commit d72d1a58f0cd6f1e971c0b3eb27bf810f235d097 Author: wehub-resource-sync Date: Mon Jul 13 13:27:18 2026 +0800 chore: import upstream snapshot with attribution diff --git a/.appveyor.yml b/.appveyor.yml new file mode 100644 index 0000000..40e4597 --- /dev/null +++ b/.appveyor.yml @@ -0,0 +1,40 @@ +version: 4.6.0.99.{build} + +image: Visual Studio 2017 +platform: x64 +configuration: + - '3.10' + +# only build on 'master' / 'main' and pull requests targeting it +branches: + only: + - master + - main + +environment: + matrix: + - COMPILER: MSVC + TASK: python + - COMPILER: MINGW + TASK: python + +clone_depth: 5 + +install: + - git submodule update --init --recursive # get `external_libs` folder + - set PATH=C:\mingw-w64\x86_64-8.1.0-posix-seh-rt_v6-rev0\mingw64\bin;%PATH% + - set PYTHON_VERSION=%CONFIGURATION% + - powershell.exe -ExecutionPolicy ByPass -c "Invoke-RestMethod https://pixi.sh/install.ps1 | Invoke-Expression" + - set PATH=C:\Users\appveyor\.pixi\bin;%PATH% + - ps: | + $env:APPVEYOR = "true" + $env:CMAKE_BUILD_PARALLEL_LEVEL = 4 + $env:BUILD_SOURCESDIRECTORY = "$env:APPVEYOR_BUILD_FOLDER" + # tell scripts where to put artifacts + # (this variable name is left over from when jobs ran on Azure DevOps) + $env:BUILD_ARTIFACTSTAGINGDIRECTORY = "$env:APPVEYOR_BUILD_FOLDER/artifacts" + +build: false + +test_script: + - powershell.exe -ExecutionPolicy Bypass -File %APPVEYOR_BUILD_FOLDER%\.ci\test-windows.ps1 diff --git a/.ci/README.md b/.ci/README.md new file mode 100644 index 0000000..fbcb229 --- /dev/null +++ b/.ci/README.md @@ -0,0 +1,6 @@ +Helper Scripts for CI +===================== + +This folder contains scripts which are run on CI services. + +Dockerfile used on CI service is maintained in a separate [GitHub repository](https://github.com/guolinke/lightgbm-ci-docker) and can be pulled from [Docker Hub](https://hub.docker.com/r/lightgbm/vsts-agent). diff --git a/.ci/append-comment.sh b/.ci/append-comment.sh new file mode 100755 index 0000000..8738495 --- /dev/null +++ b/.ci/append-comment.sh @@ -0,0 +1,44 @@ +#!/bin/bash +# +# [description] +# Post a comment to a pull request. +# +# [usage] +# append-comment.sh +# +# PULL_REQUEST_ID: ID of PR to post the comment on. +# +# BODY: Text of the comment to be posted. + +set -e -E -u -o pipefail + +if [ -z "$GITHUB_ACTIONS" ]; then + echo "Must be run inside GitHub Actions CI" + exit 1 +fi + +if [ $# -ne 2 ]; then + echo "Usage: $0 " + exit 1 +fi + +pr_id=$1 +body=$2 + +body=${body/failure/failure ❌} +body=${body/error/failure ❌} +body=${body/cancelled/failure ❌} +body=${body/timed_out/failure ❌} +body=${body/success/success ✔️} +data=$( + jq -n \ + --argjson body "\"$body\"" \ + '{"body": $body}' +) +curl -sL \ + --fail \ + -X POST \ + -H "Accept: application/vnd.github.v3+json" \ + -H "Authorization: token ${GITHUB_TOKEN}" \ + -d "$data" \ + "${GITHUB_API_URL}/repos/lightgbm-org/LightGBM/issues/${pr_id}/comments" diff --git a/.ci/build-docs.R b/.ci/build-docs.R new file mode 100644 index 0000000..d2c807b --- /dev/null +++ b/.ci/build-docs.R @@ -0,0 +1,19 @@ +loadNamespace("pkgdown") +loadNamespace("roxygen2") + +roxygen2::roxygenize( + "R-package/" + , load = "installed" +) + +pkgdown::build_site( + "R-package/" + , lazy = FALSE + , install = FALSE + , devel = FALSE + , examples = TRUE + , run_dont_run = TRUE + , seed = 42L + , preview = FALSE + , new_process = TRUE +) diff --git a/.ci/build-docs.sh b/.ci/build-docs.sh new file mode 100755 index 0000000..799ddfe --- /dev/null +++ b/.ci/build-docs.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +conda env create \ + --name test-env \ + --file ./docs/env.yml \ +|| exit 1 + +# shellcheck disable=SC1091 +source activate test-env + +make -C docs html || exit 1 diff --git a/.ci/check-dynamic-dependencies.sh b/.ci/check-dynamic-dependencies.sh new file mode 100755 index 0000000..bb83638 --- /dev/null +++ b/.ci/check-dynamic-dependencies.sh @@ -0,0 +1,129 @@ +#!/bin/bash +# +# [description] +# Helper script for checking versions in the dynamic symbol table. +# This script checks that LightGBM library is linked to the appropriate symbol versions. +# Linking to newer symbol versions at compile time is problematic because it could result +# in built artifacts being unusable on older platforms. +# +# Version history for these symbols can be found at the following: +# * GLIBC: https://sourceware.org/glibc/wiki/Glibc%20Timeline +# * GLIBCXX: https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html +# * OMP/GOMP: https://github.com/gcc-mirror/gcc/blob/master/libgomp/libgomp.map +# +# [usage] +# check-dynamic-dependencies.sh +# +# PATH: Path to the file. +# Path to the file with the dynamic symbol table entries of the file +# (result of `objdump -T` command). + +set -e -E -u -o pipefail + +if [ "$#" -ne 1 ]; then + echo "Usage: $0 " + exit 1 +fi + +INPUT_FILE="$1" + +if [ ! -f "$INPUT_FILE" ]; then + echo "Error: File '$INPUT_FILE' not found." + exit 1 +fi + +awk ' +BEGIN { + glibc_count = 0 + glibcxx_count = 0 + gomp_count = 0 + + has_error = 0 +} + +# --- Check GLIBC --- +/0{16}[ \t\(]+GLIBC_[0-9]+\.[0-9]+/ { + match($0, /GLIBC_([0-9]+)\.([0-9]+)/, parts) + if (RSTART > 0) { + match($0, /GLIBC_[0-9]+\.[0-9]+/) + ver_str = substr($0, RSTART+6, RLENGTH-6) # skip "GLIBC_" + split(ver_str, v, ".") + major = v[1] + 0 + minor = v[2] + 0 + + glibc_count++ + + if (major > 2 || (major == 2 && minor > 28)) { + print "Error: found unexpected GLIBC version: \x27" major "." minor "\x27" + has_error = 1 + } + } +} + +# --- Check GLIBCXX --- +/0{16}[ \t\(]+GLIBCXX_[0-9]+\.[0-9]+/ { + match($0, /GLIBCXX_[0-9]+\.[0-9]+(\.[0-9]+)?/) + if (RSTART > 0) { + ver_str = substr($0, RSTART+8, RLENGTH-8) # skip "GLIBCXX_" + n = split(ver_str, v, ".") + major = v[1] + 0 + minor = v[2] + 0 + patch = (n >= 3) ? v[3] + 0 : 0 + patch_str = (n >= 3) ? v[3] : "" + + glibcxx_count++ + + msg_ver = major "." minor + if (n >= 3) msg_ver = msg_ver "." patch + + if (major != 3 || minor != 4) { + print "Error: found unexpected GLIBCXX version: \x27" msg_ver "\x27" + has_error = 1 + } + if (n >= 3 && patch > 22) { + print "Error: found unexpected GLIBCXX version: \x27" msg_ver "\x27" + has_error = 1 + } + } +} + +# --- Check OMP/GOMP --- +/0{16}[ \t\(]+G?OMP_[0-9]+\.[0-9]+/ { + match($0, /G?OMP_[0-9]+\.[0-9]+/) + if (RSTART > 0) { + full_match = substr($0, RSTART, RLENGTH) + us_idx = index(full_match, "_") + ver_str = substr(full_match, us_idx + 1) + + split(ver_str, v, ".") + major = v[1] + 0 + minor = v[2] + 0 + + gomp_count++ + + if (major > 4 || (major == 4 && minor > 5)) { + print "Error: found unexpected OMP/GOMP version: \x27" major "." minor "\x27" + has_error = 1 + } + } +} + +END { + if (glibc_count <= 1) { + print "Error: Not enough GLIBC symbols found (found " glibc_count ", expected > 1)" + has_error = 1 + } + if (glibcxx_count <= 1) { + print "Error: Not enough GLIBCXX symbols found (found " glibcxx_count ", expected > 1)" + has_error = 1 + } + if (gomp_count <= 1) { + print "Error: Not enough OMP/GOMP symbols found (found " gomp_count ", expected > 1)" + has_error = 1 + } + + if (has_error == 1) { + exit 1 + } +} +' "$INPUT_FILE" diff --git a/.ci/check-omp-pragmas.sh b/.ci/check-omp-pragmas.sh new file mode 100755 index 0000000..ac83b16 --- /dev/null +++ b/.ci/check-omp-pragmas.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +set -e -u + +echo "checking that all OpenMP pragmas specify num_threads()" +get_omp_pragmas_without_num_threads() { + grep \ + -n \ + -R \ + --include='*.c' \ + --include='*.cc' \ + --include='*.cpp' \ + --include='*.h' \ + --include='*.hpp' \ + 'pragma omp parallel' \ + | grep -v ' num_threads' +} + +# 'grep' returns a non-0 exit code if 0 lines were found. +# Turning off '-e -o pipefail' options here so that bash doesn't +# consider this a failure and stop execution of the script. +# +# ref: https://www.gnu.org/software/grep/manual/html_node/Exit-Status.html +set +e +PROBLEMATIC_LINES=$( + get_omp_pragmas_without_num_threads +) +set -e +if test "${PROBLEMATIC_LINES}" != ""; then + get_omp_pragmas_without_num_threads + echo "Found '#pragma omp parallel' not using explicit num_threads() configuration. Fix those." + echo "For details, see https://www.openmp.org/spec-html/5.0/openmpse14.html#x54-800002.6" + exit 1 +fi +echo "done checking OpenMP pragmas" diff --git a/.ci/check-python-dists.sh b/.ci/check-python-dists.sh new file mode 100644 index 0000000..2cfe2c6 --- /dev/null +++ b/.ci/check-python-dists.sh @@ -0,0 +1,58 @@ +#!/bin/sh + +set -e -u + +DIST_DIR=${1} + +# defaults +METHOD=${METHOD:-""} +TASK=${TASK:-""} + +echo "checking Python-package distributions in '${DIST_DIR}'" + +pip install \ + -qq \ + check-wheel-contents \ + twine || exit 1 + +echo "twine check..." +twine check --strict "$(echo "${DIST_DIR}"/*)" || exit 1 + +if { test "${TASK}" = "bdist" || test "${METHOD}" = "wheel"; }; then + echo "check-wheel-contents..." + check-wheel-contents "$(echo "${DIST_DIR}"/*.whl)" || exit 1 +fi + +PY_MINOR_VER=$(python -c "import sys; print(sys.version_info.minor)") +if [ "$PY_MINOR_VER" -gt 7 ]; then + echo "pydistcheck..." + pip install 'pydistcheck>=0.9.1' + if { test "${TASK}" = "cuda" || test "${METHOD}" = "wheel"; }; then + pydistcheck \ + --inspect \ + --ignore 'compiled-objects-have-debug-symbols'\ + --ignore 'distro-too-large-compressed' \ + --max-allowed-size-uncompressed '550M' \ + --max-allowed-files 806 \ + "$(echo "${DIST_DIR}"/*)" || exit 1 + elif { test "$(uname -m)" = "aarch64"; }; then + pydistcheck \ + --inspect \ + --ignore 'compiled-objects-have-debug-symbols' \ + --max-allowed-size-compressed '5M' \ + --max-allowed-size-uncompressed '15M' \ + --max-allowed-files 806 \ + "$(echo "${DIST_DIR}"/*)" || exit 1 + else + pydistcheck \ + --inspect \ + --max-allowed-size-compressed '5M' \ + --max-allowed-size-uncompressed '15M' \ + --max-allowed-files 806 \ + "$(echo "${DIST_DIR}"/*)" || exit 1 + fi +else + echo "skipping pydistcheck (does not support Python 3.${PY_MINOR_VER})" +fi + +echo "done checking Python-package distributions" diff --git a/.ci/check-workflow-status.sh b/.ci/check-workflow-status.sh new file mode 100755 index 0000000..711a4a1 --- /dev/null +++ b/.ci/check-workflow-status.sh @@ -0,0 +1,51 @@ +#!/bin/bash + +# [description] +# +# Look for the last run of a given GitHub Actions workflow on a given branch. +# If there's never been one (as might be the case with optional workflows like valgrind), +# exit with 0. +# +# Otherwise, check the status of that latest run. +# If it wasn't successful, exit with a non-0 exit code. +# +# [usage] +# +# check-workflow-status.sh +# +# BRANCH: name of a branch involved in a pull request. +# +# WORKFLOW_FILE: filename (e.g. 'r_valgrind.yml') defining the GitHub Actions workflow. +# + +set -e -u -o pipefail + +BRANCH="${1}" +WORKFLOW_FILE="${2}" +PR_NUMBER="${3}" + +# Limit how much data is pulled from the API and needs to be parsed locally. +OLDEST_ALLOWED_RUN_DATE=$(date --date='7 days ago' '+%F') + +echo "Searching for latest run of '${WORKFLOW_FILE}' on branch '${BRANCH}' " + +LATEST_RUN_ID=$( + gh run list \ + --repo 'lightgbm-org/LightGBM' \ + --event 'workflow_dispatch' \ + --created ">= ${OLDEST_ALLOWED_RUN_DATE}" \ + --workflow "${WORKFLOW_FILE}" \ + --json 'createdAt,databaseId,name' \ + --jq "sort_by(.createdAt) | reverse | map(select(.name | contains (\"pr=${PR_NUMBER}\"))) | .[0] | .databaseId" +) + +if [[ "${LATEST_RUN_ID}" == "" ]]; then + echo "No runs of '${WORKFLOW_FILE}' found on branch from pull request ${PR_NUMBER} (on or after ${OLDEST_ALLOWED_RUN_DATE})." + exit 0 +fi + +echo "Checking status of workflow run '${LATEST_RUN_ID}'" +gh run view \ + --repo "lightgbm-org/LightGBM" \ + --exit-status \ + "${LATEST_RUN_ID}" diff --git a/.ci/conda-envs/README.md b/.ci/conda-envs/README.md new file mode 100644 index 0000000..4666f96 --- /dev/null +++ b/.ci/conda-envs/README.md @@ -0,0 +1,11 @@ +# conda-envs + +This directory contains files used to create `conda` environments for development +and testing of LightGBM. + +The `.txt` files here are intended to be used with `conda create --file`. + +For details on that, see the `conda` docs: + +* `conda create` docs ([link](https://conda.io/projects/conda/en/latest/commands/create.html)) +* "Managing Environments" ([link](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)) diff --git a/.ci/conda-envs/ci-core.txt b/.ci/conda-envs/ci-core.txt new file mode 100644 index 0000000..6a83c5d --- /dev/null +++ b/.ci/conda-envs/ci-core.txt @@ -0,0 +1,45 @@ +# [description] +# +# Core dependencies used across most LightGBM continuous integration (CI) jobs. +# +# 'python' constraint is intentionally omitted, so this file can be reused across +# Python versions. +# +# These floors are not the oldest versions LightGBM supports... they're here just to make conda +# solves faster, and should generally be the latest versions that work for all CI jobs using this. +# +# [usage] +# +# conda create \ +# --name test-env \ +# --file ./.ci/conda-envs/ci-core.txt \ +# python=3.10 +# + +# direct imports +cffi>=1.16 +dask>=2025.4.0 +joblib>=1.3.2 +matplotlib-base>=3.7.3 +narwhals>=1.15 +numpy>=1.24.4 +pandas>2.0 +polars>=1.0.0 +pyarrow-core>=16.0 +python-graphviz>=0.20.3 +scikit-learn>=1.3.2 +scipy>=1.7 + +# starting some time around 2026, 'pip' is no longer guaranteed to be installed +pip>=24 + +# testing-only dependencies +cloudpickle>=3.0.0 +psutil>=5.9.8 +pytest>=8.1.1 + +# other recursive dependencies, just +# pinned here to help speed up solves +pluggy>=1.4.0 +setuptools>=69.2 +wheel>=0.43 diff --git a/.ci/create-nuget.py b/.ci/create-nuget.py new file mode 100644 index 0000000..ad48d87 --- /dev/null +++ b/.ci/create-nuget.py @@ -0,0 +1,85 @@ +# coding: utf-8 +"""Script for generating files with NuGet package metadata.""" + +import datetime +import sys +from pathlib import Path +from shutil import copyfile + +if __name__ == "__main__": + source = Path(sys.argv[1]) + nuget_dir = Path(__file__).absolute().parent / "nuget" + print(f"Creating nuget directory '{nuget_dir}'") + linux_folder_path = nuget_dir / "runtimes" / "linux-x64" / "native" + linux_folder_path.mkdir(parents=True, exist_ok=True) + osx_folder_path = nuget_dir / "runtimes" / "osx-x64" / "native" + osx_folder_path.mkdir(parents=True, exist_ok=True) + windows_folder_path = nuget_dir / "runtimes" / "win-x64" / "native" + windows_folder_path.mkdir(parents=True, exist_ok=True) + build_folder_path = nuget_dir / "build" + build_folder_path.mkdir(parents=True, exist_ok=True) + print(f"Looking for libraries in '{source}'") + copyfile(source / "lib_lightgbm.so", linux_folder_path / "lib_lightgbm.so") + copyfile(source / "lib_lightgbm.dylib", osx_folder_path / "lib_lightgbm.dylib") + copyfile(source / "lib_lightgbm.dll", windows_folder_path / "lib_lightgbm.dll") + copyfile(source / "lightgbm.exe", windows_folder_path / "lightgbm.exe") + version = (nuget_dir.parents[1] / "VERSION.txt").read_text(encoding="utf-8").strip().replace("rc", "-rc") + print(f"Setting version to '{version}'") + nuget_str = rf""" + + + LightGBM + {version} + Guolin Ke + Guolin Ke + MIT + https://github.com/lightgbm-org/LightGBM + false + A fast, distributed, high performance gradient boosting framework + Copyright {datetime.datetime.now().year} @ Microsoft + machine-learning data-mining distributed native boosting gbdt + + + + + + + + """ + prop_str = r""" + + + + PreserveNewest + false + + + PreserveNewest + false + + + + """ + target_str = r""" + + + true + + + + + + """ + (nuget_dir / "LightGBM.nuspec").write_text(nuget_str, encoding="utf-8") + (nuget_dir / "build" / "LightGBM.props").write_text(prop_str, encoding="utf-8") + (nuget_dir / "build" / "LightGBM.targets").write_text(target_str, encoding="utf-8") + print("Done creating NuGet package") diff --git a/.ci/download-artifacts.sh b/.ci/download-artifacts.sh new file mode 100755 index 0000000..11f4bb9 --- /dev/null +++ b/.ci/download-artifacts.sh @@ -0,0 +1,69 @@ +#!/bin/bash + +# [description] +# Collect and download artifacts from all workflow runs for a commit. +# +# [usage] +# ./download-artifacts.sh +# + +set -e -u -E -o pipefail + +COMMIT_ID="${1}" +OUTPUT_DIR="./release-artifacts" + +get-latest-run-id() { + gh run list \ + --repo "lightgbm-org/LightGBM" \ + --commit "${1}" \ + --workflow "${2}" \ + --json 'createdAt,databaseId' \ + --jq 'sort_by(.createdAt) | reverse | .[0] | .databaseId' +} + +# ensure directory for storing artifacts exists +echo "preparing to download artifacts for commit '${COMMIT_ID}' to '${OUTPUT_DIR}'" +mkdir -p "${OUTPUT_DIR}" + +# get core artifacts +echo "downloading core artifacts" +gh run download \ + --repo "lightgbm-org/LightGBM" \ + --dir "${OUTPUT_DIR}" \ + "$(get-latest-run-id "${COMMIT_ID}" 'build.yml')" +echo "done downloading core artifacts" + +# get python-package artifacts +echo "downloading python-package artifacts and NuGet package" +gh run download \ + --repo "lightgbm-org/LightGBM" \ + --dir "${OUTPUT_DIR}" \ + "$(get-latest-run-id "${COMMIT_ID}" 'python_package.yml')" +echo "done downloading python-package artifacts and NuGet package" + +# get R-package artifacts +echo "downloading R-package artifacts" +gh run download \ + --repo "lightgbm-org/LightGBM" \ + --dir "${OUTPUT_DIR}" \ + "$(get-latest-run-id "${COMMIT_ID}" 'r_package.yml')" +echo "done downloading R-package artifacts" + +# get SWIG artifacts +echo "downloading SWIG artifacts" +gh run download \ + --repo "lightgbm-org/LightGBM" \ + --dir "${OUTPUT_DIR}" \ + "$(get-latest-run-id "${COMMIT_ID}" 'swig.yml')" +echo "done downloading SWIG artifacts" + +# 'gh run download' unpackages into nested directories like {artifact-name}/{file}. +# +# This moves all files to the top level and then deletes those {artifact-name}/ directories, +# to make it easier to bulk upload all files to a release. +echo "flattening directory structure" +find "${OUTPUT_DIR}" -type f -mindepth 2 -exec mv -i '{}' "${OUTPUT_DIR}" \; +find "${OUTPUT_DIR}" -type d -mindepth 1 -exec rm -r '{}' \+ + +echo "downloaded artifacts:" +find "${OUTPUT_DIR}" -type f diff --git a/.ci/install-opencl.ps1 b/.ci/install-opencl.ps1 new file mode 100644 index 0000000..76bcf5c --- /dev/null +++ b/.ci/install-opencl.ps1 @@ -0,0 +1,40 @@ +Write-Output "Installing OpenCL CPU platform" + +$installer = "AMD-APP-SDKInstaller-v3.0.130.135-GA-windows-F-x64.exe" + +Write-Output "Downloading OpenCL platform installer" +$ProgressPreference = "SilentlyContinue" # progress bar bug extremely slows down download speed +$params = @{ + OutFile = "$installer" + Uri = "https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/$installer" +} +Invoke-WebRequest @params + +if (Test-Path "$installer") { + Write-Output "Successfully downloaded OpenCL platform installer" +} else { + Write-Output "Unable to download OpenCL platform installer" + Write-Output "Setting EXIT" + $host.SetShouldExit(-1) + exit 1 +} + +# Install OpenCL platform from installer executable +Write-Output "Running OpenCL installer" +Invoke-Command -ScriptBlock { + Start-Process "$installer" -ArgumentList '/S /V"/quiet /norestart /passive /log opencl.log"' -Wait +} + +$property = Get-ItemProperty -Path Registry::HKEY_LOCAL_MACHINE\SOFTWARE\Khronos\OpenCL\Vendors +if ($null -eq $property) { + Write-Output "Unable to install OpenCL CPU platform" + Write-Output "OpenCL installation log:" + Get-Content "opencl.log" + Write-Output "Setting EXIT" + $host.SetShouldExit(-1) + exit 1 +} else { + Write-Output "Successfully installed OpenCL CPU platform" + Write-Output "Current OpenCL drivers:" + Write-Output $property +} diff --git a/.ci/install-r-deps.R b/.ci/install-r-deps.R new file mode 100644 index 0000000..da13aa8 --- /dev/null +++ b/.ci/install-r-deps.R @@ -0,0 +1,125 @@ +# Install R dependencies, using only base R. +# +# Supported arguments: +# +# --all Install all the 'Depends', 'Imports', 'LinkingTo', and 'Suggests' dependencies +# (automatically implies --build --test). +# +# --build Install the packages needed to build. +# +# --exclude= Comma-delimited list of packages to NOT install. +# +# --include= Comma-delimited list of additional packages to install. +# These will always be installed, unless also used in "--exclude". +# +# --test Install packages needed to run tests. +# + + +# [description] Parse command line arguments into an R list. +# Returns a list where keys are arguments and values +# are either TRUE (for flags) or a vector of values passed via a +# comma-delimited list. +.parse_args <- function(args) { + out <- list( + "--all" = FALSE + , "--build" = FALSE + , "--exclude" = character(0L) + , "--include" = character(0L) + , "--test" = FALSE + ) + for (arg in args) { + parsed_arg <- unlist(strsplit(arg, "=", fixed = TRUE)) + arg_name <- parsed_arg[[1L]] + if (!(arg_name %in% names(out))) { + stop(sprintf("Unrecognized argument: '%s'", arg_name)) + } + if (length(parsed_arg) == 2L) { + # lists, like "--include=roxygen2,testthat" + values <- unlist(strsplit(parsed_arg[[2L]], ",", fixed = TRUE)) + out[[arg_name]] <- values + } else { + # flags, like "--build" + out[[arg]] <- TRUE + } + } + return(out) +} + +args <- .parse_args( + commandArgs(trailingOnly = TRUE) +) + +# which dependencies to install +ALL_DEPS <- isTRUE(args[["--all"]]) +BUILD_DEPS <- ALL_DEPS || isTRUE(args[["--build"]]) +TEST_DEPS <- ALL_DEPS || isTRUE(args[["--test"]]) + +# force downloading of binary packages on macOS +COMPILE_FROM_SOURCE <- "both" +PACKAGE_TYPE <- getOption("pkgType") + +# CRAN has precompiled binaries for macOS and Windows... prefer those, +# for faster installation. +if (Sys.info()[["sysname"]] == "Darwin" || .Platform$OS.type == "windows") { + COMPILE_FROM_SOURCE <- "never" + PACKAGE_TYPE <- "binary" +} +options( + install.packages.check.source = "no" + , install.packages.compile.from.source = COMPILE_FROM_SOURCE +) + +# always use the same CRAN mirror +CRAN_MIRROR <- Sys.getenv("CRAN_MIRROR", unset = "https://cran.r-project.org") + +# we always want these +deps_to_install <- c( + "data.table" + , "jsonlite" + , "Matrix" + , "R6" +) + +if (isTRUE(BUILD_DEPS)) { + deps_to_install <- c( + deps_to_install + , "knitr" + , "markdown" + ) +} + +if (isTRUE(TEST_DEPS)) { + deps_to_install <- c( + deps_to_install + , "RhpcBLASctl" + , "testthat" + ) +} + +# add packages passed through '--include' +deps_to_install <- unique(c( + deps_to_install + , args[["--include"]] +)) + +# remove packages passed through '--exclude' +deps_to_install <- setdiff( + x = deps_to_install + , args[["--exclude"]] +) + +msg <- sprintf( + "[install-r-deps] installing R packages: %s\n" + , toString(sort(deps_to_install)) +) +cat(msg) + +install.packages( # nolint: undesirable_function. + pkgs = deps_to_install + , dependencies = c("Depends", "Imports", "LinkingTo") + , lib = Sys.getenv("R_LIB_PATH", unset = .libPaths()[[1L]]) + , repos = CRAN_MIRROR + , type = PACKAGE_TYPE + , Ncpus = parallel::detectCores() +) diff --git a/.ci/lint-all.sh b/.ci/lint-all.sh new file mode 100755 index 0000000..3a3bfb2 --- /dev/null +++ b/.ci/lint-all.sh @@ -0,0 +1,21 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +pwsh -command "Install-Module -Name PSScriptAnalyzer -Scope CurrentUser -SkipPublisherCheck" +echo "Linting PowerShell code" +pwsh -file ./.ci/lint-powershell.ps1 || exit 1 + +conda create -q -y -n test-env \ + "python=3.14[build=*_cp*]" \ + 'pre-commit>=3.8.0' \ + 'r-lintr>=3.3.0' + +# shellcheck disable=SC1091 +source activate test-env + +echo "Running pre-commit checks" +pre-commit run --all-files || exit 1 + +echo "Linting R code" +Rscript ./.ci/lint-r-code.R "$(pwd)" || exit 1 diff --git a/.ci/lint-powershell.ps1 b/.ci/lint-powershell.ps1 new file mode 100644 index 0000000..ff0bd0e --- /dev/null +++ b/.ci/lint-powershell.ps1 @@ -0,0 +1,87 @@ +$ErrorActionPreference = 'Stop' + +$settings = @{ + Severity = @( + 'Information', + 'Warning', + 'Error' + ) + IncludeDefaultRules = $true + ExcludeRules = @( + 'PSAvoidUsingInvokeExpression' + ) + # Additional rules that are disabled by default. + # + # Some of the skips could be replaced with inline comments if PSScriptAnalyzer + # supports that in the future (https://github.com/PowerShell/PSScriptAnalyzer/issues/849). + Rules = @{ + PSAvoidExclaimOperator = @{ + Enable = $true + } + PSAvoidLongLines = @{ + Enable = $true + MaximumLineLength = 120 + } + PSAvoidSemicolonsAsLineTerminators = @{ + Enable = $true + } + PSPlaceCloseBrace = @{ + Enable = $true + NoEmptyLineBefore = $true + IgnoreOneLineBlock = $true + NewLineAfter = $false + } + PSPlaceOpenBrace = @{ + Enable = $true + OnSameLine = $true + NewLineAfter = $true + IgnoreOneLineBlock = $true + } + PSUseConsistentIndentation = @{ + Enable = $true + IndentationSize = 4 + PipelineIndentation = 'IncreaseIndentationAfterEveryPipeline' + Kind = 'space' + } + PSUseConsistentWhitespace = @{ + Enable = $true + CheckInnerBrace = $true + CheckOpenBrace = $true + CheckOpenParen = $true + CheckOperator = $true + CheckSeparator = $true + CheckPipe = $true + CheckPipeForRedundantWhitespace = $true + CheckParameter = $true + IgnoreAssignmentOperatorInsideHashTable = $false + } + PSUseCorrectCasing = @{ + Enable = $true + } + } +} + +# this pre-listing of files can be removed whenever PSScriptAnalyzer adds support for exclusions. +# +# see: +# +# * https://github.com/PowerShell/PSScriptAnalyzer/issues/561 +# * https://github.com/PowerShell/vscode-powershell/issues/3048 +# +# lint-powershell.ps1 itself is included here because linting this script itself +# sometimes fails (non-deterministically!) with an error like "Object reference not set to an instance of an object" +# +$files = @( + Get-ChildItem -Path ./ -Recurse -Force -Filter '*.ps1' | + Where-Object { $_.FullName -notmatch '[/\\]bin[/\\]' } | + Where-Object { $_.FullName -notmatch '[/\\]external_libs[/\\]' } | + Where-Object { $_.FullName -notmatch '[/\\]\.pixi[/\\]' } | + Where-Object { $_.FullName -notmatch '[/\\]venv[/\\]' } | + Where-Object { $_.Name -ne 'lint-powershell.ps1' } | + ForEach-Object { $_.FullName } +) + +foreach ($file in $files) { + Write-Output "linting '$file'" + Invoke-ScriptAnalyzer -Path $file -EnableExit -Settings $settings +} diff --git a/.ci/lint-r-code.R b/.ci/lint-r-code.R new file mode 100755 index 0000000..9eae00a --- /dev/null +++ b/.ci/lint-r-code.R @@ -0,0 +1,154 @@ +loadNamespace("lintr") + +args <- commandArgs( + trailingOnly = TRUE +) +SOURCE_DIR <- args[[1L]] + +FILES_TO_LINT <- list.files( + path = SOURCE_DIR + , pattern = "\\.r$|\\.rmd$" + , all.files = TRUE + , ignore.case = TRUE + , full.names = TRUE + , recursive = TRUE + , include.dirs = FALSE +) + +# text to use for pipe operators from packages like 'magrittr' +pipe_text <- paste0( + "For consistency and the sake of being explicit, this project's code " + , "does not use the pipe operator." +) + +# text to use for functions that should only be called interactively +interactive_text <- paste0( + "Functions like '?', 'help', and 'install.packages()' should only be used " + , "interactively, not in package code." +) + +LINTERS_TO_USE <- list( + "absolute_path" = lintr::absolute_path_linter() + , "any_duplicated" = lintr::any_duplicated_linter() + , "any_is_na" = lintr::any_is_na_linter() + , "assignment" = lintr::assignment_linter() + , "backport" = lintr::backport_linter() + , "boolean_arithmetic" = lintr::boolean_arithmetic_linter() + , "braces" = lintr::brace_linter() + , "class_equals" = lintr::class_equals_linter() + , "commas" = lintr::commas_linter() + , "conjunct_test" = lintr::conjunct_test_linter() + , "duplicate_argument" = lintr::duplicate_argument_linter() + , "empty_assignment" = lintr::empty_assignment_linter() + , "equals_na" = lintr::equals_na_linter() + , "fixed_regex" = lintr::fixed_regex_linter() + , "for_loop_index" = lintr::for_loop_index_linter() + , "function_left" = lintr::function_left_parentheses_linter() + , "function_return" = lintr::function_return_linter() + , "implicit_assignment" = lintr::implicit_assignment_linter() + , "implicit_integers" = lintr::implicit_integer_linter() + , "infix_spaces" = lintr::infix_spaces_linter() + , "inner_combine" = lintr::inner_combine_linter() + , "is_numeric" = lintr::is_numeric_linter() + , "lengths" = lintr::lengths_linter() + , "length_levels" = lintr::length_levels_linter() + , "length_test" = lintr::length_test_linter() + , "line_length" = lintr::line_length_linter(length = 120L) + , "literal_coercion" = lintr::literal_coercion_linter() + , "matrix" = lintr::matrix_apply_linter() + , "missing_argument" = lintr::missing_argument_linter() + , "non_portable_path" = lintr::nonportable_path_linter() + , "numeric_leading_zero" = lintr::numeric_leading_zero_linter() + , "outer_negation" = lintr::outer_negation_linter() + , "package_hooks" = lintr::package_hooks_linter() + , "paren_body" = lintr::paren_body_linter() + , "paste" = lintr::paste_linter() + , "quotes" = lintr::quotes_linter() + , "redundant_equals" = lintr::redundant_equals_linter() + , "regex_subset" = lintr::regex_subset_linter() + , "routine_registration" = lintr::routine_registration_linter() + , "scalar_in" = lintr::scalar_in_linter() + , "semicolon" = lintr::semicolon_linter() + , "seq" = lintr::seq_linter() + , "spaces_inside" = lintr::spaces_inside_linter() + , "spaces_left_parens" = lintr::spaces_left_parentheses_linter() + , "sprintf" = lintr::sprintf_linter() + , "string_boundary" = lintr::string_boundary_linter() + , "todo_comments" = lintr::todo_comment_linter(c("todo", "fixme", "to-do")) + , "trailing_blank" = lintr::trailing_blank_lines_linter() + , "trailing_white" = lintr::trailing_whitespace_linter() + , "true_false" = lintr::T_and_F_symbol_linter() + , "undesirable_function" = lintr::undesirable_function_linter( + fun = c( + "cbind" = paste0( + "cbind is an unsafe way to build up a data frame. merge() or direct " + , "column assignment is preferred." + ) + , "dyn.load" = "Directly loading or unloading .dll or .so files in package code should not be necessary." + , "dyn.unload" = "Directly loading or unloading .dll or .so files in package code should not be necessary." + , "help" = interactive_text + , "ifelse" = "The use of ifelse() is dangerous because it will silently allow mixing types." + , "install.packages" = interactive_text + , "is.list" = paste0( + "This project uses data.table, and is.list(x) is TRUE for a data.table. " + , "identical(class(x), 'list') is a safer way to check that something is an R list object." + ) + , "rbind" = "data.table::rbindlist() is faster and safer than rbind(), and is preferred in this project." + , "require" = paste0( + "library() is preferred to require() because it will raise an error immediately " + , "if a package is missing." + ) + ) + ) + , "undesirable_operator" = lintr::undesirable_operator_linter( + op = c( + "%>%" = pipe_text + , "%.%" = pipe_text + , "%..%" = pipe_text + , "|>" = pipe_text + , "?" = interactive_text + , "??" = interactive_text + ) + ) + , "unnecessary_concatenation" = lintr::unnecessary_concatenation_linter() + , "unnecessary_lambda" = lintr::unnecessary_lambda_linter() + , "unreachable_code" = lintr::unreachable_code_linter() + , "unused_import" = lintr::unused_import_linter() + , "vector_logic" = lintr::vector_logic_linter() + , "whitespace" = lintr::whitespace_linter() +) + +noquote(paste0(length(FILES_TO_LINT), " R files need linting")) + +results <- NULL + +for (r_file in FILES_TO_LINT) { + + this_result <- lintr::lint( + filename = r_file + , linters = LINTERS_TO_USE + , cache = FALSE + ) + + print( + sprintf( + "Found %i linting errors in %s" + , length(this_result) + , r_file + ) + , quote = FALSE + ) + + results <- c(results, this_result) + +} + +issues_found <- length(results) + +noquote(paste0("Total linting issues found: ", issues_found)) + +if (issues_found > 0L) { + print(results) +} + +quit(save = "no", status = issues_found) diff --git a/.ci/parameter-generator.py b/.ci/parameter-generator.py new file mode 100644 index 0000000..af020ec --- /dev/null +++ b/.ci/parameter-generator.py @@ -0,0 +1,408 @@ +# coding: utf-8 +"""Helper script for generating config file and parameters list. + +This script generates LightGBM/src/io/config_auto.cpp file +with list of all parameters, aliases table and other routines +along with parameters description in LightGBM/docs/Parameters.rst file +from the information in LightGBM/include/LightGBM/config.h file. +""" + +import re +from collections import defaultdict +from pathlib import Path +from typing import Dict, List, Tuple + + +def get_parameter_infos(config_hpp: Path) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]: + """Parse config header file. + + Parameters + ---------- + config_hpp : pathlib.Path + Path to the config header file. + + Returns + ------- + infos : tuple + Tuple with names and content of sections. + """ + is_inparameter = False + cur_key = None + key_lvl = 0 + cur_info: Dict[str, List] = {} + keys = [] + member_infos: List[List[Dict[str, List]]] = [] + with open(config_hpp) as config_hpp_file: + for line in config_hpp_file: + if line.strip() in {"#ifndef __NVCC__", "#endif // __NVCC__"}: + continue + if "#pragma region Parameters" in line: + is_inparameter = True + elif "#pragma region" in line and "Parameters" in line: + key_lvl += 1 + cur_key = line.split("region")[1].strip() + keys.append((cur_key, key_lvl)) + member_infos.append([]) + elif "#pragma endregion" in line: + key_lvl -= 1 + if cur_key is not None: + cur_key = None + elif is_inparameter: + is_inparameter = False + elif cur_key is not None: + line = line.strip() + if line.startswith("//"): + key, _, val = line[2:].partition("=") + key = key.strip() + val = val.strip() + if key not in cur_info: + if key == "descl2" and "desc" not in cur_info: + cur_info["desc"] = [] + elif key != "descl2": + cur_info[key] = [] + if key == "desc": + cur_info["desc"].append(("l1", val)) + elif key == "descl2": + cur_info["desc"].append(("l2", val)) + else: + cur_info[key].append(val) + elif line: + has_eqsgn = False + tokens = line.split("=") + if len(tokens) == 2: + if "default" not in cur_info: + cur_info["default"] = [tokens[1][:-1].strip()] + has_eqsgn = True + tokens = line.split() + cur_info["inner_type"] = [tokens[0].strip()] + if "name" not in cur_info: + if has_eqsgn: + cur_info["name"] = [tokens[1].strip()] + else: + cur_info["name"] = [tokens[1][:-1].strip()] + member_infos[-1].append(cur_info) + cur_info = {} + + return keys, member_infos + + +def get_names(infos: List[List[Dict[str, List]]]) -> List[str]: + """Get names of all parameters. + + Parameters + ---------- + infos : list + Content of the config header file. + + Returns + ------- + names : list + Names of all parameters. + """ + names = [] + for x in infos: + for y in x: + names.append(y["name"][0]) + return names + + +def get_alias(infos: List[List[Dict[str, List]]]) -> List[Tuple[str, str]]: + """Get aliases of all parameters. + + Parameters + ---------- + infos : list + Content of the config header file. + + Returns + ------- + pairs : list + List of tuples (param alias, param name). + """ + pairs = [] + for x in infos: + for y in x: + if "alias" in y: + name = y["name"][0] + alias = y["alias"][0].split(",") + for name2 in alias: + pairs.append((name2.strip(), name)) + return pairs + + +def parse_check(check: str, reverse: bool = False) -> Tuple[str, str]: + """Parse the constraint. + + Parameters + ---------- + check : str + String representation of the constraint. + reverse : bool, optional (default=False) + Whether to reverse the sign of the constraint. + + Returns + ------- + pair : tuple + Parsed constraint in the form of tuple (value, sign). + """ + try: + idx = 1 + float(check[idx:]) + except ValueError: + idx = 2 + float(check[idx:]) + if reverse: + reversed_sign = {"<": ">", ">": "<", "<=": ">=", ">=": "<="} + return check[idx:], reversed_sign[check[:idx]] + else: + return check[idx:], check[:idx] + + +def set_one_var_from_string(name: str, param_type: str, checks: List[str]) -> str: + """Construct code for auto config file for one param value. + + Parameters + ---------- + name : str + Name of the parameter. + param_type : str + Type of the parameter. + checks : list + Constraints of the parameter. + + Returns + ------- + ret : str + Lines of auto config file with getting and checks of one parameter value. + """ + ret = "" + univar_mapper = {"int": "GetInt", "double": "GetDouble", "bool": "GetBool", "std::string": "GetString"} + if "vector" not in param_type: + ret += f' {univar_mapper[param_type]}(params, "{name}", &{name});\n' + if len(checks) > 0: + check_mapper = {"<": "LT", ">": "GT", "<=": "LE", ">=": "GE"} + for check in checks: + value, sign = parse_check(check) + ret += f" CHECK_{check_mapper[sign]}({name}, {value});\n" + ret += "\n" + else: + ret += f' if (GetString(params, "{name}", &tmp_str)) {{\n' + type2 = param_type.split("<")[1][:-1] + if type2 == "std::string": + ret += f" {name} = Common::Split(tmp_str.c_str(), ',');\n" + else: + ret += f" {name} = Common::StringToArray<{type2}>(tmp_str, ',');\n" + ret += " }\n\n" + return ret + + +def gen_parameter_description( + sections: List[Tuple[str, int]], descriptions: List[List[Dict[str, List]]], params_rst: Path +) -> None: + """Write descriptions of parameters to the documentation file. + + Parameters + ---------- + sections : list + Names of parameters sections. + descriptions : list + Structured descriptions of parameters. + params_rst : pathlib.Path + Path to the file with parameters documentation. + """ + params_to_write = [] + lvl_mapper = {1: "-", 2: "~"} + for (section_name, section_lvl), section_params in zip(sections, descriptions, strict=True): + heading_sign = lvl_mapper[section_lvl] + params_to_write.append(f"{section_name}\n{heading_sign * len(section_name)}") + for param_desc in section_params: + name = param_desc["name"][0] + default_raw = param_desc["default"][0] + default = default_raw.strip('"') if len(default_raw.strip('"')) > 0 else default_raw + param_type = param_desc.get("type", param_desc["inner_type"])[0].split(":")[-1].split("<")[-1].strip(">") + options = param_desc.get("options", []) + if len(options) > 0: + opts = "``, ``".join([x.strip() for x in options[0].split(",")]) + options_str = f", options: ``{opts}``" + else: + options_str = "" + aliases = param_desc.get("alias", []) + if len(aliases) > 0: + aliases_joined = "``, ``".join([x.strip() for x in aliases[0].split(",")]) + aliases_str = f", aliases: ``{aliases_joined}``" + else: + aliases_str = "" + checks = sorted(param_desc.get("check", [])) + checks_len = len(checks) + if checks_len > 1: + number1, sign1 = parse_check(checks[0]) + number2, sign2 = parse_check(checks[1], reverse=True) + checks_str = f", constraints: ``{number2} {sign2} {name} {sign1} {number1}``" + elif checks_len == 1: + number, sign = parse_check(checks[0]) + checks_str = f", constraints: ``{name} {sign} {number}``" + else: + checks_str = "" + main_desc = f'- ``{name}`` :raw-html:`🔗︎`, default = ``{default}``, type = {param_type}{options_str}{aliases_str}{checks_str}' + params_to_write.append(main_desc) + params_to_write.extend([f"{' ' * 3 * int(desc[0][-1])}- {desc[1]}" for desc in param_desc["desc"]]) + + with open(params_rst) as original_params_file: + all_lines = original_params_file.read() + before, start_sep, _ = all_lines.partition(".. start params list\n\n") + _, end_sep, after = all_lines.partition("\n\n.. end params list") + + with open(params_rst, "w") as new_params_file: + new_params_file.write(before) + new_params_file.write(start_sep) + new_params_file.write("\n\n".join(params_to_write)) + new_params_file.write(end_sep) + new_params_file.write(after) + + +def gen_parameter_code( + config_hpp: Path, config_out_cpp: Path +) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]: + """Generate auto config file. + + Parameters + ---------- + config_hpp : pathlib.Path + Path to the config header file. + config_out_cpp : pathlib.Path + Path to the auto config file. + + Returns + ------- + infos : tuple + Tuple with names and content of sections. + """ + keys, infos = get_parameter_infos(config_hpp) + names = get_names(infos) + alias = get_alias(infos) + names_with_aliases = defaultdict(list) + str_to_write = r"""/*! + * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * \note + * This file is auto generated by LightGBM\.ci\parameter-generator.py from LightGBM\include\LightGBM\config.h file. + */ +""" + str_to_write += "#include \n\n" + str_to_write += "#include \n" + str_to_write += "#include \n" + str_to_write += "#include \n" + str_to_write += "#include \n\n" + str_to_write += "namespace LightGBM {\n" + + # alias table + str_to_write += "const std::unordered_map& Config::alias_table() {\n" + str_to_write += " static std::unordered_map aliases({\n" + + for pair in alias: + str_to_write += f' {{"{pair[0]}", "{pair[1]}"}},\n' + names_with_aliases[pair[1]].append(pair[0]) + str_to_write += " });\n" + str_to_write += " return aliases;\n" + str_to_write += "}\n\n" + + # names + str_to_write += "const std::unordered_set& Config::parameter_set() {\n" + str_to_write += " static std::unordered_set params({\n" + + for name in names: + str_to_write += f' "{name}",\n' + str_to_write += " });\n" + str_to_write += " return params;\n" + str_to_write += "}\n\n" + # from strings + str_to_write += "void Config::GetMembersFromString(const std::unordered_map& params) {\n" + str_to_write += ' std::string tmp_str = "";\n' + for x in infos: + for y in x: + if "[no-automatically-extract]" in y: + continue + param_type = y["inner_type"][0] + name = y["name"][0] + checks = [] + if "check" in y: + checks = y["check"] + tmp = set_one_var_from_string(name, param_type, checks) + str_to_write += tmp + # tails + str_to_write = f"{str_to_write.strip()}\n}}\n\n" + str_to_write += "std::string Config::SaveMembersToString() const {\n" + str_to_write += " std::stringstream str_buf;\n" + for x in infos: + for y in x: + if "[no-save]" in y: + continue + param_type = y["inner_type"][0] + name = y["name"][0] + if "vector" in param_type: + if "int8" in param_type: + str_to_write += f' str_buf << "[{name}: " << Common::Join(Common::ArrayCast({name}), ",") << "]\\n";\n' + else: + str_to_write += f' str_buf << "[{name}: " << Common::Join({name}, ",") << "]\\n";\n' + else: + str_to_write += f' str_buf << "[{name}: " << {name} << "]\\n";\n' + # tails + str_to_write += " return str_buf.str();\n" + str_to_write += "}\n\n" + + str_to_write += """const std::unordered_map>& Config::parameter2aliases() { + static std::unordered_map> map({""" + for name in names: + str_to_write += '\n {"' + name + '", ' + if names_with_aliases[name]: + str_to_write += '{"' + '", "'.join(names_with_aliases[name]) + '"}},' + else: + str_to_write += "{}}," + str_to_write += """ + }); + return map; +} + +""" + str_to_write += """const std::unordered_map& Config::ParameterTypes() { + static std::unordered_map map({""" + int_t_pat = re.compile(r"int\d+_t") + # the following are stored as comma separated strings but are arrays in the wrappers + overrides = { + "categorical_feature": "vector", + "ignore_column": "vector", + "interaction_constraints": "vector>", + } + for x in infos: + for y in x: + name = y["name"][0] + if name == "task": + continue + if name in overrides: + param_type = overrides[name] + else: + param_type = int_t_pat.sub("int", y["inner_type"][0]).replace("std::", "") + str_to_write += '\n {"' + name + '", "' + param_type + '"},' + str_to_write += """ + }); + return map; +} + +""" + + str_to_write += "} // namespace LightGBM\n" + with open(config_out_cpp, "w") as config_out_cpp_file: + config_out_cpp_file.write(str_to_write) + + return keys, infos + + +if __name__ == "__main__": + current_dir = Path(__file__).absolute().parent + config_hpp = current_dir.parent / "include" / "LightGBM" / "config.h" + config_out_cpp = current_dir.parent / "src" / "io" / "config_auto.cpp" + params_rst = current_dir.parent / "docs" / "Parameters.rst" + sections, descriptions = gen_parameter_code(config_hpp, config_out_cpp) + gen_parameter_description(sections, descriptions, params_rst) diff --git a/.ci/pip-envs/requirements-latest.txt b/.ci/pip-envs/requirements-latest.txt new file mode 100644 index 0000000..c7be0aa --- /dev/null +++ b/.ci/pip-envs/requirements-latest.txt @@ -0,0 +1,24 @@ +# nightlies for: matplotlib, numpy, pandas, pyarrow, scipy, scikit-learn +--extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple + +# runtime dependencies (using `.dev0` suffix to allow nightlies) +# +# pinning rules: +# +# * latest versions of lightgbm's dependencies, +# * including pre-releases and nightlies +# +cffi>=2.0.0 +matplotlib>=3.11.0.dev0 +narwhals>=2.21.0.dev0 +numpy>=2.5.0.dev0 +pandas>=3.1.0.dev0 +polars>=1.40.1.dev0 +pyarrow>=24.0.0.dev0 +scikit-learn>=1.9.dev0 +scipy>=1.18.0.dev0 + +# testing-only dependencies +cloudpickle>=3.1.2 +psutil>=7.2.2 +pytest>=9.0.2 diff --git a/.ci/pip-envs/requirements-oldest.txt b/.ci/pip-envs/requirements-oldest.txt new file mode 100644 index 0000000..fb38b71 --- /dev/null +++ b/.ci/pip-envs/requirements-oldest.txt @@ -0,0 +1,15 @@ +# oldest versions of dependencies published after +# minimum supported Python version's first release, +# for which there are wheels compatible with the +# python:{version} image +# +# see https://devguide.python.org/versions/ +# +cffi==1.15.1 +narwhals==1.15.0 +numpy==1.22.4 +pandas==1.3.4 +polars==1.0.0 +pyarrow==16.0.0 +scikit-learn==1.0.2 +scipy==1.7.2 diff --git a/.ci/rerun-workflow.sh b/.ci/rerun-workflow.sh new file mode 100755 index 0000000..8f343df --- /dev/null +++ b/.ci/rerun-workflow.sh @@ -0,0 +1,54 @@ +#!/bin/bash +# +# [description] +# Rerun specified workflow for given pull request. +# +# [usage] +# rerun-workflow.sh +# +# WORKFLOW_ID: Identifier (config name of ID) of a workflow to be rerun. +# +# PR_BRANCH: Name of pull request's branch. + +set -e -E -u -o pipefail + +if [ -z "$GITHUB_ACTIONS" ]; then + echo "Must be run inside GitHub Actions CI" + exit 1 +fi + +if [ $# -ne 2 ]; then + echo "Usage: $0 " + exit 1 +fi + +workflow_id=$1 +pr_branch=$2 + +# --branch for some GitHub GLI commands does not respect the difference between forks and branches +# on the main repo. While some parts of the GitHub API refer to the branch of a workflow +# as '{org}:{branch}' for branches from forks, others expect only '{branch}'. +# +# This expansion trims a leading '{org}:' from 'pr_branch' if one is present. +pr_branch_no_fork_prefix="${pr_branch/*:/}" + +RUN_ID=$( + gh run list \ + --repo 'lightgbm-org/LightGBM' \ + --workflow "${workflow_id}" \ + --event "pull_request" \ + --branch "${pr_branch_no_fork_prefix}" \ + --json 'createdAt,databaseId' \ + --jq 'sort_by(.createdAt) | reverse | .[0] | .databaseId' +) + +if [[ -z "${RUN_ID}" ]]; then + echo "ERROR: failed to find a run of workflow '${workflow_id}' for branch '${pr_branch}'" + exit 1 +fi + +echo "Re-running workflow '${workflow_id}' (run ID ${RUN_ID})" + +gh run rerun \ + --repo 'lightgbm-org/LightGBM' \ + "${RUN_ID}" diff --git a/.ci/run-r-cmd-check.sh b/.ci/run-r-cmd-check.sh new file mode 100755 index 0000000..239425c --- /dev/null +++ b/.ci/run-r-cmd-check.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +set -e -u -o pipefail + +PKG_TARBALL="${1}" +declare -i ALLOWED_CHECK_NOTES=${2} + +# 'R CMD check' redirects installation logs to a file, and returns +# a non-0 exit code if ERRORs are raised. +# +# The '||' here gives us an opportunity to echo out the installation +# logs prior to exiting the script. +check_succeeded="yes" +R CMD check "${PKG_TARBALL}" \ + --as-cran \ + --run-donttest \ +|| check_succeeded="no" + +CHECK_LOG_FILE=lightgbm.Rcheck/00check.log +BUILD_LOG_FILE=lightgbm.Rcheck/00install.out + +echo "R CMD check build logs:" +cat "${BUILD_LOG_FILE}" + +if [[ $check_succeeded == "no" ]]; then + echo "R CMD check failed" + exit 1 +fi + +# WARNINGs or ERRORs should be treated as a failure +if grep -q -E "WARNING|ERROR" "${CHECK_LOG_FILE}"; then + echo "WARNINGs or ERRORs have been found by R CMD check" + exit 1 +fi + +# Allow a configurable number of NOTEs. +# Sometimes NOTEs are raised in CI that wouldn't show up on an actual CRAN submission. +set +e +NUM_CHECK_NOTES=$( + grep -o -E '[0-9]+ NOTE' "${CHECK_LOG_FILE}" \ + | sed 's/[^0-9]*//g' +) +if [[ ${NUM_CHECK_NOTES} -gt ${ALLOWED_CHECK_NOTES} ]]; then + echo "Found ${NUM_CHECK_NOTES} NOTEs from R CMD check. Only ${ALLOWED_CHECK_NOTES} are allowed" + exit 1 +fi diff --git a/.ci/set-commit-status.sh b/.ci/set-commit-status.sh new file mode 100755 index 0000000..3169b0c --- /dev/null +++ b/.ci/set-commit-status.sh @@ -0,0 +1,54 @@ +#!/bin/bash +# +# [description] +# Set a status with a given name to the specified commit. +# +# [usage] +# set-commit-status.sh +# +# NAME: Name of status. +# Status with existing name overwrites a previous one. +# +# STATUS: Status to be set. +# Can be "error", "failure", "pending" or "success". +# +# SHA: SHA of a commit to set a status on. + +set -e -E -u -o pipefail + +if [ -z "$GITHUB_ACTIONS" ]; then + echo "Must be run inside GitHub Actions CI" + exit 1 +fi + +if [ $# -ne 3 ]; then + echo "Usage: $0 " + exit 1 +fi + +name=$1 + +status=$2 +status=${status/error/failure} +status=${status/cancelled/failure} +status=${status/timed_out/failure} +status=${status/in_progress/pending} +status=${status/queued/pending} + +sha=$3 + +data=$( + jq -n \ + --arg state "${status}" \ + --arg url "${GITHUB_SERVER_URL}/lightgbm-org/LightGBM/actions/runs/${GITHUB_RUN_ID}" \ + --arg name "${name}" \ + '{"state":$state,"target_url":$url,"context":$name}' +) + +curl -sL \ + --fail \ + -X POST \ + -H "Accept: application/vnd.github.v3+json" \ + -H "Authorization: token ${GITHUB_TOKEN}" \ + -d "$data" \ + "${GITHUB_API_URL}/repos/lightgbm-org/LightGBM/statuses/$sha" diff --git a/.ci/setup.sh b/.ci/setup.sh new file mode 100755 index 0000000..f89636f --- /dev/null +++ b/.ci/setup.sh @@ -0,0 +1,192 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +# defaults +IN_UBUNTU_BASE_CONTAINER=${IN_UBUNTU_BASE_CONTAINER:-"false"} +SETUP_CONDA=${SETUP_CONDA:-"true"} + +ARCH=$(uname -m) + + +if [[ $OS_NAME == "macos" ]]; then + # Check https://github.com/actions/runner-images/tree/main/images/macos for available + # versions of Xcode + macos_ver=$(sw_vers --productVersion) + if [[ "${macos_ver}" =~ 15. ]]; then + xcode_path="/Applications/Xcode_16.0.app/Contents/Developer" + else + xcode_path="/Applications/Xcode_15.0.app/Contents/Developer" + fi + sudo xcode-select -s "${xcode_path}" || exit 1 + if [[ $COMPILER == "clang" ]]; then + brew install libomp + else # gcc + brew install 'gcc@14' + fi + if [[ $TASK == "mpi" ]]; then + brew install open-mpi + fi + if [[ $TASK == "swig" ]]; then + brew install swig + fi +else # Linux + if type -f apt > /dev/null 2>&1; then + sudo apt-get update + sudo apt-get install --no-install-recommends -y \ + ca-certificates \ + curl + else + sudo yum update -y + sudo yum install -y \ + ca-certificates \ + curl + fi + CMAKE_VERSION="3.30.0" + curl -O -L \ + "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-${ARCH}.sh" \ + || exit 1 + sudo mkdir /opt/cmake || exit 1 + sudo sh "cmake-${CMAKE_VERSION}-linux-${ARCH}.sh" --skip-license --prefix=/opt/cmake || exit 1 + sudo ln -sf /opt/cmake/bin/cmake /usr/local/bin/cmake || exit 1 + + if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then + # fixes error "unable to initialize frontend: Dialog" + # https://github.com/moby/moby/issues/27988#issuecomment-462809153 + echo 'debconf debconf/frontend select Noninteractive' | sudo debconf-set-selections + + sudo apt-get update + sudo apt-get install --no-install-recommends -y \ + software-properties-common + + sudo apt-get install --no-install-recommends -y \ + build-essential \ + git \ + libcurl4 \ + libicu-dev \ + libssl-dev \ + locales \ + locales-all || exit 1 + if [[ $COMPILER == "clang" ]]; then + sudo apt-get install --no-install-recommends -y \ + clang \ + libomp-dev + elif [[ $COMPILER == "clang-17" ]]; then + sudo apt-get install --no-install-recommends -y \ + wget + wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc + sudo apt-add-repository deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-17 main + sudo apt-add-repository deb-src http://apt.llvm.org/jammy/ llvm-toolchain-jammy-17 main + sudo apt-get update + sudo apt-get install -y \ + clang-17 \ + libomp-17-dev + fi + + export LANG="en_US.UTF-8" + sudo update-locale LANG=${LANG} + export LC_ALL="${LANG}" + fi + if [[ $TASK == "r-package" ]] && [[ $COMPILER == "clang" ]]; then + sudo apt-get install --no-install-recommends -y \ + libomp-dev + fi + if [[ $TASK == "mpi" ]]; then + if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then + sudo apt-get update + sudo apt-get install --no-install-recommends -y \ + libopenmpi-dev \ + openmpi-bin + else # in manylinux image + sudo yum update -y + sudo yum install -y \ + openmpi-devel \ + || exit 1 + fi + fi + if [[ $TASK == "gpu" ]]; then + if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then + sudo apt-get update + sudo apt-get install --no-install-recommends -y \ + libboost1.74-dev \ + libboost-filesystem1.74-dev \ + ocl-icd-opencl-dev + else # in manylinux image + sudo yum update -y + sudo yum install -y \ + boost-devel \ + ocl-icd-devel \ + opencl-headers \ + || exit 1 + fi + fi + if [[ $TASK == "gpu" || $TASK == "bdist" ]]; then + if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then + sudo apt-get update + sudo apt-get install --no-install-recommends -y \ + patchelf \ + pocl-opencl-icd + elif [[ $(uname -m) == "x86_64" ]]; then + sudo yum update -y + sudo yum install -y \ + ocl-icd-devel \ + opencl-headers \ + || exit 1 + fi + fi + if [[ $TASK == "cuda" ]]; then + echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections + apt-get update + APT_INSTALL_PACKAGES=() + + # only re-install NCCL if there wasn't one already pre-installed in the image + # (for example, nvidia/cuda 12.9.1 images excluded it) + if ! apt list --installed | grep -E 'libnccl\-dev' >/dev/null 2>&1; then + echo "libnccl-dev not found, manually installing it" + + # find a compatible libnccl-dev for this CUDA version + # + # (just 'apt-get install libnccl-dev' can result in mixing across CUDA versions) + IFS='.' read -r CUDA_MAJOR CUDA_MINOR _ <<< "${CI_CUDA_VERSION}" + NCCL_VERSION=$( + apt-cache madison libnccl-dev \ + | awk -F'|' '{print $2}' \ + | tr -d ' ' \ + | grep "+cuda${CUDA_MAJOR}.${CUDA_MINOR}$" \ + | sort -V \ + | tail -1 + ) + + APT_INSTALL_PACKAGES+=( + "libnccl-dev=${NCCL_VERSION}" + "libnccl2=${NCCL_VERSION}" + ) + else + echo "libnccl-dev already installed" + fi + if [[ $COMPILER == "clang" ]]; then + APT_INSTALL_PACKAGES+=( + clang + libomp-dev + ) + fi + apt-get install --no-install-recommends -y \ + "${APT_INSTALL_PACKAGES[@]}" + fi +fi + +if [[ "${TASK}" != "cpp-tests" ]] && [[ "${TASK}" != "r-package" ]] && [[ "${TASK}" != "swig" ]]; then + if [[ $SETUP_CONDA != "false" ]]; then + curl \ + -sL \ + -o miniforge.sh \ + "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-${ARCH}.sh" + sh miniforge.sh -b -p "${CONDA}" + fi + conda config --set always_yes yes --set changeps1 no + conda update -q -y conda + + # print output of 'conda info', to help in submitting bug reports + echo "conda info:" + conda info +fi diff --git a/.ci/test-python-latest.sh b/.ci/test-python-latest.sh new file mode 100755 index 0000000..0ff2bbd --- /dev/null +++ b/.ci/test-python-latest.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +echo "installing lightgbm and its dependencies" +pip install \ + --prefer-binary \ + --upgrade \ + -r ./.ci/pip-envs/requirements-latest.txt \ + dist/*.whl + +echo "installed package versions:" +pip freeze + +echo "" +echo "running tests" +pytest tests/c_api_test/ +pytest tests/python_package_test/ diff --git a/.ci/test-python-oldest.sh b/.ci/test-python-oldest.sh new file mode 100644 index 0000000..aa88355 --- /dev/null +++ b/.ci/test-python-oldest.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +echo "installing lightgbm and its dependencies" +pip install \ + --prefer-binary \ + --upgrade \ + --constraint ./.ci/pip-envs/requirements-oldest.txt \ + "$(echo dist/*.whl)[arrow,pandas,scikit-learn]" + +echo "installed package versions:" +pip freeze + +echo "" +echo "checking that examples run without error" + +# run a few examples to test that Python-package minimally works +echo "" +echo "--- advanced_example.py ---" +echo "" +python ./examples/python-guide/advanced_example.py || exit 1 + +echo "" +echo "--- logistic_regression.py ---" +echo "" +python ./examples/python-guide/logistic_regression.py || exit 1 + +echo "" +echo "--- simple_example.py ---" +echo "" +python ./examples/python-guide/simple_example.py || exit 1 + +echo "" +echo "--- sklearn_example.py ---" +echo "" +python ./examples/python-guide/sklearn_example.py || exit 1 +echo "" + +echo "checking that imports work without any optional dependencies" +pip uninstall --yes \ + cffi \ + dask \ + distributed \ + graphviz \ + joblib \ + matplotlib \ + pandas \ + psutil \ + pyarrow \ + scikit-learn + +python -c "import lightgbm" + +echo "" +echo "done testing on oldest supported Python version" diff --git a/.ci/test-r-package-valgrind.sh b/.ci/test-r-package-valgrind.sh new file mode 100755 index 0000000..11fb517 --- /dev/null +++ b/.ci/test-r-package-valgrind.sh @@ -0,0 +1,100 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +RDscriptvalgrind ./.ci/install-r-deps.R --test || exit 1 +sh build-cran-package.sh \ + --r-executable=RDvalgrind \ + --no-build-vignettes \ + || exit 1 + +RDvalgrind CMD INSTALL --preclean --install-tests lightgbm_*.tar.gz || exit 1 + +cd R-package/tests + +ALL_LOGS_FILE="out.log" +VALGRIND_LOGS_FILE="valgrind-logs.log" + +RDvalgrind \ + --no-readline \ + --vanilla \ + -d "valgrind --tool=memcheck --leak-check=full --track-origins=yes --gen-suppressions=all" \ + -f testthat.R \ + > ${ALL_LOGS_FILE} 2>&1 || exit 1 + +cat ${ALL_LOGS_FILE} + +echo "writing valgrind output to ${VALGRIND_LOGS_FILE}" +cat ${ALL_LOGS_FILE} | grep -E "^\=" > ${VALGRIND_LOGS_FILE} + +bytes_definitely_lost=$( + cat ${VALGRIND_LOGS_FILE} \ + | grep -E "definitely lost\: .*" \ + | sed 's/^.*definitely lost\: \(.*\) bytes.*$/\1/' \ + | tr -d "," +) +echo "valgrind found ${bytes_definitely_lost} bytes definitely lost" +if [[ ${bytes_definitely_lost} -gt 0 ]]; then + exit 1 +fi + +bytes_indirectly_lost=$( + cat ${VALGRIND_LOGS_FILE} \ + | grep -E "indirectly lost\: .*" \ + | sed 's/^.*indirectly lost\: \(.*\) bytes.*$/\1/' \ + | tr -d "," +) +echo "valgrind found ${bytes_indirectly_lost} bytes indirectly lost" +if [[ ${bytes_indirectly_lost} -gt 0 ]]; then + exit 1 +fi + +# one error caused by a false positive between valgrind and openmp is allowed +# ==2063== 352 bytes in 1 blocks are possibly lost in loss record 153 of 2,709 +# ==2063== at 0x483DD99: calloc (in /usr/lib/x86_64-linux-gnu/valgrind/vgpreload_memcheck-amd64-linux.so) +# ==2063== by 0x40149CA: allocate_dtv (dl-tls.c:286) +# ==2063== by 0x40149CA: _dl_allocate_tls (dl-tls.c:532) +# ==2063== by 0x5702322: allocate_stack (allocatestack.c:622) +# ==2063== by 0x5702322: pthread_create@@GLIBC_2.2.5 (pthread_create.c:660) +# ==2063== by 0x56D0DDA: ??? (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) +# ==2063== by 0x56C88E0: GOMP_parallel (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) +# ==2063== by 0x1544D29C: LGBM_DatasetCreateFromCSC (c_api.cpp:1286) +# ==2063== by 0x1546F980: LGBM_DatasetCreateFromCSC_R (lightgbm_R.cpp:91) +# ==2063== by 0x4941E2F: R_doDotCall (dotcode.c:634) +# ==2063== by 0x494CCC6: do_dotcall (dotcode.c:1281) +# ==2063== by 0x499FB01: bcEval (eval.c:7078) +# ==2063== by 0x498B67F: Rf_eval (eval.c:727) +# ==2063== by 0x498E414: R_execClosure (eval.c:1895) +bytes_possibly_lost=$( + cat ${VALGRIND_LOGS_FILE} \ + | grep -E "possibly lost\: .*" \ + | sed 's/^.*possibly lost\: \(.*\) bytes.*$/\1/' \ + | tr -d "," +) +echo "valgrind found ${bytes_possibly_lost} bytes possibly lost" +if [[ ${bytes_possibly_lost} -gt 1104 ]]; then + exit 1 +fi + +# ensure 'grep --count' doesn't cause failures +set +e + +echo "checking for invalid reads" +invalid_reads=$( + cat ${VALGRIND_LOGS_FILE} \ + | grep --count -i "Invalid read" +) +if [[ ${invalid_reads} -gt 0 ]]; then + echo "valgrind found invalid reads: ${invalid_reads}" + exit 1 +fi + +echo "checking for invalid writes" +invalid_writes=$( + cat ${VALGRIND_LOGS_FILE} \ + | grep --count -i "Invalid write" +) +if [[ ${invalid_writes} -gt 0 ]]; then + echo "valgrind found invalid writes: ${invalid_writes}" + exit 1 +fi diff --git a/.ci/test-r-package-windows.ps1 b/.ci/test-r-package-windows.ps1 new file mode 100644 index 0000000..2b37473 --- /dev/null +++ b/.ci/test-r-package-windows.ps1 @@ -0,0 +1,337 @@ +# Download a file and retry upon failure. This looks like +# an infinite loop but CI-level timeouts will kill it +function Get-File-With-Tenacity { + param( + [Parameter(Mandatory = $true)][string]$url, + [Parameter(Mandatory = $true)][string]$destfile + ) + $ProgressPreference = "SilentlyContinue" # progress bar bug extremely slows down download speed + do { + Write-Output "Downloading ${url}" + sleep 5 + Invoke-WebRequest -Uri $url -OutFile $destfile + } while (-not $?) +} + +# External utilities like R.exe / Rscript.exe writing to stderr (even for harmless +# status information) can cause failures in GitHub Actions PowerShell jobs. +# See https://github.community/t/powershell-steps-fail-nondeterministically/115496 +# +# Using standard PowerShell redirection does not work to avoid these errors. +# This function uses R's built-in redirection mechanism, sink(). Any place where +# this function is used is a command that writes harmless messages to stderr +function Invoke-R-Code-Redirect-Stderr { + param( + [Parameter(Mandatory = $true)][string]$rcode + ) + $decorated_code = "out_file <- file(tempfile(), open = 'wt'); sink(out_file, type = 'message'); $rcode; sink()" + Rscript --vanilla -e $decorated_code +} + +# Remove all items matching some pattern from PATH environment variable +function Remove-From-Path { + [CmdletBinding(SupportsShouldProcess)] + param( + [Parameter(Mandatory = $true)][string]$pattern_to_remove + ) + if ($PSCmdlet.ShouldProcess($env:PATH, "Removing ${pattern_to_remove}")) { + $env:PATH = ($env:PATH.Split(';') | Where-Object { $_ -notmatch "$pattern_to_remove" }) -join ';' + } +} + +# remove some details that exist in the GitHub Actions images which might +# cause conflicts with R and other components installed by this script +$env:RTOOLS40_HOME = "" +Remove-From-Path ".*Amazon.*" +Remove-From-Path ".*Anaconda.*" +Remove-From-Path ".*android.*" +Remove-From-Path ".*Android.*" +Remove-From-Path ".*chocolatey.*" +Remove-From-Path ".*Chocolatey.*" +Remove-From-Path ".*cmake.*" +Remove-From-Path ".*CMake.*" +Remove-From-Path ".*\\Git\\.*" +Remove-From-Path "(?!.*pandoc.*).*hostedtoolcache.*" +Remove-From-Path ".*Microsoft SDKs.*" +Remove-From-Path ".*mingw.*" +Remove-From-Path ".*msys64.*" +Remove-From-Path ".*PostgreSQL.*" +Remove-From-Path ".*\\R\\.*" +Remove-From-Path ".*R Client.*" +Remove-From-Path ".*rtools40.*" +Remove-From-Path ".*rtools42.*" +Remove-From-Path ".*rtools43.*" +Remove-From-Path ".*rtools44.*" +Remove-From-Path ".*rtools45.*" +Remove-From-Path ".*shells.*" +Remove-From-Path ".*Strawberry.*" +Remove-From-Path ".*tools.*" + +Remove-Item C:\rtools40 -Force -Recurse -ErrorAction Ignore +Remove-Item C:\rtools42 -Force -Recurse -ErrorAction Ignore +Remove-Item C:\rtools43 -Force -Recurse -ErrorAction Ignore +Remove-Item C:\rtools44 -Force -Recurse -ErrorAction Ignore +Remove-Item C:\rtools45 -Force -Recurse -ErrorAction Ignore + +# Get details needed for installing R components +# +# NOTES: +# * some paths and file names are different on R4.0 +$env:R_MAJOR_VERSION = $env:R_VERSION.split('.')[0] +if ($env:R_MAJOR_VERSION -eq "4") { + $RTOOLS_INSTALL_PATH = "C:\rtools43" + $env:RTOOLS_BIN = "$RTOOLS_INSTALL_PATH\usr\bin" + $env:RTOOLS_MINGW_BIN = "$RTOOLS_INSTALL_PATH\x86_64-w64-mingw32.static.posix\bin" + $env:RTOOLS_EXE_FILE = "rtools43-5550-5548.exe" + $env:R_WINDOWS_VERSION = "4.3.1" +} else { + Write-Output "[ERROR] Unrecognized R version: $env:R_VERSION" + Assert-Output $false +} +$env:CMAKE_VERSION = "3.30.0" + +$env:R_LIB_PATH = "$env:BUILD_SOURCESDIRECTORY/RLibrary" -replace '[\\]', '/' +$env:R_LIBS = "$env:R_LIB_PATH" +$env:CMAKE_PATH = "$env:BUILD_SOURCESDIRECTORY/CMake_installation" +$env:PATH = @( + "$env:RTOOLS_BIN", + "$env:RTOOLS_MINGW_BIN", + "$env:R_LIB_PATH/R/bin/x64", + "$env:CMAKE_PATH/cmake-$env:CMAKE_VERSION-windows-x86_64/bin", + "$env:PATH" +) -join ";" +$env:CRAN_MIRROR = "https://cran.rstudio.com" +$env:MIKTEX_EXCEPTION_PATH = "$env:TEMP\miktex" + +# don't fail builds for long-running examples unless they're very long. +# See https://github.com/lightgbm-org/LightGBM/issues/4049#issuecomment-793412254. +if ($env:R_BUILD_TYPE -ne "cran") { + $env:_R_CHECK_EXAMPLE_TIMING_THRESHOLD_ = 30 +} + +if (($env:COMPILER -eq "MINGW") -and ($env:R_BUILD_TYPE -eq "cmake")) { + $env:CXX = "$env:RTOOLS_MINGW_BIN/g++.exe" + $env:CC = "$env:RTOOLS_MINGW_BIN/gcc.exe" +} + +Set-Location "$env:BUILD_SOURCESDIRECTORY" +tzutil /s "GMT Standard Time" +[Void][System.IO.Directory]::CreateDirectory("$env:R_LIB_PATH") +[Void][System.IO.Directory]::CreateDirectory("$env:CMAKE_PATH") + +# download R, RTools and CMake +Write-Output "Downloading R, Rtools and CMake" +$params = @{ + url = "$env:CRAN_MIRROR/bin/windows/base/old/$env:R_WINDOWS_VERSION/R-$env:R_WINDOWS_VERSION-win.exe" + destfile = "R-win.exe" +} +Get-File-With-Tenacity @params + +$params = @{ + url = "https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/$env:RTOOLS_EXE_FILE" + destfile = "Rtools.exe" +} +Get-File-With-Tenacity @params + +$params = @{ + url = "https://github.com/Kitware/CMake/releases/download/v{0}/cmake-{0}-windows-x86_64.zip" -f $env:CMAKE_VERSION + destfile = "$env:CMAKE_PATH/cmake.zip" +} +Get-File-With-Tenacity @params + +# Install R +Write-Output "Installing R" +$params = @{ + FilePath = "R-win.exe" + NoNewWindow = $true + Wait = $true + ArgumentList = "/VERYSILENT /DIR=$env:R_LIB_PATH/R /COMPONENTS=main,x64,i386" +} +Start-Process @params ; Assert-Output $? +Write-Output "Done installing R" + +Write-Output "Installing Rtools" +$params = @{ + FilePath = "Rtools.exe" + NoNewWindow = $true + Wait = $true + ArgumentList = "/VERYSILENT /SUPPRESSMSGBOXES /DIR=$RTOOLS_INSTALL_PATH" +} +Start-Process @params; Assert-Output $? +Write-Output "Done installing Rtools" + +Write-Output "Installing CMake" +Add-Type -AssemblyName System.IO.Compression.FileSystem +[System.IO.Compression.ZipFile]::ExtractToDirectory("$env:CMAKE_PATH/cmake.zip", "$env:CMAKE_PATH") ; Assert-Output $? +# Remove old CMake shipped with RTools +Remove-Item "$env:RTOOLS_MINGW_BIN/cmake.exe" -Force -ErrorAction Ignore +Write-Output "Done installing CMake" + +Write-Output "Installing dependencies" +Rscript.exe --vanilla ".ci/install-r-deps.R" --build --include=processx --test ; Assert-Output $? + +Write-Output "Building R-package" + +# R CMD check is not used for MSVC builds +if ($env:COMPILER -ne "MSVC") { + + $PKG_FILE_NAME = "lightgbm_$env:LGB_VER.tar.gz" + $LOG_FILE_NAME = "lightgbm.Rcheck/00check.log" + + if ($env:R_BUILD_TYPE -eq "cmake") { + if ($env:TOOLCHAIN -eq "MINGW") { + Write-Output "Telling R to use MinGW" + $env:BUILD_R_FLAGS = "c('--skip-install', '--use-mingw', '-j4')" + } elseif ($env:TOOLCHAIN -eq "MSYS") { + Write-Output "Telling R to use MSYS" + $env:BUILD_R_FLAGS = "c('--skip-install', '--use-msys2', '-j4')" + } elseif ($env:TOOLCHAIN -eq "MSVC") { + $env:BUILD_R_FLAGS = "'--skip-install'" + } else { + Write-Output "[ERROR] Unrecognized toolchain: $env:TOOLCHAIN" + Assert-Output $false + } + Invoke-R-Code-Redirect-Stderr "commandArgs <- function(...){$env:BUILD_R_FLAGS}; source('build_r.R')" + Assert-Output $? + } elseif ($env:R_BUILD_TYPE -eq "cran") { + $params = -join @( + "result <- processx::run(command = 'sh', args = 'build-cran-package.sh', ", + "echo = TRUE, windows_verbatim_args = FALSE, error_on_status = TRUE)" + ) + Invoke-R-Code-Redirect-Stderr $params ; Assert-Output $? + Remove-From-Path ".*msys64.*" + # Test CRAN source .tar.gz in a directory that is not this repo or below it. + # When people install.packages('lightgbm'), they won't have the LightGBM + # git repo around. This is to protect against the use of relative paths + # like ../../CMakeLists.txt that would only work if you are in the repoo + $R_CMD_CHECK_DIR = "tmp-r-cmd-check" + New-Item -Path "C:\" -Name $R_CMD_CHECK_DIR -ItemType "directory" > $null + Move-Item -Path "$PKG_FILE_NAME" -Destination "C:\$R_CMD_CHECK_DIR\" > $null + Set-Location "C:\$R_CMD_CHECK_DIR\" + } + + Write-Output "Running R CMD check" + if ($env:R_BUILD_TYPE -eq "cran") { + # CRAN packages must pass without --no-multiarch (build on 64-bit and 32-bit) + $check_args = "c('CMD', 'check', '--as-cran', '--run-donttest', '$PKG_FILE_NAME')" + } else { + $check_args = "c('CMD', 'check', '--no-multiarch', '--as-cran', '--run-donttest', '$PKG_FILE_NAME')" + } + $params = -join ( + "result <- processx::run(command = 'R.exe', args = $check_args, ", + "echo = TRUE, windows_verbatim_args = FALSE, error_on_status = TRUE)" + ) + Invoke-R-Code-Redirect-Stderr $params ; $check_succeeded = $? + + Write-Output "R CMD check build logs:" + $INSTALL_LOG_FILE_NAME = "lightgbm.Rcheck\00install.out" + Get-Content -Path "$INSTALL_LOG_FILE_NAME" + + Assert-Output $check_succeeded + + Write-Output "Looking for issues with R CMD check results" + if (Get-Content "$LOG_FILE_NAME" | Select-String -Pattern "NOTE|WARNING|ERROR" -CaseSensitive -Quiet) { + Write-Output "NOTEs, WARNINGs, or ERRORs have been found by R CMD check" + Assert-Output $False + } +} else { + $INSTALL_LOG_FILE_NAME = "$env:BUILD_SOURCESDIRECTORY\00install_out.txt" + Invoke-R-Code-Redirect-Stderr "source('build_r.R')" 1> $INSTALL_LOG_FILE_NAME ; $install_succeeded = $? + Write-Output "----- build and install logs -----" + Get-Content -Path "$INSTALL_LOG_FILE_NAME" + Write-Output "----- end of build and install logs -----" + Assert-Output $install_succeeded + # some errors are not raised above, but can be found in the logs + if (Get-Content "$INSTALL_LOG_FILE_NAME" | Select-String -Pattern "ERROR" -CaseSensitive -Quiet) { + Write-Output "ERRORs have been found installing lightgbm" + Assert-Output $False + } +} + +# Checking that the correct R version was used +if ($env:TOOLCHAIN -ne "MSVC") { + $checks = Select-String -Path "${LOG_FILE_NAME}" -Pattern "using R version $env:R_WINDOWS_VERSION" + $checks_cnt = $checks.Matches.length +} else { + $checksParams = @{ + Path = "${INSTALL_LOG_FILE_NAME}" + Pattern = "R version passed into FindLibR.* $env:R_WINDOWS_VERSION" + } + $checks = Select-String @checksParams + $checks_cnt = $checks.Matches.length +} +if ($checks_cnt -eq 0) { + Write-Output "Wrong R version was found (expected '$env:R_WINDOWS_VERSION'). Check the build logs." + Assert-Output $False +} + +# Checking that we actually got the expected compiler. The R-package has some logic +# to fail back to MinGW if MSVC fails, but for CI builds we need to check that the correct +# compiler was used. +if ($env:R_BUILD_TYPE -eq "cmake") { + $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "Check for working CXX compiler.*$env:COMPILER" + if ($checks.Matches.length -eq 0) { + Write-Output "The wrong compiler was used. Check the build logs." + Assert-Output $False + } +} + +# Checking that we got the right toolchain for MinGW. If using MinGW, both +# MinGW and MSYS toolchains are supported +if (($env:COMPILER -eq "MINGW") -and ($env:R_BUILD_TYPE -eq "cmake")) { + $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "Trying to build with.*$env:TOOLCHAIN" + if ($checks.Matches.length -eq 0) { + Write-Output "The wrong toolchain was used. Check the build logs." + Assert-Output $False + } +} + +# Checking that MM_PREFETCH preprocessor definition is actually used in CI builds. +if ($env:R_BUILD_TYPE -eq "cran") { + $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "checking whether MM_PREFETCH work.*yes" + $checks_cnt = $checks.Matches.length +} elseif ($env:TOOLCHAIN -ne "MSVC") { + $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern ".*Performing Test MM_PREFETCH - Success" + $checks_cnt = $checks.Matches.length +} else { + $checks_cnt = 1 +} +if ($checks_cnt -eq 0) { + Write-Output "MM_PREFETCH preprocessor definition wasn't used. Check the build logs." + Assert-Output $False +} + +# Checking that MM_MALLOC preprocessor definition is actually used in CI builds. +if ($env:R_BUILD_TYPE -eq "cran") { + $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern "checking whether MM_MALLOC work.*yes" + $checks_cnt = $checks.Matches.length +} elseif ($env:TOOLCHAIN -ne "MSVC") { + $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern ".*Performing Test MM_MALLOC - Success" + $checks_cnt = $checks.Matches.length +} else { + $checks_cnt = 1 +} +if ($checks_cnt -eq 0) { + Write-Output "MM_MALLOC preprocessor definition wasn't used. Check the build logs." + Assert-Output $False +} + +# Checking that OpenMP is actually used in CMake builds. +if ($env:R_BUILD_TYPE -eq "cmake") { + $checks = Select-String -Path "${INSTALL_LOG_FILE_NAME}" -Pattern ".*Found OpenMP: TRUE.*" + if ($checks.Matches.length -eq 0) { + Write-Output "OpenMP wasn't found. Check the build logs." + Assert-Output $False + } +} + +if ($env:COMPILER -eq "MSVC") { + Write-Output "Running tests with testthat.R" + Set-Location R-package/tests + # NOTE: using Rscript.exe intentionally here, instead of Invoke-R-Code-Redirect-Stderr, + # because something about the interaction between Invoke-R-Code-Redirect-Stderr + # and testthat results in failing tests not exiting with a non-0 exit code. + Rscript.exe --vanilla "testthat.R" ; Assert-Output $? +} + +Write-Output "No issues were found checking the R-package" diff --git a/.ci/test-r-package.sh b/.ci/test-r-package.sh new file mode 100755 index 0000000..24d3991 --- /dev/null +++ b/.ci/test-r-package.sh @@ -0,0 +1,260 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +# defaults +ARCH=$(uname -m) + +# set up R environment +export CRAN_MIRROR="https://cran.rstudio.com" +export R_LIB_PATH=~/Rlib +mkdir -p $R_LIB_PATH +export R_LIBS=$R_LIB_PATH +export PATH="$R_LIB_PATH/R/bin:$PATH" + +# don't fail builds for long-running examples unless they're very long. +# See https://github.com/lightgbm-org/LightGBM/issues/4049#issuecomment-793412254. +if [[ $R_BUILD_TYPE != "cran" ]]; then + export _R_CHECK_EXAMPLE_TIMING_THRESHOLD_=30 +fi + +# Get details needed for installing R components +R_MAJOR_VERSION="${R_VERSION%.*}" +if [[ "${R_MAJOR_VERSION}" == "4" ]]; then + export R_MAC_VERSION=4.3.1 + export R_MAC_PKG_URL=${CRAN_MIRROR}/bin/macosx/big-sur-${ARCH}/base/R-${R_MAC_VERSION}-${ARCH}.pkg + export R_LINUX_VERSION="4.3.1-1.2204.0" + export R_APT_REPO="jammy-cran40/" +else + echo "Unrecognized R version: ${R_VERSION}" + exit 1 +fi + +# installing precompiled R for Ubuntu +# https://cran.r-project.org/bin/linux/ubuntu/#installation +# adding steps from https://stackoverflow.com/a/56378217/3986677 to get latest version +# +# `devscripts` is required for 'checkbashisms' (https://github.com/r-lib/actions/issues/111) +if [[ $OS_NAME == "linux" ]]; then + mkdir -p ~/.gnupg + echo "disable-ipv6" >> ~/.gnupg/dirmngr.conf + sudo apt-key adv \ + --homedir ~/.gnupg \ + --keyserver keyserver.ubuntu.com \ + --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || exit 1 + sudo add-apt-repository \ + "deb ${CRAN_MIRROR}/bin/linux/ubuntu ${R_APT_REPO}" || exit 1 + sudo apt-get update + sudo apt-get install \ + --no-install-recommends \ + -y \ + devscripts \ + libuv1-dev \ + r-base-core=${R_LINUX_VERSION} \ + r-base-dev=${R_LINUX_VERSION} \ + texinfo \ + texlive-latex-extra \ + texlive-latex-recommended \ + texlive-fonts-recommended \ + texlive-fonts-extra \ + tidy \ + qpdf \ + || exit 1 + + if [[ $R_BUILD_TYPE == "cran" ]]; then + sudo apt-get install \ + --no-install-recommends \ + -y \ + "autoconf=$(cat R-package/AUTOCONF_UBUNTU_VERSION)" \ + automake \ + || exit 1 + fi +fi + +# Installing R precompiled for Mac OS 10.11 or higher +if [[ $OS_NAME == "macos" ]]; then + brew update-reset --auto-update + brew update --auto-update + if [[ $R_BUILD_TYPE == "cran" ]]; then + brew install automake || exit 1 + fi + brew install \ + checkbashisms \ + qpdf || exit 1 + brew install basictex || exit 1 + export PATH="/Library/TeX/texbin:$PATH" + sudo tlmgr --verify-repo=none update --self || exit 1 + sudo tlmgr --verify-repo=none install inconsolata helvetic rsfs || exit 1 + + curl -sL "${R_MAC_PKG_URL}" -o R.pkg || exit 1 + sudo installer \ + -pkg "$(pwd)/R.pkg" \ + -target / || exit 1 + + # install tidy v5.8.0 + # ref: https://groups.google.com/g/r-sig-mac/c/7u_ivEj4zhM + TIDY_URL=https://github.com/htacg/tidy-html5/releases/download/5.8.0/tidy-5.8.0-macos-x86_64+arm64.pkg + curl -sL ${TIDY_URL} -o tidy.pkg + sudo installer \ + -pkg "$(pwd)/tidy.pkg" \ + -target / + + # ensure that this newer version of 'tidy' is used by 'R CMD check' + # ref: https://cran.r-project.org/doc/manuals/R-exts.html#Checking-packages + export R_TIDYCMD=/usr/local/bin/tidy +fi + +# {Matrix} needs {lattice}, so this needs to run before manually installing {Matrix}. +# This should be unnecessary on R >=4.4.0 +# ref: https://github.com/lightgbm-org/LightGBM/issues/6433 +Rscript --vanilla -e "install.packages('lattice', repos = '${CRAN_MIRROR}', lib = '${R_LIB_PATH}')" + +# manually install {Matrix}, as {Matrix}=1.7-0 raised its R floor all the way to R 4.4.0 +# ref: https://github.com/lightgbm-org/LightGBM/issues/6433 +Rscript --vanilla -e "install.packages('https://cran.r-project.org/src/contrib/Archive/Matrix/Matrix_1.6-5.tar.gz', repos = NULL, lib = '${R_LIB_PATH}')" + +# Manually install dependencies to avoid a CI-time dependency on devtools (for devtools::install_deps()) +Rscript --vanilla ./.ci/install-r-deps.R --build --test --exclude=Matrix || exit 1 + +cd "${BUILD_DIRECTORY}" +PKG_TARBALL="lightgbm_$(head -1 VERSION.txt).tar.gz" +BUILD_LOG_FILE="lightgbm.Rcheck/00install.out" +LOG_FILE_NAME="lightgbm.Rcheck/00check.log" +if [[ $R_BUILD_TYPE == "cmake" ]]; then + Rscript build_r.R -j4 --skip-install || exit 1 +elif [[ $R_BUILD_TYPE == "cran" ]]; then + + # on Linux, we recreate configure in CI to test if + # a change in a PR has changed configure.ac + if [[ $OS_NAME == "linux" ]]; then + ./R-package/recreate-configure.sh + + num_files_changed=$( + git diff --name-only | wc -l + ) + if [[ ${num_files_changed} -gt 0 ]]; then + echo "'configure' in the R-package has changed. Please recreate it and commit the changes." + echo "Changed files:" + git diff --compact-summary + echo "See R-package/README.md for details on how to recreate this script." + echo "" + exit 1 + fi + fi + + ./build-cran-package.sh || exit 1 + + # Test CRAN source .tar.gz in a directory that is not this repo or below it. + # When people install.packages('lightgbm'), they won't have the LightGBM + # git repo around. This is to protect against the use of relative paths + # like ../../CMakeLists.txt that would only work if you are in the repo + R_CMD_CHECK_DIR="${HOME}/tmp-r-cmd-check/" + mkdir -p "${R_CMD_CHECK_DIR}" + mv "${PKG_TARBALL}" "${R_CMD_CHECK_DIR}" + cd "${R_CMD_CHECK_DIR}" +fi + +if [[ $PRODUCES_ARTIFACTS == "true" ]]; then + cp "${PKG_TARBALL}" "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbm-${LGB_VER}-r-cran.tar.gz" +fi + +declare -i allowed_notes=0 +bash "${BUILD_DIRECTORY}/.ci/run-r-cmd-check.sh" \ + "${PKG_TARBALL}" \ + "${allowed_notes}" + +# ensure 'grep --count' doesn't cause failures +set +e + +used_correct_r_version=$( + cat $LOG_FILE_NAME \ + | grep --count "using R version ${R_VERSION}" +) +if [[ $used_correct_r_version -ne 1 ]]; then + echo "Unexpected R version was used. Expected '${R_VERSION}'." + exit 1 +fi + +if [[ $R_BUILD_TYPE == "cmake" ]]; then + passed_correct_r_version_to_cmake=$( + cat $BUILD_LOG_FILE \ + | grep --count "R version passed into FindLibR.cmake: ${R_VERSION}" + ) + if [[ $passed_correct_r_version_to_cmake -ne 1 ]]; then + echo "Unexpected R version was passed into cmake. Expected '${R_VERSION}'." + exit 1 + fi +fi + +# this check makes sure that CI builds of the package actually use OpenMP +if [[ $OS_NAME == "macos" ]] && [[ $R_BUILD_TYPE == "cran" ]]; then + omp_working=$( + cat $BUILD_LOG_FILE \ + | grep --count -E "checking whether OpenMP will work .*yes" + ) +elif [[ $R_BUILD_TYPE == "cmake" ]]; then + omp_working=$( + cat $BUILD_LOG_FILE \ + | grep --count -E ".*Found OpenMP: TRUE.*" + ) +else + omp_working=1 +fi +if [[ $omp_working -ne 1 ]]; then + echo "OpenMP was not found" + exit 1 +fi + +# this check makes sure that CI builds of the package +# actually use MM_PREFETCH preprocessor definition +# +# _mm_prefetch will not work on arm64 architecture +# ref: https://github.com/lightgbm-org/LightGBM/issues/4124 +if [[ $ARCH != "arm64" ]]; then + if [[ $R_BUILD_TYPE == "cran" ]]; then + mm_prefetch_working=$( + cat $BUILD_LOG_FILE \ + | grep --count -E "checking whether MM_PREFETCH work.*yes" + ) + else + mm_prefetch_working=$( + cat $BUILD_LOG_FILE \ + | grep --count -E ".*Performing Test MM_PREFETCH - Success" + ) + fi + if [[ $mm_prefetch_working -ne 1 ]]; then + echo "MM_PREFETCH test was not passed" + exit 1 + fi +fi + +# this check makes sure that CI builds of the package +# actually use MM_MALLOC preprocessor definition +if [[ $R_BUILD_TYPE == "cran" ]]; then + mm_malloc_working=$( + cat $BUILD_LOG_FILE \ + | grep --count -E "checking whether MM_MALLOC work.*yes" + ) +else + mm_malloc_working=$( + cat $BUILD_LOG_FILE \ + | grep --count -E ".*Performing Test MM_MALLOC - Success" + ) +fi +if [[ $mm_malloc_working -ne 1 ]]; then + echo "MM_MALLOC test was not passed" + exit 1 +fi + +# this check makes sure that no "warning: unknown pragma ignored" logs +# reach the user leading them to believe that something went wrong +if [[ $R_BUILD_TYPE == "cran" ]]; then + pragma_warning_present=$( + cat $BUILD_LOG_FILE \ + | grep --count -E "warning: unknown pragma ignored" + ) + if [[ $pragma_warning_present -ne 0 ]]; then + echo "Unknown pragma warning is present, pragmas should have been removed before build" + exit 1 + fi +fi diff --git a/.ci/test-windows.ps1 b/.ci/test-windows.ps1 new file mode 100644 index 0000000..33b3002 --- /dev/null +++ b/.ci/test-windows.ps1 @@ -0,0 +1,190 @@ +function Assert-Output { + param( [Parameter(Mandatory = $true)][bool]$success ) + if (-not $success) { + $host.SetShouldExit(-1) + exit 1 + } +} + +$env:LGB_VER = (Get-Content $env:BUILD_SOURCESDIRECTORY\VERSION.txt).trim() +# Use custom temp directory to avoid +# > warning MSB8029: The Intermediate directory or Output directory cannot reside under the Temporary directory +# > as it could lead to issues with incremental build. +# And make sure this directory is always clean +$env:TMPDIR = "$env:USERPROFILE\tmp" +Remove-Item $env:TMPDIR -Force -Recurse -ErrorAction Ignore +[Void][System.IO.Directory]::CreateDirectory($env:TMPDIR) + +# create the artifact upload directory if it doesn't exist yet +[Void][System.IO.Directory]::CreateDirectory($env:BUILD_ARTIFACTSTAGINGDIRECTORY) + +if ($env:TASK -eq "r-package") { + & .\.ci\test-r-package-windows.ps1 ; Assert-Output $? + exit 0 +} + +if ($env:TASK -eq "cpp-tests") { + cmake -B build -S . -DBUILD_CPP_TEST=ON -DUSE_DEBUG=ON -A x64 + cmake --build build --target testlightgbm --config Debug ; Assert-Output $? + .\Debug\testlightgbm.exe ; Assert-Output $? + exit 0 +} + +if ($env:TASK -eq "swig") { + $env:JAVA_HOME = $env:JAVA_HOME_8_X64 # there is pre-installed Eclipse Temurin 8 somewhere + $ProgressPreference = "SilentlyContinue" # progress bar bug extremely slows down download speed + $params = @{ + Uri = "https://sourceforge.net/projects/swig/files/latest/download" + OutFile = "$env:BUILD_SOURCESDIRECTORY/swig/swigwin.zip" + UserAgent = "curl" + } + Invoke-WebRequest @params + Add-Type -AssemblyName System.IO.Compression.FileSystem + [System.IO.Compression.ZipFile]::ExtractToDirectory( + "$env:BUILD_SOURCESDIRECTORY/swig/swigwin.zip", + "$env:BUILD_SOURCESDIRECTORY/swig" + ) ; Assert-Output $? + $SwigFolder = Get-ChildItem -Name -Path "$env:BUILD_SOURCESDIRECTORY/swig" -Attributes Directory + $env:PATH = @("$env:BUILD_SOURCESDIRECTORY/swig/$SwigFolder", "$env:PATH") -join ";" + $BuildLogFileName = "$env:BUILD_SOURCESDIRECTORY\cmake_build.log" + cmake -B build -S . -A x64 -DUSE_SWIG=ON *> "$BuildLogFileName" ; $build_succeeded = $? + Write-Output "CMake build logs:" + Get-Content -Path "$BuildLogFileName" + Assert-Output $build_succeeded + $checks = Select-String -Path "${BuildLogFileName}" -Pattern "-- Found SWIG.*${SwigFolder}/swig.exe" + $checks_cnt = $checks.Matches.length + if ($checks_cnt -eq 0) { + Write-Output "Wrong SWIG version was found (expected '${SwigFolder}'). Check the build logs." + Assert-Output $False + } + cmake --build build --target ALL_BUILD --config Release ; Assert-Output $? + if ($env:PRODUCES_ARTIFACTS -eq "true") { + cp ./build/lightgbmlib.jar $env:BUILD_ARTIFACTSTAGINGDIRECTORY/lightgbmlib_win.jar ; Assert-Output $? + } + exit 0 +} + +# 'pixi' is used for end-of-life Python versions +if ($env:PYTHON_VERSION -eq "3.10") { + $activation = ((& pixi shell-hook --locked -e py310 --shell powershell) -join "`n") + Invoke-Expression $activation ; Assert-Output $? +} else { + # update conda env + $env:CONDA_ENV = "test-env" + conda activate ; Assert-Output $? + conda config --set always_yes yes --set changeps1 no ; Assert-Output $? + conda config --remove channels defaults ; Assert-Output $? + conda config --add channels nodefaults ; Assert-Output $? + conda config --add channels conda-forge ; Assert-Output $? + conda config --set channel_priority strict ; Assert-Output $? + conda install -q -y conda "python=$env:PYTHON_VERSION[build=*_cp*]" ; Assert-Output $? + + # print output of 'conda info', to help in submitting bug reports + Write-Output "conda info:" + conda info + + $env:CONDA_REQUIREMENT_FILE = "$env:BUILD_SOURCESDIRECTORY/.ci/conda-envs/ci-core.txt" + + $condaParams = @( + "-y", + "-n", "$env:CONDA_ENV", + "--file", "$env:CONDA_REQUIREMENT_FILE", + "python=$env:PYTHON_VERSION[build=*_cp*]" + ) + conda create @condaParams ; Assert-Output $? + + # print output of 'conda list', to help in submitting bug reports + Write-Output "conda list:" + conda list -n $env:CONDA_ENV + + # 'bdist' job invokes 'RefreshEnv' to update PATH from the registry (which may have been modified + # by building in OpenCL support), so defer activating the conda environment until later for those builds. + if ($env:TASK -ne "bdist") { + conda activate $env:CONDA_ENV + } +} + +Set-Location "$env:BUILD_SOURCESDIRECTORY" +if ($env:TASK -eq "regular") { + cmake -B build -S . -A x64 ; Assert-Output $? + cmake --build build --target ALL_BUILD --config Release ; Assert-Output $? + sh ./build-python.sh install --precompile ; Assert-Output $? + cp ./Release/lib_lightgbm.dll "$env:BUILD_ARTIFACTSTAGINGDIRECTORY" + cp ./Release/lightgbm.exe "$env:BUILD_ARTIFACTSTAGINGDIRECTORY" +} elseif ($env:TASK -eq "sdist") { + sh ./build-python.sh sdist ; Assert-Output $? + sh ./.ci/check-python-dists.sh ./dist ; Assert-Output $? + Set-Location dist; pip install --no-deps @(Get-ChildItem *.gz) -v ; Assert-Output $? +} elseif ($env:TASK -eq "bdist") { + # Import the Chocolatey profile module so that the RefreshEnv command + # invoked below properly updates the current PowerShell session environment. + $module = "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1" + Import-Module "$module" ; Assert-Output $? + RefreshEnv + + Write-Output "Current OpenCL drivers:" + Get-ItemProperty -Path Registry::HKEY_LOCAL_MACHINE\SOFTWARE\Khronos\OpenCL\Vendors + + # (re-) activate conda environment, in case any activation logic was overridden by that 'RefreshEnv' call above + conda activate $env:CONDA_ENV + + # TODO: restore --integrated-opencl as part of https://github.com/lightgbm-org/LightGBM/issues/6968 + sh "build-python.sh" bdist_wheel ; Assert-Output $? + sh ./.ci/check-python-dists.sh ./dist ; Assert-Output $? + Set-Location dist; pip install --no-deps @(Get-ChildItem *py3-none-win_amd64.whl) ; Assert-Output $? + cp @(Get-ChildItem *py3-none-win_amd64.whl) "$env:BUILD_ARTIFACTSTAGINGDIRECTORY" +} elseif (($env:APPVEYOR -eq "true") -and ($env:TASK -eq "python")) { + if ($env:COMPILER -eq "MINGW") { + sh ./build-python.sh install --mingw ; Assert-Output $? + } else { + sh ./build-python.sh install; Assert-Output $? + } +} + +if (($env:TASK -eq "sdist") -or (($env:APPVEYOR -eq "true") -and ($env:TASK -eq "python"))) { + # cannot test C API with "sdist" task + $tests = "$env:BUILD_SOURCESDIRECTORY/tests/python_package_test" +} else { + $tests = "$env:BUILD_SOURCESDIRECTORY/tests" +} +if ($env:TASK -eq "bdist") { + # Make sure we can do both CPU and GPU; see tests/python_package_test/test_dual.py + # TODO: set LIGHTGBM_TEST_DUAL_CPU_GPU back to "1" as part of https://github.com/lightgbm-org/LightGBM/issues/6968 + $env:LIGHTGBM_TEST_DUAL_CPU_GPU = "0" +} + +pytest -ra $tests ; Assert-Output $? + +if (($env:TASK -eq "regular") -or (($env:APPVEYOR -eq "true") -and ($env:TASK -eq "python"))) { + Set-Location "$env:BUILD_SOURCESDIRECTORY/examples/python-guide" + @("import matplotlib", "matplotlib.use('Agg')") + (Get-Content "plot_example.py") | Set-Content "plot_example.py" + # Prevent interactive window mode + (Get-Content "plot_example.py").replace( + 'graph.render(view=True)', + 'graph.render(view=False)' + ) | Set-Content "plot_example.py" + + # install optional plotting libraries + # (not necessary for pixi-managed environments, where they're just installed by default) + if ($env:PYTHON_VERSION -ne "3.10") { + conda install -y -n $env:CONDA_ENV "h5py>=3.10" "ipywidgets>=8.1.2" "notebook>=7.1.2" + } + # Run all examples + foreach ($file in @(Get-ChildItem *.py)) { + @( + "import sys, warnings", + -join @( + "warnings.showwarning = lambda message, category, filename, lineno, file=None, line=None: ", + "sys.stdout.write(warnings.formatwarning(message, category, filename, lineno, line))" + ) + ) + (Get-Content $file) | Set-Content $file + python $file ; Assert-Output $? + } + # Run all notebooks + Set-Location "$env:BUILD_SOURCESDIRECTORY/examples/python-guide/notebooks" + (Get-Content "interactive_plot_example.ipynb").replace( + 'INTERACTIVE = False', + 'assert False, \"Interactive mode disabled\"' + ) | Set-Content "interactive_plot_example.ipynb" + jupyter nbconvert --ExecutePreprocessor.timeout=180 --to notebook --execute --inplace *.ipynb ; Assert-Output $? +} diff --git a/.ci/test.sh b/.ci/test.sh new file mode 100755 index 0000000..ffcd396 --- /dev/null +++ b/.ci/test.sh @@ -0,0 +1,321 @@ +#!/bin/bash + +set -e -E -o -u pipefail + +# defaults +CONDA_ENV="test-env" +IN_UBUNTU_BASE_CONTAINER=${IN_UBUNTU_BASE_CONTAINER:-"false"} +METHOD=${METHOD:-""} +PRODUCES_ARTIFACTS=${PRODUCES_ARTIFACTS:-"false"} +SANITIZERS=${SANITIZERS:-""} + +ARCH=$(uname -m) + +LGB_VER=$(head -n 1 "${BUILD_DIRECTORY}/VERSION.txt") + +# create the artifact upload directory if it doesn't exist yet +mkdir -p "${BUILD_ARTIFACTSTAGINGDIRECTORY}" + +if [[ $OS_NAME == "macos" ]] && [[ $COMPILER == "gcc" ]]; then + export CXX=g++-14 + export CC=gcc-14 +elif [[ $OS_NAME == "linux" ]] && [[ $COMPILER == "clang" ]]; then + export CXX=clang++ + export CC=clang +elif [[ $OS_NAME == "linux" ]] && [[ $COMPILER == "clang-17" ]]; then + export CXX=clang++-17 + export CC=clang-17 +fi + +if [[ $IN_UBUNTU_BASE_CONTAINER == "true" ]]; then + export LANG="en_US.UTF-8" + export LC_ALL="en_US.UTF-8" +fi + +# Setting MACOSX_DEPLOYMENT_TARGET prevents CMake from building against too-new +# macOS features, and helps tools like Python build tools determine the appropriate +# wheel compatibility tags. +# +# ref: +# * https://cmake.org/cmake/help/latest/envvar/MACOSX_DEPLOYMENT_TARGET.html +# * https://github.com/scikit-build/scikit-build-core/blob/acb7d0346e4a05bcb47a4ea3939c705ab71e3145/src/scikit_build_core/builder/macos.py#L36 +if [[ $ARCH == "x86_64" ]]; then + export MACOSX_DEPLOYMENT_TARGET=10.15 +else + export MACOSX_DEPLOYMENT_TARGET=12.0 +fi + +if [[ "${TASK}" == "r-package" ]]; then + bash "${BUILD_DIRECTORY}/.ci/test-r-package.sh" || exit 1 + exit 0 +fi + +cd "${BUILD_DIRECTORY}" + +if [[ $TASK == "swig" ]]; then + cmake -B build -S . -DUSE_SWIG=ON + cmake --build build -j4 || exit 1 + if [[ $OS_NAME == "linux" ]] && [[ $COMPILER == "gcc" ]]; then + objdump -T ./lib_lightgbm.so > ./objdump.log || exit 1 + objdump -T ./lib_lightgbm_swig.so >> ./objdump.log || exit 1 + ./.ci/check-dynamic-dependencies.sh ./objdump.log || exit 1 + fi + if [[ $PRODUCES_ARTIFACTS == "true" ]]; then + cp ./build/lightgbmlib.jar "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbmlib_${OS_NAME}.jar" + fi + exit 0 +fi + +if [[ "$TASK" == "cpp-tests" ]]; then + cmake_args=( + -DBUILD_CPP_TEST=ON + -DUSE_DEBUG=ON + ) + if [[ $METHOD == "with-sanitizers" ]]; then + cmake_args+=("-DUSE_SANITIZER=ON") + if [[ -n $SANITIZERS ]]; then + cmake_args+=("-DENABLED_SANITIZERS=$SANITIZERS") + fi + fi + cmake -B build -S . "${cmake_args[@]}" + cmake --build build --target testlightgbm -j4 || exit 1 + ./testlightgbm || exit 1 + exit 0 +fi + +# including python=version=[build=*_cp*] to ensure that conda prefers CPython and doesn't fall back to +# other implementations like pypy +CONDA_PYTHON_REQUIREMENT="python=${PYTHON_VERSION}[build=*_cp*]" + +if [[ $TASK == "if-else" ]]; then + conda create -q -y -n "${CONDA_ENV}" "${CONDA_PYTHON_REQUIREMENT}" numpy + # shellcheck disable=SC1091 + source activate "${CONDA_ENV}" + cmake -B build -S . || exit 1 + cmake --build build --target lightgbm -j4 || exit 1 + cd "$BUILD_DIRECTORY/tests/cpp_tests" + ../../lightgbm config=train.conf convert_model_language=cpp convert_model=../../src/boosting/gbdt_prediction.cpp + ../../lightgbm config=predict.conf output_result=origin.pred + ../../lightgbm config=predict.conf output_result=ifelse.pred + python test.py + exit 0 +fi + +PYTHON_ENV_MANAGER="conda" +if [[ "${PYTHON_VERSION}" == "3.10" ]]; then + PYTHON_ENV_MANAGER="pixi" + CI_PIXI_ENV="py310" +fi + +# 'pixi' is used for end-of-life Python versions +if [[ "${PYTHON_ENV_MANAGER}" == "pixi" ]]; then + eval "$(pixi shell-hook --locked -e "${CI_PIXI_ENV}")" +else + CONDA_REQUIREMENT_FILE="${BUILD_DIRECTORY}/.ci/conda-envs/ci-core.txt" + conda create \ + -y \ + -n "${CONDA_ENV}" \ + --file "${CONDA_REQUIREMENT_FILE}" \ + "${CONDA_PYTHON_REQUIREMENT}" \ + || exit 1 + + # print output of 'conda list', to help in submitting bug reports + echo "conda list:" + conda list -n ${CONDA_ENV} + + # shellcheck disable=SC1091 + source activate $CONDA_ENV +fi + +cd "${BUILD_DIRECTORY}" + +if [[ $TASK == "sdist" ]]; then + sh ./build-python.sh sdist || exit 1 + sh .ci/check-python-dists.sh ./dist || exit 1 + pip install -v --no-deps "./dist/lightgbm-${LGB_VER}.tar.gz" || exit 1 + if [[ $PRODUCES_ARTIFACTS == "true" ]]; then + cp "./dist/lightgbm-${LGB_VER}.tar.gz" "${BUILD_ARTIFACTSTAGINGDIRECTORY}" || exit 1 + fi + pytest -ra ./tests/python_package_test || exit 1 + exit 0 +elif [[ $TASK == "bdist" ]]; then + if [[ $OS_NAME == "macos" ]]; then + sh ./build-python.sh bdist_wheel || exit 1 + sh .ci/check-python-dists.sh ./dist || exit 1 + if [[ $PRODUCES_ARTIFACTS == "true" ]]; then + cp "$(echo "dist/lightgbm-${LGB_VER}-py3-none-macosx"*.whl)" "${BUILD_ARTIFACTSTAGINGDIRECTORY}" || exit 1 + fi + else + sh ./build-python.sh bdist_wheel --integrated-opencl || exit 1 + + # print some debugging logs about the wheel's GLIBC version and dependencies on shared libraries + pip install 'auditwheel>=6.5.1' + auditwheel show ./dist/lightgbm*.whl + + # pass through 'auditwheel repair' to set the appropriate wheel tags. + # + # intentionally avoid vendoring libgomp, to reduce the risk of multiple OpenMP libraries + # being loaded in the same process. + auditwheel repair \ + --exclude 'libgomp.so*' \ + --lib-sdir '' \ + --wheel-dir dist-fixed/ \ + ./dist/lightgbm*.whl + + # overwrite the original wheel with the new one + rm ./dist/lightgbm*.whl + mv ./dist-fixed/lightgbm*.whl ./dist + + # check wheel properties + sh .ci/check-python-dists.sh ./dist || exit 1 + + if [[ $PRODUCES_ARTIFACTS == "true" ]]; then + # hard-code expected tag so CI will fail if 'auditwheel repair' has a surprising result (e.g. newer + # manylinux tag than we intended) + if [[ $ARCH == "x86_64" ]]; then + PLATFORM="manylinux_2_27_x86_64.manylinux_2_28_x86_64" + else + PLATFORM="manylinux2014_aarch64.manylinux_2_17_aarch64" + fi + cp "dist/lightgbm-${LGB_VER}-py3-none-${PLATFORM}.whl" "${BUILD_ARTIFACTSTAGINGDIRECTORY}" || exit 1 + fi + # Make sure we can do both CPU and GPU; see tests/python_package_test/test_dual.py + export LIGHTGBM_TEST_DUAL_CPU_GPU=1 + fi + pip install -v --no-deps ./dist/*.whl || exit 1 + pytest -ra ./tests || exit 1 + exit 0 +fi + +if [[ $TASK == "gpu" ]]; then + sed -i'.bak' 's/std::string device_type = "cpu";/std::string device_type = "gpu";/' ./include/LightGBM/config.h + grep -q 'std::string device_type = "gpu"' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done + if [[ $METHOD == "pip" ]]; then + sh ./build-python.sh sdist || exit 1 + sh .ci/check-python-dists.sh ./dist || exit 1 + pip install \ + -v \ + --no-deps \ + --config-settings=cmake.define.USE_GPU=ON \ + "./dist/lightgbm-${LGB_VER}.tar.gz" \ + || exit 1 + pytest -ra ./tests/python_package_test || exit 1 + exit 0 + elif [[ $METHOD == "wheel" ]]; then + sh ./build-python.sh bdist_wheel --gpu || exit 1 + sh ./.ci/check-python-dists.sh ./dist || exit 1 + pip install -v --no-deps "$(echo "./dist/lightgbm-${LGB_VER}"*.whl)" || exit 1 + pytest -ra ./tests || exit 1 + exit 0 + elif [[ $METHOD == "source" ]]; then + cmake -B build -S . -DUSE_GPU=ON + fi +elif [[ $TASK == "cuda" ]]; then + sed -i'.bak' 's/std::string device_type = "cpu";/std::string device_type = "cuda";/' ./include/LightGBM/config.h + grep -q 'std::string device_type = "cuda"' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done + # by default ``gpu_use_dp=false`` for efficiency. change to ``true`` here for exact results in ci tests + sed -i'.bak' 's/gpu_use_dp = false;/gpu_use_dp = true;/' ./include/LightGBM/config.h + grep -q 'gpu_use_dp = true' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done + if [[ $METHOD == "pip" ]]; then + sh ./build-python.sh sdist || exit 1 + sh ./.ci/check-python-dists.sh ./dist || exit 1 + pip install \ + -v \ + --no-deps \ + --config-settings=cmake.define.USE_CUDA=ON \ + "./dist/lightgbm-${LGB_VER}.tar.gz" \ + || exit 1 + pytest -ra ./tests/python_package_test || exit 1 + exit 0 + elif [[ $METHOD == "wheel" ]]; then + sh ./build-python.sh bdist_wheel --cuda || exit 1 + sh ./.ci/check-python-dists.sh ./dist || exit 1 + pip install -v --no-deps "$(echo "./dist/lightgbm-${LGB_VER}"*.whl)" || exit 1 + pytest -ra ./tests || exit 1 + exit 0 + elif [[ $METHOD == "source" ]]; then + # we want at least 1 CI job testing that manual override of CMAKE_CUDA_ARCHITECTURES works + cmake \ + -B build \ + -S . \ + -DUSE_CUDA=ON \ + -DCMAKE_CUDA_ARCHITECTURES="native" + fi +elif [[ $TASK == "mpi" ]]; then + if [[ $METHOD == "pip" ]]; then + sh ./build-python.sh sdist || exit 1 + sh ./.ci/check-python-dists.sh ./dist || exit 1 + pip install \ + -v \ + --no-deps \ + --config-settings=cmake.define.USE_MPI=ON \ + "./dist/lightgbm-${LGB_VER}.tar.gz" \ + || exit 1 + pytest -ra ./tests/python_package_test || exit 1 + exit 0 + elif [[ $METHOD == "wheel" ]]; then + sh ./build-python.sh bdist_wheel --mpi || exit 1 + sh ./.ci/check-python-dists.sh ./dist || exit 1 + pip install -v --no-deps "$(echo "./dist/lightgbm-${LGB_VER}"*.whl)" || exit 1 + pytest -ra ./tests || exit 1 + exit 0 + elif [[ $METHOD == "source" ]]; then + cmake -B build -S . -DUSE_MPI=ON -DUSE_DEBUG=ON + fi +else + cmake -B build -S . +fi + +cmake --build build --target _lightgbm -j4 || exit 1 + +sh ./build-python.sh install --precompile || exit 1 +pytest -ra ./tests || exit 1 + +if [[ $TASK == "regular" ]]; then + if [[ $PRODUCES_ARTIFACTS == "true" ]]; then + if [[ $OS_NAME == "macos" ]]; then + cp ./lib_lightgbm.dylib "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.dylib" + else + if [[ $COMPILER == "gcc" ]]; then + objdump -T ./lib_lightgbm.so > ./objdump.log || exit 1 + ./.ci/check-dynamic-dependencies.sh ./objdump.log || exit 1 + fi + cp ./lib_lightgbm.so "${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.so" + fi + fi + cd "$BUILD_DIRECTORY/examples/python-guide" + sed -i'.bak' '/import lightgbm as lgb/a\ +import matplotlib\ +matplotlib.use\(\"Agg\"\)\ +' plot_example.py # prevent interactive window mode + sed -i'.bak' 's/graph.render(view=True)/graph.render(view=False)/' plot_example.py + # install optional plotting libraries + # (not necessary for pixi-managed environments, where they're just installed by default) + if [[ "${PYTHON_ENV_MANAGER}" != "pixi" ]]; then + conda install -y -n $CONDA_ENV \ + 'h5py>=3.10' \ + 'ipywidgets>=8.1.2' \ + 'notebook>=7.1.2' + fi + for f in *.py **/*.py; do python "${f}" || exit 1; done # run all examples + cd "$BUILD_DIRECTORY/examples/python-guide/notebooks" + sed -i'.bak' 's/INTERACTIVE = False/assert False, \\"Interactive mode disabled\\"/' interactive_plot_example.ipynb + jupyter nbconvert --ExecutePreprocessor.timeout=180 --to notebook --execute --inplace ./*.ipynb || exit 1 # run all notebooks + + # importing the library should succeed even if all optional dependencies are not present + if [[ "${PYTHON_ENV_MANAGER}" != "pixi" ]]; then + conda uninstall -n $CONDA_ENV --force --yes \ + cffi \ + dask \ + distributed \ + joblib \ + matplotlib-base \ + pandas \ + polars \ + psutil \ + pyarrow \ + python-graphviz \ + scikit-learn || exit 1 + fi + python -c "import lightgbm" || exit 1 +fi diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..e7191b6 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,19 @@ +root = true + +[*] +charset = utf-8 +trim_trailing_whitespace = true +insert_final_newline = true +end_of_line = lf +indent_style = space +indent_size = 2 + +[*.{py,sh,ps1,js,json}] +indent_size = 4 +max_line_length = 120 +skip = external_libs +known_first_party = lightgbm + +# Tabs matter for Makefile and .gitmodules +[{makefile*,Makefile*,*.mk,*.mak,*.makefile,*.Makefile,GNUmakefile,BSDmakefile,make.bat,Makevars*,*.gitmodules}] +indent_style = tab diff --git a/.git-blame-ignore-revs b/.git-blame-ignore-revs new file mode 100644 index 0000000..6ade88f --- /dev/null +++ b/.git-blame-ignore-revs @@ -0,0 +1,8 @@ +# introduce ruff-format (#6308) +6330d6269c81dfd4c96e664b99239b8ff39ccf91 +# enable ruff format on tests and examples (#6317) +1b792e716682254c33ddb5eb845357e84018636d +# enable ruff-format on main library Python code (#6336) +dd31208ab7a7aea86762830697b00666f843ded9 +# enable whitespace/indent_namespace rule from cpplint (#7056) +50f11a9f3c066eadee475ea567f9e9d49e6bb827 diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..351fd66 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,34 @@ +* text=auto eol=lf + +# Explicitly declare text files you want to always be normalized and converted +# to native line endings on checkout. +*.c text +*.cc text +*.cmake text +*.conf text +*.cpp text +*.cu text +*.cuh text +*.h text +*.hpp text +*.i text +*.js text +*.md text +*.py text +*.R text +*.rst text +*.sh text +*.svg text +*.toml text +*.tpp text +*.txt text +*.yaml text +*.yml text + +# help git identify which files should be treated as binary +*.ico binary +*.png binary + +# SCM syntax highlighting & preventing 3-way merges +# (added automatically by 'pixi init') +pixi.lock merge=binary linguist-language=YAML linguist-generated=true -diff diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000..4284a97 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,10 @@ +# This file controls default reviewers for LightGBM code. +# See https://help.github.com/en/articles/about-code-owners +# for details +# +# Maintainers are encouraged to use their best discretion in +# setting reviewers on PRs manually, but this file should +# offer a reasonable automatic best-guess + +# catch-all rule (this only gets matched if no rules below match) +* @guolinke @jameslamb @shiyu1994 @jmoralez @borchero @StrikerRUS @mayer79 diff --git a/.github/ISSUE_TEMPLATE/BUG_REPORT.md b/.github/ISSUE_TEMPLATE/BUG_REPORT.md new file mode 100644 index 0000000..0a90d33 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/BUG_REPORT.md @@ -0,0 +1,26 @@ +--- +name: Bug Report 🐞 +about: Something isn't working as expected? Here is the right place to report. +--- + +## Description + + +## Reproducible example + + +## Environment info + +LightGBM version or commit hash: + +Command(s) you used to install LightGBM + +```shell + +``` + + + + +## Additional Comments + diff --git a/.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md b/.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md new file mode 100644 index 0000000..206b629 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/FEATURE_REQUEST.md @@ -0,0 +1,25 @@ +--- +name: Feature Request 💡 +about: Suggest a new idea for the project. +labels: enhancement, feature-request +--- + + + +## Summary + + + +## Motivation + + + +## Description + + + +## References + + diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..99e65af --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,17 @@ +version: 2 +updates: + - package-ecosystem: github-actions + directory: / + schedule: + interval: monthly + groups: + ci-dependencies: + patterns: + - "*" + cooldown: + # only accept releases that have been out for at least 15 days + default-days: 15 + commit-message: + prefix: "[ci]" + labels: + - maintenance diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml new file mode 100644 index 0000000..4682781 --- /dev/null +++ b/.github/release-drafter.yml @@ -0,0 +1,20 @@ +name-template: 'v$NEXT_PATCH_VERSION' +tag-template: 'v$NEXT_PATCH_VERSION' +categories: + - title: '💡 New Features' + label: 'feature' + - title: '🔨 Breaking' + label: 'breaking' + - title: '🚀 Efficiency Improvement' + label: 'efficiency' + - title: '🐛 Bug Fixes' + label: 'fix' + - title: '📖 Documentation' + label: 'doc' + - title: '🧰 Maintenance' + label: 'maintenance' +change-template: '- $TITLE @$AUTHOR (#$NUMBER)' +template: | + ## Changes + + $CHANGES diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 0000000..4be5966 --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,81 @@ +# builds core artifacts, intended to be attached to releases +# or used by other workflows +name: Build + +on: + push: + branches: + - main + pull_request: + branches: + - main + - master + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +env: + # tell scripts where to put artifacts + BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' + +jobs: + archive: + runs-on: ubuntu-latest + timeout-minutes: 15 + permissions: + contents: write + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Create source archive + run: | + mkdir -p "${BUILD_ARTIFACTSTAGINGDIRECTORY}" + tar \ + -czvf \ + /tmp/LightGBM-complete_source_code_tar_gz.tar.gz \ + . + mv \ + /tmp/LightGBM-complete_source_code_tar_gz.tar.gz \ + ${BUILD_ARTIFACTSTAGINGDIRECTORY}/ + - name: Create commit.txt + shell: bash + run: | + # for pull requests, github.sha refers to the merge commit from merging the PR and + # target branch... we want the actual commit that was pushed + if [[ "${{ github.event_name }}" == "pull_request" ]]; then + COMMIT_SHA="${{ github.event.pull_request.head.sha }}" + else + COMMIT_SHA="${{ github.sha }}" + fi + echo "${COMMIT_SHA}" > "${BUILD_ARTIFACTSTAGINGDIRECTORY}/commit.txt" + - name: Upload artifacts + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: source-archive + path: | + ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/commit.txt + ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/LightGBM-complete_source_code_tar_gz.tar.gz + if-no-files-found: error + all-build-jobs-successful: + if: always() + runs-on: ubuntu-latest + needs: + - archive + permissions: + statuses: read + steps: + - name: Note that all tests succeeded + uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 + with: + jobs: ${{ toJSON(needs) }} diff --git a/.github/workflows/cpp.yml b/.github/workflows/cpp.yml new file mode 100644 index 0000000..b60a9ae --- /dev/null +++ b/.github/workflows/cpp.yml @@ -0,0 +1,158 @@ +name: C++ + +on: + push: + branches: + - main + pull_request: + branches: + - main + - master + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +env: + # tell scripts where to put artifacts + # (this variable name is left over from when jobs ran on Azure DevOps) + BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' + # where repo sources are cloned to + BUILD_DIRECTORY: '${{ github.workspace }}' + # in CMake-driven builds, parallelize compilation + CMAKE_BUILD_PARALLEL_LEVEL: 4 + +jobs: + test: + name: ${{ matrix.task }} (${{ matrix.os-display-name || matrix.os }}, ${{ matrix.compiler }}) + runs-on: ${{ matrix.os }} + container: ${{ matrix.container }} + timeout-minutes: 60 + permissions: + contents: write + strategy: + fail-fast: false + matrix: + include: + ############# + # C++ tests # + ############# + - os: ubuntu-latest + task: cpp-tests + compiler: clang-17 + method: with-sanitizers + container: 'ubuntu:22.04' + os-display-name: 'ubuntu22.04' + - os: macos-15-intel + task: cpp-tests + compiler: clang + method: with-sanitizers + sanitizers: "address;undefined" + container: null + - os: windows-2022 + task: cpp-tests + compiler: msvc + container: null + ########### + # if-else # + ########### + # These test CLI-generated C++ inference code. + # + # They need Python because they're written with pytest, but they + # do not need the LightGBM Python package + - os: ubuntu-latest + task: if-else + compiler: clang-17 + container: 'ubuntu:22.04' + os-display-name: 'ubuntu22.04' + python_version: '3.14' + setup-conda: 'true' + - os: macos-15-intel + task: if-else + compiler: clang + python_version: '3.10' + container: null + - os: ubuntu-latest + task: if-else + compiler: gcc + python_version: '3.11' + container: 'ghcr.io/lightgbm-org/lightgbm-vsts-agent:manylinux_2_28_x86_64' + os-display-name: 'manylinux_2_28' + steps: + - name: Install packages used by third-party actions + if: matrix.container == 'ubuntu:22.04' + shell: bash + run: | + apt-get update -y + apt-get install --no-install-recommends -y \ + dirmngr \ + gpg \ + gpg-agent \ + software-properties-common \ + sudo + # install newest version of git + # ref: + # - https://unix.stackexchange.com/a/170831/550004 + # - https://git-scm.com/download/linux + add-apt-repository ppa:git-core/ppa -y + apt-get update -y + apt-get install --no-install-recommends -y \ + git + - name: Trust git cloning LightGBM + if: startsWith(matrix.os, 'ubuntu') + run: | + git config --global --add safe.directory "${GITHUB_WORKSPACE}" + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Setup and run tests on Linux and macOS + if: startsWith(matrix.os, 'macos') || startsWith(matrix.os, 'ubuntu') + shell: bash + run: | + export COMPILER="${{ matrix.compiler }}" + export CONDA="${HOME}/miniforge" + export METHOD="${{ matrix.method }}" + export PATH=${CONDA}/bin:${PATH} + export PYTHON_VERSION="${{ matrix.python_version }}" + export SANITIZERS="${{ matrix.sanitizers }}" + export SETUP_CONDA="${{ matrix.setup-conda }}" + export TASK="${{ matrix.task }}" + if [[ "${{ matrix.os }}" =~ ^macos ]]; then + export OS_NAME="macos" + elif [[ "${{ matrix.os }}" == "ubuntu-latest" ]]; then + export OS_NAME="linux" + fi + if [[ "${{ matrix.container }}" == "ubuntu:22.04" ]]; then + export DEBIAN_FRONTEND=noninteractive + export IN_UBUNTU_BASE_CONTAINER="true" + fi + $GITHUB_WORKSPACE/.ci/setup.sh + $GITHUB_WORKSPACE/.ci/test.sh + - name: Setup and run tests on Windows + if: startsWith(matrix.os, 'windows') + shell: pwsh -command ". {0}" + run: | + $env:BUILD_SOURCESDIRECTORY = $env:BUILD_DIRECTORY + $env:TASK = "${{ matrix.task }}" + & "$env:GITHUB_WORKSPACE/.ci/test-windows.ps1" + all-cpp-jobs-successful: + if: always() + runs-on: ubuntu-latest + needs: + - test + permissions: + statuses: read + steps: + - name: Note that all tests succeeded + uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 + with: + jobs: ${{ toJSON(needs) }} diff --git a/.github/workflows/cuda.yml b/.github/workflows/cuda.yml new file mode 100644 index 0000000..5cdcba1 --- /dev/null +++ b/.github/workflows/cuda.yml @@ -0,0 +1,115 @@ +name: CUDA Version + +on: + push: + # run on pushes to LightGBM branches matching certain names + branches: + - cuda-* + # Run manually by clicking a button in the UI + workflow_dispatch: + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +jobs: + test: + # yamllint disable-line rule:line-length + name: ${{ matrix.task }} ${{ matrix.cuda_version }} ${{ matrix.method }} (${{ matrix.linux_version }}, ${{ matrix.compiler }}, Python ${{ matrix.python_version }}) + # yamllint disable-line rule:line-length + # ref: https://docs.github.com/en/actions/concepts/runners/about-larger-runners#specifications-for-gpu-larger-runners + runs-on: github-hosted-linux-gpu-amd64 + container: + image: nvcr.io/nvidia/cuda:${{ matrix.cuda_version }}-devel-${{ matrix.linux_version }} + env: + CMAKE_BUILD_PARALLEL_LEVEL: 4 + COMPILER: ${{ matrix.compiler }} + CONDA: /tmp/miniforge + DEBIAN_FRONTEND: noninteractive + METHOD: ${{ matrix.method }} + OS_NAME: linux + PYTHON_VERSION: ${{ matrix.python_version }} + TASK: ${{ matrix.task }} + SKBUILD_STRICT_CONFIG: true + options: --gpus all + timeout-minutes: 30 + permissions: + contents: write + strategy: + fail-fast: false + matrix: + include: + - method: wheel + compiler: gcc + python_version: "3.11" + cuda_version: "13.2.1" + linux_version: "ubuntu24.04" + task: cuda + - method: wheel + compiler: gcc + python_version: "3.11" + cuda_version: "12.9.1" + linux_version: "ubuntu22.04" + task: cuda + - method: source + compiler: gcc + python_version: "3.14" + cuda_version: "12.2.2" + linux_version: "ubuntu22.04" + task: cuda + - method: pip + compiler: gcc + python_version: "3.12" + cuda_version: "11.8.0" + linux_version: "ubuntu20.04" + task: cuda + steps: + - name: Install latest git and sudo + run: | + apt-get update + apt-get install --no-install-recommends -y \ + ca-certificates \ + software-properties-common + add-apt-repository ppa:git-core/ppa -y + apt-get update + apt-get install --no-install-recommends -y \ + git \ + sudo + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Setup and run tests + env: + CI_CUDA_VERSION: ${{ matrix.cuda_version }} + run: | + export BUILD_ARTIFACTSTAGINGDIRECTORY="${{ github.workspace }}/artifacts" + export BUILD_DIRECTORY="$GITHUB_WORKSPACE" + export PATH=$CONDA/bin:$PATH + + # check GPU usage + nvidia-smi + + # build and test + $GITHUB_WORKSPACE/.ci/setup.sh + $GITHUB_WORKSPACE/.ci/test.sh + all-cuda-jobs-successful: + if: always() + runs-on: ubuntu-latest + needs: + - test + permissions: + statuses: read + steps: + - name: Note that all tests succeeded + uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 + with: + jobs: ${{ toJSON(needs) }} diff --git a/.github/workflows/lock.yml b/.github/workflows/lock.yml new file mode 100644 index 0000000..3eab150 --- /dev/null +++ b/.github/workflows/lock.yml @@ -0,0 +1,58 @@ +name: 'Lock Inactive Threads' + +on: + schedule: + # midnight UTC, every Wednesday, for Issues + - cron: '0 0 * * 3' + # midnight UTC, every Thursday, for PRs + - cron: '0 0 * * 4' + # allow manual triggering from GitHub UI + workflow_dispatch: + +# only 1 job running in the repo at any time +concurrency: + group: lock + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +jobs: + action: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: dessant/lock-threads@89ae32b08ed1a541efecbab17912962a5e38981c # v6.0.2 + with: + github-token: ${{ github.token }} + # after how many days of inactivity should a closed issue/PR be locked? + issue-inactive-days: '365' + pr-inactive-days: '365' + # do not close feature request issues... + # we close those but track them in https://github.com/lightgbm-org/LightGBM/issues/2302 + exclude-any-issue-labels: 'feature request' + # what labels should be removed prior to locking? + remove-issue-labels: 'awaiting response,awaiting review,blocking,in progress' + remove-pr-labels: 'awaiting response,awaiting review,blocking,in progress' + # what message should be posted prior to locking? + issue-comment: > + This issue has been automatically locked + since there has not been any recent activity since it was closed. + + To start a new related discussion, + open a new issue at https://github.com/lightgbm-org/LightGBM/issues + including a reference to this. + pr-comment: > + This pull request has been automatically locked + since there has not been any recent activity since it was closed. + + To start a new related discussion, + open a new issue at https://github.com/lightgbm-org/LightGBM/issues + including a reference to this. + # what should the locking status be? + issue-lock-reason: 'resolved' + pr-lock-reason: 'resolved' + process-only: ${{ github.event.schedule == '0 0 * * 3' && 'issues' || 'prs' }} diff --git a/.github/workflows/lychee.yml b/.github/workflows/lychee.yml new file mode 100644 index 0000000..b0e7620 --- /dev/null +++ b/.github/workflows/lychee.yml @@ -0,0 +1,54 @@ +name: Link checks + +on: + # Run manually by clicking a button in the UI + workflow_dispatch: + # Run once a day at 8:00am UTC + schedule: + - cron: '0 8 * * *' + +# only 1 job running in the repo at any time +concurrency: + group: lock + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +env: + COMPILER: gcc + OS_NAME: 'linux' + TASK: 'check-links' + +jobs: + check-links: + timeout-minutes: 60 + runs-on: ubuntu-latest + permissions: + contents: read + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: false + - name: Build docs + run: | + export CONDA=${HOME}/miniforge + export PATH=${CONDA}/bin:${HOME}/.local/bin:${PATH} + $GITHUB_WORKSPACE/.ci/setup.sh || exit 1 + $GITHUB_WORKSPACE/.ci/build-docs.sh || exit 1 + - name: Check links + uses: lycheeverse/lychee-action@8646ba30535128ac92d33dfc9133794bfdd9b411 # v2.8.0 + with: + args: >- + --config=./docs/.lychee.toml + -- + "**/*.rst" + "**/*.md" + "./R-package/**/*.Rd" + "./docs/_build/html/*.html" + fail: true + failIfEmpty: true diff --git a/.github/workflows/no_response.yml b/.github/workflows/no_response.yml new file mode 100644 index 0000000..ce48a52 --- /dev/null +++ b/.github/workflows/no_response.yml @@ -0,0 +1,40 @@ +name: No Response Bot + +on: + issue_comment: + types: [created] + schedule: + # "every day at 04:00 UTC" + - cron: '0 4 * * *' + +# only 1 job running in the repo at any time +concurrency: + group: lock + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +jobs: + no-response: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: lee-dohm/no-response@9bb0a4b5e6a45046f00353d5de7d90fb8bd773bb # v0.5.0 + with: + closeComment: > + This issue has been automatically closed + because it has been awaiting a response for too long. + + When you have time to to work with the maintainers to resolve this issue, + please post a new comment and it will be re-opened. + If the issue has been locked for editing by the time you return to it, + please open a new issue and reference this one. + + Thank you for taking the time to improve LightGBM! + daysUntilClose: 30 + responseRequiredLabel: awaiting response + token: ${{ github.token }} diff --git a/.github/workflows/optional_checks.yml b/.github/workflows/optional_checks.yml new file mode 100644 index 0000000..30f27d5 --- /dev/null +++ b/.github/workflows/optional_checks.yml @@ -0,0 +1,44 @@ +name: Optional checks + +on: + pull_request: + branches: + - main + - master + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +jobs: + all-optional-checks-successful: + timeout-minutes: 30 + runs-on: ubuntu-latest + env: + GITHUB_TOKEN: ${{ github.token }} + permissions: + contents: write + id-token: write + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: false + - name: Check valgrind workflow + shell: bash + run: | + # ref: https://docs.github.com/en/actions/reference/workflows-and-actions/contexts#github-context + PR_BRANCH="${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}" + echo "checking status for branch '${PR_BRANCH}'" + ./.ci/check-workflow-status.sh \ + "${PR_BRANCH}" \ + 'r_valgrind.yml' \ + ${{ github.event.number }} diff --git a/.github/workflows/python_package.yml b/.github/workflows/python_package.yml new file mode 100644 index 0000000..57b18fc --- /dev/null +++ b/.github/workflows/python_package.yml @@ -0,0 +1,534 @@ +name: Python-package + +on: + push: + branches: + - main + pull_request: + branches: + - main + - master + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +env: + # tell scripts where to put artifacts + # (this variable name is left over from when jobs ran on Azure DevOps) + BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' + # where repo sources are cloned to + BUILD_SOURCESDIRECTORY: '${{ github.workspace }}' + CMAKE_BUILD_PARALLEL_LEVEL: 4 + # avoid interactive prompts in Debian-based distributions + DEBIAN_FRONTEND: noninteractive + # On runners using nvidia-container-runtime with Docker, ensure GPUs are available to running processes. + # + # ref: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html + NVIDIA_VISIBLE_DEVICES: 'all' + SKBUILD_STRICT_CONFIG: true + +jobs: + test-linux-aarch64: + name: bdist wheel (manylinux2014-arm, gcc, Python 3.14) + runs-on: ubuntu-24.04-arm + timeout-minutes: 60 + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Setup and run tests + shell: bash + # this uses 'docker run' instead of just setting 'container:' + # because actions/checkout requires GLIBC 2.28 and that is too + # new for manylinux2014 + env: + BUILD_DIRECTORY: /LightGBM + run: | + cat > ./docker-script.sh <> tests.log 2>&1 || exit_code=-1 + cat ./tests.log + exit ${exit_code} + test-r-extra-checks: + name: r-package (${{ matrix.image }}, R-devel) + timeout-minutes: 60 + permissions: + contents: read + strategy: + fail-fast: false + matrix: + # references: + # * CRAN "additional checks": https://cran.r-project.org/web/checks/check_issue_kinds.html + # * images: https://r-hub.github.io/containers/containers.html + # + # Generally, only images that are still supported, listed at + # https://r-hub.github.io/containers/#available-containers, should be used. + image: + - clang21 + - clang22 + - gcc16 + - rchk + - ubuntu-clang + - ubuntu-gcc12 + runs-on: ubuntu-latest + container: ghcr.io/r-hub/containers/${{ matrix.image }}:latest # zizmor: ignore[unpinned-images] + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Install pandoc + uses: r-lib/actions/setup-pandoc@a51a8012b0aab7c32ef9d19bf54da93f3254335e # v2.12.0 + - name: Install system dependencies + shell: bash + run: | + # libuv is needed for {fs} (https://github.com/lightgbm-org/LightGBM/issues/7208) + if type -f apt 2>&1 > /dev/null; then + apt-get update + apt-get install --no-install-recommends -y \ + cmake \ + devscripts \ + libcurl4-openssl-dev \ + libuv1-dev \ + texinfo \ + texlive-latex-extra \ + texlive-latex-recommended \ + texlive-fonts-recommended \ + texlive-fonts-extra \ + tidy \ + qpdf + else + yum update -y + yum install -y \ + cmake \ + devscripts \ + libcurl-devel \ + libuv-devel \ + qpdf \ + texinfo \ + texinfo-tex \ + texlive-amsmath \ + texlive-amsfonts \ + texlive-collection-fontsrecommended \ + texlive-collection-latexrecommended \ + texlive-latex \ + tidy + fi + - name: Install packages and run tests + shell: bash + run: | + Rscript ./.ci/install-r-deps.R --build --test --exclude=testthat + sh build-cran-package.sh + + # 'rchk' isn't run through 'R CMD check', use the approach documented at + # https://r-hub.github.io/containers/local.html + if [[ "${{ matrix.image }}" =~ "rchk" ]]; then + r-check "$(pwd)" \ + | tee ./rchk-logs.txt 2>&1 + + # the '-v' exceptions below are from R/rchk itself and not LightGBM: + # https://github.com/kalibera/rchk/issues/22#issuecomment-656036156 + if grep -E '\[PB\]|ERROR' ./rchk-logs.txt \ + | grep -v 'too many states' \ + > /dev/null; \ + then + echo "rchk found issues" + exit 1 + else + echo "rchk did not find any issues" + exit 0 + fi + fi + + # why these packages? + # + # - 'curl' is not a dependency of {lightgbm}, but it's needed for 'R CMD check' URL checks + # - 'testthat' is not needed by 'rchk', so avoid installing it until here + # + Rscript -e " + install.packages(c('curl', 'testthat'), + repos = 'https://cran.rstudio.com', + Ncpus = parallel::detectCores()) + " + + if [[ "${{ matrix.image }}" =~ "clang" ]]; then + # allowing the following NOTEs (produced by default in the clang images): + # + # * checking compilation flags used ... NOTE + # Compilation used the following non-portable flag(s): + # ‘-Wp,-D_FORTIFY_SOURCE=3’ + # + # even though CRAN itself sets that: + # https://www.stats.ox.ac.uk/pub/bdr/Rconfig/r-devel-linux-x86_64-fedora-clang + # + declare -i allowed_notes=1 + else + declare -i allowed_notes=0 + fi + + bash .ci/run-r-cmd-check.sh \ + "$(echo lightgbm_$(head -1 VERSION.txt).tar.gz)" \ + "${allowed_notes}" + all-r-package-jobs-successful: + if: always() + runs-on: ubuntu-latest + needs: + - test + - test-r-sanitizers + - test-r-extra-checks + permissions: + statuses: read + steps: + - name: Note that all tests succeeded + uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 + with: + jobs: ${{ toJSON(needs) }} diff --git a/.github/workflows/r_valgrind.yml b/.github/workflows/r_valgrind.yml new file mode 100644 index 0000000..8c2e64e --- /dev/null +++ b/.github/workflows/r_valgrind.yml @@ -0,0 +1,106 @@ +name: R valgrind tests + +# 'run-name' is used here to distinguish in the API between different runs from forks. +# +# When this was added, it was the only way to feed workflow inputs into something that +# would show up in the output of 'gh run list'. +# +# See https://github.com/orgs/community/discussions/73223#discussioncomment-11862624 +run-name: R valgrind tests (pr=${{ inputs.pr-number }}) + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +on: + workflow_dispatch: + inputs: + pr-branch: + type: string + description: | + Branch the PR was submitted from. + Branches from forks should be prefixed with the user/org they originate from, + like '{user}:{branch}'. + pr-number: + type: string + description: Pull request ID, found in the PR URL. + +jobs: + test-r-valgrind: + name: r-package (ubuntu-latest, R-devel, valgrind) + timeout-minutes: 360 + runs-on: ubuntu-latest + container: wch1/r-debug # zizmor: ignore[unpinned-images] + env: + GITHUB_TOKEN: ${{ github.token }} + permissions: + actions: write + checks: write + contents: read + id-token: write + pull-requests: write + statuses: write + steps: + - name: Install essential software before checkout + shell: bash + run: | + apt-get update + apt-get install --no-install-recommends -y \ + curl \ + jq + - name: Install GitHub CLI + run: | + GH_CLI_VERSION="2.83.0" + curl \ + --fail \ + -O \ + -L \ + https://github.com/cli/cli/releases/download/v${GH_CLI_VERSION}/gh_${GH_CLI_VERSION}_linux_amd64.tar.gz + tar -xvf ./gh_${GH_CLI_VERSION}_linux_amd64.tar.gz + mv ./gh_${GH_CLI_VERSION}_linux_amd64/bin/gh /usr/local/bin/ + - name: Trust git cloning LightGBM + run: | + git config --global --add safe.directory "${GITHUB_WORKSPACE}" + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + submodules: true + persist-credentials: false + repository: lightgbm-org/LightGBM + ref: "refs/pull/${{ inputs.pr-number }}/merge" + - name: Run tests with valgrind + shell: bash + run: ./.ci/test-r-package-valgrind.sh + - name: Send final status + if: ${{ always() }} + env: + GITHUB__WORKFLOW: ${{ github.workflow }} + INPUTS__PR_NUMBER: ${{ inputs.pr-number }} + JOB__STATUS: ${{ job.status }} + run: | + $GITHUB_WORKSPACE/.ci/set-commit-status.sh \ + "${GITHUB_WORKFLOW}" \ + "${JOB__STATUS}" \ + "${{ github.sha }}" + comment="Workflow **${GITHUB__WORKFLOW}** has been triggered! 🚀\r\n" + comment="${comment}\r\n${GITHUB_SERVER_URL}/lightgbm-org/LightGBM/actions/runs/${GITHUB_RUN_ID} \r\n" + comment="${comment}\r\nStatus: ${JOB__STATUS}" + $GITHUB_WORKSPACE/.ci/append-comment.sh \ + "${INPUTS__PR_NUMBER}" \ + "${comment}" + - name: Rerun workflow-indicator + if: ${{ always() }} + env: + INPUTS__PR_BRANCH: ${{ inputs.pr-branch }} + run: | + bash $GITHUB_WORKSPACE/.ci/rerun-workflow.sh \ + "optional_checks.yml" \ + "${INPUTS__PR_BRANCH}" \ + || true diff --git a/.github/workflows/release_drafter.yml b/.github/workflows/release_drafter.yml new file mode 100644 index 0000000..fa40d17 --- /dev/null +++ b/.github/workflows/release_drafter.yml @@ -0,0 +1,29 @@ +name: Release Drafter + +on: + push: + branches: + - main + +# only 1 job running in the repo at any time +concurrency: + group: lock + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +jobs: + updateReleaseDraft: + permissions: + contents: write + pull-requests: read + runs-on: ubuntu-latest + steps: + - uses: release-drafter/release-drafter@ed4bc48ec97379be2258e7b7ac2624a3e26ab809 # v7.4.0 + with: + config-name: release-drafter.yml + disable-autolabeler: true + env: + GITHUB_TOKEN: ${{ github.token }} diff --git a/.github/workflows/static_analysis.yml b/.github/workflows/static_analysis.yml new file mode 100644 index 0000000..29ad5b8 --- /dev/null +++ b/.github/workflows/static_analysis.yml @@ -0,0 +1,122 @@ +# contains non-functional tests, like checks on docs +# and code style +name: Static Analysis + +on: + push: + branches: + - main + pull_request: + branches: + - main + - master + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +env: + COMPILER: 'gcc' + MAKEFLAGS: '-j4' + OS_NAME: 'linux' + +jobs: + lint: + name: lint + runs-on: ubuntu-latest + timeout-minutes: 60 + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: false + - name: Setup and run tests + shell: bash + run: | + export TASK=lint + export CONDA=${HOME}/miniforge + export PATH=${CONDA}/bin:$HOME/.local/bin:${PATH} + $GITHUB_WORKSPACE/.ci/setup.sh || exit 1 + $GITHUB_WORKSPACE/.ci/lint-all.sh || exit 1 + check-docs: + name: check-docs + runs-on: ubuntu-latest + timeout-minutes: 60 + permissions: + contents: read + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Setup and run tests + shell: bash + run: | + export TASK=check-docs + export CONDA=${HOME}/miniforge + export PATH=${CONDA}/bin:$HOME/.local/bin:${PATH} + $GITHUB_WORKSPACE/.ci/setup.sh || exit 1 + $GITHUB_WORKSPACE/.ci/build-docs.sh || exit 1 + r-check-docs: + name: r-package-check-docs + timeout-minutes: 60 + runs-on: ubuntu-latest + container: rocker/verse # zizmor: ignore[unpinned-images] + permissions: + contents: read + steps: + - name: Trust git cloning LightGBM + run: | + git config --global --add safe.directory "${GITHUB_WORKSPACE}" + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Install packages + shell: bash + run: | + Rscript ./.ci/install-r-deps.R --build --include=roxygen2 + sh build-cran-package.sh || exit 1 + R CMD INSTALL --with-keep.source lightgbm_*.tar.gz || exit 1 + - name: Test documentation + shell: bash --noprofile --norc {0} + run: | + Rscript .ci/build-docs.R || exit 1 + num_doc_files_changed=$( + git diff --name-only | grep --count -E "\.Rd|NAMESPACE" + ) + if [[ ${num_doc_files_changed} -gt 0 ]]; then + echo "Some R documentation files have changed. Please re-generate them and commit those changes." + echo "" + echo " sh build-cran-package.sh" + echo " R CMD INSTALL --with-keep.source lightgbm_*.tar.gz" + echo " Rscript -e \"roxygen2::roxygenize('R-package/', load = 'installed')\"" + echo "" + exit 1 + fi + all-static-analysis-jobs-successful: + if: always() + runs-on: ubuntu-latest + needs: + - lint + - check-docs + - r-check-docs + permissions: + statuses: read + steps: + - name: Note that all tests succeeded + uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 + with: + jobs: ${{ toJSON(needs) }} diff --git a/.github/workflows/swig.yml b/.github/workflows/swig.yml new file mode 100644 index 0000000..0953d0f --- /dev/null +++ b/.github/workflows/swig.yml @@ -0,0 +1,106 @@ +name: SWIG + +on: + push: + branches: + - main + pull_request: + branches: + - main + - master + +# automatically cancel in-progress builds if another commit is pushed +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +# default to 0 permissions +# (job-level overrides add the minimal permissions needed) +permissions: + contents: none + +env: + # tell scripts where to put artifacts + # (this variable name is left over from when jobs ran on Azure DevOps) + BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts' + # in CMake-driven builds, parallelize compilation + CMAKE_BUILD_PARALLEL_LEVEL: 4 + # all SWIG jobs produce artifacts + PRODUCES_ARTIFACTS: 'true' + # all jobs here have the same 'TASK' + TASK: swig + +jobs: + test-swig: + name: swig (${{ matrix.os-display-name || matrix.os }}, ${{ matrix.compiler }}) + runs-on: ${{ matrix.os }} + container: ${{ matrix.container }} + timeout-minutes: 60 + permissions: + contents: write + id-token: write + strategy: + fail-fast: false + matrix: + include: + - os: ubuntu-latest + compiler: gcc + container: 'ghcr.io/lightgbm-org/lightgbm-vsts-agent:manylinux_2_28_x86_64' + artifact-name: swig-linux-x86_64-jar + os-display-name: 'manylinux_2_28' + - os: macos-15-intel + compiler: clang + container: null + artifact-name: swig-macos-x86_64-jar + # Visual Studio 2022 + - os: windows-2022 + compiler: msvc + container: null + artifact-name: swig-windows-x86_64-jar + steps: + - name: Checkout repository + uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6.0.3 + with: + fetch-depth: 5 + persist-credentials: false + submodules: true + - name: Setup and run tests on Linux and macOS + if: startsWith(matrix.os, 'macos') || startsWith(matrix.os, 'ubuntu') + shell: bash + run: | + export BUILD_DIRECTORY="${GITHUB_WORKSPACE}" + export COMPILER="${{ matrix.compiler }}" + export CONDA="${HOME}/miniforge" + export PATH=${CONDA}/bin:${PATH} + if [[ "${{ matrix.os }}" =~ ^macos ]]; then + export OS_NAME="macos" + elif [[ "${{ matrix.os }}" == "ubuntu-latest" ]]; then + export OS_NAME="linux" + fi + $GITHUB_WORKSPACE/.ci/setup.sh + $GITHUB_WORKSPACE/.ci/test.sh + - name: Setup and run tests on Windows + if: startsWith(matrix.os, 'windows') + shell: pwsh -command ". {0}" + run: | + $env:BUILD_DIRECTORY = $env:GITHUB_WORKSPACE + $env:BUILD_SOURCESDIRECTORY = $env:BUILD_DIRECTORY + & "$env:GITHUB_WORKSPACE/.ci/test-windows.ps1" + - name: Upload artifacts + uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1 + with: + name: ${{ matrix.artifact-name }} + path: ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/*.jar + if-no-files-found: error + all-swig-jobs-successful: + if: always() + runs-on: ubuntu-latest + needs: + - test-swig + permissions: + statuses: read + steps: + - name: Note that all tests succeeded + uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2 + with: + jobs: ${{ toJSON(needs) }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..458ad4e --- /dev/null +++ b/.gitignore @@ -0,0 +1,486 @@ +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. + +# User-specific files +*.suo +*.user +*.userosscache +*.sln.docstates + +# User-specific files (MonoDevelop/Xamarin Studio) +*.userprefs + +# Build results +[Dd]ebug/ +[Dd]ebugPublic/ +[Rr]elease/ +[Rr]eleases/ +x64/ +x86/ +bld/ +[Bb]in/ +[Oo]bj/ +[Ll]og/ +[Bb]uild/ + +# Visual Studio 2015 cache/options directory +.vs/ +# Uncomment if you have tasks that create the project's static files in wwwroot +#wwwroot/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +# NUNIT +*.VisualState.xml +TestResult.xml + +# Build Results of an ATL Project +[Dd]ebugPS/ +[Rr]eleasePS/ +dlldata.c + +# DNX +project.lock.json +artifacts/ + +*_i.c +*_p.c +*_i.h +*.ilk +*.meta +*.obj +*.pch +*.pdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*.log +*.vspscc +*.vssscc +.builds +*.pidb +*.svclog +*.scc + +# Chutzpah Test files +_Chutzpah* + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opendb +*.opensdf +*.sdf +*.cachefile + +# Visual Studio profiler +*.psess +*.vsp +*.vspx +*.sap + +# TFS 2012 Local Workspace +$tf/ + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper +*.DotSettings.user + +# JustCode is a .NET coding add-in +.JustCode + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# NCrunch +_NCrunch_* +.*crunch*.local.xml +nCrunchTemp_* + +# MightyMoose +*.mm.* +AutoTest.Net/ + +# Web workbench (sass) +.sass-cache/ + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +*.pubxml +*.publishproj + +# NuGet Packages +*.nupkg +nuget/ +# The packages folder can be ignored because of Package Restore +**/packages/ +# except build/, which is used as an MSBuild target. +!**/packages/build/ +# Uncomment if necessary however generally it will be regenerated when needed +#!**/packages/repositories.config +# NuGet v3's project.json files produces more ignoreable files +*.nuget.props +*.nuget.targets + +# Microsoft Azure Build Output +csx/ +*.build.csdef + +# Microsoft Azure Emulator +ecf/ +rcf/ + +# Microsoft Azure ApplicationInsights config file +ApplicationInsights.config + +# Windows Store app package directory +AppPackages/ +BundleArtifacts/ + +# Visual Studio cache files +# files ending in .cache can be ignored +*.[Cc]ache +# but keep track of directories ending in .cache +!*.[Cc]ache/ + +# Others +ClientBin/ +~$* +*~ +.*.swp +*.dbmdl +*.dbproj.schemaview +*.pfx +*.publishsettings +node_modules/ +orleans.codegen.cs + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file +# to a newer Visual Studio version. Backup files are not needed, +# because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm + +# SQL Server files +*.mdf +*.ldf + +# Business Intelligence projects +*.rdl.data +*.bim.layout +*.bim_*.settings + +# Microsoft Fakes +FakesAssemblies/ + +# GhostDoc plugin setting file +*.GhostDoc.xml + +# Node.js Tools for Visual Studio +.ntvs_analysis.dat + +# Visual Studio 6 build log +*.plg + +# Visual Studio 6 workspace options file +*.opt + +# Visual Studio LightSwitch build output +**/*.HTMLClient/GeneratedArtifacts +**/*.DesktopClient/GeneratedArtifacts +**/*.DesktopClient/ModelManifest.xml +**/*.Server/GeneratedArtifacts +**/*.Server/ModelManifest.xml +_Pvt_Extensions + +# Paket dependency manager +.paket/paket.exe + +# FAKE - F# Make +.fake/ + + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app +/windows/LightGBM.VC.db +/lightgbm +/testlightgbm + +# Created by https://www.gitignore.io/api/python + +### Python ### +!/python-package/lightgbm/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +prof/ +*.prof +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +.ruff_cache/ +cover/ +**/coverage.html +**/coverage.html.zip +**/Rplots.pdf + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +docs/pythonapi/ +*.flag + +# Doxygen documentation +docs/doxyoutput/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +Untitled*.ipynb + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# dotenv +.env + +# virtualenv +.venv/ +venv/ +ENV/ + +# Spyder project settings +.spyderproject + +# Rope project settings +.ropeproject + +# R testing artefact +lightgbm.model + +# saved or dumped model/data +*.model +*.pkl +*.bin +*.h5 + +# macOS +**/.DS_Store + +# VSCode +.vscode + +# IntelliJ/CLion +.idea +*.iml +/cmake-build-debug/ + +# Files from local Python install +lightgbm-python/ +python-package/LICENSE +python-package/build_cpp/ +python-package/compile/ +python-package/lightgbm/VERSION.txt + +# R build artefacts +**/autom4te.cache/ +R-package/conftest* +R-package/config.status +!R-package/data/agaricus.test.rda +!R-package/data/agaricus.train.rda +!R-package/data/bank.rda +R-package/docs +R-package/src/CMakeLists.txt +R-package/src/Makevars +R-package/src/lib_lightgbm.so.dSYM/ +R-package/src/src/ +R-package/src-x64 +R-package/src-i386 +R-package/**/VERSION.txt +**/Makevars.win +lightgbm_r/ +lightgbm*.tar.gz +lightgbm*.tgz +lightgbm.Rcheck/ +miktex*.zip +*.def + +# Files created by examples and tests +*.buffer +**/lgb-Dataset.data +**/lgb.Dataset.data +**/model.txt +**/lgb-model.txt +examples/**/*.txt +tests/distributed/mlist.txt +tests/distributed/train* +tests/distributed/model* +tests/distributed/predict* + + +# Files from interactive R sessions +.Rproj.user +**/.Rapp.history +**/.Rhistory +*.rda +*.RData +*.rds + +# Files generated by aspell +**/*.bak + +# GraphViz artifacts +*.gv +*.gv.* + +# Files from local Dask work +dask-worker-space/ + +# credentials and key material +*.env +*.pem +*.pub +*.rdp +*_rsa + +# hipify-perl -inplace leaves behind *.prehip files +*.prehip + +# pixi environments +.pixi +!.pixi/config.toml + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# files created by release tasks +/release-artifacts diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..63f0903 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,15 @@ +[submodule "include/boost/compute"] + path = external_libs/compute + url = https://github.com/boostorg/compute +[submodule "eigen"] + path = external_libs/eigen + url = https://gitlab.com/libeigen/eigen.git +[submodule "external_libs/fmt"] + path = external_libs/fmt + url = https://github.com/fmtlib/fmt.git +[submodule "external_libs/fast_double_parser"] + path = external_libs/fast_double_parser + url = https://github.com/lemire/fast_double_parser.git +[submodule "external_libs/nanoarrow"] + path = external_libs/nanoarrow + url = https://github.com/apache/arrow-nanoarrow.git diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..6aca01c --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,115 @@ +# exclude files which are auto-generated by build tools +exclude: | + (?x)^( + build| + external_libs| + lightgbm-python| + lightgbm_r| + \.pixi| + )$ + |R-package/configure$ + |R-package/inst/Makevars$ + |R-package/inst/Makevars.win$ + |R-package/man/.*Rd$ + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: check-toml + - id: check-xml + - id: end-of-file-fixer + - id: trailing-whitespace + - repo: https://github.com/cmake-lint/cmake-lint + rev: '1.4.3' + hooks: + - id: cmakelint + args: ["--linelength=120"] + - repo: https://github.com/cpplint/cpplint + rev: '2.0.2' + hooks: + - id: cpplint + args: + - --root=.. # workaround to get correct header guard pattern + - --recursive + - --filter=-build/include_subdir,-whitespace/line_length + - repo: local + hooks: + - id: check-omp-pragmas + name: check-omp-pragmas + entry: sh + args: + - .ci/check-omp-pragmas.sh + language: system + pass_filenames: false + - repo: https://github.com/adrienverge/yamllint + rev: v1.38.0 + hooks: + - id: yamllint + args: ["--strict"] + - repo: local + hooks: + - id: regenerate-parameters + name: regenerate-parameters + entry: python + args: + - ./.ci/parameter-generator.py + language: python + pass_filenames: false + - repo: https://github.com/rstcheck/rstcheck + rev: v6.2.5 + hooks: + - id: rstcheck + args: ["--config", "./python-package/pyproject.toml"] + additional_dependencies: + - breathe>=4.36.0 + - sphinx>=8.1.3 + - sphinx_rtd_theme>=3.0.1 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.15.20 + hooks: + - id: ruff-check + args: ["--config", "python-package/pyproject.toml"] + types_or: [python, jupyter] + - id: ruff-format + args: ["--config", "python-package/pyproject.toml"] + types_or: [python, jupyter] + - repo: https://github.com/biomejs/pre-commit + rev: v2.5.2 + hooks: + - id: biome-ci + args: + - --config-path=./biome.json + - --diagnostic-level=info + - --error-on-warnings + - repo: https://github.com/shellcheck-py/shellcheck-py + rev: v0.11.0.1 + hooks: + - id: shellcheck + - repo: https://github.com/crate-ci/typos + rev: v1.48.0 + hooks: + - id: typos + args: ["--force-exclude"] + exclude: (\.gitignore$)|(^\.editorconfig$) + - repo: https://github.com/henryiii/validate-pyproject-schema-store + rev: 2026.06.30 + hooks: + - id: validate-pyproject + files: python-package/pyproject.toml$ + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v2.1.0 + hooks: + - id: mypy + args: ["--config-file", "python-package/pyproject.toml", "python-package/"] + pass_filenames: false + verbose: true + additional_dependencies: + - matplotlib>=3.9.1 + - narwhals>=1.15 + - pandas>=2.0 + - scikit-learn>=1.5.2 + - repo: https://github.com/zizmorcore/zizmor-pre-commit + rev: v1.26.1 + hooks: + - id: zizmor diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..1915551 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,16 @@ +version: 2 +build: + os: "ubuntu-24.04" + tools: + python: "miniforge3-latest" +conda: + environment: docs/env.yml +formats: + - pdf +sphinx: + builder: html + configuration: docs/conf.py + fail_on_warning: true +submodules: + include: all + recursive: true diff --git a/.typos.toml b/.typos.toml new file mode 100644 index 0000000..426ad08 --- /dev/null +++ b/.typos.toml @@ -0,0 +1,22 @@ +default.extend-ignore-re = [ + "/Ot", + "mis-alignment", + "mis-spelled", + "posix-seh-rt", +] + +[default.extend-words] +MAPE = "MAPE" +datas = "datas" +indx = "indx" +interprete = "interprete" +mape = "mape" +splitted = "splitted" + +[default.extend-identifiers] +ERRORs = "ERRORs" +GAM = "GAM" +ND24s = "ND24s" +WARNINGs = "WARNINGs" +fullset = "fullset" +thess = "thess" diff --git a/.yamllint.yml b/.yamllint.yml new file mode 100644 index 0000000..7bb0ae9 --- /dev/null +++ b/.yamllint.yml @@ -0,0 +1,10 @@ +# default config: https://yamllint.readthedocs.io/en/stable/configuration.html#default-configuration +extends: default + +rules: + document-start: disable + line-length: + max: 120 + truthy: + # prevent treating GitHub Workflow "on" key as boolean value + check-keys: false diff --git a/CMakeLists.txt b/CMakeLists.txt new file mode 100644 index 0000000..109975c --- /dev/null +++ b/CMakeLists.txt @@ -0,0 +1,866 @@ +# Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. +# Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. +# Licensed under the MIT License. See LICENSE file in the project root for license information. + +cmake_minimum_required(VERSION 3.28) + +option(USE_MPI "Enable MPI-based distributed learning" OFF) +option(USE_OPENMP "Enable OpenMP" ON) +option(USE_GPU "Enable GPU-accelerated training" OFF) +option(USE_SWIG "Enable SWIG to generate Java API" OFF) +option(USE_TIMETAG "Set to ON to output time costs" OFF) +option(USE_CUDA "Enable CUDA-accelerated training " OFF) +option(USE_ROCM "Enable ROCm-accelerated training " OFF) +option(USE_DEBUG "Set to ON for Debug mode" OFF) +option(USE_SANITIZER "Use sanitizer flags" OFF) +set( + ENABLED_SANITIZERS + "address" "leak" "undefined" + CACHE + STRING + "Semicolon separated list of sanitizer names, e.g., 'address;leak'. \ +Supported sanitizers are address, leak, undefined and thread." +) +option(USE_HOMEBREW_FALLBACK "(macOS-only) also look in 'brew --prefix' for libraries (e.g. OpenMP)" ON) +option(BUILD_CLI "Build the 'lightgbm' command-line interface in addition to lib_lightgbm" ON) +option(BUILD_CPP_TEST "Build C++ tests with Google Test" OFF) +option(BUILD_STATIC_LIB "Build static library" OFF) +option(INSTALL_HEADERS "Install headers to CMAKE_INSTALL_PREFIX (e.g. '/usr/local/include')" ON) +option(__BUILD_FOR_PYTHON "Set to ON if building lib_lightgbm for use with the Python-package" OFF) +option(__BUILD_FOR_R "Set to ON if building lib_lightgbm for use with the R-package" OFF) +option(__INTEGRATE_OPENCL "Set to ON if building LightGBM with the OpenCL ICD Loader and its dependencies included" OFF) + +# If using Visual Studio generators, always target v10.x of the Windows SDK. +# Doing this avoids lookups that could fall back to very old versions, e.g. by finding +# outdated registry entries. +# ref: https://cmake.org/cmake/help/latest/variable/CMAKE_VS_WINDOWS_TARGET_PLATFORM_VERSION.html +if(CMAKE_GENERATOR MATCHES "Visual Studio") + set(CMAKE_SYSTEM_VERSION 10.0 CACHE INTERNAL "target Windows SDK version" FORCE) +endif() + +project(lightgbm LANGUAGES C CXX) + +include(cmake/Utils.cmake) + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake/modules") + +#-- Sanitizer +if(USE_SANITIZER) + if(MSVC) + message(FATAL_ERROR "Sanitizers are not supported with MSVC.") + endif() + include(cmake/Sanitizer.cmake) + enable_sanitizers("${ENABLED_SANITIZERS}") +endif() + +if(__INTEGRATE_OPENCL) + set(__INTEGRATE_OPENCL ON CACHE BOOL "" FORCE) + set(USE_GPU OFF CACHE BOOL "" FORCE) + message(STATUS "Building library with integrated OpenCL components") +endif() + +if(__BUILD_FOR_PYTHON OR __BUILD_FOR_R OR USE_SWIG) + # the SWIG wrapper, the Python and R packages don't require the CLI + set(BUILD_CLI OFF) + # installing the SWIG wrapper, the R and Python packages shouldn't place LightGBM's headers + # outside of where the package is installed + set(INSTALL_HEADERS OFF) +endif() + +if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU") + if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.8.2") + message(FATAL_ERROR "Insufficient gcc version (${CMAKE_CXX_COMPILER_VERSION})") + endif() +elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "3.8") + message(FATAL_ERROR "Insufficient Clang version (${CMAKE_CXX_COMPILER_VERSION})") + endif() +elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang") + if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "8.1.0") + message(FATAL_ERROR "Insufficient AppleClang version (${CMAKE_CXX_COMPILER_VERSION})") + endif() +elseif(MSVC) + if(MSVC_VERSION LESS 1900) + message(FATAL_ERROR "Insufficient MSVC version (${MSVC_VERSION})") + endif() +endif() + +if(USE_SWIG) + find_package(SWIG REQUIRED) + find_package(Java REQUIRED) + find_package(JNI REQUIRED) + include(UseJava) + include(UseSWIG) + set(SWIG_CXX_EXTENSION "cxx") + set(SWIG_EXTRA_LIBRARIES "") + set(SWIG_JAVA_EXTRA_FILE_EXTENSIONS ".java" "JNI.java") + set(SWIG_MODULE_JAVA_LANGUAGE "JAVA") + set(SWIG_MODULE_JAVA_SWIG_LANGUAGE_FLAG "java") + set(CMAKE_SWIG_OUTDIR "${CMAKE_CURRENT_BINARY_DIR}/java") + include_directories(Java_INCLUDE_DIRS) + include_directories(JNI_INCLUDE_DIRS) + include_directories($ENV{JAVA_HOME}/include) + if(WIN32) + set(LGBM_SWIG_DESTINATION_DIR "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/windows/x86_64") + include_directories($ENV{JAVA_HOME}/include/win32) + elseif(APPLE) + set(LGBM_SWIG_DESTINATION_DIR "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/osx/x86_64") + include_directories($ENV{JAVA_HOME}/include/darwin) + else() + set(LGBM_SWIG_DESTINATION_DIR "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/linux/x86_64") + include_directories($ENV{JAVA_HOME}/include/linux) + endif() + file(MAKE_DIRECTORY "${LGBM_SWIG_DESTINATION_DIR}") +endif() + +set(EIGEN_DIR "${PROJECT_SOURCE_DIR}/external_libs/eigen") +include_directories(${EIGEN_DIR}) + +# See https://gitlab.com/libeigen/eigen/-/blob/master/COPYING.README +add_definitions(-DEIGEN_MPL2_ONLY) +add_definitions(-DEIGEN_DONT_PARALLELIZE) + +set(FAST_DOUBLE_PARSER_INCLUDE_DIR "${PROJECT_SOURCE_DIR}/external_libs/fast_double_parser/include") +include_directories(${FAST_DOUBLE_PARSER_INCLUDE_DIR}) + +set(FMT_INCLUDE_DIR "${PROJECT_SOURCE_DIR}/external_libs/fmt/include") +include_directories(${FMT_INCLUDE_DIR}) + +if(BUILD_CPP_TEST AND MSVC) + # Use /MT flag to statically link the C runtime. Must be set before + # add_subdirectory(nanoarrow) so the bundled library uses the same runtime + # as the test executable; otherwise linking fails with unresolved __imp_* + # symbols (e.g. __imp_realloc). + set(CMAKE_MSVC_RUNTIME_LIBRARY "MultiThreaded$<$:Debug>") +endif() + +# nanoarrow is only needed by the Arrow-based C API entry points, which are +# disabled for the R package (the R API never calls them). Skipping the +# subdirectory keeps the package tarball free of nanoarrow sources, which +# would otherwise trigger CRAN warnings about diagnostic-suppressing pragmas. +if(NOT __BUILD_FOR_R) + add_subdirectory("${PROJECT_SOURCE_DIR}/external_libs/nanoarrow" EXCLUDE_FROM_ALL) + if(NOT BUILD_STATIC_LIB) + set_target_properties(nanoarrow_static PROPERTIES POSITION_INDEPENDENT_CODE ON) + endif() +endif() + +if(__BUILD_FOR_R) + find_package(LibR REQUIRED) + message(STATUS "LIBR_EXECUTABLE: ${LIBR_EXECUTABLE}") + message(STATUS "LIBR_INCLUDE_DIRS: ${LIBR_INCLUDE_DIRS}") + message(STATUS "LIBR_LIBS_DIR: ${LIBR_LIBS_DIR}") + message(STATUS "LIBR_CORE_LIBRARY: ${LIBR_CORE_LIBRARY}") + include_directories(${LIBR_INCLUDE_DIRS}) + add_definitions(-DLGB_R_BUILD) +endif() + +if(USE_TIMETAG) + add_definitions(-DTIMETAG) +endif() + +if(USE_DEBUG) + add_definitions(-DDEBUG) +endif() + +if(USE_MPI) + find_package(MPI REQUIRED) + add_definitions(-DUSE_MPI) +else() + add_definitions(-DUSE_SOCKET) +endif() + +if(USE_CUDA) + set(CMAKE_CUDA_HOST_COMPILER "${CMAKE_CXX_COMPILER}") + find_package(CUDAToolkit 11.8 REQUIRED) + if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + compute_cmake_cuda_archs("${CUDAToolkit_VERSION}") + endif() + enable_language(CUDA) + set(USE_OPENMP ON CACHE BOOL "CUDA requires OpenMP" FORCE) +endif() + +if(USE_ROCM) + enable_language(HIP) + set(USE_OPENMP ON CACHE BOOL "ROCm requires OpenMP" FORCE) +endif() + +if(USE_OPENMP) + if(APPLE) + find_package(OpenMP) + if(NOT OpenMP_FOUND) + if(USE_HOMEBREW_FALLBACK) + # libomp 15.0+ from brew is keg-only, so have to search in other locations. + # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927. + execute_process(COMMAND brew --prefix libomp + OUTPUT_VARIABLE HOMEBREW_LIBOMP_PREFIX + OUTPUT_STRIP_TRAILING_WHITESPACE) + set(OpenMP_C_FLAGS "-Xpreprocessor -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include") + set(OpenMP_CXX_FLAGS "-Xpreprocessor -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include") + set(OpenMP_C_LIB_NAMES omp) + set(OpenMP_CXX_LIB_NAMES omp) + set(OpenMP_omp_LIBRARY ${HOMEBREW_LIBOMP_PREFIX}/lib/libomp.dylib) + endif() + find_package(OpenMP REQUIRED) + endif() + else() + find_package(OpenMP REQUIRED) + endif() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") +endif() + +if(USE_GPU) + set(BOOST_COMPUTE_HEADER_DIR ${PROJECT_SOURCE_DIR}/external_libs/compute/include) + include_directories(${BOOST_COMPUTE_HEADER_DIR}) + find_package(OpenCL REQUIRED) + include_directories(${OpenCL_INCLUDE_DIRS}) + message(STATUS "OpenCL include directory: " ${OpenCL_INCLUDE_DIRS}) + if(WIN32) + set(Boost_USE_STATIC_LIBS ON) + endif() + find_package(Boost 1.56.0 COMPONENTS filesystem system REQUIRED) + if(WIN32) + # disable autolinking in boost + add_definitions(-DBOOST_ALL_NO_LIB) + endif() + include_directories(${Boost_INCLUDE_DIRS}) + add_definitions(-DUSE_GPU) +endif() + +if(__INTEGRATE_OPENCL) + if(APPLE) + message(FATAL_ERROR "Integrated OpenCL build is not available on macOS") + else() + include(cmake/IntegratedOpenCL.cmake) + add_definitions(-DUSE_GPU) + endif() +endif() + +if(USE_CUDA) + find_package(NCCL REQUIRED) + include_directories(${CUDAToolkit_INCLUDE_DIRS}) + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -Xcompiler=${OpenMP_CXX_FLAGS} -Xcompiler=-fPIC -Xcompiler=-Wall") + if(USE_DEBUG) + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -g") + else() + set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -O3 -lineinfo") + endif() + message(STATUS "CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}") + + add_definitions(-DUSE_CUDA) + + if(NOT DEFINED CMAKE_CUDA_STANDARD) + set(CMAKE_CUDA_STANDARD 17) + set(CMAKE_CUDA_STANDARD_REQUIRED ON) + endif() +endif() + +if(USE_ROCM) + find_package(HIP) + include_directories(${HIP_INCLUDE_DIRS}) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_AMD__") + set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} ${OpenMP_CXX_FLAGS} -fPIC -Wall") + + # avoid warning: unused variable 'mask' due to __shfl_down_sync work-around + set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-unused-variable") + # avoid warning: 'hipHostAlloc' is deprecated: use hipHostMalloc instead + set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-deprecated-declarations") + # avoid many warnings about missing overrides + set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-inconsistent-missing-override") + # avoid warning: shift count >= width of type in feature_histogram.hpp + set(DISABLED_WARNINGS "${DISABLED_WARNINGS} -Wno-shift-count-overflow") + + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${DISABLED_WARNINGS}") + set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} ${DISABLED_WARNINGS}") + + if(USE_DEBUG) + set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -g -O0") + else() + set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -O3") + endif() + message(STATUS "CMAKE_HIP_FLAGS: ${CMAKE_HIP_FLAGS}") + + # Building for ROCm almost always means USE_CUDA. + # Exceptions to this will be guarded by USE_ROCM. + add_definitions(-DUSE_CUDA) + add_definitions(-DUSE_ROCM) +endif() + +include(CheckCXXSourceCompiles) +check_cxx_source_compiles(" +#include +int main() { + int a = 0; + _mm_prefetch(&a, _MM_HINT_NTA); + return 0; +} +" MM_PREFETCH) + +if(${MM_PREFETCH}) + message(STATUS "Using _mm_prefetch") + add_definitions(-DMM_PREFETCH) +endif() + +include(CheckCXXSourceCompiles) +check_cxx_source_compiles(" +#include +int main() { + char *a = (char*)_mm_malloc(8, 16); + _mm_free(a); + return 0; +} +" MM_MALLOC) + +if(${MM_MALLOC}) + message(STATUS "Using _mm_malloc") + add_definitions(-DMM_MALLOC) +endif() + +if(UNIX OR MINGW OR CYGWIN) + set( + CMAKE_CXX_FLAGS + "${CMAKE_CXX_FLAGS} -pthread -Wextra -Wall -Wno-ignored-attributes -Wno-unknown-pragmas -Wno-return-type" + ) + if(MINGW) + # ignore this warning: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=95353 + set( + CMAKE_CXX_FLAGS + "${CMAKE_CXX_FLAGS} -Wno-stringop-overflow" + ) + endif() + if(USE_DEBUG) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O0") + else() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") + endif() + if(USE_SWIG) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-strict-aliasing") + endif() + if(NOT USE_OPENMP) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unknown-pragmas -Wno-unused-private-field") + endif() + if(__BUILD_FOR_R AND CMAKE_CXX_COMPILER_ID STREQUAL "GNU") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-cast-function-type") + endif() +endif() + +if(WIN32 AND MINGW) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -static-libstdc++") +endif() + +# Check if inet_pton is already available, to avoid conflicts with the implementation in LightGBM. +# As of 2022, MinGW started including a definition of inet_pton. +if(WIN32) + include(CheckSymbolExists) + list(APPEND CMAKE_REQUIRED_LIBRARIES "ws2_32") + check_symbol_exists(inet_pton "ws2tcpip.h" WIN_INET_PTON_FOUND) + if(WIN_INET_PTON_FOUND) + add_definitions(-DWIN_HAS_INET_PTON) + endif() + list(REMOVE_ITEM CMAKE_REQUIRED_LIBRARIES "ws2_32") +endif() + +if(MSVC) + # compiling 'fmt' on MSVC: "Unicode support requires compiling with /utf-8" + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /MP /utf-8") + if(__BUILD_FOR_R) + # MSVC does not like this commit: + # https://github.com/wch/r-source/commit/fb52ac1a610571fcb8ac92d886b9fefcffaa7d48 + # + # and raises "error C3646: 'private_data_c': unknown override specifier" + add_definitions(-DR_LEGACY_RCOMPLEX) + endif() + if(USE_DEBUG) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /Od") + else() + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /O2 /Ob2 /Oi /Ot /Oy") + endif() +else() + if(NOT BUILD_STATIC_LIB) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC") + endif() + if(NOT USE_DEBUG) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -funroll-loops") + endif() +endif() + +set(LightGBM_HEADER_DIR ${PROJECT_SOURCE_DIR}/include) + +set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}) +set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}) + +include_directories(${LightGBM_HEADER_DIR}) + +if(USE_MPI) + include_directories(${MPI_CXX_INCLUDE_PATH}) +endif() + +set( + LGBM_SOURCES + src/boosting/boosting.cpp + src/boosting/gbdt_model_text.cpp + src/boosting/gbdt_prediction.cpp + src/boosting/gbdt.cpp + src/boosting/prediction_early_stop.cpp + src/boosting/sample_strategy.cpp + src/io/bin.cpp + src/io/config_auto.cpp + src/io/config.cpp + src/io/dataset_loader.cpp + src/io/dataset.cpp + src/io/file_io.cpp + src/io/json11.cpp + src/io/metadata.cpp + src/io/parser.cpp + src/io/train_share_states.cpp + src/io/tree.cpp + src/metric/dcg_calculator.cpp + src/metric/metric.cpp + src/network/linker_topo.cpp + src/network/linkers_mpi.cpp + src/network/linkers_socket.cpp + src/network/network.cpp + src/objective/objective_function.cpp + src/treelearner/data_parallel_tree_learner.cpp + src/treelearner/feature_histogram.cpp + src/treelearner/feature_parallel_tree_learner.cpp + src/treelearner/gpu_tree_learner.cpp + src/treelearner/gradient_discretizer.cpp + src/treelearner/linear_tree_learner.cpp + src/treelearner/serial_tree_learner.cpp + src/treelearner/tree_learner.cpp + src/treelearner/voting_parallel_tree_learner.cpp + src/utils/openmp_wrapper.cpp +) +set( + LGBM_CUDA_SOURCES + src/boosting/cuda/cuda_score_updater.cpp + src/boosting/cuda/cuda_score_updater.cu + src/boosting/cuda/nccl_gbdt.cpp + src/metric/cuda/cuda_binary_metric.cpp + src/metric/cuda/cuda_pointwise_metric.cpp + src/metric/cuda/cuda_regression_metric.cpp + src/metric/cuda/cuda_pointwise_metric.cu + src/objective/cuda/cuda_binary_objective.cpp + src/objective/cuda/cuda_multiclass_objective.cpp + src/objective/cuda/cuda_rank_objective.cpp + src/objective/cuda/cuda_regression_objective.cpp + src/objective/cuda/cuda_binary_objective.cu + src/objective/cuda/cuda_multiclass_objective.cu + src/objective/cuda/cuda_rank_objective.cu + src/objective/cuda/cuda_regression_objective.cu + src/treelearner/cuda/cuda_best_split_finder.cpp + src/treelearner/cuda/cuda_data_partition.cpp + src/treelearner/cuda/cuda_histogram_constructor.cpp + src/treelearner/cuda/cuda_leaf_splits.cpp + src/treelearner/cuda/cuda_single_gpu_tree_learner.cpp + src/treelearner/cuda/cuda_best_split_finder.cu + src/treelearner/cuda/cuda_data_partition.cu + src/treelearner/cuda/cuda_gradient_discretizer.cu + src/treelearner/cuda/cuda_histogram_constructor.cu + src/treelearner/cuda/cuda_leaf_splits.cu + src/treelearner/cuda/cuda_single_gpu_tree_learner.cu + src/io/cuda/cuda_column_data.cu + src/io/cuda/cuda_tree.cu + src/io/cuda/cuda_column_data.cpp + src/io/cuda/cuda_metadata.cpp + src/io/cuda/cuda_row_data.cpp + src/io/cuda/cuda_tree.cpp + src/cuda/cuda_utils.cpp + src/cuda/cuda_algorithms.cu +) + +if(USE_CUDA OR USE_ROCM) + list(APPEND LGBM_SOURCES ${LGBM_CUDA_SOURCES}) +endif() + +if(USE_ROCM) + set(CU_FILES "") + foreach(file IN LISTS LGBM_CUDA_SOURCES) + string(REGEX MATCH "\\.cu$" is_cu_file "${file}") + if(is_cu_file) + list(APPEND CU_FILES "${file}") + endif() + endforeach() + set_source_files_properties(${CU_FILES} PROPERTIES LANGUAGE HIP) +endif() + +add_library(lightgbm_objs OBJECT ${LGBM_SOURCES}) +if(NOT __BUILD_FOR_R) + target_link_libraries(lightgbm_objs PRIVATE nanoarrow_static) +endif() + +if(BUILD_CLI) + add_executable(lightgbm src/main.cpp src/application/application.cpp) + target_link_libraries(lightgbm PRIVATE lightgbm_objs) +endif() + +set(API_SOURCES "src/c_api.cpp") +# Only build the R part of the library if building for +# use with the R-package +if(__BUILD_FOR_R) + list(APPEND API_SOURCES "src/lightgbm_R.cpp") +endif() + +add_library(lightgbm_capi_objs OBJECT ${API_SOURCES}) +if(NOT __BUILD_FOR_R) + target_link_libraries(lightgbm_capi_objs PRIVATE nanoarrow_static) +endif() + +if(BUILD_STATIC_LIB) + add_library(_lightgbm STATIC) +else() + add_library(_lightgbm SHARED) +endif() + +# R expects libraries of the form .{dll,dylib,so}, not lib_.{dll,dylib,so} +if(__BUILD_FOR_R) + set_target_properties( + _lightgbm + PROPERTIES + PREFIX "" + OUTPUT_NAME "lightgbm" + ) +endif() + +# LightGBM headers include openmp, cuda, R etc. headers, +# thus PUBLIC is required for building _lightgbm_swig target. +target_link_libraries(_lightgbm PUBLIC lightgbm_capi_objs lightgbm_objs) + +if(MSVC AND NOT __BUILD_FOR_R) + set_target_properties(_lightgbm PROPERTIES OUTPUT_NAME "lib_lightgbm") +endif() + +if(USE_SWIG) + set_property(SOURCE swig/lightgbmlib.i PROPERTY CPLUSPLUS ON) + list(APPEND swig_options -package com.microsoft.ml.lightgbm) + set_property(SOURCE swig/lightgbmlib.i PROPERTY SWIG_FLAGS "${swig_options}") + swig_add_library(_lightgbm_swig LANGUAGE java SOURCES swig/lightgbmlib.i) + swig_link_libraries(_lightgbm_swig _lightgbm) + set_target_properties( + _lightgbm_swig + PROPERTIES + # needed to ensure Linux build does not have lib prefix specified twice, e.g. liblib_lightgbm_swig + PREFIX "" + # needed in some versions of CMake for VS and MinGW builds to ensure output dll has lib prefix + OUTPUT_NAME "lib_lightgbm_swig" + ) + if(WIN32) + set(LGBM_SWIG_LIB_DESTINATION_PATH "${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.dll") + if(MINGW OR CYGWIN) + set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm.dll") + set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.dll") + else() + set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/Release/lib_lightgbm.dll") + set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/Release/lib_lightgbm_swig.dll") + endif() + elseif(APPLE) + set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm.dylib") + set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.jnilib") + set(LGBM_SWIG_LIB_DESTINATION_PATH "${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.dylib") + else() + set(LGBM_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm.so") + set(LGBM_SWIG_LIB_SOURCE_PATH "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.so") + set(LGBM_SWIG_LIB_DESTINATION_PATH "${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.so") + endif() + add_custom_command( + TARGET _lightgbm_swig + POST_BUILD + COMMAND "${Java_JAVAC_EXECUTABLE}" -d . java/*.java + COMMAND + "${CMAKE_COMMAND}" + -E + copy_if_different + "${LGBM_LIB_SOURCE_PATH}" + "${LGBM_SWIG_DESTINATION_DIR}" + COMMAND + "${CMAKE_COMMAND}" + -E + copy_if_different + "${LGBM_SWIG_LIB_SOURCE_PATH}" + "${LGBM_SWIG_LIB_DESTINATION_PATH}" + COMMAND "${Java_JAR_EXECUTABLE}" -cf lightgbmlib.jar com + ) +endif() + +if(USE_MPI) + target_link_libraries(lightgbm_objs PUBLIC ${MPI_CXX_LIBRARIES}) +endif() + +if(USE_OPENMP) + if(CMAKE_CXX_COMPILER_ID MATCHES "Clang") + target_link_libraries(lightgbm_objs PUBLIC OpenMP::OpenMP_CXX) + # c_api headers also includes OpenMP headers, thus compiling + # lightgbm_capi_objs needs include directory for OpenMP. + # Specifying OpenMP in target_link_libraries will get include directory + # requirements for compilation. + # This uses CMake's Transitive Usage Requirements. Refer to CMake doc: + # https://cmake.org/cmake/help/v3.16/manual/cmake-buildsystem.7.html#transitive-usage-requirements + target_link_libraries(lightgbm_capi_objs PUBLIC OpenMP::OpenMP_CXX) + endif() +endif() + +if(USE_GPU) + target_link_libraries(lightgbm_objs PUBLIC ${OpenCL_LIBRARY} ${Boost_LIBRARIES}) +endif() + +if(USE_CUDA) + target_link_libraries(lightgbm_objs PUBLIC NCCL::NCCL) +endif() + +if(USE_ROCM) + find_package(rccl) + target_link_libraries(lightgbm_objs PUBLIC ${RCCL_LIBRARY}) +endif() + +if(__INTEGRATE_OPENCL) + # targets OpenCL and Boost are added in IntegratedOpenCL.cmake + add_dependencies(lightgbm_objs OpenCL Boost) + # variables INTEGRATED_OPENCL_* are set in IntegratedOpenCL.cmake + target_include_directories(lightgbm_objs PRIVATE ${INTEGRATED_OPENCL_INCLUDES}) + target_compile_definitions(lightgbm_objs PRIVATE ${INTEGRATED_OPENCL_DEFINITIONS}) + target_link_libraries(lightgbm_objs PUBLIC ${INTEGRATED_OPENCL_LIBRARIES} ${CMAKE_DL_LIBS}) +endif() + +if(USE_CUDA) + + set_target_properties( + lightgbm_objs + PROPERTIES + CUDA_SEPARABLE_COMPILATION ON + ) + + set_target_properties( + _lightgbm + PROPERTIES + CUDA_RESOLVE_DEVICE_SYMBOLS ON + ) + + if(BUILD_CLI) + set_target_properties( + lightgbm + PROPERTIES + CUDA_RESOLVE_DEVICE_SYMBOLS ON + ) + endif() +endif() + +if(USE_ROCM) + target_link_libraries(lightgbm_objs PUBLIC hip::host) +endif() + +if(WIN32) + if(MINGW OR CYGWIN) + target_link_libraries(lightgbm_objs PUBLIC ws2_32 iphlpapi) + endif() +endif() + +if(__BUILD_FOR_R) + # utils/log.h and capi uses R headers, thus both object libraries need to link + # with R lib. + if(MSVC) + set(R_LIB ${LIBR_MSVC_CORE_LIBRARY}) + else() + set(R_LIB ${LIBR_CORE_LIBRARY}) + endif() + target_link_libraries(lightgbm_objs PUBLIC ${R_LIB}) + target_link_libraries(lightgbm_capi_objs PUBLIC ${R_LIB}) +endif() + +#-- Google C++ tests +if(BUILD_CPP_TEST) + find_package(GTest CONFIG) + if(NOT GTEST_FOUND) + message(STATUS "Did not find Google Test in the system root. Fetching Google Test now...") + include(FetchContent) +# lint_cmake: -readability/wonkycase + FetchContent_Declare( +# lint_cmake: +readability/wonkycase + googletest + GIT_REPOSITORY https://github.com/google/googletest.git + GIT_TAG v1.14.0 + ) +# lint_cmake: -readability/wonkycase + FetchContent_MakeAvailable(googletest) +# lint_cmake: +readability/wonkycase + add_library(GTest::GTest ALIAS gtest) + endif() + + set(LightGBM_TEST_HEADER_DIR ${PROJECT_SOURCE_DIR}/tests/cpp_tests) + include_directories(${LightGBM_TEST_HEADER_DIR}) + + set( + CPP_TEST_SOURCES + tests/cpp_tests/test_array_args.cpp + tests/cpp_tests/test_arrow.cpp + tests/cpp_tests/test_arrow_deprecated.cpp + tests/cpp_tests/test_byte_buffer.cpp + tests/cpp_tests/test_chunked_array.cpp + tests/cpp_tests/test_common.cpp + tests/cpp_tests/test_main.cpp + tests/cpp_tests/test_serialize.cpp + tests/cpp_tests/test_single_row.cpp + tests/cpp_tests/test_stream.cpp + tests/cpp_tests/testutils.cpp + ) + if(MSVC) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /permissive-") + endif() + add_executable(testlightgbm ${CPP_TEST_SOURCES}) + target_link_libraries(testlightgbm PRIVATE lightgbm_objs lightgbm_capi_objs nanoarrow_static GTest::GTest) +endif() + +if(BUILD_CLI) + install( + TARGETS lightgbm + RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/bin + ) +endif() + +if(__BUILD_FOR_PYTHON) + set(CMAKE_INSTALL_PREFIX "lightgbm") +endif() + +# The macOS linker puts an absolute path to linked libraries in lib_lightgbm.dylib. +# This block overrides that information for LightGBM's OpenMP dependency, to allow +# finding that library in more places. +# +# This reduces the risk of runtime issues resulting from multiple {libgomp,libiomp,libomp}.dylib being loaded. +# +if(APPLE AND USE_OPENMP AND NOT BUILD_STATIC_LIB) + # store path to {libgomp,libiomp,libomp}.dylib found at build time in a variable + get_target_property( + OpenMP_LIBRARY_LOCATION + OpenMP::OpenMP_CXX + INTERFACE_LINK_LIBRARIES + ) + # get just the filename of that path + # (to deal with the possibility that it might be 'libomp.dylib' or 'libgomp.dylib' or 'libiomp.dylib') + get_filename_component( + OpenMP_LIBRARY_NAME + ${OpenMP_LIBRARY_LOCATION} + NAME + ) + # get directory of that path + get_filename_component( + OpenMP_LIBRARY_DIR + ${OpenMP_LIBRARY_LOCATION} + DIRECTORY + ) + # get exact name of the library in a variable + get_target_property( + __LIB_LIGHTGBM_OUTPUT_NAME + _lightgbm + OUTPUT_NAME + ) + if(NOT __LIB_LIGHTGBM_OUTPUT_NAME) + set(__LIB_LIGHTGBM_OUTPUT_NAME "lib_lightgbm") + endif() + + if(CMAKE_SHARED_LIBRARY_SUFFIX_CXX) + set( + __LIB_LIGHTGBM_FILENAME "${__LIB_LIGHTGBM_OUTPUT_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX_CXX}" + CACHE INTERNAL "lightgbm shared library filename" + ) + else() + set( + __LIB_LIGHTGBM_FILENAME "${__LIB_LIGHTGBM_OUTPUT_NAME}.dylib" + CACHE INTERNAL "lightgbm shared library filename" + ) + endif() + + # Override the absolute path to OpenMP with a relative one using @rpath. + # + # This also ensures that if a {libgomp,libiomp,libomp}.dylib has already been loaded, it'll just use that. + add_custom_command( + TARGET _lightgbm + POST_BUILD + COMMAND + install_name_tool + -change + ${OpenMP_LIBRARY_LOCATION} + "@rpath/${OpenMP_LIBRARY_NAME}" + "${__LIB_LIGHTGBM_FILENAME}" + WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR} + COMMENT "Replacing hard-coded OpenMP install_name with '@rpath/${OpenMP_LIBRARY_NAME}'..." + ) + # add RPATH entries to ensure the loader looks in the following, in the following order: + # + # - (R-only) ${LIBR_LIBS_DIR} (wherever R for macOS stores vendored third-party libraries) + # - ${OpenMP_LIBRARY_DIR} (wherever find_package(OpenMP) found OpenMP at build time) + # - /opt/homebrew/opt/libomp/lib (where 'brew install' / 'brew link' puts libomp.dylib) + # - /opt/local/lib/libomp (where 'port install' puts libomp.dylib) + # + + # with some compilers, OpenMP ships with the compiler (e.g. libgomp with gcc) + list(APPEND __omp_install_rpaths "${OpenMP_LIBRARY_DIR}") + + # with clang, libomp doesn't ship with the compiler and might be supplied separately + if(CMAKE_CXX_COMPILER_ID MATCHES "Clang") + list( + APPEND __omp_install_rpaths + "/opt/homebrew/opt/libomp/lib" + "/opt/local/lib/libomp" + ) + # It appears that CRAN's macOS binaries compiled with -fopenmp have install names + # of the form: + # + # /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libomp.dylib + # + # That corresponds to the libomp.dylib that ships with the R framework for macOS, available + # from https://cran.r-project.org/bin/macosx/. + # + # That absolute-path install name leads to that library being loaded unconditionally. + # + # That can result in e.g. 'library(data.table)' loading R's libomp.dylib and 'library(lightgbm)' loading + # Homebrew's. Having 2 loaded in the same process can lead to segfaults and unpredictable behavior. + # + # This can't be easily avoided by forcing R-package builds in LightGBM to use R's libomp.dylib + # at build time... LightGBM's CMake uses find_package(OpenMP), and R for macOS only provides the + # library, not CMake config files for it. + # + # Best we can do, to allow CMake-based builds of the R-package here to continue to work + # alongside CRAN-prepared binaries of other packages with OpenMP dependencies, is to + # ensure that R's library directory is the first place the loader searches for + # libomp.dylib when clang is used. + # + # ref: https://github.com/lightgbm-org/LightGBM/issues/6628 + # + if(__BUILD_FOR_R) + list(PREPEND __omp_install_rpaths "${LIBR_LIBS_DIR}") + endif() + endif() + set_target_properties( + _lightgbm + PROPERTIES + BUILD_WITH_INSTALL_RPATH TRUE + INSTALL_RPATH "${__omp_install_rpaths}" + INSTALL_RPATH_USE_LINK_PATH FALSE + ) +endif() + +install( + TARGETS _lightgbm + RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/bin + LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/lib + ARCHIVE DESTINATION ${CMAKE_INSTALL_PREFIX}/lib +) + +if(INSTALL_HEADERS) + install( + DIRECTORY ${LightGBM_HEADER_DIR}/LightGBM + DESTINATION ${CMAKE_INSTALL_PREFIX}/include + ) + install( + FILES ${FAST_DOUBLE_PARSER_INCLUDE_DIR}/fast_double_parser.h + DESTINATION ${CMAKE_INSTALL_PREFIX}/include/LightGBM/utils + ) + install( + DIRECTORY ${FMT_INCLUDE_DIR}/ + DESTINATION ${CMAKE_INSTALL_PREFIX}/include/LightGBM/utils + FILES_MATCHING PATTERN "*.h" + ) +endif() diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..e066932 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,41 @@ +# Microsoft Open Source Code of Conduct + +This code of conduct outlines expectations for participation in Microsoft-managed open source communities, as well as steps for reporting unacceptable behavior. We are committed to providing a welcoming and inspiring community for all. People violating this code of conduct may be banned from the community. + +## Our open source communities strive to: + +* Be friendly and patient: Remember you might not be communicating in someone else's primary spoken or programming language, and others may not have your level of understanding. +* Be welcoming: Our communities welcome and support people of all backgrounds and identities. This includes, but is not limited to members of any race, ethnicity, culture, national origin, color, immigration status, social and economic class, educational level, sex, sexual orientation, gender identity and expression, age, size, family status, political belief, religion, and mental and physical ability. +* Be respectful: We are a world-wide community of professionals, and we conduct ourselves professionally. Disagreement is no excuse for poor behavior and poor manners. Disrespectful and unacceptable behavior includes, but is not limited to: + 1. Violent threats or language. + 2. Discriminatory or derogatory jokes and language. + 3. Posting sexually explicit or violent material. + 4. Posting, or threatening to post, people's personally identifying information ("doxing"). + 5. Insults, especially those using discriminatory terms or slurs. +Behavior that could be perceived as sexual attention. +Advocating for or encouraging any of the above behaviors. +* Understand disagreements: Disagreements, both social and technical, are useful learning opportunities. Seek to understand the other viewpoints and resolve differences constructively. +* This code is not exhaustive or complete. It serves to capture our common understanding of a productive, collaborative environment. We expect the code to be followed in spirit as much as in the letter. + +## Scope + +This code of conduct applies to all repos and communities for Microsoft-managed open source projects regardless of whether or not the repo explicitly calls out its use of this code. The code also applies in public spaces when an individual is representing a project or its community. Examples include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. + +Note: Some Microsoft-managed communities have codes of conduct that pre-date this document and issue resolution process. While communities are not required to change their code, they are expected to use the resolution process outlined here. The review team will coordinate with the communities involved to address your concerns. + +## Reporting Code of Conduct Issues + +We encourage all communities to resolve issues on their own whenever possible. This builds a broader and deeper understanding and ultimately a healthier interaction. In the event that an issue cannot be resolved locally, please feel free to report your concerns by contacting opencode@microsoft.com. Your report will be handled in accordance with the issue resolution process described in the Code of Conduct FAQ. + +In your report please include: + +* Your contact information. +* Names (real, usernames or pseudonyms) of any individuals involved. If there are additional witnesses, please include them as well. +* Your account of what occurred, and if you believe the incident is ongoing. If there is a publicly available record (e.g. a mailing list archive or a public chat log), please include a link or attachment. +* Any additional information that may be helpful. + +All reports will be reviewed by a multi-person team and will result in a response that is deemed necessary and appropriate to the circumstances. Where additional perspectives are needed, the team may seek insight from others with relevant expertise or experience. The confidentiality of the person reporting the incident will be kept at all times. Involved parties are never part of the review team. + +Anyone asked to stop unacceptable behavior is expected to comply immediately. If an individual engages in unacceptable behavior, the review team may take any action they deem appropriate, including a permanent ban from the community. + +This code of conduct is based on the template established by the TODO Group and used by numerous other large communities (e.g., Facebook, Yahoo, Twitter, GitHub) and the Scope section from the Contributor Covenant version 1.4. diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..3a25dfb --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,28 @@ +# contributing + +LightGBM has been developed and used by many active community members. + +Your help is very valuable to make it better for everyone. + +## How to Contribute + +- Check the [Feature Requests Hub](https://github.com/lightgbm-org/LightGBM/issues/2302), and submit pull requests to address chosen issue. If you need development guideline, you can check the [Development Guide](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst) or directly ask us in Issues/Pull Requests. +- Contribute to the [tests](https://github.com/lightgbm-org/LightGBM/tree/main/tests) to make it more reliable. +- Contribute to the [documentation](https://github.com/lightgbm-org/LightGBM/tree/main/docs) to make it clearer for everyone. +- Contribute to the [examples](https://github.com/lightgbm-org/LightGBM/tree/main/examples) to share your experience with other users. +- Add your stories and experience to [Awesome LightGBM](https://github.com/lightgbm-org/LightGBM/blob/main/examples/README.md). If LightGBM helped you in a machine learning competition or some research application, we want to hear about it! +- [Open an issue](https://github.com/lightgbm-org/LightGBM/issues) to report problems or recommend new features. + +## Development Guide + +### Linting + +Every commit in the repository is tested with multiple static analyzers. + +When developing locally, run some of them using `pre-commit` ([pre-commit docs](https://pre-commit.com/)). + +```shell +pre-commit run --all-files +``` + +That command will check for some issues and automatically reformat the code. diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..54a487f --- /dev/null +++ b/LICENSE @@ -0,0 +1,22 @@ +The MIT License (MIT) + +Copyright (c) Microsoft Corporation +Copyright (c) The LightGBM developers + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/MAINTAINING.md b/MAINTAINING.md new file mode 100644 index 0000000..7930cef --- /dev/null +++ b/MAINTAINING.md @@ -0,0 +1,130 @@ +# maintaining + +This document is for LightGBM maintainers. + +## Managing Dependencies + +### Locked Environments with `pixi` + +This project uses `pixi` for tasks and CI jobs that run with a locked set of dependencies. + +In general, updating these environments looks like: + +1. manually modify `pixi.toml` +2. run `pixi install` + +And running inside one looks like: + +```shell +# interactive shell +pixi shell -e py310 + +# run a task +pixi run -e py310 python -c "import pandas; print(pandas.__version__)" +``` + +See https://pixi.prefix.dev/latest/ for more details. + + +## Releasing + +### Step 1: Put up a Release PR + +Create a pull request into `main` which prepares the source code for release. + +Copy the description and checklist from the previous release PR (for example: https://github.com/lightgbm-org/LightGBM/pull/6796). + +This should usually also include a checklist of other issues and PRs that should be completed for the release, +and the PR should be used to discuss what makes it into the release. + +### Step 2: Merge the Release PR + +Once the PR is approved, merge it. + +Do not merge any other PRs into `main` until the rest of the release is complete. + +### Step 3: Wait for a New CI Run on `main` + +Wait for all CI runs triggered by the merge to `main` to complete successfully. + +These runs build and test the official artifacts that will be attached to the GitHub release and published to package managers. + +### Step 4: Create a Release + +Navigate to https://github.com/lightgbm-org/LightGBM/releases. + +Click "edit" on the draft release that `release-drafter` has created there. + +* update the tag and release title to match the version of LightGBM, in the format `v{major}.{minor}.{patch}` +* ensure that tag points at the commit on `main` created by merging the release PR + +When you're satisfied with the state of the release, click "Publish release". + +### Step 5: Upload Artifacts + +After creating a release, run the following from the root of the repo to populate it with artifacts. + +```shell +# download all artifacts to a local directory +./.ci/download-artifacts.sh ${COMMIT_ID} + +# attach them to the GitHub release +gh release upload \ + --repo lightgbm-org/LightGBM \ + "${TAG}" \ + ./release-artifacts/* +``` + +Where: + +* `COMMIT_ID` = full commit hash of the commit on `main` corresponding to the release +* `TAG` = the tag for the release (e.g. `v4.6.0`) + +### Step 6: Complete All Other Post-merge Release Steps + +These include things like publishing to package managers, updating build configs for repackagers like ``conda-forge``, and many other steps. + +See the release checklist on the PR for details. + +## Nightly Packages + +Nightly packages for the `lightgbm` Python package are uploaded to https://anaconda.org/lightgbm-packages on every merge to `main`. + +That's done using an upload token stored in a secret in CI. +Those tokens expire after 1 year. + +To generate a new one, run the following. + +```shell +# install Anaconda CLI +conda install -y -c conda-forge \ + anaconda-auth \ + anaconda-client + +# authenticate locally +anaconda auth login + +# create a token (this expires after 1 year) +TOKEN=$( + anaconda org auth \ + --create \ + --name nightly-uploads \ + --org lightgbm-packages \ + --scopes 'api:read api:write pypi:upload' +) +``` + +That token can be used by maintainers to manually upload packages as well. + +For example: + +```shell +./.ci/download-artifacts.sh $(git rev-parse HEAD) + +# NOTE: set upload token in environment variable 'ANACONDA_API_TOKEN' +anaconda upload \ + --package lightgbm \ + --force-metadata-update \ + -t pypi \ + ./release-artifacts/*.whl +``` diff --git a/R-package/.Rbuildignore b/R-package/.Rbuildignore new file mode 100644 index 0000000..54e8bad --- /dev/null +++ b/R-package/.Rbuildignore @@ -0,0 +1,52 @@ +\.appveyor\.yml +AUTOCONF_UBUNTU_VERSION +^autom4te.cache/.*$ +^.*\.bin +^build_r.R$ +\.clang-format +^.*\.clusterfuzzlite$ +^cran-comments\.md$ +^docs$ +^.*\.dll +\.drone\.yml +^.*\.dylib +\.git +\.gitkeep$ +^.*\.history +^Makefile$ +^.*\.o +^.*\.out +^pkgdown$ +^recreate-configure\.sh$ +^.*\.so +^src/build/.*$ +^src/CMakeLists.txt$ +^src/external_libs/compute/.appveyor.yml$ +^src/external_libs/compute/.coveralls.yml$ +^src/external_libs/compute/.travis.yml$ +^src/external_libs/compute/test/.*$ +^src/external_libs/compute/index.html$ +^src/external_libs/compute/.git$ +^src/external_libs/compute/.gitignore$ +^src/external_libs/compute/CONTRIBUTING.md$ +^src/external_libs/compute/README.md$ +src/external_libs/fast_double_parser/benchmarks +src/external_libs/fast_double_parser/Makefile +src/external_libs/fast_double_parser/.*\.md +src/external_libs/fast_double_parser/tests +src/external_libs/fast_double_parser/.*\.yaml +src/external_libs/fast_double_parser/.*\.yml +src/external_libs/fmt/.*\.md +src/external_libs/fmt/.travis.yml +src/external_libs/fmt/doc +src/external_libs/fmt/support/Android\.mk +src/external_libs/fmt/support/bazel/.bazel.* +src/external_libs/fmt/support/.*\.gradle +src/external_libs/fmt/support/.*\.pro +src/external_libs/fmt/support/.*\.py +src/external_libs/fmt/support/rtd +src/external_libs/fmt/support/.*sublime-syntax +src/external_libs/fmt/support/Vagrantfile +src/external_libs/fmt/support/.*\.xml +src/external_libs/fmt/support/.*\.yml +src/external_libs/fmt/test diff --git a/R-package/AUTOCONF_UBUNTU_VERSION b/R-package/AUTOCONF_UBUNTU_VERSION new file mode 100644 index 0000000..e522974 --- /dev/null +++ b/R-package/AUTOCONF_UBUNTU_VERSION @@ -0,0 +1 @@ +2.71-2 diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION new file mode 100755 index 0000000..c3f7052 --- /dev/null +++ b/R-package/DESCRIPTION @@ -0,0 +1,66 @@ +Package: lightgbm +Type: Package +Title: Light Gradient Boosting Machine +Version: ~~VERSION~~ +Date: ~~DATE~~ +Authors@R: c( + person("Yu", "Shi", email = "yushi2@microsoft.com", role = c("aut")), + person("Guolin", "Ke", email = "guolin.ke@outlook.com", role = c("aut")), + person("Damien", "Soukhavong", email = "damien.soukhavong@skema.edu", role = c("aut")), + person("James", "Lamb", email="jaylamb20@gmail.com", role = c("aut", "cre")), + person("Qi", "Meng", role = c("aut")), + person("Thomas", "Finley", role = c("aut")), + person("Taifeng", "Wang", role = c("aut")), + person("Wei", "Chen", role = c("aut")), + person("Weidong", "Ma", role = c("aut")), + person("Qiwei", "Ye", role = c("aut")), + person("Tie-Yan", "Liu", role = c("aut")), + person("Nikita", "Titov", role = c("aut")), + person("Yachen", "Yan", role = c("ctb")), + person("Microsoft Corporation", role = c("cph")), + person("Dropbox, Inc.", role = c("cph")), + person("Alberto", "Ferreira", role = c("ctb")), + person("Daniel", "Lemire", role = c("ctb")), + person("Victor", "Zverovich", role = c("cph")), + person("IBM Corporation", role = c("ctb")), + person("David", "Cortes", role = c("aut")), + person("Michael", "Mayer", role = c("ctb")) + ) +Description: Tree based algorithms can be improved by introducing boosting frameworks. + 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) . + This package offers an R interface to work with it. + It is designed to be distributed and efficient with the following advantages: + 1. Faster training speed and higher efficiency. + 2. Lower memory usage. + 3. Better accuracy. + 4. Parallel learning supported. + 5. Capable of handling large-scale data. + In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. + Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines. +Encoding: UTF-8 +License: MIT + file LICENSE +URL: https://github.com/lightgbm-org/LightGBM +BugReports: https://github.com/lightgbm-org/LightGBM/issues +NeedsCompilation: yes +Biarch: true +VignetteBuilder: knitr +Suggests: + knitr, + markdown, + processx, + RhpcBLASctl, + testthat +Depends: + R (>= 4.0) +Imports: + R6 (>= 2.4.0), + data.table (>= 1.9.6), + graphics, + jsonlite (>= 1.0), + Matrix (>= 1.1-0), + methods, + parallel, + utils +SystemRequirements: + C++17 +Config/roxygen2/version: 8.0.0 diff --git a/R-package/LICENSE b/R-package/LICENSE new file mode 100644 index 0000000..3c99fcc --- /dev/null +++ b/R-package/LICENSE @@ -0,0 +1,2 @@ +YEAR: 2016 +COPYRIGHT HOLDER: Microsoft Corporation diff --git a/R-package/NAMESPACE b/R-package/NAMESPACE new file mode 100644 index 0000000..8c5e4e1 --- /dev/null +++ b/R-package/NAMESPACE @@ -0,0 +1,66 @@ +# Generated by roxygen2: do not edit by hand + +S3method("dimnames<-",lgb.Dataset) +S3method(dim,lgb.Dataset) +S3method(dimnames,lgb.Dataset) +S3method(get_field,lgb.Dataset) +S3method(predict,lgb.Booster) +S3method(print,lgb.Booster) +S3method(set_field,lgb.Dataset) +S3method(summary,lgb.Booster) +export(getLGBMthreads) +export(get_field) +export(lgb.Dataset) +export(lgb.Dataset.construct) +export(lgb.Dataset.create.valid) +export(lgb.Dataset.save) +export(lgb.Dataset.set.categorical) +export(lgb.Dataset.set.reference) +export(lgb.configure_fast_predict) +export(lgb.convert_with_rules) +export(lgb.cv) +export(lgb.drop_serialized) +export(lgb.dump) +export(lgb.get.eval.result) +export(lgb.importance) +export(lgb.interpret) +export(lgb.interprete) +export(lgb.load) +export(lgb.make_serializable) +export(lgb.model.dt.tree) +export(lgb.plot.importance) +export(lgb.plot.interpretation) +export(lgb.restore_handle) +export(lgb.save) +export(lgb.slice.Dataset) +export(lgb.train) +export(lightgbm) +export(setLGBMthreads) +export(set_field) +import(methods) +importClassesFrom(Matrix,CsparseMatrix) +importClassesFrom(Matrix,RsparseMatrix) +importClassesFrom(Matrix,dgCMatrix) +importClassesFrom(Matrix,dgRMatrix) +importClassesFrom(Matrix,dsparseMatrix) +importClassesFrom(Matrix,dsparseVector) +importFrom(Matrix,Matrix) +importFrom(R6,R6Class) +importFrom(data.table,":=") +importFrom(data.table,as.data.table) +importFrom(data.table,data.table) +importFrom(data.table,rbindlist) +importFrom(data.table,set) +importFrom(data.table,setnames) +importFrom(data.table,setorder) +importFrom(data.table,setorderv) +importFrom(graphics,barplot) +importFrom(graphics,par) +importFrom(jsonlite,fromJSON) +importFrom(methods,is) +importFrom(methods,new) +importFrom(parallel,detectCores) +importFrom(stats,quantile) +importFrom(utils,modifyList) +importFrom(utils,read.delim) +useDynLib(lightgbm , .registration = TRUE) diff --git a/R-package/R/aliases.R b/R-package/R/aliases.R new file mode 100644 index 0000000..5251cca --- /dev/null +++ b/R-package/R/aliases.R @@ -0,0 +1,104 @@ +# Central location for parameter aliases. +# See https://lightgbm.readthedocs.io/en/latest/Parameters.html#core-parameters + +# [description] List of respected parameter aliases specific to lgb.Dataset. Wrapped in a function to +# take advantage of lazy evaluation (so it doesn't matter what order +# R sources files during installation). +# [return] A named list, where each key is a parameter relevant to lgb.Dataset and each value is a character +# vector of corresponding aliases. +.DATASET_PARAMETERS <- function() { + all_aliases <- .PARAMETER_ALIASES() + return(all_aliases[c( + "bin_construct_sample_cnt" + , "categorical_feature" + , "data_random_seed" + , "enable_bundle" + , "feature_pre_filter" + , "forcedbins_filename" + , "group_column" + , "header" + , "ignore_column" + , "is_enable_sparse" + , "label_column" + , "linear_tree" + , "max_bin" + , "max_bin_by_feature" + , "min_data_in_bin" + , "pre_partition" + , "precise_float_parser" + , "two_round" + , "use_missing" + , "weight_column" + , "zero_as_missing" + )]) +} + +# [description] Non-exported environment, used for caching details that only need to be +# computed once per R session. +.lgb_session_cache_env <- new.env() + +# [description] List of respected parameter aliases. Wrapped in a function to take advantage of +# lazy evaluation (so it doesn't matter what order R sources files during installation). +# [return] A named list, where each key is a main LightGBM parameter and each value is a character +# vector of corresponding aliases. +.PARAMETER_ALIASES <- function() { + if (exists("PARAMETER_ALIASES", where = .lgb_session_cache_env)) { + return(get("PARAMETER_ALIASES", envir = .lgb_session_cache_env)) + } + params_to_aliases <- jsonlite::fromJSON( + .Call( + LGBM_DumpParamAliases_R + ) + ) + for (main_name in names(params_to_aliases)) { + aliases_with_main_name <- c(main_name, unlist(params_to_aliases[[main_name]])) + params_to_aliases[[main_name]] <- aliases_with_main_name + } + # store in cache so the next call to `.PARAMETER_ALIASES()` doesn't need to recompute this + assign( + x = "PARAMETER_ALIASES" + , value = params_to_aliases + , envir = .lgb_session_cache_env + ) + return(params_to_aliases) +} + +# [description] +# Per https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst#metric, +# a few different strings can be used to indicate "no metrics". +# [returns] +# A character vector +.NO_METRIC_STRINGS <- function() { + return( + c( + "na" + , "None" + , "null" + , "custom" + ) + ) +} + +.MULTICLASS_OBJECTIVES <- function() { + return( + c( + "multi_logloss" + , "multiclass" + , "softmax" + , "multiclassova" + , "multiclass_ova" + , "ova" + , "ovr" + ) + ) +} + +.BINARY_OBJECTIVES <- function() { + return( + c( + "binary_logloss" + , "binary" + , "binary_error" + ) + ) +} diff --git a/R-package/R/callback.R b/R-package/R/callback.R new file mode 100644 index 0000000..ba3f742 --- /dev/null +++ b/R-package/R/callback.R @@ -0,0 +1,368 @@ +# constants that control naming in lists +.EVAL_KEY <- function() { + return("eval") +} +.EVAL_ERR_KEY <- function() { + return("eval_err") +} + +#' @importFrom R6 R6Class +CB_ENV <- R6::R6Class( + "lgb.cb_env", + cloneable = FALSE, + public = list( + model = NULL, + iteration = NULL, + begin_iteration = NULL, + end_iteration = NULL, + eval_list = list(), + eval_err_list = list(), + best_iter = -1L, + best_score = NA, + met_early_stop = FALSE + ) +) + +# Format the evaluation metric string +.format_eval_string <- function(eval_res, eval_err) { + + # Check for empty evaluation string + if (is.null(eval_res) || length(eval_res) == 0L) { + stop("no evaluation results") + } + + # Check for empty evaluation error + if (!is.null(eval_err)) { + return(sprintf("%s\'s %s:%g+%g", eval_res$data_name, eval_res$name, eval_res$value, eval_err)) + } else { + return(sprintf("%s\'s %s:%g", eval_res$data_name, eval_res$name, eval_res$value)) + } + +} + +.merge_eval_string <- function(env) { + + # Check length of evaluation list + if (length(env$eval_list) <= 0L) { + return("") + } + + # Get evaluation + msg <- list(sprintf("[%d]:", env$iteration)) + + # Set if evaluation error + is_eval_err <- length(env$eval_err_list) > 0L + + # Loop through evaluation list + for (j in seq_along(env$eval_list)) { + + # Store evaluation error + eval_err <- NULL + if (isTRUE(is_eval_err)) { + eval_err <- env$eval_err_list[[j]] + } + + # Set error message + msg <- c(msg, .format_eval_string(eval_res = env$eval_list[[j]], eval_err = eval_err)) + + } + + return(paste(msg, collapse = " ")) + +} + +cb_print_evaluation <- function(period) { + + # Create callback + callback <- function(env) { + + # Check if period is at least 1 or more + if (period > 0L) { + + # Store iteration + i <- env$iteration + + # Check if iteration matches moduo + if ((i - 1L) %% period == 0L || is.element(i, c(env$begin_iteration, env$end_iteration))) { + + # Merge evaluation string + msg <- .merge_eval_string(env = env) + + # Check if message is existing + if (nchar(msg) > 0L) { + cat(.merge_eval_string(env = env), "\n") + } + + } + + } + + return(invisible(NULL)) + + } + + # Store attributes + attr(callback, "call") <- match.call() + attr(callback, "name") <- "cb_print_evaluation" + + return(callback) + +} + +cb_record_evaluation <- function() { + + # Create callback + callback <- function(env) { + + if (length(env$eval_list) <= 0L) { + return() + } + + # Set if evaluation error + is_eval_err <- length(env$eval_err_list) > 0L + + # Check length of recorded evaluation + if (length(env$model$record_evals) == 0L) { + + # Loop through each evaluation list element + for (j in seq_along(env$eval_list)) { + + # Store names + data_name <- env$eval_list[[j]]$data_name + name <- env$eval_list[[j]]$name + env$model$record_evals$start_iter <- env$begin_iteration + + # Check if evaluation record exists + if (is.null(env$model$record_evals[[data_name]])) { + env$model$record_evals[[data_name]] <- list() + } + + # Create dummy lists + env$model$record_evals[[data_name]][[name]] <- list() + env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] <- list() + env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] <- list() + + } + + } + + # Loop through each evaluation list element + for (j in seq_along(env$eval_list)) { + + # Get evaluation data + eval_res <- env$eval_list[[j]] + eval_err <- NULL + if (isTRUE(is_eval_err)) { + eval_err <- env$eval_err_list[[j]] + } + + # Store names + data_name <- eval_res$data_name + name <- eval_res$name + + # Store evaluation data + env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] <- c( + env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] + , eval_res$value + ) + env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] <- c( + env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] + , eval_err + ) + + } + + return(invisible(NULL)) + + } + + # Store attributes + attr(callback, "call") <- match.call() + attr(callback, "name") <- "cb_record_evaluation" + + return(callback) + +} + +cb_early_stop <- function(stopping_rounds, first_metric_only, verbose) { + + factor_to_bigger_better <- NULL + best_iter <- NULL + best_score <- NULL + best_msg <- NULL + eval_len <- NULL + + # Initialization function + init <- function(env) { + + # Early stopping cannot work without metrics + if (length(env$eval_list) == 0L) { + stop("For early stopping, valids must have at least one element") + } + + # Store evaluation length + eval_len <<- length(env$eval_list) + + # Check if verbose or not + if (isTRUE(verbose)) { + msg <- paste0( + "Will train until there is no improvement in " + , stopping_rounds + , " rounds.\n" + ) + cat(msg) + } + + # Internally treat everything as a maximization task + factor_to_bigger_better <<- rep.int(1.0, eval_len) + best_iter <<- rep.int(-1L, eval_len) + best_score <<- rep.int(-Inf, eval_len) + best_msg <<- list() + + # Loop through evaluation elements + for (i in seq_len(eval_len)) { + + # Prepend message + best_msg <<- c(best_msg, "") + + # Internally treat everything as a maximization task + if (!isTRUE(env$eval_list[[i]]$higher_better)) { + factor_to_bigger_better[i] <<- -1.0 + } + + } + + return(invisible(NULL)) + + } + + # Create callback + callback <- function(env) { + + # Check for empty evaluation + if (is.null(eval_len)) { + init(env = env) + } + + # Store iteration + cur_iter <- env$iteration + + # By default, any metric can trigger early stopping. This can be disabled + # with 'first_metric_only = TRUE' + if (isTRUE(first_metric_only)) { + evals_to_check <- 1L + } else { + evals_to_check <- seq_len(eval_len) + } + + # Loop through evaluation + for (i in evals_to_check) { + + # Store score + score <- env$eval_list[[i]]$value * factor_to_bigger_better[i] + + # Check if score is better + if (score > best_score[i]) { + + # Store new scores + best_score[i] <<- score + best_iter[i] <<- cur_iter + + # Prepare to print if verbose + if (verbose) { + best_msg[[i]] <<- as.character(.merge_eval_string(env = env)) + } + + } else { + + # Check if early stopping is required + if (cur_iter - best_iter[i] >= stopping_rounds) { + + if (!is.null(env$model)) { + env$model$best_score <- best_score[i] + env$model$best_iter <- best_iter[i] + } + + if (isTRUE(verbose)) { + cat(paste0("Early stopping, best iteration is: ", best_msg[[i]], "\n")) + } + + # Store best iteration and stop + env$best_iter <- best_iter[i] + env$met_early_stop <- TRUE + } + + } + + if (!isTRUE(env$met_early_stop) && cur_iter == env$end_iteration) { + + if (!is.null(env$model)) { + env$model$best_score <- best_score[i] + env$model$best_iter <- best_iter[i] + } + + if (isTRUE(verbose)) { + cat(paste0("Did not meet early stopping, best iteration is: ", best_msg[[i]], "\n")) + } + + # Store best iteration and stop + env$best_iter <- best_iter[i] + env$met_early_stop <- TRUE + } + } + + return(invisible(NULL)) + + } + + attr(callback, "call") <- match.call() + attr(callback, "name") <- "cb_early_stop" + + return(callback) + +} + +# Extract callback names from the list of callbacks +.callback_names <- function(cb_list) { + return(unlist(lapply(cb_list, attr, "name"))) +} + +.add_cb <- function(cb_list, cb) { + + # Combine two elements + cb_list <- c(cb_list, cb) + + # Set names of elements + names(cb_list) <- .callback_names(cb_list = cb_list) + + if ("cb_early_stop" %in% names(cb_list)) { + + # Concatenate existing elements + cb_list <- c(cb_list, cb_list["cb_early_stop"]) + + # Remove only the first one + cb_list["cb_early_stop"] <- NULL + + } + + return(cb_list) + +} + +.categorize_callbacks <- function(cb_list) { + + # Check for pre-iteration or post-iteration + return( + list( + pre_iter = Filter(function(x) { + pre <- attr(x, "is_pre_iteration") + !is.null(pre) && pre + }, cb_list), + post_iter = Filter(function(x) { + pre <- attr(x, "is_pre_iteration") + is.null(pre) || !pre + }, cb_list) + ) + ) + +} diff --git a/R-package/R/lgb.Booster.R b/R-package/R/lgb.Booster.R new file mode 100644 index 0000000..6252485 --- /dev/null +++ b/R-package/R/lgb.Booster.R @@ -0,0 +1,1575 @@ +#' @importFrom R6 R6Class +#' @importFrom utils modifyList +Booster <- R6::R6Class( + classname = "lgb.Booster", + cloneable = FALSE, + public = list( + + best_iter = -1L, + best_score = NA_real_, + params = list(), + record_evals = list(), + data_processor = NULL, + + # Initialize will create a starter booster + initialize = function(params = list(), + train_set = NULL, + modelfile = NULL, + model_str = NULL) { + + handle <- NULL + + if (!is.null(train_set)) { + + if (!.is_Dataset(train_set)) { + stop("lgb.Booster: Can only use lgb.Dataset as training data") + } + train_set_handle <- train_set$.__enclos_env__$private$get_handle() + params <- utils::modifyList(params, train_set$get_params()) + params_str <- .params2str(params = params) + # Store booster handle + handle <- .Call( + LGBM_BoosterCreate_R + , train_set_handle + , params_str + ) + + # Create private booster information + private$train_set <- train_set + private$train_set_version <- train_set$.__enclos_env__$private$version + private$num_dataset <- 1L + private$init_predictor <- train_set$.__enclos_env__$private$predictor + + if (!is.null(private$init_predictor)) { + + # Merge booster + .Call( + LGBM_BoosterMerge_R + , handle + , private$init_predictor$.__enclos_env__$private$handle + ) + + } + + # Check current iteration + private$is_predicted_cur_iter <- c(private$is_predicted_cur_iter, FALSE) + + } else if (!is.null(modelfile)) { + + # Do we have a model file as character? + if (!is.character(modelfile)) { + stop("lgb.Booster: Can only use a string as model file path") + } + + modelfile <- path.expand(modelfile) + + # Create booster from model + handle <- .Call( + LGBM_BoosterCreateFromModelfile_R + , modelfile + ) + params <- private$get_loaded_param(handle) + + } else if (!is.null(model_str)) { + + # Do we have a model_str as character/raw? + if (!is.raw(model_str) && !is.character(model_str)) { + stop("lgb.Booster: Can only use a character/raw vector as model_str") + } + + # Create booster from model + handle <- .Call( + LGBM_BoosterLoadModelFromString_R + , model_str + ) + + } else { + + # Booster non existent + stop( + "lgb.Booster: Need at least either training dataset, " + , "model file, or model_str to create booster instance" + ) + + } + + class(handle) <- "lgb.Booster.handle" + private$handle <- handle + private$num_class <- 1L + .Call( + LGBM_BoosterGetNumClasses_R + , private$handle + , private$num_class + ) + + self$params <- params + + return(invisible(NULL)) + + }, + + # Set training data name + set_train_data_name = function(name) { + + # Set name + private$name_train_set <- name + return(invisible(self)) + + }, + + # Add validation data + add_valid = function(data, name) { + + if (!.is_Dataset(data)) { + stop("lgb.Booster.add_valid: Can only use lgb.Dataset as validation data") + } + + if (!identical(data$.__enclos_env__$private$predictor, private$init_predictor)) { + stop( + "lgb.Booster.add_valid: Failed to add validation data; " + , "you should use the same predictor for these data" + ) + } + + if (!is.character(name)) { + stop("lgb.Booster.add_valid: Can only use characters as data name") + } + + # Add validation data to booster + .Call( + LGBM_BoosterAddValidData_R + , private$handle + , data$.__enclos_env__$private$get_handle() + ) + + private$valid_sets <- c(private$valid_sets, data) + private$name_valid_sets <- c(private$name_valid_sets, name) + private$num_dataset <- private$num_dataset + 1L + private$is_predicted_cur_iter <- c(private$is_predicted_cur_iter, FALSE) + + return(invisible(self)) + + }, + + reset_parameter = function(params) { + + if (methods::is(self$params, "list")) { + params <- utils::modifyList(self$params, params) + } + + params_str <- .params2str(params = params) + + self$restore_handle() + + .Call( + LGBM_BoosterResetParameter_R + , private$handle + , params_str + ) + self$params <- params + + return(invisible(self)) + + }, + + # Perform boosting update iteration + update = function(train_set = NULL, fobj = NULL) { + + if (is.null(train_set)) { + if (private$train_set$.__enclos_env__$private$version != private$train_set_version) { + train_set <- private$train_set + } + } + + if (!is.null(train_set)) { + + if (!.is_Dataset(train_set)) { + stop("lgb.Booster.update: Only can use lgb.Dataset as training data") + } + + if (!identical(train_set$predictor, private$init_predictor)) { + stop("lgb.Booster.update: Change train_set failed, you should use the same predictor for these data") + } + + .Call( + LGBM_BoosterResetTrainingData_R + , private$handle + , train_set$.__enclos_env__$private$get_handle() + ) + + private$train_set <- train_set + private$train_set_version <- train_set$.__enclos_env__$private$version + + } + + # Check if objective is empty + if (is.null(fobj)) { + if (private$set_objective_to_none) { + stop("lgb.Booster.update: cannot update due to null objective function") + } + # Boost iteration from known objective + .Call( + LGBM_BoosterUpdateOneIter_R + , private$handle + ) + + } else { + + if (!is.function(fobj)) { + stop("lgb.Booster.update: fobj should be a function") + } + if (!private$set_objective_to_none) { + self$reset_parameter(params = list(objective = "none")) + private$set_objective_to_none <- TRUE + } + # Perform objective calculation + preds <- private$inner_predict(1L) + gpair <- fobj(preds, private$train_set) + + # Check for gradient and hessian as list + if (is.null(gpair$grad) || is.null(gpair$hess)) { + stop("lgb.Booster.update: custom objective should + return a list with attributes (hess, grad)") + } + + # Check grad and hess have the right shape + n_grad <- length(gpair$grad) + n_hess <- length(gpair$hess) + n_preds <- length(preds) + if (n_grad != n_preds) { + stop(sprintf("Expected custom objective function to return grad with length %d, got %d.", n_preds, n_grad)) + } + if (n_hess != n_preds) { + stop(sprintf("Expected custom objective function to return hess with length %d, got %d.", n_preds, n_hess)) + } + + # Return custom boosting gradient/hessian + .Call( + LGBM_BoosterUpdateOneIterCustom_R + , private$handle + , gpair$grad + , gpair$hess + , n_preds + ) + + } + + # Loop through each iteration + for (i in seq_along(private$is_predicted_cur_iter)) { + private$is_predicted_cur_iter[[i]] <- FALSE + } + + return(invisible(self)) + + }, + + # Return one iteration behind + rollback_one_iter = function() { + + self$restore_handle() + + .Call( + LGBM_BoosterRollbackOneIter_R + , private$handle + ) + + # Loop through each iteration + for (i in seq_along(private$is_predicted_cur_iter)) { + private$is_predicted_cur_iter[[i]] <- FALSE + } + + return(invisible(self)) + + }, + + # Get current iteration + current_iter = function() { + + self$restore_handle() + + cur_iter <- 0L + .Call( + LGBM_BoosterGetCurrentIteration_R + , private$handle + , cur_iter + ) + return(cur_iter) + + }, + + # Number of trees per iteration + num_trees_per_iter = function() { + + self$restore_handle() + + trees_per_iter <- 1L + .Call( + LGBM_BoosterNumModelPerIteration_R + , private$handle + , trees_per_iter + ) + return(trees_per_iter) + + }, + + # Total number of trees + num_trees = function() { + + self$restore_handle() + + ntrees <- 0L + .Call( + LGBM_BoosterNumberOfTotalModel_R + , private$handle + , ntrees + ) + return(ntrees) + + }, + + # Number of iterations (= rounds) + num_iter = function() { + + ntrees <- self$num_trees() + trees_per_iter <- self$num_trees_per_iter() + + return(ntrees / trees_per_iter) + + }, + + # Get upper bound + upper_bound = function() { + + self$restore_handle() + + upper_bound <- 0.0 + .Call( + LGBM_BoosterGetUpperBoundValue_R + , private$handle + , upper_bound + ) + return(upper_bound) + + }, + + # Get lower bound + lower_bound = function() { + + self$restore_handle() + + lower_bound <- 0.0 + .Call( + LGBM_BoosterGetLowerBoundValue_R + , private$handle + , lower_bound + ) + return(lower_bound) + + }, + + # Evaluate data on metrics + eval = function(data, name, feval = NULL) { + + if (!.is_Dataset(data)) { + stop("lgb.Booster.eval: Can only use lgb.Dataset to eval") + } + + # Check for identical data + data_idx <- 0L + if (identical(data, private$train_set)) { + data_idx <- 1L + } else { + + # Check for validation data + if (length(private$valid_sets) > 0L) { + + for (i in seq_along(private$valid_sets)) { + + # Check for identical validation data with training data + if (identical(data, private$valid_sets[[i]])) { + + # Found identical data, skip + data_idx <- i + 1L + break + + } + + } + + } + + } + + # Check if evaluation was not done + if (data_idx == 0L) { + + # Add validation data by name + self$add_valid(data, name) + data_idx <- private$num_dataset + + } + + # Evaluate data + return( + private$inner_eval( + data_name = name + , data_idx = data_idx + , feval = feval + ) + ) + + }, + + # Evaluation training data + eval_train = function(feval = NULL) { + return(private$inner_eval(private$name_train_set, 1L, feval)) + }, + + # Evaluation validation data + eval_valid = function(feval = NULL) { + + ret <- list() + + if (length(private$valid_sets) <= 0L) { + return(ret) + } + + for (i in seq_along(private$valid_sets)) { + ret <- append( + x = ret + , values = private$inner_eval(private$name_valid_sets[[i]], i + 1L, feval) + ) + } + + return(ret) + + }, + + # Save model + save_model = function( + filename + , num_iteration = NULL + , feature_importance_type = 0L + , start_iteration = 1L + ) { + + self$restore_handle() + + if (is.null(num_iteration)) { + num_iteration <- self$best_iter + } + + filename <- path.expand(filename) + + .Call( + LGBM_BoosterSaveModel_R + , private$handle + , as.integer(num_iteration) + , as.integer(feature_importance_type) + , filename + , as.integer(start_iteration) - 1L # Turn to 0-based + ) + + return(invisible(self)) + }, + + save_model_to_string = function( + num_iteration = NULL + , feature_importance_type = 0L + , as_char = TRUE + , start_iteration = 1L + ) { + + self$restore_handle() + + if (is.null(num_iteration)) { + num_iteration <- self$best_iter + } + + model_str <- .Call( + LGBM_BoosterSaveModelToString_R + , private$handle + , as.integer(num_iteration) + , as.integer(feature_importance_type) + , as.integer(start_iteration) - 1L # Turn to 0-based + ) + + if (as_char) { + model_str <- rawToChar(model_str) + } + + return(model_str) + + }, + + # Dump model in memory + dump_model = function( + num_iteration = NULL, feature_importance_type = 0L, start_iteration = 1L + ) { + + self$restore_handle() + + if (is.null(num_iteration)) { + num_iteration <- self$best_iter + } + + model_str <- .Call( + LGBM_BoosterDumpModel_R + , private$handle + , as.integer(num_iteration) + , as.integer(feature_importance_type) + , as.integer(start_iteration) - 1L # Turn to 0-based + ) + + return(model_str) + + }, + + # Predict on new data + predict = function(data, + start_iteration = NULL, + num_iteration = NULL, + rawscore = FALSE, + predleaf = FALSE, + predcontrib = FALSE, + header = FALSE, + params = list()) { + + self$restore_handle() + + if (is.null(num_iteration)) { + num_iteration <- self$best_iter + } + + if (is.null(start_iteration)) { + start_iteration <- 0L + } + + # possibly override keyword arguments with parameters + # + # NOTE: this length() check minimizes the latency introduced by these checks, + # for the common case where params is empty + # + # NOTE: doing this here instead of in Predictor$predict() to keep + # Predictor$predict() as fast as possible + if (length(params) > 0L) { + params <- .check_wrapper_param( + main_param_name = "predict_raw_score" + , params = params + , alternative_kwarg_value = rawscore + ) + params <- .check_wrapper_param( + main_param_name = "predict_leaf_index" + , params = params + , alternative_kwarg_value = predleaf + ) + params <- .check_wrapper_param( + main_param_name = "predict_contrib" + , params = params + , alternative_kwarg_value = predcontrib + ) + rawscore <- params[["predict_raw_score"]] + predleaf <- params[["predict_leaf_index"]] + predcontrib <- params[["predict_contrib"]] + } + + # Predict on new data + predictor <- Predictor$new( + modelfile = private$handle + , params = params + , fast_predict_config = private$fast_predict_config + ) + return( + predictor$predict( + data = data + , start_iteration = start_iteration + , num_iteration = num_iteration + , rawscore = rawscore + , predleaf = predleaf + , predcontrib = predcontrib + , header = header + ) + ) + + }, + + # Transform into predictor + to_predictor = function() { + return(Predictor$new(modelfile = private$handle)) + }, + + configure_fast_predict = function(csr = FALSE, + start_iteration = NULL, + num_iteration = NULL, + rawscore = FALSE, + predleaf = FALSE, + predcontrib = FALSE, + params = list()) { + + self$restore_handle() + ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) + + if (is.null(num_iteration)) { + num_iteration <- -1L + } + if (is.null(start_iteration)) { + start_iteration <- 0L + } + + if (!csr) { + fun <- LGBM_BoosterPredictForMatSingleRowFastInit_R + } else { + fun <- LGBM_BoosterPredictForCSRSingleRowFastInit_R + } + + fast_handle <- .Call( + fun + , private$handle + , ncols + , rawscore + , predleaf + , predcontrib + , start_iteration + , num_iteration + , .params2str(params = params) + ) + + private$fast_predict_config <- list( + handle = fast_handle + , csr = as.logical(csr) + , ncols = ncols + , start_iteration = start_iteration + , num_iteration = num_iteration + , rawscore = as.logical(rawscore) + , predleaf = as.logical(predleaf) + , predcontrib = as.logical(predcontrib) + , params = params + ) + + return(invisible(NULL)) + }, + + # Used for serialization + raw = NULL, + + # Store serialized raw bytes in model object + save_raw = function() { + if (is.null(self$raw)) { + self$raw <- self$save_model_to_string(NULL, as_char = FALSE) + } + return(invisible(NULL)) + + }, + + drop_raw = function() { + self$raw <- NULL + return(invisible(NULL)) + }, + + check_null_handle = function() { + return(.is_null_handle(private$handle)) + }, + + restore_handle = function() { + if (self$check_null_handle()) { + if (is.null(self$raw)) { + .Call(LGBM_NullBoosterHandleError_R) + } + private$handle <- .Call(LGBM_BoosterLoadModelFromString_R, self$raw) + } + return(invisible(NULL)) + }, + + get_handle = function() { + return(private$handle) + } + + ), + private = list( + handle = NULL, + train_set = NULL, + name_train_set = "training", + valid_sets = list(), + name_valid_sets = list(), + predict_buffer = list(), + is_predicted_cur_iter = list(), + num_class = 1L, + num_dataset = 0L, + init_predictor = NULL, + eval_names = NULL, + higher_better_inner_eval = NULL, + set_objective_to_none = FALSE, + train_set_version = 0L, + fast_predict_config = list(), + + # finalize() will free up the handles + finalize = function() { + .Call( + LGBM_BoosterFree_R + , private$handle + ) + private$handle <- NULL + return(invisible(NULL)) + }, + + # Predict data + inner_predict = function(idx) { + + # Store data name + data_name <- private$name_train_set + + if (idx > 1L) { + data_name <- private$name_valid_sets[[idx - 1L]] + } + + # Check for unknown dataset (over the maximum provided range) + if (idx > private$num_dataset) { + stop("data_idx should not be greater than num_dataset") + } + + # Check for prediction buffer + if (is.null(private$predict_buffer[[data_name]])) { + + # Store predictions + npred <- 0L + .Call( + LGBM_BoosterGetNumPredict_R + , private$handle + , as.integer(idx - 1L) + , npred + ) + private$predict_buffer[[data_name]] <- numeric(npred) + + } + + # Check if current iteration was already predicted + if (!private$is_predicted_cur_iter[[idx]]) { + + # Use buffer + .Call( + LGBM_BoosterGetPredict_R + , private$handle + , as.integer(idx - 1L) + , private$predict_buffer[[data_name]] + ) + private$is_predicted_cur_iter[[idx]] <- TRUE + } + + return(private$predict_buffer[[data_name]]) + }, + + # Get evaluation information + get_eval_info = function() { + + if (is.null(private$eval_names)) { + eval_names <- .Call( + LGBM_BoosterGetEvalNames_R + , private$handle + ) + + if (length(eval_names) > 0L) { + + # Parse and store privately names + private$eval_names <- eval_names + + # some metrics don't map cleanly to metric names, for example "ndcg@1" is just the + # ndcg metric evaluated at the first "query result" in learning-to-rank + metric_names <- gsub("@.*", "", eval_names) + private$higher_better_inner_eval <- .METRICS_HIGHER_BETTER()[metric_names] + + } + + } + + return(private$eval_names) + + }, + + get_loaded_param = function(handle) { + params_str <- .Call( + LGBM_BoosterGetLoadedParam_R + , handle + ) + params <- jsonlite::fromJSON(params_str) + if ("interaction_constraints" %in% names(params)) { + params[["interaction_constraints"]] <- lapply(params[["interaction_constraints"]], function(x) x + 1L) + } + + return(params) + + }, + + inner_eval = function(data_name, data_idx, feval = NULL) { + + # Check for unknown dataset (over the maximum provided range) + if (data_idx > private$num_dataset) { + stop("data_idx should not be greater than num_dataset") + } + + self$restore_handle() + + private$get_eval_info() + + ret <- list() + + if (length(private$eval_names) > 0L) { + + # Create evaluation values + tmp_vals <- numeric(length(private$eval_names)) + .Call( + LGBM_BoosterGetEval_R + , private$handle + , as.integer(data_idx - 1L) + , tmp_vals + ) + + for (i in seq_along(private$eval_names)) { + + # Store evaluation and append to return + res <- list() + res$data_name <- data_name + res$name <- private$eval_names[i] + res$value <- tmp_vals[i] + res$higher_better <- private$higher_better_inner_eval[i] + ret <- append(ret, list(res)) + + } + + } + + # Check if there are evaluation metrics + if (!is.null(feval)) { + + # Check if evaluation metric is a function + if (!is.function(feval)) { + stop("lgb.Booster.eval: feval should be a function") + } + + data <- private$train_set + + # Check if data to assess is existing differently + if (data_idx > 1L) { + data <- private$valid_sets[[data_idx - 1L]] + } + + # Perform function evaluation + res <- feval(private$inner_predict(data_idx), data) + + if (is.null(res$name) || is.null(res$value) || is.null(res$higher_better)) { + stop( + "lgb.Booster.eval: custom eval function should return a list with attribute (name, value, higher_better)" + ) + } + + # Append names and evaluation + res$data_name <- data_name + ret <- append(ret, list(res)) + } + + return(ret) + + } + + ) +) + +#' @name lgb_predict_shared_params +#' @title Shared prediction parameter docs +#' @param type Type of prediction to output. Allowed types are:\itemize{ +#' \item \code{"response"}: will output the predicted score according to the objective function being +#' optimized (depending on the link function that the objective uses), after applying any necessary +#' transformations - for example, for \code{objective="binary"}, it will output class probabilities. +#' \item \code{"class"}: for classification objectives, will output the class with the highest predicted +#' probability. For other objectives, will output the same as "response". Note that \code{"class"} is +#' not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function +#' for more details). +#' \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' +#' results) from which the "response" number is produced for a given objective function - for example, +#' for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as +#' "regression", since no transformation is applied, the output will be the same as for "response". +#' \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls +#' in each tree in the model, outputted as integers, with one column per tree. +#' \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an +#' intercept (each feature will produce one column). +#' } +#' +#' Note that, if using custom objectives, types "class" and "response" will not be available and will +#' default towards using "raw" instead. +#' +#' If the model was fit through function \link{lightgbm} and it was passed a factor as labels, +#' passing the prediction type through \code{params} instead of through this argument might +#' result in factor levels for classification objectives not being applied correctly to the +#' resulting output. +#' +#' \emph{New in version 4.0.0} +#' +#' @param start_iteration int or None, optional (default=None) +#' Start index of the iteration to predict. +#' If None or <= 0, starts from the first iteration. +#' @param num_iteration int or None, optional (default=None) +#' Limit number of iterations in the prediction. +#' If None, if the best iteration exists and start_iteration is None or <= 0, the +#' best iteration is used; otherwise, all iterations from start_iteration are used. +#' If <= 0, all iterations from start_iteration are used (no limits). +#' @param params a list of additional named parameters. See +#' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ +#' the "Predict Parameters" section of the documentation} for a list of parameters and +#' valid values. Where these conflict with the values of keyword arguments to this function, +#' the values in \code{params} take precedence. +#' @details This page contains shared documentation for prediction-related parameters used throughout the package. +#' @keywords internal +NULL + +#' @name predict.lgb.Booster +#' @title Predict method for LightGBM model +#' @description Predicted values based on class \code{lgb.Booster} +#' +#' \emph{New in version 4.0.0} +#' +#' @details If the model object has been configured for fast single-row predictions through +#' \link{lgb.configure_fast_predict}, this function will use the prediction parameters +#' that were configured for it - as such, extra prediction parameters should not be passed +#' here, otherwise the configuration will be ignored and the slow route will be taken. +#' @inheritParams lgb_predict_shared_params +#' @param object Object of class \code{lgb.Booster} +#' @param newdata a \code{matrix} object, a \code{dgCMatrix}, a \code{dgRMatrix} object, a \code{dsparseVector} object, +#' or a character representing a path to a text file (CSV, TSV, or LibSVM). +#' +#' For sparse inputs, if predictions are only going to be made for a single row, it will be faster to +#' use CSR format, in which case the data may be passed as either a single-row CSR matrix (class +#' \code{dgRMatrix} from package \code{Matrix}) or as a sparse numeric vector (class +#' \code{dsparseVector} from package \code{Matrix}). +#' +#' If single-row predictions are going to be performed frequently, it is recommended to +#' pre-configure the model object for fast single-row sparse predictions through function +#' \link{lgb.configure_fast_predict}. +#' +#' \emph{Changed from 'data', in version 4.0.0} +#' +#' @param header only used for prediction for text file. True if text file has header +#' @param ... ignored +#' @return For prediction types that are meant to always return one output per observation (e.g. when predicting +#' \code{type="response"} or \code{type="raw"} on a binary classification or regression objective), will +#' return a vector with one element per row in \code{newdata}. +#' +#' For prediction types that are meant to return more than one output per observation (e.g. when predicting +#' \code{type="response"} or \code{type="raw"} on a multi-class objective, or when predicting +#' \code{type="leaf"}, regardless of objective), will return a matrix with one row per observation in +#' \code{newdata} and one column per output. +#' +#' For \code{type="leaf"} predictions, will return a matrix with one row per observation in \code{newdata} +#' and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each +#' boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf +#' predictions for the first class can be found in columns 1, 4, 7, 10, etc. +#' +#' For \code{type="contrib"}, will return a matrix of SHAP values with one row per observation in +#' \code{newdata} and columns corresponding to features. For regression, ranking, cross-entropy, and binary +#' classification objectives, this matrix contains one column per feature plus a final column containing the +#' Shapley base value. For multiclass objectives, this matrix will represent \code{num_classes} such matrices, +#' in the order "feature contributions for first class, feature contributions for second class, feature +#' contributions for third class, etc.". +#' +#' If the model was fit through function \link{lightgbm} and it was passed a factor as labels, predictions +#' returned from this function will retain the factor levels (either as values for \code{type="class"}, or +#' as column names for \code{type="response"} and \code{type="raw"} for multi-class objectives). Note that +#' passing the requested prediction type under \code{params} instead of through \code{type} might result in +#' the factor levels not being present in the output. +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +#' params <- list( +#' objective = "regression" +#' , metric = "l2" +#' , min_data = 1L +#' , learning_rate = 1.0 +#' , num_threads = 2L +#' ) +#' valids <- list(test = dtest) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' , valids = valids +#' ) +#' preds <- predict(model, test$data) +#' +#' # pass other prediction parameters +#' preds <- predict( +#' model, +#' test$data, +#' params = list( +#' predict_disable_shape_check = TRUE +#' ) +#' ) +#' } +#' @importFrom utils modifyList +#' @export +predict.lgb.Booster <- function(object, + newdata, + type = "response", + start_iteration = NULL, + num_iteration = NULL, + header = FALSE, + params = list(), + ...) { + + if (!.is_Booster(x = object)) { + stop("predict.lgb.Booster: object should be an ", sQuote("lgb.Booster", q = FALSE)) + } + + additional_params <- list(...) + if (length(additional_params) > 0L) { + additional_params_names <- names(additional_params) + if ("reshape" %in% additional_params_names) { + stop("'reshape' argument is no longer supported.") + } + + old_args_for_type <- list( + "rawscore" = "raw" + , "predleaf" = "leaf" + , "predcontrib" = "contrib" + ) + for (arg in names(old_args_for_type)) { + if (arg %in% additional_params_names) { + stop(sprintf("Argument '%s' is no longer supported. Use type='%s' instead." + , arg + , old_args_for_type[[arg]])) + } + } + + warning(paste0( + "predict.lgb.Booster: Found the following passed through '...': " + , toString(names(additional_params)) + , ". These are ignored. Use argument 'params' instead." + )) + } + + if (!is.null(object$params$objective) && object$params$objective == "none" && type %in% c("class", "response")) { + warning("Prediction types 'class' and 'response' are not supported for custom objectives.") + type <- "raw" + } + + rawscore <- FALSE + predleaf <- FALSE + predcontrib <- FALSE + if (type == "raw") { + rawscore <- TRUE + } else if (type == "leaf") { + predleaf <- TRUE + } else if (type == "contrib") { + predcontrib <- TRUE + } + + pred <- object$predict( + data = newdata + , start_iteration = start_iteration + , num_iteration = num_iteration + , rawscore = rawscore + , predleaf = predleaf + , predcontrib = predcontrib + , header = header + , params = params + ) + if (type == "class") { + if (object$params$objective %in% .BINARY_OBJECTIVES()) { + pred <- as.integer(pred >= 0.5) + } else if (object$params$objective %in% .MULTICLASS_OBJECTIVES()) { + pred <- max.col(pred) - 1L + } + } + if (!is.null(object$data_processor)) { + pred <- object$data_processor$process_predictions( + pred = pred + , type = type + ) + } + return(pred) +} + +#' @title Configure Fast Single-Row Predictions +#' @description Pre-configures a LightGBM model object to produce fast single-row predictions +#' for a given input data type, prediction type, and parameters. +#' @details Calling this function multiple times with different parameters might not override +#' the previous configuration and might trigger undefined behavior. +#' +#' Any saved configuration for fast predictions might be lost after making a single-row +#' prediction of a different type than what was configured (except for types "response" and +#' "class", which can be switched between each other at any time without losing the configuration). +#' +#' In some situations, setting a fast prediction configuration for one type of prediction +#' might cause the prediction function to keep using that configuration for single-row +#' predictions even if the requested type of prediction is different from what was configured. +#' +#' Note that this function will not accept argument \code{type="class"} - for such cases, one +#' can pass \code{type="response"} to this function and then \code{type="class"} to the +#' \code{predict} function - the fast configuration will not be lost or altered if the switch +#' is between "response" and "class". +#' +#' The configuration does not survive de-serializations, so it has to be generated +#' anew in every R process that is going to use it (e.g. if loading a model object +#' through \code{readRDS}, whatever configuration was there previously will be lost). +#' +#' Requesting a different prediction type or passing parameters to \link{predict.lgb.Booster} +#' will cause it to ignore the fast-predict configuration and take the slow route instead +#' (but be aware that an existing configuration might not always be overridden by supplying +#' different parameters or prediction type, so make sure to check that the output is what +#' was expected when a prediction is to be made on a single row for something different than +#' what is configured). +#' +#' Note that, if configuring a non-default prediction type (such as leaf indices), +#' then that type must also be passed in the call to \link{predict.lgb.Booster} in +#' order for it to use the configuration. This also applies for \code{start_iteration} +#' and \code{num_iteration}, but \bold{the \code{params} list must be empty} in the call to \code{predict}. +#' +#' Predictions about feature contributions do not allow a fast route for CSR inputs, +#' and as such, this function will produce an error if passing \code{csr=TRUE} and +#' \code{type = "contrib"} together. +#' @inheritParams lgb_predict_shared_params +#' @param model LightGBM model object (class \code{lgb.Booster}). +#' +#' \bold{The object will be modified in-place}. +#' @param csr Whether the prediction function is going to be called on sparse CSR inputs. +#' If \code{FALSE}, will be assumed that predictions are going to be called on single-row +#' regular R matrices. +#' @return The same \code{model} that was passed as input, invisibly, with the desired +#' configuration stored inside it and available to be used in future calls to +#' \link{predict.lgb.Booster}. +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' library(lightgbm) +#' data(mtcars) +#' X <- as.matrix(mtcars[, -1L]) +#' y <- mtcars[, 1L] +#' dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) +#' params <- list( +#' min_data_in_leaf = 2L +#' , num_threads = 2L +#' ) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , obj = "regression" +#' , nrounds = 5L +#' , verbose = -1L +#' ) +#' lgb.configure_fast_predict(model) +#' +#' x_single <- X[11L, , drop = FALSE] +#' predict(model, x_single) +#' +#' # Will not use it if the prediction to be made +#' # is different from what was configured +#' predict(model, x_single, type = "leaf") +#' } +#' @export +lgb.configure_fast_predict <- function(model, + csr = FALSE, + start_iteration = NULL, + num_iteration = NULL, + type = "response", + params = list()) { + if (!.is_Booster(x = model)) { + stop("lgb.configure_fast_predict: model should be an ", sQuote("lgb.Booster", q = FALSE)) + } + if (type == "class") { + stop("type='class' is not supported for 'lgb.configure_fast_predict'. Use 'response' instead.") + } + + rawscore <- FALSE + predleaf <- FALSE + predcontrib <- FALSE + if (type == "raw") { + rawscore <- TRUE + } else if (type == "leaf") { + predleaf <- TRUE + } else if (type == "contrib") { + predcontrib <- TRUE + } + + if (csr && predcontrib) { + stop("'lgb.configure_fast_predict' does not support feature contributions for CSR data.") + } + model$configure_fast_predict( + csr = csr + , start_iteration = start_iteration + , num_iteration = num_iteration + , rawscore = rawscore + , predleaf = predleaf + , predcontrib = predcontrib + , params = params + ) + return(invisible(model)) +} + +#' @name print.lgb.Booster +#' @title Print method for LightGBM model +#' @description Show summary information about a LightGBM model object (same as \code{summary}). +#' +#' \emph{New in version 4.0.0} +#' +#' @param x Object of class \code{lgb.Booster} +#' @param ... Not used +#' @return The same input \code{x}, returned as invisible. +#' @export +print.lgb.Booster <- function(x, ...) { + # nolint start + handle <- x$.__enclos_env__$private$handle + handle_is_null <- .is_null_handle(handle) + + if (!handle_is_null) { + ntrees <- x$current_iter() + if (ntrees == 1L) { + cat("LightGBM Model (1 tree)\n") + } else { + cat(sprintf("LightGBM Model (%d trees)\n", ntrees)) + } + } else { + cat("LightGBM Model\n") + } + + if (!handle_is_null) { + obj <- x$params$objective + if (is.null(obj)) { + obj <- "(default)" + } + if (obj == "none") { + obj <- "custom" + } + num_class <- x$.__enclos_env__$private$num_class + if (num_class == 1L) { + cat(sprintf("Objective: %s\n", obj)) + } else { + cat(sprintf("Objective: %s (%d classes)\n" + , obj + , num_class)) + } + } else { + cat("(Booster handle is invalid)\n") + } + + if (!handle_is_null) { + ncols <- .Call(LGBM_BoosterGetNumFeature_R, handle) + cat(sprintf("Fitted to dataset with %d columns\n", ncols)) + } + # nolint end + + return(invisible(x)) +} + +#' @name summary.lgb.Booster +#' @title Summary method for LightGBM model +#' @description Show summary information about a LightGBM model object (same as \code{print}). +#' +#' \emph{New in version 4.0.0} +#' +#' @param object Object of class \code{lgb.Booster} +#' @param ... Not used +#' @return The same input \code{object}, returned as invisible. +#' @export +summary.lgb.Booster <- function(object, ...) { + print(object) +} + +#' @name lgb.load +#' @title Load LightGBM model +#' @description Load LightGBM takes in either a file path or model string. +#' If both are provided, Load will default to loading from file +#' @param filename path of model file +#' @param model_str a str containing the model (as a \code{character} or \code{raw} vector) +#' +#' @return lgb.Booster +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +#' params <- list( +#' objective = "regression" +#' , metric = "l2" +#' , min_data = 1L +#' , learning_rate = 1.0 +#' , num_threads = 2L +#' ) +#' valids <- list(test = dtest) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' , valids = valids +#' , early_stopping_rounds = 3L +#' ) +#' model_file <- tempfile(fileext = ".txt") +#' lgb.save(model, model_file) +#' load_booster <- lgb.load(filename = model_file) +#' model_string <- model$save_model_to_string(NULL) # saves best iteration +#' load_booster_from_str <- lgb.load(model_str = model_string) +#' } +#' @export +lgb.load <- function(filename = NULL, model_str = NULL) { + + filename_provided <- !is.null(filename) + model_str_provided <- !is.null(model_str) + + if (filename_provided) { + if (!is.character(filename)) { + stop("lgb.load: filename should be character") + } + filename <- path.expand(filename) + if (!file.exists(filename)) { + stop(sprintf("lgb.load: file '%s' passed to filename does not exist", filename)) + } + return(invisible(Booster$new(modelfile = filename))) + } + + if (model_str_provided) { + if (!is.raw(model_str) && !is.character(model_str)) { + stop("lgb.load: model_str should be a character/raw vector") + } + return(invisible(Booster$new(model_str = model_str))) + } + + stop("lgb.load: either filename or model_str must be given") +} + +#' @name lgb.save +#' @title Save LightGBM model +#' @description Save LightGBM model +#' @param booster Object of class \code{lgb.Booster} +#' @param filename Saved filename +#' @param num_iteration Number of iterations to save, NULL or <= 0 means use best iteration +#' @param start_iteration Index (1-based) of the first boosting round to save. +#' For example, passing \code{start_iteration=5, num_iteration=3} for a regression model +#' means "save the fifth, sixth, and seventh tree" +#' +#' \emph{New in version 4.4.0} +#' +#' @return lgb.Booster +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' library(lightgbm) +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +#' params <- list( +#' objective = "regression" +#' , metric = "l2" +#' , min_data = 1L +#' , learning_rate = 1.0 +#' , num_threads = 2L +#' ) +#' valids <- list(test = dtest) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 10L +#' , valids = valids +#' , early_stopping_rounds = 5L +#' ) +#' lgb.save(model, tempfile(fileext = ".txt")) +#' } +#' @export +lgb.save <- function( + booster, filename, num_iteration = NULL, start_iteration = 1L + ) { + + if (!.is_Booster(x = booster)) { + stop("lgb.save: booster should be an ", sQuote("lgb.Booster", q = FALSE)) + } + + if (!(is.character(filename) && length(filename) == 1L)) { + stop("lgb.save: filename should be a string") + } + filename <- path.expand(filename) + + # Store booster + return( + invisible(booster$save_model( + filename = filename + , num_iteration = num_iteration + , start_iteration = start_iteration + )) + ) + +} + +#' @name lgb.dump +#' @title Dump LightGBM model to json +#' @description Dump LightGBM model to json +#' @param booster Object of class \code{lgb.Booster} +#' @param num_iteration Number of iterations to be dumped. NULL or <= 0 means use best iteration +#' @param start_iteration Index (1-based) of the first boosting round to dump. +#' For example, passing \code{start_iteration=5, num_iteration=3} for a regression model +#' means "dump the fifth, sixth, and seventh tree" +#' +#' \emph{New in version 4.4.0} +#' +#' @return json format of model +#' +#' @examples +#' \donttest{ +#' library(lightgbm) +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +#' params <- list( +#' objective = "regression" +#' , metric = "l2" +#' , min_data = 1L +#' , learning_rate = 1.0 +#' , num_threads = 2L +#' ) +#' valids <- list(test = dtest) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 10L +#' , valids = valids +#' , early_stopping_rounds = 5L +#' ) +#' json_model <- lgb.dump(model) +#' } +#' @export +lgb.dump <- function(booster, num_iteration = NULL, start_iteration = 1L) { + + if (!.is_Booster(x = booster)) { + stop("lgb.dump: booster should be an ", sQuote("lgb.Booster", q = FALSE)) + } + + # Return booster at requested iteration + return( + booster$dump_model( + num_iteration = num_iteration, start_iteration = start_iteration + ) + ) + +} + +#' @name lgb.get.eval.result +#' @title Get record evaluation result from booster +#' @description Given a \code{lgb.Booster}, return evaluation results for a +#' particular metric on a particular dataset. +#' @param booster Object of class \code{lgb.Booster} +#' @param data_name Name of the dataset to return evaluation results for. +#' @param eval_name Name of the evaluation metric to return results for. +#' @param iters An integer vector of iterations you want to get evaluation results for. If NULL +#' (the default), evaluation results for all iterations will be returned. +#' @param is_err TRUE will return evaluation error instead +#' +#' @return numeric vector of evaluation result +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' # train a regression model +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +#' params <- list( +#' objective = "regression" +#' , metric = "l2" +#' , min_data = 1L +#' , learning_rate = 1.0 +#' , num_threads = 2L +#' ) +#' valids <- list(test = dtest) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' , valids = valids +#' ) +#' +#' # Examine valid data_name values +#' print(setdiff(names(model$record_evals), "start_iter")) +#' +#' # Examine valid eval_name values for dataset "test" +#' print(names(model$record_evals[["test"]])) +#' +#' # Get L2 values for "test" dataset +#' lgb.get.eval.result(model, "test", "l2") +#' } +#' @export +lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) { + + if (!.is_Booster(x = booster)) { + stop("lgb.get.eval.result: Can only use ", sQuote("lgb.Booster", q = FALSE), " to get eval result") + } + + if (!is.character(data_name) || !is.character(eval_name)) { + stop("lgb.get.eval.result: data_name and eval_name should be characters") + } + + # NOTE: "start_iter" exists in booster$record_evals but is not a valid data_name + data_names <- setdiff(names(booster$record_evals), "start_iter") + if (!(data_name %in% data_names)) { + stop(paste0( + "lgb.get.eval.result: data_name " + , shQuote(data_name) + , " not found. Only the following datasets exist in record evals: [" + , toString(data_names) + , "]" + )) + } + + # Check if evaluation result is existing + eval_names <- names(booster$record_evals[[data_name]]) + if (!(eval_name %in% eval_names)) { + stop(paste0( + "lgb.get.eval.result: eval_name " + , shQuote(eval_name) + , " not found. Only the following eval_names exist for dataset " + , shQuote(data_name) + , ": [" + , toString(eval_names) + , "]" + )) + } + + result <- booster$record_evals[[data_name]][[eval_name]][[.EVAL_KEY()]] + + # Check if error is requested + if (is_err) { + result <- booster$record_evals[[data_name]][[eval_name]][[.EVAL_ERR_KEY()]] + } + + if (is.null(iters)) { + return(as.numeric(result)) + } + + # Parse iteration and booster delta + iters <- as.integer(iters) + delta <- booster$record_evals$start_iter - 1.0 + iters <- iters - delta + + return(as.numeric(result[iters])) +} diff --git a/R-package/R/lgb.DataProcessor.R b/R-package/R/lgb.DataProcessor.R new file mode 100644 index 0000000..c35ce4f --- /dev/null +++ b/R-package/R/lgb.DataProcessor.R @@ -0,0 +1,94 @@ +DataProcessor <- R6::R6Class( + classname = "lgb.DataProcessor", + public = list( + factor_levels = NULL, + + process_label = function(label, objective, params) { + + if (is.character(label)) { + label <- factor(label) + } + + if (is.factor(label)) { + + self$factor_levels <- levels(label) + if (length(self$factor_levels) <= 1L) { + stop("Labels to predict is a factor with <2 possible values.") + } + + label <- as.numeric(label) - 1.0 + out <- list(label = label) + if (length(self$factor_levels) == 2L) { + if (objective == "auto") { + objective <- "binary" + } + if (!(objective %in% .BINARY_OBJECTIVES())) { + stop("Two-level factors as labels only allowed for objective='binary' or objective='auto'.") + } + } else { + if (objective == "auto") { + objective <- "multiclass" + } + if (!(objective %in% .MULTICLASS_OBJECTIVES())) { + stop( + sprintf( + "Factors with >2 levels as labels only allowed for multi-class objectives. Got: %s (allowed: %s)" + , objective + , toString(.MULTICLASS_OBJECTIVES()) + ) + ) + } + data_num_class <- length(self$factor_levels) + params <- .check_wrapper_param( + main_param_name = "num_class" + , params = params + , alternative_kwarg_value = data_num_class + ) + if (params[["num_class"]] != data_num_class) { + warning( + sprintf( + "Found num_class=%d in params, but 'label' is a factor with %d levels. 'num_class' will be ignored." + , params[["num_class"]] + , data_num_class + ) + ) + params$num_class <- data_num_class + } + } + out$objective <- objective + out$params <- params + return(out) + + } else { + + label <- as.numeric(label) + if (objective == "auto") { + objective <- "regression" + } + out <- list( + label = label + , objective = objective + , params = params + ) + return(out) + + } + }, + + process_predictions = function(pred, type) { + if (NROW(self$factor_levels)) { + if (type == "class") { + pred <- as.integer(pred) + 1L + attributes(pred)$levels <- self$factor_levels + attributes(pred)$class <- "factor" + } else if (type %in% c("response", "raw")) { + if (is.matrix(pred) && ncol(pred) == length(self$factor_levels)) { + colnames(pred) <- self$factor_levels + } + } + } + + return(pred) + } + ) +) diff --git a/R-package/R/lgb.Dataset.R b/R-package/R/lgb.Dataset.R new file mode 100644 index 0000000..af8aec9 --- /dev/null +++ b/R-package/R/lgb.Dataset.R @@ -0,0 +1,1290 @@ +#' @name lgb_shared_dataset_params +#' @title Shared Dataset parameter docs +#' @description Parameter docs for fields used in \code{lgb.Dataset} construction +#' @param label vector of labels to use as the target variable +#' @param weight numeric vector of sample weights +#' @param init_score initial score is the base prediction lightgbm will boost from +#' @param group used for learning-to-rank tasks. An integer vector describing how to +#' group rows together as ordered results from the same set of candidate results +#' to be ranked. For example, if you have a 100-document dataset with +#' \code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, +#' where the first 10 records are in the first group, records 11-30 are in the +#' second group, etc. +#' @details This page contains shared documentation for dataset-related parameters used throughout the package. +#' @keywords internal +NULL + +# [description] List of valid keys for "info" arguments in lgb.Dataset. +# Wrapped in a function to take advantage of lazy evaluation +# (so it doesn't matter what order R sources files during installation). +# [return] A character vector of names. +.INFO_KEYS <- function() { + return(c("label", "weight", "init_score", "group")) +} + +#' @importFrom methods is +#' @importFrom R6 R6Class +#' @importFrom utils modifyList +Dataset <- R6::R6Class( + + classname = "lgb.Dataset", + cloneable = FALSE, + public = list( + + # Initialize will create a starter dataset + initialize = function(data, + params = list(), + reference = NULL, + colnames = NULL, + categorical_feature = NULL, + predictor = NULL, + free_raw_data = TRUE, + used_indices = NULL, + label = NULL, + weight = NULL, + group = NULL, + init_score = NULL) { + + # validate inputs early to avoid unnecessary computation + if (!(is.null(reference) || .is_Dataset(reference))) { + stop("lgb.Dataset: If provided, reference must be a ", sQuote("lgb.Dataset", q = FALSE)) + } + if (!(is.null(predictor) || .is_Predictor(predictor))) { + stop("lgb.Dataset: If provided, predictor must be a ", sQuote("lgb.Predictor", q = FALSE)) + } + + info <- list() + if (!is.null(label)) { + info[["label"]] <- label + } + if (!is.null(weight)) { + info[["weight"]] <- weight + } + if (!is.null(group)) { + info[["group"]] <- group + } + if (!is.null(init_score)) { + info[["init_score"]] <- init_score + } + + # Check for matrix format + if (is.matrix(data)) { + # Check whether matrix is the correct type first ("double") + if (storage.mode(data) != "double") { + storage.mode(data) <- "double" + } + } + + # Setup private attributes + private$raw_data <- data + private$params <- params + private$reference <- reference + private$colnames <- colnames + + private$categorical_feature <- categorical_feature + private$predictor <- predictor + private$free_raw_data <- free_raw_data + private$used_indices <- sort(used_indices, decreasing = FALSE) + private$info <- info + private$version <- 0L + + return(invisible(NULL)) + + }, + + create_valid = function(data, + label = NULL, + weight = NULL, + group = NULL, + init_score = NULL, + params = list()) { + + # the Dataset's existing parameters should be overwritten by any passed in to this call + params <- modifyList(private$params, params) + + # Create new dataset + ret <- Dataset$new( + data = data + , params = params + , reference = self + , colnames = private$colnames + , categorical_feature = private$categorical_feature + , predictor = private$predictor + , free_raw_data = private$free_raw_data + , used_indices = NULL + , label = label + , weight = weight + , group = group + , init_score = init_score + ) + + return(invisible(ret)) + + }, + + # Dataset constructor + construct = function() { + + # Check for handle null + if (!.is_null_handle(x = private$handle)) { + return(invisible(self)) + } + + # Get feature names + cnames <- NULL + if (is.matrix(private$raw_data) || methods::is(private$raw_data, "dgCMatrix")) { + cnames <- colnames(private$raw_data) + } + + # set feature names if they do not exist + if (is.null(private$colnames) && !is.null(cnames)) { + private$colnames <- as.character(cnames) + } + + # Get categorical feature index + if (!is.null(private$categorical_feature)) { + + # Check for character name + if (is.character(private$categorical_feature)) { + + cate_indices <- as.list(match(private$categorical_feature, private$colnames) - 1L) + + # Provided indices, but some indices are missing? + if (anyNA(cate_indices)) { + stop( + "lgb.Dataset.construct: supplied an unknown feature in categorical_feature: " + , sQuote(private$categorical_feature[is.na(cate_indices)], q = FALSE) + ) + } + + } else { + + # Check if more categorical features were output over the feature space + data_is_not_filename <- !is.character(private$raw_data) + if ( + data_is_not_filename + && !is.null(private$raw_data) + && is.null(private$used_indices) + && max(private$categorical_feature) > ncol(private$raw_data) + ) { + stop( + "lgb.Dataset.construct: supplied a too large value in categorical_feature: " + , max(private$categorical_feature) + , " but only " + , ncol(private$raw_data) + , " features" + ) + } + + # Store indices as [0, n-1] indexed instead of [1, n] indexed + cate_indices <- as.list(private$categorical_feature - 1L) + + } + + # Store indices for categorical features + private$params$categorical_feature <- cate_indices + + } + + # Generate parameter str + params_str <- .params2str(params = private$params) + + # Get handle of reference dataset + ref_handle <- NULL + if (!is.null(private$reference)) { + ref_handle <- private$reference$.__enclos_env__$private$get_handle() + } + + # not subsetting, constructing from raw data + if (is.null(private$used_indices)) { + + if (is.null(private$raw_data)) { + stop(paste0( + "Attempting to create a Dataset without any raw data. " + , "This can happen if the Dataset's finalizer was called or if this Dataset was saved with saveRDS(). " + , "To avoid this error in the future, use lgb.Dataset.save() or " + , "Dataset$save_binary() to save lightgbm Datasets." + )) + } + + # Are we using a data file? + if (is.character(private$raw_data)) { + + handle <- .Call( + LGBM_DatasetCreateFromFile_R + , path.expand(private$raw_data) + , params_str + , ref_handle + ) + + } else if (is.matrix(private$raw_data)) { + + # Are we using a matrix? + handle <- .Call( + LGBM_DatasetCreateFromMat_R + , private$raw_data + , nrow(private$raw_data) + , ncol(private$raw_data) + , params_str + , ref_handle + ) + + } else if (methods::is(private$raw_data, "dgCMatrix")) { + if (length(private$raw_data@p) > 2147483647L) { + stop("Cannot support large CSC matrix") + } + # Are we using a dgCMatrix (sparse matrix column compressed) + handle <- .Call( + LGBM_DatasetCreateFromCSC_R + , private$raw_data@p + , private$raw_data@i + , private$raw_data@x + , length(private$raw_data@p) + , length(private$raw_data@x) + , nrow(private$raw_data) + , params_str + , ref_handle + ) + + } else { + + # Unknown data type + stop( + "lgb.Dataset.construct: does not support constructing from " + , sQuote(class(private$raw_data), q = FALSE) + ) + + } + + } else { + + # Reference is empty + if (is.null(private$reference)) { + stop("lgb.Dataset.construct: reference cannot be NULL for constructing data subset") + } + + # Construct subset + handle <- .Call( + LGBM_DatasetGetSubset_R + , ref_handle + , c(private$used_indices) + , length(private$used_indices) + , params_str + ) + + } + if (.is_null_handle(x = handle)) { + stop("lgb.Dataset.construct: cannot create Dataset handle") + } + # Setup class and private type + class(handle) <- "lgb.Dataset.handle" + private$handle <- handle + + # Set feature names + if (!is.null(private$colnames)) { + self$set_colnames(colnames = private$colnames) + } + + # Ensure that private$colnames matches the feature names on the C++ side. This line is necessary + # in cases like constructing from a file or from a matrix with no column names. + private$colnames <- .Call( + LGBM_DatasetGetFeatureNames_R + , private$handle + ) + + # Load init score if requested + if (!is.null(private$predictor) && is.null(private$used_indices)) { + + # Setup initial scores + init_score <- private$predictor$predict( + data = private$raw_data + , rawscore = TRUE + ) + + # Not needed to transpose, for is col_marjor + init_score <- as.vector(init_score) + private$info$init_score <- init_score + + } + + # Should we free raw data? + if (isTRUE(private$free_raw_data)) { + private$raw_data <- NULL + } + + # Get private information + if (length(private$info) > 0L) { + + # Set infos + for (i in seq_along(private$info)) { + + p <- private$info[i] + self$set_field( + field_name = names(p) + , data = p[[1L]] + ) + + } + + } + + # Get label information existence + if (is.null(self$get_field(field_name = "label"))) { + stop("lgb.Dataset.construct: label should be set") + } + + return(invisible(self)) + + }, + + # Dimension function + dim = function() { + + # Check for handle + if (!.is_null_handle(x = private$handle)) { + + num_row <- 0L + num_col <- 0L + + # Get numeric data and numeric features + .Call( + LGBM_DatasetGetNumData_R + , private$handle + , num_row + ) + .Call( + LGBM_DatasetGetNumFeature_R + , private$handle + , num_col + ) + return( + c(num_row, num_col) + ) + + } else if (is.matrix(private$raw_data) || methods::is(private$raw_data, "dgCMatrix")) { + + # Check if dgCMatrix (sparse matrix column compressed) + # NOTE: requires Matrix package + return(dim(private$raw_data)) + + } else { + + # Trying to work with unknown dimensions is not possible + stop( + "dim: cannot get dimensions before dataset has been constructed, " + , "please call lgb.Dataset.construct explicitly" + ) + + } + + }, + + # Get number of bins for feature + get_feature_num_bin = function(feature) { + if (.is_null_handle(x = private$handle)) { + stop("Cannot get number of bins in feature before constructing Dataset.") + } + if (is.character(feature)) { + feature_name <- feature + feature <- which(private$colnames == feature_name) + if (length(feature) == 0L) { + stop(sprintf("feature '%s' not found", feature_name)) + } + } + num_bin <- integer(1L) + .Call( + LGBM_DatasetGetFeatureNumBin_R + , private$handle + , feature - 1L + , num_bin + ) + return(num_bin) + }, + + # Get column names + get_colnames = function() { + + # Check for handle + if (!.is_null_handle(x = private$handle)) { + private$colnames <- .Call( + LGBM_DatasetGetFeatureNames_R + , private$handle + ) + return(private$colnames) + + } else if (is.matrix(private$raw_data) || methods::is(private$raw_data, "dgCMatrix")) { + + # Check if dgCMatrix (sparse matrix column compressed) + return(colnames(private$raw_data)) + + } else { + + # Trying to work with unknown formats is not possible + stop( + "Dataset$get_colnames(): cannot get column names before dataset has been constructed, please call " + , "lgb.Dataset.construct() explicitly" + ) + + } + + }, + + # Set column names + set_colnames = function(colnames) { + + # Check column names non-existence + if (is.null(colnames)) { + return(invisible(self)) + } + + # Check empty column names + colnames <- as.character(colnames) + if (length(colnames) == 0L) { + return(invisible(self)) + } + + # Write column names + private$colnames <- colnames + if (!.is_null_handle(x = private$handle)) { + + # Merge names with tab separation + merged_name <- paste(as.list(private$colnames), collapse = "\t") + .Call( + LGBM_DatasetSetFeatureNames_R + , private$handle + , merged_name + ) + + } + + return(invisible(self)) + + }, + + get_field = function(field_name) { + + # Check if attribute key is in the known attribute list + if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) { + stop( + "Dataset$get_field(): field_name must be one of the following: " + , toString(sQuote(.INFO_KEYS(), q = FALSE)) + ) + } + + # Check for info name and handle + if (is.null(private$info[[field_name]])) { + + if (.is_null_handle(x = private$handle)) { + stop("Cannot perform Dataset$get_field() before constructing Dataset.") + } + + # Get field size of info + info_len <- 0L + .Call( + LGBM_DatasetGetFieldSize_R + , private$handle + , field_name + , info_len + ) + + if (info_len > 0L) { + + # Get back fields + if (field_name == "group") { + ret <- integer(info_len) + } else { + ret <- numeric(info_len) + } + + .Call( + LGBM_DatasetGetField_R + , private$handle + , field_name + , ret + ) + + private$info[[field_name]] <- ret + + } + } + + return(private$info[[field_name]]) + + }, + + set_field = function(field_name, data) { + + # Check if attribute key is in the known attribute list + if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) { + stop( + "Dataset$set_field(): field_name must be one of the following: " + , toString(sQuote(.INFO_KEYS(), q = FALSE)) + ) + } + + # Check for type of information + data <- if (field_name == "group") { + as.integer(data) + } else { + as.numeric(data) + } + + # Store information privately + private$info[[field_name]] <- data + + if (!.is_null_handle(x = private$handle) && !is.null(data)) { + + if (length(data) > 0L) { + + .Call( + LGBM_DatasetSetField_R + , private$handle + , field_name + , data + , length(data) + ) + + private$version <- private$version + 1L + + } + + } + + return(invisible(self)) + + }, + + slice = function(idxset) { + + return( + Dataset$new( + data = NULL + , params = private$params + , reference = self + , colnames = private$colnames + , categorical_feature = private$categorical_feature + , predictor = private$predictor + , free_raw_data = private$free_raw_data + , used_indices = sort(idxset, decreasing = FALSE) + ) + ) + + }, + + # [description] Update Dataset parameters. If it has not been constructed yet, + # this operation just happens on the R side (updating private$params). + # If it has been constructed, parameters will be updated on the C++ side. + update_params = function(params) { + if (length(params) == 0L) { + return(invisible(self)) + } + new_params <- utils::modifyList(private$params, params) + if (.is_null_handle(x = private$handle)) { + private$params <- new_params + } else { + tryCatch({ + .Call( + LGBM_DatasetUpdateParamChecking_R + , .params2str(params = private$params) + , .params2str(params = new_params) + ) + private$params <- new_params + }, error = function(e) { + # If updating failed but raw data is not available, raise an error because + # achieving what the user asked for is not possible + if (is.null(private$raw_data)) { + stop(e) + } + + # If updating failed but raw data is available, modify the params + # on the R side and re-set ("deconstruct") the Dataset + private$params <- new_params + private$finalize() + }) + } + return(invisible(self)) + + }, + + # [description] Get only Dataset-specific parameters. This is primarily used by + # Booster to update its parameters based on the characteristics of + # a Dataset. It should not be used by other methods in this class, + # since "verbose" is not a Dataset parameter and needs to be passed + # through to avoid globally re-setting verbosity. + get_params = function() { + dataset_params <- unname(unlist(.DATASET_PARAMETERS())) + ret <- list() + for (param_key in names(private$params)) { + if (param_key %in% dataset_params) { + ret[[param_key]] <- private$params[[param_key]] + } + } + return(ret) + }, + + # Set categorical feature parameter + set_categorical_feature = function(categorical_feature) { + + # Check for identical input + if (identical(private$categorical_feature, categorical_feature)) { + return(invisible(self)) + } + + # Check for empty data + if (is.null(private$raw_data)) { + stop("set_categorical_feature: cannot set categorical feature after freeing raw data, + please set ", sQuote("free_raw_data = FALSE"), " when you construct lgb.Dataset") + } + + # Overwrite categorical features + private$categorical_feature <- categorical_feature + + # Finalize and return self + private$finalize() + return(invisible(self)) + + }, + + set_reference = function(reference) { + + # setting reference to this same Dataset object doesn't require any changes + if (identical(private$reference, reference)) { + return(invisible(self)) + } + + # changing the reference removes the Dataset object on the C++ side, so it should only + # be done if you still have the raw_data available, so that the new Dataset can be reconstructed + if (is.null(private$raw_data)) { + stop("set_reference: cannot set reference after freeing raw data, + please set ", sQuote("free_raw_data = FALSE"), " when you construct lgb.Dataset") + } + + if (!.is_Dataset(reference)) { + stop("set_reference: Can only use lgb.Dataset as a reference") + } + + # Set known references + self$set_categorical_feature(categorical_feature = reference$.__enclos_env__$private$categorical_feature) + self$set_colnames(colnames = reference$get_colnames()) + private$set_predictor(predictor = reference$.__enclos_env__$private$predictor) + + # Store reference + private$reference <- reference + + # Finalize and return self + private$finalize() + return(invisible(self)) + + }, + + # Save binary model + save_binary = function(fname) { + + # Store binary data + self$construct() + .Call( + LGBM_DatasetSaveBinary_R + , private$handle + , path.expand(fname) + ) + return(invisible(self)) + } + + ), + private = list( + handle = NULL, + raw_data = NULL, + params = list(), + reference = NULL, + colnames = NULL, + categorical_feature = NULL, + predictor = NULL, + free_raw_data = TRUE, + used_indices = NULL, + info = NULL, + version = 0L, + + # finalize() will free up the handles + finalize = function() { + .Call( + LGBM_DatasetFree_R + , private$handle + ) + private$handle <- NULL + return(invisible(NULL)) + }, + + get_handle = function() { + + # Get handle and construct if needed + if (.is_null_handle(x = private$handle)) { + self$construct() + } + return(private$handle) + + }, + + set_predictor = function(predictor) { + + if (identical(private$predictor, predictor)) { + return(invisible(self)) + } + + # Check for empty data + if (is.null(private$raw_data)) { + stop("set_predictor: cannot set predictor after free raw data, + please set ", sQuote("free_raw_data = FALSE"), " when you construct lgb.Dataset") + } + + # Check for empty predictor + if (!is.null(predictor)) { + + # Predictor is unknown + if (!.is_Predictor(predictor)) { + stop("set_predictor: Can only use lgb.Predictor as predictor") + } + + } + + # Store predictor + private$predictor <- predictor + + # Finalize and return self + private$finalize() + return(invisible(self)) + + } + + ) +) + +#' @title Construct \code{lgb.Dataset} object +#' @description LightGBM does not train on raw data. +#' It discretizes continuous features into histogram bins, tries to +#' combine categorical features, and automatically handles missing and +# infinite values. +#' +#' The \code{Dataset} class handles that preprocessing, and holds that +#' alternative representation of the input data. +#' @inheritParams lgb_shared_dataset_params +#' @param data a \code{matrix} object, a \code{dgCMatrix} object, +#' a character representing a path to a text file (CSV, TSV, or LibSVM), +#' or a character representing a path to a binary \code{lgb.Dataset} file +#' @param params a list of parameters. See +#' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ +#' The "Dataset Parameters" section of the documentation} for a list of parameters +#' and valid values. +#' @param reference reference dataset. When LightGBM creates a Dataset, it does some preprocessing like binning +#' continuous features into histograms. If you want to apply the same bin boundaries from an existing +#' dataset to new \code{data}, pass that existing Dataset to this argument. +#' @param colnames names of columns +#' @param categorical_feature categorical features. This can either be a character vector of feature +#' names or an integer vector with the indices of the features (e.g. +#' \code{c(1L, 10L)} to say "the first and tenth columns"). +#' @param free_raw_data LightGBM constructs its data format, called a "Dataset", from tabular data. +#' By default, that Dataset object on the R side does not keep a copy of the raw data. +#' This reduces LightGBM's memory consumption, but it means that the Dataset object +#' cannot be changed after it has been constructed. If you'd prefer to be able to +#' change the Dataset object after construction, set \code{free_raw_data = FALSE}. +#' +#' @return constructed dataset +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data_file <- tempfile(fileext = ".data") +#' lgb.Dataset.save(dtrain, data_file) +#' dtrain <- lgb.Dataset(data_file) +#' lgb.Dataset.construct(dtrain) +#' } +#' @export +lgb.Dataset <- function(data, + params = list(), + reference = NULL, + colnames = NULL, + categorical_feature = NULL, + free_raw_data = TRUE, + label = NULL, + weight = NULL, + group = NULL, + init_score = NULL) { + + return( + invisible(Dataset$new( + data = data + , params = params + , reference = reference + , colnames = colnames + , categorical_feature = categorical_feature + , predictor = NULL + , free_raw_data = free_raw_data + , used_indices = NULL + , label = label + , weight = weight + , group = group + , init_score = init_score + )) + ) + +} + +#' @name lgb.Dataset.create.valid +#' @title Construct validation data +#' @description Construct validation data according to training data +#' @inheritParams lgb_shared_dataset_params +#' @param dataset \code{lgb.Dataset} object, training data +#' @param data a \code{matrix} object, a \code{dgCMatrix} object, +#' a character representing a path to a text file (CSV, TSV, or LibSVM), +#' or a character representing a path to a binary \code{Dataset} file +#' @param params a list of parameters. See +#' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ +#' The "Dataset Parameters" section of the documentation} for a list of parameters +#' and valid values. If this is an empty list (the default), the validation Dataset +#' will have the same parameters as the Dataset passed to argument \code{dataset}. +#' +#' @return constructed dataset +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +#' +#' # parameters can be changed between the training data and validation set, +#' # for example to account for training data in a text file with a header row +#' # and validation data in a text file without it +#' train_file <- tempfile(pattern = "train_", fileext = ".csv") +#' write.table( +#' data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) +#' , file = train_file +#' , sep = "," +#' , col.names = TRUE +#' , row.names = FALSE +#' , quote = FALSE +#' ) +#' +#' valid_file <- tempfile(pattern = "valid_", fileext = ".csv") +#' write.table( +#' data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) +#' , file = valid_file +#' , sep = "," +#' , col.names = FALSE +#' , row.names = FALSE +#' , quote = FALSE +#' ) +#' +#' dtrain <- lgb.Dataset( +#' data = train_file +#' , params = list(has_header = TRUE) +#' ) +#' dtrain$construct() +#' +#' dvalid <- lgb.Dataset( +#' data = valid_file +#' , params = list(has_header = FALSE) +#' ) +#' dvalid$construct() +#' } +#' @export +lgb.Dataset.create.valid <- function(dataset, + data, + label = NULL, + weight = NULL, + group = NULL, + init_score = NULL, + params = list()) { + + if (!.is_Dataset(x = dataset)) { + stop("lgb.Dataset.create.valid: input data should be an lgb.Dataset object") + } + + # Create validation dataset + return(invisible( + dataset$create_valid( + data = data + , label = label + , weight = weight + , group = group + , init_score = init_score + , params = params + ) + )) + +} + +#' @name lgb.Dataset.construct +#' @title Construct Dataset explicitly +#' @description Construct Dataset explicitly +#' @param dataset Object of class \code{lgb.Dataset} +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' lgb.Dataset.construct(dtrain) +#' } +#' @return constructed dataset +#' @export +lgb.Dataset.construct <- function(dataset) { + + if (!.is_Dataset(x = dataset)) { + stop("lgb.Dataset.construct: input data should be an lgb.Dataset object") + } + + return(invisible(dataset$construct())) + +} + +#' @title Dimensions of an \code{lgb.Dataset} +#' @description Returns a vector of numbers of rows and of columns in an \code{lgb.Dataset}. +#' @param x Object of class \code{lgb.Dataset} +#' +#' @return a vector of numbers of rows and of columns +#' +#' @details +#' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also +#' be directly used with an \code{lgb.Dataset} object. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' +#' stopifnot(nrow(dtrain) == nrow(train$data)) +#' stopifnot(ncol(dtrain) == ncol(train$data)) +#' stopifnot(all(dim(dtrain) == dim(train$data))) +#' } +#' @rdname dim +#' @export +dim.lgb.Dataset <- function(x) { + + if (!.is_Dataset(x = x)) { + stop("dim.lgb.Dataset: input data should be an lgb.Dataset object") + } + + return(x$dim()) + +} + +#' @title Handling of column names of \code{lgb.Dataset} +#' @description Only column names are supported for \code{lgb.Dataset}, thus setting of +#' row names would have no effect and returned row names would be NULL. +#' @param x object of class \code{lgb.Dataset} +#' @param value a list of two elements: the first one is ignored +#' and the second one is column names +#' +#' @details +#' Generic \code{dimnames} methods are used by \code{colnames}. +#' Since row names are irrelevant, it is recommended to use \code{colnames} directly. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' lgb.Dataset.construct(dtrain) +#' dimnames(dtrain) +#' colnames(dtrain) +#' colnames(dtrain) <- make.names(seq_len(ncol(train$data))) +#' print(dtrain, verbose = TRUE) +#' } +#' @rdname dimnames.lgb.Dataset +#' @return A list with the dimension names of the dataset +#' @export +dimnames.lgb.Dataset <- function(x) { + + if (!.is_Dataset(x = x)) { + stop("dimnames.lgb.Dataset: input data should be an lgb.Dataset object") + } + + # Return dimension names + return(list(NULL, x$get_colnames())) + +} + +#' @rdname dimnames.lgb.Dataset +#' @export +`dimnames<-.lgb.Dataset` <- function(x, value) { + + # Check if invalid element list + if (!identical(class(value), "list") || length(value) != 2L) { + stop("invalid ", sQuote("value", q = FALSE), " given: must be a list of two elements") + } + + # Check for unknown row names + if (!is.null(value[[1L]])) { + stop("lgb.Dataset does not have rownames") + } + + if (is.null(value[[2L]])) { + + x$set_colnames(colnames = NULL) + return(x) + + } + + # Check for unmatching column size + if (ncol(x) != length(value[[2L]])) { + stop( + "can't assign " + , sQuote(length(value[[2L]]), q = FALSE) + , " colnames to an lgb.Dataset with " + , sQuote(ncol(x), q = FALSE) + , " columns" + ) + } + + # Set column names properly, and return + x$set_colnames(colnames = value[[2L]]) + return(x) + +} + +#' @title Slice a dataset +#' @description Get a new \code{lgb.Dataset} containing the specified rows of +#' original \code{lgb.Dataset} object +#' +#' \emph{Renamed from} \code{slice()} \emph{in 4.4.0} +#' +#' @param dataset Object of class \code{lgb.Dataset} +#' @param idxset an integer vector of indices of rows needed +#' @return constructed sub dataset +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' +#' dsub <- lgb.slice.Dataset(dtrain, seq_len(42L)) +#' lgb.Dataset.construct(dsub) +#' labels <- lightgbm::get_field(dsub, "label") +#' } +#' @export +lgb.slice.Dataset <- function(dataset, idxset) { + + if (!.is_Dataset(x = dataset)) { + stop("lgb.slice.Dataset: input dataset should be an lgb.Dataset object") + } + + return(invisible(dataset$slice(idxset = idxset))) + +} + +#' @name get_field +#' @title Get one attribute of a \code{lgb.Dataset} +#' @description Get one attribute of a \code{lgb.Dataset} +#' @param dataset Object of class \code{lgb.Dataset} +#' @param field_name String with the name of the attribute to get. One of the following. +#' \itemize{ +#' \item \code{label}: label lightgbm learns from ; +#' \item \code{weight}: to do a weight rescale ; +#' \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to +#' group rows together as ordered results from the same set of candidate results to be ranked. +#' For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, +#' that means that you have 6 groups, where the first 10 records are in the first group, +#' records 11-30 are in the second group, etc.} +#' \item \code{init_score}: initial score is the base prediction lightgbm will boost from. +#' } +#' @return requested attribute +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' lgb.Dataset.construct(dtrain) +#' +#' labels <- lightgbm::get_field(dtrain, "label") +#' lightgbm::set_field(dtrain, "label", 1 - labels) +#' +#' labels2 <- lightgbm::get_field(dtrain, "label") +#' stopifnot(all(labels2 == 1 - labels)) +#' } +#' @export +get_field <- function(dataset, field_name) { + UseMethod("get_field") +} + +#' @rdname get_field +#' @export +get_field.lgb.Dataset <- function(dataset, field_name) { + + # Check if dataset is not a dataset + if (!.is_Dataset(x = dataset)) { + stop("get_field.lgb.Dataset(): input dataset should be an lgb.Dataset object") + } + + return(dataset$get_field(field_name = field_name)) + +} + +#' @name set_field +#' @title Set one attribute of a \code{lgb.Dataset} object +#' @description Set one attribute of a \code{lgb.Dataset} +#' @param dataset Object of class \code{lgb.Dataset} +#' @param field_name String with the name of the attribute to set. One of the following. +#' \itemize{ +#' \item \code{label}: label lightgbm learns from ; +#' \item \code{weight}: to do a weight rescale ; +#' \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to +#' group rows together as ordered results from the same set of candidate results to be ranked. +#' For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, +#' that means that you have 6 groups, where the first 10 records are in the first group, +#' records 11-30 are in the second group, etc.} +#' \item \code{init_score}: initial score is the base prediction lightgbm will boost from. +#' } +#' @param data The data for the field. See examples. +#' @return The \code{lgb.Dataset} you passed in. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' lgb.Dataset.construct(dtrain) +#' +#' labels <- lightgbm::get_field(dtrain, "label") +#' lightgbm::set_field(dtrain, "label", 1 - labels) +#' +#' labels2 <- lightgbm::get_field(dtrain, "label") +#' stopifnot(all.equal(labels2, 1 - labels)) +#' } +#' @export +set_field <- function(dataset, field_name, data) { + UseMethod("set_field") +} + +#' @rdname set_field +#' @export +set_field.lgb.Dataset <- function(dataset, field_name, data) { + + if (!.is_Dataset(x = dataset)) { + stop("set_field.lgb.Dataset: input dataset should be an lgb.Dataset object") + } + + return(invisible(dataset$set_field(field_name = field_name, data = data))) +} + +#' @name lgb.Dataset.set.categorical +#' @title Set categorical feature of \code{lgb.Dataset} +#' @description Set the categorical features of an \code{lgb.Dataset} object. Use this function +#' to tell LightGBM which features should be treated as categorical. +#' @param dataset object of class \code{lgb.Dataset} +#' @param categorical_feature categorical features. This can either be a character vector of feature +#' names or an integer vector with the indices of the features (e.g. +#' \code{c(1L, 10L)} to say "the first and tenth columns"). +#' @return the dataset you passed in +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data_file <- tempfile(fileext = ".data") +#' lgb.Dataset.save(dtrain, data_file) +#' dtrain <- lgb.Dataset(data_file) +#' lgb.Dataset.set.categorical(dtrain, 1L:2L) +#' } +#' @rdname lgb.Dataset.set.categorical +#' @export +lgb.Dataset.set.categorical <- function(dataset, categorical_feature) { + + if (!.is_Dataset(x = dataset)) { + stop("lgb.Dataset.set.categorical: input dataset should be an lgb.Dataset object") + } + + return(invisible(dataset$set_categorical_feature(categorical_feature = categorical_feature))) + +} + +#' @name lgb.Dataset.set.reference +#' @title Set reference of \code{lgb.Dataset} +#' @description If you want to use validation data, you should set reference to training data +#' @param dataset object of class \code{lgb.Dataset} +#' @param reference object of class \code{lgb.Dataset} +#' +#' @return the dataset you passed in +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' # create training Dataset +#' data(agaricus.train, package ="lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' +#' # create a validation Dataset, using dtrain as a reference +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset(test$data, label = test$label) +#' lgb.Dataset.set.reference(dtest, dtrain) +#' } +#' @rdname lgb.Dataset.set.reference +#' @export +lgb.Dataset.set.reference <- function(dataset, reference) { + + if (!.is_Dataset(x = dataset)) { + stop("lgb.Dataset.set.reference: input dataset should be an lgb.Dataset object") + } + + return(invisible(dataset$set_reference(reference = reference))) +} + +#' @name lgb.Dataset.save +#' @title Save \code{lgb.Dataset} to a binary file +#' @description Please note that \code{init_score} is not saved in binary file. +#' If you need it, please set it again after loading Dataset. +#' @param dataset object of class \code{lgb.Dataset} +#' @param fname object filename of output file +#' +#' @return the dataset you passed in +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' lgb.Dataset.save(dtrain, tempfile(fileext = ".bin")) +#' } +#' @export +lgb.Dataset.save <- function(dataset, fname) { + + if (!.is_Dataset(x = dataset)) { + stop("lgb.Dataset.save: input dataset should be an lgb.Dataset object") + } + + if (!is.character(fname)) { + stop("lgb.Dataset.save: fname should be a character or a file connection") + } + + return(invisible(dataset$save_binary(fname = fname))) +} diff --git a/R-package/R/lgb.Predictor.R b/R-package/R/lgb.Predictor.R new file mode 100644 index 0000000..0b71a52 --- /dev/null +++ b/R-package/R/lgb.Predictor.R @@ -0,0 +1,529 @@ +#' @importFrom methods is new +#' @importFrom R6 R6Class +#' @importFrom utils read.delim +#' @importClassesFrom Matrix dsparseMatrix dsparseVector dgCMatrix dgRMatrix CsparseMatrix RsparseMatrix +Predictor <- R6::R6Class( + + classname = "lgb.Predictor", + cloneable = FALSE, + public = list( + + # Initialize will create a starter model + initialize = function(modelfile, params = list(), fast_predict_config = list()) { + private$params <- .params2str(params = params) + handle <- NULL + + if (is.character(modelfile)) { + + # Create handle on it + handle <- .Call( + LGBM_BoosterCreateFromModelfile_R + , path.expand(modelfile) + ) + private$need_free_handle <- TRUE + + } else if (methods::is(modelfile, "lgb.Booster.handle") || inherits(modelfile, "externalptr")) { + + # Check if model file is a booster handle already + handle <- modelfile + private$need_free_handle <- FALSE + + } else if (.is_Booster(modelfile)) { + + handle <- modelfile$get_handle() + private$need_free_handle <- FALSE + + } else { + + stop("lgb.Predictor: modelfile must be either a character filename or an lgb.Booster.handle") + + } + + private$fast_predict_config <- fast_predict_config + + # Override class and store it + class(handle) <- "lgb.Booster.handle" + private$handle <- handle + + return(invisible(NULL)) + + }, + + # Get current iteration + current_iter = function() { + + cur_iter <- 0L + .Call( + LGBM_BoosterGetCurrentIteration_R + , private$handle + , cur_iter + ) + return(cur_iter) + + }, + + # Predict from data + predict = function(data, + start_iteration = NULL, + num_iteration = NULL, + rawscore = FALSE, + predleaf = FALSE, + predcontrib = FALSE, + header = FALSE) { + + # Check if number of iterations is existing - if not, then set it to -1 (use all) + if (is.null(num_iteration)) { + num_iteration <- -1L + } + # Check if start iterations is existing - if not, then set it to 0 (start from the first iteration) + if (is.null(start_iteration)) { + start_iteration <- 0L + } + + # Check if data is a file name and not a matrix + if (identical(class(data), "character") && length(data) == 1L) { + + data <- path.expand(data) + + # Data is a filename, create a temporary file with a "lightgbm_" pattern in it + tmp_filename <- tempfile(pattern = "lightgbm_") + on.exit(unlink(tmp_filename), add = TRUE) + + # Predict from temporary file + .Call( + LGBM_BoosterPredictForFile_R + , private$handle + , data + , as.integer(header) + , as.integer(rawscore) + , as.integer(predleaf) + , as.integer(predcontrib) + , as.integer(start_iteration) + , as.integer(num_iteration) + , private$params + , tmp_filename + ) + + # Get predictions from file + preds <- utils::read.delim(tmp_filename, header = FALSE, sep = "\t") + num_row <- nrow(preds) + preds <- as.vector(t(preds)) + + } else if (predcontrib && inherits(data, c("dsparseMatrix", "dsparseVector"))) { + + ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) + ncols_out <- integer(1L) + .Call(LGBM_BoosterGetNumClasses_R, private$handle, ncols_out) + ncols_out <- (ncols + 1L) * max(ncols_out, 1L) + if (is.na(ncols_out)) { + ncols_out <- as.numeric(ncols + 1L) * as.numeric(max(ncols_out, 1L)) + } + if (!inherits(data, "dsparseVector") && ncols_out > .Machine$integer.max) { + stop("Resulting matrix of feature contributions is too large for R to handle.") + } + + if (inherits(data, "dsparseVector")) { + + if (length(data) > ncols) { + stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." + , ncols + , length(data))) + } + res <- .Call( + LGBM_BoosterPredictSparseOutput_R + , private$handle + , c(0L, as.integer(length(data@x))) + , data@i - 1L + , data@x + , TRUE + , 1L + , ncols + , start_iteration + , num_iteration + , private$params + ) + out <- methods::new("dsparseVector") + out@i <- res$indices + 1L + out@x <- res$data + out@length <- ncols_out + return(out) + + } else if (inherits(data, "dgRMatrix")) { + + if (ncol(data) > ncols) { + stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." + , ncols + , ncol(data))) + } + res <- .Call( + LGBM_BoosterPredictSparseOutput_R + , private$handle + , data@p + , data@j + , data@x + , TRUE + , nrow(data) + , ncols + , start_iteration + , num_iteration + , private$params + ) + out <- methods::new("dgRMatrix") + out@p <- res$indptr + out@j <- res$indices + out@x <- res$data + out@Dim <- as.integer(c(nrow(data), ncols_out)) + + } else if (inherits(data, "dgCMatrix")) { + + if (ncol(data) != ncols) { + stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." + , ncols + , ncol(data))) + } + res <- .Call( + LGBM_BoosterPredictSparseOutput_R + , private$handle + , data@p + , data@i + , data@x + , FALSE + , nrow(data) + , ncols + , start_iteration + , num_iteration + , private$params + ) + out <- methods::new("dgCMatrix") + out@p <- res$indptr + out@i <- res$indices + out@x <- res$data + out@Dim <- as.integer(c(nrow(data), length(res$indptr) - 1L)) + + } else { + + stop(sprintf("Predictions on sparse inputs are only allowed for '%s', '%s', '%s' - got: %s" + , "dsparseVector" + , "dgRMatrix" + , "dgCMatrix" + , toString(class(data)))) + } + + if (NROW(row.names(data))) { + out@Dimnames[[1L]] <- row.names(data) + } + return(out) + + } else { + + # Not a file, we need to predict from R object + num_row <- nrow(data) + if (is.null(num_row)) { + num_row <- 1L + } + + npred <- 0L + + # Check number of predictions to do + .Call( + LGBM_BoosterCalcNumPredict_R + , private$handle + , as.integer(num_row) + , as.integer(rawscore) + , as.integer(predleaf) + , as.integer(predcontrib) + , as.integer(start_iteration) + , as.integer(num_iteration) + , npred + ) + + # Pre-allocate empty vector + preds <- numeric(npred) + + # Check if data is a matrix + if (is.matrix(data)) { + # this if() prevents the memory and computational costs + # of converting something that is already "double" to "double" + if (storage.mode(data) != "double") { + storage.mode(data) <- "double" + } + + if (nrow(data) == 1L) { + + use_fast_config <- private$check_can_use_fast_predict_config( + csr = FALSE + , rawscore = rawscore + , predleaf = predleaf + , predcontrib = predcontrib + , start_iteration = start_iteration + , num_iteration = num_iteration + ) + + if (use_fast_config) { + .Call( + LGBM_BoosterPredictForMatSingleRowFast_R + , private$fast_predict_config$handle + , data + , preds + ) + } else { + .Call( + LGBM_BoosterPredictForMatSingleRow_R + , private$handle + , data + , rawscore + , predleaf + , predcontrib + , start_iteration + , num_iteration + , private$params + , preds + ) + } + + } else { + .Call( + LGBM_BoosterPredictForMat_R + , private$handle + , data + , as.integer(nrow(data)) + , as.integer(ncol(data)) + , as.integer(rawscore) + , as.integer(predleaf) + , as.integer(predcontrib) + , as.integer(start_iteration) + , as.integer(num_iteration) + , private$params + , preds + ) + } + + } else if (inherits(data, "dsparseVector")) { + + if (length(self$fast_predict_config)) { + ncols <- self$fast_predict_config$ncols + use_fast_config <- private$check_can_use_fast_predict_config( + csr = TRUE + , rawscore = rawscore + , predleaf = predleaf + , predcontrib = predcontrib + , start_iteration = start_iteration + , num_iteration = num_iteration + ) + } else { + ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) + use_fast_config <- FALSE + } + + if (length(data) > ncols) { + stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." + , ncols + , length(data))) + } + + if (use_fast_config) { + .Call( + LGBM_BoosterPredictForCSRSingleRowFast_R + , self$fast_predict_config$handle + , data@i - 1L + , data@x + , preds + ) + } else { + .Call( + LGBM_BoosterPredictForCSRSingleRow_R + , private$handle + , data@i - 1L + , data@x + , ncols + , as.integer(rawscore) + , as.integer(predleaf) + , as.integer(predcontrib) + , start_iteration + , num_iteration + , private$params + , preds + ) + } + + } else if (inherits(data, "dgRMatrix")) { + + ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) + if (ncol(data) > ncols) { + stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns." + , ncols + , ncol(data))) + } + + if (nrow(data) == 1L) { + + if (length(self$fast_predict_config)) { + ncols <- self$fast_predict_config$ncols + use_fast_config <- private$check_can_use_fast_predict_config( + csr = TRUE + , rawscore = rawscore + , predleaf = predleaf + , predcontrib = predcontrib + , start_iteration = start_iteration + , num_iteration = num_iteration + ) + } else { + ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle) + use_fast_config <- FALSE + } + + if (use_fast_config) { + .Call( + LGBM_BoosterPredictForCSRSingleRowFast_R + , self$fast_predict_config$handle + , data@j + , data@x + , preds + ) + } else { + .Call( + LGBM_BoosterPredictForCSRSingleRow_R + , private$handle + , data@j + , data@x + , ncols + , as.integer(rawscore) + , as.integer(predleaf) + , as.integer(predcontrib) + , start_iteration + , num_iteration + , private$params + , preds + ) + } + + } else { + + .Call( + LGBM_BoosterPredictForCSR_R + , private$handle + , data@p + , data@j + , data@x + , ncols + , as.integer(rawscore) + , as.integer(predleaf) + , as.integer(predcontrib) + , start_iteration + , num_iteration + , private$params + , preds + ) + + } + + } else if (methods::is(data, "dgCMatrix")) { + if (length(data@p) > 2147483647L) { + stop("Cannot support large CSC matrix") + } + # Check if data is a dgCMatrix (sparse matrix, column compressed format) + .Call( + LGBM_BoosterPredictForCSC_R + , private$handle + , data@p + , data@i + , data@x + , length(data@p) + , length(data@x) + , nrow(data) + , as.integer(rawscore) + , as.integer(predleaf) + , as.integer(predcontrib) + , as.integer(start_iteration) + , as.integer(num_iteration) + , private$params + , preds + ) + + } else { + + stop("predict: cannot predict on data of class ", sQuote(class(data), q = FALSE)) + + } + } + + # Check if number of rows is strange (not a multiple of the dataset rows) + if (length(preds) %% num_row != 0L) { + stop( + "predict: prediction length " + , sQuote(length(preds), q = FALSE) + , " is not a multiple of nrows(data): " + , sQuote(num_row, q = FALSE) + ) + } + + # Get number of cases per row + npred_per_case <- length(preds) / num_row + + # Data reshaping + if (npred_per_case > 1L || predleaf || predcontrib) { + preds <- matrix(preds, ncol = npred_per_case, byrow = TRUE) + } + + # Keep row names if possible + if (NROW(row.names(data)) && NROW(data) == NROW(preds)) { + if (is.null(dim(preds))) { + names(preds) <- row.names(data) + } else { + row.names(preds) <- row.names(data) + } + } + + return(preds) + } + + ), + private = list( + handle = NULL + , need_free_handle = FALSE + , params = "" + , fast_predict_config = list() + , check_can_use_fast_predict_config = function(csr, + rawscore, + predleaf, + predcontrib, + start_iteration, + num_iteration) { + + if (!NROW(private$fast_predict_config)) { + return(FALSE) + } + + if (.is_null_handle(private$fast_predict_config$handle)) { + warning(paste0("Model had fast CSR predict configuration, but it is inactive." + , " Try re-generating it through 'lgb.configure_fast_predict'.")) + return(FALSE) + } + + if (isTRUE(csr) != private$fast_predict_config$csr) { + return(FALSE) + } + + return( + private$params == "" && + private$fast_predict_config$rawscore == rawscore && + private$fast_predict_config$predleaf == predleaf && + private$fast_predict_config$predcontrib == predcontrib && + .equal_or_both_null(private$fast_predict_config$start_iteration, start_iteration) && + .equal_or_both_null(private$fast_predict_config$num_iteration, num_iteration) + ) + } + + # finalize() will free up the handles + , finalize = function() { + if (private$need_free_handle) { + .Call( + LGBM_BoosterFree_R + , private$handle + ) + private$handle <- NULL + } + return(invisible(NULL)) + } + ) +) diff --git a/R-package/R/lgb.convert_with_rules.R b/R-package/R/lgb.convert_with_rules.R new file mode 100644 index 0000000..1d2748a --- /dev/null +++ b/R-package/R/lgb.convert_with_rules.R @@ -0,0 +1,207 @@ +# [description] get all column classes of a data.table or data.frame. +# This function collapses the result of class() into a single string +.get_column_classes <- function(df) { + return( + vapply( + X = df + , FUN = function(x) { + paste(class(x), collapse = ",") + } + , FUN.VALUE = character(1L) + ) + ) +} + +# [description] check a data frame or data table for columns that are any +# type other than numeric and integer. This is used by lgb.convert_with_rules() +# to warn if more action is needed by users +# before a dataset can be converted to a lgb.Dataset. +.warn_for_unconverted_columns <- function(df, function_name) { + column_classes <- .get_column_classes(df = df) + unconverted_columns <- column_classes[!(column_classes %in% c("numeric", "integer"))] + if (length(unconverted_columns) > 0L) { + col_detail_string <- toString( + paste0( + names(unconverted_columns) + , " (" + , unconverted_columns + , ")" + ) + ) + msg <- paste0( + function_name + , ": " + , length(unconverted_columns) + , " columns are not numeric or integer. These need to be dropped or converted to " + , "be used in an lgb.Dataset object. " + , col_detail_string + ) + warning(msg) + } + return(invisible(NULL)) +} + +.LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA <- function() { + return(-1L) +} +.LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA <- function() { + return(0L) +} + + +#' @name lgb.convert_with_rules +#' @title Data preparator for LightGBM datasets with rules (integer) +#' @description Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. +#' Factor, character, and logical columns are converted to integer. Missing values +#' in factors and characters will be filled with 0L. Missing values in logicals +#' will be filled with -1L. +#' +#' This function returns and optionally takes in "rules" the describe exactly +#' how to convert values in columns. +#' +#' Columns that contain only NA values will be converted by this function but will +#' not show up in the returned \code{rules}. +#' +#' NOTE: In previous releases of LightGBM, this function was called \code{lgb.prepare_rules2}. +#' @param data A data.frame or data.table to prepare. +#' @param rules A set of rules from the data preparator, if already used. This should be an R list, +#' where names are column names in \code{data} and values are named character +#' vectors whose names are column values and whose values are new values to +#' replace them with. +#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). +#' Note that the data must be converted to a matrix format (\code{as.matrix}) for input in +#' \code{lgb.Dataset}. +#' +#' @examples +#' \donttest{ +#' data(iris) +#' +#' str(iris) +#' +#' new_iris <- lgb.convert_with_rules(data = iris) +#' str(new_iris$data) +#' +#' data(iris) # Erase iris dataset +#' iris$Species[1L] <- "NEW FACTOR" # Introduce junk factor (NA) +#' +#' # Use conversion using known rules +#' # Unknown factors become 0, excellent for sparse datasets +#' newer_iris <- lgb.convert_with_rules(data = iris, rules = new_iris$rules) +#' +#' # Unknown factor is now zero, perfect for sparse datasets +#' newer_iris$data[1L, ] # Species became 0 as it is an unknown factor +#' +#' newer_iris$data[1L, 5L] <- 1.0 # Put back real initial value +#' +#' # Is the newly created dataset equal? YES! +#' all.equal(new_iris$data, newer_iris$data) +#' +#' # Can we test our own rules? +#' data(iris) # Erase iris dataset +#' +#' # We remapped values differently +#' personal_rules <- list( +#' Species = c( +#' "setosa" = 3L +#' , "versicolor" = 2L +#' , "virginica" = 1L +#' ) +#' ) +#' newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules) +#' str(newest_iris$data) # SUCCESS! +#' } +#' @importFrom data.table set +#' @export +lgb.convert_with_rules <- function(data, rules = NULL) { + + column_classes <- .get_column_classes(df = data) + + is_data_table <- data.table::is.data.table(x = data) + is_data_frame <- is.data.frame(data) + + if (!(is_data_table || is_data_frame)) { + stop( + "lgb.convert_with_rules: you provided " + , paste(class(data), collapse = " & ") + , " but data should have class data.frame or data.table" + ) + } + + # if user didn't provide rules, create them + if (is.null(rules)) { + rules <- list() + columns_to_fix <- which(column_classes %in% c("character", "factor", "logical")) + + for (i in columns_to_fix) { + + col_values <- data[[i]] + + # Get unique values + if (is.factor(col_values)) { + unique_vals <- levels(col_values) + unique_vals <- unique_vals[!is.na(unique_vals)] + mini_numeric <- seq_along(unique_vals) # respect ordinal + } else if (is.character(col_values)) { + unique_vals <- as.factor(unique(col_values)) + unique_vals <- unique_vals[!is.na(unique_vals)] + mini_numeric <- as.integer(unique_vals) # no respect for ordinal + } else if (is.logical(col_values)) { + unique_vals <- c(FALSE, TRUE) + mini_numeric <- c(0L, 1L) + } + + # don't add rules for all-NA columns + if (length(unique_vals) > 0L) { + col_name <- names(data)[i] + rules[[col_name]] <- mini_numeric + names(rules[[col_name]]) <- unique_vals + } + } + } + + for (col_name in names(rules)) { + if (column_classes[[col_name]] == "logical") { + default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA() + } else { + default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA() + } + if (is_data_table) { + data.table::set( + x = data + , j = col_name + , value = unname(rules[[col_name]][data[[col_name]]]) + ) + data[is.na(get(col_name)), (col_name) := default_value_for_na] + } else { + data[[col_name]] <- unname(rules[[col_name]][data[[col_name]]]) + data[is.na(data[col_name]), col_name] <- default_value_for_na + } + } + + # if any all-NA columns exist, they won't be in rules. Convert them + all_na_cols <- which( + sapply( + X = data + , FUN = function(x) { + (is.factor(x) || is.character(x) || is.logical(x)) && all(is.na(unique(x))) + } + ) + ) + for (col_name in all_na_cols) { + if (column_classes[[col_name]] == "logical") { + default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA() + } else { + default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA() + } + if (is_data_table) { + data[, (col_name) := rep(default_value_for_na, .N)] + } else { + data[[col_name]] <- default_value_for_na + } + } + + .warn_for_unconverted_columns(df = data, function_name = "lgb.convert_with_rules") + + return(list(data = data, rules = rules)) + +} diff --git a/R-package/R/lgb.cv.R b/R-package/R/lgb.cv.R new file mode 100644 index 0000000..1f6fcf6 --- /dev/null +++ b/R-package/R/lgb.cv.R @@ -0,0 +1,617 @@ +#' @importFrom R6 R6Class +CVBooster <- R6::R6Class( + classname = "lgb.CVBooster", + cloneable = FALSE, + public = list( + best_iter = -1L, + best_score = NA, + record_evals = list(), + boosters = list(), + initialize = function(x) { + self$boosters <- x + return(invisible(NULL)) + }, + reset_parameter = function(new_params) { + for (x in self$boosters) { + x[["booster"]]$reset_parameter(params = new_params) + } + return(invisible(self)) + } + ) +) + +#' @name lgb.cv +#' @title Main CV logic for LightGBM +#' @description Cross validation logic used by LightGBM +#' @inheritParams lgb_shared_params +#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples. +#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} +#' @param showsd \code{boolean}, whether to show standard deviation of cross validation. +#' This parameter defaults to \code{TRUE}. Setting it to \code{FALSE} can lead to a +#' slight speedup by avoiding unnecessary computation. +#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified +#' by the values of outcome labels. +#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds +#' (each element must be a vector of test fold's indices). When folds are supplied, +#' the \code{nfold} and \code{stratified} parameters are ignored. +#' @param callbacks List of callback functions that are applied at each iteration. +#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the booster model +#' into a predictor model which frees up memory and the original datasets +#' @param eval_train_metric \code{boolean}, whether to add the cross validation results on the +#' training data. This parameter defaults to \code{FALSE}. Setting it to \code{TRUE} +#' will increase run time. +#' @inheritSection lgb_shared_params Early Stopping +#' @return a trained model \code{lgb.CVBooster}. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' params <- list( +#' objective = "regression" +#' , metric = "l2" +#' , min_data = 1L +#' , learning_rate = 1.0 +#' , num_threads = 2L +#' ) +#' model <- lgb.cv( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' , nfold = 3L +#' ) +#' } +#' +#' @importFrom data.table data.table setorderv +#' @export +lgb.cv <- function(params = list() + , data + , nrounds = 100L + , nfold = 3L + , obj = NULL + , eval = NULL + , verbose = 1L + , record = TRUE + , eval_freq = 1L + , showsd = TRUE + , stratified = TRUE + , folds = NULL + , init_model = NULL + , early_stopping_rounds = NULL + , callbacks = list() + , reset_data = FALSE + , serializable = TRUE + , eval_train_metric = FALSE + ) { + + if (nrounds <= 0L) { + stop("nrounds should be greater than zero") + } + if (!.is_Dataset(x = data)) { + stop("lgb.cv: data must be an lgb.Dataset instance") + } + + # set some parameters, resolving the way they were passed in with other parameters + # in `params`. + # this ensures that the model stored with Booster$save() correctly represents + # what was passed in + params <- .check_wrapper_param( + main_param_name = "verbosity" + , params = params + , alternative_kwarg_value = verbose + ) + params <- .check_wrapper_param( + main_param_name = "num_iterations" + , params = params + , alternative_kwarg_value = nrounds + ) + params <- .check_wrapper_param( + main_param_name = "metric" + , params = params + , alternative_kwarg_value = NULL + ) + params <- .check_wrapper_param( + main_param_name = "objective" + , params = params + , alternative_kwarg_value = obj + ) + params <- .check_wrapper_param( + main_param_name = "early_stopping_round" + , params = params + , alternative_kwarg_value = early_stopping_rounds + ) + early_stopping_rounds <- params[["early_stopping_round"]] + + # extract any function objects passed for objective or metric + fobj <- NULL + if (is.function(params$objective)) { + fobj <- params$objective + params$objective <- "none" + } + + # If eval is a single function, store it as a 1-element list + # (for backwards compatibility). If it is a list of functions, store + # all of them. This makes it possible to pass any mix of strings like "auc" + # and custom functions to eval + params <- .check_eval(params = params, eval = eval) + eval_functions <- list(NULL) + if (is.function(eval)) { + eval_functions <- list(eval) + } + if (methods::is(eval, "list")) { + eval_functions <- Filter( + f = is.function + , x = eval + ) + } + + # Init predictor to empty + predictor <- NULL + + # Check for boosting from a trained model + if (is.character(init_model)) { + predictor <- Predictor$new(modelfile = init_model) + } else if (.is_Booster(x = init_model)) { + predictor <- init_model$to_predictor() + } + + # Set the iteration to start from / end to (and check for boosting from a trained model, again) + begin_iteration <- 1L + if (!is.null(predictor)) { + begin_iteration <- predictor$current_iter() + 1L + } + end_iteration <- begin_iteration + params[["num_iterations"]] - 1L + + # pop interaction_constraints off of params. It needs some preprocessing on the + # R side before being passed into the Dataset object + interaction_constraints <- params[["interaction_constraints"]] + params["interaction_constraints"] <- NULL + + # Construct datasets, if needed + data$update_params(params = params) + data$construct() + + # Check interaction constraints + params[["interaction_constraints"]] <- .check_interaction_constraints( + interaction_constraints = interaction_constraints + , column_names = data$get_colnames() + ) + + # Update parameters with parsed parameters + data$update_params(params = params) + + # Create the predictor set + data$.__enclos_env__$private$set_predictor(predictor = predictor) + + if (!is.null(folds)) { + + # Check for list of folds or for single value + if (!identical(class(folds), "list") || length(folds) < 2L) { + stop(sQuote("folds"), " must be a list with 2 or more elements that are vectors of indices for each CV-fold") + } + + } else { + + if (nfold <= 1L) { + stop(sQuote("nfold"), " must be > 1") + } + + # Create folds + folds <- .generate_cv_folds( + nfold = nfold + , nrows = nrow(data) + , stratified = stratified + , label = get_field(dataset = data, field_name = "label") + , group = get_field(dataset = data, field_name = "group") + , params = params + ) + + } + + # Add printing log callback + if (params[["verbosity"]] > 0L && eval_freq > 0L) { + callbacks <- .add_cb(cb_list = callbacks, cb = cb_print_evaluation(period = eval_freq)) + } + + # Add evaluation log callback + if (record) { + callbacks <- .add_cb(cb_list = callbacks, cb = cb_record_evaluation()) + } + + # Did user pass parameters that indicate they want to use early stopping? + using_early_stopping <- !is.null(early_stopping_rounds) && early_stopping_rounds > 0L + + boosting_param_names <- .PARAMETER_ALIASES()[["boosting"]] + using_dart <- any( + sapply( + X = boosting_param_names + , FUN = function(param) { + identical(params[[param]], "dart") + } + ) + ) + + # Cannot use early stopping with 'dart' boosting + if (using_dart) { + if (using_early_stopping) { + warning("Early stopping is not available in 'dart' mode.") + } + using_early_stopping <- FALSE + + # Remove the cb_early_stop() function if it was passed in to callbacks + callbacks <- Filter( + f = function(cb_func) { + !identical(attr(cb_func, "name"), "cb_early_stop") + } + , x = callbacks + ) + } + + # If user supplied early_stopping_rounds, add the early stopping callback + if (using_early_stopping) { + callbacks <- .add_cb( + cb_list = callbacks + , cb = cb_early_stop( + stopping_rounds = early_stopping_rounds + , first_metric_only = isTRUE(params[["first_metric_only"]]) + , verbose = params[["verbosity"]] > 0L + ) + ) + } + + cb <- .categorize_callbacks(cb_list = callbacks) + + # Construct booster for each fold. The data.table() code below is used to + # guarantee that indices are sorted while keeping init_score and weight together + # with the correct indices. Note that it takes advantage of the fact that + # someDT$some_column returns NULL is 'some_column' does not exist in the data.table + bst_folds <- lapply( + X = seq_along(folds) + , FUN = function(k) { + + # For learning-to-rank, each fold is a named list with two elements: + # * `fold` = an integer vector of row indices + # * `group` = an integer vector describing which groups are in the fold + # For classification or regression tasks, it will just be an integer + # vector of row indices + folds_have_group <- "group" %in% names(folds[[k]]) + if (folds_have_group) { + test_indices <- folds[[k]]$fold + test_group_indices <- folds[[k]]$group + test_groups <- get_field(dataset = data, field_name = "group")[test_group_indices] + train_groups <- get_field(dataset = data, field_name = "group")[-test_group_indices] + } else { + test_indices <- folds[[k]] + } + train_indices <- seq_len(nrow(data))[-test_indices] + + # set up test set + indexDT <- data.table::data.table( + indices = test_indices + , weight = get_field(dataset = data, field_name = "weight")[test_indices] + , init_score = get_field(dataset = data, field_name = "init_score")[test_indices] + ) + data.table::setorderv(x = indexDT, cols = "indices", order = 1L) + dtest <- lgb.slice.Dataset(data, indexDT$indices) + set_field(dataset = dtest, field_name = "weight", data = indexDT$weight) + set_field(dataset = dtest, field_name = "init_score", data = indexDT$init_score) + + # set up training set + indexDT <- data.table::data.table( + indices = train_indices + , weight = get_field(dataset = data, field_name = "weight")[train_indices] + , init_score = get_field(dataset = data, field_name = "init_score")[train_indices] + ) + data.table::setorderv(x = indexDT, cols = "indices", order = 1L) + dtrain <- lgb.slice.Dataset(data, indexDT$indices) + set_field(dataset = dtrain, field_name = "weight", data = indexDT$weight) + set_field(dataset = dtrain, field_name = "init_score", data = indexDT$init_score) + + if (folds_have_group) { + set_field(dataset = dtest, field_name = "group", data = test_groups) + set_field(dataset = dtrain, field_name = "group", data = train_groups) + } + + booster <- Booster$new(params = params, train_set = dtrain) + if (isTRUE(eval_train_metric)) { + booster$add_valid(data = dtrain, name = "train") + } + booster$add_valid(data = dtest, name = "valid") + return( + list(booster = booster) + ) + } + ) + + # Create new booster + cv_booster <- CVBooster$new(x = bst_folds) + + # Callback env + env <- CB_ENV$new() + env$model <- cv_booster + env$begin_iteration <- begin_iteration + env$end_iteration <- end_iteration + + # Start training model using number of iterations to start and end with + for (i in seq.int(from = begin_iteration, to = end_iteration)) { + + # Overwrite iteration in environment + env$iteration <- i + env$eval_list <- list() + + for (f in cb$pre_iter) { + f(env) + } + + # Update one boosting iteration + msg <- lapply(cv_booster$boosters, function(fd) { + fd$booster$update(fobj = fobj) + out <- list() + for (eval_function in eval_functions) { + out <- append(out, fd$booster$eval_valid(feval = eval_function)) + } + return(out) + }) + + # Prepare collection of evaluation results + merged_msg <- .merge_cv_result( + msg = msg + , showsd = showsd + ) + + # Write evaluation result in environment + env$eval_list <- merged_msg$eval_list + + # Check for standard deviation requirement + if (showsd) { + env$eval_err_list <- merged_msg$eval_err_list + } + + # Loop through env + for (f in cb$post_iter) { + f(env) + } + + # Check for early stopping and break if needed + if (env$met_early_stop) break + + } + + # When early stopping is not activated, we compute the best iteration / score ourselves + # based on the first first metric + if (record && is.na(env$best_score)) { + # when using a custom eval function, the metric name is returned from the + # function, so figure it out from record_evals + if (!is.null(eval_functions[1L])) { + first_metric <- names(cv_booster$record_evals[["valid"]])[1L] + } else { + first_metric <- cv_booster$.__enclos_env__$private$eval_names[1L] + } + .find_best <- which.min + if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) { + .find_best <- which.max + } + cv_booster$best_iter <- unname( + .find_best( + unlist( + cv_booster$record_evals[["valid"]][[first_metric]][[.EVAL_KEY()]] + ) + ) + ) + cv_booster$best_score <- cv_booster$record_evals[["valid"]][[first_metric]][[.EVAL_KEY()]][[cv_booster$best_iter]] + } + # Propagate the best_iter attribute from the cv_booster to the individual boosters + for (bst in cv_booster$boosters) { + bst$booster$best_iter <- cv_booster$best_iter + } + + if (reset_data) { + lapply(cv_booster$boosters, function(fd) { + # Store temporarily model data elsewhere + booster_old <- list( + best_iter = fd$booster$best_iter + , best_score = fd$booster$best_score + , record_evals = fd$booster$record_evals + ) + # Reload model + fd$booster <- lgb.load(model_str = fd$booster$save_model_to_string()) + fd$booster$best_iter <- booster_old$best_iter + fd$booster$best_score <- booster_old$best_score + fd$booster$record_evals <- booster_old$record_evals + }) + } + + if (serializable) { + lapply(cv_booster$boosters, function(model) model$booster$save_raw()) + } + + return(cv_booster) + +} + +# Generates random (stratified if needed) CV folds +.generate_cv_folds <- function(nfold, nrows, stratified, label, group, params) { + + # Check for group existence + if (is.null(group)) { + + # Shuffle + rnd_idx <- sample.int(nrows) + + # Request stratified folds + if (isTRUE(stratified) && params$objective %in% c("binary", "multiclass") && length(label) == length(rnd_idx)) { + + y <- label[rnd_idx] + y <- as.factor(y) + folds <- .stratified_folds(y = y, k = nfold) + + } else { + + # Make simple non-stratified folds + folds <- list() + + # Loop through each fold + for (i in seq_len(nfold)) { + kstep <- length(rnd_idx) %/% (nfold - i + 1L) + folds[[i]] <- rnd_idx[seq_len(kstep)] + rnd_idx <- rnd_idx[-seq_len(kstep)] + } + + } + + } else { + + # When doing group, stratified is not possible (only random selection) + if (nfold > length(group)) { + stop("\nYou requested too many folds for the number of available groups.\n") + } + + # Degroup the groups + ungrouped <- inverse.rle(list(lengths = group, values = seq_along(group))) + + # Can't stratify, shuffle + rnd_idx <- sample.int(length(group)) + + # Make simple non-stratified folds + folds <- list() + + # Loop through each fold + for (i in seq_len(nfold)) { + kstep <- length(rnd_idx) %/% (nfold - i + 1L) + folds[[i]] <- list( + fold = which(ungrouped %in% rnd_idx[seq_len(kstep)]) + , group = rnd_idx[seq_len(kstep)] + ) + rnd_idx <- rnd_idx[-seq_len(kstep)] + } + + } + + return(folds) + +} + +# Creates CV folds stratified by the values of y. +# It was borrowed from caret::createFolds and simplified +# by always returning an unnamed list of fold indices. +#' @importFrom stats quantile +.stratified_folds <- function(y, k) { + + # Group the numeric data based on their magnitudes + # and sample within those groups. + # When the number of samples is low, we may have + # issues further slicing the numeric data into + # groups. The number of groups will depend on the + # ratio of the number of folds to the sample size. + # At most, we will use quantiles. If the sample + # is too small, we just do regular unstratified CV + if (is.numeric(y)) { + + cuts <- length(y) %/% k + if (cuts < 2L) { + cuts <- 2L + } + if (cuts > 5L) { + cuts <- 5L + } + y <- cut( + y + , unique(stats::quantile(y, probs = seq.int(0.0, 1.0, length.out = cuts))) + , include.lowest = TRUE + ) + + } + + if (k < length(y)) { + + # Reset levels so that the possible levels and + # the levels in the vector are the same + y <- as.factor(as.character(y)) + numInClass <- table(y) + foldVector <- vector(mode = "integer", length(y)) + + # For each class, balance the fold allocation as far + # as possible, then resample the remainder. + # The final assignment of folds is also randomized. + for (i in seq_along(numInClass)) { + + # Create a vector of integers from 1:k as many times as possible without + # going over the number of samples in the class. Note that if the number + # of samples in a class is less than k, nothing is produced here. + seqVector <- rep(seq_len(k), numInClass[i] %/% k) + + # Add enough random integers to get length(seqVector) == numInClass[i] + if (numInClass[i] %% k > 0L) { + seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k)) + } + + # Shuffle the integers for fold assignment and assign to this classes's data + foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector) + + } + + } else { + + foldVector <- seq(along = y) + + } + + out <- split(seq(along = y), foldVector) + names(out) <- NULL + return(out) +} + +.merge_cv_result <- function(msg, showsd) { + + if (length(msg) == 0L) { + stop("lgb.cv: size of cv result error") + } + + eval_len <- length(msg[[1L]]) + + if (eval_len == 0L) { + stop("lgb.cv: should provide at least one metric for CV") + } + + # Get evaluation results using a list apply + eval_result <- lapply(seq_len(eval_len), function(j) { + as.numeric(lapply(seq_along(msg), function(i) { + msg[[i]][[j]]$value })) + }) + + # Get evaluation. Just taking the first element here to + # get structure (name, higher_better, data_name) + ret_eval <- msg[[1L]] + + for (j in seq_len(eval_len)) { + ret_eval[[j]]$value <- mean(eval_result[[j]]) + } + + ret_eval_err <- NULL + + # Check for standard deviation + if (showsd) { + + # Parse standard deviation + for (j in seq_len(eval_len)) { + ret_eval_err <- c( + ret_eval_err + , sqrt(mean(eval_result[[j]] ^ 2L) - mean(eval_result[[j]]) ^ 2L) + ) + } + + ret_eval_err <- as.list(ret_eval_err) + + } + + return( + list( + eval_list = ret_eval + , eval_err_list = ret_eval_err + ) + ) + +} diff --git a/R-package/R/lgb.drop_serialized.R b/R-package/R/lgb.drop_serialized.R new file mode 100644 index 0000000..6100f61 --- /dev/null +++ b/R-package/R/lgb.drop_serialized.R @@ -0,0 +1,21 @@ +#' @name lgb.drop_serialized +#' @title Drop serialized raw bytes in a LightGBM model object +#' @description If a LightGBM model object was produced with argument `serializable=TRUE`, the R object will keep +#' a copy of the underlying C++ object as raw bytes, which can be used to reconstruct such object after getting +#' serialized and de-serialized, but at the cost of extra memory usage. If these raw bytes are not needed anymore, +#' they can be dropped through this function in order to save memory. Note that the object will be modified in-place. +#' +#' \emph{New in version 4.0.0} +#' +#' @param model \code{lgb.Booster} object which was produced with `serializable=TRUE`. +#' +#' @return \code{lgb.Booster} (the same `model` object that was passed as input, as invisible). +#' @seealso \link{lgb.restore_handle}, \link{lgb.make_serializable}. +#' @export +lgb.drop_serialized <- function(model) { + if (!.is_Booster(x = model)) { + stop("lgb.drop_serialized: model should be an ", sQuote("lgb.Booster", q = FALSE)) + } + model$drop_raw() + return(invisible(model)) +} diff --git a/R-package/R/lgb.importance.R b/R-package/R/lgb.importance.R new file mode 100644 index 0000000..d60507c --- /dev/null +++ b/R-package/R/lgb.importance.R @@ -0,0 +1,83 @@ +#' @name lgb.importance +#' @title Compute feature importance in a model +#' @description Creates a \code{data.table} of feature importances in a model. +#' @param model object of class \code{lgb.Booster}. +#' @param percentage whether to show importance in relative percentage. +#' +#' @return For a tree model, a \code{data.table} with the following columns: +#' \itemize{ +#' \item{\code{Feature}: Feature names in the model.} +#' \item{\code{Gain}: The total gain of this feature's splits.} +#' \item{\code{Cover}: The number of observation related to this feature.} +#' \item{\code{Frequency}: The number of times a feature split in trees.} +#' } +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' +#' params <- list( +#' objective = "binary" +#' , learning_rate = 0.1 +#' , max_depth = -1L +#' , min_data_in_leaf = 1L +#' , min_sum_hessian_in_leaf = 1.0 +#' , num_threads = 2L +#' ) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' ) +#' +#' tree_imp1 <- lgb.importance(model, percentage = TRUE) +#' tree_imp2 <- lgb.importance(model, percentage = FALSE) +#' } +#' @importFrom data.table := setnames setorderv +#' @export +lgb.importance <- function(model, percentage = TRUE) { + + if (!.is_Booster(x = model)) { + stop("'model' has to be an object of class lgb.Booster") + } + + # Setup importance + tree_dt <- lgb.model.dt.tree(model = model) + + # Extract elements + tree_imp_dt <- tree_dt[ + !is.na(split_index) + , .(Gain = sum(split_gain), Cover = sum(internal_count), Frequency = .N) + , by = "split_feature" + ] + + data.table::setnames( + x = tree_imp_dt + , old = "split_feature" + , new = "Feature" + ) + + # Sort features by Gain + data.table::setorderv( + x = tree_imp_dt + , cols = "Gain" + , order = -1L + ) + + # Check if relative values are requested + if (percentage) { + tree_imp_dt[, `:=`( + Gain = Gain / sum(Gain) + , Cover = Cover / sum(Cover) + , Frequency = Frequency / sum(Frequency) + )] + } + + # adding an empty [] to ensure the table is printed the first time print.data.table() is called + return(tree_imp_dt[]) + +} diff --git a/R-package/R/lgb.interpret.R b/R-package/R/lgb.interpret.R new file mode 100644 index 0000000..5293ab4 --- /dev/null +++ b/R-package/R/lgb.interpret.R @@ -0,0 +1,256 @@ +#' @name lgb.interpret +#' @title Compute feature contribution of prediction +#' @description Computes feature contribution components of rawscore prediction. +#' @param model object of class \code{lgb.Booster}. +#' @param data a matrix object or a dgCMatrix object. +#' @param idxset an integer vector of indices of rows needed. +#' @param num_iteration number of iteration want to predict with, NULL or <= 0 means use best iteration. +#' +#' @return For regression, binary classification and lambdarank model, a \code{list} of \code{data.table} +#' with the following columns: +#' \itemize{ +#' \item{\code{Feature}: Feature names in the model.} +#' \item{\code{Contribution}: The total contribution of this feature's splits.} +#' } +#' For multiclass classification, a \code{list} of \code{data.table} with the Feature column and +#' Contribution columns to each class. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' Logit <- function(x) log(x / (1.0 - x)) +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' set_field( +#' dataset = dtrain +#' , field_name = "init_score" +#' , data = rep(Logit(mean(train$label)), length(train$label)) +#' ) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' +#' params <- list( +#' objective = "binary" +#' , learning_rate = 0.1 +#' , max_depth = -1L +#' , min_data_in_leaf = 1L +#' , min_sum_hessian_in_leaf = 1.0 +#' , num_threads = 2L +#' ) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 3L +#' ) +#' +#' tree_interpretation <- lgb.interpret(model, test$data, 1L:5L) +#' } +#' @importFrom data.table as.data.table +#' @export +lgb.interpret <- function(model, + data, + idxset, + num_iteration = NULL) { + + # Get tree model + tree_dt <- lgb.model.dt.tree(model = model, num_iteration = num_iteration) + + # Check number of classes + num_class <- model$.__enclos_env__$private$num_class + + # Get vector list + tree_interpretation_dt_list <- vector(mode = "list", length = length(idxset)) + + # Get parsed predictions of data + pred_mat <- t( + model$predict( + data = data[idxset, , drop = FALSE] + , num_iteration = num_iteration + , predleaf = TRUE + ) + ) + leaf_index_dt <- data.table::as.data.table(x = pred_mat) + leaf_index_mat_list <- lapply( + X = leaf_index_dt + , FUN = matrix + , ncol = num_class + , byrow = TRUE + ) + + # Get list of trees + tree_index_mat_list <- lapply( + X = leaf_index_mat_list + , FUN = function(x) { + matrix(seq_along(x) - 1L, ncol = num_class, byrow = TRUE) + } + ) + + for (i in seq_along(idxset)) { + tree_interpretation_dt_list[[i]] <- .single_row_interpret( + tree_dt = tree_dt + , num_class = num_class + , tree_index_mat = tree_index_mat_list[[i]] + , leaf_index_mat = leaf_index_mat_list[[i]] + ) + } + + return(tree_interpretation_dt_list) + +} + +#' @name lgb.interprete +#' @title DEPRECATED - use lgb.interpret() instead +#' @description Alias for \code{lgb.interpret}. +#' @param ... Arguments passed through to \code{lgb.interpret} +#' @export +lgb.interprete <- function(...) { + warning("lgb.interprete() is deprecated and will be removed in a future release. Use lgb.interpret() instead.") + return(lgb.interpret(...)) +} + +#' @importFrom data.table data.table +single.tree.interpret <- function(tree_dt, + tree_id, + leaf_id) { + + # Match tree id + single_tree_dt <- tree_dt[tree_index == tree_id, ] + + # Get leaves + leaf_dt <- single_tree_dt[leaf_index == leaf_id, .(leaf_index, leaf_parent, leaf_value)] + + # Get nodes + node_dt <- single_tree_dt[!is.na(split_index), .(split_index, split_feature, node_parent, internal_value)] + + # Prepare sequences + feature_seq <- character(0L) + value_seq <- numeric(0L) + + # Get to root from leaf + leaf_to_root <- function(parent_id, current_value) { + + value_seq <<- c(current_value, value_seq) + + if (!is.na(parent_id)) { + + # Not null means existing node + this_node <- node_dt[split_index == parent_id, ] + feature_seq <<- c(this_node[["split_feature"]], feature_seq) + leaf_to_root( + parent_id = this_node[["node_parent"]] + , current_value = this_node[["internal_value"]] + ) + + } + + } + + # Perform leaf to root conversion + leaf_to_root( + parent_id = leaf_dt[["leaf_parent"]] + , current_value = leaf_dt[["leaf_value"]] + ) + + return( + data.table::data.table( + Feature = feature_seq + , Contribution = diff.default(value_seq) + ) + ) + +} + +#' @importFrom data.table := rbindlist setorder +.multiple_tree_interpret <- function(tree_dt, + tree_index, + leaf_index) { + + interp_dt <- data.table::rbindlist( + l = mapply( + FUN = single.tree.interpret + , tree_id = tree_index + , leaf_id = leaf_index + , MoreArgs = list( + tree_dt = tree_dt + ) + , SIMPLIFY = FALSE + , USE.NAMES = TRUE + ) + , use.names = TRUE + ) + + interp_dt <- interp_dt[, .(Contribution = sum(Contribution)), by = "Feature"] + + # Sort features in descending order by contribution + interp_dt[, abs_contribution := abs(Contribution)] + data.table::setorder( + x = interp_dt + , -abs_contribution + ) + + # Drop absolute value of contribution (only needed for sorting) + interp_dt[, abs_contribution := NULL] + + return(interp_dt) + +} + +#' @importFrom data.table set setnames +.single_row_interpret <- function(tree_dt, num_class, tree_index_mat, leaf_index_mat) { + + # Prepare vector list + tree_interpretation <- vector(mode = "list", length = num_class) + + # Loop throughout each class + for (i in seq_len(num_class)) { + + next_interp_dt <- .multiple_tree_interpret( + tree_dt = tree_dt + , tree_index = tree_index_mat[, i] + , leaf_index = leaf_index_mat[, i] + ) + + if (num_class > 1L) { + data.table::setnames( + x = next_interp_dt + , old = "Contribution" + , new = paste("Class", i - 1L) + ) + } + + tree_interpretation[[i]] <- next_interp_dt + + } + + if (num_class == 1L) { + + tree_interpretation_dt <- tree_interpretation[[1L]] + + } else { + + # Full interpretation elements + tree_interpretation_dt <- Reduce( + f = function(x, y) { + merge(x, y, by = "Feature", all = TRUE) + } + , x = tree_interpretation + ) + + # Loop throughout each tree + for (j in 2L:ncol(tree_interpretation_dt)) { + + data.table::set( + x = tree_interpretation_dt + , i = which(is.na(tree_interpretation_dt[[j]])) + , j = j + , value = 0.0 + ) + + } + + } + + return(tree_interpretation_dt) +} diff --git a/R-package/R/lgb.make_serializable.R b/R-package/R/lgb.make_serializable.R new file mode 100644 index 0000000..19a4252 --- /dev/null +++ b/R-package/R/lgb.make_serializable.R @@ -0,0 +1,21 @@ +#' @name lgb.make_serializable +#' @title Make a LightGBM object serializable by keeping raw bytes +#' @description If a LightGBM model object was produced with argument `serializable=FALSE`, the R object will not +#' be serializable (e.g. cannot save and load with \code{saveRDS} and \code{readRDS}) as it will lack the raw bytes +#' needed to reconstruct its underlying C++ object. This function can be used to forcibly produce those serialized +#' raw bytes and make the object serializable. Note that the object will be modified in-place. +#' +#' \emph{New in version 4.0.0} +#' +#' @param model \code{lgb.Booster} object which was produced with `serializable=FALSE`. +#' +#' @return \code{lgb.Booster} (the same `model` object that was passed as input, as invisible). +#' @seealso \link{lgb.restore_handle}, \link{lgb.drop_serialized}. +#' @export +lgb.make_serializable <- function(model) { + if (!.is_Booster(x = model)) { + stop("lgb.make_serializable: model should be an ", sQuote("lgb.Booster", q = FALSE)) + } + model$save_raw() + return(invisible(model)) +} diff --git a/R-package/R/lgb.model.dt.tree.R b/R-package/R/lgb.model.dt.tree.R new file mode 100644 index 0000000..ac1b2f9 --- /dev/null +++ b/R-package/R/lgb.model.dt.tree.R @@ -0,0 +1,192 @@ +#' @name lgb.model.dt.tree +#' @title Parse a LightGBM model json dump +#' @description Parse a LightGBM model json dump into a \code{data.table} structure. +#' @param model object of class \code{lgb.Booster}. +#' @param num_iteration Number of iterations to include. NULL or <= 0 means use best iteration. +#' @param start_iteration Index (1-based) of the first boosting round to include in the output. +#' For example, passing \code{start_iteration=5, num_iteration=3} for a regression model +#' means "return information about the fifth, sixth, and seventh trees". +#' +#' \emph{New in version 4.4.0} +#' +#' @return +#' A \code{data.table} with detailed information about model trees' nodes and leaves. +#' +#' The columns of the \code{data.table} are: +#' +#' \itemize{ +#' \item{\code{tree_index}: ID of a tree in a model (integer)} +#' \item{\code{split_index}: ID of a node in a tree (integer)} +#' \item{\code{split_feature}: for a node, it's a feature name (character); +#' for a leaf, it simply labels it as \code{"NA"}} +#' \item{\code{node_parent}: ID of the parent node for current node (integer)} +#' \item{\code{leaf_index}: ID of a leaf in a tree (integer)} +#' \item{\code{leaf_parent}: ID of the parent node for current leaf (integer)} +#' \item{\code{split_gain}: Split gain of a node} +#' \item{\code{threshold}: Splitting threshold value of a node} +#' \item{\code{decision_type}: Decision type of a node} +#' \item{\code{default_left}: Determine how to handle NA value, TRUE -> Left, FALSE -> Right} +#' \item{\code{internal_value}: Node value} +#' \item{\code{internal_count}: The number of observation collected by a node} +#' \item{\code{leaf_value}: Leaf value} +#' \item{\code{leaf_count}: The number of observation collected by a leaf} +#' } +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' +#' params <- list( +#' objective = "binary" +#' , learning_rate = 0.01 +#' , num_leaves = 63L +#' , max_depth = -1L +#' , min_data_in_leaf = 1L +#' , min_sum_hessian_in_leaf = 1.0 +#' , num_threads = 2L +#' ) +#' model <- lgb.train(params, dtrain, 10L) +#' +#' tree_dt <- lgb.model.dt.tree(model) +#' } +#' @importFrom data.table := rbindlist +#' @importFrom jsonlite fromJSON +#' @export +lgb.model.dt.tree <- function( + model, num_iteration = NULL, start_iteration = 1L + ) { + + json_model <- lgb.dump( + booster = model + , num_iteration = num_iteration + , start_iteration = start_iteration + ) + + parsed_json_model <- jsonlite::fromJSON( + txt = json_model + , simplifyVector = TRUE + , simplifyDataFrame = FALSE + , simplifyMatrix = FALSE + , flatten = FALSE + ) + + # Parse tree model + tree_list <- lapply( + X = parsed_json_model$tree_info + , FUN = .single_tree_parse + ) + + # Combine into single data.table + tree_dt <- data.table::rbindlist(l = tree_list, use.names = TRUE) + + # Substitute feature index with the actual feature name + + # Since the index comes from C++ (which is 0-indexed), be sure + # to add 1 (e.g. index 28 means the 29th feature in feature_names) + split_feature_indx <- tree_dt[, split_feature] + 1L + + # Get corresponding feature names. Positions in split_feature_indx + # which are NA will result in an NA feature name + feature_names <- parsed_json_model$feature_names[split_feature_indx] + tree_dt[, split_feature := feature_names] + + return(tree_dt) +} + + +#' @importFrom data.table := data.table rbindlist +.single_tree_parse <- function(lgb_tree) { + tree_info_cols <- c( + "split_index" + , "split_feature" + , "split_gain" + , "threshold" + , "decision_type" + , "default_left" + , "internal_value" + , "internal_count" + ) + + # Traverse tree function + pre_order_traversal <- function(env = NULL, tree_node_leaf, current_depth = 0L, parent_index = NA_integer_) { + + if (is.null(env)) { + # Setup initial default data.table with default types + env <- new.env(parent = emptyenv()) + env$single_tree_dt <- list() + env$single_tree_dt[[1L]] <- data.table::data.table( + tree_index = integer(0L) + , depth = integer(0L) + , split_index = integer(0L) + , split_feature = integer(0L) + , node_parent = integer(0L) + , leaf_index = integer(0L) + , leaf_parent = integer(0L) + , split_gain = numeric(0L) + , threshold = numeric(0L) + , decision_type = character(0L) + , default_left = character(0L) + , internal_value = integer(0L) + , internal_count = integer(0L) + , leaf_value = integer(0L) + , leaf_count = integer(0L) + ) + # start tree traversal + pre_order_traversal( + env = env + , tree_node_leaf = tree_node_leaf + , current_depth = current_depth + , parent_index = parent_index + ) + } else { + + # Check if split index is not null in leaf + if (!is.null(tree_node_leaf$split_index)) { + + # update data.table + env$single_tree_dt[[length(env$single_tree_dt) + 1L]] <- c( + tree_node_leaf[tree_info_cols] + , list("depth" = current_depth, "node_parent" = parent_index) + ) + + # Traverse tree again both left and right + pre_order_traversal( + env = env + , tree_node_leaf = tree_node_leaf$left_child + , current_depth = current_depth + 1L + , parent_index = tree_node_leaf$split_index + ) + pre_order_traversal( + env = env + , tree_node_leaf = tree_node_leaf$right_child + , current_depth = current_depth + 1L + , parent_index = tree_node_leaf$split_index + ) + } else if (!is.null(tree_node_leaf$leaf_index)) { + + # update list + env$single_tree_dt[[length(env$single_tree_dt) + 1L]] <- c( + tree_node_leaf[c("leaf_index", "leaf_value", "leaf_count")] + , list("depth" = current_depth, "leaf_parent" = parent_index) + ) + } + } + return(env$single_tree_dt) + } + + # Traverse structure and rowbind everything + single_tree_dt <- data.table::rbindlist( + pre_order_traversal(tree_node_leaf = lgb_tree$tree_structure) + , use.names = TRUE + , fill = TRUE + ) + + # Store index + single_tree_dt[, tree_index := lgb_tree$tree_index] + + return(single_tree_dt) +} diff --git a/R-package/R/lgb.plot.importance.R b/R-package/R/lgb.plot.importance.R new file mode 100644 index 0000000..b8a90ca --- /dev/null +++ b/R-package/R/lgb.plot.importance.R @@ -0,0 +1,99 @@ +#' @name lgb.plot.importance +#' @title Plot feature importance as a bar graph +#' @description Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph. +#' @param tree_imp a \code{data.table} returned by \code{\link{lgb.importance}}. +#' @param top_n maximal number of top features to include into the plot. +#' @param measure the name of importance measure to plot, can be "Gain", "Cover" or "Frequency". +#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names. +#' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{\link[graphics]{barplot}}. +#' Set a number smaller than 1.0 to make the bar labels smaller than R's default and values +#' greater than 1.0 to make them larger. +#' +#' @details +#' The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature. +#' Features are shown ranked in a decreasing importance order. +#' +#' @return +#' The \code{lgb.plot.importance} function creates a \code{barplot} +#' and silently returns a processed data.table with \code{top_n} features sorted by defined importance. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' +#' params <- list( +#' objective = "binary" +#' , learning_rate = 0.1 +#' , min_data_in_leaf = 1L +#' , min_sum_hessian_in_leaf = 1.0 +#' , num_threads = 2L +#' ) +#' +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' ) +#' +#' tree_imp <- lgb.importance(model, percentage = TRUE) +#' lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain") +#' } +#' @importFrom graphics barplot par +#' @export +lgb.plot.importance <- function(tree_imp, + top_n = 10L, + measure = "Gain", + left_margin = 10L, + cex = NULL + ) { + + # Check for measurement (column names) correctness + measure <- match.arg( + measure + , choices = c("Gain", "Cover", "Frequency") + , several.ok = FALSE + ) + + # Get top N importance (defaults to 10) + top_n <- min(top_n, nrow(tree_imp)) + + # Parse importance + tree_imp <- tree_imp[order(abs(get(measure)), decreasing = TRUE), ][seq_len(top_n), ] + + # Attempt to setup a correct cex + if (is.null(cex)) { + cex <- 2.5 / log2(1.0 + top_n) + } + + # Refresh plot + op <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(op)) + + graphics::par( + mar = c( + op$mar[1L] + , left_margin + , op$mar[3L] + , op$mar[4L] + ) + ) + + tree_imp[rev(seq_len(.N)), + graphics::barplot( + height = get(measure) + , names.arg = Feature + , horiz = TRUE + , border = NA + , main = "Feature Importance" + , xlab = measure + , cex.names = cex + , las = 1L + )] + + return(invisible(tree_imp)) + +} diff --git a/R-package/R/lgb.plot.interpretation.R b/R-package/R/lgb.plot.interpretation.R new file mode 100644 index 0000000..d9e0e3e --- /dev/null +++ b/R-package/R/lgb.plot.interpretation.R @@ -0,0 +1,166 @@ +#' @name lgb.plot.interpretation +#' @title Plot feature contribution as a bar graph +#' @description Plot previously calculated feature contribution as a bar graph. +#' @param tree_interpretation_dt a \code{data.table} returned by \code{\link{lgb.interpret}}. +#' @param top_n maximal number of top features to include into the plot. +#' @param cols the column numbers of layout, will be used only for multiclass classification feature contribution. +#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names. +#' @param cex (base R barplot) passed as \code{cex.names} parameter to \code{barplot}. +#' +#' @details +#' The graph represents each feature as a horizontal bar of length proportional to the defined +#' contribution of a feature. Features are shown ranked in a decreasing contribution order. +#' +#' @return +#' The \code{lgb.plot.interpretation} function creates a \code{barplot}. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' Logit <- function(x) { +#' log(x / (1.0 - x)) +#' } +#' data(agaricus.train, package = "lightgbm") +#' labels <- agaricus.train$label +#' dtrain <- lgb.Dataset( +#' agaricus.train$data +#' , label = labels +#' ) +#' set_field( +#' dataset = dtrain +#' , field_name = "init_score" +#' , data = rep(Logit(mean(labels)), length(labels)) +#' ) +#' +#' data(agaricus.test, package = "lightgbm") +#' +#' params <- list( +#' objective = "binary" +#' , learning_rate = 0.1 +#' , max_depth = -1L +#' , min_data_in_leaf = 1L +#' , min_sum_hessian_in_leaf = 1.0 +#' , num_threads = 2L +#' ) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' ) +#' +#' tree_interpretation <- lgb.interpret( +#' model = model +#' , data = agaricus.test$data +#' , idxset = 1L:5L +#' ) +#' lgb.plot.interpretation( +#' tree_interpretation_dt = tree_interpretation[[1L]] +#' , top_n = 3L +#' ) +#' } +#' @importFrom data.table setnames +#' @importFrom graphics barplot par +#' @export +lgb.plot.interpretation <- function(tree_interpretation_dt, + top_n = 10L, + cols = 1L, + left_margin = 10L, + cex = NULL) { + + num_class <- ncol(tree_interpretation_dt) - 1L + + # Refresh plot + op <- graphics::par(no.readonly = TRUE) + on.exit(graphics::par(op)) + + # Do some magic plotting + bottom_margin <- 3.0 + top_margin <- 2.0 + right_margin <- op$mar[4L] + + graphics::par( + mar = c( + bottom_margin + , left_margin + , top_margin + , right_margin + ) + ) + + if (num_class == 1L) { + + # Only one class, plot straight away + .multiple_tree_plot_interpretation( + tree_interpretation = tree_interpretation_dt + , top_n = top_n + , title = NULL + , cex = cex + ) + + } else { + + # More than one class, shape data first + layout_mat <- matrix( + seq.int(to = cols * ceiling(num_class / cols)) + , ncol = cols + , nrow = ceiling(num_class / cols) + ) + + # Shape output + graphics::par(mfcol = c(nrow(layout_mat), ncol(layout_mat))) + + # Loop throughout all classes + for (i in seq_len(num_class)) { + + # Prepare interpretation, perform T, get the names, and plot straight away + plot_dt <- tree_interpretation_dt[, c(1L, i + 1L), with = FALSE] + data.table::setnames( + x = plot_dt + , old = names(plot_dt) + , new = c("Feature", "Contribution") + ) + .multiple_tree_plot_interpretation( + tree_interpretation = plot_dt + , top_n = top_n + , title = paste("Class", i - 1L) + , cex = cex + ) + + } + } + return(invisible(NULL)) +} + +#' @importFrom graphics barplot +.multiple_tree_plot_interpretation <- function(tree_interpretation, + top_n, + title, + cex) { + + # Parse tree + tree_interpretation <- tree_interpretation[order(abs(Contribution), decreasing = TRUE), ][seq_len(min(top_n, .N)), ] + + # Attempt to setup a correct cex + if (is.null(cex)) { + cex <- 2.5 / log2(1.0 + top_n) + } + + # create plot + tree_interpretation[abs(Contribution) > 0.0, bar_color := "firebrick"] + tree_interpretation[Contribution == 0.0, bar_color := "steelblue"] + tree_interpretation[rev(seq_len(.N)), + graphics::barplot( + height = Contribution + , names.arg = Feature + , horiz = TRUE + , col = bar_color + , border = NA + , main = title + , cex.names = cex + , las = 1L + )] + + return(invisible(NULL)) + +} diff --git a/R-package/R/lgb.restore_handle.R b/R-package/R/lgb.restore_handle.R new file mode 100644 index 0000000..407f275 --- /dev/null +++ b/R-package/R/lgb.restore_handle.R @@ -0,0 +1,47 @@ +#' @name lgb.restore_handle +#' @title Restore the C++ component of a de-serialized LightGBM model +#' @description After a LightGBM model object is de-serialized through functions such as \code{save} or +#' \code{saveRDS}, its underlying C++ object will be blank and needs to be restored to able to use it. Such +#' object is restored automatically when calling functions such as \code{predict}, but this function can be +#' used to forcibly restore it beforehand. Note that the object will be modified in-place. +#' +#' \emph{New in version 4.0.0} +#' +#' @details Be aware that fast single-row prediction configurations are not restored through this +#' function. If you wish to make fast single-row predictions using a \code{lgb.Booster} loaded this way, +#' call \link{lgb.configure_fast_predict} on the loaded \code{lgb.Booster} object. +#' @param model \code{lgb.Booster} object which was de-serialized and whose underlying C++ object and R handle +#' need to be restored. +#' +#' @return \code{lgb.Booster} (the same `model` object that was passed as input, invisibly). +#' @seealso \link{lgb.make_serializable}, \link{lgb.drop_serialized}. +#' @examples +#' \donttest{ +#' library(lightgbm) +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data("agaricus.train") +#' model <- lightgbm( +#' agaricus.train$data +#' , agaricus.train$label +#' , params = list(objective = "binary") +#' , nrounds = 5L +#' , verbose = 0 +#' , num_threads = 2L +#' ) +#' fname <- tempfile(fileext="rds") +#' saveRDS(model, fname) +#' +#' model_new <- readRDS(fname) +#' model_new$check_null_handle() +#' lgb.restore_handle(model_new) +#' model_new$check_null_handle() +#' } +#' @export +lgb.restore_handle <- function(model) { + if (!.is_Booster(x = model)) { + stop("lgb.restore_handle: model should be an ", sQuote("lgb.Booster", q = FALSE)) + } + model$restore_handle() + return(invisible(model)) +} diff --git a/R-package/R/lgb.train.R b/R-package/R/lgb.train.R new file mode 100644 index 0000000..4b16cac --- /dev/null +++ b/R-package/R/lgb.train.R @@ -0,0 +1,383 @@ +#' @name lgb.train +#' @title Main training logic for LightGBM +#' @description Low-level R interface to train a LightGBM model. Unlike \code{\link{lightgbm}}, +#' this function is focused on performance (e.g. speed, memory efficiency). It is also +#' less likely to have breaking API changes in new releases than \code{\link{lightgbm}}. +#' @inheritParams lgb_shared_params +#' @param valids a list of \code{lgb.Dataset} objects, used for validation +#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals} +#' @param callbacks List of callback functions that are applied at each iteration. +#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the +#' booster model into a predictor model which frees up memory and the +#' original datasets +#' @inheritSection lgb_shared_params Early Stopping +#' @return a trained booster model \code{lgb.Booster}. +#' +#' @examples +#' \donttest{ +#' \dontshow{setLGBMthreads(2L)} +#' \dontshow{data.table::setDTthreads(1L)} +#' data(agaricus.train, package = "lightgbm") +#' train <- agaricus.train +#' dtrain <- lgb.Dataset(train$data, label = train$label) +#' data(agaricus.test, package = "lightgbm") +#' test <- agaricus.test +#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +#' params <- list( +#' objective = "regression" +#' , metric = "l2" +#' , min_data = 1L +#' , learning_rate = 1.0 +#' , num_threads = 2L +#' ) +#' valids <- list(test = dtest) +#' model <- lgb.train( +#' params = params +#' , data = dtrain +#' , nrounds = 5L +#' , valids = valids +#' , early_stopping_rounds = 3L +#' ) +#' } +#' +#' @export +lgb.train <- function(params = list(), + data, + nrounds = 100L, + valids = list(), + obj = NULL, + eval = NULL, + verbose = 1L, + record = TRUE, + eval_freq = 1L, + init_model = NULL, + early_stopping_rounds = NULL, + callbacks = list(), + reset_data = FALSE, + serializable = TRUE) { + + # validate inputs early to avoid unnecessary computation + if (nrounds <= 0L) { + stop("nrounds should be greater than zero") + } + if (!.is_Dataset(x = data)) { + stop("lgb.train: data must be an lgb.Dataset instance") + } + if (length(valids) > 0L) { + if (!identical(class(valids), "list") || !all(vapply(valids, .is_Dataset, logical(1L)))) { + stop("lgb.train: valids must be a list of lgb.Dataset elements") + } + evnames <- names(valids) + if (is.null(evnames) || !all(nzchar(evnames))) { + stop("lgb.train: each element of valids must have a name") + } + } + + # set some parameters, resolving the way they were passed in with other parameters + # in `params`. + # this ensures that the model stored with Booster$save() correctly represents + # what was passed in + params <- .check_wrapper_param( + main_param_name = "verbosity" + , params = params + , alternative_kwarg_value = verbose + ) + params <- .check_wrapper_param( + main_param_name = "num_iterations" + , params = params + , alternative_kwarg_value = nrounds + ) + params <- .check_wrapper_param( + main_param_name = "metric" + , params = params + , alternative_kwarg_value = NULL + ) + params <- .check_wrapper_param( + main_param_name = "objective" + , params = params + , alternative_kwarg_value = obj + ) + params <- .check_wrapper_param( + main_param_name = "early_stopping_round" + , params = params + , alternative_kwarg_value = early_stopping_rounds + ) + early_stopping_rounds <- params[["early_stopping_round"]] + + # extract any function objects passed for objective or metric + fobj <- NULL + if (is.function(params$objective)) { + fobj <- params$objective + params$objective <- "none" + } + + # If eval is a single function, store it as a 1-element list + # (for backwards compatibility). If it is a list of functions, store + # all of them. This makes it possible to pass any mix of strings like "auc" + # and custom functions to eval + params <- .check_eval(params = params, eval = eval) + eval_functions <- list(NULL) + if (is.function(eval)) { + eval_functions <- list(eval) + } + if (methods::is(eval, "list")) { + eval_functions <- Filter( + f = is.function + , x = eval + ) + } + + # Init predictor to empty + predictor <- NULL + + # Check for boosting from a trained model + if (is.character(init_model)) { + predictor <- Predictor$new(modelfile = init_model) + } else if (.is_Booster(x = init_model)) { + predictor <- init_model$to_predictor() + } + + # Set the iteration to start from / end to (and check for boosting from a trained model, again) + begin_iteration <- 1L + if (!is.null(predictor)) { + begin_iteration <- predictor$current_iter() + 1L + } + end_iteration <- begin_iteration + params[["num_iterations"]] - 1L + + # pop interaction_constraints off of params. It needs some preprocessing on the + # R side before being passed into the Dataset object + interaction_constraints <- params[["interaction_constraints"]] + params["interaction_constraints"] <- NULL + + # Construct datasets, if needed + data$update_params(params = params) + data$construct() + + # Check interaction constraints + params[["interaction_constraints"]] <- .check_interaction_constraints( + interaction_constraints = interaction_constraints + , column_names = data$get_colnames() + ) + + # Update parameters with parsed parameters + data$update_params(params) + + # Create the predictor set + data$.__enclos_env__$private$set_predictor(predictor) + + valid_contain_train <- FALSE + train_data_name <- "train" + reduced_valid_sets <- list() + + # Parse validation datasets + if (length(valids) > 0L) { + + for (key in names(valids)) { + + # Use names to get validation datasets + valid_data <- valids[[key]] + + # Check for duplicate train/validation dataset + if (identical(data, valid_data)) { + valid_contain_train <- TRUE + train_data_name <- key + next + } + + # Update parameters, data + valid_data$update_params(params) + valid_data$set_reference(data) + reduced_valid_sets[[key]] <- valid_data + + } + + } + + # Add printing log callback + if (params[["verbosity"]] > 0L && eval_freq > 0L) { + callbacks <- .add_cb( + cb_list = callbacks + , cb = cb_print_evaluation(period = eval_freq) + ) + } + + # Add evaluation log callback + if (record && length(valids) > 0L) { + callbacks <- .add_cb( + cb_list = callbacks + , cb = cb_record_evaluation() + ) + } + + # Did user pass parameters that indicate they want to use early stopping? + using_early_stopping <- !is.null(early_stopping_rounds) && early_stopping_rounds > 0L + + boosting_param_names <- .PARAMETER_ALIASES()[["boosting"]] + using_dart <- any( + sapply( + X = boosting_param_names + , FUN = function(param) { + identical(params[[param]], "dart") + } + ) + ) + + # Cannot use early stopping with 'dart' boosting + if (using_dart) { + if (using_early_stopping) { + warning("Early stopping is not available in 'dart' mode.") + } + using_early_stopping <- FALSE + + # Remove the cb_early_stop() function if it was passed in to callbacks + callbacks <- Filter( + f = function(cb_func) { + !identical(attr(cb_func, "name"), "cb_early_stop") + } + , x = callbacks + ) + } + + # If user supplied early_stopping_rounds, add the early stopping callback + if (using_early_stopping) { + callbacks <- .add_cb( + cb_list = callbacks + , cb = cb_early_stop( + stopping_rounds = early_stopping_rounds + , first_metric_only = isTRUE(params[["first_metric_only"]]) + , verbose = params[["verbosity"]] > 0L + ) + ) + } + + cb <- .categorize_callbacks(cb_list = callbacks) + + # Construct booster with datasets + booster <- Booster$new(params = params, train_set = data) + if (valid_contain_train) { + booster$set_train_data_name(name = train_data_name) + } + + for (key in names(reduced_valid_sets)) { + booster$add_valid(data = reduced_valid_sets[[key]], name = key) + } + + # Callback env + env <- CB_ENV$new() + env$model <- booster + env$begin_iteration <- begin_iteration + env$end_iteration <- end_iteration + + # Start training model using number of iterations to start and end with + for (i in seq.int(from = begin_iteration, to = end_iteration)) { + + # Overwrite iteration in environment + env$iteration <- i + env$eval_list <- list() + + # Loop through "pre_iter" element + for (f in cb$pre_iter) { + f(env) + } + + # Update one boosting iteration + booster$update(fobj = fobj) + + # Prepare collection of evaluation results + eval_list <- list() + + # Collection: Has validation dataset? + if (length(valids) > 0L) { + + # Get evaluation results with passed-in functions + for (eval_function in eval_functions) { + + # Validation has training dataset? + if (valid_contain_train) { + eval_list <- append(eval_list, booster$eval_train(feval = eval_function)) + } + + eval_list <- append(eval_list, booster$eval_valid(feval = eval_function)) + } + + # Calling booster$eval_valid() will get + # evaluation results with the metrics in params$metric by calling LGBM_BoosterGetEval_R", + # so need to be sure that gets called, which it wouldn't be above if no functions + # were passed in + if (length(eval_functions) == 0L) { + if (valid_contain_train) { + eval_list <- append(eval_list, booster$eval_train(feval = eval_function)) + } + eval_list <- append(eval_list, booster$eval_valid(feval = eval_function)) + } + + } + + # Write evaluation result in environment + env$eval_list <- eval_list + + # Loop through env + for (f in cb$post_iter) { + f(env) + } + + # Check for early stopping and break if needed + if (env$met_early_stop) break + + } + + # check if any valids were given other than the training data + non_train_valid_names <- names(valids)[!(names(valids) == train_data_name)] + first_valid_name <- non_train_valid_names[1L] + + # When early stopping is not activated, we compute the best iteration / score ourselves by + # selecting the first metric and the first dataset + if (record && length(non_train_valid_names) > 0L && is.na(env$best_score)) { + + # when using a custom eval function, the metric name is returned from the + # function, so figure it out from record_evals + if (!is.null(eval_functions[1L])) { + first_metric <- names(booster$record_evals[[first_valid_name]])[1L] + } else { + first_metric <- booster$.__enclos_env__$private$eval_names[1L] + } + + .find_best <- which.min + if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) { + .find_best <- which.max + } + booster$best_iter <- unname( + .find_best( + unlist( + booster$record_evals[[first_valid_name]][[first_metric]][[.EVAL_KEY()]] + ) + ) + ) + booster$best_score <- booster$record_evals[[first_valid_name]][[first_metric]][[.EVAL_KEY()]][[booster$best_iter]] + } + + # Check for booster model conversion to predictor model + if (reset_data) { + + # Store temporarily model data elsewhere + booster_old <- list( + best_iter = booster$best_iter + , best_score = booster$best_score + , record_evals = booster$record_evals + ) + + # Reload model + booster <- lgb.load(model_str = booster$save_model_to_string()) + booster$best_iter <- booster_old$best_iter + booster$best_score <- booster_old$best_score + booster$record_evals <- booster_old$record_evals + + } + + if (serializable) { + booster$save_raw() + } + + return(booster) + +} diff --git a/R-package/R/lightgbm.R b/R-package/R/lightgbm.R new file mode 100644 index 0000000..4c09ed7 --- /dev/null +++ b/R-package/R/lightgbm.R @@ -0,0 +1,368 @@ +#' @name lgb_shared_params +#' @title Shared parameter docs +#' @description Parameter docs shared by \code{lgb.train}, \code{lgb.cv}, and \code{lightgbm} +#' @param callbacks List of callback functions that are applied at each iteration. +#' @param data a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, +#' may allow you to pass other types of data like \code{matrix} and then separately supply +#' \code{label} as a keyword argument. +#' @param early_stopping_rounds int. Activates early stopping. When this parameter is non-null, +#' training will stop if the evaluation of any metric on any validation set +#' fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. +#' If training stops early, the returned model will have attribute \code{best_iter} +#' set to the iteration number of the best iteration. +#' @param eval evaluation function(s). This can be a character vector, function, or list with a mixture of +#' strings and functions. +#' +#' \itemize{ +#' \item{\bold{a. character vector}: +#' If you provide a character vector to this argument, it should contain strings with valid +#' evaluation metrics. +#' See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ +#' The "metric" section of the documentation} +#' for a list of valid metrics. +#' } +#' \item{\bold{b. function}: +#' You can provide a custom evaluation function. This +#' should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named +#' list with three elements: +#' \itemize{ +#' \item{\code{name}: A string with the name of the metric, used for printing +#' and storing results. +#' } +#' \item{\code{value}: A single number indicating the value of the metric for the +#' given predictions and true values +#' } +#' \item{ +#' \code{higher_better}: A boolean indicating whether higher values indicate a better fit. +#' For example, this would be \code{FALSE} for metrics like MAE or RMSE. +#' } +#' } +#' } +#' \item{\bold{c. list}: +#' If a list is given, it should only contain character vectors and functions. +#' These should follow the requirements from the descriptions above. +#' } +#' } +#' @param eval_freq evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided +#' @param init_model path of model file or \code{lgb.Booster} object, will continue training from this model +#' @param nrounds number of training rounds +#' @param obj objective function, can be character or custom objective function. Examples include +#' \code{regression}, \code{regression_l1}, \code{huber}, +#' \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass} +#' @param params a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ +#' the "Parameters" section of the documentation} for a list of parameters and valid values. +#' @param verbose verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the +#' printing of evaluation during training +#' @param serializable whether to make the resulting objects serializable through functions such as +#' \code{save} or \code{saveRDS} (see section "Model serialization"). +#' @section Early Stopping: +#' +#' "early stopping" refers to stopping the training process if the model's performance on a given +#' validation set does not improve for several consecutive iterations. +#' +#' If multiple arguments are given to \code{eval}, their order will be preserved. If you enable +#' early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all +#' metrics will be considered for early stopping. +#' +#' If you want to only consider the first metric for early stopping, pass +#' \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} +#' in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, +#' a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) +#' or \code{objective} (passed into \code{params}). +#' +#' \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored +#' and early stopping will not be performed. +#' @section Model serialization: +#' +#' LightGBM model objects can be serialized and de-serialized through functions such as \code{save} +#' or \code{saveRDS}, but similarly to libraries such as 'xgboost', serialization works a bit differently +#' from typical R objects. In order to make models serializable in R, a copy of the underlying C++ object +#' as serialized raw bytes is produced and stored in the R model object, and when this R object is +#' de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will only +#' do so once some function that uses it is called, such as \code{predict}. In order to forcibly +#' reconstruct the C++ object after deserialization (e.g. after calling \code{readRDS} or similar), one +#' can use the function \link{lgb.restore_handle} (for example, if one makes predictions in parallel or in +#' forked processes, it will be faster to restore the handle beforehand). +#' +#' Producing and keeping these raw bytes however uses extra memory, and if they are not required, +#' it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw +#' bytes can be added to the model on demand through function \link{lgb.make_serializable}. +#' +#' \emph{New in version 4.0.0} +#' +#' @keywords internal +NULL + +#' @name lightgbm +#' @title Train a LightGBM model +#' @description High-level R interface to train a LightGBM model. Unlike \code{\link{lgb.train}}, this function +#' is focused on compatibility with other statistics and machine learning interfaces in R. +#' This focus on compatibility means that this interface may experience more frequent breaking API changes +#' than \code{\link{lgb.train}}. +#' For efficiency-sensitive applications, or for applications where breaking API changes across releases +#' is very expensive, use \code{\link{lgb.train}}. +#' @inheritParams lgb_shared_params +#' @param label Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}} +#' @param weights Sample / observation weights for rows in the input data. If \code{NULL}, will assume that all +#' observations / rows have the same importance / weight. +#' +#' \emph{Changed from 'weight', in version 4.0.0} +#' +#' @param objective Optimization objective (e.g. `"regression"`, `"binary"`, etc.). +#' For a list of accepted objectives, see +#' \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{ +#' the "objective" item of the "Parameters" section of the documentation}. +#' +#' If passing \code{"auto"} and \code{data} is not of type \code{lgb.Dataset}, the objective will +#' be determined according to what is passed for \code{label}:\itemize{ +#' \item If passing a factor with two variables, will use objective \code{"binary"}. +#' \item If passing a factor with more than two variables, will use objective \code{"multiclass"} +#' (note that parameter \code{num_class} in this case will also be determined automatically from +#' \code{label}). +#' \item Otherwise (or if passing \code{lgb.Dataset} as input), will use objective \code{"regression"}. +#' } +#' +#' \emph{New in version 4.0.0} +#' +#' @param init_score initial score is the base prediction lightgbm will boost from +#' +#' \emph{New in version 4.0.0} +#' +#' @param num_threads Number of parallel threads to use. For best speed, this should be set to the number of +#' physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the +#' number of maximum threads. +#' +#' Be aware that using too many threads can result in speed degradation in smaller datasets +#' (see the parameters documentation for more details). +#' +#' If passing zero, will use the default number of threads configured for OpenMP +#' (typically controlled through an environment variable \code{OMP_NUM_THREADS}). +#' +#' If passing \code{NULL} (the default), will try to use the number of physical cores in the +#' system, but be aware that getting the number of cores detected correctly requires package +#' \code{RhpcBLASctl} to be installed. +#' +#' This parameter gets overridden by \code{num_threads} and its aliases under \code{params} +#' if passed there. +#' +#' \emph{New in version 4.0.0} +#' +#' @param colnames Character vector of features. Only used if \code{data} is not an \code{\link{lgb.Dataset}}. +#' @param categorical_feature categorical features. This can either be a character vector of feature +#' names or an integer vector with the indices of the features (e.g. +#' \code{c(1L, 10L)} to say "the first and tenth columns"). +#' Only used if \code{data} is not an \code{\link{lgb.Dataset}}. +#' +#' @param ... Additional arguments passed to \code{\link{lgb.train}}. For example +#' \itemize{ +#' \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation} +#' \item{\code{obj}: objective function, can be character or custom objective function. Examples include +#' \code{regression}, \code{regression_l1}, \code{huber}, +#' \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} +#' \item{\code{eval}: evaluation function, can be (a list of) character or custom eval function} +#' \item{\code{record}: Boolean, TRUE will record iteration message to \code{booster$record_evals}} +#' \item{\code{reset_data}: Boolean, setting it to TRUE (not the default value) will transform the booster model +#' into a predictor model which frees up memory and the original datasets} +#' } +#' @inheritSection lgb_shared_params Early Stopping +#' @return a trained \code{lgb.Booster} +#' @export +lightgbm <- function(data, + label = NULL, + weights = NULL, + params = list(), + nrounds = 100L, + verbose = 1L, + eval_freq = 1L, + early_stopping_rounds = NULL, + init_model = NULL, + callbacks = list(), + serializable = TRUE, + objective = "auto", + init_score = NULL, + num_threads = NULL, + colnames = NULL, + categorical_feature = NULL, + ...) { + + # validate inputs early to avoid unnecessary computation + if (nrounds <= 0L) { + stop("nrounds should be greater than zero") + } + + if (is.null(num_threads)) { + num_threads <- .get_default_num_threads() + } + params <- .check_wrapper_param( + main_param_name = "num_threads" + , params = params + , alternative_kwarg_value = num_threads + ) + params <- .check_wrapper_param( + main_param_name = "verbosity" + , params = params + , alternative_kwarg_value = verbose + ) + + # Process factors as labels and auto-determine objective + if (!.is_Dataset(data)) { + data_processor <- DataProcessor$new() + temp <- data_processor$process_label( + label = label + , objective = objective + , params = params + ) + label <- temp$label + objective <- temp$objective + params <- temp$params + rm(temp) + } else { + data_processor <- NULL + if (objective == "auto") { + objective <- "regression" + } + } + + # Set data to a temporary variable + dtrain <- data + + # Check whether data is lgb.Dataset, if not then create lgb.Dataset manually + if (!.is_Dataset(x = dtrain)) { + dtrain <- lgb.Dataset( + data = data + , label = label + , weight = weights + , init_score = init_score + , categorical_feature = categorical_feature + , colnames = colnames + ) + } + + train_args <- list( + "params" = params + , "data" = dtrain + , "nrounds" = nrounds + , "obj" = objective + , "verbose" = params[["verbosity"]] + , "eval_freq" = eval_freq + , "early_stopping_rounds" = early_stopping_rounds + , "init_model" = init_model + , "callbacks" = callbacks + , "serializable" = serializable + ) + train_args <- append(train_args, list(...)) + + if (! "valids" %in% names(train_args)) { + train_args[["valids"]] <- list() + } + + # Train a model using the regular way + bst <- do.call( + what = lgb.train + , args = train_args + ) + bst$data_processor <- data_processor + + return(bst) +} + +#' @name agaricus.train +#' @title Training part from Mushroom Data Set +#' @description This data set is originally from the Mushroom data set, +#' UCI Machine Learning Repository. +#' This data set includes the following fields: +#' +#' \itemize{ +#' \item{\code{label}: the label for each record} +#' \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} +#' } +#' +#' @references +#' https://archive.ics.uci.edu/ml/datasets/Mushroom +#' +#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository +#' [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, +#' School of Information and Computer Science. +#' +#' @docType data +#' @keywords datasets +#' @usage data(agaricus.train) +#' @format A list containing a label vector, and a dgCMatrix object with 6513 +#' rows and 127 variables +NULL + +#' @name agaricus.test +#' @title Test part from Mushroom Data Set +#' @description This data set is originally from the Mushroom data set, +#' UCI Machine Learning Repository. +#' This data set includes the following fields: +#' +#' \itemize{ +#' \item{\code{label}: the label for each record} +#' \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} +#' } +#' @references +#' https://archive.ics.uci.edu/ml/datasets/Mushroom +#' +#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository +#' [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, +#' School of Information and Computer Science. +#' +#' @docType data +#' @keywords datasets +#' @usage data(agaricus.test) +#' @format A list containing a label vector, and a dgCMatrix object with 1611 +#' rows and 126 variables +NULL + +#' @name bank +#' @title Bank Marketing Data Set +#' @description This data set is originally from the Bank Marketing data set, +#' UCI Machine Learning Repository. +#' +#' It contains only the following: bank.csv with 10% of the examples and 17 inputs, +#' randomly selected from 3 (older version of this dataset with less inputs). +#' +#' @references +#' https://archive.ics.uci.edu/ml/datasets/Bank+Marketing +#' +#' S. Moro, P. Cortez and P. Rita. (2014) +#' A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems +#' +#' @docType data +#' @keywords datasets +#' @usage data(bank) +#' @format A data.table with 4521 rows and 17 variables +NULL + +# Various imports +#' @import methods +#' @importFrom Matrix Matrix +#' @importFrom R6 R6Class +#' @useDynLib lightgbm , .registration = TRUE +NULL + +# Suppress false positive warnings from R CMD CHECK about +# "unrecognized global variable" +globalVariables(c( + "." + , ".N" + , ".SD" + , "abs_contribution" + , "bar_color" + , "Contribution" + , "Cover" + , "Feature" + , "Frequency" + , "Gain" + , "internal_count" + , "internal_value" + , "leaf_index" + , "leaf_parent" + , "leaf_value" + , "node_parent" + , "split_feature" + , "split_gain" + , "split_index" + , "tree_index" +)) diff --git a/R-package/R/metrics.R b/R-package/R/metrics.R new file mode 100644 index 0000000..12a8047 --- /dev/null +++ b/R-package/R/metrics.R @@ -0,0 +1,38 @@ +# [description] List of metrics known to LightGBM. The most up to date list can be found +# at https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters +# +# [return] A named logical vector, where each key is a metric name and each value is a boolean. +# TRUE if higher values of the metric are desirable, FALSE if lower values are desirable. +# Note that only the 'main' metrics are stored here, not aliases, since only the 'main' metrics +# are returned from the C++ side. For example, if you use `metric = "mse"` in your code, +# the metric name `"l2"` will be returned. +.METRICS_HIGHER_BETTER <- function() { + return( + c( + "l1" = FALSE + , "l2" = FALSE + , "mape" = FALSE + , "rmse" = FALSE + , "quantile" = FALSE + , "huber" = FALSE + , "fair" = FALSE + , "poisson" = FALSE + , "gamma" = FALSE + , "gamma_deviance" = FALSE + , "tweedie" = FALSE + , "ndcg" = TRUE + , "map" = TRUE + , "auc" = TRUE + , "average_precision" = TRUE + , "r2" = TRUE + , "binary_logloss" = FALSE + , "binary_error" = FALSE + , "auc_mu" = TRUE + , "multi_logloss" = FALSE + , "multi_error" = FALSE + , "cross_entropy" = FALSE + , "cross_entropy_lambda" = FALSE + , "kullback_leibler" = FALSE + ) + ) +} diff --git a/R-package/R/multithreading.R b/R-package/R/multithreading.R new file mode 100644 index 0000000..a8d6b51 --- /dev/null +++ b/R-package/R/multithreading.R @@ -0,0 +1,51 @@ +#' @name setLGBMThreads +#' @title Set maximum number of threads used by LightGBM +#' @description LightGBM attempts to speed up many operations by using multi-threading. +#' The number of threads used in those operations can be controlled via the +#' \code{num_threads} parameter passed through \code{params} to functions like +#' \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing +#' a model from a text file) are done via code paths that don't explicitly accept thread-control +#' configuration. +#' +#' Use this function to set the maximum number of threads LightGBM will use for such operations. +#' +#' This function affects all LightGBM operations in the same process. +#' +#' So, for example, if you call \code{setLGBMthreads(4)}, no other multi-threaded LightGBM +#' operation in the same process will use more than 4 threads. +#' +#' Call \code{setLGBMthreads(-1)} to remove this limitation. +#' @param num_threads maximum number of threads to be used by LightGBM in multi-threaded operations +#' @return NULL +#' @seealso \link{getLGBMthreads} +#' @export +setLGBMthreads <- function(num_threads) { + .Call( + LGBM_SetMaxThreads_R, + num_threads + ) + return(invisible(NULL)) +} + +#' @name getLGBMThreads +#' @title Get default number of threads used by LightGBM +#' @description LightGBM attempts to speed up many operations by using multi-threading. +#' The number of threads used in those operations can be controlled via the +#' \code{num_threads} parameter passed through \code{params} to functions like +#' \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing +#' a model from a text file) are done via code paths that don't explicitly accept thread-control +#' configuration. +#' +#' Use this function to see the default number of threads LightGBM will use for such operations. +#' @return number of threads as an integer. \code{-1} means that in situations where parameter \code{num_threads} is +#' not explicitly supplied, LightGBM will choose a number of threads to use automatically. +#' @seealso \link{setLGBMthreads} +#' @export +getLGBMthreads <- function() { + out <- 0L + .Call( + LGBM_GetMaxThreads_R, + out + ) + return(out) +} diff --git a/R-package/R/utils.R b/R-package/R/utils.R new file mode 100644 index 0000000..17e6106 --- /dev/null +++ b/R-package/R/utils.R @@ -0,0 +1,260 @@ +.is_Booster <- function(x) { + return(all(c("R6", "lgb.Booster") %in% class(x))) # nolint: class_equals. +} + +.is_Dataset <- function(x) { + return(all(c("R6", "lgb.Dataset") %in% class(x))) # nolint: class_equals. +} + +.is_Predictor <- function(x) { + return(all(c("R6", "lgb.Predictor") %in% class(x))) # nolint: class_equals. +} + +.is_null_handle <- function(x) { + if (is.null(x)) { + return(TRUE) + } + return( + isTRUE(.Call(LGBM_HandleIsNull_R, x)) + ) +} + +.params2str <- function(params) { + + if (!identical(class(params), "list")) { + stop("params must be a list") + } + + names(params) <- gsub(".", "_", names(params), fixed = TRUE) + param_names <- names(params) + ret <- list() + + # Perform key value join + for (i in seq_along(params)) { + + # If a parameter has multiple values, join those values together with commas. + # trimws() is necessary because format() will pad to make strings the same width + val <- paste( + trimws( + format( + x = unname(params[[i]]) + , scientific = FALSE + ) + ) + , collapse = "," + ) + if (nchar(val) <= 0L) next # Skip join + + # Join key value + pair <- paste(c(param_names[[i]], val), collapse = "=") + ret <- c(ret, pair) + + } + + if (length(ret) == 0L) { + return("") + } + + return(paste(ret, collapse = " ")) + +} + +# [description] +# +# Besides applying checks, this function +# +# 1. turns feature *names* into 1-based integer positions, then +# 2. adds an extra list element with skipped features, then +# 3. turns 1-based integer positions into 0-based positions, and finally +# 4. collapses the values of each list element into a string like "[0, 1]". +# +.check_interaction_constraints <- function(interaction_constraints, column_names) { + if (is.null(interaction_constraints)) { + return(list()) + } + if (!identical(class(interaction_constraints), "list")) { + stop("interaction_constraints must be a list") + } + + column_indices <- seq_along(column_names) + + # Convert feature names to 1-based integer positions and apply checks + for (j in seq_along(interaction_constraints)) { + constraint <- interaction_constraints[[j]] + + if (is.character(constraint)) { + constraint_indices <- match(constraint, column_names) + } else if (is.numeric(constraint)) { + constraint_indices <- as.integer(constraint) + } else { + stop("every element in interaction_constraints must be a character vector or numeric vector") + } + + # Features outside range? + bad <- !(constraint_indices %in% column_indices) + if (any(bad)) { + stop( + "unknown feature(s) in interaction_constraints: " + , toString(sQuote(constraint[bad], q = FALSE)) + ) + } + + interaction_constraints[[j]] <- constraint_indices + } + + # Add missing features as new interaction set + remaining_indices <- setdiff( + column_indices, sort(unique(unlist(interaction_constraints))) + ) + if (length(remaining_indices) > 0L) { + interaction_constraints <- c( + interaction_constraints, list(remaining_indices) + ) + } + + # Turn indices 0-based and convert to string + for (j in seq_along(interaction_constraints)) { + interaction_constraints[[j]] <- paste0( + "[", paste(interaction_constraints[[j]] - 1L, collapse = ","), "]" + ) + } + return(interaction_constraints) +} + + +# [description] +# Take any character values from eval and store them in params$metric. +# This has to account for the fact that `eval` could be a character vector, +# a function, a list of functions, or a list with a mix of strings and +# functions +.check_eval <- function(params, eval) { + + if (is.null(params$metric)) { + params$metric <- list() + } else if (is.character(params$metric)) { + params$metric <- as.list(params$metric) + } + + # if 'eval' is a character vector or list, find the character + # elements and add them to 'metric' + if (!is.function(eval)) { + for (i in seq_along(eval)) { + element <- eval[[i]] + if (is.character(element)) { + params$metric <- append(params$metric, element) + } + } + } + + # If more than one character metric was given, then "None" should + # not be included + if (length(params$metric) > 1L) { + params$metric <- Filter( + f = function(metric) { + !(metric %in% .NO_METRIC_STRINGS()) + } + , x = params$metric + ) + } + + # duplicate metrics should be filtered out + params$metric <- as.list(unique(unlist(params$metric))) + + return(params) +} + + +# [description] +# +# Resolve differences between passed-in keyword arguments, parameters, +# and parameter aliases. This function exists because some functions in the +# package take in parameters through their own keyword arguments other than +# the `params` list. +# +# If the same underlying parameter is provided multiple +# ways, the first item in this list is used: +# +# 1. the main (non-alias) parameter found in `params` +# 2. the alias with the highest priority found in `params` +# 3. the keyword argument passed in +# +# For example, "num_iterations" can also be provided to lgb.train() +# via keyword "nrounds". lgb.train() will choose one value for this parameter +# based on the first match in this list: +# +# 1. params[["num_iterations]] +# 2. the highest priority alias of "num_iterations" found in params +# 3. the nrounds keyword argument +# +# If multiple aliases are found in `params` for the same parameter, they are +# all removed before returning `params`. +# +# [return] +# params with num_iterations set to the chosen value, and other aliases +# of num_iterations removed +.check_wrapper_param <- function(main_param_name, params, alternative_kwarg_value) { + + aliases <- .PARAMETER_ALIASES()[[main_param_name]] + aliases_provided <- aliases[aliases %in% names(params)] + aliases_provided <- aliases_provided[aliases_provided != main_param_name] + + # prefer the main parameter + if (!is.null(params[[main_param_name]])) { + for (param in aliases_provided) { + params[[param]] <- NULL + } + return(params) + } + + # if the main parameter wasn't provided, prefer the first alias + if (length(aliases_provided) > 0L) { + first_param <- aliases_provided[1L] + params[[main_param_name]] <- params[[first_param]] + for (param in aliases_provided) { + params[[param]] <- NULL + } + return(params) + } + + # if not provided in params at all, use the alternative value provided + # through a keyword argument from lgb.train(), lgb.cv(), etc. + params[[main_param_name]] <- alternative_kwarg_value + return(params) +} + +#' @importFrom parallel detectCores +.get_default_num_threads <- function() { + if (requireNamespace("RhpcBLASctl", quietly = TRUE)) { # nolint: undesirable_function. + return(RhpcBLASctl::get_num_cores()) + } else { + msg <- "Optional package 'RhpcBLASctl' not found." + cores <- 0L + if (Sys.info()["sysname"] != "Linux") { + cores <- parallel::detectCores(logical = FALSE) + if (is.na(cores) || cores < 0L) { + cores <- 0L + } + } + if (cores == 0L) { + msg <- paste(msg, "Will use default number of OpenMP threads.", sep = " ") + } else { + msg <- paste(msg, "Detection of CPU cores might not be accurate.", sep = " ") + } + warning(msg) + return(cores) + } +} + +.equal_or_both_null <- function(a, b) { + if (is.null(a)) { + if (!is.null(b)) { + return(FALSE) + } + return(TRUE) + } else { + if (is.null(b)) { + return(FALSE) + } + return(a == b) + } +} diff --git a/R-package/README.md b/R-package/README.md new file mode 100644 index 0000000..6a40abf --- /dev/null +++ b/R-package/README.md @@ -0,0 +1,508 @@ +# LightGBM R-package + +[![CRAN Version](https://www.r-pkg.org/badges/version/lightgbm)](https://cran.r-project.org/package=lightgbm) +[![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/lightgbm)](https://cran.r-project.org/package=lightgbm) +[![API Docs](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/en/latest/R/reference/) + + + +### Contents + +* [Installation](#installation) + - [Installing the CRAN Package](#installing-the-cran-package) + - [Installing from Source with CMake](#install) + - [Installing a GPU-enabled Build](#installing-a-gpu-enabled-build) + - [Installing Precompiled Binaries](#installing-precompiled-binaries) + - [Installing from a Pre-compiled lib_lightgbm](#lib_lightgbm) +* [Examples](#examples) +* [Testing](#testing) + - [Running the Tests](#running-the-tests) + - [Code Coverage](#code-coverage) +* [Updating Documentation](#updating-documentation) +* [Preparing a CRAN Package](#preparing-a-cran-package) +* [Known Issues](#known-issues) + +Installation +------------ + +For the easiest installation, go to ["Installing the CRAN package"](#installing-the-cran-package). + +If you experience any issues with that, try ["Installing from Source with CMake"](#install). This can produce a more efficient version of the library on Windows systems with Visual Studio. + +To build a GPU-enabled version of the package, follow the steps in ["Installing a GPU-enabled Build"](#installing-a-gpu-enabled-build). + +If any of the above options do not work for you or do not meet your needs, please let the maintainers know by [opening an issue](https://github.com/lightgbm-org/LightGBM/issues). + +When your package installation is done, you can check quickly if your LightGBM R-package is working by running the following: + +```r +library(lightgbm) +data(agaricus.train, package='lightgbm') +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +model <- lgb.cv( + params = list( + objective = "regression" + , metric = "l2" + ) + , data = dtrain +) +``` + +### Installing the CRAN package + +`{lightgbm}` is [available on CRAN](https://cran.r-project.org/package=lightgbm), and can be installed with the following R code. + +```r +install.packages("lightgbm", repos = "https://cran.r-project.org") +``` + +This is the easiest way to install `{lightgbm}`. It does not require `CMake` or `Visual Studio`, and should work well on many different operating systems and compilers. + +Each CRAN package is also available on [LightGBM releases](https://github.com/lightgbm-org/LightGBM/releases), with a name like `lightgbm-{VERSION}-r-cran.tar.gz`. + +#### Custom Installation (Linux, Mac) + +The steps above should work on most systems, but users with highly-customized environments might want to change how R builds packages from source. + +To change the compiler used when installing the CRAN package, you can create a file `~/.R/Makevars` which overrides `CC` (`C` compiler) and `CXX` (`C++` compiler). + +For example, to use `gcc-14` instead of `clang` on macOS, you could use something like the following: + +```make +# ~/.R/Makevars +CC=gcc-14 +CC17=gcc-14 +CXX=g++-14 +CXX17=g++-14 +``` + +To check the values R is using, run the following: + +```shell +R CMD config --all +``` + +### Installing from Source with CMake + +You need to install git and [CMake](https://cmake.org/) first. + +Note: this method is only supported on 64-bit systems. If you need to run LightGBM on 32-bit Windows (i386), follow the instructions in ["Installing the CRAN Package"](#installing-the-cran-package). + +#### Windows Preparation + +NOTE: Windows users may need to run with administrator rights (either R or the command prompt, depending on the way you are installing this package). + +Installing a 64-bit version of [Rtools](https://cran.r-project.org/bin/windows/Rtools/) is mandatory. + +After installing `Rtools` and `CMake`, be sure the following paths are added to the environment variable `PATH`. These may have been automatically added when installing other software. + +* `Rtools` + - If you have `Rtools` 4.0, example: + - `C:\rtools40\mingw64\bin` + - `C:\rtools40\usr\bin` + - If you have `Rtools` 4.2+, example: + - `C:\rtools42\x86_64-w64-mingw32.static.posix\bin` + - `C:\rtools42\usr\bin` + - **NOTE**: this is e.g. `rtools43\` for R 4.3 +* `CMake` + - example: `C:\Program Files\CMake\bin` +* `R` + - example: `C:\Program Files\R\R-4.5.1\bin` + +NOTE: Two `Rtools` paths are required from `Rtools` 4.0 onwards because paths and the list of included software was changed in `Rtools` 4.0. + +NOTE: `Rtools42` and later take a very different approach to the compiler toolchain than previous releases, and how you install it changes what is required to build packages. See ["Howto: Building R 4.2 and packages on Windows"](https://cran.r-project.org/bin/windows/base/howto-R-4.2.html). + +#### Windows Toolchain Options + +A "toolchain" refers to the collection of software used to build the library. The R-package can be built with three different toolchains. + +**Warning for Windows users**: it is recommended to use *Visual Studio* for its better multi-threading efficiency in Windows for many core systems. For very simple systems (dual core computers or worse), MinGW64 is recommended for maximum performance. If you do not know what to choose, it is recommended to use [Visual Studio](https://visualstudio.microsoft.com/downloads/), the default compiler. **Do not try using MinGW in Windows on many core systems. It may result in 10x slower results than Visual Studio.** + +**Visual Studio (default)** + +By default, the package will be built with [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/). + +**MSYS2 (R 4.x)** + +If you are using R 4.x and installation fails with Visual Studio, `LightGBM` will fall back to using [MSYS2](https://www.msys2.org/). This should work with the tools already bundled in `Rtools` 4.0. + +If you want to force `LightGBM` to use MSYS2 (for any R version), pass `--use-msys2` to the installation script. + +```shell +Rscript build_r.R --use-msys2 +``` + +**MinGW** + +If you want to force `LightGBM` to use [MinGW](https://www.mingw-w64.org/) (for any R version), pass `--use-mingw` to the installation script. + +```shell +Rscript build_r.R --use-mingw +``` + +#### Mac OS Preparation + +You can perform installation either with **Apple Clang** or **gcc**. In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in [Installation Guide](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Installation-Guide.rst#apple-clang)) first. In case you prefer **gcc**, you need to install it (details for installation can be found in [Installation Guide](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Installation-Guide.rst#gcc)) and set some environment variables to tell R to use `gcc` and `g++`. If you install these from Homebrew, your versions of `g++` and `gcc` are most likely in `/usr/local/bin`, as shown below. + +``` +# replace 8 with version of gcc installed on your machine +export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8 +``` + +#### Install with CMake + +After following the "preparation" steps above for your operating system, build and install the R-package with the following commands: + +```sh +git clone --recursive https://github.com/lightgbm-org/LightGBM +cd LightGBM +Rscript build_r.R +``` + +The `build_r.R` script builds the package in a temporary directory called `lightgbm_r`. It will destroy and recreate that directory each time you run the script. That script supports the following command-line options: + +- `--no-build-vignettes`: Skip building vignettes. +- `-j[jobs]`: Number of threads to use when compiling LightGBM. E.g., `-j4` will try to compile 4 objects at a time. + - by default, this script uses single-thread compilation + - for best results, set `-j` to the number of physical CPUs +- `--skip-install`: Build the package tarball, but do not install it. +- `--use-gpu`: Build a GPU-enabled version of the library. +- `--use-mingw`: Force the use of MinGW toolchain, regardless of R version. +- `--use-msys2`: Force the use of MSYS2 toolchain, regardless of R version. + +Note: for the build with Visual Studio/VS Build Tools in Windows, you should use the Windows CMD or PowerShell. + +### Installing a GPU-enabled Build + +You will need to install Boost and OpenCL first: details for installation can be found in [Installation-Guide](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Installation-Guide.rst#build-gpu-version). + +After installing these other libraries, follow the steps in ["Installing from Source with CMake"](#install). When you reach the step that mentions `build_r.R`, pass the flag `--use-gpu`. + +```shell +Rscript build_r.R --use-gpu +``` + +You may also need or want to provide additional configuration, depending on your setup. For example, you may need to provide locations for Boost and OpenCL. + +```shell +Rscript build_r.R \ + --use-gpu \ + --opencl-library=/usr/lib/x86_64-linux-gnu/libOpenCL.so \ + --boost-librarydir=/usr/lib/x86_64-linux-gnu +``` + +The following options correspond to the [CMake FindBoost options](https://cmake.org/cmake/help/latest/module/FindBoost.html) by the same names. + +* `--boost-root` +* `--boost-dir` +* `--boost-include-dir` +* `--boost-librarydir` + +The following options correspond to the [CMake FindOpenCL options](https://cmake.org/cmake/help/latest/module/FindOpenCL.html) by the same names. + +* `--opencl-include-dir` +* `--opencl-library` + +### Installing Precompiled Binaries + +Precompiled binaries for Mac and Windows are prepared by CRAN a few days after each release to CRAN. They can be installed with the following R code. + +```r +install.packages( + "lightgbm" + , type = "both" + , repos = "https://cran.r-project.org" +) +``` + +These packages do not require compilation, so they will be faster and easier to install than packages that are built from source. + +CRAN does not prepare precompiled binaries for Linux, and as of this writing neither does this project. + +### Installing from a Pre-compiled lib_lightgbm + +Previous versions of LightGBM offered the ability to first compile the C++ library (`lib_lightgbm.{dll,dylib,so}`) and then build an R-package that wraps it. + +As of version 3.0.0, this is no longer supported. If building from source is difficult for you, please [open an issue](https://github.com/lightgbm-org/LightGBM/issues). + +Examples +-------- + +Please visit [demo](https://github.com/lightgbm-org/LightGBM/tree/main/R-package/demo): + +* [Basic walkthrough of wrappers](https://github.com/lightgbm-org/LightGBM/blob/main/R-package/demo/basic_walkthrough.R) +* [Boosting from existing prediction](https://github.com/lightgbm-org/LightGBM/blob/main/R-package/demo/boost_from_prediction.R) +* [Early Stopping](https://github.com/lightgbm-org/LightGBM/blob/main/R-package/demo/early_stopping.R) +* [Cross Validation](https://github.com/lightgbm-org/LightGBM/blob/main/R-package/demo/cross_validation.R) +* [Multiclass Training/Prediction](https://github.com/lightgbm-org/LightGBM/blob/main/R-package/demo/multiclass.R) +* [Leaf (in)Stability](https://github.com/lightgbm-org/LightGBM/blob/main/R-package/demo/leaf_stability.R) +* [Weight-Parameter Adjustment Relationship](https://github.com/lightgbm-org/LightGBM/blob/main/R-package/demo/weight_param.R) + +Testing +------- + +The R-package's unit tests are run automatically on every commit, via integrations like [GitHub Actions](https://github.com/lightgbm-org/LightGBM/actions). Adding new tests in `R-package/tests/testthat` is a valuable way to improve the reliability of the R-package. + +### Running the Tests + +While developing the R-package, run the code below to run the unit tests. + +```shell +sh build-cran-package.sh \ + --no-build-vignettes + +R CMD INSTALL --with-keep.source lightgbm*.tar.gz +cd R-package/tests +Rscript testthat.R +``` + +To run the tests with more verbose logs, set environment variable `LIGHTGBM_TEST_VERBOSITY` to a valid value for parameter [`verbosity`](https://lightgbm.readthedocs.io/en/latest/Parameters.html#verbosity). + +```shell +export LIGHTGBM_TEST_VERBOSITY=1 +cd R-package/tests +Rscript testthat.R +``` + +### Code Coverage + +When adding tests, you may want to use test coverage to identify untested areas and to check if the tests you've added are covering all branches of the intended code. + +The example below shows how to generate code coverage for the R-package on a macOS or Linux setup. To adjust for your environment, refer to [the customization step described above](#custom-installation-linux-mac). + +```shell +# Install +sh build-cran-package.sh \ + --no-build-vignettes + +# Get coverage +Rscript -e " \ + library(covr); + coverage <- covr::package_coverage('./lightgbm_r', type = 'tests', quiet = FALSE); + print(coverage); + covr::report(coverage, file = file.path(getwd(), 'coverage.html'), browse = TRUE); + " +``` + +Updating Documentation +---------------------- + +The R-package uses [`{roxygen2}`](https://CRAN.R-project.org/package=roxygen2) to generate its documentation. +The generated `DESCRIPTION`, `NAMESPACE`, and `man/` files are checked into source control. +To regenerate those files, run the following. + +```shell +Rscript \ + --vanilla \ + -e "install.packages('roxygen2', repos = 'https://cran.rstudio.com')" + +sh build-cran-package.sh --no-build-vignettes +R CMD INSTALL \ + --with-keep.source \ + ./lightgbm_*.tar.gz + +cd R-package +Rscript \ + --vanilla \ + -e "roxygen2::roxygenize(load = 'installed')" +``` + +Preparing a CRAN Package +------------------------ + +This section is primarily for maintainers, but may help users and contributors to understand the structure of the R-package. + +Most of `LightGBM` uses `CMake` to handle tasks like setting compiler and linker flags, including header file locations, and linking to other libraries. Because CRAN packages typically do not assume the presence of `CMake`, the R-package uses an alternative method that is in the CRAN-supported toolchain for building R packages with C++ code: `Autoconf`. + +For more information on this approach, see ["Writing R Extensions"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Configure-and-cleanup). + +### Build a CRAN Package + +From the root of the repository, run the following. + +```shell +git submodule update --init --recursive +sh build-cran-package.sh +``` + +This will create a file `lightgbm_${VERSION}.tar.gz`, where `VERSION` is the version of `LightGBM`. + +That script supports the following command-line options: + +- `--no-build-vignettes`: Skip building vignettes. +- `--r-executable=[path-to-executable]`: Use an alternative build of R. + +### Standard Installation from CRAN Package + +After building the package, install it with a command like the following: + +```shell +R CMD install lightgbm_*.tar.gz +``` + +### Changing the CRAN Package + +A lot of details are handled automatically by `R CMD build` and `R CMD install`, so it can be difficult to understand how the files in the R-package are related to each other. An extensive treatment of those details is available in ["Writing R Extensions"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html). + +This section briefly explains the key files for building a CRAN package. To update the package, edit the files relevant to your change and re-run the steps in [Build a CRAN Package](#build-a-cran-package). + +**Linux or Mac** + +At build time, `configure` will be run and used to create a file `Makevars`, using `Makevars.in` as a template. + +1. Edit `configure.ac`. +2. Create `configure` with `autoconf`. Do not edit it by hand. This file must be generated on Ubuntu 22.04. + + If you have an Ubuntu 22.04 environment available, run the provided script from the root of the `LightGBM` repository. + + ```shell + ./R-package/recreate-configure.sh + ``` + + If you do not have easy access to an Ubuntu 22.04 environment, the `configure` script can be generated using Docker by running the code below from the root of this repo. + + ```shell + docker run \ + --rm \ + -v $(pwd):/opt/LightGBM \ + -w /opt/LightGBM \ + ubuntu:22.04 \ + ./R-package/recreate-configure.sh + ``` + + The version of `autoconf` used by this project is stored in `R-package/AUTOCONF_UBUNTU_VERSION`. To update that version, update that file and run the commands above. To see available versions, see https://packages.ubuntu.com/search?keywords=autoconf. + +3. Edit `src/Makevars.in`. + +Alternatively, GitHub Actions can re-generate this file for you. + +1. navigate to https://github.com/lightgbm-org/LightGBM/actions/workflows/r_configure.yml +2. click "Run workflow" (drop-down) +3. enter the branch from the pull request for the `pr-branch` input +4. click "Run workflow" (button) + +**Configuring for Windows** + +At build time, `configure.win` will be run and used to create a file `Makevars.win`, using `Makevars.win.in` as a template. + +1. Edit `configure.win` directly. +2. Edit `src/Makevars.win.in`. + +### Testing the CRAN Package + +`{lightgbm}` is tested automatically on every commit, across many combinations of operating system, R version, and compiler. This section describes how to test the package locally while you are developing. + +#### Windows, Mac, and Linux + +```shell +sh build-cran-package.sh +R CMD check --as-cran lightgbm_*.tar.gz +``` + +#### ASAN and UBSAN + +All packages uploaded to CRAN must pass builds using `gcc` and `clang`, instrumented with two sanitizers: the Address Sanitizer (ASAN) and the Undefined Behavior Sanitizer (UBSAN). + +For more background, see + +* [this blog post](https://dirk.eddelbuettel.com/code/sanitizers.html) +* [top-level CRAN documentation on these checks](https://cran.r-project.org/web/checks/check_issue_kinds.html) +* [CRAN's configuration of these checks](https://www.stats.ox.ac.uk/pub/bdr/memtests/README.txt) + +You can replicate these checks locally using Docker. +For more information on the image used for testing, see https://github.com/wch/r-debug. + +In the code below, environment variable `R_CUSTOMIZATION` should be set to one of two values. + +* `"san"` = replicates CRAN's `gcc-ASAN` and `gcc-UBSAN` checks +* `"csan"` = replicates CRAN's `clang-ASAN` and `clang-UBSAN` checks + +```shell +docker run \ + --rm \ + -it \ + -v $(pwd):/opt/LightGBM \ + -w /opt/LightGBM \ + --env R_CUSTOMIZATION=san \ + wch1/r-debug:latest \ + /bin/bash + +# install dependencies +RDscript${R_CUSTOMIZATION} \ + -e "install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl', 'testthat'), repos = 'https://cran.r-project.org', Ncpus = parallel::detectCores())" + +# install lightgbm +sh build-cran-package.sh --r-executable=RD${R_CUSTOMIZATION} +RD${R_CUSTOMIZATION} \ + CMD INSTALL lightgbm_*.tar.gz + +# run tests +cd R-package/tests +rm -f ./tests.log +RDscript${R_CUSTOMIZATION} testthat.R >> tests.log 2>&1 + +# check that tests passed +echo "test exit code: $?" +tail -300 ./tests.log +``` + +#### Valgrind + +All packages uploaded to CRAN must be built and tested without raising any issues from `valgrind`. `valgrind` is a profiler that can catch serious issues like memory leaks and illegal writes. For more information, see [this blog post](https://reside-ic.github.io/blog/debugging-and-fixing-crans-additional-checks-errors/). + +You can replicate these checks locally using Docker. Note that instrumented versions of R built to use `valgrind` run much slower, and these tests may take as long as 20 minutes to run. + +```shell +docker run \ + --rm \ + -v $(pwd):/opt/LightGBM \ + -w /opt/LightGBM \ + -it \ + wch1/r-debug + +RDscriptvalgrind -e "install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl', 'testthat'), repos = 'https://cran.rstudio.com', Ncpus = parallel::detectCores())" + +sh build-cran-package.sh \ + --r-executable=RDvalgrind + +RDvalgrind CMD INSTALL \ + --preclean \ + --install-tests \ + lightgbm_*.tar.gz + +cd R-package/tests + +RDvalgrind \ + --no-readline \ + --vanilla \ + -d "valgrind --tool=memcheck --leak-check=full --track-origins=yes" \ + -f testthat.R \ +2>&1 \ +| tee out.log \ +| cat +``` + +These tests can also be triggered on a pull request branch, using GitHub Actions. + +1. navigate to https://github.com/lightgbm-org/LightGBM/actions/workflows/r_valgrind.yml +2. click "Run workflow" (drop-down) +3. enter the branch from the pull request for the `pr-branch` input +4. enter the pull request ID for the `pr-number` input +5. click "Run workflow" (button) + +Or by using the GitHub CLI, using a command similar to this: + +```shell +gh workflow run \ + --repo lightgbm-org/LightGBM \ + r_valgrind.yml \ + -f pr-branch=ci/fix-rerun-workflow \ + -f pr-number=7072 +``` + +Known Issues +------------ + +For information about known issues with the R-package, see the [R-package section of LightGBM's main FAQ page](https://lightgbm.readthedocs.io/en/latest/FAQ.html#r-package). diff --git a/R-package/cleanup b/R-package/cleanup new file mode 100755 index 0000000..aa84af6 --- /dev/null +++ b/R-package/cleanup @@ -0,0 +1,6 @@ +#!/bin/sh +rm -f aclocal.m4 +rm -rf ./autom4te.cache +rm -f config.log +rm -f config.status +rm -f src/Makevars diff --git a/R-package/configure b/R-package/configure new file mode 100755 index 0000000..673a785 --- /dev/null +++ b/R-package/configure @@ -0,0 +1,3072 @@ +#! /bin/sh +# Guess values for system-dependent variables and create Makefiles. +# Generated by GNU Autoconf 2.71 for lightgbm 4.6.0.99. +# +# +# Copyright (C) 1992-1996, 1998-2017, 2020-2021 Free Software Foundation, +# Inc. +# +# +# This configure script is free software; the Free Software Foundation +# gives unlimited permission to copy, distribute and modify it. +## -------------------- ## +## M4sh Initialization. ## +## -------------------- ## + +# Be more Bourne compatible +DUALCASE=1; export DUALCASE # for MKS sh +as_nop=: +if test ${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1 +then : + emulate sh + NULLCMD=: + # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which + # is contrary to our usage. Disable this feature. + alias -g '${1+"$@"}'='"$@"' + setopt NO_GLOB_SUBST +else $as_nop + case `(set -o) 2>/dev/null` in #( + *posix*) : + set -o posix ;; #( + *) : + ;; +esac +fi + + + +# Reset variables that may have inherited troublesome values from +# the environment. + +# IFS needs to be set, to space, tab, and newline, in precisely that order. +# (If _AS_PATH_WALK were called with IFS unset, it would have the +# side effect of setting IFS to empty, thus disabling word splitting.) +# Quoting is to prevent editors from complaining about space-tab. +as_nl=' +' +export as_nl +IFS=" "" $as_nl" + +PS1='$ ' +PS2='> ' +PS4='+ ' + +# Ensure predictable behavior from utilities with locale-dependent output. +LC_ALL=C +export LC_ALL +LANGUAGE=C +export LANGUAGE + +# We cannot yet rely on "unset" to work, but we need these variables +# to be unset--not just set to an empty or harmless value--now, to +# avoid bugs in old shells (e.g. pre-3.0 UWIN ksh). This construct +# also avoids known problems related to "unset" and subshell syntax +# in other old shells (e.g. bash 2.01 and pdksh 5.2.14). +for as_var in BASH_ENV ENV MAIL MAILPATH CDPATH +do eval test \${$as_var+y} \ + && ( (unset $as_var) || exit 1) >/dev/null 2>&1 && unset $as_var || : +done + +# Ensure that fds 0, 1, and 2 are open. +if (exec 3>&0) 2>/dev/null; then :; else exec 0&1) 2>/dev/null; then :; else exec 1>/dev/null; fi +if (exec 3>&2) ; then :; else exec 2>/dev/null; fi + +# The user is always right. +if ${PATH_SEPARATOR+false} :; then + PATH_SEPARATOR=: + (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && { + (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 || + PATH_SEPARATOR=';' + } +fi + + +# Find who we are. Look in the path if we contain no directory separator. +as_myself= +case $0 in #(( + *[\\/]* ) as_myself=$0 ;; + *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR +for as_dir in $PATH +do + IFS=$as_save_IFS + case $as_dir in #((( + '') as_dir=./ ;; + */) ;; + *) as_dir=$as_dir/ ;; + esac + test -r "$as_dir$0" && as_myself=$as_dir$0 && break + done +IFS=$as_save_IFS + + ;; +esac +# We did not find ourselves, most probably we were run as `sh COMMAND' +# in which case we are not to be found in the path. +if test "x$as_myself" = x; then + as_myself=$0 +fi +if test ! -f "$as_myself"; then + printf "%s\n" "$as_myself: error: cannot find myself; rerun with an absolute file name" >&2 + exit 1 +fi + + +# Use a proper internal environment variable to ensure we don't fall + # into an infinite loop, continuously re-executing ourselves. + if test x"${_as_can_reexec}" != xno && test "x$CONFIG_SHELL" != x; then + _as_can_reexec=no; export _as_can_reexec; + # We cannot yet assume a decent shell, so we have to provide a +# neutralization value for shells without unset; and this also +# works around shells that cannot unset nonexistent variables. +# Preserve -v and -x to the replacement shell. +BASH_ENV=/dev/null +ENV=/dev/null +(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV +case $- in # (((( + *v*x* | *x*v* ) as_opts=-vx ;; + *v* ) as_opts=-v ;; + *x* ) as_opts=-x ;; + * ) as_opts= ;; +esac +exec $CONFIG_SHELL $as_opts "$as_myself" ${1+"$@"} +# Admittedly, this is quite paranoid, since all the known shells bail +# out after a failed `exec'. +printf "%s\n" "$0: could not re-execute with $CONFIG_SHELL" >&2 +exit 255 + fi + # We don't want this to propagate to other subprocesses. + { _as_can_reexec=; unset _as_can_reexec;} +if test "x$CONFIG_SHELL" = x; then + as_bourne_compatible="as_nop=: +if test \${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1 +then : + emulate sh + NULLCMD=: + # Pre-4.2 versions of Zsh do word splitting on \${1+\"\$@\"}, which + # is contrary to our usage. Disable this feature. + alias -g '\${1+\"\$@\"}'='\"\$@\"' + setopt NO_GLOB_SUBST +else \$as_nop + case \`(set -o) 2>/dev/null\` in #( + *posix*) : + set -o posix ;; #( + *) : + ;; +esac +fi +" + as_required="as_fn_return () { (exit \$1); } +as_fn_success () { as_fn_return 0; } +as_fn_failure () { as_fn_return 1; } +as_fn_ret_success () { return 0; } +as_fn_ret_failure () { return 1; } + +exitcode=0 +as_fn_success || { exitcode=1; echo as_fn_success failed.; } +as_fn_failure && { exitcode=1; echo as_fn_failure succeeded.; } +as_fn_ret_success || { exitcode=1; echo as_fn_ret_success failed.; } +as_fn_ret_failure && { exitcode=1; echo as_fn_ret_failure succeeded.; } +if ( set x; as_fn_ret_success y && test x = \"\$1\" ) +then : + +else \$as_nop + exitcode=1; echo positional parameters were not saved. +fi +test x\$exitcode = x0 || exit 1 +blah=\$(echo \$(echo blah)) +test x\"\$blah\" = xblah || exit 1 +test -x / || exit 1" + as_suggested=" as_lineno_1=";as_suggested=$as_suggested$LINENO;as_suggested=$as_suggested" as_lineno_1a=\$LINENO + as_lineno_2=";as_suggested=$as_suggested$LINENO;as_suggested=$as_suggested" as_lineno_2a=\$LINENO + eval 'test \"x\$as_lineno_1'\$as_run'\" != \"x\$as_lineno_2'\$as_run'\" && + test \"x\`expr \$as_lineno_1'\$as_run' + 1\`\" = \"x\$as_lineno_2'\$as_run'\"' || exit 1" + if (eval "$as_required") 2>/dev/null +then : + as_have_required=yes +else $as_nop + as_have_required=no +fi + if test x$as_have_required = xyes && (eval "$as_suggested") 2>/dev/null +then : + +else $as_nop + as_save_IFS=$IFS; IFS=$PATH_SEPARATOR +as_found=false +for as_dir in /bin$PATH_SEPARATOR/usr/bin$PATH_SEPARATOR$PATH +do + IFS=$as_save_IFS + case $as_dir in #((( + '') as_dir=./ ;; + */) ;; + *) as_dir=$as_dir/ ;; + esac + as_found=: + case $as_dir in #( + /*) + for as_base in sh bash ksh sh5; do + # Try only shells that exist, to save several forks. + as_shell=$as_dir$as_base + if { test -f "$as_shell" || test -f "$as_shell.exe"; } && + as_run=a "$as_shell" -c "$as_bourne_compatible""$as_required" 2>/dev/null +then : + CONFIG_SHELL=$as_shell as_have_required=yes + if as_run=a "$as_shell" -c "$as_bourne_compatible""$as_suggested" 2>/dev/null +then : + break 2 +fi +fi + done;; + esac + as_found=false +done +IFS=$as_save_IFS +if $as_found +then : + +else $as_nop + if { test -f "$SHELL" || test -f "$SHELL.exe"; } && + as_run=a "$SHELL" -c "$as_bourne_compatible""$as_required" 2>/dev/null +then : + CONFIG_SHELL=$SHELL as_have_required=yes +fi +fi + + + if test "x$CONFIG_SHELL" != x +then : + export CONFIG_SHELL + # We cannot yet assume a decent shell, so we have to provide a +# neutralization value for shells without unset; and this also +# works around shells that cannot unset nonexistent variables. +# Preserve -v and -x to the replacement shell. +BASH_ENV=/dev/null +ENV=/dev/null +(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV +case $- in # (((( + *v*x* | *x*v* ) as_opts=-vx ;; + *v* ) as_opts=-v ;; + *x* ) as_opts=-x ;; + * ) as_opts= ;; +esac +exec $CONFIG_SHELL $as_opts "$as_myself" ${1+"$@"} +# Admittedly, this is quite paranoid, since all the known shells bail +# out after a failed `exec'. +printf "%s\n" "$0: could not re-execute with $CONFIG_SHELL" >&2 +exit 255 +fi + + if test x$as_have_required = xno +then : + printf "%s\n" "$0: This script requires a shell more modern than all" + printf "%s\n" "$0: the shells that I found on your system." + if test ${ZSH_VERSION+y} ; then + printf "%s\n" "$0: In particular, zsh $ZSH_VERSION has bugs and should" + printf "%s\n" "$0: be upgraded to zsh 4.3.4 or later." + else + printf "%s\n" "$0: Please tell bug-autoconf@gnu.org about your system, +$0: including any error possibly output before this +$0: message. Then install a modern shell, or manually run +$0: the script under such a shell if you do have one." + fi + exit 1 +fi +fi +fi +SHELL=${CONFIG_SHELL-/bin/sh} +export SHELL +# Unset more variables known to interfere with behavior of common tools. +CLICOLOR_FORCE= GREP_OPTIONS= +unset CLICOLOR_FORCE GREP_OPTIONS + +## --------------------- ## +## M4sh Shell Functions. ## +## --------------------- ## +# as_fn_unset VAR +# --------------- +# Portably unset VAR. +as_fn_unset () +{ + { eval $1=; unset $1;} +} +as_unset=as_fn_unset + + +# as_fn_set_status STATUS +# ----------------------- +# Set $? to STATUS, without forking. +as_fn_set_status () +{ + return $1 +} # as_fn_set_status + +# as_fn_exit STATUS +# ----------------- +# Exit the shell with STATUS, even in a "trap 0" or "set -e" context. +as_fn_exit () +{ + set +e + as_fn_set_status $1 + exit $1 +} # as_fn_exit +# as_fn_nop +# --------- +# Do nothing but, unlike ":", preserve the value of $?. +as_fn_nop () +{ + return $? +} +as_nop=as_fn_nop + +# as_fn_mkdir_p +# ------------- +# Create "$as_dir" as a directory, including parents if necessary. +as_fn_mkdir_p () +{ + + case $as_dir in #( + -*) as_dir=./$as_dir;; + esac + test -d "$as_dir" || eval $as_mkdir_p || { + as_dirs= + while :; do + case $as_dir in #( + *\'*) as_qdir=`printf "%s\n" "$as_dir" | sed "s/'/'\\\\\\\\''/g"`;; #'( + *) as_qdir=$as_dir;; + esac + as_dirs="'$as_qdir' $as_dirs" + as_dir=`$as_dirname -- "$as_dir" || +$as_expr X"$as_dir" : 'X\(.*[^/]\)//*[^/][^/]*/*$' \| \ + X"$as_dir" : 'X\(//\)[^/]' \| \ + X"$as_dir" : 'X\(//\)$' \| \ + X"$as_dir" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X"$as_dir" | + sed '/^X\(.*[^/]\)\/\/*[^/][^/]*\/*$/{ + s//\1/ + q + } + /^X\(\/\/\)[^/].*/{ + s//\1/ + q + } + /^X\(\/\/\)$/{ + s//\1/ + q + } + /^X\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + test -d "$as_dir" && break + done + test -z "$as_dirs" || eval "mkdir $as_dirs" + } || test -d "$as_dir" || as_fn_error $? "cannot create directory $as_dir" + + +} # as_fn_mkdir_p + +# as_fn_executable_p FILE +# ----------------------- +# Test if FILE is an executable regular file. +as_fn_executable_p () +{ + test -f "$1" && test -x "$1" +} # as_fn_executable_p +# as_fn_append VAR VALUE +# ---------------------- +# Append the text in VALUE to the end of the definition contained in VAR. Take +# advantage of any shell optimizations that allow amortized linear growth over +# repeated appends, instead of the typical quadratic growth present in naive +# implementations. +if (eval "as_var=1; as_var+=2; test x\$as_var = x12") 2>/dev/null +then : + eval 'as_fn_append () + { + eval $1+=\$2 + }' +else $as_nop + as_fn_append () + { + eval $1=\$$1\$2 + } +fi # as_fn_append + +# as_fn_arith ARG... +# ------------------ +# Perform arithmetic evaluation on the ARGs, and store the result in the +# global $as_val. Take advantage of shells that can avoid forks. The arguments +# must be portable across $(()) and expr. +if (eval "test \$(( 1 + 1 )) = 2") 2>/dev/null +then : + eval 'as_fn_arith () + { + as_val=$(( $* )) + }' +else $as_nop + as_fn_arith () + { + as_val=`expr "$@" || test $? -eq 1` + } +fi # as_fn_arith + +# as_fn_nop +# --------- +# Do nothing but, unlike ":", preserve the value of $?. +as_fn_nop () +{ + return $? +} +as_nop=as_fn_nop + +# as_fn_error STATUS ERROR [LINENO LOG_FD] +# ---------------------------------------- +# Output "`basename $0`: error: ERROR" to stderr. If LINENO and LOG_FD are +# provided, also output the error to LOG_FD, referencing LINENO. Then exit the +# script with STATUS, using 1 if that was 0. +as_fn_error () +{ + as_status=$1; test $as_status -eq 0 && as_status=1 + if test "$4"; then + as_lineno=${as_lineno-"$3"} as_lineno_stack=as_lineno_stack=$as_lineno_stack + printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: $2" >&$4 + fi + printf "%s\n" "$as_me: error: $2" >&2 + as_fn_exit $as_status +} # as_fn_error + +if expr a : '\(a\)' >/dev/null 2>&1 && + test "X`expr 00001 : '.*\(...\)'`" = X001; then + as_expr=expr +else + as_expr=false +fi + +if (basename -- /) >/dev/null 2>&1 && test "X`basename -- / 2>&1`" = "X/"; then + as_basename=basename +else + as_basename=false +fi + +if (as_dir=`dirname -- /` && test "X$as_dir" = X/) >/dev/null 2>&1; then + as_dirname=dirname +else + as_dirname=false +fi + +as_me=`$as_basename -- "$0" || +$as_expr X/"$0" : '.*/\([^/][^/]*\)/*$' \| \ + X"$0" : 'X\(//\)$' \| \ + X"$0" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X/"$0" | + sed '/^.*\/\([^/][^/]*\)\/*$/{ + s//\1/ + q + } + /^X\/\(\/\/\)$/{ + s//\1/ + q + } + /^X\/\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + +# Avoid depending upon Character Ranges. +as_cr_letters='abcdefghijklmnopqrstuvwxyz' +as_cr_LETTERS='ABCDEFGHIJKLMNOPQRSTUVWXYZ' +as_cr_Letters=$as_cr_letters$as_cr_LETTERS +as_cr_digits='0123456789' +as_cr_alnum=$as_cr_Letters$as_cr_digits + + + as_lineno_1=$LINENO as_lineno_1a=$LINENO + as_lineno_2=$LINENO as_lineno_2a=$LINENO + eval 'test "x$as_lineno_1'$as_run'" != "x$as_lineno_2'$as_run'" && + test "x`expr $as_lineno_1'$as_run' + 1`" = "x$as_lineno_2'$as_run'"' || { + # Blame Lee E. McMahon (1931-1989) for sed's syntax. :-) + sed -n ' + p + /[$]LINENO/= + ' <$as_myself | + sed ' + s/[$]LINENO.*/&-/ + t lineno + b + :lineno + N + :loop + s/[$]LINENO\([^'$as_cr_alnum'_].*\n\)\(.*\)/\2\1\2/ + t loop + s/-\n.*// + ' >$as_me.lineno && + chmod +x "$as_me.lineno" || + { printf "%s\n" "$as_me: error: cannot create $as_me.lineno; rerun with a POSIX shell" >&2; as_fn_exit 1; } + + # If we had to re-execute with $CONFIG_SHELL, we're ensured to have + # already done that, so ensure we don't try to do so again and fall + # in an infinite loop. This has already happened in practice. + _as_can_reexec=no; export _as_can_reexec + # Don't try to exec as it changes $[0], causing all sort of problems + # (the dirname of $[0] is not the place where we might find the + # original and so on. Autoconf is especially sensitive to this). + . "./$as_me.lineno" + # Exit status is that of the last command. + exit +} + + +# Determine whether it's possible to make 'echo' print without a newline. +# These variables are no longer used directly by Autoconf, but are AC_SUBSTed +# for compatibility with existing Makefiles. +ECHO_C= ECHO_N= ECHO_T= +case `echo -n x` in #((((( +-n*) + case `echo 'xy\c'` in + *c*) ECHO_T=' ';; # ECHO_T is single tab character. + xy) ECHO_C='\c';; + *) echo `echo ksh88 bug on AIX 6.1` > /dev/null + ECHO_T=' ';; + esac;; +*) + ECHO_N='-n';; +esac + +# For backward compatibility with old third-party macros, we provide +# the shell variables $as_echo and $as_echo_n. New code should use +# AS_ECHO(["message"]) and AS_ECHO_N(["message"]), respectively. +as_echo='printf %s\n' +as_echo_n='printf %s' + + +rm -f conf$$ conf$$.exe conf$$.file +if test -d conf$$.dir; then + rm -f conf$$.dir/conf$$.file +else + rm -f conf$$.dir + mkdir conf$$.dir 2>/dev/null +fi +if (echo >conf$$.file) 2>/dev/null; then + if ln -s conf$$.file conf$$ 2>/dev/null; then + as_ln_s='ln -s' + # ... but there are two gotchas: + # 1) On MSYS, both `ln -s file dir' and `ln file dir' fail. + # 2) DJGPP < 2.04 has no symlinks; `ln -s' creates a wrapper executable. + # In both cases, we have to default to `cp -pR'. + ln -s conf$$.file conf$$.dir 2>/dev/null && test ! -f conf$$.exe || + as_ln_s='cp -pR' + elif ln conf$$.file conf$$ 2>/dev/null; then + as_ln_s=ln + else + as_ln_s='cp -pR' + fi +else + as_ln_s='cp -pR' +fi +rm -f conf$$ conf$$.exe conf$$.dir/conf$$.file conf$$.file +rmdir conf$$.dir 2>/dev/null + +if mkdir -p . 2>/dev/null; then + as_mkdir_p='mkdir -p "$as_dir"' +else + test -d ./-p && rmdir ./-p + as_mkdir_p=false +fi + +as_test_x='test -x' +as_executable_p=as_fn_executable_p + +# Sed expression to map a string onto a valid CPP name. +as_tr_cpp="eval sed 'y%*$as_cr_letters%P$as_cr_LETTERS%;s%[^_$as_cr_alnum]%_%g'" + +# Sed expression to map a string onto a valid variable name. +as_tr_sh="eval sed 'y%*+%pp%;s%[^_$as_cr_alnum]%_%g'" + + +test -n "$DJDIR" || exec 7<&0 &1 + +# Name of the host. +# hostname on some systems (SVR3.2, old GNU/Linux) returns a bogus exit status, +# so uname gets run too. +ac_hostname=`(hostname || uname -n) 2>/dev/null | sed 1q` + +# +# Initializations. +# +ac_default_prefix=/usr/local +ac_clean_files= +ac_config_libobj_dir=. +LIBOBJS= +cross_compiling=no +subdirs= +MFLAGS= +MAKEFLAGS= + +# Identity of this package. +PACKAGE_NAME='lightgbm' +PACKAGE_TARNAME='lightgbm' +PACKAGE_VERSION='4.6.0.99' +PACKAGE_STRING='lightgbm 4.6.0.99' +PACKAGE_BUGREPORT='' +PACKAGE_URL='' + +ac_subst_vars='LTLIBOBJS +LIBOBJS +LGB_CPPFLAGS +OPENMP_LIB +OPENMP_CXXFLAGS +target_alias +host_alias +build_alias +LIBS +ECHO_T +ECHO_N +ECHO_C +DEFS +mandir +localedir +libdir +psdir +pdfdir +dvidir +htmldir +infodir +docdir +oldincludedir +includedir +runstatedir +localstatedir +sharedstatedir +sysconfdir +datadir +datarootdir +libexecdir +sbindir +bindir +program_transform_name +prefix +exec_prefix +PACKAGE_URL +PACKAGE_BUGREPORT +PACKAGE_STRING +PACKAGE_VERSION +PACKAGE_TARNAME +PACKAGE_NAME +PATH_SEPARATOR +SHELL' +ac_subst_files='' +ac_user_opts=' +enable_option_checking +' + ac_precious_vars='build_alias +host_alias +target_alias' + + +# Initialize some variables set by options. +ac_init_help= +ac_init_version=false +ac_unrecognized_opts= +ac_unrecognized_sep= +# The variables have the same names as the options, with +# dashes changed to underlines. +cache_file=/dev/null +exec_prefix=NONE +no_create= +no_recursion= +prefix=NONE +program_prefix=NONE +program_suffix=NONE +program_transform_name=s,x,x, +silent= +site= +srcdir= +verbose= +x_includes=NONE +x_libraries=NONE + +# Installation directory options. +# These are left unexpanded so users can "make install exec_prefix=/foo" +# and all the variables that are supposed to be based on exec_prefix +# by default will actually change. +# Use braces instead of parens because sh, perl, etc. also accept them. +# (The list follows the same order as the GNU Coding Standards.) +bindir='${exec_prefix}/bin' +sbindir='${exec_prefix}/sbin' +libexecdir='${exec_prefix}/libexec' +datarootdir='${prefix}/share' +datadir='${datarootdir}' +sysconfdir='${prefix}/etc' +sharedstatedir='${prefix}/com' +localstatedir='${prefix}/var' +runstatedir='${localstatedir}/run' +includedir='${prefix}/include' +oldincludedir='/usr/include' +docdir='${datarootdir}/doc/${PACKAGE_TARNAME}' +infodir='${datarootdir}/info' +htmldir='${docdir}' +dvidir='${docdir}' +pdfdir='${docdir}' +psdir='${docdir}' +libdir='${exec_prefix}/lib' +localedir='${datarootdir}/locale' +mandir='${datarootdir}/man' + +ac_prev= +ac_dashdash= +for ac_option +do + # If the previous option needs an argument, assign it. + if test -n "$ac_prev"; then + eval $ac_prev=\$ac_option + ac_prev= + continue + fi + + case $ac_option in + *=?*) ac_optarg=`expr "X$ac_option" : '[^=]*=\(.*\)'` ;; + *=) ac_optarg= ;; + *) ac_optarg=yes ;; + esac + + case $ac_dashdash$ac_option in + --) + ac_dashdash=yes ;; + + -bindir | --bindir | --bindi | --bind | --bin | --bi) + ac_prev=bindir ;; + -bindir=* | --bindir=* | --bindi=* | --bind=* | --bin=* | --bi=*) + bindir=$ac_optarg ;; + + -build | --build | --buil | --bui | --bu) + ac_prev=build_alias ;; + -build=* | --build=* | --buil=* | --bui=* | --bu=*) + build_alias=$ac_optarg ;; + + -cache-file | --cache-file | --cache-fil | --cache-fi \ + | --cache-f | --cache- | --cache | --cach | --cac | --ca | --c) + ac_prev=cache_file ;; + -cache-file=* | --cache-file=* | --cache-fil=* | --cache-fi=* \ + | --cache-f=* | --cache-=* | --cache=* | --cach=* | --cac=* | --ca=* | --c=*) + cache_file=$ac_optarg ;; + + --config-cache | -C) + cache_file=config.cache ;; + + -datadir | --datadir | --datadi | --datad) + ac_prev=datadir ;; + -datadir=* | --datadir=* | --datadi=* | --datad=*) + datadir=$ac_optarg ;; + + -datarootdir | --datarootdir | --datarootdi | --datarootd | --dataroot \ + | --dataroo | --dataro | --datar) + ac_prev=datarootdir ;; + -datarootdir=* | --datarootdir=* | --datarootdi=* | --datarootd=* \ + | --dataroot=* | --dataroo=* | --dataro=* | --datar=*) + datarootdir=$ac_optarg ;; + + -disable-* | --disable-*) + ac_useropt=`expr "x$ac_option" : 'x-*disable-\(.*\)'` + # Reject names that are not valid shell variable names. + expr "x$ac_useropt" : ".*[^-+._$as_cr_alnum]" >/dev/null && + as_fn_error $? "invalid feature name: \`$ac_useropt'" + ac_useropt_orig=$ac_useropt + ac_useropt=`printf "%s\n" "$ac_useropt" | sed 's/[-+.]/_/g'` + case $ac_user_opts in + *" +"enable_$ac_useropt" +"*) ;; + *) ac_unrecognized_opts="$ac_unrecognized_opts$ac_unrecognized_sep--disable-$ac_useropt_orig" + ac_unrecognized_sep=', ';; + esac + eval enable_$ac_useropt=no ;; + + -docdir | --docdir | --docdi | --doc | --do) + ac_prev=docdir ;; + -docdir=* | --docdir=* | --docdi=* | --doc=* | --do=*) + docdir=$ac_optarg ;; + + -dvidir | --dvidir | --dvidi | --dvid | --dvi | --dv) + ac_prev=dvidir ;; + -dvidir=* | --dvidir=* | --dvidi=* | --dvid=* | --dvi=* | --dv=*) + dvidir=$ac_optarg ;; + + -enable-* | --enable-*) + ac_useropt=`expr "x$ac_option" : 'x-*enable-\([^=]*\)'` + # Reject names that are not valid shell variable names. + expr "x$ac_useropt" : ".*[^-+._$as_cr_alnum]" >/dev/null && + as_fn_error $? "invalid feature name: \`$ac_useropt'" + ac_useropt_orig=$ac_useropt + ac_useropt=`printf "%s\n" "$ac_useropt" | sed 's/[-+.]/_/g'` + case $ac_user_opts in + *" +"enable_$ac_useropt" +"*) ;; + *) ac_unrecognized_opts="$ac_unrecognized_opts$ac_unrecognized_sep--enable-$ac_useropt_orig" + ac_unrecognized_sep=', ';; + esac + eval enable_$ac_useropt=\$ac_optarg ;; + + -exec-prefix | --exec_prefix | --exec-prefix | --exec-prefi \ + | --exec-pref | --exec-pre | --exec-pr | --exec-p | --exec- \ + | --exec | --exe | --ex) + ac_prev=exec_prefix ;; + -exec-prefix=* | --exec_prefix=* | --exec-prefix=* | --exec-prefi=* \ + | --exec-pref=* | --exec-pre=* | --exec-pr=* | --exec-p=* | --exec-=* \ + | --exec=* | --exe=* | --ex=*) + exec_prefix=$ac_optarg ;; + + -gas | --gas | --ga | --g) + # Obsolete; use --with-gas. + with_gas=yes ;; + + -help | --help | --hel | --he | -h) + ac_init_help=long ;; + -help=r* | --help=r* | --hel=r* | --he=r* | -hr*) + ac_init_help=recursive ;; + -help=s* | --help=s* | --hel=s* | --he=s* | -hs*) + ac_init_help=short ;; + + -host | --host | --hos | --ho) + ac_prev=host_alias ;; + -host=* | --host=* | --hos=* | --ho=*) + host_alias=$ac_optarg ;; + + -htmldir | --htmldir | --htmldi | --htmld | --html | --htm | --ht) + ac_prev=htmldir ;; + -htmldir=* | --htmldir=* | --htmldi=* | --htmld=* | --html=* | --htm=* \ + | --ht=*) + htmldir=$ac_optarg ;; + + -includedir | --includedir | --includedi | --included | --include \ + | --includ | --inclu | --incl | --inc) + ac_prev=includedir ;; + -includedir=* | --includedir=* | --includedi=* | --included=* | --include=* \ + | --includ=* | --inclu=* | --incl=* | --inc=*) + includedir=$ac_optarg ;; + + -infodir | --infodir | --infodi | --infod | --info | --inf) + ac_prev=infodir ;; + -infodir=* | --infodir=* | --infodi=* | --infod=* | --info=* | --inf=*) + infodir=$ac_optarg ;; + + -libdir | --libdir | --libdi | --libd) + ac_prev=libdir ;; + -libdir=* | --libdir=* | --libdi=* | --libd=*) + libdir=$ac_optarg ;; + + -libexecdir | --libexecdir | --libexecdi | --libexecd | --libexec \ + | --libexe | --libex | --libe) + ac_prev=libexecdir ;; + -libexecdir=* | --libexecdir=* | --libexecdi=* | --libexecd=* | --libexec=* \ + | --libexe=* | --libex=* | --libe=*) + libexecdir=$ac_optarg ;; + + -localedir | --localedir | --localedi | --localed | --locale) + ac_prev=localedir ;; + -localedir=* | --localedir=* | --localedi=* | --localed=* | --locale=*) + localedir=$ac_optarg ;; + + -localstatedir | --localstatedir | --localstatedi | --localstated \ + | --localstate | --localstat | --localsta | --localst | --locals) + ac_prev=localstatedir ;; + -localstatedir=* | --localstatedir=* | --localstatedi=* | --localstated=* \ + | --localstate=* | --localstat=* | --localsta=* | --localst=* | --locals=*) + localstatedir=$ac_optarg ;; + + -mandir | --mandir | --mandi | --mand | --man | --ma | --m) + ac_prev=mandir ;; + -mandir=* | --mandir=* | --mandi=* | --mand=* | --man=* | --ma=* | --m=*) + mandir=$ac_optarg ;; + + -nfp | --nfp | --nf) + # Obsolete; use --without-fp. + with_fp=no ;; + + -no-create | --no-create | --no-creat | --no-crea | --no-cre \ + | --no-cr | --no-c | -n) + no_create=yes ;; + + -no-recursion | --no-recursion | --no-recursio | --no-recursi \ + | --no-recurs | --no-recur | --no-recu | --no-rec | --no-re | --no-r) + no_recursion=yes ;; + + -oldincludedir | --oldincludedir | --oldincludedi | --oldincluded \ + | --oldinclude | --oldinclud | --oldinclu | --oldincl | --oldinc \ + | --oldin | --oldi | --old | --ol | --o) + ac_prev=oldincludedir ;; + -oldincludedir=* | --oldincludedir=* | --oldincludedi=* | --oldincluded=* \ + | --oldinclude=* | --oldinclud=* | --oldinclu=* | --oldincl=* | --oldinc=* \ + | --oldin=* | --oldi=* | --old=* | --ol=* | --o=*) + oldincludedir=$ac_optarg ;; + + -prefix | --prefix | --prefi | --pref | --pre | --pr | --p) + ac_prev=prefix ;; + -prefix=* | --prefix=* | --prefi=* | --pref=* | --pre=* | --pr=* | --p=*) + prefix=$ac_optarg ;; + + -program-prefix | --program-prefix | --program-prefi | --program-pref \ + | --program-pre | --program-pr | --program-p) + ac_prev=program_prefix ;; + -program-prefix=* | --program-prefix=* | --program-prefi=* \ + | --program-pref=* | --program-pre=* | --program-pr=* | --program-p=*) + program_prefix=$ac_optarg ;; + + -program-suffix | --program-suffix | --program-suffi | --program-suff \ + | --program-suf | --program-su | --program-s) + ac_prev=program_suffix ;; + -program-suffix=* | --program-suffix=* | --program-suffi=* \ + | --program-suff=* | --program-suf=* | --program-su=* | --program-s=*) + program_suffix=$ac_optarg ;; + + -program-transform-name | --program-transform-name \ + | --program-transform-nam | --program-transform-na \ + | --program-transform-n | --program-transform- \ + | --program-transform | --program-transfor \ + | --program-transfo | --program-transf \ + | --program-trans | --program-tran \ + | --progr-tra | --program-tr | --program-t) + ac_prev=program_transform_name ;; + -program-transform-name=* | --program-transform-name=* \ + | --program-transform-nam=* | --program-transform-na=* \ + | --program-transform-n=* | --program-transform-=* \ + | --program-transform=* | --program-transfor=* \ + | --program-transfo=* | --program-transf=* \ + | --program-trans=* | --program-tran=* \ + | --progr-tra=* | --program-tr=* | --program-t=*) + program_transform_name=$ac_optarg ;; + + -pdfdir | --pdfdir | --pdfdi | --pdfd | --pdf | --pd) + ac_prev=pdfdir ;; + -pdfdir=* | --pdfdir=* | --pdfdi=* | --pdfd=* | --pdf=* | --pd=*) + pdfdir=$ac_optarg ;; + + -psdir | --psdir | --psdi | --psd | --ps) + ac_prev=psdir ;; + -psdir=* | --psdir=* | --psdi=* | --psd=* | --ps=*) + psdir=$ac_optarg ;; + + -q | -quiet | --quiet | --quie | --qui | --qu | --q \ + | -silent | --silent | --silen | --sile | --sil) + silent=yes ;; + + -runstatedir | --runstatedir | --runstatedi | --runstated \ + | --runstate | --runstat | --runsta | --runst | --runs \ + | --run | --ru | --r) + ac_prev=runstatedir ;; + -runstatedir=* | --runstatedir=* | --runstatedi=* | --runstated=* \ + | --runstate=* | --runstat=* | --runsta=* | --runst=* | --runs=* \ + | --run=* | --ru=* | --r=*) + runstatedir=$ac_optarg ;; + + -sbindir | --sbindir | --sbindi | --sbind | --sbin | --sbi | --sb) + ac_prev=sbindir ;; + -sbindir=* | --sbindir=* | --sbindi=* | --sbind=* | --sbin=* \ + | --sbi=* | --sb=*) + sbindir=$ac_optarg ;; + + -sharedstatedir | --sharedstatedir | --sharedstatedi \ + | --sharedstated | --sharedstate | --sharedstat | --sharedsta \ + | --sharedst | --shareds | --shared | --share | --shar \ + | --sha | --sh) + ac_prev=sharedstatedir ;; + -sharedstatedir=* | --sharedstatedir=* | --sharedstatedi=* \ + | --sharedstated=* | --sharedstate=* | --sharedstat=* | --sharedsta=* \ + | --sharedst=* | --shareds=* | --shared=* | --share=* | --shar=* \ + | --sha=* | --sh=*) + sharedstatedir=$ac_optarg ;; + + -site | --site | --sit) + ac_prev=site ;; + -site=* | --site=* | --sit=*) + site=$ac_optarg ;; + + -srcdir | --srcdir | --srcdi | --srcd | --src | --sr) + ac_prev=srcdir ;; + -srcdir=* | --srcdir=* | --srcdi=* | --srcd=* | --src=* | --sr=*) + srcdir=$ac_optarg ;; + + -sysconfdir | --sysconfdir | --sysconfdi | --sysconfd | --sysconf \ + | --syscon | --sysco | --sysc | --sys | --sy) + ac_prev=sysconfdir ;; + -sysconfdir=* | --sysconfdir=* | --sysconfdi=* | --sysconfd=* | --sysconf=* \ + | --syscon=* | --sysco=* | --sysc=* | --sys=* | --sy=*) + sysconfdir=$ac_optarg ;; + + -target | --target | --targe | --targ | --tar | --ta | --t) + ac_prev=target_alias ;; + -target=* | --target=* | --targe=* | --targ=* | --tar=* | --ta=* | --t=*) + target_alias=$ac_optarg ;; + + -v | -verbose | --verbose | --verbos | --verbo | --verb) + verbose=yes ;; + + -version | --version | --versio | --versi | --vers | -V) + ac_init_version=: ;; + + -with-* | --with-*) + ac_useropt=`expr "x$ac_option" : 'x-*with-\([^=]*\)'` + # Reject names that are not valid shell variable names. + expr "x$ac_useropt" : ".*[^-+._$as_cr_alnum]" >/dev/null && + as_fn_error $? "invalid package name: \`$ac_useropt'" + ac_useropt_orig=$ac_useropt + ac_useropt=`printf "%s\n" "$ac_useropt" | sed 's/[-+.]/_/g'` + case $ac_user_opts in + *" +"with_$ac_useropt" +"*) ;; + *) ac_unrecognized_opts="$ac_unrecognized_opts$ac_unrecognized_sep--with-$ac_useropt_orig" + ac_unrecognized_sep=', ';; + esac + eval with_$ac_useropt=\$ac_optarg ;; + + -without-* | --without-*) + ac_useropt=`expr "x$ac_option" : 'x-*without-\(.*\)'` + # Reject names that are not valid shell variable names. + expr "x$ac_useropt" : ".*[^-+._$as_cr_alnum]" >/dev/null && + as_fn_error $? "invalid package name: \`$ac_useropt'" + ac_useropt_orig=$ac_useropt + ac_useropt=`printf "%s\n" "$ac_useropt" | sed 's/[-+.]/_/g'` + case $ac_user_opts in + *" +"with_$ac_useropt" +"*) ;; + *) ac_unrecognized_opts="$ac_unrecognized_opts$ac_unrecognized_sep--without-$ac_useropt_orig" + ac_unrecognized_sep=', ';; + esac + eval with_$ac_useropt=no ;; + + --x) + # Obsolete; use --with-x. + with_x=yes ;; + + -x-includes | --x-includes | --x-include | --x-includ | --x-inclu \ + | --x-incl | --x-inc | --x-in | --x-i) + ac_prev=x_includes ;; + -x-includes=* | --x-includes=* | --x-include=* | --x-includ=* | --x-inclu=* \ + | --x-incl=* | --x-inc=* | --x-in=* | --x-i=*) + x_includes=$ac_optarg ;; + + -x-libraries | --x-libraries | --x-librarie | --x-librari \ + | --x-librar | --x-libra | --x-libr | --x-lib | --x-li | --x-l) + ac_prev=x_libraries ;; + -x-libraries=* | --x-libraries=* | --x-librarie=* | --x-librari=* \ + | --x-librar=* | --x-libra=* | --x-libr=* | --x-lib=* | --x-li=* | --x-l=*) + x_libraries=$ac_optarg ;; + + -*) as_fn_error $? "unrecognized option: \`$ac_option' +Try \`$0 --help' for more information" + ;; + + *=*) + ac_envvar=`expr "x$ac_option" : 'x\([^=]*\)='` + # Reject names that are not valid shell variable names. + case $ac_envvar in #( + '' | [0-9]* | *[!_$as_cr_alnum]* ) + as_fn_error $? "invalid variable name: \`$ac_envvar'" ;; + esac + eval $ac_envvar=\$ac_optarg + export $ac_envvar ;; + + *) + # FIXME: should be removed in autoconf 3.0. + printf "%s\n" "$as_me: WARNING: you should use --build, --host, --target" >&2 + expr "x$ac_option" : ".*[^-._$as_cr_alnum]" >/dev/null && + printf "%s\n" "$as_me: WARNING: invalid host type: $ac_option" >&2 + : "${build_alias=$ac_option} ${host_alias=$ac_option} ${target_alias=$ac_option}" + ;; + + esac +done + +if test -n "$ac_prev"; then + ac_option=--`echo $ac_prev | sed 's/_/-/g'` + as_fn_error $? "missing argument to $ac_option" +fi + +if test -n "$ac_unrecognized_opts"; then + case $enable_option_checking in + no) ;; + fatal) as_fn_error $? "unrecognized options: $ac_unrecognized_opts" ;; + *) printf "%s\n" "$as_me: WARNING: unrecognized options: $ac_unrecognized_opts" >&2 ;; + esac +fi + +# Check all directory arguments for consistency. +for ac_var in exec_prefix prefix bindir sbindir libexecdir datarootdir \ + datadir sysconfdir sharedstatedir localstatedir includedir \ + oldincludedir docdir infodir htmldir dvidir pdfdir psdir \ + libdir localedir mandir runstatedir +do + eval ac_val=\$$ac_var + # Remove trailing slashes. + case $ac_val in + */ ) + ac_val=`expr "X$ac_val" : 'X\(.*[^/]\)' \| "X$ac_val" : 'X\(.*\)'` + eval $ac_var=\$ac_val;; + esac + # Be sure to have absolute directory names. + case $ac_val in + [\\/$]* | ?:[\\/]* ) continue;; + NONE | '' ) case $ac_var in *prefix ) continue;; esac;; + esac + as_fn_error $? "expected an absolute directory name for --$ac_var: $ac_val" +done + +# There might be people who depend on the old broken behavior: `$host' +# used to hold the argument of --host etc. +# FIXME: To remove some day. +build=$build_alias +host=$host_alias +target=$target_alias + +# FIXME: To remove some day. +if test "x$host_alias" != x; then + if test "x$build_alias" = x; then + cross_compiling=maybe + elif test "x$build_alias" != "x$host_alias"; then + cross_compiling=yes + fi +fi + +ac_tool_prefix= +test -n "$host_alias" && ac_tool_prefix=$host_alias- + +test "$silent" = yes && exec 6>/dev/null + + +ac_pwd=`pwd` && test -n "$ac_pwd" && +ac_ls_di=`ls -di .` && +ac_pwd_ls_di=`cd "$ac_pwd" && ls -di .` || + as_fn_error $? "working directory cannot be determined" +test "X$ac_ls_di" = "X$ac_pwd_ls_di" || + as_fn_error $? "pwd does not report name of working directory" + + +# Find the source files, if location was not specified. +if test -z "$srcdir"; then + ac_srcdir_defaulted=yes + # Try the directory containing this script, then the parent directory. + ac_confdir=`$as_dirname -- "$as_myself" || +$as_expr X"$as_myself" : 'X\(.*[^/]\)//*[^/][^/]*/*$' \| \ + X"$as_myself" : 'X\(//\)[^/]' \| \ + X"$as_myself" : 'X\(//\)$' \| \ + X"$as_myself" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X"$as_myself" | + sed '/^X\(.*[^/]\)\/\/*[^/][^/]*\/*$/{ + s//\1/ + q + } + /^X\(\/\/\)[^/].*/{ + s//\1/ + q + } + /^X\(\/\/\)$/{ + s//\1/ + q + } + /^X\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + srcdir=$ac_confdir + if test ! -r "$srcdir/$ac_unique_file"; then + srcdir=.. + fi +else + ac_srcdir_defaulted=no +fi +if test ! -r "$srcdir/$ac_unique_file"; then + test "$ac_srcdir_defaulted" = yes && srcdir="$ac_confdir or .." + as_fn_error $? "cannot find sources ($ac_unique_file) in $srcdir" +fi +ac_msg="sources are in $srcdir, but \`cd $srcdir' does not work" +ac_abs_confdir=`( + cd "$srcdir" && test -r "./$ac_unique_file" || as_fn_error $? "$ac_msg" + pwd)` +# When building in place, set srcdir=. +if test "$ac_abs_confdir" = "$ac_pwd"; then + srcdir=. +fi +# Remove unnecessary trailing slashes from srcdir. +# Double slashes in file names in object file debugging info +# mess up M-x gdb in Emacs. +case $srcdir in +*/) srcdir=`expr "X$srcdir" : 'X\(.*[^/]\)' \| "X$srcdir" : 'X\(.*\)'`;; +esac +for ac_var in $ac_precious_vars; do + eval ac_env_${ac_var}_set=\${${ac_var}+set} + eval ac_env_${ac_var}_value=\$${ac_var} + eval ac_cv_env_${ac_var}_set=\${${ac_var}+set} + eval ac_cv_env_${ac_var}_value=\$${ac_var} +done + +# +# Report the --help message. +# +if test "$ac_init_help" = "long"; then + # Omit some internal or obsolete options to make the list less imposing. + # This message is too long to be a string in the A/UX 3.1 sh. + cat <<_ACEOF +\`configure' configures lightgbm 4.6.0.99 to adapt to many kinds of systems. + +Usage: $0 [OPTION]... [VAR=VALUE]... + +To assign environment variables (e.g., CC, CFLAGS...), specify them as +VAR=VALUE. See below for descriptions of some of the useful variables. + +Defaults for the options are specified in brackets. + +Configuration: + -h, --help display this help and exit + --help=short display options specific to this package + --help=recursive display the short help of all the included packages + -V, --version display version information and exit + -q, --quiet, --silent do not print \`checking ...' messages + --cache-file=FILE cache test results in FILE [disabled] + -C, --config-cache alias for \`--cache-file=config.cache' + -n, --no-create do not create output files + --srcdir=DIR find the sources in DIR [configure dir or \`..'] + +Installation directories: + --prefix=PREFIX install architecture-independent files in PREFIX + [$ac_default_prefix] + --exec-prefix=EPREFIX install architecture-dependent files in EPREFIX + [PREFIX] + +By default, \`make install' will install all the files in +\`$ac_default_prefix/bin', \`$ac_default_prefix/lib' etc. You can specify +an installation prefix other than \`$ac_default_prefix' using \`--prefix', +for instance \`--prefix=\$HOME'. + +For better control, use the options below. + +Fine tuning of the installation directories: + --bindir=DIR user executables [EPREFIX/bin] + --sbindir=DIR system admin executables [EPREFIX/sbin] + --libexecdir=DIR program executables [EPREFIX/libexec] + --sysconfdir=DIR read-only single-machine data [PREFIX/etc] + --sharedstatedir=DIR modifiable architecture-independent data [PREFIX/com] + --localstatedir=DIR modifiable single-machine data [PREFIX/var] + --runstatedir=DIR modifiable per-process data [LOCALSTATEDIR/run] + --libdir=DIR object code libraries [EPREFIX/lib] + --includedir=DIR C header files [PREFIX/include] + --oldincludedir=DIR C header files for non-gcc [/usr/include] + --datarootdir=DIR read-only arch.-independent data root [PREFIX/share] + --datadir=DIR read-only architecture-independent data [DATAROOTDIR] + --infodir=DIR info documentation [DATAROOTDIR/info] + --localedir=DIR locale-dependent data [DATAROOTDIR/locale] + --mandir=DIR man documentation [DATAROOTDIR/man] + --docdir=DIR documentation root [DATAROOTDIR/doc/lightgbm] + --htmldir=DIR html documentation [DOCDIR] + --dvidir=DIR dvi documentation [DOCDIR] + --pdfdir=DIR pdf documentation [DOCDIR] + --psdir=DIR ps documentation [DOCDIR] +_ACEOF + + cat <<\_ACEOF +_ACEOF +fi + +if test -n "$ac_init_help"; then + case $ac_init_help in + short | recursive ) echo "Configuration of lightgbm 4.6.0.99:";; + esac + cat <<\_ACEOF + +Report bugs to the package provider. +_ACEOF +ac_status=$? +fi + +if test "$ac_init_help" = "recursive"; then + # If there are subdirs, report their specific --help. + for ac_dir in : $ac_subdirs_all; do test "x$ac_dir" = x: && continue + test -d "$ac_dir" || + { cd "$srcdir" && ac_pwd=`pwd` && srcdir=. && test -d "$ac_dir"; } || + continue + ac_builddir=. + +case "$ac_dir" in +.) ac_dir_suffix= ac_top_builddir_sub=. ac_top_build_prefix= ;; +*) + ac_dir_suffix=/`printf "%s\n" "$ac_dir" | sed 's|^\.[\\/]||'` + # A ".." for each directory in $ac_dir_suffix. + ac_top_builddir_sub=`printf "%s\n" "$ac_dir_suffix" | sed 's|/[^\\/]*|/..|g;s|/||'` + case $ac_top_builddir_sub in + "") ac_top_builddir_sub=. ac_top_build_prefix= ;; + *) ac_top_build_prefix=$ac_top_builddir_sub/ ;; + esac ;; +esac +ac_abs_top_builddir=$ac_pwd +ac_abs_builddir=$ac_pwd$ac_dir_suffix +# for backward compatibility: +ac_top_builddir=$ac_top_build_prefix + +case $srcdir in + .) # We are building in place. + ac_srcdir=. + ac_top_srcdir=$ac_top_builddir_sub + ac_abs_top_srcdir=$ac_pwd ;; + [\\/]* | ?:[\\/]* ) # Absolute name. + ac_srcdir=$srcdir$ac_dir_suffix; + ac_top_srcdir=$srcdir + ac_abs_top_srcdir=$srcdir ;; + *) # Relative name. + ac_srcdir=$ac_top_build_prefix$srcdir$ac_dir_suffix + ac_top_srcdir=$ac_top_build_prefix$srcdir + ac_abs_top_srcdir=$ac_pwd/$srcdir ;; +esac +ac_abs_srcdir=$ac_abs_top_srcdir$ac_dir_suffix + + cd "$ac_dir" || { ac_status=$?; continue; } + # Check for configure.gnu first; this name is used for a wrapper for + # Metaconfig's "Configure" on case-insensitive file systems. + if test -f "$ac_srcdir/configure.gnu"; then + echo && + $SHELL "$ac_srcdir/configure.gnu" --help=recursive + elif test -f "$ac_srcdir/configure"; then + echo && + $SHELL "$ac_srcdir/configure" --help=recursive + else + printf "%s\n" "$as_me: WARNING: no configuration information is in $ac_dir" >&2 + fi || ac_status=$? + cd "$ac_pwd" || { ac_status=$?; break; } + done +fi + +test -n "$ac_init_help" && exit $ac_status +if $ac_init_version; then + cat <<\_ACEOF +lightgbm configure 4.6.0.99 +generated by GNU Autoconf 2.71 + +Copyright (C) 2021 Free Software Foundation, Inc. +This configure script is free software; the Free Software Foundation +gives unlimited permission to copy, distribute and modify it. +_ACEOF + exit +fi + +## ------------------------ ## +## Autoconf initialization. ## +## ------------------------ ## +ac_configure_args_raw= +for ac_arg +do + case $ac_arg in + *\'*) + ac_arg=`printf "%s\n" "$ac_arg" | sed "s/'/'\\\\\\\\''/g"` ;; + esac + as_fn_append ac_configure_args_raw " '$ac_arg'" +done + +case $ac_configure_args_raw in + *$as_nl*) + ac_safe_unquote= ;; + *) + ac_unsafe_z='|&;<>()$`\\"*?[ '' ' # This string ends in space, tab. + ac_unsafe_a="$ac_unsafe_z#~" + ac_safe_unquote="s/ '\\([^$ac_unsafe_a][^$ac_unsafe_z]*\\)'/ \\1/g" + ac_configure_args_raw=` printf "%s\n" "$ac_configure_args_raw" | sed "$ac_safe_unquote"`;; +esac + +cat >config.log <<_ACEOF +This file contains any messages produced by compilers while +running configure, to aid debugging if configure makes a mistake. + +It was created by lightgbm $as_me 4.6.0.99, which was +generated by GNU Autoconf 2.71. Invocation command line was + + $ $0$ac_configure_args_raw + +_ACEOF +exec 5>>config.log +{ +cat <<_ASUNAME +## --------- ## +## Platform. ## +## --------- ## + +hostname = `(hostname || uname -n) 2>/dev/null | sed 1q` +uname -m = `(uname -m) 2>/dev/null || echo unknown` +uname -r = `(uname -r) 2>/dev/null || echo unknown` +uname -s = `(uname -s) 2>/dev/null || echo unknown` +uname -v = `(uname -v) 2>/dev/null || echo unknown` + +/usr/bin/uname -p = `(/usr/bin/uname -p) 2>/dev/null || echo unknown` +/bin/uname -X = `(/bin/uname -X) 2>/dev/null || echo unknown` + +/bin/arch = `(/bin/arch) 2>/dev/null || echo unknown` +/usr/bin/arch -k = `(/usr/bin/arch -k) 2>/dev/null || echo unknown` +/usr/convex/getsysinfo = `(/usr/convex/getsysinfo) 2>/dev/null || echo unknown` +/usr/bin/hostinfo = `(/usr/bin/hostinfo) 2>/dev/null || echo unknown` +/bin/machine = `(/bin/machine) 2>/dev/null || echo unknown` +/usr/bin/oslevel = `(/usr/bin/oslevel) 2>/dev/null || echo unknown` +/bin/universe = `(/bin/universe) 2>/dev/null || echo unknown` + +_ASUNAME + +as_save_IFS=$IFS; IFS=$PATH_SEPARATOR +for as_dir in $PATH +do + IFS=$as_save_IFS + case $as_dir in #((( + '') as_dir=./ ;; + */) ;; + *) as_dir=$as_dir/ ;; + esac + printf "%s\n" "PATH: $as_dir" + done +IFS=$as_save_IFS + +} >&5 + +cat >&5 <<_ACEOF + + +## ----------- ## +## Core tests. ## +## ----------- ## + +_ACEOF + + +# Keep a trace of the command line. +# Strip out --no-create and --no-recursion so they do not pile up. +# Strip out --silent because we don't want to record it for future runs. +# Also quote any args containing shell meta-characters. +# Make two passes to allow for proper duplicate-argument suppression. +ac_configure_args= +ac_configure_args0= +ac_configure_args1= +ac_must_keep_next=false +for ac_pass in 1 2 +do + for ac_arg + do + case $ac_arg in + -no-create | --no-c* | -n | -no-recursion | --no-r*) continue ;; + -q | -quiet | --quiet | --quie | --qui | --qu | --q \ + | -silent | --silent | --silen | --sile | --sil) + continue ;; + *\'*) + ac_arg=`printf "%s\n" "$ac_arg" | sed "s/'/'\\\\\\\\''/g"` ;; + esac + case $ac_pass in + 1) as_fn_append ac_configure_args0 " '$ac_arg'" ;; + 2) + as_fn_append ac_configure_args1 " '$ac_arg'" + if test $ac_must_keep_next = true; then + ac_must_keep_next=false # Got value, back to normal. + else + case $ac_arg in + *=* | --config-cache | -C | -disable-* | --disable-* \ + | -enable-* | --enable-* | -gas | --g* | -nfp | --nf* \ + | -q | -quiet | --q* | -silent | --sil* | -v | -verb* \ + | -with-* | --with-* | -without-* | --without-* | --x) + case "$ac_configure_args0 " in + "$ac_configure_args1"*" '$ac_arg' "* ) continue ;; + esac + ;; + -* ) ac_must_keep_next=true ;; + esac + fi + as_fn_append ac_configure_args " '$ac_arg'" + ;; + esac + done +done +{ ac_configure_args0=; unset ac_configure_args0;} +{ ac_configure_args1=; unset ac_configure_args1;} + +# When interrupted or exit'd, cleanup temporary files, and complete +# config.log. We remove comments because anyway the quotes in there +# would cause problems or look ugly. +# WARNING: Use '\'' to represent an apostrophe within the trap. +# WARNING: Do not start the trap code with a newline, due to a FreeBSD 4.0 bug. +trap 'exit_status=$? + # Sanitize IFS. + IFS=" "" $as_nl" + # Save into config.log some information that might help in debugging. + { + echo + + printf "%s\n" "## ---------------- ## +## Cache variables. ## +## ---------------- ##" + echo + # The following way of writing the cache mishandles newlines in values, +( + for ac_var in `(set) 2>&1 | sed -n '\''s/^\([a-zA-Z_][a-zA-Z0-9_]*\)=.*/\1/p'\''`; do + eval ac_val=\$$ac_var + case $ac_val in #( + *${as_nl}*) + case $ac_var in #( + *_cv_*) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: cache variable $ac_var contains a newline" >&5 +printf "%s\n" "$as_me: WARNING: cache variable $ac_var contains a newline" >&2;} ;; + esac + case $ac_var in #( + _ | IFS | as_nl) ;; #( + BASH_ARGV | BASH_SOURCE) eval $ac_var= ;; #( + *) { eval $ac_var=; unset $ac_var;} ;; + esac ;; + esac + done + (set) 2>&1 | + case $as_nl`(ac_space='\'' '\''; set) 2>&1` in #( + *${as_nl}ac_space=\ *) + sed -n \ + "s/'\''/'\''\\\\'\'''\''/g; + s/^\\([_$as_cr_alnum]*_cv_[_$as_cr_alnum]*\\)=\\(.*\\)/\\1='\''\\2'\''/p" + ;; #( + *) + sed -n "/^[_$as_cr_alnum]*_cv_[_$as_cr_alnum]*=/p" + ;; + esac | + sort +) + echo + + printf "%s\n" "## ----------------- ## +## Output variables. ## +## ----------------- ##" + echo + for ac_var in $ac_subst_vars + do + eval ac_val=\$$ac_var + case $ac_val in + *\'\''*) ac_val=`printf "%s\n" "$ac_val" | sed "s/'\''/'\''\\\\\\\\'\'''\''/g"`;; + esac + printf "%s\n" "$ac_var='\''$ac_val'\''" + done | sort + echo + + if test -n "$ac_subst_files"; then + printf "%s\n" "## ------------------- ## +## File substitutions. ## +## ------------------- ##" + echo + for ac_var in $ac_subst_files + do + eval ac_val=\$$ac_var + case $ac_val in + *\'\''*) ac_val=`printf "%s\n" "$ac_val" | sed "s/'\''/'\''\\\\\\\\'\'''\''/g"`;; + esac + printf "%s\n" "$ac_var='\''$ac_val'\''" + done | sort + echo + fi + + if test -s confdefs.h; then + printf "%s\n" "## ----------- ## +## confdefs.h. ## +## ----------- ##" + echo + cat confdefs.h + echo + fi + test "$ac_signal" != 0 && + printf "%s\n" "$as_me: caught signal $ac_signal" + printf "%s\n" "$as_me: exit $exit_status" + } >&5 + rm -f core *.core core.conftest.* && + rm -f -r conftest* confdefs* conf$$* $ac_clean_files && + exit $exit_status +' 0 +for ac_signal in 1 2 13 15; do + trap 'ac_signal='$ac_signal'; as_fn_exit 1' $ac_signal +done +ac_signal=0 + +# confdefs.h avoids OS command line length limits that DEFS can exceed. +rm -f -r conftest* confdefs.h + +printf "%s\n" "/* confdefs.h */" > confdefs.h + +# Predefined preprocessor variables. + +printf "%s\n" "#define PACKAGE_NAME \"$PACKAGE_NAME\"" >>confdefs.h + +printf "%s\n" "#define PACKAGE_TARNAME \"$PACKAGE_TARNAME\"" >>confdefs.h + +printf "%s\n" "#define PACKAGE_VERSION \"$PACKAGE_VERSION\"" >>confdefs.h + +printf "%s\n" "#define PACKAGE_STRING \"$PACKAGE_STRING\"" >>confdefs.h + +printf "%s\n" "#define PACKAGE_BUGREPORT \"$PACKAGE_BUGREPORT\"" >>confdefs.h + +printf "%s\n" "#define PACKAGE_URL \"$PACKAGE_URL\"" >>confdefs.h + + +# Let the site file select an alternate cache file if it wants to. +# Prefer an explicitly selected file to automatically selected ones. +if test -n "$CONFIG_SITE"; then + ac_site_files="$CONFIG_SITE" +elif test "x$prefix" != xNONE; then + ac_site_files="$prefix/share/config.site $prefix/etc/config.site" +else + ac_site_files="$ac_default_prefix/share/config.site $ac_default_prefix/etc/config.site" +fi + +for ac_site_file in $ac_site_files +do + case $ac_site_file in #( + */*) : + ;; #( + *) : + ac_site_file=./$ac_site_file ;; +esac + if test -f "$ac_site_file" && test -r "$ac_site_file"; then + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: loading site script $ac_site_file" >&5 +printf "%s\n" "$as_me: loading site script $ac_site_file" >&6;} + sed 's/^/| /' "$ac_site_file" >&5 + . "$ac_site_file" \ + || { { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in \`$ac_pwd':" >&5 +printf "%s\n" "$as_me: error: in \`$ac_pwd':" >&2;} +as_fn_error $? "failed to load site script $ac_site_file +See \`config.log' for more details" "$LINENO" 5; } + fi +done + +if test -r "$cache_file"; then + # Some versions of bash will fail to source /dev/null (special files + # actually), so we avoid doing that. DJGPP emulates it as a regular file. + if test /dev/null != "$cache_file" && test -f "$cache_file"; then + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: loading cache $cache_file" >&5 +printf "%s\n" "$as_me: loading cache $cache_file" >&6;} + case $cache_file in + [\\/]* | ?:[\\/]* ) . "$cache_file";; + *) . "./$cache_file";; + esac + fi +else + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: creating cache $cache_file" >&5 +printf "%s\n" "$as_me: creating cache $cache_file" >&6;} + >$cache_file +fi + +# Check that the precious variables saved in the cache have kept the same +# value. +ac_cache_corrupted=false +for ac_var in $ac_precious_vars; do + eval ac_old_set=\$ac_cv_env_${ac_var}_set + eval ac_new_set=\$ac_env_${ac_var}_set + eval ac_old_val=\$ac_cv_env_${ac_var}_value + eval ac_new_val=\$ac_env_${ac_var}_value + case $ac_old_set,$ac_new_set in + set,) + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: \`$ac_var' was set to \`$ac_old_val' in the previous run" >&5 +printf "%s\n" "$as_me: error: \`$ac_var' was set to \`$ac_old_val' in the previous run" >&2;} + ac_cache_corrupted=: ;; + ,set) + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: \`$ac_var' was not set in the previous run" >&5 +printf "%s\n" "$as_me: error: \`$ac_var' was not set in the previous run" >&2;} + ac_cache_corrupted=: ;; + ,);; + *) + if test "x$ac_old_val" != "x$ac_new_val"; then + # differences in whitespace do not lead to failure. + ac_old_val_w=`echo x $ac_old_val` + ac_new_val_w=`echo x $ac_new_val` + if test "$ac_old_val_w" != "$ac_new_val_w"; then + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: \`$ac_var' has changed since the previous run:" >&5 +printf "%s\n" "$as_me: error: \`$ac_var' has changed since the previous run:" >&2;} + ac_cache_corrupted=: + else + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: warning: ignoring whitespace changes in \`$ac_var' since the previous run:" >&5 +printf "%s\n" "$as_me: warning: ignoring whitespace changes in \`$ac_var' since the previous run:" >&2;} + eval $ac_var=\$ac_old_val + fi + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: former value: \`$ac_old_val'" >&5 +printf "%s\n" "$as_me: former value: \`$ac_old_val'" >&2;} + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: current value: \`$ac_new_val'" >&5 +printf "%s\n" "$as_me: current value: \`$ac_new_val'" >&2;} + fi;; + esac + # Pass precious variables to config.status. + if test "$ac_new_set" = set; then + case $ac_new_val in + *\'*) ac_arg=$ac_var=`printf "%s\n" "$ac_new_val" | sed "s/'/'\\\\\\\\''/g"` ;; + *) ac_arg=$ac_var=$ac_new_val ;; + esac + case " $ac_configure_args " in + *" '$ac_arg' "*) ;; # Avoid dups. Use of quotes ensures accuracy. + *) as_fn_append ac_configure_args " '$ac_arg'" ;; + esac + fi +done +if $ac_cache_corrupted; then + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: in \`$ac_pwd':" >&5 +printf "%s\n" "$as_me: error: in \`$ac_pwd':" >&2;} + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: changes in the environment can compromise the build" >&5 +printf "%s\n" "$as_me: error: changes in the environment can compromise the build" >&2;} + as_fn_error $? "run \`${MAKE-make} distclean' and/or \`rm $cache_file' + and start over" "$LINENO" 5 +fi +## -------------------- ## +## Main body of script. ## +## -------------------- ## + +ac_ext=c +ac_cpp='$CPP $CPPFLAGS' +ac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5' +ac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' +ac_compiler_gnu=$ac_cv_c_compiler_gnu + + + +########################### +# find compiler and flags # +########################### + +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking location of R" >&5 +printf %s "checking location of R... " >&6; } +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${R_HOME}" >&5 +printf "%s\n" "${R_HOME}" >&6; } + +# set up CPP flags +# find the compiler and compiler flags used by R. +: ${R_HOME=`R HOME`} +if test -z "${R_HOME}"; then + echo "could not determine R_HOME" + exit 1 +fi +CXX17=`"${R_HOME}/bin/R" CMD config CXX17` +CXX17STD=`"${R_HOME}/bin/R" CMD config CXX17STD` +CXX="${CXX17} ${CXX17STD}" +CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` +CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX17FLAGS` +LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS` +ac_ext=cpp +ac_cpp='$CXXCPP $CPPFLAGS' +ac_compile='$CXX -c $CXXFLAGS $CPPFLAGS conftest.$ac_ext >&5' +ac_link='$CXX -o conftest$ac_exeext $CXXFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' +ac_compiler_gnu=$ac_cv_cxx_compiler_gnu + + +# LightGBM-specific flags +LGB_CPPFLAGS="" + +######### +# Eigen # +######### + +LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" + +############### +# MM_PREFETCH # +############### + +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether MM_PREFETCH works" >&5 +printf %s "checking whether MM_PREFETCH works... " >&6; } +ac_mmprefetch=no +cat confdefs.h - <<_ACEOF >conftest.$ac_ext +/* end confdefs.h. */ + + + #include + +int +main (void) +{ + + int a = 0; + _mm_prefetch(&a, _MM_HINT_NTA); + return 0; + + + ; + return 0; +} + + +_ACEOF +${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mmprefetch=yes +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${ac_mmprefetch}" >&5 +printf "%s\n" "${ac_mmprefetch}" >&6; } +if test "${ac_mmprefetch}" = yes; then + LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_PREFETCH=1" +fi + +############ +# MM_ALLOC # +############ + +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether MM_MALLOC works" >&5 +printf %s "checking whether MM_MALLOC works... " >&6; } +ac_mm_malloc=no +cat confdefs.h - <<_ACEOF >conftest.$ac_ext +/* end confdefs.h. */ + + + #include + +int +main (void) +{ + + char *a = (char*)_mm_malloc(8, 16); + _mm_free(a); + return 0; + + + ; + return 0; +} + + +_ACEOF +${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc=yes +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${ac_mm_malloc}" >&5 +printf "%s\n" "${ac_mm_malloc}" >&6; } +if test "${ac_mm_malloc}" = yes; then + LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_MALLOC=1" +fi + +########## +# OpenMP # +########## + +OPENMP_CXXFLAGS="" + +if test `uname -s` = "Linux" +then + OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)" +fi + +if test `uname -s` = "Darwin" +then + OPENMP_CXXFLAGS='-Xclang -fopenmp' + OPENMP_LIB='-lomp' + + # libomp 15.0+ from brew is keg-only (i.e. not symlinked into the standard paths search by the linker), + # so need to search in other locations. + # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927. + # + # If Homebrew is found and libomp was installed with it, this code adds the necessary + # flags for the compiler to find libomp headers and for the linker to find libomp.dylib. + HOMEBREW_LIBOMP_PREFIX="" + if command -v brew >/dev/null 2>&1; then + ac_brew_openmp=no + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether OpenMP was installed via Homebrew" >&5 +printf %s "checking whether OpenMP was installed via Homebrew... " >&6; } + brew --prefix libomp >/dev/null 2>&1 && ac_brew_openmp=yes + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${ac_brew_openmp}" >&5 +printf "%s\n" "${ac_brew_openmp}" >&6; } + if test "${ac_brew_openmp}" = yes; then + HOMEBREW_LIBOMP_PREFIX=`brew --prefix libomp` + OPENMP_CXXFLAGS="${OPENMP_CXXFLAGS} -I${HOMEBREW_LIBOMP_PREFIX}/include" + OPENMP_LIB="${OPENMP_LIB} -L${HOMEBREW_LIBOMP_PREFIX}/lib" + fi + fi + ac_pkg_openmp=no + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether OpenMP will work in a package" >&5 +printf %s "checking whether OpenMP will work in a package... " >&6; } + cat confdefs.h - <<_ACEOF >conftest.$ac_ext +/* end confdefs.h. */ + + + #include + +int +main (void) +{ + + return (omp_get_max_threads() <= 1); + + + ; + return 0; +} + + +_ACEOF + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + + # -Xclang is not portable (it is clang-specific) + # if compilation above failed, try without that flag + if test "${ac_pkg_openmp}" = no; then + if test -f "./conftest"; then + rm ./conftest + fi + OPENMP_CXXFLAGS="-fopenmp" + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + fi + + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${ac_pkg_openmp}" >&5 +printf "%s\n" "${ac_pkg_openmp}" >&6; } + if test "${ac_pkg_openmp}" = no; then + OPENMP_CXXFLAGS='' + OPENMP_LIB='' + echo '***********************************************************************************************' + echo ' OpenMP is unavailable on this macOS system. LightGBM code will run single-threaded as a result.' + echo ' To use all CPU cores for training jobs, you should install OpenMP by running' + echo '' + echo ' brew install libomp' + echo '***********************************************************************************************' + fi +fi + +# substitute variables from this script into Makevars.in + + + +ac_config_files="$ac_config_files src/Makevars" + + +# write out Autoconf output +cat >confcache <<\_ACEOF +# This file is a shell script that caches the results of configure +# tests run on this system so they can be shared between configure +# scripts and configure runs, see configure's option --config-cache. +# It is not useful on other systems. If it contains results you don't +# want to keep, you may remove or edit it. +# +# config.status only pays attention to the cache file if you give it +# the --recheck option to rerun configure. +# +# `ac_cv_env_foo' variables (set or unset) will be overridden when +# loading this file, other *unset* `ac_cv_foo' will be assigned the +# following values. + +_ACEOF + +# The following way of writing the cache mishandles newlines in values, +# but we know of no workaround that is simple, portable, and efficient. +# So, we kill variables containing newlines. +# Ultrix sh set writes to stderr and can't be redirected directly, +# and sets the high bit in the cache file unless we assign to the vars. +( + for ac_var in `(set) 2>&1 | sed -n 's/^\([a-zA-Z_][a-zA-Z0-9_]*\)=.*/\1/p'`; do + eval ac_val=\$$ac_var + case $ac_val in #( + *${as_nl}*) + case $ac_var in #( + *_cv_*) { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: cache variable $ac_var contains a newline" >&5 +printf "%s\n" "$as_me: WARNING: cache variable $ac_var contains a newline" >&2;} ;; + esac + case $ac_var in #( + _ | IFS | as_nl) ;; #( + BASH_ARGV | BASH_SOURCE) eval $ac_var= ;; #( + *) { eval $ac_var=; unset $ac_var;} ;; + esac ;; + esac + done + + (set) 2>&1 | + case $as_nl`(ac_space=' '; set) 2>&1` in #( + *${as_nl}ac_space=\ *) + # `set' does not quote correctly, so add quotes: double-quote + # substitution turns \\\\ into \\, and sed turns \\ into \. + sed -n \ + "s/'/'\\\\''/g; + s/^\\([_$as_cr_alnum]*_cv_[_$as_cr_alnum]*\\)=\\(.*\\)/\\1='\\2'/p" + ;; #( + *) + # `set' quotes correctly as required by POSIX, so do not add quotes. + sed -n "/^[_$as_cr_alnum]*_cv_[_$as_cr_alnum]*=/p" + ;; + esac | + sort +) | + sed ' + /^ac_cv_env_/b end + t clear + :clear + s/^\([^=]*\)=\(.*[{}].*\)$/test ${\1+y} || &/ + t end + s/^\([^=]*\)=\(.*\)$/\1=${\1=\2}/ + :end' >>confcache +if diff "$cache_file" confcache >/dev/null 2>&1; then :; else + if test -w "$cache_file"; then + if test "x$cache_file" != "x/dev/null"; then + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: updating cache $cache_file" >&5 +printf "%s\n" "$as_me: updating cache $cache_file" >&6;} + if test ! -f "$cache_file" || test -h "$cache_file"; then + cat confcache >"$cache_file" + else + case $cache_file in #( + */* | ?:*) + mv -f confcache "$cache_file"$$ && + mv -f "$cache_file"$$ "$cache_file" ;; #( + *) + mv -f confcache "$cache_file" ;; + esac + fi + fi + else + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: not updating unwritable cache $cache_file" >&5 +printf "%s\n" "$as_me: not updating unwritable cache $cache_file" >&6;} + fi +fi +rm -f confcache + +test "x$prefix" = xNONE && prefix=$ac_default_prefix +# Let make expand exec_prefix. +test "x$exec_prefix" = xNONE && exec_prefix='${prefix}' + +# Transform confdefs.h into DEFS. +# Protect against shell expansion while executing Makefile rules. +# Protect against Makefile macro expansion. +# +# If the first sed substitution is executed (which looks for macros that +# take arguments), then branch to the quote section. Otherwise, +# look for a macro that doesn't take arguments. +ac_script=' +:mline +/\\$/{ + N + s,\\\n,, + b mline +} +t clear +:clear +s/^[ ]*#[ ]*define[ ][ ]*\([^ (][^ (]*([^)]*)\)[ ]*\(.*\)/-D\1=\2/g +t quote +s/^[ ]*#[ ]*define[ ][ ]*\([^ ][^ ]*\)[ ]*\(.*\)/-D\1=\2/g +t quote +b any +:quote +s/[ `~#$^&*(){}\\|;'\''"<>?]/\\&/g +s/\[/\\&/g +s/\]/\\&/g +s/\$/$$/g +H +:any +${ + g + s/^\n// + s/\n/ /g + p +} +' +DEFS=`sed -n "$ac_script" confdefs.h` + + +ac_libobjs= +ac_ltlibobjs= +U= +for ac_i in : $LIBOBJS; do test "x$ac_i" = x: && continue + # 1. Remove the extension, and $U if already installed. + ac_script='s/\$U\././;s/\.o$//;s/\.obj$//' + ac_i=`printf "%s\n" "$ac_i" | sed "$ac_script"` + # 2. Prepend LIBOBJDIR. When used with automake>=1.10 LIBOBJDIR + # will be set to the directory where LIBOBJS objects are built. + as_fn_append ac_libobjs " \${LIBOBJDIR}$ac_i\$U.$ac_objext" + as_fn_append ac_ltlibobjs " \${LIBOBJDIR}$ac_i"'$U.lo' +done +LIBOBJS=$ac_libobjs + +LTLIBOBJS=$ac_ltlibobjs + + + +: "${CONFIG_STATUS=./config.status}" +ac_write_fail=0 +ac_clean_files_save=$ac_clean_files +ac_clean_files="$ac_clean_files $CONFIG_STATUS" +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: creating $CONFIG_STATUS" >&5 +printf "%s\n" "$as_me: creating $CONFIG_STATUS" >&6;} +as_write_fail=0 +cat >$CONFIG_STATUS <<_ASEOF || as_write_fail=1 +#! $SHELL +# Generated by $as_me. +# Run this file to recreate the current configuration. +# Compiler output produced by configure, useful for debugging +# configure, is in config.log if it exists. + +debug=false +ac_cs_recheck=false +ac_cs_silent=false + +SHELL=\${CONFIG_SHELL-$SHELL} +export SHELL +_ASEOF +cat >>$CONFIG_STATUS <<\_ASEOF || as_write_fail=1 +## -------------------- ## +## M4sh Initialization. ## +## -------------------- ## + +# Be more Bourne compatible +DUALCASE=1; export DUALCASE # for MKS sh +as_nop=: +if test ${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1 +then : + emulate sh + NULLCMD=: + # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which + # is contrary to our usage. Disable this feature. + alias -g '${1+"$@"}'='"$@"' + setopt NO_GLOB_SUBST +else $as_nop + case `(set -o) 2>/dev/null` in #( + *posix*) : + set -o posix ;; #( + *) : + ;; +esac +fi + + + +# Reset variables that may have inherited troublesome values from +# the environment. + +# IFS needs to be set, to space, tab, and newline, in precisely that order. +# (If _AS_PATH_WALK were called with IFS unset, it would have the +# side effect of setting IFS to empty, thus disabling word splitting.) +# Quoting is to prevent editors from complaining about space-tab. +as_nl=' +' +export as_nl +IFS=" "" $as_nl" + +PS1='$ ' +PS2='> ' +PS4='+ ' + +# Ensure predictable behavior from utilities with locale-dependent output. +LC_ALL=C +export LC_ALL +LANGUAGE=C +export LANGUAGE + +# We cannot yet rely on "unset" to work, but we need these variables +# to be unset--not just set to an empty or harmless value--now, to +# avoid bugs in old shells (e.g. pre-3.0 UWIN ksh). This construct +# also avoids known problems related to "unset" and subshell syntax +# in other old shells (e.g. bash 2.01 and pdksh 5.2.14). +for as_var in BASH_ENV ENV MAIL MAILPATH CDPATH +do eval test \${$as_var+y} \ + && ( (unset $as_var) || exit 1) >/dev/null 2>&1 && unset $as_var || : +done + +# Ensure that fds 0, 1, and 2 are open. +if (exec 3>&0) 2>/dev/null; then :; else exec 0&1) 2>/dev/null; then :; else exec 1>/dev/null; fi +if (exec 3>&2) ; then :; else exec 2>/dev/null; fi + +# The user is always right. +if ${PATH_SEPARATOR+false} :; then + PATH_SEPARATOR=: + (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && { + (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 || + PATH_SEPARATOR=';' + } +fi + + +# Find who we are. Look in the path if we contain no directory separator. +as_myself= +case $0 in #(( + *[\\/]* ) as_myself=$0 ;; + *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR +for as_dir in $PATH +do + IFS=$as_save_IFS + case $as_dir in #((( + '') as_dir=./ ;; + */) ;; + *) as_dir=$as_dir/ ;; + esac + test -r "$as_dir$0" && as_myself=$as_dir$0 && break + done +IFS=$as_save_IFS + + ;; +esac +# We did not find ourselves, most probably we were run as `sh COMMAND' +# in which case we are not to be found in the path. +if test "x$as_myself" = x; then + as_myself=$0 +fi +if test ! -f "$as_myself"; then + printf "%s\n" "$as_myself: error: cannot find myself; rerun with an absolute file name" >&2 + exit 1 +fi + + + +# as_fn_error STATUS ERROR [LINENO LOG_FD] +# ---------------------------------------- +# Output "`basename $0`: error: ERROR" to stderr. If LINENO and LOG_FD are +# provided, also output the error to LOG_FD, referencing LINENO. Then exit the +# script with STATUS, using 1 if that was 0. +as_fn_error () +{ + as_status=$1; test $as_status -eq 0 && as_status=1 + if test "$4"; then + as_lineno=${as_lineno-"$3"} as_lineno_stack=as_lineno_stack=$as_lineno_stack + printf "%s\n" "$as_me:${as_lineno-$LINENO}: error: $2" >&$4 + fi + printf "%s\n" "$as_me: error: $2" >&2 + as_fn_exit $as_status +} # as_fn_error + + + +# as_fn_set_status STATUS +# ----------------------- +# Set $? to STATUS, without forking. +as_fn_set_status () +{ + return $1 +} # as_fn_set_status + +# as_fn_exit STATUS +# ----------------- +# Exit the shell with STATUS, even in a "trap 0" or "set -e" context. +as_fn_exit () +{ + set +e + as_fn_set_status $1 + exit $1 +} # as_fn_exit + +# as_fn_unset VAR +# --------------- +# Portably unset VAR. +as_fn_unset () +{ + { eval $1=; unset $1;} +} +as_unset=as_fn_unset + +# as_fn_append VAR VALUE +# ---------------------- +# Append the text in VALUE to the end of the definition contained in VAR. Take +# advantage of any shell optimizations that allow amortized linear growth over +# repeated appends, instead of the typical quadratic growth present in naive +# implementations. +if (eval "as_var=1; as_var+=2; test x\$as_var = x12") 2>/dev/null +then : + eval 'as_fn_append () + { + eval $1+=\$2 + }' +else $as_nop + as_fn_append () + { + eval $1=\$$1\$2 + } +fi # as_fn_append + +# as_fn_arith ARG... +# ------------------ +# Perform arithmetic evaluation on the ARGs, and store the result in the +# global $as_val. Take advantage of shells that can avoid forks. The arguments +# must be portable across $(()) and expr. +if (eval "test \$(( 1 + 1 )) = 2") 2>/dev/null +then : + eval 'as_fn_arith () + { + as_val=$(( $* )) + }' +else $as_nop + as_fn_arith () + { + as_val=`expr "$@" || test $? -eq 1` + } +fi # as_fn_arith + + +if expr a : '\(a\)' >/dev/null 2>&1 && + test "X`expr 00001 : '.*\(...\)'`" = X001; then + as_expr=expr +else + as_expr=false +fi + +if (basename -- /) >/dev/null 2>&1 && test "X`basename -- / 2>&1`" = "X/"; then + as_basename=basename +else + as_basename=false +fi + +if (as_dir=`dirname -- /` && test "X$as_dir" = X/) >/dev/null 2>&1; then + as_dirname=dirname +else + as_dirname=false +fi + +as_me=`$as_basename -- "$0" || +$as_expr X/"$0" : '.*/\([^/][^/]*\)/*$' \| \ + X"$0" : 'X\(//\)$' \| \ + X"$0" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X/"$0" | + sed '/^.*\/\([^/][^/]*\)\/*$/{ + s//\1/ + q + } + /^X\/\(\/\/\)$/{ + s//\1/ + q + } + /^X\/\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + +# Avoid depending upon Character Ranges. +as_cr_letters='abcdefghijklmnopqrstuvwxyz' +as_cr_LETTERS='ABCDEFGHIJKLMNOPQRSTUVWXYZ' +as_cr_Letters=$as_cr_letters$as_cr_LETTERS +as_cr_digits='0123456789' +as_cr_alnum=$as_cr_Letters$as_cr_digits + + +# Determine whether it's possible to make 'echo' print without a newline. +# These variables are no longer used directly by Autoconf, but are AC_SUBSTed +# for compatibility with existing Makefiles. +ECHO_C= ECHO_N= ECHO_T= +case `echo -n x` in #((((( +-n*) + case `echo 'xy\c'` in + *c*) ECHO_T=' ';; # ECHO_T is single tab character. + xy) ECHO_C='\c';; + *) echo `echo ksh88 bug on AIX 6.1` > /dev/null + ECHO_T=' ';; + esac;; +*) + ECHO_N='-n';; +esac + +# For backward compatibility with old third-party macros, we provide +# the shell variables $as_echo and $as_echo_n. New code should use +# AS_ECHO(["message"]) and AS_ECHO_N(["message"]), respectively. +as_echo='printf %s\n' +as_echo_n='printf %s' + +rm -f conf$$ conf$$.exe conf$$.file +if test -d conf$$.dir; then + rm -f conf$$.dir/conf$$.file +else + rm -f conf$$.dir + mkdir conf$$.dir 2>/dev/null +fi +if (echo >conf$$.file) 2>/dev/null; then + if ln -s conf$$.file conf$$ 2>/dev/null; then + as_ln_s='ln -s' + # ... but there are two gotchas: + # 1) On MSYS, both `ln -s file dir' and `ln file dir' fail. + # 2) DJGPP < 2.04 has no symlinks; `ln -s' creates a wrapper executable. + # In both cases, we have to default to `cp -pR'. + ln -s conf$$.file conf$$.dir 2>/dev/null && test ! -f conf$$.exe || + as_ln_s='cp -pR' + elif ln conf$$.file conf$$ 2>/dev/null; then + as_ln_s=ln + else + as_ln_s='cp -pR' + fi +else + as_ln_s='cp -pR' +fi +rm -f conf$$ conf$$.exe conf$$.dir/conf$$.file conf$$.file +rmdir conf$$.dir 2>/dev/null + + +# as_fn_mkdir_p +# ------------- +# Create "$as_dir" as a directory, including parents if necessary. +as_fn_mkdir_p () +{ + + case $as_dir in #( + -*) as_dir=./$as_dir;; + esac + test -d "$as_dir" || eval $as_mkdir_p || { + as_dirs= + while :; do + case $as_dir in #( + *\'*) as_qdir=`printf "%s\n" "$as_dir" | sed "s/'/'\\\\\\\\''/g"`;; #'( + *) as_qdir=$as_dir;; + esac + as_dirs="'$as_qdir' $as_dirs" + as_dir=`$as_dirname -- "$as_dir" || +$as_expr X"$as_dir" : 'X\(.*[^/]\)//*[^/][^/]*/*$' \| \ + X"$as_dir" : 'X\(//\)[^/]' \| \ + X"$as_dir" : 'X\(//\)$' \| \ + X"$as_dir" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X"$as_dir" | + sed '/^X\(.*[^/]\)\/\/*[^/][^/]*\/*$/{ + s//\1/ + q + } + /^X\(\/\/\)[^/].*/{ + s//\1/ + q + } + /^X\(\/\/\)$/{ + s//\1/ + q + } + /^X\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + test -d "$as_dir" && break + done + test -z "$as_dirs" || eval "mkdir $as_dirs" + } || test -d "$as_dir" || as_fn_error $? "cannot create directory $as_dir" + + +} # as_fn_mkdir_p +if mkdir -p . 2>/dev/null; then + as_mkdir_p='mkdir -p "$as_dir"' +else + test -d ./-p && rmdir ./-p + as_mkdir_p=false +fi + + +# as_fn_executable_p FILE +# ----------------------- +# Test if FILE is an executable regular file. +as_fn_executable_p () +{ + test -f "$1" && test -x "$1" +} # as_fn_executable_p +as_test_x='test -x' +as_executable_p=as_fn_executable_p + +# Sed expression to map a string onto a valid CPP name. +as_tr_cpp="eval sed 'y%*$as_cr_letters%P$as_cr_LETTERS%;s%[^_$as_cr_alnum]%_%g'" + +# Sed expression to map a string onto a valid variable name. +as_tr_sh="eval sed 'y%*+%pp%;s%[^_$as_cr_alnum]%_%g'" + + +exec 6>&1 +## ----------------------------------- ## +## Main body of $CONFIG_STATUS script. ## +## ----------------------------------- ## +_ASEOF +test $as_write_fail = 0 && chmod +x $CONFIG_STATUS || ac_write_fail=1 + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +# Save the log message, to keep $0 and so on meaningful, and to +# report actual input values of CONFIG_FILES etc. instead of their +# values after options handling. +ac_log=" +This file was extended by lightgbm $as_me 4.6.0.99, which was +generated by GNU Autoconf 2.71. Invocation command line was + + CONFIG_FILES = $CONFIG_FILES + CONFIG_HEADERS = $CONFIG_HEADERS + CONFIG_LINKS = $CONFIG_LINKS + CONFIG_COMMANDS = $CONFIG_COMMANDS + $ $0 $@ + +on `(hostname || uname -n) 2>/dev/null | sed 1q` +" + +_ACEOF + +case $ac_config_files in *" +"*) set x $ac_config_files; shift; ac_config_files=$*;; +esac + + + +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +# Files that config.status was made for. +config_files="$ac_config_files" + +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +ac_cs_usage="\ +\`$as_me' instantiates files and other configuration actions +from templates according to the current configuration. Unless the files +and actions are specified as TAGs, all are instantiated by default. + +Usage: $0 [OPTION]... [TAG]... + + -h, --help print this help, then exit + -V, --version print version number and configuration settings, then exit + --config print configuration, then exit + -q, --quiet, --silent + do not print progress messages + -d, --debug don't remove temporary files + --recheck update $as_me by reconfiguring in the same conditions + --file=FILE[:TEMPLATE] + instantiate the configuration file FILE + +Configuration files: +$config_files + +Report bugs to the package provider." + +_ACEOF +ac_cs_config=`printf "%s\n" "$ac_configure_args" | sed "$ac_safe_unquote"` +ac_cs_config_escaped=`printf "%s\n" "$ac_cs_config" | sed "s/^ //; s/'/'\\\\\\\\''/g"` +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +ac_cs_config='$ac_cs_config_escaped' +ac_cs_version="\\ +lightgbm config.status 4.6.0.99 +configured by $0, generated by GNU Autoconf 2.71, + with options \\"\$ac_cs_config\\" + +Copyright (C) 2021 Free Software Foundation, Inc. +This config.status script is free software; the Free Software Foundation +gives unlimited permission to copy, distribute and modify it." + +ac_pwd='$ac_pwd' +srcdir='$srcdir' +test -n "\$AWK" || AWK=awk +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +# The default lists apply if the user does not specify any file. +ac_need_defaults=: +while test $# != 0 +do + case $1 in + --*=?*) + ac_option=`expr "X$1" : 'X\([^=]*\)='` + ac_optarg=`expr "X$1" : 'X[^=]*=\(.*\)'` + ac_shift=: + ;; + --*=) + ac_option=`expr "X$1" : 'X\([^=]*\)='` + ac_optarg= + ac_shift=: + ;; + *) + ac_option=$1 + ac_optarg=$2 + ac_shift=shift + ;; + esac + + case $ac_option in + # Handling of the options. + -recheck | --recheck | --rechec | --reche | --rech | --rec | --re | --r) + ac_cs_recheck=: ;; + --version | --versio | --versi | --vers | --ver | --ve | --v | -V ) + printf "%s\n" "$ac_cs_version"; exit ;; + --config | --confi | --conf | --con | --co | --c ) + printf "%s\n" "$ac_cs_config"; exit ;; + --debug | --debu | --deb | --de | --d | -d ) + debug=: ;; + --file | --fil | --fi | --f ) + $ac_shift + case $ac_optarg in + *\'*) ac_optarg=`printf "%s\n" "$ac_optarg" | sed "s/'/'\\\\\\\\''/g"` ;; + '') as_fn_error $? "missing file argument" ;; + esac + as_fn_append CONFIG_FILES " '$ac_optarg'" + ac_need_defaults=false;; + --he | --h | --help | --hel | -h ) + printf "%s\n" "$ac_cs_usage"; exit ;; + -q | -quiet | --quiet | --quie | --qui | --qu | --q \ + | -silent | --silent | --silen | --sile | --sil | --si | --s) + ac_cs_silent=: ;; + + # This is an error. + -*) as_fn_error $? "unrecognized option: \`$1' +Try \`$0 --help' for more information." ;; + + *) as_fn_append ac_config_targets " $1" + ac_need_defaults=false ;; + + esac + shift +done + +ac_configure_extra_args= + +if $ac_cs_silent; then + exec 6>/dev/null + ac_configure_extra_args="$ac_configure_extra_args --silent" +fi + +_ACEOF +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +if \$ac_cs_recheck; then + set X $SHELL '$0' $ac_configure_args \$ac_configure_extra_args --no-create --no-recursion + shift + \printf "%s\n" "running CONFIG_SHELL=$SHELL \$*" >&6 + CONFIG_SHELL='$SHELL' + export CONFIG_SHELL + exec "\$@" +fi + +_ACEOF +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +exec 5>>config.log +{ + echo + sed 'h;s/./-/g;s/^.../## /;s/...$/ ##/;p;x;p;x' <<_ASBOX +## Running $as_me. ## +_ASBOX + printf "%s\n" "$ac_log" +} >&5 + +_ACEOF +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 + +# Handling of arguments. +for ac_config_target in $ac_config_targets +do + case $ac_config_target in + "src/Makevars") CONFIG_FILES="$CONFIG_FILES src/Makevars" ;; + + *) as_fn_error $? "invalid argument: \`$ac_config_target'" "$LINENO" 5;; + esac +done + + +# If the user did not use the arguments to specify the items to instantiate, +# then the envvar interface is used. Set only those that are not. +# We use the long form for the default assignment because of an extremely +# bizarre bug on SunOS 4.1.3. +if $ac_need_defaults; then + test ${CONFIG_FILES+y} || CONFIG_FILES=$config_files +fi + +# Have a temporary directory for convenience. Make it in the build tree +# simply because there is no reason against having it here, and in addition, +# creating and moving files from /tmp can sometimes cause problems. +# Hook for its removal unless debugging. +# Note that there is a small window in which the directory will not be cleaned: +# after its creation but before its name has been assigned to `$tmp'. +$debug || +{ + tmp= ac_tmp= + trap 'exit_status=$? + : "${ac_tmp:=$tmp}" + { test ! -d "$ac_tmp" || rm -fr "$ac_tmp"; } && exit $exit_status +' 0 + trap 'as_fn_exit 1' 1 2 13 15 +} +# Create a (secure) tmp directory for tmp files. + +{ + tmp=`(umask 077 && mktemp -d "./confXXXXXX") 2>/dev/null` && + test -d "$tmp" +} || +{ + tmp=./conf$$-$RANDOM + (umask 077 && mkdir "$tmp") +} || as_fn_error $? "cannot create a temporary directory in ." "$LINENO" 5 +ac_tmp=$tmp + +# Set up the scripts for CONFIG_FILES section. +# No need to generate them if there are no CONFIG_FILES. +# This happens for instance with `./config.status config.h'. +if test -n "$CONFIG_FILES"; then + + +ac_cr=`echo X | tr X '\015'` +# On cygwin, bash can eat \r inside `` if the user requested igncr. +# But we know of no other shell where ac_cr would be empty at this +# point, so we can use a bashism as a fallback. +if test "x$ac_cr" = x; then + eval ac_cr=\$\'\\r\' +fi +ac_cs_awk_cr=`$AWK 'BEGIN { print "a\rb" }' /dev/null` +if test "$ac_cs_awk_cr" = "a${ac_cr}b"; then + ac_cs_awk_cr='\\r' +else + ac_cs_awk_cr=$ac_cr +fi + +echo 'BEGIN {' >"$ac_tmp/subs1.awk" && +_ACEOF + + +{ + echo "cat >conf$$subs.awk <<_ACEOF" && + echo "$ac_subst_vars" | sed 's/.*/&!$&$ac_delim/' && + echo "_ACEOF" +} >conf$$subs.sh || + as_fn_error $? "could not make $CONFIG_STATUS" "$LINENO" 5 +ac_delim_num=`echo "$ac_subst_vars" | grep -c '^'` +ac_delim='%!_!# ' +for ac_last_try in false false false false false :; do + . ./conf$$subs.sh || + as_fn_error $? "could not make $CONFIG_STATUS" "$LINENO" 5 + + ac_delim_n=`sed -n "s/.*$ac_delim\$/X/p" conf$$subs.awk | grep -c X` + if test $ac_delim_n = $ac_delim_num; then + break + elif $ac_last_try; then + as_fn_error $? "could not make $CONFIG_STATUS" "$LINENO" 5 + else + ac_delim="$ac_delim!$ac_delim _$ac_delim!! " + fi +done +rm -f conf$$subs.sh + +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +cat >>"\$ac_tmp/subs1.awk" <<\\_ACAWK && +_ACEOF +sed -n ' +h +s/^/S["/; s/!.*/"]=/ +p +g +s/^[^!]*!// +:repl +t repl +s/'"$ac_delim"'$// +t delim +:nl +h +s/\(.\{148\}\)..*/\1/ +t more1 +s/["\\]/\\&/g; s/^/"/; s/$/\\n"\\/ +p +n +b repl +:more1 +s/["\\]/\\&/g; s/^/"/; s/$/"\\/ +p +g +s/.\{148\}// +t nl +:delim +h +s/\(.\{148\}\)..*/\1/ +t more2 +s/["\\]/\\&/g; s/^/"/; s/$/"/ +p +b +:more2 +s/["\\]/\\&/g; s/^/"/; s/$/"\\/ +p +g +s/.\{148\}// +t delim +' >$CONFIG_STATUS || ac_write_fail=1 +rm -f conf$$subs.awk +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +_ACAWK +cat >>"\$ac_tmp/subs1.awk" <<_ACAWK && + for (key in S) S_is_set[key] = 1 + FS = "" + +} +{ + line = $ 0 + nfields = split(line, field, "@") + substed = 0 + len = length(field[1]) + for (i = 2; i < nfields; i++) { + key = field[i] + keylen = length(key) + if (S_is_set[key]) { + value = S[key] + line = substr(line, 1, len) "" value "" substr(line, len + keylen + 3) + len += length(value) + length(field[++i]) + substed = 1 + } else + len += 1 + keylen + } + + print line +} + +_ACAWK +_ACEOF +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +if sed "s/$ac_cr//" < /dev/null > /dev/null 2>&1; then + sed "s/$ac_cr\$//; s/$ac_cr/$ac_cs_awk_cr/g" +else + cat +fi < "$ac_tmp/subs1.awk" > "$ac_tmp/subs.awk" \ + || as_fn_error $? "could not setup config files machinery" "$LINENO" 5 +_ACEOF + +# VPATH may cause trouble with some makes, so we remove sole $(srcdir), +# ${srcdir} and @srcdir@ entries from VPATH if srcdir is ".", strip leading and +# trailing colons and then remove the whole line if VPATH becomes empty +# (actually we leave an empty line to preserve line numbers). +if test "x$srcdir" = x.; then + ac_vpsub='/^[ ]*VPATH[ ]*=[ ]*/{ +h +s/// +s/^/:/ +s/[ ]*$/:/ +s/:\$(srcdir):/:/g +s/:\${srcdir}:/:/g +s/:@srcdir@:/:/g +s/^:*// +s/:*$// +x +s/\(=[ ]*\).*/\1/ +G +s/\n// +s/^[^=]*=[ ]*$// +}' +fi + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +fi # test -n "$CONFIG_FILES" + + +eval set X " :F $CONFIG_FILES " +shift +for ac_tag +do + case $ac_tag in + :[FHLC]) ac_mode=$ac_tag; continue;; + esac + case $ac_mode$ac_tag in + :[FHL]*:*);; + :L* | :C*:*) as_fn_error $? "invalid tag \`$ac_tag'" "$LINENO" 5;; + :[FH]-) ac_tag=-:-;; + :[FH]*) ac_tag=$ac_tag:$ac_tag.in;; + esac + ac_save_IFS=$IFS + IFS=: + set x $ac_tag + IFS=$ac_save_IFS + shift + ac_file=$1 + shift + + case $ac_mode in + :L) ac_source=$1;; + :[FH]) + ac_file_inputs= + for ac_f + do + case $ac_f in + -) ac_f="$ac_tmp/stdin";; + *) # Look for the file first in the build tree, then in the source tree + # (if the path is not absolute). The absolute path cannot be DOS-style, + # because $ac_f cannot contain `:'. + test -f "$ac_f" || + case $ac_f in + [\\/$]*) false;; + *) test -f "$srcdir/$ac_f" && ac_f="$srcdir/$ac_f";; + esac || + as_fn_error 1 "cannot find input file: \`$ac_f'" "$LINENO" 5;; + esac + case $ac_f in *\'*) ac_f=`printf "%s\n" "$ac_f" | sed "s/'/'\\\\\\\\''/g"`;; esac + as_fn_append ac_file_inputs " '$ac_f'" + done + + # Let's still pretend it is `configure' which instantiates (i.e., don't + # use $as_me), people would be surprised to read: + # /* config.h. Generated by config.status. */ + configure_input='Generated from '` + printf "%s\n" "$*" | sed 's|^[^:]*/||;s|:[^:]*/|, |g' + `' by configure.' + if test x"$ac_file" != x-; then + configure_input="$ac_file. $configure_input" + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: creating $ac_file" >&5 +printf "%s\n" "$as_me: creating $ac_file" >&6;} + fi + # Neutralize special characters interpreted by sed in replacement strings. + case $configure_input in #( + *\&* | *\|* | *\\* ) + ac_sed_conf_input=`printf "%s\n" "$configure_input" | + sed 's/[\\\\&|]/\\\\&/g'`;; #( + *) ac_sed_conf_input=$configure_input;; + esac + + case $ac_tag in + *:-:* | *:-) cat >"$ac_tmp/stdin" \ + || as_fn_error $? "could not create $ac_file" "$LINENO" 5 ;; + esac + ;; + esac + + ac_dir=`$as_dirname -- "$ac_file" || +$as_expr X"$ac_file" : 'X\(.*[^/]\)//*[^/][^/]*/*$' \| \ + X"$ac_file" : 'X\(//\)[^/]' \| \ + X"$ac_file" : 'X\(//\)$' \| \ + X"$ac_file" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X"$ac_file" | + sed '/^X\(.*[^/]\)\/\/*[^/][^/]*\/*$/{ + s//\1/ + q + } + /^X\(\/\/\)[^/].*/{ + s//\1/ + q + } + /^X\(\/\/\)$/{ + s//\1/ + q + } + /^X\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + as_dir="$ac_dir"; as_fn_mkdir_p + ac_builddir=. + +case "$ac_dir" in +.) ac_dir_suffix= ac_top_builddir_sub=. ac_top_build_prefix= ;; +*) + ac_dir_suffix=/`printf "%s\n" "$ac_dir" | sed 's|^\.[\\/]||'` + # A ".." for each directory in $ac_dir_suffix. + ac_top_builddir_sub=`printf "%s\n" "$ac_dir_suffix" | sed 's|/[^\\/]*|/..|g;s|/||'` + case $ac_top_builddir_sub in + "") ac_top_builddir_sub=. ac_top_build_prefix= ;; + *) ac_top_build_prefix=$ac_top_builddir_sub/ ;; + esac ;; +esac +ac_abs_top_builddir=$ac_pwd +ac_abs_builddir=$ac_pwd$ac_dir_suffix +# for backward compatibility: +ac_top_builddir=$ac_top_build_prefix + +case $srcdir in + .) # We are building in place. + ac_srcdir=. + ac_top_srcdir=$ac_top_builddir_sub + ac_abs_top_srcdir=$ac_pwd ;; + [\\/]* | ?:[\\/]* ) # Absolute name. + ac_srcdir=$srcdir$ac_dir_suffix; + ac_top_srcdir=$srcdir + ac_abs_top_srcdir=$srcdir ;; + *) # Relative name. + ac_srcdir=$ac_top_build_prefix$srcdir$ac_dir_suffix + ac_top_srcdir=$ac_top_build_prefix$srcdir + ac_abs_top_srcdir=$ac_pwd/$srcdir ;; +esac +ac_abs_srcdir=$ac_abs_top_srcdir$ac_dir_suffix + + + case $ac_mode in + :F) + # + # CONFIG_FILE + # + +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +# If the template does not know about datarootdir, expand it. +# FIXME: This hack should be removed a few years after 2.60. +ac_datarootdir_hack=; ac_datarootdir_seen= +ac_sed_dataroot=' +/datarootdir/ { + p + q +} +/@datadir@/p +/@docdir@/p +/@infodir@/p +/@localedir@/p +/@mandir@/p' +case `eval "sed -n \"\$ac_sed_dataroot\" $ac_file_inputs"` in +*datarootdir*) ac_datarootdir_seen=yes;; +*@datadir@*|*@docdir@*|*@infodir@*|*@localedir@*|*@mandir@*) + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: $ac_file_inputs seems to ignore the --datarootdir setting" >&5 +printf "%s\n" "$as_me: WARNING: $ac_file_inputs seems to ignore the --datarootdir setting" >&2;} +_ACEOF +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 + ac_datarootdir_hack=' + s&@datadir@&$datadir&g + s&@docdir@&$docdir&g + s&@infodir@&$infodir&g + s&@localedir@&$localedir&g + s&@mandir@&$mandir&g + s&\\\${datarootdir}&$datarootdir&g' ;; +esac +_ACEOF + +# Neutralize VPATH when `$srcdir' = `.'. +# Shell code in configure.ac might set extrasub. +# FIXME: do we really want to maintain this feature? +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +ac_sed_extra="$ac_vpsub +$extrasub +_ACEOF +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +:t +/@[a-zA-Z_][a-zA-Z_0-9]*@/!b +s|@configure_input@|$ac_sed_conf_input|;t t +s&@top_builddir@&$ac_top_builddir_sub&;t t +s&@top_build_prefix@&$ac_top_build_prefix&;t t +s&@srcdir@&$ac_srcdir&;t t +s&@abs_srcdir@&$ac_abs_srcdir&;t t +s&@top_srcdir@&$ac_top_srcdir&;t t +s&@abs_top_srcdir@&$ac_abs_top_srcdir&;t t +s&@builddir@&$ac_builddir&;t t +s&@abs_builddir@&$ac_abs_builddir&;t t +s&@abs_top_builddir@&$ac_abs_top_builddir&;t t +$ac_datarootdir_hack +" +eval sed \"\$ac_sed_extra\" "$ac_file_inputs" | $AWK -f "$ac_tmp/subs.awk" \ + >$ac_tmp/out || as_fn_error $? "could not create $ac_file" "$LINENO" 5 + +test -z "$ac_datarootdir_hack$ac_datarootdir_seen" && + { ac_out=`sed -n '/\${datarootdir}/p' "$ac_tmp/out"`; test -n "$ac_out"; } && + { ac_out=`sed -n '/^[ ]*datarootdir[ ]*:*=/p' \ + "$ac_tmp/out"`; test -z "$ac_out"; } && + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: $ac_file contains a reference to the variable \`datarootdir' +which seems to be undefined. Please make sure it is defined" >&5 +printf "%s\n" "$as_me: WARNING: $ac_file contains a reference to the variable \`datarootdir' +which seems to be undefined. Please make sure it is defined" >&2;} + + rm -f "$ac_tmp/stdin" + case $ac_file in + -) cat "$ac_tmp/out" && rm -f "$ac_tmp/out";; + *) rm -f "$ac_file" && mv "$ac_tmp/out" "$ac_file";; + esac \ + || as_fn_error $? "could not create $ac_file" "$LINENO" 5 + ;; + + + + esac + +done # for ac_tag + + +as_fn_exit 0 +_ACEOF +ac_clean_files=$ac_clean_files_save + +test $ac_write_fail = 0 || + as_fn_error $? "write failure creating $CONFIG_STATUS" "$LINENO" 5 + + +# configure is writing to config.log, and then calls config.status. +# config.status does its own redirection, appending to config.log. +# Unfortunately, on DOS this fails, as config.log is still kept open +# by configure, so config.status won't be able to write to it; its +# output is simply discarded. So we exec the FD to /dev/null, +# effectively closing config.log, so it can be properly (re)opened and +# appended to by config.status. When coming back to configure, we +# need to make the FD available again. +if test "$no_create" != yes; then + ac_cs_success=: + ac_config_status_args= + test "$silent" = yes && + ac_config_status_args="$ac_config_status_args --quiet" + exec 5>/dev/null + $SHELL $CONFIG_STATUS $ac_config_status_args || ac_cs_success=false + exec 5>>config.log + # Use ||, not &&, to avoid exiting from the if with $? = 1, which + # would make configure fail if this is the last instruction. + $ac_cs_success || as_fn_exit 1 +fi +if test -n "$ac_unrecognized_opts" && test "$enable_option_checking" != no; then + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: unrecognized options: $ac_unrecognized_opts" >&5 +printf "%s\n" "$as_me: WARNING: unrecognized options: $ac_unrecognized_opts" >&2;} +fi + + diff --git a/R-package/configure.ac b/R-package/configure.ac new file mode 100644 index 0000000..d0f0462 --- /dev/null +++ b/R-package/configure.ac @@ -0,0 +1,172 @@ +### configure.ac -*- Autoconf -*- +# Template used by Autoconf to generate 'configure' script. For more see: +# * https://unconj.ca/blog/an-autoconf-primer-for-r-package-authors.html +# * https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Configure-and-cleanup + +AC_PREREQ(2.69) +AC_INIT([lightgbm], [~~VERSION~~], [], [lightgbm], []) + +########################### +# find compiler and flags # +########################### + +AC_MSG_CHECKING([location of R]) +AC_MSG_RESULT([${R_HOME}]) + +# set up CPP flags +# find the compiler and compiler flags used by R. +: ${R_HOME=`R HOME`} +if test -z "${R_HOME}"; then + echo "could not determine R_HOME" + exit 1 +fi +CXX17=`"${R_HOME}/bin/R" CMD config CXX17` +CXX17STD=`"${R_HOME}/bin/R" CMD config CXX17STD` +CXX="${CXX17} ${CXX17STD}" +CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` +CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX17FLAGS` +LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS` +AC_LANG(C++) + +# LightGBM-specific flags +LGB_CPPFLAGS="" + +######### +# Eigen # +######### + +LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" + +############### +# MM_PREFETCH # +############### + +AC_MSG_CHECKING([whether MM_PREFETCH works]) +ac_mmprefetch=no +AC_LANG_CONFTEST( + [ + AC_LANG_PROGRAM( + [[ + #include + ]], + [[ + int a = 0; + _mm_prefetch(&a, _MM_HINT_NTA); + return 0; + ]] + ) + ] +) +${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mmprefetch=yes +AC_MSG_RESULT([${ac_mmprefetch}]) +if test "${ac_mmprefetch}" = yes; then + LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_PREFETCH=1" +fi + +############ +# MM_ALLOC # +############ + +AC_MSG_CHECKING([whether MM_MALLOC works]) +ac_mm_malloc=no +AC_LANG_CONFTEST( + [ + AC_LANG_PROGRAM( + [[ + #include + ]], + [[ + char *a = (char*)_mm_malloc(8, 16); + _mm_free(a); + return 0; + ]] + ) + ] +) +${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc=yes +AC_MSG_RESULT([${ac_mm_malloc}]) +if test "${ac_mm_malloc}" = yes; then + LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_MALLOC=1" +fi + +########## +# OpenMP # +########## + +OPENMP_CXXFLAGS="" + +if test `uname -s` = "Linux" +then + OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)" +fi + +if test `uname -s` = "Darwin" +then + OPENMP_CXXFLAGS='-Xclang -fopenmp' + OPENMP_LIB='-lomp' + + # libomp 15.0+ from brew is keg-only (i.e. not symlinked into the standard paths search by the linker), + # so need to search in other locations. + # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927. + # + # If Homebrew is found and libomp was installed with it, this code adds the necessary + # flags for the compiler to find libomp headers and for the linker to find libomp.dylib. + HOMEBREW_LIBOMP_PREFIX="" + if command -v brew >/dev/null 2>&1; then + ac_brew_openmp=no + AC_MSG_CHECKING([whether OpenMP was installed via Homebrew]) + brew --prefix libomp >/dev/null 2>&1 && ac_brew_openmp=yes + AC_MSG_RESULT([${ac_brew_openmp}]) + if test "${ac_brew_openmp}" = yes; then + HOMEBREW_LIBOMP_PREFIX=`brew --prefix libomp` + OPENMP_CXXFLAGS="${OPENMP_CXXFLAGS} -I${HOMEBREW_LIBOMP_PREFIX}/include" + OPENMP_LIB="${OPENMP_LIB} -L${HOMEBREW_LIBOMP_PREFIX}/lib" + fi + fi + ac_pkg_openmp=no + AC_MSG_CHECKING([whether OpenMP will work in a package]) + AC_LANG_CONFTEST( + [ + AC_LANG_PROGRAM( + [[ + #include + ]], + [[ + return (omp_get_max_threads() <= 1); + ]] + ) + ] + ) + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + + # -Xclang is not portable (it is clang-specific) + # if compilation above failed, try without that flag + if test "${ac_pkg_openmp}" = no; then + if test -f "./conftest"; then + rm ./conftest + fi + OPENMP_CXXFLAGS="-fopenmp" + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + fi + + AC_MSG_RESULT([${ac_pkg_openmp}]) + if test "${ac_pkg_openmp}" = no; then + OPENMP_CXXFLAGS='' + OPENMP_LIB='' + echo '***********************************************************************************************' + echo ' OpenMP is unavailable on this macOS system. LightGBM code will run single-threaded as a result.' + echo ' To use all CPU cores for training jobs, you should install OpenMP by running' + echo '' + echo ' brew install libomp' + echo '***********************************************************************************************' + fi +fi + +# substitute variables from this script into Makevars.in +AC_SUBST(OPENMP_CXXFLAGS) +AC_SUBST(OPENMP_LIB) +AC_SUBST(LGB_CPPFLAGS) +AC_CONFIG_FILES([src/Makevars]) + +# write out Autoconf output +AC_OUTPUT diff --git a/R-package/configure.win b/R-package/configure.win new file mode 100755 index 0000000..050d9a3 --- /dev/null +++ b/R-package/configure.win @@ -0,0 +1,102 @@ +# Script used to generate `Makevars.win` from `Makevars.win.in` +# on Windows + +########################### +# find compiler and flags # +########################### + +R_EXE="${R_HOME}/bin${R_ARCH_BIN}/R" + +CXX17=`"${R_EXE}" CMD config CXX17` +CXX17STD=`"${R_EXE}" CMD config CXX17STD` +CXX="${CXX17} ${CXX17STD}" +CXXFLAGS=`"${R_EXE}" CMD config CXX17FLAGS` +CPPFLAGS=`"${R_EXE}" CMD config CPPFLAGS` + +# LightGBM-specific flags +LGB_CPPFLAGS="" + +######### +# Eigen # +######### + +LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" + +############### +# MM_PREFETCH # +############### + +ac_mm_prefetch="no" + +cat > conftest.cpp < +int main() { + int a = 0; + _mm_prefetch(&a, _MM_HINT_NTA); + return 0; +} +EOL + +${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_prefetch="yes" +rm -f ./conftest +rm -f ./conftest.cpp +echo "checking whether MM_PREFETCH works...${ac_mm_prefetch}" + +if test "${ac_mm_prefetch}" = "yes"; +then + LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_PREFETCH=1" +fi + +############ +# MM_ALLOC # +############ +ac_mm_malloc="no" + +cat > conftest.cpp < +int main() { + char *a = (char*)_mm_malloc(8, 16); + _mm_free(a); + return 0; +} +EOL + +${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc="yes" +rm -f ./conftest +rm -f ./conftest.cpp +echo "checking whether MM_MALLOC works...${ac_mm_malloc}" + +if test "${ac_mm_malloc}" = "yes"; +then + LGB_CPPFLAGS="${LGB_CPPFLAGS} -DMM_MALLOC=1" +fi + +############# +# INET_PTON # +############# + +ac_inet_pton="no" + +cat > conftest.cpp < +int main() { + int (*fptr)(int, const char*, void*); + fptr = &inet_pton; + return 0; +} +EOL + +${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_inet_pton="yes" +rm -f ./conftest +rm -f ./conftest.cpp +echo "checking whether INET_PTON works...${ac_inet_pton}" + +if test "${ac_inet_pton}" = "yes"; +then + LGB_CPPFLAGS="${LGB_CPPFLAGS} -DWIN_HAS_INET_PTON=1" +fi + +# Generate Makevars.win from Makevars.win.in +sed -e \ + "s/@LGB_CPPFLAGS@/$LGB_CPPFLAGS/" \ + < src/Makevars.win.in > src/Makevars.win diff --git a/R-package/cran-comments.md b/R-package/cran-comments.md new file mode 100644 index 0000000..08bbad0 --- /dev/null +++ b/R-package/cran-comments.md @@ -0,0 +1,962 @@ +# CRAN Submission History + +## v4.6.0 - Submission 1 - (February 13, 2025) + +### CRAN response + +Accepted to CRAN + +### Maintainer Notes + +This release fixed several issues reported by CRAN. +Bashisms in `configure` + +```text +possible bashism in configure.ac line 63 (should be VAR="${VAR}foo"): + LGB_CPPFLAGS+=" -DMM_PREFETCH=1" +possible bashism in configure.ac line 89 (should be VAR="${VAR}foo"): + LGB_CPPFLAGS+=" -DMM_MALLOC=1" +``` + +Compilation errors on GCC 15. + +```text +io/json11.cpp:97:28: error: 'uint8_t' does not name a type + 97 | } else if (static_cast(ch) == 0xe2 && + | ^~~~~~~ +io/json11.cpp:97:28: note: 'uint8_t' is defined in header ''; this is probably fixable by adding '#include ' +io/json11.cpp:98:28: error: 'uint8_t' does not name a type + 98 | static_cast(value[i + 1]) == 0x80 && +``` + +This release contains fixes for those issues. + +## v4.5.0 - Submission 1 - (July 25, 2024) + +### CRAN response + +Accepted to CRAN + +### Maintainer Notes + +This release was a response to a request from CRAN. +On July 4, 2024, CRAN notified us that the following compiler warnings raised by `gcc` 14 needed to be fixed by August 3, 2024. + +```text +Result: WARN + Found the following significant warnings: + io/dense_bin.hpp:617:27: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] + io/multi_val_dense_bin.hpp:346:26: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] + io/multi_val_sparse_bin.hpp:433:36: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] + io/sparse_bin.hpp:785:19: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor] + See ‘/data/gannet/ripley/R/packages/tests-devel/lightgbm.Rcheck/00install.out’ for details. +``` + +This release contains fixes for those issues. + +## v4.4.0 - Submission 1 - (June 14, 2024) + +### CRAN response + +Accepted to CRAN + +### Maintainer Notes + +This was a standard release of `{lightgbm}`, not intended to fix any particular R-specific issues. + +## v4.3.0 - Submission 1 - (January 18, 2024) + +### CRAN response + +Accepted to CRAN + +### Maintainer Notes + +This submission was put up in response to CRAN saying the package would be archived if the following +warning was not fixed within 14 days. + +```text +/usr/local/clang-trunk/bin/../include/c++/v1/__fwd/string_view.h:22:41: +warning: 'char_traits' is deprecated: +char_traits for T not equal to char, wchar_t, char8_t, char16_t or char32_t is non-standard and is provided for a temporary period. +It will be removed in LLVM 19, so please migrate off of it. [-Wdeprecated-declarations] +``` + +See https://github.com/lightgbm-org/LightGBM/issues/6264. + +## v4.2.0 - Submission 1 - (December 7, 2023) + +### CRAN response + +Accepted to CRAN + +### Maintainer Notes + +This submission included many changes from the last 2 years, as well as fixes for a warning +CRAN said could cause the package to be archived: https://github.com/lightgbm-org/LightGBM/issues/6221. + +## v4.1.0 - not submitted + +v4.1.0 was not submitted to CRAN, because https://github.com/lightgbm-org/LightGBM/issues/5987 had not been resolved. + +## v4.0.0 - Submission 2 - (July 19, 2023) + +### CRAN response + +> Dear maintainer, +> package lightgbm_4.0.0.tar.gz does not pass the incoming checks automatically. + +The logs linked from those messages showed one issue remaining on Debian (0 on Windows). + +```text +* checking examples ... [7s/4s] NOTE +Examples with CPU time > 2.5 times elapsed time + user system elapsed ratio +lgb.restore_handle 1.206 0.085 0.128 10.08 +``` + +### Maintainer Notes + +Chose to document the issue and need for a fix in https://github.com/lightgbm-org/LightGBM/issues/5987, but not resubmit, +to avoid annoying CRAN maintainers. + +## v4.0.0 - Submission 1 - (July 16, 2023) + +### CRAN response + +> Dear maintainer, +> package lightgbm_4.0.0.tar.gz does not pass the incoming checks automatically. + +The logs linked from those messages showed the following issues from `R CMD check`. + +```text +* checking S3 generic/method consistency ... NOTE +Mismatches for apparent methods not registered: +merge: + function(x, y, ...) +merge.eval.string: + function(env) + +format: + function(x, ...) +format.eval.string: + function(eval_res, eval_err) +See section 'Registering S3 methods' in the 'Writing R Extensions' +manual. +``` + +```text +* checking examples ... [8s/4s] NOTE +Examples with CPU time > 2.5 times elapsed time + user system elapsed ratio +lgb.restore_handle 1.819 0.128 0.165 11.8 +``` + +### Maintainer Notes + +Attempted to fix these with https://github.com/lightgbm-org/LightGBM/pull/5988 and resubmitted. + +## v3.3.5 - Submission 2 - (January 16, 2023) + +### CRAN response + +> Reason was +> +> Flavor: r-devel-windows-x86_64 +> Check: OOverall checktime, Result: NOTE +> Overall checktime 14 min > 10 min +> +> but the maintainer cannot do much to reduce this, so I triggered revdep checks now. +> Please reply to the archival message in case the issue is not fixable easily. +> +> Best, +> Uwe Ligges + +### Maintainer Notes + +This was technically not a "resubmission". +We asked CRAN why the first v3.3.5 submission had been archived, and they responded with the response above... and then v3.3.5 passed all checks with no further work from LightGBM maintainers. + +## v3.3.5 - Submission 1 - (January 11, 2023) + +### CRAN response + +Archived without a response. + +### Maintainer Notes + +Submitted with the following comment. + +> This submission contains {lightgbm} 3.3.5 + +> Per CRAN's policies, I am submitting it on behalf of the project's maintainer (Yu Shi), with his permission. + +> This submission includes patches to address the following warnings observed on the fedora and debian CRAN checks. + +> Found the following significant warnings: +> io/json11.cpp:207:47: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] +> io/json11.cpp:216:51: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] +> io/json11.cpp:225:53: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] +> io/json11.cpp:268:60: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] +> io/json11.cpp:272:36: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] +> io/json11.cpp:276:37: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] +> io/json11.cpp:381:41: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] +> io/json11.cpp:150:39: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call] + +Thank you very much for your time and consideration. + +## v3.3.4 - Submission 1 - (December 15, 2022) + +### CRAN response + +Accepted to CRAN + +### Maintainer Notes + +Submitted with the following comment: + +> This submission contains {lightgbm} 3.3.4 + +> Per CRAN's policies, I am submitting it on behalf of the project's maintainer (Yu Shi), with his permission. + +> This submission includes patches to address the following warnings observed on the fedora and debian CRAN checks. +> +> Compiled code should not call entry points which might terminate R nor write to stdout/stderr instead of to the console, nor use Fortran I/O nor system RNGs nor [v]sprintf. + +> Thank you very much for your time and consideration. + +## v3.3.3 - Submission 1 - (October 10, 2022) + +### CRAN response + +Accepted to CRAN + +### Maintainer Notes + +Submitted with the following comment: + +> This submission contains {lightgbm} 3.3.3. + +> Per CRAN's policies, I am submitting on it on behalf of the project's maintainer (Yu Shi), with his permission (https://github.com/lightgbm-org/LightGBM/pull/5525). + +> This submission includes two patches: +> * a change to testing to avoid a failed test related to non-ASCII strings on the `r-devel-linux-x86_64-debian-clang` check flavor (https://github.com/lightgbm-org/LightGBM/pull/5526) +> * modifications to allow compatibility with the RTools42 build toolchain (https://github.com/lightgbm-org/LightGBM/pull/5503) + +> Thank you very much for your time and consideration. + +## v3.3.2 - Submission 1 - (January 7, 2022) + +### CRAN response + +Accepted to CRAN on January 14, 2022. + +### Maintainer Notes + +In this submission, we uploaded a patch that CRAN stuff provided us via e-mail. The full text of the e-mail from CRAN: + +```text +Dear maintainers, + +This concerns the CRAN packages + +Cairo cepreader gpboost httpuv ipaddress lightgbm proj4 prophet +RcppCWB RcppParallel RDieHarder re2 redux rgeolocate RGtk2 tth +udunits2 unrtf + +maintained by one of you: + +Andreas Blaette andreas.blaette@uni-due.de: RcppCWB +David Hall david.hall.physics@gmail.com: ipaddress +Dirk Eddelbuettel edd@debian.org: RDieHarder +Fabio Sigrist fabiosigrist@gmail.com: gpboost +Friedrich Leisch Friedrich.Leisch@R-project.org: tth +Girish Palya girishji@gmail.com: re2 +James Hiebert hiebert@uvic.ca: udunits2 +Jari Oksanen jhoksane@gmail.com: cepreader +Kevin Ushey kevin@rstudio.com: RcppParallel +ORPHANED: RGtk2 +Os Keyes ironholds@gmail.com: rgeolocate +Rich FitzJohn rich.fitzjohn@gmail.com: redux +Sean Taylor sjtz@pm.me: prophet +Simon Urbanek simon.urbanek@r-project.org: proj4 +Simon Urbanek Simon.Urbanek@r-project.org: Cairo +Winston Chang winston@rstudio.com: httpuv +Yu Shi yushi2@microsoft.com: lightgbm + +your packages need to be updated for R-devel/R 4.2 to work on Windows, +following the recent switch to UCRT and Rtools42. + +Sorry for the group message, please feel free to respond individually +regarding your package or ask specifically about what needs to be fixed. + +I've created patches for you, so please review them and fix your packages: + +https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsvn.r-project.org%2FR-dev-web%2Ftrunk%2FWindowsBuilds%2Fwinutf8%2Fucrt3%2Fr_packages%2Fpatches%2FCRAN%2F&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=rFGf7Y4Dvo6g1kzV%2BeAJDLGm1TUtzQsLsavElTw6H1U%3D&reserved=0 + +You can apply them as follows + +tar xfz package_1.0.0.tar.gz + +wget +https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsvn.r-project.org%2FR-dev-web%2Ftrunk%2FWindowsBuilds%2Fwinutf8%2Fucrt3%2Fr_packages%2Fpatches%2FCRAN%2Fpackage.diff&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=iyTjhoqvzj3IbQ8HGCZeh1IQl34FAGpIdVyZWkzNvO0%3D&reserved=0 + +patch --binary < package.diff + +These patches are currently automatically applied by R-devel on Windows +at installation time, which makes most of your packages pass their +checks (as OK or NOTE), but please check your results carefully and +carefully review the patches. Usually these changes were because of +newer GCC or newer MinGW in the toolchain, but some for other reasons, +and some of them will definitely have to be improved so that the package +keeps building also for older versions of R using Rtools40. We have only +been testing the patches with UCRT (and Rtools42) on Windows. + +For more information, please see + +https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdeveloper.r-project.org%2FBlog%2Fpublic%2F2021%2F12%2F07%2Fupcoming-changes-in-r-4.2-on-windows%2F&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=SY77zgtbDbHvTxTgPLOoe%2Fw5OZDhXvJoxpVOoEaKoYo%3D&reserved=0 +https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdeveloper.r-project.org%2FWindowsBuilds%2Fwinutf8%2Fucrt3%2Fhowto.html&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=dlVJ4nhQlmDPd56bHoVsWZuRfrUUorvOWxoUTmVDM%2Bg%3D&reserved=0 + +Once you add your patches/fix the issues, your package will probably +show a warning during R CMD check (as patching would be attempted to be +applied again). That's ok, at that point please let me know and I will +remove my patch from the repository of automatically applied patches. + +If you end up just applying the patch as is, there is probably no need +testing on your end, but you can do so using Winbuilder, r-hub, github +actions (e.g. https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fkalibera%2Fucrt3&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=msqoPzqDStlAUn%2Bb6gGevwFPD%2FaNL5dTxiNud2Sqzy8%3D&reserved=0). + +If you wanted to test locally on your Windows machine and do not have a +UCRT version of R-devel yet, please uninstall your old version of +R-devel, delete the old library used with that, install a new UCRT +version of R-devel , and install Rtools42. You can keep Rtools40 +installed if you need it with R 4.1 or earlier. + +Currently, the new R-devel can be downloaded from +https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.r-project.org%2Fnosvn%2Fwinutf8%2Fucrt3%2Fweb%2Frdevel.html&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=0hCwONzLmcW0GIXNqiOZQEIuhNA%2BjHhQvXsofs8J98o%3D&reserved=0 + +And Rtools42 from +https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.r-project.org%2Fnosvn%2Fwinutf8%2Fucrt3%2Fweb%2Frtools.html&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=WLWLbOyQKbaYz8gkfKz2sqoGknjIOtl1aGAhUF%2Bpylg%3D&reserved=0 + +If you end up testing locally, you can use R_INSTALL_TIME_PATCHES +environment variable to disable the automated patching, see the "howto" +document above. That way you could also see what the original issue was +causing. + +If you wanted to find libraries to link for yourself, e.g. in a newer +version of your package, please look for "Using findLinkingOrder with +Rtools42 (tiff package example)" in the "howto" document above. I +created the patches for you manually before we finished this script, so +you may be able to create a shorter version using it, but - it's +probably not worth the effort. + +If you wanted to try in a virtual machine, but did not have a license, +you can use also an automated setup of a free trial VM from +https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdeveloper.r-project.org%2FBlog%2Fpublic%2F2021%2F03%2F18%2Fvirtual-windows-machine-for-checking-r-packages&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=aFFQYuC9CoBwBiLgZHi8N3yUnSiHu5Xtdqb2YBiMIHQ%3D&reserved=0 + +(but that needs a very good and un-metered network connection to install) + +Please let us know if you have any questions. + +Thanks, +Tomas & Uwe +``` + +## v3.3.1 - Submission 1 - (October 27, 2021) + +### CRAN response + +Accepted to CRAN on October 30, 2021. + +CRAN completed its checks and preparation of binaries on November 6, 2021. + +### Maintainer Notes + +Submitted v3.3.1 to CRAN, with the following fixes for the issues that caused CRAN to reject v3.3.0 and archive the package: + +* https://github.com/lightgbm-org/LightGBM/pull/4673 +* https://github.com/lightgbm-org/LightGBM/pull/4714 + +Submitted with the following comment: + +> This submission contains {lightgbm} 3.3.1. +> Per CRAN's policies, I am submitting on it on behalf of the project's maintainer (Yu Shi), with his permission (https://github.com/lightgbm-org/LightGBM/pull/4715#issuecomment-952537783). + +> {lightgbm} was removed from CRAN on October 25, 2021 due to issues detected in the gcc-ASAN and clang-ASAN checks. To the best of our knowledge, we believe this release fixes those issues. We have introduced automated testing that we believe faithfully reproduces CRAN's tests with sanitizers (https://github.com/lightgbm-org/LightGBM/pull/4678). + +> Thank you very much for your time and consideration. + +Progress on the submission was tracked in https://github.com/lightgbm-org/LightGBM/issues/4713. + +## v3.3.0 - Submission 1 - (October 8, 2021) + +### CRAN response + +`{lightgbm}` was removed from CRAN entirely on October 25, 2021. + +On October 12, 2021, maintainers received the following message from CRAN (ripley@stats.ox.ac.uk): + +> Dear maintainer, + +> Please see the problems shown on https://cran.r-project.org/web/checks/check_results_lightgbm.html + +> Please correct before 2021-10-25 to safely retain your package on CRAN. + +> Do remember to look at the 'Additional issues'. + +> The CRAN Team + +We failed to produce a new submission prior to that date, so the package was removed entirely. + +See https://github.com/lightgbm-org/LightGBM/issues/4713 for additional background and links explaining the specific failed CRAN checks. + +### Maintainer Notes + +In this submission, we attempted to switch the maintainer of the package (in the CRAN official sense) from Guolin Ke to Yu Shi. +Did this by adding a note in the CRAN submission web form explaining Guolin's departure from Microsoft. + +## v3.2.1 - Submission 1 - (April 12, 2021) + +### CRAN response + +Accepted to CRAN. + +### Maintainer Notes + +## v3.2.0 - Submission 1 - (March 22, 2021) + +### CRAN response + +Package is failing checks in the `r-devel-linux-x86_64-debian-clang` environment (described [here](https://cran.r-project.org/web/checks/check_flavors.html#r-devel-linux-x86_64-debian-clang)). Specifically, one unit test on the use of non-ASCII feature names in `Booster$dump_model()` fails. + +> Apparently your package fails its checks in a strict Latin-1* locale, +e.g. under Linux using LANG=en_US.iso88591 (see the debian-clang +results). + +> Please correct before 2021-04-21 to safely retain your package on CRAN. + +### Maintainer Notes + +Submitted a version 3.2.1 to correct the errors noted. + +## v3.1.1 - Submission 1 - (December 7, 2020) + +### CRAN response + +Accepted to CRAN, December 8. + +### Maintainer Notes + +Submitted a fix to 3.1.0 that skips some learning-to-rank tests on 32-bit Windows. + +## v3.1.0 - Submission 1 - (November 15, 2020) + +### CRAN response + +Accepted to CRAN, November 18. + +On November 21, found out that the CRAN's `r-oldrel-windows-ix86+x86_64` check was failing, with an issue similar to the one faced on Solaris and fixed in https://github.com/lightgbm-org/LightGBM/pull/3534. + +CRAN did not ask for a re-submission, but this was fixed in 3.1.1. + +### Maintainer Notes + +This package was submitted with the following information in the "optional comments" box. + +```text +Hello, + +I'm submitting {lightgbm} 3.1.0 on behalf of the maintainer, Guolin Ke. I am a co-author on the package, and he has asked me to handle this submission. We saw in https://cran.r-project.org/web/packages/policies.html#Submission that this is permitted. + +{lightgbm} was removed from CRAN in October for issues found by valgrind checks. We have invested significant effort in addressing those issues and creating an automatic test that tries to replicate CRAN's valgrind checks: https://github.com/lightgbm-org/LightGBM/blob/742d72f8bb051105484fd5cca11620493ffb0b2b/.github/workflows/r_valgrind.yml. + +We see two warnings from valgrind that we believe are not problematic. + +==2063== Conditional jump or move depends on uninitialised value(s) +==2063== at 0x49CF138: gregexpr_Regexc (grep.c:2439) +==2063== by 0x49D1F13: do_regexpr (grep.c:3100) +==2063== by 0x49A0058: bcEval (eval.c:7121) +==2063== by 0x498B67F: Rf_eval (eval.c:727) +==2063== by 0x498E414: R_execClosure (eval.c:1895) +==2063== by 0x498E0C7: Rf_applyClosure (eval.c:1821) +==2063== by 0x499FC8C: bcEval (eval.c:7089) +==2063== by 0x498B67F: Rf_eval (eval.c:727) +==2063== by 0x498B1CB: forcePromise (eval.c:555) +==2063== by 0x49963AB: FORCE_PROMISE (eval.c:5142) +==2063== by 0x4996566: getvar (eval.c:5183) +==2063== by 0x499D1A5: bcEval (eval.c:6873) +==2063== Uninitialised value was created by a stack allocation +==2063== at 0x49CEC37: gregexpr_Regexc (grep.c:2369) + +This seems to be related to R itself and not any code in {lightgbm}. + +==2063== 336 bytes in 1 blocks are possibly lost in loss record 153 of 2,709 +==2063== at 0x483DD99: calloc (in /usr/lib/x86_64-linux-gnu/valgrind/vgpreload_memcheck-amd64-linux.so) +==2063== by 0x40149CA: allocate_dtv (dl-tls.c:286) +==2063== by 0x40149CA: _dl_allocate_tls (dl-tls.c:532) +==2063== by 0x5702322: allocate_stack (allocatestack.c:622) +==2063== by 0x5702322: pthread_create@@GLIBC_2.2.5 (pthread_create.c:660) +==2063== by 0x56D0DDA: ??? (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) +==2063== by 0x56C88E0: GOMP_parallel (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0) +==2063== by 0x1544D29C: LGBM_DatasetCreateFromCSC (c_api.cpp:1286) +==2063== by 0x1546F980: LGBM_DatasetCreateFromCSC_R (lightgbm_R.cpp:91) +==2063== by 0x4941E2F: R_doDotCall (dotcode.c:634) +==2063== by 0x494CCC6: do_dotcall (dotcode.c:1281) +==2063== by 0x499FB01: bcEval (eval.c:7078) +==2063== by 0x498B67F: Rf_eval (eval.c:727) +==2063== by 0x498E414: R_execClosure (eval.c:1895) + +We believe this is a false positive, and related to a misunderstanding between valgrind and openmp (https://gcc.gnu.org/bugzilla/show_bug.cgi?id=36298). + +We have also added automated tests with ASAN/UBSAN to our testing setup, and have checked the package on Solaris 10 and found no issues. + +Thanks for your time and consideration. +``` + +## v3.0.0.2 - Submission 1 - (September 29, 2020) + +### CRAN response + +First response was a message talking about failing checks on 3.0.0. + +```text +package lightgbm_3.0.0.2.tar.gz has been auto-processed. +The auto-check found additional issues for the last version released on CRAN: +gcc-UBSAN +valgrind +CRAN incoming checks do not test for these additional issues and you will need an appropriately instrumented build of R to reproduce these. +Hence please reply-all and explain: Have these been fixed? + +Please correct before 2020-10-05 to safely retain your package on CRAN. + +There is still a valgrind error. This did not happen when tested on +submission, but the tests did run until timeout at 4 hours. When you +write illegally, corruption is common. + +Illegal writes are serious errors. +``` + +Then in later responses to email correspondence with CRAN, CRAN expressed frustration with the number of failed submission and banned this package from new submissions for a month. + +The content of that frustrated message was regrettable and it does not need to be preserved forever in this file. + +### Maintainer Notes + +The 3.0.0.x series is officially not making it to CRAN. We will wait until November, and try again. + +Detailed plan about what will be tried before November 2020 to increase the likelihood of success for that package: https://github.com/lightgbm-org/LightGBM/pull/3338#issuecomment-702756840. + +## v3.0.0.1 - Submission 1 - (September 24, 2020) + +### CRAN response + +```text +Thanks, we see: + +Still lots of alignment errors, such as + +lightgbm.Rcheck/tests/testthat.Rout:io/dataset_loader.cpp:340:59: +runtime error: reference binding to misaligned address 0x7f51fefad81e for type 'const value_type', which requires 4 byte alignment +lightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/stl_vector.h:1198:21: +runtime error: reference binding to misaligned address 0x7f51fefad81e for type 'const int', which requires 4 byte alignment lightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/vector.tcc:449:28:runtime +error: reference binding to misaligned address 0x7f51fefad81e for type 'const type', which requires 4 byte alignment +lightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/move.h:77:36: +runtime error: reference binding to misaligned address 0x7f51fefad81e for type 'const int', which requires 4 byte alignment +lightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/alloc_traits.h:512:17: +runtime error: reference binding to misaligned address 0x7f51fefad81e for type 'const type', which requires 4 byte alignment + +Please fix and resubmit. +``` + +### Maintainer Notes + +Ok, these are the notes from the UBSAN tests. Was able to reproduce them with https://github.com/lightgbm-org/LightGBM/pull/3338#issuecomment-700399862, and they were fixed in https://github.com/lightgbm-org/LightGBM/pull/3415. + +Struggling to replicate the valgrind result (running `R CMD check --use-valgrind` returns no issues), so trying submission again. Hoping that the fixes for mis-alignment fix the other errors too. + +## v3.0.0 - Submission 6 - (September 24, 2020) + +### CRAN response + +Failing pre-checks. + +### `R CMD check` results + +```text +* checking CRAN incoming feasibility ... WARNING +Maintainer: ‘Guolin Ke ’ + +Insufficient package version (submitted: 3.0.0, existing: 3.0.0) + +Days since last update: 4 +``` + +### Maintainer Notes + +Did not think the version needed to be incremented if submitting a package in response to CRAN saying "you are failing checks and will be kicked off if you don't fix it", but I guess you do! + +This can be fixed by just re-submitting but with the version changed from `3.0.0` to `3.0.0.1`. + +## v3.0.0 - Submission 5 - (September 11, 2020) + +### CRAN Response + +Accepted to CRAN! + +Please correct the problems below before 2020-10-05 to safely retain your package on CRAN: + +```text +checking installed package size ... NOTE + installed size is 49.7Mb + sub-directories of 1Mb or more: + libs 49.1Mb + +"network/socket_wrapper.hpp", line 30: Error: Could not open include file. +"network/socket_wrapper.hpp", line 216: Error: The type "ifaddrs" is incomplete. +"network/socket_wrapper.hpp", line 217: Error: The type "ifaddrs" is incomplete. +"network/socket_wrapper.hpp", line 220: Error: The type "ifaddrs" is incomplete. +"network/socket_wrapper.hpp", line 222: Error: The type "ifaddrs" is incomplete. +"network/socket_wrapper.hpp", line 214: Error: The function "getifaddrs" must have a prototype. +"network/socket_wrapper.hpp", line 228: Error: The function "freeifaddrs" must have a prototype. +"network/linkers_socket.cpp", line 76: Warning: A non-POD object of type "std::chrono::duration>" passed as a variable argument to function "static LightGBM::Log::Info(const char*, ...)". +7 Error(s) and 1 Warning(s) detected. +*** Error code 2 +make: Fatal error: Command failed for target `network/linkers_socket.o' +Current working directory /tmp/RtmpNfaavG/R.INSTALL40a84f70130a/lightgbm/src +ERROR: compilation failed for package ‘lightgbm’ +* removing ‘/home/ripley/R/Lib32/lightgbm’ +``` + +### Maintainer Notes + +Added a patch that `psutil` has used to fix missing `ifaddrs.h` on Solaris 10: https://github.com/lightgbm-org/LightGBM/issues/629#issuecomment-665091451. + +## v3.0.0 - Submission 4 - (September 4, 2020) + +### CRAN Response + +> Thanks, if the running time is the only reason to wrap the examples in +\donttest, please replace \donttest by \donttest (\donttest examples are +not executed in the CRAN checks). + +> Please replace cat() by message() or warning() in your functions (except +for print() and summary() functions). Messages and warnings can be +suppressed if needed. + +> Missing Rd-tags: + lightgbm/man/dimnames.lgb.Dataset.Rd: \value + lightgbm/man/lgb.Dataset.construct.Rd: \value + lightgbm/man/lgb.prepare.Rd: \value + ... + +> Please add the tag and explain in detail the returned objects. + +### Maintainer Notes + +Responded to CRAN with the following: + +All examples have been wrapped with `\donttest` as requested. We have replied to Swetlana Herbrandt asking for clarification on the donttest news item in the R 4.0.2 changelog (https://cran.r-project.org/doc/manuals/r-devel/NEWS.html). + +All uses of `cat()` have been replaced with `print()`. We chose `print()` over `message()` because it's important that they be written to stdout alongside all the other logs coming from the library's C++ code. `message()` and `warning()` write to stderr. + +All exported objects now have `\value{}` statements in their documentation files in `man/`. + +**We also replied directly to CRAN's feedback email** + +> Swetlana, + +> Thank you for your comments. I've just created a new submission that I believe addresses them. + +> Can you help us understand something? In your message you said "\donttest examples are +not executed in the CRAN checks)", but in https://cran.r-project.org/doc/manuals/r-devel/NEWS.html we see the following: + +> > "`R CMD check --as-cran` now runs \donttest examples (which are run by example()) instead of instructing the tester to do so. This can be temporarily circumvented during development by setting environment variable `_R_CHECK_DONTTEST_EXAMPLES_` to a false value." + +> Could you help us understand how both of those statements can be true? + +## v3.0.0 - Submission 3 - (August 29, 2020) + +### CRAN response + +* Please write references in the description of the DESCRIPTION file in +the form + - authors (year) doi:... + - authors (year) arXiv:... + - authors (year, ISBN:...) +* if those are not available: authors (year) https:... with no space after 'doi:', 'arXiv:', 'https:' and angle brackets for auto-linking. +* (If you want to add a title as well please put it in quotes: "Title") + +* \donttest{} should only be used if the example really cannot be executed (e.g. because of missing additional software, missing API keys, ...) by the user. That's why wrapping examples in \donttest{} adds the comment ("# Not run:") as a warning for the user. Does not seem necessary. Please unwrap the examples if they are executable in < 5 sec, or replace +\donttest{} with \donttest{}. + +* Please do not modify the global environment (e.g. by using <<-) in your +functions. This is not allowed by the CRAN policies. + +* Please always add all authors, contributors and copyright holders in the Authors@R field with the appropriate roles. From CRAN policies you agreed to: "The ownership of copyright and intellectual property rights of all components of the package must be clear and unambiguous (including from the authors specification in the DESCRIPTION file). Where code is copied (or derived) from the work of others (including from R itself), care must be taken that any copyright/license statements are preserved and authorship is not misrepresented." e.g.: Microsoft Corporation, Dropbox Inc. Please explain in the submission comments what you did about this issue. + +Please fix and resubmit + +### Maintainer Notes + +Responded to CRAN with the following: + +The paper citation has been adjusted as requested. We were using 'glmnet' as a guide on how to include the URL but maybe they are no longer in compliance with CRAN policies: https://github.com/cran/glmnet/blob/b1a4b50de01e0cd24343959d7cf86452bac17b26/DESCRIPTION + +All authors from the original LightGBM paper have been added to Authors@R as `"aut"`. We have also added Microsoft and DropBox, Inc. as `"cph"` (copyright holders). These roles were chosen based on the guidance in https://journal.r-project.org/archive/2012/RJ-2012-009/index.html. + +lightgbm's code does use `<<-`, but it does not modify the global environment. The uses of `<<-` in R/lgb.interprete.R and R/callback.R are in functions which are called in an environment created by the lightgbm functions that call them, and this operator is used to reach one level up into the calling function's environment. + +We chose to wrap our examples in `\donttest{}` because we found, through testing on https://r-hub.github.io/rhub/ and in our own continuous integration environments, that their run time varies a lot between platforms, and we cannot guarantee that all examples will run in under 5 seconds. We intentionally chose `\donttest{}` over `\donttest{}` because this item in the R 4.0.0 changelog (https://cran.r-project.org/doc/manuals/r-devel/NEWS.html) seems to indicate that \donttest will be ignored by CRAN's automated checks: + +> "`R CMD check --as-cran` now runs \donttest examples (which are run by example()) instead of instructing the tester to do so. This can be temporarily circumvented during development by setting environment variable `_R_CHECK_DONTTEST_EXAMPLES_` to a false value." + +We run all examples with `R CMD check --as-cran --run-dontrun` in our continuous integration tests on every commit to the package, so we have high confidence that they are working correctly. + +## v3.0.0 - Submission 2 - (August 28, 2020) + +### CRAN response + +Failing pre-checks. + +### `R CMD check` results + +* Debian: 2 NOTEs + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: 'Guolin Ke ' + + New submission + + Possibly mis-spelled words in DESCRIPTION: + Guolin (13:52) + Ke (13:48) + LightGBM (14:20) + al (13:62) + et (13:59) + + * checking top-level files ... NOTE + Non-standard files/directories found at top level: + 'docs' 'lightgbm-hex-logo.png' 'lightgbm-hex-logo.svg' + ``` + +* Windows: 2 NOTEs + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: 'Guolin Ke ' + + New submission + + Possibly mis-spelled words in DESCRIPTION: + Guolin (13:52) + Ke (13:48) + LightGBM (14:20) + al (13:62) + et (13:59) + + * checking top-level files ... NOTE + Non-standard files/directories found at top level: + 'docs' 'lightgbm-hex-logo.png' 'lightgbm-hex-logo.svg' + ``` + +### Maintainer Notes + +We should tell them the misspellings note is a false positive. + +For the note about included files, that is my fault. I had extra files laying around when I generated the package. I'm surprised to see `docs/` in that list, since it is ignored in `.Rbuildignore`. I even tested that with [the exact code Rbuildignore uses](https://github.com/wch/r-source/blob/9d13622f41cfa0f36db2595bd6a5bf93e2010e21/src/library/tools/R/build.R#L85). For now, I added `rm -r docs/` to `build-cran-package.sh`. We can figure out what is happening with `.Rbuildignore` in the future, but it shouldn't block a release. + +## v3.0.0 - Submission 1 - (August 24, 2020) + +NOTE: 3.0.0-1 was never released to CRAN. CRAN was on vacation August 14-24, 2020, and in that time version 3.0.0-1 (a release candidate) became 3.0.0. + +### CRAN response + +> Please only ship the CRAN template for the MIT license. + +> Is there some reference about the method you can add in the Description field in the form Authors (year) doi:.....? + +> Please fix and resubmit. + +### `R CMD check` results + +* Debian: 1 NOTE + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: ‘Guolin Ke ’ + + New submission + + License components with restrictions and base license permitting such: + MIT + file LICENSE + ``` + +* Windows: 1 NOTE + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: 'Guolin Ke ' + + New submission + + License components with restrictions and base license permitting such: + MIT + file LICENSE + ``` + +### Maintainer Notes + +Tried updating `LICENSE` file to this template: + +```yaml +YEAR: 2016 +COPYRIGHT HOLDER: Microsoft Corporation +``` + +Added a citation and link for [the main paper](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html) in `DESCRIPTION`. + +## v3.0.0-1 - Submission 3 - (August 12, 2020) + +### CRAN response + +Failing pre-checks. + +### `R CMD check` results + +* Debian: 1 NOTE + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: ‘Guolin Ke ’ + + New submission + + License components with restrictions and base license permitting such: + MIT + file LICENSE + ``` + +* Windows: 1 ERROR, 1 NOTE + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: ‘Guolin Ke ’ + + New submission + + License components with restrictions and base license permitting such: + MIT + file LICENSE + + ** running tests for arch 'i386' ... [9s] ERROR + Running 'testthat.R' [8s] + Running the tests in 'tests/testthat.R' failed. + Complete output: + > library(testthat) + > library(lightgbm) + Loading required package: R6 + > + > test_check( + + package = "lightgbm" + + , stop_on_failure = TRUE + + , stop_on_warning = FALSE + + ) + -- 1. Error: predictions do not fail for integer input (@test_Predictor.R#7) -- + lgb.Dataset.construct: cannot create Dataset handle + Backtrace: + 1. lightgbm::lgb.train(...) + 2. data$construct() + ``` + +### Maintainer Notes + +The "checking CRAN incoming feasibility" NOTE can be safely ignored. It only shows up the first time you submit a package to CRAN. + +So the only thing I see broken right now is the test error on 32-bit Windows. This is documented in https://github.com/lightgbm-org/LightGBM/issues/3187. + +## v3.0.0-1 - Submission 2 - (August 10, 2020) + +### CRAN response + +Failing pre-checks. + +### `R CMD check` results + +* Debian: 2 NOTEs + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: ‘Guolin Ke ’ + + New submission + + License components with restrictions and base license permitting such: + MIT + file LICENSE + + Non-standard files/directories found at top level: + ‘cran-comments.md’ ‘docs’ + ``` + +* Windows: 1 ERROR, 2 NOTEs + + ```text + * checking CRAN incoming feasibility ... NOTE + Maintainer: 'Guolin Ke ' + + New submission + + License components with restrictions and base license permitting such: + MIT + file LICENSE + + * checking top-level files ... NOTE + Non-standard files/directories found at top level: + 'cran-comments.md' 'docs' + + ** checking whether the package can be loaded ... ERROR + Loading this package had a fatal error status code 1 + Loading log: + Error: package 'lightgbm' is not installed for 'arch = i386' + Execution halted + ``` + +### Maintainer Notes + +Seems removing `Biarch` field didn't work. Noticed this in the install logs: + +> Warning: this package has a non-empty 'configure.win' file, so building only the main architecture + +Tried adding `Biarch: true` to `DESCRIPTION` to overcome this. + +NOTE about non-standard files was the result of a mistake in `.Rbuildignore` syntax, and something strange with how `cran-comments.md` line in `.Rbuildignore` was treated. Updated `.Rbuildignore` and added an `rm cran-comments.md` to `build-cran-package.sh`. + +## v3.0.0-1 - Submission 1 - (August 9, 2020) + +### CRAN response + +Failing pre-checks. + +### `R CMD check` results + +* Debian: 1 NOTE + + ```text + Possibly mis-spelled words in DESCRIPTION: + LightGBM (12:88, 19:41, 20:60, 20:264) + ``` + +* Windows: 1 ERROR, 1 NOTE + + ```text + Possibly mis-spelled words in DESCRIPTION: + LightGBM (12:88, 19:41, 20:60, 20:264) + + ** checking whether the package can be loaded ... ERROR + Loading this package had a fatal error status code 1 + Loading log: + Error: package 'lightgbm' is not installed for 'arch = i386' + Execution halted + ``` + +### Maintainer Notes + +Thought the issue on Windows was caused by `Biarch: false` in `DESCRIPTION`. Removed `Biarch` field. + +Thought the "misspellings" issue could be resolved by adding single quotes around LightGBM, like `'LightGBM'`. diff --git a/R-package/demo/00Index b/R-package/demo/00Index new file mode 100644 index 0000000..f5b6144 --- /dev/null +++ b/R-package/demo/00Index @@ -0,0 +1,10 @@ +basic_walkthrough Basic feature walkthrough +boost_from_prediction Boosting from existing prediction +categorical_features_rules Categorical Feature Preparation with Rules +cross_validation Cross Validation +early_stopping Early Stop in training +efficient_many_training Efficiency for Many Model Trainings +multiclass Multiclass training/prediction +multiclass_custom_objective Multiclass with Custom Objective Function +leaf_stability Leaf (in)Stability example +weight_param Weight-Parameter adjustment relationship diff --git a/R-package/demo/basic_walkthrough.R b/R-package/demo/basic_walkthrough.R new file mode 100644 index 0000000..c9ac484 --- /dev/null +++ b/R-package/demo/basic_walkthrough.R @@ -0,0 +1,153 @@ +library(lightgbm) + +# We load in the agaricus dataset +# In this example, we are aiming to predict whether a mushroom is edible +data(agaricus.train, package = "lightgbm") +data(agaricus.test, package = "lightgbm") +train <- agaricus.train +test <- agaricus.test + +# The loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1} +class(train$label) +class(train$data) + +# Set parameters for model training +train_params <- list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , nthread = 2L +) + +#--------------------Basic Training using lightgbm---------------- +# This is the basic usage of lightgbm you can put matrix in data field +# Note: we are putting in sparse matrix here, lightgbm naturally handles sparse input +# Use sparse matrix when your feature is sparse (e.g. when you are using one-hot encoding vector) +print("Training lightgbm with sparseMatrix") +bst <- lightgbm( + data = train$data + , params = train_params + , label = train$label + , nrounds = 2L +) + +# Alternatively, you can put in dense matrix, i.e. basic R-matrix +print("Training lightgbm with Matrix") +bst <- lightgbm( + data = as.matrix(train$data) + , params = train_params + , label = train$label + , nrounds = 2L +) + +# You can also put in lgb.Dataset object, which stores label, data and other meta datas needed for advanced features +print("Training lightgbm with lgb.Dataset") +dtrain <- lgb.Dataset( + data = train$data + , label = train$label +) +bst <- lightgbm( + data = dtrain + , params = train_params + , nrounds = 2L +) + +# Verbose = 0,1,2 +print("Train lightgbm with verbose 0, no message") +bst <- lightgbm( + data = dtrain + , params = train_params + , nrounds = 2L + , verbose = 0L +) + +print("Train lightgbm with verbose 1, print evaluation metric") +bst <- lightgbm( + data = dtrain + , params = train_params + , nrounds = 2L + , verbose = 1L +) + +print("Train lightgbm with verbose 2, also print information about tree") +bst <- lightgbm( + data = dtrain + , params = train_params + , nrounds = 2L + , verbose = 2L +) + +# You can also specify data as file path to a LibSVM/TCV/CSV format input +# Since we do not have this file with us, the following line is just for illustration +# bst <- lightgbm( +# data = "agaricus.train.svm" +# , num_leaves = 4L +# , learning_rate = 1.0 +# , nrounds = 2L +# , objective = "binary" +# ) + +#--------------------Basic prediction using lightgbm-------------- +# You can do prediction using the following line +# You can put in Matrix, sparseMatrix, or lgb.Dataset +pred <- predict(bst, test$data) +err <- mean(as.numeric(pred > 0.5) != test$label) +print(paste("test-error=", err)) + +#--------------------Save and load models------------------------- +# Save model to binary local file +lgb.save(bst, "lightgbm.model") + +# Load binary model to R +bst2 <- lgb.load("lightgbm.model") +pred2 <- predict(bst2, test$data) + +# pred2 should be identical to pred +print(paste("sum(abs(pred2-pred))=", sum(abs(pred2 - pred)))) + +#--------------------Advanced features --------------------------- +# To use advanced features, we need to put data in lgb.Dataset +dtrain <- lgb.Dataset(data = train$data, label = train$label, free_raw_data = FALSE) +dtest <- lgb.Dataset.create.valid(dtrain, data = test$data, label = test$label) + +#--------------------Using validation set------------------------- +# valids is a list of lgb.Dataset, each of them is tagged with name +valids <- list(train = dtrain, test = dtest) + +# To train with valids, use lgb.train, which contains more advanced features +# valids allows us to monitor the evaluation result on all data in the list +print("Train lightgbm using lgb.train with valids") +bst <- lgb.train( + data = dtrain + , params = train_params + , nrounds = 2L + , valids = valids +) + +# We can change evaluation metrics, or use multiple evaluation metrics +print("Train lightgbm using lgb.train with valids, watch logloss and error") +bst <- lgb.train( + data = dtrain + , params = train_params + , nrounds = 2L + , valids = valids + , eval = c("binary_error", "binary_logloss") +) + +# lgb.Dataset can also be saved using lgb.Dataset.save +lgb.Dataset.save(dtrain, "dtrain.buffer") + +# To load it in, simply call lgb.Dataset +dtrain2 <- lgb.Dataset("dtrain.buffer") +bst <- lgb.train( + data = dtrain2 + , params = train_params + , nrounds = 2L + , valids = valids +) + +# information can be extracted from lgb.Dataset using get_field() +label <- get_field(dtest, "label") +pred <- predict(bst, test$data) +err <- as.numeric(sum(as.integer(pred > 0.5) != label)) / length(label) +print(paste("test-error=", err)) diff --git a/R-package/demo/boost_from_prediction.R b/R-package/demo/boost_from_prediction.R new file mode 100644 index 0000000..68d33a7 --- /dev/null +++ b/R-package/demo/boost_from_prediction.R @@ -0,0 +1,38 @@ +library(lightgbm) + +# Load in the agaricus dataset +data(agaricus.train, package = "lightgbm") +data(agaricus.test, package = "lightgbm") +dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) +dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label) + +valids <- list(eval = dtest, train = dtrain) +#--------------------Advanced features --------------------------- +# advanced: start from an initial base prediction +print("Start running example to start from an initial prediction") + +# Train lightgbm for 1 round +param <- list( + num_leaves = 4L + , learning_rate = 1.0 + , nthread = 2L + , objective = "binary" +) +bst <- lgb.train(param, dtrain, 1L, valids = valids) + +# Note: we need the margin value instead of transformed prediction in set_init_score +ptrain <- predict(bst, agaricus.train$data, type = "raw") +ptest <- predict(bst, agaricus.test$data, type = "raw") + +# set the init_score property of dtrain and dtest +# base margin is the base prediction we will boost from +set_field(dtrain, "init_score", ptrain) +set_field(dtest, "init_score", ptest) + +print("This is result of boost from initial prediction") +bst <- lgb.train( + params = param + , data = dtrain + , nrounds = 5L + , valids = valids +) diff --git a/R-package/demo/categorical_features_rules.R b/R-package/demo/categorical_features_rules.R new file mode 100644 index 0000000..97af5a7 --- /dev/null +++ b/R-package/demo/categorical_features_rules.R @@ -0,0 +1,100 @@ +# Here we are going to try training a model with categorical features + +# Load libraries +library(data.table) +library(lightgbm) + +# Load data and look at the structure +# +# Classes 'data.table' and 'data.frame': 4521 obs. of 17 variables: +# $ age : int 30 33 35 30 59 35 36 39 41 43 ... +# $ job : chr "unemployed" "services" "management" "management" ... +# $ marital : chr "married" "married" "single" "married" ... +# $ education: chr "primary" "secondary" "tertiary" "tertiary" ... +# $ default : chr "no" "no" "no" "no" ... +# $ balance : int 1787 4789 1350 1476 0 747 307 147 221 -88 ... +# $ housing : chr "no" "yes" "yes" "yes" ... +# $ loan : chr "no" "yes" "no" "yes" ... +# $ contact : chr "cellular" "cellular" "cellular" "unknown" ... +# $ day : int 19 11 16 3 5 23 14 6 14 17 ... +# $ month : chr "oct" "may" "apr" "jun" ... +# $ duration : int 79 220 185 199 226 141 341 151 57 313 ... +# $ campaign : int 1 1 1 4 1 2 1 2 2 1 ... +# $ pdays : int -1 339 330 -1 -1 176 330 -1 -1 147 ... +# $ previous : int 0 4 1 0 0 3 2 0 0 2 ... +# $ poutcome : chr "unknown" "failure" "failure" "unknown" ... +# $ y : chr "no" "no" "no" "no" ... +data(bank, package = "lightgbm") +str(bank) + +# We are dividing the dataset into two: one train, one validation +bank_train <- bank[1L:4000L, ] +bank_test <- bank[4001L:4521L, ] + +# We must now transform the data to fit in LightGBM +# For this task, we use lgb.convert_with_rules +# The function transforms the data into a fittable data +# +# Classes 'data.table' and 'data.frame': 521 obs. of 17 variables: +# $ age : int 53 36 58 26 34 55 55 34 41 38 ... +# $ job : num 1 10 10 9 10 2 2 3 3 4 ... +# $ marital : num 1 2 1 3 3 2 2 2 1 1 ... +# $ education: num 2 2 2 2 2 1 2 3 2 2 ... +# $ default : num 1 1 1 1 1 1 1 1 1 1 ... +# $ balance : int 26 191 -123 -147 179 1086 471 105 1588 70 ... +# $ housing : num 2 1 1 1 1 2 2 2 2 1 ... +# $ loan : num 1 1 1 1 1 1 1 1 2 1 ... +# $ contact : num 1 1 1 3 1 1 3 3 3 1 ... +# $ day : int 7 31 5 4 19 6 30 28 20 27 ... +# $ month : num 9 2 2 7 2 9 9 9 7 11 ... +# $ duration : int 56 69 131 95 294 146 58 249 10 255 ... +# $ campaign : int 1 1 2 2 3 1 2 2 8 3 ... +# $ pdays : int 359 -1 -1 -1 -1 272 -1 -1 -1 148 ... +# $ previous : int 1 0 0 0 0 2 0 0 0 1 ... +# $ poutcome : num 1 4 4 4 4 1 4 4 4 3 ... +# $ y : num 1 1 1 1 1 1 1 1 1 2 ... +bank_rules <- lgb.convert_with_rules(data = bank_train) +bank_train <- bank_rules$data +bank_test <- lgb.convert_with_rules(data = bank_test, rules = bank_rules$rules)$data +str(bank_test) + +# Remove 1 to label because it must be between 0 and 1 +bank_train$y <- bank_train$y - 1L +bank_test$y <- bank_test$y - 1L + +# Data input to LightGBM must be a matrix, without the label +my_data_train <- as.matrix(bank_train[, 1L:16L, with = FALSE]) +my_data_test <- as.matrix(bank_test[, 1L:16L, with = FALSE]) + +# Creating the LightGBM dataset with categorical features +# The categorical features can be passed to lgb.train to not copy and paste a lot +dtrain <- lgb.Dataset( + data = my_data_train + , label = bank_train$y + , categorical_feature = c(2L, 3L, 4L, 5L, 7L, 8L, 9L, 11L, 16L) +) +dtest <- lgb.Dataset.create.valid( + dtrain + , data = my_data_test + , label = bank_test$y +) + +# We can now train a model +params <- list( + objective = "binary" + , metric = "l2" + , min_data = 1L + , learning_rate = 0.1 + , min_hessian = 1.0 + , max_depth = 2L +) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 100L + , valids = list(train = dtrain, valid = dtest) +) + +# Try to find split_feature: 11 +# If you find it, it means it used a categorical feature in the first tree +lgb.dump(model, num_iteration = 1L) diff --git a/R-package/demo/cross_validation.R b/R-package/demo/cross_validation.R new file mode 100644 index 0000000..90d4379 --- /dev/null +++ b/R-package/demo/cross_validation.R @@ -0,0 +1,72 @@ +library(lightgbm) + +# load in the agaricus dataset +data(agaricus.train, package = "lightgbm") +data(agaricus.test, package = "lightgbm") +dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) +dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label) + +nrounds <- 2L +param <- list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" +) + +print("Running cross validation") +# Do cross validation, this will print result out as +# [iteration] metric_name:mean_value+std_value +# std_value is standard deviation of the metric +lgb.cv( + param + , dtrain + , nrounds + , nfold = 5L + , eval = "binary_error" +) + +print("Running cross validation, disable standard deviation display") +# do cross validation, this will print result out as +# [iteration] metric_name:mean_value+std_value +# std_value is standard deviation of the metric +lgb.cv( + param + , dtrain + , nrounds + , nfold = 5L + , eval = "binary_error" + , showsd = FALSE +) + +# You can also do cross validation with customized loss function +print("Running cross validation, with cutomsized loss function") + +logregobj <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- 1.0 / (1.0 + exp(-preds)) + grad <- preds - labels + hess <- preds * (1.0 - preds) + return(list(grad = grad, hess = hess)) +} + +# User-defined evaluation function returns a pair (metric_name, result, higher_better) +# NOTE: when you do customized loss function, the default prediction value is margin +# This may make built-in evaluation metric calculate wrong results +# For example, we are doing logistic loss, the prediction is score before logistic transformation +# Keep this in mind when you use the customization, and maybe you need write customized evaluation function +evalerror <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- 1.0 / (1.0 + exp(-preds)) + err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) + return(list(name = "error", value = err, higher_better = FALSE)) +} + +# train with customized objective +lgb.cv( + params = param + , data = dtrain + , nrounds = nrounds + , obj = logregobj + , eval = evalerror + , nfold = 5L +) diff --git a/R-package/demo/early_stopping.R b/R-package/demo/early_stopping.R new file mode 100644 index 0000000..4435dd1 --- /dev/null +++ b/R-package/demo/early_stopping.R @@ -0,0 +1,51 @@ +library(lightgbm) + +# Load in the agaricus dataset +data(agaricus.train, package = "lightgbm") +data(agaricus.test, package = "lightgbm") + +dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) +dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label) + +# Note: for customized objective function, we leave objective as default +# Note: what we are getting is margin value in prediction +# You must know what you are doing +param <- list( + num_leaves = 4L + , learning_rate = 1.0 +) +valids <- list(eval = dtest) +num_round <- 20L + +# User define objective function, given prediction, return gradient and second order gradient +# This is loglikelihood loss +logregobj <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- 1.0 / (1.0 + exp(-preds)) + grad <- preds - labels + hess <- preds * (1.0 - preds) + return(list(grad = grad, hess = hess)) +} + +# User-defined evaluation function returns a pair (metric_name, result, higher_better) +# NOTE: when you do customized loss function, the default prediction value is margin +# This may make built-in evaluation metric calculate wrong results +# For example, we are doing logistic loss, the prediction is score before logistic transformation +# The built-in evaluation error assumes input is after logistic transformation +# Keep this in mind when you use the customization, and maybe you need write customized evaluation function +evalerror <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) + return(list(name = "error", value = err, higher_better = FALSE)) +} +print("Start training with early Stopping setting") + +bst <- lgb.train( + param + , dtrain + , num_round + , valids + , obj = logregobj + , eval = evalerror + , early_stopping_round = 3L +) diff --git a/R-package/demo/efficient_many_training.R b/R-package/demo/efficient_many_training.R new file mode 100644 index 0000000..ffdae9f --- /dev/null +++ b/R-package/demo/efficient_many_training.R @@ -0,0 +1,36 @@ +# Efficient training means training without giving up too much RAM +# In the case of many trainings (like 100+ models), RAM will be eaten very quickly +# Therefore, it is essential to know a strategy to deal with such issue + +# More results can be found here: https://github.com/lightgbm-org/LightGBM/issues/879#issuecomment-326656580 +# Quote: "@Laurae2 Thanks for nice easily reproducible example (unlike mine). +# With reset=FALSE you get after 500 iterations (not 1000): OS reports 27GB usage, while R gc() reports 1.5GB. +# Just doing reset=TRUE will already improve things: OS reports 4.6GB. +# Doing reset=TRUE and calling gc() in the loop will have OS 1.3GB. Thanks for the latest tip." + +# Load library +library(lightgbm) + +# Generate fictive data of size 1M x 100 +set.seed(11111L) +x_data <- matrix(rnorm(n = 100000000L, mean = 0.0, sd = 100.0), nrow = 1000000L, ncol = 100L) +y_data <- rnorm(n = 1000000L, mean = 0.0, sd = 5.0) + +# Create lgb.Dataset for training +data <- lgb.Dataset(x_data, label = y_data) +data$construct() + +# Loop through a training of 1000 models, please check your RAM on your task manager +# It MUST remain constant (if not increasing very slightly) +gbm <- list() + +for (i in 1L:1000L) { + print(i) + gbm[[i]] <- lgb.train( + params = list(objective = "regression") + , data = data + , 1L + , reset_data = TRUE + ) + gc(verbose = FALSE) +} diff --git a/R-package/demo/leaf_stability.R b/R-package/demo/leaf_stability.R new file mode 100644 index 0000000..0733f31 --- /dev/null +++ b/R-package/demo/leaf_stability.R @@ -0,0 +1,256 @@ +# We are going to look at how iterating too much might generate observation instability. +# Obviously, we are in a controlled environment, without issues (real rules). +# Do not do this in a real scenario. + +library(lightgbm) + +# define helper functions for creating plots + +# output of `RColorBrewer::brewer.pal(10, "RdYlGn")`, hardcooded here to avoid a dependency +.diverging_palette <- c( + "#A50026", "#D73027", "#F46D43", "#FDAE61", "#FEE08B" + , "#D9EF8B", "#A6D96A", "#66BD63", "#1A9850", "#006837" +) + +.prediction_depth_plot <- function(df) { + plot( + x = df$X + , y = df$Y + , type = "p" + , main = "Prediction Depth" + , xlab = "Leaf Bin" + , ylab = "Prediction Probability" + , pch = 19L + , col = .diverging_palette[df$binned + 1L] + ) + legend( + "topright" + , title = "bin" + , legend = sort(unique(df$binned)) + , pch = 19L + , col = .diverging_palette[sort(unique(df$binned + 1L))] + , cex = 0.7 + ) +} + +.prediction_depth_spread_plot <- function(df) { + plot( + x = df$binned + , xlim = c(0L, 9L) + , y = df$Z + , type = "p" + , main = "Prediction Depth Spread" + , xlab = "Leaf Bin" + , ylab = "Logloss" + , pch = 19L + , col = .diverging_palette[df$binned + 1L] + ) + legend( + "topright" + , title = "bin" + , legend = sort(unique(df$binned)) + , pch = 19L + , col = .diverging_palette[sort(unique(df$binned + 1L))] + , cex = 0.7 + ) +} + +.depth_density_plot <- function(df) { + plot( + x = density(df$Y) + , xlim = c(min(df$Y), max(df$Y)) + , type = "p" + , main = "Depth Density" + , xlab = "Prediction Probability" + , ylab = "Bin Density" + , pch = 19L + , col = .diverging_palette[df$binned + 1L] + ) + legend( + "topright" + , title = "bin" + , legend = sort(unique(df$binned)) + , pch = 19L + , col = .diverging_palette[sort(unique(df$binned + 1L))] + , cex = 0.7 + ) +} + +# load some data +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) + +# setup parameters and we train a model +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 0.1 + , bagging_fraction = 0.1 + , bagging_freq = 1L + , bagging_seed = 1L +) +valids <- list(test = dtest) +model <- lgb.train( + params + , dtrain + , 50L + , valids +) + +# We create a data.frame with the following structure: +# X = average leaf of the observation throughout all trees +# Y = prediction probability (clamped to [1e-15, 1-1e-15]) +# Z = logloss +# binned = binned quantile of average leaf +new_data <- data.frame( + X = rowMeans(predict( + model + , agaricus.test$data + , type = "leaf" + )) + , Y = pmin( + pmax( + predict(model, agaricus.test$data) + , 1e-15 + ) + , 1.0 - 1e-15 + ) +) +new_data$Z <- -1.0 * (agaricus.test$label * log(new_data$Y) + (1L - agaricus.test$label) * log(1L - new_data$Y)) +new_data$binned <- .bincode( + x = new_data$X + , breaks = quantile( + x = new_data$X + , probs = seq_len(9L) / 10.0 + ) + , right = TRUE + , include.lowest = TRUE +) +new_data$binned[is.na(new_data$binned)] <- 0L + +# We can check the binned content +table(new_data$binned) + +# We can plot the binned content +# On the second plot, we clearly notice the lower the bin (the lower the leaf value), the higher the loss +# On the third plot, it is smooth! +.prediction_depth_plot(df = new_data) +.prediction_depth_spread_plot(df = new_data) +.depth_density_plot(df = new_data) + +# Now, let's show with other parameters +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 +) +model2 <- lgb.train( + params + , dtrain + , 100L + , valids +) + +# We create the data structure, but for model2 +new_data2 <- data.frame( + X = rowMeans(predict( + model2 + , agaricus.test$data + , type = "leaf" + )) + , Y = pmin( + pmax( + predict( + model2 + , agaricus.test$data + ) + , 1e-15 + ) + , 1.0 - 1e-15 + ) +) +new_data2$Z <- -1.0 * (agaricus.test$label * log(new_data2$Y) + (1L - agaricus.test$label) * log(1L - new_data2$Y)) +new_data2$binned <- .bincode( + x = new_data2$X + , breaks = quantile( + x = new_data2$X + , probs = seq_len(9L) / 10.0 + ) + , right = TRUE + , include.lowest = TRUE +) +new_data2$binned[is.na(new_data2$binned)] <- 0L + +# We can check the binned content +table(new_data2$binned) + +# We can plot the binned content +# On the second plot, we clearly notice the lower the bin (the lower the leaf value), the higher the loss +# On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules are +# real thus it is not an issue +# However, if the rules were not true, the loss would explode. +.prediction_depth_plot(df = new_data2) +.prediction_depth_spread_plot(df = new_data2) +.depth_density_plot(df = new_data2) + +# Now, try with very severe overfitting +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 +) +model3 <- lgb.train( + params + , dtrain + , 1000L + , valids +) + +# We create the data structure, but for model3 +new_data3 <- data.frame( + X = rowMeans(predict( + model3 + , agaricus.test$data + , type = "leaf" + )) + , Y = pmin( + pmax( + predict( + model3 + , agaricus.test$data + ) + , 1e-15 + ) + , 1.0 - 1e-15 + ) +) +new_data3$Z <- -1.0 * (agaricus.test$label * log(new_data3$Y) + (1L - agaricus.test$label) * log(1L - new_data3$Y)) +new_data3$binned <- .bincode( + x = new_data3$X + , breaks = quantile( + x = new_data3$X + , probs = seq_len(9L) / 10.0 + ) + , right = TRUE + , include.lowest = TRUE +) +new_data3$binned[is.na(new_data3$binned)] <- 0L + +# We can check the binned content +table(new_data3$binned) + +# We can plot the binned content +# On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules +# are real thus it is not an issue. +# However, if the rules were not true, the loss would explode. See the sudden spikes? +.depth_density_plot(df = new_data3) + +# Compare with our second model, the difference is severe. This is smooth. +.depth_density_plot(df = new_data2) diff --git a/R-package/demo/multiclass.R b/R-package/demo/multiclass.R new file mode 100644 index 0000000..35441cc --- /dev/null +++ b/R-package/demo/multiclass.R @@ -0,0 +1,70 @@ +library(lightgbm) + +# We load the default iris dataset shipped with R +data(iris) + +# We must convert factors to numeric +# They must be starting from number 0 to use multiclass +# For instance: 0, 1, 2, 3, 4, 5... +iris$Species <- as.numeric(as.factor(iris$Species)) - 1L + +# We cut the data set into 80% train and 20% validation +# The 10 last samples of each class are for validation + +train <- as.matrix(iris[c(1L:40L, 51L:90L, 101L:140L), ]) +test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) +dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) +dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) +valids <- list(test = dtest) + +# Method 1 of training +params <- list( + objective = "multiclass" + , metric = "multi_error" + , num_class = 3L + , min_data = 1L + , learning_rate = 1.0 +) +model <- lgb.train( + params + , dtrain + , 100L + , valids + , early_stopping_rounds = 10L +) + +# We can predict on test data, outputs a 90-length vector +# Order: obs1 class1, obs1 class2, obs1 class3, obs2 class1, obs2 class2, obs2 class3... +my_preds <- predict(model, test[, 1L:4L]) + +# Method 2 of training, identical +params <- list( + min_data = 1L + , learning_rate = 1.0 + , objective = "multiclass" + , metric = "multi_error" + , num_class = 3L +) +model <- lgb.train( + params + , dtrain + , 100L + , valids + , early_stopping_rounds = 10L +) + +# We can predict on test data, identical +my_preds <- predict(model, test[, 1L:4L]) + +# A (30x3) matrix with the predictions +# class1 class2 class3 +# obs1 obs1 obs1 +# obs2 obs2 obs2 +# .... .... .... +my_preds <- predict(model, test[, 1L:4L]) + +# We can also get the predicted scores before the Sigmoid/Softmax application +my_preds <- predict(model, test[, 1L:4L], type = "raw") + +# We can also get the leaf index +my_preds <- predict(model, test[, 1L:4L], type = "leaf") diff --git a/R-package/demo/multiclass_custom_objective.R b/R-package/demo/multiclass_custom_objective.R new file mode 100644 index 0000000..150813b --- /dev/null +++ b/R-package/demo/multiclass_custom_objective.R @@ -0,0 +1,117 @@ +library(lightgbm) + +# We load the default iris dataset shipped with R +data(iris) + +# We must convert factors to numeric +# They must be starting from number 0 to use multiclass +# For instance: 0, 1, 2, 3, 4, 5... +iris$Species <- as.numeric(as.factor(iris$Species)) - 1L + +# Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2) +train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ]) +# The 10 last samples of each class are for validation +test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) + +dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) +dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) +valids <- list(train = dtrain, test = dtest) + +# Method 1 of training with built-in multiclass objective +# Note: need to turn off boost from average to match custom objective +# (https://github.com/lightgbm-org/LightGBM/issues/1846) +params <- list( + min_data = 1L + , learning_rate = 1.0 + , num_class = 3L + , boost_from_average = FALSE + , metric = "multi_logloss" +) +model_builtin <- lgb.train( + params + , dtrain + , 100L + , valids + , early_stopping_rounds = 10L + , obj = "multiclass" +) + +preds_builtin <- predict(model_builtin, test[, 1L:4L], type = "raw") +probs_builtin <- exp(preds_builtin) / rowSums(exp(preds_builtin)) + +# Method 2 of training with custom objective function + +# User defined objective function, given prediction, return gradient and second order gradient +custom_multiclass_obj <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + + # preds is a matrix with rows corresponding to samples and columns corresponding to choices + preds <- matrix(preds, nrow = length(labels)) + + # to prevent overflow, normalize preds by row + preds <- preds - apply(preds, MARGIN = 1L, max) + prob <- exp(preds) / rowSums(exp(preds)) + + # compute gradient + grad <- prob + subset_index <- as.matrix( + data.frame( + seq_along(labels) + , labels + 1L + , fix.empty.names = FALSE + ) + , nrow = length(labels) + , dimnames = NULL + ) + grad[subset_index] <- grad[subset_index] - 1L + + # compute hessian (approximation) + hess <- 2.0 * prob * (1.0 - prob) + + return(list(grad = grad, hess = hess)) +} + +# define custom metric +custom_multiclass_metric <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- matrix(preds, nrow = length(labels)) + preds <- preds - apply(preds, 1L, max) + prob <- exp(preds) / rowSums(exp(preds)) + + subset_index <- as.matrix( + data.frame( + seq_along(labels) + , labels + 1L + , fix.empty.names = FALSE + ) + , nrow = length(labels) + , dimnames = NULL + ) + return(list( + name = "error" + , value = -mean(log(prob[subset_index])) + , higher_better = FALSE + )) +} + +params <- list( + min_data = 1L + , learning_rate = 1.0 + , num_class = 3L +) +model_custom <- lgb.train( + params + , dtrain + , 100L + , valids + , early_stopping_rounds = 10L + , obj = custom_multiclass_obj + , eval = custom_multiclass_metric +) + +preds_custom <- predict(model_custom, test[, 1L:4L], type = "raw") +probs_custom <- exp(preds_custom) / rowSums(exp(preds_custom)) + +# compare predictions +stopifnot(identical(probs_builtin, probs_custom)) +stopifnot(identical(preds_builtin, preds_custom)) diff --git a/R-package/demo/weight_param.R b/R-package/demo/weight_param.R new file mode 100644 index 0000000..8fd8ae1 --- /dev/null +++ b/R-package/demo/weight_param.R @@ -0,0 +1,104 @@ +# This demo R code is to provide a demonstration of hyperparameter adjustment +# when scaling weights for appropriate learning +# As with any optimizers, bad parameters can impair performance + +# Load library +library(lightgbm) + +# We will train a model with the following scenarii: +# - Run 1: sum of weights equal to 6513 (x 1e-5) without adjusted regularization (not learning) +# - Run 2: sum of weights equal to 6513 (x 1e-5) adjusted regularization (learning) +# - Run 3: sum of weights equal to 6513 with adjusted regularization (learning) + +# Setup small weights +weights1 <- rep(1e-5, 6513L) +weights2 <- rep(1e-5, 1611L) + +# Load data and create datasets +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label, weight = weights1) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label, weight = weights2) +valids <- list(test = dtest) + +# Run 1: sum of weights equal to 6513 (x 1e-5) without adjusted regularization (not learning) +# It cannot learn because regularization is too large! +# min_sum_hessian alone is bigger than the sum of weights, thus you will never learn anything +params <- list( + objective = "regression" + , metric = "l2" + , device = "cpu" + , min_sum_hessian = 10.0 + , num_leaves = 7L + , max_depth = 3L + , nthread = 1L + , min_data = 1L + , learning_rate = 1.0 +) +model <- lgb.train( + params + , dtrain + , 50L + , valids + , early_stopping_rounds = 10L +) +weight_loss <- as.numeric(model$record_evals$test$l2$eval) +plot(weight_loss) # Shows how poor the learning was: a straight line! + +# Run 2: sum of weights equal to 6513 (x 1e-5) with adjusted regularization (learning) +# Adjusted regularization just consisting in multiplicating results by 1e4 (x10000) +# Notice how it learns, there is no issue as we adjusted regularization ourselves +params <- list( + objective = "regression" + , metric = "l2" + , device = "cpu" + , min_sum_hessian = 1e-4 + , num_leaves = 7L + , max_depth = 3L + , nthread = 1L + , min_data = 1L + , learning_rate = 1.0 +) +model <- lgb.train( + params + , dtrain + , 50L + , valids + , early_stopping_rounds = 10L +) +small_weight_loss <- as.numeric(model$record_evals$test$l2$eval) +plot(small_weight_loss) # It learns! + +# Run 3: sum of weights equal to 6513 with adjusted regularization (learning) +dtrain <- lgb.Dataset(train$data, label = train$label) +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +valids <- list(test = dtest) + +# Setup parameters and run model... +params <- list( + objective = "regression" + , metric = "l2" + , device = "cpu" + , min_sum_hessian = 10.0 + , num_leaves = 7L + , max_depth = 3L + , nthread = 1L + , min_data = 1L + , learning_rate = 1.0 +) +model <- lgb.train( + params + , dtrain + , 50L + , valids + , early_stopping_rounds = 10L +) +large_weight_loss <- as.numeric(model$record_evals$test$l2$eval) +plot(large_weight_loss) # It learns! + + +# Do you want to compare the learning? They both converge. +plot(small_weight_loss, large_weight_loss) +curve(1.0 * x, from = 0L, to = 0.02, add = TRUE) diff --git a/R-package/inst/Makevars b/R-package/inst/Makevars new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/R-package/inst/Makevars @@ -0,0 +1 @@ + diff --git a/R-package/inst/make-r-def.R b/R-package/inst/make-r-def.R new file mode 100644 index 0000000..ddde385 --- /dev/null +++ b/R-package/inst/make-r-def.R @@ -0,0 +1,98 @@ +# [description] +# Create a definition file (.def) from a .dll file, using objdump. +# +# [usage] +# +# Rscript make-r-def.R something.dll something.def +# +# [references] +# * https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html + +args <- commandArgs(trailingOnly = TRUE) + +IN_DLL_FILE <- args[[1L]] +OUT_DEF_FILE <- args[[2L]] +DLL_BASE_NAME <- basename(IN_DLL_FILE) + +message(sprintf("Creating '%s' from '%s'", OUT_DEF_FILE, IN_DLL_FILE)) + +# system() will not raise an R exception if the process called +# fails. Wrapping it here to get that behavior. +# +# system() introduces a lot of overhead, at least on Windows, +# so trying processx if it is available +.pipe_shell_command_to_stdout <- function(command, args, out_file) { + has_processx <- suppressMessages({ + suppressWarnings({ + require("processx") # nolint: undesirable_function. + }) + }) + if (has_processx) { + p <- processx::process$new( + command = command + , args = args + , stdout = out_file + , windows_verbatim_args = FALSE + ) + invisible(p$wait()) + } else { + message(paste0( + "Using system2() to run shell commands. Installing " + , "'processx' with install.packages('processx') might " + , "make this faster." + )) + # shQuote() is necessary here since one of the arguments + # is a file-path to R.dll, which may have spaces. processx + # does such quoting but system2() does not + exit_code <- system2( + command = command + , args = shoQuote(args) + , stdout = out_file + ) + if (exit_code != 0L) { + stop(paste0("Command failed with exit code: ", exit_code)) + } + } + return(invisible(NULL)) +} + +# use objdump to dump all the symbols +OBJDUMP_FILE <- "objdump-out.txt" +.pipe_shell_command_to_stdout( + command = "objdump" + , args = c("-p", IN_DLL_FILE) + , out_file = OBJDUMP_FILE +) + +objdump_results <- readLines(OBJDUMP_FILE) +invisible(file.remove(OBJDUMP_FILE)) + +# Only one table in the objdump results matters for our purposes, +# see https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html +start_index <- which( + grepl( + pattern = "[Ordinal/Name Pointer] Table" # nolint: non_portable_path. + , x = objdump_results + , fixed = TRUE + ) +) +empty_lines <- which(objdump_results == "") +end_of_table <- empty_lines[empty_lines > start_index][1L] + +# Read the contents of the table +exported_symbols <- objdump_results[(start_index + 1L):end_of_table] +exported_symbols <- gsub("\t", "", exported_symbols, fixed = TRUE) +exported_symbols <- gsub(".*\\] ", "", exported_symbols) +exported_symbols <- gsub(" ", "", exported_symbols, fixed = TRUE) + +# Write R.def file +writeLines( + text = c( + paste0("LIBRARY \"", DLL_BASE_NAME, "\"") + , "EXPORTS" + , exported_symbols + ) + , con = OUT_DEF_FILE + , sep = "\n" +) +message(sprintf("Successfully created '%s'", OUT_DEF_FILE)) diff --git a/R-package/man/agaricus.test.Rd b/R-package/man/agaricus.test.Rd new file mode 100644 index 0000000..de0fe26 --- /dev/null +++ b/R-package/man/agaricus.test.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lightgbm.R +\docType{data} +\name{agaricus.test} +\alias{agaricus.test} +\title{Test part from Mushroom Data Set} +\format{ +A list containing a label vector, and a dgCMatrix object with 1611 +rows and 126 variables +} +\usage{ +data(agaricus.test) +} +\description{ +This data set is originally from the Mushroom data set, + UCI Machine Learning Repository. + This data set includes the following fields: + + \itemize{ + \item{\code{label}: the label for each record} + \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} + } +} +\references{ +https://archive.ics.uci.edu/ml/datasets/Mushroom + +Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository +[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, +School of Information and Computer Science. +} +\keyword{datasets} diff --git a/R-package/man/agaricus.train.Rd b/R-package/man/agaricus.train.Rd new file mode 100644 index 0000000..5af43b7 --- /dev/null +++ b/R-package/man/agaricus.train.Rd @@ -0,0 +1,31 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lightgbm.R +\docType{data} +\name{agaricus.train} +\alias{agaricus.train} +\title{Training part from Mushroom Data Set} +\format{ +A list containing a label vector, and a dgCMatrix object with 6513 +rows and 127 variables +} +\usage{ +data(agaricus.train) +} +\description{ +This data set is originally from the Mushroom data set, + UCI Machine Learning Repository. + This data set includes the following fields: + + \itemize{ + \item{\code{label}: the label for each record} + \item{\code{data}: a sparse Matrix of \code{dgCMatrix} class, with 126 columns.} + } +} +\references{ +https://archive.ics.uci.edu/ml/datasets/Mushroom + +Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository +[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, +School of Information and Computer Science. +} +\keyword{datasets} diff --git a/R-package/man/bank.Rd b/R-package/man/bank.Rd new file mode 100644 index 0000000..c74842e --- /dev/null +++ b/R-package/man/bank.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lightgbm.R +\docType{data} +\name{bank} +\alias{bank} +\title{Bank Marketing Data Set} +\format{ +A data.table with 4521 rows and 17 variables +} +\usage{ +data(bank) +} +\description{ +This data set is originally from the Bank Marketing data set, + UCI Machine Learning Repository. + + It contains only the following: bank.csv with 10% of the examples and 17 inputs, + randomly selected from 3 (older version of this dataset with less inputs). +} +\references{ +https://archive.ics.uci.edu/ml/datasets/Bank+Marketing + +S. Moro, P. Cortez and P. Rita. (2014) +A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems +} +\keyword{datasets} diff --git a/R-package/man/dim.Rd b/R-package/man/dim.Rd new file mode 100644 index 0000000..69332d0 --- /dev/null +++ b/R-package/man/dim.Rd @@ -0,0 +1,34 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{dim.lgb.Dataset} +\alias{dim.lgb.Dataset} +\title{Dimensions of an \code{lgb.Dataset}} +\usage{ +\method{dim}{lgb.Dataset}(x) +} +\arguments{ +\item{x}{Object of class \code{lgb.Dataset}} +} +\value{ +a vector of numbers of rows and of columns +} +\description{ +Returns a vector of numbers of rows and of columns in an \code{lgb.Dataset}. +} +\details{ +Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also +be directly used with an \code{lgb.Dataset} object. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) + +stopifnot(nrow(dtrain) == nrow(train$data)) +stopifnot(ncol(dtrain) == ncol(train$data)) +stopifnot(all(dim(dtrain) == dim(train$data))) +} +} diff --git a/R-package/man/dimnames.lgb.Dataset.Rd b/R-package/man/dimnames.lgb.Dataset.Rd new file mode 100644 index 0000000..85f2085 --- /dev/null +++ b/R-package/man/dimnames.lgb.Dataset.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{dimnames.lgb.Dataset} +\alias{dimnames.lgb.Dataset} +\alias{dimnames<-.lgb.Dataset} +\title{Handling of column names of \code{lgb.Dataset}} +\usage{ +\method{dimnames}{lgb.Dataset}(x) + +\method{dimnames}{lgb.Dataset}(x) <- value +} +\arguments{ +\item{x}{object of class \code{lgb.Dataset}} + +\item{value}{a list of two elements: the first one is ignored +and the second one is column names} +} +\value{ +A list with the dimension names of the dataset +} +\description{ +Only column names are supported for \code{lgb.Dataset}, thus setting of + row names would have no effect and returned row names would be NULL. +} +\details{ +Generic \code{dimnames} methods are used by \code{colnames}. +Since row names are irrelevant, it is recommended to use \code{colnames} directly. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +lgb.Dataset.construct(dtrain) +dimnames(dtrain) +colnames(dtrain) +colnames(dtrain) <- make.names(seq_len(ncol(train$data))) +print(dtrain, verbose = TRUE) +} +} diff --git a/R-package/man/figures/logo.svg b/R-package/man/figures/logo.svg new file mode 100644 index 0000000..4af2394 --- /dev/null +++ b/R-package/man/figures/logo.svg @@ -0,0 +1,41 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/R-package/man/getLGBMThreads.Rd b/R-package/man/getLGBMThreads.Rd new file mode 100644 index 0000000..21af4f4 --- /dev/null +++ b/R-package/man/getLGBMThreads.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/multithreading.R +\name{getLGBMThreads} +\alias{getLGBMThreads} +\alias{getLGBMthreads} +\title{Get default number of threads used by LightGBM} +\usage{ +getLGBMthreads() +} +\value{ +number of threads as an integer. \code{-1} means that in situations where parameter \code{num_threads} is + not explicitly supplied, LightGBM will choose a number of threads to use automatically. +} +\description{ +LightGBM attempts to speed up many operations by using multi-threading. + The number of threads used in those operations can be controlled via the + \code{num_threads} parameter passed through \code{params} to functions like + \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing + a model from a text file) are done via code paths that don't explicitly accept thread-control + configuration. + + Use this function to see the default number of threads LightGBM will use for such operations. +} +\seealso{ +\link{setLGBMthreads} +} diff --git a/R-package/man/get_field.Rd b/R-package/man/get_field.Rd new file mode 100644 index 0000000..e2562cc --- /dev/null +++ b/R-package/man/get_field.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{get_field} +\alias{get_field} +\alias{get_field.lgb.Dataset} +\title{Get one attribute of a \code{lgb.Dataset}} +\usage{ +get_field(dataset, field_name) + +\method{get_field}{lgb.Dataset}(dataset, field_name) +} +\arguments{ +\item{dataset}{Object of class \code{lgb.Dataset}} + +\item{field_name}{String with the name of the attribute to get. One of the following. +\itemize{ + \item \code{label}: label lightgbm learns from ; + \item \code{weight}: to do a weight rescale ; + \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to + group rows together as ordered results from the same set of candidate results to be ranked. + For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, + that means that you have 6 groups, where the first 10 records are in the first group, + records 11-30 are in the second group, etc.} + \item \code{init_score}: initial score is the base prediction lightgbm will boost from. +}} +} +\value{ +requested attribute +} +\description{ +Get one attribute of a \code{lgb.Dataset} +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +lgb.Dataset.construct(dtrain) + +labels <- lightgbm::get_field(dtrain, "label") +lightgbm::set_field(dtrain, "label", 1 - labels) + +labels2 <- lightgbm::get_field(dtrain, "label") +stopifnot(all(labels2 == 1 - labels)) +} +} diff --git a/R-package/man/lgb.Dataset.Rd b/R-package/man/lgb.Dataset.Rd new file mode 100644 index 0000000..16b241d --- /dev/null +++ b/R-package/man/lgb.Dataset.Rd @@ -0,0 +1,82 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb.Dataset} +\alias{lgb.Dataset} +\title{Construct \code{lgb.Dataset} object} +\usage{ +lgb.Dataset( + data, + params = list(), + reference = NULL, + colnames = NULL, + categorical_feature = NULL, + free_raw_data = TRUE, + label = NULL, + weight = NULL, + group = NULL, + init_score = NULL +) +} +\arguments{ +\item{data}{a \code{matrix} object, a \code{dgCMatrix} object, +a character representing a path to a text file (CSV, TSV, or LibSVM), +or a character representing a path to a binary \code{lgb.Dataset} file} + +\item{params}{a list of parameters. See +\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ +The "Dataset Parameters" section of the documentation} for a list of parameters +and valid values.} + +\item{reference}{reference dataset. When LightGBM creates a Dataset, it does some preprocessing like binning +continuous features into histograms. If you want to apply the same bin boundaries from an existing +dataset to new \code{data}, pass that existing Dataset to this argument.} + +\item{colnames}{names of columns} + +\item{categorical_feature}{categorical features. This can either be a character vector of feature +names or an integer vector with the indices of the features (e.g. +\code{c(1L, 10L)} to say "the first and tenth columns").} + +\item{free_raw_data}{LightGBM constructs its data format, called a "Dataset", from tabular data. +By default, that Dataset object on the R side does not keep a copy of the raw data. +This reduces LightGBM's memory consumption, but it means that the Dataset object +cannot be changed after it has been constructed. If you'd prefer to be able to +change the Dataset object after construction, set \code{free_raw_data = FALSE}.} + +\item{label}{vector of labels to use as the target variable} + +\item{weight}{numeric vector of sample weights} + +\item{group}{used for learning-to-rank tasks. An integer vector describing how to +group rows together as ordered results from the same set of candidate results +to be ranked. For example, if you have a 100-document dataset with +\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, +where the first 10 records are in the first group, records 11-30 are in the +second group, etc.} + +\item{init_score}{initial score is the base prediction lightgbm will boost from} +} +\value{ +constructed dataset +} +\description{ +LightGBM does not train on raw data. + It discretizes continuous features into histogram bins, tries to + combine categorical features, and automatically handles missing and + + The \code{Dataset} class handles that preprocessing, and holds that + alternative representation of the input data. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data_file <- tempfile(fileext = ".data") +lgb.Dataset.save(dtrain, data_file) +dtrain <- lgb.Dataset(data_file) +lgb.Dataset.construct(dtrain) +} +} diff --git a/R-package/man/lgb.Dataset.construct.Rd b/R-package/man/lgb.Dataset.construct.Rd new file mode 100644 index 0000000..e400e0a --- /dev/null +++ b/R-package/man/lgb.Dataset.construct.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb.Dataset.construct} +\alias{lgb.Dataset.construct} +\title{Construct Dataset explicitly} +\usage{ +lgb.Dataset.construct(dataset) +} +\arguments{ +\item{dataset}{Object of class \code{lgb.Dataset}} +} +\value{ +constructed dataset +} +\description{ +Construct Dataset explicitly +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +lgb.Dataset.construct(dtrain) +} +} diff --git a/R-package/man/lgb.Dataset.create.valid.Rd b/R-package/man/lgb.Dataset.create.valid.Rd new file mode 100644 index 0000000..fc50dff --- /dev/null +++ b/R-package/man/lgb.Dataset.create.valid.Rd @@ -0,0 +1,95 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb.Dataset.create.valid} +\alias{lgb.Dataset.create.valid} +\title{Construct validation data} +\usage{ +lgb.Dataset.create.valid( + dataset, + data, + label = NULL, + weight = NULL, + group = NULL, + init_score = NULL, + params = list() +) +} +\arguments{ +\item{dataset}{\code{lgb.Dataset} object, training data} + +\item{data}{a \code{matrix} object, a \code{dgCMatrix} object, +a character representing a path to a text file (CSV, TSV, or LibSVM), +or a character representing a path to a binary \code{Dataset} file} + +\item{label}{vector of labels to use as the target variable} + +\item{weight}{numeric vector of sample weights} + +\item{group}{used for learning-to-rank tasks. An integer vector describing how to +group rows together as ordered results from the same set of candidate results +to be ranked. For example, if you have a 100-document dataset with +\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, +where the first 10 records are in the first group, records 11-30 are in the +second group, etc.} + +\item{init_score}{initial score is the base prediction lightgbm will boost from} + +\item{params}{a list of parameters. See +\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{ +The "Dataset Parameters" section of the documentation} for a list of parameters +and valid values. If this is an empty list (the default), the validation Dataset +will have the same parameters as the Dataset passed to argument \code{dataset}.} +} +\value{ +constructed dataset +} +\description{ +Construct validation data according to training data +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) + +# parameters can be changed between the training data and validation set, +# for example to account for training data in a text file with a header row +# and validation data in a text file without it +train_file <- tempfile(pattern = "train_", fileext = ".csv") +write.table( + data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) + , file = train_file + , sep = "," + , col.names = TRUE + , row.names = FALSE + , quote = FALSE +) + +valid_file <- tempfile(pattern = "valid_", fileext = ".csv") +write.table( + data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) + , file = valid_file + , sep = "," + , col.names = FALSE + , row.names = FALSE + , quote = FALSE +) + +dtrain <- lgb.Dataset( + data = train_file + , params = list(has_header = TRUE) +) +dtrain$construct() + +dvalid <- lgb.Dataset( + data = valid_file + , params = list(has_header = FALSE) +) +dvalid$construct() +} +} diff --git a/R-package/man/lgb.Dataset.save.Rd b/R-package/man/lgb.Dataset.save.Rd new file mode 100644 index 0000000..b03c2c5 --- /dev/null +++ b/R-package/man/lgb.Dataset.save.Rd @@ -0,0 +1,30 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb.Dataset.save} +\alias{lgb.Dataset.save} +\title{Save \code{lgb.Dataset} to a binary file} +\usage{ +lgb.Dataset.save(dataset, fname) +} +\arguments{ +\item{dataset}{object of class \code{lgb.Dataset}} + +\item{fname}{object filename of output file} +} +\value{ +the dataset you passed in +} +\description{ +Please note that \code{init_score} is not saved in binary file. + If you need it, please set it again after loading Dataset. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +lgb.Dataset.save(dtrain, tempfile(fileext = ".bin")) +} +} diff --git a/R-package/man/lgb.Dataset.set.categorical.Rd b/R-package/man/lgb.Dataset.set.categorical.Rd new file mode 100644 index 0000000..5dfcc9a --- /dev/null +++ b/R-package/man/lgb.Dataset.set.categorical.Rd @@ -0,0 +1,35 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb.Dataset.set.categorical} +\alias{lgb.Dataset.set.categorical} +\title{Set categorical feature of \code{lgb.Dataset}} +\usage{ +lgb.Dataset.set.categorical(dataset, categorical_feature) +} +\arguments{ +\item{dataset}{object of class \code{lgb.Dataset}} + +\item{categorical_feature}{categorical features. This can either be a character vector of feature +names or an integer vector with the indices of the features (e.g. +\code{c(1L, 10L)} to say "the first and tenth columns").} +} +\value{ +the dataset you passed in +} +\description{ +Set the categorical features of an \code{lgb.Dataset} object. Use this function + to tell LightGBM which features should be treated as categorical. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data_file <- tempfile(fileext = ".data") +lgb.Dataset.save(dtrain, data_file) +dtrain <- lgb.Dataset(data_file) +lgb.Dataset.set.categorical(dtrain, 1L:2L) +} +} diff --git a/R-package/man/lgb.Dataset.set.reference.Rd b/R-package/man/lgb.Dataset.set.reference.Rd new file mode 100644 index 0000000..a4efbfa --- /dev/null +++ b/R-package/man/lgb.Dataset.set.reference.Rd @@ -0,0 +1,35 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb.Dataset.set.reference} +\alias{lgb.Dataset.set.reference} +\title{Set reference of \code{lgb.Dataset}} +\usage{ +lgb.Dataset.set.reference(dataset, reference) +} +\arguments{ +\item{dataset}{object of class \code{lgb.Dataset}} + +\item{reference}{object of class \code{lgb.Dataset}} +} +\value{ +the dataset you passed in +} +\description{ +If you want to use validation data, you should set reference to training data +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +# create training Dataset +data(agaricus.train, package ="lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) + +# create a validation Dataset, using dtrain as a reference +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset(test$data, label = test$label) +lgb.Dataset.set.reference(dtest, dtrain) +} +} diff --git a/R-package/man/lgb.configure_fast_predict.Rd b/R-package/man/lgb.configure_fast_predict.Rd new file mode 100644 index 0000000..9cd4339 --- /dev/null +++ b/R-package/man/lgb.configure_fast_predict.Rd @@ -0,0 +1,144 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{lgb.configure_fast_predict} +\alias{lgb.configure_fast_predict} +\title{Configure Fast Single-Row Predictions} +\usage{ +lgb.configure_fast_predict( + model, + csr = FALSE, + start_iteration = NULL, + num_iteration = NULL, + type = "response", + params = list() +) +} +\arguments{ +\item{model}{LightGBM model object (class \code{lgb.Booster}). + + \bold{The object will be modified in-place}.} + +\item{csr}{Whether the prediction function is going to be called on sparse CSR inputs. +If \code{FALSE}, will be assumed that predictions are going to be called on single-row +regular R matrices.} + +\item{start_iteration}{int or None, optional (default=None) +Start index of the iteration to predict. +If None or <= 0, starts from the first iteration.} + +\item{num_iteration}{int or None, optional (default=None) +Limit number of iterations in the prediction. +If None, if the best iteration exists and start_iteration is None or <= 0, the +best iteration is used; otherwise, all iterations from start_iteration are used. +If <= 0, all iterations from start_iteration are used (no limits).} + +\item{type}{Type of prediction to output. Allowed types are:\itemize{ + \item \code{"response"}: will output the predicted score according to the objective function being + optimized (depending on the link function that the objective uses), after applying any necessary + transformations - for example, for \code{objective="binary"}, it will output class probabilities. + \item \code{"class"}: for classification objectives, will output the class with the highest predicted + probability. For other objectives, will output the same as "response". Note that \code{"class"} is + not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function + for more details). + \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' + results) from which the "response" number is produced for a given objective function - for example, + for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as + "regression", since no transformation is applied, the output will be the same as for "response". + \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls + in each tree in the model, outputted as integers, with one column per tree. + \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an + intercept (each feature will produce one column). + } + + Note that, if using custom objectives, types "class" and "response" will not be available and will + default towards using "raw" instead. + + If the model was fit through function \link{lightgbm} and it was passed a factor as labels, + passing the prediction type through \code{params} instead of through this argument might + result in factor levels for classification objectives not being applied correctly to the + resulting output. + + \emph{New in version 4.0.0}} + +\item{params}{a list of additional named parameters. See +\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ +the "Predict Parameters" section of the documentation} for a list of parameters and +valid values. Where these conflict with the values of keyword arguments to this function, +the values in \code{params} take precedence.} +} +\value{ +The same \code{model} that was passed as input, invisibly, with the desired + configuration stored inside it and available to be used in future calls to + \link{predict.lgb.Booster}. +} +\description{ +Pre-configures a LightGBM model object to produce fast single-row predictions + for a given input data type, prediction type, and parameters. +} +\details{ +Calling this function multiple times with different parameters might not override + the previous configuration and might trigger undefined behavior. + + Any saved configuration for fast predictions might be lost after making a single-row + prediction of a different type than what was configured (except for types "response" and + "class", which can be switched between each other at any time without losing the configuration). + + In some situations, setting a fast prediction configuration for one type of prediction + might cause the prediction function to keep using that configuration for single-row + predictions even if the requested type of prediction is different from what was configured. + + Note that this function will not accept argument \code{type="class"} - for such cases, one + can pass \code{type="response"} to this function and then \code{type="class"} to the + \code{predict} function - the fast configuration will not be lost or altered if the switch + is between "response" and "class". + + The configuration does not survive de-serializations, so it has to be generated + anew in every R process that is going to use it (e.g. if loading a model object + through \code{readRDS}, whatever configuration was there previously will be lost). + + Requesting a different prediction type or passing parameters to \link{predict.lgb.Booster} + will cause it to ignore the fast-predict configuration and take the slow route instead + (but be aware that an existing configuration might not always be overridden by supplying + different parameters or prediction type, so make sure to check that the output is what + was expected when a prediction is to be made on a single row for something different than + what is configured). + + Note that, if configuring a non-default prediction type (such as leaf indices), + then that type must also be passed in the call to \link{predict.lgb.Booster} in + order for it to use the configuration. This also applies for \code{start_iteration} + and \code{num_iteration}, but \bold{the \code{params} list must be empty} in the call to \code{predict}. + + Predictions about feature contributions do not allow a fast route for CSR inputs, + and as such, this function will produce an error if passing \code{csr=TRUE} and + \code{type = "contrib"} together. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +library(lightgbm) +data(mtcars) +X <- as.matrix(mtcars[, -1L]) +y <- mtcars[, 1L] +dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) +params <- list( + min_data_in_leaf = 2L + , num_threads = 2L +) +model <- lgb.train( + params = params + , data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = -1L +) +lgb.configure_fast_predict(model) + +x_single <- X[11L, , drop = FALSE] +predict(model, x_single) + +# Will not use it if the prediction to be made +# is different from what was configured +predict(model, x_single, type = "leaf") +} +} diff --git a/R-package/man/lgb.convert_with_rules.Rd b/R-package/man/lgb.convert_with_rules.Rd new file mode 100644 index 0000000..a8a6a00 --- /dev/null +++ b/R-package/man/lgb.convert_with_rules.Rd @@ -0,0 +1,74 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.convert_with_rules.R +\name{lgb.convert_with_rules} +\alias{lgb.convert_with_rules} +\title{Data preparator for LightGBM datasets with rules (integer)} +\usage{ +lgb.convert_with_rules(data, rules = NULL) +} +\arguments{ +\item{data}{A data.frame or data.table to prepare.} + +\item{rules}{A set of rules from the data preparator, if already used. This should be an R list, +where names are column names in \code{data} and values are named character +vectors whose names are column values and whose values are new values to +replace them with.} +} +\value{ +A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). + Note that the data must be converted to a matrix format (\code{as.matrix}) for input in + \code{lgb.Dataset}. +} +\description{ +Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. + Factor, character, and logical columns are converted to integer. Missing values + in factors and characters will be filled with 0L. Missing values in logicals + will be filled with -1L. + + This function returns and optionally takes in "rules" the describe exactly + how to convert values in columns. + + Columns that contain only NA values will be converted by this function but will + not show up in the returned \code{rules}. + + NOTE: In previous releases of LightGBM, this function was called \code{lgb.prepare_rules2}. +} +\examples{ +\donttest{ +data(iris) + +str(iris) + +new_iris <- lgb.convert_with_rules(data = iris) +str(new_iris$data) + +data(iris) # Erase iris dataset +iris$Species[1L] <- "NEW FACTOR" # Introduce junk factor (NA) + +# Use conversion using known rules +# Unknown factors become 0, excellent for sparse datasets +newer_iris <- lgb.convert_with_rules(data = iris, rules = new_iris$rules) + +# Unknown factor is now zero, perfect for sparse datasets +newer_iris$data[1L, ] # Species became 0 as it is an unknown factor + +newer_iris$data[1L, 5L] <- 1.0 # Put back real initial value + +# Is the newly created dataset equal? YES! +all.equal(new_iris$data, newer_iris$data) + +# Can we test our own rules? +data(iris) # Erase iris dataset + +# We remapped values differently +personal_rules <- list( + Species = c( + "setosa" = 3L + , "versicolor" = 2L + , "virginica" = 1L + ) +) +newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules) +str(newest_iris$data) # SUCCESS! +} +} diff --git a/R-package/man/lgb.cv.Rd b/R-package/man/lgb.cv.Rd new file mode 100644 index 0000000..2fa22f1 --- /dev/null +++ b/R-package/man/lgb.cv.Rd @@ -0,0 +1,164 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.cv.R +\name{lgb.cv} +\alias{lgb.cv} +\title{Main CV logic for LightGBM} +\usage{ +lgb.cv( + params = list(), + data, + nrounds = 100L, + nfold = 3L, + obj = NULL, + eval = NULL, + verbose = 1L, + record = TRUE, + eval_freq = 1L, + showsd = TRUE, + stratified = TRUE, + folds = NULL, + init_model = NULL, + early_stopping_rounds = NULL, + callbacks = list(), + reset_data = FALSE, + serializable = TRUE, + eval_train_metric = FALSE +) +} +\arguments{ +\item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ +the "Parameters" section of the documentation} for a list of parameters and valid values.} + +\item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, +may allow you to pass other types of data like \code{matrix} and then separately supply +\code{label} as a keyword argument.} + +\item{nrounds}{number of training rounds} + +\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.} + +\item{obj}{objective function, can be character or custom objective function. Examples include +\code{regression}, \code{regression_l1}, \code{huber}, +\code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} + +\item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of + strings and functions. + + \itemize{ + \item{\bold{a. character vector}: + If you provide a character vector to this argument, it should contain strings with valid + evaluation metrics. + See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ + The "metric" section of the documentation} + for a list of valid metrics. + } + \item{\bold{b. function}: + You can provide a custom evaluation function. This + should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named + list with three elements: + \itemize{ + \item{\code{name}: A string with the name of the metric, used for printing + and storing results. + } + \item{\code{value}: A single number indicating the value of the metric for the + given predictions and true values + } + \item{ + \code{higher_better}: A boolean indicating whether higher values indicate a better fit. + For example, this would be \code{FALSE} for metrics like MAE or RMSE. + } + } + } + \item{\bold{c. list}: + If a list is given, it should only contain character vectors and functions. + These should follow the requirements from the descriptions above. + } + }} + +\item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the +printing of evaluation during training} + +\item{record}{Boolean, TRUE will record iteration message to \code{booster$record_evals}} + +\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} + +\item{showsd}{\code{boolean}, whether to show standard deviation of cross validation. +This parameter defaults to \code{TRUE}. Setting it to \code{FALSE} can lead to a +slight speedup by avoiding unnecessary computation.} + +\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified +by the values of outcome labels.} + +\item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds +(each element must be a vector of test fold's indices). When folds are supplied, +the \code{nfold} and \code{stratified} parameters are ignored.} + +\item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} + +\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, +training will stop if the evaluation of any metric on any validation set +fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. +If training stops early, the returned model will have attribute \code{best_iter} +set to the iteration number of the best iteration.} + +\item{callbacks}{List of callback functions that are applied at each iteration.} + +\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model +into a predictor model which frees up memory and the original datasets} + +\item{serializable}{whether to make the resulting objects serializable through functions such as +\code{save} or \code{saveRDS} (see section "Model serialization").} + +\item{eval_train_metric}{\code{boolean}, whether to add the cross validation results on the +training data. This parameter defaults to \code{FALSE}. Setting it to \code{TRUE} +will increase run time.} +} +\value{ +a trained model \code{lgb.CVBooster}. +} +\description{ +Cross validation logic used by LightGBM +} +\section{Early Stopping}{ + + + "early stopping" refers to stopping the training process if the model's performance on a given + validation set does not improve for several consecutive iterations. + + If multiple arguments are given to \code{eval}, their order will be preserved. If you enable + early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all + metrics will be considered for early stopping. + + If you want to only consider the first metric for early stopping, pass + \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} + in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, + a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) + or \code{objective} (passed into \code{params}). + + \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored + and early stopping will not be performed. +} + +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , num_threads = 2L +) +model <- lgb.cv( + params = params + , data = dtrain + , nrounds = 5L + , nfold = 3L +) +} + +} diff --git a/R-package/man/lgb.drop_serialized.Rd b/R-package/man/lgb.drop_serialized.Rd new file mode 100644 index 0000000..ab4642b --- /dev/null +++ b/R-package/man/lgb.drop_serialized.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.drop_serialized.R +\name{lgb.drop_serialized} +\alias{lgb.drop_serialized} +\title{Drop serialized raw bytes in a LightGBM model object} +\usage{ +lgb.drop_serialized(model) +} +\arguments{ +\item{model}{\code{lgb.Booster} object which was produced with `serializable=TRUE`.} +} +\value{ +\code{lgb.Booster} (the same `model` object that was passed as input, as invisible). +} +\description{ +If a LightGBM model object was produced with argument `serializable=TRUE`, the R object will keep +a copy of the underlying C++ object as raw bytes, which can be used to reconstruct such object after getting +serialized and de-serialized, but at the cost of extra memory usage. If these raw bytes are not needed anymore, +they can be dropped through this function in order to save memory. Note that the object will be modified in-place. + + \emph{New in version 4.0.0} +} +\seealso{ +\link{lgb.restore_handle}, \link{lgb.make_serializable}. +} diff --git a/R-package/man/lgb.dump.Rd b/R-package/man/lgb.dump.Rd new file mode 100644 index 0000000..e356251 --- /dev/null +++ b/R-package/man/lgb.dump.Rd @@ -0,0 +1,54 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{lgb.dump} +\alias{lgb.dump} +\title{Dump LightGBM model to json} +\usage{ +lgb.dump(booster, num_iteration = NULL, start_iteration = 1L) +} +\arguments{ +\item{booster}{Object of class \code{lgb.Booster}} + +\item{num_iteration}{Number of iterations to be dumped. NULL or <= 0 means use best iteration} + +\item{start_iteration}{Index (1-based) of the first boosting round to dump. + For example, passing \code{start_iteration=5, num_iteration=3} for a regression model + means "dump the fifth, sixth, and seventh tree" + + \emph{New in version 4.4.0}} +} +\value{ +json format of model +} +\description{ +Dump LightGBM model to json +} +\examples{ +\donttest{ +library(lightgbm) +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , num_threads = 2L +) +valids <- list(test = dtest) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 10L + , valids = valids + , early_stopping_rounds = 5L +) +json_model <- lgb.dump(model) +} +} diff --git a/R-package/man/lgb.get.eval.result.Rd b/R-package/man/lgb.get.eval.result.Rd new file mode 100644 index 0000000..0dc7eb0 --- /dev/null +++ b/R-package/man/lgb.get.eval.result.Rd @@ -0,0 +1,69 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{lgb.get.eval.result} +\alias{lgb.get.eval.result} +\title{Get record evaluation result from booster} +\usage{ +lgb.get.eval.result( + booster, + data_name, + eval_name, + iters = NULL, + is_err = FALSE +) +} +\arguments{ +\item{booster}{Object of class \code{lgb.Booster}} + +\item{data_name}{Name of the dataset to return evaluation results for.} + +\item{eval_name}{Name of the evaluation metric to return results for.} + +\item{iters}{An integer vector of iterations you want to get evaluation results for. If NULL +(the default), evaluation results for all iterations will be returned.} + +\item{is_err}{TRUE will return evaluation error instead} +} +\value{ +numeric vector of evaluation result +} +\description{ +Given a \code{lgb.Booster}, return evaluation results for a + particular metric on a particular dataset. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +# train a regression model +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , num_threads = 2L +) +valids <- list(test = dtest) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 5L + , valids = valids +) + +# Examine valid data_name values +print(setdiff(names(model$record_evals), "start_iter")) + +# Examine valid eval_name values for dataset "test" +print(names(model$record_evals[["test"]])) + +# Get L2 values for "test" dataset +lgb.get.eval.result(model, "test", "l2") +} +} diff --git a/R-package/man/lgb.importance.Rd b/R-package/man/lgb.importance.Rd new file mode 100644 index 0000000..5099643 --- /dev/null +++ b/R-package/man/lgb.importance.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.importance.R +\name{lgb.importance} +\alias{lgb.importance} +\title{Compute feature importance in a model} +\usage{ +lgb.importance(model, percentage = TRUE) +} +\arguments{ +\item{model}{object of class \code{lgb.Booster}.} + +\item{percentage}{whether to show importance in relative percentage.} +} +\value{ +For a tree model, a \code{data.table} with the following columns: +\itemize{ + \item{\code{Feature}: Feature names in the model.} + \item{\code{Gain}: The total gain of this feature's splits.} + \item{\code{Cover}: The number of observation related to this feature.} + \item{\code{Frequency}: The number of times a feature split in trees.} +} +} +\description{ +Creates a \code{data.table} of feature importances in a model. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) + +params <- list( + objective = "binary" + , learning_rate = 0.1 + , max_depth = -1L + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , num_threads = 2L +) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 5L +) + +tree_imp1 <- lgb.importance(model, percentage = TRUE) +tree_imp2 <- lgb.importance(model, percentage = FALSE) +} +} diff --git a/R-package/man/lgb.interpret.Rd b/R-package/man/lgb.interpret.Rd new file mode 100644 index 0000000..0526125 --- /dev/null +++ b/R-package/man/lgb.interpret.Rd @@ -0,0 +1,63 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.interpret.R +\name{lgb.interpret} +\alias{lgb.interpret} +\title{Compute feature contribution of prediction} +\usage{ +lgb.interpret(model, data, idxset, num_iteration = NULL) +} +\arguments{ +\item{model}{object of class \code{lgb.Booster}.} + +\item{data}{a matrix object or a dgCMatrix object.} + +\item{idxset}{an integer vector of indices of rows needed.} + +\item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration.} +} +\value{ +For regression, binary classification and lambdarank model, a \code{list} of \code{data.table} + with the following columns: + \itemize{ + \item{\code{Feature}: Feature names in the model.} + \item{\code{Contribution}: The total contribution of this feature's splits.} + } + For multiclass classification, a \code{list} of \code{data.table} with the Feature column and + Contribution columns to each class. +} +\description{ +Computes feature contribution components of rawscore prediction. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +Logit <- function(x) log(x / (1.0 - x)) +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +set_field( + dataset = dtrain + , field_name = "init_score" + , data = rep(Logit(mean(train$label)), length(train$label)) +) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test + +params <- list( + objective = "binary" + , learning_rate = 0.1 + , max_depth = -1L + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , num_threads = 2L +) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 3L +) + +tree_interpretation <- lgb.interpret(model, test$data, 1L:5L) +} +} diff --git a/R-package/man/lgb.interprete.Rd b/R-package/man/lgb.interprete.Rd new file mode 100644 index 0000000..0a0e74f --- /dev/null +++ b/R-package/man/lgb.interprete.Rd @@ -0,0 +1,14 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.interpret.R +\name{lgb.interprete} +\alias{lgb.interprete} +\title{DEPRECATED - use lgb.interpret() instead} +\usage{ +lgb.interprete(...) +} +\arguments{ +\item{...}{Arguments passed through to \code{lgb.interpret}} +} +\description{ +Alias for \code{lgb.interpret}. +} diff --git a/R-package/man/lgb.load.Rd b/R-package/man/lgb.load.Rd new file mode 100644 index 0000000..f145db5 --- /dev/null +++ b/R-package/man/lgb.load.Rd @@ -0,0 +1,52 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{lgb.load} +\alias{lgb.load} +\title{Load LightGBM model} +\usage{ +lgb.load(filename = NULL, model_str = NULL) +} +\arguments{ +\item{filename}{path of model file} + +\item{model_str}{a str containing the model (as a \code{character} or \code{raw} vector)} +} +\value{ +lgb.Booster +} +\description{ +Load LightGBM takes in either a file path or model string. + If both are provided, Load will default to loading from file +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , num_threads = 2L +) +valids <- list(test = dtest) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 5L + , valids = valids + , early_stopping_rounds = 3L +) +model_file <- tempfile(fileext = ".txt") +lgb.save(model, model_file) +load_booster <- lgb.load(filename = model_file) +model_string <- model$save_model_to_string(NULL) # saves best iteration +load_booster_from_str <- lgb.load(model_str = model_string) +} +} diff --git a/R-package/man/lgb.make_serializable.Rd b/R-package/man/lgb.make_serializable.Rd new file mode 100644 index 0000000..476b934 --- /dev/null +++ b/R-package/man/lgb.make_serializable.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.make_serializable.R +\name{lgb.make_serializable} +\alias{lgb.make_serializable} +\title{Make a LightGBM object serializable by keeping raw bytes} +\usage{ +lgb.make_serializable(model) +} +\arguments{ +\item{model}{\code{lgb.Booster} object which was produced with `serializable=FALSE`.} +} +\value{ +\code{lgb.Booster} (the same `model` object that was passed as input, as invisible). +} +\description{ +If a LightGBM model object was produced with argument `serializable=FALSE`, the R object will not +be serializable (e.g. cannot save and load with \code{saveRDS} and \code{readRDS}) as it will lack the raw bytes +needed to reconstruct its underlying C++ object. This function can be used to forcibly produce those serialized +raw bytes and make the object serializable. Note that the object will be modified in-place. + + \emph{New in version 4.0.0} +} +\seealso{ +\link{lgb.restore_handle}, \link{lgb.drop_serialized}. +} diff --git a/R-package/man/lgb.model.dt.tree.Rd b/R-package/man/lgb.model.dt.tree.Rd new file mode 100644 index 0000000..df36b6a --- /dev/null +++ b/R-package/man/lgb.model.dt.tree.Rd @@ -0,0 +1,67 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.model.dt.tree.R +\name{lgb.model.dt.tree} +\alias{lgb.model.dt.tree} +\title{Parse a LightGBM model json dump} +\usage{ +lgb.model.dt.tree(model, num_iteration = NULL, start_iteration = 1L) +} +\arguments{ +\item{model}{object of class \code{lgb.Booster}.} + +\item{num_iteration}{Number of iterations to include. NULL or <= 0 means use best iteration.} + +\item{start_iteration}{Index (1-based) of the first boosting round to include in the output. + For example, passing \code{start_iteration=5, num_iteration=3} for a regression model + means "return information about the fifth, sixth, and seventh trees". + + \emph{New in version 4.4.0}} +} +\value{ +A \code{data.table} with detailed information about model trees' nodes and leaves. + +The columns of the \code{data.table} are: + +\itemize{ + \item{\code{tree_index}: ID of a tree in a model (integer)} + \item{\code{split_index}: ID of a node in a tree (integer)} + \item{\code{split_feature}: for a node, it's a feature name (character); + for a leaf, it simply labels it as \code{"NA"}} + \item{\code{node_parent}: ID of the parent node for current node (integer)} + \item{\code{leaf_index}: ID of a leaf in a tree (integer)} + \item{\code{leaf_parent}: ID of the parent node for current leaf (integer)} + \item{\code{split_gain}: Split gain of a node} + \item{\code{threshold}: Splitting threshold value of a node} + \item{\code{decision_type}: Decision type of a node} + \item{\code{default_left}: Determine how to handle NA value, TRUE -> Left, FALSE -> Right} + \item{\code{internal_value}: Node value} + \item{\code{internal_count}: The number of observation collected by a node} + \item{\code{leaf_value}: Leaf value} + \item{\code{leaf_count}: The number of observation collected by a leaf} +} +} +\description{ +Parse a LightGBM model json dump into a \code{data.table} structure. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) + +params <- list( + objective = "binary" + , learning_rate = 0.01 + , num_leaves = 63L + , max_depth = -1L + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , num_threads = 2L +) +model <- lgb.train(params, dtrain, 10L) + +tree_dt <- lgb.model.dt.tree(model) +} +} diff --git a/R-package/man/lgb.plot.importance.Rd b/R-package/man/lgb.plot.importance.Rd new file mode 100644 index 0000000..bdf354d --- /dev/null +++ b/R-package/man/lgb.plot.importance.Rd @@ -0,0 +1,64 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.plot.importance.R +\name{lgb.plot.importance} +\alias{lgb.plot.importance} +\title{Plot feature importance as a bar graph} +\usage{ +lgb.plot.importance( + tree_imp, + top_n = 10L, + measure = "Gain", + left_margin = 10L, + cex = NULL +) +} +\arguments{ +\item{tree_imp}{a \code{data.table} returned by \code{\link{lgb.importance}}.} + +\item{top_n}{maximal number of top features to include into the plot.} + +\item{measure}{the name of importance measure to plot, can be "Gain", "Cover" or "Frequency".} + +\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.} + +\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{\link[graphics]{barplot}}. +Set a number smaller than 1.0 to make the bar labels smaller than R's default and values +greater than 1.0 to make them larger.} +} +\value{ +The \code{lgb.plot.importance} function creates a \code{barplot} +and silently returns a processed data.table with \code{top_n} features sorted by defined importance. +} +\description{ +Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph. +} +\details{ +The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature. +Features are shown ranked in a decreasing importance order. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) + +params <- list( + objective = "binary" + , learning_rate = 0.1 + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , num_threads = 2L +) + +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 5L +) + +tree_imp <- lgb.importance(model, percentage = TRUE) +lgb.plot.importance(tree_imp, top_n = 5L, measure = "Gain") +} +} diff --git a/R-package/man/lgb.plot.interpretation.Rd b/R-package/man/lgb.plot.interpretation.Rd new file mode 100644 index 0000000..b7905cc --- /dev/null +++ b/R-package/man/lgb.plot.interpretation.Rd @@ -0,0 +1,81 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.plot.interpretation.R +\name{lgb.plot.interpretation} +\alias{lgb.plot.interpretation} +\title{Plot feature contribution as a bar graph} +\usage{ +lgb.plot.interpretation( + tree_interpretation_dt, + top_n = 10L, + cols = 1L, + left_margin = 10L, + cex = NULL +) +} +\arguments{ +\item{tree_interpretation_dt}{a \code{data.table} returned by \code{\link{lgb.interpret}}.} + +\item{top_n}{maximal number of top features to include into the plot.} + +\item{cols}{the column numbers of layout, will be used only for multiclass classification feature contribution.} + +\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.} + +\item{cex}{(base R barplot) passed as \code{cex.names} parameter to \code{barplot}.} +} +\value{ +The \code{lgb.plot.interpretation} function creates a \code{barplot}. +} +\description{ +Plot previously calculated feature contribution as a bar graph. +} +\details{ +The graph represents each feature as a horizontal bar of length proportional to the defined +contribution of a feature. Features are shown ranked in a decreasing contribution order. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +Logit <- function(x) { + log(x / (1.0 - x)) +} +data(agaricus.train, package = "lightgbm") +labels <- agaricus.train$label +dtrain <- lgb.Dataset( + agaricus.train$data + , label = labels +) +set_field( + dataset = dtrain + , field_name = "init_score" + , data = rep(Logit(mean(labels)), length(labels)) +) + +data(agaricus.test, package = "lightgbm") + +params <- list( + objective = "binary" + , learning_rate = 0.1 + , max_depth = -1L + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , num_threads = 2L +) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 5L +) + +tree_interpretation <- lgb.interpret( + model = model + , data = agaricus.test$data + , idxset = 1L:5L +) +lgb.plot.interpretation( + tree_interpretation_dt = tree_interpretation[[1L]] + , top_n = 3L +) +} +} diff --git a/R-package/man/lgb.restore_handle.Rd b/R-package/man/lgb.restore_handle.Rd new file mode 100644 index 0000000..37922c0 --- /dev/null +++ b/R-package/man/lgb.restore_handle.Rd @@ -0,0 +1,54 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.restore_handle.R +\name{lgb.restore_handle} +\alias{lgb.restore_handle} +\title{Restore the C++ component of a de-serialized LightGBM model} +\usage{ +lgb.restore_handle(model) +} +\arguments{ +\item{model}{\code{lgb.Booster} object which was de-serialized and whose underlying C++ object and R handle +need to be restored.} +} +\value{ +\code{lgb.Booster} (the same `model` object that was passed as input, invisibly). +} +\description{ +After a LightGBM model object is de-serialized through functions such as \code{save} or +\code{saveRDS}, its underlying C++ object will be blank and needs to be restored to able to use it. Such +object is restored automatically when calling functions such as \code{predict}, but this function can be +used to forcibly restore it beforehand. Note that the object will be modified in-place. + + \emph{New in version 4.0.0} +} +\details{ +Be aware that fast single-row prediction configurations are not restored through this +function. If you wish to make fast single-row predictions using a \code{lgb.Booster} loaded this way, +call \link{lgb.configure_fast_predict} on the loaded \code{lgb.Booster} object. +} +\examples{ +\donttest{ +library(lightgbm) +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data("agaricus.train") +model <- lightgbm( + agaricus.train$data + , agaricus.train$label + , params = list(objective = "binary") + , nrounds = 5L + , verbose = 0 + , num_threads = 2L +) +fname <- tempfile(fileext="rds") +saveRDS(model, fname) + +model_new <- readRDS(fname) +model_new$check_null_handle() +lgb.restore_handle(model_new) +model_new$check_null_handle() +} +} +\seealso{ +\link{lgb.make_serializable}, \link{lgb.drop_serialized}. +} diff --git a/R-package/man/lgb.save.Rd b/R-package/man/lgb.save.Rd new file mode 100644 index 0000000..3a532e9 --- /dev/null +++ b/R-package/man/lgb.save.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{lgb.save} +\alias{lgb.save} +\title{Save LightGBM model} +\usage{ +lgb.save(booster, filename, num_iteration = NULL, start_iteration = 1L) +} +\arguments{ +\item{booster}{Object of class \code{lgb.Booster}} + +\item{filename}{Saved filename} + +\item{num_iteration}{Number of iterations to save, NULL or <= 0 means use best iteration} + +\item{start_iteration}{Index (1-based) of the first boosting round to save. + For example, passing \code{start_iteration=5, num_iteration=3} for a regression model + means "save the fifth, sixth, and seventh tree" + + \emph{New in version 4.4.0}} +} +\value{ +lgb.Booster +} +\description{ +Save LightGBM model +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +library(lightgbm) +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , num_threads = 2L +) +valids <- list(test = dtest) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 10L + , valids = valids + , early_stopping_rounds = 5L +) +lgb.save(model, tempfile(fileext = ".txt")) +} +} diff --git a/R-package/man/lgb.slice.Dataset.Rd b/R-package/man/lgb.slice.Dataset.Rd new file mode 100644 index 0000000..c40ec0d --- /dev/null +++ b/R-package/man/lgb.slice.Dataset.Rd @@ -0,0 +1,35 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb.slice.Dataset} +\alias{lgb.slice.Dataset} +\title{Slice a dataset} +\usage{ +lgb.slice.Dataset(dataset, idxset) +} +\arguments{ +\item{dataset}{Object of class \code{lgb.Dataset}} + +\item{idxset}{an integer vector of indices of rows needed} +} +\value{ +constructed sub dataset +} +\description{ +Get a new \code{lgb.Dataset} containing the specified rows of + original \code{lgb.Dataset} object + + \emph{Renamed from} \code{slice()} \emph{in 4.4.0} +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) + +dsub <- lgb.slice.Dataset(dtrain, seq_len(42L)) +lgb.Dataset.construct(dsub) +labels <- lightgbm::get_field(dsub, "label") +} +} diff --git a/R-package/man/lgb.train.Rd b/R-package/man/lgb.train.Rd new file mode 100644 index 0000000..2a527ce --- /dev/null +++ b/R-package/man/lgb.train.Rd @@ -0,0 +1,153 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.train.R +\name{lgb.train} +\alias{lgb.train} +\title{Main training logic for LightGBM} +\usage{ +lgb.train( + params = list(), + data, + nrounds = 100L, + valids = list(), + obj = NULL, + eval = NULL, + verbose = 1L, + record = TRUE, + eval_freq = 1L, + init_model = NULL, + early_stopping_rounds = NULL, + callbacks = list(), + reset_data = FALSE, + serializable = TRUE +) +} +\arguments{ +\item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ +the "Parameters" section of the documentation} for a list of parameters and valid values.} + +\item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, +may allow you to pass other types of data like \code{matrix} and then separately supply +\code{label} as a keyword argument.} + +\item{nrounds}{number of training rounds} + +\item{valids}{a list of \code{lgb.Dataset} objects, used for validation} + +\item{obj}{objective function, can be character or custom objective function. Examples include +\code{regression}, \code{regression_l1}, \code{huber}, +\code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} + +\item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of + strings and functions. + + \itemize{ + \item{\bold{a. character vector}: + If you provide a character vector to this argument, it should contain strings with valid + evaluation metrics. + See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ + The "metric" section of the documentation} + for a list of valid metrics. + } + \item{\bold{b. function}: + You can provide a custom evaluation function. This + should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named + list with three elements: + \itemize{ + \item{\code{name}: A string with the name of the metric, used for printing + and storing results. + } + \item{\code{value}: A single number indicating the value of the metric for the + given predictions and true values + } + \item{ + \code{higher_better}: A boolean indicating whether higher values indicate a better fit. + For example, this would be \code{FALSE} for metrics like MAE or RMSE. + } + } + } + \item{\bold{c. list}: + If a list is given, it should only contain character vectors and functions. + These should follow the requirements from the descriptions above. + } + }} + +\item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the +printing of evaluation during training} + +\item{record}{Boolean, TRUE will record iteration message to \code{booster$record_evals}} + +\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} + +\item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} + +\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, +training will stop if the evaluation of any metric on any validation set +fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. +If training stops early, the returned model will have attribute \code{best_iter} +set to the iteration number of the best iteration.} + +\item{callbacks}{List of callback functions that are applied at each iteration.} + +\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the +booster model into a predictor model which frees up memory and the +original datasets} + +\item{serializable}{whether to make the resulting objects serializable through functions such as +\code{save} or \code{saveRDS} (see section "Model serialization").} +} +\value{ +a trained booster model \code{lgb.Booster}. +} +\description{ +Low-level R interface to train a LightGBM model. Unlike \code{\link{lightgbm}}, + this function is focused on performance (e.g. speed, memory efficiency). It is also + less likely to have breaking API changes in new releases than \code{\link{lightgbm}}. +} +\section{Early Stopping}{ + + + "early stopping" refers to stopping the training process if the model's performance on a given + validation set does not improve for several consecutive iterations. + + If multiple arguments are given to \code{eval}, their order will be preserved. If you enable + early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all + metrics will be considered for early stopping. + + If you want to only consider the first metric for early stopping, pass + \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} + in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, + a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) + or \code{objective} (passed into \code{params}). + + \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored + and early stopping will not be performed. +} + +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , num_threads = 2L +) +valids <- list(test = dtest) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 5L + , valids = valids + , early_stopping_rounds = 3L +) +} + +} diff --git a/R-package/man/lgb_predict_shared_params.Rd b/R-package/man/lgb_predict_shared_params.Rd new file mode 100644 index 0000000..71a3941 --- /dev/null +++ b/R-package/man/lgb_predict_shared_params.Rd @@ -0,0 +1,57 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{lgb_predict_shared_params} +\alias{lgb_predict_shared_params} +\title{Shared prediction parameter docs} +\arguments{ +\item{type}{Type of prediction to output. Allowed types are:\itemize{ + \item \code{"response"}: will output the predicted score according to the objective function being + optimized (depending on the link function that the objective uses), after applying any necessary + transformations - for example, for \code{objective="binary"}, it will output class probabilities. + \item \code{"class"}: for classification objectives, will output the class with the highest predicted + probability. For other objectives, will output the same as "response". Note that \code{"class"} is + not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function + for more details). + \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' + results) from which the "response" number is produced for a given objective function - for example, + for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as + "regression", since no transformation is applied, the output will be the same as for "response". + \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls + in each tree in the model, outputted as integers, with one column per tree. + \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an + intercept (each feature will produce one column). + } + + Note that, if using custom objectives, types "class" and "response" will not be available and will + default towards using "raw" instead. + + If the model was fit through function \link{lightgbm} and it was passed a factor as labels, + passing the prediction type through \code{params} instead of through this argument might + result in factor levels for classification objectives not being applied correctly to the + resulting output. + + \emph{New in version 4.0.0}} + +\item{start_iteration}{int or None, optional (default=None) +Start index of the iteration to predict. +If None or <= 0, starts from the first iteration.} + +\item{num_iteration}{int or None, optional (default=None) +Limit number of iterations in the prediction. +If None, if the best iteration exists and start_iteration is None or <= 0, the +best iteration is used; otherwise, all iterations from start_iteration are used. +If <= 0, all iterations from start_iteration are used (no limits).} + +\item{params}{a list of additional named parameters. See +\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ +the "Predict Parameters" section of the documentation} for a list of parameters and +valid values. Where these conflict with the values of keyword arguments to this function, +the values in \code{params} take precedence.} +} +\description{ +Shared prediction parameter docs +} +\details{ +This page contains shared documentation for prediction-related parameters used throughout the package. +} +\keyword{internal} diff --git a/R-package/man/lgb_shared_dataset_params.Rd b/R-package/man/lgb_shared_dataset_params.Rd new file mode 100644 index 0000000..5e51d46 --- /dev/null +++ b/R-package/man/lgb_shared_dataset_params.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{lgb_shared_dataset_params} +\alias{lgb_shared_dataset_params} +\title{Shared Dataset parameter docs} +\arguments{ +\item{label}{vector of labels to use as the target variable} + +\item{weight}{numeric vector of sample weights} + +\item{init_score}{initial score is the base prediction lightgbm will boost from} + +\item{group}{used for learning-to-rank tasks. An integer vector describing how to +group rows together as ordered results from the same set of candidate results +to be ranked. For example, if you have a 100-document dataset with +\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups, +where the first 10 records are in the first group, records 11-30 are in the +second group, etc.} +} +\description{ +Parameter docs for fields used in \code{lgb.Dataset} construction +} +\details{ +This page contains shared documentation for dataset-related parameters used throughout the package. +} +\keyword{internal} diff --git a/R-package/man/lgb_shared_params.Rd b/R-package/man/lgb_shared_params.Rd new file mode 100644 index 0000000..0394f66 --- /dev/null +++ b/R-package/man/lgb_shared_params.Rd @@ -0,0 +1,115 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lightgbm.R +\name{lgb_shared_params} +\alias{lgb_shared_params} +\title{Shared parameter docs} +\arguments{ +\item{callbacks}{List of callback functions that are applied at each iteration.} + +\item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, +may allow you to pass other types of data like \code{matrix} and then separately supply +\code{label} as a keyword argument.} + +\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, +training will stop if the evaluation of any metric on any validation set +fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. +If training stops early, the returned model will have attribute \code{best_iter} +set to the iteration number of the best iteration.} + +\item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of + strings and functions. + + \itemize{ + \item{\bold{a. character vector}: + If you provide a character vector to this argument, it should contain strings with valid + evaluation metrics. + See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{ + The "metric" section of the documentation} + for a list of valid metrics. + } + \item{\bold{b. function}: + You can provide a custom evaluation function. This + should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named + list with three elements: + \itemize{ + \item{\code{name}: A string with the name of the metric, used for printing + and storing results. + } + \item{\code{value}: A single number indicating the value of the metric for the + given predictions and true values + } + \item{ + \code{higher_better}: A boolean indicating whether higher values indicate a better fit. + For example, this would be \code{FALSE} for metrics like MAE or RMSE. + } + } + } + \item{\bold{c. list}: + If a list is given, it should only contain character vectors and functions. + These should follow the requirements from the descriptions above. + } + }} + +\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} + +\item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} + +\item{nrounds}{number of training rounds} + +\item{obj}{objective function, can be character or custom objective function. Examples include +\code{regression}, \code{regression_l1}, \code{huber}, +\code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} + +\item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ +the "Parameters" section of the documentation} for a list of parameters and valid values.} + +\item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the +printing of evaluation during training} + +\item{serializable}{whether to make the resulting objects serializable through functions such as +\code{save} or \code{saveRDS} (see section "Model serialization").} +} +\description{ +Parameter docs shared by \code{lgb.train}, \code{lgb.cv}, and \code{lightgbm} +} +\section{Early Stopping}{ + + + "early stopping" refers to stopping the training process if the model's performance on a given + validation set does not improve for several consecutive iterations. + + If multiple arguments are given to \code{eval}, their order will be preserved. If you enable + early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all + metrics will be considered for early stopping. + + If you want to only consider the first metric for early stopping, pass + \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} + in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, + a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) + or \code{objective} (passed into \code{params}). + + \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored + and early stopping will not be performed. +} + +\section{Model serialization}{ + + + LightGBM model objects can be serialized and de-serialized through functions such as \code{save} + or \code{saveRDS}, but similarly to libraries such as 'xgboost', serialization works a bit differently + from typical R objects. In order to make models serializable in R, a copy of the underlying C++ object + as serialized raw bytes is produced and stored in the R model object, and when this R object is + de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will only + do so once some function that uses it is called, such as \code{predict}. In order to forcibly + reconstruct the C++ object after deserialization (e.g. after calling \code{readRDS} or similar), one + can use the function \link{lgb.restore_handle} (for example, if one makes predictions in parallel or in + forked processes, it will be faster to restore the handle beforehand). + + Producing and keeping these raw bytes however uses extra memory, and if they are not required, + it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw + bytes can be added to the model on demand through function \link{lgb.make_serializable}. + + \emph{New in version 4.0.0} +} + +\keyword{internal} diff --git a/R-package/man/lightgbm.Rd b/R-package/man/lightgbm.Rd new file mode 100644 index 0000000..7abd7f6 --- /dev/null +++ b/R-package/man/lightgbm.Rd @@ -0,0 +1,150 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lightgbm.R +\name{lightgbm} +\alias{lightgbm} +\title{Train a LightGBM model} +\usage{ +lightgbm( + data, + label = NULL, + weights = NULL, + params = list(), + nrounds = 100L, + verbose = 1L, + eval_freq = 1L, + early_stopping_rounds = NULL, + init_model = NULL, + callbacks = list(), + serializable = TRUE, + objective = "auto", + init_score = NULL, + num_threads = NULL, + colnames = NULL, + categorical_feature = NULL, + ... +) +} +\arguments{ +\item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}}, +may allow you to pass other types of data like \code{matrix} and then separately supply +\code{label} as a keyword argument.} + +\item{label}{Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}}} + +\item{weights}{Sample / observation weights for rows in the input data. If \code{NULL}, will assume that all + observations / rows have the same importance / weight. + + \emph{Changed from 'weight', in version 4.0.0}} + +\item{params}{a list of parameters. See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{ +the "Parameters" section of the documentation} for a list of parameters and valid values.} + +\item{nrounds}{number of training rounds} + +\item{verbose}{verbosity for output, if <= 0 and \code{valids} has been provided, also will disable the +printing of evaluation during training} + +\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \code{valids} has been provided} + +\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null, +training will stop if the evaluation of any metric on any validation set +fails to improve for \code{early_stopping_rounds} consecutive boosting rounds. +If training stops early, the returned model will have attribute \code{best_iter} +set to the iteration number of the best iteration.} + +\item{init_model}{path of model file or \code{lgb.Booster} object, will continue training from this model} + +\item{callbacks}{List of callback functions that are applied at each iteration.} + +\item{serializable}{whether to make the resulting objects serializable through functions such as +\code{save} or \code{saveRDS} (see section "Model serialization").} + +\item{objective}{Optimization objective (e.g. `"regression"`, `"binary"`, etc.). + For a list of accepted objectives, see + \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{ + the "objective" item of the "Parameters" section of the documentation}. + + If passing \code{"auto"} and \code{data} is not of type \code{lgb.Dataset}, the objective will + be determined according to what is passed for \code{label}:\itemize{ + \item If passing a factor with two variables, will use objective \code{"binary"}. + \item If passing a factor with more than two variables, will use objective \code{"multiclass"} + (note that parameter \code{num_class} in this case will also be determined automatically from + \code{label}). + \item Otherwise (or if passing \code{lgb.Dataset} as input), will use objective \code{"regression"}. + } + + \emph{New in version 4.0.0}} + +\item{init_score}{initial score is the base prediction lightgbm will boost from + + \emph{New in version 4.0.0}} + +\item{num_threads}{Number of parallel threads to use. For best speed, this should be set to the number of + physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the + number of maximum threads. + + Be aware that using too many threads can result in speed degradation in smaller datasets + (see the parameters documentation for more details). + + If passing zero, will use the default number of threads configured for OpenMP + (typically controlled through an environment variable \code{OMP_NUM_THREADS}). + + If passing \code{NULL} (the default), will try to use the number of physical cores in the + system, but be aware that getting the number of cores detected correctly requires package + \code{RhpcBLASctl} to be installed. + + This parameter gets overridden by \code{num_threads} and its aliases under \code{params} + if passed there. + + \emph{New in version 4.0.0}} + +\item{colnames}{Character vector of features. Only used if \code{data} is not an \code{\link{lgb.Dataset}}.} + +\item{categorical_feature}{categorical features. This can either be a character vector of feature +names or an integer vector with the indices of the features (e.g. +\code{c(1L, 10L)} to say "the first and tenth columns"). +Only used if \code{data} is not an \code{\link{lgb.Dataset}}.} + +\item{...}{Additional arguments passed to \code{\link{lgb.train}}. For example +\itemize{ + \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation} + \item{\code{obj}: objective function, can be character or custom objective function. Examples include + \code{regression}, \code{regression_l1}, \code{huber}, + \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}} + \item{\code{eval}: evaluation function, can be (a list of) character or custom eval function} + \item{\code{record}: Boolean, TRUE will record iteration message to \code{booster$record_evals}} + \item{\code{reset_data}: Boolean, setting it to TRUE (not the default value) will transform the booster model + into a predictor model which frees up memory and the original datasets} +}} +} +\value{ +a trained \code{lgb.Booster} +} +\description{ +High-level R interface to train a LightGBM model. Unlike \code{\link{lgb.train}}, this function + is focused on compatibility with other statistics and machine learning interfaces in R. + This focus on compatibility means that this interface may experience more frequent breaking API changes + than \code{\link{lgb.train}}. + For efficiency-sensitive applications, or for applications where breaking API changes across releases + is very expensive, use \code{\link{lgb.train}}. +} +\section{Early Stopping}{ + + + "early stopping" refers to stopping the training process if the model's performance on a given + validation set does not improve for several consecutive iterations. + + If multiple arguments are given to \code{eval}, their order will be preserved. If you enable + early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all + metrics will be considered for early stopping. + + If you want to only consider the first metric for early stopping, pass + \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric} + in \code{params}, that metric will be considered the "first" one. If you omit \code{metric}, + a default metric will be used based on your choice for the parameter \code{obj} (keyword argument) + or \code{objective} (passed into \code{params}). + + \bold{NOTE:} if using \code{boosting_type="dart"}, any early stopping configuration will be ignored + and early stopping will not be performed. +} + diff --git a/R-package/man/predict.lgb.Booster.Rd b/R-package/man/predict.lgb.Booster.Rd new file mode 100644 index 0000000..bcb2f3f --- /dev/null +++ b/R-package/man/predict.lgb.Booster.Rd @@ -0,0 +1,157 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{predict.lgb.Booster} +\alias{predict.lgb.Booster} +\title{Predict method for LightGBM model} +\usage{ +\method{predict}{lgb.Booster}( + object, + newdata, + type = "response", + start_iteration = NULL, + num_iteration = NULL, + header = FALSE, + params = list(), + ... +) +} +\arguments{ +\item{object}{Object of class \code{lgb.Booster}} + +\item{newdata}{a \code{matrix} object, a \code{dgCMatrix}, a \code{dgRMatrix} object, a \code{dsparseVector} object, + or a character representing a path to a text file (CSV, TSV, or LibSVM). + + For sparse inputs, if predictions are only going to be made for a single row, it will be faster to + use CSR format, in which case the data may be passed as either a single-row CSR matrix (class + \code{dgRMatrix} from package \code{Matrix}) or as a sparse numeric vector (class + \code{dsparseVector} from package \code{Matrix}). + + If single-row predictions are going to be performed frequently, it is recommended to + pre-configure the model object for fast single-row sparse predictions through function + \link{lgb.configure_fast_predict}. + + \emph{Changed from 'data', in version 4.0.0}} + +\item{type}{Type of prediction to output. Allowed types are:\itemize{ + \item \code{"response"}: will output the predicted score according to the objective function being + optimized (depending on the link function that the objective uses), after applying any necessary + transformations - for example, for \code{objective="binary"}, it will output class probabilities. + \item \code{"class"}: for classification objectives, will output the class with the highest predicted + probability. For other objectives, will output the same as "response". Note that \code{"class"} is + not a supported type for \link{lgb.configure_fast_predict} (see the documentation of that function + for more details). + \item \code{"raw"}: will output the non-transformed numbers (sum of predictions from boosting iterations' + results) from which the "response" number is produced for a given objective function - for example, + for \code{objective="binary"}, this corresponds to log-odds. For many objectives such as + "regression", since no transformation is applied, the output will be the same as for "response". + \item \code{"leaf"}: will output the index of the terminal node / leaf at which each observations falls + in each tree in the model, outputted as integers, with one column per tree. + \item \code{"contrib"}: will return the per-feature contributions for each prediction, including an + intercept (each feature will produce one column). + } + + Note that, if using custom objectives, types "class" and "response" will not be available and will + default towards using "raw" instead. + + If the model was fit through function \link{lightgbm} and it was passed a factor as labels, + passing the prediction type through \code{params} instead of through this argument might + result in factor levels for classification objectives not being applied correctly to the + resulting output. + + \emph{New in version 4.0.0}} + +\item{start_iteration}{int or None, optional (default=None) +Start index of the iteration to predict. +If None or <= 0, starts from the first iteration.} + +\item{num_iteration}{int or None, optional (default=None) +Limit number of iterations in the prediction. +If None, if the best iteration exists and start_iteration is None or <= 0, the +best iteration is used; otherwise, all iterations from start_iteration are used. +If <= 0, all iterations from start_iteration are used (no limits).} + +\item{header}{only used for prediction for text file. True if text file has header} + +\item{params}{a list of additional named parameters. See +\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{ +the "Predict Parameters" section of the documentation} for a list of parameters and +valid values. Where these conflict with the values of keyword arguments to this function, +the values in \code{params} take precedence.} + +\item{...}{ignored} +} +\value{ +For prediction types that are meant to always return one output per observation (e.g. when predicting + \code{type="response"} or \code{type="raw"} on a binary classification or regression objective), will + return a vector with one element per row in \code{newdata}. + + For prediction types that are meant to return more than one output per observation (e.g. when predicting + \code{type="response"} or \code{type="raw"} on a multi-class objective, or when predicting + \code{type="leaf"}, regardless of objective), will return a matrix with one row per observation in + \code{newdata} and one column per output. + + For \code{type="leaf"} predictions, will return a matrix with one row per observation in \code{newdata} + and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each + boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf + predictions for the first class can be found in columns 1, 4, 7, 10, etc. + + For \code{type="contrib"}, will return a matrix of SHAP values with one row per observation in + \code{newdata} and columns corresponding to features. For regression, ranking, cross-entropy, and binary + classification objectives, this matrix contains one column per feature plus a final column containing the + Shapley base value. For multiclass objectives, this matrix will represent \code{num_classes} such matrices, + in the order "feature contributions for first class, feature contributions for second class, feature + contributions for third class, etc.". + + If the model was fit through function \link{lightgbm} and it was passed a factor as labels, predictions + returned from this function will retain the factor levels (either as values for \code{type="class"}, or + as column names for \code{type="response"} and \code{type="raw"} for multi-class objectives). Note that + passing the requested prediction type under \code{params} instead of through \code{type} might result in + the factor levels not being present in the output. +} +\description{ +Predicted values based on class \code{lgb.Booster} + + \emph{New in version 4.0.0} +} +\details{ +If the model object has been configured for fast single-row predictions through + \link{lgb.configure_fast_predict}, this function will use the prediction parameters + that were configured for it - as such, extra prediction parameters should not be passed + here, otherwise the configuration will be ignored and the slow route will be taken. +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +data(agaricus.test, package = "lightgbm") +test <- agaricus.test +dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) +params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , num_threads = 2L +) +valids <- list(test = dtest) +model <- lgb.train( + params = params + , data = dtrain + , nrounds = 5L + , valids = valids +) +preds <- predict(model, test$data) + +# pass other prediction parameters +preds <- predict( + model, + test$data, + params = list( + predict_disable_shape_check = TRUE + ) +) +} +} diff --git a/R-package/man/print.lgb.Booster.Rd b/R-package/man/print.lgb.Booster.Rd new file mode 100644 index 0000000..27a2849 --- /dev/null +++ b/R-package/man/print.lgb.Booster.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{print.lgb.Booster} +\alias{print.lgb.Booster} +\title{Print method for LightGBM model} +\usage{ +\method{print}{lgb.Booster}(x, ...) +} +\arguments{ +\item{x}{Object of class \code{lgb.Booster}} + +\item{...}{Not used} +} +\value{ +The same input \code{x}, returned as invisible. +} +\description{ +Show summary information about a LightGBM model object (same as \code{summary}). + + \emph{New in version 4.0.0} +} diff --git a/R-package/man/setLGBMThreads.Rd b/R-package/man/setLGBMThreads.Rd new file mode 100644 index 0000000..53336fc --- /dev/null +++ b/R-package/man/setLGBMThreads.Rd @@ -0,0 +1,32 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/multithreading.R +\name{setLGBMThreads} +\alias{setLGBMThreads} +\alias{setLGBMthreads} +\title{Set maximum number of threads used by LightGBM} +\usage{ +setLGBMthreads(num_threads) +} +\arguments{ +\item{num_threads}{maximum number of threads to be used by LightGBM in multi-threaded operations} +} +\description{ +LightGBM attempts to speed up many operations by using multi-threading. + The number of threads used in those operations can be controlled via the + \code{num_threads} parameter passed through \code{params} to functions like + \link{lgb.train} and \link{lgb.Dataset}. However, some operations (like materializing + a model from a text file) are done via code paths that don't explicitly accept thread-control + configuration. + + Use this function to set the maximum number of threads LightGBM will use for such operations. + + This function affects all LightGBM operations in the same process. + + So, for example, if you call \code{setLGBMthreads(4)}, no other multi-threaded LightGBM + operation in the same process will use more than 4 threads. + + Call \code{setLGBMthreads(-1)} to remove this limitation. +} +\seealso{ +\link{getLGBMthreads} +} diff --git a/R-package/man/set_field.Rd b/R-package/man/set_field.Rd new file mode 100644 index 0000000..2ceebfb --- /dev/null +++ b/R-package/man/set_field.Rd @@ -0,0 +1,50 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Dataset.R +\name{set_field} +\alias{set_field} +\alias{set_field.lgb.Dataset} +\title{Set one attribute of a \code{lgb.Dataset} object} +\usage{ +set_field(dataset, field_name, data) + +\method{set_field}{lgb.Dataset}(dataset, field_name, data) +} +\arguments{ +\item{dataset}{Object of class \code{lgb.Dataset}} + +\item{field_name}{String with the name of the attribute to set. One of the following. +\itemize{ + \item \code{label}: label lightgbm learns from ; + \item \code{weight}: to do a weight rescale ; + \item{\code{group}: used for learning-to-rank tasks. An integer vector describing how to + group rows together as ordered results from the same set of candidate results to be ranked. + For example, if you have a 100-document dataset with \code{group = c(10, 20, 40, 10, 10, 10)}, + that means that you have 6 groups, where the first 10 records are in the first group, + records 11-30 are in the second group, etc.} + \item \code{init_score}: initial score is the base prediction lightgbm will boost from. +}} + +\item{data}{The data for the field. See examples.} +} +\value{ +The \code{lgb.Dataset} you passed in. +} +\description{ +Set one attribute of a \code{lgb.Dataset} +} +\examples{ +\donttest{ +\dontshow{setLGBMthreads(2L)} +\dontshow{data.table::setDTthreads(1L)} +data(agaricus.train, package = "lightgbm") +train <- agaricus.train +dtrain <- lgb.Dataset(train$data, label = train$label) +lgb.Dataset.construct(dtrain) + +labels <- lightgbm::get_field(dtrain, "label") +lightgbm::set_field(dtrain, "label", 1 - labels) + +labels2 <- lightgbm::get_field(dtrain, "label") +stopifnot(all.equal(labels2, 1 - labels)) +} +} diff --git a/R-package/man/summary.lgb.Booster.Rd b/R-package/man/summary.lgb.Booster.Rd new file mode 100644 index 0000000..bd43088 --- /dev/null +++ b/R-package/man/summary.lgb.Booster.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/lgb.Booster.R +\name{summary.lgb.Booster} +\alias{summary.lgb.Booster} +\title{Summary method for LightGBM model} +\usage{ +\method{summary}{lgb.Booster}(object, ...) +} +\arguments{ +\item{object}{Object of class \code{lgb.Booster}} + +\item{...}{Not used} +} +\value{ +The same input \code{object}, returned as invisible. +} +\description{ +Show summary information about a LightGBM model object (same as \code{print}). + + \emph{New in version 4.0.0} +} diff --git a/R-package/pkgdown/_pkgdown.yml b/R-package/pkgdown/_pkgdown.yml new file mode 100644 index 0000000..2fd6405 --- /dev/null +++ b/R-package/pkgdown/_pkgdown.yml @@ -0,0 +1,111 @@ +template: + bootstrap: 5 + params: + bootswatch: cerulean + +site: + root: '' + title: LightGBM, Light Gradient Boosting Machine + +repo: + url: + home: https://github.com/lightgbm-org/LightGBM/ + source: https://github.com/lightgbm-org/LightGBM/tree/main/R-package/ + issue: https://github.com/lightgbm-org/LightGBM/issues/ + user: https://github.com/ + +development: + mode: unreleased + +authors: + Yu Shi: + href: https://github.com/shiyu1994 + html: Yu Shi + Guolin Ke: + href: https://github.com/guolinke + html: Guolin Ke + Damien Soukhavong: + href: https://github.com/Laurae2 + html: Damien Soukhavong + Yachen Yan: + href: https://github.com/yanyachen + html: Yachen Yan + James Lamb: + href: https://github.com/jameslamb + html: James Lamb + +navbar: + title: LightGBM + type: default + left: + - icon: fa-reply fa-lg + href: ../ + - icon: fa-home fa-lg + href: index.html + - text: Articles + href: articles/index.html + - text: Reference + href: reference/index.html + right: + - icon: fa-github fa-lg + href: https://github.com/lightgbm-org/LightGBM/tree/main/R-package + +reference: + - title: Datasets + desc: Datasets included with the R-package + contents: + - '`agaricus.train`' + - '`agaricus.test`' + - '`bank`' + - title: Data Input / Output + desc: Data I/O required for LightGBM + contents: + - '`dim.lgb.Dataset`' + - '`dimnames.lgb.Dataset`' + - '`get_field`' + - '`set_field`' + - '`lgb.Dataset`' + - '`lgb.Dataset.construct`' + - '`lgb.Dataset.create.valid`' + - '`lgb.Dataset.save`' + - '`lgb.Dataset.set.categorical`' + - '`lgb.Dataset.set.reference`' + - '`lgb.convert_with_rules`' + - '`lgb.slice.Dataset`' + - title: Machine Learning + desc: Train models with LightGBM and then use them to make predictions on new data + contents: + - '`lightgbm`' + - '`lgb.train`' + - '`predict.lgb.Booster`' + - '`lgb.cv`' + - '`lgb.configure_fast_predict`' + - title: Saving / Loading Models + desc: Save and load LightGBM models + contents: + - '`lgb.dump`' + - '`lgb.save`' + - '`lgb.load`' + - '`lgb.model.dt.tree`' + - '`lgb.drop_serialized`' + - '`lgb.make_serializable`' + - '`lgb.restore_handle`' + - title: Model Interpretation + desc: Analyze your models + contents: + - '`lgb.get.eval.result`' + - '`lgb.importance`' + - '`lgb.interpret`' + - '`lgb.plot.importance`' + - '`lgb.plot.interpretation`' + - '`print.lgb.Booster`' + - '`summary.lgb.Booster`' + - title: Multithreading Control + desc: Manage degree of parallelism used by LightGBM + contents: + - '`getLGBMThreads`' + - '`setLGBMThreads`' + - title: Deprecated + desc: Functionality that will be removed in the future + contents: + - '`lgb.interprete`' diff --git a/R-package/pkgdown/favicon/apple-touch-icon-120x120.png b/R-package/pkgdown/favicon/apple-touch-icon-120x120.png new file mode 100644 index 0000000..67bafad Binary files /dev/null and b/R-package/pkgdown/favicon/apple-touch-icon-120x120.png differ diff --git a/R-package/pkgdown/favicon/apple-touch-icon-152x152.png b/R-package/pkgdown/favicon/apple-touch-icon-152x152.png new file mode 100644 index 0000000..ac348c6 Binary files /dev/null and b/R-package/pkgdown/favicon/apple-touch-icon-152x152.png differ diff --git a/R-package/pkgdown/favicon/apple-touch-icon-180x180.png b/R-package/pkgdown/favicon/apple-touch-icon-180x180.png new file mode 100644 index 0000000..a3ed94d Binary files /dev/null and b/R-package/pkgdown/favicon/apple-touch-icon-180x180.png differ diff --git a/R-package/pkgdown/favicon/apple-touch-icon-60x60.png b/R-package/pkgdown/favicon/apple-touch-icon-60x60.png new file mode 100644 index 0000000..a84c660 Binary files /dev/null and b/R-package/pkgdown/favicon/apple-touch-icon-60x60.png differ diff --git a/R-package/pkgdown/favicon/apple-touch-icon-76x76.png b/R-package/pkgdown/favicon/apple-touch-icon-76x76.png new file mode 100644 index 0000000..d7fb515 Binary files /dev/null and b/R-package/pkgdown/favicon/apple-touch-icon-76x76.png differ diff --git a/R-package/pkgdown/favicon/apple-touch-icon.png b/R-package/pkgdown/favicon/apple-touch-icon.png new file mode 100644 index 0000000..07f55fa Binary files /dev/null and b/R-package/pkgdown/favicon/apple-touch-icon.png differ diff --git a/R-package/pkgdown/favicon/favicon-16x16.png b/R-package/pkgdown/favicon/favicon-16x16.png new file mode 100644 index 0000000..05e3210 Binary files /dev/null and b/R-package/pkgdown/favicon/favicon-16x16.png differ diff --git a/R-package/pkgdown/favicon/favicon-32x32.png b/R-package/pkgdown/favicon/favicon-32x32.png new file mode 100644 index 0000000..817e0a6 Binary files /dev/null and b/R-package/pkgdown/favicon/favicon-32x32.png differ diff --git a/R-package/pkgdown/favicon/favicon.ico b/R-package/pkgdown/favicon/favicon.ico new file mode 100644 index 0000000..4f8cb7e Binary files /dev/null and b/R-package/pkgdown/favicon/favicon.ico differ diff --git a/R-package/recreate-configure.sh b/R-package/recreate-configure.sh new file mode 100755 index 0000000..5fcda5b --- /dev/null +++ b/R-package/recreate-configure.sh @@ -0,0 +1,39 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +# recreates 'configure' from 'configure.ac' +# this script should run on Ubuntu 22.04 +AUTOCONF_VERSION=$(cat R-package/AUTOCONF_UBUNTU_VERSION) + +# R packages cannot have versions like 3.0.0rc1, but +# 3.0.0-1 is acceptable +LGB_VERSION=$(sed "s/rc/-/g" < VERSION.txt) + +# this script changes configure.ac. Copying to a temporary file +# so changes to configure.ac don't get committed in git +TMP_CONFIGURE_AC=".configure.ac" + +echo "Creating 'configure' script with Autoconf ${AUTOCONF_VERSION}" + +apt update +apt-get install \ + --no-install-recommends \ + -y \ + autoconf="${AUTOCONF_VERSION}" + +cd R-package + +cp configure.ac ${TMP_CONFIGURE_AC} +sed -i.bak -e "s/~~VERSION~~/${LGB_VERSION}/" ${TMP_CONFIGURE_AC} + +autoconf \ + --output configure \ + ${TMP_CONFIGURE_AC} \ + || exit 1 + +rm ${TMP_CONFIGURE_AC} + +rm -r autom4te.cache || echo "no autoconf cache found" + +echo "done creating 'configure' script" diff --git a/R-package/src/Makevars.in b/R-package/src/Makevars.in new file mode 100644 index 0000000..ec2c067 --- /dev/null +++ b/R-package/src/Makevars.in @@ -0,0 +1,59 @@ +CXX_STD = CXX17 + +PKGROOT=. + +LGB_CPPFLAGS = \ + @LGB_CPPFLAGS@ \ + -DUSE_SOCKET \ + -DLGB_R_BUILD + +PKG_CPPFLAGS = \ + -I$(PKGROOT)/include \ + $(LGB_CPPFLAGS) + +PKG_CXXFLAGS = \ + @OPENMP_CXXFLAGS@ \ + -pthread + +PKG_LIBS = \ + @OPENMP_CXXFLAGS@ \ + @OPENMP_LIB@ \ + -pthread + +OBJECTS = \ + boosting/boosting.o \ + boosting/gbdt.o \ + boosting/gbdt_model_text.o \ + boosting/gbdt_prediction.o \ + boosting/prediction_early_stop.o \ + boosting/sample_strategy.o \ + io/bin.o \ + io/config.o \ + io/config_auto.o \ + io/dataset.o \ + io/dataset_loader.o \ + io/file_io.o \ + io/json11.o \ + io/metadata.o \ + io/parser.o \ + io/train_share_states.o \ + io/tree.o \ + metric/dcg_calculator.o \ + metric/metric.o \ + objective/objective_function.o \ + network/linker_topo.o \ + network/linkers_mpi.o \ + network/linkers_socket.o \ + network/network.o \ + treelearner/data_parallel_tree_learner.o \ + treelearner/feature_histogram.o \ + treelearner/feature_parallel_tree_learner.o \ + treelearner/gpu_tree_learner.o \ + treelearner/gradient_discretizer.o \ + treelearner/linear_tree_learner.o \ + treelearner/serial_tree_learner.o \ + treelearner/tree_learner.o \ + treelearner/voting_parallel_tree_learner.o \ + utils/openmp_wrapper.o \ + c_api.o \ + lightgbm_R.o diff --git a/R-package/src/Makevars.win.in b/R-package/src/Makevars.win.in new file mode 100644 index 0000000..efe7796 --- /dev/null +++ b/R-package/src/Makevars.win.in @@ -0,0 +1,60 @@ +CXX_STD = CXX17 + +PKGROOT=. + +LGB_CPPFLAGS = \ + @LGB_CPPFLAGS@ \ + -DUSE_SOCKET \ + -DLGB_R_BUILD + +PKG_CPPFLAGS = \ + -I$(PKGROOT)/include \ + $(LGB_CPPFLAGS) + +PKG_CXXFLAGS = \ + ${SHLIB_OPENMP_CXXFLAGS} \ + ${SHLIB_PTHREAD_FLAGS} + +PKG_LIBS = \ + ${SHLIB_OPENMP_CXXFLAGS} \ + ${SHLIB_PTHREAD_FLAGS} \ + -lws2_32 \ + -liphlpapi + +OBJECTS = \ + boosting/boosting.o \ + boosting/gbdt.o \ + boosting/gbdt_model_text.o \ + boosting/gbdt_prediction.o \ + boosting/prediction_early_stop.o \ + boosting/sample_strategy.o \ + io/bin.o \ + io/config.o \ + io/config_auto.o \ + io/dataset.o \ + io/dataset_loader.o \ + io/file_io.o \ + io/json11.o \ + io/metadata.o \ + io/parser.o \ + io/train_share_states.o \ + io/tree.o \ + metric/dcg_calculator.o \ + metric/metric.o \ + objective/objective_function.o \ + network/linker_topo.o \ + network/linkers_mpi.o \ + network/linkers_socket.o \ + network/network.o \ + treelearner/data_parallel_tree_learner.o \ + treelearner/feature_histogram.o \ + treelearner/feature_parallel_tree_learner.o \ + treelearner/gpu_tree_learner.o \ + treelearner/gradient_discretizer.o \ + treelearner/linear_tree_learner.o \ + treelearner/serial_tree_learner.o \ + treelearner/tree_learner.o \ + treelearner/voting_parallel_tree_learner.o \ + utils/openmp_wrapper.o \ + c_api.o \ + lightgbm_R.o diff --git a/R-package/src/install.libs.R b/R-package/src/install.libs.R new file mode 100644 index 0000000..1bced56 --- /dev/null +++ b/R-package/src/install.libs.R @@ -0,0 +1,242 @@ +# User options +use_gpu <- FALSE +make_args_from_build_script <- character(0L) + +# For Windows, the package will be built with Visual Studio +# unless you set one of these to TRUE +use_mingw <- FALSE +use_msys2 <- FALSE + +if (use_mingw && use_msys2) { + stop("Cannot use both MinGW and MSYS2. Please choose only one.") +} + +if (.Machine$sizeof.pointer != 8L) { + stop("LightGBM only supports 64-bit R, please check the version of R and Rtools.") +} + +# Get some paths +source_dir <- file.path(R_PACKAGE_SOURCE, "src", fsep = "/") +build_dir <- file.path(source_dir, "build", fsep = "/") +inst_dir <- file.path(R_PACKAGE_SOURCE, "inst", fsep = "/") + +# system() will not raise an R exception if the process called +# fails. Wrapping it here to get that behavior. +# +# system() introduces a lot of overhead, at least on Windows, +# so trying processx if it is available +.run_shell_command <- function(cmd, args, strict = TRUE) { + on_windows <- .Platform$OS.type == "windows" + has_processx <- suppressMessages({ + suppressWarnings({ + require("processx") # nolint: undesirable_function, unused_import. + }) + }) + if (has_processx && on_windows) { + result <- processx::run( + command = cmd + , args = args + , windows_verbatim_args = TRUE + , error_on_status = FALSE + , echo = TRUE + ) + exit_code <- result$status + } else { + if (on_windows) { + message(paste0( + "Using system() to run shell commands. Installing " + , "'processx' with install.packages('processx') might " + , "make this faster." + )) + } + cmd <- paste0(cmd, " ", paste(args, collapse = " ")) + exit_code <- system(cmd) + } + + if (exit_code != 0L && isTRUE(strict)) { + stop(paste0("Command failed with exit code: ", exit_code)) + } + return(invisible(exit_code)) +} + +# try to generate Visual Studio build files +.generate_vs_makefiles <- function(cmake_args) { + vs_versions <- c( + "Visual Studio 17 2022" + , "Visual Studio 16 2019" + , "Visual Studio 15 2017" + ) + working_vs_version <- NULL + for (vs_version in vs_versions) { + message(sprintf("Trying '%s'", vs_version)) + # if the build directory is not empty, clean it + if (file.exists("CMakeCache.txt")) { + file.remove("CMakeCache.txt") + } + vs_cmake_args <- c( + cmake_args + , "-G" + , shQuote(vs_version) + , "-A" + , "x64" + ) + exit_code <- .run_shell_command("cmake", c(vs_cmake_args, ".."), strict = FALSE) + if (exit_code == 0L) { + message(sprintf("Successfully created build files for '%s'", vs_version)) + return(invisible(TRUE)) + } + + } + return(invisible(FALSE)) +} + +# Move in CMakeLists.txt +write_succeeded <- file.copy( + file.path(inst_dir, "bin", "CMakeLists.txt") + , "CMakeLists.txt" + , overwrite = TRUE +) +if (!write_succeeded) { + stop("Copying CMakeLists.txt failed") +} + +# Prepare building package +dir.create( + build_dir + , recursive = TRUE + , showWarnings = FALSE +) +setwd(build_dir) + +use_visual_studio <- !(use_mingw || use_msys2) + +# If using MSVC to build, pull in the script used +# to create R.def from R.dll +if (WINDOWS && use_visual_studio) { + write_succeeded <- file.copy( + file.path(inst_dir, "make-r-def.R") + , file.path(build_dir, "make-r-def.R") + , overwrite = TRUE + ) + if (!write_succeeded) { + stop("Copying make-r-def.R failed") + } +} + +# Prepare installation steps +cmake_args <- c( + "-D__BUILD_FOR_R=ON" + # pass in R version, to help FindLibR find the R library + , sprintf("-DCMAKE_R_VERSION='%s.%s'", R.Version()[["major"]], R.Version()[["minor"]]) + # ensure CMake build respects how R is configured (`R CMD config SHLIB_EXT`) + , sprintf("-DCMAKE_SHARED_LIBRARY_SUFFIX_CXX='%s'", SHLIB_EXT) +) +build_cmd <- "make" +build_args <- c("_lightgbm", make_args_from_build_script) +lib_folder <- file.path(source_dir, fsep = "/") + +# add in command-line arguments +# NOTE: build_r.R replaces the line below +command_line_args <- NULL +cmake_args <- c(cmake_args, command_line_args) + +WINDOWS_BUILD_TOOLS <- list( + "MinGW" = c( + build_tool = "mingw32-make.exe" + , makefile_generator = "MinGW Makefiles" + ) + , "MSYS2" = c( + build_tool = "make.exe" + , makefile_generator = "MSYS Makefiles" + ) +) + +if (use_mingw) { + windows_toolchain <- "MinGW" +} else if (use_msys2) { + windows_toolchain <- "MSYS2" +} else { + # Rtools 4.0 moved from MinGW to MSYS toolchain. If user tries + # Visual Studio install but that fails, fall back to the toolchain + # supported in Rtools + windows_toolchain <- "MSYS2" +} +windows_build_tool <- WINDOWS_BUILD_TOOLS[[windows_toolchain]][["build_tool"]] +windows_makefile_generator <- WINDOWS_BUILD_TOOLS[[windows_toolchain]][["makefile_generator"]] + +if (use_gpu) { + cmake_args <- c(cmake_args, "-DUSE_GPU=ON") +} + +# the checks below might already run `cmake -G`. If they do, set this flag +# to TRUE to avoid re-running it later +makefiles_already_generated <- FALSE + +# Check if Windows installation (for gcc vs Visual Studio) +if (WINDOWS) { + if (!use_visual_studio) { + message(sprintf("Trying to build with %s", windows_toolchain)) + # Must build twice for Windows due sh.exe in Rtools + cmake_args <- c(cmake_args, "-G", shQuote(windows_makefile_generator)) + .run_shell_command("cmake", c(cmake_args, ".."), strict = FALSE) + build_cmd <- windows_build_tool + build_args <- c("_lightgbm", make_args_from_build_script) + } else { + visual_studio_succeeded <- .generate_vs_makefiles(cmake_args) + if (!isTRUE(visual_studio_succeeded)) { + warning(sprintf("Building with Visual Studio failed. Attempting with %s", windows_toolchain)) + # Must build twice for Windows due sh.exe in Rtools + cmake_args <- c(cmake_args, "-G", shQuote(windows_makefile_generator)) + .run_shell_command("cmake", c(cmake_args, ".."), strict = FALSE) + build_cmd <- windows_build_tool + build_args <- c("_lightgbm", make_args_from_build_script) + } else { + build_cmd <- "cmake" + build_args <- c("--build", ".", "--target", "_lightgbm", "--config", "Release") + lib_folder <- file.path(source_dir, "Release", fsep = "/") + makefiles_already_generated <- TRUE + } + } +} else { + .run_shell_command("cmake", c(cmake_args, "..")) + makefiles_already_generated <- TRUE +} + +# generate build files +if (!makefiles_already_generated) { + .run_shell_command("cmake", c(cmake_args, "..")) +} + +# build the library +message(paste0("Building lightgbm", SHLIB_EXT)) +.run_shell_command(build_cmd, build_args) +src <- file.path(lib_folder, paste0("lightgbm", SHLIB_EXT), fsep = "/") + +# Packages with install.libs.R need to copy some artifacts into the +# expected places in the package structure. +# see https://cran.r-project.org/doc/manuals/r-devel/R-exts.html#Package-subdirectories, +# especially the paragraph on install.libs.R +dest <- file.path(R_PACKAGE_DIR, paste0("libs", R_ARCH), fsep = "/") +dir.create(dest, recursive = TRUE, showWarnings = FALSE) +if (file.exists(src)) { + message(paste0("Found library file: ", src, " to move to ", dest)) + file.copy(src, dest, overwrite = TRUE) + + symbols_file <- file.path(source_dir, "symbols.rds") + if (file.exists(symbols_file)) { + file.copy(symbols_file, dest, overwrite = TRUE) + } + +} else { + stop(paste0("Cannot find lightgbm", SHLIB_EXT)) +} + +# clean up the "build" directory +if (dir.exists(build_dir)) { + message("Removing 'build/' directory") + unlink( + x = build_dir + , recursive = TRUE + , force = TRUE + ) +} diff --git a/R-package/src/lightgbm_R.cpp b/R-package/src/lightgbm_R.cpp new file mode 100644 index 0000000..1a18f4a --- /dev/null +++ b/R-package/src/lightgbm_R.cpp @@ -0,0 +1,1513 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include "lightgbm_R.h" + +#include +#include +#include +#include + +#include +#include + +#ifndef R_NO_REMAP +#define R_NO_REMAP +#endif + +#ifndef R_USE_C99_IN_CXX +#define R_USE_C99_IN_CXX +#endif + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +R_altrep_class_t lgb_altrepped_char_vec; +R_altrep_class_t lgb_altrepped_int_arr; +R_altrep_class_t lgb_altrepped_dbl_arr; + +template +void delete_cpp_array(SEXP R_ptr) { + T *ptr_to_cpp_obj = static_cast(R_ExternalPtrAddr(R_ptr)); + delete[] ptr_to_cpp_obj; + R_ClearExternalPtr(R_ptr); +} + +void delete_cpp_char_vec(SEXP R_ptr) { + std::vector *ptr_to_cpp_obj = static_cast*>(R_ExternalPtrAddr(R_ptr)); + delete ptr_to_cpp_obj; + R_ClearExternalPtr(R_ptr); +} + +// Note: MSVC has issues with Altrep classes, so they are disabled for it. +// See: https://github.com/lightgbm-org/LightGBM/pull/6213#issuecomment-2111025768 +#ifdef _MSC_VER +# define LGB_NO_ALTREP +#endif + +#ifndef LGB_NO_ALTREP +SEXP make_altrepped_raw_vec(void *void_ptr) { + std::unique_ptr> *ptr_to_cpp_vec = static_cast>*>(void_ptr); + SEXP R_ptr = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + SEXP R_raw = Rf_protect(R_new_altrep(lgb_altrepped_char_vec, R_NilValue, R_NilValue)); + + R_SetExternalPtrAddr(R_ptr, ptr_to_cpp_vec->get()); + R_RegisterCFinalizerEx(R_ptr, delete_cpp_char_vec, TRUE); + ptr_to_cpp_vec->release(); + + R_set_altrep_data1(R_raw, R_ptr); + Rf_unprotect(2); + return R_raw; +} +#else +SEXP make_r_raw_vec(void *void_ptr) { + std::unique_ptr> *ptr_to_cpp_vec = static_cast>*>(void_ptr); + R_xlen_t len = ptr_to_cpp_vec->get()->size(); + SEXP out = Rf_protect(Rf_allocVector(RAWSXP, len)); + std::copy(ptr_to_cpp_vec->get()->begin(), ptr_to_cpp_vec->get()->end(), reinterpret_cast(RAW(out))); + Rf_unprotect(1); + return out; +} +#define make_altrepped_raw_vec make_r_raw_vec +#endif + +std::vector* get_ptr_from_altrepped_raw(SEXP R_raw) { + return static_cast*>(R_ExternalPtrAddr(R_altrep_data1(R_raw))); +} + +R_xlen_t get_altrepped_raw_len(SEXP R_raw) { + return get_ptr_from_altrepped_raw(R_raw)->size(); +} + +const void* get_altrepped_raw_dataptr_or_null(SEXP R_raw) { + return get_ptr_from_altrepped_raw(R_raw)->data(); +} + +void* get_altrepped_raw_dataptr(SEXP R_raw, Rboolean writable) { + return get_ptr_from_altrepped_raw(R_raw)->data(); +} + +#ifndef LGB_NO_ALTREP +template +R_altrep_class_t get_altrep_class_for_type() { + if (std::is_same::value) { + return lgb_altrepped_dbl_arr; + } else { + return lgb_altrepped_int_arr; + } +} +#else +template +SEXPTYPE get_sexptype_class_for_type() { + if (std::is_same::value) { + return REALSXP; + } else { + return INTSXP; + } +} + +template +T* get_r_vec_ptr(SEXP x) { + if (std::is_same::value) { + return static_cast(static_cast(REAL(x))); + } else { + return static_cast(static_cast(INTEGER(x))); + } +} +#endif + +template +struct arr_and_len { + T *arr; + int64_t len; +}; + +#ifndef LGB_NO_ALTREP +template +SEXP make_altrepped_vec_from_arr(void *void_ptr) { + T *arr = static_cast*>(void_ptr)->arr; + uint64_t len = static_cast*>(void_ptr)->len; + SEXP R_ptr = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + SEXP R_len = Rf_protect(Rf_allocVector(REALSXP, 1)); + SEXP R_vec = Rf_protect(R_new_altrep(get_altrep_class_for_type(), R_NilValue, R_NilValue)); + + REAL(R_len)[0] = static_cast(len); + R_SetExternalPtrAddr(R_ptr, arr); + R_RegisterCFinalizerEx(R_ptr, delete_cpp_array, TRUE); + + R_set_altrep_data1(R_vec, R_ptr); + R_set_altrep_data2(R_vec, R_len); + Rf_unprotect(3); + return R_vec; +} +#else +template +SEXP make_R_vec_from_arr(void *void_ptr) { + T *arr = static_cast*>(void_ptr)->arr; + uint64_t len = static_cast*>(void_ptr)->len; + SEXP out = Rf_protect(Rf_allocVector(get_sexptype_class_for_type(), len)); + std::copy(arr, arr + len, get_r_vec_ptr(out)); + Rf_unprotect(1); + return out; +} +#define make_altrepped_vec_from_arr make_R_vec_from_arr +#endif + +R_xlen_t get_altrepped_vec_len(SEXP R_vec) { + return static_cast(Rf_asReal(R_altrep_data2(R_vec))); +} + +const void* get_altrepped_vec_dataptr_or_null(SEXP R_vec) { + return R_ExternalPtrAddr(R_altrep_data1(R_vec)); +} + +void* get_altrepped_vec_dataptr(SEXP R_vec, Rboolean writable) { + return R_ExternalPtrAddr(R_altrep_data1(R_vec)); +} + +#define COL_MAJOR (0) + +#define MAX_LENGTH_ERR_MSG 1024 +char R_errmsg_buffer[MAX_LENGTH_ERR_MSG]; +struct LGBM_R_ErrorClass { SEXP cont_token; }; +void LGBM_R_save_exception_msg(const std::exception &err); +void LGBM_R_save_exception_msg(const std::string &err); + +#define R_API_BEGIN() \ + try { +#define R_API_END() } \ + catch(LGBM_R_ErrorClass &cont) { R_ContinueUnwind(cont.cont_token); } \ + catch(std::exception& ex) { LGBM_R_save_exception_msg(ex); } \ + catch(std::string& ex) { LGBM_R_save_exception_msg(ex); } \ + catch(...) { Rf_error("unknown exception"); } \ + Rf_error("%s", R_errmsg_buffer); \ + return R_NilValue; /* <- won't be reached */ + +#define CHECK_CALL(x) \ + if ((x) != 0) { \ + throw std::runtime_error(LGBM_GetLastError()); \ + } + +// These are helper functions to allow doing a stack unwind +// after an R allocation error, which would trigger a long jump. +void LGBM_R_save_exception_msg(const std::exception &err) { + std::snprintf(R_errmsg_buffer, MAX_LENGTH_ERR_MSG, "%s\n", err.what()); +} + +void LGBM_R_save_exception_msg(const std::string &err) { + std::snprintf(R_errmsg_buffer, MAX_LENGTH_ERR_MSG, "%s\n", err.c_str()); +} + +SEXP wrapped_R_string(void *len) { + return Rf_allocVector(STRSXP, *(reinterpret_cast(len))); +} + +SEXP wrapped_R_raw(void *len) { + return Rf_allocVector(RAWSXP, *(reinterpret_cast(len))); +} + +SEXP wrapped_R_int(void *len) { + return Rf_allocVector(INTSXP, *(reinterpret_cast(len))); +} + +SEXP wrapped_R_real(void *len) { + return Rf_allocVector(REALSXP, *(reinterpret_cast(len))); +} + +SEXP wrapped_Rf_mkChar(void *txt) { + return Rf_mkChar(reinterpret_cast(txt)); +} + +void throw_R_memerr(void *ptr_cont_token, Rboolean jump) { + if (jump) { + LGBM_R_ErrorClass err{*(reinterpret_cast(ptr_cont_token))}; + throw err; + } +} + +SEXP safe_R_string(R_xlen_t len, SEXP *cont_token) { + return R_UnwindProtect(wrapped_R_string, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); +} + +SEXP safe_R_raw(R_xlen_t len, SEXP *cont_token) { + return R_UnwindProtect(wrapped_R_raw, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); +} + +SEXP safe_R_int(R_xlen_t len, SEXP *cont_token) { + return R_UnwindProtect(wrapped_R_int, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); +} + +SEXP safe_R_real(R_xlen_t len, SEXP *cont_token) { + return R_UnwindProtect(wrapped_R_real, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token); +} + +SEXP safe_R_mkChar(char *txt, SEXP *cont_token) { + return R_UnwindProtect(wrapped_Rf_mkChar, reinterpret_cast(txt), throw_R_memerr, cont_token, *cont_token); +} + +using LightGBM::Common::Split; +using LightGBM::Log; + +SEXP LGBM_HandleIsNull_R(SEXP handle) { + return Rf_ScalarLogical(R_ExternalPtrAddr(handle) == NULL); +} + +void _DatasetFinalizer(SEXP handle) { + LGBM_DatasetFree_R(handle); +} + +SEXP LGBM_NullBoosterHandleError_R() { + Rf_error( + "Attempting to use a Booster which no longer exists and/or cannot be restored. " + "This can happen if the Booster's finalizer was called " + "or if this Booster was saved through saveRDS() using 'serializable=FALSE'."); + return R_NilValue; +} + +void _AssertBoosterHandleNotNull(SEXP handle) { + if (Rf_isNull(handle) || !R_ExternalPtrAddr(handle)) { + LGBM_NullBoosterHandleError_R(); + } +} + +void _AssertDatasetHandleNotNull(SEXP handle) { + if (Rf_isNull(handle) || !R_ExternalPtrAddr(handle)) { + Rf_error( + "Attempting to use a Dataset which no longer exists. " + "This can happen if the Dataset's finalizer was called or if this Dataset was saved with saveRDS(). " + "To avoid this error in the future, use lgb.Dataset.save() or Dataset$save_binary() to save lightgbm Datasets."); + } +} + +SEXP LGBM_DatasetCreateFromFile_R(SEXP filename, + SEXP parameters, + SEXP reference) { + R_API_BEGIN(); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + DatasetHandle handle = nullptr; + DatasetHandle ref = nullptr; + if (!Rf_isNull(reference)) { + ref = R_ExternalPtrAddr(reference); + } + const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); + const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); + CHECK_CALL(LGBM_DatasetCreateFromFile(filename_ptr, parameters_ptr, ref, &handle)); + R_SetExternalPtrAddr(ret, handle); + R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); + Rf_unprotect(3); + return ret; + R_API_END(); +} + +SEXP LGBM_DatasetCreateFromCSC_R(SEXP indptr, + SEXP indices, + SEXP data, + SEXP num_indptr, + SEXP nelem, + SEXP num_row, + SEXP parameters, + SEXP reference) { + R_API_BEGIN(); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + const int* p_indptr = INTEGER(indptr); + const int* p_indices = INTEGER(indices); + const double* p_data = REAL(data); + int64_t nindptr = static_cast(Rf_asInteger(num_indptr)); + int64_t ndata = static_cast(Rf_asInteger(nelem)); + int64_t nrow = static_cast(Rf_asInteger(num_row)); + const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); + DatasetHandle handle = nullptr; + DatasetHandle ref = nullptr; + if (!Rf_isNull(reference)) { + ref = R_ExternalPtrAddr(reference); + } + CHECK_CALL(LGBM_DatasetCreateFromCSC(p_indptr, C_API_DTYPE_INT32, p_indices, + p_data, C_API_DTYPE_FLOAT64, nindptr, ndata, + nrow, parameters_ptr, ref, &handle)); + R_SetExternalPtrAddr(ret, handle); + R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); + Rf_unprotect(2); + return ret; + R_API_END(); +} + +SEXP LGBM_DatasetCreateFromMat_R(SEXP data, + SEXP num_row, + SEXP num_col, + SEXP parameters, + SEXP reference) { + R_API_BEGIN(); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + int32_t nrow = static_cast(Rf_asInteger(num_row)); + int32_t ncol = static_cast(Rf_asInteger(num_col)); + double* p_mat = REAL(data); + const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); + DatasetHandle handle = nullptr; + DatasetHandle ref = nullptr; + if (!Rf_isNull(reference)) { + ref = R_ExternalPtrAddr(reference); + } + CHECK_CALL(LGBM_DatasetCreateFromMat(p_mat, C_API_DTYPE_FLOAT64, nrow, ncol, COL_MAJOR, + parameters_ptr, ref, &handle)); + R_SetExternalPtrAddr(ret, handle); + R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); + Rf_unprotect(2); + return ret; + R_API_END(); +} + +SEXP LGBM_DatasetGetSubset_R(SEXP handle, + SEXP used_row_indices, + SEXP len_used_row_indices, + SEXP parameters) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + int32_t len = static_cast(Rf_asInteger(len_used_row_indices)); + std::unique_ptr idxvec(new int32_t[len]); + // convert from one-based to zero-based index + const int *used_row_indices_ = INTEGER(used_row_indices); +#ifndef _MSC_VER +#pragma omp simd +#endif + for (int32_t i = 0; i < len; ++i) { + idxvec[i] = static_cast(used_row_indices_[i] - 1); + } + const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); + DatasetHandle res = nullptr; + CHECK_CALL(LGBM_DatasetGetSubset(R_ExternalPtrAddr(handle), + idxvec.get(), len, parameters_ptr, + &res)); + R_SetExternalPtrAddr(ret, res); + R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE); + Rf_unprotect(2); + return ret; + R_API_END(); +} + +SEXP LGBM_DatasetSetFeatureNames_R(SEXP handle, + SEXP feature_names) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + auto vec_names = Split(CHAR(Rf_protect(Rf_asChar(feature_names))), '\t'); + int len = static_cast(vec_names.size()); + std::unique_ptr vec_sptr(new const char*[len]); + for (int i = 0; i < len; ++i) { + vec_sptr[i] = vec_names[i].c_str(); + } + CHECK_CALL(LGBM_DatasetSetFeatureNames(R_ExternalPtrAddr(handle), + vec_sptr.get(), len)); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetGetFeatureNames_R(SEXP handle) { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + SEXP feature_names; + int len = 0; + CHECK_CALL(LGBM_DatasetGetNumFeature(R_ExternalPtrAddr(handle), &len)); + const size_t reserved_string_size = 256; + std::vector> names(len); + std::vector ptr_names(len); + for (int i = 0; i < len; ++i) { + names[i].resize(reserved_string_size); + ptr_names[i] = names[i].data(); + } + int out_len; + size_t required_string_size; + CHECK_CALL( + LGBM_DatasetGetFeatureNames( + R_ExternalPtrAddr(handle), + len, &out_len, + reserved_string_size, &required_string_size, + ptr_names.data())); + // if any feature names were larger than allocated size, + // allow for a larger size and try again + if (required_string_size > reserved_string_size) { + for (int i = 0; i < len; ++i) { + names[i].resize(required_string_size); + ptr_names[i] = names[i].data(); + } + CHECK_CALL( + LGBM_DatasetGetFeatureNames( + R_ExternalPtrAddr(handle), + len, + &out_len, + required_string_size, + &required_string_size, + ptr_names.data())); + } + CHECK_EQ(len, out_len); + feature_names = Rf_protect(safe_R_string(static_cast(len), &cont_token)); + for (int i = 0; i < len; ++i) { + SET_STRING_ELT(feature_names, i, safe_R_mkChar(ptr_names[i], &cont_token)); + } + Rf_unprotect(2); + return feature_names; + R_API_END(); +} + +SEXP LGBM_DatasetSaveBinary_R(SEXP handle, + SEXP filename) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); + CHECK_CALL(LGBM_DatasetSaveBinary(R_ExternalPtrAddr(handle), + filename_ptr)); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetFree_R(SEXP handle) { + R_API_BEGIN(); + if (!Rf_isNull(handle) && R_ExternalPtrAddr(handle)) { + CHECK_CALL(LGBM_DatasetFree(R_ExternalPtrAddr(handle))); + R_ClearExternalPtr(handle); + } + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetSetField_R(SEXP handle, + SEXP field_name, + SEXP field_data, + SEXP num_element) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + int len = Rf_asInteger(num_element); + const char* name = CHAR(Rf_protect(Rf_asChar(field_name))); + if (!strcmp("group", name) || !strcmp("query", name)) { + CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, INTEGER(field_data), len, C_API_DTYPE_INT32)); + } else if (!strcmp("init_score", name)) { + CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, REAL(field_data), len, C_API_DTYPE_FLOAT64)); + } else { + std::unique_ptr vec(new float[len]); + std::copy(REAL(field_data), REAL(field_data) + len, vec.get()); + CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, vec.get(), len, C_API_DTYPE_FLOAT32)); + } + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetGetField_R(SEXP handle, + SEXP field_name, + SEXP field_data) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + const char* name = CHAR(Rf_protect(Rf_asChar(field_name))); + int out_len = 0; + int out_type = 0; + const void* res; + CHECK_CALL(LGBM_DatasetGetField(R_ExternalPtrAddr(handle), name, &out_len, &res, &out_type)); + if (!strcmp("group", name) || !strcmp("query", name)) { + auto p_data = reinterpret_cast(res); + // convert from boundaries to size + int *field_data_ = INTEGER(field_data); +#ifndef _MSC_VER +#pragma omp simd +#endif + for (int i = 0; i < out_len - 1; ++i) { + field_data_[i] = p_data[i + 1] - p_data[i]; + } + } else if (!strcmp("init_score", name)) { + auto p_data = reinterpret_cast(res); + std::copy(p_data, p_data + out_len, REAL(field_data)); + } else { + auto p_data = reinterpret_cast(res); + std::copy(p_data, p_data + out_len, REAL(field_data)); + } + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetGetFieldSize_R(SEXP handle, + SEXP field_name, + SEXP out) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + const char* name = CHAR(Rf_protect(Rf_asChar(field_name))); + int out_len = 0; + int out_type = 0; + const void* res; + CHECK_CALL(LGBM_DatasetGetField(R_ExternalPtrAddr(handle), name, &out_len, &res, &out_type)); + if (!strcmp("group", name) || !strcmp("query", name)) { + out_len -= 1; + } + INTEGER(out)[0] = out_len; + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetUpdateParamChecking_R(SEXP old_params, + SEXP new_params) { + R_API_BEGIN(); + const char* old_params_ptr = CHAR(Rf_protect(Rf_asChar(old_params))); + const char* new_params_ptr = CHAR(Rf_protect(Rf_asChar(new_params))); + CHECK_CALL(LGBM_DatasetUpdateParamChecking(old_params_ptr, new_params_ptr)); + Rf_unprotect(2); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetGetNumData_R(SEXP handle, SEXP out) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + int nrow; + CHECK_CALL(LGBM_DatasetGetNumData(R_ExternalPtrAddr(handle), &nrow)); + INTEGER(out)[0] = nrow; + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetGetNumFeature_R(SEXP handle, + SEXP out) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + int nfeature; + CHECK_CALL(LGBM_DatasetGetNumFeature(R_ExternalPtrAddr(handle), &nfeature)); + INTEGER(out)[0] = nfeature; + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_DatasetGetFeatureNumBin_R(SEXP handle, SEXP feature_idx, SEXP out) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(handle); + int feature = Rf_asInteger(feature_idx); + int nbins; + CHECK_CALL(LGBM_DatasetGetFeatureNumBin(R_ExternalPtrAddr(handle), feature, &nbins)); + INTEGER(out)[0] = nbins; + return R_NilValue; + R_API_END(); +} + +// --- start Booster interfaces + +void _BoosterFinalizer(SEXP handle) { + LGBM_BoosterFree_R(handle); +} + +SEXP LGBM_BoosterFree_R(SEXP handle) { + R_API_BEGIN(); + if (!Rf_isNull(handle) && R_ExternalPtrAddr(handle)) { + CHECK_CALL(LGBM_BoosterFree(R_ExternalPtrAddr(handle))); + R_ClearExternalPtr(handle); + } + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterCreate_R(SEXP train_data, + SEXP parameters) { + R_API_BEGIN(); + _AssertDatasetHandleNotNull(train_data); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); + BoosterHandle handle = nullptr; + CHECK_CALL(LGBM_BoosterCreate(R_ExternalPtrAddr(train_data), parameters_ptr, &handle)); + R_SetExternalPtrAddr(ret, handle); + R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE); + Rf_unprotect(2); + return ret; + R_API_END(); +} + +SEXP LGBM_BoosterCreateFromModelfile_R(SEXP filename) { + R_API_BEGIN(); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + int out_num_iterations = 0; + const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); + BoosterHandle handle = nullptr; + CHECK_CALL(LGBM_BoosterCreateFromModelfile(filename_ptr, &out_num_iterations, &handle)); + R_SetExternalPtrAddr(ret, handle); + R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE); + Rf_unprotect(2); + return ret; + R_API_END(); +} + +SEXP LGBM_BoosterLoadModelFromString_R(SEXP model_str) { + R_API_BEGIN(); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + SEXP temp = NULL; + int n_protected = 1; + int out_num_iterations = 0; + const char* model_str_ptr = nullptr; + switch (TYPEOF(model_str)) { + case RAWSXP: { + model_str_ptr = reinterpret_cast(RAW(model_str)); + break; + } + case CHARSXP: { + model_str_ptr = reinterpret_cast(CHAR(model_str)); + break; + } + case STRSXP: { + temp = Rf_protect(STRING_ELT(model_str, 0)); + n_protected++; + model_str_ptr = reinterpret_cast(CHAR(temp)); + } + } + BoosterHandle handle = nullptr; + CHECK_CALL(LGBM_BoosterLoadModelFromString(model_str_ptr, &out_num_iterations, &handle)); + R_SetExternalPtrAddr(ret, handle); + R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE); + Rf_unprotect(n_protected); + return ret; + R_API_END(); +} + +SEXP LGBM_BoosterMerge_R(SEXP handle, + SEXP other_handle) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + _AssertBoosterHandleNotNull(other_handle); + CHECK_CALL(LGBM_BoosterMerge(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(other_handle))); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterAddValidData_R(SEXP handle, + SEXP valid_data) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + _AssertDatasetHandleNotNull(valid_data); + CHECK_CALL(LGBM_BoosterAddValidData(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(valid_data))); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterResetTrainingData_R(SEXP handle, + SEXP train_data) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + _AssertDatasetHandleNotNull(train_data); + CHECK_CALL(LGBM_BoosterResetTrainingData(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(train_data))); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterResetParameter_R(SEXP handle, + SEXP parameters) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters))); + CHECK_CALL(LGBM_BoosterResetParameter(R_ExternalPtrAddr(handle), parameters_ptr)); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetNumClasses_R(SEXP handle, + SEXP out) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int num_class; + CHECK_CALL(LGBM_BoosterGetNumClasses(R_ExternalPtrAddr(handle), &num_class)); + INTEGER(out)[0] = num_class; + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetNumFeature_R(SEXP handle) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int out = 0; + CHECK_CALL(LGBM_BoosterGetNumFeature(R_ExternalPtrAddr(handle), &out)); + return Rf_ScalarInteger(out); + R_API_END(); +} + +SEXP LGBM_BoosterUpdateOneIter_R(SEXP handle) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int produced_empty_tree = 0; + CHECK_CALL(LGBM_BoosterUpdateOneIter(R_ExternalPtrAddr(handle), &produced_empty_tree)); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterUpdateOneIterCustom_R(SEXP handle, + SEXP grad, + SEXP hess, + SEXP len) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int produced_empty_tree = 0; + int int_len = Rf_asInteger(len); + std::unique_ptr tgrad(new float[int_len]), thess(new float[int_len]); + std::copy(REAL(grad), REAL(grad) + int_len, tgrad.get()); + std::copy(REAL(hess), REAL(hess) + int_len, thess.get()); + CHECK_CALL(LGBM_BoosterUpdateOneIterCustom(R_ExternalPtrAddr(handle), tgrad.get(), thess.get(), + &produced_empty_tree)); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterRollbackOneIter_R(SEXP handle) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + CHECK_CALL(LGBM_BoosterRollbackOneIter(R_ExternalPtrAddr(handle))); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetCurrentIteration_R(SEXP handle, SEXP out) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int out_iteration; + CHECK_CALL(LGBM_BoosterGetCurrentIteration(R_ExternalPtrAddr(handle), &out_iteration)); + INTEGER(out)[0] = out_iteration; + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterNumModelPerIteration_R(SEXP handle, SEXP out) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int models_per_iter; + CHECK_CALL(LGBM_BoosterNumModelPerIteration(R_ExternalPtrAddr(handle), &models_per_iter)); + INTEGER(out)[0] = models_per_iter; + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterNumberOfTotalModel_R(SEXP handle, SEXP out) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int total_models; + CHECK_CALL(LGBM_BoosterNumberOfTotalModel(R_ExternalPtrAddr(handle), &total_models)); + INTEGER(out)[0] = total_models; + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetUpperBoundValue_R(SEXP handle, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + double* ptr_ret = REAL(out_result); + CHECK_CALL(LGBM_BoosterGetUpperBoundValue(R_ExternalPtrAddr(handle), ptr_ret)); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetLowerBoundValue_R(SEXP handle, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + double* ptr_ret = REAL(out_result); + CHECK_CALL(LGBM_BoosterGetLowerBoundValue(R_ExternalPtrAddr(handle), ptr_ret)); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetEvalNames_R(SEXP handle) { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + SEXP eval_names; + int len; + CHECK_CALL(LGBM_BoosterGetEvalCounts(R_ExternalPtrAddr(handle), &len)); + const size_t reserved_string_size = 128; + std::vector> names(len); + std::vector ptr_names(len); + for (int i = 0; i < len; ++i) { + names[i].resize(reserved_string_size); + ptr_names[i] = names[i].data(); + } + + int out_len; + size_t required_string_size; + CHECK_CALL( + LGBM_BoosterGetEvalNames( + R_ExternalPtrAddr(handle), + len, &out_len, + reserved_string_size, &required_string_size, + ptr_names.data())); + // if any eval names were larger than allocated size, + // allow for a larger size and try again + if (required_string_size > reserved_string_size) { + for (int i = 0; i < len; ++i) { + names[i].resize(required_string_size); + ptr_names[i] = names[i].data(); + } + CHECK_CALL( + LGBM_BoosterGetEvalNames( + R_ExternalPtrAddr(handle), + len, + &out_len, + required_string_size, + &required_string_size, + ptr_names.data())); + } + CHECK_EQ(out_len, len); + eval_names = Rf_protect(safe_R_string(static_cast(len), &cont_token)); + for (int i = 0; i < len; ++i) { + SET_STRING_ELT(eval_names, i, safe_R_mkChar(ptr_names[i], &cont_token)); + } + Rf_unprotect(2); + return eval_names; + R_API_END(); +} + +SEXP LGBM_BoosterGetEval_R(SEXP handle, + SEXP data_idx, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int len; + CHECK_CALL(LGBM_BoosterGetEvalCounts(R_ExternalPtrAddr(handle), &len)); + double* ptr_ret = REAL(out_result); + int out_len; + CHECK_CALL(LGBM_BoosterGetEval(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &out_len, ptr_ret)); + CHECK_EQ(out_len, len); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetNumPredict_R(SEXP handle, + SEXP data_idx, + SEXP out) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int64_t len; + CHECK_CALL(LGBM_BoosterGetNumPredict(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &len)); + INTEGER(out)[0] = static_cast(len); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterGetPredict_R(SEXP handle, + SEXP data_idx, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + double* ptr_ret = REAL(out_result); + int64_t out_len; + CHECK_CALL(LGBM_BoosterGetPredict(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &out_len, ptr_ret)); + return R_NilValue; + R_API_END(); +} + +int GetPredictType(SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib) { + int pred_type = C_API_PREDICT_NORMAL; + if (Rf_asInteger(is_rawscore)) { + pred_type = C_API_PREDICT_RAW_SCORE; + } + if (Rf_asInteger(is_leafidx)) { + pred_type = C_API_PREDICT_LEAF_INDEX; + } + if (Rf_asInteger(is_predcontrib)) { + pred_type = C_API_PREDICT_CONTRIB; + } + return pred_type; +} + +SEXP LGBM_BoosterPredictForFile_R(SEXP handle, + SEXP data_filename, + SEXP data_has_header, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP result_filename) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + const char* data_filename_ptr = CHAR(Rf_protect(Rf_asChar(data_filename))); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + const char* result_filename_ptr = CHAR(Rf_protect(Rf_asChar(result_filename))); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + CHECK_CALL(LGBM_BoosterPredictForFile(R_ExternalPtrAddr(handle), data_filename_ptr, + Rf_asInteger(data_has_header), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, + result_filename_ptr)); + Rf_unprotect(3); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterCalcNumPredict_R(SEXP handle, + SEXP num_row, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP out_len) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + int64_t len = 0; + CHECK_CALL(LGBM_BoosterCalcNumPredict(R_ExternalPtrAddr(handle), Rf_asInteger(num_row), + pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), &len)); + INTEGER(out_len)[0] = static_cast(len); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForCSC_R(SEXP handle, + SEXP indptr, + SEXP indices, + SEXP data, + SEXP num_indptr, + SEXP nelem, + SEXP num_row, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + const int* p_indptr = INTEGER(indptr); + const int32_t* p_indices = reinterpret_cast(INTEGER(indices)); + const double* p_data = REAL(data); + int64_t nindptr = static_cast(Rf_asInteger(num_indptr)); + int64_t ndata = static_cast(Rf_asInteger(nelem)); + int64_t nrow = static_cast(Rf_asInteger(num_row)); + double* ptr_ret = REAL(out_result); + int64_t out_len; + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + CHECK_CALL(LGBM_BoosterPredictForCSC(R_ExternalPtrAddr(handle), + p_indptr, C_API_DTYPE_INT32, p_indices, + p_data, C_API_DTYPE_FLOAT64, nindptr, ndata, + nrow, pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, ptr_ret)); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForCSR_R(SEXP handle, + SEXP indptr, + SEXP indices, + SEXP data, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + int64_t out_len; + CHECK_CALL(LGBM_BoosterPredictForCSR(R_ExternalPtrAddr(handle), + INTEGER(indptr), C_API_DTYPE_INT32, INTEGER(indices), + REAL(data), C_API_DTYPE_FLOAT64, + Rf_xlength(indptr), Rf_xlength(data), Rf_asInteger(ncols), + pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), + parameter_ptr, &out_len, REAL(out_result))); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForCSRSingleRow_R(SEXP handle, + SEXP indices, + SEXP data, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + int nnz = static_cast(Rf_xlength(data)); + const int indptr[] = {0, nnz}; + int64_t out_len; + CHECK_CALL(LGBM_BoosterPredictForCSRSingleRow(R_ExternalPtrAddr(handle), + indptr, C_API_DTYPE_INT32, INTEGER(indices), + REAL(data), C_API_DTYPE_FLOAT64, + 2, nnz, Rf_asInteger(ncols), + pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), + parameter_ptr, &out_len, REAL(out_result))); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +void LGBM_FastConfigFree_wrapped(SEXP handle) { + LGBM_FastConfigFree(static_cast(R_ExternalPtrAddr(handle))); +} + +SEXP LGBM_BoosterPredictForCSRSingleRowFastInit_R(SEXP handle, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + FastConfigHandle out_fastConfig; + CHECK_CALL(LGBM_BoosterPredictForCSRSingleRowFastInit(R_ExternalPtrAddr(handle), + pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), + C_API_DTYPE_FLOAT64, Rf_asInteger(ncols), + parameter_ptr, &out_fastConfig)); + R_SetExternalPtrAddr(ret, out_fastConfig); + R_RegisterCFinalizerEx(ret, LGBM_FastConfigFree_wrapped, TRUE); + Rf_unprotect(2); + return ret; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForCSRSingleRowFast_R(SEXP handle_fastConfig, + SEXP indices, + SEXP data, + SEXP out_result) { + R_API_BEGIN(); + int nnz = static_cast(Rf_xlength(data)); + const int indptr[] = {0, nnz}; + int64_t out_len; + CHECK_CALL(LGBM_BoosterPredictForCSRSingleRowFast(R_ExternalPtrAddr(handle_fastConfig), + indptr, C_API_DTYPE_INT32, INTEGER(indices), + REAL(data), + 2, nnz, + &out_len, REAL(out_result))); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForMat_R(SEXP handle, + SEXP data, + SEXP num_row, + SEXP num_col, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + int32_t nrow = static_cast(Rf_asInteger(num_row)); + int32_t ncol = static_cast(Rf_asInteger(num_col)); + const double* p_mat = REAL(data); + double* ptr_ret = REAL(out_result); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + int64_t out_len; + CHECK_CALL(LGBM_BoosterPredictForMat(R_ExternalPtrAddr(handle), + p_mat, C_API_DTYPE_FLOAT64, nrow, ncol, COL_MAJOR, + pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, ptr_ret)); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +struct SparseOutputPointers { + void* indptr; + int32_t* indices; + void* data; + SparseOutputPointers(void* indptr, int32_t* indices, void* data) + : indptr(indptr), indices(indices), data(data) {} +}; + +void delete_SparseOutputPointers(SparseOutputPointers *ptr) { + LGBM_BoosterFreePredictSparse(ptr->indptr, ptr->indices, ptr->data, C_API_DTYPE_INT32, C_API_DTYPE_FLOAT64); + delete ptr; +} + +SEXP LGBM_BoosterPredictSparseOutput_R(SEXP handle, + SEXP indptr, + SEXP indices, + SEXP data, + SEXP is_csr, + SEXP nrows, + SEXP ncols, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter) { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + const char* out_names[] = {"indptr", "indices", "data", ""}; + SEXP out = Rf_protect(Rf_mkNamed(VECSXP, out_names)); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + + int64_t out_len[2]; + void *out_indptr; + int32_t *out_indices; + void *out_data; + + CHECK_CALL(LGBM_BoosterPredictSparseOutput(R_ExternalPtrAddr(handle), + INTEGER(indptr), C_API_DTYPE_INT32, INTEGER(indices), + REAL(data), C_API_DTYPE_FLOAT64, + Rf_xlength(indptr), Rf_xlength(data), + Rf_asLogical(is_csr)? Rf_asInteger(ncols) : Rf_asInteger(nrows), + C_API_PREDICT_CONTRIB, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), + parameter_ptr, + Rf_asLogical(is_csr)? C_API_MATRIX_TYPE_CSR : C_API_MATRIX_TYPE_CSC, + out_len, &out_indptr, &out_indices, &out_data)); + + std::unique_ptr pointers_struct = { + new SparseOutputPointers( + out_indptr, + out_indices, + out_data), + &delete_SparseOutputPointers + }; + + arr_and_len indptr_str{static_cast(out_indptr), out_len[1]}; + SET_VECTOR_ELT( + out, 0, + R_UnwindProtect(make_altrepped_vec_from_arr, + static_cast(&indptr_str), throw_R_memerr, &cont_token, cont_token)); + pointers_struct->indptr = nullptr; + + arr_and_len indices_str{static_cast(out_indices), out_len[0]}; + SET_VECTOR_ELT( + out, 1, + R_UnwindProtect(make_altrepped_vec_from_arr, + static_cast(&indices_str), throw_R_memerr, &cont_token, cont_token)); + pointers_struct->indices = nullptr; + + arr_and_len data_str{static_cast(out_data), out_len[0]}; + SET_VECTOR_ELT( + out, 2, + R_UnwindProtect(make_altrepped_vec_from_arr, + static_cast(&data_str), throw_R_memerr, &cont_token, cont_token)); + pointers_struct->data = nullptr; + + Rf_unprotect(3); + return out; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForMatSingleRow_R(SEXP handle, + SEXP data, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + double* ptr_ret = REAL(out_result); + int64_t out_len; + CHECK_CALL(LGBM_BoosterPredictForMatSingleRow(R_ExternalPtrAddr(handle), + REAL(data), C_API_DTYPE_FLOAT64, Rf_xlength(data), 1, + pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), + parameter_ptr, &out_len, ptr_ret)); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForMatSingleRowFastInit_R(SEXP handle, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib); + SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue)); + const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter))); + FastConfigHandle out_fastConfig; + CHECK_CALL(LGBM_BoosterPredictForMatSingleRowFastInit(R_ExternalPtrAddr(handle), + pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), + C_API_DTYPE_FLOAT64, Rf_asInteger(ncols), + parameter_ptr, &out_fastConfig)); + R_SetExternalPtrAddr(ret, out_fastConfig); + R_RegisterCFinalizerEx(ret, LGBM_FastConfigFree_wrapped, TRUE); + Rf_unprotect(2); + return ret; + R_API_END(); +} + +SEXP LGBM_BoosterPredictForMatSingleRowFast_R(SEXP handle_fastConfig, + SEXP data, + SEXP out_result) { + R_API_BEGIN(); + int64_t out_len; + CHECK_CALL(LGBM_BoosterPredictForMatSingleRowFast(R_ExternalPtrAddr(handle_fastConfig), + REAL(data), &out_len, REAL(out_result))); + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_BoosterSaveModel_R(SEXP handle, + SEXP num_iteration, + SEXP feature_importance_type, + SEXP filename, + SEXP start_iteration) { + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename))); + CHECK_CALL(LGBM_BoosterSaveModel(R_ExternalPtrAddr(handle), Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), Rf_asInteger(feature_importance_type), filename_ptr)); + Rf_unprotect(1); + return R_NilValue; + R_API_END(); +} + +// Note: for some reason, MSVC crashes when an error is thrown here +// if the buffer variable is defined as 'std::unique_ptr>', +// but not if it is defined as ''. +#ifndef _MSC_VER +SEXP LGBM_BoosterSaveModelToString_R(SEXP handle, + SEXP num_iteration, + SEXP feature_importance_type, + SEXP start_iteration) { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int64_t out_len = 0; + int64_t buf_len = 1024 * 1024; + int num_iter = Rf_asInteger(num_iteration); + int start_iter = Rf_asInteger(start_iteration); + int importance_type = Rf_asInteger(feature_importance_type); + std::unique_ptr> inner_char_buf(new std::vector(buf_len)); + CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf->data())); + inner_char_buf->resize(out_len); + if (out_len > buf_len) { + CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, inner_char_buf->data())); + } + SEXP out = R_UnwindProtect(make_altrepped_raw_vec, &inner_char_buf, throw_R_memerr, &cont_token, cont_token); + Rf_unprotect(1); + return out; + R_API_END(); +} +#else +SEXP LGBM_BoosterSaveModelToString_R(SEXP handle, + SEXP num_iteration, + SEXP feature_importance_type, + SEXP start_iteration) { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + int64_t out_len = 0; + int64_t buf_len = 1024 * 1024; + int num_iter = Rf_asInteger(num_iteration); + int start_iter = Rf_asInteger(start_iteration); + int importance_type = Rf_asInteger(feature_importance_type); + std::vector inner_char_buf(buf_len); + CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf.data())); + SEXP model_str = Rf_protect(safe_R_raw(out_len, &cont_token)); + // if the model string was larger than the initial buffer, call the function again, writing directly to the R object + if (out_len > buf_len) { + CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, reinterpret_cast(RAW(model_str)))); + } else { + std::copy(inner_char_buf.begin(), inner_char_buf.begin() + out_len, reinterpret_cast(RAW(model_str))); + } + Rf_unprotect(2); + return model_str; + R_API_END(); +} +#endif + +SEXP LGBM_BoosterDumpModel_R(SEXP handle, + SEXP num_iteration, + SEXP feature_importance_type, + SEXP start_iteration) { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + SEXP model_str; + int64_t out_len = 0; + int64_t buf_len = 1024 * 1024; + int num_iter = Rf_asInteger(num_iteration); + int start_iter = Rf_asInteger(start_iteration); + int importance_type = Rf_asInteger(feature_importance_type); + std::vector inner_char_buf(buf_len); + CHECK_CALL(LGBM_BoosterDumpModel(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf.data())); + // if the model string was larger than the initial buffer, allocate a bigger buffer and try again + if (out_len > buf_len) { + inner_char_buf.resize(out_len); + CHECK_CALL(LGBM_BoosterDumpModel(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, inner_char_buf.data())); + } + model_str = Rf_protect(safe_R_string(static_cast(1), &cont_token)); + SET_STRING_ELT(model_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token)); + Rf_unprotect(2); + return model_str; + R_API_END(); +} + +SEXP LGBM_DumpParamAliases_R() { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + SEXP aliases_str; + int64_t out_len = 0; + int64_t buf_len = 1024 * 1024; + std::vector inner_char_buf(buf_len); + CHECK_CALL(LGBM_DumpParamAliases(buf_len, &out_len, inner_char_buf.data())); + // if aliases string was larger than the initial buffer, allocate a bigger buffer and try again + if (out_len > buf_len) { + inner_char_buf.resize(out_len); + CHECK_CALL(LGBM_DumpParamAliases(out_len, &out_len, inner_char_buf.data())); + } + aliases_str = Rf_protect(safe_R_string(static_cast(1), &cont_token)); + SET_STRING_ELT(aliases_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token)); + Rf_unprotect(2); + return aliases_str; + R_API_END(); +} + +SEXP LGBM_BoosterGetLoadedParam_R(SEXP handle) { + SEXP cont_token = Rf_protect(R_MakeUnwindCont()); + R_API_BEGIN(); + _AssertBoosterHandleNotNull(handle); + SEXP params_str; + int64_t out_len = 0; + int64_t buf_len = 1024 * 1024; + std::vector inner_char_buf(buf_len); + CHECK_CALL(LGBM_BoosterGetLoadedParam(R_ExternalPtrAddr(handle), buf_len, &out_len, inner_char_buf.data())); + // if aliases string was larger than the initial buffer, allocate a bigger buffer and try again + if (out_len > buf_len) { + inner_char_buf.resize(out_len); + CHECK_CALL(LGBM_BoosterGetLoadedParam(R_ExternalPtrAddr(handle), out_len, &out_len, inner_char_buf.data())); + } + params_str = Rf_protect(safe_R_string(static_cast(1), &cont_token)); + SET_STRING_ELT(params_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token)); + Rf_unprotect(2); + return params_str; + R_API_END(); +} + +SEXP LGBM_GetMaxThreads_R(SEXP out) { + R_API_BEGIN(); + int num_threads; + CHECK_CALL(LGBM_GetMaxThreads(&num_threads)); + INTEGER(out)[0] = num_threads; + return R_NilValue; + R_API_END(); +} + +SEXP LGBM_SetMaxThreads_R(SEXP num_threads) { + R_API_BEGIN(); + int new_num_threads = Rf_asInteger(num_threads); + CHECK_CALL(LGBM_SetMaxThreads(new_num_threads)); + return R_NilValue; + R_API_END(); +} + +// .Call() calls +static const R_CallMethodDef CallEntries[] = { + {"LGBM_HandleIsNull_R" , (DL_FUNC) &LGBM_HandleIsNull_R , 1}, + {"LGBM_DatasetCreateFromFile_R" , (DL_FUNC) &LGBM_DatasetCreateFromFile_R , 3}, + {"LGBM_DatasetCreateFromCSC_R" , (DL_FUNC) &LGBM_DatasetCreateFromCSC_R , 8}, + {"LGBM_DatasetCreateFromMat_R" , (DL_FUNC) &LGBM_DatasetCreateFromMat_R , 5}, + {"LGBM_DatasetGetSubset_R" , (DL_FUNC) &LGBM_DatasetGetSubset_R , 4}, + {"LGBM_DatasetSetFeatureNames_R" , (DL_FUNC) &LGBM_DatasetSetFeatureNames_R , 2}, + {"LGBM_DatasetGetFeatureNames_R" , (DL_FUNC) &LGBM_DatasetGetFeatureNames_R , 1}, + {"LGBM_DatasetSaveBinary_R" , (DL_FUNC) &LGBM_DatasetSaveBinary_R , 2}, + {"LGBM_DatasetFree_R" , (DL_FUNC) &LGBM_DatasetFree_R , 1}, + {"LGBM_DatasetSetField_R" , (DL_FUNC) &LGBM_DatasetSetField_R , 4}, + {"LGBM_DatasetGetFieldSize_R" , (DL_FUNC) &LGBM_DatasetGetFieldSize_R , 3}, + {"LGBM_DatasetGetField_R" , (DL_FUNC) &LGBM_DatasetGetField_R , 3}, + {"LGBM_DatasetUpdateParamChecking_R" , (DL_FUNC) &LGBM_DatasetUpdateParamChecking_R , 2}, + {"LGBM_DatasetGetNumData_R" , (DL_FUNC) &LGBM_DatasetGetNumData_R , 2}, + {"LGBM_DatasetGetNumFeature_R" , (DL_FUNC) &LGBM_DatasetGetNumFeature_R , 2}, + {"LGBM_DatasetGetFeatureNumBin_R" , (DL_FUNC) &LGBM_DatasetGetFeatureNumBin_R , 3}, + {"LGBM_BoosterCreate_R" , (DL_FUNC) &LGBM_BoosterCreate_R , 2}, + {"LGBM_BoosterFree_R" , (DL_FUNC) &LGBM_BoosterFree_R , 1}, + {"LGBM_BoosterCreateFromModelfile_R" , (DL_FUNC) &LGBM_BoosterCreateFromModelfile_R , 1}, + {"LGBM_BoosterLoadModelFromString_R" , (DL_FUNC) &LGBM_BoosterLoadModelFromString_R , 1}, + {"LGBM_BoosterMerge_R" , (DL_FUNC) &LGBM_BoosterMerge_R , 2}, + {"LGBM_BoosterAddValidData_R" , (DL_FUNC) &LGBM_BoosterAddValidData_R , 2}, + {"LGBM_BoosterResetTrainingData_R" , (DL_FUNC) &LGBM_BoosterResetTrainingData_R , 2}, + {"LGBM_BoosterResetParameter_R" , (DL_FUNC) &LGBM_BoosterResetParameter_R , 2}, + {"LGBM_BoosterGetNumClasses_R" , (DL_FUNC) &LGBM_BoosterGetNumClasses_R , 2}, + {"LGBM_BoosterGetNumFeature_R" , (DL_FUNC) &LGBM_BoosterGetNumFeature_R , 1}, + {"LGBM_BoosterGetLoadedParam_R" , (DL_FUNC) &LGBM_BoosterGetLoadedParam_R , 1}, + {"LGBM_BoosterUpdateOneIter_R" , (DL_FUNC) &LGBM_BoosterUpdateOneIter_R , 1}, + {"LGBM_BoosterUpdateOneIterCustom_R" , (DL_FUNC) &LGBM_BoosterUpdateOneIterCustom_R , 4}, + {"LGBM_BoosterRollbackOneIter_R" , (DL_FUNC) &LGBM_BoosterRollbackOneIter_R , 1}, + {"LGBM_BoosterGetCurrentIteration_R" , (DL_FUNC) &LGBM_BoosterGetCurrentIteration_R , 2}, + {"LGBM_BoosterNumModelPerIteration_R" , (DL_FUNC) &LGBM_BoosterNumModelPerIteration_R , 2}, + {"LGBM_BoosterNumberOfTotalModel_R" , (DL_FUNC) &LGBM_BoosterNumberOfTotalModel_R , 2}, + {"LGBM_BoosterGetUpperBoundValue_R" , (DL_FUNC) &LGBM_BoosterGetUpperBoundValue_R , 2}, + {"LGBM_BoosterGetLowerBoundValue_R" , (DL_FUNC) &LGBM_BoosterGetLowerBoundValue_R , 2}, + {"LGBM_BoosterGetEvalNames_R" , (DL_FUNC) &LGBM_BoosterGetEvalNames_R , 1}, + {"LGBM_BoosterGetEval_R" , (DL_FUNC) &LGBM_BoosterGetEval_R , 3}, + {"LGBM_BoosterGetNumPredict_R" , (DL_FUNC) &LGBM_BoosterGetNumPredict_R , 3}, + {"LGBM_BoosterGetPredict_R" , (DL_FUNC) &LGBM_BoosterGetPredict_R , 3}, + {"LGBM_BoosterPredictForFile_R" , (DL_FUNC) &LGBM_BoosterPredictForFile_R , 10}, + {"LGBM_BoosterCalcNumPredict_R" , (DL_FUNC) &LGBM_BoosterCalcNumPredict_R , 8}, + {"LGBM_BoosterPredictForCSC_R" , (DL_FUNC) &LGBM_BoosterPredictForCSC_R , 14}, + {"LGBM_BoosterPredictForCSR_R" , (DL_FUNC) &LGBM_BoosterPredictForCSR_R , 12}, + {"LGBM_BoosterPredictForCSRSingleRow_R" , (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRow_R , 11}, + {"LGBM_BoosterPredictForCSRSingleRowFastInit_R", (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRowFastInit_R, 8}, + {"LGBM_BoosterPredictForCSRSingleRowFast_R" , (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRowFast_R , 4}, + {"LGBM_BoosterPredictSparseOutput_R" , (DL_FUNC) &LGBM_BoosterPredictSparseOutput_R , 10}, + {"LGBM_BoosterPredictForMat_R" , (DL_FUNC) &LGBM_BoosterPredictForMat_R , 11}, + {"LGBM_BoosterPredictForMatSingleRow_R" , (DL_FUNC) &LGBM_BoosterPredictForMatSingleRow_R , 9}, + {"LGBM_BoosterPredictForMatSingleRowFastInit_R", (DL_FUNC) &LGBM_BoosterPredictForMatSingleRowFastInit_R, 8}, + {"LGBM_BoosterPredictForMatSingleRowFast_R" , (DL_FUNC) &LGBM_BoosterPredictForMatSingleRowFast_R , 3}, + {"LGBM_BoosterSaveModel_R" , (DL_FUNC) &LGBM_BoosterSaveModel_R , 5}, + {"LGBM_BoosterSaveModelToString_R" , (DL_FUNC) &LGBM_BoosterSaveModelToString_R , 4}, + {"LGBM_BoosterDumpModel_R" , (DL_FUNC) &LGBM_BoosterDumpModel_R , 4}, + {"LGBM_NullBoosterHandleError_R" , (DL_FUNC) &LGBM_NullBoosterHandleError_R , 0}, + {"LGBM_DumpParamAliases_R" , (DL_FUNC) &LGBM_DumpParamAliases_R , 0}, + {"LGBM_GetMaxThreads_R" , (DL_FUNC) &LGBM_GetMaxThreads_R , 1}, + {"LGBM_SetMaxThreads_R" , (DL_FUNC) &LGBM_SetMaxThreads_R , 1}, + {NULL, NULL, 0} +}; + +LIGHTGBM_C_EXPORT void R_init_lightgbm(DllInfo *dll); + +void R_init_lightgbm(DllInfo *dll) { + R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); + R_useDynamicSymbols(dll, FALSE); + +#ifndef LGB_NO_ALTREP + lgb_altrepped_char_vec = R_make_altraw_class("lgb_altrepped_char_vec", "lightgbm", dll); + R_set_altrep_Length_method(lgb_altrepped_char_vec, get_altrepped_raw_len); + R_set_altvec_Dataptr_method(lgb_altrepped_char_vec, get_altrepped_raw_dataptr); + R_set_altvec_Dataptr_or_null_method(lgb_altrepped_char_vec, get_altrepped_raw_dataptr_or_null); + + lgb_altrepped_int_arr = R_make_altinteger_class("lgb_altrepped_int_arr", "lightgbm", dll); + R_set_altrep_Length_method(lgb_altrepped_int_arr, get_altrepped_vec_len); + R_set_altvec_Dataptr_method(lgb_altrepped_int_arr, get_altrepped_vec_dataptr); + R_set_altvec_Dataptr_or_null_method(lgb_altrepped_int_arr, get_altrepped_vec_dataptr_or_null); + + lgb_altrepped_dbl_arr = R_make_altreal_class("lgb_altrepped_dbl_arr", "lightgbm", dll); + R_set_altrep_Length_method(lgb_altrepped_dbl_arr, get_altrepped_vec_len); + R_set_altvec_Dataptr_method(lgb_altrepped_dbl_arr, get_altrepped_vec_dataptr); + R_set_altvec_Dataptr_or_null_method(lgb_altrepped_dbl_arr, get_altrepped_vec_dataptr_or_null); +#endif +} diff --git a/R-package/src/lightgbm_R.h b/R-package/src/lightgbm_R.h new file mode 100644 index 0000000..52143d8 --- /dev/null +++ b/R-package/src/lightgbm_R.h @@ -0,0 +1,907 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_ +#define LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_ + +#include + +#ifndef R_NO_REMAP +#define R_NO_REMAP +#endif + +#ifndef R_USE_C99_IN_CXX +#define R_USE_C99_IN_CXX +#endif + +#include + +/*! +* \brief check if an R external pointer (like a Booster or Dataset handle) is a null pointer +* \param handle handle for a Booster, Dataset, or Predictor +* \return R logical, TRUE if the handle is a null pointer +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_HandleIsNull_R( + SEXP handle +); + +/*! +* \brief Throw a standardized error message when encountering a null Booster handle +* \return No return, will throw an error +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_NullBoosterHandleError_R(); + +// --- start Dataset interface + +/*! +* \brief load Dataset from file like the command_line LightGBM does +* \param filename the name of the file +* \param parameters additional parameters +* \param reference used to align bin mapper with other Dataset, nullptr means not used +* \return Dataset handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromFile_R( + SEXP filename, + SEXP parameters, + SEXP reference +); + +/*! +* \brief create a Dataset from Compressed Sparse Column (CSC) format +* \param indptr pointer to row headers +* \param indices findex +* \param data fvalue +* \param num_indptr number of cols in the matrix + 1 +* \param nelem number of nonzero elements in the matrix +* \param num_row number of rows +* \param parameters additional parameters +* \param reference used to align bin mapper with other Dataset, nullptr means not used +* \return Dataset handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromCSC_R( + SEXP indptr, + SEXP indices, + SEXP data, + SEXP num_indptr, + SEXP nelem, + SEXP num_row, + SEXP parameters, + SEXP reference +); + +/*! +* \brief create Dataset from dense matrix +* \param data matrix data +* \param num_row number of rows +* \param num_col number columns +* \param parameters additional parameters +* \param reference used to align bin mapper with other Dataset, nullptr means not used +* \return Dataset handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromMat_R( + SEXP data, + SEXP num_row, + SEXP num_col, + SEXP parameters, + SEXP reference +); + +/*! +* \brief Create subset of a Dataset +* \param handle handle of full Dataset +* \param used_row_indices Indices used in subset +* \param len_used_row_indices length of Indices used in subset +* \param parameters additional parameters +* \return Dataset handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetSubset_R( + SEXP handle, + SEXP used_row_indices, + SEXP len_used_row_indices, + SEXP parameters +); + +/*! +* \brief save feature names to Dataset +* \param handle handle +* \param feature_names feature names +* \return R character vector of feature names +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetSetFeatureNames_R( + SEXP handle, + SEXP feature_names +); + +/*! +* \brief get feature names from Dataset +* \param handle Dataset handle +* \return an R character vector with feature names from the Dataset or NULL if no feature names +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFeatureNames_R( + SEXP handle +); + +/*! +* \brief save Dataset to binary file +* \param handle an instance of Dataset +* \param filename file name +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetSaveBinary_R( + SEXP handle, + SEXP filename +); + +/*! +* \brief free Dataset +* \param handle an instance of Dataset +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetFree_R( + SEXP handle +); + +/*! +* \brief set vector to a content in info +* Note: group and group_id only work for C_API_DTYPE_INT32 +* label and weight only work for C_API_DTYPE_FLOAT32 +* \param handle an instance of Dataset +* \param field_name field name, can be label, weight, group, group_id +* \param field_data pointer to vector +* \param num_element number of element in field_data +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetSetField_R( + SEXP handle, + SEXP field_name, + SEXP field_data, + SEXP num_element +); + +/*! +* \brief get size of info vector from Dataset +* \param handle an instance of Dataset +* \param field_name field name +* \param out size of info vector from Dataset +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFieldSize_R( + SEXP handle, + SEXP field_name, + SEXP out +); + +/*! +* \brief get info vector from Dataset +* \param handle an instance of Dataset +* \param field_name field name +* \param field_data pointer to vector +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetField_R( + SEXP handle, + SEXP field_name, + SEXP field_data +); + +/*! + * \brief Raise errors for attempts to update Dataset parameters. + * Some parameters cannot be updated after construction. + * \param old_params Current Dataset parameters + * \param new_params New Dataset parameters + * \return R NULL value + */ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetUpdateParamChecking_R( + SEXP old_params, + SEXP new_params +); + +/*! +* \brief get number of data. +* \param handle the handle to the Dataset +* \param out The address to hold number of data +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetNumData_R( + SEXP handle, + SEXP out +); + +/*! +* \brief get number of features +* \param handle the handle to the Dataset +* \param out The output of number of features +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetNumFeature_R( + SEXP handle, + SEXP out +); + +/*! +* \brief get number of bins for feature +* \param handle the handle to the Dataset +* \param feature the index of the feature +* \param out The output of number of bins +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFeatureNumBin_R( + SEXP handle, + SEXP feature, + SEXP out +); + +// --- start Booster interfaces + +/*! +* \brief create a new boosting learner +* \param train_data training Dataset +* \param parameters format: 'key1=value1 key2=value2' +* \return Booster handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterCreate_R( + SEXP train_data, + SEXP parameters +); + +/*! +* \brief free Booster +* \param handle handle to be freed +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterFree_R( + SEXP handle +); + +/*! +* \brief load an existing Booster from model file +* \param filename filename of model +* \return Booster handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterCreateFromModelfile_R( + SEXP filename +); + +/*! +* \brief load an existing Booster from a string +* \param model_str string containing the model +* \return Booster handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterLoadModelFromString_R( + SEXP model_str +); + +/*! +* \brief Get parameters as JSON string. +* \param handle Booster handle +* \return R character vector (length=1) with parameters in JSON format +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetLoadedParam_R( + SEXP handle +); + +/*! +* \brief Merge model in two Boosters to first handle +* \param handle handle primary Booster handle, will merge other handle to this +* \param other_handle secondary Booster handle +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterMerge_R( + SEXP handle, + SEXP other_handle +); + +/*! +* \brief Add new validation to Booster +* \param handle Booster handle +* \param valid_data validation Dataset +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterAddValidData_R( + SEXP handle, + SEXP valid_data +); + +/*! +* \brief Reset training data for Booster +* \param handle Booster handle +* \param train_data training Dataset +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterResetTrainingData_R( + SEXP handle, + SEXP train_data +); + +/*! +* \brief Reset config for current Booster +* \param handle Booster handle +* \param parameters format: 'key1=value1 key2=value2' +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterResetParameter_R( + SEXP handle, + SEXP parameters +); + +/*! +* \brief Get number of classes +* \param handle Booster handle +* \param out number of classes +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumClasses_R( + SEXP handle, + SEXP out +); + +/*! +* \brief Get number of features. +* \param handle Booster handle +* \return Total number of features, as R integer +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumFeature_R( + SEXP handle +); + +/*! +* \brief update the model in one round +* \param handle Booster handle +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterUpdateOneIter_R( + SEXP handle +); + +/*! +* \brief update the model, by directly specifying gradient and second order gradient, +* this can be used to support customized loss function +* \param handle Booster handle +* \param grad gradient statistics +* \param hess second order gradient statistics +* \param len length of grad/hess +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterUpdateOneIterCustom_R( + SEXP handle, + SEXP grad, + SEXP hess, + SEXP len +); + +/*! +* \brief Rollback one iteration +* \param handle Booster handle +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterRollbackOneIter_R( + SEXP handle +); + +/*! +* \brief Get iteration of current boosting rounds +* \param handle Booster handle +* \param out iteration of boosting rounds +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetCurrentIteration_R( + SEXP handle, + SEXP out +); + +/*! + * \brief Get number of trees per iteration + * \param handle Booster handle + * \param out Number of trees per iteration + * \return R NULL value + */ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterNumModelPerIteration_R( + SEXP handle, + SEXP out +); + +/*! + * \brief Get total number of trees + * \param handle Booster handle + * \param out Total number of trees of Booster + * \return R NULL value + */ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterNumberOfTotalModel_R( + SEXP handle, + SEXP out +); + +/*! +* \brief Get model upper bound value. +* \param handle Handle of Booster +* \param[out] out_results Result pointing to max value +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetUpperBoundValue_R( + SEXP handle, + SEXP out_result +); + +/*! +* \brief Get model lower bound value. +* \param handle Handle of Booster +* \param[out] out_results Result pointing to min value +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetLowerBoundValue_R( + SEXP handle, + SEXP out_result +); + +/*! +* \brief Get names of eval metrics +* \param handle Handle of booster +* \return R character vector with names of eval metrics +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetEvalNames_R( + SEXP handle +); + +/*! +* \brief get evaluation for training data and validation data +* \param handle Booster handle +* \param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ... +* \param out_result float array containing result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetEval_R( + SEXP handle, + SEXP data_idx, + SEXP out_result +); + +/*! +* \brief Get number of prediction for training data and validation data +* \param handle Booster handle +* \param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ... +* \param out size of predict +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumPredict_R( + SEXP handle, + SEXP data_idx, + SEXP out +); + +/*! +* \brief Get prediction for training data and validation data. +* This can be used to support customized eval function +* \param handle Booster handle +* \param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ... +* \param out_result, used to store predict result, should pre-allocate memory +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetPredict_R( + SEXP handle, + SEXP data_idx, + SEXP out_result +); + +/*! +* \brief make prediction for file +* \param handle Booster handle +* \param data_filename filename of data file +* \param data_has_header data file has header or not +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration Start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \param result_filename filename of file to write predictions to +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForFile_R( + SEXP handle, + SEXP data_filename, + SEXP data_has_header, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP result_filename +); + +/*! +* \brief Get number of prediction +* \param handle Booster handle +* \param num_row number of rows in input +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration Start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param out_len length of prediction +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterCalcNumPredict_R( + SEXP handle, + SEXP num_row, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP out_len +); + +/*! +* \brief make prediction for a new Dataset +* Note: should pre-allocate memory for out_result, +* for normal and raw score: its length is equal to num_class * num_data +* for leaf index, its length is equal to num_class * num_data * num_iteration +* for feature contributions, its length is equal to num_class * num_data * (num_features + 1) +* \param handle Booster handle +* \param indptr pointer to row headers +* \param indices findex +* \param data fvalue +* \param num_indptr number of cols in the matrix + 1 +* \param nelem number of non-zero elements in the matrix +* \param num_row number of rows +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \param out_result prediction result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSC_R( + SEXP handle, + SEXP indptr, + SEXP indices, + SEXP data, + SEXP num_indptr, + SEXP nelem, + SEXP num_row, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result +); + +/*! +* \brief make prediction for a new Dataset +* Note: should pre-allocate memory for out_result, +* for normal and raw score: its length is equal to num_class * num_data +* for leaf index, its length is equal to num_class * num_data * num_iteration +* for feature contributions, its length is equal to num_class * num_data * (num_features + 1) +* \param handle Booster handle +* \param indptr array with the index pointer of the data in CSR format +* \param indices array with the non-zero indices of the data in CSR format +* \param data array with the non-zero values of the data in CSR format +* \param ncols number of columns in the data +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \param out_result prediction result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSR_R( + SEXP handle, + SEXP indptr, + SEXP indices, + SEXP data, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result +); + +/*! +* \brief make prediction for a single row of data +* Note: should pre-allocate memory for out_result, +* for normal and raw score: its length is equal to num_class +* for leaf index, its length is equal to num_class * num_iteration +* for feature contributions, its length is equal to num_class * (num_features + 1) +* \param handle Booster handle +* \param indices array corresponding to the indices of the columns with non-zero values of the row to predict on +* \param data array corresponding to the non-zero values of row to predict on +* \param ncols number of columns in the data +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \param out_result prediction result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRow_R( + SEXP handle, + SEXP indices, + SEXP data, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result +); + +/*! +* \brief Initialize and return a fast configuration handle to use with ``LGBM_BoosterPredictForCSRSingleRowFast_R``. +* \param handle Booster handle +* \param ncols number columns in the data +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \return Fast configuration handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRowFastInit_R( + SEXP handle, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter +); + +/*! +* \brief make prediction for a single row of data +* Note: should pre-allocate memory for out_result, +* for normal and raw score: its length is equal to num_class +* for leaf index, its length is equal to num_class * num_iteration +* for feature contributions, its length is equal to num_class * (num_features + 1) +* \param handle_fastConfig Fast configuration handle +* \param indices array corresponding to the indices of the columns with non-zero values of the row to predict on +* \param data array corresponding to the non-zero values of row to predict on +* \param out_result prediction result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRowFast_R( + SEXP handle_fastConfig, + SEXP indices, + SEXP data, + SEXP out_result +); + +/*! +* \brief make feature contribution prediction for a new Dataset +* \param handle Booster handle +* \param indptr array with the index pointer of the data in CSR or CSC format +* \param indices array with the non-zero indices of the data in CSR or CSC format +* \param data array with the non-zero values of the data in CSR or CSC format +* \param is_csr whether the input data is in CSR format or not (pass FALSE for CSC) +* \param nrows number of rows in the data +* \param ncols number of columns in the data +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \return An R list with entries "indptr", "indices", "data", constituting the +* feature contributions in sparse format, in the same storage order as +* the input data. +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictSparseOutput_R( + SEXP handle, + SEXP indptr, + SEXP indices, + SEXP data, + SEXP is_csr, + SEXP nrows, + SEXP ncols, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter +); + +/*! +* \brief make prediction for a new Dataset +* Note: should pre-allocate memory for out_result, +* for normal and raw score: its length is equal to num_class * num_data +* for leaf index, its length is equal to num_class * num_data * num_iteration +* for feature contributions, its length is equal to num_class * num_data * (num_features + 1) +* \param handle Booster handle +* \param data pointer to the data space +* \param num_row number of rows +* \param num_col number columns +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \param out_result prediction result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMat_R( + SEXP handle, + SEXP data, + SEXP num_row, + SEXP num_col, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result +); + +/*! +* \brief make prediction for a single row of data +* Note: should pre-allocate memory for out_result, +* for normal and raw score: its length is equal to num_class +* for leaf index, its length is equal to num_class * num_iteration +* for feature contributions, its length is equal to num_class * (num_features + 1) +* \param handle Booster handle +* \param data array corresponding to the row to predict on +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \param out_result prediction result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRow_R( + SEXP handle, + SEXP data, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter, + SEXP out_result +); + +/*! +* \brief Initialize and return a fast configuration handle to use with ``LGBM_BoosterPredictForMatSingleRowFast_R``. +* \param handle Booster handle +* \param ncols number columns in the data +* \param is_rawscore 1 to get raw predictions, before transformations like +* converting to probabilities, 0 otherwise +* \param is_leafidx 1 to get record of which leaf in each tree +* observations fell into, 0 otherwise +* \param is_predcontrib 1 to get feature contributions, 0 otherwise +* \param start_iteration start index of the iteration to predict +* \param num_iteration number of iteration for prediction, <= 0 means no limit +* \param parameter additional parameters +* \return Fast configuration handle +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRowFastInit_R( + SEXP handle, + SEXP ncols, + SEXP is_rawscore, + SEXP is_leafidx, + SEXP is_predcontrib, + SEXP start_iteration, + SEXP num_iteration, + SEXP parameter +); + +/*! +* \brief make prediction for a single row of data +* Note: should pre-allocate memory for out_result, +* for normal and raw score: its length is equal to num_class +* for leaf index, its length is equal to num_class * num_iteration +* for feature contributions, its length is equal to num_class * (num_features + 1) +* \param handle_fastConfig Fast configuration handle +* \param data array corresponding to the row to predict on +* \param out_result prediction result +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRowFast_R( + SEXP handle_fastConfig, + SEXP data, + SEXP out_result +); + +/*! +* \brief save model into file +* \param handle Booster handle +* \param num_iteration, <= 0 means save all +* \param feature_importance_type type of feature importance, 0: split, 1: gain +* \param filename file name +* \param start_iteration Starting iteration (0 based) +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterSaveModel_R( + SEXP handle, + SEXP num_iteration, + SEXP feature_importance_type, + SEXP filename, + SEXP start_iteration +); + +/*! +* \brief create string containing model +* \param handle Booster handle +* \param num_iteration, <= 0 means save all +* \param feature_importance_type type of feature importance, 0: split, 1: gain +* \param start_iteration Starting iteration (0 based) +* \return R character vector (length=1) with model string +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterSaveModelToString_R( + SEXP handle, + SEXP num_iteration, + SEXP feature_importance_type, + SEXP start_iteration +); + +/*! +* \brief dump model to JSON +* \param handle Booster handle +* \param num_iteration, <= 0 means save all +* \param feature_importance_type type of feature importance, 0: split, 1: gain +* \param start_iteration Index of starting iteration (0 based) +* \return R character vector (length=1) with model JSON +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_BoosterDumpModel_R( + SEXP handle, + SEXP num_iteration, + SEXP feature_importance_type, + SEXP start_iteration +); + +/*! +* \brief Dump parameter aliases to JSON +* \return R character vector (length=1) with aliases JSON +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_DumpParamAliases_R(); + +/*! +* \brief Get current maximum number of threads used by LightGBM routines in this process. +* \param[out] out current maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_GetMaxThreads_R( + SEXP out +); + + +/*! +* \brief Set maximum number of threads used by LightGBM routines in this process. +* \param num_threads maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). +* \return R NULL value +*/ +LIGHTGBM_C_EXPORT SEXP LGBM_SetMaxThreads_R( + SEXP num_threads +); + +#endif // LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_ diff --git a/R-package/tests/testthat.R b/R-package/tests/testthat.R new file mode 100644 index 0000000..c5393f8 --- /dev/null +++ b/R-package/tests/testthat.R @@ -0,0 +1,9 @@ +library(testthat) +library(lightgbm) # nolint: unused_import. + +test_check( + package = "lightgbm" + , stop_on_failure = TRUE + , stop_on_warning = FALSE + , reporter = testthat::SummaryReporter$new() +) diff --git a/R-package/tests/testthat/helper.R b/R-package/tests/testthat/helper.R new file mode 100644 index 0000000..9d91892 --- /dev/null +++ b/R-package/tests/testthat/helper.R @@ -0,0 +1,54 @@ +# ref for this file: +# +# * https://r-pkgs.org/testing-design.html#testthat-helper-files +# * https://r-pkgs.org/testing-design.html#testthat-setup-files + +# LightGBM-internal fix to comply with CRAN policy of only using up to 2 threads in tests and example. +# +# per https://cran.r-project.org/web/packages/policies.html +# +# > If running a package uses multiple threads/cores it must never use more than two simultaneously: +# the check farm is a shared resource and will typically be running many checks simultaneously. +# +.LGB_MAX_THREADS <- 2L +setLGBMthreads(.LGB_MAX_THREADS) + +# control data.table parallelism +# ref: https://github.com/Rdatatable/data.table/issues/5658 +data.table::setDTthreads(1L) + +# by default, how much should results in tests be allowed to differ from hard-coded expected numbers? +.LGB_NUMERIC_TOLERANCE <- 1e-6 + +# are the tests running on Windows? +.LGB_ON_WINDOWS <- .Platform$OS.type == "windows" +.LGB_ON_32_BIT_WINDOWS <- .LGB_ON_WINDOWS && .Machine$sizeof.pointer != 8L + +# are the tests running in a UTF-8 locale? +.LGB_UTF8_LOCALE <- all(endsWith( + Sys.getlocale(category = "LC_CTYPE") + , "UTF-8" +)) + +# control how many loud LightGBM's logger is in tests +.LGB_VERBOSITY <- as.integer( + Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1") +) + + +# [description] +# test that every element of 'x' is in 'y' +# +# testthat::expect_in() was added in {testthat} v3.1.19. +# This is here to support a similar interface on older {testthat} versions. +.expect_in <- function(x, y) { + if (exists("expect_in")) { + expect_in(x, y) + } else { + missing_items <- x[!(x %in% y)] + if (length(missing_items) != 0L) { + error_msg <- paste0("Some expected items not found: ", toString(missing_items)) + stop(error_msg) + } + } +} diff --git a/R-package/tests/testthat/test_Predictor.R b/R-package/tests/testthat/test_Predictor.R new file mode 100644 index 0000000..99cae26 --- /dev/null +++ b/R-package/tests/testthat/test_Predictor.R @@ -0,0 +1,706 @@ +library(Matrix) + +test_that("Predictor's finalizer should not fail", { + X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L) + y <- iris[["Sepal.Length"]] + dtrain <- lgb.Dataset(X, label = y) + bst <- lgb.train( + data = dtrain + , params = list( + objective = "regression" + , num_threads = .LGB_MAX_THREADS + ) + , verbose = .LGB_VERBOSITY + , nrounds = 3L + ) + model_file <- tempfile(fileext = ".model") + bst$save_model(filename = model_file) + predictor <- Predictor$new(modelfile = model_file) + + expect_true(.is_Predictor(predictor)) + + expect_false(.is_null_handle(predictor$.__enclos_env__$private$handle)) + + predictor$.__enclos_env__$private$finalize() + expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle)) + + # calling finalize() a second time shouldn't cause any issues + predictor$.__enclos_env__$private$finalize() + expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle)) +}) + +test_that("predictions do not fail for integer input", { + X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L) + y <- iris[["Sepal.Length"]] + dtrain <- lgb.Dataset(X, label = y) + fit <- lgb.train( + data = dtrain + , params = list( + objective = "regression" + , num_threads = .LGB_MAX_THREADS + ) + , verbose = .LGB_VERBOSITY + , nrounds = 3L + ) + X_double <- X[c(1L, 51L, 101L), , drop = FALSE] + X_integer <- X_double + storage.mode(X_double) <- "double" + pred_integer <- predict(fit, X_integer) + pred_double <- predict(fit, X_double) + expect_equal(pred_integer, pred_double) +}) + +test_that("start_iteration works correctly", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + train <- agaricus.train + test <- agaricus.test + dtrain <- lgb.Dataset( + agaricus.train$data + , label = agaricus.train$label + ) + dtest <- lgb.Dataset.create.valid( + dtrain + , agaricus.test$data + , label = agaricus.test$label + ) + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 4L + , learning_rate = 0.6 + , objective = "binary" + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 50L + , valids = list("test" = dtest) + , early_stopping_rounds = 2L + ) + expect_true(.is_Booster(bst)) + pred1 <- predict(bst, newdata = test$data, type = "raw") + pred_contrib1 <- predict(bst, test$data, type = "contrib") + pred2 <- rep(0.0, length(pred1)) + pred_contrib2 <- rep(0.0, length(pred2)) + step <- 11L + end_iter <- 49L + if (bst$best_iter != -1L) { + end_iter <- bst$best_iter - 1L + } + start_iters <- seq(0L, end_iter, by = step) + for (start_iter in start_iters) { + n_iter <- min(c(end_iter - start_iter + 1L, step)) + inc_pred <- predict(bst, test$data + , start_iteration = start_iter + , num_iteration = n_iter + , type = "raw" + ) + inc_pred_contrib <- bst$predict(test$data + , start_iteration = start_iter + , num_iteration = n_iter + , predcontrib = TRUE + ) + pred2 <- pred2 + inc_pred + pred_contrib2 <- pred_contrib2 + inc_pred_contrib + } + expect_equal(pred2, pred1) + expect_equal(pred_contrib2, pred_contrib1) + + pred_leaf1 <- predict(bst, test$data, type = "leaf") + pred_leaf2 <- predict(bst, test$data, start_iteration = 0L, num_iteration = end_iter + 1L, type = "leaf") + expect_equal(pred_leaf1, pred_leaf2) +}) + +test_that("Feature contributions from sparse inputs produce sparse outputs", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- as.numeric(mtcars[, 1L]) + dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) + bst <- lgb.train( + data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS) + ) + + pred_dense <- predict(bst, X, type = "contrib") + + Xcsc <- as(X, "CsparseMatrix") + pred_csc <- predict(bst, Xcsc, type = "contrib") + expect_s4_class(pred_csc, "dgCMatrix") + expect_equal(unname(pred_dense), unname(as.matrix(pred_csc))) + + Xcsr <- as(X, "RsparseMatrix") + pred_csr <- predict(bst, Xcsr, type = "contrib") + expect_s4_class(pred_csr, "dgRMatrix") + expect_equal(as(pred_csr, "CsparseMatrix"), pred_csc) + + Xspv <- as(X[1L, , drop = FALSE], "sparseVector") + pred_spv <- predict(bst, Xspv, type = "contrib") + expect_s4_class(pred_spv, "dsparseVector") + expect_equal(Matrix::t(as(pred_spv, "CsparseMatrix")), unname(pred_csc[1L, , drop = FALSE])) +}) + +test_that("Sparse feature contribution predictions do not take inputs with wrong number of columns", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- as.numeric(mtcars[, 1L]) + dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) + bst <- lgb.train( + data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS) + ) + + X_wrong <- X[, c(1L:10L, 1L:10L)] + X_wrong <- as(X_wrong, "CsparseMatrix") + expect_error(predict(bst, X_wrong, type = "contrib"), regexp = "input data has 20 columns") + + X_wrong <- as(X_wrong, "RsparseMatrix") + expect_error(predict(bst, X_wrong, type = "contrib"), regexp = "input data has 20 columns") + + X_wrong <- as(X_wrong, "CsparseMatrix") + X_wrong <- X_wrong[, 1L:3L] + expect_error(predict(bst, X_wrong, type = "contrib"), regexp = "input data has 3 columns") +}) + +test_that("Feature contribution predictions do not take non-general CSR or CSC inputs", { + set.seed(123L) + y <- runif(25L) + Dmat <- matrix(runif(625L), nrow = 25L, ncol = 25L) + Dmat <- crossprod(Dmat) + Dmat <- as(Dmat, "symmetricMatrix") + SmatC <- as(Dmat, "sparseMatrix") + SmatR <- as(SmatC, "RsparseMatrix") + + dtrain <- lgb.Dataset(as.matrix(Dmat), label = y, params = list(max_bins = 5L)) + bst <- lgb.train( + data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS) + ) + + expect_error( + predict(bst, SmatC, type = "contrib") + , regexp = "Predictions on sparse inputs are only allowed for 'dsparseVector', 'dgRMatrix', 'dgCMatrix' - got: dsCMatrix" # nolint: line_length. + ) + expect_error( + predict(bst, SmatR, type = "contrib") + , regexp = "Predictions on sparse inputs are only allowed for 'dsparseVector', 'dgRMatrix', 'dgCMatrix' - got: dsRMatrix" # nolint: line_length. + ) +}) + +test_that("predict() params should override keyword argument for raw-score predictions", { + data(agaricus.train, package = "lightgbm") + X <- agaricus.train$data + y <- agaricus.train$label + bst <- lgb.train( + data = lgb.Dataset( + data = X + , label = y + , params = list( + data_seed = 708L + , min_data_in_bin = 5L + ) + ) + , params = list( + objective = "binary" + , min_data_in_leaf = 1L + , seed = 708L + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 10L + , verbose = .LGB_VERBOSITY + ) + + # check that the predictions from predict.lgb.Booster() really look like raw score predictions + preds_prob <- predict(bst, X) + preds_raw_s3_keyword <- predict(bst, X, type = "raw") + preds_prob_from_raw <- 1.0 / (1.0 + exp(-preds_raw_s3_keyword)) + expect_equal(preds_prob, preds_prob_from_raw, tolerance = .LGB_NUMERIC_TOLERANCE) + accuracy <- sum(as.integer(preds_prob_from_raw > 0.5) == y) / length(y) + expect_equal(accuracy, 1.0) + + # should get the same results from Booster$predict() method + preds_raw_r6_keyword <- bst$predict(X, rawscore = TRUE) + expect_equal(preds_raw_s3_keyword, preds_raw_r6_keyword) + + # using a parameter alias of predict_raw_score should result in raw scores being returned + aliases <- .PARAMETER_ALIASES()[["predict_raw_score"]] + expect_true(length(aliases) > 1L) + for (rawscore_alias in aliases) { + params <- as.list( + stats::setNames( + object = TRUE + , nm = rawscore_alias + ) + ) + preds_raw_s3_param <- predict(bst, X, params = params) + preds_raw_r6_param <- bst$predict(X, params = params) + expect_equal(preds_raw_s3_keyword, preds_raw_s3_param) + expect_equal(preds_raw_s3_keyword, preds_raw_r6_param) + } +}) + +test_that("predict() params should override keyword argument for leaf-index predictions", { + data(mtcars) + X <- as.matrix(mtcars[, which(names(mtcars) != "mpg")]) + y <- as.numeric(mtcars[, "mpg"]) + bst <- lgb.train( + data = lgb.Dataset( + data = X + , label = y + , params = list( + min_data_in_bin = 1L + , data_seed = 708L + ) + ) + , params = list( + objective = "regression" + , min_data_in_leaf = 1L + , seed = 708L + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 10L + , verbose = .LGB_VERBOSITY + ) + + # check that predictions really look like leaf index predictions + preds_leaf_s3_keyword <- predict(bst, X, type = "leaf") + expect_true(is.matrix(preds_leaf_s3_keyword)) + expect_equal(dim(preds_leaf_s3_keyword), c(nrow(X), bst$current_iter())) + expect_true(min(preds_leaf_s3_keyword) >= 0L) + trees_dt <- lgb.model.dt.tree(bst) + max_leaf_by_tree_from_dt <- trees_dt[, .(idx = max(leaf_index, na.rm = TRUE)), by = tree_index]$idx + max_leaf_by_tree_from_preds <- apply(preds_leaf_s3_keyword, 2L, max, na.rm = TRUE) + expect_equal(max_leaf_by_tree_from_dt, max_leaf_by_tree_from_preds) + + # should get the same results from Booster$predict() method + preds_leaf_r6_keyword <- bst$predict(X, predleaf = TRUE) + expect_equal(preds_leaf_s3_keyword, preds_leaf_r6_keyword) + + # using a parameter alias of predict_leaf_index should result in leaf indices being returned + aliases <- .PARAMETER_ALIASES()[["predict_leaf_index"]] + expect_true(length(aliases) > 1L) + for (predleaf_alias in aliases) { + params <- as.list( + stats::setNames( + object = TRUE + , nm = predleaf_alias + ) + ) + preds_leaf_s3_param <- predict(bst, X, params = params) + preds_leaf_r6_param <- bst$predict(X, params = params) + expect_equal(preds_leaf_s3_keyword, preds_leaf_s3_param) + expect_equal(preds_leaf_s3_keyword, preds_leaf_r6_param) + } +}) + +test_that("predict() params should override keyword argument for feature contributions", { + data(mtcars) + X <- as.matrix(mtcars[, which(names(mtcars) != "mpg")]) + y <- as.numeric(mtcars[, "mpg"]) + bst <- lgb.train( + data = lgb.Dataset( + data = X + , label = y + , params = list( + min_data_in_bin = 1L + , data_seed = 708L + ) + ) + , params = list( + objective = "regression" + , min_data_in_leaf = 1L + , seed = 708L + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 10L + , verbose = .LGB_VERBOSITY + ) + + # check that predictions really look like feature contributions + preds_contrib_s3_keyword <- predict(bst, X, type = "contrib") + num_features <- ncol(X) + shap_base_value <- unname(preds_contrib_s3_keyword[, ncol(preds_contrib_s3_keyword)]) + expect_true(is.matrix(preds_contrib_s3_keyword)) + expect_equal(dim(preds_contrib_s3_keyword), c(nrow(X), num_features + 1L)) + expect_equal(length(unique(shap_base_value)), 1L) + expect_equal(mean(y), shap_base_value[1L]) + expect_equal(predict(bst, X), rowSums(preds_contrib_s3_keyword)) + + # should get the same results from Booster$predict() method + preds_contrib_r6_keyword <- bst$predict(X, predcontrib = TRUE) + expect_equal(preds_contrib_s3_keyword, preds_contrib_r6_keyword) + + # using a parameter alias of predict_contrib should result in feature contributions being returned + aliases <- .PARAMETER_ALIASES()[["predict_contrib"]] + expect_true(length(aliases) > 1L) + for (predcontrib_alias in aliases) { + params <- as.list( + stats::setNames( + object = TRUE + , nm = predcontrib_alias + ) + ) + preds_contrib_s3_param <- predict(bst, X, params = params) + preds_contrib_r6_param <- bst$predict(X, params = params) + expect_equal(preds_contrib_s3_keyword, preds_contrib_s3_param) + expect_equal(preds_contrib_s3_keyword, preds_contrib_r6_param) + } +}) + +.expect_has_row_names <- function(pred, X) { + if (is.vector(pred)) { + rnames <- names(pred) + } else { + rnames <- row.names(pred) + } + expect_false(is.null(rnames)) + expect_true(is.vector(rnames)) + expect_true(length(rnames) > 0L) + expect_equal(row.names(X), rnames) +} + +.expect_doesnt_have_row_names <- function(pred) { + if (is.vector(pred)) { + expect_null(names(pred)) + } else { + expect_null(row.names(pred)) + } +} + +.check_all_row_name_expectations <- function(bst, X) { + + # dense matrix with row names + pred <- predict(bst, X) + .expect_has_row_names(pred, X) + pred <- predict(bst, X, type = "raw") + .expect_has_row_names(pred, X) + pred <- predict(bst, X, type = "leaf") + .expect_has_row_names(pred, X) + pred <- predict(bst, X, type = "contrib") + .expect_has_row_names(pred, X) + + # dense matrix without row names + Xcopy <- X + row.names(Xcopy) <- NULL + pred <- predict(bst, Xcopy) + .expect_doesnt_have_row_names(pred) + + # sparse matrix with row names + Xcsc <- as(X, "CsparseMatrix") + pred <- predict(bst, Xcsc) + .expect_has_row_names(pred, Xcsc) + pred <- predict(bst, Xcsc, type = "raw") + .expect_has_row_names(pred, Xcsc) + pred <- predict(bst, Xcsc, type = "leaf") + .expect_has_row_names(pred, Xcsc) + pred <- predict(bst, Xcsc, type = "contrib") + .expect_has_row_names(pred, Xcsc) + pred <- predict(bst, as(Xcsc, "RsparseMatrix"), type = "contrib") + .expect_has_row_names(pred, Xcsc) + + # sparse matrix without row names + Xcopy <- Xcsc + row.names(Xcopy) <- NULL + pred <- predict(bst, Xcopy) + .expect_doesnt_have_row_names(pred) +} + +test_that("predict() keeps row names from data (regression)", { + data("mtcars") + X <- as.matrix(mtcars[, -1L]) + y <- as.numeric(mtcars[, 1L]) + dtrain <- lgb.Dataset( + X + , label = y + , params = list( + max_bins = 5L + , min_data_in_bin = 1L + ) + ) + bst <- lgb.train( + data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS) + ) + .check_all_row_name_expectations(bst, X) +}) + +test_that("predict() keeps row names from data (binary classification)", { + data(agaricus.train, package = "lightgbm") + X <- as.matrix(agaricus.train$data) + y <- agaricus.train$label + row.names(X) <- paste0("rname", seq(1L, nrow(X))) + dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) + bst <- lgb.train( + data = dtrain + , obj = "binary" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = .LGB_MAX_THREADS) + ) + .check_all_row_name_expectations(bst, X) +}) + +test_that("predict() keeps row names from data (multi-class classification)", { + data(iris) + y <- as.numeric(iris$Species) - 1.0 + X <- as.matrix(iris[, names(iris) != "Species"]) + row.names(X) <- paste0("rname", seq(1L, nrow(X))) + dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) + bst <- lgb.train( + data = dtrain + , obj = "multiclass" + , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS) + , nrounds = 5L + , verbose = .LGB_VERBOSITY + ) + .check_all_row_name_expectations(bst, X) +}) + +test_that("predictions for regression and binary classification are returned as vectors", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- as.numeric(mtcars[, 1L]) + dtrain <- lgb.Dataset( + X + , label = y + , params = list( + max_bins = 5L + , min_data_in_bin = 1L + ) + ) + model <- lgb.train( + data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS) + ) + pred <- predict(model, X) + expect_true(is.vector(pred)) + expect_equal(length(pred), nrow(X)) + pred <- predict(model, X, type = "raw") + expect_true(is.vector(pred)) + expect_equal(length(pred), nrow(X)) + + data(agaricus.train, package = "lightgbm") + X <- agaricus.train$data + y <- agaricus.train$label + dtrain <- lgb.Dataset(X, label = y) + model <- lgb.train( + data = dtrain + , obj = "binary" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = .LGB_MAX_THREADS) + ) + pred <- predict(model, X) + expect_true(is.vector(pred)) + expect_equal(length(pred), nrow(X)) + pred <- predict(model, X, type = "raw") + expect_true(is.vector(pred)) + expect_equal(length(pred), nrow(X)) +}) + +test_that("predictions for multiclass classification are returned as matrix", { + data(iris) + X <- as.matrix(iris[, -5L]) + y <- as.numeric(iris$Species) - 1.0 + dtrain <- lgb.Dataset(X, label = y) + model <- lgb.train( + data = dtrain + , obj = "multiclass" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS) + ) + pred <- predict(model, X) + expect_true(is.matrix(pred)) + expect_equal(nrow(pred), nrow(X)) + expect_equal(ncol(pred), 3L) + pred <- predict(model, X, type = "raw") + expect_true(is.matrix(pred)) + expect_equal(nrow(pred), nrow(X)) + expect_equal(ncol(pred), 3L) +}) + +test_that("Single-row predictions are identical to multi-row ones", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- mtcars[, 1L] + dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) + params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS) + model <- lgb.train( + params = params + , data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = -1L + ) + + x1 <- X[1L, , drop = FALSE] + x11 <- X[11L, , drop = FALSE] + x1_spv <- as(x1, "sparseVector") + x11_spv <- as(x11, "sparseVector") + x1_csr <- as(x1, "RsparseMatrix") + x11_csr <- as(x11, "RsparseMatrix") + + pred_all <- predict(model, X) + pred1_wo_config <- predict(model, x1) + pred11_wo_config <- predict(model, x11) + pred1_spv_wo_config <- predict(model, x1_spv) + pred11_spv_wo_config <- predict(model, x11_spv) + pred1_csr_wo_config <- predict(model, x1_csr) + pred11_csr_wo_config <- predict(model, x11_csr) + + lgb.configure_fast_predict(model) + pred1_w_config <- predict(model, x1) + pred11_w_config <- predict(model, x11) + + model <- lgb.train( + params = params + , data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = -1L + ) + lgb.configure_fast_predict(model, csr = TRUE) + pred1_spv_w_config <- predict(model, x1_spv) + pred11_spv_w_config <- predict(model, x11_spv) + pred1_csr_w_config <- predict(model, x1_csr) + pred11_csr_w_config <- predict(model, x11_csr) + + expect_equal(pred1_wo_config, pred_all[1L]) + expect_equal(pred11_wo_config, pred_all[11L]) + expect_equal(pred1_spv_wo_config, unname(pred_all[1L])) + expect_equal(pred11_spv_wo_config, unname(pred_all[11L])) + expect_equal(pred1_csr_wo_config, pred_all[1L]) + expect_equal(pred11_csr_wo_config, pred_all[11L]) + + expect_equal(pred1_w_config, pred_all[1L]) + expect_equal(pred11_w_config, pred_all[11L]) + expect_equal(pred1_spv_w_config, unname(pred_all[1L])) + expect_equal(pred11_spv_w_config, unname(pred_all[11L])) + expect_equal(pred1_csr_w_config, pred_all[1L]) + expect_equal(pred11_csr_w_config, pred_all[11L]) +}) + +test_that("Fast-predict configuration accepts non-default prediction types", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- mtcars[, 1L] + dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) + params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS) + model <- lgb.train( + params = params + , data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = -1L + ) + + x1 <- X[1L, , drop = FALSE] + x11 <- X[11L, , drop = FALSE] + + pred_all <- predict(model, X, type = "leaf") + pred1_wo_config <- predict(model, x1, type = "leaf") + pred11_wo_config <- predict(model, x11, type = "leaf") + expect_equal(pred1_wo_config, pred_all[1L, , drop = FALSE]) + expect_equal(pred11_wo_config, pred_all[11L, , drop = FALSE]) + + lgb.configure_fast_predict(model, type = "leaf") + pred1_w_config <- predict(model, x1, type = "leaf") + pred11_w_config <- predict(model, x11, type = "leaf") + expect_equal(pred1_w_config, pred_all[1L, , drop = FALSE]) + expect_equal(pred11_w_config, pred_all[11L, , drop = FALSE]) +}) + +test_that("Fast-predict configuration does not block other prediction types", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- mtcars[, 1L] + dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L)) + params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS) + model <- lgb.train( + params = params + , data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = -1L + ) + + x1 <- X[1L, , drop = FALSE] + x11 <- X[11L, , drop = FALSE] + + pred_all <- predict(model, X) + pred_all_leaf <- predict(model, X, type = "leaf") + + lgb.configure_fast_predict(model) + pred1_w_config <- predict(model, x1) + pred11_w_config <- predict(model, x11) + pred1_leaf_w_config <- predict(model, x1, type = "leaf") + pred11_leaf_w_config <- predict(model, x11, type = "leaf") + + expect_equal(pred1_w_config, pred_all[1L]) + expect_equal(pred11_w_config, pred_all[11L]) + expect_equal(pred1_leaf_w_config, pred_all_leaf[1L, , drop = FALSE]) + expect_equal(pred11_leaf_w_config, pred_all_leaf[11L, , drop = FALSE]) +}) + +test_that("predict type='class' returns predicted class for classification objectives", { + data(agaricus.train, package = "lightgbm") + X <- as.matrix(agaricus.train$data) + y <- agaricus.train$label + dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) + bst <- lgb.train( + data = dtrain + , obj = "binary" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = .LGB_MAX_THREADS) + ) + pred <- predict(bst, X, type = "class") + expect_true(all(pred %in% c(0L, 1L))) + + data(iris) + X <- as.matrix(iris[, -5L]) + y <- as.numeric(iris$Species) - 1.0 + dtrain <- lgb.Dataset(X, label = y) + model <- lgb.train( + data = dtrain + , obj = "multiclass" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS) + ) + pred <- predict(model, X, type = "class") + expect_true(all(pred %in% c(0L, 1L, 2L))) +}) + +test_that("predict type='class' returns values in the target's range for regression objectives", { + data(agaricus.train, package = "lightgbm") + X <- as.matrix(agaricus.train$data) + y <- agaricus.train$label + dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L)) + bst <- lgb.train( + data = dtrain + , obj = "regression" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = .LGB_MAX_THREADS) + ) + pred <- predict(bst, X, type = "class") + expect_true(!any(pred %in% c(0.0, 1.0))) +}) diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R new file mode 100644 index 0000000..4029b7b --- /dev/null +++ b/R-package/tests/testthat/test_basic.R @@ -0,0 +1,3863 @@ +data(agaricus.train, package = "lightgbm") +data(agaricus.test, package = "lightgbm") +train <- agaricus.train +test <- agaricus.test + +set.seed(708L) + +# [description] Every time this function is called, it adds 0.1 +# to an accumulator then returns the current value. +# This is used to mock the situation where an evaluation +# metric increases every iteration +ACCUMULATOR_NAME <- "INCREASING_METRIC_ACCUMULATOR" +assign(x = ACCUMULATOR_NAME, value = 0.0, envir = .GlobalEnv) + +.increasing_metric <- function(preds, dtrain) { + if (!exists(ACCUMULATOR_NAME, envir = .GlobalEnv)) { + assign(ACCUMULATOR_NAME, 0.0, envir = .GlobalEnv) + } + assign( + x = ACCUMULATOR_NAME + , value = get(ACCUMULATOR_NAME, envir = .GlobalEnv) + 0.1 + , envir = .GlobalEnv + ) + return(list( + name = "increasing_metric" + , value = get(ACCUMULATOR_NAME, envir = .GlobalEnv) + , higher_better = TRUE + )) +} + +# [description] Evaluation function that always returns the +# same value +CONSTANT_METRIC_VALUE <- 0.2 +.constant_metric <- function(preds, dtrain) { + return(list( + name = "constant_metric" + , value = CONSTANT_METRIC_VALUE + , higher_better = FALSE + )) +} + +# sample datasets to test early stopping +DTRAIN_RANDOM_REGRESSION <- lgb.Dataset( + data = as.matrix(rnorm(100L), ncol = 1L, drop = FALSE) + , label = rnorm(100L) + , params = list(num_threads = .LGB_MAX_THREADS) +) +DVALID_RANDOM_REGRESSION <- lgb.Dataset( + data = as.matrix(rnorm(50L), ncol = 1L, drop = FALSE) + , label = rnorm(50L) + , params = list(num_threads = .LGB_MAX_THREADS) +) +DTRAIN_RANDOM_CLASSIFICATION <- lgb.Dataset( + data = as.matrix(rnorm(120L), ncol = 1L, drop = FALSE) + , label = sample(c(0L, 1L), size = 120L, replace = TRUE) + , params = list(num_threads = .LGB_MAX_THREADS) +) +DVALID_RANDOM_CLASSIFICATION <- lgb.Dataset( + data = as.matrix(rnorm(37L), ncol = 1L, drop = FALSE) + , label = sample(c(0L, 1L), size = 37L, replace = TRUE) + , params = list(num_threads = .LGB_MAX_THREADS) +) + +test_that("train and predict binary classification", { + nrounds <- 10L + bst <- lightgbm( + data = train$data + , label = train$label + , params = list( + num_leaves = 5L + , objective = "binary" + , metric = "binary_error" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + , valids = list( + "train" = lgb.Dataset( + data = train$data + , label = train$label + ) + ) + ) + expect_false(is.null(bst$record_evals)) + record_results <- lgb.get.eval.result(bst, "train", "binary_error") + expect_lt(min(record_results), 0.02) + + pred <- predict(bst, test$data) + expect_equal(length(pred), 1611L) + + pred1 <- predict(bst, train$data, num_iteration = 1L) + expect_equal(length(pred1), 6513L) + err_pred1 <- sum((pred1 > 0.5) != train$label) / length(train$label) + err_log <- record_results[1L] + expect_lt(abs(err_pred1 - err_log), .LGB_NUMERIC_TOLERANCE) +}) + + +test_that("train and predict softmax", { + set.seed(708L) + X_mat <- as.matrix(iris[, -5L]) + lb <- as.numeric(iris$Species) - 1L + + bst <- lightgbm( + data = X_mat + , label = lb + , params = list( + num_leaves = 4L + , learning_rate = 0.05 + , min_data = 20L + , min_hessian = 10.0 + , objective = "multiclass" + , metric = "multi_error" + , num_class = 3L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 20L + , valids = list( + "train" = lgb.Dataset( + data = X_mat + , label = lb + ) + ) + ) + + expect_false(is.null(bst$record_evals)) + record_results <- lgb.get.eval.result(bst, "train", "multi_error") + expect_lt(min(record_results), 0.06) + + pred <- predict(bst, as.matrix(iris[, -5L])) + expect_equal(length(pred), nrow(iris) * 3L) +}) + + +test_that("use of multiple eval metrics works", { + metrics <- list("binary_error", "auc", "binary_logloss") + bst <- lightgbm( + data = train$data + , label = train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , metric = metrics + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 10L + , valids = list( + "train" = lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + ) + ) + expect_false(is.null(bst$record_evals)) + expect_named( + bst$record_evals[["train"]] + , unlist(metrics) + , ignore.order = FALSE + , ignore.case = FALSE + ) +}) + +test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for binary classification", { + set.seed(708L) + nrounds <- 10L + bst <- lightgbm( + data = train$data + , label = train$label + , params = list( + num_leaves = 5L + , objective = "binary" + , metric = "binary_error" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + ) + expect_true(abs(bst$lower_bound() - -1.590853) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(bst$upper_bound() - 1.871015) < .LGB_NUMERIC_TOLERANCE) +}) + +test_that("lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for regression", { + set.seed(708L) + nrounds <- 10L + bst <- lightgbm( + data = train$data + , label = train$label + , params = list( + num_leaves = 5L + , objective = "regression" + , metric = "l2" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + ) + expect_true(abs(bst$lower_bound() - 0.1513859) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(bst$upper_bound() - 0.9080349) < .LGB_NUMERIC_TOLERANCE) +}) + +test_that("lightgbm() rejects negative or 0 value passed to nrounds", { + dtrain <- lgb.Dataset(train$data, label = train$label) + params <- list(objective = "regression", metric = "l2,l1", num_threads = .LGB_MAX_THREADS) + for (nround_value in c(-10L, 0L)) { + expect_error({ + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = nround_value + ) + }, "nrounds should be greater than zero") + } +}) + +test_that("lightgbm() accepts nrounds as either a top-level argument or parameter", { + nrounds <- 15L + + set.seed(708L) + top_level_bst <- lightgbm( + data = train$data + , label = train$label + , nrounds = nrounds + , params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 5L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + + set.seed(708L) + param_bst <- lightgbm( + data = train$data + , label = train$label + , params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 5L + , nrounds = nrounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + + set.seed(708L) + both_customized <- lightgbm( + data = train$data + , label = train$label + , nrounds = 20L + , params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 5L + , nrounds = nrounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + + top_level_l2 <- top_level_bst$eval_train()[[1L]][["value"]] + params_l2 <- param_bst$eval_train()[[1L]][["value"]] + both_l2 <- both_customized$eval_train()[[1L]][["value"]] + + # check type just to be sure the subsetting didn't return a NULL + expect_true(is.numeric(top_level_l2)) + expect_true(is.numeric(params_l2)) + expect_true(is.numeric(both_l2)) + + # check that model produces identical performance + expect_identical(top_level_l2, params_l2) + expect_identical(both_l2, params_l2) + + expect_identical(param_bst$current_iter(), top_level_bst$current_iter()) + expect_identical(param_bst$current_iter(), both_customized$current_iter()) + expect_identical(param_bst$current_iter(), nrounds) + +}) + +test_that("lightgbm() performs evaluation on validation sets if they are provided", { + set.seed(708L) + dvalid1 <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid2 <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + bst <- lightgbm( + data = train$data + , label = train$label + , params = list( + num_leaves = 5L + , objective = "binary" + , metric = c( + "binary_error" + , "auc" + ) + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + , valids = list( + "valid1" = dvalid1 + , "valid2" = dvalid2 + , "train" = lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + ) + ) + + expect_named( + bst$record_evals + , c("train", "valid1", "valid2", "start_iter") + , ignore.order = TRUE + , ignore.case = FALSE + ) + for (valid_name in c("train", "valid1", "valid2")) { + eval_results <- bst$record_evals[[valid_name]][["binary_error"]] + expect_length(eval_results[["eval"]], nrounds) + } + expect_true(abs(bst$record_evals[["train"]][["binary_error"]][["eval"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(bst$record_evals[["valid1"]][["binary_error"]][["eval"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(bst$record_evals[["valid2"]][["binary_error"]][["eval"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE) +}) + +test_that("training continuation works", { + dtrain <- lgb.Dataset( + train$data + , label = train$label + , free_raw_data = FALSE + , params = list(num_threads = .LGB_MAX_THREADS) + ) + watchlist <- list(train = dtrain) + param <- list( + objective = "binary" + , metric = "binary_logloss" + , num_leaves = 5L + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + + # train for 10 consecutive iterations + bst <- lgb.train(param, dtrain, nrounds = 10L, watchlist) + err_bst <- lgb.get.eval.result(bst, "train", "binary_logloss", 10L) + + # train for 5 iterations, save, load, train for 5 more + bst1 <- lgb.train(param, dtrain, nrounds = 5L, watchlist) + model_file <- tempfile(fileext = ".model") + lgb.save(bst1, model_file) + bst2 <- lgb.train(param, dtrain, nrounds = 5L, watchlist, init_model = bst1) + err_bst2 <- lgb.get.eval.result(bst2, "train", "binary_logloss", 10L) + + # evaluation metrics should be nearly identical for the model trained in 10 coonsecutive + # iterations and the one trained in 5-then-5. + expect_lt(abs(err_bst - err_bst2), 0.01) +}) + +test_that("cv works", { + dtrain <- lgb.Dataset(train$data, label = train$label) + params <- list( + objective = "regression" + , metric = "l2,l1" + , min_data = 1L + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lgb.cv( + params + , dtrain + , 10L + , nfold = 5L + , early_stopping_rounds = 10L + ) + expect_false(is.null(bst$record_evals)) +}) + +test_that("CVBooster$reset_parameter() works as expected", { + dtrain <- lgb.Dataset(train$data, label = train$label) + n_folds <- 2L + cv_bst <- lgb.cv( + params = list( + objective = "regression" + , min_data = 1L + , num_leaves = 7L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = 3L + , nfold = n_folds + ) + expect_true(methods::is(cv_bst, "lgb.CVBooster")) + expect_length(cv_bst$boosters, n_folds) + for (bst in cv_bst$boosters) { + expect_equal(bst[["booster"]]$params[["num_leaves"]], 7L) + } + cv_bst$reset_parameter(list(num_leaves = 11L)) + for (bst in cv_bst$boosters) { + expect_equal(bst[["booster"]]$params[["num_leaves"]], 11L) + } +}) + +test_that("lgb.cv() rejects negative or 0 value passed to nrounds", { + dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = 2L)) + params <- list( + objective = "regression" + , metric = "l2,l1" + , min_data = 1L + , num_threads = .LGB_MAX_THREADS + ) + for (nround_value in c(-10L, 0L)) { + expect_error({ + bst <- lgb.cv( + params + , dtrain + , nround_value + , nfold = 5L + ) + }, "nrounds should be greater than zero") + } +}) + +test_that("lgb.cv() throws an informative error if 'data' is not an lgb.Dataset", { + bad_values <- list( + 4L + , "hello" + , list(a = TRUE, b = seq_len(10L)) + , data.frame(x = seq_len(5L), y = seq_len(5L)) + , data.table::data.table(x = seq_len(5L), y = seq_len(5L)) + , matrix(data = seq_len(10L), 2L, 5L) + ) + for (val in bad_values) { + expect_error({ + bst <- lgb.cv( + params = list( + objective = "regression" + , metric = "l2,l1" + , min_data = 1L + ) + , data = val + , 10L + , nfold = 5L + ) + }, regexp = "lgb.cv: data must be an lgb.Dataset instance", fixed = TRUE) + } +}) + +test_that("lightgbm.cv() gives the correct best_score and best_iter for a metric where higher values are better", { + set.seed(708L) + dtrain <- lgb.Dataset( + data = as.matrix(runif(n = 500L, min = 0.0, max = 15.0), drop = FALSE) + , label = rep(c(0L, 1L), 250L) + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + cv_bst <- lgb.cv( + data = dtrain + , nfold = 5L + , nrounds = nrounds + , params = list( + objective = "binary" + , metric = "auc,binary_error" + , learning_rate = 1.5 + , num_leaves = 5L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + expect_true(methods::is(cv_bst, "lgb.CVBooster")) + expect_named( + cv_bst$record_evals + , c("start_iter", "valid") + , ignore.order = FALSE + , ignore.case = FALSE + ) + auc_scores <- unlist(cv_bst$record_evals[["valid"]][["auc"]][["eval"]]) + expect_length(auc_scores, nrounds) + expect_identical(cv_bst$best_iter, which.max(auc_scores)) + expect_identical(cv_bst$best_score, auc_scores[which.max(auc_scores)]) +}) + +test_that("lgb.cv() fit on linearly-relatead data improves when using linear learners", { + set.seed(708L) + .new_dataset <- function() { + X <- matrix(rnorm(1000L), ncol = 1L) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + , params = list(num_threads = .LGB_MAX_THREADS) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , num_threads = .LGB_MAX_THREADS + ) + + dtrain <- .new_dataset() + cv_bst <- lgb.cv( + data = dtrain + , nrounds = 10L + , params = params + , nfold = 5L + ) + expect_true(methods::is(cv_bst, "lgb.CVBooster")) + + dtrain <- .new_dataset() + cv_bst_linear <- lgb.cv( + data = dtrain + , nrounds = 10L + , params = utils::modifyList(params, list(linear_tree = TRUE)) + , nfold = 5L + ) + expect_true(methods::is(cv_bst_linear, "lgb.CVBooster")) + + expect_true(cv_bst_linear$best_score < cv_bst$best_score) +}) + +test_that("lgb.cv() respects showsd argument", { + dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS)) + params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + nrounds <- 5L + set.seed(708L) + bst_showsd <- lgb.cv( + params = params + , data = dtrain + , nrounds = nrounds + , nfold = 3L + , showsd = TRUE + ) + evals_showsd <- bst_showsd$record_evals[["valid"]][["l2"]] + set.seed(708L) + bst_no_showsd <- lgb.cv( + params = params + , data = dtrain + , nrounds = nrounds + , nfold = 3L + , showsd = FALSE + ) + evals_no_showsd <- bst_no_showsd$record_evals[["valid"]][["l2"]] + expect_equal( + evals_showsd[["eval"]] + , evals_no_showsd[["eval"]] + ) + expect_true(methods::is(evals_showsd[["eval_err"]], "list")) + expect_equal(length(evals_showsd[["eval_err"]]), nrounds) + expect_identical(evals_no_showsd[["eval_err"]], list()) +}) + +test_that("lgb.cv() raises an informative error for unrecognized objectives", { + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + expect_error({ + capture.output({ + bst <- lgb.cv( + data = dtrain + , params = list( + objective_type = "not_a_real_objective" + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + }, type = "message") + }, regexp = "Unknown objective type name: not_a_real_objective") +}) + +test_that("lgb.cv() respects parameter aliases for objective", { + nrounds <- 3L + nfold <- 4L + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + cv_bst <- lgb.cv( + data = dtrain + , params = list( + num_leaves = 5L + , application = "binary" + , num_iterations = nrounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nfold = nfold + ) + expect_equal(cv_bst$best_iter, nrounds) + expect_named(cv_bst$record_evals[["valid"]], "binary_logloss") + expect_length(cv_bst$record_evals[["valid"]][["binary_logloss"]][["eval"]], nrounds) + expect_length(cv_bst$boosters, nfold) +}) + +test_that("lgb.cv() prefers objective in params to keyword argument", { + data("EuStockMarkets") + cv_bst <- lgb.cv( + data = lgb.Dataset( + data = EuStockMarkets[, c("SMI", "CAC", "FTSE")] + , label = EuStockMarkets[, "DAX"] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + , params = list( + application = "regression_l1" + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 5L + , obj = "regression_l2" + ) + for (bst_list in cv_bst$boosters) { + bst <- bst_list[["booster"]] + expect_equal(bst$params$objective, "regression_l1") + # NOTE: using save_model_to_string() since that is the simplest public API in the R-package + # allowing access to the "objective" attribute of the Booster object on the C++ side + model_txt_lines <- strsplit( + x = bst$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(model_txt_lines == "objective=regression_l1")) + expect_false(any(model_txt_lines == "objective=regression_l2")) + } +}) + +test_that("lgb.cv() respects parameter aliases for metric", { + nrounds <- 3L + nfold <- 4L + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + cv_bst <- lgb.cv( + data = dtrain + , params = list( + num_leaves = 5L + , objective = "binary" + , num_iterations = nrounds + , metric_types = c("auc", "binary_logloss") + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nfold = nfold + ) + expect_equal(cv_bst$best_iter, nrounds) + expect_named(cv_bst$record_evals[["valid"]], c("auc", "binary_logloss")) + expect_length(cv_bst$record_evals[["valid"]][["binary_logloss"]][["eval"]], nrounds) + expect_length(cv_bst$record_evals[["valid"]][["auc"]][["eval"]], nrounds) + expect_length(cv_bst$boosters, nfold) +}) + +test_that("lgb.cv() respects eval_train_metric argument", { + dtrain <- lgb.Dataset(train$data, label = train$label) + params <- list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + nrounds <- 5L + set.seed(708L) + bst_train <- lgb.cv( + params = params + , data = dtrain + , nrounds = nrounds + , nfold = 3L + , showsd = FALSE + , eval_train_metric = TRUE + ) + set.seed(708L) + bst_no_train <- lgb.cv( + params = params + , data = dtrain + , nrounds = nrounds + , nfold = 3L + , showsd = FALSE + , eval_train_metric = FALSE + ) + expect_equal( + bst_train$record_evals[["valid"]][["l2"]] + , bst_no_train$record_evals[["valid"]][["l2"]] + ) + expect_true("train" %in% names(bst_train$record_evals)) + expect_false("train" %in% names(bst_no_train$record_evals)) + expect_true(methods::is(bst_train$record_evals[["train"]][["l2"]][["eval"]], "list")) + expect_equal( + length(bst_train$record_evals[["train"]][["l2"]][["eval"]]) + , nrounds + ) +}) + +test_that("lgb.train() works as expected with multiple eval metrics", { + metrics <- c("binary_error", "auc", "binary_logloss") + bst <- lgb.train( + data = lgb.Dataset( + train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + , nrounds = 10L + , params = list( + objective = "binary" + , metric = metrics + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , valids = list( + "train" = lgb.Dataset( + train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + ) + ) + expect_false(is.null(bst$record_evals)) + expect_named( + bst$record_evals[["train"]] + , unlist(metrics) + , ignore.order = FALSE + , ignore.case = FALSE + ) +}) + +test_that("lgb.train() raises an informative error for unrecognized objectives", { + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + ) + expect_error({ + capture.output({ + bst <- lgb.train( + data = dtrain + , params = list( + objective_type = "not_a_real_objective" + , verbosity = .LGB_VERBOSITY + ) + ) + }, type = "message") + }, regexp = "Unknown objective type name: not_a_real_objective") +}) + +test_that("lgb.train() respects parameter aliases for objective", { + nrounds <- 3L + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + bst <- lgb.train( + data = dtrain + , params = list( + num_leaves = 5L + , application = "binary" + , num_iterations = nrounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , valids = list( + "the_training_data" = dtrain + ) + ) + expect_named(bst$record_evals[["the_training_data"]], "binary_logloss") + expect_length(bst$record_evals[["the_training_data"]][["binary_logloss"]][["eval"]], nrounds) + expect_equal(bst$params[["objective"]], "binary") +}) + +test_that("lgb.train() prefers objective in params to keyword argument", { + data("EuStockMarkets") + bst <- lgb.train( + data = lgb.Dataset( + data = EuStockMarkets[, c("SMI", "CAC", "FTSE")] + , label = EuStockMarkets[, "DAX"] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + , params = list( + loss = "regression_l1" + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 5L + , obj = "regression_l2" + ) + expect_equal(bst$params$objective, "regression_l1") + # NOTE: using save_model_to_string() since that is the simplest public API in the R-package + # allowing access to the "objective" attribute of the Booster object on the C++ side + model_txt_lines <- strsplit( + x = bst$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(model_txt_lines == "objective=regression_l1")) + expect_false(any(model_txt_lines == "objective=regression_l2")) +}) + +test_that("lgb.train() respects parameter aliases for metric", { + nrounds <- 3L + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + bst <- lgb.train( + data = dtrain + , params = list( + num_leaves = 5L + , objective = "binary" + , num_iterations = nrounds + , metric_types = c("auc", "binary_logloss") + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , valids = list( + "train" = dtrain + ) + ) + record_results <- bst$record_evals[["train"]] + expect_equal(sort(names(record_results)), c("auc", "binary_logloss")) + expect_length(record_results[["auc"]][["eval"]], nrounds) + expect_length(record_results[["binary_logloss"]][["eval"]], nrounds) + expect_equal(bst$params[["metric"]], list("auc", "binary_logloss")) +}) + +test_that("lgb.train() rejects negative or 0 value passed to nrounds", { + dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS)) + params <- list( + objective = "regression" + , metric = "l2,l1" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + for (nround_value in c(-10L, 0L)) { + expect_error({ + bst <- lgb.train( + params + , dtrain + , nround_value + ) + }, "nrounds should be greater than zero") + } +}) + + +test_that("lgb.train() accepts nrounds as either a top-level argument or parameter", { + nrounds <- 15L + + set.seed(708L) + top_level_bst <- lgb.train( + data = lgb.Dataset( + train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + , nrounds = nrounds + , params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 5L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + + set.seed(708L) + param_bst <- lgb.train( + data = lgb.Dataset( + train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + , params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 5L + , nrounds = nrounds + , verbose = .LGB_VERBOSITY + ) + ) + + set.seed(708L) + both_customized <- lgb.train( + data = lgb.Dataset( + train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + , nrounds = 20L + , params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 5L + , nrounds = nrounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + + top_level_l2 <- top_level_bst$eval_train()[[1L]][["value"]] + params_l2 <- param_bst$eval_train()[[1L]][["value"]] + both_l2 <- both_customized$eval_train()[[1L]][["value"]] + + # check type just to be sure the subsetting didn't return a NULL + expect_true(is.numeric(top_level_l2)) + expect_true(is.numeric(params_l2)) + expect_true(is.numeric(both_l2)) + + # check that model produces identical performance + expect_identical(top_level_l2, params_l2) + expect_identical(both_l2, params_l2) + + expect_identical(param_bst$current_iter(), top_level_bst$current_iter()) + expect_identical(param_bst$current_iter(), both_customized$current_iter()) + expect_identical(param_bst$current_iter(), nrounds) + +}) + + +test_that("lgb.train() throws an informative error if 'data' is not an lgb.Dataset", { + bad_values <- list( + 4L + , "hello" + , list(a = TRUE, b = seq_len(10L)) + , data.frame(x = seq_len(5L), y = seq_len(5L)) + , data.table::data.table(x = seq_len(5L), y = seq_len(5L)) + , matrix(data = seq_len(10L), 2L, 5L) + ) + for (val in bad_values) { + expect_error({ + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "l2,l1" + , verbose = .LGB_VERBOSITY + ) + , data = val + , 10L + ) + }, regexp = "data must be an lgb.Dataset instance", fixed = TRUE) + } +}) + +test_that("lgb.train() throws an informative error if 'valids' is not a list of lgb.Dataset objects", { + valids <- list( + "valid1" = data.frame(x = rnorm(5L), y = rnorm(5L)) + , "valid2" = data.frame(x = rnorm(5L), y = rnorm(5L)) + ) + expect_error({ + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "l2,l1" + , verbose = .LGB_VERBOSITY + ) + , data = lgb.Dataset(train$data, label = train$label) + , 10L + , valids = valids + ) + }, regexp = "valids must be a list of lgb.Dataset elements") +}) + +test_that("lgb.train() errors if 'valids' is a list of lgb.Dataset objects but some do not have names", { + valids <- list( + "valid1" = lgb.Dataset(matrix(rnorm(10L), 5L, 2L)) + , lgb.Dataset(matrix(rnorm(10L), 2L, 5L)) + ) + expect_error({ + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "l2,l1" + , verbose = .LGB_VERBOSITY + ) + , data = lgb.Dataset(train$data, label = train$label) + , 10L + , valids = valids + ) + }, regexp = "each element of valids must have a name") +}) + +test_that("lgb.train() throws an informative error if 'valids' contains lgb.Dataset objects but none have names", { + valids <- list( + lgb.Dataset(matrix(rnorm(10L), 5L, 2L)) + , lgb.Dataset(matrix(rnorm(10L), 2L, 5L)) + ) + expect_error({ + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "l2,l1" + , verbose = .LGB_VERBOSITY + ) + , data = lgb.Dataset(train$data, label = train$label) + , 10L + , valids = valids + ) + }, regexp = "each element of valids must have a name") +}) + +test_that("lgb.train() works with force_col_wise and force_row_wise", { + set.seed(1234L) + nrounds <- 10L + dtrain <- lgb.Dataset( + train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + params <- list( + objective = "binary" + , metric = "binary_error" + , force_col_wise = TRUE + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst_col_wise <- lgb.train( + params = params + , data = dtrain + , nrounds = nrounds + ) + + params <- list( + objective = "binary" + , metric = "binary_error" + , force_row_wise = TRUE + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst_row_wise <- lgb.train( + params = params + , data = dtrain + , nrounds = nrounds + ) + + expected_error <- 0.003070782 + expect_equal(bst_col_wise$eval_train()[[1L]][["value"]], expected_error) + expect_equal(bst_row_wise$eval_train()[[1L]][["value"]], expected_error) + + # check some basic details of the boosters just to be sure force_col_wise + # and force_row_wise are not causing any weird side effects + for (bst in list(bst_row_wise, bst_col_wise)) { + expect_equal(bst$current_iter(), nrounds) + parsed_model <- jsonlite::fromJSON(bst$dump_model()) + expect_equal(parsed_model$objective, "binary sigmoid:1") + expect_false(parsed_model$average_output) + } +}) + +test_that("lgb.train() works as expected with sparse features", { + set.seed(708L) + num_obs <- 70000L + trainDF <- data.frame( + y = sample(c(0L, 1L), size = num_obs, replace = TRUE) + , x = sample(c(1.0:10.0, rep(NA_real_, 50L)), size = num_obs, replace = TRUE) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["x"]], drop = FALSE) + , label = trainDF[["y"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 1L + bst <- lgb.train( + params = list( + objective = "binary" + , min_data = 1L + , min_data_in_bin = 1L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + ) + + expect_true(.is_Booster(bst)) + expect_equal(bst$current_iter(), nrounds) + parsed_model <- jsonlite::fromJSON(bst$dump_model()) + expect_equal(parsed_model$objective, "binary sigmoid:1") + expect_false(parsed_model$average_output) + expected_error <- 0.6931268 + expect_true(abs(bst$eval_train()[[1L]][["value"]] - expected_error) < .LGB_NUMERIC_TOLERANCE) +}) + +test_that("lgb.train() works with early stopping for classification", { + trainDF <- data.frame( + "feat1" = rep(c(5.0, 10.0), 500L) + , "target" = rep(c(0L, 1L), 500L) + ) + validDF <- data.frame( + "feat1" = rep(c(5.0, 10.0), 50L) + , "target" = rep(c(0L, 1L), 50L) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["feat1"]], drop = FALSE) + , label = trainDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid <- lgb.Dataset( + data = as.matrix(validDF[["feat1"]], drop = FALSE) + , label = validDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + + ################################ + # train with no early stopping # + ################################ + bst <- lgb.train( + params = list( + objective = "binary" + , metric = "binary_error" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + + # a perfect model should be trivial to obtain, but all 10 rounds + # should happen + expect_equal(bst$best_score, 0.0) + expect_equal(bst$best_iter, 1L) + expect_equal(length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]), nrounds) + + ############################# + # train with early stopping # + ############################# + early_stopping_rounds <- 5L + bst <- lgb.train( + params = list( + objective = "binary" + , metric = "binary_error" + , early_stopping_rounds = early_stopping_rounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + + # a perfect model should be trivial to obtain, and only 6 rounds + # should have happen (1 with improvement, 5 consecutive with no improvement) + expect_equal(bst$best_score, 0.0) + expect_equal(bst$best_iter, 1L) + expect_equal( + length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]) + , early_stopping_rounds + 1L + ) + +}) + +test_that("lgb.train() treats early_stopping_rounds<=0 as disabling early stopping", { + set.seed(708L) + trainDF <- data.frame( + "feat1" = rep(c(5.0, 10.0), 500L) + , "target" = rep(c(0L, 1L), 500L) + ) + validDF <- data.frame( + "feat1" = rep(c(5.0, 10.0), 50L) + , "target" = rep(c(0L, 1L), 50L) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["feat1"]], drop = FALSE) + , label = trainDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid <- lgb.Dataset( + data = as.matrix(validDF[["feat1"]], drop = FALSE) + , label = validDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 5L + + for (value in c(-5L, 0L)) { + + #----------------------------# + # passed as keyword argument # + #----------------------------# + bst <- lgb.train( + params = list( + objective = "binary" + , metric = "binary_error" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + , early_stopping_rounds = value + ) + + # a perfect model should be trivial to obtain, but all 10 rounds + # should happen + expect_equal(bst$best_score, 0.0) + expect_equal(bst$best_iter, 1L) + expect_equal(length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]), nrounds) + + #---------------------------# + # passed as parameter alias # + #---------------------------# + bst <- lgb.train( + params = list( + objective = "binary" + , metric = "binary_error" + , n_iter_no_change = value + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + + # a perfect model should be trivial to obtain, but all 10 rounds + # should happen + expect_equal(bst$best_score, 0.0) + expect_equal(bst$best_iter, 1L) + expect_equal(length(bst$record_evals[["valid1"]][["binary_error"]][["eval"]]), nrounds) + } +}) + +test_that("lgb.train() works with early stopping for classification with a metric that should be maximized", { + set.seed(708L) + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid <- lgb.Dataset( + data = test$data + , label = test$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + + ############################# + # train with early stopping # + ############################# + early_stopping_rounds <- 5L + # the harsh max_depth guarantees that AUC improves over at least the first few iterations + bst_auc <- lgb.train( + params = list( + objective = "binary" + , metric = "auc" + , max_depth = 3L + , early_stopping_rounds = early_stopping_rounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + bst_binary_error <- lgb.train( + params = list( + objective = "binary" + , metric = "binary_error" + , max_depth = 3L + , early_stopping_rounds = early_stopping_rounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + + # early stopping should have been hit for binary_error (higher_better = FALSE) + eval_info <- bst_binary_error$.__enclos_env__$private$get_eval_info() + expect_identical(eval_info, "binary_error") + expect_identical( + unname(bst_binary_error$.__enclos_env__$private$higher_better_inner_eval) + , FALSE + ) + expect_identical(bst_binary_error$best_iter, 1L) + expect_identical(bst_binary_error$current_iter(), early_stopping_rounds + 1L) + expect_true(abs(bst_binary_error$best_score - 0.01613904) < .LGB_NUMERIC_TOLERANCE) + + # early stopping should not have been hit for AUC (higher_better = TRUE) + eval_info <- bst_auc$.__enclos_env__$private$get_eval_info() + expect_identical(eval_info, "auc") + expect_identical( + unname(bst_auc$.__enclos_env__$private$higher_better_inner_eval) + , TRUE + ) + expect_identical(bst_auc$best_iter, 9L) + expect_identical(bst_auc$current_iter(), nrounds) + expect_true(abs(bst_auc$best_score - 0.9999969) < .LGB_NUMERIC_TOLERANCE) +}) + +test_that("lgb.train() works with early stopping for regression", { + set.seed(708L) + trainDF <- data.frame( + "feat1" = rep(c(10.0, 100.0), 500L) + , "target" = rep(c(-50.0, 50.0), 500L) + ) + validDF <- data.frame( + "feat1" = rep(50.0, 4L) + , "target" = rep(50.0, 4L) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["feat1"]], drop = FALSE) + , label = trainDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid <- lgb.Dataset( + data = as.matrix(validDF[["feat1"]], drop = FALSE) + , label = validDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + + ################################ + # train with no early stopping # + ################################ + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "rmse" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + + # the best possible model should come from the first iteration, but + # all 10 training iterations should happen + expect_equal(bst$best_score, 55.0) + expect_equal(bst$best_iter, 1L) + expect_equal(length(bst$record_evals[["valid1"]][["rmse"]][["eval"]]), nrounds) + + ############################# + # train with early stopping # + ############################# + early_stopping_rounds <- 5L + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "rmse" + , early_stopping_rounds = early_stopping_rounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + + # the best model should be from the first iteration, and only 6 rounds + # should have happen (1 with improvement, 5 consecutive with no improvement) + expect_equal(bst$best_score, 55.0) + expect_equal(bst$best_iter, 1L) + expect_equal( + length(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) + , early_stopping_rounds + 1L + ) +}) + +test_that("lgb.train() does not stop early if early_stopping_rounds is not given", { + set.seed(708L) + + increasing_metric_starting_value <- get( + ACCUMULATOR_NAME + , envir = .GlobalEnv + ) + nrounds <- 10L + metrics <- list( + .constant_metric + , .increasing_metric + ) + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "None" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_REGRESSION + , nrounds = nrounds + , valids = list("valid1" = DVALID_RANDOM_REGRESSION) + , eval = metrics + ) + + # Only the two functions provided to "eval" should have been evaluated + expect_equal(length(bst$record_evals[["valid1"]]), 2L) + + # all 10 iterations should have happen, and the best_iter should be + # the first one (based on constant_metric) + best_iter <- 1L + expect_equal(bst$best_iter, best_iter) + + # best_score should be taken from the first metric + expect_equal( + bst$best_score + , bst$record_evals[["valid1"]][["constant_metric"]][["eval"]][[best_iter]] + ) + + # early stopping should not have happened. Even though constant_metric + # had 9 consecutive iterations with no improvement, it is ignored because of + # first_metric_only = TRUE + expect_equal( + length(bst$record_evals[["valid1"]][["constant_metric"]][["eval"]]) + , nrounds + ) + expect_equal( + length(bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]]) + , nrounds + ) +}) + +test_that("If first_metric_only is not given or is FALSE, lgb.train() decides to stop early based on all metrics", { + set.seed(708L) + + early_stopping_rounds <- 3L + param_variations <- list( + list( + objective = "regression" + , metric = "None" + , early_stopping_rounds = early_stopping_rounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , list( + objective = "regression" + , metric = "None" + , early_stopping_rounds = early_stopping_rounds + , first_metric_only = FALSE + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + + for (params in param_variations) { + + nrounds <- 10L + bst <- lgb.train( + params = params + , data = DTRAIN_RANDOM_REGRESSION + , nrounds = nrounds + , valids = list( + "valid1" = DVALID_RANDOM_REGRESSION + ) + , eval = list( + .increasing_metric + , .constant_metric + ) + ) + + # Only the two functions provided to "eval" should have been evaluated + expect_equal(length(bst$record_evals[["valid1"]]), 2L) + + # early stopping should have happened, and should have stopped early_stopping_rounds + 1 rounds in + # because constant_metric never improves + # + # the best iteration should be the last one, because increasing_metric was first + # and gets better every iteration + best_iter <- early_stopping_rounds + 1L + expect_equal(bst$best_iter, best_iter) + + # best_score should be taken from "increasing_metric" because it was first + expect_equal( + bst$best_score + , bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]][[best_iter]] + ) + + # early stopping should not have happened. even though increasing_metric kept + # getting better, early stopping should have happened because "constant_metric" + # did not improve + expect_equal( + length(bst$record_evals[["valid1"]][["constant_metric"]][["eval"]]) + , early_stopping_rounds + 1L + ) + expect_equal( + length(bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]]) + , early_stopping_rounds + 1L + ) + } + +}) + +test_that("If first_metric_only is TRUE, lgb.train() decides to stop early based on only the first metric", { + set.seed(708L) + nrounds <- 10L + early_stopping_rounds <- 3L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "None" + , early_stopping_rounds = early_stopping_rounds + , first_metric_only = TRUE + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_REGRESSION + , nrounds = nrounds + , valids = list( + "valid1" = DVALID_RANDOM_REGRESSION + ) + , eval = list( + .increasing_metric + , .constant_metric + ) + ) + + # Only the two functions provided to "eval" should have been evaluated + expect_equal(length(bst$record_evals[["valid1"]]), 2L) + + # all 10 iterations should happen, and the best_iter should be the final one + expect_equal(bst$best_iter, nrounds) + + # best_score should be taken from "increasing_metric" + expect_equal( + bst$best_score + , increasing_metric_starting_value + 0.1 * nrounds + ) + + # early stopping should not have happened. Even though constant_metric + # had 9 consecutive iterations with no improvement, it is ignored because of + # first_metric_only = TRUE + expect_equal( + length(bst$record_evals[["valid1"]][["constant_metric"]][["eval"]]) + , nrounds + ) + expect_equal( + length(bst$record_evals[["valid1"]][["increasing_metric"]][["eval"]]) + , nrounds + ) +}) + +test_that("lgb.train() works when a mixture of functions and strings are passed to eval", { + set.seed(708L) + nrounds <- 10L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "None" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_REGRESSION + , nrounds = nrounds + , valids = list( + "valid1" = DVALID_RANDOM_REGRESSION + ) + , eval = list( + .increasing_metric + , "rmse" + , .constant_metric + , "l2" + ) + ) + + # all 4 metrics should have been used + expect_named( + bst$record_evals[["valid1"]] + , expected = c("rmse", "l2", "increasing_metric", "constant_metric") + , ignore.order = TRUE + , ignore.case = FALSE + ) + + # the difference metrics shouldn't have been mixed up with each other + results <- bst$record_evals[["valid1"]] + expect_true(abs(results[["rmse"]][["eval"]][[1L]] - 1.105012) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(results[["l2"]][["eval"]][[1L]] - 1.221051) < .LGB_NUMERIC_TOLERANCE) + expected_increasing_metric <- increasing_metric_starting_value + 0.1 + expect_true( + abs( + results[["increasing_metric"]][["eval"]][[1L]] - expected_increasing_metric + ) < .LGB_NUMERIC_TOLERANCE + ) + expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE) + +}) + +test_that("lgb.train() works when a list of strings or a character vector is passed to eval", { + + # testing list and character vector, as well as length-1 and length-2 + eval_variations <- list( + c("binary_error", "binary_logloss") + , "binary_logloss" + , list("binary_error", "binary_logloss") + , list("binary_logloss") + ) + + for (eval_variation in eval_variations) { + + set.seed(708L) + nrounds <- 10L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.train( + params = list( + objective = "binary" + , metric = "None" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_CLASSIFICATION + , nrounds = nrounds + , valids = list( + "valid1" = DVALID_RANDOM_CLASSIFICATION + ) + , eval = eval_variation + ) + + # both metrics should have been used + expect_named( + bst$record_evals[["valid1"]] + , expected = unlist(eval_variation) + , ignore.order = TRUE + , ignore.case = FALSE + ) + + # the difference metrics shouldn't have been mixed up with each other + results <- bst$record_evals[["valid1"]] + if ("binary_error" %in% unlist(eval_variation)) { + expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.4864865) < .LGB_NUMERIC_TOLERANCE) + } + if ("binary_logloss" %in% unlist(eval_variation)) { + expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6932548) < .LGB_NUMERIC_TOLERANCE) + } + } +}) + +test_that("lgb.train() works when you specify both 'metric' and 'eval' with strings", { + set.seed(708L) + nrounds <- 10L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.train( + params = list( + objective = "binary" + , metric = "binary_error" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_CLASSIFICATION + , nrounds = nrounds + , valids = list( + "valid1" = DVALID_RANDOM_CLASSIFICATION + ) + , eval = "binary_logloss" + ) + + # both metrics should have been used + expect_named( + bst$record_evals[["valid1"]] + , expected = c("binary_error", "binary_logloss") + , ignore.order = TRUE + , ignore.case = FALSE + ) + + # the difference metrics shouldn't have been mixed up with each other + results <- bst$record_evals[["valid1"]] + expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.4864865) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6932548) < .LGB_NUMERIC_TOLERANCE) +}) + +test_that("lgb.train() works when you give a function for eval", { + set.seed(708L) + nrounds <- 10L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.train( + params = list( + objective = "binary" + , metric = "None" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_CLASSIFICATION + , nrounds = nrounds + , valids = list( + "valid1" = DVALID_RANDOM_CLASSIFICATION + ) + , eval = .constant_metric + ) + + # the difference metrics shouldn't have been mixed up with each other + results <- bst$record_evals[["valid1"]] + expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE) +}) + +test_that("lgb.train() works with early stopping for regression with a metric that should be minimized", { + set.seed(708L) + trainDF <- data.frame( + "feat1" = rep(c(10.0, 100.0), 500L) + , "target" = rep(c(-50.0, 50.0), 500L) + ) + validDF <- data.frame( + "feat1" = rep(50.0, 4L) + , "target" = rep(50.0, 4L) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["feat1"]], drop = FALSE) + , label = trainDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid <- lgb.Dataset( + data = as.matrix(validDF[["feat1"]], drop = FALSE) + , label = validDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + + ############################# + # train with early stopping # + ############################# + early_stopping_rounds <- 5L + bst <- lgb.train( + params = list( + objective = "regression" + , metric = c( + "mape" + , "rmse" + , "mae" + ) + , min_data_in_bin = 5L + , early_stopping_rounds = early_stopping_rounds + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid + ) + ) + + # the best model should be from the first iteration, and only 6 rounds + # should have happened (1 with improvement, 5 consecutive with no improvement) + expect_equal(bst$best_score, 1.1) + expect_equal(bst$best_iter, 1L) + expect_equal( + length(bst$record_evals[["valid1"]][["mape"]][["eval"]]) + , early_stopping_rounds + 1L + ) + + # Booster should understand that all three of these metrics should be minimized + eval_info <- bst$.__enclos_env__$private$get_eval_info() + expect_identical(eval_info, c("mape", "rmse", "l1")) + expect_identical( + unname(bst$.__enclos_env__$private$higher_better_inner_eval) + , rep(FALSE, 3L) + ) +}) + + +test_that("lgb.train() supports non-ASCII feature names", { + # content below is equivalent to + # + # feature_names <- c("F_零", "F_一", "F_二", "F_三") + # + # but using rawToChar() to avoid weird issues when {testthat} + # sources files and converts their encodings prior to evaluating the code + feature_names <- c( + rawToChar(as.raw(c(0x46, 0x5f, 0xe9, 0x9b, 0xb6))) + , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xb8, 0x80))) + , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xba, 0x8c))) + , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xb8, 0x89))) + ) + dtrain <- lgb.Dataset( + data = matrix(rnorm(400L), ncol = 4L) + , label = rnorm(100L) + , params = list(num_threads = .LGB_MAX_THREADS) + , colnames = feature_names + ) + bst <- lgb.train( + data = dtrain + , nrounds = 5L + , obj = "regression" + , params = list( + metric = "rmse" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + expect_true(.is_Booster(bst)) + dumped_model <- jsonlite::fromJSON(bst$dump_model()) + + # UTF-8 strings are not well-supported on Windows + # * https://developer.r-project.org/Blog/public/2020/05/02/utf-8-support-on-windows/ + # * https://developer.r-project.org/Blog/public/2020/07/30/windows/utf-8-build-of-r-and-cran-packages/index.html + if (.LGB_UTF8_LOCALE && !.LGB_ON_WINDOWS) { + expect_identical( + dumped_model[["feature_names"]] + , feature_names + ) + } else { + expect_identical( + dumped_model[["feature_names"]] + , iconv(feature_names, to = "UTF-8") + ) + } +}) + +test_that("lgb.train() works with integer, double, and numeric data", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- mtcars[, 1L, drop = TRUE] + expected_mae <- 4.263667 + for (data_mode in c("numeric", "double", "integer")) { + mode(X) <- data_mode + nrounds <- 10L + bst <- lightgbm( + data = X + , label = y + , params = list( + objective = "regression" + , min_data_in_bin = 1L + , min_data_in_leaf = 1L + , learning_rate = 0.01 + , seed = 708L + , verbose = .LGB_VERBOSITY + ) + , nrounds = nrounds + ) + + # should have trained for 10 iterations and found splits + modelDT <- lgb.model.dt.tree(bst) + expect_equal(modelDT[, max(tree_index)], nrounds - 1L) + expect_gt(nrow(modelDT), nrounds * 3L) + + # should have achieved expected performance + preds <- predict(bst, X) + mae <- mean(abs(y - preds)) + expect_true(abs(mae - expected_mae) < .LGB_NUMERIC_TOLERANCE) + } +}) + +test_that("lgb.train() updates params based on keyword arguments", { + dtrain <- lgb.Dataset( + data = matrix(rnorm(400L), ncol = 4L) + , label = rnorm(100L) + , params = list(num_threads = .LGB_MAX_THREADS) + ) + + # defaults from keyword arguments should be used if not specified in params + invisible( + capture.output({ + bst <- lgb.train( + data = dtrain + , obj = "regression" + , params = list(num_threads = .LGB_MAX_THREADS) + ) + }) + ) + expect_equal(bst$params[["verbosity"]], 1L) + expect_equal(bst$params[["num_iterations"]], 100L) + + # main param names should be preferred to keyword arguments + invisible( + capture.output({ + bst <- lgb.train( + data = dtrain + , obj = "regression" + , params = list( + "verbosity" = 5L + , "num_iterations" = 2L + , num_threads = .LGB_MAX_THREADS + ) + ) + }) + ) + expect_equal(bst$params[["verbosity"]], 5L) + expect_equal(bst$params[["num_iterations"]], 2L) + + # aliases should be preferred to keyword arguments, and converted to main parameter name + invisible( + capture.output({ + bst <- lgb.train( + data = dtrain + , obj = "regression" + , params = list( + "verbose" = 5L + , "num_boost_round" = 2L + , num_threads = .LGB_MAX_THREADS + ) + ) + }) + ) + expect_equal(bst$params[["verbosity"]], 5L) + expect_false("verbose" %in% bst$params) + expect_equal(bst$params[["num_iterations"]], 2L) + expect_false("num_boost_round" %in% bst$params) +}) + +test_that("when early stopping is not activated, best_iter and best_score come from valids and not training data", { + set.seed(708L) + trainDF <- data.frame( + "feat1" = rep(c(10.0, 100.0), 500L) + , "target" = rep(c(-50.0, 50.0), 500L) + ) + validDF <- data.frame( + "feat1" = rep(50.0, 4L) + , "target" = rep(50.0, 4L) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["feat1"]], drop = FALSE) + , label = trainDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid1 <- lgb.Dataset( + data = as.matrix(validDF[["feat1"]], drop = FALSE) + , label = validDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid2 <- lgb.Dataset( + data = as.matrix(validDF[1L:10L, "feat1"], drop = FALSE) + , label = validDF[1L:10L, "target"] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + train_params <- list( + objective = "regression" + , metric = "rmse" + , learning_rate = 1.5 + , num_leaves = 5L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + + # example 1: two valids, neither are the training data + bst <- lgb.train( + data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid1 + , "valid2" = dvalid2 + ) + , params = train_params + ) + expect_named( + bst$record_evals + , c("start_iter", "valid1", "valid2") + , ignore.order = FALSE + , ignore.case = FALSE + ) + rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) + expect_length(rmse_scores, nrounds) + expect_identical(bst$best_iter, which.min(rmse_scores)) + expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) + + # example 2: train first (called "train") and two valids + bst <- lgb.train( + data = dtrain + , nrounds = nrounds + , valids = list( + "train" = dtrain + , "valid1" = dvalid1 + , "valid2" = dvalid2 + ) + , params = train_params + ) + expect_named( + bst$record_evals + , c("start_iter", "train", "valid1", "valid2") + , ignore.order = FALSE + , ignore.case = FALSE + ) + rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) + expect_length(rmse_scores, nrounds) + expect_identical(bst$best_iter, which.min(rmse_scores)) + expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) + + # example 3: train second (called "train") and two valids + bst <- lgb.train( + data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid1 + , "train" = dtrain + , "valid2" = dvalid2 + ) + , params = train_params + ) + # note that "train" still ends up as the first one + expect_named( + bst$record_evals + , c("start_iter", "train", "valid1", "valid2") + , ignore.order = FALSE + , ignore.case = FALSE + ) + rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) + expect_length(rmse_scores, nrounds) + expect_identical(bst$best_iter, which.min(rmse_scores)) + expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) + + # example 4: train third (called "train") and two valids + bst <- lgb.train( + data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid1 + , "valid2" = dvalid2 + , "train" = dtrain + ) + , params = train_params + ) + # note that "train" still ends up as the first one + expect_named( + bst$record_evals + , c("start_iter", "train", "valid1", "valid2") + , ignore.order = FALSE + , ignore.case = FALSE + ) + rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) + expect_length(rmse_scores, nrounds) + expect_identical(bst$best_iter, which.min(rmse_scores)) + expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) + + # example 5: train second (called "something-random-we-would-not-hardcode") and two valids + bst <- lgb.train( + data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid1 + , "something-random-we-would-not-hardcode" = dtrain + , "valid2" = dvalid2 + ) + , params = train_params + ) + # note that "something-random-we-would-not-hardcode" was recognized as the training + # data even though it isn't named "train" + expect_named( + bst$record_evals + , c("start_iter", "something-random-we-would-not-hardcode", "valid1", "valid2") + , ignore.order = FALSE + , ignore.case = FALSE + ) + rmse_scores <- unlist(bst$record_evals[["valid1"]][["rmse"]][["eval"]]) + expect_length(rmse_scores, nrounds) + expect_identical(bst$best_iter, which.min(rmse_scores)) + expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)]) + + # example 6: the only valid supplied is the training data + bst <- lgb.train( + data = dtrain + , nrounds = nrounds + , valids = list( + "train" = dtrain + ) + , params = train_params + ) + expect_identical(bst$best_iter, -1L) + expect_identical(bst$best_score, NA_real_) +}) + +test_that("lightgbm.train() gives the correct best_score and best_iter for a metric where higher values are better", { + set.seed(708L) + trainDF <- data.frame( + "feat1" = runif(n = 500L, min = 0.0, max = 15.0) + , "target" = rep(c(0L, 1L), 500L) + ) + validDF <- data.frame( + "feat1" = runif(n = 50L, min = 0.0, max = 15.0) + , "target" = rep(c(0L, 1L), 50L) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["feat1"]], drop = FALSE) + , label = trainDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid1 <- lgb.Dataset( + data = as.matrix(validDF[1L:25L, "feat1"], drop = FALSE) + , label = validDF[1L:25L, "target"] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + bst <- lgb.train( + data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid1 + , "something-random-we-would-not-hardcode" = dtrain + ) + , params = list( + objective = "binary" + , metric = "auc" + , learning_rate = 1.5 + , num_leaves = 5L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + # note that "something-random-we-would-not-hardcode" was recognized as the training + # data even though it isn't named "train" + expect_named( + bst$record_evals + , c("start_iter", "something-random-we-would-not-hardcode", "valid1") + , ignore.order = FALSE + , ignore.case = FALSE + ) + auc_scores <- unlist(bst$record_evals[["valid1"]][["auc"]][["eval"]]) + expect_length(auc_scores, nrounds) + expect_identical(bst$best_iter, which.max(auc_scores)) + expect_identical(bst$best_score, auc_scores[which.max(auc_scores)]) +}) + +test_that("using lightgbm() without early stopping, best_iter and best_score come from valids and not training data", { + set.seed(708L) + # example: train second (called "something-random-we-would-not-hardcode"), two valids, + # and a metric where higher values are better ("auc") + trainDF <- data.frame( + "feat1" = runif(n = 500L, min = 0.0, max = 15.0) + , "target" = rep(c(0L, 1L), 500L) + ) + validDF <- data.frame( + "feat1" = runif(n = 50L, min = 0.0, max = 15.0) + , "target" = rep(c(0L, 1L), 50L) + ) + dtrain <- lgb.Dataset( + data = as.matrix(trainDF[["feat1"]], drop = FALSE) + , label = trainDF[["target"]] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid1 <- lgb.Dataset( + data = as.matrix(validDF[1L:25L, "feat1"], drop = FALSE) + , label = validDF[1L:25L, "target"] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dvalid2 <- lgb.Dataset( + data = as.matrix(validDF[26L:50L, "feat1"], drop = FALSE) + , label = validDF[26L:50L, "target"] + , params = list(num_threads = .LGB_MAX_THREADS) + ) + nrounds <- 10L + bst <- lightgbm( + data = dtrain + , nrounds = nrounds + , valids = list( + "valid1" = dvalid1 + , "something-random-we-would-not-hardcode" = dtrain + , "valid2" = dvalid2 + ) + , params = list( + objective = "binary" + , metric = "auc" + , learning_rate = 1.5 + , num_leaves = 5L + , num_threads = .LGB_MAX_THREADS + ) + , verbose = -7L + ) + # when verbose <= 0 is passed to lightgbm(), 'valids' is passed through to lgb.train() + # untouched. If you set verbose to > 0, the training data will still be first but called "train" + expect_named( + bst$record_evals + , c("start_iter", "something-random-we-would-not-hardcode", "valid1", "valid2") + , ignore.order = FALSE + , ignore.case = FALSE + ) + auc_scores <- unlist(bst$record_evals[["valid1"]][["auc"]][["eval"]]) + expect_length(auc_scores, nrounds) + expect_identical(bst$best_iter, which.max(auc_scores)) + expect_identical(bst$best_score, auc_scores[which.max(auc_scores)]) +}) + +test_that("lgb.cv() works when you specify both 'metric' and 'eval' with strings", { + set.seed(708L) + nrounds <- 10L + nfolds <- 4L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.cv( + params = list( + objective = "binary" + , metric = "binary_error" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_CLASSIFICATION + , nrounds = nrounds + , nfold = nfolds + , eval = "binary_logloss" + ) + + # both metrics should have been used + expect_named( + bst$record_evals[["valid"]] + , expected = c("binary_error", "binary_logloss") + , ignore.order = TRUE + , ignore.case = FALSE + ) + + # the difference metrics shouldn't have been mixed up with each other + results <- bst$record_evals[["valid"]] + expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.5005654) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.7011232) < .LGB_NUMERIC_TOLERANCE) + + # all boosters should have been created + expect_length(bst$boosters, nfolds) +}) + +test_that("lgb.cv() works when you give a function for eval", { + set.seed(708L) + nrounds <- 10L + nfolds <- 3L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.cv( + params = list( + objective = "binary" + , metric = "None" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_CLASSIFICATION + , nfold = nfolds + , nrounds = nrounds + , eval = .constant_metric + ) + + # the difference metrics shouldn't have been mixed up with each other + results <- bst$record_evals[["valid"]] + expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE) + expect_named(results, "constant_metric") +}) + +test_that("If first_metric_only is TRUE, lgb.cv() decides to stop early based on only the first metric", { + set.seed(708L) + nrounds <- 10L + nfolds <- 5L + early_stopping_rounds <- 3L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.cv( + params = list( + objective = "regression" + , metric = "None" + , early_stopping_rounds = early_stopping_rounds + , first_metric_only = TRUE + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_REGRESSION + , nfold = nfolds + , nrounds = nrounds + , eval = list( + .increasing_metric + , .constant_metric + ) + ) + + # Only the two functions provided to "eval" should have been evaluated + expect_named(bst$record_evals[["valid"]], c("increasing_metric", "constant_metric")) + + # all 10 iterations should happen, and the best_iter should be the final one + expect_equal(bst$best_iter, nrounds) + + # best_score should be taken from "increasing_metric" + # + # this expected value looks magical and confusing, but it's because + # evaluation metrics are averaged over all folds. + # + # consider 5-fold CV with a metric that adds 0.1 to a global accumulator + # each time it's called + # + # * iter 1: [0.1, 0.2, 0.3, 0.4, 0.5] (mean = 0.3) + # * iter 2: [0.6, 0.7, 0.8, 0.9, 1.0] (mean = 1.3) + # * iter 3: [1.1, 1.2, 1.3, 1.4, 1.5] (mean = 1.8) + # + cv_value <- increasing_metric_starting_value + mean(seq_len(nfolds) / 10.0) + (nrounds - 1L) * 0.1 * nfolds + expect_equal(bst$best_score, cv_value) + + # early stopping should not have happened. Even though constant_metric + # had 9 consecutive iterations with no improvement, it is ignored because of + # first_metric_only = TRUE + expect_equal( + length(bst$record_evals[["valid"]][["constant_metric"]][["eval"]]) + , nrounds + ) + expect_equal( + length(bst$record_evals[["valid"]][["increasing_metric"]][["eval"]]) + , nrounds + ) +}) + +test_that("early stopping works with lgb.cv()", { + set.seed(708L) + nrounds <- 10L + nfolds <- 5L + early_stopping_rounds <- 3L + increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv) + bst <- lgb.cv( + params = list( + objective = "regression" + , metric = "None" + , early_stopping_rounds = early_stopping_rounds + , first_metric_only = TRUE + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = DTRAIN_RANDOM_REGRESSION + , nfold = nfolds + , nrounds = nrounds + , eval = list( + .constant_metric + , .increasing_metric + ) + ) + + # only the two functions provided to "eval" should have been evaluated + expect_named(bst$record_evals[["valid"]], c("constant_metric", "increasing_metric")) + + # best_iter should be based on the first metric. Since constant_metric + # never changes, its first iteration was the best oone + expect_equal(bst$best_iter, 1L) + + # best_score should be taken from the first metric + expect_equal(bst$best_score, 0.2) + + # early stopping should have happened, since constant_metric was the first + # one passed to eval and it will not improve over consecutive iterations + # + # note that this test is identical to the previous one, but with the + # order of the eval metrics switched + expect_equal( + length(bst$record_evals[["valid"]][["constant_metric"]][["eval"]]) + , early_stopping_rounds + 1L + ) + expect_equal( + length(bst$record_evals[["valid"]][["increasing_metric"]][["eval"]]) + , early_stopping_rounds + 1L + ) + + # every booster's predict method should use best_iter as num_iteration in predict + random_data <- as.matrix(rnorm(10L), ncol = 1L, drop = FALSE) + for (x in bst$boosters) { + expect_equal(x$booster$best_iter, bst$best_iter) + expect_gt(x$booster$current_iter(), bst$best_iter) + preds_iter <- predict(x$booster, random_data, num_iteration = bst$best_iter) + preds_no_iter <- predict(x$booster, random_data) + expect_equal(preds_iter, preds_no_iter) + } +}) + +test_that("lgb.cv() respects changes to logging verbosity", { + dtrain <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + # (verbose = 1) should be INFO and WARNING level logs + lgb_cv_logs <- capture.output({ + cv_bst <- lgb.cv( + params = list(num_threads = .LGB_MAX_THREADS) + , nfold = 2L + , nrounds = 5L + , data = dtrain + , obj = "binary" + , verbose = 1L + ) + }) + expect_true(any(grepl("[LightGBM] [Info]", lgb_cv_logs, fixed = TRUE))) + expect_true(any(grepl("[LightGBM] [Warning]", lgb_cv_logs, fixed = TRUE))) + + # (verbose = 0) should be WARNING level logs only + lgb_cv_logs <- capture.output({ + cv_bst <- lgb.cv( + params = list(num_threads = .LGB_MAX_THREADS) + , nfold = 2L + , nrounds = 5L + , data = dtrain + , obj = "binary" + , verbose = 0L + ) + }) + expect_false(any(grepl("[LightGBM] [Info]", lgb_cv_logs, fixed = TRUE))) + expect_true(any(grepl("[LightGBM] [Warning]", lgb_cv_logs, fixed = TRUE))) + + # (verbose = -1) no logs + lgb_cv_logs <- capture.output({ + cv_bst <- lgb.cv( + params = list(num_threads = .LGB_MAX_THREADS) + , nfold = 2L + , nrounds = 5L + , data = dtrain + , obj = "binary" + , verbose = -1L + ) + }) + # NOTE: this is not length(lgb_cv_logs) == 0 because lightgbm's + # dependencies might print other messages + expect_false(any(grepl("[LightGBM] [Info]", lgb_cv_logs, fixed = TRUE))) + expect_false(any(grepl("[LightGBM] [Warning]", lgb_cv_logs, fixed = TRUE))) +}) + +test_that("lgb.cv() updates params based on keyword arguments", { + dtrain <- lgb.Dataset( + data = matrix(rnorm(400L), ncol = 4L) + , label = rnorm(100L) + , params = list(num_threads = .LGB_MAX_THREADS) + ) + + # defaults from keyword arguments should be used if not specified in params + invisible( + capture.output({ + cv_bst <- lgb.cv( + data = dtrain + , obj = "regression" + , params = list(num_threads = .LGB_MAX_THREADS) + , nfold = 2L + ) + }) + ) + + for (bst in cv_bst$boosters) { + bst_params <- bst[["booster"]]$params + expect_equal(bst_params[["verbosity"]], 1L) + expect_equal(bst_params[["num_iterations"]], 100L) + } + + # main param names should be preferred to keyword arguments + invisible( + capture.output({ + cv_bst <- lgb.cv( + data = dtrain + , obj = "regression" + , params = list( + "verbosity" = 5L + , "num_iterations" = 2L + , num_threads = .LGB_MAX_THREADS + ) + , nfold = 2L + ) + }) + ) + for (bst in cv_bst$boosters) { + bst_params <- bst[["booster"]]$params + expect_equal(bst_params[["verbosity"]], 5L) + expect_equal(bst_params[["num_iterations"]], 2L) + } + + # aliases should be preferred to keyword arguments, and converted to main parameter name + invisible( + capture.output({ + cv_bst <- lgb.cv( + data = dtrain + , obj = "regression" + , params = list( + "verbose" = 5L + , "num_boost_round" = 2L + , num_threads = .LGB_MAX_THREADS + ) + , nfold = 2L + ) + }) + ) + for (bst in cv_bst$boosters) { + bst_params <- bst[["booster"]]$params + expect_equal(bst_params[["verbosity"]], 5L) + expect_false("verbose" %in% bst_params) + expect_equal(bst_params[["num_iterations"]], 2L) + expect_false("num_boost_round" %in% bst_params) + } + +}) + +test_that("lgb.train() fit on linearly-relatead data improves when using linear learners", { + set.seed(708L) + .new_dataset <- function() { + X <- matrix(rnorm(100L), ncol = 1L) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + , params = list(num_threads = .LGB_MAX_THREADS) + )) + } + + params <- list( + objective = "regression" + , verbose = .LGB_VERBOSITY + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , num_threads = .LGB_MAX_THREADS + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = utils::modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + + +test_that("lgb.train() with linear learner fails already-constructed dataset with linear=false", { + set.seed(708L) + params <- list( + objective = "regression" + , verbose = .LGB_VERBOSITY + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , num_threads = .LGB_MAX_THREADS + ) + + dtrain <- lgb.Dataset( + data = matrix(rnorm(100L), ncol = 1L) + , label = rnorm(100L) + , params = list(num_threads = .LGB_MAX_THREADS) + ) + dtrain$construct() + expect_error({ + capture.output({ + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = utils::modifyList(params, list(linear_tree = TRUE)) + ) + }, type = "message") + }, regexp = "Cannot change linear_tree after constructed Dataset handle") +}) + +test_that("lgb.train() works with linear learners even if Dataset has missing values", { + set.seed(708L) + .new_dataset <- function() { + values <- rnorm(100L) + values[sample(seq_along(values), size = 10L)] <- NA_real_ + X <- matrix( + data = sample(values, size = 100L) + , ncol = 1L + ) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + , params = list(num_threads = .LGB_MAX_THREADS) + )) + } + + params <- list( + objective = "regression" + , verbose = .LGB_VERBOSITY + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , num_threads = .LGB_MAX_THREADS + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = utils::modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + +test_that("lgb.train() works with linear learners, bagging, and a Dataset that has missing values", { + set.seed(708L) + .new_dataset <- function() { + values <- rnorm(100L) + values[sample(seq_along(values), size = 10L)] <- NA_real_ + X <- matrix( + data = sample(values, size = 100L) + , ncol = 1L + ) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + , params = list(num_threads = .LGB_MAX_THREADS) + )) + } + + params <- list( + objective = "regression" + , verbose = .LGB_VERBOSITY + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , bagging_freq = 1L + , subsample = 0.8 + , num_threads = .LGB_MAX_THREADS + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = utils::modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + +test_that("lgb.train() works with linear learners and data where a feature has only 1 non-NA value", { + set.seed(708L) + .new_dataset <- function() { + values <- c(rnorm(100L), rep(NA_real_, 100L)) + values[118L] <- rnorm(1L) + X <- matrix( + data = values + , ncol = 2L + ) + return(lgb.Dataset( + data = X + , label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1) + , params = list( + feature_pre_filter = FALSE + , num_threads = .LGB_MAX_THREADS + ) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , num_threads = .LGB_MAX_THREADS + ) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = utils::modifyList(params, list(linear_tree = TRUE)) + ) + expect_true(.is_Booster(bst_linear)) +}) + +test_that("lgb.train() works with linear learners when Dataset has categorical features", { + set.seed(708L) + .new_dataset <- function() { + X <- matrix(numeric(200L), nrow = 100L, ncol = 2L) + X[, 1L] <- rnorm(100L) + X[, 2L] <- sample(seq_len(4L), size = 100L, replace = TRUE) + return(lgb.Dataset( + data = X + , label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1) + , params = list(num_threads = .LGB_MAX_THREADS) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , categorical_feature = 1L + , num_threads = .LGB_MAX_THREADS + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = utils::modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(.is_Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + +test_that("lgb.train() throws an informative error if interaction_constraints is not a list", { + dtrain <- lgb.Dataset(train$data, label = train$label) + params <- list(objective = "regression", interaction_constraints = "[1,2],[3]") + expect_error({ + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = 2L + ) + }, "interaction_constraints must be a list") +}) + +test_that(paste0("lgb.train() throws an informative error if the members of interaction_constraints ", + "are not character or numeric vectors"), { + dtrain <- lgb.Dataset(train$data, label = train$label) + params <- list(objective = "regression", interaction_constraints = list(list(1L, 2L), list(3L))) + expect_error({ + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = 2L + ) + }, "every element in interaction_constraints must be a character vector or numeric vector") +}) + +test_that("lgb.train() throws an informative error if interaction_constraints contains a too large index", { + dtrain <- lgb.Dataset(train$data, label = train$label) + params <- list(objective = "regression", + interaction_constraints = list(c(1L, ncol(train$data) + 1L:2L), 3L)) + expect_error( + lightgbm(data = dtrain, params = params, nrounds = 2L) + , "unknown feature(s) in interaction_constraints: '127', '128'" + , fixed = TRUE + ) +}) + +test_that(paste0("lgb.train() gives same result when interaction_constraints is specified as a list of ", + "character vectors, numeric vectors, or a combination"), { + set.seed(1L) + dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS)) + + params <- list( + objective = "regression" + , interaction_constraints = list(c(1L, 2L), 3L) + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = 2L + ) + pred1 <- bst$predict(test$data) + + cnames <- colnames(train$data) + params <- list( + objective = "regression" + , interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), cnames[[3L]]) + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = 2L + ) + pred2 <- bst$predict(test$data) + + params <- list( + objective = "regression" + , interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), 3L) + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = 2L + ) + pred3 <- bst$predict(test$data) + + expect_equal(pred1, pred2) + expect_equal(pred2, pred3) + +}) + +test_that(paste0("lgb.train() gives same results when using interaction_constraints and specifying colnames"), { + set.seed(1L) + dtrain <- lgb.Dataset( + train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + + params <- list( + objective = "regression" + , interaction_constraints = list(c(1L, 2L), 3L) + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = 2L + ) + pred1 <- bst$predict(test$data) + + new_colnames <- paste0(colnames(train$data), "_x") + dtrain$set_colnames(new_colnames) + params <- list( + objective = "regression" + , interaction_constraints = list(c(new_colnames[1L], new_colnames[2L]), new_colnames[3L]) + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lightgbm( + data = dtrain + , params = params + , nrounds = 2L + ) + pred2 <- bst$predict(test$data) + + expect_equal(pred1, pred2) + +}) + +test_that("Interaction constraints add missing features correctly as new group", { + dtrain <- lgb.Dataset( + train$data[, 1L:6L] # Pick only some columns + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + + list_of_constraints <- list( + list(3L, 1L:2L) + , list("cap-shape=convex", c("cap-shape=bell", "cap-shape=conical")) + ) + + for (constraints in list_of_constraints) { + params <- list( + objective = "regression" + , interaction_constraints = constraints + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lightgbm(data = dtrain, params = params, nrounds = 10L) + + expected_list <- list("[2]", "[0,1]", "[3,4,5]") + expect_equal(bst$params$interaction_constraints, expected_list) + + expected_string <- "[interaction_constraints: [2],[0,1],[3,4,5]]" + expect_true( + grepl(expected_string, bst$save_model_to_string(), fixed = TRUE) + ) + } +}) + +.generate_trainset_for_monotone_constraints_tests <- function(x3_to_categorical) { + n_samples <- 3000L + x1_positively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0) + x2_negatively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0) + x3_negatively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0) + if (x3_to_categorical) { + x3_negatively_correlated_with_y <- as.integer(x3_negatively_correlated_with_y / 0.01) + categorical_features <- "feature_3" + } else { + categorical_features <- NULL + } + X <- matrix( + data = c( + x1_positively_correlated_with_y + , x2_negatively_correlated_with_y + , x3_negatively_correlated_with_y + ) + , ncol = 3L + ) + zs <- rnorm(n = n_samples, mean = 0.0, sd = 0.01) + scales <- 10.0 * (runif(n = 6L, min = 0.0, max = 1.0) + 0.5) + y <- ( + scales[1L] * x1_positively_correlated_with_y + + sin(scales[2L] * pi * x1_positively_correlated_with_y) + - scales[3L] * x2_negatively_correlated_with_y + - cos(scales[4L] * pi * x2_negatively_correlated_with_y) + - scales[5L] * x3_negatively_correlated_with_y + - cos(scales[6L] * pi * x3_negatively_correlated_with_y) + + zs + ) + return(lgb.Dataset( + data = X + , label = y + , categorical_feature = categorical_features + , free_raw_data = FALSE + , colnames = c("feature_1", "feature_2", "feature_3") + , params = list(num_threads = .LGB_MAX_THREADS) + )) +} + +.is_increasing <- function(y) { + return(all(diff(y) >= 0.0)) +} + +.is_decreasing <- function(y) { + return(all(diff(y) <= 0.0)) +} + +.is_non_monotone <- function(y) { + return(any(diff(y) < 0.0) & any(diff(y) > 0.0)) +} + +# R equivalent of numpy.linspace() +.linspace <- function(start_val, stop_val, num) { + weights <- (seq_len(num) - 1L) / (num - 1L) + return(start_val + weights * (stop_val - start_val)) +} + +.is_correctly_constrained <- function(learner, x3_to_categorical) { + iterations <- 10L + n <- 1000L + variable_x <- .linspace(0L, 1L, n) + fixed_xs_values <- .linspace(0L, 1L, n) + for (i in seq_len(iterations)) { + fixed_x <- fixed_xs_values[i] * rep(1.0, n) + monotonically_increasing_x <- matrix( + data = c(variable_x, fixed_x, fixed_x) + , ncol = 3L + ) + monotonically_increasing_y <- predict( + learner + , monotonically_increasing_x + ) + + monotonically_decreasing_x <- matrix( + data = c(fixed_x, variable_x, fixed_x) + , ncol = 3L + ) + monotonically_decreasing_y <- predict( + learner + , monotonically_decreasing_x + ) + + if (x3_to_categorical) { + non_monotone_data <- c( + fixed_x + , fixed_x + , as.integer(variable_x / 0.01) + ) + } else { + non_monotone_data <- c(fixed_x, fixed_x, variable_x) + } + non_monotone_x <- matrix( + data = non_monotone_data + , ncol = 3L + ) + non_monotone_y <- predict( + learner + , non_monotone_x + ) + if (!(.is_increasing(monotonically_increasing_y) && + .is_decreasing(monotonically_decreasing_y) && + .is_non_monotone(non_monotone_y) + )) { + return(FALSE) + } + } + return(TRUE) +} + +for (x3_to_categorical in c(TRUE, FALSE)) { + set.seed(708L) + dtrain <- .generate_trainset_for_monotone_constraints_tests( + x3_to_categorical = x3_to_categorical + ) + for (monotone_constraints_method in c("basic", "intermediate", "advanced")) { + test_msg <- paste0( + "lgb.train() supports monotone constraints (" + , "categoricals=" + , x3_to_categorical + , ", method=" + , monotone_constraints_method + , ")" + ) + test_that(test_msg, { + params <- list( + min_data = 20L + , num_leaves = 20L + , monotone_constraints = c(1L, -1L, 0L) + , monotone_constraints_method = monotone_constraints_method + , use_missing = FALSE + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + constrained_model <- lgb.train( + params = params + , data = dtrain + , obj = "regression_l2" + , nrounds = 100L + ) + expect_true({ + .is_correctly_constrained( + learner = constrained_model + , x3_to_categorical = x3_to_categorical + ) + }) + }) + } +} + +test_that("lightgbm() accepts objective as function argument and under params", { + bst1 <- lightgbm( + data = train$data + , label = train$label + , params = list(objective = "regression_l1", num_threads = .LGB_MAX_THREADS) + , nrounds = 5L + , verbose = .LGB_VERBOSITY + ) + expect_equal(bst1$params$objective, "regression_l1") + model_txt_lines <- strsplit( + x = bst1$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(model_txt_lines == "objective=regression_l1")) + expect_false(any(model_txt_lines == "objective=regression_l2")) + + bst2 <- lightgbm( + data = train$data + , label = train$label + , objective = "regression_l1" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + ) + expect_equal(bst2$params$objective, "regression_l1") + model_txt_lines <- strsplit( + x = bst2$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(model_txt_lines == "objective=regression_l1")) + expect_false(any(model_txt_lines == "objective=regression_l2")) +}) + +test_that("lightgbm() prioritizes objective under params over objective as function argument", { + bst1 <- lightgbm( + data = train$data + , label = train$label + , objective = "regression" + , params = list(objective = "regression_l1", num_threads = .LGB_MAX_THREADS) + , nrounds = 5L + , verbose = .LGB_VERBOSITY + ) + expect_equal(bst1$params$objective, "regression_l1") + model_txt_lines <- strsplit( + x = bst1$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(model_txt_lines == "objective=regression_l1")) + expect_false(any(model_txt_lines == "objective=regression_l2")) + + bst2 <- lightgbm( + data = train$data + , label = train$label + , objective = "regression" + , params = list(loss = "regression_l1", num_threads = .LGB_MAX_THREADS) + , nrounds = 5L + , verbose = .LGB_VERBOSITY + ) + expect_equal(bst2$params$objective, "regression_l1") + model_txt_lines <- strsplit( + x = bst2$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(model_txt_lines == "objective=regression_l1")) + expect_false(any(model_txt_lines == "objective=regression_l2")) +}) + +test_that("lightgbm() accepts init_score as function argument", { + bst1 <- lightgbm( + data = train$data + , label = train$label + , objective = "binary" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = .LGB_MAX_THREADS) + ) + pred1 <- predict(bst1, train$data, type = "raw") + + bst2 <- lightgbm( + data = train$data + , label = train$label + , init_score = pred1 + , objective = "binary" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = .LGB_MAX_THREADS) + ) + pred2 <- predict(bst2, train$data, type = "raw") + + expect_true(any(pred1 != pred2)) +}) + +test_that("lightgbm() defaults to 'regression' objective if objective not otherwise provided", { + bst <- lightgbm( + data = train$data + , label = train$label + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = .LGB_MAX_THREADS) + ) + expect_equal(bst$params$objective, "regression") + model_txt_lines <- strsplit( + x = bst$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(model_txt_lines == "objective=regression")) + expect_false(any(model_txt_lines == "objective=regression_l1")) +}) + +test_that("lightgbm() accepts 'num_threads' as either top-level argument or under params", { + bst <- lightgbm( + data = train$data + , label = train$label + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , num_threads = 1L + ) + expect_equal(bst$params$num_threads, 1L) + model_txt_lines <- strsplit( + x = bst$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(grepl("[num_threads: 1]", model_txt_lines, fixed = TRUE))) + + bst <- lightgbm( + data = train$data + , label = train$label + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list(num_threads = 1L) + ) + expect_equal(bst$params$num_threads, 1L) + model_txt_lines <- strsplit( + x = bst$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(grepl("[num_threads: 1]", model_txt_lines, fixed = TRUE))) + + bst <- lightgbm( + data = train$data + , label = train$label + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , num_threads = 10L + , params = list(num_threads = 1L) + ) + expect_equal(bst$params$num_threads, 1L) + model_txt_lines <- strsplit( + x = bst$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(grepl("[num_threads: 1]", model_txt_lines, fixed = TRUE))) +}) + +test_that("lightgbm() accepts 'weight' and 'weights'", { + data(mtcars) + X <- as.matrix(mtcars[, -1L]) + y <- as.numeric(mtcars[, 1L]) + w <- rep(1.0, nrow(X)) + model <- lightgbm( + X + , y + , weights = w + , obj = "regression" + , nrounds = 5L + , verbose = .LGB_VERBOSITY + , params = list( + min_data_in_bin = 1L + , min_data_in_leaf = 1L + , num_threads = .LGB_MAX_THREADS + ) + ) + expect_equal(model$.__enclos_env__$private$train_set$get_field("weight"), w) + + # Avoid a bad CRAN check due to partial argument matches + lgb_args <- list( + X + , y + , weight = w + , obj = "regression" + , nrounds = 5L + , verbose = -1L + ) + model <- do.call(lightgbm, lgb_args) + expect_equal(model$.__enclos_env__$private$train_set$get_field("weight"), w) +}) + +.assert_has_expected_logs <- function(log_txt, lgb_info, lgb_warn, early_stopping, valid_eval_msg) { + expect_identical( + object = any(grepl("[LightGBM] [Info]", log_txt, fixed = TRUE)) + , expected = lgb_info + ) + expect_identical( + object = any(grepl("[LightGBM] [Warning]", log_txt, fixed = TRUE)) + , expected = lgb_warn + ) + expect_identical( + object = any(grepl("Will train until there is no improvement in 5 rounds", log_txt, fixed = TRUE)) + , expected = early_stopping + ) + expect_identical( + object = any(grepl("Did not meet early stopping", log_txt, fixed = TRUE)) + , expected = early_stopping + ) + expect_identical( + object = any(grepl("valid's auc\\:[0-9]+", log_txt)) + , expected = valid_eval_msg + ) +} + +.assert_has_expected_record_evals <- function(fitted_model) { + record_evals <- fitted_model$record_evals + expect_equal(record_evals$start_iter, 1L) + if (inherits(fitted_model, "lgb.CVBooster")) { + expected_valid_auc <- c(0.979056, 0.9844697, 0.9900813, 0.9908026, 0.9935588) + } else { + expected_valid_auc <- c(0.9805752, 0.9805752, 0.9934957, 0.9934957, 0.9949372) + } + expect_equal( + object = unlist(record_evals[["valid"]][["auc"]][["eval"]]) + , expected = expected_valid_auc + , tolerance = .LGB_NUMERIC_TOLERANCE + ) + expect_named(record_evals, c("start_iter", "valid"), ignore.order = TRUE, ignore.case = FALSE) + expect_equal(record_evals[["valid"]][["auc"]][["eval_err"]], list()) +} + +.train_for_verbosity_test <- function(train_function, verbose_kwarg, verbose_param) { + set.seed(708L) + nrounds <- 5L + params <- list( + num_leaves = 5L + , objective = "binary" + , metric = "auc" + , early_stopping_round = nrounds + , num_threads = .LGB_MAX_THREADS + # include a nonsense parameter just to trigger a WARN-level log + , nonsense_param = 1.0 + ) + if (!is.null(verbose_param)) { + params[["verbose"]] <- verbose_param + } + train_kwargs <- list( + params = params + , nrounds = nrounds + ) + if (!is.null(verbose_kwarg)) { + train_kwargs[["verbose"]] <- verbose_kwarg + } + function_name <- deparse(substitute(train_function)) + if (function_name == "lgb.train") { + train_kwargs[["data"]] <- lgb.Dataset( + data = train$data + , label = train$label + , params = list(num_threads = .LGB_MAX_THREADS) + ) + train_kwargs[["valids"]] <- list( + "valid" = lgb.Dataset(data = test$data, label = test$label) + ) + } else if (function_name == "lightgbm") { + train_kwargs[["data"]] <- train$data + train_kwargs[["label"]] <- train$label + train_kwargs[["valids"]] <- list( + "valid" = lgb.Dataset(data = test$data, label = test$label) + ) + } else if (function_name == "lgb.cv") { + train_kwargs[["data"]] <- lgb.Dataset( + data = train$data + , label = train$label + ) + train_kwargs[["nfold"]] <- 3L + train_kwargs[["showsd"]] <- FALSE + } + log_txt <- capture.output({ + bst <- do.call( + what = train_function + , args = train_kwargs + ) + }) + return(list(booster = bst, logs = log_txt)) +} + +test_that("lgb.train() only prints eval metrics when expected to", { + + # regardless of value passed to keyword argument 'verbose', value in params + # should take precedence + for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) { + + # (verbose = -1) should not be any logs, should be record evals + out <- .train_for_verbosity_test( + train_function = lgb.train + , verbose_kwarg = verbose_keyword_arg + , verbose_param = -1L + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = FALSE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose = 0) should be only WARN-level LightGBM logs + out <- .train_for_verbosity_test( + train_function = lgb.train + , verbose_kwarg = verbose_keyword_arg + , verbose_param = 0L + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = TRUE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages + out <- .train_for_verbosity_test( + train_function = lgb.train + , verbose_kwarg = verbose_keyword_arg + , verbose_param = 1L + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = TRUE + , lgb_warn = TRUE + , early_stopping = TRUE + , valid_eval_msg = TRUE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + } + + # if verbosity isn't specified in `params`, changing keyword argument `verbose` should + # alter what messages are printed + + # (verbose = -1) should not be any logs, should be record evals + out <- .train_for_verbosity_test( + train_function = lgb.train + , verbose_kwarg = -1L + , verbose_param = NULL + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = FALSE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose = 0) should be only WARN-level LightGBM logs + out <- .train_for_verbosity_test( + train_function = lgb.train + , verbose_kwarg = 0L + , verbose_param = NULL + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = TRUE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages + out <- .train_for_verbosity_test( + train_function = lgb.train + , verbose_kwarg = 1L + , verbose_param = NULL + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = TRUE + , lgb_warn = TRUE + , early_stopping = TRUE + , valid_eval_msg = TRUE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) +}) + +test_that("lightgbm() only prints eval metrics when expected to", { + + # regardless of value passed to keyword argument 'verbose', value in params + # should take precedence + for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) { + + # (verbose = -1) should not be any logs, train should not be in valids + out <- .train_for_verbosity_test( + train_function = lightgbm + , verbose_kwarg = verbose_keyword_arg + , verbose_param = -1L + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = FALSE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose = 0) should be only WARN-level LightGBM logs, train should not be in valids + out <- .train_for_verbosity_test( + train_function = lightgbm + , verbose_kwarg = verbose_keyword_arg + , verbose_param = 0L + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = TRUE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages, and + # train should be in valids + out <- .train_for_verbosity_test( + train_function = lightgbm + , verbose_kwarg = verbose_keyword_arg + , verbose_param = 1L + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = TRUE + , lgb_warn = TRUE + , early_stopping = TRUE + , valid_eval_msg = TRUE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + } + + # if verbosity isn't specified in `params`, changing keyword argument `verbose` should + # alter what messages are printed + + # (verbose = -1) should not be any logs, train should not be in valids + out <- .train_for_verbosity_test( + train_function = lightgbm + , verbose_kwarg = -1L + , verbose_param = NULL + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = FALSE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose = 0) should be only WARN-level LightGBM logs, train should not be in valids + out <- .train_for_verbosity_test( + train_function = lightgbm + , verbose_kwarg = 0L + , verbose_param = NULL + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = TRUE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages, and + # train should be in valids + out <- .train_for_verbosity_test( + train_function = lightgbm + , verbose_kwarg = 1L + , verbose_param = NULL + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = TRUE + , lgb_warn = TRUE + , early_stopping = TRUE + , valid_eval_msg = TRUE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) +}) + +test_that("lgb.cv() only prints eval metrics when expected to", { + + # regardless of value passed to keyword argument 'verbose', value in params + # should take precedence + for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) { + + # (verbose = -1) should not be any logs, should be record evals + out <- .train_for_verbosity_test( + verbose_kwarg = verbose_keyword_arg + , verbose_param = -1L + , train_function = lgb.cv + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = FALSE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose = 0) should be only WARN-level LightGBM logs + out <- .train_for_verbosity_test( + verbose_kwarg = verbose_keyword_arg + , verbose_param = 0L + , train_function = lgb.cv + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = TRUE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages + out <- .train_for_verbosity_test( + verbose_kwarg = verbose_keyword_arg + , verbose_param = 1L + , train_function = lgb.cv + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = TRUE + , lgb_warn = TRUE + , early_stopping = TRUE + , valid_eval_msg = TRUE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + } + + # if verbosity isn't specified in `params`, changing keyword argument `verbose` should + # alter what messages are printed + + # (verbose = -1) should not be any logs, should be record evals + out <- .train_for_verbosity_test( + verbose_kwarg = verbose_keyword_arg + , verbose_param = -1L + , train_function = lgb.cv + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = FALSE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose = 0) should be only WARN-level LightGBM logs + out <- .train_for_verbosity_test( + verbose_kwarg = verbose_keyword_arg + , verbose_param = 0L + , train_function = lgb.cv + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = FALSE + , lgb_warn = TRUE + , early_stopping = FALSE + , valid_eval_msg = FALSE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) + + # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages + out <- .train_for_verbosity_test( + verbose_kwarg = verbose_keyword_arg + , verbose_param = 1L + , train_function = lgb.cv + ) + .assert_has_expected_logs( + log_txt = out[["logs"]] + , lgb_info = TRUE + , lgb_warn = TRUE + , early_stopping = TRUE + , valid_eval_msg = TRUE + ) + .assert_has_expected_record_evals( + fitted_model = out[["booster"]] + ) +}) + +test_that("lightgbm() changes objective='auto' appropriately", { + # Regression + data("mtcars") + y <- mtcars$mpg + x <- as.matrix(mtcars[, -1L]) + model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) + expect_equal(model$params$objective, "regression") + model_txt_lines <- strsplit( + x = model$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(grepl("objective=regression", model_txt_lines, fixed = TRUE))) + expect_false(any(grepl("objective=regression_l1", model_txt_lines, fixed = TRUE))) + + # Binary classification + x <- train$data + y <- factor(train$label) + model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) + expect_equal(model$params$objective, "binary") + model_txt_lines <- strsplit( + x = model$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(grepl("objective=binary", model_txt_lines, fixed = TRUE))) + + # Multi-class classification + data("iris") + y <- factor(iris$Species) + x <- as.matrix(iris[, -5L]) + model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) + expect_equal(model$params$objective, "multiclass") + expect_equal(model$params$num_class, 3L) + model_txt_lines <- strsplit( + x = model$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(grepl("objective=multiclass", model_txt_lines, fixed = TRUE))) +}) + +test_that("lightgbm() determines number of classes for non-default multiclass objectives", { + data("iris") + y <- factor(iris$Species) + x <- as.matrix(iris[, -5L]) + model <- lightgbm( + x + , y + , objective = "multiclassova" + , verbose = .LGB_VERBOSITY + , nrounds = 5L + , num_threads = .LGB_MAX_THREADS + ) + expect_equal(model$params$objective, "multiclassova") + expect_equal(model$params$num_class, 3L) + model_txt_lines <- strsplit( + x = model$save_model_to_string() + , split = "\n" + , fixed = TRUE + )[[1L]] + expect_true(any(grepl("objective=multiclassova", model_txt_lines, fixed = TRUE))) +}) + +test_that("lightgbm() doesn't accept binary classification with non-binary factors", { + data("iris") + y <- factor(iris$Species) + x <- as.matrix(iris[, -5L]) + expect_error({ + lightgbm(x, y, objective = "binary", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) + }, regexp = "Factors with >2 levels as labels only allowed for multi-class objectives") +}) + +test_that("lightgbm() doesn't accept multi-class classification with binary factors", { + data("iris") + y <- as.character(iris$Species) + y[y == "setosa"] <- "versicolor" + y <- factor(y) + x <- as.matrix(iris[, -5L]) + expect_error({ + lightgbm(x, y, objective = "multiclass", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) + }, regexp = "Two-level factors as labels only allowed for objective='binary'") +}) + +test_that("lightgbm() model predictions retain factor levels for multiclass classification", { + data("iris") + y <- factor(iris$Species) + x <- as.matrix(iris[, -5L]) + model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) + + pred <- predict(model, x, type = "class") + expect_true(is.factor(pred)) + expect_equal(levels(pred), levels(y)) + + pred <- predict(model, x, type = "response") + expect_equal(colnames(pred), levels(y)) + + pred <- predict(model, x, type = "raw") + expect_equal(colnames(pred), levels(y)) +}) + +test_that("lightgbm() model predictions retain factor levels for binary classification", { + data("iris") + y <- as.character(iris$Species) + y[y == "setosa"] <- "versicolor" + y <- factor(y) + x <- as.matrix(iris[, -5L]) + model <- lightgbm(x, y, objective = "auto", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS) + + pred <- predict(model, x, type = "class") + expect_true(is.factor(pred)) + expect_equal(levels(pred), levels(y)) + + pred <- predict(model, x, type = "response") + expect_true(is.vector(pred)) + expect_true(is.numeric(pred)) + expect_false(any(pred %in% y)) + + pred <- predict(model, x, type = "raw") + expect_true(is.vector(pred)) + expect_true(is.numeric(pred)) + expect_false(any(pred %in% y)) +}) + +test_that("lightgbm() accepts named categorical_features", { + data(mtcars) + y <- mtcars$mpg + x <- as.matrix(mtcars[, -1L]) + model <- lightgbm( + x + , y + , categorical_feature = "cyl" + , verbose = .LGB_VERBOSITY + , nrounds = 5L + , num_threads = .LGB_MAX_THREADS + ) + expect_true(length(model$params$categorical_feature) > 0L) +}) + +test_that("lightgbm() correctly sets objective when passing lgb.Dataset as input", { + data(mtcars) + y <- mtcars$mpg + x <- as.matrix(mtcars[, -1L]) + ds <- lgb.Dataset(x, label = y) + model <- lightgbm( + ds + , objective = "auto" + , verbose = .LGB_VERBOSITY + , nrounds = 5L + , num_threads = .LGB_MAX_THREADS + ) + expect_equal(model$params$objective, "regression") +}) + +test_that("Evaluation metrics aren't printed as a single-element vector", { + log_txt <- capture_output({ + data(mtcars) + y <- mtcars$mpg + x <- as.matrix(mtcars[, -1L]) + cv_result <- lgb.cv( + data = lgb.Dataset(x, label = y) + , params = list( + objective = "regression" + , metric = "l2" + , min_data_in_leaf = 5L + , max_depth = 3L + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 2L + , nfold = 3L + , verbose = 1L + , eval_train_metric = TRUE + ) + }) + expect_false(grepl("[1] \"[1]", log_txt, fixed = TRUE)) +}) diff --git a/R-package/tests/testthat/test_custom_objective.R b/R-package/tests/testthat/test_custom_objective.R new file mode 100644 index 0000000..a1baf00 --- /dev/null +++ b/R-package/tests/testthat/test_custom_objective.R @@ -0,0 +1,85 @@ +data(agaricus.train, package = "lightgbm") +data(agaricus.test, package = "lightgbm") +dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label) +dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label) +watchlist <- list(eval = dtest, train = dtrain) + +logregobj <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- 1.0 / (1.0 + exp(-preds)) + grad <- preds - labels + hess <- preds * (1.0 - preds) + return(list(grad = grad, hess = hess)) +} + +# User-defined evaluation function returns a pair (metric_name, result, higher_better) +# NOTE: when you do customized loss function, the default prediction value is margin +# This may make built-in evaluation metric calculate wrong results +# Keep this in mind when you use the customization, and maybe you need write customized evaluation function +evalerror <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- 1.0 / (1.0 + exp(-preds)) + err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) + return(list( + name = "error" + , value = err + , higher_better = FALSE + )) +} + +param <- list( + num_leaves = 8L + , learning_rate = 1.0 + , objective = logregobj + , metric = "auc" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS +) +num_round <- 10L + +test_that("custom objective works", { + bst <- lgb.train(param, dtrain, num_round, watchlist, eval = evalerror) + expect_false(is.null(bst$record_evals)) +}) + +test_that("using a custom objective, custom eval, and no other metrics works", { + set.seed(708L) + bst <- lgb.train( + params = list( + num_leaves = 8L + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = 4L + , valids = watchlist + , obj = logregobj + , eval = evalerror + ) + expect_false(is.null(bst$record_evals)) + expect_equal(bst$best_iter, 4L) + expect_true(abs(bst$best_score - 0.000621) < .LGB_NUMERIC_TOLERANCE) + + eval_results <- bst$eval_valid(feval = evalerror)[[1L]] + expect_true(eval_results[["data_name"]] == "eval") + expect_true(abs(eval_results[["value"]] - 0.0006207325) < .LGB_NUMERIC_TOLERANCE) + expect_true(eval_results[["name"]] == "error") + expect_false(eval_results[["higher_better"]]) +}) + +test_that("using a custom objective that returns wrong shape grad or hess raises an informative error", { + bad_grad <- function(preds, dtrain) { + return(list(grad = numeric(0L), hess = rep(1.0, length(preds)))) + } + bad_hess <- function(preds, dtrain) { + return(list(grad = rep(1.0, length(preds)), hess = numeric(0L))) + } + params <- list(num_leaves = 3L, verbose = .LGB_VERBOSITY) + expect_error({ + lgb.train(params = params, data = dtrain, obj = bad_grad) + }, sprintf("Expected custom objective function to return grad with length %d, got 0.", nrow(dtrain))) + expect_error({ + lgb.train(params = params, data = dtrain, obj = bad_hess) + }, sprintf("Expected custom objective function to return hess with length %d, got 0.", nrow(dtrain))) +}) diff --git a/R-package/tests/testthat/test_dataset.R b/R-package/tests/testthat/test_dataset.R new file mode 100644 index 0000000..0676061 --- /dev/null +++ b/R-package/tests/testthat/test_dataset.R @@ -0,0 +1,665 @@ +data(agaricus.train, package = "lightgbm") +train_data <- agaricus.train$data[seq_len(1000L), ] +train_label <- agaricus.train$label[seq_len(1000L)] + +data(agaricus.test, package = "lightgbm") +test_data <- agaricus.test$data[1L:100L, ] +test_label <- agaricus.test$label[1L:100L] + +test_that("lgb.Dataset: basic construction, saving, loading", { + # from sparse matrix + dtest1 <- lgb.Dataset( + test_data + , label = test_label + , params = list( + verbose = .LGB_VERBOSITY + ) + ) + # from dense matrix + dtest2 <- lgb.Dataset(as.matrix(test_data), label = test_label) + expect_equal(get_field(dtest1, "label"), get_field(dtest2, "label")) + + # save to a local file + tmp_file <- tempfile("lgb.Dataset_") + lgb.Dataset.save(dtest1, tmp_file) + # read from a local file + dtest3 <- lgb.Dataset( + tmp_file + , params = list( + verbose = .LGB_VERBOSITY + ) + ) + lgb.Dataset.construct(dtest3) + unlink(tmp_file) + expect_equal(get_field(dtest1, "label"), get_field(dtest3, "label")) +}) + +test_that("lgb.Dataset: get_field & set_field", { + dtest <- lgb.Dataset(test_data) + dtest$construct() + + set_field(dtest, "label", test_label) + labels <- get_field(dtest, "label") + expect_equal(test_label, get_field(dtest, "label")) + + expect_true(length(get_field(dtest, "weight")) == 0L) + expect_true(length(get_field(dtest, "init_score")) == 0L) + + # any other label should error + expect_error( + set_field(dtest, "asdf", test_label) + , regexp = "Dataset$set_field(): field_name must be one of the following: 'label', 'weight', 'init_score', 'group'" # nolint: line_length. + , fixed = TRUE + ) +}) + +test_that("lgb.Dataset: slice, dim", { + dtest <- lgb.Dataset(test_data, label = test_label) + lgb.Dataset.construct(dtest) + expect_equal(dim(dtest), dim(test_data)) + dsub1 <- lgb.slice.Dataset(dtest, seq_len(42L)) + lgb.Dataset.construct(dsub1) + expect_equal(nrow(dsub1), 42L) + expect_equal(ncol(dsub1), ncol(test_data)) +}) + +test_that("Dataset$set_reference() on a constructed Dataset fails if raw data has been freed", { + dtrain <- lgb.Dataset(train_data, label = train_label) + dtrain$construct() + dtest <- lgb.Dataset(test_data, label = test_label) + dtest$construct() + expect_error({ + dtest$set_reference(dtrain) + }, regexp = "cannot set reference after freeing raw data") +}) + +test_that("Dataset$set_reference() fails if reference is not a Dataset", { + dtrain <- lgb.Dataset( + train_data + , label = train_label + , free_raw_data = FALSE + ) + expect_error({ + dtrain$set_reference(reference = data.frame(x = rnorm(10L))) + }, regexp = "Can only use lgb.Dataset as a reference") + + # passing NULL when the Dataset already has a reference raises an error + dtest <- lgb.Dataset( + test_data + , label = test_label + , free_raw_data = FALSE + ) + dtrain$set_reference(dtest) + expect_error({ + dtrain$set_reference(reference = NULL) + }, regexp = "Can only use lgb.Dataset as a reference") +}) + +test_that("Dataset$set_reference() setting reference to the same Dataset has no side effects", { + dtrain <- lgb.Dataset( + train_data + , label = train_label + , free_raw_data = FALSE + , categorical_feature = c(2L, 3L) + ) + dtrain$construct() + + cat_features_before <- dtrain$.__enclos_env__$private$categorical_feature + colnames_before <- dtrain$get_colnames() + predictor_before <- dtrain$.__enclos_env__$private$predictor + + dtrain$set_reference(dtrain) + expect_identical( + cat_features_before + , dtrain$.__enclos_env__$private$categorical_feature + ) + expect_identical( + colnames_before + , dtrain$get_colnames() + ) + expect_identical( + predictor_before + , dtrain$.__enclos_env__$private$predictor + ) +}) + +test_that("Dataset$set_reference() updates categorical_feature, colnames, and predictor", { + dtrain <- lgb.Dataset( + train_data + , label = train_label + , free_raw_data = FALSE + , categorical_feature = c(2L, 3L) + ) + dtrain$construct() + bst <- Booster$new( + train_set = dtrain + , params = list(verbose = -1L, num_threads = .LGB_MAX_THREADS) + ) + dtrain$.__enclos_env__$private$predictor <- bst$to_predictor() + + test_original_feature_names <- paste0("feature_col_", seq_len(ncol(test_data))) + dtest <- lgb.Dataset( + test_data + , label = test_label + , free_raw_data = FALSE + , colnames = test_original_feature_names + ) + dtest$construct() + + # at this point, dtest should not have categorical_feature + expect_null(dtest$.__enclos_env__$private$predictor) + expect_null(dtest$.__enclos_env__$private$categorical_feature) + expect_identical( + dtest$get_colnames() + , test_original_feature_names + ) + + dtest$set_reference(dtrain) + + # after setting reference to dtrain, those attributes should have dtrain's values + expect_true(methods::is( + dtest$.__enclos_env__$private$predictor + , "lgb.Predictor" + )) + expect_identical( + dtest$.__enclos_env__$private$predictor$.__enclos_env__$private$handle + , dtrain$.__enclos_env__$private$predictor$.__enclos_env__$private$handle + ) + expect_identical( + dtest$.__enclos_env__$private$categorical_feature + , dtrain$.__enclos_env__$private$categorical_feature + ) + expect_identical( + dtest$get_colnames() + , dtrain$get_colnames() + ) + expect_false( + identical(dtest$get_colnames(), test_original_feature_names) + ) +}) + +test_that("lgb.Dataset: colnames", { + dtest <- lgb.Dataset(test_data, label = test_label) + expect_equal(colnames(dtest), colnames(test_data)) + lgb.Dataset.construct(dtest) + expect_equal(colnames(dtest), colnames(test_data)) + expect_error({ + colnames(dtest) <- "asdf" + }, regexp = "can't assign '1' colnames to an lgb.Dataset with '126' columns") + new_names <- make.names(seq_len(ncol(test_data))) + expect_silent({ + colnames(dtest) <- new_names + }) + expect_equal(colnames(dtest), new_names) +}) + +test_that("lgb.Dataset: nrow is correct for a very sparse matrix", { + nr <- 1000L + x <- Matrix::rsparsematrix(nr, 100L, density = 0.0005) + # we want it very sparse, so that last rows are empty + expect_lt(max(x@i), nr) + dtest <- lgb.Dataset(x) + expect_equal(dim(dtest), dim(x)) +}) + +test_that("lgb.Dataset: Dataset should be able to construct from matrix and return non-null handle", { + rawData <- matrix(runif(1000L), ncol = 10L) + ref_handle <- NULL + handle <- .Call( + LGBM_DatasetCreateFromMat_R + , rawData + , nrow(rawData) + , ncol(rawData) + , lightgbm:::.params2str(params = list()) + , ref_handle + ) + expect_true(methods::is(handle, "externalptr")) + expect_false(is.null(handle)) + .Call(LGBM_DatasetFree_R, handle) + handle <- NULL +}) + +test_that("cpp errors should be raised as proper R errors", { + testthat::skip_if( + Sys.getenv("COMPILER", "") == "MSVC" + , message = "Skipping on Visual Studio" + ) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset( + train$data + , label = train$label + , init_score = seq_len(10L) + ) + expect_error({ + capture.output({ + dtrain$construct() + }, type = "message") + }, regexp = "Initial score size doesn't match data size") +}) + +test_that("lgb.Dataset$set_field() should convert 'group' to integer", { + ds <- lgb.Dataset( + data = matrix(rnorm(100L), nrow = 50L, ncol = 2L) + , label = sample(c(0L, 1L), size = 50L, replace = TRUE) + ) + ds$construct() + current_group <- ds$get_field("group") + expect_null(current_group) + group_as_numeric <- rep(25.0, 2L) + ds$set_field("group", group_as_numeric) + expect_identical(ds$get_field("group"), as.integer(group_as_numeric)) +}) + +test_that("lgb.Dataset should throw an error if 'reference' is provided but of the wrong format", { + data(agaricus.test, package = "lightgbm") + test_data <- agaricus.test$data[1L:100L, ] + test_label <- agaricus.test$label[1L:100L] + # Try to trick lgb.Dataset() into accepting bad input + expect_error({ + dtest <- lgb.Dataset( + data = test_data + , label = test_label + , reference = data.frame(x = seq_len(10L), y = seq_len(10L)) + ) + }, regexp = "reference must be a") +}) + +test_that("Dataset$new() should throw an error if 'predictor' is provided but of the wrong format", { + data(agaricus.test, package = "lightgbm") + test_data <- agaricus.test$data[1L:100L, ] + test_label <- agaricus.test$label[1L:100L] + expect_error({ + dtest <- Dataset$new( + data = test_data + , label = test_label + , predictor = data.frame(x = seq_len(10L), y = seq_len(10L)) + ) + }, regexp = "predictor must be a", fixed = TRUE) +}) + +test_that("Dataset$get_params() successfully returns parameters if you passed them", { + # note that this list uses one "main" parameter (feature_pre_filter) and one that + # is an alias (is_sparse), to check that aliases are handled correctly + params <- list( + "feature_pre_filter" = TRUE + , "is_sparse" = FALSE + ) + ds <- lgb.Dataset( + test_data + , label = test_label + , params = params + ) + returned_params <- ds$get_params() + expect_identical(class(returned_params), "list") + expect_identical(length(params), length(returned_params)) + expect_identical(sort(names(params)), sort(names(returned_params))) + for (param_name in names(params)) { + expect_identical(params[[param_name]], returned_params[[param_name]]) + } +}) + +test_that("Dataset$get_params() ignores irrelevant parameters", { + params <- list( + "feature_pre_filter" = TRUE + , "is_sparse" = FALSE + , "nonsense_parameter" = c(1.0, 2.0, 5.0) + ) + ds <- lgb.Dataset( + test_data + , label = test_label + , params = params + ) + returned_params <- ds$get_params() + expect_false("nonsense_parameter" %in% names(returned_params)) +}) + +test_that("Dataset$update_parameters() does nothing for empty inputs", { + ds <- lgb.Dataset( + test_data + , label = test_label + ) + initial_params <- ds$get_params() + expect_identical(initial_params, list()) + + # update_params() should return "self" so it can be chained + res <- ds$update_params( + params = list() + ) + expect_true(.is_Dataset(res)) + + new_params <- ds$get_params() + expect_identical(new_params, initial_params) +}) + +test_that("Dataset$update_params() works correctly for recognized Dataset parameters", { + ds <- lgb.Dataset( + test_data + , label = test_label + ) + initial_params <- ds$get_params() + expect_identical(initial_params, list()) + + new_params <- list( + "data_random_seed" = 708L + , "enable_bundle" = FALSE + ) + res <- ds$update_params( + params = new_params + ) + expect_true(.is_Dataset(res)) + + updated_params <- ds$get_params() + for (param_name in names(new_params)) { + expect_identical(new_params[[param_name]], updated_params[[param_name]]) + } +}) + +test_that("Dataset's finalizer should not fail on an already-finalized Dataset", { + dtest <- lgb.Dataset( + data = test_data + , label = test_label + ) + expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle)) + + dtest$construct() + expect_false(.is_null_handle(dtest$.__enclos_env__$private$handle)) + + dtest$.__enclos_env__$private$finalize() + expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle)) + + # calling finalize() a second time shouldn't cause any issues + dtest$.__enclos_env__$private$finalize() + expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle)) +}) + +test_that("lgb.Dataset: should be able to run lgb.train() immediately after using lgb.Dataset() on a file", { + dtest <- lgb.Dataset( + data = test_data + , label = test_label + , params = list( + verbose = .LGB_VERBOSITY + ) + ) + tmp_file <- tempfile(pattern = "lgb.Dataset_") + lgb.Dataset.save( + dataset = dtest + , fname = tmp_file + ) + + # read from a local file + dtest_read_in <- lgb.Dataset(data = tmp_file) + + param <- list( + objective = "binary" + , metric = "binary_logloss" + , num_leaves = 5L + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + + # should be able to train right away + bst <- lgb.train( + params = param + , data = dtest_read_in + ) + + expect_true(.is_Booster(x = bst)) +}) + +test_that("lgb.Dataset: should be able to run lgb.cv() immediately after using lgb.Dataset() on a file", { + dtest <- lgb.Dataset( + data = test_data + , label = test_label + , params = list( + verbosity = .LGB_VERBOSITY + ) + ) + tmp_file <- tempfile(pattern = "lgb.Dataset_") + lgb.Dataset.save( + dataset = dtest + , fname = tmp_file + ) + + # read from a local file + dtest_read_in <- lgb.Dataset(data = tmp_file) + + param <- list( + objective = "binary" + , metric = "binary_logloss" + , num_leaves = 5L + , learning_rate = 1.0 + , num_iterations = 5L + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + + # should be able to train right away + bst <- lgb.cv( + params = param + , data = dtest_read_in + ) + + expect_true(methods::is(bst, "lgb.CVBooster")) +}) + +test_that("lgb.Dataset: should be able to be used in lgb.cv() when constructed with categorical feature indices", { + data("mtcars") + y <- mtcars$mpg + x <- as.matrix(mtcars[, -1L]) + categorical_feature <- which(names(mtcars) %in% c("cyl", "vs", "am", "gear", "carb")) - 1L + dtrain <- lgb.Dataset( + data = x + , label = y + , categorical_feature = categorical_feature + , free_raw_data = TRUE + , params = list(num_threads = .LGB_MAX_THREADS) + ) + # constructing the Dataset frees the raw data + dtrain$construct() + params <- list( + objective = "regression" + , num_leaves = 2L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + # cv should reuse the same categorical features without checking the indices + bst <- lgb.cv(params = params, data = dtrain, stratified = FALSE, nrounds = 1L) + expect_equal( + unlist(bst$boosters[[1L]]$booster$params$categorical_feature) + , categorical_feature - 1L # 0-based + ) +}) + + +test_that("lgb.Dataset: should be able to use and retrieve long feature names", { + # set one feature to a value longer than the default buffer size used + # in LGBM_DatasetGetFeatureNames_R + feature_names <- names(iris) + long_name <- strrep("a", 1000L) + feature_names[1L] <- long_name + names(iris) <- feature_names + # check that feature name survived the trip from R to C++ and back + dtrain <- lgb.Dataset( + data = as.matrix(iris[, -5L]) + , label = as.numeric(iris$Species) - 1L + ) + dtrain$construct() + col_names <- dtrain$get_colnames() + expect_equal(col_names[1L], long_name) + expect_equal(nchar(col_names[1L]), 1000L) +}) + +test_that("lgb.Dataset: should be able to create a Dataset from a text file with a header", { + train_file <- tempfile(pattern = "train_", fileext = ".csv") + write.table( + data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) + , file = train_file + , sep = "," + , col.names = TRUE + , row.names = FALSE + , quote = FALSE + ) + + dtrain <- lgb.Dataset( + data = train_file + , params = list( + header = TRUE + , verbosity = .LGB_VERBOSITY + ) + ) + dtrain$construct() + expect_identical(dtrain$get_colnames(), c("x1", "x2")) + expect_identical(dtrain$get_params(), list(header = TRUE)) + expect_identical(dtrain$dim(), c(100L, 2L)) +}) + +test_that("lgb.Dataset: should be able to create a Dataset from a text file without a header", { + train_file <- tempfile(pattern = "train_", fileext = ".csv") + write.table( + data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L)) + , file = train_file + , sep = "," + , col.names = FALSE + , row.names = FALSE + , quote = FALSE + ) + + dtrain <- lgb.Dataset( + data = train_file + , params = list( + header = FALSE + , verbosity = .LGB_VERBOSITY + ) + ) + dtrain$construct() + expect_identical(dtrain$get_colnames(), c("Column_0", "Column_1")) + expect_identical(dtrain$get_params(), list(header = FALSE)) + expect_identical(dtrain$dim(), c(100L, 2L)) +}) + +test_that("Dataset: method calls on a Dataset with a null handle should raise an informative error and not segfault", { + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + dtrain$construct() + dvalid <- dtrain$create_valid( + data = train$data[seq_len(100L), ] + , label = train$label[seq_len(100L)] + ) + dvalid$construct() + tmp_file <- tempfile(fileext = ".rds") + saveRDS(dtrain, tmp_file) + rm(dtrain) + dtrain <- readRDS(tmp_file) + expect_error({ + dtrain$construct() + }, regexp = "Attempting to create a Dataset without any raw data") + expect_error({ + dtrain$dim() + }, regexp = "cannot get dimensions before dataset has been constructed") + expect_error({ + dtrain$get_colnames() + }, regexp = "cannot get column names before dataset has been constructed") + expect_error({ + dtrain$get_feature_num_bin(1L) + }, regexp = "Cannot get number of bins in feature before constructing Dataset.") + expect_error({ + dtrain$save_binary(fname = tempfile(fileext = ".bin")) + }, regexp = "Attempting to create a Dataset without any raw data") + expect_error({ + dtrain$set_categorical_feature(categorical_feature = 1L) + }, regexp = "cannot set categorical feature after freeing raw data") + expect_error({ + dtrain$set_reference(reference = dvalid) + }, regexp = "cannot set reference after freeing raw data") + + tmp_valid_file <- tempfile(fileext = ".rds") + saveRDS(dvalid, tmp_valid_file) + rm(dvalid) + dvalid <- readRDS(tmp_valid_file) + dtrain <- lgb.Dataset( + train$data + , label = train$label + , free_raw_data = FALSE + ) + dtrain$construct() + expect_error({ + dtrain$set_reference(reference = dvalid) + }, regexp = "cannot get column names before dataset has been constructed") +}) + +test_that("lgb.Dataset$get_feature_num_bin() works", { + raw_df <- data.frame( + all_random = runif(100L) + , two_vals = rep(c(1.0, 2.0), 50L) + , three_vals = c(rep(c(0.0, 1.0, 2.0), 33L), 0.0) + , two_vals_plus_missing = c(rep(c(1.0, 2.0), 49L), NA_real_, NA_real_) + , all_zero = rep(0.0, 100L) + , categorical = sample.int(2L, 100L, replace = TRUE) + ) + n_features <- ncol(raw_df) + raw_mat <- data.matrix(raw_df) + min_data_in_bin <- 2L + ds <- lgb.Dataset( + raw_mat + , params = list(min_data_in_bin = min_data_in_bin) + , categorical_feature = n_features + ) + ds$construct() + expected_num_bins <- c( + 100L %/% min_data_in_bin + 1L # extra bin for zero + , 3L # 0, 1, 2 + , 3L # 0, 1, 2 + , 4L # 0, 1, 2 + NA + , 0L # unused + , 3L # 1, 2 + NA + ) + actual_num_bins <- sapply(1L:n_features, ds$get_feature_num_bin) + expect_identical(actual_num_bins, expected_num_bins) + # test using defined feature names + bins_by_name <- sapply(colnames(raw_mat), ds$get_feature_num_bin) + expect_identical(unname(bins_by_name), expected_num_bins) + # test using default feature names + no_names_mat <- raw_mat + colnames(no_names_mat) <- NULL + ds_no_names <- lgb.Dataset( + no_names_mat + , params = list(min_data_in_bin = min_data_in_bin) + , categorical_feature = n_features + ) + ds_no_names$construct() + default_names <- lapply( + X = seq(1L, ncol(raw_mat)) + , FUN = function(i) { + sprintf("Column_%d", i - 1L) + } + ) + bins_by_default_name <- sapply(default_names, ds_no_names$get_feature_num_bin) + expect_identical(bins_by_default_name, expected_num_bins) +}) + +test_that("lgb.Dataset can be constructed with categorical features and without colnames", { + # check that dataset can be constructed + raw_mat <- matrix(rep(c(0L, 1L), 50L), ncol = 1L) + ds <- lgb.Dataset(raw_mat, categorical_feature = 1L)$construct() + sparse_mat <- as(raw_mat, "dgCMatrix") + ds2 <- lgb.Dataset(sparse_mat, categorical_feature = 1L)$construct() + # check that the column names are the default ones + expect_equal(ds$.__enclos_env__$private$colnames, "Column_0") + expect_equal(ds2$.__enclos_env__$private$colnames, "Column_0") + # check for error when index is greater than the number of columns + expect_error({ + lgb.Dataset(raw_mat, categorical_feature = 2L)$construct() + }, regexp = "supplied a too large value in categorical_feature: 2 but only 1 features") +}) + +test_that("lgb.Dataset.slice fails with a categorical feature index greater than the number of features", { + data <- matrix(runif(100L), nrow = 50L, ncol = 2L) + ds <- lgb.Dataset(data = data, categorical_feature = 3L) + subset <- ds$slice(1L:20L) + expect_error({ + subset$construct() + }, regexp = "supplied a too large value in categorical_feature: 3 but only 2 features") +}) diff --git a/R-package/tests/testthat/test_learning_to_rank.R b/R-package/tests/testthat/test_learning_to_rank.R new file mode 100644 index 0000000..e99aff4 --- /dev/null +++ b/R-package/tests/testthat/test_learning_to_rank.R @@ -0,0 +1,157 @@ +test_that("learning-to-rank with lgb.train() works as expected", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + # just keep a few features,to generate an model with imperfect fit + train <- agaricus.train + train_data <- train$data[1L:6000L, 1L:20L] + dtrain <- lgb.Dataset( + train_data + , label = train$label[1L:6000L] + , group = rep(150L, 40L) + ) + ndcg_at <- "1,2,3" + eval_names <- paste0("ndcg@", strsplit(ndcg_at, ",", fixed = TRUE)[[1L]]) + params <- list( + objective = "lambdarank" + , metric = "ndcg" + , ndcg_at = ndcg_at + , lambdarank_truncation_level = 3L + , learning_rate = 0.001 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + model <- lgb.train( + params = params + , data = dtrain + , nrounds = 10L + ) + expect_true(.is_Booster(model)) + + dumped_model <- jsonlite::fromJSON( + model$dump_model() + ) + expect_equal(dumped_model[["objective"]], "lambdarank") + expect_equal(dumped_model[["max_feature_idx"]], ncol(train_data) - 1L) + + # check that evaluation results make sense (0.0 < nDCG < 1.0) + eval_results <- model$eval_train() + expect_equal(length(eval_results), length(eval_names)) + for (result in eval_results) { + expect_true(result[["value"]] > 0.0) + expect_true(result[["value"]] < 1.0) + expect_true(result[["higher_better"]]) + expect_identical(result[["data_name"]], "training") + } + expect_identical( + sapply( + X = eval_results + , FUN = function(x) { + x$name + } + ) + , eval_names + ) + expect_equal(eval_results[[1L]][["value"]], 0.775) + if (!.LGB_ON_32_BIT_WINDOWS) { + expect_true(abs(eval_results[[2L]][["value"]] - 0.745986) < .LGB_NUMERIC_TOLERANCE) + expect_true(abs(eval_results[[3L]][["value"]] - 0.7351959) < .LGB_NUMERIC_TOLERANCE) + } +}) + +test_that("learning-to-rank with lgb.cv() works as expected", { + testthat::skip_if( + .LGB_ON_32_BIT_WINDOWS + , message = "Skipping on 32-bit Windows" + ) + set.seed(708L) + data(agaricus.train, package = "lightgbm") + # just keep a few features,to generate an model with imperfect fit + train <- agaricus.train + train_data <- train$data[1L:6000L, 1L:20L] + dtrain <- lgb.Dataset( + train_data + , label = train$label[1L:6000L] + , group = rep(150L, 40L) + ) + ndcg_at <- "1,2,3" + eval_names <- paste0("ndcg@", strsplit(ndcg_at, ",", fixed = TRUE)[[1L]]) + params <- list( + objective = "lambdarank" + , metric = "ndcg" + , ndcg_at = ndcg_at + , lambdarank_truncation_level = 3L + , label_gain = "0,1,3" + , min_data = 1L + , learning_rate = 0.01 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + nfold <- 4L + nrounds <- 10L + cv_bst <- lgb.cv( + params = params + , data = dtrain + , nrounds = nrounds + , nfold = nfold + ) + expect_true(methods::is(cv_bst, "lgb.CVBooster")) + expect_equal(length(cv_bst$boosters), nfold) + + # "valid" should contain results for each metric + eval_results <- cv_bst$record_evals[["valid"]] + eval_names <- c("ndcg@1", "ndcg@2", "ndcg@3") + expect_identical(names(eval_results), eval_names) + + # check that best score and iter make sense (0.0 < nDCG < 1.0) + best_iter <- cv_bst$best_iter + best_score <- cv_bst$best_score + expect_true(best_iter > 0L) + expect_true(best_iter <= nrounds) + expect_true(best_score > 0.0) + expect_true(best_score < 1.0) + expect_true(abs(best_score - 0.75) < .LGB_NUMERIC_TOLERANCE) + + # best_score should be set for the first metric + first_metric <- eval_names[[1L]] + expect_equal(best_score, eval_results[[first_metric]][["eval"]][[best_iter]]) + + for (eval_name in eval_names) { + results_for_this_metric <- eval_results[[eval_name]] + + # each set of metrics should have eval and eval_err + expect_identical(names(results_for_this_metric), c("eval", "eval_err")) + + # there should be one "eval" and "eval_err" per round + expect_equal(length(results_for_this_metric[["eval"]]), nrounds) + expect_equal(length(results_for_this_metric[["eval_err"]]), nrounds) + + # check that evaluation results make sense (0.0 < nDCG < 1.0) + all_evals <- unlist(results_for_this_metric[["eval"]]) + expect_true(all(all_evals > 0.0 & all_evals < 1.0)) + } + + # first and last value of each metric should be as expected + ndcg1_values <- c(0.675, 0.725, 0.65, 0.725, 0.75, 0.725, 0.75, 0.725, 0.75, 0.75) + expect_true(all(abs(unlist(eval_results[["ndcg@1"]][["eval"]]) - ndcg1_values) < .LGB_NUMERIC_TOLERANCE)) + + ndcg2_values <- c( + 0.6556574, 0.6669721, 0.6306574, 0.6476294, 0.6629581, + 0.6476294, 0.6629581, 0.6379581, 0.7113147, 0.6823008 + ) + expect_true(all(abs(unlist(eval_results[["ndcg@2"]][["eval"]]) - ndcg2_values) < .LGB_NUMERIC_TOLERANCE)) + + ndcg3_values <- c( + 0.6484639, 0.6571238, 0.6469279, 0.6540516, 0.6481857, + 0.6481857, 0.6481857, 0.6466496, 0.7027939, 0.6629898 + ) + expect_true(all(abs(unlist(eval_results[["ndcg@3"]][["eval"]]) - ndcg3_values) < .LGB_NUMERIC_TOLERANCE)) + + # check details of each booster + for (bst in cv_bst$boosters) { + dumped_model <- jsonlite::fromJSON( + bst$booster$dump_model() + ) + expect_equal(dumped_model[["objective"]], "lambdarank") + expect_equal(dumped_model[["max_feature_idx"]], ncol(train_data) - 1L) + } +}) diff --git a/R-package/tests/testthat/test_lgb.Booster.R b/R-package/tests/testthat/test_lgb.Booster.R new file mode 100644 index 0000000..7b178ca --- /dev/null +++ b/R-package/tests/testthat/test_lgb.Booster.R @@ -0,0 +1,1800 @@ +test_that("Booster's finalizer should not fail", { + X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L) + y <- iris[["Sepal.Length"]] + dtrain <- lgb.Dataset(X, label = y) + bst <- lgb.train( + data = dtrain + , params = list( + objective = "regression" + , num_threads = .LGB_MAX_THREADS + ) + , verbose = .LGB_VERBOSITY + , nrounds = 3L + ) + expect_true(.is_Booster(bst)) + + expect_false(.is_null_handle(bst$.__enclos_env__$private$handle)) + + bst$.__enclos_env__$private$finalize() + expect_true(.is_null_handle(bst$.__enclos_env__$private$handle)) + + # calling finalize() a second time shouldn't cause any issues + bst$.__enclos_env__$private$finalize() + expect_true(.is_null_handle(bst$.__enclos_env__$private$handle)) +}) + +test_that("lgb.get.eval.result() should throw an informative error if booster is not an lgb.Booster", { + bad_inputs <- list( + matrix(1.0:10.0, 2L, 5L) + , TRUE + , c("a", "b") + , NA + , 10L + , lgb.Dataset( + data = matrix(1.0:10.0, 2L, 5L) + , params = list() + ) + ) + for (bad_input in bad_inputs) { + expect_error({ + lgb.get.eval.result( + booster = bad_input + , data_name = "test" + , eval_name = "l2" + ) + }, regexp = "Can only use", fixed = TRUE) + } +}) + +test_that("lgb.get.eval.result() should throw an informative error for incorrect data_name", { + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + dtrain <- lgb.Dataset( + agaricus.train$data + , label = agaricus.train$label + ) + model <- lgb.train( + params = list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = 5L + , valids = list( + "test" = lgb.Dataset.create.valid( + dtrain + , agaricus.test$data + , label = agaricus.test$label + ) + ) + ) + expect_error({ + eval_results <- lgb.get.eval.result( + booster = model + , data_name = "testing" + , eval_name = "l2" + ) + }, regexp = "Only the following datasets exist in record evals: [test]", fixed = TRUE) +}) + +test_that("lgb.get.eval.result() should throw an informative error for incorrect eval_name", { + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + dtrain <- lgb.Dataset( + agaricus.train$data + , label = agaricus.train$label + ) + model <- lgb.train( + params = list( + objective = "regression" + , metric = "l2" + , min_data = 1L + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + ) + , data = dtrain + , nrounds = 5L + , valids = list( + "test" = lgb.Dataset.create.valid( + dtrain + , agaricus.test$data + , label = agaricus.test$label + ) + ) + ) + expect_error({ + eval_results <- lgb.get.eval.result( + booster = model + , data_name = "test" + , eval_name = "l1" + ) + }, regexp = "Only the following eval_names exist for dataset.*\\: \\[l2\\]", fixed = FALSE) +}) + +test_that("lgb.load() gives the expected error messages given different incorrect inputs", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + train <- agaricus.train + test <- agaricus.test + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + objective = "binary" + , num_leaves = 4L + , learning_rate = 1.0 + , verbose = .LGB_VERBOSITY + ) + , nrounds = 2L + ) + + # you have to give model_str or filename + expect_error({ + lgb.load() + }, regexp = "either filename or model_str must be given") + expect_error({ + lgb.load(filename = NULL, model_str = NULL) + }, regexp = "either filename or model_str must be given") + + # if given, filename should be a string that points to an existing file + model_file <- tempfile(fileext = ".model") + expect_error({ + lgb.load(filename = list(model_file)) + }, regexp = "filename should be character") + file_to_check <- paste0("a.model") + while (file.exists(file_to_check)) { + file_to_check <- paste0("a", file_to_check) + } + expect_error({ + lgb.load(filename = file_to_check) + }, regexp = "passed to filename does not exist") + + # if given, model_str should be a string + expect_error({ + lgb.load(model_str = c(4.0, 5.0, 6.0)) + }, regexp = "lgb.load: model_str should be a character/raw vector") + +}) + +test_that("Loading a Booster from a text file works", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + train <- agaricus.train + test <- agaricus.test + params <- list( + num_leaves = 4L + , boosting = "rf" + , bagging_fraction = 0.8 + , bagging_freq = 1L + , boost_from_average = FALSE + , categorical_feature = c(1L, 2L) + , interaction_constraints = list(1L:2L, 3L, 4L:ncol(train$data)) + , feature_contri = rep(0.5, ncol(train$data)) + , metric = c("mape", "average_precision") + , learning_rate = 1.0 + , objective = "binary" + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = params + , nrounds = 2L + ) + expect_true(.is_Booster(bst)) + + pred <- predict(bst, test$data) + model_file <- tempfile(fileext = ".model") + lgb.save(bst, model_file) + + # finalize the booster and destroy it so you know we aren't cheating + bst$.__enclos_env__$private$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + bst2 <- lgb.load( + filename = model_file + ) + pred2 <- predict(bst2, test$data) + expect_identical(pred, pred2) + + # check that the parameters are loaded correctly + expect_equal(bst2$params[names(params)], params) +}) + +test_that("boosters with linear models at leaves can be written to text file and re-loaded successfully", { + X <- matrix(rnorm(100L), ncol = 1L) + labels <- 2L * X + runif(nrow(X), 0L, 0.1) + dtrain <- lgb.Dataset( + data = X + , label = labels + ) + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , num_threads = .LGB_MAX_THREADS + ) + + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , verbose = .LGB_VERBOSITY + ) + expect_true(.is_Booster(bst)) + + # save predictions, then write the model to a file and destroy it in R + preds <- predict(bst, X) + model_file <- tempfile(fileext = ".model") + lgb.save(bst, model_file) + bst$.__enclos_env__$private$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + # load the booster and make predictions...should be the same + bst2 <- lgb.load( + filename = model_file + ) + preds2 <- predict(bst2, X) + expect_identical(preds, preds2) +}) + + +test_that("Loading a Booster from a string works", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + train <- agaricus.train + test <- agaricus.test + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 2L + ) + expect_true(.is_Booster(bst)) + + pred <- predict(bst, test$data) + model_string <- bst$save_model_to_string() + + # finalize the booster and destroy it so you know we aren't cheating + bst$.__enclos_env__$private$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + bst2 <- lgb.load( + model_str = model_string + ) + pred2 <- predict(bst2, test$data) + expect_identical(pred, pred2) +}) + +test_that("Saving a large model to string should work", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 100L + , learning_rate = 0.01 + , objective = "binary" + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 500L + , verbose = .LGB_VERBOSITY + ) + + pred <- predict(bst, train$data) + pred_leaf_indx <- predict(bst, train$data, type = "leaf") + pred_raw_score <- predict(bst, train$data, type = "raw") + model_string <- bst$save_model_to_string() + + # make sure this test is still producing a model bigger than the default + # buffer size used in LGBM_BoosterSaveModelToString_R + expect_gt(nchar(model_string), 1024L * 1024L) + + # finalize the booster and destroy it so you know we aren't cheating + bst$.__enclos_env__$private$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + # make sure a new model can be created from this string, and that it + # produces expected results + bst2 <- lgb.load( + model_str = model_string + ) + pred2 <- predict(bst2, train$data) + pred2_leaf_indx <- predict(bst2, train$data, type = "leaf") + pred2_raw_score <- predict(bst2, train$data, type = "raw") + expect_identical(pred, pred2) + expect_identical(pred_leaf_indx, pred2_leaf_indx) + expect_identical(pred_raw_score, pred2_raw_score) +}) + +test_that("Saving a large model to JSON should work", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 100L + , learning_rate = 0.01 + , objective = "binary" + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 200L + , verbose = .LGB_VERBOSITY + ) + + model_json <- bst$dump_model() + + # make sure this test is still producing a model bigger than the default + # buffer size used in LGBM_BoosterDumpModel_R + expect_gt(nchar(model_json), 1024L * 1024L) + + # check that it is valid JSON that looks like a LightGBM model + model_list <- jsonlite::fromJSON(model_json) + expect_equal(model_list[["objective"]], "binary sigmoid:1") +}) + +test_that("If a string and a file are both passed to lgb.load() the file is used model_str is totally ignored", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + train <- agaricus.train + test <- agaricus.test + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 2L + ) + expect_true(.is_Booster(bst)) + + pred <- predict(bst, test$data) + model_file <- tempfile(fileext = ".model") + lgb.save(bst, model_file) + + # finalize the booster and destroy it so you know we aren't cheating + bst$.__enclos_env__$private$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + bst2 <- lgb.load( + filename = model_file + , model_str = 4.0 + ) + pred2 <- predict(bst2, test$data) + expect_identical(pred, pred2) +}) + +test_that("Creating a Booster from a Dataset should work", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + dtrain <- lgb.Dataset( + agaricus.train$data + , label = agaricus.train$label + ) + bst <- Booster$new( + params = list( + objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ), + train_set = dtrain + ) + expect_true(.is_Booster(bst)) + expect_equal(bst$current_iter(), 0L) + expect_true(is.na(bst$best_score)) + expect_true(all(bst$predict(agaricus.train$data) == 0.5)) +}) + +test_that("Creating a Booster from a Dataset with an existing predictor should work", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + nrounds <- 2L + bst <- lightgbm( + data = as.matrix(agaricus.train$data) + , label = agaricus.train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + ) + data(agaricus.test, package = "lightgbm") + dtest <- Dataset$new( + data = agaricus.test$data + , label = agaricus.test$label + , predictor = bst$to_predictor() + ) + bst_from_ds <- Booster$new( + train_set = dtest + , params = list( + verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + ) + expect_true(.is_Booster(bst)) + expect_equal(bst$current_iter(), nrounds) + expect_equal(bst$eval_train()[[1L]][["value"]], 0.1115352) + expect_true(.is_Booster(bst_from_ds)) + expect_equal(bst_from_ds$current_iter(), nrounds) + expect_equal(bst_from_ds$eval_train()[[1L]][["value"]], 5.65704892) + dumped_model <- jsonlite::fromJSON(bst$dump_model()) +}) + +test_that("Booster$eval() should work on a Dataset stored in a binary file", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 4L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , nrounds = 2L + ) + + data(agaricus.test, package = "lightgbm") + test <- agaricus.test + dtest <- lgb.Dataset.create.valid( + dataset = dtrain + , data = test$data + , label = test$label + ) + dtest$construct() + + eval_in_mem <- bst$eval( + data = dtest + , name = "test" + ) + + test_file <- tempfile(pattern = "lgb.Dataset_") + lgb.Dataset.save( + dataset = dtest + , fname = test_file + ) + rm(dtest) + + eval_from_file <- bst$eval( + data = lgb.Dataset( + data = test_file + , params = list(verbose = .LGB_VERBOSITY, num_threads = .LGB_MAX_THREADS) + )$construct() + , name = "test" + ) + + expect_true(abs(eval_in_mem[[1L]][["value"]] - 0.1744423) < .LGB_NUMERIC_TOLERANCE) + # refer to https://github.com/lightgbm-org/LightGBM/issues/4680 + if (isTRUE(.LGB_ON_WINDOWS)) { + expect_equal(eval_in_mem, eval_from_file) + } else { + expect_identical(eval_in_mem, eval_from_file) + } +}) + +test_that("Booster$rollback_one_iter() should work as expected", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + data(agaricus.test, package = "lightgbm") + train <- agaricus.train + test <- agaricus.test + nrounds <- 5L + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + ) + expect_equal(bst$current_iter(), nrounds) + expect_true(.is_Booster(bst)) + logloss <- bst$eval_train()[[1L]][["value"]] + expect_equal(logloss, 0.01904786) + + x <- bst$rollback_one_iter() + + # rollback_one_iter() should return a booster and modify the original + # booster in place + expect_true(.is_Booster(x)) + expect_equal(bst$current_iter(), nrounds - 1L) + + # score should now come from the model as of 4 iterations + logloss <- bst$eval_train()[[1L]][["value"]] + expect_equal(logloss, 0.027915146) +}) + +test_that("Booster$update() passing a train_set works as expected", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + nrounds <- 2L + + # train with 2 rounds and then update + bst <- lightgbm( + data = as.matrix(agaricus.train$data) + , label = agaricus.train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + ) + expect_true(.is_Booster(bst)) + expect_equal(bst$current_iter(), nrounds) + bst$update( + train_set = Dataset$new( + data = agaricus.train$data + , label = agaricus.train$label + , params = list(verbose = .LGB_VERBOSITY) + ) + ) + expect_true(.is_Booster(bst)) + expect_equal(bst$current_iter(), nrounds + 1L) + + # train with 3 rounds directly + bst2 <- lightgbm( + data = as.matrix(agaricus.train$data) + , label = agaricus.train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + 1L + ) + expect_true(.is_Booster(bst2)) + expect_equal(bst2$current_iter(), nrounds + 1L) + + # model with 2 rounds + 1 update should be identical to 3 rounds + expect_equal(bst2$eval_train()[[1L]][["value"]], 0.04806585) + expect_equal(bst$eval_train()[[1L]][["value"]], bst2$eval_train()[[1L]][["value"]]) +}) + +test_that("Booster$update() throws an informative error if you provide a non-Dataset to update()", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + nrounds <- 2L + + # train with 2 rounds and then update + bst <- lightgbm( + data = as.matrix(agaricus.train$data) + , label = agaricus.train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = nrounds + ) + expect_error({ + bst$update( + train_set = data.frame(x = rnorm(10L)) + ) + }, regexp = "lgb.Booster.update: Only can use lgb.Dataset", fixed = TRUE) +}) + +test_that("Booster$num_trees_per_iter() works as expected", { + set.seed(708L) + + X <- data.matrix(iris[2L:4L]) + y_reg <- iris[, 1L] + y_binary <- as.integer(y_reg > median(y_reg)) + y_class <- as.integer(iris[, 5L]) - 1L + num_class <- 3L + + nrounds <- 10L + + # Regression and binary probabilistic classification (1 iteration = 1 tree) + fit_reg <- lgb.train( + params = list( + objective = "mse" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset(X, label = y_reg) + , nrounds = nrounds + ) + + fit_binary <- lgb.train( + params = list( + objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset(X, label = y_binary) + , nrounds = nrounds + ) + + # Multiclass probabilistic classification (1 iteration = num_class trees) + fit_class <- lgb.train( + params = list( + objective = "multiclass" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + , num_class = num_class + ) + , data = lgb.Dataset(X, label = y_class) + , nrounds = nrounds + ) + + expect_equal(fit_reg$num_trees_per_iter(), 1L) + expect_equal(fit_binary$num_trees_per_iter(), 1L) + expect_equal(fit_class$num_trees_per_iter(), num_class) +}) + +test_that("Booster$num_trees() and $num_iter() works (no early stopping)", { + set.seed(708L) + + X <- data.matrix(iris[2L:4L]) + y_reg <- iris[, 1L] + y_binary <- as.integer(y_reg > median(y_reg)) + y_class <- as.integer(iris[, 5L]) - 1L + num_class <- 3L + nrounds <- 10L + + # Regression and binary probabilistic classification (1 iteration = 1 tree) + fit_reg <- lgb.train( + params = list( + objective = "mse" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset(X, label = y_reg) + , nrounds = nrounds + ) + + fit_binary <- lgb.train( + params = list( + objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset(X, label = y_binary) + , nrounds = nrounds + ) + + # Multiclass probabilistic classification (1 iteration = num_class trees) + fit_class <- lgb.train( + params = list( + objective = "multiclass" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + , num_class = num_class + ) + , data = lgb.Dataset(X, label = y_class) + , nrounds = nrounds + ) + + expect_equal(fit_reg$num_trees(), nrounds) + expect_equal(fit_binary$num_trees(), nrounds) + expect_equal(fit_class$num_trees(), num_class * nrounds) + + expect_equal(fit_reg$num_iter(), nrounds) + expect_equal(fit_binary$num_iter(), nrounds) + expect_equal(fit_class$num_iter(), nrounds) +}) + +test_that("Booster$num_trees() and $num_iter() work (with early stopping)", { + set.seed(708L) + + X <- data.matrix(iris[2L:4L]) + y_reg <- iris[, 1L] + y_binary <- as.integer(y_reg > median(y_reg)) + y_class <- as.integer(iris[, 5L]) - 1L + train_ix <- c(1L:40L, 51L:90L, 101L:140L) + X_train <- X[train_ix, ] + X_valid <- X[-train_ix, ] + + num_class <- 3L + nrounds <- 1000L + early_stopping <- 2L + + # Regression and binary probabilistic classification (1 iteration = 1 tree) + fit_reg <- lgb.train( + params = list( + objective = "mse" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset(X_train, label = y_reg[train_ix]) + , valids = list(valid = lgb.Dataset(X_valid, label = y_reg[-train_ix])) + , nrounds = nrounds + , early_stopping_round = early_stopping + ) + + fit_binary <- lgb.train( + params = list( + objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset(X_train, label = y_binary[train_ix]) + , valids = list(valid = lgb.Dataset(X_valid, label = y_binary[-train_ix])) + , nrounds = nrounds + , early_stopping_round = early_stopping + ) + + # Multiclass probabilistic classification (1 iteration = num_class trees) + fit_class <- lgb.train( + params = list( + objective = "multiclass" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + , num_class = num_class + ) + , data = lgb.Dataset(X_train, label = y_class[train_ix]) + , valids = list(valid = lgb.Dataset(X_valid, label = y_class[-train_ix])) + , nrounds = nrounds + , early_stopping_round = early_stopping + ) + + expected_trees_reg <- fit_reg$best_iter + early_stopping + expected_trees_binary <- fit_binary$best_iter + early_stopping + expected_trees_class <- (fit_class$best_iter + early_stopping) * num_class + + expect_equal(fit_reg$num_trees(), expected_trees_reg) + expect_equal(fit_binary$num_trees(), expected_trees_binary) + expect_equal(fit_class$num_trees(), expected_trees_class) + + expect_equal(fit_reg$num_iter(), expected_trees_reg) + expect_equal(fit_binary$num_iter(), expected_trees_binary) + expect_equal(fit_class$num_iter(), expected_trees_class / num_class) +}) + +test_that("Booster should store parameters and Booster$reset_parameter() should update them", { + data(agaricus.train, package = "lightgbm") + dtrain <- lgb.Dataset( + agaricus.train$data + , label = agaricus.train$label + ) + # testing that this works for some cases that could break it: + # - multiple metrics + # - using "metric", "boosting", "num_class" in params + params <- list( + objective = "multiclass" + , max_depth = 4L + , bagging_fraction = 0.8 + , metric = c("multi_logloss", "multi_error") + , boosting = "gbdt" + , num_class = 5L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- Booster$new( + params = params + , train_set = dtrain + ) + expect_identical(bst$params, params) + + params[["bagging_fraction"]] <- 0.9 + ret_bst <- bst$reset_parameter(params = params) + expect_identical(ret_bst$params, params) + expect_identical(bst$params, params) +}) + +test_that("Booster$params should include dataset params, before and after Booster$reset_parameter()", { + data(agaricus.train, package = "lightgbm") + dtrain <- lgb.Dataset( + agaricus.train$data + , label = agaricus.train$label + , params = list( + max_bin = 17L + ) + ) + params <- list( + objective = "binary" + , max_depth = 4L + , bagging_fraction = 0.8 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- Booster$new( + params = params + , train_set = dtrain + ) + expect_identical( + bst$params + , list( + objective = "binary" + , max_depth = 4L + , bagging_fraction = 0.8 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + , max_bin = 17L + ) + ) + + params[["bagging_fraction"]] <- 0.9 + ret_bst <- bst$reset_parameter(params = params) + expected_params <- list( + objective = "binary" + , max_depth = 4L + , bagging_fraction = 0.9 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + , max_bin = 17L + ) + expect_identical(ret_bst$params, expected_params) + expect_identical(bst$params, expected_params) +}) + +test_that("Saving a model with different feature importance types works", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 2L + ) + expect_true(.is_Booster(bst)) + + .feat_importance_from_string <- function(model_string) { + file_lines <- strsplit(model_string, "\n", fixed = TRUE)[[1L]] + start_indx <- which(file_lines == "feature_importances:") + 1L + blank_line_indices <- which(file_lines == "") + end_indx <- blank_line_indices[blank_line_indices > start_indx][1L] - 1L + importances <- file_lines[start_indx: end_indx] + return(importances) + } + + GAIN_IMPORTANCE <- 1L + model_string <- bst$save_model_to_string(feature_importance_type = GAIN_IMPORTANCE) + expect_equal( + .feat_importance_from_string(model_string) + , c( + "odor=none=4010" + , "stalk-root=club=1163" + , "stalk-root=rooted=573" + , "stalk-surface-above-ring=silky=450" + , "spore-print-color=green=397" + , "gill-color=buff=281" + ) + ) + + SPLIT_IMPORTANCE <- 0L + model_string <- bst$save_model_to_string(feature_importance_type = SPLIT_IMPORTANCE) + expect_equal( + .feat_importance_from_string(model_string) + , c( + "odor=none=1" + , "gill-color=buff=1" + , "stalk-root=club=1" + , "stalk-root=rooted=1" + , "stalk-surface-above-ring=silky=1" + , "spore-print-color=green=1" + ) + ) +}) + +test_that("Saving a model with unknown importance type fails", { + set.seed(708L) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 2L + ) + expect_true(.is_Booster(bst)) + + UNSUPPORTED_IMPORTANCE <- 2L + expect_error({ + capture.output({ + model_string <- bst$save_model_to_string( + feature_importance_type = UNSUPPORTED_IMPORTANCE + ) + }, type = "message") + }, "Unknown importance type") +}) + + +.params_from_model_string <- function(model_str) { + file_lines <- strsplit(model_str, "\n", fixed = TRUE)[[1L]] + start_indx <- which(file_lines == "parameters:") + 1L + blank_line_indices <- which(file_lines == "") + end_indx <- blank_line_indices[blank_line_indices > start_indx][1L] - 1L + params <- file_lines[start_indx: end_indx] + return(params) +} + +test_that("all parameters are stored correctly with save_model_to_string()", { + dtrain <- lgb.Dataset( + data = matrix(rnorm(500L), nrow = 100L) + , label = rnorm(100L) + ) + bst <- lgb.train( + params = list( + objective = "mape" + , metric = c("l2", "mae") + , num_threads = .LGB_MAX_THREADS + , seed = 708L + , data_sample_strategy = "bagging" + , sub_row = 0.8234 + ) + , data = dtrain + , nrounds = 3L + , verbose = .LGB_VERBOSITY + ) + + # entries whose values should reflect params passed to lgb.train() + non_default_param_entries <- c( + "[objective: mape]" + # 'l1' was passed in with alias 'mae' + , "[metric: l2,l1]" + , "[data_sample_strategy: bagging]" + , "[seed: 708]" + # this was passed in with alias 'sub_row' + , "[bagging_fraction: 0.8234]" + , "[num_iterations: 3]" + ) + + # entries with default values of params + default_param_entries <- c( + "[boosting: gbdt]" + , "[tree_learner: serial]" + , "[device_type: cpu]" + , "[data: ]" + , "[valid: ]" + , "[learning_rate: 0.1]" + , "[num_leaves: 31]" + , sprintf("[num_threads: %i]", .LGB_MAX_THREADS) + , "[deterministic: 0]" + , "[histogram_pool_size: -1]" + , "[max_depth: -1]" + , "[min_data_in_leaf: 20]" + , "[min_sum_hessian_in_leaf: 0.001]" + , "[pos_bagging_fraction: 1]" + , "[neg_bagging_fraction: 1]" + , "[bagging_freq: 0]" + , "[bagging_seed: 15415]" + , "[feature_fraction: 1]" + , "[feature_fraction_bynode: 1]" + , "[feature_fraction_seed: 32671]" + , "[extra_trees: 0]" + , "[extra_seed: 6642]" + , "[early_stopping_round: 0]" + , "[early_stopping_min_delta: 0]" + , "[first_metric_only: 0]" + , "[max_delta_step: 0]" + , "[lambda_l1: 0]" + , "[lambda_l2: 0]" + , "[linear_lambda: 0]" + , "[min_gain_to_split: 0]" + , "[drop_rate: 0.1]" + , "[max_drop: 50]" + , "[skip_drop: 0.5]" + , "[xgboost_dart_mode: 0]" + , "[uniform_drop: 0]" + , "[drop_seed: 20623]" + , "[top_rate: 0.2]" + , "[other_rate: 0.1]" + , "[min_data_per_group: 100]" + , "[max_cat_threshold: 32]" + , "[cat_l2: 10]" + , "[cat_smooth: 10]" + , "[max_cat_to_onehot: 4]" + , "[top_k: 20]" + , "[monotone_constraints: ]" + , "[monotone_constraints_method: basic]" + , "[monotone_penalty: 0]" + , "[feature_contri: ]" + , "[forcedsplits_filename: ]" + , "[force_col_wise: 0]" + , "[force_row_wise: 0]" + , "[refit_decay_rate: 0.9]" + , "[cegb_tradeoff: 1]" + , "[cegb_penalty_split: 0]" + , "[cegb_penalty_feature_lazy: ]" + , "[cegb_penalty_feature_coupled: ]" + , "[path_smooth: 0]" + , "[interaction_constraints: ]" + , sprintf("[verbosity: %i]", .LGB_VERBOSITY) + , "[saved_feature_importance_type: 0]" + , "[use_quantized_grad: 0]" + , "[num_grad_quant_bins: 4]" + , "[quant_train_renew_leaf: 0]" + , "[stochastic_rounding: 1]" + , "[linear_tree: 0]" + , "[max_bin: 255]" + , "[max_bin_by_feature: ]" + , "[min_data_in_bin: 3]" + , "[bin_construct_sample_cnt: 200000]" + , "[data_random_seed: 2350]" + , "[is_enable_sparse: 1]" + , "[enable_bundle: 1]" + , "[use_missing: 1]" + , "[zero_as_missing: 0]" + , "[feature_pre_filter: 1]" + , "[pre_partition: 0]" + , "[two_round: 0]" + , "[header: 0]" + , "[label_column: ]" + , "[weight_column: ]" + , "[group_column: ]" + , "[ignore_column: ]" + , "[categorical_feature: ]" + , "[forcedbins_filename: ]" + , "[precise_float_parser: 0]" + , "[parser_config_file: ]" + , "[objective_seed: 4309]" + , "[num_class: 1]" + , "[is_unbalance: 0]" + , "[scale_pos_weight: 1]" + , "[sigmoid: 1]" + , "[boost_from_average: 1]" + , "[reg_sqrt: 0]" + , "[alpha: 0.9]" + , "[fair_c: 1]" + , "[poisson_max_delta_step: 0.7]" + , "[tweedie_variance_power: 1.5]" + , "[lambdarank_truncation_level: 30]" + , "[lambdarank_norm: 1]" + , "[label_gain: ]" + , "[lambdarank_position_bias_regularization: 0]" + , "[eval_at: ]" + , "[multi_error_top_k: 1]" + , "[auc_mu_weights: ]" + , "[num_machines: 1]" + , "[local_listen_port: 12400]" + , "[time_out: 120]" + , "[machine_list_filename: ]" + , "[machines: ]" + , "[gpu_platform_id: -1]" + , "[gpu_device_id: -1]" + , "[gpu_use_dp: 0]" + , "[num_gpu: 1]" + ) + all_param_entries <- c(non_default_param_entries, default_param_entries) + + # parameters should match what was passed from the R-package + model_str <- bst$save_model_to_string() + params_in_file <- .params_from_model_string(model_str = model_str) + .expect_in(all_param_entries, params_in_file) + + # early stopping should be off by default + expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L) + expect_equal(sum(params_in_file == "[early_stopping_round: 0]"), 1L) + + # since save_model_to_string() is used when serializing with saveRDS(), check that parameters all + # roundtrip saveRDS()/loadRDS() successfully + rds_file <- tempfile() + saveRDS(bst, rds_file) + bst_rds <- readRDS(rds_file) + model_str <- bst_rds$save_model_to_string() + params_in_file <- .params_from_model_string(model_str = model_str) + .expect_in(all_param_entries, params_in_file) +}) + +test_that("early_stopping, num_iterations are stored correctly in model string even with aliases", { + dtrain <- lgb.Dataset( + data = matrix(rnorm(500L), nrow = 100L) + , label = rnorm(100L) + ) + dvalid <- lgb.Dataset( + data = matrix(rnorm(500L), nrow = 100L) + , label = rnorm(100L) + ) + + # num_iterations values (all different) + num_iterations <- 4L + num_boost_round <- 2L + n_iter <- 3L + nrounds_kwarg <- 6L + + # early_stopping_round values (all different) + early_stopping_round <- 2L + early_stopping_round_kwarg <- 3L + n_iter_no_change <- 4L + + params <- list( + objective = "regression" + , metric = "l2" + , num_boost_round = num_boost_round + , num_iterations = num_iterations + , n_iter = n_iter + , early_stopping_round = early_stopping_round + , n_iter_no_change = n_iter_no_change + , num_threads = .LGB_MAX_THREADS + ) + + bst <- lgb.train( + params = params + , data = dtrain + , nrounds = nrounds_kwarg + , early_stopping_rounds = early_stopping_round_kwarg + , valids = list( + "random_valid" = dvalid + ) + , verbose = .LGB_VERBOSITY + ) + + model_str <- bst$save_model_to_string() + params_in_file <- .params_from_model_string(model_str = model_str) + + # parameters should match what was passed from the R-package, and the "main" (non-alias) + # params values in `params` should be preferred to keyword argumentts or aliases + expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L) + expect_equal(sum(params_in_file == sprintf("[num_iterations: %s]", num_iterations)), 1L) + expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L) + expect_equal(sum(params_in_file == sprintf("[early_stopping_round: %s]", early_stopping_round)), 1L) + + # none of the aliases shouold have been written to the model file + expect_equal(sum(startsWith(params_in_file, "[num_boost_round:")), 0L) + expect_equal(sum(startsWith(params_in_file, "[n_iter:")), 0L) + expect_equal(sum(startsWith(params_in_file, "[n_iter_no_change:")), 0L) + +}) + +test_that("Booster: method calls Booster with a null handle should raise an informative error and not segfault", { + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + bst <- lgb.train( + params = list( + objective = "regression" + , metric = "l2" + , num_leaves = 8L + , num_threads = .LGB_MAX_THREADS + ) + , data = dtrain + , verbose = .LGB_VERBOSITY + , nrounds = 5L + , valids = list( + train = dtrain + ) + , serializable = FALSE + ) + tmp_file <- tempfile(fileext = ".rds") + saveRDS(bst, tmp_file) + rm(bst) + bst <- readRDS(tmp_file) + .expect_booster_error <- function(object) { + error_regexp <- "Attempting to use a Booster which no longer exists" + expect_error(object, regexp = error_regexp) + } + .expect_booster_error({ + bst$current_iter() + }) + .expect_booster_error({ + bst$dump_model() + }) + .expect_booster_error({ + bst$eval(data = dtrain, name = "valid") + }) + .expect_booster_error({ + bst$eval_train() + }) + .expect_booster_error({ + bst$lower_bound() + }) + .expect_booster_error({ + bst$predict(data = train$data[seq_len(5L), ]) + }) + .expect_booster_error({ + bst$reset_parameter(params = list(learning_rate = 0.123)) + }) + .expect_booster_error({ + bst$rollback_one_iter() + }) + .expect_booster_error({ + bst$save_raw() + }) + .expect_booster_error({ + bst$save_model(filename = tempfile(fileext = ".model")) + }) + .expect_booster_error({ + bst$save_model_to_string() + }) + .expect_booster_error({ + bst$update() + }) + .expect_booster_error({ + bst$upper_bound() + }) + predictor <- bst$to_predictor() + .expect_booster_error({ + predictor$current_iter() + }) + .expect_booster_error({ + predictor$predict(data = train$data[seq_len(5L), ]) + }) +}) + +test_that("Booster$new() using a Dataset with a null handle should raise an informative error and not segfault", { + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + dtrain$construct() + tmp_file <- tempfile(fileext = ".bin") + saveRDS(dtrain, tmp_file) + rm(dtrain) + dtrain <- readRDS(tmp_file) + expect_error({ + bst <- Booster$new( + train_set = dtrain + , params = list( + verbose = .LGB_VERBOSITY + ) + ) + }, regexp = "Attempting to create a Dataset without any raw data") +}) + +test_that("Booster$new() raises informative errors for malformed inputs", { + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + + # no inputs + expect_error({ + Booster$new() + }, regexp = "lgb.Booster: Need at least either training dataset, model file, or model_str") + + # unrecognized objective + expect_error({ + capture.output({ + Booster$new( + params = list(objective = "not_a_real_objective") + , train_set = dtrain + ) + }, type = "message") + }, regexp = "Unknown objective type name: not_a_real_objective") + + # train_set is not a Dataset + expect_error({ + Booster$new( + train_set = data.table::data.table(rnorm(1L:10L)) + ) + }, regexp = "lgb.Booster: Can only use lgb.Dataset as training data") + + # model file isn't a string + expect_error({ + Booster$new( + modelfile = list() + ) + }, regexp = "lgb.Booster: Can only use a string as model file path") + + # model file doesn't exist + expect_error({ + capture.output({ + Booster$new( + params = list() + , modelfile = "file-that-does-not-exist.model" + ) + }, type = "message") + }, regexp = "Could not open file-that-does-not-exist.model") + + # model file doesn't contain a valid LightGBM model + model_file <- tempfile(fileext = ".model") + writeLines( + text = c("make", "good", "predictions") + , con = model_file + ) + expect_error({ + capture.output({ + Booster$new( + params = list() + , modelfile = model_file + ) + }, type = "message") + }, regexp = "Unknown model format or submodel type in model file") + + # malformed model string + expect_error({ + capture.output({ + Booster$new( + params = list() + , model_str = "a\nb\n" + ) + }, type = "message") + }, regexp = "Model file doesn't specify the number of classes") + + # model string isn't character or raw + expect_error({ + Booster$new( + model_str = numeric() + ) + }, regexp = "lgb.Booster: Can only use a character/raw vector as model_str") +}) + +# this is almost identical to the test above it, but for lgb.cv(). A lot of code +# is duplicated between lgb.train() and lgb.cv(), and this will catch cases where +# one is updated and the other isn't +test_that("lgb.cv() correctly handles passing through params to the model file", { + dtrain <- lgb.Dataset( + data = matrix(rnorm(500L), nrow = 100L) + , label = rnorm(100L) + ) + + # num_iterations values (all different) + num_iterations <- 4L + num_boost_round <- 2L + n_iter <- 3L + nrounds_kwarg <- 6L + + # early_stopping_round values (all different) + early_stopping_round <- 2L + early_stopping_round_kwarg <- 3L + n_iter_no_change <- 4L + + params <- list( + objective = "regression" + , metric = "l2" + , num_boost_round = num_boost_round + , num_iterations = num_iterations + , n_iter = n_iter + , early_stopping_round = early_stopping_round + , n_iter_no_change = n_iter_no_change + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + + cv_bst <- lgb.cv( + params = params + , data = dtrain + , nrounds = nrounds_kwarg + , early_stopping_rounds = early_stopping_round_kwarg + , nfold = 3L + , verbose = .LGB_VERBOSITY + ) + + for (bst in cv_bst$boosters) { + model_str <- bst[["booster"]]$save_model_to_string() + params_in_file <- .params_from_model_string(model_str = model_str) + + # parameters should match what was passed from the R-package, and the "main" (non-alias) + # params values in `params` should be preferred to keyword argumentts or aliases + expect_equal(sum(startsWith(params_in_file, "[num_iterations:")), 1L) + expect_equal(sum(params_in_file == sprintf("[num_iterations: %s]", num_iterations)), 1L) + expect_equal(sum(startsWith(params_in_file, "[early_stopping_round:")), 1L) + expect_equal(sum(params_in_file == sprintf("[early_stopping_round: %s]", early_stopping_round)), 1L) + + # none of the aliases shouold have been written to the model file + expect_equal(sum(startsWith(params_in_file, "[num_boost_round:")), 0L) + expect_equal(sum(startsWith(params_in_file, "[n_iter:")), 0L) + expect_equal(sum(startsWith(params_in_file, "[n_iter_no_change:")), 0L) + } + +}) + +test_that("params (including dataset params) should be stored in .rds file for Booster", { + data(agaricus.train, package = "lightgbm") + dtrain <- lgb.Dataset( + agaricus.train$data + , label = agaricus.train$label + , params = list( + max_bin = 17L + ) + ) + params <- list( + objective = "binary" + , max_depth = 4L + , bagging_fraction = 0.8 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + bst <- Booster$new( + params = params + , train_set = dtrain + ) + bst_file <- tempfile(fileext = ".rds") + saveRDS(bst, file = bst_file) + + bst_from_file <- readRDS(file = bst_file) + expect_identical( + bst_from_file$params + , list( + objective = "binary" + , max_depth = 4L + , bagging_fraction = 0.8 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + , max_bin = 17L + ) + ) +}) + +test_that("Handle is automatically restored when calling predict", { + data(agaricus.train, package = "lightgbm") + bst <- lightgbm( + agaricus.train$data + , agaricus.train$label + , nrounds = 5L + , obj = "binary" + , params = list( + verbose = .LGB_VERBOSITY + ) + , num_threads = .LGB_MAX_THREADS + ) + bst_file <- tempfile(fileext = ".rds") + saveRDS(bst, file = bst_file) + + bst_from_file <- readRDS(file = bst_file) + + pred_before <- predict(bst, agaricus.train$data) + pred_after <- predict(bst_from_file, agaricus.train$data) + expect_equal(pred_before, pred_after) +}) + +test_that("boosters with linear models at leaves can be written to RDS and re-loaded successfully", { + X <- matrix(rnorm(100L), ncol = 1L) + labels <- 2L * X + runif(nrow(X), 0L, 0.1) + dtrain <- lgb.Dataset( + data = X + , label = labels + ) + + params <- list( + objective = "regression" + , verbose = .LGB_VERBOSITY + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , num_threads = .LGB_MAX_THREADS + ) + + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + ) + expect_true(.is_Booster(bst)) + + # save predictions, then write the model to a file and destroy it in R + preds <- predict(bst, X) + model_file <- tempfile(fileext = ".rds") + saveRDS(bst, file = model_file) + bst$.__enclos_env__$private$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + # load the booster and make predictions...should be the same + bst2 <- readRDS(file = model_file) + preds2 <- predict(bst2, X) + expect_identical(preds, preds2) +}) + +.have_same_handle <- function(model, other_model) { + expect_equal( + model$.__enclos_env__$private$handle + , other_model$.__enclos_env__$private$handle + ) +} + +.has_expected_content_for_fitted_model <- function(printed_txt) { + expect_true(any(startsWith(printed_txt, "LightGBM Model"))) + expect_true(any(startsWith(printed_txt, "Fitted to dataset"))) +} + +.has_expected_content_for_finalized_model <- function(printed_txt) { + expect_true(any(printed_txt == "LightGBM Model")) + expect_true(any(grepl("Booster handle is invalid", printed_txt, fixed = TRUE))) +} + +.check_methods_work <- function(model) { + + #--- should work for fitted models --- # + + # print() + log_txt <- capture.output({ + ret <- print(model) + }) + .have_same_handle(ret, model) + .has_expected_content_for_fitted_model(log_txt) + + # show() + log_txt <- capture.output({ + ret <- show(model) + }) + expect_null(ret) + .has_expected_content_for_fitted_model(log_txt) + + # summary() + log_txt <- capture.output({ + ret <- summary(model) + }) + .have_same_handle(ret, model) + .has_expected_content_for_fitted_model(log_txt) + + #--- should not fail for finalized models ---# + model$.__enclos_env__$private$finalize() + + # print() + log_txt <- capture.output({ + ret <- print(model) + }) + .has_expected_content_for_finalized_model(log_txt) + + # show() + .have_same_handle(ret, model) + log_txt <- capture.output({ + ret <- show(model) + }) + expect_null(ret) + .has_expected_content_for_finalized_model(log_txt) + + # summary() + log_txt <- capture.output({ + ret <- summary(model) + }) + .have_same_handle(ret, model) + .has_expected_content_for_finalized_model(log_txt) +} + +test_that("Booster's print, show, and summary work correctly for built-in objectives", { + data("mtcars") + model <- lgb.train( + params = list( + objective = "regression" + , min_data_in_leaf = 1L + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset( + as.matrix(mtcars[, -1L]) + , label = mtcars$mpg + , params = list( + min_data_in_bin = 1L + ) + ) + , verbose = .LGB_VERBOSITY + , nrounds = 5L + ) + .check_methods_work(model) + + data("iris") + model <- lgb.train( + params = list(objective = "multiclass", num_class = 3L, num_threads = .LGB_MAX_THREADS) + , data = lgb.Dataset( + as.matrix(iris[, -5L]) + , label = as.numeric(factor(iris$Species)) - 1.0 + ) + , verbose = .LGB_VERBOSITY + , nrounds = 5L + ) + .check_methods_work(model) +}) + +test_that("Booster's print, show, and summary work correctly for custom objective", { + .logregobj <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- 1.0 / (1.0 + exp(-preds)) + grad <- preds - labels + hess <- preds * (1.0 - preds) + return(list(grad = grad, hess = hess)) + } + + .evalerror <- function(preds, dtrain) { + labels <- get_field(dtrain, "label") + preds <- 1.0 / (1.0 + exp(-preds)) + err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels) + return(list( + name = "error" + , value = err + , higher_better = FALSE + )) + } + + data("iris") + model <- lgb.train( + data = lgb.Dataset( + as.matrix(iris[, -5L]) + , label = as.numeric(iris$Species == "virginica") + ) + , obj = .logregobj + , eval = .evalerror + , verbose = .LGB_VERBOSITY + , nrounds = 5L + , params = list(num_threads = .LGB_MAX_THREADS) + ) + + .check_methods_work(model) +}) + +test_that("Booster's print, show, and summary work correctly when objective is not provided", { + data("iris") + model <- lgb.train( + data = lgb.Dataset( + as.matrix(iris[, seq_len(3L)]) + , label = iris[, 4L] + ) + , verbose = .LGB_VERBOSITY + , nrounds = 5L + , params = list(num_threads = .LGB_MAX_THREADS) + ) + + log_txt <- capture.output(print(model)) + expect_true(any(log_txt == "Objective: (default)")) + + .check_methods_work(model) +}) + +test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", { + data("mtcars") + model <- lgb.train( + params = list( + objective = "regression" + , min_data_in_leaf = 1L + , num_threads = .LGB_MAX_THREADS + ) + , data = lgb.Dataset( + as.matrix(mtcars[, -1L]) + , label = mtcars$mpg + , params = list( + min_data_in_bin = 1L + ) + ) + , verbose = .LGB_VERBOSITY + , nrounds = 5L + ) + ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle) + expect_equal(ncols, ncol(mtcars) - 1L) + + data("iris") + model <- lgb.train( + params = list(objective = "multiclass", num_class = 3L) + , data = lgb.Dataset( + as.matrix(iris[, -5L]) + , label = as.numeric(factor(iris$Species)) - 1.0 + ) + , verbose = .LGB_VERBOSITY + , nrounds = 5L + ) + ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle) + expect_equal(ncols, ncol(iris) - 1L) +}) + +# Helper function that creates a fitted model with nrounds boosting rounds +.get_test_model <- function(nrounds) { + set.seed(1L) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list(objective = "binary", num_threads = .LGB_MAX_THREADS) + , nrounds = nrounds + , verbose = .LGB_VERBOSITY + ) + return(bst) +} + +# Simplified version of lgb.model.dt.tree() +.get_trees_from_dump <- function(x) { + parsed <- jsonlite::fromJSON( + txt = x + , simplifyVector = TRUE + , simplifyDataFrame = FALSE + , simplifyMatrix = FALSE + , flatten = FALSE + ) + return(lapply(parsed$tree_info, FUN = .single_tree_parse)) +} + +test_that("num_iteration and start_iteration work for lgb.dump()", { + bst <- .get_test_model(5L) + + first2 <- .get_trees_from_dump(lgb.dump(bst, num_iteration = 2L)) + last3 <- .get_trees_from_dump( + lgb.dump(bst, num_iteration = 3L, start_iteration = 3L) + ) + all5 <- .get_trees_from_dump(lgb.dump(bst)) + too_many <- .get_trees_from_dump(lgb.dump(bst, num_iteration = 10L)) + + expect_equal( + data.table::rbindlist(c(first2, last3)), data.table::rbindlist(all5) + ) + expect_equal(too_many, all5) +}) + +test_that("num_iteration and start_iteration work for lgb.save()", { + .get_n_trees <- function(x) { + return(length(.get_trees_from_dump(lgb.dump(x)))) + } + + .save_and_load <- function(bst, ...) { + model_file <- tempfile(fileext = ".model") + lgb.save(bst, model_file, ...) + return(lgb.load(model_file)) + } + + bst <- .get_test_model(5L) + n_first2 <- .get_n_trees(.save_and_load(bst, num_iteration = 2L)) + n_last3 <- .get_n_trees( + .save_and_load(bst, num_iteration = 3L, start_iteration = 3L) + ) + n_all5 <- .get_n_trees(.save_and_load(bst)) + n_too_many <- .get_n_trees(.save_and_load(bst, num_iteration = 10L)) + + expect_equal(n_first2, 2L) + expect_equal(n_last3, 3L) + expect_equal(n_all5, 5L) + expect_equal(n_too_many, 5L) +}) + +test_that("num_iteration and start_iteration work for save_model_to_string()", { + .get_n_trees_from_string <- function(x) { + return(sum(gregexpr("Tree=", x, fixed = TRUE)[[1L]] > 0L)) + } + + bst <- .get_test_model(5L) + + n_first2 <- .get_n_trees_from_string( + bst$save_model_to_string(num_iteration = 2L) + ) + n_last3 <- .get_n_trees_from_string( + bst$save_model_to_string(num_iteration = 3L, start_iteration = 3L) + ) + n_all5 <- .get_n_trees_from_string(bst$save_model_to_string()) + n_too_many <- .get_n_trees_from_string( + bst$save_model_to_string(num_iteration = 10L) + ) + + expect_equal(n_first2, 2L) + expect_equal(n_last3, 3L) + expect_equal(n_all5, 5L) + expect_equal(n_too_many, 5L) +}) diff --git a/R-package/tests/testthat/test_lgb.convert_with_rules.R b/R-package/tests/testthat/test_lgb.convert_with_rules.R new file mode 100644 index 0000000..39438f0 --- /dev/null +++ b/R-package/tests/testthat/test_lgb.convert_with_rules.R @@ -0,0 +1,246 @@ +test_that("lgb.convert_with_rules() rejects inputs that are not a data.table or data.frame", { + bad_inputs <- list( + matrix(1.0:10.0, 2L, 5L) + , TRUE + , c("a", "b") + , NA + , 10L + , lgb.Dataset( + data = matrix(1.0:10.0, 2L, 5L) + , params = list() + ) + ) + for (bad_input in bad_inputs) { + expect_error({ + conversion_result <- lgb.convert_with_rules(bad_input) + }, regexp = "lgb.convert_with_rules: you provided", fixed = TRUE) + } +}) + +test_that("lgb.convert_with_rules() should work correctly for a dataset with only character columns", { + testDF <- data.frame( + col1 = c("a", "b", "c") + , col2 = c("green", "green", "red") + , stringsAsFactors = FALSE + ) + testDT <- data.table::as.data.table(testDF) + for (input_data in list(testDF, testDT)) { + conversion_result <- lgb.convert_with_rules(input_data) + # dataset should have been converted to integer + converted_dataset <- conversion_result[["data"]] + expect_identical(class(input_data), class(converted_dataset)) + expect_identical(class(converted_dataset[["col1"]]), "integer") + expect_identical(class(converted_dataset[["col2"]]), "integer") + expect_identical(converted_dataset[["col1"]], c(1L, 2L, 3L)) + expect_identical(converted_dataset[["col2"]], c(1L, 1L, 2L)) + # rules should be returned and correct + rules <- conversion_result$rules + expect_true(methods::is(rules, "list")) + expect_length(rules, ncol(input_data)) + expect_identical(rules[["col1"]], c("a" = 1L, "b" = 2L, "c" = 3L)) + expect_identical(rules[["col2"]], c("green" = 1L, "red" = 2L)) + } +}) + +test_that("lgb.convert_with_rules() should work correctly for a dataset with only factor columns", { + testDF <- data.frame( + col1 = as.factor(c("a", "b", "c")) + , col2 = as.factor(c("green", "green", "red")) + , stringsAsFactors = FALSE + ) + testDT <- data.table::as.data.table(testDF) + for (input_data in list(testDF, testDT)) { + conversion_result <- lgb.convert_with_rules(input_data) + # dataset should have been converted to integer + converted_dataset <- conversion_result[["data"]] + expect_identical(class(input_data), class(converted_dataset)) + expect_identical(class(converted_dataset[["col1"]]), "integer") + expect_identical(class(converted_dataset[["col2"]]), "integer") + expect_identical(converted_dataset[["col1"]], c(1L, 2L, 3L)) + expect_identical(converted_dataset[["col2"]], c(1L, 1L, 2L)) + # rules should be returned and correct + rules <- conversion_result$rules + expect_true(methods::is(rules, "list")) + expect_length(rules, ncol(input_data)) + expect_identical(rules[["col1"]], c("a" = 1L, "b" = 2L, "c" = 3L)) + expect_identical(rules[["col2"]], c("green" = 1L, "red" = 2L)) + } +}) + +test_that("lgb.convert_with_rules() should not change a dataset with only integer columns", { + testDF <- data.frame( + col1 = 11L:15L + , col2 = 16L:20L + , stringsAsFactors = FALSE + ) + testDT <- data.table::as.data.table(testDF) + for (input_data in list(testDF, testDT)) { + conversion_result <- lgb.convert_with_rules(input_data) + # dataset should have been converted to integer + converted_dataset <- conversion_result[["data"]] + expect_identical(converted_dataset, input_data) + # rules should be returned and correct + rules <- conversion_result$rules + expect_identical(rules, list()) + } +}) + +test_that("lgb.convert_with_rules() should work correctly for a dataset with numeric, factor, and character columns", { + testDF <- data.frame( + character_col = c("a", "b", "c") + , numeric_col = c(1.0, 9.0, 10.0) + , factor_col = as.factor(c("n", "n", "y")) + , stringsAsFactors = FALSE + ) + testDT <- data.table::as.data.table(testDF) + for (input_data in list(testDF, testDT)) { + conversion_result <- lgb.convert_with_rules(input_data) + # dataset should have been converted to numeric + converted_dataset <- conversion_result[["data"]] + expect_identical(class(input_data), class(converted_dataset)) + expect_identical(class(converted_dataset[["character_col"]]), "integer") + expect_identical(class(converted_dataset[["factor_col"]]), "integer") + expect_identical(converted_dataset[["character_col"]], c(1L, 2L, 3L)) + expect_identical(converted_dataset[["factor_col"]], c(1L, 1L, 2L)) + # rules should be returned and correct + rules <- conversion_result$rules + expect_true(methods::is(rules, "list")) + expect_length(rules, 2L) + expect_identical(rules[["character_col"]], c("a" = 1L, "b" = 2L, "c" = 3L)) + expect_identical(rules[["factor_col"]], c("n" = 1L, "y" = 2L)) + + # today, lgb.convert_with_rules() does not convert numeric columns + expect_identical(class(converted_dataset[["numeric_col"]]), "numeric") + expect_identical(converted_dataset[["numeric_col"]], c(1.0, 9.0, 10.0)) + } +}) + +test_that("lgb.convert_with_rules() should convert missing values to the expected value", { + testDF <- data.frame( + character_col = c("a", NA_character_, "c") + , na_col = rep(NA, 3L) + , na_real_col = rep(NA_real_, 3L) + , na_int_col = rep(NA_integer_, 3L) + , na_character_col = rep(NA_character_, 3L) + , numeric_col = c(1.0, 9.0, NA_real_) + , factor_col = as.factor(c("n", "n", "y")) + , integer_col = c(1L, 9L, NA_integer_) + , stringsAsFactors = FALSE + ) + testDT <- data.table::as.data.table(testDF) + for (input_data in list(testDF, testDT)) { + conversion_result <- lgb.convert_with_rules(input_data) + # dataset should have been converted to integer + converted_dataset <- conversion_result[["data"]] + expect_identical(class(input_data), class(converted_dataset)) + + expect_identical(class(converted_dataset[["character_col"]]), "integer") + expect_identical(converted_dataset[["character_col"]], c(1L, 0L, 2L)) + + # does not try to fill 0s in for already-integer columns + expect_identical(class(converted_dataset[["integer_col"]]), "integer") + expect_identical(converted_dataset[["integer_col"]], c(1L, 9L, NA_integer_)) + expect_identical(class(converted_dataset[["na_int_col"]]), "integer") + expect_identical(converted_dataset[["na_int_col"]], rep(NA_integer_, nrow(converted_dataset))) + + expect_identical(class(converted_dataset[["factor_col"]]), "integer") + expect_identical(converted_dataset[["factor_col"]], c(1L, 1L, 2L)) + + # NAs in character columns should be converted to 0 + expect_identical(class(converted_dataset[["na_character_col"]]), "integer") + expect_identical(converted_dataset[["na_character_col"]], rep(0L, nrow(converted_dataset))) + + # logical should be converted to integer + expect_identical(class(converted_dataset[["na_col"]]), "integer") + expect_identical(converted_dataset[["na_col"]], rep(-1L, 3L)) + + # lgb.convert_with_rules() should not convert numeric columns to integer + expect_identical(class(converted_dataset[["na_real_col"]]), "numeric") + expect_identical(converted_dataset[["na_real_col"]], rep(NA_real_, nrow(converted_dataset))) + expect_identical(class(converted_dataset[["numeric_col"]]), "numeric") + expect_identical(converted_dataset[["numeric_col"]], c(1.0, 9.0, NA_real_)) + + # rules should be returned and correct + rules <- conversion_result$rules + expect_true(methods::is(rules, "list")) + expect_length(rules, 3L) + expect_identical(rules[["character_col"]], c("a" = 1L, "c" = 2L)) + expect_identical(rules[["factor_col"]], c("n" = 1L, "y" = 2L)) + expect_identical(rules[["na_col"]], stats::setNames(c(0L, 1L), c(FALSE, TRUE))) + } +}) + +test_that("lgb.convert_with_rules() should work correctly if you provide your own well-formed rules", { + testDF <- data.frame( + character_col = c("a", NA_character_, "c", "a", "a", "c") + , na_col = rep(NA, 6L) + , na_real_col = rep(NA_real_, 6L) + , na_int_col = rep(NA_integer_, 6L) + , na_character_col = rep(NA_character_, 6L) + , numeric_col = c(1.0, 9.0, NA_real_, 10.0, 11.0, 12.0) + , factor_col = as.factor(c("n", "n", "y", "y", "n", "n")) + , integer_col = c(1L, 9L, NA_integer_, 1L, 1L, 1L) + , stringsAsFactors = FALSE + ) + testDT <- data.table::as.data.table(testDF) + + # value used by lgb.convert_with_rules() when it encounters a categorical value that + # is not in the provided rules + UNKNOWN_FACTOR_VALUE <- 0L + UNKNOWN_LOGICAL_VALUE <- -1L + for (input_data in list(testDF, testDT)) { + custom_rules <- list( + "character_col" = c( + "a" = 5L + , "c" = -10L + ) + , "factor_col" = c( + "n" = 65L + , "y" = 66L + ) + ) + conversion_result <- lgb.convert_with_rules( + data = input_data + , rules = custom_rules + ) + + # dataset should have been converted to integer + converted_dataset <- conversion_result[["data"]] + expect_identical(class(input_data), class(converted_dataset)) + + expect_identical(class(converted_dataset[["character_col"]]), "integer") + expect_identical(converted_dataset[["character_col"]], c(5L, UNKNOWN_FACTOR_VALUE, -10L, 5L, 5L, -10L)) + + expect_identical(class(converted_dataset[["factor_col"]]), "integer") + expect_identical(converted_dataset[["factor_col"]], c(65L, 65L, 66L, 66L, 65L, 65L)) + + # columns not specified in rules are not going to be converted, unless they are all NA + for (col in c("na_real_col", "na_int_col", "numeric_col", "integer_col")) { + expect_identical(converted_dataset[[col]], input_data[[col]]) + } + + # non-numeric/integer columns that are all NA should have been filled in + expect_identical(converted_dataset[["na_col"]], rep(UNKNOWN_LOGICAL_VALUE, 6L)) + expect_identical(converted_dataset[["na_character_col"]], rep(UNKNOWN_FACTOR_VALUE, 6L)) + + # the rules you passed in should be returned unchanged + rules <- conversion_result$rules + expect_identical(rules, custom_rules) + } +}) + +test_that("lgb.convert_with_rules() should modify data.tables in-place", { + testDT <- data.table::data.table( + character_col = c("a", NA_character_, "c") + , na_col = rep(NA, 3L) + , na_real_col = rep(NA_real_, 3L) + , na_int_col = rep(NA_integer_, 3L) + , na_character_col = rep(NA_character_, 3L) + , numeric_col = c(1.0, 9.0, NA_real_) + , factor_col = as.factor(c("n", "n", "y")) + , integer_col = c(1L, 9L, NA_integer_) + ) + conversion_result <- lgb.convert_with_rules(testDT) + resultDT <- conversion_result[["data"]] + expect_identical(resultDT, testDT) +}) diff --git a/R-package/tests/testthat/test_lgb.importance.R b/R-package/tests/testthat/test_lgb.importance.R new file mode 100644 index 0000000..7dcf756 --- /dev/null +++ b/R-package/tests/testthat/test_lgb.importance.R @@ -0,0 +1,37 @@ +test_that("lgb.importance() should reject bad inputs", { + bad_inputs <- list( + .Machine$integer.max + , Inf + , -Inf + , NA + , NA_real_ + , -10L:10L + , list(c("a", "b", "c")) + , data.frame( + x = rnorm(20L) + , y = sample( + x = c(1L, 2L) + , size = 20L + , replace = TRUE + ) + ) + , data.table::data.table( + x = rnorm(20L) + , y = sample( + x = c(1L, 2L) + , size = 20L + , replace = TRUE + ) + ) + , lgb.Dataset( + data = matrix(rnorm(100L), ncol = 2L) + , label = matrix(sample(c(0L, 1L), 50L, replace = TRUE)) + ) + , "lightgbm.model" + ) + for (input in bad_inputs) { + expect_error({ + lgb.importance(input) + }, regexp = "'model' has to be an object of class lgb\\.Booster") + } +}) diff --git a/R-package/tests/testthat/test_lgb.interpret.R b/R-package/tests/testthat/test_lgb.interpret.R new file mode 100644 index 0000000..377ad3a --- /dev/null +++ b/R-package/tests/testthat/test_lgb.interpret.R @@ -0,0 +1,115 @@ +.sigmoid <- function(x) { + 1.0 / (1.0 + exp(-x)) +} +.logit <- function(x) { + log(x / (1.0 - x)) +} + +test_that("lgb.interpret works as expected for binary classification", { + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + set_field( + dataset = dtrain + , field_name = "init_score" + , data = rep( + .logit(mean(train$label)) + , length(train$label) + ) + ) + data(agaricus.test, package = "lightgbm") + test <- agaricus.test + params <- list( + objective = "binary" + , learning_rate = 0.01 + , num_leaves = 63L + , max_depth = -1L + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + model <- lgb.train( + params = params + , data = dtrain + , nrounds = 3L + ) + num_trees <- 5L + tree_interpretation <- lgb.interpret( + model = model + , data = test$data + , idxset = seq_len(num_trees) + ) + expect_identical(class(tree_interpretation), "list") + expect_true(length(tree_interpretation) == num_trees) + expect_null(names(tree_interpretation)) + expect_true(all( + sapply( + X = tree_interpretation + , FUN = function(treeDT) { + checks <- c( + data.table::is.data.table(treeDT) + , identical(names(treeDT), c("Feature", "Contribution")) + , is.character(treeDT[, Feature]) + , is.numeric(treeDT[, Contribution]) + ) + return(all(checks)) + } + ) + )) +}) + +test_that("lgb.intereprete works as expected for multiclass classification", { + data(iris) + + # We must convert factors to numeric + # They must be starting from number 0 to use multiclass + # For instance: 0, 1, 2, 3, 4, 5... + iris$Species <- as.numeric(as.factor(iris$Species)) - 1L + + # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2) + train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ]) + # The 10 last samples of each class are for validation + test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) + dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) + dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) + params <- list( + objective = "multiclass" + , metric = "multi_logloss" + , num_class = 3L + , learning_rate = 0.00001 + , min_data = 1L + , verbose = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + model <- lgb.train( + params = params + , data = dtrain + , nrounds = 3L + ) + num_trees <- 5L + tree_interpretation <- lgb.interpret( + model = model + , data = test[, 1L:4L] + , idxset = seq_len(num_trees) + ) + expect_identical(class(tree_interpretation), "list") + expect_true(length(tree_interpretation) == num_trees) + expect_null(names(tree_interpretation)) + expect_true(all( + sapply( + X = tree_interpretation + , FUN = function(treeDT) { + checks <- c( + data.table::is.data.table(treeDT) + , identical(names(treeDT), c("Feature", "Class 0", "Class 1", "Class 2")) + , is.character(treeDT[, Feature]) + , is.numeric(treeDT[, `Class 0`]) + , is.numeric(treeDT[, `Class 1`]) + , is.numeric(treeDT[, `Class 2`]) + ) + return(all(checks)) + } + ) + )) +}) diff --git a/R-package/tests/testthat/test_lgb.model.dt.tree.R b/R-package/tests/testthat/test_lgb.model.dt.tree.R new file mode 100644 index 0000000..c27703e --- /dev/null +++ b/R-package/tests/testthat/test_lgb.model.dt.tree.R @@ -0,0 +1,184 @@ +NROUNDS <- 10L +MAX_DEPTH <- 3L +N <- nrow(iris) +X <- data.matrix(iris[2L:4L]) +FEAT <- colnames(X) +NCLASS <- nlevels(iris[, 5L]) + +model_reg <- lgb.train( + params = list( + objective = "regression" + , num_threads = .LGB_MAX_THREADS + , max.depth = MAX_DEPTH + ) + , data = lgb.Dataset(X, label = iris[, 1L]) + , verbose = .LGB_VERBOSITY + , nrounds = NROUNDS +) + +model_binary <- lgb.train( + params = list( + objective = "binary" + , num_threads = .LGB_MAX_THREADS + , max.depth = MAX_DEPTH + ) + , data = lgb.Dataset(X, label = iris[, 5L] == "setosa") + , verbose = .LGB_VERBOSITY + , nrounds = NROUNDS +) + +model_multiclass <- lgb.train( + params = list( + objective = "multiclass" + , num_threads = .LGB_MAX_THREADS + , max.depth = MAX_DEPTH + , num_classes = NCLASS + ) + , data = lgb.Dataset(X, label = as.integer(iris[, 5L]) - 1L) + , verbose = .LGB_VERBOSITY + , nrounds = NROUNDS +) + +model_rank <- lgb.train( + params = list( + objective = "lambdarank" + , num_threads = .LGB_MAX_THREADS + , max.depth = MAX_DEPTH + , lambdarank_truncation_level = 3L + ) + , data = lgb.Dataset( + X + , label = as.integer(iris[, 1L] > 5.8) + , group = rep(10L, times = 15L) + ) + , verbose = .LGB_VERBOSITY + , nrounds = NROUNDS +) + +models <- list( + reg = model_reg + , bin = model_binary + , multi = model_multiclass + , rank = model_rank +) + +for (model_name in names(models)) { + model <- models[[model_name]] + expected_n_trees <- NROUNDS + if (model_name == "multi") { + expected_n_trees <- NROUNDS * NCLASS + } + df <- as.data.frame(lgb.model.dt.tree(model)) + df_list <- split(df, f = df$tree_index, drop = TRUE) + + df_leaf <- df[!is.na(df$leaf_index), ] + df_internal <- df[is.na(df$leaf_index), ] + + test_that("lgb.model.dt.tree() returns the right number of trees", { + expect_equal(length(unique(df$tree_index)), expected_n_trees) + }) + + test_that("num_iteration can return less trees", { + expect_equal( + length(unique(lgb.model.dt.tree(model, num_iteration = 2L)$tree_index)) + , 2L * (if (model_name == "multi") NCLASS else 1L) + ) + }) + + test_that("Tree index from lgb.model.dt.tree() is in 0:(NROUNS-1)", { + expect_equal(unique(df$tree_index), (0L:(expected_n_trees - 1L))) + }) + + test_that("Depth calculated from lgb.model.dt.tree() respects max.depth", { + expect_true(max(df$depth) <= MAX_DEPTH) + }) + + test_that("Each tree from lgb.model.dt.tree() has single root node", { + expect_equal( + unname(sapply(df_list, function(df) sum(df$depth == 0L))) + , rep(1L, expected_n_trees) + ) + }) + + test_that("Each tree from lgb.model.dt.tree() has two depth 1 nodes", { + expect_equal( + unname(sapply(df_list, function(df) sum(df$depth == 1L))) + , rep(2L, expected_n_trees) + ) + }) + + test_that("leaves from lgb.model.dt.tree() do not have split info", { + internal_node_cols <- c( + "split_index" + , "split_feature" + , "split_gain" + , "threshold" + , "decision_type" + , "default_left" + , "internal_value" + , "internal_count" + ) + expect_true(all(is.na(df_leaf[internal_node_cols]))) + }) + + test_that("leaves from lgb.model.dt.tree() have valid leaf info", { + expect_true(all(df_leaf$leaf_index %in% 0L:(2.0^MAX_DEPTH - 1.0))) + expect_true(all(is.finite(df_leaf$leaf_value))) + expect_true(all(df_leaf$leaf_count > 0L & df_leaf$leaf_count <= N)) + }) + + test_that("non-leaves from lgb.model.dt.tree() do not have leaf info", { + leaf_node_cols <- c( + "leaf_index", "leaf_parent", "leaf_value", "leaf_count" + ) + expect_true(all(is.na(df_internal[leaf_node_cols]))) + }) + + test_that("non-leaves from lgb.model.dt.tree() have valid split info", { + expect_true( + all( + sapply( + split(df_internal, df_internal$tree_index), + function(x) all(x$split_index %in% 0L:(nrow(x) - 1L)) + ) + ) + ) + + expect_true(all(df_internal$split_feature %in% FEAT)) + + num_cols <- c("split_gain", "threshold", "internal_value") + expect_true(all(is.finite(unlist(df_internal[, num_cols])))) + + # range of decision type? + expect_true(all(df_internal$default_left %in% c(TRUE, FALSE))) + + counts <- df_internal$internal_count + expect_true(all(counts > 1L & counts <= N)) + }) +} + +test_that("num_iteration and start_iteration work as expected", { + set.seed(1L) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + bst <- lightgbm( + data = as.matrix(train$data) + , label = train$label + , params = list(objective = "binary", num_threads = .LGB_MAX_THREADS) + , nrounds = 5L + , verbose = .LGB_VERBOSITY + ) + + first2 <- lgb.model.dt.tree(bst, num_iteration = 2L) + last3 <- lgb.model.dt.tree(bst, num_iteration = 3L, start_iteration = 3L) + all5 <- lgb.model.dt.tree(bst) + too_many <- lgb.model.dt.tree(bst, num_iteration = 10L) + + expect_equal(data.table::rbindlist(list(first2, last3)), all5) + expect_equal(too_many, all5) + + # Check tree indices + expect_equal(unique(first2[["tree_index"]]), 0L:1L) + expect_equal(unique(last3[["tree_index"]]), 2L:4L) + expect_equal(unique(all5[["tree_index"]]), 0L:4L) +}) diff --git a/R-package/tests/testthat/test_lgb.plot.importance.R b/R-package/tests/testthat/test_lgb.plot.importance.R new file mode 100644 index 0000000..e7ff63f --- /dev/null +++ b/R-package/tests/testthat/test_lgb.plot.importance.R @@ -0,0 +1,47 @@ +test_that("lgb.plot.importance() should run without error for well-formed inputs", { + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + params <- list( + objective = "binary" + , learning_rate = 0.01 + , num_leaves = 63L + , max_depth = -1L + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + model <- lgb.train(params, dtrain, 3L) + tree_imp <- lgb.importance(model, percentage = TRUE) + + # Check that there are no plots present before plotting + expect_null(dev.list()) + + args_no_cex <- list( + "tree_imp" = tree_imp + , top_n = 10L + , measure = "Gain" + ) + args_cex <- args_no_cex + args_cex[["cex"]] <- 0.75 + + for (arg_list in list(args_no_cex, args_cex)) { + + resDT <- do.call( + what = lgb.plot.importance + , args = arg_list + ) + + # Check that lgb.plot.importance() returns the data.table of the plotted data + expect_true(data.table::is.data.table(resDT)) + expect_named(resDT, c("Feature", "Gain", "Cover", "Frequency")) + + # Check that a plot was produced + expect_false(is.null(dev.list())) + + # remove all plots + dev.off() + expect_null(dev.list()) + } +}) diff --git a/R-package/tests/testthat/test_lgb.plot.interpretation.R b/R-package/tests/testthat/test_lgb.plot.interpretation.R new file mode 100644 index 0000000..b8359d5 --- /dev/null +++ b/R-package/tests/testthat/test_lgb.plot.interpretation.R @@ -0,0 +1,99 @@ +.sigmoid <- function(x) { + 1.0 / (1.0 + exp(-x)) +} +.logit <- function(x) { + log(x / (1.0 - x)) +} + +test_that("lgb.plot.interpretation works as expected for binary classification", { + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + dtrain <- lgb.Dataset(train$data, label = train$label) + set_field( + dataset = dtrain + , field_name = "init_score" + , data = rep( + .logit(mean(train$label)) + , length(train$label) + ) + ) + data(agaricus.test, package = "lightgbm") + test <- agaricus.test + params <- list( + objective = "binary" + , learning_rate = 0.01 + , num_leaves = 63L + , max_depth = -1L + , min_data_in_leaf = 1L + , min_sum_hessian_in_leaf = 1.0 + , verbosity = .LGB_VERBOSITY + , num_threads = .LGB_MAX_THREADS + ) + model <- lgb.train( + params = params + , data = dtrain + , nrounds = 3L + ) + num_trees <- 5L + tree_interpretation <- lgb.interpret( + model = model + , data = test$data + , idxset = seq_len(num_trees) + ) + expect_true({ + lgb.plot.interpretation( + tree_interpretation_dt = tree_interpretation[[1L]] + , top_n = 5L + ) + TRUE + }) + + # should also work when you explicitly pass cex + plot_res <- lgb.plot.interpretation( + tree_interpretation_dt = tree_interpretation[[1L]] + , top_n = 5L + , cex = 0.95 + ) + expect_null(plot_res) +}) + +test_that("lgb.plot.interpretation works as expected for multiclass classification", { + data(iris) + + # We must convert factors to numeric + # They must be starting from number 0 to use multiclass + # For instance: 0, 1, 2, 3, 4, 5... + iris$Species <- as.numeric(as.factor(iris$Species)) - 1L + + # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2) + train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ]) + # The 10 last samples of each class are for validation + test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ]) + dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L]) + dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L]) + params <- list( + objective = "multiclass" + , metric = "multi_logloss" + , num_class = 3L + , learning_rate = 0.00001 + , min_data = 1L + , num_threads = .LGB_MAX_THREADS + ) + model <- lgb.train( + params = params + , data = dtrain + , nrounds = 3L + , verbose = .LGB_VERBOSITY + ) + num_trees <- 5L + tree_interpretation <- lgb.interpret( + model = model + , data = test[, 1L:4L] + , idxset = seq_len(num_trees) + ) + plot_res <- lgb.plot.interpretation( + tree_interpretation_dt = tree_interpretation[[1L]] + , top_n = 5L + ) + expect_null(plot_res) +}) diff --git a/R-package/tests/testthat/test_metrics.R b/R-package/tests/testthat/test_metrics.R new file mode 100644 index 0000000..2974ec0 --- /dev/null +++ b/R-package/tests/testthat/test_metrics.R @@ -0,0 +1,10 @@ +test_that(".METRICS_HIGHER_BETTER() should be well formed", { + metrics <- .METRICS_HIGHER_BETTER() + metric_names <- names(.METRICS_HIGHER_BETTER()) + # should be a logical vector + expect_true(is.logical(metrics)) + # no metrics should be repeated + expect_true(length(unique(metric_names)) == length(metrics)) + # should not be any NAs + expect_false(anyNA(metrics)) +}) diff --git a/R-package/tests/testthat/test_multithreading.R b/R-package/tests/testthat/test_multithreading.R new file mode 100644 index 0000000..e2f3169 --- /dev/null +++ b/R-package/tests/testthat/test_multithreading.R @@ -0,0 +1,16 @@ +test_that("getLGBMthreads() and setLGBMthreads() work as expected", { + # works with integer input + ret <- setLGBMthreads(2L) + expect_null(ret) + expect_equal(getLGBMthreads(), 2L) + + # works with float input + ret <- setLGBMthreads(1.0) + expect_null(ret) + expect_equal(getLGBMthreads(), 1L) + + # setting to any negative number sets max threads to -1 + ret <- setLGBMthreads(-312L) + expect_null(ret) + expect_equal(getLGBMthreads(), -1L) +}) diff --git a/R-package/tests/testthat/test_parameters.R b/R-package/tests/testthat/test_parameters.R new file mode 100644 index 0000000..2e3aaa3 --- /dev/null +++ b/R-package/tests/testthat/test_parameters.R @@ -0,0 +1,169 @@ +data(agaricus.train, package = "lightgbm") +data(agaricus.test, package = "lightgbm") +train <- agaricus.train +test <- agaricus.test + +test_that("Feature penalties work properly", { + # Fit a series of models with varying penalty on most important variable + var_name <- "odor=none" + var_index <- which(train$data@Dimnames[[2L]] == var_name) + + bst <- lapply(seq(1.0, 0.0, by = -0.1), function(x) { + feature_penalties <- rep(1.0, ncol(train$data)) + feature_penalties[var_index] <- x + lightgbm( + data = train$data + , label = train$label + , params = list( + num_leaves = 5L + , learning_rate = 0.05 + , objective = "binary" + , feature_penalty = paste(feature_penalties, collapse = ",") + , metric = "binary_error" + , num_threads = .LGB_MAX_THREADS + ) + , nrounds = 5L + , verbose = -1L + ) + }) + + var_gain <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Gain]) + var_cover <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Cover]) + var_freq <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Frequency]) + + # Ensure that feature gain, cover, and frequency decreases with stronger penalties + expect_true(all(diff(unlist(var_gain)) <= 0.0)) + expect_true(all(diff(unlist(var_cover)) <= 0.0)) + expect_true(all(diff(unlist(var_freq)) <= 0.0)) + + expect_lt(min(diff(unlist(var_gain))), 0.0) + expect_lt(min(diff(unlist(var_cover))), 0.0) + expect_lt(min(diff(unlist(var_freq))), 0.0) + + # Ensure that feature is not used when feature_penalty = 0 + expect_length(var_gain[[length(var_gain)]], 0L) +}) + +test_that(".PARAMETER_ALIASES() returns a named list of character vectors, where names are unique", { + param_aliases <- .PARAMETER_ALIASES() + expect_identical(class(param_aliases), "list") + expect_true(length(param_aliases) > 100L) + expect_true(is.character(names(param_aliases))) + expect_true(is.character(param_aliases[["boosting"]])) + expect_true(is.character(param_aliases[["early_stopping_round"]])) + expect_true(is.character(param_aliases[["num_iterations"]])) + expect_true(is.character(param_aliases[["pre_partition"]])) + expect_true(length(names(param_aliases)) == length(param_aliases)) + expect_true(all(sapply(param_aliases, is.character))) + expect_true(length(unique(names(param_aliases))) == length(param_aliases)) + expect_equal(sort(param_aliases[["task"]]), c("task", "task_type")) + expect_equal(param_aliases[["bagging_fraction"]], c("bagging_fraction", "bagging", "sub_row", "subsample")) +}) + +test_that(".PARAMETER_ALIASES() uses the internal session cache", { + + cache_key <- "PARAMETER_ALIASES" + + # clear cache, so this test isn't reliant on the order unit tests are run in + if (exists(cache_key, where = .lgb_session_cache_env)) { + rm(list = cache_key, envir = .lgb_session_cache_env) + } + expect_false(exists(cache_key, where = .lgb_session_cache_env)) + + # check that result looks correct for at least one parameter + iter_aliases <- .PARAMETER_ALIASES()[["num_iterations"]] + expect_true(is.character(iter_aliases)) + expect_true(all(c("num_round", "nrounds") %in% iter_aliases)) + + # patch the cache to check that .PARAMETER_ALIASES() checks it + assign( + x = cache_key + , value = list(num_iterations = c("test", "other_test")) + , envir = .lgb_session_cache_env + ) + iter_aliases <- .PARAMETER_ALIASES()[["num_iterations"]] + expect_equal(iter_aliases, c("test", "other_test")) + + # re-set cache so this doesn't interfere with other unit tests + if (exists(cache_key, where = .lgb_session_cache_env)) { + rm(list = cache_key, envir = .lgb_session_cache_env) + } + expect_false(exists(cache_key, where = .lgb_session_cache_env)) +}) + +test_that("training should warn if you use 'dart' boosting with early stopping", { + for (boosting_param in .PARAMETER_ALIASES()[["boosting"]]) { + params <- list( + num_leaves = 5L + , learning_rate = 0.05 + , objective = "binary" + , metric = "binary_error" + , num_threads = .LGB_MAX_THREADS + ) + params[[boosting_param]] <- "dart" + + # warning: early stopping requested + expect_warning({ + result <- lightgbm( + data = train$data + , label = train$label + , params = params + , nrounds = 2L + , verbose = .LGB_VERBOSITY + , early_stopping_rounds = 1L + ) + }, regexp = "Early stopping is not available in 'dart' mode") + + # no warning: early stopping not requested + expect_silent({ + result <- lightgbm( + data = train$data + , label = train$label + , params = params + , nrounds = 2L + , verbose = .LGB_VERBOSITY + , early_stopping_rounds = NULL + ) + }) + } +}) + +test_that("lgb.cv() should warn if you use 'dart' boosting with early stopping", { + for (boosting_param in .PARAMETER_ALIASES()[["boosting"]]) { + params <- list( + num_leaves = 5L + , objective = "binary" + , metric = "binary_error" + , num_threads = .LGB_MAX_THREADS + ) + params[[boosting_param]] <- "dart" + + # warning: early stopping requested + expect_warning({ + result <- lgb.cv( + data = lgb.Dataset( + data = train$data + , label = train$label + ) + , params = params + , nrounds = 2L + , verbose = .LGB_VERBOSITY + , early_stopping_rounds = 1L + ) + }, regexp = "Early stopping is not available in 'dart' mode") + + # no warning: early stopping not requested + expect_silent({ + result <- lgb.cv( + data = lgb.Dataset( + data = train$data + , label = train$label + ) + , params = params + , nrounds = 2L + , verbose = .LGB_VERBOSITY + , early_stopping_rounds = NULL + ) + }) + } +}) diff --git a/R-package/tests/testthat/test_utils.R b/R-package/tests/testthat/test_utils.R new file mode 100644 index 0000000..5da5cff --- /dev/null +++ b/R-package/tests/testthat/test_utils.R @@ -0,0 +1,167 @@ +test_that(".params2str() works as expected for empty lists", { + out_str <- .params2str( + params = list() + ) + expect_identical(class(out_str), "character") + expect_equal(out_str, "") +}) + +test_that(".params2str() works as expected for a key in params with multiple different-length elements", { + metrics <- c("a", "ab", "abc", "abcdefg") + params <- list( + objective = "magic" + , metric = metrics + , nrounds = 10L + , learning_rate = 0.0000001 + ) + out_str <- .params2str( + params = params + ) + expect_identical(class(out_str), "character") + expect_identical( + out_str + , "objective=magic metric=a,ab,abc,abcdefg nrounds=10 learning_rate=0.0000001" + ) +}) + +test_that(".params2str() passes through duplicated params", { + out_str <- .params2str( + params = list( + objective = "regression" + , bagging_fraction = 0.8 + , bagging_fraction = 0.5 # nolint: duplicate_argument. + ) + ) + expect_equal(out_str, "objective=regression bagging_fraction=0.8 bagging_fraction=0.5") +}) + +test_that(".check_eval works as expected with no metric", { + params <- .check_eval( + params = list(device = "cpu") + , eval = "binary_error" + ) + expect_named(params, c("device", "metric")) + expect_identical(params[["metric"]], list("binary_error")) +}) + +test_that(".check_eval adds eval to metric in params", { + params <- .check_eval( + params = list(metric = "auc") + , eval = "binary_error" + ) + expect_named(params, "metric") + expect_identical(params[["metric"]], list("auc", "binary_error")) +}) + +test_that(".check_eval adds eval to metric in params if two evaluation names are provided", { + params <- .check_eval( + params = list(metric = "auc") + , eval = c("binary_error", "binary_logloss") + ) + expect_named(params, "metric") + expect_identical(params[["metric"]], list("auc", "binary_error", "binary_logloss")) +}) + +test_that(".check_eval adds eval to metric in params if a list is provided", { + params <- .check_eval( + params = list(metric = "auc") + , eval = list("binary_error", "binary_logloss") + ) + expect_named(params, "metric") + expect_identical(params[["metric"]], list("auc", "binary_error", "binary_logloss")) +}) + +test_that(".check_eval drops duplicate metrics and preserves order", { + params <- .check_eval( + params = list(metric = "l1") + , eval = list("l2", "rmse", "l1", "rmse") + ) + expect_named(params, "metric") + expect_identical(params[["metric"]], list("l1", "l2", "rmse")) +}) + +test_that(".check_wrapper_param() uses passed-in keyword arg if no alias found in params", { + kwarg_val <- sample(seq_len(100L), size = 1L) + params <- .check_wrapper_param( + main_param_name = "num_iterations" + , params = list() + , alternative_kwarg_value = kwarg_val + ) + expect_equal(params[["num_iterations"]], kwarg_val) +}) + +test_that(".check_wrapper_param() prefers main parameter to alias and keyword arg", { + num_iterations <- sample(seq_len(100L), size = 1L) + kwarg_val <- sample(seq_len(100L), size = 1L) + params <- .check_wrapper_param( + main_param_name = "num_iterations" + , params = list( + num_iterations = num_iterations + , num_tree = sample(seq_len(100L), size = 1L) + , n_estimators = sample(seq_len(100L), size = 1L) + ) + , alternative_kwarg_value = kwarg_val + ) + expect_equal(params[["num_iterations"]], num_iterations) + + # aliases should be removed + expect_identical(params, list(num_iterations = num_iterations)) +}) + +test_that(".check_wrapper_param() prefers alias to keyword arg", { + n_estimators <- sample(seq_len(100L), size = 1L) + num_tree <- sample(seq_len(100L), size = 1L) + kwarg_val <- sample(seq_len(100L), size = 1L) + params <- .check_wrapper_param( + main_param_name = "num_iterations" + , params = list( + num_tree = num_tree + , n_estimators = n_estimators + ) + , alternative_kwarg_value = kwarg_val + ) + expect_equal(params[["num_iterations"]], num_tree) + expect_identical(params, list(num_iterations = num_tree)) + + # switching the order shouldn't switch which one is chosen + params2 <- .check_wrapper_param( + main_param_name = "num_iterations" + , params = list( + n_estimators = n_estimators + , num_tree = num_tree + ) + , alternative_kwarg_value = kwarg_val + ) + expect_equal(params2[["num_iterations"]], num_tree) + expect_identical(params2, list(num_iterations = num_tree)) +}) + +test_that(".equal_or_both_null produces expected results", { + expect_true(.equal_or_both_null(NULL, NULL)) + expect_false(.equal_or_both_null(1.0, NULL)) + expect_false(.equal_or_both_null(NULL, 1.0)) + expect_true(.equal_or_both_null(1.0, 1.0)) + expect_true(.equal_or_both_null(1.0, 1L)) + expect_false(.equal_or_both_null(NA, NULL)) + expect_false(.equal_or_both_null(NULL, NA)) + expect_false(.equal_or_both_null(10.0, 1L)) + expect_true(.equal_or_both_null(0L, 0L)) +}) + +test_that(".check_interaction_constraints() adds skipped features", { + ref <- letters[1L:5L] + ic_num <- list(1L, c(2L, 3L)) + ic_char <- list("a", c("b", "c")) + expected <- list("[0]", "[1,2]", "[3,4]") + + ic_checked_num <- .check_interaction_constraints( + interaction_constraints = ic_num, column_names = ref + ) + + ic_checked_char <- .check_interaction_constraints( + interaction_constraints = ic_char, column_names = ref + ) + + expect_equal(ic_checked_num, expected) + expect_equal(ic_checked_char, expected) +}) diff --git a/R-package/tests/testthat/test_weighted_loss.R b/R-package/tests/testthat/test_weighted_loss.R new file mode 100644 index 0000000..f9f9675 --- /dev/null +++ b/R-package/tests/testthat/test_weighted_loss.R @@ -0,0 +1,65 @@ +test_that("Gamma regression reacts on 'weight'", { + n <- 100L + set.seed(87L) + X <- matrix(runif(2L * n), ncol = 2L) + y <- X[, 1L] + X[, 2L] + runif(n) + X_pred <- X[1L:5L, ] + + params <- list(objective = "gamma", num_threads = .LGB_MAX_THREADS) + + # Unweighted + dtrain <- lgb.Dataset(X, label = y) + bst <- lgb.train( + params = params + , data = dtrain + , nrounds = 4L + , verbose = .LGB_VERBOSITY + ) + pred_unweighted <- predict(bst, X_pred) + + # Constant weight 1 + dtrain <- lgb.Dataset( + X + , label = y + , weight = rep(1.0, n) + ) + bst <- lgb.train( + params = params + , data = dtrain + , nrounds = 4L + , verbose = .LGB_VERBOSITY + ) + pred_weighted_1 <- predict(bst, X_pred) + + # Constant weight 2 + dtrain <- lgb.Dataset( + X + , label = y + , weight = rep(2.0, n) + ) + bst <- lgb.train( + params = params + , data = dtrain + , nrounds = 4L + , verbose = .LGB_VERBOSITY + ) + pred_weighted_2 <- predict(bst, X_pred) + + # Non-constant weights + dtrain <- lgb.Dataset( + X + , label = y + , weight = seq(0.0, 1.0, length.out = n) + ) + bst <- lgb.train( + params = params + , data = dtrain + , nrounds = 4L + , verbose = .LGB_VERBOSITY + ) + pred_weighted <- predict(bst, X_pred) + + expect_equal(pred_unweighted, pred_weighted_1) + expect_equal(pred_weighted_1, pred_weighted_2) + expect_false(all(pred_unweighted == pred_weighted)) +}) diff --git a/R-package/vignettes/basic_walkthrough.Rmd b/R-package/vignettes/basic_walkthrough.Rmd new file mode 100644 index 0000000..f9f4720 --- /dev/null +++ b/R-package/vignettes/basic_walkthrough.Rmd @@ -0,0 +1,127 @@ +--- +title: + "Basic Walkthrough" +description: > + This vignette describes how to train a LightGBM model for binary classification. +output: + markdown::html_format: + options: + toc: true + number_sections: true +vignette: > + %\VignetteIndexEntry{Basic Walkthrough} + %\VignetteEngine{knitr::knitr} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE + , comment = "#>" + , warning = FALSE + , message = FALSE +) +``` + +## Introduction + +Welcome to the world of [LightGBM](https://lightgbm.readthedocs.io/en/latest/), a highly efficient gradient boosting implementation (Ke et al. 2017). + +```{r} +library(lightgbm) +``` + +```{r, include=FALSE} +# limit number of threads used, to be respectful of CRAN's resources when it checks this vignette +data.table::setDTthreads(1L) +setLGBMthreads(2L) +``` + +This vignette will guide you through its basic usage. It will show how to build a simple binary classification model based on a subset of the `bank` dataset (Moro, Cortez, and Rita 2014). You will use the two input features "age" and "balance" to predict whether a client has subscribed a term deposit. + +## The dataset + +The dataset looks as follows. + +```{r} +data(bank, package = "lightgbm") + +bank[1L:5L, c("y", "age", "balance")] + +# Distribution of the response +table(bank$y) +``` + +## Training the model + +The R-package of LightGBM offers two functions to train a model: + +- `lgb.train()`: This is the main training logic. It offers full flexibility but requires a `Dataset` object created by the `lgb.Dataset()` function. +- `lightgbm()`: Simpler, but less flexible. Data can be passed without having to bother with `lgb.Dataset()`. + +### Using the `lightgbm()` function + +In a first step, you need to convert data to numeric. Afterwards, you are ready to fit the model by the `lightgbm()` function. + +```{r} +# Numeric response and feature matrix +y <- as.numeric(bank$y == "yes") +X <- data.matrix(bank[, c("age", "balance")]) + +# Train +fit <- lightgbm( + data = X + , label = y + , params = list( + num_leaves = 4L + , learning_rate = 1.0 + , objective = "binary" + ) + , nrounds = 10L + , verbose = -1L +) + +# Result +summary(predict(fit, X)) +``` + +It seems to have worked! And the predictions are indeed probabilities between 0 and 1. + +### Using the `lgb.train()` function + +Alternatively, you can go for the more flexible interface `lgb.train()`. Here, as an additional step, you need to prepare `y` and `X` by the data API `lgb.Dataset()` of LightGBM. Parameters are passed to `lgb.train()` as a named list. + +```{r} +# Data interface +dtrain <- lgb.Dataset(X, label = y) + +# Parameters +params <- list( + objective = "binary" + , num_leaves = 4L + , learning_rate = 1.0 +) + +# Train +fit <- lgb.train( + params + , data = dtrain + , nrounds = 10L + , verbose = -1L +) +``` + +Try it out! If stuck, visit LightGBM's [documentation](https://lightgbm.readthedocs.io/en/latest/R/index.html) for more details. + +```{r, echo = FALSE, results = "hide"} +# Cleanup +if (file.exists("lightgbm.model")) { + file.remove("lightgbm.model") +} +``` + +## References + +Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." In Advances in Neural Information Processing Systems 30 (NIPS 2017). + +Moro, Sérgio, Paulo Cortez, and Paulo Rita. 2014. "A Data-Driven Approach to Predict the Success of Bank Telemarketing." Decision Support Systems 62: 22–31. diff --git a/README.md b/README.md new file mode 100644 index 0000000..8d3b18f --- /dev/null +++ b/README.md @@ -0,0 +1,195 @@ + + +> [!NOTE] +> This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` in March 2026. +> This repository is still the official LightGBM source code, managed by the same maintainers (including the creator of LightGBM). +> For details, see https://github.com/lightgbm-org/LightGBM/issues/7187 + +Light Gradient Boosting Machine +=============================== + +[![C++ GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/cpp.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/cpp.yml) +[![Python-package GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/python_package.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/python_package.yml) +[![R-package GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/r_package.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/r_package.yml) +[![CUDA Version GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/cuda.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/cuda.yml) +[![SWIG Wrapper GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/swig.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/swig.yml) +[![Static Analysis GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/static_analysis.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/static_analysis.yml) +[![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/1ys5ot401m0fep6l/branch/main?svg=true)](https://ci.appveyor.com/project/guolinke/lightgbm/branch/main) +[![Documentation Status](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/) +[![Link checks](https://github.com/lightgbm-org/LightGBM/actions/workflows/lychee.yml/badge.svg?branch=main)](https://github.com/lightgbm-org/LightGBM/actions/workflows/lychee.yml) +[![License](https://img.shields.io/github/license/lightgbm-org/lightgbm.svg)](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE) +[![EffVer Versioning](https://img.shields.io/badge/version_scheme-EffVer-0097a7)](https://jacobtomlinson.dev/effver) +[![StackOverflow questions](https://img.shields.io/stackexchange/stackoverflow/t/lightgbm?logo=stackoverflow&logoColor=white&label=StackOverflow%20questions)](https://stackoverflow.com/questions/tagged/lightgbm?sort=votes) +[![Python Versions](https://img.shields.io/pypi/pyversions/lightgbm.svg?logo=python&logoColor=white)](https://pypi.org/project/lightgbm) +[![PyPI Version](https://img.shields.io/pypi/v/lightgbm.svg?logo=pypi&logoColor=white)](https://pypi.org/project/lightgbm) +[![conda Version](https://img.shields.io/conda/vn/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda)](https://anaconda.org/conda-forge/lightgbm) +[![CRAN Version](https://www.r-pkg.org/badges/version/lightgbm)](https://cran.r-project.org/package=lightgbm) +[![NuGet Version](https://img.shields.io/nuget/v/lightgbm?logo=nuget&logoColor=white)](https://www.nuget.org/packages/LightGBM) +[![Winget Version](https://img.shields.io/winget/v/Microsoft.LightGBM)](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Microsoft/LightGBM) + +LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: + +- Faster training speed and higher efficiency. +- Lower memory usage. +- Better accuracy. +- Support of parallel, distributed, and GPU learning. +- Capable of handling large-scale data. + +For further details, please refer to [Features](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst). + +Benefiting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/lightgbm-org/LightGBM/blob/main/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions. + +[Comparison experiments](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, [distributed learning experiments](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings. + +Get Started and Documentation +----------------------------- + +Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository. If you are new to LightGBM, follow [the installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) on that site. + +Next you may want to read: + +- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/main/examples) showing command line usage of common tasks. +- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Features.rst) and algorithms supported by LightGBM. +- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst) is an exhaustive list of customization you can make. +- [**Distributed Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/GPU-Tutorial.rst) can speed up computation. +- [**FLAML**](https://www.microsoft.com/en-us/research/project/fast-and-lightweight-automl-for-large-scale-data/articles/flaml-a-fast-and-lightweight-automl-library/) provides automated tuning for LightGBM ([code examples](https://microsoft.github.io/FLAML/docs/Examples/AutoML-for-LightGBM/)). +- [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)). +- [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide). + +Documentation for contributors: + +- [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/README.rst). +- Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Development-Guide.rst). + +News +---- + +Please refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page. + +External (Unofficial) Repositories +---------------------------------- + +Projects listed here offer alternative ways to use LightGBM. +They are not maintained or officially endorsed by the `LightGBM` development team. + +JPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm + +Nyoka (Python PMML converter): https://github.com/SoftwareAG/nyoka + +Treelite (model compiler for efficient deployment): https://github.com/dmlc/treelite + +lleaves (LLVM-based model compiler for efficient inference): https://github.com/siboehm/lleaves + +Hummingbird (model compiler into tensor computations): https://github.com/microsoft/hummingbird + +GBNet (use `LightGBM` as a [PyTorch Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html)): https://github.com/mthorrell/gbnet + +cuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml + +nvForest (GPU-accelerated inference): https://github.com/rapidsai/nvforest + +daal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn-intelex/tree/master/daal4py + +m2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen + +leaves (Go model applier): https://github.com/dmitryikh/leaves + +ONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools + +SHAP (model output explainer): https://github.com/slundberg/shap + +Shapash (model visualization and interpretation): https://github.com/MAIF/shapash + +dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz + +supertree (interactive visualization of decision trees): https://github.com/mljar/supertree + +SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML + +Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing + +Kubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator + +lightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray + +Ray (distributed computing framework): https://github.com/ray-project/ray + +Mars (LightGBM on Mars): https://github.com/mars-project/mars + +ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning + +LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net + +LightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby + +LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j + +LightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j + +Julia-package: https://github.com/IQVIA-ML/LightGBM.jl + +lightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs + +MLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer + +MLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow + +FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML + +MLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised + +Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna + +LightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS + +LightGBM-MoE (Mixture-of-Experts / regime-switching extension): https://github.com/kyo219/LightGBM-MoE + +darts (time series forecasting and anomaly detection with LightGBM): https://github.com/unit8co/darts + +mlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast + +skforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast + +`{bonsai}` (R `{parsnip}`-compliant interface): https://github.com/tidymodels/bonsai + +`{mlr3extralearners}` (R `{mlr3}`-compliant interface): https://github.com/mlr-org/mlr3extralearners + +lightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform + +`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml + +`pyodide` (run `lightgbm` Python-package in a web browser): https://github.com/pyodide/pyodide + +`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex + +Support +------- + +- Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions. +- Open **bug reports** and **feature requests** on [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues). + +How to Contribute +----------------- + +Check [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/main/CONTRIBUTING.md) page. + +Microsoft Open Source Code of Conduct +------------------------------------- + +This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. + +Reference Papers +---------------- + +Yu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. "Quantized Training of Gradient Boosting Decision Trees" ([link](https://proceedings.neurips.cc/paper/2022/hash/77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833. + +Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html)". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157. + +Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. "[A Communication-Efficient Parallel Algorithm for Decision Tree](https://proceedings.neurips.cc/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html)". Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287. + +Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)". SysML Conference, 2018. + +License +------- + +This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE) for additional details. diff --git a/README.wehub.md b/README.wehub.md new file mode 100644 index 0000000..90707b3 --- /dev/null +++ b/README.wehub.md @@ -0,0 +1,7 @@ +# WeHub 来源说明 + +- 原始项目：`lightgbm-org/LightGBM` +- 原始仓库：https://github.com/lightgbm-org/LightGBM +- 导入方式：上游默认分支的最新快照 +- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准 +- 本文件仅用于记录来源，不代表 WeHub 是原项目作者 diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..45edf19 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,30 @@ +# Security + +## Reporting Security Issues + +> [!WARNING] +> Do not report security vulnerabilities through public GitHub issues! + +Instead, please open a private vulnerability report in this repository, on the "Security and quality" tab. + +See https://docs.github.com/en/code-security/how-tos/report-and-fix-vulnerabilities/privately-reporting-a-security-vulnerability + +## Report Details + +We prefer all communications to be in English. + +Reports should include the following: + +* reproducible example showing how the vulnerability can be exploited +* statement about the impact (including affected versions) + +And we'd appreciate if they also include: + +* statement about whether you are interested in implementing the fix yourself + +## Disclosure Policy + +This project is staffed exclusively by volunteers. +Please be patient and allow us time to respond before disclosing vulnerabilities. + +We prefer to coordinate disclosure privately, and are committed to giving credit for confirmed vulnerabilities. diff --git a/VERSION.txt b/VERSION.txt new file mode 100644 index 0000000..6db5f0c --- /dev/null +++ b/VERSION.txt @@ -0,0 +1 @@ +4.6.0.99 diff --git a/biome.json b/biome.json new file mode 100644 index 0000000..31a4ccf --- /dev/null +++ b/biome.json @@ -0,0 +1,41 @@ +{ + "root": false, + "vcs": { + "enabled": true, + "clientKind": "git", + "useIgnoreFile": true + }, + "files": { + "includes": [ + "**", + "!**/lib", + "!build", + "!external_libs", + "!lightgbm-python", + "!lightgbm_r", + "!.pixi", + "!R-package/docs" + ] + }, + "formatter": { + "enabled": true, + "expand": "always", + "useEditorconfig": true, + "lineWidth": 120 + }, + "assist": { + "enabled": true, + "actions": { + "recommended": true + } + }, + "linter": { + "enabled": true, + "domains": { + "project": "all" + }, + "rules": { + "recommended": true + } + } +} diff --git a/build-cran-package.sh b/build-cran-package.sh new file mode 100755 index 0000000..fd6385b --- /dev/null +++ b/build-cran-package.sh @@ -0,0 +1,214 @@ +#!/bin/sh + +# [description] +# Prepare a source distribution of the R-package +# to be submitted to CRAN. +# +# [arguments] +# +# --r-executable Customize the R executable used by `R CMD build`. +# Useful if building the R-package in an environment with +# non-standard builds of R, such as those provided in +# https://github.com/wch/r-debug. +# +# --no-build-vignettes Pass this flag to skip creating vignettes. +# You might want to do this to avoid installing +# vignette-only dependencies, or to avoid +# portability issues. +# +# [usage] +# +# # default usage +# sh build-cran-package.sh +# +# # custom R build +# sh build-cran-package.sh --r-executable=RDvalgrind +# +# # skip vignette building +# sh build-cran-package.sh --no-build-vignettes + +set -e -u + +# Default values of arguments +BUILD_VIGNETTES=true +LGB_R_EXECUTABLE=R + +while [ $# -gt 0 ]; do + case "$1" in + --r-executable=*) + LGB_R_EXECUTABLE="${1#*=}" + ;; + --no-build-vignettes*) + BUILD_VIGNETTES=false + ;; + *) + echo "invalid argument '${1}'" + exit 1 + ;; + esac + shift +done + +echo "Building lightgbm with R executable: ${LGB_R_EXECUTABLE}" + +ORIG_WD="$(pwd)" +TEMP_R_DIR="$(pwd)/lightgbm_r" + +if test -d "${TEMP_R_DIR}"; then + rm -r "${TEMP_R_DIR}" +fi +mkdir -p "${TEMP_R_DIR}" + +CURRENT_DATE=$(date +'%Y-%m-%d') + +# R packages cannot have versions like 3.0.0rc1, but +# 3.0.0-1 is acceptable +LGB_VERSION=$(head -1 ./VERSION.txt | sed "s/rc/-/g") + +# move relevant files +cp -R R-package/* "${TEMP_R_DIR}" +cp -R include "${TEMP_R_DIR}/src/" +cp -R src/* "${TEMP_R_DIR}/src/" + +if ${BUILD_VIGNETTES} ; then + cp docs/logo/LightGBM_logo_black_text.svg "${TEMP_R_DIR}/vignettes/" +fi + +cp \ + external_libs/fast_double_parser/include/fast_double_parser.h \ + "${TEMP_R_DIR}/src/include/LightGBM/utils" + +mkdir -p "${TEMP_R_DIR}/src/include/LightGBM/utils/fmt" +cp \ + external_libs/fmt/include/fmt/*.h \ + "${TEMP_R_DIR}/src/include/LightGBM/utils/fmt" + +# including only specific files from Eigen, to keep the R-package +# small and avoid redistributing code with licenses incompatible with +# LightGBM's license +EIGEN_R_DIR="${TEMP_R_DIR}/src/include/Eigen" +mkdir -p "${EIGEN_R_DIR}" + +modules="Cholesky Core Dense Eigenvalues Geometry Householder Jacobi LU QR SVD" +for eigen_module in ${modules}; do + cp "external_libs/eigen/Eigen/${eigen_module}" "${EIGEN_R_DIR}/${eigen_module}" + if [ "${eigen_module}" != "Dense" ]; then + mkdir -p "${EIGEN_R_DIR}/src/${eigen_module}/" + cp -R "external_libs/eigen/Eigen/src/${eigen_module}"/* "${EIGEN_R_DIR}/src/${eigen_module}/" + fi +done + +mkdir -p "${EIGEN_R_DIR}/src/misc" +cp -R external_libs/eigen/Eigen/src/misc/* "${EIGEN_R_DIR}/src/misc/" + +mkdir -p "${EIGEN_R_DIR}/src/plugins" +cp -R external_libs/eigen/Eigen/src/plugins/* "${EIGEN_R_DIR}/src/plugins/" + +cd "${TEMP_R_DIR}" + + # Remove files not needed for CRAN + echo "Removing files not needed for CRAN" + rm src/install.libs.R + rm -r inst/ + rm -r pkgdown/ + rm cran-comments.md + rm AUTOCONF_UBUNTU_VERSION + rm recreate-configure.sh + + # files only used by the lightgbm CLI aren't needed for + # the R-package + rm src/application/application.cpp + rm src/include/LightGBM/application.h + rm src/main.cpp + + # configure.ac and DESCRIPTION have placeholders for version + # and date so they don't have to be updated manually + sed -i.bak -e "s/~~VERSION~~/${LGB_VERSION}/" configure.ac + sed -i.bak -e "s/~~VERSION~~/${LGB_VERSION}/" DESCRIPTION + sed -i.bak -e "s/~~DATE~~/${CURRENT_DATE}/" DESCRIPTION + + # Remove 'region', 'endregion', and 'warning' pragmas. + # This won't change the correctness of the code. CRAN does + # not allow you to use compiler flag '-Wno-unknown-pragmas' or + # pragmas that suppress warnings. + echo "Removing unknown pragmas in headers" + find . \( -name '*.h' -o -name '*.hpp' -o -name '*.cpp' \) -exec \ + sed \ + -i.bak \ + -e 's/^.*#pragma clang diagnostic.*$//' \ + -e 's/^.*#pragma diag_suppress.*$//' \ + -e 's/^.*#pragma GCC diagnostic.*$//' \ + -e 's/^.*#pragma region.*$//' \ + -e 's/^.*#pragma endregion.*$//' \ + -e 's/^.*#pragma warning.*$//' \ + {} + + + # 'processx' is listed as a 'Suggests' dependency in DESCRIPTION + # because it is used in install.libs.R, a file that is not + # included in the CRAN distribution of the package + sed \ + -i.bak \ + '/processx/d' \ + DESCRIPTION + + echo "Cleaning sed backup files" + find . -name '*.bak' -exec rm {} \; + +cd "${ORIG_WD}" + +if ${BUILD_VIGNETTES} ; then + "${LGB_R_EXECUTABLE}" CMD build \ + --keep-empty-dirs \ + lightgbm_r + + echo "removing object files created by vignettes" + rm -rf ./_tmp + mkdir _tmp + TARBALL_NAME="lightgbm_${LGB_VERSION}.tar.gz" + mv "${TARBALL_NAME}" _tmp/ + + echo "untarring ${TARBALL_NAME}" + cd _tmp + tar -xf "${TARBALL_NAME}" > /dev/null 2>&1 + rm -f "${TARBALL_NAME}" + echo "done untarring ${TARBALL_NAME}" + + # Object files are left behind from compiling the library to generate vignettes. + # Approaches like using tar --exclude=*.so to exclude them are not portable + # (for example, don't work with some versions of tar on Windows). + # + # Removing them manually here removes the need to use tar --exclude. + # + # For background, see https://github.com/lightgbm-org/LightGBM/pull/3946#pullrequestreview-799415812. + rm -f ./lightgbm/src/*.o + rm -f ./lightgbm/src/boosting/*.o + rm -f ./lightgbm/src/io/*.o + rm -f ./lightgbm/src/metric/*.o + rm -f ./lightgbm/src/network/*.o + rm -f ./lightgbm/src/objective/*.o + rm -f ./lightgbm/src/treelearner/*.o + rm -f ./lightgbm/src/utils/*.o + + echo "re-tarring ${TARBALL_NAME}" + # --no-xattrs is the default in GNU tar but not some distributions of BSD tar. + # Enable it here to avoid errors on macOS. + # ref: https://stackoverflow.com/a/74373784/3986677 + tar \ + -cz \ + --no-xattrs \ + -f "${TARBALL_NAME}" \ + lightgbm \ + > /dev/null 2>&1 + mv "${TARBALL_NAME}" ../ + cd .. + echo "Done creating ${TARBALL_NAME}" + + rm -rf ./_tmp +else + "${LGB_R_EXECUTABLE}" CMD build \ + --keep-empty-dirs \ + --no-build-vignettes \ + lightgbm_r +fi + +echo "Done building R-package" diff --git a/build-python.sh b/build-python.sh new file mode 100755 index 0000000..4c8247c --- /dev/null +++ b/build-python.sh @@ -0,0 +1,435 @@ +#!/bin/sh + +# [description] +# +# Prepare a source distribution (sdist) or built distribution (wheel) +# of the Python-package, and optionally install it. +# +# [usage] +# +# # build sdist and put it in dist/ +# sh ./build-python.sh sdist +# +# # build wheel and put it in dist/ +# sh ./build-python.sh bdist_wheel [OPTIONS] +# +# # compile lib_lightgbm and install the Python-package wrapping it +# sh ./build-python.sh install [OPTIONS] +# +# # install the Python-package using a pre-compiled lib_lightgbm +# # (assumes lib_lightgbm.{dll,so} is located at the root of the repo) +# sh ./build-python.sh install --precompile +# +# [options] +# +# --boost-dir=FILEPATH +# Directory with Boost package configuration file. +# --boost-include-dir=FILEPATH +# Directory containing Boost headers. +# --boost-librarydir=FILEPATH +# Preferred Boost library directory. +# --boost-root=FILEPATH +# Boost preferred installation prefix. +# --opencl-include-dir=FILEPATH +# OpenCL include directory. +# --opencl-library=FILEPATH +# Path to OpenCL library. +# --bit32 +# Compile 32-bit version. +# --cuda +# Compile CUDA version. +# --gpu +# Compile GPU version. +# --integrated-opencl +# Compile integrated OpenCL version. +# --mingw +# Compile with MinGW. +# --mpi +# Compile MPI version. +# --no-isolation +# Assume all build and install dependencies are already installed, +# don't go to the internet to get them. +# --nomp +# Compile version without OpenMP support. +# --precompile +# Use precompiled library. +# Only used with 'install' command. +# --rocm +# Compile ROCm version. +# --time-costs +# Compile version that outputs time costs for different internal routines. +# --user +# Install into user-specific instead of global site-packages directory. +# Only used with 'install' command. + +set -e -u + +echo "[INFO] building lightgbm" + +# Default values of arguments +INSTALL="false" +BUILD_SDIST="false" +BUILD_WHEEL="false" + +PIP_INSTALL_ARGS="" +BUILD_ARGS="" +PRECOMPILE="false" + +while [ $# -gt 0 ]; do + case "$1" in + ############################ + # sub-commands of setup.py # + ############################ + install) + INSTALL="true" + ;; + sdist) + BUILD_SDIST="true" + ;; + bdist_wheel) + BUILD_WHEEL="true" + ;; + ############################ + # customized library paths # + ############################ + --boost-dir|--boost-dir=*) + if echo "$1" | grep -q '^*=*$'; + then shift; + fi + BOOST_DIR="${1#*=}" + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.Boost_DIR='${BOOST_DIR}'" + ;; + --boost-include-dir|--boost-include-dir=*) + if echo "$1" | grep -q '^*=*$'; + then shift; + fi + BOOST_INCLUDE_DIR="${1#*=}" + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.Boost_INCLUDE_DIR='${BOOST_INCLUDE_DIR}'" + ;; + --boost-librarydir|--boost-librarydir=*) + if echo "$1" | grep -q '^*=*$'; + then shift; + fi + BOOST_LIBRARY_DIR="${1#*=}" + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.BOOST_LIBRARYDIR='${BOOST_LIBRARY_DIR}'" + ;; + --boost-root|--boost-root=*) + if echo "$1" | grep -q '^*=*$'; + then shift; + fi + BOOST_ROOT="${1#*=}" + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.Boost_ROOT='${BOOST_ROOT}'" + ;; + --opencl-include-dir|--opencl-include-dir=*) + if echo "$1" | grep -q '^*=*$'; + then shift; + fi + OPENCL_INCLUDE_DIR="${1#*=}" + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.OpenCL_INCLUDE_DIR='${OPENCL_INCLUDE_DIR}'" + ;; + --opencl-library|--opencl-library=*) + if echo "$1" | grep -q '^*=*$'; + then shift; + fi + OPENCL_LIBRARY="${1#*=}" + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.OpenCL_LIBRARY='${OPENCL_LIBRARY}'" + ;; + ######### + # flags # + ######### + --bit32) + echo "[INFO] Attempting to build 32-bit version of LightGBM, which is only supported on Windows with Visual Studio." + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.args=-AWin32" + ;; + --cuda) + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_CUDA=ON" + ;; + --rocm) + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_ROCM=ON" + ;; + --gpu) + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_GPU=ON" + ;; + --integrated-opencl) + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.__INTEGRATE_OPENCL=ON" + ;; + --mingw) + # ref: https://stackoverflow.com/a/45104058/3986677 + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.CMAKE_SH=CMAKE_SH-NOTFOUND" + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.args=-G'MinGW Makefiles'" + ;; + --mpi) + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_MPI=ON" + ;; + --no-isolation) + BUILD_ARGS="${BUILD_ARGS} --no-isolation" + PIP_INSTALL_ARGS="${PIP_INSTALL_ARGS} --no-build-isolation" + ;; + --nomp) + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_OPENMP=OFF" + ;; + --precompile) + PRECOMPILE="true" + ;; + --time-costs) + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.USE_TIMETAG=ON" + ;; + --user) + PIP_INSTALL_ARGS="${PIP_INSTALL_ARGS} --user" + ;; + *) + echo "[ERROR] invalid argument '${1}'. Aborting" + exit 1 + ;; + esac + shift +done + +# ref: https://cmake.org/cmake/help/latest/variable/CMAKE_CUDA_ARCHITECTURES.html +if [ -n "${CUDAARCHS:-}" ]; then + BUILD_ARGS="${BUILD_ARGS} --config-setting=cmake.define.CMAKE_CUDA_ARCHITECTURES=${CUDAARCHS}" +fi + +python -m pip install --prefer-binary 'build>=0.10.0' + +# create a new directory that just contains the files needed +# to build the Python-package +create_isolated_source_dir() { + rm -rf \ + ./lightgbm-python \ + ./lightgbm \ + ./python-package/build \ + ./python-package/build_cpp \ + ./python-package/compile \ + ./python-package/dist \ + ./python-package/lightgbm.egg-info + + cp -R ./python-package ./lightgbm-python + + cp LICENSE ./lightgbm-python/ + cp VERSION.txt ./lightgbm-python/lightgbm/VERSION.txt + + cp -R ./cmake ./lightgbm-python + cp CMakeLists.txt ./lightgbm-python + cp -R ./include ./lightgbm-python + cp -R ./src ./lightgbm-python + cp -R ./swig ./lightgbm-python + + # include only specific files from external_libs, to keep the package + # small and avoid redistributing code with licenses incompatible with + # LightGBM's license + + ###################### + # fast_double_parser # + ###################### + mkdir -p ./lightgbm-python/external_libs/fast_double_parser + cp \ + external_libs/fast_double_parser/CMakeLists.txt \ + ./lightgbm-python/external_libs/fast_double_parser/CMakeLists.txt + cp \ + external_libs/fast_double_parser/LICENSE* \ + ./lightgbm-python/external_libs/fast_double_parser/ + + mkdir -p ./lightgbm-python/external_libs/fast_double_parser/include/ + cp \ + external_libs/fast_double_parser/include/fast_double_parser.h \ + ./lightgbm-python/external_libs/fast_double_parser/include/ + + ####### + # fmt # + ####### + mkdir -p ./lightgbm-python/external_libs/fmt + cp \ + external_libs/fast_double_parser/CMakeLists.txt \ + ./lightgbm-python/external_libs/fmt/CMakeLists.txt + cp \ + external_libs/fmt/LICENSE* \ + ./lightgbm-python/external_libs/fmt/ + + mkdir -p ./lightgbm-python/external_libs/fmt/include/fmt + cp \ + external_libs/fmt/include/fmt/*.h \ + ./lightgbm-python/external_libs/fmt/include/fmt/ + + ######### + # Eigen # + ######### + mkdir -p ./lightgbm-python/external_libs/eigen/Eigen + cp \ + external_libs/eigen/CMakeLists.txt \ + ./lightgbm-python/external_libs/eigen/CMakeLists.txt + + modules="Cholesky Core Dense Eigenvalues Geometry Householder Jacobi LU QR SVD" + for eigen_module in ${modules}; do + cp \ + "external_libs/eigen/Eigen/${eigen_module}" \ + "./lightgbm-python/external_libs/eigen/Eigen/${eigen_module}" + if [ "${eigen_module}" != "Dense" ]; then + mkdir -p "./lightgbm-python/external_libs/eigen/Eigen/src/${eigen_module}/" + cp \ + -R \ + "external_libs/eigen/Eigen/src/${eigen_module}"/* \ + "./lightgbm-python/external_libs/eigen/Eigen/src/${eigen_module}/" + fi + done + + mkdir -p ./lightgbm-python/external_libs/eigen/Eigen/misc + cp \ + -R \ + external_libs/eigen/Eigen/src/misc \ + ./lightgbm-python/external_libs/eigen/Eigen/src/misc/ + + mkdir -p ./lightgbm-python/external_libs/eigen/Eigen/plugins + cp \ + -R \ + external_libs/eigen/Eigen/src/plugins \ + ./lightgbm-python/external_libs/eigen/Eigen/src/plugins/ + + ################### + # compute (Boost) # + ################### + mkdir -p ./lightgbm-python/external_libs/compute + cp \ + -R \ + external_libs/compute/include \ + ./lightgbm-python/external_libs/compute/include/ + + ############# + # nanoarrow # + ############# + mkdir -p ./lightgbm-python/external_libs/nanoarrow + cp \ + external_libs/nanoarrow/CMakeLists.txt \ + external_libs/nanoarrow/LICENSE.txt \ + external_libs/nanoarrow/NOTICE.txt \ + ./lightgbm-python/external_libs/nanoarrow/ + cp -R \ + external_libs/nanoarrow/cmake \ + ./lightgbm-python/external_libs/nanoarrow/cmake/ + mkdir -p ./lightgbm-python/external_libs/nanoarrow/src/nanoarrow + cp \ + external_libs/nanoarrow/src/nanoarrow/nanoarrow.h \ + external_libs/nanoarrow/src/nanoarrow/nanoarrow.hpp \ + external_libs/nanoarrow/src/nanoarrow/nanoarrow_config.h.in \ + ./lightgbm-python/external_libs/nanoarrow/src/nanoarrow/ + mkdir -p ./lightgbm-python/external_libs/nanoarrow/src/nanoarrow/common + cp -R \ + external_libs/nanoarrow/src/nanoarrow/common/*.h \ + ./lightgbm-python/external_libs/nanoarrow/src/nanoarrow/common + cp -R \ + external_libs/nanoarrow/src/nanoarrow/common/*.c \ + ./lightgbm-python/external_libs/nanoarrow/src/nanoarrow/common + mkdir -p ./lightgbm-python/external_libs/nanoarrow/src/nanoarrow/hpp + cp -R \ + external_libs/nanoarrow/src/nanoarrow/hpp/*.hpp \ + ./lightgbm-python/external_libs/nanoarrow/src/nanoarrow/hpp +} + +create_isolated_source_dir + +cd ./lightgbm-python + +# if 'install' was passed, choose the type of package to build and install +if test "${INSTALL}" = true; then + if test "${PRECOMPILE}" = true; then + BUILD_SDIST=false + BUILD_WHEEL=true + BUILD_ARGS="" + rm -rf \ + ./cmake \ + ./CMakeLists.txt \ + ./external_libs \ + ./include \ + ./src \ + ./swig + # avoid trying to recompile, just use hatchling and copy in relevant files + sed -i.bak -e '/start:build-system/,/end:build-system/d' pyproject.toml + +# replace build backend configuration +cat >> ./pyproject.toml <=1.27.0"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +# do not consider .gitignore when choosing files to include / exclude +ignore-vcs = true +packages = ["lightgbm"] + +EOF + mkdir -p ./lightgbm/lib + if test -f ../lib_lightgbm.so; then + echo "[INFO] found pre-compiled lib_lightgbm.so" + cp ../lib_lightgbm.so ./lightgbm/lib/lib_lightgbm.so + elif test -f ../lib_lightgbm.dylib; then + echo "[INFO] found pre-compiled lib_lightgbm.dylib" + cp ../lib_lightgbm.dylib ./lightgbm/lib/lib_lightgbm.dylib + elif test -f ../lib_lightgbm.dll; then + echo "[INFO] found pre-compiled lib_lightgbm.dll" + cp ../lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll + elif test -f ../Release/lib_lightgbm.dll; then + echo "[INFO] found pre-compiled Release/lib_lightgbm.dll" + cp ../Release/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll + elif test -f ../windows/x64/DLL/lib_lightgbm.dll; then + echo "[INFO] found pre-compiled windows/x64/DLL/lib_lightgbm.dll" + cp ../windows/x64/DLL/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll + cp ../windows/x64/DLL/lib_lightgbm.lib ./lightgbm/lib/lib_lightgbm.lib + elif test -f ../windows/x64/Debug_DLL/lib_lightgbm.dll; then + echo "[INFO] found pre-compiled windows/x64/Debug_DLL/lib_lightgbm.dll" + cp ../windows/x64/Debug_DLL/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll + cp ../windows/x64/Debug_DLL/lib_lightgbm.lib ./lightgbm/lib/lib_lightgbm.lib + else + echo "[ERROR] cannot find pre-compiled library. Aborting" + exit 1 + fi + rm -f ./*.bak + fi + + # at this point, if 'install' was passed but the package type wasn't indicated, prefer wheel + if test "${BUILD_SDIST}" = false && test "${BUILD_WHEEL}" = false; then + echo "[INFO] 'install' passed but no package type ('bdist_wheel', 'sdist') chosen. Defaulting to 'bdist_wheel'." + BUILD_SDIST="false" + BUILD_WHEEL="true" + fi +fi + +if test "${BUILD_SDIST}" = true; then + echo "[INFO] --- building sdist ---" + rm -f ../dist/*.tar.gz + # use xargs to work with args that contain whitespaces + # note that empty echo string leads to that xargs doesn't run the command + # in some implementations of xargs + # ref: https://stackoverflow.com/a/8296746 + echo "--sdist --outdir ../dist ${BUILD_ARGS} ." | xargs python -m build +fi + +if test "${BUILD_WHEEL}" = true; then + echo "[INFO] --- building wheel ---" + rm -f ../dist/*.whl || true + # use xargs to work with args that contain whitespaces + # note that empty echo string leads to that xargs doesn't run the command + # in some implementations of xargs + # ref: https://stackoverflow.com/a/8296746 + echo "--wheel --outdir ../dist ${BUILD_ARGS} ." | xargs python -m build +fi + +if test "${INSTALL}" = true; then + echo "[INFO] --- installing lightgbm ---" + cd .. + if test "${BUILD_WHEEL}" = true; then + PACKAGE_FILE="$(echo dist/lightgbm*.whl)" + else + PACKAGE_FILE="$(echo dist/lightgbm*.tar.gz)" + fi + # shellcheck disable=SC2086 + python -m pip install \ + ${PIP_INSTALL_ARGS} \ + --force-reinstall \ + --no-cache-dir \ + --no-deps \ + "${PACKAGE_FILE}" +fi + +echo "[INFO] cleaning up" +rm -rf ./lightgbm-python diff --git a/build_r.R b/build_r.R new file mode 100644 index 0000000..94bb75f --- /dev/null +++ b/build_r.R @@ -0,0 +1,435 @@ +# For macOS users who have decided to use gcc +# (replace 8 with version of gcc installed on your machine) +# NOTE: your gcc / g++ from Homebrew is probably in /usr/local/bin +#export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8 +# Sys.setenv("CXX" = "/usr/local/bin/g++-8") +# Sys.setenv("CC" = "/usr/local/bin/gcc-8") + +args <- commandArgs(trailingOnly = TRUE) +INSTALL_AFTER_BUILD <- !("--skip-install" %in% args) +TEMP_R_DIR <- file.path(getwd(), "lightgbm_r") +TEMP_SOURCE_DIR <- file.path(TEMP_R_DIR, "src") + +# [description] +# Parse the content of commandArgs() into a structured +# list. This returns a list with two sections. +# * "flags" = a character of vector of flags like "--use-gpu" +# * "keyword_args" = a named character vector, where names +# refer to options and values are the option values. For +# example, c("--boost-librarydir" = "/usr/lib/x86_64-linux-gnu") +.parse_args <- function(args) { + out_list <- list( + "flags" = character(0L) + , "keyword_args" = character(0L) + , "make_args" = character(0L) + ) + for (arg in args) { + if (any(grepl("^\\-j[0-9]+", arg))) { # nolint: non_portable_path. + out_list[["make_args"]] <- arg + } else if (any(grepl("=", arg, fixed = TRUE))) { + split_arg <- strsplit(arg, "=", fixed = TRUE)[[1L]] + arg_name <- split_arg[[1L]] + arg_value <- split_arg[[2L]] + out_list[["keyword_args"]][[arg_name]] <- arg_value + } else { + out_list[["flags"]] <- c(out_list[["flags"]], arg) + } + } + return(out_list) +} +parsed_args <- .parse_args(args) + +SKIP_VIGNETTES <- "--no-build-vignettes" %in% parsed_args[["flags"]] +USING_GPU <- "--use-gpu" %in% parsed_args[["flags"]] +USING_MINGW <- "--use-mingw" %in% parsed_args[["flags"]] +USING_MSYS2 <- "--use-msys2" %in% parsed_args[["flags"]] + +# this maps command-line arguments to defines passed into CMake, +ARGS_TO_DEFINES <- c( + "--boost-root" = "-DBOOST_ROOT" + , "--boost-dir" = "-DBoost_DIR" + , "--boost-include-dir" = "-DBoost_INCLUDE_DIR" + , "--boost-librarydir" = "-DBOOST_LIBRARYDIR" + , "--opencl-include-dir" = "-DOpenCL_INCLUDE_DIR" + , "--opencl-library" = "-DOpenCL_LIBRARY" +) + +recognized_args <- c( + "--no-build-vignettes" + , "--skip-install" + , "--use-gpu" + , "--use-mingw" + , "--use-msys2" + , names(ARGS_TO_DEFINES) +) +given_args <- c( + parsed_args[["flags"]] + , names(parsed_args[["keyword_args"]]) +) +unrecognized_args <- setdiff(given_args, recognized_args) +if (length(unrecognized_args) > 0L) { + msg <- paste0( + "Unrecognized arguments: " + , toString(unrecognized_args) + ) + stop(msg) +} + +# [description] Replace statements in install.libs.R code based on +# command-line flags +.replace_flag <- function(variable_name, value, content) { + out <- gsub( + pattern = paste0(variable_name, " <-.*") + , replacement = paste0(variable_name, " <- ", as.character(value)) + , x = content + ) + return(out) +} + +install_libs_content <- readLines( + file.path("R-package", "src", "install.libs.R") +) +install_libs_content <- .replace_flag("use_gpu", USING_GPU, install_libs_content) +install_libs_content <- .replace_flag("use_mingw", USING_MINGW, install_libs_content) +install_libs_content <- .replace_flag("use_msys2", USING_MSYS2, install_libs_content) + +# set up extra flags based on keyword arguments +keyword_args <- parsed_args[["keyword_args"]] +if (length(keyword_args) > 0L) { + cmake_args_to_add <- NULL + for (i in seq_along(keyword_args)) { + arg_name <- names(keyword_args)[[i]] + define_name <- ARGS_TO_DEFINES[[arg_name]] + arg_value <- shQuote(normalizePath(keyword_args[[arg_name]], winslash = "/")) + cmake_args_to_add <- c(cmake_args_to_add, paste0(define_name, "=", arg_value)) + } + install_libs_content <- gsub( + pattern = paste0("command_line_args <- NULL") + , replacement = paste0( + "command_line_args <- c(\'" + , paste(cmake_args_to_add, collapse = "', '") + , "')" + ) + , x = install_libs_content + , fixed = TRUE + ) +} + +# if provided, set '-j' in 'make' commands in install.libs.R +if (length(parsed_args[["make_args"]]) > 0L) { + install_libs_content <- gsub( + pattern = "make_args_from_build_script <- character(0L)" + , replacement = paste0( + "make_args_from_build_script <- c(\"" + , paste(parsed_args[["make_args"]], collapse = "\", \"") + , "\")" + ) + , x = install_libs_content + , fixed = TRUE + ) +} + +# R returns FALSE (not a non-zero exit code) if a file copy operation +# breaks. Let's fix that +.handle_result <- function(res) { + if (!all(res)) { + stop("Copying files failed!") + } + return(invisible(NULL)) +} + +# system() will not raise an R exception if the process called +# fails. Wrapping it here to get that behavior. +# +# system() introduces a lot of overhead, at least on Windows, +# so trying processx if it is available +.run_shell_command <- function(cmd, args, strict = TRUE) { + on_windows <- .Platform$OS.type == "windows" + has_processx <- suppressMessages({ + suppressWarnings({ + require("processx") # nolint: undesirable_function, unused_import. + }) + }) + if (has_processx && on_windows) { + result <- processx::run( + command = cmd + , args = args + , windows_verbatim_args = TRUE + , error_on_status = FALSE + , echo = TRUE + ) + exit_code <- result$status + } else { + if (on_windows) { + message(paste0( + "Using system() to run shell commands. Installing " + , "'processx' with install.packages('processx') might " + , "make this faster." + )) + } + cmd <- paste0(cmd, " ", paste(args, collapse = " ")) + exit_code <- system(cmd) + } + + if (exit_code != 0L && isTRUE(strict)) { + stop(paste0("Command failed with exit code: ", exit_code)) + } + return(invisible(exit_code)) +} + +# Make a new temporary folder to work in +unlink(x = TEMP_R_DIR, recursive = TRUE) +dir.create(TEMP_R_DIR) + +# copy in the relevant files +result <- file.copy( + from = "R-package/./" + , to = sprintf("%s/", TEMP_R_DIR) + , recursive = TRUE + , overwrite = TRUE +) +.handle_result(result) + +# overwrite src/install.libs.R with new content based on command-line flags +writeLines( + text = install_libs_content + , con = file.path(TEMP_SOURCE_DIR, "install.libs.R") +) + +# Add blank Makevars files +result <- file.copy( + from = file.path(TEMP_R_DIR, "inst", "Makevars") + , to = file.path(TEMP_SOURCE_DIR, "Makevars") + , overwrite = TRUE +) +.handle_result(result) +result <- file.copy( + from = file.path(TEMP_R_DIR, "inst", "Makevars.win") + , to = file.path(TEMP_SOURCE_DIR, "Makevars.win") + , overwrite = TRUE +) +.handle_result(result) + +result <- file.copy( + from = "include/" + , to = sprintf("%s/", TEMP_SOURCE_DIR) + , recursive = TRUE + , overwrite = TRUE +) +.handle_result(result) + +result <- file.copy( + from = "src/" + , to = sprintf("%s/", TEMP_SOURCE_DIR) + , recursive = TRUE + , overwrite = TRUE +) +.handle_result(result) + +EIGEN_R_DIR <- file.path(TEMP_SOURCE_DIR, "include", "Eigen") +dir.create(EIGEN_R_DIR) + +eigen_modules <- c( + "Cholesky" + , "Core" + , "Dense" + , "Eigenvalues" + , "Geometry" + , "Householder" + , "Jacobi" + , "LU" + , "QR" + , "SVD" +) +for (eigen_module in eigen_modules) { + result <- file.copy( + from = file.path("external_libs", "eigen", "Eigen", eigen_module) + , to = EIGEN_R_DIR + , recursive = FALSE + , overwrite = TRUE + ) + .handle_result(result) +} + +dir.create(file.path(EIGEN_R_DIR, "src")) + +for (eigen_module in c(eigen_modules, "misc", "plugins")) { + if (eigen_module == "Dense") { + next + } + module_dir <- file.path(EIGEN_R_DIR, "src", eigen_module) + dir.create(module_dir, recursive = TRUE) + result <- file.copy( + from = sprintf("%s/", file.path("external_libs", "eigen", "Eigen", "src", eigen_module)) + , to = sprintf("%s/", file.path(EIGEN_R_DIR, "src")) + , recursive = TRUE + , overwrite = TRUE + ) + .handle_result(result) +} + +.replace_pragmas <- function(filepath) { + pragma_patterns <- c( + "^.*#pragma clang diagnostic.*$" + , "^.*#pragma diag_suppress.*$" + , "^.*#pragma GCC diagnostic.*$" + , "^.*#pragma region.*$" + , "^.*#pragma endregion.*$" + , "^.*#pragma warning.*$" + ) + content <- readLines(filepath) + for (pragma_pattern in pragma_patterns) { + content <- content[!grepl(pragma_pattern, content)] + } + writeLines(content, filepath) +} + +# remove pragmas that suppress warnings, to appease R CMD check +.replace_pragmas( + file.path(EIGEN_R_DIR, "src", "Core", "arch", "SSE", "Complex.h") +) +.replace_pragmas( + file.path(EIGEN_R_DIR, "src", "Core", "util", "DisableStupidWarnings.h") +) + +result <- file.copy( + from = "CMakeLists.txt" + , to = file.path(TEMP_R_DIR, "inst", "bin/") + , overwrite = TRUE +) +.handle_result(result) + +# remove CRAN-specific files +result <- file.remove( + file.path(TEMP_R_DIR, "cleanup") + , file.path(TEMP_R_DIR, "configure") + , file.path(TEMP_R_DIR, "configure.ac") + , file.path(TEMP_R_DIR, "configure.win") + , file.path(TEMP_SOURCE_DIR, "Makevars.in") + , file.path(TEMP_SOURCE_DIR, "Makevars.win.in") +) +.handle_result(result) + +#------------# +# submodules # +#------------# +EXTERNAL_LIBS_R_DIR <- file.path(TEMP_SOURCE_DIR, "external_libs") +dir.create(EXTERNAL_LIBS_R_DIR) +for (submodule in list.dirs( + path = "external_libs" + , full.names = FALSE + , recursive = FALSE +)) { + # compute/ is a submodule with boost, only needed if + # building the R-package with GPU support; + # eigen/ has a special treatment due to licensing aspects; + # nanoarrow/ is only needed by the Arrow-based C API entry points, which + # are excluded from the R build (the R API never calls into them). + if ((submodule == "compute" && !USING_GPU) + || submodule == "eigen" + || submodule == "nanoarrow") { + next + } + result <- file.copy( + from = sprintf("%s/", file.path("external_libs", submodule)) + , to = sprintf("%s/", EXTERNAL_LIBS_R_DIR) + , recursive = TRUE + , overwrite = TRUE + ) + .handle_result(result) +} + +# copy files into the place CMake expects +CMAKE_R_DIR <- file.path(TEMP_SOURCE_DIR, "cmake") +CMAKE_MODULES_R_DIR <- file.path(TEMP_SOURCE_DIR, "cmake", "modules") +dir.create(CMAKE_MODULES_R_DIR, recursive = TRUE) +result <- file.copy( + from = file.path("cmake", "modules", "FindLibR.cmake") + , to = sprintf("%s/", CMAKE_MODULES_R_DIR) + , overwrite = TRUE +) +result <- file.copy( + from = file.path("cmake", "Utils.cmake") + , to = sprintf("%s/", CMAKE_R_DIR) + , overwrite = TRUE +) +.handle_result(result) +for (src_file in c("lightgbm_R.cpp", "lightgbm_R.h")) { + result <- file.copy( + from = file.path(TEMP_SOURCE_DIR, src_file) + , to = file.path(TEMP_SOURCE_DIR, "src", src_file) + , overwrite = TRUE + ) + .handle_result(result) + result <- file.remove( + file.path(TEMP_SOURCE_DIR, src_file) + ) + .handle_result(result) +} + +result <- file.copy( + from = file.path("R-package", "inst", "make-r-def.R") + , to = file.path(TEMP_R_DIR, "inst", "bin/") + , overwrite = TRUE +) +.handle_result(result) + +# R packages cannot have versions like 3.0.0rc1, but +# 3.0.0-1 is acceptable +LGB_VERSION <- readLines("VERSION.txt")[1L] +LGB_VERSION <- gsub( + pattern = "rc" + , replacement = "-" + , x = LGB_VERSION + , fixed = TRUE +) + +# DESCRIPTION has placeholders for version +# and date so it doesn't have to be updated manually +DESCRIPTION_FILE <- file.path(TEMP_R_DIR, "DESCRIPTION") +description_contents <- readLines(DESCRIPTION_FILE) +description_contents <- gsub( + pattern = "~~VERSION~~" + , replacement = LGB_VERSION + , x = description_contents + , fixed = TRUE +) +description_contents <- gsub( + pattern = "~~DATE~~" + , replacement = as.character(Sys.Date()) + , x = description_contents + , fixed = TRUE +) +writeLines(description_contents, DESCRIPTION_FILE) + +# NOTE: --keep-empty-dirs is necessary to keep the deep paths expected +# by CMake while also meeting the CRAN req to create object files +# on demand +r_build_args <- c("CMD", "build", TEMP_R_DIR, "--keep-empty-dirs") +if (isTRUE(SKIP_VIGNETTES)) { + r_build_args <- c(r_build_args, "--no-build-vignettes") +} +.run_shell_command("R", r_build_args) + +# Install the package +version <- gsub( + pattern = "Version: ", + replacement = "", + x = grep( + pattern = "Version: " + , x = readLines(con = file.path(TEMP_R_DIR, "DESCRIPTION")) + , value = TRUE + , fixed = TRUE + ) + , fixed = TRUE +) +tarball <- file.path(getwd(), sprintf("lightgbm_%s.tar.gz", version)) + +install_cmd <- "R" +install_args <- c("CMD", "INSTALL", "--no-multiarch", "--with-keep.source", tarball) +if (INSTALL_AFTER_BUILD) { + .run_shell_command(install_cmd, install_args) +} else { + cmd <- paste0(install_cmd, " ", paste(install_args, collapse = " ")) + print(sprintf("Skipping installation. Install the package with command '%s'", cmd)) +} diff --git a/cmake/IntegratedOpenCL.cmake b/cmake/IntegratedOpenCL.cmake new file mode 100644 index 0000000..a4c47a1 --- /dev/null +++ b/cmake/IntegratedOpenCL.cmake @@ -0,0 +1,217 @@ +set(BUILD_SHARED_LIBS OFF CACHE BOOL "" FORCE) +set(BOOST_VERSION_DOT "1.74") +string(REPLACE "." "_" BOOST_VERSION_UNDERSCORE ${BOOST_VERSION_DOT}) + +set(OPENCL_HEADER_REPOSITORY "https://github.com/KhronosGroup/OpenCL-Headers.git") +set(OPENCL_HEADER_TAG "1b2a1850f410aaaaeaa56cead5a179b5aea4918e") + +set(OPENCL_LOADER_REPOSITORY "https://github.com/KhronosGroup/OpenCL-ICD-Loader.git") +set(OPENCL_LOADER_TAG "98ca71fb9f8484f1cd1999f55224bf9e8d18693b") + +set(BOOST_REPOSITORY "https://github.com/boostorg/boost.git") +set(BOOST_TAG "boost-${BOOST_VERSION_DOT}.0") + +# Build Independent OpenCL library +include(FetchContent) +# lint_cmake: -readability/wonkycase +FetchContent_Declare(OpenCL-Headers GIT_REPOSITORY ${OPENCL_HEADER_REPOSITORY} GIT_TAG ${OPENCL_HEADER_TAG}) +FetchContent_GetProperties(OpenCL-Headers) +# lint_cmake: +readability/wonkycase +if(NOT OpenCL-Headers_POPULATED) +# lint_cmake: -readability/wonkycase + FetchContent_MakeAvailable(OpenCL-Headers) +# lint_cmake: +readability/wonkycase + message(STATUS "Populated OpenCL Headers") +endif() +set(OPENCL_ICD_LOADER_HEADERS_DIR ${opencl-headers_SOURCE_DIR} CACHE PATH "") # for OpenCL ICD Loader +set(OpenCL_INCLUDE_DIR ${opencl-headers_SOURCE_DIR} CACHE PATH "") # for Boost::Compute + +# lint_cmake: -readability/wonkycase +FetchContent_Declare( +# lint_cmake: +readability/wonkycase + OpenCL-ICD-Loader + GIT_REPOSITORY + ${OPENCL_LOADER_REPOSITORY} + GIT_TAG + ${OPENCL_LOADER_TAG} + EXCLUDE_FROM_ALL +) +# lint_cmake: -readability/wonkycase +FetchContent_GetProperties(OpenCL-ICD-Loader) +# lint_cmake: +readability/wonkycase +if(NOT OpenCL-ICD-Loader_POPULATED) +# lint_cmake: -readability/wonkycase + FetchContent_MakeAvailable(OpenCL-ICD-Loader) +# lint_cmake: +readability/wonkycase + if(WIN32) + set(USE_DYNAMIC_VCXX_RUNTIME ON) + endif() + message(STATUS "Populated OpenCL ICD Loader") +endif() +list(APPEND INTEGRATED_OPENCL_INCLUDES ${OPENCL_ICD_LOADER_HEADERS_DIR}) +list(APPEND INTEGRATED_OPENCL_DEFINITIONS CL_TARGET_OPENCL_VERSION=120) +if(WIN32) + list( + APPEND + INTEGRATED_OPENCL_LIBRARIES + ${opencl-icd-loader_BINARY_DIR}/Release/OpenCL.lib + cfgmgr32.lib + runtimeobject.lib + ) +else() + list( + APPEND + INTEGRATED_OPENCL_LIBRARIES + ${opencl-icd-loader_BINARY_DIR}/libOpenCL.a + ) + set_property(TARGET OpenCL PROPERTY POSITION_INDEPENDENT_CODE ON) +endif() + +# Build Independent Boost libraries +include(ExternalProject) +include(ProcessorCount) +# lint_cmake: -readability/wonkycase +ProcessorCount(J) +# lint_cmake: +readability/wonkycase +set(BOOST_BASE "${PROJECT_BINARY_DIR}/Boost") +set(BOOST_INCLUDE "${BOOST_BASE}/source" CACHE PATH "") +set(BOOST_LIBRARY "${BOOST_BASE}/source/stage/lib" CACHE PATH "") +if(WIN32) + if(MSVC) + # references: + # + # * range of MSVC versions: https://learn.microsoft.com/en-us/cpp/overview/compiler-versions + # * MSVC toolchain IDs: not sure... + # comments like https://learn.microsoft.com/en-us/answers/questions/769911/visual-studio-2019-build-tools-v143 + # + if(${MSVC_VERSION} GREATER 1929) + set(MSVC_TOOLCHAIN_ID "143") + elseif(${MSVC_VERSION} GREATER 1919) + set(MSVC_TOOLCHAIN_ID "142") + elseif(${MSVC_VERSION} GREATER 1909) + set(MSVC_TOOLCHAIN_ID "141") + else() + message(FATAL_ERROR "Unsupported MSVC version number: ${MSVC_VERSION}") + endif() + list( + APPEND + BOOST_BUILD_BYPRODUCTS + ${BOOST_LIBRARY}/libboost_filesystem-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib + ${BOOST_LIBRARY}/libboost_system-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib + ${BOOST_LIBRARY}/libboost_chrono-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib + ) + else() + message(FATAL_ERROR "Integrated OpenCL build is not yet available for MinGW") + endif() + set(BOOST_BOOTSTRAP "${BOOST_BASE}/source/bootstrap.bat") + set(BOOST_BUILD "${BOOST_BASE}/source/b2.exe") + set(BOOST_FLAGS "") +else() + set(BOOST_BOOTSTRAP "${BOOST_BASE}/source/bootstrap.sh") + set(BOOST_BUILD "${BOOST_BASE}/source/b2") + set(BOOST_FLAGS "-fPIC") + list( + APPEND + BOOST_BUILD_BYPRODUCTS + ${BOOST_LIBRARY}/libboost_filesystem.a + ${BOOST_LIBRARY}/libboost_system.a + ${BOOST_LIBRARY}/libboost_chrono.a + ) +endif() +list( + APPEND + BOOST_SUBMODULES + "libs/algorithm" + "libs/align" + "libs/any" + "libs/array" + "libs/assert" + "libs/bind" + "libs/chrono" + "libs/compute" + "libs/concept_check" + "libs/config" + "libs/container" + "libs/container_hash" + "libs/core" + "libs/detail" + "libs/filesystem" + "libs/foreach" + "libs/format" + "libs/function" + "libs/function_types" + "libs/fusion" + "libs/headers" + "libs/integer" + "libs/io" + "libs/iterator" + "libs/lexical_cast" + "libs/math" + "libs/move" + "libs/mpl" + "libs/multi_index" + "libs/numeric/conversion" + "libs/optional" + "libs/predef" + "libs/preprocessor" + "libs/property_tree" + "libs/range" + "libs/ratio" + "libs/serialization" + "libs/smart_ptr" + "libs/static_assert" + "libs/system" + "libs/throw_exception" + "libs/tuple" + "libs/typeof" + "libs/type_index" + "libs/type_traits" + "libs/utility" + "libs/uuid" + "libs/winapi" + "tools/boost_install" + "tools/build" +) +# lint_cmake: -readability/wonkycase +ExternalProject_Add( +# lint_cmake: +readability/wonkycase + Boost + TMP_DIR "${BOOST_BASE}/tmp" + STAMP_DIR "${BOOST_BASE}/stamp" + DOWNLOAD_DIR "${BOOST_BASE}/download" + SOURCE_DIR "${BOOST_BASE}/source" + BINARY_DIR "${BOOST_BASE}/source" + INSTALL_DIR "${BOOST_BASE}/install" + GIT_REPOSITORY ${BOOST_REPOSITORY} + GIT_TAG ${BOOST_TAG} + GIT_SUBMODULES ${BOOST_SUBMODULES} + GIT_SHALLOW ON + UPDATE_COMMAND "" + PATCH_COMMAND "" + CONFIGURE_COMMAND ${BOOST_BOOTSTRAP} + BUILD_COMMAND + ${BOOST_BUILD} + -sBOOST_ROOT=${BOOST_BASE}/source + -a + -q + -j ${J} + --with-headers + --with-chrono + --with-filesystem + --with-system + link=static + runtime-link=shared + variant=release + threading=multi + cxxflags="${BOOST_FLAGS}" + INSTALL_COMMAND "" + # BUILD_BYPRODUCTS is necessary to support 'Ninja' builds. + # ref: + # - https://cmake.org/cmake/help/latest/module/ExternalProject.html + # - https://stackoverflow.com/a/65803911/3986677 + BUILD_BYPRODUCTS ${BOOST_BUILD_BYPRODUCTS} +) +list(APPEND INTEGRATED_OPENCL_INCLUDES ${BOOST_INCLUDE}) +list(APPEND INTEGRATED_OPENCL_LIBRARIES ${BOOST_BUILD_BYPRODUCTS}) + +set(BUILD_SHARED_LIBS ON CACHE BOOL "" FORCE) diff --git a/cmake/Sanitizer.cmake b/cmake/Sanitizer.cmake new file mode 100644 index 0000000..f990484 --- /dev/null +++ b/cmake/Sanitizer.cmake @@ -0,0 +1,51 @@ +# Set appropriate compiler and linker flags for sanitizers. +# +# Usage of this module: +# enable_sanitizers("address;leak") + +# Add flags +macro(enable_sanitizer sanitizer) + if(${sanitizer} MATCHES "address") + set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=address") + + elseif(${sanitizer} MATCHES "thread") + set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=thread") + + elseif(${sanitizer} MATCHES "leak") + set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=leak") + + elseif(${sanitizer} MATCHES "undefined") + set(SAN_COMPILE_FLAGS "${SAN_COMPILE_FLAGS} -fsanitize=undefined -fno-sanitize-recover=undefined") + + else() + message(FATAL_ERROR "Sanitizer ${sanitizer} not supported.") + endif() +endmacro() + +macro(enable_sanitizers SANITIZERS) + # Check sanitizers compatibility. + foreach(_san ${SANITIZERS}) + string(TOLOWER ${_san} _san) + if(_san MATCHES "thread") + if(${_use_other_sanitizers}) + message(FATAL_ERROR "thread sanitizer is not compatible with ${_san} sanitizer.") + endif() + set(_use_thread_sanitizer 1) + else() + if(${_use_thread_sanitizer}) + message(FATAL_ERROR "${_san} sanitizer is not compatible with thread sanitizer.") + endif() + set(_use_other_sanitizers 1) + endif() + endforeach() + + message(STATUS "Sanitizers: ${SANITIZERS}") + + foreach(_san ${SANITIZERS}) + string(TOLOWER ${_san} _san) + enable_sanitizer(${_san}) + endforeach() + message(STATUS "Sanitizers compile flags: ${SAN_COMPILE_FLAGS}") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SAN_COMPILE_FLAGS}") + set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SAN_COMPILE_FLAGS}") +endmacro() diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake new file mode 100644 index 0000000..1eaf9aa --- /dev/null +++ b/cmake/Utils.cmake @@ -0,0 +1,43 @@ +# Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. +# Copyright (c) 2026 The XGBoost developers. +# Licensed under the MIT License. See LICENSE file in the project root for license information. + +# Generate CMAKE_CUDA_ARCHITECTURES form a list of architectures. +# +# lint_cmake: -linelength +# Adopted from XGBoost, see https://github.com/dmlc/xgboost/blob/41591eeff5b10caabe82d65fc7164b447eabd249/cmake/Utils.cmake#L62 +# lint_cmake: +linelength +# +function(compute_cmake_cuda_archs _cuda_version) + + # Set up defaults based on CUDA version + # Remember to update arch-specific tunings when supporting new archs. + # + # Reference for mapping of CUDA toolkit component versions to supported architectures ("compute capabilities"): + # https://en.wikipedia.org/wiki/CUDA#GPUs_supported + # + if(_cuda_version VERSION_GREATER_EQUAL "13.0") + set(CMAKE_CUDA_ARCHITECTURES 75 80 86 89 90 100 120) + elseif(_cuda_version VERSION_GREATER_EQUAL "12.9") + set(CMAKE_CUDA_ARCHITECTURES 70 75 80 86 89 90 100 120) + elseif(_cuda_version VERSION_GREATER_EQUAL "12.8") + set(CMAKE_CUDA_ARCHITECTURES 60 61 70 80 86 89 90 100 120) + elseif(_cuda_version VERSION_GREATER_EQUAL "11.8") + set(CMAKE_CUDA_ARCHITECTURES 60 61 70 80 86 89 90) + else() + message(FATAL_ERROR + "No default architecture list configured for CUDA version '${_cuda_version}'. " + "You can avoid this error and attempt building by manually passing '-DCMAKE_CUDA_ARCHITECTURES'." + ) + endif() + + # For the last architectures, generate both: + # + # - SASS via '-real', for performance + # - PTX via '-virtual', for forward compatibility + # + list(TRANSFORM CMAKE_CUDA_ARCHITECTURES APPEND "-real") + list(TRANSFORM CMAKE_CUDA_ARCHITECTURES REPLACE "([0-9]+)-real" "\\0;\\1-virtual" AT -1) + set(CMAKE_CUDA_ARCHITECTURES "${CMAKE_CUDA_ARCHITECTURES}" PARENT_SCOPE) + message(STATUS "CMAKE_CUDA_ARCHITECTURES: ${CMAKE_CUDA_ARCHITECTURES}") +endfunction() diff --git a/cmake/modules/FindLibR.cmake b/cmake/modules/FindLibR.cmake new file mode 100644 index 0000000..b52a5c1 --- /dev/null +++ b/cmake/modules/FindLibR.cmake @@ -0,0 +1,267 @@ +# CMake module used to find the location of R's +# dll and header files. +# +# Borrows heavily from xgboost's R package: +# +# * https://github.com/dmlc/xgboost/blob/master/cmake/modules/FindLibR.cmake +# +# Defines the following: +# LIBR_FOUND +# LIBR_HOME +# LIBR_EXECUTABLE +# LIBR_MSVC_CORE_LIBRARY +# LIBR_INCLUDE_DIRS +# LIBR_LIBS_DIR +# LIBR_CORE_LIBRARY +# and a CMake function to create R.lib for MSVC + +# lint_cmake: -convention/filename + +if(NOT R_ARCH) + if("${CMAKE_SIZEOF_VOID_P}" STREQUAL "4") + set(R_ARCH "i386") + else() + set(R_ARCH "x64") + endif() +endif() + +if(NOT ("${R_ARCH}" STREQUAL "x64")) + message(FATAL_ERROR "LightGBM's R-package currently only supports 64-bit operating systems") +endif() + +# Creates R.lib and R.def in the build directory for linking with MSVC +# https://docs.microsoft.com/en-us/cpp/build/reference/link-input-files?redirectedfrom=MSDN&view=vs-2019 +function(create_rlib_for_msvc) + + message(STATUS "Creating R.lib and R.def") + + # various checks and warnings + if(NOT WIN32 OR NOT MSVC) + message(FATAL_ERROR "create_rlib_for_msvc() can only be used with MSVC") + endif() + + if(NOT EXISTS "${LIBR_CORE_LIBRARY}") + message(FATAL_ERROR "LIBR_CORE_LIBRARY, '${LIBR_CORE_LIBRARY}', not found") + endif() + + find_program(DLLTOOL_EXE dlltool) + + if(NOT DLLTOOL_EXE) + message(FATAL_ERROR "dlltool.exe not found!\nDo you have Rtools installed with its MinGW's bin/ in PATH?") + endif() + + set(LIBR_MSVC_CORE_LIBRARY "${CMAKE_CURRENT_BINARY_DIR}/R.lib" CACHE PATH "R.lib filepath") + + get_filename_component( + LIBR_RSCRIPT_EXECUTABLE_DIR + ${LIBR_EXECUTABLE} + DIRECTORY + ) + set(LIBR_RSCRIPT_EXECUTABLE "${LIBR_RSCRIPT_EXECUTABLE_DIR}/Rscript") + + execute_process( + COMMAND ${LIBR_RSCRIPT_EXECUTABLE} + "${CMAKE_CURRENT_BINARY_DIR}/make-r-def.R" + "${LIBR_CORE_LIBRARY}" "${CMAKE_CURRENT_BINARY_DIR}/R.def" + ) + execute_process( + COMMAND ${DLLTOOL_EXE} + "--input-def" "${CMAKE_CURRENT_BINARY_DIR}/R.def" + "--output-lib" "${LIBR_MSVC_CORE_LIBRARY}" + ) +endfunction() + +# R version information is used to search for R's libraries in +# the registry on Windows. Since this code is orchestrated by +# an R script (src/install.libs.R), that script uses R's built-ins to +# find the version of R and pass it through as a CMake variable +if(CMAKE_R_VERSION) + message(STATUS "R version passed into FindLibR.cmake: ${CMAKE_R_VERSION}") +elseif(WIN32) + message( + FATAL_ERROR + "Expected CMAKE_R_VERSION to be passed in on Windows but none was provided. Check src/install.libs.R" + ) +endif() + + +if(NOT LIBR_EXECUTABLE) + find_program( + LIBR_EXECUTABLE + NAMES R R.exe + ) + + # CRAN may run RD CMD CHECK instead of R CMD CHECK, + # which can lead to this infamous error: + # 'R' should not be used without a path -- see par. 1.6 of the manual + if(LIBR_EXECUTABLE MATCHES ".*\\.Rcheck.*") + unset(LIBR_EXECUTABLE CACHE) + endif() + + # ignore the R bundled with R.app on Mac, since that is GUI-only + if(LIBR_EXECUTABLE MATCHES ".+R\\.app.*") + unset(LIBR_EXECUTABLE CACHE) + endif() +endif() + +# Find R executable unless it has been provided directly or already found +if(NOT LIBR_EXECUTABLE) + if(APPLE) + + find_library(LIBR_LIBRARIES R) + + if(LIBR_LIBRARIES MATCHES ".*\\.framework") + set(LIBR_HOME "${LIBR_LIBRARIES}/Resources") + set(LIBR_EXECUTABLE "${LIBR_HOME}/R") + else() + get_filename_component(_LIBR_LIBRARIES "${LIBR_LIBRARIES}" REALPATH) + get_filename_component(_LIBR_LIBRARIES_DIR "${_LIBR_LIBRARIES}" DIRECTORY) + set(LIBR_EXECUTABLE "${_LIBR_LIBRARIES_DIR}/../bin/R") + endif() + + elseif(UNIX) + + # attempt to find R executable + if(NOT LIBR_EXECUTABLE) + find_program( + LIBR_EXECUTABLE + NO_DEFAULT_PATH + HINTS "${CMAKE_CURRENT_BINARY_DIR}" "/usr/bin" "/usr/lib/" "/usr/local/bin/" + NAMES R + ) + endif() + + # Windows + else() + + # if R executable not available, query R_HOME path from registry + if(NOT LIBR_HOME) + + # Try to find R's location in the registry + # ref: https://cran.r-project.org/bin/windows/base/rw-FAQ.html#Does-R-use-the-Registry_003f + get_filename_component( + LIBR_HOME + "[HKEY_LOCAL_MACHINE\\SOFTWARE\\R-core\\R\\${CMAKE_R_VERSION};InstallPath]" + ABSOLUTE + ) + endif() + + if(NOT LIBR_HOME) + get_filename_component( + LIBR_HOME + "[HKEY_CURRENT_USER\\SOFTWARE\\R-core\\R\\${CMAKE_R_VERSION};InstallPath]" + ABSOLUTE + ) + endif() + + if(NOT LIBR_HOME) + message( + FATAL_ERROR + "Unable to locate R executable.\ +\nEither add its location to PATH or provide it through the LIBR_EXECUTABLE CMake variable" + ) + endif() + + # set exe location based on R_ARCH + set(LIBR_EXECUTABLE "${LIBR_HOME}/bin/${R_ARCH}/R.exe") + + endif() + + if(NOT LIBR_EXECUTABLE) + message( + FATAL_ERROR + "Unable to locate R executable.\ +\nEither add its location to PATH or provide it through the LIBR_EXECUTABLE CMake variable" + ) + endif() + +endif() + +# ask R for the home path +execute_process( + COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(normalizePath(R.home(), winslash='/'))" + OUTPUT_VARIABLE LIBR_HOME +) + +# ask R for the include dir +execute_process( + COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(normalizePath(R.home('include'), winslash='/'))" + OUTPUT_VARIABLE LIBR_INCLUDE_DIRS +) + +# ask R for the lib dir +execute_process( + COMMAND ${LIBR_EXECUTABLE} "--slave" "--vanilla" "-e" "cat(normalizePath(R.home('lib'), winslash='/'))" + OUTPUT_VARIABLE LIBR_LIBS_DIR +) + +set(LIBR_HOME ${LIBR_HOME} CACHE PATH "R home directory") +set(LIBR_EXECUTABLE ${LIBR_EXECUTABLE} CACHE PATH "R executable") +set(LIBR_INCLUDE_DIRS ${LIBR_INCLUDE_DIRS} CACHE PATH "R include directory") +set(LIBR_LIBS_DIR ${LIBR_LIBS_DIR} CACHE PATH "Where R stores vendored third-party libraries") + +# where is R.so / R.dll / libR.so likely to be found? +set( + LIBR_PATH_HINTS + "${CMAKE_CURRENT_BINARY_DIR}" + "${LIBR_HOME}/lib" + "${LIBR_HOME}/bin/${R_ARCH}" + "${LIBR_HOME}/bin" + "${LIBR_LIBRARIES}" +) + +# look for the core R library +find_library( + LIBR_CORE_LIBRARY + NAMES R R.dll + HINTS ${LIBR_PATH_HINTS} +) + +# starting from CMake 3.17, find_library() will not find .dll files by default +# https://cmake.org/cmake/help/v3.17/release/3.17.html#other-changes +if(WIN32 AND NOT LIBR_CORE_LIBRARY) + find_file( + LIBR_CORE_LIBRARY + NAME R.dll + HINTS ${LIBR_PATH_HINTS} + ) +endif() + +set(LIBR_CORE_LIBRARY ${LIBR_CORE_LIBRARY} CACHE PATH "R core shared library") + +if(WIN32 AND MSVC) + + # create a local R.lib import library for R.dll if it doesn't exist + if(NOT EXISTS "${CMAKE_CURRENT_BINARY_DIR}/R.lib") + create_rlib_for_msvc() + endif() + +endif() + +# define find requirements +include(FindPackageHandleStandardArgs) + +if(WIN32 AND MSVC) +# lint_cmake: -package/stdargs + find_package_handle_standard_args( +# lint_cmake: +package/stdargs + LibR DEFAULT_MSG + LIBR_HOME + LIBR_EXECUTABLE + LIBR_INCLUDE_DIRS + LIBR_LIBS_DIR + LIBR_CORE_LIBRARY + LIBR_MSVC_CORE_LIBRARY + ) +else() +# lint_cmake: -package/stdargs + find_package_handle_standard_args( +# lint_cmake: +package/stdargs + LibR DEFAULT_MSG + LIBR_HOME + LIBR_EXECUTABLE + LIBR_INCLUDE_DIRS + LIBR_LIBS_DIR + LIBR_CORE_LIBRARY + ) +endif() diff --git a/cmake/modules/FindNCCL.cmake b/cmake/modules/FindNCCL.cmake new file mode 100644 index 0000000..fe2928a --- /dev/null +++ b/cmake/modules/FindNCCL.cmake @@ -0,0 +1,84 @@ +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Tries to find NCCL headers and libraries. +# +# Usage of this module as follows: +# +# find_package(NCCL) +# +# Variables used by this module, they can change the default behaviour and need +# to be set before calling find_package: +# +# NCCL_ROOT - When set, this path is inspected instead of standard library +# locations as the root of the NCCL installation. +# The environment variable NCCL_ROOT overrides this variable. +# +# This module defines +# NCCL_FOUND, whether nccl has been found +# NCCL_INCLUDE_DIR, directory containing header +# NCCL_LIBRARY, directory containing nccl library +# NCCL_LIB_NAME, nccl library name +# USE_NCCL_LIB_PATH, when set, NCCL_LIBRARY path is also inspected for the +# location of the nccl library. This would disable +# switching between static and shared. +# +# It creates an imported target 'NCCL::NCCL' for convenience in linking. +# +# This module assumes that the CUDA toolkit has already been found. +# + +if(NCCL_LIBRARY) + if(NOT USE_NCCL_LIB_PATH) + # Don't cache NCCL_LIBRARY to enable switching between static and shared. + unset(NCCL_LIBRARY CACHE) + endif() +endif() + +if(BUILD_WITH_SHARED_NCCL) + # libnccl.so + set(NCCL_LIB_NAME nccl) + set(_nccl_lib_type "SHARED") +else() + # libnccl_static.a + set(NCCL_LIB_NAME nccl_static) + set(_nccl_lib_type "STATIC") +endif() + +find_path(NCCL_INCLUDE_DIR + NAMES nccl.h + HINTS $ENV{NCCL_ROOT}/include ${NCCL_ROOT}/include) + +find_library(NCCL_LIBRARY + NAMES ${NCCL_LIB_NAME} + HINTS $ENV{NCCL_ROOT}/lib ${NCCL_ROOT}/lib) + +message(STATUS "Using nccl library: ${NCCL_LIBRARY}") + +include(FindPackageHandleStandardArgs) +find_package_handle_standard_args(NCCL DEFAULT_MSG + NCCL_INCLUDE_DIR NCCL_LIBRARY) + +mark_as_advanced( + NCCL_INCLUDE_DIR + NCCL_LIBRARY +) + +if(NCCL_FOUND AND NOT TARGET NCCL::NCCL) + add_library(NCCL::NCCL ${_nccl_lib_type} IMPORTED) + set_target_properties(NCCL::NCCL PROPERTIES + IMPORTED_LOCATION "${NCCL_LIBRARY}" + INTERFACE_INCLUDE_DIRECTORIES "${NCCL_INCLUDE_DIR}") +endif() + +unset(_nccl_lib_type) diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000..54744d4 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,196 @@ +# Using LightGBM via Docker + +This directory contains `Dockerfile`s to make it easy to build and run LightGBM via [Docker](https://www.docker.com/). + +These builds of LightGBM all train on the CPU. For GPU-enabled builds, see [the gpu/ directory](./gpu). + +## Installing Docker + +Follow the general installation instructions [on the Docker site](https://docs.docker.com/install/): + +* [macOS](https://docs.docker.com/docker-for-mac/install/) +* [Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/) +* [Windows](https://docs.docker.com/docker-for-windows/install/) + +## Using CLI Version of LightGBM via Docker + +Build an image with the LightGBM CLI. + +```shell +mkdir lightgbm-docker +cd lightgbm-docker +wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/main/docker/dockerfile-cli +docker build \ + -t lightgbm-cli \ + -f dockerfile-cli \ + . +``` + +Once that completes, the built image can be used to run the CLI in a container. +To try it out, run the following. + +```shell +# configure the CLI +cat << EOF > train.conf +task = train +objective = binary +data = binary.train +num_trees = 10 +output_model = LightGBM-CLI-model.txt +EOF + +# get training data +curl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/main/examples/binary_classification/binary.train + +# train, and save model to a text file +docker run \ + --rm \ + --volume "${PWD}":/opt/training \ + --workdir /opt/training \ + lightgbm-cli \ + config=train.conf +``` + +After this runs, a LightGBM model can be found at `LightGBM-CLI-model.txt`. + +For more details on how to configure and use the LightGBM CLI, see https://lightgbm.readthedocs.io/en/latest/Quick-Start.html. + +## Running the Python-package Container + +Build an image with the LightGBM Python-package installed. + +```shell +mkdir lightgbm-docker +cd lightgbm-docker +wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/main/docker/dockerfile-python +docker build \ + -t lightgbm-python \ + -f dockerfile-python \ + . +``` + +Once that completes, the built image can be used to run LightGBM's Python-package in a container. +Run the following to produce a model using the Python-package. + +```shell +# get training data +curl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/main/examples/binary_classification/binary.train + +# create training script +cat << EOF > train.py +import lightgbm as lgb +import numpy as np +params = { + "objective": "binary", + "num_trees": 10 +} + +bst = lgb.train( + train_set=lgb.Dataset("binary.train"), + params=params +) +bst.save_model("LightGBM-python-model.txt") +EOF + +# run training in a container +docker run \ + --rm \ + --volume "${PWD}":/opt/training \ + --workdir /opt/training \ + lightgbm-python \ + python train.py +``` + +After this runs, a LightGBM model can be found at `LightGBM-python-model.txt`. + +Or run an interactive Python session in a container. + +```shell +docker run \ + --rm \ + --volume "${PWD}":/opt/training \ + --workdir /opt/training \ + -it lightgbm-python \ + python +``` + +## Running the R-package Container + +Build an image with the LightGBM R-package installed. + +```shell +mkdir lightgbm-docker +cd lightgbm-docker +wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/main/docker/dockerfile-r + +docker build \ + -t lightgbm-r \ + -f dockerfile-r \ + . +``` + +Once that completes, the built image can be used to run LightGBM's R-package in a container. +Run the following to produce a model using the R-package. + +```shell +# get training data +curl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/main/examples/binary_classification/binary.train + +# create training script +cat << EOF > train.R +library(lightgbm) +params <- list( + objective = "binary" + , num_trees = 10L +) + +bst <- lgb.train( + data = lgb.Dataset("binary.train"), + params = params +) +lgb.save(bst, "LightGBM-R-model.txt") +EOF + +# run training in a container +docker run \ + --rm \ + --volume "${PWD}":/opt/training \ + --workdir /opt/training \ + lightgbm-r \ + Rscript train.R +``` + +After this runs, a LightGBM model can be found at `LightGBM-R-model.txt`. + +Run the following to get an interactive R session in a container. + +```shell +docker run \ + --rm \ + -it lightgbm-r \ + R +``` + +To use [RStudio](https://www.rstudio.com/products/rstudio/), an interactive development environment, run the following. + +```shell +docker run \ + --rm \ + --env PASSWORD="lightgbm" \ + -p 8787:8787 \ + lightgbm-r +``` + +Then navigate to `localhost:8787` in your local web browser, and log in with username `rstudio` and password `lightgbm`. + +To target a different R version, pass any [valid rocker/verse tag](https://hub.docker.com/r/rocker/verse/tags) to `docker build`. + +For example, to test LightGBM with R 4.5: + +```shell +docker build \ + -t lightgbm-r-45 \ + -f dockerfile-r \ + --build-arg R_VERSION=4.5 \ + . +``` diff --git a/docker/dockerfile-cli b/docker/dockerfile-cli new file mode 100644 index 0000000..c76b8c8 --- /dev/null +++ b/docker/dockerfile-cli @@ -0,0 +1,36 @@ +FROM ubuntu:20.04 + +ENV \ + DEBIAN_FRONTEND=noninteractive \ + LANG=C.UTF-8 \ + LC_ALL=C.UTF-8 + +RUN apt-get update -y && \ + apt-get install -y --no-install-recommends \ + ca-certificates \ + curl \ + build-essential \ + gcc \ + g++ \ + git \ + libomp-dev && \ + rm -rf /var/lib/apt/lists/* + +RUN curl -L -o cmake.sh https://github.com/Kitware/CMake/releases/download/v3.29.2/cmake-3.29.2-linux-x86_64.sh && \ + chmod +x cmake.sh && \ + sh ./cmake.sh --prefix=/usr/local --skip-license && \ + rm cmake.sh + +RUN git clone \ + --recursive \ + --branch stable \ + --depth 1 \ + https://github.com/lightgbm-org/LightGBM && \ + cd ./LightGBM && \ + cmake -B build -S . && \ + cmake --build build -j4 && \ + cmake --install build && \ + cd "${HOME}" && \ + rm -rf LightGBM + +ENTRYPOINT ["lightgbm"] diff --git a/docker/dockerfile-python b/docker/dockerfile-python new file mode 100644 index 0000000..5696f77 --- /dev/null +++ b/docker/dockerfile-python @@ -0,0 +1,34 @@ +FROM ubuntu:20.04 + +ARG CONDA_DIR=/opt/miniforge + +ENV \ + DEBIAN_FRONTEND=noninteractive \ + LANG=C.UTF-8 \ + LC_ALL=C.UTF-8 \ + PATH=$CONDA_DIR/bin:$PATH + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + ca-certificates \ + cmake \ + build-essential \ + gcc \ + g++ \ + curl \ + git \ + libomp-dev && \ + # python environment + curl -sL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -o miniforge.sh && \ + /bin/bash miniforge.sh -f -b -p $CONDA_DIR && \ + export PATH="$CONDA_DIR/bin:$PATH" && \ + conda config --set always_yes yes --set changeps1 no && \ + # lightgbm + conda install -q -y numpy scipy scikit-learn pandas && \ + git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ + cd ./LightGBM && \ + sh ./build-python.sh install && \ + # clean + apt-get autoremove -y && apt-get clean && \ + conda clean -a -y && \ + rm -rf /usr/local/src/* diff --git a/docker/dockerfile-r b/docker/dockerfile-r new file mode 100644 index 0000000..ec13929 --- /dev/null +++ b/docker/dockerfile-r @@ -0,0 +1,16 @@ +ARG R_VERSION=latest +FROM rocker/verse:${R_VERSION} + +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + build-essential \ + libomp-dev && \ + git clone \ + --recursive \ + --branch stable \ + --depth 1 https://github.com/lightgbm-org/LightGBM && \ + cd ./LightGBM && \ + sh build-cran-package.sh --no-build-vignettes && \ + R CMD INSTALL ./lightgbm_*.tar.gz && \ + cd .. && \ + rm -rf ./LightGBM diff --git a/docker/gpu/README.md b/docker/gpu/README.md new file mode 100644 index 0000000..d359c9f --- /dev/null +++ b/docker/gpu/README.md @@ -0,0 +1,58 @@ +# Tiny Distroless Dockerfile for LightGBM GPU CLI-only Version + +`dockerfile-cli-only-distroless.gpu` - A multi-stage build based on the `nvidia/opencl:devel-ubuntu18.04` (build) and `distroless/cc-debian10` (production) images. LightGBM (CLI-only) can be utilized in GPU and CPU modes. The resulting image size is around 15 MB. + +--- + +# Small Dockerfile for LightGBM GPU CLI-only Version + +`dockerfile-cli-only.gpu` - A multi-stage build based on the `nvidia/opencl:devel` (build) and `nvidia/opencl:runtime` (production) images. LightGBM (CLI-only) can be utilized in GPU and CPU modes. The resulting image size is around 100 MB. + +--- + +# Dockerfile for LightGBM GPU Version with Python + +`dockerfile.gpu` - A docker file with LightGBM utilizing nvidia-docker. The file is based on the `nvidia/cuda:8.0-cudnn5-devel` image. +LightGBM can be utilized in GPU and CPU modes and via Python. + +## Contents + +- LightGBM (cpu + gpu) +- Python (conda) + scikit-learn, notebooks, pandas, matplotlib + +Running the container starts a Jupyter Notebook at `localhost:8888`. + +Jupyter password: `keras`. + +## Requirements + +Requires docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on host machine. + +## Quickstart + +### Build Docker Image + +```sh +mkdir lightgbm-docker +cd lightgbm-docker +wget https://raw.githubusercontent.com/lightgbm-org/LightGBM/main/docker/gpu/dockerfile.gpu +docker build -f dockerfile.gpu -t lightgbm-gpu . +``` + +### Run Image + +```sh +nvidia-docker run --rm -d --name lightgbm-gpu -p 8888:8888 -v /home:/home lightgbm-gpu +``` + +### Attach with Command Line Access (if required) + +```sh +docker exec -it lightgbm-gpu bash +``` + +### Jupyter Notebook + +```sh +localhost:8888 +``` diff --git a/docker/gpu/dockerfile-cli-only-distroless.gpu b/docker/gpu/dockerfile-cli-only-distroless.gpu new file mode 100644 index 0000000..f8b07af --- /dev/null +++ b/docker/gpu/dockerfile-cli-only-distroless.gpu @@ -0,0 +1,75 @@ +# Copyright (c) 2020 The Rector and Visitors of the University of Virginia +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without +# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +# Software, and to permit persons to whom the Software is furnished to do so, subject to the following +# conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions +# of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED +# TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF +# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. + +FROM nvidia/opencl:devel-ubuntu18.04 AS build +ARG DEBIAN_FRONTEND=noninteractive +ARG OPENCL_LIBRARIES=/usr/lib/x86_64-linux-gnu +ARG OPENCL_INCLUDE_DIR=/usr/include/CL + +# SYSTEM +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + git \ + ca-certificates \ + libglib2.0-0 \ + libxext6 \ + libsm6 \ + libxrender1 \ + cmake \ + libboost-dev \ + libboost-system-dev \ + libboost-filesystem-dev \ + gcc \ + g++ && \ + rm -rf /var/lib/apt/lists/* + +# LightGBM +WORKDIR /opt +RUN git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ + cd LightGBM && \ + cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=${OPENCL_LIBRARIES}/libOpenCL.so.1 -DOpenCL_INCLUDE_DIR=$OPENCL_INCLUDE_DIR && \ + OPENCL_HEADERS=$OPENCL_INCLUDE_DIR LIBOPENCL=$OPENCL_LIBRARIES cmake --build build + +FROM gcr.io/distroless/cc-debian10 +COPY --from=build \ + /opt/LightGBM/lightgbm \ + /opt/LightGBM/lib_lightgbm.so \ + /opt/LightGBM/ +COPY --from=build \ + /usr/lib/x86_64-linux-gnu/libOpenCL.so.1 \ + /usr/lib/x86_64-linux-gnu/libboost_filesystem.so.1.65.1 \ + /usr/lib/x86_64-linux-gnu/libboost_system.so.1.65.1 \ + /usr/lib/x86_64-linux-gnu/libgomp.so.1 \ + /usr/lib/x86_64-linux-gnu/libstdc++.so.6 \ + /usr/lib/x86_64-linux-gnu/ +COPY --from=build \ + /lib/x86_64-linux-gnu/libm.so.6 \ + /lib/x86_64-linux-gnu/libgcc_s.so.1 \ + /lib/x86_64-linux-gnu/libpthread.so.0 \ + /lib/x86_64-linux-gnu/libc.so.6 \ + /lib/x86_64-linux-gnu/libdl.so.2 \ + /lib/x86_64-linux-gnu/ +COPY --from=build \ + /lib64/ld-linux-x86-64.so.2 \ + /lib64/ +COPY --from=build /etc/OpenCL/vendors/nvidia.icd /etc/OpenCL/vendors/nvidia.icd + +ENV PATH /opt/LightGBM:${PATH} +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 + +ENTRYPOINT ["lightgbm"] diff --git a/docker/gpu/dockerfile-cli-only.gpu b/docker/gpu/dockerfile-cli-only.gpu new file mode 100644 index 0000000..8bd12a3 --- /dev/null +++ b/docker/gpu/dockerfile-cli-only.gpu @@ -0,0 +1,67 @@ +# Copyright (c) 2020 The Rector and Visitors of the University of Virginia +# +# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated +# documentation files (the "Software"), to deal in the Software without restriction, including without +# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the +# Software, and to permit persons to whom the Software is furnished to do so, subject to the following +# conditions: +# +# The above copyright notice and this permission notice shall be included in all copies or substantial portions +# of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED +# TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL +# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF +# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER +# DEALINGS IN THE SOFTWARE. + +FROM nvidia/opencl:devel AS build +ARG DEBIAN_FRONTEND=noninteractive +ARG OPENCL_LIBRARIES=/usr/lib/x86_64-linux-gnu +ARG OPENCL_INCLUDE_DIR=/usr/include/CL + +# SYSTEM +RUN apt-get update && apt-get install -y --no-install-recommends \ + build-essential \ + git \ + ca-certificates \ + libglib2.0-0 \ + libxext6 \ + libsm6 \ + libxrender1 \ + cmake \ + libboost-dev \ + libboost-system-dev \ + libboost-filesystem-dev \ + gcc \ + g++ && \ + rm -rf /var/lib/apt/lists/* + +# LightGBM +WORKDIR /opt +RUN git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ + cd LightGBM && \ + cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=${OPENCL_LIBRARIES}/libOpenCL.so.1 -DOpenCL_INCLUDE_DIR=$OPENCL_INCLUDE_DIR && \ + OPENCL_HEADERS=$OPENCL_INCLUDE_DIR LIBOPENCL=$OPENCL_LIBRARIES cmake --build build + +FROM nvidia/opencl:runtime +RUN apt-get update && apt-get install -y --no-install-recommends \ + libxext6 \ + libsm6 \ + libxrender1 \ + libboost-system-dev \ + libboost-filesystem-dev \ + gcc \ + g++ && \ + rm -rf /var/lib/apt/lists/* + +COPY --from=build \ + /opt/LightGBM/lightgbm \ + /opt/LightGBM/lib_lightgbm.so \ + /opt/LightGBM/ + +ENV PATH /opt/LightGBM:${PATH} +ENV LANG C.UTF-8 +ENV LC_ALL C.UTF-8 + +ENTRYPOINT ["lightgbm"] diff --git a/docker/gpu/dockerfile.gpu b/docker/gpu/dockerfile.gpu new file mode 100644 index 0000000..cd4be66 --- /dev/null +++ b/docker/gpu/dockerfile.gpu @@ -0,0 +1,122 @@ +FROM nvidia/cuda:8.0-cudnn5-devel + +################################################################################################################# +# Global +################################################################################################################# +# apt-get to skip any interactive post-install configuration steps with DEBIAN_FRONTEND=noninteractive and apt-get install -y + +ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 +ARG DEBIAN_FRONTEND=noninteractive + +################################################################################################################# +# Global Path Setting +################################################################################################################# + +ENV CUDA_HOME /usr/local/cuda +ENV LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:${CUDA_HOME}/lib64 +ENV LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:/usr/local/lib + +ENV OPENCL_LIBRARIES /usr/local/cuda/lib64 +ENV OPENCL_INCLUDE_DIR /usr/local/cuda/include + +################################################################################################################# +# TINI +################################################################################################################# + +# Install tini +ENV TINI_VERSION v0.14.0 +ADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /tini +RUN chmod +x /tini + +################################################################################################################# +# SYSTEM +################################################################################################################# +# update: downloads the package lists from the repositories and "updates" them to get information on the newest versions of packages and their +# dependencies. It will do this for all repositories and PPAs. + +RUN apt-get update && \ +apt-get install -y --no-install-recommends \ +build-essential \ +curl \ +bzip2 \ +ca-certificates \ +libglib2.0-0 \ +libxext6 \ +libsm6 \ +libxrender1 \ +git \ +vim \ +mercurial \ +subversion \ +cmake \ +libboost-dev \ +libboost-system-dev \ +libboost-filesystem-dev \ +gcc \ +g++ + +# Add OpenCL ICD files for LightGBM +RUN mkdir -p /etc/OpenCL/vendors && \ + echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd + +################################################################################################################# +# CONDA +################################################################################################################# + +ARG CONDA_DIR=/opt/miniforge +# add to path +ENV PATH $CONDA_DIR/bin:$PATH + +# Install miniforge +RUN echo "export PATH=$CONDA_DIR/bin:"'$PATH' > /etc/profile.d/conda.sh && \ + curl -sL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -o ~/miniforge.sh && \ + /bin/bash ~/miniforge.sh -b -p $CONDA_DIR && \ + rm ~/miniforge.sh + +RUN conda config --set always_yes yes --set changeps1 no && \ + conda create -y -q -n py3 numpy scipy scikit-learn jupyter notebook ipython pandas matplotlib + +################################################################################################################# +# LightGBM +################################################################################################################# + +RUN cd /usr/local/src && mkdir lightgbm && cd lightgbm && \ + git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \ + cd LightGBM && \ + cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ && \ + OPENCL_HEADERS=/usr/local/cuda-8.0/targets/x86_64-linux/include LIBOPENCL=/usr/local/cuda-8.0/targets/x86_64-linux/lib cmake --build build + +ENV PATH /usr/local/src/lightgbm/LightGBM:${PATH} + +RUN /bin/bash -c "source activate py3 && cd /usr/local/src/lightgbm/LightGBM && sh ./build-python.sh install --precompile && source deactivate" + +################################################################################################################# +# System CleanUp +################################################################################################################# +# apt-get autoremove: used to remove packages that were automatically installed to satisfy dependencies for some package and that are no more needed. +# apt-get clean: removes the aptitude cache in /var/cache/apt/archives. You'd be amazed how much is in there! the only drawback is that the packages +# have to be downloaded again if you reinstall them. + +RUN apt-get autoremove -y && apt-get clean && \ + rm -rf /var/lib/apt/lists/* && \ + conda clean -a -y + +################################################################################################################# +# JUPYTER +################################################################################################################# + +# password: keras +# password key: --NotebookApp.password='sha1:98b767162d34:8da1bc3c75a0f29145769edc977375a373407824' + +# Add a notebook profile. +RUN mkdir -p -m 700 ~/.jupyter/ && \ + echo "c.NotebookApp.ip = '*'" >> ~/.jupyter/jupyter_notebook_config.py + +VOLUME /home +WORKDIR /home + +# IPython +EXPOSE 8888 + +ENTRYPOINT [ "/tini", "--" ] +CMD /bin/bash -c "source activate py3 && jupyter notebook --allow-root --no-browser --NotebookApp.password='sha1:98b767162d34:8da1bc3c75a0f29145769edc977375a373407824' && source deactivate" diff --git a/docs/.lychee.toml b/docs/.lychee.toml new file mode 100644 index 0000000..5b68c46 --- /dev/null +++ b/docs/.lychee.toml @@ -0,0 +1,32 @@ +verbose = "info" +no_progress = false +cache = false +scheme = ["http", "https", "file"] +include_mail = false +include_fragments = true +no_ignore = true +insecure = false +require_https = true +accept = ["100..=103", "200..=299"] +user_agent = "curl/7.88.1" +header = {"User-Agent" = "curl/7.88.1"} +timeout = 30 +retry_wait_time = 10 +max_concurrency = 10 +# remove anchors from GitHub URLs to overcome https://github.com/lycheeverse/lychee/issues/1729 +remap = [ + '(?P^https://github\.com)/(?P.*)#(?P.*)$ $host/$path/', +] +exclude = [ + '^https://www\.swig\.org/download\.html$', + '^https://proceedings\.neurips\.cc/.*', + '^https://www\.amd\.com/en/support\.html$', + '^https://www\.jstor\.org/stable/2281952$', + '^https://dl\.acm\.org/doi/10\.1145/3298689\.3347033$', + '^https://packages\.ubuntu\.com/search.*', + '^https://stackoverflow\.com/.*', + '^https://.*\.stackexchange\.com/.*', +] +exclude_path = [ + "(^|/)docs/.*\\.rst", +] diff --git a/docs/Advanced-Topics.rst b/docs/Advanced-Topics.rst new file mode 100644 index 0000000..b2e8bc4 --- /dev/null +++ b/docs/Advanced-Topics.rst @@ -0,0 +1,121 @@ +Advanced Topics +=============== + +Missing Value Handle +-------------------- + +- LightGBM enables the missing value handle by default. Disable it by setting ``use_missing=false``. + +- LightGBM uses NA (NaN) to represent missing values by default. Change it to use zero by setting ``zero_as_missing=true``. + +- When ``zero_as_missing=false`` (default), the unrecorded values in sparse matrices (and LightSVM) are treated as zeros. + +- When ``zero_as_missing=true``, NA and zeros (including unrecorded values in sparse matrices (and LightSVM)) are treated as missing. + +Categorical Feature Support +--------------------------- + +- LightGBM offers good accuracy with integer-encoded categorical features. LightGBM applies + `Fisher (1958) `_ + to find the optimal split over categories as + `described here <./Features.rst#optimal-split-for-categorical-features>`_. This often performs better than one-hot encoding. + +- Use ``categorical_feature`` to specify the categorical features. + Refer to the parameter ``categorical_feature`` in `Parameters <./Parameters.rst#categorical_feature>`__. + +- Categorical features will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package) so they must be encoded as non-negative integers (negative values will be treated as missing) + less than ``Int32.MaxValue`` (2147483647). + It is best to use a contiguous range of integers started from zero. + Floating point numbers in categorical features will be rounded towards 0. + +- When using ``pandas.DataFrame`` inputs with columns of dtype ``category``, LightGBM will + align categories to those observed during training before converting them to integer values. + This ensures consistent encoding between training and prediction without additional preprocessing. + +- At ``predict()`` time, categories not seen during training will be treated as missing values. + +- Use ``min_data_per_group``, ``cat_smooth`` to deal with over-fitting (when ``#data`` is small or ``#category`` is large). + +- For a categorical feature with high cardinality (``#category`` is large), it often works best to + treat the feature as numeric, either by simply ignoring the categorical interpretation of the integers or + by embedding the categories in a low-dimensional numeric space. + +LambdaRank +---------- + +- The label should be of type ``int``, such that larger numbers correspond to higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect). + +- Use ``label_gain`` to set the gain(weight) of ``int`` label. + +- Use ``lambdarank_truncation_level`` to truncate the max DCG. + +Cost Efficient Gradient Boosting +-------------------------------- + +`Cost Efficient Gradient Boosting `_ (CEGB) makes it possible to penalise boosting based on the cost of obtaining feature values. +CEGB penalises learning in the following ways: + +- Each time a tree is split, a penalty of ``cegb_penalty_split`` is applied. +- When a feature is used for the first time, ``cegb_penalty_feature_coupled`` is applied. This penalty can be different for each feature and should be specified as one ``double`` per feature. +- When a feature is used for the first time for a data row, ``cegb_penalty_feature_lazy`` is applied. Like ``cegb_penalty_feature_coupled``, this penalty is specified as one ``double`` per feature. + +Each of the penalties above is scaled by ``cegb_tradeoff``. +Using this parameter, it is possible to change the overall strength of the CEGB penalties by changing only one parameter. + +Parameters Tuning +----------------- + +- Refer to `Parameters Tuning <./Parameters-Tuning.rst>`__. + +.. _Parallel Learning: + +Distributed Learning +-------------------- + +- Refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__. + +GPU Support +----------- + +- Refer to `GPU Tutorial <./GPU-Tutorial.rst>`__ and `GPU Targets <./GPU-Targets.rst>`__. + +Support for Position Bias Treatment +------------------------------------ + +Often the relevance labels provided in Learning-to-Rank tasks might be derived from implicit user feedback (e.g., clicks) and therefore might be biased due to their position/location on the screen when having been presented to a user. +LightGBM can make use of positional data. + +For example, consider the case where you expect that the first 3 results from a search engine will be visible in users' browsers without scrolling, and all other results for a query would require scrolling. + +LightGBM could be told to account for the position bias from results being "above the fold" by providing a ``positions`` array encoded as follows: + +:: + + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + ... + +Where ``0 = "above the fold"`` and ``1 = "requires scrolling"``. +The specific values are not important, as long as they are consistent across all observations in the training data. +An encoding like ``100 = "above the fold"`` and ``17 = "requires scrolling"`` would result in exactly the same trained model. + +In that way, ``positions`` in LightGBM's API are similar to a categorical feature. +Just as with non-ordinal categorical features, an integer representation is just used for memory and computational efficiency... LightGBM does not care about the absolute or relative magnitude of the values. + +Unlike a categorical feature, however, ``positions`` are used to adjust the target to reduce the bias in predictions made by the trained model. + +The position file corresponds with training data file line by line, and has one position per line. And if the name of training data file is ``train.txt``, the position file should be named as ``train.txt.position`` and placed in the same folder as the data file. +In this case, LightGBM will load the position file automatically if it exists. The positions can also be specified through the ``Dataset`` constructor when using Python API. If the positions are specified in both approaches, the ``.position`` file will be ignored. + +Currently, implemented is an approach to model position bias by using an idea of Generalized Additive Models (`GAM `_) to linearly decompose the document score ``s`` into the sum of a relevance component ``f`` and a positional component ``g``: ``s(x, pos) = f(x) + g(pos)`` where the former component depends on the original query-document features and the latter depends on the position of an item. +During the training, the compound scoring function ``s(x, pos)`` is fit with a standard ranking algorithm (e.g., LambdaMART) which boils down to jointly learning the relevance component ``f(x)`` (it is later returned as an unbiased model) and the position factors ``g(pos)`` that help better explain the observed (biased) labels. +Similar score decomposition ideas have previously been applied for classification & pointwise ranking tasks with assumptions of binary labels and binary relevance (a.k.a. "two-tower" models, refer to the papers: `Towards Disentangling Relevance and Bias in Unbiased Learning to Rank `_, `PAL: a position-bias aware learning framework for CTR prediction in live recommender systems `_, `A General Framework for Debiasing in CTR Prediction `_). +In LightGBM, we adapt this idea to general pairwise Lerarning-to-Rank with arbitrary ordinal relevance labels. +Besides, GAMs have been used in the context of explainable ML (`Accurate Intelligible Models with Pairwise Interactions `_) to linearly decompose the contribution of each feature (and possibly their pairwise interactions) to the overall score, for subsequent analysis and interpretation of their effects in the trained models. diff --git a/docs/C-API.rst b/docs/C-API.rst new file mode 100644 index 0000000..2145cb6 --- /dev/null +++ b/docs/C-API.rst @@ -0,0 +1,4 @@ +C API +===== + +.. doxygenfile:: c_api.h diff --git a/docs/Development-Guide.rst b/docs/Development-Guide.rst new file mode 100644 index 0000000..ddcd509 --- /dev/null +++ b/docs/Development-Guide.rst @@ -0,0 +1,95 @@ +Development Guide +================= + +Algorithms +---------- + +Refer to `Features <./Features.rst>`__ for understanding of important algorithms used in LightGBM. + +Classes and Code Structure +-------------------------- + +Important Classes +~~~~~~~~~~~~~~~~~ + ++-------------------------+----------------------------------------------------------------------------------------+ +| Class | Description | ++=========================+========================================================================================+ +| ``Application`` | The entrance of application, including training and prediction logic | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``Bin`` | Data structure used for storing feature discrete values (converted from float values) | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``Boosting`` | Boosting interface (GBDT, DART, etc.) | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``Config`` | Stores parameters and configurations | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``Dataset`` | Stores information of dataset | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``DatasetLoader`` | Used to construct dataset | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``FeatureGroup`` | Stores the data of feature, could be multiple features | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``Metric`` | Evaluation metrics | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``Network`` | Network interfaces and communication algorithms | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``ObjectiveFunction`` | Objective functions used to train | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``Tree`` | Stores information of tree model | ++-------------------------+----------------------------------------------------------------------------------------+ +| ``TreeLearner`` | Used to learn trees | ++-------------------------+----------------------------------------------------------------------------------------+ + +Code Structure +~~~~~~~~~~~~~~ + ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| Path | Description | ++=====================+====================================================================================================================================+ +| ./include | Header files | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./include/utils | Some common functions | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./src/application | Implementations of training and prediction logic | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./src/boosting | Implementations of Boosting | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./src/io | Implementations of IO related classes, including ``Bin``, ``Config``, ``Dataset``, ``DatasetLoader``, ``Feature`` and ``Tree`` | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./src/metric | Implementations of metrics | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./src/network | Implementations of network functions | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./src/objective | Implementations of objective functions | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ +| ./src/treelearner | Implementations of tree learners | ++---------------------+------------------------------------------------------------------------------------------------------------------------------------+ + +Documents API +------------- + +Refer to `docs README <./README.rst>`__. + +C API +----- + +Refer to `C API <./C-API.rst>`__ or the comments in `c\_api.h `__ file, from which the documentation is generated. + +Tests +----- + +C++ unit tests are located in the ``./tests/cpp_tests`` folder and written with the help of Google Test framework. +To run tests locally first refer to the `Installation Guide <./Installation-Guide.rst#build-c-unit-tests>`__ for how to build tests and then simply run compiled executable file. +It is highly recommended to build tests with `sanitizers <./Installation-Guide.rst#sanitizers>`__. + +High Level Language Package +--------------------------- + +See the implementations at `Python-package `__ and `R-package `__. + +Questions +--------- + +Refer to `FAQ <./FAQ.rst>`__. + +Also feel free to open `issues `__ if you met problems. diff --git a/docs/Experiments.rst b/docs/Experiments.rst new file mode 100644 index 0000000..8350e31 --- /dev/null +++ b/docs/Experiments.rst @@ -0,0 +1,253 @@ +Experiments +=========== + +Comparison Experiment +--------------------- + +For the detailed experiment scripts and output logs, please refer to this `repo`_. + +History +^^^^^^^ + +08 Mar, 2020: update according to the latest master branch (`1b97eaf `__ for XGBoost, `bcad692 `__ for LightGBM). (``xgboost_exact`` is not updated for it is too slow.) + +27 Feb, 2017: first version. + +Data +^^^^ + +We used 5 datasets to conduct our comparison experiments. Details of data are listed in the following table: + ++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ +| Data | Task | Link | #Train\_Set | #Feature | Comments | ++===========+=======================+=================================================================================+=============+==========+==============================================+ +| Higgs | Binary classification | `link `__ | 10,500,000 | 28 | last 500,000 samples were used as test set | ++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ +| Yahoo LTR | Learning to rank | `link `__ | 473,134 | 700 | set1.train as train, set1.test as test | ++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ +| MS LTR | Learning to rank | `link `__ | 2,270,296 | 137 | {S1,S2,S3} as train set, {S5} as test set | ++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ +| Expo | Binary classification | `link `__ | 11,000,000 | 700 | last 1,000,000 samples were used as test set | ++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ +| Allstate | Binary classification | `link `__ | 13,184,290 | 4228 | last 1,000,000 samples were used as test set | ++-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+ + +Environment +^^^^^^^^^^^ + +We ran all experiments on a single Linux server (Azure ND24s) with the following specifications: + ++------------------+-----------------+---------------------+ +| OS | CPU | Memory | ++==================+=================+=====================+ +| Ubuntu 16.04 LTS | 2 \* E5-2690 v4 | 448GB | ++------------------+-----------------+---------------------+ + +Baseline +^^^^^^^^ + +We used `xgboost`_ as a baseline. + +Both xgboost and LightGBM were built with OpenMP support. + +Settings +^^^^^^^^ + +We set up total 3 settings for experiments. The parameters of these settings are: + +1. xgboost: + + .. code:: text + + eta = 0.1 + max_depth = 8 + num_round = 500 + nthread = 16 + tree_method = exact + min_child_weight = 100 + +2. xgboost\_hist (using histogram based algorithm): + + .. code:: text + + eta = 0.1 + num_round = 500 + nthread = 16 + min_child_weight = 100 + tree_method = hist + grow_policy = lossguide + max_depth = 0 + max_leaves = 255 + +3. LightGBM: + + .. code:: text + + learning_rate = 0.1 + num_leaves = 255 + num_trees = 500 + num_threads = 16 + min_data_in_leaf = 0 + min_sum_hessian_in_leaf = 100 + +xgboost grows trees depth-wise and controls model complexity by ``max_depth``. +LightGBM uses a leaf-wise algorithm instead and controls model complexity by ``num_leaves``. +So we cannot compare them in the exact same model setting. For the tradeoff, we use xgboost with ``max_depth=8``, which will have max number leaves to 255, to compare with LightGBM with ``num_leaves=255``. + +Other parameters are default values. + +Result +^^^^^^ + +Speed +''''' + +We compared speed using only the training task without any test or metric output. We didn't count the time for IO. +For the ranking tasks, since XGBoost and LightGBM implement different ranking objective functions, we used ``regression`` objective for speed benchmark, for the fair comparison. + +The following table is the comparison of time cost: + ++-----------+-----------+---------------+---------------+ +| Data | xgboost | xgboost\_hist | LightGBM | ++===========+===========+===============+===============+ +| Higgs | 3794.34 s | 165.575 s | **130.094 s** | ++-----------+-----------+---------------+---------------+ +| Yahoo LTR | 674.322 s | 131.462 s | **76.229 s** | ++-----------+-----------+---------------+---------------+ +| MS LTR | 1251.27 s | 98.386 s | **70.417 s** | ++-----------+-----------+---------------+---------------+ +| Expo | 1607.35 s | 137.65 s | **62.607 s** | ++-----------+-----------+---------------+---------------+ +| Allstate | 2867.22 s | 315.256 s | **148.231 s** | ++-----------+-----------+---------------+---------------+ + +LightGBM ran faster than xgboost on all experiment data sets. + +Accuracy +'''''''' + +We computed all accuracy metrics only on the test data set. + ++-----------+-----------------+----------+-------------------+--------------+ +| Data | Metric | xgboost | xgboost\_hist | LightGBM | ++===========+=================+==========+===================+==============+ +| Higgs | AUC | 0.839593 | 0.845314 | **0.845724** | ++-----------+-----------------+----------+-------------------+--------------+ +| Yahoo LTR | NDCG\ :sub:`1` | 0.719748 | 0.720049 | **0.732981** | +| +-----------------+----------+-------------------+--------------+ +| | NDCG\ :sub:`3` | 0.717813 | 0.722573 | **0.735689** | +| +-----------------+----------+-------------------+--------------+ +| | NDCG\ :sub:`5` | 0.737849 | 0.740899 | **0.75352** | +| +-----------------+----------+-------------------+--------------+ +| | NDCG\ :sub:`10` | 0.78089 | 0.782957 | **0.793498** | ++-----------+-----------------+----------+-------------------+--------------+ +| MS LTR | NDCG\ :sub:`1` | 0.483956 | 0.485115 | **0.517767** | +| +-----------------+----------+-------------------+--------------+ +| | NDCG\ :sub:`3` | 0.467951 | 0.47313 | **0.501063** | +| +-----------------+----------+-------------------+--------------+ +| | NDCG\ :sub:`5` | 0.472476 | 0.476375 | **0.504648** | +| +-----------------+----------+-------------------+--------------+ +| | NDCG\ :sub:`10` | 0.492429 | 0.496553 | **0.524252** | ++-----------+-----------------+----------+-------------------+--------------+ +| Expo | AUC | 0.756713 | 0.776224 | **0.776935** | ++-----------+-----------------+----------+-------------------+--------------+ +| Allstate | AUC | 0.607201 | **0.609465** | 0.609072 | ++-----------+-----------------+----------+-------------------+--------------+ + +Memory Consumption +'''''''''''''''''' + +We monitored RES while running training task. And we set ``two_round=true`` (this will increase data-loading time and +reduce peak memory usage but not affect training speed or accuracy) in LightGBM to reduce peak memory usage. + ++-----------+---------+---------------+--------------------+--------------------+ +| Data | xgboost | xgboost\_hist | LightGBM (col-wise)|LightGBM (row-wise) | ++===========+=========+===============+====================+====================+ +| Higgs | 4.853GB | 7.335GB | **0.897GB** | 1.401GB | ++-----------+---------+---------------+--------------------+--------------------+ +| Yahoo LTR | 1.907GB | 4.023GB | **1.741GB** | 2.161GB | ++-----------+---------+---------------+--------------------+--------------------+ +| MS LTR | 5.469GB | 7.491GB | **0.940GB** | 1.296GB | ++-----------+---------+---------------+--------------------+--------------------+ +| Expo | 1.553GB | 2.606GB | **0.555GB** | 0.711GB | ++-----------+---------+---------------+--------------------+--------------------+ +| Allstate | 6.237GB | 12.090GB | **1.116GB** | 1.755GB | ++-----------+---------+---------------+--------------------+--------------------+ + +Parallel Experiment +------------------- + +History +^^^^^^^ + +27 Feb, 2017: first version. + +Data +^^^^ + +We used a terabyte click log dataset to conduct parallel experiments. Details are listed in following table: + ++--------+-----------------------+---------+---------------+----------+ +| Data | Task | Link | #Data | #Feature | ++========+=======================+=========+===============+==========+ +| Criteo | Binary classification | `link`_ | 1,700,000,000 | 67 | ++--------+-----------------------+---------+---------------+----------+ + +This data contains 13 integer features and 26 categorical features for 24 days of click logs. +We statisticized the click-through rate (CTR) and count for these 26 categorical features from the first ten days. +Then we used next ten days' data, after replacing the categorical features by the corresponding CTR and count, as training data. +The processed training data have a total of 1.7 billions records and 67 features. + +Environment +^^^^^^^^^^^ + +We ran our experiments on 16 Windows servers with the following specifications: + ++---------------------+-----------------+---------------------+-------------------------------------------+ +| OS | CPU | Memory | Network Adapter | ++=====================+=================+=====================+===========================================+ +| Windows Server 2012 | 2 \* E5-2670 v2 | DDR3 1600Mhz, 256GB | Mellanox ConnectX-3, 54Gbps, RDMA support | ++---------------------+-----------------+---------------------+-------------------------------------------+ + +Settings +^^^^^^^^ + +.. code:: text + + learning_rate = 0.1 + num_leaves = 255 + num_trees = 100 + num_thread = 16 + tree_learner = data + +We used data parallel here because this data is large in ``#data`` but small in ``#feature``. Other parameters were default values. + +Results +^^^^^^^ + ++----------+---------------+---------------------------+ +| #Machine | Time per Tree | Memory Usage(per Machine) | ++==========+===============+===========================+ +| 1 | 627.8 s | 176GB | ++----------+---------------+---------------------------+ +| 2 | 311 s | 87GB | ++----------+---------------+---------------------------+ +| 4 | 156 s | 43GB | ++----------+---------------+---------------------------+ +| 8 | 80 s | 22GB | ++----------+---------------+---------------------------+ +| 16 | 42 s | 11GB | ++----------+---------------+---------------------------+ + +The results show that LightGBM achieves a linear speedup with distributed learning. + +GPU Experiments +--------------- + +Refer to `GPU Performance <./GPU-Performance.rst>`__. + +.. _repo: https://github.com/guolinke/boosting_tree_benchmarks + +.. _xgboost: https://github.com/dmlc/xgboost + +.. _link: https://ailab.criteo.com/download-criteo-1tb-click-logs-dataset/ diff --git a/docs/FAQ.rst b/docs/FAQ.rst new file mode 100644 index 0000000..c9354f4 --- /dev/null +++ b/docs/FAQ.rst @@ -0,0 +1,420 @@ +.. role:: raw-html(raw) + :format: html + +LightGBM FAQ +############ + +.. contents:: LightGBM Frequently Asked Questions + :depth: 1 + :local: + :backlinks: none + +------ + +Please post questions, feature requests, and bug reports at https://github.com/lightgbm-org/LightGBM/issues. + +This project is mostly maintained by volunteers, so please be patient. +If your request is time-sensitive or more than a month goes by without a response, please tag the maintainers below for help. + +- `@guolinke `__ **Guolin Ke** +- `@shiyu1994 `__ **Yu Shi** +- `@jameslamb `__ **James Lamb** +- `@jmoralez `__ **José Morales** +- `@borchero `__ **Oliver Borchert** +- `@mayer79 `__ **Michael Mayer** + +-------------- + +General LightGBM Questions +========================== + +.. contents:: + :local: + :backlinks: none + +1. Where do I find more details about LightGBM parameters? +---------------------------------------------------------- + +Take a look at `Parameters <./Parameters.rst>`__. + +2. On datasets with millions of features, training does not start (or starts after a very long time). +----------------------------------------------------------------------------------------------------- + +Use a smaller value for ``bin_construct_sample_cnt`` and a larger value for ``min_data``. + +3. When running LightGBM on a large dataset, my computer runs out of RAM. +------------------------------------------------------------------------- + +**Multiple Solutions**: set the ``histogram_pool_size`` parameter to the MB you want to use for LightGBM (histogram\_pool\_size + dataset size = approximately RAM used), +lower ``num_leaves`` or lower ``max_bin`` (see `lightgbm-org/LightGBM#562 `__). + +4. I am using Windows. Should I use Visual Studio or MinGW for compiling LightGBM? +---------------------------------------------------------------------------------- + +Visual Studio `performs best for LightGBM `__. + +5. When using LightGBM GPU, I cannot reproduce results over several runs. +------------------------------------------------------------------------- + +This is normal and expected behaviour, but you may try to use ``gpu_use_dp = true`` for reproducibility +(see `lightgbm-org/LightGBM#560 `__). +You may also use the CPU version. + +6. Bagging is not reproducible when changing the number of threads. +------------------------------------------------------------------- + +:raw-html:`` +LightGBM bagging is multithreaded, so its output depends on the number of threads used. +There is `no workaround currently `__. +:raw-html:`` + +Starting from `#2804 `__ bagging result doesn't depend on the number of threads. +So this issue should be solved in the latest version. + +7. I tried to use Random Forest mode, and LightGBM crashes! +----------------------------------------------------------- + +This is expected behaviour for arbitrary parameters. To enable Random Forest, +you must use ``bagging_fraction`` and ``feature_fraction`` different from 1, along with a ``bagging_freq``. +`This thread `__ includes an example. + +8. CPU usage is low (like 10%) in Windows when using LightGBM on very large datasets with many-core systems. +------------------------------------------------------------------------------------------------------------ + +Please use `Visual Studio `__ +as it may be `10x faster than MinGW `__ especially for very large trees. + +9. When I'm trying to specify a categorical column with the ``categorical_feature`` parameter, I get the following sequence of warnings, but there are no negative values in the column. +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + +.. code-block:: console + + [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN + [LightGBM] [Warning] There are no meaningful features, as all feature values are constant. + +The column you're trying to pass via ``categorical_feature`` likely contains very large values. +Categorical features in LightGBM are limited by int32 range, +so you cannot pass values that are greater than ``Int32.MaxValue`` (2147483647) as categorical features (see `lightgbm-org/LightGBM#1359 `__). +You should convert them to integers ranging from zero to the number of categories first. + +10. LightGBM crashes randomly with the error like: ``Initializing libiomp5.dylib, but found libomp.dylib already initialized.`` +------------------------------------------------------------------------------------------------------------------------------- + +.. code-block:: console + + OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized. + OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/. + +**Possible Cause**: This error means that you have multiple OpenMP libraries installed on your machine and they conflict with each other. +(File extensions in the error message may differ depending on the operating system). + +If you are using Python distributed by Conda, then it is highly likely that the error is caused by the ``numpy`` package from Conda which includes the ``mkl`` package which in turn conflicts with the system-wide library. +In this case you can update the ``numpy`` package in Conda or replace the Conda's OpenMP library instance with system-wide one by creating a symlink to it in Conda environment folder ``$CONDA_PREFIX/lib``. + +**Solution**: Assuming you are using macOS with Homebrew, the command which overwrites OpenMP library files in the current active Conda environment with symlinks to the system-wide library ones installed by Homebrew: + +.. code-block:: bash + + ln -sf `ls -d "$(brew --cellar libomp)"/*/lib`/* $CONDA_PREFIX/lib + +The described above fix worked fine before the release of OpenMP 8.0.0 version. +Starting from 8.0.0 version, Homebrew formula for OpenMP includes ``-DLIBOMP_INSTALL_ALIASES=OFF`` option which leads to that the fix doesn't work anymore. +However, you can create symlinks to library aliases manually: + +.. code-block:: bash + + for LIBOMP_ALIAS in libgomp.dylib libiomp5.dylib libomp.dylib; do sudo ln -sf "$(brew --cellar libomp)"/*/lib/libomp.dylib $CONDA_PREFIX/lib/$LIBOMP_ALIAS; done + +Another workaround would be removing MKL optimizations from Conda's packages completely: + +.. code-block:: bash + + conda install nomkl + +If this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them. + +11. LightGBM hangs when multithreading (OpenMP) and using forking in Linux at the same time. +-------------------------------------------------------------------------------------------- + +Use ``nthreads=1`` to disable multithreading of LightGBM. There is a bug with OpenMP which hangs forked sessions +with multithreading activated. A more expensive solution is to use new processes instead of using fork, however, +keep in mind it is creating new processes where you have to copy memory and load libraries (example: if you want to +fork 16 times your current process, then you will require to make 16 copies of your dataset in memory) +(see `lightgbm-org/LightGBM#1789 `__). + +An alternative, if multithreading is really necessary inside the forked sessions, would be to compile LightGBM with +Intel toolchain. Intel compilers are unaffected by this bug. + +For C/C++ users, any OpenMP feature cannot be used before the fork happens. If an OpenMP feature is used before the +fork happens (example: using OpenMP for forking), OpenMP will hang inside the forked sessions. Use new processes instead +and copy memory as required by creating new processes instead of forking (or, use Intel compilers). + +Cloud platform container services may cause LightGBM to hang, if they use Linux fork to run multiple containers on a +single instance. For example, LightGBM hangs in AWS Batch array jobs, which `use the ECS agent +`__ to manage multiple running jobs. Setting ``nthreads=1`` mitigates the issue. + +12. Why is early stopping not enabled by default in LightGBM? +------------------------------------------------------------- + +Early stopping involves choosing a validation set, a special type of holdout which is used to evaluate the current state of the model after each iteration to see if training can stop. + +In ``LightGBM``, `we have decided to require that users specify this set directly <./Parameters.rst#valid>`_. Many options exist for splitting training data into training, test, and validation sets. + +The appropriate splitting strategy depends on the task and domain of the data, information that a modeler has but which ``LightGBM`` as a general-purpose tool does not. + +13. Does LightGBM support direct loading data from zero-based or one-based LibSVM format file? +---------------------------------------------------------------------------------------------- + +LightGBM supports loading data from zero-based LibSVM format file directly. + +14. Why CMake cannot find the compiler when compiling LightGBM with MinGW? +-------------------------------------------------------------------------- + +.. code-block:: bash + + CMake Error: CMAKE_C_COMPILER not set, after EnableLanguage + CMake Error: CMAKE_CXX_COMPILER not set, after EnableLanguage + +This is a known issue of CMake when using MinGW. The easiest solution is to run again your ``cmake`` command to bypass the one time stopper from CMake. Or you can upgrade your version of CMake to at least version 3.17.0. + +See `lightgbm-org/LightGBM#3060 `__ for more details. + +15. Where can I find LightGBM's logo to use it in my presentation? +------------------------------------------------------------------ + +You can find LightGBM's logo in different file formats and resolutions `here `__. + +16. LightGBM crashes randomly or operating system hangs during or after running LightGBM. +----------------------------------------------------------------------------------------- + +**Possible Cause**: This behavior may indicate that you have multiple OpenMP libraries installed on your machine and they conflict with each other, similarly to the ``FAQ #10``. + +If you are using any Python-package that depends on ``threadpoolctl``, you also may see the following warning in your logs in this case: + +.. code-block:: console + + /root/miniconda/envs/test-env/lib/python3.8/site-packages/threadpoolctl.py:546: RuntimeWarning: + Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at + the same time. Both libraries are known to be incompatible and this + can cause random crashes or deadlocks on Linux when loaded in the + same Python program. + Using threadpoolctl may cause crashes or deadlocks. For more + information and possible workarounds, please see + https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md + +Detailed description of conflicts between multiple OpenMP instances is provided in the `following document `__. + +**Solution**: Assuming you are using LightGBM Python-package and conda as a package manager, we strongly recommend using ``conda-forge`` channel as the only source of all your Python package installations because it contains built-in patches to workaround OpenMP conflicts. Some other workarounds are listed `here `__ under the "Workarounds for Intel OpenMP and LLVM OpenMP case" section. + +If this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them. + +17. Loading LightGBM fails like: ``cannot allocate memory in static TLS block`` +------------------------------------------------------------------------------- + +When loading LightGBM, you may encounter errors like the following. + +.. code-block:: console + + lib/libgomp.so.1: cannot allocate memory in static TLS block + +This most commonly happens on aarch64 Linux systems. + +``gcc``'s OpenMP library (``libgomp.so``) tries to allocate a small amount of static thread-local storage ("TLS") +when it's dynamically loaded. + +That error can happen when the loader isn't able to find a large enough block of memory. + +On aarch64 Linux, processes and loaded libraries share the same pool of static TLS, +which makes such failures more likely. See these discussions: + +* https://bugzilla.redhat.com/show_bug.cgi?id=1722181#c6 +* https://gcc.gcc.gnu.narkive.com/vOXMQqLA/failure-to-dlopen-libgomp-due-to-static-tls-data + +If you are experiencing this issue when using the ``lightgbm`` Python-package, try upgrading +to at least ``v4.6.0``. + +For older versions of the Python-package, or for other LightGBM APIs, this issue can +often be avoided by loading ``libgomp.so.1``. That can be done directly by setting environment +variable ``LD_PRELOAD``, like this: + +.. code-block:: console + + export LD_PRELOAD=/root/miniconda3/envs/test-env/lib/libgomp.so.1 + +It can also be done indirectly by changing the order that other libraries are loaded +into processes, which varies by programming language and application type. + +For more details, see these discussions: + +* https://github.com/lightgbm-org/LightGBM/pull/6654#issuecomment-2352014275 +* https://github.com/lightgbm-org/LightGBM/issues/6509 +* https://maskray.me/blog/2021-02-14-all-about-thread-local-storage +* https://bugzilla.redhat.com/show_bug.cgi?id=1722181#c6 + +------ + +R-package +========= + +.. contents:: + :local: + :backlinks: none + +1. Any training command using LightGBM does not work after an error occurred during the training of a previous LightGBM model. +------------------------------------------------------------------------------------------------------------------------------ + +In older versions of the R-package (prior to ``v3.3.0``), this could happen occasionally and the solution was to run ``lgb.unloader(wipe = TRUE)`` to remove all LightGBM-related objects. Some conversation about this could be found in `lightgbm-org/LightGBM#698 `__. + +That is no longer necessary as of ``v3.3.0``, and function ``lgb.unloader()`` has since been removed from the R-package. + +2. I used ``setinfo()``, tried to print my ``lgb.Dataset``, and now the R console froze! +---------------------------------------------------------------------------------------- + +As of at least LightGBM v3.3.0, this issue has been resolved and printing a ``Dataset`` object does not cause the console to freeze. + +In older versions, avoid printing the ``Dataset`` after calling ``setinfo()``. + +As of LightGBM v4.0.0, ``setinfo()`` has been replaced by a new method, ``set_field()``. + +3. ``error in data.table::data.table()...argument 2 is NULL``. +-------------------------------------------------------------- + +If you are experiencing this error when running ``lightgbm``, you may be facing the same issue reported in `#2715 `_ and later in `#2989 `_. We have seen that in some situations, using ``data.table`` 1.11.x results in this error. To get around this, you can upgrade your version of ``data.table`` to at least version 1.12.0. + +4. ``package/dependency ‘Matrix’ is not available ...`` +------------------------------------------------------- + +In April 2024, ``Matrix==1.7-0`` was published to CRAN. +That version had a floor of ``R (>=4.4.0)``. +``{Matrix}`` is a hard runtime dependency of ``{lightgbm}``, so on any version of R older than ``4.4.0``, running ``install.packages("lightgbm")`` results in something like the following. + +.. code-block:: text + + package ‘Matrix’ is not available for this version of R + +To fix that without upgrading to R 4.4.0 or greater, manually install an older version of ``{Matrix}``. + +.. code-block:: R + + install.packages('https://cran.r-project.org/src/contrib/Archive/Matrix/Matrix_1.6-5.tar.gz', repos = NULL) + +------ + +Python-package +============== + +.. contents:: + :local: + :backlinks: none + +1. ``Error: setup script specifies an absolute path`` when installing from GitHub using ``python setup.py install``. +-------------------------------------------------------------------------------------------------------------------- + +.. note:: + As of v4.0.0, ``lightgbm`` does not support directly invoking ``setup.py``. + This answer refers only to versions of ``lightgbm`` prior to v4.0.0. + +.. code-block:: console + + error: Error: setup script specifies an absolute path: + /Users/Microsoft/LightGBM/python-package/lightgbm/../../lib_lightgbm.so + setup() arguments must *always* be /-separated paths relative to the setup.py directory, *never* absolute paths. + +This error should be solved in latest version. +If you still meet this error, try to remove ``lightgbm.egg-info`` folder in your Python-package and reinstall, +or check `this thread on stackoverflow `__. + +2. Error messages: ``Cannot ... before construct dataset``. +----------------------------------------------------------- + +I see error messages like... + +.. code-block:: console + + Cannot get/set label/weight/init_score/group/num_data/num_feature before construct dataset + +but I've already constructed a dataset by some code like: + +.. code-block:: python + + train = lightgbm.Dataset(X_train, y_train) + +or error messages like + +.. code-block:: console + + Cannot set predictor/reference/categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this. + +**Solution**: Because LightGBM constructs bin mappers to build trees, and train and valid Datasets within one Booster share the same bin mappers, +categorical features and feature names etc., the Dataset objects are constructed when constructing a Booster. +If you set ``free_raw_data=True`` (default), the raw data (with Python data struct) will be freed. +So, if you want to: + +- get label (or weight/init\_score/group/data) before constructing a dataset, it's same as get ``self.label``; + +- set label (or weight/init\_score/group) before constructing a dataset, it's same as ``self.label=some_label_array``; + +- get num\_data (or num\_feature) before constructing a dataset, you can get data with ``self.data``. + Then, if your data is ``numpy.ndarray``, use some code like ``self.data.shape``. But do not do this after subsetting the Dataset, because you'll get always ``None``; + +- set predictor (or reference/categorical feature) after constructing a dataset, + you should set ``free_raw_data=False`` or init a Dataset object with the same raw data. + +3. I encounter segmentation faults (segfaults) randomly after installing LightGBM from PyPI using ``pip install lightgbm``. +--------------------------------------------------------------------------------------------------------------------------- + +We are doing our best to provide universal wheels which have high running speed and are compatible with any hardware, OS, compiler, etc. at the same time. +However, sometimes it's just impossible to guarantee the possibility of usage of LightGBM in any specific environment (see `lightgbm-org/LightGBM#1743 `__). + +Therefore, the first thing you should try in case of segfaults is **compiling from the source** using ``pip install --no-binary lightgbm lightgbm``. +For the OS-specific prerequisites see https://github.com/lightgbm-org/LightGBM/blob/main/python-package/README.rst. + +Also, feel free to post a new issue in our GitHub repository. We always look at each case individually and try to find a root cause. + +4. I would like to install LightGBM from conda. What channel should I choose? +----------------------------------------------------------------------------- + +We strongly recommend installation from the ``conda-forge`` channel and not from the ``default`` one. + +For some specific examples, see `this comment `__. + +In addition, as of ``lightgbm==4.4.0``, the ``conda-forge`` package automatically supports CUDA-based GPU acceleration. + +5. How do I subclass ``scikit-learn`` estimators? +------------------------------------------------- + +For ``lightgbm <= 4.5.0``, copy all of the constructor arguments from the corresponding +``lightgbm`` class into the constructor of your custom estimator. + +For later versions, just ensure that the constructor of your custom estimator calls ``super().__init__()``. + +Consider the example below, which implements a regressor that allows creation of truncated predictions. +This pattern will work with ``lightgbm > 4.5.0``. + +.. code-block:: python + + import numpy as np + from lightgbm import LGBMRegressor + from sklearn.datasets import make_regression + + class TruncatedRegressor(LGBMRegressor): + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def predict(self, X, max_score: float = np.inf): + preds = super().predict(X) + np.clip(preds, a_min=None, a_max=max_score, out=preds) + return preds + + X, y = make_regression(n_samples=1_000, n_features=4) + + reg_trunc = TruncatedRegressor().fit(X, y) + + preds = reg_trunc.predict(X) + print(f"mean: {preds.mean():.2f}, max: {preds.max():.2f}") + # mean: -6.81, max: 345.10 + + preds_trunc = reg_trunc.predict(X, max_score=preds.mean()) + print(f"mean: {preds_trunc.mean():.2f}, max: {preds_trunc.max():.2f}") + # mean: -56.50, max: -6.81 diff --git a/docs/Features.rst b/docs/Features.rst new file mode 100644 index 0000000..5b20f0f --- /dev/null +++ b/docs/Features.rst @@ -0,0 +1,298 @@ +Features +======== + +This is a conceptual overview of how LightGBM works\ `[1] <#references>`__. We assume familiarity with decision tree boosting algorithms to focus instead on aspects of LightGBM that may differ from other boosting packages. For detailed algorithms, please refer to the citations or source code. + +Optimization in Speed and Memory Usage +-------------------------------------- + +Many boosting tools use pre-sort-based algorithms\ `[2, 3] <#references>`__ (e.g. default algorithm in xgboost) for decision tree learning. It is a simple solution, but not easy to optimize. + +LightGBM uses histogram-based algorithms\ `[4, 5, 6] <#references>`__, which bucket continuous feature (attribute) values into discrete bins. This speeds up training and reduces memory usage. Advantages of histogram-based algorithms include the following: + +- **Reduced cost of calculating the gain for each split** + + - Pre-sort-based algorithms have time complexity ``O(#data)`` + + - Computing the histogram has time complexity ``O(#data)``, but this involves only a fast sum-up operation. Once the histogram is constructed, a histogram-based algorithm has time complexity ``O(#bins)``, and ``#bins`` is far smaller than ``#data``. + +- **Use histogram subtraction for further speedup** + + - To get one leaf's histograms in a binary tree, use the histogram subtraction of its parent and its neighbor + + - So it needs to construct histograms for only one leaf (with smaller ``#data`` than its neighbor). It then can get histograms of its neighbor by histogram subtraction with small cost (``O(#bins)``) + +- **Reduce memory usage** + + - Replaces continuous values with discrete bins. If ``#bins`` is small, can use small data type, e.g. uint8\_t, to store training data + + - No need to store additional information for pre-sorting feature values + +- **Reduce communication cost for distributed learning** + +Sparse Optimization +------------------- + +- Need only ``O(2 * #non_zero_data)`` to construct histogram for sparse features + +Optimization in Accuracy +------------------------ + +Leaf-wise (Best-first) Tree Growth +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Most decision tree learning algorithms grow trees by level (depth)-wise, like the following image: + +.. image:: ./_static/images/level-wise.png + :align: center + :alt: A diagram depicting level wise tree growth in which the best possible node is split one level down. The strategy results in a symmetric tree, where every node in a level has child nodes resulting in an additional layer of depth. + +LightGBM grows trees leaf-wise (best-first)\ `[7] <#references>`__. It will choose the leaf with max delta loss to grow. +Holding ``#leaf`` fixed, leaf-wise algorithms tend to achieve lower loss than level-wise algorithms. + +Leaf-wise may cause over-fitting when ``#data`` is small, so LightGBM includes the ``max_depth`` parameter to limit tree depth. However, trees still grow leaf-wise even when ``max_depth`` is specified. + +.. image:: ./_static/images/leaf-wise.png + :align: center + :alt: A diagram depicting leaf wise tree growth in which only the node with the highest loss change is split and not bother with the rest of the nodes in the same level. This results in an asymmetrical tree where subsequent splitting is happening only on one side of the tree. + +Optimal Split for Categorical Features +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +It is common to represent categorical features with one-hot encoding, but this approach is suboptimal for tree learners. Particularly for high-cardinality categorical features, a tree built on one-hot features tends to be unbalanced and needs to grow very deep to achieve good accuracy. + +Instead of one-hot encoding, the optimal solution is to split on a categorical feature by partitioning its categories into 2 subsets. If the feature has ``k`` categories, there are ``2^(k-1) - 1`` possible partitions. +But there is an efficient solution for regression trees\ `[8] <#references>`__. It needs about ``O(k * log(k))`` to find the optimal partition. + +The basic idea is to sort the categories according to the training objective at each split. +More specifically, LightGBM sorts the histogram (for a categorical feature) according to its accumulated values (``sum_gradient / sum_hessian``) and then finds the best split on the sorted histogram. + +Optimization in Network Communication +------------------------------------- + +It only needs to use some collective communication algorithms, like "All reduce", "All gather" and "Reduce scatter", in distributed learning of LightGBM. +LightGBM implements state-of-the-art algorithms\ `[9] <#references>`__. +These collective communication algorithms can provide much better performance than point-to-point communication. + +.. _Optimization in Parallel Learning: + +Optimization in Distributed Learning +------------------------------------ + +LightGBM provides the following distributed learning algorithms. + +Feature Parallel +~~~~~~~~~~~~~~~~ + +Traditional Algorithm +^^^^^^^^^^^^^^^^^^^^^ + +Feature parallel aims to parallelize the "Find Best Split" in the decision tree. The procedure of traditional feature parallel is: + +1. Partition data vertically (different machines have different feature set). + +2. Workers find local best split point {feature, threshold} on local feature set. + +3. Communicate local best splits with each other and get the best one. + +4. Worker with best split to perform split, then send the split result of data to other workers. + +5. Other workers split data according to received data. + +The shortcomings of traditional feature parallel: + +- Has computation overhead, since it cannot speed up "split", whose time complexity is ``O(#data)``. + Thus, feature parallel cannot speed up well when ``#data`` is large. + +- Need communication of split result, which costs about ``O(#data / 8)`` (one bit for one data). + +Feature Parallel in LightGBM +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Since feature parallel cannot speed up well when ``#data`` is large, we make a little change: instead of partitioning data vertically, every worker holds the full data. +Thus, LightGBM doesn't need to communicate for split result of data since every worker knows how to split data. +And ``#data`` won't be larger, so it is reasonable to hold the full data in every machine. + +The procedure of feature parallel in LightGBM: + +1. Workers find local best split point {feature, threshold} on local feature set. + +2. Communicate local best splits with each other and get the best one. + +3. Perform best split. + +However, this feature parallel algorithm still suffers from computation overhead for "split" when ``#data`` is large. +So it will be better to use data parallel when ``#data`` is large. + +Data Parallel +~~~~~~~~~~~~~ + +Traditional Algorithm +^^^^^^^^^^^^^^^^^^^^^ + +Data parallel aims to parallelize the whole decision learning. The procedure of data parallel is: + +1. Partition data horizontally. + +2. Workers use local data to construct local histograms. + +3. Merge global histograms from all local histograms. + +4. Find best split from merged global histograms, then perform splits. + +The shortcomings of traditional data parallel: + +- High communication cost. + If using point-to-point communication algorithm, communication cost for one machine is about ``O(#machine * #feature * #bin)``. + If using collective communication algorithm (e.g. "All Reduce"), communication cost is about ``O(2 * #feature * #bin)`` (check cost of "All Reduce" in chapter 4.5 at `[9] <#references>`__). + +Data Parallel in LightGBM +^^^^^^^^^^^^^^^^^^^^^^^^^ + +We reduce communication cost of data parallel in LightGBM: + +1. Instead of "Merge global histograms from all local histograms", LightGBM uses "Reduce Scatter" to merge histograms of different (non-overlapping) features for different workers. + Then workers find the local best split on local merged histograms and sync up the global best split. + +2. As aforementioned, LightGBM uses histogram subtraction to speed up training. + Based on this, we can communicate histograms only for one leaf, and get its neighbor's histograms by subtraction as well. + +All things considered, data parallel in LightGBM has time complexity ``O(0.5 * #feature * #bin)``. + +Voting Parallel +~~~~~~~~~~~~~~~ + +Voting parallel further reduces the communication cost in `Data Parallel <#data-parallel>`__ to constant cost. +It uses two-stage voting to reduce the communication cost of feature histograms\ `[10] <#references>`__. + +GPU Support +----------- + +Thanks `@huanzhang12 `__ for contributing this feature. Please read `[11] <#references>`__ to get more details. + +- `GPU Installation <./Installation-Guide.rst#build-gpu-version>`__ + +- `GPU Tutorial <./GPU-Tutorial.rst>`__ + +Applications and Metrics +------------------------ + +LightGBM supports the following applications: + +- regression, the objective function is L2 loss + +- binary classification, the objective function is logloss + +- multi classification + +- cross-entropy, the objective function is logloss and supports training on non-binary labels + +- LambdaRank, the objective function is LambdaRank with NDCG + +LightGBM supports the following metrics: + +- L1 loss + +- L2 loss + +- Log loss + +- Classification error rate + +- AUC + +- NDCG + +- MAP + +- Multi-class log loss + +- Multi-class error rate + +- AUC-mu ``(new in v3.0.0)`` + +- Average precision ``(new in v3.1.0)`` + +- Fair + +- Huber + +- Poisson + +- Quantile + +- MAPE + +- Kullback-Leibler + +- Gamma + +- Tweedie + +For more details, please refer to `Parameters <./Parameters.rst#metric-parameters>`__. + +Other Features +-------------- + +- Limit ``max_depth`` of tree while grows tree leaf-wise + +- `DART `__ + +- L1/L2 regularization + +- Bagging + +- Column (feature) sub-sample + +- Continued train with input GBDT model + +- Continued train with the input score file + +- Weighted training + +- Validation metric output during training + +- Multiple validation data + +- Multiple metrics + +- Early stopping (both training and prediction) + +- Prediction for leaf index + +For more details, please refer to `Parameters <./Parameters.rst>`__. + +References +---------- + +[1] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. "`LightGBM\: A Highly Efficient Gradient Boosting Decision Tree`_." Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157. + +[2] Mehta, Manish, Rakesh Agrawal, and Jorma Rissanen. "SLIQ: A fast scalable classifier for data mining." International Conference on Extending Database Technology. Springer Berlin Heidelberg, 1996. + +[3] Shafer, John, Rakesh Agrawal, and Manish Mehta. "SPRINT: A scalable parallel classifier for data mining." Proc. 1996 Int. Conf. Very Large Data Bases. 1996. + +[4] Ranka, Sanjay, and V. Singh. "CLOUDS: A decision tree classifier for large datasets." Proceedings of the 4th Knowledge Discovery and Data Mining Conference. 1998. + +[5] Machado, F. P. "Communication and memory efficient parallel decision tree construction." (2003). + +[6] Li, Ping, Qiang Wu, and Christopher J. Burges. "Mcrank: Learning to rank using multiple classification and gradient boosting." Advances in Neural Information Processing Systems 20 (NIPS 2007). + +[7] Shi, Haijian. "Best-first decision tree learning." Diss. The University of Waikato, 2007. + +[8] Walter D. Fisher. "`On Grouping for Maximum Homogeneity`_." Journal of the American Statistical Association. Vol. 53, No. 284 (Dec., 1958), pp. 789-798. + +[9] Thakur, Rajeev, Rolf Rabenseifner, and William Gropp. "`Optimization of collective communication operations in MPICH`_." International Journal of High Performance Computing Applications 19.1 (2005), pp. 49-66. + +[10] Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. "`A Communication-Efficient Parallel Algorithm for Decision Tree`_." Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287. + +[11] Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." SysML Conference, 2018. + +.. _LightGBM\: A Highly Efficient Gradient Boosting Decision Tree: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html + +.. _On Grouping for Maximum Homogeneity: https://www.jstor.org/stable/2281952 + +.. _Optimization of collective communication operations in MPICH: https://www.mpich.org/2012/10/24/optimization-of-collective-communication-operations-in-mpich/ + +.. _A Communication-Efficient Parallel Algorithm for Decision Tree: https://proceedings.neurips.cc/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html + +.. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359 diff --git a/docs/GPU-Performance.rst b/docs/GPU-Performance.rst new file mode 100644 index 0000000..1ece191 --- /dev/null +++ b/docs/GPU-Performance.rst @@ -0,0 +1,211 @@ +GPU Tuning Guide and Performance Comparison +=========================================== + +How It Works? +------------- + +In LightGBM, the main computation cost during training is building the feature histograms. We use an efficient algorithm on GPU to accelerate this process. +The implementation is highly modular, and works for all learning tasks (classification, ranking, regression, etc). GPU acceleration also works in distributed learning settings. +GPU algorithm implementation is based on OpenCL and can work with a wide range of GPUs. + +Supported Hardware +------------------ + +We target AMD Graphics Core Next (GCN) architecture and NVIDIA Maxwell and Pascal architectures. +Most AMD GPUs released after 2012 and NVIDIA GPUs released after 2014 should be supported. We have tested the GPU implementation on the following GPUs: + +- AMD RX 480 with AMDGPU-pro driver 16.60 on Ubuntu 16.10 + +- AMD R9 280X (aka Radeon HD 7970) with fglrx driver 15.302.2301 on Ubuntu 16.10 + +- NVIDIA GTX 1080 with driver 375.39 and CUDA 8.0 on Ubuntu 16.10 + +- NVIDIA Titan X (Pascal) with driver 367.48 and CUDA 8.0 on Ubuntu 16.04 + +- NVIDIA Tesla M40 with driver 375.39 and CUDA 7.5 on Ubuntu 16.04 + +Using the following hardware is discouraged: + +- NVIDIA Kepler (K80, K40, K20, most GeForce GTX 700 series GPUs) or earlier NVIDIA GPUs. They don't support hardware atomic operations in local memory space and thus histogram construction will be slow. + +- AMD VLIW4-based GPUs, including Radeon HD 6xxx series and earlier GPUs. These GPUs have been discontinued for years and are rarely seen nowadays. + +How to Achieve Good Speedup on GPU +---------------------------------- + +#. You want to run a few datasets that we have verified with good speedup (including Higgs, epsilon, Bosch, etc) to ensure your setup is correct. + If you have multiple GPUs, make sure to set ``gpu_platform_id`` and ``gpu_device_id`` to use the desired GPU. + Also make sure your system is idle (especially when using a shared computer) to get accuracy performance measurements. + +#. GPU works best on large scale and dense datasets. If dataset is too small, computing it on GPU is inefficient as the data transfer overhead can be significant. + If you have categorical features, use the ``categorical_column`` option and input them into LightGBM directly; do not convert them into one-hot variables. + +#. To get good speedup with GPU, it is suggested to use a smaller number of bins. + Setting ``max_bin=63`` is recommended, as it usually does not noticeably affect training accuracy on large datasets, but GPU training can be significantly faster than using the default bin size of 255. + For some dataset, even using 15 bins is enough (``max_bin=15``); using 15 bins will maximize GPU performance. Make sure to check the run log and verify that the desired number of bins is used. + +#. Try to use single precision training (``gpu_use_dp=false``) when possible, because most GPUs (especially NVIDIA consumer GPUs) have poor double-precision performance. + +Performance Comparison +---------------------- + +We evaluate the training performance of GPU acceleration on the following datasets: + ++-----------+----------------+----------+------------+-----------+------------+ +| Data | Task | Link | #Examples | #Features | Comments | ++===========+================+==========+============+===========+============+ +| Higgs | Binary | `link1`_ | 10,500,000 | 28 | use last | +| | classification | | | | 500,000 | +| | | | | | samples | +| | | | | | as test | +| | | | | | set | ++-----------+----------------+----------+------------+-----------+------------+ +| Epsilon | Binary | `link2`_ | 400,000 | 2,000 | use the | +| | classification | | | | provided | +| | | | | | test set | ++-----------+----------------+----------+------------+-----------+------------+ +| Bosch | Binary | `link3`_ | 1,000,000 | 968 | use the | +| | classification | | | | provided | +| | | | | | test set | ++-----------+----------------+----------+------------+-----------+------------+ +| Yahoo LTR | Learning to | `link4`_ | 473,134 | 700 | set1.train | +| | rank | | | | as train, | +| | | | | | set1.test | +| | | | | | as test | ++-----------+----------------+----------+------------+-----------+------------+ +| MS LTR | Learning to | `link5`_ | 2,270,296 | 137 | {S1,S2,S3} | +| | rank | | | | as train | +| | | | | | set, {S5} | +| | | | | | as test | +| | | | | | set | ++-----------+----------------+----------+------------+-----------+------------+ +| Expo | Binary | `link6`_ | 11,000,000 | 700 | use last | +| | classification | | | | 1,000,000 | +| | (Categorical) | | | | as test | +| | | | | | set | ++-----------+----------------+----------+------------+-----------+------------+ + +We used the following hardware to evaluate the performance of LightGBM GPU training. +Our CPU reference is **a high-end dual socket Haswell-EP Xeon server with 28 cores**; +GPUs include a budget GPU (RX 480) and a mainstream (GTX 1080) GPU installed on the same server. +It is worth mentioning that **the GPUs used are not the best GPUs in the market**; +if you are using a better GPU (like AMD RX 580, NVIDIA GTX 1080 Ti, Titan X Pascal, Titan Xp, Tesla P100, etc), you are likely to get a better speedup. + ++--------------------------------+----------------+------------------+---------------+ +| Hardware | Peak FLOPS | Peak Memory BW | Cost (MSRP) | ++================================+================+==================+===============+ +| AMD Radeon RX 480 | 5,161 GFLOPS | 256 GB/s | $199 | ++--------------------------------+----------------+------------------+---------------+ +| NVIDIA GTX 1080 | 8,228 GFLOPS | 320 GB/s | $499 | ++--------------------------------+----------------+------------------+---------------+ +| 2x Xeon E5-2683v3 (28 cores) | 1,792 GFLOPS | 133 GB/s | $3,692 | ++--------------------------------+----------------+------------------+---------------+ + +During benchmarking on CPU we used only 28 physical cores of the CPU, and did not use hyper-threading cores, +because we found that using too many threads actually makes performance worse. +The following shows the training configuration we used: + +:: + + max_bin = 63 + num_leaves = 255 + num_iterations = 500 + learning_rate = 0.1 + tree_learner = serial + task = train + is_training_metric = false + min_data_in_leaf = 1 + min_sum_hessian_in_leaf = 100 + ndcg_eval_at = 1,3,5,10 + device = gpu + gpu_platform_id = 0 + gpu_device_id = 0 + num_thread = 28 + +We use the configuration shown above, except for the Bosch dataset, we use a smaller ``learning_rate=0.015`` and set ``min_sum_hessian_in_leaf=5``. +For all GPU training we vary the max number of bins (255, 63 and 15). +The GPU implementation is from commit `0bb4a82`_ of LightGBM, when the GPU support was just merged in. + +The following table lists the accuracy on test set that CPU and GPU learner can achieve after 500 iterations. +GPU with the same number of bins can achieve a similar level of accuracy as on the CPU, despite using single precision arithmetic. +For most datasets, using 63 bins is sufficient. + ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| | CPU 255 bins | CPU 63 bins | CPU 15 bins | GPU 255 bins | GPU 63 bins | GPU 15 bins | ++===========================+================+===============+===============+================+===============+===============+ +| Higgs AUC | 0.845612 | 0.845239 | 0.841066 | 0.845612 | 0.845209 | 0.840748 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| Epsilon AUC | 0.950243 | 0.949952 | 0.948365 | 0.950057 | 0.949876 | 0.948365 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| Yahoo-LTR NDCG\ :sub:`1` | 0.730824 | 0.730165 | 0.729647 | 0.730936 | 0.732257 | 0.73114 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| Yahoo-LTR NDCG\ :sub:`3` | 0.738687 | 0.737243 | 0.736445 | 0.73698 | 0.739474 | 0.735868 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| Yahoo-LTR NDCG\ :sub:`5` | 0.756609 | 0.755729 | 0.754607 | 0.756206 | 0.757007 | 0.754203 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| Yahoo-LTR NDCG\ :sub:`10` | 0.79655 | 0.795827 | 0.795273 | 0.795894 | 0.797302 | 0.795584 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| Expo AUC | 0.776217 | 0.771566 | 0.743329 | 0.776285 | 0.77098 | 0.744078 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| MS-LTR NDCG\ :sub:`1` | 0.521265 | 0.521392 | 0.518653 | 0.521789 | 0.522163 | 0.516388 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| MS-LTR NDCG\ :sub:`3` | 0.503153 | 0.505753 | 0.501697 | 0.503886 | 0.504089 | 0.501691 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| MS-LTR NDCG\ :sub:`5` | 0.509236 | 0.510391 | 0.507193 | 0.509861 | 0.510095 | 0.50663 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| MS-LTR NDCG\ :sub:`10` | 0.527835 | 0.527304 | 0.524603 | 0.528009 | 0.527059 | 0.524722 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ +| Bosch AUC | 0.718115 | 0.721791 | 0.716677 | 0.717184 | 0.724761 | 0.717005 | ++---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+ + +We record the wall clock time after 500 iterations, as shown in the figure below: + +.. image:: ./_static/images/gpu-performance-comparison.png + :align: center + :target: ./_static/images/gpu-performance-comparison.png + :alt: A performance chart which is a record of the wall clock time after 500 iterations on G P U for Higgs, epsilon, Bosch, Microsoft L T R, Expo and Yahoo L T R and bin size of 63 performs comparatively better. + +When using a GPU, it is advisable to use a bin size of 63 rather than 255, because it can speed up training significantly without noticeably affecting accuracy. +On CPU, using a smaller bin size only marginally improves performance, sometimes even slows down training, +like in Higgs (we can reproduce the same slowdown on two different machines, with different GCC versions). +We found that GPU can achieve impressive acceleration on large and dense datasets like Higgs and Epsilon. +Even on smaller and sparse datasets, a *budget* GPU can still compete and be faster than a 28-core Haswell server. + +Memory Usage +------------ + +The next table shows GPU memory usage reported by ``nvidia-smi`` during training with 63 bins. +We can see that even the largest dataset just uses about 1 GB of GPU memory, +indicating that our GPU implementation can scale to huge datasets over 10x larger than Bosch or Epsilon. +Also, we can observe that generally a larger dataset (using more GPU memory, like Epsilon or Bosch) has better speedup, +because the overhead of invoking GPU functions becomes significant when the dataset is small. + ++-------------------------+---------+-----------+---------+----------+--------+-------------+ +| Datasets | Higgs | Epsilon | Bosch | MS-LTR | Expo | Yahoo-LTR | ++=========================+=========+===========+=========+==========+========+=============+ +| GPU Memory Usage (MB) | 611 | 901 | 1067 | 413 | 405 | 291 | ++-------------------------+---------+-----------+---------+----------+--------+-------------+ + +Further Reading +--------------- + +You can find more details about the GPU algorithm and benchmarks in the +following article: + +Huan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boosting`_. SysML Conference, 2018. + +.. _link1: https://archive.ics.uci.edu/dataset/280/higgs + +.. _link2: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html + +.. _link3: https://www.kaggle.com/c/bosch-production-line-performance/data + +.. _link4: https://proceedings.mlr.press/v14/chapelle11a.html + +.. _link5: https://www.microsoft.com/en-us/research/project/mslr/ + +.. _link6: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009 + +.. _0bb4a82: https://github.com/lightgbm-org/LightGBM/commit/0bb4a82 + +.. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359 diff --git a/docs/GPU-Targets.rst b/docs/GPU-Targets.rst new file mode 100644 index 0000000..973575c --- /dev/null +++ b/docs/GPU-Targets.rst @@ -0,0 +1,174 @@ +GPU SDK Correspondence and Device Targeting Table +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +GPU Targets Table +================= + +OpenCL is a universal massively parallel programming framework that targets multiple backends (GPU, CPU, FPGA, etc). +Basically, to use a device from a vendor, you have to install drivers from that specific vendor. +Intel's and AMD's OpenCL runtime also include x86 CPU target support. +NVIDIA's OpenCL runtime only supports NVIDIA GPU (no CPU support). +In general, OpenCL CPU backends are quite slow, and should be used for testing and debugging only. + +You can find below a table of correspondence: + ++---------------------------+-----------------+-----------------+-----------------+--------------+ +| SDK | CPU Intel/AMD | GPU Intel | GPU AMD | GPU NVIDIA | ++===========================+=================+=================+=================+==============+ +| `Intel SDK for OpenCL`_ | Supported | Supported | Not Supported | Not Supported| ++---------------------------+-----------------+-----------------+-----------------+--------------+ +| AMD APP SDK \* | Supported | Not Supported | Supported | Not Supported| ++---------------------------+-----------------+-----------------+-----------------+--------------+ +| `PoCL`_ | Supported | Not Supported | Supported | Not Supported| ++---------------------------+-----------------+-----------------+-----------------+--------------+ +| `NVIDIA CUDA Toolkit`_ | Not Supported | Not Supported | Not Supported | Supported | ++---------------------------+-----------------+-----------------+-----------------+--------------+ + +Legend: + +\* AMD APP SDK is deprecated. On Windows, OpenCL is included in AMD graphics driver. On Linux, newer generation AMD cards are supported by the `ROCm`_ driver. You can download an archived copy of AMD APP SDK from our GitHub repo (`for Linux`_ and `for Windows`_). + + +-------------- + +Query OpenCL Devices in Your System +=================================== + +Your system might have multiple GPUs from different vendors ("platforms") installed. Setting up LightGBM GPU device requires two parameters: `OpenCL Platform ID <./Parameters.rst#gpu_platform_id>`__ (``gpu_platform_id``) and `OpenCL Device ID <./Parameters.rst#gpu_device_id>`__ (``gpu_device_id``). Generally speaking, each vendor provides an OpenCL platform, and devices from the same vendor have different device IDs under that platform. For example, if your system has an Intel integrated GPU and two discrete GPUs from AMD, you will have two OpenCL platforms (with ``gpu_platform_id=0`` and ``gpu_platform_id=1``). If the platform 0 is Intel, it has one device (``gpu_device_id=0``) representing the Intel GPU; if the platform 1 is AMD, it has two devices (``gpu_device_id=0``, ``gpu_device_id=1``) representing the two AMD GPUs. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct ``gpu_platform_id`` to use the discrete GPU as it usually provides better performance. + +On Windows, OpenCL devices can be queried using `GPUCapsViewer`_, under the OpenCL tab. Note that the platform and device IDs reported by this utility start from 1. So you should minus the reported IDs by 1. + +On Linux, OpenCL devices can be listed using the ``clinfo`` command. On Ubuntu, you can install ``clinfo`` by executing ``sudo apt-get install clinfo``. + + +Examples +=============== + +We provide test R code below, but you can use the language of your choice with the examples of your choices: + +.. code:: r + + library(lightgbm) + data(agaricus.train, package = "lightgbm") + train <- agaricus.train + train$data[, 1] <- 1:6513 + dtrain <- lgb.Dataset(train$data, label = train$label) + data(agaricus.test, package = "lightgbm") + test <- agaricus.test + dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) + valids <- list(test = dtest) + + params <- list(objective = "regression", + metric = "rmse", + device = "gpu", + gpu_platform_id = 0, + gpu_device_id = 0, + nthread = 1, + boost_from_average = FALSE, + num_tree_per_iteration = 10, + max_bin = 32) + model <- lgb.train(params, + dtrain, + 2, + valids, + min_data = 1, + learning_rate = 1, + early_stopping_rounds = 10) + +Make sure you list the OpenCL devices in your system and set ``gpu_platform_id`` and ``gpu_device_id`` correctly. In the following examples, our system has 1 GPU platform (``gpu_platform_id = 0``) from AMD APP SDK. The first device ``gpu_device_id = 0`` is a GPU device (AMD Oland), and the second device ``gpu_device_id = 1`` is the x86 CPU backend. + +Example of using GPU (``gpu_platform_id = 0`` and ``gpu_device_id = 0`` in our system): + +.. code:: r + + > params <- list(objective = "regression", + + metric = "rmse", + + device = "gpu", + + gpu_platform_id = 0, + + gpu_device_id = 0, + + nthread = 1, + + boost_from_average = FALSE, + + num_tree_per_iteration = 10, + + max_bin = 32) + > model <- lgb.train(params, + + dtrain, + + 2, + + valids, + + min_data = 1, + + learning_rate = 1, + + early_stopping_rounds = 10) + [LightGBM] [Info] This is the GPU trainer!! + [LightGBM] [Info] Total Bins 232 + [LightGBM] [Info] Number of data: 6513, number of used features: 116 + [LightGBM] [Info] Using GPU Device: Oland, Vendor: Advanced Micro Devices, Inc. + [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins... + [LightGBM] [Info] GPU programs have been built + [LightGBM] [Info] Size of histogram bin entry: 12 + [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004211 secs. 76 sparse feature groups. + [LightGBM] [Info] No further splits with positive gain, best gain: -inf + [LightGBM] [Info] Trained a tree with leaves=16 and depth=8 + [1]: test's rmse:1.10643e-17 + [LightGBM] [Info] No further splits with positive gain, best gain: -inf + [LightGBM] [Info] Trained a tree with leaves=7 and depth=5 + [2]: test's rmse:0 + +Running on OpenCL CPU backend devices is in generally slow, and we observe crashes on some Windows and macOS systems. Make sure you check the ``Using GPU Device`` line in the log and it is not using a CPU. The above log shows that we are using ``Oland`` GPU from AMD and not CPU. + +Example of using CPU (``gpu_platform_id = 0``, ``gpu_device_id = 1``). The GPU device reported is ``Intel(R) Core(TM) i7-4600U CPU``, so it is using the CPU backend rather than a real GPU. + +.. code:: r + + > params <- list(objective = "regression", + + metric = "rmse", + + device = "gpu", + + gpu_platform_id = 0, + + gpu_device_id = 1, + + nthread = 1, + + boost_from_average = FALSE, + + num_tree_per_iteration = 10, + + max_bin = 32) + > model <- lgb.train(params, + + dtrain, + + 2, + + valids, + + min_data = 1, + + learning_rate = 1, + + early_stopping_rounds = 10) + [LightGBM] [Info] This is the GPU trainer!! + [LightGBM] [Info] Total Bins 232 + [LightGBM] [Info] Number of data: 6513, number of used features: 116 + [LightGBM] [Info] Using requested OpenCL platform 0 device 1 + [LightGBM] [Info] Using GPU Device: Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz, Vendor: GenuineIntel + [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins... + [LightGBM] [Info] GPU programs have been built + [LightGBM] [Info] Size of histogram bin entry: 12 + [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004540 secs. 76 sparse feature groups. + [LightGBM] [Info] No further splits with positive gain, best gain: -inf + [LightGBM] [Info] Trained a tree with leaves=16 and depth=8 + [1]: test's rmse:1.10643e-17 + [LightGBM] [Info] No further splits with positive gain, best gain: -inf + [LightGBM] [Info] Trained a tree with leaves=7 and depth=5 + [2]: test's rmse:0 + + +Known issues: + +- Using a bad combination of ``gpu_platform_id`` and ``gpu_device_id`` can potentially lead to a **crash** due to OpenCL driver issues on some machines (you will lose your entire session content). Beware of it. + +- On some systems, if you have integrated graphics card (Intel HD Graphics) and a dedicated graphics card (AMD, NVIDIA), the dedicated graphics card will automatically override the integrated graphics card. The workaround is to disable your dedicated graphics card to be able to use your integrated graphics card. + +.. _Intel SDK for OpenCL: https://software.intel.com/en-us/articles/opencl-drivers + +.. _ROCm: https://rocmdocs.amd.com/en/latest/ + +.. _for Linux: https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/AMD-APP-SDKInstaller-v3.0.130.136-GA-linux64.tar.bz2 + +.. _for Windows: https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/AMD-APP-SDKInstaller-v3.0.130.135-GA-windows-F-x64.exe + +.. _NVIDIA CUDA Toolkit: https://developer.nvidia.com/cuda-downloads + +.. _clinfo: https://github.com/Oblomov/clinfo + +.. _GPUCapsViewer: https://www.ozone3d.net/gpu_caps_viewer/ + +.. _PoCL: https://portablecl.org/ diff --git a/docs/GPU-Tutorial.rst b/docs/GPU-Tutorial.rst new file mode 100644 index 0000000..4749c1e --- /dev/null +++ b/docs/GPU-Tutorial.rst @@ -0,0 +1,192 @@ +LightGBM GPU Tutorial +===================== + +The purpose of this document is to give you a quick step-by-step tutorial on GPU training. + +We will use the GPU instance on `Microsoft Azure cloud computing platform`_ for demonstration, +but you can use any machine with modern AMD or NVIDIA GPUs. + +GPU Setup +--------- + +You need to launch a ``NV`` type instance on Azure (available in East US, North Central US, South Central US, West Europe and Southeast Asia zones) +and select Ubuntu 16.04 LTS as the operating system. + +For testing, the smallest ``NV6`` type virtual machine is sufficient, which includes 1/2 M60 GPU, with 8 GB memory, 180 GB/s memory bandwidth and 4,825 GFLOPS peak computation power. +Don't use the ``NC`` type instance as the GPUs (K80) are based on an older architecture (Kepler). + +First we need to install minimal NVIDIA drivers and OpenCL development environment: + +:: + + sudo apt-get update + sudo apt-get install --no-install-recommends nvidia-375 + sudo apt-get install --no-install-recommends nvidia-opencl-icd-375 nvidia-opencl-dev opencl-headers + +After installing the drivers you need to restart the server. + +:: + + sudo init 6 + +After about 30 seconds, the server should be up again. + +If you are using an AMD GPU, you should download and install the `AMDGPU-Pro`_ driver and also install packages ``ocl-icd-libopencl1`` and ``ocl-icd-opencl-dev``. + +Build LightGBM +-------------- + +Now install necessary building tools and dependencies: + +:: + + sudo apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev + +The ``NV6`` GPU instance has a 320 GB ultra-fast SSD mounted at ``/mnt``. +Let's use it as our workspace (skip this if you are using your own machine): + +:: + + sudo mkdir -p /mnt/workspace + sudo chown $(whoami):$(whoami) /mnt/workspace + cd /mnt/workspace + +Now we are ready to checkout LightGBM and compile it with GPU support: + +:: + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_GPU=1 +   # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: + # cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ + cmake --build build -j$(nproc) + +You will see two binaries are generated, ``lightgbm`` and ``lib_lightgbm.so``. + +If you are building on macOS, you probably need to remove macro ``BOOST_COMPUTE_USE_OFFLINE_CACHE`` in ``src/treelearner/gpu_tree_learner.h`` to avoid a known crash bug in Boost.Compute. + +Install Python Interface (optional) +----------------------------------- + +If you want to use the Python interface of LightGBM, you can install it now (along with some necessary Python-package dependencies): + +:: + + sudo apt-get -y install python3-pip python3-venv + sudo -H pip install numpy scipy scikit-learn -U + sudo sh ./build-python.sh install --precompile + +You need to set an additional parameter ``"device" : "gpu"`` (along with your other options like ``learning_rate``, ``num_leaves``, etc) to use GPU in Python. + +You can read our `Python-package Examples`_ for more information on how to use the Python interface. + +Dataset Preparation +------------------- + +Using the following commands to prepare the Higgs dataset: + +:: + + git clone https://github.com/guolinke/boosting_tree_benchmarks.git + cd boosting_tree_benchmarks/data + wget "https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz" + gunzip HIGGS.csv.gz + python higgs2libsvm.py + cd ../.. + ln -s boosting_tree_benchmarks/data/higgs.train + ln -s boosting_tree_benchmarks/data/higgs.test + +Now we create a configuration file for LightGBM by running the following commands (please copy the entire block and run it as a whole): + +:: + + cat > lightgbm_gpu.conf <> lightgbm_gpu.conf + +GPU is enabled in the configuration file we just created by setting ``device=gpu``. +In this configuration we use the first GPU installed on the system (``gpu_platform_id=0`` and ``gpu_device_id=0``). If ``gpu_platform_id`` or ``gpu_device_id`` is not set, the default platform and GPU will be selected. +You might have multiple platforms (AMD/Intel/NVIDIA) or GPUs. You can use the `clinfo`_ utility to identify the GPUs on each platform. On Ubuntu, you can install ``clinfo`` by executing ``sudo apt-get install clinfo``. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct ``gpu_platform_id`` to use the discrete GPU. + +Run Your First Learning Task on GPU +----------------------------------- + +Now we are ready to start GPU training! + +First we want to verify the GPU works correctly. +Run the following command to train on GPU, and take a note of the AUC after 50 iterations: + +:: + + ./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc + +Now train the same dataset on CPU using the following command. You should observe a similar AUC: + +:: + + ./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc device=cpu + +Now we can make a speed test on GPU without calculating AUC after each iteration. + +:: + + ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc + +Speed test on CPU: + +:: + + ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc device=cpu + +You should observe over three times speedup on this GPU. + +The GPU acceleration can be used on other tasks/metrics (regression, multi-class classification, ranking, etc) as well. +For example, we can train the Higgs dataset on GPU as a regression task: + +:: + + ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2 + +Also, you can compare the training speed with CPU: + +:: + + ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2 device=cpu + +Further Reading +--------------- + +- `GPU Tuning Guide and Performance Comparison <./GPU-Performance.rst>`__ + +- `GPU SDK Correspondence and Device Targeting Table <./GPU-Targets.rst>`__ + +Reference +--------- + +Please kindly cite the following article in your publications if you find the GPU acceleration useful: + +Huan Zhang, Si Si and Cho-Jui Hsieh. "`GPU Acceleration for Large-scale Tree Boosting`_." SysML Conference, 2018. + +.. _Microsoft Azure cloud computing platform: https://azure.microsoft.com/ + +.. _AMDGPU-Pro: https://www.amd.com/en/support.html + +.. _Python-package Examples: https://github.com/lightgbm-org/LightGBM/tree/main/examples/python-guide + +.. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359 + +.. _clinfo: https://github.com/Oblomov/clinfo diff --git a/docs/GPU-Windows.rst b/docs/GPU-Windows.rst new file mode 100644 index 0000000..283b7f3 --- /dev/null +++ b/docs/GPU-Windows.rst @@ -0,0 +1,3 @@ +The content of this document was very outdated and is no longer available to avoid any misleadings. + +Starting from the ``3.2.0`` version LightGBM Python packages have been having built-in support of training on GPU devices. diff --git a/docs/Installation-Guide.rst b/docs/Installation-Guide.rst new file mode 100644 index 0000000..7dfe61a --- /dev/null +++ b/docs/Installation-Guide.rst @@ -0,0 +1,1143 @@ +Installation Guide +================== + +Versioning +~~~~~~~~~~ + +LightGBM releases use a 3-part version number, with this format: + +.. code:: + + {major}.{minor}.{patch} + +This version follows a scheme called Intended Effort Versioning ("Effver" for short). +Changes to a component of the version indicate how much effort it will likely take to update +code using a previous version. + +* ``major`` = updating will require significant effort +* ``minor`` = some effort +* ``patch`` = no or very little effort + +This means that **new minor versions can contain breaking changes**, but these are typically +small or limited to less-frequently-used parts of the project. + +For more details on why LightGBM uses EffVer instead of other schemes like semantic versioning, +see https://jacobtomlinson.dev/effver/. + +Nightly Packages +~~~~~~~~~~~~~~~~ + +When built from source on an unreleased commit, the package version takes the following form: + +.. code:: + + {major}.{minor}.{patch}.99 + +That ``.99`` is added to ensure that a version built from an unreleased commit is considered "newer" +than all previous releases, and "older" than all future releases. + +.. _nightly-builds: + +To download such artifacts, run the following from the root of this repository. + +.. code:: sh + + bash .ci/download-artifacts.sh ${COMMIT_ID} + +Where ``COMMIT_ID`` is the full commit SHA pointing to a commit on ``main``. +The artifacts can then be found in the ``release-artifacts/`` directory. + +For the Python package, nightly packages are also available via installers like ``pip``. +See `the python-package documentation`_ for details. + +General Installation Notes +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +All instructions below are aimed at compiling the 64-bit version of LightGBM. +It is worth compiling the 32-bit version only in very rare special cases involving environmental limitations. +The 32-bit version is slow and untested, so use it at your own risk and don't forget to adjust some of the commands below when installing. + +By default, instructions below will use **VS Build Tools** or **make** tool to compile the code. +It it possible to use `Ninja`_ tool instead of make on all platforms, but VS Build Tools cannot be replaced with Ninja. +You can add ``-G Ninja`` to CMake flags to use Ninja. + +By default, instructions below will produce a shared library file and an executable file with command-line interface. +You can add ``-DBUILD_CLI=OFF`` to CMake flags to disable the executable compilation. + +If you need to build a static library instead of a shared one, you can add ``-DBUILD_STATIC_LIB=ON`` to CMake flags. + +By default, instructions below will place header files into system-wide folder. +You can add ``-DINSTALL_HEADERS=OFF`` to CMake flags to disable headers installation. + +By default, on macOS, CMake is looking into Homebrew standard folders for finding dependencies (e.g. OpenMP). +You can add ``-DUSE_HOMEBREW_FALLBACK=OFF`` to CMake flags to disable this behaviour. + +Users who want to perform benchmarking can make LightGBM output time costs for different internal routines by adding ``-DUSE_TIMETAG=ON`` to CMake flags. + +It is possible to build LightGBM in debug mode. +In this mode all compiler optimizations are disabled and LightGBM performs more checks internally. +To enable debug mode you can add ``-DUSE_DEBUG=ON`` to CMake flags or choose ``Debug_*`` configuration (e.g. ``Debug_DLL``, ``Debug_mpi``) in Visual Studio depending on how you are building LightGBM. + +.. _sanitizers: + +In addition to the debug mode, LightGBM can be built with compiler sanitizers. +To enable them add ``-DUSE_SANITIZER=ON -DENABLED_SANITIZERS="address;leak;undefined"`` to CMake flags. +These values refer to the following supported sanitizers: + +- ``address`` - AddressSanitizer (ASan); +- ``leak`` - LeakSanitizer (LSan); +- ``undefined`` - UndefinedBehaviorSanitizer (UBSan); +- ``thread`` - ThreadSanitizer (TSan). + +Please note, that ThreadSanitizer cannot be used together with other sanitizers. +For more info and additional sanitizers' parameters please refer to the `following docs`_. +It is very useful to build `C++ unit tests <#build-c-unit-tests>`__ with sanitizers. + +.. contents:: **Contents** + :depth: 1 + :local: + :backlinks: none + +Windows +~~~~~~~ + +On Windows, LightGBM can be built using + +- **Visual Studio**; +- **CMake** and **VS Build Tools**; +- **CMake** and **MinGW**. + +Visual Studio (or VS Build Tools) +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +With GUI +******** + +1. Install `Visual Studio`_. + +2. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it. + +3. Go to ``LightGBM-complete_source_code_zip/windows`` folder. + +4. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release`` configuration if you need executable file or ``DLL`` configuration if you need shared library and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``. + + If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine. + + If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine. + +The ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release`` folder. +The ``.dll`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/DLL`` folder. + +From Command Line +***************** + +1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). + +2. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -A x64 + cmake --build build --target ALL_BUILD --config Release + +The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. + +MinGW-w64 +^^^^^^^^^ + +1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. + +2. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -G "MinGW Makefiles" + cmake --build build -j4 + +The ``.exe`` and ``.dll`` files will be in ``LightGBM/`` folder. + +**Note**: You may need to run the ``cmake -B build -S . -G "MinGW Makefiles"`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error. + +It is recommended that you use **Visual Studio** since it has better multithreading efficiency in **Windows** for many-core systems +(see `Question 4 <./FAQ.rst#i-am-using-windows-should-i-use-visual-studio-or-mingw-for-compiling-lightgbm>`__ and `Question 8 <./FAQ.rst#cpu-usage-is-low-like-10-in-windows-when-using-lightgbm-on-very-large-datasets-with-many-core-systems>`__). + +Linux +~~~~~ + +On Linux, LightGBM can be built using + +- **CMake** and **gcc**; +- **CMake** and **Clang**. + +After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. + +gcc +^^^ + +1. Install `CMake`_ and **gcc**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . + cmake --build build -j4 + +Clang +^^^^^ + +1. Install `CMake`_, **Clang** and **OpenMP**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . + cmake --build build -j4 + +macOS +~~~~~ + +On macOS, LightGBM can be installed using + +- **Homebrew**; +- **MacPorts**; + +or can be built using + +- **CMake** and **Apple Clang**; +- **CMake** and **gcc**. + +Install Using ``Homebrew`` +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: sh + + brew install lightgbm + +Refer to https://formulae.brew.sh/formula/lightgbm for more details. + +Install Using ``MacPorts`` +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: sh + + sudo port install LightGBM + +Refer to https://ports.macports.org/port/LightGBM for more details. + +**Note**: Port for LightGBM is not maintained by LightGBM's maintainers. + +Build from GitHub +^^^^^^^^^^^^^^^^^ + +After compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder. + +Apple Clang +*********** + +1. Install `CMake`_ and **OpenMP**: + + .. code:: sh + + brew install cmake libomp + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . + cmake --build build -j4 + +gcc +*** + +1. Install `CMake`_ and **gcc**: + + .. code:: sh + + brew install cmake gcc + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine + cmake -B build -S . + cmake --build build -j4 + +Docker +~~~~~~ + +Refer to `Docker folder `__. + +Build Threadless Version (not Recommended) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The default build version of LightGBM is based on OpenMP. +You can build LightGBM without OpenMP support but it is **strongly not recommended**. + +Windows +^^^^^^^ + +On Windows, a version of LightGBM without OpenMP support can be built using + +- **Visual Studio**; +- **CMake** and **VS Build Tools**; +- **CMake** and **MinGW**. + +Visual Studio (or VS Build Tools) +********************************* + +With GUI +-------- + +1. Install `Visual Studio`_. + +2. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it. + +3. Go to ``LightGBM-complete_source_code_zip/windows`` folder. + +4. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release`` configuration if you need executable file or ``DLL`` configuration if you need shared library. + +5. Go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``C/C++`` -> ``Language`` and change the ``OpenMP Support`` property to ``No (/openmp-)``. + +6. Get back to the project's main screen and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``. + + If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine. + + If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine. + +The ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release`` folder. +The ``.dll`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/DLL`` folder. + +From Command Line +----------------- + +1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). + +2. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -A x64 -DUSE_OPENMP=OFF + cmake --build build --target ALL_BUILD --config Release + +The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. + +MinGW-w64 +********* + +1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. + +2. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -G "MinGW Makefiles" -DUSE_OPENMP=OFF + cmake --build build -j4 + +The ``.exe`` and ``.dll`` files will be in ``LightGBM/`` folder. + +**Note**: You may need to run the ``cmake -B build -S . -G "MinGW Makefiles" -DUSE_OPENMP=OFF`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error. + +Linux +^^^^^ + +On Linux, a version of LightGBM without OpenMP support can be built using + +- **CMake** and **gcc**; +- **CMake** and **Clang**. + +After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. + +gcc +*** + +1. Install `CMake`_ and **gcc**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_OPENMP=OFF + cmake --build build -j4 + +Clang +***** + +1. Install `CMake`_ and **Clang**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . -DUSE_OPENMP=OFF + cmake --build build -j4 + +macOS +^^^^^ + +On macOS, a version of LightGBM without OpenMP support can be built using + +- **CMake** and **Apple Clang**; +- **CMake** and **gcc**. + +After compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder. + +Apple Clang +*********** + +1. Install `CMake`_: + + .. code:: sh + + brew install cmake + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_OPENMP=OFF + cmake --build build -j4 + +gcc +*** + +1. Install `CMake`_ and **gcc**: + + .. code:: sh + + brew install cmake gcc + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine + cmake -B build -S . -DUSE_OPENMP=OFF + cmake --build build -j4 + +Build MPI Version +~~~~~~~~~~~~~~~~~ + +The default build version of LightGBM is based on socket. LightGBM also supports MPI. +`MPI`_ is a high performance communication approach with `RDMA`_ support. + +If you need to run a distributed learning application with high performance communication, you can build the LightGBM with MPI support. + +Windows +^^^^^^^ + +On Windows, an MPI version of LightGBM can be built using + +- **MS MPI** and **Visual Studio**; +- **MS MPI**, **CMake** and **VS Build Tools**. + +**Note**: Building MPI version by **MinGW** is not supported due to the miss of MPI library in it. + +With GUI +******** + +1. You need to install `MS MPI`_ first. Both ``msmpisdk.msi`` and ``msmpisetup.exe`` are needed. + +2. Install `Visual Studio`_. + +3. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it. + +4. Go to ``LightGBM-complete_source_code_zip/windows`` folder. + +5. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release_mpi`` configuration and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``. + + If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine. + + If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine. + +The ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release_mpi`` folder. + +From Command Line +***************** + +1. You need to install `MS MPI`_ first. Both ``msmpisdk.msi`` and ``msmpisetup.exe`` are needed. + +2. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). + +3. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -A x64 -DUSE_MPI=ON + cmake --build build --target ALL_BUILD --config Release + +The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. + +Linux +^^^^^ + +On Linux, an MPI version of LightGBM can be built using + +- **CMake**, **gcc** and **Open MPI**; +- **CMake**, **Clang** and **Open MPI**. + +After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. + +gcc +*** + +1. Install `CMake`_, **gcc** and `Open MPI`_. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_MPI=ON + cmake --build build -j4 + +Clang +***** + +1. Install `CMake`_, **Clang**, **OpenMP** and `Open MPI`_. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . -DUSE_MPI=ON + cmake --build build -j4 + +macOS +^^^^^ + +On macOS, an MPI version of LightGBM can be built using + +- **CMake**, **Open MPI** and **Apple Clang**; +- **CMake**, **Open MPI** and **gcc**. + +After compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder. + +Apple Clang +*********** + +1. Install `CMake`_, **OpenMP** and `Open MPI`_: + + .. code:: sh + + brew install cmake libomp open-mpi + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_MPI=ON + cmake --build build -j4 + +gcc +*** + +1. Install `CMake`_, `Open MPI`_ and **gcc**: + + .. code:: sh + + brew install cmake open-mpi gcc + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine + cmake -B build -S . -DUSE_MPI=ON + cmake --build build -j4 + +Build GPU Version +~~~~~~~~~~~~~~~~~ + +Windows +^^^^^^^ + +On Windows, a GPU version of LightGBM (``device_type=gpu``) can be built using + +- **OpenCL**, **Boost**, **CMake** and **VS Build Tools**; +- **OpenCL**, **Boost**, **CMake** and **MinGW**. + +If you use **MinGW**, the build procedure is similar to the build on Linux. + +Following procedure is for the **MSVC** (Microsoft Visual C++) build. + +1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is installed). + +2. Install **OpenCL** for Windows. The installation depends on the brand (NVIDIA, AMD, Intel) of your GPU card. + + - For running on Intel, get `Intel SDK for OpenCL`_. + + - For running on AMD, get AMD APP SDK. + + - For running on NVIDIA, get `CUDA Toolkit`_. + + Further reading and correspondence table: `GPU SDK Correspondence and Device Targeting Table <./GPU-Targets.rst>`__. + +3. Install `Boost Binaries`_. + + **Note**: Match your Visual C++ version: + + Visual Studio 2017 -> ``msvc-14.1-64.exe``, + + Visual Studio 2019 -> ``msvc-14.2-64.exe``, + + Visual Studio 2022 -> ``msvc-14.3-64.exe``. + +4. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -A x64 -DUSE_GPU=ON -DBOOST_ROOT=C:/local/boost_1_63_0 -DBOOST_LIBRARYDIR=C:/local/boost_1_63_0/lib64-msvc-14.3 + # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: + # cmake -B build -S . -A x64 -DUSE_GPU=ON -DBOOST_ROOT=C:/local/boost_1_63_0 -DBOOST_LIBRARYDIR=C:/local/boost_1_63_0/lib64-msvc-14.3 -DOpenCL_LIBRARY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.0/lib/x64/OpenCL.lib" -DOpenCL_INCLUDE_DIR="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.0/include" + cmake --build build --target ALL_BUILD --config Release + + **Note**: ``C:/local/boost_1_63_0`` and ``C:/local/boost_1_63_0/lib64-msvc-14.3`` are locations of your **Boost** binaries (assuming you've downloaded 1.63.0 version for Visual Studio 2022). + +The ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder. + +Linux +^^^^^ + +On Linux, a GPU version of LightGBM (``device_type=gpu``) can be built using + +- **CMake**, **OpenCL**, **Boost** and **gcc**; +- **CMake**, **OpenCL**, **Boost** and **Clang**. + +**OpenCL** headers and libraries are usually provided by GPU manufacture. +The generic OpenCL ICD packages (for example, Debian packages ``ocl-icd-libopencl1``, ``ocl-icd-opencl-dev``, ``pocl-opencl-icd``) can also be used. + +Required **Boost** libraries (Boost.Align, Boost.System, Boost.Filesystem, Boost.Chrono) should be provided by the following Debian packages: ``libboost-dev``, ``libboost-system-dev``, ``libboost-filesystem-dev``, ``libboost-chrono-dev``. + +After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. + +gcc +*** + +1. Install `CMake`_, **gcc**, **OpenCL** and **Boost**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_GPU=ON + # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: + # cmake -B build -S . -DUSE_GPU=ON -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ + cmake --build build -j4 + +Clang +***** + +1. Install `CMake`_, **Clang**, **OpenMP**, **OpenCL** and **Boost**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . -DUSE_GPU=ON + # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following: + # cmake -B build -S . -DUSE_GPU=ON -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ + cmake --build build -j4 + +macOS +^^^^^ + +The GPU version is not supported on macOS. + +Docker +^^^^^^ + +Refer to `GPU Docker folder `__. + +Build CUDA Version +~~~~~~~~~~~~~~~~~~ + +The `original GPU version <#build-gpu-version>`__ of LightGBM (``device_type=gpu``) is based on OpenCL, and only computes histograms on GPUs, with other parts of training in CPUs. + +The CUDA-based version (``device_type=cuda``) is a separate implementation that runs significantly faster by putting all the training process on GPUs. It also supports multi-GPU, and multi-node multi-GPU training. +Use this version in Linux environments with an NVIDIA GPU with compute capability 6.0 or higher. + +Windows +^^^^^^^ + +The CUDA version is not supported on Windows. +Use the `GPU version <#build-gpu-version>`__ (``device_type=gpu``) for GPU acceleration on Windows. + +Linux +^^^^^ + +On Linux, a CUDA version of LightGBM can be built using + +- **CMake**, **gcc** and **CUDA**; +- **CMake**, **Clang** and **CUDA**. + +Please refer to `this detailed guide`_ for **CUDA** libraries installation. + +After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. + +.. note:: + + By default, the library will be built with support for a hard-coded list of GPU architectures + based on the detected CUDA Toolkit version. + + To build the library with support for more architectures, set ``CMAKE_CUDA_ARCHITECTURES``. + + .. code:: sh + + # example: all Blackwell arches, including DGX Spark + cmake -B build -S . -DUSE_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="100;120;121-real;121-virtual" + + # example: just the local GPU + cmake -B build -S . -DUSE_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="native" + +gcc +*** + +1. Install `CMake`_, **gcc** and **CUDA**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_CUDA=ON + cmake --build build -j4 + +Clang +***** + +1. Install `CMake`_, **Clang**, **OpenMP** and **CUDA**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . -DUSE_CUDA=ON + cmake --build build -j4 + +macOS +^^^^^ + +The CUDA version is not supported on macOS. + +Build ROCm Version +~~~~~~~~~~~~~~~~~~ + +The `original GPU version <#build-gpu-version>`__ of LightGBM (``device_type=gpu``) is based on OpenCL. + +The ROCm-based version (``device_type=cuda``) is a separate implementation. Yes, the ROCm version reuses the ``device_type=cuda`` as a convenience for users. Use this version in Linux environments with an AMD GPU. + +Windows +^^^^^^^ + +The ROCm version is not supported on Windows. +Use the `GPU version <#build-gpu-version>`__ (``device_type=gpu``) for GPU acceleration on Windows. + +Linux +^^^^^ + +On Linux, a ROCm version of LightGBM can be built using + +- **CMake**, **gcc** and **ROCm**; +- **CMake**, **Clang** and **ROCm**. + +Please refer to `the ROCm docs`_ for **ROCm** libraries installation. + +After compilation the executable and ``.so`` files will be in ``LightGBM/`` folder. + +gcc +*** + +1. Install `CMake`_, **gcc** and **ROCm**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_ROCM=ON + cmake --build build -j4 + +Clang +***** + +1. Install `CMake`_, **Clang**, **OpenMP** and **ROCm**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . -DUSE_ROCM=ON + cmake --build build -j4 + +macOS +^^^^^ + +The ROCm version is not supported on macOS. + +Build Java Wrapper +~~~~~~~~~~~~~~~~~~ + +Using the following instructions you can generate a JAR file containing the LightGBM `C API <./Development-Guide.rst#c-api>`__ wrapped by **SWIG**. + +After compilation the ``.jar`` file will be in ``LightGBM/build`` folder. + +Windows +^^^^^^^ + +On Windows, a Java wrapper of LightGBM can be built using + +- **Java**, **SWIG**, **CMake** and **VS Build Tools**; +- **Java**, **SWIG**, **CMake** and **MinGW**. + +VS Build Tools +************** + +1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). + +2. Install `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). + +3. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -A x64 -DUSE_SWIG=ON + cmake --build build --target ALL_BUILD --config Release + +MinGW-w64 +********* + +1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. + +2. Install `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). + +3. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -G "MinGW Makefiles" -DUSE_SWIG=ON + cmake --build build -j4 + +**Note**: You may need to run the ``cmake -B build -S . -G "MinGW Makefiles" -DUSE_SWIG=ON`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error. + +It is recommended to use **VS Build Tools (Visual Studio)** since it has better multithreading efficiency in **Windows** for many-core systems +(see `Question 4 <./FAQ.rst#i-am-using-windows-should-i-use-visual-studio-or-mingw-for-compiling-lightgbm>`__ and `Question 8 <./FAQ.rst#cpu-usage-is-low-like-10-in-windows-when-using-lightgbm-on-very-large-datasets-with-many-core-systems>`__). + +Linux +^^^^^ + +On Linux, a Java wrapper of LightGBM can be built using + +- **CMake**, **gcc**, **Java** and **SWIG**; +- **CMake**, **Clang**, **Java** and **SWIG**. + +gcc +*** + +1. Install `CMake`_, **gcc**, `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_SWIG=ON + cmake --build build -j4 + +Clang +***** + +1. Install `CMake`_, **Clang**, **OpenMP**, `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly). + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . -DUSE_SWIG=ON + cmake --build build -j4 + +macOS +^^^^^ + +On macOS, a Java wrapper of LightGBM can be built using + +- **CMake**, **Java**, **SWIG** and **Apple Clang**; +- **CMake**, **Java**, **SWIG** and **gcc**. + +Apple Clang +*********** + +1. Install `CMake`_, **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly), `SWIG`_ and **OpenMP**: + + .. code:: sh + + brew install cmake openjdk swig libomp + export JAVA_HOME="$(brew --prefix openjdk)/libexec/openjdk.jdk/Contents/Home/" + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DUSE_SWIG=ON + cmake --build build -j4 + +gcc +*** + +1. Install `CMake`_, **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly), `SWIG`_ and **gcc**: + + .. code:: sh + + brew install cmake openjdk swig gcc + export JAVA_HOME="$(brew --prefix openjdk)/libexec/openjdk.jdk/Contents/Home/" + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine + cmake -B build -S . -DUSE_SWIG=ON + cmake --build build -j4 + +Build Python-package +~~~~~~~~~~~~~~~~~~~~ + +Refer to `the python-package documentation`_. + +Build R-package +~~~~~~~~~~~~~~~ + +Refer to `R-package folder `__. + +Build C++ Unit Tests +~~~~~~~~~~~~~~~~~~~~ + +Windows +^^^^^^^ + +On Windows, C++ unit tests of LightGBM can be built using + +- **CMake** and **VS Build Tools**; +- **CMake** and **MinGW**. + +VS Build Tools +************** + +1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed). + +2. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -A x64 -DBUILD_CPP_TEST=ON + cmake --build build --target testlightgbm --config Debug + +The ``.exe`` file will be in ``LightGBM/Debug`` folder. + +MinGW-w64 +********* + +1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_. + +2. Run the following commands: + + .. code:: console + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -G "MinGW Makefiles" -DBUILD_CPP_TEST=ON + cmake --build build --target testlightgbm -j4 + +The ``.exe`` file will be in ``LightGBM/`` folder. + +**Note**: You may need to run the ``cmake -B build -S . -G "MinGW Makefiles" -DBUILD_CPP_TEST=ON`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error. + +Linux +^^^^^ + +On Linux, a C++ unit tests of LightGBM can be built using + +- **CMake** and **gcc**; +- **CMake** and **Clang**. + +After compilation the executable file will be in ``LightGBM/`` folder. + +gcc +*** + +1. Install `CMake`_ and **gcc**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DBUILD_CPP_TEST=ON + cmake --build build --target testlightgbm -j4 + +Clang +***** + +1. Install `CMake`_, **Clang** and **OpenMP**. + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=clang++-14 CC=clang-14 # replace "14" with version of Clang installed on your machine + cmake -B build -S . -DBUILD_CPP_TEST=ON + cmake --build build --target testlightgbm -j4 + +macOS +^^^^^ + +On macOS, a C++ unit tests of LightGBM can be built using + +- **CMake** and **Apple Clang**; +- **CMake** and **gcc**. + +After compilation the executable file will be in ``LightGBM/`` folder. + +Apple Clang +*********** + +1. Install `CMake`_ and **OpenMP**: + + .. code:: sh + + brew install cmake libomp + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + cmake -B build -S . -DBUILD_CPP_TEST=ON + cmake --build build --target testlightgbm -j4 + +gcc +*** + +1. Install `CMake`_ and **gcc**: + + .. code:: sh + + brew install cmake gcc + +2. Run the following commands: + + .. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM + cd LightGBM + export CXX=g++-7 CC=gcc-7 # replace "7" with version of gcc installed on your machine + cmake -B build -S . -DBUILD_CPP_TEST=ON + cmake --build build --target testlightgbm -j4 + +.. _Visual Studio: https://visualstudio.microsoft.com/downloads/ + +.. _Git for Windows: https://git-scm.com/download/win + +.. _CMake: https://cmake.org/ + +.. _VS Build Tools: https://visualstudio.microsoft.com/downloads/ + +.. _MinGW-w64: https://www.mingw-w64.org/downloads/ + +.. _MPI: https://en.wikipedia.org/wiki/Message_Passing_Interface + +.. _RDMA: https://en.wikipedia.org/wiki/Remote_direct_memory_access + +.. _MS MPI: https://learn.microsoft.com/en-us/message-passing-interface/microsoft-mpi-release-notes + +.. _Open MPI: https://www.open-mpi.org/ + +.. _Intel SDK for OpenCL: https://software.intel.com/en-us/articles/opencl-drivers + +.. _CUDA Toolkit: https://developer.nvidia.com/cuda-downloads + +.. _Boost Binaries: https://sourceforge.net/projects/boost/files/boost-binaries/ + +.. _SWIG: https://www.swig.org/download.html + +.. _this detailed guide: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html + +.. _the python-package documentation: https://github.com/lightgbm-org/LightGBM/tree/main/python-package + +.. _the ROCm docs: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/ + +.. _following docs: https://github.com/google/sanitizers/wiki + +.. _Ninja: https://ninja-build.org diff --git a/docs/Key-Events.md b/docs/Key-Events.md new file mode 100644 index 0000000..1d4b6fc --- /dev/null +++ b/docs/Key-Events.md @@ -0,0 +1 @@ +The content of this document was very outdated and is no longer available to avoid any misleadings. diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..627e704 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = -W +SPHINXBUILD = sphinx-build +SPHINXPROJ = LightGBM +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/Parallel-Learning-Guide.rst b/docs/Parallel-Learning-Guide.rst new file mode 100644 index 0000000..965e257 --- /dev/null +++ b/docs/Parallel-Learning-Guide.rst @@ -0,0 +1,549 @@ +Distributed Learning Guide +========================== + +.. _Parallel Learning Guide: + +This guide describes distributed learning in LightGBM. Distributed learning allows the use of multiple machines to produce a single model. + +Follow the `Quick Start <./Quick-Start.rst>`__ to know how to use LightGBM first. + +How Distributed LightGBM Works +------------------------------ + +This section describes how distributed learning in LightGBM works. To learn how to do this in various programming languages and frameworks, please see `Integrations <#integrations>`__. + +Choose Appropriate Parallel Algorithm +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +LightGBM provides 3 distributed learning algorithms now. + ++--------------------+---------------------------+ +| Parallel Algorithm | How to Use | ++====================+===========================+ +| Data parallel | ``tree_learner=data`` | ++--------------------+---------------------------+ +| Feature parallel | ``tree_learner=feature`` | ++--------------------+---------------------------+ +| Voting parallel | ``tree_learner=voting`` | ++--------------------+---------------------------+ + +These algorithms are suited for different scenarios, which is listed in the following table: + ++-------------------------+-------------------+-----------------+ +| | #data is small | #data is large | ++=========================+===================+=================+ +| **#feature is small** | Feature Parallel | Data Parallel | ++-------------------------+-------------------+-----------------+ +| **#feature is large** | Feature Parallel | Voting Parallel | ++-------------------------+-------------------+-----------------+ + +More details about these parallel algorithms can be found in `optimization in distributed learning <./Features.rst#optimization-in-distributed-learning>`__. + +Integrations +------------ + +This section describes how to run distributed LightGBM training in various programming languages and frameworks. To learn how distributed learning in LightGBM works generally, please see `How Distributed LightGBM Works <#how-distributed-lightgbm-works>`__. + +Apache Spark +^^^^^^^^^^^^ + +Apache Spark users can use `SynapseML`_ for machine learning workflows with LightGBM. This project is not maintained by LightGBM's maintainers. + +See `this SynapseML example`_ for additional information on using LightGBM on Spark. + +.. note:: + + ``SynapseML`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/microsoft/SynapseML/issues. + +Dask +^^^^ + +.. versionadded:: 3.2.0 + +LightGBM's Python-package supports distributed learning via `Dask`_. This integration is maintained by LightGBM's maintainers. + +.. warning:: + + Dask integration is only tested on macOS and Linux. + +Dask Examples +''''''''''''' + +For sample code using ``lightgbm.dask``, see `these Dask examples`_. + +Training with Dask +'''''''''''''''''' + +This section contains detailed information on performing LightGBM distributed training using Dask. + +Configuring the Dask Cluster +**************************** + +**Allocating Threads** + +When setting up a Dask cluster for training, give each Dask worker process at least two threads. If you do not do this, training might be substantially slower because communication work and training work will block each other. + +If you do not have other significant processes competing with Dask for resources, just accept the default ``nthreads`` from your chosen ``dask.distributed`` cluster. + +.. code:: python + + from distributed import Client, LocalCluster + + cluster = LocalCluster(n_workers=3) + client = Client(cluster) + +**Managing Memory** + +Use the Dask diagnostic dashboard or your preferred monitoring tool to monitor Dask workers' memory consumption during training. As described in `the Dask worker documentation`_, Dask workers will automatically start spilling data to disk if memory consumption gets too high. This can substantially slow down computations, since disk I/O is usually much slower than reading the same data from memory. + + `At 60% of memory load, [Dask will] spill least recently used data to disk` + +To reduce the risk of hitting memory limits, consider restarting each worker process before running any data loading or training code. + +.. code:: python + + client.restart() + +Setting Up Training Data +************************* + +The estimators in ``lightgbm.dask`` expect that matrix-like or array-like data are provided in Dask DataFrame, Dask Array, or (in some cases) Dask Series format. See `the Dask DataFrame documentation`_ and `the Dask Array documentation`_ for more information on how to create such data structures. + +.. image:: ./_static/images/dask-initial-setup.svg + :align: center + :width: 600px + :alt: On the left, rectangles showing a 5 by 5 grid for a local dataset. On the right, two circles representing Dask workers, one with a 3 by 5 grid and one with a 2 by 5 grid. + :target: ./_static/images/dask-initial-setup.svg + +While setting up for training, ``lightgbm`` will concatenate all of the partitions on a worker into a single dataset. Distributed training then proceeds with one LightGBM worker process per Dask worker. + +.. image:: ./_static/images/dask-concat.svg + :align: center + :width: 600px + :alt: A section labeled "before" showing two grids and a section labeled "after" showing a single grid that looks like the two from "before" stacked one on top of the other. + :target: ./_static/images/dask-concat.svg + +When setting up data partitioning for LightGBM training with Dask, try to follow these suggestions: + +* ensure that each worker in the cluster has some of the training data +* try to give each worker roughly the same amount of data, especially if your dataset is small +* if you plan to train multiple models (for example, to tune hyperparameters) on the same data, use ``client.persist()`` before training to materialize the data one time + +Using a Specific Dask Client +**************************** + +In most situations, you should not need to tell ``lightgbm.dask`` to use a specific Dask client. By default, the client returned by ``distributed.default_client()`` will be used. + +However, you might want to explicitly control the Dask client used by LightGBM if you have multiple active clients in the same session. This is useful in more complex workflows like running multiple training jobs on different Dask clusters. + +LightGBM's Dask estimators support setting an attribute ``client`` to control the client that is used. + +.. code:: python + + import lightgbm as lgb + from distributed import Client, LocalCluster + + cluster = LocalCluster() + client = Client(cluster) + + # option 1: keyword argument in constructor + dask_model = lgb.DaskLGBMClassifier(client=client) + + # option 2: set_params() after construction + dask_model = lgb.DaskLGBMClassifier() + dask_model.set_params(client=client) + +Using Specific Ports +******************** + +At the beginning of training, ``lightgbm.dask`` sets up a LightGBM network where each Dask worker runs one long-running task that acts as a LightGBM worker. During training, LightGBM workers communicate with each other over TCP sockets. By default, random open ports are used when creating these sockets. + +If the communication between Dask workers in the cluster used for training is restricted by firewall rules, you must tell LightGBM exactly what ports to use. + +**Option 1: provide a specific list of addresses and ports** + +LightGBM supports a parameter ``machines``, a comma-delimited string where each entry refers to one worker (host name or IP) and a port that that worker will accept connections on. If you provide this parameter to the estimators in ``lightgbm.dask``, LightGBM will not search randomly for ports. + +For example, consider the case where you are running one Dask worker process on each of the following IP addresses: + +.. code:: text + + 10.0.1.0 + 10.0.2.0 + 10.0.3.0 + +You could edit your firewall rules to allow traffic on one additional port on each of these hosts, then provide ``machines`` directly. + +.. code:: python + + import lightgbm as lgb + + machines = "10.0.1.0:12401,10.0.2.0:12402,10.0.3.0:15000" + dask_model = lgb.DaskLGBMRegressor(machines=machines) + +If you are running multiple Dask worker processes on physical host in the cluster, be sure that there are multiple entries for that IP address, with different ports. For example, if you were running a cluster with ``nprocs=2`` (2 Dask worker processes per machine), you might open two additional ports on each of these hosts, then provide ``machines`` as follows. + +.. code:: python + + import lightgbm as lgb + + machines = ",".join([ + "10.0.1.0:16000", + "10.0.1.0:16001", + "10.0.2.0:16000", + "10.0.2.0:16001", + ]) + dask_model = lgb.DaskLGBMRegressor(machines=machines) + +.. warning:: + + Providing ``machines`` gives you complete control over the networking details of training, but it also makes the training process fragile. Training will fail if you use ``machines`` and any of the following are true: + + * any of the ports mentioned in ``machines`` are not open when training begins + * some partitions of the training data are held by machines that that are not present in ``machines`` + * some machines mentioned in ``machines`` do not hold any of the training data + +**Option 2: specify one port to use on every worker** + +If you are only running one Dask worker process on each host, and if you can reliably identify a port that is open on every host, using ``machines`` is unnecessarily complicated. If ``local_listen_port`` is given and ``machines`` is not, LightGBM will not search for ports randomly, but it will limit the list of addresses in the LightGBM network to those Dask workers that have a piece of the training data. + +For example, consider the case where you are running one Dask worker process on each of the following IP addresses: + +.. code:: text + + 10.0.1.0 + 10.0.2.0 + 10.0.3.0 + +You could edit your firewall rules to allow communication between any of the workers over one port, then provide that port via parameter ``local_listen_port``. + +.. code:: python + + import lightgbm as lgb + + dask_model = lgb.DaskLGBMRegressor(local_listen_port=12400) + +.. warning:: + + Providing ``local_listen_port`` is slightly less fragile than ``machines`` because LightGBM will automatically figure out which workers have pieces of the training data. However, using this method, training can fail if any of the following are true: + + * the port ``local_listen_port`` is not open on any of the worker hosts + * any machine has multiple Dask worker processes running on it + +Using Custom Objective Functions with Dask +****************************************** + +.. versionadded:: 4.0.0 + +It is possible to customize the boosting process by providing a custom objective function written in Python. +See the Dask API's documentation for details on how to implement such functions. + +.. warning:: + + Custom objective functions used with ``lightgbm.dask`` will be called by each worker process on only that worker's local data. + +Follow the example below to use a custom implementation of the ``regression_l2`` objective. + +.. code:: python + + import dask.array as da + import lightgbm as lgb + import numpy as np + from distributed import Client, LocalCluster + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + X = da.random.random((1000, 10), (500, 10)) + y = da.random.random((1000,), (500,)) + + def custom_l2_obj(y_true, y_pred): + grad = y_pred - y_true + hess = np.ones(len(y_true)) + return grad, hess + + dask_model = lgb.DaskLGBMRegressor( + objective=custom_l2_obj + ) + dask_model.fit(X, y) + +Prediction with Dask +'''''''''''''''''''' + +The estimators from ``lightgbm.dask`` can be used to create predictions based on data stored in Dask collections. In that interface, ``.predict()`` expects a Dask Array or Dask DataFrame, and returns a Dask Array of predictions. + +See `the Dask prediction example`_ for some sample code that shows how to perform Dask-based prediction. + +For model evaluation, consider using `the metrics functions from dask-ml`_. Those functions are intended to provide the same API as equivalent functions in ``sklearn.metrics``, but they use distributed computation powered by Dask to compute metrics without all of the input data ever needing to be on a single machine. + +Saving Dask Models +'''''''''''''''''' + +After training with Dask, you have several options for saving a fitted model. + +**Option 1: pickle the Dask estimator** + +LightGBM's Dask estimators can be pickled directly with ``cloudpickle``, ``joblib``, or ``pickle``. + +.. code:: python + + import dask.array as da + import pickle + import lightgbm as lgb + from distributed import Client, LocalCluster + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + X = da.random.random((1000, 10), (500, 10)) + y = da.random.random((1000,), (500,)) + + dask_model = lgb.DaskLGBMRegressor() + dask_model.fit(X, y) + + with open("dask-model.pkl", "wb") as f: + pickle.dump(dask_model, f) + +A model saved this way can then later be loaded with whichever serialization library you used to save it. + +.. code:: python + + import pickle + with open("dask-model.pkl", "rb") as f: + dask_model = pickle.load(f) + +.. note:: + + If you explicitly set a Dask client (see `Using a Specific Dask Client <#using-a-specific-dask-client>`__), it will not be saved when pickling the estimator. When loading a Dask estimator from disk, if you need to use a specific client you can add it after loading with ``dask_model.set_params(client=client)``. + +**Option 2: pickle the sklearn estimator** + +The estimators available from ``lightgbm.dask`` can be converted to an instance of the equivalent class from ``lightgbm.sklearn``. Choosing this option allows you to use Dask for training but avoid depending on any Dask libraries at scoring time. + +.. code:: python + + import dask.array as da + import joblib + import lightgbm as lgb + from distributed import Client, LocalCluster + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + X = da.random.random((1000, 10), (500, 10)) + y = da.random.random((1000,), (500,)) + + dask_model = lgb.DaskLGBMRegressor() + dask_model.fit(X, y) + + # convert to sklearn equivalent + sklearn_model = dask_model.to_local() + + print(type(sklearn_model)) + #> lightgbm.sklearn.LGBMRegressor + + joblib.dump(sklearn_model, "sklearn-model.joblib") + +A model saved this way can then later be loaded with whichever serialization library you used to save it. + +.. code:: python + + import joblib + + sklearn_model = joblib.load("sklearn-model.joblib") + +**Option 3: save the LightGBM Booster** + +The lowest-level model object in LightGBM is the ``lightgbm.Booster``. After training, you can extract a Booster from the Dask estimator. + +.. code:: python + + import dask.array as da + import lightgbm as lgb + from distributed import Client, LocalCluster + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + X = da.random.random((1000, 10), (500, 10)) + y = da.random.random((1000,), (500,)) + + dask_model = lgb.DaskLGBMRegressor() + dask_model.fit(X, y) + + # get underlying Booster object + bst = dask_model.booster_ + +From the point forward, you can use any of the following methods to save the Booster: + +* serialize with ``cloudpickle``, ``joblib``, or ``pickle`` +* ``bst.dump_model()``: dump the model to a dictionary which could be written out as JSON +* ``bst.model_to_string()``: dump the model to a string in memory +* ``bst.save_model()``: write the output of ``bst.model_to_string()`` to a text file + +Kubeflow +^^^^^^^^ + +Kubeflow users can also use the `Kubeflow XGBoost Operator`_ for machine learning workflows with LightGBM. You can see `this example`_ for more details. + +Kubeflow integrations for LightGBM are not maintained by LightGBM's maintainers. + +.. note:: + + The Kubeflow integrations for LightGBM are not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/kubeflow/fairing/issues or https://github.com/kubeflow/xgboost-operator/issues. + +LightGBM CLI +^^^^^^^^^^^^ + +.. _Build Parallel Version: + +Preparation +''''''''''' + +By default, distributed learning with LightGBM uses socket-based communication. + +If you need to build distributed version with MPI support, please refer to `Installation Guide <./Installation-Guide.rst#build-mpi-version>`__. + +Socket Version +************** + +It needs to collect IP of all machines that want to run distributed learning in and allocate one TCP port (assume 12345 here) for all machines, +and change firewall rules to allow income of this port (12345). Then write these IP and ports in one file (assume ``mlist.txt``), like following: + +.. code:: text + + machine1_ip 12345 + machine2_ip 12345 + +MPI Version +*********** + +It needs to collect IP (or hostname) of all machines that want to run distributed learning in. +Then write these IP in one file (assume ``mlist.txt``) like following: + +.. code:: text + + machine1_ip + machine2_ip + +**Note**: For Windows users, need to start "smpd" to start MPI service. More details can be found `here`_. + +Run Distributed Learning +'''''''''''''''''''''''' + +.. _Run Parallel Learning: + +Socket Version +************** + +1. Edit following parameters in config file: + + ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here. + + ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here. + + ``machine_list_file=mlist.txt``, ``mlist.txt`` is created in `Preparation section <#preparation>`__. + + ``local_listen_port=12345``, ``12345`` is allocated in `Preparation section <#preparation>`__. + +2. Copy data file, executable file, config file and ``mlist.txt`` to all machines. + +3. Run following command on all machines, you need to change ``your_config_file`` to real config file. + + For Windows: ``lightgbm.exe config=your_config_file`` + + For Linux: ``./lightgbm config=your_config_file`` + +MPI Version +*********** + +1. Edit following parameters in config file: + + ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here. + + ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here. + +2. Copy data file, executable file, config file and ``mlist.txt`` to all machines. + + **Note**: MPI needs to be run in the **same path on all machines**. + +3. Run following command on one machine (not need to run on all machines), need to change ``your_config_file`` to real config file. + + For Windows: + + .. code:: console + + mpiexec.exe /machinefile mlist.txt lightgbm.exe config=your_config_file + + For Linux: + + .. code:: console + + mpiexec --machinefile mlist.txt ./lightgbm config=your_config_file + +Example +''''''' + +- `A simple distributed learning example`_ + +Ray +^^^ + +`Ray`_ is a Python-based framework for distributed computing. Ray provides LightGBM support through the Ray Train API with ``LightGBMTrainer`` and the `lightgbm_ray`_ project maintained within the official Ray GitHub organization. + +For the Ray Train API, see `the Ray documentation`_ for usage examples. + +For the lightgbm_ray project, see `the lightgbm_ray documentation`_ for usage examples. + +.. note:: + + ``lightgbm_ray`` and ``ray`` are not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/ray-project/lightgbm_ray/issues and https://github.com/ray-project/ray/issues respectively. + +Mars +^^^^ + +`Mars`_ is a tensor-based framework for large-scale data computation. LightGBM integration, maintained within the Mars GitHub repository, can be used to perform distributed LightGBM training using ``pymars``. + +See `the mars documentation`_ for usage examples. + +.. note:: + + ``Mars`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/mars-project/mars/issues. + +.. _Dask: https://docs.dask.org/en/latest/ + +.. _SynapseML: https://aka.ms/spark + +.. _this SynapseML example: https://github.com/microsoft/SynapseML/tree/master/docs/Explore%20Algorithms/LightGBM + +.. _the Dask Array documentation: https://docs.dask.org/en/latest/array.html + +.. _the Dask DataFrame documentation: https://docs.dask.org/en/latest/dataframe.html + +.. _the Dask prediction example: https://github.com/lightgbm-org/LightGBM/blob/main/examples/python-guide/dask/prediction.py + +.. _the Dask worker documentation: https://distributed.dask.org/en/stable/worker-memory.html + +.. _the metrics functions from dask-ml: https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics + +.. _these Dask examples: https://github.com/lightgbm-org/LightGBM/tree/main/examples/python-guide/dask + +.. _Kubeflow XGBoost Operator: https://github.com/kubeflow/xgboost-operator + +.. _this example: https://github.com/kubeflow/xgboost-operator/tree/master/config/samples/lightgbm-dist + +.. _here: https://www.youtube.com/watch?v=iqzXhp5TxUY + +.. _A simple distributed learning example: https://github.com/lightgbm-org/LightGBM/tree/main/examples/parallel_learning + +.. _lightgbm_ray: https://github.com/ray-project/lightgbm_ray + +.. _Ray: https://www.ray.io/ + +.. _the lightgbm_ray documentation: https://docs.ray.io/en/latest/tune/api_docs/integration.html#lightgbm-tune-integration-lightgbm + +.. _the Ray documentation: https://docs.ray.io/en/latest/train/api/api.html#lightgbm + +.. _Mars: https://mars-project.readthedocs.io/en/latest/ + +.. _the mars documentation: https://mars-project.readthedocs.io/en/latest/user_guide/learn/lightgbm.html diff --git a/docs/Parameters-Tuning.rst b/docs/Parameters-Tuning.rst new file mode 100644 index 0000000..9a3593f --- /dev/null +++ b/docs/Parameters-Tuning.rst @@ -0,0 +1,221 @@ +Parameters Tuning +================= + +This page contains parameters tuning guides for different scenarios. + +**List of other helpful links** + +- `Parameters <./Parameters.rst>`__ +- `Python API <./Python-API.rst>`__ +- `FLAML`_ for automated hyperparameter tuning +- `Optuna`_ for automated hyperparameter tuning + +Tune Parameters for the Leaf-wise (Best-first) Tree +--------------------------------------------------- + +LightGBM uses the `leaf-wise <./Features.rst#leaf-wise-best-first-tree-growth>`__ tree growth algorithm, while many other popular tools use depth-wise tree growth. +Compared with depth-wise growth, the leaf-wise algorithm can converge much faster. +However, the leaf-wise growth may be over-fitting if not used with the appropriate parameters. + +To get good results using a leaf-wise tree, these are some important parameters: + +1. ``num_leaves``. This is the main parameter to control the complexity of the tree model. + Theoretically, we can set ``num_leaves = 2^(max_depth)`` to obtain the same number of leaves as depth-wise tree. + However, this simple conversion is not good in practice. + A leaf-wise tree is typically much deeper than a depth-wise tree for a fixed number of leaves. Unconstrained depth can induce over-fitting. + Thus, when trying to tune the ``num_leaves``, we should let it be smaller than ``2^(max_depth)``. + For example, when the ``max_depth=7`` the depth-wise tree can get good accuracy, + but setting ``num_leaves`` to ``127`` may cause over-fitting, and setting it to ``70`` or ``80`` may get better accuracy than depth-wise. + +2. ``min_data_in_leaf``. This is a very important parameter to prevent over-fitting in a leaf-wise tree. + Its optimal value depends on the number of training samples and ``num_leaves``. + Setting it to a large value can avoid growing too deep a tree, but may cause under-fitting. + In practice, setting it to hundreds or thousands is enough for a large dataset. + +3. ``max_depth``. You also can use ``max_depth`` to limit the tree depth explicitly. + If you set ``max_depth``, also explicitly set ``num_leaves`` to some value ``<= 2^max_depth``. + +For Faster Speed +---------------- + +Add More Computational Resources +'''''''''''''''''''''''''''''''' + +On systems where it is available, LightGBM uses OpenMP to parallelize many operations. The maximum number of threads used by LightGBM is controlled by the parameter ``num_threads``. By default, this will defer to the default behavior of OpenMP (one thread per real CPU core or the value in environment variable ``OMP_NUM_THREADS``, if it is set). For best performance, set this to the number of **real** CPU cores available. + +You might be able to achieve faster training by moving to a machine with more available CPU cores. + +Using distributed (multi-machine) training might also reduce training time. See the `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`_ for details. + +Use a GPU-enabled version of LightGBM +''''''''''''''''''''''''''''''''''''' + +You might find that training is faster using a GPU-enabled build of LightGBM. See the `GPU Tutorial <./GPU-Tutorial.rst>`__ for details. + +Grow Shallower Trees +'''''''''''''''''''' + +The total training time for LightGBM increases with the total number of tree nodes added. LightGBM comes with several parameters that can be used to control the number of nodes per tree. + +The suggestions below will speed up training, but might hurt training accuracy. + +Decrease ``max_depth`` +********************** + +This parameter is an integer that controls the maximum distance between the root node of each tree and a leaf node. Decrease ``max_depth`` to reduce training time. + +Decrease ``num_leaves`` +*********************** + +LightGBM adds nodes to trees based on the gain from adding that node, regardless of depth. This figure from `the feature documentation <./Features.rst#leaf-wise-best-first-tree-growth>`__ illustrates the process. + +.. image:: ./_static/images/leaf-wise.png + :align: center + :alt: Three consecutive images of decision trees, where each shows the tree with an additional two leaf nodes added. Shows that leaf-wise growth can result in trees that have some branches which are longer than others. + +Because of this growth strategy, it isn't straightforward to use ``max_depth`` alone to limit the complexity of trees. The ``num_leaves`` parameter sets the maximum number of nodes per tree. Decrease ``num_leaves`` to reduce training time. + +Increase ``min_gain_to_split`` +****************************** + +When adding a new tree node, LightGBM chooses the split point that has the largest gain. Gain is basically the reduction in training loss that results from adding a split point. By default, LightGBM sets ``min_gain_to_split`` to 0.0, which means "there is no improvement that is too small". However, in practice you might find that very small improvements in the training loss don't have a meaningful impact on the generalization error of the model. Increase ``min_gain_to_split`` to reduce training time. + +Increase ``min_data_in_leaf`` and ``min_sum_hessian_in_leaf`` +************************************************************* + +Depending on the size of the training data and the distribution of features, it's possible for LightGBM to add tree nodes that only describe a small number of observations. In the most extreme case, consider the addition of a tree node that only a single observation from the training data falls into. This is very unlikely to generalize well, and probably is a sign of overfitting. + +This can be prevented indirectly with parameters like ``max_depth`` and ``num_leaves``, but LightGBM also offers parameters to help you directly avoid adding these overly-specific tree nodes. + +- ``min_data_in_leaf``: Minimum number of observations that must fall into a tree node for it to be added. +- ``min_sum_hessian_in_leaf``: Minimum sum of the Hessian (second derivative of the objective function evaluated for each observation) for observations in a leaf. For some regression objectives, this is just the minimum number of records that have to fall into each node. For classification objectives, it represents a sum over a distribution of probabilities. See `this Stack Overflow answer `_ for a good description of how to reason about values of this parameter. + +Grow Less Trees +''''''''''''''' + +Decrease ``num_iterations`` +*************************** + +The ``num_iterations`` parameter controls the number of boosting rounds that will be performed. Since LightGBM uses decision trees as the learners, this can also be thought of as "number of trees". + +If you try changing ``num_iterations``, change the ``learning_rate`` as well. ``learning_rate`` will not have any impact on training time, but it will impact the training accuracy. As a general rule, if you reduce ``num_iterations``, you should increase ``learning_rate``. + +Choosing the right value of ``num_iterations`` and ``learning_rate`` is highly dependent on the data and objective, so these parameters are often chosen from a set of possible values through hyperparameter tuning. + +Decrease ``num_iterations`` to reduce training time. + +Use Early Stopping +****************** + +If early stopping is enabled, after each boosting round the model's training accuracy is evaluated against a validation set that contains data not available to the training process. That accuracy is then compared to the accuracy as of the previous boosting round. If the model's accuracy fails to improve for some number of consecutive rounds, LightGBM stops the training process. + +That "number of consecutive rounds" is controlled by the parameter ``early_stopping_round``. For example, ``early_stopping_round=1`` says "the first time accuracy on the validation set does not improve, stop training". + +Set ``early_stopping_round`` and provide a validation set to possibly reduce training time. + +Consider Fewer Splits +''''''''''''''''''''' + +The parameters described in previous sections control how many trees are constructed and how many nodes are constructed per tree. Training time can be further reduced by reducing the amount of time needed to add a tree node to the model. + +The suggestions below will speed up training, but might hurt training accuracy. + +Enable Feature Pre-Filtering When Creating Dataset +************************************************** + +By default, when a LightGBM ``Dataset`` object is constructed, some features will be filtered out based on the value of ``min_data_in_leaf``. + +For a simple example, consider a 1000-observation dataset with a feature called ``feature_1``. ``feature_1`` takes on only two values: 25.0 (995 observations) and 50.0 (5 observations). If ``min_data_in_leaf = 10``, there is no split for this feature which will result in a valid split at least one of the leaf nodes will only have 5 observations. + +Instead of reconsidering this feature and then ignoring it every iteration, LightGBM filters this feature out at before training, when the ``Dataset`` is constructed. + +If this default behavior has been overridden by setting ``feature_pre_filter=False``, set ``feature_pre_filter=True`` to reduce training time. + +Decrease ``max_bin`` or ``max_bin_by_feature`` When Creating Dataset +******************************************************************** + +LightGBM training `buckets continuous features into discrete bins <./Features.rst#optimization-in-speed-and-memory-usage>`_ to improve training speed and reduce memory requirements for training. This binning is done one time during ``Dataset`` construction. The number of splits considered when adding a node is ``O(#feature * #bin)``, so reducing the number of bins per feature can reduce the number of splits that need to be evaluated. + +``max_bin`` is controls the maximum number of bins that features will bucketed into. It is also possible to set this maximum feature-by-feature, by passing ``max_bin_by_feature``. + +Reduce ``max_bin`` or ``max_bin_by_feature`` to reduce training time. + +Increase ``min_data_in_bin`` When Creating Dataset +************************************************** + +Some bins might contain a small number of observations, which might mean that the effort of evaluating that bin's boundaries as possible split points isn't likely to change the final model very much. You can control the granularity of the bins by setting ``min_data_in_bin``. + +Increase ``min_data_in_bin`` to reduce training time. + +Decrease ``feature_fraction`` +***************************** + +By default, LightGBM considers all features in a ``Dataset`` during the training process. This behavior can be changed by setting ``feature_fraction`` to a value ``> 0`` and ``<= 1.0``. Setting ``feature_fraction`` to ``0.5``, for example, tells LightGBM to randomly select ``50%`` of features at the beginning of constructing each tree. This reduces the total number of splits that have to be evaluated to add each tree node. + +Decrease ``feature_fraction`` to reduce training time. + +Decrease ``max_cat_threshold`` +****************************** + +LightGBM uses a `custom approach for finding optimal splits for categorical features <./Advanced-Topics.html#categorical-feature-support>`_. In this process, LightGBM explores splits that break a categorical feature into two groups. These are sometimes called "k-vs.-rest" splits. Higher ``max_cat_threshold`` values correspond to more split points and larger possible group sizes to search. + +Decrease ``max_cat_threshold`` to reduce training time. + +Use Less Data +''''''''''''' + +Use Bagging +*********** + +By default, LightGBM uses all observations in the training data for each iteration. It is possible to instead tell LightGBM to randomly sample the training data. This process of training over multiple random samples without replacement is called "bagging". + +Set ``bagging_freq`` to an integer greater than 0 to control how often a new sample is drawn. Set ``bagging_fraction`` to a value ``> 0.0`` and ``< 1.0`` to control the size of the sample. For example, ``{"bagging_freq": 5, "bagging_fraction": 0.75}`` tells LightGBM "re-sample without replacement every 5 iterations, and draw samples of 75% of the training data". + +Decrease ``bagging_fraction`` to reduce training time. + + +Save Constructed Datasets with ``save_binary`` +'''''''''''''''''''''''''''''''''''''''''''''' + +This only applies to the LightGBM CLI. If you pass parameter ``save_binary``, the training dataset and all validations sets will be saved in a binary format understood by LightGBM. This can speed up training next time, because binning and other work done when constructing a ``Dataset`` does not have to be re-done. + + +For Better Accuracy +------------------- + +- Use large ``max_bin`` (may be slower) + +- Use small ``learning_rate`` with large ``num_iterations`` + +- Use large ``num_leaves`` (may cause over-fitting) + +- Use bigger training data + +- Try ``dart`` + +Deal with Over-fitting +---------------------- + +- Use small ``max_bin`` + +- Use small ``num_leaves`` + +- Use ``min_data_in_leaf`` and ``min_sum_hessian_in_leaf`` + +- Use bagging by set ``bagging_fraction`` and ``bagging_freq`` + +- Use feature sub-sampling by set ``feature_fraction`` + +- Use bigger training data + +- Try ``lambda_l1``, ``lambda_l2`` and ``min_gain_to_split`` for regularization + +- Try ``max_depth`` to avoid growing deep tree + +- Try ``extra_trees`` + +- Try increasing ``path_smooth`` + +.. _Optuna: https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258 + +.. _FLAML: https://github.com/microsoft/FLAML diff --git a/docs/Parameters.rst b/docs/Parameters.rst new file mode 100644 index 0000000..9aaea5b --- /dev/null +++ b/docs/Parameters.rst @@ -0,0 +1,1483 @@ +.. List of parameters is auto generated by LightGBM\.ci\parameter-generator.py from LightGBM\include\LightGBM\config.h file. + +.. role:: raw-html(raw) + :format: html + +Parameters +========== + +This page contains descriptions of all parameters in LightGBM. + +**List of other helpful links** + +- `Python API <./Python-API.rst>`__ + +- `Parameters Tuning <./Parameters-Tuning.rst>`__ + +Parameters Format +----------------- + +Parameters are merged together in the following order (later items overwrite earlier ones): + +1. LightGBM's default values +2. special files for ``weight``, ``init_score``, ``query``, and ``positions`` (see `Others <#others>`__) +3. (CLI only) configuration in a file passed like ``config=train.conf`` +4. (CLI only) configuration passed via the command line +5. (Python, R) special keyword arguments to some functions (e.g. ``num_boost_round`` in ``train()``) +6. (Python, R) ``params`` function argument (including ``**kwargs`` in Python and ``...`` in R) +7. (C API) ``parameters`` or ``params`` function argument + +Many parameters have "aliases", alternative names which refer to the same configuration. + +Where a mix of the primary parameter name and aliases are given, the primary parameter name is always preferred to any aliases. + +For example, in Python: + +.. code-block:: python + + # use learning rate of 0.07, because 'learning_rate' + # is the primary parameter name + lgb.train( + params={ + "learning_rate": 0.07, + "shrinkage_rate": 0.12 + }, + train_set=dtrain + ) + +Where multiple aliases are given, and the primary parameter name is not, the first alias +appearing in the lists returned by ``Config::parameter2aliases()`` in the C++ library is used. +Those lists are hard-coded in a fairly arbitrary way... wherever possible, avoid relying on this behavior. + +For example, in Python: + +.. code-block:: python + + # use learning rate of 0.12, LightGBM has a hard-coded preference for 'shrinkage_rate' + # over any other aliases, and 'learning_rate' is not provided + lgb.train( + params={ + "eta": 0.19, + "shrinkage_rate": 0.12 + }, + train_set=dtrain + ) + +**CLI** + +The parameters format is ``key1=value1 key2=value2 ...``. +Parameters can be set both in config file and command line. +By using command line, parameters should not have spaces before and after ``=``. +By using config files, one line can only contain one parameter. You can use ``#`` to comment. + +**Python** + +Any parameters that accept multiple values should be passed as a Python list. + +.. code-block:: python + + params = { + "monotone_constraints": [-1, 0, 1] + } + + +**R** + +Any parameters that accept multiple values should be passed as an R list. + +.. code-block:: r + + params <- list( + monotone_constraints = c(-1, 0, 1) + ) + +.. start params list + +Core Parameters +--------------- + +- ``config`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``config_file`` + + - path of config file + + - **Note**: can be used only in CLI version + +- ``task`` :raw-html:`🔗︎`, default = ``train``, type = enum, options: ``train``, ``predict``, ``convert_model``, ``refit``, aliases: ``task_type`` + + - ``train``, for training, aliases: ``training`` + + - ``predict``, for prediction, aliases: ``prediction``, ``test`` + + - ``convert_model``, for converting model file into if-else format, see more information in `Convert Parameters <#convert-parameters>`__ + + - ``refit``, for refitting existing models with new data, aliases: ``refit_tree`` + + - ``save_binary``, load train (and validation) data then save dataset to binary file. Typical usage: ``save_binary`` first, then run multiple ``train`` tasks in parallel using the saved binary file + + - **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions + +- ``objective`` :raw-html:`🔗︎`, default = ``regression``, type = enum, options: ``regression``, ``regression_l1``, ``huber``, ``fair``, ``poisson``, ``quantile``, ``mape``, ``gamma``, ``tweedie``, ``binary``, ``multiclass``, ``multiclassova``, ``cross_entropy``, ``cross_entropy_lambda``, ``lambdarank``, ``rank_xendcg``, aliases: ``objective_type``, ``app``, ``application``, ``loss`` + + - regression application + + - ``regression``, L2 loss, aliases: ``regression_l2``, ``l2``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse`` + + - ``regression_l1``, L1 loss, aliases: ``l1``, ``mean_absolute_error``, ``mae`` + + - ``huber``, `Huber loss `__ + + - ``fair``, `Fair loss `__ + + - ``poisson``, `Poisson regression `__ + + - ``quantile``, `Quantile regression `__ + + - ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` + + - ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed `__ + + - ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed `__ + + - binary classification application + + - ``binary``, binary `log loss `__ classification (or logistic regression) + + - requires labels in {0, 1}; see ``cross-entropy`` application for general probability labels in [0, 1] + + - multi-class classification application + + - ``multiclass``, `softmax `__ objective function, aliases: ``softmax`` + + - ``multiclassova``, `One-vs-All `__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr`` + + - ``num_class`` should be set as well + + - cross-entropy application + + - ``cross_entropy``, objective function for cross-entropy (with optional linear weights), aliases: ``xentropy`` + + - ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda`` + + - label is anything in interval [0, 1] + + - ranking application + + - ``lambdarank``, `lambdarank `__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain`` + + - ``rank_xendcg``, `XE_NDCG_MART `__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` + + - ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank`` + + - label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect) + + - custom objective function (gradients and hessians not computed directly by LightGBM) + + - ``custom`` + + - must be passed through parameters explicitly in the C API + + - **Note**: cannot be used in CLI version + +- ``boosting`` :raw-html:`🔗︎`, default = ``gbdt``, type = enum, options: ``gbdt``, ``rf``, ``dart``, aliases: ``boosting_type``, ``boost`` + + - ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt`` + + - ``rf``, Random Forest, aliases: ``random_forest`` + + - ``dart``, `Dropouts meet Multiple Additive Regression Trees `__ + + - **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations + +- ``data_sample_strategy`` :raw-html:`🔗︎`, default = ``bagging``, type = enum, options: ``bagging``, ``goss`` + + - ``bagging``, Randomly Bagging Sampling + + - **Note**: ``bagging`` is only effective when ``bagging_freq > 0`` and ``bagging_fraction < 1.0`` + + - ``goss``, Gradient-based One-Side Sampling + + - *New in version 4.0.0* + +- ``data`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``train``, ``train_data``, ``train_data_file``, ``data_filename`` + + - path of training data, LightGBM will train from this data + + - **Note**: can be used only in CLI version + +- ``valid`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``test``, ``valid_data``, ``valid_data_file``, ``test_data``, ``test_data_file``, ``valid_filenames`` + + - path(s) of validation/test data, LightGBM will output metrics for these data + + - support multiple validation data, separated by ``,`` + + - **Note**: can be used only in CLI version + +- ``num_iterations`` :raw-html:`🔗︎`, default = ``100``, type = int, aliases: ``num_iteration``, ``n_iter``, ``num_tree``, ``num_trees``, ``num_round``, ``num_rounds``, ``nrounds``, ``num_boost_round``, ``n_estimators``, ``max_iter``, constraints: ``num_iterations >= 0`` + + - number of boosting iterations + + - **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems + +- ``learning_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, aliases: ``shrinkage_rate``, ``eta``, constraints: ``learning_rate > 0.0`` + + - shrinkage rate + + - in ``dart``, it also affects on normalization weights of dropped trees + +- ``num_leaves`` :raw-html:`🔗︎`, default = ``31``, type = int, aliases: ``num_leaf``, ``max_leaves``, ``max_leaf``, ``max_leaf_nodes``, constraints: ``1 < num_leaves <= 131072`` + + - max number of leaves in one tree + +- ``tree_learner`` :raw-html:`🔗︎`, default = ``serial``, type = enum, options: ``serial``, ``feature``, ``data``, ``voting``, aliases: ``tree``, ``tree_type``, ``tree_learner_type`` + + - ``serial``, single machine tree learner + + - ``feature``, feature parallel tree learner, aliases: ``feature_parallel`` + + - ``data``, data parallel tree learner, aliases: ``data_parallel`` + + - ``voting``, voting parallel tree learner, aliases: ``voting_parallel`` + + - refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details + +- ``num_threads`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``num_thread``, ``nthread``, ``nthreads``, ``n_jobs`` + + - used only in ``train``, ``prediction`` and ``refit`` tasks or in correspondent functions of language-specific packages + + - number of threads for LightGBM + + - ``0`` means default number of threads in OpenMP + + - for the best speed, set this to the number of **real CPU cores**, not the number of threads (most CPUs use `hyper-threading `__ to generate 2 threads per CPU core) + + - do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows) + + - be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal** + + - for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication + + - **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors + +- ``device_type`` :raw-html:`🔗︎`, default = ``cpu``, type = enum, options: ``cpu``, ``gpu``, ``cuda``, aliases: ``device`` + + - device for the tree learning + + - ``cpu`` supports all LightGBM functionality and is portable across the widest range of operating systems and hardware + + - ``cuda`` offers faster training than ``gpu`` or ``cpu``, but only works on GPUs supporting CUDA or ROCm + + - ``gpu`` can be faster than ``cpu`` and works on a wider range of GPUs than CUDA + + - **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up + + - **Note**: for the faster speed, GPU uses 32-bit float point to sum up by default, so this may affect the accuracy for some tasks. You can set ``gpu_use_dp=true`` to enable 64-bit float point, but it will slow down the training + + - **Note**: refer to `Installation Guide <./Installation-Guide.rst>`__ to build LightGBM with GPU, CUDA, or ROCm support + +- ``seed`` :raw-html:`🔗︎`, default = ``None``, type = int, aliases: ``random_seed``, ``random_state`` + + - this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc. + + - by default, this seed is unused in favor of default values of other seeds + + - this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly + +- ``deterministic`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only with ``cpu`` device type + + - setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``) + + - when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different + + - you can `raise issues `__ in LightGBM GitHub repo when you meet the unstable results + + - **Note**: setting this to ``true`` may slow down the training + + - **Note**: to avoid potential instability due to numerical issues, please set ``force_col_wise=true`` or ``force_row_wise=true`` when setting ``deterministic=true`` + +Learning Control Parameters +--------------------------- + +- ``force_col_wise`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only with ``cpu`` device type + + - set this to ``true`` to force col-wise histogram building + + - enabling this is recommended when: + + - the number of columns is large, or the total number of bins is large + + - ``num_threads`` is large, e.g. ``> 20`` + + - you want to reduce memory cost + + - **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually + + - **Note**: this parameter cannot be used at the same time with ``force_row_wise``, choose only one of them + +- ``force_row_wise`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only with ``cpu`` device type + + - set this to ``true`` to force row-wise histogram building + + - enabling this is recommended when: + + - the number of data points is large, and the total number of bins is relatively small + + - ``num_threads`` is relatively small, e.g. ``<= 16`` + + - you want to use small ``bagging_fraction`` or ``goss`` sample strategy to speed up + + - **Note**: setting this to ``true`` will double the memory cost for Dataset object. If you have not enough memory, you can try setting ``force_col_wise=true`` + + - **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually + + - **Note**: this parameter cannot be used at the same time with ``force_col_wise``, choose only one of them + +- ``histogram_pool_size`` :raw-html:`🔗︎`, default = ``-1.0``, type = double, aliases: ``hist_pool_size`` + + - max cache size in MB for historical histogram + + - ``< 0`` means no limit + +- ``max_depth`` :raw-html:`🔗︎`, default = ``-1``, type = int + + - limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise + + - ``<= 0`` means no limit + +- ``min_data_in_leaf`` :raw-html:`🔗︎`, default = ``20``, type = int, aliases: ``min_data_per_leaf``, ``min_data``, ``min_child_samples``, ``min_samples_leaf``, constraints: ``min_data_in_leaf >= 0`` + + - minimal number of data in one leaf. Can be used to deal with over-fitting + + - **Note**: this is an approximation based on the Hessian, so occasionally you may observe splits which produce leaf nodes that have less than this many observations + +- ``min_sum_hessian_in_leaf`` :raw-html:`🔗︎`, default = ``1e-3``, type = double, aliases: ``min_sum_hessian_per_leaf``, ``min_sum_hessian``, ``min_hessian``, ``min_child_weight``, constraints: ``min_sum_hessian_in_leaf >= 0.0`` + + - minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting + +- ``bagging_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_row``, ``subsample``, ``bagging``, constraints: ``0.0 < bagging_fraction <= 1.0`` + + - like ``feature_fraction``, but this will randomly select part of data without resampling + + - can be used to speed up training + + - can be used to deal with over-fitting + + - **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well + +- ``pos_bagging_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``pos_sub_row``, ``pos_subsample``, ``pos_bagging``, constraints: ``0.0 < pos_bagging_fraction <= 1.0`` + + - used only in ``binary`` application + + - used for imbalanced binary classification problem, will randomly sample ``#pos_samples * pos_bagging_fraction`` positive samples in bagging + + - should be used together with ``neg_bagging_fraction`` + + - set this to ``1.0`` to disable + + - **Note**: to enable this, you need to set ``bagging_freq`` and ``neg_bagging_fraction`` as well + + - **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled + + - **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored + +- ``neg_bagging_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``neg_sub_row``, ``neg_subsample``, ``neg_bagging``, constraints: ``0.0 < neg_bagging_fraction <= 1.0`` + + - used only in ``binary`` application + + - used for imbalanced binary classification problem, will randomly sample ``#neg_samples * neg_bagging_fraction`` negative samples in bagging + + - should be used together with ``pos_bagging_fraction`` + + - set this to ``1.0`` to disable + + - **Note**: to enable this, you need to set ``bagging_freq`` and ``pos_bagging_fraction`` as well + + - **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled + + - **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored + +- ``bagging_freq`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``subsample_freq`` + + - frequency for bagging + + - ``0`` means disable bagging; ``k`` means perform bagging at every ``k`` iteration. Every ``k``-th iteration, LightGBM will randomly select ``bagging_fraction * 100%`` of the data to use for the next ``k`` iterations + + - **Note**: bagging is only effective when ``0.0 < bagging_fraction < 1.0`` + +- ``bagging_seed`` :raw-html:`🔗︎`, default = ``3``, type = int, aliases: ``bagging_fraction_seed`` + + - random seed for bagging + +- ``bagging_by_query`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - whether to do bagging sample by query + + - *New in version 4.6.0* + +- ``feature_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_feature``, ``colsample_bytree``, constraints: ``0.0 < feature_fraction <= 1.0`` + + - LightGBM will randomly select a subset of features on each iteration (tree) if ``feature_fraction`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features before training each tree + + - can be used to speed up training + + - can be used to deal with over-fitting + +- ``feature_fraction_bynode`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_feature_bynode``, ``colsample_bynode``, constraints: ``0.0 < feature_fraction_bynode <= 1.0`` + + - LightGBM will randomly select a subset of features on each tree node if ``feature_fraction_bynode`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features at each tree node + + - can be used to deal with over-fitting + + - **Note**: unlike ``feature_fraction``, this cannot speed up training + + - **Note**: if both ``feature_fraction`` and ``feature_fraction_bynode`` are smaller than ``1.0``, the final fraction of each node is ``feature_fraction * feature_fraction_bynode`` + +- ``feature_fraction_seed`` :raw-html:`🔗︎`, default = ``2``, type = int + + - random seed for ``feature_fraction`` + +- ``extra_trees`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``extra_tree`` + + - use extremely randomized trees + + - if set to ``true``, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature + + - can be used to speed up training + + - can be used to deal with over-fitting + +- ``extra_seed`` :raw-html:`🔗︎`, default = ``6``, type = int + + - random seed for selecting thresholds when ``extra_trees`` is true + +- ``early_stopping_round`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``early_stopping_rounds``, ``early_stopping``, ``n_iter_no_change`` + + - will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds + + - ``<= 0`` means disable + + - can be used to speed up training + +- ``early_stopping_min_delta`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``early_stopping_min_delta >= 0.0`` + + - when early stopping is used (i.e. ``early_stopping_round > 0``), require the early stopping metric to improve by at least this delta to be considered an improvement + + - *New in version 4.4.0* + +- ``first_metric_only`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - LightGBM allows you to provide multiple evaluation metrics. Set this to ``true``, if you want to use only the first metric for early stopping + +- ``max_delta_step`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``max_tree_output``, ``max_leaf_output`` + + - used to limit the max output of tree leaves + + - ``<= 0`` means no constraint + + - the final max output of leaves is ``learning_rate * max_delta_step`` + +- ``lambda_l1`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``reg_alpha``, ``l1_regularization``, constraints: ``lambda_l1 >= 0.0`` + + - L1 regularization + +- ``lambda_l2`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``reg_lambda``, ``lambda``, ``l2_regularization``, constraints: ``lambda_l2 >= 0.0`` + + - L2 regularization + +- ``linear_lambda`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``linear_lambda >= 0.0`` + + - linear tree regularization, corresponds to the parameter ``lambda`` in Eq. 3 of `Gradient Boosting with Piece-Wise Linear Regression Trees `__ + +- ``min_gain_to_split`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``min_split_gain``, constraints: ``min_gain_to_split >= 0.0`` + + - the minimal gain to perform split + + - can be used to speed up training + +- ``drop_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, aliases: ``rate_drop``, constraints: ``0.0 <= drop_rate <= 1.0`` + + - used only in ``dart`` + + - dropout rate: a fraction of previous trees to drop during the dropout + +- ``max_drop`` :raw-html:`🔗︎`, default = ``50``, type = int + + - used only in ``dart`` + + - max number of dropped trees during one boosting iteration + + - ``<=0`` means no limit + +- ``skip_drop`` :raw-html:`🔗︎`, default = ``0.5``, type = double, constraints: ``0.0 <= skip_drop <= 1.0`` + + - used only in ``dart`` + + - probability of skipping the dropout procedure during a boosting iteration + +- ``xgboost_dart_mode`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only in ``dart`` + + - set this to ``true``, if you want to use XGBoost DART mode + +- ``uniform_drop`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only in ``dart`` + + - set this to ``true``, if you want to use uniform drop + +- ``drop_seed`` :raw-html:`🔗︎`, default = ``4``, type = int + + - used only in ``dart`` + + - random seed to choose dropping models + +- ``top_rate`` :raw-html:`🔗︎`, default = ``0.2``, type = double, constraints: ``0.0 <= top_rate <= 1.0`` + + - used only in ``goss`` + + - the retain ratio of large gradient data + +- ``other_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, constraints: ``0.0 <= other_rate <= 1.0`` + + - used only in ``goss`` + + - the retain ratio of small gradient data + +- ``min_data_per_group`` :raw-html:`🔗︎`, default = ``100``, type = int, constraints: ``min_data_per_group > 0`` + + - used for the categorical features + + - minimal number of data per categorical group + +- ``max_cat_threshold`` :raw-html:`🔗︎`, default = ``32``, type = int, constraints: ``max_cat_threshold > 0`` + + - used for the categorical features + + - limit number of split points considered for categorical features. See `the documentation on how LightGBM finds optimal splits for categorical features <./Features.rst#optimal-split-for-categorical-features>`_ for more details + + - can be used to speed up training + +- ``cat_l2`` :raw-html:`🔗︎`, default = ``10.0``, type = double, constraints: ``cat_l2 >= 0.0`` + + - used for the categorical features + + - L2 regularization in categorical split + +- ``cat_smooth`` :raw-html:`🔗︎`, default = ``10.0``, type = double, constraints: ``cat_smooth >= 0.0`` + + - used for the categorical features + + - this can reduce the effect of noises in categorical features, especially for categories with few data + +- ``max_cat_to_onehot`` :raw-html:`🔗︎`, default = ``4``, type = int, constraints: ``max_cat_to_onehot > 0`` + + - used for the categorical features + + - when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used + +- ``top_k`` :raw-html:`🔗︎`, default = ``20``, type = int, aliases: ``topk``, constraints: ``top_k > 0`` + + - used only in ``voting`` tree learner, refer to `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__ + + - set this to larger value for more accurate result, but it will slow down the training speed + +- ``monotone_constraints`` :raw-html:`🔗︎`, default = ``None``, type = multi-int, aliases: ``mc``, ``monotone_constraint``, ``monotonic_cst`` + + - used for constraints of monotonic features + + - ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint + + - you need to specify all features in order. For example, ``mc=-1,0,1`` means decreasing for the 1st feature, non-constraint for the 2nd feature and increasing for the 3rd feature + +- ``monotone_constraints_method`` :raw-html:`🔗︎`, default = ``basic``, type = enum, options: ``basic``, ``intermediate``, ``advanced``, aliases: ``monotone_constraining_method``, ``mc_method`` + + - used only if ``monotone_constraints`` is set + + - monotone constraints method + + - ``basic``, the most basic monotone constraints method. It does not slow down the training speed at all, but over-constrains the predictions + + - ``intermediate``, a `more advanced method `__, which may slow down the training speed very slightly. However, this method is much less constraining than the basic method and should significantly improve the results + + - ``advanced``, an `even more advanced method `__, which may slow down the training speed. However, this method is even less constraining than the intermediate method and should again significantly improve the results + +- ``monotone_penalty`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``monotone_splits_penalty``, ``ms_penalty``, ``mc_penalty``, constraints: ``monotone_penalty >= 0.0`` + + - used only if ``monotone_constraints`` is set + + - `monotone penalty `__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter + + - if ``0.0`` (the default), no penalization is applied + +- ``feature_contri`` :raw-html:`🔗︎`, default = ``None``, type = multi-double, aliases: ``feature_contrib``, ``fc``, ``fp``, ``feature_penalty`` + + - used to control feature's split gain, will use ``gain[i] = max(0, feature_contri[i]) * gain[i]`` to replace the split gain of i-th feature + + - you need to specify all features in order + +- ``forcedsplits_filename`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``fs``, ``forced_splits_filename``, ``forced_splits_file``, ``forced_splits`` + + - path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences + + - ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits + + - categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values + + - **Note**: the forced split logic will be ignored, if the split makes gain worse + + - see `this file `__ as an example + +- ``refit_decay_rate`` :raw-html:`🔗︎`, default = ``0.9``, type = double, constraints: ``0.0 <= refit_decay_rate <= 1.0`` + + - decay rate of ``refit`` task, will use ``leaf_output = refit_decay_rate * old_leaf_output + (1.0 - refit_decay_rate) * new_leaf_output`` to refit trees + + - used only in ``refit`` task in CLI version or as argument in ``refit`` function in language-specific package + +- ``cegb_tradeoff`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``cegb_tradeoff >= 0.0`` + + - cost-effective gradient boosting multiplier for all penalties + +- ``cegb_penalty_split`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``cegb_penalty_split >= 0.0`` + + - cost-effective gradient-boosting penalty for splitting a node + +- ``cegb_penalty_feature_lazy`` :raw-html:`🔗︎`, default = ``0,0,...,0``, type = multi-double + + - cost-effective gradient boosting penalty for using a feature + + - applied per data point + +- ``cegb_penalty_feature_coupled`` :raw-html:`🔗︎`, default = ``0,0,...,0``, type = multi-double + + - cost-effective gradient boosting penalty for using a feature + + - applied once per forest + +- ``path_smooth`` :raw-html:`🔗︎`, default = ``0``, type = double, constraints: ``path_smooth >= 0.0`` + + - controls smoothing applied to tree nodes + + - helps prevent overfitting on leaves with few samples + + - if ``0.0`` (the default), no smoothing is applied + + - if ``path_smooth > 0`` then ``min_data_in_leaf`` must be at least ``2`` + + - larger values give stronger regularization + + - the weight of each node is ``w * (n / path_smooth) / (n / path_smooth + 1) + w_p / (n / path_smooth + 1)``, where ``n`` is the number of samples in the node, ``w`` is the optimal node weight to minimise the loss (approximately ``-sum_gradients / sum_hessians``), and ``w_p`` is the weight of the parent node + + - note that the parent output ``w_p`` itself has smoothing applied, unless it is the root node, so that the smoothing effect accumulates with the tree depth + +- ``interaction_constraints`` :raw-html:`🔗︎`, default = ``""``, type = string + + - controls which features can appear in the same branch + + - by default interaction constraints are disabled, to enable them you can specify + + - for CLI, lists separated by commas, e.g. ``[0,1,2],[2,3]`` + + - for Python-package, list of lists, e.g. ``[[0, 1, 2], [2, 3]]`` + + - for R-package, list of character or numeric vectors, e.g. ``list(c("var1", "var2", "var3"), c("var3", "var4"))`` or ``list(c(1L, 2L, 3L), c(3L, 4L))``. Numeric vectors should use 1-based indexing, where ``1L`` is the first feature, ``2L`` is the second feature, etc. + + - any two features can only appear in the same branch only if there exists a constraint containing both features + +- ``verbosity`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``verbose`` + + - controls the level of LightGBM's verbosity + + - ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug + +- ``input_model`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``model_input``, ``model_in`` + + - filename of input model + + - for ``prediction`` task, this model will be applied to prediction data + + - for ``train`` task, training will be continued from this model + + - **Note**: can be used only in CLI version + +- ``output_model`` :raw-html:`🔗︎`, default = ``LightGBM_model.txt``, type = string, aliases: ``model_output``, ``model_out`` + + - filename of output model in training + + - **Note**: can be used only in CLI version + +- ``saved_feature_importance_type`` :raw-html:`🔗︎`, default = ``0``, type = int + + - the feature importance type in the saved model file + + - ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted) + + - **Note**: can be used only in CLI version + +- ``snapshot_freq`` :raw-html:`🔗︎`, default = ``-1``, type = int, aliases: ``save_period`` + + - frequency of saving model file snapshot + + - set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1`` + + - **Note**: can be used only in CLI version + +- ``use_quantized_grad`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - whether to use gradient quantization when training + + - enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins`` + + - with quantized training, most arithmetics in the training process will be integer operations + + - gradient quantization can accelerate training, with little accuracy drop in most cases + + - **Note**: works only with ``cpu`` and ``cuda`` device type + + - *New in version 4.0.0* + +- ``num_grad_quant_bins`` :raw-html:`🔗︎`, default = ``4``, type = int + + - used only if ``use_quantized_grad=true`` + + - number of bins to quantization gradients and hessians + + - with more bins, the quantized training will be closer to full precision training + + - **Note**: works only with ``cpu`` and ``cuda`` device type + + - *New in version 4.0.0* + +- ``quant_train_renew_leaf`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only if ``use_quantized_grad=true`` + + - whether to renew the leaf values with original gradients when quantized training + + - renewing is very helpful for good quantized training accuracy for ranking objectives + + - **Note**: works only with ``cpu`` and ``cuda`` device type + + - *New in version 4.0.0* + +- ``stochastic_rounding`` :raw-html:`🔗︎`, default = ``true``, type = bool + + - used only if ``use_quantized_grad=true`` + + - whether to use stochastic rounding in gradient quantization + + - **Note**: works only with ``cpu`` and ``cuda`` device type + + - *New in version 4.0.0* + +IO Parameters +------------- + +Dataset Parameters +~~~~~~~~~~~~~~~~~~ + +- ``linear_tree`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``linear_trees`` + + - fit piecewise linear gradient boosting tree + + - tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant + + - the linear model at each leaf includes all the numerical features in that leaf's branch + + - the first tree has constant leaf values + + - categorical features are used for splits as normal but are not used in the linear models + + - missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R + + - it is recommended to rescale data before training so that features have similar mean and standard deviation + + - **Note**: works only with ``cpu``, ``gpu`` device type and ``serial`` tree learner + + - **Note**: ``regression_l1`` objective is not supported with linear tree boosting + + - **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM + + - **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves + +- ``max_bin`` :raw-html:`🔗︎`, default = ``255``, type = int, aliases: ``max_bins``, constraints: ``max_bin > 1`` + + - max number of bins that feature values will be bucketed in + + - small number of bins may reduce training accuracy but may increase general power (deal with over-fitting) + + - LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255`` + +- ``max_bin_by_feature`` :raw-html:`🔗︎`, default = ``None``, type = multi-int + + - max number of bins for each feature + + - if not specified, will use ``max_bin`` for all features + +- ``min_data_in_bin`` :raw-html:`🔗︎`, default = ``3``, type = int, constraints: ``min_data_in_bin > 0`` + + - minimal number of data inside one bin + + - use this to avoid one-data-one-bin (potential over-fitting) + +- ``bin_construct_sample_cnt`` :raw-html:`🔗︎`, default = ``200000``, type = int, aliases: ``subsample_for_bin``, constraints: ``bin_construct_sample_cnt > 0`` + + - number of data that sampled to construct feature discrete bins + + - setting this to larger value will give better training result, but may increase data loading time + + - set this to larger value if data is very sparse + + - **Note**: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy + +- ``data_random_seed`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``data_seed`` + + - random seed for sampling data to construct histogram bins + +- ``is_enable_sparse`` :raw-html:`🔗︎`, default = ``true``, type = bool, aliases: ``is_sparse``, ``enable_sparse``, ``sparse`` + + - used to enable/disable sparse optimization + +- ``enable_bundle`` :raw-html:`🔗︎`, default = ``true``, type = bool, aliases: ``is_enable_bundle``, ``bundle`` + + - set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree `__ + + - **Note**: disabling this may cause the slow training speed for sparse datasets + +- ``use_missing`` :raw-html:`🔗︎`, default = ``true``, type = bool + + - set this to ``false`` to disable the special handle of missing value + +- ``zero_as_missing`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices) + + - set this to ``false`` to use ``na`` for representing missing values + +- ``feature_pre_filter`` :raw-html:`🔗︎`, default = ``true``, type = bool + + - set this to ``true`` (the default) to tell LightGBM to ignore the features that are unsplittable based on ``min_data_in_leaf`` + + - as dataset object is initialized only once and cannot be changed after that, you may need to set this to ``false`` when searching parameters with ``min_data_in_leaf``, otherwise features are filtered by ``min_data_in_leaf`` firstly if you don't reconstruct dataset object + + - **Note**: setting this to ``false`` may slow down the training + +- ``pre_partition`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_pre_partition`` + + - used for distributed learning (excluding the ``feature_parallel`` mode) + + - ``true`` if training data are pre-partitioned, and different machines use different partitions + +- ``two_round`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``two_round_loading``, ``use_two_round_loading`` + + - set this to ``true`` if data file is too big to fit in memory + + - by default, LightGBM will map data file to memory and load features from memory. This will provide faster data loading speed, but may cause run out of memory error when the data file is very big + + - **Note**: works only in case of loading data directly from text file + +- ``header`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``has_header`` + + - set this to ``true`` if input data has header + + - **Note**: works only in case of loading data directly from text file + +- ``label_column`` :raw-html:`🔗︎`, default = ``""``, type = int or string, aliases: ``label`` + + - used to specify the label column + + - use number for index, e.g. ``label=0`` means column\_0 is the label + + - add a prefix ``name:`` for column name, e.g. ``label=name:is_click`` + + - if omitted, the first column in the training data is used as the label + + - **Note**: works only in case of loading data directly from text file + +- ``weight_column`` :raw-html:`🔗︎`, default = ``""``, type = int or string, aliases: ``weight`` + + - used to specify the weight column + + - use number for index, e.g. ``weight=0`` means column\_0 is the weight + + - add a prefix ``name:`` for column name, e.g. ``weight=name:weight`` + + - **Note**: works only in case of loading data directly from text file + + - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0, and weight is column\_1, the correct parameter is ``weight=0`` + + - **Note**: weights should be non-negative + +- ``group_column`` :raw-html:`🔗︎`, default = ``""``, type = int or string, aliases: ``group``, ``group_id``, ``query_column``, ``query``, ``query_id`` + + - used to specify the query/group id column + + - use number for index, e.g. ``query=0`` means column\_0 is the query id + + - add a prefix ``name:`` for column name, e.g. ``query=name:query_id`` + + - **Note**: works only in case of loading data directly from text file + + - **Note**: data should be grouped by query\_id, for more information, see `Query Data <#query-data>`__ + + - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0 and query\_id is column\_1, the correct parameter is ``query=0`` + +- ``ignore_column`` :raw-html:`🔗︎`, default = ``""``, type = multi-int or string, aliases: ``ignore_feature``, ``blacklist`` + + - used to specify some ignoring columns in training + + - use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored + + - add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored + + - **Note**: works only in case of loading data directly from text file + + - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` + + - **Note**: despite the fact that specified columns will be completely ignored during the training, they still should have a valid format allowing LightGBM to load file successfully + +- ``categorical_feature`` :raw-html:`🔗︎`, default = ``""``, type = multi-int or string, aliases: ``cat_feature``, ``categorical_column``, ``cat_column``, ``categorical_features`` + + - used to specify categorical features + + - use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features + + - add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features + + - **Note**: all values will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package) + + - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` + + - **Note**: all values should be less than ``Int32.MaxValue`` (2147483647) + + - **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero + + - **Note**: all negative values will be treated as **missing values** + + - **Note**: the output cannot be monotonically constrained with respect to a categorical feature + + - **Note**: floating point numbers in categorical features will be rounded towards 0 + +- ``forcedbins_filename`` :raw-html:`🔗︎`, default = ``""``, type = string + + - path to a ``.json`` file that specifies bin upper bounds for some or all features + + - ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning) + + - see `this file `__ as an example + +- ``save_binary`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_save_binary``, ``is_save_binary_file`` + + - if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time + + - **Note**: ``init_score`` is not saved in binary file + + - **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function + +- ``precise_float_parser`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - use precise floating point number parsing for text parser (e.g. CSV, TSV, LibSVM input) + + - **Note**: setting this to ``true`` may lead to much slower text parsing + +- ``parser_config_file`` :raw-html:`🔗︎`, default = ``""``, type = string + + - path to a ``.json`` file that specifies customized parser initialized configuration + + - see `lightgbm-transform `__ for usage examples + + - **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page `__ + + - *New in version 4.0.0* + +Predict Parameters +~~~~~~~~~~~~~~~~~~ + +- ``start_iteration_predict`` :raw-html:`🔗︎`, default = ``0``, type = int + + - used only in ``prediction`` task + + - used to specify from which iteration to start the prediction + + - ``<= 0`` means from the first iteration + +- ``num_iteration_predict`` :raw-html:`🔗︎`, default = ``-1``, type = int + + - used only in ``prediction`` task + + - used to specify how many trained iterations will be used in prediction + + - ``<= 0`` means no limit + +- ``predict_raw_score`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_raw_score``, ``predict_rawscore``, ``raw_score`` + + - used only in ``prediction`` task + + - set this to ``true`` to predict only the raw scores + + - set this to ``false`` to predict transformed scores + +- ``predict_leaf_index`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_leaf_index``, ``leaf_index`` + + - used only in ``prediction`` task + + - set this to ``true`` to predict with leaf index of all trees + +- ``predict_contrib`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_contrib``, ``contrib`` + + - used only in ``prediction`` task + + - set this to ``true`` to estimate `SHAP values `__, which represent how each feature contributes to each prediction + + - produces ``#features + 1`` values where the last value is the expected value of the model output over the training data + + - **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package `__ + + - **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value + + - **Note**: this feature is not implemented for linear trees + +- ``predict_disable_shape_check`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only in ``prediction`` task + + - control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data + + - if ``false`` (the default), a fatal error will be raised if the number of features in the dataset you predict on differs from the number seen during training + + - if ``true``, LightGBM will attempt to predict on whatever data you provide. This is dangerous because you might get incorrect predictions, but you could use it in situations where it is difficult or expensive to generate some features and you are very confident that they were never chosen for splits in the model + + - **Note**: be very careful setting this parameter to ``true`` + +- ``pred_early_stop`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only in ``prediction`` task + + - used only in ``classification`` and ``ranking`` applications + + - used only for predicting normal or raw scores + + - if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy + + - **Note**: cannot be used with ``rf`` boosting type or custom objective function + +- ``pred_early_stop_freq`` :raw-html:`🔗︎`, default = ``10``, type = int + + - used only in ``prediction`` task and if ``pred_early_stop=true`` + + - the frequency of checking early-stopping prediction + +- ``pred_early_stop_margin`` :raw-html:`🔗︎`, default = ``10.0``, type = double + + - used only in ``prediction`` task and if ``pred_early_stop=true`` + + - the threshold of margin in early-stopping prediction + +- ``output_result`` :raw-html:`🔗︎`, default = ``LightGBM_predict_result.txt``, type = string, aliases: ``predict_result``, ``prediction_result``, ``predict_name``, ``prediction_name``, ``pred_name``, ``name_pred`` + + - used only in ``prediction`` task + + - filename of prediction result + + - **Note**: can be used only in CLI version + +Convert Parameters +~~~~~~~~~~~~~~~~~~ + +- ``convert_model_language`` :raw-html:`🔗︎`, default = ``""``, type = string + + - used only in ``convert_model`` task + + - only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen `__ utility + + - if ``convert_model_language`` is set and ``task=train``, the model will be also converted + + - **Note**: can be used only in CLI version + +- ``convert_model`` :raw-html:`🔗︎`, default = ``gbdt_prediction.cpp``, type = string, aliases: ``convert_model_file`` + + - used only in ``convert_model`` task + + - output filename of converted model + + - **Note**: can be used only in CLI version + +Objective Parameters +-------------------- + +- ``objective_seed`` :raw-html:`🔗︎`, default = ``5``, type = int + + - used only in ``rank_xendcg`` objective + + - random seed for objectives, if random process is needed + +- ``num_class`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``num_classes``, constraints: ``num_class > 0`` + + - used only in ``multi-class`` classification application + +- ``is_unbalance`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``unbalance``, ``unbalanced_sets`` + + - used only in ``binary`` and ``multiclassova`` applications + + - set this to ``true`` if training data are unbalanced + + - **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities + + - **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them + +- ``scale_pos_weight`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``scale_pos_weight > 0.0`` + + - used only in ``binary`` and ``multiclassova`` applications + + - weight of labels with positive class + + - **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities + + - **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them + +- ``sigmoid`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``sigmoid > 0.0`` + + - used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications + + - parameter for the sigmoid function + +- ``boost_from_average`` :raw-html:`🔗︎`, default = ``true``, type = bool + + - used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications + + - adjusts initial score to the mean of labels for faster convergence + +- ``reg_sqrt`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - used only in ``regression`` application + + - used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2`` + + - might be useful in case of large-range labels + +- ``alpha`` :raw-html:`🔗︎`, default = ``0.9``, type = double, constraints: ``alpha > 0.0`` + + - used only in ``huber`` and ``quantile`` ``regression`` applications + + - parameter for `Huber loss `__ and `Quantile regression `__ + +- ``fair_c`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``fair_c > 0.0`` + + - used only in ``fair`` ``regression`` application + + - parameter for `Fair loss `__ + +- ``poisson_max_delta_step`` :raw-html:`🔗︎`, default = ``0.7``, type = double, constraints: ``poisson_max_delta_step > 0.0`` + + - used only in ``poisson`` ``regression`` application + + - parameter for `Poisson regression `__ to safeguard optimization + +- ``tweedie_variance_power`` :raw-html:`🔗︎`, default = ``1.5``, type = double, constraints: ``1.0 <= tweedie_variance_power < 2.0`` + + - used only in ``tweedie`` ``regression`` application + + - used to control the variance of the tweedie distribution + + - set this closer to ``2`` to shift towards a **Gamma** distribution + + - set this closer to ``1`` to shift towards a **Poisson** distribution + +- ``lambdarank_truncation_level`` :raw-html:`🔗︎`, default = ``30``, type = int, constraints: ``lambdarank_truncation_level > 0`` + + - used only in ``lambdarank`` application + + - controls the number of top-results to focus on during training, refer to "truncation level" in the Sec. 3 of `LambdaMART paper `__ + + - this parameter is closely related to the desirable cutoff ``k`` in the metric **NDCG@k** that we aim at optimizing the ranker for. The optimal setting for this parameter is likely to be slightly higher than ``k`` (e.g., ``k + 3``) to include more pairs of documents to train on, but perhaps not too high to avoid deviating too much from the desired target metric **NDCG@k** + +- ``lambdarank_norm`` :raw-html:`🔗︎`, default = ``true``, type = bool + + - used only in ``lambdarank`` application + + - set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data + + - set this to ``false`` to enforce the original lambdarank algorithm + +- ``label_gain`` :raw-html:`🔗︎`, default = ``0,1,3,7,15,31,63,...,2^30-1``, type = multi-double + + - used only in ``lambdarank`` application + + - relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains + + - separate by ``,`` + +- ``lambdarank_position_bias_regularization`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``lambdarank_position_bias_regularization >= 0.0`` + + - used only in ``lambdarank`` application when positional information is provided and position bias is modeled + + - larger values reduce the inferred position bias factors + + - *New in version 4.1.0* + +Metric Parameters +----------------- + +- ``metric`` :raw-html:`🔗︎`, default = ``""``, type = multi-enum, aliases: ``metrics``, ``metric_types`` + + - metric(s) to be evaluated on the evaluation set(s) + + - ``""`` (empty string or not specified) means that metric corresponding to specified ``objective`` will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added) + + - ``"None"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom`` + + - ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1`` + + - ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression`` + + - ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root`` + + - ``quantile``, `Quantile regression `__ + + - ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` + + - ``huber``, `Huber loss `__ + + - ``fair``, `Fair loss `__ + + - ``poisson``, negative log-likelihood for `Poisson regression `__ + + - ``gamma``, negative log-likelihood for **Gamma** regression + + - ``gamma_deviance``, residual deviance for **Gamma** regression + + - ``tweedie``, negative log-likelihood for **Tweedie** regression + + - ``ndcg``, `NDCG `__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` + + - ``map``, `MAP `__, aliases: ``mean_average_precision`` + + - ``auc``, `AUC `__ + + - ``average_precision``, `average precision score `__ + + - ``r2``, `R-squared `__ + + - ``binary_logloss``, `log loss `__, aliases: ``binary`` + + - ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification + + - ``auc_mu``, `AUC-mu `__ + + - ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr`` + + - ``multi_error``, error rate for multi-class classification + + - ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy`` + + - ``cross_entropy_lambda``, "intensity-weighted" cross-entropy, aliases: ``xentlambda`` + + - ``kullback_leibler``, `Kullback-Leibler divergence `__, aliases: ``kldiv`` + + - support multiple metrics, separated by ``,`` + +- ``metric_freq`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``output_freq``, constraints: ``metric_freq > 0`` + + - frequency for metric output + + - **Note**: can be used only in CLI version + +- ``is_provide_training_metric`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``training_metric``, ``is_training_metric``, ``train_metric`` + + - set this to ``true`` to output metric result over training dataset + + - **Note**: can be used only in CLI version + +- ``eval_at`` :raw-html:`🔗︎`, default = ``1,2,3,4,5``, type = multi-int, aliases: ``ndcg_eval_at``, ``ndcg_at``, ``map_eval_at``, ``map_at`` + + - used only with ``ndcg`` and ``map`` metrics + + - `NDCG `__ and `MAP `__ evaluation positions, separated by ``,`` + +- ``multi_error_top_k`` :raw-html:`🔗︎`, default = ``1``, type = int, constraints: ``multi_error_top_k > 0`` + + - used only with ``multi_error`` metric + + - threshold for top-k multi-error metric + + - the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise + + - more precisely, the error on a sample is ``0`` if there are at least ``num_classes - multi_error_top_k`` predictions strictly less than the prediction on the true class + + - when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric + +- ``auc_mu_weights`` :raw-html:`🔗︎`, default = ``None``, type = multi-double + + - used only with ``auc_mu`` metric + + - list representing flattened matrix (in row-major order) giving loss weights for classification errors + + - list should have ``n * n`` elements, where ``n`` is the number of classes + + - the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list + + - if not specified, will use equal weights for all classes + +Network Parameters +------------------ + +- ``num_machines`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``num_machine``, constraints: ``num_machines > 0`` + + - the number of machines for distributed learning application + + - this parameter is needed to be set in both **socket** and **MPI** versions + +- ``local_listen_port`` :raw-html:`🔗︎`, default = ``12400 (random for Dask-package)``, type = int, aliases: ``local_port``, ``port``, constraints: ``local_listen_port > 0`` + + - TCP listen port for local machines + + - **Note**: don't forget to allow this port in firewall settings before training + +- ``time_out`` :raw-html:`🔗︎`, default = ``120``, type = int, constraints: ``time_out > 0`` + + - socket time-out in minutes + +- ``machine_list_filename`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``machine_list_file``, ``machine_list``, ``mlist`` + + - path of file that lists machines for this distributed learning application + + - each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator) + + - **Note**: can be used only in CLI version + +- ``machines`` :raw-html:`🔗︎`, default = ``""``, type = string, aliases: ``workers``, ``nodes`` + + - list of machines in the following format: ``ip1:port1,ip2:port2`` + +GPU Parameters +-------------- + +- ``gpu_platform_id`` :raw-html:`🔗︎`, default = ``-1``, type = int + + - used only with ``gpu`` device type + + - OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform + + - ``-1`` means the system-wide default platform + + - **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details + +- ``gpu_device_id`` :raw-html:`🔗︎`, default = ``-1``, type = int + + - OpenCL device ID in the specified platform or CUDA device ID. Each GPU in the selected platform has a unique device ID + + - ``-1`` means the default device in the selected platform + + - in multi-GPU case (``num_gpu>1``) means ID of the master GPU + + - **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details + +- ``gpu_device_id_list`` :raw-html:`🔗︎`, default = ``""``, type = string + + - list of CUDA device IDs + + - **Note**: can be used only in CUDA implementation (``device_type="cuda"``) and when ``num_gpu>1`` + + - if empty, the devices with the smallest IDs will be used + +- ``gpu_use_dp`` :raw-html:`🔗︎`, default = ``false``, type = bool + + - set this to ``true`` to use double precision math on GPU (by default single precision is used) + + - **Note**: can be used only in OpenCL implementation (``device_type="gpu"``), in CUDA implementation only double precision is currently supported + +- ``num_gpu`` :raw-html:`🔗︎`, default = ``1``, type = int, constraints: ``num_gpu > 0`` + + - number of GPUs used for training in this node + + - **Note**: can be used only in CUDA implementation (``device_type="cuda"``) + + - if ``0``, only 1 GPU will be used + + - used in both single-machine and distributed learning applications + + - in distributed learning application, each machine can use different number of GPUs + +.. end params list + +Others +------ + +Continued Training with Input Score +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +LightGBM supports continued training with initial scores. +It uses an additional file to store these initial scores, like the following: + +:: + + 0.5 + -0.1 + 0.9 + ... + +It means the initial score of the first data row is ``0.5``, second is ``-0.1``, and so on. +The initial score file corresponds with data file line by line, and has per score per line. + +If the name of data file is ``train.txt``, the initial score file should be named as ``train.txt.init`` and placed in the same folder as the data file. +In this case, LightGBM will auto load initial score file if it exists. + +If binary data files exist for raw data file ``train.txt``, for example in the name ``train.txt.bin``, then the initial score file should be named as ``train.txt.bin.init``. + +Weight Data +~~~~~~~~~~~ + +LightGBM supports weighted training. +It uses an additional file to store weight data, like the following: + +:: + + 1.0 + 0.5 + 0.8 + ... + +It means the weight of the first data row is ``1.0``, second is ``0.5``, and so on. Weights should be non-negative. + +The weight file corresponds with data file line by line, and has per weight per line. + +And if the name of data file is ``train.txt``, the weight file should be named as ``train.txt.weight`` and placed in the same folder as the data file. +In this case, LightGBM will load the weight file automatically if it exists. + +Also, you can include weight column in your data file. +Please refer to the ``weight_column`` `parameter <#weight_column>`__ in above. + +Query Data +~~~~~~~~~~ + +For learning to rank, it needs query information for training data. + +LightGBM uses an additional file to store query data, like the following: + +:: + + 27 + 18 + 67 + ... + +For wrapper libraries like in Python and R, this information can also be provided as an array-like via the Dataset parameter ``group``. + +:: + + [27, 18, 67, ...] + +For example, if you have a 112-document dataset with ``group = [27, 18, 67]``, that means that you have 3 groups, where the first 27 records are in the first group, records 28-45 are in the second group, and records 46-112 are in the third group. + +**Note**: data should be ordered by the query. + +If the name of data file is ``train.txt``, the query file should be named as ``train.txt.query`` and placed in the same folder as the data file. +In this case, LightGBM will load the query file automatically if it exists. + +Also, you can include query/group id column in your data file. +Please refer to the ``group_column`` `parameter <#group_column>`__ in above. diff --git a/docs/Python-API.rst b/docs/Python-API.rst new file mode 100644 index 0000000..3e0dab7 --- /dev/null +++ b/docs/Python-API.rst @@ -0,0 +1,78 @@ +Python API +========== + +.. currentmodule:: lightgbm + +Data Structure API +------------------ + +.. autosummary:: + :toctree: pythonapi/ + + Dataset + Booster + CVBooster + Sequence + +Training API +------------ + +.. autosummary:: + :toctree: pythonapi/ + + train + cv + +Scikit-learn API +---------------- + +.. autosummary:: + :toctree: pythonapi/ + + LGBMModel + LGBMClassifier + LGBMRegressor + LGBMRanker + +Dask API +-------- + +.. versionadded:: 3.2.0 + +.. autosummary:: + :toctree: pythonapi/ + + DaskLGBMClassifier + DaskLGBMRegressor + DaskLGBMRanker + +Callbacks +--------- + +.. autosummary:: + :toctree: pythonapi/ + + early_stopping + log_evaluation + record_evaluation + reset_parameter + +Plotting +-------- + +.. autosummary:: + :toctree: pythonapi/ + + plot_importance + plot_split_value_histogram + plot_metric + plot_tree + create_tree_digraph + +Utilities +--------- + +.. autosummary:: + :toctree: pythonapi/ + + register_logger diff --git a/docs/Python-Intro.rst b/docs/Python-Intro.rst new file mode 100644 index 0000000..071aa5c --- /dev/null +++ b/docs/Python-Intro.rst @@ -0,0 +1,267 @@ +Python-package Introduction +=========================== + +This document gives a basic walk-through of LightGBM Python-package. + +**List of other helpful links** + +- `Python Examples `__ + +- `Python API <./Python-API.rst>`__ + +- `Parameters Tuning <./Parameters-Tuning.rst>`__ + +Install +------- + +The preferred way to install LightGBM is via pip: + +:: + + pip install lightgbm + +Refer to `Python-package`_ folder for the detailed installation guide. + +To verify your installation, try to ``import lightgbm`` in Python: + +:: + + import lightgbm as lgb + +Data Interface +-------------- + +The LightGBM Python module can load data from: + +- LibSVM (zero-based) / TSV / CSV format text file + +- NumPy 2D array(s), SciPy sparse matrix + +- pandas DataFrame, polars DataFrame, pyarrow Table + +- LightGBM binary file + +- LightGBM ``Sequence`` object(s) + +The data is stored in a ``Dataset`` object. + +Many of the examples in this page use functionality from ``numpy``. To run the examples, be sure to import ``numpy`` in your session. + +.. code:: python + + import numpy as np + +**To load a LibSVM (zero-based) text file or a LightGBM binary file into Dataset:** + +.. code:: python + + train_data = lgb.Dataset('train.svm.bin') + +**To load a numpy array into Dataset:** + +.. code:: python + + rng = np.random.default_rng() + data = rng.uniform(size=(500, 10)) # 500 entities, each contains 10 features + label = rng.integers(low=0, high=2, size=(500, )) # binary target + train_data = lgb.Dataset(data, label=label) + +**To load a scipy.sparse.csr\_matrix array into Dataset:** + +.. code:: python + + import scipy + csr = scipy.sparse.csr_matrix((dat, (row, col))) + train_data = lgb.Dataset(csr) + +**Load from Sequence objects:** + +We can implement ``Sequence`` interface to read binary files. The following example shows reading HDF5 file with ``h5py``. + +.. code:: python + + import h5py + + class HDFSequence(lgb.Sequence): + def __init__(self, hdf_dataset, batch_size): + self.data = hdf_dataset + self.batch_size = batch_size + + def __getitem__(self, idx): + return self.data[idx] + + def __len__(self): + return len(self.data) + + f = h5py.File('train.hdf5', 'r') + train_data = lgb.Dataset(HDFSequence(f['X'], 8192), label=f['Y'][:]) + +Features of using ``Sequence`` interface: + +- Data sampling uses random access, thus does not go through the whole dataset +- Reading data in batch, thus saves memory when constructing ``Dataset`` object +- Supports creating ``Dataset`` from multiple data files + +Please refer to ``Sequence`` `API doc <./Python-API.rst#data-structure-api>`__. + +`dataset_from_multi_hdf5.py `__ is a detailed example. + +**Saving Dataset into a LightGBM binary file will make loading faster:** + +.. code:: python + + train_data = lgb.Dataset('train.svm.txt') + train_data.save_binary('train.bin') + +**Create validation data:** + +.. code:: python + + validation_data = train_data.create_valid('validation.svm') + +or + +.. code:: python + + validation_data = lgb.Dataset('validation.svm', reference=train_data) + +In LightGBM, the validation data should be aligned with training data. + +**Specific feature names and categorical features:** + +.. code:: python + + train_data = lgb.Dataset(data, label=label, feature_name=['c1', 'c2', 'c3'], categorical_feature=['c3']) + +LightGBM can use categorical features as input directly. +It doesn't need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up). + +**Note**: You should convert your categorical features to ``int`` type before you construct ``Dataset``. + +**Weights can be set when needed:** + +.. code:: python + + rng = np.random.default_rng() + w = rng.uniform(size=(500, )) + train_data = lgb.Dataset(data, label=label, weight=w) + +or + +.. code:: python + + train_data = lgb.Dataset(data, label=label) + rng = np.random.default_rng() + w = rng.uniform(size=(500, )) + train_data.set_weight(w) + +And you can use ``Dataset.set_init_score()`` to set initial score, and ``Dataset.set_group()`` to set group/query data for ranking tasks. + +**Memory efficient usage:** + +The ``Dataset`` object in LightGBM is very memory-efficient, it only needs to save discrete bins. +However, Numpy/Array/Pandas object is memory expensive. +If you are concerned about your memory consumption, you can save memory by: + +1. Set ``free_raw_data=True`` (default is ``True``) when constructing the ``Dataset`` + +2. Explicitly set ``raw_data=None`` after the ``Dataset`` has been constructed + +3. Call ``gc`` + +Setting Parameters +------------------ + +LightGBM can use a dictionary to set `Parameters <./Parameters.rst>`__. +For instance: + +- Booster parameters: + + .. code:: python + + param = {'num_leaves': 31, 'objective': 'binary'} + param['metric'] = 'auc' + +- You can also specify multiple eval metrics: + + .. code:: python + + param['metric'] = ['auc', 'binary_logloss'] + +Training +-------- + +Training a model requires a parameter list and data set: + +.. code:: python + + num_round = 10 + bst = lgb.train(param, train_data, num_round, valid_sets=[validation_data]) + +After training, the model can be saved: + +.. code:: python + + bst.save_model('model.txt') + +The trained model can also be dumped to JSON format: + +.. code:: python + + json_model = bst.dump_model() + +A saved model can be loaded: + +.. code:: python + + bst = lgb.Booster(model_file='model.txt') # init model + +CV +-- + +Training with 5-fold CV: + +.. code:: python + + lgb.cv(param, train_data, num_round, nfold=5) + +Early Stopping +-------------- + +If you have a validation set, you can use early stopping to find the optimal number of boosting rounds. +Early stopping requires at least one set in ``valid_sets``. If there is more than one, it will use all of them except the training data: + +.. code:: python + + bst = lgb.train(param, train_data, num_round, valid_sets=valid_sets, callbacks=[lgb.early_stopping(stopping_rounds=5)]) + bst.save_model('model.txt', num_iteration=bst.best_iteration) + +The model will train until the validation score stops improving. +Validation score needs to improve at least every ``stopping_rounds`` to continue training. + +The index of iteration that has the best performance will be saved in the ``best_iteration`` field if early stopping logic is enabled by setting ``early_stopping`` callback. +Note that ``train()`` will return a model from the best iteration. + +This works with both metrics to minimize (L2, log loss, etc.) and to maximize (NDCG, AUC, etc.). +Note that if you specify more than one evaluation metric, all of them will be used for early stopping. +However, you can change this behavior and make LightGBM check only the first metric for early stopping by passing ``first_metric_only=True`` in ``early_stopping`` callback constructor. + +Prediction +---------- + +A model that has been trained or loaded can perform predictions on datasets: + +.. code:: python + + # 7 entities, each contains 10 features + rng = np.random.default_rng() + data = rng.uniform(size=(7, 10)) + ypred = bst.predict(data) + +If early stopping is enabled during training, you can get predictions from the best iteration with ``bst.best_iteration``: + +.. code:: python + + ypred = bst.predict(data, num_iteration=bst.best_iteration) + +.. _Python-package: https://github.com/lightgbm-org/LightGBM/tree/main/python-package diff --git a/docs/Quick-Start.rst b/docs/Quick-Start.rst new file mode 100644 index 0000000..0c2b676 --- /dev/null +++ b/docs/Quick-Start.rst @@ -0,0 +1,88 @@ +Quick Start +=========== + +This is a quick start guide for LightGBM CLI version. + +Follow the `Installation Guide <./Installation-Guide.rst>`__ to install LightGBM first. + +**List of other helpful links** + +- `Parameters <./Parameters.rst>`__ + +- `Parameters Tuning <./Parameters-Tuning.rst>`__ + +- `Python-package Quick Start <./Python-Intro.rst>`__ + +- `Python API <./Python-API.rst>`__ + +Training Data Format +-------------------- + +LightGBM supports input data files with `CSV`_, `TSV`_ and `LibSVM`_ (zero-based) formats. + +Files could be both with and without `headers <./Parameters.rst#header>`__. + +`Label column <./Parameters.rst#label_column>`__ could be specified both by index and by name. + +Some columns could be `ignored <./Parameters.rst#ignore_column>`__. + +Categorical Feature Support +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +LightGBM can use categorical features directly (without one-hot encoding). +The experiment on `Expo data`_ shows about 8x speed-up compared with one-hot encoding. + +For the setting details, please refer to the ``categorical_feature`` `parameter <./Parameters.rst#categorical_feature>`__. + +Weight and Query/Group Data +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +LightGBM also supports weighted training, it needs an additional `weight data <./Parameters.rst#weight-data>`__. +And it needs an additional `query data <./Parameters.rst#query-data>`_ for ranking task. + +Also, `weight <./Parameters.rst#weight_column>`__ and `query <./Parameters.rst#group_column>`__ data could be specified as columns in training data in the same manner as label. + +Parameters Quick Look +--------------------- + +The parameters format is ``key1=value1 key2=value2 ...``. + +Parameters can be set both in config file and command line. +If one parameter appears in both command line and config file, LightGBM will use the parameter from the command line. + +The most important parameters which new users should take a look at are located into `Core Parameters <./Parameters.rst#core-parameters>`__ +and the top of `Learning Control Parameters <./Parameters.rst#learning-control-parameters>`__ +sections of the full detailed list of `LightGBM's parameters <./Parameters.rst>`__. + +Run LightGBM +------------ + +:: + + lightgbm config=your_config_file other_args ... + +Parameters can be set both in the config file and command line, and the parameters in command line have higher priority than in the config file. +For example, the following command line will keep ``num_trees=10`` and ignore the same parameter in the config file. + +:: + + lightgbm config=train.conf num_trees=10 + +Examples +-------- + +- `Binary Classification `__ + +- `Regression `__ + +- `Lambdarank `__ + +- `Distributed Learning `__ + +.. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values + +.. _TSV: https://en.wikipedia.org/wiki/Tab-separated_values + +.. _LibSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/ + +.. _Expo data: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009 diff --git a/docs/README.rst b/docs/README.rst new file mode 100644 index 0000000..5eaac9e --- /dev/null +++ b/docs/README.rst @@ -0,0 +1,69 @@ +Documentation +============= + +Documentation for LightGBM is generated using `Sphinx `__ +and `Breathe `__, which works on top of `Doxygen `__ output. + +List of parameters and their descriptions in `Parameters.rst <./Parameters.rst>`__ +is generated automatically from comments in `config file `__ +by `this script `__. + +After each commit on ``main``, documentation is updated and published to `Read the Docs `__. + +Build +----- + +It is not necessary to re-build this documentation while modifying LightGBM's source code. +The HTML files generated using ``Sphinx`` are not checked into source control. +However, you may want to build them locally during development to test changes. + +Docker +^^^^^^ + +The most reliable way to build the documentation locally is with Docker, using `the same images Read the Docs uses `_. + +Run the following from the root of this repository to pull the relevant image and run a container locally. + +.. code:: sh + + docker run \ + --rm \ + --user=0 \ + -v $(pwd):/opt/LightGBM \ + --env C_API=true \ + --env CONDA=/opt/miniforge \ + --env READTHEDOCS=true \ + --workdir=/opt/LightGBM/docs \ + --entrypoint="" \ + readthedocs/build:ubuntu-24.04-2024.06.17 \ + /bin/bash build-docs.sh + +When that code completes, open ``docs/_build/html/index.html`` in your browser. + +.. note:: + + The navigation in these locally-built docs does not link to the local copy of the R documentation. To view the local version of the R docs, open ``docs/_build/html/R/index.html`` in your browser. + +Without Docker +^^^^^^^^^^^^^^ + +You can build the documentation locally without Docker. Just install Doxygen and run in ``docs`` folder + +.. code:: sh + + pip install breathe sphinx 'sphinx_rtd_theme>=0.5' + make html + +Note that this will not build the R documentation. +Consider using common R utilities for documentation generation, if you need it. +Or use the Docker-based approach described above to build the R documentation locally. + +Optionally, you may also install ``scikit-learn`` and get richer documentation for the classes in ``Scikit-learn API``. + +If you faced any problems with Doxygen installation or you simply do not need documentation for C code, it is possible to build the documentation without it: + +.. code:: sh + + pip install sphinx 'sphinx_rtd_theme>=0.5' + export C_API=NO || set C_API=NO + make html diff --git a/docs/_static/images/dask-concat.svg b/docs/_static/images/dask-concat.svg new file mode 100644 index 0000000..7e0ca1f --- /dev/null +++ b/docs/_static/images/dask-concat.svg @@ -0,0 +1 @@ + diff --git a/docs/_static/images/dask-initial-setup.svg b/docs/_static/images/dask-initial-setup.svg new file mode 100644 index 0000000..ed18049 --- /dev/null +++ b/docs/_static/images/dask-initial-setup.svg @@ -0,0 +1 @@ + diff --git a/docs/_static/images/favicon.ico b/docs/_static/images/favicon.ico new file mode 100644 index 0000000..7aa6981 Binary files /dev/null and b/docs/_static/images/favicon.ico differ diff --git a/docs/_static/images/gpu-performance-comparison.png b/docs/_static/images/gpu-performance-comparison.png new file mode 100644 index 0000000..f26d46b Binary files /dev/null and b/docs/_static/images/gpu-performance-comparison.png differ diff --git a/docs/_static/images/leaf-wise.png b/docs/_static/images/leaf-wise.png new file mode 100644 index 0000000..aa0fd32 Binary files /dev/null and b/docs/_static/images/leaf-wise.png differ diff --git a/docs/_static/images/level-wise.png b/docs/_static/images/level-wise.png new file mode 100644 index 0000000..b014ccb Binary files /dev/null and b/docs/_static/images/level-wise.png differ diff --git a/docs/_static/js/script.js b/docs/_static/js/script.js new file mode 100644 index 0000000..5babca3 --- /dev/null +++ b/docs/_static/js/script.js @@ -0,0 +1,52 @@ +$(() => { + /* Use wider container for the page content */ + $(".wy-nav-content").each(function () { + this.style.setProperty("max-width", "none", "important"); + }); + + /* List each class property item on a new line + https://github.com/lightgbm-org/LightGBM/issues/5073 */ + if (window.location.pathname.toLocaleLowerCase().indexOf("pythonapi") !== -1) { + $(".py.property").each(function () { + this.style.setProperty("display", "inline", "important"); + }); + } + + /* Collapse specified sections in the installation guide */ + if (window.location.pathname.toLocaleLowerCase().indexOf("installation-guide") !== -1) { + $( + '', + ).appendTo("body"); + const collapsible = [ + "#build-threadless-version-not-recommended", + "#build-mpi-version", + "#build-gpu-version", + "#build-cuda-version", + "#build-rocm-version", + "#build-java-wrapper", + "#build-python-package", + "#build-r-package", + "#build-c-unit-tests", + ]; + $.each(collapsible, (_, val) => { + const header = `${val} > :header:first`; + const content = `${val} :not(:header:first)`; + $(header).addClass("closed"); + $(content).hide(); + $(header).click(() => { + $(header).toggleClass("closed opened"); + $(content).slideToggle(0); + }); + }); + /* Uncollapse parent sections when nested section is specified in the URL or before navigate to it from navbar */ + function uncollapse(section) { + section.parents().each((_, val) => { + $(val).children(".closed").click(); + }); + } + uncollapse($(window.location.hash)); + $(".wy-menu.wy-menu-vertical li a.reference.internal").click(function () { + uncollapse($($(this).attr("href"))); + }); + } +}); diff --git a/docs/build-docs.sh b/docs/build-docs.sh new file mode 100644 index 0000000..682c800 --- /dev/null +++ b/docs/build-docs.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +set -e -E -u -o pipefail + +rm -f ./_FIRST_RUN.flag + +export PATH="${CONDA}/bin:${PATH}" + +curl \ + -sL \ + -o "${HOME}/miniforge.sh" \ + https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh + +/bin/bash "${HOME}/miniforge.sh" -b -p "${CONDA}" + +conda config --set always_yes yes --set changeps1 no +conda update -q -y conda + +conda env create \ + --name docs-env \ + --file env.yml || exit 1 + +# shellcheck disable=SC1091 +source activate docs-env +make clean html || exit 1 + +echo "Done building docs. Open docs/_build/html/index.html in a web browser to view them." diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..bff6781 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,335 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# LightGBM documentation build configuration file, created by +# sphinx-quickstart on Thu May 4 14:30:58 2017. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute. +"""Sphinx configuration file.""" + +import datetime +import os +import sys +from pathlib import Path +from re import compile +from shutil import copytree +from subprocess import PIPE, Popen +from typing import Any, List + +import sphinx +from docutils.nodes import reference +from docutils.parsers.rst import Directive +from docutils.transforms import Transform +from sphinx.application import Sphinx +from sphinx.errors import VersionRequirementError + +CURR_PATH = Path(__file__).absolute().parent +LIB_PATH = CURR_PATH.parent / "python-package" +sys.path.insert(0, str(LIB_PATH)) + +INTERNAL_REF_REGEX = compile(r"(?P\.\/.+)(?P\.rst)(?P$|#)") +RTD_R_REF_REGEX = compile(r"(?Phttps://.+/)(?Platest)(?P/R/reference/)") + + +class InternalRefTransform(Transform): + """Replaces '.rst' with '.html' in all internal links like './[Something].rst[#anchor]'.""" + + default_priority = 210 + """Numerical priority of this transform, 0 through 999.""" + + def apply(self, **kwargs: Any) -> None: + """Apply the transform to the document tree.""" + for section in self.document.traverse(reference): + if section.get("refuri") is not None: + section["refuri"] = INTERNAL_REF_REGEX.sub(r"\g.html\g", section["refuri"]) + + +class IgnoredDirective(Directive): + """Stub for unknown directives.""" + + has_content = True + + def run(self) -> List: + """Do nothing.""" + return [] + + +# -- General configuration ------------------------------------------------ + +os.environ["LIGHTGBM_BUILD_DOC"] = "True" +C_API = os.environ.get("C_API", "").lower().strip() != "no" +RTD = bool(os.environ.get("READTHEDOCS", "")) +RTD_VERSION = os.environ.get("READTHEDOCS_VERSION", "stable") + +# If your documentation needs a minimal Sphinx version, state it here. +needs_sphinx = "2.1.0" # Due to sphinx.ext.napoleon, autodoc_typehints +if needs_sphinx > sphinx.__version__: + message = f"This project needs at least Sphinx v{needs_sphinx}" + raise VersionRequirementError(message) + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.autosummary", + "sphinx.ext.todo", + "sphinx.ext.viewcode", + "sphinx.ext.napoleon", + "sphinx.ext.intersphinx", +] + +autodoc_default_flags = ["members", "inherited-members", "show-inheritance"] +autodoc_default_options = { + "members": True, + "inherited-members": True, + "show-inheritance": True, +} +# mock out modules +autodoc_mock_imports = [ + "dask", + "dask.distributed", + "graphviz", + "matplotlib", + "narwhals", + "numpy", + "pandas", + "scipy", + "scipy.sparse", +] +try: + import sklearn # noqa: F401 +except ImportError: + autodoc_mock_imports.append("sklearn") +# hide type hints in API docs +autodoc_typehints = "none" + +# Generate autosummary pages. Output should be set with: `:toctree: pythonapi/` +autosummary_generate = ["Python-API.rst"] + +# Only the class' docstring is inserted. +autoclass_content = "class" + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + +# The master toctree document. +master_doc = "index" + +# General information about the project. +project = "LightGBM" +copyright = f"{datetime.datetime.now().year}, Microsoft Corporation" +author = "Microsoft Corporation" + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +html_logo = str(CURR_PATH / "logo" / "LightGBM_logo_grey_text.svg") + +# The name of an image file (relative to this directory) to use as a favicon of +# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +html_favicon = str(CURR_PATH / "_static" / "images" / "favicon.ico") + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# The short X.Y version. +version = (CURR_PATH.parent / "VERSION.txt").read_text(encoding="utf-8").strip().replace("rc", "-rc") +# The full version, including alpha/beta/rc tags. +release = version + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = "en" + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = "default" + +# -- Configuration for C API docs generation ------------------------------ + +if C_API: + extensions.extend( + [ + "breathe", + ] + ) + breathe_projects = {"LightGBM": str(CURR_PATH / "doxyoutput" / "xml")} + breathe_default_project = "LightGBM" + breathe_domain_by_extension = { + "h": "c", + } + breathe_show_define_initializer = True + c_id_attributes = ["LIGHTGBM_C_EXPORT"] + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +html_theme = "sphinx_rtd_theme" + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +html_theme_options = { + "includehidden": False, + "logo_only": True, +} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] + +# -- Options for HTMLHelp output ------------------------------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = "LightGBMdoc" + +# -- Options for LaTeX output --------------------------------------------- + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +latex_logo = str(CURR_PATH / "logo" / "LightGBM_logo_black_text_small.png") + +# intersphinx configuration +intersphinx_mapping = { + "sklearn": ("https://scikit-learn.org/stable/", None), +} + + +def generate_doxygen_xml(app: Sphinx) -> None: + """Generate XML documentation for C API by Doxygen. + + Parameters + ---------- + app : sphinx.application.Sphinx + The application object representing the Sphinx process. + """ + doxygen_args = [ + f"INPUT={CURR_PATH.parent / 'include' / 'LightGBM' / 'c_api.h'}", + f"OUTPUT_DIRECTORY={CURR_PATH / 'doxyoutput'}", + "GENERATE_HTML=NO", + "GENERATE_LATEX=NO", + "GENERATE_XML=YES", + "XML_OUTPUT=xml", + "XML_PROGRAMLISTING=YES", + r'ALIASES="rst=\verbatim embed:rst:leading-asterisk"', + r'ALIASES+="endrst=\endverbatim"', + "ENABLE_PREPROCESSING=YES", + "MACRO_EXPANSION=YES", + "EXPAND_ONLY_PREDEF=NO", + "SKIP_FUNCTION_MACROS=NO", + "PREDEFINED=__cplusplus", + "SORT_BRIEF_DOCS=YES", + "WARN_AS_ERROR=YES", + ] + doxygen_input = "\n".join(doxygen_args) + doxygen_input = bytes(doxygen_input, "utf-8") + (CURR_PATH / "doxyoutput").mkdir(parents=True, exist_ok=True) + try: + # Warning! The following code can cause buffer overflows on RTD. + # Consider suppressing output completely if RTD project silently fails. + # Refer to https://github.com/svenevs/exhale + # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111 + process = Popen(["doxygen", "-"], stdin=PIPE, stdout=PIPE, stderr=PIPE) + stdout, stderr = process.communicate(doxygen_input) + output = "\n".join([i.decode("utf-8") for i in (stdout, stderr) if i is not None]) + if process.returncode != 0: + raise RuntimeError(output) + print(output) + except BaseException as e: + raise Exception(f"An error has occurred while executing Doxygen\n{e}") + + +def generate_r_docs(app: Sphinx) -> None: + """Generate documentation for R-package. + + Parameters + ---------- + app : sphinx.application.Sphinx + The application object representing the Sphinx process. + """ + commands = f""" + export TAR=/bin/tar + cd {CURR_PATH.parent} + export R_LIBS="$CONDA_PREFIX/lib/R/library" + sh build-cran-package.sh || exit 1 + R CMD INSTALL --with-keep.source lightgbm_*.tar.gz || exit 1 + Rscript .ci/build-docs.R || exit 1 + """ + try: + print("Building R-package documentation") + # Warning! The following code can cause buffer overflows on RTD. + # Consider suppressing output completely if RTD project silently fails. + # Refer to https://github.com/svenevs/exhale + # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111 + process = Popen(["/bin/bash"], stdin=PIPE, stdout=PIPE, stderr=PIPE, universal_newlines=True) + stdout, stderr = process.communicate(commands) + output = "\n".join([i for i in (stdout, stderr) if i is not None]) + if process.returncode != 0: + raise RuntimeError(output) + print(output) + print("Done building R-package documentation") + except BaseException as e: + raise Exception(f"An error has occurred while generating documentation for R-package\n{e}") + + +def replace_reference_to_r_docs(app: Sphinx) -> None: + """Make reference to R-package documentation point to the actual version. + + Parameters + ---------- + app : sphinx.application.Sphinx + The application object representing the Sphinx process. + """ + index_doc_path = CURR_PATH / "index.rst" + with open(index_doc_path, "r+t", encoding="utf-8") as index_doc: + content = index_doc.read() + content = RTD_R_REF_REGEX.sub(rf"\g{RTD_VERSION}\g", content) + index_doc.seek(0) + index_doc.write(content) + + +def setup(app: Sphinx) -> None: + """Add new elements at Sphinx initialization time. + + Parameters + ---------- + app : sphinx.application.Sphinx + The application object representing the Sphinx process. + """ + first_run = not (CURR_PATH / "_FIRST_RUN.flag").exists() + if first_run and RTD: + (CURR_PATH / "_FIRST_RUN.flag").touch() + if C_API: + app.connect("builder-inited", generate_doxygen_xml) + else: + app.add_directive("doxygenfile", IgnoredDirective) + if first_run: + app.connect("builder-inited", generate_r_docs) + app.connect( + "build-finished", lambda app, _: copytree(CURR_PATH.parent / "R-package" / "docs", Path(app.outdir) / "R") + ) + app.connect("builder-inited", replace_reference_to_r_docs) + app.add_transform(InternalRefTransform) + add_js_file = getattr(app, "add_js_file", False) or app.add_javascript + add_js_file("js/script.js") diff --git a/docs/env.yml b/docs/env.yml new file mode 100644 index 0000000..1e76529 --- /dev/null +++ b/docs/env.yml @@ -0,0 +1,19 @@ +name: docs-env +channels: + - nodefaults + - conda-forge +dependencies: + - breathe>=4.36 + - doxygen>=1.13.2 + - python=3.14 + - r-base>=4.4 + - r-data.table=1.17.8 + - r-jsonlite=2.0.0 + - r-knitr=1.51 + - r-markdown=2.0 + - r-matrix=1.7_4 + - r-pkgdown=2.2.0 + - r-roxygen2=8.0.0 + - scikit-learn>=1.8.0 + - sphinx>=8.1.3 + - sphinx_rtd_theme>=3.0.1 diff --git a/docs/gcc-Tips.rst b/docs/gcc-Tips.rst new file mode 100644 index 0000000..1d4b6fc --- /dev/null +++ b/docs/gcc-Tips.rst @@ -0,0 +1 @@ +The content of this document was very outdated and is no longer available to avoid any misleadings. diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..54ca853 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,58 @@ +.. LightGBM documentation master file, created by + sphinx-quickstart on Thu May 4 14:30:58 2017. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +.. image:: ./logo/LightGBM_logo_black_text.svg + :align: center + :width: 600 + :alt: Light Gradient Boosting Machine logo. + +| + +Welcome to LightGBM's documentation! +==================================== + +**LightGBM** is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: + +- Faster training speed and higher efficiency. +- Lower memory usage. +- Better accuracy. +- Support of parallel, distributed, and GPU learning. +- Capable of handling large-scale data. + +For more details, please refer to `Features <./Features.rst>`__. + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + Installation Guide + Quick Start + Python Quick Start + Features + Experiments + Parameters + Parameters Tuning + C API + Python API + R API + Distributed Learning Guide + GPU Tutorial + Advanced Topics + FAQ + Development Guide + +.. toctree:: + :hidden: + + GPU-Performance + GPU-Targets + GPU-Windows + gcc-Tips + README + +Indices and Tables +================== + +* :ref:`genindex` diff --git a/docs/logo/LightGBM-logo-hex.cdr b/docs/logo/LightGBM-logo-hex.cdr new file mode 100644 index 0000000..97ba157 Binary files /dev/null and b/docs/logo/LightGBM-logo-hex.cdr differ diff --git a/docs/logo/LightGBM-logo-hex.svg b/docs/logo/LightGBM-logo-hex.svg new file mode 100644 index 0000000..4af2394 --- /dev/null +++ b/docs/logo/LightGBM-logo-hex.svg @@ -0,0 +1,41 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/logo/LightGBM_logo-hex.png b/docs/logo/LightGBM_logo-hex.png new file mode 100644 index 0000000..2093663 Binary files /dev/null and b/docs/logo/LightGBM_logo-hex.png differ diff --git a/docs/logo/LightGBM_logo.cdr b/docs/logo/LightGBM_logo.cdr new file mode 100644 index 0000000..b22abf2 Binary files /dev/null and b/docs/logo/LightGBM_logo.cdr differ diff --git a/docs/logo/LightGBM_logo_black_text.svg b/docs/logo/LightGBM_logo_black_text.svg new file mode 100644 index 0000000..a341128 --- /dev/null +++ b/docs/logo/LightGBM_logo_black_text.svg @@ -0,0 +1,39 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/logo/LightGBM_logo_black_text_huge.png b/docs/logo/LightGBM_logo_black_text_huge.png new file mode 100644 index 0000000..508028b Binary files /dev/null and b/docs/logo/LightGBM_logo_black_text_huge.png differ diff --git a/docs/logo/LightGBM_logo_black_text_large.png b/docs/logo/LightGBM_logo_black_text_large.png new file mode 100644 index 0000000..17d9634 Binary files /dev/null and b/docs/logo/LightGBM_logo_black_text_large.png differ diff --git a/docs/logo/LightGBM_logo_black_text_medium.png b/docs/logo/LightGBM_logo_black_text_medium.png new file mode 100644 index 0000000..ca61213 Binary files /dev/null and b/docs/logo/LightGBM_logo_black_text_medium.png differ diff --git a/docs/logo/LightGBM_logo_black_text_small.png b/docs/logo/LightGBM_logo_black_text_small.png new file mode 100644 index 0000000..3e1d34c Binary files /dev/null and b/docs/logo/LightGBM_logo_black_text_small.png differ diff --git a/docs/logo/LightGBM_logo_black_text_tiny.png b/docs/logo/LightGBM_logo_black_text_tiny.png new file mode 100644 index 0000000..e681515 Binary files /dev/null and b/docs/logo/LightGBM_logo_black_text_tiny.png differ diff --git a/docs/logo/LightGBM_logo_grey_text.svg b/docs/logo/LightGBM_logo_grey_text.svg new file mode 100644 index 0000000..1a9e421 --- /dev/null +++ b/docs/logo/LightGBM_logo_grey_text.svg @@ -0,0 +1,37 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/logo/LightGBM_logo_grey_text_huge.png b/docs/logo/LightGBM_logo_grey_text_huge.png new file mode 100644 index 0000000..49fcd79 Binary files /dev/null and b/docs/logo/LightGBM_logo_grey_text_huge.png differ diff --git a/docs/logo/LightGBM_logo_grey_text_large.png b/docs/logo/LightGBM_logo_grey_text_large.png new file mode 100644 index 0000000..57a02eb Binary files /dev/null and b/docs/logo/LightGBM_logo_grey_text_large.png differ diff --git a/docs/logo/LightGBM_logo_grey_text_medium.png b/docs/logo/LightGBM_logo_grey_text_medium.png new file mode 100644 index 0000000..776d4ea Binary files /dev/null and b/docs/logo/LightGBM_logo_grey_text_medium.png differ diff --git a/docs/logo/LightGBM_logo_grey_text_small.png b/docs/logo/LightGBM_logo_grey_text_small.png new file mode 100644 index 0000000..700509c Binary files /dev/null and b/docs/logo/LightGBM_logo_grey_text_small.png differ diff --git a/docs/logo/LightGBM_logo_grey_text_tiny.png b/docs/logo/LightGBM_logo_grey_text_tiny.png new file mode 100644 index 0000000..fda5712 Binary files /dev/null and b/docs/logo/LightGBM_logo_grey_text_tiny.png differ diff --git a/docs/logo/LightGBM_logo_no_text.svg b/docs/logo/LightGBM_logo_no_text.svg new file mode 100644 index 0000000..c9640de --- /dev/null +++ b/docs/logo/LightGBM_logo_no_text.svg @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + diff --git a/docs/logo/LightGBM_logo_no_text_huge.png b/docs/logo/LightGBM_logo_no_text_huge.png new file mode 100644 index 0000000..fd67adc Binary files /dev/null and b/docs/logo/LightGBM_logo_no_text_huge.png differ diff --git a/docs/logo/LightGBM_logo_no_text_large.png b/docs/logo/LightGBM_logo_no_text_large.png new file mode 100644 index 0000000..9b6c77a Binary files /dev/null and b/docs/logo/LightGBM_logo_no_text_large.png differ diff --git a/docs/logo/LightGBM_logo_no_text_medium.png b/docs/logo/LightGBM_logo_no_text_medium.png new file mode 100644 index 0000000..55f22e5 Binary files /dev/null and b/docs/logo/LightGBM_logo_no_text_medium.png differ diff --git a/docs/logo/LightGBM_logo_no_text_small.png b/docs/logo/LightGBM_logo_no_text_small.png new file mode 100644 index 0000000..0241e8e Binary files /dev/null and b/docs/logo/LightGBM_logo_no_text_small.png differ diff --git a/docs/logo/LightGBM_logo_no_text_tiny.png b/docs/logo/LightGBM_logo_no_text_tiny.png new file mode 100644 index 0000000..513d76e Binary files /dev/null and b/docs/logo/LightGBM_logo_no_text_tiny.png differ diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..c21fbdd --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,37 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build +set SPHINXPROJ=LightGBM +set SPHINXOPTS=-W + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..50748de --- /dev/null +++ b/examples/README.md @@ -0,0 +1,72 @@ +Examples +======== + +You can learn how to use LightGBM by these examples. + +Comments in configuration files might be outdated. Actual information about parameters always can be found [here](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parameters.rst). + +Machine Learning Challenge Winning Solutions +============================================ + +**LightGBM is used in many winning solutions, but this table is updated very infrequently.** + +| Place         | Competition   | Solution | Date | +|---------|:------------- | --------- | -----| +| 3rd | [Water Supply Forecast Rodeo: Forecast Stage](https://drivendata.co/blog/water-supply-forecast-and-final-winners) | [link](https://github.com/drivendataorg/water-supply-forecast-rodeo/blob/main/overall/3rd%20place/) | 2024.3 | +| 1st | [American Express - Default Prediction](https://www.drivendata.org/competitions/group/competition-nasa-airport-pushback/) | [link](https://www.kaggle.com/competitions/amex-default-prediction/writeups/lucky-shake-1st-solution-update-github-code) | 2022.8 | +| 2nd | [American Express - Default Prediction](https://www.drivendata.org/competitions/group/competition-nasa-airport-pushback/) | [link](https://www.kaggle.com/competitions/amex-default-prediction/writeups/bydefault-junehomes-2nd-place-solution-team-juneho) | 2022.8 | +| 3rd | [American Express - Default Prediction](https://www.drivendata.org/competitions/group/competition-nasa-airport-pushback/) | [link](https://www.kaggle.com/competitions/amex-default-prediction/writeups/aibank-3rd-solution-simple-is-the-best) | 2022.8 | +| 1st | [Ubiquant Market Prediction](https://www.kaggle.com/competitions/ubiquant-market-prediction/) | [link](https://www.kaggle.com/competitions/ubiquant-market-prediction/writeups/k-i-y-1st-place-solution-our-betting-strategy) | 2022.7 | +| 2nd | [Ubiquant Market Prediction](https://www.kaggle.com/competitions/ubiquant-market-prediction/) | [link](https://www.kaggle.com/competitions/ubiquant-market-prediction/writeups/davide-stenner-2nd-place-solution-robust-cv-and-lg) | 2022.7 | +| 2nd | [G-Research Crypto Forecasting](https://www.kaggle.com/competitions/g-research-crypto-forecasting/) | [link](https://www.kaggle.com/competitions/g-research-crypto-forecasting/writeups/nathaniel-maddux-2nd-place-solution) | 2022.5 | +| 3rd | [G-Research Crypto Forecasting](https://www.kaggle.com/competitions/g-research-crypto-forecasting/) | [link](https://www.kaggle.com/competitions/g-research-crypto-forecasting/writeups/gaba-3rd-place-solution) | 2022.5 | +| 1st | [NASA Airathon: Predict Air Quality (Particulate Track)](https://www.drivendata.org/competitions/88/competition-air-quality-pm/) | [link](https://github.com/drivendataorg/nasa-airathon/tree/main/pm25/1st%20Place) | 2022.3 | +| 2nd | [NASA Airathon: Predict Air Quality (Particulate Track)](https://www.drivendata.org/competitions/88/competition-air-quality-pm/) | [link](https://github.com/drivendataorg/nasa-airathon/tree/main/pm25/2nd%20Place) | 2022.3 | +| 1st | [M5 Forecasting - Uncertainty](https://www.kaggle.com/c/m5-forecasting-uncertainty) | [link](https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/163368) | 2020.7 | +| 3rd | [M5 Forecasting - Uncertainty](https://www.kaggle.com/c/m5-forecasting-uncertainty) | [link](https://www.kaggle.com/competitions/m5-forecasting-uncertainty/writeups/ouranos-3rd-place-solution) | 2020.7 | +| 3rd | [ALASKA2 Image Steganalysis](https://www.kaggle.com/c/alaska2-image-steganalysis) | [link](https://www.kaggle.com/competitions/alaska2-image-steganalysis/writeups/kaizaburochubachi-3rd-place-solution) | 2020.7 | +| 1st | [M5 Forecasting - Accuracy](https://www.kaggle.com/c/m5-forecasting-accuracy) | [link](https://www.kaggle.com/competitions/m5-forecasting-accuracy/writeups/yeonjun-in-stu-1st-place-solution) | 2020.6 | +| 2nd | [COVID19 Global Forecasting (Week 5)](https://www.kaggle.com/c/covid19-global-forecasting-week-5) | [link](https://www.kaggle.com/competitions/covid19-global-forecasting-week-5/writeups/kaz-some-ml-a-lot-of-judgement-and-luck) | 2020.5 | +| 3rd | [COVID19 Global Forecasting (Week 5)](https://www.kaggle.com/c/covid19-global-forecasting-week-5) | [link](https://www.kaggle.com/c/covid19-global-forecasting-week-5/discussion/143029) | 2020.5 | +| 1st | [COVID19 Global Forecasting (Week 4)](https://www.kaggle.com/c/covid19-global-forecasting-week-4) | [link](https://www.kaggle.com/c/covid19-global-forecasting-week-5/discussion/154804) | 2020.5 | +| 2nd | [COVID19 Global Forecasting (Week 4)](https://www.kaggle.com/c/covid19-global-forecasting-week-4) | [link](https://www.kaggle.com/c/covid19-global-forecasting-week-5/discussion/144081) | 2020.5 | +| 2nd | [2019 Data Science Bowl](https://www.kaggle.com/c/data-science-bowl-2019) | [link](https://www.kaggle.com/competitions/data-science-bowl-2019/writeups/fuson-2nd-place-solution) | 2020.1 | +| 3rd | [RSNA Intracranial Hemorrhage Detection](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection) | [link](https://www.kaggle.com/competitions/rsna-intracranial-hemorrhage-detection/writeups/takuoko-3rd-place-solution-become-gm-updated-with-) | 2019.11 | +| 1st | [IEEE-CIS Fraud Detection](https://www.kaggle.com/c/ieee-fraud-detection) | [link](https://www.kaggle.com/competitions/ieee-fraud-detection/writeups/fraudsquad-1st-place-solution-part-2) | 2019.10 | +| 2nd | [IEEE-CIS Fraud Detection](https://www.kaggle.com/c/ieee-fraud-detection) | [link](https://www.kaggle.com/competitions/ieee-fraud-detection/writeups/2-uncles-and-3-puppies-2nd-solution-cpmp-view) | 2019.10 | +| 2nd | [Kuzushiji Recognition](https://www.kaggle.com/c/kuzushiji-recognition) | [link](https://www.kaggle.com/c/kuzushiji-recognition/discussion/112712) | 2019.10 | +| 1st | [Los Alamos National Laboratory Earthquake Prediction](https://www.kaggle.com/c/LANL-Earthquake-Prediction) | [link](https://www.kaggle.com/competitions/LANL-Earthquake-Prediction/writeups/the-zoo-1st-place-solution) | 2019.6 | +| 3rd | [Los Alamos National Laboratory Earthquake Prediction](https://www.kaggle.com/c/LANL-Earthquake-Prediction) | [link](https://www.kaggle.com/competitions/LANL-Earthquake-Prediction/writeups/character-ranking-3rd-place-memo) | 2019.6 | +| 1st | [Santander Customer Transaction Prediction](https://www.kaggle.com/c/santander-customer-transaction-prediction) | [link](https://www.kaggle.com/competitions/santander-customer-transaction-prediction/writeups/wizardry-1-solution) | 2019.4 | +| 2nd | [Santander Customer Transaction Prediction](https://www.kaggle.com/c/santander-customer-transaction-prediction) | [link](https://www.kaggle.com/competitions/santander-customer-transaction-prediction/writeups/2nd-place-solution) | 2019.4 | +| 3rd | [Santander Customer Transaction Prediction](https://www.kaggle.com/c/santander-customer-transaction-prediction) | [link](https://www.kaggle.com/competitions/santander-customer-transaction-prediction/writeups/rock-physics-science-3rd-place-solution-summary-an) | 2019.4 | +| 1st | [PetFinder.my Adoption Prediction](https://www.kaggle.com/c/petfinder-adoption-prediction) | [link](https://www.kaggle.com/competitions/petfinder-adoption-prediction/writeups/kaggler-ja-wodori-1st-place-solution-summary) | 2019.4 | +| 1st | [Google Analytics Customer Revenue Prediction](https://www.kaggle.com/c/ga-customer-revenue-prediction) | [link](https://www.kaggle.com/competitions/ga-customer-revenue-prediction/writeups/ml-keksika-winning-solution-link-to-kernel-inside) | 2019.3 | +| 1st | [VSB Power Line Fault Detection](https://www.kaggle.com/c/vsb-power-line-fault-detection) | [link](https://www.kaggle.com/competitions/vsb-power-line-fault-detection/writeups/mark4h-overview-of-1st-place-solution) | 2019.3 | +| 5th | [Elo Merchant Category Recommendation](https://www.kaggle.com/c/elo-merchant-category-recommendation) | [link](https://www.kaggle.com/competitions/elo-merchant-category-recommendation/writeups/evgeny-patekha-5-solution) | 2019.2 | +| 2nd | [PLAsTiCC Astronomical Classification](https://www.kaggle.com/c/PLAsTiCC-2018) | [link](https://www.kaggle.com/competitions/PLAsTiCC-2018/writeups/mike-silogram-2nd-place-solution-notes) | 2018.12 | +| 1st | [Google Research Doodle Recognition Challenge](https://www.kaggle.com/c/quickdraw-doodle-recognition) | [link](https://www.kaggle.com/competitions/quickdraw-doodle-recognition/writeups/ods-ai-pablos-1st-place-solution) | 2018.12 | +| 1st | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/competitions/home-credit-default-risk/writeups/home-aloan-1st-place-solution) | 2018.8 | +| 2nd | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/competitions/home-credit-default-risk/writeups/ikiri-ds-2nd-place-solution-team-ikiri-ds) | 2018.8 | +| 3rd | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/competitions/home-credit-default-risk/writeups/alijs-evgeny-3rd-place-solution) | 2018.8 | +| 2nd | [Google AI Open Images - Visual Relationship Track](https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track) | [link](https://www.kaggle.com/competitions/google-ai-open-images-visual-relationship-track/writeups/tito-brief-summary-of-2nd-place) | 2018.8 | +| 2nd | [Santander Value Prediction Challenge](https://www.kaggle.com/c/santander-value-prediction-challenge) | [link](https://www.kaggle.com/competitions/santander-value-prediction-challenge/writeups/adilism-2nd-place-solution-overview) | 2018.8 | +| 1st | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/competitions/avito-demand-prediction/writeups/dance-with-ensemble-dance-with-ensemble-sharing-th) | 2018.6 | +| 2nd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/competitions/avito-demand-prediction/writeups/song-and-dance-ensemble-second-place-solution) | 2018.6 | +| 3rd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/competitions/avito-demand-prediction/writeups/superanova-3-place-solution) | 2018.6 | +| 1st | [TalkingData AdTracking Fraud Detection Challenge](https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection) | [link](https://www.kaggle.com/competitions/talkingdata-adtracking-fraud-detection/writeups/flowlight-komaki-shuffle-1st-place-solution)| 2018.5 | +| 1st | [DonorsChoose.org Application Screening](https://www.kaggle.com/c/donorschoose-application-screening)| [link](https://www.kaggle.com/shadowwarrior/1st-place-solution/notebook) | 2018.4 | +| 1st | [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)| [link](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/writeups/toxic-crusaders-1st-place-solution-overview) | 2018.3 | +| 1st | [Mercari Price Suggestion Challenge](https://www.kaggle.com/c/mercari-price-suggestion-challenge) | [link](https://www.kaggle.com/competitions/mercari-price-suggestion-challenge/writeups/pawe-and-konstantin-1st-place-solution) | 2018.2 | +| 1st | [IEEE's Signal Processing Society, Camera Model Identification](https://www.kaggle.com/c/sp-society-camera-model-identification)| [link](https://www.kaggle.com/competitions/sp-society-camera-model-identification/writeups/ods-ai-stamp-1st-place-solution) | 2018.2 | +| 1st | [Recruit Restaurant Visitor Forecasting](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) | [link](https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/writeups/pppp-solution-public-0-471-private-0-505) | 2018.2| +| 1st | [WSDM CUP 2018 - KKBox's Music Recommendation Challenge](https://www.kaggle.com/c/kkbox-music-recommendation-challenge) | [link](https://www.kaggle.com/competitions/kkbox-music-recommendation-challenge/writeups/bing-bai-a-brief-introduction-to-the-1st-place-sol) | 2017.12 | +| 1st | [Porto Seguro’s Safe Driver Prediction](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction) | [link](https://www.kaggle.com/competitions/porto-seguro-safe-driver-prediction/writeups/michael-jahrer-1st-place-with-representation-learn) |2017.11 | +| 1st | [Quora Question Pairs](https://www.kaggle.com/c/quora-question-pairs) | [link](https://www.kaggle.com/competitions/quora-question-pairs/writeups/dl-guys-1st-place-solution) | 2017.6 | +| 1st | [Two Sigma Connect: Rental Listing Inquiries](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries) | [link](https://www.kaggle.com/competitions/two-sigma-connect-rental-listing-inquiries/writeups/plantsgo-my-best-single-model-and-solution) | 2017.4 | +| 1st | [CIKM2017 AnalytiCup - Lazada Product Title Quality Challenge](https://cikm2017.org/CIKM_AnalytiCup_task3.html) | [link](https://arxiv.org/abs/1804.01000) | 2017.9 | +| 2nd | [Two Sigma Connect: Rental Listing Inquiries](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries) | [link](https://www.kaggle.com/competitions/two-sigma-connect-rental-listing-inquiries/writeups/faron-2nd-place-solution) | 2017.4 | +| 3rd | [Two Sigma Connect: Rental Listing Inquiries](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries) | [link](https://www.kaggle.com/competitions/two-sigma-connect-rental-listing-inquiries/writeups/little-boat-3rd-place-solution-summary) | 2017.4 | +| 3rd | [Dogs vs. Cats Redux: Kernels Edition](https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition) | [link](https://medium.com/kaggle-blog/dogs-vs-cats-redux-playground-competition-3rd-place-interview-marco-lugo-74893739b10f) | - | +| 3rd | [Bosch Production Line Performance](https://www.kaggle.com/c/bosch-production-line-performance) | [link](https://www.kaggle.com/competitions/bosch-production-line-performance/writeups/data-property-avengers-3-place-solution) | 2016.11 | +| 1st | [The 1st Di-Tech Competitions](https://web.archive.org/web/20170311212917/https://research.xiaojukeji.com/competition/main.action?competitionId=DiTech2016) | - | 2016.7 | diff --git a/examples/binary_classification/README.md b/examples/binary_classification/README.md new file mode 100644 index 0000000..5ce64ba --- /dev/null +++ b/examples/binary_classification/README.md @@ -0,0 +1,27 @@ +Binary Classification Example +============================= + +Here is an example for LightGBM to run binary classification task. + +***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) +for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** + +Training +-------- + +Run the following command in this folder: + +```bash +"../../lightgbm" config=train.conf +``` + +Prediction +---------- + +You should finish training first. + +Run the following command in this folder: + +```bash +"../../lightgbm" config=predict.conf +``` diff --git a/examples/binary_classification/binary.test b/examples/binary_classification/binary.test new file mode 100644 index 0000000..c9674d6 --- /dev/null +++ b/examples/binary_classification/binary.test @@ -0,0 +1,500 @@ +1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796 +0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116 +0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696 +1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 1.231 1.234 +0 0.897 -1.703 -1.306 1.022 -0.729 0.836 0.859 -0.333 2.173 1.336 -0.965 0.972 2.215 0.671 1.021 -1.439 0.000 0.493 -2.019 -0.289 0.000 0.805 0.930 0.984 1.430 2.198 1.934 1.684 +0 0.756 1.126 -0.945 2.355 -0.555 0.889 0.800 1.440 0.000 0.585 0.271 0.631 2.215 0.722 1.744 1.051 0.000 0.618 0.924 0.698 1.551 0.976 0.864 0.988 0.803 0.234 0.822 0.911 +0 1.141 -0.741 0.953 1.478 -0.524 1.197 -0.871 1.689 2.173 0.875 1.321 -0.518 1.107 0.540 0.037 -0.987 0.000 0.879 1.187 0.245 0.000 0.888 0.701 1.747 1.358 2.479 1.491 1.223 +1 0.606 -0.936 -0.384 1.257 -1.162 2.719 -0.600 0.100 2.173 3.303 -0.284 1.561 1.107 0.689 1.786 -0.326 0.000 0.780 -0.532 1.216 0.000 0.936 2.022 0.985 1.574 4.323 2.263 1.742 +1 0.603 0.429 -0.279 1.448 1.301 1.008 2.423 -1.295 0.000 0.452 1.305 0.533 0.000 1.076 1.011 1.256 2.548 2.021 1.260 -0.343 0.000 0.890 0.969 1.281 0.763 0.652 0.827 0.785 +0 1.171 -0.962 0.521 0.841 -0.315 1.196 -0.744 -0.882 2.173 0.726 -1.305 1.377 1.107 0.643 -1.790 -1.264 0.000 1.257 0.222 0.817 0.000 0.862 0.911 0.987 0.846 1.293 0.899 0.756 +1 1.392 -0.358 0.235 1.494 -0.461 0.895 -0.848 1.549 2.173 0.841 -0.384 0.666 1.107 1.199 2.509 -0.891 0.000 1.109 -0.364 -0.945 0.000 0.693 2.135 1.170 1.362 0.959 2.056 1.842 +1 1.024 1.076 -0.886 0.851 1.530 0.673 -0.449 0.187 1.087 0.628 -0.895 1.176 2.215 0.696 -0.232 -0.875 0.000 0.411 1.501 0.048 0.000 0.842 0.919 1.063 1.193 0.777 0.964 0.807 +1 0.890 -0.760 1.182 1.369 0.751 0.696 -0.959 -0.710 1.087 0.775 -0.130 -1.409 2.215 0.701 -0.110 -0.739 0.000 0.508 -0.451 0.390 0.000 0.762 0.738 0.998 1.126 0.788 0.940 0.790 +1 0.460 0.537 0.636 1.442 -0.269 0.585 0.323 -1.731 2.173 0.503 1.034 -0.927 0.000 0.928 -1.024 1.006 2.548 0.513 -0.618 -1.336 0.000 0.802 0.831 0.992 1.019 0.925 1.056 0.833 +1 0.364 1.648 0.560 1.720 0.829 1.110 0.811 -0.588 0.000 0.408 1.045 1.054 2.215 0.319 -1.138 1.545 0.000 0.423 1.025 -1.265 3.102 1.656 0.928 1.003 0.544 0.327 0.670 0.746 +1 0.525 -0.096 1.206 0.948 -1.103 1.519 -0.582 0.606 2.173 1.274 -0.572 -0.934 0.000 0.855 -1.028 -1.222 0.000 0.578 -1.000 -1.725 3.102 0.896 0.878 0.981 0.498 0.909 0.772 0.668 +0 0.536 -0.821 -1.029 0.703 1.113 0.363 -0.711 0.022 1.087 0.325 1.503 1.249 2.215 0.673 1.041 -0.401 0.000 0.480 2.127 1.681 0.000 0.767 1.034 0.990 0.671 0.836 0.669 0.663 +1 1.789 -0.583 1.641 0.897 0.799 0.515 -0.100 -1.483 0.000 1.101 0.031 -0.326 2.215 1.195 0.001 0.126 2.548 0.768 -0.148 0.601 0.000 0.916 0.921 1.207 1.069 0.483 0.934 0.795 +1 1.332 -0.571 0.986 0.580 1.508 0.582 0.634 -0.746 1.087 1.084 -0.964 -0.489 0.000 0.785 0.274 0.343 2.548 0.779 0.721 1.489 0.000 1.733 1.145 0.990 1.270 0.715 0.897 0.915 +0 1.123 0.629 -1.708 0.597 -0.882 0.752 0.195 1.522 2.173 1.671 1.515 -0.003 0.000 0.778 0.514 0.139 1.274 0.801 1.260 1.600 0.000 1.495 0.976 0.988 0.676 0.921 1.010 0.943 +0 1.816 -0.515 0.171 0.980 -0.454 0.870 0.202 -1.399 2.173 1.130 1.066 -1.593 0.000 0.844 0.735 1.275 2.548 1.125 -1.133 0.348 0.000 0.837 0.693 0.988 1.112 0.784 1.009 0.974 +1 0.364 0.694 0.445 1.862 0.159 0.963 -1.356 1.260 1.087 0.887 -0.540 -1.533 2.215 0.658 -2.544 -1.236 0.000 0.516 -0.807 0.039 0.000 0.891 1.004 0.991 1.092 0.976 1.000 0.953 +1 0.790 -1.175 0.475 1.846 0.094 0.999 -1.090 0.257 0.000 1.422 0.854 1.112 2.215 1.302 1.004 -1.702 1.274 2.557 -0.787 -1.048 0.000 0.890 1.429 0.993 2.807 0.840 2.248 1.821 +1 0.765 -0.500 -0.603 1.843 -0.560 1.068 0.007 0.746 2.173 1.154 -0.017 1.329 0.000 1.165 1.791 -1.585 0.000 1.116 0.441 -0.886 0.000 0.774 0.982 0.989 1.102 0.633 1.178 1.021 +1 1.407 1.293 -1.418 0.502 -1.527 2.005 -2.122 0.622 0.000 1.699 1.508 -0.649 2.215 1.665 0.748 -0.755 0.000 2.555 0.811 1.423 1.551 7.531 5.520 0.985 1.115 1.881 4.487 3.379 +1 0.772 -0.186 -1.372 0.823 -0.140 0.781 0.763 0.046 2.173 1.128 0.516 1.380 0.000 0.797 -0.640 -0.134 2.548 2.019 -0.972 -1.670 0.000 2.022 1.466 0.989 0.856 0.808 1.230 0.991 +1 0.546 -0.954 0.715 1.335 -1.689 0.783 -0.443 -1.735 2.173 1.081 0.185 -0.435 0.000 1.433 -0.662 -0.389 0.000 0.969 0.924 1.099 0.000 0.910 0.879 0.988 0.683 0.753 0.878 0.865 +1 0.596 0.276 -1.054 1.358 1.355 1.444 1.813 -0.208 0.000 1.175 -0.949 -1.573 0.000 0.855 -1.228 -0.925 2.548 1.837 -0.400 0.913 0.000 0.637 0.901 1.028 0.553 0.790 0.679 0.677 +0 0.458 2.292 1.530 0.291 1.283 0.749 -0.930 -0.198 0.000 0.300 -1.560 0.990 0.000 0.811 -0.176 0.995 2.548 1.085 -0.178 -1.213 3.102 0.891 0.648 0.999 0.732 0.655 0.619 0.620 +0 0.638 -0.575 -1.048 0.125 0.178 0.846 -0.753 -0.339 1.087 0.799 -0.727 1.182 0.000 0.888 0.283 0.717 0.000 1.051 -1.046 -1.557 3.102 0.889 0.871 0.989 0.884 0.923 0.836 0.779 +1 0.434 -1.119 -0.313 2.427 0.461 0.497 0.261 -1.177 2.173 0.618 -0.737 -0.688 0.000 1.150 -1.237 -1.652 2.548 0.757 -0.054 1.700 0.000 0.809 0.741 0.982 1.450 0.936 1.086 0.910 +1 0.431 -1.144 -1.030 0.778 -0.655 0.490 0.047 -1.546 0.000 1.583 -0.014 0.891 2.215 0.516 0.956 0.567 2.548 0.935 -1.123 -0.082 0.000 0.707 0.995 0.995 0.700 0.602 0.770 0.685 +1 1.894 0.222 1.224 1.578 1.715 0.966 2.890 -0.013 0.000 0.922 -0.703 -0.844 0.000 0.691 2.056 1.039 0.000 0.900 -0.733 -1.240 3.102 1.292 1.992 1.026 0.881 0.684 1.759 1.755 +0 0.985 -0.316 0.141 1.067 -0.946 0.819 -1.177 1.307 2.173 1.080 -0.429 0.557 1.107 1.726 1.435 -1.075 0.000 1.100 1.547 -0.647 0.000 0.873 1.696 1.179 1.146 1.015 1.538 1.270 +0 0.998 -0.187 -0.236 0.882 0.755 0.468 0.950 -0.439 2.173 0.579 -0.550 -0.624 0.000 1.847 1.196 1.384 1.274 0.846 1.273 -1.072 0.000 1.194 0.797 1.013 1.319 1.174 0.963 0.898 +0 0.515 0.246 -0.593 1.082 1.591 0.912 -0.623 -0.957 2.173 0.858 0.418 0.844 0.000 0.948 2.519 1.599 0.000 1.158 1.385 -0.095 3.102 0.973 1.033 0.988 0.998 1.716 1.054 0.901 +0 0.919 -1.001 1.506 1.389 0.653 0.507 -0.616 -0.689 2.173 0.808 0.536 -0.467 2.215 0.496 2.187 -0.859 0.000 0.822 0.807 1.163 0.000 0.876 0.861 1.088 0.947 0.614 0.911 1.087 +0 0.794 0.051 1.477 1.504 -1.695 0.716 0.315 0.264 1.087 0.879 -0.135 -1.094 2.215 1.433 -0.741 0.201 0.000 1.566 0.534 -0.989 0.000 0.627 0.882 0.974 0.807 1.130 0.929 0.925 +1 0.455 -0.946 -1.175 1.453 -0.580 0.763 -0.856 0.840 0.000 0.829 1.223 1.174 2.215 0.714 0.638 -0.466 0.000 1.182 0.223 -1.333 0.000 0.977 0.938 0.986 0.713 0.714 0.796 0.843 +1 0.662 -0.296 -1.287 1.212 -0.707 0.641 1.457 0.222 0.000 0.600 0.525 -1.700 2.215 0.784 -0.835 -0.961 2.548 0.865 1.131 1.162 0.000 0.854 0.877 0.978 0.740 0.734 0.888 0.811 +0 0.390 0.698 -1.629 1.888 0.298 0.990 1.614 -1.572 0.000 1.666 0.170 0.719 2.215 1.590 1.064 -0.886 1.274 0.952 0.305 -1.216 0.000 1.048 0.897 1.173 0.891 1.936 1.273 1.102 +0 1.014 0.117 1.384 0.686 -1.047 0.609 -1.245 -0.850 0.000 1.076 -1.158 0.814 1.107 1.598 -0.389 -0.111 0.000 0.907 1.688 -1.673 0.000 1.333 0.866 0.989 0.975 0.442 0.797 0.788 +0 1.530 -1.408 -0.207 0.440 -1.357 0.902 -0.647 1.325 1.087 1.320 -0.819 0.246 1.107 0.503 1.407 -1.683 0.000 1.189 -0.972 -0.925 0.000 0.386 1.273 0.988 0.829 1.335 1.173 1.149 +1 1.689 -0.590 0.915 2.076 1.202 0.644 -0.478 -0.238 0.000 0.809 -1.660 -1.184 0.000 1.227 -0.224 -0.808 2.548 1.655 1.047 -0.623 0.000 0.621 1.192 0.988 1.309 0.866 0.924 1.012 +0 1.102 0.402 -1.622 1.262 1.022 0.576 0.271 -0.269 0.000 0.591 0.495 -1.278 0.000 1.271 0.209 0.575 2.548 0.941 0.964 -0.685 3.102 0.989 0.963 1.124 0.857 0.858 0.716 0.718 +0 2.491 0.825 0.581 1.593 0.205 0.782 -0.815 1.499 0.000 1.179 -0.999 -1.509 0.000 0.926 0.920 -0.522 2.548 2.068 -1.021 -1.050 3.102 0.874 0.943 0.980 0.945 1.525 1.570 1.652 +0 0.666 0.254 1.601 1.303 -0.250 1.236 -1.929 0.793 0.000 1.074 0.447 -0.871 0.000 0.991 1.059 -0.342 0.000 1.703 -0.393 -1.419 3.102 0.921 0.945 1.285 0.931 0.462 0.770 0.729 +0 0.937 -1.126 1.424 1.395 1.743 0.760 0.428 -0.238 2.173 0.846 0.494 1.320 2.215 0.872 -1.826 -0.507 0.000 0.612 1.860 1.403 0.000 3.402 2.109 0.985 1.298 1.165 1.404 1.240 +1 0.881 -1.086 -0.870 0.513 0.266 2.049 -1.870 1.160 0.000 2.259 -0.428 -0.935 2.215 1.321 -0.655 -0.449 2.548 1.350 -1.766 -0.108 0.000 0.911 1.852 0.987 1.167 0.820 1.903 1.443 +0 0.410 0.835 -0.819 1.257 1.112 0.871 -1.737 -0.401 0.000 0.927 0.158 1.253 0.000 1.183 0.405 -1.570 0.000 0.807 -0.704 -0.438 3.102 0.932 0.962 0.987 0.653 0.315 0.616 0.648 +1 0.634 0.196 -1.679 1.379 -0.967 2.260 -0.273 1.114 0.000 1.458 1.070 -0.278 1.107 1.195 0.110 -0.688 2.548 0.907 0.298 -1.359 0.000 0.949 1.129 0.984 0.675 0.877 0.938 0.824 +1 0.632 -1.254 1.201 0.496 -0.106 0.235 2.731 -0.955 0.000 0.615 -0.805 0.600 0.000 0.633 -0.934 1.641 0.000 1.407 -0.483 -0.962 1.551 0.778 0.797 0.989 0.578 0.722 0.576 0.539 +0 0.714 1.122 1.566 2.399 -1.431 1.665 0.299 0.323 0.000 1.489 1.087 -0.861 2.215 1.174 0.140 1.083 2.548 0.404 -0.968 1.105 0.000 0.867 0.969 0.981 1.039 1.552 1.157 1.173 +1 0.477 -0.321 -0.471 1.966 1.034 2.282 1.359 -0.874 0.000 1.672 -0.258 1.109 0.000 1.537 0.604 0.231 2.548 1.534 -0.640 0.827 0.000 0.746 1.337 1.311 0.653 0.721 0.795 0.742 +1 1.351 0.460 0.031 1.194 -1.185 0.670 -1.157 -1.637 2.173 0.599 -0.823 0.680 0.000 0.478 0.373 1.716 0.000 0.809 -0.919 0.010 1.551 0.859 0.839 1.564 0.994 0.777 0.971 0.826 +1 0.520 -1.442 -0.348 0.840 1.654 1.273 -0.760 1.317 0.000 0.861 2.579 -0.791 0.000 1.779 0.257 -0.703 0.000 2.154 1.928 0.457 0.000 1.629 3.194 0.992 0.730 1.107 2.447 2.747 +0 0.700 -0.308 0.920 0.438 -0.879 0.516 1.409 1.101 0.000 0.960 0.701 -0.049 2.215 1.442 -0.416 -1.439 2.548 0.628 1.009 -0.364 0.000 0.848 0.817 0.987 0.759 1.421 0.937 0.920 +1 0.720 1.061 -0.546 0.798 -1.521 1.066 0.173 0.271 1.087 1.453 0.114 1.336 1.107 0.702 0.616 -0.367 0.000 0.543 -0.386 -1.301 0.000 0.653 0.948 0.989 1.031 1.500 0.965 0.790 +1 0.735 -0.416 0.588 1.308 -0.382 1.042 0.344 1.609 0.000 0.926 0.163 -0.520 1.107 1.050 -0.427 1.159 2.548 0.834 0.613 0.948 0.000 0.848 1.189 1.042 0.844 1.099 0.829 0.843 +1 0.777 -0.396 1.540 1.608 0.638 0.955 0.040 0.918 2.173 1.315 1.116 -0.823 0.000 0.781 -0.762 0.564 2.548 0.945 -0.573 1.379 0.000 0.679 0.706 1.124 0.608 0.593 0.515 0.493 +1 0.934 0.319 -0.257 0.970 -0.980 0.726 0.774 0.731 0.000 0.896 0.038 -1.465 1.107 0.773 -0.055 -0.831 2.548 1.439 -0.229 0.698 0.000 0.964 1.031 0.995 0.845 0.480 0.810 0.762 +0 0.461 0.771 0.019 2.055 -1.288 1.043 0.147 0.261 2.173 0.833 -0.156 1.425 0.000 0.832 0.805 -0.491 2.548 0.589 1.252 1.414 0.000 0.850 0.906 1.245 1.364 0.850 0.908 0.863 +1 0.858 -0.116 -0.937 0.966 1.167 0.825 -0.108 1.111 1.087 0.733 1.163 -0.634 0.000 0.894 0.771 0.020 0.000 0.846 -1.124 -1.195 3.102 0.724 1.194 1.195 0.813 0.969 0.985 0.856 +0 0.720 -0.335 -0.307 1.445 0.540 1.108 -0.034 -1.691 1.087 0.883 -1.356 -0.678 2.215 0.440 1.093 0.253 0.000 0.389 -1.582 -1.097 0.000 1.113 1.034 0.988 1.256 1.572 1.062 0.904 +1 0.750 -0.811 -0.542 0.985 0.408 0.471 0.477 0.355 0.000 1.347 -0.875 -1.556 2.215 0.564 1.082 -0.724 0.000 0.793 -0.958 -0.020 3.102 0.836 0.825 0.986 1.066 0.924 0.927 0.883 +0 0.392 -0.468 -0.216 0.680 1.565 1.086 -0.765 -0.581 1.087 1.264 -1.035 1.189 2.215 0.986 -0.338 0.747 0.000 0.884 -1.328 -0.965 0.000 1.228 0.988 0.982 1.135 1.741 1.108 0.956 +1 0.434 -1.269 0.643 0.713 0.608 0.597 0.832 1.627 0.000 0.708 -0.422 0.079 2.215 1.533 -0.823 -1.127 2.548 0.408 -1.357 -0.828 0.000 1.331 1.087 0.999 1.075 1.015 0.875 0.809 +0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184 +1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822 +1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948 +0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885 +1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871 +1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794 +0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679 +1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903 +1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840 +0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909 +1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892 +1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836 +1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886 +1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661 +1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883 +1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783 +1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838 +0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106 +0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895 +0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352 +1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836 +1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645 +1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866 +0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706 +0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750 +0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720 +0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006 +1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826 +1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908 +0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041 +0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898 +1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823 +0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498 +0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062 +0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863 +1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358 +0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655 +0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089 +1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806 +0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779 +1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832 +1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713 +1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977 +0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749 +1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926 +0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150 +0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107 +1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791 +1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781 +0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692 +1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824 +0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754 +0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969 +0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786 +1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791 +1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798 +1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777 +0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687 +0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806 +1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694 +1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906 +1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770 +1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656 +1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785 +0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827 +0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745 +0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650 +1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489 +1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876 +0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041 +1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242 +0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769 +1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078 +1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623 +1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137 +1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589 +0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816 +1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638 +0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057 +0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774 +1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526 +1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958 +0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902 +0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707 +1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740 +1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949 +0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815 +1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474 +0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183 +1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715 +0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094 +1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777 +0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008 +1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807 +1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879 +1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798 +0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661 +0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870 +1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963 +1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705 +1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866 +0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280 +1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866 +1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878 +0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676 +0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608 +1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726 +0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578 +0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059 +1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939 +0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035 +0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145 +0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276 +0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466 +1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776 +0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278 +0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934 +0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430 +1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841 +1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906 +0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325 +1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786 +0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950 +0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762 +0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861 +0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851 +0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079 +1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724 +1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744 +0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912 +0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012 +0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665 +1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034 +0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095 +0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221 +1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777 +1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917 +1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771 +0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395 +1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865 +1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890 +1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815 +0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131 +0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154 +1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717 +0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775 +1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893 +0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901 +0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769 +1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828 +0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748 +1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599 +0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120 +1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833 +0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051 +1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126 +1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881 +1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135 +1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797 +0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612 +0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556 +0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789 +1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708 +0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878 +0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087 +0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846 +1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028 +0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994 +0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868 +1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913 +0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291 +0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123 +1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878 +0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511 +0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076 +0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112 +1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046 +1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009 +1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891 +1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140 +0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744 +1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238 +0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306 +0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758 +1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172 +1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992 +1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786 +1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922 +0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609 +1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351 +1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836 +0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252 +1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122 +0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042 +0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662 +1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626 +1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920 +1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059 +1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722 +1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941 +0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072 +1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053 +0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310 +0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847 +0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873 +0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183 +1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807 +0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936 +1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013 +1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822 +1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803 +0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176 +0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714 +0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815 +0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918 +0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056 +0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513 +0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773 +0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799 +1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813 +1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978 +1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743 +1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817 +1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875 +0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078 +1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619 +1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726 +0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950 +0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860 +0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034 +0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846 +0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920 +1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668 +0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313 +0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671 +1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224 +1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779 +0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851 +1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860 +0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958 +1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671 +1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127 +1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807 +1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823 +1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872 +1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061 +1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833 +1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780 +0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143 +1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709 +1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858 +1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091 +0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838 +0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661 +0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729 +0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878 +1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838 +1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792 +0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702 +0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068 +1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931 +0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522 +0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807 +1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054 +1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606 +1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961 +0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308 +0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146 +1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709 +0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180 +1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868 +1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779 +0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213 +1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168 +1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238 +0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891 +0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561 +1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911 +0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303 +1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094 +1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825 +1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995 +1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151 +1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280 +1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893 +1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043 +0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226 +1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851 +0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733 +0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764 +0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028 +1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774 +1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661 +1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776 +0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675 +1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931 +1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715 +0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889 +1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886 +0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087 +0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760 +0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006 +1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811 +0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176 +0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096 +0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721 +1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732 +1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874 +1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753 +0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883 +0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018 +1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806 +0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472 +0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827 +1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835 +1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826 +1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640 +1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764 +1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025 +1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223 +1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169 +1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003 +1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908 +1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836 +0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833 +0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294 +1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845 +1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510 +0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445 +1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926 +1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933 +1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853 +0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678 +1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038 +1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197 +1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746 +0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772 +1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983 +1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759 +1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825 +1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776 +0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892 +0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110 +1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762 +0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756 +1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831 +1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748 +0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756 +1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993 +1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881 +1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786 +0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983 +0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983 +1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867 +0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937 +0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293 +1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105 +0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949 +1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895 +0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722 +1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809 +0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007 +1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717 +0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656 +0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856 +1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832 +1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753 +0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954 +1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874 +0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848 +1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518 +1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787 +1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834 +0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722 +1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040 +0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758 +1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854 +1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828 +1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745 +0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701 +1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215 +1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837 +1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867 +0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119 +1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914 +0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373 +1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623 +1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133 +0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079 +0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919 +1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651 +0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207 +0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660 +1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951 +1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910 +0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715 +1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026 +0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239 +1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696 +0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662 +1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910 +0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050 +0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258 +1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884 +0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026 +1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119 +1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926 +0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079 +1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674 +0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733 +0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795 +0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329 +0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161 +0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200 +1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875 +0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071 +1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993 +1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866 +1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978 +1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982 +1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853 +1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122 +1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719 +1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739 +0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690 +1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643 +0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142 +1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758 +1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823 +0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258 +1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836 +0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349 +0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961 diff --git a/examples/binary_classification/binary.test.weight b/examples/binary_classification/binary.test.weight new file mode 100644 index 0000000..da91e8a --- /dev/null +++ b/examples/binary_classification/binary.test.weight @@ -0,0 +1,500 @@ +1.2 +1.1 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 diff --git a/examples/binary_classification/binary.train b/examples/binary_classification/binary.train new file mode 100644 index 0000000..6ce09b8 --- /dev/null +++ b/examples/binary_classification/binary.train @@ -0,0 +1,7000 @@ +1 0.869 -0.635 0.226 0.327 -0.690 0.754 -0.249 -1.092 0.000 1.375 -0.654 0.930 1.107 1.139 -1.578 -1.047 0.000 0.658 -0.010 -0.046 3.102 1.354 0.980 0.978 0.920 0.722 0.989 0.877 +1 0.908 0.329 0.359 1.498 -0.313 1.096 -0.558 -1.588 2.173 0.813 -0.214 1.271 2.215 0.500 -1.261 0.732 0.000 0.399 -1.139 -0.001 0.000 0.302 0.833 0.986 0.978 0.780 0.992 0.798 +1 0.799 1.471 -1.636 0.454 0.426 1.105 1.282 1.382 0.000 0.852 1.541 -0.820 2.215 0.993 0.356 -0.209 2.548 1.257 1.129 0.900 0.000 0.910 1.108 0.986 0.951 0.803 0.866 0.780 +0 1.344 -0.877 0.936 1.992 0.882 1.786 -1.647 -0.942 0.000 2.423 -0.676 0.736 2.215 1.299 -1.431 -0.365 0.000 0.745 -0.678 -1.360 0.000 0.947 1.029 0.999 0.728 0.869 1.027 0.958 +1 1.105 0.321 1.522 0.883 -1.205 0.681 -1.070 -0.922 0.000 0.801 1.021 0.971 2.215 0.597 -0.350 0.631 0.000 0.480 -0.374 0.113 0.000 0.756 1.361 0.987 0.838 1.133 0.872 0.808 +0 1.596 -0.608 0.007 1.818 -0.112 0.848 -0.566 1.581 2.173 0.755 0.643 1.426 0.000 0.922 -1.190 -1.616 0.000 0.651 -0.654 -1.274 3.102 0.824 0.938 0.972 0.789 0.431 0.961 0.958 +1 0.409 -1.885 -1.027 1.672 -1.605 1.338 0.055 0.013 2.173 0.510 -1.038 0.708 0.000 0.747 -0.358 -1.647 0.000 0.367 0.069 1.377 3.102 0.869 1.222 1.001 0.545 0.699 0.977 0.829 +1 0.934 0.629 0.528 0.238 -0.967 0.548 -0.059 -1.707 2.173 0.941 -2.654 -0.157 0.000 1.030 -0.176 0.523 2.548 1.374 1.291 -1.467 0.000 0.902 1.084 0.980 0.783 0.849 0.894 0.775 +1 1.405 0.537 0.690 1.180 -0.110 3.202 -1.527 -1.576 0.000 2.932 0.567 -0.130 2.215 1.787 0.899 0.585 2.548 0.402 -0.151 1.163 0.000 1.667 4.039 1.176 1.045 1.543 3.535 2.741 +1 1.177 0.104 1.397 0.480 0.266 1.136 1.535 -0.253 0.000 1.027 0.534 1.180 0.000 2.406 0.088 -0.977 2.548 1.250 0.269 0.530 0.000 0.833 0.774 0.986 1.104 0.849 0.937 0.812 +1 0.946 1.111 1.218 0.908 0.822 1.153 -0.365 -1.566 0.000 0.745 0.721 -0.376 2.215 0.609 0.308 -1.282 0.000 1.598 -0.451 0.064 3.102 0.829 0.981 0.994 0.908 0.776 0.783 0.725 +0 0.739 -0.178 0.830 0.505 -0.130 0.961 -0.356 -1.717 2.173 0.621 -0.482 -1.199 0.000 0.983 0.081 -0.290 0.000 1.065 0.774 0.399 3.102 0.945 1.026 0.982 0.542 1.251 0.830 0.761 +1 1.384 0.117 -1.180 0.763 -0.080 1.020 0.877 1.277 2.173 0.331 1.410 -1.474 0.000 1.283 0.737 -0.225 0.000 1.560 0.847 0.505 3.102 0.959 0.807 1.192 1.221 0.861 0.929 0.838 +1 1.384 0.889 0.619 1.082 0.345 0.956 0.855 -1.129 2.173 0.546 -0.308 -0.623 2.215 0.348 1.024 0.184 0.000 0.781 -1.636 1.144 0.000 0.522 0.738 0.986 1.350 0.813 0.953 0.780 +1 1.344 0.839 -1.061 2.472 -0.573 1.513 1.144 0.856 0.000 0.884 1.475 -1.361 1.107 1.587 2.235 0.078 0.000 1.609 2.396 0.757 0.000 0.934 0.845 1.078 1.400 0.948 1.008 0.901 +0 0.547 -0.350 -0.647 2.040 0.276 0.545 0.839 1.729 0.000 0.653 1.472 1.243 0.000 0.786 -0.044 -1.020 2.548 0.419 -0.629 1.571 3.102 0.689 0.867 1.082 0.664 0.354 0.580 0.817 +1 1.484 1.700 -1.059 2.700 -1.056 2.409 0.457 0.345 0.000 1.415 1.114 -1.449 0.000 1.013 -2.057 1.131 0.000 0.905 2.182 1.043 0.000 1.654 0.994 0.983 0.741 0.163 0.592 0.745 +0 1.058 -0.161 -0.195 2.705 -0.751 1.910 -1.032 0.865 0.000 1.301 0.147 -1.119 1.107 0.967 -0.367 1.108 0.000 0.555 -0.714 1.505 3.102 0.954 0.651 1.125 0.894 0.672 1.182 1.316 +0 0.675 1.121 -0.280 1.540 0.735 0.615 -0.507 0.795 2.173 0.219 -1.894 -0.581 0.000 1.246 -0.348 -0.856 2.548 0.753 -1.146 -1.375 0.000 0.907 0.898 1.120 1.269 1.089 1.015 0.915 +1 0.643 -1.430 1.519 0.941 0.887 1.615 -1.337 -0.267 1.087 1.667 0.656 -1.588 0.000 0.828 1.836 0.408 0.000 1.709 -0.347 -1.183 3.102 0.921 1.373 0.985 1.423 1.547 1.783 1.438 +1 1.102 0.427 1.717 0.934 0.776 1.279 -0.250 -0.926 2.173 1.067 0.434 0.681 0.000 1.054 0.004 0.255 0.000 0.743 1.208 -1.151 0.000 0.709 0.522 1.054 1.273 0.835 0.935 0.865 +1 1.330 0.202 1.173 0.135 -1.083 0.728 1.109 -0.540 1.087 0.462 0.133 -0.561 0.000 0.479 1.187 0.658 0.000 0.670 1.007 0.055 3.102 0.782 0.672 0.990 0.734 0.379 0.765 0.643 +0 1.290 -1.423 -0.687 0.131 -1.136 0.821 0.296 0.168 2.173 0.696 -0.469 -1.151 1.107 0.940 0.273 1.641 0.000 0.720 1.106 0.727 0.000 1.007 0.868 0.999 1.110 1.125 0.883 0.859 +1 1.048 -1.119 -0.957 0.996 -1.550 0.733 0.283 0.919 2.173 1.050 -0.041 0.109 2.215 0.943 0.320 -0.858 0.000 0.628 -0.325 1.217 0.000 0.873 0.873 0.976 1.373 0.888 1.207 0.999 +0 0.488 1.698 0.791 0.894 -0.709 1.563 -0.076 1.739 2.173 0.624 2.395 0.523 0.000 1.661 0.266 -0.218 2.548 0.947 -0.077 0.285 0.000 1.675 1.414 0.988 1.333 2.004 1.551 1.217 +0 1.413 -0.852 0.310 1.128 -1.510 0.820 1.153 -1.670 2.173 1.170 0.100 0.266 0.000 0.852 0.401 -1.334 0.000 1.370 0.960 -0.632 0.000 0.890 0.938 1.745 0.974 0.677 1.136 0.973 +1 0.770 -0.449 -0.986 0.966 -1.301 0.739 -1.033 0.875 1.087 1.369 -1.181 0.167 1.107 1.257 -0.122 -1.588 0.000 0.600 0.611 0.116 0.000 1.048 1.106 0.993 1.132 0.892 0.974 0.951 +0 2.468 0.664 1.024 0.317 1.407 0.996 -0.453 -0.500 0.000 0.348 1.016 -0.161 0.000 0.978 -2.634 -0.285 0.000 1.245 -0.472 1.464 3.102 1.006 0.795 0.996 0.945 0.322 0.735 1.470 +1 1.014 0.013 -0.485 0.695 1.701 0.597 0.076 0.143 2.173 0.917 0.685 1.713 2.215 0.531 -0.987 -1.654 0.000 0.963 1.295 0.264 0.000 1.576 1.067 1.072 0.806 1.130 0.838 0.752 +0 1.251 -0.750 1.090 0.462 -0.381 0.677 0.340 -0.711 0.000 0.601 -0.461 -1.247 0.000 0.822 0.985 -1.653 0.000 0.754 -0.907 0.279 3.102 0.848 0.842 1.021 0.666 0.411 0.607 0.638 +1 1.114 1.782 1.450 0.653 1.513 0.825 1.851 -0.480 0.000 0.846 1.158 0.514 2.215 0.520 2.685 1.542 0.000 1.042 0.549 -0.463 1.551 1.321 1.037 0.997 0.824 0.692 0.804 0.831 +1 0.657 -0.901 -0.855 1.176 1.487 0.745 -1.236 1.649 2.173 0.661 -2.099 0.137 0.000 1.780 -1.036 -0.213 0.000 1.236 -0.185 0.784 3.102 0.861 1.016 1.045 0.759 0.898 0.849 0.765 +0 1.009 -0.660 -1.539 1.316 -1.693 1.146 2.025 0.137 0.000 1.063 -0.539 1.052 2.215 1.124 0.548 -0.887 2.548 1.017 -0.057 0.172 0.000 1.076 0.939 0.974 0.932 1.346 0.854 0.822 +0 2.122 0.792 0.723 2.438 1.064 2.692 0.361 -0.993 2.173 1.725 1.204 0.488 2.215 0.267 -0.767 -1.134 0.000 1.372 0.601 -0.568 0.000 0.727 0.981 0.989 2.837 3.398 2.152 1.568 +1 0.304 -1.425 -1.646 1.166 -1.469 1.458 -0.472 0.510 2.173 0.867 -0.309 -1.605 0.000 1.317 0.136 -0.332 2.548 0.853 0.744 -1.365 0.000 0.760 0.980 0.986 1.376 1.309 1.081 0.957 +1 1.167 0.556 -0.911 0.908 0.051 1.078 0.387 1.253 0.000 1.213 0.155 -0.673 2.215 0.489 -1.384 0.704 0.000 1.348 0.692 -1.502 3.102 0.868 0.829 1.087 0.782 0.878 0.642 0.621 +1 0.880 0.617 -0.649 1.724 1.104 1.213 -0.576 1.216 2.173 0.782 -0.913 -0.102 0.000 1.183 -0.576 -0.783 0.000 0.432 1.286 -0.204 0.000 0.879 0.616 1.706 1.435 0.598 0.911 1.007 +0 0.313 1.256 -0.904 1.002 1.290 1.383 1.295 -1.528 2.173 1.160 -0.765 0.080 1.107 1.060 2.309 -0.340 0.000 0.852 1.129 0.378 0.000 0.911 1.480 0.988 1.000 2.976 1.837 1.444 +0 1.263 0.596 0.460 1.063 1.060 0.709 -0.613 -0.688 0.000 1.464 1.079 1.174 2.215 1.411 0.369 -0.596 1.274 0.611 0.293 -0.894 0.000 1.175 1.244 0.988 0.905 1.623 1.442 1.222 +1 1.121 -0.379 1.363 1.451 0.782 1.088 -0.803 -0.793 1.087 0.515 0.368 -0.665 0.000 0.708 -1.372 1.449 0.000 0.579 0.441 0.238 3.102 1.336 0.869 0.984 1.459 0.905 0.950 0.863 +0 1.205 0.916 -1.209 0.354 -0.706 1.124 1.045 0.787 0.000 0.489 -0.457 -1.033 2.215 0.388 1.276 0.000 0.000 0.443 -0.889 1.403 0.000 0.842 0.653 0.986 0.500 0.532 0.580 0.589 +1 0.420 -0.722 0.732 0.885 -0.724 0.741 1.244 1.619 0.000 1.248 0.281 0.076 2.215 1.085 0.331 1.242 0.000 1.025 0.086 -0.955 1.551 0.919 0.927 0.989 0.744 0.824 0.923 0.798 +0 1.380 1.427 1.105 1.788 0.982 1.955 -0.205 -0.852 1.087 0.901 -0.193 0.854 0.000 1.172 0.352 -0.512 1.274 0.445 -0.158 1.421 0.000 0.403 0.882 1.000 2.450 0.804 1.608 1.272 +1 0.704 0.369 -0.230 1.167 -1.430 0.721 0.012 1.508 2.173 0.683 0.028 0.688 2.215 1.013 -0.764 -0.222 0.000 0.930 0.082 -0.753 0.000 0.865 0.748 1.107 0.835 0.696 0.681 0.604 +1 0.695 0.420 1.203 0.769 -0.911 0.830 1.168 0.076 0.000 0.394 0.392 0.510 2.215 0.747 1.559 0.835 0.000 1.090 -0.422 -1.161 3.102 0.973 0.654 0.987 0.688 0.652 0.784 0.703 +1 0.312 1.722 1.411 1.133 1.163 0.756 1.210 -0.700 2.173 0.755 -0.053 -0.139 2.215 0.812 -0.193 1.153 0.000 0.847 1.298 1.682 0.000 1.010 1.000 0.996 1.118 0.931 0.860 0.794 +0 0.431 0.572 -0.684 2.262 0.155 1.178 0.178 -1.429 2.173 0.463 0.649 0.544 2.215 0.757 0.955 1.552 0.000 0.658 1.073 1.064 0.000 0.344 0.840 0.986 0.580 1.096 0.957 0.821 +0 0.309 -1.951 -1.229 1.592 0.770 0.633 -0.197 -1.568 1.087 0.898 -1.885 -0.257 0.000 0.897 -0.933 0.931 2.548 1.280 -0.431 -0.799 0.000 0.921 0.862 0.990 0.812 0.831 1.026 0.895 +1 0.458 0.129 -0.519 1.195 0.737 0.534 -1.316 -1.729 0.000 0.687 0.351 1.103 2.215 0.911 1.049 -0.219 2.548 0.808 -1.014 -0.367 0.000 0.888 1.371 0.984 0.871 0.852 1.238 1.006 +0 0.637 -0.037 -1.732 1.254 -0.425 0.486 0.090 0.024 2.173 0.675 -1.119 1.644 0.000 0.494 -2.085 0.544 0.000 0.386 -0.239 1.092 0.000 0.913 0.912 1.144 0.698 0.525 0.741 0.726 +1 0.976 0.291 -1.128 0.668 -0.540 0.950 2.026 1.060 0.000 0.678 -0.571 1.307 2.215 1.199 1.293 -0.273 0.000 0.602 1.124 0.825 3.102 1.891 1.026 0.990 0.814 0.693 1.131 1.181 +1 0.535 -1.391 -0.825 1.343 -1.449 1.111 -0.852 -0.484 0.000 1.677 -0.700 1.069 2.215 0.623 0.018 -1.653 0.000 0.925 0.350 0.169 0.000 0.852 1.025 0.986 1.447 0.755 1.273 1.138 +0 2.638 1.289 -0.280 0.991 0.872 1.152 -0.702 1.551 2.173 0.643 -0.767 -1.689 0.000 0.747 -2.603 0.907 0.000 1.259 0.986 -0.759 0.000 0.889 0.937 1.931 2.569 0.709 1.666 1.322 +0 1.541 0.058 1.227 1.217 0.660 0.524 1.040 -0.640 0.000 0.709 -0.226 -0.727 2.215 0.543 1.360 1.720 0.000 0.981 0.326 -0.429 3.102 0.842 0.839 0.988 0.882 0.311 0.754 0.792 +0 2.559 -0.021 -1.615 2.095 -1.335 1.720 -0.641 0.033 2.173 0.737 -0.414 -0.379 0.000 1.158 -0.598 -1.608 2.548 0.847 1.549 0.847 0.000 0.980 0.951 1.004 0.748 1.751 1.606 1.295 +1 1.925 -0.859 1.353 1.769 -1.452 0.756 -0.342 -0.809 2.173 1.734 -0.850 0.151 0.000 0.944 -0.376 0.932 0.000 0.606 0.624 -1.039 0.000 0.964 0.931 1.474 1.062 0.530 0.907 0.819 +1 1.545 0.059 -1.732 1.034 0.807 2.467 -1.237 -0.565 0.000 1.933 2.370 -1.639 0.000 3.921 -0.645 0.727 2.548 1.843 -0.219 -0.527 3.102 2.292 2.692 1.319 1.447 1.914 3.176 2.387 +0 1.200 -1.018 -1.173 0.845 -0.439 0.601 -0.814 1.627 0.000 0.706 -1.103 0.845 0.000 1.111 -0.536 0.424 2.548 1.038 -0.456 -0.630 3.102 0.923 0.890 0.990 0.887 0.667 0.658 0.694 +0 0.609 -0.521 0.287 0.650 0.198 0.511 1.237 -0.670 2.173 0.648 -1.193 -1.686 2.215 0.364 1.444 0.064 0.000 0.451 1.152 0.677 0.000 0.433 0.925 0.983 0.770 1.497 0.925 0.731 +0 0.318 -1.381 -0.250 2.482 0.957 1.383 0.001 -0.222 2.173 1.045 -1.565 1.525 2.215 0.904 2.253 1.645 0.000 1.349 -0.541 -1.383 0.000 0.992 2.146 1.091 0.821 2.375 2.313 2.267 +1 0.947 -0.329 -0.033 0.020 -1.381 1.245 0.865 0.799 2.173 1.130 -0.013 -1.688 0.000 1.371 0.681 -0.931 0.000 0.982 0.958 0.019 0.000 1.001 0.587 0.525 0.860 0.892 0.820 0.697 +0 1.147 0.502 -1.131 1.237 -1.061 0.869 0.812 0.520 0.000 1.011 0.808 1.346 2.215 0.635 1.284 -0.138 0.000 0.538 0.612 0.124 3.102 0.848 0.987 0.993 0.677 0.595 0.704 0.778 +1 1.028 -0.732 1.243 1.198 -0.032 0.756 -1.491 1.404 0.000 1.343 -1.475 -0.263 2.215 0.483 -2.591 1.686 0.000 0.707 -0.687 -1.342 1.551 0.831 0.686 1.402 1.093 0.791 0.829 0.856 +1 0.303 1.225 0.629 1.256 -0.602 0.897 0.529 0.974 2.173 0.913 -0.667 -0.299 2.215 0.991 0.560 1.376 0.000 0.534 -1.176 -0.672 0.000 0.771 1.006 0.988 0.700 1.491 0.876 0.757 +0 0.534 -0.766 -0.533 0.974 -1.501 0.797 -1.574 0.323 2.173 1.137 0.271 -0.998 2.215 2.434 2.003 1.210 0.000 1.956 0.216 -0.272 0.000 3.588 2.573 0.989 1.251 1.990 2.742 2.023 +0 0.459 -1.448 -0.858 0.262 -0.304 0.760 1.090 -0.338 2.173 1.076 -1.079 1.151 2.215 0.357 -0.614 1.522 0.000 0.506 1.609 -1.293 0.000 0.842 0.866 0.988 0.935 2.209 1.120 0.920 +0 1.076 1.912 -0.667 0.618 -0.665 0.496 -1.524 1.127 0.000 0.944 -0.870 0.103 2.215 0.935 1.243 1.271 2.548 1.235 -0.512 -1.578 0.000 0.961 1.036 0.975 0.872 1.634 1.178 1.285 +1 0.442 1.823 -1.466 0.988 -1.565 1.444 -2.428 0.846 0.000 2.252 0.525 -0.141 1.107 2.366 0.328 -1.663 0.000 1.064 -0.091 -0.788 0.000 0.657 0.900 0.991 0.834 1.460 1.053 0.845 +1 0.575 -0.588 1.555 0.501 0.137 0.407 -1.782 1.262 0.000 0.348 -1.980 0.111 0.000 0.942 -0.695 -1.028 2.548 0.607 0.406 -0.667 3.102 0.695 0.884 0.987 0.705 0.428 0.634 0.590 +0 0.999 1.633 1.532 1.019 -0.793 0.613 -0.171 1.109 1.087 0.817 0.619 0.904 0.000 1.225 0.506 -0.244 0.000 1.189 1.033 0.553 0.000 0.992 0.948 1.211 1.278 0.973 1.015 0.924 +1 1.175 -0.643 0.099 1.273 -0.627 0.584 -0.133 -1.130 0.000 0.561 0.226 1.221 0.000 1.565 1.090 1.382 2.548 0.522 0.666 0.624 0.000 0.936 1.043 1.030 0.500 1.077 1.064 0.882 +0 0.733 -0.490 1.685 2.278 1.609 1.372 -1.278 -0.212 0.000 1.102 0.960 1.197 2.215 1.219 -0.308 -0.175 2.548 0.483 -0.242 -0.916 0.000 0.982 0.782 0.988 1.978 1.458 1.476 1.445 +1 1.792 -0.344 0.136 0.841 -0.813 1.685 0.625 1.499 0.000 0.548 0.587 -1.315 0.000 0.806 2.248 -0.160 0.000 1.011 1.329 -0.285 3.102 1.160 0.878 1.283 1.102 0.299 0.793 1.010 +1 0.641 1.633 0.001 1.118 1.010 1.013 0.750 1.516 0.000 1.438 0.526 0.358 2.215 1.649 0.175 -0.915 0.000 1.605 -0.493 -0.864 1.551 0.845 0.645 0.987 0.815 1.472 1.009 0.965 +0 0.442 0.276 0.929 1.638 -1.072 1.752 0.460 -0.802 2.173 1.436 -2.551 0.752 0.000 1.424 0.493 0.587 0.000 1.545 0.634 1.463 3.102 0.521 0.675 1.148 0.917 1.574 1.078 0.926 +1 1.152 0.873 -1.400 0.290 -0.264 0.831 0.373 -0.288 0.000 1.157 0.599 0.723 2.215 1.550 0.878 1.527 1.274 1.283 0.871 -0.714 0.000 0.798 1.181 0.988 0.758 0.975 0.987 0.872 +0 0.546 0.444 -0.292 1.429 -1.480 1.474 0.659 -1.104 2.173 2.622 0.481 0.538 0.000 0.685 -0.777 1.058 2.548 0.564 -1.013 -1.035 0.000 0.413 1.265 1.073 0.854 1.565 0.917 0.799 +1 1.274 -0.150 -0.628 1.824 -0.101 2.833 1.929 -1.628 0.000 1.361 0.040 0.111 2.215 2.690 0.230 0.574 1.274 0.776 0.382 -1.153 0.000 2.074 3.255 0.990 1.344 0.851 2.496 2.299 +1 0.625 -0.506 1.263 0.814 -1.314 1.228 -0.925 -0.091 0.000 1.217 0.430 1.588 2.215 0.976 0.010 -0.291 2.548 0.518 -1.251 0.127 0.000 0.921 0.750 0.986 0.647 1.177 1.064 0.929 +0 0.667 1.941 -0.188 0.446 0.506 1.049 0.577 1.737 1.087 1.508 0.766 -0.323 2.215 0.930 0.075 1.093 0.000 0.677 -0.442 -0.886 0.000 0.930 1.235 0.988 0.754 1.785 1.221 1.047 +1 1.864 0.056 -0.290 0.550 0.224 0.604 0.555 0.877 0.000 1.060 -0.375 1.727 2.215 0.824 -1.420 -0.485 0.000 0.817 0.925 1.318 0.000 0.510 0.916 0.990 0.821 0.441 0.842 0.785 +0 0.732 -0.712 -0.454 0.451 -0.392 1.167 0.448 0.949 2.173 0.920 0.120 1.609 0.000 0.926 1.528 -0.666 2.548 0.615 0.689 -0.687 0.000 0.930 0.983 0.987 1.117 1.539 0.967 0.852 +1 1.065 -0.611 -0.375 1.116 0.990 0.582 -1.434 -0.946 0.000 0.986 -0.550 -1.030 0.000 1.145 1.286 0.130 0.000 1.169 0.648 1.056 3.102 0.936 0.946 1.424 0.845 0.724 0.728 0.717 +1 0.910 -1.631 -0.125 1.964 -0.646 1.310 -0.927 1.357 2.173 0.445 -0.372 0.368 0.000 1.188 -1.481 0.595 0.000 1.407 -0.139 -1.529 3.102 0.984 0.993 0.996 1.619 0.930 1.159 0.979 +0 0.512 0.589 -1.486 0.552 -0.637 0.439 -0.923 -0.210 2.173 1.266 0.445 1.368 2.215 0.366 0.425 -0.052 0.000 0.641 -0.054 0.686 0.000 0.360 0.633 0.983 0.645 1.362 0.814 0.639 +1 1.377 -0.587 -0.869 1.735 -1.399 0.433 -0.277 0.236 2.173 0.921 0.321 1.152 1.107 0.330 -0.051 1.366 0.000 1.935 -2.212 0.028 0.000 0.635 0.758 0.988 0.980 0.740 0.923 0.794 +1 1.825 0.661 -0.885 1.030 0.833 1.565 2.020 -0.009 0.000 1.341 0.817 1.398 1.107 1.286 0.089 -1.706 0.000 1.295 1.032 -1.295 0.000 1.000 0.904 1.900 1.043 0.663 0.883 0.810 +1 1.477 0.870 0.367 0.643 0.024 0.425 0.141 0.632 0.000 1.340 0.221 -1.515 0.000 0.334 0.049 -1.312 2.548 1.172 1.080 -1.022 3.102 1.499 1.109 0.984 0.654 0.340 0.633 0.750 +1 1.074 -0.203 0.943 1.242 -1.727 0.952 -0.813 -0.239 2.173 0.629 -1.616 1.494 0.000 0.759 -0.793 -1.276 2.548 0.668 -0.085 -0.832 0.000 0.921 0.765 1.075 0.735 0.852 0.866 0.765 +1 0.652 0.084 -0.285 0.344 -0.839 1.105 0.260 1.644 2.173 0.700 0.765 -0.311 1.107 0.762 1.143 0.745 0.000 0.977 1.361 0.130 0.000 0.532 1.219 0.991 0.562 1.316 0.871 0.769 +1 1.748 -1.259 -1.568 1.159 -1.308 2.531 -0.895 -0.116 2.173 1.097 -0.529 1.515 1.107 1.602 0.505 1.042 0.000 0.954 -0.732 -1.359 0.000 1.553 1.095 0.985 2.288 2.479 1.717 1.644 +1 0.653 0.816 1.491 1.173 0.353 0.999 0.795 0.099 2.173 1.032 1.716 -0.995 0.000 1.052 0.893 -1.388 0.000 1.044 -0.757 -1.378 0.000 0.849 1.122 1.037 0.773 1.037 1.016 0.879 +1 0.603 -1.305 -0.295 1.986 -0.397 1.038 0.458 1.221 2.173 0.430 0.015 1.719 2.215 0.470 0.031 -0.543 0.000 0.524 -1.371 0.515 0.000 0.682 1.045 0.984 1.363 0.480 1.875 1.364 +0 0.510 -0.400 1.364 1.352 -0.990 0.630 -0.448 0.685 2.173 0.594 -0.795 -0.770 2.215 0.600 0.602 0.801 0.000 0.456 -0.936 1.413 0.000 0.659 0.725 0.988 0.901 0.886 0.668 0.599 +1 0.664 -0.216 0.435 1.156 1.437 1.839 -2.034 0.306 0.000 2.575 0.989 -1.165 2.215 1.506 1.083 -1.623 0.000 0.631 0.661 0.674 3.102 0.839 0.945 0.988 0.541 1.154 0.998 0.837 +1 1.436 1.090 0.733 0.278 -0.823 2.421 1.483 0.320 0.000 2.447 -1.403 -1.503 2.215 2.000 2.287 -1.506 0.000 2.205 1.306 -0.221 0.000 1.660 2.246 0.983 2.974 1.665 3.841 2.825 +1 0.709 0.850 0.672 0.949 -1.138 1.241 0.417 1.582 2.173 0.957 0.470 -0.037 2.215 0.877 0.102 0.661 0.000 1.705 1.461 -0.759 0.000 0.972 0.856 1.134 0.950 1.595 1.049 0.923 +0 1.135 0.285 -1.109 1.089 -0.896 1.103 0.127 0.964 0.000 0.731 -0.489 0.048 2.215 0.754 0.464 0.380 0.000 0.715 -1.183 -0.956 1.551 0.883 0.926 0.987 1.058 0.600 0.887 0.971 +1 1.124 0.354 0.040 1.132 1.620 0.956 1.375 0.416 0.000 1.543 0.437 -0.805 2.215 1.724 1.678 -1.636 0.000 2.128 -0.175 1.562 0.000 0.852 1.251 1.546 0.743 0.139 0.718 0.746 +1 0.341 -1.223 -1.373 0.994 0.692 1.086 0.319 -1.186 0.000 1.213 1.562 0.163 2.215 1.057 0.491 1.657 2.548 0.565 1.305 0.426 0.000 1.430 0.975 0.988 1.257 1.353 1.040 0.963 +0 1.218 -0.308 -1.602 1.532 -1.007 0.556 -0.059 0.820 2.173 0.840 -1.431 0.502 0.000 0.463 -0.801 -0.215 2.548 0.407 -1.488 0.811 0.000 0.627 0.812 0.989 0.704 0.573 0.709 0.765 +1 0.352 -1.440 0.063 0.644 1.519 1.138 0.660 1.460 2.173 1.127 -0.034 -0.520 0.000 0.931 0.095 0.137 0.000 0.496 0.796 -0.591 3.102 0.883 0.568 0.995 0.906 0.773 0.907 0.786 +1 0.637 0.994 1.198 0.755 1.183 0.646 -1.285 0.844 0.000 1.288 0.139 -1.166 2.215 0.826 -1.664 -0.400 0.000 0.702 0.662 -0.712 0.000 0.925 0.712 1.001 0.989 0.705 0.810 0.732 +0 0.802 -0.507 0.519 1.249 -1.030 1.596 0.474 0.732 0.000 1.052 -0.734 -1.455 0.000 1.868 0.130 -0.997 2.548 0.906 -0.195 -0.393 3.102 0.782 0.968 1.366 0.968 0.548 1.045 0.989 +0 1.178 -1.468 -0.931 1.113 0.177 0.710 -1.096 0.586 2.173 0.659 0.755 1.437 0.000 0.792 -1.703 -0.403 0.000 1.131 -0.379 -1.623 3.102 0.736 0.788 1.333 0.960 0.921 0.798 0.689 +1 0.775 -1.014 -0.295 0.804 -1.630 0.743 -0.163 1.621 2.173 0.559 -2.107 -1.004 0.000 0.450 -1.627 0.467 0.000 0.984 0.124 0.343 3.102 0.761 0.978 1.020 0.841 0.839 0.853 0.737 +0 0.928 -0.440 -0.658 1.570 -1.652 1.047 0.218 -0.527 2.173 0.696 -1.161 1.125 0.000 1.395 -0.149 0.858 0.000 0.653 0.972 0.477 1.551 0.890 0.900 1.304 1.137 0.811 0.990 0.961 +0 0.812 -0.292 0.794 0.639 -0.177 1.133 -0.240 -0.137 1.087 1.422 -0.659 1.663 2.215 1.218 0.176 -1.177 0.000 1.184 0.265 1.585 0.000 0.903 0.981 0.995 0.817 1.910 1.048 0.878 +1 1.263 -0.147 -1.176 1.318 -1.711 1.417 0.788 -0.284 2.173 1.114 -2.680 0.734 0.000 0.449 1.895 1.423 0.000 0.646 -0.064 1.098 1.551 6.084 3.149 0.989 1.753 1.062 2.739 2.117 +0 1.277 -0.164 0.024 0.660 -1.226 0.841 -0.443 1.136 2.173 0.788 0.152 -1.739 2.215 0.597 -0.002 -0.640 0.000 0.701 2.486 -1.042 0.000 1.528 1.286 1.148 0.997 0.725 1.191 1.061 +0 1.447 -1.048 1.596 1.251 -1.497 1.650 -0.449 -0.184 0.000 1.313 -0.575 1.523 2.215 0.635 -1.123 0.221 2.548 0.422 0.784 1.142 0.000 1.526 0.981 0.986 0.612 0.949 1.060 1.045 +1 0.923 0.825 -1.263 0.812 -0.403 0.916 0.648 1.435 2.173 0.737 1.715 -0.442 0.000 1.649 1.150 0.529 0.000 1.227 -1.071 -1.294 0.000 0.923 0.931 0.989 0.829 0.607 0.739 0.695 +0 0.863 0.002 -0.218 1.329 -1.344 0.364 1.067 -0.937 0.000 0.431 -0.404 1.713 0.000 0.611 -0.045 0.202 2.548 1.152 -0.293 1.027 0.000 0.897 0.744 1.260 0.757 0.171 0.557 0.563 +0 0.621 0.645 0.474 0.697 0.467 1.350 2.065 -1.640 0.000 1.298 0.237 -0.505 2.215 1.846 0.267 0.252 0.000 2.003 -0.829 1.240 0.000 1.049 1.014 0.994 1.248 0.588 0.902 1.115 +1 0.664 0.845 1.330 0.592 -0.368 1.532 0.473 -1.002 0.000 1.232 1.295 1.302 1.107 1.087 2.506 0.963 0.000 2.042 0.245 0.262 0.000 0.706 1.086 0.987 0.617 1.044 0.747 0.678 +1 1.302 -0.364 0.124 0.147 1.571 0.709 -1.391 0.877 0.000 0.720 0.189 -0.711 0.000 0.985 -0.285 -1.221 1.274 1.175 0.418 1.662 3.102 2.017 1.317 0.990 0.784 0.547 0.909 0.824 +0 1.073 1.932 -1.676 0.803 0.434 1.031 0.949 -1.336 2.173 1.064 0.704 0.255 2.215 0.560 -0.953 -0.640 0.000 1.275 -0.461 0.544 0.000 0.848 0.988 1.216 1.111 1.537 1.088 1.172 +1 0.415 1.473 -0.724 0.829 1.331 0.606 0.368 -0.583 0.000 0.600 -0.576 0.055 1.107 0.394 1.805 0.495 0.000 1.022 -0.054 -0.292 1.551 0.837 0.722 0.991 1.183 0.288 1.121 0.948 +1 1.565 0.915 -1.256 0.485 1.501 0.549 1.244 -0.414 0.000 1.305 1.168 0.520 2.215 0.468 -0.242 -0.108 2.548 0.459 0.355 0.876 0.000 0.920 0.893 0.986 0.723 0.801 0.836 0.726 +0 0.684 0.813 -1.557 0.770 0.439 1.436 0.458 0.763 2.173 0.908 -0.660 -1.353 2.215 1.145 2.230 -1.641 0.000 1.706 -0.599 -0.662 0.000 0.705 0.853 0.988 0.890 1.882 1.347 1.173 +1 0.818 -0.868 0.519 0.705 0.776 1.202 0.951 -1.058 2.173 0.606 0.685 1.517 2.215 0.989 0.391 0.207 0.000 1.054 0.289 0.975 0.000 0.720 0.695 0.993 1.427 0.933 0.971 0.842 +1 0.847 -0.389 0.605 0.414 -0.117 0.884 0.391 1.665 2.173 0.436 -0.325 -0.601 0.000 0.765 -1.419 0.035 0.000 0.418 0.291 1.276 0.000 0.889 0.796 0.978 1.225 0.682 0.863 0.774 +0 0.823 1.163 1.226 1.047 0.010 0.742 1.415 -1.439 2.173 0.798 -1.929 1.047 0.000 0.402 1.814 -1.194 0.000 1.063 -0.775 -0.249 3.102 0.841 0.844 1.144 0.947 1.613 1.691 1.591 +0 0.964 0.029 -0.104 0.350 0.913 0.730 0.093 -1.261 2.173 0.442 1.332 -1.143 2.215 0.661 2.238 0.373 0.000 2.534 1.524 1.156 0.000 1.033 0.915 0.987 0.883 0.578 1.050 0.935 +1 1.240 0.152 0.496 0.770 -0.247 0.771 2.734 -0.009 0.000 0.775 1.245 1.534 0.000 1.190 -0.475 1.513 2.548 1.941 0.445 -1.070 3.102 2.091 1.879 0.990 0.922 1.061 1.663 1.504 +1 0.617 1.431 -1.119 0.798 -0.421 1.166 0.250 -0.175 2.173 1.347 0.837 1.512 0.000 0.416 1.295 1.737 0.000 0.500 0.890 -0.447 0.000 0.801 0.700 0.993 0.812 1.091 1.019 0.885 +1 0.445 -1.120 -1.676 0.837 0.217 1.127 -0.084 -0.095 2.173 0.933 0.224 1.700 0.000 0.526 -0.430 1.275 0.000 0.767 0.662 1.526 0.000 0.557 1.286 0.983 0.931 0.728 0.952 0.939 +1 1.349 -1.565 -0.602 1.033 -1.097 1.380 -0.398 0.928 2.173 0.385 0.596 1.480 0.000 0.600 -0.880 -1.298 0.000 1.038 -0.545 -0.047 0.000 0.982 1.003 0.977 0.661 0.848 1.050 0.904 +1 0.524 -1.114 1.010 0.394 1.008 0.602 -1.107 0.556 2.173 1.212 -0.314 -1.062 2.215 0.666 -0.798 -0.794 0.000 0.464 -1.154 -1.287 0.000 0.307 0.675 0.975 0.940 1.351 0.836 0.680 +1 1.215 0.744 0.537 0.534 -1.342 1.031 0.398 -1.499 2.173 0.964 1.233 0.606 0.000 0.837 1.197 -1.060 2.548 0.521 -2.332 -0.709 0.000 0.617 1.543 1.108 0.994 0.706 1.188 1.068 +0 0.348 -1.520 0.332 0.486 -1.136 1.203 -0.862 -1.639 0.000 0.895 -1.623 -1.204 2.215 1.856 -0.118 0.433 2.548 0.685 -1.014 0.913 0.000 0.935 0.794 0.985 0.818 1.792 1.106 0.912 +1 1.599 -1.367 -1.364 0.376 0.956 0.414 -0.288 0.354 0.000 0.710 0.691 -0.085 2.215 0.572 -0.480 1.130 0.000 0.617 0.713 -1.179 3.102 0.573 0.700 0.988 0.850 0.500 0.873 0.743 +1 1.038 1.267 -0.809 1.920 -1.217 2.402 -1.935 0.666 0.000 1.667 0.815 -1.069 0.000 1.486 2.160 -1.727 0.000 2.174 0.159 -0.459 1.551 0.702 0.889 0.989 1.392 0.525 1.130 1.038 +1 1.040 -0.629 -1.578 0.873 1.250 1.270 -0.737 -0.440 1.087 0.587 -1.312 -1.565 0.000 1.391 -0.061 0.561 2.548 0.701 -1.555 1.379 0.000 0.449 1.047 0.985 1.287 1.419 1.071 0.910 +0 0.488 -1.191 1.315 0.574 -0.503 0.440 0.489 1.144 1.087 0.723 0.911 1.517 0.000 1.571 0.206 -0.050 2.548 0.838 0.142 -0.965 0.000 0.892 1.049 0.988 1.256 0.923 1.148 1.103 +1 0.395 1.580 -0.387 1.730 1.123 0.801 -0.316 -0.692 2.173 0.687 0.675 1.167 1.107 0.557 -0.132 0.231 0.000 0.462 0.820 -1.510 0.000 0.884 0.829 1.120 1.598 1.230 1.081 0.927 +0 0.368 -0.881 0.232 1.259 1.729 0.726 0.981 -0.303 0.000 0.540 2.865 0.353 0.000 1.324 -0.082 1.687 1.274 0.819 1.872 -0.461 0.000 0.807 0.757 0.987 0.882 0.625 0.870 1.198 +1 0.932 0.790 1.624 0.789 -0.636 0.646 -0.122 1.068 1.087 0.691 0.694 -1.189 2.215 0.854 2.067 -0.058 0.000 1.282 0.913 0.298 0.000 0.805 0.995 1.062 0.888 0.975 0.943 0.820 +1 0.876 -0.059 -0.512 0.623 1.056 0.787 0.152 -0.092 0.000 1.525 0.151 -1.709 2.215 0.951 0.673 0.986 1.274 0.770 1.160 -0.496 0.000 0.892 0.926 1.011 0.912 0.918 0.946 0.803 +0 1.861 -0.667 -0.277 1.997 -0.758 0.721 -1.048 1.412 0.000 0.659 -1.964 1.670 0.000 1.140 0.067 0.124 2.548 1.639 -1.440 1.187 0.000 0.807 0.936 1.121 0.968 0.639 0.911 1.058 +0 0.331 1.639 1.172 0.843 1.560 0.537 -0.534 -1.733 0.000 0.811 -0.430 -0.969 0.000 2.548 0.670 0.310 2.548 1.293 0.781 -0.813 3.102 0.895 0.947 0.999 1.144 1.184 1.131 0.959 +1 0.378 -1.116 -0.289 1.450 0.283 0.884 0.966 -1.630 2.173 0.369 1.390 0.267 0.000 0.825 0.734 -0.766 2.548 0.383 0.864 1.276 0.000 0.399 0.669 0.986 1.920 0.753 2.084 1.657 +0 1.399 0.506 1.676 1.639 1.095 1.759 1.332 -1.423 2.173 1.874 0.352 -0.292 2.215 2.006 0.346 0.377 0.000 1.793 0.421 0.028 0.000 0.659 0.947 1.048 1.462 2.654 1.669 1.487 +0 1.763 -1.140 0.063 0.717 -0.207 1.013 -0.222 -1.663 0.000 0.689 -1.374 1.153 1.107 0.649 0.861 -1.090 1.274 0.903 -0.892 -1.149 0.000 0.913 0.969 0.992 1.084 1.228 0.926 0.942 +0 0.598 -1.099 -0.297 0.887 1.681 1.774 -0.119 -0.024 2.173 1.206 -0.239 -1.702 0.000 1.209 0.951 -1.688 1.274 0.810 0.557 1.213 0.000 0.879 0.810 0.987 1.298 2.139 1.309 1.136 +1 0.685 0.643 -1.637 0.794 0.241 0.645 -1.297 -0.422 2.173 0.442 -0.656 1.553 0.000 0.661 -0.569 1.007 2.548 1.208 -0.553 0.371 0.000 0.859 0.709 1.014 0.691 0.831 0.802 0.688 +0 0.381 -0.498 -0.126 0.379 -0.311 0.544 0.665 1.639 2.173 0.913 -0.448 -0.753 2.215 0.277 -0.388 -1.183 0.000 0.723 0.095 0.255 0.000 0.494 0.590 0.984 0.889 1.063 0.753 0.598 +1 0.490 -2.359 -0.439 0.467 -0.508 1.020 -1.376 -1.350 0.000 0.414 0.153 -0.294 0.000 1.583 0.262 0.810 2.548 1.155 -0.548 0.533 1.551 1.628 1.221 0.985 1.198 0.568 1.080 0.939 +0 1.974 -0.520 -0.274 1.046 -0.317 1.240 0.975 1.141 1.087 1.082 0.029 -1.722 0.000 0.638 0.247 0.819 0.000 0.732 0.334 -1.273 3.102 0.971 1.057 1.002 0.898 0.881 1.491 1.227 +1 0.464 -2.190 0.863 1.148 -0.355 0.479 0.768 0.208 2.173 0.599 -1.474 -1.680 0.000 0.808 -0.366 -1.517 2.548 0.430 -0.155 0.625 0.000 0.747 1.047 0.990 0.857 0.913 0.925 0.783 +0 3.524 0.413 1.358 0.502 1.397 1.182 0.445 -0.197 2.173 2.001 0.849 -0.630 0.000 1.133 -0.419 1.143 2.548 0.681 0.639 -0.851 0.000 0.311 0.811 0.987 0.708 1.509 1.232 1.297 +1 1.032 -0.978 0.678 0.604 -1.027 0.743 -1.006 -0.794 1.087 1.060 -0.218 1.430 2.215 1.286 0.382 0.398 0.000 0.456 1.251 -0.015 0.000 0.570 1.009 1.093 0.820 1.298 0.992 0.883 +1 0.796 -0.391 0.418 1.192 -1.625 0.721 0.456 -0.743 0.000 0.782 0.667 0.955 2.215 0.757 1.490 -0.856 0.000 1.124 -0.781 0.166 3.102 0.838 1.091 1.301 0.825 0.944 0.923 0.897 +1 0.583 1.687 -0.278 0.980 1.532 0.654 2.241 0.179 0.000 0.916 0.640 -1.499 2.215 0.730 0.337 -0.457 2.548 0.398 1.153 0.935 0.000 0.604 0.858 1.045 0.820 0.713 0.803 0.722 +0 1.212 0.266 0.342 0.968 0.934 0.798 -0.127 -1.372 2.173 0.661 -0.117 -0.256 0.000 0.689 -0.790 -0.920 1.274 0.748 -0.763 1.364 0.000 0.985 0.905 0.993 0.855 0.506 0.797 0.703 +0 2.110 0.969 0.013 0.566 -1.518 2.075 -1.121 1.561 0.000 1.281 0.153 -0.573 2.215 0.664 -0.617 1.216 0.000 0.524 -2.282 0.079 0.000 0.971 0.762 1.487 1.092 0.546 0.840 1.086 +0 1.006 0.503 -0.135 0.709 0.959 0.661 1.545 -0.028 1.087 0.637 2.118 -1.141 0.000 0.605 1.485 1.235 1.274 1.753 -0.224 -1.704 0.000 2.243 1.345 0.988 0.800 0.716 1.039 0.913 +0 0.628 -0.317 -1.678 0.587 -0.446 1.237 0.947 1.659 2.173 1.313 0.604 0.225 0.000 1.009 -2.638 -1.139 0.000 0.412 1.205 0.642 3.102 5.249 3.002 0.979 1.526 0.629 2.510 1.855 +1 0.464 -1.107 -0.410 0.525 1.402 0.962 -1.204 -0.155 2.173 0.769 -0.136 -1.737 0.000 0.774 0.955 -1.494 0.000 0.918 2.343 0.106 0.000 0.789 0.724 0.984 0.914 1.006 1.024 1.087 +1 1.031 1.057 -0.080 1.233 -0.598 0.590 -0.332 -1.222 2.173 0.506 -0.231 0.536 0.000 1.498 0.105 1.593 0.000 1.397 1.207 0.338 0.000 0.963 0.824 0.990 1.114 0.507 0.998 1.061 +0 0.990 0.753 1.671 0.664 -0.394 0.730 -0.244 -0.925 2.173 0.966 0.689 0.358 2.215 0.345 2.219 1.238 0.000 0.618 0.003 0.968 0.000 0.795 1.088 1.076 0.836 1.287 0.824 0.731 +0 2.066 0.020 0.385 1.332 0.659 1.167 0.934 -1.575 1.087 0.818 0.447 -1.135 1.107 0.788 -2.658 -0.248 0.000 0.462 1.233 -0.762 0.000 2.981 2.199 0.974 1.680 0.658 1.993 1.799 +0 0.872 0.717 1.067 0.425 -1.153 0.856 -1.057 0.204 0.000 1.407 -1.190 -1.741 2.215 1.543 -1.330 -0.421 2.548 0.617 0.194 0.694 0.000 0.907 0.986 0.987 1.742 1.465 1.549 1.312 +0 0.625 0.276 0.272 2.293 0.929 0.991 0.450 1.653 2.173 0.437 -0.252 -1.166 0.000 0.645 -1.489 -0.402 0.000 2.805 0.934 -0.404 3.102 0.901 0.978 0.990 1.051 1.797 1.183 0.965 +1 1.067 -0.284 0.666 0.562 -0.683 0.824 -0.122 -0.604 0.000 1.345 0.099 1.294 2.215 0.612 -1.449 1.524 2.548 1.019 -1.009 -0.562 0.000 0.802 0.958 1.006 0.869 0.939 0.980 0.830 +1 0.471 0.532 1.703 0.764 -1.341 0.834 -0.732 -0.231 0.000 1.166 -0.564 1.506 1.107 1.265 -1.159 0.236 2.548 0.599 -2.162 -1.436 0.000 1.498 1.029 0.996 0.689 1.261 0.980 0.866 +0 0.923 1.977 0.963 0.348 -1.165 0.964 1.353 0.241 2.173 0.877 0.460 -1.350 0.000 0.577 1.670 -0.859 0.000 0.755 -0.619 1.732 3.102 0.928 0.879 0.991 0.829 1.439 0.958 0.842 +1 0.735 1.285 -0.067 0.604 0.201 0.385 -0.484 -1.267 2.173 0.689 0.040 -1.695 0.000 0.531 0.878 -1.477 2.548 0.480 1.001 -0.409 0.000 0.889 0.662 0.985 0.687 0.458 0.590 0.566 +1 0.470 0.019 -0.417 1.425 0.555 1.138 -0.645 -1.303 2.173 0.393 -1.135 0.464 1.107 0.640 -1.579 -0.002 0.000 0.969 -0.878 1.387 0.000 0.878 1.064 0.982 0.778 1.016 1.005 0.918 +1 0.690 -0.866 -1.684 0.493 0.323 0.739 1.529 -0.242 0.000 1.197 0.469 -1.467 2.215 0.924 1.691 0.195 0.000 0.929 -0.031 1.188 3.102 0.818 0.932 0.985 1.281 0.695 0.857 1.106 +1 1.052 0.423 -0.511 1.364 1.328 1.202 -0.309 -1.602 2.173 0.932 0.014 -0.157 0.000 0.499 0.470 1.229 2.548 1.654 -0.738 0.246 0.000 0.947 0.864 1.654 1.248 0.676 0.973 0.956 +1 0.675 -0.414 0.782 0.170 1.003 1.559 1.024 1.680 0.000 2.118 0.665 -0.359 1.107 0.864 0.753 1.272 0.000 0.828 -1.272 -0.183 0.000 0.803 0.914 0.989 1.107 1.024 0.875 0.802 +1 1.645 -0.421 -0.496 0.371 -0.958 1.178 -0.788 1.118 2.173 0.568 -0.388 -1.170 0.000 0.632 -0.688 0.177 1.274 0.485 -0.146 -1.556 0.000 0.245 0.880 0.979 0.657 0.807 0.925 0.743 +1 0.353 1.209 0.993 0.777 0.593 0.734 -0.475 0.589 1.087 0.994 0.301 -1.078 0.000 0.716 1.057 -0.503 0.000 0.625 0.274 -1.448 0.000 0.950 0.804 0.996 0.760 0.903 0.613 0.587 +1 0.424 0.476 1.372 2.397 -1.284 0.818 -0.252 0.083 2.173 1.090 0.366 0.771 2.215 0.430 0.300 -0.351 0.000 0.523 -1.048 -1.537 0.000 0.650 0.811 0.989 1.260 0.922 1.038 0.816 +0 1.330 1.084 0.755 0.416 0.198 0.500 0.181 0.414 0.000 0.819 -0.310 -1.592 2.215 1.121 0.477 -0.994 2.548 1.493 0.929 -0.537 0.000 0.728 0.752 0.990 1.000 0.684 0.822 0.842 +1 1.274 -1.011 -0.817 0.246 -0.339 0.754 -0.031 0.790 2.173 0.961 0.020 -1.485 2.215 0.323 0.139 0.422 0.000 0.435 -2.003 1.055 0.000 0.721 0.887 0.989 1.027 1.111 0.829 0.731 +0 1.045 -1.383 -0.047 0.145 0.425 1.032 -0.769 1.020 0.000 1.052 -1.520 -0.524 2.215 1.971 0.564 -1.104 2.548 1.216 0.284 1.074 0.000 0.969 1.347 0.980 1.303 2.163 1.358 1.110 +1 0.965 0.728 -0.924 0.797 -1.577 0.774 -0.019 0.805 0.000 0.989 -0.081 0.423 2.215 1.061 0.606 -1.266 2.548 0.889 -1.991 0.894 0.000 1.884 1.313 0.997 0.564 1.165 1.148 1.047 +0 1.842 -0.780 -0.583 1.509 -1.214 0.591 -1.562 0.471 0.000 0.575 -0.674 0.889 0.000 0.701 -1.324 0.855 0.000 1.099 -0.173 -1.407 3.102 1.034 0.858 1.243 0.798 0.324 0.578 0.770 +1 0.832 1.305 1.098 1.187 0.502 0.977 2.932 0.745 0.000 1.699 0.550 -1.112 0.000 1.104 0.231 -0.695 0.000 0.945 -0.453 -1.724 3.102 0.839 0.901 0.983 1.166 0.555 1.017 1.085 +1 0.399 0.185 -1.260 1.759 -0.670 0.969 1.055 0.589 0.000 1.884 -0.363 1.360 0.000 1.489 -1.122 -0.162 2.548 1.935 -0.867 0.686 3.102 2.311 1.624 0.994 0.778 0.906 1.241 1.116 +1 0.694 -0.023 0.247 0.844 0.997 0.852 -0.371 1.057 1.087 0.578 -0.083 -0.531 0.000 1.575 -0.947 -0.407 0.000 1.338 -0.710 -1.604 3.102 0.745 0.934 0.982 0.675 0.811 0.897 0.792 +1 0.351 -1.542 -1.212 1.485 1.533 0.972 -0.071 0.116 0.000 1.430 0.320 -1.621 2.215 0.872 -0.511 -0.508 1.274 0.505 1.521 0.384 0.000 0.788 0.976 0.987 1.314 1.140 1.776 1.579 +1 0.878 -0.497 0.312 1.068 0.529 1.362 -0.567 -1.019 2.173 0.496 -1.036 0.853 0.000 0.619 -0.368 1.506 2.548 0.800 0.672 0.349 0.000 0.956 0.676 0.978 1.514 0.876 1.003 0.872 +0 0.701 -0.652 0.516 2.586 0.100 0.739 -0.411 -1.204 2.173 0.664 0.324 -1.287 0.000 0.553 -0.857 1.365 0.000 0.451 1.125 -1.626 0.000 0.897 0.681 0.974 0.923 0.659 0.969 0.906 +0 3.291 0.586 -0.841 0.297 -0.460 1.598 -1.229 1.031 0.000 0.792 -0.490 0.765 2.215 0.757 -0.612 -0.369 2.548 0.775 -0.974 1.610 0.000 0.848 1.126 0.988 1.354 0.705 0.929 1.339 +1 1.037 -0.375 -0.433 1.485 -1.244 0.969 0.344 0.853 0.000 0.798 2.616 1.576 0.000 1.285 0.920 0.123 2.548 1.324 -0.055 -0.813 3.102 0.811 1.015 1.146 1.247 0.933 0.831 0.890 +0 0.465 -0.020 0.313 1.280 -1.013 0.857 0.325 1.216 2.173 1.457 -0.428 -0.829 1.107 1.949 -1.452 0.568 0.000 0.747 -1.525 -0.120 0.000 0.789 1.608 0.994 0.965 1.711 1.409 1.169 +0 1.149 0.542 -0.779 0.412 1.454 0.634 0.510 1.730 2.173 0.806 -0.062 0.175 2.215 0.538 -0.076 1.196 0.000 0.484 -0.450 0.622 0.000 0.307 0.525 0.983 0.769 1.082 0.688 0.567 +1 1.224 -1.015 1.297 0.718 0.394 1.455 -0.730 1.726 0.000 2.353 -0.191 0.087 2.215 0.833 0.346 -0.651 2.548 0.856 -1.437 -1.468 0.000 0.982 1.277 0.987 1.243 1.017 1.457 1.187 +0 0.940 0.183 1.100 1.314 0.247 0.533 -0.899 -0.670 0.000 1.232 0.200 1.522 2.215 1.625 0.124 -0.710 2.548 0.554 -0.782 0.893 0.000 0.819 1.086 1.070 1.061 1.361 0.942 0.861 +1 1.126 -0.754 1.556 1.080 1.743 2.352 -0.618 -0.337 0.000 1.093 -1.694 0.298 0.000 1.396 -1.219 -1.700 0.000 2.313 0.810 1.721 1.551 0.863 0.848 0.985 1.841 0.872 1.406 1.290 +1 0.380 -1.178 0.873 1.237 -1.067 0.855 0.226 0.970 0.000 1.376 -0.159 -0.635 2.215 0.499 1.295 -0.745 0.000 0.804 -0.397 0.357 1.551 1.389 0.915 0.988 0.786 0.756 0.881 0.820 +1 0.793 1.258 -1.374 0.374 0.317 0.564 0.302 1.099 2.173 0.461 2.217 0.043 0.000 0.656 1.539 1.448 0.000 0.836 -0.529 -0.186 3.102 0.950 0.880 0.989 0.700 0.754 0.655 0.600 +1 0.738 -0.628 -1.413 1.892 -0.735 0.694 -0.090 1.403 0.000 1.238 -0.707 0.465 1.107 0.466 0.374 0.372 0.000 0.553 0.863 0.929 3.102 0.860 0.954 0.985 0.983 0.806 0.928 0.860 +1 0.730 -0.971 0.273 1.771 1.435 0.726 -0.923 -0.457 0.000 0.966 0.787 -0.450 0.000 0.788 -0.719 0.811 2.548 0.994 -0.349 -1.088 0.000 0.911 0.946 1.364 0.714 0.529 0.641 0.662 +0 0.669 0.758 -0.462 0.211 0.475 1.541 -0.041 0.031 2.173 2.259 0.322 -1.715 0.000 1.302 -1.141 -1.197 2.548 1.192 2.222 -0.355 0.000 0.899 0.959 0.978 0.815 1.945 1.363 1.107 +1 1.337 0.073 -1.373 0.259 0.715 1.040 0.171 1.256 0.000 1.326 -0.272 0.207 2.215 0.615 -2.401 -0.242 0.000 1.529 0.254 -0.705 3.102 0.934 1.114 0.985 1.032 1.015 0.992 0.864 +0 0.299 -1.604 -0.905 1.220 -0.732 1.313 2.107 1.474 0.000 1.890 -0.639 -0.258 2.215 1.279 1.384 1.245 2.548 1.910 0.672 0.574 0.000 0.975 0.890 0.990 0.905 2.710 1.452 1.214 +0 0.617 -0.042 -1.005 0.378 1.614 0.672 0.984 0.245 1.087 0.383 -0.813 -0.481 0.000 0.847 -1.144 1.130 2.548 0.873 0.749 -1.374 0.000 0.917 0.964 0.989 0.662 1.460 0.958 0.828 +0 0.940 -0.703 1.332 1.113 -0.912 0.504 -0.749 -1.210 2.173 0.601 -0.754 0.402 2.215 0.513 0.242 -0.533 0.000 0.650 -0.944 0.979 0.000 0.789 0.657 1.275 0.839 0.804 0.628 0.563 +0 0.892 1.239 0.822 0.256 -1.493 0.848 0.461 -0.262 1.087 0.330 0.790 0.309 0.000 1.024 1.600 -1.499 0.000 1.017 0.236 1.575 3.102 0.991 1.084 0.978 0.579 0.983 0.758 0.684 +1 0.892 0.780 -1.602 0.373 -0.768 0.603 1.175 -1.077 2.173 0.462 -0.122 1.005 2.215 0.526 -1.449 1.257 0.000 1.674 0.657 0.181 0.000 1.053 0.973 0.985 0.725 0.921 0.780 0.763 +1 0.481 1.083 -1.015 1.993 -0.354 1.192 0.755 1.679 0.000 1.114 -0.428 -0.413 2.215 0.921 0.227 0.266 2.548 2.434 -1.435 0.893 0.000 0.890 1.097 0.983 1.730 0.725 1.186 1.202 +0 1.577 2.017 -1.364 0.560 0.205 0.470 1.358 0.410 0.000 0.607 -0.958 0.699 0.000 1.002 1.173 -0.519 2.548 0.582 0.038 0.567 0.000 0.580 0.687 1.286 0.753 0.661 0.631 0.668 +0 0.517 1.836 0.705 2.437 0.849 0.604 -1.041 -0.422 1.087 0.586 0.199 -0.563 0.000 1.296 -0.246 -1.353 2.548 0.862 1.006 -1.193 0.000 0.694 0.983 0.997 1.394 0.932 1.306 1.065 +1 0.871 0.166 -0.658 1.662 0.113 0.883 -0.065 -1.450 0.000 0.552 0.324 -0.054 2.215 1.552 0.404 0.809 0.000 2.551 -0.779 -1.530 0.000 0.880 1.017 1.068 1.031 0.837 0.759 0.837 +0 1.033 -0.488 -1.260 0.761 0.365 1.177 -1.283 -1.533 2.173 1.050 -0.422 0.232 2.215 0.597 -0.926 1.075 0.000 0.724 -2.056 -0.037 0.000 0.838 0.909 1.222 1.057 1.790 0.996 0.872 +0 2.931 0.181 -0.816 1.290 -1.261 1.699 1.014 1.091 0.000 1.008 1.223 -0.743 1.107 0.968 -0.651 0.752 2.548 1.141 0.309 0.233 0.000 0.856 1.289 1.048 0.972 1.587 1.212 1.367 +1 0.866 -1.386 1.434 0.824 0.737 0.823 0.116 -1.009 2.173 0.318 -1.644 0.333 0.000 0.422 -0.399 0.623 2.548 0.727 -0.996 -0.526 0.000 0.477 0.749 0.978 0.667 0.759 1.033 0.774 +1 0.526 -0.454 -0.254 1.847 0.634 0.835 0.982 -1.610 2.173 0.623 1.186 -0.714 0.000 0.570 0.919 0.517 0.000 0.857 -0.324 -1.116 3.102 0.824 0.886 0.988 0.791 0.772 1.004 0.914 +0 0.697 -1.126 0.719 0.884 -0.586 0.677 0.291 0.103 0.000 1.430 -0.933 1.128 1.107 0.658 0.649 -0.868 0.000 1.880 0.246 -1.217 1.551 0.955 0.936 1.003 0.926 1.610 1.102 0.977 +1 0.877 0.080 0.958 1.354 -0.028 0.608 -0.044 1.594 0.000 0.897 0.333 0.204 0.000 0.878 2.132 -1.621 0.000 2.168 0.305 -1.249 0.000 1.024 0.774 1.171 0.851 0.682 0.813 0.818 +1 0.838 -0.008 -0.861 0.536 1.464 1.230 0.532 -0.581 2.173 1.309 0.320 1.087 1.107 0.717 0.807 1.713 0.000 0.375 2.376 1.015 0.000 0.739 0.920 0.984 0.842 1.873 1.057 0.870 +1 0.693 0.460 1.485 0.579 0.584 1.160 1.208 -0.611 2.173 0.821 2.256 1.369 0.000 1.488 2.077 0.782 0.000 0.801 -0.015 -0.918 0.000 0.855 1.276 0.993 1.405 0.778 1.178 1.257 +0 0.753 -0.642 0.580 0.686 -0.609 0.760 0.133 -1.106 2.173 0.846 0.890 1.018 1.107 0.564 0.343 0.032 0.000 0.795 -0.282 1.402 0.000 0.748 0.774 0.986 1.185 1.207 0.973 0.776 +1 0.493 -1.340 -1.280 1.537 1.615 1.157 -0.847 -0.157 2.173 0.739 0.345 0.179 2.215 1.269 0.123 0.981 0.000 0.876 -2.150 -1.471 0.000 1.185 0.839 0.996 1.672 0.963 1.468 1.594 +0 0.438 -0.827 1.361 0.702 -1.052 0.949 0.046 0.781 2.173 1.777 -0.999 -1.409 0.000 1.263 -0.628 0.189 2.548 1.393 -0.299 0.000 0.000 2.079 1.526 0.990 0.905 0.864 1.213 1.022 +0 0.585 0.462 -0.588 1.346 0.294 1.039 -0.231 -1.064 2.173 1.147 1.579 1.041 2.215 1.686 -0.438 1.187 0.000 1.818 -0.603 -0.517 0.000 0.910 0.941 0.989 1.323 2.294 1.598 1.284 +1 0.307 -1.864 0.730 0.868 -0.308 0.897 -0.021 -0.367 0.000 1.445 0.238 1.102 2.215 0.716 -0.256 -0.948 0.000 1.022 0.253 -1.404 1.551 0.744 0.726 0.981 1.047 0.848 0.894 0.797 +1 0.687 0.401 -1.718 0.229 -0.528 1.048 -0.493 0.765 1.087 0.918 0.993 -1.088 0.000 0.291 -0.495 -0.631 0.000 0.498 0.513 -0.293 3.102 0.756 1.447 0.988 0.579 0.769 0.844 0.743 +0 2.058 0.191 1.474 0.653 -0.064 0.814 1.760 -0.065 0.000 0.554 1.753 -0.800 0.000 0.467 0.595 -1.692 1.274 0.703 1.335 0.524 3.102 0.878 0.839 1.579 0.960 0.453 0.626 0.859 +1 0.647 1.255 -1.699 1.302 0.762 0.717 1.893 0.460 0.000 0.972 -0.472 -0.198 2.215 1.854 -0.757 -1.187 1.274 0.898 0.444 -1.050 0.000 1.507 1.657 1.014 1.754 1.137 1.526 1.340 +0 0.580 -0.676 1.715 1.587 1.682 1.179 1.109 -0.145 2.173 0.661 1.213 0.764 0.000 0.937 0.093 0.099 0.000 1.402 -0.245 -0.345 3.102 0.962 1.289 0.972 3.017 1.073 1.935 1.784 +1 0.288 -2.154 -0.071 1.130 -1.398 0.907 -0.507 -0.392 2.173 1.303 -1.171 0.840 0.000 1.030 -0.936 1.407 0.000 0.973 -0.773 -0.690 1.551 0.883 0.976 0.986 0.873 0.336 0.918 0.806 +1 1.423 1.049 -1.648 1.105 -0.791 0.488 1.094 -0.311 2.173 1.218 0.504 0.584 2.215 0.277 0.555 0.241 0.000 0.693 -0.047 -0.201 0.000 0.903 0.884 1.213 1.267 0.886 0.895 0.821 +0 0.759 0.896 0.919 1.366 -1.734 0.565 0.676 0.154 0.000 0.579 -0.872 -1.190 2.215 0.695 0.123 -0.635 2.548 0.961 -0.907 0.064 0.000 1.136 1.035 0.990 0.784 0.487 0.700 0.781 +1 1.109 -0.751 0.057 0.535 1.697 0.348 -2.114 0.209 0.000 0.646 -0.625 -1.280 1.107 0.476 1.509 1.123 0.000 1.036 -1.012 1.392 0.000 0.914 0.857 1.062 0.854 0.749 0.859 0.747 +1 0.624 -0.150 1.539 0.554 1.434 0.528 0.985 -1.187 0.000 1.411 0.536 -0.142 1.107 1.313 0.494 1.293 0.000 1.824 0.571 -0.790 3.102 1.135 1.180 0.990 1.515 0.804 1.167 1.044 +0 0.569 2.386 1.580 1.233 -0.223 0.426 0.838 0.922 2.173 0.451 -1.694 -1.301 0.000 0.707 -0.124 -0.199 2.548 0.863 -1.071 0.854 0.000 0.780 1.113 1.159 1.264 0.676 0.917 1.449 +0 0.863 -1.453 0.273 1.565 0.306 1.009 -0.750 -0.070 2.173 3.236 0.454 -1.606 2.215 0.696 -2.400 1.145 0.000 0.854 0.322 0.265 0.000 0.915 1.116 0.986 4.261 3.133 2.832 2.157 +1 0.586 -1.273 1.394 0.040 0.717 0.992 -0.297 0.132 2.173 1.030 0.176 -1.342 0.000 0.988 0.370 -0.731 0.000 1.313 0.129 0.964 3.102 0.857 0.844 0.907 0.846 0.868 0.890 0.756 +1 1.674 -0.130 0.319 1.216 0.949 0.840 -0.960 -0.382 2.173 0.754 -0.284 1.270 1.107 1.872 -0.409 -1.069 0.000 0.998 0.776 1.455 0.000 1.609 1.159 1.063 1.175 1.235 1.016 1.016 +0 0.551 1.170 -0.672 1.159 0.359 0.734 0.593 1.340 0.000 0.813 -0.633 0.108 2.215 1.183 -0.654 -1.264 2.548 0.548 -1.177 -0.394 0.000 1.502 1.153 0.984 0.813 0.985 0.874 0.850 +0 0.389 -0.963 1.296 0.386 -0.569 0.804 -1.083 0.729 0.000 0.708 0.570 -0.022 2.215 0.308 2.066 -0.021 0.000 0.629 -0.614 -1.068 0.000 0.905 1.042 0.986 0.581 0.736 0.666 0.634 +1 0.782 -0.170 -0.049 0.863 1.490 1.023 0.352 -1.291 2.173 0.891 0.106 0.827 2.215 1.264 1.808 -0.002 0.000 1.432 1.041 1.642 0.000 1.143 0.971 1.119 0.956 1.337 0.819 0.759 +1 0.632 -1.118 -1.367 0.996 0.795 1.022 -0.367 -1.664 0.000 1.673 -0.159 0.051 2.215 0.512 0.009 1.316 0.000 0.430 -0.703 1.390 3.102 1.112 0.617 1.021 1.129 0.763 0.877 0.796 +1 0.680 1.366 -0.416 1.004 1.071 1.351 -0.737 0.691 0.000 2.494 1.615 -0.978 0.000 1.081 0.734 0.653 2.548 0.867 0.945 -1.476 3.102 0.671 0.595 1.114 0.697 0.706 0.873 0.810 +0 1.369 0.372 1.691 0.342 -0.556 0.510 2.220 0.416 0.000 0.746 1.225 -0.587 2.215 0.764 1.067 -1.405 2.548 1.085 0.682 0.606 0.000 0.899 0.906 0.990 0.559 0.541 0.675 0.705 +0 0.794 1.949 0.703 0.324 -1.319 0.979 -0.166 1.632 1.087 1.493 0.478 -0.283 2.215 0.972 0.729 1.614 0.000 0.518 -0.186 0.244 0.000 0.849 1.047 0.992 1.153 1.856 1.106 0.890 +0 0.534 -0.706 -1.284 1.574 -0.386 0.963 -1.186 0.795 0.000 1.090 -1.384 1.648 0.000 0.758 -0.493 1.047 0.000 1.181 0.782 -1.090 1.551 0.959 1.039 0.991 0.788 1.021 0.976 0.889 +1 1.950 -0.143 -0.222 0.999 -0.740 0.557 -0.133 1.610 0.000 1.007 0.326 0.946 2.215 1.021 0.549 -1.434 2.548 0.412 0.964 0.419 0.000 0.925 0.978 0.996 0.953 0.916 0.914 0.878 +0 0.415 1.655 -1.666 1.441 -0.762 0.630 1.022 0.873 0.000 0.365 -1.530 1.388 0.000 0.450 1.452 0.366 0.000 0.871 -0.061 -0.898 3.102 0.837 0.820 0.996 0.562 0.429 0.529 0.596 +0 0.301 -0.435 1.176 2.128 -1.580 1.009 -0.016 -1.030 2.173 0.990 -0.748 0.609 0.000 0.370 -2.559 1.214 0.000 1.733 -0.414 -0.050 0.000 0.982 0.637 0.981 1.359 0.477 0.878 0.985 +0 0.630 0.685 0.468 0.919 -1.254 0.516 2.425 0.838 0.000 1.141 -0.024 1.388 2.215 1.271 1.927 -0.691 0.000 1.019 1.328 0.085 0.000 0.874 0.501 1.054 0.854 0.701 0.980 0.822 +1 0.454 -1.220 1.122 0.662 1.024 0.894 -0.380 -0.425 0.000 0.683 -0.914 0.068 0.000 1.402 -0.409 1.181 2.548 1.717 -0.250 -1.428 3.102 0.853 1.096 0.987 0.618 0.848 0.914 0.821 +0 1.904 -0.093 -1.471 1.313 -1.123 3.175 -0.233 0.654 0.000 2.244 0.018 -1.012 2.215 1.520 -0.705 -0.819 0.000 1.227 0.523 0.844 3.102 3.983 2.262 0.989 0.780 1.560 2.031 1.754 +1 0.787 -0.891 0.909 0.873 -1.643 2.202 -0.040 -0.601 0.000 1.164 0.923 0.477 0.000 1.368 0.684 1.033 0.000 1.596 0.457 1.509 3.102 0.949 0.918 0.988 0.586 0.731 0.901 1.072 +1 1.474 0.491 0.744 0.392 -0.420 1.040 1.308 0.470 0.000 1.285 -0.710 -1.403 1.107 0.690 -0.517 -0.637 2.548 0.987 0.084 -0.828 0.000 0.626 0.692 0.987 0.819 0.643 0.904 0.738 +0 0.463 1.242 -1.314 2.791 1.720 0.748 1.157 0.372 0.000 0.601 1.056 -0.308 0.000 0.898 0.007 0.125 1.274 0.934 -1.066 -0.693 3.102 0.822 0.723 0.979 1.207 0.674 0.957 1.029 +0 1.079 -0.575 0.495 0.313 0.078 0.524 0.421 -0.528 0.000 0.869 -0.779 -0.583 0.000 2.022 -1.058 1.205 1.274 1.743 -0.434 -1.414 3.102 0.937 0.942 0.985 1.118 1.114 1.064 0.958 +1 0.920 0.674 0.591 0.417 -1.276 0.574 1.485 1.671 0.000 1.258 1.206 -1.256 0.000 0.849 1.905 0.081 0.000 1.387 0.827 -0.189 3.102 0.899 0.980 0.989 0.654 0.569 0.770 0.688 +1 0.697 0.436 1.342 0.199 -1.430 1.067 -1.009 0.838 2.173 0.982 -1.186 -0.471 0.000 0.841 -0.398 -0.803 0.000 0.644 -0.439 -1.181 3.102 0.695 1.354 0.977 0.536 0.878 0.821 0.744 +1 1.002 0.348 -1.265 1.269 1.351 1.317 0.496 -0.267 2.173 0.646 -0.247 -1.579 0.000 0.580 0.400 0.681 0.000 0.854 -0.422 0.340 3.102 0.930 0.915 1.103 1.352 0.830 0.933 0.883 +1 0.401 2.052 -1.082 1.207 0.977 0.463 1.540 0.750 0.000 1.100 1.368 -0.910 1.107 1.013 0.296 -0.526 2.548 0.414 1.733 -0.760 0.000 0.678 0.831 0.988 0.913 0.742 0.773 0.675 +0 0.645 -1.241 0.485 2.162 1.506 1.458 -1.382 -0.385 0.000 0.868 -1.593 -1.584 2.215 1.103 0.572 0.574 2.548 0.775 -0.624 -0.384 0.000 0.533 1.190 1.302 1.419 1.742 1.274 1.213 +1 0.934 -0.787 1.514 0.149 1.322 0.499 0.464 -0.702 0.000 1.009 1.198 -0.343 2.215 0.447 0.787 0.885 2.548 0.900 -1.064 0.468 0.000 1.348 0.904 0.994 1.143 0.652 0.808 0.786 +1 0.801 0.080 -0.663 0.347 -0.820 0.847 -0.042 0.308 0.000 1.145 1.244 -1.143 2.215 1.157 0.661 0.984 0.000 1.263 0.118 -1.674 0.000 0.987 1.133 0.992 0.606 0.600 0.672 0.660 +0 1.440 -0.196 -0.599 1.265 -0.270 0.918 0.980 1.698 0.000 0.608 -0.048 0.489 2.215 0.658 0.829 0.983 0.000 0.470 1.057 -0.584 3.102 0.843 0.956 0.986 0.546 0.523 0.617 0.790 +1 0.928 0.249 -1.091 0.799 1.270 1.109 -0.534 -0.220 0.000 0.883 -0.368 1.682 2.215 0.734 0.227 0.242 0.000 1.005 0.853 1.170 1.551 0.927 1.173 1.012 0.657 0.749 0.936 0.826 +1 0.417 -0.870 -1.429 0.710 -0.594 1.157 -0.427 -1.612 2.173 1.013 0.668 0.813 0.000 1.154 -1.378 -0.811 0.000 1.113 0.527 0.270 0.000 0.820 1.207 0.979 0.831 0.948 0.944 0.823 +1 0.943 -0.893 -0.091 0.943 -1.307 0.968 -0.080 0.616 0.000 0.773 -1.127 -0.775 0.000 1.554 -0.724 -1.512 1.274 0.710 0.497 1.221 1.551 0.892 0.701 1.162 0.834 0.787 0.794 0.780 +0 0.671 -1.476 -1.489 1.633 -0.633 0.479 -1.200 0.092 0.000 0.804 -0.665 1.629 1.107 0.875 -1.185 1.122 1.274 0.666 -1.675 -0.445 0.000 0.536 0.897 1.011 0.882 0.483 0.727 0.671 +1 0.932 0.036 -1.741 1.614 -1.197 0.845 -0.520 0.913 2.173 0.668 0.388 -0.294 2.215 0.953 0.397 0.185 0.000 0.831 0.852 1.054 0.000 0.751 0.900 0.986 0.855 1.113 0.957 0.838 +1 0.810 1.075 -0.641 1.238 1.640 0.436 2.313 -0.797 0.000 0.875 0.733 0.239 2.215 0.535 2.417 0.696 0.000 0.846 0.050 1.516 3.102 0.858 1.056 1.227 0.729 0.761 0.827 0.787 +0 1.256 0.559 0.839 0.545 1.684 0.596 0.949 -0.895 2.173 0.445 0.967 -0.212 0.000 0.640 0.068 0.987 0.000 1.251 -0.582 -1.032 1.551 0.821 0.849 0.982 0.888 0.854 0.827 0.710 +0 1.305 0.256 0.817 0.433 -1.628 0.698 1.286 -0.805 0.000 1.097 -0.728 -0.882 0.000 2.087 -0.792 0.778 2.548 0.716 -1.157 -0.155 0.000 0.798 0.783 0.989 0.840 1.194 0.905 0.829 +1 0.742 0.523 1.054 1.258 -1.253 0.981 -0.809 1.417 2.173 1.091 0.634 -0.318 0.000 0.806 -0.074 0.290 2.548 0.472 2.198 -0.358 0.000 1.090 0.941 1.170 1.160 1.030 1.250 1.058 +0 0.777 2.260 1.450 1.346 -1.116 0.701 1.864 0.312 0.000 0.772 0.291 1.113 0.000 1.467 1.356 -0.605 2.548 0.846 0.553 0.381 0.000 0.765 0.939 1.046 0.941 0.914 0.801 0.841 +0 0.451 -2.143 -0.834 1.143 0.252 1.105 -0.367 0.946 1.087 1.451 -1.128 -0.730 0.000 0.413 1.078 1.694 0.000 1.474 -0.703 1.739 1.551 1.988 1.327 0.990 1.117 0.941 1.146 1.027 +0 0.818 -0.663 1.109 1.043 1.611 0.778 -2.913 -0.636 0.000 1.510 -0.061 0.308 0.000 1.067 -0.136 -0.453 2.548 2.652 0.039 -1.536 3.102 0.792 0.866 0.976 0.760 1.072 0.975 0.878 +1 1.788 0.170 -0.595 0.320 -0.650 0.803 0.713 1.321 2.173 1.119 -0.299 0.652 1.107 0.937 0.698 -0.704 0.000 0.525 1.308 1.318 0.000 0.815 0.864 0.986 1.087 1.090 1.023 0.894 +1 0.558 -0.627 0.091 1.325 -1.043 0.614 -0.332 0.386 0.000 0.691 0.335 -0.040 0.000 0.921 2.498 1.066 0.000 1.627 0.759 1.491 3.102 0.706 0.938 1.015 1.115 0.793 0.811 0.799 +0 0.741 0.706 1.546 0.985 -0.670 0.879 1.520 -0.497 0.000 1.305 1.542 1.493 0.000 0.511 -0.274 0.652 1.274 0.966 1.260 -0.069 3.102 2.219 1.318 1.079 0.708 0.645 0.957 0.840 +1 0.744 -0.659 0.502 0.939 -0.808 1.124 0.855 0.982 0.000 1.157 -0.776 -0.976 0.000 0.662 -0.139 -0.561 2.548 0.894 -0.604 1.446 0.000 1.085 0.966 1.071 0.588 0.305 0.600 0.589 +1 1.780 0.235 -0.147 0.885 0.597 0.338 0.551 1.357 0.000 1.500 1.307 1.716 2.215 0.715 1.376 -0.480 0.000 1.220 0.869 0.342 0.000 0.983 0.940 1.080 0.761 0.960 1.047 0.871 +1 0.721 -1.425 0.859 0.327 -1.571 1.067 -1.671 -0.301 0.000 0.890 -0.866 -1.106 1.107 0.827 -1.035 1.543 2.548 0.669 -1.794 0.511 0.000 0.915 1.037 0.989 0.770 0.634 0.800 0.758 +0 1.954 -0.564 -1.073 0.619 1.349 2.851 0.887 0.839 0.000 1.725 0.325 -0.817 2.215 1.199 1.304 -0.860 0.000 0.647 0.368 -0.191 3.102 1.596 0.900 1.247 1.124 0.512 0.765 0.842 +0 1.815 -1.727 1.495 1.537 -1.659 1.308 0.802 0.074 0.000 1.014 0.513 -0.236 0.000 1.117 -1.150 0.905 2.548 1.721 0.944 -0.701 1.551 0.760 0.933 1.003 0.847 1.912 1.599 1.859 +0 0.439 1.898 0.440 0.923 -1.309 0.589 -0.361 1.084 0.000 0.874 1.102 -0.024 1.107 0.672 0.243 -0.408 2.548 0.398 0.327 0.021 0.000 0.670 0.945 0.984 1.030 0.458 0.807 0.947 +1 0.597 -1.663 -0.154 0.568 -0.946 1.420 -0.949 1.455 0.000 1.441 -0.599 -0.582 0.000 1.014 -1.290 1.064 2.548 1.675 -1.080 0.650 3.102 0.887 0.965 0.985 0.811 0.368 0.724 0.668 +0 1.971 0.855 0.367 0.062 -1.284 0.873 -0.975 -0.945 0.000 0.922 0.088 0.865 2.215 1.025 -1.606 -1.251 0.000 0.987 -0.066 -1.730 3.102 0.822 0.884 0.986 0.719 0.624 0.954 1.118 +0 2.475 -1.448 -0.413 0.593 -1.307 0.890 -0.545 1.193 2.173 0.741 -1.699 0.980 0.000 0.614 -0.074 -1.439 2.548 0.477 -0.576 -0.653 0.000 0.881 0.856 1.210 0.968 0.676 1.024 0.865 +1 0.939 -0.172 1.040 0.513 1.625 1.879 0.740 -1.057 0.000 1.489 -0.058 0.641 2.215 0.848 0.914 0.063 0.000 0.919 0.074 -0.271 0.000 0.744 1.445 0.994 0.913 0.625 1.322 1.195 +1 1.530 0.627 1.239 0.681 -1.076 0.681 -0.039 -0.386 2.173 0.470 0.094 0.035 0.000 0.452 1.011 -0.274 0.000 0.971 -0.738 1.140 3.102 0.607 0.655 1.231 1.063 0.924 0.820 0.664 +1 0.729 0.312 0.462 1.264 1.574 1.043 -0.424 -1.411 0.000 1.822 -0.677 0.286 2.215 0.552 -0.622 -0.767 0.000 0.864 -0.678 1.218 3.102 0.868 0.718 1.121 1.283 0.846 0.929 0.891 +1 0.879 0.320 -0.376 2.413 -0.045 2.062 -0.411 1.571 0.000 1.145 0.065 -0.994 2.215 1.274 -0.835 -0.179 0.000 2.082 -0.950 -1.092 3.102 0.950 1.329 0.994 1.210 0.902 1.353 1.574 +1 0.669 0.353 -1.717 0.385 -1.529 0.786 0.237 0.715 2.173 0.652 2.350 -1.499 0.000 1.169 0.929 -0.239 1.274 1.153 1.550 -0.582 0.000 0.890 0.946 0.984 0.941 1.025 1.024 1.148 +0 1.539 -0.825 -1.022 0.991 -0.049 1.007 0.232 0.363 2.173 0.778 0.959 0.655 0.000 1.578 -0.855 -1.510 1.274 1.366 0.533 1.276 0.000 0.975 0.989 1.316 1.338 1.840 1.142 1.011 +1 2.168 0.073 -0.688 0.725 -0.384 1.384 1.738 0.897 0.000 1.365 0.620 0.905 2.215 0.614 0.677 -0.853 1.274 0.715 -0.493 -1.424 0.000 0.676 0.960 0.998 0.513 0.974 0.922 0.810 +1 1.896 -0.414 -0.963 0.636 0.168 1.093 0.303 0.748 2.173 0.290 -0.549 -1.624 2.215 0.332 0.902 1.217 0.000 0.544 0.131 -0.511 0.000 0.510 0.645 1.297 0.680 0.791 0.895 0.694 +1 0.855 -1.294 0.416 1.312 1.036 0.257 -2.689 -0.308 0.000 0.920 -1.012 -0.838 2.215 0.568 1.189 -0.558 0.000 0.543 -1.191 1.299 0.000 0.688 0.758 0.995 1.534 1.142 1.158 0.954 +1 4.335 -0.902 -0.530 0.835 -1.042 0.836 -1.622 1.248 0.000 1.913 -0.266 0.005 2.215 4.189 1.139 1.302 0.000 1.276 -0.454 -0.891 0.000 0.822 1.007 1.172 0.755 0.774 0.945 0.807 +0 0.656 0.761 -1.076 0.990 -0.311 0.513 -1.057 0.361 0.000 0.794 0.382 1.265 2.215 1.490 -0.942 -0.820 2.548 0.754 -0.484 1.629 0.000 0.894 0.932 0.990 1.005 1.421 1.246 1.129 +0 2.683 -0.605 0.494 0.890 -0.386 1.066 -0.617 -0.904 2.173 0.939 0.447 0.950 2.215 1.416 -0.399 -1.565 0.000 1.330 -1.534 1.240 0.000 1.067 0.832 1.524 1.198 1.687 1.216 1.095 +1 0.565 -0.095 0.713 0.188 -1.350 0.733 -1.734 -0.394 0.000 0.755 -0.288 -1.514 2.215 0.799 -1.464 1.537 2.548 1.071 -0.961 0.253 0.000 0.850 0.913 0.985 0.736 0.661 0.800 0.695 +1 0.621 -1.016 0.577 0.776 -0.343 0.619 0.017 1.555 0.000 0.560 1.141 -0.607 2.215 1.421 -0.690 -1.669 0.000 1.291 -0.424 0.013 3.102 0.776 1.069 0.993 0.798 0.832 0.896 0.798 +0 1.013 -0.552 1.710 0.265 -0.847 0.588 1.633 1.525 1.087 0.975 -0.568 0.404 2.215 1.554 0.756 -0.158 0.000 1.127 0.070 -1.269 0.000 1.350 1.276 0.990 1.616 1.789 1.246 1.156 +0 2.391 -1.293 0.948 1.210 0.949 1.694 -0.775 -0.618 2.173 0.754 -1.064 -1.365 1.107 0.359 -0.875 -1.729 0.000 0.637 0.448 0.233 0.000 0.675 1.017 0.996 1.130 1.068 1.480 1.157 +1 0.948 -0.014 0.759 0.496 -1.070 0.728 0.192 -0.249 1.087 0.995 0.341 -0.718 0.000 1.259 -0.109 1.494 0.000 0.670 0.740 1.408 1.551 1.617 0.975 0.988 0.770 0.782 0.777 0.699 +0 0.950 -1.377 1.522 1.153 0.926 0.463 -0.557 -0.747 0.000 0.866 0.338 -1.272 1.107 0.635 -2.558 -0.132 0.000 1.089 -0.297 0.784 3.102 1.301 1.048 0.984 1.088 0.899 0.988 0.897 +1 0.529 -0.590 -0.818 1.141 1.610 0.849 0.508 0.943 0.000 1.363 -0.856 -0.392 2.215 0.734 -0.058 1.704 0.000 0.747 0.771 -0.168 3.102 0.989 0.835 0.984 1.085 0.957 1.006 0.871 +1 0.865 -0.429 -0.818 0.711 0.995 0.885 -0.933 -0.535 0.000 0.687 -2.766 0.626 0.000 2.097 -1.141 1.208 2.548 1.836 0.759 -0.514 0.000 0.841 0.931 1.084 0.985 0.854 0.914 0.792 +0 2.584 -0.257 -0.364 2.446 -0.585 1.610 0.245 1.426 2.173 0.801 -0.660 1.577 0.000 0.482 -1.194 1.077 0.000 0.539 0.287 0.313 3.102 0.516 0.953 1.000 0.700 0.833 1.426 1.217 +1 0.626 1.159 -0.123 1.036 -1.551 0.809 0.047 0.559 0.000 0.826 -0.442 1.379 2.215 0.758 0.461 -1.064 2.548 0.590 1.088 -1.052 0.000 1.264 0.946 1.071 1.037 0.798 0.738 0.752 +1 0.685 0.490 0.258 2.455 0.996 1.325 1.383 -1.287 0.000 0.740 2.909 1.030 0.000 1.233 0.078 -0.595 2.548 1.876 0.885 -0.234 3.102 2.610 1.936 1.109 1.200 0.698 1.478 1.467 +1 1.567 0.410 -0.444 0.838 0.329 0.804 -2.841 -0.263 0.000 1.123 -0.900 -1.702 2.215 1.271 -0.449 1.271 0.000 1.168 0.468 1.398 1.551 3.238 2.198 1.020 0.882 0.914 1.662 1.689 +0 0.983 1.151 -0.541 1.881 -1.054 0.766 0.843 1.393 2.173 0.876 2.034 0.671 0.000 0.931 1.825 0.212 0.000 1.074 0.867 -1.294 0.000 0.908 1.005 0.981 0.906 0.452 0.823 0.794 +1 0.749 -0.875 -1.195 0.766 -0.553 0.394 1.605 0.657 0.000 0.879 -0.696 1.599 2.215 0.811 -0.739 0.055 2.548 1.469 -1.674 0.157 0.000 0.816 1.087 0.983 0.899 0.884 0.896 1.080 +0 0.697 0.026 -1.442 0.788 0.333 0.893 -0.052 0.780 0.000 1.273 0.831 -1.102 2.215 0.696 -0.198 -0.021 0.000 1.165 -0.198 1.723 0.000 0.945 0.850 1.026 0.909 0.570 0.903 0.766 +0 1.791 -0.412 1.684 0.433 0.714 0.749 -0.996 -0.356 1.087 0.330 1.009 0.025 2.215 0.539 -0.604 1.012 0.000 0.429 -0.283 -1.057 0.000 0.515 0.657 0.989 0.806 0.919 0.837 0.647 +1 0.880 -1.111 -1.408 2.535 -1.146 1.650 2.276 0.851 0.000 1.416 -1.525 -0.564 0.000 1.318 0.221 0.246 2.548 0.906 -0.210 1.615 3.102 1.712 1.206 0.987 1.966 0.816 1.292 1.182 +0 1.210 0.383 -1.246 0.442 -1.513 0.971 -0.784 0.149 1.087 1.459 -0.110 1.579 2.215 0.592 -0.193 -0.015 0.000 0.932 -0.639 -0.294 0.000 0.916 1.085 0.993 1.196 1.788 1.045 0.890 +1 1.166 0.383 -0.953 0.770 -0.463 1.174 1.892 0.787 0.000 1.117 -0.070 -0.848 2.215 1.190 1.062 1.516 0.000 0.919 1.712 -0.028 0.000 1.071 0.958 0.980 0.771 0.548 1.222 1.051 +0 0.662 0.429 1.475 1.126 0.524 1.216 0.360 -1.648 1.087 1.672 0.175 0.058 2.215 0.735 -0.270 -1.222 0.000 0.488 0.375 0.582 0.000 0.706 0.899 0.989 1.065 2.106 1.136 0.894 +1 1.842 -1.461 0.324 0.521 -1.223 0.971 -0.175 1.405 2.173 0.540 -0.776 -1.251 0.000 0.760 0.386 -0.453 2.548 0.868 0.281 -1.269 0.000 0.535 0.790 1.336 1.140 1.113 1.085 0.911 +0 1.781 0.188 1.399 0.115 0.478 0.706 0.339 -0.364 2.173 0.883 0.814 0.372 2.215 0.694 1.483 -1.025 0.000 1.101 1.350 0.928 0.000 0.931 1.001 0.983 1.035 0.772 0.897 0.898 +1 1.542 0.725 -1.055 0.750 0.365 1.171 1.251 1.177 2.173 1.266 -0.753 -0.372 0.000 0.615 0.711 0.725 0.000 1.246 1.012 -1.536 3.102 0.788 0.900 1.428 0.838 0.820 0.878 0.761 +1 1.283 -1.669 0.700 0.582 -0.312 1.563 1.064 -1.179 0.000 2.011 -0.751 0.245 2.215 1.446 -1.420 -1.636 0.000 1.081 -1.181 1.058 0.000 0.908 0.720 0.986 0.894 0.831 0.959 0.830 +1 1.462 -0.491 1.483 0.307 -1.384 0.931 -1.114 -1.440 0.000 1.163 0.044 0.734 2.215 1.423 -1.297 -0.229 0.000 1.308 -0.330 -0.400 3.102 0.961 0.936 0.986 0.997 0.982 0.867 0.826 +1 0.705 -1.570 0.177 1.320 0.677 0.990 -0.449 -1.302 0.000 0.498 0.136 0.772 1.107 0.751 -0.535 -0.324 0.000 0.685 -0.037 -1.726 0.000 0.785 0.637 0.991 1.543 0.443 1.153 0.967 +1 1.210 -1.128 -0.022 0.570 -1.092 0.738 -1.743 1.661 0.000 0.603 -1.304 1.010 0.000 1.044 -0.185 -0.078 2.548 0.883 -0.846 -1.243 3.102 0.841 0.700 0.987 0.728 0.706 0.782 0.729 +1 1.165 -0.590 1.582 0.222 -0.160 0.930 -1.602 -0.033 0.000 1.069 -0.254 0.951 1.107 1.406 -0.435 -1.094 2.548 1.433 -1.433 -1.366 0.000 1.646 1.404 0.990 0.782 1.263 1.175 0.959 +1 1.449 1.070 0.521 0.371 1.673 2.089 -0.175 -0.979 0.000 2.539 0.602 0.127 2.215 2.081 -0.017 -1.639 0.000 1.040 1.750 1.238 0.000 0.935 0.767 0.982 1.070 0.915 1.183 1.015 +1 0.717 -0.863 1.272 1.338 -0.299 0.845 -0.658 0.194 0.000 1.566 0.017 -1.366 2.215 0.439 -1.509 0.879 0.000 0.841 -0.450 1.129 3.102 0.851 0.660 1.340 1.258 0.858 0.927 0.857 +0 0.837 -0.180 1.222 0.715 -1.527 0.643 -0.963 -0.231 2.173 0.678 -2.048 0.504 0.000 1.049 -0.427 -0.928 2.548 0.375 -2.198 1.707 0.000 0.607 1.020 0.990 1.125 0.656 0.814 0.950 +1 1.432 -0.539 -0.390 0.872 0.266 1.837 -0.474 1.632 0.000 1.443 -0.817 0.128 0.000 1.037 0.585 -0.899 2.548 0.887 -0.981 0.743 0.000 0.816 0.914 0.985 0.851 0.646 0.876 0.774 +0 0.371 -1.945 -1.516 1.786 -1.677 0.495 1.134 0.234 1.087 0.884 0.065 0.222 0.000 0.611 0.361 1.535 1.274 0.737 -0.802 0.224 0.000 0.598 0.664 0.975 0.662 0.684 0.899 0.745 +0 0.510 -0.044 0.945 0.957 -0.555 0.859 0.636 0.131 1.087 0.792 -0.678 -1.614 0.000 0.586 -1.731 1.503 0.000 0.462 0.642 -1.662 3.102 0.750 0.694 0.989 0.716 0.668 0.957 0.824 +1 0.731 0.709 -1.671 1.584 0.953 0.627 2.045 -1.655 0.000 1.078 -0.650 -0.547 0.000 0.685 -0.592 0.772 0.000 1.178 0.537 -0.631 3.102 1.223 0.969 1.045 0.649 0.283 0.613 0.791 +0 1.076 0.028 0.125 1.631 -0.604 0.931 0.793 1.415 0.000 1.008 0.624 -1.311 0.000 1.294 1.626 0.248 0.000 1.117 0.707 1.128 1.551 1.311 0.867 1.120 0.911 0.566 0.762 0.871 +1 0.785 -1.019 0.790 0.688 -1.245 0.669 -2.245 -1.196 0.000 0.560 -0.492 -0.182 2.215 1.150 -0.404 1.195 2.548 0.745 -1.913 0.074 0.000 0.982 1.033 0.988 0.653 0.808 0.911 0.772 +1 0.680 0.577 0.657 0.565 -0.732 0.987 1.558 1.467 0.000 0.910 -0.330 -0.488 0.000 0.750 0.910 0.481 2.548 0.982 0.541 -1.308 3.102 0.544 0.791 0.984 0.637 0.666 0.609 0.626 +0 1.612 0.907 -0.118 1.128 0.649 0.525 -1.080 -1.293 0.000 0.386 -1.181 0.488 0.000 2.023 -0.913 1.520 2.548 1.865 0.752 -0.686 3.102 0.957 0.878 1.192 0.937 2.127 1.493 1.302 +0 1.324 0.518 -1.578 0.692 -0.548 0.973 0.664 -0.698 0.000 1.513 -0.837 0.978 0.000 1.712 -0.406 0.401 2.548 1.711 -0.303 -0.530 0.000 0.972 0.829 1.062 1.201 1.117 0.910 0.822 +0 0.952 -1.832 0.426 1.383 1.005 0.655 1.308 -0.956 0.000 0.503 -0.889 -0.591 1.107 0.277 1.172 1.346 0.000 0.759 1.305 -1.325 0.000 0.974 0.676 0.985 1.044 0.617 0.837 0.857 +1 1.674 0.081 -1.698 1.277 -1.290 0.903 0.286 0.439 2.173 0.641 0.513 1.006 0.000 1.194 -0.183 -0.444 2.548 0.431 0.327 -0.186 0.000 0.604 0.753 0.978 1.386 0.978 1.029 0.833 +0 1.436 0.264 -0.970 1.420 -0.439 1.127 -0.676 0.638 2.173 1.465 -0.458 1.314 2.215 0.629 -1.381 -1.115 0.000 0.377 0.698 -0.052 0.000 0.903 1.000 0.988 1.641 1.099 1.355 1.086 +0 1.763 1.187 0.443 0.876 0.808 0.564 -0.542 -1.087 0.000 0.703 0.665 -1.075 2.215 0.799 1.860 1.442 0.000 0.586 -2.287 -0.446 0.000 1.079 0.913 0.991 1.029 0.717 0.739 0.812 +1 0.951 -0.172 0.304 0.352 0.219 1.295 -0.804 0.925 2.173 1.121 -0.104 -0.956 0.000 1.316 -0.407 -0.269 0.000 1.345 -2.407 -1.548 0.000 0.904 0.980 0.989 0.873 0.951 0.867 0.782 +0 1.690 1.438 0.949 0.772 -1.109 0.720 1.100 0.316 1.087 0.717 -1.314 -1.479 2.215 0.553 -1.301 -0.125 0.000 0.915 -0.059 -1.219 0.000 0.881 0.807 1.520 1.002 1.966 1.437 1.233 +0 1.552 0.228 0.776 1.065 -1.439 0.885 0.266 -1.087 2.173 1.055 -0.966 1.285 0.000 0.881 -0.021 -0.509 0.000 1.628 -0.524 0.302 3.102 0.826 1.113 1.623 1.170 1.343 0.984 0.922 +1 0.730 0.076 -0.712 0.749 1.059 0.626 0.958 0.557 1.087 0.663 -0.371 1.699 0.000 0.785 -1.320 -1.239 2.548 1.186 -0.283 0.285 0.000 1.105 1.040 1.024 0.813 1.593 0.902 0.761 +1 0.562 0.301 -1.472 0.944 -0.477 1.072 0.134 -1.675 0.000 1.040 0.045 -0.025 0.000 1.083 -0.337 -0.560 0.000 0.955 1.001 0.855 3.102 0.968 0.935 0.984 0.894 1.024 0.759 0.709 +1 0.667 -0.357 -1.584 1.287 -0.108 1.336 0.390 -0.162 2.173 1.069 0.453 1.255 2.215 1.344 0.321 -1.683 0.000 0.588 1.082 -1.729 0.000 0.484 0.578 1.247 1.018 1.684 1.022 0.942 +1 0.934 0.111 -1.263 2.339 1.684 0.759 1.012 -0.094 2.173 1.016 -0.570 -0.576 0.000 0.911 0.696 0.805 0.000 1.817 2.067 0.498 0.000 0.958 1.122 0.985 0.746 0.420 0.897 0.874 +0 0.463 0.022 -1.153 0.431 -0.343 0.612 -1.019 -0.760 0.000 1.267 -0.497 0.817 2.215 1.331 0.114 -0.745 0.000 2.139 0.837 1.193 0.000 0.990 0.927 0.984 1.293 0.680 0.923 0.939 +0 0.610 -0.721 0.047 1.126 0.749 0.809 0.640 0.567 1.087 0.703 1.973 0.077 0.000 1.261 -0.972 -1.063 0.000 1.639 -1.359 -1.232 0.000 0.787 0.881 0.993 0.981 1.326 0.937 0.881 +1 1.468 0.169 0.736 0.469 1.395 0.628 -0.368 -1.245 2.173 1.108 -0.791 1.408 2.215 1.204 -0.358 -0.170 0.000 0.588 -0.948 -0.524 0.000 1.009 0.908 0.988 1.031 0.881 0.876 0.898 +1 0.380 1.267 1.541 0.874 0.535 0.950 -0.452 1.424 2.173 1.049 -2.570 -1.208 0.000 1.162 -0.563 0.083 0.000 0.815 -0.269 -0.378 0.000 1.057 0.836 0.988 2.190 0.355 1.564 1.363 +1 1.131 -0.147 0.081 2.441 -1.303 1.351 -0.135 -0.277 2.173 0.607 0.242 1.464 0.000 1.307 0.486 0.137 2.548 2.348 -0.362 1.247 0.000 0.988 1.726 2.182 1.525 0.826 1.352 1.312 +0 0.518 2.315 0.329 1.341 -1.002 1.079 0.369 0.688 0.000 0.643 0.470 -1.616 0.000 0.734 1.039 -0.324 2.548 0.863 0.178 -0.994 3.102 0.742 0.831 1.076 0.991 0.452 0.694 0.875 +0 1.107 1.540 -1.021 0.954 -0.132 0.880 0.266 -1.645 0.000 1.130 0.018 -0.303 0.000 0.966 1.813 1.514 0.000 2.144 2.210 0.483 0.000 0.816 0.776 1.023 0.717 0.523 0.722 0.775 +0 2.144 -0.219 -1.342 0.564 -1.422 0.759 0.172 0.152 2.173 1.137 -0.799 -0.749 2.215 1.417 -0.633 0.593 0.000 2.100 0.169 0.967 0.000 1.084 0.984 0.999 1.002 1.221 1.034 1.091 +1 1.197 -0.081 0.919 0.618 -0.264 0.433 -1.525 0.096 2.173 0.618 -0.334 -0.438 0.000 0.632 -0.709 1.717 2.548 1.101 -2.163 -1.515 0.000 0.813 0.821 1.043 0.688 0.696 0.625 0.593 +1 1.061 -0.736 1.441 0.997 0.986 1.499 -0.812 -0.800 2.173 0.569 -0.875 0.247 2.215 0.916 0.182 0.750 0.000 0.448 -0.348 -1.589 0.000 0.646 1.264 0.997 0.748 1.102 1.033 0.900 +1 0.796 2.221 1.734 0.569 0.178 0.432 0.667 0.774 2.173 0.356 0.846 -1.658 0.000 0.576 0.075 1.243 0.000 1.217 -0.336 -0.537 3.102 0.449 0.686 0.991 0.735 0.833 0.823 0.679 +1 0.586 -1.090 -0.937 1.280 0.093 1.070 -0.254 -1.575 0.000 0.740 -1.108 1.684 0.000 0.939 0.358 0.158 1.274 1.243 -0.716 -0.168 0.000 0.944 0.821 0.988 0.960 0.821 0.848 0.895 +0 0.953 -0.957 0.828 1.468 1.565 0.998 -0.700 -0.762 2.173 0.474 -0.139 0.643 0.000 0.426 -0.026 -0.852 0.000 0.810 0.079 0.122 3.102 0.673 0.808 1.011 0.822 0.785 0.877 0.722 +0 1.127 1.349 -0.949 1.410 -0.310 0.641 -0.372 1.146 1.087 0.442 2.154 0.874 0.000 1.092 0.622 1.157 0.000 1.001 0.087 -0.881 3.102 0.939 0.999 0.985 1.408 0.845 0.935 0.925 +1 0.571 -0.457 -1.127 1.556 -0.934 0.586 0.263 1.002 0.000 0.785 0.834 -1.295 0.000 0.699 -0.105 0.709 2.548 1.227 1.004 1.116 3.102 0.860 0.821 0.992 0.887 0.562 0.747 0.742 +1 1.883 -0.760 1.158 1.173 1.232 0.765 -1.050 -0.035 2.173 1.043 -0.192 -0.977 2.215 0.648 -0.252 1.740 0.000 0.871 -0.488 -0.458 0.000 0.771 0.810 0.991 1.391 1.147 1.126 0.902 +1 0.609 -0.069 -1.094 0.776 0.386 1.228 -0.194 0.991 0.000 1.191 -0.244 -0.561 1.107 1.030 1.151 -1.008 0.000 1.297 -0.224 0.443 0.000 0.919 0.697 0.990 0.772 0.577 0.856 0.752 +0 0.805 1.150 1.305 1.911 0.250 0.471 -1.387 1.229 0.000 0.545 -1.035 -0.475 2.215 0.600 -0.439 -1.436 2.548 1.271 1.168 -0.627 0.000 0.907 0.885 1.399 1.137 0.497 1.029 1.465 +0 0.640 0.104 -1.140 1.011 1.345 0.592 0.328 -0.640 2.173 0.910 -1.596 0.378 2.215 0.384 0.133 0.787 0.000 0.865 -0.529 -0.620 0.000 0.658 0.784 0.988 0.845 1.514 0.929 0.731 +1 0.462 0.106 -0.497 0.753 -0.976 0.819 1.280 0.529 0.000 1.223 1.442 -1.138 2.215 0.528 1.678 0.819 0.000 1.265 0.419 1.570 3.102 0.449 0.833 0.985 0.659 0.924 0.855 0.766 +1 1.193 -0.881 -0.713 0.668 1.275 2.249 1.337 0.157 0.000 1.605 -0.887 1.577 1.107 0.660 -0.415 -1.709 2.548 2.670 -1.459 1.743 0.000 0.672 1.197 1.207 0.679 0.319 0.738 0.692 +0 1.116 1.277 0.465 0.612 -1.667 0.891 0.205 1.204 2.173 0.766 1.342 -0.306 2.215 0.514 -0.063 -1.553 0.000 1.328 0.772 -0.698 0.000 0.792 1.010 1.076 0.751 1.409 0.835 0.764 +0 0.566 -0.800 -0.360 1.095 1.021 0.571 0.507 -1.188 2.173 0.518 -1.229 -1.066 0.000 0.839 0.201 0.979 1.274 0.405 -1.411 0.356 0.000 0.586 0.874 1.033 0.677 0.809 0.708 0.661 +0 0.654 -0.280 -1.240 1.077 -0.091 0.792 0.294 0.708 2.173 0.412 -0.304 1.168 0.000 0.541 0.954 1.561 2.548 0.793 1.879 -1.109 0.000 1.407 0.813 1.000 0.903 0.647 0.710 0.766 +0 0.780 -0.675 1.168 0.783 -1.458 1.448 -0.427 0.471 1.087 0.770 0.130 -1.340 0.000 0.678 -1.095 -1.018 0.000 1.022 -1.272 -0.559 0.000 0.858 0.514 0.987 1.255 0.714 0.869 0.837 +1 0.651 -0.058 -0.599 1.620 0.657 1.273 -1.425 -0.619 0.000 1.827 -0.251 0.944 2.215 3.026 2.116 -1.487 0.000 1.516 -0.575 -0.180 0.000 0.886 1.854 1.287 0.963 0.858 1.446 1.203 +1 0.998 0.385 -0.033 1.496 1.038 0.935 1.078 1.641 2.173 0.683 0.925 0.017 2.215 1.047 0.565 -0.977 0.000 0.876 -2.352 -0.508 0.000 0.497 0.773 1.392 0.846 1.172 0.883 0.805 +0 1.818 1.089 1.715 1.050 -0.959 2.660 0.985 0.185 2.173 2.121 0.205 -1.620 1.107 0.620 0.966 0.474 0.000 0.857 1.063 -0.285 0.000 0.516 1.318 1.280 2.142 3.762 1.971 1.439 +1 1.116 0.725 0.711 1.180 1.478 1.636 -2.434 0.743 0.000 1.676 2.760 -1.144 0.000 0.990 0.595 -1.250 0.000 2.721 0.667 -0.725 3.102 0.700 0.636 1.014 0.843 0.451 0.848 0.696 +1 0.488 0.183 -1.314 1.377 -0.839 1.020 0.497 1.353 2.173 1.009 -0.135 -0.349 0.000 0.720 0.280 0.475 2.548 0.483 0.630 -1.636 0.000 0.934 1.218 0.990 0.864 0.767 0.870 0.835 +0 0.963 0.638 1.729 0.933 0.178 1.512 -0.013 -0.216 2.173 2.022 0.660 1.005 2.215 0.805 0.215 -0.809 0.000 1.309 -0.408 1.688 0.000 0.976 1.275 1.293 1.019 2.466 1.348 1.111 +1 1.042 -0.348 -0.758 1.613 -1.110 1.710 -0.231 -0.539 2.173 1.172 -0.332 1.671 1.107 0.596 -2.224 0.319 0.000 1.442 2.169 0.928 0.000 0.930 1.995 0.999 1.087 1.904 2.008 1.918 +0 0.408 -2.071 1.086 1.695 0.551 0.520 -2.288 -1.098 0.000 0.653 -0.978 -1.530 2.215 0.623 -1.358 -0.614 2.548 0.658 -2.475 -0.044 0.000 0.787 0.879 0.979 0.762 0.525 0.660 0.747 +0 1.003 0.687 -1.158 0.648 0.335 1.169 0.570 0.912 1.087 1.743 -0.246 -0.998 2.215 0.345 2.216 0.807 0.000 0.553 -0.369 0.554 0.000 0.965 0.847 1.088 1.015 2.261 1.201 0.983 +0 0.950 -0.796 0.636 0.391 -1.445 0.262 1.049 -1.691 0.000 0.740 -0.349 -0.902 2.215 1.093 1.759 0.988 0.000 1.162 1.066 -0.425 3.102 0.771 0.813 0.988 0.743 0.841 0.857 0.862 +1 1.089 -0.397 -1.419 0.292 0.574 1.074 -0.134 1.248 0.000 0.926 -0.930 -1.088 1.107 2.335 0.291 0.078 0.000 1.343 -1.071 -0.056 0.000 1.760 1.430 0.983 0.746 0.563 1.071 0.910 +1 0.302 1.168 0.252 0.226 0.045 0.722 -0.160 -0.810 2.173 0.751 -2.118 1.693 0.000 0.544 -1.115 0.888 0.000 1.003 0.038 0.725 3.102 0.812 0.986 0.984 0.745 0.889 0.915 0.789 +1 0.652 0.543 -1.645 0.683 0.227 0.712 -0.044 1.111 2.173 0.948 1.066 -0.356 0.000 0.927 1.266 -1.276 0.000 0.785 0.993 0.411 0.000 0.944 1.058 0.986 0.568 0.737 0.724 0.642 +1 1.559 0.828 -1.298 1.116 -0.771 0.923 0.179 0.519 2.173 0.798 -0.285 -0.051 2.215 2.043 0.635 1.316 0.000 0.597 0.107 0.879 0.000 0.713 0.746 0.979 1.015 0.690 0.974 0.801 +0 0.451 1.024 -1.245 0.855 0.187 0.461 0.596 0.217 2.173 0.689 0.160 -1.070 0.000 1.022 0.988 1.031 0.000 0.826 -1.129 -1.409 1.551 1.006 1.009 0.991 0.723 0.989 0.981 0.863 +0 1.579 0.002 -1.705 0.922 1.466 0.838 -0.732 0.980 2.173 1.163 -0.666 -0.354 0.000 1.051 -1.594 -0.423 0.000 1.539 -0.131 0.212 3.102 0.952 0.972 0.987 1.113 0.845 0.928 1.150 +1 0.809 -0.003 0.685 0.716 -1.615 0.763 -0.973 -1.510 0.000 1.279 0.281 0.131 2.215 0.856 -0.457 -0.792 2.548 0.680 -0.836 1.116 0.000 0.770 0.727 0.988 0.915 0.938 0.905 0.777 +0 0.595 1.803 0.639 0.851 -0.324 0.872 -0.593 0.349 2.173 0.891 -0.767 -1.686 0.000 1.360 1.389 -1.475 2.548 0.639 -0.289 -0.446 0.000 0.910 1.059 0.979 0.889 2.196 1.241 1.098 +0 0.775 0.764 -1.566 0.592 1.375 1.671 -0.304 -0.108 2.173 1.266 -0.480 1.463 2.215 0.785 -1.017 -0.154 0.000 0.886 0.808 0.761 0.000 0.873 0.962 0.991 1.371 2.123 1.172 0.959 +1 0.544 -1.350 -0.754 0.636 0.688 1.030 -1.729 0.976 0.000 1.353 -0.723 -0.407 2.215 1.588 0.296 -1.119 0.000 0.981 -0.656 0.865 3.102 0.769 0.606 0.988 1.053 0.948 0.939 0.837 +0 1.037 0.854 0.339 0.543 0.675 1.020 0.228 1.326 2.173 0.529 -0.171 -0.546 0.000 0.470 0.172 0.575 0.000 0.968 -1.496 -1.171 0.000 0.977 1.221 0.989 1.214 1.363 1.034 1.150 +1 0.669 -0.383 -1.047 0.892 0.760 1.403 -1.426 -1.164 0.000 1.645 -1.142 0.322 2.215 0.390 -0.969 -0.689 0.000 0.408 0.074 0.606 0.000 0.912 0.690 1.069 0.972 0.307 0.755 0.685 +0 0.552 1.123 0.951 1.039 -1.464 0.667 0.616 0.033 2.173 0.576 0.449 1.545 0.000 1.125 1.337 0.336 0.000 1.127 -0.101 -1.002 3.102 1.272 0.967 0.990 0.966 0.816 0.836 0.797 +1 0.933 -0.060 -1.544 0.469 -0.324 1.087 -1.162 0.156 2.173 1.023 -1.258 -1.330 0.000 0.733 -0.558 1.640 0.000 1.179 -0.261 0.827 3.102 0.754 0.896 0.985 1.039 0.863 0.916 0.800 +1 0.648 0.439 0.041 1.678 0.075 0.943 0.638 -0.162 0.000 0.774 0.645 1.562 2.215 1.878 -2.326 1.112 0.000 0.857 -0.085 -1.281 0.000 0.588 0.798 1.007 1.556 0.979 1.139 0.996 +0 1.038 -1.641 0.223 0.790 -0.829 1.051 1.667 1.001 0.000 0.937 0.075 -0.693 0.000 0.521 1.437 0.175 0.000 0.743 -0.313 -1.107 3.102 0.906 1.031 1.018 0.715 0.328 0.826 1.448 +1 1.106 0.303 -0.341 1.223 -0.646 0.469 -0.722 1.392 0.000 1.034 -0.496 0.599 1.107 1.321 0.236 1.545 2.548 0.371 -0.723 -0.797 0.000 0.588 0.677 0.996 1.128 1.056 0.942 0.773 +1 0.299 0.479 1.727 1.487 1.049 1.138 -0.706 -0.896 0.000 0.886 -1.420 -1.493 0.000 1.033 0.265 0.616 2.548 1.431 -1.125 0.675 0.000 1.363 1.149 0.981 0.942 0.733 1.075 1.601 +0 0.907 0.334 -0.775 0.580 -1.343 0.339 0.326 0.912 0.000 0.690 2.388 0.191 0.000 0.878 0.636 1.686 2.548 0.453 1.925 -0.183 0.000 0.855 0.695 0.986 0.562 0.219 0.503 0.591 +1 0.558 0.003 1.133 1.205 0.211 0.695 -1.330 0.741 1.087 1.294 -0.261 -1.009 2.215 0.388 -0.428 -0.165 0.000 1.152 0.337 1.688 0.000 0.881 1.172 0.990 1.224 1.602 1.251 1.090 +1 0.696 0.696 0.731 0.731 -1.701 1.863 0.404 -0.606 2.173 2.216 0.396 0.907 0.000 0.865 1.314 1.335 0.000 0.806 1.042 -0.025 0.000 0.871 0.753 0.992 1.298 0.788 0.895 0.843 +1 0.440 -0.663 0.928 1.982 -0.061 0.484 0.416 -1.585 2.173 1.092 1.215 1.643 1.107 0.512 1.192 0.099 0.000 0.416 0.329 1.392 0.000 0.526 0.644 1.006 1.067 0.519 1.164 0.895 +1 1.078 0.892 0.670 1.059 -0.855 0.948 1.322 -1.627 2.173 1.076 1.386 0.246 0.000 0.837 0.249 0.669 0.000 1.508 0.025 -1.268 1.551 0.836 0.914 1.451 1.094 0.974 0.858 0.788 +1 1.242 0.567 -0.166 1.531 1.725 0.626 1.722 -0.065 0.000 0.978 0.245 1.551 2.215 0.365 0.165 0.746 0.000 0.683 -0.294 -0.744 3.102 0.936 0.855 1.893 1.110 0.687 0.782 0.822 +1 0.538 1.149 1.727 0.914 1.430 0.532 -0.276 0.474 0.000 0.610 -0.806 -0.154 0.000 1.149 0.860 -0.788 2.548 0.721 1.107 -0.643 3.102 0.732 0.885 0.984 0.915 0.162 0.736 0.758 +1 1.693 0.392 1.691 1.251 1.241 1.189 0.564 -0.424 2.173 0.647 0.382 0.145 0.000 0.443 -2.715 -1.276 0.000 0.615 0.647 0.578 3.102 2.285 1.470 1.001 1.530 0.717 1.326 1.373 +0 0.917 -0.250 0.669 0.803 1.350 1.759 -2.767 -0.384 0.000 0.872 -0.621 1.370 0.000 1.085 -0.883 -1.464 1.274 1.234 -0.208 -1.544 3.102 1.104 0.949 0.992 0.783 0.331 0.660 0.634 +1 1.305 1.039 1.573 1.512 -1.052 0.812 0.768 0.675 2.173 0.775 1.466 -0.012 2.215 0.637 0.386 -0.719 0.000 0.454 1.074 0.389 0.000 0.564 0.690 1.365 1.095 0.807 0.948 0.746 +1 0.911 0.276 -0.320 1.795 -0.442 0.603 0.832 1.302 0.000 0.613 -0.271 0.813 2.215 0.752 -0.004 1.535 2.548 0.517 1.076 -1.603 0.000 0.783 0.635 0.976 0.954 0.448 0.750 0.757 +1 0.686 -0.500 0.939 1.350 -1.333 0.745 -0.845 -0.527 2.173 0.846 1.423 -1.663 0.000 0.998 -1.130 0.519 2.548 0.946 1.231 0.935 0.000 0.941 1.519 1.185 0.933 0.894 1.173 1.002 +0 1.154 -0.132 -1.172 1.205 -0.379 0.479 -1.096 -0.309 2.173 1.043 -0.651 0.596 1.107 0.975 -0.205 1.443 0.000 0.816 -1.059 1.388 0.000 1.048 0.959 1.072 1.092 0.791 0.781 0.764 +1 0.895 -1.061 0.742 0.840 -1.061 1.095 0.918 -0.389 2.173 0.839 0.630 -1.238 0.000 0.934 0.439 -1.651 0.000 0.673 0.306 0.526 1.551 0.715 1.312 1.200 0.758 0.715 1.035 0.956 +1 1.776 1.181 -1.527 0.775 -1.263 0.720 -0.094 -0.055 2.173 0.604 0.422 0.387 0.000 0.604 1.440 0.657 2.548 0.448 1.799 1.255 0.000 0.815 0.870 0.984 0.858 0.915 0.924 0.795 +1 0.999 -2.310 0.531 0.385 -0.514 0.448 1.585 -1.032 0.000 0.688 -0.803 -1.696 0.000 0.773 -0.943 0.955 0.000 0.621 -2.074 1.410 0.000 0.843 0.813 0.979 0.653 0.223 0.535 0.582 +1 0.879 1.530 -0.715 0.825 0.221 0.556 1.070 1.175 2.173 0.480 1.791 0.360 0.000 1.072 0.322 -1.236 2.548 0.379 -1.707 -1.581 0.000 1.984 1.301 0.986 0.890 0.868 0.910 0.978 +1 2.368 0.796 0.542 1.064 1.237 0.410 1.171 -1.301 0.000 0.462 -0.457 1.680 0.000 1.201 0.601 -0.696 2.548 0.616 -0.965 -1.238 3.102 0.945 0.820 1.291 1.188 0.750 0.978 0.874 +1 1.663 -0.508 0.134 0.601 0.287 0.795 -0.459 1.634 0.000 1.157 0.111 -0.902 2.215 0.776 0.324 0.912 2.548 0.449 0.664 -1.671 0.000 0.631 0.923 1.002 0.701 1.013 0.797 0.802 +0 0.413 0.203 1.624 1.527 1.027 0.677 -0.037 -0.241 2.173 1.438 0.046 -1.649 2.215 1.186 1.332 -0.158 0.000 0.626 1.445 -0.770 0.000 0.923 0.949 0.983 1.040 1.387 0.999 1.078 +1 1.084 0.762 1.222 0.897 -1.221 0.584 0.973 0.305 0.000 0.424 -0.638 -0.397 0.000 0.739 0.724 -0.945 2.548 1.455 0.211 -0.150 0.000 0.886 0.859 1.104 0.672 0.797 0.789 0.882 +1 0.952 -0.757 1.033 0.522 -0.883 0.821 -0.303 1.364 2.173 0.752 -0.788 -0.168 0.000 1.113 0.237 -0.274 0.000 0.976 -1.176 -1.303 3.102 0.799 0.935 0.987 0.686 0.842 0.872 0.742 +1 1.502 1.209 0.702 0.759 1.018 0.895 1.605 -0.734 0.000 0.429 0.898 1.597 0.000 0.429 2.044 -1.154 0.000 0.453 2.215 0.697 0.000 1.074 0.777 0.983 0.613 0.241 0.480 0.681 +0 0.510 -0.845 -0.825 0.923 1.316 0.627 0.712 0.285 2.173 0.923 -1.091 -1.590 2.215 0.531 -2.126 -0.594 0.000 0.983 -0.569 0.180 0.000 0.896 0.926 0.987 0.875 1.622 1.032 0.863 +0 0.937 0.030 -0.446 0.814 1.215 0.240 0.395 1.715 0.000 0.525 -0.233 1.012 2.215 0.735 0.064 0.077 2.548 1.730 -0.389 -0.968 0.000 0.838 0.707 1.207 0.692 0.503 0.514 0.522 +1 1.154 -0.586 -0.910 1.279 -0.295 1.508 -0.209 1.276 2.173 0.619 -0.247 0.484 0.000 0.289 2.100 -0.482 0.000 0.553 -0.039 -0.364 3.102 1.186 0.709 0.984 1.547 0.965 0.977 0.926 +1 1.046 -0.419 -0.646 0.379 -0.816 0.884 -0.646 1.732 0.000 2.073 -1.193 -0.000 0.000 1.723 -0.541 1.309 2.548 0.396 -2.305 -0.722 0.000 1.230 1.440 0.988 1.139 0.682 1.164 1.099 +0 1.434 -0.903 -0.793 0.328 0.160 0.625 -0.750 -1.657 0.000 0.810 -0.641 1.026 2.215 0.901 0.306 0.206 2.548 0.445 -0.961 -0.075 0.000 0.813 0.902 0.988 0.900 0.772 0.714 0.667 +0 1.759 1.471 -1.264 1.191 1.650 0.904 -0.670 0.183 2.173 0.427 1.040 0.708 2.215 0.819 -1.790 -0.173 0.000 0.375 -1.798 1.201 0.000 0.581 0.807 0.988 0.815 0.996 1.383 1.565 +0 1.088 -0.611 1.523 0.421 -0.635 1.282 0.556 -1.466 2.173 1.269 -0.915 -0.515 0.000 2.838 -0.261 0.369 2.548 0.996 -0.471 1.077 0.000 0.872 0.920 0.993 1.208 2.586 1.363 1.051 +0 3.865 -1.225 -0.169 0.486 0.408 2.683 -0.363 1.606 1.087 1.958 -1.265 0.264 2.215 1.240 -2.103 -1.370 0.000 0.837 -0.928 -1.044 0.000 0.784 1.563 0.989 2.964 3.554 2.220 1.832 +0 0.928 0.878 -0.710 2.000 -0.880 0.622 -0.708 1.700 2.173 1.604 -0.162 0.867 0.000 0.733 -0.312 0.269 1.274 0.526 1.809 1.632 0.000 1.113 1.023 0.976 0.885 0.823 0.850 0.960 +0 1.040 0.987 1.700 1.159 -0.835 0.590 -0.071 0.455 0.000 0.777 0.459 0.033 2.215 0.614 1.220 0.784 0.000 1.232 0.106 -1.287 3.102 0.888 0.942 1.151 0.934 0.834 0.690 0.737 +0 2.217 -0.194 0.202 0.677 -0.312 1.576 -0.361 1.544 2.173 0.499 -0.504 -0.912 2.215 0.954 0.392 -0.859 0.000 0.374 0.289 -1.345 0.000 0.281 1.065 0.999 0.800 1.048 1.146 0.916 +0 3.530 -1.640 -0.277 1.379 1.114 3.102 -0.125 1.149 1.087 1.597 -0.746 1.243 1.107 0.826 1.343 -1.556 0.000 1.646 -0.667 -0.759 0.000 1.990 1.895 2.903 3.876 1.104 2.520 2.291 +1 0.656 -0.074 -0.380 0.778 1.296 0.957 -0.518 -0.730 2.173 0.808 0.910 -1.476 0.000 1.184 0.268 1.486 0.000 0.786 1.260 0.584 0.000 0.840 0.850 0.988 0.846 0.838 0.894 0.760 +1 1.246 0.206 -1.344 0.424 -0.901 1.368 0.872 1.430 2.173 1.848 0.918 -0.372 0.000 1.497 0.392 0.981 2.548 1.154 -0.863 0.132 0.000 1.020 1.389 0.984 1.313 0.813 1.255 1.152 +0 0.607 0.109 1.623 0.180 -0.713 0.492 0.652 0.752 0.000 1.722 0.905 -0.711 2.215 0.814 0.403 -1.359 0.000 1.841 1.442 0.404 0.000 0.934 0.907 0.985 1.423 1.404 1.113 1.140 +0 0.686 -1.481 1.477 1.200 -1.048 0.755 -0.235 -1.156 2.173 0.328 0.687 0.285 0.000 1.227 -1.036 0.522 2.548 0.487 -0.995 -0.596 0.000 0.684 0.880 0.988 0.957 1.321 0.916 0.831 +1 0.899 -0.758 0.336 0.892 -1.491 0.954 -0.567 -1.630 0.000 1.337 -0.688 0.071 2.215 1.042 -0.162 -0.929 2.548 0.810 -0.912 1.466 0.000 1.103 0.837 1.237 0.928 1.039 0.878 0.787 +1 1.036 1.431 -0.434 0.735 -1.208 0.853 -0.021 0.667 2.173 0.657 0.803 -1.566 0.000 0.813 0.116 -0.345 0.000 0.803 1.398 1.111 0.000 1.077 1.105 0.986 0.465 0.651 0.733 0.685 +0 1.920 -0.262 -0.501 0.125 -1.455 1.435 1.362 0.847 0.000 0.709 -0.778 -1.399 0.000 0.803 1.271 -0.271 1.274 0.543 1.654 1.534 0.000 0.886 0.980 0.985 0.768 0.448 0.714 1.073 +1 0.935 -0.677 1.227 1.685 0.671 1.040 1.335 -0.993 1.087 0.531 1.327 1.499 0.000 1.149 0.405 -0.811 0.000 0.990 0.792 0.209 3.102 0.915 0.868 0.986 0.828 0.973 1.213 0.983 +1 0.289 -1.962 -0.349 0.403 -0.806 1.102 -0.800 0.764 2.173 0.793 -1.894 -1.180 0.000 0.611 -0.043 -1.295 2.548 0.590 -0.553 0.267 0.000 1.056 0.843 0.978 0.976 1.058 0.859 0.770 +1 0.929 -0.410 -1.322 0.358 0.787 0.907 -0.861 1.236 2.173 1.703 1.927 -0.007 0.000 0.589 -2.041 -0.842 0.000 1.586 -1.496 -1.547 0.000 0.676 1.083 0.991 0.591 0.601 0.731 0.779 +0 0.604 -0.716 -0.886 0.424 0.105 0.489 -0.521 -0.287 2.173 0.617 -0.564 0.672 1.107 1.298 -1.322 1.363 0.000 0.868 -1.460 -1.005 0.000 1.006 1.013 0.982 0.710 0.615 0.708 0.738 +0 1.520 0.177 -0.041 0.385 -1.280 0.763 -0.247 -1.729 0.000 1.466 -0.945 0.210 2.215 0.832 -0.771 -1.434 0.000 1.637 -1.318 1.574 3.102 0.576 0.761 0.986 0.943 1.387 1.006 0.971 +1 1.015 0.441 0.518 0.486 -0.352 0.638 -1.062 1.286 2.173 0.600 0.017 -0.315 0.000 1.295 -0.793 -1.104 1.274 0.554 0.676 0.687 0.000 0.665 0.964 0.987 0.947 0.951 0.808 0.722 +1 0.930 -0.507 -0.821 1.348 1.604 0.279 -0.406 -0.183 0.000 0.620 0.573 -0.425 2.215 0.952 0.460 1.508 1.274 0.958 -1.824 0.475 0.000 0.912 1.111 1.267 0.839 0.805 0.898 0.846 +0 1.520 -0.439 1.458 0.798 1.134 0.687 -0.530 -0.491 0.000 0.494 0.426 0.417 2.215 0.316 1.026 -0.442 2.548 0.606 0.683 0.074 0.000 0.866 0.694 0.986 0.731 0.329 0.565 0.661 +1 0.802 -0.480 0.089 0.713 1.225 2.125 0.779 0.466 0.000 2.200 0.875 -1.341 2.215 1.891 0.187 -1.631 2.548 1.643 1.121 -0.408 0.000 0.889 1.109 0.991 0.931 0.951 0.994 0.924 +0 0.547 -1.312 -0.111 1.370 1.409 1.299 2.403 -0.697 0.000 1.469 0.111 -1.584 2.215 2.116 0.956 0.545 0.000 0.945 0.363 0.680 3.102 3.481 2.120 1.175 1.273 0.964 1.864 2.242 +0 0.485 0.296 1.047 0.483 0.932 0.500 -0.995 -0.115 2.173 0.750 0.038 -1.036 2.215 0.655 1.879 -1.513 0.000 0.578 0.760 -1.625 0.000 0.697 0.729 0.987 0.874 0.827 0.685 0.607 +0 0.806 -0.819 -0.676 0.840 -1.487 1.184 -0.611 -0.043 2.173 1.369 -0.494 1.612 2.215 1.187 -0.002 0.484 0.000 0.483 -0.102 1.476 0.000 0.653 0.913 0.989 1.132 1.870 1.092 0.940 +0 0.819 0.204 0.528 0.681 0.399 0.364 -0.645 -0.344 1.087 1.127 -0.122 -0.919 2.215 0.855 -0.996 1.038 0.000 1.343 -0.409 -1.585 0.000 0.904 1.033 0.990 0.902 0.530 0.859 0.903 +1 0.808 0.326 -1.705 0.460 0.211 0.703 -0.672 0.739 2.173 0.907 0.208 -0.624 0.000 1.145 -0.488 -1.145 0.000 0.995 -0.784 0.255 0.000 0.927 0.718 0.989 0.740 0.531 0.760 0.669 +1 0.794 -1.488 -0.678 0.386 -0.735 0.667 0.554 1.703 2.173 0.698 0.322 0.659 0.000 0.555 -0.807 0.380 0.000 0.616 -0.439 1.695 3.102 0.662 0.928 0.979 0.584 0.387 0.652 0.665 +1 0.299 1.251 -1.641 0.713 -0.331 0.875 -0.458 -1.640 0.000 1.323 -0.835 0.371 0.000 0.711 -1.060 1.310 2.548 1.053 -0.014 -0.401 3.102 2.266 1.302 0.991 0.546 0.773 0.889 0.779 +1 0.770 0.238 -1.724 0.857 -0.297 1.253 -0.041 0.474 1.087 1.185 1.875 1.483 0.000 1.244 -0.300 -0.849 0.000 1.078 0.390 -0.298 1.551 0.632 0.565 1.080 1.016 0.850 0.877 0.765 +1 0.729 -0.410 -0.092 0.613 -1.276 1.212 0.098 1.722 2.173 0.845 0.853 0.273 0.000 0.736 0.321 -0.329 0.000 0.539 -0.419 0.514 3.102 0.703 0.553 0.993 0.951 0.803 0.840 0.733 +1 1.380 1.749 1.237 0.261 -0.646 1.317 0.820 0.146 2.173 1.013 2.789 -1.036 0.000 0.890 1.811 -1.211 0.000 0.919 0.822 1.088 1.551 0.702 1.069 0.992 1.161 0.878 1.293 1.062 +0 1.212 -0.170 -0.364 0.344 -0.602 0.649 -1.609 0.900 0.000 1.054 -0.528 -0.579 2.215 1.369 -0.245 -1.438 0.000 2.053 0.221 0.656 3.102 1.092 1.030 0.986 1.039 1.313 0.977 0.859 +0 2.060 0.441 0.342 0.723 -0.460 1.045 0.925 -1.156 0.000 1.269 -0.149 0.971 2.215 0.617 0.134 -1.540 0.000 0.745 -0.585 -0.977 3.102 0.761 0.753 1.117 1.061 0.897 0.920 0.955 +1 0.897 2.121 0.592 1.129 1.328 1.486 0.322 -1.045 0.000 1.498 1.112 0.683 1.107 0.730 -1.578 -1.656 0.000 0.646 -1.104 -0.347 3.102 1.277 1.218 0.984 0.790 1.568 1.331 1.300 +1 0.911 1.825 1.033 0.276 0.217 0.779 0.936 -0.498 0.000 0.897 0.972 -1.069 0.000 1.146 1.040 1.347 2.548 1.413 0.870 0.427 1.551 0.875 1.004 0.999 0.625 0.719 0.818 0.741 +0 0.568 0.545 -1.368 3.152 -0.728 1.429 0.490 0.741 2.173 0.530 0.157 0.252 0.000 1.888 1.186 1.644 0.000 1.628 1.345 -0.333 3.102 0.962 1.251 1.012 1.860 1.635 1.343 1.246 +0 0.495 -0.522 0.831 0.778 -1.338 0.416 -1.958 -0.059 0.000 0.588 0.936 -1.432 1.107 0.561 0.428 -0.195 1.274 0.465 1.257 0.828 0.000 1.922 1.177 0.986 1.067 0.568 0.925 0.873 +1 2.287 -0.206 -0.855 1.496 0.296 1.100 0.074 0.650 0.000 0.942 0.181 -1.407 0.000 0.991 -0.509 1.578 2.548 0.374 -0.678 -1.486 0.000 1.023 0.933 2.208 1.345 1.049 0.985 0.934 +1 0.487 1.476 -0.178 0.848 -1.075 0.872 0.103 0.873 0.000 1.173 0.796 -0.840 2.215 1.040 0.547 1.395 0.000 1.247 0.393 0.039 3.102 0.886 0.907 0.981 0.624 0.800 0.918 0.798 +1 1.847 -0.658 -0.506 0.289 -1.092 1.252 -0.533 1.050 2.173 0.590 -1.709 1.563 0.000 0.356 -1.737 -1.208 0.000 0.644 -1.067 -1.351 3.102 0.743 0.921 0.989 0.586 0.867 0.917 0.763 +1 0.916 -1.572 1.325 0.251 -0.371 0.624 -1.203 -0.810 0.000 1.074 -0.130 1.187 2.215 1.870 -0.493 0.160 2.548 0.854 -1.943 -1.216 0.000 0.768 1.293 0.992 0.778 1.242 1.104 0.905 +1 0.632 1.011 -1.116 1.253 1.276 0.877 0.580 0.117 2.173 1.001 0.446 -0.349 0.000 1.070 0.747 -1.429 2.548 1.088 -0.787 1.509 0.000 1.703 1.239 1.028 1.034 1.196 0.965 0.906 +1 0.496 -0.645 -0.882 0.955 1.070 0.602 0.041 -1.731 0.000 1.136 -0.089 -0.037 2.215 0.682 1.005 0.572 2.548 0.638 0.757 -1.152 0.000 0.916 1.059 0.986 0.949 0.768 0.880 0.814 +0 1.619 0.557 -1.233 1.899 -0.925 2.482 0.130 0.869 1.087 1.486 0.724 -0.510 2.215 0.758 0.402 -0.067 0.000 0.550 1.708 -1.484 0.000 0.933 0.821 0.976 2.565 2.818 1.887 1.428 +1 0.610 -1.762 -0.635 0.832 0.164 0.988 -0.941 1.129 0.000 0.933 -0.445 -0.569 2.215 0.874 0.405 -1.210 0.000 0.700 2.340 -0.587 0.000 1.158 0.946 0.996 0.661 0.712 0.628 0.639 +0 0.951 1.868 -0.565 1.429 -1.374 1.180 0.888 0.859 2.173 0.590 -0.433 0.306 2.215 0.705 0.993 -1.305 0.000 0.960 1.256 0.216 0.000 0.910 1.003 1.075 1.473 1.065 1.261 0.995 +0 1.031 0.730 0.885 1.183 -0.781 0.457 1.358 -0.144 2.173 0.907 0.032 -1.634 2.215 0.618 0.518 -0.807 0.000 0.673 1.537 1.003 0.000 0.860 0.852 1.527 0.875 1.147 0.810 0.694 +1 0.801 -0.320 -0.450 0.124 -1.296 0.862 0.546 0.511 1.087 1.553 0.375 1.646 0.000 1.094 0.281 0.029 0.000 0.956 1.089 1.405 3.102 0.861 0.879 0.993 0.728 0.785 0.690 0.643 +1 0.748 1.009 -0.466 1.164 1.214 0.684 1.051 1.639 2.173 0.649 0.309 0.300 0.000 0.696 -0.611 -0.612 1.274 0.600 -2.133 0.054 0.000 1.630 1.009 1.290 0.807 1.141 1.121 1.083 +1 1.193 -1.403 -0.085 0.816 -1.139 0.949 -0.738 0.008 2.173 1.086 -1.325 -1.530 0.000 1.686 -2.134 0.954 0.000 0.816 -0.341 -0.807 3.102 1.944 1.422 1.111 0.870 0.643 1.204 1.008 +1 1.839 -1.074 1.122 0.806 1.498 1.029 -1.038 -0.306 2.173 0.822 1.729 -0.471 0.000 1.135 -0.985 -1.399 0.000 1.271 -1.652 0.831 0.000 1.349 0.839 1.001 1.404 0.705 0.919 0.855 +1 1.063 1.222 0.546 1.183 1.151 1.248 -0.357 0.025 0.000 1.352 0.337 -1.118 2.215 0.570 0.225 1.504 2.548 0.389 1.878 0.147 0.000 1.827 1.229 0.981 1.259 0.657 1.032 1.005 +1 1.111 -1.797 0.036 0.529 -0.940 0.751 -1.832 0.580 0.000 0.509 -1.418 1.428 2.215 1.410 -0.030 -1.366 2.548 0.694 -0.463 1.197 0.000 0.961 0.685 0.986 1.125 0.874 0.877 0.822 +1 0.436 1.636 1.156 0.631 -0.584 0.744 0.446 1.137 1.087 0.955 0.376 -1.404 1.107 0.816 0.640 -0.252 0.000 0.622 0.678 0.130 0.000 0.558 0.922 0.981 0.762 0.933 0.806 0.689 +1 1.699 0.749 0.180 0.391 1.545 0.379 -1.137 -0.713 2.173 1.090 0.368 -1.426 2.215 0.672 -0.754 0.404 0.000 0.488 -0.996 1.387 0.000 0.503 0.933 1.064 1.051 0.976 0.902 0.789 +1 0.712 1.437 -0.315 0.527 -1.501 0.490 -1.566 -0.437 1.087 0.450 -0.086 0.472 0.000 0.632 -0.536 1.711 2.548 0.972 0.194 1.352 0.000 0.630 0.962 0.992 1.195 0.740 1.485 1.142 +1 1.067 0.978 0.296 0.821 1.644 1.242 0.979 1.709 2.173 0.908 0.877 -0.734 0.000 0.726 1.093 0.830 2.548 1.254 -0.110 -0.301 0.000 0.919 0.947 1.216 1.010 0.853 0.951 0.848 +1 2.015 -0.442 -0.686 0.868 0.486 0.329 1.213 -1.220 0.000 1.093 0.182 1.307 2.215 0.429 -0.187 0.882 0.000 0.395 0.669 -0.501 0.000 0.828 0.740 1.596 0.855 0.595 0.861 0.756 +1 0.369 -1.946 -1.683 0.662 0.100 0.696 -1.520 -0.623 0.000 1.639 -0.705 1.190 0.000 1.087 -2.389 -0.667 0.000 1.033 -0.630 -1.279 3.102 0.897 0.920 0.980 0.587 0.720 0.815 0.735 +1 1.133 0.679 -1.641 1.875 -1.571 1.967 1.177 0.287 0.000 0.867 2.400 1.151 0.000 1.338 -0.202 -1.070 2.548 1.564 -1.309 -0.843 3.102 1.791 1.956 1.001 1.346 0.836 1.803 1.601 +1 1.279 -0.508 -1.397 0.827 -0.518 0.955 -0.014 -0.854 1.087 0.733 -1.671 0.266 0.000 0.906 -0.311 1.592 2.548 0.952 1.784 0.625 0.000 0.826 0.916 1.015 0.691 0.953 0.943 0.818 +0 0.593 1.520 0.195 0.986 -1.719 0.901 0.099 0.068 2.173 0.869 1.364 -0.191 0.000 1.478 -0.479 1.319 2.548 1.914 -0.832 -1.567 0.000 0.769 1.118 1.047 1.300 1.376 1.167 1.046 +1 0.806 0.343 0.691 1.028 -1.710 0.876 -0.303 0.237 2.173 0.912 1.447 -0.795 0.000 0.597 -0.293 -1.563 1.274 0.397 0.279 -1.466 0.000 0.653 0.749 1.047 0.979 0.900 0.866 0.789 +1 0.645 -1.552 0.848 0.814 -0.964 1.174 -1.072 -1.720 2.173 1.398 -0.661 -1.308 2.215 2.026 -0.015 0.443 0.000 2.718 0.270 -0.077 0.000 0.935 1.633 1.002 0.872 0.784 1.289 1.105 +1 0.862 -0.460 0.781 1.114 -0.301 0.960 -0.170 -1.666 0.000 1.435 -1.251 0.024 2.215 0.969 -1.776 1.203 0.000 0.823 -0.013 -0.717 3.102 1.978 1.305 1.123 0.867 0.902 1.168 0.998 +1 0.799 -0.496 1.631 0.505 1.055 2.129 -0.470 0.093 0.000 1.757 -0.395 -1.518 0.000 1.672 0.337 -1.476 2.548 1.597 0.961 0.797 0.000 0.937 0.959 0.980 0.732 0.636 1.127 1.001 +1 1.883 0.739 -1.420 1.654 0.888 0.328 0.103 -1.184 0.000 0.646 0.844 -0.333 0.000 1.210 -0.167 -0.383 1.274 1.231 -0.634 0.686 3.102 0.779 0.938 2.134 1.481 0.813 1.119 0.928 +0 1.042 -1.796 -1.740 0.744 -0.539 1.752 1.248 0.027 0.000 2.031 -1.125 1.676 2.215 0.516 1.901 -0.225 0.000 0.620 0.533 1.352 3.102 0.824 0.944 1.077 1.012 1.079 2.281 2.187 +1 1.763 1.357 -1.215 0.237 -0.008 0.816 1.278 0.268 2.173 0.394 0.314 0.241 0.000 0.540 2.243 1.471 0.000 0.847 0.376 1.437 1.551 1.109 0.881 0.989 0.683 0.854 0.753 0.699 +0 1.048 0.167 1.301 0.879 -0.834 0.753 0.035 -0.849 0.000 0.995 -0.663 -0.531 0.000 2.804 -1.293 1.050 0.000 1.763 -0.006 0.176 3.102 0.852 0.989 1.248 0.719 1.032 0.749 0.749 +0 0.772 -1.315 0.988 0.614 -0.389 1.371 -1.362 -0.232 2.173 0.884 -2.188 1.679 0.000 1.084 -2.520 -1.257 0.000 1.347 -0.988 1.448 3.102 0.815 0.912 0.983 0.891 1.442 1.169 0.942 +1 0.830 1.685 -0.317 1.009 0.743 0.922 -0.428 1.680 0.000 1.237 0.735 0.026 2.215 1.311 1.807 1.113 0.000 1.210 1.379 -1.370 0.000 0.807 0.943 1.035 0.855 0.754 0.954 1.042 +0 0.438 -0.581 -1.598 1.106 0.362 0.900 -0.164 -1.394 2.173 0.636 0.517 0.367 0.000 0.803 0.589 -1.005 2.548 0.736 0.008 1.362 0.000 0.735 0.980 0.989 0.747 0.572 0.683 0.647 +1 1.208 -1.938 0.301 0.617 1.694 0.651 0.087 0.301 1.087 0.737 -1.155 -1.454 0.000 0.841 -0.535 -1.140 2.548 0.465 -1.413 1.263 0.000 0.525 1.045 1.137 0.941 0.943 0.948 0.797 +0 1.009 0.489 -0.344 0.186 1.110 1.015 0.395 -1.268 1.087 0.755 0.124 0.525 1.107 0.407 -0.249 -0.562 0.000 0.779 -0.857 1.550 0.000 0.635 0.807 0.988 0.750 1.298 0.814 0.732 +0 0.332 -0.778 -0.149 1.561 1.368 0.991 -0.288 0.515 2.173 0.895 -0.173 -0.683 1.107 0.672 -0.894 -0.348 0.000 1.190 -2.350 -1.613 0.000 0.744 0.926 0.988 0.926 1.224 0.836 0.741 +1 0.363 1.356 1.085 1.256 0.275 1.007 0.365 -1.242 0.000 1.293 0.655 0.687 2.215 0.992 -0.142 -0.724 0.000 0.774 1.065 -1.575 3.102 0.941 0.766 0.986 0.672 0.853 0.945 0.859 +0 1.176 0.484 -1.026 1.331 -1.547 0.532 0.422 0.827 2.173 1.039 -1.007 1.358 1.107 1.281 -0.297 -0.239 0.000 1.843 0.669 -0.376 0.000 0.990 0.872 0.996 1.120 1.008 0.909 0.888 +1 1.259 -1.345 1.469 0.534 -0.900 1.459 -2.898 0.759 0.000 2.024 -1.099 -0.817 1.107 0.855 -0.624 1.630 0.000 2.036 -0.473 -0.282 3.102 3.096 2.726 0.990 1.076 0.998 2.072 1.571 +0 1.235 0.497 -0.416 0.199 0.552 0.635 1.995 -1.100 0.000 0.891 0.123 0.941 1.107 1.358 -1.445 0.873 0.000 0.998 0.956 -0.477 0.000 0.846 0.884 0.989 0.847 0.636 0.878 0.827 +1 0.910 1.750 -0.856 1.089 -1.368 0.842 0.083 1.142 2.173 0.472 0.358 0.473 2.215 0.292 1.583 -0.477 0.000 0.414 2.071 -0.488 0.000 0.914 0.811 0.976 1.151 0.543 1.279 1.083 +0 2.233 0.447 1.075 1.085 -0.234 2.101 -0.109 -0.154 0.000 1.976 -0.249 1.414 1.107 1.123 0.113 -0.847 0.000 2.001 0.377 -1.424 3.102 0.843 0.785 1.993 1.519 1.184 1.154 1.033 +1 0.355 -2.258 -0.727 0.916 1.169 1.029 -1.102 -0.624 2.173 0.416 -2.371 1.191 0.000 0.560 -0.656 -0.320 0.000 0.681 1.521 1.707 0.000 1.020 0.995 0.991 0.572 0.749 0.660 0.634 +0 0.312 1.518 -0.017 1.033 1.697 1.179 0.583 0.073 2.173 0.873 -0.603 -1.708 2.215 0.631 -1.421 1.234 0.000 0.601 0.526 -0.828 0.000 1.113 0.799 0.989 1.036 1.773 1.041 0.892 +0 1.134 0.540 0.211 0.764 0.174 1.170 -0.832 -1.057 2.173 1.225 -0.467 1.597 0.000 1.099 0.245 0.907 2.548 0.786 -0.703 0.014 0.000 1.286 0.988 0.983 1.345 1.611 1.048 0.962 +1 1.043 -0.499 1.052 0.746 0.285 0.667 -0.071 -1.052 2.173 0.662 2.284 -1.676 0.000 0.873 -0.518 0.084 1.274 1.043 -2.102 0.883 0.000 0.970 0.943 0.989 0.947 0.847 0.902 0.899 +1 0.409 1.029 -0.046 1.288 0.841 0.940 0.643 -1.218 0.000 1.298 0.371 0.025 2.215 0.804 1.194 -1.713 0.000 0.495 -0.927 1.021 3.102 0.855 0.979 0.987 0.797 0.820 0.941 0.859 +0 1.599 -0.018 0.014 0.778 -1.280 0.849 -0.839 1.739 2.173 1.350 0.311 -0.859 1.107 1.771 -0.740 0.658 0.000 0.551 -0.125 1.633 0.000 0.916 1.015 1.420 0.977 1.491 1.081 0.995 +0 1.974 -0.448 -0.526 1.002 -0.852 0.876 -0.634 1.166 2.173 0.415 -1.481 0.271 2.215 0.901 0.037 1.547 0.000 0.607 -0.573 0.268 0.000 0.804 0.710 0.987 1.401 0.759 0.983 0.820 +0 1.430 0.120 0.450 1.019 -1.307 0.893 -0.345 -1.481 1.087 1.210 0.138 -0.590 2.215 0.716 1.340 1.072 0.000 0.855 -0.465 1.272 0.000 1.047 1.110 1.673 1.103 1.165 0.946 0.904 +0 1.815 0.926 -0.662 0.132 -0.394 1.203 1.021 0.705 0.000 0.851 0.540 1.095 0.000 1.735 0.749 -1.074 2.548 0.630 0.138 1.638 3.102 0.900 0.753 0.979 0.686 0.579 0.938 0.915 +0 0.841 0.636 1.168 1.073 0.417 0.836 0.149 -1.182 0.000 0.829 -0.105 -1.723 2.215 0.891 1.469 0.202 0.000 0.968 0.309 -0.344 3.102 1.924 1.125 0.986 0.824 0.790 0.852 0.823 +1 1.313 -0.893 -1.258 0.920 -0.460 0.413 0.123 1.358 0.000 1.160 0.363 0.554 2.215 0.324 0.818 0.149 2.548 0.417 0.767 -0.727 0.000 0.684 0.812 1.003 0.767 0.289 0.863 0.698 +1 0.986 -0.412 -1.647 0.678 0.520 0.910 -0.711 0.528 0.000 1.273 0.507 -1.145 2.215 0.798 -0.469 -0.136 2.548 0.373 -0.080 -1.123 0.000 0.930 0.668 1.051 0.975 1.025 0.889 0.781 +1 0.382 -1.484 -0.178 0.268 -0.258 0.791 0.137 -1.506 1.087 1.132 -0.921 0.772 2.215 1.268 0.047 -0.749 0.000 1.064 0.386 0.755 0.000 1.279 1.041 0.983 0.876 1.466 0.989 0.828 +0 1.480 -1.407 -1.710 0.685 -0.874 0.381 0.035 -0.047 2.173 0.554 -1.361 0.097 2.215 0.509 0.565 1.204 0.000 0.724 -0.637 0.939 0.000 0.526 0.704 0.986 1.012 0.536 0.742 0.704 +1 1.057 -0.750 -1.238 1.709 1.552 0.723 1.124 0.143 2.173 0.590 1.808 0.371 0.000 0.488 -0.659 1.666 0.000 0.995 -0.540 -0.599 3.102 0.931 1.075 1.094 1.717 1.068 1.143 1.120 +1 0.673 -0.030 -0.051 1.008 -1.395 0.732 -0.725 0.806 0.000 0.882 0.367 -1.089 2.215 1.510 0.104 0.578 0.000 0.791 -0.843 -0.812 3.102 0.910 0.978 1.067 0.661 0.598 0.895 0.789 +0 0.509 2.377 -1.043 0.824 1.685 1.132 0.635 -0.696 2.173 1.184 0.222 0.997 2.215 1.313 -0.632 0.780 0.000 0.709 0.377 -0.125 0.000 1.008 0.850 0.997 1.015 1.738 1.069 1.045 +0 0.674 0.092 0.431 0.362 1.076 1.068 0.104 0.043 2.173 1.022 -1.504 -1.682 0.000 0.626 -0.494 0.535 2.548 1.326 -1.342 -0.858 0.000 1.027 0.969 0.986 0.922 0.551 1.123 0.919 +1 0.557 -0.410 -0.034 0.825 0.782 1.324 0.211 -0.554 2.173 0.481 -0.954 0.556 0.000 1.195 -0.279 1.252 0.000 1.071 -0.192 -1.481 1.551 0.792 0.722 0.987 1.453 0.976 1.008 0.919 +1 0.301 2.128 1.126 1.421 0.364 0.580 -0.401 -0.190 2.173 0.538 0.145 -1.080 0.000 1.145 -1.000 -1.047 2.548 0.936 -2.157 0.859 0.000 0.868 0.839 0.981 1.411 0.795 0.996 0.851 +0 1.192 -0.449 -1.693 1.264 -1.267 1.166 1.036 0.275 0.000 0.867 0.355 -0.236 0.000 1.510 0.639 1.220 1.274 0.895 0.680 -0.886 3.102 0.814 1.119 0.988 0.665 0.843 0.724 0.749 +0 0.750 1.800 -1.700 1.218 0.896 0.631 -0.063 0.765 2.173 0.960 0.526 -1.076 2.215 0.805 -0.068 0.109 0.000 0.411 0.958 -1.465 0.000 0.751 0.737 0.988 1.041 1.194 0.937 0.787 +1 0.705 -0.898 1.526 0.626 0.688 0.850 0.157 0.353 0.000 1.342 0.490 -1.238 2.215 0.843 -1.412 -0.721 0.000 0.681 0.196 1.248 3.102 0.678 0.604 0.995 0.993 0.686 0.826 0.735 +1 0.661 -2.283 -0.198 1.152 -0.090 1.541 -1.150 -1.728 2.173 0.944 -1.168 0.323 0.000 0.884 -0.314 0.595 2.548 0.903 -0.209 -1.041 0.000 1.127 0.778 0.996 1.496 1.399 1.094 0.936 +0 2.199 1.614 -0.241 1.571 -0.502 0.936 -0.010 1.277 2.173 0.913 1.460 1.511 0.000 0.798 1.263 -1.424 2.548 0.822 0.400 0.860 0.000 0.869 0.968 0.993 0.934 1.073 1.298 1.108 +1 1.191 0.989 0.518 1.176 1.150 0.413 -0.268 1.063 0.000 1.446 -0.464 -0.603 2.215 0.375 -0.248 -0.992 2.548 0.940 0.615 -1.255 0.000 0.966 1.125 0.990 0.825 0.281 1.133 0.941 +1 1.050 -1.317 -0.205 0.725 0.486 0.455 -0.260 0.756 0.000 1.160 -0.952 1.435 1.107 0.314 -0.962 0.007 0.000 0.669 1.544 -0.734 0.000 0.813 1.333 0.992 1.220 1.060 1.010 1.092 +0 0.580 -1.170 0.179 3.078 -0.363 1.778 -0.790 1.458 1.087 0.988 -1.800 1.164 0.000 0.672 -0.779 -0.365 1.274 0.875 -1.647 -0.785 0.000 1.193 0.938 0.985 2.092 1.358 1.323 1.211 +1 1.011 -1.861 -0.189 1.571 -0.818 1.393 -0.656 -1.475 2.173 1.303 1.281 0.233 2.215 1.160 -2.356 0.562 0.000 2.296 -0.989 1.227 0.000 0.947 1.736 0.989 2.881 3.037 2.301 1.989 +0 0.720 0.723 -0.954 0.482 -0.483 0.471 0.969 0.947 2.173 0.605 0.106 -1.387 2.215 0.400 -0.362 0.493 0.000 0.714 0.703 0.242 0.000 0.591 0.630 0.992 0.815 0.763 0.696 0.642 +1 0.561 -0.420 1.258 0.147 -0.322 0.740 0.494 -1.164 0.000 1.141 0.799 -0.712 0.000 0.679 0.136 0.328 1.274 0.663 -1.141 1.570 3.102 0.836 0.937 0.991 0.754 0.629 0.806 0.724 +1 1.002 0.494 0.151 1.539 0.876 1.007 1.178 -0.989 1.087 0.755 0.040 1.274 0.000 0.646 2.427 -0.058 0.000 1.261 0.179 -1.550 3.102 2.097 1.403 1.047 1.371 0.836 1.051 1.032 +1 0.883 0.562 1.699 1.298 1.200 1.130 -0.247 -0.472 2.173 0.774 -0.236 0.480 0.000 0.961 0.332 -1.219 1.274 0.435 -0.170 -1.701 0.000 0.698 0.934 0.989 0.838 0.905 1.117 0.937 +0 0.903 1.260 -1.738 0.151 1.045 0.684 -0.091 0.408 0.000 0.776 -0.095 -0.429 2.215 0.458 -1.085 0.819 0.000 0.377 -2.003 -1.166 0.000 0.686 0.829 0.991 0.842 0.623 0.636 0.686 +1 2.631 1.183 0.485 0.401 -0.144 0.981 0.545 1.675 2.173 1.456 -1.294 -1.231 0.000 0.747 0.623 -0.938 0.000 0.892 0.712 0.207 3.102 0.855 1.026 0.994 0.488 0.972 0.887 0.779 +0 0.402 -1.692 0.078 0.632 -1.720 0.785 -1.067 -0.747 0.000 0.898 0.773 0.695 2.215 1.291 0.563 1.532 2.548 0.542 2.256 0.179 0.000 0.893 0.899 0.995 0.940 0.790 0.758 0.807 +1 0.828 -0.961 -0.164 1.830 -1.374 1.110 -0.991 0.660 1.087 1.310 0.073 -1.368 2.215 0.786 -1.699 0.160 0.000 0.438 1.429 0.048 0.000 1.888 1.441 1.511 1.143 1.987 1.280 1.174 +0 1.285 -1.618 -0.050 0.488 -1.005 0.748 -1.306 -0.923 0.000 1.028 1.738 1.213 0.000 1.776 1.136 1.594 2.548 1.381 -0.005 0.220 3.102 4.556 2.569 0.988 2.740 1.386 1.788 2.032 +1 0.982 1.026 0.656 1.963 -1.379 0.553 0.280 0.942 2.173 0.898 2.721 -0.860 0.000 0.676 -0.067 -0.614 0.000 0.800 -0.612 -1.471 3.102 2.389 1.892 1.858 1.169 0.689 1.346 1.199 +1 1.091 1.280 1.624 1.175 -1.139 0.951 0.962 0.185 2.173 0.955 2.604 0.895 0.000 0.707 0.810 -1.679 0.000 1.522 0.267 -0.433 3.102 1.007 1.012 0.989 1.234 0.795 0.944 0.833 +1 0.966 0.273 0.402 1.043 1.419 1.141 1.634 1.460 2.173 1.050 -0.243 -0.726 0.000 1.049 1.112 0.145 0.000 0.630 1.141 -0.288 3.102 1.044 1.177 1.103 0.724 0.906 0.847 0.822 +0 0.448 1.679 1.504 1.756 -0.115 0.888 -0.269 -1.070 0.000 0.901 -0.260 0.855 2.215 0.306 0.844 0.935 2.548 0.443 -1.456 -1.329 0.000 0.804 1.111 1.221 0.656 0.356 0.872 1.086 +0 0.879 -0.948 0.250 0.758 1.048 1.244 0.589 -1.733 1.087 1.253 -0.218 -0.373 0.000 0.380 0.778 -0.974 0.000 0.706 -0.042 0.226 3.102 0.811 0.576 0.981 1.862 1.026 1.188 1.077 +0 1.412 -0.519 0.589 0.874 -0.316 0.785 0.400 -0.827 0.000 1.766 -0.403 1.612 2.215 0.455 0.529 -0.173 1.274 0.594 1.253 0.268 0.000 1.059 0.620 1.120 1.295 1.073 0.940 0.938 +1 1.307 0.127 0.223 0.472 1.399 1.694 -2.066 0.511 0.000 2.344 0.364 -1.028 2.215 2.016 0.062 -1.515 2.548 0.932 -0.255 1.629 0.000 0.659 0.908 0.985 1.061 1.043 1.034 0.810 +1 0.994 0.562 1.612 0.748 0.186 0.504 0.049 -0.551 2.173 1.022 -0.060 -1.419 0.000 0.479 2.088 -0.185 0.000 1.839 0.059 0.436 3.102 0.768 0.965 1.146 0.792 0.791 0.793 0.721 +0 0.398 0.529 -0.313 5.039 -0.763 1.475 0.033 0.790 0.000 1.848 -0.333 1.128 0.000 1.571 1.478 -1.056 2.548 2.096 0.345 1.138 3.102 1.190 0.909 0.982 0.811 1.555 1.276 1.516 +1 1.436 0.204 0.466 1.121 1.171 1.136 0.153 -1.441 1.087 1.030 1.459 0.235 0.000 1.047 -0.051 -0.516 0.000 0.736 -0.404 -1.238 3.102 0.994 1.067 1.043 0.797 0.361 0.845 0.823 +0 0.423 -0.607 -0.638 0.273 1.480 0.721 -0.608 0.209 2.173 0.977 -0.514 -1.727 2.215 0.690 0.439 0.899 0.000 0.966 1.936 1.378 0.000 1.024 1.432 0.988 0.909 1.217 1.192 0.957 +1 1.279 1.695 0.754 0.976 -1.491 0.699 -1.401 1.398 0.000 0.778 -0.389 -0.673 2.215 0.887 0.547 -1.413 0.000 2.165 0.802 0.136 1.551 0.527 0.912 1.393 1.513 1.162 1.125 0.906 +0 1.500 -1.729 -0.283 0.019 -0.582 0.464 -0.285 -1.665 0.000 0.895 0.072 1.251 2.215 0.572 -1.110 0.721 0.000 1.075 0.147 -0.737 3.102 0.895 0.792 0.998 1.217 0.865 0.892 0.774 +0 1.405 -0.187 1.623 1.148 0.628 0.562 1.633 -1.131 0.000 0.764 1.009 0.257 2.215 0.606 0.696 1.296 0.000 1.730 -0.361 -0.255 1.551 0.981 0.928 1.374 1.078 0.954 0.903 0.960 +0 0.960 -0.237 1.295 0.514 0.684 0.414 0.725 1.453 0.000 0.805 -1.280 -0.623 0.000 1.114 -0.283 -0.315 2.548 0.631 -0.389 0.667 1.551 0.952 0.890 0.979 0.502 0.498 0.568 0.597 +1 0.524 0.870 1.305 1.065 -0.394 2.409 -0.012 -1.642 1.087 3.340 1.512 0.178 0.000 0.686 0.325 -0.480 0.000 1.006 -0.100 0.677 0.000 0.822 0.713 1.034 1.519 0.829 0.971 0.888 +1 1.047 -0.444 -0.637 0.346 0.503 0.951 -0.460 0.555 0.000 0.835 -0.422 1.266 0.000 0.888 0.164 -0.336 2.548 2.631 0.261 -1.469 3.102 1.130 1.077 0.988 1.110 0.998 1.022 0.918 +1 0.649 -1.028 0.278 0.624 -1.366 0.968 -0.061 0.972 0.000 0.731 0.831 -0.275 2.215 0.751 -0.307 -1.193 0.000 0.589 -0.952 -1.349 0.000 1.196 0.874 0.990 1.242 0.697 0.790 0.825 +0 1.609 -0.811 -1.614 0.527 -0.440 0.894 -0.232 1.216 2.173 0.788 -0.564 -0.418 0.000 0.465 0.111 0.677 1.274 1.448 -1.666 -0.272 0.000 1.089 0.938 1.112 0.960 0.403 0.947 0.865 +0 0.633 -0.569 -1.082 0.861 -0.066 0.607 1.311 -1.466 2.173 0.436 1.565 -0.016 0.000 1.002 -0.084 1.031 1.274 0.724 -1.265 -0.342 0.000 1.079 0.804 0.991 1.530 1.062 1.078 0.923 +1 2.545 1.411 -1.622 0.611 1.254 2.032 -0.686 0.063 0.000 1.202 -0.022 -1.354 2.215 0.902 1.518 1.417 0.000 1.004 0.650 -0.342 3.102 0.856 1.004 0.984 0.973 0.884 0.985 0.828 +1 2.268 1.403 -0.518 0.437 -0.299 1.423 -1.183 1.099 0.000 0.682 1.323 -0.856 0.000 0.839 -0.139 -0.911 2.548 0.770 -0.330 0.449 3.102 0.926 0.833 0.992 0.877 0.583 0.746 0.689 +1 0.522 -1.186 0.970 1.017 -1.117 1.086 2.217 -0.099 0.000 2.026 -0.854 1.623 1.107 0.789 0.406 0.328 0.000 0.914 -0.982 0.442 0.000 0.853 0.752 0.986 0.936 0.843 0.881 0.803 +0 0.844 0.260 -0.758 0.919 1.612 0.726 2.215 -0.165 0.000 0.581 -0.146 0.499 0.000 0.656 1.367 0.538 0.000 1.443 -0.281 1.558 3.102 0.875 0.943 1.030 0.574 0.665 0.667 0.636 +0 1.722 -0.697 1.243 0.834 1.258 0.765 -0.485 0.585 2.173 1.837 0.156 -0.913 2.215 0.891 0.788 -0.280 0.000 0.482 -1.043 -0.238 0.000 0.917 0.956 0.993 0.898 1.797 1.348 1.145 +0 0.772 0.305 0.990 1.195 -0.510 1.097 -1.251 0.818 2.173 0.745 -0.892 -0.527 0.000 0.881 0.495 1.720 0.000 1.018 0.602 -1.080 0.000 0.693 0.892 1.299 1.456 1.639 1.078 0.953 +0 0.688 0.216 1.378 0.392 1.109 0.543 -0.854 -0.680 0.000 0.930 -0.549 1.728 2.215 0.844 -0.523 -0.175 2.548 0.655 -1.196 0.413 0.000 0.804 0.893 0.989 0.743 0.932 0.627 0.617 +1 1.047 -0.745 -0.368 1.504 -1.445 1.159 -0.826 1.220 2.173 1.018 -0.507 0.464 0.000 0.524 -2.457 -1.289 0.000 0.509 -1.017 -0.665 0.000 0.865 1.024 1.434 0.858 0.593 0.854 0.785 +0 0.849 0.632 0.655 0.427 -0.225 0.812 -0.749 0.041 0.000 1.112 1.177 -1.698 2.215 1.222 -1.500 -0.706 0.000 0.610 -1.398 0.818 3.102 1.132 0.754 0.994 1.087 1.596 1.430 1.162 +0 0.629 0.004 1.257 1.422 0.356 0.708 -0.664 -1.682 2.173 0.909 1.207 -1.668 0.000 1.006 0.713 -0.157 2.548 0.768 1.084 0.757 0.000 0.889 0.918 0.987 0.949 1.326 0.951 0.915 +1 0.473 -1.476 -1.269 1.489 0.857 0.538 -0.103 -1.303 0.000 0.854 0.777 -0.176 2.215 0.549 0.883 1.105 2.548 0.531 -0.308 0.855 0.000 0.767 0.890 1.094 1.096 0.668 1.092 0.891 +1 1.677 0.129 0.400 0.886 1.061 0.808 -0.882 -1.385 2.173 0.703 -0.689 0.385 0.000 0.917 1.251 -0.693 0.000 1.389 -0.369 -0.570 0.000 0.991 1.077 0.986 0.976 0.949 1.002 0.871 +1 0.403 -1.524 -0.423 0.179 0.134 0.722 -0.265 -1.027 1.087 0.855 -1.027 0.768 0.000 1.299 -0.509 1.611 2.548 1.569 -0.866 0.115 0.000 0.837 1.048 0.984 0.708 0.852 0.893 0.771 +0 0.766 1.705 1.333 1.245 0.465 1.304 0.965 -0.952 2.173 0.759 0.528 0.963 0.000 0.747 -0.319 0.129 2.548 0.500 0.209 1.525 0.000 0.406 1.184 0.991 1.213 1.343 1.194 0.983 +0 1.471 -0.052 0.010 0.417 -1.663 0.795 -0.381 0.576 0.000 1.033 0.571 -1.097 0.000 0.881 -0.509 -1.680 1.274 0.745 -0.209 1.351 3.102 0.903 0.816 1.082 0.668 0.265 0.561 0.609 +0 0.924 0.804 0.949 2.844 0.574 1.006 0.319 -1.077 0.000 0.756 -0.525 -1.423 2.215 1.035 0.808 -0.667 0.000 0.542 1.319 1.316 3.102 0.847 0.921 0.991 0.594 0.819 1.064 1.133 +0 2.039 -0.500 1.510 2.119 1.485 2.993 -0.704 1.712 2.173 3.991 -0.281 -0.192 0.000 1.751 -1.763 0.714 0.000 0.574 0.619 -1.358 0.000 2.015 1.163 0.974 1.157 1.955 2.211 1.913 +0 0.559 -1.689 -0.223 1.257 -0.142 0.458 0.998 0.525 0.000 0.379 0.721 0.878 2.215 1.139 -0.842 -1.510 0.000 0.514 1.216 -1.728 0.000 0.685 0.622 0.984 0.823 0.506 0.651 0.691 +1 1.779 -0.989 -0.983 0.410 -0.168 0.828 -0.107 1.078 2.173 0.347 -1.834 0.879 0.000 0.378 0.032 -0.703 2.548 0.626 -1.652 -0.074 0.000 0.460 0.963 0.991 0.512 0.699 0.758 0.711 +1 0.531 1.348 1.628 1.125 0.808 0.575 1.047 -0.075 0.000 1.004 0.596 -0.642 0.000 1.638 0.311 -1.616 2.548 1.627 0.952 1.172 3.102 0.868 1.144 0.985 0.835 0.895 0.936 0.832 +1 2.287 -0.327 1.010 0.560 0.648 0.492 -0.601 -0.038 0.000 0.697 -1.186 -0.752 2.215 0.822 2.214 -1.395 0.000 1.644 -0.489 -1.352 3.102 0.746 0.715 0.986 1.206 0.587 0.933 0.770 +0 0.757 -1.171 1.622 1.045 0.412 0.508 -0.373 -1.306 0.000 0.857 -1.222 0.699 2.215 0.973 0.306 -0.470 1.274 0.946 -1.543 -1.245 0.000 0.857 0.992 1.092 0.631 1.203 0.860 0.789 +0 1.840 0.130 0.400 0.929 0.338 1.292 -0.661 -1.251 2.173 0.771 -0.334 -0.309 0.000 0.740 -1.042 -1.620 0.000 1.245 -0.175 1.460 3.102 1.172 1.039 0.984 0.888 0.916 1.112 0.955 +1 0.529 -0.260 -0.615 0.935 0.768 0.845 -0.527 0.597 0.000 0.934 0.364 -1.099 0.000 0.620 -1.135 0.846 0.000 0.724 1.184 -1.373 3.102 0.556 1.163 0.990 0.499 0.325 0.696 0.625 +1 0.670 -1.182 -1.325 0.915 1.046 0.571 -0.138 1.443 2.173 0.856 -2.268 -0.118 0.000 0.819 -0.315 -0.330 2.548 0.486 -1.855 -0.628 0.000 0.954 1.052 0.986 0.624 0.856 0.972 0.838 +0 0.413 0.291 0.041 1.193 -0.366 0.434 -0.542 0.367 1.087 0.707 -0.193 1.132 0.000 0.737 -0.859 -1.639 2.548 1.140 0.025 -1.675 0.000 0.685 0.731 0.992 0.765 0.699 0.586 0.652 +1 0.801 1.721 0.108 0.865 -1.604 0.636 -0.492 1.591 2.173 0.638 0.819 -0.945 0.000 0.720 -0.154 0.643 0.000 0.770 0.664 0.238 3.102 0.879 0.680 1.153 1.368 0.865 0.905 0.837 +1 0.725 -1.768 0.455 0.344 -0.315 1.074 -1.423 -0.064 2.173 1.658 -0.936 -1.447 2.215 0.963 -2.331 1.379 0.000 0.517 -0.513 0.694 0.000 0.990 1.196 0.982 1.066 1.919 1.141 0.947 +1 0.554 0.461 -0.626 1.569 1.021 0.508 0.505 -0.987 0.000 0.594 -0.149 -0.880 0.000 0.738 -0.986 0.728 2.548 1.126 0.326 1.213 3.102 0.923 0.957 1.287 0.669 0.634 0.666 0.670 +0 1.347 0.691 -0.587 0.128 -1.020 0.494 -0.152 0.568 1.087 1.015 -0.255 -0.891 0.000 1.171 0.323 -1.648 2.548 2.220 0.885 1.045 0.000 1.224 1.012 0.979 0.797 0.896 0.796 0.710 +1 0.619 -0.955 1.499 2.304 0.646 1.729 -1.755 -0.690 0.000 1.276 0.434 1.140 2.215 0.368 0.144 -1.174 2.548 0.711 0.580 -0.391 0.000 0.183 0.823 1.150 0.833 0.643 0.875 0.788 +0 3.324 -0.673 -0.990 0.444 0.717 2.085 -0.158 1.118 0.000 0.854 2.388 0.184 0.000 0.963 -0.667 -0.377 0.000 0.930 0.078 -1.483 0.000 0.982 0.738 1.683 0.935 0.325 0.728 0.655 +1 0.506 -0.169 0.456 1.688 1.549 2.896 -1.339 1.687 0.000 3.969 -0.389 -0.054 1.107 0.818 -1.375 -0.081 0.000 1.053 -0.051 -0.597 1.551 2.775 2.042 1.067 1.908 0.923 2.424 1.903 +0 1.529 0.612 1.162 0.925 -1.310 1.713 -0.112 1.467 2.173 2.798 -1.190 -0.305 0.000 1.462 -0.076 -0.480 2.548 1.831 -1.483 -0.042 0.000 0.873 1.127 1.306 1.092 1.938 1.782 1.668 +1 0.609 0.022 -0.221 1.247 -1.435 0.511 1.060 0.783 1.087 0.284 -0.889 0.960 0.000 0.551 1.712 -0.446 0.000 0.523 -1.789 0.760 0.000 1.290 0.907 1.072 0.607 0.793 0.661 0.667 +0 0.754 -0.922 -0.779 1.369 1.696 1.165 1.732 -0.227 0.000 0.892 -0.422 1.617 2.215 1.055 -1.325 0.714 2.548 0.899 -0.145 0.297 0.000 0.998 1.901 1.113 0.902 0.930 1.754 1.595 +1 0.801 -1.643 -1.022 0.428 -0.115 0.465 -2.446 -0.140 0.000 0.826 -1.072 1.146 2.215 0.969 -1.933 1.421 0.000 0.889 -2.385 -1.282 0.000 0.864 0.947 0.992 0.813 0.298 0.757 0.724 +1 0.558 1.595 -0.901 1.174 -0.242 0.695 0.478 0.127 0.000 1.061 -0.591 1.006 2.215 0.662 0.612 -1.430 1.274 1.482 0.055 0.898 0.000 0.909 1.069 0.982 0.965 0.946 1.571 1.237 +1 1.571 0.860 -1.020 1.754 1.663 1.193 0.637 1.047 2.173 0.600 -0.436 0.128 0.000 0.953 0.845 -0.632 0.000 0.860 0.923 -0.204 3.102 0.805 0.820 1.522 0.964 1.001 0.980 0.833 +1 0.401 2.244 -1.036 0.387 -0.595 1.097 1.629 1.600 2.173 1.537 0.234 0.146 2.215 1.315 0.016 -1.606 0.000 0.947 1.132 -0.032 0.000 0.371 0.944 0.979 0.978 2.350 1.169 0.944 +0 2.150 -0.120 -0.014 0.601 -0.461 0.843 0.469 1.338 0.000 0.786 0.436 -0.754 2.215 1.344 0.833 -1.252 2.548 1.946 1.240 1.607 0.000 1.137 1.043 0.975 0.834 0.539 0.890 1.135 +0 0.921 0.714 0.347 1.167 1.464 0.900 0.951 -1.423 2.173 0.911 1.700 -0.067 0.000 0.506 2.066 1.493 0.000 0.694 0.645 0.102 3.102 1.062 1.204 1.214 0.666 0.824 0.759 0.766 +0 0.816 -0.033 1.174 0.643 1.055 1.187 -1.315 -0.455 0.000 1.308 -1.020 -1.624 2.215 1.318 -0.503 -0.418 1.274 0.671 -1.267 0.975 0.000 0.809 0.927 0.989 1.174 1.284 1.154 0.928 +0 1.738 -0.994 -1.384 2.604 -1.554 1.229 -1.332 0.369 2.173 0.791 -1.036 0.747 0.000 1.634 -1.641 0.059 0.000 1.970 -0.542 -0.926 3.102 1.207 0.827 0.975 1.002 1.623 1.374 1.288 +0 0.720 2.012 -1.469 0.695 -1.509 1.025 0.928 0.025 2.173 0.618 0.574 -0.727 2.215 1.040 0.832 1.399 0.000 1.053 1.524 1.587 0.000 1.011 1.048 0.983 0.685 0.762 0.784 0.743 +1 0.861 -0.006 -1.528 2.018 1.669 1.027 -0.340 0.471 1.087 0.341 -0.258 -1.085 0.000 0.662 0.621 -0.535 2.548 1.117 -1.014 0.134 0.000 0.819 0.845 0.977 0.835 0.978 1.058 0.958 +0 0.882 0.672 -0.704 0.539 1.024 0.396 2.133 1.030 0.000 0.648 -1.635 -0.751 0.000 0.641 0.322 -0.130 2.548 0.936 -1.002 1.455 0.000 0.909 0.751 0.986 0.705 0.443 0.512 0.570 +1 1.666 -0.272 -1.441 0.830 1.219 0.494 -1.346 0.426 2.173 0.650 -1.086 -1.006 0.000 0.799 0.184 0.125 2.548 0.689 -0.563 0.202 0.000 0.796 0.736 1.103 1.043 0.707 0.805 0.712 +0 0.630 -1.064 -0.570 0.612 1.687 0.523 -0.144 1.378 2.173 0.782 0.246 -0.064 0.000 0.782 0.693 0.677 0.000 1.022 0.964 -1.021 3.102 0.804 0.871 0.989 0.762 0.839 0.677 0.665 +0 0.517 -0.170 0.380 0.472 -1.000 1.124 -0.353 1.526 2.173 1.623 0.311 0.243 2.215 1.601 -0.342 -0.053 0.000 1.413 -1.872 -1.250 0.000 1.045 0.879 0.984 0.983 1.945 1.118 0.957 +0 0.385 0.705 1.303 1.534 0.256 1.033 0.751 1.573 2.173 1.228 0.430 -0.227 2.215 1.601 0.740 -1.213 0.000 1.317 -0.229 1.231 0.000 1.584 1.153 0.986 0.941 1.675 1.085 1.044 +1 0.794 0.715 1.444 0.500 -0.958 0.912 0.779 -0.365 1.087 1.066 0.237 0.747 2.215 1.990 -1.218 -1.018 0.000 1.854 1.217 0.921 0.000 0.817 1.023 0.988 0.949 1.286 0.911 0.803 +1 2.172 0.379 0.295 0.618 0.826 1.011 1.058 -1.160 1.087 0.997 0.915 1.040 2.215 1.002 -1.083 -0.797 0.000 1.070 0.184 -1.576 0.000 1.144 1.435 0.985 1.511 1.357 1.164 1.138 +1 1.744 -1.480 0.661 0.889 -0.443 1.301 -2.118 -1.511 0.000 1.756 1.506 0.675 0.000 1.648 -1.363 -1.076 2.548 1.030 -0.538 -0.445 0.000 1.097 0.926 1.447 0.719 0.761 0.789 0.855 +1 0.748 1.087 -1.668 0.021 0.322 0.524 1.945 0.958 0.000 0.776 0.106 -1.249 2.215 0.834 0.624 -0.197 1.274 1.300 1.421 0.122 0.000 0.878 0.840 0.690 0.490 0.738 0.848 0.714 +1 1.617 0.979 -1.741 1.253 -1.586 3.405 -1.444 0.365 2.173 1.975 1.107 -1.166 0.000 1.295 -0.045 -0.804 2.548 1.548 1.527 1.626 0.000 1.558 1.539 1.006 5.880 3.028 3.975 3.496 +0 1.477 -0.002 0.347 0.880 -1.315 1.873 0.939 -1.655 2.173 1.982 0.217 -0.102 2.215 0.516 0.721 0.250 0.000 0.672 0.521 0.894 0.000 0.576 1.008 1.575 1.154 2.985 1.591 1.149 +1 0.982 -2.058 -0.774 0.973 -1.436 0.958 -0.267 1.530 0.000 0.853 -1.245 0.380 0.000 1.048 0.263 -0.489 2.548 0.706 -1.503 -1.146 0.000 1.027 0.880 0.993 1.183 0.931 1.002 0.879 +1 1.477 0.231 1.666 0.759 -1.208 0.697 -0.795 0.604 2.173 0.620 -0.667 -0.180 0.000 0.873 -1.018 -0.486 2.548 0.614 -1.329 1.188 0.000 0.847 0.684 0.984 1.114 0.823 0.989 0.858 +1 0.690 -0.281 -1.113 0.448 -0.466 1.039 1.168 0.555 2.173 0.968 0.857 1.148 2.215 1.002 0.729 -1.465 0.000 0.865 -1.735 -0.748 0.000 0.974 1.028 0.994 0.881 0.783 0.819 0.741 +1 1.136 0.062 -0.641 0.160 0.421 1.112 0.948 1.643 0.000 0.890 -1.225 1.085 1.107 0.821 0.140 0.256 0.000 1.796 0.486 -0.225 0.000 0.633 0.678 0.986 1.235 0.737 0.834 0.783 +1 0.691 0.721 -1.738 0.830 -0.919 2.469 0.751 0.581 0.000 1.873 0.557 -1.002 0.000 1.120 0.660 -1.378 1.274 1.560 1.375 1.727 3.102 1.700 1.740 0.989 0.621 0.592 1.268 1.167 +0 0.849 -1.216 1.586 2.412 -1.307 1.565 -0.936 0.083 0.000 0.956 -0.382 1.445 2.215 0.693 2.635 -0.690 0.000 0.983 -1.383 0.564 0.000 1.055 0.932 1.011 0.947 0.484 0.911 1.086 +0 1.220 -0.315 0.096 2.332 0.648 2.569 0.952 -1.323 2.173 0.862 -0.556 1.176 2.215 0.453 1.568 -0.142 0.000 0.464 -1.440 0.170 0.000 1.384 1.116 1.117 2.930 2.512 1.994 1.579 +1 1.451 -0.627 1.115 1.625 1.591 1.349 -0.182 -0.080 1.087 0.888 0.364 -0.970 2.215 0.417 -0.787 -1.001 0.000 0.517 -0.209 -1.525 0.000 0.282 0.818 0.986 1.293 1.244 1.310 0.953 +0 0.664 1.185 -0.422 1.481 -1.207 1.031 1.249 0.369 2.173 0.544 1.675 1.094 0.000 0.516 1.502 -0.633 0.000 1.680 -0.411 1.696 3.102 0.813 0.825 0.990 1.006 1.896 1.135 0.960 +0 1.043 0.372 -0.267 0.310 0.072 0.619 -0.301 0.649 0.000 0.553 0.387 -0.545 2.215 0.678 2.457 1.286 0.000 1.260 0.880 -1.509 3.102 0.882 0.834 0.996 0.579 0.628 0.596 0.650 +1 0.391 1.039 1.704 0.533 -1.271 1.051 0.932 0.418 0.000 1.285 -0.727 0.382 0.000 1.198 0.517 1.507 2.548 2.418 1.054 -1.001 1.551 0.778 1.044 0.977 1.084 1.106 0.957 0.917 +1 0.927 -1.161 1.473 1.308 -1.306 1.304 -1.396 0.051 2.173 1.694 -0.536 -1.449 0.000 2.041 -0.964 0.478 2.548 0.552 -1.847 0.517 0.000 1.013 1.733 0.990 1.380 0.843 1.414 1.241 +0 2.127 -0.129 0.684 2.429 0.732 3.401 -0.736 -0.819 1.087 4.316 -0.465 0.838 0.000 0.704 -2.230 1.401 0.000 0.506 -0.891 1.672 1.551 3.346 1.795 0.998 3.206 1.108 2.564 2.174 +0 1.605 0.629 -1.432 0.182 -0.363 0.458 1.947 1.285 0.000 1.246 -0.400 -0.050 1.107 0.748 0.302 0.453 0.000 0.686 -0.031 -1.728 3.102 1.197 0.863 0.986 1.305 0.848 0.952 0.896 +0 0.469 -0.238 -1.226 1.624 1.343 0.399 -0.451 0.614 1.087 0.544 0.182 0.110 0.000 1.182 0.726 -0.970 0.000 0.735 -0.546 -0.170 3.102 1.086 0.932 0.987 0.785 0.377 0.597 0.671 +1 0.656 0.643 1.658 0.385 -0.191 1.029 -0.732 -0.989 1.087 1.102 -0.426 0.943 0.000 0.771 -0.553 0.397 0.000 0.592 -1.219 0.749 0.000 0.679 0.604 0.991 1.483 0.653 0.952 0.968 +0 0.383 1.191 1.183 1.646 -1.455 0.734 0.222 0.737 2.173 0.790 -0.841 0.192 2.215 1.241 -0.794 -0.722 0.000 0.551 -1.286 -1.725 0.000 0.814 0.881 0.984 2.127 0.832 1.514 1.317 +1 2.841 0.463 -1.484 0.259 0.488 0.834 1.575 -0.038 2.173 0.608 1.416 0.777 0.000 0.376 0.169 -0.359 0.000 0.703 0.060 0.977 3.102 1.013 0.990 1.164 0.751 0.933 0.990 0.875 +0 1.423 -0.578 1.443 0.463 -0.952 1.136 1.436 0.083 0.000 0.708 -1.394 -1.078 0.000 0.667 0.009 0.782 2.548 0.780 1.126 -1.093 0.000 0.808 0.631 0.986 0.675 0.531 0.522 0.550 +1 0.736 -0.884 -1.632 0.530 0.101 1.294 -0.024 -0.928 2.173 0.860 0.057 0.589 0.000 1.158 -0.599 1.088 1.274 0.486 1.858 0.227 0.000 1.138 1.106 0.989 0.895 1.558 1.134 0.926 +1 0.793 -0.120 0.990 2.447 0.409 0.750 0.916 -0.711 2.173 0.486 1.452 -1.697 0.000 0.620 0.689 1.494 2.548 1.253 2.151 -1.475 0.000 0.869 0.729 0.988 0.881 0.780 1.000 0.880 +1 1.013 -0.437 -1.047 0.905 0.585 1.257 0.083 1.734 2.173 1.329 -0.690 -0.305 0.000 0.878 -0.125 0.820 0.000 0.971 -0.693 0.338 3.102 1.486 0.871 1.320 1.122 1.244 1.064 0.913 +1 1.119 -0.804 0.620 1.092 -0.100 0.924 0.228 -1.687 1.087 0.561 1.888 -1.183 0.000 0.314 -1.223 -0.359 0.000 1.009 0.563 -0.729 0.000 0.896 1.057 0.981 1.400 0.801 0.951 1.195 +1 1.152 -0.448 -0.949 1.240 -1.623 0.702 -0.169 0.634 2.173 0.838 -1.033 -0.071 2.215 0.580 -0.581 -0.467 0.000 0.802 0.094 1.236 0.000 0.953 1.009 0.986 0.965 0.848 0.874 0.788 +0 1.299 -1.578 0.576 0.506 -0.744 0.747 -0.239 -0.193 0.000 0.988 -1.110 -1.409 2.215 1.467 -0.578 0.895 2.548 0.820 0.937 -1.522 0.000 0.870 0.939 1.042 0.933 1.166 0.816 0.757 +0 1.631 0.635 -0.306 0.215 -0.452 0.550 0.697 -1.597 2.173 0.520 0.087 1.156 0.000 0.705 -1.460 1.293 0.000 1.216 -0.483 0.347 3.102 0.891 1.011 1.001 0.976 1.039 0.831 0.928 +0 1.011 -0.372 -0.521 0.203 -1.241 1.022 0.620 -1.302 0.000 1.172 -1.167 -0.005 2.215 1.998 0.005 0.858 2.548 1.364 0.729 1.280 0.000 1.308 1.061 0.985 1.041 1.544 1.072 0.953 +0 1.698 0.254 0.090 1.329 -0.614 0.651 0.267 -1.228 0.000 1.012 -0.919 0.686 2.215 1.434 2.257 1.730 0.000 0.419 1.549 -1.509 0.000 0.730 0.624 1.232 1.267 0.703 0.897 0.890 +0 1.687 -1.122 -0.905 0.405 0.100 1.399 -0.955 -0.228 2.173 1.835 -0.159 1.432 0.000 1.108 0.552 0.820 2.548 1.018 0.322 1.717 0.000 0.671 0.879 0.989 0.883 1.842 1.399 1.270 +1 0.704 -0.200 -0.918 1.037 1.565 0.544 -1.831 0.627 0.000 0.828 -0.151 -0.573 0.000 1.228 -0.568 0.795 2.548 1.541 -0.004 -1.162 3.102 1.095 1.039 0.988 0.598 1.084 0.779 0.714 +1 0.785 -1.415 0.797 0.675 -1.088 0.358 0.167 0.618 2.173 0.638 -1.648 -1.198 0.000 0.819 -0.841 -0.793 0.000 1.364 -0.596 -1.383 3.102 0.857 0.996 1.001 0.783 0.795 0.644 0.663 +0 0.629 0.532 -1.103 0.638 0.875 0.655 -0.656 -0.131 1.087 0.517 -1.705 0.400 0.000 0.967 -0.912 1.497 2.548 0.687 -1.628 -1.119 0.000 0.765 0.780 0.985 1.048 1.001 0.916 0.946 +0 0.580 -0.427 -0.570 1.226 0.234 0.888 0.334 1.126 0.000 0.700 0.935 -1.360 2.215 0.427 -0.247 0.210 0.000 1.429 -0.134 -0.890 3.102 0.883 0.948 0.990 0.847 0.650 0.909 0.897 +1 0.406 -0.898 -0.210 0.259 0.314 1.769 -0.435 0.680 0.000 1.905 -0.717 -1.166 1.107 1.270 0.100 -1.704 2.548 0.649 0.367 -0.290 0.000 1.460 1.442 0.990 1.229 1.056 1.388 1.138 +1 0.554 0.356 -0.511 2.467 0.625 1.100 -0.547 -1.426 1.087 0.500 0.123 1.641 1.107 0.884 -0.084 0.054 0.000 1.244 0.883 -0.908 0.000 1.128 0.865 1.384 1.641 0.552 1.057 0.964 +1 0.906 0.291 -0.761 1.419 -1.296 0.613 -0.392 0.068 0.000 0.579 0.050 0.867 2.215 0.936 -0.955 0.972 2.548 0.452 -0.751 1.326 0.000 0.756 0.657 1.000 0.892 0.458 0.758 0.682 +1 0.295 1.668 -0.353 0.459 0.955 1.234 -0.381 -1.072 2.173 1.093 -0.698 1.074 0.000 1.028 -0.634 0.497 0.000 0.981 0.007 -0.667 0.000 0.808 0.688 0.990 0.995 0.900 0.950 0.814 +0 0.703 -0.281 0.924 1.084 -0.744 1.216 -0.799 -1.426 2.173 1.182 -1.027 0.247 2.215 2.000 0.072 0.222 0.000 0.627 -0.109 1.175 0.000 0.943 0.960 1.206 1.016 1.775 1.196 0.991 +0 0.837 -1.376 -0.253 0.859 -0.871 0.699 1.501 0.691 0.000 0.699 0.106 -1.391 0.000 0.772 -0.047 0.981 2.548 0.777 -1.102 -0.759 0.000 0.910 0.818 0.992 0.680 0.193 0.747 0.753 +0 0.425 1.198 -0.499 1.453 0.767 0.893 0.060 -0.704 2.173 1.395 -0.141 0.785 1.107 1.660 -0.318 -1.193 0.000 0.720 -1.126 -1.608 0.000 0.769 0.893 0.990 1.053 1.609 1.079 1.113 +1 0.591 1.441 -1.402 1.680 0.797 0.913 -1.437 -1.301 0.000 1.172 0.257 -0.222 1.107 0.610 -0.734 0.554 0.000 0.534 0.646 0.663 3.102 0.859 1.021 1.266 1.292 0.543 0.953 1.323 +1 0.703 -0.798 -1.426 0.580 -1.334 0.634 0.633 -1.243 2.173 1.110 1.030 0.336 0.000 0.552 -0.812 -0.805 2.548 0.543 -0.363 0.821 0.000 0.933 0.997 0.988 0.607 0.683 0.799 0.740 +1 0.430 1.295 -1.078 0.335 -0.265 1.109 -0.608 -1.155 2.173 1.182 0.003 0.553 2.215 0.616 1.083 0.512 0.000 0.427 -0.350 1.000 0.000 0.561 1.178 0.999 0.842 1.765 0.946 0.795 +1 0.319 0.162 -0.815 1.435 1.057 0.997 -0.224 -0.995 0.000 1.139 -1.030 -0.785 0.000 2.209 0.224 0.815 0.000 1.389 -0.245 0.181 1.551 1.061 1.152 0.987 1.067 0.905 0.818 0.964 +0 0.645 1.492 1.414 1.287 0.782 0.345 -0.197 -0.207 2.173 0.390 -0.162 0.309 2.215 0.672 1.796 -1.656 0.000 1.027 1.113 -0.733 0.000 0.748 0.837 0.988 0.649 0.242 0.596 0.635 +1 0.976 -0.919 0.680 0.233 -0.734 1.077 -0.738 1.454 2.173 1.119 -0.310 -1.023 2.215 1.600 -0.531 -0.202 0.000 0.580 0.222 1.051 0.000 1.067 1.004 0.984 0.934 1.320 0.977 0.893 +0 0.340 -0.962 0.132 1.928 -0.755 0.965 -0.793 -0.103 2.173 1.168 -0.821 1.158 0.000 1.510 -0.867 1.656 2.548 1.138 -0.579 0.490 0.000 0.858 0.865 0.989 0.878 1.508 0.976 0.962 +1 1.072 0.289 1.245 0.781 -1.440 0.679 -0.312 1.664 2.173 0.852 1.236 0.084 0.000 0.616 -0.362 0.255 0.000 0.784 1.013 -0.987 0.000 0.876 1.088 0.988 0.725 0.849 0.922 0.832 +0 0.471 -0.701 -0.227 1.114 1.618 0.811 -0.490 0.476 0.000 1.142 -0.170 -1.430 2.215 1.490 0.476 -0.090 0.000 0.979 0.443 1.637 0.000 0.965 1.163 0.999 0.646 0.698 0.734 0.673 +0 0.706 1.344 -0.790 0.354 1.581 1.100 0.724 1.582 2.173 0.982 1.139 -0.201 1.107 1.000 2.159 0.824 0.000 1.262 1.888 -0.437 0.000 1.126 1.031 0.993 0.839 1.565 1.147 0.974 +0 0.692 0.556 0.290 1.077 0.173 0.700 0.330 -1.043 2.173 0.490 -0.794 0.859 0.000 0.725 0.636 1.539 0.000 0.752 -0.782 -1.427 3.102 0.924 0.959 0.976 1.218 0.583 0.965 0.896 +1 1.444 -0.011 0.918 0.786 0.033 2.253 -0.561 -0.087 0.000 2.255 -0.565 -1.542 2.215 1.410 0.507 -1.294 2.548 1.322 0.260 1.190 0.000 2.687 2.199 1.056 1.477 1.213 1.804 1.458 +0 0.282 0.276 -0.532 0.852 0.820 0.330 -0.286 -0.258 2.173 0.777 0.804 1.500 0.000 0.662 0.426 -1.512 2.548 0.907 1.056 -0.308 0.000 1.118 0.889 0.989 0.736 0.573 0.659 0.632 +1 0.664 -0.406 -0.535 1.312 -0.146 0.978 0.558 0.113 0.000 1.107 -1.199 1.502 0.000 1.071 0.003 1.405 0.000 0.534 1.065 1.111 0.000 0.953 0.858 0.982 1.001 0.699 0.820 0.751 +0 3.356 -0.497 0.920 2.241 1.150 1.484 -2.553 -0.746 0.000 0.621 -1.832 -0.397 0.000 0.563 -0.851 -1.497 2.548 0.824 -0.435 -1.207 3.102 0.958 1.094 0.981 1.045 0.173 0.851 1.768 +1 0.691 -1.415 1.141 0.553 0.480 1.007 -0.459 -0.737 1.087 0.853 -1.377 0.648 0.000 0.865 -0.344 0.946 0.000 1.649 -0.685 -1.487 3.102 0.797 0.999 0.977 1.031 0.887 0.943 0.814 +1 1.190 -0.865 -1.286 0.551 1.221 1.078 -1.198 0.458 0.000 0.979 -2.270 -0.730 0.000 1.116 -0.884 1.405 2.548 1.137 -0.885 -1.046 3.102 1.008 1.103 0.985 0.572 0.693 0.713 0.656 +1 0.603 -0.301 1.575 0.651 0.827 2.186 -1.834 1.026 0.000 3.067 0.263 -0.854 2.215 1.088 -0.333 -0.127 0.000 0.686 -0.094 -1.218 1.551 0.609 1.094 0.986 0.664 0.492 1.166 0.963 +1 1.447 -1.325 -0.344 0.917 0.129 1.137 -0.756 1.313 1.087 0.330 -1.542 -0.826 0.000 0.715 -1.161 -1.284 2.548 0.594 -1.498 -1.708 0.000 0.416 0.799 0.987 0.792 0.857 1.014 0.774 +0 2.057 1.335 1.524 1.329 0.278 2.270 0.973 -0.001 0.000 1.256 0.942 -1.229 0.000 1.114 -1.910 1.428 0.000 1.918 0.188 -1.473 3.102 1.383 2.253 2.064 1.208 1.569 2.755 2.870 +1 1.539 -0.726 0.540 0.785 0.528 1.167 0.037 -1.166 0.000 0.684 -1.443 1.193 0.000 1.417 0.120 0.092 2.548 0.679 -0.367 -1.647 3.102 2.263 1.198 0.974 1.036 0.781 1.015 1.053 +1 0.841 0.120 -0.965 1.057 1.716 0.914 -0.341 -1.639 1.087 0.830 -0.454 1.237 0.000 1.051 0.813 -0.410 2.548 1.269 -0.804 -0.324 0.000 0.949 0.806 0.992 0.612 1.356 0.914 0.835 +0 0.555 -0.876 -1.496 1.017 1.122 0.737 -1.337 1.015 0.000 0.506 0.767 -0.742 2.215 0.795 -0.222 0.380 0.000 1.143 -0.254 -1.058 3.102 0.872 0.843 0.988 0.694 0.440 0.578 0.598 +0 3.968 -0.884 1.743 1.228 1.674 1.135 1.306 -0.194 0.000 1.021 0.732 0.053 1.107 1.950 -0.837 1.245 2.548 2.057 -1.085 -0.549 0.000 0.824 0.666 0.962 0.915 1.928 1.559 1.909 +1 1.083 -0.136 1.574 0.804 0.047 0.779 0.070 0.346 0.000 1.254 0.487 -1.579 2.215 1.179 0.845 -0.805 1.274 1.028 1.132 0.305 0.000 0.944 1.068 1.268 0.968 0.875 0.957 0.883 +0 0.789 1.014 0.446 0.338 -1.201 0.794 1.247 -1.205 2.173 0.778 -0.132 -1.351 0.000 2.059 0.149 0.616 2.548 0.640 0.209 0.231 0.000 0.927 0.967 0.985 0.758 1.835 1.005 0.824 +0 1.148 0.037 0.824 1.506 0.223 1.823 -0.662 -1.529 0.000 1.535 -0.879 -0.542 0.000 0.947 -0.956 0.725 0.000 1.128 1.048 -0.077 3.102 1.108 1.061 0.988 0.784 0.973 0.745 0.704 +1 0.442 0.931 -1.322 1.243 0.125 1.017 0.422 1.651 0.000 1.439 0.039 0.491 1.107 1.271 -1.064 -0.772 0.000 0.740 2.471 -0.838 0.000 0.900 1.068 0.991 0.899 0.626 0.953 0.911 +0 1.705 -0.324 -1.051 0.503 1.617 0.646 -1.234 0.642 2.173 0.729 1.340 -0.247 0.000 0.545 -2.303 -0.655 0.000 1.065 -1.820 1.396 0.000 0.821 0.872 0.987 0.856 0.760 0.849 0.952 +1 0.365 -0.571 -0.945 0.094 -1.114 0.731 0.746 -0.469 0.000 0.673 0.163 0.140 0.000 1.116 0.286 1.000 2.548 1.431 -0.532 -1.733 3.102 0.895 1.010 0.749 0.680 0.772 0.858 0.704 +1 0.815 0.519 0.070 1.116 -0.367 0.338 -1.486 -1.500 0.000 1.138 -1.056 1.444 2.215 0.707 -1.203 0.435 2.548 1.267 -1.382 -0.943 0.000 0.750 0.804 0.993 1.368 0.760 1.396 1.211 +0 0.950 -0.274 0.300 1.403 -0.356 0.601 -2.283 -0.202 0.000 1.082 -0.716 0.791 2.215 0.636 -0.696 -1.691 0.000 0.739 0.165 -0.373 1.551 1.435 1.133 0.989 1.009 0.802 0.893 0.945 +0 0.919 0.236 -0.612 1.227 0.866 0.665 1.327 0.908 2.173 0.907 1.335 -1.274 2.215 0.726 1.873 -0.761 0.000 0.777 1.537 0.308 0.000 0.686 0.792 1.429 1.128 1.054 0.902 0.822 +0 0.770 0.759 0.736 0.736 -1.027 0.330 -0.940 -0.272 2.173 0.295 0.326 1.407 0.000 0.530 1.248 -0.710 2.548 0.974 1.705 1.241 0.000 0.681 1.113 1.042 0.589 0.775 0.718 0.638 +0 1.045 0.237 -1.715 1.000 0.388 1.114 0.780 -1.023 2.173 1.398 -1.402 0.506 0.000 1.070 2.054 -1.315 0.000 0.955 -1.307 -0.241 0.000 0.943 0.807 1.342 1.162 1.755 1.551 1.295 +1 0.737 0.111 -1.599 1.047 -1.551 0.383 0.519 -1.677 0.000 0.917 -0.411 0.415 0.000 0.505 1.139 0.488 1.274 0.466 -0.195 -1.269 3.102 1.347 0.900 0.991 0.569 0.478 0.567 0.675 +1 0.430 -0.795 0.753 0.833 1.400 0.561 -0.519 1.514 1.087 0.685 1.162 -1.049 0.000 1.267 -1.348 0.172 0.000 1.024 -0.045 -0.336 3.102 0.804 0.872 0.984 0.862 0.820 0.819 1.297 +0 1.267 1.312 -0.193 0.674 1.350 0.788 2.235 1.519 0.000 0.603 1.016 -1.227 2.215 1.217 0.161 0.022 0.000 0.542 0.396 -0.487 0.000 0.956 0.902 1.259 0.752 0.347 0.568 0.654 +0 0.411 -2.314 1.243 0.286 -1.179 1.442 -0.166 0.026 0.000 1.954 -1.225 -1.559 1.107 0.416 0.637 1.702 2.548 0.632 1.717 -0.888 0.000 0.953 0.987 0.997 0.839 1.114 1.345 1.065 +0 0.568 -0.484 -0.507 0.522 -0.660 0.847 -0.634 0.902 2.173 0.953 0.712 1.556 0.000 1.203 -0.088 -0.938 2.548 1.134 -0.078 0.125 0.000 1.423 1.120 0.983 1.069 1.299 0.941 1.019 +1 0.746 0.534 -1.727 0.757 0.116 0.757 -0.347 -0.058 2.173 0.942 -0.169 0.665 1.107 1.354 0.284 -1.322 0.000 0.712 1.602 1.640 0.000 1.088 1.261 1.037 0.841 0.761 1.000 0.830 +1 0.603 1.177 -0.435 1.395 0.143 0.510 0.599 -1.483 2.173 0.647 0.824 -1.093 0.000 0.675 -0.530 0.131 0.000 2.241 -0.716 1.488 3.102 1.200 0.870 0.993 2.475 1.037 1.606 1.301 +0 0.564 -0.290 0.604 0.391 -0.364 1.268 0.363 -1.401 2.173 0.705 -1.356 0.811 0.000 0.652 -2.372 0.147 0.000 1.087 -0.631 -0.013 3.102 0.888 0.773 0.979 1.441 1.391 1.387 1.108 +1 1.091 -0.295 0.673 0.296 0.463 0.853 -0.749 -0.960 1.087 0.495 -0.678 1.098 0.000 0.962 -0.557 0.059 0.000 1.003 -1.329 1.602 0.000 0.853 0.997 0.996 0.625 0.539 0.691 0.640 +0 1.251 -0.903 -1.037 0.290 1.112 0.733 -0.671 0.212 1.087 0.796 -0.140 1.510 0.000 0.835 -0.034 0.612 0.000 0.819 1.161 -1.006 3.102 0.908 0.939 0.981 0.858 1.246 0.818 0.770 +1 0.380 2.223 -1.632 1.200 1.213 0.662 1.012 -1.054 2.173 2.048 0.629 -0.434 2.215 0.956 0.456 0.782 0.000 1.391 1.210 1.111 0.000 0.730 1.054 0.992 1.387 0.965 1.036 0.940 +1 1.337 -0.088 -1.053 0.569 -0.497 1.002 -0.563 1.051 2.173 0.578 -0.901 -1.281 0.000 1.331 -1.320 0.302 2.548 0.389 2.295 1.080 0.000 1.971 1.675 0.987 1.367 1.113 1.350 1.199 +0 2.183 -0.597 0.334 0.133 0.096 1.253 -1.244 -1.085 0.000 0.697 0.675 0.368 0.000 1.001 -1.381 -1.556 0.000 0.939 0.149 1.238 3.102 0.869 0.848 0.987 0.738 0.581 0.777 0.922 +0 1.576 1.712 -1.165 0.511 1.131 1.031 0.142 0.366 0.000 0.437 -0.409 -1.272 2.215 0.393 0.616 -1.734 0.000 0.920 0.091 0.686 0.000 1.128 0.933 1.092 0.682 0.182 0.659 0.818 +0 1.148 -0.158 0.480 0.532 -0.813 0.656 -0.504 -1.600 2.173 0.892 -0.495 -0.264 0.000 0.808 0.685 1.512 2.548 0.452 -0.881 0.842 0.000 0.732 0.928 0.994 0.850 0.680 0.709 0.659 +1 1.229 -1.187 0.889 0.634 1.715 1.019 -0.305 -0.382 0.000 0.850 0.536 1.684 2.215 0.640 0.079 -0.059 2.548 0.883 -1.634 -1.333 0.000 0.717 0.812 0.994 0.957 0.805 0.819 0.747 +0 0.898 -0.837 -0.229 0.972 0.753 2.051 0.304 1.445 1.087 1.007 -1.240 -0.751 0.000 1.833 -0.029 -0.390 1.274 0.563 -1.022 0.596 0.000 0.920 1.031 1.002 1.647 2.442 1.546 1.273 +1 1.808 -1.086 -1.533 0.992 0.029 2.164 -1.137 1.155 2.173 2.107 1.035 -0.170 0.000 1.183 -1.559 -0.868 0.000 1.322 -0.453 -0.846 3.102 4.772 2.620 1.831 1.637 1.826 2.599 2.091 +0 1.280 -0.733 -1.638 1.119 1.336 0.774 -0.951 -0.297 0.000 0.628 -0.707 0.770 2.215 0.986 -0.025 -0.015 0.000 1.391 -0.084 -1.051 3.102 0.855 0.886 0.976 0.737 0.883 0.701 0.809 +0 1.132 1.830 1.010 5.547 1.284 2.728 0.134 -0.603 1.087 1.193 0.327 -0.146 0.000 1.829 2.140 -0.235 0.000 2.644 0.982 1.489 3.102 2.693 2.274 0.980 4.509 3.117 2.898 2.586 +0 0.283 -0.993 1.421 1.151 -1.511 0.728 -0.930 0.526 0.000 0.875 0.909 1.584 2.215 1.163 -0.685 -0.131 0.000 0.888 0.136 -0.668 1.551 0.938 0.861 0.986 0.614 0.780 0.983 0.910 +0 2.298 0.328 -0.186 0.289 0.452 0.705 0.860 0.475 2.173 1.522 0.658 1.728 2.215 0.492 0.228 1.577 0.000 1.233 1.768 -1.530 0.000 0.947 0.910 0.991 1.459 1.385 1.082 0.952 +1 0.824 -1.436 -0.667 0.897 0.891 0.973 -0.852 -1.125 0.000 1.406 0.101 0.925 2.215 0.461 -0.331 -0.634 2.548 0.453 -1.172 -0.297 0.000 0.744 0.490 1.175 1.270 0.868 0.882 0.850 +0 1.644 -1.171 -1.352 0.854 1.194 1.289 -0.954 -0.691 1.087 1.059 -0.819 1.127 2.215 1.345 0.220 0.448 0.000 1.245 -0.709 0.359 0.000 0.827 0.950 1.231 1.197 1.718 1.131 1.077 +0 2.114 -0.490 -1.230 0.143 -0.704 1.362 -0.346 0.591 1.087 0.794 -1.431 0.319 0.000 1.385 -1.097 -0.813 2.548 0.760 -0.571 1.403 0.000 0.933 0.978 0.989 0.812 1.798 1.174 1.010 +1 2.173 0.688 1.385 0.494 0.071 0.449 -0.254 -1.673 0.000 0.778 -0.364 -0.812 2.215 1.611 0.560 0.121 2.548 0.611 -0.493 0.713 0.000 0.683 0.985 1.329 1.166 1.078 0.971 0.825 +0 0.362 -0.623 -0.094 0.652 -0.853 0.726 1.193 1.301 0.000 0.446 0.007 -1.514 1.107 0.989 0.706 -0.233 2.548 0.490 -0.058 0.537 0.000 0.849 0.903 0.989 0.626 0.703 0.631 0.605 +0 0.722 -1.687 1.609 0.718 -0.895 0.614 -0.123 -1.434 2.173 0.972 -0.556 1.142 0.000 0.773 1.853 -0.171 0.000 0.712 0.842 -0.137 3.102 2.581 1.443 0.988 1.239 0.768 1.148 1.561 +0 0.501 2.095 -0.656 3.431 -1.148 0.667 0.755 0.373 2.173 0.720 1.580 1.359 0.000 1.383 1.522 0.393 0.000 0.930 0.064 1.347 3.102 1.165 1.122 0.985 1.450 0.703 1.160 2.031 +1 0.774 -1.179 0.285 0.495 -1.259 0.698 0.033 -1.026 1.087 0.933 -0.441 1.151 2.215 0.910 -1.478 -0.938 0.000 0.706 1.990 -0.314 0.000 3.264 1.910 0.989 0.897 1.136 1.327 1.259 +1 0.974 0.339 -1.483 0.262 0.377 1.363 -1.036 0.863 0.000 2.333 -0.469 -0.472 2.215 1.337 -0.766 -1.492 2.548 1.009 -0.055 0.706 0.000 0.948 1.278 0.982 1.408 1.530 1.396 1.282 +1 0.471 -1.274 -1.286 1.530 -1.608 1.679 0.282 0.426 2.173 0.470 0.560 -0.707 0.000 1.042 1.839 -1.662 0.000 0.683 -0.353 -0.568 1.551 1.173 1.002 0.982 3.275 0.975 2.001 2.262 +1 1.987 0.656 -0.793 0.319 0.549 0.832 1.639 0.155 0.000 1.178 -0.323 -1.722 1.107 1.029 0.371 0.657 2.548 0.947 1.495 -0.278 0.000 0.974 0.893 1.032 1.123 1.079 1.102 0.992 +1 1.098 1.986 0.505 1.088 -0.151 1.104 1.758 -1.154 0.000 1.076 1.207 0.607 0.000 0.975 1.459 -0.352 2.548 1.103 -0.872 -1.398 0.000 0.826 0.885 0.985 0.701 0.779 0.738 0.801 +1 1.879 -0.505 -1.528 0.267 0.397 1.926 -2.132 0.358 0.000 1.979 -0.797 -1.689 2.215 1.056 -0.679 -0.355 0.000 1.159 -0.324 0.376 0.000 0.778 0.788 0.987 0.691 0.862 0.911 0.783 +1 0.675 0.805 1.619 1.846 -1.444 0.391 0.688 -0.920 0.000 1.352 1.136 0.148 2.215 1.016 1.491 -0.337 0.000 1.519 0.426 0.619 3.102 0.794 0.942 0.976 1.538 0.686 1.078 0.964 +0 0.554 0.619 1.040 3.079 1.734 1.070 1.061 0.049 2.173 0.653 -1.762 0.307 0.000 0.731 0.096 -0.934 2.548 0.599 1.712 0.434 0.000 0.970 0.898 1.059 1.652 1.017 1.211 1.324 +0 0.749 -0.649 -0.470 1.787 -0.549 1.492 -0.448 1.201 2.173 0.956 -1.578 -1.133 2.215 0.861 -1.604 0.803 0.000 1.030 -0.922 0.180 0.000 0.905 1.030 0.994 1.383 1.866 1.457 1.195 +1 2.447 -0.507 1.655 0.301 -1.339 0.716 -0.085 -0.443 2.173 0.818 -0.895 0.367 2.215 0.834 -0.837 -0.174 0.000 0.639 -0.099 0.999 0.000 0.776 0.700 0.986 1.098 0.893 0.952 0.789 +1 1.113 -0.430 -1.397 0.714 1.551 0.729 0.332 -0.191 2.173 0.382 -0.755 -0.532 0.000 0.760 -1.037 0.908 0.000 0.726 -0.057 1.149 3.102 0.811 0.929 0.980 0.630 0.737 0.805 0.701 +1 0.336 -1.917 0.471 0.572 -1.013 0.950 -0.600 1.223 0.000 1.313 0.083 -0.504 2.215 0.561 -0.328 0.454 0.000 0.645 0.151 -1.081 3.102 0.856 0.774 0.990 0.812 0.415 0.837 0.722 +1 0.892 0.137 -0.084 0.509 1.530 0.793 1.255 0.144 0.000 0.916 -0.066 -1.342 2.215 1.625 0.534 1.311 2.548 0.379 2.063 -0.849 0.000 0.846 1.186 0.987 0.770 0.984 0.990 0.825 +1 0.395 0.578 0.747 1.939 -0.228 0.831 0.010 -1.220 2.173 0.745 1.928 1.187 0.000 0.387 -1.868 0.355 0.000 0.587 -1.185 0.884 3.102 0.562 0.880 0.991 0.798 0.906 0.790 0.713 +0 0.424 -0.714 0.029 2.095 -0.111 0.609 -0.530 1.204 0.000 0.799 0.599 1.294 0.000 0.592 -0.348 -0.872 2.548 0.813 0.645 -1.435 3.102 0.909 0.867 0.990 0.821 0.414 0.606 0.781 +0 0.582 0.167 1.192 1.301 -0.880 0.395 0.450 -0.209 2.173 0.561 0.658 1.575 0.000 0.830 -0.414 0.728 2.548 0.610 1.342 0.048 0.000 0.834 0.808 1.154 0.695 0.630 0.608 0.607 +0 1.225 -0.893 -1.589 2.172 -1.489 1.795 0.438 0.813 2.173 2.186 -1.190 -1.400 2.215 2.578 1.482 -0.119 0.000 1.288 0.683 0.403 0.000 0.633 0.867 0.990 0.642 3.801 2.230 1.798 +1 2.054 1.082 0.131 0.331 -0.898 0.790 -0.398 0.630 0.000 0.641 0.015 -1.563 0.000 1.588 0.730 -1.139 2.548 1.213 0.538 -1.737 3.102 1.425 1.032 0.986 1.064 0.550 0.886 0.968 +0 1.319 0.241 1.057 1.784 1.604 0.810 -0.905 -0.576 0.000 0.593 -0.062 -0.008 2.215 1.083 -0.516 0.509 0.000 1.332 0.431 -1.042 1.551 1.428 0.897 1.006 0.921 0.686 0.782 0.952 +1 1.471 -1.212 -1.277 1.476 -0.918 0.295 -1.307 0.596 0.000 0.699 0.250 1.208 2.215 1.025 -0.877 0.145 0.000 0.636 0.254 0.223 3.102 0.429 0.843 0.982 1.077 0.467 1.039 0.894 +0 1.424 1.123 -1.552 0.217 -1.560 1.198 1.323 -0.368 0.000 0.558 0.936 -0.906 0.000 1.132 0.134 0.672 2.548 1.592 -0.588 1.259 0.000 0.872 0.785 0.998 0.924 0.255 0.784 0.807 +0 0.440 -0.386 -0.493 1.082 -1.375 0.707 -1.804 -0.421 0.000 1.262 0.290 0.561 2.215 1.206 -0.315 1.091 0.000 1.411 -0.188 -1.576 3.102 2.106 1.459 0.993 1.048 1.173 1.219 1.131 +0 1.237 -0.208 -0.792 0.863 -1.743 1.641 -0.479 0.974 2.173 2.302 0.330 -0.681 2.215 1.387 -0.328 0.530 0.000 0.665 0.754 0.998 0.000 0.837 0.885 1.081 1.017 3.098 1.510 1.241 +0 0.376 -1.987 -1.713 0.334 -1.229 1.472 0.897 -0.083 1.087 1.631 0.038 1.210 1.107 1.729 -0.566 -1.217 0.000 0.508 2.080 0.333 0.000 2.562 1.945 0.979 1.494 2.330 1.661 1.353 +1 0.728 -0.776 1.739 0.607 -0.097 1.218 -1.424 1.078 0.000 1.531 0.614 -1.048 2.215 2.581 -0.009 0.154 1.274 1.906 -0.212 -1.437 0.000 2.305 2.340 0.984 1.302 1.989 1.899 1.457 +1 0.424 -2.146 -0.253 0.266 -1.690 0.755 -0.358 0.744 0.000 0.992 -0.434 -1.321 1.107 0.685 0.315 0.167 1.274 0.404 -0.654 0.898 0.000 1.013 1.008 0.995 0.712 0.924 0.692 0.651 +0 1.152 1.118 0.030 0.354 1.742 0.860 -0.251 1.570 0.000 0.899 0.847 -0.980 2.215 0.921 0.027 0.827 0.000 1.090 0.388 -0.132 3.102 1.020 1.010 0.986 0.775 0.645 0.850 0.842 +0 0.425 0.551 0.925 1.083 -0.025 1.386 0.275 1.186 2.173 1.158 0.717 -0.674 2.215 0.748 -0.445 -1.391 0.000 0.766 0.004 0.735 0.000 0.872 1.113 0.991 1.008 1.903 1.113 0.917 +1 0.892 -0.333 0.294 1.400 0.912 0.369 -0.190 -0.816 0.000 0.570 -0.783 1.161 0.000 1.675 -1.180 -1.046 1.274 0.860 -0.189 -1.234 3.102 0.999 0.948 0.984 0.796 0.537 0.967 0.809 +0 1.548 -1.045 -0.035 0.455 0.117 0.941 -0.445 1.621 0.000 0.460 -0.491 0.883 0.000 0.614 -0.933 -0.659 2.548 1.069 -0.628 -1.110 1.551 0.865 0.853 0.987 0.769 0.259 0.582 0.703 +0 2.321 -0.783 -0.100 1.382 0.565 1.046 -0.550 -0.727 1.087 1.487 -0.711 1.577 0.000 0.640 -0.143 0.451 0.000 0.974 -0.551 -1.675 0.000 0.931 0.941 1.401 1.443 1.536 1.213 0.988 +0 1.642 -0.917 -0.783 0.243 -0.061 1.615 -0.349 1.021 2.173 0.927 -0.055 -0.550 2.215 0.888 0.197 1.673 0.000 0.610 1.555 -0.138 0.000 1.104 0.997 0.987 1.497 1.799 1.134 1.061 +0 0.900 0.096 1.066 1.948 0.350 0.672 0.443 -1.085 2.173 0.918 -2.441 -1.246 0.000 0.405 -1.067 1.671 0.000 0.569 -0.688 0.607 0.000 0.816 1.816 1.103 0.743 0.672 1.390 1.360 +0 0.712 -0.227 0.050 1.660 1.078 1.040 0.764 -1.236 2.173 1.019 -2.099 0.095 0.000 1.103 1.036 -0.736 0.000 1.793 -0.762 1.545 3.102 0.956 0.967 1.205 0.886 1.616 1.102 1.043 +1 1.075 0.719 -0.168 0.938 1.391 1.006 0.266 -0.973 0.000 1.000 0.634 0.852 0.000 1.129 -0.088 1.684 2.548 1.466 0.073 0.307 3.102 2.163 1.391 1.372 0.842 0.935 0.962 0.875 +0 2.243 -1.695 -0.020 1.125 0.059 1.318 -1.387 -1.604 0.000 1.218 -1.428 -1.245 0.000 1.487 -1.289 0.735 1.274 0.626 -0.393 0.823 3.102 0.863 0.977 0.988 1.024 0.366 0.942 1.171 +1 0.531 0.554 1.743 1.299 -0.321 0.922 1.827 -0.462 0.000 0.771 1.311 1.488 0.000 1.145 1.275 1.023 2.548 0.749 0.937 0.134 0.000 0.936 0.783 1.103 0.934 0.810 0.696 0.650 +0 0.399 1.909 -1.123 1.100 1.127 0.595 0.555 1.011 2.173 0.449 2.066 -0.598 0.000 0.805 -0.167 -0.519 2.548 1.426 -0.024 -1.391 0.000 0.857 0.844 0.983 0.876 0.908 0.673 0.646 +1 0.331 0.758 1.528 1.575 -0.447 0.653 0.506 1.115 0.000 0.925 -0.416 0.914 0.000 0.893 0.449 -0.995 2.548 1.245 -0.567 -0.702 3.102 0.858 1.065 0.989 0.899 0.543 0.824 0.850 +0 0.561 -1.671 0.303 1.009 -0.794 1.524 -0.881 0.071 2.173 1.164 -0.005 -1.721 1.107 0.723 1.929 1.327 0.000 0.713 0.784 -1.152 0.000 0.796 1.070 0.986 0.892 2.153 1.618 1.377 +0 0.513 1.738 1.001 1.013 -0.600 0.654 0.783 -1.149 2.173 0.650 -0.163 1.734 0.000 2.189 0.679 0.651 2.548 1.772 0.919 -0.430 0.000 0.820 1.046 0.990 0.795 1.489 0.931 0.827 +1 0.757 0.022 -0.775 1.862 -0.070 1.395 -0.516 1.154 1.087 1.058 1.919 -1.489 0.000 0.757 0.364 -0.395 0.000 1.589 -0.477 -1.284 0.000 0.804 1.225 0.988 0.670 0.467 0.980 0.825 +0 1.177 1.021 0.804 1.538 0.044 1.845 0.051 -1.582 1.087 0.436 0.056 -0.534 1.107 0.564 2.532 0.159 0.000 0.811 -0.892 0.122 0.000 0.523 0.544 1.179 1.986 1.070 1.280 1.171 +0 3.211 -1.053 -1.274 0.813 -1.617 3.254 0.430 0.376 0.000 3.664 -0.912 -1.600 2.215 3.134 0.888 0.385 0.000 1.896 1.036 -1.472 3.102 1.334 1.170 0.988 0.858 3.156 2.371 2.063 +0 1.917 -0.638 1.101 0.687 1.539 0.906 0.047 -0.920 0.000 1.027 0.515 -0.205 2.215 0.544 0.615 -1.685 0.000 1.233 -0.315 0.328 3.102 0.899 0.984 0.991 1.480 0.670 0.985 0.983 +1 0.573 1.144 -0.806 1.199 1.644 0.869 0.572 -0.790 2.173 1.125 0.577 -0.033 2.215 1.109 -0.573 1.332 0.000 0.737 1.324 0.771 0.000 0.774 1.101 0.992 0.976 0.918 0.925 0.979 +0 1.536 -0.761 -0.482 2.613 -0.144 1.034 -1.478 1.349 0.000 0.870 -2.741 1.663 0.000 0.733 0.152 0.849 2.548 0.573 0.084 1.225 0.000 1.047 0.943 0.990 0.936 0.433 0.861 1.078 +0 0.687 1.583 -0.708 1.385 -1.613 0.889 1.434 0.248 0.000 0.404 0.328 -0.463 2.215 0.641 -0.559 -1.331 2.548 1.164 -0.127 0.942 0.000 1.653 1.051 0.986 0.996 0.465 0.842 0.891 +0 1.155 -0.234 -0.225 1.264 -0.759 0.906 -1.571 1.637 2.173 0.784 -0.632 -1.240 0.000 1.311 -0.049 0.522 2.548 1.510 -1.779 0.649 0.000 1.817 1.301 0.983 0.985 1.620 1.135 1.086 +0 0.670 -0.980 -0.184 0.480 1.275 1.140 0.324 -0.626 2.173 0.711 0.551 1.229 0.000 0.758 1.302 0.612 0.000 0.680 -0.862 1.331 0.000 0.811 1.296 0.983 0.583 0.844 0.798 0.706 +1 0.380 -0.978 0.445 0.734 -1.036 1.221 0.040 -0.264 0.000 1.085 -0.433 0.985 0.000 0.999 -0.838 1.426 2.548 1.754 0.886 -1.355 3.102 1.062 0.736 0.982 0.776 1.321 1.042 0.876 +0 0.347 -2.251 0.510 1.561 -1.457 1.496 0.562 1.067 2.173 1.687 -1.045 -0.144 2.215 1.103 0.672 -0.846 0.000 0.902 -0.972 -1.094 0.000 0.678 0.987 1.000 2.922 2.984 2.181 1.584 +1 1.528 0.425 -0.742 0.329 -1.267 1.570 -0.698 1.034 2.173 0.489 -0.703 -0.528 0.000 0.273 2.610 -0.776 0.000 0.453 -0.663 0.060 3.102 1.168 0.741 0.989 1.915 0.688 1.188 1.038 +1 0.766 0.317 -0.285 0.773 -1.326 0.778 -0.231 1.325 2.173 0.446 1.290 -0.571 0.000 0.532 -1.283 -1.319 1.274 1.118 0.325 0.276 0.000 0.778 0.999 0.985 0.874 0.743 0.807 0.721 +0 0.514 -0.069 -0.630 0.477 -1.693 0.998 0.226 0.548 0.000 0.788 1.281 0.044 2.215 1.669 0.616 -1.390 0.000 0.448 0.797 -1.073 3.102 0.759 0.859 0.991 0.473 0.464 0.572 0.598 +0 0.940 -0.694 1.330 0.895 -0.610 0.658 0.214 -0.862 0.000 0.426 -0.015 1.434 0.000 0.898 -0.959 0.407 2.548 0.840 1.838 -0.224 0.000 0.996 1.011 1.251 0.674 0.142 0.655 0.640 +1 0.407 2.138 1.044 0.743 0.819 2.112 0.373 -1.540 0.000 2.009 1.203 0.381 0.000 1.791 0.603 -0.251 1.274 1.107 0.137 -1.202 3.102 0.841 1.017 0.995 0.778 0.857 0.883 0.806 +1 0.787 1.108 -1.695 0.872 1.703 0.854 0.754 -0.130 2.173 0.644 0.076 1.677 2.215 0.548 -0.565 -0.584 0.000 0.744 -1.106 1.149 0.000 0.897 0.779 0.995 1.313 1.154 1.033 0.842 +0 1.365 1.113 1.709 0.438 -0.509 0.784 1.249 1.034 2.173 1.186 1.278 -0.682 2.215 0.377 1.205 -0.290 0.000 1.466 0.056 0.292 0.000 0.687 0.933 0.987 0.789 1.419 0.857 0.781 +1 0.717 -0.525 -0.352 1.629 -0.280 1.262 0.201 1.026 0.000 0.930 -1.141 -0.897 0.000 0.927 -0.576 1.247 2.548 1.330 -0.767 -1.323 1.551 0.909 1.119 0.993 1.078 0.636 0.875 0.922 +1 0.908 0.115 1.149 1.646 1.724 1.225 -0.293 0.400 2.173 1.041 -0.484 -0.481 2.215 0.557 0.108 -1.742 0.000 1.101 -0.088 -1.007 0.000 0.540 1.114 0.993 1.156 1.197 1.073 0.878 +0 0.326 -1.718 -0.762 1.476 -1.081 1.009 -0.526 0.085 2.173 0.807 0.924 -1.573 1.107 0.401 2.102 0.554 0.000 0.737 -2.058 1.475 0.000 0.859 0.825 1.000 1.087 1.706 1.022 0.976 +1 0.410 -0.630 -0.621 0.106 -1.494 0.960 0.757 1.360 0.000 1.260 -0.039 -0.345 2.215 1.099 0.236 0.839 2.548 0.997 1.162 -1.681 0.000 1.058 1.246 0.894 0.728 1.111 1.100 0.872 +1 0.815 0.465 1.062 1.180 -1.456 0.486 -2.050 -0.383 0.000 1.334 0.492 -1.611 1.107 0.702 1.159 -0.504 0.000 0.596 -1.142 -0.013 0.000 1.281 0.743 1.042 0.652 0.855 0.767 0.713 +0 0.677 -1.636 -0.558 0.793 -1.238 1.291 0.166 0.707 0.000 0.964 0.674 1.077 0.000 1.057 -0.317 -0.370 2.548 1.123 -0.462 -1.004 1.551 0.998 1.314 0.981 0.522 0.458 0.976 0.969 +1 0.716 -0.196 -1.450 0.547 0.205 1.360 0.703 1.173 2.173 1.445 1.036 -0.685 2.215 0.664 1.985 -0.341 0.000 0.908 1.186 0.695 0.000 0.979 0.779 0.987 0.947 2.084 1.108 0.918 +1 0.821 -1.303 -0.662 0.255 0.587 0.949 -1.584 0.074 0.000 1.314 -1.036 1.250 1.107 1.682 -0.910 -1.430 2.548 0.405 -2.460 1.057 0.000 1.007 1.273 0.995 0.816 1.050 1.046 0.907 +1 3.017 -0.240 -0.278 0.742 -0.499 1.309 -1.166 1.318 0.000 0.456 -0.627 -1.579 0.000 1.095 -0.002 0.621 2.548 1.182 0.617 -1.491 1.551 0.939 1.133 0.991 1.091 0.886 0.948 1.190 +1 0.525 0.090 -1.129 1.475 1.199 1.179 -0.271 0.808 1.087 1.038 -1.332 -0.208 0.000 1.146 -1.507 -0.625 0.000 1.108 -0.720 -0.937 0.000 0.924 0.923 1.053 0.872 0.798 0.960 0.900 +0 0.541 1.873 0.034 0.982 -1.227 0.820 0.544 -1.361 2.173 0.948 -1.400 0.751 0.000 1.107 1.254 -0.361 0.000 1.776 0.724 0.680 3.102 0.987 0.990 0.988 0.801 1.248 0.832 0.729 +1 0.861 1.019 -0.607 0.939 -1.434 1.869 0.577 0.981 0.000 0.782 0.873 -1.518 0.000 1.714 0.141 -0.476 2.548 1.681 1.314 -1.022 0.000 0.867 0.985 0.988 1.053 0.880 0.885 0.774 +1 0.382 -1.725 1.542 2.375 -0.686 0.412 -0.814 0.416 0.000 0.622 0.718 0.890 1.107 0.843 -1.152 1.337 0.000 0.614 -1.151 1.673 3.102 0.817 0.930 1.197 0.727 0.796 1.106 0.926 +0 1.356 -0.852 0.561 0.841 0.171 1.106 -1.225 -1.355 0.000 0.685 -0.324 -0.375 1.107 0.518 -1.494 0.743 0.000 1.197 -0.464 -1.234 3.102 1.321 0.862 0.977 0.850 0.579 0.741 0.830 +1 1.890 -0.933 0.683 0.671 1.065 0.655 0.224 -1.491 2.173 0.613 -0.680 -1.113 0.000 0.688 -1.341 -0.954 0.000 0.841 -0.399 0.284 0.000 0.896 0.827 1.000 0.760 0.741 0.914 0.781 +1 0.809 -0.053 -0.717 1.257 0.552 0.864 -0.223 1.572 0.000 0.650 0.470 -1.404 0.000 0.960 0.132 -0.166 2.548 1.513 -0.573 0.553 3.102 0.910 1.073 1.272 0.775 0.682 0.831 0.781 +0 0.655 1.421 -0.148 1.282 0.766 0.921 0.374 1.032 2.173 1.782 -0.174 -0.749 0.000 0.993 0.704 -0.810 0.000 1.219 -0.802 1.357 0.000 1.014 0.968 0.987 0.831 0.347 1.017 0.965 +1 0.589 -1.288 0.882 0.706 -1.282 0.912 -0.280 1.587 0.000 1.092 -1.156 1.702 2.215 2.027 1.958 -0.071 0.000 1.698 -1.373 0.314 0.000 2.229 1.465 0.985 1.327 1.471 1.195 1.070 +1 0.464 2.117 1.455 1.277 -0.532 0.492 0.348 1.679 0.000 0.620 -2.247 -0.951 0.000 1.811 0.032 0.499 2.548 0.626 1.016 -1.669 3.102 0.577 0.811 1.041 0.637 0.911 1.034 0.860 +0 0.883 0.282 1.525 0.923 -1.277 0.667 0.378 -0.304 0.000 0.604 -0.516 -0.880 1.107 1.025 -0.050 0.353 0.000 1.043 0.576 0.908 1.551 0.892 0.832 0.994 0.772 0.854 0.651 0.709 +0 1.139 1.250 -0.936 0.608 -1.478 2.087 0.351 1.160 1.087 2.580 0.803 -0.582 1.107 1.468 0.184 0.788 0.000 1.053 -0.524 1.014 0.000 0.649 0.844 0.987 0.961 3.510 1.749 1.419 +0 2.183 1.059 -1.225 1.576 -0.637 1.106 -1.504 0.383 2.173 0.680 -0.080 0.720 0.000 0.640 -2.077 0.819 0.000 1.254 -1.310 1.662 3.102 1.355 1.001 1.299 3.215 1.139 2.248 1.881 +1 0.950 -0.127 1.076 1.207 0.094 1.257 0.335 -1.062 2.173 0.612 1.214 0.474 0.000 0.712 -0.390 0.511 0.000 0.597 -0.307 1.683 3.102 0.954 0.739 1.148 1.321 0.657 0.850 0.824 +0 1.061 -0.398 -1.582 1.375 -1.049 0.949 0.906 0.811 0.000 0.788 -1.621 -0.817 0.000 0.569 2.087 0.815 0.000 0.930 -0.289 0.216 3.102 0.984 1.042 0.983 0.723 0.375 0.918 0.986 +1 0.625 -0.581 0.952 1.814 1.733 1.494 -0.925 -0.238 1.087 0.768 -0.362 1.299 2.215 0.441 -0.396 1.636 0.000 0.484 -1.153 -1.126 0.000 0.644 0.873 0.989 0.543 1.613 1.133 0.867 +1 0.282 -0.038 0.753 0.129 1.713 1.088 1.135 -0.984 2.173 1.153 1.030 1.002 2.215 0.656 0.901 -0.295 0.000 0.736 -0.278 0.232 0.000 0.648 0.966 0.608 0.488 1.610 0.912 0.708 +1 0.728 1.193 1.172 1.265 -1.518 1.591 -0.702 0.414 0.000 1.005 2.186 0.308 0.000 1.749 1.475 -1.567 0.000 1.780 0.022 1.448 3.102 0.923 0.960 0.988 0.764 0.287 0.719 0.981 +0 0.521 -1.252 -1.330 1.137 -1.274 1.091 -0.228 -0.701 2.173 1.189 -1.964 0.395 0.000 0.946 -0.794 0.713 0.000 0.618 -0.506 1.521 1.551 1.088 0.866 0.995 1.823 0.806 1.170 1.186 +0 0.425 -0.942 -0.408 1.318 -1.666 0.931 2.046 -0.693 0.000 1.869 0.591 0.778 2.215 1.306 0.330 1.302 0.000 2.187 0.303 -0.676 3.102 0.738 1.018 0.989 1.878 1.777 1.458 1.139 +1 1.152 -0.412 -0.081 0.687 0.449 0.871 -1.778 -1.543 0.000 0.630 -0.600 -1.460 0.000 0.619 -1.361 0.102 0.000 1.057 -1.064 1.658 1.551 0.988 1.231 0.985 0.813 0.780 0.745 0.815 +1 1.073 1.471 -0.864 0.993 0.145 0.655 2.136 0.577 0.000 1.569 0.246 -1.630 2.215 0.466 2.203 1.451 0.000 1.315 0.437 -0.125 1.551 0.715 1.036 1.129 1.403 1.279 1.183 1.034 +0 0.688 0.289 -0.299 0.329 -1.276 0.630 2.799 0.546 0.000 0.519 -1.509 -1.140 2.215 1.299 -1.524 0.950 0.000 0.580 -0.520 -0.535 0.000 0.899 1.022 0.992 0.659 1.087 1.650 1.429 +0 1.109 0.433 -0.468 1.175 0.241 0.701 -1.039 1.730 0.000 0.377 1.571 1.412 2.215 0.591 0.098 0.501 2.548 0.502 -1.305 -0.971 0.000 0.634 0.792 0.985 0.794 0.555 0.787 0.868 +0 0.946 1.536 0.058 0.724 -0.300 1.641 1.067 0.425 2.173 1.580 -0.824 -1.154 0.000 2.171 0.012 -1.604 2.548 1.229 -2.323 1.184 0.000 2.579 2.312 0.994 0.914 2.625 2.842 2.275 +1 0.617 1.733 -0.695 1.110 1.042 1.355 0.182 -1.722 1.087 1.529 -1.026 -0.209 0.000 0.911 -0.820 0.991 2.548 0.591 1.563 -0.394 0.000 2.547 1.688 1.147 1.466 1.186 1.458 1.530 +1 1.852 0.057 -1.327 1.108 1.144 1.124 -0.445 0.673 2.173 0.650 0.023 -0.280 2.215 0.468 0.818 0.158 0.000 1.160 -1.182 -0.682 0.000 0.670 0.666 1.572 1.054 0.999 1.013 0.796 +0 0.869 0.150 -0.228 1.577 0.290 1.197 1.356 -1.683 1.087 0.635 1.798 1.358 0.000 0.743 2.004 0.641 0.000 0.851 -1.142 -1.462 3.102 0.799 1.347 0.987 1.212 1.979 1.515 1.322 +0 1.687 -0.743 0.796 1.179 1.013 2.338 0.190 -0.846 2.173 1.664 -1.004 0.541 0.000 0.810 0.648 -0.799 2.548 1.504 -0.334 -0.332 0.000 1.602 1.480 0.999 2.247 0.457 1.549 1.466 +0 1.525 0.148 -0.799 0.902 -1.485 1.169 -1.336 1.029 1.087 0.726 -1.975 0.299 0.000 1.173 -0.496 -0.983 0.000 1.220 -0.316 1.184 1.551 0.972 0.808 0.984 1.646 0.660 1.081 0.914 +0 0.348 -0.907 0.642 0.978 -0.753 1.609 0.838 -0.160 1.087 2.021 -0.391 1.533 2.215 0.926 0.143 0.604 0.000 1.303 0.069 -1.558 0.000 1.127 1.077 0.989 2.618 3.186 2.117 1.618 +0 0.701 -0.799 -0.975 0.621 -1.159 1.172 -1.742 1.184 0.000 1.609 0.733 -0.314 2.215 0.452 -0.745 0.490 0.000 0.637 -0.571 1.438 3.102 0.998 0.662 0.996 0.982 1.162 1.587 1.303 +1 0.299 1.678 -0.207 1.484 -0.899 0.966 0.987 -0.482 2.173 0.297 2.175 1.023 0.000 0.919 -0.041 1.075 0.000 0.379 1.336 1.696 3.102 0.924 0.783 0.993 0.736 0.624 0.902 0.799 +0 1.447 0.708 -0.072 0.402 0.199 1.086 0.723 -0.923 2.173 1.208 0.486 1.071 2.215 0.656 2.218 1.494 0.000 0.553 0.968 -1.527 0.000 0.527 0.928 0.989 1.044 1.653 1.042 0.912 +0 1.186 1.102 0.204 0.191 -0.201 0.836 -2.042 0.858 0.000 0.837 -0.530 -0.917 2.215 1.103 1.021 -1.524 0.000 1.402 0.403 -1.154 0.000 0.632 0.919 0.983 0.510 0.750 0.705 0.727 +1 1.178 0.413 -0.465 1.224 -1.463 1.037 0.708 0.693 0.000 0.561 -0.348 0.362 0.000 1.109 0.341 -1.441 2.548 0.671 -1.031 -0.785 3.102 1.028 1.123 1.301 0.740 0.695 0.879 0.868 +1 0.598 -1.435 0.398 1.088 -1.164 0.684 -0.904 0.101 2.173 0.911 -1.215 1.288 0.000 1.052 -0.182 1.330 0.000 1.886 -0.185 -0.505 3.102 0.906 0.966 1.102 0.922 0.755 0.729 0.747 +0 0.847 1.179 -1.039 1.368 -1.346 1.598 1.575 0.489 2.173 1.243 -0.272 -1.603 1.107 0.324 0.160 0.237 0.000 0.768 1.562 -0.414 0.000 0.608 0.847 0.983 1.764 2.981 1.777 1.306 +0 0.714 1.317 -1.712 0.959 1.663 1.010 0.010 -1.075 2.173 0.863 1.840 0.742 0.000 1.623 0.889 0.299 2.548 0.858 1.811 0.358 0.000 0.789 0.952 0.988 0.893 1.708 1.250 1.090 +1 0.359 1.363 0.130 0.783 1.588 1.052 -1.057 0.267 0.000 0.948 0.501 -0.992 2.215 0.832 -0.601 1.344 2.548 0.524 -0.414 1.721 0.000 1.143 0.902 0.981 0.747 1.003 0.949 0.830 +1 0.790 -0.299 1.378 1.729 -1.729 1.012 -0.899 0.180 2.173 0.687 -0.128 0.039 0.000 1.620 -0.390 -1.362 2.548 0.739 -1.797 0.015 0.000 0.788 1.008 0.999 0.797 1.616 1.064 1.023 +1 0.552 1.138 0.037 1.701 -0.060 0.905 0.310 1.545 2.173 0.868 1.133 1.550 0.000 1.112 -0.243 -1.115 2.548 1.414 -0.557 -0.012 0.000 2.081 1.395 0.998 1.270 0.922 0.986 0.992 +0 1.524 1.498 -1.736 0.578 -1.106 0.594 -0.362 -0.811 0.000 0.767 0.914 1.116 1.107 1.181 0.357 -0.364 0.000 2.530 0.838 0.506 3.102 0.831 1.183 0.981 1.264 0.658 0.972 1.090 +0 0.918 -1.183 1.706 0.657 -0.908 0.633 -1.169 -0.809 0.000 0.776 -1.040 0.794 1.107 1.051 -1.412 0.211 2.548 0.740 -1.330 1.560 0.000 0.911 0.911 0.989 0.895 0.535 0.689 0.666 +1 0.957 0.098 1.073 1.421 -1.659 1.546 -0.670 -0.233 2.173 1.539 1.046 1.485 2.215 0.667 -0.376 -0.640 0.000 0.911 -0.877 0.274 0.000 0.690 0.640 1.016 0.902 3.200 1.604 1.263 +1 0.511 0.488 0.454 1.708 -0.561 0.567 0.471 0.924 0.000 1.259 -0.153 1.512 2.215 0.547 -0.798 -0.891 2.548 0.372 -1.061 0.558 0.000 0.732 0.759 1.025 0.722 0.799 0.839 0.754 +1 0.468 0.755 -0.715 0.455 -0.858 0.648 1.114 0.868 0.000 1.271 0.497 1.190 2.215 1.252 1.627 -0.608 0.000 1.438 -0.436 -0.099 0.000 1.665 1.045 0.983 1.084 0.587 0.882 0.978 +1 0.947 -1.734 1.738 0.854 -0.164 1.258 1.374 -0.534 0.000 1.244 -0.345 1.712 0.000 1.372 -1.133 1.463 2.548 1.180 -0.476 0.451 3.102 0.887 1.146 1.233 0.879 0.838 0.735 0.851 +1 0.672 -0.078 1.170 1.109 -0.142 0.923 -0.331 1.683 2.173 0.715 -0.892 -0.063 0.000 0.861 -2.060 -0.595 0.000 0.633 -0.388 -0.371 3.102 0.941 0.778 1.106 0.993 0.779 0.871 0.880 +1 1.321 -0.501 -1.721 0.785 0.955 0.419 -0.989 0.146 0.000 1.245 -1.063 -0.729 1.107 0.397 -0.758 -1.328 2.548 0.892 -0.100 0.052 0.000 0.874 1.030 0.990 0.530 0.396 0.673 0.649 +0 0.512 1.691 -1.331 0.552 0.373 1.198 0.838 1.315 2.173 1.404 0.310 -0.260 0.000 0.540 -0.149 -0.932 0.000 0.851 -0.241 1.537 1.551 0.827 0.880 0.980 0.856 0.674 0.990 0.823 +1 0.982 -0.229 -0.513 2.079 -1.097 0.958 1.377 -0.057 0.000 1.838 -0.866 1.253 1.107 0.511 0.332 -1.728 2.548 0.694 -1.417 0.006 0.000 0.704 1.081 0.993 1.624 0.825 1.040 1.113 +1 1.091 -1.010 0.509 0.501 1.338 0.808 0.018 -0.627 2.173 0.425 0.724 1.649 0.000 0.837 2.038 0.871 0.000 0.425 -0.526 1.116 0.000 0.953 0.771 0.992 1.003 0.790 0.878 0.992 +1 0.372 -1.892 0.776 0.680 -0.785 0.806 0.746 -0.040 2.173 0.646 -0.934 -1.720 0.000 1.362 -0.355 1.195 0.000 0.634 -0.010 -0.593 0.000 0.822 0.554 0.989 1.039 0.671 0.862 0.772 +0 1.050 -0.001 -1.376 0.020 0.805 0.742 0.660 0.805 0.000 0.449 -1.209 -0.417 2.215 0.679 -0.535 -0.673 1.274 0.445 -0.135 0.680 0.000 0.388 0.918 0.461 0.428 0.238 0.653 0.585 +0 0.904 1.056 0.005 0.710 0.367 1.104 0.943 1.376 0.000 0.911 1.294 -0.810 2.215 0.370 1.300 -1.609 1.274 0.767 1.655 -0.306 0.000 1.597 0.873 0.990 0.853 0.408 0.734 0.763 +1 0.541 -1.546 -0.236 1.359 1.513 1.211 -1.154 -0.146 2.173 1.203 -1.542 1.533 0.000 1.003 -0.439 -0.550 2.548 0.660 -0.964 0.666 0.000 0.855 1.091 1.187 1.188 0.684 0.964 0.879 +0 1.036 0.784 1.434 1.159 0.645 0.872 0.520 -1.043 2.173 0.852 0.779 -0.635 0.000 1.874 0.418 0.517 1.274 1.150 -0.359 1.514 0.000 0.874 1.007 0.990 0.752 1.572 0.957 0.895 +1 0.914 -0.525 0.572 0.679 -1.285 1.191 0.337 1.619 2.173 0.820 0.434 0.604 0.000 1.828 -0.379 -0.370 0.000 1.657 -0.428 -0.949 1.551 0.399 0.587 1.086 1.021 1.274 0.962 0.836 +1 0.620 0.647 -0.146 1.161 -1.632 0.655 -1.134 0.078 0.000 1.126 -0.900 -1.117 2.215 1.084 -0.192 1.127 0.000 0.637 0.863 -0.773 3.102 1.066 1.102 1.144 1.128 0.908 0.957 0.960 +0 0.756 -0.342 -0.004 0.726 1.286 0.692 0.599 -0.880 2.173 0.485 1.135 -0.486 2.215 0.582 -0.243 0.536 0.000 0.731 1.190 1.430 0.000 0.859 0.887 0.984 0.727 0.384 0.620 0.604 +1 0.810 -0.507 0.384 0.888 1.317 0.867 -1.311 -0.965 0.000 0.920 -1.167 0.570 2.215 1.116 -0.425 -0.699 0.000 1.072 0.097 1.420 3.102 0.887 1.098 0.991 0.790 0.888 0.923 0.865 +0 1.542 1.087 0.611 0.407 0.153 0.475 -2.321 1.049 0.000 1.036 -1.229 -0.647 1.107 1.142 -0.144 -1.375 2.548 0.503 -1.309 1.622 0.000 1.177 1.130 0.982 1.068 0.975 1.145 1.194 +1 0.385 0.857 -1.130 1.611 1.466 1.363 0.351 0.016 2.173 0.393 -0.509 1.735 0.000 0.379 0.639 1.053 2.548 1.131 0.402 -0.900 0.000 0.755 1.138 0.994 0.579 0.737 1.061 0.940 +1 1.010 -0.625 -0.011 0.383 1.620 0.822 -0.547 -1.667 0.000 0.354 -1.294 1.425 0.000 0.917 0.342 0.170 2.548 0.900 0.191 -1.117 3.102 0.755 0.684 0.990 0.632 0.639 0.517 0.506 +0 0.824 -0.613 -0.018 0.446 -1.459 1.084 -0.982 -0.750 1.087 1.622 1.438 0.938 0.000 1.488 2.255 -1.242 0.000 2.901 0.512 1.231 3.102 0.938 0.959 0.990 0.765 2.483 1.806 1.545 +1 1.115 -0.809 -1.350 0.766 -0.213 1.114 -1.178 1.240 0.000 1.833 -0.548 -0.970 2.215 2.052 -0.726 0.318 0.000 1.338 -0.088 1.632 3.102 0.917 0.824 1.094 0.784 1.062 0.839 0.738 +0 2.743 0.288 -1.303 0.672 -0.736 1.816 -1.435 0.680 1.087 0.704 -1.449 0.044 1.107 1.750 -0.910 -1.157 0.000 1.002 -0.533 0.585 0.000 1.484 1.106 0.993 2.713 0.904 1.800 1.471 +0 0.418 0.799 1.673 0.528 -1.454 0.977 -0.306 -0.048 1.087 0.768 -1.166 -1.382 0.000 0.583 -1.048 0.599 0.000 0.697 0.807 1.098 1.551 1.003 0.978 0.987 0.990 0.958 0.845 0.754 +0 1.177 -1.178 -0.344 0.161 1.077 0.549 0.131 -0.908 0.000 0.851 -0.954 0.771 2.215 0.507 -0.232 1.512 0.000 0.725 -0.570 1.208 3.102 0.937 1.033 0.994 0.639 0.292 0.630 0.620 +1 0.594 0.915 0.073 0.854 -1.619 1.197 -0.380 0.974 1.087 0.848 -0.812 -1.081 0.000 0.648 -0.581 -0.152 2.548 0.519 -0.368 1.466 0.000 0.887 1.222 0.987 0.790 0.943 0.866 0.804 +1 1.288 -0.597 -1.190 2.446 -1.338 1.357 1.603 0.658 0.000 0.688 -0.030 -1.473 0.000 1.176 0.181 -0.238 0.000 1.301 -0.251 0.723 1.551 0.830 0.737 0.982 1.092 0.267 0.935 0.925 +1 1.105 1.192 1.173 0.676 0.359 0.945 0.736 1.559 2.173 1.215 2.299 -1.104 0.000 1.028 2.122 -0.095 0.000 0.922 1.277 0.574 3.102 0.791 0.653 0.985 0.930 0.865 0.887 0.782 +0 0.936 1.106 -1.311 0.717 1.476 0.441 0.054 -1.381 0.000 0.635 1.199 -0.002 1.107 1.050 -0.509 0.130 2.548 0.443 1.650 0.312 0.000 1.012 0.993 0.991 0.830 0.893 0.992 0.840 +0 0.724 -0.133 -1.375 1.353 -0.875 1.534 0.876 0.684 2.173 0.517 1.248 0.471 0.000 1.621 -0.399 -0.908 0.000 1.863 0.000 1.507 3.102 0.683 0.977 0.985 1.535 1.458 1.152 0.939 +0 0.711 0.427 1.648 0.862 1.368 0.456 -1.998 -0.954 0.000 1.134 -1.110 0.147 2.215 0.535 -1.132 -0.851 2.548 0.470 -0.463 1.201 0.000 0.867 0.918 0.992 0.700 0.649 0.752 0.701 +0 0.441 1.675 -1.415 0.804 0.367 1.767 0.190 -0.725 2.173 1.638 1.090 0.970 2.215 0.812 1.275 0.296 0.000 1.388 0.750 -1.514 0.000 0.909 1.200 0.986 2.148 2.776 1.741 1.426 +1 1.700 -0.424 -1.294 0.572 -1.057 0.740 -0.733 -0.783 0.000 1.960 -1.319 0.250 2.215 1.264 1.533 1.297 0.000 0.891 -1.819 0.660 0.000 0.868 0.879 0.983 0.542 1.278 1.195 0.996 +0 1.246 -1.193 1.361 0.574 0.328 0.594 -0.467 -1.062 2.173 0.557 -0.922 -0.554 0.000 0.969 -0.370 0.499 2.548 0.443 -1.947 0.746 0.000 0.770 0.738 0.990 0.665 0.933 0.686 0.630 +1 0.538 0.690 -0.179 1.195 -0.928 0.419 1.353 1.032 0.000 0.581 -0.160 -0.501 2.215 1.067 -0.490 0.765 0.000 0.849 0.233 -1.287 1.551 1.357 0.970 0.987 0.874 0.436 0.711 0.831 +0 0.992 -1.496 -0.831 0.806 1.709 1.118 -0.779 -1.099 2.173 1.240 0.182 0.337 2.215 1.330 1.281 0.537 0.000 1.106 0.452 1.061 0.000 0.852 0.902 0.986 0.884 1.884 1.381 1.496 +1 1.963 0.507 0.777 1.179 -0.738 1.353 -0.155 -1.415 2.173 0.549 0.923 1.473 2.215 0.579 -0.164 -0.941 0.000 1.763 -1.087 0.212 0.000 1.004 1.143 2.063 1.677 0.984 1.115 1.090 +0 1.304 1.487 -0.030 1.077 0.961 1.363 1.330 -1.425 0.000 1.301 0.623 0.497 1.107 1.045 0.547 -0.864 2.548 0.643 0.682 1.645 0.000 0.647 0.795 1.280 0.933 1.167 1.022 0.994 +0 0.396 -0.606 0.851 0.564 -0.887 0.757 0.072 -1.294 0.000 1.554 0.412 0.504 2.215 0.460 -0.665 0.179 1.274 0.966 1.096 -1.247 0.000 0.866 0.886 0.983 1.592 0.607 0.983 1.086 +1 1.411 -2.003 -0.452 0.329 0.224 0.687 -1.568 -1.417 2.173 1.022 -0.311 1.391 2.215 0.734 -0.960 -0.794 0.000 0.823 2.281 0.925 0.000 0.613 0.849 0.975 0.868 1.091 0.947 0.777 +0 0.878 -0.751 1.327 3.579 1.408 1.228 -1.540 -0.574 0.000 1.223 1.277 -0.747 0.000 1.139 1.249 0.496 2.548 1.530 1.266 -0.142 0.000 0.942 0.998 0.987 0.852 0.746 1.038 1.340 +0 0.675 -1.009 -0.347 1.295 -0.716 0.946 1.286 1.189 0.000 0.512 2.521 -1.234 0.000 0.459 0.754 -0.861 2.548 0.894 -0.051 1.146 0.000 1.033 0.848 0.986 0.872 0.610 0.603 0.831 +1 1.667 -0.565 0.759 1.695 1.215 1.524 2.534 -0.887 0.000 1.595 -1.159 -0.227 2.215 0.680 -1.005 -1.595 2.548 1.409 -1.178 0.974 0.000 0.729 1.160 0.987 0.815 1.046 1.081 0.876 +1 0.822 0.304 -0.666 1.194 -0.132 1.024 0.116 1.673 2.173 0.587 0.497 0.754 0.000 0.488 -0.871 -0.080 2.548 0.659 0.414 1.192 0.000 0.311 0.692 0.993 0.518 1.011 0.835 0.730 +0 1.133 -0.647 -0.998 0.112 1.470 0.848 -0.343 1.082 0.000 1.305 0.181 -0.200 1.107 1.737 1.726 -1.128 0.000 1.603 0.174 0.595 3.102 0.912 0.922 0.979 0.861 0.858 0.992 0.903 +0 0.593 0.250 -0.005 0.593 -1.536 1.027 -0.787 0.746 2.173 1.521 0.237 -0.282 2.215 2.315 -1.293 -1.360 0.000 1.642 -1.183 0.918 0.000 1.905 1.676 0.987 0.876 1.779 1.613 1.448 +0 1.550 -0.517 -0.243 1.418 -1.149 1.416 -1.681 -1.687 0.000 2.057 -0.067 0.424 2.215 0.548 -0.472 -1.688 2.548 0.938 1.006 0.208 0.000 0.819 0.752 1.495 1.549 1.097 1.546 1.396 +0 0.769 -2.183 0.341 0.621 0.870 0.685 0.452 -0.699 1.087 0.925 -1.167 -1.569 2.215 0.698 -0.263 0.496 0.000 0.554 -0.487 -1.667 0.000 0.646 0.790 0.981 0.888 1.364 1.041 0.818 +0 0.767 -0.494 1.477 1.613 0.797 0.715 -1.306 -1.343 0.000 0.910 -1.513 -0.492 0.000 0.794 -0.889 1.631 1.274 1.404 -0.996 -0.028 3.102 1.029 0.871 0.987 0.721 0.809 0.730 0.775 +1 1.079 -1.245 -1.356 0.789 -1.554 1.017 -0.603 0.136 2.173 0.555 -0.534 0.685 0.000 0.703 -0.828 1.088 2.548 0.448 0.813 -1.102 0.000 0.847 0.841 0.978 0.694 0.813 0.841 0.727 +0 1.117 -1.143 1.593 1.739 -1.417 0.309 -0.462 1.325 0.000 0.831 -1.035 -0.004 2.215 0.940 0.674 0.599 2.548 0.578 -0.890 0.290 0.000 0.989 0.877 0.982 1.666 1.083 1.220 1.185 +1 0.433 -0.543 0.418 0.843 1.568 1.073 0.753 -0.890 0.000 0.701 0.248 -0.434 2.215 0.724 -2.226 0.824 0.000 1.954 0.645 0.614 3.102 1.299 0.973 0.993 1.435 0.900 1.062 1.212 +1 1.255 -0.382 -1.548 0.619 -0.308 1.062 0.288 0.378 2.173 1.067 -1.041 -1.599 0.000 0.765 -0.695 -0.946 1.274 0.835 -0.466 0.414 0.000 1.009 0.666 1.099 1.151 1.214 0.994 0.854 +0 1.279 0.539 -1.468 0.312 0.991 0.667 -1.582 -0.684 0.000 0.945 -0.145 1.385 2.215 1.060 -0.101 -0.180 2.548 0.624 -2.206 0.456 0.000 1.002 1.129 0.984 0.662 1.050 1.015 0.981 +0 2.094 0.416 -1.638 0.462 -0.603 1.339 1.348 -0.356 2.173 1.630 1.087 1.506 0.000 1.023 -0.107 0.183 0.000 1.380 0.423 0.493 3.102 0.944 0.954 1.095 1.484 1.181 1.088 1.044 +0 0.680 -0.768 0.564 1.169 1.527 0.524 -0.053 -0.583 0.000 0.801 0.341 0.220 0.000 1.258 0.538 -1.330 2.548 1.138 -0.083 0.470 1.551 0.952 1.012 0.985 0.733 0.968 0.837 0.832 +1 2.188 0.051 -1.652 0.517 0.457 1.206 0.545 0.158 2.173 0.465 -1.881 0.693 0.000 1.532 0.412 -1.069 2.548 0.531 0.804 0.654 0.000 1.305 1.398 1.394 1.465 1.516 1.174 1.097 +1 1.760 -0.554 0.291 0.506 0.694 1.160 0.212 -1.118 2.173 0.438 0.053 -1.481 0.000 0.443 0.119 0.815 0.000 0.631 0.753 1.224 3.102 0.647 0.689 0.974 0.870 0.840 1.065 0.824 +1 1.685 0.469 -0.039 0.196 0.391 1.476 1.456 1.373 2.173 0.731 0.138 -0.689 0.000 0.820 0.594 -1.669 0.000 0.582 2.296 -0.783 0.000 0.967 0.686 0.993 1.476 1.074 0.994 0.930 +0 1.534 1.079 0.443 0.823 1.317 0.412 2.041 1.690 0.000 0.678 0.288 -0.426 2.215 0.429 0.380 -1.171 0.000 0.649 0.673 -1.384 3.102 0.946 0.949 1.104 0.714 0.481 0.647 0.653 +1 0.590 -0.970 -0.212 1.752 1.189 0.708 -0.681 -1.202 0.000 0.655 -0.142 1.271 1.107 0.765 -1.462 -0.995 0.000 1.625 2.174 -0.213 0.000 0.676 1.006 1.342 0.790 0.409 0.726 0.780 +0 1.707 0.327 -1.150 0.274 0.035 1.279 -1.306 0.536 0.000 0.510 -1.738 -0.003 0.000 0.999 -0.884 -1.137 0.000 1.211 -0.034 1.572 3.102 0.899 1.280 0.990 0.854 0.530 0.750 0.955 +0 0.422 0.755 -0.656 1.486 -0.123 0.597 0.739 1.603 2.173 0.661 -0.740 0.777 0.000 0.728 -1.106 -1.175 2.548 0.514 2.157 1.515 0.000 2.012 1.281 0.989 1.804 1.060 1.274 1.225 +0 0.962 1.619 -1.155 0.727 0.245 0.492 1.882 0.800 0.000 0.593 0.460 -1.629 2.215 0.673 0.232 -0.533 2.548 0.753 0.898 1.112 0.000 0.696 0.756 1.104 0.750 0.565 0.618 0.593 +0 0.412 0.253 -1.542 1.399 -0.477 1.000 0.007 0.499 2.173 0.800 1.572 -1.377 0.000 0.727 0.762 1.587 1.274 0.465 0.210 0.159 0.000 0.976 0.667 0.988 1.143 0.992 0.844 0.792 +1 0.953 0.033 -1.679 1.534 -1.613 0.872 0.529 0.135 1.087 1.122 0.872 0.961 2.215 0.880 -1.567 -0.103 0.000 0.937 0.480 -0.312 0.000 0.705 0.730 0.989 1.304 1.021 0.994 0.789 +0 0.297 1.121 1.505 1.861 -0.493 0.685 -0.992 1.602 0.000 0.759 0.430 0.642 2.215 0.564 -0.762 0.440 0.000 1.102 0.159 -1.512 3.102 0.973 1.009 1.003 0.813 0.777 0.727 0.937 +0 0.860 0.689 -0.496 0.426 0.476 1.227 0.027 0.623 1.087 0.886 -0.615 1.652 2.215 0.383 0.137 0.360 0.000 1.674 -0.877 -1.114 0.000 1.027 0.830 0.988 0.899 1.331 0.941 0.832 +1 0.812 0.543 0.987 0.471 -0.626 0.669 -1.155 0.297 0.000 0.908 0.179 -1.363 2.215 0.975 0.443 0.024 2.548 0.642 -1.721 -1.582 0.000 1.098 1.191 0.987 0.731 0.961 0.975 0.828 +0 0.318 1.916 -1.528 1.226 -0.246 0.461 0.163 1.165 2.173 0.359 1.899 1.459 0.000 0.665 1.098 0.421 2.548 0.536 2.082 -0.759 0.000 0.544 0.830 0.984 0.595 0.570 0.580 0.582 +0 0.901 0.831 -1.387 4.221 -1.484 1.743 -1.194 0.087 0.000 0.867 -0.058 0.357 2.215 0.780 0.780 1.346 2.548 0.887 -0.033 0.785 0.000 1.272 1.104 0.988 0.872 0.797 1.252 1.708 +1 1.020 -0.240 -0.819 1.867 0.123 1.243 2.916 1.640 0.000 1.422 -0.629 -0.174 0.000 1.607 -0.531 1.497 0.000 1.218 0.206 -0.076 3.102 2.315 1.445 1.436 1.076 0.673 1.020 0.949 +0 0.911 -1.205 0.164 0.787 -1.690 1.524 -0.580 -0.014 1.087 1.700 0.042 -1.671 0.000 0.839 0.887 -0.367 0.000 1.374 -1.011 0.986 0.000 0.578 1.540 1.167 1.115 1.804 1.354 1.185 +1 0.792 0.613 1.578 1.155 -1.316 2.242 -0.825 -0.035 0.000 1.424 -0.087 1.543 1.107 1.393 -0.092 0.973 2.548 1.249 0.508 -1.268 0.000 0.827 0.894 0.981 0.697 0.735 0.664 0.626 +1 0.996 -0.088 -1.723 0.728 -0.582 0.423 0.613 -0.434 2.173 0.772 0.651 -1.406 2.215 0.608 -0.967 -0.144 0.000 1.086 0.429 0.340 0.000 0.870 0.995 1.011 0.683 0.646 0.653 0.624 +1 0.819 -1.525 -0.766 0.873 1.129 0.731 -0.920 -0.389 0.000 0.500 -0.073 1.730 0.000 0.974 -0.295 1.031 0.000 0.936 0.894 -1.058 3.102 0.645 0.790 1.161 0.680 1.014 0.868 0.750 +0 0.382 1.907 0.741 0.098 -0.924 0.461 0.789 0.404 0.000 1.287 0.096 -1.020 2.215 0.434 -0.826 0.254 2.548 0.821 0.044 1.141 0.000 0.681 1.071 0.990 0.639 0.836 0.700 0.636 +0 0.625 0.544 -1.069 0.506 0.281 0.560 -0.650 -1.569 0.000 0.936 -0.797 0.513 2.215 0.875 0.185 1.166 2.548 0.863 1.837 -0.844 0.000 0.835 0.856 0.985 0.764 0.742 0.859 0.840 +1 1.636 -0.107 1.626 0.151 0.679 0.668 0.901 -0.472 2.173 0.392 -0.551 1.025 0.000 1.585 0.536 0.402 2.548 1.238 -0.244 -1.031 0.000 0.881 0.965 0.988 1.001 0.932 0.871 0.792 +1 1.095 -0.028 1.404 0.586 0.748 0.653 -0.676 0.787 0.000 1.153 0.041 -1.451 2.215 1.093 0.184 0.472 0.000 1.069 0.617 -0.444 3.102 0.815 0.953 0.978 0.863 0.866 0.885 0.793 +0 1.015 0.154 1.097 0.784 1.205 1.176 0.768 -0.982 2.173 1.263 1.747 1.063 0.000 1.677 1.427 -0.314 0.000 0.875 1.182 0.679 0.000 0.861 1.159 0.991 0.811 0.326 0.991 1.065 +1 1.665 -0.167 0.929 0.991 -0.138 1.314 -1.467 -0.968 2.173 0.623 -2.753 0.752 0.000 0.679 0.065 1.592 0.000 0.685 -1.024 -1.653 3.102 2.128 1.185 1.459 1.800 0.594 1.136 1.198 +0 0.655 -0.854 -1.038 1.626 -1.230 0.813 -0.356 0.861 1.087 0.322 2.792 -0.137 0.000 0.803 -0.822 0.517 2.548 0.524 0.857 -0.547 0.000 0.600 1.371 1.003 1.497 0.414 1.086 1.649 +0 2.814 1.249 -0.195 0.544 1.183 1.116 0.659 0.255 2.173 2.117 -0.086 1.738 1.107 0.688 -2.364 1.356 0.000 0.883 1.439 -1.264 0.000 3.737 2.546 1.623 1.105 2.367 1.996 2.005 +0 0.984 -0.227 -0.807 1.883 -1.121 0.602 -1.129 0.765 0.000 0.679 0.790 0.117 2.215 0.633 0.278 0.605 0.000 0.569 -0.912 1.471 1.551 0.888 0.975 0.993 0.665 0.814 0.869 0.874 +1 0.724 -0.539 0.979 1.230 0.258 0.406 -1.160 -0.796 1.087 0.618 1.000 -1.460 0.000 0.530 -0.930 1.683 1.274 0.561 0.033 0.316 0.000 0.863 0.941 0.996 0.665 0.457 0.628 0.742 +0 1.771 1.430 -1.317 1.005 -1.657 2.017 0.973 0.329 2.173 1.638 0.053 -1.562 2.215 1.384 -0.076 -0.440 0.000 1.237 0.301 0.910 0.000 1.392 1.383 0.996 1.966 2.946 1.685 1.427 +1 0.741 -0.897 -1.012 0.570 -1.041 1.318 -0.287 -0.145 0.000 1.097 -0.642 1.584 2.215 1.767 -1.274 0.871 2.548 1.251 0.010 1.320 0.000 0.830 1.074 0.976 1.007 1.048 0.912 0.868 +0 1.203 -1.280 1.044 0.561 1.738 1.449 -0.130 0.895 2.173 1.366 -0.196 -0.703 0.000 1.280 0.381 -1.051 2.548 0.602 -0.918 -0.229 0.000 0.921 0.856 0.992 0.936 1.733 1.205 1.062 +0 0.943 -1.171 -0.799 0.607 -0.149 0.934 -1.570 -1.099 2.173 1.199 -2.631 0.883 0.000 1.309 -1.370 0.718 2.548 0.655 -0.508 -0.482 0.000 1.809 1.185 0.986 0.914 1.375 1.081 1.058 +0 0.532 -0.051 0.299 0.856 -1.708 1.207 1.661 0.543 0.000 0.793 1.166 -0.169 2.215 0.800 0.905 0.968 0.000 3.044 0.617 -1.456 3.102 0.901 0.941 0.987 0.805 1.326 1.153 0.962 +1 0.283 1.950 -0.709 0.896 1.250 1.061 1.257 -1.517 0.000 1.402 0.759 0.363 2.215 0.983 0.656 -0.450 2.548 0.562 1.300 1.050 0.000 0.886 0.986 0.991 0.858 0.835 0.948 0.803 +1 1.039 -0.094 0.708 1.258 1.289 1.013 -0.687 -0.574 2.173 0.537 -0.562 0.516 0.000 0.551 -1.487 0.632 0.000 0.605 0.553 -1.311 3.102 0.469 0.948 0.997 0.658 0.793 0.935 0.831 +0 1.053 -0.766 -0.682 0.479 0.737 0.855 -0.834 0.172 1.087 0.464 -1.537 1.207 2.215 0.974 -1.017 -1.032 0.000 1.060 -1.535 1.701 0.000 0.913 1.114 0.986 0.709 0.822 0.759 0.670 +1 0.844 0.445 -0.041 0.150 1.562 1.254 1.260 -0.134 2.173 1.849 1.394 -1.716 2.215 0.449 1.315 -1.013 0.000 0.497 0.879 0.495 0.000 0.640 0.783 0.993 1.509 2.225 1.368 1.028 +1 0.919 -0.426 -0.336 0.495 -0.147 0.816 -0.468 1.345 0.000 0.634 -0.630 -1.334 2.215 0.712 1.031 1.021 2.548 0.909 -2.463 -0.486 0.000 2.467 1.512 0.980 1.205 0.945 1.310 1.112 +1 1.391 1.046 -0.479 1.945 -1.100 1.225 0.565 0.811 2.173 0.638 0.566 -1.656 1.107 0.375 -0.242 0.845 0.000 0.538 0.111 -0.022 0.000 0.447 0.552 1.209 0.872 1.034 1.135 0.842 +0 0.347 -1.265 -0.788 0.666 1.196 0.738 -2.518 -1.357 0.000 0.402 -2.857 -0.316 0.000 1.011 -0.839 0.566 2.548 0.629 -0.604 -0.347 0.000 0.884 0.852 0.979 0.558 0.357 0.592 0.672 +0 0.476 0.704 -0.342 1.287 0.069 0.871 -1.401 1.684 2.173 0.574 0.591 -1.326 2.215 0.587 -2.209 0.612 0.000 0.678 -0.578 -0.482 0.000 0.895 0.945 0.989 0.882 1.314 1.003 0.910 +1 0.773 0.965 -0.158 0.632 -1.162 0.660 0.879 -1.466 0.000 0.775 1.477 -0.984 1.107 1.732 0.174 0.534 2.548 0.705 0.592 0.940 0.000 0.891 1.074 0.982 1.142 1.493 0.903 0.844 +0 0.930 0.192 0.893 0.632 -0.962 0.527 -1.255 -0.858 2.173 0.759 0.773 0.234 2.215 0.813 2.611 1.099 0.000 1.362 1.627 -1.434 0.000 0.851 0.929 1.056 0.917 1.378 1.279 1.027 +1 1.297 -0.025 -0.510 0.437 -0.018 1.131 0.322 0.067 0.000 1.249 2.397 1.243 0.000 2.017 0.420 -1.127 2.548 1.207 0.985 0.101 0.000 0.950 1.211 0.984 0.678 0.713 0.739 0.695 +1 1.719 -0.291 -0.375 1.296 0.956 1.201 -0.230 -0.860 2.173 0.996 0.259 1.646 2.215 0.864 0.070 0.257 0.000 0.688 -1.330 -1.538 0.000 0.913 0.795 1.928 1.384 1.308 1.116 0.959 +0 0.843 1.415 0.073 1.151 -0.419 1.254 -1.318 -1.035 0.000 1.018 -0.151 0.908 2.215 0.964 1.235 1.555 0.000 1.038 -1.372 -1.494 0.000 0.909 1.388 0.984 1.266 0.673 1.174 1.077 +1 1.149 0.932 1.718 0.739 -0.164 1.262 0.461 0.631 0.000 1.337 -1.573 -0.825 0.000 1.944 -0.064 1.718 2.548 1.338 0.521 0.132 3.102 0.901 1.275 1.267 1.059 1.296 1.089 1.108 +0 0.805 0.035 -0.385 0.649 0.696 1.300 -0.584 -0.927 2.173 0.792 0.124 0.849 2.215 1.405 -1.619 -1.484 0.000 2.625 -1.612 0.762 0.000 1.912 1.833 0.990 0.981 1.589 1.547 1.237 +1 1.275 2.137 -1.509 1.461 -0.172 0.933 0.510 1.019 2.173 0.455 0.867 -1.676 0.000 0.726 1.134 -0.557 0.000 0.559 0.538 -0.213 1.551 0.762 0.966 1.766 0.979 0.687 1.126 0.906 +1 0.415 1.146 1.261 0.559 -0.892 0.908 -0.319 0.543 0.000 0.716 -0.645 1.135 0.000 1.925 -1.073 -1.116 1.274 0.683 -1.525 -0.713 0.000 0.919 0.854 0.992 0.974 0.966 0.995 0.858 +1 0.574 -2.051 1.238 1.484 -1.151 0.537 -2.667 -0.899 0.000 1.060 0.053 0.433 2.215 0.413 -0.213 0.076 0.000 0.397 -1.119 0.203 0.000 0.782 1.329 1.068 1.681 0.755 1.211 1.120 +1 1.169 0.480 -0.727 0.768 -0.599 1.149 -0.167 1.358 1.087 0.387 -0.989 0.083 2.215 0.436 1.265 -0.976 0.000 0.555 0.956 0.173 0.000 0.471 1.036 1.002 0.676 0.993 0.896 0.786 +0 1.162 1.122 -1.049 0.480 1.708 1.543 0.872 -0.492 2.173 1.163 -1.207 1.004 0.000 1.064 -0.916 1.492 0.000 1.125 0.296 0.678 1.551 0.755 0.969 0.983 1.011 1.268 1.639 1.351 +0 0.488 -2.176 -0.504 3.286 -0.107 1.953 -1.331 1.631 1.087 0.478 -0.872 -1.358 0.000 0.779 -0.706 0.380 2.548 0.373 -1.455 -0.322 0.000 0.504 0.840 0.972 0.746 1.457 1.467 1.080 +1 1.404 -0.574 1.044 1.573 1.541 0.380 0.203 -0.777 0.000 0.585 -0.865 1.486 0.000 1.587 -0.374 -0.224 2.548 1.071 0.836 -0.622 3.102 1.063 0.985 0.989 1.324 0.841 1.102 0.922 +0 0.977 1.238 1.071 0.767 0.128 0.679 0.653 -1.073 2.173 0.725 0.453 0.220 2.215 0.593 0.823 -1.467 0.000 1.519 0.189 1.562 0.000 0.810 0.775 0.989 0.919 0.955 0.697 0.637 +0 0.713 -0.977 -0.273 0.562 1.301 0.801 -0.281 0.032 2.173 0.982 -1.762 -1.240 0.000 0.644 1.129 1.193 2.548 0.810 -1.071 0.628 0.000 0.826 1.421 0.991 1.174 1.078 1.195 0.999 +0 0.473 0.280 0.937 0.552 -1.172 0.824 -0.120 0.750 0.000 0.935 -0.335 1.594 0.000 0.576 -2.485 -0.541 0.000 1.131 0.080 0.314 3.102 1.301 0.929 0.985 0.659 0.572 0.714 0.662 +1 1.880 1.683 1.391 0.678 0.438 0.687 -0.475 -1.475 0.000 1.250 1.326 -0.565 2.215 1.190 0.499 0.131 2.548 1.072 1.984 0.315 0.000 0.705 0.871 1.183 1.257 0.936 1.001 0.821 +1 1.223 0.954 0.544 1.366 1.125 0.967 0.249 -1.510 2.173 1.215 -1.295 -0.534 0.000 0.364 0.059 -0.533 1.274 0.399 -0.818 -1.490 0.000 0.707 1.390 0.992 0.756 0.575 0.876 1.221 +0 2.197 -1.095 -0.518 0.249 1.504 0.968 -0.454 0.142 2.173 0.966 -0.598 -1.230 0.000 1.666 -1.038 1.355 2.548 1.039 -2.367 1.283 0.000 0.992 0.951 0.993 0.950 1.509 1.013 0.906 +0 0.747 -1.363 1.295 0.653 -0.908 0.449 -2.227 0.369 0.000 0.596 0.449 1.153 2.215 0.621 -0.001 -0.581 2.548 0.671 -1.282 -1.073 0.000 0.863 0.903 0.982 1.094 0.664 0.837 0.783 +1 0.563 0.766 -1.404 1.130 1.064 0.829 -0.451 0.429 2.173 0.792 -0.924 -0.754 2.215 0.383 -1.168 1.656 0.000 0.424 -2.226 1.678 0.000 0.334 0.884 0.984 0.982 1.086 0.840 0.773 +0 0.348 -1.274 0.396 0.933 -1.576 0.795 0.939 1.230 0.000 0.977 0.737 -0.302 0.000 1.224 -0.104 -1.431 2.548 1.868 0.413 0.191 1.551 1.847 1.241 0.991 1.252 1.204 1.346 1.581 +1 1.946 1.190 -0.132 1.177 -0.592 0.930 0.963 -1.705 1.087 1.092 1.496 0.644 2.215 0.576 1.659 1.678 0.000 0.366 2.036 -1.492 0.000 0.214 0.650 0.982 1.383 1.337 1.101 0.854 +0 1.871 0.765 -1.514 0.093 1.481 0.586 -1.006 -0.479 2.173 0.856 -1.500 1.166 0.000 0.907 0.698 -0.032 2.548 1.215 -0.918 0.327 0.000 0.958 0.984 0.977 0.892 0.988 0.939 1.056 +1 2.088 0.777 0.525 0.263 -0.637 0.729 -0.605 -1.381 2.173 0.478 0.891 -0.095 2.215 0.670 -0.218 -0.937 0.000 0.803 0.961 -1.712 0.000 0.801 0.759 0.983 0.564 1.080 0.932 0.795 +0 0.608 0.357 1.542 0.345 0.454 0.829 1.626 0.251 2.173 0.742 1.222 -0.653 0.000 1.383 0.645 -1.690 0.000 1.023 1.454 -1.327 0.000 0.828 0.698 0.990 0.795 1.106 0.865 0.736 +0 0.789 0.004 -0.300 0.997 -1.326 0.858 0.317 0.012 0.000 0.968 -0.259 1.356 2.215 0.854 -0.112 -1.243 0.000 0.724 -0.281 0.855 3.102 1.434 0.925 0.987 0.854 0.331 0.758 0.704 +1 1.450 -0.258 0.094 0.563 0.632 0.576 0.869 -0.985 2.173 0.358 -1.118 -0.906 0.000 0.551 0.514 -1.692 2.548 0.580 0.814 1.134 0.000 0.950 0.836 0.996 0.752 0.432 0.688 0.642 +1 2.154 -0.461 -0.590 0.639 -0.435 0.983 0.066 1.562 2.173 0.824 0.221 0.443 2.215 0.575 0.325 -1.633 0.000 0.807 -0.417 0.470 0.000 0.786 0.727 0.988 0.966 1.126 1.031 0.825 +0 1.407 0.002 -1.705 0.547 -1.212 0.702 -0.442 -0.424 2.173 0.615 1.400 -1.461 0.000 0.722 1.071 0.382 2.548 0.732 1.314 0.078 0.000 0.862 1.220 0.995 0.858 0.994 0.833 0.794 +1 2.178 -0.161 -1.600 0.512 -0.812 0.457 0.257 -0.280 0.000 1.012 1.729 0.320 0.000 0.344 -0.889 -0.042 2.548 0.645 0.847 0.944 3.102 1.417 0.865 0.985 0.691 0.505 0.682 0.926 +0 0.805 0.089 -0.785 1.437 -0.133 0.768 -2.669 0.096 0.000 1.747 -0.814 1.323 2.215 1.233 -1.311 -1.076 2.548 1.490 0.217 -1.565 0.000 0.876 1.013 0.987 1.388 1.380 1.375 1.071 +1 0.916 0.395 1.343 1.113 -0.929 0.928 -1.044 1.565 2.173 0.815 -1.244 0.479 0.000 1.458 2.320 1.283 0.000 1.979 -0.948 -0.804 0.000 0.901 0.770 1.244 1.217 1.264 0.901 0.891 +0 0.666 1.839 1.718 0.602 0.661 1.133 0.581 -1.072 1.087 1.360 -0.479 0.289 0.000 0.745 -1.339 1.359 2.548 0.370 0.445 1.067 0.000 0.775 0.888 0.991 1.010 1.679 1.120 1.051 +1 0.848 -1.166 -1.525 0.438 -0.142 0.837 0.250 0.587 2.173 0.657 0.924 1.739 2.215 1.151 -0.229 -0.705 0.000 0.427 0.219 0.227 0.000 0.610 0.885 0.992 0.877 1.017 0.815 0.717 +0 1.611 -1.495 0.112 0.640 0.646 0.756 -0.446 -1.533 0.000 0.513 1.221 -0.239 0.000 0.725 0.808 1.002 2.548 1.709 0.310 -1.260 3.102 1.760 1.099 0.989 1.567 0.791 1.362 1.324 +1 0.748 0.541 1.116 1.108 0.246 0.888 -0.448 -0.682 0.000 0.502 1.019 -1.734 0.000 0.796 -1.341 -1.467 1.274 0.488 1.466 1.009 0.000 0.464 1.175 0.985 0.498 0.826 0.748 0.707 +1 0.371 -0.504 -0.644 1.314 0.718 0.728 0.555 1.234 2.173 0.864 0.594 -0.840 0.000 1.094 1.112 -1.212 0.000 1.594 0.142 0.302 3.102 0.674 1.087 0.987 0.731 0.879 0.870 0.794 +1 0.479 1.532 1.644 1.258 0.374 0.596 -1.460 -1.411 0.000 0.777 0.164 -0.827 2.215 0.426 0.755 1.325 0.000 0.922 -0.491 -0.872 3.102 1.072 0.926 0.986 1.140 0.299 0.881 0.800 +1 0.559 -1.952 -1.249 0.327 0.073 1.554 -0.992 0.946 2.173 1.168 -0.464 -0.416 0.000 0.913 1.058 -0.803 0.000 0.518 -1.177 -1.010 0.000 0.847 0.699 0.986 1.047 0.762 0.885 0.771 +0 0.920 0.594 0.958 0.506 -0.675 0.760 -0.224 0.955 0.000 0.830 -1.259 -0.470 2.215 0.632 -0.610 -1.658 0.000 0.938 0.310 -0.793 3.102 0.924 0.878 0.989 1.214 0.778 0.813 0.803 +0 0.725 -0.151 -1.293 1.845 -0.418 0.614 -0.416 0.599 2.173 0.844 0.909 1.447 0.000 0.721 -0.906 -0.983 0.000 0.850 -1.024 1.437 0.000 0.979 0.979 1.137 0.898 0.638 0.767 0.766 +0 1.142 -0.388 -1.531 0.440 0.548 0.909 -1.397 -1.004 2.173 1.388 -0.665 0.578 2.215 0.483 -2.008 -1.532 0.000 0.498 1.733 0.364 0.000 2.337 1.698 0.984 0.870 1.747 1.382 1.088 +0 0.407 1.116 1.122 0.508 1.593 0.728 -1.158 -1.629 2.173 0.693 0.003 -0.526 0.000 1.245 -0.138 0.018 0.000 0.485 1.244 0.761 3.102 0.680 0.728 0.984 0.796 1.224 0.887 0.773 +0 0.941 1.236 0.309 0.421 -1.729 1.072 -0.268 -1.018 2.173 1.029 -0.012 1.112 2.215 0.714 0.390 -0.114 0.000 0.766 0.124 0.651 0.000 0.531 0.964 0.989 1.058 1.466 1.172 0.919 +1 1.898 1.209 1.677 1.083 1.127 0.789 0.204 0.343 2.173 0.829 0.597 0.044 0.000 0.896 0.837 -1.000 2.548 0.472 1.382 -0.956 0.000 0.778 0.728 0.987 0.854 1.052 0.938 0.823 +0 2.078 0.116 1.589 0.687 -1.296 1.041 0.058 0.074 1.087 0.618 0.450 0.478 1.107 0.591 -0.480 -1.365 0.000 0.771 -0.715 -0.310 0.000 0.619 0.852 0.984 0.900 0.483 0.951 0.810 +0 1.543 0.633 -0.593 0.699 -0.723 1.187 1.320 1.010 2.173 0.527 1.689 0.190 0.000 1.164 -1.048 -1.006 1.274 1.050 -0.152 1.015 0.000 1.265 1.093 0.973 0.950 2.724 1.485 1.248 +0 1.528 -0.490 0.185 1.757 -0.094 0.714 -0.966 -0.363 2.173 1.141 0.650 1.302 0.000 1.764 -1.653 -1.292 0.000 1.726 -0.474 1.288 3.102 0.878 0.930 0.989 0.858 1.195 1.040 1.068 +1 2.925 -0.099 0.405 1.998 0.024 2.810 -0.445 -1.491 0.000 0.753 0.831 -0.707 0.000 0.887 -0.433 1.572 2.548 1.124 -0.502 -0.151 3.102 1.068 0.937 1.126 0.707 0.764 0.828 0.849 +0 0.583 -0.771 -0.660 2.118 -0.751 1.121 0.582 0.760 2.173 0.651 1.387 1.012 0.000 1.812 0.609 1.425 2.548 0.794 0.839 -0.147 0.000 0.834 0.879 0.980 1.516 1.004 1.221 1.018 +1 1.063 -1.308 0.583 1.148 0.628 1.123 -0.293 -1.074 2.173 0.592 -0.667 1.394 0.000 0.411 -1.775 0.286 0.000 1.318 -0.084 -0.206 1.551 0.815 0.838 0.983 1.907 0.917 1.345 1.053 +1 0.688 -0.766 -0.142 2.263 -0.518 0.956 -0.227 1.537 2.173 0.352 1.092 1.367 0.000 0.784 0.362 -0.140 0.000 0.639 -0.198 -1.442 0.000 0.842 1.001 0.977 0.889 0.837 0.988 0.842 +0 0.481 -0.444 -1.645 0.562 1.072 0.723 -1.154 -0.496 2.173 0.868 -0.175 -1.482 0.000 0.819 -0.779 0.873 0.000 0.529 0.107 -1.363 3.102 1.188 1.156 0.985 0.670 0.646 0.694 0.696 +1 0.565 0.263 0.543 1.933 0.794 0.721 0.563 -1.108 2.173 1.281 2.108 -0.517 0.000 0.846 0.139 1.511 0.000 0.569 1.448 -1.175 0.000 0.841 0.714 0.994 0.757 0.722 0.938 0.848 +1 0.441 0.802 -0.334 1.660 0.779 0.985 0.869 -1.440 0.000 0.892 0.055 -0.348 2.215 0.425 2.491 -0.158 0.000 0.574 -0.522 0.822 3.102 1.636 1.272 1.000 0.908 0.605 0.971 0.892 +1 0.534 -0.669 -0.692 0.991 0.309 0.906 0.144 -1.325 0.000 1.372 0.508 0.583 2.215 0.923 0.809 -0.903 0.000 1.382 0.131 1.189 3.102 0.875 0.989 0.987 1.412 0.685 1.009 1.112 +0 0.647 -0.899 0.728 1.828 0.395 1.032 -0.623 -1.280 1.087 0.824 -0.388 1.724 0.000 1.067 -1.951 -0.370 0.000 0.593 0.244 0.752 3.102 1.971 1.278 0.996 1.435 0.892 0.963 1.060 +1 1.044 -0.750 0.623 0.229 0.514 0.900 0.209 -1.175 0.000 0.701 -1.290 1.335 2.215 0.875 0.304 -0.646 0.000 1.105 -0.078 0.451 3.102 0.740 0.948 0.976 0.809 0.771 0.879 0.802 +0 0.451 0.161 -0.277 0.826 1.434 0.494 0.729 0.237 2.173 0.472 1.295 0.603 0.000 0.663 -0.876 -1.177 2.548 0.724 0.652 -1.408 0.000 0.769 0.900 0.985 0.665 0.964 0.683 0.612 +0 0.881 0.500 0.990 2.439 0.462 1.042 2.367 -0.563 0.000 1.659 0.682 1.662 0.000 1.394 -0.484 -0.332 2.548 1.389 0.204 -1.146 0.000 0.834 0.851 0.990 1.040 0.992 1.027 0.965 +0 1.409 -0.207 0.458 1.114 -0.008 1.252 0.411 -1.519 1.087 0.700 1.425 0.797 2.215 0.447 -1.605 -0.844 0.000 0.569 1.222 -0.920 0.000 1.344 1.243 0.995 1.308 1.417 1.319 1.137 +0 0.456 1.473 -1.394 1.272 -0.052 0.935 -2.623 -1.252 0.000 1.038 -0.064 0.183 2.215 0.903 0.756 -1.410 0.000 1.317 0.516 1.177 0.000 0.880 1.103 0.988 0.583 0.460 0.680 0.713 +0 0.484 0.081 -1.641 1.783 1.640 1.009 0.608 -0.083 0.000 1.281 0.111 -0.410 0.000 0.931 0.394 1.129 2.548 0.788 0.493 -1.679 3.102 0.929 1.001 0.991 0.947 0.379 0.813 1.092 +0 1.496 -1.081 0.476 0.509 0.031 0.897 0.243 -1.641 2.173 1.461 -0.152 0.787 1.107 1.100 0.839 -1.206 0.000 1.552 -0.189 -0.703 0.000 1.078 0.978 0.992 1.165 1.414 1.256 1.263 +1 0.533 1.277 0.857 0.958 1.422 0.837 0.584 -0.682 1.087 0.913 -0.999 0.251 0.000 1.016 -0.729 1.535 2.548 0.442 -0.983 -0.898 0.000 0.714 0.810 0.981 1.028 1.357 0.911 0.863 +1 1.070 0.556 -0.033 0.922 -1.274 0.976 0.463 -0.768 2.173 0.916 1.619 0.815 0.000 1.265 1.312 1.316 0.000 1.773 0.869 0.292 0.000 0.920 0.981 1.237 0.795 0.976 0.998 0.885 +1 1.064 -0.151 1.454 0.656 -1.265 0.819 -0.729 -0.449 0.000 1.092 0.621 0.436 2.215 0.789 1.178 -1.253 0.000 1.003 0.747 0.995 0.000 0.903 0.916 0.995 0.740 0.713 0.810 0.766 +1 1.625 0.938 -0.590 0.931 -1.687 0.934 1.613 -0.024 0.000 1.573 2.497 1.499 0.000 1.336 0.678 0.011 0.000 2.557 -0.621 0.823 1.551 0.919 1.162 1.423 1.747 1.029 1.367 1.238 +0 0.396 0.388 0.182 2.253 -1.550 1.976 0.037 -0.864 2.173 2.089 0.743 0.726 0.000 1.490 -0.135 0.498 2.548 1.115 -0.152 0.919 0.000 1.036 0.886 1.309 1.315 2.024 1.605 1.379 +1 1.777 -1.437 0.545 0.324 0.447 0.584 -0.888 0.906 0.000 1.861 -0.663 -1.038 2.215 0.332 0.051 -0.936 0.000 0.402 -1.405 -1.715 3.102 0.881 1.174 0.991 0.612 0.605 0.934 0.802 +1 0.593 -0.792 0.136 1.532 -0.676 0.681 0.531 1.226 1.087 0.536 -0.048 0.391 0.000 0.997 -0.818 1.686 2.548 0.487 1.149 -0.720 0.000 0.762 0.906 0.987 1.116 0.901 0.868 0.752 +1 0.879 -1.438 0.430 0.637 -0.212 0.715 0.352 -1.681 2.173 1.232 1.380 1.275 0.000 1.090 0.707 -0.688 0.000 0.853 0.444 0.489 3.102 1.063 1.113 0.983 0.689 0.769 0.781 0.789 +0 2.928 -0.278 0.129 0.425 1.354 1.341 -0.324 1.666 1.087 0.633 -0.717 -0.765 2.215 0.860 0.126 -1.065 0.000 0.432 0.053 0.757 0.000 0.671 0.831 1.380 0.968 1.137 1.145 0.897 +0 0.496 0.908 0.904 0.830 -0.068 1.198 0.234 0.905 2.173 2.000 0.634 -0.922 2.215 0.973 0.890 1.337 0.000 0.680 1.283 -1.458 0.000 0.995 0.993 0.986 1.319 2.321 1.379 1.088 +1 1.563 -1.398 0.235 0.742 0.283 1.325 -0.746 1.307 2.173 0.506 1.028 -0.961 0.000 1.667 -1.021 -1.202 0.000 1.395 0.609 0.019 0.000 0.907 1.355 0.983 0.786 0.705 0.887 0.880 +1 0.578 1.509 0.206 1.197 1.189 1.189 0.429 -0.579 2.173 1.082 1.004 1.388 2.215 0.472 -0.632 0.596 0.000 0.367 -0.190 -0.902 0.000 0.461 0.802 0.983 1.572 1.712 1.193 0.991 +0 1.841 2.155 0.303 0.209 0.282 1.315 -1.245 -1.571 0.000 0.625 1.725 -1.725 0.000 1.717 -0.084 -0.283 2.548 0.722 0.715 -0.385 1.551 4.184 2.371 0.986 1.601 0.427 1.611 1.885 +0 0.871 -1.296 1.016 0.655 -1.494 0.565 -1.164 -0.190 2.173 0.534 -1.752 0.463 0.000 0.780 -0.413 -1.238 2.548 1.080 -1.257 1.644 0.000 0.877 0.824 0.985 0.820 0.737 0.639 0.617 +0 0.663 -0.509 0.123 1.114 0.846 0.808 -1.095 -1.147 2.173 0.966 -0.181 -0.418 2.215 0.842 -0.571 1.143 0.000 0.610 -1.639 1.217 0.000 0.573 1.013 0.988 1.042 1.008 0.885 0.777 +1 0.756 -0.407 -0.691 0.920 -1.523 0.617 -1.247 -0.132 0.000 0.603 -1.122 1.412 2.215 0.983 -1.259 0.828 0.000 1.068 0.581 0.977 3.102 1.070 0.865 0.983 0.979 0.819 0.809 0.779 +0 0.862 0.015 -1.363 1.406 1.358 0.469 -0.987 0.583 2.173 0.865 0.850 -0.402 0.000 0.834 1.324 0.098 2.548 0.874 0.589 -0.959 0.000 0.878 1.163 0.986 1.017 1.257 0.898 0.838 +1 1.089 0.926 1.253 0.411 -0.863 0.948 0.260 0.973 0.000 1.369 0.240 -0.998 2.215 0.684 0.766 0.266 2.548 0.499 1.237 -1.226 0.000 0.956 0.696 0.987 1.020 0.984 0.831 0.781 +1 1.014 1.240 -0.514 0.689 -0.232 1.706 0.788 1.366 2.173 0.730 0.083 -0.645 0.000 0.657 2.468 -0.237 0.000 0.744 0.800 0.362 3.102 1.870 1.091 0.979 1.756 0.944 1.193 1.122 +0 0.795 -0.492 0.453 0.395 -1.642 0.408 -0.771 1.406 0.000 1.040 -0.318 -0.968 2.215 0.901 -0.754 -0.073 2.548 0.640 -0.111 1.002 0.000 0.379 0.801 0.991 0.632 0.787 0.652 0.600 +0 1.831 -0.047 -1.168 1.818 -1.282 1.438 0.037 0.238 0.000 0.794 -0.241 -0.187 2.215 1.335 -1.116 1.294 0.000 2.173 2.007 0.991 0.000 0.684 1.108 1.001 0.824 1.176 0.884 0.970 +1 1.931 0.996 0.211 0.586 -0.922 1.242 1.125 -1.636 2.173 0.755 1.642 -0.675 0.000 1.369 -0.356 1.010 0.000 0.484 0.782 0.748 0.000 0.814 1.030 1.256 0.887 0.900 0.971 0.826 +1 0.749 -0.553 0.766 0.880 -0.468 0.526 -0.102 -1.634 2.173 1.311 -0.985 1.262 2.215 1.163 -1.190 -0.574 0.000 1.058 -0.735 -0.162 0.000 0.515 0.955 1.008 0.935 0.845 0.870 0.766 +0 1.049 -1.407 1.592 0.688 1.318 0.897 0.353 -0.221 0.000 0.327 -0.336 0.303 2.215 0.296 1.299 -0.030 0.000 0.367 1.160 0.415 0.000 0.672 0.739 0.977 0.847 0.353 0.550 1.020 +0 0.654 -1.450 0.128 0.826 -1.374 0.777 -1.263 -1.186 0.000 1.035 -2.167 -1.257 0.000 1.778 1.053 0.698 1.274 1.582 -0.023 0.122 3.102 0.953 1.697 0.994 1.861 1.026 2.076 1.601 +0 1.066 -0.232 -1.486 0.040 -1.530 0.763 -0.264 0.437 1.087 0.361 1.669 0.550 0.000 1.345 0.276 -1.322 2.548 0.715 1.024 -0.832 0.000 0.648 0.979 0.692 0.435 1.313 0.821 0.705 +1 1.928 0.024 -0.101 0.851 -0.782 0.772 0.495 -1.334 2.173 0.630 0.549 -1.649 2.215 1.472 0.272 1.112 0.000 0.637 0.872 0.033 0.000 0.973 1.032 1.021 0.952 0.289 0.793 0.805 +1 1.909 2.020 0.450 0.509 1.310 0.747 1.331 -1.513 2.173 0.864 0.538 -0.642 2.215 0.805 2.021 1.631 0.000 0.946 1.718 -0.347 0.000 0.944 1.052 0.990 1.114 0.964 0.999 0.856 +1 0.363 -1.188 -0.139 2.492 -1.121 0.929 0.323 1.157 2.173 0.643 -0.660 0.235 0.000 0.942 -0.420 1.228 0.000 1.603 0.069 0.501 0.000 0.906 0.902 1.019 1.677 1.130 1.072 0.997 +0 0.706 0.967 -1.518 1.296 1.045 0.368 1.534 -0.688 0.000 0.690 -1.752 -0.161 0.000 1.451 -1.397 1.330 2.548 1.427 -0.117 -0.691 0.000 0.897 0.872 0.988 1.605 1.279 1.139 1.021 +1 1.347 -0.565 1.673 0.457 1.554 2.792 -0.745 0.474 0.000 2.670 0.405 -0.969 2.215 1.847 0.243 -1.408 2.548 0.595 0.439 1.097 0.000 0.671 0.948 0.986 1.101 0.926 1.283 0.992 +0 0.956 0.597 -1.681 0.213 0.039 0.859 0.074 -0.077 2.173 0.829 -0.366 -1.297 2.215 0.767 0.036 1.026 0.000 0.895 0.790 1.580 0.000 0.951 0.976 0.988 0.895 1.142 0.805 0.720 +1 1.698 -0.884 -0.603 0.720 0.064 0.674 -0.693 1.060 2.173 0.636 -0.198 0.027 0.000 1.237 -0.221 -1.697 1.274 1.113 0.415 1.551 0.000 1.144 0.939 0.988 1.018 0.742 0.861 0.806 +1 1.044 -1.352 -1.229 0.896 -0.263 1.325 -0.786 -1.511 2.173 0.737 -1.496 0.552 0.000 1.154 -0.684 0.172 2.548 1.219 1.058 0.747 0.000 0.661 0.671 1.025 1.018 1.539 1.002 0.870 +1 1.321 -0.470 0.926 0.517 -0.192 1.395 -0.534 -1.425 0.000 1.694 0.479 0.037 2.215 1.220 1.999 1.167 0.000 1.883 0.593 -0.386 3.102 0.770 1.500 0.986 1.010 0.625 1.358 1.135 +1 0.787 0.460 -0.567 1.286 -1.215 1.339 -0.164 -0.732 2.173 2.103 -2.307 0.867 0.000 0.949 -0.867 1.099 0.000 1.086 -0.604 0.172 0.000 0.841 0.785 0.990 0.688 0.850 0.862 0.773 +1 2.035 -0.779 0.207 1.149 0.771 1.028 -0.347 -1.063 1.087 0.701 1.888 -0.165 0.000 0.712 -0.047 -1.640 1.274 2.066 1.162 1.706 0.000 0.993 1.066 1.031 0.979 0.553 1.016 0.852 +0 0.858 -0.158 -0.991 0.605 1.371 0.806 0.158 1.463 0.000 0.744 -0.008 0.007 2.215 1.390 1.293 -0.586 2.548 0.838 0.855 0.922 0.000 0.813 0.987 0.991 1.272 1.003 0.925 0.898 +0 1.084 0.343 1.013 1.366 1.570 0.522 -0.205 0.056 2.173 0.774 -0.523 -0.741 0.000 0.458 -2.346 -0.399 0.000 1.218 0.507 1.621 3.102 1.135 0.970 0.986 0.593 0.905 0.896 0.915 +0 1.823 -0.803 0.155 0.827 0.512 0.969 0.091 -1.276 2.173 0.530 0.507 1.288 0.000 0.437 1.185 -0.948 0.000 0.402 -0.971 1.170 3.102 0.729 0.785 0.978 0.560 0.693 0.991 0.937 +1 0.454 0.409 1.624 0.502 1.660 1.147 -1.001 1.029 2.173 1.228 -0.602 -0.428 2.215 1.590 1.235 -0.293 0.000 0.803 -0.842 -1.686 0.000 0.538 0.919 0.987 1.700 1.723 1.711 1.392 +1 1.254 0.433 -0.735 0.152 1.100 0.928 -0.161 0.625 2.173 0.399 -0.565 1.568 1.107 0.427 -2.404 1.732 0.000 0.501 -1.540 -1.247 0.000 0.306 1.219 0.986 0.652 0.698 0.744 0.782 +0 0.491 1.219 -1.519 1.551 1.009 1.063 0.179 1.736 2.173 1.348 0.804 -0.252 2.215 1.319 -0.218 -0.323 0.000 0.620 -0.769 -1.553 0.000 0.958 1.075 0.989 1.287 1.813 1.222 1.179 +0 0.569 -1.331 -1.239 1.098 1.564 1.255 -1.641 -0.270 0.000 1.206 1.206 1.414 0.000 0.876 0.350 0.811 2.548 0.839 0.617 -0.481 3.102 1.114 0.875 0.982 1.478 0.613 1.215 1.608 +1 1.307 -0.702 -1.619 1.493 -0.015 0.427 0.363 -0.015 2.173 0.552 -0.650 0.640 0.000 0.690 1.035 -0.882 2.548 1.265 -0.056 1.413 0.000 0.920 0.779 1.920 1.110 0.546 0.896 0.770 +1 1.596 -0.384 1.026 1.259 1.449 0.437 -0.998 -0.115 2.173 0.700 -1.327 -1.342 2.215 0.615 1.240 -0.582 0.000 0.390 -0.686 -1.375 0.000 0.798 1.031 0.982 0.943 0.742 0.773 0.837 +1 0.411 0.919 -1.575 1.321 0.230 0.725 1.090 0.992 2.173 0.660 1.415 -0.544 0.000 0.756 0.622 1.572 2.548 0.415 1.639 -0.396 0.000 0.886 0.906 1.020 0.708 0.499 0.602 0.591 +1 0.616 1.620 -0.370 1.340 -0.566 2.037 0.733 0.902 2.173 1.775 -2.561 -1.128 0.000 0.893 0.510 -1.560 0.000 0.748 0.577 0.450 0.000 0.878 1.104 0.990 0.454 0.789 1.026 0.850 +0 1.293 1.119 0.048 0.432 1.649 0.875 1.609 1.048 2.173 1.277 1.158 -1.120 1.107 0.427 0.394 -0.721 0.000 0.366 0.018 0.269 0.000 0.351 0.804 1.027 0.921 1.481 0.886 0.704 +1 2.075 -1.040 -1.303 1.249 -0.448 0.669 -2.452 0.456 0.000 1.050 -1.154 0.270 2.215 1.211 0.337 1.439 0.000 0.711 0.310 -0.863 0.000 0.896 0.707 1.555 1.276 0.655 0.855 0.931 +1 1.613 1.361 1.703 0.769 1.245 0.911 1.745 -0.243 0.000 0.637 0.889 0.579 2.215 0.482 0.751 -1.353 0.000 0.510 0.226 -1.186 1.551 1.158 0.943 0.982 0.709 0.541 0.639 0.750 +0 0.842 0.981 1.457 1.649 0.522 0.521 -0.264 -1.033 0.000 0.773 0.682 -0.528 2.215 0.782 -0.671 -1.576 0.000 0.424 -0.598 1.281 0.000 0.646 0.710 1.218 0.652 0.494 0.646 0.697 +1 1.868 0.681 -1.647 0.801 -1.618 0.470 -0.395 0.517 0.000 1.147 0.901 -0.425 2.215 0.933 1.144 -0.773 0.000 1.020 0.979 0.397 3.102 0.874 0.765 0.982 0.908 0.670 0.868 0.747 +1 0.346 1.307 0.018 0.214 -1.352 0.991 0.202 -0.075 0.000 0.805 0.938 1.566 2.215 0.949 0.075 0.665 0.000 1.309 0.275 -1.189 3.102 0.910 1.094 0.984 0.643 0.641 0.742 0.665 +1 0.679 0.765 0.946 0.806 1.301 0.673 -0.101 -0.330 2.173 0.400 -0.349 0.813 0.000 0.679 1.904 -0.443 0.000 1.145 1.108 -1.406 3.102 1.439 0.989 0.988 0.938 1.053 0.823 0.839 +0 0.538 -1.744 -0.236 1.491 -1.139 0.290 -1.425 1.730 0.000 0.767 -0.734 0.362 2.215 0.522 -2.175 0.131 0.000 0.565 -1.360 0.855 3.102 0.775 0.762 0.989 0.658 0.371 0.622 0.574 +0 1.056 0.704 1.188 1.549 -1.729 1.054 -2.542 -0.295 0.000 0.831 0.013 1.146 2.215 1.535 -0.066 -0.085 0.000 1.038 0.360 -0.620 1.551 1.747 1.031 0.989 0.633 0.856 0.810 0.814 +0 1.974 -0.507 -1.319 0.254 1.283 0.744 1.521 0.952 0.000 0.890 0.823 -0.003 2.215 0.482 1.107 -1.527 0.000 0.513 0.491 -0.386 3.102 0.865 0.957 0.988 0.645 0.221 0.747 0.864 +0 1.419 0.386 0.329 3.072 0.026 1.708 1.157 -1.722 1.087 1.067 0.698 -1.057 0.000 1.743 2.727 1.667 0.000 0.895 -1.022 0.044 3.102 1.043 1.026 0.977 2.329 2.366 1.745 1.381 +1 0.893 0.521 -0.511 0.605 0.911 1.038 1.163 -0.473 2.173 1.056 1.469 -1.092 2.215 1.573 0.378 1.236 0.000 1.675 0.259 0.664 0.000 0.888 1.482 0.987 0.839 0.855 1.184 0.957 +1 0.618 -0.963 -1.669 0.662 1.741 1.219 -0.260 0.422 0.000 0.545 -0.145 -1.129 2.215 0.947 1.119 -1.358 0.000 1.085 0.558 -0.184 3.102 0.474 0.700 0.983 1.463 0.598 1.006 1.294 +0 0.439 -1.789 1.531 1.177 -0.823 0.950 -1.118 1.248 1.087 0.886 0.575 0.250 0.000 1.231 0.870 -1.059 0.000 0.494 -1.366 -0.438 0.000 1.277 0.801 0.991 0.958 1.116 0.917 0.852 +1 0.467 0.548 1.247 0.581 -0.217 2.671 1.131 -1.362 0.000 1.570 -0.929 0.798 0.000 2.021 -0.485 -0.082 2.548 2.711 -0.010 0.284 1.551 0.689 1.126 0.987 0.765 0.746 0.911 0.781 +0 0.326 -1.490 -1.234 0.604 0.494 0.686 -1.240 -0.310 2.173 0.886 -1.046 1.651 0.000 0.479 0.088 -0.726 2.548 0.956 -1.241 0.996 0.000 0.713 1.034 0.997 0.558 0.581 0.704 0.669 +1 0.837 -2.246 0.393 0.690 -0.157 0.650 -1.549 0.027 1.087 1.376 -0.913 -1.272 1.107 1.409 -1.481 -1.662 0.000 0.654 -2.086 1.566 0.000 0.538 1.028 0.974 1.141 1.356 0.864 0.831 +1 1.227 0.764 -1.256 0.257 0.513 0.852 -0.716 0.440 0.000 0.826 0.276 -1.117 2.215 0.530 0.377 0.920 0.000 0.524 -0.111 1.663 3.102 0.884 1.166 0.986 0.517 0.375 0.694 0.679 +1 0.403 1.827 0.337 0.616 -1.165 1.102 1.084 -0.087 0.000 1.028 0.895 -0.420 0.000 1.754 0.517 1.109 0.000 0.901 -0.249 1.556 0.000 0.825 0.755 0.987 0.638 0.757 0.564 0.596 +0 0.781 1.599 0.839 1.846 -0.348 0.996 -0.652 1.677 1.087 0.921 -0.492 -0.703 0.000 1.300 0.219 0.537 1.274 0.587 -0.083 1.263 0.000 0.959 0.973 1.458 2.288 1.381 1.554 1.301 +0 2.006 0.241 -0.748 0.146 -1.374 1.470 -0.488 -1.108 2.173 1.771 -2.059 0.661 0.000 1.613 -0.483 0.148 2.548 3.528 0.089 1.173 0.000 0.906 1.192 0.992 0.839 1.737 1.259 1.025 +1 1.635 -1.594 0.945 0.478 1.327 0.892 -0.844 -0.162 2.173 1.745 -0.917 -1.204 0.000 0.430 -0.671 -0.847 0.000 1.013 0.092 0.555 3.102 0.443 1.057 0.991 1.308 0.787 1.017 1.055 +1 0.811 -0.938 0.851 0.877 -1.434 0.743 -1.007 -0.032 2.173 1.564 -0.496 -1.350 0.000 0.850 0.437 0.425 2.548 1.328 1.149 0.049 0.000 2.721 1.677 1.032 0.879 0.904 1.267 1.094 +0 1.600 -0.949 -0.531 1.396 -0.598 2.468 -1.353 -0.604 0.000 1.386 -0.191 0.995 1.107 2.251 0.605 1.290 1.274 0.976 -0.967 0.802 0.000 2.276 2.531 0.999 1.884 0.970 2.270 1.826 +0 0.482 2.308 1.577 1.585 1.346 0.935 1.048 -1.272 2.173 0.817 -1.365 0.000 0.000 1.947 -0.490 0.495 2.548 1.852 -0.219 -0.640 0.000 1.303 1.236 1.004 0.946 2.245 1.463 1.505 +0 2.797 -0.806 0.021 1.439 0.487 1.557 1.758 1.079 0.000 1.273 -1.175 -1.049 0.000 1.544 -0.657 -1.552 2.548 2.237 -0.361 -1.071 3.102 6.909 4.090 1.134 1.526 0.632 2.554 2.418 +0 0.577 -1.617 0.550 1.626 0.418 0.806 0.805 -1.389 1.087 0.512 1.808 -1.115 0.000 0.608 1.704 0.415 0.000 0.777 -0.875 -1.338 3.102 0.840 0.906 0.981 1.004 0.899 2.068 2.443 +1 1.149 1.214 -1.089 0.707 -0.564 0.576 1.837 0.412 0.000 0.545 0.172 -1.410 2.215 0.674 -0.338 1.301 0.000 1.139 0.437 0.784 3.102 1.720 1.012 0.981 0.858 0.665 0.757 0.851 +1 1.623 0.303 -0.872 0.340 -0.466 2.228 1.041 1.409 0.000 2.718 -0.044 0.143 0.000 0.804 -0.673 -0.818 2.548 1.006 2.146 -1.477 0.000 2.248 1.759 0.993 0.564 0.467 1.448 1.345 +1 0.436 -0.509 -1.555 1.165 -0.011 0.800 0.078 -0.738 0.000 0.890 0.289 1.498 1.107 0.749 1.136 -0.742 0.000 1.384 -0.087 0.693 1.551 0.861 1.090 0.989 0.857 0.695 0.837 0.752 +1 0.662 0.176 -0.210 0.819 1.440 0.913 -0.704 -1.337 2.173 0.548 -1.024 0.729 0.000 1.171 0.214 0.782 0.000 1.452 -0.949 -0.495 1.551 0.850 1.054 1.016 0.906 0.879 0.915 0.808 +0 0.617 -1.438 -1.351 1.439 -0.095 0.629 -0.615 1.157 2.173 0.767 -1.190 -0.608 1.107 0.648 -2.146 1.180 0.000 0.524 -1.539 1.741 0.000 0.353 0.736 1.182 1.026 1.068 0.765 0.682 +0 1.246 -0.426 1.734 0.712 0.462 1.037 -0.780 -0.976 2.173 1.107 2.167 0.348 0.000 0.591 -1.772 0.876 0.000 0.880 0.164 -1.067 0.000 1.290 1.085 1.189 0.677 1.037 0.766 0.730 +1 0.583 0.393 -1.245 1.586 1.218 0.877 -0.834 0.087 1.087 0.929 0.768 -0.582 0.000 0.726 -0.502 -1.354 2.548 1.071 -0.601 1.087 0.000 1.687 1.086 1.062 1.302 0.968 0.922 0.912 +1 0.950 1.222 0.941 0.366 0.017 1.032 2.527 -1.316 0.000 0.722 2.208 -0.447 0.000 1.637 1.281 0.310 2.548 1.297 0.852 1.530 1.551 1.319 1.301 0.989 0.757 1.015 1.116 1.045 +1 0.837 1.281 1.703 1.308 0.426 1.582 1.094 -0.995 0.000 1.412 0.366 0.597 0.000 0.984 1.162 0.785 1.274 1.365 1.303 -0.757 3.102 1.270 0.884 1.324 0.704 0.881 0.878 0.894 +1 1.037 1.955 0.489 0.492 -0.897 1.252 0.563 -1.509 2.173 1.371 -0.159 0.300 0.000 0.998 -1.029 0.804 0.000 1.181 0.540 -0.581 1.551 1.015 0.973 0.987 1.275 0.957 1.048 1.049 +1 1.408 0.505 0.460 1.324 -0.996 0.710 1.561 1.053 2.173 0.825 1.205 0.531 0.000 1.201 1.778 -1.342 0.000 1.353 0.037 -0.293 3.102 0.689 0.963 1.829 0.983 1.307 0.975 0.967 +0 0.627 -0.368 -0.204 1.086 -1.131 0.674 0.176 0.406 2.173 1.090 0.561 -1.586 2.215 0.871 1.295 -0.232 0.000 0.791 -1.121 1.086 0.000 1.911 1.228 0.991 1.117 1.255 0.993 0.990 +0 0.906 0.053 -1.025 1.530 -0.868 0.543 0.146 0.708 0.000 0.869 -0.134 0.025 2.215 1.025 -0.736 1.142 1.274 0.635 0.139 1.171 0.000 0.824 0.809 0.975 1.005 0.914 0.837 0.836 +1 0.848 -0.304 0.395 0.451 1.429 0.383 0.502 0.928 0.000 0.620 1.057 -1.583 1.107 0.982 -0.149 -0.422 0.000 0.805 -0.573 -0.405 1.551 1.101 0.932 0.989 0.667 0.851 0.646 0.609 +1 0.560 -0.423 -1.118 0.705 0.651 0.681 -0.886 -1.730 2.173 1.277 -0.933 -0.917 2.215 0.999 -1.344 0.436 0.000 1.095 -1.613 0.896 0.000 0.822 0.935 0.989 0.830 0.920 0.785 0.686 +0 0.292 -1.473 0.212 2.889 -0.722 0.814 -1.387 0.609 2.173 1.048 -1.752 -1.058 0.000 0.844 -0.834 -0.199 2.548 2.633 -1.540 0.917 0.000 0.922 0.974 0.989 1.293 0.734 0.888 1.047 +0 0.660 -1.565 -0.146 1.221 0.924 0.965 -0.930 0.053 2.173 1.485 -0.516 -1.422 2.215 1.038 -1.196 1.543 0.000 0.792 -1.485 -0.900 0.000 0.841 0.891 1.022 0.850 1.748 1.077 0.910 +0 0.512 -0.689 0.559 1.678 1.386 1.204 -0.541 -1.189 2.173 0.957 0.222 0.093 2.215 0.851 -1.175 -0.108 0.000 0.865 1.165 0.767 0.000 1.820 1.166 0.986 1.175 1.576 1.139 1.027 +1 1.583 0.158 -0.597 0.434 0.915 0.736 -1.038 -1.544 2.173 0.449 -1.431 0.069 2.215 0.316 -1.781 0.437 0.000 0.454 -1.104 0.603 0.000 0.307 0.682 1.124 0.845 0.859 0.814 0.740 +1 0.929 0.634 -1.486 0.290 -0.406 1.568 -1.725 1.243 0.000 1.726 -0.290 -0.298 2.215 1.115 1.417 -0.738 0.000 1.302 0.083 0.645 0.000 0.922 1.200 0.988 1.357 0.920 1.016 0.993 +0 1.532 0.972 1.010 0.872 1.291 0.811 0.208 -1.183 0.000 0.816 1.054 -0.234 2.215 0.846 -0.674 0.228 2.548 0.386 -0.663 -0.375 0.000 0.724 0.909 0.969 1.358 0.990 1.029 0.985 +1 0.468 -0.263 0.809 0.941 -0.254 0.824 0.866 1.305 2.173 1.317 0.874 -1.557 0.000 1.041 0.901 0.192 2.548 1.156 0.116 -0.414 0.000 1.513 1.215 0.994 0.902 0.976 0.907 0.803 +1 3.280 -0.404 1.408 0.578 -0.635 0.672 -0.732 -0.046 2.173 2.028 -0.005 -0.710 0.000 0.519 -0.747 0.972 2.548 0.399 1.031 0.346 0.000 1.230 1.076 1.838 1.409 0.585 0.889 1.044 +1 0.966 -0.702 -1.111 0.749 0.882 0.524 0.466 0.339 2.173 0.294 -1.392 1.628 0.000 0.665 -2.021 0.159 0.000 0.713 0.352 -1.684 3.102 0.713 0.879 1.149 0.900 0.626 0.788 0.714 +0 2.338 -1.420 0.795 0.438 0.075 1.305 -0.920 -0.845 2.173 0.876 -1.080 -0.207 0.000 1.423 -0.980 1.453 2.548 0.642 -1.350 -1.646 0.000 0.959 1.006 0.986 0.865 1.494 1.151 0.966 +0 0.433 0.579 1.001 0.812 -1.199 1.224 1.279 1.078 1.087 1.404 -0.135 -0.652 0.000 0.740 0.493 -1.177 0.000 0.938 -0.359 0.403 3.102 0.895 0.896 0.993 1.355 1.279 1.223 1.027 +0 1.920 -0.563 0.188 0.787 0.596 0.835 -1.803 -1.487 0.000 0.900 -0.889 1.736 1.107 0.349 1.577 -1.561 0.000 0.685 -0.170 -0.885 0.000 0.682 0.899 0.986 0.687 1.195 0.872 0.802 +0 1.451 -0.442 -0.441 0.354 0.333 1.132 -0.626 -0.885 2.173 1.479 -0.780 1.024 0.000 0.770 0.958 1.257 1.274 0.513 -0.180 1.054 0.000 0.339 0.915 0.986 0.811 1.547 1.085 0.976 +1 0.768 -0.836 1.352 1.233 -0.152 0.774 -0.076 1.142 0.000 1.419 -0.195 -0.787 2.215 0.752 -0.260 0.547 0.000 0.857 -0.114 1.683 3.102 0.716 1.340 1.317 0.797 0.790 0.815 0.799 +1 0.973 0.524 1.577 0.499 1.650 1.053 0.959 0.079 0.000 0.518 1.071 1.192 1.107 0.436 -0.327 1.740 2.548 0.577 -2.212 -1.244 0.000 0.928 0.998 0.984 0.710 0.468 0.701 0.807 +1 0.607 -1.734 1.443 1.019 0.645 0.752 0.781 -1.011 2.173 0.520 -0.568 -1.528 0.000 0.629 -0.682 0.615 1.274 0.449 1.810 0.758 0.000 0.845 0.786 0.988 0.515 1.119 0.930 0.769 +1 0.695 1.537 -0.672 1.545 -0.794 0.799 1.111 0.742 2.173 1.166 0.030 1.022 2.215 0.657 1.287 -0.261 0.000 0.681 1.884 1.696 0.000 0.793 0.831 0.989 2.218 0.887 1.554 1.173 +0 2.717 -0.525 0.903 2.327 0.516 1.663 -0.042 -0.959 2.173 1.006 -0.092 -0.360 2.215 0.762 0.082 -1.431 0.000 0.945 -0.021 1.463 0.000 0.480 0.900 1.189 1.402 0.980 1.637 1.265 +1 0.804 0.922 -0.244 0.241 0.738 1.167 0.144 -1.053 2.173 0.715 0.576 0.686 0.000 0.888 0.382 1.720 2.548 0.391 1.332 1.203 0.000 0.476 1.166 0.985 0.728 0.779 0.747 0.693 +1 0.488 1.230 1.552 0.227 1.537 0.510 0.869 -0.654 0.000 0.613 1.645 -0.152 0.000 0.612 -0.717 1.200 2.548 0.481 1.281 1.027 3.102 0.719 1.133 0.989 0.599 0.602 0.698 0.673 +0 0.999 -0.495 -0.580 0.305 0.785 1.170 2.173 -0.569 0.000 1.042 0.916 0.537 2.215 0.889 1.326 1.586 0.000 2.062 -0.591 1.339 3.102 1.842 1.641 0.988 0.879 1.503 1.814 1.715 +1 1.038 -1.460 0.819 0.586 -1.021 1.135 -0.043 -0.539 0.000 0.713 -0.447 1.647 0.000 0.944 -0.206 -1.401 2.548 1.261 0.646 0.346 0.000 1.399 0.983 1.076 0.521 0.418 0.614 0.714 +1 0.485 1.309 -0.784 0.545 -1.079 0.965 -0.525 0.158 0.000 0.934 0.702 -0.993 0.000 1.659 -0.335 1.118 2.548 1.181 0.056 -0.299 0.000 0.923 1.042 0.996 0.981 0.827 0.931 0.807 +1 1.403 1.477 1.714 0.929 -0.956 1.201 1.254 -0.256 2.173 1.164 1.502 1.196 0.000 1.003 0.255 0.500 2.548 0.919 1.642 -1.047 0.000 1.247 1.181 1.062 1.222 1.110 1.039 0.969 +1 0.538 1.221 0.603 0.786 -1.049 1.723 1.436 1.285 0.000 2.041 0.755 -0.481 0.000 1.668 0.233 -1.071 2.548 1.448 0.654 -0.090 0.000 0.772 1.111 0.990 0.844 1.028 0.982 0.925 +0 1.496 1.807 -0.762 0.586 -1.348 0.803 1.347 0.745 0.000 0.433 1.002 -0.730 2.215 0.599 1.272 1.059 0.000 1.012 0.841 1.471 1.551 0.883 0.777 0.991 0.876 0.547 0.618 0.696 +1 0.623 -2.071 -0.523 1.502 0.475 0.701 -0.932 -1.679 2.173 0.380 -1.715 0.910 0.000 1.050 -0.794 -1.222 1.274 0.409 -1.416 -1.203 0.000 0.486 0.569 1.050 1.184 0.430 0.909 0.691 +0 0.869 -1.243 -1.520 1.482 0.181 1.598 -0.898 -1.025 1.087 1.036 1.073 1.064 1.107 0.718 0.844 0.159 0.000 0.680 2.340 0.641 0.000 0.898 0.847 1.571 1.386 2.877 1.926 1.842 +0 0.601 0.889 1.639 0.792 -0.093 0.861 0.359 1.598 2.173 1.388 0.463 1.014 0.000 2.567 -0.430 -0.316 0.000 1.168 1.369 -1.719 0.000 1.459 1.054 0.987 0.662 0.950 0.871 0.766 +0 2.442 -0.542 -0.318 0.938 0.615 0.572 -1.347 0.699 0.000 0.840 -1.013 1.191 0.000 1.383 -1.054 -1.459 2.548 0.802 1.240 1.684 0.000 0.673 0.974 1.563 0.851 0.180 0.843 0.853 +0 0.748 -0.178 -0.751 1.096 -1.097 0.534 1.159 -1.022 0.000 0.552 1.885 -1.740 0.000 1.430 1.143 0.869 2.548 1.566 -0.111 0.134 3.102 0.830 1.010 0.978 0.926 1.111 0.938 0.875 +0 2.328 -0.849 -0.787 0.845 -1.232 1.507 -0.506 0.771 0.000 0.920 -1.056 -1.622 2.215 0.736 -1.174 0.017 2.548 1.017 0.271 0.583 0.000 0.874 0.940 0.978 0.834 0.875 0.920 1.084 +0 0.530 1.968 -0.666 1.719 -1.336 0.811 1.924 1.282 0.000 1.155 0.304 0.217 0.000 1.035 0.932 0.643 1.274 1.272 0.275 -0.747 1.551 2.514 1.423 0.983 1.440 0.889 1.158 1.325 +1 0.714 1.096 -1.608 1.130 -0.665 0.467 0.632 1.157 0.000 0.513 -0.905 0.889 2.215 0.483 -0.154 -1.198 0.000 1.120 0.142 -0.386 3.102 0.922 1.011 0.992 0.772 0.740 0.870 0.763 +1 1.123 1.909 -0.819 0.267 1.591 1.142 2.741 1.347 0.000 1.035 0.886 -1.704 0.000 2.641 0.299 -0.137 2.548 0.800 0.012 -0.542 0.000 0.869 1.075 0.982 0.473 0.762 0.787 0.853 +0 1.014 -1.994 1.303 0.856 0.353 0.458 -2.789 0.058 0.000 0.620 -2.831 -1.428 0.000 0.346 0.246 -0.085 0.000 0.642 -0.897 -1.050 1.551 1.103 0.872 0.988 0.843 0.309 0.800 0.743 +0 1.028 -0.064 0.594 1.347 1.069 0.815 -1.695 -1.162 0.000 1.054 -1.246 -0.660 1.107 0.443 -0.840 0.870 2.548 0.392 2.087 0.055 0.000 3.586 1.938 0.995 1.591 0.725 1.315 1.315 +1 0.590 1.583 0.880 2.002 1.030 1.245 0.454 -0.029 0.000 0.925 -1.633 -1.702 0.000 0.975 0.704 -1.381 0.000 1.409 0.543 -0.534 3.102 0.605 0.666 1.002 2.159 0.625 1.537 1.286 +0 0.573 0.002 -1.054 1.101 -0.091 1.234 0.158 -0.794 2.173 1.285 2.792 0.701 0.000 0.913 1.425 1.564 0.000 1.650 -1.087 1.193 0.000 0.490 0.552 0.985 0.891 0.531 0.849 0.925 +0 0.728 2.079 0.378 1.072 -1.296 0.603 1.038 -0.997 2.173 0.515 -0.350 -0.123 0.000 1.324 0.646 1.181 2.548 0.833 1.004 0.568 0.000 0.891 0.908 1.222 1.095 1.046 0.855 0.855 +0 0.860 1.951 0.577 1.180 -1.724 1.987 0.010 -0.103 0.000 1.173 -2.492 1.607 0.000 0.740 0.815 -1.006 1.274 0.411 2.024 1.505 0.000 0.259 0.600 1.223 0.746 0.437 0.615 0.534 +1 0.788 -0.503 -1.554 0.544 -1.085 0.764 0.483 -0.384 1.087 0.716 -0.335 1.422 2.215 0.622 1.316 -0.784 0.000 0.985 0.401 1.459 0.000 0.892 0.880 0.983 0.834 1.182 0.734 0.681 +1 1.602 -0.482 0.950 1.322 0.472 0.677 1.982 -1.200 0.000 0.700 -1.234 -0.934 2.215 0.898 0.361 -0.506 2.548 0.811 -0.927 1.513 0.000 0.425 0.690 0.985 1.082 0.848 0.917 0.735 +1 1.225 0.116 0.092 0.736 0.873 1.149 -0.047 1.182 0.000 2.263 0.312 -0.762 0.000 0.911 0.663 0.838 2.548 1.177 2.419 -1.470 0.000 0.899 0.789 0.984 0.588 0.397 0.512 0.546 +1 1.898 -0.746 0.746 0.312 -1.590 1.130 -0.815 -0.686 2.173 0.491 0.552 -1.143 2.215 0.779 -0.315 1.072 0.000 0.374 -0.082 0.318 0.000 0.381 0.964 0.988 0.908 0.934 0.934 0.745 +0 1.794 -0.438 0.286 0.532 0.221 0.548 1.236 -1.684 0.000 1.048 0.250 -1.356 2.215 0.857 -0.319 1.316 2.548 1.275 0.023 -0.424 0.000 1.424 1.003 0.988 0.821 0.742 0.853 0.874 +1 1.048 -0.133 -0.045 1.171 -0.480 1.119 -0.481 1.593 0.000 1.116 0.704 1.170 0.000 1.673 -0.078 -0.633 2.548 0.851 0.712 0.208 3.102 0.819 0.671 0.982 0.698 0.769 0.876 0.818 +0 2.582 -1.329 1.319 1.076 1.038 1.456 -1.316 -0.651 0.000 0.828 -0.905 -0.453 0.000 0.724 -0.299 1.223 2.548 0.745 -0.345 0.074 3.102 0.624 1.217 0.993 0.967 0.484 0.760 1.087 +0 0.291 1.048 -0.077 1.333 -0.877 0.546 1.539 0.439 0.000 0.816 -0.192 1.308 2.215 0.828 1.119 1.270 0.000 1.163 0.661 -0.763 3.102 0.846 1.052 0.989 0.843 0.955 1.154 0.989 +1 2.282 -1.030 -1.371 0.678 -0.296 1.450 -1.066 1.378 2.173 1.634 -0.654 -0.119 0.000 0.751 0.007 0.628 2.548 0.701 -1.102 -0.813 0.000 0.938 0.883 1.420 1.338 1.099 1.120 1.077 +1 0.880 1.128 -1.297 0.348 -0.336 0.178 1.480 0.302 0.000 0.498 -0.573 -0.498 2.215 1.024 0.453 0.889 2.548 1.012 2.151 -1.219 0.000 0.850 1.163 0.984 0.673 0.840 1.065 0.912 +1 0.669 -1.065 0.454 1.022 1.188 1.509 0.252 1.534 0.000 2.303 -0.294 -0.544 2.215 0.437 -0.311 0.681 0.000 0.732 0.767 -0.359 3.102 1.108 1.033 0.987 1.376 0.787 1.256 1.075 +1 0.783 0.716 0.830 0.547 -0.838 1.076 0.502 1.729 2.173 0.893 0.347 -0.239 1.107 0.896 1.196 0.098 0.000 0.520 1.227 -1.239 0.000 0.706 1.060 0.985 0.685 1.417 0.837 0.719 +0 1.150 0.215 1.537 0.602 -1.123 0.707 -0.017 0.190 0.000 0.931 -0.567 -0.611 0.000 0.876 0.895 0.484 2.548 0.801 1.320 1.267 0.000 0.910 0.936 0.994 0.568 0.666 0.622 0.627 +1 1.341 -0.542 1.479 0.908 -1.407 0.429 0.149 -0.619 0.000 0.839 -0.121 -0.161 2.215 1.292 0.830 0.684 1.274 0.610 1.166 -0.352 0.000 0.552 0.813 0.986 1.006 0.971 0.907 0.788 +0 0.878 1.195 0.358 2.148 0.590 1.251 1.208 -0.915 2.173 0.741 0.016 -1.312 2.215 0.278 0.172 1.371 0.000 0.716 1.457 1.271 0.000 0.427 0.888 0.992 1.617 1.032 1.394 1.039 +1 0.652 -0.281 0.657 1.086 0.993 0.889 0.225 1.086 1.087 0.756 0.826 -0.581 1.107 1.388 1.540 -0.913 0.000 1.020 2.040 -0.675 0.000 0.581 0.700 0.994 1.056 1.262 1.100 1.572 +0 0.892 -0.959 -1.325 1.056 0.391 0.781 -0.377 0.155 2.173 0.948 0.355 -1.283 0.000 1.174 1.167 0.904 1.274 0.838 0.391 -0.029 0.000 1.052 1.068 1.344 0.920 1.337 1.083 0.982 +0 0.644 -0.473 -0.491 1.978 -0.976 1.653 -0.471 1.029 2.173 1.675 -0.450 -0.770 1.107 0.937 -0.700 0.537 0.000 1.118 -1.380 0.549 0.000 0.864 1.243 0.992 1.837 2.445 1.463 1.229 +1 1.206 -1.561 -1.529 1.907 1.452 1.315 -1.673 -0.019 0.000 0.549 -0.500 0.583 2.215 0.700 -0.725 -1.086 2.548 0.980 -1.242 -0.838 0.000 1.179 0.997 0.994 1.050 0.664 0.782 0.961 +1 2.290 0.260 0.112 1.207 -0.719 1.533 -0.816 1.460 1.087 0.657 -0.482 -0.676 2.215 0.425 0.206 -1.051 0.000 0.488 2.195 1.268 0.000 0.879 1.013 1.568 2.128 1.405 1.400 1.277 +1 3.214 -0.930 -1.477 1.183 1.547 2.422 -0.739 0.156 0.000 0.647 -0.140 -1.317 1.107 0.373 -0.664 0.576 0.000 1.099 0.390 -0.627 0.000 0.805 0.849 1.094 0.749 0.606 0.623 0.847 +0 1.425 -0.176 -1.441 0.432 -0.142 0.677 -0.217 -0.086 2.173 0.835 -1.093 1.179 0.000 0.555 1.103 -0.518 0.000 0.692 0.468 1.065 3.102 1.370 0.849 1.001 0.842 0.688 0.714 0.685 +1 1.636 -1.937 0.185 0.694 0.468 0.998 -0.321 -0.558 2.173 0.954 0.975 -1.685 0.000 0.414 -2.065 0.633 0.000 1.420 -0.648 1.635 3.102 0.672 0.976 1.002 1.047 1.193 1.054 0.840 +0 1.002 -0.503 -1.552 0.504 -0.050 1.645 -2.624 0.577 0.000 1.578 0.388 -1.533 2.215 1.186 1.992 -0.585 0.000 0.956 0.541 0.238 3.102 0.853 0.893 0.989 0.847 1.117 0.979 0.962 +1 0.351 1.357 -0.471 0.487 -0.196 1.034 0.005 1.672 0.000 1.425 0.087 -0.229 2.215 0.977 0.567 -1.479 0.000 1.201 -1.747 0.672 0.000 0.780 0.663 0.979 0.670 0.870 0.935 0.804 +0 0.727 2.288 0.495 0.266 1.052 0.654 0.232 -0.396 2.173 0.746 -1.066 1.568 1.107 0.540 0.318 1.398 0.000 0.827 0.763 -0.684 0.000 0.734 0.908 0.991 0.956 1.247 1.101 0.861 +0 0.401 0.784 0.041 1.706 0.462 1.345 0.185 -0.932 2.173 0.651 0.167 -1.603 0.000 0.938 0.512 1.133 2.548 0.415 -0.183 0.867 0.000 0.554 0.836 0.997 0.970 1.366 1.378 1.101 +0 0.424 0.868 -0.998 1.639 1.255 1.474 0.713 -0.037 2.173 0.339 1.612 -1.394 0.000 0.834 0.376 1.566 2.548 0.570 -0.532 -1.266 0.000 0.816 1.177 1.035 0.567 1.387 0.960 0.818 +0 0.369 0.964 -1.426 0.948 0.991 0.420 -2.202 0.725 0.000 1.112 -0.077 -0.739 1.107 0.621 -0.510 -1.648 0.000 0.863 0.195 0.240 3.102 1.153 1.012 0.988 0.916 0.697 0.894 0.804 +0 0.333 0.151 -0.921 1.240 0.133 1.089 -0.398 -1.031 2.173 0.860 0.725 1.421 0.000 1.010 0.153 0.411 2.548 1.383 1.097 0.808 0.000 0.852 0.847 0.989 1.427 1.314 1.096 1.002 +0 0.861 0.869 1.377 1.051 -0.251 0.697 0.169 -1.222 0.000 0.881 -0.049 0.640 2.215 0.402 -0.966 1.587 0.000 0.776 -0.105 0.063 3.102 0.828 0.995 1.311 0.744 0.372 0.638 0.705 +0 0.954 -0.909 -1.450 0.967 1.149 0.639 0.269 0.909 1.087 1.148 -0.412 -0.625 1.107 0.790 1.038 -0.179 0.000 0.624 1.746 0.893 0.000 0.744 0.849 0.987 0.986 1.316 0.937 0.964 +1 0.540 2.262 0.484 0.673 -0.187 1.044 1.247 -0.608 0.000 1.147 1.438 1.410 2.215 1.899 0.869 1.039 2.548 0.518 0.294 -0.477 0.000 0.543 1.271 0.992 0.899 0.662 1.026 0.873 +1 1.150 0.782 -0.107 0.213 1.400 1.096 0.686 1.405 0.000 0.275 -2.784 -0.645 0.000 0.748 1.283 0.073 2.548 0.882 0.442 -1.498 0.000 0.762 0.973 0.988 0.525 0.683 0.763 0.729 +1 1.225 0.129 -0.695 0.800 -1.683 0.647 -0.224 1.404 0.000 1.348 -1.116 0.879 1.107 0.639 -2.416 -0.604 0.000 0.759 0.269 0.468 0.000 0.869 0.633 1.065 1.352 0.921 0.955 0.885 +1 0.973 -0.601 1.536 1.202 -1.158 1.513 -0.246 -1.580 2.173 2.024 -1.088 0.096 0.000 1.537 -1.033 0.859 0.000 1.341 1.201 -0.222 0.000 1.716 1.158 0.988 0.740 0.979 1.445 1.187 +1 1.394 -0.406 -1.088 0.359 -0.745 1.600 -1.160 0.445 0.000 1.849 0.376 -0.966 2.215 1.368 -0.595 1.068 0.000 0.722 0.627 1.096 0.000 0.836 0.613 0.994 0.686 0.822 0.920 0.806 +0 0.441 -0.672 1.115 1.701 -0.577 0.586 -1.313 1.345 0.000 0.580 0.866 -1.567 1.107 0.771 -0.720 -0.032 2.548 0.744 -1.531 0.496 0.000 0.747 0.745 1.199 1.026 0.967 0.848 0.809 +0 1.050 0.816 0.893 1.093 1.623 1.096 0.049 -1.359 1.087 0.746 0.660 -0.191 2.215 1.012 -1.555 -0.394 0.000 0.969 -2.002 0.292 0.000 0.930 1.320 0.987 1.186 1.233 1.163 1.259 +1 0.687 1.388 -1.210 1.089 0.304 1.684 2.240 1.040 0.000 1.894 -0.240 -0.942 1.107 0.990 -0.276 -0.072 1.274 0.530 -0.935 -1.615 0.000 0.457 0.584 1.173 1.000 1.028 1.111 0.861 +1 0.655 1.750 -1.468 0.571 1.107 1.263 0.466 -0.936 0.000 0.535 2.260 -0.187 0.000 1.054 0.311 0.322 2.548 2.889 1.295 1.024 1.551 1.202 1.110 0.996 0.833 1.166 1.141 0.931 +0 1.265 -0.516 -1.174 0.383 -0.287 1.131 1.131 1.030 0.000 1.633 -0.958 -0.456 2.215 1.490 0.718 1.424 0.000 1.244 0.968 0.275 3.102 0.910 0.961 0.994 0.799 1.840 1.728 1.498 +0 0.362 0.894 0.866 1.503 -0.534 0.805 -0.622 0.985 1.087 1.271 0.207 1.562 2.215 1.174 -0.496 -0.513 0.000 0.869 -0.523 -0.110 0.000 0.399 1.001 0.988 1.174 0.979 1.107 1.015 +1 1.013 0.278 1.406 1.450 1.146 1.421 0.747 -0.911 1.087 0.690 2.763 0.803 0.000 1.361 0.938 -0.404 0.000 0.485 1.149 -0.266 0.000 0.864 1.474 0.988 1.762 1.408 1.309 1.435 +0 0.478 1.732 0.908 0.918 -0.729 0.779 -0.271 0.023 0.000 0.980 0.064 1.456 0.000 1.231 0.744 0.444 2.548 1.220 -0.823 -1.533 0.000 1.000 0.861 0.983 0.741 0.730 0.829 0.810 +1 0.970 1.230 0.418 0.289 0.569 1.060 0.664 -1.239 2.173 0.517 -1.119 0.592 2.215 0.632 0.260 0.822 0.000 0.756 -1.191 -1.335 0.000 1.024 1.204 0.993 0.841 1.572 0.974 0.868 +0 1.638 -0.777 -1.571 1.072 1.424 0.943 0.731 0.182 1.087 0.389 0.215 0.882 0.000 0.478 -1.303 -0.423 2.548 0.729 -0.670 -0.809 0.000 0.786 0.905 0.991 0.804 1.179 1.098 0.858 +1 3.481 0.207 -1.183 1.274 -1.497 1.965 -1.060 0.283 0.000 0.705 0.225 1.663 0.000 0.853 0.888 -0.927 2.548 0.890 2.063 0.237 0.000 1.729 1.119 0.991 1.429 0.914 0.953 1.036 +1 2.031 -0.675 -1.619 0.112 -1.193 0.588 -0.435 -0.641 2.173 0.931 0.709 -0.049 2.215 0.669 -0.902 -0.133 0.000 1.021 2.021 0.980 0.000 0.813 0.859 0.994 0.841 0.872 0.905 0.772 +1 1.005 1.425 -0.123 1.039 -1.255 1.135 1.172 0.147 2.173 1.229 1.453 1.724 0.000 0.635 2.167 -1.737 0.000 0.624 0.912 1.352 3.102 0.619 0.454 1.206 1.018 0.789 0.892 0.824 +1 1.319 -0.586 -0.225 0.620 -0.100 0.767 0.911 1.021 2.173 0.685 1.278 -1.405 2.215 0.720 0.546 1.503 0.000 0.759 1.458 -0.645 0.000 0.908 0.815 0.999 1.602 0.896 1.359 1.152 +0 1.399 -0.020 1.234 1.141 1.494 1.211 -0.726 -0.221 0.000 0.739 0.120 -0.127 0.000 0.640 0.815 1.138 2.548 1.165 -1.098 -1.364 1.551 0.928 1.079 0.980 0.752 1.026 0.755 0.790 +0 0.977 -1.463 1.416 0.427 -1.196 2.019 -1.580 1.110 1.087 1.305 -1.052 -0.989 2.215 1.271 -1.762 -0.881 0.000 1.901 1.908 -0.110 0.000 0.925 0.967 0.995 1.092 2.349 1.354 1.099 +0 0.327 -1.517 0.850 2.173 0.106 0.652 -1.360 -0.293 2.173 1.150 0.641 -1.420 0.000 0.678 0.153 0.744 2.548 0.574 2.042 -1.724 0.000 1.117 0.998 0.987 0.909 0.968 1.273 2.115 +0 0.790 -0.711 0.733 0.422 0.855 0.479 0.009 -1.261 2.173 0.483 0.195 0.324 0.000 1.651 -0.270 -0.407 2.548 1.574 -0.168 1.617 0.000 1.068 1.084 0.988 1.022 0.790 0.911 0.855 +1 0.524 1.129 0.683 1.532 0.579 1.106 0.050 -1.019 2.173 1.058 0.051 1.201 0.000 0.577 -1.155 -1.054 0.000 1.348 -0.265 -0.362 3.102 1.369 1.077 0.994 1.343 0.762 0.928 0.913 +0 0.686 1.798 0.218 1.167 0.756 0.722 0.143 1.338 0.000 0.830 0.445 -0.257 0.000 0.693 1.901 -0.683 0.000 0.569 0.784 0.271 0.000 0.898 0.812 0.976 0.618 0.395 0.564 0.588 +0 2.098 -0.024 -0.138 0.901 -0.242 0.483 -0.642 -0.965 0.000 0.853 0.180 -1.508 0.000 1.094 1.961 1.391 0.000 1.367 -2.052 0.947 0.000 0.867 0.737 0.982 0.805 0.486 0.699 0.733 +0 1.694 0.868 -0.740 0.356 1.339 0.701 -0.015 0.206 2.173 0.571 -0.924 0.726 0.000 0.942 0.176 -1.732 2.548 0.935 -2.316 1.516 0.000 0.909 1.209 1.027 0.963 1.002 1.020 1.161 +0 3.216 0.626 -0.738 0.103 0.336 1.587 -0.781 0.943 1.087 0.888 -1.003 1.308 0.000 1.093 0.045 -0.073 2.548 0.574 -1.396 -1.298 0.000 0.730 0.908 0.986 0.847 1.481 1.572 1.362 +0 0.947 -0.204 -1.015 2.041 -1.426 1.353 0.363 0.454 2.173 0.485 -0.387 -0.335 0.000 0.426 0.817 1.357 0.000 0.459 -1.315 0.760 3.102 0.847 0.920 0.984 0.942 0.974 1.151 0.905 +0 0.830 0.657 -1.403 1.241 -0.608 0.799 -2.113 0.972 0.000 0.928 0.963 -0.805 0.000 0.868 0.457 0.499 2.548 1.060 1.036 1.233 1.551 4.552 2.654 0.984 0.843 0.530 1.757 1.450 +1 1.141 0.353 1.269 0.539 -0.392 1.197 0.839 -0.778 0.000 1.274 0.173 0.266 2.215 1.516 0.271 1.534 2.548 0.728 -0.369 0.690 0.000 1.697 1.427 1.083 0.716 1.347 1.109 0.916 +1 1.359 -0.796 1.287 0.771 -0.849 1.825 -0.994 -1.226 1.087 1.206 -0.107 0.538 0.000 1.763 0.488 0.245 0.000 0.841 -0.676 0.869 0.000 0.938 0.637 1.331 1.164 1.259 1.528 1.258 +1 1.198 1.925 1.155 0.920 0.991 2.569 0.090 -0.804 0.000 1.241 0.633 1.189 2.215 0.945 1.074 0.059 0.000 0.665 0.994 1.000 0.000 0.656 0.812 0.973 0.925 0.760 0.702 0.640 +0 0.495 0.543 0.284 1.707 1.079 0.645 -0.462 -0.194 0.000 0.817 -2.483 0.897 0.000 0.846 0.741 -1.163 2.548 1.112 -0.489 -1.396 0.000 1.146 0.963 0.989 1.019 0.992 0.851 0.799 +0 0.444 -1.225 0.682 0.373 -1.422 0.880 -0.436 1.679 0.000 0.797 -0.863 -0.174 2.215 0.560 -0.516 0.751 0.000 0.854 0.654 -0.240 3.102 0.941 0.976 0.988 0.753 0.700 0.779 0.674 +1 0.564 0.655 1.138 0.603 -0.910 0.906 0.728 -1.291 2.173 0.796 1.149 0.800 0.000 1.414 0.364 0.329 0.000 0.605 -0.859 -0.603 3.102 0.935 1.013 0.983 0.777 0.908 0.953 0.839 +0 0.647 -1.068 -1.451 1.266 -0.865 1.234 -0.729 1.448 1.087 0.781 -1.330 -0.465 0.000 1.134 -0.147 0.717 2.548 1.220 -1.522 -0.014 0.000 0.583 1.078 0.988 1.128 1.001 1.047 0.981 +1 0.966 -0.214 0.730 1.067 -1.154 0.839 0.439 0.846 0.000 0.833 -0.658 -1.447 2.215 0.596 0.216 -0.023 0.000 2.347 -0.015 -0.950 3.102 0.907 1.164 1.396 0.950 0.691 0.921 0.832 +0 0.778 1.505 -1.427 1.772 -1.601 0.478 0.946 0.815 1.087 0.675 0.519 0.119 0.000 0.901 1.209 -0.566 2.548 0.941 -0.433 0.184 0.000 0.576 0.789 0.978 1.127 0.790 0.878 1.084 +0 1.096 1.605 -0.616 0.827 0.033 0.770 0.843 -1.358 2.173 0.930 1.284 0.140 2.215 1.012 0.521 1.229 0.000 0.842 1.650 1.553 0.000 0.831 0.965 0.978 1.122 1.251 0.879 0.857 +0 0.310 0.353 -1.552 0.491 0.828 0.803 0.184 -1.105 2.173 0.789 -0.714 0.713 1.107 0.486 -1.915 0.348 0.000 0.642 -1.649 -0.862 0.000 0.548 1.206 0.984 0.638 1.295 0.881 0.756 +1 1.182 1.084 0.747 1.266 0.237 0.719 0.242 -1.707 2.173 0.542 -2.673 -1.115 0.000 1.166 -0.534 -0.824 0.000 0.590 0.708 1.301 0.000 1.119 0.900 0.985 0.663 0.991 0.894 0.944 +1 0.418 -1.167 0.756 1.154 -0.455 0.574 -0.421 1.280 2.173 0.817 -1.214 -1.259 2.215 0.623 1.179 0.160 0.000 0.535 0.682 1.707 0.000 0.645 0.809 0.989 0.810 0.871 0.822 0.756 +0 1.326 -0.350 -0.204 0.339 0.596 1.056 -0.409 0.869 2.173 1.692 0.569 -1.426 0.000 1.176 0.001 -1.036 2.548 0.627 -1.191 0.728 0.000 2.026 1.245 0.984 0.978 1.403 1.151 1.009 +1 1.242 -0.650 1.320 1.163 0.811 1.024 -0.227 -0.909 2.173 0.493 -1.095 -1.189 0.000 1.006 -0.971 -0.101 2.548 0.684 -0.608 0.243 0.000 0.742 0.772 0.985 0.966 1.003 0.983 0.795 +0 0.623 -1.176 1.542 0.480 0.318 1.275 0.446 0.087 0.000 1.404 -0.055 1.470 0.000 1.996 -0.259 -1.005 2.548 0.636 0.239 -0.768 3.102 2.789 1.542 0.988 1.325 0.309 1.203 1.317 +0 0.636 1.164 0.589 0.595 0.662 1.077 -0.344 -1.399 0.000 1.394 -0.245 0.098 2.215 1.146 -0.175 -0.939 0.000 1.110 -0.835 1.059 3.102 0.879 0.919 1.000 0.791 0.958 0.809 0.736 +1 1.205 -1.088 0.959 1.898 1.151 0.606 -0.653 -0.869 2.173 0.862 0.206 -0.683 0.000 0.786 -0.199 0.203 1.274 0.734 -0.438 -0.342 0.000 0.501 0.560 1.005 1.187 0.733 0.845 0.820 +1 0.333 0.505 0.415 1.146 -0.152 1.701 -0.030 -0.886 0.000 1.798 -1.329 0.652 2.215 1.683 -0.953 1.162 2.548 0.668 -0.054 -1.646 0.000 1.030 1.763 0.991 3.278 0.872 2.361 2.027 +0 0.930 0.179 -0.595 0.271 0.854 0.654 -0.498 1.515 2.173 0.983 0.292 -1.281 2.215 0.667 -0.904 -0.113 0.000 1.184 0.355 0.278 0.000 0.877 0.922 0.985 0.947 0.841 0.727 0.694 +1 1.758 0.195 -1.519 0.551 0.858 1.057 -1.284 -0.245 2.173 0.770 -0.633 -1.630 0.000 1.627 -0.779 0.707 2.548 0.608 0.614 -0.852 0.000 0.883 1.102 1.146 1.586 1.289 1.213 1.020 +1 0.750 -1.544 -0.300 1.308 -1.184 0.505 -0.494 1.389 0.000 0.369 2.001 -1.651 0.000 1.821 0.203 0.657 1.274 1.298 0.184 -0.292 3.102 0.828 0.887 0.987 1.095 0.887 1.188 0.972 +1 0.413 0.727 0.878 1.121 -1.302 1.039 -0.926 0.361 2.173 0.763 -1.196 1.271 2.215 1.300 -0.632 -1.082 0.000 1.142 1.280 0.276 0.000 2.224 1.752 0.988 1.889 0.977 1.439 1.364 +1 0.907 -0.730 -0.315 1.014 0.551 0.922 0.499 -1.373 2.173 0.410 -0.888 1.098 0.000 1.207 0.233 1.535 0.000 0.951 -1.587 -0.162 0.000 0.952 0.993 0.984 0.751 0.238 0.890 0.788 +1 0.336 0.838 -0.813 1.411 1.658 1.419 -0.235 -0.961 2.173 1.061 -0.700 0.977 2.215 1.965 0.650 0.535 0.000 0.403 -2.196 -0.124 0.000 0.854 1.154 0.991 0.847 1.831 0.989 0.862 +1 1.811 -1.649 -1.623 0.461 -0.025 1.072 -0.689 0.366 2.173 0.491 1.920 -0.657 0.000 0.594 -1.202 1.233 0.000 0.416 -0.378 -1.255 3.102 0.762 0.978 1.255 0.656 0.708 0.872 0.729 +0 1.358 0.970 -1.289 1.618 -0.619 0.569 -2.297 -1.411 0.000 1.040 0.206 0.788 0.000 1.280 -0.426 0.552 0.000 1.125 0.924 1.530 3.102 0.718 0.986 1.166 1.453 1.308 1.062 1.098 +1 0.888 0.821 -1.451 0.397 0.583 1.160 -0.424 -0.610 2.173 0.838 1.161 1.378 0.000 0.573 1.619 0.951 0.000 0.713 1.575 0.076 0.000 0.995 0.791 0.982 1.368 0.984 1.076 0.940 +0 1.728 0.897 1.389 1.463 0.518 0.703 1.017 -0.954 2.173 0.653 1.356 -1.345 2.215 0.272 1.139 0.888 0.000 1.260 0.765 0.074 0.000 0.450 0.649 1.556 1.068 0.391 0.902 0.713 +0 1.155 -0.459 1.456 1.320 1.218 1.098 -0.537 -0.472 1.087 0.510 1.885 -0.063 0.000 0.885 0.894 -1.058 0.000 1.186 1.230 0.322 0.000 0.955 0.717 0.989 1.466 0.443 0.960 1.231 +1 0.804 1.268 -1.253 1.299 0.970 0.889 0.453 -0.086 2.173 1.434 0.919 1.437 0.000 0.711 -0.073 -1.488 2.548 1.054 -1.918 -0.587 0.000 0.816 0.784 1.285 1.169 0.982 0.874 0.820 +1 0.570 0.954 -0.795 1.633 -0.072 0.528 -0.750 0.228 0.000 0.716 0.529 1.389 2.215 1.341 -0.920 1.545 1.274 0.750 -0.456 -1.269 0.000 0.943 0.941 0.988 2.060 0.906 1.367 1.189 +1 0.780 0.752 -0.252 0.789 1.520 2.944 1.272 -1.181 0.000 2.788 0.759 0.525 0.000 1.074 0.627 0.020 0.000 1.434 -0.485 1.218 3.102 1.173 0.778 1.086 0.872 0.447 0.876 0.817 +1 1.029 -0.684 -0.892 0.187 -1.685 1.504 -0.338 -0.004 0.000 0.858 0.091 1.026 1.107 2.675 -0.274 -1.719 2.548 1.060 -0.625 0.528 0.000 0.974 1.149 0.977 0.917 1.047 1.272 1.049 +1 1.292 -0.407 -1.136 2.004 -1.701 0.623 2.174 0.223 0.000 1.124 0.286 0.386 2.215 0.498 1.293 -0.336 0.000 0.966 -0.994 1.201 3.102 1.020 0.983 1.084 1.434 0.992 0.995 0.918 +0 1.959 0.647 0.013 1.699 -0.083 1.780 -0.534 1.704 0.000 0.602 -0.068 -0.034 1.107 0.811 0.640 1.584 1.274 0.598 -0.803 -1.209 0.000 0.846 0.917 0.975 0.750 0.794 0.847 1.354 +0 1.792 -0.006 -0.203 0.223 -0.563 1.338 -0.056 1.478 0.000 0.543 -2.690 -0.391 0.000 0.698 1.099 0.132 2.548 0.797 -0.662 1.096 0.000 0.800 0.892 0.998 0.821 0.599 0.802 0.897 +1 0.298 1.472 0.100 1.350 -1.572 0.931 0.243 -0.576 2.173 0.657 -1.300 0.078 0.000 1.254 1.135 0.998 0.000 1.252 0.099 1.390 3.102 0.908 0.813 0.986 1.555 1.121 1.176 0.978 +1 0.476 0.590 -0.322 2.835 0.297 0.669 -0.851 1.474 0.000 0.749 1.646 -1.262 2.215 0.975 -0.904 1.047 0.000 1.849 0.273 -1.327 0.000 1.152 0.808 0.986 0.804 0.803 1.005 0.973 +1 0.555 -0.496 -1.694 0.710 0.315 0.833 -1.168 0.761 0.000 1.086 -1.317 -1.537 1.107 0.986 -1.431 -0.815 2.548 0.788 -1.887 1.049 0.000 0.774 1.018 0.985 1.042 0.677 0.825 0.869 +1 1.117 1.341 -1.362 0.224 -1.573 1.124 0.695 1.193 2.173 0.817 2.147 -0.143 0.000 0.502 0.738 -0.313 0.000 0.444 -0.139 -0.022 0.000 0.759 0.693 0.979 0.942 0.874 0.896 0.809 +1 2.050 -0.109 -1.427 0.683 -1.557 1.620 -1.244 0.798 0.000 1.193 0.134 -1.169 0.000 1.865 -0.135 -0.078 2.548 0.999 -1.282 -0.369 0.000 0.760 0.853 0.977 1.358 0.647 0.932 1.186 +0 1.664 -0.938 0.546 1.304 -1.587 0.527 0.786 1.640 0.000 0.620 0.500 0.916 0.000 1.133 -0.974 -0.054 2.548 1.393 0.013 -0.947 3.102 0.909 0.867 1.917 1.245 0.882 0.925 0.821 +1 0.749 -1.522 -1.680 0.651 0.567 0.670 -1.083 -0.644 0.000 0.841 -0.611 1.414 2.215 1.037 -1.678 -1.167 0.000 1.864 -0.165 0.339 3.102 0.891 1.115 0.989 1.134 0.963 0.967 0.898 +0 0.682 -0.017 0.639 1.210 -1.299 1.350 0.163 1.143 2.173 1.114 -0.852 -0.364 2.215 0.961 0.098 -0.203 0.000 0.691 -1.169 -1.265 0.000 1.047 0.785 1.240 1.050 2.020 1.103 0.948 +0 4.066 0.724 -1.381 0.448 -1.100 2.328 0.087 0.316 1.087 1.025 0.946 -0.799 0.000 0.740 0.737 1.184 0.000 0.593 -0.064 1.110 3.102 0.807 1.282 0.982 0.819 0.822 1.660 1.278 +0 0.374 0.432 0.988 0.244 -1.146 0.946 -1.506 0.236 0.000 0.986 1.123 -1.166 0.000 1.221 -0.511 0.846 0.000 0.599 -1.049 0.750 1.551 0.913 1.051 0.995 0.528 0.762 0.650 0.587 +1 0.920 -0.320 -0.241 0.289 -0.303 0.985 0.635 1.418 0.000 0.599 1.390 -1.545 0.000 0.761 -0.044 -0.787 2.548 0.844 -0.936 0.236 0.000 0.986 1.037 0.999 0.737 0.371 0.686 0.998 +1 0.578 0.643 -1.119 0.853 1.090 1.110 2.041 -0.083 0.000 1.330 0.460 1.081 0.000 0.666 -0.113 1.424 0.000 1.935 -1.051 -1.242 3.102 0.981 0.779 0.987 0.968 0.815 0.971 0.808 +0 0.863 0.927 -1.666 1.122 1.691 1.484 -1.540 0.417 0.000 0.954 0.374 -0.321 2.215 0.785 0.072 1.494 0.000 1.400 0.052 -0.945 3.102 0.874 0.850 1.003 1.291 0.581 0.989 1.083 +0 0.844 -2.102 0.735 1.315 1.312 0.949 -0.769 0.747 1.087 1.383 0.451 -0.375 0.000 0.794 1.383 -1.597 0.000 2.914 0.191 -1.077 1.551 0.879 1.271 0.998 0.835 1.983 1.367 1.568 +1 1.164 0.534 -1.645 0.443 1.258 1.100 0.639 -0.056 2.173 0.711 0.444 -0.872 2.215 0.779 0.113 1.220 0.000 0.495 1.325 0.194 0.000 0.775 0.909 0.987 0.768 0.881 0.859 0.737 +0 0.781 0.153 0.493 0.344 -1.506 0.617 -0.523 -0.380 2.173 0.842 0.453 -0.690 0.000 0.869 -0.828 -1.737 2.548 0.754 0.064 1.116 0.000 0.988 0.948 0.983 0.659 0.875 0.671 0.613 +1 1.065 0.515 0.639 0.692 -1.696 0.721 -0.928 -0.742 0.000 0.752 0.683 1.414 2.215 1.197 -0.483 -0.133 0.000 1.078 -0.028 0.338 3.102 0.935 0.828 1.025 0.605 0.736 0.870 0.825 +1 1.339 -0.342 -1.533 0.894 -0.442 0.787 0.619 1.162 2.173 0.707 0.586 0.207 2.215 0.552 0.512 -0.962 0.000 1.253 -2.279 -0.121 0.000 0.714 0.756 1.262 0.991 0.833 0.882 0.788 +0 1.079 -0.630 -1.332 1.576 -0.588 0.455 -0.411 0.247 2.173 0.531 -0.255 1.211 0.000 0.675 -1.487 1.342 0.000 0.617 0.698 0.617 1.551 0.689 0.732 1.123 0.869 0.417 0.696 0.709 +0 0.779 -1.248 0.918 1.726 0.282 0.945 -0.533 -1.302 2.173 1.093 -0.354 1.267 0.000 0.878 0.686 -1.237 0.000 1.095 0.334 -0.381 3.102 1.460 1.098 0.986 1.490 0.952 1.161 1.184 +0 0.713 1.338 1.365 0.943 -1.454 1.157 1.881 0.033 0.000 1.054 0.877 0.652 1.107 0.790 -0.573 -1.271 2.548 1.528 0.966 -1.278 0.000 2.030 1.531 0.981 1.425 1.259 1.305 1.190 +1 0.650 1.832 1.593 0.369 -1.284 0.644 -0.897 1.493 2.173 0.525 -0.149 -0.162 0.000 0.973 -0.490 0.330 2.548 0.744 0.125 -0.800 0.000 1.008 1.042 0.990 0.974 0.874 0.876 0.925 +0 0.785 -0.020 1.287 0.729 -1.372 0.852 0.733 0.200 1.087 0.647 1.203 -0.543 2.215 0.809 1.110 1.262 0.000 0.375 -1.735 1.283 0.000 0.701 0.864 0.989 1.090 0.732 0.960 0.808 +1 0.411 1.328 -0.967 0.223 0.745 0.560 -0.355 -0.856 2.173 0.969 0.739 -0.450 2.215 1.220 -0.440 0.977 0.000 0.419 -0.791 1.096 0.000 0.203 1.106 0.993 0.656 0.748 0.786 0.677 +1 1.623 0.033 0.952 1.043 0.614 0.782 -1.507 -0.790 2.173 0.587 0.255 0.297 0.000 0.563 0.193 -0.339 2.548 1.057 -0.069 -1.461 0.000 1.041 1.240 0.984 0.776 0.873 0.983 0.867 +1 0.861 -0.887 0.213 0.961 0.893 0.685 1.039 -1.252 2.173 0.598 0.611 1.670 0.000 0.703 -0.287 -0.356 2.548 1.172 0.096 -0.832 0.000 0.892 0.720 0.984 1.220 0.892 0.840 0.745 +0 0.798 0.515 -1.094 1.228 1.126 0.707 1.348 1.252 2.173 1.185 -0.773 -0.810 2.215 1.480 -1.050 -0.117 0.000 0.418 1.077 0.519 0.000 1.427 1.156 1.247 0.838 2.175 1.370 1.169 +1 1.458 -0.595 -0.506 0.854 1.555 0.533 0.683 0.622 2.173 1.355 -0.133 1.223 2.215 0.721 0.704 -1.132 0.000 0.785 0.637 -0.813 0.000 0.236 1.013 1.483 1.133 0.835 0.938 0.836 +1 1.794 -1.064 -0.431 0.537 0.679 1.514 1.337 0.798 0.000 0.644 0.409 -0.759 1.107 1.102 0.477 1.730 2.548 0.563 -1.147 -1.481 0.000 2.865 1.768 1.145 0.953 0.702 1.176 1.396 +1 0.619 -1.821 0.384 1.252 1.212 2.304 -2.713 -0.520 0.000 1.066 -0.611 -1.682 2.215 2.077 -0.602 0.972 0.000 1.120 0.534 1.383 3.102 0.980 2.564 0.993 1.000 0.763 2.300 1.787 +1 0.673 0.434 -0.059 0.832 -1.338 1.018 0.450 0.660 0.000 1.294 0.330 -0.887 2.215 0.601 1.435 0.383 0.000 1.274 0.484 1.620 3.102 0.901 0.923 0.990 0.679 0.906 0.954 0.817 +1 1.110 -0.907 -1.025 0.352 0.389 0.786 0.468 0.767 2.173 0.914 -0.388 -0.803 0.000 1.343 2.121 0.498 0.000 1.326 -0.915 -1.274 0.000 0.781 0.656 0.986 1.252 0.730 0.859 0.798 +1 0.805 -0.718 1.344 0.307 1.602 1.721 -1.175 1.516 2.173 1.713 -0.993 -0.391 0.000 2.114 -0.503 0.466 0.000 1.177 -0.125 0.184 1.551 0.914 0.822 0.984 1.194 1.625 1.306 1.045 +1 1.041 -2.224 1.377 0.756 -0.949 0.675 -0.052 -0.143 2.173 0.609 -1.055 -1.376 2.215 0.537 -0.611 1.262 0.000 0.516 -1.641 -0.054 0.000 0.673 0.794 1.064 0.739 0.987 1.004 0.789 +0 0.280 -1.592 0.254 1.711 0.531 0.563 -1.450 -0.469 0.000 0.915 -1.550 -1.166 0.000 0.760 -1.088 1.050 0.000 0.549 1.284 -0.374 3.102 0.901 0.850 1.003 0.802 0.625 0.903 0.936 +0 0.737 -1.765 -0.399 1.033 0.828 1.196 -2.173 -1.580 0.000 1.495 -0.162 0.657 2.215 2.093 -0.843 -0.938 2.548 1.491 -0.040 0.165 0.000 3.331 2.220 1.081 1.264 2.001 1.804 1.440 +0 2.636 0.430 -0.661 0.213 -0.954 1.642 0.619 1.096 2.173 0.730 -0.451 -1.251 2.215 0.493 0.143 1.195 0.000 0.423 -1.620 0.560 0.000 0.689 1.146 0.989 0.777 1.658 1.293 1.058 +0 0.975 1.711 1.031 1.228 0.575 1.385 -0.090 -0.856 2.173 0.347 -1.088 1.171 0.000 1.281 0.526 1.551 2.548 0.478 0.661 -0.260 0.000 0.779 0.975 0.990 1.296 1.483 1.813 1.446 +0 0.417 -1.488 -1.493 1.198 -0.219 1.453 -0.458 0.890 0.000 1.364 -0.271 -1.145 2.215 0.822 -0.215 -0.073 2.548 0.403 -0.244 1.291 0.000 1.008 0.981 0.986 0.884 0.926 0.899 0.906 +0 1.421 1.507 1.074 0.793 1.700 0.577 2.405 -0.377 0.000 0.317 2.214 -1.606 0.000 0.609 -2.336 -0.280 0.000 0.530 0.335 1.053 3.102 0.815 0.987 0.991 0.520 0.404 0.669 0.711 +1 0.660 -1.796 -0.047 1.053 -0.868 0.926 -1.217 -1.465 2.173 0.937 -1.742 0.874 0.000 1.021 -0.614 -0.768 2.548 1.692 0.104 0.370 0.000 0.897 0.910 0.986 1.122 0.789 0.928 1.167 +1 1.726 -0.551 -0.581 0.320 1.573 0.635 1.003 0.851 0.000 0.386 0.410 -0.683 0.000 0.613 -0.657 0.721 2.548 0.379 0.123 0.271 3.102 1.079 0.859 0.986 0.549 0.220 0.495 0.653 +1 0.339 -1.324 -1.092 1.114 -1.029 0.905 1.047 0.626 0.000 0.852 -0.969 -1.194 2.215 1.030 -0.500 -1.503 2.548 0.447 0.066 -1.549 0.000 1.026 1.212 0.975 0.737 0.355 1.021 0.878 +0 0.889 -0.590 1.653 0.665 -0.396 0.313 -1.403 1.366 0.000 0.763 -0.568 -0.624 2.215 0.782 -1.820 0.231 0.000 0.809 0.973 1.383 1.551 0.804 0.897 1.026 0.798 0.986 0.898 0.761 +1 0.551 0.306 0.773 0.579 1.444 0.506 0.059 -0.257 0.000 0.858 0.202 -1.001 2.215 0.916 -1.411 1.528 0.000 0.374 -0.546 0.180 3.102 1.642 0.883 0.988 0.946 0.502 0.713 0.901 +1 0.688 -0.666 -1.491 0.740 -0.899 0.872 -0.831 -0.302 0.000 1.328 -0.079 1.061 2.215 0.604 0.692 -1.474 0.000 0.809 -0.812 0.272 1.551 1.622 1.001 0.993 1.312 0.748 0.912 0.935 +0 0.966 -0.670 -1.189 0.459 0.749 0.854 0.276 -0.594 2.173 0.825 0.453 0.410 2.215 0.816 -0.206 -1.144 0.000 1.007 -0.215 1.064 0.000 0.913 0.930 0.986 0.959 0.978 0.838 0.739 +1 1.076 0.378 0.100 0.844 -1.085 1.123 0.159 -0.895 2.173 1.050 1.359 0.533 2.215 1.080 -0.560 1.443 0.000 1.038 -0.181 0.682 0.000 0.906 1.455 1.156 0.995 1.860 1.366 1.108 +1 0.468 -1.378 0.424 1.023 -1.355 0.832 -1.234 1.545 2.173 1.172 0.122 -0.276 0.000 0.455 -0.727 -0.560 2.548 0.975 1.466 0.981 0.000 1.789 1.152 0.987 0.744 0.746 1.240 1.201 +0 0.548 1.570 -1.559 1.629 1.487 0.689 -0.913 0.624 2.173 0.789 -1.021 -0.852 2.215 0.881 -0.781 0.018 0.000 0.821 -0.205 -0.719 0.000 0.648 0.735 0.983 1.303 1.056 1.123 0.946 +1 0.636 1.143 0.287 0.579 -0.436 1.003 -0.497 1.733 2.173 0.761 0.424 -0.114 0.000 1.201 -0.382 0.459 0.000 1.110 0.478 -1.323 3.102 0.971 0.992 0.989 1.108 0.757 0.933 0.819 +1 1.441 -1.328 1.689 0.991 1.428 0.950 -0.965 -0.704 0.000 1.093 -1.130 0.268 2.215 0.919 -0.693 1.375 1.274 0.998 0.184 0.304 0.000 0.998 1.179 0.977 1.206 0.920 1.079 1.362 +1 1.977 1.239 -1.385 0.571 -1.705 1.065 -0.115 0.386 2.173 0.565 1.512 0.721 0.000 0.526 0.729 -0.820 2.548 0.880 0.682 -0.375 0.000 0.842 1.060 1.003 0.585 0.937 1.029 0.882 +1 1.201 -1.196 -0.715 1.085 -1.069 0.804 -0.872 0.784 1.087 0.823 -0.217 0.734 2.215 0.571 -0.263 -1.734 0.000 0.584 -0.558 -0.298 0.000 0.625 0.717 0.983 1.069 0.404 0.909 0.716 +0 0.938 -0.932 0.265 1.236 -0.339 0.574 -0.364 1.050 0.000 0.633 -1.026 1.602 1.107 0.699 -2.141 1.324 0.000 0.972 -1.145 -1.345 0.000 0.766 1.004 0.988 0.926 0.777 0.674 0.764 +0 0.577 0.473 -1.180 0.282 -0.146 0.751 1.066 0.510 2.173 0.736 0.866 -0.370 0.000 0.686 0.486 1.613 0.000 0.413 2.440 -1.324 0.000 0.927 0.938 0.984 0.803 0.849 0.703 0.665 +0 0.292 -1.476 0.440 1.559 -1.172 1.234 -1.153 0.216 2.173 1.414 -2.351 0.673 0.000 2.057 -0.359 -1.577 2.548 0.861 -1.230 -0.559 0.000 1.467 1.383 0.988 0.782 2.131 1.539 1.266 +0 1.134 -0.732 1.521 1.380 -1.253 1.184 1.167 0.517 0.000 0.840 -1.872 -0.189 0.000 1.436 -1.119 -1.041 1.274 1.050 -0.562 0.467 0.000 1.064 0.952 1.039 0.758 0.499 0.735 0.763 +1 0.489 -0.899 0.400 0.856 1.635 0.895 -1.208 1.494 0.000 1.475 -1.215 0.028 2.215 0.823 0.278 -0.513 2.548 0.711 -0.900 -1.661 0.000 0.855 1.163 0.993 1.126 1.146 1.006 0.894 +1 1.077 1.583 0.818 1.470 0.143 1.116 1.002 -1.354 2.173 0.332 1.837 -0.894 0.000 0.326 -0.145 -0.366 0.000 0.428 0.842 -0.791 0.000 0.656 0.726 0.995 0.782 0.942 0.982 0.788 +0 0.755 1.796 -0.050 0.452 1.022 0.616 1.000 1.689 2.173 0.591 0.250 1.609 0.000 0.414 1.849 -1.662 0.000 1.932 0.907 -0.041 3.102 0.779 0.986 0.997 0.781 1.155 0.708 0.654 +1 1.098 -0.043 -0.473 0.285 1.440 1.174 0.047 0.125 2.173 1.211 -1.520 -1.220 1.107 0.987 -0.210 1.563 0.000 0.914 -1.317 1.371 0.000 0.982 1.231 0.993 1.295 2.274 1.361 1.108 +1 1.409 0.396 1.067 0.894 0.401 0.634 0.785 -1.389 2.173 0.408 -0.918 -0.078 0.000 0.930 -0.052 -0.581 2.548 0.776 -0.643 -1.739 0.000 0.733 0.930 0.981 0.889 0.763 0.797 0.723 +0 1.415 -1.155 -0.957 0.390 -0.482 1.020 -1.176 0.785 2.173 0.590 -1.812 -1.380 0.000 0.884 -1.319 0.323 0.000 0.696 0.129 -1.676 3.102 1.131 0.928 0.987 1.221 0.960 0.902 0.803 +0 0.507 0.120 -0.589 0.530 1.388 0.509 -0.890 1.412 2.173 0.521 -0.636 -0.572 0.000 0.636 -0.850 0.703 2.548 0.575 -2.096 -0.269 0.000 0.788 0.847 0.990 0.587 0.423 0.563 0.539 +0 1.720 -1.069 0.806 2.594 0.334 0.925 -0.436 -1.617 2.173 1.071 -1.275 -1.139 0.000 1.465 -1.350 -0.025 2.548 2.893 -0.426 -1.079 0.000 0.951 0.972 1.206 0.921 1.640 1.229 1.324 +0 1.059 -0.486 1.117 1.098 1.283 1.081 -0.740 -1.518 0.000 1.692 -0.664 -0.172 2.215 0.615 0.296 -1.077 2.548 0.782 0.153 -0.360 0.000 1.390 0.866 0.978 1.398 0.974 1.019 0.993 +1 0.997 0.217 -1.436 0.470 0.855 0.859 -0.056 0.203 0.000 0.530 -1.229 -0.010 0.000 0.744 1.068 -0.931 2.548 0.824 0.802 0.581 0.000 0.959 1.050 0.991 0.756 0.701 0.856 0.761 +1 1.469 1.811 -0.762 0.532 -0.972 0.833 2.170 1.116 0.000 0.490 2.798 0.060 0.000 1.047 1.418 1.273 2.548 1.315 1.150 -0.456 3.102 1.203 0.894 1.001 0.562 0.900 0.780 0.838 +1 0.388 1.212 -1.048 0.877 1.007 2.610 2.126 -1.301 0.000 3.651 1.052 0.455 2.215 0.375 0.866 0.171 0.000 0.425 1.743 0.560 0.000 1.169 1.381 0.988 1.347 1.396 2.231 1.776 +0 1.322 -0.798 0.213 1.357 1.674 1.610 -0.550 -0.248 2.173 1.051 -0.030 1.508 0.000 1.541 1.063 1.565 2.548 0.616 0.480 -1.280 0.000 0.696 0.769 1.796 1.498 2.734 1.626 1.304 +0 0.449 -1.449 0.341 0.615 -1.252 1.246 -0.923 -1.613 2.173 0.952 0.943 0.171 0.000 1.490 -0.653 0.279 2.548 0.456 -1.908 -1.633 0.000 0.915 0.927 0.982 0.758 1.692 0.895 0.754 +1 0.540 0.208 -0.465 0.992 -1.630 0.468 -0.125 1.540 0.000 0.756 0.784 0.326 2.215 0.920 -0.794 -0.477 0.000 0.932 -1.032 0.917 1.551 0.851 0.878 0.988 0.709 0.995 0.733 0.681 +0 0.915 0.081 0.770 0.242 0.399 0.345 -1.206 -0.313 0.000 1.115 -0.708 -1.016 0.000 1.005 -0.074 1.428 2.548 0.442 0.006 -1.636 3.102 0.845 1.005 0.989 0.570 0.190 0.591 0.753 +1 2.215 -1.195 0.434 1.127 1.137 1.129 -0.780 -0.969 1.087 0.488 -1.027 -0.574 0.000 0.938 -0.854 -1.714 0.000 0.768 -0.914 1.406 1.551 0.890 0.769 1.296 0.737 0.844 1.031 0.862 +1 1.592 0.091 -1.384 0.379 -0.320 0.594 -0.927 0.496 2.173 0.260 0.569 -1.073 0.000 0.862 -1.507 -0.825 2.548 0.912 0.083 0.533 0.000 0.685 0.904 0.988 1.023 0.891 0.894 0.821 +1 1.413 0.724 -1.335 0.307 1.605 1.060 1.686 -0.631 0.000 1.367 0.436 0.721 2.215 0.594 -0.047 -1.629 0.000 2.197 0.305 1.100 3.102 0.940 0.970 0.997 1.111 0.529 0.818 0.767 +1 0.700 -0.136 0.282 1.209 1.320 0.886 0.211 -0.738 2.173 0.463 -1.145 -0.021 0.000 1.031 -0.580 0.899 0.000 0.852 1.200 -1.511 1.551 0.834 1.162 1.026 1.060 0.842 0.917 0.826 +1 0.692 1.943 0.264 0.484 1.602 2.655 -1.037 -0.766 0.000 3.523 1.392 0.882 2.215 1.453 0.600 -1.687 1.274 0.712 0.861 0.108 0.000 2.995 2.608 0.979 1.021 2.010 3.562 2.610 +1 0.775 -1.310 -0.310 0.179 1.211 1.434 -1.137 0.759 0.000 1.369 -0.684 -0.586 0.000 2.098 -0.005 1.118 0.000 4.529 -0.947 -0.989 0.000 0.726 0.991 0.987 0.560 0.727 0.793 0.736 +0 0.503 1.408 0.821 1.373 -1.309 1.176 1.725 -0.823 0.000 1.204 0.656 1.145 2.215 0.766 0.764 0.474 0.000 1.306 -0.227 0.157 3.102 1.761 1.604 1.082 0.955 1.041 1.221 1.038 +0 0.826 0.115 -0.612 1.188 -1.217 0.704 0.552 0.689 1.087 1.247 0.671 -0.916 2.215 1.110 2.340 0.588 0.000 2.232 1.033 1.251 0.000 0.585 1.190 0.990 0.902 1.371 1.130 1.335 +1 1.099 1.414 -0.113 0.379 1.688 0.803 0.439 0.908 2.173 1.053 1.122 -1.247 0.000 1.015 0.318 -0.854 2.548 0.947 1.185 0.368 0.000 0.813 0.747 0.986 0.871 1.126 0.741 0.632 +1 0.664 0.326 -1.298 0.483 1.138 1.000 -0.144 0.156 0.000 1.288 -0.319 -1.405 2.215 0.736 -0.988 -0.162 0.000 1.094 -0.586 1.047 3.102 0.871 0.858 0.990 1.029 0.884 0.924 0.963 +0 0.980 1.726 -0.762 0.966 1.267 0.445 1.965 -0.196 0.000 0.773 0.619 1.039 1.107 0.516 -0.541 -0.403 2.548 0.635 1.066 1.656 0.000 0.859 0.937 1.303 0.950 0.782 0.837 0.742 +1 0.607 -0.025 0.188 0.854 -1.272 1.279 0.472 -1.594 0.000 1.175 0.924 0.160 2.215 1.260 -0.096 0.896 2.548 1.400 2.350 -0.643 0.000 3.360 2.336 0.988 0.770 1.070 1.649 1.376 +0 0.641 1.328 -0.710 0.859 1.091 1.350 1.452 1.484 1.087 1.488 0.655 -1.371 0.000 3.466 0.598 -0.208 2.548 1.295 -0.010 -0.222 0.000 0.960 1.158 1.027 1.194 2.916 1.539 1.195 +0 0.842 -1.910 -0.245 0.222 1.665 1.245 -2.030 -0.630 0.000 2.142 -0.869 1.191 2.215 0.784 -1.486 -1.196 2.548 0.915 -1.342 0.234 0.000 1.080 0.929 0.979 1.140 1.263 0.874 0.799 +1 0.841 0.792 1.305 0.582 -0.907 0.695 0.746 -0.157 0.000 0.682 0.933 -1.063 1.107 1.514 0.407 0.484 1.274 1.723 0.358 -1.693 0.000 1.354 0.987 0.990 0.887 1.100 0.825 0.741 +1 0.658 0.433 -0.926 0.797 -0.157 0.723 1.702 -1.419 0.000 0.662 0.986 -0.297 2.215 1.090 -0.712 0.702 2.548 0.938 1.231 1.198 0.000 0.900 0.939 0.993 0.808 1.164 1.105 1.023 +1 1.935 0.637 -0.310 0.436 -1.062 0.637 0.216 1.727 0.000 0.751 -0.851 1.512 2.215 1.302 0.669 0.821 2.548 0.820 1.394 -0.150 0.000 0.772 1.085 0.991 0.999 1.123 1.025 0.841 +1 1.215 1.178 1.589 0.313 0.430 0.799 -0.674 -1.105 0.000 0.779 1.097 -0.009 0.000 0.634 0.122 0.714 2.548 0.658 -0.857 -1.037 1.551 1.003 0.831 0.986 0.820 0.579 0.813 0.739 +0 0.677 -0.195 0.490 0.996 1.322 1.217 0.296 1.308 0.000 1.638 -0.958 -0.557 0.000 0.813 1.002 1.372 2.548 1.948 0.189 -0.355 1.551 3.624 2.240 0.996 0.597 1.055 1.472 1.200 +1 2.172 0.282 -0.300 0.248 -0.357 0.688 -0.654 1.675 2.173 0.700 2.202 -1.560 0.000 1.343 -2.216 0.808 0.000 0.590 -0.253 -0.016 0.000 0.781 0.868 0.981 0.876 0.934 0.957 0.774 +0 1.415 1.665 -1.552 0.265 0.548 0.800 0.950 0.896 0.000 0.956 0.660 -0.979 2.215 1.308 1.215 -0.288 2.548 0.697 1.370 0.690 0.000 0.451 0.927 0.987 0.787 0.799 0.817 0.763 +0 0.572 0.408 -1.450 1.050 0.570 0.404 1.404 -0.035 0.000 0.482 0.914 -0.976 1.107 1.515 1.520 -0.676 2.548 1.026 1.656 -1.401 0.000 0.969 0.741 1.040 0.689 0.416 0.710 0.666 +0 0.507 -1.546 0.422 0.878 -0.233 1.277 1.155 -0.802 0.000 1.033 -0.615 1.345 2.215 1.053 -1.783 1.190 0.000 1.606 -0.296 -0.567 0.000 0.745 0.715 0.988 0.750 0.287 0.654 0.633 +0 0.694 -1.242 -0.850 0.463 -0.761 0.798 -2.154 -1.497 0.000 0.356 -1.876 1.704 0.000 1.092 -0.923 0.667 2.548 0.452 -0.442 0.546 1.551 0.723 0.618 1.003 0.572 0.143 0.563 0.530 +1 1.300 -1.294 -0.204 0.438 -0.190 1.311 -0.237 1.519 2.173 0.624 0.074 -0.847 1.107 0.471 0.725 0.945 0.000 0.600 0.559 0.207 0.000 0.364 0.871 0.984 0.733 1.145 0.993 0.822 +1 0.384 0.716 -1.380 1.174 0.370 0.712 -1.267 -0.360 0.000 1.068 0.238 1.299 2.215 0.923 -1.585 0.358 0.000 1.144 2.175 1.577 0.000 0.929 0.691 0.990 0.922 0.709 0.778 0.988 +0 0.718 0.087 -0.395 1.370 -1.528 1.276 -0.449 0.185 2.173 1.162 0.024 -1.510 2.215 0.706 -0.399 1.613 0.000 0.886 -0.431 -0.416 0.000 0.845 0.989 1.171 0.715 1.841 1.067 0.856 +1 0.449 -1.027 -0.489 1.103 0.777 1.370 -2.122 0.362 0.000 2.044 1.304 -1.289 0.000 1.942 0.319 -0.654 0.000 1.781 0.203 1.153 3.102 1.623 1.539 0.985 0.725 0.595 1.341 1.080 +1 0.544 0.325 -1.527 0.901 0.787 0.635 0.851 0.318 2.173 0.728 1.678 -0.685 0.000 0.651 1.374 1.716 0.000 0.560 -0.688 -1.339 3.102 0.944 0.816 0.989 0.728 0.867 0.626 0.608 +0 0.956 0.657 -0.187 1.152 0.695 0.985 0.160 -1.007 2.173 0.785 -0.056 -1.605 1.107 0.621 -1.649 -0.923 0.000 0.828 0.405 1.176 0.000 0.848 1.116 1.038 0.971 0.679 0.870 0.795 +1 2.229 0.763 -0.619 0.160 -1.406 0.464 1.431 -1.562 0.000 0.653 1.267 0.301 0.000 1.207 0.755 0.837 2.548 0.943 -0.280 1.728 0.000 1.039 0.945 0.989 0.789 0.224 0.731 0.732 +0 0.584 -1.026 -1.383 1.154 0.977 1.075 -0.564 -0.839 2.173 1.317 1.527 0.455 2.215 1.015 1.157 -1.347 0.000 0.626 0.702 0.586 0.000 0.887 1.001 0.986 1.052 2.770 1.570 1.275 +1 0.580 -1.148 -0.756 1.396 0.364 0.471 -0.133 1.427 2.173 0.423 0.719 -0.365 0.000 0.773 -0.281 -1.282 2.548 1.244 1.260 0.992 0.000 0.964 0.948 1.056 0.906 0.488 0.695 0.902 +1 0.730 -1.363 0.848 1.617 -1.484 0.672 -1.202 -0.443 1.087 0.843 -0.999 -0.761 0.000 1.461 0.037 1.544 2.548 2.116 -0.173 0.233 0.000 1.545 1.048 1.299 1.125 1.461 1.026 1.019 +0 0.893 -1.485 -1.668 0.734 0.542 0.713 -1.776 -0.422 0.000 0.581 -1.913 0.212 0.000 1.169 -1.365 0.955 1.274 1.892 -0.330 -1.303 3.102 0.749 0.943 1.024 0.916 1.211 0.954 0.815 +0 0.448 0.257 -0.056 1.386 1.109 0.750 -1.301 0.226 0.000 1.330 0.450 1.655 2.215 0.781 -2.594 -1.561 0.000 1.077 -2.352 -0.309 0.000 0.915 1.473 0.989 0.873 1.301 1.765 1.449 +1 1.093 1.771 -0.293 2.109 -0.823 1.365 -2.081 1.324 0.000 0.538 0.551 -0.041 0.000 0.676 0.777 0.448 2.548 1.441 0.368 -1.703 3.102 0.895 0.916 0.985 0.957 0.721 0.942 0.796 +1 0.643 -0.142 1.359 0.685 0.623 0.386 0.180 -0.727 2.173 0.505 -2.623 0.609 0.000 0.917 -0.303 1.631 1.274 0.984 -1.565 -0.611 0.000 0.905 1.154 0.991 0.743 0.659 0.907 1.075 +0 0.772 -0.470 -1.466 0.390 -0.344 0.716 -0.303 1.083 2.173 0.785 -0.622 -0.972 0.000 1.154 -1.033 0.117 2.548 0.717 -0.331 1.560 0.000 0.750 0.879 0.984 0.820 0.992 0.774 0.702 +1 0.449 1.490 -0.194 0.734 1.296 0.788 0.953 -1.032 0.000 0.742 1.157 1.083 2.215 0.394 1.927 1.184 0.000 1.080 -0.020 0.432 3.102 1.099 0.946 0.988 1.123 0.687 0.792 0.794 +1 0.765 -0.199 -0.829 0.860 0.921 1.295 0.209 -1.616 0.000 0.735 1.490 -0.805 0.000 1.514 0.697 0.478 1.274 0.812 0.724 1.534 0.000 0.930 1.031 1.124 0.721 0.682 0.720 0.740 +0 0.503 1.577 -1.050 0.824 -0.058 0.665 -0.098 0.936 2.173 0.527 1.422 1.391 0.000 0.641 1.141 0.020 0.000 1.135 0.938 -1.248 3.102 0.847 0.929 0.978 0.644 1.038 0.710 0.659 +0 0.619 -1.216 0.456 1.224 1.275 0.932 -1.042 -0.563 2.173 1.534 0.529 0.956 1.107 1.504 -0.461 -1.186 0.000 0.610 0.788 -1.023 0.000 0.851 1.023 0.987 1.926 2.323 1.560 1.341 +0 0.391 0.134 -1.658 0.595 1.362 0.417 1.242 -1.293 1.087 0.351 0.804 -0.060 0.000 0.759 0.382 0.505 2.548 0.724 -1.061 0.932 0.000 0.971 0.970 0.981 0.876 0.761 0.880 0.763 +0 0.416 0.739 -1.653 0.235 -0.289 0.704 0.339 -0.116 2.173 0.705 -1.881 1.630 0.000 0.476 -0.671 0.696 2.548 0.581 -0.795 -1.050 0.000 0.698 0.615 0.989 0.809 0.631 0.826 0.707 +0 0.384 -0.267 -0.624 1.101 1.228 0.786 1.177 -1.282 2.173 0.987 1.181 0.239 2.215 0.635 1.695 -0.662 0.000 0.602 2.212 0.843 0.000 0.722 0.824 0.985 1.357 1.271 1.199 1.131 +0 1.077 0.114 1.659 0.682 1.302 1.013 -1.251 0.078 0.000 0.900 -0.339 -1.071 1.107 0.735 -1.950 -0.299 0.000 0.543 -2.018 -0.931 0.000 0.912 1.173 0.988 0.596 0.441 0.674 0.737 +0 1.012 -0.965 -1.604 1.170 1.370 0.675 0.819 -0.442 0.000 0.669 0.740 0.003 0.000 0.466 -0.385 -1.542 2.548 0.998 -0.698 0.016 3.102 0.559 0.821 0.995 0.488 0.526 0.615 0.794 +0 1.015 -0.595 1.307 0.910 -1.265 0.863 2.131 -0.320 0.000 1.561 0.250 1.715 2.215 0.577 0.906 -0.700 0.000 1.449 -1.779 0.112 0.000 0.885 0.791 0.988 0.787 0.970 1.146 1.185 +0 0.780 0.545 1.715 1.604 0.482 0.804 0.389 -0.840 2.173 0.697 -0.059 0.589 0.000 0.799 -0.579 -1.157 2.548 0.445 0.174 1.333 0.000 0.734 1.005 1.388 1.045 0.606 0.864 0.774 +0 0.787 -0.604 0.796 0.847 0.961 0.646 -0.146 0.734 2.173 1.011 -0.237 -1.017 0.000 1.408 -0.452 -0.556 2.548 1.068 -0.470 -1.707 0.000 0.818 1.063 0.997 1.072 1.111 0.794 0.809 +1 1.195 -0.031 0.647 0.565 -1.480 0.861 -0.187 -1.335 2.173 0.568 -0.701 0.859 0.000 0.506 -0.865 -1.497 2.548 0.976 -0.499 -0.583 0.000 0.937 0.945 1.071 0.662 0.342 0.613 0.609 +0 0.453 -1.189 0.754 1.448 0.084 0.810 0.213 -0.608 2.173 0.659 0.171 -1.299 0.000 0.656 -0.549 -1.456 0.000 1.084 -1.114 1.152 3.102 0.583 0.786 0.994 0.888 1.305 0.822 0.749 +1 1.064 -1.090 -1.618 1.356 1.291 2.083 2.016 -0.136 0.000 1.653 0.038 1.319 2.215 0.603 -0.956 -0.314 0.000 0.826 -0.801 1.418 3.102 4.514 3.202 0.984 1.334 0.558 2.371 2.682 +1 1.932 1.712 -1.541 0.636 -1.617 0.584 1.076 0.138 2.173 0.507 1.826 -0.118 0.000 0.771 0.866 0.562 2.548 0.887 0.442 -0.346 0.000 0.833 0.608 0.991 0.919 0.317 0.809 0.692 +1 1.985 0.162 0.788 1.081 0.113 0.503 0.446 -0.006 0.000 1.587 0.052 -1.090 2.215 1.268 -0.399 1.594 2.548 0.970 -1.076 -0.470 0.000 1.138 1.137 1.158 1.518 1.065 1.119 1.006 +0 0.334 0.953 -0.717 0.591 1.219 0.594 -1.421 -1.672 0.000 0.803 -1.057 0.279 1.107 1.207 0.950 -0.127 2.548 1.359 2.431 1.483 0.000 5.986 3.388 0.992 0.748 1.390 2.338 1.748 +1 1.679 -1.408 -0.511 0.822 -1.065 1.003 -0.322 1.143 2.173 0.388 -1.111 -0.041 2.215 0.557 -0.366 -0.278 0.000 0.813 -0.366 -1.505 0.000 0.909 0.971 0.996 0.579 0.892 0.930 0.793 +1 1.207 -0.283 1.639 0.398 -0.001 1.356 1.150 -0.442 0.000 2.351 -0.712 1.393 2.215 0.693 -0.532 -0.601 0.000 0.536 0.247 0.602 1.551 0.766 0.686 0.987 0.841 0.858 0.885 0.754 +0 1.031 0.231 -0.085 0.536 -1.741 0.875 0.250 1.214 0.000 0.472 -1.161 0.472 2.215 1.505 1.172 -0.719 2.548 0.439 0.576 -0.928 0.000 0.911 0.906 1.027 0.863 1.623 0.990 0.823 +1 1.304 -0.363 0.540 0.772 -0.769 2.221 -1.045 1.057 0.000 1.280 0.156 -0.854 2.215 1.243 0.961 -0.702 2.548 1.376 -0.513 -1.092 0.000 0.687 0.813 1.284 1.032 0.646 0.858 0.714 +1 2.371 -0.559 -1.223 1.430 -0.543 0.883 -0.637 -0.823 0.000 1.121 1.482 1.128 0.000 2.190 -0.821 -0.307 2.548 4.220 0.594 1.136 0.000 1.184 1.563 1.469 1.191 0.710 1.840 1.790 +1 0.510 1.186 1.656 0.974 0.364 0.924 1.271 -1.012 1.087 0.761 -0.447 1.541 0.000 0.772 -0.257 0.749 0.000 0.772 0.162 0.042 3.102 0.777 0.684 0.986 0.982 0.889 0.922 0.795 +1 0.519 -0.106 0.006 0.694 -1.625 0.390 -1.574 -0.700 0.000 0.842 0.459 -1.033 2.215 0.351 0.323 1.234 0.000 1.326 0.860 0.340 3.102 0.999 0.963 0.984 0.681 0.941 0.843 0.735 +0 1.598 -1.168 1.597 0.933 -1.407 1.194 0.306 0.287 1.087 0.885 -0.698 -0.121 2.215 1.452 0.864 -1.351 0.000 1.601 -0.438 1.029 0.000 0.913 1.300 0.995 1.689 0.977 1.201 1.204 +0 0.858 -0.039 -0.693 1.275 -0.338 0.478 2.232 1.210 0.000 0.734 -2.713 -0.543 0.000 1.031 -0.389 1.373 2.548 2.497 1.262 0.927 0.000 0.998 0.729 0.984 1.107 0.596 1.005 0.978 +1 1.479 -1.297 0.740 0.364 1.719 2.749 -1.327 -0.785 0.000 2.422 -0.789 1.241 2.215 0.863 -1.337 0.348 2.548 1.208 -1.938 0.231 0.000 0.929 1.298 0.987 1.013 1.221 1.686 1.372 +1 0.423 -0.785 -0.613 0.346 -0.819 0.809 0.019 0.523 2.173 0.653 -0.586 -1.320 0.000 1.366 0.112 1.636 0.000 0.499 0.470 0.196 0.000 0.900 0.914 0.989 0.565 0.798 0.679 0.646 +1 1.392 0.046 -0.482 0.210 -0.411 1.475 -0.710 0.876 0.000 0.904 -1.383 -1.196 0.000 0.868 0.751 0.208 2.548 1.133 -0.129 -0.802 0.000 1.004 1.314 1.001 0.524 0.586 0.746 0.680 +1 0.430 1.650 -0.751 0.219 -1.475 0.561 0.691 0.710 0.000 0.755 0.842 -1.543 2.215 1.864 0.578 -0.624 2.548 0.508 2.431 0.024 0.000 1.199 1.044 0.987 0.726 0.940 0.903 0.778 +0 1.652 0.570 0.108 1.134 1.387 1.038 -0.437 0.261 2.173 2.429 -0.059 -1.571 2.215 0.730 1.591 -0.153 0.000 0.860 -1.380 -1.145 0.000 2.417 1.776 1.734 1.725 2.371 1.576 1.404 +1 0.597 0.635 -0.623 0.917 0.484 0.808 0.670 -1.165 1.087 0.853 0.068 0.032 0.000 0.872 -0.869 0.983 2.548 0.851 0.666 1.542 0.000 1.162 0.970 0.985 0.916 1.363 0.873 0.758 +0 0.509 0.729 -1.274 1.057 0.635 1.161 0.682 0.586 1.087 1.301 -0.899 -0.880 2.215 1.542 1.376 -1.479 0.000 1.381 1.204 0.790 0.000 1.131 1.668 1.004 1.306 2.389 1.815 1.376 +1 0.318 2.059 1.091 0.821 0.042 0.636 -2.368 -0.946 0.000 0.883 -0.797 1.707 0.000 0.456 -0.980 0.265 1.274 1.365 1.225 0.310 3.102 0.772 0.707 0.994 0.619 0.994 0.858 0.772 +1 0.865 -1.015 -1.715 0.854 -0.252 0.566 -0.749 1.306 2.173 0.756 -1.099 -1.270 0.000 0.761 -1.120 0.438 0.000 1.983 -0.424 -0.049 3.102 1.033 0.905 1.153 0.780 1.063 0.726 0.665 +1 1.093 0.984 -0.528 1.378 -1.113 1.481 0.419 -1.042 0.000 3.647 -0.986 0.888 0.000 1.097 1.233 -0.045 0.000 1.406 0.896 -1.253 3.102 2.107 1.259 0.981 0.952 1.006 0.993 0.842 +0 2.238 -0.052 0.735 0.379 0.098 0.954 0.616 -0.864 1.087 1.076 0.135 -1.647 2.215 0.840 -1.061 1.175 0.000 1.231 0.775 -0.525 0.000 0.708 0.794 0.987 1.338 1.033 1.048 0.906 +0 0.750 1.574 -0.926 0.796 1.049 0.639 0.736 0.275 1.087 0.753 -0.857 0.257 1.107 0.937 -1.612 -1.532 0.000 0.607 2.471 -0.972 0.000 0.721 0.926 1.047 0.832 0.924 0.974 1.163 +1 1.569 -1.973 -1.496 1.589 -1.347 1.228 0.867 0.654 0.000 0.736 -1.719 -0.613 0.000 1.013 -0.502 -0.151 2.548 0.902 -0.506 1.004 3.102 0.962 0.921 0.975 1.205 0.630 0.883 1.487 +1 1.035 -0.387 0.439 1.890 0.641 0.636 0.712 1.479 0.000 1.179 -0.531 -1.299 2.215 0.373 2.291 0.398 0.000 1.061 0.174 -0.828 3.102 0.725 1.266 0.970 1.104 0.571 1.026 1.103 +1 0.720 -1.027 0.996 0.193 -0.975 0.660 0.884 1.399 0.000 0.845 -0.543 -0.456 2.215 0.559 1.153 -1.398 0.000 1.117 -1.240 0.100 3.102 0.666 1.230 0.994 0.748 0.597 1.034 0.854 +1 1.496 -0.636 -1.432 0.439 -1.604 0.445 -0.627 0.383 2.173 0.289 -2.606 0.633 0.000 0.651 -1.087 0.022 2.548 0.372 -2.326 1.192 0.000 0.207 0.646 0.977 0.860 0.287 0.689 0.730 +0 1.296 -1.759 0.877 0.657 -1.299 1.173 1.038 -0.034 2.173 0.728 -0.049 1.362 2.215 1.886 -1.972 -1.349 0.000 0.995 -1.993 -0.632 0.000 0.926 1.630 1.182 2.610 1.513 2.387 1.932 +0 0.660 -1.418 -1.155 0.338 1.442 1.167 -0.763 -0.356 1.087 1.626 1.326 1.452 0.000 0.983 -1.605 0.338 0.000 0.901 -0.465 0.989 3.102 0.957 0.986 0.995 0.612 1.023 0.718 0.686 +1 1.453 0.019 -0.545 0.610 0.792 2.542 1.641 -1.658 0.000 1.000 1.702 0.264 0.000 1.331 0.860 0.169 0.000 1.267 -1.132 0.054 3.102 1.013 0.793 1.217 0.890 0.913 1.033 0.899 +1 1.205 0.638 -1.497 1.355 -1.554 0.721 -0.710 0.143 0.000 0.482 -1.038 -1.402 2.215 1.095 1.144 -0.040 0.000 0.827 0.331 0.977 3.102 0.920 1.076 0.982 0.726 0.661 0.678 0.745 +0 1.408 -0.338 -0.110 0.783 -1.196 0.563 -1.437 -1.224 0.000 1.242 0.576 0.053 1.107 1.202 -0.528 1.576 2.548 1.204 1.266 1.234 0.000 0.767 0.900 1.206 0.978 1.506 1.310 1.101 +1 0.863 0.702 1.097 1.589 0.376 1.197 1.058 -1.344 2.173 0.344 2.481 -0.142 0.000 0.931 0.640 -0.462 2.548 0.439 1.658 1.488 0.000 0.524 0.927 0.987 0.818 0.970 0.970 0.822 +1 1.247 -0.710 1.130 0.748 0.494 0.907 -0.397 -0.660 2.173 0.479 0.788 0.052 2.215 0.350 -1.445 1.350 0.000 0.496 -2.332 -1.408 0.000 0.739 0.941 0.991 1.015 0.853 0.940 0.829 +0 0.479 2.001 0.272 0.621 0.925 0.627 0.212 -0.307 2.173 0.735 0.189 1.336 0.000 0.941 0.119 -1.119 2.548 0.416 -1.537 0.605 0.000 0.975 1.005 0.985 0.821 0.641 0.703 0.721 +0 0.528 0.582 -1.453 0.983 -0.241 0.529 -2.811 -1.024 0.000 1.419 0.263 0.966 2.215 0.683 0.621 -0.263 0.000 0.482 -0.209 0.971 3.102 3.126 1.722 0.988 0.993 0.198 1.543 1.253 +1 0.374 -0.148 -1.673 1.089 0.726 0.934 1.145 -0.074 0.000 0.881 0.527 1.191 0.000 1.311 0.045 -0.503 2.548 1.239 -0.617 -1.331 0.000 1.398 0.864 0.993 1.041 0.785 0.822 0.829 +0 1.812 1.235 1.393 1.242 -1.598 0.930 1.847 -0.139 0.000 0.702 2.191 0.414 0.000 0.541 2.214 -0.681 0.000 1.003 0.935 -0.829 3.102 0.886 0.955 0.984 0.821 0.517 0.682 0.854 +0 0.369 -0.782 -0.128 0.980 -0.584 0.525 -1.186 1.289 2.173 0.420 0.550 -0.695 2.215 0.957 -0.155 1.486 0.000 0.865 -0.744 0.358 0.000 0.931 0.788 0.975 1.145 0.966 0.774 0.717 +0 0.543 0.952 -1.358 0.507 -0.189 0.541 -1.063 1.246 0.000 0.454 -1.086 0.354 0.000 0.760 -0.090 -1.373 1.274 0.399 -0.834 -0.213 1.551 0.922 0.730 0.987 0.905 0.414 0.730 0.846 +1 0.873 0.914 -1.094 0.738 0.216 0.934 -0.252 1.512 0.000 0.909 0.637 -0.354 2.215 1.099 0.476 0.758 2.548 1.042 -0.728 -1.420 0.000 0.863 1.044 1.029 0.645 0.898 0.944 0.866 +0 0.637 -1.593 1.524 1.188 -1.031 0.600 -1.311 -0.437 2.173 0.647 -0.309 0.690 0.000 1.107 -1.170 0.182 1.274 0.720 -0.421 1.284 0.000 0.997 0.870 0.986 0.931 0.538 0.704 0.713 +0 0.640 0.746 0.003 0.705 -1.690 1.065 0.654 1.034 2.173 0.816 1.513 -1.114 0.000 1.144 -0.427 1.021 0.000 2.143 0.748 -0.768 0.000 0.869 0.924 0.985 0.853 1.600 0.970 0.921 +0 5.331 1.077 0.157 1.011 -0.269 1.522 -0.836 1.559 0.000 1.617 -0.800 -1.230 0.000 1.657 0.058 -1.373 0.000 0.796 -0.060 0.579 3.102 1.196 0.845 1.202 0.976 0.474 0.840 1.604 +1 0.421 0.840 1.604 1.006 1.055 1.271 -2.596 0.445 0.000 1.199 -0.135 0.681 2.215 2.365 0.493 -1.078 0.000 1.256 0.039 -0.296 3.102 7.876 4.278 0.984 1.549 0.860 2.653 3.166 +1 0.694 -0.218 -1.193 1.476 1.305 0.916 -0.660 0.138 1.087 0.629 -1.090 -0.728 2.215 0.443 -1.080 0.169 0.000 1.890 0.219 -1.221 0.000 1.246 0.900 1.090 1.141 0.827 0.859 0.819 +0 0.592 0.660 -1.632 0.779 0.354 0.687 -0.053 -0.199 1.087 1.240 -0.852 0.560 2.215 1.371 -1.811 -1.430 0.000 1.737 0.096 -1.249 0.000 2.158 1.747 0.991 1.302 1.032 1.300 1.310 +0 0.794 1.209 0.682 1.928 0.618 0.998 2.468 -1.284 0.000 1.253 -0.331 -0.450 0.000 0.842 -0.368 1.101 2.548 0.791 1.068 -1.541 3.102 0.841 0.916 0.987 0.871 0.731 0.703 0.808 +1 0.628 1.124 -1.620 0.997 -0.472 0.672 0.167 -1.689 0.000 0.896 -0.652 -1.342 0.000 1.899 -0.643 0.283 2.548 0.840 0.234 -0.489 1.551 0.875 0.806 0.990 1.665 0.793 1.051 1.066 +1 0.877 1.810 1.579 0.904 1.248 1.109 0.490 -0.423 2.173 0.527 0.097 0.300 2.215 0.679 0.200 -1.152 0.000 1.130 0.194 1.108 0.000 0.864 1.043 0.973 0.831 0.718 0.914 0.790 +0 0.659 -0.548 0.212 0.729 -1.662 1.049 0.346 -0.591 2.173 0.709 0.519 1.234 0.000 0.655 0.433 0.604 0.000 0.698 -0.680 -1.290 1.551 0.563 1.136 0.989 0.540 0.772 0.756 0.737 +1 1.594 -0.349 0.503 0.278 0.557 0.962 0.740 -1.020 1.087 0.744 -0.073 1.513 2.215 0.323 0.380 1.090 0.000 0.543 -0.691 -0.051 0.000 0.501 0.820 0.987 0.807 1.079 0.934 0.721 +1 0.956 -0.337 -1.392 1.317 -0.877 0.678 0.692 0.480 2.173 0.589 1.442 0.753 0.000 1.067 1.321 -0.145 2.548 0.924 -1.592 1.390 0.000 0.958 1.011 0.977 1.570 0.698 1.223 0.989 +0 0.489 -0.806 0.339 0.688 -0.603 0.926 1.535 1.062 0.000 1.010 -0.008 -1.016 2.215 0.570 0.287 -0.143 2.548 0.389 1.464 0.368 0.000 0.844 0.881 0.987 1.167 0.586 0.890 1.382 +1 0.590 -0.228 0.443 0.763 -1.724 1.037 0.448 -1.135 2.173 1.572 1.109 1.592 0.000 2.479 0.264 0.186 0.000 0.734 1.285 -0.153 0.000 1.090 1.018 0.986 0.859 0.897 1.007 0.848 +1 2.256 -0.273 -1.452 0.682 0.865 0.437 -0.433 1.308 0.000 1.103 0.006 -0.006 2.215 0.397 -2.701 0.748 0.000 1.308 -1.440 0.065 0.000 0.989 0.975 1.494 0.836 0.234 0.819 0.817 +1 0.607 0.534 -0.418 1.583 0.705 0.547 0.367 0.975 2.173 0.908 0.780 -0.025 2.215 1.168 -0.616 -1.365 0.000 2.258 0.308 -1.236 0.000 0.907 0.945 1.152 0.733 0.844 0.813 0.775 +0 1.026 -1.834 -0.754 1.788 -1.661 0.489 -1.053 -0.534 0.000 0.760 -2.085 1.297 0.000 0.579 -1.253 0.701 2.548 0.788 0.051 -0.045 3.102 1.485 0.899 1.368 1.205 0.516 0.830 0.806 +1 0.524 -1.310 0.683 1.178 -0.708 0.941 0.451 1.734 2.173 0.924 0.300 0.408 2.215 0.750 -1.428 1.098 0.000 0.589 -2.395 -0.731 0.000 0.893 1.469 1.034 1.408 1.281 1.320 1.130 +0 0.777 -0.038 0.681 1.287 0.300 0.994 1.242 -1.187 0.000 0.846 -1.037 0.483 2.215 1.353 0.260 -0.853 2.548 1.301 -0.761 1.678 0.000 0.873 0.866 0.989 1.040 1.347 0.992 0.880 +0 1.083 -0.383 1.261 0.464 -0.084 0.907 -0.437 0.384 1.087 1.264 -0.424 -1.240 2.215 0.894 -0.915 -0.709 0.000 0.744 -0.992 1.112 0.000 0.901 0.934 0.985 0.888 1.566 0.864 0.751 +0 0.823 1.424 -0.732 0.649 0.092 0.353 -0.740 0.472 2.173 0.508 0.912 0.178 2.215 1.437 0.174 -1.612 0.000 0.418 -2.289 0.458 0.000 0.451 0.837 0.989 0.559 0.612 0.645 0.652 +0 0.870 -0.729 0.752 2.530 0.243 1.424 -0.672 -1.506 0.000 0.951 -0.733 -0.339 2.215 1.180 0.035 1.486 2.548 0.830 -0.693 -1.162 0.000 0.524 0.817 0.978 0.870 1.213 0.966 1.092 +0 0.651 -0.395 -0.291 1.377 -0.267 0.988 0.022 -1.353 2.173 0.678 0.243 1.494 0.000 1.028 0.419 0.717 2.548 0.740 -0.905 0.778 0.000 0.865 0.947 0.991 1.262 1.233 1.180 0.996 +1 0.806 -0.368 0.793 1.212 1.500 1.476 -0.012 -0.359 2.173 0.618 0.230 1.155 0.000 0.938 -1.455 -1.604 0.000 0.733 -0.002 0.110 0.000 0.717 1.085 0.981 0.719 0.965 0.957 0.796 +1 0.745 0.643 0.591 0.623 -0.423 0.946 0.974 -0.224 0.000 0.851 1.168 -1.382 1.107 0.840 0.127 -1.714 2.548 0.861 0.992 1.562 0.000 0.832 1.033 0.992 0.831 0.566 0.797 0.712 +0 0.614 0.388 0.588 0.979 1.345 0.756 -0.273 -0.589 0.000 0.626 -1.678 -1.065 0.000 0.438 0.553 1.544 2.548 0.839 -1.592 0.123 0.000 0.833 0.971 0.987 1.081 0.517 0.682 1.036 +0 0.889 0.356 0.261 0.916 0.397 0.856 1.345 -1.114 0.000 1.205 0.903 1.027 1.107 0.790 -0.040 -1.180 2.548 0.606 0.383 -1.528 0.000 0.649 1.141 0.987 0.960 1.084 0.803 0.828 +0 0.704 -0.279 -0.729 0.627 1.103 0.516 0.241 -1.699 0.000 0.471 1.168 0.237 0.000 0.547 2.005 1.304 0.000 0.853 -1.037 -0.009 3.102 0.757 0.989 0.989 0.695 0.205 0.847 0.724 +1 2.526 -0.833 -1.682 0.209 -0.637 0.802 0.034 -0.411 2.173 1.193 -1.641 0.530 0.000 0.580 -0.340 -1.717 2.548 1.087 -1.217 -0.822 0.000 0.908 0.800 0.986 0.480 0.804 0.785 0.744 +1 2.038 -1.164 1.137 1.410 0.222 2.995 0.174 -0.913 0.000 0.851 1.501 0.817 0.000 0.977 0.188 1.027 0.000 0.999 0.088 -0.056 3.102 1.042 0.915 1.724 1.222 0.580 0.906 1.156 +0 1.040 -0.649 1.586 0.540 -1.161 0.626 1.370 1.515 2.173 0.941 2.025 0.161 0.000 0.434 -1.200 -0.653 0.000 1.004 0.933 0.153 3.102 0.792 0.915 0.984 1.213 0.798 1.160 1.334 +0 1.064 -1.689 1.144 0.774 0.844 1.131 -1.562 0.571 2.173 1.159 -1.515 -0.859 0.000 1.715 -1.839 -1.231 0.000 0.455 -0.065 -0.978 3.102 0.872 0.790 0.999 0.803 0.973 1.063 0.988 +1 1.147 -0.364 -1.192 0.174 1.682 0.882 -0.511 1.662 1.087 0.880 0.179 0.271 2.215 1.011 1.140 0.066 0.000 0.461 0.564 -0.417 0.000 0.386 1.304 0.976 1.007 1.316 0.894 0.895 +0 1.253 1.917 -1.135 0.745 -1.330 0.678 -0.123 0.643 0.000 0.924 0.620 0.192 0.000 0.409 -0.843 -1.167 1.274 0.636 0.463 1.276 3.102 0.934 0.896 0.975 0.681 0.446 0.664 0.853 +0 0.830 1.469 -0.503 0.790 0.614 0.966 -0.371 1.505 2.173 0.674 -0.448 -0.643 0.000 0.998 -0.427 -0.145 2.548 0.741 -0.885 0.757 0.000 0.921 1.009 0.989 1.161 1.220 1.222 1.072 +1 0.882 0.743 0.530 0.795 1.574 0.705 0.783 -1.649 0.000 0.422 -0.027 1.480 0.000 1.350 -0.699 -0.073 2.548 1.270 0.792 -0.260 3.102 0.609 0.901 0.990 1.219 0.983 0.911 0.846 +1 0.481 -0.687 -1.417 0.697 -0.018 0.860 0.458 1.046 0.000 1.119 -2.334 -0.377 0.000 0.568 0.788 -0.005 0.000 1.417 1.827 1.735 0.000 1.052 0.694 0.991 0.725 0.210 0.716 0.818 +1 1.004 0.661 -0.862 1.004 1.641 1.170 -0.101 -0.188 2.173 1.154 0.637 -1.689 0.000 1.368 0.143 1.315 2.548 1.259 -1.151 0.404 0.000 2.411 1.491 1.077 1.213 1.553 1.267 1.107 +1 1.074 0.080 0.283 0.860 -0.355 1.057 1.095 1.663 0.000 0.629 0.280 -1.165 2.215 0.734 0.970 -0.486 2.548 0.418 1.224 0.019 0.000 1.028 0.891 0.986 0.785 0.505 0.630 0.710 +1 2.355 -0.491 -0.653 0.748 0.112 0.693 0.664 1.242 1.087 0.835 -0.763 0.666 1.107 0.471 1.108 -1.516 0.000 0.470 1.563 1.150 0.000 0.390 1.024 1.170 1.421 1.049 1.062 0.954 +0 0.821 1.390 0.233 1.465 0.789 1.752 1.034 0.742 2.173 2.724 1.138 -0.872 2.215 1.443 0.503 -1.349 0.000 0.368 1.066 -1.499 0.000 0.318 0.861 0.976 0.682 3.200 1.600 1.286 +0 1.555 1.856 0.835 1.152 0.433 1.091 -0.162 -1.717 0.000 1.276 0.651 -0.331 2.215 0.921 1.470 -0.557 0.000 0.415 0.174 1.275 0.000 0.856 0.763 1.001 0.995 0.824 1.111 0.904 +0 1.705 0.794 0.171 0.232 -0.914 0.926 -0.951 -1.608 0.000 0.501 2.782 0.186 0.000 0.775 0.001 -1.652 0.000 0.498 -1.883 0.961 0.000 1.051 0.954 0.984 0.726 0.529 0.651 0.877 +1 0.918 1.679 1.592 1.745 -1.170 0.529 1.763 -1.473 0.000 1.267 -1.273 -0.291 0.000 1.521 1.078 0.352 2.548 0.876 1.254 1.059 0.000 0.800 0.986 1.066 1.181 0.903 1.021 0.852 +1 0.753 0.058 0.740 0.384 -1.121 0.855 -0.061 -0.586 0.000 1.086 -0.021 1.392 2.215 0.588 -0.959 -0.995 0.000 1.249 -0.863 0.676 3.102 0.803 1.003 0.990 0.763 0.842 0.873 0.807 +1 0.370 -0.660 -0.511 1.211 -0.886 1.205 -0.304 1.491 2.173 1.448 -0.450 0.761 0.000 1.083 0.035 -0.607 1.274 1.105 0.365 -1.257 0.000 0.963 1.042 0.986 1.303 1.373 0.950 0.955 +1 0.392 1.670 0.039 1.678 1.494 1.621 2.615 -0.604 0.000 1.342 0.065 1.014 2.215 0.973 0.800 0.837 0.000 0.932 -0.502 1.531 3.102 1.048 2.494 1.087 1.284 0.570 2.116 1.636 +0 1.950 0.691 0.445 1.452 0.301 2.038 0.179 -1.391 2.173 1.090 0.991 0.058 2.215 0.431 1.294 1.679 0.000 0.455 0.615 1.317 0.000 0.231 0.808 0.991 0.624 2.319 1.669 1.199 +1 0.764 -0.556 0.755 1.054 -0.453 0.385 -0.849 -1.702 1.087 0.598 0.286 -0.822 0.000 0.370 -1.939 0.960 0.000 0.488 -0.495 -0.986 3.102 1.211 0.841 1.101 0.572 0.283 0.496 0.533 +1 0.539 -0.857 -0.632 1.126 0.478 0.911 -0.886 -0.352 1.087 0.720 -0.651 0.485 0.000 2.128 -0.846 -1.536 2.548 0.922 -1.424 -1.471 0.000 1.191 1.046 0.991 1.119 1.520 0.949 0.844 +1 1.475 -0.350 -1.109 1.150 -1.724 1.279 -0.374 0.491 0.000 0.529 -0.017 1.515 0.000 0.689 -0.704 -1.131 0.000 1.108 0.420 -0.669 3.102 0.836 0.996 0.989 0.741 0.463 0.639 0.797 +1 0.465 -0.023 -1.457 0.784 1.389 0.978 -1.036 1.142 0.000 1.218 -1.319 -0.551 0.000 1.211 -0.161 -0.656 1.274 1.100 0.418 0.464 3.102 0.848 0.992 0.986 0.920 0.807 0.851 0.777 +0 0.634 0.703 -0.950 0.989 0.006 0.956 1.559 1.692 2.173 0.611 1.524 -0.640 0.000 1.436 0.935 0.583 2.548 0.714 0.759 1.384 0.000 0.882 0.863 0.986 1.026 1.294 0.865 0.735 +1 2.057 0.872 1.346 0.548 0.381 0.661 0.662 0.675 0.000 1.042 1.068 -0.532 2.215 1.097 -0.064 -1.008 2.548 0.586 -0.113 -0.389 0.000 0.873 0.953 1.123 1.088 0.847 0.938 0.827 +1 0.923 1.122 -0.993 0.633 0.399 0.967 0.482 -1.740 1.087 1.023 1.628 0.609 0.000 0.520 0.787 -0.206 0.000 0.943 -0.374 -0.822 3.102 0.884 1.135 1.007 0.929 0.892 0.966 0.821 +1 1.005 -0.044 1.160 1.181 -1.242 1.010 0.190 -0.615 0.000 0.993 -0.740 1.592 1.107 1.265 -0.512 0.708 1.274 0.945 0.031 0.055 0.000 0.856 1.145 1.252 0.806 0.860 0.967 0.881 +1 1.113 -0.707 -1.194 1.771 -1.438 0.828 -0.182 1.042 0.000 0.778 2.012 0.107 0.000 0.859 -1.119 -0.014 0.000 1.621 -0.477 -0.611 0.000 0.978 0.973 0.975 0.642 0.634 0.785 0.722 +0 2.182 -1.602 -0.583 1.435 -0.473 1.169 -1.218 1.402 0.000 0.590 -0.264 1.383 0.000 0.947 -0.482 0.471 2.548 0.415 -0.702 0.051 0.000 0.915 1.008 0.979 0.790 0.362 0.856 0.968 +0 2.074 0.884 -1.511 0.753 -0.825 1.227 -0.493 0.374 0.000 1.026 -0.828 0.704 0.000 1.526 0.334 -0.805 2.548 0.722 -0.626 1.603 1.551 0.815 0.847 1.004 0.804 0.814 1.020 1.213 +0 0.294 -1.159 0.495 1.037 -1.135 1.006 -0.175 1.154 0.000 0.531 -0.930 0.938 0.000 1.234 1.150 -0.377 1.274 0.803 0.544 0.052 1.551 1.089 0.779 0.989 2.515 0.371 1.643 1.304 +0 0.449 0.050 1.620 1.278 1.619 0.686 -0.266 -0.097 2.173 0.772 1.188 -1.270 0.000 1.218 0.702 -0.608 0.000 0.683 2.243 1.131 0.000 0.902 1.150 1.002 1.422 0.816 1.149 1.075 +1 0.443 0.601 -1.628 0.561 -0.854 0.480 0.671 -0.679 0.000 1.378 -0.830 1.417 2.215 0.980 -0.828 0.102 2.548 0.661 1.792 0.195 0.000 0.922 1.243 0.989 1.989 1.145 1.494 1.272 +0 1.233 -0.165 -0.892 1.341 0.272 0.962 0.106 0.537 2.173 1.849 -0.902 -1.300 0.000 0.923 -0.343 1.190 0.000 0.840 -0.903 0.465 3.102 1.663 1.184 1.545 1.085 0.604 1.107 1.038 +0 1.019 0.081 -0.809 0.778 0.039 0.929 -0.389 0.953 2.173 0.741 0.474 -1.119 0.000 0.399 0.068 -1.648 0.000 0.544 -0.742 1.188 0.000 0.949 1.036 0.988 0.584 0.987 0.761 0.720 +0 0.864 0.543 -0.813 0.968 -0.123 0.537 0.251 1.591 0.000 0.770 -0.331 0.613 0.000 0.705 0.973 -1.677 2.548 0.936 0.091 0.991 1.551 0.896 0.693 0.987 0.819 0.520 0.620 0.649 +1 0.479 -2.138 0.061 0.971 0.883 1.069 -0.727 -0.471 0.000 1.187 -2.466 1.128 0.000 0.899 -0.652 -1.030 2.548 1.115 0.079 -1.290 3.102 0.925 1.269 0.979 0.902 0.365 1.058 0.898 +1 1.385 0.238 0.378 2.601 0.890 1.076 -2.164 -0.774 0.000 0.937 -0.441 -1.099 0.000 1.306 -0.204 1.436 2.548 0.915 -1.354 -1.006 0.000 0.755 0.981 1.172 1.034 1.051 0.879 1.050 +0 0.399 1.075 -1.644 0.639 0.131 0.617 0.670 -1.337 1.087 0.490 1.280 -0.141 2.215 0.684 0.511 0.739 0.000 0.375 -0.881 -0.132 0.000 0.699 0.747 0.986 0.682 0.760 0.611 0.544 +1 1.078 -2.326 1.728 0.597 -1.364 0.514 -1.403 0.600 0.000 0.709 -0.791 -0.278 1.107 0.725 -0.764 -0.736 2.548 1.247 -0.145 0.509 0.000 0.954 0.814 0.985 0.748 0.306 0.671 0.664 +0 0.610 -2.247 -1.013 0.971 1.479 0.642 0.690 -0.186 2.173 0.321 -1.363 -0.113 0.000 0.441 1.121 -1.338 0.000 0.841 -0.344 0.947 3.102 1.125 0.910 0.987 0.723 0.803 1.013 0.886 +0 0.421 -0.788 -1.499 1.773 -1.416 0.983 -0.249 0.713 0.000 0.516 0.241 1.331 0.000 0.671 0.602 -0.132 2.548 0.758 -0.682 -0.304 1.551 0.885 0.856 0.981 0.828 0.449 1.015 1.114 +0 1.796 0.407 0.703 1.094 0.130 0.732 -0.270 -1.028 2.173 0.964 0.219 -0.179 0.000 1.070 0.675 -1.710 2.548 1.646 -0.394 -1.721 0.000 1.717 1.161 0.985 1.007 0.862 0.967 0.957 +0 1.861 -1.475 0.304 1.122 -0.292 1.421 -1.800 -1.176 0.000 1.196 -1.517 0.826 2.215 0.387 -0.858 -1.298 1.274 1.023 -1.107 1.287 0.000 1.535 0.847 1.024 0.926 0.718 0.897 0.961 +1 0.380 -0.735 -0.242 0.247 -0.432 0.676 0.299 0.958 0.000 1.035 0.290 -1.185 1.107 1.483 -0.659 -0.525 2.548 0.637 1.200 0.922 0.000 0.730 1.042 0.986 0.754 1.019 0.805 0.724 +0 0.571 -0.893 1.560 1.094 1.578 0.791 0.563 -0.492 1.087 0.809 0.561 0.503 0.000 0.295 -0.781 0.390 0.000 0.500 -0.982 1.070 1.551 0.980 0.920 0.985 0.609 0.930 0.760 0.699 +1 0.660 -0.125 -0.763 0.941 1.624 0.917 -0.353 0.559 0.000 0.733 -0.488 0.013 0.000 1.139 -1.089 -1.241 2.548 0.748 -1.972 0.974 0.000 0.833 1.099 0.988 0.871 0.826 0.916 0.833 +0 1.394 0.462 -1.523 1.050 1.399 0.548 -0.382 0.343 0.000 0.763 1.041 -0.651 2.215 0.727 -0.911 0.372 2.548 0.916 2.494 -1.211 0.000 1.050 0.918 0.998 0.963 1.162 1.150 1.119 +1 0.610 -0.906 0.438 0.526 1.651 0.793 0.447 0.342 0.000 1.057 0.437 -0.436 1.107 1.805 0.570 1.423 1.274 1.334 0.868 -1.376 0.000 1.640 1.178 0.993 1.575 1.466 1.319 1.258 +1 1.535 0.160 -0.645 1.262 -0.237 0.482 0.607 -1.267 0.000 1.342 -0.792 1.007 2.215 0.630 0.760 1.394 2.548 0.612 -2.304 -1.264 0.000 1.031 1.000 0.988 0.964 0.961 1.034 0.985 +0 0.629 0.488 0.729 0.379 0.944 0.793 -0.728 -0.968 2.173 0.700 -0.127 1.491 0.000 1.086 0.305 -0.199 2.548 0.704 -0.830 0.632 0.000 0.758 0.940 0.998 0.881 0.984 1.091 0.963 +1 0.960 -0.119 0.945 1.133 -1.350 0.731 0.588 -0.320 0.000 0.845 0.730 0.081 0.000 1.872 -0.127 -1.317 1.274 1.493 0.294 1.067 3.102 0.604 0.985 1.271 0.866 1.117 0.982 0.900 +1 3.006 -0.010 -1.063 1.535 -1.244 2.765 0.714 0.673 0.000 1.650 0.647 -0.466 2.215 0.878 -1.643 1.444 0.000 0.753 0.687 1.491 3.102 0.972 1.022 0.972 0.824 0.991 0.912 0.874 +1 1.016 1.157 -1.390 1.023 -0.977 1.063 -0.735 0.448 2.173 0.666 0.426 1.513 0.000 1.370 -2.642 -1.021 0.000 1.352 0.573 0.103 1.551 0.898 1.022 0.991 0.879 1.059 1.094 0.922 +1 1.766 1.984 -1.349 0.532 -0.506 0.491 1.094 -0.631 0.000 0.992 1.698 0.655 0.000 1.038 0.324 1.423 2.548 0.603 0.113 0.171 0.000 0.998 0.975 0.988 0.884 0.447 0.850 0.836 +0 3.221 1.677 -1.400 0.486 -0.899 0.543 0.440 0.221 2.173 0.773 0.678 0.556 2.215 0.957 1.208 0.223 0.000 1.166 1.591 1.321 0.000 1.033 0.899 0.991 1.327 0.308 1.092 0.951 +1 0.464 -0.490 -1.380 1.247 0.801 0.809 2.880 -1.099 0.000 0.681 -0.439 -0.658 0.000 1.196 -0.354 0.182 2.548 1.044 -2.333 0.914 0.000 1.941 1.067 0.986 0.705 0.539 0.840 0.800 +0 0.665 0.741 -0.440 0.778 -1.616 0.575 0.489 1.327 2.173 0.684 -0.924 -0.958 0.000 1.145 0.435 0.028 0.000 0.600 1.046 0.236 0.000 0.623 0.750 0.987 0.556 0.386 0.490 0.548 +0 0.485 -0.845 -0.326 1.232 1.268 0.594 0.879 1.080 2.173 0.772 -0.842 -1.159 2.215 0.992 0.428 -0.332 0.000 0.832 1.164 -0.167 0.000 0.856 0.904 1.062 0.763 1.337 0.892 0.896 +0 1.153 -0.408 -1.306 2.303 -1.040 0.947 -1.214 0.950 0.000 0.772 -1.580 0.505 2.215 1.177 0.212 -0.361 2.548 1.915 -1.973 0.576 0.000 1.415 0.847 0.988 0.883 1.297 1.205 1.545 +0 0.840 -0.388 -1.053 0.413 0.274 0.618 1.072 -1.111 0.000 0.985 -0.816 0.936 2.215 0.475 -0.055 -0.591 0.000 0.630 -0.379 0.707 3.102 0.735 0.759 0.981 0.810 0.201 0.813 0.759 +1 0.412 1.151 0.527 1.116 -0.554 2.422 -1.472 -1.297 2.173 2.455 -0.709 0.734 1.107 1.017 -0.123 0.572 0.000 0.979 0.095 -0.105 0.000 0.645 0.947 0.986 4.949 3.734 3.626 2.589 +0 0.770 1.641 -0.700 0.924 -1.204 0.420 0.855 1.264 2.173 0.710 0.833 -0.086 0.000 1.816 -0.072 0.654 2.548 0.620 0.128 -1.107 0.000 0.755 0.956 0.983 0.761 0.780 0.881 0.751 +1 1.255 -0.090 -0.767 0.691 -0.668 1.566 1.338 1.572 0.000 1.046 0.776 0.395 2.215 1.587 0.292 -0.080 2.548 1.301 -0.214 0.011 0.000 1.091 0.911 0.992 0.964 0.657 0.941 1.061 +0 0.561 1.386 -0.096 0.466 1.011 1.040 0.655 1.690 2.173 1.069 -0.441 0.095 0.000 0.927 -0.214 -0.639 0.000 0.393 -0.949 -1.197 3.102 0.953 0.654 0.990 0.905 0.776 0.929 0.800 +1 1.773 -0.322 -1.192 0.078 0.606 1.200 -0.149 0.664 2.173 0.755 -0.115 -0.416 2.215 0.585 0.949 0.988 0.000 0.753 0.392 -1.216 0.000 0.703 0.960 0.984 0.710 1.158 0.924 0.787 +1 0.873 0.202 -1.363 0.867 0.561 0.839 -2.207 -1.458 0.000 0.873 -1.636 0.479 2.215 0.721 -1.165 0.063 0.000 1.106 -0.907 -0.606 3.102 0.793 1.051 1.189 0.862 0.783 0.860 1.004 +1 1.093 -1.223 -0.568 1.004 1.268 1.552 -0.568 -1.552 0.000 1.946 0.885 -0.241 0.000 1.499 -2.576 1.125 0.000 1.797 -0.739 -0.144 0.000 0.886 1.178 1.446 0.712 0.575 0.727 0.776 +0 0.279 1.305 -0.004 1.147 -1.332 0.827 -1.044 1.238 2.173 0.664 -1.403 -0.669 2.215 0.956 0.073 0.409 0.000 0.973 -1.346 -1.292 0.000 0.871 0.857 0.987 1.038 1.099 0.860 0.755 +1 1.584 -0.159 -1.264 0.965 1.680 0.728 -1.062 0.082 0.000 0.412 -0.225 1.581 0.000 1.459 0.823 -0.361 0.000 0.753 0.042 0.947 3.102 0.983 0.785 0.990 0.820 0.266 0.585 0.675 +1 0.785 -0.150 -1.408 0.870 -0.293 1.235 0.551 -1.405 2.173 1.747 -0.117 0.515 0.000 1.123 -0.034 1.416 2.548 0.910 0.862 -0.964 0.000 1.884 1.315 0.988 0.885 0.937 1.174 0.970 +1 1.688 0.776 -0.115 0.780 0.731 1.259 1.362 -1.541 2.173 0.621 1.077 0.100 2.215 0.349 2.439 1.318 0.000 0.473 1.168 0.586 0.000 0.412 0.772 1.098 0.559 1.308 0.996 0.788 +0 0.374 -1.282 1.521 1.163 -0.217 0.672 0.266 0.861 0.000 1.153 -1.100 -1.525 1.107 0.823 -0.502 0.104 0.000 0.614 -0.999 0.116 3.102 1.015 0.705 0.985 0.960 0.757 0.831 0.864 +1 0.697 0.363 0.412 0.750 -0.788 0.475 0.970 1.516 0.000 0.885 -1.424 0.378 0.000 0.799 0.925 -1.061 2.548 0.767 1.332 -1.461 3.102 2.440 1.713 0.992 0.789 0.275 1.151 0.938 +0 0.927 0.369 0.480 0.409 0.346 1.005 -0.698 -0.954 2.173 0.901 1.030 0.955 0.000 0.803 -0.770 1.366 2.548 1.103 0.065 -0.625 0.000 1.445 1.161 0.990 1.566 0.974 1.131 1.039 +0 0.582 -0.701 0.489 0.402 -1.271 0.773 1.056 1.017 2.173 1.022 0.555 -1.521 2.215 1.177 -2.369 -0.529 0.000 0.555 1.325 0.737 0.000 0.847 0.941 0.989 1.615 1.039 1.238 1.032 +0 1.087 -1.739 -0.368 1.715 0.006 0.743 -0.931 1.510 2.173 0.525 -2.379 -1.570 0.000 0.866 -1.017 -1.039 0.000 1.275 -1.153 0.790 3.102 0.881 0.898 0.986 0.823 0.665 0.875 0.842 +1 0.579 0.926 1.156 1.082 -0.451 0.700 -0.396 1.526 2.173 0.796 -0.513 0.156 2.215 0.951 -0.490 -1.152 0.000 0.746 -0.046 0.672 0.000 0.954 0.801 1.088 1.057 1.040 0.866 0.759 +0 2.940 -0.749 -0.158 1.248 -0.440 3.201 -0.491 1.321 0.000 1.977 -0.697 -0.619 1.107 1.459 -1.106 1.096 2.548 1.200 -0.742 -1.234 0.000 1.026 1.052 0.976 1.413 1.860 1.164 1.071 +1 0.964 0.515 -1.484 0.076 -1.650 0.533 -0.962 0.267 2.173 0.879 -0.584 -0.986 1.107 0.581 0.739 -0.002 0.000 1.172 -0.493 0.950 0.000 0.978 0.986 0.985 0.876 0.930 0.715 0.685 +1 0.348 0.655 -1.708 1.663 1.341 0.973 0.542 -0.788 2.173 0.926 0.423 0.797 2.215 0.746 0.781 0.194 0.000 1.615 1.159 -0.328 0.000 0.813 0.958 1.002 1.383 1.384 1.064 1.001 +1 1.255 -0.446 0.556 1.346 0.581 0.745 -0.451 -1.494 2.173 0.507 -0.705 -0.199 0.000 1.217 1.087 -1.019 2.548 0.772 -0.425 1.303 0.000 0.801 1.093 0.982 1.233 1.199 1.353 1.038 +1 0.892 0.445 1.054 0.665 -1.270 0.831 0.499 -0.968 0.000 0.790 1.227 -1.393 0.000 1.524 1.180 0.340 2.548 0.540 1.179 1.400 0.000 0.927 0.905 0.989 0.874 0.757 0.890 0.774 +0 1.046 1.044 -1.439 0.448 -1.579 0.568 1.320 1.458 0.000 0.698 1.162 0.107 0.000 0.940 0.365 -0.543 2.548 0.814 -0.441 0.540 3.102 1.258 1.018 0.996 0.778 0.641 0.742 0.729 +1 0.501 1.726 0.726 1.090 -0.423 0.820 -0.187 -0.217 2.173 0.764 1.408 1.493 0.000 0.900 -0.310 -1.448 0.000 0.736 0.371 -1.081 3.102 1.428 0.850 0.982 1.675 0.635 1.088 1.093 +0 1.345 0.102 -0.419 0.473 0.006 0.726 0.569 1.415 0.000 1.108 0.958 0.906 2.215 0.892 1.099 -0.807 2.548 0.775 -0.134 -0.953 0.000 1.059 0.972 0.995 0.649 1.062 0.775 0.781 +1 0.799 1.094 0.758 1.387 1.252 1.665 -0.267 -0.671 0.000 1.052 0.414 1.474 0.000 1.724 0.208 0.676 0.000 2.032 0.589 0.008 3.102 1.263 0.997 1.000 0.991 1.261 0.916 0.846 +0 0.770 -2.237 0.267 0.631 0.631 0.531 -1.300 0.633 0.000 1.357 -0.799 -1.406 2.215 0.701 -0.540 -0.179 0.000 1.144 -1.123 -1.333 3.102 0.838 0.836 0.973 1.158 0.308 0.787 0.747 +0 1.141 0.389 0.124 1.332 0.901 1.243 -0.986 -1.471 2.173 0.648 1.108 -0.211 2.215 0.270 1.246 0.881 0.000 0.860 -0.297 -0.516 0.000 0.922 1.008 1.101 0.804 2.075 1.331 1.035 +0 0.437 1.138 -0.669 1.578 -0.367 0.635 1.443 1.194 0.000 0.638 1.520 -1.359 2.215 0.708 2.567 1.076 0.000 0.831 0.449 0.748 3.102 0.896 0.845 0.983 1.007 0.715 0.764 0.798 +1 0.699 1.188 0.119 0.414 -0.856 0.875 0.166 0.611 0.000 0.911 0.231 -0.040 0.000 1.215 0.642 -1.540 2.548 1.327 -1.193 -0.924 0.000 0.914 1.138 0.986 0.800 0.725 0.699 0.739 +1 0.837 -0.774 -1.407 0.914 1.055 0.764 0.296 0.998 2.173 0.984 -1.284 -0.783 0.000 0.713 -0.605 -0.265 2.548 0.452 0.101 0.236 0.000 0.972 1.329 0.989 0.716 0.954 0.824 0.746 +1 0.843 -0.778 -0.682 0.635 -1.702 0.669 0.383 0.653 1.087 0.543 -1.775 0.537 0.000 0.527 -1.515 -0.974 0.000 1.221 0.196 -1.358 3.102 0.806 1.033 0.980 1.162 0.931 0.907 0.828 +0 1.088 1.003 -0.458 0.775 -0.458 0.847 -0.348 1.406 2.173 0.344 1.679 1.118 0.000 0.583 -0.977 -0.762 2.548 0.521 -0.249 0.758 0.000 0.683 0.853 0.989 1.223 0.874 0.859 0.770 +1 1.053 1.552 1.206 0.309 0.197 0.463 -0.530 0.357 2.173 0.558 1.181 -0.222 0.000 0.570 1.460 -1.445 0.000 0.850 0.599 -1.217 3.102 0.790 1.014 0.998 0.656 0.793 0.673 0.648 +1 0.724 0.630 0.129 0.876 1.058 0.667 1.255 1.668 0.000 1.165 -0.627 0.055 2.215 1.023 0.230 -1.368 1.274 1.029 1.898 -0.634 0.000 1.084 0.921 0.985 1.264 1.236 1.066 0.955 +0 1.280 0.201 -1.389 0.264 -0.098 0.601 0.489 0.363 0.000 0.364 0.130 1.580 0.000 0.751 -0.550 -0.704 2.548 0.682 0.936 0.460 3.102 0.902 0.828 0.991 0.660 0.714 0.571 0.583 +0 1.262 -0.761 0.463 0.878 1.448 1.036 1.468 0.609 0.000 0.989 1.118 -0.074 0.000 1.199 1.701 1.437 0.000 3.596 -0.757 -1.070 3.102 1.062 0.976 1.132 1.314 0.349 1.519 1.339 +0 1.720 -0.734 -1.009 1.157 -0.648 0.410 0.131 0.245 0.000 1.365 0.530 1.067 2.215 0.557 0.839 0.798 2.548 0.370 -2.466 1.079 0.000 0.903 0.901 0.980 1.007 0.283 1.065 0.869 +0 0.800 -1.072 1.459 0.526 0.352 0.667 -0.304 1.585 2.173 0.543 -1.339 -0.046 0.000 0.799 -0.045 0.070 2.548 0.612 0.141 -0.417 0.000 0.687 0.921 0.981 0.656 0.898 0.633 0.591 +0 1.049 -0.446 -1.071 0.943 1.152 0.553 0.386 0.958 2.173 0.337 -1.020 -0.559 0.000 0.564 0.139 -0.524 1.274 0.447 -1.521 1.231 0.000 0.543 0.802 1.251 0.732 0.682 0.640 0.586 +0 1.140 -0.340 0.879 0.637 1.601 0.853 0.626 -0.406 0.000 0.948 0.460 -1.298 2.215 1.142 0.056 -1.008 2.548 0.647 1.534 1.170 0.000 0.701 0.891 0.989 0.875 0.366 0.690 0.731 +0 0.660 0.523 0.503 0.993 0.228 0.564 -0.514 1.294 2.173 0.664 -0.307 -1.541 0.000 1.057 -0.006 -0.918 2.548 0.904 -0.560 -0.485 0.000 0.842 0.762 0.988 0.874 0.912 0.724 0.664 +0 0.489 0.172 -0.062 1.035 1.569 0.613 -0.344 -1.131 2.173 0.561 1.017 0.864 0.000 1.079 0.798 0.341 2.548 0.840 0.622 -0.677 0.000 0.890 0.943 0.987 0.784 1.184 0.722 0.664 +1 0.716 -0.646 -1.613 1.112 0.969 1.180 -0.546 0.889 1.087 1.170 -0.415 -1.134 0.000 1.288 1.506 -0.689 0.000 1.386 -0.055 0.098 3.102 2.445 1.636 0.985 0.788 0.946 1.489 1.323 +0 0.600 -0.217 -1.315 2.162 -0.306 1.336 -0.637 1.499 2.173 0.932 -1.264 -1.595 0.000 1.161 -0.864 0.022 2.548 1.782 0.122 0.429 0.000 0.889 0.699 1.246 1.572 1.526 1.144 0.975 +0 0.690 0.674 0.300 1.425 -0.792 0.481 1.638 -0.924 0.000 0.877 -0.038 1.577 0.000 0.988 0.601 -1.472 2.548 1.309 -1.824 1.007 0.000 0.955 0.939 1.143 0.761 0.844 0.720 0.695 +1 0.618 0.679 -0.928 0.364 -0.722 1.717 0.170 -0.590 0.000 1.624 0.512 0.521 0.000 2.327 -0.250 1.522 1.274 2.395 1.940 1.165 0.000 1.068 0.677 0.998 1.006 0.800 1.053 0.863 +1 1.066 0.573 -0.987 1.731 -0.329 0.749 0.082 1.204 1.087 0.577 0.609 0.473 0.000 0.594 -0.194 -0.700 0.000 1.439 0.675 1.614 0.000 0.873 0.856 1.053 0.781 0.721 0.857 0.737 +1 1.524 0.978 -1.113 1.403 -1.173 1.467 0.806 0.657 2.173 0.421 1.719 -0.330 0.000 0.549 0.524 0.340 2.548 0.758 0.490 -1.572 0.000 0.806 1.111 1.000 0.920 0.342 1.185 0.929 +1 0.586 -0.235 1.458 1.141 0.087 1.140 1.377 1.641 0.000 1.219 0.912 -1.474 2.215 2.613 0.311 -0.183 2.548 1.033 -0.596 -1.554 0.000 0.980 1.147 1.069 0.944 1.833 1.083 0.931 +0 0.525 0.274 -1.558 0.587 0.530 0.678 0.697 -1.152 0.000 0.614 0.161 -0.042 0.000 1.485 -1.272 0.788 1.274 0.829 -0.093 -0.573 0.000 0.747 0.853 0.990 1.549 0.603 1.012 0.969 +1 0.535 1.657 0.294 0.931 -0.844 0.815 0.069 0.722 2.173 0.584 0.741 -0.405 0.000 0.672 -0.247 -1.373 0.000 0.990 -0.394 1.661 3.102 0.900 1.009 0.993 1.405 0.757 1.241 1.048 +0 0.327 0.849 -1.103 1.033 0.372 1.390 -2.313 0.642 0.000 1.539 -1.009 -0.842 1.107 0.834 -1.509 -1.551 0.000 0.654 -0.624 -1.732 0.000 0.405 0.765 0.985 0.675 1.078 1.496 1.354 +0 1.009 -0.406 -0.260 0.421 0.948 0.788 0.509 0.732 2.173 0.821 -0.512 -1.448 2.215 0.493 1.307 -0.401 0.000 1.033 0.243 -1.586 0.000 0.838 0.894 0.991 0.815 1.268 0.776 0.709 +1 0.762 -0.592 -0.426 1.483 0.410 0.911 -0.180 1.554 2.173 0.496 -1.206 -1.300 2.215 0.438 0.911 -1.018 0.000 0.607 -0.145 0.019 0.000 0.578 0.814 1.008 0.822 0.765 0.833 0.715 +1 0.348 -1.296 -1.022 1.345 -0.434 2.399 -1.018 0.815 0.000 0.614 -0.151 1.356 0.000 1.882 0.862 -0.971 2.548 0.775 -0.253 0.415 0.000 1.065 1.618 0.985 0.936 0.353 1.802 1.440 +1 1.363 -1.564 1.381 0.684 1.135 0.473 -0.569 1.595 0.000 1.290 -0.308 -0.074 2.215 0.553 -2.670 -0.082 0.000 1.328 -0.626 -0.881 0.000 0.961 1.106 0.982 0.657 0.641 0.836 0.763 +1 0.727 -1.194 1.245 0.328 0.288 0.630 -1.006 -1.277 2.173 0.545 0.091 -0.154 1.107 0.675 -1.954 -0.218 0.000 0.646 0.264 1.542 0.000 1.354 0.980 0.980 0.669 0.888 0.733 0.682 +1 0.530 -1.092 0.801 1.341 -0.818 0.847 -0.485 1.583 0.000 0.638 -0.559 -0.060 0.000 0.987 0.924 0.335 2.548 1.000 0.717 -1.472 3.102 1.556 1.134 1.160 1.274 0.760 0.950 0.925 +0 0.533 -1.281 -0.041 1.600 -1.042 1.059 -0.774 0.147 2.173 0.770 -1.950 -1.631 0.000 1.161 0.138 1.258 2.548 0.593 2.049 1.062 0.000 0.524 1.106 1.003 1.076 1.342 1.025 0.931 +0 0.830 -0.425 0.532 1.999 0.204 1.503 0.159 -1.544 1.087 0.266 -0.121 1.425 0.000 0.347 -2.244 -0.209 0.000 0.639 0.955 -1.269 3.102 0.821 0.909 0.991 2.083 0.590 1.401 1.117 +0 1.344 0.591 -0.856 0.606 0.859 0.246 -1.636 -0.545 0.000 0.537 0.577 1.456 1.107 1.276 -0.889 0.805 1.274 0.404 -0.847 -0.723 0.000 0.173 0.745 1.249 1.210 0.906 0.842 0.745 +1 0.667 0.527 0.146 1.385 -0.883 0.871 -0.105 1.250 2.173 0.742 0.983 -0.355 2.215 0.899 1.597 -1.502 0.000 0.756 0.776 0.861 0.000 0.862 1.116 1.064 0.620 1.366 0.875 0.823 +1 1.470 -0.196 1.517 1.281 -1.078 0.498 1.118 0.817 0.000 0.880 0.466 -0.025 2.215 0.354 0.695 0.613 2.548 0.741 1.589 -0.024 0.000 0.738 0.750 1.369 0.810 0.334 0.772 0.811 +0 0.824 -0.572 0.445 1.562 1.177 0.424 0.986 -0.285 0.000 1.084 -0.533 -1.585 1.107 0.458 1.272 0.093 2.548 1.387 -0.421 -0.625 0.000 0.983 0.867 0.986 0.933 1.132 0.867 0.754 +0 1.107 0.185 0.511 1.841 0.997 1.114 -0.310 0.163 2.173 1.480 -0.102 -1.394 2.215 0.816 1.242 -1.247 0.000 1.930 -0.401 -0.870 0.000 1.537 1.136 0.982 1.146 1.873 1.256 1.215 +1 1.160 0.148 -1.481 1.052 1.393 1.282 0.482 0.127 2.173 1.187 0.880 -0.744 0.000 1.524 -0.554 1.214 2.548 0.577 1.423 -1.029 0.000 0.521 1.197 0.992 0.950 1.760 1.240 1.101 +0 1.125 -0.023 0.571 2.183 0.659 1.180 -2.621 -1.354 0.000 0.493 -0.921 -0.156 0.000 0.536 -1.246 -1.282 2.548 1.003 -0.080 1.445 3.102 0.375 0.671 0.994 1.268 0.520 0.865 0.831 +0 0.572 -1.082 1.237 0.366 0.884 1.160 -0.942 -1.194 2.173 0.931 1.636 -0.235 0.000 0.937 1.232 0.317 0.000 0.795 0.560 1.101 3.102 0.724 0.766 0.983 1.114 1.281 1.517 1.205 +1 0.822 -0.424 0.850 0.847 -0.121 0.648 -0.534 0.507 0.000 0.555 0.537 0.487 0.000 1.544 0.765 -1.156 2.548 1.155 -0.493 -1.511 0.000 0.731 0.859 0.988 1.040 0.877 0.894 0.778 +1 0.814 1.536 0.768 1.029 1.320 0.423 0.643 -0.126 0.000 0.542 0.904 1.058 0.000 0.757 0.133 -1.652 2.548 2.039 -0.514 -0.615 1.551 0.901 1.127 0.993 1.126 0.850 1.502 1.165 +0 0.435 0.596 0.383 1.381 -0.633 1.185 0.587 -0.911 2.173 1.912 0.653 1.163 1.107 0.779 -0.074 0.126 0.000 1.163 0.050 0.725 0.000 0.795 0.931 0.990 0.862 2.118 1.217 0.963 +0 0.282 1.717 -0.049 0.691 1.475 0.545 -1.093 -1.644 2.173 0.375 -0.914 0.318 1.107 0.851 1.328 -1.305 0.000 0.644 0.662 0.129 0.000 0.895 0.889 0.978 0.837 0.654 0.787 0.688 +0 0.967 -1.856 1.209 1.110 0.524 0.561 -1.422 -1.119 2.173 1.007 -1.102 -0.664 1.107 0.479 2.386 0.009 0.000 1.347 -1.531 0.952 0.000 4.126 2.738 0.994 1.213 0.472 1.778 1.882 +1 1.550 0.155 -0.658 1.057 -1.623 1.366 -0.592 0.869 1.087 0.390 0.430 1.415 0.000 1.817 -0.810 -0.788 2.548 0.895 -0.593 0.340 0.000 0.789 1.015 1.354 1.588 1.978 1.294 1.013 +0 0.663 0.443 1.595 1.656 -0.814 0.932 0.949 0.868 0.000 1.306 0.267 -0.527 2.215 0.668 1.585 1.476 0.000 0.520 -0.273 0.949 3.102 0.932 0.713 1.199 0.824 0.757 0.912 0.875 +0 1.217 0.824 -0.703 1.283 -1.079 1.068 0.234 0.445 0.000 0.762 1.072 -1.408 2.215 1.784 -0.205 1.039 2.548 0.703 0.144 -0.338 0.000 0.898 0.833 0.986 0.736 1.334 0.995 0.961 +1 0.364 -0.536 -0.680 1.876 -1.693 1.025 0.700 -0.189 2.173 0.331 1.325 -1.679 0.000 1.138 0.324 0.764 2.548 0.392 0.200 0.503 0.000 0.514 0.733 0.993 0.901 1.046 0.962 0.743 +1 0.464 -1.659 -1.709 0.751 0.889 1.336 -1.347 -0.664 2.173 0.551 -0.034 1.036 0.000 0.652 -0.085 1.577 0.000 0.711 -1.050 0.464 3.102 0.432 0.545 0.980 1.201 0.878 0.882 0.781 +0 0.818 -0.167 0.712 0.375 0.669 0.776 0.948 -0.412 1.087 0.571 1.797 -0.796 0.000 0.582 2.000 1.229 0.000 1.362 0.164 1.638 3.102 0.870 1.002 0.980 1.376 1.129 1.003 1.122 +0 0.685 -0.068 -0.902 0.591 -0.364 1.134 -0.288 -1.151 1.087 0.667 -0.881 0.815 0.000 0.767 0.432 1.133 0.000 1.338 -1.114 -0.134 3.102 0.875 0.984 0.983 0.849 1.257 0.961 0.833 +1 0.759 -2.048 -0.172 0.868 -0.740 0.627 -0.155 -1.540 1.087 0.765 -1.188 0.586 0.000 2.008 -0.541 0.957 2.548 1.348 -0.227 -0.781 0.000 0.674 0.985 0.987 1.005 1.129 0.981 0.805 +1 1.083 -0.260 1.273 0.503 -0.938 0.838 0.314 0.178 0.000 1.490 -0.108 -1.602 2.215 1.038 -0.642 -0.265 0.000 0.515 -0.791 -0.581 0.000 0.929 0.797 0.987 0.714 0.947 0.884 0.753 +1 1.112 0.889 -0.073 1.016 1.533 0.928 1.041 1.197 0.000 0.386 0.631 -1.521 0.000 1.284 0.808 -0.347 1.274 0.615 -0.752 -0.584 1.551 0.850 1.012 1.461 0.920 0.707 0.825 0.781 +1 0.888 0.302 -0.757 0.950 1.723 2.275 -1.051 -0.134 0.000 2.279 0.623 1.436 2.215 1.289 -0.329 1.498 2.548 1.043 0.236 0.040 0.000 1.742 1.911 1.002 1.039 0.958 2.108 1.631 +1 1.774 1.677 1.585 0.296 -0.720 0.859 0.300 0.499 2.173 0.982 -2.216 -0.213 0.000 0.972 -0.283 -1.237 2.548 0.447 -0.110 1.004 0.000 0.713 0.957 0.988 1.104 1.193 1.037 0.987 +0 1.123 -0.804 0.993 1.180 -0.034 0.726 0.198 -1.618 2.173 0.817 0.311 -0.535 2.215 0.309 1.781 0.748 0.000 0.444 -2.015 -0.788 0.000 1.211 0.916 1.274 1.190 0.941 0.938 0.843 +0 0.548 0.383 -1.439 4.161 1.566 1.719 -0.062 -0.525 2.173 0.570 1.069 1.366 2.215 1.896 0.179 0.217 0.000 0.396 -0.309 -0.680 0.000 0.744 0.925 0.995 2.199 1.699 1.489 1.285 +0 0.401 -0.234 -0.005 1.731 1.015 0.416 -0.996 -0.812 2.173 0.466 -2.770 0.696 0.000 0.667 -0.318 -1.051 2.548 0.629 -1.451 1.302 0.000 0.630 0.873 0.984 0.843 0.260 0.634 0.686 +1 3.088 -0.690 0.845 1.949 0.139 3.365 -2.020 -1.150 0.000 1.050 -0.216 -0.058 2.215 1.296 0.027 0.427 2.548 0.948 -0.508 -0.425 0.000 0.825 0.738 2.018 1.291 0.547 0.887 0.823 +0 1.214 -0.221 0.144 0.294 -0.296 0.551 -0.193 -0.874 2.173 0.826 0.409 1.310 2.215 0.813 1.491 0.852 0.000 1.044 -1.803 1.513 0.000 1.050 0.927 0.980 0.767 0.966 0.804 0.767 +1 0.618 -0.874 -0.632 0.707 0.888 0.812 -0.659 0.467 0.000 0.617 -0.591 1.535 0.000 0.586 1.257 -1.571 0.000 1.051 1.229 -0.555 0.000 0.897 0.669 0.984 0.696 0.517 0.489 0.512 +1 1.461 -0.927 0.604 0.819 -0.063 0.969 -0.121 -1.382 2.173 0.406 0.795 -1.081 0.000 0.892 0.500 -0.225 2.548 1.390 -0.188 1.154 0.000 0.941 1.037 0.992 1.043 1.076 1.068 0.888 +0 0.494 -1.134 0.454 0.806 -0.221 1.302 -0.679 -0.981 0.000 1.184 0.148 0.750 2.215 0.719 -1.043 -0.384 2.548 1.168 0.614 1.115 0.000 1.055 0.820 0.992 1.679 1.079 1.096 1.017 +1 1.475 -0.207 -1.642 0.893 -1.355 0.452 -2.122 0.539 0.000 1.489 0.460 0.157 2.215 0.699 0.305 1.118 2.548 0.369 -2.205 -1.591 0.000 0.607 1.058 0.996 1.602 0.829 1.200 1.136 +1 1.268 -0.136 -0.667 0.937 -1.623 0.825 1.929 0.691 0.000 1.436 1.513 -0.343 2.215 1.266 1.040 -1.732 2.548 1.296 0.259 1.166 0.000 1.584 1.217 1.145 1.484 1.393 1.134 1.208 +0 0.293 -0.924 1.135 0.274 0.274 0.999 0.256 1.573 0.000 1.266 1.105 -0.451 2.215 0.565 0.237 -0.070 2.548 0.677 0.956 1.216 0.000 0.701 0.815 0.995 0.851 0.508 0.876 0.743 +0 0.344 -1.962 0.923 0.828 -1.016 1.002 -1.092 1.741 2.173 0.847 0.504 0.631 2.215 1.083 0.537 0.023 0.000 1.290 -0.527 -0.405 0.000 0.979 0.904 0.985 0.770 1.674 1.129 0.941 +1 2.268 0.002 -1.467 0.716 1.054 1.014 -2.028 0.571 0.000 1.280 -0.803 0.197 2.215 0.318 0.778 1.463 0.000 1.311 -0.198 -0.398 0.000 0.818 0.888 1.349 0.697 0.828 0.941 0.795 +0 0.652 -1.514 -0.848 0.754 0.345 0.646 0.049 1.311 2.173 0.846 -0.052 -0.945 0.000 0.978 -1.747 1.403 0.000 1.598 0.232 0.565 3.102 1.027 1.060 0.992 1.345 0.682 1.097 1.177 +0 1.156 -0.306 -0.768 0.686 0.001 0.734 -1.533 1.189 0.000 0.621 -0.917 -1.632 2.215 1.237 -0.003 -1.656 2.548 2.604 -0.559 0.245 0.000 0.977 1.170 0.990 0.758 0.454 0.723 0.761 +1 0.481 1.567 1.452 0.725 0.217 0.786 1.649 0.035 0.000 1.263 1.379 -1.362 0.000 1.261 0.675 -0.105 0.000 1.911 0.935 1.177 3.102 0.886 1.213 0.981 0.740 0.988 0.967 0.802 +1 2.003 -1.935 -0.837 0.898 -1.114 1.113 -1.472 0.662 2.173 0.408 -1.500 -0.126 0.000 0.734 -1.541 1.083 0.000 0.581 -0.202 -1.125 3.102 0.747 0.659 0.990 0.927 1.021 1.128 0.895 +0 1.419 -0.405 1.666 0.851 0.666 1.103 0.506 0.017 2.173 0.974 0.270 -1.143 2.215 0.398 2.606 -1.545 0.000 0.748 -0.638 1.109 0.000 1.844 1.333 1.194 1.333 1.332 1.101 1.090 +0 1.712 0.807 -0.387 0.438 -0.023 0.852 1.157 -1.426 1.087 0.816 0.855 1.522 0.000 0.499 -0.900 0.458 0.000 1.457 0.734 0.614 3.102 1.324 0.960 0.974 1.116 1.149 0.882 0.881 +0 0.997 -0.060 -1.725 0.858 0.967 1.708 -0.072 1.255 1.087 2.161 0.228 -0.285 2.215 1.086 -0.314 -0.575 0.000 1.001 0.343 -1.034 0.000 0.641 0.805 0.987 0.772 2.814 1.408 1.166 +1 0.897 -1.121 -0.198 0.872 -0.064 1.023 0.182 1.481 2.173 0.467 0.571 0.041 0.000 1.482 -1.082 -1.506 2.548 0.438 -0.909 -0.544 0.000 0.629 0.970 0.980 1.120 1.318 1.076 0.875 +0 0.777 -1.080 0.919 0.336 1.664 0.667 -0.843 0.029 1.087 1.013 -2.107 1.360 0.000 1.308 0.078 -0.461 2.548 1.412 -0.102 -1.201 0.000 2.213 1.628 0.995 0.879 0.753 1.230 1.024 +1 1.037 -0.235 1.710 1.065 0.770 1.152 0.552 -0.441 0.000 0.639 0.679 0.018 1.107 1.456 1.574 1.675 0.000 0.697 0.064 0.839 3.102 0.959 0.800 1.091 0.550 0.449 0.572 0.714 +0 0.776 -1.201 1.187 0.940 0.985 1.015 -0.859 1.525 2.173 1.029 0.177 -0.514 0.000 1.678 0.589 -0.118 0.000 1.456 0.357 -1.136 3.102 0.847 0.897 0.984 0.768 1.246 1.214 1.079 +1 0.300 1.489 0.710 1.582 -1.352 0.734 -0.527 -0.915 2.173 1.004 -0.589 0.136 0.000 1.089 0.133 1.458 0.000 0.991 0.513 -0.156 3.102 0.801 0.841 0.990 0.937 0.791 1.233 1.026 +1 1.054 1.127 -0.895 1.464 -1.064 2.065 0.745 -0.767 0.000 1.484 -2.224 0.989 0.000 1.532 0.459 -0.188 1.274 1.428 0.041 1.127 0.000 1.009 1.090 0.987 0.983 1.135 0.857 0.853 +1 0.771 -0.245 -0.555 0.331 0.539 1.323 -0.321 0.821 2.173 1.378 -0.030 -1.411 0.000 1.633 0.826 -0.092 0.000 2.254 0.500 -0.667 3.102 0.791 0.915 0.987 1.085 1.983 1.236 0.999 +0 0.497 -0.892 0.853 2.375 1.693 1.054 -0.912 -1.481 2.173 0.999 1.871 0.139 0.000 1.160 1.508 -0.491 0.000 2.352 0.802 0.198 3.102 0.915 0.898 1.034 0.747 2.460 1.887 1.907 +0 0.416 0.392 -0.759 1.281 0.400 2.364 -0.627 -0.194 1.087 3.397 -0.943 1.511 0.000 0.901 -0.626 -1.349 2.548 0.423 -1.178 -1.569 0.000 0.660 0.805 0.992 1.891 1.570 1.798 1.762 +0 0.708 -0.845 1.199 1.922 0.701 0.879 -0.527 -1.126 0.000 0.753 -0.050 -0.809 0.000 0.689 -0.932 -0.206 2.548 0.438 0.034 -1.389 0.000 0.644 0.779 0.985 0.784 0.582 0.640 0.894 +0 0.660 0.162 1.201 1.598 0.578 1.440 -0.323 -1.692 2.173 0.865 0.521 -0.439 2.215 0.667 2.015 1.666 0.000 2.001 -0.441 -0.057 0.000 0.847 1.015 0.988 1.275 1.655 1.177 1.062 +1 1.633 1.751 0.564 1.253 -0.034 0.486 0.342 -0.395 2.173 1.362 1.139 1.569 2.215 1.600 0.252 -0.935 0.000 0.543 0.501 1.391 0.000 0.905 1.104 1.015 1.037 1.280 1.070 1.034 +1 1.128 -0.570 1.282 1.171 0.793 0.715 -1.000 -0.976 2.173 0.488 0.186 -0.468 0.000 0.902 -2.204 0.123 0.000 1.001 -0.340 -1.231 0.000 0.641 0.617 0.982 0.568 0.654 0.715 0.691 +0 0.668 2.430 0.580 1.473 0.531 0.747 0.524 -1.263 2.173 0.631 1.073 -0.965 0.000 0.630 0.185 0.573 2.548 0.419 0.223 -0.927 0.000 0.290 0.597 0.998 1.304 0.863 0.893 0.748 +0 0.287 1.741 -0.970 1.391 0.141 0.686 -0.264 0.999 2.173 0.831 0.041 -0.889 2.215 0.463 -1.589 -1.335 0.000 0.710 -0.002 1.576 0.000 0.698 0.727 0.988 0.809 1.115 0.792 0.755 +0 1.119 -0.729 1.165 1.498 0.525 1.347 1.253 -1.165 2.173 0.626 1.865 1.032 0.000 1.274 2.024 -0.686 0.000 1.222 0.620 0.374 3.102 1.386 1.127 0.986 2.416 1.382 1.578 1.660 +1 1.094 0.030 1.577 0.553 -0.142 0.943 0.329 0.409 2.173 0.663 -1.068 -1.164 0.000 0.553 1.249 1.526 2.548 0.819 -0.117 -0.550 0.000 0.695 0.961 1.078 0.889 0.904 0.873 0.758 +0 3.461 -0.384 0.379 0.272 0.551 1.042 0.218 -1.297 0.000 0.528 -0.126 -0.579 0.000 0.753 -1.071 -1.543 2.548 0.827 -0.030 1.056 3.102 0.990 0.898 0.994 0.664 0.564 0.762 0.943 +1 0.816 1.238 -0.400 0.827 -0.963 1.249 0.408 1.130 0.000 0.587 0.197 -0.826 0.000 0.477 1.234 -1.710 0.000 0.629 -0.359 0.194 3.102 1.020 0.866 0.983 0.794 0.221 0.712 0.877 +0 2.460 1.484 0.505 0.705 1.606 0.960 0.153 -0.947 2.173 0.578 -0.123 1.218 1.107 1.338 -0.841 -0.801 0.000 0.539 0.403 1.285 0.000 1.144 0.943 1.528 1.173 1.030 1.231 1.233 +0 0.920 0.014 1.698 2.818 -1.393 1.419 -0.921 0.246 1.087 0.723 0.599 0.035 0.000 0.823 0.240 0.364 0.000 1.108 -1.221 -1.034 0.000 0.405 1.118 0.987 0.759 0.845 1.449 1.193 +0 1.293 0.725 1.252 1.644 0.755 0.892 0.657 0.060 0.000 1.631 0.363 -1.043 2.215 0.850 -0.021 1.585 0.000 0.710 0.950 -0.992 3.102 1.630 1.022 0.990 1.574 0.384 1.008 0.987 +0 0.330 0.885 1.074 1.794 0.388 0.730 0.185 -1.184 0.000 0.712 -0.286 -1.466 2.215 1.013 0.494 -0.057 2.548 0.564 0.327 1.417 0.000 0.708 0.840 0.987 1.601 0.945 1.117 1.022 +0 0.656 1.392 1.554 0.670 -0.518 0.514 -1.339 1.739 0.000 1.201 0.789 0.734 2.215 1.125 -0.120 -0.368 2.548 0.560 0.553 -1.163 0.000 1.056 0.980 0.984 0.833 1.199 0.998 0.885 +1 0.423 -2.302 0.267 1.010 -1.158 0.883 -1.273 1.721 0.000 1.078 0.181 -0.416 2.215 0.841 -0.170 0.839 2.548 0.853 -1.063 0.834 0.000 0.952 0.876 0.991 1.177 0.935 0.971 0.896 +1 1.873 1.060 0.675 0.788 0.154 0.610 0.769 1.590 0.000 0.918 1.403 -1.431 2.215 0.815 0.108 -0.711 2.548 0.664 1.562 -0.701 0.000 1.023 0.861 0.990 1.165 0.858 0.891 0.800 +1 1.276 0.166 1.592 0.361 -0.825 1.056 0.197 -0.731 1.087 1.859 0.913 0.689 0.000 1.087 0.805 -1.682 2.548 1.234 0.867 -0.443 0.000 1.684 1.341 0.987 0.953 1.111 1.175 0.985 +1 1.089 1.490 -0.347 0.779 0.861 0.859 0.031 1.633 2.173 0.863 -0.023 -0.767 0.000 1.479 -0.004 0.621 0.000 0.679 -0.575 -0.870 3.102 1.221 1.028 1.131 0.979 0.693 0.938 0.890 +0 0.953 -0.251 0.904 0.959 1.672 0.336 0.816 -1.456 0.000 0.754 0.256 -0.385 1.107 0.403 1.815 1.181 0.000 1.197 -0.583 -0.058 3.102 0.614 1.006 0.989 0.893 0.497 0.705 0.702 +1 0.308 -2.192 0.890 0.521 0.757 1.301 0.095 0.658 2.173 0.951 -0.301 -0.651 0.000 1.563 -0.420 -1.353 0.000 0.750 0.164 -1.030 3.102 1.116 0.643 1.001 0.860 1.045 1.035 0.884 +0 1.462 0.573 -0.047 1.309 -0.472 0.849 -0.441 1.178 0.000 0.702 0.437 0.660 0.000 1.074 -0.263 1.728 2.548 0.977 0.632 -0.350 0.000 0.868 0.945 0.981 0.713 0.659 0.762 0.708 +0 1.580 -1.134 -0.592 0.443 -1.655 0.964 -0.532 -1.665 2.173 1.195 -1.272 0.341 2.215 0.653 0.048 0.520 0.000 0.792 0.922 0.883 0.000 0.770 0.897 0.989 0.951 1.658 0.989 0.922 +1 1.909 0.355 1.698 1.529 0.946 2.153 1.530 0.204 0.000 1.132 0.278 -1.241 0.000 1.810 -1.254 -1.553 0.000 1.594 -0.946 -1.131 3.102 0.997 0.870 1.482 1.005 0.843 1.002 0.889 +1 1.415 0.643 -0.145 1.454 -0.794 0.945 -1.441 0.416 0.000 0.430 -0.744 -0.801 0.000 2.393 -0.038 1.508 1.274 1.212 -0.407 -1.299 3.102 1.296 1.114 1.095 1.590 0.803 1.109 1.267 +1 0.652 0.252 -1.499 1.384 1.618 1.177 0.362 -0.256 2.173 0.479 -1.218 0.398 0.000 1.138 -0.128 1.507 0.000 0.645 -1.022 -0.100 1.551 1.160 0.789 0.984 1.490 0.825 0.987 0.902 +0 1.189 -0.130 -0.622 0.682 1.450 1.221 -0.767 -0.096 1.087 1.489 0.185 1.665 2.215 0.916 0.674 0.708 0.000 0.595 1.868 -0.986 0.000 1.042 1.008 1.193 1.062 2.220 1.291 1.050 +1 0.968 -0.533 1.530 0.743 0.462 0.711 -0.684 -0.377 2.173 0.833 -1.159 0.458 1.107 0.885 0.270 -1.274 0.000 0.626 1.109 1.129 0.000 0.812 1.061 0.987 0.681 0.826 0.891 0.793 +0 0.406 1.366 1.610 0.781 0.135 0.616 1.246 -0.532 0.000 1.021 -0.456 1.117 2.215 0.897 0.084 0.704 0.000 1.844 0.040 -1.681 0.000 1.132 1.023 0.992 1.603 0.813 1.417 1.279 +1 0.447 1.908 0.460 1.002 -0.167 1.029 0.659 -1.673 0.000 0.899 1.239 1.330 2.215 1.184 0.792 -0.067 0.000 0.914 0.673 -0.429 3.102 1.980 1.181 0.993 0.902 0.841 0.859 0.793 +0 1.383 -0.570 -0.270 0.350 1.194 1.593 -1.290 1.526 2.173 1.433 0.163 0.100 0.000 2.176 -0.969 -0.894 1.274 0.883 -0.867 0.942 0.000 0.775 1.077 0.989 1.328 1.918 1.145 0.943 +1 0.544 1.659 1.034 0.499 -1.600 1.212 0.812 -0.784 2.173 1.615 1.098 1.119 2.215 1.523 -2.438 0.353 0.000 0.732 0.806 -0.075 0.000 0.826 0.948 0.987 0.994 2.063 1.040 0.842 +0 0.970 -0.085 0.092 1.275 -0.960 0.467 -0.884 1.163 2.173 0.394 1.020 -1.021 2.215 0.346 -2.272 0.798 0.000 0.734 2.057 0.700 0.000 0.715 0.590 1.251 0.965 0.922 0.850 0.877 +0 0.350 -1.924 0.103 1.524 0.105 0.508 0.522 -0.361 2.173 0.735 -1.044 0.887 0.000 1.191 0.078 -1.191 0.000 0.507 -0.510 -1.129 0.000 0.793 0.951 0.985 0.541 0.432 0.565 0.583 +1 0.871 -0.232 -0.624 0.370 0.847 0.765 -0.658 -1.395 1.087 0.675 -0.359 0.830 0.000 0.990 0.208 -0.784 2.548 0.718 0.587 0.312 0.000 1.158 1.027 0.987 0.754 0.756 0.881 0.792 +0 1.059 0.150 0.915 1.409 -0.156 0.652 -1.456 1.143 0.000 0.845 -0.882 -0.937 2.215 0.757 0.419 -0.969 2.548 0.527 -0.347 -1.527 0.000 0.777 1.006 1.391 1.134 0.628 0.799 0.850 +0 1.475 -0.470 -1.039 1.301 -1.536 1.157 -0.068 0.485 0.000 0.847 0.087 -0.255 2.215 0.747 0.678 0.841 0.000 1.108 -0.294 1.553 3.102 0.875 0.938 0.995 0.682 0.894 0.763 0.966 +0 4.249 0.396 1.708 1.297 -0.379 1.441 0.454 -0.078 0.000 0.455 -0.362 -0.369 0.000 1.290 -0.386 1.415 2.548 1.151 1.283 0.252 1.551 0.861 0.934 3.098 1.675 1.339 1.335 1.356 +1 1.019 0.379 -1.129 0.510 1.353 0.875 0.427 1.390 0.000 0.886 0.573 0.858 0.000 1.797 0.046 -0.869 2.548 1.704 1.894 1.105 0.000 0.878 0.627 0.989 0.732 0.715 0.864 0.752 +1 0.596 0.983 0.343 0.540 -0.977 0.629 -2.631 -1.197 0.000 1.758 -0.941 0.639 1.107 1.330 -0.368 0.057 2.548 0.767 0.184 -0.398 0.000 0.817 1.827 0.988 2.226 0.936 1.507 2.233 +0 1.130 0.638 -0.230 1.695 -0.253 1.325 -1.135 1.463 0.000 0.406 -0.831 0.943 0.000 0.592 0.541 -1.213 1.274 0.484 0.488 1.500 3.102 0.740 1.088 0.995 0.756 0.262 0.681 1.357 +0 0.722 1.565 -0.654 0.779 0.432 1.042 1.780 1.664 0.000 1.244 0.873 0.284 1.107 1.105 0.803 -0.945 2.548 0.627 1.493 -1.101 0.000 0.929 1.124 0.987 0.912 1.115 1.034 0.916 +0 0.618 0.383 -1.335 1.571 -0.298 0.660 0.171 0.944 1.087 0.670 -1.042 0.537 2.215 0.802 0.935 -1.276 0.000 0.910 -0.943 -1.571 0.000 1.252 1.072 1.098 1.042 0.739 0.839 0.843 +0 0.914 0.618 -0.486 0.910 0.350 1.025 0.352 -1.717 0.000 1.079 1.035 1.060 2.215 0.734 2.538 -0.335 0.000 0.958 0.000 -0.795 3.102 2.819 1.724 0.987 0.988 1.047 1.185 1.066 +0 0.503 1.340 0.288 0.767 -0.302 0.733 -1.903 -0.029 0.000 0.782 -0.136 -1.124 0.000 0.776 0.783 1.027 2.548 0.606 -1.304 0.874 3.102 2.110 1.223 0.999 0.716 0.807 1.076 0.977 +1 0.843 0.279 0.455 1.342 1.635 0.753 -0.342 0.191 0.000 1.429 0.595 -0.822 2.215 1.472 0.128 -1.667 2.548 0.500 0.816 0.463 0.000 0.705 1.115 1.288 1.174 1.126 0.946 0.882 +1 0.694 0.222 -1.514 0.771 -0.154 0.805 -0.337 0.935 0.000 1.305 0.524 -0.245 2.215 1.259 0.043 1.434 0.000 0.735 -0.033 -0.698 0.000 0.862 0.984 0.985 0.777 1.021 1.008 0.844 +1 0.392 2.157 -1.651 0.639 0.438 0.568 0.011 -1.332 2.173 0.924 0.867 0.160 0.000 1.248 0.298 1.271 1.274 1.293 0.871 -0.495 0.000 0.799 1.056 0.995 0.728 0.768 0.837 0.732 +0 2.519 -0.075 0.013 0.113 -0.626 2.054 1.511 1.508 0.000 0.960 -0.519 -0.686 2.215 0.596 1.439 0.432 2.548 0.693 0.218 -0.778 0.000 2.025 1.332 0.994 0.810 1.223 1.465 1.489 +0 0.989 -0.344 0.276 0.696 1.670 0.826 -2.074 -0.574 0.000 0.508 0.217 -0.368 2.215 0.810 -0.302 1.527 2.548 1.378 -0.359 0.802 0.000 1.349 0.938 1.093 0.652 0.702 0.647 0.622 +1 0.917 -0.215 0.091 0.536 0.541 0.955 -0.554 0.910 2.173 1.088 -0.155 1.590 1.107 1.552 -2.015 -0.527 0.000 1.027 1.195 -0.815 0.000 0.648 1.548 0.987 1.007 0.912 1.226 1.236 +1 0.709 0.204 0.206 0.982 1.589 1.418 0.142 -0.682 2.173 0.838 0.549 0.972 0.000 0.886 1.217 0.702 0.000 0.833 -0.757 1.378 1.551 0.981 0.870 1.096 1.126 1.273 0.965 0.834 +1 0.820 1.391 0.467 0.880 -1.580 2.628 2.237 -0.579 0.000 3.050 1.546 1.326 0.000 1.393 0.804 0.922 2.548 0.868 0.483 -0.348 1.551 1.041 1.227 1.133 0.776 0.776 1.336 1.126 +1 0.975 0.385 0.742 1.025 1.438 0.602 1.012 -0.824 2.173 0.413 0.299 -0.906 0.000 0.716 1.701 -0.263 0.000 0.655 1.932 1.408 0.000 0.852 0.592 0.990 0.647 0.513 0.711 0.694 +0 1.113 0.476 0.374 0.462 -0.116 1.361 1.590 0.563 0.000 0.984 1.043 -0.980 2.215 1.617 1.001 -1.661 2.548 1.197 0.014 -1.068 0.000 2.592 1.802 0.985 1.047 0.772 1.239 1.040 +1 1.436 -0.799 -0.729 0.619 -0.095 1.255 0.655 -0.688 0.000 1.760 -1.056 1.257 0.000 1.691 0.195 0.897 2.548 1.056 -0.334 -1.738 3.102 4.404 2.358 0.991 1.187 0.778 1.561 1.311 +0 1.126 1.239 -0.945 2.182 -1.471 1.819 0.275 0.252 1.087 1.497 1.521 1.626 0.000 1.109 -0.460 -0.198 0.000 0.928 -0.001 0.887 3.102 3.132 1.788 0.989 2.199 0.768 1.510 1.494 +1 0.597 -1.139 0.845 0.864 -0.867 1.552 0.544 0.719 1.087 1.664 -2.370 -0.557 0.000 1.811 -0.605 -1.699 2.548 0.837 0.348 -1.617 0.000 3.274 2.378 0.995 1.555 2.179 2.579 1.871 +0 0.602 1.322 0.398 0.505 1.426 0.354 1.744 1.191 0.000 0.647 -0.111 -0.249 2.215 0.371 1.241 -1.244 2.548 0.562 1.285 1.714 0.000 0.317 0.898 0.997 0.587 0.585 0.550 0.544 +1 0.845 0.653 -1.409 0.498 0.293 0.548 0.949 -0.228 0.000 1.120 -0.211 1.432 1.107 1.283 -0.044 0.005 0.000 1.150 1.070 -1.657 3.102 0.852 1.072 0.986 0.927 0.916 0.960 0.819 +0 1.273 -0.436 0.729 0.781 -0.075 0.876 1.009 -1.640 0.000 0.582 0.669 -0.311 2.215 1.126 1.430 -1.307 0.000 1.197 -1.355 0.344 1.551 1.004 0.783 0.992 0.780 1.159 1.084 0.956 +0 1.311 -1.875 0.238 0.473 -1.436 1.248 -1.164 0.708 1.087 1.093 0.493 -1.143 0.000 1.019 -0.499 -0.567 2.548 0.532 -1.380 1.368 0.000 0.819 0.782 1.089 0.968 1.358 0.890 0.865 +0 1.038 1.163 -1.259 0.345 0.395 0.401 -0.649 1.058 0.000 0.484 -0.698 -1.510 2.215 0.757 0.942 -0.035 0.000 0.977 0.774 0.673 1.551 0.767 0.892 0.992 0.744 0.809 0.606 0.672 +0 0.729 0.411 0.935 0.267 0.955 0.832 0.260 -0.252 2.173 0.989 0.802 -1.649 2.215 0.490 1.250 -1.364 0.000 0.674 1.059 0.356 0.000 0.634 0.772 0.992 0.980 1.326 0.906 0.770 +1 1.175 0.725 1.159 1.683 0.737 1.061 0.286 -0.972 1.087 0.577 -2.075 -1.139 0.000 0.603 -0.589 -1.068 0.000 1.510 -0.106 0.369 3.102 0.740 1.105 0.998 1.453 1.284 1.031 1.149 +1 0.284 -1.252 -1.640 0.604 -1.592 0.937 -0.452 -0.748 2.173 0.718 0.566 0.849 0.000 1.118 -0.026 1.265 0.000 1.149 0.537 0.026 3.102 0.660 0.775 0.994 0.932 0.949 0.889 0.781 +0 0.734 -1.009 -1.301 0.942 0.395 1.148 -0.681 -1.740 2.173 1.007 -1.120 -0.363 2.215 0.957 -0.751 0.637 0.000 0.448 -1.576 -0.071 0.000 0.591 1.042 1.151 0.778 1.543 0.892 0.760 +1 0.361 -0.003 1.168 1.350 -1.245 0.941 2.218 1.145 0.000 1.639 0.319 -1.209 2.215 0.665 0.600 -0.188 0.000 1.720 -1.481 0.614 0.000 0.924 1.211 0.988 1.375 1.438 1.111 1.110 +1 0.413 1.253 1.293 1.546 -1.119 1.008 -0.320 -0.074 1.087 1.049 -0.609 1.383 0.000 0.345 0.588 1.636 0.000 1.035 0.810 0.186 1.551 0.859 0.935 0.986 1.822 0.790 1.176 1.151 +0 1.018 0.183 -0.998 0.784 1.332 1.249 1.100 0.689 0.000 1.234 -1.629 -1.732 0.000 2.852 -0.151 -0.650 2.548 1.478 1.144 1.091 0.000 0.784 0.970 1.067 1.042 1.451 1.489 1.223 +0 1.709 0.490 -0.954 1.522 -1.049 3.217 0.960 -0.845 2.173 2.449 -0.078 1.016 0.000 1.798 1.779 1.466 0.000 1.647 -0.025 0.762 0.000 0.597 1.381 0.993 0.789 2.929 2.437 1.981 +1 0.289 1.879 1.184 0.791 -0.736 1.091 0.048 0.471 0.000 1.275 -0.105 -1.148 1.107 0.511 0.691 -0.162 0.000 0.398 -0.830 0.888 0.000 0.874 1.012 0.989 0.777 0.915 0.959 0.830 +1 1.161 0.041 1.101 1.481 -1.570 0.802 0.608 -0.833 2.173 0.745 -1.236 0.541 0.000 0.866 -0.528 -0.304 0.000 0.727 0.155 0.405 1.551 0.955 0.672 1.219 1.113 0.747 0.826 0.885 +0 1.326 1.110 -0.130 0.570 -1.387 0.687 2.670 -1.412 0.000 0.392 2.874 1.073 0.000 0.517 0.619 -1.592 2.548 0.612 1.445 0.359 0.000 0.876 0.922 1.090 0.755 0.439 0.941 0.865 +1 1.096 -1.541 0.984 1.706 0.726 1.683 -2.458 -1.181 0.000 1.308 -0.025 0.414 0.000 0.489 -1.034 -1.460 0.000 1.167 -0.117 -0.588 3.102 1.250 1.475 1.000 1.472 0.284 1.189 1.259 +1 1.191 -0.268 -1.276 2.063 -0.507 0.805 0.697 0.834 2.173 0.732 -0.337 1.161 1.107 0.568 0.681 -0.293 0.000 0.580 1.085 0.393 0.000 0.407 0.741 1.389 1.506 0.702 1.081 0.869 +0 0.759 -1.418 1.172 0.650 -0.214 0.647 -0.817 -1.471 0.000 0.405 0.303 0.819 0.000 0.649 -0.937 -0.021 2.548 1.058 -0.971 -0.933 3.102 0.792 0.692 0.987 0.643 0.465 0.470 0.493 +0 1.236 1.322 0.317 0.169 -0.612 0.633 0.634 -0.859 2.173 0.756 -0.008 1.497 2.215 0.352 2.066 0.476 0.000 0.517 1.124 1.722 0.000 0.480 0.731 0.996 0.816 0.929 0.747 0.640 +0 0.450 -2.087 1.150 1.544 -1.348 0.416 0.324 1.526 2.173 0.961 -1.152 0.051 2.215 0.326 -0.962 -0.975 0.000 1.190 -0.302 0.324 0.000 0.678 0.692 0.987 1.011 1.189 0.913 0.758 +0 2.366 -0.595 -1.355 0.526 -0.868 1.111 -0.462 0.165 2.173 0.840 -1.443 -1.740 0.000 1.345 0.239 0.702 2.548 0.678 -1.505 0.156 0.000 0.986 1.309 0.982 1.256 0.908 1.135 1.071 +1 0.359 -1.239 1.738 1.588 -0.422 1.116 -0.081 0.950 0.000 0.617 -0.084 0.318 0.000 0.610 0.431 1.271 0.000 1.700 0.277 -0.596 3.102 0.952 1.089 0.987 0.852 0.743 0.890 0.886 +1 1.344 -1.030 1.373 0.906 -1.352 0.776 -1.175 -0.471 1.087 0.866 -0.480 0.040 2.215 0.557 -2.021 -1.692 0.000 1.154 -0.582 1.000 0.000 0.940 0.988 0.988 1.030 0.685 0.855 0.780 +1 1.208 0.075 -0.296 0.776 -1.691 1.130 -0.875 0.296 2.173 0.530 0.630 -0.526 0.000 0.633 1.059 -1.442 0.000 1.224 -0.023 1.345 0.000 0.991 1.097 1.275 1.195 0.996 1.174 0.991 +0 1.140 0.471 1.050 0.777 0.305 0.398 1.622 0.588 0.000 0.973 0.858 -0.487 2.215 1.606 0.128 -1.619 0.000 1.290 1.291 1.525 0.000 0.819 0.951 0.984 0.996 0.582 0.747 0.767 +1 1.304 1.335 0.020 0.452 -1.309 0.983 -0.200 1.480 2.173 0.808 0.800 -0.493 0.000 0.730 1.265 1.419 0.000 0.979 0.145 0.615 0.000 0.863 0.945 0.990 0.544 0.696 0.857 0.741 +0 1.495 0.446 -1.127 0.129 -0.859 1.627 0.837 0.914 2.173 2.293 -0.455 -0.872 2.215 1.420 1.459 1.302 0.000 1.444 -0.259 -0.286 0.000 0.925 1.302 0.991 1.532 3.474 1.640 1.306 +0 0.404 -1.346 -0.448 1.259 1.599 0.535 -0.632 0.901 2.173 0.533 0.054 -0.814 0.000 1.517 -0.503 -0.122 0.000 1.518 0.685 -1.693 3.102 0.911 0.970 0.987 0.990 1.023 0.878 0.824 +1 0.782 0.424 1.487 0.621 -1.014 1.219 0.611 0.322 2.173 0.764 -1.163 -1.349 0.000 0.555 0.261 -0.943 0.000 0.543 -0.590 1.115 3.102 0.886 0.638 0.982 1.166 0.833 0.966 0.830 +1 0.680 -0.393 0.703 1.655 1.011 1.635 0.717 -0.549 0.000 1.320 0.530 1.333 1.107 0.939 -1.891 -1.114 0.000 0.610 -1.099 1.256 0.000 0.776 0.972 0.982 0.694 0.370 1.056 1.036 +0 0.556 -0.320 0.031 1.343 -0.890 0.766 1.774 1.062 0.000 0.533 1.496 0.289 0.000 0.747 -0.732 1.595 1.274 0.846 1.209 -0.690 3.102 0.889 0.818 0.987 0.728 0.996 0.954 1.171 +0 1.772 0.215 -1.304 0.590 -0.558 1.651 0.335 0.409 0.000 2.030 -0.513 0.383 2.215 2.939 -0.116 -0.854 2.548 4.223 -0.638 1.673 0.000 4.347 2.862 0.989 0.734 2.389 2.189 1.733 +0 1.744 -0.554 -0.571 1.660 -1.072 0.830 -0.535 0.923 0.000 0.662 0.280 0.460 1.107 0.519 -0.810 0.569 2.548 1.089 -0.586 1.722 0.000 0.963 0.862 1.026 0.845 0.393 0.786 0.840 +1 0.711 -1.553 -0.870 0.532 -0.491 0.953 0.089 -0.191 2.173 0.595 -1.818 1.694 0.000 0.722 -0.364 1.029 2.548 1.682 -0.524 1.489 0.000 0.895 0.696 0.976 0.844 0.956 0.977 0.851 +1 1.110 -1.111 1.663 0.573 -0.899 1.023 -1.027 -1.239 2.173 0.760 -2.363 0.948 0.000 1.549 -0.053 0.626 0.000 1.209 -0.479 -0.375 3.102 0.721 0.968 0.986 0.713 0.871 0.896 0.774 +1 1.752 -0.150 0.386 0.593 1.175 1.517 0.492 -1.101 2.173 0.868 0.568 0.421 0.000 0.730 0.403 1.345 2.548 0.472 -1.094 0.523 0.000 0.919 0.736 0.987 1.619 1.057 1.074 0.946 +0 0.473 0.506 -1.549 0.935 -0.117 1.359 0.422 -0.596 2.173 1.270 0.044 1.400 0.000 1.263 0.745 1.371 1.274 0.783 0.443 -0.006 0.000 0.675 1.042 0.988 0.885 1.631 0.906 0.803 +1 0.917 -1.681 0.145 0.334 -1.622 0.742 -1.436 -1.634 0.000 1.001 -1.782 1.281 0.000 0.706 0.402 -0.983 2.548 1.897 -1.075 -0.240 3.102 0.967 1.315 0.985 0.847 1.025 1.062 0.878 +1 0.971 0.163 0.778 1.031 0.206 1.092 0.199 -1.013 2.173 1.799 1.554 1.452 0.000 0.894 0.365 -0.064 1.274 2.427 1.105 -0.756 0.000 0.808 1.267 0.984 1.269 0.938 1.244 1.089 +1 1.590 0.352 -1.421 2.307 1.672 3.202 -1.932 0.194 0.000 0.810 -0.943 -0.542 1.107 0.913 1.072 -1.353 2.548 0.789 0.289 1.626 0.000 0.248 0.750 0.983 0.779 1.330 1.004 0.744 +1 0.568 1.035 0.160 0.449 0.972 1.114 0.668 -1.360 2.173 0.750 -0.121 -1.572 0.000 1.433 1.031 0.459 0.000 1.392 0.290 0.100 0.000 0.799 0.852 0.976 1.050 0.595 0.929 0.815 +1 1.557 -0.936 0.623 1.151 0.265 0.989 -0.450 -0.873 2.173 0.392 1.086 1.712 0.000 0.669 -1.024 -1.689 1.274 0.666 -0.842 1.052 0.000 0.929 1.061 0.993 0.850 0.763 0.989 0.936 +0 1.505 -0.075 0.554 0.589 -0.844 0.882 -0.618 -1.269 0.000 1.403 -0.222 -0.119 0.000 0.942 0.509 1.691 2.548 0.845 -0.539 -0.482 0.000 0.860 0.951 1.241 0.887 0.454 0.693 0.733 +1 0.449 1.020 1.663 1.393 1.238 1.210 -0.425 -0.367 1.087 0.845 -0.547 -1.164 2.215 0.643 -0.714 0.661 0.000 0.439 -2.061 1.302 0.000 0.642 1.108 0.987 0.869 0.984 0.973 0.869 +1 1.106 0.979 0.037 0.767 -0.411 0.363 2.416 0.862 0.000 1.432 -0.508 -1.699 1.107 0.639 1.755 -0.415 0.000 0.762 -0.891 0.671 1.551 0.822 1.453 0.990 1.364 0.838 1.401 1.181 +1 1.638 0.507 0.156 0.644 -0.676 0.941 -0.565 -1.536 1.087 0.415 0.887 0.797 2.215 0.365 0.765 1.182 0.000 0.738 0.602 -0.108 0.000 0.918 0.935 0.988 0.641 1.088 0.910 0.780 +0 0.579 -0.464 0.589 0.457 -0.355 0.781 0.703 0.868 2.173 0.591 1.108 -0.789 0.000 0.910 -0.345 -0.719 0.000 0.968 0.211 1.576 0.000 0.917 0.807 0.983 0.628 0.752 0.578 0.568 +0 0.803 0.002 -0.447 1.649 0.249 0.846 0.976 -1.695 0.000 0.462 1.131 -0.586 0.000 0.854 -0.287 1.186 2.548 0.434 0.458 1.525 3.102 1.123 1.042 0.990 0.637 0.250 0.590 0.738 +0 1.647 1.027 -0.920 0.341 -1.263 1.123 -0.971 0.379 2.173 0.364 -0.521 0.178 2.215 1.387 0.555 -1.598 0.000 0.730 -0.737 -1.588 0.000 0.909 0.833 0.978 1.758 0.271 1.099 1.018 +1 0.765 0.253 -0.110 0.346 -1.634 2.050 0.329 1.508 0.000 1.016 -1.591 -0.400 0.000 1.612 0.129 -0.611 2.548 3.584 -1.926 0.040 0.000 0.903 1.133 0.989 0.770 0.805 0.956 0.984 +1 1.332 0.749 -0.878 1.932 -0.338 0.615 1.444 1.292 0.000 0.715 0.596 1.086 1.107 0.470 1.427 -0.286 0.000 1.154 1.005 0.689 3.102 0.946 0.628 1.041 0.972 0.378 0.830 0.778 +0 0.997 2.070 1.388 0.426 -0.498 0.658 0.033 -0.875 0.000 1.414 1.111 0.772 2.215 0.991 -0.959 -0.068 2.548 0.695 -1.091 -1.192 0.000 0.802 0.828 0.984 0.870 1.888 1.253 1.212 +1 0.746 -1.397 -0.180 0.698 1.158 0.492 0.183 -1.609 2.173 0.636 -0.387 0.555 2.215 0.385 -0.941 1.735 0.000 0.778 2.377 1.592 0.000 0.843 0.734 0.988 0.843 0.802 0.634 0.618 +0 0.471 -0.644 0.501 0.810 -0.872 0.965 0.908 0.704 2.173 0.483 -0.035 -1.179 0.000 1.442 0.988 -1.549 0.000 1.679 0.462 -0.273 0.000 0.924 0.951 0.986 1.683 0.516 1.142 1.014 +0 1.629 0.723 -0.881 0.637 -0.912 1.604 -0.492 0.651 0.000 1.530 0.931 -0.266 2.215 2.833 -0.321 1.578 0.000 1.378 -0.324 -0.833 3.102 2.855 1.969 0.990 0.893 1.147 1.791 1.668 +0 0.979 -1.288 0.852 0.351 0.048 1.152 -0.334 1.566 1.087 0.831 -1.359 -1.050 0.000 0.753 0.301 -0.127 2.548 0.971 1.001 0.454 0.000 0.588 0.559 0.978 0.954 1.225 0.998 0.946 +1 1.017 0.103 1.154 0.107 0.242 0.684 -0.558 0.420 0.000 1.387 0.289 -1.200 2.215 0.432 -2.718 0.149 0.000 1.070 -0.963 -0.039 0.000 0.795 0.677 0.982 1.034 0.890 1.075 0.924 +0 3.398 -0.119 -0.253 3.695 -0.278 3.287 0.509 -1.710 2.173 0.771 0.009 1.272 0.000 1.322 -0.582 -0.063 2.548 1.015 1.020 0.627 0.000 0.976 1.168 0.939 4.146 3.033 2.678 2.062 +0 1.522 0.834 -1.504 0.274 0.381 0.556 0.029 -1.065 0.000 0.841 0.843 -0.335 2.215 1.753 0.351 0.521 1.274 0.592 0.367 1.097 0.000 0.834 1.000 0.990 0.823 0.953 0.826 0.744 +0 1.129 -0.231 -0.809 0.209 1.050 1.385 0.473 -1.171 1.087 2.062 -0.908 0.756 2.215 0.817 -1.883 0.521 0.000 0.596 -0.777 -0.935 0.000 0.875 1.028 0.983 1.049 3.107 1.636 1.264 +1 1.012 -0.457 0.739 0.919 -0.361 0.619 -0.098 -0.584 1.087 0.838 0.381 -1.402 2.215 1.246 -0.656 1.557 0.000 1.255 -1.218 0.478 0.000 1.254 1.215 1.118 0.972 0.759 0.924 0.833 +1 0.983 0.664 0.766 1.162 1.190 0.874 0.747 -1.048 2.173 0.763 0.446 -0.147 2.215 0.830 1.261 -1.443 0.000 1.281 -0.671 0.082 0.000 1.878 1.191 0.996 1.201 0.891 0.926 0.983 +1 1.660 -0.640 -1.128 0.695 0.479 0.549 -1.133 -0.401 2.173 1.081 1.198 0.266 0.000 1.485 -0.380 1.099 0.000 0.660 -2.237 1.422 0.000 0.793 0.987 1.477 0.816 0.550 0.696 0.701 +1 0.493 -0.006 -1.448 1.581 0.234 0.563 1.084 1.118 0.000 0.921 0.462 -0.715 2.215 1.380 -0.010 -1.194 2.548 1.345 0.256 0.587 0.000 0.815 1.094 1.221 0.985 0.581 0.857 0.814 +1 0.656 -0.550 -1.136 0.410 0.751 1.814 -2.044 1.624 0.000 1.062 -0.642 0.696 2.215 1.593 0.826 -0.134 0.000 1.467 0.281 -0.861 0.000 0.705 0.923 0.984 0.805 1.022 0.858 0.835 +1 0.685 -0.058 0.780 1.137 -0.133 0.939 0.587 1.658 0.000 0.997 0.780 0.050 0.000 0.955 0.557 -1.266 2.548 0.372 0.787 1.231 3.102 0.916 0.633 0.985 0.540 0.362 0.534 0.595 +1 0.346 1.534 -1.647 0.851 -1.109 1.364 -0.246 0.346 0.000 0.349 0.260 -1.317 2.215 0.532 -0.227 1.083 0.000 1.098 -1.353 -1.482 3.102 0.944 0.913 0.992 0.887 0.616 0.844 0.812 +1 1.036 1.643 -1.299 0.907 1.242 1.101 0.492 -0.410 1.087 1.996 -2.365 1.484 0.000 1.984 1.264 0.286 2.548 1.184 1.474 -0.664 0.000 0.713 0.850 1.010 1.328 1.373 1.102 0.925 +0 0.841 -1.386 0.869 0.799 0.375 1.197 -0.728 -1.722 2.173 1.046 -1.021 -0.171 2.215 0.796 -1.115 -1.040 0.000 0.750 -2.071 0.869 0.000 1.020 0.940 0.976 1.451 1.644 1.162 0.958 +0 0.719 0.399 0.387 1.283 -0.309 0.973 0.275 1.341 0.000 1.040 1.144 -0.653 0.000 0.999 -1.567 0.800 0.000 1.574 0.882 -1.426 1.551 2.243 1.288 0.991 0.914 0.842 1.127 1.148 +1 1.137 -1.144 -1.640 0.909 -0.835 0.924 -0.785 0.159 2.173 0.532 -1.322 -1.184 0.000 0.427 -0.360 1.243 0.000 0.978 0.408 0.651 3.102 0.838 1.009 0.983 1.075 0.814 0.927 0.791 +0 0.898 -0.293 -0.494 2.126 -1.239 1.519 -0.659 -1.528 0.000 1.561 -0.245 0.707 2.215 2.845 -1.564 0.010 0.000 0.675 -0.414 1.560 1.551 4.212 2.253 1.190 1.518 0.654 1.684 1.497 +1 2.111 -0.336 -1.378 0.443 0.018 1.185 -1.030 -0.932 0.000 1.326 -0.787 0.721 0.000 1.083 -0.896 0.391 2.548 1.033 -0.251 1.206 1.551 0.689 0.597 1.275 1.070 0.610 0.748 0.762 +0 1.455 0.570 0.623 1.174 1.459 0.750 -0.137 -1.127 0.000 1.151 1.288 -1.022 2.215 1.356 -0.461 0.210 2.548 0.423 0.746 0.072 0.000 0.890 0.967 1.239 1.097 1.833 1.158 0.993 +1 0.477 -1.491 -0.775 0.511 -0.515 1.223 -0.347 -1.353 2.173 0.908 -0.784 0.476 2.215 0.389 -2.144 0.479 0.000 0.700 0.001 0.991 0.000 0.885 1.193 0.974 0.810 1.587 0.912 0.799 +0 0.596 -2.202 0.706 1.468 0.635 0.816 -0.868 -1.131 2.173 0.602 -1.357 -1.717 0.000 0.773 -1.700 -0.755 0.000 0.726 0.163 0.291 3.102 0.836 0.906 0.995 1.202 0.905 0.853 0.787 +1 1.320 0.495 -1.630 0.256 -1.696 0.718 -1.938 -0.631 0.000 0.588 0.308 1.388 0.000 1.066 -0.453 -0.353 0.000 2.300 -0.249 0.498 1.551 1.311 0.696 0.984 1.056 0.702 0.850 0.943 +1 0.275 -1.842 -0.284 0.575 0.946 1.446 -0.038 -0.987 2.173 1.322 -0.139 0.715 0.000 1.099 -0.508 -0.369 1.274 1.234 0.427 0.992 0.000 0.690 1.087 0.985 1.061 0.927 1.157 0.934 +1 0.298 0.755 1.272 0.564 0.646 0.814 -0.310 0.199 0.000 1.484 -1.117 -1.114 0.000 0.904 -0.360 -1.353 1.274 0.921 -1.146 1.222 3.102 1.170 0.898 0.999 0.722 0.622 0.741 0.653 +1 0.605 0.477 -1.504 0.683 1.189 0.939 -0.829 -0.731 2.173 0.574 -1.443 -1.519 0.000 0.798 -1.491 1.026 0.000 2.162 -0.248 -0.076 3.102 0.989 1.106 0.987 1.650 0.934 1.242 1.184 +0 0.431 -1.204 1.617 1.120 1.627 0.845 -1.832 0.237 0.000 0.914 -1.215 -0.152 0.000 1.523 -2.588 -1.567 0.000 1.049 -0.254 -0.472 3.102 0.856 0.912 0.979 0.495 0.447 0.629 0.878 +1 1.804 1.536 -0.598 0.427 0.265 1.160 0.782 1.061 2.173 0.621 1.538 1.524 0.000 0.577 2.062 0.119 0.000 0.840 0.130 -0.866 0.000 0.932 1.042 0.981 0.575 0.734 0.864 0.773 +1 0.787 -1.593 0.591 0.926 -0.781 0.706 -0.597 1.714 2.173 0.486 -1.140 -0.609 0.000 0.543 -2.282 0.372 0.000 0.651 0.877 0.908 3.102 0.830 1.043 1.117 1.120 0.817 0.877 0.819 +1 0.832 0.801 -1.079 0.788 -0.169 1.112 -0.437 0.655 2.173 1.165 0.176 -0.912 0.000 1.521 -2.001 1.487 0.000 1.319 0.391 -0.257 0.000 0.930 0.927 0.982 1.520 0.961 1.066 0.988 +1 0.988 0.650 1.654 1.047 1.306 0.940 1.356 -1.574 0.000 1.347 1.423 -0.353 0.000 0.825 1.085 0.772 0.000 1.538 0.445 -0.156 3.102 1.362 1.074 0.984 1.052 0.273 0.814 0.785 +1 0.481 0.002 -0.523 0.985 1.712 0.510 0.873 -0.590 0.000 1.011 -0.675 0.887 2.215 0.762 0.605 1.306 0.000 0.835 1.202 0.298 0.000 0.778 1.042 0.982 0.941 1.432 0.863 0.813 +1 0.852 0.609 1.601 2.043 -1.186 0.863 -0.247 0.730 2.173 0.401 1.394 0.675 0.000 0.532 -0.477 -0.506 2.548 0.792 -0.234 0.008 0.000 0.823 0.796 1.079 0.813 0.766 0.942 0.816 +0 1.614 -0.840 1.436 1.172 0.253 1.512 -0.191 1.730 0.000 1.681 -0.324 0.011 2.215 1.078 -1.057 -0.520 2.548 1.039 -0.047 -0.394 0.000 1.806 1.489 1.667 1.349 0.897 1.249 1.166 +0 0.893 0.463 -0.874 0.901 0.931 0.769 0.862 -0.525 0.000 0.749 -0.899 1.066 2.215 0.519 1.120 -1.688 0.000 0.480 1.006 1.089 0.000 0.935 0.665 1.241 0.961 0.604 0.778 0.720 +1 0.638 -1.060 -1.400 1.158 1.291 1.077 -0.964 -0.273 2.173 0.588 -1.725 -1.459 0.000 0.894 -0.491 0.689 0.000 1.157 -0.323 1.662 0.000 0.879 1.020 0.983 1.247 0.830 0.983 0.890 +0 0.451 2.071 1.727 0.804 -0.440 0.842 1.198 1.204 2.173 0.379 -0.216 -0.619 1.107 0.937 0.496 -0.334 0.000 0.607 -0.227 -1.330 0.000 0.739 1.066 0.993 0.661 1.052 0.709 0.661 +1 0.804 -1.456 -1.009 1.128 0.360 0.885 0.438 -0.260 1.087 1.255 0.373 1.570 0.000 0.368 -0.735 -0.515 0.000 0.515 1.184 1.535 0.000 0.800 0.771 1.245 0.751 0.787 0.937 0.810 +0 0.536 -0.998 1.227 0.992 -1.220 0.729 -1.640 -1.672 0.000 1.518 -0.161 0.118 2.215 1.067 -1.698 -1.057 0.000 1.027 -0.261 1.626 3.102 0.852 0.880 0.994 1.074 1.105 1.180 0.985 +1 1.024 -0.655 -1.617 0.612 -0.070 0.905 -0.158 -0.583 2.173 1.069 -0.957 1.204 0.000 0.793 -0.542 0.557 2.548 0.395 1.142 -0.629 0.000 1.504 0.944 1.080 0.843 0.933 0.876 0.764 +0 0.447 1.887 1.520 0.064 0.653 0.657 -0.650 -0.567 0.000 1.267 -0.628 -0.047 0.000 2.474 0.115 1.581 2.548 0.393 0.594 -0.823 3.102 0.875 0.678 0.978 0.846 0.662 1.067 0.931 +1 1.062 -0.732 1.541 0.972 0.834 0.576 -0.622 0.768 0.000 1.548 0.570 -0.587 1.107 0.443 -0.406 -0.634 0.000 0.607 -0.753 -0.974 0.000 0.912 0.678 0.995 1.739 0.869 1.119 0.927 +0 2.048 -0.606 -1.430 3.840 -1.519 2.456 0.716 -0.074 2.173 2.147 -1.408 0.680 0.000 0.838 2.286 -1.080 0.000 1.132 -0.968 1.674 3.102 0.884 1.692 0.986 0.590 2.609 2.618 2.402 +0 0.877 -0.551 -1.313 0.592 0.528 1.000 -0.935 0.655 2.173 0.518 0.133 1.463 0.000 0.676 0.938 -0.823 2.548 1.164 -0.519 -1.050 0.000 0.873 1.126 0.995 0.767 1.547 0.903 0.759 +1 1.307 0.191 -0.930 0.093 -0.685 0.575 1.046 0.353 0.000 0.552 -1.204 -0.910 1.107 1.085 -1.240 0.407 0.000 1.040 0.637 1.372 0.000 0.953 0.692 0.978 0.620 0.540 0.773 0.738 +1 0.401 1.158 -1.619 1.021 0.640 0.648 0.438 1.196 0.000 1.400 0.652 -1.723 0.000 1.800 -0.086 -0.471 1.274 1.079 1.222 -0.428 3.102 1.015 1.166 0.985 0.953 0.920 1.084 0.909 +0 0.318 2.288 -1.204 0.506 0.671 0.628 0.411 -0.889 0.000 0.928 -0.285 0.690 2.215 0.749 -0.623 -1.519 2.548 0.596 0.342 0.061 0.000 0.706 0.951 0.982 0.817 0.827 0.711 0.672 +1 0.824 2.006 -1.011 2.079 -0.861 0.961 0.697 0.903 2.173 0.791 1.422 0.080 0.000 0.744 1.179 1.119 0.000 0.583 2.085 0.496 0.000 0.952 0.885 0.986 0.604 0.909 0.977 0.877 +0 0.832 -1.220 -0.674 0.304 1.055 0.686 0.345 0.267 2.173 0.880 -0.321 1.389 2.215 0.835 0.067 -1.229 0.000 0.506 -1.727 -1.510 0.000 0.943 1.133 0.981 0.765 1.046 0.805 0.712 +1 0.583 1.537 0.599 1.405 0.275 1.120 0.671 -1.695 2.173 1.117 -0.081 -1.142 2.215 0.930 0.415 0.637 0.000 0.865 0.314 -0.504 0.000 0.848 0.966 0.997 1.906 1.016 1.732 1.373 +0 0.890 0.643 -1.607 0.617 -0.406 0.710 0.123 -0.489 1.087 0.566 -1.063 -0.684 0.000 2.073 -0.458 1.073 2.548 0.521 -0.990 1.147 0.000 0.706 0.936 0.987 0.787 1.567 1.023 0.898 +1 0.389 1.614 1.232 1.334 -0.058 0.688 1.454 -0.734 1.087 0.471 0.264 -1.439 2.215 0.478 2.102 -1.297 0.000 0.701 -0.987 -0.108 0.000 1.864 1.101 0.991 0.822 0.727 0.842 0.947 +0 0.955 -0.204 0.316 1.851 0.537 0.900 -0.066 -1.363 1.087 0.954 0.645 -1.216 0.000 0.924 -0.366 1.433 2.548 1.608 0.113 -0.542 0.000 0.874 0.916 0.988 0.953 0.688 0.994 0.857 +0 0.410 -1.222 -0.060 1.116 0.003 0.628 1.872 0.972 0.000 0.399 2.762 -0.983 0.000 0.525 1.020 -1.120 1.274 0.807 2.433 1.518 0.000 0.778 1.147 1.001 0.734 0.428 0.730 0.932 +0 1.193 0.269 1.529 0.484 1.628 0.751 -0.975 -0.886 0.000 0.661 -0.244 0.273 2.215 0.888 -1.101 0.570 2.548 0.509 -2.078 -0.419 0.000 0.873 0.979 0.992 1.228 0.459 0.886 1.042 +1 1.433 0.048 -0.693 0.881 0.125 1.097 1.032 1.307 2.173 0.687 0.855 -0.201 0.000 0.518 -0.623 1.468 2.548 0.517 1.270 -1.315 0.000 0.703 1.035 1.047 0.772 0.926 0.975 0.832 +1 0.661 0.055 -0.781 1.280 -0.355 1.582 1.092 -0.933 2.173 2.791 2.338 1.036 0.000 0.924 -0.541 0.981 2.548 1.018 0.858 -0.435 0.000 0.681 0.864 0.980 0.886 2.070 1.406 1.082 +0 1.235 -0.272 -0.718 0.739 1.204 1.551 -0.563 0.765 0.000 1.371 -1.067 -0.981 2.215 0.613 -1.731 1.341 0.000 1.023 -0.538 -1.353 3.102 1.527 1.225 1.306 0.988 0.427 1.130 0.997 +1 0.686 -0.001 -0.856 0.462 0.139 0.644 -0.960 0.145 1.087 1.110 -0.650 -1.676 0.000 1.190 0.501 -1.472 2.548 0.472 -1.500 -0.729 0.000 0.907 0.972 0.985 1.033 1.415 0.900 0.888 +1 1.590 1.571 1.733 0.358 -0.930 1.873 -0.241 0.479 0.000 1.309 1.156 -0.760 1.107 0.842 2.248 -1.469 0.000 0.581 -0.238 -1.293 1.551 4.655 2.502 0.987 0.995 0.732 1.753 1.580 +0 1.002 -0.940 -1.429 1.313 1.320 0.526 -1.253 -0.523 0.000 0.701 -0.500 0.199 0.000 0.599 -1.186 1.282 2.548 0.622 0.596 -0.493 3.102 0.925 0.803 0.988 0.853 0.724 0.606 0.688 +1 2.266 0.833 1.241 0.858 1.728 0.520 0.692 1.689 2.173 1.323 0.272 0.130 0.000 1.607 0.423 -0.800 2.548 1.450 -2.137 -0.125 0.000 3.552 2.591 0.984 1.265 0.900 1.786 1.732 +1 0.996 -0.118 -1.449 1.212 1.240 0.667 0.565 -0.007 0.000 1.171 0.338 1.576 1.107 1.012 -0.918 -0.880 0.000 1.731 -0.431 -0.171 0.000 0.943 1.092 1.002 0.622 0.809 0.968 0.861 +0 2.045 0.116 -1.137 0.681 0.203 1.414 0.645 -1.562 2.173 2.243 -0.440 0.339 0.000 0.928 -0.054 0.135 0.000 0.630 -0.059 1.567 3.102 0.596 0.848 1.528 1.218 0.485 1.359 1.202 +1 1.890 -0.302 -0.156 0.895 -0.695 2.438 -1.429 1.669 0.000 1.210 -0.951 0.460 0.000 1.885 0.220 -0.500 2.548 0.660 -0.432 0.866 1.551 1.089 0.965 0.984 0.621 0.870 1.541 1.467 +0 2.911 0.747 -0.821 0.334 0.703 1.003 -0.238 1.580 2.173 0.510 -1.136 0.853 0.000 0.632 0.788 0.555 2.548 0.626 0.382 0.140 0.000 0.800 0.923 1.339 0.923 0.980 1.037 0.940 +1 0.631 0.058 1.386 1.100 0.201 1.302 -0.053 -1.075 1.087 0.355 1.358 0.797 0.000 0.414 -1.318 0.515 0.000 0.772 -0.486 -0.186 3.102 1.158 0.765 1.011 1.143 0.814 0.821 0.756 +1 0.283 -0.180 0.734 1.511 1.625 1.185 1.686 -0.022 0.000 0.954 0.215 1.390 1.107 0.891 2.159 -1.568 0.000 0.977 0.404 -0.050 3.102 0.760 0.890 0.982 0.683 0.847 0.856 0.759 +1 1.394 -1.457 -0.054 0.751 -0.305 1.419 0.278 1.667 0.000 0.693 -1.318 -0.473 0.000 0.943 -0.356 0.253 0.000 0.649 0.229 -1.350 3.102 0.979 0.816 0.993 0.712 0.296 0.589 0.571 +1 0.387 1.246 0.986 1.072 -0.762 1.154 -1.089 1.723 2.173 0.821 0.336 -1.074 2.215 1.110 2.676 0.279 0.000 0.963 -1.019 0.746 0.000 1.047 0.855 0.989 2.568 1.402 1.653 1.331 +1 0.475 -2.102 -1.668 1.675 0.796 1.313 -0.593 -0.919 1.087 0.654 -1.245 -0.316 0.000 0.695 -0.331 0.697 0.000 0.716 -1.043 1.347 1.551 0.954 1.131 0.988 0.559 0.975 1.081 0.927 +0 0.363 1.314 -0.960 0.716 0.587 0.746 -0.276 -1.424 2.173 0.507 -0.964 -1.518 2.215 1.283 -0.498 0.207 0.000 0.570 -2.084 0.972 0.000 0.827 0.853 0.986 0.821 0.338 0.770 0.734 +0 0.477 0.809 -0.827 1.018 1.108 1.103 1.368 0.583 2.173 1.419 1.420 -1.127 0.000 1.057 1.768 -0.656 0.000 0.956 1.208 1.568 3.102 0.898 0.828 0.986 0.945 0.843 1.003 0.913 +0 0.417 -0.707 0.795 1.265 -0.864 0.581 0.325 0.403 2.173 1.372 0.410 -0.541 2.215 1.430 -1.118 1.257 0.000 0.508 1.207 -1.044 0.000 0.825 1.034 1.004 0.944 0.989 0.996 1.090 +0 0.405 -2.066 -0.178 0.885 1.195 0.738 0.608 -1.006 0.000 0.516 -0.228 0.494 0.000 1.112 0.086 1.228 2.548 1.213 0.386 0.378 3.102 0.568 0.533 0.981 0.875 0.637 0.690 0.557 +1 1.189 -0.838 -0.889 1.581 -1.023 1.048 -0.695 0.954 2.173 0.497 -0.343 1.501 0.000 1.260 0.502 0.296 1.274 0.514 -1.281 0.799 0.000 0.568 0.869 0.974 1.480 1.248 1.181 0.930 +1 0.850 1.294 -0.536 0.899 -1.062 1.464 -1.338 0.841 0.000 0.687 -0.422 -0.700 0.000 0.655 -2.677 0.951 0.000 1.439 0.292 -1.329 0.000 0.868 0.833 0.990 1.537 0.777 1.336 1.130 +0 0.849 -0.880 0.092 0.247 -0.102 0.604 -1.365 -0.641 1.087 1.814 -0.707 1.064 2.215 1.308 -0.313 -1.451 0.000 0.556 1.484 -0.558 0.000 1.377 1.382 0.993 1.087 1.622 1.254 1.040 +1 0.619 0.743 0.405 0.681 -0.542 0.815 0.044 1.137 1.087 0.934 -0.883 -0.655 2.215 0.486 -1.332 1.131 0.000 1.143 0.058 -1.706 0.000 0.829 0.913 0.988 0.869 1.432 0.827 0.738 +0 1.048 0.274 0.417 0.837 0.535 0.776 0.467 -1.132 0.000 0.405 -1.236 1.485 2.215 0.338 0.849 0.017 0.000 0.602 -0.767 -1.588 3.102 0.820 0.931 0.979 0.694 0.180 0.585 0.694 +1 2.206 -0.128 1.003 1.607 1.178 1.455 1.667 -1.077 0.000 1.485 -0.243 0.309 0.000 1.816 -1.818 -0.709 0.000 0.821 0.391 -0.542 3.102 3.195 1.926 0.994 0.978 0.432 1.216 1.391 +0 0.667 -1.030 0.426 1.345 -0.453 0.403 -1.029 -0.241 0.000 1.098 -0.577 -1.565 0.000 0.623 -0.763 1.478 1.274 1.010 -0.092 1.160 3.102 1.249 0.912 0.989 0.902 0.283 0.652 0.724 +0 0.802 0.918 1.739 0.459 0.572 0.326 -0.203 -0.012 2.173 0.317 -0.706 -1.374 0.000 0.694 -0.791 0.556 0.000 0.637 0.590 -1.257 3.102 0.825 0.626 0.989 0.842 0.491 0.573 0.607 +0 1.743 0.750 -1.401 0.212 0.763 1.472 -1.209 1.250 0.000 1.875 0.432 -0.122 1.107 1.145 0.278 -0.538 0.000 0.492 -0.588 1.528 3.102 0.641 0.751 0.988 0.674 1.012 0.906 0.730 +0 0.357 1.196 -0.728 1.289 1.367 1.045 -0.264 1.302 2.173 1.384 -0.219 -0.473 0.000 1.156 -0.235 -0.073 0.000 0.955 -0.560 -1.268 3.102 0.685 0.794 0.988 1.586 0.805 1.181 1.280 +1 0.989 -0.715 -1.586 1.540 1.571 1.080 -0.647 -0.398 2.173 0.718 2.572 0.688 0.000 0.823 -0.864 -0.613 2.548 0.833 -0.606 0.611 0.000 0.899 1.871 0.993 1.444 0.286 1.665 1.877 +0 1.168 -0.724 -0.487 0.648 -0.254 0.491 -1.196 1.083 2.173 0.870 0.752 1.486 1.107 0.644 0.060 0.520 0.000 0.562 0.426 -1.731 0.000 1.013 0.859 0.991 0.885 1.167 1.102 1.057 +0 1.621 0.739 -0.830 0.520 1.277 0.677 -0.785 0.929 0.000 0.425 0.624 -1.299 2.215 0.369 0.005 0.273 0.000 0.927 -1.044 0.118 3.102 0.622 0.793 1.204 1.100 0.824 0.736 0.756 +1 0.607 0.563 -1.442 0.838 -0.469 1.257 0.583 0.631 0.000 0.757 2.106 -0.909 0.000 2.248 -0.277 1.554 0.000 2.328 0.203 -0.976 3.102 1.197 0.723 0.995 0.833 0.803 0.867 0.733 +1 1.016 0.561 1.313 0.516 -1.149 0.536 -0.610 -1.517 0.000 0.529 -0.496 1.222 1.107 1.529 0.297 -0.034 0.000 0.680 1.145 -0.472 0.000 0.753 0.953 0.981 0.695 0.480 0.644 0.651 +1 0.451 -1.803 -0.552 1.332 -1.160 1.196 -0.541 0.787 0.000 0.989 -0.134 -1.679 2.215 0.728 -0.873 -0.891 2.548 0.776 -1.863 -0.113 0.000 1.041 0.767 0.988 0.807 0.700 0.753 0.693 +1 0.356 -0.619 -1.621 1.085 0.098 1.120 1.011 1.672 0.000 1.139 0.278 -0.520 2.215 0.503 0.196 0.622 2.548 0.695 0.547 1.021 0.000 0.787 0.725 0.985 1.150 0.689 0.834 1.079 +0 0.566 -1.818 0.342 0.944 -1.202 0.614 -0.915 0.612 2.173 0.699 -1.486 1.494 0.000 0.609 -0.323 -1.296 0.000 1.115 -0.745 -0.377 3.102 0.856 0.871 0.996 0.671 0.682 0.644 0.642 +1 2.422 1.580 0.178 0.782 -0.303 0.471 1.530 1.672 0.000 0.909 1.350 -1.297 1.107 1.255 2.262 -1.403 0.000 1.019 1.332 1.144 0.000 0.839 0.884 0.976 1.181 0.632 0.776 0.859 +1 1.485 0.808 1.386 1.839 0.816 1.137 -0.339 -1.309 0.000 1.154 -0.711 -0.122 2.215 1.263 0.144 -0.429 2.548 0.455 0.664 -1.483 0.000 0.682 0.933 1.123 1.707 0.686 1.239 1.170 +0 2.024 0.862 0.082 0.600 -0.013 2.114 -0.935 1.521 0.000 1.189 -0.352 -0.533 0.000 1.043 -0.307 1.081 2.548 1.259 -1.423 -0.499 0.000 0.902 0.950 0.994 0.792 1.015 0.907 0.922 +0 1.680 -1.235 0.440 0.278 -1.025 0.782 2.034 1.254 0.000 1.182 0.046 -1.022 2.215 0.693 -0.749 -0.806 0.000 0.507 -1.843 1.314 0.000 0.789 0.950 0.990 0.742 0.472 0.812 0.720 +0 1.560 -0.136 -1.101 1.725 -1.354 2.184 1.417 0.287 0.000 0.783 1.619 0.812 0.000 2.469 -0.230 -1.647 2.548 0.870 0.956 -0.143 3.102 1.306 0.869 1.000 0.921 1.386 1.811 1.691 +1 1.057 -0.479 0.365 1.194 -0.796 0.809 -1.656 0.949 0.000 1.078 -0.285 -1.598 2.215 1.568 -0.196 -0.572 1.274 0.745 -0.643 1.230 0.000 0.655 0.819 1.346 1.049 1.104 0.814 0.709 +1 0.949 0.954 0.460 1.049 -0.383 1.384 1.044 1.711 0.000 1.100 1.386 0.781 2.215 0.963 0.253 -0.655 0.000 0.695 -1.191 -0.629 0.000 0.868 0.469 0.987 0.868 0.827 0.895 0.789 +1 0.547 -0.881 0.903 1.451 0.394 1.216 -0.659 0.597 1.087 1.434 -1.754 -1.241 0.000 1.576 -0.348 -1.162 2.548 0.396 -0.074 1.350 0.000 1.185 1.099 0.990 0.759 1.742 1.240 1.176 +1 2.122 0.908 1.423 0.475 -1.112 0.940 -0.540 -0.428 2.173 0.462 1.106 -0.319 0.000 0.720 -0.073 0.698 0.000 0.565 0.378 1.003 3.102 0.907 1.011 1.052 0.559 0.843 0.999 0.845 +1 1.506 0.590 0.447 0.967 -0.680 0.962 0.100 -1.727 2.173 0.496 -0.849 0.971 0.000 1.289 1.641 0.621 0.000 2.345 -0.075 -1.015 3.102 0.932 1.136 1.420 1.278 0.966 1.011 1.043 +1 1.296 0.129 1.413 0.500 1.144 1.311 -0.940 -0.698 2.173 0.218 -1.719 -0.582 0.000 0.487 -2.102 0.662 0.000 0.399 0.953 1.342 3.102 0.470 0.901 0.977 0.581 1.208 0.954 0.841 +1 1.390 0.412 -1.286 0.688 -0.066 0.802 0.956 1.022 2.173 1.336 0.216 -0.688 2.215 1.052 0.008 0.100 0.000 0.978 0.792 -1.543 0.000 1.241 1.020 1.208 1.060 1.629 0.942 0.836 +0 0.904 -0.231 -1.618 0.373 -1.435 1.662 -0.378 0.532 0.000 2.011 -0.354 -1.137 2.215 0.510 0.136 1.030 0.000 1.070 -0.722 0.087 3.102 0.844 0.717 0.994 0.899 1.229 1.239 1.064 +0 0.859 -0.310 0.029 1.435 -1.124 1.643 -0.524 0.892 0.000 1.372 -0.811 0.531 1.107 2.826 -1.992 -1.029 0.000 1.392 0.405 1.671 0.000 0.842 0.771 1.325 1.210 0.946 0.856 0.827 +0 0.800 -0.473 1.620 0.500 -0.271 1.017 -0.846 1.077 0.000 1.118 1.058 -0.440 0.000 1.147 -1.163 -0.098 2.548 1.323 0.666 -1.120 0.000 0.939 1.025 0.990 0.862 1.088 1.099 0.899 +1 0.912 -1.842 -1.458 0.269 0.425 0.914 -1.496 0.530 0.000 0.947 -1.245 -0.999 2.215 0.713 -0.456 0.220 2.548 0.747 0.360 -1.649 0.000 1.844 1.084 0.995 0.664 0.853 0.894 0.778 +1 0.947 0.643 0.575 1.133 -0.306 0.529 2.160 -1.050 0.000 0.479 1.283 0.190 2.215 0.956 1.329 -1.570 0.000 0.754 0.790 1.335 0.000 1.026 0.798 1.023 0.686 0.566 0.549 0.633 +1 0.827 1.568 -0.283 0.336 0.449 0.779 1.352 -1.716 0.000 0.938 -0.427 -0.034 2.215 0.569 0.323 -1.587 2.548 0.456 1.656 0.693 0.000 0.804 0.612 0.998 0.859 0.828 0.894 0.787 +0 1.547 -0.128 -0.252 0.553 1.078 0.878 0.359 0.962 2.173 1.484 -0.198 -0.851 2.215 1.164 1.171 1.385 0.000 0.378 1.479 -1.030 0.000 0.629 0.816 1.194 0.956 1.744 1.058 0.966 +1 1.194 -1.422 -0.170 0.934 -0.883 0.608 -2.400 -1.456 0.000 1.254 0.813 1.472 2.215 1.348 -0.748 0.402 1.274 0.628 -1.356 -0.689 0.000 0.715 1.207 0.986 1.773 1.713 1.612 1.341 +1 0.981 1.375 0.223 0.726 -0.516 0.344 0.021 -1.143 1.087 0.509 -1.405 1.130 2.215 0.596 -0.359 1.314 0.000 0.587 -0.274 0.452 0.000 0.458 0.521 0.983 1.892 0.737 1.243 0.982 +0 1.027 -0.277 -0.714 0.404 0.850 0.801 -0.042 1.142 2.173 0.733 -1.935 1.518 0.000 2.160 0.839 -0.170 1.274 0.614 -0.957 -1.030 0.000 0.756 1.161 0.988 1.158 1.725 1.524 1.184 +1 1.057 0.728 1.152 0.772 0.088 1.602 -0.026 -1.359 2.173 0.777 -0.032 -0.316 0.000 0.870 2.350 -0.213 0.000 0.710 -0.045 1.047 3.102 1.470 0.990 1.024 1.369 0.933 1.088 0.961 +1 2.091 0.008 0.445 0.727 0.091 1.694 -0.339 -1.664 2.173 0.805 1.621 -0.421 0.000 0.682 0.078 0.962 2.548 1.014 -0.173 -0.531 0.000 1.279 1.047 0.991 1.854 0.979 1.255 1.200 +1 1.759 -0.752 -1.552 1.105 -1.515 0.605 -0.845 1.581 2.173 1.533 -0.400 0.146 0.000 1.216 -1.361 -0.341 0.000 1.200 -0.315 1.145 3.102 0.809 0.782 0.985 0.619 0.408 0.587 0.626 +0 0.691 -0.381 0.565 0.834 -1.226 0.863 -0.314 1.648 1.087 0.863 -1.677 -0.010 0.000 0.514 -2.066 0.400 0.000 1.375 -0.613 -0.451 3.102 0.799 1.248 1.051 0.683 1.120 0.845 0.744 +0 0.897 -0.222 1.451 0.656 -0.303 0.712 -0.261 -0.248 0.000 1.160 -0.670 0.910 2.215 0.701 -1.017 1.139 2.548 2.426 -0.595 -1.007 0.000 1.348 1.142 1.062 0.797 0.285 0.950 0.807 +1 1.434 -1.847 1.445 0.681 0.156 0.531 -1.465 0.586 0.000 0.776 -0.117 -1.068 0.000 0.959 -0.150 -0.123 2.548 0.381 -0.546 -1.678 3.102 1.687 0.910 1.256 1.172 0.470 0.741 0.800 +0 0.518 0.202 1.052 1.631 -1.446 0.410 -0.799 0.956 0.000 0.742 -1.244 0.096 0.000 1.063 -0.703 1.626 0.000 1.331 0.560 -0.138 3.102 0.868 1.060 0.991 0.803 0.585 0.691 0.777 +1 0.624 0.939 -0.487 1.463 0.139 1.027 0.883 1.537 2.173 0.639 1.261 0.486 0.000 1.135 1.083 -0.960 2.548 0.383 0.985 -1.582 0.000 0.618 0.783 0.982 0.838 1.064 0.967 0.768 +0 0.655 -0.480 -0.945 1.768 -1.636 0.962 1.347 0.031 2.173 0.503 0.586 -0.343 0.000 0.842 -0.906 1.565 0.000 1.573 -0.490 0.428 3.102 0.752 0.968 0.982 2.180 1.547 1.609 1.424 +0 1.202 0.208 -0.256 1.442 -1.018 0.697 0.066 1.056 0.000 0.896 0.730 1.485 0.000 0.682 2.002 -1.586 0.000 0.581 -0.782 0.607 3.102 0.861 0.801 1.155 0.811 0.391 0.584 0.755 +0 1.012 -0.729 -0.280 1.220 0.383 2.443 -0.434 0.100 2.173 4.423 -0.701 -1.583 0.000 0.956 0.205 1.700 2.548 0.643 0.275 0.596 0.000 2.363 1.451 0.986 0.975 1.994 2.150 1.729 +1 0.694 0.871 -0.528 1.741 -0.581 0.752 -0.304 0.933 2.173 0.591 1.207 0.624 1.107 1.455 -1.240 -1.456 0.000 0.539 0.603 1.101 0.000 1.414 1.180 0.978 0.894 0.881 1.219 1.502 +0 1.353 -1.235 -1.170 1.069 1.729 0.850 0.542 0.364 0.000 0.235 -1.177 0.040 2.215 0.504 -0.188 -0.098 0.000 0.662 0.889 -1.585 3.102 0.659 0.751 0.990 0.645 0.613 0.641 0.816 +1 0.962 -0.703 0.609 1.007 1.614 0.876 -0.962 -1.544 2.173 0.850 -1.950 -0.233 0.000 0.955 -0.060 -0.797 2.548 1.132 0.611 0.609 0.000 2.512 1.591 1.074 0.853 0.889 1.174 1.000 +0 1.840 0.612 -0.438 2.640 -0.071 1.415 1.742 -1.638 0.000 0.770 -0.474 0.104 1.107 1.455 0.913 0.779 0.000 2.160 0.304 -1.720 3.102 0.941 1.410 0.995 0.957 1.269 1.490 1.571 +1 0.479 0.668 -1.534 1.117 0.400 1.106 -0.072 1.684 0.000 1.019 -0.545 -0.010 2.215 1.410 -0.800 -0.955 2.548 1.093 0.042 1.014 0.000 0.962 1.237 0.999 0.899 0.979 1.016 0.942 +1 0.385 1.364 0.641 1.333 -0.802 1.307 1.073 1.415 0.000 1.215 0.802 0.081 2.215 1.485 0.661 -0.794 2.548 0.667 -1.656 0.180 0.000 0.810 1.268 0.988 0.942 1.015 1.025 0.933 +1 1.519 0.448 1.207 0.539 0.561 0.767 -0.684 -0.729 2.173 0.757 0.867 -1.093 0.000 1.512 1.207 1.467 0.000 1.475 0.324 0.137 3.102 0.937 0.938 0.994 0.795 1.016 0.970 0.848 +1 0.571 -1.684 -0.218 1.728 1.013 0.446 -0.528 0.429 0.000 0.600 -0.621 1.299 0.000 1.734 1.256 -0.866 0.000 1.585 0.411 -0.738 3.102 0.848 1.134 1.233 0.868 0.821 1.079 0.890 +0 2.930 -0.533 0.818 1.549 0.762 1.678 0.389 -0.805 0.000 1.130 -0.422 -1.052 1.107 1.830 0.055 0.316 2.548 1.906 0.318 -1.308 0.000 0.952 0.885 0.992 1.127 1.491 1.255 1.201 +0 1.885 -0.389 0.009 0.220 -0.824 1.082 0.350 -0.585 1.087 2.181 0.844 1.524 0.000 1.321 0.612 -1.609 0.000 0.980 0.078 0.804 3.102 0.879 0.990 0.997 0.678 1.045 0.733 0.748 +1 2.172 0.438 -0.966 0.944 -0.172 2.939 0.942 1.304 0.000 1.460 0.414 -0.480 2.215 2.575 0.582 0.041 2.548 0.601 -0.265 1.658 0.000 1.516 2.416 1.303 1.218 0.957 1.836 1.594 +1 0.672 0.598 0.115 0.943 1.079 0.688 -0.581 -0.907 2.173 1.092 -0.843 -0.025 1.107 1.076 -0.923 1.321 0.000 1.178 0.571 0.933 0.000 1.007 0.983 0.990 0.888 0.928 0.807 0.751 +1 2.051 0.531 -0.896 0.185 -0.644 0.808 -0.219 0.918 2.173 0.938 2.256 -0.072 0.000 0.932 0.565 0.568 0.000 1.568 0.523 1.481 3.102 1.328 0.973 0.986 1.224 0.777 0.910 0.834 +1 0.610 -1.119 0.534 0.926 -0.917 0.595 -0.460 -0.039 0.000 0.773 -1.045 1.647 0.000 1.117 -1.363 -0.403 2.548 1.088 1.732 1.647 0.000 0.833 0.837 1.005 0.624 0.784 0.806 0.724 +1 0.581 1.722 0.739 1.170 1.353 0.734 1.635 1.179 0.000 0.910 0.923 -0.329 2.215 1.102 0.753 -0.829 2.548 0.911 0.882 -1.575 0.000 0.972 0.776 0.980 0.903 0.468 0.747 0.662 +0 0.634 0.622 -0.290 1.591 -0.827 0.671 -0.311 -0.235 2.173 0.420 2.155 1.066 0.000 0.837 0.210 0.847 0.000 1.870 -0.846 0.888 0.000 0.919 0.890 0.994 0.650 0.631 0.739 0.773 +1 0.343 -1.060 0.319 1.061 1.425 1.490 0.296 -0.142 0.000 1.381 0.285 1.174 2.215 1.227 0.056 -0.827 2.548 1.213 -2.235 -1.618 0.000 4.769 2.750 0.986 1.814 1.356 1.971 1.810 +1 1.317 0.666 1.713 0.112 -1.100 0.585 0.129 0.369 0.000 0.922 -0.322 -0.521 2.215 0.643 1.231 -1.313 0.000 0.376 -0.843 0.047 3.102 1.315 1.088 0.995 0.636 0.319 0.655 0.678 +1 1.184 0.012 1.149 1.011 -0.052 0.701 -0.466 0.874 0.000 1.035 -0.754 -1.443 2.215 1.154 -1.394 -0.804 2.548 0.602 -0.573 -0.183 0.000 0.817 0.986 1.338 1.230 0.776 0.952 0.838 +0 0.418 -0.272 -1.486 1.111 0.666 0.470 -0.045 -0.039 0.000 0.671 0.506 -1.731 2.215 0.781 0.403 -0.510 2.548 0.577 -1.111 1.622 0.000 0.965 0.876 0.989 0.855 0.686 0.722 0.655 +1 0.825 0.305 -0.935 1.268 -0.176 0.918 -0.708 1.433 1.087 0.401 -1.018 -0.819 0.000 0.580 1.124 1.479 0.000 1.051 0.107 0.607 3.102 0.771 1.134 0.991 0.750 0.838 0.958 0.809 +1 0.424 1.210 0.173 1.267 -1.654 0.956 -0.936 0.158 2.173 1.005 -0.694 -0.903 2.215 0.889 -0.764 1.483 0.000 0.981 0.095 0.940 0.000 0.703 0.968 1.013 1.692 1.190 1.285 1.065 +1 0.812 0.440 0.750 0.874 -1.270 1.054 -1.921 0.081 0.000 1.339 -0.841 -1.560 1.107 0.724 -0.100 -1.350 0.000 0.926 -0.247 0.084 1.551 0.931 0.943 1.131 1.082 1.042 1.059 1.155 +1 2.141 -1.231 -1.289 0.967 0.891 0.556 -1.623 0.161 0.000 0.879 -1.023 0.764 1.107 1.004 -0.364 0.131 2.548 1.275 -1.546 1.616 0.000 0.628 0.892 1.840 1.183 0.633 0.927 0.778 +0 0.822 -0.076 -1.314 1.813 -1.063 0.930 0.771 0.770 0.000 1.106 1.292 0.587 0.000 0.604 0.066 0.378 2.548 0.920 1.916 -0.792 0.000 0.698 0.657 0.984 0.873 0.456 0.590 0.849 +1 1.491 -1.054 -0.510 0.413 -1.371 2.830 1.459 0.211 0.000 2.099 -0.418 0.887 0.000 4.630 -0.136 -1.329 2.548 2.131 -1.218 -1.252 0.000 0.741 2.407 0.994 1.644 1.345 2.791 2.593 +0 1.158 0.766 0.727 4.707 0.447 3.421 -0.590 -1.387 0.000 1.118 0.581 0.126 2.215 0.545 2.616 -0.460 0.000 0.845 -1.238 -1.073 3.102 6.985 3.830 0.975 0.742 1.337 2.501 2.489 +1 0.988 0.732 -1.311 0.840 -0.120 0.802 1.058 -0.919 0.000 1.325 0.984 1.080 2.215 0.618 2.140 -0.518 0.000 0.761 -0.435 0.282 3.102 0.977 1.162 1.109 1.034 0.971 1.013 0.866 +0 0.402 0.619 -1.270 1.127 -0.503 1.307 -0.150 1.047 0.000 1.550 0.124 -1.158 1.107 1.006 0.331 0.800 2.548 2.300 0.050 -0.124 0.000 1.533 1.069 0.988 0.836 1.312 0.958 0.859 +0 0.622 -1.138 0.533 0.103 -0.119 0.747 -0.373 1.595 0.000 0.749 0.916 0.936 2.215 0.877 1.318 -0.602 0.000 1.048 -0.654 -1.112 0.000 0.915 0.959 0.983 0.760 0.705 0.802 0.709 +0 0.448 0.139 -0.803 2.074 -1.706 2.206 0.160 0.179 1.087 1.598 -1.102 1.473 2.215 1.999 2.394 -0.685 0.000 2.166 1.355 -1.004 0.000 0.810 1.162 0.990 1.902 3.183 1.725 1.331 +0 0.774 1.613 -1.342 0.556 1.363 1.102 0.020 -0.461 2.173 2.044 0.286 1.692 2.215 1.644 -0.129 0.112 0.000 0.762 -0.557 0.951 0.000 0.909 1.015 0.977 0.849 2.082 1.255 1.082 +1 1.618 0.054 -1.427 0.413 -0.858 0.621 -1.896 0.151 0.000 0.441 -0.133 1.657 0.000 0.834 0.237 0.975 1.274 0.622 -1.269 0.541 0.000 0.771 0.629 0.990 0.622 0.614 0.585 0.599 +1 0.598 -2.307 1.234 0.302 -0.511 0.632 -0.150 0.204 1.087 0.626 -0.571 1.140 0.000 0.696 0.807 -1.224 2.548 0.547 -1.632 -1.610 0.000 0.735 0.947 0.990 0.894 0.911 0.841 0.742 +1 0.674 1.085 1.262 0.264 1.034 0.778 0.596 -0.377 2.173 1.533 1.063 1.030 0.000 1.280 0.658 1.462 0.000 1.278 1.304 -1.298 0.000 0.909 1.486 0.978 0.864 0.957 1.244 0.995 +0 0.711 0.376 -1.292 0.325 0.082 1.291 0.500 1.690 2.173 0.937 0.358 -0.451 2.215 0.767 1.798 0.283 0.000 1.271 0.416 0.301 0.000 0.879 0.920 0.991 0.934 1.518 1.069 0.877 +1 0.871 0.973 -1.083 0.349 1.286 0.987 0.967 1.418 2.173 0.577 -0.401 -0.167 0.000 0.971 -0.312 -0.497 0.000 1.313 -0.454 0.439 3.102 0.339 0.567 0.995 0.847 1.373 0.970 0.828 +1 0.734 -0.537 0.833 0.257 -0.129 0.625 -0.770 -1.167 0.000 0.501 -1.973 -0.716 0.000 0.730 1.016 0.698 2.548 0.617 -0.944 1.394 3.102 0.922 0.682 0.993 1.109 0.768 0.895 0.813 +1 0.851 -0.040 0.135 0.330 -0.916 0.627 1.824 -1.496 0.000 0.998 0.923 0.387 1.107 0.483 -0.102 -1.165 2.548 0.787 0.658 -1.445 0.000 0.593 1.098 0.985 0.586 0.835 0.738 0.886 +0 1.059 -0.943 -1.225 1.735 1.167 1.697 -0.192 -0.421 2.173 0.707 -1.367 0.410 0.000 1.906 -0.023 1.420 2.548 0.449 -0.205 -1.381 0.000 0.861 1.075 1.565 1.822 2.240 1.448 1.154 +1 0.377 -0.365 -1.472 1.242 -0.681 0.635 1.217 -1.527 1.087 0.859 1.554 1.012 2.215 1.287 -0.173 0.175 0.000 0.664 1.655 0.410 0.000 0.742 1.097 0.984 2.190 0.843 1.649 1.279 +1 0.328 -1.948 -0.733 1.707 -1.225 0.773 2.657 1.732 0.000 0.746 0.632 0.215 2.215 2.115 -0.687 0.631 2.548 0.504 -2.227 0.716 0.000 6.951 3.743 0.992 1.321 1.129 2.677 2.224 +0 0.642 0.760 1.289 0.632 -1.458 0.789 0.723 0.311 1.087 0.704 -0.792 -0.759 2.215 1.045 -0.281 1.483 0.000 0.525 0.400 -0.343 0.000 0.879 0.963 0.989 1.379 1.297 1.078 0.918 +0 1.240 -1.275 0.946 0.340 1.308 0.345 -0.769 -1.089 1.087 0.687 0.217 -0.276 2.215 0.296 2.258 -0.969 0.000 0.482 -1.238 -1.309 0.000 1.492 0.999 0.973 0.724 0.607 0.691 0.790 +1 0.875 -0.360 0.872 0.819 -1.389 0.631 -0.729 0.355 0.000 0.959 -0.188 -0.184 0.000 1.445 0.366 1.554 2.548 0.927 0.938 -0.716 3.102 0.895 1.020 1.048 0.738 0.852 0.933 0.821 +0 0.755 -0.250 -0.273 3.584 -0.737 1.257 0.128 1.271 2.173 0.616 0.664 0.690 0.000 1.725 -0.605 0.992 0.000 0.638 0.596 -1.233 0.000 0.824 0.930 0.988 0.856 0.875 1.231 0.977 +1 0.529 1.162 -1.186 0.281 -0.927 1.010 0.724 1.580 0.000 1.313 0.339 0.340 1.107 0.956 0.019 -0.223 0.000 0.573 -0.528 -0.864 3.102 1.876 1.135 0.991 0.948 0.800 0.925 0.802 +1 0.777 -2.102 -0.247 0.774 0.624 1.268 0.130 -1.248 2.173 0.667 -0.770 1.198 2.215 0.387 -1.490 -0.494 0.000 0.831 0.035 0.362 0.000 0.798 0.975 0.985 0.762 1.268 1.127 0.883 +1 0.594 -0.188 1.651 0.420 0.147 0.965 0.249 0.791 0.000 0.466 -0.116 0.054 0.000 1.532 0.930 -1.227 1.274 0.515 -0.511 -1.221 0.000 0.918 0.872 0.983 1.270 0.532 0.866 0.872 +0 0.910 -0.398 1.689 0.375 -1.181 1.002 -0.126 -0.236 0.000 1.247 -1.085 1.338 1.107 0.709 -0.907 0.049 0.000 0.612 -0.572 -1.590 1.551 0.781 0.775 0.992 1.044 0.417 0.890 0.854 +0 0.946 -0.272 -1.557 0.238 0.026 1.451 0.505 0.756 0.000 1.405 0.488 -1.012 2.215 1.483 1.364 -0.255 2.548 0.538 2.417 1.605 0.000 0.817 0.883 0.982 0.751 1.253 0.854 0.797 +1 0.880 -0.100 -0.730 0.704 -0.077 0.942 1.062 0.730 2.173 0.766 -0.228 -1.390 1.107 0.699 -1.526 -0.854 0.000 0.581 -0.896 1.111 0.000 0.722 0.718 0.997 1.490 1.468 1.116 0.996 +1 0.871 -0.714 -1.039 0.566 0.450 1.114 -0.992 0.611 1.087 1.405 -1.290 -1.352 2.215 1.114 0.193 0.651 0.000 1.405 0.874 -1.366 0.000 0.818 0.934 0.988 0.806 1.829 1.127 0.915 +0 1.237 0.613 -0.408 1.270 0.156 0.896 -1.076 1.540 0.000 1.052 1.226 0.319 2.215 0.863 -1.972 1.611 0.000 1.462 -0.302 -1.517 3.102 0.902 0.906 0.987 0.856 1.506 1.605 1.438 +1 2.128 -0.042 1.686 1.284 -1.273 1.151 0.414 0.191 2.173 0.632 -0.423 0.848 2.215 0.745 0.614 -0.711 0.000 0.473 0.035 0.494 0.000 0.616 0.695 1.049 0.932 0.891 1.120 0.881 +1 0.829 -0.657 -1.270 1.127 0.825 3.386 1.441 1.408 0.000 3.409 -0.309 -0.254 1.107 2.389 0.287 -0.205 0.000 0.930 0.637 -0.364 3.102 2.023 1.193 1.272 1.573 0.927 1.073 1.057 +0 0.975 0.825 1.687 0.343 -0.682 0.375 -0.872 -1.065 2.173 0.549 -0.938 0.117 2.215 0.264 0.759 0.248 0.000 0.819 -1.047 0.817 0.000 0.688 0.651 0.983 1.165 0.585 0.894 0.775 +0 0.604 -0.512 0.195 0.317 -0.767 0.600 -1.102 -1.565 2.173 0.395 -2.850 0.159 0.000 1.309 -0.557 1.172 2.548 0.508 -0.917 -1.095 0.000 0.809 1.050 0.989 0.772 0.744 0.744 0.682 +1 2.344 -1.597 -0.415 0.899 0.074 0.347 -1.683 0.886 0.000 0.593 -0.451 0.596 1.107 1.233 -1.702 -1.705 0.000 1.511 0.101 1.491 3.102 0.896 1.073 0.993 0.947 0.667 1.039 0.944 +1 1.185 0.016 1.614 0.672 -1.226 0.667 -0.123 -1.633 2.173 1.695 -1.275 0.143 0.000 1.352 -0.693 -0.336 0.000 1.409 1.022 1.104 3.102 0.737 0.928 0.995 1.042 0.984 0.935 0.825 +1 0.693 -0.672 0.285 0.213 0.178 0.653 -0.153 0.035 2.173 0.792 -0.328 -1.498 0.000 1.493 0.656 -1.492 0.000 0.881 0.420 -0.276 0.000 0.924 0.726 0.981 0.609 0.578 0.776 0.714 +0 1.017 -0.526 -1.057 5.700 -1.371 3.733 -1.007 0.421 2.173 1.432 0.185 -1.571 2.215 0.800 -1.297 -0.200 0.000 1.142 -1.579 0.438 0.000 0.626 1.153 0.993 1.211 3.968 2.896 2.177 +0 0.833 -1.005 -0.875 0.647 -0.531 0.758 0.232 0.688 0.000 1.031 1.290 0.792 2.215 1.532 -0.027 -1.431 2.548 0.657 -0.354 -0.171 0.000 0.836 0.938 0.976 0.770 1.558 1.035 0.916 +0 1.848 0.713 -0.654 3.006 -0.820 1.255 -0.800 0.878 0.000 1.180 0.097 1.010 0.000 1.408 -0.014 1.297 2.548 1.158 1.274 0.810 0.000 1.077 0.926 0.982 0.724 1.214 1.172 1.531 +1 0.521 0.502 -1.284 0.682 0.471 0.519 -1.008 -0.325 0.000 0.477 1.051 0.728 2.215 1.031 0.384 1.606 1.274 0.933 0.582 -0.274 0.000 0.917 0.720 0.988 0.559 0.586 0.521 0.510 +0 0.836 1.675 -0.549 0.750 -1.631 0.610 1.345 0.805 2.173 0.712 0.273 -1.684 0.000 0.982 0.820 0.318 2.548 0.873 1.056 -0.904 0.000 0.848 0.950 0.984 0.877 0.466 0.698 0.730 +0 1.286 -0.032 1.465 0.269 -0.833 0.643 -0.725 0.464 2.173 0.790 0.984 -1.301 2.215 0.397 0.474 0.583 0.000 0.841 -0.334 -0.268 0.000 0.537 0.764 0.986 0.819 1.474 0.860 0.699 +1 0.969 -0.407 -0.911 0.265 1.281 0.855 -1.414 -0.371 0.000 1.334 -0.159 1.093 0.000 0.710 -1.056 1.368 2.548 0.524 1.001 -0.090 0.000 0.810 0.852 0.993 0.490 0.357 0.544 0.619 +0 0.459 0.263 0.758 0.625 -0.288 0.931 -1.251 0.366 2.173 1.621 0.716 -1.344 0.000 0.497 0.768 -0.970 1.274 0.438 -2.359 1.077 0.000 3.302 1.816 0.982 1.767 1.329 1.538 1.422 +1 0.874 0.654 -1.102 2.128 -0.335 1.107 -0.047 1.233 0.000 0.907 -0.072 -0.226 2.215 0.720 1.473 1.656 0.000 0.962 -0.595 -1.158 0.000 1.420 0.992 1.205 0.790 0.844 0.858 0.965 +1 1.293 -1.676 -0.608 0.248 -1.008 0.998 -0.158 1.461 0.000 0.803 -0.565 -0.729 0.000 0.732 -0.817 0.604 1.274 1.026 -0.130 0.681 3.102 1.151 0.885 0.976 0.854 0.255 0.621 0.745 +0 1.170 1.729 -1.559 0.957 -0.607 0.421 -1.208 1.141 0.000 0.535 0.304 0.533 2.215 0.608 -0.644 -0.457 2.548 0.446 -1.902 -0.618 0.000 0.757 0.851 1.110 1.220 0.572 0.902 1.183 +0 0.895 -1.934 0.879 1.047 -0.273 0.510 -0.575 0.194 0.000 1.195 -0.948 1.556 2.215 1.031 -1.287 -0.867 1.274 0.691 0.370 -0.956 0.000 0.919 0.885 1.155 1.142 1.000 0.858 0.871 +0 0.422 -1.331 1.415 0.978 -1.122 1.086 -2.026 1.159 0.000 0.866 -0.216 0.307 0.000 0.747 -0.374 -0.699 0.000 1.695 0.791 -0.767 3.102 0.892 0.952 0.987 1.489 0.806 1.586 1.248 +1 1.107 -0.050 1.570 0.268 1.357 1.485 -0.803 0.872 1.087 1.285 -2.397 -0.449 0.000 0.831 -1.152 1.461 0.000 1.398 0.428 -0.449 0.000 0.801 0.942 0.987 0.932 2.011 1.193 1.060 +0 0.603 -1.436 1.464 0.826 0.544 0.902 -0.697 0.907 1.087 1.118 0.180 -1.334 2.215 0.530 -1.617 -1.630 0.000 0.919 1.592 -0.501 0.000 0.878 1.068 0.981 1.693 1.494 1.261 1.064 +0 1.167 -0.572 1.323 0.273 -0.272 0.814 -0.018 -0.493 0.000 0.747 -0.451 0.771 2.215 0.461 -1.622 -0.327 0.000 0.894 0.550 -1.432 3.102 1.114 0.970 0.987 0.612 0.804 0.768 0.706 +0 1.361 0.050 -0.308 0.047 0.824 0.726 0.953 0.640 0.000 1.220 0.138 -0.893 2.215 1.015 -0.804 1.672 0.000 1.002 0.661 1.510 1.551 1.176 0.814 0.821 0.696 0.890 0.841 0.770 +1 1.247 1.334 -0.085 1.039 0.219 2.487 1.050 0.934 0.000 1.273 -0.276 -0.865 0.000 2.975 -1.880 -1.351 0.000 1.578 0.363 -0.353 3.102 1.530 0.971 0.991 0.993 0.580 0.679 0.961 +1 1.385 0.466 0.474 0.799 0.724 0.936 0.622 -0.669 2.173 0.568 1.058 -1.732 2.215 0.287 0.636 -1.038 0.000 0.375 1.535 1.573 0.000 0.472 0.742 1.001 0.819 0.913 0.867 0.679 +1 1.595 0.289 -0.478 1.425 -1.099 0.932 0.668 0.465 1.087 0.958 0.711 -1.681 0.000 0.815 1.048 1.413 2.548 1.206 0.754 -0.054 0.000 0.892 0.888 1.107 1.291 0.859 0.971 0.832 +0 0.713 0.664 0.821 0.747 0.014 0.574 -0.469 -0.223 2.173 0.584 -1.687 -1.379 0.000 0.957 -0.808 1.645 0.000 1.137 -0.789 -0.592 3.102 0.827 1.194 0.988 0.752 0.346 0.897 0.780 +1 0.681 0.595 -0.971 0.848 0.441 0.676 -0.335 1.523 0.000 0.792 0.756 0.297 2.215 1.115 -0.083 -0.492 2.548 0.891 -1.141 -1.464 0.000 0.821 1.010 1.006 0.624 0.791 0.886 0.789 +1 1.059 0.074 1.231 0.696 0.047 1.458 0.921 -1.516 0.000 1.866 1.773 -0.528 0.000 1.352 -0.534 -0.066 1.274 1.713 1.402 1.015 0.000 0.806 0.472 1.041 0.825 0.821 0.951 0.819 +0 1.100 1.703 -0.578 0.682 0.101 0.750 1.468 0.153 0.000 0.886 -0.135 -1.739 1.107 0.551 0.776 1.584 2.548 0.740 2.056 0.796 0.000 0.853 0.803 0.979 1.622 0.398 1.041 0.955 +1 0.800 0.355 0.423 0.113 -0.151 0.626 -2.030 -1.070 0.000 0.742 -0.749 1.560 2.215 0.627 -0.347 -0.017 2.548 0.681 1.363 1.468 0.000 0.840 1.158 0.993 0.527 0.731 0.761 0.702 +0 0.833 -0.626 -0.286 1.520 -0.716 0.829 -1.838 1.639 0.000 0.621 -1.434 -0.072 0.000 1.070 -0.524 0.866 2.548 0.624 -0.593 -1.720 3.102 1.551 0.928 0.997 1.036 0.454 0.728 0.965 +1 1.744 0.759 0.988 0.542 0.325 1.105 0.264 -0.896 2.173 0.604 -0.602 0.459 2.215 0.331 0.498 1.736 0.000 0.399 -0.958 1.693 0.000 0.383 0.671 0.993 0.938 1.257 1.066 0.820 +1 1.543 -0.299 -1.358 0.792 -1.229 0.767 0.036 0.436 2.173 0.376 -1.499 0.181 0.000 0.765 -0.647 -1.600 0.000 0.770 -0.820 1.140 1.551 0.894 0.964 0.985 0.701 0.648 0.846 0.727 +1 0.796 1.813 1.170 0.609 -1.121 0.751 0.367 0.223 2.173 0.864 0.060 -0.996 2.215 0.957 1.161 0.446 0.000 0.867 0.036 -1.509 0.000 0.696 1.039 0.984 1.289 1.071 1.111 0.906 +1 1.018 0.391 0.618 1.363 -1.491 0.514 0.738 -1.185 0.000 0.371 -0.433 0.697 2.215 0.709 0.729 -0.125 0.000 0.986 -0.993 0.347 1.551 0.887 1.062 1.544 0.839 0.269 0.718 0.728 +1 0.745 0.432 0.523 1.164 1.223 1.871 1.400 -0.503 2.173 1.198 -1.655 1.100 0.000 1.353 0.944 -1.732 2.548 0.953 1.253 -1.196 0.000 0.639 0.733 0.989 0.986 1.812 1.466 1.101 +0 0.872 -0.695 -0.014 0.701 -0.579 0.858 -0.860 0.840 2.173 0.808 -1.560 -1.532 2.215 0.499 -2.007 0.941 0.000 0.864 -1.355 -0.890 0.000 0.752 0.884 0.984 1.123 1.132 0.954 0.838 +0 0.587 -0.621 1.007 1.651 -1.306 0.647 -0.756 -0.798 2.173 0.847 -0.109 0.867 2.215 0.676 1.191 0.901 0.000 0.537 -1.385 0.195 0.000 1.447 0.940 1.189 0.790 1.145 0.852 0.832 +0 1.119 -0.452 0.458 0.750 0.374 1.002 1.001 -1.561 0.000 0.568 1.142 1.091 0.000 1.056 1.120 -0.224 2.548 1.486 -0.703 -0.972 3.102 1.100 1.139 0.996 0.938 1.324 1.103 1.291 +1 0.677 0.195 -0.300 0.639 -1.232 1.061 -0.791 -1.246 2.173 0.289 2.112 0.299 0.000 0.797 -0.333 0.215 0.000 0.717 0.504 1.475 3.102 1.209 0.808 0.989 1.281 0.915 0.997 0.894 +0 0.760 -1.909 1.008 0.428 -0.981 0.894 -1.311 -0.647 1.087 0.558 -0.924 0.227 0.000 0.391 1.635 1.441 0.000 0.771 -0.935 1.593 3.102 0.782 0.972 0.995 0.534 0.797 0.644 0.603 +0 0.858 -1.415 0.799 2.026 0.184 1.542 1.646 -1.669 0.000 1.251 -0.466 -1.330 0.000 1.713 -0.543 0.263 1.274 1.866 -0.846 -0.411 3.102 0.813 0.938 0.988 0.861 0.832 0.714 0.691 +0 0.778 -0.136 -0.224 1.067 1.272 1.272 -2.125 -0.789 0.000 0.886 -1.116 1.299 2.215 2.398 -0.133 0.515 2.548 2.225 -0.488 -1.363 0.000 1.276 1.015 1.231 0.922 1.292 1.022 0.877 +1 1.520 -0.901 0.514 0.532 0.964 0.621 0.825 -1.570 2.173 0.765 -0.077 -0.729 0.000 0.399 0.605 -0.403 2.548 0.478 0.868 1.294 0.000 0.894 0.771 0.982 0.885 0.541 1.037 0.895 +1 0.404 -1.089 -1.274 2.084 -1.561 0.633 1.273 0.145 2.173 0.236 1.676 -0.411 0.000 0.711 0.169 0.882 0.000 0.425 -0.052 0.093 0.000 0.786 0.663 0.993 0.795 0.881 0.938 0.781 +1 1.612 0.476 0.066 0.285 -0.744 1.120 0.629 -1.673 1.087 0.613 0.540 -0.420 0.000 0.472 -0.093 1.651 0.000 1.740 0.308 1.062 3.102 0.924 1.119 0.989 1.266 0.945 1.025 0.993 +1 0.971 -0.658 0.081 0.805 0.734 0.982 -0.115 -1.250 2.173 0.502 0.160 -0.085 0.000 0.698 0.181 1.703 2.548 0.549 0.264 0.882 0.000 0.526 0.868 0.995 0.906 0.503 0.912 0.761 +0 0.767 1.529 -0.039 1.189 1.076 0.691 0.383 1.544 1.087 0.531 1.572 -0.523 0.000 0.618 -0.237 -1.240 2.548 0.741 -0.035 -0.088 0.000 0.821 0.993 1.116 0.987 0.550 0.791 0.744 +1 0.301 -1.149 -0.120 1.764 -0.871 0.520 -0.664 -0.983 0.000 0.890 0.621 0.765 2.215 0.567 -2.163 1.034 0.000 1.878 -0.658 0.913 3.102 1.331 1.094 0.983 1.074 0.920 1.025 1.008 +1 0.968 1.215 -1.037 0.483 -0.207 0.535 0.467 -0.105 0.000 0.667 1.409 0.371 0.000 1.338 0.146 1.401 2.548 0.785 -0.353 -1.328 3.102 0.834 0.928 0.987 0.902 0.546 0.776 0.718 +1 0.704 0.048 0.349 0.604 1.299 1.511 -0.145 -0.623 2.173 1.211 -0.434 0.779 0.000 0.438 0.899 -1.229 2.548 0.745 0.012 -1.615 0.000 1.071 0.877 0.988 1.198 0.802 0.971 0.859 +0 0.571 1.326 -1.342 0.465 0.854 0.597 0.708 -0.960 2.173 0.638 -0.862 1.306 2.215 0.681 0.389 -0.545 0.000 0.960 1.538 0.368 0.000 0.949 0.807 0.987 0.769 1.143 0.867 0.736 +1 1.068 1.916 0.911 0.905 0.564 1.374 1.058 -0.888 2.173 0.598 1.145 0.753 0.000 0.368 2.600 -1.536 0.000 0.734 0.931 1.625 3.102 0.928 1.267 0.975 0.620 0.817 0.932 0.814 +1 0.662 -1.046 -1.445 1.390 0.908 1.083 -0.347 0.305 2.173 1.506 2.686 -1.067 0.000 0.593 -2.265 1.547 0.000 1.176 -0.733 1.075 3.102 0.838 0.812 1.133 1.063 0.828 0.908 0.814 +1 1.696 -0.640 -0.789 1.464 -0.545 1.428 -0.677 0.603 2.173 0.466 -1.002 0.837 2.215 0.739 -1.617 -0.171 0.000 1.564 0.515 1.614 0.000 0.759 1.119 0.972 0.974 0.327 1.105 0.940 +1 1.091 0.472 -1.066 1.289 0.290 0.958 0.628 -1.556 0.000 1.207 0.427 0.642 1.107 0.721 1.285 0.081 0.000 0.700 1.088 -0.864 3.102 1.602 0.930 1.544 1.043 0.891 0.854 0.834 +1 1.447 -0.413 0.012 1.033 -0.945 0.897 -0.645 1.072 2.173 0.460 -0.931 -1.489 1.107 0.659 0.289 -1.513 0.000 0.720 -1.495 0.195 0.000 1.225 1.012 1.285 0.817 0.714 0.826 0.755 +0 0.750 0.588 0.398 2.234 0.575 0.813 -0.607 -0.803 2.173 0.885 -0.225 -1.421 0.000 1.170 -0.294 1.515 2.548 0.855 -1.325 -0.045 0.000 1.357 0.967 0.981 0.975 1.070 1.003 0.945 +0 0.474 0.540 -1.253 2.915 1.324 0.748 1.480 -0.392 0.000 1.125 -1.506 0.191 0.000 0.655 -1.844 -0.506 0.000 1.413 0.702 0.918 3.102 0.836 1.020 1.190 0.735 1.014 1.138 1.346 +0 0.344 -1.296 0.276 1.257 0.597 0.599 -0.066 1.674 2.173 0.716 1.144 -0.651 0.000 0.923 -0.472 -0.786 2.548 0.545 1.227 1.618 0.000 0.734 0.898 0.989 0.857 0.768 0.721 0.752 +1 1.435 -0.897 -0.695 1.221 -0.819 0.964 -0.248 1.445 2.173 0.825 -0.264 -0.413 0.000 1.354 -1.044 0.783 0.000 1.175 -1.565 0.442 0.000 0.908 0.916 0.998 0.694 0.475 0.839 0.872 +0 0.718 -0.014 -0.392 0.919 1.614 1.231 0.054 0.169 2.173 0.872 -1.130 1.693 2.215 1.031 -1.357 0.860 0.000 1.845 -1.180 -1.227 0.000 1.449 0.956 1.094 1.020 1.791 1.254 1.054 +1 1.278 1.183 0.901 0.237 -1.123 0.718 1.142 -0.407 0.000 0.630 1.348 0.280 0.000 0.839 -0.035 -1.624 2.548 1.093 0.423 -1.288 0.000 0.845 0.930 0.990 0.738 0.516 0.759 0.686 +0 0.738 -0.700 1.165 1.122 0.557 0.754 0.905 -0.558 2.173 0.375 0.285 -0.819 0.000 1.073 0.607 -1.532 2.548 0.512 0.565 1.196 0.000 0.565 0.583 0.991 0.902 0.873 0.863 0.668 +1 1.674 -0.675 0.718 1.564 1.139 0.502 -1.122 -0.583 0.000 1.063 -0.242 -1.110 2.215 1.062 0.870 -0.442 2.548 0.597 0.069 1.691 0.000 0.918 1.021 0.979 1.356 0.965 1.251 1.019 +0 1.360 0.986 1.067 0.376 -0.677 0.888 0.437 -0.861 2.173 1.347 1.525 0.111 2.215 1.326 0.351 1.610 0.000 1.224 0.346 -1.459 0.000 0.840 1.042 0.990 0.918 1.564 1.044 0.882 +1 0.403 -1.843 -0.391 0.840 -0.591 0.956 -1.662 0.906 0.000 1.180 -0.595 0.111 2.215 1.346 -1.809 -1.191 0.000 0.921 -0.232 -1.697 3.102 0.989 0.896 0.984 0.743 0.953 0.862 0.834 +0 0.474 -1.836 1.716 1.584 -1.732 1.074 -0.878 0.533 0.000 1.450 -0.802 -0.841 0.000 1.335 -0.694 -0.047 0.000 1.548 -0.554 1.296 3.102 0.872 0.678 0.977 0.806 0.808 0.618 0.601 +0 0.659 0.090 -0.285 1.295 0.752 0.678 0.685 -0.808 0.000 1.042 -0.114 -1.042 0.000 0.819 0.020 -1.659 1.274 0.996 -2.012 0.909 0.000 0.831 0.745 1.030 0.595 0.904 0.853 0.802 +0 0.793 -0.007 -1.605 1.278 0.044 1.074 1.432 0.130 1.087 1.913 -0.763 -1.285 1.107 0.977 2.610 1.514 0.000 1.603 -0.688 0.575 0.000 0.896 1.519 1.389 1.313 3.511 1.741 1.370 +0 1.829 0.021 1.555 2.846 -1.551 1.839 -0.557 0.085 2.173 0.861 -0.075 -0.480 0.000 0.862 2.436 -1.290 0.000 1.185 0.134 0.247 0.000 0.817 0.991 1.065 2.470 1.503 1.609 1.315 +0 0.779 0.270 -1.020 0.773 1.725 0.300 0.556 -0.073 0.000 0.790 -1.039 0.528 2.215 0.784 -0.249 1.584 2.548 0.557 0.640 -1.228 0.000 0.542 0.850 0.990 0.722 0.762 0.879 0.712 +1 2.224 0.801 0.972 0.917 -1.486 0.880 1.219 -0.242 2.173 1.085 0.151 -0.987 2.215 0.812 1.041 1.365 0.000 0.584 1.689 -0.342 0.000 0.835 1.016 1.584 1.385 1.208 1.140 0.931 +1 1.003 -1.132 1.153 1.325 1.322 0.727 -0.783 -0.284 1.087 0.391 -1.431 -0.199 2.215 0.575 -1.816 -1.077 0.000 0.704 -0.603 0.220 0.000 0.803 0.692 0.989 0.787 0.279 0.854 0.712 +1 1.340 -0.225 0.253 1.498 0.771 1.259 -0.343 -1.201 1.087 0.430 -0.642 -0.471 0.000 0.762 -1.597 1.000 0.000 0.529 -0.042 1.167 3.102 0.990 1.231 0.980 0.535 0.741 0.973 0.903 +0 0.449 1.190 -1.245 0.534 1.027 1.551 0.334 -0.557 2.173 1.287 -0.401 1.067 0.000 1.277 0.451 0.914 0.000 0.612 0.050 -0.974 3.102 0.962 0.877 0.988 1.063 0.405 1.162 0.933 +1 0.504 -1.356 0.964 1.089 0.401 0.551 0.889 1.357 1.087 1.251 0.266 -1.105 2.215 0.373 -1.322 -1.625 0.000 0.598 -1.334 0.420 0.000 0.504 1.002 0.985 2.223 1.047 1.860 1.337 +1 0.371 -0.753 1.642 1.145 0.949 0.849 0.913 -1.129 2.173 0.929 1.244 -0.539 0.000 1.283 0.677 0.577 2.548 1.078 0.498 1.395 0.000 0.857 0.850 0.998 1.007 1.305 0.821 0.753 +1 0.718 1.295 -0.166 0.548 -1.369 0.538 1.438 -1.523 0.000 0.674 -0.940 0.146 2.215 0.599 1.589 0.875 0.000 0.651 0.556 1.096 3.102 0.860 0.587 0.988 0.921 0.708 0.881 0.760 +0 0.731 0.197 1.122 0.999 0.205 0.607 -0.271 -0.595 0.000 1.349 -0.302 1.693 2.215 1.114 -0.478 -0.052 2.548 0.611 0.506 -1.610 0.000 0.850 0.970 0.982 0.654 1.310 0.804 0.742 +1 0.469 -1.268 1.090 1.136 0.012 0.974 -0.539 0.820 2.173 1.046 -0.271 -1.049 0.000 1.117 0.659 -1.378 2.548 0.452 2.279 -0.329 0.000 1.999 1.220 0.987 0.753 1.481 1.238 1.079 +0 0.592 0.410 -1.529 1.128 -0.815 1.011 1.054 -0.189 2.173 1.502 0.514 1.044 2.215 0.904 0.431 0.365 0.000 1.622 -1.923 -1.509 0.000 2.846 2.541 0.981 0.899 1.697 2.076 1.785 +1 0.996 -0.710 0.155 0.892 1.351 1.438 -0.756 1.580 2.173 0.879 -1.360 -0.192 0.000 0.414 -1.924 0.316 0.000 1.450 -0.301 -0.287 3.102 0.530 0.663 1.150 1.045 1.549 1.035 0.886 +1 0.753 0.827 0.749 0.402 -0.597 0.902 0.002 -0.110 0.000 1.199 0.328 -1.603 0.000 1.122 0.099 0.516 2.548 0.551 0.112 -0.527 3.102 2.185 1.166 0.984 0.815 0.486 0.825 0.823 +1 1.018 1.080 0.393 0.716 0.980 0.873 0.149 0.910 0.000 0.767 0.873 -1.196 0.000 1.795 0.995 -0.645 1.274 0.554 -0.194 -1.293 3.102 1.149 0.786 0.984 1.095 0.687 0.833 0.852 +1 2.620 -0.787 -0.453 0.539 -0.867 0.926 -0.026 0.834 2.173 0.729 -2.047 -1.186 0.000 1.674 0.568 1.236 0.000 0.706 -0.617 -1.040 0.000 0.803 0.884 0.978 0.556 0.771 0.902 0.765 +1 2.149 0.844 0.295 0.389 -1.740 0.879 -0.103 1.675 0.000 1.261 0.705 -0.822 1.107 0.500 1.281 -1.586 0.000 0.484 -0.626 -1.514 3.102 1.017 1.169 1.224 0.862 0.703 0.815 0.859 +0 0.731 -0.363 -0.142 1.813 0.676 0.459 1.213 -0.532 0.000 0.513 2.070 0.477 0.000 1.956 -0.543 -1.459 2.548 1.015 0.169 -1.603 3.102 0.945 0.958 1.073 1.308 0.464 1.143 1.135 +0 0.417 -1.590 0.521 1.357 -1.110 0.624 0.461 -0.769 0.000 0.784 -0.448 1.557 2.215 0.966 0.288 0.701 0.000 0.573 -0.059 0.135 3.102 1.358 1.108 1.037 0.737 0.592 0.669 0.857 +0 2.342 1.059 1.722 0.424 1.623 0.906 0.886 -0.445 2.173 1.128 1.518 0.277 0.000 0.500 1.241 0.674 2.548 0.373 1.601 -0.385 0.000 0.830 0.948 0.981 0.700 0.736 0.877 0.824 +1 1.601 0.998 1.435 1.008 -1.328 0.584 1.886 -0.453 0.000 0.524 -0.216 1.558 2.215 0.802 1.009 -0.224 2.548 0.752 1.143 -1.417 0.000 0.819 1.077 1.068 0.902 0.847 0.705 0.735 +0 0.392 1.378 1.567 0.171 1.657 0.864 0.888 1.480 2.173 1.201 0.949 -0.301 2.215 1.277 0.403 0.507 0.000 0.506 -0.554 -0.706 0.000 0.942 0.945 0.984 0.780 1.499 0.917 0.803 +1 1.283 -0.376 1.243 1.343 0.732 0.810 -0.356 -1.151 2.173 0.695 2.033 -0.577 0.000 1.039 -0.166 0.395 0.000 1.247 0.797 -0.559 3.102 0.909 0.994 0.987 1.189 0.927 1.047 0.876 +1 1.168 -0.405 1.234 0.981 1.270 0.961 1.449 -0.624 0.000 0.817 0.830 1.583 2.215 2.374 -0.999 -0.755 2.548 0.489 -0.386 0.520 0.000 1.253 0.985 0.975 1.400 2.112 1.587 1.482 +1 0.805 -1.106 -0.142 0.461 -0.063 0.550 -2.857 -0.241 0.000 0.332 2.482 0.587 0.000 0.979 -0.514 0.890 2.548 2.005 -0.310 -1.515 3.102 0.934 1.098 0.985 0.734 0.891 0.875 0.854 +0 0.668 -0.759 -0.651 1.006 0.633 1.151 -0.233 -0.701 2.173 1.397 0.741 1.411 1.107 0.528 0.042 1.402 0.000 0.648 0.211 -0.363 0.000 1.107 1.103 1.040 1.295 2.012 1.299 1.076 +0 0.569 -1.719 -0.297 0.940 -0.971 1.159 1.841 1.116 0.000 1.190 -0.052 1.528 2.215 1.437 -0.257 0.073 0.000 1.379 -2.480 -1.250 0.000 0.928 0.733 0.995 1.050 1.417 0.936 0.828 +1 0.581 0.390 -1.349 0.917 0.055 0.513 -0.528 1.547 0.000 0.965 -1.286 0.096 2.215 0.772 0.850 1.657 0.000 0.778 -1.116 -1.395 3.102 0.913 0.807 0.986 0.931 0.762 0.883 0.791 +1 1.238 -0.305 1.241 0.539 -0.440 0.839 -2.620 -1.243 0.000 0.510 0.901 -0.070 0.000 1.298 -0.612 -1.525 2.548 2.198 -0.038 0.129 0.000 0.740 0.843 1.129 0.779 0.923 0.874 0.774 +0 2.246 0.114 -0.374 0.575 -0.633 1.481 0.635 -1.590 0.000 1.319 0.993 0.432 2.215 1.371 -0.638 0.147 2.548 2.085 -0.381 1.557 0.000 0.611 1.000 0.994 1.354 1.435 1.087 1.045 +0 0.385 -1.293 1.299 1.246 1.243 0.509 0.022 -0.274 0.000 0.381 1.422 -1.048 0.000 0.585 -0.046 0.206 2.548 0.750 -0.573 0.851 3.102 0.948 0.838 0.972 0.656 0.323 0.530 0.608 +1 0.759 0.450 -0.263 1.068 0.767 0.356 0.536 -0.498 2.173 0.711 -1.272 1.471 2.215 0.937 -1.563 -0.940 0.000 0.833 1.007 1.413 0.000 0.697 0.894 0.998 1.079 1.071 0.793 0.824 +0 3.409 -0.390 0.291 1.448 0.591 1.895 -0.905 -1.437 1.087 0.379 0.392 -0.469 2.215 0.766 1.237 -1.177 0.000 0.658 -0.812 -0.843 0.000 0.757 1.027 0.986 0.884 1.300 1.594 1.191 +1 1.703 -0.370 -1.020 0.819 -1.061 0.637 0.590 0.093 2.173 1.171 0.081 0.789 0.000 0.760 0.355 1.075 2.548 0.829 0.163 -1.733 0.000 0.982 0.929 0.976 0.900 0.677 0.805 0.803 +0 6.067 -0.106 0.526 1.353 -0.793 2.974 -0.253 -0.876 2.173 1.201 -1.684 1.612 0.000 1.581 -0.873 -1.261 0.000 0.753 -0.254 0.927 3.102 1.390 1.065 3.682 3.566 1.582 2.186 2.097 +1 0.643 -1.963 0.790 0.862 -1.660 0.762 0.236 -0.785 2.173 0.378 -1.384 1.607 0.000 1.146 -0.073 0.562 2.548 0.462 -2.013 0.229 0.000 0.587 0.840 0.984 1.962 1.107 1.468 1.101 +1 0.625 0.939 0.327 1.252 -0.750 1.544 -0.923 -1.731 0.000 0.737 -0.239 -0.549 0.000 1.742 -0.626 0.659 2.548 1.876 -0.851 0.109 3.102 0.936 0.986 1.011 1.279 0.697 1.086 0.897 +1 2.660 0.241 -1.365 0.623 0.512 1.043 0.742 1.058 0.000 0.918 -1.016 0.120 1.107 1.930 -1.525 -0.380 0.000 0.949 -0.525 1.488 3.102 0.628 0.722 1.770 1.516 0.814 1.004 0.991 +1 0.686 1.039 1.628 1.186 0.767 0.679 0.481 -0.771 0.000 0.907 -0.063 -0.320 2.215 1.308 -0.233 -1.415 2.548 0.545 0.385 1.043 0.000 0.927 0.818 0.987 0.963 0.972 0.827 0.732 +1 1.086 0.966 -0.067 0.992 1.197 0.911 0.217 -0.697 2.173 0.652 1.786 -1.730 0.000 1.170 0.714 1.601 0.000 1.331 0.414 0.357 3.102 0.763 1.008 1.307 1.124 0.962 0.941 0.864 +0 0.759 1.417 1.312 1.406 -1.574 0.833 1.324 -0.237 2.173 0.407 2.524 -1.313 0.000 0.628 1.438 0.412 2.548 0.730 1.594 0.091 0.000 0.958 0.885 0.991 0.771 0.509 0.818 0.704 +0 1.809 -0.096 1.147 0.382 -1.229 0.685 1.531 0.362 0.000 0.666 0.931 -1.224 2.215 0.767 0.164 -0.362 0.000 0.749 -0.776 -0.852 3.102 1.245 1.039 0.986 0.737 0.727 0.822 0.858 +1 0.965 -1.023 1.664 0.447 1.214 1.124 -0.504 0.021 2.173 0.830 -0.544 1.350 0.000 0.658 -1.076 -0.990 2.548 0.674 -1.400 -0.704 0.000 1.099 0.748 1.000 1.144 0.923 0.817 0.761 +1 0.513 0.943 1.607 1.385 -1.648 0.556 1.589 0.566 2.173 0.570 -0.014 -1.454 0.000 1.605 0.060 -0.138 0.000 0.603 0.449 0.257 1.551 1.362 0.801 0.977 0.908 0.381 0.764 1.002 +0 1.125 0.391 -1.106 0.286 -0.160 0.783 0.796 0.550 2.173 0.691 0.117 0.648 2.215 0.422 1.726 -0.894 0.000 0.781 0.946 1.600 0.000 0.812 0.925 0.980 0.795 0.389 0.728 0.679 +1 0.836 -0.253 -0.008 1.122 -1.322 0.537 -1.391 -0.035 0.000 1.045 0.046 1.432 1.107 0.756 1.400 0.595 0.000 0.586 0.869 -0.921 3.102 0.763 0.841 1.242 0.935 0.705 0.785 0.726 +1 0.305 0.267 -0.111 0.678 0.813 1.114 -1.429 -1.728 0.000 1.401 -0.736 -0.603 0.000 0.515 -1.662 1.131 0.000 1.051 -0.828 0.720 1.551 0.773 0.918 0.997 0.541 0.378 0.636 0.604 +1 0.537 -0.973 -0.549 1.827 1.549 0.965 -1.384 0.461 0.000 1.491 -1.086 -1.328 2.215 1.065 1.502 -0.239 0.000 0.952 -0.937 1.169 3.102 0.484 1.362 1.302 0.683 0.836 0.840 0.799 +0 0.467 0.585 -1.522 2.223 1.341 2.053 0.858 1.129 0.000 3.993 -0.465 -0.346 0.000 1.053 -1.054 -1.441 2.548 1.037 1.484 -1.062 0.000 2.304 1.400 0.997 1.770 0.919 1.417 1.254 +0 0.886 1.185 1.065 1.818 0.495 1.086 -0.379 -0.803 2.173 0.409 0.327 1.451 0.000 0.775 0.556 0.115 1.274 1.163 1.320 -1.626 0.000 1.003 0.765 0.984 2.125 1.029 1.342 1.083 +1 1.226 -0.925 -1.634 1.048 0.239 0.872 -0.574 0.053 2.173 0.849 -0.658 -1.257 0.000 1.116 -0.064 0.806 2.548 0.816 -1.385 1.576 0.000 0.839 0.920 1.559 0.994 0.833 0.823 0.731 +0 0.645 -0.635 0.250 1.541 -0.836 0.607 -1.067 1.393 0.000 0.727 -0.678 0.672 1.107 0.715 -0.139 -0.664 2.548 0.412 0.189 -1.465 0.000 0.942 0.844 1.146 0.882 0.746 0.629 0.682 +1 1.227 0.305 -0.007 0.053 1.055 1.924 -1.222 1.077 0.000 2.580 1.001 -0.383 2.215 1.558 1.097 -1.411 0.000 1.262 0.261 -1.116 3.102 0.558 0.593 0.937 1.211 1.163 0.913 0.918 +0 3.000 0.595 -1.143 0.286 0.726 0.985 -1.055 0.158 0.000 0.528 -0.814 1.258 2.215 0.335 2.681 -0.565 0.000 0.695 0.369 -1.694 0.000 0.966 1.209 1.274 1.492 0.440 1.057 1.042 +1 0.825 0.634 0.124 0.545 -0.789 1.098 -0.240 -1.390 1.087 1.113 0.281 1.102 0.000 1.077 0.234 0.530 1.274 1.329 0.087 -0.431 0.000 1.563 0.994 0.980 0.982 1.380 0.974 0.839 +0 2.047 -0.013 -0.207 0.522 -0.897 0.932 -1.085 1.225 1.087 1.249 -0.972 1.545 0.000 0.986 -0.543 -1.440 2.548 1.034 0.003 0.069 0.000 1.380 1.118 0.983 1.512 0.858 1.056 1.018 +0 2.845 -1.917 -0.008 0.431 -0.350 1.209 -0.040 1.537 2.173 0.425 -0.941 -1.648 2.215 0.609 -0.593 -0.113 0.000 0.740 0.327 -1.468 0.000 0.805 0.940 0.976 0.968 0.580 1.455 1.154 +1 1.950 0.475 0.793 0.076 -1.070 0.575 -2.178 -1.684 0.000 0.962 -0.113 -0.747 1.107 0.336 1.134 -0.055 0.000 0.874 -0.009 0.266 3.102 0.795 0.989 0.987 1.119 0.657 0.843 1.076 +0 1.075 -0.872 1.082 0.660 0.189 1.434 -1.452 -1.460 2.173 1.472 -0.490 0.051 2.215 0.457 -0.861 -0.822 0.000 0.888 -1.148 1.308 0.000 0.678 0.926 0.986 1.198 2.348 1.226 0.930 +1 0.333 1.779 0.070 1.431 -0.077 1.082 -0.065 -0.269 0.000 2.209 0.935 -1.606 0.000 2.272 0.104 0.976 2.548 1.346 0.864 0.484 3.102 1.210 1.360 0.975 1.129 0.859 1.166 1.101 +1 0.455 -1.187 0.661 2.285 0.138 1.165 -0.943 1.411 0.000 2.011 0.426 -0.801 2.215 0.727 0.666 1.662 2.548 0.908 0.857 0.642 0.000 2.082 1.331 0.987 1.551 1.041 1.370 1.281 +0 1.336 0.635 -0.449 0.724 0.378 1.004 -0.661 1.712 0.000 0.408 0.208 -1.299 0.000 0.925 -0.108 0.537 2.548 0.685 -0.817 -1.265 3.102 0.836 0.990 0.987 0.784 0.665 0.616 0.762 +0 0.699 -0.868 0.562 1.028 1.283 0.716 -0.144 -0.818 0.000 0.894 -0.264 1.654 2.215 1.019 0.554 -0.310 1.274 0.612 0.166 0.478 0.000 0.946 0.910 0.986 1.364 1.095 1.016 0.912 +0 0.899 -1.004 -0.112 0.621 0.868 0.861 -1.034 -1.707 1.087 0.942 -0.486 0.195 2.215 0.580 0.883 -0.144 0.000 1.289 -0.169 -1.310 0.000 1.017 0.975 0.989 0.973 1.362 0.922 0.891 +0 1.803 -0.892 0.053 0.736 -0.340 1.116 -1.420 -0.361 0.000 2.177 -0.331 1.409 2.215 1.370 -1.347 -1.695 2.548 0.415 -1.849 -1.699 0.000 1.058 1.147 0.986 1.804 1.264 1.314 1.219 +1 0.518 -0.616 0.407 0.686 -1.274 1.295 0.813 0.575 0.000 1.403 -1.609 1.461 0.000 3.306 -1.150 -0.372 1.274 2.111 -0.349 -1.233 1.551 0.579 1.071 0.986 1.336 1.664 1.263 1.105 +0 0.530 -0.330 -0.209 0.772 -1.498 0.330 -0.054 -1.228 0.000 1.242 0.192 -0.376 2.215 1.449 -0.409 1.246 1.274 0.428 2.266 1.434 0.000 1.145 1.146 0.986 0.862 1.493 1.021 0.836 +0 0.973 -1.433 0.925 0.681 -0.881 0.930 0.844 -1.415 2.173 0.756 0.122 -0.451 0.000 0.936 0.524 0.684 2.548 0.452 -0.253 1.380 0.000 0.927 1.026 1.126 1.097 1.115 1.199 1.077 +0 1.442 0.728 0.963 0.074 -1.734 0.840 0.090 -1.242 2.173 0.534 0.448 -0.247 0.000 1.250 0.016 0.201 2.548 0.647 -2.187 -0.948 0.000 0.853 0.904 0.984 0.760 1.231 0.826 0.708 +1 0.951 2.086 0.781 0.506 -0.813 1.241 0.934 -0.073 2.173 0.898 -2.554 1.363 0.000 0.746 0.347 -0.784 0.000 1.348 1.558 -1.684 0.000 1.207 0.766 0.988 1.017 0.945 0.841 0.782 +0 0.407 -0.196 -0.381 1.596 -1.649 0.908 1.446 0.231 0.000 0.731 -1.539 1.512 0.000 0.495 -0.789 0.203 0.000 0.677 2.424 -0.132 0.000 0.917 0.852 1.015 0.896 0.597 0.907 0.792 +0 1.620 -0.612 -0.866 0.129 1.166 0.573 -0.460 0.499 2.173 0.801 0.472 -1.462 0.000 0.378 0.110 -0.326 0.000 1.540 0.642 1.048 3.102 0.737 0.903 0.992 1.208 0.806 0.892 0.789 +0 1.027 2.419 -0.386 2.278 0.987 0.735 1.310 0.744 1.087 0.569 1.418 1.345 0.000 1.273 0.556 1.563 2.548 0.471 -0.377 0.464 0.000 0.889 0.719 2.004 1.749 0.921 1.221 1.040 +0 0.630 -0.699 0.366 0.684 -0.442 0.802 0.057 0.522 0.000 1.350 -1.436 -1.481 2.215 0.529 -0.602 1.606 2.548 0.548 -0.404 -1.031 0.000 1.036 0.752 0.994 1.357 0.491 0.885 0.848 +0 0.287 -0.084 0.945 1.606 1.096 2.767 -1.438 1.397 2.173 3.320 -0.742 -0.536 0.000 1.811 -0.781 -0.066 2.548 1.377 -1.342 -0.521 0.000 1.218 1.073 0.984 0.939 2.829 2.267 1.790 +0 1.830 -1.026 -1.290 0.601 -1.187 1.151 -0.762 0.224 2.173 1.188 -0.612 1.176 2.215 0.404 -0.274 -0.567 0.000 0.408 -1.537 0.183 0.000 0.474 0.729 0.980 1.413 1.307 1.116 0.841 +1 1.284 -0.304 -1.009 1.383 1.674 1.039 -1.248 -0.509 0.000 0.718 0.078 -0.588 0.000 1.188 -0.231 0.254 2.548 2.875 -0.040 1.029 3.102 0.824 1.069 1.222 1.094 0.920 0.953 0.835 +0 1.873 -0.405 -0.734 5.037 -0.783 2.091 -1.926 0.908 0.000 0.794 1.952 0.919 0.000 1.490 0.587 1.362 2.548 1.089 0.420 -1.185 3.102 0.896 0.935 1.017 0.781 0.732 1.226 1.551 +1 0.941 0.719 0.277 2.166 0.508 1.637 -1.743 -1.687 0.000 1.249 -0.130 -0.279 0.000 0.685 -0.511 1.234 2.548 1.235 0.005 -1.189 3.102 0.991 0.926 0.996 1.069 0.609 0.765 0.767 +0 2.112 0.773 -1.230 0.451 0.127 0.948 -0.661 0.476 1.087 0.323 0.727 1.659 0.000 0.480 -0.260 1.415 2.548 0.562 -1.530 -0.449 0.000 1.072 0.998 1.272 0.795 0.650 1.017 0.884 +1 1.164 1.204 -0.343 1.028 0.109 1.750 1.018 -0.532 0.000 1.141 0.499 1.572 0.000 1.365 0.763 1.015 2.548 0.898 1.016 1.642 0.000 0.482 0.642 0.983 0.975 0.352 0.624 0.717 +0 0.623 0.531 0.124 1.550 1.079 0.898 0.180 1.689 1.087 1.272 -0.578 -0.446 2.215 0.696 -0.136 0.290 0.000 0.711 0.746 -1.622 0.000 0.877 0.958 1.032 0.908 1.602 1.069 0.859 +1 0.322 1.112 -0.512 1.270 0.863 0.491 -1.520 -0.488 0.000 1.224 -0.968 -1.178 0.000 1.152 -0.406 1.026 1.274 1.006 0.803 0.041 3.102 1.058 1.245 0.984 0.696 0.896 1.036 1.532 +0 0.824 0.028 1.210 1.445 0.720 1.552 1.340 -0.965 2.173 2.269 0.800 0.531 1.107 1.594 0.769 -1.359 0.000 0.810 -1.336 -1.075 0.000 0.816 0.873 0.999 0.809 2.792 1.487 1.222 +1 0.612 1.032 -0.800 0.748 1.451 0.852 0.642 0.980 0.000 1.496 0.254 -0.794 2.215 1.131 0.540 0.007 2.548 0.859 1.372 0.467 0.000 0.897 0.874 0.989 0.830 0.942 0.977 0.831 +1 0.727 -0.875 -1.518 0.727 -0.390 0.900 0.075 0.537 2.173 0.674 0.996 -0.795 1.107 0.408 0.889 1.239 0.000 0.462 -0.494 -0.011 0.000 0.600 0.620 0.990 0.945 1.208 0.813 0.645 +1 1.042 -1.986 -1.397 0.952 -0.878 0.899 -1.170 0.376 2.173 0.501 -0.424 0.671 0.000 0.738 2.352 -1.695 0.000 0.811 -1.434 -0.021 0.000 0.854 0.807 0.976 0.792 0.641 0.816 0.698 +0 0.999 1.453 -1.555 0.359 0.825 0.825 1.195 0.438 2.173 1.129 0.549 1.244 2.215 1.372 1.919 -0.614 0.000 1.122 0.726 -0.320 0.000 0.970 1.099 0.991 0.713 1.050 1.035 0.885 +0 3.618 -0.465 0.565 4.835 0.336 4.140 0.036 -1.378 0.000 0.352 0.531 1.739 0.000 1.077 -0.133 -0.872 2.548 0.950 -0.229 -0.157 3.102 1.066 1.048 1.185 0.881 0.467 1.096 2.152 +0 0.332 1.310 -1.286 1.285 -0.030 2.277 0.658 -0.628 2.173 2.259 1.037 1.217 2.215 0.781 1.856 1.392 0.000 0.820 0.134 1.215 0.000 0.929 0.859 0.988 1.746 3.391 1.844 1.422 +0 0.571 1.152 0.449 0.791 1.593 0.531 0.073 0.775 2.173 0.722 1.570 -1.247 0.000 1.517 0.635 -0.640 2.548 0.605 -1.621 0.705 0.000 2.624 1.605 0.989 0.881 1.127 1.140 0.934 +0 0.478 1.680 1.198 2.412 1.349 1.775 -1.052 -0.650 2.173 1.017 0.053 0.900 2.215 1.296 -0.732 -0.072 0.000 0.446 -1.087 -1.197 0.000 0.743 0.988 0.990 6.583 2.268 4.134 3.190 +0 1.328 0.701 0.777 0.836 -0.270 1.105 0.059 -0.799 2.173 0.241 -1.497 0.845 0.000 0.315 -0.604 1.663 0.000 0.841 0.625 1.336 3.102 0.365 0.912 1.181 0.691 1.022 0.844 0.787 +1 0.895 0.999 0.482 0.778 -1.540 1.073 0.445 0.691 0.000 1.331 0.729 -1.598 1.107 0.720 0.881 0.049 0.000 1.744 0.309 -0.704 1.551 0.960 1.157 1.120 0.858 1.026 1.018 0.865 +1 1.148 -0.766 1.489 1.176 0.647 1.364 0.217 -0.663 2.173 0.945 -0.117 1.154 0.000 0.761 0.970 0.377 2.548 0.737 -0.528 -1.308 0.000 0.912 0.914 1.107 1.598 1.163 1.165 0.989 +0 0.664 -0.190 -0.437 1.647 -1.126 0.681 0.062 0.669 0.000 1.138 0.301 -0.483 0.000 2.077 -0.143 1.180 2.548 0.495 0.566 0.213 3.102 1.629 0.875 0.986 1.287 0.680 0.864 0.886 +0 1.102 1.059 0.257 0.428 1.388 0.595 0.527 -0.508 1.087 0.577 0.528 0.054 0.000 1.268 0.743 1.722 2.548 0.635 1.360 -1.128 0.000 0.824 0.829 0.991 0.742 0.991 0.700 0.619 +1 0.517 -0.862 0.411 0.951 0.500 1.804 -0.179 -0.641 0.000 1.324 1.172 1.297 2.215 1.105 0.230 0.732 0.000 2.076 0.800 -1.547 3.102 0.968 1.281 0.990 1.046 0.839 1.003 0.904 +1 0.881 -0.557 0.838 2.421 1.349 3.062 -0.162 -0.253 0.000 1.425 -0.508 1.668 0.000 1.334 -0.248 -1.198 2.548 1.484 0.373 1.282 3.102 4.452 2.613 0.996 0.627 0.935 1.712 1.557 +1 1.176 -0.152 0.820 0.838 -0.125 0.646 1.004 0.932 2.173 0.892 -1.545 -0.046 0.000 1.731 1.129 -0.870 0.000 1.762 0.580 1.607 3.102 1.003 1.005 1.034 0.866 0.669 0.794 0.898 +1 0.917 0.023 -1.116 1.062 1.622 0.773 -0.634 1.183 0.000 1.307 0.055 0.939 0.000 1.712 0.154 -0.453 0.000 1.092 -0.298 -0.752 3.102 0.926 0.980 0.991 0.845 0.492 0.680 0.707 +1 0.285 1.180 -1.395 0.874 -0.100 1.047 -2.411 -1.456 0.000 0.906 -0.403 -1.228 0.000 1.665 -1.129 0.549 1.274 1.266 -0.444 1.465 0.000 0.918 0.992 0.995 0.943 0.597 0.856 0.766 +0 0.615 0.321 -1.440 0.940 0.117 0.659 -1.756 -0.873 0.000 0.574 -1.221 0.205 2.215 0.429 -0.208 1.479 0.000 1.329 1.066 1.215 3.102 1.152 0.881 1.039 0.785 1.434 1.158 0.983 +0 0.849 0.025 0.907 0.311 -0.883 0.814 0.406 0.112 0.000 0.390 -2.292 0.965 0.000 1.352 0.548 -1.257 2.548 0.413 1.892 -0.914 0.000 0.885 0.996 0.992 0.926 1.063 0.845 0.751 +0 0.294 -0.629 0.163 2.151 1.221 1.490 -0.007 -0.838 1.087 0.850 0.225 0.611 1.107 0.867 -0.469 -0.329 0.000 0.511 0.436 -1.707 0.000 0.801 0.809 0.982 1.064 1.610 1.363 1.054 +1 0.620 0.702 0.930 0.622 -0.923 1.023 0.645 -1.191 2.173 0.534 1.050 0.506 0.000 1.407 -0.002 0.496 0.000 1.170 0.452 -0.045 0.000 0.769 0.991 0.985 0.839 0.797 0.928 0.835 +0 0.548 -0.322 0.010 1.802 -1.035 0.598 1.508 0.557 1.087 0.970 0.880 1.381 2.215 0.521 -0.267 -0.805 0.000 0.396 2.116 -1.478 0.000 1.013 0.936 1.113 1.221 0.835 1.103 0.923 +0 2.205 -0.455 -0.427 0.647 -0.381 0.949 -1.460 -1.681 2.173 1.238 -1.046 1.401 0.000 1.317 -1.515 1.001 0.000 1.864 -0.953 -0.049 3.102 0.905 0.914 0.991 0.661 1.420 1.017 1.096 +0 0.721 0.826 -1.392 1.266 1.213 1.075 1.297 0.154 0.000 0.837 -0.933 -1.317 0.000 1.256 -1.335 -0.773 1.274 1.878 -0.874 -0.271 3.102 0.722 2.096 0.990 1.424 0.569 1.793 1.475 +1 0.721 0.562 -0.322 1.328 -1.392 0.858 0.966 0.977 2.173 0.994 1.559 -0.548 0.000 1.513 -0.135 0.304 2.548 0.837 -0.252 1.456 0.000 0.800 0.881 1.113 1.063 1.166 0.946 0.776 +1 0.790 -0.268 -1.410 0.680 -0.373 0.989 -0.245 0.137 0.000 1.361 0.623 -1.661 2.215 0.775 0.082 0.790 2.548 0.575 0.437 -0.362 0.000 0.677 0.640 0.981 1.120 0.927 0.903 0.852 +0 0.618 -1.474 0.951 0.628 -0.559 0.960 -1.308 1.498 2.173 0.710 0.138 1.406 0.000 1.552 0.321 -0.597 2.548 1.267 -0.188 -0.431 0.000 0.879 0.803 0.990 0.913 2.030 1.269 1.085 +1 0.792 0.723 -0.290 0.843 0.910 0.680 1.042 1.454 0.000 1.246 1.393 -0.379 0.000 1.099 0.319 -1.216 1.274 0.720 0.775 0.322 3.102 1.983 1.134 1.000 0.800 0.698 0.839 0.744 +0 1.860 1.421 1.642 1.251 -0.864 0.983 -0.763 0.147 1.087 0.814 -0.613 0.684 0.000 0.306 0.007 1.657 0.000 0.616 -0.032 -1.125 3.102 0.639 0.739 1.635 0.941 0.811 1.467 1.209 +0 2.804 0.565 -1.336 1.919 -0.972 1.694 0.491 0.428 2.173 1.404 0.029 0.801 2.215 0.502 -0.445 -0.925 0.000 0.517 -0.515 -0.082 0.000 0.389 1.024 1.037 1.822 0.918 1.705 1.265 +0 2.910 1.083 1.245 1.379 1.296 2.551 -0.269 -0.662 0.000 0.688 1.028 0.558 2.215 0.565 -0.904 0.096 2.548 0.665 -1.428 0.953 0.000 2.519 1.474 0.982 0.847 0.848 1.272 1.889 +1 0.861 -0.359 0.845 0.846 0.155 0.820 -1.640 -0.177 0.000 1.087 -1.049 -1.540 0.000 1.652 -1.230 1.515 0.000 0.968 0.549 0.551 3.102 0.826 0.651 0.985 0.783 0.586 0.812 0.790 +0 0.956 -0.152 -1.150 0.555 -1.418 1.297 -1.710 -0.251 0.000 1.170 -0.117 0.858 0.000 1.392 0.198 1.643 2.548 0.711 -0.598 -1.183 1.551 1.068 0.921 0.990 0.882 0.563 1.169 0.985 +0 0.596 -1.391 1.520 1.163 -0.743 1.394 0.759 0.781 0.000 0.900 -0.589 -0.682 1.107 1.520 -1.228 0.126 2.548 0.853 0.264 1.446 0.000 1.012 1.611 1.029 0.872 0.953 1.396 1.300 +1 1.526 -0.977 0.157 0.228 0.633 1.495 0.267 -0.743 0.000 1.683 -1.606 1.627 0.000 0.707 -2.103 1.176 0.000 0.945 -1.581 0.180 0.000 0.866 0.844 0.988 1.050 0.639 1.056 0.977 +0 0.593 0.032 0.394 0.441 0.554 0.380 -0.361 1.396 0.000 0.892 0.491 -1.609 0.000 1.050 0.866 0.114 0.000 1.215 0.780 -1.108 3.102 0.958 0.669 0.991 0.555 0.175 0.495 0.555 +0 0.986 -0.946 -1.270 0.806 0.198 0.852 0.076 -0.508 2.173 0.750 0.074 -1.498 0.000 0.867 0.000 0.397 2.548 0.497 -1.956 0.656 0.000 1.426 1.057 1.198 0.956 0.780 0.856 0.786 +1 0.921 0.034 0.288 0.574 1.675 0.503 0.319 -0.303 0.000 0.805 -0.121 1.683 2.215 1.029 0.927 1.167 2.548 0.885 -0.516 -0.630 0.000 0.579 0.994 0.988 0.695 0.727 0.741 0.666 +1 0.611 1.623 -0.025 0.763 1.499 0.712 0.973 0.684 0.000 0.909 1.810 -1.112 0.000 1.402 0.022 -1.252 2.548 0.952 0.029 0.121 3.102 0.857 1.113 0.990 0.700 0.834 0.712 0.682 +0 1.207 -0.395 -1.344 0.876 -0.429 0.853 1.022 0.542 2.173 0.410 -1.478 1.344 0.000 0.531 -0.216 -0.400 2.548 0.454 -0.115 -1.573 0.000 0.503 1.235 1.047 0.558 0.848 0.869 0.784 +1 0.561 0.541 -0.475 0.494 0.499 1.439 0.775 0.797 0.000 1.218 0.362 -1.458 0.000 1.705 -0.470 -0.501 2.548 1.245 -0.982 -1.260 3.102 2.583 2.069 0.982 0.708 0.797 1.515 1.187 +1 1.959 0.669 1.468 0.814 0.414 0.773 0.094 -0.952 2.173 1.370 -0.065 0.060 2.215 0.785 -0.271 1.280 0.000 0.432 2.020 -1.011 0.000 1.300 1.092 1.422 1.336 1.204 1.091 0.966 +0 0.543 0.367 -1.562 0.658 1.292 1.034 0.882 0.056 2.173 1.147 -1.689 -1.376 0.000 0.511 -2.201 -1.110 0.000 0.450 -0.744 0.245 0.000 1.016 0.687 0.999 1.074 0.870 1.320 1.362 +0 1.132 0.770 -1.601 2.048 1.131 0.575 -0.119 -0.270 0.000 0.856 0.511 -0.576 2.215 1.056 -1.033 -0.761 0.000 1.242 0.393 0.474 3.102 0.955 1.007 1.326 1.208 0.755 0.866 1.028 +1 0.992 0.323 1.360 1.309 1.312 0.792 -0.236 -0.180 2.173 0.714 1.669 0.099 0.000 0.366 0.148 0.004 2.548 0.494 -0.639 -1.375 0.000 1.269 1.181 0.991 0.682 0.175 0.761 0.865 +0 1.263 0.591 1.721 0.541 -1.539 0.917 -0.626 0.873 2.173 0.972 -0.950 0.014 0.000 0.949 0.442 -0.122 0.000 1.157 -0.602 -1.547 3.102 0.886 0.838 0.975 1.029 0.894 0.808 0.780 +1 0.613 0.834 1.542 1.257 0.423 1.187 0.826 -1.292 0.000 1.176 0.306 0.291 1.107 0.461 0.964 -0.492 0.000 1.263 -0.080 1.586 3.102 0.891 0.908 1.029 0.735 1.037 0.941 0.848 +0 1.766 -0.540 1.634 0.886 1.251 0.822 0.443 -0.761 0.000 0.932 0.486 0.327 2.215 0.277 0.057 0.010 1.274 0.411 1.708 -0.231 0.000 0.890 0.952 0.994 0.688 0.193 0.840 0.962 +1 0.380 0.822 -0.459 1.136 1.030 0.731 0.463 -1.290 0.000 0.926 -0.227 0.281 2.215 0.993 0.536 1.547 0.000 1.876 -0.024 -0.391 3.102 0.939 0.956 0.984 1.115 0.688 0.908 0.803 +1 1.031 -0.232 1.388 0.919 1.384 2.153 0.966 -0.186 0.000 1.244 1.030 -1.638 1.107 1.028 -0.378 -1.479 2.548 1.241 0.707 1.205 0.000 1.265 1.002 0.991 1.534 0.985 1.020 0.949 +1 0.766 -0.842 0.669 1.097 -0.613 0.959 -0.425 -0.203 2.173 0.470 1.606 1.172 0.000 1.423 0.735 -1.590 0.000 0.806 -1.824 1.458 0.000 0.937 0.637 1.161 0.781 0.691 0.959 0.967 +0 3.148 -1.131 0.928 3.036 0.855 2.728 0.630 -0.797 1.087 0.728 -1.028 1.383 2.215 0.600 1.368 -1.094 0.000 1.221 0.215 -0.431 0.000 0.821 0.873 0.988 0.803 2.750 3.214 2.472 +0 0.652 2.075 0.301 1.056 -0.176 0.761 -2.139 -0.095 0.000 1.745 0.164 -1.541 2.215 0.490 1.889 -1.521 0.000 1.363 1.005 -1.522 1.551 4.618 3.088 0.983 1.495 0.755 2.050 1.846 +0 1.365 -0.329 -1.385 0.978 -0.489 0.604 0.335 0.728 0.000 1.087 0.735 1.613 2.215 1.603 0.065 -0.537 0.000 1.375 1.136 0.658 1.551 1.625 1.251 1.157 1.104 0.903 0.996 0.988 +0 0.414 1.082 -0.073 0.779 -1.015 0.867 0.837 0.944 0.000 0.639 0.331 1.341 2.215 1.114 0.017 -0.787 2.548 0.488 -1.642 0.905 0.000 0.500 0.760 0.982 0.565 0.856 0.632 0.595 +1 1.443 0.081 1.518 0.492 -0.696 0.874 -0.659 0.523 2.173 0.970 1.618 -0.069 0.000 1.579 0.947 -1.335 2.548 0.537 0.431 -0.922 0.000 0.867 1.013 1.064 1.032 2.022 1.258 1.066 +1 0.959 0.631 0.626 1.297 -1.127 1.776 0.034 1.244 2.173 1.614 -1.275 -0.638 0.000 1.121 0.037 -0.239 0.000 1.118 -0.052 0.511 0.000 0.829 0.933 1.546 1.436 0.946 0.996 1.042 +1 1.024 0.823 1.306 0.614 -0.710 1.011 0.085 -0.113 0.000 1.355 0.340 -1.386 2.215 1.332 1.536 0.826 2.548 0.582 -0.634 -0.690 0.000 0.870 0.993 1.066 0.817 1.658 0.907 0.767 +0 0.398 1.393 0.134 0.795 0.231 0.499 0.779 -0.254 0.000 0.733 0.096 1.676 2.215 0.772 1.431 1.270 1.274 0.701 0.409 -1.421 0.000 0.956 1.070 0.999 0.896 0.704 0.728 0.759 +1 1.459 -0.547 0.110 0.413 -1.093 1.113 -0.163 -0.666 2.173 0.903 -0.139 0.892 0.000 0.985 2.192 -1.390 0.000 1.015 -1.096 1.263 0.000 0.892 0.826 0.986 0.859 0.867 0.884 0.806 +1 0.493 -0.955 -1.647 1.599 0.260 0.941 -0.965 1.318 0.000 1.555 0.627 -0.940 0.000 1.586 -0.640 -0.041 1.274 0.783 -1.401 -1.542 0.000 0.845 0.706 1.216 0.773 0.697 0.796 0.744 +1 0.953 -0.214 -1.640 0.630 -0.143 0.994 0.531 -1.572 0.000 1.183 -1.841 -0.164 0.000 2.359 0.271 0.646 2.548 1.014 0.662 -0.970 3.102 4.168 2.462 1.047 1.032 1.211 1.833 1.381 +1 0.690 0.073 -0.358 0.712 -1.421 0.696 -0.193 1.278 0.000 0.911 1.353 0.614 1.107 0.596 0.921 -0.608 2.548 0.379 1.303 -0.896 0.000 0.862 1.016 0.980 0.702 0.714 0.835 0.717 +1 0.837 -0.660 -0.878 0.797 -1.566 1.424 -0.747 -0.627 2.173 0.912 -0.623 1.347 0.000 0.739 0.024 0.358 2.548 1.156 -1.230 1.050 0.000 0.675 0.796 0.988 1.001 1.116 1.018 0.911 +1 0.455 -2.335 -1.625 1.149 0.431 1.018 -0.877 -1.405 2.173 0.617 -1.589 0.333 0.000 0.833 -1.966 -0.548 0.000 0.724 -0.047 1.135 1.551 0.837 0.913 0.989 1.183 0.786 0.856 0.810 +0 0.278 1.453 -1.511 2.099 -0.632 1.225 -0.131 1.121 2.173 0.943 -0.911 -1.204 0.000 0.865 -0.764 0.191 0.000 0.616 0.104 1.663 3.102 1.320 0.859 0.987 1.505 0.447 0.941 0.970 +0 2.265 -1.757 -0.714 0.806 -0.618 1.390 -0.433 1.142 2.173 0.940 -1.120 -0.305 1.107 0.472 2.270 1.601 0.000 0.436 -1.739 0.499 0.000 2.481 2.014 0.985 0.861 1.735 1.580 1.963 +0 1.511 -0.571 -0.841 3.200 -0.505 2.003 -2.480 1.124 0.000 1.232 -0.015 -0.219 0.000 1.349 1.177 1.361 1.274 1.762 0.235 1.220 3.102 1.428 1.279 0.996 2.343 0.639 1.671 1.415 +0 1.460 -0.679 -1.205 0.550 1.183 0.963 -1.503 -0.133 2.173 0.904 -0.649 0.959 2.215 0.449 1.168 1.454 0.000 0.431 -1.594 1.726 0.000 0.718 0.811 1.038 0.846 1.291 0.925 0.739 +0 0.707 -0.365 -0.849 0.528 -0.872 0.202 -1.131 0.550 0.000 0.551 -0.005 0.506 2.215 0.370 -1.459 1.690 2.548 0.408 -1.730 0.927 0.000 0.478 0.529 0.978 0.547 0.598 0.592 0.491 +1 1.283 0.246 -0.730 0.757 -0.500 1.092 -0.297 1.398 2.173 0.971 -1.796 -1.065 0.000 1.445 -0.090 0.664 1.274 0.988 -0.787 0.154 0.000 1.283 1.453 0.989 1.476 0.975 1.157 1.303 +0 1.075 1.250 -0.088 0.981 1.190 0.766 0.624 1.260 1.087 1.473 0.879 -0.849 0.000 0.483 -0.602 0.590 0.000 0.374 0.018 0.398 3.102 1.674 0.950 1.299 0.930 0.435 0.816 0.811 +0 0.574 -1.110 1.061 1.313 -0.371 0.664 0.733 -1.635 2.173 0.639 -2.206 -0.650 0.000 1.122 -0.043 1.164 2.548 0.597 -0.105 0.312 0.000 1.205 1.256 1.155 1.338 0.759 1.132 1.002 +1 0.812 0.883 -1.651 0.479 -0.442 0.824 0.736 -0.537 0.000 1.140 0.584 0.911 0.000 0.649 -1.313 0.870 2.548 1.115 0.573 -1.425 3.102 1.521 1.071 0.989 1.328 1.008 0.916 0.946 +1 1.084 1.831 -1.312 0.792 -0.704 0.550 0.742 0.246 0.000 0.561 0.238 -0.470 1.107 0.552 0.139 1.347 2.548 1.189 -0.563 1.011 0.000 1.056 0.857 0.989 0.777 0.591 0.619 0.706 +1 0.596 -1.860 1.307 0.964 -1.402 0.667 0.614 0.534 1.087 0.431 0.099 0.916 0.000 0.633 1.008 -1.317 2.548 0.925 0.805 -0.609 0.000 0.886 0.711 0.979 1.004 0.830 0.950 0.816 +0 1.242 0.717 -0.164 0.667 -0.984 0.662 0.844 -1.482 2.173 0.750 -0.283 1.444 2.215 1.191 -0.707 0.155 0.000 0.374 -0.665 1.508 0.000 0.691 1.166 0.989 0.936 0.802 0.781 0.773 +0 1.830 0.236 0.039 1.430 -0.142 1.040 -0.831 1.678 0.000 0.868 -0.133 -1.180 2.215 1.221 -0.241 1.342 0.000 0.554 -0.946 0.875 3.102 0.845 0.959 0.980 0.745 0.684 0.772 0.978 +0 1.269 0.041 -1.239 1.690 -0.722 1.347 0.915 -1.022 0.000 1.165 -1.096 0.618 2.215 1.505 -0.247 0.368 0.000 2.723 -0.177 0.979 3.102 0.759 0.822 0.984 1.613 0.919 1.287 1.027 +0 0.802 -0.011 -0.135 1.578 0.973 0.704 -0.933 1.249 2.173 0.754 0.409 -0.745 0.000 0.781 1.651 -0.811 0.000 1.126 1.201 -1.361 3.102 0.890 0.670 1.311 0.960 1.559 1.115 1.038 +1 1.021 -0.482 -0.027 1.195 -1.223 0.555 -1.247 0.522 0.000 0.794 0.484 1.425 2.215 0.641 -1.858 -0.334 0.000 1.284 -0.773 -1.525 3.102 0.863 0.906 1.348 1.049 0.824 0.915 0.860 +1 0.381 -0.514 0.996 1.168 1.562 0.569 -2.456 -0.025 0.000 1.022 -0.725 -0.457 2.215 1.221 0.630 0.502 0.000 2.809 0.878 1.735 0.000 0.792 0.763 0.980 1.255 1.109 0.945 0.862 +1 0.640 -0.490 -0.596 0.723 1.635 1.690 0.027 0.142 0.000 1.304 -0.819 -1.177 0.000 1.765 0.224 1.474 2.548 1.075 -1.213 -0.636 0.000 0.868 0.876 0.984 0.760 0.406 0.905 0.769 +1 0.295 1.251 0.264 2.782 1.122 0.417 -0.247 -0.206 2.173 0.822 1.009 -1.059 2.215 0.469 1.438 -1.375 0.000 1.461 1.324 -0.751 0.000 0.908 0.996 0.990 1.221 0.844 1.160 0.955 +1 1.495 -0.338 -1.049 1.067 -0.902 0.345 1.336 1.473 2.173 1.312 0.626 0.330 2.215 0.510 2.368 0.532 0.000 0.791 1.938 1.293 0.000 0.454 1.081 0.986 0.923 0.921 0.970 1.015 +0 0.659 0.936 -1.531 0.878 -0.677 0.714 -1.147 0.217 2.173 0.325 -1.954 -1.574 0.000 0.423 -0.376 1.603 2.548 0.546 0.206 0.947 0.000 0.868 0.826 0.990 0.830 0.700 1.236 1.091 +1 1.086 -0.797 -0.167 0.570 -1.222 0.990 -1.778 1.459 0.000 0.358 2.543 0.116 0.000 0.716 -0.101 0.458 2.548 0.705 -2.197 -1.679 0.000 0.678 0.963 0.988 0.715 0.576 0.834 0.772 +1 0.935 1.386 0.314 0.735 -1.534 1.892 0.725 0.813 1.087 1.135 -0.049 -1.257 0.000 0.497 0.862 1.026 2.548 2.018 0.889 -0.022 0.000 0.849 1.029 1.144 1.167 0.264 1.433 1.233 +0 1.013 0.642 0.595 0.476 1.088 0.822 2.012 1.408 0.000 1.645 0.881 -0.386 2.215 0.593 1.630 -0.867 0.000 1.310 0.438 -1.521 3.102 1.124 1.010 0.985 1.207 1.159 1.050 1.057 +0 0.492 -1.491 -0.259 1.513 0.715 0.761 0.454 -0.750 0.000 0.830 -1.158 1.700 1.107 1.047 -0.294 0.060 2.548 1.044 0.441 -1.605 0.000 0.948 0.981 0.985 0.853 1.082 0.909 0.925 +1 1.189 1.158 -1.077 0.895 1.303 0.864 0.852 -1.384 0.000 1.442 1.201 0.230 2.215 1.511 0.514 0.997 2.548 1.444 0.809 -0.732 0.000 0.951 1.266 1.200 1.159 1.136 1.084 0.948 +1 0.785 0.175 0.246 1.043 1.381 1.160 -2.034 -0.698 0.000 1.622 -0.842 -1.298 0.000 3.071 -1.208 0.647 2.548 1.542 0.219 -1.643 3.102 0.993 0.764 1.070 1.429 2.072 1.254 1.051 +1 0.520 0.637 1.495 1.219 -1.409 1.301 -0.523 0.278 1.087 0.746 -0.121 0.968 0.000 2.130 0.774 -1.221 0.000 1.967 0.661 0.207 3.102 0.906 1.188 0.978 1.316 1.206 1.313 1.168 +0 0.598 -1.858 -0.120 0.249 -1.365 0.331 -2.332 -1.239 0.000 0.923 0.373 1.524 2.215 0.522 -1.171 0.652 2.548 0.976 -0.581 -0.056 0.000 1.106 0.728 0.986 0.976 0.865 0.863 0.742 +0 0.645 1.182 -1.216 1.166 0.587 0.544 0.695 0.444 2.173 0.338 -2.558 -0.931 0.000 0.582 -0.128 -0.874 0.000 0.401 1.258 1.659 0.000 1.084 0.695 1.200 0.719 0.807 0.850 1.021 +0 0.552 1.397 0.414 0.884 -1.056 0.426 1.775 -0.323 0.000 0.921 -1.982 1.169 0.000 1.629 -0.163 -1.031 2.548 0.846 0.545 0.381 0.000 0.773 0.754 0.986 1.267 1.025 0.899 0.807 +0 1.453 2.074 1.410 0.411 -1.723 0.994 1.816 -0.922 0.000 0.765 1.453 0.732 2.215 0.880 0.306 -0.638 0.000 1.424 -0.200 0.687 1.551 0.849 0.982 0.995 0.726 0.926 0.873 0.851 +1 1.563 -0.440 -0.525 0.291 -1.027 0.937 0.746 1.284 0.000 0.487 -0.326 -0.300 0.000 0.857 -0.483 0.819 2.548 1.091 1.375 1.299 0.000 0.913 0.876 0.984 0.562 0.486 0.573 0.627 +0 0.616 -1.337 0.361 1.222 0.980 1.020 -0.433 -0.552 2.173 0.567 2.504 -1.572 0.000 1.157 0.088 1.524 2.548 0.711 0.658 0.351 0.000 1.173 1.226 0.984 1.613 1.344 1.336 2.007 +1 0.698 1.866 -1.198 0.694 0.469 0.418 0.794 -0.729 0.000 0.937 0.245 1.196 2.215 0.502 1.118 1.245 0.000 0.796 -1.329 -0.627 1.551 0.824 1.063 0.988 0.938 1.136 1.046 0.857 +1 0.486 0.996 -0.332 0.500 0.087 1.116 0.145 0.181 2.173 0.752 -0.105 -1.482 0.000 1.271 0.831 1.606 0.000 0.800 -1.031 1.243 0.000 0.879 1.305 0.987 0.537 0.734 0.791 0.689 +0 0.555 0.308 -1.614 1.483 -0.530 1.186 0.670 1.559 1.087 1.550 0.791 0.397 0.000 0.569 1.139 -0.434 0.000 0.594 -0.199 -0.976 3.102 1.031 0.886 1.043 1.158 0.792 0.945 0.881 +1 0.853 0.725 -0.242 1.191 0.202 1.176 1.081 -0.358 0.000 0.952 0.627 0.952 1.107 0.707 1.639 1.459 0.000 1.298 0.015 -1.662 3.102 1.010 0.977 0.978 1.120 0.779 0.868 0.808 +0 0.713 2.002 1.506 0.589 0.324 0.964 -0.887 -1.201 0.000 0.739 1.144 -0.181 2.215 1.182 0.567 0.992 0.000 1.161 0.799 -1.313 3.102 0.910 0.859 0.989 0.684 0.718 0.634 0.618 +1 0.955 1.692 -0.461 0.558 -0.006 0.977 0.592 -1.369 0.000 1.003 1.527 0.991 2.215 1.252 0.947 0.159 2.548 0.423 -1.318 1.251 0.000 1.086 0.993 0.977 0.951 0.873 0.836 0.775 +0 0.417 1.279 0.590 1.059 -0.408 1.202 -0.306 1.116 1.087 1.499 0.935 -0.514 2.215 1.081 0.351 1.150 0.000 0.524 1.908 1.201 0.000 0.917 1.156 0.989 0.775 2.379 1.265 1.041 +1 0.498 1.290 -1.594 0.869 0.886 1.033 0.239 -0.595 0.000 1.067 0.687 1.450 2.215 0.974 0.933 -0.867 0.000 1.473 0.011 0.478 3.102 0.847 1.111 0.986 0.635 0.957 0.972 0.838 +1 0.995 -0.144 -1.352 0.625 1.117 0.783 0.840 -0.587 0.000 0.647 -0.298 -0.415 2.215 1.401 0.195 1.113 1.274 0.928 -1.547 0.842 0.000 2.662 1.510 0.990 0.701 1.028 1.112 0.937 +0 0.588 0.490 -0.137 1.607 0.439 0.457 -0.449 -1.272 0.000 1.017 -1.317 -0.695 1.107 1.084 -1.229 1.550 0.000 1.156 -0.334 1.638 3.102 0.923 0.967 0.996 1.155 0.974 1.414 1.296 +1 0.361 1.296 0.253 2.219 -0.519 1.460 0.541 1.522 2.173 0.748 0.440 -1.186 2.215 1.310 -0.781 -0.054 0.000 1.239 -0.517 0.422 0.000 1.034 1.124 0.989 2.119 0.992 1.440 1.647 +0 0.522 0.673 1.375 0.810 -1.038 0.827 -1.139 0.225 2.173 0.536 -0.735 -0.198 2.215 1.062 -0.609 1.450 0.000 1.218 0.313 -1.486 0.000 0.908 0.911 0.987 1.033 0.413 0.836 0.752 +0 0.673 -0.093 -0.641 0.895 0.400 0.580 -0.460 -1.628 0.000 0.442 -0.782 -0.766 1.107 1.048 0.022 0.823 2.548 1.249 0.747 -0.067 0.000 0.999 0.876 0.988 0.718 0.780 0.671 0.687 +0 0.323 -0.612 -0.674 1.572 -1.658 1.085 -1.065 0.500 0.000 1.298 -1.658 -0.932 0.000 1.056 -0.243 0.949 2.548 1.301 -1.402 0.851 3.102 0.788 1.140 0.982 1.247 0.694 1.206 1.022 +0 0.795 0.140 -0.749 1.015 -1.068 0.493 -1.159 0.144 2.173 0.667 -0.358 -0.624 0.000 0.699 0.239 1.413 0.000 1.435 -0.861 1.190 3.102 1.065 0.924 0.975 1.341 0.722 1.098 0.908 +1 0.975 2.058 0.686 1.015 0.081 0.831 0.705 -0.999 2.173 0.593 1.054 1.663 0.000 0.716 -2.126 -1.049 0.000 1.030 1.131 0.573 0.000 0.858 0.975 0.992 1.016 0.986 0.945 0.801 +0 0.845 1.026 -0.231 1.208 0.641 1.112 2.071 -1.213 0.000 0.741 -0.818 -0.355 2.215 0.913 -0.531 1.544 0.000 1.169 0.895 1.272 0.000 0.836 0.994 0.991 0.616 0.868 0.785 0.693 +0 0.352 0.838 0.984 2.691 1.306 1.038 1.023 -0.630 2.173 0.880 0.241 -0.729 0.000 1.183 -0.001 0.413 2.548 0.868 0.122 -1.661 0.000 0.849 0.940 0.985 1.826 1.341 1.268 1.051 +0 1.195 0.272 -1.599 0.357 -1.185 0.477 -1.237 1.368 2.173 0.810 0.329 -0.163 0.000 1.148 -0.846 0.348 2.548 0.474 0.662 1.223 0.000 0.788 1.027 0.999 1.264 0.748 1.001 0.861 +0 2.342 -1.590 1.625 0.750 -1.145 1.513 0.440 -0.019 2.173 0.600 -0.055 0.146 0.000 1.147 -0.900 -1.612 2.548 0.371 -0.439 -1.550 0.000 0.632 0.760 1.106 2.743 2.069 1.794 1.322 +0 0.500 -0.872 1.264 0.767 -1.713 1.014 -0.311 0.633 2.173 1.145 -0.142 -1.509 0.000 1.050 0.233 -0.153 1.274 1.684 -1.317 -0.525 0.000 1.022 1.028 0.990 1.393 0.915 1.165 1.148 +0 0.281 0.263 1.035 2.106 -1.301 0.952 -0.352 -0.688 2.173 0.771 -0.351 -0.023 0.000 1.716 -0.995 1.204 2.548 1.554 0.096 0.505 0.000 0.778 0.944 0.986 0.849 1.688 0.992 0.889 +0 1.464 1.744 -0.451 1.253 0.104 1.117 1.434 -1.687 0.000 1.552 0.783 0.142 1.107 1.420 0.664 1.364 2.548 1.304 0.290 -1.564 0.000 1.086 0.876 0.981 1.117 1.409 1.154 1.230 +1 1.150 -1.556 -1.051 0.368 -0.464 0.686 -0.252 -0.469 2.173 0.880 -1.415 0.361 0.000 0.705 -2.230 0.958 0.000 1.166 -0.699 -1.460 3.102 0.891 0.825 1.001 0.751 0.788 0.874 0.780 +1 0.829 0.175 -1.635 1.634 -1.267 0.485 2.156 -0.234 0.000 0.536 -0.679 0.732 0.000 0.626 1.387 0.337 0.000 1.066 -1.199 1.292 3.102 1.243 0.801 0.976 0.819 0.691 0.740 0.814 +0 0.840 -0.872 0.152 0.473 1.329 1.468 -1.238 -0.137 2.173 1.043 0.327 1.603 0.000 1.352 -0.170 -1.531 1.274 0.587 -0.644 1.243 0.000 0.679 0.587 0.990 1.051 1.934 1.262 1.010 +0 0.374 -0.832 0.956 0.923 -0.913 1.463 0.317 0.483 2.173 0.855 -0.982 -1.439 2.215 1.010 0.498 -0.739 0.000 1.367 0.239 -1.502 0.000 0.839 0.962 0.988 1.846 2.006 1.352 1.212 +1 0.875 0.761 -1.431 0.296 -0.013 0.953 0.639 0.586 0.000 1.389 1.333 -0.975 2.215 0.827 1.584 0.685 0.000 0.816 0.992 1.372 3.102 0.929 0.709 0.982 0.928 0.827 0.939 0.852 +1 1.004 2.119 1.087 0.962 -0.192 1.337 1.017 -1.592 2.173 0.932 -0.968 0.263 0.000 0.820 -1.595 0.130 0.000 0.585 1.275 -0.779 1.551 0.949 0.980 1.244 1.435 0.670 1.157 1.290 +0 0.852 0.020 0.730 0.506 -0.891 1.080 0.026 -0.156 2.173 0.883 1.895 -1.649 0.000 1.700 -1.733 -1.540 0.000 1.210 0.614 0.424 0.000 1.553 1.299 0.984 0.816 0.861 1.145 0.916 +0 0.835 -0.911 0.622 0.436 -0.916 1.437 -0.935 -0.856 0.000 1.295 -0.086 1.105 2.215 0.875 0.678 1.310 2.548 0.780 0.642 0.578 0.000 2.205 1.714 0.988 0.787 0.528 1.260 0.999 +1 1.273 0.578 -1.504 0.366 1.536 2.400 0.606 -0.424 0.000 1.108 0.053 0.323 0.000 1.946 0.526 1.486 0.000 2.766 0.983 1.287 3.102 0.937 0.908 0.981 0.792 0.770 0.678 0.641 +1 0.788 -1.615 -0.225 1.188 -0.385 2.395 -1.534 1.187 0.000 2.656 -0.660 -0.647 0.000 1.113 -0.674 0.983 2.548 0.812 0.456 -0.910 3.102 1.158 0.876 0.987 1.323 0.877 1.222 1.265 +1 0.966 0.666 0.055 1.455 0.828 0.355 2.038 0.708 0.000 0.691 -0.369 -1.654 2.215 0.862 0.788 -1.095 2.548 0.713 1.280 -1.219 0.000 0.784 1.113 1.055 0.909 0.674 0.795 0.768 +1 0.404 -1.174 -0.508 1.436 1.090 0.701 0.343 1.637 0.000 0.899 1.893 -1.050 0.000 1.101 -0.514 0.251 2.548 1.519 0.943 -0.096 3.102 0.827 1.134 1.046 1.444 0.991 0.987 0.979 +1 0.706 -0.230 1.538 0.754 1.107 0.891 -0.082 0.774 2.173 1.396 -0.678 -1.064 0.000 1.086 -1.149 -0.332 2.548 1.056 -1.679 -0.950 0.000 0.816 1.237 0.984 0.881 1.283 0.870 0.786 +1 0.996 0.662 0.845 0.471 -0.146 0.621 1.557 -1.517 0.000 1.330 -0.083 0.244 2.215 1.157 -1.036 1.411 0.000 1.063 0.064 -0.977 3.102 0.780 0.767 0.984 0.691 0.961 0.983 0.856 +1 0.290 -0.999 1.421 0.347 0.151 0.555 -0.649 -0.479 2.173 0.756 1.104 1.371 0.000 0.757 0.844 0.486 0.000 1.026 0.756 -1.168 1.551 0.839 0.757 0.983 0.812 0.831 0.805 0.689 +1 0.642 2.091 0.036 1.298 1.111 1.525 2.086 -0.899 0.000 1.078 1.173 1.374 2.215 0.831 0.308 0.465 2.548 1.121 -1.012 1.543 0.000 5.320 2.996 1.042 0.859 0.862 1.872 1.586 +0 0.668 -0.119 0.887 0.428 0.775 0.802 0.245 -1.497 0.000 0.602 0.118 -0.051 2.215 0.885 1.320 -0.178 0.000 1.605 2.452 1.455 0.000 1.255 1.023 0.979 0.516 0.353 0.718 0.828 +1 1.408 1.125 -0.476 0.578 -0.702 2.153 -0.830 0.871 0.000 2.059 0.651 -1.087 2.215 0.966 -0.058 -1.050 2.548 0.990 1.746 0.495 0.000 1.046 1.005 0.985 0.899 0.565 0.861 0.774 +1 0.897 1.228 -0.801 0.264 0.572 0.471 -0.912 -0.001 2.173 0.278 -0.996 1.029 0.000 0.830 0.153 -1.384 2.548 1.308 1.011 -1.550 0.000 1.217 1.157 0.982 0.629 0.863 0.746 0.691 +1 0.887 0.011 1.354 0.823 -1.716 1.108 -0.253 -0.734 0.000 1.137 -0.437 0.405 2.215 0.749 0.785 0.507 0.000 0.449 -1.066 -1.243 3.102 1.761 1.064 0.990 1.172 0.700 0.865 0.918 +0 0.624 0.900 -0.234 2.429 0.436 0.979 1.221 -1.717 2.173 0.638 0.876 -0.794 0.000 0.584 1.727 -1.434 0.000 0.397 -0.132 -1.021 1.551 0.928 0.879 0.989 0.698 0.630 0.922 0.807 +1 0.474 0.890 1.571 0.753 -0.275 0.790 0.082 -1.345 0.000 0.779 0.992 -1.193 0.000 0.667 1.842 0.395 0.000 1.522 0.857 0.746 3.102 0.905 0.863 0.988 0.660 0.309 0.537 0.568 +1 1.151 0.982 1.237 0.765 1.066 1.529 1.164 -0.245 0.000 0.495 -2.483 1.638 0.000 1.267 0.962 -1.540 2.548 0.612 0.551 -0.719 0.000 0.723 1.226 1.000 0.485 0.515 0.734 0.836 +0 0.438 -0.097 -1.222 1.760 0.533 1.386 0.266 -0.953 0.000 1.041 -1.272 1.042 2.215 1.764 0.294 0.465 0.000 0.962 0.695 -0.856 0.000 0.717 1.097 1.217 1.025 1.364 1.001 0.880 +1 1.499 0.005 -0.254 1.205 0.613 0.593 -0.132 1.740 0.000 0.593 -0.122 -0.742 0.000 1.467 0.442 1.566 2.548 0.378 1.176 -0.001 1.551 0.989 0.857 1.311 0.695 0.625 0.777 0.758 +1 0.567 -0.666 -1.415 1.213 0.197 0.799 1.065 -0.010 2.173 0.644 2.845 1.415 0.000 0.950 -0.027 -1.320 0.000 0.392 0.750 -0.840 3.102 0.430 1.212 1.141 0.672 0.406 0.766 1.221 +1 1.073 -1.687 -0.875 1.510 -1.101 1.968 -1.202 0.589 1.087 1.587 -0.716 -0.764 1.107 1.570 -0.107 -1.205 0.000 2.795 0.091 1.105 0.000 2.033 1.855 1.001 2.214 2.521 1.845 1.960 +1 0.638 -1.434 1.738 2.542 1.402 2.976 -0.052 -0.193 0.000 1.138 0.046 -1.440 2.215 1.392 -0.780 1.732 0.000 0.852 -0.084 -0.916 0.000 0.945 0.748 0.993 1.537 1.421 1.164 0.999 +0 0.746 -1.983 0.262 0.211 -0.771 1.027 -2.452 -1.185 0.000 0.413 -1.113 -0.857 0.000 1.133 -1.067 0.053 2.548 3.278 -0.419 1.122 3.102 1.068 1.254 0.977 1.035 1.309 1.406 1.104 +1 1.440 -0.205 -0.386 1.177 -0.009 0.785 2.121 -1.136 0.000 1.739 0.231 1.151 2.215 0.755 -0.148 -1.263 2.548 0.687 -0.996 0.893 0.000 3.069 1.831 0.985 1.595 1.032 1.426 1.485 +1 0.878 0.321 1.706 1.395 0.884 0.773 -0.377 -0.788 1.087 1.270 -0.078 -0.125 2.215 0.928 -0.250 1.059 0.000 0.415 -1.460 -0.935 0.000 0.866 0.987 1.034 1.148 0.850 0.958 0.837 +1 0.778 0.921 -1.002 0.566 0.163 0.825 0.010 1.277 0.000 0.579 -0.708 0.780 0.000 1.415 0.312 -0.736 2.548 0.983 1.105 -1.663 3.102 0.956 0.776 0.989 0.629 0.812 0.778 0.699 +1 0.712 -0.705 0.465 1.298 -1.028 0.642 -0.294 -0.910 0.000 0.756 0.213 1.325 1.107 1.037 -0.471 -0.328 2.548 0.615 2.148 0.963 0.000 2.132 1.404 1.298 0.752 1.003 1.027 0.989 +1 0.964 1.066 0.887 0.490 -0.425 1.223 0.168 -0.097 2.173 0.850 0.358 -1.503 1.107 0.535 -2.569 1.450 0.000 0.893 0.908 1.607 0.000 0.502 0.959 0.987 0.887 1.439 0.962 0.761 +1 0.948 -0.370 0.162 0.995 1.310 0.961 -0.575 -0.146 0.000 1.047 -0.217 1.288 2.215 1.032 0.388 -1.416 2.548 0.930 -0.956 -0.978 0.000 1.068 1.169 1.157 0.752 0.803 0.945 0.839 +1 0.607 0.032 0.377 1.100 0.707 2.024 2.319 -1.078 0.000 1.445 0.319 -0.405 2.215 2.327 1.214 1.023 2.548 1.651 0.514 0.781 0.000 1.157 1.286 0.977 1.941 2.131 1.557 1.249 +0 1.980 -0.593 -1.501 1.676 -1.065 1.343 -0.054 0.357 0.000 0.857 -0.250 -0.102 0.000 1.001 1.020 0.286 2.548 1.124 -0.439 1.096 3.102 0.945 0.970 0.986 0.925 0.921 1.112 1.180 +1 0.494 0.866 0.434 0.534 -1.488 1.251 1.123 0.195 0.000 0.583 0.287 -1.264 2.215 0.865 -0.268 1.394 2.548 0.402 1.372 0.792 0.000 1.182 1.109 0.991 0.602 0.560 0.876 0.768 +1 0.740 -0.267 0.981 1.515 0.088 0.475 1.067 1.192 0.000 0.602 1.909 -1.589 0.000 0.761 0.401 0.283 0.000 1.297 -0.557 -1.317 3.102 0.908 0.771 1.057 0.780 0.872 0.730 0.636 +1 0.418 -1.341 -1.195 0.993 0.620 0.653 -0.192 1.007 2.173 0.878 -0.415 -0.016 2.215 0.466 -2.093 -1.325 0.000 0.398 -0.352 -1.013 0.000 0.531 0.866 0.988 0.977 0.898 0.869 0.738 +0 1.466 -1.441 1.026 1.494 0.805 0.783 1.658 -0.686 0.000 0.631 -0.129 0.439 2.215 1.826 0.418 -0.916 0.000 1.771 -1.033 1.640 3.102 0.433 0.934 1.006 0.943 1.009 0.975 1.426 +1 0.779 -0.316 1.595 0.344 -1.433 1.283 0.948 0.873 0.000 0.787 2.707 -1.023 0.000 0.956 0.137 -0.212 0.000 0.931 -1.955 -0.728 0.000 0.531 0.941 0.986 0.548 0.471 0.600 0.595 +0 1.097 0.508 1.591 1.625 -1.387 0.619 0.752 -0.070 2.173 0.671 0.273 -0.408 2.215 0.771 0.922 1.020 0.000 1.280 1.800 0.904 0.000 0.674 0.939 0.983 0.949 0.365 0.817 0.847 +1 0.941 1.258 -0.703 0.478 0.833 0.905 1.467 0.876 2.173 0.780 2.348 -1.281 0.000 0.454 0.211 -0.214 1.274 0.530 2.477 -0.550 0.000 0.565 0.894 0.988 0.879 0.851 0.831 0.749 +1 0.495 1.051 -0.732 0.939 1.523 0.880 0.234 -0.857 2.173 0.776 2.259 1.686 0.000 0.951 0.020 1.588 2.548 1.479 0.290 0.551 0.000 2.007 1.441 0.986 0.786 0.927 1.161 0.939 +1 0.645 1.438 0.116 1.023 -1.228 1.050 0.312 1.008 0.000 0.713 -0.451 -1.144 2.215 1.051 0.915 -0.063 0.000 1.297 1.072 -1.542 3.102 0.846 0.923 1.052 1.049 0.913 0.927 0.806 +1 1.819 0.386 0.099 0.568 1.696 1.056 -1.300 -0.140 0.000 1.465 0.224 1.503 0.000 0.805 -0.480 0.574 0.000 2.158 0.365 -1.295 3.102 1.197 1.388 1.396 1.111 0.400 1.173 1.078 +1 0.893 0.540 1.113 1.274 0.383 1.452 0.231 -0.396 2.173 1.120 0.231 -1.027 2.215 2.629 -1.920 1.427 0.000 0.613 1.443 1.333 0.000 4.675 3.190 0.991 1.316 1.010 2.430 2.107 +0 1.034 -0.326 -1.629 1.085 0.544 0.566 2.165 1.739 0.000 0.804 0.242 -0.395 2.215 0.371 0.048 0.020 2.548 0.368 -1.210 -0.755 0.000 1.039 0.718 1.358 0.696 0.220 0.614 0.602 +0 1.629 -1.807 0.711 0.336 -1.223 0.668 -0.779 0.984 2.173 1.133 1.442 -1.302 1.107 1.712 2.119 -0.563 0.000 0.421 -1.723 -1.678 0.000 4.241 2.552 1.010 0.777 2.115 2.018 2.377 +1 0.298 1.146 -0.522 1.039 0.127 0.796 0.352 -1.369 2.173 0.565 -0.528 -0.300 0.000 1.141 -0.494 1.251 2.548 0.885 -1.876 -0.097 0.000 0.902 1.036 0.989 0.999 1.000 0.995 0.869 +1 2.461 -0.146 -0.536 0.933 -0.950 0.608 2.724 1.339 0.000 0.550 0.385 0.750 1.107 1.179 -0.735 0.773 1.274 0.508 -0.189 -1.394 0.000 1.863 1.343 0.996 1.282 0.547 1.396 1.438 +0 1.007 -0.208 1.699 0.385 0.259 1.108 0.938 -1.401 2.173 0.929 -1.030 0.158 0.000 0.682 -0.843 0.940 0.000 1.016 0.329 -0.155 3.102 0.795 0.813 0.985 0.892 1.060 1.188 0.955 +0 0.607 -1.016 -0.654 1.997 -1.594 0.729 -0.657 1.229 2.173 0.818 0.157 0.043 2.215 0.852 0.087 0.600 0.000 0.852 0.649 -0.674 0.000 0.917 0.980 1.141 1.229 1.107 0.946 0.887 +0 0.593 0.627 -0.314 1.696 -1.175 0.863 -1.563 1.272 0.000 0.715 0.231 1.258 2.215 0.924 -0.358 -0.121 2.548 0.960 -1.478 0.320 0.000 1.062 1.117 0.987 0.871 0.863 0.906 1.079 +1 0.354 1.548 -1.558 1.560 -0.314 0.760 1.134 0.025 0.000 2.273 -0.074 -1.737 2.215 1.561 0.597 0.667 2.548 1.780 1.083 -0.582 0.000 0.935 1.131 0.986 1.604 1.819 1.481 1.219 +1 0.732 0.451 0.106 1.338 -0.226 1.057 0.191 -0.067 0.000 1.008 -0.448 1.632 2.215 0.932 0.773 0.876 0.000 0.891 0.896 1.554 0.000 0.767 1.106 0.989 1.526 0.428 1.206 1.110 +1 1.328 -0.711 0.349 0.674 1.086 1.126 -1.116 -1.325 2.173 0.625 -1.471 0.091 0.000 0.654 -0.233 1.032 0.000 0.553 -0.737 -0.313 3.102 0.988 1.216 0.983 0.577 0.668 0.831 0.754 +1 1.383 0.264 0.959 0.529 -0.163 0.477 2.741 1.362 0.000 1.543 0.606 -0.769 1.107 0.824 1.165 -0.040 2.548 0.594 -0.253 0.858 0.000 0.783 0.976 1.004 0.743 0.834 0.815 0.713 +1 2.025 0.145 0.064 0.499 -0.305 0.436 1.215 -1.554 0.000 1.252 0.598 1.198 2.215 0.944 0.154 -0.966 0.000 0.886 0.437 0.520 0.000 0.963 0.799 0.995 0.892 0.544 0.896 0.797 +0 0.396 -0.918 0.496 0.672 -1.344 1.037 0.122 0.279 2.173 1.309 -1.081 -0.374 0.000 1.549 -0.890 0.268 0.000 1.874 -1.722 -1.226 0.000 0.928 1.097 0.990 0.729 1.707 1.153 1.016 +0 1.511 -0.077 -0.626 0.982 -0.780 0.587 -1.109 0.956 0.000 0.946 -0.810 0.376 2.215 0.918 -0.374 1.435 0.000 0.949 0.208 -1.616 1.551 0.729 0.797 0.983 0.725 0.965 0.871 0.912 +1 0.676 0.287 -1.097 0.527 1.254 1.203 0.431 0.524 2.173 0.949 0.257 0.025 0.000 1.786 0.264 -1.421 2.548 0.958 -0.922 -1.312 0.000 1.480 1.301 0.982 1.030 1.801 1.152 0.985 +1 0.752 -0.269 -1.204 0.882 0.513 0.607 0.137 0.207 2.173 0.992 0.892 -0.429 2.215 1.258 0.420 1.432 0.000 0.911 1.241 1.647 0.000 0.674 1.020 1.128 0.957 0.770 0.833 0.793 +0 1.300 0.079 -0.980 0.480 1.038 1.511 -1.462 -1.504 0.000 0.987 -0.498 0.515 0.000 0.947 0.055 -0.173 0.000 1.528 0.539 0.954 3.102 0.963 0.950 1.061 0.839 0.615 0.772 0.770 +0 0.566 0.628 -0.331 1.025 -0.687 1.647 -1.046 -0.800 2.173 1.488 -1.590 1.142 0.000 1.365 0.594 0.251 0.000 2.429 0.279 1.140 3.102 0.656 1.496 0.991 0.905 2.622 1.586 1.306 +0 1.744 0.293 -0.501 1.144 -0.967 0.801 1.246 0.922 1.087 1.027 0.370 0.307 0.000 1.556 1.001 1.588 1.274 0.485 -0.372 -1.700 0.000 0.975 0.979 0.993 1.214 0.793 1.067 0.955 +1 1.636 0.355 0.033 0.537 -0.373 2.874 -0.931 1.150 0.000 1.774 -1.398 -0.680 1.107 1.537 -0.201 -0.764 2.548 0.598 -1.045 -1.054 0.000 0.871 0.708 0.992 1.455 1.144 1.033 0.902 +0 1.394 -0.688 1.419 0.428 -1.458 0.627 -2.480 -0.795 0.000 1.055 0.207 0.508 1.107 0.609 1.782 -1.461 0.000 1.314 1.667 0.032 0.000 0.962 0.911 0.989 0.951 0.743 0.841 0.942 +1 2.178 1.001 1.288 0.613 -1.633 1.476 2.505 0.087 0.000 1.890 0.439 -1.052 2.215 0.586 -0.707 0.514 2.548 0.399 -2.246 -0.173 0.000 7.960 4.417 0.983 1.362 1.324 2.875 2.237 +1 1.165 -0.681 0.410 0.326 -0.928 1.231 -0.423 0.099 0.000 1.372 -0.162 -1.586 0.000 1.591 -0.332 -0.959 2.548 1.437 -0.057 1.102 3.102 0.918 1.032 0.994 0.880 1.122 0.914 0.768 +1 0.657 0.125 -0.506 0.259 -0.471 0.698 -0.749 -0.190 2.173 0.999 -1.674 1.528 0.000 0.997 -0.425 0.475 0.000 1.778 -0.347 -1.740 3.102 1.613 1.194 0.987 0.614 1.178 0.961 0.829 +1 0.581 -1.114 0.022 0.488 1.513 1.093 -1.188 0.520 2.173 1.281 -0.653 -1.029 0.000 0.826 -0.825 1.493 2.548 0.552 -1.555 -1.332 0.000 0.755 0.751 0.987 0.887 0.926 0.915 0.789 +0 0.878 0.769 -1.108 0.292 0.224 0.591 -0.751 -0.003 2.173 0.753 -1.743 1.546 0.000 0.810 -0.828 1.041 2.548 0.574 0.627 -0.866 0.000 1.576 0.988 0.988 0.802 0.700 0.764 0.764 +0 1.810 -0.784 -1.512 0.388 1.489 0.456 0.607 -0.996 2.173 0.699 -0.644 0.548 0.000 0.543 -0.419 0.182 1.274 0.779 0.632 0.207 0.000 0.798 0.893 0.980 0.762 0.644 0.646 0.692 +0 1.273 0.617 0.148 0.395 1.155 0.807 0.802 0.645 1.087 0.848 0.857 -1.008 2.215 0.895 1.969 -1.208 0.000 0.462 1.382 -1.728 0.000 0.629 0.854 0.989 0.885 1.213 0.753 0.663 +1 0.713 -1.241 -1.592 1.148 0.674 0.836 -0.711 -1.384 1.087 0.633 -1.450 -0.412 0.000 1.139 -2.346 0.018 0.000 0.718 -1.416 1.015 3.102 0.890 0.744 1.117 0.947 0.800 0.895 0.813 +1 0.934 0.768 1.314 0.395 0.217 0.945 0.540 -1.464 2.173 1.020 -0.419 0.522 2.215 0.416 -0.531 -0.347 0.000 0.845 0.359 -0.124 0.000 0.370 0.863 0.991 1.100 1.588 1.000 0.816 +1 1.049 -0.619 1.501 0.127 -1.021 1.048 -1.099 1.100 0.000 1.186 0.038 -1.022 2.215 1.320 -0.382 0.226 2.548 1.896 -0.103 -0.363 0.000 2.360 1.482 0.982 0.817 1.238 1.186 0.989 +0 1.056 0.171 1.094 0.575 -1.594 1.652 1.936 1.598 0.000 1.352 0.234 0.028 2.215 0.798 -0.013 -0.878 0.000 1.457 -0.311 -0.357 1.551 0.833 0.971 0.979 0.851 0.585 0.788 0.728 +0 1.183 1.136 -1.018 1.394 -0.232 0.707 0.753 1.281 2.173 0.570 0.119 -0.310 0.000 0.452 2.703 -1.515 0.000 0.668 1.460 0.379 0.000 0.965 0.747 1.158 0.765 0.323 0.759 0.679 +0 1.160 -0.248 -0.572 0.633 0.332 0.850 -0.849 -1.077 2.173 1.132 -0.816 0.782 0.000 0.933 -1.308 0.288 0.000 1.249 -0.815 1.742 1.551 0.830 0.912 0.991 0.824 0.620 0.868 0.797 +1 1.114 1.168 1.580 1.282 -0.966 1.040 -0.600 0.056 2.173 0.469 -1.764 1.296 0.000 0.513 -1.080 0.700 0.000 0.802 0.308 -0.793 3.102 0.454 0.921 1.242 0.714 0.829 1.128 1.142 +1 0.632 0.830 -0.929 0.630 -0.487 1.347 -0.019 -0.315 1.087 1.026 -1.208 1.417 0.000 2.047 -0.079 1.015 0.000 1.061 0.000 1.551 3.102 1.570 0.961 0.988 1.520 1.257 1.272 1.495 +0 2.021 0.557 -0.801 0.303 0.156 1.628 1.339 0.979 0.000 2.136 0.843 -1.130 2.215 2.051 -0.069 0.188 0.000 1.661 0.292 1.200 3.102 1.561 1.396 0.990 0.889 1.534 1.519 1.228 +1 0.807 1.393 1.361 0.858 0.326 0.723 1.972 0.026 0.000 0.446 -0.827 -0.041 1.107 1.608 0.265 -1.280 2.548 1.349 -0.389 -1.523 0.000 0.840 0.959 0.986 0.912 0.974 0.841 0.725 +1 0.954 0.667 -1.664 1.061 -0.499 1.025 0.579 0.917 0.000 1.470 0.576 -0.741 1.107 0.502 0.138 0.531 2.548 0.955 1.398 0.888 0.000 0.868 0.597 1.210 0.802 0.856 0.937 0.852 +0 0.283 -0.694 -1.511 1.221 0.586 0.623 -0.662 -0.846 0.000 0.872 -0.529 1.497 2.215 0.486 1.096 -0.440 0.000 0.721 1.291 0.395 3.102 0.805 0.912 0.986 1.527 1.077 1.086 0.982 +0 0.799 -1.774 0.943 1.203 -0.704 0.636 -1.062 -0.047 2.173 0.581 -0.067 1.128 2.215 0.499 -2.052 1.472 0.000 1.106 -0.572 -1.563 0.000 0.886 0.915 1.353 1.093 0.909 0.823 0.798 +1 0.642 1.009 1.411 1.663 -0.376 0.552 0.698 -0.574 2.173 0.764 -0.215 1.252 0.000 1.262 0.268 1.527 0.000 0.708 -0.299 0.254 3.102 0.539 1.063 1.430 0.869 0.582 0.692 0.791 +1 0.480 -1.077 -0.547 0.640 -1.646 0.772 0.024 0.285 0.000 1.193 -0.651 0.543 0.000 2.547 -0.044 -1.158 2.548 1.289 0.800 1.327 3.102 0.860 1.201 0.992 1.602 1.308 1.413 1.370 +1 1.029 -0.317 -1.317 0.819 -0.308 0.905 -0.063 0.898 0.000 0.552 -0.648 0.423 0.000 0.668 0.527 1.537 2.548 2.087 0.160 -0.862 3.102 0.817 1.123 1.005 0.720 0.770 0.721 0.689 +1 1.005 0.449 -1.316 0.636 0.040 0.622 1.018 -0.615 2.173 0.779 -0.606 1.380 0.000 0.909 0.954 0.907 2.548 0.646 -1.102 0.359 0.000 0.805 0.991 1.042 0.680 0.918 0.918 0.794 +1 0.795 1.450 0.435 0.702 -1.369 0.907 0.644 -1.069 0.000 0.755 1.000 -0.687 0.000 1.786 -2.033 1.245 0.000 1.869 0.142 -0.163 3.102 0.682 0.947 1.033 0.995 0.929 0.946 0.876 +1 0.925 0.674 1.630 0.644 -0.917 0.733 0.263 1.162 0.000 1.059 0.492 0.260 0.000 0.822 -0.881 -0.503 2.548 0.528 0.618 -1.197 0.000 0.949 0.942 0.990 0.614 0.149 0.611 0.632 +1 0.818 1.860 0.766 0.462 1.071 0.735 -0.352 -1.232 2.173 0.343 1.304 -0.802 0.000 0.379 1.013 -1.309 0.000 0.775 0.116 -0.268 3.102 0.252 0.663 1.002 0.708 0.642 0.808 0.646 +1 0.309 -0.578 0.052 1.897 0.034 1.197 0.639 -1.522 2.173 0.434 -0.138 -0.028 0.000 0.581 0.680 1.269 0.000 0.413 0.589 -0.007 0.000 0.961 1.039 0.982 1.657 0.812 1.919 1.573 +1 2.452 -0.781 0.204 0.652 0.594 0.444 -0.446 1.300 0.000 0.572 -1.248 -1.144 2.215 0.788 -2.123 -1.248 0.000 1.095 -0.230 -1.413 3.102 1.402 0.979 0.978 1.020 0.413 0.812 0.872 +1 1.815 -1.781 0.031 0.317 1.597 0.720 -0.263 -1.685 2.173 0.661 -2.122 1.219 0.000 1.083 -0.673 -1.189 1.274 0.424 -0.938 0.139 0.000 0.686 1.037 1.038 0.995 0.544 0.948 0.822 +0 1.002 0.257 0.346 0.609 0.424 0.438 -0.647 -1.047 0.000 0.652 -0.127 1.209 2.215 1.243 0.210 -0.523 2.548 0.794 0.742 -1.643 0.000 0.893 0.775 0.990 0.829 0.972 0.750 0.745 +1 0.663 1.282 0.372 0.981 -1.618 0.464 2.050 -1.356 0.000 0.444 0.894 -1.040 2.215 0.450 1.970 -0.214 0.000 0.811 -0.709 1.114 3.102 0.705 1.304 1.090 0.628 0.743 0.788 0.705 +0 1.500 -0.052 -0.710 0.447 -0.970 1.012 0.312 0.632 0.000 1.630 0.414 -1.367 2.215 0.988 1.159 0.693 0.000 0.398 0.482 -1.682 3.102 0.900 0.679 1.002 1.066 0.211 0.951 1.009 +1 0.768 0.027 -0.585 1.095 1.499 0.819 -1.588 0.878 2.173 0.618 -0.358 -1.033 0.000 0.530 -0.470 0.219 2.548 0.588 -1.725 -0.739 0.000 0.780 1.091 1.211 0.714 0.657 0.835 0.774 +0 1.011 1.403 1.410 0.525 0.414 0.695 0.147 0.149 2.173 0.771 -1.548 -0.591 0.000 0.818 0.230 1.474 0.000 1.524 0.723 -0.875 3.102 1.007 0.908 0.985 1.145 0.956 0.892 0.806 +1 0.622 0.798 -0.993 0.353 1.468 0.568 1.235 -0.134 0.000 0.509 0.766 0.466 2.215 1.612 0.824 1.365 2.548 1.804 1.415 -0.647 0.000 0.934 1.088 0.995 0.711 0.700 0.701 0.752 +1 0.617 -0.880 -1.739 1.248 0.669 1.116 0.165 -0.675 1.087 0.815 -0.045 1.301 0.000 0.704 0.018 0.427 0.000 0.458 0.852 -0.708 0.000 0.893 1.091 1.004 0.567 0.425 0.806 0.740 +1 0.976 -0.338 -0.840 0.266 0.011 1.287 0.008 0.746 1.087 0.905 -0.822 -1.277 0.000 0.670 -1.304 -0.510 0.000 0.467 -1.145 1.678 1.551 0.841 0.542 0.986 1.122 0.865 0.931 0.856 +1 1.380 0.468 -0.083 2.257 -0.477 2.122 0.291 -1.013 2.173 1.557 -2.280 0.812 0.000 3.721 -0.845 1.211 0.000 0.916 0.233 0.415 0.000 0.826 0.558 0.992 1.483 1.052 1.097 1.030 +1 0.564 0.065 0.700 0.949 1.742 0.800 0.111 0.145 2.173 0.649 -1.409 -1.368 0.000 0.730 -1.122 -0.364 0.000 0.478 0.843 1.446 3.102 0.839 0.899 0.984 0.930 0.676 0.812 0.842 +0 0.774 -1.904 0.443 1.010 -1.563 0.675 0.459 -0.973 0.000 0.941 0.914 -0.402 2.215 1.028 -0.577 1.687 2.548 0.502 0.056 1.180 0.000 0.847 0.801 1.191 1.985 1.349 1.330 1.131 +1 0.795 0.308 1.304 1.933 0.721 1.018 -0.089 -0.620 2.173 0.628 0.246 -1.405 0.000 0.496 2.611 -1.724 0.000 0.774 0.709 0.007 0.000 0.911 0.859 0.991 0.738 0.759 0.910 0.796 +1 0.908 0.872 -0.430 0.679 0.658 0.418 -2.499 -1.145 0.000 0.371 1.920 -0.982 0.000 0.855 0.376 0.616 2.548 0.755 -0.919 0.975 3.102 0.218 0.939 0.985 0.656 0.548 0.676 0.637 +0 0.293 -1.776 -0.717 1.418 0.858 1.179 -1.578 -0.258 0.000 1.017 -0.472 1.519 2.215 1.014 0.034 -1.400 2.548 0.694 1.898 -0.447 0.000 1.177 1.388 0.992 0.771 0.600 1.056 0.908 +1 0.457 -1.194 -1.139 1.075 -1.722 0.752 -2.528 -1.705 0.000 1.787 0.795 0.412 2.215 1.193 1.079 0.305 2.548 2.026 0.583 -0.628 0.000 0.684 3.049 0.977 1.395 0.323 2.556 1.914 +1 1.825 0.002 -1.336 0.527 1.003 0.620 -0.688 -0.161 2.173 0.475 -1.117 0.068 2.215 1.310 -0.175 0.806 0.000 0.568 -0.791 -1.118 0.000 1.009 0.875 1.167 0.924 0.245 0.757 0.710 +1 1.785 0.701 -1.416 0.053 -1.156 0.897 0.429 -0.283 0.000 1.235 0.491 1.163 2.215 0.568 -0.484 0.163 0.000 0.466 0.998 0.057 0.000 0.817 1.217 1.001 0.939 1.324 1.044 0.965 +1 0.865 -0.648 1.073 2.164 1.625 0.844 -0.216 -0.033 2.173 0.761 -0.293 -1.221 0.000 0.723 0.345 -1.048 2.548 1.089 0.759 0.372 0.000 0.965 0.952 0.988 0.843 0.826 0.928 0.820 +1 0.688 -0.688 -1.275 0.140 -0.418 1.549 -0.583 -0.142 2.173 0.985 -0.287 -1.730 0.000 0.822 1.819 1.080 0.000 1.078 -0.363 -1.080 3.102 0.897 0.737 0.992 1.132 1.028 1.004 0.851 +0 1.738 0.359 1.689 0.741 1.375 0.673 0.258 -1.264 2.173 0.780 -0.461 0.151 0.000 0.649 -0.183 0.539 0.000 0.853 -1.944 -0.817 0.000 1.304 1.313 0.984 1.366 1.290 1.252 1.140 +0 0.576 0.343 1.317 1.394 0.070 1.741 -0.614 -1.353 2.173 1.566 -0.199 0.407 2.215 0.749 -0.813 1.647 0.000 0.688 -0.578 -0.198 0.000 0.793 0.952 1.120 1.601 2.482 1.372 1.061 +1 1.244 -1.721 -0.998 0.505 -0.936 0.874 -1.178 0.721 2.173 0.764 -1.168 0.287 0.000 0.478 -1.487 1.527 0.000 0.500 0.302 -1.336 0.000 0.857 0.698 0.990 1.135 0.698 0.803 0.701 +1 1.525 0.442 1.474 0.419 0.150 0.761 -0.618 -1.002 2.173 0.724 -0.719 0.101 1.107 0.890 0.346 -0.543 0.000 1.087 -0.085 0.530 0.000 0.931 0.953 1.030 0.946 0.918 0.853 0.757 +1 0.313 -0.852 -0.562 0.433 0.533 0.762 0.471 -0.145 2.173 1.144 0.030 -1.449 2.215 0.907 -1.451 1.595 0.000 0.496 -0.354 1.229 0.000 0.517 0.861 0.977 0.663 1.305 0.949 0.847 +0 0.322 -1.405 -0.196 0.868 1.689 0.565 -0.334 0.132 0.000 1.011 -1.308 0.047 1.107 0.828 1.058 -0.940 2.548 0.488 0.055 1.424 0.000 0.912 0.705 0.989 0.954 1.738 1.306 1.053 +0 1.459 -0.899 -1.256 0.285 0.707 0.943 0.720 0.691 2.173 0.500 -1.382 -1.367 0.000 1.204 0.396 -0.543 0.000 0.693 0.098 1.188 0.000 0.865 1.263 0.986 0.687 0.377 0.941 0.817 +0 0.620 0.199 -0.401 0.635 1.359 1.354 0.445 -0.923 2.173 1.631 -1.158 0.437 2.215 1.196 0.065 1.491 0.000 0.762 -2.213 1.149 0.000 1.952 1.622 0.990 0.912 2.871 1.777 1.453 +1 1.961 0.451 1.730 0.609 -1.442 0.798 0.394 -0.097 2.173 0.330 0.117 0.442 0.000 0.596 -0.153 0.753 2.548 0.659 -0.320 -0.722 0.000 0.549 0.519 0.983 0.741 0.644 0.837 0.669 +1 0.691 1.177 0.070 1.096 -0.845 1.005 0.354 1.204 2.173 1.196 -0.020 1.730 0.000 1.209 0.327 -0.670 0.000 1.616 0.431 -0.028 0.000 0.852 0.808 0.986 1.120 0.843 0.871 0.800 +1 0.527 1.443 0.909 0.867 -0.265 0.444 1.925 -0.243 0.000 0.910 0.449 1.309 1.107 0.863 2.241 -1.198 0.000 1.305 -0.240 1.530 3.102 0.893 1.329 0.988 0.832 0.425 1.072 0.904 +1 0.493 -0.642 0.359 0.247 -0.793 0.884 0.643 -1.413 0.000 1.033 0.436 0.617 2.215 1.269 0.929 -0.321 0.000 1.611 -0.050 1.742 1.551 1.626 1.240 0.990 0.693 1.032 0.995 0.825 +1 0.625 -1.036 0.379 0.728 1.580 1.636 0.336 -1.537 2.173 0.719 -0.020 0.561 0.000 1.682 -0.064 -0.115 1.274 0.371 0.151 -0.518 0.000 0.560 1.212 0.990 0.868 2.026 1.098 0.885 +1 0.479 0.731 0.745 2.542 1.423 0.781 0.081 -0.563 2.173 0.319 2.145 -0.077 0.000 0.271 0.549 -0.949 2.548 0.520 1.439 0.292 0.000 0.317 0.757 0.994 0.648 0.247 0.925 0.756 +1 0.787 0.347 -0.441 0.846 0.716 0.596 -0.626 -1.361 2.173 0.680 1.006 0.247 0.000 0.471 2.102 1.325 0.000 1.024 0.648 -1.269 3.102 0.929 0.807 0.988 0.872 0.636 0.874 0.777 +1 0.603 -0.206 1.061 0.813 -0.166 1.174 0.743 1.193 2.173 1.281 0.196 -0.303 0.000 0.905 0.830 -1.384 1.274 0.978 0.123 -0.989 0.000 0.846 0.878 0.987 1.272 0.943 0.996 0.954 +1 0.729 -1.717 -0.464 0.917 -1.510 2.182 -1.620 -0.910 0.000 2.733 -1.271 0.737 2.215 0.994 -0.325 1.068 2.548 0.749 -1.669 1.316 0.000 0.736 0.860 0.987 1.081 1.007 1.122 0.867 +1 0.313 0.134 -1.634 1.235 -0.795 0.955 0.316 1.159 2.173 0.761 -0.944 0.024 2.215 0.344 0.057 -1.025 0.000 0.383 -0.618 -1.676 0.000 0.276 0.602 0.985 0.743 1.378 1.010 0.740 +0 1.242 -0.467 1.335 0.830 -0.557 0.591 0.432 -1.201 2.173 0.769 1.174 0.488 0.000 0.307 0.099 -1.501 1.274 0.586 -2.141 1.225 0.000 2.881 1.527 1.394 0.938 0.166 1.058 0.933 +0 0.867 -0.305 0.857 1.095 -0.599 0.524 0.725 -0.741 0.000 0.773 -0.984 0.094 2.215 0.713 -0.562 -1.381 2.548 0.547 -1.369 1.134 0.000 1.463 0.920 1.305 0.808 0.782 0.738 0.697 +1 1.217 -0.038 0.862 0.793 -1.092 0.745 1.435 1.377 0.000 0.709 -0.380 0.104 0.000 1.246 0.942 -0.445 2.548 1.403 -0.124 -0.834 3.102 0.782 0.820 1.337 0.854 0.722 0.767 0.669 +0 0.696 0.741 1.294 1.161 -0.657 0.798 -0.459 1.189 1.087 0.762 -0.542 -0.597 2.215 0.568 -0.267 0.697 0.000 0.572 1.332 0.245 0.000 0.996 0.914 1.224 0.912 1.148 0.901 0.871 +0 0.749 0.968 0.010 0.992 1.232 0.770 1.412 0.251 1.087 0.554 1.392 -1.208 0.000 1.163 0.183 -1.281 2.548 1.040 -0.613 1.223 0.000 0.864 0.980 1.065 0.748 1.389 1.190 1.030 +1 0.803 1.840 1.150 0.977 1.288 1.547 1.265 0.087 0.000 0.898 0.378 -1.426 0.000 1.745 -0.739 -1.297 2.548 0.799 -0.249 -1.409 3.102 1.204 1.051 0.973 0.791 0.251 1.008 0.877 +1 0.533 -0.703 0.400 1.730 0.180 0.887 0.057 -1.199 2.173 0.793 -1.095 -0.321 0.000 1.593 0.900 1.655 2.548 1.054 -0.279 1.207 0.000 1.266 1.199 0.984 2.469 1.071 1.786 1.409 +1 0.774 -1.057 0.736 0.657 -0.227 0.968 -0.605 -1.141 0.000 1.371 -1.474 1.140 0.000 0.621 -1.151 0.042 2.548 0.744 -0.616 -0.347 1.551 2.450 1.435 0.987 0.524 0.225 0.899 0.810 +0 1.735 -0.522 0.285 0.122 0.516 1.273 0.062 1.657 2.173 0.403 0.051 1.257 0.000 0.736 -2.530 -0.739 0.000 1.688 0.970 1.654 3.102 1.021 0.979 0.993 1.314 0.898 1.064 0.914 +1 0.793 0.503 -0.920 1.837 -0.247 1.180 1.558 0.630 0.000 1.787 1.462 -1.419 2.215 0.914 1.180 1.272 2.548 0.597 0.553 1.730 0.000 1.230 0.816 0.990 1.566 0.903 1.130 1.135 +1 0.793 -0.557 -0.341 0.871 -1.317 1.137 0.152 -1.143 2.173 1.562 0.794 0.960 0.000 2.009 0.559 0.181 2.548 0.410 0.527 -1.601 0.000 0.779 1.065 0.984 0.961 1.805 1.163 1.156 +0 0.826 0.508 -1.254 0.892 -0.563 1.564 -1.330 0.874 0.000 1.390 -0.063 -1.131 2.215 1.442 -0.655 0.304 2.548 1.049 -2.275 -0.879 0.000 0.865 0.976 0.987 0.641 1.532 1.267 1.132 +1 0.827 -1.069 -1.510 1.764 1.710 0.938 0.240 1.347 1.087 0.686 -1.915 -0.125 0.000 1.592 2.013 0.512 0.000 1.298 0.577 -0.182 0.000 0.685 0.767 1.000 0.839 1.273 1.129 1.054 +0 0.401 -0.731 0.449 0.365 0.795 0.764 -0.010 -1.187 2.173 0.844 0.429 1.097 0.000 0.996 0.378 0.070 2.548 0.699 -1.952 -0.297 0.000 2.116 1.445 0.995 0.910 1.012 1.062 0.930 +0 0.336 1.414 -0.213 1.781 1.715 0.706 0.713 -1.307 0.000 0.894 1.808 0.578 0.000 1.047 -0.382 -1.198 0.000 0.982 -0.914 -0.221 0.000 0.942 0.840 1.057 1.479 0.390 0.948 0.941 +1 0.669 -0.352 0.358 0.544 -1.347 0.849 1.402 0.426 0.000 1.410 0.475 -0.315 0.000 2.084 0.167 -1.637 2.548 1.029 0.433 1.173 3.102 0.987 0.794 0.987 1.020 0.668 0.902 0.949 +0 3.480 -0.174 -1.289 0.637 -1.577 1.741 -1.803 0.434 0.000 0.819 -0.354 -0.649 1.107 0.906 -0.814 0.514 2.548 0.911 -1.688 1.211 0.000 1.254 0.910 0.978 0.875 0.830 1.035 1.555 +0 1.373 -2.041 0.824 0.092 0.709 0.858 0.292 -0.222 0.000 0.688 -1.219 1.451 2.215 0.924 -0.274 -1.475 2.548 0.543 -2.269 -1.456 0.000 0.684 0.995 0.989 0.657 0.587 0.950 1.097 +0 0.562 0.972 1.152 0.420 1.356 0.710 -0.650 0.291 2.173 1.035 -0.765 -0.758 1.107 1.535 0.518 -1.509 0.000 0.401 -1.142 0.690 0.000 1.259 1.172 0.992 0.816 1.026 0.946 0.825 +1 0.336 -1.189 -1.503 0.911 -0.072 1.164 0.578 -0.743 2.173 1.233 -0.006 1.452 0.000 1.033 -0.342 0.837 0.000 0.392 -0.351 0.402 3.102 0.966 0.614 0.993 0.876 0.719 0.957 0.813 +1 0.458 0.628 -0.427 0.409 -0.463 0.685 -0.305 -1.486 0.000 1.109 -1.441 0.488 2.215 0.689 -0.845 1.569 0.000 0.609 0.931 1.432 0.000 0.903 0.805 0.986 0.898 0.617 0.907 0.781 +0 0.379 0.037 -0.744 1.226 1.027 0.546 -2.800 0.928 0.000 0.892 0.724 -1.217 2.215 0.702 -0.355 -0.303 1.274 1.372 1.011 -0.723 0.000 0.955 0.899 0.988 0.804 0.796 0.657 0.656 +1 1.727 -0.498 -1.120 0.731 1.664 1.132 -0.573 0.390 2.173 0.658 0.027 -0.251 0.000 0.381 -1.458 0.621 0.000 0.657 0.243 1.679 3.102 0.884 0.781 0.989 0.556 0.932 0.889 0.770 +0 0.628 -1.046 -0.454 0.896 0.196 1.142 0.554 1.387 0.000 2.028 -0.329 -0.851 2.215 0.570 -0.358 1.015 0.000 1.018 0.074 0.501 3.102 0.862 0.768 0.989 1.005 1.249 1.142 0.982 +1 0.574 0.600 0.940 0.846 -0.593 1.459 -0.399 -0.477 2.173 1.322 -0.926 0.987 0.000 1.068 1.908 1.300 0.000 1.023 -0.858 -1.248 3.102 1.190 0.908 0.988 0.922 0.925 1.014 0.882 +0 0.540 1.383 1.538 0.611 -0.367 1.084 0.822 1.311 2.173 1.041 -0.598 -0.529 2.215 0.868 -1.656 0.416 0.000 0.808 -0.847 -1.249 0.000 0.997 0.932 0.986 1.115 1.985 1.388 1.560 +0 1.941 0.336 -0.060 0.369 0.758 1.039 -0.226 -1.656 2.173 0.505 -0.450 1.456 0.000 0.592 -1.327 -0.648 0.000 1.205 -0.858 0.161 3.102 0.906 0.874 0.980 0.870 1.278 1.030 0.897 +1 0.841 -0.136 -1.431 1.450 0.052 0.738 -0.381 -0.146 0.000 1.347 0.766 1.697 2.215 1.095 0.616 0.263 2.548 0.686 -1.326 0.829 0.000 1.089 1.016 1.488 1.312 1.243 1.107 0.984 +1 1.647 -0.522 0.343 0.513 0.082 0.417 0.383 -1.170 1.087 1.214 1.590 -1.348 0.000 0.659 0.371 0.427 2.548 0.533 1.081 0.990 0.000 0.918 0.942 0.984 1.039 0.648 0.733 1.119 +1 0.420 -1.688 -1.293 0.450 -0.670 1.235 -0.211 0.750 0.000 0.857 0.061 -0.893 2.215 1.005 -1.233 -1.687 2.548 0.810 -0.575 0.338 0.000 0.751 0.758 0.981 0.722 0.997 0.648 0.588 +1 0.851 -1.194 -0.806 0.930 0.067 0.610 -0.822 -0.048 0.000 0.422 -0.448 -0.251 0.000 0.619 1.253 -1.673 0.000 0.421 -2.354 -1.614 0.000 0.963 0.976 0.986 0.879 0.372 0.715 0.699 +1 0.424 1.480 -1.248 0.361 -0.496 0.973 -0.054 1.611 0.000 1.458 -0.650 -0.229 2.215 0.596 0.222 0.608 2.548 0.710 -0.578 0.483 0.000 1.156 0.797 0.991 0.933 0.824 0.892 0.785 +0 0.305 -0.054 1.522 0.948 -0.956 0.563 -0.221 -0.141 0.000 0.977 0.415 0.961 2.215 0.714 -2.367 0.901 0.000 1.427 0.344 -1.133 3.102 0.864 1.019 0.988 0.650 1.013 0.738 0.730 +0 0.861 0.407 0.623 2.301 1.114 0.943 1.078 -0.827 0.000 0.569 2.089 -1.018 0.000 1.373 0.271 -1.122 2.548 2.117 -0.388 0.398 3.102 0.904 0.964 0.987 1.201 1.375 1.247 1.246 +1 1.908 -0.359 0.491 1.126 1.018 0.894 -1.042 -0.945 1.087 0.573 -0.377 -0.488 0.000 0.769 -0.626 -1.623 1.274 0.397 0.100 -1.191 0.000 0.400 0.524 0.988 0.868 0.622 0.954 0.765 +0 0.409 -1.936 1.310 1.316 0.147 0.685 0.575 -1.016 2.173 0.737 0.751 0.423 0.000 1.178 -0.024 -1.495 2.548 0.653 -0.977 1.226 0.000 1.187 1.031 0.990 2.364 0.581 1.696 1.495 +1 1.577 0.788 1.222 0.762 0.461 2.274 0.441 -0.055 0.000 1.816 0.993 -1.307 0.000 0.718 0.101 1.606 2.548 0.626 1.497 -0.735 0.000 0.879 0.858 0.986 0.622 0.460 0.539 0.700 +0 1.440 0.466 0.995 0.082 -1.377 0.680 0.374 -0.093 2.173 1.227 -0.834 -1.212 0.000 1.033 -1.169 -0.756 0.000 1.034 -1.883 0.574 0.000 0.783 0.861 0.984 0.861 0.867 0.892 0.881 +1 0.900 -0.534 0.823 0.896 0.038 0.824 1.035 1.216 1.087 1.112 0.683 -1.437 2.215 0.706 -0.547 0.224 0.000 0.872 1.988 -0.706 0.000 0.853 0.922 0.993 1.100 0.989 0.914 0.785 +1 1.722 1.003 0.098 0.698 0.134 0.351 -1.591 0.470 0.000 0.653 0.606 -0.733 1.107 0.872 -0.497 -1.650 2.548 0.753 0.974 -0.734 0.000 0.775 0.740 0.986 1.418 0.771 0.973 0.877 +0 1.564 1.728 1.652 1.153 0.960 0.671 -0.041 -0.412 0.000 0.450 -0.670 1.662 0.000 0.637 1.267 -0.230 2.548 0.475 -0.035 0.331 3.102 1.181 0.973 1.086 0.853 0.386 0.669 0.939 +0 0.519 1.588 -0.921 1.333 1.568 0.617 0.904 0.956 2.173 0.613 -0.101 -0.590 0.000 1.036 1.217 -0.257 2.548 1.038 0.054 0.395 0.000 0.811 0.904 0.986 0.851 0.910 0.701 0.735 +0 0.609 -0.595 1.181 0.710 -0.059 0.585 0.045 -0.519 0.000 0.768 -0.779 -1.297 2.215 0.733 1.140 0.778 2.548 0.925 -0.392 0.380 0.000 0.865 0.896 0.985 0.762 1.234 0.883 0.786 +0 2.584 0.731 -1.123 0.620 0.831 1.953 -0.881 0.541 0.000 1.262 1.422 -1.009 1.107 0.666 -0.483 0.204 0.000 0.564 -0.459 -0.660 0.000 0.717 1.399 1.722 1.075 0.896 1.824 1.658 +1 0.766 -0.440 0.946 0.500 -1.116 1.247 -0.274 1.499 2.173 1.012 -0.504 -0.395 2.215 0.520 0.117 -1.114 0.000 1.899 -0.364 0.178 0.000 1.054 0.771 0.987 0.799 1.650 0.980 0.816 +0 0.440 -0.129 0.770 1.682 -0.567 1.283 0.719 1.450 0.000 0.851 1.030 -0.576 2.215 0.818 0.524 0.645 2.548 1.634 2.092 0.170 0.000 0.832 0.877 1.112 0.848 0.820 0.861 0.910 +1 1.565 1.356 -0.034 0.116 0.649 0.787 -0.072 1.510 2.173 0.913 0.598 -1.229 2.215 0.521 1.789 -0.819 0.000 0.967 1.090 0.592 0.000 0.793 0.851 0.975 1.204 0.891 0.916 0.810 +0 1.516 0.896 -1.496 0.484 0.453 0.345 0.406 1.613 0.000 0.976 0.125 0.125 1.107 0.770 1.186 0.362 2.548 0.465 -1.146 -1.014 0.000 0.755 0.876 1.166 1.034 0.604 0.739 0.710 +0 0.571 1.042 0.164 1.559 0.010 0.547 -0.074 0.884 2.173 0.595 -0.796 -1.468 2.215 0.737 -0.318 1.591 0.000 1.381 0.648 -1.149 0.000 0.958 0.898 0.991 1.916 0.784 1.406 1.202 +1 0.945 2.182 1.374 0.906 1.208 1.373 1.123 -0.480 2.173 0.563 0.904 1.688 0.000 0.452 1.230 0.690 0.000 0.665 0.285 0.460 3.102 0.627 1.088 0.983 0.703 0.862 0.959 0.779 +1 1.131 0.286 -1.370 0.294 1.044 0.474 -0.275 -0.277 2.173 0.825 -0.613 -0.808 2.215 0.941 1.070 0.586 0.000 0.905 0.477 0.192 0.000 0.471 1.103 0.989 0.826 0.454 0.715 0.723 +1 1.120 -1.164 0.433 0.568 1.577 0.887 -1.363 -1.012 0.000 1.696 -0.944 0.711 2.215 1.162 -2.197 -1.052 0.000 0.959 -0.640 1.616 3.102 0.993 0.964 0.987 0.688 0.846 1.165 0.966 +0 0.903 1.364 1.229 1.289 -0.145 0.881 1.112 -1.707 2.173 0.795 1.656 0.149 0.000 0.693 0.571 -0.762 0.000 0.640 1.244 -1.040 0.000 0.822 1.040 1.413 1.212 1.449 1.043 0.906 +0 0.637 1.299 -1.708 1.014 -0.413 1.056 1.213 0.361 2.173 0.826 0.639 -0.599 0.000 1.386 -0.041 -1.595 2.548 1.509 -0.804 1.233 0.000 0.843 0.762 1.024 0.931 1.798 1.118 1.025 +0 0.500 1.707 0.262 0.432 1.336 1.235 0.838 0.571 2.173 1.594 0.653 -0.756 2.215 1.060 0.757 -1.364 0.000 0.931 1.258 1.442 0.000 0.735 1.004 0.989 0.765 1.929 1.098 0.893 +0 1.571 0.271 0.681 0.093 1.619 1.373 0.667 -0.902 0.000 1.394 -0.074 1.265 2.215 0.959 0.047 -0.760 0.000 0.954 0.310 0.177 3.102 0.704 0.887 0.996 0.861 0.896 1.064 0.968 +0 0.420 -1.611 -0.946 0.618 0.873 0.800 -0.321 -0.701 0.000 1.166 -1.162 1.424 2.215 1.302 -0.434 0.078 2.548 0.373 1.367 0.616 0.000 1.232 0.979 0.995 0.769 1.317 1.021 0.835 +1 0.889 -1.466 0.976 0.467 -0.578 1.016 -0.257 -1.222 0.000 1.841 -1.352 -0.295 2.215 0.391 -0.847 1.178 0.000 0.810 -0.121 1.455 0.000 0.925 0.917 0.985 0.946 0.775 1.046 0.877 +0 1.937 -0.778 0.707 0.901 0.859 1.242 1.138 -1.278 0.000 0.526 1.318 -0.886 2.215 0.603 0.573 0.666 0.000 1.016 -0.389 -0.301 3.102 1.368 0.875 0.987 1.696 0.764 1.119 0.975 +0 1.270 -1.031 0.683 0.939 1.614 0.496 1.005 -1.686 0.000 0.524 -1.149 -0.202 1.107 0.995 0.420 -0.313 2.548 1.106 -0.749 -0.976 0.000 0.757 0.906 1.125 0.784 0.708 0.904 1.054 +0 0.588 1.402 -0.394 0.782 1.406 0.435 -0.927 1.446 1.087 1.060 -1.121 -0.440 0.000 0.927 -0.547 0.993 2.548 0.745 -0.145 -0.724 0.000 0.767 0.889 0.987 0.950 0.342 0.723 0.850 +1 0.398 -0.917 -0.986 0.205 0.010 0.844 0.101 -0.141 0.000 1.157 2.254 1.592 0.000 1.188 0.828 -0.235 0.000 1.240 -1.856 -1.654 0.000 0.882 0.879 0.986 0.655 1.001 0.857 0.981 +1 1.103 1.345 -1.159 1.544 0.566 1.054 1.123 -0.203 0.000 0.407 -1.612 0.593 0.000 0.516 1.450 0.877 2.548 1.865 1.016 -1.724 1.551 0.857 0.728 1.808 1.120 0.555 0.766 0.799 +0 2.092 0.338 -0.879 1.476 -1.655 2.391 -0.403 0.713 1.087 1.542 0.150 -1.473 2.215 1.845 0.795 -0.577 0.000 2.320 0.172 0.264 0.000 1.750 1.796 1.566 2.450 2.723 1.862 1.677 +1 0.741 -0.218 -0.446 0.681 0.705 0.381 0.061 -1.139 2.173 0.267 2.094 -0.120 0.000 0.737 -1.243 0.724 2.548 0.550 1.229 -1.637 0.000 0.552 1.081 0.984 0.695 0.833 0.717 0.688 +0 5.039 -0.657 1.147 0.310 1.306 1.227 0.461 -0.717 0.000 1.134 0.935 -0.452 0.000 1.854 -0.180 -0.177 2.548 0.617 -0.408 -1.260 0.000 0.909 0.980 1.007 1.788 1.036 1.180 1.303 +1 0.840 -1.240 -0.221 1.017 0.119 1.294 -1.102 0.029 0.000 0.964 -1.306 -1.578 0.000 1.544 -0.662 1.523 1.274 1.082 -0.845 -0.881 3.102 2.370 1.383 0.989 1.100 0.830 1.063 0.961 +0 1.894 -0.111 1.603 1.188 -1.630 1.198 0.947 -0.274 2.173 0.590 -0.829 1.156 1.107 0.542 0.188 0.516 0.000 0.831 0.038 -0.795 0.000 0.688 0.798 0.989 0.831 1.745 1.255 0.962 +1 0.849 -1.358 -0.567 1.136 -1.581 1.503 -1.430 0.551 2.173 0.539 -0.734 -0.928 0.000 1.240 -2.185 -1.653 0.000 1.250 -1.463 -0.086 0.000 0.923 1.211 1.076 0.662 0.725 0.869 0.790 +1 1.278 -0.699 -1.423 0.984 1.325 0.924 -0.334 0.111 1.087 0.735 -1.070 1.051 0.000 0.361 -0.704 -0.266 0.000 1.041 -0.272 -0.732 3.102 0.857 0.976 0.990 0.726 0.715 0.813 0.726 +1 0.431 1.550 -0.705 1.190 0.815 1.356 0.144 1.419 2.173 0.968 0.455 -0.146 0.000 2.486 0.332 -0.901 0.000 1.294 0.473 0.615 3.102 1.502 1.283 0.987 1.172 0.977 1.255 1.092 +1 0.786 -0.167 0.934 0.946 -1.732 0.915 -0.951 -0.256 1.087 0.644 -2.034 1.467 0.000 1.191 -0.534 -1.102 2.548 1.037 0.363 0.004 0.000 0.641 0.788 0.987 1.293 0.931 0.945 0.778 +0 0.812 -1.023 0.164 1.224 0.842 0.687 -0.900 -1.244 2.173 0.720 0.156 -1.629 0.000 0.528 0.776 0.388 0.000 1.028 0.129 -0.732 3.102 0.976 1.005 0.998 0.821 0.634 0.767 0.743 +0 1.330 -0.807 1.360 1.665 0.244 0.932 0.081 1.474 0.000 1.667 0.867 -0.634 0.000 1.351 0.499 -0.328 1.274 0.651 0.362 -0.986 0.000 0.972 0.928 1.741 1.441 0.391 1.047 0.992 +0 0.681 0.222 -0.517 0.977 0.425 1.144 0.271 -1.348 2.173 1.556 -0.269 0.406 2.215 0.922 0.166 0.866 0.000 0.953 -0.720 -0.514 0.000 1.003 0.946 0.990 1.130 2.038 1.121 0.923 +0 1.059 0.052 0.123 0.730 1.344 0.987 -0.051 1.386 2.173 0.718 -0.729 -1.160 2.215 0.901 0.105 -0.492 0.000 1.027 0.747 0.049 0.000 0.654 0.882 1.086 0.866 1.027 0.863 0.769 +0 1.518 1.227 -1.711 0.913 1.616 1.608 0.493 0.322 0.000 1.558 1.440 -1.213 2.215 0.491 0.027 -0.453 2.548 0.843 0.206 -0.054 0.000 0.896 0.747 0.980 0.976 0.942 1.180 1.099 +0 1.918 -0.086 -0.813 3.775 -0.917 4.190 0.138 0.786 1.087 1.531 -0.861 -1.335 0.000 0.859 1.124 -1.095 2.548 0.781 -1.041 -0.382 0.000 0.928 0.942 0.981 3.987 2.712 2.566 1.882 +1 1.261 -0.336 1.479 0.725 -0.786 0.982 -1.012 -1.582 1.087 1.085 -0.492 0.528 0.000 0.709 2.672 0.411 0.000 1.566 -0.728 -0.758 3.102 0.890 0.928 1.182 0.840 0.894 0.897 0.808 +1 1.194 0.165 0.667 0.634 1.614 0.981 0.797 -0.868 0.000 1.130 -0.396 1.020 2.215 0.731 1.368 -1.340 0.000 0.523 0.473 1.096 0.000 0.831 0.950 0.987 0.718 0.646 1.025 0.889 +1 0.960 0.282 1.241 1.069 -1.214 1.038 0.458 0.729 2.173 1.034 -2.815 -0.873 0.000 0.743 0.805 -0.640 0.000 1.516 0.213 -1.333 0.000 0.780 1.230 1.125 1.140 1.185 1.037 0.925 +1 0.673 -0.414 -1.389 1.812 -0.565 0.515 -0.317 0.450 2.173 0.780 1.019 1.298 2.215 0.584 -1.072 -0.962 0.000 0.811 -1.459 0.079 0.000 0.651 0.724 1.036 1.260 0.944 0.931 0.867 +1 0.942 -0.537 0.408 0.540 -0.258 0.847 -0.308 -1.087 0.000 1.405 -0.552 -0.555 2.215 1.465 -0.960 1.474 0.000 3.348 -0.785 0.539 0.000 1.123 0.727 0.986 0.896 0.809 0.958 0.975 +0 1.249 -1.702 0.479 0.740 0.174 1.648 -0.284 -1.184 0.000 1.383 -0.782 0.301 0.000 1.206 -0.151 1.143 1.274 1.010 -0.967 -0.777 0.000 1.031 0.883 0.993 0.846 0.606 0.732 0.724 +0 1.170 1.016 -0.510 0.573 -1.337 0.673 1.421 0.430 2.173 0.822 -0.102 -1.565 2.215 0.538 -1.736 1.257 0.000 0.384 0.025 -0.250 0.000 0.748 0.764 0.986 0.938 1.413 1.023 0.919 +0 0.534 -1.050 1.221 0.904 -0.219 0.881 -1.299 -0.820 2.173 0.555 2.173 -1.357 0.000 1.954 0.148 0.618 1.274 1.681 0.625 -1.581 0.000 1.054 1.630 0.984 0.840 2.052 1.750 1.376 +0 0.641 0.611 -0.420 0.069 -1.501 1.363 -0.887 1.603 0.000 1.616 1.026 -0.363 1.107 0.911 1.225 -0.021 2.548 0.798 -0.248 -0.187 0.000 0.792 1.837 0.904 0.982 0.434 1.677 1.266 +0 0.876 -0.699 -0.337 1.168 1.427 0.293 1.454 0.424 0.000 0.848 0.980 1.181 1.107 0.293 1.919 -0.382 0.000 0.593 0.986 -1.008 3.102 0.390 0.581 1.401 0.922 0.592 0.844 0.786 +1 0.666 -0.179 0.021 0.839 -1.618 0.727 0.738 -0.316 2.173 0.749 -0.586 0.067 2.215 0.726 -0.343 -1.636 0.000 1.806 0.532 1.458 0.000 0.799 1.092 1.031 0.852 0.870 0.893 0.764 +0 0.467 -1.656 0.367 1.882 1.167 0.903 -1.543 -1.237 0.000 0.882 -1.805 -0.776 0.000 1.434 -0.572 0.461 2.548 0.666 -0.317 -0.578 3.102 0.817 0.746 0.992 0.773 0.609 0.901 0.908 +0 0.543 0.269 -1.743 0.978 0.425 0.918 0.498 -1.502 0.000 1.004 -0.219 -1.563 0.000 1.261 -0.096 -0.053 0.000 0.712 -0.690 -0.079 3.102 0.768 0.981 0.990 0.513 0.442 0.717 0.702 +1 0.331 -0.543 1.403 1.619 -1.716 1.076 -0.451 -0.166 2.173 0.844 0.979 -1.107 0.000 1.408 -0.098 0.595 2.548 0.884 -0.773 1.035 0.000 0.952 0.972 0.988 1.444 1.007 1.297 1.074 +0 0.879 0.034 -1.648 0.513 -0.488 0.604 0.062 -0.019 2.173 0.640 0.600 0.920 2.215 0.383 -0.751 0.782 0.000 0.516 0.356 1.428 0.000 0.424 0.570 0.988 0.700 0.731 0.623 0.522 +0 1.573 0.466 -1.588 0.369 -0.918 0.734 1.324 0.521 0.000 0.724 0.306 0.677 1.107 0.514 1.718 -0.574 0.000 0.662 -0.166 -0.980 1.551 0.971 0.917 0.989 0.876 0.644 0.665 0.767 +1 0.421 -1.576 -0.240 0.774 0.245 0.835 -0.101 1.489 2.173 0.716 -1.025 1.201 0.000 1.243 1.068 -0.274 2.548 1.175 -1.986 -0.188 0.000 0.935 0.759 0.998 0.953 1.536 0.981 0.870 +0 0.881 -1.523 1.146 0.436 -0.732 1.077 -1.301 -0.897 2.173 0.396 -1.149 1.051 0.000 1.610 -0.381 0.709 1.274 0.697 -2.142 -0.841 0.000 0.851 0.898 0.990 0.796 1.797 0.979 0.811 +1 2.210 0.185 -1.499 0.471 -0.443 0.905 -0.123 0.182 0.000 0.759 -0.475 -1.201 0.000 1.015 0.553 0.830 2.548 0.506 0.396 0.064 0.000 0.861 0.932 1.152 0.815 0.320 0.695 0.654 +0 0.830 0.827 0.531 0.880 -1.565 0.923 0.303 -0.603 0.000 0.893 -0.223 -0.888 0.000 1.214 1.214 0.853 0.000 0.827 -0.233 0.952 3.102 0.885 0.725 1.125 0.565 0.510 0.512 0.557 +1 0.936 -0.947 -1.606 0.688 -0.593 0.858 -0.267 -1.444 2.173 0.950 -1.353 0.054 0.000 1.457 -0.542 0.454 0.000 1.124 -0.919 1.097 3.102 0.975 0.831 0.986 0.832 0.900 0.956 0.857 +1 1.994 -0.311 1.146 0.565 -1.536 1.196 -0.666 0.067 2.173 1.313 -0.276 -0.750 2.215 1.147 0.062 1.725 0.000 0.389 -0.705 -1.493 0.000 0.815 0.871 0.989 1.310 1.290 1.123 0.930 +0 1.214 -0.822 0.808 0.553 1.018 1.310 0.553 -1.343 2.173 1.275 0.238 0.106 0.000 0.459 0.319 1.038 0.000 0.813 0.777 -0.575 1.551 0.876 0.699 0.989 1.401 0.727 0.962 0.907 +0 0.793 0.185 0.057 2.109 -0.676 1.061 -0.530 -0.981 2.173 1.052 -0.504 0.852 0.000 1.578 -0.506 1.454 2.548 1.422 0.629 0.821 0.000 0.874 0.942 1.098 0.972 1.308 1.053 0.985 +0 0.866 -0.591 -1.366 0.500 0.339 1.083 -0.511 1.351 2.173 1.474 -1.515 -0.284 2.215 0.725 0.000 1.642 0.000 0.697 0.559 -0.161 0.000 0.827 0.901 0.986 1.081 2.110 1.174 0.945 +0 0.351 1.212 -0.899 1.282 0.761 3.184 0.477 0.074 2.173 3.749 1.111 -1.404 0.000 1.829 -0.155 1.477 0.000 0.575 -0.274 -1.245 0.000 0.860 0.855 0.986 1.832 0.929 1.165 1.103 +0 2.253 0.120 1.482 0.242 -0.943 0.585 1.054 0.307 0.000 0.799 0.592 -1.124 2.215 0.708 -0.341 -0.830 2.548 0.950 1.365 -0.268 0.000 0.654 0.922 0.984 0.801 0.460 0.693 0.793 +1 2.327 -1.339 -0.998 1.421 -1.426 2.703 0.901 0.842 0.000 1.470 -1.536 -0.502 0.000 0.554 -0.781 0.666 2.548 0.708 -0.482 -0.158 1.551 0.834 0.792 0.985 0.835 0.331 0.706 0.673 +0 0.412 -1.225 1.320 0.458 -0.643 0.659 -0.503 1.056 0.000 0.690 -1.304 -0.225 2.215 0.909 -0.862 -1.009 1.274 0.457 -1.314 -1.519 0.000 0.773 0.867 0.980 0.663 0.572 0.631 0.613 +0 1.107 0.512 -0.151 1.110 0.803 0.820 0.639 -1.279 2.173 0.438 0.355 1.629 0.000 0.708 -0.510 -1.686 1.274 0.673 0.996 -0.597 0.000 0.929 0.956 1.165 0.909 0.696 0.820 0.747 +0 0.439 1.428 -0.750 0.801 -1.326 0.997 0.371 0.416 2.173 1.053 0.369 1.544 0.000 0.532 0.863 -1.723 0.000 1.480 -0.695 -0.361 3.102 0.400 1.116 0.999 0.828 1.170 0.962 0.847 +0 0.557 0.046 0.769 1.813 -1.251 0.815 0.145 1.069 0.000 0.838 0.626 -0.573 0.000 1.290 1.198 0.362 1.274 1.071 1.460 1.460 0.000 0.843 0.938 1.350 1.277 1.642 1.055 0.873 +0 1.760 0.062 1.516 1.945 1.172 1.839 -1.119 -0.276 0.000 0.364 -0.882 -1.563 2.215 0.339 -0.975 -0.889 2.548 0.418 -0.357 -0.386 0.000 0.497 0.851 0.984 0.780 0.215 0.550 1.038 +0 0.861 0.523 1.494 1.122 0.162 0.891 -0.998 -1.272 2.173 1.049 0.415 -0.341 2.215 0.763 -1.500 1.383 0.000 0.562 0.391 0.895 0.000 0.966 0.970 1.269 0.896 1.534 1.075 0.979 +1 0.941 0.719 0.837 0.853 -1.300 0.667 -0.281 0.885 2.173 0.851 0.274 0.297 0.000 2.032 0.616 -0.836 2.548 0.766 0.645 1.646 0.000 1.020 1.127 1.164 0.884 1.621 0.938 0.819 +1 0.984 -1.358 -0.851 0.819 1.444 0.574 -0.362 0.316 1.087 0.741 -1.205 0.753 2.215 1.390 -1.706 -1.391 0.000 1.203 -1.337 -0.370 0.000 0.980 0.772 1.092 0.946 0.569 0.702 0.660 +1 0.634 1.476 1.324 0.764 1.313 0.987 1.062 -0.242 1.087 0.723 0.291 -0.622 0.000 1.074 0.638 -1.622 0.000 0.420 -0.286 0.442 0.000 0.842 0.816 1.001 0.636 1.213 0.859 0.731 +0 1.177 -0.160 0.154 3.206 0.497 1.710 -0.932 -1.071 0.000 1.043 1.379 -1.416 0.000 1.712 -0.541 0.648 2.548 1.982 -0.016 -1.328 0.000 0.645 0.912 0.993 0.849 0.984 0.875 1.227 +1 0.991 -0.259 0.322 0.984 1.517 1.253 -0.143 -0.662 0.000 0.518 -0.297 1.470 0.000 0.613 -1.260 1.037 2.548 1.066 0.379 0.500 3.102 0.627 0.912 1.205 0.710 0.712 0.776 0.755 +1 1.908 -1.067 0.395 0.852 0.775 1.158 -0.621 -1.210 1.087 0.427 -0.087 -0.905 0.000 0.808 -0.649 1.078 0.000 0.445 -1.507 -0.495 0.000 0.924 0.918 0.978 0.604 0.758 1.007 0.849 +1 1.091 -1.196 1.560 0.432 -0.523 0.869 -1.486 -0.951 1.087 0.613 -0.964 0.348 0.000 1.456 -1.443 0.892 0.000 0.881 -0.117 -0.312 3.102 0.811 0.887 0.983 0.801 0.846 0.867 0.759 +1 0.947 -1.305 -0.869 1.161 -1.435 0.930 -0.709 1.045 0.000 1.072 -0.187 0.733 1.107 0.894 1.450 -1.010 0.000 0.456 0.498 0.248 3.102 1.015 1.021 0.985 1.006 0.370 1.024 1.044 +0 1.451 -0.692 0.994 0.361 1.178 1.129 0.937 -0.620 0.000 1.459 1.813 1.634 0.000 0.861 -0.077 -1.014 2.548 1.113 0.592 -0.085 0.000 0.820 0.860 0.980 0.848 0.770 0.664 0.860 +1 1.269 -0.268 1.241 1.617 -1.679 0.866 -0.150 -0.472 0.000 0.449 0.468 1.073 2.215 1.565 0.494 0.323 0.000 0.647 0.955 -0.863 3.102 1.562 1.011 0.986 0.656 0.507 0.707 0.895 +0 1.448 0.965 -0.487 2.697 -0.073 1.300 0.295 1.481 0.000 1.441 -0.689 1.743 2.215 0.552 -0.110 0.455 2.548 0.847 -1.679 0.924 0.000 2.006 1.305 1.002 2.334 0.914 1.460 1.460 +0 0.590 -1.376 1.092 1.360 1.324 1.128 -0.535 -0.045 2.173 1.033 0.701 -1.301 2.215 0.623 -0.519 -1.679 0.000 0.716 0.679 -0.268 0.000 0.894 0.992 0.986 1.116 1.792 1.157 0.923 +0 0.865 0.487 1.738 0.740 0.934 1.112 -0.136 -0.952 0.000 1.150 -1.535 0.288 0.000 0.709 -1.080 1.369 1.274 1.040 0.703 0.204 3.102 2.868 1.712 0.995 0.699 0.972 1.217 1.259 +1 0.740 0.949 -0.060 2.050 0.755 0.951 0.166 -1.259 2.173 0.808 -0.488 1.338 2.215 0.936 0.024 -0.862 0.000 0.822 -0.818 -0.307 0.000 0.727 0.790 1.145 1.442 1.026 1.109 0.895 +1 0.493 -0.478 0.990 1.512 -1.057 0.646 1.120 1.258 0.000 0.552 0.940 -1.476 0.000 2.510 -0.589 0.120 2.548 0.429 -0.076 -1.434 3.102 0.801 0.560 1.152 1.203 0.813 1.098 0.992 +1 1.553 -0.881 -1.612 0.653 0.592 0.502 0.056 -0.091 0.000 0.864 0.566 0.356 2.215 1.564 1.418 -1.488 0.000 0.892 0.553 -0.529 0.000 1.160 1.194 1.276 0.759 0.523 0.874 1.070 +1 0.516 -0.842 -0.676 1.048 0.772 0.531 0.651 -1.292 2.173 0.859 -0.383 -0.318 2.215 0.675 -1.213 0.982 0.000 0.908 -0.679 0.347 0.000 1.005 0.964 0.987 0.717 0.940 0.733 0.675 +1 1.209 0.610 0.479 0.644 1.319 0.784 -0.549 -0.882 2.173 0.324 0.652 -0.556 0.000 0.916 1.399 1.624 0.000 0.678 -1.082 0.216 0.000 0.857 1.226 0.987 0.495 0.855 0.751 0.741 +0 0.965 -1.175 1.416 1.102 1.108 1.205 -1.353 -0.624 0.000 0.706 -1.077 0.327 1.107 0.478 -1.661 -1.339 0.000 0.734 -0.150 1.159 3.102 0.879 0.967 0.985 0.773 0.542 0.739 0.836 +1 2.073 0.007 -1.585 0.668 -1.542 1.095 0.093 0.030 2.173 0.439 0.488 0.362 0.000 0.820 0.454 -0.954 2.548 0.896 -1.177 0.654 0.000 0.932 0.923 0.985 1.483 0.946 0.978 0.920 +1 0.550 1.012 0.376 0.513 -0.586 1.114 2.050 -1.254 0.000 1.266 0.195 1.378 2.215 1.336 0.492 -0.656 1.274 2.194 -0.078 0.709 0.000 2.350 1.971 0.989 0.943 1.355 1.517 1.748 +1 2.048 0.231 -1.410 0.208 0.580 0.998 -0.615 -0.081 2.173 0.563 0.426 0.170 0.000 0.661 -1.065 1.034 2.548 0.699 0.911 1.559 0.000 0.959 0.828 0.985 0.851 0.899 0.912 0.785 +0 0.618 -0.641 1.163 0.123 -1.245 0.843 2.839 -1.400 0.000 0.601 -0.942 0.284 0.000 0.640 1.194 0.841 1.274 0.601 0.466 -0.616 3.102 0.847 0.891 0.989 0.624 0.493 0.923 0.826 +1 0.506 -2.134 0.773 2.364 0.911 1.042 -1.430 -1.323 2.173 0.914 -1.238 0.133 2.215 0.527 1.629 -1.055 0.000 0.383 -0.067 -1.022 0.000 1.118 1.375 0.975 1.391 1.393 1.265 1.259 +0 1.516 -0.713 -1.531 0.723 1.410 1.088 -0.722 0.471 2.173 0.825 -0.537 -0.788 2.215 0.669 -1.880 -0.585 0.000 0.399 -0.016 0.110 0.000 0.767 0.927 0.979 0.823 1.270 0.963 0.836 +0 2.129 0.105 0.268 0.595 0.415 1.650 0.844 -1.510 1.087 0.980 0.474 -0.425 2.215 0.465 0.979 0.574 0.000 0.586 -0.754 1.300 0.000 0.754 0.824 0.976 2.028 1.590 1.407 1.086 +1 0.531 -1.204 -0.660 0.899 1.201 0.552 -0.760 -0.924 0.000 1.103 0.804 1.565 1.107 1.418 0.296 0.194 2.548 0.930 1.366 1.671 0.000 0.891 0.975 0.987 1.086 1.299 0.967 0.864 +0 0.366 -1.671 1.642 2.169 -1.083 1.025 -0.783 -0.068 2.173 1.336 -0.860 1.690 0.000 1.192 -0.844 0.602 2.548 0.787 -1.541 0.279 0.000 1.092 0.969 0.985 1.755 0.787 1.325 1.253 +0 1.049 0.231 0.630 1.188 0.483 0.698 -1.771 -0.726 0.000 0.838 0.766 1.274 2.215 1.271 0.633 -0.968 2.548 0.665 1.818 -1.367 0.000 3.762 2.305 0.994 0.998 0.989 1.548 1.360 +0 0.536 1.071 1.154 2.406 -1.595 0.433 0.935 -0.529 0.000 0.952 1.247 0.549 2.215 0.617 -1.526 -0.549 0.000 0.814 -0.265 0.189 0.000 0.990 0.833 0.984 0.867 0.590 0.816 0.755 +1 0.367 -0.946 0.783 0.763 0.280 1.083 -0.028 -1.680 2.173 0.963 -0.678 0.216 0.000 1.192 0.365 -0.900 2.548 0.651 -0.140 1.024 0.000 0.740 1.021 0.985 1.063 0.966 0.910 0.824 +1 1.647 -0.275 0.131 1.107 -0.388 0.715 -0.537 1.055 2.173 0.353 0.537 -0.920 0.000 0.547 0.296 -1.611 2.548 1.160 -0.347 -1.681 0.000 0.915 0.925 0.981 0.811 0.630 0.783 0.739 +1 0.722 -1.195 1.588 1.556 -0.985 1.537 -0.143 0.171 2.173 1.306 -0.145 -1.020 0.000 0.996 -0.411 1.214 2.548 1.526 -2.102 1.173 0.000 3.216 1.935 1.077 1.670 1.267 1.683 1.399 +0 0.696 -0.626 1.022 0.347 -0.622 0.608 -0.146 -0.360 0.000 1.235 -0.288 1.622 2.215 1.095 -0.204 0.373 0.000 0.895 -0.121 -1.149 3.102 0.902 0.772 0.989 0.771 0.575 0.784 0.682 +1 0.734 0.336 -0.446 1.303 0.675 1.052 0.158 -0.917 0.000 0.769 -0.128 1.035 2.215 1.698 -0.299 1.615 0.000 1.385 -0.948 0.299 1.551 1.068 1.220 1.148 0.752 0.754 0.930 0.881 +0 1.506 -0.605 0.967 0.307 -1.503 0.925 0.001 -0.457 0.000 0.523 -1.035 -0.984 2.215 0.569 0.902 1.306 2.548 0.824 -0.361 -1.596 0.000 1.177 0.979 0.987 0.751 0.872 0.691 0.758 +0 1.815 -0.957 -0.074 0.830 0.360 1.510 -1.544 -1.573 0.000 1.295 0.248 1.260 1.107 1.726 -0.469 -0.487 1.274 0.495 1.119 0.585 0.000 2.932 2.116 0.976 1.363 1.708 1.603 1.407 +1 0.891 2.240 -0.237 1.022 -1.255 0.728 -0.032 -0.109 2.173 1.405 -0.485 -1.735 0.000 0.513 2.107 1.130 0.000 0.707 -1.554 -0.763 0.000 0.547 0.661 1.048 1.567 0.934 1.106 1.029 +0 2.514 -0.234 -0.993 0.469 1.493 0.989 -0.682 0.267 2.173 0.476 -0.818 0.792 0.000 0.855 0.129 1.248 2.548 0.589 -0.406 -1.572 0.000 0.598 0.719 1.180 0.922 1.012 0.989 0.784 +0 0.653 -0.978 -0.182 1.204 -1.692 0.750 -0.853 1.134 0.000 1.888 -0.614 -0.567 1.107 1.080 0.349 1.497 2.548 1.061 -0.471 0.413 0.000 0.850 0.905 1.201 1.080 1.661 1.097 0.950 +0 0.348 -0.881 -1.509 2.117 -1.564 0.736 -0.542 0.479 1.087 1.016 0.979 -0.199 1.107 0.541 0.646 0.764 0.000 0.612 0.836 -1.221 0.000 0.627 0.827 0.982 1.214 1.314 1.118 0.861 +1 0.487 0.570 0.401 1.204 -1.029 0.686 1.424 -1.044 2.173 1.223 0.046 0.500 0.000 0.995 0.896 1.587 2.548 0.527 0.571 0.097 0.000 0.503 0.916 1.019 0.720 0.754 0.875 0.766 +1 0.335 1.116 0.228 0.826 1.666 1.327 1.339 -0.764 1.087 1.331 0.246 1.042 0.000 0.339 0.805 -0.619 0.000 0.569 0.098 -0.293 0.000 1.061 0.712 0.987 1.308 0.894 1.025 0.901 +1 1.800 0.443 -0.776 0.216 1.320 0.591 0.624 0.740 2.173 0.703 -0.231 -1.212 0.000 0.528 -0.442 0.675 2.548 0.668 0.132 1.261 0.000 0.729 0.839 0.987 0.781 0.411 0.680 0.633 +0 1.061 -1.299 1.631 2.761 1.591 1.543 1.823 0.423 0.000 2.550 1.417 -0.431 2.215 1.589 0.062 1.685 1.274 1.741 -0.543 -1.182 0.000 1.145 1.615 0.998 1.813 2.583 3.883 3.894 +0 0.722 0.080 -1.689 1.179 -0.974 0.619 0.129 0.516 0.000 0.811 1.050 0.085 0.000 1.223 1.005 -1.232 2.548 1.086 -0.468 1.094 3.102 0.915 1.067 0.986 0.745 1.120 0.799 0.849 +0 1.345 0.738 0.518 0.362 0.762 0.507 0.836 -0.793 2.173 1.029 0.212 -1.697 2.215 0.673 -0.775 -0.985 0.000 0.516 0.972 0.076 0.000 0.938 0.858 0.986 0.890 0.844 0.790 0.712 +0 1.410 -2.134 0.812 0.583 0.177 0.664 -1.013 1.144 1.087 1.198 -0.294 -0.733 0.000 1.360 -0.793 -1.649 2.548 1.650 -0.867 -0.681 0.000 0.752 0.958 0.989 0.773 0.697 0.802 0.910 +0 1.009 0.735 0.973 1.358 0.389 1.978 -0.707 -0.996 2.173 0.747 -1.136 0.431 2.215 0.742 -0.291 1.133 0.000 0.697 -1.073 1.374 0.000 0.434 1.303 0.997 1.460 1.765 1.846 1.406 +1 0.610 0.291 1.128 1.010 -0.127 0.800 1.272 -1.370 1.087 1.209 0.425 0.373 1.107 0.535 2.208 -1.078 0.000 0.907 0.652 1.703 0.000 0.840 1.182 0.987 0.986 1.580 0.903 0.813 +0 0.416 0.559 1.691 0.526 -1.409 0.613 0.907 1.534 2.173 0.917 1.523 -0.365 2.215 0.806 1.822 -1.442 0.000 2.042 0.334 -0.188 0.000 1.794 1.173 0.988 0.967 1.151 1.076 1.076 +0 0.643 -1.666 -0.532 1.340 1.096 1.030 1.136 -0.410 0.000 1.673 -0.891 -0.839 2.215 2.478 -0.293 1.094 0.000 1.215 -0.914 1.362 0.000 0.888 0.837 1.279 1.278 0.948 1.110 1.035 +1 1.121 -0.096 -1.619 1.256 0.935 0.581 -0.862 0.468 2.173 0.941 -1.434 -0.521 0.000 0.742 1.483 -1.237 0.000 1.099 -0.301 0.257 0.000 1.154 0.996 1.224 0.923 0.758 0.702 0.796 +1 1.920 0.327 0.134 0.415 -0.144 0.664 0.531 1.403 2.173 0.549 0.135 1.602 2.215 0.619 1.275 -1.284 0.000 0.461 -0.630 -0.889 0.000 0.794 0.751 0.998 0.910 0.237 0.774 0.697 +1 1.061 -0.613 1.342 0.824 1.192 1.812 1.051 -0.517 0.000 1.040 0.408 0.559 0.000 1.215 0.599 -1.686 2.548 2.248 -0.380 1.690 3.102 0.930 1.063 0.981 0.709 0.752 0.841 0.733 +1 0.722 -0.535 1.040 0.498 -0.360 0.874 0.351 -0.937 0.000 0.753 -0.396 -1.188 0.000 1.532 0.093 0.650 2.548 1.270 0.575 1.561 0.000 0.774 1.024 0.980 0.871 0.633 0.919 0.841 +0 0.910 0.021 1.541 0.421 0.206 0.663 0.597 -0.809 0.000 0.505 1.601 -1.614 0.000 0.903 0.884 0.473 2.548 0.814 -0.249 -0.040 3.102 1.057 0.964 0.990 0.609 0.532 0.683 0.724 +1 1.668 0.993 0.367 0.657 -1.029 2.060 1.715 -1.160 0.000 1.055 0.709 1.387 0.000 1.609 0.484 -0.159 2.548 1.290 -0.098 1.638 0.000 0.746 1.315 1.380 0.657 0.380 0.750 0.770 +1 0.775 -0.824 -1.436 0.873 0.704 0.967 -0.055 1.398 0.000 1.404 -0.757 -0.603 0.000 0.994 -0.696 -0.096 0.000 1.377 0.528 1.060 1.551 0.800 0.621 1.067 0.862 0.700 0.826 0.777 +0 0.907 0.770 0.647 0.126 1.345 0.748 -0.424 -1.040 0.000 0.818 0.804 -0.144 2.215 0.471 -0.124 1.613 0.000 1.021 1.339 1.344 0.000 0.880 1.067 0.979 0.620 0.734 0.695 0.667 +1 1.088 -1.507 0.353 0.544 1.670 1.976 0.784 -1.416 0.000 1.874 -1.169 0.750 0.000 2.659 -0.925 -0.825 2.548 1.683 -0.295 0.487 0.000 0.989 0.883 0.988 1.140 0.738 1.069 0.897 +0 3.344 0.008 -0.364 0.606 -0.566 0.973 0.289 1.363 2.173 0.776 -0.914 1.410 2.215 0.370 1.136 0.338 0.000 0.594 -0.999 -1.692 0.000 0.919 0.873 0.992 1.363 0.840 1.268 1.011 +0 0.842 -0.228 0.625 0.690 -0.464 0.457 -0.745 1.293 0.000 0.648 0.006 -1.536 2.215 1.283 -0.763 -1.443 2.548 1.325 -0.271 -0.150 0.000 1.174 0.977 0.988 0.745 0.429 0.678 0.679 +1 0.933 0.232 0.800 0.365 -0.091 0.770 -0.631 0.940 0.000 0.511 -0.074 0.407 0.000 1.841 -0.267 -1.192 2.548 0.982 1.058 -1.398 0.000 0.724 0.935 0.984 1.001 0.744 0.873 0.761 +1 0.788 -0.858 1.049 0.621 0.426 0.594 0.209 -1.582 0.000 1.148 0.508 0.749 2.215 0.786 -1.002 -1.409 0.000 0.993 0.618 -0.375 0.000 0.886 0.811 0.986 1.410 0.744 1.131 1.022 +1 0.675 0.406 0.824 0.478 1.650 0.772 -0.831 -0.619 2.173 0.711 0.059 0.190 0.000 1.328 -0.398 -1.466 0.000 0.711 -2.262 -1.402 0.000 0.753 1.274 0.983 0.623 0.579 0.900 0.823 +1 0.560 -1.418 -1.552 0.692 -0.663 0.786 0.919 0.431 2.173 0.585 1.478 1.361 2.215 0.640 1.586 -1.054 0.000 0.606 0.037 -0.288 0.000 0.993 1.020 0.989 1.077 0.800 0.939 0.830 +0 0.901 -0.165 -0.759 0.902 1.631 1.112 -1.641 0.223 0.000 1.395 -1.017 -1.421 2.215 1.295 0.872 0.245 0.000 1.088 1.943 0.820 0.000 1.169 1.226 1.042 0.841 0.556 1.583 1.247 +0 0.579 2.041 1.214 0.366 0.264 0.549 1.181 0.083 2.173 0.849 0.136 -1.333 2.215 0.520 0.302 1.574 0.000 0.547 -1.417 0.703 0.000 0.820 1.064 0.977 0.857 1.109 0.791 0.751 +1 1.836 0.054 0.056 1.316 0.152 1.516 -0.374 1.503 0.000 0.906 -0.030 -1.046 0.000 1.403 0.542 -1.280 2.548 1.054 -0.335 -0.485 0.000 0.867 0.750 0.968 0.742 0.847 0.938 0.779 +0 0.925 0.309 -0.652 1.670 -1.509 1.285 -0.494 0.584 2.173 0.714 -0.339 1.248 0.000 1.246 1.204 -1.009 2.548 0.592 1.714 0.044 0.000 1.487 1.238 1.200 1.586 2.266 1.319 1.148 +1 0.359 1.266 1.109 0.561 -0.989 1.047 0.044 -1.335 2.173 1.283 -0.260 1.405 0.000 1.075 0.352 -0.482 2.548 1.809 -0.021 -0.059 0.000 0.903 1.278 0.978 0.733 0.947 1.171 0.950 +0 0.643 0.100 0.608 0.688 -1.504 0.689 -0.457 1.320 2.173 0.712 0.467 -0.720 1.107 1.384 0.208 -0.015 0.000 0.807 -2.393 1.149 0.000 0.874 1.026 0.984 0.760 1.114 1.012 0.837 +0 1.118 1.031 1.539 0.506 0.420 1.159 1.199 -1.089 2.173 0.933 0.534 0.348 1.107 0.915 -0.809 0.541 0.000 0.621 -1.097 -1.167 0.000 0.850 0.988 0.990 0.997 1.560 1.235 1.094 +0 0.356 -0.139 -0.042 1.344 -0.933 0.859 1.091 1.249 2.173 0.441 0.533 -0.476 0.000 0.692 0.703 0.128 0.000 0.537 1.963 -0.039 0.000 0.698 0.941 0.983 0.865 0.807 1.183 1.099 +1 0.963 1.779 -0.894 1.085 -1.076 1.093 0.117 0.958 2.173 0.596 0.785 -0.595 0.000 0.479 -2.333 1.698 0.000 0.687 -0.513 -0.505 3.102 2.254 1.230 0.994 1.487 0.953 1.090 1.233 +0 0.829 0.569 0.657 0.211 -1.478 0.459 -1.329 -1.074 0.000 1.018 0.198 0.290 2.215 0.909 -0.042 -0.700 0.000 1.444 0.064 1.605 3.102 0.893 0.929 0.992 0.816 1.017 0.846 0.891 +1 1.841 0.666 0.930 0.686 -0.355 1.382 0.329 -0.473 2.173 0.465 -0.425 -1.124 0.000 0.599 0.680 -1.615 2.548 0.435 -1.177 -1.203 0.000 0.481 0.811 1.425 0.821 1.000 0.940 0.761 +1 0.683 -2.110 -0.451 0.511 -0.834 0.499 -0.782 1.652 1.087 0.655 0.968 -1.358 2.215 0.515 -1.688 0.574 0.000 2.438 -0.145 0.381 0.000 1.184 1.076 0.980 1.216 0.924 0.983 0.933 +0 0.499 1.128 0.946 0.444 1.220 1.105 0.529 -1.205 2.173 1.158 0.780 0.247 2.215 0.689 0.762 1.407 0.000 0.664 -0.022 -0.276 0.000 0.816 0.851 1.002 0.890 1.623 0.970 0.780 +1 2.721 1.091 0.725 0.523 0.944 0.950 2.432 -0.909 0.000 0.978 1.136 -0.273 1.107 1.025 1.044 1.626 0.000 0.366 0.473 -0.581 0.000 1.017 1.019 0.992 1.188 0.801 0.953 1.056 +0 0.713 -0.752 -0.208 0.853 0.758 0.865 -0.902 -0.886 2.173 0.883 0.348 -1.457 2.215 1.042 -1.191 1.355 0.000 0.651 1.479 -0.264 0.000 0.802 1.060 0.991 0.901 1.077 0.939 0.863 +0 0.794 0.465 0.881 0.667 -0.885 1.590 -0.302 0.394 2.173 0.806 -0.049 1.636 0.000 2.130 0.321 -1.207 2.548 0.603 1.021 0.382 0.000 0.950 0.915 1.008 1.076 2.400 1.179 0.941 +0 0.326 -1.446 0.350 0.375 1.518 1.199 0.057 1.637 2.173 1.089 -0.655 -0.399 2.215 1.077 -0.311 0.562 0.000 0.988 -2.048 -0.599 0.000 0.644 1.152 0.983 0.837 1.741 0.974 0.802 +1 0.916 -1.352 -0.720 0.320 -1.328 0.579 0.012 1.281 2.173 0.452 -1.914 1.714 0.000 0.979 -1.788 1.122 0.000 1.242 -1.082 -0.140 0.000 1.027 1.112 0.984 0.542 0.676 0.700 0.657 +1 0.965 -0.629 1.720 1.446 -0.529 0.981 -1.063 0.751 0.000 1.020 0.041 -0.431 0.000 1.980 -0.631 1.363 1.274 1.695 -0.993 -0.602 3.102 0.844 0.834 1.470 1.190 1.417 0.941 0.860 +1 1.191 0.296 0.117 0.739 -0.683 1.997 1.225 -1.134 0.000 1.033 -0.331 -0.776 0.000 1.862 0.275 1.168 2.548 2.322 0.114 0.630 3.102 0.902 0.877 0.990 0.877 0.753 0.827 0.776 +0 2.178 -0.851 -0.902 0.262 0.985 0.931 -1.386 0.343 2.173 0.861 -1.596 1.596 0.000 1.291 -1.635 -0.001 0.000 0.655 -0.240 0.969 3.102 1.607 1.055 1.037 1.170 0.654 0.803 0.858 +1 0.441 2.150 1.061 1.092 0.345 0.766 1.479 -1.504 0.000 1.028 0.064 -0.042 2.215 0.792 0.416 1.619 0.000 0.818 1.016 -0.900 3.102 0.882 0.652 0.983 0.881 0.768 0.844 0.798 +0 0.305 -0.863 -0.353 2.308 -1.499 1.387 -0.425 0.353 2.173 0.418 -1.168 -1.682 2.215 0.374 0.697 -0.938 0.000 0.516 0.015 -0.457 0.000 0.680 0.985 0.998 0.498 1.169 1.033 0.817 +1 0.821 1.143 -0.065 2.526 0.449 0.959 0.420 -1.343 2.173 1.207 -0.644 1.365 0.000 2.575 1.138 -0.695 0.000 1.398 1.033 1.171 3.102 3.966 2.364 0.990 0.876 1.072 1.516 1.444 +0 0.623 1.018 1.167 1.208 0.277 0.948 -0.727 -1.524 0.000 1.147 0.600 -0.313 2.215 0.829 -0.282 1.579 0.000 0.702 -0.189 0.345 3.102 0.636 0.788 0.993 0.831 0.574 0.921 0.875 +1 0.743 0.543 -0.294 1.523 0.663 0.835 0.027 -0.667 0.000 0.881 -0.160 -1.179 2.215 0.894 0.778 1.239 0.000 1.947 -0.224 1.501 3.102 0.913 0.921 1.119 1.049 0.788 0.878 0.756 +1 2.089 -0.100 -0.859 1.001 0.721 0.707 0.168 1.377 2.173 0.540 -1.098 0.983 2.215 0.403 0.763 0.172 0.000 1.013 -0.923 -0.478 0.000 0.890 0.983 1.982 1.196 0.708 0.930 0.811 +0 0.681 -0.662 1.072 1.462 -1.702 0.786 1.635 0.410 0.000 0.709 1.318 1.059 0.000 1.326 -0.529 -0.724 0.000 1.135 0.593 -1.278 3.102 0.904 1.041 0.992 1.620 0.736 1.140 1.366 +1 0.351 1.397 1.177 2.619 0.702 0.797 -0.949 -1.074 0.000 0.757 -0.930 -0.433 0.000 0.816 0.897 0.538 1.274 1.395 0.589 -1.060 3.102 0.902 1.106 0.987 0.904 0.816 1.042 2.029 +1 1.100 0.399 -0.221 0.474 -1.012 0.803 0.870 -1.327 1.087 0.559 0.625 1.020 2.215 0.613 -1.017 -0.214 0.000 0.974 -0.517 0.665 0.000 0.644 0.753 0.983 0.943 0.851 0.832 0.755 +1 0.759 -0.198 -1.478 0.650 0.587 0.848 2.268 -1.723 0.000 1.543 0.714 -0.523 0.000 2.222 0.819 0.427 2.548 0.956 -1.180 -1.418 0.000 0.753 0.915 0.988 0.965 0.869 0.958 0.798 +0 1.040 -1.543 1.658 0.336 1.525 0.499 -1.047 -0.175 2.173 0.431 -1.398 0.469 0.000 0.344 -2.100 1.392 0.000 0.950 -0.165 -0.496 3.102 0.511 0.689 0.988 0.839 0.383 0.631 0.574 +0 1.038 -1.032 -0.346 0.469 -0.217 1.000 -0.937 0.433 2.173 1.065 -1.583 1.560 0.000 1.364 -0.349 -1.378 2.548 0.841 -0.364 1.624 0.000 0.783 0.864 0.991 0.959 1.510 0.996 0.947 +0 3.587 -0.434 -0.315 2.020 -0.352 0.987 1.635 1.020 0.000 1.659 -0.023 -1.612 0.000 1.312 -0.573 -1.634 0.000 2.954 0.900 1.538 0.000 0.698 0.984 0.957 0.857 0.176 0.912 1.206 +1 0.630 0.889 1.165 1.084 -1.366 1.784 0.840 0.193 0.000 0.418 1.614 1.136 0.000 1.204 -0.695 -1.101 2.548 0.828 1.025 -0.874 3.102 0.903 0.888 0.985 1.314 0.907 0.870 0.840 +0 1.130 -1.702 -0.326 0.521 0.615 0.600 2.505 -0.952 0.000 0.719 -1.466 1.258 2.215 0.856 1.007 1.572 0.000 1.374 0.180 0.778 3.102 0.792 0.829 0.988 0.790 0.951 1.004 1.070 +1 0.621 0.523 -0.117 0.654 -1.100 0.700 0.018 0.522 0.000 1.314 -0.695 -1.074 2.215 0.358 -1.429 0.805 0.000 0.723 -0.271 1.484 3.102 0.965 0.730 0.991 0.746 0.676 0.889 0.763 +0 0.719 -0.560 1.136 2.187 0.795 1.225 0.529 -0.860 0.000 0.845 0.491 1.676 2.215 1.189 -0.646 0.029 2.548 0.463 -0.129 -0.555 0.000 0.511 0.918 0.984 0.939 1.265 1.072 1.241 +0 0.828 -0.387 -0.018 1.212 1.501 0.460 -0.152 -1.027 0.000 1.095 0.395 -1.699 2.215 1.352 1.071 0.319 2.548 0.562 -1.669 0.228 0.000 1.085 1.147 1.359 1.225 1.353 1.083 0.963 +1 0.470 0.733 -0.287 0.872 0.939 0.669 -1.721 -0.688 0.000 0.793 0.828 -0.989 0.000 1.842 0.501 1.062 2.548 1.375 0.313 0.326 3.102 0.805 1.040 0.987 0.881 0.757 0.878 0.790 +1 0.881 1.237 -1.179 1.781 1.428 0.291 1.805 -1.470 0.000 0.543 1.105 -0.166 2.215 1.171 -0.012 0.501 2.548 0.376 -1.829 0.823 0.000 1.904 1.271 1.235 1.220 0.704 0.880 0.966 +0 1.579 -0.834 1.583 0.343 0.590 1.461 -0.754 -1.514 2.173 1.666 -1.289 0.240 1.107 0.765 -1.516 -0.823 0.000 1.180 -2.175 -0.118 0.000 0.807 1.053 0.988 0.881 2.387 1.349 1.125 +1 0.762 0.005 1.172 0.647 -1.651 0.493 1.057 -0.003 2.173 0.489 -1.030 1.174 2.215 0.531 -1.808 -1.126 0.000 0.664 -1.345 0.131 0.000 0.605 1.335 0.994 0.849 1.117 0.869 0.901 +0 0.292 1.288 -0.783 1.748 -0.468 0.478 -2.605 1.314 0.000 0.521 0.449 1.508 2.215 0.990 -1.193 0.496 1.274 0.717 -1.327 -1.437 0.000 0.732 0.824 1.001 0.868 0.978 0.888 1.027 +0 2.183 1.530 0.851 0.237 -0.622 1.283 0.174 -1.023 2.173 1.206 -1.242 1.518 0.000 1.409 -1.634 0.115 0.000 2.140 1.057 -0.884 3.102 0.817 2.119 0.986 1.148 1.030 1.869 1.981 +0 0.894 1.460 -0.680 0.444 0.771 1.434 0.335 -1.416 0.000 1.494 1.168 0.368 0.000 1.830 0.763 1.733 1.274 1.818 1.785 0.338 0.000 0.845 1.026 0.982 1.018 1.377 1.011 0.886 +0 1.700 0.521 -1.286 3.820 -1.622 2.414 -0.217 0.206 0.000 1.502 -0.694 0.387 2.215 1.652 0.225 1.725 2.548 0.926 -0.531 -0.193 0.000 0.939 0.835 1.049 0.674 1.773 1.574 1.810 +0 2.008 -0.041 0.394 0.865 -0.297 0.855 -1.227 1.652 0.000 0.830 0.925 1.460 0.000 0.962 -1.267 -0.797 2.548 1.126 -0.488 -1.338 0.000 0.816 1.078 1.064 1.107 0.549 0.751 0.923 +0 0.787 -0.228 1.161 0.754 0.328 0.848 -1.115 -0.920 2.173 0.614 -0.738 0.420 0.000 0.566 -1.606 1.533 0.000 0.559 -1.535 0.305 0.000 0.897 0.988 0.988 0.844 0.632 0.889 0.822 +0 1.462 -0.502 0.378 0.783 -1.730 1.478 -1.935 -0.907 0.000 2.290 -0.780 0.956 2.215 0.697 -1.074 -0.619 0.000 1.115 0.450 -1.222 1.551 0.863 1.690 1.403 1.074 1.692 1.700 1.404 +0 1.107 1.087 -0.917 0.558 0.209 0.682 -0.115 -0.549 0.000 1.431 -0.392 0.719 2.215 1.156 -0.007 -1.590 2.548 0.707 -0.465 1.107 0.000 1.083 0.892 0.986 1.244 1.223 0.954 0.851 +0 0.351 -1.149 -0.034 0.680 -1.177 1.457 1.061 -1.117 0.000 1.027 -0.449 0.880 0.000 1.882 0.061 0.329 2.548 0.904 -0.157 1.190 3.102 3.347 1.896 0.997 0.887 0.709 1.384 1.089 +1 0.525 0.435 0.544 0.497 -1.068 1.844 -0.943 1.084 2.173 1.646 0.387 -0.039 0.000 1.789 -0.380 -0.927 0.000 1.466 0.124 1.537 3.102 1.005 1.072 0.991 1.097 1.221 1.231 0.991 +0 1.021 0.245 0.585 0.615 -1.644 0.702 0.564 -1.165 2.173 0.805 2.192 -0.097 0.000 0.747 0.813 -0.735 2.548 0.439 0.316 1.619 0.000 1.141 0.817 0.995 0.831 0.371 0.707 0.744 +0 1.270 0.349 1.181 0.911 1.057 0.762 1.532 0.415 0.000 0.717 -0.728 -1.150 2.215 1.564 0.636 -0.620 2.548 0.526 0.430 -0.950 0.000 0.897 1.064 1.000 1.252 1.028 1.057 1.015 +1 1.142 -1.185 0.664 0.773 -0.214 1.147 -0.046 -1.380 0.000 0.707 -0.501 -0.566 0.000 0.788 -0.203 1.156 0.000 1.148 0.119 0.459 1.551 1.157 0.814 0.986 0.518 0.119 0.478 0.532 +0 1.117 -1.139 -0.119 0.637 -1.085 0.830 -0.845 -1.426 2.173 1.237 -0.809 0.900 1.107 0.494 -1.591 -0.385 0.000 0.599 -1.335 0.665 0.000 0.488 0.788 0.990 0.982 1.289 0.865 0.707 +1 0.453 0.662 -0.419 0.878 1.440 0.868 -0.245 -0.142 2.173 1.313 -0.759 -0.749 2.215 1.126 -0.590 1.143 0.000 1.026 0.143 1.231 0.000 0.959 1.091 0.988 0.861 0.921 0.862 0.775 +0 0.442 -0.460 -0.865 1.286 0.697 0.681 2.319 -0.588 0.000 0.679 0.346 -1.534 1.107 0.748 -1.062 1.411 0.000 0.626 0.718 1.625 0.000 0.915 0.959 1.030 0.820 0.880 0.678 0.654 +1 0.353 1.429 0.165 2.087 -1.037 0.816 1.046 0.632 0.000 1.207 -0.203 1.347 2.215 0.942 -0.117 -0.142 2.548 1.164 -2.019 -1.380 0.000 0.729 0.797 1.050 1.557 1.105 1.148 1.016 +0 0.703 -2.264 -1.551 1.113 -0.651 0.537 -0.513 1.143 1.087 0.546 -2.426 0.276 0.000 0.619 0.015 -0.627 2.548 0.511 1.274 0.515 0.000 0.779 0.993 0.984 1.385 0.744 1.107 0.911 +0 0.803 0.056 1.001 0.489 -0.415 0.846 -0.022 -1.109 2.173 1.129 0.870 0.474 0.000 0.697 0.828 1.611 2.548 1.061 1.246 -0.234 0.000 0.953 0.878 0.986 0.856 0.764 0.909 0.843 +1 1.317 -0.640 1.167 0.833 0.914 1.395 0.277 -0.988 2.173 0.596 0.389 0.938 0.000 0.605 0.453 0.086 0.000 0.796 -0.488 -0.235 3.102 0.641 1.191 0.986 0.759 0.860 0.994 0.839 +0 1.259 1.036 -1.153 0.301 0.058 0.975 0.948 1.567 2.173 0.945 2.372 -0.312 0.000 1.113 0.767 -0.163 1.274 1.636 0.832 0.638 0.000 0.902 0.940 0.984 0.706 1.300 0.821 0.775 +0 0.436 1.839 1.296 1.263 -1.498 0.552 1.532 -0.190 0.000 1.187 0.457 -0.452 1.107 1.359 -0.175 1.260 1.274 0.596 -1.420 1.725 0.000 2.310 1.508 0.978 0.872 1.424 1.144 1.014 +0 0.958 -0.691 -0.016 0.249 -1.485 1.483 -0.226 -1.365 0.000 1.265 -1.146 0.613 2.215 2.291 -0.095 0.886 0.000 1.288 -0.894 -0.627 0.000 1.028 0.947 0.988 0.767 1.892 1.030 0.861 +0 0.600 0.233 -1.674 1.774 -0.814 0.521 -0.416 1.142 2.173 0.353 0.192 0.991 2.215 0.988 -1.526 -0.046 0.000 0.482 -2.272 1.258 0.000 0.818 0.964 1.001 0.722 0.216 0.676 0.898 +1 0.979 0.406 -1.731 0.612 0.038 0.718 0.972 -0.509 2.173 1.038 -0.454 1.118 2.215 0.827 -0.247 -0.977 0.000 0.481 1.805 0.365 0.000 1.256 0.910 1.072 0.840 1.613 0.962 0.808 +0 3.062 0.432 -1.004 0.312 0.142 1.045 0.342 1.206 1.087 0.748 0.596 0.298 2.215 0.512 1.902 0.914 0.000 0.554 1.531 0.302 0.000 0.954 0.989 1.164 1.065 0.966 1.062 0.899 +0 0.790 0.390 0.084 0.566 -1.420 1.241 0.719 -0.842 2.173 0.781 0.920 0.801 0.000 1.301 0.046 0.488 0.000 1.556 -0.504 1.173 3.102 0.835 0.917 0.992 0.821 1.773 1.155 0.934 +1 0.690 -0.174 1.030 2.228 1.726 0.599 -0.795 0.145 0.000 0.963 -1.316 -0.713 2.215 0.433 0.221 0.252 2.548 0.456 -1.459 0.045 0.000 0.400 0.683 1.009 0.745 0.803 0.884 0.809 +0 1.475 -0.243 1.490 0.318 1.128 0.297 0.681 1.366 0.000 0.841 0.559 -0.328 2.215 0.936 -0.380 -0.516 1.274 0.923 1.675 -1.310 0.000 0.779 0.891 0.988 0.911 0.513 0.770 0.775 +0 0.912 0.892 0.121 1.597 -0.811 0.599 -1.777 0.810 0.000 0.913 1.569 0.419 2.215 1.022 0.731 1.550 2.548 1.335 0.131 -0.208 0.000 1.895 1.682 1.244 1.002 0.974 1.509 1.358 +1 0.843 -1.378 1.368 0.634 -0.730 1.086 -0.384 -0.976 0.000 0.671 -2.050 0.390 0.000 1.012 -0.304 -0.495 2.548 2.086 -0.307 0.858 3.102 0.922 1.290 0.987 0.836 1.043 0.826 0.836 +0 0.549 -1.856 -1.549 0.495 -0.030 1.290 -0.594 0.680 0.000 1.222 -0.850 -0.150 2.215 2.027 -0.585 1.644 0.000 2.941 0.059 -1.386 1.551 2.220 1.842 0.990 1.009 1.749 1.473 1.155 +0 0.783 0.564 0.989 0.678 -0.482 0.677 -0.900 -0.354 2.173 0.332 -1.533 -0.691 0.000 0.713 -1.920 0.987 0.000 1.574 -0.379 -1.430 3.102 0.772 0.891 0.988 0.994 0.935 0.804 0.861 +0 2.494 -0.012 1.649 0.298 -1.417 1.246 -0.048 1.142 2.173 1.443 0.206 -0.425 0.000 1.818 -0.385 -0.418 0.000 1.145 0.304 0.484 3.102 0.802 0.964 0.983 0.908 0.755 1.182 1.149 +1 0.664 0.861 -0.011 1.508 0.508 0.735 -0.913 -1.446 0.000 0.507 0.294 1.166 2.215 0.550 -1.928 -0.649 0.000 0.617 -1.208 -0.508 3.102 1.045 1.073 0.995 1.476 0.711 0.997 1.385 +0 1.076 0.153 -1.384 0.376 -0.618 1.171 1.424 0.179 0.000 1.658 -1.096 -1.659 2.215 0.747 -0.331 0.165 2.548 0.463 0.189 0.607 0.000 0.839 0.950 0.990 0.901 1.267 1.687 1.338 +1 1.007 -0.996 -1.567 1.078 0.914 1.508 0.107 -1.473 2.173 0.987 -0.357 -0.897 2.215 1.580 2.371 0.434 0.000 1.593 0.136 0.442 0.000 2.708 2.636 1.136 1.302 0.990 2.085 1.964 +1 0.692 1.009 -1.675 0.757 -0.655 1.020 0.637 0.198 1.087 1.341 0.571 -1.128 1.107 0.681 1.474 0.585 0.000 1.023 2.356 1.170 0.000 0.881 0.857 0.991 0.859 1.601 0.984 0.868 +0 0.308 2.046 -1.009 2.050 0.414 0.830 0.855 -1.346 0.000 0.610 2.188 1.617 0.000 0.780 0.458 0.542 2.548 1.268 -0.202 -1.043 3.102 1.327 1.169 1.055 1.545 0.806 1.046 1.055 +0 0.536 1.957 -1.682 1.257 0.581 0.579 -0.306 -1.316 0.000 0.762 0.724 -0.380 1.107 0.751 -0.843 1.219 1.274 0.863 -0.351 -0.514 0.000 0.718 0.798 1.015 1.496 1.097 1.081 1.040 +1 0.875 0.592 0.706 0.127 0.215 1.417 0.946 0.844 0.000 1.160 -0.134 0.632 2.215 2.252 0.951 -1.051 2.548 1.466 -0.301 -0.560 0.000 0.813 0.884 0.982 1.273 2.025 1.140 0.951 +1 2.207 1.249 -0.892 0.772 0.459 0.917 1.187 0.961 2.173 0.791 1.280 -1.638 0.000 0.677 -0.089 0.420 2.548 0.797 0.557 -0.294 0.000 1.027 1.008 1.697 1.138 0.823 0.993 0.857 +0 1.238 -2.110 0.375 0.528 -1.117 0.572 0.007 -0.914 0.000 0.625 -0.907 1.180 2.215 0.338 0.017 0.377 0.000 0.828 -0.439 -1.723 3.102 0.726 0.803 1.091 0.863 0.352 0.661 0.755 +1 0.420 2.222 1.714 1.435 0.203 0.815 0.962 1.369 0.000 0.930 0.252 -1.706 2.215 0.885 1.268 -0.314 0.000 0.998 1.099 -0.694 0.000 1.342 0.997 1.052 1.121 0.964 1.083 0.970 +0 1.214 -0.675 0.853 0.833 0.570 0.840 -0.445 -1.412 0.000 0.623 -0.228 1.444 1.107 1.624 -0.247 -0.338 2.548 0.692 0.615 -0.696 0.000 1.015 1.044 0.987 0.666 1.068 0.775 0.813 +0 1.034 -0.919 -1.362 1.716 -0.783 1.869 -0.767 0.672 0.000 0.670 -2.766 -1.286 0.000 0.798 2.690 1.314 0.000 1.042 0.575 -0.800 3.102 0.973 0.938 0.990 0.798 1.146 0.920 0.875 +1 0.345 -1.529 0.763 0.622 1.602 1.043 -0.486 -0.875 2.173 1.002 0.125 -0.197 2.215 1.368 -0.629 0.616 0.000 0.795 1.562 0.837 0.000 0.984 1.041 0.998 0.954 0.985 0.849 0.762 +0 0.907 -1.692 0.629 2.663 1.169 1.134 -2.243 -0.572 0.000 1.188 -0.054 -1.018 0.000 1.028 -0.108 1.637 2.548 0.713 0.999 0.863 0.000 1.101 1.706 1.007 1.778 0.808 1.374 1.393 +0 0.412 1.642 -1.515 1.506 -0.418 0.762 0.037 0.309 2.173 0.745 0.255 1.047 2.215 1.470 0.159 -1.248 0.000 1.546 -0.779 1.194 0.000 0.587 1.026 0.988 0.959 0.695 0.813 0.780 +1 0.571 0.470 0.782 0.507 -0.725 0.649 1.297 1.192 2.173 0.512 2.902 -1.568 0.000 1.245 0.303 0.419 0.000 0.915 -0.310 -0.875 1.551 2.456 1.517 0.990 0.780 1.097 1.141 0.913 +1 1.616 -0.881 -0.445 0.057 -1.499 1.309 -0.560 1.241 2.173 0.423 -0.318 -0.248 0.000 0.728 -0.168 -1.257 2.548 0.546 0.622 1.402 0.000 0.716 0.949 0.985 0.651 0.973 0.893 0.739 +1 1.125 0.794 0.630 0.104 -0.432 1.160 1.038 1.579 2.173 1.167 1.260 -0.294 0.000 0.971 0.352 -0.872 1.274 1.128 -0.082 1.140 0.000 0.805 1.152 0.988 0.781 1.151 0.842 0.761 +0 0.710 -1.557 -0.409 2.510 -1.025 1.189 -1.080 1.220 0.000 0.612 -0.408 1.041 0.000 0.987 -0.658 0.155 2.548 0.628 -0.427 -0.283 3.102 0.685 0.965 0.984 0.726 0.239 0.725 0.976 +1 0.873 0.267 -1.547 1.460 0.584 0.988 -1.047 0.359 2.173 1.576 -1.102 -1.044 0.000 0.501 -1.715 -0.774 0.000 1.294 -0.172 1.184 3.102 0.617 1.142 1.470 1.301 0.964 1.019 1.093 +1 0.788 -0.083 -1.618 0.631 0.518 1.231 0.305 0.370 2.173 0.799 2.394 -0.855 0.000 1.390 -0.010 -1.081 0.000 0.822 0.616 1.364 3.102 0.842 0.764 0.987 0.892 0.862 0.826 0.717 +0 0.352 -2.175 0.962 2.027 0.308 0.656 0.048 -1.538 0.000 0.953 -1.113 -0.983 2.215 0.698 0.030 1.195 0.000 0.423 -0.710 -0.693 3.102 0.763 1.013 0.980 0.597 0.170 0.677 0.812 +1 0.361 -2.057 0.907 0.563 -0.832 1.042 -0.152 0.251 0.000 1.138 -0.436 -0.871 2.215 1.153 -2.410 1.639 0.000 0.798 -0.718 1.490 3.102 0.893 0.916 0.987 0.729 0.752 0.851 0.756 +0 0.671 -0.885 -1.547 0.682 0.185 0.820 0.378 -1.151 2.173 0.644 -0.351 -0.575 0.000 0.479 0.938 0.801 0.000 1.030 1.235 0.290 3.102 1.030 0.894 0.988 0.932 1.092 0.809 0.717 +1 1.040 -0.061 0.060 0.253 -1.110 1.365 -0.222 -1.568 2.173 1.067 0.058 0.383 2.215 1.128 0.118 -0.291 0.000 0.979 0.394 1.144 0.000 1.133 0.845 0.991 1.146 1.763 1.026 0.887 +1 1.363 0.329 -1.226 0.255 -1.001 0.828 -1.143 0.482 2.173 1.469 0.810 -0.794 0.000 1.282 0.524 1.109 2.548 0.900 0.676 0.346 0.000 0.704 0.871 0.981 0.903 1.430 1.174 0.914 +0 0.536 -0.422 0.445 2.634 1.221 1.317 -1.970 -0.911 0.000 0.872 0.328 0.344 0.000 1.066 0.509 0.888 2.548 1.251 -0.205 -0.345 0.000 0.889 0.924 1.061 0.794 0.969 0.878 0.862 +1 0.879 0.437 -1.656 0.147 -0.207 0.666 0.552 0.083 0.000 0.413 1.393 0.742 0.000 1.587 -0.445 -0.737 2.548 1.124 -0.324 1.409 3.102 0.801 0.937 0.995 0.787 0.955 0.886 0.777 +1 0.401 -0.352 0.840 2.332 -0.370 1.307 0.475 1.511 2.173 0.859 0.597 0.771 0.000 1.035 1.043 -1.400 2.548 1.101 0.482 -0.383 0.000 1.092 0.992 1.188 1.669 0.868 1.219 1.049 +1 0.944 0.599 -0.786 0.972 0.136 1.024 1.151 -1.535 2.173 0.751 1.796 -0.144 0.000 1.477 1.396 1.147 2.548 0.655 1.439 0.429 0.000 0.455 0.832 0.988 1.096 1.054 0.952 0.856 +0 4.126 1.032 -0.120 0.484 -0.605 1.783 0.645 0.120 0.000 5.193 0.059 1.673 2.215 0.613 1.437 1.353 0.000 0.624 0.379 1.723 3.102 1.912 1.239 0.985 3.793 0.330 2.262 2.030 +0 0.346 -1.226 -0.886 1.020 -0.535 0.746 -0.762 -0.785 2.173 0.689 -1.203 1.572 0.000 1.387 -0.077 0.774 2.548 0.844 -2.312 0.104 0.000 0.729 0.979 0.976 0.939 1.326 0.935 0.922 +0 2.247 0.237 -0.648 0.455 0.487 0.642 -0.839 -1.723 1.087 1.400 -0.051 0.978 2.215 0.952 1.521 -0.764 0.000 0.718 -0.629 0.582 0.000 1.639 1.482 1.197 1.177 1.069 1.129 1.063 +0 1.980 -0.614 0.791 0.708 -0.161 1.123 -0.415 0.163 2.173 1.409 -0.920 -1.570 2.215 0.483 0.635 -1.031 0.000 1.238 -0.056 -0.640 0.000 0.697 0.937 1.240 1.292 1.915 1.113 0.911 +1 0.565 0.759 0.197 0.370 -0.298 0.592 1.245 0.483 0.000 1.132 1.303 -0.938 2.215 1.376 1.091 1.738 2.548 1.908 0.507 0.944 0.000 0.845 1.040 0.983 1.153 0.889 0.959 0.954 +1 1.002 0.122 -1.331 0.768 0.872 0.830 1.125 -0.397 1.087 0.570 -1.048 0.303 0.000 0.664 1.816 0.884 0.000 1.915 -0.564 1.482 0.000 1.220 0.876 1.113 1.062 0.870 1.077 0.912 +0 0.700 -1.188 1.138 1.419 1.081 1.411 -0.737 1.570 2.173 2.075 -0.223 -0.626 0.000 1.059 0.025 -0.397 2.548 1.452 2.055 0.014 0.000 1.265 0.766 0.990 1.399 1.608 1.343 1.428 +1 1.591 -0.168 0.294 0.765 1.656 0.294 0.637 0.646 0.000 0.506 0.438 -0.463 0.000 1.026 0.344 -1.213 2.548 0.902 -0.769 -1.652 3.102 0.855 1.047 1.439 1.003 0.586 0.725 0.771 +0 0.973 0.269 -0.492 0.699 -0.831 0.835 -0.331 -1.738 2.173 0.575 0.972 0.474 0.000 1.499 -0.013 0.677 0.000 0.558 1.000 -0.791 3.102 0.798 0.767 0.983 0.957 0.818 0.809 0.825 +1 0.393 -0.576 -1.286 1.527 0.802 2.742 -1.467 -1.003 0.000 0.960 -0.933 -0.088 2.215 1.850 -0.181 0.446 2.548 2.824 -0.567 1.023 0.000 0.741 1.002 1.021 0.844 0.858 0.897 0.759 +0 1.125 -0.104 -0.521 0.849 0.782 0.955 0.055 1.241 2.173 0.555 -0.510 -0.071 0.000 0.884 0.764 -0.582 0.000 1.223 1.071 1.621 3.102 0.923 1.024 1.250 1.020 0.840 0.876 0.819 +1 0.384 -1.197 0.736 0.250 0.678 1.268 0.464 0.316 2.173 0.886 0.162 -1.018 0.000 0.446 1.944 -1.530 0.000 1.349 -0.923 -1.733 0.000 0.947 0.608 0.981 0.798 0.832 0.865 0.731 +1 1.005 -1.101 0.590 0.802 1.058 0.909 0.312 -0.834 2.173 0.857 -0.484 -1.266 2.215 0.647 0.441 0.445 0.000 0.560 -1.336 0.011 0.000 0.865 1.022 0.986 1.119 0.733 1.223 1.004 +0 0.441 0.840 -1.403 0.957 0.783 0.831 -0.990 -1.689 2.173 0.630 -1.125 -0.687 0.000 0.819 -0.179 0.420 0.000 0.399 0.076 -1.377 1.551 1.077 1.088 0.989 0.614 0.388 1.079 1.035 +0 0.520 0.756 -1.121 1.774 -0.091 0.451 0.832 0.664 0.000 0.564 -0.084 1.382 0.000 0.943 0.547 1.566 1.274 1.013 -0.234 -0.859 3.102 0.825 0.827 1.066 0.923 0.699 0.695 0.685 +0 1.791 0.107 -1.054 0.521 -0.554 0.903 0.763 0.597 0.000 0.466 1.282 1.284 0.000 0.818 -0.429 -0.207 2.548 0.669 -0.604 -1.710 3.102 0.886 0.937 0.994 0.772 0.557 0.748 0.810 +1 1.020 -1.592 -0.635 0.144 -0.862 1.925 0.059 1.613 0.000 1.403 -0.704 -0.250 2.215 1.732 0.086 0.164 2.548 1.106 -1.231 0.789 0.000 0.598 1.016 0.981 1.023 0.922 0.867 0.786 +0 0.436 -0.535 0.091 1.171 1.617 0.963 -0.938 -0.493 2.173 0.926 -0.655 0.855 0.000 0.993 -0.652 -1.270 2.548 0.565 -1.680 -0.028 0.000 0.959 0.942 0.987 0.959 0.797 0.794 0.722 +0 1.456 0.204 0.175 1.031 0.797 0.785 -0.964 -1.254 2.173 1.364 -0.643 1.658 0.000 1.673 0.560 -0.359 2.548 0.712 -1.095 0.980 0.000 0.852 0.863 0.984 0.969 1.626 1.145 1.086 +1 0.625 -0.195 0.908 2.331 0.250 1.177 -0.027 -1.330 1.087 0.396 -0.634 -0.655 0.000 0.517 0.523 -1.076 0.000 0.532 0.377 0.402 1.551 0.910 0.955 0.992 0.454 0.861 0.956 0.860 +0 1.384 0.493 0.157 0.734 0.945 0.332 1.101 1.190 0.000 0.320 -0.255 0.019 0.000 0.718 -0.115 -1.037 2.548 1.031 0.343 -1.549 0.000 0.791 0.783 0.988 0.837 0.374 0.727 0.631 +0 0.615 -1.738 -0.412 1.358 0.341 0.531 -1.458 0.885 0.000 0.680 -0.029 -1.570 2.215 0.574 -1.255 -1.507 0.000 1.024 -0.945 -0.855 3.102 0.828 0.876 0.983 0.694 0.629 0.713 0.679 +0 1.047 0.897 0.034 1.458 -0.696 0.790 1.880 1.304 0.000 0.901 0.078 -1.022 2.215 0.980 0.538 0.557 2.548 1.039 0.753 1.351 0.000 0.719 0.886 1.046 0.850 1.021 0.931 0.924 +1 1.147 -0.900 -1.286 1.640 -0.659 1.071 -0.831 0.693 2.173 0.358 0.564 0.098 2.215 0.422 1.479 -1.550 0.000 0.838 0.250 0.740 0.000 0.832 1.242 1.020 0.881 0.844 0.979 0.921 +0 1.336 0.075 1.051 1.103 0.338 1.059 1.423 -1.205 0.000 0.807 1.052 0.427 0.000 0.908 0.589 -1.541 0.000 0.964 -0.219 -0.444 1.551 0.887 1.080 1.007 0.538 0.173 0.679 0.856 +1 0.796 1.024 -1.168 1.188 0.753 1.332 0.351 0.406 2.173 0.602 0.270 -0.743 0.000 0.956 0.219 -1.223 0.000 0.493 -1.134 1.303 0.000 0.943 1.176 1.330 0.675 0.958 0.795 0.800 +0 1.388 1.803 0.595 0.246 1.096 1.173 0.244 -0.971 0.000 1.260 1.009 -0.987 2.215 2.460 -1.408 1.155 0.000 1.332 0.521 0.208 3.102 0.921 0.958 0.987 1.131 1.058 0.829 0.890 +0 0.661 1.740 -0.153 1.012 0.073 0.788 -0.962 -1.415 0.000 1.455 1.098 1.000 2.215 0.464 -2.027 -1.141 0.000 0.859 -0.264 -0.611 3.102 0.776 0.719 0.986 1.041 1.279 1.410 1.287 +1 0.908 -0.466 -0.623 0.763 1.232 0.961 -0.804 1.294 2.173 0.820 -0.018 0.225 0.000 0.784 0.478 -0.573 0.000 0.850 -2.445 -0.897 0.000 0.884 1.153 1.147 0.559 0.520 0.691 0.698 +1 0.914 -0.387 0.049 0.546 1.635 1.172 -0.487 1.024 2.173 1.683 -0.510 -1.071 0.000 1.310 0.901 0.168 0.000 0.917 -0.058 -0.462 0.000 0.923 0.711 0.989 0.845 0.856 0.982 0.828 +0 0.469 1.385 -0.976 0.841 -1.175 0.835 -0.473 1.463 0.000 1.302 1.168 -0.600 2.215 0.949 0.500 1.421 0.000 1.142 0.301 0.422 3.102 0.861 0.903 0.984 0.906 0.997 1.088 0.920 +1 1.350 -2.050 0.497 0.890 -1.703 0.613 0.727 -0.808 1.087 0.521 -0.884 1.096 1.107 0.808 -1.236 0.184 0.000 1.017 -1.047 -1.624 0.000 0.986 1.053 1.392 0.846 1.123 1.326 1.097 +0 0.522 -2.269 -1.317 1.079 1.498 0.950 -0.255 0.015 2.173 0.973 -0.611 0.501 2.215 2.040 -1.305 -1.342 0.000 0.756 0.613 -1.503 0.000 0.699 1.478 0.975 0.966 0.656 1.135 1.264 +0 0.605 -1.998 0.774 0.461 -0.538 0.508 -0.574 1.305 0.000 0.891 -1.615 1.022 0.000 0.933 -0.500 -0.757 2.548 0.878 0.346 -0.822 3.102 0.889 1.072 0.993 0.789 0.349 0.809 0.716 +0 1.956 -1.069 -1.357 0.611 -0.235 1.931 -0.851 -1.715 2.173 1.623 -1.087 0.024 2.215 1.986 -0.269 0.091 0.000 1.362 -0.383 0.680 0.000 0.930 0.956 1.284 1.148 2.627 1.567 1.334 +1 1.442 0.181 1.361 1.048 -1.648 1.291 0.522 -0.096 2.173 0.704 1.255 -1.050 0.000 0.710 1.171 0.541 1.274 0.527 1.563 1.209 0.000 0.747 1.168 0.989 0.980 0.796 1.093 0.974 +1 0.580 -0.264 0.170 0.985 1.340 0.840 0.824 -1.093 0.000 0.790 -0.334 0.773 2.215 1.016 0.622 0.168 2.548 1.332 -0.183 -1.372 0.000 0.975 1.112 0.989 0.616 0.711 0.901 0.781 +0 0.330 0.914 0.964 1.966 -0.627 0.874 0.031 0.586 0.000 0.727 -0.142 -1.194 2.215 0.806 -0.033 1.419 0.000 0.583 -1.757 -1.283 0.000 1.030 1.051 1.104 0.941 0.581 0.724 0.813 +1 0.627 1.152 -1.300 1.295 -1.001 2.262 -1.843 1.337 0.000 1.231 -0.920 0.039 1.107 2.733 0.158 -0.272 2.548 0.478 0.738 1.702 0.000 2.980 2.413 0.991 1.089 1.270 2.305 1.959 +0 0.477 0.153 -0.336 1.240 -1.604 0.561 -1.229 -1.185 1.087 0.599 -0.729 -0.280 0.000 1.280 -1.158 1.430 1.274 1.074 0.865 -0.424 0.000 1.073 1.132 0.988 0.867 0.746 0.960 0.824 +1 1.638 0.269 1.191 0.948 -1.579 0.968 0.375 -0.395 0.000 0.768 2.728 1.241 0.000 0.866 0.143 -0.922 2.548 0.704 0.819 0.880 1.551 1.054 0.885 1.041 0.833 0.648 0.633 0.820 +0 0.392 2.166 -0.353 0.894 1.589 0.650 0.326 0.409 2.173 0.659 1.779 -1.275 0.000 0.744 0.396 1.514 0.000 0.912 -0.267 0.009 3.102 1.029 1.040 0.982 0.871 0.394 0.792 0.721 +0 0.386 0.345 -1.207 1.151 1.078 1.726 -0.581 -0.410 0.000 1.545 -0.244 1.030 1.107 1.522 0.321 1.556 1.274 1.307 -0.656 -0.951 0.000 1.100 1.806 0.988 1.126 0.896 1.438 1.351 +1 1.256 0.932 0.351 0.428 -1.070 1.968 -0.547 -0.124 0.000 1.659 1.008 1.584 2.215 1.031 1.597 1.148 0.000 1.800 0.036 1.550 3.102 0.934 0.965 0.989 1.049 0.810 0.840 0.753 +0 0.950 -0.660 -1.628 0.493 -0.511 0.517 0.048 0.338 1.087 0.968 0.697 1.623 0.000 1.532 -0.395 -0.294 2.548 0.405 0.316 1.326 0.000 0.257 1.158 0.993 0.892 0.657 0.782 0.809 +1 0.721 0.466 -1.301 0.728 0.733 0.745 -0.496 1.463 0.000 1.001 -0.673 -0.237 0.000 1.033 -0.458 0.917 2.548 0.797 -0.882 1.020 1.551 1.841 1.076 0.988 0.683 0.206 0.707 0.683 +0 0.401 0.685 -0.441 1.583 1.556 0.790 -0.568 0.032 1.087 0.686 -1.108 1.127 0.000 1.030 -0.430 -0.572 0.000 1.067 -1.028 -1.469 3.102 1.361 1.032 1.075 1.000 1.004 0.961 0.907 +1 1.067 1.068 -1.688 0.890 1.706 0.607 1.538 0.147 2.173 0.274 -1.542 -1.394 0.000 0.451 0.896 0.153 2.548 0.643 -0.119 -0.020 0.000 0.672 1.194 0.986 0.723 0.188 0.755 0.766 +0 0.661 1.474 -1.575 1.284 -1.045 0.644 0.572 1.524 2.173 1.239 0.658 -0.003 1.107 0.937 -0.822 1.448 0.000 0.577 -0.354 0.757 0.000 0.512 1.175 0.981 0.753 1.291 0.871 0.863 +1 0.489 -0.206 -0.509 1.307 1.678 0.915 0.091 -0.121 0.000 1.197 0.385 1.637 2.215 0.923 -0.777 0.175 0.000 0.438 -1.599 0.362 0.000 0.920 0.968 1.020 0.714 0.397 0.937 0.825 +1 0.676 1.380 -0.709 1.109 1.736 0.584 -0.269 -0.326 2.173 0.657 0.404 0.962 2.215 0.651 -0.178 -1.457 0.000 0.633 -0.206 1.173 0.000 0.494 0.683 0.988 0.869 0.896 0.891 0.755 +1 0.484 0.673 -0.910 0.957 0.980 0.814 -0.069 -0.076 0.000 2.188 -0.315 -1.329 1.107 0.912 -0.076 1.434 0.000 2.825 -1.584 0.242 0.000 0.945 0.973 0.987 0.555 1.543 1.035 0.860 +0 0.776 0.751 -0.628 2.167 -1.088 1.057 -0.216 0.860 2.173 0.861 -0.403 -0.028 2.215 0.352 -0.902 1.493 0.000 0.456 -1.644 1.055 0.000 0.283 0.651 0.985 1.925 1.017 1.429 1.219 +0 1.429 1.336 -1.245 0.457 0.230 1.306 0.983 -0.583 1.087 2.069 1.084 1.062 0.000 0.519 1.677 1.162 0.000 0.573 0.045 0.052 3.102 0.606 0.828 1.088 0.899 0.667 1.123 0.961 +0 1.122 -1.277 1.510 1.002 -1.205 0.994 0.647 0.894 0.000 0.766 -1.204 -0.649 2.215 0.840 0.465 0.323 0.000 1.222 2.054 0.127 0.000 0.819 0.852 0.985 0.784 1.009 1.010 1.178 +0 0.461 0.122 -0.279 1.024 1.236 0.834 0.682 -0.221 1.087 0.619 0.241 0.393 0.000 0.625 0.438 -1.253 0.000 0.956 1.200 1.302 1.551 0.957 0.813 0.988 0.592 0.995 0.667 0.616 +1 0.556 -1.299 -1.630 1.338 -1.145 1.144 0.898 0.861 2.173 0.694 0.935 -0.178 0.000 0.553 0.496 -1.537 2.548 0.662 0.316 -0.582 0.000 0.851 1.159 0.984 1.328 0.842 2.049 1.509 +0 0.317 2.149 0.739 1.641 -1.433 0.423 -1.035 -0.504 0.000 1.163 -0.187 -0.263 0.000 0.635 -0.869 0.827 1.274 0.800 1.954 1.093 0.000 0.729 0.810 0.990 1.492 0.215 1.535 1.716 +1 0.409 -0.276 0.736 0.852 1.559 0.865 0.254 -0.310 2.173 0.894 1.051 1.317 0.000 0.591 -1.981 0.378 0.000 0.626 1.623 -0.540 0.000 0.928 0.904 0.989 1.348 0.295 0.897 1.249 +1 0.599 -0.784 0.997 0.303 -0.814 0.496 -0.771 1.473 0.000 1.182 1.397 -0.043 2.215 0.542 0.096 0.305 0.000 0.592 1.236 -1.121 3.102 0.915 0.858 0.986 1.028 0.623 0.910 0.779 +0 1.848 0.276 0.337 0.516 -0.365 0.843 1.159 1.726 0.000 0.950 0.117 -0.830 1.107 0.972 0.658 1.100 2.548 0.695 0.606 -1.147 0.000 0.665 0.941 0.987 0.824 1.054 0.784 0.844 +1 0.917 0.789 1.371 0.864 1.197 0.416 0.251 1.201 0.000 0.563 -0.322 -0.908 0.000 0.623 1.803 -1.439 0.000 2.020 1.005 -0.082 3.102 1.018 1.137 0.986 0.938 0.682 0.820 0.848 +0 0.555 -0.253 1.152 2.195 0.489 0.741 -0.146 -0.516 2.173 0.810 -0.456 -1.311 0.000 1.743 -1.090 1.665 0.000 1.581 0.652 -0.049 3.102 0.797 0.993 0.992 1.124 0.724 0.949 0.916 +1 0.981 -0.696 1.671 1.532 1.065 0.582 -0.996 -0.808 0.000 0.347 -1.215 0.402 2.215 0.329 -0.338 -0.059 0.000 0.381 -0.418 -0.509 3.102 0.553 0.520 0.986 0.626 0.271 0.493 0.556 +0 0.493 -0.712 -0.901 1.298 1.704 0.786 -0.057 -0.006 2.173 0.200 -0.698 0.361 0.000 0.427 0.615 -1.371 0.000 1.435 -0.596 1.112 3.102 0.562 0.615 0.986 0.764 1.022 0.913 0.737 +0 2.304 0.012 0.524 0.754 0.170 1.234 -0.375 -1.241 1.087 0.316 0.369 -0.577 0.000 0.529 -0.318 -1.633 1.274 0.714 -0.813 1.031 0.000 0.764 0.876 0.993 0.823 0.348 1.035 0.812 +1 2.610 -0.694 -1.074 0.729 -1.250 1.206 1.450 0.524 0.000 1.321 -0.530 0.129 0.000 1.521 -0.669 0.735 2.548 1.287 -0.897 -1.636 3.102 0.525 0.983 0.976 1.383 0.921 0.923 0.924 +0 0.373 2.046 1.675 1.153 0.281 0.867 1.063 -1.290 1.087 1.052 0.743 0.103 2.215 1.291 -2.498 -1.102 0.000 1.069 0.303 0.808 0.000 1.071 0.987 0.985 0.959 1.354 0.905 0.777 +1 1.186 -0.661 -0.822 0.166 -1.686 0.724 0.370 0.867 0.000 0.562 -1.127 -0.458 0.000 0.848 0.204 1.665 2.548 0.831 0.378 0.368 3.102 0.925 0.677 0.987 0.691 0.595 0.560 0.584 +1 0.447 -0.694 0.017 0.862 -1.088 0.761 -1.006 0.972 2.173 0.565 0.572 1.372 2.215 0.490 -1.322 -0.517 0.000 0.554 0.200 -0.332 0.000 0.555 0.815 0.985 1.130 0.925 0.885 0.738 +1 0.652 -1.915 0.285 0.696 -0.015 0.886 -0.240 0.384 2.173 0.710 -1.899 1.647 0.000 1.549 2.103 1.363 0.000 2.459 -0.424 -0.760 1.551 0.683 1.079 1.000 0.756 1.357 1.024 0.881 +0 1.729 -0.186 -0.093 0.462 -1.656 1.188 2.887 -1.378 0.000 0.984 -1.442 0.049 2.215 1.301 -1.180 0.463 0.000 1.523 -0.819 -1.714 3.102 0.882 0.872 1.222 0.985 1.139 0.873 0.792 +1 1.055 -0.247 -0.316 1.754 -1.028 1.198 -0.354 1.109 1.087 0.328 -1.034 -0.719 0.000 0.685 1.354 0.972 0.000 0.862 0.550 0.270 0.000 0.864 1.042 1.127 0.545 0.782 0.922 0.781 +0 0.655 -0.356 0.174 0.918 -1.066 0.308 1.580 1.162 0.000 0.487 -2.458 0.076 0.000 0.723 -0.632 0.475 2.548 1.173 0.742 1.607 3.102 0.814 0.920 0.987 0.875 0.861 0.666 0.689 +1 1.085 0.333 -0.317 0.501 1.572 0.697 0.037 1.500 2.173 0.612 -1.198 0.696 2.215 0.742 1.336 -0.096 0.000 0.441 -0.496 -1.613 0.000 0.989 0.949 1.013 0.887 0.912 0.817 0.732 +0 1.740 0.324 1.688 1.304 1.336 1.865 0.323 -0.065 0.000 0.903 0.135 -1.164 2.215 0.341 0.448 -0.667 0.000 0.884 0.466 0.977 1.551 0.750 0.911 0.975 0.872 0.774 0.836 1.015 +0 1.737 -0.024 1.516 1.275 0.953 0.966 -0.540 -0.262 0.000 0.854 -0.441 0.212 0.000 0.934 -0.068 -0.902 2.548 1.401 0.727 -1.450 3.102 0.805 0.857 1.001 0.902 0.597 0.872 0.974 +1 0.893 -0.581 0.846 0.812 -0.545 0.985 -0.866 1.530 0.000 1.645 -0.624 -0.461 2.215 0.910 0.352 1.385 2.548 0.366 1.994 0.112 0.000 2.424 1.393 1.121 0.891 1.471 1.298 1.056 +0 1.065 -0.997 0.559 0.153 -0.966 1.146 -0.596 -0.978 2.173 1.102 0.460 0.667 2.215 0.783 -1.333 1.472 0.000 0.565 -0.247 -0.151 0.000 0.859 0.966 0.996 0.797 1.892 1.027 0.864 +0 0.636 -1.883 -0.146 1.284 -0.019 2.254 1.181 1.656 2.173 1.822 -0.160 -0.200 0.000 1.209 -0.561 0.301 1.274 1.276 -0.129 1.702 0.000 1.966 1.285 0.987 3.068 2.873 2.101 1.803 +1 0.786 -1.464 1.067 0.565 0.133 0.694 0.004 -1.141 2.173 0.475 0.292 1.153 0.000 0.533 -0.845 0.859 0.000 1.556 0.721 -0.481 3.102 0.543 0.922 0.984 0.952 0.787 0.860 0.742 +0 0.361 -0.339 0.437 0.654 -0.546 0.731 0.101 0.595 2.173 0.696 1.421 -0.553 0.000 0.826 1.427 1.164 2.548 0.452 0.921 1.599 0.000 0.741 0.964 0.991 0.719 0.909 0.699 0.638 +1 0.345 1.528 0.427 1.419 1.252 0.978 -0.692 -1.234 2.173 0.996 0.831 -0.220 1.107 0.882 0.054 0.192 0.000 1.213 0.131 1.292 0.000 0.956 0.946 0.994 1.240 1.696 1.094 0.920 +0 0.372 -0.195 -1.456 1.011 -1.691 0.932 0.506 -0.805 0.000 1.122 1.854 1.043 0.000 1.598 0.509 0.514 2.548 0.981 0.567 -0.210 0.000 1.042 0.787 0.983 1.641 0.197 1.138 1.070 +1 1.489 2.036 -1.683 0.856 1.334 0.712 1.058 0.162 2.173 0.741 0.509 -0.371 2.215 0.622 1.554 -1.110 0.000 0.479 0.999 1.026 0.000 0.585 0.703 0.997 1.148 0.575 0.938 0.730 +1 1.743 -1.008 -0.589 0.828 -0.764 0.862 -0.725 -1.629 2.173 1.274 -0.737 1.033 2.215 1.154 -2.057 0.765 0.000 0.805 0.393 -0.201 0.000 2.111 1.476 0.983 1.056 1.042 1.108 1.116 +1 2.203 0.650 1.706 0.918 -1.202 1.191 0.550 0.100 2.173 0.420 1.270 1.022 1.107 0.619 -0.353 -1.129 0.000 0.735 0.138 0.538 0.000 0.799 1.058 0.987 0.724 0.867 1.020 0.853 +0 0.742 1.048 -1.047 0.467 -0.103 0.433 -0.422 1.422 0.000 0.813 1.227 1.151 2.215 0.735 -0.206 -0.024 2.548 0.892 2.147 -0.084 0.000 1.074 1.053 0.982 0.874 0.980 0.770 0.726 +1 1.150 -1.362 0.377 1.319 1.627 0.643 -0.113 -0.835 2.173 0.522 0.982 0.440 2.215 0.364 1.204 -0.746 0.000 0.562 -0.931 1.595 0.000 0.883 0.743 1.541 1.274 0.928 1.109 0.913 +0 1.379 0.617 -1.381 0.776 1.400 1.317 0.085 0.220 0.000 0.446 -0.008 -0.937 1.107 0.585 0.660 0.629 0.000 0.442 2.083 1.689 0.000 0.765 0.880 0.986 0.507 0.238 0.634 0.736 +1 0.297 0.724 -1.269 1.171 0.679 1.588 -1.342 -1.295 0.000 2.789 -0.320 0.296 0.000 1.924 0.323 -0.904 2.548 1.378 2.169 -1.634 0.000 0.874 0.814 0.986 1.294 1.028 1.123 1.260 +0 1.408 -0.113 1.082 1.619 1.568 0.622 0.167 -0.907 0.000 0.585 -1.109 0.531 1.107 0.755 2.130 -0.370 0.000 1.207 -0.285 -0.126 0.000 0.923 0.946 0.990 0.795 0.365 0.702 0.776 +0 0.670 -0.590 -0.341 1.301 1.427 1.311 -0.117 0.037 2.173 0.945 0.698 1.614 1.107 1.181 -0.133 0.828 0.000 1.650 0.379 -1.400 0.000 1.215 0.994 1.293 1.216 1.767 1.091 0.928 +0 0.745 1.082 0.093 0.862 -0.011 1.527 1.023 0.351 2.173 1.120 1.059 -0.800 0.000 2.215 0.856 -1.621 2.548 0.500 -1.609 1.126 0.000 0.211 2.084 0.983 1.031 2.245 1.946 1.884 +0 0.415 -0.907 -0.839 2.005 -1.355 1.321 0.471 0.721 0.000 0.954 -0.345 0.520 0.000 1.389 0.404 -0.690 2.548 0.683 -0.865 -1.484 3.102 1.107 1.157 0.996 1.834 0.777 1.126 1.529 +1 1.227 0.815 -0.708 0.335 1.163 0.578 0.851 1.294 0.000 0.674 -0.251 -0.160 2.215 1.387 0.129 -1.269 1.274 1.888 1.167 0.671 0.000 0.957 1.198 0.989 0.786 0.889 0.975 0.847 +0 0.795 0.534 1.104 0.866 -0.139 0.806 0.759 -0.317 2.173 0.592 -1.381 1.566 0.000 0.452 1.040 0.326 0.000 1.178 -0.078 1.735 3.102 0.575 0.660 1.034 0.760 1.092 0.965 0.876 +1 0.642 -0.568 -1.703 0.553 -1.021 1.315 0.048 1.187 0.000 1.425 0.561 -0.116 2.215 0.869 1.417 -0.693 2.548 0.983 -0.299 -0.894 0.000 0.836 1.155 0.989 0.776 0.842 0.932 0.829 +0 0.502 -1.035 1.401 0.729 0.143 1.650 -0.443 0.879 2.173 1.418 -0.075 -1.115 2.215 1.266 1.159 -0.756 0.000 1.375 -1.834 -0.846 0.000 0.959 1.425 0.986 1.320 2.232 1.440 1.217 +1 0.709 0.823 0.063 1.784 0.816 0.445 1.439 0.295 0.000 0.652 1.119 -1.560 2.215 0.754 1.515 -1.219 0.000 1.406 0.467 -1.062 3.102 1.025 0.861 0.987 0.886 0.456 0.757 0.710 +1 1.118 -0.684 -0.217 1.177 0.243 0.403 -1.348 -0.891 0.000 0.776 0.898 1.479 2.215 0.717 -1.001 1.353 2.548 0.737 -0.216 1.406 0.000 0.875 1.086 0.979 0.885 0.950 0.880 0.805 +1 0.575 0.350 -0.243 0.704 -1.117 0.716 -0.082 -0.387 0.000 0.881 0.353 0.559 1.107 0.793 -0.565 -1.202 0.000 1.309 0.822 1.352 3.102 0.883 0.860 0.983 0.955 0.705 0.768 0.679 +1 0.600 0.338 -0.789 0.593 0.388 0.952 0.420 1.694 0.000 1.041 0.291 -0.225 2.215 1.086 0.482 1.211 0.000 1.124 2.349 -0.661 0.000 0.780 0.847 0.990 0.712 0.853 0.880 0.801 +1 0.340 1.440 0.456 0.832 -1.118 0.635 0.450 1.291 2.173 1.549 0.366 0.139 1.107 0.827 0.177 -0.527 0.000 1.158 -0.757 -1.636 0.000 1.103 1.007 0.989 0.909 1.259 0.942 0.799 +0 0.862 -2.244 -0.375 1.042 -0.309 0.776 0.521 1.532 2.173 0.557 -1.370 -1.230 0.000 1.166 1.242 0.550 0.000 1.266 0.879 -1.254 3.102 1.132 1.048 1.003 1.736 0.678 1.384 1.111 +1 2.773 0.260 -0.638 1.701 -1.050 1.092 0.722 0.751 2.173 0.543 -0.389 -1.650 0.000 0.883 1.254 1.089 0.000 0.485 1.189 0.500 3.102 0.752 0.832 1.089 1.824 0.325 1.162 1.091 +0 0.816 1.420 -0.547 0.711 -0.983 0.652 0.841 -1.592 1.087 1.545 -1.595 0.497 0.000 0.753 -1.152 -0.153 0.000 0.843 -1.168 -1.641 3.102 0.966 0.931 0.983 1.011 1.082 1.337 2.137 +0 1.834 -0.687 0.012 0.957 0.474 1.776 1.074 -1.559 0.000 1.465 -0.097 -0.123 2.215 1.096 -2.023 1.515 0.000 1.209 -1.077 -0.136 1.551 0.830 0.811 0.988 0.901 0.761 0.966 0.860 +0 0.561 -1.760 -1.367 0.645 0.888 1.158 -0.472 -0.313 1.087 1.187 -0.356 1.701 0.000 1.095 -0.723 1.349 0.000 0.955 -1.614 0.056 0.000 1.034 0.862 0.980 1.017 0.783 0.851 0.766 +0 1.046 1.144 -0.362 1.288 -0.562 0.787 -1.168 1.053 1.087 0.687 -1.225 -1.687 0.000 0.760 0.828 1.108 2.548 1.014 -0.449 -0.392 0.000 1.078 0.981 0.995 0.976 1.202 1.739 1.575 +0 0.847 -0.348 -0.207 1.067 -1.039 0.582 -1.844 1.412 0.000 1.251 -1.160 -0.930 2.215 1.071 0.508 0.587 0.000 2.035 -0.488 0.878 3.102 2.372 1.436 0.984 0.918 1.504 1.231 1.099 +1 0.840 0.073 0.640 1.419 0.600 0.345 -0.144 1.611 2.173 1.259 -0.909 -1.073 2.215 0.474 -1.535 -1.588 0.000 1.147 -1.068 -0.142 0.000 0.801 0.781 0.987 0.757 0.752 0.880 0.762 +1 1.074 -0.135 0.069 0.593 0.888 0.989 -0.873 -0.524 2.173 0.599 1.102 0.970 0.000 0.794 1.369 1.713 0.000 1.083 0.394 -1.603 3.102 0.685 0.600 0.986 0.899 1.208 1.125 1.006 +0 0.953 -1.059 -0.864 0.925 -0.357 1.012 -0.556 0.423 2.173 0.796 0.359 1.715 0.000 0.465 -0.881 -1.696 2.548 0.445 -0.174 1.135 0.000 0.709 1.159 0.985 0.661 0.824 0.840 0.910 +0 1.519 0.204 -1.361 1.493 -1.495 1.843 1.109 0.476 2.173 0.903 0.248 -0.915 2.215 1.581 0.730 0.792 0.000 0.992 1.545 -0.194 0.000 0.914 0.863 0.991 2.015 1.992 1.435 1.141 +1 0.439 0.988 0.008 2.500 -0.421 0.509 1.472 0.973 0.000 0.772 0.751 1.632 2.215 0.779 1.283 1.725 2.548 0.508 0.293 -1.286 0.000 0.835 0.632 0.991 0.982 0.276 0.917 0.804 +1 1.362 1.360 0.778 1.545 1.035 1.002 0.787 -0.860 2.173 0.820 0.859 -0.323 0.000 0.508 -0.438 -0.073 0.000 1.133 0.204 1.614 3.102 0.762 0.855 0.990 1.458 0.945 0.993 0.903 +1 0.639 1.330 -0.725 1.630 1.657 2.171 1.906 0.107 0.000 1.605 0.681 1.631 2.215 0.788 1.743 -1.477 0.000 0.743 -0.782 -1.331 0.000 1.661 1.239 1.185 1.403 1.564 1.090 1.011 +1 1.780 0.330 -0.093 0.331 -1.100 1.341 -0.144 1.292 2.173 1.423 1.336 -0.205 0.000 1.350 -0.181 -1.256 1.274 0.966 1.193 1.495 0.000 1.317 1.663 0.991 1.412 1.253 1.535 1.259 +1 0.393 1.365 1.576 0.792 -1.182 1.192 0.668 -0.137 2.173 1.072 0.375 1.395 0.000 0.586 -0.489 0.568 2.548 0.704 -0.223 1.707 0.000 0.965 0.737 0.998 1.088 0.912 0.823 0.762 +1 0.311 -1.178 0.766 0.660 -1.371 0.764 -0.327 1.188 2.173 1.232 -0.753 -0.274 2.215 0.312 1.222 0.967 0.000 0.798 0.346 -0.818 0.000 0.611 0.925 0.987 0.742 1.420 0.850 0.725 +1 1.192 -1.240 -0.664 1.233 1.285 0.531 -0.545 -0.138 2.173 0.855 -1.865 1.417 0.000 0.971 -1.377 0.035 0.000 0.952 -0.522 -1.542 1.551 1.355 0.991 1.651 1.034 0.718 0.772 0.777 +0 0.435 0.373 -1.099 2.466 -0.458 1.032 0.183 0.983 1.087 0.601 -0.639 1.715 0.000 1.078 1.516 -1.276 0.000 1.663 0.485 0.669 3.102 1.857 1.390 0.989 1.574 0.477 1.100 1.123 +0 0.489 -0.560 -0.287 1.635 -0.867 0.447 0.430 0.168 2.173 1.026 0.881 1.484 0.000 0.699 -0.317 0.185 2.548 0.800 -0.090 1.413 0.000 0.629 0.864 0.987 1.182 0.280 0.809 1.025 +1 0.805 1.554 -0.598 1.345 -1.659 0.490 1.458 -0.087 0.000 0.746 0.212 -1.295 2.215 1.509 0.636 0.379 0.000 1.031 0.314 1.328 1.551 0.879 0.896 1.177 0.900 0.559 0.776 0.827 +0 1.334 -1.125 1.309 0.263 -0.309 0.487 -1.430 0.135 0.000 0.660 0.410 -1.008 2.215 0.430 -0.294 -1.517 0.000 0.384 0.760 0.142 3.102 0.954 0.962 0.984 0.670 0.407 0.625 0.628 +0 1.466 -1.111 1.287 2.576 1.130 1.982 0.371 -0.394 0.000 0.822 -0.059 1.675 2.215 0.875 1.011 -0.483 0.000 0.419 1.416 -1.234 3.102 0.911 0.837 0.979 1.276 0.591 1.261 1.893 +1 0.869 -0.329 1.598 0.669 -0.111 0.816 -1.098 -0.969 0.000 0.745 -0.341 -0.898 0.000 1.503 0.346 0.979 2.548 1.455 0.121 0.019 3.102 1.038 1.001 1.056 0.722 0.871 0.703 0.655 +1 1.333 1.213 1.469 0.725 -1.231 0.800 0.517 0.058 2.173 0.419 -2.324 -0.905 0.000 0.953 0.766 -1.631 0.000 0.778 0.750 -0.763 3.102 2.419 1.525 0.993 1.095 0.585 1.170 1.129 +0 1.927 1.637 0.422 0.302 0.328 1.063 -0.364 -1.678 0.000 1.436 1.680 -0.055 0.000 2.099 0.430 -1.366 1.274 0.597 -0.325 1.477 0.000 0.357 0.944 1.001 1.507 0.930 0.950 1.069 +1 0.650 0.699 -0.098 0.861 -0.605 1.050 -0.275 1.510 0.000 0.973 0.562 1.205 0.000 1.555 0.493 -0.402 2.548 1.143 -0.856 -0.131 3.102 0.982 1.050 0.986 0.802 0.918 0.996 1.174 +1 0.424 -0.694 1.732 1.077 -1.672 0.697 1.163 -0.407 0.000 0.763 0.606 0.867 2.215 0.746 1.843 0.088 0.000 0.834 -0.409 -1.489 3.102 0.798 0.994 0.989 0.720 0.744 1.057 1.630 +0 1.416 -0.247 1.096 0.743 0.092 0.898 0.816 0.945 2.173 1.650 0.240 -1.197 0.000 1.268 -0.115 -0.495 2.548 1.180 -0.074 -1.668 0.000 0.809 0.967 1.116 0.917 1.438 1.058 0.982 +1 0.895 -1.225 -1.193 1.138 -0.531 0.812 -0.412 1.030 2.173 0.771 0.885 0.526 2.215 0.561 -0.913 -1.698 0.000 0.872 -0.199 -0.563 0.000 0.725 0.955 0.993 1.335 0.976 1.333 1.023 +1 0.852 0.515 0.282 0.285 -0.301 0.696 -0.385 -0.909 2.173 0.706 0.717 -0.185 0.000 1.395 -0.472 1.702 2.548 0.906 1.105 0.834 0.000 0.884 1.115 0.990 0.910 0.874 0.927 0.814 +1 0.725 -1.110 -0.840 0.824 1.371 0.477 -0.265 0.721 0.000 1.081 -0.928 -1.331 2.215 0.996 2.482 -0.203 0.000 2.149 0.164 0.240 0.000 0.746 0.612 0.989 0.649 0.761 0.846 0.767 +0 1.464 -0.882 -1.234 1.799 1.631 0.634 -0.446 0.391 0.000 0.789 0.238 1.689 2.215 1.198 -0.880 -0.195 0.000 1.567 2.331 0.760 0.000 0.890 0.919 1.193 0.906 1.225 1.078 1.053 +1 1.383 -0.559 -1.567 2.071 -1.593 1.012 1.868 0.643 0.000 0.542 -0.730 -0.841 0.000 1.768 -0.991 -0.381 2.548 0.854 -0.274 0.621 0.000 0.879 0.797 0.995 1.582 1.411 1.205 0.962 +1 0.685 -2.266 0.131 1.021 -1.211 1.119 -1.195 1.413 2.173 1.132 -1.278 0.217 2.215 0.847 -1.743 -0.462 0.000 1.192 -1.472 -0.940 0.000 0.522 0.995 1.084 0.993 1.463 1.023 0.818 +0 0.690 0.819 0.605 0.214 0.684 0.735 -0.101 -1.670 0.000 1.343 0.341 -0.849 2.215 1.068 -0.258 0.192 2.548 0.756 2.064 0.693 0.000 0.275 1.230 0.982 0.994 1.106 1.073 0.943 +1 1.682 -0.221 1.179 1.701 0.565 2.008 -0.450 -1.437 0.000 1.325 -0.427 0.268 2.215 1.312 0.222 0.744 0.000 2.042 0.521 -1.248 0.000 0.918 1.018 1.231 0.952 0.832 1.140 1.144 +1 0.323 1.083 -1.132 1.769 -0.368 0.976 -0.403 0.707 1.087 1.034 -0.152 1.513 2.215 0.701 -0.297 -0.541 0.000 0.591 -0.383 -1.178 0.000 0.518 0.819 0.993 1.090 0.998 0.960 0.753 +1 0.437 1.255 0.864 1.663 -1.110 1.156 1.154 0.529 2.173 0.984 0.059 1.704 0.000 0.507 0.738 -0.949 0.000 0.650 0.424 -0.420 3.102 0.848 0.660 1.156 1.240 0.757 0.845 0.831 +0 0.418 0.510 1.657 1.129 0.147 0.747 1.242 -0.132 0.000 1.307 1.336 1.542 2.215 1.004 0.559 -0.694 0.000 1.248 -0.149 -1.172 1.551 0.910 0.986 0.987 0.934 1.234 0.998 0.850 +0 0.825 1.625 1.420 0.976 0.629 0.939 0.213 -0.717 2.173 0.309 0.544 -0.961 2.215 0.593 2.447 0.626 0.000 0.702 -0.144 0.584 0.000 0.809 0.852 0.986 0.660 0.220 0.774 0.675 +1 0.616 0.460 -0.636 0.517 1.071 0.792 0.634 0.672 0.000 1.066 0.274 0.166 0.000 0.908 -0.077 -1.740 2.548 1.324 0.862 -0.994 3.102 0.930 1.098 0.988 0.771 0.719 0.858 0.754 +0 1.706 -0.782 1.019 0.541 0.232 0.697 -0.927 1.636 1.087 0.511 0.559 -1.243 0.000 0.867 1.762 -0.874 0.000 1.260 -1.286 -0.111 0.000 0.820 0.838 0.987 0.794 1.072 1.034 1.129 +0 1.156 0.245 0.911 0.656 -0.040 0.733 1.714 -1.412 0.000 0.918 -1.427 -1.586 0.000 1.412 -1.054 -0.314 2.548 2.424 -0.701 0.651 3.102 0.825 0.909 0.982 0.763 1.104 0.854 0.850 +1 1.053 0.575 -0.351 0.275 1.049 0.869 1.137 -1.556 2.173 0.784 -0.192 1.121 0.000 0.737 1.381 0.257 0.000 0.803 -0.695 0.205 0.000 0.830 0.865 0.985 1.014 0.790 0.875 0.771 +0 1.064 -0.335 -0.335 0.990 1.396 2.076 -0.748 -0.152 1.087 1.820 -0.707 1.583 0.000 1.372 -0.310 1.299 0.000 1.124 -1.059 -1.269 3.102 0.773 0.891 1.422 1.354 1.430 1.512 1.234 +1 0.401 0.609 -0.829 0.718 0.151 0.636 2.561 -0.757 0.000 1.013 0.470 1.396 0.000 1.320 -0.779 -0.747 2.548 1.228 -1.153 1.466 3.102 0.810 1.017 0.988 1.630 0.924 1.427 1.199 +0 0.664 -0.231 0.695 0.483 -1.531 0.798 0.855 -0.095 2.173 0.963 -0.941 1.474 2.215 0.904 -2.099 -1.225 0.000 0.445 -1.466 1.006 0.000 0.662 0.788 0.992 0.823 1.858 1.362 1.151 +0 0.729 -0.868 0.536 0.921 -0.171 1.066 0.649 0.139 2.173 2.081 -0.874 -1.616 2.215 1.005 -2.073 -0.629 0.000 1.344 -1.217 1.327 0.000 0.975 1.272 0.985 0.808 2.888 1.687 1.344 +1 1.248 0.981 0.711 1.717 0.664 0.414 1.352 -0.738 2.173 1.052 1.446 1.714 0.000 0.930 -1.834 -0.531 0.000 0.776 1.133 0.110 0.000 1.170 0.941 0.996 0.979 0.628 0.907 0.829 +0 0.844 -0.803 1.380 0.253 -0.546 1.404 -0.079 -1.584 0.000 1.493 -0.961 0.027 1.107 1.636 -0.029 0.043 2.548 0.735 0.553 1.268 0.000 1.025 1.524 0.992 1.024 0.826 1.322 1.043 +1 0.479 -0.725 0.590 2.003 0.007 0.902 2.292 -0.915 0.000 1.512 1.013 1.357 2.215 0.566 0.358 1.408 0.000 0.880 1.114 0.277 3.102 0.642 0.715 0.984 1.601 0.875 1.831 1.428 +1 0.537 -0.241 0.591 0.270 -0.989 1.145 -1.268 0.819 2.173 0.802 -0.345 -1.482 0.000 0.727 -0.691 -0.805 2.548 0.413 -0.911 -1.612 0.000 0.903 0.721 0.992 1.472 1.170 1.035 0.891 +1 0.612 -1.829 1.678 0.552 -0.283 0.943 -0.557 -0.789 2.173 1.378 -0.989 1.252 2.215 0.836 -2.241 0.557 0.000 0.931 -0.463 0.095 0.000 0.916 0.970 0.991 0.804 1.662 1.026 0.835 +0 2.949 0.811 -0.457 0.658 1.456 0.995 0.879 -1.022 2.173 1.296 0.246 1.119 0.000 1.144 0.656 1.451 2.548 2.096 0.860 0.707 0.000 1.158 0.851 1.907 1.159 1.057 1.042 1.134 +0 0.891 -0.762 0.792 0.829 -1.319 0.535 -0.727 -0.906 2.173 0.407 -2.079 -1.269 0.000 0.856 -1.094 0.412 2.548 0.821 -1.518 0.885 0.000 0.873 0.742 1.126 0.719 0.807 0.617 0.578 +0 1.248 -0.447 -0.304 0.772 -1.134 1.085 0.649 1.356 0.000 1.004 0.430 0.639 2.215 1.350 0.924 -0.639 0.000 0.595 -0.743 -1.520 3.102 2.156 1.367 0.988 1.003 0.820 0.976 0.944 +1 0.420 2.058 -0.506 1.262 0.833 0.695 0.002 -0.567 2.173 0.373 0.776 -0.834 0.000 0.953 -1.615 0.929 0.000 0.737 -1.222 -1.297 1.551 1.781 1.078 0.989 1.698 0.764 1.604 1.736 +0 1.374 0.140 -0.684 0.030 -1.145 0.430 -0.240 -1.364 0.000 1.293 -0.216 0.566 0.000 1.593 0.428 1.125 2.548 0.683 1.307 -1.154 0.000 0.871 0.962 0.657 0.466 1.018 0.739 0.679 +0 1.773 0.222 -0.089 1.547 -0.695 0.532 -1.432 1.563 0.000 1.905 0.669 1.082 0.000 0.883 0.030 -1.064 2.548 0.748 -0.810 -0.657 0.000 0.905 0.828 1.191 0.748 0.379 0.576 0.773 +0 0.598 -0.766 -1.703 0.239 -1.381 0.277 -0.584 0.222 1.087 0.526 0.597 -0.065 2.215 0.403 2.198 -1.191 0.000 0.674 0.695 0.756 0.000 0.756 0.831 0.974 0.688 0.387 0.546 0.575 +1 1.806 0.111 -0.461 0.862 0.321 1.045 1.139 1.127 2.173 0.375 1.051 -0.670 0.000 0.593 0.224 1.574 1.274 0.460 2.137 -1.490 0.000 0.571 0.888 1.120 0.832 0.598 0.971 0.844 +1 1.190 -0.245 -1.345 1.290 -1.183 1.205 0.602 0.877 2.173 0.339 2.125 0.830 0.000 1.233 0.268 -0.045 2.548 0.626 -0.883 0.082 0.000 0.953 1.060 0.977 1.025 1.148 1.084 0.890 +0 0.535 1.078 -0.160 1.198 0.640 1.029 0.636 1.291 2.173 0.966 1.269 -0.633 2.215 1.299 -0.293 -0.562 0.000 1.166 0.221 -1.674 0.000 1.072 1.021 0.992 1.192 1.529 1.014 0.878 +0 2.409 -0.060 -1.559 1.080 0.099 0.602 2.163 0.643 0.000 0.974 -0.823 0.122 2.215 1.067 -0.799 -1.157 2.548 0.924 -0.568 1.211 0.000 0.818 0.768 2.228 1.479 0.990 1.047 0.892 +1 0.501 0.046 0.612 0.933 -1.190 0.799 0.680 0.679 0.000 0.637 1.285 -1.433 0.000 0.540 1.341 -0.400 0.000 1.290 0.181 0.276 3.102 1.086 0.876 0.988 0.718 0.597 0.587 0.643 +0 0.670 -1.361 1.200 0.293 -0.196 0.519 1.010 -1.116 0.000 0.770 -0.157 1.563 2.215 0.690 0.707 0.000 0.000 1.348 -1.675 0.352 0.000 0.919 0.959 0.985 0.691 0.722 0.807 0.742 +1 1.531 -0.285 -1.141 0.560 0.651 1.803 0.620 0.358 1.087 1.910 0.260 -1.711 0.000 0.619 0.571 -0.380 2.548 0.576 1.330 -0.730 0.000 1.438 1.061 1.282 1.589 0.813 1.254 1.131 +1 0.755 0.479 0.080 1.012 1.160 0.525 -0.201 -1.567 1.087 0.495 1.542 -1.523 0.000 0.815 -1.232 -0.201 2.548 1.126 1.008 -0.029 0.000 0.966 1.011 1.002 0.997 0.914 0.976 0.852 +1 0.744 0.002 -0.444 0.592 0.883 1.899 -0.348 0.423 0.000 0.909 2.561 -1.191 0.000 2.154 0.573 -1.670 2.548 1.717 -0.449 -0.339 0.000 0.872 0.823 0.982 1.118 0.866 0.843 0.765 +1 0.547 -0.885 -1.384 1.065 0.113 0.602 -0.335 0.744 1.087 1.408 -0.149 1.494 0.000 1.212 0.808 -0.582 1.274 0.883 -0.344 -1.045 0.000 1.111 0.976 1.031 1.061 1.209 0.931 0.868 +1 1.454 0.157 0.903 1.814 -0.021 0.765 1.432 -1.321 1.087 0.954 0.535 -0.005 0.000 1.469 -1.334 -0.952 0.000 0.915 0.615 -1.529 3.102 2.556 1.641 1.663 1.618 0.360 1.439 1.342 +0 1.427 0.261 0.237 0.230 -0.665 1.018 0.458 -1.631 2.173 0.683 2.535 0.201 0.000 0.795 0.912 -1.164 2.548 0.508 0.847 0.217 0.000 0.687 0.924 0.990 0.768 0.551 0.787 0.711 +1 0.628 -1.305 0.374 1.051 1.120 0.369 -0.104 1.621 0.000 0.969 -0.148 -1.204 2.215 1.134 0.777 -0.680 0.000 1.242 0.774 0.454 0.000 1.057 1.013 0.984 0.615 0.709 0.899 1.028 +0 0.447 -0.446 -1.740 0.436 -1.175 1.253 0.355 -0.135 2.173 0.985 -2.165 -1.653 0.000 1.154 -1.600 1.051 0.000 1.134 -1.708 -0.985 0.000 0.790 1.485 0.992 1.733 0.572 1.657 1.343 +1 1.326 1.209 1.193 0.864 0.525 0.442 -0.085 -0.105 0.000 0.838 -0.694 1.523 2.215 0.991 -0.546 -0.533 2.548 1.257 -1.497 -0.950 0.000 0.672 0.954 0.989 1.177 0.931 0.976 0.822 +1 1.126 -0.333 -1.289 0.469 -1.625 1.061 -1.032 0.407 2.173 0.793 -0.931 1.538 2.215 0.416 -0.053 -1.005 0.000 0.628 0.498 0.029 0.000 0.490 0.899 0.981 0.635 1.151 0.860 0.752 +1 1.216 -2.332 1.277 0.179 -1.732 0.591 -1.144 -0.693 0.000 1.010 -0.393 -1.199 2.215 0.496 0.410 1.523 2.548 0.878 -0.578 0.170 0.000 0.904 0.765 0.981 1.140 0.582 0.862 0.776 +1 1.261 -0.304 0.338 1.056 -0.260 2.285 -2.057 0.603 0.000 2.335 -0.612 -0.983 2.215 1.680 -0.733 -1.497 2.548 1.715 -0.059 -1.702 0.000 4.440 2.977 0.993 1.358 0.958 2.343 1.854 +0 0.403 -1.388 0.173 0.641 1.020 0.539 0.510 -1.033 0.000 0.740 -1.074 -1.261 2.215 1.017 0.485 1.201 2.548 0.666 -0.358 1.146 0.000 0.952 0.916 0.996 0.843 1.118 0.905 0.803 +1 0.822 0.391 -1.279 0.727 1.719 0.538 0.858 -0.033 0.000 0.794 -0.250 1.243 1.107 1.194 -0.571 0.242 2.548 0.988 1.036 -0.895 0.000 0.808 1.078 0.990 0.948 0.835 0.950 0.878 +1 1.644 0.072 -0.242 0.865 -0.047 1.376 0.837 -1.131 1.087 0.796 0.724 1.371 2.215 0.977 0.678 0.713 0.000 0.443 0.967 0.446 0.000 0.921 0.875 0.976 1.298 1.195 1.049 0.973 +0 0.846 -0.177 0.966 0.747 -1.374 1.899 -0.695 0.158 0.000 2.111 0.577 -1.351 0.000 0.368 -1.144 -0.027 0.000 1.817 -0.982 1.235 3.102 0.491 0.730 0.989 0.694 0.519 0.800 0.770 +0 0.565 0.888 -1.468 0.909 -0.716 0.725 0.752 1.159 2.173 0.665 -0.011 -1.322 0.000 1.704 0.247 -0.119 2.548 0.686 -0.947 0.826 0.000 0.974 0.991 0.984 0.839 1.311 0.883 0.790 +1 1.101 -0.071 -0.113 0.707 1.039 0.621 -0.828 -0.119 0.000 1.051 0.011 1.564 2.215 0.592 1.143 -1.206 2.548 0.508 -1.249 -1.307 0.000 0.807 1.045 1.054 0.891 0.750 0.830 0.756 +0 0.546 -0.495 0.337 1.251 -1.362 0.963 -0.128 1.330 0.000 1.587 -0.616 -0.327 2.215 1.991 1.908 0.143 0.000 1.093 -1.047 -1.531 3.102 0.766 0.841 1.144 0.990 1.117 1.104 0.914 +0 0.594 0.790 -0.860 1.312 1.255 0.920 -0.057 -0.961 2.173 1.263 0.288 0.690 2.215 0.692 0.693 -0.541 0.000 0.668 1.808 0.875 0.000 0.919 1.015 1.154 1.062 1.606 0.991 0.875 +0 1.799 -1.747 -0.641 0.529 1.738 0.625 -1.521 1.098 0.000 0.530 -0.657 -0.260 2.215 1.192 -0.706 -1.529 1.274 1.121 -2.079 0.597 0.000 0.848 0.966 1.135 0.924 0.770 0.811 0.813 +0 1.904 0.013 0.734 0.936 1.332 0.468 -2.108 -1.264 0.000 1.020 -0.022 -0.804 2.215 0.339 -1.191 0.684 0.000 0.613 -0.739 -0.135 0.000 0.882 1.101 0.986 0.752 0.354 0.799 0.908 +0 0.529 -1.709 1.061 0.860 0.300 0.696 2.226 1.022 0.000 0.944 1.794 -0.681 0.000 0.520 -2.153 -0.440 0.000 0.897 0.001 -1.046 1.551 0.983 0.932 0.996 0.745 0.574 0.633 0.616 +1 1.948 0.490 -1.236 0.703 -0.706 0.718 0.647 0.421 1.087 0.629 1.184 1.540 0.000 0.462 1.617 0.284 0.000 1.269 0.373 1.042 1.551 0.784 0.791 0.999 0.948 0.546 0.857 0.751 +1 0.450 1.186 0.304 1.164 1.652 0.779 -0.545 -1.735 1.087 1.602 0.800 -0.271 2.215 0.757 -0.473 -0.252 0.000 1.073 0.488 1.307 0.000 1.138 0.988 0.985 1.173 2.006 1.272 1.067 +0 2.894 -0.523 1.368 1.396 1.541 0.985 0.182 -0.838 0.000 1.697 0.172 -0.472 0.000 1.682 -0.226 0.784 0.000 1.072 -0.376 -0.161 1.551 0.933 0.688 1.001 0.897 0.217 0.808 0.874 +1 0.736 -0.112 -0.629 0.656 1.114 0.894 0.153 -1.400 0.000 1.739 0.729 0.414 2.215 0.980 0.822 -0.823 0.000 0.886 1.090 -0.520 3.102 0.944 0.885 0.989 0.939 0.893 1.000 0.834 +0 1.233 -1.565 -0.693 0.616 -1.021 0.596 -0.842 1.698 0.000 0.840 -0.009 0.860 2.215 1.340 -0.875 0.463 2.548 0.714 -0.878 -0.786 0.000 0.788 0.934 0.985 1.117 0.683 0.865 0.771 +0 0.342 -1.382 0.717 1.717 -0.962 0.557 2.094 0.791 0.000 0.383 -0.280 1.280 2.215 0.461 0.811 0.101 0.000 0.820 -0.220 -1.581 3.102 0.842 0.665 1.059 0.774 0.270 0.560 0.601 +0 1.008 0.642 0.945 0.575 -0.294 0.886 0.419 -1.434 2.173 0.812 0.100 0.478 2.215 0.567 0.986 -0.167 0.000 0.389 0.025 -1.192 0.000 0.503 0.663 0.985 0.595 1.250 0.750 0.611 +1 1.656 0.172 -0.791 0.337 0.718 0.584 -1.250 1.354 1.087 0.248 -0.550 1.706 0.000 0.756 -0.462 -0.235 0.000 0.470 0.660 1.166 3.102 0.655 0.722 1.012 0.618 0.681 0.734 0.612 +1 0.587 -0.346 -1.266 0.921 0.391 0.774 -0.484 1.570 0.000 0.799 0.712 -1.272 0.000 0.605 2.459 -0.785 0.000 0.704 -0.364 0.292 3.102 0.836 1.027 1.016 0.568 0.265 0.599 0.723 +1 1.068 -0.668 0.501 0.071 -0.769 0.378 -1.749 -1.357 0.000 0.644 -2.378 1.233 0.000 1.374 -0.612 -0.577 2.548 0.435 -1.289 1.625 0.000 0.840 0.769 0.990 0.834 0.606 0.769 0.804 +0 0.523 -1.018 -1.124 0.510 1.477 0.908 -0.347 0.935 1.087 0.799 -0.304 -0.601 0.000 0.956 1.016 -0.948 2.548 0.932 0.004 0.435 0.000 0.921 1.030 0.983 0.751 1.480 0.889 0.766 +0 0.345 1.548 0.043 1.686 -1.351 1.395 -0.953 1.297 0.000 0.882 0.610 0.186 2.215 1.432 0.071 -0.646 0.000 0.883 -0.240 0.844 0.000 0.891 0.845 1.004 1.046 0.771 0.952 1.328 +1 2.088 1.289 0.858 0.958 0.457 0.832 1.065 -0.915 0.000 0.904 0.807 -1.475 2.215 0.612 1.031 -0.298 2.548 0.380 0.065 0.125 0.000 0.824 0.721 1.001 0.765 0.700 0.800 0.785 +0 2.507 -0.757 -0.963 2.588 -0.874 2.588 0.703 0.872 1.087 0.399 0.679 0.585 0.000 0.513 0.764 -1.120 2.548 0.772 -0.089 0.665 0.000 0.304 0.573 0.983 0.830 1.403 2.159 1.557 +1 0.407 -1.400 0.338 1.440 0.913 1.143 -1.127 -1.181 2.173 0.997 0.861 -0.977 0.000 0.678 -0.857 -0.348 0.000 2.496 0.568 0.819 3.102 0.838 1.151 0.982 1.357 2.546 1.371 1.172 +0 0.628 -1.674 -0.693 0.793 1.374 0.653 -0.211 -0.844 0.000 1.081 0.371 0.898 0.000 0.811 -0.322 0.071 2.548 0.682 -0.001 1.643 1.551 0.793 0.744 0.987 0.798 0.571 0.694 0.686 +1 1.464 0.279 -0.404 0.690 -1.184 1.603 0.269 0.842 2.173 1.519 -1.421 -0.299 0.000 1.235 0.757 -0.221 0.000 3.206 -1.055 -1.630 0.000 0.949 1.021 0.990 1.455 0.852 1.320 1.224 +0 0.759 -0.775 1.341 0.344 -1.499 0.698 -0.226 0.215 2.173 0.617 -1.174 1.444 2.215 1.060 1.074 -0.358 0.000 0.711 0.508 -0.504 0.000 0.304 0.804 0.993 0.699 0.995 0.876 0.789 +0 0.829 1.170 -0.587 0.902 -1.469 1.067 0.519 -1.454 1.087 1.056 1.053 1.045 0.000 1.290 1.516 -0.021 0.000 1.033 0.535 0.481 3.102 1.282 0.819 0.986 0.716 1.096 0.801 0.734 +1 1.283 -0.249 0.742 1.618 0.947 0.486 -1.422 -0.534 0.000 1.031 -0.424 -1.070 0.000 0.535 -1.120 0.696 0.000 0.414 -1.397 -1.653 0.000 0.826 0.592 0.993 0.820 0.243 0.618 0.616 +1 0.492 0.549 0.341 1.355 0.763 2.219 -1.413 -0.549 0.000 2.157 0.611 1.441 2.215 0.788 0.099 -1.421 0.000 1.411 0.353 0.957 3.102 2.621 2.345 0.979 1.323 0.686 2.305 2.299 +1 1.218 0.527 0.882 0.262 -0.059 0.481 1.531 -1.516 0.000 0.338 1.167 0.229 0.000 0.588 -1.582 -1.327 2.548 0.570 0.371 0.122 3.102 0.868 0.627 0.990 0.934 0.724 0.859 0.771 +0 0.981 0.952 0.703 0.816 1.731 0.980 -0.480 1.596 2.173 0.511 -0.823 -0.139 0.000 1.812 0.719 -0.131 2.548 0.722 -0.029 -1.287 0.000 0.756 0.959 0.991 1.108 2.009 1.119 0.945 +1 0.995 1.532 -0.700 0.369 -1.635 1.041 1.730 1.485 0.000 1.488 1.033 0.378 1.107 0.679 -0.078 0.572 0.000 1.365 0.476 -1.010 0.000 1.003 0.907 0.983 0.512 0.831 0.687 0.634 +1 1.149 0.211 0.669 0.173 1.675 2.568 -0.941 -0.965 0.000 2.653 -0.095 1.242 1.107 1.790 -1.989 0.370 0.000 0.947 -0.686 0.260 3.102 3.344 2.009 0.989 0.930 1.229 2.008 1.541 +1 0.368 -0.662 -0.424 0.591 -1.015 0.803 0.723 1.073 2.173 1.028 0.283 0.235 2.215 0.846 -1.172 -0.593 0.000 0.887 -0.107 -1.491 0.000 0.906 1.085 0.985 1.970 0.963 1.474 1.180 +0 0.790 -0.202 0.020 1.301 -1.325 0.967 0.183 0.786 2.173 0.371 1.794 0.190 0.000 1.071 0.977 -1.039 2.548 0.460 0.011 -1.575 0.000 0.780 0.872 1.315 0.933 1.392 0.950 0.803 +0 1.416 0.944 -0.630 2.342 -1.052 1.215 1.634 0.733 0.000 0.783 0.834 0.494 2.215 1.201 0.663 -1.574 1.274 1.070 1.429 1.226 0.000 0.749 0.736 0.995 0.835 0.989 0.888 1.110 +1 0.796 1.070 0.404 1.290 0.998 0.995 -0.250 -0.249 0.000 0.904 -2.881 -1.447 0.000 0.993 1.766 -0.909 0.000 0.790 -1.002 -1.703 0.000 1.129 1.397 0.995 0.669 0.554 1.288 1.448 +1 0.676 -1.062 -1.710 1.258 0.175 0.790 -0.031 -0.911 0.000 0.296 0.387 1.288 0.000 0.668 1.123 -1.587 0.000 1.435 -0.713 0.888 3.102 0.967 0.927 1.267 0.755 0.442 0.717 0.717 +1 1.412 -1.356 -0.659 0.778 -1.675 1.062 -1.669 0.612 0.000 0.729 -1.263 1.508 2.215 0.269 -1.139 0.246 2.548 0.468 -0.557 -0.825 0.000 1.107 0.894 1.150 0.606 0.427 0.541 0.652 +1 1.847 0.374 1.454 1.345 0.977 0.724 -0.296 -0.401 2.173 0.630 0.358 -0.516 2.215 0.458 0.211 0.708 0.000 1.136 1.224 -0.794 0.000 0.937 0.961 0.993 1.042 0.353 0.979 0.850 +0 1.673 1.448 0.804 0.860 -1.162 0.598 0.251 -1.130 2.173 0.528 1.805 -1.403 0.000 0.603 1.127 0.326 0.000 1.042 0.129 -0.089 3.102 0.907 0.905 1.629 1.075 0.675 0.895 0.771 +1 1.677 0.192 0.934 1.398 0.205 0.940 0.460 -0.784 2.173 0.517 1.215 -1.356 2.215 1.036 -1.322 -0.710 0.000 0.868 -0.274 1.541 0.000 0.963 1.007 1.294 1.079 0.653 0.988 0.855 +0 0.727 -1.847 0.471 0.702 1.324 0.706 -0.526 -1.372 2.173 0.729 -0.646 0.881 1.107 1.158 -0.217 -0.447 0.000 0.751 0.423 -0.072 0.000 0.889 0.888 0.994 0.897 0.948 0.693 0.710 +0 0.701 0.098 1.256 1.258 0.532 0.731 -0.094 -0.565 1.087 0.702 1.544 0.820 0.000 0.833 0.119 -1.428 2.548 0.943 0.991 -1.127 0.000 1.064 0.910 0.995 1.061 0.691 0.815 0.792 +0 2.237 -0.619 -0.890 0.918 -0.631 0.446 0.364 1.217 0.000 0.690 -0.784 1.407 0.000 1.159 -0.366 0.217 2.548 0.548 -0.579 1.099 3.102 0.759 0.899 0.998 0.772 0.443 0.732 0.810 +0 0.297 2.165 -1.474 0.794 -0.278 1.329 0.977 -1.032 2.173 0.900 -0.297 1.494 2.215 2.527 0.610 0.663 0.000 0.535 -2.451 -0.075 0.000 3.883 2.299 0.977 0.830 1.656 2.025 1.558 +0 1.389 0.383 0.957 1.272 1.351 0.955 1.338 0.420 1.087 1.271 0.848 -1.272 0.000 1.451 1.002 -0.790 0.000 1.699 0.142 -0.515 3.102 0.909 0.930 0.986 1.341 1.313 1.107 1.174 +0 0.725 0.315 -0.538 0.329 0.509 0.548 -0.241 0.701 0.000 0.426 -0.221 0.341 0.000 0.701 -0.439 -0.725 0.000 0.880 -0.445 1.384 3.102 1.078 0.758 0.997 0.644 0.215 0.493 0.513 +1 1.614 -0.040 0.156 0.205 1.047 1.127 -0.891 -1.410 2.173 0.653 -0.648 -0.415 2.215 0.786 -0.380 0.523 0.000 0.901 -0.419 1.629 0.000 0.780 0.978 0.981 0.646 0.998 0.881 0.755 +0 0.451 1.747 -1.208 0.963 0.886 1.021 0.527 -0.594 1.087 0.865 -0.320 1.191 0.000 0.459 -0.396 -0.858 2.548 0.929 -1.356 1.138 0.000 0.815 0.729 0.987 1.005 0.473 0.956 0.933 +1 0.693 1.841 1.348 0.851 -0.082 0.579 0.656 -0.564 2.173 0.563 0.911 1.063 0.000 0.725 -0.691 -1.607 2.548 1.068 0.819 -0.027 0.000 0.841 0.958 1.021 0.857 0.903 0.915 0.778 +1 0.428 -1.772 0.043 0.698 0.888 0.751 -0.714 0.365 0.000 1.367 -0.764 -1.361 2.215 1.041 -0.600 1.279 0.000 1.880 -0.518 -0.592 3.102 0.907 0.951 0.992 0.981 0.932 0.782 0.718 +0 1.904 0.156 0.048 5.442 0.090 3.277 0.743 -1.599 0.000 0.545 0.044 -1.580 0.000 0.514 0.655 0.599 2.548 0.728 0.052 1.050 3.102 1.022 1.054 1.004 0.683 0.242 0.825 1.770 +0 0.411 0.894 -1.290 1.617 0.138 1.047 0.129 -0.611 0.000 1.011 -1.367 -1.736 0.000 1.205 -2.364 1.450 0.000 1.401 -0.166 1.466 3.102 1.160 1.305 1.084 0.651 0.634 1.077 1.461 +0 2.078 1.411 -1.461 0.707 -1.535 1.255 0.319 0.637 0.000 0.956 -1.826 -0.442 0.000 0.560 0.113 -1.089 2.548 0.693 0.844 0.692 3.102 3.656 2.058 0.978 0.817 0.524 1.277 1.835 +1 0.562 -1.089 1.314 0.554 -1.724 2.335 -0.941 0.020 0.000 2.775 -0.744 1.727 0.000 0.928 -1.303 -0.674 2.548 0.689 -2.390 -1.487 0.000 0.846 1.159 0.997 0.510 0.602 0.735 0.714 +1 1.857 0.990 1.110 0.445 0.480 0.342 1.495 -1.319 0.000 0.724 0.324 -0.304 0.000 0.708 0.780 -0.218 2.548 0.564 -0.859 1.675 0.000 1.020 0.722 0.985 1.243 0.728 0.867 0.813 +1 1.212 -0.959 -0.200 0.712 0.808 0.729 -1.404 -1.149 2.173 0.662 -0.656 0.774 0.000 1.520 -1.081 1.560 1.274 0.376 -2.070 -0.697 0.000 0.921 0.924 1.015 0.969 0.857 0.808 0.715 +0 0.842 0.709 -0.515 0.946 1.355 1.221 -0.023 0.457 2.173 0.885 -0.442 -1.133 0.000 1.267 0.069 -1.664 0.000 1.372 -0.787 -0.455 0.000 0.918 0.826 1.229 1.124 0.846 0.855 0.819 +1 1.134 1.405 1.239 0.979 1.709 1.095 0.324 -0.195 2.173 0.794 0.710 0.311 0.000 1.102 -2.046 -1.182 0.000 0.603 -0.111 0.991 3.102 3.328 1.793 0.997 1.364 0.780 1.404 1.425 +1 1.423 -0.755 1.451 1.630 1.387 0.761 -0.631 -0.400 0.000 0.565 -1.532 1.084 2.215 1.918 -1.922 -0.651 0.000 1.575 -0.301 0.206 3.102 1.053 0.992 0.960 1.188 0.817 0.784 0.862 +0 1.298 -0.523 -1.253 0.836 -0.648 0.648 -0.274 0.957 0.000 0.455 0.101 -1.000 2.215 0.478 -1.291 0.568 2.548 0.457 -0.201 0.522 0.000 0.319 0.652 0.986 0.723 0.642 0.545 0.608 +0 0.350 1.113 0.170 1.392 1.472 0.669 0.734 -0.790 2.173 0.448 -0.229 0.413 0.000 1.239 0.037 1.393 2.548 1.440 0.523 -0.327 0.000 0.975 0.793 0.989 1.058 1.122 0.921 0.868 +1 1.092 -0.204 0.106 1.789 0.860 1.372 -1.326 -0.464 0.000 1.666 0.692 1.537 2.215 0.616 0.270 -1.397 0.000 0.543 0.941 -0.303 0.000 0.598 0.788 1.218 0.922 1.059 1.018 0.823 +0 1.875 -0.424 -0.981 0.872 0.531 0.371 1.855 -1.530 0.000 0.484 0.856 0.658 0.000 0.600 1.330 0.596 2.548 1.430 1.671 0.968 0.000 0.951 1.254 1.734 1.244 1.100 0.885 0.947 +1 2.467 -0.070 -0.110 0.273 -0.264 0.556 -0.208 -1.536 2.173 0.673 -1.756 -1.533 0.000 0.512 -1.221 1.526 0.000 0.721 0.368 1.087 3.102 0.399 0.796 1.001 1.071 0.520 0.751 0.892 +1 0.555 -1.600 0.130 1.224 1.262 0.499 0.901 -1.019 2.173 0.386 -0.421 -1.631 0.000 0.805 0.193 0.574 2.548 1.070 -0.761 -0.546 0.000 0.724 0.815 0.988 0.854 0.831 0.958 0.792 +0 1.078 1.109 -0.011 0.395 -1.106 0.665 1.531 1.702 0.000 0.729 0.889 0.662 2.215 0.590 0.727 -1.366 0.000 0.910 -0.410 -0.192 3.102 0.597 1.047 0.986 0.728 0.759 0.756 0.732 +0 1.106 -1.305 -1.238 0.572 -1.465 0.633 0.096 0.255 0.000 0.429 -0.762 1.137 1.107 0.858 0.744 -1.038 2.548 1.213 -0.924 0.274 0.000 0.858 1.106 0.997 0.663 0.827 0.733 0.774 +0 1.533 -0.753 0.693 1.104 1.281 0.767 0.418 -0.236 0.000 1.800 -0.426 -1.471 2.215 1.329 2.273 0.525 0.000 1.653 -0.097 -0.861 3.102 2.517 2.050 0.989 1.286 0.851 1.925 1.759 +0 1.606 0.682 0.108 1.261 0.828 0.785 -1.712 -1.143 0.000 0.487 -1.208 -1.462 2.215 0.640 -0.612 0.723 0.000 0.659 0.842 -1.377 3.102 1.449 0.849 1.192 0.818 0.707 0.880 1.149 +0 0.576 -2.140 -0.756 0.973 1.212 0.815 -0.371 -0.644 2.173 0.665 0.344 0.781 0.000 0.833 0.286 -0.137 0.000 1.346 -0.500 1.236 3.102 0.840 0.975 1.016 0.869 1.107 0.953 1.003 +1 0.787 1.001 -1.558 0.905 -0.247 0.626 1.754 0.470 0.000 0.973 0.758 -0.455 0.000 1.704 0.985 0.197 0.000 2.533 0.523 -1.491 3.102 0.794 0.516 1.082 0.847 0.662 0.920 0.802 +1 1.589 -0.298 0.738 1.615 1.115 1.163 0.425 -0.779 1.087 0.208 1.707 -0.697 0.000 0.698 -0.443 1.705 2.548 1.167 -0.513 0.160 0.000 1.053 1.023 0.997 0.711 1.029 1.175 0.994 +1 1.046 0.225 -0.708 1.166 1.188 0.650 -0.375 -0.458 0.000 1.385 -0.559 -1.235 0.000 1.218 0.316 1.151 2.548 2.399 -0.150 0.499 1.551 1.313 1.405 1.515 1.077 0.804 1.095 0.985 +1 0.572 1.719 1.115 0.765 -0.332 0.739 -0.689 0.020 2.173 0.311 -0.750 -1.035 0.000 0.963 0.438 -1.247 0.000 1.416 -0.766 1.195 3.102 0.709 0.818 0.989 1.856 0.951 1.571 1.180 +0 1.057 -0.048 0.847 1.397 1.628 1.016 0.382 0.486 0.000 0.666 0.310 -1.461 1.107 2.196 0.923 -0.595 2.548 0.424 -0.954 -0.751 0.000 1.234 1.086 1.090 1.535 1.012 1.025 0.994 +1 0.802 -0.561 0.469 0.777 -0.659 0.830 0.209 -1.117 2.173 2.087 0.186 1.386 0.000 1.296 -1.495 -0.256 0.000 0.977 -0.279 0.184 1.551 0.986 0.874 0.983 0.848 0.917 1.050 0.882 +1 2.366 0.640 -0.621 1.002 0.284 1.441 2.203 0.800 0.000 1.140 0.734 -1.732 1.107 0.596 -0.855 1.421 2.548 0.655 2.241 1.069 0.000 0.497 1.640 1.555 1.234 0.874 1.499 1.492 +0 1.957 -0.818 1.536 0.493 0.038 2.023 -1.044 0.185 0.000 1.888 -0.432 -1.439 2.215 0.877 -2.202 -1.220 0.000 0.986 -1.429 -0.050 0.000 0.965 0.829 1.327 1.073 0.877 0.981 0.925 +0 1.038 -0.167 -1.699 0.698 0.531 0.553 -1.483 1.533 0.000 1.106 -2.332 -1.118 0.000 1.947 -1.100 1.083 0.000 2.520 -0.094 -0.398 3.102 0.787 0.852 1.067 0.983 0.944 1.091 0.938 +1 0.636 0.074 0.832 1.040 -0.443 1.107 0.141 -1.310 0.000 1.301 0.378 0.432 2.215 0.863 0.692 1.741 0.000 0.557 1.066 0.814 0.000 0.868 0.746 1.027 0.764 0.871 0.935 0.815 +0 1.142 -0.844 -1.249 1.270 -0.488 2.854 -1.150 -0.707 0.000 3.864 -1.573 0.925 0.000 1.245 -1.393 1.342 2.548 1.097 -0.667 0.732 3.102 7.203 3.792 1.057 1.050 0.574 2.238 1.779 +0 0.908 -1.856 -1.583 1.557 -1.201 1.564 0.789 0.443 2.173 1.094 -1.165 1.520 2.215 0.961 1.258 -0.123 0.000 1.266 2.196 -0.704 0.000 0.840 0.938 0.989 0.793 2.759 1.903 1.593 +1 2.224 0.287 -1.669 1.127 1.662 2.207 -0.706 -0.573 2.173 1.393 0.231 0.956 2.215 1.768 -0.589 -0.096 0.000 1.590 -0.795 1.173 0.000 1.229 1.612 1.002 2.128 2.835 1.801 1.684 +1 0.764 -1.039 -0.447 1.064 1.691 0.759 -1.213 0.142 0.000 0.979 -0.049 1.422 2.215 0.634 0.845 -0.732 2.548 0.617 2.471 1.097 0.000 4.260 2.326 1.171 0.915 0.889 1.505 1.363 +0 1.652 0.814 1.446 0.376 -1.712 1.119 -0.687 -0.198 0.000 0.725 0.253 -0.647 0.000 0.941 0.049 1.366 2.548 0.854 0.682 0.575 3.102 0.827 0.880 0.997 0.732 0.523 0.751 0.981 +0 0.512 -0.464 -0.754 0.358 0.853 0.545 2.665 -1.546 0.000 0.580 1.154 0.409 2.215 0.657 1.455 0.944 0.000 1.080 -0.114 0.250 1.551 1.043 0.933 0.997 0.743 0.519 0.898 1.443 +1 1.035 -0.466 -0.236 1.760 -0.739 0.844 -1.083 0.909 2.173 0.385 -1.398 1.593 0.000 0.987 -0.121 1.734 1.274 0.676 0.692 1.233 0.000 0.951 0.875 0.984 0.941 0.956 1.023 0.869 +1 0.580 -0.270 0.736 0.402 0.011 0.894 -0.949 -0.967 2.173 0.368 -0.077 -1.696 0.000 0.649 0.604 0.337 0.000 0.657 0.422 -0.586 3.102 0.778 1.046 0.993 0.764 0.704 0.704 0.701 +0 0.429 1.159 -0.135 1.571 1.643 0.828 -0.363 1.232 2.173 1.534 0.228 -0.480 2.215 0.380 -1.813 0.674 0.000 0.382 -2.177 0.111 0.000 0.244 0.854 1.137 1.288 1.734 1.168 1.207 +0 1.058 0.270 -0.292 0.886 -0.511 0.976 -0.567 1.035 2.173 0.690 -0.043 0.464 0.000 0.653 0.494 -0.904 0.000 0.371 2.351 -1.053 0.000 0.938 1.073 0.989 1.551 1.562 1.155 0.985 +1 0.345 0.576 -1.477 0.770 -0.569 3.540 1.146 1.298 0.000 2.513 -0.592 -0.053 0.000 2.216 -0.324 -0.367 2.548 1.431 0.292 -0.957 3.102 2.053 1.301 0.984 0.706 0.847 0.981 0.838 +0 3.106 -1.365 0.473 0.357 1.208 1.713 -2.031 -1.033 0.000 0.538 -0.850 0.858 2.215 0.619 -1.070 1.601 2.548 0.479 -2.337 -1.089 0.000 0.539 0.859 0.986 0.566 0.392 0.828 1.046 +0 0.866 0.094 -0.447 1.019 -1.505 0.745 -0.436 0.201 2.173 0.753 -1.474 -1.454 2.215 1.045 -0.884 0.796 0.000 0.647 0.019 -1.724 0.000 0.838 0.842 1.060 0.944 1.263 0.878 0.777 +0 1.415 -0.584 1.725 0.608 -1.018 0.861 0.379 -0.074 0.000 0.450 -1.309 0.826 2.215 0.840 0.113 -0.943 0.000 0.513 -0.451 1.210 0.000 0.965 1.069 0.998 0.583 0.173 0.620 0.709 +0 2.119 -0.329 0.960 1.120 -0.820 1.141 -1.046 -0.641 2.173 1.476 0.332 0.582 0.000 1.236 -0.918 -1.388 2.548 0.842 1.190 -1.699 0.000 1.528 1.679 2.133 1.618 0.922 1.456 1.319 +1 0.425 -0.293 -0.671 0.776 0.900 0.996 0.540 -0.646 0.000 1.127 1.178 0.696 1.107 0.485 0.465 -1.373 0.000 1.259 1.036 -1.615 3.102 0.806 0.777 0.991 1.234 0.938 1.161 1.000 +0 0.427 -1.207 -0.972 2.425 -0.391 0.638 -0.612 1.625 2.173 0.445 1.105 0.400 0.000 0.617 -0.431 -0.315 0.000 2.112 1.032 1.160 0.000 0.934 0.862 0.984 0.745 0.328 0.757 0.691 +0 1.641 -0.177 1.676 1.574 1.217 0.829 -0.908 -0.224 1.087 0.666 -1.074 0.361 0.000 0.785 0.048 -0.981 2.548 0.942 0.495 -0.373 0.000 0.790 0.947 0.990 1.495 0.812 1.018 0.996 +1 1.108 -1.314 0.622 0.196 0.812 1.178 -1.138 -1.417 0.000 0.190 -1.010 -1.031 2.215 0.965 -0.738 0.166 0.000 0.449 -0.287 -0.114 3.102 1.925 0.997 0.981 0.518 0.215 0.617 0.649 +0 1.050 -0.933 -0.031 1.909 -1.300 0.570 -0.076 0.059 0.000 0.736 -0.115 1.700 2.215 0.927 0.731 0.208 2.548 1.057 1.401 1.251 0.000 1.576 0.963 1.785 1.105 0.952 1.027 1.099 +1 2.024 -0.276 -1.364 0.495 -0.713 0.367 -1.145 1.240 0.000 0.691 -0.510 -0.662 0.000 0.968 -0.723 0.538 2.548 0.788 -0.038 0.011 0.000 0.877 0.732 0.988 0.990 0.610 0.793 0.697 +0 1.217 -0.018 0.806 1.435 -0.042 0.849 -1.584 1.479 0.000 0.849 0.785 -0.505 2.215 0.757 -1.712 -1.445 0.000 0.795 -0.430 -0.375 3.102 0.726 0.911 1.266 0.983 0.535 1.176 1.133 +0 1.788 0.664 1.540 1.052 -0.936 0.673 1.589 -0.966 0.000 1.241 1.625 -0.567 0.000 1.086 -0.439 -1.309 2.548 0.962 0.490 0.030 3.102 0.686 0.846 1.501 1.009 0.851 1.008 0.988 +0 1.356 0.434 -1.267 0.388 0.175 0.942 -0.767 1.098 2.173 0.297 -0.394 -1.427 0.000 0.873 -0.598 -0.567 2.548 1.162 -0.255 0.227 0.000 0.764 0.814 0.988 0.703 1.129 0.845 0.700 +0 1.043 -0.709 0.227 0.694 1.734 0.903 -0.891 -0.183 1.087 0.737 -0.726 -0.967 0.000 1.364 -0.545 1.498 0.000 0.785 -1.285 1.152 1.551 0.917 0.854 1.153 0.850 0.881 0.792 0.712 +0 2.891 0.816 0.489 1.355 0.903 1.508 0.814 -1.233 0.000 0.805 0.250 -0.535 2.215 0.526 0.706 1.697 2.548 0.488 1.320 -1.163 0.000 0.519 0.865 1.002 0.820 0.651 0.852 1.040 +0 1.416 0.839 1.323 0.330 0.103 0.690 -0.960 -0.354 0.000 0.650 -0.057 0.930 2.215 0.660 -0.226 -0.481 2.548 0.524 -1.518 -1.228 0.000 0.766 0.953 0.988 0.851 0.669 0.647 0.868 +0 0.764 -1.590 -0.880 0.980 1.331 1.045 -0.945 -0.873 2.173 0.752 -0.543 0.168 1.107 0.808 -1.859 0.593 0.000 1.970 0.712 1.229 0.000 0.899 0.882 1.093 0.962 1.084 0.834 0.769 +1 1.742 0.277 0.223 0.695 -0.414 0.697 0.145 -1.195 2.173 0.438 1.788 1.506 0.000 1.507 0.263 1.447 2.548 0.415 0.286 -0.175 0.000 0.718 0.813 0.984 1.121 0.884 0.908 0.800 +0 1.599 0.469 1.600 1.500 1.024 1.245 -0.991 -0.162 0.000 0.376 0.265 1.077 0.000 0.901 0.865 -1.343 0.000 1.473 1.328 -0.698 3.102 0.794 0.803 1.063 0.856 0.421 0.854 0.710 +1 0.479 0.335 -1.445 0.776 -0.200 1.766 -0.752 1.536 2.173 1.854 -1.284 0.046 0.000 0.827 -1.221 -0.516 0.000 0.426 -0.450 -0.337 3.102 0.921 0.556 0.993 1.177 0.917 1.252 1.016 +0 0.556 0.693 -0.262 0.859 -1.039 0.648 -0.099 0.397 1.087 0.768 0.528 -1.519 2.215 0.681 -0.226 1.238 0.000 0.565 1.612 -0.245 0.000 1.123 0.904 0.982 0.734 1.080 0.717 0.699 +1 1.189 0.199 1.027 1.276 -0.073 1.916 -2.051 -1.527 0.000 1.521 -1.546 -0.047 2.215 1.020 -0.127 0.676 0.000 0.971 -0.388 -0.357 0.000 0.899 1.145 1.427 0.907 0.900 1.097 0.896 +0 1.838 0.222 -1.685 0.875 0.168 1.226 1.229 -1.715 0.000 1.736 0.075 -0.486 1.107 2.821 0.352 0.609 0.000 0.838 -1.375 1.638 0.000 1.458 1.088 1.748 1.380 0.644 1.217 1.144 +0 0.519 -0.573 1.063 0.681 -0.118 1.176 -0.184 0.933 2.173 0.906 0.012 -1.271 0.000 0.772 -1.137 -0.204 2.548 0.674 -1.157 -1.505 0.000 0.860 0.970 0.984 0.774 1.211 0.845 0.730 +0 0.368 1.732 -0.137 0.180 -1.403 1.230 0.608 0.947 2.173 0.764 1.155 -0.401 0.000 0.742 -0.043 0.052 2.548 0.735 -0.190 -1.452 0.000 1.092 0.790 0.987 0.921 0.949 0.842 0.723 +1 0.645 -0.406 1.578 0.704 -0.925 0.334 1.429 1.032 0.000 0.507 0.883 -0.057 1.107 0.357 0.566 -0.846 2.548 0.384 1.708 0.589 0.000 0.272 0.457 0.985 0.659 0.302 0.666 0.746 +1 0.299 1.036 0.844 1.103 1.552 0.670 0.070 0.303 2.173 0.728 -0.431 -0.561 0.000 0.808 0.325 -1.497 2.548 0.776 -1.874 0.511 0.000 0.601 0.825 0.988 1.082 0.925 1.153 1.292 +1 0.893 0.582 0.554 0.810 -0.961 1.367 0.240 0.117 2.173 1.068 0.302 -1.215 0.000 1.075 -0.038 -1.728 0.000 0.589 2.255 -1.507 0.000 1.042 0.724 1.153 0.946 0.984 0.856 0.758 +0 0.886 1.074 -1.738 0.775 0.074 0.748 -0.333 -1.235 0.000 1.091 0.407 0.336 2.215 0.665 -0.549 0.019 0.000 1.192 -0.661 0.646 0.000 1.022 0.825 1.146 0.705 0.944 0.687 0.648 +1 0.731 0.517 0.217 0.579 -0.509 0.623 -0.173 1.437 1.087 0.825 0.953 -0.377 0.000 0.788 0.846 0.797 1.274 0.510 1.669 -1.256 0.000 0.752 1.128 0.986 0.766 0.695 0.743 0.741 +1 0.678 -0.339 -0.972 1.115 0.854 0.782 -1.001 0.187 1.087 0.965 -1.203 1.301 0.000 0.835 -1.442 -0.214 2.548 0.932 -0.377 -0.731 0.000 0.982 0.978 1.201 0.874 0.461 0.685 0.714 +0 1.500 -0.536 -1.566 0.720 -1.487 1.136 -0.465 0.180 2.173 0.950 -0.031 1.507 2.215 1.354 0.483 0.206 0.000 0.592 0.463 -0.838 0.000 0.798 1.005 1.009 1.421 1.462 1.033 0.934 +0 0.902 0.639 1.399 1.332 0.895 0.979 1.377 -0.038 0.000 0.442 1.340 -0.801 0.000 0.964 0.594 -1.364 2.548 0.520 -0.160 -1.325 3.102 0.887 1.005 0.998 0.751 0.236 0.672 0.765 +1 1.124 -0.769 0.169 2.204 0.918 1.460 0.826 -0.652 0.000 0.965 -0.687 1.496 2.215 0.768 0.476 -1.457 2.548 0.484 -1.064 -1.618 0.000 1.896 1.172 1.364 1.005 0.733 1.081 1.271 +0 0.723 -1.497 0.511 0.183 -0.608 0.662 -0.199 1.667 0.000 0.831 0.053 1.010 0.000 0.500 0.625 -0.501 2.548 1.943 -0.812 -0.571 3.102 0.903 0.845 0.995 0.755 0.702 0.842 0.745 +1 1.363 0.637 1.516 1.524 1.080 0.488 0.439 0.413 0.000 1.419 -0.550 -0.315 2.215 0.743 0.091 1.571 0.000 1.103 -0.456 -0.707 0.000 0.944 0.968 0.979 0.839 0.788 1.049 0.870 +1 1.281 0.748 0.223 1.008 -1.311 1.075 2.833 1.072 0.000 1.192 1.035 -0.743 2.215 2.160 1.099 1.640 0.000 1.804 0.184 -0.136 0.000 2.447 1.366 1.547 0.996 0.681 1.002 0.920 +1 0.656 -0.245 -0.850 0.939 0.341 0.849 -0.271 0.141 0.000 0.937 -0.312 -0.239 2.215 2.038 -0.005 -1.600 2.548 1.921 -1.223 1.394 0.000 0.919 0.900 0.987 0.999 1.402 0.877 0.764 +1 1.036 -1.069 0.030 0.269 0.794 1.341 -1.554 -0.982 2.173 0.737 -2.128 -1.204 0.000 0.907 -1.065 0.633 0.000 2.559 1.137 0.805 0.000 1.417 0.970 0.976 1.263 0.542 0.836 0.853 +0 1.263 0.043 1.639 0.826 1.042 1.106 0.908 0.219 0.000 0.888 0.707 -0.806 2.215 0.490 0.549 -1.378 2.548 0.383 0.235 0.685 0.000 0.517 0.940 0.983 0.563 0.348 0.636 0.725 +1 1.628 -0.066 -0.130 0.774 0.215 0.980 -0.840 -1.607 2.173 0.503 -0.532 -1.001 0.000 0.276 0.817 0.819 0.000 1.491 -0.783 0.996 3.102 0.726 0.789 0.992 1.091 0.915 1.120 0.875 +0 1.815 1.433 -0.602 1.080 -0.851 0.710 0.731 1.663 1.087 0.918 1.724 0.764 0.000 1.314 -1.060 -1.620 0.000 1.050 -0.168 0.467 0.000 0.634 1.042 0.979 1.322 0.832 1.087 0.994 +0 0.297 1.365 -1.078 2.092 0.743 1.495 -0.738 -0.300 2.173 1.860 0.856 1.099 2.215 3.441 0.490 -1.054 0.000 1.859 -0.000 1.648 0.000 1.964 2.179 1.089 2.371 3.225 2.074 1.838 +0 1.863 -0.297 -0.984 0.335 -1.412 1.184 -0.459 -1.686 2.173 0.896 0.782 -0.037 0.000 1.828 0.473 0.506 0.000 1.149 -0.392 0.332 3.102 0.965 0.798 0.986 0.903 1.196 1.176 1.122 +0 1.793 -0.871 0.894 0.473 -0.593 1.182 -0.577 -0.596 0.000 1.032 -1.359 -1.488 2.215 0.499 -0.641 0.324 2.548 0.431 -0.849 -1.711 0.000 0.950 1.067 1.242 0.622 0.808 0.711 0.773 +0 0.417 -1.630 0.356 1.734 0.368 0.681 0.726 -1.658 2.173 0.759 -1.555 -1.078 0.000 0.663 -0.676 1.257 2.548 0.795 -0.185 0.700 0.000 0.841 0.833 0.987 0.693 0.791 0.865 0.753 +1 1.282 1.329 1.090 1.073 -1.209 1.106 -1.065 0.462 0.000 2.159 0.690 -0.692 2.215 2.048 0.698 1.518 0.000 0.711 0.234 -0.280 0.000 1.026 0.662 1.426 1.449 0.625 0.932 0.902 +1 0.998 -1.171 -0.297 1.224 -0.069 0.836 0.684 -1.340 0.000 0.689 0.176 1.036 0.000 0.774 0.528 1.529 1.274 1.118 0.854 0.584 3.102 0.907 0.867 0.983 1.018 0.558 0.821 0.763 +0 1.354 1.946 -0.456 0.269 -0.761 0.632 0.107 1.412 0.000 0.671 0.858 0.565 0.000 0.509 1.237 0.808 2.548 0.715 -0.867 -1.224 3.102 1.104 0.969 0.976 0.667 0.826 0.806 0.823 +1 0.477 0.119 0.976 1.381 -0.164 1.233 -2.489 0.912 0.000 1.092 1.651 -1.529 2.215 1.048 0.513 -1.008 0.000 1.407 1.210 -0.676 3.102 0.739 0.788 0.985 0.789 0.791 0.852 0.710 +0 0.694 -0.613 -0.388 0.897 1.338 0.582 -0.205 0.641 1.087 0.338 -0.699 -0.819 0.000 0.535 0.535 1.512 0.000 0.646 0.391 0.241 0.000 0.725 0.705 1.093 0.790 0.923 0.660 0.572 +1 1.927 0.094 0.287 0.250 1.320 0.693 0.044 -1.167 2.173 0.938 -1.206 -0.698 2.215 0.638 -0.363 -1.326 0.000 0.930 0.096 0.989 0.000 0.922 0.937 0.986 1.227 0.951 0.970 0.850 +0 0.430 -1.025 -0.694 1.900 0.124 1.032 0.317 -1.373 2.173 1.586 0.392 1.470 0.000 1.265 -1.032 -0.047 2.548 0.384 0.812 1.246 0.000 1.267 1.044 0.987 0.646 1.740 1.158 1.084 +1 1.008 0.201 0.104 0.483 1.541 1.482 0.194 -0.518 0.000 0.780 0.702 1.196 0.000 1.413 -0.406 0.979 1.274 1.864 -0.867 0.093 0.000 0.972 0.895 0.985 0.549 0.568 0.577 0.565 +0 1.176 -0.273 -0.299 1.586 0.321 1.168 -1.057 1.220 1.087 0.961 -1.470 1.662 0.000 0.850 -0.057 -1.136 2.548 1.381 0.381 -0.669 0.000 0.962 0.785 1.004 1.408 1.240 1.044 0.996 +1 0.451 -0.403 0.147 1.334 1.275 0.754 -0.982 -1.440 2.173 0.745 -1.105 0.255 2.215 1.107 -0.186 0.515 0.000 1.818 -0.696 -0.705 0.000 0.699 0.768 0.988 0.821 1.105 0.715 0.668 +0 1.375 -1.651 0.499 0.766 1.182 1.126 -0.573 -1.363 0.000 1.089 -0.986 -0.093 2.215 0.819 -0.904 1.478 0.000 1.490 -0.436 -0.723 3.102 1.013 0.916 0.988 1.006 0.681 0.902 1.026 +1 1.006 0.844 -0.845 0.823 1.359 0.989 0.628 -0.235 0.000 1.134 1.412 1.026 2.215 1.093 0.212 1.569 2.548 0.602 1.529 -0.120 0.000 0.762 1.159 1.153 0.934 0.948 0.957 0.842 +1 0.992 1.151 0.070 0.079 1.568 0.392 2.324 0.818 0.000 0.686 2.007 -1.509 0.000 0.950 -0.179 -1.567 2.548 1.051 0.722 -0.501 3.102 0.964 0.932 0.987 0.822 0.757 0.900 0.816 +1 1.600 -1.336 1.445 1.207 0.954 0.717 2.694 -0.623 0.000 0.475 0.562 1.299 0.000 0.530 0.776 -0.501 2.548 0.964 -0.590 -1.086 0.000 0.913 0.639 0.995 0.514 0.523 0.724 0.685 +0 0.906 0.008 0.076 1.392 -0.544 0.669 1.253 1.731 0.000 0.709 0.812 0.746 2.215 0.707 2.044 1.239 0.000 0.389 -0.229 -0.808 1.551 0.826 0.842 0.990 0.507 0.543 0.615 0.778 +0 1.168 -0.133 0.060 0.829 -0.944 2.449 -0.266 -0.413 0.000 1.828 -0.653 1.186 2.215 1.751 -1.390 1.241 0.000 1.678 -0.597 1.621 1.551 0.897 1.059 1.071 0.953 0.606 0.925 0.940 +1 1.149 -1.038 0.695 0.522 0.480 0.840 -0.661 -0.713 2.173 0.929 -0.614 1.280 0.000 1.398 -0.217 -1.601 2.548 0.815 -1.760 -0.464 0.000 0.861 1.023 0.986 0.971 1.011 0.880 0.765 +0 3.015 -0.123 0.462 0.390 -0.040 1.515 -0.694 -1.217 2.173 1.193 -1.224 -1.132 0.000 1.543 -0.650 0.788 2.548 0.411 -1.188 1.119 0.000 0.822 1.134 0.974 1.957 1.852 1.395 1.224 +1 1.116 -1.186 0.426 0.812 -0.429 1.155 -0.647 -1.615 2.173 0.750 -1.116 -0.021 0.000 0.320 -1.049 -1.258 0.000 0.766 0.906 0.933 1.551 0.674 0.931 0.984 1.294 1.230 1.084 0.885 +0 0.322 -1.074 -0.104 2.046 0.885 0.545 -1.123 -1.026 0.000 0.815 0.836 -1.045 2.215 0.458 -0.126 0.364 0.000 0.601 -0.001 -0.372 3.102 0.973 1.062 0.989 0.902 0.457 1.261 1.018 +1 1.630 0.466 0.639 0.773 0.003 0.638 -0.176 -1.492 2.173 0.623 -0.594 -0.627 2.215 0.387 -1.345 -0.304 0.000 0.779 -0.148 1.350 0.000 0.736 0.671 0.996 1.004 0.683 0.887 0.760 +0 0.742 -0.919 -0.800 1.034 0.453 0.755 0.464 1.678 0.000 0.798 0.806 0.357 2.215 0.882 0.332 -0.548 2.548 0.843 1.322 1.254 0.000 0.850 0.967 1.097 0.824 0.682 0.771 0.898 +0 0.411 -0.878 1.694 0.878 0.560 1.854 -0.116 1.252 2.173 2.836 -0.119 -0.602 1.107 0.377 0.869 -0.560 0.000 0.373 0.677 0.847 0.000 0.396 0.940 0.987 1.317 3.357 1.551 1.127 +0 0.877 0.477 -0.761 0.667 -0.068 0.764 0.591 0.520 2.173 0.787 0.037 1.695 0.000 1.274 -0.420 1.161 0.000 1.022 1.698 -0.650 0.000 0.887 1.148 0.984 0.635 0.974 0.790 0.727 +1 2.099 -0.197 0.759 0.334 -0.532 1.009 0.375 -1.296 2.173 0.550 -0.003 -0.525 0.000 0.600 0.924 -1.613 0.000 0.549 0.295 -0.117 3.102 0.869 0.730 1.065 0.604 0.688 0.824 0.720 +0 1.083 -0.436 -1.120 0.322 -0.084 1.363 0.158 0.397 2.173 1.197 -2.649 -1.200 0.000 0.881 -0.504 0.811 0.000 0.876 0.094 1.524 3.102 0.897 0.941 0.980 0.694 0.982 0.907 0.764 +0 0.561 -0.838 0.270 1.010 1.245 0.708 -0.547 -0.632 2.173 0.908 0.914 -1.446 2.215 0.559 1.755 -0.058 0.000 0.547 0.326 0.716 0.000 0.648 1.086 0.990 0.950 1.246 0.868 0.808 +0 1.070 0.384 -1.126 0.950 1.650 1.099 -0.269 0.000 0.000 1.761 0.152 1.443 2.215 1.164 0.676 -0.056 0.000 2.042 -0.394 0.279 3.102 1.082 0.847 0.989 0.853 1.581 1.216 1.095 +0 0.277 1.125 -0.627 1.124 -1.666 0.597 -0.437 -0.063 0.000 0.556 -1.270 -0.779 2.215 1.308 -0.297 1.011 2.548 0.401 -1.610 -0.392 0.000 0.649 0.877 0.989 0.734 1.016 0.688 0.669 +1 3.325 0.647 -0.479 0.178 -1.475 0.663 -0.132 -1.711 1.087 1.396 1.928 0.600 0.000 1.205 1.419 1.595 0.000 0.627 0.267 0.366 3.102 1.177 0.850 0.989 1.231 0.670 0.979 1.070 +0 1.464 -0.890 -1.704 0.725 -1.664 0.745 0.239 0.415 0.000 0.646 -0.645 -0.238 1.107 0.952 -0.032 1.680 1.274 1.309 2.134 -0.168 0.000 0.783 0.837 0.984 0.975 0.865 0.774 0.850 +1 1.620 1.667 1.668 0.546 1.172 1.100 1.327 -0.813 2.173 0.737 1.029 -0.226 1.107 0.657 1.124 0.583 0.000 0.874 1.791 1.289 0.000 1.038 1.053 0.995 1.078 0.694 0.967 0.843 +1 0.696 -0.023 0.604 0.521 0.918 0.955 0.286 1.673 2.173 1.260 0.550 -0.292 0.000 1.115 -0.486 1.602 0.000 0.563 0.580 -0.681 1.551 0.861 0.516 0.991 0.939 0.679 0.876 0.799 +0 0.543 0.166 1.541 1.835 1.009 1.304 -0.571 -0.881 1.087 0.721 -1.364 0.960 0.000 0.624 -0.879 0.154 0.000 0.548 0.689 -0.940 0.000 0.850 0.912 0.979 0.764 0.271 1.186 0.954 +0 0.705 -0.531 -0.945 0.842 1.367 0.751 0.679 0.457 1.087 1.457 0.084 -1.244 1.107 0.555 -2.395 0.958 0.000 0.918 -0.987 0.380 0.000 0.733 1.641 0.986 0.899 1.608 1.377 1.098 +1 1.067 -1.176 0.600 0.799 0.368 0.940 -0.190 -1.100 2.173 0.343 -1.237 -1.526 2.215 0.607 -1.568 1.223 0.000 0.908 -0.655 -0.513 0.000 0.911 1.002 1.000 0.721 0.568 1.019 0.827 +1 0.952 0.081 -1.263 0.400 0.930 0.826 -0.254 -0.798 2.173 0.492 0.066 1.025 2.215 0.447 -0.061 0.082 0.000 0.443 -1.901 0.660 0.000 0.896 1.114 0.991 0.622 0.947 0.747 0.667 +0 0.626 0.103 -0.067 1.652 0.180 0.712 0.380 1.588 2.173 1.178 -1.147 -0.610 2.215 1.749 -0.348 1.102 0.000 1.459 -0.092 -1.285 0.000 1.493 1.023 0.984 1.822 1.698 1.389 1.269 +0 0.458 -1.626 -0.020 1.310 -1.232 0.452 -1.032 -0.359 0.000 1.214 -0.914 0.847 0.000 0.524 0.697 -1.458 2.548 0.452 -0.950 1.135 1.551 1.394 1.177 0.987 0.597 0.490 0.760 0.812 +0 2.525 -0.638 -0.603 2.642 -0.361 1.260 0.677 1.301 0.000 1.070 0.096 1.077 2.215 0.582 -1.456 -1.685 0.000 0.807 -1.136 0.884 3.102 1.149 0.957 0.995 1.069 0.686 1.229 1.567 +1 0.738 -0.398 -0.901 0.339 0.768 1.202 0.031 0.123 0.000 1.429 1.983 1.680 0.000 1.323 0.601 1.419 2.548 1.172 -1.709 -0.717 0.000 0.673 0.647 0.989 1.078 1.084 1.082 0.879 +0 0.732 2.051 0.179 1.143 -0.655 1.575 0.685 1.372 2.173 0.926 1.219 -0.547 2.215 0.671 0.654 -0.165 0.000 0.449 2.015 0.884 0.000 0.759 1.170 0.989 0.866 1.826 1.456 1.109 +1 2.046 -0.362 -0.017 0.589 -0.281 0.956 -0.919 1.507 2.173 0.700 -0.297 -1.369 0.000 0.508 -0.849 1.017 2.548 0.461 0.789 -1.025 0.000 0.544 0.874 0.972 0.712 0.372 0.870 0.799 +1 0.406 1.463 0.023 0.905 -1.376 1.043 0.030 -0.602 0.000 1.706 0.222 0.974 2.215 1.119 -0.386 -0.379 0.000 1.171 -1.198 1.581 0.000 0.591 0.886 0.989 1.771 0.241 1.101 1.318 +1 0.680 -0.137 -1.609 1.010 0.605 1.195 0.374 0.782 1.087 2.291 1.407 -1.129 0.000 2.011 0.478 0.368 2.548 1.130 1.897 0.162 0.000 0.906 1.158 1.045 0.810 0.719 0.953 0.905 +1 1.266 -0.645 -0.640 0.819 -0.121 1.370 0.433 1.618 2.173 1.535 -0.321 0.850 0.000 0.942 0.954 -0.201 0.000 1.441 -0.170 -0.967 3.102 1.479 1.242 0.980 1.465 1.182 1.053 1.025 +0 0.363 1.846 1.737 1.889 -0.546 0.396 -0.046 -1.673 0.000 1.208 0.844 1.127 2.215 1.742 0.078 0.722 2.548 0.757 1.632 -1.223 0.000 1.040 0.968 1.014 1.645 0.828 1.236 1.051 +0 0.512 1.422 1.695 0.726 -0.405 0.880 -0.371 1.688 2.173 0.720 0.995 -1.649 2.215 1.423 0.291 0.047 0.000 0.652 1.383 0.387 0.000 0.831 1.004 0.991 1.757 0.902 1.144 1.056 +0 2.565 0.022 -0.718 0.832 1.646 1.067 -1.288 0.988 2.173 0.446 0.136 0.288 0.000 0.329 0.948 -0.436 2.548 0.564 -0.618 0.491 0.000 0.321 0.779 1.715 0.850 1.289 1.228 0.936 +1 0.981 -0.080 0.206 1.335 0.277 1.262 -0.676 1.689 2.173 0.403 1.840 1.396 0.000 1.275 -0.531 0.909 2.548 1.226 0.732 -0.876 0.000 0.947 1.313 0.985 1.447 1.026 1.186 1.199 +1 0.832 0.139 -0.783 0.657 0.048 0.903 -0.363 0.908 2.173 0.986 -0.582 -1.191 0.000 0.794 0.694 1.042 0.000 0.927 -0.027 -0.347 3.102 1.580 1.014 0.995 0.965 0.891 0.845 0.773 +0 0.504 1.073 -1.421 2.067 -1.208 0.758 1.518 0.460 0.000 0.835 2.517 -0.793 0.000 0.740 2.499 0.540 0.000 1.759 1.174 -1.579 3.102 0.914 1.009 0.982 0.723 0.782 0.726 0.883 +0 1.076 0.255 -0.091 1.516 -0.191 0.742 0.085 0.825 0.000 0.873 0.922 1.419 0.000 1.337 0.860 -1.166 2.548 1.228 0.161 -1.259 3.102 1.156 1.018 0.997 1.287 0.386 0.898 1.005 +1 0.640 0.679 -0.351 1.063 -0.729 1.911 0.541 0.735 0.000 1.388 -1.230 1.741 0.000 0.997 -0.206 -0.626 2.548 0.752 -0.199 1.692 0.000 0.716 1.034 0.982 1.277 0.304 0.927 1.267 +0 1.413 -0.483 -0.117 1.367 -0.663 0.881 1.634 -1.587 0.000 0.928 -1.824 0.177 0.000 1.188 0.559 1.394 2.548 0.902 -0.278 1.034 3.102 0.891 0.933 0.987 0.841 0.464 0.842 1.056 +0 0.936 -1.635 -0.006 0.289 -1.504 0.459 0.149 1.122 0.000 1.021 0.237 -1.392 2.215 0.936 0.080 0.074 2.548 0.898 1.384 1.429 0.000 0.856 0.900 0.994 0.744 1.010 0.805 0.871 +0 0.938 0.448 -1.670 1.957 -1.014 0.831 0.532 0.638 0.000 0.971 0.716 -0.084 2.215 0.547 -0.939 1.282 2.548 0.451 1.489 1.132 0.000 0.741 0.875 1.048 1.065 1.067 0.879 0.873 +0 0.868 1.262 1.383 1.460 -1.298 1.157 0.631 -0.349 0.000 2.060 0.358 1.431 2.215 1.436 0.105 -0.086 0.000 1.564 1.619 0.711 0.000 0.819 0.865 1.036 1.139 1.664 1.346 1.265 +0 0.465 2.136 0.846 1.772 -1.572 0.352 -1.001 -0.121 0.000 0.722 -0.167 0.390 0.000 1.193 1.030 -0.003 2.548 1.279 0.563 1.270 3.102 0.964 1.296 1.033 1.130 0.887 1.170 1.652 +1 1.215 1.156 -0.810 1.979 -0.258 2.467 0.741 1.555 0.000 0.920 1.055 -0.222 2.215 1.158 0.215 0.351 2.548 0.765 0.328 -1.473 0.000 0.919 1.526 1.028 0.565 0.723 1.237 1.269 +1 1.640 0.997 -0.587 0.783 -1.455 0.909 -2.309 0.859 0.000 0.809 0.943 -1.021 2.215 0.769 -0.010 0.137 0.000 1.659 -0.102 1.383 1.551 0.669 0.813 1.105 0.588 1.054 0.795 0.733 +1 0.334 -2.183 1.004 1.495 -0.587 1.260 -0.677 -1.713 2.173 0.806 -0.326 -0.196 0.000 0.836 -1.548 0.137 0.000 1.873 -0.811 1.349 3.102 0.855 0.933 0.989 1.363 0.650 0.996 0.910 +1 0.568 1.234 1.207 0.099 1.684 0.716 1.007 -0.302 0.000 0.515 1.950 -0.749 0.000 1.053 -0.004 -1.641 2.548 1.079 0.178 0.631 3.102 0.894 0.942 0.986 0.584 0.729 0.657 0.605 +1 0.900 -0.113 1.540 1.502 -1.635 0.334 0.057 -0.812 2.173 1.922 -0.399 0.050 2.215 0.748 0.481 0.860 0.000 0.828 -0.250 -1.424 0.000 0.851 1.153 0.979 0.772 0.873 1.051 0.887 +0 0.738 -1.292 0.131 0.252 1.197 1.694 -0.723 -1.620 0.000 2.302 -0.354 0.188 2.215 0.690 0.320 -0.807 2.548 0.574 0.118 -1.718 0.000 0.689 0.932 0.991 0.833 1.155 1.352 1.067 +1 0.578 -0.954 1.579 0.608 1.694 0.517 -1.020 1.138 0.000 0.743 -0.663 0.306 1.107 1.414 -0.117 -0.320 2.548 1.086 0.979 -1.046 0.000 1.070 1.590 0.988 0.863 0.658 1.270 1.083 +1 1.418 -0.267 0.490 1.853 1.035 1.269 -2.623 -0.746 0.000 0.918 -0.958 -1.060 2.215 0.869 0.437 -1.738 2.548 0.548 -0.339 1.478 0.000 0.603 0.890 1.059 0.929 0.934 0.962 0.761 +0 0.782 -0.290 1.366 0.585 -1.551 1.083 -0.906 -0.218 2.173 0.634 -2.411 -1.033 0.000 0.562 -0.521 0.808 0.000 0.991 0.621 1.478 3.102 0.698 0.956 0.980 1.108 1.495 0.957 0.815 +0 2.899 -1.209 -0.507 0.512 -1.417 1.270 1.338 0.653 0.000 0.689 -1.206 0.437 2.215 0.950 -0.958 -1.519 2.548 0.881 -1.489 -1.025 0.000 0.859 0.934 1.233 0.916 0.848 0.784 0.792 +0 0.706 1.759 -1.685 2.058 -0.103 0.582 -0.133 0.260 1.087 0.816 1.162 1.586 2.215 0.761 -0.286 -0.915 0.000 0.539 -0.641 0.848 0.000 0.724 0.936 1.652 1.464 1.189 1.111 1.037 +1 0.455 -0.118 1.525 0.992 -0.173 2.415 -1.635 -1.567 0.000 2.366 0.737 -0.047 2.215 0.962 -0.767 0.920 1.274 1.358 -0.092 0.212 0.000 0.773 0.937 0.984 0.676 1.881 1.068 0.848 +1 0.761 2.184 0.117 0.847 1.177 1.023 0.632 -1.151 2.173 0.417 1.107 0.809 0.000 1.086 1.550 -0.396 0.000 0.501 0.605 1.215 0.000 0.908 1.003 0.989 0.733 0.715 0.842 0.733 +1 0.339 -1.498 -1.719 1.190 -1.072 0.676 0.340 1.365 2.173 1.249 -0.550 -0.205 0.000 0.575 -1.102 1.099 2.548 0.483 -0.844 0.702 0.000 0.876 1.134 0.991 0.733 0.691 0.741 0.715 +1 0.710 -0.820 -1.476 0.226 -0.731 0.994 -0.748 1.439 0.000 1.092 -0.596 -0.179 2.215 1.027 0.141 0.251 0.000 1.124 0.642 -0.783 3.102 0.911 1.152 0.975 0.862 0.911 0.990 0.823 +0 2.123 -0.589 1.587 0.710 0.865 0.537 1.648 0.089 0.000 2.134 0.627 -0.589 2.215 1.338 0.483 1.256 2.548 0.642 0.314 -0.456 0.000 0.840 1.110 1.030 1.969 1.791 1.398 1.430 +0 0.410 -1.787 -1.492 1.128 -0.651 1.846 -0.244 -1.352 2.173 1.511 0.448 0.332 0.000 1.448 0.441 0.745 1.274 1.647 0.524 -0.229 0.000 1.010 0.905 0.980 1.018 2.078 1.502 1.274 +0 1.178 -1.318 0.280 1.194 1.625 0.784 -0.940 -1.459 2.173 0.861 0.316 0.275 2.215 0.398 -0.401 0.263 0.000 0.392 0.009 -0.909 0.000 0.684 0.887 1.538 1.057 1.462 1.055 0.831 +1 0.808 -0.482 -0.967 1.203 0.680 0.313 0.680 -0.981 2.173 0.477 -1.173 -1.327 0.000 1.749 1.035 0.405 2.548 0.862 0.109 1.156 0.000 0.922 0.939 1.361 0.856 0.899 0.922 0.807 +0 1.338 -0.377 -0.606 1.093 1.637 0.497 -1.627 0.871 0.000 0.772 -0.804 -0.216 0.000 0.757 0.366 1.327 2.548 1.050 -0.864 1.723 1.551 0.871 1.056 1.508 0.843 0.584 0.707 0.742 +0 0.707 -1.349 -0.309 0.760 1.600 0.746 -0.703 0.242 2.173 0.707 -0.558 -1.277 2.215 0.809 -1.193 -0.812 0.000 1.140 -1.060 1.172 0.000 1.035 0.952 1.004 0.683 1.049 0.718 0.648 +0 0.592 1.128 0.099 0.541 -1.414 1.166 0.440 -0.243 1.087 1.274 0.036 -1.414 2.215 1.351 -0.617 1.111 0.000 0.620 1.373 1.153 0.000 0.937 0.989 0.989 0.793 1.603 0.939 0.776 +1 0.563 -1.613 -0.890 0.557 -0.352 0.977 -0.709 1.416 2.173 0.388 -0.750 -0.805 0.000 1.119 -1.150 0.325 0.000 0.512 2.171 1.742 0.000 0.896 1.085 0.984 0.501 0.670 0.654 0.642 +0 0.392 -0.908 0.968 2.635 0.190 1.103 -1.444 -0.312 1.087 1.186 -0.245 -1.499 0.000 1.812 -0.799 -1.680 2.548 1.245 0.283 1.369 0.000 0.961 0.862 0.983 1.174 1.741 1.249 1.228 +1 0.957 -0.405 -1.194 1.028 1.340 0.887 0.299 -0.900 2.173 1.188 -1.157 0.832 2.215 0.985 0.244 0.722 0.000 0.660 1.493 -0.568 0.000 0.802 1.036 1.039 0.984 1.951 1.078 0.944 +0 0.484 -0.636 1.620 1.631 0.726 0.904 0.636 1.573 2.173 0.746 -2.595 -0.569 0.000 1.148 1.456 -0.078 0.000 1.377 0.438 0.309 1.551 0.964 0.967 0.990 1.455 1.075 1.091 1.414 +0 0.900 -0.421 -1.539 0.720 -0.862 1.054 2.103 0.656 0.000 0.563 2.077 -0.844 0.000 0.357 1.488 1.013 2.548 0.960 1.029 -1.329 3.102 1.598 0.879 0.989 1.056 0.394 0.835 1.449 +1 1.086 0.012 0.124 0.992 -1.318 1.535 0.635 0.706 2.173 0.860 0.774 1.088 1.107 0.642 -1.270 0.642 0.000 0.566 -1.991 0.098 0.000 0.979 1.245 1.385 1.328 0.585 1.136 1.024 +1 1.738 -0.787 -0.346 0.945 -0.002 0.683 -0.268 1.347 2.173 0.597 0.444 -0.026 0.000 0.903 0.478 -1.280 2.548 1.535 -2.092 1.493 0.000 0.839 0.855 0.982 1.229 0.792 1.040 0.932 +0 0.383 0.794 -1.539 1.899 -0.420 1.101 -0.158 1.508 0.000 0.606 -0.393 0.397 2.215 0.741 -0.347 -0.416 1.274 0.646 -1.231 1.563 0.000 0.915 0.999 1.000 0.895 0.477 0.717 0.892 +1 1.079 -0.215 -1.190 0.261 -0.099 1.505 0.381 -1.685 2.173 1.596 -1.609 0.314 0.000 1.323 0.139 0.301 2.548 0.673 0.968 -0.347 0.000 1.188 1.498 0.983 1.155 1.728 1.806 1.386 +1 0.461 2.079 0.765 0.840 0.293 1.307 -0.337 -1.111 2.173 0.311 2.747 -1.356 0.000 0.570 -0.326 0.769 0.000 1.037 0.175 1.046 3.102 0.616 1.113 0.974 0.632 1.197 1.015 0.810 +1 1.757 1.046 0.989 0.222 -1.463 1.163 1.104 -1.233 2.173 2.186 2.139 -0.133 0.000 1.404 0.697 1.556 2.548 1.315 1.352 0.566 0.000 0.783 1.053 0.985 0.715 0.976 0.837 0.738 +0 3.054 -0.487 1.606 0.303 0.819 1.005 0.064 -0.103 1.087 0.588 0.292 0.550 2.215 0.793 -1.514 -0.354 0.000 0.494 1.755 0.055 0.000 1.306 1.151 0.989 1.536 0.641 1.042 1.022 +0 0.733 -1.257 1.157 0.876 -0.994 2.305 -0.557 0.032 2.173 3.278 -1.087 -1.639 2.215 1.252 0.275 1.289 0.000 1.186 -0.221 0.611 0.000 0.863 1.689 1.036 0.999 4.194 2.074 1.573 +1 0.395 -0.893 0.025 1.478 -1.672 1.244 0.377 -0.171 0.000 1.481 0.435 1.009 2.215 0.640 0.378 -0.837 0.000 0.756 0.403 -1.465 3.102 0.908 0.824 1.058 1.236 0.756 0.926 0.984 +1 2.147 -0.242 -1.011 0.602 1.242 0.597 -0.886 0.527 0.000 0.446 0.239 0.757 0.000 0.926 -0.499 1.570 0.000 1.046 -0.453 0.008 1.551 0.772 0.721 1.411 0.745 0.305 0.601 0.619 +0 1.392 -0.645 -1.725 0.301 -1.152 0.547 -1.506 -0.507 0.000 0.816 -0.900 -0.032 1.107 0.846 -0.304 0.616 2.548 0.701 0.145 1.368 0.000 1.300 0.937 0.987 0.926 0.554 0.712 0.701 +0 0.613 -0.687 -1.133 0.798 -1.659 1.495 -0.564 -0.020 0.000 1.370 0.836 1.578 2.215 0.472 0.187 0.081 0.000 1.103 -0.052 1.486 0.000 0.767 0.819 0.994 0.630 1.755 1.323 1.031 +1 0.835 -1.163 0.925 1.230 -0.218 1.763 -1.054 -0.312 1.087 1.918 -2.133 1.640 0.000 1.020 -0.396 1.027 0.000 1.460 -0.882 -1.391 3.102 2.421 1.522 1.203 1.007 1.403 1.643 1.319 +0 0.867 1.098 -1.554 0.456 1.190 2.115 1.008 1.445 2.173 1.933 1.174 -0.506 0.000 1.753 1.589 0.033 0.000 0.995 0.568 -0.035 1.551 0.614 0.587 0.981 0.945 1.515 1.177 1.003 +1 0.555 0.756 -1.112 1.398 0.877 1.097 0.021 1.223 1.087 1.808 -2.346 -0.315 0.000 1.025 -0.373 1.513 0.000 1.541 0.011 -1.006 3.102 0.845 0.804 1.190 0.948 1.246 0.852 0.761 +0 1.122 0.884 0.015 0.375 -0.060 1.155 1.394 0.720 2.173 0.936 0.769 -1.413 0.000 1.650 1.044 -0.764 2.548 1.480 1.635 -1.620 0.000 0.867 0.940 0.985 0.890 1.689 0.952 0.842 +0 1.274 -0.528 -1.570 0.677 -0.341 1.296 0.973 0.824 0.000 1.476 -0.289 -0.897 0.000 0.950 -0.331 -0.129 1.274 1.131 -0.535 0.358 3.102 2.300 1.507 1.152 0.793 0.355 0.979 0.850 +0 0.778 0.133 0.347 1.030 -0.259 1.000 -1.329 -1.680 2.173 0.274 -0.148 1.141 0.000 0.649 -1.433 0.150 2.548 0.381 -1.992 -0.756 0.000 0.711 0.714 0.984 0.614 1.007 0.867 0.694 +1 1.756 0.642 0.282 1.108 0.077 1.110 1.043 1.646 2.173 0.965 0.805 -1.292 0.000 0.404 1.559 0.620 0.000 0.448 0.911 -0.113 1.551 1.043 0.889 0.976 0.516 0.746 0.977 0.884 +0 0.685 -0.181 -0.498 2.083 0.249 0.892 -1.825 1.465 0.000 0.526 0.498 -1.689 2.215 0.484 -0.721 -0.531 2.548 0.559 -1.720 -0.975 0.000 0.880 0.805 1.032 0.958 0.596 0.816 0.973 +1 1.583 -0.427 -1.379 0.255 0.558 0.732 0.715 0.737 2.173 0.654 -0.465 -0.436 2.215 1.041 0.240 0.153 0.000 0.995 2.069 1.568 0.000 1.861 1.263 0.990 0.715 1.099 1.034 1.006 +1 0.921 0.663 -0.987 0.541 1.422 0.859 -0.150 -0.129 0.000 1.011 -0.112 -1.483 1.107 0.626 0.998 1.432 2.548 0.897 -0.160 0.913 0.000 0.927 1.043 0.992 0.567 0.685 0.742 0.731 +1 0.346 0.766 1.130 0.719 -1.001 0.842 -0.252 -0.241 1.087 0.628 -1.295 -0.133 2.215 1.109 1.294 1.690 0.000 1.949 -0.670 1.524 0.000 2.214 1.851 0.985 1.481 0.614 1.366 1.410 +1 0.911 -1.375 0.636 0.662 -0.203 0.741 0.866 1.284 2.173 0.969 -0.435 1.735 2.215 0.450 -1.083 -1.728 0.000 0.560 1.238 1.580 0.000 0.987 0.836 0.990 0.926 1.013 0.951 0.810 +1 0.775 -0.696 -0.158 1.488 0.447 1.236 -0.086 -1.613 2.173 0.752 0.954 0.140 0.000 0.594 -1.402 1.522 0.000 0.743 -0.177 -0.729 3.102 0.766 1.215 0.992 0.656 0.729 0.902 0.808 +1 0.920 -0.744 -1.567 2.355 0.847 1.277 -1.250 -0.488 1.087 0.237 -0.685 0.772 2.215 0.684 -0.866 -1.190 0.000 0.529 0.440 -1.452 0.000 0.559 0.967 1.678 0.751 0.769 1.046 0.867 +0 0.791 -2.041 -0.855 0.859 0.184 0.486 0.226 0.775 0.000 0.472 0.015 1.604 2.215 0.363 -1.713 -0.586 0.000 1.137 -0.801 -1.345 3.102 1.189 0.898 0.985 1.173 0.455 0.831 0.857 +1 0.578 -0.271 1.443 0.983 -1.189 0.888 -0.289 -0.474 2.173 0.930 -0.741 0.209 2.215 0.674 -1.033 -0.823 0.000 0.871 -0.865 1.309 0.000 0.795 0.879 0.996 0.922 0.835 0.801 0.691 +0 1.026 0.621 -0.093 0.134 0.998 1.511 1.427 0.242 2.173 2.045 -0.858 -1.419 0.000 1.119 0.210 -1.697 2.548 1.171 0.816 0.910 0.000 2.774 1.613 0.989 1.209 1.904 2.067 1.557 +0 2.296 0.265 0.258 0.405 0.910 0.913 1.898 -1.350 0.000 1.077 -0.054 0.971 2.215 1.212 -0.770 -0.610 1.274 1.674 -0.488 -1.283 0.000 0.985 1.043 0.981 1.162 1.298 0.934 0.933 +0 2.664 -1.238 0.932 0.893 0.932 1.532 -0.344 -1.079 2.173 1.037 0.571 -0.802 0.000 1.471 -0.587 0.380 2.548 0.509 1.564 0.976 0.000 1.149 1.402 0.970 2.233 1.832 1.562 1.631 +0 0.343 0.937 -0.988 1.529 0.839 1.088 0.155 0.739 0.000 2.426 -0.795 -0.951 2.215 0.580 0.481 0.237 2.548 0.666 -0.807 1.442 0.000 1.089 0.746 1.001 2.044 1.435 1.323 1.208 +1 0.505 -0.756 0.069 1.239 -1.562 1.317 0.915 1.646 1.087 1.061 0.698 0.283 0.000 1.230 1.661 0.344 0.000 1.784 -0.731 -0.708 3.102 1.023 1.712 1.090 0.751 2.166 1.586 1.421 +1 1.071 0.359 -0.694 0.620 1.471 1.074 0.065 0.747 0.000 1.055 0.460 -1.503 1.107 0.718 0.789 0.346 0.000 1.323 -0.336 -0.620 3.102 0.857 1.100 1.048 0.696 0.902 0.938 0.812 +0 0.296 2.274 0.008 1.767 -0.385 1.472 -0.120 1.196 0.000 1.572 0.949 -0.536 0.000 1.391 -0.754 1.251 2.548 0.671 -1.121 -1.356 1.551 3.729 2.220 0.977 1.567 0.560 1.494 1.390 +1 0.791 -0.505 1.396 0.589 -0.218 1.552 0.409 -1.516 0.000 1.655 1.044 0.327 1.107 1.180 0.716 -0.283 2.548 0.698 0.672 1.249 0.000 1.011 1.287 0.987 1.418 0.805 1.229 1.138 +0 0.554 -1.033 1.216 1.045 -0.324 0.514 -2.368 -0.646 0.000 0.567 -1.952 -1.483 0.000 0.939 -1.377 0.752 0.000 0.547 -0.986 -0.716 0.000 0.815 0.648 1.037 1.074 0.539 1.134 0.928 +0 0.780 -0.486 -0.127 1.335 -1.237 0.705 -2.426 1.632 0.000 1.222 -1.634 0.880 0.000 0.982 0.605 -0.136 2.548 1.178 -0.858 -0.572 3.102 0.911 1.014 1.189 0.913 0.845 0.997 0.920 +0 0.804 -1.187 -0.133 0.717 -1.596 0.680 0.997 0.836 0.000 0.721 0.715 -1.127 2.215 0.439 -2.427 -0.354 0.000 0.497 -0.395 1.732 3.102 3.126 1.648 1.018 1.022 0.452 1.153 1.008 +1 0.804 -1.537 -1.094 0.894 -1.336 1.271 -0.130 0.251 2.173 1.560 -0.479 1.151 1.107 1.027 1.710 -0.990 0.000 1.385 -0.239 -1.098 0.000 1.181 1.091 0.982 1.367 1.548 1.156 0.977 +0 0.571 -1.364 0.908 1.134 -1.599 0.978 -0.356 -0.280 0.000 0.797 0.726 1.449 2.215 0.686 0.304 -0.823 0.000 0.791 -0.879 0.676 3.102 0.866 0.848 0.991 0.916 0.869 0.840 0.815 +1 1.095 0.228 1.138 0.693 0.882 0.866 0.787 0.974 0.000 1.320 -0.296 -0.058 2.215 0.522 0.071 1.736 0.000 1.814 0.944 -1.346 0.000 0.885 1.107 0.980 1.035 0.955 0.968 0.904 +0 1.800 -0.128 0.412 1.130 -0.204 1.293 0.082 -1.152 2.173 0.716 -0.169 1.678 1.107 1.309 -1.095 1.447 0.000 1.752 0.911 0.368 0.000 2.741 1.622 1.040 1.481 0.809 1.287 1.210 +1 1.754 -1.471 -0.990 0.450 -0.065 0.648 -1.830 -1.286 0.000 0.846 -1.294 1.440 0.000 1.019 -1.149 0.374 2.548 1.030 0.087 0.553 3.102 0.910 0.796 0.988 0.852 0.591 0.755 0.700 +1 0.495 -0.259 1.553 1.594 -1.062 0.942 1.332 0.782 0.000 0.709 0.618 0.080 2.215 1.019 2.101 -1.110 0.000 0.963 1.915 -0.054 0.000 0.890 1.048 0.985 1.184 0.353 0.918 1.259 +0 0.366 1.094 0.155 1.786 1.074 0.651 -0.847 0.132 0.000 0.751 -0.843 -0.678 0.000 0.884 -0.030 -1.104 2.548 1.092 0.024 -1.625 3.102 0.991 0.985 0.985 1.268 0.340 0.952 1.286 +0 0.576 -0.147 -1.482 1.026 1.318 1.050 -0.121 -0.394 1.087 1.080 1.126 0.996 2.215 0.517 -0.982 -0.423 0.000 0.482 -0.856 -1.663 0.000 0.893 0.963 0.990 1.481 1.836 1.297 1.015 +1 0.850 -0.303 0.739 0.987 1.683 0.685 0.444 -0.743 2.173 0.746 1.209 0.123 0.000 0.840 1.244 1.722 2.548 0.484 0.722 -0.455 0.000 0.417 0.704 0.989 0.940 0.876 0.754 0.708 +0 1.653 -0.613 0.452 1.092 -0.207 1.325 1.167 -1.041 2.173 0.684 1.605 -1.639 0.000 0.881 -0.793 1.091 1.274 0.594 0.626 0.735 0.000 0.799 0.959 1.040 0.790 2.067 1.502 1.285 +0 0.940 -0.689 -0.975 0.522 -0.994 0.425 -0.501 -1.319 0.000 0.561 -0.593 1.120 2.215 1.121 0.381 0.606 0.000 0.860 -0.456 0.441 3.102 1.361 0.884 0.992 0.773 0.361 0.627 0.745 +1 0.810 -0.512 0.267 0.709 -0.885 1.008 -1.029 1.296 0.000 1.121 -0.239 -0.403 2.215 1.049 -1.868 1.028 0.000 1.445 -1.261 -1.079 1.551 1.074 1.149 0.984 0.631 1.019 1.141 0.999 +1 0.581 -0.941 -0.052 0.410 -1.488 1.048 -0.450 0.642 2.173 0.670 -0.428 -0.410 1.107 1.470 -1.003 1.200 0.000 0.762 -1.037 -0.718 0.000 0.890 1.050 0.993 0.607 1.002 0.737 0.652 +1 1.018 0.768 -0.770 0.363 0.563 1.141 -0.452 0.186 2.173 0.904 -1.695 -1.573 0.000 1.053 0.768 -1.386 0.000 1.029 -0.266 1.156 1.551 0.708 0.703 0.984 1.302 0.883 0.935 0.849 +1 0.546 -1.655 1.016 0.470 0.746 0.601 0.151 0.846 0.000 0.544 0.281 -0.122 0.000 1.576 -1.311 -0.884 0.000 0.554 -0.892 -1.176 3.102 0.934 0.778 0.981 0.481 0.216 0.469 0.492 +0 1.141 0.248 0.323 1.058 1.262 0.792 1.243 -0.565 0.000 0.600 1.167 -1.736 1.107 0.765 0.869 -1.354 1.274 0.575 2.324 -1.395 0.000 0.924 0.788 1.140 0.850 0.258 0.648 0.684 +0 0.596 0.780 -0.925 0.754 -0.272 0.785 1.160 -1.031 0.000 1.090 0.215 0.867 0.000 0.589 1.118 0.448 0.000 0.439 1.106 -1.215 3.102 0.920 0.665 0.999 0.508 0.254 0.389 0.501 +1 0.760 0.598 0.270 1.110 1.325 0.659 -0.381 0.015 0.000 0.794 0.216 0.595 0.000 1.157 0.165 -0.944 2.548 0.823 0.907 -1.683 3.102 0.900 0.974 1.036 0.895 0.578 0.756 0.720 +1 1.948 -1.594 -1.045 0.209 -0.670 1.400 -1.326 0.921 2.173 0.588 -1.145 -0.013 0.000 0.607 -0.338 0.156 2.548 0.557 1.673 -1.255 0.000 1.883 1.091 0.976 1.475 0.932 1.237 1.207 +0 0.639 1.236 -1.405 0.314 1.641 1.320 0.143 1.517 0.000 1.144 1.126 -0.645 2.215 0.563 1.903 -0.501 0.000 1.367 0.609 0.203 0.000 0.912 0.788 0.978 0.647 0.796 0.665 0.661 +0 0.652 -0.443 1.706 1.111 -0.951 1.586 -0.937 1.544 0.000 2.081 -0.640 -0.664 2.215 2.181 -0.707 0.771 2.548 1.605 -2.342 0.353 0.000 3.382 2.334 0.989 0.923 2.181 1.996 1.531 +1 0.503 -0.808 -1.388 2.097 1.471 1.049 -0.631 -0.399 2.173 0.423 -1.657 -1.068 0.000 0.647 -0.387 0.729 0.000 1.277 -0.008 0.476 1.551 0.971 0.955 0.994 1.168 0.953 1.140 0.916 +1 0.382 -0.107 1.441 1.569 -0.289 1.317 0.532 -0.903 2.173 1.208 0.643 1.056 1.107 0.597 -0.585 0.987 0.000 0.535 -1.374 0.789 0.000 0.346 0.882 1.072 0.996 1.824 1.113 0.960 +0 0.761 0.033 0.694 0.299 0.556 0.486 -0.329 -1.572 2.173 0.828 0.075 -0.830 2.215 0.398 -0.933 0.933 0.000 0.410 -0.797 -0.761 0.000 0.446 0.578 0.991 0.765 0.610 0.689 0.543 +0 0.472 1.413 -0.726 1.475 0.141 0.734 -0.142 0.862 0.000 0.485 -1.395 -1.246 0.000 0.742 0.522 1.550 0.000 1.166 0.443 -0.319 3.102 0.911 0.967 0.994 0.888 0.713 0.795 0.988 +1 0.480 -1.707 -0.662 0.253 -0.908 1.094 -1.223 0.520 0.000 1.100 -1.347 -1.690 0.000 0.900 -0.942 -1.101 1.274 0.575 -0.089 -0.610 3.102 0.861 0.698 0.980 0.590 0.354 0.470 0.501 +0 2.020 0.547 -0.208 1.077 -0.833 1.704 -1.441 1.408 0.000 0.897 -1.399 -1.637 0.000 2.869 -0.744 -0.063 0.000 0.753 0.950 -1.414 3.102 1.014 0.859 1.088 0.766 0.932 1.098 1.548 +1 1.526 -0.919 0.478 0.697 -0.381 2.496 -0.339 -1.004 0.000 1.768 0.519 0.978 1.107 1.328 -0.282 0.838 0.000 0.694 0.030 -1.634 0.000 0.859 0.892 0.998 1.460 0.877 1.088 0.894 +1 1.923 0.374 0.880 0.499 -1.012 0.407 -0.273 0.817 0.000 0.775 0.347 -1.165 2.215 0.938 -1.128 -0.851 2.548 0.438 -0.607 -0.716 0.000 0.650 0.725 1.345 1.235 0.841 0.910 0.735 +0 2.738 0.149 -1.638 0.834 -1.410 1.423 -0.167 0.188 2.173 0.838 0.861 0.086 0.000 0.340 -0.193 1.156 0.000 1.064 0.779 -0.926 3.102 0.972 0.855 1.008 0.757 1.334 1.284 1.084 +1 0.440 -1.808 -1.046 1.605 1.478 0.984 -0.914 1.094 2.173 0.776 -1.930 -0.799 0.000 1.001 1.219 -1.522 0.000 2.339 -1.915 0.129 0.000 0.729 1.132 0.991 0.821 0.846 0.886 0.793 +0 0.936 -1.643 -1.134 0.276 -1.676 0.621 0.664 1.015 0.000 0.791 -1.204 -0.004 2.215 0.375 -0.989 0.815 0.000 1.198 0.879 -1.221 3.102 0.879 0.935 0.989 0.800 1.470 0.926 0.889 +1 0.429 1.376 -1.513 2.616 -0.793 0.873 0.052 1.170 2.173 1.347 0.789 0.470 2.215 0.604 -0.298 -0.823 0.000 0.689 0.464 1.685 0.000 0.638 0.974 0.989 1.956 1.128 1.505 1.193 +0 0.481 -0.835 0.471 1.247 -0.120 0.699 1.539 -1.634 2.173 0.535 -0.772 -0.794 0.000 0.487 -0.924 0.767 0.000 0.465 -0.523 1.398 0.000 0.995 0.719 0.983 1.249 0.306 0.808 0.764 +1 0.619 1.022 -0.666 1.206 0.995 1.035 0.486 -1.253 0.000 0.563 1.041 1.329 2.215 0.620 1.935 0.430 0.000 1.349 -0.461 0.373 1.551 1.310 0.939 1.194 0.985 0.929 0.784 0.733 +1 0.876 1.565 0.638 0.745 1.647 0.918 1.218 -0.977 2.173 1.029 1.294 1.729 2.215 1.244 0.881 0.133 0.000 1.798 -1.880 0.303 0.000 0.753 0.956 0.984 0.961 0.927 0.758 0.712 +0 0.653 0.272 -1.625 1.052 -0.762 1.289 0.508 1.228 0.000 1.063 0.949 -1.087 0.000 1.129 1.245 -0.511 0.000 1.311 0.705 0.429 3.102 0.897 1.002 0.993 0.691 0.298 0.686 0.638 +1 0.512 -0.945 1.294 1.118 -0.907 1.163 0.711 0.796 2.173 0.973 -0.883 -0.537 0.000 1.072 0.790 -1.027 2.548 0.729 1.972 0.748 0.000 2.898 1.756 0.987 1.684 1.391 1.363 1.361 +1 0.937 0.893 1.654 0.902 -0.462 1.896 0.805 0.606 0.000 1.432 0.199 -0.915 0.000 1.331 1.492 -1.496 0.000 1.237 0.135 -0.171 1.551 1.962 1.123 1.203 0.789 0.700 0.846 0.760 +1 0.611 -0.692 1.699 2.237 -1.210 1.286 -0.624 0.964 2.173 0.687 -1.168 0.055 2.215 0.566 -2.279 -0.151 0.000 0.484 -0.115 0.565 0.000 0.922 1.150 0.986 0.997 1.087 1.105 0.936 +1 0.410 -0.695 -1.683 1.544 -0.202 0.983 0.498 1.264 2.173 1.111 0.104 1.690 0.000 1.258 -0.391 -0.264 2.548 0.616 0.875 -0.242 0.000 1.186 0.945 1.071 0.610 1.516 0.982 0.918 +1 0.620 -1.523 -0.159 1.901 -1.293 1.184 -1.678 0.492 0.000 1.073 -1.200 -0.881 2.215 1.186 -0.535 -1.735 2.548 1.089 -0.673 0.791 0.000 0.918 1.295 1.283 0.749 0.923 1.053 0.994 +1 1.155 -0.319 -0.471 1.473 0.752 0.894 1.531 0.801 0.000 2.013 -1.213 1.539 0.000 1.548 -0.770 1.281 0.000 1.981 -0.559 0.356 0.000 0.830 0.738 1.613 1.549 1.447 1.438 1.311 +0 0.843 0.874 0.215 0.467 1.649 0.684 -0.654 1.738 2.173 0.761 1.156 -0.849 1.107 1.053 -0.378 0.474 0.000 0.688 -1.408 0.706 0.000 1.086 0.978 0.988 0.763 1.364 1.021 0.874 +1 1.200 -0.983 0.244 0.566 0.984 0.784 0.377 -0.659 2.173 0.533 0.142 0.661 0.000 1.093 0.088 -1.712 2.548 0.852 0.776 -0.983 0.000 0.943 0.815 0.986 1.107 0.952 1.060 0.926 +1 1.828 -0.030 -0.244 0.243 -0.428 1.166 1.151 1.424 2.173 0.677 0.591 0.243 1.107 0.570 2.274 -1.322 0.000 0.609 0.887 -0.493 0.000 0.672 0.976 0.980 0.622 1.202 1.016 0.903 +0 0.784 -1.159 0.438 1.004 0.140 1.657 -0.199 0.618 2.173 1.525 -2.254 -1.635 0.000 2.077 -1.026 -1.215 0.000 1.639 -1.153 -0.464 0.000 1.303 1.178 0.995 1.769 1.346 1.430 1.374 +0 1.914 1.142 -1.138 0.769 -0.896 0.697 1.114 0.781 0.000 0.602 -0.770 0.258 1.107 0.748 0.539 -1.622 0.000 0.378 1.380 -0.361 3.102 0.788 0.915 0.973 0.517 0.708 0.976 0.910 +0 0.903 0.281 1.575 0.452 -0.421 1.015 0.538 -0.926 0.000 1.140 0.637 0.490 2.215 0.508 -0.618 0.607 2.548 0.472 -1.363 1.095 0.000 1.731 1.138 0.987 0.887 0.587 0.941 0.797 +0 0.909 1.101 1.568 1.043 -0.444 1.003 0.916 0.086 2.173 0.837 2.528 1.583 0.000 0.600 1.522 1.579 2.548 0.567 0.789 -0.631 0.000 1.165 0.696 1.310 1.000 1.012 0.888 0.816 +1 0.327 0.610 -0.821 1.419 1.498 0.611 -1.214 1.269 0.000 1.421 -0.776 -0.519 2.215 0.725 -1.515 -0.055 0.000 1.252 0.029 1.213 0.000 0.901 0.930 0.991 1.973 0.872 1.219 1.211 +1 0.653 1.786 0.236 1.148 1.143 0.712 -0.170 -0.866 2.173 0.824 1.035 1.653 0.000 0.306 -2.021 1.135 0.000 0.608 0.922 -0.358 3.102 0.865 0.944 0.984 0.610 0.567 0.797 0.702 +0 1.850 0.215 1.006 0.406 1.010 1.252 -1.367 -0.132 2.173 1.604 -0.989 -0.791 0.000 0.939 0.470 -1.205 2.548 1.000 -0.913 0.772 0.000 1.630 1.328 0.971 0.901 1.852 1.467 1.365 +1 1.337 0.078 1.017 1.061 -0.145 1.375 0.171 0.141 0.000 1.637 0.826 -1.177 0.000 0.860 0.603 1.249 2.548 0.398 0.775 -0.457 0.000 0.727 0.962 1.429 0.802 0.563 0.691 0.687 +1 0.503 2.127 1.070 1.729 0.983 0.666 0.392 -0.767 2.173 0.870 -0.305 -0.461 2.215 0.848 0.841 1.689 0.000 0.851 0.188 1.005 0.000 0.839 0.880 0.973 1.179 0.507 1.027 0.832 +1 1.230 0.709 1.481 1.110 0.936 0.662 -1.646 -0.848 0.000 1.615 0.628 -0.314 2.215 1.037 0.230 1.307 0.000 0.684 0.109 0.682 3.102 1.035 0.910 0.997 1.398 0.781 1.041 1.228 +1 1.033 -1.045 1.342 0.171 -0.054 0.530 -0.415 1.048 0.000 0.867 -0.136 -0.744 2.215 1.493 0.575 0.080 2.548 0.414 0.734 1.009 0.000 0.499 0.865 0.983 1.027 0.947 0.813 0.708 +0 1.739 -0.310 -1.341 1.198 0.964 2.075 0.820 0.726 2.173 2.064 1.293 -1.123 2.215 3.014 -1.795 -0.383 0.000 1.223 -0.212 0.983 0.000 0.934 4.758 1.748 2.015 3.129 3.897 2.900 +0 1.671 -0.286 -1.726 1.346 -0.498 1.060 0.900 -0.254 0.000 0.773 0.460 0.910 1.107 1.460 0.796 1.320 2.548 0.775 1.718 0.220 0.000 0.856 1.002 1.859 1.403 0.467 1.001 1.019 +1 1.232 -0.490 1.050 0.888 1.417 1.657 0.424 -0.602 0.000 0.882 -1.280 -0.798 0.000 1.044 -1.135 1.372 2.548 1.552 0.686 0.177 1.551 1.182 0.916 0.996 1.330 1.472 1.141 1.001 +1 1.207 1.403 0.437 0.980 -1.223 1.451 1.847 1.563 0.000 2.569 0.941 -0.480 2.215 0.697 0.705 -1.410 2.548 0.568 0.289 1.074 0.000 0.551 0.639 1.503 1.334 1.067 0.969 0.966 +1 0.555 -0.117 1.592 0.564 0.609 0.908 -0.846 -1.515 0.000 0.947 -0.928 -0.722 2.215 0.750 0.142 1.118 0.000 2.535 -0.153 0.393 3.102 1.071 1.044 0.988 0.826 1.304 0.963 0.887 +0 0.663 0.901 0.754 0.744 -1.421 1.374 0.144 0.352 2.173 1.316 -0.411 -1.546 2.215 0.727 -0.845 -0.250 0.000 0.856 0.181 -1.245 0.000 0.860 0.864 0.990 1.248 2.041 1.252 1.044 +1 0.333 -1.521 0.744 0.817 -1.524 1.543 0.299 0.699 0.000 0.963 -0.626 -0.627 0.000 1.927 -0.405 -1.286 2.548 0.673 0.358 -0.055 3.102 0.765 0.723 0.986 0.628 0.873 0.602 0.566 +1 0.693 2.048 -1.734 0.509 1.310 0.603 0.754 -0.421 0.000 0.583 2.016 -0.186 0.000 1.335 0.726 1.694 1.274 0.733 -0.194 0.550 3.102 0.926 0.912 0.990 0.610 0.766 0.812 0.743 +1 0.873 0.251 -0.607 0.980 -1.533 0.843 0.560 1.673 2.173 0.670 0.569 0.970 0.000 1.475 -0.385 0.065 1.274 0.655 0.992 0.026 0.000 0.977 0.890 0.984 0.927 1.553 0.891 0.755 +1 0.733 0.267 -1.614 0.575 -0.186 1.003 0.439 1.412 2.173 1.286 0.933 0.193 0.000 1.430 0.415 -0.207 0.000 2.257 1.713 -1.445 0.000 0.904 0.718 0.988 0.883 0.702 0.933 0.839 +1 0.555 -0.663 0.483 0.887 -0.568 0.764 -0.328 1.675 2.173 0.541 -1.720 0.209 0.000 0.652 -2.496 1.200 0.000 1.188 -0.538 -0.583 3.102 0.846 0.974 0.992 1.033 0.915 0.927 0.804 +0 1.000 0.540 -0.168 0.518 -0.860 0.530 2.619 0.512 0.000 0.801 0.373 -1.661 1.107 0.816 0.832 -0.526 0.000 1.155 -0.289 1.167 1.551 0.885 1.224 0.982 0.785 0.584 0.992 0.976 +1 1.444 0.528 1.475 1.394 1.063 0.576 0.966 0.903 0.000 1.118 -0.380 -1.168 0.000 0.931 1.839 -0.028 0.000 1.956 1.147 -0.666 3.102 1.018 1.006 0.995 0.590 0.669 0.901 0.820 +0 0.499 -0.777 -1.083 1.272 1.219 0.875 -0.633 0.496 2.173 1.030 0.344 1.698 0.000 0.928 0.046 -0.488 2.548 1.294 1.878 -0.410 0.000 0.849 0.965 0.986 0.872 0.956 0.998 0.957 +0 0.735 -0.014 0.571 1.776 -0.046 1.565 -0.861 -1.686 0.000 0.471 -1.044 -1.014 0.000 0.951 -1.134 0.334 2.548 0.494 -0.128 0.346 3.102 1.057 0.910 0.991 1.026 0.300 0.795 1.057 +0 3.056 -0.780 -0.264 1.062 -0.318 2.004 -0.771 1.545 0.000 1.645 0.006 -0.078 2.215 1.145 -1.312 1.124 1.274 0.994 0.468 -1.498 0.000 0.743 0.944 0.967 0.812 1.727 1.116 1.042 +0 1.868 -1.121 1.249 0.873 1.110 1.074 0.603 -0.732 2.173 0.660 0.436 -0.150 0.000 0.441 -0.412 1.578 0.000 0.563 -0.304 -0.544 3.102 0.913 0.886 0.996 0.848 0.439 1.441 1.156 +1 0.487 -0.160 -1.622 1.242 0.298 0.451 0.542 -0.291 0.000 0.675 -0.472 0.757 0.000 1.275 1.342 -1.641 2.548 0.975 -1.688 -1.038 0.000 1.024 0.591 1.064 1.162 0.771 1.024 0.871 +1 1.641 -0.086 0.907 0.353 -0.349 3.066 -0.254 -0.852 0.000 1.624 -0.273 1.510 0.000 2.305 0.993 0.664 2.548 1.197 -0.676 0.934 3.102 4.020 2.510 0.988 0.960 1.431 2.239 1.708 +0 0.715 0.275 -1.482 1.382 -1.448 0.592 -1.139 1.311 2.173 0.891 -0.564 0.018 2.215 0.944 0.583 0.509 0.000 1.086 0.904 -0.558 0.000 0.949 0.967 0.988 0.790 1.029 0.943 0.950 +0 0.914 -0.161 1.365 1.649 -1.404 0.809 -1.091 0.390 0.000 0.584 -1.398 1.138 0.000 1.096 -1.446 -0.422 2.548 1.099 0.019 -0.489 3.102 0.944 0.954 1.025 0.817 0.753 0.859 0.917 +1 0.780 1.603 -0.435 0.723 0.750 0.716 0.411 0.059 1.087 1.000 1.738 1.513 0.000 0.595 1.262 -1.316 0.000 1.017 0.113 -1.376 3.102 0.702 0.809 0.986 0.969 0.878 0.854 0.819 +1 0.662 0.681 -0.308 0.264 0.653 0.939 0.565 1.081 0.000 0.796 1.529 -1.122 2.215 1.123 0.389 -0.866 1.274 0.625 1.239 0.293 0.000 0.943 1.102 0.979 1.024 0.650 0.845 0.807 +1 0.413 -1.246 1.529 1.073 -0.989 0.349 -1.536 -1.084 0.000 1.003 -1.620 1.414 0.000 0.533 -0.542 0.284 0.000 0.591 -0.997 0.045 3.102 0.979 0.736 0.986 0.805 0.685 0.897 0.803 +0 1.141 0.072 0.334 1.007 1.193 1.943 1.346 -1.120 0.000 1.450 -2.735 -0.014 0.000 1.536 -0.060 -0.047 0.000 2.812 -0.996 -1.681 3.102 0.800 1.866 1.039 1.325 1.220 1.754 1.376 +1 0.884 0.715 1.001 1.227 0.150 0.895 0.641 1.308 2.173 1.270 0.687 -0.538 0.000 1.122 0.291 -1.024 0.000 0.589 0.684 -1.668 0.000 0.961 0.772 1.000 0.873 0.678 0.774 0.735 +1 0.480 1.358 0.070 0.889 1.143 0.503 1.256 -1.103 0.000 0.422 -1.406 0.465 2.215 0.403 0.549 -0.638 2.548 0.369 0.911 1.008 0.000 0.624 1.118 0.991 0.581 0.647 0.665 0.630 +1 0.804 0.664 0.744 1.333 -0.505 0.556 1.999 -1.656 0.000 0.398 -0.419 0.629 2.215 0.630 0.363 -1.083 2.548 0.717 1.270 1.200 0.000 0.571 1.008 1.294 0.734 0.579 0.680 0.716 +0 1.752 0.212 -1.001 0.695 -1.237 0.782 -0.985 0.711 2.173 1.304 0.507 0.622 0.000 1.372 -0.538 -1.068 2.548 1.571 -0.325 0.431 0.000 0.937 1.041 0.997 0.574 1.316 1.054 1.074 +0 1.234 0.157 0.881 0.475 -0.377 1.699 0.551 0.261 0.000 2.461 -1.606 -1.295 0.000 0.490 -0.774 -1.263 2.548 0.992 -1.071 1.303 3.102 7.235 3.684 0.989 0.805 0.410 2.128 1.665 +1 1.129 0.872 -1.256 2.698 0.838 1.651 -2.427 0.613 0.000 2.573 1.181 -0.458 0.000 2.391 0.495 1.101 1.274 0.981 1.468 -1.117 0.000 1.291 0.868 2.297 1.334 0.825 1.275 1.287 +1 1.114 0.343 0.921 0.909 -1.488 1.196 0.589 -1.430 0.000 0.886 1.239 -0.027 1.107 1.530 0.798 0.468 2.548 1.179 -0.338 -0.467 0.000 0.912 1.284 1.151 0.926 0.589 1.026 0.922 +1 0.304 -0.386 -0.965 0.824 -0.295 1.784 0.849 -1.361 0.000 0.896 1.599 0.134 0.000 1.809 0.690 0.100 0.000 1.920 -0.351 1.198 1.551 0.898 0.688 0.983 1.145 0.877 1.065 1.508 +0 2.631 -0.619 0.392 0.673 0.324 1.849 0.761 -1.260 2.173 0.369 0.942 0.259 2.215 0.679 1.090 1.446 0.000 0.370 -0.942 -1.466 0.000 0.845 1.044 0.982 0.939 1.196 1.744 1.334 +1 0.525 1.541 -0.231 0.619 0.919 0.696 -0.459 -1.532 2.173 0.371 -0.009 0.230 0.000 0.528 -1.049 0.694 2.548 1.299 1.305 -0.855 0.000 1.088 1.091 0.988 0.947 0.734 0.868 0.769 +0 0.469 -0.085 0.815 1.358 -1.195 1.340 0.937 1.479 2.173 1.714 -1.179 -0.492 0.000 1.600 -0.664 0.435 1.274 0.462 1.024 -0.578 0.000 1.029 0.864 1.074 1.160 2.262 1.215 1.009 +1 2.277 0.728 -1.009 0.928 -1.618 0.652 -0.415 0.048 0.000 0.934 0.735 0.202 1.107 1.004 0.687 1.012 1.274 0.580 -0.775 0.613 0.000 0.828 0.874 1.048 1.025 0.687 0.905 0.801 +1 0.648 -0.060 -0.206 1.690 0.533 0.697 -1.041 -0.849 2.173 0.638 -0.389 1.506 2.215 0.929 0.051 -1.309 0.000 0.656 -0.993 1.517 0.000 0.859 0.901 0.987 0.887 0.897 0.930 0.768 +0 1.031 0.424 1.175 1.310 -1.679 1.639 1.104 -0.567 0.000 0.901 -0.131 -1.579 2.215 1.547 0.912 0.801 0.000 1.320 0.121 0.082 3.102 0.975 0.958 0.985 0.757 0.991 0.966 0.970 +1 0.835 0.469 -0.615 0.863 0.592 1.419 -0.218 1.116 2.173 1.561 -1.101 -1.532 0.000 1.568 -0.291 -0.306 2.548 1.212 -0.302 -1.051 0.000 1.034 1.343 1.041 1.143 1.784 1.294 1.146 +1 0.292 1.297 0.874 0.780 -1.429 1.078 -0.969 -0.784 1.087 1.403 -0.932 0.616 1.107 0.880 -0.470 -1.558 0.000 0.485 0.569 0.545 0.000 0.823 1.003 0.982 1.026 1.724 1.019 0.851 +1 0.593 0.319 -0.866 0.916 -0.096 0.847 -0.358 -1.579 0.000 0.858 -0.748 -0.238 2.215 1.382 -0.352 1.432 0.000 1.119 0.380 0.684 3.102 0.806 0.962 0.979 0.567 0.871 0.879 0.804 +0 0.964 1.773 -0.733 0.945 -0.956 0.712 0.013 -1.361 0.000 1.264 -0.546 1.027 0.000 1.191 1.221 0.023 2.548 0.570 -0.116 0.478 1.551 1.781 1.029 0.974 0.801 0.568 1.026 1.036 +0 0.279 1.027 -0.611 2.222 -0.826 0.789 0.896 1.144 0.000 0.872 0.068 0.135 2.215 1.031 0.656 0.566 2.548 1.228 1.175 -1.653 0.000 0.954 0.853 0.987 1.853 0.506 1.342 1.212 +1 0.964 0.153 0.614 0.744 -0.684 1.052 0.220 1.496 2.173 0.789 -2.224 -0.400 0.000 0.675 1.339 -0.043 0.000 0.518 0.673 0.475 3.102 3.601 2.010 1.080 0.987 0.664 1.533 1.213 +1 1.270 -0.618 1.024 0.227 -1.674 1.019 0.097 -0.599 1.087 0.972 -1.060 -0.259 2.215 0.659 -0.419 0.708 0.000 1.472 -1.266 1.486 0.000 0.924 1.028 0.992 1.122 1.023 0.998 0.910 +1 1.204 -1.807 -0.729 1.023 -0.604 0.717 -1.520 1.032 2.173 0.647 -2.406 1.121 0.000 1.311 -0.368 0.335 2.548 1.458 -0.717 -1.328 0.000 1.526 1.079 0.998 1.053 1.011 0.952 0.962 +0 0.781 -0.168 -0.989 0.736 0.790 0.896 0.747 0.191 2.173 0.713 0.647 1.528 0.000 0.858 1.118 -1.396 0.000 0.486 -0.867 -0.723 3.102 0.674 1.150 1.050 0.566 0.880 0.797 0.729 +0 0.498 -0.383 -1.115 1.368 1.644 0.654 0.839 -1.635 2.173 1.582 1.342 -0.230 0.000 0.900 1.308 0.812 0.000 1.399 0.554 0.478 1.551 1.478 1.023 0.987 1.295 0.961 1.088 1.430 +0 1.254 0.400 1.364 1.811 1.457 0.608 -1.143 -0.698 0.000 0.687 0.027 -0.532 0.000 1.123 -1.298 0.157 2.548 0.380 2.245 0.271 0.000 0.883 0.932 1.000 0.702 0.612 0.878 0.918 +0 0.928 0.346 -0.669 2.447 -0.930 1.418 -1.319 0.951 2.173 0.770 -1.771 0.412 0.000 0.469 -2.518 1.240 0.000 1.244 -0.746 -0.324 3.102 0.826 0.788 0.990 0.749 1.320 1.375 1.136 +1 0.926 0.177 -0.941 1.615 -0.265 1.308 -2.348 1.301 0.000 2.782 -1.449 -0.746 0.000 2.745 -0.620 0.887 2.548 0.704 0.646 1.410 0.000 3.055 2.192 0.987 1.673 0.981 1.751 1.560 +1 0.407 1.971 1.404 0.714 -1.104 0.984 0.699 0.142 0.000 1.091 0.534 -1.455 2.215 1.041 1.131 1.260 1.274 0.921 1.281 0.056 0.000 0.633 0.970 0.992 0.642 0.827 0.913 0.785 +0 0.561 0.827 -1.057 0.768 1.181 0.883 0.634 0.734 1.087 1.318 0.538 -0.857 0.000 1.227 -1.325 1.376 0.000 2.571 -0.178 -0.312 3.102 1.022 1.025 0.989 0.813 1.469 1.027 0.869 +0 0.770 -0.209 -0.338 1.020 -1.568 0.880 -0.650 1.347 2.173 1.037 -0.905 0.376 0.000 1.801 0.276 0.344 0.000 2.045 1.071 -1.196 1.551 0.772 1.232 1.098 0.912 1.926 1.165 0.985 +0 1.031 -0.663 1.254 0.699 1.738 1.240 0.234 0.841 2.173 1.269 -1.182 -0.366 2.215 1.449 -0.627 -1.065 0.000 1.036 0.190 -0.510 0.000 0.912 0.983 0.977 0.883 2.194 1.271 1.094 +1 0.531 0.612 -0.072 1.121 1.070 0.836 -0.149 -0.649 0.000 1.238 1.139 1.535 0.000 1.295 -1.452 -0.141 2.548 0.927 -1.193 -1.381 3.102 0.887 1.130 0.987 0.870 0.756 0.856 0.811 +0 0.426 -1.745 -0.400 0.549 0.884 0.462 0.004 -1.455 0.000 0.556 -0.054 1.298 0.000 0.706 0.940 -0.531 2.548 1.205 0.125 0.090 3.102 0.660 0.788 0.995 0.830 0.493 0.596 0.611 +0 1.816 -0.146 -1.418 0.712 -0.766 0.910 -0.372 -0.515 2.173 1.237 -0.862 1.255 0.000 1.928 -0.709 0.447 1.274 0.704 -1.346 0.808 0.000 0.658 0.901 0.978 0.871 1.302 1.022 1.000 +1 0.948 0.387 -1.154 0.546 0.478 0.630 0.432 1.368 0.000 0.809 -0.100 -0.184 2.215 0.661 1.166 0.284 2.548 1.635 0.095 -1.530 0.000 0.822 0.935 0.992 0.693 0.663 0.782 0.678 +1 0.934 -1.487 -0.052 0.878 -0.797 1.030 -0.054 1.656 2.173 0.470 -0.525 -0.644 0.000 1.035 -0.144 0.870 2.548 0.626 1.279 -0.240 0.000 0.909 0.878 0.985 1.263 0.841 0.930 0.867 +0 2.965 -0.962 0.278 0.883 -0.931 0.837 -1.377 -1.111 2.173 0.684 -1.555 1.738 0.000 0.935 -0.599 0.854 1.274 1.147 0.725 -1.316 0.000 0.718 0.763 1.986 1.109 1.156 1.053 0.919 +1 1.180 0.275 1.429 0.339 -1.397 0.464 0.709 -1.072 2.173 1.130 1.237 -0.152 2.215 0.549 0.048 -1.299 0.000 0.516 -1.240 -0.655 0.000 0.593 1.162 0.988 0.762 0.841 0.900 0.784 +1 0.603 -0.709 -0.320 0.387 -0.822 0.556 0.112 -0.984 2.173 0.597 0.450 0.856 0.000 0.598 -0.827 0.353 0.000 0.932 1.027 1.374 3.102 0.789 0.899 0.982 0.743 0.789 0.669 0.629 +1 0.445 0.693 -1.156 0.599 -1.446 0.502 -1.833 0.411 0.000 1.565 -0.131 1.660 2.215 0.869 -1.072 -0.380 2.548 1.430 0.660 0.175 0.000 0.853 0.869 0.995 1.628 1.373 1.479 1.217 +0 0.869 -2.209 -0.723 3.064 -0.744 1.429 -0.145 0.688 0.000 1.255 -1.626 -1.343 0.000 1.274 0.603 0.628 2.548 2.117 0.073 1.277 3.102 3.641 2.283 1.013 2.293 0.783 1.787 1.835 +0 0.439 0.442 -0.875 0.514 -0.326 1.398 -1.025 -1.473 0.000 2.047 1.602 1.504 0.000 2.425 0.680 -0.205 1.274 2.631 0.612 0.221 3.102 1.740 1.790 0.989 1.242 0.723 1.455 1.497 +0 0.902 -0.043 -0.626 0.788 1.089 0.479 -0.134 -1.490 2.173 0.857 0.694 -0.201 0.000 0.482 0.674 -1.162 0.000 1.086 -0.254 1.090 3.102 0.751 0.852 1.168 0.711 0.559 0.630 0.607 +1 1.006 -1.638 1.026 1.900 1.666 0.743 -0.869 0.092 0.000 0.358 -0.889 -0.171 2.215 0.812 -0.519 -1.012 0.000 0.604 0.552 -0.765 1.551 1.191 0.841 1.045 0.851 0.425 0.834 0.849 +0 0.833 0.846 -0.541 1.245 -0.609 0.822 0.252 1.571 0.000 0.834 -0.623 1.433 0.000 1.390 0.256 0.748 2.548 0.927 0.136 -0.171 1.551 0.843 0.977 1.003 1.069 0.641 0.742 0.852 +1 0.764 1.178 -1.511 1.760 -0.028 1.777 1.214 1.445 0.000 2.387 1.648 -0.548 0.000 1.495 0.957 0.401 1.274 0.614 1.648 -1.568 0.000 0.925 0.931 1.563 0.783 0.723 0.676 0.739 +1 1.205 0.662 -0.192 2.442 -1.077 2.719 0.009 1.135 1.087 1.857 -1.207 -0.368 0.000 0.843 0.397 -1.188 0.000 1.075 1.006 -0.552 0.000 0.705 0.686 1.698 2.599 1.324 1.656 1.328 +1 0.671 -1.052 -0.795 0.238 1.685 0.871 -0.164 0.099 2.173 1.185 -0.629 -1.447 0.000 1.447 0.003 0.821 2.548 0.369 0.931 -1.444 0.000 0.864 1.105 0.979 0.823 0.855 0.923 0.784 +0 1.532 -0.927 1.398 1.346 -0.250 0.725 0.157 1.133 2.173 0.775 -0.191 -0.424 2.215 0.421 0.275 -1.248 0.000 0.632 -1.302 -0.761 0.000 0.658 0.872 1.982 1.179 1.105 1.003 0.810 +0 0.907 0.905 0.079 1.399 0.604 0.503 -2.003 1.714 0.000 0.546 -0.724 -0.687 2.215 0.710 2.563 -1.572 0.000 1.034 1.227 -0.849 1.551 0.663 0.767 0.999 0.813 0.922 1.067 1.426 +1 0.439 -0.706 -1.292 0.098 -1.362 1.956 0.460 -0.814 0.000 2.094 1.347 0.899 2.215 2.130 1.072 0.304 2.548 1.106 -1.228 -1.148 0.000 0.569 1.141 0.901 0.981 1.170 0.996 0.971 +1 1.095 -1.338 -0.560 1.322 -1.486 1.453 -0.933 0.854 1.087 0.563 -0.946 -0.349 2.215 0.350 -1.087 -0.015 0.000 1.055 -0.877 -1.016 0.000 0.735 1.134 1.234 0.750 1.177 1.023 0.849 +0 1.929 0.669 0.189 0.768 -0.395 0.597 0.752 1.593 0.000 1.467 0.548 -1.049 2.215 0.871 0.421 1.230 2.548 0.698 1.151 0.946 0.000 0.918 0.974 0.984 0.877 1.065 0.917 0.810 +1 1.072 -1.381 0.138 1.003 -0.844 0.646 -0.060 1.366 1.087 1.514 -1.236 1.103 0.000 2.117 -0.990 -0.589 2.548 0.472 -0.997 1.614 0.000 0.491 0.787 1.111 0.753 1.630 1.067 0.970 +1 2.189 0.374 -0.344 0.344 -1.726 0.845 0.621 1.737 2.173 1.011 0.732 0.863 2.215 0.717 1.452 -1.732 0.000 0.704 2.039 -0.133 0.000 0.850 0.988 1.138 1.070 0.969 0.939 0.892 +1 2.755 -0.144 0.809 0.632 0.459 2.459 0.048 -1.186 0.000 1.781 -0.731 0.201 2.215 0.769 -0.675 -0.261 2.548 1.052 -1.146 -1.649 0.000 1.569 1.099 0.982 1.012 0.503 0.973 0.938 +0 0.933 -0.110 -1.470 0.555 -0.132 0.518 1.182 0.892 0.000 1.289 -0.559 1.592 2.215 1.208 0.278 -0.260 2.548 1.481 1.266 0.062 0.000 0.937 0.963 0.986 0.791 1.451 1.183 1.001 +1 1.334 -0.980 1.401 0.743 -1.034 0.616 -1.027 0.881 0.000 0.705 0.804 -0.182 0.000 0.916 -0.979 -0.640 2.548 1.057 -0.859 0.270 0.000 0.906 0.720 1.120 0.617 0.130 0.515 0.534 +0 0.334 -1.396 -1.231 2.396 -0.101 0.779 2.891 1.433 0.000 0.417 0.732 -0.609 2.215 0.524 0.395 1.301 2.548 0.385 2.002 -1.348 0.000 0.542 0.986 1.055 1.135 0.498 0.900 2.277 +0 0.975 -0.839 -1.163 1.959 -0.336 0.868 -0.028 0.518 0.000 0.904 -0.751 1.579 1.107 1.460 0.341 1.713 0.000 1.586 -0.095 -0.222 3.102 1.035 1.093 1.299 0.819 1.139 0.912 1.043 +1 1.153 0.312 -1.227 0.988 1.587 0.546 -0.244 1.473 0.000 1.342 -0.752 0.192 2.215 0.372 -1.064 -0.051 2.548 0.476 1.772 -0.701 0.000 0.924 1.049 0.986 0.845 0.221 0.980 0.825 +1 0.450 0.546 1.041 1.672 -0.685 0.432 2.149 -1.709 0.000 0.399 2.816 0.429 0.000 0.613 0.868 -0.097 2.548 1.282 -0.149 1.283 3.102 0.890 0.825 1.201 0.932 0.759 0.895 0.885 +1 0.802 -2.184 -0.199 2.655 1.638 0.740 1.454 1.414 0.000 1.023 -2.068 -1.644 0.000 0.782 -0.883 -1.631 0.000 0.725 0.431 0.030 3.102 0.840 0.836 2.014 1.830 0.672 1.173 1.008 +1 0.663 -0.287 1.299 0.949 1.428 1.580 -1.219 0.958 0.000 2.823 -0.258 -0.716 2.215 0.518 0.344 1.710 0.000 1.014 -1.717 0.172 0.000 1.465 0.888 0.988 1.786 0.474 1.113 1.029 +1 0.929 0.930 -0.772 0.667 -1.682 1.089 0.468 0.852 0.000 1.042 0.466 -1.122 2.215 0.726 0.840 0.379 0.000 0.996 -0.383 -0.228 3.102 0.756 0.929 0.980 0.581 0.800 0.890 0.797 +0 0.366 -1.676 -0.593 0.283 0.147 0.546 0.535 -0.734 2.173 1.104 -0.147 1.323 0.000 0.831 2.600 -0.014 0.000 0.786 -0.623 1.574 0.000 0.833 0.976 0.999 0.664 0.535 0.693 0.655 +0 0.734 0.101 1.259 1.608 0.570 0.302 -0.692 1.648 0.000 0.478 -1.110 -0.429 0.000 1.224 0.401 -1.434 2.548 1.288 -0.491 -0.836 3.102 0.934 0.754 0.989 0.935 0.714 0.828 0.709 +1 1.853 -0.819 -0.688 1.015 -0.823 2.329 -0.247 -1.041 2.173 3.533 -1.198 0.683 0.000 1.025 0.123 -0.198 0.000 2.463 -0.234 1.064 0.000 0.939 1.588 0.974 1.374 1.867 2.165 1.798 +0 0.654 0.590 0.003 0.733 -1.674 1.044 -0.047 0.645 2.173 1.137 -1.657 -0.831 0.000 0.912 1.157 -1.617 0.000 1.375 0.321 1.046 3.102 0.725 0.815 0.987 0.958 0.526 0.841 0.731 +0 0.927 0.991 1.247 1.380 0.977 0.526 -2.561 0.946 0.000 0.872 2.470 -1.165 0.000 1.120 0.660 0.004 0.000 0.733 -0.401 -0.724 1.551 0.807 0.893 0.996 0.962 0.661 1.333 1.315 +0 0.432 0.696 0.121 0.676 -1.199 0.719 0.391 1.003 2.173 0.709 1.137 -1.338 2.215 0.959 0.176 0.051 0.000 0.434 1.554 -0.775 0.000 0.823 0.869 0.985 0.828 0.993 0.745 0.693 +1 1.403 0.054 0.108 1.591 0.424 1.279 -0.415 1.583 2.173 0.691 1.318 -1.341 0.000 0.914 0.632 -0.393 0.000 0.637 -0.022 -0.541 1.551 1.007 0.667 0.991 1.529 0.918 0.995 1.073 +0 0.796 -0.454 -0.932 0.797 0.833 0.992 -0.317 1.311 2.173 1.287 0.177 -0.032 2.215 0.999 0.816 -0.894 0.000 0.378 -1.445 -1.057 0.000 1.175 1.071 1.104 0.842 1.612 1.023 0.863 +1 0.433 -1.220 -0.991 0.919 0.327 0.611 -0.372 1.070 0.000 0.578 -0.495 -0.490 2.215 1.039 0.343 -1.114 2.548 0.621 0.199 -1.453 0.000 0.833 0.783 0.984 0.580 0.579 0.558 0.541 +0 1.521 -0.271 -1.704 0.830 -1.177 0.945 -2.048 1.033 0.000 0.752 0.249 -0.550 0.000 1.019 -0.361 -0.012 0.000 2.160 0.647 0.021 3.102 0.769 1.021 0.999 1.448 1.104 1.063 0.941 +1 0.592 -0.208 1.647 1.415 -0.546 2.399 -1.259 1.624 0.000 1.023 -0.608 0.060 2.215 1.257 0.254 0.619 0.000 3.246 -0.299 -0.451 3.102 1.037 1.055 1.166 0.814 0.763 0.916 0.838 +0 0.875 0.652 -0.157 0.932 -0.619 0.655 0.855 1.209 0.000 0.748 -0.041 -0.541 2.215 0.823 0.159 -1.644 0.000 0.561 -0.798 1.047 3.102 0.850 1.030 0.987 0.700 0.643 0.676 0.702 +1 0.421 -1.178 0.588 0.950 -1.209 0.833 0.151 -0.251 0.000 0.722 -0.146 0.712 2.215 1.495 -0.290 1.549 2.548 0.968 -0.895 -0.203 0.000 0.898 0.894 0.989 0.720 0.761 0.859 0.749 +0 0.884 1.594 1.703 0.210 0.293 0.920 -1.007 -0.870 0.000 0.370 -2.584 1.203 0.000 1.344 0.946 0.599 2.548 1.087 -0.447 -0.263 3.102 1.136 0.854 0.995 0.755 1.033 1.171 1.103 +0 1.720 -0.230 0.661 2.222 0.416 1.258 -2.050 -1.143 0.000 0.808 -1.139 -1.203 0.000 0.736 -0.804 -0.796 2.548 1.240 0.259 1.226 3.102 0.999 0.798 0.999 0.800 0.845 1.079 1.549 +1 0.667 -0.133 -0.360 1.796 0.465 1.566 0.075 -1.351 2.173 0.824 -0.145 0.223 0.000 1.019 0.231 1.314 2.548 0.451 0.522 -0.226 0.000 0.445 0.725 1.027 1.575 1.069 1.090 0.904 +0 1.100 -0.252 0.581 0.324 0.455 1.065 0.769 -0.247 0.000 1.332 -0.848 1.448 2.215 0.830 0.805 -1.087 0.000 0.669 0.862 1.592 3.102 1.166 0.892 0.983 1.146 0.949 1.198 1.011 +0 0.721 0.481 -0.224 0.743 -0.978 0.507 1.080 0.597 0.000 0.570 1.605 1.085 0.000 0.534 -0.338 -1.023 2.548 0.669 0.951 -1.694 3.102 0.584 0.903 0.995 0.720 0.460 0.568 0.689 +0 0.893 -0.235 1.081 0.987 0.573 1.201 -0.774 -0.696 0.000 0.868 -0.491 -1.391 2.215 0.265 -0.813 -1.559 0.000 0.504 -0.784 0.821 3.102 0.714 0.731 0.981 0.447 0.560 0.594 0.702 +1 0.621 -1.404 0.422 0.993 -0.118 1.447 -0.471 1.679 2.173 0.793 -1.242 -1.336 0.000 0.555 -1.027 -0.043 0.000 0.716 2.058 0.472 0.000 0.938 1.146 0.993 0.548 1.006 0.888 0.816 +1 0.769 0.960 1.263 0.792 -0.409 0.684 0.454 -0.197 2.173 0.909 0.169 -0.985 2.215 0.843 0.358 1.028 0.000 1.452 1.483 1.140 0.000 0.924 1.147 1.079 0.815 0.775 0.913 0.778 +0 0.734 0.746 0.560 1.819 0.747 1.348 0.043 -0.789 1.087 1.107 2.557 1.622 0.000 1.525 0.491 1.297 2.548 2.213 -0.378 -0.484 0.000 1.111 0.875 0.994 1.027 1.759 1.472 1.340 +1 0.663 -0.285 0.814 0.754 -0.480 0.794 -1.620 0.915 0.000 0.999 -0.255 -1.007 2.215 0.863 -0.588 0.146 1.274 0.744 -1.053 -1.295 0.000 1.093 0.912 0.987 0.784 0.871 0.863 0.737 +1 0.777 -1.335 -1.331 1.254 0.999 0.814 -0.179 -0.425 2.173 0.549 1.103 1.109 0.000 0.503 0.611 -1.416 2.548 0.922 -0.019 0.450 0.000 0.768 1.027 1.180 0.943 0.713 0.891 0.887 +0 1.647 0.047 -1.042 1.589 -0.529 1.686 0.250 1.281 2.173 0.689 -1.782 -0.554 0.000 0.911 -0.500 0.925 0.000 1.889 1.021 -0.689 0.000 1.446 0.981 1.002 1.815 1.095 1.228 1.243 +0 0.671 -0.371 1.346 1.062 -1.399 0.676 -1.032 -1.086 2.173 0.540 -1.101 0.434 0.000 0.598 -0.735 -0.153 0.000 1.673 0.050 0.459 3.102 0.824 0.849 0.983 0.897 1.284 0.865 0.748 +1 1.103 -0.972 -1.158 1.158 1.654 0.884 -1.121 0.809 0.000 1.148 -0.992 -1.561 0.000 1.122 -0.598 -0.116 0.000 2.865 -0.647 0.354 3.102 0.899 0.883 0.993 1.274 0.933 0.868 0.805 +0 0.552 0.551 1.364 1.034 0.373 0.850 -1.025 1.594 2.173 1.235 -1.270 -0.189 0.000 0.546 -0.243 0.365 0.000 1.473 -0.005 -1.537 3.102 0.913 1.197 0.986 0.965 0.736 0.955 0.862 +0 0.852 0.225 -0.290 1.351 -1.086 0.792 1.341 0.978 0.000 1.542 1.399 -0.775 1.107 1.620 1.353 0.404 2.548 1.460 2.220 1.109 0.000 0.888 0.938 0.989 0.933 1.468 1.040 1.045 +1 1.136 -0.102 -0.831 2.029 -1.549 0.999 0.659 0.363 2.173 0.858 -0.467 0.601 1.107 0.439 0.679 -1.592 0.000 0.604 1.325 0.262 0.000 0.735 1.000 1.266 1.213 0.874 1.159 0.907 +1 1.449 1.598 1.170 0.818 -0.989 0.835 1.346 0.023 2.173 1.005 0.743 0.672 2.215 0.848 1.362 1.635 0.000 1.398 -0.011 -0.777 0.000 0.859 1.172 1.405 1.041 0.847 0.933 0.868 +1 0.649 -1.287 -0.975 2.096 0.024 1.129 0.862 -1.533 0.000 0.395 -0.162 0.574 0.000 0.493 1.415 0.758 0.000 0.904 0.766 1.235 1.551 0.667 0.685 1.267 1.377 0.528 1.109 0.979 +0 1.160 -0.085 -1.221 0.967 -0.519 1.060 0.459 -1.630 0.000 0.971 -0.657 0.329 2.215 1.609 0.082 0.906 1.274 1.679 -0.009 -0.221 0.000 2.007 1.524 0.989 1.061 0.841 1.116 1.001 +1 1.990 -0.417 -1.137 0.438 -0.833 1.040 -0.868 -0.117 2.173 0.796 -0.836 -1.680 0.000 1.471 -2.412 1.255 0.000 1.035 -1.133 0.930 3.102 0.955 0.703 0.987 1.214 0.929 0.959 1.200 +1 0.682 0.285 -0.112 1.035 1.414 0.429 -0.407 0.847 0.000 1.048 -1.060 -1.194 0.000 1.512 -0.279 -1.737 2.548 1.815 -0.354 0.127 1.551 0.714 0.964 1.142 0.861 1.261 0.795 0.725 +1 2.565 -0.182 -0.534 1.503 -1.626 1.361 0.545 0.620 0.000 1.674 -1.387 1.435 2.215 1.073 -1.764 -0.327 0.000 0.684 -1.015 1.122 3.102 3.825 2.081 2.264 2.077 0.287 1.675 1.642 +0 1.031 0.303 -1.613 1.500 -0.926 0.400 -1.149 0.176 0.000 0.430 -1.492 -1.725 2.215 0.762 -0.258 0.560 2.548 0.816 0.517 0.482 0.000 0.914 0.863 1.001 0.911 0.674 0.764 0.753 +1 1.371 -0.064 1.142 1.761 1.740 1.344 -0.863 -0.114 2.173 0.604 -0.050 -1.071 2.215 0.411 -0.749 -0.999 0.000 0.423 -1.365 1.401 0.000 0.428 0.783 1.106 0.834 1.151 1.195 0.895 +0 1.662 -0.327 0.290 0.880 0.277 1.109 0.738 -0.678 2.173 0.856 -0.187 1.294 0.000 0.849 0.879 0.530 2.548 0.384 1.456 -1.001 0.000 1.069 0.834 0.982 1.657 1.080 1.176 1.045 +0 0.447 -0.303 -0.141 1.109 -1.233 1.248 2.055 0.145 0.000 1.436 -0.035 1.579 2.215 0.619 -1.114 0.733 0.000 0.545 -1.962 1.650 0.000 0.830 1.245 0.985 1.067 0.782 1.039 0.898 +0 1.499 -0.134 -1.461 0.397 -0.008 0.734 1.056 -1.737 2.173 0.758 -0.407 0.283 0.000 0.847 -1.801 0.403 0.000 0.875 -0.095 -1.221 0.000 0.823 0.761 1.033 0.593 0.797 0.641 0.571 +1 0.558 -2.016 0.570 0.533 -0.264 1.156 -1.449 -1.640 1.087 0.892 -1.300 0.435 0.000 0.736 0.200 -0.312 0.000 1.063 -0.049 -1.557 3.102 0.543 0.856 0.987 1.056 0.897 0.846 0.759 +1 0.793 -0.204 -0.140 0.177 0.291 1.078 0.876 0.657 1.087 1.139 -0.345 -1.062 0.000 0.585 0.509 1.285 0.000 0.750 0.664 -1.572 1.551 1.224 0.769 0.985 0.838 0.863 0.927 0.803 +0 0.848 -0.070 -1.657 1.096 -1.122 0.477 -0.497 0.378 1.087 0.709 -1.141 1.337 2.215 0.484 0.983 -0.023 0.000 0.788 -0.897 -0.407 0.000 0.915 0.981 0.977 0.886 0.714 0.683 0.696 +1 0.719 0.747 1.486 1.079 -0.216 0.896 0.633 -0.256 0.000 0.708 0.942 -0.854 0.000 1.439 0.231 1.630 1.274 0.719 0.304 0.930 3.102 0.913 0.840 1.219 0.926 0.461 0.802 0.729 +1 2.564 -0.232 -0.071 0.669 0.122 0.661 -2.363 -1.401 0.000 0.542 0.693 -1.693 2.215 0.734 -0.935 1.303 2.548 0.745 -0.384 1.023 0.000 0.747 0.763 0.985 1.060 0.719 0.940 1.180 +0 0.604 -0.210 0.962 0.977 -0.163 0.969 0.659 -1.230 2.173 0.516 0.604 0.440 0.000 0.943 1.456 -0.080 2.548 1.062 1.125 1.499 0.000 0.865 1.005 0.988 1.168 1.176 1.049 0.912 +1 0.572 -1.787 1.359 0.925 -1.172 2.476 -1.142 -0.138 0.000 2.412 -0.644 -1.660 0.000 1.799 -0.843 1.291 2.548 1.409 -0.976 0.668 0.000 1.894 1.150 0.984 1.221 0.899 1.213 1.276 +0 0.861 0.127 -0.743 0.616 -0.044 0.920 1.146 -0.826 1.087 1.562 -0.794 1.211 2.215 0.507 0.364 1.002 0.000 0.660 -1.300 0.126 0.000 0.860 0.851 0.985 1.181 2.655 1.363 1.085 +1 0.355 1.404 -0.759 1.588 -0.931 0.461 -0.314 0.133 0.000 0.511 0.245 1.229 0.000 0.824 -0.916 0.617 2.548 1.047 -0.911 1.389 3.102 0.910 0.760 1.001 1.000 0.456 0.777 0.710 +0 2.973 2.252 0.375 0.143 -1.633 1.660 0.641 -1.127 1.087 0.762 1.340 0.945 1.107 0.543 0.041 -0.640 0.000 0.947 1.180 1.530 0.000 0.939 0.969 0.987 0.830 1.700 1.599 1.252 +1 1.696 0.572 0.185 1.072 0.975 0.472 -0.443 -0.569 0.000 0.717 -0.221 -1.234 2.215 0.959 1.138 -1.551 2.548 0.772 -0.178 1.488 0.000 0.891 0.941 1.222 1.115 0.754 0.884 0.802 +0 1.320 0.542 0.808 0.443 -1.039 0.491 0.502 -1.231 2.173 0.498 -0.780 0.705 0.000 0.675 -0.993 1.566 2.548 0.553 -0.109 -0.748 0.000 0.701 0.738 1.055 0.835 0.760 0.667 0.602 +0 0.494 1.846 0.751 0.757 -0.958 0.585 0.240 1.503 0.000 0.672 1.049 -0.148 2.215 0.881 0.076 0.969 2.548 0.693 -0.708 -0.812 0.000 1.013 1.019 0.990 0.762 0.811 0.691 0.673 +1 1.681 1.053 0.940 1.210 0.635 1.834 2.026 -0.576 0.000 1.653 0.662 -1.310 2.215 1.584 1.206 1.233 2.548 1.362 0.632 0.605 0.000 2.687 2.117 0.990 1.609 1.409 1.635 1.455 +0 1.250 1.515 1.504 0.613 -0.503 0.713 0.629 0.619 1.087 0.647 -0.348 -1.645 0.000 0.842 -0.241 -0.149 0.000 0.376 -0.202 -0.524 3.102 1.106 1.052 1.179 0.723 0.531 0.669 0.775 +1 0.685 -0.096 0.379 0.306 -0.763 1.091 -0.923 1.362 2.173 1.239 0.855 0.027 0.000 1.448 -0.193 -1.605 0.000 1.481 -0.841 -0.285 3.102 1.353 1.119 0.985 0.919 1.341 0.940 0.816 +1 2.504 0.375 0.868 1.007 -1.213 1.136 1.734 -0.605 0.000 0.425 0.057 -1.067 0.000 1.262 0.453 -0.849 2.548 0.674 0.158 0.399 3.102 1.525 1.061 2.100 1.004 0.645 0.874 1.049 +1 0.656 0.693 0.866 1.562 0.152 0.904 -0.277 -1.690 2.173 0.752 0.675 -1.099 2.215 0.593 -0.353 -0.339 0.000 0.378 -0.608 1.446 0.000 0.530 0.678 0.983 0.960 0.872 0.918 0.721 +0 0.699 1.203 -1.586 0.406 0.865 0.531 -0.773 0.288 2.173 0.813 -0.021 1.574 1.107 0.842 -0.229 -0.317 0.000 0.822 -1.347 -1.158 0.000 0.925 0.951 0.977 0.872 0.962 0.707 0.712 +1 0.998 0.012 -0.781 0.747 1.377 0.528 1.688 0.902 0.000 0.756 -0.218 -0.284 0.000 0.833 -0.054 0.606 0.000 0.671 0.672 -1.256 3.102 0.881 0.896 1.113 0.593 0.248 0.587 0.585 +1 0.829 -0.101 -1.396 0.229 0.335 0.759 0.541 0.615 2.173 0.809 0.449 -1.257 0.000 1.027 0.897 -0.600 0.000 0.911 -0.949 0.671 3.102 0.864 1.155 0.986 0.638 0.834 0.905 0.839 +1 0.804 -0.909 -0.162 0.325 0.733 0.849 0.259 -1.151 0.000 1.447 1.199 0.813 2.215 0.757 0.415 -0.468 0.000 0.679 0.563 -1.676 3.102 0.845 0.607 0.985 2.242 0.744 1.424 1.246 +1 1.244 0.334 -1.315 0.564 0.710 0.692 -0.623 0.903 0.000 1.139 -1.202 0.263 0.000 0.648 1.377 -0.891 0.000 1.341 0.319 1.160 0.000 0.846 1.113 1.123 0.863 0.838 0.881 0.799 +0 1.365 0.707 0.068 0.979 0.372 0.481 0.238 -0.934 0.000 0.421 -0.926 -1.024 1.107 0.281 -1.176 -1.609 0.000 0.624 0.926 -1.456 0.000 0.801 0.584 1.001 0.893 0.275 0.843 0.737 +1 1.279 -1.124 -1.104 0.498 -0.430 1.396 0.150 1.085 1.087 0.885 -1.931 -0.997 0.000 1.215 -0.208 -0.035 0.000 0.859 -0.643 0.374 3.102 0.813 1.131 0.994 0.754 0.889 1.187 0.972 +0 1.473 -0.386 1.581 0.799 -0.974 0.714 1.275 -0.017 2.173 0.782 -0.030 0.628 0.000 0.776 -0.860 -0.355 2.548 0.471 0.425 -1.335 0.000 0.805 0.909 1.119 0.828 1.295 1.037 0.851 +0 0.919 -0.731 -1.431 0.882 0.153 0.612 -1.158 1.667 2.173 1.472 -1.180 -0.539 0.000 1.059 1.787 0.800 0.000 1.403 -0.503 1.206 1.551 0.661 1.072 1.235 0.787 0.486 0.835 0.758 +0 0.509 0.962 1.148 1.353 0.014 0.384 0.139 1.651 0.000 0.602 0.290 -0.658 1.107 0.868 -0.981 0.368 0.000 1.084 0.775 -1.300 3.102 0.999 0.947 0.988 0.735 0.463 0.633 0.713 +1 0.758 -0.346 0.767 1.894 -0.099 0.782 1.033 -1.625 2.173 1.097 1.244 -0.943 2.215 1.066 1.174 1.287 0.000 1.139 1.726 -1.649 0.000 1.057 1.059 1.167 1.490 0.803 1.232 1.039 +1 0.714 0.928 0.306 0.947 0.477 1.116 0.251 -1.695 0.000 1.101 2.261 0.083 0.000 1.112 0.934 -1.084 2.548 0.378 0.773 0.999 1.551 3.737 1.908 0.988 0.999 0.473 1.197 1.087 +0 0.457 1.637 1.439 0.668 -0.571 0.662 2.685 0.637 0.000 0.908 0.852 1.396 2.215 1.213 -0.859 -1.194 0.000 0.801 -1.888 0.543 0.000 0.933 0.574 0.984 0.724 0.772 0.915 0.864 +1 0.934 1.631 0.385 1.771 0.055 0.469 1.214 -1.011 0.000 1.305 0.448 -1.497 2.215 0.394 1.345 0.863 0.000 1.110 0.259 1.048 3.102 0.773 0.806 1.000 1.292 0.819 1.382 1.059 +1 2.389 -0.970 -1.607 0.638 -0.415 0.992 -0.899 0.156 2.173 0.588 -1.401 -1.246 0.000 0.638 -0.987 1.203 2.548 0.813 -1.348 -0.006 0.000 0.814 0.917 1.505 0.836 0.807 0.929 0.784 +0 0.902 0.999 0.163 0.806 1.157 0.931 0.632 -0.968 0.000 1.241 -2.606 0.160 0.000 0.626 1.650 -0.802 0.000 1.245 0.637 1.468 3.102 0.878 0.925 0.985 0.827 0.489 0.739 0.745 +1 0.702 -1.839 -0.259 1.542 0.461 0.472 -2.136 -0.808 0.000 0.534 0.014 -0.911 2.215 1.068 -0.686 -1.461 1.274 0.583 -1.714 1.187 0.000 0.782 0.959 0.989 1.021 0.496 0.810 0.757 +0 0.449 0.090 -0.438 1.032 1.427 0.483 1.126 0.305 0.000 1.138 1.237 -0.726 2.215 0.553 -1.115 0.640 2.548 0.406 1.667 1.464 0.000 0.989 1.104 0.989 0.785 1.566 0.907 0.771 +0 0.688 -2.172 -0.599 0.156 0.688 0.969 2.204 0.975 0.000 1.114 -0.889 -0.422 1.107 1.157 -1.108 -1.355 2.548 0.809 -1.279 0.939 0.000 4.333 3.414 0.995 0.691 0.917 2.367 1.979 +0 1.470 1.012 -1.075 0.594 0.056 1.305 -0.542 -1.145 0.000 1.452 -0.508 0.595 2.215 0.275 2.382 1.550 0.000 0.870 0.281 0.585 0.000 0.862 1.260 1.104 0.657 0.252 0.929 0.827 +1 0.453 -1.567 -0.140 1.007 -0.618 0.787 -0.715 1.199 1.087 0.821 -1.107 -1.706 0.000 0.884 0.727 0.507 0.000 1.336 -0.374 -0.400 3.102 1.895 1.271 0.981 0.517 1.087 0.921 0.843 +1 0.351 -1.095 -0.055 1.109 -1.010 1.555 -0.263 0.602 2.173 0.748 0.241 -1.630 0.000 0.897 0.681 -0.796 2.548 0.688 -0.318 -0.900 0.000 0.736 0.801 0.977 2.011 1.602 1.612 1.311 +1 0.988 0.400 -1.711 0.532 0.251 0.934 0.138 1.147 0.000 0.589 2.666 0.024 0.000 1.594 0.911 -0.794 2.548 0.792 1.180 1.523 0.000 0.997 1.047 0.987 0.838 0.757 0.927 0.771 +1 0.857 -0.393 -0.032 0.526 -1.456 1.741 -0.837 0.410 1.087 0.543 -1.486 1.254 0.000 1.246 -0.401 -1.658 1.274 1.979 -0.570 -1.118 0.000 0.913 0.955 0.991 0.997 1.796 1.162 0.947 +0 0.650 -0.142 -1.074 0.378 0.414 0.769 0.554 0.463 0.000 1.385 0.331 -1.561 1.107 0.898 1.494 0.301 0.000 0.833 0.452 -0.803 3.102 0.876 0.838 0.988 0.805 0.620 0.926 0.772 +0 1.491 0.386 0.097 1.124 0.672 0.691 1.179 -1.463 0.000 0.624 0.457 -0.571 2.215 0.981 -0.361 -1.320 0.000 0.894 -0.506 1.205 3.102 1.317 0.959 0.979 0.829 0.774 0.733 0.850 +1 1.940 -0.109 0.535 0.604 -0.162 1.045 0.084 -1.608 2.173 0.699 0.055 -0.712 2.215 0.670 0.301 -1.183 0.000 0.781 0.906 0.449 0.000 0.852 0.893 0.987 0.868 0.909 0.951 0.807 +1 1.251 -0.453 0.410 0.154 0.433 0.706 -0.089 1.270 1.087 0.606 -0.888 -0.389 0.000 0.339 0.225 -0.502 0.000 1.053 -1.226 -1.270 0.000 0.798 1.075 0.983 0.782 0.693 0.682 0.722 +0 0.557 -0.522 0.956 0.978 -1.175 0.567 2.527 1.658 0.000 0.716 0.086 -0.620 2.215 1.231 -0.566 0.377 2.548 0.956 -1.669 0.242 0.000 0.928 0.934 0.987 0.813 0.860 0.677 0.659 +1 0.606 -0.905 1.691 0.938 0.324 1.095 -0.286 -0.213 2.173 1.076 0.295 1.146 0.000 1.455 0.413 -1.445 2.548 0.720 -0.286 -1.101 0.000 1.097 0.910 0.987 0.902 1.530 0.993 0.887 +1 0.612 -0.987 -1.538 1.312 -0.575 1.071 -0.547 0.651 2.173 0.475 -1.135 -0.687 0.000 0.760 -0.656 1.560 2.548 0.764 0.831 -1.395 0.000 1.127 0.818 0.986 1.220 0.826 0.851 0.832 +0 0.883 0.387 0.738 0.488 1.716 0.892 -0.089 0.149 0.000 0.990 0.457 -1.504 1.107 0.617 -0.058 -0.914 0.000 0.472 -1.009 0.844 0.000 0.825 1.220 0.986 0.542 0.428 0.704 0.702 +1 1.312 0.833 -0.411 1.082 -0.330 0.641 0.643 0.825 2.173 0.373 -0.739 0.726 0.000 0.728 1.587 -1.550 0.000 0.959 0.418 -1.156 1.551 1.300 0.876 1.005 1.094 0.813 0.799 0.801 +1 0.974 0.319 -1.504 0.371 -0.227 0.606 -0.136 -1.114 0.000 0.591 0.664 0.111 0.000 1.618 1.344 0.995 2.548 0.591 1.311 -0.095 3.102 1.255 0.849 0.982 1.178 0.623 0.855 0.806 +1 0.720 -0.736 -1.428 1.399 -0.594 1.457 -0.710 0.496 2.173 0.864 -0.727 1.158 0.000 0.969 0.250 1.627 1.274 1.446 0.842 -1.421 0.000 0.971 0.713 0.989 1.347 1.474 1.086 1.041 +0 0.430 -1.101 1.154 0.999 -0.724 1.260 -1.456 -1.190 2.173 1.762 0.179 0.475 0.000 1.499 0.763 1.107 0.000 1.462 0.071 -1.010 3.102 0.985 1.015 0.989 0.794 1.255 1.346 1.192 +1 0.601 0.201 0.130 0.817 -0.528 1.226 0.610 1.514 0.000 1.379 0.449 -0.060 2.215 0.691 1.392 -1.622 0.000 0.658 0.074 -1.465 3.102 0.935 0.641 0.988 0.734 0.836 0.961 0.870 +1 0.595 0.755 1.308 1.187 -0.270 0.986 -0.738 -0.209 2.173 1.076 0.059 1.528 0.000 0.792 1.087 1.473 0.000 0.556 0.444 0.905 3.102 0.849 0.547 1.151 1.168 0.851 0.979 0.891 +0 1.882 -0.367 0.321 1.090 0.740 0.690 0.338 -1.466 2.173 0.705 -0.723 -0.963 0.000 1.149 -0.613 -1.578 1.274 0.804 -2.347 -0.684 0.000 1.235 1.037 1.000 1.341 0.598 1.006 1.084 +1 1.031 0.338 -0.140 0.135 -1.406 1.030 -0.625 0.869 1.087 1.133 0.262 -1.009 2.215 0.621 -0.015 1.137 0.000 1.362 -0.940 -1.009 0.000 1.119 1.005 0.993 0.952 1.742 0.987 0.846 +1 0.937 -0.606 0.581 1.334 1.018 0.525 0.746 -0.622 2.173 0.695 -1.290 -1.696 0.000 0.645 -1.055 -0.294 2.548 0.665 -1.697 -0.782 0.000 0.724 1.301 0.988 0.745 0.831 0.959 0.880 +0 1.476 -0.564 -1.178 2.767 -0.921 1.256 -0.044 0.578 0.000 1.324 -0.444 1.294 2.215 0.387 0.779 0.568 0.000 0.589 -0.497 -0.175 0.000 0.903 1.102 0.989 1.082 0.561 1.073 1.111 +1 0.414 -1.340 -0.405 3.093 0.084 0.666 -0.456 -1.370 2.173 0.834 1.130 -1.656 0.000 0.960 0.462 1.037 2.548 0.733 -1.302 -1.217 0.000 1.901 1.218 0.975 1.147 0.963 1.027 1.117 +1 0.954 -0.228 1.727 0.344 -0.275 0.983 -0.501 -1.376 0.000 1.246 -0.499 -0.122 1.107 1.142 -0.135 0.357 0.000 1.388 -0.970 1.188 3.102 0.798 1.007 0.989 0.957 1.166 0.821 0.761 +1 1.417 0.391 -0.436 1.364 0.081 1.253 0.451 1.473 1.087 0.458 0.654 1.635 2.215 0.697 0.835 0.401 0.000 0.811 1.455 -1.087 0.000 0.882 1.121 0.994 0.864 0.201 0.995 0.845 +1 1.182 0.284 -1.610 1.247 -1.680 0.781 0.736 0.592 2.173 0.784 0.279 -0.184 0.000 0.384 0.608 -0.542 2.548 0.467 1.036 -0.683 0.000 0.520 0.744 0.985 0.647 0.583 0.748 0.699 +0 1.089 0.390 1.676 0.844 1.092 1.529 -1.237 0.072 1.087 0.505 -1.907 -0.689 0.000 1.010 -1.248 -1.264 2.548 1.938 -0.209 1.657 0.000 0.991 0.986 0.988 1.389 1.449 1.647 1.367 +1 1.010 0.013 0.356 0.253 -0.560 0.617 -0.255 -1.079 0.000 0.995 -1.063 0.321 1.107 0.986 -1.215 1.359 1.274 1.154 0.406 -1.547 0.000 0.719 1.010 0.988 1.018 0.857 0.913 0.899 +0 0.880 1.159 -1.225 0.973 -0.635 0.485 2.474 0.752 0.000 0.695 -0.033 0.053 2.215 0.384 0.578 -1.666 0.000 0.831 0.000 1.493 3.102 1.047 0.968 0.992 1.150 0.661 0.868 0.852 +0 0.867 0.410 -1.278 2.469 1.686 1.847 0.077 0.010 0.000 0.765 1.488 1.656 0.000 0.914 -0.512 0.950 2.548 1.049 -1.060 -0.298 3.102 3.214 2.019 0.993 1.201 0.727 1.361 1.337 +0 1.033 -1.845 1.135 0.887 1.202 0.705 -1.071 -1.063 0.000 1.080 -1.711 -0.419 0.000 1.351 -1.051 -1.575 0.000 1.172 -0.807 0.076 3.102 0.902 0.878 1.004 0.796 0.360 0.593 0.696 +0 0.299 -1.400 -0.350 0.623 1.437 0.764 -0.938 -0.703 2.173 1.121 0.803 1.001 0.000 0.486 0.494 -0.621 2.548 1.131 -0.234 0.816 0.000 0.893 0.869 0.993 0.580 0.622 0.606 0.594 +0 0.689 -0.615 1.012 0.424 -0.617 0.669 -1.359 -0.433 0.000 0.991 -0.389 -0.941 2.215 1.228 -1.252 1.191 1.274 0.706 -1.721 0.410 0.000 0.822 0.896 0.988 0.840 1.251 0.791 0.688 +1 0.573 -0.082 1.354 0.744 0.310 0.685 1.439 0.756 0.000 1.094 0.763 -1.065 1.107 1.073 1.346 -0.402 2.548 1.105 2.323 -0.995 0.000 1.067 0.914 0.989 1.229 0.765 1.066 1.085 +0 0.642 -0.672 -1.224 1.428 0.954 0.585 0.027 -0.732 2.173 0.516 -1.207 1.505 0.000 0.481 -0.494 0.335 1.274 0.588 -1.534 -0.535 0.000 0.726 0.876 1.225 0.654 0.575 0.655 0.617 +0 0.402 -1.052 0.474 2.160 -1.116 0.886 0.839 0.441 0.000 0.482 0.410 1.153 1.107 0.577 1.743 0.843 0.000 1.291 0.802 -0.964 3.102 0.851 1.011 1.278 1.062 0.700 0.952 1.191 +0 0.364 2.045 -0.758 1.858 -1.715 0.727 0.010 -0.526 2.173 1.099 0.244 1.110 0.000 0.811 2.030 -0.024 0.000 0.527 -1.275 -0.946 3.102 2.083 1.678 0.983 1.264 0.607 1.170 1.127 +0 0.338 0.915 -0.926 1.250 1.138 0.725 -0.259 -0.720 2.173 0.274 1.279 1.401 1.107 0.370 1.777 -0.025 0.000 1.091 1.076 0.436 0.000 0.368 1.063 0.984 0.478 0.844 0.878 0.739 +1 0.674 0.985 -1.543 1.054 0.937 0.768 0.877 -0.174 0.000 1.031 0.863 -0.642 1.107 0.991 1.314 1.362 0.000 1.199 -0.371 1.368 1.551 1.603 1.158 0.988 0.665 1.212 0.962 0.839 +1 0.565 -0.595 0.086 1.378 0.012 1.136 0.179 -0.848 0.000 2.397 1.029 1.414 2.215 0.955 -0.294 -0.425 0.000 1.593 0.033 0.533 3.102 0.845 1.160 0.978 1.647 1.570 1.462 1.238 +0 0.696 -0.219 1.653 0.292 -0.733 0.551 0.230 -1.347 2.173 1.445 0.739 0.885 1.107 0.402 -2.129 1.671 0.000 0.519 -0.616 -0.881 0.000 1.214 0.920 0.996 0.878 1.238 0.855 0.733 +1 1.967 -1.011 -0.126 0.612 -0.448 0.896 -0.369 1.201 2.173 0.634 -0.674 -1.200 0.000 0.919 0.277 -0.361 0.000 0.771 1.292 1.264 0.000 1.007 1.170 0.989 1.208 0.844 1.098 0.965 +1 0.688 -0.580 0.343 0.164 -1.239 1.304 -0.540 1.450 2.173 0.754 -0.951 -0.098 0.000 0.465 2.499 1.057 0.000 0.657 -1.353 -0.440 0.000 0.702 1.131 0.995 0.558 0.938 0.782 0.736 +1 0.593 0.082 -0.844 0.444 1.380 0.534 -1.562 0.800 0.000 0.850 -0.984 -0.313 2.215 0.706 -0.262 -1.308 2.548 0.565 -2.159 1.192 0.000 0.531 0.888 0.988 0.560 0.713 0.706 0.647 +0 0.726 0.670 1.161 0.280 -1.589 1.050 -1.423 -1.482 0.000 1.379 0.428 -0.344 2.215 1.930 0.566 0.675 2.548 0.429 -0.808 1.550 0.000 0.474 1.841 0.986 0.877 1.387 1.543 1.195 +1 0.701 1.407 0.913 1.086 -1.579 0.481 0.559 0.433 1.087 1.130 1.164 -1.170 2.215 1.253 -2.459 -0.376 0.000 2.310 0.025 1.062 0.000 1.374 0.855 0.985 0.810 1.131 0.916 0.884 +1 0.507 0.281 1.717 3.300 1.488 1.883 -1.679 0.007 0.000 0.537 0.254 -1.432 2.215 0.607 -0.933 -0.073 1.274 0.543 -1.300 -0.633 0.000 0.849 0.555 0.993 0.776 0.707 0.950 1.276 +1 0.396 -1.013 -1.223 0.146 -0.381 1.367 0.838 -0.174 0.000 1.297 -0.045 -1.130 0.000 3.360 -0.739 1.322 0.000 1.205 0.171 0.447 3.102 0.785 0.892 0.978 0.661 0.447 0.637 0.630 +1 1.184 0.721 -1.023 0.570 -1.394 0.984 1.088 0.008 1.087 0.571 0.007 1.385 0.000 0.723 1.289 1.165 2.548 0.429 1.116 0.502 0.000 0.652 0.941 0.980 0.734 0.922 0.790 0.718 +0 0.500 0.660 0.944 1.270 -1.246 1.477 -0.555 -0.350 2.173 1.942 -0.320 1.460 2.215 0.844 -1.053 -0.032 0.000 0.565 -0.352 0.720 0.000 0.557 1.133 1.017 1.410 2.504 1.384 1.106 +0 1.645 -0.684 -0.131 0.050 0.657 0.542 -0.008 1.500 2.173 0.494 -1.524 0.337 0.000 1.353 -0.644 -1.258 2.548 0.855 -0.366 1.166 0.000 0.764 0.900 0.980 0.919 0.753 0.772 0.713 +1 0.514 -1.314 1.231 1.251 -0.478 1.778 -0.356 -1.630 2.173 1.266 -0.825 0.430 2.215 1.684 -2.074 0.289 0.000 0.687 -0.384 -0.778 0.000 0.737 1.125 1.110 1.424 2.187 1.692 1.321 +1 0.319 -1.741 -1.316 1.523 0.152 0.884 -0.322 1.357 0.000 1.514 -1.436 -0.858 2.215 0.709 -2.338 1.168 0.000 1.601 -0.113 0.687 3.102 1.561 1.070 0.987 0.969 1.716 1.290 1.108 +1 0.954 0.356 0.303 1.442 -0.269 0.917 -0.456 1.568 2.173 0.642 0.689 -1.081 2.215 0.626 -2.117 -0.915 0.000 1.003 -0.574 0.361 0.000 1.128 1.222 0.980 0.800 1.040 1.027 1.156 +0 1.160 -0.281 0.445 1.828 1.004 1.646 -0.959 -1.347 2.173 0.751 -0.412 -0.608 0.000 0.903 0.078 -0.117 2.548 1.202 -0.842 0.857 0.000 1.254 0.872 0.989 1.763 1.598 1.262 1.061 +1 1.017 1.586 -0.767 1.555 -0.346 1.072 0.469 1.295 2.173 0.295 1.613 -1.301 0.000 0.273 0.309 1.596 1.274 0.758 0.343 0.333 0.000 0.738 0.838 0.983 0.654 0.186 0.910 0.728 +1 0.380 1.700 1.080 2.886 1.315 0.594 -2.663 0.007 0.000 1.392 -0.342 -1.446 0.000 1.290 0.106 -0.740 2.548 1.578 0.958 0.026 3.102 0.800 1.677 0.977 1.562 0.911 1.757 4.073 +1 0.296 2.169 -0.415 0.982 0.429 1.498 -0.367 -1.313 0.000 2.287 0.142 0.241 2.215 0.858 1.295 -1.268 0.000 1.294 0.233 0.928 3.102 0.833 0.769 0.982 0.880 0.906 0.917 0.781 +1 0.730 -0.269 0.029 0.738 -1.016 1.058 -1.417 1.323 2.173 0.932 -1.623 -0.274 0.000 0.495 -0.589 -0.824 0.000 0.437 -0.115 1.549 3.102 0.746 1.268 0.986 0.550 0.519 0.880 0.869 +0 0.849 0.602 -0.231 1.561 -0.170 0.920 0.900 0.810 2.173 0.860 -0.411 1.480 2.215 0.872 2.443 -1.406 0.000 1.120 1.922 0.952 0.000 0.949 1.301 0.989 1.118 1.196 1.313 1.426 +1 0.927 -0.944 -1.174 0.627 1.488 0.862 -0.239 -0.207 0.000 1.066 0.930 0.291 0.000 0.744 0.023 1.088 2.548 1.800 0.834 -0.718 3.102 1.014 0.906 0.989 0.658 0.991 0.759 0.833 +0 0.931 1.431 -0.446 1.248 -0.101 0.614 1.441 1.072 2.173 0.642 0.232 1.409 0.000 0.901 0.663 -1.492 2.548 0.378 -0.645 1.140 0.000 0.637 0.703 0.992 0.871 0.765 0.802 0.693 +1 0.693 -0.999 -0.300 0.495 -1.128 1.481 -1.455 -1.563 2.173 1.104 1.887 0.437 0.000 1.386 0.007 0.648 2.548 2.096 -1.931 -0.058 0.000 0.982 0.982 0.987 1.303 2.174 1.194 0.966 +1 1.542 -0.429 -1.444 0.799 -0.573 1.378 -0.586 0.552 2.173 0.542 -1.485 1.689 0.000 0.552 -0.292 -0.783 0.000 0.566 -0.656 -0.349 3.102 0.855 1.212 1.088 0.603 0.685 0.893 0.785 +1 0.643 -0.694 0.966 1.074 -1.351 0.649 -0.073 1.197 2.173 1.322 0.730 -0.278 0.000 0.800 -0.329 -1.170 2.548 0.581 0.877 0.652 0.000 0.866 0.950 1.001 0.721 0.770 0.820 0.813 +0 0.665 -0.350 -0.985 0.374 1.605 0.706 0.703 0.348 2.173 0.817 0.003 0.096 2.215 0.787 0.536 -1.512 0.000 1.123 2.178 -1.649 0.000 1.246 1.323 0.980 0.788 0.476 1.029 0.877 +0 0.730 0.827 0.174 0.491 1.453 1.076 0.390 1.426 2.173 1.159 -0.315 -0.863 2.215 1.264 -0.404 0.478 0.000 1.268 -0.237 -1.432 0.000 0.940 0.965 0.985 1.244 1.568 1.072 0.887 +1 0.703 0.124 -0.096 0.516 -1.083 0.927 -0.668 1.345 0.000 0.728 -0.553 0.800 0.000 1.549 -0.978 -0.385 2.548 1.075 0.219 -1.058 3.102 0.830 1.022 0.994 1.213 0.905 0.945 0.968 +1 1.164 1.075 0.793 1.683 0.199 1.278 0.552 -1.326 2.173 0.703 0.561 -0.328 0.000 0.456 -0.406 1.697 0.000 0.792 0.020 0.591 0.000 0.960 1.004 0.988 0.635 0.865 1.023 0.886 +0 0.448 -1.906 -0.585 0.665 1.106 0.523 -1.055 0.462 0.000 1.108 -0.872 -0.788 1.107 0.590 -0.990 1.320 0.000 1.084 0.406 -1.662 3.102 0.697 0.979 0.981 0.793 1.020 0.786 0.688 +1 1.116 -1.284 1.466 2.313 -0.139 0.741 0.106 -0.089 2.173 1.622 -0.749 -1.682 0.000 1.155 -2.464 -1.415 0.000 1.617 -1.042 -0.298 0.000 0.885 0.876 2.208 1.526 0.777 1.132 0.914 +1 1.035 0.821 -1.549 0.653 -0.921 0.306 1.056 0.419 0.000 0.749 -0.590 0.545 1.107 0.680 0.267 1.219 0.000 1.222 0.152 -0.772 3.102 1.018 1.087 0.979 0.759 0.874 0.906 0.804 +0 0.847 0.235 1.575 0.565 -1.447 0.605 0.986 1.261 0.000 0.731 0.950 0.326 2.215 0.586 0.222 -0.268 2.548 0.994 -1.044 -0.664 0.000 0.842 0.790 0.991 0.724 0.440 0.723 0.692 +1 0.480 -0.357 -1.123 2.404 0.471 1.780 0.346 -1.293 0.000 1.384 -0.800 0.835 0.000 1.584 0.426 -0.394 1.274 0.418 2.051 -1.300 0.000 1.666 1.475 1.475 0.722 0.790 1.112 1.200 +0 2.774 -0.067 -1.332 0.840 -1.302 1.603 -1.229 0.556 0.000 1.461 -0.999 0.149 0.000 1.964 0.397 -1.619 2.548 1.298 -1.285 -0.203 1.551 1.218 0.984 1.008 0.693 1.834 1.646 1.709 +1 0.839 0.427 1.306 0.962 0.475 1.242 0.348 -0.423 2.173 1.311 -0.334 1.054 0.000 1.243 -1.395 -1.117 0.000 0.858 -0.532 -1.691 0.000 0.880 0.680 0.985 1.159 0.916 0.985 0.843 +0 0.846 0.908 -0.961 0.370 1.563 0.670 -0.130 0.286 1.087 0.489 -1.099 0.931 0.000 1.126 -0.031 -1.067 2.548 0.596 -1.820 1.241 0.000 0.434 0.957 0.983 0.853 1.017 0.772 0.755 +0 1.577 0.332 -0.220 0.232 1.369 0.834 -0.246 1.559 0.000 0.767 -0.726 -0.998 1.107 0.539 -1.302 1.187 0.000 1.092 1.161 0.235 3.102 0.854 0.905 0.990 0.636 1.295 0.963 0.901 +0 0.479 -2.150 0.327 0.616 -1.174 0.835 -1.005 1.624 2.173 0.494 -1.227 -0.116 0.000 0.652 -1.666 -1.368 0.000 1.836 -0.765 0.352 3.102 0.825 0.874 0.995 0.742 1.197 0.750 0.660 +0 2.209 -0.247 0.513 0.520 -0.248 0.896 2.815 0.301 0.000 1.802 0.735 -1.709 1.107 1.683 0.385 -1.041 0.000 2.338 0.825 -1.247 0.000 0.746 1.012 0.988 0.800 2.345 1.456 1.369 +0 1.063 1.338 0.762 0.321 -0.428 0.786 0.516 -1.534 0.000 0.569 2.577 1.355 0.000 1.147 -0.876 -0.056 2.548 0.948 0.490 0.059 3.102 0.687 0.810 0.983 1.633 0.687 1.032 0.973 +1 0.731 -1.408 0.973 0.703 0.585 0.903 -0.132 -1.468 0.000 0.751 -1.508 0.469 2.215 0.899 -2.600 -0.515 0.000 1.391 0.444 -0.762 0.000 0.976 1.005 0.974 0.685 0.890 0.858 0.763 +0 1.140 0.158 -0.861 0.843 -0.246 0.354 -1.243 0.775 0.000 0.583 -0.397 0.604 2.215 0.775 -0.109 -1.692 2.548 0.825 -2.088 1.626 0.000 0.794 0.883 0.986 0.776 0.636 0.645 0.755 +1 0.562 -0.792 -1.528 2.153 1.321 1.526 -0.101 0.499 0.000 2.259 0.528 -1.186 2.215 1.110 -0.122 0.087 0.000 2.230 -0.202 -0.577 3.102 0.850 1.303 0.984 2.321 1.335 1.648 1.631 +0 0.560 0.287 0.280 1.995 1.005 0.882 -0.947 -0.246 0.000 0.974 -0.975 -1.178 2.215 1.127 0.944 1.654 2.548 0.386 -2.207 -0.660 0.000 0.909 0.931 0.982 0.792 1.480 1.200 1.293 +0 0.884 1.674 0.582 0.561 1.640 0.765 2.687 -0.469 0.000 0.369 -0.863 0.969 0.000 1.299 0.436 1.628 2.548 1.666 -0.712 -0.628 1.551 0.387 1.043 0.986 1.967 1.290 1.366 1.403 +0 0.582 -2.138 -0.872 0.511 -1.476 0.791 -1.059 0.363 2.173 0.844 0.056 0.799 0.000 0.943 0.536 1.711 0.000 1.528 0.413 -0.747 3.102 1.067 1.034 0.993 0.913 1.404 0.986 0.940 +1 0.677 -0.289 -1.397 2.952 -0.154 1.131 -0.364 0.877 0.000 1.087 1.098 -1.305 2.215 0.333 0.849 1.457 0.000 0.475 0.278 1.104 1.551 0.946 0.522 1.764 1.686 0.598 1.066 1.061 +1 1.006 -0.665 -0.120 1.248 -0.424 1.184 0.872 1.701 2.173 0.698 0.097 -0.769 0.000 0.465 -0.698 1.068 2.548 0.797 0.642 0.134 0.000 0.859 0.961 0.976 0.743 0.987 1.037 0.847 +1 0.779 0.693 -0.840 1.179 -0.104 0.604 0.016 0.989 0.000 0.943 1.065 1.675 1.107 1.201 -0.077 1.375 2.548 0.675 -2.350 -0.025 0.000 0.992 0.923 0.989 0.970 0.769 0.852 0.799 +1 0.906 0.172 0.708 0.768 1.580 0.623 -0.013 1.139 0.000 0.683 -1.353 -0.375 1.107 0.681 -0.385 -1.035 0.000 1.265 -1.462 -1.065 0.000 0.734 0.681 0.987 1.212 0.483 0.792 0.818 +1 1.520 1.104 0.293 0.710 1.053 1.063 2.829 -0.804 0.000 0.841 0.929 1.306 0.000 1.207 0.411 -1.557 2.548 0.423 1.767 -0.106 0.000 0.884 0.807 0.990 0.696 0.679 0.710 0.658 +1 2.354 -1.849 -0.282 0.520 -1.174 0.422 -1.869 0.999 0.000 0.573 -1.395 -1.385 0.000 1.235 -0.871 0.692 2.548 1.387 -0.989 1.625 3.102 0.906 0.839 1.102 1.073 0.754 0.909 0.795 +0 1.186 -1.354 1.684 0.339 -0.553 1.196 -0.736 1.386 2.173 1.582 -1.168 0.124 0.000 0.974 1.423 -1.243 0.000 1.114 -1.212 -0.601 3.102 3.982 2.367 0.989 0.959 1.272 1.728 1.467 +1 0.733 -1.729 0.379 0.932 1.131 2.048 -0.649 1.731 2.173 2.796 -0.797 -0.432 0.000 0.604 -1.872 -0.352 0.000 1.644 -1.475 0.979 0.000 1.036 0.725 0.988 1.290 0.864 1.027 0.891 +1 0.880 1.540 0.212 1.336 -1.667 1.930 0.098 -1.633 0.000 0.741 0.438 -0.271 0.000 1.178 -0.399 0.355 1.274 1.632 0.483 1.370 3.102 2.434 1.520 1.491 1.477 1.011 1.178 1.241 +0 2.679 -0.294 0.333 0.823 -0.135 1.140 -0.234 -0.972 2.173 0.594 -0.345 1.522 2.215 0.672 0.194 1.059 0.000 1.295 0.191 -1.450 0.000 0.794 0.958 0.987 0.988 0.946 1.059 0.925 +1 0.916 0.561 0.980 0.569 0.091 0.462 0.222 -1.301 1.087 0.640 2.170 0.876 0.000 0.345 -0.002 1.319 0.000 1.118 1.159 -0.524 0.000 0.856 0.682 0.990 0.620 0.405 0.572 0.530 +0 0.716 -0.356 1.344 1.289 -1.659 0.801 -0.817 0.244 0.000 0.929 -0.671 -1.021 2.215 0.972 -0.341 0.660 2.548 0.913 0.052 -0.431 0.000 0.957 1.010 0.994 0.864 1.021 0.815 0.857 +0 0.661 -0.586 0.075 1.154 0.984 0.705 -1.089 -1.487 2.173 0.419 -1.041 0.653 0.000 0.734 -1.145 -1.021 2.548 0.493 2.240 -0.439 0.000 0.706 0.814 0.985 0.857 0.373 0.747 0.698 +0 2.172 -0.877 -0.751 0.162 -0.709 0.598 -0.084 0.535 2.173 0.702 0.068 1.571 2.215 0.368 -1.920 0.902 0.000 0.457 -0.363 0.374 0.000 0.472 0.678 0.998 1.039 0.770 0.839 0.706 +0 0.697 -0.264 -0.446 1.803 -1.472 0.694 -0.519 0.490 0.000 1.068 1.063 0.631 2.215 1.377 0.353 -0.888 2.548 0.403 0.116 1.729 0.000 0.775 0.983 1.238 0.811 1.345 1.033 0.921 +1 0.599 0.255 0.425 1.440 -0.646 2.299 1.635 0.800 0.000 1.358 -0.028 -1.177 2.215 0.777 -0.130 1.697 0.000 1.897 -0.929 -0.950 3.102 0.901 0.967 1.057 0.887 0.878 0.805 0.736 +0 1.329 0.083 -1.451 1.941 1.412 0.904 -1.995 -0.068 0.000 0.721 -1.358 -0.384 2.215 1.074 -1.152 0.615 2.548 0.553 1.011 -1.252 0.000 0.285 0.988 1.185 1.276 0.735 1.097 0.935 +1 1.105 0.217 -1.316 0.276 1.097 0.618 0.808 -0.190 0.000 0.651 1.743 0.627 0.000 1.615 -0.529 -0.961 1.274 1.143 -1.187 0.839 3.102 0.858 1.219 0.995 0.829 1.131 0.990 0.847 +0 0.656 1.252 1.684 1.163 -1.618 0.920 1.130 0.127 0.000 0.785 0.639 -1.468 2.215 0.355 -1.993 0.323 0.000 0.898 1.599 0.905 0.000 1.052 1.185 0.988 0.755 0.500 0.697 0.826 +1 0.861 0.603 -0.470 1.125 0.783 0.644 1.325 0.001 0.000 0.886 0.988 1.622 1.107 0.754 -0.791 1.407 2.548 0.647 1.115 -1.046 0.000 0.798 0.940 1.233 0.942 0.960 0.878 0.799 +1 1.299 -0.948 0.998 0.186 1.023 0.657 -0.546 0.272 1.087 1.047 -0.417 -0.330 2.215 1.237 -0.241 -1.571 0.000 0.743 -0.642 -1.406 0.000 0.313 0.964 0.980 0.965 0.636 0.745 0.727 +1 2.234 -0.063 0.720 0.848 0.328 0.387 -0.236 1.738 0.000 0.876 0.241 -0.687 0.000 1.233 0.549 -1.468 0.000 0.880 -0.671 -1.002 3.102 1.055 0.688 0.996 0.609 0.220 0.639 0.701 +0 1.408 -1.646 1.288 0.608 0.023 0.425 -0.921 1.310 1.087 0.654 -1.810 -0.734 0.000 1.160 -0.043 -0.572 2.548 0.422 -0.928 -0.428 0.000 0.322 0.734 1.163 0.681 0.955 0.841 0.722 +1 0.794 0.403 0.028 1.134 -1.051 1.744 -0.228 1.081 2.173 1.247 0.425 -0.623 0.000 1.566 -2.382 -0.975 0.000 1.836 -0.937 0.632 1.551 0.897 0.871 1.086 1.473 1.151 1.155 1.002 +1 0.915 -0.852 1.300 0.450 -1.252 0.677 0.215 -1.174 2.173 2.013 -0.493 -0.826 2.215 3.341 -1.751 0.515 0.000 0.705 0.861 1.456 0.000 0.555 2.349 0.977 1.221 0.829 1.875 1.444 +1 0.527 1.056 1.362 1.859 -0.782 0.870 -0.754 1.149 2.173 0.855 -0.012 0.083 0.000 0.289 -1.301 1.077 0.000 0.480 1.925 -1.419 0.000 0.837 0.919 1.283 1.204 0.810 1.210 1.004 +1 2.151 0.217 -0.811 0.482 -0.913 1.069 -0.260 0.480 2.173 1.096 0.085 1.670 2.215 0.347 -1.043 -0.569 0.000 1.079 0.093 1.116 0.000 0.836 0.828 0.971 1.456 1.426 1.151 0.955 +1 1.205 -0.288 -0.995 1.697 -1.442 1.093 0.139 0.153 2.173 1.429 0.537 0.884 2.215 0.526 0.766 -1.601 0.000 0.558 0.184 -0.666 0.000 0.484 0.842 0.985 1.647 1.188 1.366 1.030 +1 0.425 -2.314 -0.753 1.825 0.557 0.674 -0.267 -1.657 2.173 0.754 -0.511 -0.900 0.000 0.582 -1.296 0.173 0.000 1.164 -0.049 0.821 3.102 0.960 0.926 1.129 1.318 0.745 1.214 1.033 +0 0.366 -1.579 -0.317 0.605 1.616 1.058 -1.021 -0.752 2.173 0.602 1.003 -1.361 0.000 0.590 1.589 1.006 0.000 2.120 0.652 0.665 1.551 0.842 0.885 0.989 0.878 2.236 1.379 1.094 +1 0.495 -1.149 -1.630 1.694 1.001 0.653 0.769 -0.095 1.087 0.397 -1.499 -0.781 0.000 1.106 0.144 -1.239 2.548 0.602 1.033 1.163 0.000 1.355 0.947 0.986 1.219 0.968 0.936 0.853 +0 0.402 0.087 -0.223 0.608 1.650 0.380 0.012 1.321 2.173 0.429 2.868 -1.680 0.000 0.398 -1.297 -0.938 0.000 0.693 1.351 -0.087 0.000 0.868 1.098 0.992 0.551 0.517 0.889 1.051 +0 0.711 -0.785 0.042 1.553 -0.873 1.102 1.521 0.972 2.173 0.707 0.975 0.567 0.000 1.249 -0.765 -0.954 2.548 1.070 0.526 -1.739 0.000 1.012 0.909 1.068 0.616 2.610 1.652 1.329 +1 0.986 -1.067 1.663 0.366 0.481 0.575 0.031 1.132 2.173 0.547 -2.349 -0.415 0.000 0.636 0.856 -1.234 0.000 0.517 0.500 -0.719 0.000 1.501 0.887 0.983 0.899 0.185 0.779 0.727 +0 0.431 1.121 1.177 0.348 0.254 0.592 1.514 -0.160 0.000 0.689 -0.418 -1.441 2.215 0.672 -0.271 0.939 1.274 0.955 -0.632 1.643 0.000 1.984 1.250 0.994 0.701 0.609 0.882 0.755 +0 1.044 1.391 1.531 0.374 0.906 1.852 0.426 1.417 2.173 3.674 -0.917 -0.132 1.107 2.025 0.181 -1.387 0.000 1.060 -1.691 0.014 0.000 1.753 1.698 0.990 2.500 4.733 2.711 2.076 +1 1.003 0.162 -0.705 1.304 0.769 0.678 -2.607 0.930 0.000 1.210 -1.250 -0.751 2.215 1.307 0.608 1.662 2.548 1.154 -0.541 0.242 0.000 0.982 1.261 1.538 1.046 1.880 1.555 1.447 +1 2.018 0.352 -0.465 0.540 0.032 1.174 0.485 1.329 2.173 0.687 0.228 -1.174 0.000 0.700 -0.286 1.170 0.000 0.867 -0.015 0.232 3.102 0.958 0.934 0.988 0.586 0.934 0.955 0.831 +1 0.617 -0.348 1.157 0.869 -0.484 0.661 -1.390 1.644 1.087 0.885 -0.478 1.703 0.000 0.980 -0.801 -0.269 2.548 0.557 -1.711 0.595 0.000 0.896 0.829 1.010 0.870 1.026 0.693 0.636 +1 0.738 -0.389 0.702 0.966 -0.752 1.045 -0.371 1.340 2.173 0.447 1.903 -0.874 0.000 0.646 -0.655 -0.022 2.548 0.707 0.437 -0.323 0.000 0.670 0.874 1.130 0.987 0.981 0.958 0.849 +1 0.851 0.658 0.398 0.365 -0.536 1.012 0.533 1.573 0.000 0.949 1.322 0.146 2.215 1.031 1.601 0.760 0.000 1.313 0.058 -1.476 3.102 1.063 0.854 0.990 0.899 1.220 0.949 0.790 +0 0.463 0.526 -0.130 1.051 1.410 0.524 0.586 0.484 0.000 0.746 -0.062 0.711 2.215 1.823 0.247 -0.916 2.548 0.754 1.245 -0.890 0.000 1.013 1.049 0.986 0.625 1.250 0.816 0.726 +1 0.694 -1.192 -1.266 2.247 -1.438 0.817 -1.375 0.364 0.000 0.776 0.086 0.195 2.215 0.532 -0.751 -0.710 1.274 0.762 0.111 0.728 0.000 0.973 0.876 1.010 0.643 0.593 0.771 0.790 +1 0.363 -0.092 -1.626 0.178 1.557 0.657 -1.220 1.193 0.000 0.946 0.219 0.035 0.000 1.246 -1.096 0.019 2.548 1.688 -0.323 -1.068 0.000 0.921 1.008 0.795 0.384 1.087 0.888 1.027 +0 0.346 0.170 1.101 2.501 -1.555 0.936 -1.165 0.522 1.087 0.557 1.141 -0.030 0.000 0.607 0.173 -0.848 1.274 0.832 -0.654 -0.256 0.000 1.032 0.682 0.983 1.391 1.126 0.969 0.961 +1 0.671 -0.148 -0.808 0.591 -1.700 1.054 -0.658 0.475 0.000 1.076 0.468 -0.491 0.000 1.623 -0.210 -1.671 2.548 1.344 -0.999 1.162 3.102 2.207 1.608 0.996 0.762 0.847 1.199 1.006 +0 1.742 1.881 1.391 0.290 -0.918 0.674 1.506 -0.478 0.000 0.904 1.095 -1.446 1.107 1.120 0.477 -0.159 0.000 1.344 0.896 0.794 3.102 0.924 0.965 0.992 0.817 0.897 0.805 0.869 +1 0.742 -1.044 1.447 0.513 -0.787 0.448 -0.380 -1.293 2.173 0.462 -2.690 -0.649 0.000 0.739 -0.247 0.681 2.548 1.098 -1.704 0.622 0.000 0.912 1.046 0.985 0.722 0.702 0.829 0.720 +0 1.107 1.615 0.786 1.182 0.934 0.884 0.783 0.232 2.173 2.251 -0.058 -1.170 0.000 0.895 1.169 -0.046 0.000 1.082 -0.652 -1.147 1.551 1.043 1.029 0.992 0.832 1.332 0.977 0.826 +1 0.713 -1.450 0.817 0.364 -1.184 0.464 -0.875 1.604 1.087 0.794 2.701 -1.259 0.000 0.664 2.301 0.997 0.000 2.949 -0.517 0.041 3.102 1.007 2.403 0.994 0.867 1.233 2.225 1.830 +1 0.372 -1.721 1.294 0.749 -1.649 0.674 0.133 -1.081 0.000 0.802 0.827 -0.248 0.000 1.470 -1.117 0.521 2.548 1.103 -0.902 -1.106 0.000 0.852 0.936 0.988 0.937 0.475 0.865 0.758 +1 0.832 -1.514 0.993 1.754 0.182 0.629 -1.983 -1.035 0.000 1.338 0.815 -1.665 2.215 0.491 0.437 -1.243 0.000 1.091 -0.591 0.612 0.000 0.951 1.083 1.115 1.018 1.103 1.494 1.152 +1 0.371 -0.122 0.822 1.548 -0.158 0.857 0.628 -1.162 2.173 0.809 -0.156 1.531 0.000 0.508 0.219 -0.683 0.000 1.211 0.537 0.561 3.102 0.918 0.846 0.987 0.924 1.078 1.009 0.862 +1 0.533 0.150 -0.653 0.370 0.791 1.087 -0.747 -1.189 0.000 1.091 -0.434 0.593 2.215 0.727 -0.803 -0.589 0.000 0.700 0.024 1.686 3.102 0.912 0.938 0.991 0.630 0.684 0.713 0.647 +0 1.188 -0.158 -1.590 0.839 -0.584 0.372 -1.353 1.731 0.000 0.750 -0.420 0.950 0.000 1.073 -0.302 -0.355 2.548 1.043 -0.023 0.176 3.102 0.907 0.974 1.090 0.767 0.392 0.652 0.673 +1 1.012 1.947 0.915 0.906 -1.731 0.777 -0.325 -0.094 0.000 0.615 0.406 0.926 0.000 0.872 0.120 -1.384 2.548 0.900 0.632 -1.078 0.000 0.957 0.771 0.985 1.179 0.438 1.108 0.914 +1 0.668 0.057 1.735 2.306 -0.608 1.030 0.344 1.227 0.000 1.222 0.865 0.676 2.215 0.593 -0.201 -0.609 1.274 0.375 0.387 -1.161 0.000 0.795 0.868 1.475 0.696 0.983 0.938 0.893 +1 2.024 -0.778 -1.446 1.060 -0.240 0.606 -1.286 0.151 2.173 0.377 -1.198 1.644 0.000 0.553 -1.836 0.454 0.000 1.384 -0.103 0.675 3.102 0.814 0.738 1.797 1.202 0.746 0.933 0.767 +1 1.278 1.864 -0.153 1.162 -0.151 1.045 0.578 1.419 2.173 0.506 0.075 0.405 0.000 1.129 0.683 -1.420 2.548 0.490 1.424 -0.967 0.000 0.851 0.930 0.990 1.057 0.752 1.070 0.866 +1 0.693 0.234 1.419 0.768 -1.209 0.635 0.287 0.770 2.173 0.920 -2.213 -0.078 0.000 0.432 -1.082 -0.537 0.000 0.814 1.064 -1.317 3.102 0.939 1.717 0.993 0.769 0.823 1.400 1.128 +0 2.060 -0.587 0.380 2.553 1.076 0.877 0.841 -0.321 0.000 1.431 2.537 -1.189 0.000 1.042 0.885 -1.413 1.274 1.006 -1.137 -0.421 3.102 2.912 1.801 1.864 1.267 1.291 1.904 2.378 +1 1.018 1.554 0.979 1.489 -0.657 0.479 0.501 -1.092 1.087 0.625 0.562 0.079 0.000 0.996 -0.748 -0.760 2.548 0.989 0.463 1.327 0.000 0.920 0.851 1.698 1.678 0.665 1.103 0.944 +1 0.779 0.003 -1.265 0.445 0.015 1.259 0.914 -1.537 2.173 1.050 0.093 0.676 0.000 1.477 0.520 0.114 0.000 0.517 -0.117 -0.617 3.102 1.033 0.745 0.986 1.238 0.789 1.037 0.942 +0 0.647 2.022 -1.498 0.823 -0.678 0.791 1.230 0.310 2.173 0.347 0.428 -1.707 0.000 0.484 -1.214 1.385 0.000 0.741 -0.454 0.075 3.102 0.671 1.209 0.991 0.865 0.842 0.765 0.803 +1 0.405 -1.193 -0.681 0.795 1.145 1.365 0.655 0.358 0.000 2.257 -0.772 -1.523 2.215 0.917 0.697 -0.290 0.000 0.542 0.172 -0.281 3.102 1.115 0.672 0.993 1.008 1.038 1.523 1.173 +0 0.595 1.815 1.601 0.456 -1.364 0.636 0.185 0.609 0.000 0.601 -1.790 1.151 0.000 0.675 1.599 0.119 0.000 2.257 0.466 -0.924 3.102 0.892 1.013 0.997 0.544 0.615 0.614 0.598 +1 0.909 -0.257 -0.572 1.501 -1.423 1.137 -1.803 -0.227 0.000 1.445 -0.367 1.241 2.215 0.598 -0.761 -0.088 0.000 1.538 -0.577 1.480 3.102 0.915 1.018 1.121 1.141 0.358 0.792 0.835 +1 0.554 -0.603 0.571 0.430 -1.657 1.108 0.888 -0.505 0.000 1.208 -0.539 1.449 0.000 1.466 0.390 -1.434 2.548 1.055 0.411 -0.084 0.000 0.698 1.100 0.991 0.748 0.880 0.886 0.771 +1 2.221 1.381 0.384 0.319 -1.660 1.808 0.175 -0.991 0.000 1.001 0.216 1.320 2.215 0.692 0.645 0.549 0.000 0.753 -0.198 -0.203 3.102 0.761 0.619 1.123 0.874 0.789 0.839 0.711 +1 0.504 0.625 -0.848 1.359 1.598 2.850 -0.941 1.549 0.000 2.634 1.093 -0.168 1.107 1.602 -2.049 0.065 0.000 1.444 1.120 -0.787 0.000 1.347 1.289 0.987 1.641 0.993 1.108 1.106 +1 1.042 0.122 -1.545 0.824 -1.079 1.171 -0.493 0.188 2.173 0.768 -0.800 0.989 2.215 0.276 -0.549 0.577 0.000 0.805 1.355 -1.544 0.000 0.861 0.971 0.988 1.506 0.950 1.125 0.949 +0 0.664 -0.025 1.616 1.605 -1.083 0.805 -2.581 -0.510 0.000 0.710 -2.405 -1.567 0.000 1.025 -0.747 0.996 0.000 1.687 0.574 0.052 1.551 1.039 1.290 0.982 1.167 0.851 1.121 1.040 +1 0.828 1.095 1.563 0.413 -1.513 0.383 -1.034 0.185 1.087 0.597 0.837 0.697 0.000 0.690 0.521 -1.145 1.274 0.394 2.378 0.398 0.000 0.758 0.776 0.981 0.890 0.829 0.808 0.743 +1 1.125 -0.595 1.383 1.259 1.311 1.069 -0.625 0.548 2.173 1.073 -0.706 -0.068 2.215 2.577 -0.690 -1.170 0.000 0.538 1.090 -1.119 0.000 0.637 0.947 0.996 1.209 0.835 0.968 0.908 +0 0.799 1.486 -0.681 0.653 -0.326 1.030 0.871 0.393 0.000 1.748 0.342 -1.550 2.215 0.693 0.659 1.067 0.000 0.605 -0.702 -0.217 1.551 0.921 1.036 0.991 1.100 1.046 1.148 0.975 +0 1.397 1.504 -1.592 0.707 1.161 0.933 1.702 0.055 0.000 0.639 0.342 -0.887 2.215 0.671 2.130 0.661 0.000 0.859 1.045 -1.197 3.102 0.864 0.886 0.994 0.832 0.362 0.778 0.793 +0 1.208 1.782 0.655 0.994 1.267 0.441 -1.476 0.113 0.000 1.051 -0.151 -1.297 2.215 0.432 1.073 -0.657 2.548 0.692 1.547 -0.797 0.000 2.256 1.320 0.996 1.881 0.645 1.178 1.408 +0 4.114 -1.136 0.450 2.004 -1.726 1.308 -0.879 0.148 2.173 1.928 -0.140 -1.105 0.000 3.007 -0.566 -1.483 2.548 0.593 0.534 0.163 0.000 1.386 1.326 3.680 2.102 2.480 1.967 1.801 +1 1.399 0.606 0.867 1.374 1.353 0.733 1.666 -0.996 0.000 0.746 1.539 -0.368 0.000 0.455 0.557 -1.640 0.000 0.765 0.642 -0.210 1.551 0.931 0.587 0.988 0.594 0.152 0.528 0.632 +1 1.264 1.592 1.627 0.641 -0.991 0.620 -0.262 -0.512 2.173 0.599 1.987 0.752 0.000 1.310 -0.283 -0.005 0.000 0.628 1.085 -1.595 3.102 0.684 0.534 0.991 1.422 0.791 0.891 0.833 +0 1.966 0.808 -0.016 0.658 1.676 0.869 0.184 -0.743 2.173 0.894 1.435 0.678 0.000 1.127 0.708 -1.472 2.548 1.110 -1.667 1.421 0.000 0.401 1.375 1.574 1.115 0.839 1.085 1.171 +1 0.668 -0.818 0.959 0.964 1.486 0.434 -0.861 0.515 1.087 1.381 -0.350 -0.362 2.215 0.754 0.192 1.710 0.000 0.601 1.186 -1.508 0.000 0.510 0.874 0.991 1.403 0.861 0.941 0.984 +0 0.950 -0.329 -1.336 0.631 0.380 0.792 -0.509 0.365 1.087 0.270 -0.188 -0.822 0.000 0.704 -0.269 -1.692 0.000 0.389 1.039 -1.396 3.102 0.473 0.798 1.072 0.614 0.830 0.621 0.542 +0 0.457 -0.153 -1.248 2.186 -0.492 1.606 -0.810 1.150 1.087 1.493 0.732 -0.908 0.000 1.704 -0.727 0.748 1.274 0.514 -0.278 -0.373 0.000 0.841 1.682 0.988 2.009 0.732 1.501 1.380 +1 1.601 0.400 -0.393 0.310 -0.852 0.615 0.275 0.933 1.087 0.558 1.776 -0.774 0.000 0.771 1.275 1.400 2.548 0.891 1.698 1.049 0.000 0.921 1.071 0.988 1.000 0.616 0.803 0.844 +1 0.921 -1.862 0.970 1.384 1.476 0.471 -0.625 1.482 0.000 1.057 0.213 -0.073 2.215 1.496 0.037 -0.841 0.000 0.950 -1.101 0.494 3.102 1.405 1.110 0.994 2.272 0.886 1.417 1.380 +0 0.609 0.134 0.525 0.958 -1.037 0.947 -0.031 -0.127 2.173 0.795 2.651 0.915 0.000 1.442 -0.879 1.616 2.548 0.516 -1.276 -0.280 0.000 3.540 2.550 1.044 0.936 1.618 2.028 1.519 +1 0.467 0.148 1.668 1.245 -0.452 0.916 -1.138 -1.444 1.087 0.834 -1.150 0.448 2.215 0.660 1.114 0.506 0.000 0.493 -1.294 0.267 0.000 1.191 1.029 0.997 1.079 1.275 1.014 0.906 +0 1.084 -0.446 -0.619 0.392 0.302 1.103 -2.219 1.275 0.000 1.380 -0.586 -1.614 2.215 2.583 -1.392 -0.098 0.000 1.768 -2.130 -0.471 0.000 1.486 1.634 0.994 0.993 0.327 1.425 1.124 +1 0.516 -0.346 -0.670 0.711 -1.096 0.662 1.828 0.474 0.000 1.329 1.070 -1.259 2.215 0.849 0.822 1.317 2.548 0.525 -0.438 -0.055 0.000 0.727 0.722 0.978 0.691 0.833 0.840 0.775 +1 0.529 0.466 -0.520 1.165 0.379 0.905 -2.117 -0.369 0.000 0.757 0.476 0.949 0.000 1.933 0.297 1.579 1.274 0.790 1.301 1.562 0.000 1.033 0.941 0.981 0.762 0.710 0.793 0.734 +1 0.820 -1.966 -1.108 0.529 1.227 0.727 0.361 0.863 2.173 0.628 0.478 -0.408 2.215 0.506 -1.657 0.296 0.000 0.450 -0.396 -1.589 0.000 0.647 0.903 0.983 1.047 0.908 0.964 0.783 +0 0.914 0.142 1.209 0.550 1.024 0.897 2.419 0.917 0.000 0.957 1.602 -0.538 2.215 1.718 0.970 -0.906 0.000 1.013 0.157 -0.401 3.102 2.777 1.741 0.990 0.803 0.717 1.181 1.063 +0 1.488 -1.054 1.496 2.561 1.415 1.283 0.340 -0.236 0.000 0.718 -1.456 -0.103 1.107 1.731 -0.431 -1.520 2.548 1.088 -0.685 -0.145 0.000 0.860 0.986 1.011 1.247 1.299 1.105 1.086 +1 0.699 -0.625 -1.286 1.429 -0.411 0.499 -0.014 1.183 0.000 0.806 0.849 1.647 2.215 0.532 -0.354 0.658 2.548 0.539 0.284 -0.063 0.000 0.729 0.690 0.987 0.684 0.716 0.767 0.664 +0 0.841 -1.892 0.086 1.365 0.973 0.711 -0.163 -1.426 2.173 0.830 -0.213 0.104 0.000 1.188 -0.263 -0.644 0.000 0.975 -0.354 1.614 3.102 0.951 1.050 1.064 0.922 0.363 0.965 0.991 +0 0.595 -1.338 -0.997 0.576 -0.474 0.722 -0.540 0.353 0.000 1.074 -1.348 -1.236 2.215 0.893 -0.052 -0.721 0.000 1.102 2.214 0.971 0.000 1.042 1.249 0.979 0.826 0.120 1.590 1.273 +1 0.496 -1.100 1.616 0.091 1.526 0.678 -0.243 -1.641 2.173 0.992 0.663 0.467 0.000 0.743 -0.377 -0.473 2.548 0.548 2.144 -1.410 0.000 0.977 0.960 0.977 0.630 0.772 0.865 0.746 +0 0.513 -0.406 0.299 1.193 -0.665 0.966 1.443 0.629 0.000 1.057 1.082 1.029 2.215 1.962 -0.561 -1.183 2.548 0.562 0.943 -1.051 0.000 1.138 0.834 0.990 0.980 2.046 1.390 1.143 +1 2.090 -0.309 -1.316 1.446 -0.931 0.558 2.211 0.413 0.000 0.923 -1.032 -0.168 2.215 1.064 1.948 1.488 0.000 1.217 -0.207 0.102 0.000 0.735 0.940 0.999 1.205 1.625 1.142 1.039 +0 0.698 2.076 -0.820 0.984 -0.834 0.825 0.417 -1.448 2.173 1.525 0.786 1.103 2.215 1.077 1.786 0.507 0.000 1.264 -0.240 -0.418 0.000 1.029 0.940 1.005 0.876 1.273 0.986 0.918 +1 0.647 -0.037 0.256 1.037 -1.223 0.847 -0.524 0.693 0.000 0.666 0.284 -1.031 2.215 0.502 -1.533 1.225 0.000 0.941 0.132 -0.080 0.000 0.897 1.143 1.103 0.655 0.531 0.726 0.691 +0 0.959 0.989 -0.703 1.886 -1.037 1.833 0.870 -0.484 2.173 1.565 -1.105 1.454 0.000 0.602 -1.598 0.861 0.000 2.223 -0.225 0.953 1.551 0.889 0.998 0.997 1.032 2.430 2.039 1.715 +1 2.171 -0.507 0.843 0.521 -0.981 1.178 -0.703 -0.462 2.173 0.695 -0.649 -1.216 1.107 0.584 0.948 1.070 0.000 0.656 -1.362 -1.407 0.000 1.327 0.927 1.469 1.357 0.837 0.969 0.903 +0 1.174 -0.070 -1.173 1.393 -1.021 0.891 -0.608 0.808 2.173 0.752 0.214 1.371 0.000 1.095 0.919 0.140 2.548 0.370 0.780 0.493 0.000 0.550 0.727 0.982 1.310 1.312 1.186 0.957 +1 1.038 0.306 1.079 0.815 -1.532 0.433 -0.648 1.425 2.173 0.996 -1.295 -0.319 2.215 0.652 0.426 -1.156 0.000 0.412 -0.615 -0.164 0.000 0.577 0.808 0.989 0.570 1.023 0.824 0.686 +0 0.428 -0.635 -0.364 2.359 0.468 0.509 -0.051 0.529 0.000 1.242 0.176 1.464 2.215 1.931 1.810 -1.059 0.000 1.200 -0.454 -1.020 3.102 2.782 1.996 0.990 1.391 0.957 1.418 1.659 +1 0.653 -0.948 -0.506 1.566 -1.326 2.561 1.832 -0.196 0.000 2.335 -0.486 1.281 1.107 1.135 -1.079 1.099 0.000 2.133 -0.511 -1.688 3.102 1.060 3.405 0.989 1.344 0.912 3.093 2.527 +1 0.595 0.316 1.569 1.497 0.466 0.542 -1.361 -0.291 2.173 0.800 0.816 0.997 0.000 0.868 -0.255 -1.253 2.548 0.903 1.006 -0.939 0.000 1.109 0.954 1.096 1.133 0.816 0.975 0.885 +0 0.951 -0.856 0.223 1.308 1.218 2.035 -0.539 -0.913 2.173 2.561 -0.533 0.793 2.215 0.730 -0.796 -0.526 0.000 0.696 0.364 -1.013 0.000 0.890 0.967 1.207 0.911 3.357 1.653 1.268 +0 6.695 -1.608 -0.184 0.835 -1.599 3.701 -1.419 1.443 2.173 1.586 -1.140 1.131 2.215 3.735 0.336 -1.072 0.000 1.133 -1.375 0.203 0.000 3.048 3.870 3.133 4.193 1.096 4.272 4.316 +1 0.824 -0.100 0.108 1.057 0.922 1.720 0.565 0.786 0.000 4.604 -0.181 -0.861 2.215 1.645 0.812 1.254 0.000 2.053 -0.485 0.424 3.102 0.988 1.551 0.982 1.982 2.603 1.947 1.556 +1 0.883 -0.700 -1.093 0.509 1.376 0.785 -1.312 -1.480 0.000 1.399 -1.234 0.394 2.215 0.826 -1.797 0.987 0.000 2.217 -0.949 -0.384 3.102 0.719 1.090 0.988 1.165 1.031 0.953 0.892 +1 0.994 0.404 1.591 0.916 0.679 0.825 1.875 -1.461 0.000 0.560 -0.332 0.850 2.215 1.631 -1.171 -0.153 2.548 1.579 -0.208 -0.388 0.000 2.668 1.813 0.986 1.460 0.941 1.740 1.382 +1 0.787 -0.358 -0.844 0.666 -1.729 1.051 -0.234 -1.547 2.173 1.301 -0.010 0.269 0.000 0.624 -1.774 -0.570 0.000 1.051 -0.292 -0.358 0.000 0.856 1.093 0.987 0.731 1.117 1.038 0.901 +1 0.572 -0.671 0.740 0.721 1.534 0.783 0.553 0.035 2.173 0.648 -0.117 -1.529 1.107 0.439 -1.403 -0.625 0.000 0.484 0.319 -1.401 0.000 0.656 0.894 0.988 0.862 1.096 1.053 0.828 +1 1.323 -0.271 0.767 0.836 -0.650 0.733 0.414 0.672 0.000 0.863 -0.900 -0.784 0.000 0.840 0.087 -1.253 2.548 0.748 -0.709 1.324 1.551 0.700 0.785 1.394 0.758 0.534 0.615 0.599 +1 1.004 0.891 1.219 1.196 -0.309 0.412 1.035 -0.145 0.000 1.127 0.184 1.252 2.215 1.440 0.336 -0.675 0.000 0.686 -0.465 0.313 3.102 0.796 0.735 1.489 1.086 0.667 0.793 0.789 +1 2.658 -1.671 -0.341 0.201 -0.145 1.061 -1.303 1.148 1.087 0.470 -1.076 0.451 0.000 0.859 -1.230 -1.446 2.548 0.618 -1.160 1.513 0.000 0.582 0.581 0.996 1.453 0.855 1.014 0.792 +1 0.525 0.670 -0.169 0.988 0.545 1.234 0.411 -0.375 0.000 0.681 -0.416 0.038 0.000 2.111 -0.879 1.556 2.548 1.295 0.084 -1.164 3.102 0.895 1.032 0.984 0.814 1.070 0.876 0.752 +0 0.850 -0.117 -1.529 1.068 -0.572 0.338 1.628 -1.643 0.000 0.719 1.360 -0.590 2.215 0.906 0.273 1.003 2.548 0.925 -1.042 0.450 0.000 1.107 0.772 1.003 0.882 0.984 0.787 0.758 +1 0.662 2.000 -0.216 1.771 -0.932 1.471 1.444 0.966 1.087 0.669 0.786 -0.664 2.215 0.598 1.548 -0.021 0.000 0.984 1.234 1.653 0.000 0.849 0.928 0.992 0.960 1.530 1.267 0.982 +0 0.453 -1.992 -1.022 1.046 0.951 0.992 -1.053 0.083 2.173 1.115 0.164 -1.565 2.215 0.514 -0.214 -0.554 0.000 0.700 -1.374 1.463 0.000 0.818 0.874 0.986 1.167 1.845 1.400 1.067 +1 1.530 -0.776 1.711 0.090 0.952 1.037 -0.468 -0.173 2.173 0.742 0.865 0.725 2.215 0.929 1.422 -1.279 0.000 0.427 -1.051 1.042 0.000 1.469 1.072 0.980 1.163 1.332 1.050 0.983 +1 1.064 -2.158 -0.937 0.100 1.302 0.553 -2.114 0.481 0.000 1.692 -0.962 -1.236 1.107 1.688 -1.003 0.702 0.000 0.412 -0.374 0.257 3.102 0.998 0.639 0.979 0.830 0.763 1.018 0.878 +1 1.623 0.496 -0.838 0.500 -0.332 0.987 -0.189 0.780 0.000 1.350 0.097 -1.491 2.215 0.791 -0.852 0.303 0.000 0.577 -0.653 -0.383 3.102 0.898 0.710 0.991 1.022 0.763 0.882 0.973 +0 1.831 1.342 1.419 1.523 0.906 1.197 -0.273 -0.334 0.000 0.621 0.689 -0.005 0.000 0.306 0.251 -0.605 0.000 1.026 0.866 -1.159 3.102 0.858 0.758 1.031 1.384 0.645 0.956 0.864 +1 1.025 -0.581 -1.061 0.166 0.973 0.883 -0.590 0.235 2.173 1.278 -0.376 1.411 0.000 1.169 0.009 -0.462 1.274 0.537 -0.896 1.051 0.000 0.512 1.064 0.983 0.805 0.840 0.858 0.797 +0 0.460 -1.792 -0.436 0.542 0.701 1.044 -0.278 -0.669 2.173 0.531 -1.229 -1.169 0.000 0.929 0.849 1.513 0.000 1.518 0.073 0.343 1.551 0.929 1.395 0.979 0.868 1.082 1.128 0.912 +0 0.621 -0.508 -1.679 2.017 0.693 2.477 -0.998 1.368 0.000 2.410 0.297 -0.309 0.000 2.525 1.967 -0.854 0.000 1.123 0.833 -0.365 3.102 1.177 1.903 1.307 1.147 0.444 1.511 1.235 +0 0.712 1.145 -1.266 2.816 1.635 0.984 0.443 -0.169 2.173 0.961 1.071 0.072 2.215 0.467 -0.459 -0.455 0.000 0.955 1.754 1.023 0.000 1.447 1.032 0.987 1.618 0.568 1.205 1.044 +0 0.884 1.111 -0.624 0.325 0.382 0.467 -0.472 1.127 2.173 0.706 -0.099 -0.887 2.215 0.893 -0.957 0.642 0.000 0.639 0.588 1.366 0.000 0.973 0.911 0.986 1.203 0.835 0.925 0.913 +0 1.354 -0.864 0.207 0.362 0.970 0.453 -0.688 -0.610 2.173 1.137 -2.424 -1.248 0.000 0.699 -0.887 1.193 0.000 1.103 0.245 1.496 3.102 1.590 1.264 0.990 0.964 0.807 1.076 0.963 +0 0.448 0.593 -1.605 1.847 -1.423 0.982 0.619 -0.073 2.173 0.428 0.176 0.785 0.000 0.939 0.513 -0.769 2.548 0.494 -1.713 1.194 0.000 0.847 0.961 0.985 1.492 0.704 1.007 0.919 +1 0.506 -0.144 0.562 1.011 -1.057 0.740 -1.048 0.785 0.000 0.569 -0.551 1.457 0.000 1.198 -0.518 -0.044 2.548 1.169 0.767 -1.411 3.102 0.855 0.946 0.987 0.708 1.127 0.899 0.774 +1 0.910 -0.679 0.815 1.748 -0.054 0.680 -0.066 -0.845 0.000 1.251 0.107 1.668 0.000 0.818 -1.345 -1.439 2.548 0.554 0.691 0.183 0.000 0.946 0.939 1.231 0.820 0.224 0.700 0.762 +1 2.991 -0.038 1.261 0.502 0.192 2.866 1.837 -0.359 0.000 1.506 -0.869 -1.554 2.215 0.964 -0.683 1.306 1.274 0.517 -1.369 -1.277 0.000 5.363 3.906 1.392 1.355 0.690 3.164 2.575 +1 0.908 -1.732 -1.690 0.103 0.007 1.106 -0.572 -1.402 2.173 1.804 -1.367 -1.315 2.215 0.874 -0.495 -0.456 0.000 3.539 0.667 0.412 0.000 0.834 1.554 0.988 0.787 0.902 1.206 0.952 +1 1.563 -1.103 0.259 1.207 0.722 1.666 0.205 -1.059 0.000 0.630 0.108 1.219 0.000 1.388 -0.443 -1.381 1.274 1.429 -0.262 0.363 0.000 0.901 1.034 0.985 0.577 0.686 0.786 0.713 +1 0.901 0.728 0.150 0.262 -1.654 0.953 0.263 1.110 0.000 1.582 0.709 -0.907 2.215 0.736 0.930 1.434 1.274 0.846 -0.110 -0.037 0.000 1.211 0.843 0.981 0.954 0.998 0.955 0.845 +1 1.035 -0.281 -1.450 0.468 1.676 0.851 -0.759 -0.963 2.173 0.779 -1.508 -0.406 0.000 0.913 -0.275 1.168 2.548 1.878 1.075 0.320 0.000 1.086 0.999 0.979 0.734 1.063 0.832 0.750 +0 1.211 1.032 -0.717 0.630 1.017 1.913 1.043 -0.157 0.000 1.888 2.323 -1.738 0.000 1.369 -0.538 1.578 1.274 2.015 0.464 0.564 3.102 0.625 1.782 1.210 1.206 1.265 1.753 1.377 +1 0.778 0.192 1.361 0.628 0.559 0.852 -0.649 -0.050 0.000 0.705 1.031 1.642 2.215 1.160 0.101 -0.991 2.548 0.533 -0.464 1.009 0.000 0.841 0.951 0.996 0.908 0.819 0.870 0.792 +1 0.999 1.000 1.506 1.084 -1.032 0.700 0.185 0.554 0.000 0.630 0.848 1.209 1.107 1.588 1.736 -1.388 0.000 2.356 1.041 -0.244 3.102 1.035 1.064 1.089 0.707 1.083 0.786 0.804 +0 0.511 0.703 -0.614 1.148 -0.535 1.136 -0.598 1.424 2.173 0.686 -1.403 1.201 0.000 1.283 -1.543 -1.691 0.000 2.524 0.359 -0.282 0.000 0.755 1.015 0.997 1.249 1.124 0.945 0.938 +1 0.980 -0.305 -1.177 0.647 0.212 0.980 -0.843 -0.499 0.000 1.687 -1.095 0.978 2.215 0.742 -0.591 -1.494 2.548 0.554 0.056 0.219 0.000 0.864 0.812 1.047 1.135 0.984 0.949 0.828 +1 1.148 1.428 0.726 0.795 1.648 0.851 0.334 -1.157 2.173 0.547 0.554 -0.025 0.000 0.722 -0.322 -0.239 2.548 0.917 0.712 1.111 0.000 0.799 0.946 0.988 0.988 0.798 0.883 0.748 +1 0.923 -1.337 -1.720 0.720 -0.078 1.008 0.634 0.693 1.087 0.863 0.411 -0.822 0.000 1.064 -0.003 -1.420 0.000 1.178 2.076 0.353 0.000 0.817 1.375 1.125 0.999 0.980 1.082 0.992 +0 0.683 0.934 -0.071 2.487 0.408 0.807 0.622 -1.480 0.000 1.067 0.155 1.512 1.107 1.751 0.311 -0.749 2.548 0.701 -1.002 0.937 0.000 1.530 1.009 0.988 1.488 1.304 1.308 1.259 +0 1.283 -0.676 -1.231 0.516 -0.752 0.846 0.582 0.433 2.173 0.933 2.126 -1.547 0.000 1.013 -0.693 0.017 2.548 0.520 -1.510 1.008 0.000 3.365 2.190 0.981 0.825 0.940 1.527 1.548 +1 0.617 0.118 1.076 0.933 -1.679 0.976 -0.352 -0.392 2.173 1.066 -0.317 0.225 0.000 1.673 0.419 1.544 0.000 0.704 0.847 1.542 0.000 1.173 0.964 0.986 0.711 0.683 0.757 0.788 +0 0.438 -0.780 -0.328 0.136 1.133 0.935 -0.005 -0.238 1.087 1.769 0.838 1.466 1.107 0.974 0.638 -0.831 0.000 1.110 0.764 0.444 0.000 1.054 0.928 0.977 0.978 2.072 1.101 0.884 +1 0.636 -1.047 -0.807 0.804 0.298 0.901 -0.050 1.145 1.087 1.102 -0.549 -1.613 2.215 1.008 -0.439 -0.172 0.000 0.452 0.129 -0.445 0.000 0.301 0.939 0.986 1.029 0.972 0.980 0.840 +0 1.406 -1.022 1.699 0.183 0.979 0.779 -0.181 0.632 0.000 0.615 -1.101 -0.518 2.215 0.753 -2.681 -1.142 0.000 0.724 0.358 -0.078 3.102 2.915 1.698 0.976 0.767 0.570 1.117 0.981 +1 1.830 0.146 -0.872 0.716 -0.258 0.852 -0.428 0.871 2.173 0.556 0.857 1.557 2.215 0.587 -0.013 -0.004 0.000 0.512 1.550 -0.471 0.000 0.699 1.012 0.986 0.918 0.924 0.928 0.796 +1 1.591 0.464 0.523 1.143 0.779 2.247 0.279 0.974 2.173 4.049 0.231 -1.017 0.000 1.372 0.940 0.122 0.000 1.583 2.290 -0.885 0.000 0.992 1.274 0.991 1.090 0.896 1.949 1.570 +1 1.225 -0.285 0.866 0.852 0.577 1.015 0.207 -1.630 2.173 1.175 -0.251 -0.357 0.000 1.070 -0.493 -1.017 2.548 0.931 0.577 0.519 0.000 1.178 0.984 0.978 1.305 0.845 0.975 0.971 +0 0.842 -0.397 -0.624 0.829 -0.928 0.505 0.427 0.250 0.000 0.728 0.135 1.599 2.215 1.260 -0.809 1.189 1.274 0.770 -0.791 0.626 0.000 0.779 0.856 0.996 1.010 0.658 0.819 0.801 +1 0.898 0.536 -1.164 0.828 -0.418 0.798 0.440 0.307 2.173 0.875 0.059 1.506 0.000 0.449 -0.851 1.557 2.548 0.408 -0.289 0.768 0.000 0.506 0.855 0.993 0.884 0.873 0.790 0.724 +1 1.520 0.393 -0.208 1.042 -0.858 0.906 1.480 -0.707 0.000 1.341 0.592 1.221 2.215 1.192 -0.458 1.274 1.274 1.158 1.988 0.810 0.000 1.694 1.717 0.987 1.184 0.789 1.401 1.249 +1 0.593 0.325 0.843 0.534 1.163 0.794 -0.817 1.728 0.000 1.289 -0.935 -0.523 2.215 0.932 0.607 -0.497 2.548 0.372 -0.540 0.744 0.000 0.648 1.039 0.992 1.018 1.058 0.877 0.808 +1 0.891 -1.507 1.261 0.780 -0.591 0.692 -0.493 -0.114 2.173 1.154 -0.311 0.692 2.215 1.054 -0.440 -1.535 0.000 1.255 -1.370 -1.152 0.000 0.890 1.093 1.149 1.046 0.881 0.929 0.851 +1 0.335 2.040 -0.850 2.260 -0.386 0.506 0.050 -1.673 2.173 1.142 0.496 0.673 2.215 0.779 1.843 1.205 0.000 0.588 1.106 1.737 0.000 0.435 0.851 1.000 1.057 0.991 0.951 0.861 +0 1.889 -0.138 1.150 1.012 1.711 0.836 -0.163 -0.226 1.087 0.719 -0.914 -0.871 0.000 0.571 -1.758 0.111 0.000 0.526 0.487 -0.789 3.102 0.914 0.948 0.990 0.744 0.434 0.838 0.851 +0 0.642 0.476 1.149 0.954 -0.913 1.620 0.137 -1.562 2.173 1.707 -0.328 0.262 0.000 0.582 -0.829 0.117 0.000 0.864 -0.783 -1.143 3.102 0.787 0.976 1.040 0.910 0.847 1.282 1.061 +0 4.682 1.002 -1.371 1.893 -1.244 1.743 0.674 0.110 0.000 2.114 1.093 0.599 0.000 0.760 0.134 -1.208 2.548 1.396 0.367 1.136 3.102 1.963 1.484 0.995 0.826 0.684 1.136 1.698 +1 1.579 -0.703 -1.104 0.579 -1.000 1.557 0.677 1.071 2.173 0.983 1.087 0.002 1.107 0.531 0.215 -0.254 0.000 0.820 -0.410 -0.843 0.000 0.457 0.736 0.989 1.712 1.548 1.351 1.045 +1 3.715 0.702 1.626 0.515 0.860 1.897 2.346 -0.378 0.000 0.675 0.865 0.915 0.000 0.849 -0.254 0.684 2.548 0.798 0.839 -0.647 3.102 0.989 0.872 1.219 1.077 0.729 1.295 1.531 +0 1.767 0.515 -0.613 1.365 -0.317 3.272 -0.745 0.899 0.000 1.620 0.341 -1.032 1.107 1.635 -2.659 -0.919 0.000 0.664 0.044 1.676 3.102 6.848 3.664 0.982 0.910 0.620 2.934 2.543 +1 0.629 -1.419 1.419 0.595 -1.345 1.223 -2.453 0.211 0.000 0.682 -1.972 -1.519 0.000 1.253 -0.517 -1.510 2.548 1.270 -0.324 0.461 3.102 0.803 1.294 0.989 0.585 0.948 1.186 1.066 +1 0.872 1.353 0.808 0.844 0.543 0.334 -1.017 0.308 0.000 1.209 0.229 -0.934 2.215 0.952 -0.150 1.731 2.548 0.454 -1.473 -0.532 0.000 0.467 0.968 0.988 1.422 0.802 1.281 1.308 +0 0.825 0.030 -1.071 0.533 -0.432 0.662 0.209 1.556 0.000 1.127 0.646 0.696 2.215 0.550 0.019 -0.291 0.000 0.914 0.941 -0.698 3.102 1.084 0.981 0.994 0.517 0.898 0.712 0.687 +1 0.510 1.140 -1.013 0.928 -0.318 0.588 -0.153 -1.261 0.000 0.815 -0.626 0.611 2.215 0.581 -0.467 -0.128 0.000 1.024 -1.231 1.473 1.551 0.916 0.917 0.978 2.151 0.677 1.625 1.317 +1 0.628 -0.155 -0.774 1.385 0.836 0.803 -1.028 -0.234 2.173 1.048 0.441 1.389 0.000 1.431 -0.196 -0.002 0.000 1.266 -0.988 -1.650 3.102 0.916 1.109 1.282 1.038 1.024 1.043 0.905 +1 0.709 0.275 1.536 0.093 -0.809 0.706 0.724 -1.149 1.087 0.765 1.682 0.177 0.000 0.848 1.039 0.991 0.000 0.940 -0.107 -0.504 3.102 0.906 0.975 0.935 0.862 0.609 0.796 0.832 +1 0.495 0.914 1.361 1.013 0.052 1.353 -0.039 0.188 2.173 1.157 -0.651 1.515 0.000 1.108 -0.748 -1.236 0.000 1.671 2.292 -1.266 0.000 0.822 1.436 0.990 0.863 0.968 1.126 0.906 +1 3.884 -0.229 1.101 0.210 0.569 1.021 1.231 -0.752 0.000 0.546 -0.552 -0.278 2.215 2.028 0.574 -0.620 0.000 0.760 -0.235 -0.925 0.000 0.862 1.109 0.992 0.484 0.625 0.952 1.319 +0 0.943 1.422 -1.220 0.743 1.134 0.611 0.457 0.878 1.087 0.900 0.757 -1.008 2.215 0.671 2.603 0.379 0.000 0.743 0.842 0.439 0.000 0.828 0.998 0.988 0.744 1.096 0.873 0.768 +0 1.642 -1.163 -0.703 0.295 -1.548 1.217 0.259 1.111 2.173 0.792 -0.088 -0.547 2.215 0.636 -0.102 1.578 0.000 0.845 0.189 0.085 0.000 0.800 0.799 0.986 0.672 1.463 1.086 0.862 +0 1.078 -0.581 1.257 1.322 -1.424 0.632 -0.451 0.600 0.000 1.354 0.112 -0.376 0.000 0.655 0.497 -0.134 2.548 1.195 -0.256 1.728 3.102 1.617 0.936 1.098 0.558 0.735 0.768 0.837 +1 1.392 0.525 -0.445 1.232 -0.989 2.957 -0.287 1.538 0.000 1.594 0.292 0.427 2.215 0.385 -1.839 -0.156 0.000 0.959 1.219 -0.313 3.102 0.297 1.150 0.985 0.729 0.973 0.967 0.956 +0 0.469 0.137 -1.226 0.718 -0.009 0.867 0.936 1.357 1.087 1.321 -0.164 -0.137 2.215 1.149 1.601 -1.497 0.000 0.930 0.879 0.636 0.000 0.967 0.912 0.989 0.759 1.792 1.113 1.088 +1 0.504 -0.674 -0.015 0.663 -0.738 1.001 -0.318 0.660 2.173 1.266 0.614 -1.485 1.107 0.690 1.435 -0.189 0.000 0.483 1.151 -1.502 0.000 0.729 1.067 0.983 1.733 1.752 1.378 1.228 +0 2.450 1.863 0.223 0.080 1.329 1.098 0.937 1.419 0.000 0.736 0.754 -1.513 0.000 0.920 0.829 -0.340 0.000 0.981 -0.897 -1.542 1.551 0.925 1.174 0.994 1.813 0.806 1.244 1.193 +0 0.281 -1.028 1.735 0.685 -1.409 0.500 0.246 -0.617 1.087 0.595 0.856 1.036 0.000 0.645 -0.639 0.187 2.548 0.372 -1.289 1.374 0.000 0.920 0.769 0.993 0.800 0.585 0.604 0.579 +0 0.872 -1.795 1.334 1.056 0.136 0.577 0.578 -0.804 0.000 0.737 1.506 1.057 2.215 0.523 -0.628 -0.618 2.548 0.756 0.851 0.443 0.000 0.936 0.924 1.172 0.766 1.118 1.424 1.248 +1 0.974 -1.169 1.065 0.678 -0.116 1.190 -0.241 0.913 0.000 1.131 -0.156 -1.139 2.215 2.624 -1.122 -0.793 2.548 1.437 -1.642 0.677 0.000 1.995 1.909 0.987 1.096 1.172 1.563 1.227 +1 0.899 1.367 0.973 0.815 0.054 1.133 -1.063 -1.019 0.000 0.734 1.501 -1.093 2.215 1.236 1.101 0.453 1.274 1.649 0.271 -1.709 0.000 1.210 1.030 0.986 0.850 1.011 0.832 0.734 +1 0.865 0.067 -0.502 1.459 0.067 0.946 1.222 1.339 2.173 0.606 0.021 0.617 2.215 0.451 1.096 0.805 0.000 0.483 2.167 -0.651 0.000 0.632 0.769 0.985 1.293 0.986 0.904 0.769 +1 0.781 -0.174 -0.170 0.435 0.657 0.845 0.380 -1.723 0.000 0.605 1.355 0.397 2.215 1.549 1.883 -0.775 0.000 1.388 -0.598 0.959 1.551 0.680 0.929 0.983 0.626 1.121 0.698 0.632 +0 2.163 -0.110 1.211 0.310 -0.041 0.699 1.147 -1.402 0.000 0.941 1.974 -0.467 0.000 1.139 -0.769 0.260 2.548 1.227 -1.056 -1.649 3.102 1.035 1.752 1.025 0.851 0.914 1.413 1.226 +1 0.347 -0.411 1.356 0.739 0.051 1.719 -0.337 -1.466 0.000 0.827 0.456 0.147 2.215 1.159 -1.175 0.577 0.000 1.707 -0.648 -0.007 0.000 0.878 1.029 0.979 1.105 0.792 0.936 0.866 +1 2.388 -0.558 -0.717 0.983 0.208 1.005 -1.236 0.890 0.000 1.158 -0.992 1.733 2.215 0.457 -1.124 -1.513 0.000 0.525 -1.346 0.465 3.102 1.009 0.931 1.573 0.890 0.675 0.904 0.902 +1 0.375 0.611 -1.260 0.401 1.111 1.119 0.185 -0.568 2.173 0.777 0.274 1.654 0.000 0.911 0.171 0.096 2.548 1.306 0.270 0.672 0.000 0.821 0.717 0.984 1.016 0.708 0.799 0.747 +0 2.982 -1.084 -0.989 1.200 -1.739 1.283 -0.458 0.709 0.000 1.061 -0.195 -1.234 2.215 1.526 -0.950 0.533 2.548 1.466 0.193 0.316 0.000 1.038 0.867 1.637 1.053 1.474 1.155 1.313 +0 1.025 -0.811 0.303 1.781 0.763 1.096 -0.753 1.031 2.173 1.218 -1.031 -1.112 1.107 1.785 -0.146 -1.256 0.000 2.085 -0.711 -0.510 0.000 1.524 1.060 0.986 0.833 1.611 1.189 1.199 +0 0.626 -1.363 -0.986 1.620 1.542 0.341 -1.759 -0.783 0.000 0.896 -0.626 0.356 2.215 0.704 0.045 0.364 2.548 0.473 0.139 -1.436 0.000 0.773 0.837 1.061 0.981 0.298 0.802 0.705 +1 0.456 -2.149 0.139 1.536 1.017 0.873 0.276 -0.745 2.173 0.445 -0.660 0.470 0.000 0.873 -0.358 1.733 2.548 0.478 -1.888 -1.596 0.000 0.782 1.148 0.988 0.842 0.935 1.072 0.868 +1 1.219 -1.349 -0.283 1.135 0.238 0.963 0.477 1.562 2.173 0.589 -0.713 -1.057 0.000 0.447 0.106 0.617 1.274 0.487 -2.252 -1.420 0.000 0.835 0.796 0.988 1.617 0.633 1.012 0.957 +0 0.593 -0.770 1.033 1.078 -0.210 1.794 0.159 -1.256 2.173 0.940 0.142 0.571 2.215 1.094 -1.372 1.077 0.000 0.779 1.546 -0.418 0.000 0.819 1.467 0.996 1.421 1.905 1.753 1.355 +1 0.845 -0.685 -1.325 0.722 0.995 0.737 -0.516 -0.030 2.173 0.608 1.254 1.252 0.000 0.455 1.310 -0.306 0.000 0.720 -0.890 1.730 3.102 0.798 0.949 0.987 0.861 0.800 0.844 0.765 +1 0.595 -0.962 -0.632 0.412 -1.535 0.644 -0.072 1.670 2.173 0.584 -0.069 0.512 2.215 0.806 -2.006 1.172 0.000 0.749 -0.895 -0.940 0.000 0.957 0.973 0.989 0.698 0.781 0.790 0.745 +1 0.383 -0.470 0.104 1.169 -0.941 1.033 2.355 1.454 0.000 1.428 1.051 -0.056 2.215 1.389 2.502 1.068 0.000 3.052 0.957 -1.709 3.102 0.991 1.499 0.983 1.031 1.879 1.312 1.188 +0 0.469 1.239 -0.446 0.975 0.558 0.681 -0.235 -1.068 0.000 0.602 -0.766 0.514 0.000 0.974 0.502 -0.429 1.274 1.649 1.403 1.438 3.102 0.741 0.781 0.992 0.975 1.123 0.899 0.809 +0 1.584 0.929 1.170 0.716 0.249 1.641 -0.316 -1.166 0.000 2.496 0.970 0.580 2.215 1.352 -0.544 -0.598 2.548 0.987 -0.984 -1.151 0.000 0.865 0.863 1.088 0.863 2.429 1.928 1.608 +0 0.694 -2.236 -0.003 1.110 0.550 0.708 -1.367 -1.344 0.000 0.848 -1.149 1.132 2.215 0.796 -0.320 -1.683 0.000 1.407 -0.107 -0.305 3.102 0.830 1.032 0.996 0.759 1.094 0.803 0.832 +1 1.580 -0.382 0.353 1.484 -0.194 0.999 -2.563 -1.574 0.000 1.224 0.399 1.371 2.215 1.079 -0.741 -0.192 0.000 0.480 0.352 -1.165 0.000 0.803 1.150 1.002 0.844 0.878 0.964 0.818 +0 1.294 0.637 1.016 1.545 1.425 1.018 0.374 -0.741 0.000 1.230 -0.296 0.546 1.107 0.927 -1.527 -0.641 0.000 1.038 0.956 -0.583 1.551 1.493 0.897 0.991 1.315 1.185 1.023 1.063 +1 0.304 -1.851 -1.119 1.287 -0.544 0.916 -0.426 -0.858 2.173 1.069 -0.867 1.010 2.215 1.159 0.318 1.135 0.000 0.732 -2.125 0.964 0.000 1.083 0.825 0.988 0.612 1.486 0.925 0.848 +0 0.762 -1.328 1.639 1.575 -1.320 1.004 2.830 1.582 0.000 2.089 0.464 0.142 0.000 1.067 0.230 0.697 0.000 1.491 0.105 -0.199 3.102 1.128 0.837 0.983 1.395 0.621 1.201 1.566 +0 0.406 -0.819 0.591 0.865 -0.401 0.855 0.977 1.244 2.173 0.897 -0.615 -1.517 0.000 0.934 -0.845 -0.134 2.548 0.556 -0.432 -1.077 0.000 0.952 0.950 0.987 0.984 1.625 0.901 0.770 +0 1.867 0.678 0.605 0.232 -1.037 0.826 -1.507 -0.800 2.173 0.665 -0.752 0.617 2.215 0.548 -2.062 -1.026 0.000 1.281 -1.854 1.454 0.000 0.728 0.918 0.991 1.700 1.125 1.142 1.190 +0 0.808 1.560 -1.694 0.832 0.318 0.974 0.835 0.083 2.173 1.204 -1.009 -1.391 2.215 0.383 -0.269 0.163 0.000 0.784 -0.470 0.998 0.000 0.421 0.789 1.103 1.881 2.321 1.477 1.113 +1 0.434 -0.154 1.046 1.016 -0.089 1.199 -1.760 0.690 0.000 1.408 -0.136 -1.345 2.215 1.617 -0.315 -0.598 0.000 0.882 -0.465 1.410 3.102 3.012 1.737 0.982 1.099 0.651 1.408 1.313 +0 0.338 -1.998 1.649 1.187 -0.265 0.795 -0.499 0.825 1.087 0.598 2.274 -0.931 0.000 0.601 1.950 1.233 0.000 1.054 0.518 -1.298 3.102 0.859 0.822 0.992 0.919 1.078 1.180 1.354 +0 0.604 0.235 -0.684 0.856 0.393 1.291 -0.864 -1.145 0.000 0.767 -1.966 1.022 0.000 1.269 -1.229 1.426 2.548 2.153 -0.633 0.144 1.551 2.343 1.481 0.985 0.646 1.216 1.180 1.013 +0 1.376 0.523 -0.122 1.959 0.300 0.878 0.803 0.706 2.173 1.125 1.185 1.703 0.000 2.663 0.940 -1.083 2.548 1.109 0.450 -1.530 0.000 0.603 0.933 0.984 0.911 1.915 1.324 1.203 +0 1.517 -0.242 -0.451 0.531 -0.391 0.596 2.512 1.646 0.000 0.864 1.331 1.111 2.215 0.799 -0.557 0.604 2.548 0.513 2.476 -0.517 0.000 0.806 0.897 0.987 0.764 1.097 1.158 1.530 +1 1.235 -0.130 0.821 0.571 -0.243 0.595 0.553 -0.451 2.173 0.758 1.116 -1.449 2.215 0.510 -1.303 0.290 0.000 0.370 -0.554 1.385 0.000 0.554 1.067 0.986 0.788 0.829 0.779 0.727 +1 0.789 -0.956 -0.995 1.743 -1.171 0.403 -2.172 -1.180 0.000 1.297 -0.565 0.171 2.215 0.600 -1.362 -1.679 0.000 0.481 -1.221 0.225 1.551 0.837 0.861 0.991 0.678 0.327 0.936 0.790 +1 0.519 0.668 -1.175 0.505 0.513 0.857 -1.439 -1.561 0.000 0.703 -1.062 -0.863 0.000 1.390 -0.177 0.166 1.274 1.352 -1.351 0.605 3.102 1.016 1.092 0.986 1.071 0.908 1.205 1.351 +1 1.141 -0.488 -0.226 0.652 0.837 0.890 0.157 -1.478 1.087 0.538 0.036 0.081 0.000 0.822 0.988 1.287 2.548 0.514 -0.903 -0.261 0.000 0.455 0.930 0.989 0.967 0.822 0.859 0.728 +0 0.537 -1.408 1.207 0.168 -1.435 0.300 -1.745 0.919 0.000 0.498 -1.628 -1.184 0.000 1.150 -0.370 -0.404 2.548 1.118 0.170 0.252 3.102 0.779 0.961 0.978 0.754 0.553 0.703 0.654 +1 0.706 0.814 1.364 1.370 -1.038 0.777 -0.918 0.549 1.087 0.586 -0.715 -0.792 2.215 0.503 0.403 0.216 0.000 0.371 0.831 -1.642 0.000 0.492 0.764 1.129 0.920 0.934 1.028 0.784 +1 1.005 -0.048 -0.263 0.331 -1.726 0.897 0.251 1.337 2.173 1.272 1.652 -1.610 0.000 0.942 -1.063 -0.038 0.000 1.559 0.081 0.530 3.102 0.398 1.209 0.984 0.726 0.839 1.009 1.033 +0 1.084 1.800 -1.726 0.459 0.283 0.732 0.034 1.222 0.000 0.809 -0.678 -1.030 2.215 0.891 1.611 -0.152 0.000 0.980 0.335 0.135 3.102 0.925 0.935 0.987 0.854 0.838 1.056 0.924 +1 0.692 -1.421 0.463 0.484 0.768 1.721 -0.427 -0.832 0.000 0.583 -0.807 0.690 0.000 0.928 -0.588 -1.481 2.548 1.799 -0.130 0.271 0.000 0.672 0.940 0.973 0.649 0.686 0.647 0.593 +1 0.636 0.312 0.525 0.565 -0.245 0.697 -0.009 0.194 0.000 1.349 0.087 -1.292 2.215 0.643 0.910 0.735 0.000 1.417 -0.561 -1.446 0.000 0.848 1.301 0.989 0.559 0.697 0.784 0.731 +1 0.278 0.267 0.024 1.320 1.467 1.074 1.551 -1.343 2.173 1.138 1.901 0.454 0.000 0.832 0.741 -0.571 2.548 0.912 1.258 -0.312 0.000 0.896 0.873 0.990 1.832 0.881 1.228 1.291 +1 0.467 1.329 -1.705 1.220 -0.992 1.168 0.129 0.759 2.173 0.385 0.333 0.021 0.000 0.481 -0.411 -0.244 2.548 0.656 0.771 -1.012 0.000 0.559 0.872 0.994 0.640 0.784 0.818 0.667 +0 0.497 1.030 -1.296 0.338 0.335 0.979 0.759 -0.699 2.173 2.599 1.084 0.898 2.215 1.386 -0.227 -1.024 0.000 1.240 1.002 0.341 0.000 1.303 1.032 0.987 1.303 2.362 1.409 1.123 +1 0.958 -0.207 0.378 0.449 -1.323 1.083 -1.150 -1.619 2.173 1.135 -0.504 -0.183 0.000 0.662 -1.490 -0.166 0.000 0.935 -1.133 0.914 3.102 0.783 0.808 0.989 0.984 0.813 0.909 0.783 +1 0.765 -0.378 0.170 0.382 1.690 1.818 -2.245 -1.552 0.000 2.478 0.860 -0.246 0.000 0.751 1.718 1.483 0.000 1.124 0.896 0.856 0.000 0.688 0.862 0.986 0.589 0.634 0.592 0.585 +1 0.379 -1.546 0.285 0.872 -0.953 2.658 -0.959 1.091 0.000 1.897 -1.108 -0.805 1.107 0.982 -0.212 -0.635 2.548 1.102 -1.497 -0.778 0.000 0.971 1.614 0.983 0.830 0.716 1.557 1.223 +0 0.807 1.114 0.425 0.833 1.034 1.317 1.457 0.249 0.000 0.672 1.472 -0.647 2.215 1.076 0.758 -1.383 1.274 0.802 -2.008 -1.734 0.000 1.193 1.272 0.986 0.923 0.639 1.069 0.949 +0 0.332 -0.251 0.874 1.217 -1.324 0.594 0.733 0.907 2.173 1.437 -1.019 -0.086 2.215 0.908 2.118 -1.704 0.000 0.476 0.492 -1.355 0.000 0.747 0.888 0.986 1.478 1.745 1.555 1.232 +0 0.721 -0.275 -0.484 0.410 1.493 1.018 -1.039 -0.154 2.173 1.153 0.516 0.952 0.000 0.922 -0.229 1.369 0.000 1.624 0.309 -1.486 3.102 0.861 0.921 0.989 1.074 1.646 1.178 0.963 +1 1.291 0.183 0.608 0.121 -1.695 1.066 -1.234 -0.185 0.000 0.926 0.000 -1.639 2.215 1.431 -0.633 1.733 2.548 0.969 0.047 0.897 0.000 1.137 1.009 0.983 1.062 0.452 0.839 0.796 +0 3.018 -0.100 1.015 0.451 -1.266 1.001 0.624 -0.648 2.173 0.357 0.773 1.470 0.000 0.333 -0.129 -0.763 1.274 1.181 1.560 -0.636 0.000 0.933 0.917 1.431 0.833 0.294 1.007 0.960 +1 0.814 -0.217 0.838 1.581 -1.685 1.207 -0.413 -0.729 2.173 1.628 1.497 0.938 0.000 0.873 -0.464 0.169 0.000 1.190 0.733 -0.666 3.102 0.859 1.062 1.201 1.266 0.884 1.377 1.235 +1 0.866 -0.979 0.565 1.015 -0.546 0.961 -0.560 0.154 2.173 1.339 0.312 -1.714 0.000 0.466 2.047 0.735 0.000 0.704 -0.533 -1.381 3.102 1.670 1.122 1.093 0.748 0.856 1.150 1.089 +1 0.453 -1.405 -0.724 2.920 -0.214 0.803 -2.643 -1.549 0.000 0.921 -2.890 1.168 0.000 1.306 -1.162 1.193 1.274 0.812 -2.151 -1.157 0.000 1.000 0.862 0.989 1.314 0.397 0.815 1.145 +1 0.540 -0.597 -0.998 0.668 0.995 0.583 -0.926 0.781 0.000 1.035 -1.474 -1.372 2.215 1.163 -0.480 -0.672 2.548 0.656 -2.222 0.084 0.000 1.079 1.114 0.987 1.033 0.913 0.867 0.857 +1 0.600 -0.008 0.801 0.348 -1.689 0.617 0.821 1.027 1.087 1.051 0.536 0.268 1.107 2.350 1.051 -1.185 0.000 0.412 -0.024 0.529 0.000 1.280 1.184 0.983 0.758 0.767 0.947 0.789 +0 0.741 -0.091 -0.886 0.306 -1.697 0.722 -2.032 -1.354 0.000 0.718 1.508 1.463 0.000 1.299 0.321 0.506 2.548 1.705 0.511 -0.196 3.102 0.747 0.941 0.988 1.005 0.688 0.824 0.906 +0 1.162 1.105 -1.153 2.117 -0.554 0.802 1.233 1.530 0.000 1.023 1.273 1.027 0.000 1.309 0.494 0.692 2.548 1.635 0.931 0.036 3.102 0.845 0.915 1.120 0.887 0.703 0.900 0.999 +1 1.509 0.778 -1.597 0.496 -1.150 0.951 0.448 0.252 1.087 0.693 1.217 -1.079 0.000 1.042 -0.313 0.218 0.000 1.471 1.357 1.170 3.102 1.664 1.234 0.990 0.956 1.206 0.991 0.956 +1 0.346 -1.470 0.489 0.747 -0.821 0.615 -0.396 1.182 0.000 0.674 0.949 0.736 2.215 1.355 -0.874 -0.917 0.000 0.533 -0.417 1.685 3.102 0.894 0.847 0.996 0.545 0.599 0.576 0.548 +0 0.380 -1.076 -0.065 2.097 -0.635 0.994 1.306 0.439 0.000 0.849 0.927 -1.197 1.107 0.741 1.005 0.864 0.000 2.178 0.669 1.433 3.102 0.877 1.123 0.992 2.424 0.861 2.132 2.366 +0 1.541 0.524 0.392 2.444 0.115 1.003 0.840 -1.635 0.000 1.536 -0.315 -1.629 2.215 0.516 1.046 -0.529 2.548 0.927 -0.927 0.323 0.000 0.669 1.167 0.987 0.794 1.098 1.194 1.175 +1 0.714 1.725 -1.396 0.737 0.739 0.523 0.840 1.612 2.173 0.504 -0.655 -0.298 2.215 0.385 1.562 1.035 0.000 0.643 1.212 -0.217 0.000 0.501 0.784 0.988 0.753 0.978 0.906 0.708 +1 0.606 0.198 1.699 0.964 -0.334 1.242 1.002 0.408 0.000 1.265 1.240 1.669 2.215 1.014 2.128 1.702 0.000 1.116 0.714 -0.714 0.000 1.354 1.042 1.023 0.536 1.064 0.846 0.824 +0 0.647 1.381 0.573 0.873 -0.771 0.756 0.377 1.519 0.000 1.015 0.780 -1.295 2.215 0.646 -1.119 -0.255 0.000 1.323 0.052 -0.385 3.102 0.943 1.036 0.988 0.716 0.862 0.833 0.770 +1 0.640 -1.090 -1.182 1.402 -0.927 0.936 0.051 0.851 0.000 0.619 -0.430 -0.589 2.215 0.748 -1.004 0.498 0.000 1.151 -1.220 1.442 0.000 1.032 1.039 0.986 0.640 0.419 0.670 0.991 +0 1.048 -0.715 1.080 0.741 -1.520 1.127 0.978 -0.173 2.173 0.569 0.058 1.091 2.215 0.817 0.389 -1.381 0.000 0.578 -1.424 1.143 0.000 1.131 0.782 0.988 1.452 1.211 0.990 0.907 +1 0.605 -0.265 -1.416 0.756 -0.384 1.474 0.452 -0.855 0.000 1.622 1.683 1.341 0.000 1.421 1.284 0.652 2.548 0.604 -0.439 1.024 3.102 3.755 2.184 0.987 1.593 0.831 1.465 1.504 +1 1.040 0.702 -0.386 0.369 -1.635 0.572 -0.391 -1.031 2.173 0.839 0.423 0.956 2.215 0.532 0.991 -1.452 0.000 1.298 1.654 0.771 0.000 0.934 0.860 0.987 0.887 1.085 0.901 0.797 +0 1.179 0.565 -0.277 0.678 0.145 0.667 0.225 1.248 2.173 1.217 -0.610 -1.366 2.215 0.287 1.593 -0.392 0.000 0.608 -1.243 0.182 0.000 1.144 0.962 0.991 1.529 1.104 1.134 0.967 +1 0.559 -1.813 0.167 0.511 -0.327 1.033 0.776 -1.449 2.173 0.353 -0.779 1.475 0.000 0.828 -0.380 -0.002 0.000 0.848 0.125 0.853 3.102 0.821 1.198 0.988 0.669 0.924 0.925 0.787 +1 0.965 0.244 0.038 0.745 1.427 0.866 -0.539 0.046 2.173 0.681 1.077 1.484 0.000 1.053 0.008 -1.666 0.000 0.866 -0.201 1.302 3.102 0.934 1.048 1.115 0.635 0.841 0.688 0.666 +1 0.922 -2.005 0.530 0.536 -1.467 0.856 -0.699 0.932 2.173 0.868 -1.003 0.152 2.215 1.152 -1.756 -0.917 0.000 0.878 -1.068 -1.422 0.000 0.845 0.996 0.988 0.870 0.846 0.814 0.786 +0 2.922 -0.164 0.530 1.773 0.496 2.284 0.154 -1.447 2.173 1.570 1.183 -0.076 0.000 0.697 -0.822 1.314 0.000 1.640 -0.821 1.693 0.000 1.251 0.789 0.995 2.636 1.199 1.706 1.352 +0 0.640 -1.085 0.597 0.546 -0.483 0.658 -1.920 -1.038 0.000 0.883 -0.094 0.646 1.107 1.236 -0.588 1.251 0.000 1.211 -0.281 -1.452 3.102 1.817 1.178 0.988 1.044 0.893 0.973 0.906 +0 0.304 2.175 -1.621 0.785 0.753 1.072 0.546 -0.666 2.173 0.649 0.657 1.672 2.215 0.783 1.293 0.769 0.000 0.440 -0.172 0.879 0.000 0.590 1.051 0.992 0.623 1.058 0.755 0.673 +0 0.783 1.646 -0.924 0.535 1.280 0.546 0.562 -1.682 0.000 0.926 1.242 1.074 1.107 1.342 0.039 -0.351 2.548 1.326 0.311 0.025 0.000 1.305 1.004 0.987 0.829 1.378 1.009 0.931 +1 0.354 -1.404 0.210 4.521 0.906 3.115 -1.455 -0.914 0.000 1.436 0.054 0.925 1.107 1.984 1.274 -0.634 0.000 0.963 -1.123 0.941 0.000 2.637 1.675 1.029 1.605 0.263 1.798 1.798 +0 1.035 1.406 -0.724 0.833 -1.274 0.700 1.249 1.325 2.173 0.439 -0.426 0.919 0.000 1.060 0.485 0.754 2.548 0.870 1.637 -0.253 0.000 0.682 0.720 0.988 0.938 0.663 0.776 0.678 +0 0.487 0.597 -0.925 2.710 -1.659 1.634 1.142 0.261 0.000 0.724 0.948 0.641 0.000 1.223 2.258 -0.772 0.000 0.986 -0.853 1.226 3.102 0.800 0.919 0.988 1.129 0.791 1.029 1.131 +1 1.656 -0.268 1.441 0.393 1.029 1.693 -1.917 0.134 0.000 1.505 1.383 -1.297 1.107 0.953 1.967 -0.298 0.000 0.727 0.805 -0.746 0.000 0.995 1.064 0.988 0.570 0.807 0.880 0.927 +1 0.472 1.150 0.226 1.809 -0.403 1.113 -0.507 1.401 2.173 0.860 0.314 -1.396 2.215 0.315 0.028 1.234 0.000 1.015 -0.828 0.083 0.000 0.632 0.836 0.993 1.341 1.040 1.723 1.403 +0 1.371 -0.969 0.236 1.248 -1.078 0.802 0.788 0.950 0.000 0.701 0.783 -1.016 2.215 0.866 -0.549 -1.187 1.274 0.565 -1.522 0.489 0.000 1.819 1.329 1.678 1.327 0.645 0.954 1.037 +1 0.651 0.388 -1.364 0.688 -0.656 0.534 -0.618 -1.625 2.173 0.624 0.697 0.351 0.000 0.819 -0.955 0.415 2.548 0.449 -0.418 1.225 0.000 0.662 0.820 0.991 0.766 0.813 0.620 0.625 +0 1.790 1.406 1.377 0.770 0.712 0.896 1.340 -0.294 2.173 1.149 0.820 -0.677 0.000 0.352 -0.166 0.666 2.548 0.487 -0.161 -1.055 0.000 0.621 0.751 0.986 0.670 0.796 0.825 0.814 +1 1.001 -1.259 -1.235 1.686 -0.553 0.729 -1.044 0.109 2.173 1.165 2.595 1.599 0.000 1.165 0.305 0.744 2.548 0.794 -0.173 -1.331 0.000 2.567 1.951 1.039 0.931 1.063 2.042 2.267 +1 2.324 0.275 -0.179 0.442 -0.875 0.900 0.560 1.596 2.173 0.890 -0.272 0.741 0.000 0.848 -0.720 -1.703 2.548 1.229 0.287 -0.829 0.000 0.846 0.901 0.991 1.095 0.817 1.005 0.821 +1 1.052 -1.171 0.309 1.157 0.046 0.935 -0.301 -1.087 2.173 0.556 -0.089 0.853 0.000 1.234 -0.022 -0.097 0.000 1.645 -0.441 -1.713 0.000 0.923 0.988 0.993 1.617 0.997 1.338 1.100 +1 0.489 -0.121 0.475 1.099 -1.437 1.553 -0.400 0.247 2.173 0.950 -0.347 -1.209 0.000 0.962 -1.602 1.168 0.000 0.867 1.096 -0.980 0.000 1.134 0.790 1.004 1.169 1.025 1.060 0.886 +0 1.175 -0.294 1.320 0.834 -0.271 0.975 0.889 -0.965 2.173 0.735 1.594 1.580 0.000 1.389 0.655 0.194 2.548 0.936 1.160 0.685 0.000 0.871 0.982 1.358 1.256 1.260 0.999 0.905 +1 0.898 0.140 1.504 0.899 -0.141 0.965 0.478 -0.929 0.000 1.299 0.184 1.093 2.215 0.510 -2.382 0.283 0.000 0.972 1.054 -0.532 0.000 0.788 0.738 1.240 0.890 0.832 0.902 0.801 +0 0.930 0.252 -0.816 0.772 -1.401 0.495 1.306 0.386 2.173 0.856 0.083 0.124 2.215 0.697 -0.732 -1.679 0.000 0.697 0.166 1.242 0.000 0.549 0.951 0.984 0.887 0.668 0.835 0.741 +1 1.476 1.568 -0.078 0.447 0.399 1.883 -0.118 -1.072 0.000 2.140 0.793 0.956 2.215 1.098 0.481 0.643 2.548 0.790 0.254 -0.330 0.000 1.222 1.507 0.980 1.219 0.511 1.519 1.331 +0 0.408 -0.006 -0.604 1.358 1.418 1.644 -0.568 -1.521 2.173 1.737 -0.687 0.404 0.000 0.805 -1.159 -0.656 0.000 2.236 -0.445 -0.017 3.102 1.571 1.022 0.999 0.946 1.983 1.376 1.147 +0 0.716 1.172 1.551 1.366 1.627 0.755 -0.034 -1.242 0.000 1.030 -0.473 -0.108 2.215 0.398 2.166 -0.520 0.000 0.624 -0.976 0.794 3.102 0.777 0.832 0.999 1.629 0.583 1.573 1.196 +0 0.529 0.569 0.242 0.801 1.425 1.554 1.491 -1.574 0.000 0.848 0.358 0.701 2.215 0.978 0.377 -0.617 2.548 1.912 -0.483 0.240 0.000 1.038 1.078 0.987 0.648 0.898 0.856 0.756 +0 0.767 -0.513 -0.216 1.728 0.502 0.881 -1.602 1.356 2.173 0.732 -0.981 -1.110 2.215 0.435 -1.506 -1.410 0.000 0.574 1.302 -0.898 0.000 1.346 0.972 0.987 1.329 1.007 1.012 0.985 +0 0.548 1.261 -1.678 2.130 1.332 1.263 -0.548 -0.101 2.173 1.020 -0.249 -1.532 0.000 0.960 -0.284 -0.570 1.274 0.615 1.860 -0.325 0.000 1.144 0.899 0.995 1.758 0.587 1.198 1.063 +1 0.633 0.454 -0.153 2.363 1.139 1.706 1.004 -0.364 0.000 1.312 0.039 0.926 2.215 1.940 1.173 -1.251 0.000 0.636 -1.083 -1.067 0.000 0.747 0.881 1.556 0.900 0.873 0.717 0.719 +0 1.036 0.162 1.057 0.980 1.248 0.653 -0.576 1.666 1.087 0.742 -1.088 -0.453 2.215 1.412 -2.447 -0.101 0.000 1.220 0.209 -1.163 0.000 0.487 0.829 0.989 1.010 1.004 1.052 1.611 +1 1.447 -0.404 0.954 1.265 1.289 0.704 -0.658 -1.309 0.000 0.622 1.512 -1.019 0.000 1.879 1.395 -0.124 0.000 1.268 -0.077 1.525 3.102 1.198 1.430 0.980 1.191 1.148 1.454 1.529 +1 1.593 -0.829 -0.117 0.370 0.548 2.089 1.986 1.554 0.000 1.248 -1.053 0.135 1.107 1.225 -0.265 -0.019 0.000 2.031 -0.380 -1.095 3.102 0.721 0.861 0.980 0.654 1.365 0.806 0.666 +1 0.840 0.098 -0.423 0.974 -0.670 1.541 0.126 -0.165 2.173 1.544 0.450 1.301 0.000 1.057 -0.279 1.657 0.000 0.753 -1.256 -0.230 0.000 0.990 0.769 0.989 0.978 0.884 1.166 1.066 +1 0.629 0.733 1.078 0.766 1.733 1.110 0.394 0.927 0.000 1.041 0.310 -1.192 2.215 1.801 0.423 -0.405 2.548 0.876 0.928 0.522 0.000 0.771 1.295 0.983 1.080 0.954 1.038 0.912 +0 0.840 -2.103 1.356 1.345 -1.621 1.604 -1.130 0.555 2.173 1.297 -0.003 -0.846 2.215 0.759 0.432 -0.466 0.000 0.768 -0.654 -0.439 0.000 0.907 0.966 0.983 1.416 2.390 1.497 1.443 +1 0.875 -0.064 1.001 0.366 0.113 1.112 0.240 -1.741 2.173 1.296 0.167 0.279 0.000 0.679 -2.589 -0.639 0.000 1.834 0.121 -0.918 1.551 0.966 1.089 0.989 0.935 1.025 1.050 0.900 +1 0.731 1.426 0.457 0.537 -1.074 0.797 0.371 1.211 0.000 0.945 1.077 -1.326 2.215 1.534 0.887 -0.468 2.548 0.947 0.623 0.631 0.000 0.709 1.026 0.985 0.829 0.897 0.891 0.836 +1 0.875 0.472 -0.990 0.521 0.757 0.374 0.856 0.491 2.173 1.010 0.443 -0.100 2.215 0.668 -1.545 1.447 0.000 1.309 0.536 -1.476 0.000 1.583 1.226 0.987 0.731 0.496 0.942 0.814 +1 2.396 0.717 -0.491 0.480 -1.022 0.738 0.440 1.367 1.087 0.557 -0.834 1.037 0.000 0.374 0.546 1.049 1.274 1.121 1.494 1.006 0.000 1.802 0.947 0.993 1.216 0.192 0.789 0.884 +0 1.776 0.593 -0.124 0.651 -0.310 0.710 0.948 -1.633 0.000 1.127 0.570 1.129 2.215 1.216 0.489 0.630 2.548 0.716 0.612 -0.765 0.000 0.777 0.966 0.988 1.177 0.542 0.830 0.840 +0 0.900 0.091 -1.078 0.772 1.526 1.529 0.685 0.521 0.000 0.615 1.407 0.028 0.000 1.119 0.919 -1.426 2.548 1.044 0.832 -0.981 3.102 0.823 1.030 0.986 0.582 0.323 0.848 0.781 +0 0.447 -0.436 0.519 1.800 1.402 1.370 0.670 0.562 2.173 1.992 0.601 -0.909 0.000 0.812 0.315 -0.290 0.000 1.003 0.466 -1.325 3.102 1.067 0.731 0.992 1.075 1.232 1.132 1.062 +1 0.461 2.236 0.998 0.623 1.720 0.697 0.434 0.636 0.000 0.948 -0.127 -1.199 0.000 1.096 -0.277 -0.038 0.000 1.330 0.411 0.066 3.102 1.079 0.690 0.980 0.636 1.031 0.861 0.795 +0 2.362 1.842 0.820 1.941 0.692 1.687 2.717 -1.244 0.000 1.032 -0.834 -0.731 2.215 0.796 1.187 0.179 1.274 0.612 1.310 -0.613 0.000 0.929 1.203 0.978 0.853 1.440 2.163 1.605 +0 0.940 0.985 -1.660 1.077 1.299 0.544 0.055 0.593 0.000 0.987 0.528 0.008 0.000 1.261 0.595 -0.866 2.548 0.405 -0.907 -1.450 3.102 0.873 1.038 0.989 0.966 0.604 0.809 0.893 +1 1.198 -1.432 1.002 0.523 0.588 2.532 -0.509 -1.078 0.000 1.107 -0.760 0.656 2.215 2.078 -1.826 0.602 0.000 0.686 1.246 -1.618 0.000 1.035 0.992 1.000 0.591 0.483 0.622 0.621 +0 0.910 -0.301 -1.104 0.698 1.283 1.076 -0.816 -0.953 2.173 1.483 -0.244 0.469 0.000 0.986 1.018 1.053 0.000 0.665 0.030 1.371 0.000 0.958 0.838 0.985 0.897 0.520 0.915 0.807 +1 0.542 0.428 0.866 1.325 -0.417 0.516 0.840 1.287 0.000 1.299 1.411 -0.473 2.215 1.215 0.083 1.630 0.000 0.520 -0.276 1.037 3.102 0.701 0.489 1.074 0.895 1.046 0.901 0.814 +0 0.999 -0.359 -1.350 0.839 0.327 0.612 -0.540 0.782 0.000 1.045 0.005 1.302 1.107 1.060 0.211 0.282 0.000 3.140 0.423 -0.869 3.102 0.855 0.876 1.266 1.097 1.571 1.084 0.934 +1 0.859 1.294 -0.430 0.802 1.612 0.351 -0.218 -0.883 0.000 0.910 2.266 1.057 0.000 0.483 0.569 -1.389 2.548 0.709 0.065 -0.292 1.551 0.923 0.731 1.109 0.697 0.394 0.482 0.672 +0 0.888 -0.256 1.000 1.243 0.203 0.731 -0.179 -1.514 2.173 0.708 0.163 -0.605 0.000 0.426 0.294 -0.950 2.548 0.425 1.382 0.553 0.000 0.849 0.914 0.988 0.692 0.382 0.690 0.677 +0 0.433 0.977 -1.303 1.957 -0.298 0.687 -0.015 1.295 0.000 0.578 0.888 1.109 2.215 1.255 -1.136 -0.329 2.548 1.568 -0.780 1.596 0.000 0.866 0.878 1.004 1.495 1.467 1.120 1.140 +0 1.600 -0.040 -0.314 0.844 0.906 1.197 -2.905 1.189 0.000 1.550 0.644 -1.168 2.215 0.707 1.879 1.653 0.000 1.105 -0.178 0.410 3.102 10.018 5.222 1.435 1.309 1.285 3.675 2.753 +1 0.744 0.660 -0.123 1.376 -1.602 0.654 0.895 0.686 0.000 0.992 1.088 -1.525 2.215 1.150 0.996 -0.631 2.548 1.578 1.634 0.497 0.000 0.898 1.087 1.362 0.823 0.818 0.896 0.856 +1 2.190 0.602 1.577 2.602 -1.692 3.165 -0.914 0.075 0.000 1.212 0.352 -1.655 0.000 0.684 0.466 -0.831 2.548 1.411 0.330 -0.349 3.102 0.771 1.005 0.969 0.920 0.319 0.921 0.833 +1 0.777 -0.015 0.275 1.059 1.347 0.533 0.139 -0.192 1.087 0.712 -0.969 1.401 0.000 1.203 0.048 -1.444 2.548 0.706 1.715 0.019 0.000 0.388 0.935 1.034 0.772 0.903 0.692 0.720 +0 1.222 -0.601 0.475 0.856 -0.182 0.609 -1.256 -1.026 0.000 0.957 -0.447 1.270 2.215 0.727 2.446 -1.397 0.000 0.535 -1.737 -0.164 0.000 0.714 1.036 0.989 0.534 0.173 0.626 0.699 +1 1.239 -0.256 -1.178 0.567 0.319 1.388 1.982 1.389 0.000 1.662 -0.080 -0.243 2.215 1.860 -0.528 0.165 2.548 0.554 0.561 -1.237 0.000 0.622 0.863 1.133 0.930 0.820 0.788 0.683 +1 0.729 -0.603 -1.321 0.620 0.403 2.046 -2.134 0.693 0.000 1.093 -1.442 -1.497 2.215 1.457 -0.777 1.538 0.000 3.858 -0.679 -0.521 3.102 0.906 1.598 0.985 0.910 1.562 1.681 1.293 +0 0.370 -0.694 0.657 0.094 -0.272 1.674 1.120 0.761 2.173 2.037 0.327 -0.879 0.000 0.721 0.617 -1.714 2.548 0.490 0.907 -0.611 0.000 0.590 0.754 0.830 0.864 1.125 1.292 0.990 +1 0.725 -0.266 0.322 2.285 1.034 0.513 0.128 -0.150 0.000 1.324 0.480 -1.444 1.107 0.876 1.170 -0.444 2.548 0.573 0.482 -0.949 0.000 0.578 0.882 1.067 1.261 1.010 1.109 0.906 +1 0.885 -1.423 1.186 0.773 -0.969 0.835 1.075 0.572 2.173 0.691 0.228 -1.461 0.000 0.868 -0.699 -0.474 2.548 0.508 -1.913 -0.597 0.000 1.372 0.914 1.068 1.774 1.419 1.211 1.079 +1 0.688 -1.923 0.359 0.362 0.375 0.990 -0.965 0.936 2.173 0.890 -0.939 -0.935 0.000 0.835 -0.684 -0.088 2.548 0.368 -0.753 -1.377 0.000 0.290 1.052 0.996 0.577 0.912 0.717 0.651 +0 0.601 0.486 -1.037 1.800 -0.090 0.465 -0.109 -0.730 2.173 0.862 -0.329 -1.439 2.215 0.609 -1.453 0.349 0.000 0.929 -0.190 1.008 0.000 0.769 0.882 1.086 0.674 0.565 0.703 0.765 +0 0.465 0.925 1.191 0.736 -0.762 0.998 2.336 -1.640 0.000 1.773 1.662 -0.083 0.000 1.208 0.884 0.362 2.548 1.674 -0.583 -1.427 3.102 0.825 0.852 0.991 0.797 1.488 1.109 1.314 +1 1.742 0.523 1.733 1.093 1.055 0.748 1.424 0.261 2.173 0.237 0.823 0.177 0.000 1.138 1.723 -0.761 0.000 1.021 0.269 -0.831 3.102 0.738 0.773 1.096 0.849 0.942 0.898 0.838 +0 0.924 -1.270 -1.392 1.934 -1.215 3.614 -0.825 0.670 0.000 3.100 -0.509 -1.070 1.107 1.425 -0.503 0.358 2.548 1.784 -1.705 -0.724 0.000 4.459 2.474 1.005 1.872 2.144 2.535 2.255 +0 1.660 -0.592 -0.034 0.537 -0.909 0.877 -2.540 -1.692 0.000 1.236 -0.830 -1.204 2.215 1.495 -1.411 0.639 1.274 1.908 -1.111 1.143 0.000 1.300 0.864 0.986 0.980 1.527 0.979 0.928 +0 2.144 0.100 -0.768 0.690 -1.415 0.693 -0.377 0.097 0.000 0.570 0.804 0.546 0.000 1.416 0.206 1.288 2.548 1.003 0.578 0.200 3.102 0.930 0.961 0.986 0.854 0.786 0.837 0.761 +0 0.339 2.327 -0.609 0.732 0.577 1.173 0.314 0.577 2.173 1.272 0.830 -0.945 2.215 0.913 1.552 1.604 0.000 0.674 -1.568 -1.198 0.000 0.963 0.939 0.986 0.860 1.829 1.005 0.831 +1 0.809 -0.801 1.018 0.449 -0.819 1.119 0.745 -1.236 2.173 0.910 0.285 0.764 0.000 0.470 1.011 -0.496 2.548 0.703 -0.678 0.170 0.000 0.909 1.120 0.993 0.680 0.582 0.722 0.688 +1 1.857 0.222 -1.552 1.416 1.463 1.049 -0.319 -0.430 1.087 0.949 0.113 0.637 1.107 0.333 0.920 -0.178 0.000 0.390 0.688 0.425 0.000 0.309 0.786 0.994 1.088 1.246 1.158 0.889 +0 0.522 -1.696 -0.140 1.285 0.534 0.707 0.039 -1.123 0.000 1.076 1.622 -0.152 0.000 1.190 0.324 1.728 0.000 1.334 -0.454 -1.720 1.551 0.930 0.826 0.991 0.862 0.518 0.560 0.741 +0 0.546 0.346 0.875 0.982 -0.879 0.556 0.747 1.006 2.173 0.759 0.369 -0.888 0.000 0.731 -0.490 0.188 2.548 0.868 -0.431 1.669 0.000 0.926 0.934 1.015 0.686 0.767 0.680 0.621 +1 1.482 -0.171 0.270 0.861 -0.097 0.509 -0.883 0.082 2.173 1.341 -0.781 -1.414 2.215 1.248 0.822 1.456 0.000 0.702 1.376 -1.063 0.000 0.884 1.348 0.980 1.457 1.187 1.147 1.080 +1 0.430 -0.670 1.499 0.456 0.103 0.867 -1.514 -1.709 0.000 0.628 -1.330 -1.029 2.215 1.189 -1.129 -0.092 1.274 1.342 -2.226 0.014 0.000 1.925 1.212 0.989 0.710 0.689 0.893 0.755 +1 0.755 -0.517 -0.381 3.202 -1.002 0.895 0.037 1.459 2.173 1.332 0.240 0.846 0.000 0.571 -0.622 1.489 0.000 0.949 0.987 0.124 0.000 0.946 1.040 1.141 1.438 1.239 1.004 1.014 +0 1.486 0.284 -1.541 0.718 0.122 0.861 -0.668 -0.106 2.173 0.628 -0.493 1.009 0.000 0.751 -0.708 -1.237 0.000 1.193 -0.376 0.510 3.102 0.958 1.000 1.428 0.927 0.577 0.835 0.765 +1 0.609 -0.643 -1.518 0.919 1.312 1.122 0.280 1.631 1.087 1.284 1.244 -0.720 2.215 1.299 0.321 0.235 0.000 1.342 -0.272 -0.394 0.000 0.930 1.255 0.981 0.634 1.764 1.195 1.010 +1 1.099 0.508 1.145 0.660 -1.630 1.227 0.256 -1.515 0.000 2.024 -1.269 0.041 0.000 1.299 1.409 0.696 1.274 1.543 0.174 -0.878 3.102 4.389 2.541 0.992 1.048 1.323 2.074 1.595 +1 0.382 -1.186 1.277 0.523 0.655 0.990 0.113 -1.456 2.173 0.745 0.766 0.246 0.000 0.877 1.452 -0.412 0.000 0.533 -0.830 0.738 3.102 0.862 0.927 1.000 0.899 0.836 0.921 0.799 +0 0.585 1.053 1.593 0.253 -1.195 1.145 0.245 0.349 0.000 1.958 -0.055 -1.371 2.215 2.408 -0.419 -0.087 0.000 1.189 -1.116 -0.191 0.000 0.865 0.952 1.000 0.806 0.478 1.134 0.952 +1 1.340 0.480 -1.731 0.448 0.930 0.988 -0.727 -0.450 1.087 0.532 -1.555 -1.554 2.215 0.803 -0.850 0.276 0.000 0.564 0.417 0.561 0.000 0.609 0.808 0.994 1.218 1.014 0.923 0.792 +0 0.863 -1.714 -1.669 0.227 0.384 0.398 0.100 -0.268 0.000 0.816 0.196 1.501 2.215 1.047 -0.546 0.567 2.548 1.434 -0.891 -0.666 0.000 0.821 1.066 0.993 0.740 0.837 0.765 0.722 +0 1.350 0.840 1.470 0.266 1.728 0.861 -1.170 -0.853 2.173 0.867 -2.180 -0.134 0.000 0.901 0.102 0.903 2.548 0.682 -0.811 0.005 0.000 0.691 0.904 0.987 0.802 1.335 1.319 1.521 +1 0.610 -1.273 -0.606 0.273 0.268 1.218 1.717 -1.523 0.000 1.106 -0.556 -0.491 2.215 0.880 1.153 1.079 0.000 1.566 -0.363 0.335 3.102 0.922 0.945 0.993 0.652 0.809 0.896 0.767 +1 0.441 1.438 -1.216 1.243 0.678 0.449 1.144 -0.723 0.000 0.521 0.958 0.504 0.000 0.679 -0.383 0.876 0.000 0.640 0.481 -0.440 3.102 0.922 0.926 1.015 0.640 0.503 0.687 0.631 +0 1.280 -0.474 -0.203 1.654 -0.722 0.899 -0.086 1.315 2.173 0.492 0.303 0.803 1.107 0.769 0.696 0.210 0.000 0.378 0.629 -0.750 0.000 0.452 0.858 0.989 0.891 0.479 0.924 0.754 +1 0.375 0.035 1.562 1.854 -0.327 0.838 -1.238 0.634 2.173 0.539 -0.855 -1.291 0.000 0.716 -0.797 -1.667 1.274 0.555 -1.061 -0.329 0.000 0.664 0.669 1.145 0.873 0.865 0.896 0.707 +0 0.834 0.192 0.717 0.858 -0.192 0.696 -1.246 1.494 0.000 0.883 -1.604 -0.670 0.000 1.004 0.048 -1.694 2.548 0.489 -0.502 0.480 3.102 1.581 0.958 0.982 0.844 0.527 0.780 0.940 +0 1.750 1.370 0.368 0.582 0.879 0.636 1.132 -1.639 0.000 0.477 2.454 -1.366 0.000 0.477 0.477 1.170 1.274 1.037 0.936 -0.467 0.000 0.920 0.742 0.984 0.650 0.402 0.517 0.651 +1 0.890 -0.737 1.551 0.729 -0.638 0.924 0.251 -0.685 1.087 0.313 -1.167 1.234 0.000 0.678 -2.092 1.004 0.000 1.094 -0.657 0.760 1.551 0.952 0.654 1.028 0.933 1.178 0.927 0.794 +1 1.144 0.466 -1.417 1.713 -1.573 1.543 0.032 -1.425 0.000 3.927 -2.156 0.209 0.000 0.975 0.126 -1.688 0.000 1.501 -0.571 -0.211 3.102 0.530 0.940 0.972 1.103 0.901 0.918 0.810 +1 1.995 -0.914 -0.352 0.483 1.417 0.951 -1.037 1.410 2.173 0.416 -1.616 -0.372 0.000 0.835 -0.820 0.764 0.000 0.382 0.129 -0.827 3.102 0.849 0.909 1.359 0.681 0.704 0.793 0.696 +0 2.339 1.351 -1.071 0.376 -1.258 0.705 -1.583 0.396 0.000 0.672 0.481 1.499 2.215 1.342 -0.371 0.815 1.274 0.983 -0.877 -0.276 0.000 0.867 0.811 0.980 1.832 0.750 1.219 1.088 +1 1.279 0.942 0.293 0.327 1.550 0.744 -0.591 -1.094 2.173 0.438 -0.529 -0.389 0.000 0.777 -0.369 1.052 2.548 0.640 -0.598 1.393 0.000 0.692 0.633 0.987 0.858 0.889 0.951 0.787 +1 0.429 -1.118 1.288 2.003 0.341 1.123 -1.946 -1.480 0.000 0.769 -0.771 -0.248 1.107 0.671 -0.832 -1.489 0.000 0.584 -1.239 -0.130 3.102 0.859 0.792 0.986 0.771 0.223 0.753 0.825 +0 0.684 0.255 1.428 1.156 1.251 1.330 -0.256 0.195 2.173 1.474 0.875 -1.484 2.215 0.732 -0.637 -0.303 0.000 1.169 0.421 -0.765 0.000 0.772 0.976 0.983 0.890 2.413 1.393 1.169 +1 0.680 0.346 0.863 1.610 0.085 0.951 0.897 -1.156 2.173 0.454 0.784 -0.643 2.215 0.465 1.640 1.644 0.000 0.444 1.313 1.039 0.000 0.267 0.655 0.989 0.666 0.435 0.771 0.651 +1 0.672 0.258 -0.037 0.602 1.520 1.438 0.525 -0.297 0.000 1.309 0.542 1.158 0.000 1.865 0.857 -1.636 2.548 0.960 -1.030 1.396 0.000 1.526 1.433 0.986 0.939 1.324 1.207 0.999 +1 0.644 0.375 0.727 1.277 -1.665 1.216 0.507 -0.139 0.000 1.005 0.730 1.468 2.215 0.770 0.063 -1.008 2.548 0.729 -0.143 -0.009 0.000 1.015 0.808 1.049 0.629 0.807 0.821 0.762 +1 1.025 -0.156 0.395 0.555 -0.689 1.617 -0.384 -0.805 1.087 1.186 0.085 1.025 0.000 1.271 -0.836 1.411 0.000 0.817 0.798 0.587 3.102 0.793 0.628 0.990 1.033 1.454 1.044 0.889 +1 1.229 -0.987 0.984 1.625 0.459 1.840 -0.156 -0.814 2.173 0.804 -1.306 1.252 0.000 1.340 -1.950 -1.413 0.000 1.006 -2.377 0.904 0.000 1.036 1.027 0.990 2.115 1.129 1.523 1.357 +1 1.224 0.125 1.468 1.754 0.910 1.006 -0.017 -0.838 0.000 1.224 -0.446 0.042 2.215 0.962 0.128 -1.266 0.000 0.380 -2.194 0.640 0.000 0.678 1.213 0.985 0.731 0.673 0.819 0.921 +0 1.026 -0.837 -1.322 0.871 -0.833 0.778 -0.554 0.961 0.000 0.502 -1.477 0.541 0.000 1.895 -1.229 -0.065 0.000 2.026 -1.270 1.503 3.102 0.825 0.902 0.978 0.569 1.290 0.871 0.872 +1 1.260 0.639 -0.257 1.682 -0.013 1.895 0.822 -0.043 2.173 0.592 2.276 -0.690 0.000 3.260 -0.058 -1.552 2.548 1.826 1.721 1.160 0.000 0.877 1.013 0.992 1.828 3.349 1.716 1.345 +0 0.620 0.314 -0.192 1.328 -0.820 0.422 0.413 1.735 2.173 0.476 0.709 1.095 2.215 0.655 -1.324 0.088 0.000 0.869 -0.361 1.102 0.000 0.795 0.842 0.979 0.788 0.375 0.621 0.796 +1 0.560 -1.264 0.255 0.782 -1.350 1.316 -0.236 0.324 2.173 0.965 -0.090 -1.393 0.000 0.819 2.619 1.632 0.000 1.167 0.052 -0.580 0.000 0.931 0.871 0.985 1.336 0.828 1.067 1.015 +1 0.749 0.204 1.742 0.553 -0.350 1.494 0.653 -1.541 2.173 2.007 0.257 0.570 1.107 2.025 0.668 -0.590 0.000 1.355 -0.515 -0.187 0.000 1.482 1.739 0.990 0.845 2.461 1.593 1.217 +1 0.685 0.424 0.338 0.627 -1.232 0.840 0.925 0.196 0.000 0.901 -0.615 1.411 2.215 0.789 -0.605 -1.231 0.000 0.858 0.527 -1.217 3.102 1.913 1.158 0.988 0.762 0.771 0.938 0.778 +0 1.524 1.163 0.206 0.892 0.930 0.856 2.530 -0.344 0.000 0.875 0.443 -1.453 0.000 0.814 1.232 -1.300 2.548 1.493 0.946 1.337 3.102 0.863 1.083 0.988 0.776 0.591 0.827 0.883 +1 2.726 -0.959 1.515 0.993 0.857 0.689 0.048 -0.499 1.087 0.399 0.377 0.182 0.000 1.044 -0.874 -0.032 0.000 1.665 -0.493 -1.152 3.102 0.864 1.093 1.272 1.508 0.729 1.076 1.039 +1 0.697 -0.862 -1.005 1.485 1.106 1.777 -0.538 1.455 2.173 1.495 -0.146 -1.191 0.000 2.955 -2.213 -0.262 0.000 2.188 0.859 0.280 0.000 0.889 0.654 1.333 1.026 0.824 1.123 1.039 +0 1.584 -0.268 -0.318 2.260 0.082 1.848 0.813 -1.672 2.173 0.909 0.064 1.524 0.000 1.443 0.306 0.379 2.548 0.827 -0.046 -1.237 0.000 0.941 1.015 0.995 2.383 2.019 1.625 1.344 +1 1.610 0.241 0.168 0.129 -0.424 0.883 -1.800 -1.532 0.000 0.933 -1.216 1.120 2.215 0.729 -0.186 -1.606 2.548 0.525 1.093 -0.906 0.000 1.372 0.950 0.980 1.318 0.734 0.920 1.071 +1 0.950 -0.781 -1.158 0.787 -0.639 0.861 0.983 1.330 2.173 0.970 1.000 -0.055 2.215 0.410 1.177 -1.657 0.000 0.481 2.316 0.583 0.000 0.595 0.741 0.994 1.930 1.276 1.557 1.389 +1 1.338 0.583 -1.372 1.228 -0.050 0.819 -0.233 0.465 2.173 0.479 -1.045 -0.913 0.000 0.541 0.606 -1.127 0.000 1.051 0.133 1.188 0.000 0.784 1.007 1.650 0.880 0.575 0.817 0.746 +1 0.753 0.368 -1.717 1.226 0.573 0.918 -0.947 -0.861 2.173 0.525 1.152 -1.702 0.000 0.961 -0.474 0.189 0.000 1.373 0.258 -0.348 3.102 0.834 0.838 1.172 0.829 0.962 0.937 0.795 +0 0.668 -0.091 -0.005 1.274 1.444 0.696 1.454 -0.428 0.000 0.806 0.785 1.439 2.215 0.439 2.187 0.597 0.000 1.025 -1.397 1.628 0.000 0.928 0.968 1.234 0.810 1.103 0.837 0.895 +0 0.898 1.499 -1.176 0.614 -1.661 0.397 0.951 -0.280 2.173 0.591 -0.235 1.231 0.000 0.933 -0.329 0.198 0.000 0.483 0.593 0.886 1.551 0.916 0.859 0.974 0.574 0.406 0.519 0.626 +0 1.626 0.256 1.590 1.225 1.056 0.786 -0.760 -0.263 1.087 1.168 -0.744 -0.741 2.215 1.096 -0.033 -1.398 0.000 1.893 -0.202 0.147 0.000 0.940 1.027 0.992 1.346 0.588 1.103 0.925 +1 0.565 -0.087 1.533 1.256 -0.860 0.984 1.911 0.693 0.000 0.825 0.638 -0.920 0.000 0.367 1.448 1.276 1.274 0.783 0.823 -0.267 3.102 1.876 1.220 0.988 0.682 0.422 0.821 0.720 +0 1.655 0.577 1.033 2.347 1.377 1.478 -0.847 -0.517 0.000 0.503 0.263 -1.367 2.215 0.562 0.668 -0.668 0.000 0.394 -0.173 0.798 1.551 1.460 0.947 0.993 0.815 0.386 0.697 1.105 +1 0.875 -0.071 0.384 0.425 -1.563 1.362 0.424 1.481 1.087 1.129 0.526 -0.258 0.000 1.246 -0.957 -0.650 0.000 0.793 0.611 0.620 0.000 0.884 0.713 0.983 1.067 0.667 0.877 0.797 +1 2.092 -0.951 -1.724 0.926 1.157 0.908 -1.167 -0.445 1.087 0.904 -0.543 0.041 0.000 0.780 -0.269 1.273 0.000 0.429 0.966 0.098 3.102 1.167 1.108 0.999 0.984 1.009 0.998 0.907 +0 0.322 2.013 1.676 2.849 1.332 1.346 -0.884 -0.195 1.087 0.563 0.988 -1.600 0.000 0.609 0.303 0.423 0.000 0.841 -0.451 -0.699 0.000 0.924 0.662 0.985 2.528 1.052 1.598 1.236 +0 0.752 -0.129 -1.204 0.627 -1.436 0.431 0.717 1.551 2.173 0.539 -0.220 -0.295 0.000 1.098 1.507 0.512 2.548 0.630 0.007 0.311 0.000 0.404 0.867 0.984 0.599 0.805 0.690 0.675 +1 0.691 -1.621 -0.259 1.555 0.158 1.274 -0.914 1.587 2.173 0.335 -2.450 -0.486 0.000 0.796 -0.268 -0.984 0.000 0.485 0.649 -1.525 3.102 0.829 0.823 0.993 1.436 0.842 0.989 0.849 +1 1.876 0.909 -1.176 0.559 1.672 0.722 -0.774 0.419 0.000 1.183 1.444 0.731 2.215 0.612 0.041 -0.285 0.000 0.560 0.550 1.452 3.102 0.873 0.757 0.983 1.264 0.543 0.938 0.983 +1 1.331 -0.092 -0.994 1.308 1.712 1.007 0.822 0.281 0.000 0.859 0.135 -1.635 0.000 1.124 -0.660 0.525 1.274 0.603 -1.931 -0.359 0.000 1.697 0.980 1.181 1.099 0.494 0.739 0.776 +1 0.419 2.187 -1.220 1.085 1.131 1.022 0.362 -0.539 0.000 0.755 1.364 1.547 2.215 0.877 0.941 -0.136 0.000 1.038 -0.168 1.066 3.102 0.837 1.057 0.993 0.563 0.788 0.895 0.835 +1 0.691 2.044 -1.157 0.901 0.396 0.849 1.435 1.142 0.000 1.073 0.789 -0.638 2.215 0.798 -0.091 1.287 2.548 1.099 1.484 -0.395 0.000 1.471 1.207 1.077 0.978 1.075 0.964 0.895 +0 1.158 -0.507 -0.255 0.761 1.359 0.912 1.854 -1.017 0.000 0.292 2.200 -0.192 0.000 0.738 0.857 1.122 2.548 0.942 -0.419 1.363 3.102 0.773 0.940 1.292 0.738 0.520 0.911 1.059 +0 1.587 -0.621 -0.508 1.242 -0.873 1.131 -0.936 0.923 0.000 0.952 -0.051 -1.591 2.215 1.112 -0.473 0.509 0.000 0.737 -0.502 1.430 3.102 0.854 1.291 0.984 0.807 0.373 0.769 0.958 +0 1.535 -0.133 1.283 0.386 -1.361 0.879 -0.178 -0.664 2.173 0.844 -0.463 0.324 1.107 0.964 0.076 0.049 0.000 1.198 -0.759 -0.964 0.000 1.031 1.035 0.985 1.069 1.004 0.844 0.802 +0 1.235 1.100 0.371 0.195 1.652 1.328 0.457 -0.068 0.000 1.284 -2.278 -1.038 0.000 1.674 -1.786 -1.418 0.000 1.538 -0.206 0.959 0.000 0.945 0.837 0.984 0.586 0.813 1.082 1.295 +1 0.907 -1.094 1.692 0.265 -0.079 1.223 -0.780 -1.534 0.000 1.545 1.597 0.086 0.000 0.851 -0.122 0.047 2.548 0.932 0.058 -1.118 1.551 1.514 0.987 0.982 0.881 0.594 0.778 0.760 +1 0.734 1.633 0.916 0.758 -1.131 0.888 -0.235 0.128 2.173 0.433 1.692 -1.438 0.000 0.654 0.427 0.970 0.000 0.730 -0.174 -0.965 3.102 0.873 1.187 0.995 0.787 0.710 0.908 0.795 +1 0.726 -0.462 -1.239 3.277 -0.698 2.209 -0.448 1.021 0.000 0.813 0.613 0.920 2.215 1.079 -0.514 -0.117 2.548 0.756 -1.246 -0.849 0.000 2.238 1.577 1.003 0.819 1.019 1.133 1.342 +1 2.003 0.130 0.243 1.402 -0.472 0.413 1.107 -0.456 2.173 0.870 -0.677 -1.525 0.000 1.314 0.099 1.133 0.000 0.897 0.654 -1.409 3.102 0.926 0.697 1.393 1.007 0.499 0.719 0.655 +1 1.214 0.251 -1.419 0.777 -0.443 0.685 1.359 0.071 2.173 0.669 0.347 1.588 0.000 0.444 1.465 0.648 0.000 1.101 1.230 1.150 3.102 0.838 0.916 1.038 0.890 0.760 0.801 0.711 +0 1.751 0.332 -0.335 2.592 -1.139 0.967 0.260 0.921 1.087 1.492 0.565 0.405 0.000 1.208 -0.033 1.388 2.548 0.931 -0.513 -1.369 0.000 1.820 1.278 1.955 1.433 0.585 1.247 1.180 +1 0.939 0.927 -1.455 0.326 -0.624 0.590 1.211 0.906 1.087 1.171 1.130 1.715 2.215 2.160 2.301 -0.013 0.000 0.700 1.796 -0.840 0.000 0.954 1.230 0.990 0.720 0.815 1.108 0.931 +1 0.981 -0.254 0.941 1.470 -1.699 0.966 1.144 -0.794 0.000 1.527 -0.037 0.404 2.215 1.035 0.751 -1.393 0.000 0.708 0.458 -0.085 3.102 0.970 0.762 1.150 1.174 0.488 1.017 1.041 +0 0.523 1.008 1.355 0.833 -0.562 0.564 0.528 -0.076 0.000 0.705 -0.533 1.532 2.215 0.894 0.743 -1.314 2.548 1.540 -0.012 0.720 0.000 1.015 1.006 0.985 0.776 0.773 0.774 0.684 +0 0.639 -0.478 -1.467 1.897 1.313 0.291 -0.117 -0.337 2.173 0.429 1.468 -0.930 0.000 0.886 0.872 -0.204 0.000 0.382 -1.038 0.100 1.551 0.702 0.711 0.987 0.837 0.250 0.562 0.768 +0 0.385 -1.086 0.594 0.279 0.292 1.881 0.494 -1.581 2.173 2.330 -0.184 0.320 2.215 1.086 0.456 -1.139 0.000 0.742 0.610 -0.280 0.000 0.702 1.012 0.982 0.880 3.234 1.517 1.173 +0 0.570 0.216 0.671 0.733 -0.971 0.980 -0.654 -0.909 2.173 0.551 0.333 0.954 0.000 0.562 1.042 -1.561 0.000 2.065 0.946 0.443 3.102 0.931 0.975 0.989 1.006 2.091 1.092 0.889 +0 0.330 -1.826 1.621 1.614 -1.733 1.749 0.072 0.221 0.000 1.558 -0.933 -1.128 2.215 0.696 -0.514 0.733 0.000 0.711 1.300 -0.801 0.000 1.092 0.965 0.992 0.923 0.894 1.239 1.115 +0 1.117 -0.543 0.562 0.337 -1.426 0.551 -0.984 -1.050 2.173 0.858 1.271 1.476 2.215 0.609 1.675 0.018 0.000 0.559 -0.095 -0.603 0.000 0.810 0.863 0.988 0.775 1.631 1.034 0.953 +0 1.211 0.751 -1.263 0.819 1.254 1.252 0.622 0.011 2.173 1.275 -0.417 -1.538 2.215 0.805 0.097 0.416 0.000 0.677 0.647 1.158 0.000 0.576 0.984 1.057 1.243 2.100 1.175 0.932 +0 0.642 -0.181 -1.409 0.687 -1.123 0.893 1.362 0.201 0.000 1.003 -0.090 0.652 0.000 1.150 -0.642 1.589 0.000 0.919 0.347 1.454 0.000 0.888 0.994 0.982 1.559 0.497 1.098 1.000 +1 2.163 0.414 -1.161 1.134 -0.615 0.820 -0.852 0.502 2.173 0.666 -0.323 -1.693 0.000 0.766 0.936 0.893 0.000 0.970 0.736 0.250 3.102 1.131 0.856 1.027 1.601 0.955 1.098 0.980 +0 0.543 0.473 0.242 1.231 -0.853 0.700 0.799 1.131 1.087 0.454 1.647 0.350 0.000 0.566 1.963 1.072 0.000 1.578 0.035 -1.547 3.102 0.930 0.943 0.990 0.924 0.859 0.749 0.693 +0 1.111 -0.047 0.415 0.190 0.984 0.449 1.469 0.037 0.000 0.889 1.165 1.382 1.107 0.935 0.902 -1.419 2.548 1.379 1.439 -0.859 0.000 0.880 1.003 0.985 1.038 0.570 0.899 0.959 +0 1.060 -0.651 -1.154 0.427 -1.155 0.586 0.321 0.817 2.173 0.858 -0.083 0.352 2.215 0.778 1.006 -0.724 0.000 0.801 -0.130 -1.327 0.000 0.738 0.900 0.978 1.193 0.476 0.924 0.878 +0 1.039 -0.349 -1.377 1.023 -0.560 0.831 -1.378 0.530 0.000 1.469 0.009 -1.686 1.107 1.089 -0.490 0.349 0.000 0.898 -0.533 -0.677 3.102 0.803 0.849 0.986 0.879 0.887 1.050 0.945 +0 0.344 -1.812 0.167 0.333 -0.778 1.433 -0.548 -1.281 2.173 1.115 -0.761 0.591 2.215 0.837 1.918 0.265 0.000 0.719 -1.674 1.157 0.000 0.839 1.563 0.990 0.952 1.860 1.569 1.213 +0 3.324 0.471 -0.979 0.980 -0.596 1.497 0.292 0.818 2.173 0.641 -0.249 0.424 0.000 1.615 -0.527 1.039 0.000 1.385 1.067 -1.176 1.551 0.861 0.913 0.997 0.697 1.678 1.398 1.351 +1 0.536 -0.242 1.194 1.174 -0.234 0.598 -0.563 -0.689 0.000 0.871 -1.289 1.003 2.215 1.345 -0.048 0.986 2.548 1.015 -1.673 -0.679 0.000 0.933 1.118 1.055 0.803 0.784 0.945 0.837 +1 1.147 -1.031 -1.239 0.426 1.431 1.639 1.635 1.527 0.000 1.115 -0.408 0.007 2.215 1.803 0.503 0.069 2.548 0.682 -0.751 -0.161 0.000 0.609 0.669 0.984 0.975 0.766 0.928 0.733 +1 0.755 -2.203 -0.889 0.666 0.354 0.754 -0.376 0.014 0.000 0.927 -2.655 -1.572 0.000 0.771 -0.054 -0.311 2.548 1.589 -0.171 1.429 3.102 3.143 1.997 0.987 1.044 0.848 1.378 1.117 +1 0.999 -1.843 -0.413 1.144 -0.594 0.794 -0.472 0.977 2.173 0.446 -2.088 0.772 0.000 0.714 0.197 -1.179 2.548 0.530 -0.491 1.640 0.000 0.705 0.857 1.008 1.230 0.934 0.913 0.791 +0 0.971 0.068 -0.483 0.753 0.269 0.696 0.891 -1.638 0.000 0.539 -0.637 -1.343 0.000 1.221 -0.147 0.808 2.548 0.517 0.452 0.041 1.551 1.168 0.823 0.979 0.845 0.444 0.706 0.715 +0 1.011 -1.399 0.504 0.375 1.333 1.412 -1.194 -0.135 2.173 0.951 -1.113 -1.635 0.000 1.828 -0.520 1.504 0.000 0.429 0.079 -0.420 3.102 0.857 0.790 0.993 1.001 0.611 1.116 0.930 +0 1.798 -0.380 1.201 0.292 -0.363 1.298 0.556 1.519 1.087 2.321 0.781 -0.286 2.215 0.683 0.506 -1.300 0.000 0.561 -1.441 -0.454 0.000 1.072 1.300 0.991 1.694 2.569 1.472 1.226 +1 1.381 1.042 0.792 0.943 -1.682 0.741 2.007 -0.813 0.000 1.353 1.019 -0.145 2.215 0.623 0.896 -1.256 0.000 0.948 1.111 -1.029 1.551 0.818 1.058 1.252 0.812 0.745 0.814 0.801 +1 0.662 -2.291 1.023 0.997 -0.962 0.777 -0.641 -0.630 2.173 0.789 -0.531 0.225 2.215 1.397 -1.186 1.103 0.000 0.782 -1.886 1.742 0.000 0.852 1.025 1.099 1.160 0.804 0.949 0.903 +1 0.745 -0.227 -0.881 0.279 -0.044 0.737 0.507 -1.677 0.000 0.858 -0.464 -0.222 0.000 1.288 -0.537 1.277 2.548 1.624 0.185 0.268 0.000 0.856 1.118 0.988 0.530 0.256 0.664 0.674 +0 0.585 1.509 1.709 1.521 0.616 0.919 -0.517 -1.398 0.000 0.881 0.698 0.136 2.215 0.471 0.250 0.554 2.548 1.016 -0.143 -0.071 0.000 1.397 0.943 1.089 0.844 0.295 0.800 0.969 +1 0.600 -0.148 0.432 0.574 -0.715 1.086 -1.158 0.691 0.000 0.567 -1.347 -1.365 0.000 0.360 -0.871 -0.234 0.000 1.902 -0.289 -1.110 3.102 0.934 0.748 0.986 0.516 0.242 0.537 0.596 +1 1.006 0.090 -0.264 2.000 0.367 1.047 -0.827 -1.487 2.173 0.576 -0.059 1.492 0.000 0.882 0.245 -0.815 0.000 0.572 -0.433 0.832 3.102 0.972 0.961 1.058 0.615 0.722 1.003 0.868 +0 1.356 1.606 -1.658 0.958 0.660 0.526 -2.811 1.407 0.000 0.814 0.816 -0.492 2.215 1.240 0.211 -0.078 2.548 1.254 0.867 0.145 0.000 0.816 0.710 1.372 1.067 0.510 0.920 0.751 +1 0.912 -0.332 1.580 0.317 -1.246 2.927 1.235 -0.135 0.000 2.463 1.021 1.407 0.000 2.055 0.537 -1.557 2.548 1.019 0.660 0.911 0.000 0.932 1.154 0.991 0.860 0.985 1.013 0.865 +0 0.953 -1.152 -0.398 0.699 1.200 0.912 -0.385 1.061 2.173 1.943 -0.983 -0.879 2.215 0.901 -1.101 1.192 0.000 1.052 -1.166 0.464 0.000 0.663 0.717 1.121 0.990 2.025 1.097 0.911 +1 0.830 1.119 1.314 0.918 -1.198 0.641 1.276 -1.065 0.000 0.810 0.948 0.253 0.000 1.145 0.031 0.868 1.274 0.858 1.868 -0.782 0.000 1.140 1.172 0.990 0.606 0.584 0.691 0.689 +1 0.485 1.001 -0.506 1.485 -0.200 1.615 -0.301 -1.710 2.173 1.470 1.471 0.276 0.000 0.860 1.008 1.581 0.000 1.107 0.533 -1.132 3.102 1.631 1.223 0.998 2.858 0.989 1.773 1.573 +1 1.273 -1.399 0.647 1.112 1.260 0.818 -0.549 0.056 0.000 0.611 -0.030 -0.510 2.215 1.634 -0.886 -1.443 2.548 0.983 -0.742 -0.980 0.000 1.125 1.185 0.986 1.186 0.947 0.986 0.941 +1 1.581 0.452 0.538 1.062 -0.067 1.022 -0.940 -1.242 2.173 0.604 -0.481 1.160 2.215 0.484 -1.251 -1.686 0.000 0.686 -0.369 -0.222 0.000 0.687 0.678 0.986 0.823 0.994 1.081 0.854 +1 0.762 0.590 -1.425 0.709 -1.520 0.799 -0.360 -1.533 0.000 1.223 -1.587 0.577 0.000 1.923 0.010 0.079 2.548 1.845 -0.478 -0.506 3.102 2.443 1.691 0.981 1.138 0.841 1.285 1.112 +0 2.481 -0.420 -0.702 0.443 0.261 0.954 -1.506 1.408 0.000 0.771 -1.134 -0.060 2.215 0.826 -0.002 0.978 2.548 0.881 -2.041 0.574 0.000 1.171 1.142 1.108 0.982 0.860 0.881 1.017 +1 0.549 1.171 -1.446 0.446 1.438 0.999 -0.156 0.346 2.173 0.925 -1.075 -0.886 0.000 0.865 -0.039 -1.292 0.000 1.364 0.555 1.112 3.102 0.908 1.215 0.982 0.979 0.945 1.017 0.863 +0 0.839 -0.076 -1.000 1.018 0.212 1.104 -1.153 0.632 0.000 1.291 -1.067 -1.402 2.215 0.647 -1.951 -1.503 0.000 0.539 -1.163 -0.248 1.551 1.623 0.948 1.136 1.109 0.659 0.876 0.907 +1 0.669 -0.427 -1.252 1.465 -0.204 1.268 -1.237 -1.576 0.000 0.862 2.113 0.638 0.000 0.442 -0.467 -1.631 0.000 2.089 0.217 0.292 3.102 0.511 1.583 1.112 0.905 0.650 1.251 1.045 +1 0.348 -1.474 0.773 1.901 1.470 0.905 0.374 -0.524 2.173 0.297 1.349 -0.780 0.000 0.720 0.884 0.592 2.548 0.397 -0.545 0.546 0.000 0.683 0.632 0.983 0.915 0.902 0.973 0.775 +1 1.554 0.932 -0.865 1.540 -1.449 1.958 1.826 0.528 0.000 2.703 -0.172 -1.515 1.107 2.068 0.939 0.287 0.000 0.780 -0.152 -0.372 3.102 1.703 1.640 1.074 1.479 1.124 2.453 1.979 +1 1.881 0.567 -0.339 0.602 0.492 1.177 1.298 -1.643 2.173 0.762 1.127 0.313 0.000 0.436 0.680 -1.477 0.000 1.389 0.582 1.146 3.102 0.902 1.081 1.004 0.909 0.900 1.009 0.839 +1 1.013 0.085 1.562 0.378 -1.614 0.794 0.421 0.840 2.173 1.266 0.298 -0.331 0.000 0.889 -1.103 -1.588 2.548 0.719 -0.501 0.625 0.000 1.071 1.177 0.980 0.804 1.281 1.010 0.953 +1 0.845 0.052 1.722 0.596 0.105 0.774 -0.227 -1.466 0.000 1.048 -0.525 0.707 2.215 1.057 0.223 0.073 2.548 1.064 -0.860 -0.858 0.000 0.922 1.086 0.988 0.778 0.754 0.878 0.755 +0 1.440 -0.114 -0.678 0.885 0.995 1.367 1.746 1.486 0.000 1.816 0.385 0.238 2.215 1.337 0.434 -0.242 0.000 1.819 0.802 -1.450 3.102 2.951 1.793 1.561 1.228 1.705 1.561 1.412 +0 0.749 -0.900 0.788 0.755 1.183 1.346 1.337 -0.415 0.000 1.588 -0.639 -1.277 2.215 1.406 -1.171 1.127 2.548 1.621 -0.831 0.438 0.000 3.653 2.929 0.979 0.603 1.409 2.111 1.927 +1 0.282 -2.020 -0.779 1.574 1.269 0.648 0.499 1.567 0.000 0.582 2.487 -0.403 0.000 0.607 -0.959 0.530 0.000 1.515 0.109 -0.495 0.000 0.795 0.767 0.982 1.892 0.677 1.745 1.319 +0 1.108 1.331 0.422 0.520 -1.624 0.971 0.792 -0.401 2.173 0.829 0.435 1.535 1.107 0.531 -0.780 -1.026 0.000 0.758 -0.841 1.273 0.000 0.616 1.179 1.012 0.796 1.321 0.876 0.859 +0 0.952 -0.554 -1.594 0.613 0.427 0.627 -1.082 1.178 0.000 1.048 -0.184 -1.271 0.000 1.337 0.213 0.303 2.548 0.988 -1.199 -0.643 3.102 0.806 1.062 1.026 0.699 1.057 0.808 0.726 +0 1.206 1.413 0.767 0.807 -0.228 0.678 -1.047 -1.740 2.173 0.654 -2.534 -1.244 0.000 0.407 -1.589 0.291 0.000 0.821 0.108 -0.424 1.551 0.853 0.915 1.067 0.765 0.884 1.138 1.531 +1 0.544 -0.966 -1.593 0.794 0.410 0.717 -0.413 -0.900 0.000 0.616 -1.050 -0.369 2.215 1.436 -0.612 1.264 2.548 0.485 1.061 0.494 0.000 1.210 0.907 0.991 0.695 1.016 0.823 0.709 +0 0.363 -0.747 1.667 2.057 0.504 0.800 1.871 -1.625 0.000 0.662 -2.131 -0.480 0.000 0.675 0.420 -0.526 0.000 1.585 -1.073 -1.154 3.102 0.802 0.894 1.038 1.001 1.209 1.101 1.003 +0 1.639 -0.467 1.004 0.571 1.324 1.114 -1.460 -0.219 0.000 1.803 1.201 -1.306 2.215 0.882 1.389 -0.710 0.000 1.657 0.451 0.892 3.102 0.994 1.044 0.987 0.931 1.534 1.493 1.237 +1 0.613 0.155 1.293 0.884 -1.449 0.899 1.719 0.660 0.000 1.131 0.161 -0.856 2.215 0.884 1.479 -0.989 2.548 0.589 1.568 -0.327 0.000 0.946 0.984 0.981 1.216 0.856 0.909 0.843 +1 0.633 0.586 -0.472 1.327 -1.028 0.627 1.325 1.302 0.000 0.702 0.635 -0.245 2.215 0.470 2.160 -1.545 0.000 0.862 1.523 0.281 0.000 0.936 0.933 0.984 0.721 0.593 0.619 0.799 +1 2.111 0.434 1.391 0.402 0.331 0.708 0.673 0.910 2.173 0.767 1.198 -1.023 0.000 1.143 -1.323 -0.468 2.548 1.346 -0.209 -0.556 0.000 0.752 0.986 1.041 0.644 1.786 1.124 0.964 +1 0.385 -0.358 1.250 1.778 -1.309 1.193 -0.567 -0.392 2.173 0.951 0.110 0.513 2.215 0.461 -1.429 1.370 0.000 0.382 -1.831 -1.391 0.000 0.318 0.961 0.983 1.211 1.266 1.060 0.857 +1 0.666 -1.386 -0.955 1.362 -0.234 1.263 1.129 1.526 0.000 0.845 -0.932 1.108 1.107 2.164 -0.741 -0.076 2.548 0.555 -0.862 -0.630 0.000 0.768 1.003 0.996 0.967 1.263 0.821 0.752 +0 0.725 0.784 1.106 1.408 0.360 0.685 0.274 1.622 2.173 0.689 0.670 -0.063 2.215 0.405 0.649 -1.362 0.000 1.231 -0.537 -1.199 0.000 0.582 0.800 0.987 0.887 1.032 0.713 0.701 +1 2.101 -0.007 -1.083 1.104 -0.630 0.621 -0.279 1.610 0.000 1.390 -0.933 0.582 2.215 0.813 -2.067 -0.753 0.000 1.541 0.436 0.069 3.102 0.943 1.057 0.991 1.030 1.224 1.157 1.093 +0 0.600 -2.395 -1.504 0.886 -0.305 0.662 -0.205 0.920 2.173 0.571 -0.840 -1.041 0.000 0.681 -0.337 1.335 2.548 0.928 -1.002 0.024 0.000 0.793 0.925 0.985 0.850 0.312 0.812 0.712 +0 0.719 -1.899 -0.204 1.242 0.404 0.810 -0.340 -1.164 2.173 0.643 -0.457 -0.700 2.215 0.679 -1.522 1.304 0.000 0.379 1.506 0.617 0.000 1.579 1.097 0.991 1.175 0.436 0.807 0.868 +1 0.628 0.172 0.227 2.754 -0.474 0.823 0.935 1.016 2.173 1.062 -0.269 1.596 1.107 0.914 -0.262 -1.233 0.000 0.469 -0.006 0.939 0.000 0.676 0.937 1.076 1.368 1.126 1.196 0.943 +1 0.483 1.215 -1.188 0.843 1.565 0.622 0.565 -1.394 0.000 1.203 0.517 1.515 0.000 1.271 0.390 -0.194 2.548 2.168 -0.426 0.515 3.102 0.912 1.239 0.980 2.053 0.977 1.430 1.270 +0 1.640 -2.185 1.590 0.717 1.696 0.664 -0.320 -0.715 1.087 0.743 1.023 0.377 2.215 0.725 -2.235 -0.107 0.000 0.625 0.184 0.284 0.000 1.342 1.138 1.005 2.166 1.153 1.522 1.305 +1 1.563 -1.177 -1.545 1.261 -1.092 1.345 -1.148 0.644 2.173 0.596 -0.555 -1.537 0.000 1.068 -0.561 -0.214 2.548 0.608 -1.996 -0.044 0.000 1.121 0.897 0.980 1.582 1.121 1.131 0.950 +1 0.756 0.605 -1.013 1.022 1.101 0.725 0.638 -0.511 0.000 0.687 -0.257 0.249 2.215 0.794 -1.046 1.235 0.000 1.756 -0.175 1.718 1.551 1.951 1.219 1.151 0.784 0.961 0.899 0.826 +0 2.354 0.365 -0.899 0.603 -0.924 1.411 -0.270 1.065 2.173 0.528 0.354 0.823 0.000 0.438 1.329 -0.296 0.000 0.393 1.939 0.762 0.000 0.755 1.066 0.979 0.698 0.641 1.080 0.912 +1 0.844 1.553 0.847 0.809 1.698 0.798 1.020 -1.444 0.000 1.025 0.514 -0.277 2.215 1.220 1.293 0.055 0.000 0.695 0.799 1.479 3.102 0.893 1.039 0.992 0.486 0.780 0.668 0.667 +1 1.098 -1.293 0.346 1.101 -1.129 0.931 0.762 1.685 2.173 1.323 0.046 0.531 0.000 1.739 -1.029 -0.476 0.000 0.961 -0.940 1.610 3.102 0.838 0.604 1.479 1.808 1.102 1.175 0.995 +1 0.406 -0.710 0.939 0.446 -1.314 0.370 -0.512 0.680 0.000 0.565 0.470 1.268 2.215 0.692 -0.124 -0.770 2.548 0.434 -1.402 -0.375 0.000 0.626 0.692 0.981 0.622 0.674 0.526 0.539 +1 0.403 0.775 -0.469 0.559 0.224 0.943 1.609 -0.698 0.000 1.022 -1.756 1.265 0.000 1.376 1.272 0.771 1.274 1.185 1.079 -1.248 0.000 0.813 0.980 0.982 1.189 0.848 0.881 0.982 +1 0.733 1.283 0.610 1.413 0.944 1.168 -0.014 -0.626 2.173 0.543 0.726 -1.698 2.215 0.738 -0.035 -1.306 0.000 0.514 0.947 1.062 0.000 0.713 0.893 0.978 0.929 1.066 1.422 1.107 +0 0.399 0.973 -0.820 1.063 1.682 0.440 1.814 -0.866 0.000 0.933 0.623 0.785 2.215 1.257 0.852 -0.297 2.548 0.509 2.081 1.221 0.000 0.731 0.994 0.979 0.971 0.968 0.869 0.747 +0 0.409 1.925 -0.728 1.518 1.453 0.758 1.030 0.561 2.173 0.681 1.091 -0.676 2.215 0.607 0.024 -0.872 0.000 0.915 1.150 -1.654 0.000 0.946 0.873 1.008 0.865 0.950 0.805 0.736 +1 1.042 -0.999 0.802 0.743 -1.088 0.578 -1.182 -0.591 0.000 1.167 0.105 0.742 1.107 0.695 0.857 -0.882 2.548 0.666 -0.870 -1.154 0.000 0.657 0.948 1.209 1.015 1.037 0.897 0.832 +1 0.389 2.026 -0.986 1.851 -0.766 0.716 -0.741 0.840 2.173 0.457 0.363 -0.794 0.000 0.730 0.275 0.635 0.000 1.330 0.409 1.642 3.102 0.851 0.882 0.980 0.863 0.964 1.054 0.849 +0 0.876 0.135 0.655 1.153 0.480 0.572 -0.981 -1.483 2.173 0.895 -1.483 -0.575 2.215 0.497 0.533 -1.201 0.000 0.533 -0.874 1.483 0.000 0.639 0.869 0.985 0.990 0.821 0.863 0.740 +0 0.400 0.378 1.059 1.725 0.163 0.341 1.893 -1.604 0.000 0.560 0.825 0.586 0.000 1.541 0.615 -1.023 2.548 0.511 0.264 -1.296 0.000 0.998 0.998 0.984 0.587 0.734 0.785 0.828 +1 0.876 0.822 0.658 1.272 0.414 1.271 -0.435 -1.038 2.173 0.473 -1.231 -1.509 0.000 1.247 0.218 0.603 0.000 0.760 -1.202 1.395 3.102 1.478 0.959 0.989 2.221 1.006 1.617 1.383 +1 1.030 -1.509 0.131 0.715 1.329 0.555 -0.837 0.182 2.173 1.358 -1.405 0.838 1.107 2.240 -1.104 -1.520 0.000 1.743 0.347 -0.059 0.000 0.705 1.170 1.048 0.712 0.811 1.007 0.864 +1 1.240 0.483 -1.655 0.731 -0.635 1.010 0.222 -1.017 2.173 1.083 -0.622 0.519 0.000 1.235 1.397 0.360 0.000 0.730 1.749 1.485 0.000 0.938 0.828 1.049 0.709 0.804 0.936 0.821 +0 2.324 0.426 -1.190 0.297 1.053 0.884 0.179 0.373 2.173 0.686 -0.244 0.770 0.000 1.030 1.233 1.507 2.548 1.412 -1.085 0.139 0.000 0.850 0.927 1.036 0.858 1.254 0.986 0.980 +1 0.420 -0.987 0.130 1.341 1.544 1.233 -2.941 1.598 0.000 1.932 -0.011 -0.268 2.215 0.672 0.217 0.066 0.000 1.342 -0.323 0.647 0.000 0.621 0.878 0.994 0.866 0.663 0.948 0.806 +1 0.371 -2.272 -1.504 0.777 -1.741 0.698 -0.976 0.608 0.000 0.990 -0.458 -0.313 1.107 1.638 -0.399 -1.108 2.548 1.031 0.310 0.558 0.000 0.951 0.977 0.995 0.757 0.889 0.905 0.835 +1 0.620 1.262 -1.627 0.367 -0.538 1.096 0.846 -0.300 0.000 1.155 0.925 1.229 2.215 0.691 0.993 0.308 0.000 0.551 -0.777 1.598 3.102 0.837 1.046 0.993 0.827 0.819 0.923 0.794 +1 1.433 -0.277 1.504 0.976 -1.332 1.215 0.453 0.057 0.000 0.660 -0.281 -1.194 2.215 0.596 0.505 0.505 0.000 1.297 0.480 -0.795 0.000 0.893 0.751 0.986 0.706 0.411 0.511 0.601 +0 1.116 0.106 -0.597 3.477 -0.913 1.676 -0.304 0.201 0.000 1.384 1.358 1.636 2.215 1.354 2.386 1.245 0.000 1.700 1.438 1.177 0.000 1.001 1.024 0.991 2.162 1.409 1.567 1.382 +0 0.569 -0.610 0.127 0.645 -0.711 0.636 0.541 -1.697 0.000 1.456 -0.411 0.375 0.000 1.508 -0.865 -1.436 2.548 1.333 -1.163 0.562 0.000 0.889 1.204 0.999 0.528 0.725 0.749 0.663 +0 1.394 -1.095 -0.022 0.527 -1.116 0.690 0.136 1.641 0.000 1.014 -0.914 1.608 0.000 1.970 -0.147 0.980 2.548 1.187 2.280 -0.394 0.000 1.006 1.047 0.989 0.773 1.464 1.018 0.986 +0 0.760 -0.377 -1.609 0.829 -0.808 0.623 -0.158 -0.575 1.087 0.864 0.840 0.947 2.215 0.558 0.767 0.417 0.000 0.372 -0.415 1.119 0.000 0.469 0.648 0.992 1.274 1.205 0.943 0.755 +1 0.998 1.339 -0.989 0.611 -1.345 0.785 0.579 1.094 2.173 1.375 0.751 -0.209 0.000 1.600 0.467 -1.644 1.274 1.250 1.165 0.622 0.000 1.284 1.257 1.003 1.129 0.871 1.028 1.065 +0 0.519 -0.939 1.271 0.238 1.268 0.796 0.634 -0.462 0.000 1.300 0.011 -1.707 2.215 2.143 -0.264 0.047 2.548 1.127 1.106 -1.440 0.000 0.827 0.902 0.989 0.988 1.794 1.030 0.853 +1 0.706 -0.760 1.114 0.423 0.203 0.883 2.281 -0.338 0.000 0.716 0.480 1.445 2.215 0.741 -0.926 -1.068 2.548 0.708 -1.320 -0.026 0.000 0.737 1.231 0.985 0.705 0.876 0.792 0.734 +1 0.937 0.241 1.678 0.614 0.919 0.885 0.729 0.012 0.000 1.196 -0.603 1.566 2.215 1.207 0.044 -0.309 0.000 0.623 -2.277 -1.378 0.000 0.841 0.849 0.987 0.634 0.944 1.037 0.907 +0 0.767 0.028 0.703 1.457 -1.151 0.728 0.738 -1.577 0.000 1.225 -0.532 0.499 2.215 1.016 2.083 0.332 0.000 0.970 1.044 -0.184 0.000 0.769 1.658 1.457 1.166 0.920 1.361 1.191 +0 0.953 -1.325 -0.761 1.863 -1.563 0.403 -0.268 -0.126 2.173 1.137 0.855 0.660 2.215 0.758 -0.751 0.988 0.000 0.639 0.892 -0.336 0.000 0.947 0.865 1.219 2.095 0.887 1.361 1.159 +1 1.419 -2.151 1.506 0.740 0.455 1.292 -0.850 -0.793 2.173 0.523 -1.185 -0.164 1.107 0.827 0.344 1.340 0.000 0.671 -0.141 0.827 0.000 1.195 0.963 1.152 1.604 0.686 1.054 1.061 +1 1.416 0.413 1.361 1.339 -0.090 1.308 0.945 -0.590 0.000 0.452 1.165 1.620 2.215 1.103 2.017 1.284 0.000 0.814 -0.825 0.577 3.102 2.592 1.498 1.842 1.053 0.851 1.240 1.154 +0 1.261 -1.439 -0.828 0.310 0.515 0.664 0.752 0.062 0.000 0.534 -0.255 0.682 0.000 0.908 -0.919 1.051 2.548 1.463 -0.000 -1.079 3.102 0.900 0.876 0.988 0.771 0.949 0.685 0.643 +0 0.609 -0.347 -0.301 0.453 1.602 0.660 -1.038 0.933 0.000 1.056 -0.105 1.166 2.215 0.880 -0.307 -0.613 0.000 1.337 1.392 -0.881 0.000 0.691 0.750 0.986 0.786 0.763 0.579 0.611 +0 1.342 -0.382 0.018 0.121 0.349 1.148 -0.237 -1.104 2.173 1.576 -0.448 0.609 2.215 0.324 -1.028 1.099 0.000 0.977 -0.009 -1.666 0.000 0.524 0.821 0.986 1.091 1.991 1.073 0.853 +0 0.597 1.864 0.287 0.639 0.851 0.625 2.111 1.015 0.000 1.152 -0.277 -0.401 2.215 0.991 -0.655 -1.276 1.274 0.489 -0.562 1.129 0.000 1.616 1.709 0.980 1.066 0.843 1.310 1.076 +0 1.663 -1.109 0.455 1.582 -0.158 1.294 -1.463 -1.708 0.000 1.191 -0.720 -0.447 2.215 1.000 -0.989 1.089 2.548 1.481 -1.864 -1.466 0.000 0.945 0.992 1.177 0.924 1.157 1.092 1.146 +1 1.787 -1.516 -1.071 0.775 0.157 0.627 0.184 0.296 2.173 0.782 -0.841 -1.153 0.000 1.017 -1.065 0.828 0.000 1.138 -0.429 1.496 3.102 1.352 0.872 1.459 1.412 0.851 0.999 0.898 +0 0.516 -0.215 0.373 0.976 -1.309 0.891 0.835 0.635 0.000 0.473 -1.489 -1.224 2.215 0.522 0.093 -0.953 2.548 0.496 1.658 1.498 0.000 0.940 0.866 0.988 0.704 0.498 0.944 0.798 +0 0.375 1.376 -1.710 1.723 0.649 0.538 2.044 -0.581 0.000 0.772 -1.063 0.964 0.000 1.486 0.839 -1.290 2.548 1.611 0.623 -0.559 3.102 0.839 0.876 0.989 0.918 0.732 0.814 0.751 +1 0.331 -0.424 1.182 0.817 0.365 1.313 0.890 -1.076 0.000 0.631 1.284 0.301 2.215 0.480 2.490 0.921 0.000 1.161 1.001 1.532 3.102 2.047 1.267 0.992 0.565 0.694 0.899 0.807 +0 0.401 -0.206 0.531 1.813 1.584 0.597 -0.818 0.016 0.000 0.609 -1.123 0.819 0.000 0.780 0.267 -0.577 2.548 0.441 0.393 -1.465 3.102 0.874 0.898 0.987 0.554 0.324 0.596 0.663 +0 1.962 -0.173 -1.116 0.133 1.145 1.027 -1.829 0.465 0.000 1.834 0.530 -0.927 2.215 1.700 0.814 0.958 0.000 0.818 1.670 0.500 0.000 0.923 0.944 0.990 0.943 1.286 1.124 1.087 +1 1.282 0.212 -0.174 1.081 -0.729 0.741 -1.618 0.297 0.000 1.173 -0.451 -1.462 2.215 1.139 -1.160 1.159 0.000 0.642 1.236 1.113 0.000 1.196 1.018 0.991 1.194 0.461 0.915 1.133 +1 0.942 -1.139 -1.001 0.896 1.549 1.742 -0.839 -1.253 1.087 2.248 2.024 0.509 0.000 1.161 0.015 0.350 2.548 1.983 0.085 -0.391 0.000 1.828 2.187 0.988 0.774 1.926 3.371 2.752 +1 1.201 1.506 -0.147 0.870 -0.726 1.266 0.539 0.853 2.173 0.425 1.782 -1.006 0.000 0.488 1.972 -1.465 0.000 0.510 -0.478 -0.668 1.551 0.304 0.712 0.990 1.317 0.972 0.907 0.834 +1 2.373 -1.468 0.390 0.986 -0.130 0.628 -0.702 -1.114 2.173 0.536 -1.777 -1.105 0.000 0.906 2.009 -1.450 0.000 1.390 -1.222 1.338 1.551 3.963 2.391 0.991 0.971 0.882 1.678 1.837 +0 0.907 0.421 -0.059 0.777 -0.550 0.477 -0.041 -1.590 0.000 1.100 -0.403 1.117 2.215 1.054 0.021 -0.800 2.548 0.572 -1.149 0.721 0.000 0.902 0.793 0.992 1.330 1.158 0.967 0.871 +0 0.340 0.441 0.190 1.619 1.262 0.778 0.773 -0.641 2.173 0.506 1.526 0.255 0.000 0.749 0.399 -1.071 2.548 0.665 -0.271 0.565 0.000 0.854 0.887 0.987 0.807 0.395 0.824 0.766 +0 3.825 -0.254 -1.436 0.872 -1.264 2.110 0.581 0.278 0.000 0.603 -0.123 -0.744 0.000 1.207 0.283 0.716 2.548 0.728 -0.922 1.405 3.102 2.099 1.333 0.999 0.828 0.691 1.015 1.385 +1 1.337 0.712 1.628 1.613 1.731 2.029 0.363 1.741 2.173 1.664 0.890 -0.041 0.000 0.889 1.076 0.366 0.000 1.417 -0.656 0.394 0.000 0.713 0.633 0.986 1.128 1.214 1.365 1.239 +1 0.303 0.242 0.273 0.470 0.527 1.034 -1.528 -0.907 2.173 1.104 -0.889 0.824 2.215 1.218 -0.962 -0.470 0.000 1.225 -0.876 1.638 0.000 1.056 1.072 0.975 2.876 1.650 2.057 1.742 +0 1.123 1.850 -1.063 2.117 -1.537 1.130 0.740 0.143 0.000 1.637 0.496 0.533 2.215 1.498 1.004 -1.674 2.548 0.708 1.327 -0.494 0.000 0.951 0.963 0.982 0.934 1.601 1.484 1.360 +1 1.021 -0.538 -1.726 0.297 -0.440 1.078 -0.232 -0.088 2.173 0.779 -0.924 1.293 0.000 1.341 0.421 0.535 2.548 0.829 -1.372 -1.381 0.000 0.887 1.291 0.991 1.084 0.958 1.032 0.915 +0 1.322 -1.823 0.492 1.347 0.637 0.820 0.157 -1.262 0.000 0.941 -0.833 -0.869 1.107 0.797 -0.740 1.566 2.548 0.395 -1.166 0.341 0.000 1.142 0.880 0.971 1.119 0.747 1.098 1.230 +1 0.561 -1.440 1.539 0.513 0.208 0.565 -0.538 1.297 2.173 0.855 -0.992 -1.166 0.000 0.819 -1.528 -0.535 0.000 1.239 0.402 0.314 3.102 0.814 0.999 0.985 0.720 0.834 0.875 0.740 +0 2.036 0.714 1.101 0.234 1.061 0.592 1.254 0.298 2.173 0.607 0.528 -0.939 0.000 0.998 -2.243 -0.841 0.000 0.863 1.120 -1.196 3.102 0.814 1.058 0.994 0.770 0.738 0.702 0.772 +0 0.726 -0.595 0.928 1.469 0.039 0.593 -1.574 -0.952 2.173 0.451 0.715 1.636 0.000 0.612 -0.967 -1.307 0.000 0.498 1.036 1.198 3.102 0.909 0.996 1.026 0.792 1.237 0.844 0.782 +0 2.145 -0.361 -1.499 0.824 -1.360 1.884 -0.190 0.515 0.000 2.011 0.325 -1.203 1.107 1.666 -0.443 0.138 2.548 1.187 0.219 0.909 0.000 0.951 0.925 0.966 1.135 1.996 1.517 1.453 +1 0.658 -0.108 0.317 0.842 1.307 1.469 0.634 -1.111 0.000 1.151 -0.321 0.772 2.215 1.058 0.169 -0.668 2.548 1.221 2.202 0.707 0.000 3.156 1.966 0.986 0.742 1.171 1.630 1.272 +1 0.638 -1.221 0.768 0.976 -0.904 0.680 0.371 1.017 0.000 1.059 -0.818 0.027 2.215 0.697 0.556 -1.710 0.000 1.631 -0.026 -0.741 3.102 0.794 1.010 1.091 0.798 0.903 0.909 0.876 +0 0.820 0.273 0.965 0.895 -1.306 0.648 0.037 0.413 2.173 1.342 0.009 -0.622 2.215 0.688 1.405 -1.677 0.000 0.601 -0.205 1.344 0.000 0.778 0.902 1.055 0.949 1.103 0.836 0.747 +0 0.765 -0.919 0.449 1.191 1.480 0.614 1.017 -0.021 2.173 1.212 -0.541 -1.182 2.215 0.593 1.935 0.380 0.000 0.580 -0.205 1.698 0.000 1.132 0.850 1.059 0.989 1.565 1.097 1.073 +1 0.579 1.620 0.366 0.592 0.234 2.496 0.569 -1.099 2.173 1.186 1.014 0.792 0.000 1.072 -0.341 1.587 2.548 1.343 0.094 0.691 0.000 0.913 1.210 0.998 1.559 1.678 1.495 1.219 +1 0.910 0.006 -1.343 0.452 0.252 1.069 -0.444 0.140 2.173 0.422 -1.018 0.980 0.000 1.229 0.295 1.592 2.548 0.913 0.277 -0.327 0.000 0.960 0.881 0.985 0.895 1.490 0.838 0.719 +1 1.726 0.627 -1.212 0.734 -0.413 0.714 2.244 1.300 0.000 1.363 0.527 1.688 0.000 2.367 0.220 0.213 2.548 2.095 0.956 -0.105 3.102 0.525 1.312 1.028 1.290 0.932 1.101 1.009 +1 1.212 -0.213 -1.188 1.116 -0.843 1.388 0.195 0.740 2.173 0.801 -0.367 0.256 0.000 0.604 -0.617 -0.871 0.000 1.180 -0.429 1.668 0.000 0.921 1.134 0.989 0.703 0.833 1.000 0.877 +1 0.328 0.149 1.386 1.461 -0.325 1.902 2.817 1.724 0.000 1.248 -0.976 0.133 2.215 1.200 -2.240 1.732 0.000 2.612 0.099 0.740 0.000 1.243 0.991 0.988 1.137 0.559 0.817 0.822 +1 0.651 0.939 1.122 0.484 0.841 1.242 0.783 -0.809 1.087 0.525 0.085 0.948 0.000 0.762 0.231 -1.417 0.000 0.992 -0.422 -0.188 1.551 0.825 1.097 0.973 0.694 1.028 0.880 0.764 +1 0.827 1.549 -0.312 0.925 0.211 1.135 1.107 -1.580 1.087 0.762 1.345 1.067 2.215 0.863 0.700 -0.675 0.000 0.772 1.515 0.146 0.000 0.783 1.058 0.984 0.902 0.956 1.020 0.871 +0 1.104 0.022 0.911 1.388 0.149 0.836 -0.806 -1.384 2.173 0.492 -1.350 1.309 0.000 0.990 -0.977 -0.710 1.274 0.756 -0.409 0.035 0.000 0.813 0.846 1.087 1.030 0.664 0.951 0.795 +1 0.749 -0.727 0.155 1.437 -0.975 1.558 1.341 0.960 2.173 2.399 -0.392 -1.013 2.215 0.659 -0.693 1.186 0.000 1.948 -0.374 0.481 0.000 0.767 1.646 1.222 2.354 3.973 2.083 1.669 +1 1.918 0.720 -1.026 0.225 0.388 0.971 0.652 1.214 2.173 0.503 2.050 0.304 0.000 0.765 0.084 -0.634 0.000 0.797 -0.753 1.272 0.000 0.963 1.076 0.988 0.584 0.767 0.743 0.710 +1 0.402 -0.271 -1.086 1.673 0.093 0.403 -1.100 -0.778 0.000 1.756 -0.977 1.709 2.215 0.634 -1.356 -0.360 0.000 0.979 -1.149 0.373 0.000 0.836 1.124 0.992 0.636 0.889 0.915 0.803 +1 0.873 -0.488 0.996 0.878 -0.448 0.781 0.495 -1.240 2.173 0.646 0.549 0.945 2.215 0.731 -0.471 -1.050 0.000 0.684 0.896 0.315 0.000 1.008 0.832 1.169 0.808 0.964 0.785 0.697 +1 2.351 0.072 -0.404 0.872 -1.037 0.885 -0.053 1.106 2.173 0.380 -0.343 1.510 0.000 0.276 -0.332 0.810 0.000 0.966 0.298 -1.298 3.102 0.907 0.854 1.071 0.711 0.835 0.918 0.785 +1 0.730 -1.946 -0.031 1.203 0.459 1.136 -1.154 -1.233 1.087 0.932 -0.757 1.417 2.215 0.510 -1.463 -0.653 0.000 0.815 0.286 0.465 0.000 1.007 0.904 0.994 1.270 1.076 0.977 0.854 +1 1.047 -0.894 0.676 0.477 -1.095 3.077 -0.057 -0.140 0.000 1.403 -0.097 1.687 2.215 1.809 -0.634 1.307 1.274 3.955 -0.817 -1.612 0.000 5.763 3.590 0.988 0.920 0.765 2.288 1.689 +0 0.539 0.494 0.228 1.143 0.576 0.702 -0.619 1.173 0.000 1.029 -0.491 -0.597 2.215 1.152 -0.230 -1.710 0.000 0.503 0.857 -0.455 3.102 0.888 0.875 0.984 1.533 0.555 0.943 1.074 +0 0.509 0.955 -1.231 1.442 -1.368 0.761 -1.545 -0.515 0.000 1.345 0.987 0.221 2.215 1.009 -0.867 1.111 2.548 1.017 0.758 -1.725 0.000 0.607 0.907 1.001 2.139 1.681 1.600 1.201 +1 1.692 1.393 1.065 1.550 1.099 0.903 1.055 -1.490 0.000 1.847 0.614 -0.425 1.107 0.875 1.224 -0.635 0.000 0.652 0.924 -0.375 1.551 1.063 0.974 1.010 0.875 0.243 1.289 1.049 +1 0.736 1.938 -0.331 2.826 1.072 0.658 0.546 -1.685 2.173 0.661 0.373 0.645 1.107 0.647 1.091 1.271 0.000 0.908 0.772 -1.184 0.000 0.687 0.695 1.906 1.523 0.841 1.130 0.895 +1 0.533 -0.921 -1.009 1.196 -0.277 1.428 0.527 1.627 0.000 1.258 1.469 0.130 2.215 0.663 0.245 -0.275 0.000 0.766 0.855 1.069 3.102 0.565 0.565 0.980 2.824 0.698 1.823 1.621 +1 0.735 1.419 0.312 1.027 1.470 0.743 -0.255 -0.223 2.173 0.290 0.433 0.738 0.000 1.229 -0.652 -1.203 1.274 0.679 1.373 1.711 0.000 0.577 0.967 1.041 1.249 0.961 1.115 0.886 +0 0.533 -0.863 0.926 0.389 0.438 0.798 1.531 0.869 0.000 1.332 0.625 0.243 1.107 1.187 -1.006 -1.719 0.000 0.826 2.049 -1.244 0.000 1.188 0.819 0.981 0.749 1.778 1.232 1.034 +0 0.526 -1.174 0.986 0.561 -0.930 1.164 -0.578 0.580 2.173 0.979 -0.733 -0.885 0.000 1.583 -0.545 1.445 2.548 1.320 1.968 -0.756 0.000 0.847 0.940 0.985 0.880 1.188 0.944 0.789 +1 0.590 0.909 0.724 0.576 1.324 0.940 0.248 -0.967 0.000 0.637 0.377 -0.319 0.000 1.169 -1.108 1.385 1.274 1.295 0.104 0.419 3.102 0.914 0.971 0.990 1.981 0.989 1.312 1.205 +0 0.410 -0.529 1.583 1.783 -0.911 1.045 -1.480 1.028 0.000 0.791 0.540 -0.152 2.215 0.442 -1.377 0.365 0.000 0.798 -0.249 -0.662 3.102 0.902 0.851 0.990 0.512 0.448 0.549 0.595 +1 0.838 -0.410 -0.720 0.318 0.464 0.974 0.206 -1.291 2.173 0.787 0.175 0.959 0.000 0.865 -0.338 -0.014 0.000 2.099 -0.825 -1.323 3.102 0.843 1.060 0.993 0.941 0.963 0.886 0.772 +1 0.791 0.565 1.542 0.707 -0.519 0.699 -0.718 -0.124 2.173 0.420 0.133 1.147 0.000 0.479 1.746 0.940 0.000 0.923 -0.867 -1.495 3.102 0.708 0.946 0.994 0.945 0.814 0.845 0.750 +1 1.049 -0.695 0.610 0.243 0.832 0.818 -0.633 -0.020 0.000 1.509 -0.192 1.494 2.215 1.155 -0.770 -0.576 2.548 0.747 -1.146 -1.263 0.000 1.165 0.775 0.981 0.903 1.420 0.969 0.862 +1 0.763 0.362 -1.422 0.640 -0.624 0.617 1.341 -0.687 0.000 0.790 -0.067 0.312 2.215 0.610 1.799 -1.558 0.000 1.890 0.636 1.053 3.102 0.854 1.090 0.986 0.937 0.830 0.899 0.791 +1 1.008 0.747 -1.499 0.785 -0.734 1.165 0.099 -1.131 0.000 0.895 -0.999 1.026 2.215 2.449 -0.020 0.307 1.274 0.490 0.779 1.551 0.000 0.911 1.354 0.985 1.473 1.259 1.297 1.171 +0 1.662 0.115 -1.137 0.771 0.625 0.649 0.406 -0.201 2.173 0.324 1.861 0.335 0.000 0.286 0.475 -1.685 0.000 0.370 -0.403 -0.184 3.102 0.918 1.172 1.568 0.750 0.240 0.666 0.726 +1 1.694 1.163 -1.231 0.333 0.621 0.753 -0.003 1.250 2.173 0.576 -0.819 0.187 1.107 0.739 0.308 0.230 0.000 1.171 1.063 -0.470 0.000 0.863 1.036 1.035 1.178 0.897 0.947 0.838 +0 0.780 -1.154 1.627 1.226 -1.422 1.497 0.620 -0.024 0.000 2.840 -0.332 -1.419 0.000 1.854 -2.246 0.138 0.000 2.026 0.170 -0.520 3.102 0.659 1.268 0.984 1.654 0.768 1.295 1.093 +1 0.773 0.590 -1.211 1.406 -1.271 0.889 -0.142 0.337 2.173 0.707 0.155 -0.037 0.000 1.335 -0.311 1.074 2.548 0.998 1.980 -0.918 0.000 0.718 1.566 1.004 1.642 0.847 1.304 1.198 +1 1.119 1.135 -1.456 0.793 0.680 1.037 1.132 0.807 2.173 1.384 0.132 -1.246 0.000 1.509 -1.383 -0.417 0.000 1.989 -0.629 0.152 0.000 0.903 0.793 1.224 0.920 0.769 1.513 1.394 +1 0.850 -1.271 -0.412 1.566 1.470 1.066 -0.332 1.195 2.173 1.035 -1.270 0.028 2.215 0.808 0.342 -1.616 0.000 0.771 -0.593 -1.127 0.000 0.620 0.868 1.586 1.108 1.553 1.053 0.933 +0 0.951 2.276 -1.208 0.861 1.387 0.897 0.935 0.668 2.173 0.342 1.734 -0.950 0.000 0.671 1.894 0.453 0.000 1.501 0.553 -0.676 3.102 0.709 0.831 0.989 0.965 1.162 0.935 0.766 +0 0.909 -0.732 -0.083 0.873 -0.649 0.879 -0.344 0.632 1.087 1.072 0.226 1.493 2.215 0.470 0.747 1.069 0.000 0.509 -0.832 -1.348 0.000 0.715 0.745 0.989 1.383 1.084 1.077 0.863 +0 0.479 1.464 1.540 1.131 -0.830 0.959 0.843 1.522 0.000 1.843 0.698 -0.197 2.215 0.576 1.278 0.383 2.548 0.973 -1.281 -1.353 0.000 0.873 0.820 0.984 0.966 0.667 1.039 0.897 +1 0.538 -1.586 1.063 1.116 -1.422 0.903 -0.663 -0.859 0.000 1.074 -0.494 0.364 2.215 0.753 1.052 0.797 2.548 0.703 -0.778 -1.647 0.000 0.809 1.138 0.987 1.928 0.961 1.388 1.180 +0 0.350 -2.067 -1.083 1.232 -0.030 1.314 -1.491 0.887 0.000 1.372 -1.007 -1.054 2.215 0.863 -1.292 1.292 0.000 0.769 0.369 -0.207 0.000 0.977 0.955 0.982 0.601 0.350 0.586 0.617 +1 1.138 -0.202 -0.785 1.315 0.050 1.101 -0.226 1.536 2.173 1.914 -0.582 0.878 0.000 0.887 1.496 -0.246 0.000 1.173 -1.106 -1.511 0.000 0.795 0.636 1.159 1.319 0.846 0.917 0.891 +0 0.324 2.143 -0.796 0.683 -0.168 1.085 -0.081 -1.115 2.173 1.456 2.211 0.624 0.000 0.665 -1.157 -1.230 2.548 0.640 -0.125 0.535 0.000 1.932 2.456 0.990 0.951 0.676 1.853 1.400 +0 0.421 2.067 0.697 0.876 -0.027 1.117 0.587 1.136 1.087 0.387 -0.810 0.818 0.000 1.818 0.968 -0.580 2.548 1.174 -0.739 -1.115 0.000 0.865 1.249 0.989 0.810 1.824 1.192 1.012 +1 0.488 -1.026 -0.184 0.456 -0.058 0.418 -1.253 1.741 0.000 1.109 -0.360 -1.112 0.000 0.805 0.381 -0.117 2.548 1.390 0.827 0.925 3.102 1.026 1.061 0.991 0.786 0.694 0.933 0.809 +1 0.732 -0.578 -1.659 0.856 1.170 0.735 -0.973 1.361 2.173 1.202 -0.551 -1.422 2.215 0.794 -1.781 0.892 0.000 1.762 -0.487 -0.648 0.000 0.992 1.314 0.982 0.586 0.868 1.050 0.983 +0 2.001 -0.593 -1.208 0.748 1.522 1.968 0.630 0.353 2.173 1.474 0.246 -1.538 0.000 0.340 -0.084 -0.350 1.274 0.543 0.960 -0.282 0.000 1.186 0.764 1.068 2.203 0.714 1.334 1.195 +1 0.367 1.523 -0.965 0.962 1.078 0.707 0.125 0.092 1.087 1.132 -0.594 -1.414 2.215 0.482 -0.168 -0.427 0.000 0.427 2.002 0.453 0.000 0.917 0.779 0.989 2.239 1.379 1.631 1.243 +0 1.329 -0.572 -0.110 1.954 -0.926 0.567 -0.343 -1.183 2.173 1.499 -0.579 0.667 0.000 1.119 -0.940 1.228 1.274 0.883 -1.325 -1.349 0.000 1.648 1.043 1.497 0.877 0.888 0.870 0.958 +0 2.287 -0.233 1.342 1.056 1.651 1.079 0.379 -0.110 2.173 0.500 1.028 -1.092 0.000 0.787 -2.528 -0.340 0.000 0.602 1.287 -0.262 0.000 0.517 0.789 0.980 0.810 0.894 1.123 0.982 +1 2.962 -1.207 -1.704 1.348 1.470 2.632 -1.671 -0.163 0.000 1.405 -0.001 1.717 0.000 1.319 -1.450 0.297 2.548 1.226 -0.513 1.087 0.000 0.917 0.999 0.983 0.660 0.863 0.931 0.934 +0 0.551 0.114 -0.638 1.996 -1.352 0.861 -0.652 0.740 0.000 0.963 -0.073 -1.730 2.215 0.990 0.712 0.101 2.548 0.382 -0.676 0.319 0.000 1.146 1.210 0.988 0.949 1.131 0.982 1.058 +1 0.830 0.826 -1.064 0.351 1.371 0.776 -0.061 1.358 0.000 0.912 0.323 -0.484 1.107 0.809 -1.005 -0.056 2.548 0.750 0.923 0.937 0.000 0.842 1.086 0.979 0.866 0.791 0.923 0.903 +0 0.848 0.112 1.029 0.261 1.168 0.500 0.490 -0.244 2.173 0.804 0.823 1.178 2.215 0.828 1.121 -0.997 0.000 1.166 -0.321 -0.678 0.000 1.022 1.058 0.979 0.773 0.909 0.720 0.693 +0 0.787 0.788 -0.347 0.764 -1.488 0.506 0.862 1.464 0.000 1.049 -0.598 -0.272 2.215 1.308 -0.200 1.003 1.274 0.634 -2.240 1.319 0.000 2.495 1.532 0.987 0.874 1.163 1.151 1.023 +1 0.468 0.835 -0.064 1.246 1.375 0.824 -0.719 0.777 0.000 1.299 0.197 -0.731 1.107 0.809 -0.195 1.287 0.000 0.643 -1.581 -0.635 0.000 0.753 0.763 1.019 1.044 0.624 0.882 0.871 +0 0.652 1.362 -0.107 0.962 -1.489 0.664 2.038 0.940 0.000 0.853 -0.236 -0.175 2.215 0.830 1.406 1.672 0.000 0.867 0.642 -0.614 3.102 0.888 0.920 1.038 1.038 0.508 0.992 0.845 +1 0.377 -0.142 -1.187 0.519 1.030 2.284 -0.639 1.501 0.000 2.306 0.477 0.027 0.000 1.283 0.941 -0.335 1.274 1.911 -0.091 -0.951 3.102 1.719 1.096 0.989 0.761 0.959 0.940 0.900 +1 0.631 -0.270 -0.485 0.570 -1.364 0.547 0.843 0.370 0.000 0.778 -0.615 0.054 1.107 1.181 0.839 -1.607 2.548 1.207 -0.184 -1.725 0.000 1.370 1.038 0.979 0.855 1.341 0.879 0.766 +0 2.025 0.586 0.678 1.328 1.288 1.085 0.705 -0.805 0.000 1.239 0.048 -0.691 0.000 1.248 0.826 -1.244 2.548 2.045 -0.155 0.836 3.102 0.869 0.844 1.188 0.802 1.360 1.085 1.171 +0 0.761 -0.477 0.787 1.127 -0.269 0.702 0.149 -1.023 0.000 1.126 -1.665 0.509 0.000 1.085 0.027 1.723 2.548 2.292 -0.536 -0.590 0.000 1.081 1.065 1.044 0.595 0.173 0.650 0.690 +1 1.375 0.268 0.717 0.745 -1.640 0.667 1.209 0.191 0.000 0.935 -1.058 -1.124 2.215 0.804 -1.126 -1.702 0.000 0.389 -0.269 0.023 3.102 2.436 1.278 1.194 1.179 0.514 1.044 0.940 +1 0.806 -0.145 0.914 1.158 0.931 0.851 0.996 -1.104 2.173 0.212 2.477 0.194 0.000 0.915 -0.323 -1.071 2.548 0.784 0.730 0.727 0.000 0.556 0.951 0.974 1.744 0.808 1.166 1.073 +1 0.593 -0.043 0.958 0.716 -1.048 0.737 0.419 0.647 2.173 0.756 0.184 -0.305 2.215 0.500 0.955 1.285 0.000 1.209 -1.325 -0.274 0.000 0.660 0.745 0.985 0.738 0.841 0.697 0.681 +0 0.456 1.554 -0.409 0.711 -0.555 0.519 0.655 1.717 1.087 0.592 1.271 0.135 2.215 0.727 0.269 0.943 0.000 0.399 -0.683 1.385 0.000 0.418 0.670 0.992 0.790 0.851 0.648 0.588 +0 0.435 -1.375 -0.136 0.842 1.268 2.540 -1.040 0.805 1.087 2.839 -0.367 -0.790 2.215 1.292 -1.039 -1.132 0.000 0.665 1.718 -1.496 0.000 2.457 2.009 0.986 1.424 4.143 2.422 2.040 +1 0.905 -0.636 0.545 0.620 -0.516 1.036 -0.412 -1.629 0.000 0.610 -0.509 0.262 0.000 0.779 -0.770 -0.794 2.548 1.059 0.224 1.110 1.551 1.675 1.071 0.989 0.639 0.798 0.750 0.731 +1 0.575 1.621 -1.491 0.456 -1.726 0.940 0.715 0.258 0.000 0.698 -0.218 0.505 0.000 1.312 0.570 -0.775 2.548 0.791 0.840 -1.698 3.102 0.926 0.946 0.997 0.736 0.594 0.795 0.750 +1 0.772 0.068 1.383 0.832 0.170 1.742 0.156 -1.187 0.000 1.928 0.664 0.685 0.000 1.639 -0.491 -0.493 2.548 0.594 -0.112 1.008 3.102 4.001 2.105 0.988 0.914 0.752 1.497 1.168 +0 1.166 -0.732 -1.148 0.936 -0.215 0.631 0.189 1.666 2.173 0.576 0.082 0.532 2.215 0.860 0.801 -0.269 0.000 0.854 -1.640 1.184 0.000 1.177 0.986 1.079 0.845 0.759 0.756 0.741 +1 1.682 -1.750 1.151 0.794 0.808 1.447 -0.818 -1.009 1.087 0.814 -0.727 -0.111 1.107 0.334 -0.977 1.543 0.000 0.456 1.813 0.652 0.000 1.095 1.062 1.003 1.626 1.158 1.173 1.191 +1 0.474 0.161 1.666 1.038 0.233 0.950 1.217 0.908 0.000 2.694 1.395 -1.047 0.000 1.761 0.200 0.619 1.274 1.024 -0.444 -0.237 3.102 0.900 1.079 0.990 0.585 0.818 0.911 0.836 +0 2.158 -1.337 1.011 0.686 0.790 1.696 -0.384 -0.812 2.173 0.637 -0.240 -1.685 0.000 1.118 -0.459 0.732 2.548 0.720 -0.022 -0.340 0.000 0.831 0.943 1.005 0.621 1.691 1.328 1.052 +0 0.585 -1.624 -0.556 0.599 -0.396 0.946 -0.741 -0.208 1.087 1.217 -1.242 1.172 2.215 0.997 -0.551 -0.615 0.000 1.531 -2.461 0.913 0.000 0.954 1.068 0.982 0.645 1.554 0.907 0.791 +0 1.699 1.935 -0.751 1.956 -0.339 0.763 0.419 1.374 0.000 1.075 0.886 0.890 0.000 1.062 1.254 -1.667 2.548 1.073 1.343 0.253 0.000 0.909 0.955 0.987 1.199 0.623 0.940 0.949 +0 0.366 0.045 0.321 2.054 1.539 0.671 0.693 0.113 0.000 0.637 1.601 -0.376 0.000 1.077 -0.889 -1.329 1.274 1.361 -0.223 -0.499 1.551 0.905 0.949 1.070 0.895 0.713 1.003 0.990 +1 0.365 -0.938 -1.087 1.190 1.318 0.910 -0.350 0.593 2.173 1.437 0.212 -1.348 0.000 1.071 0.114 -0.628 2.548 0.636 -0.184 -0.131 0.000 0.950 0.773 0.983 1.238 1.136 1.093 1.105 +1 0.906 0.234 -0.209 0.645 1.010 1.355 0.816 -0.762 2.173 1.274 1.003 1.294 1.107 0.572 0.666 0.526 0.000 0.715 0.302 1.140 0.000 0.393 1.047 0.987 0.994 1.867 1.084 0.844 +1 1.194 -1.871 1.459 0.287 -0.154 1.175 -1.211 0.298 2.173 0.778 -2.156 -1.586 0.000 0.799 -1.151 -0.341 0.000 0.522 -0.378 -1.359 3.102 1.243 0.798 0.990 0.999 0.888 0.841 0.752 +1 0.619 1.214 0.814 0.817 -1.121 0.420 0.447 1.158 0.000 0.598 0.958 0.026 0.000 1.459 -0.506 -1.360 2.548 0.433 -0.846 0.517 3.102 0.952 1.189 0.986 0.803 0.619 0.839 0.771 +1 0.576 -1.656 -0.325 1.879 0.642 1.566 -1.512 -0.882 2.173 0.934 -1.350 0.763 0.000 1.042 -0.994 1.325 0.000 1.214 -0.652 -1.693 3.102 0.768 0.740 1.103 1.500 1.120 1.088 1.007 +1 0.758 1.534 -1.038 1.156 0.013 1.141 0.857 0.961 1.087 0.488 2.527 -0.774 0.000 0.768 1.772 -1.200 0.000 0.520 -0.023 -0.862 3.102 0.527 1.206 1.053 0.685 0.896 0.824 0.767 +1 1.318 1.612 1.403 0.933 0.800 0.561 0.898 -1.728 0.000 0.970 -0.092 -0.851 2.215 0.669 0.698 0.195 2.548 0.574 0.991 -0.731 0.000 0.690 0.823 0.986 0.833 0.791 1.094 0.881 +1 0.943 0.075 -0.223 0.403 1.262 0.729 0.317 0.734 0.000 0.681 0.642 0.098 0.000 1.926 -0.439 -1.487 2.548 0.452 1.138 -1.006 3.102 0.852 0.717 0.987 0.914 0.814 0.911 0.785 +1 2.451 -0.367 -0.039 1.504 0.527 0.624 1.780 -1.690 0.000 0.866 -1.035 1.163 2.215 2.093 0.684 -1.227 2.548 1.115 -1.091 -1.193 0.000 0.848 0.974 1.300 1.195 1.906 1.462 1.429 +0 0.707 -0.145 -0.218 0.523 -0.621 1.199 0.829 1.345 0.000 1.223 -0.205 -1.090 1.107 1.314 -0.607 -1.555 0.000 2.384 -1.007 0.363 3.102 1.603 1.188 0.973 0.795 1.690 1.094 0.919 +0 0.939 1.628 0.479 1.494 -0.246 0.952 -0.312 -1.406 2.173 1.002 1.353 0.995 0.000 0.899 -0.921 -0.762 2.548 0.495 -0.653 1.469 0.000 1.267 1.456 0.996 1.678 0.754 1.461 1.282 +1 2.006 0.036 -1.637 0.703 -0.504 1.058 0.269 -0.111 2.173 0.476 0.936 -1.107 0.000 1.126 -0.424 0.547 2.548 0.854 0.114 1.097 0.000 0.838 0.981 1.403 1.103 0.917 0.994 0.837 +1 0.759 -0.716 -0.232 0.504 0.759 0.794 -0.173 -1.379 0.000 1.121 -0.015 0.147 0.000 1.106 -0.733 -0.516 2.548 1.692 -0.158 1.399 3.102 0.870 0.917 0.985 0.709 1.084 0.753 0.659 +1 1.160 1.255 1.022 1.608 -1.283 0.619 1.654 1.359 0.000 0.658 0.947 -0.508 2.215 1.001 1.255 0.389 0.000 1.455 0.184 -0.404 3.102 1.106 0.993 1.656 1.205 0.351 0.836 0.849 +1 1.283 1.785 1.356 0.418 0.784 1.014 -0.055 -0.329 2.173 0.649 1.246 -0.667 0.000 0.578 2.541 1.257 0.000 0.579 -0.159 -1.464 3.102 0.728 0.982 0.992 0.727 0.694 0.958 0.795 +1 0.916 -1.414 1.696 1.007 0.197 0.904 -0.788 0.351 2.173 0.564 -1.690 0.637 0.000 0.808 -1.874 -0.937 0.000 1.060 -0.814 1.284 3.102 1.037 1.073 1.298 0.732 0.777 0.726 0.690 +1 1.739 1.296 0.266 0.567 0.161 3.012 -1.344 -1.230 0.000 2.255 1.638 -0.824 0.000 1.623 1.476 0.632 0.000 5.594 0.786 0.889 3.102 0.804 2.557 0.983 1.392 0.474 3.157 3.025 +1 1.349 -0.455 1.468 0.761 0.842 0.683 -2.795 -1.054 0.000 0.614 -0.718 -0.148 0.000 0.526 -1.201 -1.415 0.000 0.757 0.129 -0.283 3.102 0.952 1.328 0.985 0.768 0.477 1.202 1.060 +0 1.196 -0.952 -0.900 1.649 -0.844 1.667 -0.044 1.123 2.173 0.958 0.567 -0.652 1.107 0.429 -0.572 0.046 0.000 0.857 -0.580 0.780 0.000 0.907 1.088 0.989 1.629 1.951 1.809 1.405 +0 1.457 0.510 -1.430 0.508 -0.952 0.838 0.737 0.119 2.173 0.714 0.175 0.981 2.215 0.303 -0.237 1.574 0.000 0.552 -1.398 1.731 0.000 0.353 1.058 0.985 0.911 0.861 0.857 0.808 +1 0.389 0.970 1.127 1.173 -0.171 0.664 -1.028 1.328 2.173 0.806 -0.904 -0.986 2.215 0.333 1.214 0.263 0.000 0.626 0.167 -0.667 0.000 1.040 1.491 0.989 1.028 0.938 1.231 1.017 +0 1.105 1.288 0.671 0.631 -0.705 1.206 0.437 0.597 2.173 0.792 0.653 -1.143 0.000 1.407 0.889 -1.560 2.548 0.827 -0.096 -0.202 0.000 0.898 0.837 1.094 0.957 1.573 0.964 0.862 +0 0.951 0.977 -1.008 0.872 0.109 0.695 0.075 -0.039 1.087 1.305 0.705 -1.527 2.215 2.243 -0.145 0.646 0.000 0.792 -0.009 -1.399 0.000 0.950 1.053 1.066 0.939 1.441 0.860 0.764 +1 0.969 0.852 1.435 1.112 0.804 0.343 1.388 -0.525 0.000 0.963 0.016 -0.783 0.000 0.348 0.916 0.244 2.548 0.481 -2.260 0.590 0.000 0.958 0.655 0.985 0.577 0.294 0.419 0.605 +1 0.917 -0.467 -1.628 0.324 -1.624 0.896 -0.199 -0.056 2.173 0.706 1.810 1.411 0.000 0.947 -1.211 -0.480 0.000 0.951 0.752 0.682 0.000 0.842 1.083 0.990 1.157 0.983 1.021 1.124 +0 0.554 -0.453 1.161 0.681 -0.287 1.544 0.567 0.953 0.000 0.819 1.790 1.525 0.000 0.979 0.487 1.357 0.000 2.407 -0.423 -0.935 3.102 0.945 1.178 0.988 0.944 1.166 1.054 0.873 +1 1.024 0.159 0.272 0.791 -1.099 0.804 -1.337 -0.296 2.173 0.685 0.382 0.793 1.107 1.400 -0.120 1.286 0.000 0.791 0.815 1.616 0.000 0.758 0.627 1.177 1.092 1.408 1.024 0.896 +0 1.047 0.598 1.172 0.275 -1.620 0.798 -2.041 -0.320 0.000 0.751 -0.371 -1.317 1.107 0.668 -0.493 0.438 2.548 0.472 -1.560 -1.688 0.000 0.889 0.875 0.983 0.721 0.755 0.779 0.868 +0 0.513 0.861 -0.746 0.371 1.478 0.610 -0.985 -1.114 0.000 0.816 -0.252 0.262 2.215 0.657 -1.392 -0.315 0.000 2.007 0.911 1.446 3.102 0.919 0.887 0.990 0.910 1.316 1.019 0.834 +1 0.865 0.198 0.923 0.718 -0.111 0.913 0.902 -1.199 2.173 0.868 -0.381 0.149 0.000 0.485 0.728 1.623 1.274 0.449 -1.275 1.021 0.000 0.755 0.776 0.984 1.129 0.469 0.863 0.772 +0 2.633 0.596 0.014 0.259 0.372 1.671 0.434 -1.541 0.000 0.701 0.037 1.576 0.000 0.884 -0.789 0.011 1.274 0.608 0.336 0.483 0.000 0.881 0.912 0.982 0.821 0.293 0.918 1.040 +1 0.768 0.980 -0.000 2.006 -0.613 0.932 0.644 1.593 0.000 0.831 1.052 0.690 2.215 0.620 0.252 1.045 0.000 0.707 0.040 -1.128 3.102 0.698 0.821 0.983 0.622 0.786 0.709 0.798 +1 0.513 0.786 1.049 0.349 0.282 1.100 -0.483 -1.309 2.173 0.901 2.337 0.252 0.000 0.594 -1.162 1.165 2.548 0.853 0.429 -0.901 0.000 1.164 1.426 0.989 0.971 0.895 1.220 1.009 +1 0.499 -0.179 1.166 1.121 -0.842 0.787 0.758 0.537 2.173 0.519 -1.465 -1.501 0.000 0.650 -0.925 -0.136 0.000 0.414 0.343 -1.452 1.551 0.868 0.634 1.007 0.991 0.599 0.824 0.729 +1 0.460 1.520 -1.482 0.966 1.572 1.272 2.029 -0.545 0.000 0.996 1.461 0.858 0.000 2.143 1.021 -1.310 1.274 2.003 0.477 0.474 0.000 0.760 0.849 0.987 0.865 0.973 0.884 0.778 +0 1.217 -0.616 1.330 1.447 1.667 0.794 0.199 0.190 0.000 0.731 0.955 -0.593 0.000 1.021 -1.140 -0.783 2.548 0.711 -0.152 0.743 3.102 1.235 0.842 0.990 0.955 0.735 0.850 1.065 +1 0.795 0.690 1.324 1.607 1.471 0.746 1.214 -0.463 2.173 0.282 -0.135 0.773 2.215 0.433 0.061 -0.091 0.000 0.370 1.893 -1.187 0.000 0.711 1.324 0.982 0.540 0.784 0.905 0.880 +1 2.277 -1.311 0.358 1.029 0.447 1.064 -1.545 1.538 2.173 1.140 -1.709 -0.320 0.000 1.179 -0.093 -1.613 2.548 1.040 -1.296 -1.288 0.000 1.095 1.334 0.997 1.341 1.184 1.207 1.145 +0 1.044 -0.167 -1.518 1.361 -1.246 1.274 0.305 1.619 2.173 2.629 -0.072 0.331 0.000 1.079 -2.728 -0.143 0.000 1.119 -0.269 -0.602 3.102 5.378 2.954 0.974 1.187 1.217 2.412 1.915 +1 0.520 -1.957 -0.062 0.933 0.900 0.715 -1.766 0.309 0.000 1.270 -0.954 -1.198 2.215 1.226 -0.921 1.606 0.000 1.672 -2.403 -0.501 0.000 1.477 1.217 0.980 0.999 0.435 1.071 0.930 +0 0.670 -0.366 0.707 0.993 -0.622 0.374 0.463 -0.199 2.173 0.612 -0.141 0.944 0.000 1.379 -0.050 1.635 1.274 0.834 -1.927 -1.050 0.000 1.520 1.206 1.052 0.873 0.923 0.898 0.786 +1 0.545 -0.230 -0.803 0.774 1.253 0.827 0.745 -1.501 2.173 1.343 -0.002 -0.066 0.000 1.210 -0.478 0.395 2.548 0.885 -2.191 1.591 0.000 1.457 1.071 0.986 1.045 1.500 1.031 0.937 +0 1.025 -0.419 1.496 0.105 -0.615 0.720 -1.146 0.752 0.000 0.834 -1.541 -0.083 0.000 1.614 0.997 -1.605 2.548 1.233 0.452 -0.189 3.102 0.819 0.954 0.978 0.821 1.075 1.097 0.898 +1 0.612 -0.351 1.460 0.474 -0.898 1.234 -0.448 0.578 0.000 1.290 -0.287 -1.654 0.000 0.557 2.635 -0.070 0.000 1.510 -0.420 -0.236 3.102 2.431 1.359 0.990 0.773 0.424 0.946 0.836 +1 0.602 -0.257 0.300 1.074 -1.353 0.604 -1.830 0.889 0.000 0.773 0.058 -1.695 1.107 1.467 -0.588 -0.756 2.548 1.540 -1.019 0.293 0.000 0.887 1.276 1.110 0.684 0.941 1.018 0.888 +0 0.451 -2.210 0.224 0.669 -1.028 0.835 -0.653 0.434 2.173 1.377 -2.179 1.391 0.000 0.729 -0.673 -1.175 0.000 0.962 0.411 -1.035 0.000 0.735 0.661 0.989 0.847 0.372 0.778 0.826 +1 0.458 0.705 1.663 0.929 0.001 1.038 1.028 -0.143 0.000 1.959 -0.056 1.589 0.000 1.960 0.240 0.714 0.000 1.602 0.951 1.425 3.102 0.750 0.876 0.990 0.845 1.805 0.940 0.765 +0 0.568 0.112 -1.410 1.840 1.210 1.530 0.533 -0.313 1.087 0.874 -0.638 1.530 2.215 1.171 -0.150 -0.231 0.000 1.271 -0.767 -1.484 0.000 1.072 0.971 0.996 0.662 2.009 1.223 1.033 +1 0.984 -0.777 -0.096 1.824 -0.040 0.867 -0.008 -1.385 2.173 0.791 -0.385 1.169 2.215 0.729 0.009 1.703 0.000 0.428 -1.530 0.779 0.000 0.797 0.784 0.974 1.195 0.937 1.212 0.968 +0 0.777 -1.392 0.349 0.882 -0.612 1.691 -0.720 0.287 1.087 2.260 -0.001 -1.666 0.000 0.379 0.534 -1.195 0.000 1.260 -0.579 -0.919 3.102 0.722 0.889 0.986 0.894 1.368 1.410 1.160 +1 1.291 -0.342 0.966 0.595 0.608 1.952 -2.488 -0.860 0.000 1.395 -0.117 0.303 0.000 1.238 0.634 1.295 2.548 1.597 -0.125 1.624 3.102 0.866 1.068 0.997 1.038 0.564 0.829 0.806 +1 0.579 -0.909 1.027 2.006 1.739 0.913 -0.225 1.276 0.000 1.373 0.748 -0.593 2.215 1.604 -0.176 0.243 0.000 1.419 1.413 -0.375 3.102 1.746 1.818 0.991 2.361 0.653 1.840 1.603 +1 1.151 1.498 -0.214 0.495 1.352 1.223 1.392 -1.022 2.173 1.051 1.236 1.346 0.000 1.114 1.508 0.731 0.000 0.498 0.214 -0.046 3.102 0.930 0.785 1.033 0.932 0.811 0.926 0.801 +1 0.624 0.969 0.058 1.147 1.196 0.934 -2.611 0.089 0.000 0.761 -0.208 -0.630 0.000 0.792 -1.283 -1.242 2.548 1.029 0.042 1.217 1.551 0.824 0.835 1.003 0.630 0.778 0.840 0.769 +0 0.666 1.372 -1.349 1.462 -0.509 0.743 0.632 1.481 0.000 1.000 0.423 0.646 2.215 0.672 -0.314 0.367 2.548 0.624 0.164 -0.739 0.000 0.973 0.916 0.987 1.113 0.411 0.940 0.865 +0 0.407 -1.179 0.094 1.431 1.375 0.702 -1.271 -0.225 0.000 1.104 0.883 -1.520 2.215 0.575 -0.047 -0.378 0.000 1.125 0.929 0.288 0.000 0.765 1.039 0.989 1.700 0.324 1.124 1.082 +1 0.993 -0.991 -1.278 1.566 0.525 1.540 -0.459 0.508 2.173 0.937 -0.230 -1.088 0.000 0.422 -1.139 -1.720 0.000 1.071 -0.384 -0.575 3.102 0.729 0.575 1.725 1.291 1.125 0.942 0.915 +1 0.477 -0.571 -1.629 1.341 -0.294 1.016 -0.225 1.182 0.000 1.144 -0.734 -0.664 0.000 1.381 -0.354 0.588 1.274 0.668 -0.832 -1.295 3.102 2.361 1.310 1.034 0.837 0.763 0.933 0.825 +1 0.850 0.493 1.697 0.336 0.546 0.846 -0.575 -0.913 2.173 0.973 -2.895 -0.636 0.000 1.432 -1.541 -0.202 0.000 2.153 0.408 1.071 0.000 1.189 1.225 0.990 1.215 1.019 0.981 1.201 +1 1.289 -0.220 -0.282 0.256 -0.829 1.085 -1.938 1.533 0.000 0.329 -0.965 -0.294 0.000 0.531 -1.182 1.373 2.548 0.960 -1.028 0.455 3.102 1.134 0.926 0.994 0.717 0.402 0.558 0.736 +1 0.897 0.621 1.579 0.371 1.252 0.969 0.670 -1.679 2.173 0.892 -0.226 -0.756 1.107 1.700 -0.335 0.799 0.000 0.416 -1.486 0.151 0.000 0.878 1.097 0.991 0.784 1.200 1.049 0.870 +0 1.519 -0.989 -1.164 1.261 -0.370 0.748 0.328 0.828 2.173 0.491 0.470 -0.742 1.107 0.666 -0.004 1.596 0.000 0.932 -0.828 0.530 0.000 0.841 0.768 1.258 1.462 0.884 1.001 0.842 +1 0.984 0.832 -1.606 1.409 -0.848 1.217 0.208 0.482 0.000 0.938 0.515 -0.380 0.000 0.632 0.062 -1.385 0.000 1.032 1.119 1.225 3.102 0.970 0.928 1.031 0.817 0.650 0.660 0.656 +0 0.847 0.955 0.702 1.281 -1.608 0.916 0.478 -0.335 2.173 0.479 -0.360 0.948 2.215 0.493 -0.685 -0.489 0.000 0.919 -0.049 -1.340 0.000 0.578 0.750 1.259 0.843 0.990 0.856 0.746 +1 0.688 -1.592 -1.111 0.681 1.649 0.553 0.828 -1.246 0.000 1.195 -0.889 0.093 2.215 0.482 0.123 -0.250 0.000 1.638 -1.668 -1.580 0.000 0.968 0.927 0.987 0.602 0.511 0.654 0.648 +1 1.652 0.143 0.034 0.339 -1.554 0.395 0.219 -0.745 0.000 0.667 -1.206 1.322 1.107 0.441 2.664 -0.129 0.000 1.115 0.031 1.268 0.000 0.950 0.687 1.026 0.733 0.224 0.676 0.615 +0 0.660 -1.887 -0.622 0.606 -1.742 0.472 -0.853 -1.664 2.173 0.797 -2.271 0.456 0.000 0.836 -1.807 -0.328 0.000 0.437 0.835 1.370 1.551 0.843 1.079 0.991 0.761 0.549 0.909 0.775 +0 0.320 0.283 -0.752 0.375 1.354 1.095 1.613 -0.173 2.173 0.989 0.278 -1.058 0.000 1.413 -0.061 1.317 2.548 1.815 0.666 -1.661 0.000 1.009 1.057 0.994 2.126 2.112 1.530 1.313 +1 0.818 -0.534 -1.705 0.947 -0.577 0.525 -1.429 0.869 2.173 0.806 -1.211 1.584 0.000 1.280 -0.009 -0.128 1.274 1.351 -0.868 -0.281 0.000 0.939 0.810 1.037 0.802 1.129 0.770 0.670 +1 1.816 -0.887 -0.305 0.736 -0.626 0.759 -1.346 0.817 1.087 0.921 -1.189 1.248 0.000 1.027 -1.324 1.717 2.548 0.512 -1.477 -0.223 0.000 0.903 0.674 0.982 0.998 0.799 0.882 0.772 +1 1.958 0.542 0.806 0.037 -0.474 0.966 -0.259 -1.092 2.173 0.502 2.869 1.309 0.000 0.884 0.920 -0.598 0.000 1.674 -0.331 -0.303 3.102 1.125 0.954 0.997 1.269 0.884 0.956 0.852 +0 0.499 0.061 1.194 1.030 0.729 1.512 -0.823 1.357 2.173 2.180 0.143 -0.644 2.215 0.659 0.094 -0.014 0.000 0.733 -1.106 -0.588 0.000 0.707 0.880 0.993 1.829 2.933 1.764 1.360 +0 0.911 -1.719 -1.503 0.331 0.737 1.601 -1.058 -0.936 2.173 1.028 -1.387 1.116 0.000 2.172 -2.451 0.492 0.000 0.652 -0.636 0.332 3.102 1.972 1.263 0.986 0.971 0.998 1.553 1.213 +1 1.023 -0.601 -0.153 0.687 1.002 0.765 0.634 1.187 0.000 0.783 0.232 -1.599 2.215 0.887 -1.026 -0.326 1.274 0.720 0.662 -0.729 0.000 1.123 0.826 1.002 0.633 1.038 0.844 0.788 +0 1.293 -1.678 0.809 0.832 1.735 0.947 -1.166 -1.303 0.000 1.314 -1.138 -0.521 2.215 0.962 1.147 0.732 0.000 0.621 -0.310 0.731 3.102 3.123 1.681 1.066 1.180 0.809 1.306 1.251 +1 0.749 -0.440 1.533 1.713 -1.634 0.686 1.548 -0.524 0.000 0.565 -0.400 -1.628 2.215 0.607 -1.273 0.061 0.000 1.854 0.373 0.317 0.000 0.671 0.794 0.973 0.514 0.420 0.746 0.834 +1 0.630 0.298 1.353 1.529 0.815 0.715 -1.308 -1.372 0.000 0.916 -0.996 0.710 0.000 1.068 0.430 -0.541 0.000 2.433 -0.606 -0.653 3.102 1.659 0.928 0.983 1.222 0.860 0.843 0.854 +0 0.602 0.495 0.366 0.763 -1.107 0.574 -0.439 1.693 2.173 0.779 -1.105 -1.122 0.000 0.742 1.985 0.368 0.000 1.220 0.641 0.193 3.102 3.165 1.829 0.988 0.890 1.036 1.236 1.008 +1 0.651 -0.245 0.145 0.658 1.105 1.105 -0.581 -1.325 2.173 0.470 -1.169 -0.506 2.215 0.576 -2.518 0.861 0.000 1.049 -0.815 0.683 0.000 0.883 0.768 0.994 1.047 0.788 0.894 0.784 +0 0.494 -1.311 -1.247 1.854 -1.688 0.978 0.231 0.290 0.000 0.787 -0.477 -0.911 2.215 0.266 1.828 -1.127 0.000 0.891 -0.510 -0.141 3.102 1.274 0.890 0.985 0.730 0.486 0.750 0.858 +0 1.039 -1.339 -1.273 0.361 -0.074 0.800 -1.330 1.257 2.173 0.610 -0.272 -1.522 1.107 0.767 -1.439 0.073 0.000 0.804 0.059 -0.306 0.000 0.850 1.049 0.996 0.620 0.837 0.755 0.683 +1 1.219 0.772 1.575 1.122 -1.321 0.857 0.413 -0.287 2.173 0.788 0.942 0.534 1.107 0.501 -0.341 -0.531 0.000 0.707 0.012 0.762 0.000 0.616 0.648 0.986 1.199 0.883 0.919 0.772 +1 0.630 -0.129 0.406 0.589 -0.626 1.036 0.590 -1.389 2.173 0.862 1.395 1.162 0.000 1.527 0.927 0.582 0.000 0.470 -1.539 -0.066 0.000 0.952 1.041 0.981 0.930 0.933 0.993 0.848 +1 0.746 -0.817 0.878 0.972 0.511 1.092 0.070 -0.781 2.173 1.078 0.207 -1.482 2.215 0.985 -0.368 0.443 0.000 0.463 0.204 1.569 0.000 0.679 1.012 0.981 1.508 0.949 1.341 1.053 +1 0.959 0.278 -0.420 1.214 -1.431 1.327 -0.209 0.963 2.173 0.268 0.596 -1.688 0.000 0.587 0.054 0.039 2.548 0.829 -0.439 -0.823 0.000 0.822 1.241 1.180 0.715 0.827 0.896 0.785 +1 1.346 -0.636 -0.631 1.769 0.142 1.031 -0.440 1.149 2.173 0.646 0.075 0.757 0.000 0.872 0.232 -1.276 2.548 0.499 -0.852 -0.913 0.000 0.854 0.773 1.372 1.067 1.052 1.047 0.843 +0 0.754 0.530 -1.713 1.211 0.712 0.880 0.047 -0.458 2.173 0.727 -0.539 -0.558 2.215 1.023 -1.433 1.403 0.000 0.388 -1.339 -1.570 0.000 0.804 0.935 1.082 1.074 0.375 0.845 0.888 +1 0.454 0.614 -1.567 0.644 -1.634 1.131 -0.289 -1.383 0.000 2.769 0.318 0.537 1.107 1.333 -0.715 -1.089 0.000 0.991 0.692 0.106 0.000 0.799 0.595 0.988 1.567 1.224 1.460 1.180 +0 0.747 0.047 1.658 0.369 -0.946 0.495 -0.047 0.751 2.173 0.480 0.041 -0.684 0.000 1.154 -0.158 -0.163 2.548 0.614 1.495 1.468 0.000 0.964 0.874 0.977 0.768 0.693 0.685 0.643 +1 0.679 -1.702 -0.404 1.032 -0.435 0.782 -1.066 1.055 1.087 0.706 -0.674 -1.605 2.215 0.897 -0.336 -0.450 0.000 0.931 0.173 1.212 0.000 1.047 1.023 0.989 0.834 0.769 0.804 0.750 +0 1.462 -0.329 0.499 1.970 1.076 1.009 -0.502 -0.947 2.173 1.110 0.248 0.093 0.000 1.416 0.510 -1.439 2.548 1.235 -1.313 -1.724 0.000 0.795 1.095 1.168 1.535 1.049 1.219 1.087 +0 1.862 1.526 0.951 0.362 -0.381 0.597 2.298 -0.372 0.000 0.686 1.149 -1.146 1.107 0.407 0.385 -1.542 0.000 0.769 0.532 0.311 3.102 1.242 0.878 1.060 0.892 0.659 0.635 0.683 +0 0.915 -1.307 -0.365 0.800 0.804 1.026 -0.335 -1.180 1.087 0.881 -1.835 0.695 0.000 0.663 -2.595 1.198 0.000 0.690 -1.013 0.994 1.551 0.779 0.552 1.030 1.133 0.918 1.086 0.911 +1 0.725 0.888 1.282 1.432 -1.360 1.067 0.026 0.986 0.000 1.339 0.128 -0.072 0.000 1.095 0.981 -0.582 2.548 0.873 -0.274 -0.884 0.000 0.918 1.251 0.988 0.713 0.697 0.737 0.859 +1 0.585 -0.769 0.927 0.534 -0.942 0.639 1.348 0.139 2.173 0.673 0.368 -0.588 2.215 0.909 -0.688 1.573 0.000 0.719 -0.082 1.448 0.000 0.315 0.798 0.986 1.676 0.768 1.137 0.949 +0 0.304 0.865 0.520 1.296 -1.450 0.472 0.667 0.735 0.000 0.904 0.435 1.727 2.215 0.701 1.170 -0.399 0.000 1.674 -0.206 -0.318 3.102 0.940 0.935 0.985 0.815 1.141 0.785 0.723 +0 0.617 -0.295 1.333 0.701 -1.162 0.885 0.519 -0.278 1.087 0.443 1.405 1.056 2.215 0.942 2.248 1.252 0.000 0.391 1.343 -1.020 0.000 0.664 1.298 0.990 1.108 0.965 1.004 1.177 +0 1.244 -0.669 -1.374 2.193 1.607 2.901 0.308 -0.031 0.000 2.223 -0.619 1.616 2.215 0.937 -0.201 -0.420 0.000 0.609 -1.464 1.283 0.000 1.092 0.663 1.006 0.644 0.906 0.797 0.756 +0 1.276 0.274 0.013 0.593 -0.821 0.771 0.879 0.701 2.173 0.665 1.155 1.203 0.000 0.930 1.221 -1.194 0.000 1.435 1.811 -0.779 0.000 1.006 0.964 0.989 0.981 0.897 0.834 0.809 +0 0.698 -1.181 0.216 0.995 1.541 0.854 -1.149 -1.719 0.000 0.658 -0.826 -0.958 0.000 0.442 -0.169 0.674 0.000 1.723 0.561 0.175 3.102 0.869 0.959 1.074 0.536 0.718 0.763 0.671 +0 1.271 0.454 0.345 0.962 -1.012 0.497 -0.749 -1.400 0.000 0.897 -0.646 0.486 0.000 1.038 0.494 -1.508 2.548 0.955 -0.524 1.110 3.102 1.407 0.853 1.440 0.924 0.714 0.734 0.801 +0 0.874 -0.513 1.471 0.978 -0.445 0.645 -1.407 -0.801 2.173 0.998 0.294 0.340 2.215 0.527 -0.247 1.037 0.000 0.651 -0.155 -0.986 0.000 0.626 0.747 1.266 0.965 1.530 0.899 0.715 +1 0.449 -2.133 -0.985 2.042 -0.405 0.455 -0.866 1.234 0.000 0.820 -0.650 1.638 2.215 1.179 -0.924 0.641 2.548 0.771 0.035 -1.537 0.000 0.691 0.727 0.979 1.081 0.837 0.871 0.782 +0 0.537 2.307 0.512 1.049 -0.668 0.738 1.057 1.379 0.000 0.783 0.145 0.417 1.107 0.717 -0.011 -0.962 2.548 0.637 1.437 -0.588 0.000 1.084 0.923 0.986 1.032 0.757 0.810 0.779 +1 1.378 1.272 -0.687 2.179 -0.041 0.642 0.138 0.476 2.173 0.753 2.599 -1.682 0.000 0.775 0.285 -1.349 0.000 0.636 1.109 0.752 0.000 0.981 1.042 1.321 1.213 0.790 0.984 1.049 +1 1.548 -1.061 -1.359 1.032 -0.777 0.622 -1.039 0.520 2.173 0.486 -1.615 1.299 0.000 1.819 -0.103 0.334 2.548 1.048 -0.977 0.019 0.000 0.880 1.024 0.988 1.077 0.680 1.071 0.885 +0 0.876 0.470 -0.838 1.228 1.543 1.063 -0.382 -0.439 2.173 1.122 1.139 1.545 0.000 0.756 0.875 0.302 0.000 0.806 0.561 0.841 3.102 1.099 1.120 1.206 0.714 1.048 0.856 0.797 +0 2.049 -1.019 -0.809 0.721 0.813 0.530 0.954 0.852 0.000 0.449 -0.439 0.830 2.215 0.563 1.343 -1.181 0.000 0.989 -1.182 0.977 3.102 0.982 0.832 1.675 0.996 0.313 0.790 0.998 +1 0.806 1.488 -0.120 0.184 1.327 0.415 1.390 0.234 1.087 0.968 1.599 -1.696 2.215 0.574 -0.413 1.543 0.000 1.009 -0.402 -0.072 0.000 0.857 1.123 0.988 0.580 0.927 0.780 0.685 +0 1.743 0.849 0.053 2.571 -0.146 1.951 1.238 1.613 0.000 3.401 0.530 -0.116 1.107 1.130 0.925 1.019 1.274 1.357 1.265 -1.396 0.000 1.085 1.038 0.996 0.612 1.846 1.916 1.728 +1 1.117 -0.491 0.267 1.459 -0.330 0.897 2.426 -0.931 0.000 1.030 0.190 0.694 0.000 1.810 1.006 1.324 2.548 0.686 2.406 1.516 0.000 1.000 0.918 0.988 1.847 0.799 1.175 1.729 +0 0.812 0.295 -0.520 0.628 -0.827 0.501 0.899 -1.411 2.173 0.897 0.111 -0.040 1.107 0.986 -0.034 0.880 0.000 1.102 1.045 1.353 0.000 0.926 0.991 0.985 0.898 1.015 0.756 0.799 +1 0.525 0.830 1.108 0.467 -0.779 3.107 1.466 0.438 0.000 3.680 -0.037 -1.337 1.107 1.070 -0.404 -1.525 2.548 0.773 0.283 -1.630 0.000 0.924 1.233 0.980 0.650 0.559 0.755 0.723 +1 0.441 -1.535 0.789 0.313 -1.443 2.026 0.478 -0.636 0.000 1.272 -0.921 -1.408 1.107 3.355 -0.429 0.985 0.000 1.605 -0.721 0.682 3.102 1.520 0.906 0.980 0.811 1.228 1.022 0.845 +1 1.172 0.328 -0.798 0.439 0.756 1.201 0.538 -0.583 0.000 1.877 0.485 0.915 0.000 1.327 -0.211 1.559 2.548 1.135 -1.220 -0.429 0.000 1.115 1.068 0.988 0.840 0.685 0.911 0.765 +0 1.445 -0.437 1.664 0.213 -1.605 0.798 -0.355 0.468 2.173 0.737 -0.252 -0.154 0.000 0.696 -0.923 -0.460 2.548 1.104 -0.161 -0.883 0.000 0.892 0.745 0.975 0.747 0.756 0.749 0.667 +1 0.897 -0.727 -0.032 0.420 -0.592 1.391 -0.142 -1.582 0.000 1.763 -0.110 -0.220 2.215 1.126 -0.872 1.156 1.274 0.611 -1.382 0.738 0.000 0.457 1.074 0.990 0.916 1.559 0.850 0.755 +1 1.029 -0.904 0.352 1.720 0.233 1.501 0.243 -1.406 2.173 0.853 0.435 -1.711 0.000 1.462 0.135 0.374 2.548 0.368 1.136 -0.590 0.000 0.709 0.962 1.002 2.504 1.846 1.766 1.456 +0 0.490 -2.188 -0.962 0.846 -1.112 1.087 -0.530 -0.161 2.173 0.894 -0.541 0.395 2.215 1.554 -1.117 1.492 0.000 0.429 -1.883 1.472 0.000 0.487 0.969 0.985 0.990 0.697 0.947 0.862 +1 1.317 0.051 1.278 0.400 1.496 0.792 0.387 -0.037 2.173 0.795 0.359 -0.892 0.000 0.644 -0.157 -1.405 0.000 0.763 -0.480 0.389 3.102 0.574 0.888 0.982 0.627 0.516 0.733 0.721 +1 0.838 -0.744 0.423 1.073 1.395 0.466 -1.154 -1.500 0.000 0.975 -0.086 0.487 0.000 1.910 0.521 -1.285 2.548 1.347 1.020 0.088 3.102 0.927 0.859 1.010 1.282 1.228 1.079 0.934 +1 0.332 -0.853 -1.678 0.086 0.383 0.486 -0.906 0.740 0.000 1.113 1.058 -1.687 2.215 1.299 -0.444 -0.897 2.548 0.392 2.105 0.212 0.000 1.829 1.444 0.826 0.663 1.393 1.137 0.880 +0 0.757 0.402 0.671 0.423 1.240 0.474 1.175 -1.685 2.173 0.833 -0.434 -0.562 0.000 0.708 1.743 1.431 0.000 0.711 -0.125 0.503 0.000 0.834 1.038 0.990 0.885 0.622 0.678 0.743 +1 0.906 -0.765 1.426 0.162 0.861 1.115 0.881 -0.134 2.173 0.912 -0.189 -1.493 1.107 0.985 1.543 0.723 0.000 0.646 -1.635 -0.777 0.000 0.334 0.821 0.980 1.203 1.632 1.315 1.073 +0 1.000 -0.885 -0.096 0.923 -1.056 0.810 -0.495 0.510 1.087 0.771 -1.950 0.302 0.000 1.975 -0.135 -1.391 2.548 0.861 -1.057 -1.180 0.000 0.754 1.037 1.012 0.986 1.585 0.953 0.847 +0 1.545 -0.060 0.394 0.335 -1.085 0.997 0.434 -0.517 2.173 0.813 0.606 -1.695 0.000 0.872 -0.389 0.886 2.548 1.272 -0.418 -1.556 0.000 0.803 0.833 0.987 0.921 1.223 0.883 0.810 +0 2.294 0.573 -0.676 0.142 1.381 0.941 0.746 1.118 0.000 1.048 0.471 0.690 0.000 0.585 -0.416 -0.011 2.548 2.049 0.139 -1.405 3.102 0.845 0.924 0.987 0.863 0.839 0.883 0.926 +1 0.943 -1.601 -0.014 0.155 -0.967 1.585 -0.723 1.500 2.173 1.705 -0.683 -1.107 0.000 1.145 -0.394 -0.366 1.274 2.083 -0.284 0.379 0.000 0.731 0.983 0.982 0.631 1.686 0.997 0.801 +0 0.507 1.101 1.372 1.546 -1.139 1.091 -0.461 0.193 2.173 1.030 -0.927 1.705 0.000 0.860 0.178 1.555 2.548 0.981 1.764 -0.051 0.000 3.183 1.775 0.989 1.838 1.207 1.395 1.366 +0 1.012 0.183 -1.683 1.439 1.092 0.703 1.708 -0.456 0.000 0.923 -0.565 1.081 0.000 0.920 0.606 -0.876 0.000 0.722 2.018 0.418 0.000 0.963 1.070 1.000 0.740 0.476 0.674 0.844 +1 1.142 -0.288 -0.503 1.143 -0.358 1.044 -0.848 0.732 2.173 0.760 -2.464 -1.340 0.000 1.595 -0.700 1.456 1.274 0.940 -0.982 -0.980 0.000 0.879 1.185 0.987 1.206 0.977 1.065 1.054 +1 0.697 -1.508 0.202 0.506 0.518 0.923 0.122 -1.467 0.000 0.680 0.235 0.218 2.215 1.100 -0.703 -1.577 2.548 1.471 -0.772 -0.236 0.000 1.871 1.193 0.993 0.640 1.039 0.903 0.825 +0 0.480 0.095 0.479 0.917 -1.481 0.700 0.088 0.108 2.173 0.797 0.518 -1.061 1.107 0.559 -0.243 1.297 0.000 0.734 0.851 0.558 0.000 1.040 0.891 0.988 0.834 0.986 0.697 0.654 +0 1.248 0.525 -0.242 1.576 -1.064 0.857 -1.099 1.321 1.087 0.485 0.220 1.475 0.000 0.749 -1.291 0.132 0.000 0.869 -0.357 -0.107 0.000 0.883 0.925 1.309 0.930 0.321 1.092 0.877 +1 0.622 0.279 -0.040 0.658 -1.205 1.306 1.456 0.600 0.000 1.221 1.056 -1.659 0.000 0.655 0.716 0.365 0.000 1.348 1.141 -0.596 3.102 0.938 0.827 0.990 0.615 0.682 0.601 0.570 +1 1.472 0.198 1.370 0.803 -0.211 0.732 1.372 -0.006 0.000 0.597 -0.137 -0.009 2.215 1.264 -0.470 -1.408 2.548 1.182 1.285 1.252 0.000 0.913 0.942 1.490 1.003 0.896 0.890 0.839 +0 0.745 -1.481 -1.571 0.862 0.863 1.320 -1.638 1.571 0.000 1.763 -1.501 -0.041 0.000 2.054 1.150 -0.475 0.000 1.094 -0.760 0.312 3.102 0.832 0.993 0.990 0.658 0.730 0.786 0.677 +0 0.655 -0.480 -0.551 1.810 -0.023 0.660 -0.315 1.133 2.173 0.655 0.128 -1.327 0.000 0.511 2.374 -1.481 0.000 1.242 0.921 -1.146 3.102 0.926 0.770 0.990 1.138 1.127 1.146 1.353 +0 1.485 0.791 0.358 0.864 -0.783 0.895 0.740 1.112 2.173 0.883 0.428 -0.984 0.000 0.501 -0.301 -0.547 0.000 0.631 -0.673 -0.990 3.102 0.562 1.146 1.344 0.895 1.017 0.836 0.776 +1 0.827 -0.119 0.324 0.354 -1.525 1.129 0.229 -1.292 2.173 0.960 1.006 0.413 0.000 1.011 1.030 -0.249 0.000 0.695 2.395 1.308 0.000 0.852 0.717 0.991 0.943 0.831 0.923 0.783 +0 0.620 0.693 -0.546 2.155 0.051 0.927 -0.147 1.577 0.000 0.629 -2.201 -0.981 0.000 0.508 -0.451 0.728 2.548 0.910 -0.464 -1.312 0.000 0.991 0.848 0.982 0.897 0.573 0.968 1.014 +1 1.119 -0.185 1.582 1.461 0.830 1.310 1.121 -0.595 2.173 0.507 -0.599 -0.179 0.000 0.493 -1.139 1.714 0.000 1.127 -0.025 1.121 3.102 0.799 0.692 1.109 1.815 1.509 1.208 1.041 +1 0.769 0.524 -1.224 0.964 0.043 2.024 -0.256 -0.078 0.000 1.791 -0.154 1.137 0.000 1.661 -0.679 -1.547 2.548 1.652 -0.208 1.551 0.000 0.824 1.087 1.085 0.685 0.858 0.920 0.924 +0 1.335 -0.274 0.813 0.599 0.380 0.622 1.964 -0.975 0.000 0.803 2.281 -1.514 0.000 0.527 -1.570 -1.130 0.000 1.596 -0.416 -0.135 3.102 0.753 0.884 0.996 0.770 0.873 1.225 1.447 +0 0.466 0.259 0.837 3.177 1.304 0.893 -0.030 -0.502 0.000 0.486 2.463 -1.333 0.000 0.511 1.394 -0.535 1.274 1.198 -0.604 0.327 3.102 2.517 1.367 0.988 0.898 0.928 1.108 1.356 +0 0.959 0.213 1.514 0.486 0.725 0.920 -0.818 -1.567 2.173 0.796 0.290 -0.520 0.000 0.999 1.287 0.656 0.000 0.442 0.710 0.985 0.000 0.882 0.707 0.984 1.193 1.507 1.266 1.022 +0 0.785 0.899 -0.859 0.984 -0.942 1.400 0.763 0.628 2.173 0.919 0.526 -1.318 2.215 0.643 0.297 0.971 0.000 0.605 1.499 -1.275 0.000 0.830 0.927 0.987 0.886 1.652 1.196 0.940 +1 0.815 -0.281 1.207 0.346 -1.407 0.687 -0.338 -0.432 2.173 0.432 1.885 0.975 0.000 0.627 0.988 -1.372 0.000 1.178 0.192 0.494 1.551 0.768 0.788 0.991 0.898 0.757 0.795 0.880 +1 1.221 -0.739 -0.118 1.345 -0.655 2.465 -1.114 1.121 2.173 2.094 -0.470 -0.703 0.000 1.033 -0.614 0.455 0.000 1.529 0.672 -1.468 0.000 0.650 1.050 0.990 0.632 1.144 1.338 1.057 +1 1.662 -0.322 0.283 0.599 -0.951 1.559 -0.159 1.649 2.173 0.718 -1.067 -1.290 2.215 0.778 1.096 -0.410 0.000 1.527 -0.817 0.863 0.000 0.809 0.964 1.239 1.431 1.054 1.035 0.960 +0 2.307 0.588 -1.143 0.474 0.797 0.557 1.574 0.973 0.000 1.007 0.924 0.230 2.215 0.523 0.233 1.157 0.000 0.640 -0.877 -0.774 3.102 0.714 1.031 1.426 1.181 1.027 0.883 0.848 +0 0.324 -0.991 1.427 3.103 -1.264 2.521 -0.239 0.513 2.173 0.981 -0.534 1.645 2.215 1.246 -2.152 -0.778 0.000 0.814 0.583 -0.045 0.000 1.317 1.289 0.987 2.883 2.004 1.907 1.650 +0 0.393 -1.487 1.508 3.241 -1.659 1.855 0.377 0.158 2.173 2.043 -0.361 0.373 0.000 2.605 -0.003 -1.475 2.548 1.211 0.797 -0.160 0.000 1.289 1.867 0.986 2.997 2.776 3.434 3.564 +1 2.485 -1.087 0.688 0.118 1.500 0.487 -0.116 -1.623 0.000 0.573 -0.026 -0.990 2.215 1.022 -0.956 -0.399 1.274 0.498 -1.858 -1.638 0.000 0.952 0.887 0.997 1.029 0.599 0.795 0.764 +0 0.559 0.649 -0.521 0.262 0.168 0.825 1.025 -1.594 1.087 1.195 1.016 0.252 2.215 0.470 0.454 -0.095 0.000 0.808 -0.632 -1.679 0.000 0.813 1.002 0.986 1.095 1.454 0.997 0.831 +0 3.255 -0.315 0.635 1.214 -0.969 1.268 -0.719 -1.027 2.173 1.506 -0.177 1.071 2.215 0.808 -0.006 -0.648 0.000 0.925 -1.452 -0.462 0.000 0.950 0.866 2.732 1.570 2.009 1.524 1.257 +1 0.384 0.406 0.077 0.588 -0.959 0.673 -0.155 -1.485 0.000 0.810 0.893 1.115 2.215 0.755 0.711 -0.558 0.000 0.862 -0.661 0.296 0.000 0.854 0.842 0.988 0.710 0.289 0.719 0.658 +1 0.900 0.188 -1.630 1.654 -1.084 0.974 0.069 0.405 2.173 0.744 0.597 1.151 2.215 0.851 -0.429 1.137 0.000 0.763 -0.120 -0.994 0.000 0.922 1.093 0.979 1.368 0.852 0.971 0.979 +1 0.924 -0.200 -1.134 0.513 1.320 0.881 -0.275 -0.036 1.087 1.035 1.101 1.358 0.000 0.573 -0.488 -0.568 0.000 0.369 0.797 -0.657 0.000 0.784 1.318 0.992 0.683 0.867 0.787 0.806 +0 1.107 -0.038 1.209 0.764 0.335 0.672 0.089 0.088 0.000 0.821 0.771 -1.191 2.215 0.982 -0.038 -1.238 2.548 0.662 1.268 -0.290 0.000 0.864 0.947 0.985 0.824 0.412 0.733 0.752 +0 0.299 1.073 -0.302 1.292 1.514 1.198 1.125 1.075 2.173 1.673 -0.046 -0.671 1.107 0.799 0.463 -0.460 0.000 0.743 0.164 -1.043 0.000 0.818 0.862 0.992 0.920 2.458 1.465 1.280 +0 0.462 -1.385 -0.656 0.805 0.407 1.613 0.133 -0.394 2.173 1.001 0.172 1.350 2.215 0.954 0.942 -1.562 0.000 1.945 0.546 -0.880 0.000 0.913 1.061 0.989 0.958 1.870 1.127 0.989 +0 2.078 1.567 -1.241 0.731 -0.619 0.768 0.118 1.609 2.173 0.712 -0.315 1.080 0.000 1.414 1.029 0.459 0.000 0.795 -0.752 0.122 3.102 0.873 0.964 0.986 1.380 0.918 1.204 1.093 +0 0.708 -0.021 0.108 0.803 1.360 0.766 -0.291 -1.223 2.173 0.386 -1.065 -0.810 0.000 0.548 -1.694 0.792 0.000 1.326 -1.172 0.229 1.551 0.752 0.882 0.985 0.736 1.205 0.752 0.666 +1 1.709 -0.286 0.393 1.551 -0.574 1.180 -0.530 1.410 0.000 0.950 -0.132 -1.603 2.215 0.988 -2.257 0.806 0.000 0.562 -0.745 0.215 0.000 0.834 0.898 1.725 1.299 0.544 0.875 1.000 +1 0.761 0.834 0.945 0.437 1.329 1.929 -0.148 -1.256 0.000 1.857 0.385 0.399 2.215 1.246 -0.867 -0.291 2.548 1.091 1.532 1.456 0.000 0.787 1.275 0.980 0.968 1.507 1.173 0.977 +0 0.839 0.219 1.120 0.394 -0.580 0.822 1.144 -1.350 2.173 0.893 -0.228 -0.081 0.000 1.163 -0.231 0.636 0.000 1.148 -0.493 -1.219 3.102 0.942 0.960 0.991 0.801 1.024 1.024 0.860 +1 0.650 1.778 -0.991 0.361 -1.194 1.388 0.797 1.133 0.000 1.779 0.314 -0.331 2.215 1.076 0.859 -1.460 0.000 0.629 0.351 0.488 3.102 1.589 0.985 0.976 0.947 0.644 1.159 0.962 +1 0.724 -1.401 -0.903 0.787 1.375 0.933 -0.332 0.091 2.173 0.677 -0.123 -0.923 0.000 1.097 -0.520 0.900 2.548 1.040 -1.796 -1.151 0.000 1.004 0.997 0.986 0.732 0.852 0.752 0.703 +1 0.713 -0.694 0.416 1.053 -1.438 1.403 0.996 -0.328 0.000 1.325 -0.707 0.834 1.107 1.430 0.518 -1.343 0.000 2.091 -0.154 1.408 3.102 2.095 1.945 1.194 0.917 0.849 1.601 1.325 +0 0.935 0.431 0.085 2.172 -0.629 0.808 0.485 0.852 1.087 0.909 -0.322 1.146 0.000 0.823 -0.245 -1.646 0.000 1.616 0.229 -1.190 3.102 0.778 0.855 1.182 1.242 1.173 0.942 0.930 +1 0.781 -1.344 -0.423 1.650 -1.191 1.309 -0.756 1.624 1.087 0.918 -2.102 0.166 0.000 1.258 -0.145 0.051 0.000 0.911 -0.561 0.314 0.000 0.985 1.075 1.002 1.203 0.535 1.044 0.993 +1 0.989 0.064 -0.136 1.499 -0.029 0.966 -0.801 1.731 1.087 0.605 -1.268 -1.198 0.000 0.389 -1.478 1.064 0.000 0.490 -1.050 0.525 3.102 0.675 0.660 0.973 0.852 0.666 1.140 1.020 +1 0.974 -0.986 -0.615 1.136 1.374 1.051 0.801 0.956 2.173 0.733 0.924 -1.443 2.215 1.121 -0.334 -0.641 0.000 0.669 0.307 0.031 0.000 1.101 0.934 1.421 1.621 1.075 1.210 1.277 +1 0.392 -1.473 -1.145 0.698 -0.798 1.313 -0.273 1.091 2.173 0.598 0.150 -0.197 0.000 0.730 1.010 -1.216 0.000 0.602 0.209 -0.639 0.000 0.949 0.861 1.000 1.155 0.556 0.865 0.797 +1 1.601 0.911 -1.635 0.794 -0.078 0.791 -0.005 0.842 2.173 0.684 0.012 -1.079 0.000 1.739 -0.008 0.221 0.000 0.748 -0.396 0.093 3.102 0.988 0.936 1.540 0.976 0.545 0.839 0.818 +0 0.448 0.983 -0.805 1.479 -1.718 0.917 -0.481 0.399 0.000 0.660 -0.286 -0.684 2.215 0.539 -0.306 1.180 0.000 0.625 0.033 0.021 3.102 0.982 0.858 0.982 0.669 0.358 0.548 0.661 +0 0.606 0.358 -1.016 0.891 0.400 1.730 0.749 -0.354 2.173 2.240 0.465 1.166 0.000 1.294 0.420 -1.633 2.548 0.767 0.616 1.466 0.000 0.508 0.805 0.988 0.971 1.726 1.408 1.110 +1 1.228 1.232 0.843 1.033 0.615 2.574 1.336 -1.646 0.000 2.692 0.855 -0.151 2.215 0.427 -0.277 0.147 2.548 0.903 -0.219 -1.176 0.000 0.604 1.212 0.986 0.939 0.776 1.101 0.974 +0 0.933 0.478 -1.239 0.682 0.481 0.510 -0.337 -1.188 0.000 0.928 -0.572 0.983 2.215 0.849 -0.110 -0.354 0.000 0.606 -1.219 0.920 1.551 0.818 1.006 1.105 0.808 0.308 0.663 0.674 +0 0.605 -0.096 -1.398 1.718 0.390 0.991 -2.133 0.306 0.000 1.131 -0.056 1.530 1.107 0.651 -1.299 -0.871 0.000 1.926 0.962 -1.107 1.551 0.804 1.792 1.411 1.285 1.261 1.897 1.531 +0 0.866 -0.913 -0.056 1.060 -0.776 1.016 -1.071 -1.110 2.173 1.500 -0.545 1.297 2.215 0.792 2.214 0.088 0.000 0.690 -0.833 0.218 0.000 2.198 2.230 0.984 0.887 1.575 1.845 1.467 +0 0.621 0.901 0.883 1.117 -0.886 0.367 2.443 0.622 0.000 0.809 -0.787 1.143 2.215 0.694 0.631 -0.339 2.548 0.832 0.748 -1.055 0.000 1.049 0.880 1.153 0.640 1.015 0.805 0.731 +0 0.940 -1.425 1.447 0.668 -0.891 0.534 0.861 -0.145 0.000 1.014 -1.135 -1.138 2.215 0.903 -2.194 -0.050 0.000 1.184 0.126 0.806 1.551 0.441 1.144 0.986 0.851 1.199 1.086 0.904 +1 0.808 0.149 0.766 0.392 -0.784 0.596 -0.199 -0.692 2.173 0.546 2.100 1.285 0.000 0.828 -0.310 1.477 2.548 0.942 0.239 -1.389 0.000 0.855 1.004 0.985 0.717 0.814 0.850 0.784 +1 1.815 0.313 -0.515 1.559 -1.143 1.969 0.672 1.408 0.000 0.615 -0.863 0.401 0.000 0.999 -1.143 0.947 2.548 0.413 -0.244 -0.213 0.000 0.398 0.495 1.248 1.121 0.618 1.057 0.840 +0 0.700 0.899 0.637 1.122 1.652 1.027 -0.521 -1.018 2.173 1.067 1.710 0.020 0.000 0.351 -1.597 0.678 0.000 0.920 1.338 1.223 0.000 1.145 1.136 0.987 1.241 0.654 1.296 1.066 +1 0.979 -0.429 -1.720 1.277 -0.401 0.651 0.940 -0.004 2.173 0.779 0.657 1.455 0.000 0.976 -1.066 1.248 0.000 1.802 0.417 -0.694 0.000 1.440 1.171 1.438 1.171 0.740 0.978 0.983 +1 0.773 -0.046 0.888 0.472 -1.432 2.017 -1.593 -0.435 0.000 1.300 -0.891 1.027 2.215 0.972 -1.974 -1.632 0.000 1.992 -0.145 1.531 3.102 0.828 1.024 0.984 0.669 0.843 0.902 0.771 +1 1.549 0.841 -0.854 0.671 0.763 0.916 2.506 -0.491 0.000 1.376 0.250 0.958 2.215 1.383 0.635 1.520 2.548 1.236 -0.384 1.490 0.000 0.860 0.885 1.404 0.987 0.783 0.889 0.812 +1 0.809 -0.754 -0.774 1.760 -0.247 0.566 -1.765 -1.408 0.000 1.102 -0.157 -1.681 1.107 0.759 -0.521 -0.374 0.000 3.412 -0.600 0.863 3.102 1.211 1.163 1.001 1.446 1.404 1.169 1.088 +1 0.515 0.454 -0.582 0.816 0.400 0.634 0.187 -1.273 2.173 1.158 -0.642 -1.037 0.000 1.444 0.583 0.524 0.000 1.581 0.420 1.672 3.102 0.998 1.172 0.987 0.953 0.523 0.919 0.881 +1 1.937 1.301 -1.301 0.507 0.744 1.204 1.344 -0.303 2.173 0.688 0.741 0.310 2.215 1.433 1.654 1.500 0.000 0.753 -0.418 0.943 0.000 1.737 1.212 1.322 1.211 0.810 1.092 1.005 +1 0.366 1.900 0.901 0.652 0.774 1.237 0.102 -1.481 0.000 1.552 0.659 0.254 1.107 0.706 -0.099 -0.907 0.000 0.500 -0.200 1.354 3.102 0.962 1.130 0.995 0.526 0.765 0.721 0.664 +1 0.451 1.055 -0.298 1.039 -0.141 0.753 -0.727 1.432 0.000 0.678 -0.082 -1.500 0.000 0.631 0.346 -0.521 2.548 1.255 1.004 0.424 0.000 0.881 0.905 0.994 0.664 0.290 0.614 1.182 +0 1.728 1.460 1.198 0.843 -1.619 1.335 -0.940 -0.682 0.000 1.012 0.220 1.562 0.000 0.674 0.864 0.574 2.548 0.587 1.198 -1.404 3.102 2.699 1.738 0.982 0.714 0.485 1.174 1.398 +1 1.138 0.426 -0.047 0.939 -0.774 1.590 0.548 -0.590 2.173 2.121 -0.104 0.804 0.000 1.749 -1.319 1.529 1.274 1.508 0.568 1.423 0.000 1.552 1.919 0.987 0.774 3.134 1.964 1.548 +0 0.943 0.249 1.039 2.680 1.038 1.811 0.576 -0.778 2.173 0.783 0.654 -1.407 2.215 1.240 0.084 0.536 0.000 0.547 0.767 -0.312 0.000 0.738 0.909 0.971 2.398 0.944 1.589 1.253 +0 0.442 0.118 -1.021 1.661 0.789 0.748 1.272 -1.731 1.087 0.640 1.780 -1.121 0.000 1.116 -0.562 0.119 2.548 1.422 1.052 -0.357 0.000 0.867 0.942 1.185 0.824 1.690 1.098 1.017 +0 0.526 -1.433 -0.055 1.779 0.173 1.261 -0.431 1.570 0.000 1.210 0.243 1.299 0.000 2.945 -0.607 -0.394 2.548 0.753 -1.041 -1.463 3.102 1.116 0.926 0.990 0.930 0.993 1.370 1.220 +1 0.844 -1.034 -0.227 0.507 1.204 1.012 0.917 -1.121 0.000 1.206 -1.223 0.858 2.215 0.578 -2.273 -1.053 0.000 0.806 -2.134 0.741 0.000 0.753 0.940 0.988 0.515 0.352 0.591 0.564 +0 0.566 1.264 -0.838 1.090 0.278 0.788 0.854 0.933 0.000 0.864 -0.711 1.682 2.215 1.534 -0.624 -1.077 0.000 0.828 -0.532 -0.172 3.102 1.322 1.038 0.987 1.013 0.761 1.099 1.319 +0 0.723 0.550 0.356 1.616 -0.220 1.122 2.230 -1.471 0.000 1.321 -0.530 0.525 2.215 1.763 1.059 1.229 0.000 1.319 1.308 -1.071 3.102 2.190 1.375 0.982 1.435 1.916 1.901 1.559 +1 1.104 0.483 -1.025 1.548 -1.299 0.860 0.952 0.427 2.173 1.300 -2.416 0.467 0.000 0.549 0.149 -1.422 2.548 0.569 0.143 0.582 0.000 0.504 0.524 0.992 0.483 0.922 0.934 0.759 +0 0.661 -1.443 -0.722 0.529 1.433 0.881 0.465 -0.735 1.087 0.968 0.399 0.961 1.107 0.485 1.478 1.507 0.000 1.256 0.194 0.296 0.000 0.832 0.838 0.990 1.559 1.358 1.428 1.174 +1 0.634 -1.699 1.673 1.496 -0.663 1.558 0.256 0.128 1.087 1.180 -2.581 1.628 0.000 0.888 -0.391 1.360 0.000 0.698 0.585 -1.380 3.102 2.165 1.881 1.162 2.128 1.107 2.082 1.711 +1 1.007 -0.436 -0.640 0.988 0.299 1.680 -0.705 -1.320 0.000 0.836 -0.653 0.287 0.000 1.992 0.207 0.355 2.548 1.046 -0.234 1.488 0.000 1.102 0.986 1.035 0.779 0.688 0.679 0.681 +1 0.943 -0.374 0.316 0.970 -1.180 0.811 0.819 1.608 0.000 1.379 -0.016 0.050 2.215 0.766 1.063 -1.229 0.000 0.560 -0.802 -1.736 3.102 0.817 0.808 1.292 0.942 0.883 0.950 0.882 +1 1.066 -0.699 -1.410 1.098 -0.629 0.700 -0.923 0.866 0.000 1.083 0.247 0.431 2.215 0.737 0.983 -0.712 0.000 1.089 -0.085 0.885 3.102 0.294 0.887 0.986 0.867 0.428 0.842 0.820 +1 0.787 0.894 0.280 1.232 0.433 2.828 -0.055 -1.070 0.000 1.383 -0.503 0.585 2.215 2.322 0.330 1.144 0.000 0.981 -0.440 -0.525 3.102 0.836 1.152 0.984 0.725 0.885 0.947 0.836 +1 1.675 -0.256 -0.504 1.428 0.208 1.302 0.107 1.180 0.000 0.597 0.971 -0.679 1.107 0.443 -0.770 0.030 1.274 0.819 0.629 -1.664 0.000 1.005 0.931 1.284 0.950 0.665 0.795 0.916 +0 2.030 -1.210 -1.250 0.706 1.027 1.388 0.381 0.360 2.173 0.459 -0.087 1.230 0.000 0.653 2.335 1.572 0.000 0.377 0.562 -0.533 3.102 0.773 0.543 1.470 2.077 0.563 1.283 1.425 +0 0.962 0.549 -1.365 1.342 -0.029 1.360 0.340 0.806 2.173 1.613 -0.506 -1.315 2.215 0.899 -0.358 0.602 0.000 1.344 -0.937 -0.540 0.000 1.134 1.244 1.470 1.292 2.272 1.335 1.177 +0 0.673 0.836 -1.359 1.474 -1.417 1.204 -1.465 0.232 2.173 0.615 -0.822 -0.220 0.000 0.986 1.973 -1.602 0.000 1.450 -0.718 1.156 3.102 0.694 0.859 0.999 1.850 1.122 2.452 1.764 +0 0.608 -0.716 1.379 1.963 -1.037 0.921 -0.845 0.721 1.087 0.684 -1.500 -0.469 2.215 0.446 -0.779 -0.408 0.000 0.954 0.635 1.597 0.000 0.953 1.008 1.244 1.260 1.105 0.934 0.854 +1 0.398 1.379 -1.038 1.199 -1.407 0.810 0.054 -0.396 2.173 0.911 1.429 1.013 0.000 0.767 0.619 1.630 0.000 1.417 0.667 0.171 0.000 0.854 0.731 0.977 0.871 0.741 0.844 0.795 +1 0.360 -0.935 -1.049 0.734 1.482 0.813 0.247 1.581 2.173 1.053 -0.524 -0.621 0.000 1.058 -1.117 -0.151 0.000 0.900 0.299 0.311 3.102 0.874 0.858 0.992 1.606 0.826 1.183 1.100 +0 0.594 0.228 1.371 1.115 -1.081 0.946 0.603 -0.196 2.173 0.394 1.126 1.047 0.000 0.432 -2.336 -1.247 0.000 0.452 1.564 -0.074 0.000 0.508 0.727 0.988 0.632 0.832 0.739 0.682 +1 1.605 -0.278 0.033 0.924 0.371 0.959 0.924 -0.880 2.173 0.949 0.559 1.499 2.215 0.698 0.011 1.497 0.000 0.382 0.035 -0.933 0.000 0.464 0.759 0.987 1.315 1.206 1.269 0.951 +1 0.466 1.073 -0.374 0.022 -0.121 0.903 -0.434 0.074 2.173 0.359 2.071 0.647 0.000 1.086 0.905 1.430 1.274 0.506 1.180 -1.405 0.000 0.571 1.289 0.543 0.580 1.506 0.928 0.713 +1 2.477 -1.667 -1.127 0.890 -1.543 1.325 1.062 0.696 0.000 1.611 -0.987 -0.218 2.215 0.731 -1.443 0.961 0.000 0.627 0.026 -1.671 0.000 0.849 1.111 0.986 0.722 1.171 0.989 0.873 +0 2.232 0.127 0.041 3.490 0.385 3.415 -0.387 -1.400 0.000 2.419 0.155 0.468 1.107 2.632 -0.932 -1.261 2.548 1.779 0.313 1.049 0.000 0.898 1.059 1.182 2.644 3.165 1.975 1.544 +0 1.451 -0.835 0.892 1.078 1.329 0.846 -0.055 -0.026 2.173 0.448 -2.645 -0.629 0.000 0.965 -1.019 -1.428 2.548 0.825 0.029 -1.055 0.000 0.671 0.732 0.979 1.118 1.243 0.918 0.941 +1 0.540 -0.885 -0.417 0.279 0.685 0.840 0.785 1.173 0.000 1.233 -0.070 -0.270 2.215 1.109 0.743 -1.710 0.000 0.442 0.175 -1.564 3.102 0.896 0.553 0.999 0.699 0.620 0.866 0.750 +1 0.717 -0.190 0.302 0.557 -0.273 1.164 0.120 0.608 0.000 1.179 2.272 -1.428 0.000 1.671 -0.662 -1.443 2.548 0.711 -0.284 0.022 0.000 0.773 0.689 0.995 1.230 0.894 0.907 0.862 +1 1.233 -0.697 1.293 0.549 1.215 0.907 -0.988 -0.596 0.000 0.931 0.300 1.174 2.215 0.708 -1.311 0.313 0.000 1.110 -1.523 -0.598 0.000 0.982 1.276 0.989 0.597 0.580 0.736 0.721 +0 0.659 -0.998 1.229 0.911 -0.812 1.239 -0.845 -1.251 2.173 1.275 -0.483 0.373 1.107 1.005 -0.942 0.104 0.000 1.532 0.045 1.077 0.000 1.311 0.918 1.035 0.792 1.868 1.127 0.932 +1 1.412 -0.313 1.244 0.694 0.393 1.399 0.640 -0.405 2.173 0.426 2.748 0.748 0.000 0.744 -0.602 -1.601 0.000 0.582 1.486 -1.588 3.102 2.507 1.380 0.987 1.527 1.014 1.198 1.223 +0 0.529 1.170 0.667 1.147 -0.515 1.077 2.521 -0.522 0.000 1.252 -0.133 0.904 2.215 0.825 1.030 1.469 0.000 0.502 -1.983 -1.537 0.000 2.014 1.125 0.991 1.344 0.791 0.946 1.071 +0 1.386 -2.416 0.101 0.602 -1.724 1.413 -0.782 -1.511 2.173 0.879 -0.108 -0.614 0.000 1.813 1.650 0.733 0.000 0.571 -0.294 0.926 1.551 0.974 1.011 1.262 1.772 0.798 1.797 2.525 +0 2.187 0.673 1.662 0.505 0.321 0.735 1.450 -0.108 1.087 0.383 2.292 -0.451 0.000 0.410 1.580 0.814 0.000 0.717 2.177 1.133 0.000 0.681 0.753 1.361 1.248 0.566 0.823 0.795 +0 0.915 0.056 -0.133 1.101 -1.008 0.877 1.444 1.690 2.173 0.636 2.792 0.359 0.000 1.089 1.144 1.110 0.000 0.559 -0.067 -0.480 3.102 1.399 1.191 0.988 1.245 0.930 0.912 1.041 +0 0.476 -2.349 -0.470 0.715 -1.711 1.079 -0.516 -0.157 1.087 0.792 -0.852 1.428 2.215 0.593 -0.036 -1.165 0.000 0.623 0.920 1.280 0.000 0.676 1.047 0.989 0.698 1.368 0.862 0.820 +0 0.372 0.717 0.157 0.535 -1.076 0.701 0.089 1.662 0.000 1.392 -0.564 -0.016 1.107 1.210 0.768 -1.723 0.000 1.465 -0.948 1.550 0.000 0.999 0.921 0.988 0.779 0.502 0.853 0.737 +1 2.200 -0.197 -1.460 1.999 -1.405 1.657 2.025 0.491 0.000 0.485 -0.826 -0.979 0.000 0.892 -0.987 -0.531 2.548 1.535 -0.591 1.242 1.551 1.441 0.921 0.975 1.098 0.910 0.971 1.275 +0 2.318 0.861 1.100 0.884 1.596 0.942 0.426 -0.528 0.000 0.795 -0.322 -0.490 2.215 1.025 0.266 0.412 2.548 1.449 0.047 -1.396 0.000 1.300 0.889 0.985 0.862 0.760 0.921 0.975 +0 1.012 -0.030 0.894 0.879 -0.320 1.125 0.806 1.292 0.000 1.089 1.753 1.613 0.000 2.021 -0.856 -0.884 0.000 0.980 0.452 0.320 3.102 1.384 1.154 1.161 0.629 0.615 0.837 0.897 +1 0.865 -1.037 1.186 0.394 -0.583 0.502 1.204 1.336 2.173 0.684 0.496 0.259 2.215 0.278 0.413 0.987 0.000 0.451 -1.085 0.179 0.000 0.957 1.126 0.990 0.935 0.777 0.985 0.829 +1 1.240 1.307 1.299 1.528 0.079 0.944 0.338 0.051 1.087 1.051 1.890 -1.138 0.000 0.910 2.392 1.443 0.000 0.379 1.399 -1.526 0.000 1.210 1.035 1.698 1.250 0.296 1.158 1.066 +1 0.643 -0.182 -0.340 1.487 0.861 0.603 -0.911 -1.638 2.173 0.604 0.230 -1.586 0.000 0.648 0.136 -0.994 2.548 0.366 0.027 0.884 0.000 0.489 0.552 1.196 0.798 0.611 0.705 0.584 +1 0.829 -0.071 0.543 0.553 1.265 0.747 -0.035 0.101 0.000 1.006 -2.127 0.258 0.000 2.299 -0.580 -1.358 2.548 0.841 -0.419 -0.850 0.000 0.959 1.100 0.994 1.334 0.664 1.004 0.947 +0 1.720 -0.296 -0.169 1.160 -0.078 0.851 -0.393 -1.717 1.087 0.350 -0.436 -0.834 0.000 0.716 0.336 1.027 2.548 0.830 1.407 1.257 0.000 0.779 0.840 0.983 1.382 0.713 0.953 0.885 +0 0.672 0.625 -0.526 0.644 1.011 1.386 -0.151 0.156 2.173 1.178 0.875 1.688 2.215 0.802 0.169 -1.621 0.000 0.615 -1.230 -1.088 0.000 0.805 0.996 0.990 0.867 2.117 1.161 0.923 +0 0.373 0.109 0.285 1.698 -1.451 0.381 -0.396 -0.530 0.000 0.551 0.916 1.497 2.215 0.566 -2.507 0.323 0.000 0.719 -0.878 0.240 3.102 1.085 0.664 1.103 0.698 0.842 0.866 0.866 +1 0.672 -0.009 1.347 1.585 -0.722 0.665 1.433 -0.558 2.173 0.780 -0.339 0.651 0.000 0.818 -0.811 1.463 2.548 0.877 0.943 0.905 0.000 0.907 0.853 1.369 1.109 1.606 0.977 0.916 +0 0.825 0.336 1.379 1.440 -1.182 0.664 -0.103 0.606 2.173 0.384 -1.116 -0.635 0.000 0.480 -2.299 0.784 0.000 0.595 1.104 -0.435 3.102 0.799 1.018 1.119 0.708 0.745 0.852 0.910 +0 0.524 1.551 0.146 1.132 0.718 1.108 -0.010 -0.407 2.173 1.169 0.581 1.459 2.215 0.611 1.057 -0.709 0.000 0.766 -0.025 -1.707 0.000 0.759 0.880 0.990 0.874 1.742 1.009 0.829 +0 1.233 -0.207 1.298 0.877 -1.291 0.460 0.408 -0.213 0.000 0.377 -1.409 0.111 0.000 0.441 -0.590 0.364 2.548 0.963 -0.696 -1.611 0.000 0.981 1.026 1.043 0.661 0.654 0.691 0.709 +0 1.106 -2.254 -1.116 1.357 -1.637 0.854 -0.450 -0.322 1.087 0.558 -0.066 0.288 0.000 1.010 -0.905 1.157 0.000 0.654 1.092 0.727 3.102 0.982 1.032 0.985 1.712 1.019 1.329 1.124 +1 0.748 1.891 -1.163 0.187 0.575 0.598 0.381 1.678 0.000 0.582 -2.101 0.373 0.000 0.606 0.757 -0.258 2.548 1.340 1.884 -0.704 0.000 1.871 1.139 0.996 0.727 0.635 0.816 0.701 +1 0.355 0.772 -0.669 0.158 0.164 1.297 -0.633 -1.256 2.173 1.239 -0.477 1.126 0.000 1.004 -0.484 0.571 0.000 1.413 1.011 -0.150 0.000 0.821 0.744 0.988 0.881 0.990 0.966 0.808 +1 0.675 0.590 -1.736 1.093 0.373 0.393 0.561 -0.798 0.000 0.445 -0.475 -0.058 2.215 0.678 2.048 -0.626 0.000 1.079 -0.640 1.513 1.551 0.888 0.965 1.126 0.838 0.624 0.861 0.774 +0 1.614 0.625 -0.385 0.725 -1.054 1.083 0.623 0.844 0.000 1.065 0.623 -1.284 1.107 0.981 -0.443 0.573 2.548 1.159 0.621 1.397 0.000 0.952 0.946 0.986 0.799 1.259 0.913 0.920 +0 5.215 -0.529 -0.590 0.950 -1.016 2.811 -0.064 1.194 2.173 0.355 -2.657 1.024 0.000 1.084 0.081 0.582 0.000 0.597 -0.591 0.253 3.102 0.970 1.257 1.153 0.838 1.123 2.055 1.554 +0 0.329 -0.022 1.457 1.778 -0.820 1.066 -0.844 0.527 1.087 0.329 0.205 0.964 0.000 0.604 -1.070 -1.117 1.274 0.543 -2.355 1.584 0.000 1.233 1.085 0.989 0.781 1.009 1.012 0.978 +0 0.775 0.068 1.509 0.316 -1.264 1.272 -1.673 -1.235 0.000 0.951 -1.276 0.708 2.215 1.863 -0.539 1.219 2.548 1.413 -0.922 -0.364 0.000 0.904 1.159 0.988 0.817 0.817 0.764 0.722 +0 3.963 -1.376 -1.235 0.880 -0.869 1.393 -0.173 0.660 0.000 1.043 -0.323 0.359 0.000 0.891 -1.368 -1.695 2.548 1.305 0.987 0.400 3.102 0.718 1.051 0.979 0.699 1.668 1.632 1.701 +0 0.992 -0.653 0.341 0.352 -0.721 0.704 -0.348 0.950 0.000 0.655 -2.866 -0.991 0.000 0.928 2.360 1.466 0.000 1.094 1.415 0.681 0.000 0.886 0.850 0.980 1.147 0.223 0.897 1.297 +1 1.554 0.726 -1.122 0.344 -1.194 0.944 1.634 -0.295 0.000 1.908 -0.085 0.964 2.215 0.768 -0.699 -0.675 0.000 1.071 0.390 0.555 0.000 1.111 0.810 0.974 1.665 0.842 1.109 0.981 +0 0.978 -0.864 1.592 0.484 -1.055 0.389 2.279 0.018 0.000 0.494 0.321 -0.083 2.215 0.698 -0.003 1.127 2.548 0.500 0.857 1.398 0.000 0.785 0.868 0.993 0.743 0.563 0.608 0.744 +0 0.598 -0.274 0.944 0.450 1.638 0.505 -1.200 0.496 0.000 0.619 -1.484 1.403 2.215 1.635 -0.882 -0.175 2.548 2.615 1.133 -1.172 0.000 3.416 2.437 0.984 1.260 1.100 1.699 1.363 +1 0.897 -0.100 -0.850 0.931 -1.674 0.684 -0.937 0.423 0.000 0.894 0.137 0.007 0.000 0.823 1.058 -1.600 2.548 0.792 -0.340 -1.740 3.102 1.126 0.945 0.985 0.626 0.542 0.871 0.811 +0 0.891 -0.682 1.531 0.993 1.040 1.086 -0.848 0.122 0.000 1.023 -0.962 -1.575 2.215 0.646 0.195 -0.867 2.548 0.837 -0.897 -0.593 0.000 0.890 0.878 1.000 0.897 0.755 0.838 0.865 +0 0.997 -1.911 -0.309 1.684 0.583 0.604 -0.448 -0.910 1.087 1.524 -0.335 -1.424 0.000 1.157 -0.531 -0.032 0.000 2.897 -0.563 0.560 0.000 0.942 0.885 1.293 1.324 0.980 1.250 1.161 +0 0.377 -2.261 -1.261 1.582 -1.492 0.870 -0.337 -0.204 0.000 0.581 -0.663 0.919 2.215 0.502 -0.212 1.246 2.548 1.082 0.243 0.576 0.000 1.069 0.876 0.990 0.653 0.212 0.577 0.762 +0 1.160 0.945 1.576 1.372 -1.654 1.036 -1.065 -0.700 2.173 1.564 0.551 0.637 0.000 0.744 0.350 0.060 0.000 1.196 1.383 0.149 0.000 0.836 1.341 0.977 2.612 0.719 1.747 1.547 +0 1.610 -0.073 -1.629 0.814 -1.420 2.192 -0.153 -1.307 2.173 3.965 1.303 0.176 1.107 2.119 1.825 1.033 0.000 0.447 1.065 0.413 0.000 0.695 1.726 0.986 0.849 5.516 2.838 2.447 +0 0.959 -0.669 1.719 0.801 0.826 1.088 -0.240 -0.124 0.000 0.584 -0.900 0.473 0.000 1.175 0.170 -1.372 2.548 0.941 -0.666 -1.509 3.102 1.049 1.016 0.987 0.752 0.424 0.842 0.780 +0 0.812 -0.776 0.509 1.106 1.722 0.790 0.735 -1.210 2.173 0.902 0.099 0.543 2.215 0.645 1.288 -0.192 0.000 0.466 0.578 -0.779 0.000 0.739 0.977 1.166 1.217 1.306 0.962 0.963 +1 1.053 2.065 1.531 1.121 0.316 0.493 0.507 -0.865 0.000 0.932 0.351 -0.383 2.215 0.566 0.842 1.712 0.000 0.393 -1.438 1.564 0.000 0.913 0.929 1.337 1.027 0.827 0.987 0.975 +1 1.174 -0.802 -0.337 1.093 0.256 1.047 0.130 -1.520 2.173 0.801 -0.233 1.435 0.000 0.383 -1.093 1.367 0.000 0.601 0.295 0.063 0.000 0.896 0.872 0.988 0.507 0.798 0.837 0.744 +1 1.156 -0.156 -0.606 1.499 0.145 2.677 0.015 -0.843 0.000 1.242 -1.108 1.207 2.215 0.758 -1.393 0.597 2.548 1.185 0.961 1.226 0.000 0.803 0.930 1.142 1.376 0.575 0.953 0.864 +1 0.550 -2.264 1.230 0.503 -0.145 1.215 -0.624 -1.222 2.173 1.072 -0.943 0.632 1.107 0.752 -0.690 -0.021 0.000 1.171 -1.685 0.460 0.000 0.966 1.271 0.988 0.708 1.695 1.178 1.059 +0 0.401 -2.170 0.637 1.628 -1.533 0.400 -0.649 0.903 0.000 0.565 -0.986 -1.469 2.215 0.929 0.369 -0.025 1.274 0.591 -0.580 -0.310 0.000 0.659 0.643 1.038 0.719 0.950 1.074 0.867 +0 1.697 0.760 1.489 0.765 0.524 1.060 0.974 -1.460 2.173 0.774 -0.353 -0.303 2.215 0.637 -0.150 0.456 0.000 2.099 -1.236 0.024 0.000 1.025 0.805 1.205 1.014 1.504 1.383 1.288 +1 1.728 0.082 0.566 0.268 0.951 0.826 -0.327 1.027 0.000 1.018 0.457 -0.752 2.215 1.185 -1.349 -1.349 0.000 1.526 -0.322 0.104 0.000 1.021 1.171 0.979 0.621 0.705 0.748 0.712 +0 0.746 -1.031 -0.754 1.150 1.560 0.592 0.279 0.149 0.000 0.866 0.044 0.974 1.107 0.889 1.420 0.139 0.000 1.161 0.632 -0.784 3.102 0.892 0.974 1.117 1.020 0.963 0.843 0.962 +0 0.891 -0.593 -0.392 0.485 0.351 1.158 0.271 -1.208 2.173 1.146 -0.844 0.889 1.107 0.688 -1.315 0.328 0.000 0.384 -2.013 0.486 0.000 0.729 1.248 0.991 0.969 1.907 1.056 0.903 +0 0.478 -0.632 -0.146 1.589 -1.345 0.691 0.843 0.462 0.000 0.910 0.245 1.391 2.215 1.121 0.984 -0.146 0.000 0.432 -0.723 -1.314 3.102 0.844 0.883 1.064 0.927 0.496 0.754 0.874 +0 0.592 0.791 -1.145 0.650 0.146 1.251 0.221 -1.593 2.173 1.100 0.831 0.272 1.107 0.856 -1.370 0.227 0.000 0.885 1.491 0.811 0.000 0.802 0.879 0.987 1.208 1.803 1.057 0.899 +0 0.656 -1.537 -1.612 0.096 -1.614 0.918 -0.853 0.673 0.000 0.584 -1.514 0.175 1.107 1.423 -0.912 -0.939 0.000 0.900 -0.716 1.589 3.102 1.072 0.793 0.986 0.765 0.668 0.586 0.599 +1 0.446 1.033 -1.526 2.178 1.015 3.186 1.633 -0.909 0.000 1.849 0.000 1.030 2.215 1.969 -0.300 1.519 2.548 1.671 0.216 0.202 0.000 0.710 0.939 1.027 1.228 0.929 0.969 0.930 +0 1.265 1.128 -1.196 0.559 -0.730 1.291 0.474 0.517 2.173 1.436 0.823 -1.658 1.107 1.061 0.350 -0.286 0.000 0.674 -0.161 1.243 0.000 0.955 0.958 0.977 0.808 1.890 1.124 0.941 +0 0.621 -0.943 -1.698 0.950 0.592 0.901 0.176 -1.422 0.000 1.203 -2.449 0.403 0.000 0.736 1.116 0.183 2.548 1.693 0.217 -0.951 3.102 0.530 0.897 0.989 1.093 0.845 0.971 0.861 +1 1.604 -0.042 0.970 0.463 1.134 0.727 -0.174 -0.451 2.173 1.065 0.404 -1.276 2.215 0.559 1.098 0.018 0.000 0.485 0.432 -1.026 0.000 0.633 0.868 0.991 1.036 0.959 0.925 0.783 +1 0.884 -0.477 -0.295 1.082 -1.223 0.539 0.909 -0.914 0.000 0.683 0.727 -1.479 0.000 0.885 -1.670 1.099 0.000 0.888 0.779 0.507 3.102 0.960 0.817 1.004 0.935 1.211 1.038 0.926 +1 0.574 -1.679 -0.752 0.456 -1.193 1.050 1.109 0.936 0.000 0.588 0.079 -1.199 2.215 0.822 0.009 -0.269 2.548 0.604 2.456 0.865 0.000 1.275 1.359 0.999 0.605 0.549 0.995 1.089 +1 0.628 0.652 -1.154 0.795 0.311 0.974 0.533 1.577 0.000 1.365 0.877 0.648 2.215 1.344 0.660 -0.061 2.548 1.977 -1.075 -0.970 0.000 0.971 1.204 0.989 0.837 0.867 0.950 0.801 +0 2.281 -0.391 -0.926 0.356 1.734 0.571 -1.426 1.185 0.000 0.462 -1.721 0.448 0.000 0.726 0.200 0.247 2.548 0.881 0.483 0.602 3.102 0.698 0.969 0.989 0.879 0.222 0.713 0.826 +1 1.951 0.946 1.742 0.614 -1.618 1.437 1.022 -0.045 2.173 0.811 -0.232 0.555 0.000 0.897 0.277 -1.092 0.000 0.645 0.187 1.012 3.102 0.830 1.149 1.000 0.618 0.932 1.063 0.867 +1 0.348 -0.011 1.007 2.427 -0.467 2.984 -2.227 1.355 0.000 2.892 -0.042 -0.274 1.107 0.680 -0.400 1.681 0.000 0.867 0.222 -0.914 3.102 0.852 0.728 1.236 0.867 0.811 0.886 0.798 +1 0.852 1.071 -1.257 0.713 -0.345 0.951 0.552 0.857 2.173 0.836 -1.309 -1.691 1.107 1.256 0.577 -0.185 0.000 1.225 -0.351 -0.315 0.000 0.773 1.184 0.989 1.872 1.748 1.420 1.214 +1 1.495 0.348 -1.077 0.346 0.257 0.268 0.184 -0.569 0.000 0.676 1.381 0.814 0.000 0.783 1.340 1.267 2.548 1.740 1.229 -0.181 3.102 1.043 0.838 0.988 0.858 0.861 0.769 0.704 +1 0.952 -1.752 1.478 1.641 1.348 1.390 -0.572 0.447 0.000 1.845 -0.858 -1.297 0.000 1.668 1.379 -0.023 0.000 1.178 -0.467 1.505 0.000 1.152 0.684 0.974 1.641 0.591 1.185 1.198 +0 0.918 0.801 0.742 0.588 0.864 1.137 -1.025 -1.319 2.173 1.036 -0.269 -0.071 1.107 0.636 -1.627 0.624 0.000 0.705 -0.782 -1.709 0.000 0.714 0.910 0.996 1.379 1.569 1.081 0.907 +1 1.054 1.332 -0.105 1.000 0.479 1.386 -2.825 -0.913 0.000 1.295 1.411 1.422 2.215 1.326 0.304 1.291 2.548 1.076 0.411 -0.576 0.000 0.876 1.087 0.984 0.960 0.836 0.889 0.784 +1 0.472 -1.140 -0.598 2.016 -1.664 0.877 -0.612 0.644 1.087 0.704 0.802 0.349 2.215 0.955 -2.447 -0.773 0.000 0.868 -0.936 -1.373 0.000 1.010 1.495 1.108 1.442 0.959 1.363 1.195 +0 0.599 -0.378 0.609 0.830 -1.452 1.162 -0.054 -1.682 2.173 1.327 -0.377 -0.020 2.215 1.110 -1.464 -0.473 0.000 0.550 -1.066 0.371 0.000 0.908 1.021 0.989 0.750 1.848 1.153 0.952 +1 1.020 0.300 0.840 0.753 -1.363 0.783 -1.019 -1.215 2.173 0.668 -0.246 0.607 0.000 1.556 -0.218 -1.718 2.548 1.998 -0.315 -0.132 0.000 0.935 1.237 1.112 1.069 0.820 0.957 0.873 +0 1.453 -0.314 0.218 1.704 -0.313 1.050 -0.412 1.660 2.173 0.763 -0.628 -0.842 0.000 0.828 0.688 1.099 0.000 1.082 0.971 0.741 0.000 1.053 0.912 1.004 0.915 0.508 1.003 0.863 +1 0.768 0.782 -1.544 0.755 0.748 0.995 0.694 0.800 2.173 1.148 0.494 -1.111 0.000 0.908 -0.255 -0.756 0.000 0.580 0.637 0.329 0.000 0.802 0.681 0.991 0.773 0.693 0.890 0.793 +0 1.902 -0.179 0.551 1.064 0.948 1.233 -0.027 -0.830 0.000 0.539 -1.097 -1.700 0.000 0.487 0.165 -1.225 2.548 0.592 1.229 0.428 3.102 1.581 0.869 0.998 0.887 0.498 0.751 0.934 +0 0.647 0.818 -0.818 2.915 -0.204 1.553 -2.329 1.288 0.000 1.578 0.596 0.900 0.000 0.488 0.147 -0.977 0.000 1.822 0.612 -1.459 3.102 1.370 1.081 1.001 1.164 0.859 0.850 0.955 +1 0.812 -0.887 1.734 1.268 0.883 0.952 0.213 -0.949 0.000 0.920 -0.480 0.935 2.215 1.025 -0.932 -0.109 2.548 1.085 -0.664 0.448 0.000 0.893 0.738 0.987 0.853 0.879 0.642 0.593 +1 0.770 0.035 -0.561 0.875 0.583 1.244 0.125 -1.087 2.173 0.744 -0.151 -1.586 0.000 1.123 -0.287 0.742 0.000 0.873 -0.299 -0.479 1.551 0.896 0.914 0.987 1.012 0.635 1.026 0.852 +1 1.204 1.415 -0.119 1.989 0.617 0.959 0.890 -1.356 2.173 1.079 -0.238 -1.013 0.000 1.148 0.271 0.723 0.000 1.294 -0.599 1.203 3.102 1.770 1.202 1.321 1.504 1.376 1.334 1.275 +1 1.834 0.115 0.306 0.416 -0.469 1.443 -0.025 -1.364 2.173 0.643 0.541 1.070 0.000 0.436 -0.683 -0.209 2.548 0.553 -1.892 0.378 0.000 1.570 0.928 0.987 1.452 0.928 0.973 0.940 +0 1.136 0.164 -1.637 0.889 0.688 0.541 1.327 -0.971 0.000 0.851 0.184 -0.359 2.215 0.484 -1.901 1.110 0.000 0.522 1.891 0.862 0.000 0.899 0.964 1.205 0.804 0.484 0.675 0.734 +0 0.729 1.566 0.191 0.968 0.576 0.915 0.610 -1.256 1.087 0.549 -0.350 1.017 2.215 0.446 -0.861 -1.023 0.000 0.552 0.116 -0.156 0.000 0.496 0.720 0.974 0.741 1.066 0.850 0.712 +1 0.583 -1.451 -0.358 0.547 -1.738 0.853 -0.912 1.626 2.173 0.423 1.488 -0.051 0.000 0.606 -0.139 0.098 2.548 0.551 0.647 -0.401 0.000 0.312 1.353 0.984 0.896 0.948 0.836 1.071 +1 0.753 0.139 -0.183 0.779 0.726 0.536 -0.360 -0.554 1.087 0.940 0.487 1.191 2.215 1.086 0.363 -1.215 0.000 0.545 1.121 0.794 0.000 0.923 0.840 0.986 0.865 1.143 0.734 0.715 +1 0.364 -1.982 -0.273 0.440 -0.697 1.024 -0.066 0.831 2.173 1.204 0.173 -1.315 2.215 0.829 0.270 -0.642 0.000 0.490 0.687 0.438 0.000 0.611 0.905 0.983 0.862 1.540 0.925 0.768 +1 1.360 -1.930 0.538 0.163 -1.234 0.885 -0.353 -0.881 2.173 0.546 -1.685 0.311 0.000 0.528 -1.626 -1.579 0.000 0.825 -0.200 1.333 3.102 0.816 1.091 0.980 0.746 0.826 0.854 0.746 +0 1.384 1.017 -1.205 1.897 -1.704 0.786 0.145 -0.035 0.000 1.220 -0.688 0.655 2.215 0.748 -0.914 0.010 0.000 0.596 -1.108 1.513 0.000 0.839 0.939 0.988 0.578 0.821 1.207 1.183 +0 0.327 2.181 -1.356 2.407 -1.392 0.911 0.478 -0.364 0.000 0.796 1.053 1.677 0.000 1.357 -0.548 0.615 1.274 1.239 -1.373 0.343 0.000 1.830 1.193 1.002 1.594 0.346 1.081 1.058 +1 0.609 -0.033 0.509 0.981 1.509 1.266 0.981 -1.244 0.000 1.427 1.113 0.567 2.215 0.414 -0.418 -0.136 0.000 0.378 0.583 -1.636 3.102 1.502 0.797 0.987 0.848 0.625 0.949 0.822 +1 0.980 -1.254 0.007 0.773 -1.212 1.615 -0.722 1.223 2.173 0.946 -0.850 -0.379 0.000 1.071 -0.966 -1.132 2.548 0.853 -1.535 -0.410 0.000 0.815 0.766 1.073 1.297 1.421 1.003 0.881 +0 1.108 0.045 -0.460 1.435 1.527 0.765 -0.464 1.697 2.173 1.369 0.542 0.243 2.215 0.792 0.027 -0.957 0.000 0.876 -0.879 1.056 0.000 1.034 0.803 1.705 1.287 1.664 1.068 0.906 +1 0.817 0.459 1.471 0.778 -0.129 0.820 0.147 0.339 1.087 0.861 -0.063 -1.314 0.000 1.322 -0.805 -1.587 0.000 2.254 0.391 -0.423 3.102 0.934 1.170 1.095 0.778 0.941 0.939 0.817 +0 1.464 -1.012 -1.334 2.120 1.720 1.255 -0.766 -0.207 0.000 1.186 -1.870 1.135 0.000 1.679 0.315 -0.359 0.000 2.195 -0.984 1.234 3.102 0.752 0.956 0.992 0.923 0.794 0.971 1.058 +0 2.269 -0.404 1.167 0.285 0.009 1.002 0.326 -0.787 0.000 0.726 0.460 -1.479 2.215 0.770 -0.277 0.459 2.548 1.073 -0.167 -0.256 0.000 0.844 0.895 0.986 0.907 0.844 0.699 0.834 +1 2.387 0.556 1.586 0.348 1.673 0.520 0.219 0.398 2.173 0.554 0.663 -0.568 0.000 0.914 -0.421 -0.653 2.548 0.671 1.333 0.270 0.000 0.662 0.757 0.986 1.008 0.760 0.900 0.790 +0 1.259 1.948 -0.756 0.728 -1.416 1.063 0.152 1.585 2.173 1.770 1.575 0.112 2.215 0.527 -0.371 1.015 0.000 0.979 0.224 -1.006 0.000 0.815 0.775 0.989 1.278 2.541 1.628 1.352 +1 0.949 -0.297 -1.008 1.249 -1.150 0.896 -0.684 0.666 1.087 0.545 -0.484 1.298 0.000 0.475 0.171 -0.463 2.548 0.467 -0.004 0.424 0.000 0.494 0.505 0.984 0.554 0.784 0.907 0.733 +1 0.527 1.595 -1.432 1.334 0.709 1.859 -0.529 1.466 2.173 1.124 -1.906 0.076 0.000 1.578 -0.913 -0.157 0.000 1.573 -1.678 -0.627 0.000 0.818 0.941 1.087 2.229 1.115 1.504 1.750 +0 0.327 -1.836 -0.562 1.947 -0.350 0.805 -0.515 0.929 1.087 1.087 0.140 1.266 0.000 0.849 -0.927 1.641 2.548 1.267 0.220 -0.254 0.000 0.759 0.778 0.994 0.944 0.668 0.863 0.841 +1 1.008 0.178 0.694 0.802 -1.168 0.630 1.890 -0.538 0.000 0.481 0.550 -0.438 0.000 0.754 0.344 -1.470 1.274 1.461 -1.362 1.334 0.000 0.852 0.876 1.238 0.733 0.689 0.805 0.767 +1 1.215 0.416 0.546 0.626 -1.677 1.261 -0.650 0.797 2.173 3.439 0.274 -0.576 0.000 3.059 2.110 1.132 0.000 0.943 -0.380 -0.853 0.000 1.060 0.887 1.097 1.037 0.910 1.427 1.209 +0 0.712 -0.662 -0.306 1.765 -0.459 1.566 1.126 1.437 2.173 0.899 0.199 0.296 0.000 0.473 -0.070 -1.130 0.000 0.631 1.122 -1.604 3.102 0.971 0.774 0.984 3.416 0.424 2.154 1.620 +1 0.686 1.003 0.430 0.775 1.716 1.132 -0.735 -0.703 2.173 0.981 -1.004 1.238 2.215 0.941 0.089 -1.350 0.000 1.403 0.751 -0.051 0.000 0.881 0.869 0.988 1.677 1.543 1.388 1.105 +0 0.493 0.427 1.602 1.336 -1.093 0.683 -0.489 1.142 2.173 0.764 -0.886 0.035 0.000 0.417 0.919 -1.252 0.000 1.222 -0.025 0.172 3.102 1.250 1.046 0.987 0.980 0.776 0.951 0.912 +1 0.662 -0.642 0.436 0.751 -1.041 1.345 0.844 1.396 2.173 0.731 0.700 0.088 2.215 0.565 0.846 -0.491 0.000 0.802 0.489 -0.934 0.000 0.315 1.028 0.988 0.744 1.353 0.943 0.776 +0 1.833 2.245 -0.796 1.833 -0.725 1.312 1.418 1.095 0.000 0.529 2.679 0.345 0.000 1.001 0.715 0.821 0.000 0.890 1.190 -1.686 3.102 0.937 0.714 0.995 1.299 0.696 0.901 0.924 +0 1.344 0.701 0.969 1.865 0.841 0.946 1.539 -1.064 0.000 0.774 0.651 -1.092 2.215 2.045 0.696 -0.157 2.548 1.347 0.398 1.636 0.000 1.485 0.929 0.970 1.357 1.000 1.090 1.111 +0 1.478 1.441 1.425 0.896 0.523 1.339 0.407 -0.300 2.173 0.751 0.660 -1.327 2.215 0.615 -0.589 -1.040 0.000 0.402 1.131 -0.743 0.000 0.994 1.328 1.157 1.578 1.195 1.280 1.479 +0 1.430 0.561 -0.506 1.467 -1.097 0.623 0.833 1.461 0.000 1.022 0.693 0.866 2.215 1.105 -0.047 0.476 0.000 0.558 -0.645 -1.198 3.102 1.348 0.938 1.018 1.216 0.857 0.835 0.851 +1 0.472 -1.393 1.435 1.598 -0.689 0.957 0.529 0.236 2.173 1.063 2.096 -1.643 0.000 0.781 -0.592 -0.356 2.548 0.409 -1.248 0.773 0.000 1.009 1.563 1.134 0.688 0.870 1.211 1.611 +0 0.487 -0.589 -1.605 0.258 -0.404 1.175 1.186 -0.936 2.173 0.861 -1.700 0.861 0.000 1.001 -0.858 1.089 0.000 0.825 0.662 0.510 3.102 0.658 1.095 0.987 0.866 1.027 1.674 1.285 +0 1.660 -0.138 -1.048 0.668 -1.678 0.996 -0.922 0.848 2.173 1.123 -0.463 -0.571 2.215 1.422 0.065 0.862 0.000 0.482 0.127 -0.152 0.000 0.724 1.035 0.990 1.270 1.532 1.014 0.909 +1 2.171 0.518 0.144 0.377 -0.252 1.118 0.954 -1.617 1.087 0.558 1.229 0.880 0.000 0.310 0.832 0.473 2.548 0.535 0.831 -0.413 0.000 0.661 0.886 0.989 0.471 0.698 0.886 0.713 +1 0.579 0.473 0.370 0.504 1.061 1.808 2.217 -0.302 0.000 2.174 0.463 1.063 2.215 0.876 -0.458 -1.258 0.000 0.790 -1.975 -1.292 0.000 0.988 0.889 0.989 1.060 0.852 1.241 1.246 +0 1.617 0.139 1.335 0.484 0.102 0.801 1.232 0.288 2.173 0.707 1.641 -0.519 0.000 1.376 -0.378 -1.202 2.548 0.655 0.880 -1.658 0.000 0.810 0.860 1.098 0.948 1.759 1.021 0.938 +0 1.280 -1.517 0.647 0.508 0.572 1.237 -1.686 0.190 2.173 0.891 -1.339 -1.494 0.000 0.719 -0.980 1.674 0.000 0.956 -0.663 -0.261 1.551 0.394 0.735 1.001 0.894 0.706 0.881 0.811 +0 0.711 -1.732 1.238 1.023 0.297 0.590 -0.159 0.782 0.000 0.683 -0.244 -1.544 1.107 0.710 -0.814 -0.827 0.000 0.715 0.843 -0.931 3.102 0.751 0.806 0.988 0.893 0.539 0.795 0.696 +1 0.722 -0.199 -0.403 1.462 0.562 1.406 -1.478 -1.701 0.000 0.823 -0.788 -0.080 0.000 1.486 -0.899 -1.021 2.548 0.817 -0.056 0.313 0.000 1.015 1.081 1.088 0.677 0.920 0.786 0.723 +1 2.102 -0.947 0.254 0.322 0.742 1.126 -0.466 -1.363 2.173 0.530 -0.882 -0.603 0.000 0.400 0.498 1.122 2.548 0.373 0.085 -1.613 0.000 0.550 0.626 0.978 0.825 0.793 1.006 0.784 +1 0.968 -0.534 1.576 0.475 -0.810 0.820 -1.138 -0.713 0.000 1.156 -1.040 -0.164 1.107 1.696 -0.912 1.067 2.548 1.181 0.418 1.221 0.000 0.933 0.953 0.988 1.023 1.333 0.909 0.811 +1 1.336 -1.104 1.706 0.666 -1.135 1.054 -1.363 0.136 2.173 1.201 -1.169 0.823 2.215 1.438 -1.665 -1.458 0.000 0.422 -0.787 0.357 0.000 0.943 1.051 0.982 1.276 0.970 0.987 0.896 +0 0.656 -1.462 -0.762 0.969 -0.093 0.767 -0.732 0.881 2.173 0.795 -0.150 1.637 2.215 1.218 0.347 -1.140 0.000 0.566 0.399 0.740 0.000 0.910 1.108 0.992 1.404 0.800 1.107 1.142 +0 0.854 0.759 1.508 1.592 -1.559 0.612 1.712 0.641 0.000 0.877 0.833 -0.358 1.107 0.734 2.618 -1.542 0.000 1.559 1.405 -0.055 0.000 0.877 0.984 0.980 1.104 0.591 0.787 0.965 +1 1.857 -0.763 -1.541 1.158 1.363 0.782 1.231 0.132 0.000 1.496 -1.237 -0.028 0.000 1.014 0.672 -1.613 1.274 1.191 -0.144 0.243 1.551 1.146 1.054 1.017 0.993 0.924 0.861 1.185 +1 0.789 -1.094 1.277 0.589 0.096 0.397 -1.307 0.891 0.000 0.762 0.701 -0.496 2.215 0.682 0.389 1.018 2.548 0.485 0.279 -0.650 0.000 0.904 0.985 0.982 0.893 0.759 0.933 0.777 +1 1.576 0.585 0.017 0.424 -0.450 1.348 -0.980 -0.961 0.000 1.031 -0.258 -1.484 0.000 1.681 -0.200 0.771 2.548 1.265 0.124 0.354 0.000 0.949 1.012 0.975 0.992 0.631 0.818 0.800 +1 0.718 -1.455 -1.153 0.823 0.267 1.047 -0.159 0.085 1.087 1.401 -0.631 1.709 0.000 0.430 -0.988 -0.989 0.000 0.607 -0.260 -1.338 3.102 0.819 0.490 1.021 1.057 0.812 0.851 0.804 +1 3.508 -0.092 -0.884 0.834 -0.741 1.219 -0.922 0.715 2.173 1.116 -0.738 1.435 0.000 0.937 -0.704 -1.080 0.000 1.580 -1.512 1.115 0.000 0.914 0.932 0.967 0.629 0.787 1.221 1.085 +1 0.788 0.311 -0.603 2.352 -0.597 1.723 -0.020 0.884 0.000 0.878 0.473 -1.242 0.000 0.997 0.100 0.502 1.274 0.550 0.911 -1.220 3.102 0.961 0.823 1.003 0.807 0.636 0.760 0.764 +0 1.171 0.305 -0.155 0.680 -1.067 0.983 -0.072 0.980 2.173 0.633 0.346 1.515 0.000 1.534 0.555 -1.337 2.548 1.337 -1.287 -0.039 0.000 0.891 0.888 0.989 0.815 1.431 0.939 0.779 +1 0.861 0.184 -0.681 0.589 0.888 0.701 -0.011 1.684 2.173 0.866 -0.570 -0.046 0.000 0.538 0.890 0.955 2.548 0.728 -1.008 -0.908 0.000 0.796 0.905 0.986 0.763 0.612 0.751 0.673 +0 1.676 1.033 -0.634 0.244 -1.397 0.741 -2.939 -0.472 0.000 1.048 -0.557 1.181 0.000 1.339 0.798 1.482 2.548 0.769 -0.861 0.387 0.000 0.814 1.131 0.985 0.680 0.189 0.664 0.939 +1 0.771 1.496 1.268 0.877 -0.296 1.658 0.706 -0.314 1.087 0.313 0.915 1.024 0.000 1.290 1.179 -1.738 0.000 0.786 -1.238 1.058 0.000 0.954 1.150 1.124 1.152 1.710 1.195 1.018 +1 0.584 -2.056 1.693 0.361 -1.084 0.956 -1.191 -1.630 1.087 1.182 -1.291 0.581 0.000 1.217 -1.012 -0.147 2.548 0.525 -0.195 -0.862 0.000 1.151 0.854 0.986 0.662 1.309 0.891 0.764 +0 1.059 -0.443 0.136 1.203 -1.700 1.351 2.711 -1.740 0.000 0.589 -2.666 -0.758 0.000 0.714 -1.720 0.111 0.000 1.474 0.300 -0.536 3.102 0.882 1.920 1.559 0.867 0.781 1.899 1.888 +0 1.458 0.275 -1.008 0.537 1.526 0.524 0.415 1.118 2.173 0.743 -0.642 0.638 0.000 0.897 -0.304 0.014 2.548 1.012 -0.436 -0.916 0.000 1.116 0.922 0.985 0.801 0.789 0.681 0.682 +1 0.722 1.079 1.303 1.547 0.656 0.546 -0.263 0.040 0.000 0.779 0.299 1.562 0.000 0.808 0.155 -0.951 0.000 0.799 0.697 -0.807 3.102 0.939 0.926 0.986 0.776 0.763 0.872 0.779 +1 0.970 -0.304 0.373 0.337 0.410 1.155 -0.040 -1.169 2.173 1.284 -0.363 -0.065 1.107 1.400 -1.374 1.418 0.000 1.163 -0.609 1.048 0.000 0.863 1.043 0.986 0.768 1.533 1.020 0.893 +0 1.344 0.397 0.949 1.828 1.165 2.156 0.833 -0.635 2.173 0.823 -1.144 1.035 2.215 0.418 1.067 0.611 0.000 0.466 0.611 -1.226 0.000 0.497 0.902 0.995 2.410 3.044 1.856 1.333 +1 0.961 0.261 0.073 0.578 -1.152 0.688 0.181 -1.238 0.000 0.958 -0.286 -0.728 0.000 1.414 1.262 0.471 0.000 1.727 0.421 0.928 3.102 0.871 0.996 0.989 0.779 0.761 0.869 0.785 +1 1.958 0.099 -1.694 0.308 -0.744 0.895 -0.581 0.025 2.173 0.577 -0.447 0.566 0.000 0.435 -1.329 -1.317 0.000 0.580 0.735 0.218 3.102 0.861 0.790 0.988 0.708 0.626 0.849 0.754 +1 0.679 1.220 -0.669 0.765 0.508 1.249 -0.541 1.192 2.173 0.916 -2.121 -0.813 0.000 0.473 -0.603 0.202 0.000 0.687 -0.045 -1.283 0.000 1.073 0.617 0.987 1.876 0.830 1.259 1.530 +1 0.523 -1.556 0.132 2.268 1.671 0.748 0.270 -0.627 2.173 0.827 -0.026 -1.454 0.000 0.964 0.111 0.365 0.000 0.587 -0.392 1.196 0.000 1.050 1.130 1.484 0.974 0.679 1.119 0.953 +1 0.503 -2.304 -0.071 1.606 -1.099 1.179 2.970 1.552 0.000 1.622 -1.088 -0.274 1.107 1.420 -1.442 0.684 0.000 0.755 -1.917 1.255 0.000 0.701 0.887 0.995 1.212 0.792 0.880 0.901 +1 0.904 -1.791 -0.776 0.673 -1.229 2.019 -0.864 -1.733 0.000 1.186 -0.699 0.496 0.000 2.406 -2.106 -0.373 0.000 3.396 -1.848 -0.020 0.000 0.997 1.079 0.979 0.762 0.875 1.001 0.970 +1 1.168 1.256 -0.248 0.520 1.508 1.513 0.105 1.249 2.173 0.773 -0.462 -0.139 2.215 0.765 0.973 -0.530 0.000 1.107 1.389 -1.063 0.000 0.561 1.057 1.079 1.389 1.582 1.179 1.028 +1 0.923 -1.510 -1.655 0.631 -1.476 0.350 -1.638 -0.137 0.000 0.913 0.215 0.197 2.215 0.776 -0.426 0.949 2.548 0.488 0.028 -1.329 0.000 0.815 0.875 0.989 0.702 0.643 0.760 0.682 +0 1.230 0.746 -1.214 0.153 0.758 0.662 -0.108 0.093 0.000 1.035 1.080 1.332 2.215 0.847 -1.160 -0.077 0.000 0.772 -0.057 -1.307 3.102 0.856 0.813 0.990 0.786 0.752 0.985 0.943 +1 1.172 0.238 0.180 0.846 0.172 1.251 -2.722 -0.447 0.000 2.845 0.841 1.357 0.000 1.133 0.456 -0.590 2.548 1.555 -0.781 1.604 3.102 0.976 0.801 0.999 1.022 1.225 0.874 0.881 +1 0.824 -0.827 1.468 0.701 -0.671 0.784 1.125 -0.762 2.173 0.922 0.397 0.766 0.000 0.444 -0.114 0.400 0.000 0.404 0.973 1.091 3.102 0.413 1.104 0.987 0.684 0.593 0.827 0.767 +1 0.525 -0.000 -0.714 0.202 -1.606 1.157 0.216 1.178 2.173 1.026 -0.387 -1.124 0.000 1.041 -0.814 -1.735 0.000 1.619 0.996 -0.300 0.000 0.920 1.134 0.989 1.061 0.945 0.981 0.958 +1 0.648 -1.535 -1.506 0.945 -0.955 0.482 0.488 0.858 2.173 0.804 -0.442 -0.498 2.215 0.619 -2.584 0.606 0.000 0.610 -0.706 0.973 0.000 0.822 1.084 0.987 0.969 0.971 0.961 0.873 +0 0.780 -1.577 -0.982 0.593 -0.333 0.818 -0.638 1.031 2.173 0.961 -0.454 -1.569 0.000 1.199 -1.030 0.254 2.548 0.669 -0.812 -0.355 0.000 0.966 0.972 0.996 0.767 0.852 0.772 0.732 +1 0.343 1.356 1.125 1.748 0.857 1.831 -1.169 -1.131 0.000 1.796 -0.551 0.169 1.107 1.205 0.683 1.318 2.548 1.001 -0.281 -1.641 0.000 0.950 1.141 0.992 1.304 1.741 2.377 1.794 +0 1.633 -1.022 -0.099 0.629 1.458 1.351 -1.022 -0.635 2.173 1.606 -1.312 1.530 2.215 1.581 -0.873 1.169 0.000 0.452 2.124 -0.090 0.000 2.749 2.403 1.385 1.070 2.042 1.885 1.518 +0 1.001 -0.629 -1.353 0.715 1.007 0.856 0.934 0.469 2.173 1.135 0.136 -0.709 2.215 1.062 0.468 1.469 0.000 1.618 1.888 -1.113 0.000 0.804 1.043 0.995 1.237 1.401 1.087 0.964 +1 1.207 0.579 -0.441 0.771 0.937 1.010 -0.226 -1.077 2.173 1.173 0.882 0.478 2.215 1.241 -1.600 1.033 0.000 0.755 0.594 -1.158 0.000 1.946 1.598 1.265 0.852 1.847 1.447 1.227 +0 1.283 -1.366 0.903 1.769 0.126 1.178 -0.731 -0.676 2.173 1.062 0.016 -0.379 2.215 1.168 -0.791 1.698 0.000 2.336 -0.116 1.543 0.000 0.725 1.374 1.343 1.454 0.770 1.156 1.228 +0 0.543 -0.411 -0.164 1.388 0.699 0.618 -0.992 -0.629 1.087 0.513 0.952 -1.660 0.000 1.554 -0.660 -1.433 2.548 1.214 -0.735 0.610 0.000 1.432 1.196 0.988 0.853 0.826 0.892 0.871 +0 0.758 1.313 -0.571 0.563 -1.134 1.047 -1.160 1.631 2.173 0.667 0.233 -0.026 2.215 0.413 -1.078 0.272 0.000 0.414 -1.838 0.404 0.000 0.712 0.814 0.997 0.671 1.545 1.067 0.859 +1 1.352 0.734 -1.591 0.961 -0.909 1.381 -1.379 -0.253 0.000 1.435 1.137 1.088 2.215 0.672 0.976 -0.302 1.274 1.507 1.850 1.154 0.000 0.831 0.925 0.994 0.701 0.993 0.815 0.714 +1 1.107 -0.919 0.978 1.181 -1.373 0.939 -0.657 -0.145 0.000 1.068 -0.549 0.280 2.215 1.773 -0.369 -1.335 2.548 0.777 -2.495 -1.507 0.000 1.217 0.794 1.353 0.953 1.457 0.949 0.926 +0 1.294 -1.717 0.590 1.636 -1.479 0.698 -1.063 -0.934 0.000 1.874 -0.810 0.589 2.215 1.027 -0.165 -0.841 2.548 1.062 -1.743 -1.358 0.000 0.855 0.864 1.930 1.613 1.498 1.284 1.136 +1 0.607 -1.066 -1.262 1.093 0.558 0.755 0.649 0.855 2.173 0.939 -1.251 -0.870 0.000 0.702 -0.549 -1.637 0.000 0.731 -0.809 -0.125 1.551 0.906 0.676 1.125 1.158 0.937 0.935 0.840 +0 0.521 -1.814 0.525 0.792 -0.408 0.476 -2.736 0.907 0.000 0.455 -2.594 -0.373 0.000 0.576 0.634 -1.376 2.548 0.864 -0.678 -1.677 3.102 0.904 0.962 0.992 0.823 0.470 1.049 0.895 +0 0.619 0.453 -1.257 1.498 1.209 1.178 0.616 -0.136 2.173 0.521 0.038 0.276 0.000 1.165 -0.462 -0.781 0.000 1.862 0.158 1.474 3.102 0.943 0.968 1.060 0.599 1.593 0.971 0.805 +0 1.191 -0.940 0.272 0.635 1.199 0.940 -1.923 -1.028 0.000 0.419 -1.332 -0.424 2.215 0.480 -0.755 1.191 2.548 0.530 -2.339 1.208 0.000 1.086 0.887 0.988 0.623 0.491 0.585 0.652 +0 0.768 0.372 0.409 0.875 1.174 1.264 0.302 -1.530 1.087 1.564 0.744 0.037 2.215 0.675 -0.188 -1.180 0.000 0.483 1.733 0.573 0.000 1.089 1.020 0.984 0.909 2.099 1.143 0.943 +0 2.210 -1.143 -0.318 1.097 0.030 1.688 0.967 1.430 1.087 0.677 -0.533 -1.075 2.215 0.450 -0.819 0.724 0.000 0.444 0.956 -1.083 0.000 0.775 1.133 0.997 0.876 1.794 1.863 1.359 +1 1.238 -0.192 1.263 0.437 -0.043 0.958 -0.181 1.740 2.173 1.545 0.095 -0.125 0.000 0.862 0.043 -1.123 0.000 0.603 -0.672 -0.157 3.102 1.387 0.811 0.990 0.777 0.836 0.893 0.795 +0 1.446 0.988 1.096 0.681 -0.104 0.784 1.815 -1.354 0.000 0.774 0.082 0.056 1.107 1.132 0.136 1.566 2.548 1.593 1.180 -0.330 0.000 1.409 1.414 1.213 0.876 0.973 1.071 0.948 +0 0.925 -0.618 -0.475 0.529 0.150 0.740 0.137 1.246 0.000 0.323 -0.917 1.669 0.000 0.300 0.153 -1.025 2.548 0.566 0.902 -0.302 3.102 0.704 0.760 0.985 0.607 0.243 0.577 0.672 +1 2.620 -0.353 -0.954 1.730 -1.222 2.205 -0.553 0.231 0.000 2.486 -0.455 1.017 0.000 1.374 -1.349 -1.339 2.548 0.373 -0.769 -1.280 3.102 3.243 1.808 0.984 1.165 0.157 1.513 1.638 +0 0.710 0.407 1.069 0.794 -0.264 0.563 0.103 0.270 0.000 0.425 1.023 -0.556 0.000 1.041 0.496 -1.096 2.548 1.262 -0.250 1.509 3.102 0.870 0.911 0.988 0.728 0.732 0.674 0.613 +1 0.969 -0.018 0.378 0.293 -1.147 1.109 -0.112 -0.140 0.000 1.240 0.001 1.319 1.107 1.398 -0.470 -1.398 2.548 0.707 -0.526 -0.839 0.000 0.872 1.130 0.988 0.860 0.967 0.996 0.836 +1 0.844 -0.616 -0.133 0.584 0.271 1.007 -0.744 -1.248 0.000 1.811 -0.237 1.077 0.000 1.127 -0.432 -0.678 2.548 1.600 0.573 -0.069 0.000 0.489 0.840 0.982 0.674 0.645 0.586 0.567 +0 0.550 0.056 0.770 0.801 -1.468 0.799 0.012 0.190 0.000 1.312 -0.891 1.708 2.215 0.700 -0.814 -0.083 2.548 1.297 -0.372 -0.941 0.000 0.957 0.853 0.988 0.662 1.017 0.651 0.593 +1 0.329 1.696 1.392 1.044 -0.886 0.621 -0.438 -1.047 2.173 0.425 1.169 1.056 0.000 0.393 -0.125 1.270 2.548 0.801 2.371 0.292 0.000 0.848 0.828 0.996 0.731 0.543 0.969 0.822 +0 0.716 -0.730 0.546 0.742 -0.046 0.682 -0.676 1.150 1.087 0.348 -2.190 -0.462 0.000 0.597 -1.470 1.131 0.000 1.373 -1.254 -1.249 3.102 0.729 0.803 0.976 0.818 0.954 0.746 0.638 +1 0.624 1.748 -0.489 0.894 0.278 1.285 -0.404 -1.719 2.173 0.530 -1.039 -0.319 1.107 1.140 -0.373 -0.100 0.000 0.825 0.770 -1.727 0.000 1.312 0.930 0.999 1.533 1.227 1.131 0.980 +1 0.447 0.520 0.302 0.362 0.286 0.835 -0.450 0.745 2.173 1.122 -1.459 -1.190 0.000 0.977 -0.792 -1.711 0.000 0.385 -0.879 -0.485 1.551 0.892 0.590 0.992 0.696 0.568 0.796 0.722 +1 0.663 0.344 0.493 0.851 -1.032 0.847 0.393 1.680 0.000 1.061 0.045 -0.083 0.000 1.351 0.301 1.258 0.000 1.417 -0.166 -0.532 3.102 0.882 0.670 1.020 0.648 0.520 0.475 0.515 +1 1.339 1.387 0.467 0.434 -1.261 0.919 -0.473 -1.144 2.173 0.539 0.589 -1.721 0.000 0.543 -0.780 0.719 0.000 0.986 -0.130 0.033 3.102 0.943 0.953 1.056 0.805 0.894 1.004 0.864 +0 1.085 0.552 1.398 0.903 -1.324 0.692 -0.187 -0.456 2.173 0.580 0.578 -0.186 0.000 1.359 0.379 0.818 2.548 0.465 -0.797 -1.156 0.000 0.778 0.835 0.989 0.956 1.163 0.823 0.707 +1 0.566 1.432 0.936 0.418 1.534 0.582 -0.014 -0.035 1.087 0.512 1.329 -0.757 0.000 0.491 2.138 1.590 0.000 0.862 -0.883 1.581 1.551 0.766 1.065 0.975 0.713 0.851 0.912 0.783 +0 0.743 -1.553 -0.241 0.608 -0.784 0.888 -1.358 0.573 2.173 1.151 0.323 1.684 2.215 0.997 0.786 -0.955 0.000 1.177 -1.475 0.917 0.000 0.936 0.975 0.988 0.955 1.897 1.205 1.017 +0 2.597 -0.003 -0.788 0.897 0.664 0.973 0.604 0.046 0.000 1.023 0.465 -1.450 2.215 0.880 -0.670 1.742 0.000 0.379 -0.254 -0.032 0.000 0.651 0.717 2.043 1.106 0.638 0.875 0.740 +1 0.639 0.882 0.924 1.584 -1.193 1.170 -0.152 -0.360 2.173 1.108 0.528 -1.513 0.000 1.066 0.360 1.422 0.000 1.725 1.344 0.541 3.102 0.928 1.212 1.316 1.337 1.861 1.173 1.021 +1 0.646 -0.809 0.126 0.353 0.143 0.562 -1.886 1.300 0.000 0.814 -0.990 -0.644 2.215 1.047 -1.670 -1.246 0.000 1.227 -0.878 1.271 3.102 0.829 1.103 0.988 0.847 0.892 0.913 0.786 +0 0.449 -1.586 -1.570 1.374 -0.919 1.455 -0.916 0.556 0.000 1.308 -2.904 -1.235 0.000 0.636 -0.240 -0.246 2.548 0.693 -0.811 1.036 0.000 0.643 0.891 0.984 0.631 0.551 0.646 0.794 +0 1.050 1.968 -1.704 0.611 -0.058 0.529 1.135 0.079 0.000 1.090 0.889 -1.314 2.215 0.957 0.840 0.993 2.548 0.826 1.636 -0.173 0.000 0.599 1.052 1.105 0.813 0.948 0.745 0.714 +0 0.705 -1.903 0.205 2.272 -0.243 0.557 -0.142 1.618 0.000 1.417 0.631 1.144 2.215 1.092 -1.346 -1.006 2.548 0.725 0.558 -1.125 0.000 0.725 0.992 0.992 3.753 2.091 2.386 1.933 +1 1.410 -0.826 1.118 0.142 -1.159 0.485 0.156 -0.440 0.000 0.607 0.703 -1.306 2.215 0.861 1.294 0.080 2.548 0.485 0.812 -0.157 0.000 0.363 0.551 0.994 1.111 0.778 0.831 0.702 +1 2.047 1.124 -1.365 1.138 -0.537 1.896 -2.868 1.353 0.000 0.998 0.741 0.169 0.000 1.396 0.403 -0.483 2.548 1.289 -0.261 0.591 0.000 0.937 0.751 1.437 1.015 0.864 0.904 0.804 +1 0.559 0.070 -0.519 0.213 -1.411 0.850 -0.104 0.648 2.173 0.475 0.516 -1.052 0.000 0.527 -1.161 -0.360 1.274 0.830 0.752 1.368 0.000 0.946 0.894 0.986 0.929 0.836 0.830 0.757 +0 0.637 -0.505 -1.539 2.538 -0.871 0.744 -0.623 1.149 0.000 0.546 -1.052 0.407 0.000 0.471 0.927 0.317 2.548 0.433 1.857 0.895 0.000 0.896 0.879 0.999 0.626 0.278 0.636 0.773 +1 0.563 0.727 -1.148 0.955 0.405 0.949 1.834 -0.976 0.000 0.770 -0.604 1.025 0.000 1.761 0.625 0.565 2.548 1.021 -0.781 -1.289 0.000 1.022 1.007 1.001 0.714 0.737 0.882 0.778 +0 0.691 1.543 0.106 0.995 1.010 1.251 0.610 0.862 2.173 0.622 -0.408 -0.137 0.000 1.934 1.463 -1.113 0.000 2.082 0.393 -1.055 1.551 1.086 1.011 0.986 0.733 1.691 0.976 0.845 +0 0.461 -0.550 -0.218 0.743 1.066 1.464 -0.130 1.578 2.173 0.892 0.222 0.104 0.000 0.920 1.560 -0.522 0.000 1.061 0.140 -0.597 3.102 1.352 0.881 0.985 0.983 1.235 1.195 0.957 +1 0.766 -1.026 -0.231 1.050 1.394 0.518 0.450 0.106 0.000 1.015 1.068 -1.355 2.215 1.111 0.674 1.099 2.548 0.710 -2.336 -0.474 0.000 0.767 0.865 1.236 1.117 0.927 1.089 0.903 +0 4.233 0.579 0.834 1.008 1.313 1.841 1.613 -0.641 0.000 1.221 0.599 -1.416 1.107 0.514 2.295 -1.312 0.000 0.673 0.490 0.051 3.102 1.281 1.022 1.197 1.544 0.793 1.017 1.485 +1 0.917 -0.616 -0.385 0.629 0.799 0.689 0.191 -1.624 1.087 0.693 -0.923 -1.560 1.107 1.113 -0.758 -0.101 0.000 0.604 -2.088 1.025 0.000 1.144 0.965 0.988 0.973 0.616 0.881 0.774 +0 0.667 -1.880 -0.206 1.170 -0.271 0.844 0.693 1.590 0.000 0.586 -0.084 0.365 2.215 0.366 -0.967 -1.721 1.274 0.432 -0.095 -1.638 0.000 0.441 0.809 0.993 0.646 0.531 0.547 0.783 +1 0.841 -1.475 1.131 0.324 -0.386 1.920 2.867 -0.416 0.000 0.802 -0.430 1.268 0.000 2.063 0.648 1.010 2.548 1.046 -0.147 1.705 1.551 0.934 1.275 0.994 0.640 0.836 0.817 0.755 +1 0.996 0.207 -0.734 1.212 0.745 0.367 1.199 -1.205 0.000 0.882 0.982 -0.348 0.000 1.660 0.121 1.105 2.548 0.827 0.173 -1.350 3.102 0.853 1.270 1.479 0.813 0.718 0.784 0.775 +0 0.343 -1.797 -0.557 2.075 0.126 1.257 0.649 -1.612 2.173 0.664 -0.330 0.806 2.215 0.548 1.136 -0.722 0.000 0.662 -0.157 0.145 0.000 1.067 0.935 0.982 4.330 1.304 2.718 2.415 +0 0.620 1.248 -1.074 1.403 -0.839 0.852 -0.071 0.966 0.000 0.565 0.767 0.362 2.215 0.569 -0.455 -1.594 2.548 0.693 -0.946 0.421 0.000 0.858 0.784 0.993 1.381 0.723 1.010 1.258 +1 0.368 0.145 1.520 0.758 -1.027 1.041 0.646 0.680 2.173 1.622 1.383 -0.999 0.000 1.347 -0.303 0.430 2.548 0.697 -0.305 -0.916 0.000 0.915 0.952 0.996 0.924 0.826 1.018 0.898 +1 1.416 0.242 0.637 0.634 -1.608 0.612 1.377 -0.081 1.087 0.934 1.376 -1.080 2.215 0.351 -1.001 1.181 0.000 0.784 -0.316 -1.407 0.000 0.651 1.157 1.181 1.116 0.871 0.927 0.865 +1 0.709 0.650 0.243 0.671 -0.887 0.797 0.743 0.956 0.000 0.557 1.503 1.495 0.000 0.966 1.334 -0.828 2.548 1.658 -0.170 -0.558 3.102 0.876 1.040 0.988 0.612 0.934 0.942 0.824 +1 2.392 0.939 0.195 1.114 0.514 1.552 1.726 -1.143 0.000 0.911 0.969 0.983 2.215 1.059 0.070 -1.287 2.548 0.401 0.204 1.349 0.000 1.365 1.275 0.998 0.861 1.053 1.039 1.196 +0 0.509 0.785 -0.762 1.793 1.463 1.204 1.012 -0.338 2.173 1.148 1.314 1.240 2.215 0.790 0.088 -0.670 0.000 0.776 1.518 0.780 0.000 1.184 1.012 1.201 0.801 1.735 1.073 0.920 +1 0.343 1.313 -0.568 0.935 1.593 0.937 0.326 1.187 0.000 0.940 0.162 -0.223 2.215 1.305 -0.781 -0.845 2.548 0.574 -0.498 0.536 0.000 0.823 1.073 0.991 0.877 0.888 0.925 0.799 +0 0.461 0.561 1.039 0.132 -0.264 1.203 0.657 -0.542 2.173 0.935 -0.234 1.687 0.000 0.993 -0.658 0.489 2.548 0.840 -2.041 1.445 0.000 0.808 0.790 0.991 1.182 1.495 1.041 0.874 +0 1.072 0.338 0.978 0.782 -1.325 0.763 0.479 -1.471 1.087 0.516 -0.653 -1.014 0.000 1.313 -0.299 0.455 2.548 1.020 1.105 -0.528 0.000 1.174 1.007 1.111 0.746 1.335 1.026 0.924 +0 0.596 -1.673 1.040 0.760 -0.643 0.567 -0.327 1.380 2.173 1.013 -2.015 -1.245 0.000 0.937 -0.135 0.180 0.000 0.626 0.660 -0.019 0.000 0.867 0.909 0.985 1.154 0.464 0.874 0.797 +1 0.885 -1.354 0.225 1.051 1.433 1.114 -0.150 0.312 2.173 0.576 -0.767 1.549 0.000 1.164 -0.953 -1.305 2.548 0.718 0.653 -0.671 0.000 1.055 0.847 1.184 1.193 1.555 0.998 0.895 +0 0.578 0.990 -0.798 0.972 -1.503 0.405 0.521 1.311 2.173 0.516 -1.066 0.700 0.000 0.743 0.580 0.349 2.548 1.494 1.534 -1.302 0.000 0.929 0.869 0.991 0.883 0.522 0.693 0.649 +0 1.223 0.409 1.367 0.740 -1.163 0.937 0.048 -1.049 0.000 1.591 -0.219 0.827 2.215 0.818 0.624 -0.022 0.000 1.137 0.066 -0.503 3.102 1.358 0.803 1.001 1.032 1.146 0.988 0.883 +1 1.635 -0.051 1.565 0.596 -1.497 0.734 1.351 0.287 2.173 0.267 -0.261 1.056 0.000 1.227 -0.004 -0.227 2.548 0.519 -2.114 -0.772 0.000 0.831 0.924 0.977 1.238 1.021 1.107 1.054 +1 0.647 -1.062 1.419 0.091 -0.101 0.771 -0.733 -1.079 2.173 0.977 -2.428 0.863 0.000 0.713 -0.617 0.227 2.548 0.596 1.117 -0.570 0.000 3.454 1.900 0.992 0.804 0.854 1.309 1.078 +1 1.408 1.144 1.329 1.585 0.658 0.755 1.256 -0.208 2.173 0.803 1.334 -0.707 0.000 0.998 0.672 -1.143 1.274 0.660 0.649 -1.733 0.000 0.809 0.751 1.176 1.063 0.858 0.914 0.798 +1 0.684 1.472 -0.848 1.176 -1.545 1.057 0.545 0.603 2.173 0.687 0.872 -0.895 0.000 0.598 0.155 1.290 2.548 0.988 1.332 0.079 0.000 0.910 1.122 0.993 0.989 0.605 1.092 0.929 +1 0.761 0.601 0.391 0.963 -0.493 0.543 0.646 -0.435 2.173 0.733 0.701 1.014 2.215 0.299 1.953 0.659 0.000 0.453 0.683 1.662 0.000 0.431 0.582 0.988 0.785 0.896 0.618 0.518 +0 0.776 0.487 0.887 0.467 -0.878 0.603 1.183 -0.982 1.087 0.548 -1.032 0.813 0.000 0.517 0.966 0.809 0.000 1.501 0.595 -1.592 3.102 1.038 0.992 0.989 0.714 0.580 0.832 0.729 +1 0.416 -0.702 -1.377 1.533 1.337 1.514 0.339 -0.465 2.173 0.808 0.012 1.437 0.000 0.613 -1.023 0.896 0.000 1.111 0.787 0.302 3.102 0.821 0.927 0.986 1.432 0.970 1.034 0.950 +1 0.322 -0.072 -1.528 1.850 -0.617 1.373 -1.529 0.463 2.173 1.881 -1.478 1.501 0.000 1.040 -1.252 -0.798 0.000 0.924 1.076 -1.725 0.000 0.827 0.669 0.988 2.519 0.674 1.524 1.253 +0 0.454 0.236 0.073 0.933 1.034 0.938 0.342 -0.839 2.173 0.804 -0.978 1.380 2.215 0.539 -1.152 0.958 0.000 0.800 -0.716 -1.142 0.000 0.913 0.986 0.986 0.650 1.489 0.918 0.769 +0 0.284 -1.170 0.025 3.605 1.133 1.614 -0.708 -1.181 0.000 1.564 0.698 -0.416 0.000 1.891 -1.126 1.653 2.548 1.311 -1.239 -1.030 0.000 0.931 1.105 1.179 0.949 1.505 1.136 1.232 +1 0.846 -0.236 0.118 0.274 -0.835 1.845 -1.438 -1.235 0.000 1.728 -1.466 0.188 0.000 2.693 0.602 1.011 0.000 1.979 0.894 0.634 0.000 0.954 1.090 0.981 0.677 0.549 0.640 0.724 +0 0.596 -2.088 -0.819 1.921 -1.611 0.733 -0.124 -0.569 1.087 1.343 0.197 0.184 2.215 0.921 -2.326 0.911 0.000 0.708 0.845 0.923 0.000 2.578 1.918 0.988 2.459 0.948 1.768 1.647 +0 0.599 1.160 0.066 1.875 0.687 0.951 1.303 -1.038 0.000 0.813 -0.126 1.682 2.215 0.389 0.432 1.513 2.548 0.796 1.005 -0.406 0.000 0.721 1.161 0.990 0.740 0.204 0.928 0.926 +0 1.044 0.272 -0.841 1.630 0.141 0.483 0.014 0.588 2.173 0.521 1.074 1.633 0.000 0.724 -0.378 1.560 0.000 0.595 1.288 -0.764 0.000 1.040 0.827 1.399 0.862 0.618 0.660 0.652 +0 0.347 -0.450 1.474 1.609 0.762 0.774 -0.878 -0.815 2.173 0.918 -1.542 -1.307 0.000 0.807 -0.629 0.845 0.000 1.474 -0.638 0.028 1.551 1.374 1.082 0.994 0.839 0.781 0.846 0.828 +1 0.563 -1.318 -0.425 1.549 1.291 0.881 0.141 -1.176 2.173 0.647 -0.540 0.119 0.000 0.985 0.392 1.051 2.548 0.373 0.652 0.763 0.000 0.579 0.934 1.294 1.104 1.065 1.040 0.862 +1 0.776 -0.253 -0.699 1.230 1.174 0.950 -1.146 1.506 2.173 1.159 -0.570 -0.261 2.215 0.569 0.711 -0.674 0.000 0.596 -0.750 0.075 0.000 0.727 1.142 1.344 1.023 1.607 0.981 0.833 +0 1.214 0.234 -1.582 1.360 1.385 1.587 -0.548 0.132 2.173 1.936 2.554 -1.687 0.000 1.127 -0.932 -0.194 0.000 1.344 0.038 -0.519 0.000 0.855 0.916 0.989 1.065 1.688 1.288 1.123 +0 0.614 0.466 1.330 1.278 -1.398 0.446 0.012 -0.574 2.173 0.657 -1.562 0.597 0.000 0.649 -0.740 0.049 2.548 0.560 -1.561 -0.653 0.000 0.722 0.885 0.993 1.080 0.456 0.787 0.998 +1 1.679 0.894 0.260 0.741 1.318 1.172 -0.228 -1.124 2.173 0.815 0.348 -1.591 0.000 1.437 -0.118 1.540 2.548 1.798 0.406 -0.156 0.000 0.824 1.218 1.260 1.555 1.094 1.152 1.030 +1 1.033 0.852 0.754 0.183 -0.580 0.505 -0.870 -0.145 2.173 0.746 -0.788 -1.722 2.215 0.438 0.501 -1.196 0.000 0.417 0.322 1.459 0.000 0.323 0.639 0.992 0.871 0.893 0.734 0.589 +0 0.362 -1.062 -0.439 0.478 -0.943 0.516 -0.628 1.028 2.173 0.683 -0.681 -1.551 2.215 0.687 0.329 -0.573 0.000 0.933 0.976 0.496 0.000 0.813 0.966 0.979 0.833 0.637 0.721 0.665 +0 1.429 -0.043 -1.625 0.263 1.354 0.875 1.046 0.462 0.000 0.395 1.737 -0.108 0.000 0.565 2.089 -0.649 0.000 1.739 0.023 -0.628 3.102 0.772 0.893 0.981 0.883 1.238 0.892 0.995 +1 1.074 0.538 -0.339 1.476 -0.567 0.948 0.139 1.456 0.000 0.630 -0.021 1.008 0.000 1.170 0.660 -0.796 2.548 1.047 0.852 1.331 0.000 0.714 0.852 0.977 0.702 0.688 0.764 0.882 +0 1.125 -0.867 -0.472 0.697 0.281 1.685 -1.719 -0.149 0.000 1.626 -0.340 1.511 0.000 2.250 0.425 1.517 2.548 1.504 -0.791 -1.280 3.102 0.601 0.827 0.984 1.721 1.356 1.193 1.020 +1 0.292 1.219 1.480 0.883 -0.114 1.297 0.883 0.545 0.000 1.121 1.074 -1.199 0.000 1.080 0.391 -1.050 2.548 0.511 -0.839 1.192 3.102 2.576 1.634 0.987 0.714 0.677 1.083 0.916 +0 0.332 1.870 0.124 2.092 0.297 0.771 -0.232 -1.301 2.173 0.433 0.443 -0.948 0.000 1.658 0.103 0.816 1.274 2.187 0.957 -1.345 0.000 0.631 0.837 0.981 0.831 1.352 1.013 1.007 +0 1.188 -0.198 0.165 1.091 -0.009 1.241 0.050 -1.661 1.087 0.544 0.931 -0.735 0.000 1.013 1.230 1.052 2.548 0.436 -1.282 -0.473 0.000 1.026 1.099 0.986 1.587 1.323 1.330 1.094 +1 0.787 0.340 -0.263 0.780 1.703 0.629 0.451 0.005 2.173 0.779 1.171 1.645 0.000 1.510 0.938 -1.226 0.000 2.552 0.814 0.749 3.102 0.887 1.198 1.064 0.938 0.905 0.977 0.841 +1 1.071 0.295 -0.971 1.556 -1.172 1.559 0.359 -0.742 2.173 1.477 0.156 0.977 0.000 0.802 -0.663 0.995 1.274 0.932 -1.166 0.112 0.000 0.638 0.579 0.988 0.973 1.606 1.107 0.983 +0 0.936 -1.135 -0.334 0.634 1.043 0.439 0.298 -1.732 0.000 0.705 -0.040 -1.205 0.000 1.022 -0.437 0.459 2.548 0.811 0.627 -0.199 3.102 0.578 0.921 1.010 0.814 0.599 0.634 0.682 +1 0.676 -1.307 -1.546 0.948 0.414 0.533 -1.489 -0.797 0.000 0.563 1.020 -0.622 0.000 1.297 -0.413 0.938 2.548 1.083 0.582 1.663 3.102 0.782 0.972 1.088 1.028 0.779 0.823 0.757 +1 0.627 -0.944 0.512 1.643 -0.266 1.310 0.937 -1.139 2.173 1.351 1.202 1.062 0.000 0.691 2.481 0.880 0.000 0.862 0.232 -1.421 0.000 0.903 1.108 0.982 0.626 0.749 1.416 1.294 +1 1.282 -0.606 -0.657 0.271 1.222 0.567 -0.624 1.068 0.000 0.861 -0.495 0.575 0.000 1.130 -0.693 -1.380 1.274 1.403 0.634 -1.304 3.102 0.646 0.977 0.984 0.941 0.808 0.853 0.794 +0 0.498 -0.861 1.680 1.601 -0.163 0.838 1.235 -1.544 0.000 1.141 -0.826 -0.515 2.215 1.102 0.575 0.582 0.000 0.847 -0.265 0.967 3.102 0.863 1.235 1.232 0.764 0.895 0.772 0.765 +1 0.550 -0.294 -1.344 0.797 0.828 0.715 -0.823 0.459 2.173 0.978 0.643 1.445 0.000 1.569 -0.975 -0.775 2.548 0.498 -1.504 -0.824 0.000 1.643 1.347 0.987 0.847 1.194 1.081 0.892 +1 0.597 -0.032 0.672 0.709 -0.327 0.886 0.123 1.118 0.000 0.599 1.001 1.059 0.000 0.942 0.927 -0.478 1.274 0.905 0.825 -1.309 0.000 0.811 0.895 0.989 0.970 0.807 0.693 0.751 +1 0.615 1.102 -0.556 2.133 -1.262 1.255 0.261 0.274 0.000 1.437 0.574 1.469 2.215 1.212 0.699 0.642 2.548 0.528 -1.296 -1.149 0.000 1.197 1.044 0.986 1.223 0.959 1.020 0.978 +0 0.680 -0.718 -1.579 0.522 0.042 0.772 -1.156 1.340 2.173 0.690 1.144 -0.371 2.215 0.700 -0.358 0.128 0.000 0.809 0.348 -0.386 0.000 0.915 0.957 0.985 0.803 1.900 1.073 0.868 +1 1.207 0.279 -1.732 0.917 1.383 1.070 0.038 -0.186 0.000 0.472 -2.192 -0.128 0.000 0.544 -0.775 0.836 2.548 0.930 0.480 -0.891 1.551 2.139 1.279 0.996 0.775 0.688 0.901 0.942 +1 0.359 0.784 1.375 1.216 -0.794 0.594 -1.031 0.711 2.173 0.741 -0.203 -0.722 0.000 0.507 -0.400 1.392 1.274 0.386 0.632 0.719 0.000 0.757 0.891 0.987 0.616 0.445 0.635 0.587 +1 0.578 1.557 0.509 0.392 -1.574 0.927 0.465 -0.005 1.087 0.630 0.676 -1.110 0.000 1.043 0.068 0.783 2.548 1.913 0.679 1.734 0.000 0.787 0.979 0.978 0.795 0.835 0.889 0.746 +0 2.028 1.164 -1.360 0.311 -1.713 0.772 0.345 0.129 0.000 0.845 0.731 0.720 0.000 1.125 1.276 1.296 2.548 1.462 1.078 -0.455 3.102 0.937 0.967 0.998 0.817 0.982 0.812 0.884 +1 0.321 1.373 1.054 2.149 -1.656 0.563 -0.520 0.211 0.000 0.759 -0.994 -0.051 1.107 0.837 -2.008 1.586 0.000 0.685 0.651 0.376 3.102 0.919 0.744 0.992 0.941 0.713 1.696 1.438 +0 0.559 0.557 1.525 0.670 -0.725 0.618 1.959 -0.005 0.000 0.403 0.275 1.056 1.107 0.751 -1.252 1.254 0.000 0.511 1.137 -0.253 3.102 3.352 1.785 0.987 0.602 0.444 1.050 0.907 +1 0.906 0.488 1.492 0.626 0.678 1.072 -0.840 -0.628 2.173 0.510 -1.201 1.137 1.107 0.314 1.993 -0.202 0.000 0.386 -1.119 1.705 0.000 0.988 1.011 0.985 1.189 1.108 0.929 0.843 +0 0.297 -0.904 -0.088 1.908 1.165 1.124 0.974 -0.974 0.000 1.098 0.005 0.400 2.215 0.637 -0.205 0.703 2.548 0.482 -0.615 -1.255 0.000 1.129 1.083 0.990 1.156 0.261 0.922 1.182 +0 1.472 -0.754 -0.812 1.399 -0.203 0.649 0.482 1.134 0.000 0.636 -1.003 0.754 1.107 0.437 0.043 1.700 0.000 0.755 -0.972 -1.622 1.551 0.926 0.948 1.038 0.754 0.528 0.679 0.824 +0 0.783 -1.773 -0.593 0.982 -0.673 0.578 -0.741 0.671 0.000 0.968 -1.483 1.172 0.000 0.525 0.304 1.684 0.000 0.818 -1.215 -1.044 3.102 0.939 0.879 0.979 0.452 0.162 0.565 0.653 +0 0.537 -0.910 0.559 3.811 0.437 1.901 -1.933 -0.856 0.000 1.274 1.841 1.682 0.000 1.240 2.451 1.593 0.000 0.955 0.077 1.319 3.102 0.804 0.932 0.984 0.848 0.562 0.863 1.641 +0 1.896 0.715 1.332 0.633 0.360 2.523 1.018 -0.985 0.000 1.813 0.479 0.693 0.000 1.481 1.168 0.800 2.548 0.576 0.747 0.242 0.000 0.682 0.795 1.165 0.758 0.947 1.109 1.117 +0 0.707 -1.212 1.634 2.007 -1.174 0.434 -0.823 -0.193 2.173 0.711 0.370 1.108 0.000 0.905 0.259 0.037 0.000 1.889 -0.172 0.609 3.102 0.953 1.008 0.990 1.238 0.703 0.878 0.908 +0 0.736 1.712 1.602 1.198 -1.645 0.951 -0.468 -0.339 2.173 1.081 0.556 0.769 2.215 1.229 0.321 -0.937 0.000 0.483 -0.960 1.723 0.000 0.908 0.928 0.992 0.956 1.496 1.145 0.973 +0 0.714 -0.080 0.761 0.250 0.138 0.625 1.641 -0.085 0.000 1.065 0.270 -1.379 2.215 0.631 2.650 0.404 0.000 0.890 1.097 1.731 3.102 0.896 0.881 0.979 0.903 0.560 0.991 0.860 +1 1.488 0.973 0.943 0.607 0.511 0.649 0.164 -0.965 2.173 0.507 0.360 1.629 2.215 0.807 1.291 -0.856 0.000 0.769 0.601 0.297 0.000 0.811 0.768 0.976 0.766 0.614 0.836 0.727 +0 1.422 0.180 1.035 1.363 1.437 1.143 -0.936 -0.435 2.173 0.570 1.501 0.681 0.000 1.042 -0.215 -0.755 0.000 0.610 0.465 -0.556 3.102 0.805 0.828 0.982 0.761 0.734 1.206 0.969 +1 0.799 -1.018 -0.845 0.239 0.814 0.846 -0.092 -0.968 2.173 1.088 -0.784 1.207 0.000 0.963 0.110 0.931 0.000 1.285 0.214 -0.015 3.102 0.971 0.969 0.988 0.684 0.858 0.853 0.729 +1 0.591 0.026 0.859 1.712 1.497 2.606 1.432 -0.081 0.000 0.978 -0.637 0.368 1.107 1.667 0.250 -1.656 0.000 2.482 1.801 -1.214 0.000 0.766 0.961 0.987 0.920 0.892 0.905 0.799 +1 1.408 -0.310 -0.394 0.470 -0.029 0.969 -1.922 0.094 0.000 1.767 0.089 1.722 2.215 1.363 -0.703 -1.065 0.000 1.779 -0.488 1.034 1.551 0.924 0.924 0.984 1.453 1.082 1.069 0.902 +1 0.559 0.832 0.750 0.904 -0.365 1.723 -0.451 -1.393 2.173 1.426 -1.015 0.591 0.000 0.658 -0.385 0.860 0.000 1.255 -0.897 -0.829 1.551 0.811 0.975 0.990 1.976 0.904 1.413 1.370 +1 1.292 0.504 -0.877 0.684 -0.661 1.155 0.179 0.854 2.173 0.453 1.003 -0.440 0.000 0.777 -0.209 0.403 0.000 1.092 0.659 1.741 3.102 0.878 0.951 0.995 0.794 0.927 0.938 0.807 +0 0.463 1.709 -1.338 0.328 -0.803 0.683 0.665 0.797 2.173 0.439 -1.026 -1.017 0.000 1.128 0.262 1.558 1.274 0.796 0.279 -0.187 0.000 0.780 0.981 0.978 0.681 0.724 0.712 0.659 +1 0.318 1.315 -1.725 1.022 0.699 1.103 0.154 -1.109 0.000 0.723 -0.594 1.005 2.215 0.746 0.664 -0.606 0.000 1.637 0.653 0.314 3.102 0.858 1.180 0.993 0.619 0.938 0.953 0.830 +1 0.699 -0.707 1.685 1.759 -1.572 1.222 -0.358 0.371 0.000 0.895 -0.251 -0.442 2.215 1.021 -0.478 -1.125 0.000 1.109 -0.895 1.062 3.102 1.966 1.257 1.006 1.280 0.956 0.900 1.063 +0 0.524 -2.417 0.264 1.034 1.627 0.654 -0.498 -0.295 2.173 0.497 -1.470 -0.705 0.000 0.451 0.388 -0.927 0.000 0.638 0.879 0.576 0.000 0.832 0.852 0.986 1.129 1.034 0.820 0.764 +1 0.525 -0.807 1.104 0.342 0.305 0.782 0.142 1.024 2.173 0.824 1.698 -0.307 0.000 1.130 0.485 -0.937 2.548 1.027 -1.461 -0.904 0.000 0.894 0.976 0.981 0.642 1.171 0.891 0.780 +1 1.057 0.037 -0.141 1.795 0.513 0.908 -0.855 1.690 2.173 1.248 0.236 -0.953 2.215 0.510 0.565 0.865 0.000 0.706 0.159 1.642 0.000 0.450 0.775 1.062 1.412 1.410 1.181 0.909 +1 0.955 -0.709 0.622 0.880 -0.465 0.644 -1.743 1.532 0.000 0.448 1.841 -0.645 0.000 1.352 0.109 -0.038 1.274 0.751 -0.474 -1.404 0.000 0.838 0.955 1.054 0.740 0.820 0.924 0.817 +1 0.615 0.980 0.296 1.022 -0.971 0.437 -1.195 -0.991 2.173 0.680 -0.345 0.806 2.215 0.343 0.536 -0.072 0.000 0.928 0.674 1.461 0.000 0.615 0.859 0.998 0.911 0.875 0.849 0.697 +1 0.813 -1.079 1.510 0.638 0.281 1.349 -0.442 -1.003 2.173 1.015 -0.220 0.697 0.000 0.997 -0.618 0.068 2.548 0.846 0.660 1.294 0.000 0.891 0.857 0.991 1.235 1.200 1.028 0.963 +0 2.044 1.388 -1.557 0.157 0.224 0.318 -0.465 -0.540 0.000 1.068 -0.058 0.303 0.000 0.627 0.548 1.726 2.548 0.720 0.498 0.109 3.102 0.890 0.864 0.994 0.796 0.510 0.560 0.820 +0 1.150 -1.414 -0.283 0.221 1.447 0.424 1.418 1.143 0.000 0.882 0.060 1.142 1.107 2.048 -0.449 -1.075 2.548 1.166 0.740 0.583 0.000 0.901 0.945 0.984 0.898 1.361 1.082 1.054 +1 0.982 1.267 1.184 1.007 0.246 1.044 0.161 -1.075 2.173 0.797 -0.859 -0.065 1.107 0.351 -1.548 1.447 0.000 0.365 1.398 0.705 0.000 1.061 0.905 1.031 1.337 1.291 1.178 0.990 +1 1.305 -1.001 1.264 0.344 -0.732 0.459 0.699 -1.109 2.173 0.891 -0.655 -0.587 1.107 0.858 -1.908 1.215 0.000 0.786 -0.049 -0.852 0.000 0.988 1.008 0.987 0.850 0.822 0.750 0.697 +0 2.151 0.191 -1.021 0.140 -0.338 0.746 1.324 -1.358 2.173 1.545 -0.480 0.475 0.000 0.765 1.390 0.899 0.000 0.931 -1.010 1.175 0.000 1.085 1.010 0.994 1.019 0.549 1.199 1.077 +1 1.096 0.244 0.715 0.319 -1.298 0.678 0.768 -0.238 0.000 1.321 0.500 1.625 0.000 0.888 -0.207 -1.208 2.548 0.942 -0.304 0.217 3.102 2.015 1.290 0.994 0.643 0.672 0.868 0.762 +0 0.338 2.163 -1.278 1.560 0.649 0.464 2.030 -1.629 0.000 0.735 1.025 -1.332 2.215 0.994 0.476 -0.378 2.548 0.921 1.310 0.549 0.000 0.953 0.991 0.992 0.974 0.730 0.852 0.748 +1 0.513 -0.168 -0.071 1.061 1.014 1.557 -0.558 -0.732 2.173 0.565 1.249 1.548 0.000 0.807 -0.192 1.141 0.000 0.603 -0.895 0.538 1.551 0.927 0.810 0.993 1.431 0.971 1.057 0.943 +0 1.641 0.717 1.329 0.630 0.732 1.082 1.218 -0.978 2.173 0.770 0.531 -0.528 0.000 1.294 -0.616 1.037 2.548 1.334 1.099 -0.094 0.000 0.725 0.886 0.983 1.112 2.166 1.245 1.099 +1 0.894 -0.474 -1.000 1.526 -1.260 1.156 -0.602 1.284 2.173 1.289 0.168 -0.187 0.000 1.041 -0.062 0.572 2.548 1.320 -0.612 0.147 0.000 0.944 0.791 0.997 1.352 0.899 1.021 1.049 +1 0.364 1.454 -0.411 0.631 -1.593 0.837 -0.839 0.571 2.173 0.544 0.446 0.114 2.215 0.333 0.356 -1.051 0.000 0.422 -0.618 -0.410 0.000 0.410 0.570 0.991 0.655 0.802 0.718 0.555 +0 0.516 -0.211 0.119 0.475 1.592 0.726 0.806 -0.200 0.000 0.850 0.869 -1.252 1.107 1.090 0.757 0.389 2.548 0.938 0.408 -1.735 0.000 1.165 1.036 0.993 0.641 1.019 0.744 0.657 +0 2.374 1.171 1.062 2.077 0.665 1.217 0.848 -0.764 0.000 1.411 0.543 -1.084 0.000 2.557 -0.345 -0.420 2.548 3.163 1.282 1.246 3.102 0.894 1.603 1.076 0.871 3.248 1.949 1.848 +0 0.799 0.368 -0.482 0.467 -0.352 0.316 -0.988 -0.934 0.000 0.657 0.243 0.090 0.000 0.786 1.265 1.706 2.548 0.785 -0.017 -1.676 0.000 0.906 0.696 0.985 1.044 0.428 0.898 0.759 +1 1.235 -1.050 -1.651 2.194 -1.243 1.117 -0.622 0.476 2.173 0.756 -2.133 0.000 0.000 0.332 -0.958 0.323 2.548 0.446 -0.124 -0.911 0.000 1.070 1.173 0.989 0.747 0.194 1.009 0.953 +1 1.127 -1.192 -1.016 1.348 -0.880 2.671 -0.242 0.583 0.000 2.840 -0.907 -1.188 2.215 2.267 -0.721 -1.701 2.548 1.467 -0.686 -0.269 0.000 0.822 1.215 0.986 1.177 1.218 1.093 1.085 +0 2.276 0.991 1.723 0.913 1.206 1.618 2.134 -0.080 0.000 1.048 -0.663 -0.238 1.107 2.018 0.037 1.667 2.548 0.491 -1.077 0.579 0.000 3.775 3.206 0.990 1.691 1.632 2.308 1.900 +0 1.483 -0.213 0.091 0.796 -0.037 0.985 -0.077 -1.649 0.000 0.339 -1.571 -1.605 2.215 0.842 0.679 1.578 0.000 0.653 -1.408 -0.002 3.102 0.790 0.876 0.991 0.859 0.422 0.762 0.908 +0 0.615 1.114 1.079 1.099 1.470 0.639 1.289 -0.238 2.173 0.366 2.484 1.666 0.000 0.428 2.048 -1.092 0.000 0.751 -0.073 1.091 0.000 1.077 0.901 0.987 0.700 0.288 0.695 0.682 +0 0.849 0.276 0.851 0.635 0.040 0.730 -0.870 -1.679 2.173 0.758 -1.595 -1.352 0.000 2.077 -0.270 0.237 2.548 0.789 0.614 1.505 0.000 1.596 1.014 0.989 0.973 1.581 1.086 1.100 +0 1.077 -1.263 -0.858 0.720 -0.059 1.068 -0.075 0.169 0.000 0.501 0.684 -1.698 0.000 1.469 -0.108 1.271 2.548 0.729 0.127 -0.127 3.102 1.013 0.888 0.986 1.308 0.760 0.904 1.371 +1 1.266 -0.335 1.009 0.620 0.061 0.845 -0.400 -0.762 2.173 1.073 0.082 -1.410 2.215 0.728 -0.369 0.625 0.000 0.369 0.606 -1.515 0.000 0.992 0.995 0.987 1.010 0.846 0.852 0.766 +1 0.480 0.801 -0.159 0.422 -1.456 1.339 0.731 0.988 2.173 0.886 2.709 -0.635 0.000 0.944 1.516 -0.678 0.000 1.237 0.214 1.667 3.102 0.832 1.460 0.981 1.181 0.849 1.396 1.101 +1 0.890 0.494 0.776 0.709 -0.527 0.685 -0.550 0.238 0.000 1.269 0.562 1.722 2.215 0.700 -0.345 -0.875 2.548 0.848 0.881 -0.506 0.000 0.898 0.788 1.014 0.908 0.875 0.909 0.811 +1 2.806 0.620 0.101 0.453 1.103 0.413 0.025 -0.835 0.000 0.917 0.771 -1.359 1.107 0.965 -0.365 1.289 2.548 0.822 1.291 -1.608 0.000 0.939 0.923 1.228 1.223 0.933 0.981 0.860 +1 0.604 -1.043 -0.827 1.384 1.471 0.893 -0.674 0.736 2.173 0.799 -0.186 -0.531 2.215 0.651 0.513 -0.863 0.000 0.705 -1.569 -1.643 0.000 1.148 0.982 1.112 0.943 1.171 0.851 0.760 +0 0.834 -2.062 1.347 0.374 -0.352 1.033 -0.577 -1.516 1.087 1.606 -0.199 -0.028 0.000 0.574 -0.691 0.479 0.000 0.588 0.687 1.728 3.102 0.770 0.888 0.990 0.950 0.654 0.955 0.913 +0 1.232 1.907 0.835 0.193 0.742 0.449 0.029 1.720 0.000 0.791 -0.628 -0.046 2.215 0.579 -1.515 -1.671 0.000 1.152 0.672 -1.012 3.102 0.876 0.967 0.997 0.841 0.948 0.942 1.004 +1 1.229 2.078 -0.216 0.620 0.677 0.988 0.953 1.605 1.087 0.531 1.286 -0.967 2.215 0.688 0.536 0.273 0.000 0.682 0.577 -1.434 0.000 0.756 0.821 0.984 0.704 0.805 0.834 0.716 +0 2.532 -0.217 0.619 0.653 -0.989 1.040 1.135 -0.973 0.000 1.169 0.088 -0.978 1.107 1.378 0.893 0.753 2.548 0.593 0.691 1.384 0.000 1.038 0.947 1.768 1.209 1.484 1.125 1.121 +0 0.627 -1.430 0.114 1.034 0.642 0.748 -0.286 -0.797 0.000 1.539 0.593 -1.224 1.107 1.883 0.088 0.926 2.548 0.705 -1.818 0.458 0.000 0.815 0.902 0.978 1.888 1.753 2.048 1.564 +1 0.283 1.427 0.075 0.425 -0.866 1.130 0.452 1.346 2.173 0.678 -0.155 -0.161 2.215 1.124 1.134 -0.832 0.000 0.876 0.792 0.804 0.000 1.101 0.947 0.992 1.000 1.322 0.913 0.809 +1 0.778 -1.916 0.691 1.311 0.581 0.925 -0.145 -1.053 2.173 0.665 -0.694 1.558 1.107 0.645 -1.017 0.863 0.000 0.637 0.616 0.132 0.000 0.869 1.032 0.999 0.805 0.883 0.966 0.819 +1 0.551 2.247 -0.698 0.750 1.720 0.506 2.941 1.718 0.000 1.288 0.181 0.193 2.215 0.858 0.369 1.264 1.274 0.831 0.975 -1.014 0.000 0.955 0.979 0.979 0.745 0.927 0.847 0.785 +0 1.441 0.200 -1.023 0.467 1.211 0.846 -0.203 0.900 0.000 0.509 0.768 -1.608 0.000 1.307 -0.939 -0.578 2.548 0.829 -0.832 0.119 3.102 1.295 0.988 1.027 0.920 0.468 0.876 0.811 +1 0.773 -0.170 -1.302 0.821 0.541 0.955 0.922 0.635 0.000 1.074 -0.331 -0.732 2.215 0.619 -1.309 1.157 0.000 1.043 1.059 -1.140 3.102 0.616 0.914 1.099 0.814 0.923 0.922 0.814 +0 0.883 -0.428 -0.326 0.947 0.900 0.470 0.075 1.686 1.087 1.412 -1.894 -0.530 0.000 1.331 -1.205 1.495 1.274 1.249 -1.679 0.841 0.000 0.740 1.089 1.132 0.943 0.766 0.731 0.758 +1 0.756 1.551 1.190 1.277 -1.475 0.439 1.712 -0.152 0.000 0.483 0.122 -1.141 2.215 0.465 0.562 0.844 2.548 0.507 -1.055 0.298 0.000 0.501 0.597 0.992 0.614 0.507 0.523 0.718 +1 0.538 1.339 -0.884 0.305 -0.102 1.148 -0.012 1.392 0.000 1.312 -0.787 -0.553 2.215 0.440 2.354 -0.548 0.000 0.584 -0.218 0.755 3.102 2.505 1.400 0.983 0.892 0.762 1.345 1.057 +0 0.381 -1.726 -0.785 1.179 1.206 0.851 -0.432 -1.358 2.173 0.774 -0.610 -0.515 0.000 1.045 -0.941 0.361 0.000 1.235 0.546 0.674 3.102 1.019 1.045 0.987 0.857 1.219 0.913 0.834 +0 1.247 0.511 0.706 0.241 -1.230 1.034 -0.166 -0.018 2.173 1.387 0.527 -1.684 2.215 0.845 -0.583 -0.844 0.000 0.456 0.593 1.242 0.000 0.821 0.888 0.983 0.853 1.870 0.987 0.821 +1 1.318 0.765 -0.151 1.082 -1.420 1.142 0.543 0.703 0.000 0.818 0.695 -1.469 2.215 1.447 -0.088 -0.668 2.548 0.448 0.169 1.130 0.000 0.452 1.001 1.505 0.990 0.904 0.906 0.875 +1 0.563 1.065 1.005 0.844 -0.793 0.517 0.318 1.367 0.000 0.449 0.978 -0.391 2.215 0.776 1.953 -1.742 0.000 1.355 -0.586 0.105 3.102 0.910 0.760 0.990 0.555 0.744 0.610 0.567 +0 0.833 0.442 -0.225 0.456 0.511 0.813 0.345 0.351 0.000 0.898 0.498 -1.580 2.215 1.242 -1.945 1.324 0.000 1.501 0.022 -0.902 3.102 0.804 1.048 0.995 0.858 0.655 0.747 0.718 +1 1.157 0.183 0.884 1.141 -1.690 2.526 -0.386 -0.286 0.000 1.508 0.198 1.264 2.215 1.547 0.356 -1.201 2.548 1.584 -0.168 1.604 0.000 1.133 1.015 1.166 0.710 1.301 0.894 0.800 +1 2.328 0.425 -1.043 0.560 -1.328 0.831 -1.625 0.213 0.000 1.646 1.078 1.251 2.215 0.407 0.944 0.779 0.000 0.640 0.372 -0.262 3.102 1.926 1.203 0.986 1.552 0.959 1.508 1.381 +0 0.365 -0.841 -1.541 1.557 -0.314 1.042 0.021 1.306 2.173 0.521 -0.311 -0.439 0.000 0.875 -0.316 0.802 2.548 0.525 0.908 0.837 0.000 0.943 0.852 0.989 1.174 0.567 0.822 0.797 +1 0.430 -0.455 -0.570 0.815 0.553 1.132 2.503 -0.304 0.000 1.072 1.114 0.909 0.000 1.280 0.782 1.550 2.548 2.319 -0.075 -1.371 1.551 2.714 2.020 0.994 0.966 0.913 1.773 1.387 +0 0.856 -2.098 1.384 0.484 0.701 1.015 -1.954 -1.572 0.000 1.188 -0.449 -0.108 2.215 0.972 1.463 -0.076 0.000 0.556 0.802 -1.442 3.102 0.895 1.244 0.998 1.075 0.889 1.097 0.952 +0 1.025 0.897 -0.912 0.524 0.696 0.797 -0.436 -0.180 0.000 0.928 1.565 1.715 2.215 1.148 -0.015 0.360 0.000 1.035 -0.719 1.359 3.102 0.884 0.963 1.008 0.789 1.424 1.183 0.983 +0 0.511 -0.434 0.166 0.640 -1.714 0.918 -2.725 0.394 0.000 0.951 0.625 -1.362 2.215 1.180 -0.370 -0.608 0.000 1.095 0.657 1.490 3.102 0.802 0.869 0.989 0.594 0.503 0.638 0.590 +1 0.397 -0.696 -1.173 0.872 0.475 0.849 -2.905 -0.749 0.000 1.295 -1.408 1.239 0.000 1.736 -0.465 0.881 2.548 1.464 -0.166 -0.866 3.102 1.197 1.673 0.987 0.893 1.233 1.492 1.153 +1 0.870 0.361 1.113 0.346 -0.233 0.506 -1.152 1.644 0.000 0.601 1.296 -0.370 2.215 0.421 -0.451 0.859 0.000 0.764 -1.909 -1.335 0.000 0.861 0.690 0.988 0.862 0.746 0.892 0.766 +0 1.682 0.128 0.970 0.416 0.076 0.586 2.046 -0.572 0.000 0.479 0.998 -0.740 2.215 0.723 1.211 1.736 2.548 0.389 -0.334 -1.296 0.000 0.850 0.641 0.987 0.870 0.503 0.699 0.630 +0 0.468 -0.815 -0.828 0.749 1.548 1.598 -0.947 1.533 2.173 1.297 -2.146 -0.058 0.000 1.237 -1.172 0.041 2.548 0.757 -2.101 -0.844 0.000 0.864 0.804 0.989 1.001 1.733 1.341 1.064 +1 2.764 1.050 -0.550 0.151 -0.416 2.144 1.522 0.999 1.087 1.191 1.292 -1.276 2.215 1.279 -0.728 -0.640 0.000 1.179 1.113 0.185 0.000 1.218 1.106 0.975 2.149 2.100 1.562 1.257 +0 2.062 0.014 -0.600 0.400 -0.976 1.204 -0.282 1.243 0.000 0.836 0.717 0.073 2.215 1.183 0.450 -1.570 1.274 0.847 -0.239 0.423 0.000 1.039 1.054 0.980 0.934 1.061 0.911 0.963 +1 1.337 -0.155 1.073 0.332 -0.145 1.713 0.572 0.028 0.000 1.420 0.283 -1.242 1.107 0.844 -0.075 0.641 0.000 2.043 -0.510 -1.462 3.102 0.796 0.968 0.992 0.860 0.780 0.828 0.741 +0 3.188 0.565 -0.604 0.343 1.289 1.171 -0.856 0.975 2.173 1.215 -1.590 1.140 0.000 1.383 0.444 -1.281 2.548 0.776 -1.364 0.321 0.000 0.851 0.825 1.435 0.961 1.829 1.452 1.539 +0 0.394 -0.168 -1.084 1.191 1.338 0.870 -0.620 -0.112 2.173 0.780 1.481 -1.417 0.000 0.766 0.087 0.461 0.000 0.717 0.819 0.753 3.102 1.494 0.887 0.992 1.251 0.951 0.967 0.867 +1 0.658 0.039 -1.411 0.387 -0.695 1.244 1.141 0.649 2.173 0.937 0.882 -1.310 0.000 0.433 0.021 -0.952 0.000 0.507 0.151 -0.117 1.551 0.585 1.250 0.992 0.589 0.683 1.069 0.940 +0 0.436 1.956 -1.347 1.790 0.403 0.511 0.255 0.113 2.173 0.395 1.232 0.113 0.000 0.611 -0.921 -1.693 0.000 1.136 0.056 -0.812 0.000 0.942 0.876 1.224 1.169 0.935 0.943 0.955 +0 0.718 0.965 -1.612 1.311 -0.839 0.350 0.261 0.365 0.000 0.357 1.368 -0.437 2.215 0.916 -0.385 1.403 0.000 1.261 0.737 0.716 3.102 0.894 0.826 0.985 0.877 0.546 0.607 0.652 +1 0.497 -1.434 -0.793 0.964 1.629 0.830 -0.568 1.511 2.173 0.563 0.620 -0.467 0.000 0.918 1.857 0.159 0.000 1.287 0.613 0.005 0.000 0.855 0.726 0.986 1.114 0.748 1.073 1.666 +0 0.658 -0.495 -1.309 0.750 0.946 0.876 0.225 -0.841 2.173 0.790 -1.090 0.250 2.215 1.828 -1.017 1.133 0.000 0.698 -0.498 -0.612 0.000 1.037 0.810 0.984 0.850 1.352 0.887 0.787 +1 0.356 1.631 -0.610 0.621 0.080 0.564 0.349 -1.296 1.087 0.596 -0.956 0.615 0.000 0.586 1.346 0.885 1.274 0.757 -1.327 -1.509 0.000 0.868 1.016 0.988 0.696 0.786 0.854 0.764 +1 0.427 0.188 1.017 0.791 0.332 1.285 0.661 -0.763 0.000 1.649 0.496 1.248 1.107 1.441 1.003 0.656 2.548 0.698 2.192 -0.972 0.000 0.995 1.392 0.996 1.296 0.967 1.165 1.196 +0 2.552 -0.487 1.658 1.232 -1.677 1.837 -1.508 0.922 2.173 2.644 -0.250 -0.485 2.215 1.559 -0.601 -0.852 0.000 1.947 1.637 -0.085 0.000 0.863 0.909 1.001 2.105 3.794 2.214 1.807 +0 0.840 -1.319 -1.679 0.838 -1.306 1.391 -0.813 0.719 0.000 1.611 -0.955 -0.779 2.215 0.570 -0.575 1.128 0.000 0.846 0.488 0.116 3.102 0.596 0.887 0.993 1.166 1.189 1.121 1.182 +0 0.739 1.087 -1.282 1.109 -1.450 1.483 0.446 0.142 0.000 1.470 -0.108 1.493 1.107 0.846 0.132 -0.686 0.000 0.803 0.230 0.994 3.102 0.515 0.681 0.983 0.859 0.467 0.955 0.928 +1 0.898 -0.664 1.556 0.401 -0.545 0.972 0.123 -0.467 2.173 0.638 1.021 0.354 2.215 0.872 -0.025 -1.681 0.000 0.658 -1.955 -1.604 0.000 0.864 0.958 0.988 0.833 0.959 0.758 0.683 +1 0.864 0.614 0.351 1.013 0.405 1.575 -0.541 -1.472 0.000 1.050 -2.306 0.149 0.000 1.868 0.354 1.285 1.274 3.688 0.712 0.162 0.000 1.247 0.907 1.002 1.174 0.921 0.937 0.869 +0 0.778 -1.619 0.257 0.378 0.972 0.490 -0.673 1.066 2.173 0.916 -1.440 -1.250 2.215 0.633 -0.819 -0.366 0.000 0.524 -2.262 -1.065 0.000 0.758 0.832 0.997 0.899 0.948 0.685 0.639 +0 0.832 0.422 0.321 1.179 1.260 0.630 -0.598 0.019 2.173 1.309 -0.206 -1.431 0.000 0.981 0.225 -0.914 0.000 0.879 -0.129 0.607 3.102 0.878 0.931 1.029 0.925 0.436 0.789 0.796 +1 1.141 0.635 -1.644 1.268 0.673 0.584 0.147 -0.083 0.000 0.361 -0.637 -0.006 0.000 0.951 0.217 -0.911 0.000 0.815 -0.486 -1.181 1.551 0.911 0.764 1.448 0.930 0.380 0.617 0.671 +1 1.026 0.159 1.676 1.249 0.824 0.395 -0.639 0.484 0.000 0.863 0.156 -1.167 1.107 0.897 0.452 -0.108 0.000 1.267 -0.699 1.606 0.000 0.922 0.953 1.089 0.891 1.097 0.804 0.718 +0 1.296 -1.030 -0.858 0.654 0.402 1.186 1.973 1.195 0.000 1.049 -0.218 -0.004 2.215 0.474 1.870 0.840 0.000 1.551 -1.948 -0.987 0.000 0.427 0.955 1.156 0.865 1.005 1.261 1.537 +1 1.078 -0.025 -1.273 0.359 -1.625 0.771 0.132 0.473 0.000 1.017 -1.006 -1.164 2.215 0.964 0.680 1.008 0.000 0.576 -0.394 1.385 0.000 0.865 0.846 0.999 0.621 0.321 0.919 0.831 +0 0.884 1.192 1.472 1.034 -1.087 0.461 -0.508 -0.539 2.173 0.336 2.143 1.462 0.000 0.295 -1.268 0.483 2.548 0.687 -2.103 0.732 0.000 0.317 1.005 0.987 0.968 0.421 0.808 0.733 +1 1.067 0.321 0.776 0.499 -0.763 0.971 0.551 -0.717 0.000 0.991 -1.113 0.787 2.215 0.460 -0.328 -1.228 0.000 1.102 -0.248 -0.243 3.102 0.781 0.672 0.994 0.938 0.860 0.931 0.811 +0 1.888 -0.475 -1.610 0.323 0.913 0.684 0.302 0.071 0.000 0.791 0.507 1.203 2.215 0.559 2.589 -0.027 0.000 0.894 -1.106 -0.344 1.551 1.583 0.978 0.986 0.867 1.099 0.881 0.863 +1 0.490 0.415 -0.537 0.749 1.116 0.534 0.910 -0.279 2.173 0.819 -1.406 -0.643 2.215 0.861 -0.765 0.891 0.000 0.753 -1.167 -1.495 0.000 0.785 0.828 0.986 0.814 1.472 0.929 0.782 +0 0.316 -1.311 -1.613 1.057 -1.597 0.803 -0.175 0.214 0.000 1.136 0.740 -0.709 2.215 1.049 -0.626 0.718 0.000 1.245 -0.716 1.490 3.102 0.840 0.925 1.001 0.905 1.381 1.014 0.913 +1 1.509 0.583 -1.271 0.372 0.720 1.105 0.828 -0.514 0.000 1.513 1.241 1.663 2.215 1.115 2.273 0.793 0.000 1.204 -0.977 0.672 0.000 0.985 0.816 1.012 0.852 0.854 0.896 0.787 +0 0.857 -1.512 0.859 1.709 1.270 1.112 -1.249 -0.802 2.173 0.426 -1.308 1.644 0.000 0.774 -0.828 -0.413 2.548 0.825 -0.165 0.220 0.000 0.876 0.997 0.988 1.079 0.446 1.089 0.923 +1 1.914 0.512 1.178 0.482 -0.335 0.856 2.169 -0.792 0.000 0.531 1.089 0.409 2.215 0.366 2.585 -0.632 0.000 0.692 1.270 1.553 0.000 1.083 0.964 1.303 0.687 0.185 0.576 0.774 +1 0.593 0.578 0.587 1.288 -1.495 0.890 -0.151 0.006 0.000 0.868 -1.000 0.969 2.215 0.799 1.066 -1.692 0.000 1.150 -0.580 -1.003 3.102 0.850 0.984 1.156 1.115 0.895 0.935 0.837 +1 2.169 0.924 -0.398 1.800 0.691 0.682 2.837 1.251 0.000 1.286 0.438 -1.730 2.215 0.575 -0.385 0.653 1.274 0.534 -2.218 -1.633 0.000 7.300 3.858 2.276 1.706 0.870 2.333 2.017 +1 0.670 -0.159 0.845 1.888 0.349 0.710 -1.093 -0.980 2.173 0.973 -0.508 -1.646 2.215 0.743 -1.494 -0.178 0.000 0.597 -1.360 -1.658 0.000 0.714 0.850 0.981 1.145 0.778 0.948 0.791 +0 0.643 -0.160 0.658 0.928 -0.854 1.666 0.048 -0.297 2.173 1.737 1.364 1.365 2.215 1.240 0.589 1.091 0.000 0.435 -0.050 -1.033 0.000 0.817 0.905 1.047 0.912 3.095 1.496 1.171 +0 1.329 0.368 -1.029 0.887 0.702 0.549 -1.242 -0.139 0.000 0.628 1.698 1.511 0.000 1.272 0.438 1.487 2.548 0.577 -0.290 -0.968 0.000 0.757 0.809 1.504 0.941 0.888 0.799 0.794 +1 0.646 -0.079 0.632 0.908 0.366 1.965 -1.816 -0.858 0.000 1.585 0.914 1.186 0.000 2.287 -0.214 0.381 1.274 0.670 0.470 -1.525 0.000 0.901 0.781 0.988 0.812 0.844 0.949 0.995 +0 0.701 1.397 1.626 1.127 0.368 0.998 1.010 1.035 0.000 0.678 -2.830 -0.874 0.000 1.099 1.285 -0.450 0.000 0.646 0.820 -0.920 3.102 1.870 1.004 1.115 0.668 0.244 0.639 0.612 +1 0.370 -0.049 -0.070 1.259 -1.072 0.369 -2.238 -1.130 0.000 0.646 -1.036 -0.590 0.000 1.338 -0.597 0.622 2.548 0.704 -0.615 1.167 0.000 0.890 0.808 0.993 1.498 0.555 1.123 0.944 +1 1.285 2.083 -0.454 0.720 -0.119 1.384 0.829 1.555 2.173 0.665 1.172 0.535 2.215 0.387 1.807 0.631 0.000 0.854 0.912 -0.705 0.000 0.660 1.008 1.002 0.762 1.153 1.070 0.832 +0 1.794 -0.777 -0.021 0.726 0.703 0.740 -1.722 -1.728 0.000 0.688 -1.157 -1.512 2.215 0.536 -1.177 -1.076 0.000 0.803 -0.716 0.360 3.102 0.679 0.773 0.988 0.978 0.676 0.638 0.724 +1 0.697 1.341 1.042 1.133 -1.384 0.481 0.391 1.698 2.173 0.549 1.089 -0.615 0.000 0.863 -0.714 0.304 1.274 0.534 -2.428 -0.670 0.000 2.609 1.545 1.006 0.665 0.914 1.078 1.208 +0 1.805 -0.439 -1.416 0.964 1.546 0.661 1.121 0.040 0.000 1.099 -0.119 0.511 0.000 1.699 -0.747 1.469 2.548 2.365 -0.120 -0.179 3.102 0.904 0.975 0.976 0.774 1.616 1.118 1.066 +0 2.005 -0.780 1.343 1.628 -1.403 0.899 -0.035 -0.554 2.173 0.744 -0.269 0.379 0.000 1.063 -0.835 0.114 1.274 0.556 0.215 1.083 0.000 0.574 0.724 1.552 1.248 0.885 1.130 0.911 +1 1.792 0.188 1.387 0.805 0.387 1.272 0.332 -0.892 2.173 0.804 -1.737 -0.031 0.000 0.939 0.473 -1.612 0.000 0.587 0.418 0.229 1.551 0.884 1.108 1.304 0.702 0.779 0.908 0.808 +0 1.584 0.397 -0.001 0.252 1.398 0.840 0.213 -0.808 0.000 1.381 -0.530 1.105 2.215 0.490 0.491 -1.086 2.548 0.860 -0.746 1.456 0.000 1.385 0.793 0.986 1.180 0.946 0.852 0.846 +0 0.309 -1.721 1.566 1.169 0.150 1.557 -0.371 1.438 2.173 0.775 -1.204 -0.409 0.000 1.584 -0.062 -0.199 2.548 0.759 -0.579 -1.349 0.000 0.797 0.857 0.990 1.170 1.971 1.127 0.940 +0 0.887 -0.630 -0.941 0.995 -0.273 0.858 -1.629 1.065 0.000 0.905 -0.649 1.520 2.215 1.480 -0.679 -1.267 1.274 3.871 0.355 -0.349 0.000 1.084 1.419 0.979 0.968 0.727 1.109 1.037 +1 1.758 0.308 1.438 0.345 0.628 1.307 0.344 -0.917 0.000 0.924 0.460 0.473 1.107 0.270 1.208 -0.793 2.548 0.391 2.107 0.136 0.000 1.694 0.887 0.987 0.808 0.536 0.817 0.830 +1 2.097 0.802 -1.582 0.214 -0.718 0.456 0.363 0.713 2.173 0.539 -0.042 -1.229 0.000 0.728 1.051 0.119 0.000 0.941 0.309 0.322 0.000 0.935 0.710 0.992 0.632 0.381 0.590 0.573 +1 0.414 1.530 -0.780 0.994 0.935 0.516 1.260 -0.115 0.000 0.571 -0.540 0.635 2.215 1.095 -0.984 -0.978 2.548 0.869 -1.115 1.713 0.000 2.023 1.271 0.986 2.051 0.864 1.439 1.317 +0 1.402 -1.683 1.204 1.805 0.599 0.769 -0.076 -0.732 2.173 0.595 0.165 0.438 2.215 1.074 -0.628 -1.115 0.000 1.104 -1.570 -0.880 0.000 0.803 0.894 1.142 1.224 0.875 1.254 1.108 +1 0.475 -0.712 -1.352 1.288 -0.334 0.712 1.198 1.027 2.173 0.571 1.816 0.409 0.000 0.654 -2.393 -0.742 0.000 0.867 2.139 1.645 0.000 0.885 0.798 0.989 1.269 0.486 1.287 1.438 +1 0.742 1.852 1.386 0.639 0.232 0.490 0.552 0.033 1.087 0.492 1.611 -1.620 0.000 0.409 -0.026 -1.017 0.000 0.727 -0.617 -1.678 3.102 0.743 0.769 0.989 1.363 0.769 0.995 0.828 +1 1.247 -0.716 0.696 0.583 0.179 0.794 -1.050 -1.493 2.173 0.537 0.040 -0.112 0.000 0.607 -0.864 0.922 0.000 1.242 -0.075 -0.759 3.102 0.848 0.741 0.988 1.049 0.835 0.844 0.732 +1 0.377 0.446 -0.612 2.216 -0.188 1.277 -0.178 1.713 2.173 0.295 -0.052 -0.094 1.107 0.553 -1.251 1.230 0.000 0.612 0.355 0.667 0.000 0.729 0.876 0.995 0.724 0.903 1.343 1.162 +0 1.483 0.433 1.268 0.545 0.255 0.897 0.396 -0.972 2.173 0.570 1.255 0.318 0.000 0.757 0.136 0.540 0.000 0.521 -0.534 -0.327 3.102 0.640 1.089 0.987 0.674 0.560 0.717 0.686 +1 0.528 -1.512 1.715 0.989 0.051 1.072 -0.679 -1.286 0.000 0.485 0.512 0.327 2.215 0.826 -0.771 -0.759 0.000 1.118 -0.743 1.184 3.102 0.787 0.912 0.999 0.909 0.691 0.780 0.770 +0 0.545 -0.423 -0.510 1.760 -0.147 0.555 -1.948 0.966 0.000 0.721 -0.480 0.987 2.215 1.208 -1.157 -1.244 2.548 0.937 0.104 -1.626 0.000 0.868 0.910 0.993 1.042 0.980 1.046 1.148 +0 0.887 0.530 -0.956 0.824 1.335 1.367 1.612 -1.144 2.173 1.163 2.373 -0.251 0.000 1.802 0.177 1.052 2.548 1.108 1.003 -0.451 0.000 1.011 1.326 1.043 0.865 2.354 1.553 1.263 +0 0.527 -0.374 -0.694 1.869 -1.658 1.068 1.084 0.710 2.173 0.981 -0.077 0.079 2.215 0.814 0.055 0.900 0.000 2.625 0.687 -0.914 0.000 1.729 1.333 1.048 1.609 1.240 1.197 1.137 +1 0.860 -0.528 -0.252 1.554 0.298 1.009 -0.986 -1.312 1.087 0.286 -0.434 0.575 0.000 0.450 -0.105 1.179 2.548 0.619 -2.089 1.500 0.000 0.864 1.373 0.984 0.715 0.758 0.861 0.919 +0 0.529 1.705 1.662 0.511 1.182 1.452 1.152 -0.234 0.000 1.243 -1.470 1.059 0.000 2.123 1.197 0.251 0.000 2.588 0.747 -1.293 1.551 1.357 1.865 0.981 0.984 1.395 1.819 1.404 +1 0.392 1.103 0.857 2.116 -0.239 0.851 -0.056 -0.898 2.173 1.349 1.445 0.024 0.000 1.478 2.381 -1.717 0.000 0.926 0.314 1.667 3.102 0.843 1.023 1.053 0.896 0.723 0.858 0.776 +1 0.623 0.360 -1.365 2.851 1.630 2.164 0.858 -0.136 0.000 0.727 0.195 0.221 2.215 0.607 1.407 0.750 0.000 0.941 -0.301 -1.676 3.102 0.833 1.175 0.987 0.762 0.770 0.923 0.933 +0 0.841 0.707 -1.089 0.359 -0.476 0.831 0.144 0.148 0.000 1.344 0.650 1.663 1.107 1.388 0.130 0.688 0.000 0.897 -0.099 -0.782 3.102 0.903 0.881 0.993 0.965 0.894 0.941 0.935 +0 0.553 1.368 1.203 2.258 -1.112 1.361 0.528 0.458 2.173 0.667 0.917 -0.262 0.000 1.262 1.369 -1.497 1.274 1.013 1.434 0.519 0.000 0.813 0.963 1.347 1.733 1.802 1.243 1.021 +0 1.224 0.618 1.165 1.217 -0.165 1.174 -0.306 0.066 0.000 3.201 0.394 1.724 1.107 1.916 0.476 -0.123 2.548 0.635 -0.432 -0.511 0.000 0.669 0.787 1.575 1.661 2.625 1.643 1.370 +1 1.062 -0.368 -0.273 0.586 0.334 0.794 0.652 1.304 2.173 1.295 -1.553 -0.774 0.000 1.079 -0.608 1.732 2.548 0.918 0.682 -1.670 0.000 0.926 0.839 0.990 1.318 0.929 0.960 0.829 +0 1.890 -1.100 0.880 1.248 1.383 1.303 -0.241 -0.302 0.000 0.830 -0.872 -1.581 2.215 0.931 -0.651 -1.013 2.548 0.555 0.784 -0.541 0.000 0.861 0.847 0.995 0.883 0.466 0.803 1.067 +0 0.588 -0.276 -0.088 0.839 -1.011 0.673 0.712 0.626 2.173 0.501 -2.060 1.521 0.000 0.927 -0.517 -0.966 0.000 0.670 -0.090 1.232 3.102 1.214 0.843 0.981 0.840 0.477 0.939 0.880 +1 0.708 0.755 1.368 0.743 -1.644 1.104 1.171 -0.757 2.173 0.606 -0.025 0.501 1.107 0.546 2.076 0.271 0.000 0.550 1.854 1.346 0.000 0.498 0.956 1.000 1.004 1.336 0.958 0.808 +0 3.514 -0.145 -0.297 2.003 -0.307 2.268 -0.268 1.350 0.000 1.383 -0.430 -1.705 0.000 1.337 0.900 0.265 2.548 0.765 -1.000 -0.929 3.102 1.153 0.908 1.022 1.185 1.231 0.987 1.184 +0 1.357 0.731 1.307 0.664 -0.481 0.774 -0.248 -1.477 2.173 0.794 -0.632 -0.685 0.000 1.414 0.133 0.231 2.548 0.522 -1.026 1.021 0.000 0.874 0.915 1.314 1.006 1.329 0.897 0.842 +1 0.934 0.165 -1.524 0.660 1.114 0.892 -0.409 -1.015 2.173 0.833 1.285 0.759 0.000 1.139 1.147 0.103 0.000 1.196 0.920 -0.743 3.102 0.835 0.863 0.988 0.833 0.949 1.070 0.995 +0 1.368 -1.408 -1.029 0.714 -0.942 0.587 0.520 0.622 0.000 0.761 -0.936 0.100 2.215 1.275 -1.902 1.018 0.000 0.687 1.600 -0.570 0.000 0.773 0.928 0.998 0.488 0.508 0.594 0.736 +0 0.717 -0.650 -0.737 0.321 1.169 0.749 -0.160 0.282 0.000 0.720 -0.875 0.795 0.000 1.187 -0.282 -1.540 1.274 0.887 0.443 -1.080 3.102 0.911 0.991 0.989 0.674 0.464 0.788 0.691 +0 0.542 0.941 -1.728 0.745 0.486 0.511 1.309 0.718 0.000 0.611 0.862 -1.237 2.215 0.795 -0.545 -0.930 1.274 1.044 1.852 0.018 0.000 0.839 0.923 0.989 1.128 0.635 0.892 0.800 +0 0.408 1.841 -0.468 1.123 0.406 0.765 0.233 1.708 0.000 0.651 1.080 -0.635 2.215 0.730 -0.728 0.766 2.548 0.844 0.035 -0.914 0.000 0.869 0.875 0.991 0.846 1.071 0.740 0.742 +0 0.856 1.730 -1.730 1.044 0.652 0.647 1.154 -1.230 0.000 0.537 -0.506 -0.172 2.215 0.685 0.735 0.373 0.000 0.572 0.032 0.174 3.102 0.741 0.916 1.098 0.781 0.209 0.809 0.758 +1 1.398 2.073 -1.214 0.434 1.740 0.714 1.332 -0.240 2.173 0.976 0.850 0.639 2.215 1.148 1.357 1.321 0.000 0.639 1.662 -0.918 0.000 0.885 0.957 0.974 1.097 0.922 0.871 0.766 +1 0.559 -0.851 -1.583 0.328 -0.803 0.910 0.304 0.667 2.173 0.629 -0.249 0.859 0.000 0.463 0.462 -0.101 2.548 0.685 2.314 -1.369 0.000 0.597 0.553 0.986 0.575 0.523 0.609 0.525 +0 1.663 1.538 -0.389 0.305 -1.516 1.009 -0.560 -0.434 2.173 1.197 0.481 -1.532 0.000 1.106 0.311 0.359 2.548 1.572 0.760 1.465 0.000 0.972 0.958 0.990 1.782 1.061 1.229 1.226 +0 1.938 -0.591 1.466 0.822 1.004 0.961 -0.454 -0.354 0.000 0.863 0.568 -0.305 2.215 1.906 -0.804 -1.551 2.548 1.266 -0.793 0.191 0.000 0.898 0.927 0.979 0.924 1.645 1.077 1.084 +0 1.167 1.783 0.030 0.351 1.732 0.416 0.938 1.281 1.087 1.013 -0.385 -1.461 0.000 0.876 1.115 -1.232 2.548 0.589 -0.460 0.944 0.000 0.840 0.685 0.984 0.704 0.587 0.572 0.562 +1 0.990 -0.835 -1.465 0.734 -0.932 1.838 -0.029 0.694 2.173 1.594 -0.916 -1.165 0.000 0.564 0.685 -0.190 0.000 0.487 -0.836 1.678 1.551 1.780 1.003 0.984 1.925 0.926 1.285 1.204 +1 0.485 1.319 -1.041 1.556 1.382 0.999 0.650 -0.249 2.173 1.094 -0.010 -1.419 0.000 1.414 -0.660 0.703 0.000 0.489 -0.435 -0.457 1.551 1.928 1.085 0.987 1.207 0.481 1.006 1.050 +0 0.594 -0.004 1.207 0.869 -0.512 0.926 -1.453 1.233 2.173 0.859 -2.684 -0.654 0.000 1.067 -0.297 -0.096 0.000 0.899 -1.031 -1.290 3.102 0.906 0.770 0.996 1.128 0.745 0.788 0.953 +1 1.291 -0.469 1.256 0.469 1.557 1.836 -1.349 0.097 0.000 0.685 -0.022 -1.690 0.000 1.020 0.438 -1.310 2.548 0.693 -0.865 1.609 1.551 0.831 0.978 0.987 0.757 0.622 1.132 1.114 +0 0.868 0.627 0.592 0.610 1.451 0.677 -0.728 -0.224 2.173 0.720 -0.419 -1.569 2.215 0.651 0.646 -1.739 0.000 0.935 0.400 -0.212 0.000 0.850 0.924 0.993 0.739 0.975 0.732 0.678 +1 0.703 -1.245 1.163 1.668 -1.532 0.579 -0.761 0.810 0.000 1.964 -0.696 -0.072 2.215 1.074 -1.236 -0.786 2.548 1.698 -0.651 1.516 0.000 0.898 1.075 0.987 1.513 1.054 1.045 0.980 +0 0.716 -0.978 0.200 1.050 1.346 0.849 0.058 1.121 0.000 0.862 -2.592 -0.072 0.000 1.019 -1.172 -1.331 0.000 1.027 -0.264 -0.950 0.000 0.964 1.180 1.032 0.526 0.497 0.702 0.706 +1 0.917 0.842 -0.130 0.501 -1.405 1.104 -0.256 -1.358 0.000 0.809 -0.799 0.742 2.215 1.258 0.413 0.178 2.548 0.781 1.803 1.164 0.000 1.675 1.356 0.990 0.671 0.899 1.081 0.941 +1 1.326 -0.563 -1.171 0.340 -0.340 1.137 1.143 1.175 0.000 1.178 -0.776 0.130 0.000 1.314 -0.077 0.490 0.000 1.184 0.016 -0.874 3.102 0.924 1.026 0.984 0.797 0.765 0.915 0.832 +0 1.934 0.779 1.371 0.737 1.156 0.972 0.190 -1.561 2.173 0.607 -2.804 0.086 0.000 2.011 0.353 -0.281 2.548 1.132 0.865 -0.506 0.000 3.806 2.781 1.007 1.525 1.602 1.954 2.001 +1 1.225 0.256 -0.608 0.881 1.632 0.898 -0.756 0.013 0.000 0.376 -2.484 0.975 0.000 1.842 0.315 1.530 2.548 0.831 -0.587 -0.875 0.000 0.948 0.983 1.297 0.974 0.919 0.997 0.895 +1 0.539 -1.577 -1.265 0.799 1.298 0.989 -1.023 0.331 0.000 1.523 -0.945 1.589 2.215 1.371 -0.996 -0.564 0.000 0.896 2.078 -0.669 0.000 0.692 0.823 0.991 0.682 0.926 0.928 0.799 +0 2.334 1.293 -0.019 1.183 0.293 1.236 -0.786 -1.599 2.173 0.493 0.472 -0.753 0.000 1.005 -1.500 1.686 0.000 0.919 0.239 1.229 3.102 1.636 1.094 0.974 2.542 0.898 1.593 1.485 +1 0.413 1.511 1.706 2.402 0.955 1.013 -1.805 -0.802 0.000 1.075 0.203 -0.972 1.107 0.838 0.168 0.162 0.000 1.127 0.653 0.909 3.102 2.329 1.836 0.994 0.820 1.028 1.363 2.339 +0 1.202 0.037 -1.413 0.803 0.671 0.474 -0.763 -1.189 0.000 0.624 0.572 0.328 2.215 0.609 -0.685 0.131 2.548 0.420 0.124 0.900 0.000 0.725 0.795 1.297 0.794 0.487 0.620 0.584 +1 0.449 -0.118 -1.668 1.308 -0.073 0.689 -0.098 0.390 0.000 0.966 -1.208 -1.169 2.215 1.167 -0.072 -1.208 2.548 1.161 -0.831 0.846 0.000 0.836 1.108 1.052 0.974 0.696 0.903 0.805 +0 1.249 0.091 1.097 1.708 0.650 0.732 2.362 -0.307 0.000 1.063 -0.443 -0.543 1.107 0.825 -1.444 -1.586 0.000 1.767 0.024 -1.304 1.551 5.156 2.949 0.999 1.226 0.840 1.909 1.704 +0 0.537 0.644 1.467 0.587 -1.407 0.716 1.003 0.837 2.173 0.917 -1.062 -0.452 0.000 0.473 -0.603 0.115 2.548 0.628 1.015 -0.906 0.000 1.467 0.859 0.980 1.023 0.810 0.930 0.831 +1 2.472 0.928 1.701 0.584 -0.326 0.481 -0.275 1.691 0.000 1.092 1.350 0.111 2.215 0.608 -0.277 0.255 0.000 1.066 -0.340 -0.865 1.551 0.936 0.709 1.611 1.333 1.250 1.043 0.948 +1 1.000 1.469 0.261 1.867 1.012 0.708 0.019 -0.894 2.173 0.487 2.141 -1.231 0.000 0.507 0.817 -1.691 2.548 0.468 -1.426 -1.022 0.000 2.163 1.195 1.187 1.520 0.596 0.972 1.051 +1 0.850 -0.237 -0.382 0.637 -1.503 1.128 2.147 0.884 0.000 1.200 0.428 0.080 2.215 2.040 0.007 -1.190 1.274 1.093 -0.507 -1.715 0.000 3.412 2.342 0.986 0.795 1.557 1.813 1.559 +1 0.627 1.697 -0.055 1.096 1.695 0.320 -0.088 1.175 2.173 0.319 0.186 -0.473 2.215 0.292 0.539 1.416 0.000 0.386 -2.239 -1.075 0.000 0.964 0.698 1.148 0.762 0.473 0.642 0.889 +0 1.575 -0.050 1.272 0.972 1.511 1.048 -0.777 0.973 2.173 2.090 0.055 -0.800 2.215 0.934 -1.374 -0.001 0.000 0.488 0.378 -0.060 0.000 0.855 1.015 0.967 1.581 2.372 1.408 1.212 +0 0.782 0.063 -0.724 1.321 0.190 1.207 -0.575 -0.053 0.000 1.095 -1.320 1.469 0.000 1.798 -0.189 1.512 2.548 1.488 -0.252 -1.327 3.102 2.584 1.693 1.034 1.138 0.691 1.219 1.096 +0 0.489 1.233 -0.441 2.737 -0.426 1.564 0.436 1.624 0.000 1.289 -0.223 0.108 2.215 0.849 0.901 1.278 0.000 1.327 -0.532 1.424 3.102 0.861 0.926 0.989 0.917 1.121 1.138 1.232 +0 1.150 -1.923 -1.481 0.482 0.158 1.051 -0.782 1.208 2.173 0.531 -0.362 0.894 0.000 1.475 -0.673 -0.398 2.548 1.018 0.867 -0.867 0.000 1.207 1.119 1.028 1.076 1.540 1.028 1.053 +1 1.157 0.517 0.064 0.803 1.511 0.963 0.605 1.346 0.000 1.280 -0.657 -0.821 0.000 0.389 -1.208 0.563 1.274 0.787 0.492 -0.646 3.102 2.724 1.537 1.289 0.821 0.601 0.973 0.895 +0 1.136 -0.207 -1.172 0.820 1.312 0.642 -0.194 -0.171 2.173 0.405 -1.910 1.253 0.000 0.388 -1.515 -0.831 0.000 1.273 -0.110 1.006 3.102 0.721 0.871 1.050 0.710 0.836 0.690 0.659 +1 0.924 1.972 1.221 1.063 -0.028 1.089 1.253 0.102 2.173 1.126 1.079 1.714 0.000 1.082 0.772 -1.177 0.000 1.216 1.251 -0.689 3.102 0.893 0.838 1.239 0.974 0.810 0.946 0.914 +1 0.899 0.220 0.951 0.376 -0.773 0.926 -0.837 -0.703 0.000 0.798 -0.841 0.663 1.107 0.886 0.089 -0.947 0.000 1.178 -0.544 1.491 0.000 0.872 1.015 0.988 0.879 0.635 0.832 0.806 +1 1.149 0.669 1.100 0.554 -1.500 2.786 1.485 0.108 0.000 2.707 1.144 1.385 0.000 2.373 0.957 -1.160 0.000 1.056 -2.364 -1.308 0.000 2.926 1.712 0.985 0.721 0.265 1.092 0.907 +1 1.249 -0.865 -0.948 0.702 0.526 0.698 -0.765 -1.284 0.000 0.801 -0.938 0.672 2.215 1.142 -1.349 0.997 2.548 0.534 -1.826 -1.401 0.000 1.003 0.872 1.259 0.855 0.396 0.678 0.674 +1 0.916 -1.093 -0.018 1.275 1.686 0.937 -0.548 -1.737 2.173 1.417 -0.327 0.162 0.000 0.722 0.134 -0.574 0.000 0.402 -0.347 0.575 3.102 1.028 0.593 1.496 1.017 0.567 0.825 0.839 +0 0.803 1.453 -1.386 0.608 -0.116 0.477 -0.345 1.064 2.173 0.338 -2.186 -0.366 0.000 0.873 0.715 0.367 2.548 0.656 0.179 0.666 0.000 1.081 1.070 0.988 0.887 0.674 0.712 0.811 +0 0.799 -1.367 1.261 1.395 0.987 0.612 -0.613 -1.039 2.173 0.499 0.597 -0.253 1.107 0.318 0.669 -0.673 0.000 0.637 2.046 0.482 0.000 1.009 0.692 0.995 1.326 0.752 1.250 1.557 +1 0.568 0.436 0.794 0.367 -1.492 0.823 2.219 0.840 0.000 0.822 1.915 0.358 0.000 0.893 0.790 -0.933 2.548 0.749 -1.784 -1.255 0.000 0.774 1.003 0.990 0.829 0.181 0.826 1.052 +0 0.459 -0.592 0.764 1.310 -1.506 0.702 0.644 0.740 0.000 0.803 0.229 -0.354 2.215 0.789 1.334 -0.255 2.548 0.816 2.492 -1.267 0.000 0.694 0.865 0.988 1.004 0.558 0.754 0.726 +1 0.457 -1.456 0.938 0.423 0.662 1.501 -0.469 -0.758 2.173 0.927 0.542 0.736 2.215 0.933 -0.332 1.373 0.000 0.500 0.819 -1.532 0.000 0.664 1.223 0.984 0.686 1.934 1.048 0.877 +0 1.003 -1.368 -1.222 0.939 -0.202 0.711 -0.116 -0.428 1.087 0.821 -0.708 0.018 0.000 0.987 -0.404 1.148 0.000 1.317 -1.126 1.042 3.102 0.858 1.080 1.070 0.836 1.200 0.808 0.754 +0 1.193 -0.149 1.353 0.356 -0.903 2.676 1.070 0.240 1.087 2.697 -0.702 -1.271 0.000 2.205 -0.517 0.917 0.000 1.539 0.373 0.127 0.000 0.789 0.927 0.991 1.038 4.053 2.031 1.595 +0 1.214 -0.297 0.064 0.646 1.040 0.882 -0.044 -0.888 2.173 0.773 0.393 1.242 2.215 1.105 -0.980 1.701 0.000 0.400 1.798 0.414 0.000 0.838 0.932 0.987 0.751 1.175 0.803 0.771 +1 1.118 1.477 1.658 0.585 -0.664 0.915 -1.711 1.079 0.000 1.666 -0.082 0.007 1.107 0.598 0.901 -0.912 0.000 0.631 -0.016 -1.426 3.102 2.744 1.528 0.987 1.435 0.890 1.276 1.379 +1 0.767 -0.951 -0.522 0.550 1.404 0.643 0.956 1.295 0.000 0.876 0.195 0.823 2.215 1.300 0.336 -1.001 0.000 1.706 -0.501 -0.240 3.102 1.526 1.179 0.987 0.672 1.011 0.980 0.848 +1 0.574 -0.288 0.769 0.936 -0.699 0.480 0.033 -0.445 0.000 1.013 -0.232 1.389 0.000 1.458 0.789 -1.521 1.274 1.918 0.224 0.252 1.551 1.489 1.156 0.987 0.969 1.335 0.952 0.826 +1 0.770 -0.528 0.257 0.403 -1.139 0.808 0.294 -1.449 2.173 1.604 -0.745 0.862 0.000 0.906 -0.685 -0.614 2.548 0.413 -0.614 1.734 0.000 0.750 0.991 0.989 1.068 0.935 0.921 0.818 +0 1.052 0.823 -0.622 0.475 -1.122 0.987 0.023 0.307 2.173 0.806 -1.480 -1.353 0.000 0.874 0.903 1.043 2.548 1.311 0.245 1.035 0.000 0.884 1.290 0.995 0.996 0.920 1.060 0.937 +0 1.245 0.017 0.427 2.898 0.460 2.037 -0.983 -1.201 0.000 0.595 -0.714 1.229 2.215 0.515 -0.852 -1.474 2.548 0.571 -0.249 -0.375 0.000 1.259 1.185 0.988 1.181 0.386 0.927 1.428 +0 0.497 -1.109 -0.813 0.622 0.912 0.885 0.961 -1.543 0.000 0.648 1.177 -0.918 1.107 0.881 1.249 0.237 0.000 1.576 0.357 0.626 1.551 1.619 1.024 0.984 1.282 0.970 1.254 1.382 +0 0.627 -0.806 0.570 0.444 -0.870 1.686 -0.202 0.861 1.087 1.282 0.785 -1.672 1.107 2.086 -1.263 -0.213 0.000 1.596 -0.216 -1.264 0.000 0.873 0.938 0.983 0.965 1.996 1.301 1.037 +1 0.732 0.103 -1.694 0.442 1.073 0.589 1.730 -1.121 0.000 0.997 0.087 0.400 2.215 0.429 2.324 0.085 0.000 0.751 0.194 -1.123 3.102 0.884 0.789 0.986 0.895 0.768 0.873 0.772 +1 2.187 -1.674 -1.242 0.508 0.758 0.887 -1.146 0.623 0.000 0.524 -0.306 0.707 1.107 0.467 -0.787 -0.969 0.000 1.136 -1.898 0.348 0.000 0.898 0.854 1.421 0.920 0.748 0.813 0.726 +1 0.976 -0.383 -0.937 0.627 -0.076 0.783 -0.240 -1.295 0.000 0.699 -2.329 0.987 0.000 0.699 -2.615 1.330 0.000 1.039 0.977 -1.310 0.000 0.801 0.905 0.988 0.718 0.451 0.623 0.637 +1 1.147 0.376 -0.871 0.459 0.569 0.915 -0.247 1.244 0.000 0.672 -0.255 -1.632 0.000 1.238 0.810 -0.252 2.548 0.893 -0.728 -0.057 3.102 0.868 0.934 0.987 0.679 0.817 0.917 0.790 +0 0.863 1.191 1.441 1.987 1.107 1.193 1.860 -0.307 0.000 0.392 1.701 0.184 0.000 0.408 0.545 -0.952 2.548 1.166 1.188 -1.222 1.551 0.628 0.851 0.982 0.842 0.258 0.675 0.839 +1 0.475 0.939 0.181 1.085 -0.446 0.526 1.053 -1.211 1.087 0.775 1.599 0.963 2.215 0.987 0.451 0.658 0.000 0.719 -0.300 -1.329 0.000 0.852 0.810 0.983 1.164 0.913 0.882 0.759 +1 1.593 -0.447 -1.070 0.591 -0.913 2.729 -0.351 0.820 0.000 2.010 -0.187 -0.632 2.215 0.957 0.028 -0.865 0.000 1.553 -0.010 -1.379 3.102 0.731 0.748 0.989 0.722 1.004 0.753 0.638 +0 1.430 -0.756 1.228 0.663 0.051 0.648 -0.352 -0.144 2.173 0.654 -1.387 1.558 0.000 0.748 -0.932 -1.244 2.548 0.603 0.221 -0.823 0.000 1.038 0.974 1.177 0.783 0.784 0.697 0.665 +0 0.553 -0.163 0.480 0.628 -1.325 1.236 1.285 0.651 0.000 1.539 0.386 -1.301 2.215 0.686 0.912 0.149 2.548 0.898 -0.391 -0.573 0.000 1.325 0.785 0.989 1.057 1.106 1.055 1.054 +0 0.903 -1.396 -0.128 1.468 -0.683 1.207 -2.117 1.289 0.000 1.540 2.113 1.186 0.000 2.432 -1.202 -0.396 2.548 0.817 0.205 -1.334 0.000 1.003 1.134 0.988 0.741 0.313 1.481 1.582 +0 0.335 1.491 -1.411 1.135 0.797 0.623 0.564 -0.606 2.173 0.640 -0.924 0.112 2.215 0.772 -0.661 1.669 0.000 0.615 -1.820 -1.618 0.000 0.620 0.770 0.988 1.273 0.958 1.450 1.620 +0 0.370 -0.661 1.430 1.327 -0.485 0.799 0.058 -1.737 0.000 1.192 -0.228 0.401 2.215 0.953 0.455 -1.171 0.000 0.770 0.827 0.609 3.102 0.843 0.852 0.987 0.838 0.587 0.850 0.778 +0 1.105 -0.950 -1.484 1.009 1.146 1.312 2.468 0.069 0.000 0.614 -0.452 -0.536 0.000 1.160 -2.041 -0.189 0.000 2.239 0.318 1.606 3.102 0.918 0.981 1.019 0.759 0.513 0.700 0.712 +0 0.472 -1.690 -0.247 1.678 0.694 1.361 -0.736 0.085 2.173 1.194 -0.443 -0.759 0.000 2.373 -1.255 -1.693 1.274 0.929 -0.027 -1.562 0.000 0.955 1.345 0.984 0.899 2.352 1.332 1.162 +0 0.723 -0.930 -0.912 1.104 0.766 0.558 -0.766 1.291 2.173 0.568 -1.413 0.080 0.000 0.675 -1.556 -0.837 0.000 0.462 -1.327 -1.309 3.102 0.709 0.868 1.236 0.644 0.446 0.525 0.553 +0 1.122 -0.987 0.586 0.284 1.648 0.756 -1.002 -0.486 2.173 0.642 -1.047 -1.518 1.107 0.787 -0.244 -0.831 0.000 1.092 0.481 0.884 0.000 0.980 0.904 0.989 0.888 0.822 0.765 0.735 +0 0.730 -0.551 -0.826 0.911 -0.290 0.927 -1.017 0.083 2.173 0.830 -2.559 1.259 0.000 1.437 -0.379 -1.618 0.000 1.241 -0.702 1.314 1.551 2.424 1.406 0.985 1.047 1.022 1.184 1.230 +1 1.748 0.058 1.071 0.155 1.152 0.705 -0.459 -0.565 2.173 0.567 -1.951 0.996 0.000 0.364 -1.289 -0.197 2.548 1.560 0.412 -1.215 0.000 2.262 1.274 0.990 1.148 0.374 0.888 0.956 +1 0.669 -0.844 -0.874 1.200 0.448 0.815 -1.631 1.157 0.000 0.718 -0.701 -0.287 2.215 1.909 -0.347 -1.386 2.548 1.321 -1.111 0.548 0.000 0.866 1.060 1.152 1.084 1.063 1.041 0.899 +1 0.858 0.863 1.331 1.113 -0.570 1.019 0.256 -0.535 0.000 1.735 0.420 0.696 2.215 1.020 -0.505 -1.401 0.000 0.600 0.085 -1.313 3.102 1.500 0.837 1.340 1.172 0.907 1.042 0.948 +1 0.788 0.913 0.877 0.180 1.320 1.248 0.392 -0.841 0.000 0.813 0.165 1.126 2.215 0.763 0.541 1.357 2.548 0.498 -0.713 -1.022 0.000 0.828 0.966 0.981 0.569 0.249 0.795 0.726 +0 0.878 -0.547 0.671 0.471 1.136 0.841 -1.023 -1.613 2.173 0.586 -1.612 0.957 0.000 0.854 1.315 -0.722 0.000 2.465 0.047 -0.193 3.102 0.895 0.963 0.992 0.918 1.701 1.233 1.136 +1 0.783 -0.886 0.272 1.072 1.366 1.213 -0.525 1.055 0.000 1.253 -0.233 -1.244 2.215 2.327 2.022 -0.521 0.000 1.926 -1.583 -0.910 0.000 0.792 1.070 1.059 0.643 1.443 0.967 0.859 +0 0.600 -1.022 0.330 0.999 -1.190 1.656 0.394 1.163 0.000 1.197 0.625 -0.014 2.215 0.680 1.552 0.686 0.000 1.670 -0.607 -0.650 0.000 1.558 1.046 1.051 1.218 0.878 0.990 1.121 +1 0.646 -0.498 1.592 2.034 1.557 0.458 1.382 0.142 2.173 0.565 0.574 0.327 0.000 0.617 0.384 -0.203 0.000 1.093 0.229 -0.722 3.102 0.644 0.563 0.976 0.881 0.687 0.828 0.695 +0 1.070 1.278 -1.053 0.458 0.407 0.556 -0.781 -0.737 2.173 0.571 -0.950 0.531 0.000 0.751 -1.035 1.539 0.000 1.434 0.222 1.147 3.102 0.797 0.836 0.986 0.808 1.073 0.819 0.845 +0 0.978 0.036 0.285 0.594 -0.230 0.565 0.889 -1.471 1.087 0.421 0.733 1.463 2.215 0.805 -2.166 -0.126 0.000 0.436 -1.294 -1.568 0.000 0.688 1.208 0.980 1.114 0.346 1.109 0.958 +0 0.281 -0.030 -1.589 1.904 0.830 0.999 0.441 1.460 2.173 1.741 0.194 -0.651 2.215 1.199 0.210 0.632 0.000 2.255 -0.753 -0.755 0.000 2.035 1.586 0.989 1.194 1.849 1.347 1.255 +0 2.386 -0.512 -0.060 1.210 -0.400 0.455 2.552 1.582 0.000 0.835 -0.298 1.441 0.000 0.509 -0.067 1.123 2.548 1.094 -0.817 -1.099 3.102 0.905 0.766 0.987 0.837 0.583 0.708 0.791 +0 0.282 -1.559 0.832 1.498 0.252 1.201 0.585 -1.358 0.000 0.798 1.429 -1.324 0.000 1.738 0.831 0.471 2.548 1.323 1.070 -0.520 3.102 0.947 0.965 0.999 0.883 0.928 1.047 1.085 +1 1.055 -0.653 -0.688 1.296 -0.809 0.371 0.373 -0.524 0.000 0.787 0.061 0.616 0.000 1.358 0.636 -1.721 2.548 1.464 0.855 0.691 0.000 0.999 1.027 0.981 0.719 0.868 1.053 0.982 +1 0.764 -0.039 -1.021 0.735 -0.583 1.349 0.644 0.974 2.173 0.366 0.499 -1.700 0.000 0.328 0.885 -1.018 2.548 0.498 -0.895 -0.876 0.000 0.618 1.026 0.979 0.660 0.818 1.056 0.825 +1 0.674 1.048 -1.549 1.030 -0.231 1.428 0.094 1.159 1.087 0.581 0.258 -0.894 0.000 0.557 1.282 -0.225 0.000 0.777 -0.364 0.440 3.102 0.722 0.698 1.071 1.327 0.738 0.900 0.818 +1 0.865 0.412 1.511 1.103 -0.849 0.940 0.372 0.295 0.000 1.051 1.211 1.603 2.215 1.065 0.944 -0.097 0.000 0.762 -0.000 -1.141 3.102 0.868 0.886 1.150 0.882 0.735 0.895 0.827 +0 0.577 1.174 -0.049 0.676 1.238 0.518 -0.340 -1.677 0.000 0.588 0.596 -0.265 2.215 0.521 -0.443 0.503 2.548 0.466 1.110 -1.192 0.000 0.773 0.773 0.982 0.575 0.507 0.548 0.536 +1 1.043 0.347 -1.555 1.751 -1.009 0.621 -0.731 0.187 2.173 0.756 0.709 0.739 0.000 0.466 -0.459 1.640 0.000 0.692 0.460 0.434 1.551 0.891 0.916 0.985 0.805 0.503 0.807 0.762 +0 1.167 0.346 1.669 0.806 0.657 1.014 0.861 -0.300 2.173 0.668 -1.183 1.498 0.000 0.836 0.724 0.627 2.548 1.646 1.766 -0.994 0.000 3.654 2.091 1.062 1.144 0.852 1.445 1.187 +0 0.738 1.166 0.550 1.749 1.125 0.518 -2.507 -0.079 0.000 0.570 2.009 -0.984 0.000 1.234 2.294 -1.675 0.000 1.541 0.400 -0.447 3.102 0.805 0.644 0.988 1.211 0.133 0.769 0.838 +0 0.756 0.655 1.156 1.404 -1.559 0.774 -1.715 0.350 0.000 1.094 0.691 -0.786 2.215 1.153 -1.164 1.272 2.548 0.435 -0.812 -0.480 0.000 0.694 0.821 0.990 0.949 1.804 1.233 1.155 +1 0.906 -1.266 -1.600 0.320 0.493 0.449 -0.417 0.235 1.087 0.566 -1.789 -0.858 0.000 1.260 -0.577 -1.597 2.548 1.353 0.593 0.662 0.000 0.808 0.978 0.988 0.704 0.939 0.676 0.642 +1 2.672 0.822 0.926 0.589 0.806 0.830 0.373 -0.601 0.000 0.542 2.024 -0.923 0.000 0.742 0.124 -0.071 0.000 0.947 0.033 -1.593 1.551 1.093 0.762 0.979 0.909 0.684 0.764 0.725 +1 1.448 0.084 1.032 0.540 -0.388 0.478 0.655 0.683 0.000 0.838 0.233 -1.134 2.215 1.386 0.995 -0.519 2.548 0.959 0.283 -1.701 0.000 0.882 0.980 1.173 0.897 0.785 0.812 0.731 +0 1.926 -0.705 1.543 0.467 -0.408 1.291 0.433 -0.489 2.173 2.097 -0.491 0.939 2.215 0.685 0.859 -0.676 0.000 0.781 1.249 -0.895 0.000 0.277 0.572 1.291 1.107 2.609 1.454 1.259 +0 1.087 -0.256 0.022 0.810 1.147 0.819 0.556 -1.158 2.173 0.572 0.927 0.054 0.000 0.831 0.154 1.226 0.000 0.590 0.973 -0.564 3.102 1.015 1.024 1.103 0.730 0.438 0.724 0.688 +0 0.816 -1.556 0.435 1.133 -1.513 0.762 1.570 -0.253 0.000 0.725 0.120 0.753 1.107 0.863 -1.595 -0.495 0.000 1.516 0.658 -1.330 0.000 0.835 1.043 1.310 0.773 0.620 0.763 0.724 +0 0.864 -1.260 0.634 1.216 -0.279 1.181 -0.506 1.451 0.000 0.566 -1.475 -1.592 1.107 1.470 -1.296 -0.598 0.000 0.470 0.106 -0.304 0.000 0.821 0.792 1.041 0.637 0.450 0.571 0.590 +1 0.499 -1.286 0.496 2.036 1.324 2.429 -1.612 -0.519 0.000 2.190 -1.011 1.019 2.215 1.238 -0.684 -0.909 0.000 1.097 -0.444 1.672 3.102 0.836 0.644 0.985 0.827 0.855 0.881 0.859 +1 0.688 -0.530 -0.787 0.254 1.499 0.830 -0.125 -0.096 2.173 1.431 -0.336 1.510 0.000 0.673 -0.701 0.869 1.274 0.463 1.245 -0.738 0.000 1.470 0.981 0.987 0.788 0.773 0.889 0.771 +1 0.841 0.378 -0.212 0.727 -1.375 0.923 0.411 -1.478 0.000 1.128 1.205 0.007 2.215 0.496 -0.741 1.082 0.000 0.677 1.695 -1.014 0.000 0.930 0.905 0.987 0.797 0.737 0.826 0.722 +0 0.584 -1.556 0.625 1.531 0.713 0.577 -1.233 -1.165 1.087 0.474 -1.495 1.552 0.000 1.159 -1.297 -0.514 2.548 0.366 2.416 0.056 0.000 0.270 0.646 0.997 1.071 0.572 0.862 0.716 +0 0.883 -0.139 0.696 1.162 -0.065 0.789 1.363 1.520 2.173 0.872 0.291 -1.461 0.000 1.164 -0.175 -0.550 2.548 0.396 -0.195 0.220 0.000 0.791 0.902 0.988 0.752 1.542 1.111 0.906 +0 1.037 1.273 -1.294 1.058 0.496 1.203 -0.131 0.938 1.087 0.594 0.269 1.259 0.000 0.935 -2.331 -0.562 0.000 2.043 1.311 -0.121 3.102 2.499 2.110 1.450 0.957 2.091 2.122 1.884 +0 1.536 1.372 0.723 0.252 -0.832 1.002 1.644 -0.141 2.173 1.200 1.526 -1.259 2.215 1.571 1.369 -1.722 0.000 2.244 2.348 0.190 0.000 1.121 1.014 0.987 0.899 1.366 1.121 0.978 +1 0.859 -0.860 0.895 1.772 0.283 1.075 -1.054 -1.174 2.173 0.781 -0.546 1.593 0.000 0.535 -0.044 -0.141 2.548 0.371 -1.568 0.328 0.000 0.809 0.891 0.988 0.592 0.908 0.930 0.796 +0 0.600 -1.055 1.710 1.509 -0.510 1.877 -1.129 -1.277 2.173 1.506 -1.297 0.822 0.000 1.310 -1.552 0.258 0.000 1.778 -0.381 0.591 3.102 1.121 0.962 1.199 1.075 2.035 1.486 1.222 +1 1.438 -0.154 -0.687 0.817 -1.689 1.054 -1.212 0.700 0.000 1.104 0.641 -1.571 0.000 2.269 -0.533 0.404 2.548 0.735 0.482 -0.482 3.102 3.217 1.865 1.178 1.282 0.932 1.330 1.169 +1 0.770 1.247 1.158 1.514 1.604 1.379 -1.216 0.478 2.173 1.520 0.389 -0.619 1.107 1.197 0.098 -1.204 0.000 0.567 0.903 -0.365 0.000 0.858 0.894 0.988 3.779 2.627 2.603 1.929 +1 0.798 -0.208 1.463 0.716 -1.277 0.503 -0.974 -0.465 0.000 0.660 -0.680 0.400 0.000 0.846 0.029 -1.442 2.548 0.872 -1.263 0.590 0.000 0.878 1.088 0.985 0.624 0.363 0.638 0.757 +1 1.436 0.216 -1.109 1.003 -0.698 0.902 0.526 1.449 0.000 1.274 -0.118 0.834 2.215 2.421 1.046 0.129 0.000 1.727 0.062 -1.482 0.000 1.013 0.790 0.997 1.286 0.811 0.828 0.863 +1 0.670 -2.150 0.565 0.605 1.695 0.787 -0.437 0.553 0.000 1.003 -0.345 -1.156 2.215 0.737 0.943 -1.494 2.548 0.994 -1.036 0.125 0.000 0.967 1.045 0.990 0.964 0.741 0.910 0.846 +1 1.032 -0.507 -1.220 1.039 1.153 1.510 0.177 0.232 2.173 1.022 2.516 -1.134 0.000 0.940 0.647 1.359 2.548 0.764 -0.578 -0.997 0.000 2.865 1.785 1.209 1.409 1.323 1.690 1.552 +1 0.643 0.086 -1.641 0.362 -0.030 0.770 2.702 1.529 0.000 1.633 -1.075 -0.288 2.215 1.243 -0.219 -0.335 2.548 3.611 0.022 1.205 0.000 0.817 1.278 0.985 0.913 0.679 1.273 1.012 +1 1.483 -1.157 0.931 0.865 -0.763 1.774 0.917 -0.014 0.000 0.958 -0.218 1.465 0.000 1.495 0.265 -1.541 2.548 2.171 -0.638 -1.288 1.551 3.173 2.185 1.567 1.100 0.824 1.622 1.564 +0 0.533 -0.374 -1.210 0.965 1.034 0.494 -2.378 -0.537 0.000 0.739 0.737 -0.186 0.000 0.679 1.471 -0.815 0.000 1.569 0.610 0.685 3.102 0.755 0.861 0.986 0.973 0.724 0.892 0.907 +0 0.429 -1.663 -0.629 0.495 0.883 0.546 -0.122 -1.022 0.000 0.575 2.019 1.451 0.000 0.851 0.291 -0.590 0.000 1.203 -0.232 0.523 3.102 0.761 0.836 0.979 0.646 0.290 0.566 0.565 +1 1.347 -1.055 0.461 0.372 1.661 1.341 -2.340 -1.606 0.000 1.293 -0.799 -0.795 2.215 1.690 -1.193 -0.172 2.548 1.634 -0.826 1.147 0.000 0.848 1.151 0.988 1.005 0.922 0.956 0.823 +0 1.914 -1.517 0.373 0.144 -0.907 1.880 0.584 -0.992 2.173 0.566 0.833 1.743 0.000 1.093 -1.474 0.779 0.000 0.958 -0.684 0.889 3.102 0.939 1.208 0.983 2.438 1.780 1.587 1.580 +1 0.497 -0.833 1.149 1.709 0.131 0.971 0.756 -1.622 0.000 0.396 0.344 -0.548 0.000 0.408 2.066 0.218 0.000 0.901 0.577 1.156 3.102 0.840 1.001 1.014 0.883 0.655 0.872 0.793 +1 0.754 -0.001 1.460 0.978 0.473 0.530 2.321 -1.417 0.000 0.637 1.522 -0.595 0.000 0.814 0.115 -0.694 2.548 1.186 0.358 0.740 1.551 0.968 1.065 0.992 0.599 0.731 0.846 0.966 +1 0.606 0.238 0.945 1.266 -1.351 0.997 0.316 -0.192 1.087 0.615 0.053 1.523 0.000 0.848 -0.327 -0.248 0.000 1.253 -1.008 1.471 0.000 0.775 0.536 1.067 1.051 0.666 0.821 0.747 +0 0.500 1.415 -1.067 0.200 1.704 0.748 -1.263 1.115 0.000 1.002 -0.583 -0.082 1.107 1.301 -0.112 -0.788 2.548 1.054 -1.600 -1.432 0.000 1.108 1.267 0.995 0.679 0.778 0.993 0.924 +1 1.135 -0.308 0.574 0.275 -0.845 0.382 2.606 -1.453 0.000 0.494 -1.318 -1.453 1.107 0.886 -1.125 -0.441 2.548 1.093 -0.005 0.222 0.000 0.845 0.739 0.988 0.685 0.558 0.562 0.545 +1 0.349 1.255 -0.985 0.646 -0.931 0.801 0.927 -0.820 0.000 1.319 -0.162 0.855 0.000 0.517 -1.108 0.861 0.000 0.786 0.203 0.366 3.102 0.696 0.686 0.987 0.648 0.440 0.465 0.582 +1 1.631 0.190 -0.315 1.076 0.096 0.758 0.498 -1.246 2.173 0.645 1.546 1.183 0.000 1.556 0.483 1.021 1.274 0.788 0.971 -1.275 0.000 0.769 0.836 1.002 1.203 1.204 1.033 0.965 +0 2.055 -0.883 0.806 1.140 0.667 1.059 -1.429 -0.809 2.173 0.482 -1.413 0.191 0.000 0.879 -1.058 1.620 2.548 1.301 -0.448 -1.242 0.000 0.856 0.873 0.973 0.871 0.993 1.137 0.957 +0 1.559 -1.783 0.402 0.418 -0.779 0.772 0.259 1.646 2.173 0.789 1.013 -1.347 0.000 0.587 -0.185 0.296 2.548 0.424 2.461 -0.731 0.000 0.925 1.005 0.989 1.602 0.811 1.047 1.477 +1 0.293 2.157 -0.273 0.628 -1.059 1.154 -1.415 0.559 0.000 1.966 0.441 -1.233 2.215 0.835 0.453 0.672 0.000 0.649 -0.824 0.245 0.000 0.728 0.749 0.985 0.815 0.934 0.878 0.751 +0 1.387 0.395 -1.230 1.583 -0.159 1.134 -0.359 1.243 0.000 1.060 1.433 0.314 2.215 1.873 1.004 -1.528 2.548 0.877 1.566 -0.351 0.000 2.578 1.912 1.689 1.324 1.517 1.422 1.299 +0 1.404 -1.323 -1.366 1.188 -0.624 0.397 -2.917 -0.844 0.000 0.743 -1.432 1.255 2.215 1.820 -1.778 0.828 0.000 1.068 -1.122 0.228 3.102 1.719 1.118 1.110 0.935 0.644 0.774 0.879 +0 1.434 0.604 1.658 1.044 -0.194 1.498 2.030 0.028 0.000 1.551 0.294 -1.154 1.107 1.656 0.056 1.292 0.000 0.738 -0.768 -1.730 0.000 0.797 0.864 1.687 1.150 0.988 0.855 0.865 +0 1.175 -0.859 1.664 0.437 -0.777 1.091 -0.473 -0.915 2.173 1.137 -1.338 1.101 2.215 0.533 -2.031 0.746 0.000 1.583 1.255 0.410 0.000 3.219 2.342 0.993 0.784 1.763 1.713 1.384 +1 1.932 0.823 1.054 0.749 0.397 0.542 -0.752 -0.503 0.000 0.448 -0.920 0.286 2.215 0.410 0.928 -0.078 0.000 1.611 0.266 -1.226 0.000 0.913 0.686 0.986 0.914 0.526 0.663 0.719 +0 0.786 0.991 -1.721 0.926 -0.158 0.557 0.261 1.297 2.173 0.786 1.550 -0.360 0.000 0.449 -1.112 1.008 2.548 0.722 0.231 -1.056 0.000 0.903 1.032 1.167 0.934 0.532 0.804 0.738 +0 1.107 0.538 -1.297 0.803 0.959 1.191 0.950 -0.308 2.173 1.461 0.697 1.326 2.215 0.535 -0.241 0.550 0.000 0.648 0.322 -0.673 0.000 0.618 0.872 1.169 1.152 1.946 1.063 0.844 +0 0.662 -0.450 1.677 0.496 0.276 0.896 1.570 1.189 0.000 1.231 -0.736 -0.532 2.215 0.789 1.958 -1.401 0.000 0.930 0.939 0.452 0.000 0.919 0.868 0.987 0.956 0.832 1.268 1.000 +1 0.608 -1.260 0.484 0.594 -1.028 1.014 1.378 -1.740 0.000 1.258 0.629 -0.073 2.215 0.611 1.707 -0.218 0.000 0.779 -0.187 1.037 0.000 1.177 0.939 0.987 1.062 0.686 1.176 1.136 +1 0.321 -1.394 1.010 0.690 -0.991 0.462 1.260 -1.023 0.000 0.537 0.456 -0.572 0.000 1.028 -0.680 0.418 1.274 1.492 0.419 1.103 3.102 0.874 0.951 0.988 0.745 0.835 0.877 0.771 +0 0.904 0.816 0.585 0.300 -0.584 0.869 0.916 -0.872 2.173 0.412 -1.497 1.436 0.000 0.541 2.124 -1.282 0.000 0.588 -0.628 0.910 3.102 2.502 1.437 0.985 0.927 1.039 1.082 0.907 +1 0.451 -0.318 1.440 1.212 -1.287 3.069 -1.133 -0.251 0.000 1.661 1.728 1.537 0.000 1.819 -2.332 1.036 0.000 1.707 -0.030 -1.591 3.102 0.873 0.887 0.998 0.833 0.617 0.845 1.348 +0 0.396 -1.290 -0.945 0.868 1.010 0.486 0.698 -1.297 1.087 0.348 -1.152 0.163 0.000 0.664 -0.224 0.595 0.000 1.279 0.797 -0.363 3.102 0.456 0.861 0.986 1.763 0.631 1.368 1.044 +1 0.925 1.663 1.353 0.569 -1.333 0.441 -1.412 1.673 0.000 1.047 0.328 -0.853 0.000 0.904 -0.381 0.772 0.000 1.952 0.582 0.294 1.551 0.922 1.193 0.992 0.649 0.492 0.714 0.801 +1 1.289 0.147 -1.295 1.515 -1.500 1.028 -0.945 1.042 0.000 1.408 0.797 -0.004 1.107 0.929 -0.670 -0.277 2.548 0.565 0.408 -1.587 0.000 1.223 1.092 0.969 1.650 1.085 1.159 1.147 +1 0.384 -2.007 -1.648 0.629 -0.075 0.800 -0.170 -0.212 1.087 0.811 -1.081 1.690 2.215 0.926 -0.686 -1.119 0.000 1.390 0.568 0.971 0.000 1.541 1.166 0.992 0.764 1.307 0.949 0.810 +1 0.937 -0.781 1.214 0.742 0.051 0.558 0.022 0.114 2.173 0.918 -1.582 1.491 0.000 1.185 -0.304 -1.718 1.274 1.067 0.174 -0.889 0.000 0.854 0.866 1.001 0.775 1.026 0.717 0.693 +1 1.304 -1.634 -1.187 0.228 0.938 1.030 -1.150 0.121 1.087 1.676 -0.531 1.393 0.000 1.383 -1.051 -0.379 0.000 0.526 2.376 1.403 0.000 2.443 1.276 0.987 1.034 0.691 1.041 0.911 +1 1.044 -0.440 0.700 0.313 0.247 0.729 -2.426 -1.354 0.000 0.646 -1.016 0.206 2.215 0.586 0.370 -1.119 0.000 1.043 0.530 1.114 3.102 0.820 1.028 0.984 0.588 0.884 1.092 1.077 +1 2.080 -0.087 -0.289 1.684 -1.713 0.781 0.547 1.462 2.173 0.642 1.252 0.557 2.215 0.472 0.501 -1.018 0.000 1.039 -0.688 0.334 0.000 0.926 0.978 2.486 1.554 0.855 1.163 0.960 +1 0.949 1.439 1.214 1.132 0.723 1.165 1.059 -0.690 2.173 0.901 1.085 1.066 2.215 0.842 0.660 -1.307 0.000 0.646 1.454 -0.198 0.000 0.810 0.833 0.985 1.329 1.508 0.979 0.816 +0 1.062 0.518 -0.468 0.982 0.363 0.849 0.447 -1.435 2.173 0.537 -0.094 -1.231 2.215 0.892 0.303 0.469 0.000 1.242 -0.183 0.973 0.000 0.605 1.062 0.990 0.784 0.331 0.727 0.731 +1 1.709 1.279 1.052 0.163 -0.870 0.889 -0.099 -0.822 1.087 0.641 0.464 -1.623 0.000 0.892 -1.224 0.047 0.000 0.917 -0.003 0.664 0.000 0.915 0.974 0.991 0.596 0.813 0.848 0.740 +1 0.807 0.503 1.346 0.801 -1.045 0.839 -0.392 -1.424 2.173 1.522 -0.022 0.296 0.000 0.756 -0.391 -0.254 0.000 0.725 -0.916 1.003 3.102 0.852 0.789 0.987 0.877 0.735 0.875 0.868 +0 0.491 0.347 -1.264 1.723 -1.184 1.500 0.902 0.215 0.000 1.450 -0.014 -1.689 2.215 1.075 1.346 0.902 2.548 0.852 1.088 -0.276 0.000 0.815 0.940 0.976 0.901 1.447 1.248 1.392 +1 0.599 -0.405 0.601 0.209 -0.102 0.938 0.079 -1.629 2.173 0.783 1.071 0.808 2.215 2.003 0.049 0.556 0.000 2.261 -0.157 -0.626 0.000 1.104 1.098 0.986 0.637 1.223 0.950 0.822 +1 0.894 0.066 -1.633 0.668 0.738 1.339 0.358 -0.323 2.173 0.801 -0.388 1.408 0.000 0.931 -0.349 0.672 0.000 0.580 1.062 -1.351 3.102 0.815 0.822 0.988 1.120 0.866 0.941 0.818 +0 0.494 1.436 -1.058 2.502 -1.318 0.722 0.050 0.362 0.000 0.841 -0.528 0.947 0.000 0.948 -1.570 0.346 0.000 1.543 -0.165 -1.671 3.102 0.971 1.043 0.980 1.459 0.797 1.447 1.668 +1 0.981 -0.917 -0.497 3.311 -0.103 1.579 -0.201 1.577 0.000 0.361 -1.123 -1.244 1.107 0.944 -1.009 1.072 0.000 0.650 0.600 -1.226 3.102 1.412 0.982 0.981 1.228 0.473 0.840 1.222 +1 1.876 0.581 -0.727 0.859 -0.518 0.589 0.890 0.940 2.173 0.846 -0.403 1.613 2.215 0.738 0.874 -0.273 0.000 1.000 -0.337 1.151 0.000 1.146 0.952 0.977 1.109 0.941 1.029 0.893 +1 2.237 -1.294 -1.213 0.199 -1.634 0.849 -0.112 0.654 2.173 0.565 -0.972 0.987 0.000 0.647 -0.244 -0.436 2.548 0.996 -1.192 0.342 0.000 0.577 0.710 0.996 0.745 0.772 0.931 0.809 +1 0.826 0.578 1.616 0.634 0.648 0.932 0.395 0.021 1.087 0.483 0.952 -0.443 0.000 0.703 0.453 -1.442 0.000 1.198 1.320 1.350 3.102 0.734 0.851 0.982 0.558 1.255 0.773 0.691 +0 1.790 -0.021 -0.393 0.500 -1.332 0.656 0.242 0.822 2.173 0.741 1.101 -1.512 2.215 0.620 -0.267 1.138 0.000 0.467 0.895 -0.283 0.000 0.719 0.715 0.987 0.982 1.001 0.849 0.693 +0 0.542 1.145 -1.048 1.179 0.272 0.813 1.206 -1.634 2.173 0.442 0.594 -1.185 0.000 0.272 0.973 1.408 0.000 0.657 0.057 0.691 0.000 0.723 0.744 1.028 1.039 1.529 0.964 0.764 +0 0.466 0.286 1.089 1.319 -0.360 0.609 0.165 0.310 2.173 0.835 0.994 -1.219 2.215 0.700 0.889 0.864 0.000 1.302 2.046 -1.381 0.000 1.275 0.981 1.047 0.664 1.130 0.933 0.867 +0 2.307 0.643 0.837 0.836 1.167 1.330 1.339 -1.080 0.000 0.567 0.532 -0.083 2.215 0.547 1.169 0.702 2.548 0.568 0.806 -0.497 0.000 0.714 0.882 0.981 0.581 0.444 0.656 0.920 +1 0.711 1.297 -1.040 0.813 0.875 1.568 0.960 -1.460 2.173 0.637 -0.367 0.392 0.000 0.930 1.874 -0.736 0.000 2.544 0.971 0.712 3.102 0.917 1.047 1.041 0.984 1.968 1.203 0.972 +0 0.866 -0.016 0.807 0.882 1.110 0.730 0.968 0.121 1.087 0.854 0.119 -1.392 0.000 0.604 -0.523 -0.606 2.548 0.563 -2.466 -0.410 0.000 0.894 1.100 0.987 0.879 0.870 0.760 0.746 +0 1.080 -1.796 0.564 1.837 -0.031 0.926 -0.098 -1.706 2.173 0.653 -0.948 -1.453 0.000 0.919 0.167 -0.389 0.000 0.511 -0.299 -0.164 1.551 0.931 0.989 0.997 0.684 0.722 1.167 0.984 +0 1.484 -0.046 -1.002 0.657 -1.362 2.276 -0.243 1.192 0.000 1.508 1.026 0.040 2.215 2.877 0.616 -0.740 0.000 1.742 0.559 0.881 1.551 1.990 1.785 0.976 1.609 1.044 1.337 1.245 +0 1.019 -0.195 -1.032 0.725 -0.206 0.599 -0.240 0.356 2.173 0.746 0.911 0.391 2.215 0.699 -1.836 -1.460 0.000 1.194 1.359 -0.225 0.000 0.480 0.963 0.979 1.029 0.615 0.754 0.793 +0 0.380 -0.510 -1.706 1.566 -1.321 1.666 0.654 0.846 0.000 1.116 0.332 0.100 2.215 1.736 -0.132 -0.997 2.548 1.095 -1.983 -0.935 0.000 4.946 2.935 1.000 0.846 1.288 1.931 1.627 +0 0.445 -1.332 -0.835 2.036 -1.278 0.691 -0.843 1.692 2.173 0.653 -1.435 -0.272 0.000 1.340 -0.341 0.606 2.548 1.247 -1.643 0.482 0.000 0.795 0.975 0.996 0.779 1.036 0.868 0.947 +1 0.475 -1.115 -1.001 1.167 -1.629 3.671 -0.743 0.358 0.000 3.563 1.157 -1.488 2.215 1.045 2.429 -0.898 0.000 0.661 0.343 1.117 0.000 1.550 1.008 0.987 4.829 0.881 3.016 2.788 +1 0.699 1.625 -1.430 1.074 1.427 1.004 1.033 0.400 0.000 0.448 0.045 -0.779 0.000 1.269 -0.043 1.718 2.548 1.243 0.240 0.159 0.000 0.755 0.972 0.990 1.555 0.904 1.023 1.113 +0 1.079 0.753 -1.345 0.585 -0.316 0.799 0.679 0.481 2.173 0.562 0.900 1.499 0.000 0.518 -0.408 -0.859 2.548 0.547 0.918 0.976 0.000 0.334 0.627 0.984 0.669 0.890 0.715 0.614 +1 0.784 0.785 -0.352 1.044 1.388 1.318 -0.060 -1.513 0.000 1.585 0.267 0.010 2.215 1.744 0.576 0.558 0.000 0.794 1.312 0.856 0.000 0.722 1.006 1.253 0.836 1.011 0.863 0.813 +1 0.588 0.381 -1.398 0.577 -1.697 0.682 -0.130 1.347 0.000 1.718 1.170 -0.363 0.000 0.770 0.004 0.039 1.274 1.099 0.938 0.110 0.000 0.743 0.782 0.997 1.005 1.021 0.889 1.033 +0 1.328 0.723 -1.549 0.442 0.934 0.695 0.745 -0.142 0.000 1.373 0.967 0.677 2.215 2.287 0.449 -1.104 2.548 0.639 -0.068 0.893 0.000 0.927 0.900 0.987 0.899 1.942 1.049 0.917 +1 1.194 0.175 -0.627 0.865 1.711 0.779 -1.010 -1.636 2.173 1.275 0.203 0.571 0.000 0.882 0.980 -0.214 0.000 0.747 -1.030 0.530 3.102 0.634 0.759 1.210 1.025 0.754 0.820 0.777 +1 0.539 -0.219 -1.499 1.333 0.589 0.672 0.839 1.036 2.173 0.509 1.277 -1.368 0.000 1.462 0.211 -1.059 0.000 1.064 -2.342 -0.420 0.000 0.835 1.041 1.118 0.842 0.749 0.826 0.838 +1 0.393 -0.960 -1.464 0.845 0.155 2.371 0.017 -0.575 0.000 3.276 1.408 1.466 0.000 2.470 -0.519 -0.037 2.548 1.681 1.608 1.013 0.000 1.425 1.447 0.987 1.114 0.890 2.211 2.423 +0 0.475 1.613 -0.811 1.170 1.030 1.208 2.203 -1.023 0.000 0.594 -1.646 1.073 0.000 1.231 1.123 0.749 2.548 0.614 1.455 -0.083 0.000 1.036 1.350 1.029 1.151 1.107 1.333 1.079 +0 0.572 0.671 -0.659 0.233 1.209 0.889 0.716 0.138 2.173 0.641 1.764 -1.474 0.000 0.586 1.468 0.780 0.000 0.935 0.042 -1.623 1.551 0.848 1.087 0.990 0.587 1.020 0.788 0.783 +1 0.427 1.496 -1.304 0.448 -0.090 0.978 1.157 0.663 2.173 1.012 0.240 -1.327 0.000 0.825 -0.659 -1.352 0.000 1.020 0.553 1.456 3.102 0.942 0.967 0.987 0.939 0.743 1.086 0.886 +1 0.416 -1.640 0.468 1.435 -0.778 0.955 -1.340 0.849 2.173 1.182 -1.132 0.172 2.215 1.864 -1.418 -1.251 0.000 0.702 -0.214 -1.436 0.000 0.904 1.335 0.987 1.053 0.909 1.049 0.901 +1 0.963 0.668 1.355 0.980 -0.253 0.620 1.028 -1.223 2.173 0.283 1.696 1.417 0.000 0.397 -0.774 -0.524 2.548 0.788 -0.025 0.279 0.000 0.813 0.784 1.336 0.794 0.767 0.672 0.608 +1 0.551 1.117 0.119 0.616 1.554 0.690 0.442 0.628 0.000 1.442 0.712 -0.888 1.107 0.659 -0.841 0.979 0.000 1.094 -0.183 -1.428 0.000 0.972 0.650 0.988 0.897 0.959 0.911 0.765 +1 0.349 -0.501 1.044 0.972 0.565 0.404 0.600 0.866 0.000 1.168 0.257 -0.478 2.215 0.513 0.529 -1.025 0.000 0.473 -0.558 0.491 3.102 0.811 0.836 0.985 0.604 0.608 0.663 0.593 +0 1.797 -0.504 -0.458 0.147 0.716 1.233 -0.853 0.963 0.000 0.500 -1.473 0.575 0.000 1.555 0.495 -0.924 2.548 1.131 -0.157 1.554 3.102 0.798 0.833 0.985 0.834 0.885 1.128 1.013 +0 1.713 0.072 0.879 1.254 -0.532 0.885 -1.776 1.086 0.000 0.907 -2.102 -0.849 0.000 0.811 0.318 -0.465 2.548 1.499 -1.525 1.608 0.000 0.796 0.832 1.940 1.035 0.586 0.964 1.148 +1 0.597 0.711 0.211 0.412 -1.388 0.903 -0.637 0.129 2.173 1.371 -0.349 -1.119 2.215 1.132 -1.200 1.319 0.000 0.600 -1.230 -1.327 0.000 1.191 1.019 0.988 1.325 1.495 1.233 1.026 +0 1.122 0.029 0.755 0.437 -0.552 0.562 -0.061 -0.915 0.000 1.011 -0.815 -0.472 2.215 1.532 1.102 1.212 2.548 1.514 -0.634 1.703 0.000 0.999 0.889 0.987 1.018 2.079 1.066 0.892 +1 0.571 -0.418 -0.193 1.144 0.658 0.823 -0.256 -1.364 2.173 0.721 -0.792 0.970 0.000 0.527 -2.386 0.446 0.000 0.730 -0.595 -0.741 0.000 0.946 0.921 0.983 0.500 0.832 0.706 0.678 +1 1.315 0.375 -0.329 0.574 -0.806 1.210 2.197 -1.210 0.000 0.698 0.430 0.560 0.000 2.124 0.316 1.149 2.548 1.356 -0.322 0.545 3.102 2.774 2.337 0.986 1.290 0.829 1.669 1.368 +0 2.645 0.416 0.161 0.199 1.039 1.712 -0.476 -1.507 0.000 0.884 -0.828 -0.854 2.215 2.169 0.877 0.672 1.274 0.772 -0.458 1.676 0.000 0.476 0.883 0.989 0.845 2.101 1.494 1.400 +0 0.378 -1.194 1.414 1.637 -0.548 1.673 -1.406 0.685 2.173 1.071 -0.259 -0.926 0.000 0.511 1.649 -1.352 0.000 0.884 -1.594 1.519 0.000 1.197 0.821 1.069 1.353 1.151 1.368 1.199 +1 1.245 0.617 1.198 0.967 -1.512 0.961 1.253 -0.247 0.000 0.281 1.391 1.563 2.215 0.946 -0.470 1.706 2.548 0.749 1.505 0.513 0.000 0.894 0.735 0.988 0.751 0.626 0.881 0.822 +1 1.054 -1.099 0.942 0.632 0.379 0.997 2.270 0.751 0.000 1.936 0.131 -0.815 2.215 1.000 0.123 -1.543 2.548 0.698 -0.611 1.174 0.000 0.791 1.041 0.976 0.865 0.902 0.989 0.824 +0 1.932 1.526 0.238 1.138 0.527 1.119 -0.093 -1.162 0.000 0.756 -0.537 -1.702 0.000 0.648 1.308 1.333 2.548 0.646 0.370 -1.588 0.000 1.018 0.950 0.980 0.790 0.961 1.065 1.436 +1 1.857 1.302 -1.007 1.235 1.690 2.371 0.758 0.903 1.087 1.341 0.703 -0.665 0.000 1.350 0.850 -0.131 2.548 0.653 1.559 -0.620 0.000 0.740 0.682 1.369 2.061 1.801 1.499 1.318 +1 1.969 0.641 0.818 0.880 1.480 1.070 0.316 -1.055 2.173 0.851 0.602 -0.611 0.000 0.589 -0.530 0.219 2.548 0.623 -0.936 1.445 0.000 1.307 1.015 1.026 0.821 1.014 0.975 0.898 +1 0.726 -0.378 1.391 1.567 -1.332 1.097 0.761 1.068 0.000 0.676 -0.042 -1.015 0.000 1.704 -0.225 -0.394 2.548 1.802 0.219 0.336 1.551 0.835 0.813 0.987 1.080 0.888 0.947 0.806 +0 1.502 -0.500 -0.017 0.423 1.280 0.679 -0.362 1.679 2.173 0.930 -0.141 1.193 2.215 0.647 0.292 -0.431 0.000 1.290 -0.591 -0.546 0.000 0.553 0.974 1.016 0.920 0.515 0.725 0.702 +0 0.877 1.154 1.697 0.389 0.981 0.629 1.136 0.239 2.173 0.316 0.042 1.384 2.215 0.539 0.291 0.101 0.000 0.761 1.732 -1.044 0.000 0.930 0.736 0.980 0.487 0.679 0.577 0.584 +0 0.919 -0.044 -0.793 1.492 -1.632 0.764 -0.610 1.576 0.000 0.516 -0.826 0.783 0.000 1.518 0.000 -0.203 2.548 0.889 -0.698 -0.429 0.000 0.890 0.862 1.111 1.029 0.859 0.834 0.820 +0 1.040 -1.422 -1.407 1.740 1.243 1.386 1.277 -0.213 0.000 1.367 -1.200 0.628 0.000 1.177 0.487 -0.778 0.000 1.229 0.341 -1.632 3.102 0.959 0.899 1.275 1.186 0.931 0.846 0.852 +1 1.712 0.523 -0.076 1.063 -0.488 1.122 0.778 1.561 2.173 0.486 0.646 0.783 0.000 0.635 1.269 -1.399 0.000 0.481 0.490 -0.562 3.102 0.848 0.764 0.996 0.475 0.736 0.910 0.763 +0 2.331 0.735 0.119 0.647 1.025 1.242 -0.924 -1.485 1.087 0.400 -0.469 0.928 0.000 0.747 0.415 1.235 2.548 1.398 -0.440 -1.120 0.000 0.938 0.860 1.241 0.834 1.181 1.361 1.088 +0 1.218 1.112 -1.160 0.469 1.294 0.741 0.178 -1.347 2.173 0.389 0.685 0.313 1.107 0.582 0.650 0.778 0.000 0.966 -0.867 1.013 0.000 0.841 0.970 0.988 0.698 0.815 0.667 0.758 +0 0.760 -0.145 1.081 0.668 1.098 0.543 0.083 -0.538 0.000 0.639 0.441 -1.313 0.000 1.039 1.232 -0.061 1.274 0.777 -1.967 1.085 0.000 0.837 0.909 0.979 0.848 0.263 0.960 0.862 +1 0.625 -1.161 1.191 0.876 -0.007 1.068 0.048 -0.815 0.000 0.929 0.759 1.559 2.215 0.671 0.689 1.068 0.000 0.675 0.299 -1.053 3.102 0.703 0.742 0.987 0.850 0.529 0.994 0.841 +1 1.574 -0.335 -0.280 0.594 -1.169 0.506 0.511 1.575 0.000 0.759 0.985 -1.586 2.215 0.557 -0.446 0.593 0.000 1.854 1.158 0.654 3.102 0.893 0.966 0.988 1.008 0.986 0.989 0.853 +0 0.935 -1.681 -1.001 0.610 -0.004 0.602 -0.799 1.193 0.000 0.556 -0.639 0.313 0.000 0.763 -0.228 -0.846 2.548 1.222 0.451 1.585 0.000 0.921 1.067 0.992 0.905 0.687 1.046 0.912 +0 3.361 1.523 1.110 0.621 0.654 1.575 2.176 -0.739 0.000 1.231 1.488 -0.202 0.000 1.155 1.682 1.309 0.000 0.782 0.928 0.213 3.102 0.987 0.999 0.981 0.835 0.376 0.617 1.013 +1 2.148 -0.714 -0.349 1.279 0.164 0.401 2.841 1.575 0.000 0.696 -1.656 1.723 0.000 0.839 0.137 -1.601 2.548 0.609 -0.948 1.355 0.000 0.582 0.931 1.024 0.574 0.510 0.710 0.815 +1 1.413 -1.015 -1.027 1.429 -1.424 1.032 -0.242 0.949 2.173 1.044 -0.175 -0.485 0.000 0.665 0.231 0.501 0.000 1.064 0.818 0.737 3.102 1.031 0.904 0.989 1.602 0.753 1.334 1.172 +0 0.912 -0.411 0.378 0.243 0.751 0.885 -1.215 0.439 1.087 0.369 -0.448 1.142 0.000 1.277 -0.358 -1.491 2.548 1.479 1.111 -1.301 0.000 0.893 0.896 0.992 0.929 1.423 0.937 0.788 +0 0.412 0.827 1.322 0.941 -0.562 0.871 0.053 0.184 2.173 0.733 -0.056 -1.342 0.000 0.986 -2.088 1.613 0.000 1.232 0.519 -0.546 3.102 1.081 0.975 0.990 0.772 0.742 0.811 0.700 +0 2.024 -1.353 -0.612 0.156 1.579 0.497 -1.243 1.738 0.000 0.743 0.450 0.938 2.215 0.642 -0.328 1.016 2.548 0.489 -1.433 0.086 0.000 0.884 0.879 0.983 0.905 0.315 0.957 0.802 +1 0.918 -1.014 1.192 1.667 0.842 2.428 -1.842 -0.829 0.000 2.075 -1.258 0.853 1.107 0.642 -0.573 -0.712 1.274 0.399 -2.105 0.309 0.000 1.384 1.059 0.991 0.968 1.280 1.511 1.515 +0 1.313 1.007 -1.461 1.551 -0.896 0.747 -0.422 0.797 2.173 0.739 -0.582 -0.087 0.000 0.774 -2.290 0.484 0.000 1.001 -0.402 1.477 1.551 0.777 0.709 0.989 1.052 0.526 1.095 0.976 +0 1.914 0.188 0.807 0.732 1.394 1.397 0.523 -0.695 2.173 0.711 1.926 -0.811 0.000 1.370 1.118 1.148 1.274 0.478 -0.002 -0.065 0.000 1.003 1.028 0.987 0.908 1.824 1.254 1.115 +1 0.509 -1.066 0.302 0.718 -0.854 1.088 0.863 0.578 0.000 0.709 -2.171 -1.519 0.000 1.432 0.108 -0.518 2.548 1.389 -0.779 -1.515 3.102 0.663 0.803 0.983 0.785 1.032 0.903 0.771 +1 1.134 0.932 -1.637 0.443 1.517 0.969 -0.732 -0.403 2.173 0.923 -0.239 0.522 0.000 0.643 -0.524 1.351 0.000 0.470 -0.474 -1.632 3.102 0.827 1.110 0.987 0.513 0.642 0.799 0.767 +1 1.046 -0.298 -0.199 1.641 0.558 1.040 -0.490 -1.353 0.000 0.692 -1.069 1.723 0.000 0.839 -0.928 -0.764 1.274 1.454 -2.111 0.545 0.000 0.854 0.770 1.145 0.626 0.515 0.606 0.796 +1 0.345 -0.956 -0.146 0.973 -1.306 0.572 1.835 0.584 0.000 1.230 0.774 -1.403 2.215 0.749 1.253 -0.379 1.274 1.040 -0.224 1.296 0.000 0.750 0.773 0.987 0.732 0.867 1.003 1.008 +0 0.807 -0.118 1.385 1.270 -0.937 1.163 0.994 0.627 2.173 0.742 0.754 -1.190 0.000 0.330 0.446 0.302 0.000 1.389 0.796 -0.394 3.102 0.792 0.901 1.216 1.410 1.071 1.014 0.911 +0 1.369 1.925 0.721 0.634 -0.125 0.548 0.580 -1.371 0.000 0.740 1.201 -1.598 2.215 1.125 0.946 -0.253 2.548 0.408 -0.383 1.011 0.000 0.723 0.830 0.981 0.893 0.913 0.728 0.735 +0 2.508 0.626 -0.440 0.163 1.067 0.958 -0.070 1.410 1.087 0.664 0.255 0.317 2.215 0.447 1.721 1.528 0.000 0.479 2.356 -1.492 0.000 0.324 0.882 0.984 1.402 0.998 0.965 0.930 +1 0.493 -0.629 -0.194 0.345 -0.718 1.526 -0.553 -1.251 2.173 1.513 -2.145 0.550 0.000 0.795 -0.206 1.725 0.000 1.247 0.455 -0.386 0.000 1.126 0.927 0.975 1.188 1.223 0.920 0.822 +0 1.860 -0.161 -0.642 0.998 -1.204 0.504 1.528 1.289 0.000 1.045 0.121 0.951 2.215 0.355 1.412 -0.508 0.000 0.731 0.826 0.053 1.551 0.760 0.919 0.984 0.825 0.671 0.872 0.889 +1 0.476 -0.032 0.723 0.508 -0.595 0.513 0.239 -0.606 2.173 0.336 2.667 1.584 0.000 0.365 2.358 -0.938 0.000 0.646 1.194 0.765 3.102 0.652 0.849 0.987 0.925 0.692 0.711 0.830 +1 0.829 -0.029 -0.027 0.810 1.322 0.893 -0.978 1.012 2.173 0.904 0.108 -1.026 0.000 1.265 -0.550 -0.578 0.000 0.626 -1.261 -1.070 3.102 0.922 1.072 1.065 0.726 0.783 0.675 0.680 +1 0.345 2.270 0.764 0.492 -0.533 1.388 1.350 -1.639 0.000 0.889 2.735 0.526 0.000 1.005 0.153 0.430 2.548 1.081 1.006 0.545 0.000 0.780 0.717 0.979 0.686 0.914 0.602 0.539 +1 0.927 -0.744 -0.197 0.262 -1.506 0.511 -1.821 1.546 0.000 0.670 0.317 -1.538 2.215 1.150 0.281 0.244 2.548 0.572 -1.239 0.467 0.000 0.698 1.050 0.986 0.675 0.932 0.907 0.788 +0 0.361 2.091 1.046 1.170 -0.362 0.672 0.024 -1.610 0.000 0.794 -0.035 0.640 2.215 0.510 0.036 1.433 2.548 0.568 0.670 -0.414 0.000 0.914 0.903 0.990 0.738 0.444 0.665 0.672 +0 1.848 0.639 0.656 0.623 -1.653 0.655 0.057 -0.019 2.173 0.675 0.524 1.398 0.000 0.779 -2.109 -1.088 0.000 1.384 -1.005 -1.511 0.000 0.793 0.747 1.298 0.951 0.595 0.858 1.117 +0 0.886 -0.192 1.596 0.957 -1.705 0.486 -0.095 -1.289 0.000 0.662 -1.336 0.546 1.107 0.944 0.549 0.164 2.548 1.018 -0.741 -0.351 0.000 0.916 0.929 0.989 0.899 1.013 0.941 0.851 +0 1.469 -0.493 1.613 0.508 -1.050 0.713 0.625 -0.139 0.000 1.139 0.105 1.221 0.000 0.892 -0.438 -0.380 2.548 1.388 0.936 -0.898 3.102 1.093 0.889 0.985 0.812 0.849 0.750 0.735 +1 0.685 -0.890 0.054 1.076 -1.305 0.889 0.655 0.481 1.087 1.233 1.214 -1.364 2.215 0.357 1.026 0.004 0.000 0.660 1.546 1.281 0.000 0.547 0.727 1.118 1.293 1.601 1.277 1.002 +1 0.675 0.531 1.568 0.658 0.266 1.407 -1.345 1.189 0.000 1.020 -0.438 0.010 2.215 0.728 0.328 -0.415 0.000 1.733 0.613 -1.157 0.000 0.803 1.052 0.990 0.929 0.997 0.913 0.808 +0 0.343 1.638 -1.553 1.313 -0.794 1.419 0.318 0.109 2.173 0.874 1.139 1.040 2.215 0.809 0.496 -1.205 0.000 0.596 1.746 1.425 0.000 0.846 0.777 0.980 1.140 1.415 0.994 0.885 +0 2.759 -0.717 0.845 0.717 1.360 1.243 -1.454 -0.623 0.000 0.720 0.186 1.424 2.215 1.244 -0.865 -0.395 0.000 1.044 -0.304 -1.167 3.102 0.742 0.849 0.985 0.863 0.607 0.972 1.112 +1 1.054 0.201 -0.831 0.557 0.435 0.983 -0.047 1.435 1.087 0.910 -1.366 0.004 0.000 1.351 -0.018 -0.223 2.548 0.981 0.158 -1.720 0.000 0.875 0.871 0.988 0.979 1.432 0.968 0.858 +0 0.508 -0.259 -0.653 0.521 0.648 0.909 -0.359 0.138 2.173 1.000 0.241 1.471 2.215 0.988 0.403 -1.370 0.000 0.411 -2.192 -0.984 0.000 1.577 1.237 0.990 0.864 1.379 1.043 0.916 +0 0.749 -1.210 -1.109 0.680 1.308 0.801 0.935 -0.865 0.000 1.246 -1.171 0.021 2.215 1.411 -2.214 1.336 0.000 1.429 -1.601 0.689 0.000 0.952 0.955 0.990 0.975 0.861 0.906 0.860 +1 1.174 -0.384 -0.297 0.137 -0.145 2.363 1.128 -1.631 0.000 1.885 0.084 0.139 1.107 1.756 -0.930 0.403 0.000 1.685 -1.591 -0.822 0.000 1.905 1.717 1.000 0.785 0.936 1.376 1.135 +1 0.639 0.277 0.556 1.983 -0.042 0.475 1.314 -1.125 0.000 0.980 0.411 -1.320 2.215 0.659 -1.605 1.007 0.000 0.661 0.699 -1.739 0.000 1.318 0.982 0.991 1.196 0.628 0.850 0.861 +0 0.746 -0.928 -0.648 0.947 -1.706 1.091 -0.120 0.023 2.173 0.690 -0.313 1.455 0.000 0.607 -1.377 -1.218 0.000 1.024 0.183 0.856 3.102 0.913 0.795 0.986 1.068 0.786 0.827 0.762 +0 0.529 2.248 -1.281 0.669 -1.276 0.807 -0.629 0.699 0.000 1.448 1.510 -0.547 2.215 1.123 0.734 1.094 2.548 0.782 -0.154 1.611 0.000 0.929 0.916 0.973 0.856 1.446 1.303 1.105 +1 0.568 -2.042 0.675 1.082 -1.296 0.586 -0.526 -0.310 0.000 0.386 -0.933 -1.353 0.000 0.527 1.366 1.174 2.548 0.861 -0.741 0.344 3.102 0.844 1.040 1.063 0.763 0.849 1.100 0.916 +0 0.468 -1.269 1.199 0.828 -1.160 0.870 0.357 -0.229 0.000 0.621 -0.018 0.481 0.000 0.767 -1.829 1.160 0.000 1.424 -0.297 -1.203 3.102 0.979 1.046 0.983 0.629 0.716 0.727 0.684 +1 0.883 -1.111 0.012 1.921 1.456 1.481 0.146 -0.912 0.000 1.301 -0.756 -0.182 0.000 2.130 -0.712 1.507 2.548 1.191 -0.562 0.552 0.000 0.999 0.680 1.739 1.087 0.860 0.895 0.910 +0 1.558 -0.802 1.346 0.662 0.604 1.497 -0.342 0.306 0.000 1.383 0.616 -1.416 0.000 1.125 0.543 -0.580 1.274 0.670 1.490 -1.160 0.000 0.829 0.664 0.980 0.607 0.433 0.805 0.784 +1 1.809 -0.320 -1.566 0.846 1.009 1.637 -0.703 0.419 2.173 1.638 0.986 -1.128 0.000 0.861 -0.690 -0.775 0.000 0.658 0.184 1.101 3.102 0.675 1.040 1.254 0.659 0.824 0.975 0.846 +0 0.929 1.278 -1.016 0.900 1.008 0.962 0.648 1.590 2.173 1.085 0.441 -0.144 0.000 0.585 -0.718 0.903 2.548 0.735 -0.701 -0.642 0.000 0.953 0.852 1.227 0.903 0.913 0.904 0.892 +1 1.185 -2.115 0.376 0.245 -0.246 0.813 -1.042 -1.166 2.173 0.617 1.995 1.418 0.000 0.951 -0.809 0.125 0.000 1.370 -0.489 1.417 1.551 0.734 0.888 1.000 1.036 0.853 0.823 0.729 +1 1.011 0.570 0.528 0.777 1.664 0.862 0.060 0.562 2.173 0.973 1.172 -1.006 0.000 1.025 0.161 -1.216 2.548 0.405 0.189 0.038 0.000 1.022 0.694 1.049 0.759 1.172 0.864 0.780 +1 0.904 0.222 -1.270 0.105 1.392 1.670 0.427 0.698 1.087 0.714 -0.920 -1.700 0.000 1.725 0.219 -0.504 2.548 1.750 -1.701 -1.628 0.000 0.844 1.827 0.992 1.360 1.879 1.847 1.404 +1 0.957 -0.444 -1.638 1.378 -0.931 0.651 -0.112 0.674 2.173 0.477 -0.842 1.521 0.000 1.248 -0.443 -0.083 2.548 1.370 -0.813 0.358 0.000 0.915 0.803 0.986 1.068 0.740 0.831 0.748 +0 0.594 0.279 -1.625 1.771 -0.917 1.524 -0.010 1.530 2.173 0.852 0.126 0.925 0.000 1.608 1.806 0.647 0.000 3.537 -0.813 -0.512 0.000 1.208 0.860 0.984 1.211 1.499 1.013 1.001 +1 0.572 0.728 -1.670 1.083 -0.776 1.771 0.957 -0.247 0.000 1.376 -1.777 1.384 0.000 1.789 1.006 1.232 2.548 1.392 0.896 -0.749 0.000 0.861 0.615 0.989 1.137 1.650 1.491 1.248 +0 1.637 -1.729 -1.164 0.583 -0.943 0.927 -1.060 -0.584 2.173 1.142 -0.902 0.906 0.000 1.464 -0.509 0.600 2.548 1.332 -1.326 1.390 0.000 0.869 0.829 1.001 0.779 1.325 0.978 0.983 +0 0.432 0.215 0.388 0.992 -1.255 1.053 0.114 -0.370 0.000 1.460 -0.002 0.873 2.215 0.598 0.305 -1.737 2.548 1.178 -1.404 -1.404 0.000 1.585 1.092 0.991 0.962 0.725 0.973 0.864 +1 1.223 -2.041 -1.470 0.378 0.467 0.516 0.542 0.929 0.000 0.667 0.626 0.181 1.107 1.132 -0.804 0.166 0.000 1.132 -0.854 -1.009 0.000 1.016 0.900 0.988 1.644 0.805 1.057 1.238 +0 0.413 1.608 1.617 1.680 0.990 0.856 -1.138 -0.474 0.000 0.314 -0.305 0.497 0.000 0.746 0.419 -1.623 2.548 0.866 -0.175 -0.799 3.102 0.969 1.057 0.987 0.850 0.466 0.643 0.855 +1 1.769 0.418 -1.228 2.167 -1.310 1.623 -1.297 -0.016 0.000 0.449 1.276 -1.631 0.000 1.589 -0.567 1.439 2.548 1.215 -0.754 0.979 0.000 1.470 1.082 0.959 1.162 1.089 1.110 1.009 +1 0.381 -0.934 -0.282 0.599 1.164 0.932 0.765 -0.446 0.000 0.457 -0.162 -0.589 0.000 1.129 0.656 1.492 1.274 1.428 0.361 1.075 3.102 0.699 1.066 0.990 0.678 0.382 0.814 0.706 +0 1.013 0.645 -0.404 0.556 1.484 0.955 -0.003 1.294 2.173 1.126 -0.633 -0.576 1.107 0.551 -0.725 0.314 0.000 0.655 0.461 -1.087 0.000 0.794 0.856 1.031 0.940 1.597 0.951 0.767 +1 0.767 1.091 1.651 0.969 0.502 0.673 2.045 -1.729 0.000 1.523 -0.148 0.124 2.215 0.791 -1.558 1.286 0.000 1.634 0.010 -0.577 3.102 4.271 2.535 1.027 1.158 0.850 1.764 1.414 +0 0.385 1.122 0.766 2.140 0.351 0.635 1.000 1.399 2.173 0.931 1.848 -1.119 0.000 1.256 0.922 -1.006 0.000 0.482 0.718 -0.559 3.102 0.780 1.035 0.988 0.740 0.576 0.797 0.905 +1 0.932 -0.963 -0.625 0.936 0.623 1.200 -0.913 0.441 1.087 1.287 -0.418 -1.459 0.000 0.815 -1.228 -1.692 0.000 0.697 0.179 -0.812 1.551 0.816 0.717 1.168 0.855 1.050 0.998 0.873 +0 0.847 -0.443 0.433 0.864 -1.357 0.468 -1.590 1.458 0.000 0.549 1.268 -0.121 2.215 0.633 -1.122 -1.097 0.000 0.630 -0.249 -0.960 3.102 0.751 0.595 1.184 0.957 0.597 0.857 0.754 +0 0.675 -1.974 0.779 2.568 0.053 0.632 -0.248 -1.670 2.173 0.537 1.365 -0.663 0.000 0.662 -1.191 -1.728 2.548 0.913 0.806 -1.489 0.000 0.650 0.884 1.110 0.998 0.449 1.124 1.526 +0 0.677 -1.548 -1.146 0.709 0.861 0.491 -1.646 -0.412 2.173 0.932 -0.740 0.888 2.215 0.496 0.743 -1.152 0.000 0.565 -0.695 1.679 0.000 0.855 0.837 0.987 0.679 1.027 0.696 0.612 +0 1.210 -0.953 0.543 0.969 -0.087 0.997 -0.552 0.820 2.173 1.342 -1.199 -1.598 2.215 1.502 -0.674 -0.994 0.000 1.071 -1.103 -0.304 0.000 0.914 1.032 0.980 0.778 1.514 1.053 0.970 +1 1.176 -0.064 0.093 0.749 -1.443 0.802 -1.073 -1.533 0.000 0.679 -0.115 -1.672 2.215 0.568 1.043 -0.156 2.548 0.394 2.007 -0.025 0.000 1.095 0.828 1.278 0.754 0.785 0.791 0.753 +0 0.873 0.939 -0.974 0.887 -1.246 0.467 0.859 -0.483 2.173 1.190 0.556 0.843 0.000 0.403 0.314 0.024 2.548 1.132 -0.759 1.474 0.000 1.473 0.908 0.981 0.681 0.278 0.751 0.762 +1 1.297 1.167 -0.085 1.130 -0.562 1.288 0.345 1.358 2.173 0.605 1.197 -0.947 0.000 0.900 1.041 0.567 0.000 0.442 0.747 -0.548 3.102 0.847 0.930 0.982 0.485 0.820 1.081 0.872 +0 0.541 -0.261 0.984 0.284 1.303 0.853 0.475 -0.641 2.173 0.677 1.057 1.026 0.000 0.738 1.215 0.765 2.548 0.566 -0.147 -1.361 0.000 0.867 0.992 0.978 1.256 1.040 1.114 0.956 +0 1.096 0.912 -0.939 1.643 -0.367 1.055 -0.577 1.088 0.000 0.657 0.714 -1.591 2.215 0.712 0.835 0.773 0.000 0.792 -0.991 -0.582 3.102 0.956 1.008 0.985 0.922 0.891 0.763 0.928 +0 1.075 -2.015 -0.381 1.527 -0.759 0.657 0.589 1.185 1.087 0.719 -0.626 0.872 0.000 0.317 -0.341 1.554 0.000 0.432 -1.313 0.824 3.102 0.434 0.603 0.991 0.657 0.762 1.136 0.905 +1 2.221 -2.021 1.376 0.428 -0.882 0.648 -0.493 -0.630 2.173 0.818 -2.141 -0.095 0.000 0.764 -1.251 0.628 2.548 0.807 -1.540 -1.461 0.000 1.016 1.087 1.209 0.816 0.888 0.952 0.855 +0 1.203 -1.061 0.774 0.600 1.731 0.379 -0.515 0.369 1.087 0.510 0.993 1.043 0.000 1.041 -0.054 -0.907 2.548 1.134 1.021 -1.052 0.000 0.946 0.940 0.985 0.897 0.737 0.698 0.784 +1 1.489 -1.282 -0.121 0.714 -0.748 0.883 -0.242 0.822 2.173 0.622 -1.083 -1.577 2.215 1.034 -0.843 1.213 0.000 0.723 -0.774 -1.156 0.000 0.806 0.840 0.995 0.834 1.029 0.877 0.756 +1 0.664 -1.265 1.159 0.892 -1.532 1.205 -0.434 -1.470 2.173 2.423 0.616 0.297 0.000 1.348 1.300 1.308 2.548 1.169 0.530 -0.512 0.000 0.725 0.887 0.977 1.259 1.950 1.780 1.546 +0 1.127 -0.113 0.938 0.363 0.945 0.987 -0.891 -0.693 2.173 0.705 -0.734 1.579 0.000 0.373 -1.144 -0.395 0.000 1.306 -0.720 0.493 3.102 0.898 0.921 0.985 0.813 1.054 0.996 0.826 +0 0.876 -0.480 -1.407 0.419 1.075 1.880 1.646 1.672 0.000 2.026 -0.998 -0.076 0.000 0.758 1.140 0.742 2.548 1.493 -0.429 0.331 3.102 1.013 0.833 0.992 0.763 0.867 1.056 0.976 +0 0.428 -0.097 -1.504 0.850 0.201 0.977 1.417 0.578 2.173 0.434 0.007 -0.959 0.000 1.198 -1.237 -0.528 2.548 0.389 -0.396 1.533 0.000 0.438 1.067 0.990 1.160 2.793 1.314 0.991 +1 0.529 -1.463 -0.067 1.024 1.616 0.476 -2.255 0.566 0.000 1.429 -0.271 -0.994 2.215 0.742 -0.243 0.199 2.548 0.942 2.407 1.356 0.000 0.711 0.824 1.018 1.083 0.963 0.995 0.854 +1 1.532 0.109 -0.312 0.712 1.355 0.810 -1.470 1.470 2.173 1.141 -1.268 -0.592 0.000 0.605 0.119 0.272 0.000 0.545 0.284 1.174 3.102 1.040 0.859 1.444 1.431 0.757 0.915 0.879 +1 0.879 -0.125 1.452 1.540 -0.019 0.712 1.124 -1.717 0.000 0.997 -1.006 -0.860 0.000 1.370 0.200 -0.243 2.548 0.968 0.020 0.989 0.000 0.911 0.990 1.563 0.820 0.502 0.646 0.693 +1 0.762 0.161 -0.436 0.600 0.846 0.931 -1.238 -0.524 0.000 1.280 -0.502 1.093 2.215 1.407 -0.050 -1.661 0.000 1.132 -1.167 0.179 0.000 0.936 1.173 0.990 0.825 1.014 1.037 0.856 +1 1.280 0.360 -0.905 1.982 -1.294 1.508 1.474 0.401 0.000 1.123 -0.007 1.260 2.215 0.488 1.204 -1.315 2.548 0.730 0.442 0.414 0.000 0.798 0.918 0.988 1.170 0.804 0.994 1.234 +1 1.933 -0.432 0.170 0.753 -0.480 1.187 -0.420 -1.379 2.173 1.389 -0.477 1.435 1.107 0.724 -1.118 -1.502 0.000 1.249 0.343 0.194 0.000 1.336 1.101 0.984 1.380 1.077 1.149 0.993 +1 1.605 -1.031 0.505 0.629 0.458 0.639 0.150 -1.654 1.087 0.678 -0.218 -1.014 0.000 1.116 -0.682 -0.883 2.548 1.120 0.104 0.939 0.000 1.132 0.880 0.979 1.108 0.833 0.888 0.795 +1 1.527 -2.084 -1.126 0.474 1.595 0.494 -1.911 0.207 0.000 0.757 -0.308 0.442 0.000 1.006 -1.275 1.465 0.000 0.710 -0.449 -0.343 3.102 1.153 0.770 0.987 0.664 0.435 0.556 0.624 +1 1.382 0.293 -1.403 0.937 0.834 0.657 -0.098 0.246 2.173 0.923 0.761 0.719 2.215 0.286 -0.683 -0.843 0.000 0.519 1.213 -0.563 0.000 0.579 0.717 1.422 1.030 0.704 0.789 0.668 +0 1.161 -0.071 -0.808 2.102 -1.214 1.242 -1.930 0.629 0.000 0.933 0.192 1.532 0.000 1.547 0.553 -0.253 2.548 1.168 1.205 0.673 3.102 1.514 1.051 0.996 1.018 0.881 0.944 0.991 +1 1.036 -0.229 1.722 0.800 -0.127 1.059 -0.066 1.238 2.173 1.570 -0.265 -0.567 0.000 0.600 0.606 0.671 0.000 0.596 -0.523 0.457 3.102 1.521 0.926 1.256 0.953 0.595 0.928 0.817 +1 0.902 -0.805 -1.174 0.160 -0.660 0.899 -0.505 -1.556 2.173 0.559 0.863 -0.295 0.000 1.310 0.389 0.203 2.548 1.771 -1.283 -1.520 0.000 0.951 0.918 0.989 0.716 1.503 1.260 1.064 +1 1.028 -0.108 -0.566 0.278 -0.387 1.293 0.840 -0.862 2.173 1.516 0.291 0.369 0.000 2.391 0.338 1.631 1.274 0.714 1.197 1.039 0.000 1.063 1.599 0.989 1.324 1.788 1.536 1.276 +1 0.503 2.280 0.135 2.093 0.688 0.440 1.226 -0.608 1.087 0.302 2.283 -1.362 0.000 0.842 1.077 1.579 0.000 0.947 -0.294 -1.358 3.102 0.933 0.775 1.000 0.892 0.738 0.901 0.787 +1 1.001 -0.717 -1.244 0.461 -0.004 0.838 0.150 0.451 2.173 0.490 -1.210 1.469 0.000 0.849 0.017 -1.364 0.000 0.588 0.198 -1.702 0.000 0.865 1.085 0.988 0.920 0.809 0.859 0.762 +0 0.421 1.109 -1.720 0.612 -0.693 0.525 -1.433 1.046 2.173 0.525 -1.608 -0.742 0.000 0.826 1.061 1.029 1.274 1.137 -0.689 -0.165 0.000 0.658 0.834 0.994 0.799 1.402 0.985 0.848 +1 0.696 -0.207 1.712 0.642 1.307 0.897 -2.334 -0.200 0.000 1.147 0.290 0.983 2.215 1.291 0.130 -0.798 0.000 1.611 0.731 -1.482 3.102 0.821 0.881 0.982 0.719 1.041 0.699 0.640 +0 0.352 0.510 -1.026 1.753 1.285 0.732 0.148 -0.141 2.173 0.746 -0.080 -0.844 2.215 1.124 0.916 1.092 0.000 0.471 1.064 -0.496 0.000 0.802 0.934 0.984 0.974 0.656 0.855 0.751 +1 0.602 -1.540 0.219 0.247 -0.972 1.735 -1.302 0.422 2.173 1.171 -0.820 -1.633 0.000 0.957 -1.241 -1.008 0.000 0.550 -0.070 1.415 3.102 0.966 0.689 0.990 1.008 1.053 1.114 0.913 +1 0.793 1.167 -0.754 0.871 0.495 0.698 -0.540 -1.732 0.000 1.072 1.082 0.178 0.000 1.384 0.930 1.518 2.548 1.080 1.298 -0.241 0.000 0.604 0.993 1.040 0.862 0.761 0.840 0.732 +1 2.264 -0.102 -0.530 1.173 -1.559 0.746 -0.195 0.654 2.173 0.534 1.295 0.905 2.215 0.550 -0.236 1.638 0.000 0.600 1.133 -1.001 0.000 0.720 0.863 1.805 1.338 0.809 1.065 0.857 +1 1.971 -1.403 -0.427 0.870 -0.710 0.744 -1.138 1.454 0.000 0.651 -0.848 0.885 2.215 0.593 -1.311 1.705 2.548 0.449 0.124 1.026 0.000 0.698 0.536 0.995 0.821 0.483 0.735 0.739 +0 0.407 1.071 -0.437 2.735 -0.183 1.358 -0.042 -1.633 1.087 0.667 -0.888 1.308 0.000 0.777 0.553 1.337 0.000 1.396 -0.761 0.252 3.102 0.923 0.942 0.988 2.853 1.587 2.253 1.873 +1 0.792 -0.440 1.480 0.889 0.278 0.665 1.189 -0.391 1.087 0.526 -0.718 -1.473 1.107 0.689 -1.261 0.672 0.000 0.818 1.367 -1.182 0.000 1.969 1.233 1.027 1.119 1.218 1.020 0.901 +1 0.403 -1.974 -0.535 0.365 -0.793 1.021 -1.137 1.394 2.173 0.924 -0.156 -1.062 2.215 1.332 -0.932 -0.065 0.000 0.481 0.395 0.793 0.000 0.952 0.986 0.978 0.980 1.359 0.951 0.802 +1 0.708 -0.678 -0.475 1.088 1.546 0.677 -1.424 1.716 2.173 0.755 -0.036 0.576 2.215 1.093 -0.202 -0.044 0.000 1.324 -0.121 -1.126 0.000 1.099 0.909 1.178 0.787 1.204 0.885 0.779 +1 0.716 0.384 -1.463 0.910 -0.178 0.855 -0.307 1.305 2.173 0.447 0.630 -0.456 0.000 0.746 -0.505 -0.760 0.000 1.146 0.573 0.736 1.551 0.614 1.029 1.025 0.704 0.749 0.725 0.676 +1 0.870 -0.355 -1.737 1.753 -1.128 0.753 2.075 1.196 0.000 1.360 -1.039 0.427 2.215 0.749 0.914 0.060 0.000 1.135 -0.704 -0.220 0.000 0.858 0.872 0.987 0.540 0.845 0.874 0.735 +0 0.515 0.358 1.191 0.697 0.412 0.958 0.213 0.583 1.087 1.166 1.676 -0.754 0.000 0.736 0.564 -0.968 0.000 1.227 1.194 1.731 1.551 0.870 0.840 0.991 0.791 1.234 1.053 1.098 +1 0.395 -1.107 1.010 1.202 -0.101 0.770 -1.068 -0.687 0.000 0.919 0.665 1.366 2.215 1.340 0.167 -1.538 2.548 1.043 -0.229 0.228 0.000 1.153 1.219 0.987 0.967 0.661 1.007 0.877 +1 2.351 0.281 -0.077 0.436 0.494 0.883 0.257 1.178 1.087 0.806 1.256 -0.558 0.000 1.343 0.389 -1.677 0.000 0.703 0.977 0.817 0.000 0.929 0.967 0.984 1.178 1.026 0.950 0.870 +1 0.934 0.895 1.092 0.309 -1.536 0.826 0.822 -0.252 0.000 1.079 0.044 1.121 2.215 0.965 0.424 -1.370 2.548 0.522 1.745 -0.746 0.000 0.803 0.881 0.985 0.963 0.878 0.883 0.824 +1 0.505 -2.290 -1.683 1.270 0.423 0.606 -2.375 -0.850 0.000 0.409 -1.894 -1.080 0.000 1.359 -0.211 1.112 2.548 0.767 -1.975 0.509 0.000 0.980 0.672 1.050 1.341 0.791 0.931 0.870 +1 0.620 1.533 1.443 0.961 -0.090 1.542 -1.041 -1.611 0.000 0.903 1.421 1.006 0.000 2.225 0.541 -0.440 2.548 1.512 0.329 0.366 3.102 0.822 0.877 1.050 1.004 0.941 0.932 0.808 +1 1.229 0.345 -0.481 0.613 -1.691 0.907 0.630 1.118 2.173 1.004 -0.356 -1.131 2.215 1.712 0.813 0.048 0.000 1.573 1.698 1.297 0.000 1.967 1.493 1.067 0.746 1.459 1.346 1.111 +1 1.748 0.020 -0.467 1.432 -1.328 1.175 -0.281 1.065 2.173 0.670 -1.150 1.641 0.000 0.448 -0.275 0.625 0.000 0.508 -0.744 -1.009 3.102 0.768 0.769 1.533 0.745 0.822 0.991 0.846 +0 0.647 0.768 0.969 1.633 0.469 1.752 0.531 0.690 0.000 0.802 1.808 0.910 0.000 2.705 -0.469 -1.247 2.548 3.334 -0.735 -0.848 3.102 1.859 3.034 0.990 1.562 0.914 2.357 1.825 +0 0.404 -0.198 -0.839 1.507 1.275 0.533 0.024 -0.455 2.173 0.778 -0.837 0.368 2.215 0.833 -0.766 1.730 0.000 0.754 -1.146 -0.490 0.000 0.829 0.807 1.021 0.823 0.773 0.708 0.666 +0 3.288 1.124 -1.203 0.257 -0.304 1.738 0.389 0.381 1.087 0.492 -0.131 0.039 0.000 0.823 -0.271 1.211 0.000 1.106 0.224 -1.722 3.102 0.853 0.985 0.988 0.795 1.394 1.392 1.156 +1 0.647 -0.415 -1.729 1.521 -0.576 0.785 -0.185 0.496 2.173 0.807 -1.535 1.667 2.215 0.827 0.286 -0.778 0.000 0.737 -1.555 0.550 0.000 1.391 1.093 1.185 1.014 1.348 0.938 0.861 +1 0.435 0.675 0.884 0.872 -0.188 0.499 0.407 -0.129 0.000 0.475 0.901 1.713 2.215 1.140 0.902 -1.383 2.548 0.739 1.260 1.045 0.000 0.970 0.894 0.990 0.777 0.270 0.686 0.665 +1 0.543 0.339 -0.448 1.041 -1.225 0.406 -0.494 -1.530 0.000 0.379 0.720 0.664 2.215 0.748 -0.776 0.213 0.000 0.917 -1.017 0.686 3.102 1.006 0.776 0.987 0.763 0.610 0.628 0.590 +1 0.645 1.238 1.271 0.650 0.202 0.899 0.922 -0.624 2.173 0.905 0.352 0.974 0.000 1.798 0.353 1.645 0.000 1.388 -0.374 -0.347 3.102 1.114 1.282 0.992 0.911 0.929 1.080 0.894 +0 1.504 1.813 1.651 0.977 -0.922 0.675 0.369 -0.324 0.000 1.203 1.246 0.318 0.000 0.737 0.991 -1.451 0.000 0.820 0.218 0.782 1.551 1.173 0.849 1.232 0.885 0.194 0.718 0.760 +1 0.449 -0.835 -0.988 1.179 -0.174 1.605 -0.745 1.189 0.000 1.212 1.049 -0.438 2.215 1.018 0.565 -0.787 0.000 0.735 -1.206 0.892 0.000 0.872 0.894 0.994 1.626 0.686 1.592 1.229 +1 1.278 -1.050 -1.423 1.866 -1.037 0.608 0.045 0.198 2.173 1.119 -0.433 1.031 2.215 0.432 -0.169 -0.696 0.000 0.811 0.497 0.748 0.000 0.682 0.720 0.996 1.474 0.880 1.213 1.000 +1 1.317 0.291 -0.656 1.144 -0.130 0.850 -0.117 1.182 1.087 0.852 1.104 1.554 2.215 0.381 1.370 -0.210 0.000 0.464 1.691 0.321 0.000 0.459 0.844 0.987 1.079 0.933 1.012 0.789 +1 1.113 1.527 1.484 0.505 -0.585 0.695 -0.094 -0.666 2.173 0.390 -0.226 -0.082 0.000 1.022 -0.104 0.977 2.548 0.822 0.735 0.890 0.000 0.713 0.766 0.995 0.952 1.046 0.901 0.744 +1 0.583 0.917 0.940 1.274 -1.702 1.024 -0.073 0.110 2.173 0.718 -0.368 -0.791 2.215 0.797 0.362 0.858 0.000 1.026 0.265 -1.297 0.000 0.931 1.020 0.990 0.846 0.936 0.867 0.754 +0 0.873 -1.649 0.437 0.731 1.410 1.224 -0.545 -0.187 1.087 0.536 1.192 -1.452 0.000 0.987 0.717 -0.770 0.000 0.484 -1.980 1.100 0.000 0.689 0.855 0.992 1.096 0.801 0.936 0.981 +1 0.783 -0.201 0.450 0.614 0.187 1.944 -0.076 -1.253 2.173 1.710 -0.816 0.652 1.107 1.110 -2.328 -0.103 0.000 1.087 -1.467 1.293 0.000 0.898 1.439 0.982 1.587 2.854 1.840 1.648 +0 1.517 -0.242 -0.299 0.723 -0.159 0.855 -0.660 1.607 2.173 0.875 -0.081 0.852 2.215 0.395 -1.363 -1.150 0.000 0.454 -0.804 -1.513 0.000 0.196 0.674 0.986 1.220 0.885 0.944 0.729 +0 1.311 0.108 -1.666 0.365 -0.507 0.821 -0.182 -0.349 2.173 1.114 -0.454 0.999 2.215 0.759 2.213 -1.587 0.000 1.009 -1.002 0.467 0.000 0.846 1.506 0.986 0.854 1.334 1.317 1.106 +1 0.619 1.689 -1.406 1.007 -0.411 0.710 1.836 0.233 0.000 0.985 -0.012 -1.431 2.215 1.787 0.102 1.299 0.000 1.612 1.160 0.830 0.000 0.919 0.851 0.985 1.467 0.686 0.993 0.986 +1 0.763 0.739 0.345 0.712 1.100 0.965 -0.157 -1.258 2.173 0.581 0.568 -0.852 0.000 0.969 0.235 0.858 0.000 1.094 -1.311 -1.039 3.102 0.887 0.983 0.987 1.040 0.863 0.863 0.775 +0 1.394 -1.038 -1.604 1.092 1.128 0.676 -0.637 0.517 2.173 0.368 -1.611 -1.475 0.000 1.282 0.085 -0.473 2.548 0.642 -1.286 -0.273 0.000 0.560 0.839 1.074 0.953 1.003 0.941 0.777 +0 0.905 0.948 0.036 1.082 -1.163 0.841 0.468 1.122 1.087 0.691 -0.403 -0.040 0.000 0.565 0.362 0.596 0.000 0.700 1.814 1.603 0.000 0.890 0.962 1.209 1.059 1.130 0.833 0.749 +0 0.775 0.464 1.354 0.991 0.188 1.498 0.949 0.419 2.173 2.314 0.365 -1.302 0.000 0.845 0.306 -1.571 2.548 0.818 -0.172 -0.343 0.000 1.463 0.865 1.053 0.858 1.437 1.347 1.089 +0 0.526 -0.687 1.554 2.766 -0.764 0.881 1.487 0.927 0.000 0.987 -0.148 0.712 2.215 0.849 0.786 -1.491 2.548 0.877 -2.044 1.140 0.000 1.811 1.231 1.452 1.360 1.029 1.061 1.363 +1 0.991 0.004 0.705 1.195 -0.739 0.639 -0.749 1.691 2.173 0.743 -2.249 1.451 0.000 1.105 0.962 0.385 0.000 0.877 0.302 -1.044 3.102 3.797 2.172 1.453 1.040 0.680 1.352 1.172 +0 0.363 -1.732 -0.789 2.071 1.102 0.662 -1.157 0.205 0.000 0.644 -1.213 0.958 2.215 0.957 -0.371 -0.962 0.000 0.859 1.400 -0.255 0.000 1.363 1.012 1.191 1.051 0.642 0.720 0.817 +0 0.529 -0.252 -0.961 1.650 -0.564 0.924 -1.192 0.098 2.173 1.720 -0.150 1.353 0.000 0.968 1.435 1.724 0.000 1.087 0.326 -0.767 3.102 0.968 0.895 0.981 1.609 1.198 1.387 1.186 +1 1.227 0.492 0.443 1.480 -1.238 1.484 0.400 -1.114 2.173 0.804 2.285 0.043 0.000 0.369 0.330 0.814 2.548 1.282 -0.980 1.703 0.000 0.624 0.651 1.863 1.317 0.909 1.106 1.059 +1 0.604 -1.295 0.069 0.793 0.783 0.758 -0.485 -1.104 2.173 0.747 -0.168 0.700 2.215 1.424 0.157 -0.467 0.000 0.375 1.645 -1.681 0.000 1.096 1.011 0.986 1.305 1.120 1.051 1.202 +0 1.566 -0.173 0.736 1.850 -1.315 0.733 0.444 -1.518 2.173 1.041 0.453 0.138 0.000 0.877 -1.071 0.416 0.000 1.936 1.121 -1.301 0.000 1.063 0.904 2.268 1.357 0.888 0.998 0.979 +0 0.397 -0.803 0.027 1.017 -0.058 0.490 -0.044 1.621 0.000 0.676 -0.539 -1.050 2.215 1.301 1.268 -0.577 2.548 0.706 -0.213 0.803 0.000 0.887 0.936 0.987 2.764 1.204 1.754 1.438 +1 0.606 -1.310 1.567 0.917 -0.246 1.486 2.334 0.093 0.000 1.601 -0.851 -1.301 2.215 1.417 -0.446 1.714 1.274 1.524 -0.723 0.884 0.000 0.765 0.874 1.031 0.920 0.728 0.821 0.765 +0 1.559 -0.187 -1.727 0.333 1.738 0.647 -0.253 -0.359 1.087 1.119 0.421 0.512 0.000 0.977 -0.301 0.921 2.548 1.915 0.972 -0.799 0.000 1.273 1.059 0.984 0.767 0.906 0.774 0.776 +1 0.624 -0.479 0.230 1.307 -0.850 1.333 -0.620 -0.358 0.000 1.790 0.135 1.596 2.215 1.533 0.116 0.931 2.548 0.649 -0.872 1.013 0.000 0.961 1.431 1.034 1.245 0.992 1.318 1.092 +0 0.915 1.588 -1.124 1.047 -1.461 1.061 1.106 1.244 2.173 0.591 0.995 -0.688 0.000 0.991 1.064 0.223 1.274 1.342 2.022 -0.074 0.000 1.073 0.808 0.975 1.025 1.018 0.909 0.893 +0 0.368 1.543 0.216 1.509 -1.572 0.305 -0.777 1.080 2.173 0.627 -0.336 -1.081 2.215 0.478 0.754 -0.453 0.000 1.119 0.413 0.381 0.000 0.567 0.713 1.031 1.098 0.615 0.873 0.746 +0 1.269 -0.307 -1.573 0.426 0.500 0.770 -0.155 0.422 0.000 0.779 -1.450 -1.536 2.215 0.634 -1.304 0.235 0.000 0.949 -0.294 -0.613 3.102 0.883 0.769 0.987 0.776 0.739 0.773 0.726 +1 1.984 0.777 0.494 0.578 -0.956 0.469 1.264 1.164 2.173 0.553 1.389 -1.713 1.107 0.643 -1.235 -1.324 0.000 1.435 0.705 -1.168 0.000 0.762 0.995 1.431 0.950 0.394 0.721 0.794 +1 0.284 1.928 0.052 1.889 1.004 0.906 -2.170 -0.222 0.000 1.468 0.631 -0.971 2.215 2.292 0.130 0.430 0.000 0.993 -0.086 -1.440 3.102 4.024 2.385 0.996 1.299 0.614 1.978 1.840 +1 1.517 -0.218 -1.492 0.985 1.528 0.904 -0.639 0.343 2.173 0.665 1.220 0.307 0.000 0.414 -1.732 1.467 0.000 0.939 -0.335 -0.995 0.000 0.899 1.009 0.996 0.675 0.647 0.869 0.818 +0 2.636 0.295 -1.045 2.473 -0.724 1.982 -0.327 0.751 2.173 0.626 -0.162 0.125 0.000 0.770 -0.139 1.366 2.548 0.708 -1.922 1.675 0.000 1.405 0.983 1.012 2.683 0.821 1.703 1.481 +1 2.015 -0.460 0.973 1.126 0.680 0.796 -0.749 -1.239 2.173 1.262 -0.171 -0.298 2.215 0.888 1.153 -0.822 0.000 0.961 0.314 1.730 0.000 0.893 1.097 0.989 1.338 1.189 1.135 1.064 +0 0.791 0.797 0.013 0.266 -1.042 0.850 -1.778 0.691 0.000 0.597 2.123 -1.468 0.000 1.134 1.726 -0.248 0.000 1.332 0.934 -1.221 3.102 0.987 0.879 0.986 0.694 0.961 0.726 0.664 +0 0.803 2.166 0.242 0.871 1.314 0.741 1.405 -0.247 2.173 0.563 0.229 1.150 0.000 0.322 0.471 1.535 0.000 0.595 -1.646 -0.634 0.000 0.892 0.604 0.986 0.951 1.079 0.959 1.223 +1 0.585 -1.843 -0.910 1.232 0.606 1.188 -2.283 0.372 0.000 1.033 0.340 -1.432 1.107 0.444 -1.106 0.079 2.548 0.824 -1.012 -1.327 0.000 1.740 0.989 1.152 1.597 0.940 1.418 1.171 +0 1.398 0.743 -0.171 0.585 -0.429 0.568 -0.732 1.420 2.173 0.422 -1.919 -1.366 0.000 0.527 -1.652 0.132 0.000 1.077 0.459 1.389 3.102 0.708 1.082 0.984 1.450 0.572 0.984 1.165 +0 0.534 0.725 0.560 2.122 0.940 2.734 0.648 -0.848 2.173 1.059 0.826 -0.460 0.000 4.288 0.213 1.023 2.548 0.435 0.136 -1.249 0.000 0.655 0.788 0.991 1.708 4.332 2.398 1.795 +0 1.160 0.434 -1.459 1.680 1.470 1.069 1.285 -0.135 1.087 0.818 2.849 1.073 0.000 0.596 0.143 -1.021 0.000 0.562 0.095 0.862 0.000 0.633 0.928 0.989 0.818 0.561 1.052 0.814 +0 0.879 -0.837 -0.121 0.823 -0.525 1.227 -0.833 -1.071 0.000 1.174 1.004 0.787 0.000 1.610 0.220 0.692 2.548 1.789 -0.932 -1.623 0.000 1.124 0.822 0.992 0.953 0.537 1.057 0.960 +1 0.324 -0.596 0.920 1.207 -1.088 1.154 -0.055 1.276 2.173 0.967 1.614 0.190 0.000 0.644 0.821 -0.513 2.548 0.729 -0.005 -0.497 0.000 1.202 0.741 0.989 1.312 1.199 1.059 1.265 +0 1.546 -0.825 -0.568 2.154 -0.029 1.267 -0.241 1.358 0.000 1.672 0.028 -1.608 2.215 0.714 0.768 1.528 2.548 1.949 -0.346 0.243 0.000 1.431 1.132 1.182 1.821 0.605 1.315 1.237 +0 0.481 -1.166 -1.721 1.030 1.628 0.954 1.204 0.594 0.000 0.891 0.615 -1.120 2.215 0.734 1.173 -0.541 2.548 0.476 -0.122 0.081 0.000 0.885 0.819 0.976 2.095 0.515 1.792 1.841 +1 0.433 -0.259 1.216 0.952 0.120 0.886 1.038 -0.364 0.000 0.807 0.860 -0.943 0.000 1.632 1.090 1.035 2.548 1.040 -0.115 -1.604 3.102 0.907 1.008 0.991 0.785 0.993 0.963 0.824 +0 1.067 -1.079 0.736 0.948 1.289 0.942 -0.975 -1.187 0.000 2.344 -0.818 -0.591 0.000 1.712 0.088 0.674 2.548 2.121 -1.799 1.478 0.000 1.634 1.648 0.990 0.760 0.560 1.403 1.224 +1 0.369 0.660 1.732 0.098 -0.648 2.617 1.111 0.657 0.000 2.247 -0.542 -1.032 0.000 1.207 -1.331 1.595 1.274 2.166 -0.944 -0.633 3.102 1.625 1.095 0.838 0.654 1.138 0.939 0.792 +0 0.589 -0.595 0.733 1.041 0.104 0.833 -1.210 1.452 2.173 0.751 -1.220 -0.814 0.000 0.924 -0.510 -0.467 2.548 0.425 -1.480 1.725 0.000 0.588 0.801 0.986 0.745 1.140 0.922 0.825 +0 0.999 -1.420 0.534 1.497 0.946 0.598 -1.028 -0.054 0.000 0.760 -0.382 -1.221 2.215 0.615 -0.411 -0.659 0.000 1.450 0.184 -1.487 1.551 0.652 0.955 0.994 1.350 0.371 1.216 1.028 +0 0.531 -0.441 -0.710 2.335 0.111 2.794 0.285 -1.611 2.173 2.270 0.258 0.288 0.000 0.526 0.987 1.388 1.274 0.747 -0.531 -1.332 0.000 1.863 1.252 1.041 2.405 0.889 1.601 1.500 +1 0.754 -1.222 -0.374 0.476 -1.187 0.820 -0.084 0.025 0.000 1.207 -0.880 1.238 2.215 0.769 -0.809 -1.227 0.000 0.994 0.279 -1.723 3.102 0.899 0.851 0.990 1.125 0.794 0.931 0.816 +0 0.639 -0.527 1.259 0.868 0.950 0.595 -0.285 -1.492 2.173 1.137 1.394 -0.539 2.215 0.780 2.084 -0.248 0.000 0.680 1.990 0.314 0.000 0.392 0.628 0.986 0.826 1.492 1.032 0.990 +1 2.215 0.371 0.053 0.907 -0.404 1.580 0.454 1.648 0.000 0.498 -0.433 0.813 1.107 0.320 0.168 -0.892 0.000 0.579 0.777 -0.967 3.102 0.978 0.952 0.998 0.648 0.604 0.629 0.893 +0 0.645 0.501 -1.263 0.470 -0.245 1.073 1.419 -1.669 0.000 1.529 0.139 0.169 1.107 0.928 -0.721 0.683 0.000 0.947 -0.304 -1.067 3.102 2.875 1.727 0.984 1.111 1.014 1.355 1.131 +1 1.935 0.093 0.397 1.395 0.186 1.188 -0.360 -1.446 2.173 0.370 -1.038 -1.369 0.000 0.617 -2.203 -1.591 0.000 0.463 0.538 -0.421 0.000 0.661 0.669 0.977 0.538 0.739 1.098 0.874 +0 1.827 0.076 1.436 1.031 -0.468 1.050 0.140 0.054 0.000 0.560 -0.822 -0.593 0.000 0.398 -0.633 -0.107 2.548 0.604 -0.070 -1.111 3.102 1.226 0.823 1.881 0.960 0.317 0.625 0.729 +1 0.830 -1.064 -0.818 0.439 1.202 0.905 -1.727 1.367 0.000 1.443 -1.642 -0.299 2.215 0.949 -1.426 -1.509 0.000 1.170 -1.171 0.641 3.102 0.882 0.839 0.986 0.811 0.894 0.942 0.786 +0 0.660 -1.986 -1.308 1.060 0.162 1.050 -0.837 0.965 2.173 1.470 -1.975 -0.168 0.000 2.136 -0.449 1.586 2.548 3.257 -1.096 -0.812 0.000 1.878 2.170 1.124 1.406 1.043 1.692 1.366 +1 1.161 -0.814 1.692 2.167 -1.197 0.816 0.357 0.087 1.087 0.938 -0.816 0.828 2.215 0.894 -1.494 0.382 0.000 0.613 -0.641 -1.181 0.000 0.885 1.154 1.128 1.179 1.143 1.195 0.999 +1 0.577 1.015 -1.222 0.825 -0.058 1.370 1.053 -0.489 2.173 0.998 0.490 1.029 0.000 1.455 -1.684 1.472 0.000 1.267 1.072 1.499 0.000 0.842 0.673 0.984 0.828 1.003 0.987 0.860 +0 0.577 -0.263 -1.424 0.794 0.305 0.809 -1.081 1.041 2.173 1.392 0.387 -0.881 2.215 0.584 -0.307 1.352 0.000 0.878 0.700 0.073 0.000 0.874 0.962 0.984 0.756 1.999 1.031 0.854 +1 2.383 -0.322 -0.920 0.528 -0.697 1.099 1.574 1.009 2.173 0.769 -0.772 -0.922 0.000 1.151 -0.693 0.826 0.000 0.447 0.822 1.344 1.551 1.443 0.962 0.978 2.062 0.316 1.310 1.279 +0 0.739 1.142 1.284 0.945 0.128 0.527 0.726 -0.150 2.173 0.823 2.255 -1.497 0.000 1.057 0.780 0.843 2.548 0.946 -0.033 -0.668 0.000 0.859 0.851 0.999 0.668 0.727 0.591 0.574 +1 0.608 0.729 1.648 1.127 -0.947 0.937 1.175 1.427 2.173 0.506 -0.311 -0.507 1.107 0.420 2.247 -0.371 0.000 0.936 0.722 0.217 0.000 0.708 0.980 0.983 0.861 1.303 0.826 0.756 +1 0.687 0.285 -0.667 0.233 0.478 0.733 0.154 1.666 0.000 1.403 -0.573 1.026 2.215 1.546 -0.861 -0.568 1.274 1.071 -0.625 -1.294 0.000 0.885 1.067 0.987 1.266 1.576 1.197 1.047 +0 1.546 -1.760 -0.550 1.508 -0.105 1.498 -1.324 1.555 0.000 0.225 -1.402 1.027 0.000 0.547 -0.005 0.184 2.548 1.135 -0.254 1.528 0.000 1.062 0.783 0.989 0.490 0.252 0.525 0.638 +0 0.499 -0.452 -1.221 0.313 0.282 1.121 0.623 -0.851 2.173 1.631 -0.668 0.860 2.215 0.607 -0.407 -0.592 0.000 0.517 0.080 1.407 0.000 0.625 0.866 0.990 0.782 2.436 1.198 0.904 +0 1.145 0.971 0.265 0.506 -0.732 0.849 1.028 1.009 2.173 1.129 -0.613 -1.529 2.215 0.776 -0.653 -0.383 0.000 0.796 -1.168 -0.643 0.000 0.364 0.798 0.990 0.882 1.730 1.115 0.991 +0 2.109 0.829 -0.232 0.609 1.696 1.414 0.064 0.022 0.000 1.270 0.706 -1.580 0.000 1.581 -0.256 1.576 2.548 1.337 1.008 1.549 3.102 0.861 1.270 1.549 1.419 0.908 1.047 1.024 +0 1.140 0.551 -0.655 1.880 -1.215 0.953 0.253 -0.513 0.000 1.284 -2.202 1.102 0.000 1.912 -0.345 0.436 2.548 1.244 0.269 1.558 3.102 1.634 1.149 0.988 1.573 1.084 1.084 1.014 +0 0.957 0.902 -1.418 0.476 1.723 0.937 -2.683 0.256 0.000 1.366 -0.873 1.740 2.215 1.197 1.489 -0.350 2.548 0.675 -1.540 0.429 0.000 0.570 1.805 0.987 1.074 2.571 2.666 2.088 +1 0.285 0.871 0.008 1.298 0.277 0.832 -1.822 -1.554 0.000 0.925 -1.033 -1.404 0.000 1.057 -0.677 0.980 2.548 1.038 -0.476 -0.308 3.102 0.784 1.025 0.983 2.140 0.737 1.576 2.339 +1 1.974 -0.009 -0.801 0.329 -1.380 0.860 -0.239 0.808 0.000 0.663 0.801 0.883 0.000 0.887 -1.066 -1.197 0.000 0.813 0.359 -1.300 3.102 0.902 0.884 0.988 0.796 0.587 0.587 0.756 +1 0.643 -0.689 -0.362 2.068 -0.793 0.317 0.398 1.269 0.000 0.937 -1.152 0.801 2.215 0.895 1.312 0.272 0.000 0.907 -0.660 1.212 0.000 0.913 0.874 0.987 1.169 0.754 0.901 1.157 +1 0.990 1.161 0.151 0.918 1.508 1.090 0.764 1.599 2.173 0.771 0.372 -0.533 2.215 0.668 1.667 1.614 0.000 1.432 1.915 -0.112 0.000 1.105 1.011 1.242 0.981 1.293 0.991 0.864 +0 1.774 0.690 -1.043 0.504 -0.332 0.560 -1.958 0.803 0.000 1.170 0.962 -1.685 0.000 0.940 -1.038 -0.531 2.548 2.253 -0.039 0.351 3.102 1.423 1.417 0.989 1.206 1.018 1.131 1.087 +1 0.657 1.143 1.170 0.666 -1.016 0.937 1.058 0.130 0.000 1.357 1.256 -1.206 2.215 0.419 1.559 0.399 0.000 0.739 0.153 1.576 3.102 0.719 0.788 0.990 0.805 0.752 0.824 0.721 +1 0.777 0.438 0.259 0.587 -1.717 0.946 -0.706 -1.299 2.173 1.515 -0.181 0.647 2.215 0.582 -1.415 -0.684 0.000 0.546 -0.314 -0.692 0.000 0.388 1.015 0.983 0.925 1.794 0.943 0.791 +0 1.806 1.848 -0.973 0.721 -1.389 1.046 -0.340 0.813 2.173 0.397 -0.501 0.088 0.000 0.451 1.388 0.438 1.274 0.366 2.068 0.718 0.000 1.089 1.117 0.973 0.766 0.964 1.603 1.271 +0 0.657 -0.367 0.936 0.993 -0.222 0.812 0.583 0.071 1.087 1.121 -1.529 1.740 0.000 1.218 -0.617 -1.519 0.000 0.457 2.474 -1.053 0.000 0.906 0.856 0.989 0.858 0.714 1.161 0.955 +1 1.031 -0.302 -1.196 0.198 0.173 0.794 -0.337 1.579 0.000 1.047 0.890 0.097 2.215 0.582 -0.634 0.666 0.000 0.516 1.035 -0.906 3.102 0.923 0.844 0.987 0.903 0.532 0.837 0.742 +1 0.845 -0.561 1.269 2.064 -1.637 1.052 0.221 0.198 0.000 1.569 -0.774 -0.650 1.107 0.530 -1.475 0.760 0.000 0.949 -0.739 0.418 3.102 0.471 0.988 0.996 0.851 0.906 0.941 0.787 +0 0.802 -0.450 -0.275 0.579 -0.531 1.010 -1.537 1.483 0.000 0.969 -1.947 0.020 0.000 1.048 -0.602 0.493 1.274 1.649 -1.100 -1.629 0.000 0.690 1.132 0.984 0.503 0.679 0.765 0.768 +0 1.493 0.571 0.319 0.585 -0.417 0.634 1.473 -1.575 1.087 0.718 2.164 -1.040 0.000 0.581 1.629 1.425 0.000 1.018 0.546 0.666 3.102 0.818 0.899 0.983 1.031 0.848 0.718 0.742 +0 1.021 -1.082 -1.035 0.562 -1.476 1.414 -0.256 -1.709 0.000 1.786 -1.686 -0.008 0.000 1.735 -0.087 0.085 1.274 2.224 -2.212 -1.541 0.000 0.857 0.774 0.992 1.051 0.878 0.871 0.731 +1 0.578 0.163 -0.534 1.293 0.570 0.881 1.792 -0.549 0.000 1.435 -1.246 -1.540 1.107 0.622 -1.453 -0.113 0.000 1.875 -0.910 1.175 1.551 0.426 0.870 1.005 0.988 0.959 1.036 0.850 +1 0.621 0.102 0.586 0.716 -0.506 0.598 0.753 0.296 0.000 0.913 1.240 -1.672 2.215 0.778 0.028 -0.334 0.000 1.417 0.077 1.672 3.102 0.803 0.965 0.987 1.208 0.657 0.844 0.813 +0 1.440 0.186 -0.506 0.837 1.305 0.470 0.424 -1.630 0.000 1.173 0.102 0.129 2.215 1.036 0.414 1.192 0.000 0.781 2.451 1.501 0.000 0.706 1.097 1.518 0.804 0.736 0.733 0.735 +0 3.021 0.011 -1.714 2.751 -1.559 1.541 -1.833 0.142 0.000 3.094 -0.046 0.351 2.215 1.617 -0.485 -1.203 1.274 0.819 -0.782 1.141 0.000 1.022 0.902 0.998 2.874 2.414 1.952 1.463 +0 1.388 -0.457 1.176 1.108 1.724 0.875 0.918 0.362 2.173 0.629 1.295 -0.486 0.000 0.839 -0.709 -1.047 2.548 0.853 -0.291 0.177 0.000 1.049 0.989 0.984 1.563 1.446 1.127 1.036 +1 0.633 -0.776 -1.364 0.315 0.241 0.829 0.163 1.377 2.173 0.345 -0.389 0.854 0.000 1.438 0.878 -0.480 2.548 0.476 -1.467 -0.117 0.000 0.561 1.072 0.989 0.754 1.464 0.892 0.749 +1 0.903 0.326 0.049 0.403 -1.484 0.611 -0.417 -1.443 1.087 0.917 -1.658 1.005 0.000 0.732 1.469 -0.915 0.000 0.876 0.067 -0.143 0.000 0.936 1.025 0.988 0.573 0.781 0.661 0.620 +1 0.630 1.030 -0.339 0.917 0.750 0.648 0.887 0.590 0.000 0.910 0.250 -1.073 2.215 1.158 -0.581 -1.598 2.548 0.786 -0.532 0.660 0.000 0.924 1.118 0.988 1.358 0.709 0.996 0.925 +1 0.643 1.652 1.314 0.822 0.297 0.949 0.731 0.478 1.087 1.002 0.431 -1.018 2.215 0.723 -0.149 -1.346 0.000 0.466 -0.259 -0.217 0.000 0.547 0.933 0.987 0.930 1.416 0.821 0.722 +0 0.658 -0.020 -1.433 0.813 0.078 1.526 0.241 -0.966 0.000 1.173 0.134 0.854 1.107 0.269 -1.591 0.202 0.000 0.458 1.051 -0.866 3.102 1.678 0.985 0.991 0.800 0.769 0.992 0.815 +1 0.890 1.176 1.335 0.709 -1.082 1.161 0.864 -0.627 2.173 1.487 0.019 1.130 0.000 0.339 -0.132 0.043 0.000 0.688 0.629 0.643 3.102 0.908 0.601 0.987 0.957 0.863 0.945 0.825 +1 0.693 0.131 -0.041 0.912 1.508 0.814 -0.120 -1.213 0.000 0.569 -1.287 -1.297 0.000 1.389 -0.088 0.797 2.548 1.727 -0.497 0.009 3.102 0.930 1.126 1.084 0.721 0.828 0.928 0.814 +1 2.035 -0.933 0.827 0.557 -1.480 0.816 0.160 -1.175 2.173 0.774 -0.006 -0.110 2.215 0.411 0.680 -0.906 0.000 0.910 -1.093 -1.685 0.000 0.932 0.854 1.289 1.327 0.963 1.000 0.837 +1 0.792 -0.079 -1.313 0.521 -1.564 1.441 -0.161 0.316 2.173 0.711 -1.261 0.674 0.000 0.617 -1.004 1.514 2.548 0.850 -1.879 -1.694 0.000 0.994 0.648 0.986 1.424 1.184 1.036 1.123 +1 0.625 0.090 -1.275 0.819 -1.611 0.971 0.055 0.864 2.173 0.539 -0.242 -0.336 2.215 0.780 1.290 0.507 0.000 0.749 0.381 -0.520 0.000 0.794 0.950 0.987 0.743 0.955 0.832 0.850 +1 1.448 0.108 -0.536 0.756 -1.610 1.154 -1.456 -1.528 0.000 1.595 -2.176 1.144 0.000 1.290 1.185 -0.238 0.000 2.161 -0.678 0.066 3.102 0.884 0.621 1.193 1.055 0.887 0.945 0.877 +1 0.587 1.140 0.195 0.717 0.219 0.518 2.926 0.858 0.000 0.768 1.038 -0.686 2.215 0.803 0.395 1.680 1.274 0.702 1.983 -1.429 0.000 0.855 1.134 1.003 0.781 0.756 0.892 1.021 +1 0.408 -1.375 0.076 0.948 -0.243 0.522 -0.554 -0.568 0.000 0.644 0.614 -0.393 0.000 1.518 0.163 1.436 0.000 1.183 -1.180 1.071 3.102 0.801 0.878 0.986 0.910 0.301 0.749 0.693 +0 1.530 -1.100 -0.551 0.348 -0.695 0.569 -0.851 0.123 2.173 0.769 -0.141 1.499 0.000 1.758 0.126 1.029 0.000 0.570 -1.303 -1.424 3.102 0.775 0.862 0.990 0.773 0.631 0.763 0.941 +0 0.777 0.196 -0.328 1.498 -0.489 0.748 -0.445 1.671 0.000 0.709 -1.393 1.526 0.000 0.958 1.166 0.833 0.000 1.489 -0.134 -0.690 3.102 0.947 0.944 0.976 0.738 0.760 0.707 0.755 +1 0.626 -1.904 -1.432 1.225 0.771 0.526 -0.317 -0.356 2.173 0.461 -0.545 -1.666 0.000 0.836 0.300 -0.775 0.000 1.195 0.984 0.487 3.102 0.813 0.928 1.111 1.091 0.900 1.243 1.040 +1 0.440 -0.738 1.663 1.597 -0.026 1.622 -2.425 1.112 0.000 1.062 -0.972 -0.861 2.215 0.982 -2.080 -1.701 0.000 1.901 -0.662 -0.239 3.102 0.592 0.849 1.160 0.675 0.695 0.779 0.787 +0 1.840 -0.674 -0.705 1.012 -1.247 1.074 -0.300 0.621 0.000 0.949 0.099 1.238 2.215 0.843 0.146 -1.444 2.548 0.544 0.373 -0.156 0.000 0.933 0.951 0.988 0.785 0.631 0.857 0.945 +1 0.402 -1.780 -1.184 0.276 0.724 0.907 -1.129 -0.413 0.000 0.872 -0.434 -1.249 2.215 0.903 -0.906 0.230 0.000 1.141 0.020 0.912 0.000 0.898 1.071 0.981 0.642 0.763 0.837 0.716 +0 0.753 -0.153 -0.866 1.029 1.640 0.530 -2.260 -0.153 0.000 0.725 -0.955 -1.033 0.000 0.671 0.343 1.604 2.548 2.069 -0.270 0.504 3.102 1.312 1.345 0.991 0.951 0.820 1.039 0.909 +1 1.696 -1.657 -0.665 0.683 -1.214 0.944 -1.153 0.973 2.173 0.558 -0.517 -1.514 2.215 0.705 -2.316 0.732 0.000 0.918 -1.335 -0.329 0.000 0.909 0.864 0.990 1.366 0.904 0.984 0.847 +0 1.646 -0.031 -0.719 1.286 -0.239 1.056 -0.256 1.362 0.000 0.475 -0.389 -0.197 1.107 0.663 -0.130 -1.624 0.000 1.408 -0.159 0.545 0.000 0.988 0.834 0.978 0.556 0.596 0.572 0.630 +1 1.824 -0.111 -0.650 0.632 -0.048 0.733 -0.050 0.069 0.000 1.313 -0.163 1.231 2.215 0.747 -0.504 1.502 1.274 0.622 0.561 -1.723 0.000 0.902 1.046 0.990 0.902 0.327 0.890 0.783 +0 0.745 -0.859 -1.354 1.732 -0.704 0.606 -0.246 0.180 2.173 0.799 0.825 1.468 0.000 0.980 -0.888 1.257 2.548 0.572 -0.105 -0.999 0.000 0.831 0.960 0.987 0.963 0.869 0.801 0.806 +0 0.478 1.131 1.149 0.361 -0.886 0.730 1.409 -1.430 2.173 0.535 1.238 1.485 0.000 0.873 0.641 0.092 2.548 0.910 -0.269 -0.784 0.000 0.904 0.879 0.994 0.678 1.041 0.726 0.653 +1 1.052 -0.045 -0.633 1.212 0.147 0.932 0.467 1.185 2.173 0.994 -0.533 -0.941 0.000 0.728 -0.616 -1.656 2.548 1.024 -0.097 0.056 0.000 1.050 1.246 1.012 0.837 0.839 0.859 0.814 +0 0.896 -0.111 0.195 0.826 1.419 0.587 -0.522 -1.080 1.087 0.647 1.124 -0.247 0.000 1.332 0.240 -1.554 2.548 0.716 0.378 0.813 0.000 0.790 0.997 1.064 0.830 0.639 0.739 0.709 +1 1.775 -0.030 0.599 0.271 0.043 0.910 -0.155 -1.689 2.173 0.716 -0.084 -0.931 0.000 0.763 -0.336 0.191 0.000 1.058 0.870 -1.184 3.102 0.974 1.020 0.988 0.903 0.805 0.862 0.796 +1 0.626 -0.067 -1.208 0.278 -0.607 1.271 0.654 0.304 2.173 1.486 1.891 -1.193 0.000 0.867 1.916 1.361 0.000 1.507 -0.029 1.044 3.102 1.303 1.738 0.989 1.504 1.043 1.468 1.586 +1 1.210 0.137 0.766 0.401 1.559 1.041 0.338 -0.644 2.173 1.438 -0.696 1.601 0.000 0.619 0.526 -0.252 2.548 0.711 -0.284 -0.086 0.000 1.064 1.066 0.987 1.168 0.366 0.990 0.881 +1 0.497 -0.887 0.723 2.221 1.539 1.334 0.042 -0.065 0.000 0.630 0.141 -1.521 2.215 0.692 0.860 -1.423 0.000 0.772 -0.975 -0.921 3.102 1.814 1.256 0.988 0.744 0.550 0.864 0.970 +1 1.039 -1.923 0.051 0.449 0.432 1.010 0.144 1.585 2.173 0.646 -0.010 0.361 0.000 0.580 0.392 -0.894 0.000 0.721 0.055 -0.618 3.102 0.875 1.017 0.975 0.731 0.827 0.963 0.838 +1 0.694 -2.157 0.736 1.403 -1.709 0.831 -0.920 -0.128 2.173 1.125 -0.348 -0.765 1.107 0.991 -0.364 1.395 0.000 0.546 -0.050 0.880 0.000 0.968 0.993 1.104 1.498 0.875 1.166 0.984 +0 0.840 -0.786 -1.486 0.870 1.032 1.204 0.669 1.065 0.000 1.165 -0.657 -0.938 2.215 1.538 0.023 -0.217 0.000 1.127 -1.033 -0.732 3.102 2.381 1.834 0.986 0.906 0.353 1.325 1.170 +0 0.637 0.411 0.111 0.364 -1.464 1.092 0.249 1.136 1.087 0.909 1.050 -0.209 2.215 1.122 0.755 -0.964 0.000 1.074 1.079 -0.941 0.000 0.985 0.829 0.994 0.922 1.509 0.910 0.859 +0 0.654 -0.750 -0.137 0.381 1.168 0.993 -0.711 -0.524 0.000 1.434 -0.496 1.278 0.000 0.754 -1.207 -1.176 2.548 0.451 0.812 0.568 1.551 1.128 1.066 0.985 0.810 0.775 0.706 0.762 +0 0.720 0.518 0.217 0.693 1.653 0.493 1.074 -0.493 2.173 0.459 1.908 1.400 0.000 0.393 0.110 1.122 0.000 0.416 1.017 1.294 0.000 0.708 0.871 0.990 0.683 0.615 0.637 0.587 +1 0.463 -0.246 1.310 0.474 0.726 0.606 -0.496 -1.322 1.087 0.379 2.417 0.079 0.000 1.048 0.045 -0.158 2.548 0.764 1.027 1.716 0.000 0.841 1.015 0.980 0.977 0.904 0.934 0.812 +1 1.708 -0.077 -0.098 1.133 -0.715 0.741 0.639 1.336 2.173 0.982 0.347 -1.433 2.215 0.522 0.547 0.255 0.000 0.674 -0.597 1.113 0.000 0.650 0.791 1.015 1.273 0.776 0.972 0.792 +0 0.676 0.556 1.494 0.478 -0.453 0.549 -0.256 -0.020 0.000 0.472 1.039 -0.222 0.000 0.494 -0.965 1.466 1.274 1.218 -0.892 -1.401 3.102 0.797 1.055 0.987 0.859 0.314 0.778 0.767 +1 0.795 -0.427 0.789 1.876 1.411 1.426 -0.569 -0.225 2.173 0.353 0.057 -0.790 0.000 0.347 -0.219 -1.627 2.548 1.003 -0.894 -1.684 0.000 0.726 1.051 0.983 0.539 0.849 0.981 0.811 +0 0.416 -1.827 1.318 1.242 -0.210 0.762 -1.094 0.086 2.173 1.006 -0.006 1.374 0.000 1.572 -0.373 -1.505 0.000 0.585 -1.556 -0.515 0.000 0.796 0.843 0.986 0.665 0.533 0.788 0.746 +0 0.449 -0.968 1.600 1.430 -1.015 0.641 -0.304 0.146 2.173 1.125 0.870 0.869 2.215 0.780 0.598 -1.528 0.000 0.701 1.071 -0.312 0.000 0.792 0.979 0.992 0.916 1.100 0.941 0.791 +1 0.563 -1.594 1.430 1.950 0.708 0.698 -1.555 -1.534 2.173 0.861 -0.268 -1.014 2.215 0.610 0.180 0.241 0.000 0.721 -1.510 -0.699 0.000 1.020 1.008 0.987 1.528 0.944 1.102 0.996 +0 0.537 -0.800 -0.244 2.371 -0.687 2.183 -0.319 0.979 2.173 1.136 -0.053 0.591 0.000 2.228 0.134 -0.705 0.000 1.679 -0.227 -1.401 1.551 0.625 1.066 0.996 2.106 1.701 1.450 1.183 +0 2.459 1.308 -1.340 1.364 -1.241 1.315 0.556 0.611 1.087 0.723 1.092 0.198 0.000 1.236 -0.018 -0.012 0.000 0.626 -0.417 -1.019 3.102 0.917 0.940 0.991 0.829 1.095 1.253 1.144 +1 2.337 -1.525 -0.126 0.234 -1.309 1.031 -1.407 0.576 2.173 1.611 -1.296 -1.399 0.000 1.092 -0.786 1.469 2.548 0.704 -2.340 -0.663 0.000 1.408 1.134 0.985 0.949 1.023 1.078 1.019 +1 0.600 1.295 0.591 1.280 -1.266 0.674 0.655 -0.029 2.173 1.058 1.395 1.344 0.000 1.416 0.552 -0.559 2.548 1.347 0.268 1.220 0.000 0.929 1.198 1.208 0.901 0.562 0.946 0.858 +0 1.497 1.063 -0.932 0.237 -0.578 0.334 -0.029 -0.360 0.000 0.786 1.375 1.053 0.000 0.621 -0.407 1.460 2.548 0.996 1.308 0.162 3.102 1.346 0.973 0.998 0.779 0.903 0.697 0.717 +1 0.912 -0.890 -0.370 0.775 1.073 1.174 -0.448 -1.236 2.173 1.015 -1.093 0.341 0.000 0.688 -0.285 0.919 0.000 1.000 -0.613 1.569 1.551 0.832 0.761 1.122 1.039 0.680 0.887 0.780 +1 0.903 -1.971 0.797 0.768 -0.526 1.085 -0.717 -1.125 2.173 0.814 -1.644 -1.681 0.000 1.067 1.878 0.246 0.000 1.590 -1.104 0.526 0.000 0.852 0.952 1.072 0.988 0.869 0.935 0.797 +0 0.744 -1.368 0.370 1.613 0.450 0.965 -0.274 -1.424 2.173 0.518 -0.546 -0.783 0.000 0.699 -0.698 1.512 2.548 0.781 0.410 0.006 0.000 0.709 0.862 0.976 0.776 0.548 0.892 0.771 +0 1.044 -1.073 0.189 0.531 -0.968 0.734 -0.644 -0.936 0.000 0.900 -0.792 0.911 2.215 1.117 -0.855 1.563 1.274 0.790 -1.364 -0.191 0.000 0.939 0.944 0.986 0.761 0.595 0.761 0.693 +0 0.800 0.321 -1.096 1.269 -0.678 1.208 -0.513 1.720 2.173 0.736 0.835 0.268 2.215 0.634 -1.360 0.379 0.000 0.440 1.958 -0.038 0.000 1.962 1.257 0.978 1.096 1.693 1.210 1.078 +1 0.365 -0.761 -0.734 2.465 -1.611 1.754 0.834 -1.695 2.173 1.693 -0.098 0.336 0.000 2.509 1.093 -0.294 0.000 0.954 1.275 0.190 3.102 1.057 0.658 0.985 1.339 1.441 1.214 1.344 +1 0.669 -0.898 -1.284 1.160 -0.208 0.933 -0.155 0.481 0.000 1.371 -0.595 -1.654 2.215 0.794 1.802 -0.557 0.000 0.896 0.057 -0.749 3.102 2.345 1.395 1.007 0.982 0.807 1.278 1.137 +0 0.593 -1.430 0.834 1.488 -0.807 0.912 -0.901 -0.943 2.173 1.585 -0.005 0.583 2.215 1.028 -0.014 1.083 0.000 0.477 0.360 -1.307 0.000 0.668 1.043 1.296 1.526 1.923 1.208 1.012 +1 0.773 0.898 1.701 0.790 -0.497 0.845 1.549 1.128 0.000 0.951 0.468 -0.318 2.215 0.631 0.300 0.851 0.000 0.772 -0.449 -1.283 3.102 0.923 1.084 0.993 0.727 0.721 0.890 0.768 +1 0.953 -0.205 0.999 0.514 -0.066 0.975 1.539 -1.562 0.000 1.088 0.311 0.196 1.107 0.917 -0.624 0.525 0.000 0.562 -0.398 -1.441 3.102 1.216 0.850 0.985 0.814 0.760 0.880 0.750 +1 0.740 -0.851 -1.295 0.760 0.334 1.425 -0.150 -0.200 2.173 1.188 -2.420 1.521 0.000 0.992 -0.366 1.478 0.000 0.628 0.329 0.895 3.102 0.939 1.347 1.034 0.640 0.880 0.819 0.756 +0 0.601 -1.811 1.188 0.845 -1.672 0.501 -1.613 -0.132 0.000 1.170 -0.392 0.773 2.215 0.902 0.059 -1.111 2.548 1.262 -0.720 -0.678 0.000 0.745 0.883 1.001 0.856 1.113 0.859 0.795 +0 0.335 2.180 0.357 1.986 -0.138 0.969 -0.732 1.553 1.087 0.981 -1.400 1.617 0.000 0.665 0.207 -0.711 0.000 1.153 0.159 1.632 3.102 0.935 0.935 0.979 1.871 0.544 1.228 1.284 +1 0.813 0.474 -1.013 1.236 -0.331 1.305 0.664 1.287 0.000 0.892 0.544 0.598 0.000 1.797 0.399 -0.268 2.548 1.384 1.237 1.539 0.000 0.974 0.886 0.990 0.715 0.865 1.051 0.964 +1 0.610 -0.597 1.201 1.076 0.011 1.130 -2.213 1.445 0.000 1.039 -0.982 -0.399 2.215 0.604 -1.938 0.163 0.000 1.937 -0.205 -0.464 3.102 0.932 1.051 0.988 0.757 0.507 0.884 0.788 +1 1.267 -1.487 -1.231 1.257 1.674 0.665 -0.560 -1.738 0.000 1.138 -2.013 -0.075 0.000 1.479 -0.409 -0.142 2.548 1.503 -0.313 0.921 3.102 0.832 0.980 0.989 1.154 0.933 1.082 0.908 +1 0.494 -1.155 0.730 0.291 -0.382 0.419 -2.230 1.185 0.000 0.193 2.663 1.315 0.000 0.527 0.397 -1.079 2.548 0.535 0.184 -0.423 1.551 0.817 1.031 0.988 0.517 0.230 0.678 0.654 +0 0.518 1.730 0.911 0.345 1.156 0.945 0.403 -0.689 2.173 0.793 0.152 -1.149 0.000 0.529 2.288 -0.075 0.000 1.216 0.151 1.459 3.102 1.675 1.199 0.989 0.971 1.068 0.904 0.813 +0 0.940 -1.159 -0.192 1.566 0.495 0.450 0.594 -1.455 0.000 0.505 1.042 1.069 1.107 0.685 -0.128 -0.671 0.000 1.198 -0.516 -1.479 3.102 0.799 1.023 0.989 0.940 0.850 0.910 0.779 +1 1.045 -0.183 1.449 0.646 0.844 0.886 -0.112 -0.739 1.087 1.648 0.383 1.183 2.215 1.819 0.327 -0.112 0.000 2.607 2.496 -1.091 0.000 1.321 1.125 0.981 0.627 1.813 1.145 0.972 +1 0.715 0.040 -0.879 0.991 -1.116 1.281 -0.171 0.983 2.173 0.729 -0.431 0.148 2.215 1.033 -0.706 -0.884 0.000 0.776 -0.744 0.879 0.000 0.989 0.783 0.983 1.378 0.992 0.982 0.840 +0 0.396 -0.917 -1.506 1.370 -0.624 0.631 -0.191 1.740 1.087 0.481 -1.302 0.024 0.000 0.750 -0.852 0.948 0.000 1.072 0.483 0.439 3.102 0.707 0.880 0.993 0.769 0.874 0.686 0.713 +1 0.833 -0.059 1.128 0.517 -1.132 0.651 -0.583 -0.397 0.000 0.844 1.043 1.240 2.215 1.080 -0.339 0.222 0.000 0.963 0.200 -1.022 3.102 0.873 0.676 0.986 0.662 0.806 0.796 0.699 +0 0.591 -0.857 0.600 2.762 1.196 0.587 1.427 -0.377 0.000 0.840 -0.034 -0.806 2.215 0.845 -0.655 -1.392 1.274 1.437 0.413 0.035 0.000 0.964 0.853 0.983 1.238 0.549 0.872 0.820 +0 0.759 1.242 1.069 0.951 0.333 0.814 1.009 -1.573 0.000 0.767 0.396 0.547 2.215 1.221 0.361 -0.975 1.274 1.045 0.828 -0.233 0.000 1.318 0.932 0.982 0.540 1.008 0.792 0.753 +1 0.856 0.447 -0.077 1.302 -1.442 1.142 1.284 1.128 2.173 0.510 1.350 0.311 2.215 0.639 -0.182 0.333 0.000 0.686 0.925 -1.400 0.000 0.893 1.032 1.378 0.894 0.757 0.891 0.766 +0 1.562 0.969 0.909 0.129 0.377 0.548 1.241 -0.864 0.000 0.906 -0.062 -1.711 2.215 1.017 0.961 -0.335 0.000 1.297 0.677 0.127 3.102 0.819 0.862 0.980 0.671 1.071 0.725 0.726 +0 0.387 1.397 -1.144 0.456 0.690 0.968 -0.080 0.298 0.000 0.888 -1.142 -1.340 2.215 0.415 0.561 -1.147 2.548 1.189 -0.827 0.573 0.000 0.871 0.874 0.984 0.906 0.668 0.866 0.762 +1 0.765 -0.213 1.689 2.183 0.964 1.078 -0.524 -0.751 1.087 0.482 0.936 -1.214 0.000 0.605 0.433 -0.035 2.548 0.703 0.241 0.692 0.000 0.795 1.040 1.088 0.844 0.803 0.996 0.844 +0 0.606 0.321 0.148 1.729 -0.963 0.625 -0.140 -1.638 1.087 0.868 1.379 1.512 2.215 0.951 1.900 0.524 0.000 0.389 2.293 -0.395 0.000 0.544 0.780 1.194 0.883 0.989 0.924 0.947 +1 0.369 -1.109 0.247 0.463 -0.160 0.929 0.474 0.509 0.000 0.849 1.317 -1.151 0.000 0.774 1.293 0.427 2.548 1.905 -0.063 -1.102 3.102 0.776 0.871 0.987 0.804 1.187 0.730 0.677 +1 0.982 -1.068 1.308 0.139 1.734 0.721 0.231 -1.268 2.173 0.991 -0.842 0.505 2.215 1.270 -0.962 -0.440 0.000 1.076 0.020 1.594 0.000 0.968 0.867 0.992 0.831 1.436 1.005 0.868 +1 0.305 -0.761 1.686 0.766 0.801 0.893 0.173 -0.688 2.173 0.989 0.037 0.060 2.215 1.398 0.446 1.117 0.000 1.028 1.012 -1.709 0.000 0.889 1.146 0.980 0.969 0.869 0.947 0.810 +1 0.944 0.400 0.208 0.419 -1.015 1.030 0.747 -1.156 2.173 0.839 -2.069 1.034 0.000 1.197 0.729 1.426 0.000 1.140 -2.028 0.325 0.000 0.947 1.200 0.990 0.661 0.899 0.965 0.865 +0 0.919 -0.262 0.255 0.808 0.438 0.912 0.340 -0.974 0.000 1.453 -0.358 0.981 2.215 0.927 -1.190 -1.191 2.548 0.831 -1.155 1.321 0.000 0.852 0.954 0.987 0.917 1.290 0.997 0.950 +1 1.515 0.690 0.523 0.219 1.372 0.751 0.051 0.186 0.000 1.183 -0.857 -0.740 1.107 1.660 -0.850 1.497 0.000 0.654 -0.276 1.576 1.551 2.124 1.167 0.985 1.566 0.725 1.011 1.091 +0 0.681 1.038 0.305 0.330 0.687 1.214 0.966 1.260 2.173 1.431 0.502 -0.382 2.215 0.915 1.866 -1.723 0.000 0.772 1.718 -0.298 0.000 0.890 1.140 0.996 0.871 1.983 1.164 1.025 +1 1.270 -0.071 -0.585 1.650 -1.052 0.216 1.627 1.617 0.000 0.622 0.405 1.209 0.000 0.585 1.136 0.062 0.000 0.878 -0.743 1.177 0.000 0.888 0.582 0.977 1.109 0.369 0.731 0.735 +1 1.295 -0.150 1.639 0.233 -0.943 0.647 -1.047 -0.415 0.000 0.803 -0.841 -1.398 1.107 0.600 -0.616 1.475 0.000 0.673 -0.005 -1.511 0.000 0.837 0.890 0.981 0.535 0.498 0.546 0.639 +1 1.452 -0.083 0.584 1.055 -0.079 0.754 0.685 1.608 2.173 0.513 -0.368 -1.176 2.215 0.604 0.570 0.155 0.000 1.226 0.682 -1.445 0.000 0.946 0.786 0.990 0.837 0.745 0.855 0.763 +1 0.672 -0.178 -0.724 0.901 1.398 0.947 0.848 0.589 2.173 0.905 0.909 1.257 0.000 1.970 0.164 -0.590 2.548 0.706 2.380 -1.176 0.000 0.880 0.786 1.016 0.900 1.599 0.967 0.846 +1 4.570 -0.893 -1.657 0.317 -0.391 2.550 1.647 -0.294 0.000 1.738 -1.288 0.796 0.000 2.580 0.079 0.304 0.000 2.134 0.001 -0.904 3.102 1.274 0.979 1.517 1.377 0.808 1.080 1.169 +0 2.702 -0.626 0.023 0.558 -1.570 0.501 -0.246 1.144 0.000 0.856 0.371 1.643 2.215 0.982 0.977 1.020 0.000 2.027 -0.467 -0.830 3.102 0.907 0.722 1.685 1.108 1.106 1.033 1.032 +1 1.282 2.147 0.804 1.817 0.530 1.523 -0.824 -0.848 0.000 0.756 1.596 1.128 0.000 1.316 0.683 1.547 1.274 0.744 1.036 -0.410 1.551 0.744 0.723 1.006 0.968 0.766 1.138 1.086 +1 1.008 -0.508 1.009 0.899 -0.122 0.577 -0.318 -1.233 0.000 1.023 -0.047 1.631 2.215 1.375 -1.106 0.174 1.274 0.796 -0.572 -0.843 0.000 0.405 0.974 1.123 0.921 1.446 0.838 0.769 +1 0.918 -0.848 0.232 1.577 0.881 0.446 -0.470 0.018 2.173 0.544 0.362 -0.874 0.000 0.536 1.451 -0.861 0.000 1.736 0.047 -1.263 3.102 0.538 0.791 0.987 1.122 0.888 0.788 0.818 +1 0.370 -0.720 0.219 1.117 -0.370 0.606 1.721 -1.626 0.000 0.838 -0.442 -0.637 2.215 0.894 1.114 1.059 0.000 1.342 0.082 0.690 0.000 0.904 0.954 0.984 0.579 0.546 0.681 0.676 +0 0.945 0.508 1.439 0.697 -1.727 0.697 -2.295 -0.014 0.000 0.829 -0.009 -0.757 1.107 1.016 0.612 0.521 0.000 0.940 1.396 1.003 0.000 0.724 1.083 0.994 0.826 0.579 0.742 0.779 +1 0.579 -0.923 0.002 0.773 1.016 0.415 -0.513 -0.038 1.087 0.470 -2.422 1.375 0.000 1.253 -0.501 -1.130 2.548 1.107 -0.496 -0.638 0.000 1.377 0.996 0.996 0.855 0.748 0.749 0.748 +1 1.938 0.364 0.469 0.542 -1.632 0.703 1.773 -1.705 0.000 0.675 -0.380 -1.469 0.000 1.203 1.055 -0.626 2.548 1.034 1.144 0.329 1.551 0.931 0.861 1.347 0.792 0.653 0.743 0.781 +0 0.945 -1.192 1.499 0.697 -0.304 0.608 1.008 0.114 2.173 0.812 0.158 -1.591 2.215 0.430 1.722 1.670 0.000 0.376 0.610 -1.087 0.000 0.384 0.611 1.122 0.916 1.130 0.994 0.854 +1 1.541 0.598 1.354 2.462 0.874 0.829 2.569 -0.668 0.000 1.274 0.290 -0.918 2.215 0.372 1.628 -1.195 0.000 0.449 0.762 0.674 3.102 0.616 0.716 1.129 1.582 0.709 0.970 1.264 +1 0.654 2.053 -1.470 0.708 0.579 0.287 1.354 1.022 0.000 0.705 -0.978 -0.327 2.215 0.703 -0.006 1.699 0.000 0.773 0.819 -1.194 1.551 0.945 1.104 0.988 0.600 0.908 0.930 0.789 +0 0.832 -0.576 -1.577 0.349 -0.105 0.802 0.667 0.800 0.000 1.083 -0.557 -1.072 2.215 1.332 0.035 0.091 0.000 0.802 -1.445 -1.367 0.000 1.262 0.831 0.985 0.711 0.817 0.932 0.880 +1 1.098 -0.550 -0.049 0.474 -0.290 0.762 -0.597 -1.711 2.173 0.614 2.766 -0.925 0.000 1.538 0.135 1.316 2.548 1.209 0.861 -0.032 0.000 0.572 0.911 0.989 1.073 0.749 0.875 0.807 +0 0.741 -0.209 1.287 1.010 -1.238 1.197 -1.366 0.387 1.087 0.626 -1.182 -0.866 2.215 1.126 -0.565 -1.553 0.000 0.382 -1.553 -0.491 0.000 0.762 1.201 0.988 0.832 1.156 1.067 0.883 +1 3.455 0.643 -1.443 0.775 -0.804 1.646 -2.010 0.302 0.000 0.483 0.393 0.138 2.215 0.629 0.343 0.944 0.000 0.567 -1.066 -1.710 1.551 0.709 0.558 1.237 1.062 0.649 0.845 0.732 +0 0.703 -0.687 -0.346 0.709 1.580 0.600 0.049 1.551 0.000 0.607 0.795 -0.395 1.107 0.486 0.624 -0.058 2.548 0.406 -1.973 -0.495 0.000 1.360 0.984 0.989 0.885 0.176 0.751 0.698 +0 0.699 0.928 -1.346 0.880 -0.442 0.256 -2.104 0.838 0.000 1.140 -1.001 0.791 2.215 0.357 0.987 0.013 0.000 0.528 -0.227 -0.800 1.551 1.349 0.999 0.990 0.683 0.745 1.188 1.103 +0 0.513 0.802 1.343 0.426 -0.056 0.470 0.242 1.484 0.000 0.646 1.497 -1.107 1.107 0.395 0.999 0.445 0.000 1.103 -1.015 0.612 0.000 1.051 0.975 0.989 0.911 0.285 0.832 0.731 +0 1.075 1.119 0.848 0.756 0.643 0.405 2.018 -0.119 0.000 0.407 2.783 0.422 0.000 1.460 1.064 -1.398 2.548 0.836 0.642 1.682 3.102 0.545 1.085 0.975 0.733 0.346 0.728 0.726 +1 1.029 0.826 0.278 1.173 0.909 1.250 1.061 -1.067 2.173 0.686 0.469 0.870 0.000 0.788 2.202 -0.415 0.000 1.122 1.123 -1.694 3.102 0.879 0.799 0.985 1.370 0.693 0.923 0.832 +0 1.799 -0.712 0.066 0.367 -0.453 0.498 -0.155 1.693 2.173 0.310 -0.527 1.214 0.000 0.446 -0.088 0.544 0.000 0.430 0.672 1.471 0.000 0.658 0.574 0.981 0.948 0.490 0.759 0.615 +0 1.071 0.549 -0.399 0.766 -0.690 0.942 -0.171 1.598 2.173 0.399 -1.462 1.126 0.000 0.442 -0.217 -0.588 0.000 1.326 0.130 0.496 3.102 0.777 0.832 0.995 0.907 1.010 1.021 0.932 +0 0.319 -1.132 -0.671 0.340 1.426 1.189 0.075 0.401 2.173 1.267 0.303 -1.265 2.215 1.297 -0.696 -0.838 0.000 0.699 0.560 -0.655 0.000 0.877 0.894 0.998 0.911 1.815 0.934 0.777 +1 0.579 -0.063 0.278 0.839 1.691 0.705 -0.242 1.419 0.000 1.817 -0.765 -0.721 0.000 1.051 0.439 1.057 1.274 2.335 0.712 0.200 3.102 0.983 1.454 0.988 0.846 0.865 1.273 1.032 +1 0.704 -2.014 1.178 1.301 -1.727 0.967 -0.841 -0.010 0.000 0.387 -0.821 -0.709 2.215 1.000 -1.425 0.416 0.000 1.009 -1.534 -1.689 0.000 0.926 0.759 0.996 0.709 0.233 0.515 0.689 +1 1.455 0.167 -1.445 0.736 0.315 1.449 -0.086 0.485 1.087 0.656 0.019 -0.622 0.000 1.792 -1.451 -1.291 0.000 1.317 0.898 1.121 3.102 1.764 1.951 1.433 1.300 1.200 1.568 1.300 +1 2.367 -0.639 -0.656 0.359 0.399 0.799 -1.369 0.807 2.173 0.551 -0.029 -1.734 2.215 0.797 -1.370 -1.661 0.000 0.988 1.780 0.928 0.000 0.788 1.047 1.040 0.865 1.023 0.920 0.810 +0 0.633 0.064 -1.334 1.000 0.128 0.956 0.640 1.676 0.000 1.116 0.059 0.347 2.215 0.807 0.506 -1.144 0.000 0.848 0.965 -0.014 3.102 0.893 0.912 1.068 0.735 0.579 0.863 0.765 +1 2.776 -0.612 -1.194 0.130 0.147 0.598 0.271 0.362 1.087 0.998 -0.636 1.221 2.215 0.538 -2.306 0.394 0.000 0.706 -0.257 0.754 0.000 0.948 0.873 0.992 1.229 0.964 0.977 0.923 +0 0.517 1.690 -0.345 1.126 0.991 0.962 1.444 -0.741 1.087 0.702 2.302 -1.503 0.000 1.103 1.332 1.021 2.548 0.504 1.099 0.182 0.000 0.884 0.887 0.988 0.593 1.283 0.786 0.699 +0 0.619 0.140 0.921 0.478 1.671 0.796 -0.763 -1.140 0.000 1.577 -0.569 0.292 0.000 0.708 0.332 -0.609 2.548 0.627 -1.767 -1.658 0.000 0.923 0.927 0.981 0.651 0.732 0.754 0.884 +0 1.358 -0.433 1.557 0.709 -1.410 1.011 0.054 0.316 2.173 0.761 0.178 -1.249 2.215 0.974 0.602 0.946 0.000 0.626 2.399 -0.256 0.000 1.030 0.949 0.994 0.629 1.277 0.890 0.770 +0 0.711 0.307 -1.384 1.191 -0.290 0.678 -1.436 0.519 0.000 0.814 -1.387 -1.071 2.215 0.844 -0.407 1.104 2.548 0.518 -0.393 1.587 0.000 0.873 0.940 1.063 0.845 0.931 0.821 0.813 +0 0.411 0.118 0.407 1.156 -1.227 0.507 -1.526 -0.127 2.173 1.049 -1.288 1.504 2.215 0.745 -2.649 -0.060 0.000 0.437 -0.133 1.116 0.000 1.287 0.850 0.990 0.885 1.074 0.784 0.828 +0 1.904 0.464 -0.985 0.931 -1.030 0.882 -0.853 0.867 1.087 0.555 -0.469 0.405 0.000 0.506 -1.149 0.516 2.548 0.599 1.486 -1.244 0.000 0.836 0.641 0.989 1.226 0.310 1.280 1.142 +1 0.280 1.475 0.951 1.422 -0.105 0.612 1.236 -1.432 2.173 0.546 0.602 -0.068 0.000 0.970 1.411 1.467 0.000 1.295 -0.348 -1.658 3.102 1.068 0.928 0.986 1.974 0.899 1.343 1.104 +1 0.301 -2.034 -1.341 0.852 0.695 1.232 -1.152 -0.576 0.000 1.628 -1.197 1.534 0.000 1.434 0.055 0.590 1.274 1.130 -0.470 -1.314 3.102 0.637 0.728 0.984 0.748 1.011 0.894 0.786 +0 1.048 1.480 -1.298 0.850 0.010 0.609 -1.433 1.158 0.000 0.635 -2.776 -1.014 0.000 0.433 1.830 1.411 0.000 0.631 -0.505 0.354 3.102 0.889 1.111 1.208 0.943 0.435 0.925 0.807 +1 0.604 0.406 0.792 0.334 -0.189 0.632 -1.063 -1.682 2.173 1.026 0.953 -0.612 1.107 0.783 1.419 0.474 0.000 0.868 1.648 -0.054 0.000 0.873 0.993 0.992 0.798 1.741 0.978 0.840 +0 2.120 -1.368 0.042 2.335 0.393 2.222 -0.456 -1.395 0.000 0.515 -0.928 -1.144 0.000 0.372 -1.131 1.194 0.000 1.393 0.000 0.444 3.102 0.764 0.907 0.989 1.013 0.490 0.940 1.396 +1 1.006 0.266 1.642 1.124 -0.368 0.612 0.551 1.027 0.000 1.015 1.014 1.590 0.000 1.105 -0.132 -0.157 2.548 1.332 1.229 -0.109 3.102 0.911 1.097 1.431 0.875 0.837 0.909 0.860 +0 1.420 -0.501 0.440 0.356 1.736 0.545 0.531 1.440 1.087 0.884 -1.106 -0.778 2.215 0.674 -0.342 -0.379 0.000 0.855 -0.477 -1.025 0.000 0.741 0.805 0.989 0.879 1.335 0.845 0.690 +0 0.396 1.911 -1.499 0.799 0.018 0.454 -0.315 1.142 0.000 0.654 -0.866 0.015 2.215 0.672 1.393 -1.712 0.000 0.768 0.841 -0.990 0.000 1.064 0.948 0.984 0.550 0.490 0.613 0.611 +1 1.275 -1.356 0.157 0.891 0.976 1.042 -0.621 1.638 2.173 0.748 -0.825 -0.763 0.000 0.318 0.081 -1.538 0.000 0.486 0.637 1.217 3.102 0.604 0.852 0.993 0.840 0.633 0.838 0.753 +0 0.689 1.186 0.035 1.170 0.691 0.803 -1.749 -1.060 0.000 0.493 -0.998 1.166 2.215 0.564 -1.342 -0.333 0.000 1.103 0.856 -1.457 3.102 0.759 0.839 0.990 0.880 0.936 1.072 1.717 +0 0.716 -1.393 0.142 1.331 1.429 0.685 0.347 0.598 0.000 0.705 -0.617 -1.412 2.215 1.289 -1.925 -0.919 0.000 1.844 -1.020 -0.077 3.102 3.125 1.838 1.240 0.937 1.009 1.209 1.061 +1 0.653 -0.375 -0.971 1.129 0.284 1.125 -0.497 0.906 2.173 1.623 2.058 -1.151 0.000 0.458 -2.183 -1.330 0.000 0.535 -0.698 -0.257 3.102 0.678 0.982 1.076 0.526 0.725 0.634 0.631 +0 1.636 0.395 1.456 0.537 1.192 1.137 0.539 -0.590 2.173 0.514 0.067 -1.061 0.000 1.047 1.153 0.429 0.000 0.666 -0.698 0.778 3.102 1.308 0.956 0.988 1.353 1.109 0.973 0.881 +0 2.065 0.397 -0.001 0.170 1.646 1.295 0.827 -1.700 1.087 1.091 1.893 1.429 0.000 2.255 0.064 -0.203 1.274 0.532 0.457 1.125 0.000 0.932 0.975 0.986 1.432 2.238 1.220 0.968 +1 0.452 1.371 1.491 1.617 -1.174 0.583 0.002 0.871 0.000 1.121 -0.417 -0.020 2.215 0.316 -1.185 -1.076 0.000 1.109 -0.321 -1.678 1.551 0.931 0.881 0.987 1.381 1.004 1.489 1.285 +0 1.787 -1.180 0.895 0.940 -0.456 0.732 -1.569 -0.190 0.000 0.929 -2.198 1.490 0.000 1.139 -1.086 -0.979 2.548 1.283 0.250 1.697 0.000 0.937 0.899 1.684 1.108 0.896 0.814 0.770 +1 0.635 0.503 -0.542 0.739 -1.367 1.340 1.346 0.265 0.000 0.438 -0.236 1.594 0.000 0.742 0.179 -0.777 0.000 1.359 0.698 1.636 3.102 0.911 0.705 0.994 0.811 0.537 0.630 0.607 +0 0.973 0.327 -0.518 0.455 -1.571 0.844 1.084 1.171 0.000 0.710 1.038 -0.334 2.215 0.865 0.373 0.611 2.548 0.778 0.984 -1.439 0.000 0.880 0.978 0.991 0.739 0.687 0.677 0.655 +0 0.765 0.361 -1.549 1.167 0.163 1.345 -0.125 0.889 0.000 0.826 0.644 -0.807 2.215 0.970 -0.505 0.248 0.000 1.731 -0.631 -1.055 3.102 0.753 0.948 1.308 1.020 0.867 0.770 0.729 +1 0.336 1.354 -0.566 0.831 1.434 0.984 -0.012 0.076 2.173 1.216 -0.191 -1.240 0.000 1.359 0.128 0.921 2.548 0.659 -0.593 1.633 0.000 0.735 1.153 0.996 0.921 1.001 0.968 0.822 +0 1.407 -1.371 0.408 0.634 -1.677 1.116 0.733 1.631 2.173 0.720 1.381 -1.536 0.000 1.806 -0.378 -0.133 2.548 0.973 1.753 -0.217 0.000 1.081 1.173 1.248 1.080 2.072 1.444 1.561 +1 1.656 -0.070 -1.498 0.966 -0.970 2.020 -0.544 0.410 0.000 1.693 -0.491 1.579 2.215 0.995 2.592 -0.032 0.000 1.602 -0.325 -1.132 1.551 0.850 0.887 0.979 0.919 0.960 0.921 0.827 +0 1.852 1.435 1.312 0.565 -1.389 0.512 -1.138 0.121 0.000 1.111 -1.763 -0.415 0.000 0.676 1.017 -1.277 2.548 0.767 0.107 -0.069 3.102 0.921 0.885 0.983 0.649 0.564 1.025 1.604 +0 1.047 -0.364 -0.131 0.370 -0.995 1.206 0.459 0.454 2.173 0.873 1.891 1.611 0.000 1.166 -0.216 -1.113 2.548 0.643 0.175 1.688 0.000 0.917 1.559 0.987 0.918 1.552 1.311 1.111 +0 0.614 1.162 1.725 0.771 -0.870 0.734 -0.086 0.077 0.000 0.940 -0.341 1.291 0.000 0.775 -0.163 -0.134 2.548 0.683 -0.895 1.411 1.551 1.584 0.976 0.994 0.700 0.605 0.661 0.691 +1 0.914 -0.659 -0.239 1.033 -1.726 0.672 -0.513 1.045 2.173 0.586 0.765 -0.235 2.215 0.515 -1.035 -0.598 0.000 0.747 -2.340 -0.271 0.000 0.867 1.011 1.310 0.963 1.063 0.989 0.874 +1 0.556 -0.714 -0.800 0.410 -1.511 1.106 0.208 0.436 1.087 1.638 -0.694 -1.050 1.107 0.723 -0.387 1.697 0.000 0.743 -0.569 -0.374 0.000 0.781 1.009 0.984 0.913 2.149 1.104 0.875 +1 0.917 -1.972 0.019 0.745 -0.337 2.405 -0.846 -1.740 0.000 1.706 -1.105 0.185 0.000 1.867 -1.027 -1.049 2.548 1.408 -0.518 0.603 0.000 0.910 0.652 0.991 0.939 0.758 0.918 0.795 +0 0.394 1.102 1.476 1.632 -0.466 0.460 0.137 0.644 2.173 1.118 0.030 -1.537 1.107 1.132 -0.839 -0.125 0.000 0.865 2.110 0.765 0.000 1.274 0.890 1.093 1.103 0.976 0.860 0.899 +0 1.537 0.542 1.310 1.399 1.440 0.666 0.402 -0.244 2.173 0.608 2.321 -0.837 0.000 0.913 2.007 0.036 0.000 0.813 0.150 0.309 1.551 0.979 0.986 0.982 0.766 0.383 0.819 0.942 +0 0.570 0.954 1.581 1.197 1.336 0.429 2.124 -0.084 0.000 0.710 -0.604 -0.194 2.215 1.353 0.092 -1.436 2.548 0.941 -0.260 0.576 0.000 0.901 0.904 0.988 1.742 1.013 1.304 1.086 +1 0.686 -0.222 1.560 0.609 0.543 0.354 -0.544 0.796 0.000 0.553 -0.251 -0.237 0.000 1.159 1.215 -1.451 2.548 1.224 0.839 -0.479 3.102 0.767 0.810 0.991 1.318 0.715 0.992 0.848 +1 0.427 -1.746 1.015 1.573 -1.232 0.582 -0.866 1.379 0.000 0.605 -1.310 -0.024 2.215 0.448 -0.211 -0.913 2.548 0.646 0.699 0.353 0.000 1.174 0.995 1.022 0.702 0.514 0.631 0.751 +1 0.955 -0.511 -0.868 1.260 -0.194 1.996 -2.038 1.143 0.000 1.498 0.295 -1.239 0.000 1.590 0.519 0.016 2.548 0.816 -0.800 0.792 3.102 6.231 3.213 0.993 0.808 0.923 2.444 1.941 +1 1.019 -1.098 1.240 0.763 -0.958 0.968 -1.272 -0.120 0.000 1.342 -0.116 1.466 2.215 0.651 1.678 -0.913 0.000 0.917 -1.254 0.509 0.000 0.789 0.565 1.121 0.945 0.950 0.961 0.839 +1 1.334 -0.657 1.439 0.861 -0.095 1.208 -1.472 0.714 0.000 1.549 -0.173 -1.026 2.215 0.827 0.310 -1.466 0.000 1.124 0.398 -0.443 3.102 0.700 0.751 1.458 0.983 0.717 0.869 0.731 +1 2.062 0.140 0.671 2.363 -0.111 1.191 -0.090 -1.593 0.000 1.276 0.767 -1.277 2.215 0.494 -0.775 0.838 0.000 1.348 0.730 -0.218 3.102 1.046 0.984 1.980 1.109 0.967 1.185 0.997 +0 1.141 -0.595 1.585 1.179 -1.715 0.589 -0.194 0.737 2.173 0.817 -1.029 0.806 2.215 0.484 -0.193 -0.862 0.000 1.420 -0.571 -0.198 0.000 0.909 0.931 0.977 0.862 0.458 0.746 0.757 +1 0.818 -0.283 1.682 0.505 -1.113 1.152 -0.844 0.915 0.000 2.214 -1.481 -0.915 0.000 1.439 -0.762 -0.285 2.548 2.616 -0.543 0.371 3.102 0.839 0.953 0.993 1.077 0.838 0.955 0.958 +1 0.877 -0.834 1.592 1.111 0.421 1.369 -1.683 -1.692 0.000 1.760 -1.538 0.033 0.000 0.703 -1.942 -1.351 0.000 1.272 -1.110 0.645 3.102 1.006 1.039 1.190 0.867 0.935 0.890 0.829 +1 1.092 0.676 0.961 0.240 -0.417 0.560 1.096 0.510 1.087 0.654 -0.073 0.393 0.000 1.298 0.608 -1.332 2.548 2.198 1.471 -0.889 0.000 2.182 1.411 0.988 0.834 1.084 1.002 0.889 +0 0.472 0.464 -0.268 1.190 1.564 0.871 -1.347 0.863 0.000 1.619 -0.331 -1.293 2.215 1.647 -1.247 0.233 0.000 1.538 -1.053 -0.475 1.551 1.160 1.111 1.035 0.956 1.175 1.257 1.200 +1 0.436 -1.117 1.209 1.619 -1.575 0.949 -1.495 -0.406 2.173 0.726 -2.053 0.246 0.000 0.864 -1.175 0.730 1.274 1.095 -1.251 1.484 0.000 1.104 1.075 0.989 0.836 0.971 0.881 0.794 +1 0.661 0.244 0.604 1.937 -0.023 1.170 -0.891 1.661 0.000 1.024 0.746 -1.188 2.215 0.642 1.386 0.570 2.548 0.386 -0.558 -1.290 0.000 0.956 0.833 0.987 0.941 0.924 0.948 0.809 +1 0.645 -0.781 0.115 1.583 0.508 1.415 0.974 -1.264 0.000 0.722 0.725 0.961 1.107 0.585 0.279 -0.907 0.000 1.065 1.136 -0.281 3.102 1.031 0.964 0.980 0.729 0.754 0.784 0.936 +0 0.550 0.603 1.577 1.366 0.567 0.754 0.628 -1.207 2.173 0.619 1.111 -0.478 0.000 1.035 -0.155 1.660 0.000 1.012 -0.222 0.815 0.000 0.781 0.957 0.990 0.994 1.166 0.819 0.743 +1 0.741 -0.434 -0.860 1.170 0.247 1.466 -0.263 -0.052 2.173 2.672 -0.383 -1.687 1.107 0.718 0.700 0.158 0.000 0.394 -0.513 -0.361 0.000 0.836 1.194 1.084 1.447 2.904 1.498 1.210 +1 0.531 1.787 -1.180 0.865 0.931 0.958 0.391 -0.655 1.087 0.542 2.188 -1.568 0.000 0.386 0.839 1.185 2.548 0.719 2.411 0.197 0.000 0.855 0.659 0.986 1.008 0.781 0.900 0.775 +1 0.915 -0.405 -1.526 0.527 0.251 0.589 -0.101 0.511 0.000 1.083 0.120 -1.702 2.215 1.305 -0.118 -0.721 2.548 1.806 -0.979 0.415 0.000 0.898 1.171 0.988 0.694 0.990 1.003 0.825 +0 1.951 -0.613 -0.040 2.305 -0.070 1.823 0.132 -1.706 2.173 1.764 -0.740 1.717 1.107 2.614 -0.401 0.307 0.000 0.801 0.324 -1.161 0.000 1.691 1.863 1.007 2.685 1.229 1.972 1.696 +1 1.031 0.601 1.615 0.392 -0.502 0.945 0.542 0.669 0.000 1.144 -0.286 -0.629 2.215 0.984 0.273 0.080 0.000 1.492 -0.145 -1.631 3.102 0.905 1.163 0.985 1.022 0.928 0.966 0.875 +0 0.568 0.449 0.855 0.613 -1.653 0.509 -0.490 0.436 2.173 0.423 0.099 -1.424 0.000 1.402 0.735 -0.646 2.548 0.797 1.031 -0.262 0.000 0.971 0.865 0.987 1.014 1.140 0.918 0.787 +1 0.706 0.757 -0.685 0.406 1.502 1.178 0.254 -0.906 0.000 0.940 -0.292 0.794 2.215 0.681 0.637 0.260 0.000 0.689 1.632 0.402 0.000 0.818 0.682 0.993 1.137 0.716 0.833 0.726 +0 0.473 -0.610 -1.034 1.456 -0.124 0.363 -2.775 1.336 0.000 0.373 -1.301 0.853 0.000 0.506 0.128 -1.040 1.274 0.724 -0.652 1.520 3.102 0.706 0.996 0.988 0.716 0.408 0.616 0.663 +1 0.727 1.145 -0.634 1.505 -1.102 1.193 -0.666 0.272 2.173 0.803 -1.866 -1.451 0.000 0.796 -0.475 1.400 2.548 0.604 -0.190 0.650 0.000 1.219 0.850 0.976 1.534 1.036 1.079 1.091 +1 0.557 -0.667 -1.469 1.845 -0.794 0.600 0.890 0.493 2.173 0.935 0.584 1.678 0.000 1.268 1.904 0.017 0.000 1.683 0.111 1.231 3.102 0.781 0.899 0.982 1.016 0.777 0.898 0.771 +0 0.578 0.030 0.066 1.544 1.736 0.778 1.144 -1.245 0.000 0.901 0.081 -1.640 2.215 1.426 -0.866 0.549 2.548 2.184 -2.013 -0.258 0.000 0.890 0.915 1.306 1.102 1.286 1.161 0.988 +1 0.764 1.096 -1.676 1.307 -0.256 2.111 0.676 0.319 2.173 1.380 -0.199 -1.136 2.215 1.573 2.025 -1.074 0.000 1.273 -0.442 -0.410 0.000 0.801 0.821 1.326 1.359 2.687 1.435 1.139 +0 0.618 0.543 1.613 1.852 0.789 0.676 -1.577 -0.484 0.000 0.743 -0.340 -1.064 2.215 0.666 -0.099 0.061 2.548 0.375 -1.760 1.301 0.000 0.867 0.869 1.001 0.727 0.641 0.745 0.925 +1 0.871 -0.395 0.418 0.535 -0.287 0.401 -1.893 1.028 0.000 0.639 0.313 -0.640 2.215 0.792 -0.604 1.289 2.548 1.512 -0.046 -1.112 0.000 0.977 0.799 0.986 0.767 0.840 0.618 0.658 +1 0.654 -1.317 1.702 0.344 -0.203 0.741 0.819 -0.661 2.173 0.582 2.131 1.591 0.000 0.572 0.129 0.917 0.000 0.706 -2.481 -0.749 0.000 0.698 0.888 0.992 0.510 0.723 0.665 0.780 +1 0.448 -2.179 0.003 1.617 1.630 0.761 -1.256 -0.196 0.000 0.935 -0.876 0.953 2.215 1.071 -0.941 -0.749 2.548 0.666 -1.101 -1.519 0.000 1.011 1.002 1.173 1.021 1.065 0.891 0.825 +1 1.389 0.236 1.470 0.467 0.354 1.224 -1.919 1.018 0.000 1.465 -0.752 -0.787 1.107 1.546 -0.206 -0.196 2.548 0.808 -1.149 -1.152 0.000 0.774 1.736 0.985 1.284 0.925 1.354 1.299 +1 1.238 -1.766 -1.391 0.273 1.399 0.548 -1.441 0.571 0.000 0.577 -1.126 -1.076 2.215 1.485 0.438 0.453 0.000 0.932 0.851 -1.114 0.000 0.876 1.298 0.984 0.562 0.251 0.743 0.903 +1 0.786 0.317 0.930 0.535 -0.546 0.674 0.687 1.702 0.000 0.793 1.034 -0.345 2.215 0.904 0.802 1.276 2.548 2.090 0.245 -0.772 0.000 0.791 0.942 0.985 0.803 0.898 0.671 0.648 +1 0.781 0.685 -1.089 1.346 -1.670 0.493 2.484 0.344 0.000 0.927 -0.427 -0.536 2.215 0.917 0.194 -1.356 0.000 1.015 0.442 0.954 1.551 1.305 0.867 0.995 1.322 0.961 0.936 0.845 +0 0.623 1.449 -0.108 0.975 -1.454 0.774 -0.200 1.551 2.173 0.493 -0.761 -0.947 0.000 0.816 0.846 0.243 2.548 0.993 0.115 0.306 0.000 0.932 0.922 1.011 0.712 1.089 0.847 0.812 +1 1.115 -0.073 0.703 1.453 -0.261 0.683 -0.467 -1.071 2.173 0.655 0.088 1.554 2.215 0.480 -0.262 1.122 0.000 0.719 -1.283 -0.338 0.000 0.762 0.736 1.345 0.970 0.744 0.818 0.706 +1 0.765 -0.216 -0.502 1.352 0.606 0.781 -0.233 0.011 2.173 0.747 0.591 1.393 0.000 1.318 0.027 -1.467 0.000 1.114 -1.061 -0.812 3.102 0.928 1.107 1.185 0.709 0.855 0.930 0.852 +0 0.366 -1.177 -1.438 0.465 -1.258 0.410 -1.570 0.101 0.000 0.571 -0.688 0.832 0.000 0.683 -0.334 1.660 2.548 0.891 -0.358 -0.590 3.102 0.783 0.744 0.979 0.603 0.535 0.543 0.613 +1 1.604 0.424 0.948 0.730 -1.122 1.063 -0.320 -1.143 2.173 0.700 -0.905 0.809 1.107 0.623 2.553 0.081 0.000 0.587 -0.242 -0.222 0.000 1.020 1.139 1.435 1.252 1.307 1.000 0.928 +1 1.387 -0.621 0.866 0.549 1.364 0.909 -0.722 -0.449 2.173 0.318 -1.708 -0.420 0.000 0.594 -1.075 1.309 0.000 1.006 -0.403 -1.546 3.102 0.694 0.801 0.985 0.691 0.854 0.817 0.706 +1 0.695 0.197 1.125 0.722 -0.202 0.961 1.248 -1.651 0.000 1.814 1.299 0.185 0.000 1.447 0.037 -1.278 2.548 1.003 0.336 -1.701 0.000 0.666 0.862 0.987 0.544 0.880 0.828 0.792 +1 0.754 0.615 -1.346 0.179 1.705 1.028 0.048 0.836 2.173 0.848 -0.654 -0.570 1.107 1.133 0.352 -0.196 0.000 1.692 0.347 1.597 0.000 1.526 1.212 0.979 0.950 1.406 0.996 0.850 +1 0.379 1.340 0.562 1.061 -1.682 1.405 0.579 0.814 2.173 1.318 0.125 0.359 2.215 1.593 1.967 -1.027 0.000 1.293 0.189 -1.267 0.000 0.914 0.878 0.992 1.530 0.923 1.327 1.104 +0 0.464 -2.161 -0.966 1.196 0.693 0.574 -0.551 0.723 0.000 0.862 -0.843 -0.748 2.215 0.722 0.124 -1.322 2.548 0.591 -1.410 1.456 0.000 0.763 0.928 1.029 1.161 0.604 0.885 0.793 +0 0.314 -0.863 1.325 1.115 0.690 0.337 -2.718 -0.632 0.000 0.552 -0.320 0.567 2.215 1.126 0.508 -0.445 2.548 0.640 1.503 1.551 0.000 0.490 0.919 0.985 1.855 0.766 1.222 1.052 +1 0.658 -1.330 0.029 0.982 0.937 1.461 0.109 -1.368 0.000 0.667 -0.512 -0.441 2.215 0.895 -0.414 0.638 2.548 0.971 0.858 0.745 0.000 1.924 1.401 0.987 0.535 0.679 0.984 0.919 +1 1.868 0.019 0.780 0.638 -1.454 2.141 -1.253 -0.081 0.000 1.518 0.639 -0.500 0.000 2.824 -0.343 1.640 2.548 2.813 -1.023 -1.701 3.102 1.024 1.972 1.367 1.156 0.986 1.718 1.499 +1 1.233 -0.275 0.346 1.311 -0.446 0.709 0.816 -1.461 2.173 1.190 1.228 1.629 2.215 0.696 0.084 -0.210 0.000 0.502 0.891 0.102 0.000 0.378 0.953 1.152 1.252 0.558 1.162 0.900 +1 0.419 1.335 0.926 1.191 -1.131 0.799 1.834 -0.351 0.000 1.044 0.512 0.481 2.215 1.179 0.469 1.462 2.548 0.786 0.611 -0.774 0.000 0.843 1.147 0.989 0.705 0.911 0.933 0.812 +1 2.457 0.526 -0.511 0.491 -1.341 1.153 -0.223 0.796 2.173 0.806 1.127 -1.548 0.000 1.040 0.970 -0.579 0.000 1.927 0.977 1.292 3.102 1.078 1.021 1.034 1.538 1.375 1.235 1.108 +0 0.526 0.795 1.480 1.650 1.522 0.437 2.017 -1.312 0.000 0.986 0.451 0.169 2.215 0.593 2.521 -0.360 0.000 0.618 0.699 -0.075 1.551 0.772 0.681 0.993 1.150 0.203 0.784 1.085 +1 0.375 -0.719 0.736 0.819 -0.455 1.523 -1.116 1.504 0.000 0.649 0.145 -0.305 0.000 0.441 -0.464 -0.688 1.274 1.233 -0.577 0.359 0.000 0.829 0.767 0.983 0.611 0.347 0.657 0.668 +0 0.535 0.851 -0.880 1.795 -0.047 0.987 -2.791 1.532 0.000 1.020 -1.514 1.359 0.000 0.432 0.230 0.377 2.548 0.852 -0.508 -0.570 3.102 0.911 0.884 0.983 0.603 0.406 0.641 1.213 +1 1.243 0.351 1.288 1.593 -1.708 0.447 1.109 0.108 2.173 0.481 -0.776 0.344 0.000 1.086 -0.392 -0.366 1.274 0.580 0.496 -0.744 0.000 0.773 0.720 0.991 1.231 0.825 0.936 0.804 +0 0.911 1.078 0.184 1.293 -0.268 1.055 -0.240 -1.250 0.000 0.734 0.336 -1.634 0.000 1.495 0.559 0.869 2.548 1.001 -0.269 0.533 3.102 0.844 1.023 0.988 0.939 0.534 0.908 0.923 +1 0.471 -0.708 0.088 0.343 -1.521 1.074 -0.006 1.648 2.173 1.002 0.437 0.246 1.107 1.044 -0.178 -0.518 0.000 0.808 -1.488 0.775 0.000 0.906 1.032 0.997 0.869 1.497 0.937 0.800 +0 0.551 -1.093 -1.685 0.148 -0.479 0.846 -0.773 -0.540 2.173 0.502 0.255 0.727 0.000 0.687 1.620 1.034 0.000 0.382 2.296 -1.585 0.000 1.037 0.865 0.988 0.840 0.575 0.925 0.777 +1 1.356 -1.317 0.178 2.624 -1.196 0.882 -0.984 -1.432 2.173 0.957 0.664 -1.716 0.000 0.401 -1.476 1.644 0.000 0.389 -1.721 0.173 0.000 1.360 1.168 2.470 1.171 0.518 0.893 1.084 +0 0.406 -0.913 -1.043 1.175 1.127 0.619 0.138 -0.003 2.173 1.197 -0.928 0.478 1.107 1.126 1.021 -1.485 0.000 0.420 -1.370 -1.015 0.000 1.452 1.178 0.989 0.861 0.901 1.054 0.887 +1 1.240 1.038 1.180 2.124 0.604 0.511 0.891 -1.216 2.173 0.808 1.635 -0.872 0.000 0.648 1.067 -0.461 0.000 0.934 0.134 -0.439 3.102 0.785 0.724 1.114 1.103 0.547 0.842 0.772 +1 0.444 0.405 -1.127 1.115 0.611 1.338 -0.545 0.637 2.173 0.745 1.130 -1.608 0.000 0.993 -0.837 -1.184 0.000 0.752 -1.527 -0.527 0.000 0.919 1.372 0.987 0.536 0.844 0.819 0.723 +0 0.706 0.490 -1.417 0.927 -1.560 0.618 -0.236 0.624 2.173 0.463 0.724 0.218 1.107 0.928 0.281 -0.589 0.000 0.398 1.569 -1.264 0.000 0.699 0.924 1.004 0.802 0.491 0.856 0.735 +0 1.022 -1.246 -0.177 0.164 -1.328 0.921 -0.838 -1.616 2.173 1.643 -0.547 0.605 0.000 1.008 -0.447 0.076 2.548 0.771 0.088 -0.898 0.000 1.006 0.883 0.999 0.945 1.218 0.755 0.685 +1 1.171 0.252 0.823 1.653 1.150 1.375 0.971 -1.256 2.173 2.392 2.049 -0.306 0.000 1.578 0.399 1.390 0.000 0.566 1.316 -1.536 0.000 0.805 1.065 0.978 0.573 0.840 1.130 0.937 +1 0.667 -1.332 -0.712 0.877 0.899 0.751 -0.433 0.663 0.000 0.736 -0.534 -0.649 2.215 0.728 0.335 0.203 0.000 3.025 -0.648 -1.501 3.102 0.768 0.930 1.052 0.976 0.948 0.972 0.866 +0 0.526 0.938 -0.817 2.830 -0.253 1.106 -1.670 1.372 0.000 0.847 -0.445 1.122 2.215 1.021 -0.483 -0.648 2.548 0.937 -0.748 1.634 0.000 0.725 0.828 0.988 1.435 0.989 1.369 1.957 +1 1.015 0.316 -0.998 0.958 0.173 1.249 -0.331 1.287 0.000 0.693 1.639 -0.988 0.000 1.835 0.222 0.207 2.548 1.215 0.373 -0.666 3.102 0.454 0.667 1.189 0.851 0.816 0.835 0.768 +0 1.019 0.840 0.317 2.432 1.115 0.448 -0.270 -0.251 2.173 0.510 -1.684 -0.617 0.000 1.206 0.392 -1.192 2.548 0.920 -1.482 -1.455 0.000 0.612 1.167 1.437 1.196 0.762 0.989 1.236 +1 0.749 0.170 -1.286 0.730 1.386 0.492 0.634 1.058 0.000 0.762 0.837 0.172 2.215 1.665 1.125 -0.797 2.548 0.849 -0.365 1.087 0.000 0.560 0.745 0.979 1.272 0.945 0.978 0.863 +1 1.676 -0.182 -0.485 0.958 1.099 0.493 0.861 -0.319 0.000 1.314 0.219 1.088 2.215 0.536 0.232 -1.122 0.000 1.341 0.914 -1.672 3.102 0.999 1.193 1.738 1.256 0.907 0.981 0.943 +0 2.412 0.291 1.412 1.998 1.743 1.097 0.904 0.243 0.000 0.928 0.661 -1.233 0.000 1.426 0.705 -0.549 0.000 1.785 -1.380 0.362 1.551 0.863 0.578 0.972 2.138 0.720 1.388 1.467 +0 1.784 0.008 1.346 0.375 -0.189 0.982 0.676 -0.910 0.000 0.680 -0.188 0.098 0.000 0.674 0.248 0.039 2.548 0.775 0.264 1.694 3.102 0.593 0.593 1.113 0.750 0.551 0.520 0.500 +1 1.376 -0.850 -0.897 0.560 -0.062 0.831 1.151 0.249 0.000 1.130 0.236 -1.596 2.215 0.959 -0.557 1.187 0.000 1.158 0.144 0.758 3.102 2.015 1.145 0.996 0.982 0.881 0.964 0.977 +0 1.139 -0.488 -0.055 1.071 0.467 0.663 -0.732 -1.076 0.000 0.979 -0.905 1.447 2.215 0.761 0.632 -0.897 2.548 0.379 0.523 -1.470 0.000 0.624 0.807 0.994 1.055 1.143 0.919 0.807 +0 1.576 0.206 -0.132 2.022 0.205 1.505 1.250 -1.459 2.173 0.738 0.401 1.219 1.107 0.371 -1.488 0.138 0.000 0.493 0.521 -1.641 0.000 0.806 0.727 0.979 2.331 1.235 1.547 1.223 +1 0.764 1.591 1.305 0.632 -0.078 0.643 -0.061 -1.078 2.173 0.431 -0.960 0.648 2.215 0.674 1.528 0.141 0.000 0.656 0.570 1.585 0.000 0.803 0.940 0.987 1.273 0.859 1.048 0.853 +0 1.511 -0.730 0.934 1.315 -0.907 0.849 0.211 0.984 2.173 2.691 0.527 -0.596 2.215 1.298 -0.996 1.304 0.000 0.785 1.200 -0.167 0.000 1.243 1.047 1.945 2.069 2.231 1.589 1.345 +1 0.657 1.036 0.003 0.551 -1.155 0.365 -0.160 0.364 2.173 0.502 -2.299 0.590 0.000 0.447 -1.503 -1.682 0.000 1.283 -0.867 1.135 0.000 0.893 0.843 0.989 1.276 0.795 0.926 1.324 +0 0.677 -0.636 1.039 0.907 -0.170 0.828 0.114 0.246 2.173 1.146 -0.193 -1.388 2.215 0.726 0.600 -0.607 0.000 0.690 0.760 1.251 0.000 0.783 0.833 0.987 0.677 1.445 0.819 0.727 +1 0.493 0.388 0.925 1.150 0.377 0.914 0.382 -1.323 1.087 0.564 0.633 -0.535 2.215 0.623 -0.108 -1.011 0.000 0.919 1.241 -1.627 0.000 0.903 0.957 0.988 1.249 0.704 0.899 1.151 +0 0.583 -1.298 -0.854 1.265 0.284 0.810 -0.518 -1.452 2.173 0.946 -1.523 -0.244 0.000 1.168 -0.865 1.082 2.548 0.962 -1.733 0.670 0.000 0.967 0.989 1.018 1.057 0.953 0.930 0.816 +1 2.510 0.206 1.077 0.165 -0.323 1.068 -0.954 -0.160 0.000 0.607 0.727 1.643 2.215 1.054 1.011 -0.899 0.000 0.444 0.443 -1.317 3.102 0.849 0.753 0.989 0.661 0.220 0.770 0.857 +1 1.263 0.988 -0.617 0.615 0.309 1.295 1.185 0.630 2.173 0.987 1.045 -0.966 0.000 0.952 -2.625 -1.494 0.000 0.871 1.708 -0.419 0.000 0.837 0.735 0.988 1.041 0.966 0.908 0.795 +0 0.608 -0.054 -0.064 1.794 0.192 0.725 2.000 -0.692 0.000 0.941 1.014 1.082 2.215 1.098 -1.028 -1.288 0.000 0.370 0.642 -0.731 1.551 0.415 0.588 0.983 0.934 0.536 0.909 1.019 +0 1.308 0.667 0.904 0.767 0.140 2.330 0.109 0.682 2.173 3.897 0.335 -1.120 0.000 1.434 0.684 1.240 2.548 2.419 -0.098 -0.519 0.000 1.067 1.762 0.988 0.819 1.324 2.068 1.604 +0 0.594 2.205 -1.599 1.070 1.647 0.866 0.434 1.135 2.173 1.050 0.356 0.382 2.215 1.774 0.455 -0.731 0.000 0.553 0.845 -0.510 0.000 1.028 1.105 0.983 0.883 0.884 0.956 0.967 +0 1.183 0.516 1.575 0.667 -0.826 0.881 -2.022 1.025 0.000 0.681 -1.032 -0.057 2.215 1.134 1.123 -0.700 2.548 0.523 -1.853 -0.074 0.000 0.873 0.919 1.021 0.786 1.418 1.572 1.394 +1 0.760 0.832 1.732 0.826 1.085 0.781 0.988 0.709 2.173 1.464 -1.091 -0.452 0.000 0.608 -0.580 -1.688 0.000 1.582 -0.387 -1.067 3.102 0.771 0.837 0.977 0.848 1.505 1.223 1.062 +1 0.557 0.144 -0.881 1.781 -0.172 0.740 0.959 1.169 2.173 0.500 -0.061 -0.618 0.000 0.975 0.727 -1.536 1.274 0.552 0.388 0.783 0.000 0.823 0.842 0.979 1.105 0.690 0.828 0.780 +0 3.208 -0.165 1.046 2.096 0.836 1.375 0.220 -0.793 0.000 1.625 -0.315 -0.431 2.215 0.685 -0.865 1.631 2.548 1.116 -1.161 -0.912 0.000 0.833 0.982 0.964 1.951 1.132 1.282 1.308 +0 2.028 1.070 1.307 0.746 -1.000 1.553 0.256 -0.548 2.173 1.253 0.108 1.046 2.215 0.848 0.591 -0.945 0.000 0.958 -0.344 0.431 0.000 0.779 1.093 1.489 1.138 2.040 1.378 1.081 +0 1.359 -0.948 1.077 0.145 -0.854 0.548 -1.557 -1.279 2.173 0.614 0.617 0.206 0.000 0.790 0.740 -1.008 2.548 0.520 -0.725 0.511 0.000 0.640 1.126 0.989 0.924 1.243 0.840 0.775 +0 0.481 -0.396 -0.783 0.879 0.976 1.717 -0.718 0.110 2.173 2.501 -1.304 -0.309 0.000 4.993 -0.355 1.696 2.548 2.238 0.214 1.648 0.000 4.079 2.769 0.984 1.224 3.663 2.564 1.899 +0 0.315 -1.370 0.926 1.848 -0.979 0.976 -0.262 0.017 2.173 0.580 -0.321 0.607 0.000 0.658 -0.470 1.608 0.000 0.674 1.002 1.579 3.102 0.749 0.885 1.045 1.195 1.091 1.049 0.875 +1 0.556 -0.208 -1.669 1.334 0.758 0.639 0.616 -0.222 2.173 0.566 -0.387 -0.870 0.000 0.654 0.341 -1.699 0.000 0.878 0.410 1.245 3.102 0.822 0.706 0.988 0.586 0.770 0.682 0.661 +1 0.989 -1.787 -0.247 0.848 -1.586 0.471 0.121 -1.333 2.173 0.854 0.496 0.237 2.215 0.709 -0.977 0.768 0.000 0.879 0.348 1.663 0.000 0.949 0.910 1.185 1.093 0.940 1.097 0.929 +0 1.283 -0.647 -1.623 0.398 1.279 0.633 0.736 -1.126 1.087 0.760 0.217 -0.300 2.215 0.981 -1.344 0.630 0.000 0.722 1.000 1.341 0.000 0.859 0.902 0.989 0.899 0.742 0.722 0.744 +1 0.507 1.448 -0.020 1.083 -1.092 0.538 -0.297 -0.548 0.000 1.142 0.998 1.096 2.215 0.586 -1.057 0.449 0.000 0.923 1.300 -0.994 3.102 0.909 1.105 0.988 1.114 0.917 0.986 1.102 +0 1.517 -0.472 1.501 1.597 1.333 1.022 0.214 -0.357 0.000 1.096 0.070 1.619 2.215 1.008 -0.750 0.141 0.000 1.603 0.972 -0.070 3.102 0.940 1.265 0.981 1.383 1.379 1.124 1.096 +0 1.365 -0.908 1.734 0.941 1.236 3.074 -0.423 1.359 2.173 4.498 1.345 -0.478 2.215 2.485 1.593 -0.147 0.000 0.871 0.939 0.887 0.000 1.188 1.367 0.987 0.848 7.860 3.740 2.959 +1 0.672 -0.656 -1.592 1.342 0.353 1.020 0.468 1.511 0.000 0.860 -0.666 0.064 2.215 1.153 1.387 -1.044 0.000 0.777 -1.820 1.100 0.000 0.745 0.825 1.294 0.844 0.682 0.763 0.723 +0 0.647 0.910 1.638 0.767 -1.437 0.750 0.874 0.088 2.173 0.876 0.887 0.873 2.215 1.008 0.335 -0.962 0.000 1.089 -0.528 -0.516 0.000 0.755 0.998 0.988 0.897 0.776 0.837 0.811 +1 0.916 1.690 -0.634 0.552 1.147 0.748 1.313 -0.865 2.173 1.027 1.136 -1.462 0.000 1.636 1.648 0.380 0.000 1.195 0.611 1.039 3.102 1.150 0.926 0.987 0.729 1.035 0.738 0.661 +0 0.760 -1.133 -0.067 0.769 1.512 1.124 2.093 0.633 0.000 1.058 1.553 -0.522 0.000 1.400 0.468 -1.261 1.274 1.090 1.439 1.381 3.102 0.850 0.887 1.047 1.411 0.891 1.064 1.098 +0 0.812 1.438 0.576 1.265 -0.323 1.119 -0.448 1.418 0.000 0.661 -1.304 -0.785 0.000 0.882 -1.166 1.008 0.000 2.425 0.281 -0.987 3.102 0.999 1.072 1.017 1.156 0.267 1.008 1.221 +1 1.193 0.713 -1.367 1.371 1.463 1.236 -0.612 -0.820 2.173 1.725 0.458 0.154 2.215 1.215 -1.625 0.692 0.000 0.415 -1.908 0.884 0.000 0.884 1.408 0.987 1.443 2.059 1.474 1.468 +1 1.898 0.517 0.949 1.760 0.203 1.243 0.149 -1.353 2.173 1.327 -0.172 1.636 0.000 1.651 0.433 -0.068 0.000 0.933 0.777 0.583 0.000 0.821 0.588 1.575 1.760 0.922 1.161 1.002 +1 0.814 0.462 -0.230 1.436 -0.244 1.475 -0.026 -1.246 0.000 0.757 -1.239 1.238 0.000 1.514 -1.122 0.799 2.548 1.271 -0.319 0.890 0.000 0.709 0.619 0.977 2.115 0.703 1.460 1.292 +1 0.337 -0.054 0.805 1.145 -0.355 0.738 1.140 1.334 2.173 1.058 1.230 -1.579 0.000 1.619 0.039 -0.005 2.548 0.369 0.869 -0.758 0.000 0.556 1.238 0.988 1.567 1.500 1.157 1.112 +1 0.702 0.186 1.242 1.406 -1.603 1.552 -0.553 -0.571 0.000 2.401 0.629 0.935 2.215 1.615 1.044 0.598 2.548 2.641 0.269 -0.751 0.000 0.839 0.886 0.984 1.436 0.819 1.175 1.081 +1 0.821 0.710 1.120 0.679 -1.004 0.749 0.279 -1.359 2.173 0.611 -0.901 0.433 0.000 0.818 0.696 0.263 0.000 0.870 1.151 -1.428 3.102 0.927 0.951 0.989 0.717 0.496 0.728 0.667 +1 1.136 0.719 1.366 0.626 -1.006 0.708 0.728 0.127 1.087 0.815 -0.432 1.490 0.000 1.085 1.123 -0.755 2.548 0.370 1.205 -0.094 0.000 1.055 1.040 0.988 0.757 0.826 0.810 0.737 +1 0.997 1.079 -1.146 0.728 0.204 0.483 -1.041 1.024 2.173 0.472 -0.341 -0.341 2.215 0.810 1.125 1.252 0.000 0.749 2.021 -1.219 0.000 0.871 1.065 1.107 1.217 0.708 1.026 0.901 +1 3.071 -1.038 0.676 0.620 0.675 3.127 -0.650 -1.471 0.000 2.361 -0.480 0.096 1.107 0.442 0.150 -1.294 0.000 0.974 -2.292 -0.005 0.000 0.954 1.249 0.979 1.402 1.137 1.738 1.735 +0 0.923 1.275 -1.046 1.126 -0.379 0.635 1.499 0.862 0.000 0.919 1.058 -0.354 2.215 1.165 0.122 1.188 1.274 0.646 0.884 -1.623 0.000 0.799 0.936 0.984 1.280 1.211 0.924 0.829 +0 0.295 -0.063 -1.391 3.780 -0.320 1.040 -0.865 1.657 0.000 1.172 -0.028 1.183 2.215 0.825 -1.221 1.037 0.000 0.745 1.806 -1.241 0.000 0.969 0.988 1.204 0.755 0.743 0.995 1.128 +0 0.998 -0.746 0.068 0.051 1.718 1.133 -1.050 1.403 2.173 1.674 0.547 -0.348 2.215 0.651 0.711 1.528 0.000 0.701 -1.141 -0.082 0.000 0.808 0.923 0.982 0.883 2.731 1.300 1.036 +0 1.267 -0.708 -1.331 1.436 -0.949 1.664 -0.753 0.541 2.173 0.496 -1.117 -0.732 0.000 0.462 -0.584 0.938 0.000 0.831 0.697 -1.460 3.102 0.756 0.991 0.976 0.680 1.638 1.256 0.976 +1 0.708 -0.807 1.189 0.347 0.755 0.659 -0.668 -0.939 0.000 0.864 0.621 1.064 1.107 1.342 -0.438 -0.339 2.548 1.051 0.523 0.288 0.000 0.954 0.970 0.998 0.626 1.283 0.933 0.820 +1 0.357 -1.029 1.040 1.081 -1.183 0.855 0.323 0.924 0.000 0.287 1.509 0.632 0.000 0.518 -1.317 -1.117 2.548 0.726 0.643 -0.323 3.102 0.739 1.119 0.987 0.633 0.704 0.729 0.687 +1 0.912 -1.211 -0.673 2.702 -0.548 1.063 -0.718 0.673 2.173 1.063 0.719 1.420 0.000 0.774 0.524 -1.649 2.548 0.670 1.081 1.350 0.000 0.662 1.252 0.992 1.833 1.257 1.555 1.557 +1 1.836 -0.141 0.592 0.575 -0.104 0.407 -0.466 -0.892 0.000 0.870 -0.865 -1.670 2.215 0.434 -1.775 0.675 0.000 1.655 0.216 -1.088 3.102 0.965 0.932 0.979 1.044 0.853 0.893 0.792 +0 0.924 -0.301 1.626 0.182 -0.953 1.124 -0.460 -1.121 0.000 0.798 -0.529 0.673 1.107 1.850 0.264 -0.152 2.548 0.848 -1.937 1.224 0.000 1.950 1.476 0.981 0.981 1.036 1.104 0.932 +1 0.485 0.618 -1.639 0.948 -0.102 1.249 0.582 -1.363 0.000 2.288 0.407 0.612 0.000 1.936 -0.389 -0.654 2.548 2.623 -0.173 -1.186 3.102 0.900 0.933 0.989 0.783 0.815 0.938 0.840 +0 0.841 1.305 -0.536 1.533 0.646 0.714 0.483 1.479 1.087 0.477 -2.140 0.091 0.000 0.512 0.803 0.019 0.000 0.714 1.462 -1.182 0.000 0.661 0.799 1.377 1.008 0.411 0.821 0.688 +1 0.570 0.721 0.779 0.601 1.658 0.526 1.125 -0.772 0.000 0.631 1.037 0.235 0.000 1.175 -0.105 1.599 2.548 1.121 -0.223 -0.683 0.000 0.897 1.021 0.985 0.571 0.594 0.717 0.667 +1 0.515 -1.143 -1.551 0.608 -0.138 2.469 -0.342 -1.450 0.000 1.104 0.223 -0.036 0.000 1.793 -1.322 0.449 2.548 1.453 -0.611 1.169 3.102 1.415 1.560 0.987 0.851 0.872 1.518 1.475 +1 0.703 -0.760 1.218 1.352 1.486 0.366 0.445 1.161 0.000 0.758 -0.263 -0.380 2.215 0.550 0.624 0.543 0.000 1.569 1.041 -0.471 3.102 0.438 0.770 0.980 0.995 0.827 0.919 0.745 +1 1.053 -1.488 0.202 1.039 -1.233 0.350 -2.444 -1.188 0.000 0.847 0.096 1.426 2.215 0.879 -0.801 0.445 2.548 0.798 -0.847 -0.592 0.000 0.760 0.830 1.393 1.247 0.849 0.873 0.811 +1 0.506 -1.981 -0.869 0.332 1.418 1.312 -0.826 1.664 2.173 0.990 -1.358 0.088 0.000 0.669 -1.862 0.394 0.000 0.788 1.360 0.090 0.000 0.534 0.496 0.986 0.839 0.900 0.920 0.772 +0 0.576 -1.816 -0.981 0.434 0.782 0.764 -0.078 -0.026 2.173 0.805 -0.749 1.274 0.000 1.067 -0.156 1.739 2.548 0.922 -0.929 -0.219 0.000 0.748 0.963 0.980 0.723 1.126 0.734 0.656 +0 0.779 -0.425 0.558 0.662 -0.622 0.972 -1.031 1.136 2.173 0.784 -0.074 0.181 0.000 1.209 0.626 -1.128 2.548 0.744 -0.532 -1.735 0.000 1.023 0.993 0.988 0.879 1.784 1.003 0.852 +1 0.582 -0.450 -0.398 1.704 0.139 2.506 0.479 -1.057 2.173 3.251 1.373 0.855 0.000 1.588 2.155 -0.417 0.000 1.126 0.038 0.538 0.000 1.007 0.864 0.986 2.596 1.086 1.699 1.364 +1 0.665 0.934 1.095 1.052 -1.646 0.637 1.850 0.035 0.000 0.975 0.258 -0.405 2.215 0.828 -0.153 -1.703 2.548 0.532 1.578 1.279 0.000 0.801 1.078 0.989 0.543 0.904 0.891 0.850 +0 0.829 1.805 -0.112 0.585 1.605 0.319 1.455 0.736 0.000 0.457 -0.395 -0.722 2.215 0.523 0.159 -1.725 0.000 0.448 -0.165 -0.268 0.000 0.776 0.754 0.989 0.735 0.439 0.716 0.626 +1 0.487 0.043 -0.651 0.157 -0.174 0.763 0.428 -1.464 2.173 0.740 0.086 -0.138 2.215 0.892 -1.604 0.528 0.000 1.639 -0.709 1.049 0.000 0.870 1.100 0.851 0.941 1.047 1.058 0.871 +0 1.677 -0.637 -1.491 0.966 -1.148 0.898 -0.832 0.490 2.173 0.640 0.426 0.309 0.000 0.971 0.207 -1.732 0.000 0.930 -0.883 1.157 1.551 0.860 1.098 0.984 1.356 0.557 0.943 1.028 +1 0.729 0.406 -1.293 0.645 1.192 0.751 0.715 1.387 2.173 0.907 0.534 -0.571 2.215 1.363 -0.316 -0.311 0.000 0.861 -0.668 0.766 0.000 1.024 0.937 0.990 0.651 1.197 0.922 0.854 +0 0.741 0.264 0.828 0.930 -1.201 0.476 -0.322 1.091 0.000 1.257 -0.099 0.090 2.215 1.260 -0.016 -1.523 2.548 0.648 -0.498 -0.595 0.000 0.855 0.842 1.112 0.696 1.329 0.800 0.695 +0 0.318 0.096 -0.821 0.529 -1.037 1.175 -1.865 -1.722 0.000 0.836 -0.543 0.460 2.215 1.280 0.961 0.323 2.548 0.803 0.383 -0.583 0.000 2.509 1.785 0.999 1.601 0.996 1.677 1.358 +1 2.360 -0.588 -0.550 0.371 -1.581 1.103 -0.628 1.267 2.173 0.563 -1.421 -0.493 0.000 1.237 0.190 1.466 0.000 1.663 -0.618 0.266 3.102 0.926 1.073 1.038 0.873 1.128 1.006 0.852 +1 1.047 -0.157 0.034 0.452 1.293 0.735 -0.670 -0.557 2.173 0.728 1.142 0.579 0.000 0.791 1.045 1.595 0.000 0.688 0.872 -1.215 3.102 0.923 0.677 0.987 0.831 0.844 0.888 0.765 +0 0.341 1.150 -0.992 0.599 -1.008 0.640 0.458 1.257 2.173 0.894 -0.296 0.045 0.000 0.759 0.571 -1.379 2.548 0.594 0.759 0.500 0.000 0.721 0.894 0.993 0.635 0.606 0.666 0.663 +0 1.458 -0.559 -0.535 0.581 1.639 0.608 -1.230 -1.679 1.087 0.725 -2.228 0.772 0.000 0.755 -0.076 -1.582 2.548 1.352 0.686 0.250 0.000 2.986 1.805 1.180 0.866 0.522 1.192 1.021 +0 0.541 -1.259 1.619 0.384 0.767 0.790 -0.951 0.164 0.000 0.990 -0.278 -0.830 2.215 1.668 0.410 1.697 2.548 1.058 0.112 0.161 0.000 0.802 1.020 0.995 0.775 1.159 1.040 0.865 +0 0.840 2.235 0.046 0.668 -1.252 1.428 0.009 1.485 1.087 1.711 1.268 -0.194 2.215 0.964 -0.503 -1.202 0.000 0.546 1.178 -0.588 0.000 0.992 0.967 0.990 0.882 2.796 1.554 1.301 +0 1.492 -0.796 0.539 0.688 0.323 0.807 -0.794 -1.342 1.087 0.837 -0.473 -0.502 2.215 0.519 -0.229 -0.938 0.000 0.814 -0.902 1.726 0.000 0.573 0.607 0.988 1.202 0.853 0.886 0.722 +0 0.756 1.039 1.335 1.841 -1.470 1.581 0.449 0.232 2.173 1.419 -2.511 -1.624 0.000 1.687 -2.676 -0.559 0.000 1.352 0.069 0.770 0.000 0.646 0.764 0.990 0.441 0.824 1.013 0.819 +0 1.495 0.737 -1.221 0.935 1.017 0.581 0.525 0.065 2.173 0.270 1.377 -1.042 0.000 0.460 0.855 0.345 0.000 0.564 -0.997 0.516 3.102 0.529 0.658 1.477 1.028 0.638 0.796 0.641 +0 1.325 -1.343 0.828 0.525 -0.032 0.440 0.711 -1.146 0.000 0.713 0.467 -0.194 0.000 0.916 -0.675 -1.573 2.548 0.525 1.200 1.394 3.102 0.911 0.960 0.981 0.980 0.740 0.750 0.818 +0 1.082 -0.577 -1.740 0.330 0.107 0.831 -2.592 -0.137 0.000 1.700 -0.147 0.803 2.215 1.931 -0.869 -1.088 2.548 0.553 0.158 0.579 0.000 2.076 1.795 0.990 0.908 2.067 1.687 1.348 +0 0.779 0.473 1.546 0.902 -0.723 0.858 0.541 0.304 0.000 1.111 0.146 -0.244 2.215 1.835 -0.126 -1.553 2.548 0.989 1.099 0.970 0.000 0.970 1.008 1.034 0.785 1.419 1.069 0.897 +1 0.510 -0.502 1.283 1.428 0.220 2.544 0.099 -1.212 0.000 0.744 -0.489 0.404 0.000 0.903 0.149 -0.113 2.548 3.322 -1.220 0.836 1.551 3.037 1.886 0.987 0.880 1.561 1.929 1.510 +1 0.654 -1.137 0.053 0.690 -1.699 1.923 -1.043 1.300 0.000 1.346 -0.194 -0.178 0.000 2.390 -1.264 -0.493 1.274 1.875 -0.952 -1.139 3.102 0.828 1.168 0.986 0.877 0.911 0.953 0.853 +0 1.597 0.091 -0.844 1.228 -1.200 1.056 -0.199 -0.409 2.173 1.127 -0.818 1.677 0.000 1.160 -0.861 0.571 2.548 2.065 -1.092 1.010 0.000 1.220 1.000 0.998 0.881 1.188 1.136 1.114 +1 0.948 -0.751 -0.939 1.035 1.715 0.963 0.521 0.620 2.173 0.440 -0.758 -0.458 0.000 0.683 0.896 -1.288 2.548 0.534 1.898 1.308 0.000 0.745 0.751 0.991 0.748 1.026 0.902 0.817 +0 0.460 0.494 -0.403 1.473 -0.370 1.159 -2.180 0.951 0.000 1.352 -0.261 -1.132 2.215 0.792 0.071 0.243 0.000 0.830 2.429 0.608 0.000 0.590 2.131 0.980 1.321 1.213 2.017 1.621 +1 2.549 -0.524 1.716 2.156 -1.269 1.851 -2.412 0.412 0.000 1.480 0.195 -0.645 0.000 0.568 0.796 -0.816 2.548 0.809 0.908 0.271 3.102 1.109 0.850 1.419 1.105 0.434 0.996 0.946 +0 0.866 0.122 0.781 1.087 1.529 0.661 0.638 -1.143 0.000 0.886 0.425 -0.236 2.215 0.550 -0.187 -0.458 1.274 0.837 0.900 0.715 0.000 0.985 0.896 0.995 0.755 0.284 0.668 0.659 +0 0.557 0.274 1.131 1.700 -1.611 1.087 -1.086 -1.181 1.087 1.343 0.701 0.347 0.000 1.170 0.465 0.802 2.548 1.406 0.734 -0.160 0.000 0.803 0.772 0.993 1.703 1.859 1.433 1.296 +0 1.450 -1.520 0.347 0.942 -0.428 1.532 -0.545 -1.176 2.173 0.449 0.212 -1.331 0.000 1.018 -2.413 1.023 0.000 1.601 0.476 0.932 0.000 1.005 0.649 1.040 1.597 1.100 1.083 1.098 +1 0.634 2.178 -0.858 0.966 -0.583 2.775 1.462 1.187 0.000 1.010 0.737 -0.567 0.000 0.957 0.092 -1.136 2.548 2.613 -0.708 -0.585 0.000 0.916 0.723 0.989 0.691 0.517 0.588 0.589 +1 1.245 2.112 0.378 0.708 1.646 0.332 -2.529 1.463 0.000 0.776 1.141 -0.440 1.107 0.472 -0.204 -1.565 1.274 0.652 -0.035 0.175 0.000 0.603 0.548 1.183 1.061 0.732 0.817 0.692 +0 1.041 -0.311 1.188 0.728 -0.160 0.384 0.206 0.032 0.000 0.488 -0.541 -0.434 2.215 1.271 0.150 1.722 2.548 0.915 -0.397 -1.437 0.000 0.933 0.804 1.131 0.684 0.838 0.640 0.590 +0 1.005 1.683 0.469 0.278 1.217 1.266 0.937 -1.076 0.000 1.105 0.762 0.857 2.215 0.633 0.135 -0.169 2.548 0.668 1.216 1.717 0.000 0.898 0.897 0.988 0.643 0.766 0.886 0.800 +0 0.392 -0.226 0.907 1.461 -1.627 1.673 -1.974 0.185 0.000 0.564 -1.894 1.587 0.000 1.579 -0.865 -1.590 2.548 0.862 -0.944 0.145 0.000 0.955 0.888 0.988 0.597 0.431 0.573 0.549 +1 0.919 0.194 -0.976 0.735 0.382 0.619 -0.242 -0.367 0.000 1.303 -0.022 1.671 2.215 0.823 0.662 0.927 1.274 0.746 1.643 1.380 0.000 1.742 1.123 1.071 0.927 0.804 0.920 0.805 +1 0.539 -0.549 0.493 1.731 -1.450 0.562 0.148 1.189 1.087 0.698 0.483 -0.384 2.215 0.691 -0.212 -0.043 0.000 0.544 0.980 -1.328 0.000 0.807 0.745 1.317 1.005 0.925 0.790 0.688 +0 1.408 1.020 -0.673 0.693 1.157 0.720 -0.442 1.195 0.000 0.777 0.516 1.439 0.000 0.619 -1.233 -0.543 2.548 0.546 -1.350 -0.211 0.000 0.877 0.880 1.364 1.212 0.644 0.796 0.868 +0 0.936 0.174 -0.733 0.654 1.317 1.084 2.003 1.262 0.000 1.201 -0.749 -1.250 0.000 2.018 -0.592 -0.345 1.274 0.969 0.519 0.195 3.102 1.725 1.476 1.043 0.959 0.883 1.100 0.959 +1 1.504 -0.080 1.626 1.061 -0.971 1.701 1.540 0.941 0.000 1.558 -1.209 -0.349 2.215 0.954 0.701 -0.229 2.548 1.038 -0.640 1.254 0.000 0.834 1.145 1.257 1.017 1.544 1.158 0.999 +0 0.463 -0.265 1.143 0.445 -0.907 0.744 -0.470 1.684 2.173 0.614 0.926 0.170 0.000 1.077 1.540 -0.324 2.548 0.794 0.216 1.120 0.000 0.758 1.014 0.985 1.693 1.817 1.256 1.037 +1 1.601 0.205 0.104 0.109 -1.544 1.666 -0.458 -1.544 0.000 1.588 0.041 1.066 2.215 1.751 -0.171 -0.327 0.000 2.025 0.564 -0.577 0.000 1.037 0.986 0.987 1.012 0.488 1.061 0.881 +1 0.436 1.226 1.020 0.521 -0.517 1.356 0.539 -1.599 0.000 0.735 -0.010 0.176 0.000 0.883 0.236 1.151 2.548 1.066 2.313 0.267 0.000 0.802 0.794 0.996 0.746 0.972 0.633 0.594 +0 1.448 -0.116 1.070 0.596 0.270 0.818 -0.288 1.665 2.173 1.961 -0.396 -0.277 1.107 0.594 -0.065 -1.308 0.000 1.084 1.934 -1.711 0.000 1.368 1.404 0.988 1.278 1.836 1.488 1.232 +1 0.876 -0.925 -1.729 1.207 -1.274 0.400 -0.663 1.022 0.000 1.077 -0.110 -0.024 2.215 1.045 0.515 -1.287 0.000 1.649 1.061 0.477 3.102 0.723 0.897 1.001 1.326 1.044 1.047 0.849 +1 1.435 0.393 0.264 1.023 -0.161 1.010 -0.169 1.415 2.173 1.092 -0.949 -1.259 2.215 0.520 -1.350 0.455 0.000 0.936 0.069 -1.405 0.000 1.015 0.925 0.998 1.644 1.213 1.323 1.076 +1 1.611 -1.168 1.419 0.335 -1.111 0.684 -0.301 -0.237 2.173 0.465 -1.417 -0.548 0.000 0.577 -0.161 -0.688 0.000 0.655 -1.573 1.516 0.000 0.901 1.026 0.989 1.041 0.846 0.898 0.781 +1 0.903 0.807 -1.008 0.494 -0.413 0.724 -0.108 0.934 2.173 0.708 0.312 -1.544 1.107 0.614 0.583 0.815 0.000 1.206 -0.479 -0.400 0.000 1.043 0.877 0.988 1.253 0.861 0.896 0.836 +1 0.718 -2.143 0.263 0.519 -1.504 0.640 -1.512 1.171 1.087 0.651 -1.488 -1.527 0.000 1.198 -0.662 -0.494 2.548 0.741 -1.181 0.086 0.000 0.900 0.803 0.989 0.685 1.176 0.707 0.629 +1 0.277 -1.792 -1.324 0.678 0.779 0.678 0.015 1.185 0.000 0.491 0.181 -1.581 0.000 1.481 -0.749 -0.264 2.548 1.146 0.234 -0.895 1.551 0.874 1.035 0.980 0.702 0.788 0.730 0.653 +1 0.534 1.390 0.768 0.365 -1.331 0.538 -0.117 1.730 2.173 0.368 -0.597 -0.171 0.000 0.459 -1.697 -0.146 0.000 0.753 -0.838 0.520 0.000 0.465 0.882 0.993 0.662 0.553 0.661 0.638 +1 0.721 0.578 0.266 0.972 -0.725 1.138 -0.255 0.999 2.173 1.135 0.440 -1.085 0.000 0.515 -0.702 -0.144 2.548 0.803 -0.533 -1.113 0.000 0.715 0.718 0.983 1.305 0.855 0.896 0.883 +1 1.097 0.681 1.297 1.372 0.567 0.933 0.037 -0.984 2.173 0.680 0.846 -0.212 2.215 0.743 -0.531 1.707 0.000 0.869 -0.022 -0.375 0.000 0.882 0.834 1.038 1.299 0.905 0.928 0.813 +1 1.035 1.047 1.431 0.462 0.161 0.849 -0.227 -1.550 2.173 0.704 0.324 -0.087 2.215 0.730 0.715 0.756 0.000 0.820 0.078 -0.655 0.000 0.869 0.943 0.986 0.738 1.148 0.767 0.670 +1 0.692 0.540 -0.531 1.278 -1.293 1.535 0.164 -1.470 0.000 1.471 -0.735 0.074 2.215 2.209 -1.331 0.637 2.548 0.455 0.511 0.620 0.000 1.248 1.866 0.985 1.616 1.158 1.610 1.336 +1 0.485 0.212 -0.200 1.908 0.257 0.802 -0.636 1.648 2.173 0.869 0.125 -1.063 2.215 0.730 -0.801 0.742 0.000 0.563 -1.518 -1.227 0.000 0.775 0.925 0.991 1.695 0.926 1.217 1.117 +1 1.157 0.867 -0.518 0.963 -0.392 0.319 0.850 -1.468 0.000 0.895 1.433 1.368 0.000 0.741 1.901 1.567 0.000 1.201 0.348 0.465 3.102 0.721 0.969 0.980 0.754 0.473 0.683 0.774 +1 0.937 -1.100 -1.091 0.739 1.553 1.115 -0.749 -1.658 2.173 1.389 -0.319 0.297 0.000 1.457 0.794 -0.775 0.000 2.499 -0.112 1.023 0.000 0.960 1.164 0.987 0.659 0.650 1.075 0.908 +1 0.599 -0.499 0.117 1.374 -0.984 1.014 -0.507 1.025 2.173 1.122 -0.790 -0.525 2.215 0.919 -0.917 0.196 0.000 0.819 -1.326 1.691 0.000 0.975 0.948 1.052 0.656 1.564 0.925 0.813 +0 0.564 -1.735 0.062 0.185 -1.231 0.728 0.057 0.910 1.087 1.064 -0.011 -0.908 2.215 1.102 0.774 -1.185 0.000 1.361 1.520 -0.029 0.000 0.968 1.103 0.982 0.803 1.293 0.986 0.952 +1 0.430 1.524 -0.996 0.378 0.658 0.792 -0.227 -0.691 2.173 1.342 -0.792 0.629 0.000 0.457 0.705 -1.642 0.000 0.600 -0.987 1.417 3.102 1.503 0.896 0.994 0.776 0.780 0.832 0.753 +1 0.305 1.173 -0.190 0.257 1.042 1.543 0.999 -1.675 0.000 2.166 0.502 0.171 2.215 1.184 -0.161 -1.684 2.548 0.717 -0.442 0.325 0.000 0.919 0.931 0.987 1.039 1.797 1.231 0.970 +0 1.044 0.869 -0.758 0.706 -1.460 1.206 0.122 0.883 1.087 0.846 -0.110 0.121 0.000 1.604 -1.693 -0.994 0.000 1.269 0.767 0.635 3.102 2.323 2.043 0.987 1.469 0.609 1.539 1.585 +0 0.606 0.522 0.922 1.061 -0.271 0.886 -0.650 1.209 2.173 0.764 0.292 -0.921 2.215 1.407 0.285 0.008 0.000 2.007 -1.438 -1.473 0.000 0.919 0.964 0.988 1.147 1.287 1.034 0.874 +0 0.774 -0.606 -0.130 1.562 0.644 1.005 -1.452 -1.726 2.173 1.106 -1.241 -0.103 2.215 0.780 -0.703 -1.298 0.000 1.100 -0.844 -0.644 0.000 0.909 1.044 0.988 0.832 1.550 1.057 0.947 +0 0.461 -0.009 -1.427 1.320 -0.494 1.618 -0.034 1.169 2.173 0.660 2.458 -0.572 0.000 0.331 -2.591 1.552 0.000 1.307 0.225 -0.360 3.102 0.834 0.995 0.987 1.498 1.529 1.084 1.093 +0 1.651 -0.724 -0.003 0.637 0.604 1.031 0.861 -1.730 2.173 0.465 1.314 -1.063 2.215 0.492 0.012 0.324 0.000 0.464 1.624 0.322 0.000 0.594 0.923 0.989 1.073 0.629 1.106 0.881 +0 1.977 1.600 0.129 0.370 -1.618 0.680 1.261 0.726 0.000 1.168 1.240 -1.281 2.215 0.537 1.682 -1.667 0.000 1.043 0.640 -1.585 3.102 0.959 1.046 1.185 0.922 0.371 0.807 0.755 +0 1.487 1.030 0.198 1.399 0.269 1.832 -1.063 1.564 0.000 1.162 0.535 -0.713 2.215 0.874 1.375 -0.457 0.000 1.005 -0.050 -1.469 3.102 0.510 0.729 0.985 1.268 0.685 1.032 0.790 +0 0.472 1.839 0.474 1.223 1.343 1.371 -0.387 -0.755 0.000 1.335 0.164 0.388 2.215 0.977 1.012 -1.692 0.000 0.435 2.489 -1.348 0.000 0.884 1.307 0.996 0.978 0.675 1.041 1.006 +0 1.267 1.363 -1.494 0.472 -1.248 0.607 0.370 -0.679 0.000 0.969 1.704 1.317 0.000 1.369 -0.885 0.100 2.548 0.843 -0.060 0.258 0.000 0.853 0.925 0.982 0.592 1.038 1.384 1.120 +1 0.408 -1.291 0.096 1.091 -0.296 1.418 0.673 1.507 2.173 0.599 -0.510 0.252 0.000 0.449 0.171 -1.385 0.000 0.400 0.378 -0.516 3.102 0.844 1.203 0.985 0.472 0.777 0.891 0.768 +1 0.813 0.110 1.020 0.796 -0.482 0.547 1.183 -1.261 0.000 0.703 -1.389 1.390 2.215 0.998 1.613 0.525 0.000 1.160 -0.234 0.070 3.102 0.650 0.830 1.088 0.940 0.904 1.047 0.863 +0 1.206 -0.267 0.208 0.594 1.402 0.687 0.551 -1.704 0.000 0.364 0.328 -1.372 2.215 0.783 -1.079 -0.608 2.548 0.405 0.444 0.281 0.000 0.786 0.986 1.032 0.669 0.597 0.594 0.621 +1 0.466 -0.732 -0.382 1.825 0.995 1.036 1.133 -1.170 2.173 0.702 -1.457 0.795 0.000 1.408 -0.127 -0.783 2.548 0.526 0.372 0.947 0.000 0.904 1.126 1.209 1.829 1.154 1.289 1.166 +0 0.308 0.478 -1.365 2.144 -1.700 0.817 -0.306 0.005 0.000 0.654 1.038 0.420 0.000 0.747 0.129 1.410 0.000 0.754 -1.077 -0.157 0.000 0.981 0.656 0.990 0.472 0.240 0.421 0.606 +1 2.065 -0.545 -0.907 0.428 0.719 0.978 -0.843 1.035 2.173 0.897 -1.602 1.690 2.215 0.515 -0.340 0.774 0.000 0.794 0.141 -0.096 0.000 0.994 1.103 1.295 1.084 0.952 0.978 0.887 +1 0.660 -0.712 1.606 0.471 -0.562 1.135 0.341 0.404 0.000 1.730 -1.129 -0.872 2.215 1.478 -1.252 0.958 0.000 1.463 -0.185 -1.369 3.102 2.519 1.792 0.985 1.049 0.936 1.538 1.199 +0 0.662 -1.682 0.254 1.132 -0.909 0.914 -0.743 0.479 0.000 1.280 -0.157 -1.538 0.000 1.070 -0.257 1.129 0.000 1.036 2.429 -1.490 0.000 1.068 0.912 1.039 0.919 0.912 0.983 0.961 +0 0.568 -1.189 -0.872 1.624 1.393 1.026 -0.455 -1.399 0.000 1.313 -1.154 0.613 1.107 0.762 0.205 -0.133 1.274 0.617 1.868 -0.300 0.000 0.258 0.557 1.186 1.000 1.050 1.162 1.213 +1 1.111 1.560 -0.689 0.660 -0.530 2.317 -0.741 0.779 1.087 1.047 -0.114 -0.517 1.107 2.357 0.695 -1.527 0.000 1.710 0.649 -0.207 0.000 0.964 1.491 0.992 2.492 2.233 2.093 1.754 +1 1.802 0.828 -1.468 1.536 1.439 1.159 1.380 0.151 2.173 0.652 0.227 -0.712 2.215 0.779 0.289 -0.025 0.000 0.674 1.729 0.605 0.000 0.906 0.802 1.148 1.646 1.192 1.167 0.965 +1 1.632 1.196 0.552 0.288 0.171 0.768 1.277 -1.299 2.173 1.137 1.306 1.263 0.000 0.458 0.968 -0.376 0.000 1.484 2.035 -0.692 0.000 0.725 0.827 0.981 0.844 1.291 0.962 0.929 +0 0.315 1.183 -0.662 0.470 0.255 0.335 0.951 0.798 2.173 0.619 1.629 1.596 0.000 0.963 1.123 -0.992 2.548 0.398 0.134 0.438 0.000 0.776 0.688 0.994 0.697 0.714 0.664 0.638 +0 2.002 0.597 0.906 1.390 0.357 0.869 0.347 0.251 2.173 2.277 -0.351 -1.134 0.000 0.925 -0.819 -1.700 0.000 0.972 0.262 -1.009 3.102 1.245 0.827 1.096 0.765 0.882 1.087 1.243 +0 0.342 2.116 0.135 1.931 1.302 0.726 1.235 0.395 2.173 0.928 0.558 -0.234 2.215 1.202 1.278 -0.786 0.000 0.865 1.385 -1.581 0.000 0.752 1.001 0.986 1.445 0.770 1.043 0.928 +1 0.969 0.322 0.500 1.210 -0.313 0.645 -1.398 1.215 2.173 0.649 -0.395 -0.355 0.000 1.271 1.244 -1.693 0.000 1.180 1.073 -0.684 0.000 1.067 0.844 1.003 1.311 1.016 1.241 1.088 +0 0.775 -0.724 -1.033 0.673 -0.156 0.870 0.448 0.142 2.173 0.915 -1.909 -0.805 0.000 1.133 -1.390 1.476 2.548 1.443 0.253 1.188 0.000 0.900 0.896 0.994 0.831 1.840 1.462 1.215 +1 0.806 -0.786 1.701 0.751 1.413 1.216 -1.195 -1.073 0.000 1.739 -0.405 0.756 2.215 0.694 -0.475 0.263 0.000 0.934 -0.982 0.216 3.102 0.880 0.956 0.992 0.746 0.701 0.862 0.736 +1 0.470 -0.240 1.010 0.799 -1.036 0.679 0.048 -1.211 2.173 0.845 -0.505 0.266 0.000 0.954 0.319 0.024 0.000 0.450 0.162 0.401 0.000 0.686 1.056 0.993 0.662 0.731 0.832 0.731 +1 2.191 0.725 0.379 0.715 0.114 0.967 0.582 -1.446 1.087 1.482 -0.554 1.133 2.215 1.411 -2.391 -0.642 0.000 0.588 0.530 -1.152 0.000 1.181 1.839 0.986 1.425 1.677 1.819 1.818 +1 1.156 -0.205 0.784 1.604 0.364 1.566 -0.411 -1.711 2.173 1.754 -0.152 -0.684 0.000 0.648 1.293 -0.456 0.000 0.938 -0.906 0.935 3.102 1.465 1.392 0.997 1.588 0.983 1.294 1.265 +1 0.563 -0.399 -1.400 1.293 -0.076 1.038 -1.101 -0.993 0.000 1.166 -0.222 0.356 0.000 1.003 -0.313 -0.364 0.000 2.819 0.076 1.286 1.551 1.006 1.229 1.098 1.124 0.841 0.997 0.897 +1 1.862 -0.351 0.695 0.952 0.257 1.168 0.014 -1.066 2.173 1.166 0.597 1.478 1.107 0.437 -1.017 -0.364 0.000 1.007 0.316 -0.503 0.000 0.607 1.051 0.994 1.574 1.391 1.284 1.019 +0 0.458 0.953 -0.724 1.262 0.721 0.606 0.490 -1.157 2.173 0.958 0.898 -0.005 0.000 1.330 -0.950 1.586 0.000 0.528 1.709 -0.726 0.000 0.785 0.892 1.016 0.983 0.997 0.883 0.798 +0 1.030 0.716 0.225 1.720 0.895 1.390 -0.051 -0.632 0.000 0.430 -2.800 1.186 0.000 1.586 -1.360 -1.354 0.000 1.222 0.058 1.496 3.102 0.808 0.858 1.049 0.822 0.183 0.723 0.871 +1 1.278 2.224 0.845 0.491 -1.106 0.453 1.028 0.098 1.087 0.701 0.462 -1.077 2.215 0.786 0.692 1.222 0.000 0.862 0.876 -0.566 0.000 0.917 0.716 1.079 0.828 0.760 0.809 0.713 +0 0.318 0.709 -1.278 2.134 -0.401 0.990 -1.377 1.107 1.087 0.444 -1.660 1.711 0.000 0.708 -0.428 -0.913 0.000 0.449 0.684 0.884 3.102 0.829 0.965 0.995 0.673 0.960 1.808 1.512 +1 0.639 -0.651 1.540 1.018 1.589 0.403 0.046 -1.731 0.000 0.735 1.250 -0.300 2.215 1.597 0.246 -0.152 0.000 1.102 1.247 -1.640 0.000 0.849 0.971 0.980 1.064 0.916 1.333 1.029 +1 1.749 -1.253 -1.248 0.213 -0.697 1.072 -1.216 -0.634 0.000 1.700 -0.450 1.186 2.215 1.778 0.166 0.270 2.548 0.468 1.604 1.164 0.000 2.727 1.941 0.977 1.235 1.488 1.496 1.301 +1 0.711 1.387 -0.550 0.682 1.169 0.840 0.675 -0.416 0.000 0.670 0.423 1.652 2.215 1.162 -1.547 0.720 2.548 1.221 -1.052 -1.394 0.000 0.820 0.941 0.986 2.025 1.390 1.340 1.174 +1 2.221 -0.485 -0.529 1.659 -1.097 1.174 -0.588 1.232 2.173 0.516 0.873 0.734 2.215 0.480 0.239 0.155 0.000 0.727 -0.675 0.738 0.000 0.491 0.729 1.301 1.295 1.061 1.266 0.968 +1 0.646 1.537 0.597 0.585 -0.993 0.415 -0.661 -0.411 2.173 1.071 1.309 -1.148 0.000 1.105 -0.176 1.601 2.548 0.742 1.322 -0.218 0.000 0.874 1.170 0.984 0.830 0.844 0.915 0.784 +0 1.087 -0.371 -1.135 0.986 1.413 1.557 -0.756 0.358 1.087 0.805 -0.740 -0.141 0.000 1.329 0.961 -1.615 0.000 2.037 -0.953 -0.881 1.551 0.841 1.651 1.073 1.399 1.733 1.539 1.251 +0 1.526 0.015 0.377 1.189 0.370 2.183 -0.313 0.248 1.087 1.187 -1.013 -1.736 0.000 2.235 -0.533 -1.236 2.548 0.977 0.086 1.505 0.000 0.903 0.936 0.989 0.698 2.703 1.564 1.326 +0 0.480 1.213 -1.015 0.266 -1.263 1.325 -0.053 0.257 2.173 0.915 0.854 -1.458 2.215 1.408 2.276 -1.334 0.000 0.644 1.589 0.852 0.000 1.018 1.045 1.000 1.052 1.799 1.584 1.294 +1 0.477 1.135 0.031 0.819 1.296 1.230 0.902 -1.424 2.173 1.009 0.063 0.280 2.215 0.398 1.060 0.973 0.000 0.510 -1.712 -0.307 0.000 0.564 0.803 0.987 1.128 1.787 1.139 0.863 +1 0.847 -0.198 0.589 0.753 -1.645 0.926 -0.963 0.268 0.000 1.480 -0.879 -1.251 0.000 0.834 -0.740 0.969 2.548 0.775 -0.250 -0.407 1.551 2.437 1.362 1.000 0.594 0.602 0.897 0.798 +1 1.502 1.235 -0.315 0.909 0.387 0.979 1.103 -0.781 2.173 1.137 1.371 0.526 2.215 1.361 1.278 1.279 0.000 2.196 -1.677 1.656 0.000 0.478 3.022 0.985 0.742 1.453 2.434 2.159 +1 1.409 -1.383 0.839 1.938 1.094 1.174 -2.623 -1.023 0.000 1.580 -0.498 -0.586 2.215 0.838 -0.857 0.256 0.000 0.943 1.605 1.586 0.000 2.146 1.171 0.992 1.680 0.806 1.074 1.228 +1 1.165 -0.383 -0.555 0.299 1.551 0.830 -0.577 0.269 2.173 0.562 -0.921 1.593 2.215 0.724 1.309 -1.327 0.000 0.623 0.373 1.196 0.000 0.682 0.879 0.990 0.848 0.952 0.858 0.758 +1 1.842 0.028 0.346 0.722 -0.478 0.706 2.140 1.680 0.000 0.740 0.565 -1.321 1.107 1.094 0.686 0.842 0.000 0.618 -0.298 -1.207 0.000 1.021 0.830 1.079 0.693 0.373 0.674 0.654 +1 2.836 -0.194 1.465 0.223 0.455 1.387 -1.969 -0.347 0.000 1.212 0.981 0.516 2.215 0.613 0.204 -1.408 0.000 0.791 0.940 -1.302 3.102 2.610 2.268 0.994 1.309 0.883 2.074 1.746 +1 1.802 1.481 -0.873 0.561 -1.042 1.125 1.176 0.878 2.173 0.758 0.715 -1.725 1.107 0.378 1.608 0.596 0.000 1.163 -0.248 -0.123 0.000 0.999 0.927 0.976 1.490 1.019 1.076 1.006 +1 0.365 0.334 1.060 1.061 -0.253 0.770 1.179 -0.724 2.173 0.593 1.424 0.614 0.000 1.072 1.216 1.351 0.000 1.174 1.919 -0.404 0.000 0.865 0.902 0.985 0.852 1.124 0.894 0.770 +0 0.543 0.053 0.195 1.206 1.437 1.140 -1.940 0.242 0.000 1.352 -1.015 -1.244 2.215 1.086 -0.936 1.184 2.548 0.782 -1.885 -0.817 0.000 0.844 1.038 1.009 1.099 1.050 0.925 0.825 +0 0.460 -1.249 1.561 0.825 0.232 0.783 0.029 0.693 0.000 0.972 -0.197 -0.220 2.215 0.819 -0.243 1.455 0.000 1.181 -1.435 -1.644 0.000 0.937 0.928 0.986 0.709 0.747 0.762 0.679 +1 1.110 0.944 -0.582 1.145 0.092 1.085 0.364 1.453 1.087 0.762 -1.966 -0.135 0.000 0.877 -0.946 -1.470 2.548 0.739 -0.754 -0.896 0.000 0.845 0.801 0.986 1.404 1.099 1.213 1.420 +0 0.928 -0.271 -1.611 0.958 1.294 1.261 0.916 -0.415 2.173 1.222 1.148 -0.091 0.000 2.110 1.517 1.609 0.000 1.554 -0.114 0.474 3.102 0.465 1.552 0.986 1.358 1.346 1.321 1.150 +1 1.328 0.741 1.358 1.446 -0.177 0.695 -0.337 -0.899 2.173 0.632 -0.778 0.155 2.215 0.505 1.032 -0.979 0.000 0.468 1.421 -0.519 0.000 0.266 0.849 1.886 1.319 0.824 1.001 0.810 +0 1.292 -1.242 1.562 0.352 1.736 0.422 0.220 -0.691 2.173 0.764 -0.320 0.758 0.000 1.267 -0.812 0.169 2.548 0.742 -1.429 0.039 0.000 0.944 0.921 0.987 0.959 0.832 0.775 0.718 +0 0.722 0.769 0.402 0.755 -0.706 0.623 0.240 -1.417 2.173 0.492 0.178 1.470 0.000 0.640 -0.381 -0.142 2.548 0.724 1.265 0.620 0.000 0.854 0.786 0.985 0.758 0.765 0.696 0.639 +1 0.615 0.839 -0.767 1.261 0.258 2.247 -0.196 1.439 0.000 3.664 -0.834 -0.517 0.000 1.221 -0.394 0.854 0.000 1.195 0.374 1.203 3.102 0.904 0.634 0.987 0.546 0.108 0.483 0.522 +1 0.414 0.829 -0.924 0.812 -0.354 1.055 0.086 0.211 2.173 1.301 -0.861 1.657 0.000 0.761 -0.024 1.094 2.548 0.581 -1.138 1.554 0.000 0.968 0.819 0.995 1.558 0.800 1.134 1.454 +0 0.556 1.301 0.363 0.855 1.312 0.528 1.230 -0.839 2.173 0.672 2.038 -0.520 0.000 0.500 1.972 0.509 0.000 0.796 0.322 1.557 3.102 0.712 0.844 0.986 0.867 0.646 0.680 0.634 +1 0.951 -2.027 -0.086 0.406 1.271 0.854 -0.939 0.919 0.000 1.204 -1.137 -1.059 0.000 0.441 1.163 1.452 2.548 0.697 -1.798 1.102 0.000 0.982 0.963 0.987 1.150 0.294 0.834 0.784 +1 0.490 0.540 -0.263 0.761 0.846 1.462 -0.835 -1.371 2.173 0.709 -1.214 -0.249 0.000 0.898 -1.101 1.377 1.274 1.007 -1.279 0.585 0.000 0.768 0.783 0.994 2.085 0.919 1.477 1.341 +0 0.749 -0.572 -0.668 1.894 -1.532 1.614 -0.586 0.459 2.173 0.869 -0.400 -1.574 0.000 1.260 0.484 -0.837 2.548 0.665 -1.141 0.698 0.000 1.009 1.073 1.158 1.649 1.944 1.299 1.072 +0 0.821 -1.325 0.287 0.716 -1.569 0.349 -1.035 -1.267 0.000 0.647 -1.459 -0.655 0.000 0.567 -2.189 0.688 0.000 1.204 -0.708 -0.098 0.000 0.866 0.800 1.057 0.594 0.289 0.553 0.552 +1 0.509 1.327 -1.693 0.261 0.069 0.828 -0.338 1.494 0.000 0.961 0.013 0.129 0.000 1.116 -0.056 1.034 1.274 1.278 -1.651 -0.434 0.000 1.818 1.112 0.980 0.651 1.014 0.898 0.755 +0 0.438 1.392 0.296 2.501 0.006 0.946 -0.136 -1.315 2.173 0.829 -0.224 1.425 0.000 0.455 -0.551 -0.014 0.000 1.017 0.473 1.533 3.102 0.924 0.912 0.979 0.950 0.676 1.000 0.878 +0 0.874 0.845 1.136 2.139 -1.655 0.594 -0.141 0.162 2.173 1.010 -1.047 -0.470 2.215 0.570 0.475 -0.735 0.000 1.204 1.149 0.589 0.000 0.940 0.843 1.113 1.803 0.827 1.284 1.084 +0 1.209 -1.003 -1.401 0.251 1.298 1.398 -0.061 0.506 0.000 1.385 -0.492 -1.182 2.215 0.925 0.268 -0.001 0.000 1.727 -0.218 0.953 3.102 0.975 0.864 0.985 0.873 1.321 1.129 1.128 +1 0.964 0.281 0.602 1.107 -1.560 1.464 -0.313 0.539 0.000 0.962 -0.725 -1.175 1.107 1.006 -1.261 -1.132 0.000 1.348 0.285 -0.761 3.102 0.832 1.123 1.331 0.862 0.696 0.758 0.728 +1 0.431 -0.695 0.622 0.647 0.781 0.545 -1.487 -1.200 0.000 0.808 0.113 -0.897 2.215 0.409 -1.359 0.985 0.000 0.929 1.136 1.453 0.000 0.782 0.946 0.992 0.753 0.675 0.691 0.806 +0 1.288 -1.203 -0.728 0.266 -1.432 1.381 -0.259 1.235 2.173 1.336 -2.223 -0.227 0.000 0.576 -0.603 -1.654 0.000 0.698 -1.369 0.126 3.102 1.775 0.987 0.978 1.297 1.169 1.366 1.136 +0 0.533 0.083 -0.154 1.295 0.886 0.924 0.542 -1.511 0.000 0.939 -0.309 0.467 0.000 1.128 -1.054 -0.244 2.548 0.807 0.042 -0.752 0.000 0.898 0.694 0.986 1.080 1.538 0.959 0.891 +1 1.401 0.795 -1.709 1.392 1.551 0.663 0.490 0.272 0.000 0.993 1.206 -0.554 2.215 0.770 0.213 -0.077 2.548 0.840 0.401 1.160 0.000 0.817 0.987 0.997 0.965 0.623 0.927 0.839 +1 0.491 -2.116 0.564 1.785 1.368 0.724 -1.219 -0.406 2.173 0.558 -0.992 -0.975 0.000 0.330 1.140 -0.514 2.548 0.429 -1.989 1.166 0.000 0.761 0.876 0.986 1.403 0.967 1.438 1.093 +1 1.185 -0.447 -1.355 0.318 -0.041 1.166 -0.122 0.094 0.000 0.960 0.011 1.678 2.215 0.491 1.296 1.077 0.000 0.670 0.470 0.721 0.000 0.863 0.983 0.989 0.676 0.631 0.848 0.750 +1 0.915 0.172 -1.613 1.108 -1.053 0.831 0.679 -0.599 0.000 1.694 -0.129 0.975 2.215 1.026 0.419 0.329 1.274 0.447 -1.170 -0.981 0.000 1.202 0.990 0.979 1.230 0.878 1.006 0.958 +0 1.819 -0.873 1.669 0.093 -0.379 1.068 0.666 -0.428 2.173 0.519 -0.546 1.119 0.000 0.295 -0.108 -0.523 2.548 0.458 -1.402 1.003 0.000 0.381 1.307 0.978 0.591 0.291 0.901 0.792 +1 1.832 -0.448 1.628 1.037 -1.564 2.573 2.052 0.737 0.000 1.335 -0.605 -0.853 2.215 2.406 0.527 -0.645 0.000 1.077 0.223 -0.172 3.102 0.834 0.754 0.984 1.139 0.797 0.987 1.052 +0 1.228 0.097 -1.122 0.861 -0.232 0.760 0.013 1.359 2.173 0.747 -0.389 0.161 0.000 0.882 -0.196 1.009 2.548 0.720 -1.327 -0.615 0.000 0.862 1.101 1.024 0.851 0.338 0.726 0.745 +1 1.362 1.350 0.263 1.986 0.918 2.850 -0.546 -0.965 0.000 0.797 0.727 1.172 1.107 0.665 0.529 0.050 2.548 1.399 1.716 0.689 0.000 1.007 0.829 1.268 0.807 0.659 0.655 0.656 +0 0.880 0.372 -1.197 0.660 0.721 0.436 -0.144 -1.471 0.000 1.185 0.119 1.283 2.215 1.173 1.506 0.045 0.000 1.089 -0.241 -0.037 1.551 1.145 0.913 1.042 0.767 0.975 0.884 0.760 +1 0.425 0.146 1.031 2.097 0.343 1.395 0.643 0.035 0.000 1.223 0.811 -1.114 1.107 0.988 -0.154 1.702 2.548 1.034 -0.938 -1.730 0.000 0.529 0.948 0.995 1.090 0.902 0.998 0.879 +1 0.370 -2.328 0.629 2.233 1.262 0.836 -1.265 -0.280 1.087 0.515 -0.831 -1.678 0.000 1.718 -0.877 -0.933 2.548 0.820 -0.253 0.467 0.000 0.827 0.921 0.991 1.289 0.862 1.062 0.879 +0 1.577 -0.364 0.068 1.957 0.034 1.099 0.561 -1.465 0.000 1.063 0.618 1.579 0.000 1.334 -0.084 1.353 1.274 1.117 -0.842 -0.188 3.102 0.892 0.905 0.971 0.504 1.020 0.973 1.305 +1 0.474 1.571 0.085 0.867 -0.462 0.802 1.437 1.598 0.000 0.650 1.786 0.680 0.000 0.874 0.808 -1.272 0.000 1.209 0.411 1.249 3.102 0.900 0.755 0.982 0.738 0.989 0.976 0.868 +1 0.677 -1.667 1.654 1.042 -0.514 1.427 -1.426 -1.463 2.173 1.021 -1.133 0.884 2.215 1.524 -1.698 0.318 0.000 1.573 -2.043 0.102 0.000 0.959 0.972 1.080 0.929 1.537 1.073 0.899 +0 0.854 0.573 -0.283 1.384 0.507 0.892 -0.370 0.267 2.173 1.442 -0.608 -1.724 2.215 1.311 -0.062 -1.429 0.000 0.605 1.295 -1.278 0.000 0.897 1.009 0.987 0.776 1.641 1.105 1.023 +0 0.448 0.449 1.720 0.832 0.398 2.069 -0.561 -1.310 2.173 2.121 -2.766 0.091 0.000 2.703 -0.060 0.915 2.548 1.121 -0.479 -1.665 0.000 0.892 0.841 0.985 0.809 2.773 1.345 1.087 +1 0.766 0.628 1.011 0.421 -1.630 0.810 2.112 -0.804 0.000 1.212 0.335 1.562 0.000 1.088 0.045 0.357 2.548 1.099 -1.062 -0.199 3.102 1.054 0.978 0.995 0.826 0.718 0.795 0.698 +0 0.591 0.922 1.036 1.039 -0.689 0.704 0.259 1.020 2.173 0.559 0.678 -0.453 2.215 1.613 0.172 -1.292 0.000 0.820 0.587 0.616 0.000 1.297 0.893 1.085 0.886 0.919 0.744 0.695 +1 0.960 0.729 -0.016 0.790 -0.715 1.048 -0.770 1.200 2.173 0.411 -1.410 -1.195 0.000 0.619 0.585 1.522 2.548 1.331 -0.435 0.102 0.000 0.971 1.104 0.995 0.738 0.826 0.888 0.797 +1 1.233 1.278 0.160 1.310 -0.216 0.541 0.748 1.499 1.087 0.471 0.838 -1.040 0.000 0.799 0.266 -1.524 0.000 0.961 -1.564 1.309 0.000 0.482 0.538 0.997 1.156 0.769 0.905 0.814 +1 1.687 -0.032 0.254 0.751 0.044 0.888 0.762 -1.276 2.173 1.019 -0.157 1.560 2.215 0.753 -0.267 0.556 0.000 1.117 1.372 -1.381 0.000 0.868 0.991 0.976 1.275 1.027 1.049 0.883 +1 0.753 -0.278 0.418 0.779 -1.083 1.607 0.057 0.934 0.000 1.101 -0.017 -1.124 2.215 2.497 -0.851 -0.356 0.000 1.357 -1.332 -0.471 0.000 0.874 0.955 1.036 0.754 0.850 0.966 0.843 +1 0.641 0.894 1.076 1.262 -1.306 0.849 0.481 0.724 0.000 0.928 0.418 0.204 0.000 1.250 0.869 -1.227 2.548 0.445 -0.611 -0.535 0.000 0.854 0.900 1.046 0.628 0.663 0.846 0.796 +1 0.939 0.243 0.636 0.797 -0.596 0.995 0.316 -0.478 2.173 0.844 0.985 -1.236 2.215 1.759 -0.144 1.364 0.000 1.026 -0.197 0.530 0.000 1.014 1.224 1.073 0.790 0.976 1.040 0.877 +0 0.523 0.402 0.055 1.156 -0.851 0.590 0.575 0.939 2.173 0.735 -0.073 -1.619 2.215 0.776 2.705 -0.308 0.000 0.406 -1.968 1.296 0.000 0.618 0.879 0.987 0.959 0.788 0.896 0.814 +0 0.962 -0.803 -0.057 0.509 0.990 1.105 -1.112 -0.401 1.087 0.932 -0.275 1.454 2.215 1.082 -0.801 1.220 0.000 0.999 0.929 -1.400 0.000 1.567 0.993 0.989 0.910 1.620 1.163 0.957 +1 0.431 0.736 0.022 0.675 -0.068 0.711 1.214 0.405 2.173 1.391 1.022 -1.371 1.107 0.550 2.699 -0.015 0.000 0.931 1.963 1.681 0.000 0.825 0.964 0.996 1.167 1.468 0.962 0.851 +1 1.154 0.646 -0.386 0.780 0.370 0.762 -1.064 1.006 0.000 2.105 -0.086 -1.260 2.215 0.636 1.311 0.866 0.000 0.905 1.365 0.034 0.000 0.695 1.188 0.984 0.673 1.076 1.009 0.834 +1 0.784 -1.019 1.146 1.009 0.261 1.210 0.596 -1.057 2.173 0.712 -1.112 0.682 0.000 0.707 -0.204 1.380 0.000 0.669 1.216 0.743 3.102 0.834 0.840 0.986 1.903 1.040 1.350 1.052 +1 0.586 -1.396 0.349 0.313 1.579 2.581 0.616 -1.511 0.000 3.263 -0.619 0.165 2.215 0.852 -0.910 0.781 0.000 1.613 -0.005 0.294 3.102 0.696 0.759 0.990 1.060 0.707 0.788 0.721 +0 0.530 -1.176 1.471 0.725 0.255 0.395 -1.423 -0.125 0.000 0.696 -0.091 0.191 0.000 1.521 -1.182 -1.532 2.548 0.516 0.697 1.417 3.102 0.864 0.811 0.989 0.916 0.924 0.818 0.714 +0 1.729 1.223 1.279 0.698 0.605 0.715 2.818 -0.420 0.000 0.698 0.166 -1.076 2.215 0.953 -1.072 0.501 1.274 1.008 -0.207 1.404 0.000 1.124 1.006 0.987 1.388 1.063 1.043 1.051 +1 1.504 -1.419 -1.247 0.639 0.984 0.845 -0.824 0.557 2.173 0.449 -1.895 -0.400 0.000 0.401 -0.856 1.434 0.000 0.458 -2.364 -1.005 0.000 0.734 0.800 1.229 1.106 0.792 0.878 0.732 +0 1.809 -0.711 -0.993 1.336 -1.384 0.668 1.511 1.026 0.000 1.034 0.392 0.924 0.000 1.199 -0.140 -0.389 2.548 1.444 0.854 0.224 1.551 1.056 0.897 0.975 0.869 0.820 0.971 1.193 +0 0.527 0.226 1.589 1.443 0.747 1.136 0.720 -0.819 2.173 0.989 0.653 -1.296 2.215 1.059 1.683 0.856 0.000 0.850 0.140 -0.312 0.000 0.837 0.912 0.991 1.247 0.651 0.915 0.829 +0 1.483 -0.707 1.307 0.474 1.044 0.759 -1.456 -0.409 0.000 0.835 -0.601 -0.993 2.215 1.169 0.338 1.008 0.000 1.368 -1.634 -0.959 0.000 0.835 0.843 0.976 0.787 0.697 0.721 0.862 +1 0.374 0.785 -0.924 0.676 -0.936 1.064 -0.433 1.499 0.000 1.564 -0.472 0.292 2.215 1.415 -0.171 -0.729 1.274 1.423 -0.505 0.981 0.000 0.863 1.325 0.994 1.031 1.280 1.106 0.946 +1 0.708 0.893 -0.011 0.756 1.357 1.118 2.309 -0.994 0.000 1.163 -0.176 0.835 2.215 0.606 0.411 -0.326 0.000 0.933 -0.299 1.515 3.102 0.483 0.690 0.987 0.756 0.547 0.691 0.610 +0 0.580 -0.226 -1.304 0.701 -1.535 1.785 -1.710 1.491 0.000 1.356 -1.185 0.061 0.000 1.144 -2.057 -0.438 0.000 1.187 -0.881 -0.521 1.551 1.331 0.867 0.977 0.875 0.674 0.793 1.195 +1 0.604 1.936 0.798 1.423 1.163 3.059 -1.635 -0.905 0.000 1.436 0.200 1.149 0.000 1.633 1.355 0.266 2.548 1.285 1.177 0.693 3.102 2.213 1.298 0.978 0.900 0.418 0.964 0.823 +1 1.030 -0.164 1.538 0.990 0.337 0.661 0.549 -0.920 2.173 1.047 -0.100 -1.464 0.000 1.108 -0.591 0.570 2.548 0.783 0.529 0.118 0.000 1.249 0.898 1.235 0.739 1.242 0.826 0.777 +0 0.443 -0.559 -0.051 0.778 0.730 1.317 0.922 -0.553 1.087 0.862 0.112 1.372 0.000 0.983 0.789 0.929 2.548 1.195 0.605 -1.470 0.000 0.834 0.738 0.998 1.040 1.379 0.981 0.846 +1 1.280 0.126 1.440 0.604 -0.592 1.085 0.657 0.015 0.000 0.798 0.709 -0.382 2.215 1.492 1.010 1.235 0.000 1.468 0.537 -0.948 3.102 0.875 1.062 1.177 0.785 0.478 0.798 0.777 +0 1.185 0.703 0.414 0.733 0.877 0.796 -0.357 1.575 2.173 1.131 0.572 -1.007 0.000 1.365 0.102 -0.495 2.548 0.526 -0.393 0.661 0.000 1.147 1.088 0.989 0.920 1.280 0.882 0.837 +0 1.634 -0.262 1.191 0.357 0.689 1.073 1.440 -0.750 0.000 0.762 -0.357 -1.108 2.215 1.235 0.623 0.596 0.000 2.182 -0.614 0.568 1.551 1.095 1.115 0.997 0.852 1.182 0.966 0.845 +0 0.632 0.571 0.179 1.788 1.099 0.626 0.052 -0.082 0.000 0.926 0.015 -0.945 0.000 0.779 -0.735 -1.454 2.548 0.568 0.820 1.422 3.102 1.135 0.932 1.086 0.555 0.583 0.650 0.768 +0 0.758 0.096 1.217 1.324 -1.089 0.729 -1.443 0.866 0.000 1.081 0.165 -0.561 1.107 0.334 -1.128 -0.639 0.000 0.873 -0.159 0.345 3.102 0.872 0.701 1.214 0.881 0.659 0.811 0.827 +1 0.619 0.775 -0.830 1.351 1.384 0.750 0.793 0.252 1.087 0.709 1.523 0.327 0.000 0.698 -0.904 -0.917 0.000 0.595 0.787 -1.588 3.102 0.925 0.704 1.155 0.555 0.706 0.638 0.600 +0 1.284 1.278 1.390 0.774 -1.665 0.546 0.939 -0.692 2.173 0.545 1.829 -0.243 0.000 0.754 1.805 0.910 0.000 0.696 -1.639 0.883 0.000 0.918 0.823 0.981 0.969 0.648 0.824 0.713 +0 0.949 -0.383 -1.405 1.087 -0.543 0.382 0.446 0.917 1.087 0.652 -0.957 0.696 2.215 0.402 0.779 -0.006 0.000 0.484 -2.257 -0.822 0.000 0.591 1.013 0.987 0.838 0.591 0.694 0.719 +1 0.808 2.013 0.922 0.063 -0.171 1.653 -0.099 -1.181 0.000 1.198 -0.184 0.720 2.215 0.653 0.762 -0.182 0.000 1.656 -0.159 0.073 3.102 0.879 1.185 0.987 1.034 0.700 0.966 1.271 +0 0.412 -1.222 -0.002 0.481 0.737 0.599 -2.505 -1.188 0.000 0.600 -0.024 1.415 0.000 1.222 0.447 -0.086 2.548 1.663 0.179 -1.426 3.102 2.239 1.798 0.983 0.665 1.029 1.433 1.167 +0 1.664 -0.185 1.530 0.582 -1.372 0.535 -0.719 0.248 2.173 0.919 0.686 -0.651 2.215 0.366 2.151 -0.170 0.000 0.471 -0.924 -0.395 0.000 1.262 0.879 0.983 1.013 1.098 0.894 0.847 +1 2.207 -0.601 1.026 1.519 0.635 1.247 -1.247 -1.461 2.173 1.555 -0.706 -0.510 0.000 0.809 0.429 0.199 2.548 0.757 -2.343 -0.943 0.000 1.838 1.602 0.992 1.703 1.747 1.300 1.380 +1 0.640 -1.416 -1.310 0.130 -1.191 0.837 -0.160 0.790 2.173 1.247 -0.978 -0.920 0.000 0.558 -0.867 0.272 2.548 0.714 0.590 -0.115 0.000 1.152 0.834 0.977 0.890 0.516 0.843 0.738 +0 0.999 0.071 0.489 0.356 1.003 1.454 2.324 0.476 0.000 1.706 -0.525 -1.200 2.215 0.909 -0.658 -1.631 0.000 1.170 -1.370 -0.955 0.000 0.848 0.784 0.994 0.825 1.211 0.925 0.799 +0 1.391 -0.717 -0.436 0.578 -1.218 0.375 -0.769 0.347 2.173 0.369 -2.243 -1.240 0.000 1.078 -0.776 -1.637 2.548 0.667 0.510 0.961 0.000 0.847 0.887 0.984 0.693 0.774 0.660 0.754 +1 0.734 0.330 1.378 0.841 0.744 0.516 -1.005 1.306 0.000 0.764 -0.213 -1.606 0.000 1.799 0.100 -0.594 2.548 1.619 -0.703 -0.077 3.102 0.854 1.061 0.992 1.208 0.872 1.043 1.028 +0 1.327 0.496 0.449 1.389 0.374 0.970 1.084 -1.176 0.000 0.943 0.053 -0.115 2.215 1.811 0.894 -1.591 0.000 1.201 0.348 1.353 3.102 0.880 0.909 0.979 0.917 0.948 0.977 1.060 +0 0.496 1.440 -0.982 0.670 -1.459 0.652 0.440 0.833 2.173 0.680 2.504 -0.101 0.000 0.547 -0.859 1.087 0.000 0.965 -0.359 -1.294 0.000 0.827 0.760 0.983 0.457 0.481 0.539 0.542 +1 1.464 -0.523 -0.775 1.184 -1.364 0.938 -0.750 0.403 2.173 0.980 -0.647 1.192 2.215 0.943 -0.250 -1.390 0.000 0.443 2.332 0.519 0.000 0.954 0.901 0.987 1.283 0.922 1.003 0.870 +1 0.784 0.895 0.018 2.781 -0.299 1.149 0.310 1.392 0.000 1.562 1.002 -1.213 2.215 1.376 1.474 0.758 0.000 0.944 1.251 -0.519 0.000 1.150 0.702 0.990 1.481 0.933 1.000 1.010 +0 0.400 1.064 -1.597 0.865 0.295 0.924 0.610 -0.532 2.173 1.465 -0.479 1.143 0.000 0.603 -2.263 -0.459 0.000 1.109 0.343 -1.541 3.102 2.211 1.586 0.986 0.823 0.852 1.413 1.123 +0 1.156 -0.041 -0.575 1.367 -1.061 0.594 -1.300 0.424 0.000 0.706 0.319 0.712 2.215 0.841 0.563 1.313 2.548 0.574 -1.266 -1.659 0.000 0.855 0.939 0.990 0.897 0.439 0.756 0.903 +1 0.857 -0.640 -1.063 0.658 1.466 0.967 -0.748 -0.140 0.000 0.785 0.235 1.507 2.215 0.811 -1.038 0.697 1.274 0.722 -1.493 -0.287 0.000 0.712 0.740 0.991 0.863 0.850 0.844 0.786 +0 0.671 -0.350 -0.605 1.624 1.560 0.962 -1.645 -0.537 0.000 0.719 1.343 1.004 1.107 1.373 0.370 1.280 2.548 1.003 1.205 -0.970 0.000 0.745 0.831 1.343 1.191 0.596 0.836 0.777 +1 1.444 1.002 -0.881 1.068 -0.535 0.547 1.616 1.605 0.000 0.372 0.793 0.537 0.000 1.499 0.635 1.216 1.274 0.911 1.412 0.195 3.102 0.883 0.717 0.983 0.835 0.849 0.874 0.792 +0 2.015 -0.132 0.491 0.673 0.867 0.875 -0.540 -1.119 2.173 0.613 -1.157 -1.674 0.000 0.508 1.780 0.854 0.000 1.507 -0.672 -0.542 3.102 0.842 0.768 0.986 1.079 0.625 1.016 0.853 +0 0.305 2.263 -1.680 2.012 1.192 1.433 -0.890 -0.279 2.173 0.745 -0.158 -1.295 0.000 0.639 -1.364 1.107 0.000 1.069 -0.170 0.991 3.102 1.161 0.818 0.987 2.445 1.279 1.550 1.311 +0 0.552 0.146 -1.597 1.442 0.270 1.141 1.529 0.445 0.000 0.552 1.310 -0.793 0.000 1.880 -0.881 -1.142 2.548 2.129 0.337 1.488 3.102 1.243 1.119 1.228 1.274 1.556 1.055 0.958 +1 0.519 -2.270 -1.371 0.972 1.709 0.854 -1.342 0.229 0.000 0.898 -0.981 -0.543 1.107 1.500 -1.198 1.652 0.000 0.628 -0.597 1.180 0.000 0.919 0.893 0.992 0.596 0.211 0.561 0.635 +0 0.844 1.450 0.161 0.640 -1.186 0.890 0.781 1.731 2.173 0.398 0.200 0.690 0.000 1.047 0.139 0.185 2.548 0.758 1.315 -0.858 0.000 0.882 0.850 0.987 0.755 1.246 0.787 0.672 +0 1.516 0.080 -0.274 0.688 -0.223 1.002 -0.886 -1.578 2.173 1.157 -0.450 0.085 2.215 1.109 -1.480 1.114 0.000 1.555 -1.178 1.591 0.000 0.623 0.842 0.972 0.624 1.618 1.016 1.036 +0 1.426 -1.334 0.582 0.567 -1.037 0.470 -0.226 -0.062 2.173 0.820 -0.749 -1.443 0.000 0.519 0.469 1.277 0.000 0.666 0.186 -1.210 1.551 0.938 0.896 1.238 0.860 0.527 0.663 0.689 +1 0.327 -1.821 1.187 0.640 -1.144 1.036 0.407 -0.127 0.000 1.164 -0.523 -1.639 0.000 0.831 -0.994 0.886 2.548 0.693 -0.289 0.447 1.551 2.555 1.418 0.994 0.686 0.315 0.969 0.818 +1 0.882 0.082 -0.390 1.029 -1.526 1.350 -0.396 -0.136 2.173 1.812 0.335 1.632 0.000 0.728 -2.378 0.491 0.000 0.660 -1.703 0.758 0.000 0.286 0.616 1.126 1.094 0.924 0.936 0.989 +0 0.510 0.903 -0.820 1.152 1.334 1.228 1.279 0.212 2.173 0.848 1.816 -1.210 0.000 0.628 2.430 1.549 0.000 1.455 0.604 1.697 0.000 0.823 0.716 0.990 1.068 0.174 0.845 0.788 +1 1.438 1.638 -0.800 1.245 -0.589 1.279 1.648 1.260 2.173 1.089 1.895 0.760 0.000 0.705 0.090 1.140 0.000 2.331 0.353 -0.272 0.000 1.371 0.991 0.997 1.602 2.166 1.693 1.497 +0 0.721 -0.194 1.034 0.816 -0.549 0.863 0.888 0.795 2.173 1.653 0.100 -1.482 0.000 1.283 0.339 0.200 2.548 1.677 1.323 -0.341 0.000 0.979 1.018 1.052 0.953 0.756 0.880 0.841 +0 1.233 -1.140 -0.060 0.296 -1.683 0.673 -1.851 1.298 0.000 0.885 -1.206 0.584 2.215 1.123 -0.953 -1.129 2.548 0.861 -0.668 1.671 0.000 0.960 0.935 0.982 0.680 1.064 0.712 0.665 +1 0.945 -1.717 -0.925 0.415 0.363 0.551 0.516 -0.277 2.173 0.503 0.548 1.313 2.215 0.419 -1.147 0.789 0.000 0.410 1.085 0.373 0.000 0.777 0.710 0.987 0.941 0.767 0.814 0.693 +1 1.086 0.952 0.313 0.339 -0.992 1.345 0.805 -0.767 0.000 1.690 0.420 0.584 2.215 1.513 0.315 -1.379 0.000 1.390 1.043 1.242 3.102 0.966 1.010 0.980 0.749 0.969 0.704 0.655 +1 1.263 -0.030 1.347 0.977 -1.504 0.987 1.434 -0.683 2.173 0.549 0.702 -0.176 0.000 0.888 -1.697 0.838 0.000 0.443 0.696 0.799 3.102 0.720 0.764 0.986 0.621 0.714 1.040 0.890 +1 1.048 1.027 1.150 0.176 -1.648 0.830 1.132 0.484 0.000 1.429 0.723 1.669 0.000 1.423 0.627 -0.663 2.548 0.873 -0.414 -0.542 0.000 0.893 0.965 0.990 0.927 0.837 0.768 0.723 +0 0.865 0.191 0.409 2.226 0.680 0.858 -0.144 -1.088 0.000 0.532 1.085 -1.274 2.215 0.278 -0.648 -0.915 0.000 0.495 0.964 1.638 0.000 0.937 0.687 1.005 0.605 0.427 0.795 0.845 +1 0.891 -0.318 0.746 1.014 1.541 1.920 -0.895 -1.278 0.000 0.895 0.242 0.592 0.000 0.408 -1.421 0.220 0.000 0.439 -0.519 -1.584 3.102 1.005 1.093 0.992 0.509 0.226 0.644 0.609 +0 0.787 -0.598 -1.676 0.618 -0.302 0.414 -1.767 1.693 0.000 0.995 -0.362 -0.136 2.215 0.679 0.259 1.518 2.548 0.628 -1.355 0.192 0.000 0.763 0.986 0.988 0.604 0.918 0.752 0.677 +1 0.831 -1.128 0.391 1.009 0.398 0.759 -0.347 1.662 1.087 0.818 -1.414 -1.203 0.000 0.275 -1.142 0.028 0.000 1.396 -0.568 -1.004 3.102 0.656 0.843 0.996 1.093 0.755 1.042 0.844 +1 1.690 0.137 -0.577 0.918 0.663 0.622 -0.609 -1.541 2.173 0.954 -1.106 1.168 2.215 0.531 -0.590 -0.688 0.000 0.909 0.353 0.511 0.000 0.802 0.907 1.551 1.130 0.789 0.989 0.812 +1 0.634 0.806 -1.685 1.727 0.746 1.481 -0.587 -0.646 2.173 0.752 -0.666 -1.158 2.215 1.896 -0.514 0.737 0.000 0.834 -0.784 -1.607 0.000 1.218 1.088 1.180 1.889 0.695 1.303 1.205 +0 1.231 -1.796 1.718 0.770 0.158 0.591 -0.226 1.478 2.173 0.657 -1.048 -0.151 0.000 0.548 0.357 -0.227 1.274 0.529 -0.125 -1.183 0.000 0.718 0.834 1.330 1.107 0.743 0.891 0.758 +1 2.556 -0.630 -1.508 0.222 1.467 0.588 -0.500 -0.295 2.173 0.927 -1.198 0.556 2.215 0.651 -0.583 0.545 0.000 0.463 0.114 -0.294 0.000 0.481 0.514 0.978 1.037 0.855 0.926 0.745 +0 0.747 -1.255 0.086 0.519 -0.710 0.918 -0.143 -1.143 2.173 1.092 -0.950 1.247 0.000 1.109 0.130 1.350 2.548 0.587 -0.065 -0.228 0.000 1.122 0.854 0.981 1.369 0.997 1.142 0.988 +0 1.276 -1.162 1.668 1.523 1.615 1.978 -1.024 -1.610 2.173 2.155 0.757 0.309 0.000 1.364 1.332 -0.060 2.548 0.773 1.776 -0.576 0.000 1.221 1.008 1.003 0.847 3.843 1.978 1.651 +0 0.932 -0.139 -1.716 0.365 0.206 1.416 0.108 0.650 2.173 0.882 -1.330 -0.637 0.000 0.992 -0.604 -0.924 0.000 1.006 0.468 -1.280 3.102 0.633 0.898 0.988 0.969 1.278 1.177 0.974 +1 0.982 1.102 -0.291 0.418 0.391 0.690 -0.278 -0.732 1.087 0.634 0.148 0.273 1.107 0.902 -0.237 -1.505 0.000 0.868 0.896 -1.624 0.000 0.709 0.850 0.981 0.789 0.795 0.673 0.659 +0 0.770 -0.170 1.456 0.616 -1.556 0.380 -2.023 0.737 0.000 0.514 -0.697 -0.511 2.215 0.764 2.563 -0.793 0.000 0.656 1.127 -0.290 0.000 0.711 0.862 0.980 0.713 0.541 0.909 1.168 +0 0.994 0.363 0.982 0.555 -0.116 2.053 -0.014 -0.940 2.173 1.648 -0.863 1.175 2.215 0.632 0.696 0.351 0.000 0.763 -1.619 0.341 0.000 1.384 1.270 0.991 1.367 2.832 1.551 1.231 +0 0.559 -0.065 -0.580 0.948 0.871 0.981 -0.053 -1.239 1.087 0.693 1.084 0.256 0.000 0.751 1.922 0.249 0.000 1.140 0.563 1.677 0.000 1.139 1.233 0.988 0.682 0.996 0.790 0.744 +1 0.876 0.437 1.186 0.778 -0.270 0.979 0.375 -1.254 1.087 1.213 -0.456 0.749 2.215 0.790 -0.364 -0.714 0.000 0.958 -0.823 0.136 0.000 0.725 1.052 1.105 0.887 1.710 0.959 0.845 +1 0.587 -1.062 1.328 0.401 -0.768 1.534 -1.172 0.523 0.000 1.188 -1.205 -0.436 0.000 1.274 -0.784 -0.944 0.000 1.103 0.299 1.672 3.102 0.908 0.892 0.991 0.600 0.637 0.804 0.726 +0 2.581 -0.894 1.179 0.958 0.766 2.637 -0.904 -0.347 0.000 1.570 -0.262 1.319 2.215 1.804 -1.188 -0.801 2.548 0.669 1.041 1.348 0.000 1.230 0.954 0.984 1.506 1.944 1.237 1.084 +0 0.404 0.447 -1.001 0.165 -0.160 0.494 -0.952 -1.079 0.000 0.674 0.248 0.332 2.215 0.977 0.879 0.980 2.548 0.421 1.543 0.429 0.000 1.500 1.050 0.983 1.009 0.569 0.779 0.764 +0 2.327 -0.841 0.010 1.353 0.114 0.955 -1.533 0.427 2.173 1.900 -0.234 -1.267 1.107 1.884 0.034 1.462 0.000 1.089 -2.412 -0.870 0.000 0.789 1.160 0.988 1.783 2.419 1.491 1.420 +0 1.898 -1.418 0.112 0.963 -0.251 0.783 0.576 1.584 0.000 0.847 0.518 -1.427 0.000 0.855 0.085 -0.526 2.548 0.859 -0.371 1.180 3.102 0.717 0.921 0.980 0.852 0.679 0.716 1.057 +1 0.866 0.480 0.888 0.755 -1.527 1.104 -0.944 -0.583 0.000 1.023 -0.667 1.277 0.000 0.863 0.059 0.673 0.000 2.828 0.154 -0.773 1.551 0.929 0.675 0.984 1.043 1.137 0.938 0.838 +0 0.831 0.491 1.544 1.572 -1.131 0.965 -0.138 -0.308 1.087 1.114 -0.378 1.292 2.215 0.451 -1.666 -0.625 0.000 0.990 -0.793 0.454 0.000 0.695 0.899 1.059 1.146 1.525 1.031 0.949 +1 0.924 -1.055 0.200 0.279 -0.219 0.807 1.016 -1.446 2.173 0.321 -0.385 1.639 2.215 0.479 0.903 0.019 0.000 0.446 -0.276 0.729 0.000 0.474 0.782 0.993 0.623 0.636 0.797 0.643 +1 1.027 -0.287 -0.757 1.149 0.000 0.855 -0.690 1.268 2.173 0.561 -0.472 -0.177 0.000 0.686 -0.878 -1.608 2.548 0.502 0.619 -1.697 0.000 0.814 0.916 0.987 0.764 0.514 0.774 0.678 +1 0.684 -0.117 1.393 1.871 1.332 1.344 0.339 0.212 2.173 0.509 0.286 -0.876 2.215 0.725 2.343 -1.433 0.000 1.437 0.826 -0.611 0.000 0.980 0.961 0.983 1.803 1.012 1.240 1.483 +0 0.276 -2.108 1.689 1.908 0.589 1.263 1.364 -1.162 0.000 0.570 0.675 -0.769 0.000 1.007 1.524 1.528 0.000 2.206 -0.469 0.276 3.102 0.887 0.706 0.995 0.769 0.596 1.152 1.440 +0 1.653 0.876 1.657 0.545 -0.732 0.875 0.483 0.253 2.173 0.442 -1.485 -0.012 0.000 0.559 -2.070 -0.967 0.000 0.637 -0.634 -0.408 0.000 0.795 1.154 1.099 0.789 0.851 0.820 0.862 +0 1.319 1.010 -1.498 0.022 -0.765 0.331 0.130 -1.672 0.000 0.311 2.369 1.327 0.000 1.603 0.251 -0.775 0.000 0.927 -2.127 0.842 0.000 0.953 0.872 0.888 0.688 0.252 0.600 0.592 +0 0.650 -1.336 -0.668 0.554 1.674 1.244 -0.634 1.459 2.173 1.537 0.093 0.034 0.000 1.509 -0.512 -0.145 2.548 1.196 0.984 -1.334 0.000 0.688 1.151 0.980 1.238 1.695 1.122 1.159 +0 0.445 1.216 -0.759 1.128 1.311 0.678 -0.164 -1.272 0.000 0.719 0.184 1.449 2.215 0.809 1.891 -0.058 0.000 1.506 -0.422 0.086 3.102 0.984 1.050 0.985 1.289 0.945 0.927 0.864 +1 0.662 2.345 -1.062 0.367 -0.008 0.447 -0.114 0.125 2.173 0.452 1.458 1.188 0.000 0.652 1.251 -0.585 0.000 0.751 -0.108 1.419 1.551 0.834 0.846 0.980 0.789 0.563 0.686 0.620 +1 1.226 0.005 0.918 1.842 1.474 1.348 0.175 -0.868 1.087 1.100 0.024 0.131 2.215 0.432 -2.592 0.926 0.000 0.386 2.395 -0.400 0.000 3.945 2.359 1.002 1.564 1.411 1.696 1.493 +1 0.639 -0.021 1.638 1.143 -0.909 0.893 -0.104 -0.161 0.000 0.790 1.891 1.519 0.000 1.819 -0.651 1.266 2.548 0.539 0.432 -1.380 3.102 2.806 1.504 0.989 1.142 0.719 1.344 1.096 +1 0.890 -0.229 -1.301 0.243 0.873 1.570 1.334 -1.696 0.000 1.453 -0.585 0.022 2.215 1.431 -0.776 0.237 0.000 1.853 0.017 0.560 0.000 0.959 0.923 0.990 0.968 0.726 0.669 0.722 +0 0.857 -1.210 1.098 0.632 1.665 1.762 -0.425 0.306 0.000 1.646 -1.407 -1.702 1.107 2.692 -1.539 -0.998 0.000 0.893 -0.378 0.813 0.000 0.846 0.937 0.980 0.801 1.451 1.337 1.217 +0 2.480 -1.516 1.025 0.154 -1.418 0.528 -0.365 -0.686 0.000 0.860 -2.363 -0.748 0.000 1.186 -1.054 -0.166 1.274 0.756 0.205 1.623 1.551 1.096 0.952 0.988 0.871 0.909 0.813 0.803 +1 1.722 1.501 0.186 0.724 1.328 2.403 -1.116 -1.203 2.173 1.845 0.100 0.646 2.215 1.165 0.463 0.272 0.000 0.652 0.615 1.565 0.000 0.890 0.828 1.326 3.880 3.691 2.756 2.029 +0 1.408 -0.358 -0.896 0.411 0.070 0.876 -0.173 -1.564 2.173 0.501 -1.207 0.045 0.000 1.220 0.233 1.024 1.274 0.710 0.347 0.232 0.000 0.732 1.056 0.986 1.011 0.972 0.824 0.759 +0 0.668 0.054 1.590 2.750 -0.462 0.704 -0.261 0.705 2.173 0.940 -0.928 1.627 2.215 0.858 0.327 1.314 0.000 0.584 -1.352 -0.628 0.000 1.190 0.912 1.805 1.459 0.979 1.116 0.948 +0 1.546 -0.990 0.609 2.915 0.994 2.469 0.197 -0.758 2.173 1.335 -2.855 0.137 0.000 1.791 -0.426 -1.180 0.000 1.369 -0.887 1.519 1.551 1.806 1.179 1.002 3.168 2.168 2.052 1.711 +1 1.218 -0.456 -0.737 1.077 -0.032 0.375 -0.507 1.439 0.000 0.647 0.114 0.529 2.215 0.736 1.074 1.685 0.000 0.380 1.164 -0.900 0.000 0.922 0.806 0.988 0.615 0.253 0.541 0.663 +0 1.164 1.141 -0.828 0.412 0.413 0.588 0.430 1.616 0.000 0.702 -0.009 0.145 2.215 0.487 0.778 -1.122 2.548 0.505 -1.660 0.976 0.000 1.356 0.933 0.990 0.739 0.630 0.711 0.724 +1 0.649 -1.279 -0.166 1.104 0.824 0.492 -1.061 -1.429 0.000 0.735 0.368 -0.016 2.215 0.325 0.756 0.108 0.000 0.742 -0.163 -1.123 0.000 1.061 0.936 0.989 1.062 0.689 0.925 0.824 +0 1.933 -0.233 0.941 1.352 0.322 1.189 0.335 -1.385 2.173 0.827 -0.627 -0.966 0.000 0.443 0.175 0.017 0.000 0.649 -1.224 -0.248 3.102 0.823 0.977 1.185 0.863 1.246 1.126 0.947 +0 0.486 -0.556 -1.281 1.151 0.989 0.456 -1.836 -0.807 0.000 0.951 -0.815 -0.669 1.107 1.447 -0.916 0.236 0.000 0.665 -0.647 1.298 1.551 1.341 0.991 0.988 0.486 0.704 0.681 0.679 +1 0.517 1.424 0.029 1.372 1.459 1.410 -0.184 -1.236 0.000 0.920 0.882 0.499 1.107 0.759 0.938 -0.423 2.548 0.736 -1.223 0.378 0.000 1.879 1.428 1.120 0.820 0.658 1.161 1.149 +1 0.704 0.521 1.507 0.456 0.387 0.529 -0.713 -0.524 0.000 0.855 0.411 -0.527 0.000 1.527 0.533 0.925 2.548 0.934 1.142 -1.407 3.102 0.861 0.993 0.988 0.712 0.869 0.915 0.837 +0 0.626 -0.988 0.356 0.241 -0.292 1.501 1.921 1.408 0.000 1.527 -1.003 -0.640 0.000 1.867 -0.399 0.106 2.548 0.677 -1.320 -1.355 0.000 0.878 0.883 0.993 0.696 0.791 0.816 0.747 +0 0.625 -1.759 1.054 1.130 1.647 0.480 0.533 -1.488 0.000 0.742 0.255 -0.584 0.000 1.280 -1.345 -0.005 0.000 1.653 -0.200 1.612 3.102 0.939 0.884 0.981 0.950 1.138 0.846 0.842 +1 0.962 -0.027 -0.561 0.969 0.974 1.095 1.146 -1.609 2.173 1.239 0.000 0.068 1.107 0.596 2.083 0.939 0.000 0.427 1.124 1.407 0.000 0.611 0.979 1.314 0.876 2.006 1.134 1.008 +1 0.571 -0.230 0.124 0.529 1.583 0.937 -0.056 -0.909 0.000 0.896 -2.173 -0.081 0.000 2.361 0.211 0.875 2.548 0.591 -0.687 1.500 3.102 0.921 0.745 0.988 1.071 0.694 0.960 0.874 +0 1.253 0.502 -0.029 0.513 0.928 0.423 2.152 -0.648 0.000 0.774 -0.592 -1.672 2.215 0.286 -0.133 0.194 1.274 0.989 -1.181 -1.352 0.000 0.878 0.595 0.985 0.493 0.511 0.666 0.670 +1 1.961 -1.125 -0.280 1.254 0.309 1.482 -1.159 1.090 2.173 1.201 -0.451 -1.233 2.215 1.497 0.484 -1.559 0.000 0.899 2.377 0.101 0.000 2.241 2.175 1.102 1.535 1.841 2.342 2.171 +0 0.413 0.596 1.637 1.278 -0.669 0.937 1.609 -1.586 0.000 0.979 0.530 0.276 2.215 0.685 0.592 -0.223 2.548 0.588 0.285 1.253 0.000 0.956 0.824 0.987 0.611 0.381 0.593 0.650 +1 0.761 0.544 0.936 1.476 -0.278 1.242 0.661 1.705 0.000 1.137 -0.087 -0.930 2.215 1.414 -1.298 -0.075 2.548 1.734 -0.685 0.776 0.000 0.906 0.837 1.303 1.409 1.348 1.089 0.931 +0 0.605 -0.872 0.083 0.225 1.670 1.212 -0.341 -1.729 2.173 1.483 0.907 -0.407 1.107 0.962 -0.024 0.738 0.000 0.558 -0.478 0.634 0.000 0.236 0.936 0.982 0.927 2.275 1.156 0.925 +0 0.470 -0.217 -1.674 2.262 1.437 1.223 -0.350 -0.173 0.000 0.480 -0.607 -1.011 2.215 0.720 0.418 -0.526 0.000 0.796 -0.456 1.079 0.000 0.938 0.669 1.004 0.797 0.411 0.708 0.669 +0 0.854 1.291 -1.089 1.650 -0.330 0.595 0.391 1.127 2.173 0.643 1.048 1.597 2.215 0.526 1.699 1.057 0.000 0.684 -0.212 1.072 0.000 0.841 0.608 1.041 1.140 0.490 0.826 0.727 +0 0.727 -0.465 -0.480 2.265 0.034 1.057 0.034 -1.528 0.000 1.258 -0.658 -1.416 0.000 1.508 0.763 0.747 0.000 0.952 -0.927 1.120 3.102 0.923 0.985 0.978 0.615 0.274 0.699 0.959 +0 1.689 0.952 -1.425 0.654 0.994 0.482 -1.318 0.112 0.000 0.414 -1.055 1.175 0.000 0.771 1.025 -0.191 1.274 0.590 0.099 0.283 3.102 0.785 1.162 1.194 0.764 0.345 0.676 0.883 +0 1.775 0.392 -1.264 1.961 -0.918 1.126 1.666 -1.157 0.000 2.051 0.556 0.702 0.000 0.990 -2.311 -0.138 0.000 3.277 1.298 0.857 0.000 0.894 0.756 0.985 1.097 0.905 0.858 0.863 +1 0.313 -1.366 1.045 1.315 -1.067 0.671 -0.658 0.028 1.087 1.232 1.039 1.395 2.215 0.955 0.154 -0.498 0.000 0.472 -1.247 0.549 0.000 0.916 0.680 0.985 1.246 1.820 1.082 0.912 +1 0.630 0.869 -1.192 0.963 -0.426 2.050 1.436 1.515 0.000 1.989 0.470 0.113 2.215 0.944 0.254 -1.146 0.000 1.372 -0.145 -0.288 3.102 0.731 1.081 0.991 0.592 0.726 0.700 0.661 +1 0.842 -0.139 0.830 0.643 -0.608 1.098 0.151 1.308 2.173 1.197 0.169 -0.596 0.000 0.491 -0.891 -0.228 0.000 0.486 0.641 -1.110 3.102 0.811 0.573 0.987 0.879 0.678 0.843 0.728 +0 0.604 -0.865 -1.217 0.942 1.496 0.749 0.653 0.071 2.173 0.758 0.309 -0.844 0.000 0.911 -0.781 0.360 1.274 0.789 -0.319 1.676 0.000 0.851 0.970 0.984 0.835 0.898 0.805 0.734 +1 0.638 -1.868 -1.625 1.467 1.470 0.668 -0.854 -0.358 1.087 0.631 -0.875 0.911 0.000 0.726 -1.555 0.346 0.000 0.698 0.003 -0.468 0.000 0.913 0.868 0.983 1.060 0.898 1.001 0.830 +0 1.118 -0.456 -0.446 1.629 -0.956 0.838 -1.229 0.959 1.087 0.704 -0.127 1.198 0.000 0.685 -0.621 0.397 0.000 0.671 0.138 -1.152 3.102 0.781 0.721 0.987 0.509 0.964 0.943 0.796 +1 0.929 0.065 0.522 0.725 -1.283 0.634 -0.035 1.494 0.000 1.298 0.504 -0.376 2.215 0.734 -1.217 1.601 1.274 0.819 -0.042 -0.719 0.000 1.002 1.121 1.135 0.812 1.501 0.889 0.772 +0 1.337 1.197 0.421 1.101 1.441 0.630 1.113 1.409 2.173 1.211 -0.178 -0.684 0.000 0.754 1.508 -0.259 0.000 0.383 -0.359 1.571 3.102 1.640 1.003 1.337 0.796 0.452 0.868 0.895 +0 0.387 -0.890 -0.640 0.293 0.184 0.976 -0.763 -0.365 2.173 0.591 -1.133 -0.890 0.000 2.271 -1.304 1.459 2.548 0.461 -0.763 0.488 0.000 0.649 0.929 0.997 0.949 1.953 1.252 0.959 +1 1.115 0.908 0.313 1.618 -0.319 0.698 -2.746 1.432 0.000 0.794 0.273 -1.427 2.215 1.106 1.309 -0.410 0.000 1.908 1.026 1.591 3.102 0.785 0.930 1.003 1.174 0.713 0.927 0.799 +1 0.694 -0.687 0.664 0.726 -0.744 1.008 -1.205 -0.288 2.173 1.082 -0.327 1.309 2.215 1.154 -0.563 -1.605 0.000 0.517 -1.769 -0.368 0.000 1.040 0.949 0.986 0.834 1.676 0.958 0.811 +0 0.908 -0.352 -0.021 1.681 0.383 1.203 -0.624 -1.644 0.000 0.756 -0.471 -0.837 0.000 0.600 0.514 0.821 2.548 0.432 1.011 -1.166 0.000 0.743 0.735 0.995 0.499 0.380 0.540 0.673 +0 1.288 -1.001 1.127 4.239 0.862 2.040 -0.783 -0.808 0.000 0.506 -0.779 1.713 2.215 1.522 -0.056 -0.522 2.548 0.699 -0.774 -1.399 0.000 0.933 0.955 0.982 0.868 0.913 1.178 1.406 +1 0.382 -0.503 -0.564 1.086 0.363 0.846 -0.035 -1.529 0.000 0.537 -0.218 0.594 2.215 1.192 1.002 0.500 0.000 1.015 0.816 -0.979 3.102 2.054 1.257 0.982 0.735 0.784 0.888 1.097 +1 1.374 0.620 1.203 0.506 -1.541 0.930 -0.018 -0.630 2.173 0.682 -0.175 0.986 1.107 0.555 0.109 0.036 0.000 0.569 -1.026 -0.352 0.000 0.499 0.608 0.986 1.240 1.168 0.920 0.782 +1 1.147 1.810 0.844 0.393 0.190 0.864 1.684 -0.702 2.173 1.126 1.137 1.027 0.000 1.211 1.194 -1.026 0.000 1.444 1.117 1.609 0.000 0.842 1.096 0.978 1.015 0.839 0.959 0.835 +0 1.009 -0.051 1.551 0.463 1.690 0.745 -0.504 1.016 0.000 0.663 -2.576 0.231 0.000 0.551 1.782 0.135 0.000 1.148 0.520 -1.144 3.102 1.020 0.734 0.980 0.791 0.400 0.560 0.598 +1 0.303 -1.054 -1.134 1.234 -0.603 0.749 0.254 -0.137 2.173 0.697 0.333 1.354 0.000 0.618 1.806 -0.757 0.000 0.924 1.016 1.412 0.000 0.942 0.890 0.992 1.823 0.555 1.560 1.450 +0 1.167 1.003 -1.440 0.264 -1.274 0.307 2.156 -1.468 0.000 0.773 0.994 -0.396 0.000 1.698 1.562 0.919 0.000 0.975 0.843 0.546 1.551 1.038 0.812 0.989 0.742 0.661 0.624 0.611 +1 2.120 -0.154 1.328 1.425 1.098 1.473 0.945 -0.736 0.000 0.339 -1.713 -0.042 0.000 0.525 -1.183 1.268 2.548 0.489 2.281 -1.088 0.000 1.377 1.186 0.982 0.566 0.336 1.100 1.339 +0 1.063 1.166 0.270 1.231 0.893 1.272 -0.753 -1.019 2.173 0.823 -0.533 1.637 0.000 0.348 -0.800 -0.055 0.000 1.238 0.122 -0.105 3.102 0.830 0.947 0.993 0.742 1.157 1.227 1.018 +0 0.547 0.599 1.371 3.854 0.864 3.200 0.012 -0.905 0.000 1.316 -1.211 0.966 2.215 1.047 1.099 0.668 2.548 0.384 1.456 -0.008 0.000 0.986 1.992 0.990 2.358 1.973 1.945 2.061 +1 0.570 0.447 0.275 0.103 -0.301 1.132 -0.133 1.729 2.173 0.852 1.034 -0.394 0.000 0.739 -0.721 0.782 2.548 0.672 0.675 -0.760 0.000 0.343 0.975 0.932 0.971 0.941 0.939 0.823 +1 0.888 -1.424 1.050 0.964 -1.704 1.237 0.566 1.637 2.173 2.572 0.081 0.031 0.000 1.173 -0.019 -0.904 0.000 0.702 1.323 -0.505 3.102 1.994 1.350 0.993 1.278 1.061 1.382 1.324 +1 0.369 -0.734 -1.233 0.432 -1.552 0.493 -0.723 -1.700 2.173 0.484 0.298 0.463 2.215 0.558 2.160 0.798 0.000 1.361 0.900 -0.004 0.000 0.921 0.728 0.981 0.783 0.773 0.894 0.777 +0 0.657 0.715 1.037 0.678 -1.669 0.446 -0.653 0.559 0.000 1.109 -0.359 -0.451 2.215 0.691 -1.205 1.632 0.000 0.712 0.003 -0.630 1.551 0.888 1.013 0.984 0.627 0.198 0.630 0.648 +1 0.917 -0.077 -1.717 0.770 0.616 0.901 -1.412 -0.866 2.173 0.757 -1.012 -0.142 2.215 0.666 0.443 0.669 0.000 0.551 -1.437 0.995 0.000 0.920 0.819 1.003 1.170 0.775 0.859 0.789 +1 0.486 -0.517 0.991 1.006 -1.126 0.505 -1.238 0.940 0.000 1.083 -0.382 -0.262 2.215 0.393 -2.238 0.115 0.000 1.050 1.525 1.559 0.000 0.748 1.045 0.991 0.720 0.835 0.828 0.737 +0 1.263 1.327 1.411 0.367 -0.945 0.479 1.259 -1.612 0.000 1.312 1.309 0.291 2.215 0.998 0.465 -0.833 0.000 0.670 1.001 -0.017 3.102 0.938 1.239 0.991 0.638 0.240 0.727 0.698 +0 0.285 1.213 -0.991 1.201 0.723 0.521 1.139 1.520 0.000 0.626 -0.224 0.571 0.000 1.426 0.136 -0.428 2.548 1.779 -0.538 -1.156 3.102 1.289 1.196 0.987 0.945 0.894 0.936 0.815 +1 1.496 0.286 1.390 1.155 0.887 1.071 0.806 -0.515 2.173 0.455 -0.493 -0.205 0.000 0.368 1.077 1.644 2.548 0.410 -0.707 0.572 0.000 0.375 0.847 0.994 0.619 0.742 0.959 0.785 +0 1.984 0.259 -1.545 1.152 1.168 1.226 1.067 0.086 0.000 0.507 0.001 -0.847 2.215 0.563 -0.305 -0.527 2.548 0.809 -1.628 0.939 0.000 0.682 0.742 1.344 0.908 0.188 0.656 0.720 +0 0.485 1.854 -0.409 0.706 -0.910 0.973 1.577 0.778 2.173 0.783 1.220 1.403 0.000 0.553 -0.190 -0.619 0.000 0.709 1.753 -1.323 0.000 1.071 0.951 0.990 1.117 1.495 0.956 0.841 +0 1.265 0.272 1.608 1.288 0.456 1.286 0.404 -0.480 0.000 0.749 -1.348 1.554 2.215 1.215 0.159 -0.037 0.000 1.200 1.555 -1.612 0.000 0.913 1.393 1.523 1.236 0.535 1.196 1.114 +1 0.511 0.288 -1.522 1.224 0.695 0.637 -1.024 1.618 1.087 0.496 -0.170 0.041 0.000 0.276 1.257 0.456 1.274 0.824 0.193 -0.311 0.000 0.733 0.882 0.997 0.524 0.915 0.663 0.659 +0 0.488 -1.409 -1.229 1.234 -0.358 0.607 -1.324 -0.734 2.173 1.511 -0.763 1.245 2.215 0.534 -0.712 0.184 0.000 0.532 -1.874 0.749 0.000 0.552 0.802 0.984 0.683 1.432 1.066 0.835 +1 1.381 -1.134 -1.200 0.979 -0.837 0.747 -0.485 1.315 2.173 0.529 0.023 0.918 0.000 1.336 -0.022 0.142 2.548 1.103 -0.331 -0.826 0.000 0.921 0.950 0.997 1.091 1.123 0.939 0.798 +0 0.424 1.658 1.697 0.588 0.395 0.619 0.496 0.670 0.000 0.419 1.478 -0.247 0.000 0.788 -0.357 -1.247 2.548 0.840 -0.176 1.220 3.102 0.983 1.009 0.994 0.589 0.497 0.658 0.595 +0 1.099 0.267 0.858 0.941 1.270 1.220 -0.452 -0.775 0.000 1.046 -0.130 0.263 2.215 0.710 0.169 1.326 2.548 0.577 -1.975 -1.655 0.000 1.197 1.036 0.979 0.981 0.764 0.845 0.951 +1 0.327 2.427 -0.049 1.459 0.912 0.471 0.442 0.625 0.000 0.716 0.311 -0.532 0.000 1.072 0.830 -1.502 2.548 0.930 0.379 -1.195 0.000 1.008 0.802 0.985 1.149 0.864 0.863 0.750 +1 1.930 -1.199 -0.882 1.893 -0.406 1.430 -1.442 1.263 0.000 0.861 -0.130 0.561 2.215 0.528 -0.954 -0.087 0.000 0.883 0.454 -0.741 0.000 0.786 0.795 1.104 1.380 0.823 1.033 0.865 +1 0.934 -0.643 0.199 0.397 -1.479 0.955 -0.495 -1.464 0.000 1.444 0.010 0.109 2.215 0.823 0.561 -1.249 0.000 1.027 -0.285 1.222 3.102 0.986 0.829 0.988 0.732 0.947 0.975 0.814 +1 2.113 -0.792 -1.488 1.221 1.410 0.811 -0.157 0.139 1.087 0.281 -0.591 -0.918 2.215 0.821 -0.945 0.080 0.000 0.461 -0.070 -0.292 0.000 0.403 0.419 1.125 0.644 0.594 0.892 0.747 +1 0.384 -0.051 0.008 1.563 1.213 0.808 0.040 -0.696 0.000 0.771 1.146 1.543 2.215 1.558 1.504 -0.558 0.000 1.617 0.680 1.138 3.102 0.868 1.250 0.988 0.983 0.405 0.934 0.880 +1 1.076 -1.608 1.373 0.329 -0.024 0.424 -0.160 0.928 1.087 0.265 -2.045 -0.659 0.000 0.455 0.232 -0.458 0.000 1.012 0.068 -0.943 3.102 0.790 0.750 0.987 1.034 0.694 0.803 0.704 +0 1.442 1.224 -1.654 1.550 -1.278 1.174 0.290 0.453 2.173 0.535 0.274 1.642 0.000 0.691 0.874 -0.520 0.000 0.484 2.393 -0.540 0.000 0.925 1.079 0.978 0.760 0.428 1.024 0.841 +0 0.604 0.921 0.437 1.140 -0.590 0.873 1.927 -1.572 0.000 1.100 1.476 -0.091 2.215 1.365 0.837 1.404 2.548 0.543 -1.737 -0.273 0.000 0.893 0.866 0.992 0.794 1.330 0.913 0.907 +0 1.115 1.355 -1.633 1.025 0.596 0.688 -0.342 -0.800 0.000 1.038 -0.653 -0.154 2.215 0.956 0.380 0.951 2.548 0.877 0.174 -1.688 0.000 0.913 0.949 1.341 1.608 1.075 1.081 0.991 +1 1.316 0.365 1.270 0.187 -0.262 0.668 -0.245 -0.196 2.173 0.860 -0.649 -1.264 2.215 0.988 0.816 0.807 0.000 0.709 -0.083 -0.903 0.000 1.040 0.922 0.985 0.824 0.945 0.780 0.724 +1 1.517 -1.215 -0.639 0.286 -1.136 1.179 -0.458 -0.746 0.000 1.235 -0.560 -0.336 0.000 4.053 -0.702 1.419 2.548 1.567 -0.594 0.755 0.000 0.938 0.912 0.989 1.763 1.626 1.534 1.335 +0 0.405 -0.436 -0.566 2.191 -1.508 0.840 1.022 0.058 0.000 1.145 1.618 0.265 0.000 1.291 0.702 1.463 2.548 1.171 -0.050 1.513 0.000 0.775 0.805 0.988 0.937 0.696 0.842 1.102 +1 1.872 0.275 -1.494 0.445 0.573 1.145 0.875 0.186 2.173 0.587 1.603 -1.738 0.000 0.559 -0.470 -0.965 0.000 0.684 -1.878 -1.457 0.000 0.937 1.021 1.211 0.826 0.842 0.920 0.807 +1 1.311 -0.749 1.343 0.229 -0.769 0.726 0.878 -0.452 1.087 0.663 -0.388 -0.156 0.000 0.485 1.243 0.512 0.000 1.027 0.266 -1.557 3.102 0.911 0.934 0.991 0.620 0.811 0.774 0.704 +1 0.707 0.655 0.215 0.397 1.720 1.596 0.289 1.373 1.087 0.919 -1.346 -0.455 0.000 1.518 -0.128 -0.692 0.000 0.681 -0.861 0.343 3.102 1.262 0.868 0.993 0.984 1.183 1.332 1.047 +0 0.812 -1.686 -0.768 0.866 1.600 0.823 0.714 -0.167 2.173 0.755 -0.390 1.303 0.000 0.941 -0.196 0.938 2.548 0.627 -0.354 -0.585 0.000 0.889 1.092 0.987 0.967 1.063 1.220 0.974 +0 1.284 0.596 -1.017 0.169 -0.280 1.466 0.932 -0.437 1.087 1.417 0.933 1.525 0.000 1.853 0.973 0.777 2.548 1.151 0.216 1.055 0.000 0.906 0.958 0.982 0.985 1.829 1.279 1.110 +0 0.617 -0.564 1.060 1.101 0.253 0.372 -0.053 0.305 0.000 0.863 0.067 -0.967 2.215 0.492 0.755 1.572 1.274 0.571 -0.365 -1.322 0.000 0.713 0.665 0.993 0.940 0.587 0.838 0.682 +0 0.433 1.946 1.148 2.090 -0.837 1.783 0.931 0.415 0.000 1.021 0.896 -1.582 1.107 0.930 0.175 -1.039 2.548 0.830 -0.276 1.717 0.000 2.127 1.599 1.287 1.079 0.621 1.167 1.247 +0 1.848 0.807 1.122 1.029 0.123 0.765 -2.911 -0.528 0.000 0.803 -1.244 -1.723 0.000 1.060 -1.364 -0.712 2.548 1.121 -0.376 1.168 3.102 2.130 1.336 1.496 0.975 0.944 1.202 1.925 +0 0.516 -0.463 -1.336 0.118 -0.368 1.246 -0.071 0.458 2.173 1.316 -0.104 -1.646 1.107 0.456 1.011 0.613 0.000 0.383 0.811 -0.393 0.000 0.364 0.821 0.978 1.054 1.785 0.995 0.770 +1 1.405 -1.623 -1.482 0.355 -0.434 0.843 -1.337 0.078 2.173 0.539 -1.129 1.170 0.000 0.635 -0.357 0.798 2.548 0.548 -0.826 -1.019 0.000 0.655 0.795 0.985 0.881 0.713 0.799 0.665 +1 1.126 -0.151 -0.153 1.247 -0.770 0.713 -0.489 0.871 2.173 0.437 -0.035 1.127 0.000 0.883 0.637 1.432 2.548 1.055 -0.718 -1.426 0.000 0.759 0.713 0.981 0.938 0.788 0.859 0.752 +1 1.304 -0.921 -0.486 1.420 -0.886 0.688 0.329 1.112 2.173 0.771 -1.362 1.197 1.107 0.459 -2.619 0.306 0.000 0.589 -0.637 -1.176 0.000 0.915 0.762 0.985 1.594 1.053 1.159 0.986 +1 0.387 -1.681 1.141 0.462 0.893 1.539 0.080 -1.099 0.000 1.155 -0.647 0.984 0.000 1.274 0.044 0.602 1.274 0.790 -0.186 -1.698 0.000 0.902 0.883 0.990 0.646 0.408 0.862 0.781 +0 1.813 0.697 -0.105 0.595 0.050 1.313 0.976 -1.570 1.087 0.547 -1.232 1.157 0.000 0.474 0.376 -0.626 0.000 1.119 0.085 0.589 3.102 1.073 0.771 0.993 1.577 1.329 1.085 1.010 +1 1.577 0.103 0.514 0.304 -1.598 1.355 0.796 -1.201 2.173 0.724 -1.358 -0.582 0.000 0.886 -0.264 0.171 0.000 0.884 0.757 1.394 3.102 0.941 1.070 0.988 1.308 0.834 1.207 1.027 +1 2.559 -0.760 -1.062 0.168 -1.673 1.639 -1.164 0.447 1.087 0.583 -1.710 1.537 0.000 0.383 -0.003 1.613 0.000 0.474 -0.093 -0.527 3.102 0.723 1.214 0.978 0.530 0.887 1.112 0.935 +1 0.512 2.003 -1.193 0.580 1.629 0.790 0.940 0.571 0.000 1.074 0.441 1.239 2.215 1.296 -0.135 -1.076 2.548 0.941 -0.203 -0.570 0.000 0.889 0.855 0.987 0.820 1.156 0.842 0.752 +0 1.237 0.096 -0.398 0.749 -1.372 0.473 1.170 1.165 0.000 1.095 1.036 0.697 2.215 1.150 0.337 -1.724 0.000 0.977 -1.004 -0.893 3.102 0.875 0.916 1.025 0.705 1.599 0.974 0.895 +1 0.421 1.651 -0.894 0.500 -0.813 1.527 0.155 0.372 2.173 0.922 0.060 -1.201 2.215 0.955 -0.768 1.652 0.000 1.020 -1.738 -1.722 0.000 0.729 1.045 0.987 1.138 1.727 1.334 1.117 +1 2.447 0.492 1.136 0.786 0.485 0.733 -0.083 -1.068 0.000 0.892 0.364 -0.445 2.215 1.129 0.671 0.398 0.000 1.745 1.071 -1.176 3.102 1.728 1.111 1.062 1.233 0.869 1.006 0.988 +0 3.276 -0.698 0.157 0.350 1.531 2.247 -1.092 -1.584 1.087 0.595 -0.312 1.463 0.000 0.598 -0.878 -1.355 2.548 1.716 -1.236 0.146 0.000 1.169 0.778 1.402 2.281 0.314 1.409 1.154 +0 0.426 -1.842 1.027 1.309 -1.209 0.869 -0.880 0.284 2.173 0.547 -0.305 0.715 2.215 0.547 -0.956 1.345 0.000 0.941 -1.430 -1.194 0.000 0.762 0.978 0.988 1.099 0.484 0.969 0.801 +0 0.711 -0.606 -1.401 0.559 1.559 1.157 -0.637 -0.798 1.087 1.375 -0.382 0.587 2.215 0.694 0.893 1.407 0.000 1.371 0.495 0.340 0.000 0.907 0.986 0.994 1.051 1.776 1.144 0.981 +1 0.575 1.471 1.241 1.147 -1.157 0.885 -1.095 0.506 2.173 0.485 -1.130 1.292 1.107 0.741 -0.551 -0.698 0.000 0.631 -0.592 -1.269 0.000 0.373 0.884 0.988 1.084 0.627 1.172 0.932 +0 0.417 0.017 -0.260 2.364 -1.152 1.086 1.530 0.761 2.173 0.449 1.709 0.187 0.000 0.584 0.352 -0.593 1.274 0.798 0.463 1.298 0.000 0.824 0.726 0.989 0.557 1.113 1.179 0.957 +1 0.485 0.495 1.011 0.929 -1.153 0.599 -0.231 1.219 0.000 0.855 -0.264 -1.407 0.000 1.049 -1.136 -0.319 2.548 0.825 -1.609 -0.212 0.000 1.062 0.944 0.989 1.340 0.556 0.962 0.906 +0 0.383 1.378 -0.309 3.185 0.576 1.240 1.408 -1.196 0.000 1.147 0.630 -0.794 2.215 1.143 0.831 1.090 2.548 0.708 1.028 1.601 0.000 0.843 0.927 1.097 0.823 1.218 1.040 1.078 +0 1.977 0.276 0.875 1.501 0.437 1.080 -1.059 -0.973 0.000 0.432 -0.351 -0.337 0.000 0.585 0.835 -1.618 1.274 0.688 0.398 -1.077 3.102 0.947 1.098 0.993 0.856 0.252 0.678 1.023 +0 1.488 -0.569 -1.039 0.694 1.479 0.829 0.106 0.304 0.000 0.669 -0.861 0.710 0.000 1.246 0.589 -0.914 2.548 1.266 1.118 0.071 0.000 0.994 0.993 1.078 0.901 1.282 0.969 0.905 +1 0.846 -0.911 0.275 2.294 0.570 0.760 -0.018 -1.738 0.000 0.999 -0.309 -1.184 1.107 1.244 0.447 -1.128 2.548 0.722 1.077 -0.315 0.000 1.346 1.008 1.000 1.915 0.496 1.424 1.343 +1 0.463 -0.458 -1.189 1.113 0.314 1.157 0.018 -1.139 1.087 0.925 -0.549 0.312 1.107 2.565 1.161 1.687 0.000 2.207 0.538 0.119 0.000 1.570 1.086 0.989 0.998 1.537 1.056 0.866 +0 1.575 -0.133 0.690 0.911 1.393 1.700 0.238 -0.678 0.000 1.468 -0.736 1.038 2.215 1.132 0.077 -1.286 1.274 0.494 -0.526 -0.296 0.000 0.788 0.823 0.988 0.695 1.327 1.220 1.108 +1 2.218 -0.819 1.543 0.627 1.024 1.113 -1.041 -0.222 2.173 0.436 -0.638 0.788 0.000 0.910 -1.035 -1.065 2.548 0.449 -0.494 -0.081 0.000 0.407 0.636 0.992 0.874 0.866 1.030 0.786 +0 3.067 -0.053 -1.572 0.692 1.486 1.103 -1.995 0.104 0.000 0.760 0.132 -0.850 2.215 0.695 -1.754 0.704 0.000 1.490 -0.793 0.169 3.102 0.814 0.765 0.979 0.875 0.940 1.040 1.442 +0 2.943 -0.994 1.227 2.299 1.335 3.010 0.280 -0.440 1.087 1.422 -1.647 1.489 0.000 1.146 -0.189 0.038 0.000 0.634 0.990 -0.130 0.000 0.725 1.031 1.011 0.837 0.918 2.221 1.682 +0 2.993 0.024 -0.013 0.531 -0.358 0.810 0.737 1.447 0.000 1.150 1.142 1.680 2.215 1.319 0.129 -0.664 2.548 0.682 -0.802 1.324 0.000 1.099 0.966 0.993 0.794 1.326 1.131 1.095 +1 0.783 -1.438 -1.408 1.690 -1.742 0.815 -1.032 -0.147 2.173 0.892 -0.123 0.578 0.000 0.687 -0.632 0.654 2.548 1.031 -0.209 -0.515 0.000 0.915 0.988 0.975 0.801 0.638 0.850 0.784 +1 0.588 0.577 0.923 1.654 1.678 0.701 1.985 -0.311 0.000 0.642 1.383 0.548 1.107 0.746 0.840 -0.532 1.274 0.796 2.027 -1.418 0.000 0.989 0.876 0.987 0.887 0.636 0.767 0.946 +0 0.555 -1.112 -1.107 0.067 0.634 0.608 0.469 0.617 0.000 0.711 -0.694 0.928 2.215 1.323 0.276 1.555 0.000 1.498 0.452 -0.649 3.102 0.944 0.859 0.998 0.737 1.113 0.694 0.637 +1 1.782 1.678 -0.348 0.701 -0.861 0.411 2.264 -1.468 0.000 0.997 -1.550 1.179 2.215 1.347 0.463 -1.252 1.274 3.225 0.687 0.544 0.000 2.209 1.703 0.983 3.452 1.845 2.222 1.984 +1 1.085 -0.040 -0.512 1.608 -0.953 0.952 -0.662 1.107 2.173 0.568 0.907 1.513 2.215 0.791 -0.338 0.529 0.000 0.578 -2.193 -0.143 0.000 1.120 1.053 0.990 0.890 1.038 1.078 1.112 +1 1.554 -0.281 1.428 0.869 -1.557 0.965 0.685 -0.005 2.173 0.658 0.051 0.640 0.000 0.821 0.624 -1.023 2.548 0.694 -0.719 -0.828 0.000 0.951 0.968 0.989 0.775 0.880 0.933 0.818 +0 0.282 1.135 0.761 0.814 -0.187 0.983 1.931 0.756 0.000 1.244 -0.186 1.685 0.000 1.267 1.297 -0.257 2.548 0.885 -0.349 -0.805 0.000 1.079 0.727 0.978 1.018 0.922 0.919 0.841 +1 2.015 0.074 0.196 0.616 0.850 0.955 0.800 -1.024 2.173 0.329 0.745 0.092 0.000 0.528 -2.331 -1.494 0.000 0.988 0.667 1.568 3.102 0.791 0.918 0.988 0.842 0.741 0.909 0.757 +0 1.381 0.573 0.569 0.751 -0.271 0.683 0.110 1.279 2.173 0.910 -0.546 -1.439 0.000 0.899 0.687 -0.118 2.548 0.822 0.279 -0.967 0.000 0.699 0.923 0.986 0.892 0.983 0.744 0.773 +0 1.675 -0.134 1.429 0.649 -1.330 0.425 -0.200 -0.842 0.000 0.676 0.743 0.559 2.215 1.064 -0.859 -0.133 1.274 0.391 0.491 -0.234 0.000 0.411 0.667 0.989 1.013 1.015 0.858 0.707 +1 0.844 -0.452 1.709 0.991 0.751 1.308 -0.721 -0.545 2.173 0.928 -0.903 1.573 0.000 0.538 0.505 0.257 1.274 0.420 -1.963 0.732 0.000 0.843 0.935 0.989 1.248 0.996 0.933 0.856 +1 1.189 -0.798 -1.716 0.420 0.571 1.276 -0.038 -0.865 2.173 0.731 -0.396 -1.603 2.215 1.377 -1.194 0.208 0.000 0.938 0.130 0.601 0.000 0.853 1.079 0.985 1.201 0.916 1.070 0.934 +1 0.784 0.187 0.216 0.725 -0.398 0.804 1.435 1.632 0.000 0.619 0.380 -1.060 2.215 0.560 1.374 0.779 0.000 0.832 -0.327 0.520 3.102 0.841 1.010 0.986 0.779 0.692 0.739 0.873 +1 1.668 1.308 0.883 0.558 0.885 0.984 0.666 -1.143 2.173 0.800 2.514 -0.599 0.000 0.633 0.751 0.467 2.548 1.028 1.631 0.800 0.000 1.187 0.946 0.978 1.454 0.979 0.987 0.962 +0 1.563 1.440 -1.146 0.238 0.378 0.852 -0.031 0.332 2.173 0.742 0.675 1.345 2.215 0.735 -1.261 0.940 0.000 0.745 -0.744 -0.083 0.000 0.681 0.999 0.993 1.409 1.022 1.006 1.089 +1 1.714 0.914 -0.905 0.505 1.285 1.448 -0.050 1.444 2.173 2.560 0.028 -0.185 2.215 1.294 -1.127 1.120 0.000 2.151 1.269 1.278 0.000 0.864 0.874 1.186 1.390 2.821 1.556 1.251 +0 0.759 0.290 0.245 1.818 -1.090 0.569 -0.706 1.294 2.173 0.719 -1.317 0.788 0.000 0.862 -0.980 -0.112 0.000 1.285 -0.937 -1.461 3.102 0.891 0.882 1.518 1.153 0.585 0.872 0.898 +1 1.395 0.929 0.919 0.583 -1.520 1.351 1.069 -0.869 0.000 1.194 -0.328 -0.335 2.215 0.782 -1.000 0.794 2.548 0.389 0.168 -1.727 0.000 0.922 1.336 1.013 1.032 0.961 1.141 1.064 +1 0.391 2.002 -0.362 1.186 1.525 0.892 0.331 -0.263 2.173 0.682 1.288 -1.468 0.000 0.518 1.891 0.615 0.000 1.185 0.286 0.938 3.102 0.936 0.838 0.988 1.141 0.960 0.838 0.769 +1 1.108 -1.298 1.717 1.306 0.659 0.950 0.075 -0.881 2.173 1.415 -1.919 -0.270 0.000 0.995 2.042 1.537 0.000 1.258 -1.076 1.127 3.102 0.932 1.468 1.358 0.689 1.412 1.330 1.484 +0 1.755 -1.230 1.250 0.664 -0.171 1.111 -2.133 1.553 0.000 0.867 -0.757 -0.383 0.000 1.012 -0.210 -0.919 2.548 1.086 0.309 -0.196 0.000 0.796 0.836 1.432 1.087 0.980 0.831 0.817 +0 2.186 -0.417 0.810 0.954 0.537 2.336 1.234 -0.971 2.173 1.785 -1.049 0.924 1.107 0.522 1.868 -0.974 0.000 0.551 0.181 -0.158 0.000 0.738 0.904 0.981 1.010 5.260 2.620 1.949 +1 0.877 0.086 0.219 1.018 0.250 1.034 1.231 -1.305 0.000 1.131 1.298 0.639 2.215 1.132 0.867 -1.709 0.000 1.358 0.987 -0.072 1.551 0.886 0.909 0.984 1.475 0.674 1.068 1.156 +0 0.303 -1.653 -1.154 0.863 -1.648 0.478 1.243 -0.902 0.000 0.880 -0.822 0.608 0.000 0.754 -0.333 -0.984 2.548 0.683 -1.134 0.835 0.000 0.334 0.776 0.991 0.767 0.621 0.595 0.632 +1 0.724 -0.782 1.216 1.603 -1.699 0.458 2.936 -1.739 0.000 1.464 -0.479 0.215 2.215 1.319 0.586 -0.303 2.548 0.374 -0.214 -0.219 0.000 1.640 1.438 0.987 1.295 1.107 1.582 1.466 +1 1.472 1.506 0.538 1.702 -0.137 1.362 1.593 -1.572 0.000 0.432 0.222 -1.127 1.107 0.607 0.400 -0.047 0.000 0.794 0.480 0.560 1.551 1.897 1.187 1.252 0.740 0.536 0.810 0.949 +0 0.398 -0.991 -0.681 0.656 -0.492 0.607 0.050 1.651 1.087 0.932 0.307 0.721 0.000 0.704 0.780 -1.288 2.548 0.772 -0.446 -0.194 0.000 0.945 0.917 0.993 0.615 0.511 0.664 0.649 +1 2.553 -1.408 0.026 0.592 -0.596 2.056 -1.070 1.049 0.000 1.208 2.483 1.582 0.000 1.226 -1.033 -0.439 0.000 1.880 -1.003 -1.322 1.551 1.056 0.739 0.985 1.148 0.460 0.841 1.054 +1 1.508 -0.041 0.642 0.926 -0.090 1.080 0.458 -1.451 2.173 0.976 1.671 -0.843 0.000 1.088 0.961 1.176 0.000 1.025 0.680 -0.612 1.551 0.588 0.847 1.003 1.341 0.790 0.923 0.934 +1 0.286 1.733 1.709 1.188 0.277 3.223 -0.279 1.616 0.000 3.382 0.276 -0.094 2.215 2.138 1.739 -1.046 0.000 1.926 0.824 0.283 1.551 2.765 2.764 0.982 1.059 1.130 2.564 1.916 +1 1.376 0.906 -0.810 1.142 -0.269 1.449 1.848 1.601 0.000 0.570 0.901 0.504 1.107 0.306 1.439 1.420 0.000 0.462 0.264 0.507 3.102 1.284 0.986 0.987 0.799 0.144 0.733 0.908 +1 0.403 1.239 0.483 1.209 -0.257 1.969 -0.551 1.230 0.000 1.331 0.531 -0.167 2.215 1.386 -0.077 -1.294 0.000 1.525 -1.022 -1.148 3.102 1.052 0.775 0.984 1.296 1.634 1.789 1.554 +0 0.681 -1.348 -1.704 2.095 1.236 2.960 0.096 -0.321 2.173 1.069 1.220 -1.719 0.000 1.014 0.537 0.132 0.000 1.746 1.385 1.121 0.000 1.045 0.722 0.988 3.675 1.966 2.413 2.556 +1 0.536 0.762 0.245 0.990 0.032 1.285 0.794 -1.571 2.173 0.737 0.403 -0.663 0.000 1.276 -0.098 1.114 0.000 0.716 -0.203 -0.374 0.000 1.029 1.278 0.996 0.971 1.105 1.085 1.038 +0 1.405 -0.103 -0.714 0.520 -0.875 0.828 0.161 1.030 1.087 1.526 -0.694 -1.394 2.215 1.207 0.306 0.140 0.000 1.335 -0.700 0.422 0.000 0.941 0.975 0.984 0.863 1.545 1.104 1.005 +1 2.404 -1.208 -1.262 0.375 1.151 0.765 -1.140 0.444 2.173 0.410 -1.886 0.143 0.000 1.061 -1.400 1.158 0.000 0.794 -0.010 -0.369 3.102 0.828 0.873 1.083 1.194 0.741 0.858 0.783 +0 1.051 0.688 1.035 1.562 -1.569 0.645 -0.118 0.542 0.000 1.062 -0.865 -0.669 0.000 0.777 0.926 -1.672 2.548 1.159 -1.904 -0.123 0.000 1.289 1.269 1.269 0.642 0.635 1.142 1.245 +1 0.536 -2.161 -0.523 0.782 0.176 1.007 -0.966 -0.371 1.087 0.774 -0.930 1.006 0.000 1.172 -0.155 1.686 2.548 0.607 0.509 0.937 0.000 0.942 1.091 0.992 0.980 1.415 0.871 0.776 +1 0.755 -1.317 0.774 0.848 -0.680 1.553 -0.864 1.529 0.000 0.833 -0.699 0.070 2.215 0.961 0.540 -0.303 0.000 1.455 -0.317 0.506 3.102 0.912 0.913 1.071 0.706 0.417 0.629 0.691 +0 0.836 1.515 0.305 0.708 -0.432 0.670 0.054 1.031 0.000 0.974 1.228 -1.310 2.215 0.909 -0.941 0.230 0.000 1.018 -1.030 -1.200 0.000 1.025 1.040 0.988 0.986 0.214 1.016 1.277 +1 0.793 -0.546 -1.602 1.018 -1.047 0.707 -0.915 -1.001 2.173 1.143 -0.554 0.737 2.215 0.841 -1.581 0.898 0.000 0.557 -2.003 0.261 0.000 0.484 0.953 0.986 1.122 1.343 0.863 0.792 +0 0.712 1.462 -1.368 0.176 -1.675 1.370 0.889 -1.105 2.173 1.956 1.630 0.438 0.000 0.547 0.076 0.383 0.000 0.958 -0.472 -1.509 3.102 0.623 1.050 0.998 0.654 1.061 0.726 0.649 +1 0.726 0.403 -1.133 1.431 0.362 0.780 -2.228 -1.138 0.000 1.424 0.596 -0.508 2.215 1.182 0.328 1.000 0.000 1.278 1.383 1.202 0.000 0.976 0.718 1.377 0.987 0.532 0.850 0.795 +1 0.478 0.458 1.405 1.527 -0.316 0.742 -0.429 -1.424 0.000 1.032 0.113 1.625 0.000 1.484 -0.256 0.599 2.548 1.374 0.664 -0.187 3.102 0.879 1.235 1.184 0.957 0.941 0.977 0.896 +0 1.003 -0.158 -1.601 1.332 -0.997 0.894 0.334 -1.050 1.087 1.504 -0.424 0.566 0.000 1.177 0.108 0.765 0.000 0.983 -0.978 -0.136 1.551 0.681 0.862 0.991 0.584 1.099 1.043 1.005 +0 0.562 0.100 0.720 0.518 -0.365 0.625 -1.732 0.369 0.000 0.890 0.027 -1.510 1.107 0.867 -1.437 -1.547 2.548 0.667 -0.301 -0.162 0.000 0.869 0.900 0.988 0.857 0.834 0.858 0.740 +0 0.838 -1.188 1.059 0.926 -1.380 0.399 0.190 1.717 0.000 1.029 -0.077 0.161 2.215 0.676 0.698 -1.363 2.548 0.458 1.136 -0.192 0.000 0.762 0.844 0.989 0.913 0.951 0.855 0.757 +1 0.891 -0.684 0.886 0.742 -0.267 0.800 -1.376 1.379 2.173 0.970 -1.909 -0.522 0.000 1.011 -0.042 0.239 1.274 1.366 -1.209 -1.214 0.000 0.958 1.192 0.985 0.591 1.256 1.030 0.893 +1 1.344 1.360 -0.688 0.637 -0.182 2.759 -0.138 1.005 0.000 2.252 0.646 -0.479 2.215 1.341 -1.885 -1.248 0.000 1.029 1.310 -1.575 0.000 1.013 2.467 0.991 0.707 0.850 1.932 1.587 +0 1.293 2.129 -0.515 0.631 1.574 0.695 1.435 0.573 2.173 0.323 0.644 0.630 0.000 1.501 0.622 -1.638 0.000 0.671 0.759 -1.304 3.102 0.951 1.023 1.191 0.713 0.744 0.695 0.759 +1 0.895 1.219 -1.735 0.245 -1.280 0.920 0.198 -0.698 0.000 0.921 1.530 0.830 1.107 0.899 0.245 0.431 2.548 1.430 -1.771 -1.739 0.000 1.402 1.079 0.986 0.909 0.758 0.923 0.833 +1 1.218 0.879 0.954 1.150 1.721 0.609 -0.442 -0.915 0.000 0.633 2.455 0.676 0.000 1.078 0.001 -0.167 1.274 1.212 0.204 -1.244 3.102 0.878 1.124 1.046 1.053 0.729 0.891 0.850 +0 0.278 0.542 -0.238 0.912 0.377 1.701 -1.049 0.808 2.173 0.897 1.528 -0.487 2.215 1.604 -2.647 -1.243 0.000 2.155 2.077 -1.719 0.000 1.065 0.827 0.979 0.910 3.562 1.912 1.533 +1 1.125 0.984 1.054 0.448 0.851 2.358 1.631 0.640 0.000 0.992 0.253 -0.796 1.107 3.351 1.235 -1.003 2.548 0.964 0.560 -1.341 0.000 2.535 2.318 0.984 1.468 1.173 1.884 1.484 +1 1.157 -0.196 -1.152 0.807 0.818 1.395 0.341 1.354 2.173 0.548 0.222 0.567 2.215 0.908 -0.759 -0.178 0.000 2.051 0.204 -0.537 0.000 0.977 0.861 1.311 1.094 0.843 1.066 0.926 +1 0.522 -0.542 1.219 1.440 -0.577 1.053 0.123 1.659 1.087 1.131 -0.649 0.167 0.000 1.092 0.115 0.469 0.000 0.915 -0.565 -0.843 3.102 0.959 0.729 1.200 1.134 0.911 0.834 0.774 +0 0.718 -2.132 1.315 2.040 -1.600 0.732 -1.385 -0.009 2.173 0.705 -1.960 -0.383 0.000 1.103 -0.919 0.486 0.000 0.597 -1.425 0.601 0.000 0.668 0.657 0.990 1.221 0.771 0.802 0.720 +1 0.982 0.610 0.263 0.861 1.364 1.761 1.128 1.404 0.000 1.577 0.660 -0.708 0.000 2.121 -0.678 -0.585 2.548 1.505 -1.030 0.844 0.000 0.974 1.137 1.067 1.368 1.248 1.650 1.302 +1 0.540 0.975 0.523 0.616 -0.129 3.044 0.098 -0.814 2.173 3.624 -0.386 0.425 0.000 2.641 0.796 1.597 0.000 3.132 0.267 1.092 3.102 0.911 1.661 0.984 1.431 3.252 2.331 1.700 +1 1.174 0.696 -1.681 0.560 0.124 1.240 0.971 -1.154 1.087 1.037 0.936 0.729 0.000 1.223 1.191 -0.498 2.548 1.351 -0.995 0.922 0.000 0.639 2.036 1.121 0.906 0.892 1.744 1.370 +1 1.524 0.257 -1.190 0.305 -1.703 0.485 0.862 -1.477 0.000 0.643 1.213 0.704 2.215 0.954 1.234 0.377 0.000 1.315 -0.116 0.160 3.102 1.253 1.040 0.988 0.896 0.736 0.762 0.739 +0 2.042 1.477 0.032 0.224 0.749 0.629 0.153 1.679 2.173 0.690 1.531 -1.692 0.000 0.864 1.288 -1.157 0.000 1.277 0.191 0.026 3.102 0.557 0.995 0.990 1.207 0.946 0.852 0.837 +1 0.852 -0.490 1.494 0.988 0.771 1.084 0.834 -0.587 2.173 0.833 -0.204 0.318 0.000 0.844 -0.733 -1.395 0.000 0.853 0.035 0.793 3.102 1.343 0.837 0.991 1.750 1.051 1.123 0.982 +1 1.554 0.416 1.645 0.750 -0.629 0.996 -0.950 0.696 2.173 0.902 -1.119 -0.325 0.000 0.578 -1.015 -1.500 0.000 0.454 0.737 -0.900 1.551 0.965 1.104 1.329 0.647 1.031 0.951 0.925 +0 0.941 -1.055 -1.462 0.971 0.983 0.975 -0.475 -0.833 1.087 0.986 -1.313 0.475 0.000 0.548 -1.422 -0.477 0.000 1.208 -0.443 0.819 3.102 0.861 1.200 1.067 0.678 1.145 0.835 0.787 +1 0.749 -0.137 1.733 0.557 0.613 0.737 0.926 -1.017 2.173 0.868 -0.348 1.191 0.000 1.137 -0.320 -0.556 2.548 0.765 -1.122 0.469 0.000 0.845 0.975 0.983 1.137 0.910 0.953 0.838 +1 0.764 -0.882 -0.082 1.263 -1.291 0.946 -0.184 0.273 0.000 0.582 -0.344 -1.363 0.000 0.951 -1.591 -0.855 0.000 1.172 0.208 0.853 0.000 0.875 0.846 1.206 0.604 0.150 0.555 0.688 +1 0.803 -0.512 -0.836 0.504 0.497 0.865 -0.159 1.331 0.000 0.615 -0.448 0.463 2.215 1.123 0.683 -0.287 1.274 0.501 -0.316 -0.297 0.000 1.006 1.097 0.988 0.653 0.793 0.730 0.722 +1 0.735 -1.123 1.440 0.679 -0.575 1.272 -0.160 1.407 0.000 1.142 -0.158 0.114 2.215 1.284 -0.031 -0.726 1.274 0.821 2.164 -0.496 0.000 0.820 1.232 0.987 0.999 0.888 0.981 0.914 +1 0.628 -0.892 -0.077 1.314 -0.946 1.313 -0.751 1.277 1.087 0.550 -0.508 -1.522 0.000 0.436 -1.033 -0.525 0.000 0.755 0.621 0.290 0.000 1.013 1.106 0.989 0.690 0.780 0.891 0.843 +1 1.231 0.140 1.388 1.043 -1.195 0.885 0.232 -0.466 2.173 1.700 -2.329 0.530 0.000 1.392 0.744 -1.232 2.548 0.854 0.139 0.751 0.000 2.630 2.794 1.143 0.793 0.971 2.240 1.828 +1 0.546 -0.274 0.313 0.564 1.044 1.113 0.444 1.048 2.173 0.767 1.483 -0.372 0.000 1.101 0.456 -0.518 0.000 1.026 -0.347 -0.799 0.000 0.949 0.668 0.983 0.732 0.528 0.782 0.683 +1 0.927 -2.132 0.691 1.810 0.550 2.071 0.753 -0.957 0.000 1.980 -0.844 -1.682 0.000 2.015 -0.503 0.548 2.548 2.177 0.770 1.635 1.551 0.714 0.976 1.001 2.251 1.854 1.552 1.284 +0 0.556 1.352 -1.036 1.079 0.420 0.701 0.250 -1.082 0.000 0.614 -0.456 0.818 2.215 1.095 -0.191 -1.663 0.000 1.380 -0.087 -0.162 3.102 0.869 0.967 1.037 0.865 0.660 0.751 0.832 +1 0.648 -0.867 0.110 0.256 1.742 1.128 -1.005 -1.677 0.000 1.031 -0.228 -0.122 2.215 1.440 -2.508 0.929 0.000 2.177 -0.201 0.585 3.102 0.925 1.405 0.994 0.678 0.804 1.082 0.890 +0 0.771 -0.232 0.068 0.993 -1.725 0.843 -0.053 -1.217 2.173 1.066 -0.847 0.438 0.000 0.796 -0.153 1.740 2.548 0.798 0.167 0.479 0.000 0.677 0.856 1.211 0.845 0.471 0.820 0.733 +0 1.284 -0.095 -1.055 0.862 0.078 0.485 -1.734 0.653 0.000 0.879 -1.155 1.380 2.215 0.374 -2.442 -0.714 0.000 0.424 0.267 -0.152 3.102 0.811 0.808 1.243 0.592 0.708 0.711 0.790 +1 1.030 1.361 1.228 0.191 -0.570 0.507 0.163 -1.098 2.173 0.770 1.844 1.193 0.000 1.052 1.391 0.484 0.000 1.811 0.911 -0.289 3.102 0.863 1.030 0.984 0.762 0.833 0.887 0.769 +0 0.328 0.676 0.970 2.030 -1.372 0.790 -0.667 0.740 0.000 1.020 -0.832 0.201 0.000 1.263 0.452 -0.616 2.548 1.848 -0.193 -0.293 3.102 0.909 0.989 0.986 0.845 0.549 0.896 1.119 +1 0.370 2.240 -0.983 0.504 -1.527 0.739 1.574 0.135 0.000 0.865 1.110 1.509 2.215 1.186 0.614 0.487 0.000 1.583 -0.132 -1.213 3.102 0.955 1.123 0.998 0.729 0.997 1.015 0.855 +0 1.610 1.244 -0.963 0.917 -0.865 1.014 0.305 0.824 0.000 0.448 0.563 -0.703 2.215 0.774 0.823 1.269 0.000 0.708 0.909 0.514 3.102 0.786 0.904 1.007 0.811 0.471 0.589 0.891 +1 0.670 0.044 1.193 1.258 0.166 1.219 -0.697 -1.654 2.173 1.372 -1.160 -0.085 0.000 0.727 0.430 1.285 2.548 0.384 -2.021 -1.288 0.000 1.041 1.270 1.016 1.256 0.916 1.091 1.003 +0 0.906 -0.455 0.828 0.616 -1.454 0.593 -0.053 0.031 2.173 0.476 0.879 -1.464 0.000 0.613 -0.939 -0.062 0.000 0.664 -0.345 -0.879 3.102 0.703 1.083 0.991 0.591 0.500 0.714 0.690 +1 0.850 -1.142 1.593 0.884 -0.304 0.577 -2.021 0.813 0.000 0.606 -1.016 1.314 2.215 0.622 0.041 -0.442 0.000 0.991 -0.318 -0.946 1.551 0.948 1.107 1.189 0.701 0.669 0.741 0.791 +1 0.548 0.608 0.617 0.143 0.972 1.222 -0.858 -0.988 0.000 1.008 -0.140 1.019 2.215 0.768 -1.176 -0.362 2.548 0.629 -1.059 0.574 0.000 1.349 0.867 0.992 0.658 1.052 0.904 0.783 +1 0.502 0.184 0.905 0.300 0.853 0.818 0.337 -0.563 2.173 1.053 -1.063 0.299 2.215 1.003 -0.543 -1.080 0.000 0.709 -2.013 1.443 0.000 0.843 0.956 0.989 0.693 1.432 0.971 0.871 +1 0.768 -0.710 -1.372 2.113 -0.830 1.058 -0.076 0.956 2.173 0.305 0.492 0.961 0.000 0.441 0.242 0.037 0.000 0.505 -2.495 -0.133 0.000 1.281 1.386 0.978 1.447 0.790 1.052 1.077 +1 0.280 -0.000 0.180 1.718 -1.344 1.089 1.111 1.473 2.173 1.045 1.546 -0.847 0.000 1.048 -0.754 0.319 0.000 1.119 0.644 0.524 3.102 0.873 1.054 0.987 0.964 0.906 1.019 0.853 +1 0.599 0.673 0.220 0.789 -0.813 0.640 -1.287 -1.735 2.173 0.988 -0.398 -1.231 0.000 1.046 -0.279 0.180 0.000 0.855 -1.171 0.414 0.000 0.684 0.911 0.989 0.823 1.647 0.938 0.767 +1 2.046 0.547 1.262 1.213 1.184 1.292 -1.961 -0.739 0.000 1.364 -0.719 -0.171 2.215 0.655 -0.538 1.735 0.000 0.890 -0.133 0.702 3.102 1.805 1.476 0.982 1.646 0.762 1.101 1.468 +1 0.893 -0.013 0.018 1.731 1.153 2.113 0.500 0.430 0.000 4.139 0.983 -1.132 0.000 1.228 -0.033 -0.428 2.548 1.460 2.321 1.464 0.000 1.148 1.614 1.471 1.075 0.882 1.277 1.220 +0 2.224 -1.541 0.973 0.827 0.589 1.046 -1.133 -0.776 0.000 1.026 -0.509 -0.839 0.000 0.722 -0.810 1.586 2.548 0.380 -0.821 0.117 3.102 0.704 0.915 0.987 0.546 0.389 0.568 0.886 +0 0.650 2.150 0.122 0.313 -0.968 1.054 -1.727 -0.603 0.000 1.804 1.197 0.715 2.215 1.438 1.245 -1.565 2.548 1.548 1.204 1.107 0.000 1.421 0.933 0.986 0.914 1.519 1.001 0.857 +0 0.358 -0.760 0.247 1.364 -1.066 0.777 0.145 0.548 0.000 0.843 0.538 0.964 2.215 1.636 0.219 -1.148 2.548 0.561 1.296 1.003 0.000 0.869 1.234 0.987 0.921 1.196 0.830 0.792 +1 1.413 0.070 -1.608 0.641 -1.479 2.098 1.907 0.326 0.000 1.160 1.223 -1.443 2.215 1.792 0.391 -1.100 1.274 1.203 0.219 0.343 0.000 0.777 1.099 0.989 1.214 0.803 0.892 0.954 +0 1.452 -0.446 0.091 0.374 -1.082 0.516 0.567 -0.996 2.173 0.855 -0.657 0.575 1.107 1.058 0.413 1.730 0.000 0.752 -0.837 1.565 0.000 0.797 0.934 0.985 0.794 1.164 0.755 0.726 +1 0.459 -0.580 0.109 0.831 -1.377 0.674 -1.678 -0.483 0.000 0.533 -0.085 1.383 0.000 1.211 1.283 1.254 2.548 0.992 0.625 0.376 3.102 1.701 1.392 0.982 1.616 0.659 1.334 1.168 +0 0.827 -1.997 0.287 1.068 1.401 0.842 0.622 -1.245 1.087 0.560 -0.518 -1.499 0.000 1.361 -0.902 0.173 0.000 1.064 0.313 0.540 3.102 1.371 0.999 1.099 2.045 1.009 1.412 1.179 +0 0.703 -1.039 -1.515 1.416 1.047 1.089 -1.443 0.011 2.173 0.231 -1.010 -0.214 2.215 0.274 -1.638 1.584 0.000 1.070 -0.555 -1.132 0.000 0.522 0.937 1.022 0.613 0.215 0.731 0.643 +1 0.952 -1.600 -0.784 0.575 1.351 0.613 -1.327 -0.195 2.173 0.856 -0.070 1.016 2.215 1.360 -0.679 -0.630 0.000 0.837 -1.608 1.331 0.000 0.828 0.956 0.988 0.708 1.190 0.790 0.698 +0 0.365 -0.408 0.146 1.319 -1.675 0.988 0.314 1.361 2.173 1.168 1.005 0.241 2.215 0.881 2.078 -0.689 0.000 1.266 1.432 -0.216 0.000 0.585 0.908 0.987 0.938 1.459 1.130 1.275 +1 2.138 0.432 0.271 0.399 1.190 1.441 0.969 -0.982 0.000 0.893 1.032 -1.737 0.000 2.921 -0.751 -0.380 0.000 3.389 0.204 1.200 3.102 1.020 0.993 0.987 1.065 0.872 0.913 0.867 +0 1.203 1.930 -0.110 1.060 0.559 0.564 -2.826 1.301 0.000 1.686 0.253 -1.219 2.215 0.773 -0.116 -0.531 0.000 0.826 0.543 0.257 3.102 0.851 0.949 0.982 0.604 1.056 1.097 1.035 +0 0.624 0.911 0.788 2.753 0.122 1.299 1.350 -1.320 0.000 0.612 1.397 1.072 2.215 0.602 1.779 1.512 0.000 0.548 -0.438 -1.395 3.102 1.002 0.987 1.024 0.849 0.730 0.751 0.980 +0 0.329 -2.328 -0.489 2.053 -0.903 1.278 -2.321 1.237 0.000 1.013 -0.592 -0.034 2.215 0.470 -1.863 0.568 0.000 0.545 -0.718 -1.541 3.102 0.819 0.804 0.983 0.938 0.661 1.013 1.033 +0 1.752 -0.927 -1.454 0.722 1.305 0.481 -0.012 1.076 0.000 1.005 -0.690 0.002 2.215 0.813 -0.735 -0.380 2.548 0.476 0.568 -0.972 0.000 0.749 0.881 0.988 0.839 0.328 0.788 0.744 +1 0.593 1.487 -1.644 1.839 1.427 0.851 0.437 -0.405 2.173 0.385 1.149 -0.045 0.000 0.869 -0.150 0.996 2.548 0.648 1.720 -0.058 0.000 0.297 0.807 0.972 1.249 1.073 0.901 0.798 +1 0.491 0.770 0.611 1.057 0.617 0.615 -1.133 0.720 2.173 0.604 -0.720 -0.804 2.215 0.859 1.267 -1.004 0.000 0.411 -2.197 -1.509 0.000 2.433 1.433 0.983 0.646 0.898 1.090 0.947 +0 0.829 -1.553 -0.452 0.317 -0.641 0.701 -1.823 0.641 0.000 1.377 -1.058 -1.441 1.107 0.792 -0.827 0.091 2.548 0.855 1.660 0.928 0.000 1.030 1.119 0.982 0.902 1.094 1.144 0.985 +1 1.856 1.283 0.428 0.544 -1.521 1.035 0.807 -1.411 1.087 0.361 -0.884 -0.885 1.107 0.803 1.054 0.885 0.000 1.296 0.025 0.018 0.000 1.037 0.899 1.368 1.274 0.968 1.013 0.905 +1 1.000 -0.512 -1.416 0.584 -0.015 0.931 -0.925 1.117 0.000 0.448 -1.912 0.711 0.000 0.617 -1.375 -0.552 2.548 1.002 0.750 -0.607 3.102 0.937 0.853 1.009 0.744 0.914 0.810 0.708 +0 0.550 0.637 1.143 1.323 -0.815 0.484 -0.741 -0.217 1.087 0.607 -0.289 0.557 2.215 0.711 -1.555 -1.670 0.000 0.869 -0.008 1.569 0.000 0.850 0.867 1.160 0.867 0.543 0.706 0.761 +0 0.428 -0.488 1.082 1.301 -0.386 0.478 -0.628 -1.453 0.000 0.765 0.232 0.488 2.215 0.575 0.922 -0.325 2.548 0.752 -2.228 1.676 0.000 0.920 0.912 1.002 0.696 0.548 0.659 0.660 +1 1.004 -2.255 0.249 0.635 0.764 1.375 -1.283 -1.009 0.000 0.451 -1.861 1.297 0.000 1.388 0.152 0.532 2.548 0.529 0.278 -1.533 3.102 0.869 0.982 0.983 0.881 0.630 0.866 0.762 +1 0.996 -0.802 0.466 0.788 -0.786 1.626 -0.873 0.186 0.000 2.490 -1.061 -1.495 2.215 0.713 -1.875 1.241 0.000 0.804 -0.559 1.307 3.102 0.765 0.996 1.109 0.670 0.786 0.857 0.738 +0 0.700 -1.020 -0.370 0.707 1.328 1.121 -0.525 -0.200 2.173 1.027 0.151 1.190 0.000 1.493 0.461 -1.556 0.000 0.866 0.694 0.622 3.102 1.221 0.902 0.988 0.853 1.040 1.087 0.930 +1 0.752 0.347 0.920 0.340 -1.173 1.016 0.117 -0.248 1.087 0.964 0.357 0.086 0.000 1.736 0.100 1.379 2.548 2.182 0.382 -1.548 0.000 0.977 0.913 0.983 0.914 1.646 0.889 0.767 +1 0.827 0.848 1.455 1.473 1.543 1.284 0.526 0.076 2.173 0.930 0.902 -0.887 2.215 0.440 0.814 -0.329 0.000 0.427 1.521 1.289 0.000 0.528 0.724 1.000 1.088 1.269 1.150 0.879 +1 0.520 0.381 -1.379 1.028 1.153 0.802 -1.235 -0.748 2.173 0.620 -2.032 0.736 0.000 0.379 0.010 1.467 0.000 0.749 -0.455 -0.531 3.102 0.822 0.719 0.987 0.855 0.334 1.101 0.946 +1 1.468 -0.625 0.233 1.181 -0.277 0.839 2.804 -1.364 0.000 1.400 -0.430 1.185 2.215 0.793 0.726 -1.100 2.548 0.854 0.232 -0.004 0.000 0.585 0.771 0.985 1.166 1.235 1.076 0.827 +1 0.933 1.262 -0.164 0.417 0.814 0.518 -0.250 1.410 2.173 0.466 1.327 -0.893 0.000 0.749 0.670 1.295 0.000 1.146 -0.972 -0.458 3.102 0.883 0.822 0.993 0.967 0.897 0.799 0.754 +1 0.979 -0.755 -0.507 1.468 -1.458 0.870 -1.213 -0.142 0.000 1.129 -0.422 0.123 0.000 1.852 -0.338 1.711 0.000 2.136 -0.587 0.728 1.551 0.989 1.049 1.254 0.875 1.364 1.106 1.031 +0 1.599 0.746 -1.571 1.297 1.247 0.587 -0.515 -0.179 2.173 0.682 0.811 0.105 0.000 0.753 0.773 -0.437 2.548 0.531 1.328 1.563 0.000 0.817 0.931 1.131 0.921 0.641 0.924 0.795 +1 0.829 1.447 1.599 0.221 -1.565 1.473 -2.364 -0.738 0.000 1.360 0.811 1.565 0.000 2.473 0.210 0.222 2.548 1.109 0.859 0.929 0.000 0.881 0.765 0.985 1.161 0.998 0.974 0.834 +1 1.230 -2.115 1.270 0.887 0.717 0.777 -1.067 -0.060 0.000 0.600 -0.492 -0.335 2.215 0.564 -0.915 -1.647 2.548 1.090 -0.098 -1.020 0.000 0.728 0.552 0.977 0.659 0.593 0.690 0.650 +1 0.725 -0.602 -1.143 1.057 0.170 0.956 -0.497 1.723 0.000 0.915 -0.710 -0.704 0.000 1.275 -0.251 0.261 2.548 1.387 -0.646 1.213 3.102 0.790 1.032 1.123 0.797 0.810 0.672 0.677 +0 0.642 0.888 -0.279 0.848 1.307 0.801 1.405 -0.526 2.173 0.476 1.743 0.494 0.000 0.588 2.072 1.671 0.000 1.295 -0.601 1.599 1.551 0.735 0.882 1.012 0.882 1.719 1.078 0.875 +0 0.492 0.150 -1.313 0.819 -0.098 0.709 -0.148 -1.646 0.000 0.905 -0.352 0.720 2.215 0.420 -1.137 -0.997 0.000 0.949 0.652 0.129 3.102 0.785 0.952 0.993 0.561 0.660 0.723 0.746 +0 0.737 -1.336 1.561 0.974 -0.454 0.808 -0.632 0.739 2.173 0.510 0.186 0.444 0.000 1.313 0.094 -1.345 0.000 1.123 0.015 -1.038 3.102 0.762 0.785 1.139 0.961 1.062 0.785 0.685 +0 0.607 -2.180 1.128 0.096 -0.062 1.271 -0.991 1.622 2.173 1.444 -0.295 -0.400 2.215 0.773 0.827 -0.187 0.000 0.469 -1.103 0.834 0.000 0.863 1.045 0.995 0.847 2.056 1.249 1.065 +1 1.130 0.768 0.132 0.347 1.455 1.421 0.130 1.482 2.173 1.036 0.348 -0.405 0.000 0.540 -1.784 -0.248 0.000 0.507 0.986 -0.820 3.102 1.655 1.077 0.988 1.094 0.929 1.155 0.970 +1 0.611 -1.305 -0.121 0.697 0.494 1.343 0.738 -1.289 2.173 0.680 0.729 1.283 2.215 0.894 -0.652 0.095 0.000 1.034 0.233 0.330 0.000 0.845 1.029 0.981 1.413 1.030 1.079 0.968 +0 1.877 1.268 -1.203 0.627 1.454 1.064 0.772 0.527 2.173 1.503 -1.440 -0.624 0.000 1.365 0.250 1.022 0.000 1.076 1.257 -0.014 3.102 0.541 0.816 1.021 1.340 0.678 0.922 0.826 +1 1.338 0.144 -0.177 0.338 -1.198 0.917 0.141 0.877 1.087 0.845 -0.133 -0.548 0.000 1.335 0.741 1.559 2.548 1.256 1.806 -1.644 0.000 0.840 1.283 0.985 0.946 0.923 1.034 0.908 +1 1.694 -0.752 0.165 0.844 1.005 0.706 0.110 1.408 0.000 0.727 0.294 -1.367 0.000 0.726 -0.010 -0.608 2.548 0.777 0.464 -1.012 3.102 0.920 0.836 1.138 0.925 0.263 0.675 0.770 +0 0.445 1.682 -0.952 0.670 -1.073 0.569 1.101 0.296 0.000 0.703 -0.073 -0.871 0.000 0.766 0.033 1.305 1.274 0.608 0.341 1.580 1.551 1.440 1.034 1.000 0.557 0.161 0.630 0.609 +0 0.879 0.731 -0.161 1.550 -0.734 0.858 1.300 0.741 2.173 0.701 1.133 1.707 2.215 0.519 2.360 1.449 0.000 0.776 0.572 0.371 0.000 0.966 0.763 0.985 0.993 0.875 0.977 0.870 +1 0.465 -0.542 0.995 1.102 -1.410 0.774 1.078 1.721 0.000 0.957 0.039 -0.222 2.215 1.327 0.637 0.369 2.548 0.774 -0.209 0.602 0.000 0.889 1.061 0.987 0.877 0.729 0.850 0.779 +1 0.982 -0.028 -0.451 1.189 0.560 1.085 0.683 1.312 2.173 0.718 0.507 0.562 2.215 1.370 0.449 -0.501 0.000 1.408 0.610 -1.117 0.000 0.829 0.972 1.183 1.209 0.820 0.924 0.883 +0 0.772 -0.035 -0.370 0.665 1.344 1.072 0.343 1.011 0.000 1.143 0.193 -0.789 0.000 0.735 0.994 -0.315 2.548 0.770 -0.404 1.600 3.102 2.354 1.346 0.992 0.684 0.758 0.909 0.764 +1 1.030 -0.752 1.278 0.891 1.737 0.500 0.275 -0.379 2.173 0.289 0.792 0.369 0.000 0.326 -0.812 0.873 2.548 0.878 -0.862 -0.457 0.000 0.828 0.598 0.985 0.514 0.551 0.740 0.682 +1 1.174 1.794 0.459 0.792 1.346 1.255 1.092 -0.776 2.173 0.423 0.084 0.031 0.000 0.478 1.347 -1.710 2.548 0.923 -2.391 1.741 0.000 0.918 1.112 0.987 0.608 0.742 0.878 0.787 +0 1.561 -1.619 1.471 0.318 -1.273 0.656 -0.016 0.185 2.173 0.571 -0.232 1.322 1.107 1.175 -1.711 -0.366 0.000 0.439 -0.742 -1.167 0.000 0.664 0.932 0.986 1.175 0.776 0.806 0.800 +1 2.081 0.738 0.887 0.583 1.512 0.602 -0.270 -0.970 1.087 0.735 -0.822 -1.203 0.000 2.013 -0.264 0.024 2.548 1.059 -0.124 1.721 0.000 0.692 1.209 0.984 1.150 1.070 1.053 0.964 +0 1.168 1.045 -1.410 0.729 -1.101 0.705 0.749 0.373 2.173 0.996 0.809 -0.385 0.000 1.465 0.085 -1.502 0.000 2.173 -0.136 1.100 3.102 0.870 0.939 0.978 1.143 1.012 1.130 1.009 +1 0.837 -1.009 1.163 1.118 -1.660 0.555 -0.238 0.132 2.173 0.698 -2.019 -0.931 0.000 1.366 0.955 0.170 0.000 2.147 -0.229 -1.106 0.000 0.883 0.956 0.987 1.096 0.739 0.924 1.056 +0 2.381 -1.203 0.390 0.738 1.013 1.205 0.018 -1.425 0.000 0.848 -1.173 -0.616 0.000 1.165 -0.776 0.945 2.548 1.615 1.759 -0.650 0.000 2.002 1.043 0.986 0.646 0.360 0.878 1.011 +0 0.630 -1.053 -0.137 0.832 1.206 0.949 -0.516 -0.697 2.173 1.199 -1.140 1.142 0.000 0.463 0.312 0.091 2.548 0.637 -0.171 -1.313 0.000 1.077 0.884 0.990 1.004 0.652 0.835 0.765 +1 0.747 -0.331 0.636 1.303 1.250 1.015 0.049 -0.293 0.000 0.643 0.550 0.987 2.215 0.687 -0.399 -0.905 2.548 0.974 0.977 -1.456 0.000 1.593 0.992 0.990 0.855 0.792 0.778 0.921 +0 0.664 -0.213 -1.731 0.881 -1.082 0.563 0.285 -1.016 0.000 1.087 0.665 0.496 1.107 0.541 0.038 0.114 0.000 1.305 -0.369 1.049 3.102 0.854 0.950 0.985 0.779 0.820 0.937 0.830 +0 0.860 1.835 -0.534 0.723 0.849 1.307 0.502 0.801 2.173 1.341 0.842 -0.913 2.215 0.676 0.888 1.580 0.000 0.481 -1.975 -1.115 0.000 1.670 1.587 1.035 1.245 1.979 1.363 1.294 +1 1.913 -0.467 1.698 0.906 -1.395 0.912 -0.042 -0.348 2.173 0.427 1.181 0.668 2.215 0.858 0.548 -1.659 0.000 1.843 -1.988 0.172 0.000 0.485 0.969 0.998 1.189 0.954 1.082 0.892 +1 1.451 0.077 -0.796 0.620 1.665 0.813 -0.071 1.047 2.173 0.622 -1.494 0.611 2.215 0.338 1.395 1.164 0.000 0.640 -2.084 -0.433 0.000 1.962 1.291 1.048 1.012 0.929 0.959 0.940 +0 1.067 0.278 0.954 0.488 -0.887 1.573 -0.315 -1.626 2.173 1.747 0.941 -0.121 0.000 0.377 0.592 0.028 2.548 1.158 -0.059 0.651 0.000 1.558 0.805 0.996 1.037 1.072 1.336 1.049 +1 0.811 0.071 -0.966 0.796 1.316 1.005 0.119 -0.423 1.087 0.743 0.832 -0.512 0.000 1.333 0.707 1.358 0.000 1.652 0.058 -1.691 3.102 1.045 0.941 0.986 0.904 1.241 0.964 0.811 +1 0.969 -0.318 0.620 0.354 1.708 0.935 0.123 -0.883 1.087 0.663 -0.764 -1.063 0.000 0.613 1.893 1.252 0.000 0.921 -0.906 0.383 0.000 0.939 0.807 0.996 0.493 0.752 0.711 0.631 +0 1.487 -0.224 -1.573 1.524 1.538 0.526 0.813 0.970 0.000 1.506 -0.882 -0.251 0.000 0.702 -0.152 0.400 2.548 0.452 -0.893 -0.936 3.102 2.500 1.368 0.993 0.907 0.450 0.825 0.950 +0 1.047 -0.816 -0.010 0.827 0.333 1.003 -0.867 1.124 2.173 0.558 -0.049 1.413 1.107 0.928 0.213 -1.155 0.000 1.893 0.500 -0.671 0.000 0.673 0.840 0.989 1.069 0.546 0.985 1.081 +1 1.226 -0.234 -0.511 0.240 -1.164 0.573 -0.625 1.407 0.000 0.880 0.877 0.085 2.215 1.184 0.031 0.727 2.548 0.938 -0.699 -1.120 0.000 0.859 0.891 0.981 1.053 0.767 0.849 0.822 +0 1.847 0.495 -0.590 0.105 -0.422 1.248 0.153 -1.143 0.000 2.363 0.502 1.526 0.000 1.841 0.540 0.243 2.548 1.670 1.102 0.115 3.102 1.056 1.337 0.988 0.950 0.523 1.022 0.885 +1 1.537 -0.330 0.614 0.831 0.185 0.707 -0.560 -1.445 0.000 0.580 -0.555 1.482 0.000 0.921 0.475 -0.742 2.548 0.386 0.280 -1.199 3.102 0.826 0.879 0.979 0.680 0.187 0.690 0.635 +1 1.338 0.809 -1.659 0.176 -0.641 1.262 0.039 -0.301 2.173 0.896 1.196 0.609 0.000 0.689 1.211 -1.305 0.000 0.605 0.287 -1.686 3.102 1.192 0.774 0.985 1.367 0.888 0.904 0.860 +0 1.150 0.764 0.534 2.573 0.700 1.181 -0.827 -1.126 0.000 1.569 0.205 -1.146 2.215 0.576 1.437 1.146 0.000 0.644 -0.911 0.061 0.000 1.181 0.832 1.001 2.006 0.931 1.331 1.550 +0 1.250 0.715 0.441 0.117 -0.880 2.270 0.313 -1.210 0.000 1.246 0.010 0.574 2.215 0.967 2.219 0.008 0.000 0.672 -0.183 1.174 3.102 3.096 1.735 0.998 0.650 0.436 1.373 1.140 +1 0.646 -1.430 1.648 0.300 0.766 1.251 -0.441 -0.891 0.000 1.297 -1.001 0.558 2.215 1.055 -0.439 -1.525 2.548 1.064 -0.057 -0.027 0.000 0.905 0.878 0.979 0.824 1.235 0.737 0.665 +1 0.570 -0.383 -0.135 0.935 1.683 0.572 0.077 1.611 2.173 0.426 -1.591 -0.121 0.000 0.844 -0.521 -0.647 2.548 0.838 -2.192 0.441 0.000 0.552 0.790 1.009 0.644 0.825 0.852 0.753 +0 1.381 -1.826 0.712 0.589 0.939 0.962 -0.478 -1.145 2.173 0.811 -1.026 -0.236 0.000 0.370 0.392 1.558 0.000 0.496 -0.618 1.739 3.102 1.083 0.987 1.001 0.607 0.389 0.836 0.772 +1 0.904 1.680 -1.541 1.132 -1.041 1.572 -0.151 -0.135 0.000 2.819 1.405 0.387 0.000 2.705 -0.257 -1.685 2.548 2.540 0.652 -1.612 0.000 0.847 1.043 0.998 2.629 0.325 1.651 1.687 +0 1.299 -0.694 1.183 0.765 0.181 0.531 -2.772 -0.286 0.000 0.229 -1.205 -0.751 0.000 0.729 -0.893 -1.603 2.548 0.366 0.171 -1.480 3.102 0.705 0.862 1.084 0.605 0.252 0.629 0.723 +1 0.816 -1.119 0.202 0.792 -1.373 0.831 -0.059 -0.635 0.000 1.534 0.971 1.511 0.000 1.396 0.950 -0.170 0.000 0.661 0.323 0.674 3.102 1.373 0.939 1.101 0.810 0.137 0.671 0.823 +0 2.579 1.044 0.380 0.514 -0.477 0.911 0.070 -1.724 2.173 0.907 -0.753 -1.734 0.000 0.689 1.431 -0.881 0.000 0.375 -0.467 -1.062 0.000 0.724 0.873 1.112 1.072 1.060 1.113 0.913 +1 1.344 -1.256 1.333 0.535 0.291 0.850 2.465 -0.917 0.000 0.931 -0.180 -0.170 2.215 1.136 -0.302 0.998 0.000 0.484 0.877 1.350 3.102 3.826 1.978 0.986 1.061 0.715 1.475 1.788 +0 0.898 -0.140 -1.102 0.982 0.024 1.452 -0.587 0.800 0.000 2.042 0.511 -1.421 2.215 1.195 1.249 -0.310 0.000 1.548 -0.227 -0.015 1.551 0.994 1.070 1.106 1.182 1.666 1.065 0.948 +0 0.415 -1.384 -1.051 0.692 0.473 1.044 -0.323 -1.191 1.087 0.792 1.571 0.322 2.215 1.124 0.476 0.854 0.000 0.394 1.990 -0.651 0.000 1.066 0.797 0.989 0.882 2.002 1.126 0.974 +1 0.947 0.672 0.171 1.010 0.997 1.010 0.416 -1.586 2.173 1.910 0.278 0.290 0.000 1.192 -0.394 1.720 2.548 1.101 1.899 -0.050 0.000 1.247 1.431 0.990 1.073 0.640 1.168 0.983 +0 0.964 0.141 0.787 1.260 -0.184 0.572 -0.173 -1.123 2.173 0.636 0.115 1.470 0.000 0.735 0.856 -0.901 1.274 0.933 -1.133 0.858 0.000 0.967 1.028 1.171 0.932 0.500 0.716 0.748 +0 0.591 1.725 -0.151 1.602 0.229 1.456 -0.315 1.341 0.000 0.618 0.453 -0.874 2.215 0.581 -0.942 1.331 0.000 1.134 -1.688 -0.899 0.000 0.933 1.110 0.985 0.665 0.369 0.954 1.078 +1 1.049 2.093 -1.375 0.922 1.368 0.707 0.737 0.516 1.087 0.728 1.069 -0.613 2.215 0.588 0.616 1.656 0.000 0.853 -0.454 -0.310 0.000 0.918 0.866 0.994 0.857 0.919 0.836 0.814 +0 1.128 -1.356 0.629 0.494 -1.614 0.572 -0.965 -0.153 1.087 0.775 -1.510 -1.382 0.000 0.655 -1.384 -0.698 0.000 0.914 -0.113 1.261 3.102 0.631 0.816 0.985 0.766 0.803 0.653 0.646 +0 0.941 -1.123 -1.040 2.095 0.802 1.836 -0.672 -1.644 1.087 1.276 -0.633 -0.164 0.000 1.282 -0.071 -0.454 0.000 1.198 0.278 0.410 3.102 0.763 0.831 1.938 1.678 1.720 1.321 1.285 +1 0.511 -0.449 1.122 0.284 0.557 1.041 -2.229 0.984 0.000 2.503 -0.361 -0.643 2.215 1.992 -0.106 1.343 0.000 0.756 0.502 0.318 0.000 1.189 0.860 0.989 1.429 0.813 1.123 0.983 +0 1.709 -0.562 -0.247 1.014 0.140 0.631 0.776 -1.329 2.173 0.944 1.234 1.623 0.000 0.323 -1.074 1.320 2.548 0.622 0.791 1.099 0.000 0.482 0.760 0.993 1.465 0.751 0.948 1.059 +0 0.690 2.262 0.632 1.367 -0.377 0.829 1.437 0.563 2.173 0.967 0.913 1.680 0.000 0.956 0.548 -0.780 2.548 0.709 1.449 -0.789 0.000 0.964 1.090 1.061 1.033 1.146 0.873 0.862 +0 0.350 -1.405 0.272 1.234 -1.399 0.990 0.161 -0.262 0.000 0.712 -1.581 1.588 0.000 1.015 -0.704 1.114 0.000 1.683 0.254 0.250 3.102 0.788 1.259 0.991 0.517 0.710 0.765 0.704 +0 1.415 0.037 1.240 0.926 0.690 1.024 0.933 -0.636 0.000 1.092 0.501 -0.982 2.215 0.975 -0.548 0.993 2.548 0.798 0.489 0.035 0.000 0.823 0.742 0.989 0.514 1.252 0.905 0.982 +1 1.085 -0.172 -0.509 0.299 1.402 1.361 -0.408 1.363 0.000 0.784 -1.006 0.008 2.215 1.087 0.292 -0.630 2.548 0.546 -0.095 0.136 0.000 1.194 1.228 0.991 0.668 0.894 0.960 0.817 +1 0.856 -0.263 -0.637 0.927 1.480 0.794 0.272 -1.508 2.173 1.246 -0.871 0.723 0.000 0.830 -0.515 -0.251 2.548 0.621 -0.742 0.191 0.000 0.529 0.672 1.165 0.780 1.016 0.865 0.773 +1 0.773 -0.461 -0.169 0.771 0.663 0.975 -0.060 1.362 0.000 1.377 0.461 -0.877 2.215 0.864 -1.139 0.306 0.000 1.177 -0.569 -1.433 3.102 0.733 0.869 0.987 1.339 0.894 0.931 0.847 +0 0.592 -0.341 -1.697 0.574 0.492 1.049 -0.183 -0.766 2.173 1.029 -0.208 0.636 0.000 1.185 0.145 1.246 2.548 0.760 -0.842 -0.944 0.000 1.234 0.946 0.979 1.019 1.369 0.921 0.821 +1 0.799 -1.181 -0.953 0.058 1.540 1.267 0.052 1.393 0.000 1.605 0.229 -0.192 0.000 1.369 -0.917 -1.305 2.548 1.188 -0.736 -0.103 3.102 0.943 0.763 0.981 0.665 0.863 0.869 0.749 +0 0.486 0.917 -1.428 0.953 0.209 0.477 -0.218 1.431 2.173 0.977 0.292 0.635 0.000 1.078 0.262 -0.502 0.000 0.552 -1.696 -1.136 0.000 1.311 1.029 0.988 0.550 0.556 0.625 0.632 +1 1.416 -0.021 0.339 1.366 0.062 2.933 2.773 1.684 0.000 2.287 0.487 -0.305 1.107 1.477 -0.253 0.013 2.548 1.081 -0.116 -1.267 0.000 5.742 5.296 0.988 0.961 0.956 3.801 3.027 +0 0.879 -0.469 1.725 1.782 1.294 0.822 -0.636 -0.305 0.000 1.469 -0.459 0.256 2.215 0.905 -0.280 -1.149 0.000 1.283 -0.763 -1.042 3.102 1.100 1.134 0.990 0.984 1.174 1.031 0.982 +0 0.558 0.054 -0.966 1.552 0.063 1.944 -1.411 -1.711 2.173 2.035 0.348 -0.189 2.215 1.361 -1.597 1.012 0.000 0.407 0.691 1.267 0.000 1.394 1.504 1.032 0.726 4.128 2.052 1.661 +1 1.555 -1.215 1.137 1.050 1.113 1.293 -2.137 -0.846 0.000 0.974 -0.521 1.120 0.000 1.654 -0.520 -0.562 1.274 1.343 0.070 0.315 3.102 0.839 1.103 0.977 1.260 0.895 1.167 0.989 +0 1.372 -1.143 1.132 0.217 0.252 1.012 -1.329 0.152 2.173 1.175 -1.867 -1.175 0.000 0.574 0.077 0.748 2.548 0.464 1.776 -1.343 0.000 0.814 0.641 0.977 0.952 0.881 0.974 0.860 +0 1.455 -0.422 0.302 0.582 -1.719 1.187 -0.801 -1.720 2.173 1.139 -1.354 -0.258 2.215 0.772 -0.060 0.998 0.000 0.743 -0.531 -1.029 0.000 0.844 1.008 1.235 1.154 1.733 1.061 0.866 +0 0.555 0.969 0.917 1.102 1.643 0.656 -0.543 -1.088 2.173 1.208 0.587 -0.206 2.215 0.590 -0.101 1.229 0.000 0.483 1.454 1.091 0.000 0.892 0.940 0.980 1.259 1.228 1.236 0.973 +1 2.378 0.123 0.742 0.116 1.479 1.044 -0.611 -1.045 2.173 0.560 -0.612 0.118 1.107 0.686 1.684 1.528 0.000 0.623 0.829 0.963 0.000 0.474 0.985 0.978 1.424 0.975 1.032 0.985 +1 1.042 0.051 0.154 0.870 -0.234 1.077 0.370 -1.568 2.173 0.419 -0.289 1.095 1.107 0.346 -0.246 1.630 0.000 0.443 -1.044 0.189 0.000 0.471 0.765 0.987 0.761 0.748 0.866 0.705 +0 0.613 -1.118 1.415 1.209 1.334 1.133 0.753 -0.738 0.000 1.112 -1.066 0.801 1.107 1.404 -0.312 -0.849 2.548 1.102 0.393 -0.156 0.000 0.889 0.919 0.986 0.935 1.419 1.237 1.616 +0 0.386 0.831 -1.302 1.893 0.274 1.105 0.233 0.397 2.173 1.095 -2.515 -1.581 0.000 1.545 0.424 -1.506 2.548 0.990 1.367 -1.249 0.000 5.555 3.481 1.171 0.828 1.622 2.418 2.069 +1 0.845 -1.609 0.810 1.558 1.451 0.335 -1.312 0.191 0.000 0.554 0.584 -0.726 1.107 0.354 0.910 -1.239 2.548 0.427 0.697 1.375 0.000 0.913 0.721 0.994 1.336 0.230 1.216 0.992 +0 0.388 1.704 1.305 0.284 0.853 0.952 1.375 0.538 0.000 1.331 0.463 -1.178 2.215 0.822 1.208 -0.846 2.548 0.676 0.770 1.394 0.000 0.900 0.934 0.997 0.879 0.587 0.913 0.802 +1 1.442 -0.925 1.259 0.212 -0.909 3.287 -1.614 -0.248 0.000 1.999 -0.292 1.527 2.215 0.926 -1.405 -1.179 0.000 0.380 -1.185 0.925 3.102 0.604 1.032 0.984 0.463 0.618 0.630 0.621 +1 1.187 -0.438 1.363 1.574 -1.497 1.670 -0.946 -1.536 2.173 2.983 -0.570 0.003 0.000 1.277 -0.760 0.631 2.548 0.373 0.043 -0.297 0.000 0.577 0.912 1.015 0.832 1.692 1.510 1.331 +1 0.384 -1.929 -1.431 1.918 0.866 0.788 -1.299 -1.085 2.173 0.428 -0.593 -0.693 0.000 0.336 0.424 0.490 1.274 0.665 -1.755 0.121 0.000 0.736 0.704 1.043 0.953 0.906 0.898 0.755 +0 0.485 1.476 -1.048 1.986 -0.266 1.090 -0.293 0.950 0.000 1.013 0.149 -1.665 2.215 1.553 1.353 0.014 0.000 1.163 -1.037 -0.822 3.102 0.820 0.951 0.984 2.193 1.004 1.606 1.529 +1 0.800 -0.470 0.891 1.003 -0.598 0.661 1.229 0.785 0.000 1.181 -0.482 -1.298 2.215 0.840 -0.409 0.633 1.274 0.472 -0.167 0.004 0.000 0.868 0.780 1.209 0.905 1.044 0.913 0.833 +0 0.785 0.352 -0.465 1.626 1.507 1.154 0.304 0.362 0.000 1.221 0.906 -1.094 2.215 1.395 -0.789 -1.121 0.000 0.558 -1.105 0.432 0.000 0.986 0.741 1.532 1.072 1.004 0.902 0.878 +1 2.118 -1.267 0.630 0.610 -1.467 1.291 -0.730 -0.591 0.000 0.990 -1.643 1.628 0.000 0.911 -0.166 -1.588 0.000 1.620 0.229 1.410 3.102 0.915 0.701 1.496 1.264 0.843 0.928 0.964 +1 1.359 -0.088 -0.511 0.603 1.091 1.219 -0.497 -0.967 2.173 1.148 0.278 0.880 2.215 0.777 -0.136 1.147 0.000 1.278 -0.402 1.737 0.000 0.928 0.744 1.244 0.976 1.871 1.033 0.891 +0 1.021 0.166 1.100 0.922 -1.050 0.688 0.604 -0.003 2.173 0.474 0.346 -0.505 0.000 1.134 -0.526 1.316 0.000 0.967 0.963 -0.925 1.551 1.247 0.995 1.255 0.951 0.681 0.765 0.727 +1 0.419 1.625 -1.273 0.463 1.674 0.917 1.419 -0.246 0.000 0.915 0.232 -0.165 1.107 1.746 0.267 1.515 2.548 0.731 -2.251 0.989 0.000 4.994 2.768 0.974 0.685 1.342 1.850 1.409 +1 0.953 0.066 1.573 0.384 -0.934 0.594 -0.620 -0.595 2.173 0.327 -1.509 1.617 2.215 0.387 -1.051 0.022 0.000 0.920 0.640 -0.516 0.000 0.796 0.764 0.994 0.755 0.669 0.553 0.568 +0 2.077 -0.626 -0.641 0.502 -0.547 1.123 -0.659 0.790 1.087 0.956 -0.083 -1.168 0.000 0.681 -1.111 0.373 2.548 0.953 -0.643 -1.639 0.000 1.151 0.970 0.986 1.426 0.504 0.949 0.910 +1 0.282 -1.190 -0.474 1.056 1.229 0.628 0.866 -0.013 2.173 1.082 0.182 -1.174 2.215 1.039 -0.194 0.324 0.000 0.932 -0.784 1.711 0.000 1.107 1.081 0.987 2.156 1.132 1.646 1.291 +0 0.958 -0.018 -0.031 1.109 -0.873 1.060 1.405 1.241 2.173 0.769 -0.539 1.697 0.000 1.335 -0.749 -0.514 2.548 0.630 0.246 0.386 0.000 0.928 0.952 0.989 1.466 2.514 1.316 1.085 +0 0.608 -0.582 0.273 1.292 0.192 0.736 -0.183 1.162 2.173 0.650 -0.253 -1.144 0.000 1.173 0.293 -0.879 0.000 0.880 1.246 1.027 0.000 0.909 0.785 0.977 1.195 1.203 1.256 1.708 +0 0.909 0.076 1.658 2.207 -1.278 1.372 -0.332 0.411 2.173 0.360 -1.976 0.681 0.000 0.702 0.406 -1.497 0.000 1.236 0.116 -0.386 3.102 1.413 1.045 0.988 1.772 0.966 1.182 1.071 +0 0.905 0.422 1.021 0.617 -0.916 0.649 1.023 -0.100 0.000 0.731 0.714 -0.785 0.000 1.585 1.220 1.633 1.274 0.714 1.075 0.914 3.102 0.875 0.759 1.020 0.822 0.491 0.757 0.693 +1 1.363 0.719 -0.809 0.356 -0.158 1.253 1.426 0.815 2.173 0.690 1.173 -0.548 0.000 0.589 0.046 1.433 2.548 0.921 0.195 -1.329 0.000 0.853 0.731 0.989 1.264 0.984 0.911 0.826 +1 0.398 1.952 1.666 0.457 -1.278 1.591 0.129 -0.196 0.000 1.003 0.564 -1.111 1.107 1.124 0.503 1.295 2.548 1.835 -0.513 -1.425 0.000 1.009 1.056 0.989 0.654 0.933 0.748 0.671 +0 0.515 0.347 -1.396 1.738 -1.448 0.720 0.807 -0.248 2.173 0.516 -1.173 0.779 0.000 0.865 0.830 1.198 0.000 1.254 0.325 0.306 3.102 0.946 0.947 0.991 1.079 0.527 0.883 0.929 +1 0.554 -0.461 0.015 0.637 -1.568 0.520 -1.282 1.703 0.000 0.584 -1.070 0.012 0.000 0.971 -0.016 1.527 2.548 0.486 1.170 0.409 3.102 1.175 0.963 0.988 0.578 0.601 0.754 0.687 +0 0.356 1.693 0.726 1.208 -0.809 0.436 0.743 1.252 2.173 0.868 0.099 -1.308 2.215 0.589 -0.071 -0.129 0.000 0.393 0.809 0.167 0.000 0.323 0.635 0.990 0.982 0.733 0.992 0.828 +1 3.036 -0.339 -1.591 0.431 -1.148 1.845 0.268 -0.085 0.000 0.952 -0.010 1.685 2.215 1.540 -0.455 0.300 2.548 1.620 -0.145 1.259 0.000 0.746 0.756 0.994 1.405 1.261 0.973 0.811 +1 1.472 0.834 -0.360 1.221 0.076 1.352 0.558 1.724 2.173 0.605 0.892 0.415 0.000 0.362 -0.271 0.589 2.548 0.514 -0.533 -1.592 0.000 0.943 1.076 0.978 0.607 0.839 1.000 0.830 +0 0.529 -1.785 -0.663 0.412 0.155 0.494 -0.538 -1.395 2.173 0.723 -1.027 1.140 0.000 0.502 -1.215 -1.166 2.548 0.754 -1.703 0.158 0.000 0.894 0.881 0.983 0.567 0.283 0.557 0.560 +0 0.382 -0.738 -1.493 1.946 1.048 0.935 0.890 -0.828 2.173 0.784 1.611 -0.011 0.000 0.651 0.465 -1.480 2.548 0.548 2.411 0.940 0.000 0.856 0.921 0.984 1.399 0.571 0.905 1.044 +0 0.387 1.501 -0.468 2.235 0.531 0.812 0.882 1.185 2.173 0.997 0.874 -0.865 0.000 0.772 1.414 -1.628 0.000 0.580 0.021 -1.035 0.000 0.964 1.112 1.009 0.995 0.898 0.833 0.867 +1 0.376 2.073 0.598 1.021 -1.246 0.911 0.941 -0.626 2.173 0.858 0.243 0.499 0.000 1.401 0.739 -1.440 2.548 1.062 -0.426 1.285 0.000 0.943 1.145 0.983 0.773 0.947 0.976 0.857 +1 0.314 1.057 -0.787 0.841 1.269 0.663 -1.155 1.391 0.000 1.266 -0.931 -0.405 2.215 0.970 0.240 -0.126 2.548 0.598 0.289 1.494 0.000 0.804 1.047 0.988 2.557 0.820 1.636 1.514 +1 0.992 0.438 -0.627 1.443 1.623 0.784 -0.125 1.073 0.000 1.158 0.491 1.364 0.000 1.035 0.872 0.155 2.548 1.034 -0.272 -0.695 3.102 0.833 1.074 1.487 1.034 0.772 0.854 0.847 +0 0.568 0.736 -0.123 1.885 0.807 0.808 2.065 -0.861 0.000 0.487 1.332 1.459 2.215 0.388 2.030 -0.465 0.000 0.858 -0.354 -1.713 3.102 0.359 0.727 1.066 0.866 0.619 0.807 0.872 +1 1.210 -0.124 1.445 0.413 1.641 0.473 -0.929 0.941 0.000 0.550 1.226 -0.848 2.215 0.984 -0.267 -0.197 2.548 0.636 -1.724 -0.962 0.000 0.921 0.886 1.002 0.903 0.799 0.852 0.731 +1 1.016 0.492 -0.322 0.531 1.569 0.730 -0.866 -0.028 0.000 1.075 -0.048 1.436 2.215 2.102 0.459 0.653 2.548 2.269 -0.021 -1.312 0.000 0.489 1.224 1.009 0.895 1.128 1.075 0.936 +0 1.623 -0.061 0.983 0.969 0.476 0.626 0.925 -0.640 0.000 1.009 0.106 -0.512 0.000 1.086 0.428 -1.703 2.548 0.599 0.243 -1.172 0.000 1.039 0.972 0.993 0.880 0.773 0.742 0.722 +0 0.475 -1.581 1.691 1.960 -0.557 1.004 -0.232 1.481 2.173 0.708 -0.396 0.500 0.000 0.566 -1.066 -0.585 2.548 0.381 1.992 1.079 0.000 1.331 1.093 1.201 1.538 1.009 0.986 1.142 +0 0.290 0.429 0.626 0.529 -1.402 0.600 0.672 0.139 2.173 0.519 2.277 1.671 0.000 0.675 0.301 -0.868 2.548 0.997 0.892 1.213 0.000 0.757 0.759 0.989 0.719 0.641 0.731 0.788 +0 0.287 -0.457 0.905 1.937 -1.665 1.984 0.503 -1.193 0.000 1.504 0.153 0.622 2.215 2.698 -0.625 0.520 2.548 1.772 0.294 -0.066 0.000 2.445 2.256 0.992 1.665 0.951 1.857 1.530 +1 0.900 0.531 -0.660 1.408 0.124 1.369 -0.232 -1.486 0.000 0.582 -0.822 -0.197 1.107 1.054 -0.543 0.777 0.000 0.522 0.792 0.535 0.000 0.720 0.712 1.013 0.707 0.676 0.616 0.609 +1 1.370 -0.455 -0.731 0.558 -1.726 1.043 0.124 0.722 2.173 1.292 -0.862 0.068 2.215 0.971 -0.716 1.725 0.000 1.158 0.031 -1.361 0.000 0.960 1.219 0.987 1.157 1.310 1.089 0.947 +1 2.366 -0.719 -1.389 0.746 -0.676 0.680 -0.352 -0.380 0.000 1.615 -0.190 0.703 2.215 0.298 -1.079 1.028 0.000 0.541 -0.525 -0.889 3.102 0.846 1.040 1.105 0.524 0.856 0.981 0.836 +0 1.442 -0.783 -0.616 0.735 -1.374 0.587 -0.044 0.031 0.000 0.860 -1.968 -1.660 0.000 0.882 -0.943 0.775 2.548 0.915 0.305 -1.692 3.102 2.311 1.388 0.985 0.728 0.755 0.962 0.881 +0 0.413 -0.407 0.016 1.470 -0.182 1.656 0.595 -0.703 2.173 2.424 0.866 1.118 1.107 1.107 0.698 1.433 0.000 1.508 1.112 -1.718 0.000 0.819 1.329 0.991 0.965 2.972 1.532 1.289 +0 0.517 1.589 1.444 0.934 -0.764 0.561 -0.916 -1.150 2.173 0.721 0.703 1.601 2.215 0.806 -0.711 0.957 0.000 1.004 -0.652 0.388 0.000 0.869 0.937 0.992 0.897 1.036 1.237 1.632 +1 0.580 -0.841 -1.196 0.523 0.098 0.727 0.047 -0.479 0.000 1.467 0.391 0.919 2.215 0.890 0.828 -0.920 0.000 1.320 0.803 1.631 3.102 0.852 0.980 0.989 1.542 0.832 1.216 1.151 +1 2.135 1.293 0.039 1.032 0.817 0.679 -1.441 -0.703 0.000 0.964 0.788 1.156 0.000 1.121 0.045 1.665 2.548 0.419 0.729 1.554 0.000 0.718 0.731 1.327 1.354 0.755 0.930 0.810 +0 0.561 -0.668 0.907 0.542 -1.451 0.886 -0.495 0.160 2.173 1.210 -0.257 -1.304 2.215 0.703 0.179 1.164 0.000 0.497 -1.239 -0.064 0.000 0.849 0.893 0.984 0.998 1.487 0.958 0.798 +1 0.773 0.715 1.362 1.246 -0.970 0.956 -0.857 -1.139 2.173 2.339 1.960 0.307 0.000 0.806 0.502 1.721 0.000 0.883 0.211 0.050 0.000 0.939 0.851 1.174 1.242 0.781 0.941 0.822 +1 0.595 -0.458 -0.582 1.226 1.728 0.831 -0.954 0.445 0.000 1.064 -1.000 -1.139 2.215 1.169 -0.825 1.103 0.000 0.877 -1.872 0.113 0.000 0.963 0.855 1.032 0.713 0.349 0.730 0.659 +0 1.032 -0.614 0.328 1.380 0.915 0.482 -1.255 -1.401 2.173 0.512 -0.076 -0.661 2.215 0.409 -1.339 -0.122 0.000 1.625 0.052 -1.407 0.000 1.068 0.761 0.988 1.026 0.646 0.763 0.725 +0 1.345 1.439 1.728 0.773 1.176 0.713 -0.585 -0.405 2.173 0.727 -0.278 -1.314 2.215 1.075 -0.658 1.125 0.000 1.208 -0.294 0.286 0.000 0.893 0.965 0.985 0.946 0.790 1.018 0.970 +0 1.379 -0.393 1.219 1.394 0.463 0.641 0.740 -0.442 0.000 0.537 1.435 0.833 2.215 1.123 0.433 -1.391 0.000 1.332 -0.743 -0.780 3.102 1.170 1.028 1.209 1.048 1.363 0.968 0.984 +1 0.607 -0.033 0.814 1.553 -1.663 0.495 0.845 1.535 0.000 1.410 0.916 -0.026 1.107 0.428 0.727 -0.472 0.000 0.495 -0.512 -0.918 3.102 0.804 0.969 1.063 0.603 0.846 0.878 0.747 +0 1.040 -1.192 1.466 1.968 -1.434 1.343 -0.463 0.245 0.000 1.590 -1.632 -0.685 0.000 0.850 -1.037 -0.021 0.000 1.632 -0.115 1.400 3.102 0.801 1.253 1.002 0.644 0.224 0.708 0.948 +1 0.462 -0.495 -1.067 0.718 0.884 0.924 1.623 -1.573 0.000 1.301 -0.248 -0.370 0.000 0.825 -1.039 -0.451 0.000 2.921 -0.440 1.019 3.102 0.737 0.646 0.988 0.869 0.820 0.909 0.828 +1 0.825 -0.127 -1.227 0.832 1.343 1.099 0.047 1.348 0.000 0.995 0.840 -0.884 2.215 0.902 -2.159 -0.483 0.000 1.750 -0.024 0.128 3.102 1.318 1.172 0.988 1.048 1.091 0.969 0.899 +1 1.194 0.168 0.819 0.174 -0.744 0.388 0.895 0.141 0.000 1.140 -0.106 -1.513 2.215 1.206 0.079 -0.357 2.548 0.473 -1.752 1.258 0.000 1.506 1.062 0.980 0.937 1.082 0.877 0.809 +1 0.404 -0.694 -0.389 0.426 -0.307 0.419 -2.583 1.002 0.000 0.377 0.178 -0.842 2.215 0.302 -1.403 1.655 0.000 0.785 -1.116 0.851 3.102 0.645 1.104 0.984 0.891 0.640 0.683 0.996 +0 1.522 1.345 1.522 1.025 1.269 0.733 1.734 1.246 0.000 0.889 0.612 -1.259 2.215 2.506 -0.758 -0.130 0.000 0.852 -0.496 -0.525 0.000 0.772 0.857 0.999 1.088 0.619 0.951 1.607 +1 1.772 0.622 0.317 0.525 -0.582 0.858 1.422 1.522 2.173 0.554 0.250 -0.761 0.000 1.121 0.452 -1.348 2.548 0.798 0.134 1.717 0.000 0.697 1.193 0.986 0.950 0.876 0.918 0.844 +1 0.514 -1.289 0.752 0.382 0.939 0.911 -0.062 -1.072 0.000 0.941 -0.925 -1.305 2.215 2.229 1.928 -0.237 0.000 2.514 0.109 1.289 0.000 1.096 2.290 0.974 0.890 0.485 2.140 1.687 +1 0.880 -0.823 1.590 1.395 -0.832 0.578 -1.066 -0.072 0.000 0.747 0.413 1.349 2.215 0.906 -1.275 0.717 0.000 1.267 0.792 -0.313 3.102 0.874 1.202 1.258 1.168 0.906 0.983 0.951 +0 0.366 -0.851 1.117 1.262 0.701 0.884 0.445 -0.961 0.000 0.873 0.608 -0.354 0.000 0.522 -1.654 0.537 0.000 1.181 1.400 1.621 3.102 0.986 0.866 0.980 2.692 0.174 1.802 1.684 +0 0.514 -1.180 -1.719 4.347 -1.296 2.763 -0.199 0.429 2.173 0.846 -1.426 -1.091 2.215 0.309 0.527 0.176 0.000 0.923 -0.567 0.758 0.000 0.494 0.871 0.991 3.938 2.680 2.511 1.813 +0 0.705 0.344 -0.601 0.751 -0.177 0.846 2.057 1.042 0.000 0.846 1.341 0.405 2.215 1.689 0.487 -1.085 2.548 0.637 2.295 -1.566 0.000 0.886 0.916 1.000 0.874 1.355 1.088 0.944 +0 0.909 1.050 0.943 0.719 -0.721 1.242 1.158 -0.084 0.000 1.280 0.067 1.036 2.215 1.401 1.246 -0.869 0.000 1.012 -1.241 1.728 0.000 0.358 1.168 1.117 1.427 1.606 1.139 1.122 +1 0.623 -0.155 1.012 1.447 -0.742 2.547 -0.840 1.098 0.000 2.741 0.399 -0.515 0.000 1.212 -0.405 -1.276 2.548 0.584 0.988 -1.156 0.000 1.102 0.843 1.316 0.669 0.593 0.562 0.567 +0 0.334 -1.496 -0.004 0.958 -1.511 1.015 0.587 1.475 1.087 1.105 -0.662 -0.048 2.215 0.385 1.071 0.970 0.000 0.393 0.743 -0.101 0.000 0.727 1.103 0.990 1.321 1.861 1.785 1.349 +0 0.654 -0.166 0.400 0.448 1.535 0.886 0.299 0.772 0.000 0.626 2.377 -0.374 0.000 1.220 0.409 -1.186 2.548 0.801 -1.100 -1.331 1.551 2.451 1.737 0.989 0.860 0.766 1.330 1.045 +0 0.516 -0.268 1.597 0.954 0.845 1.086 -1.932 -0.602 0.000 0.999 1.206 1.284 1.107 0.946 -0.990 -0.215 0.000 0.843 -0.081 0.482 0.000 0.846 0.645 0.993 0.649 0.943 0.902 0.806 +0 1.145 -0.463 -0.498 0.567 1.050 0.905 1.445 1.471 0.000 1.397 0.389 -0.695 2.215 1.249 -0.663 0.187 2.548 1.475 -0.432 1.259 0.000 2.072 1.898 1.100 0.707 1.305 1.422 1.175 +0 0.309 1.803 1.452 2.374 0.539 1.059 1.085 -0.903 2.173 0.445 0.644 -1.628 0.000 0.391 -0.906 1.227 2.548 0.448 2.496 1.473 0.000 0.865 0.977 0.982 1.380 1.253 1.060 0.913 +1 0.619 0.904 0.362 0.835 1.260 0.644 -0.173 -1.608 2.173 0.624 0.435 0.543 0.000 0.516 -0.665 -0.340 2.548 0.737 1.487 -0.388 0.000 0.914 1.083 0.989 1.036 0.684 0.915 0.804 +0 1.550 1.100 1.585 0.744 -0.921 0.691 0.894 -1.007 0.000 1.059 0.833 0.829 2.215 1.109 -0.017 0.216 2.548 0.846 1.056 -0.464 0.000 0.590 0.952 1.150 0.965 0.798 0.866 0.819 +1 0.468 0.275 1.620 1.108 -0.690 1.480 -2.234 0.616 0.000 1.908 0.620 -0.762 2.215 1.983 -1.956 1.097 0.000 1.327 -0.359 -0.505 3.102 1.303 1.960 0.992 0.747 0.871 2.870 2.490 +0 0.478 1.998 0.034 0.805 -0.718 0.654 -0.970 0.912 0.000 1.167 0.555 -1.637 2.215 1.224 -0.086 -0.141 2.548 0.683 -1.148 -0.285 0.000 0.923 0.926 0.983 0.917 1.312 0.995 0.970 +1 0.791 0.754 -0.208 0.801 -0.751 0.818 -0.097 0.456 0.000 1.365 0.153 1.460 1.107 0.825 0.703 -1.243 1.274 1.185 0.275 -0.131 0.000 0.826 1.001 0.998 1.116 0.812 0.884 0.819 +0 1.452 0.792 -0.916 1.250 -0.155 0.763 -0.843 1.094 1.087 1.690 0.110 -0.688 2.215 2.089 -1.000 0.561 0.000 1.147 1.788 1.710 0.000 0.973 0.800 1.182 0.882 1.867 1.308 1.417 +0 0.355 0.299 -1.341 1.512 0.258 0.618 -0.660 -1.286 0.000 0.769 -0.071 0.655 2.215 0.772 -1.575 -1.021 0.000 0.848 -0.775 1.272 3.102 0.898 1.138 1.006 0.763 0.502 0.702 0.775 +1 0.810 0.667 -1.653 1.012 -1.178 0.332 0.963 -1.077 0.000 0.460 1.498 0.459 0.000 0.957 -0.686 -0.112 2.548 0.881 0.299 0.983 3.102 0.964 0.855 0.995 0.791 0.716 0.930 0.797 +1 0.653 -0.442 -0.217 1.122 -0.934 1.005 0.420 0.256 2.173 0.858 -0.793 -1.734 0.000 0.646 2.484 -0.725 0.000 1.065 0.346 1.210 3.102 0.897 0.901 0.988 1.381 0.829 1.012 1.242 +1 0.684 0.358 0.030 0.585 -1.570 0.884 0.658 0.731 2.173 1.279 0.223 -0.961 0.000 0.550 -0.845 0.970 1.274 0.654 -0.039 -1.733 0.000 0.781 0.861 0.989 0.790 0.789 0.856 0.752 +1 0.794 -1.088 0.258 1.056 -0.807 1.039 -0.007 1.427 1.087 0.814 -1.152 -0.361 0.000 0.785 -1.058 0.855 1.274 0.516 -2.063 -1.127 0.000 0.794 0.777 1.039 1.262 0.883 0.949 0.852 +1 0.854 1.155 0.099 0.023 -0.410 0.684 0.866 -1.231 0.000 1.025 -0.127 0.837 2.215 0.959 1.278 -0.626 0.000 0.646 0.907 1.539 3.102 0.851 0.651 0.779 0.715 0.644 0.834 0.710 +1 0.430 0.011 1.016 0.891 -1.160 1.518 -1.830 0.377 0.000 1.334 -0.540 -1.095 0.000 0.924 1.142 1.332 2.548 1.056 1.913 -0.783 0.000 0.803 0.911 0.989 0.642 0.763 1.494 1.442 +1 0.390 1.113 -0.115 1.235 -1.016 0.751 0.199 -1.598 0.000 0.816 -0.362 -0.098 2.215 2.008 -0.449 0.891 2.548 0.862 0.124 -1.138 0.000 0.497 0.973 0.986 1.185 1.062 0.908 0.827 +0 0.673 -0.537 1.268 0.263 0.949 1.806 -1.038 1.397 2.173 1.559 -0.646 -0.302 1.107 1.771 -1.650 -0.337 0.000 0.732 -1.104 -0.985 0.000 0.758 0.955 0.976 1.337 2.513 1.455 1.336 +0 0.740 -0.545 0.661 0.955 -0.522 0.586 -0.058 -0.225 0.000 0.932 -0.887 -1.740 2.215 0.784 0.276 -1.180 0.000 1.142 -0.616 1.277 3.102 0.950 0.926 1.019 0.874 0.391 0.732 0.688 +0 0.633 1.154 1.016 2.141 0.848 0.940 0.536 -0.154 2.173 1.112 1.522 -1.295 0.000 0.940 0.004 1.573 0.000 0.729 0.257 -0.724 3.102 0.926 1.150 0.996 0.832 0.443 0.823 0.801 +0 1.811 2.088 -0.245 0.239 -1.275 1.315 2.161 1.648 0.000 0.804 2.225 0.660 0.000 0.819 0.969 -0.975 2.548 0.475 1.376 0.953 3.102 0.765 0.827 0.983 0.606 0.491 0.523 0.543 +0 0.294 1.270 1.325 1.633 -0.714 0.922 1.495 -0.616 2.173 1.486 1.173 1.007 0.000 1.170 2.585 1.226 0.000 1.021 1.221 1.591 0.000 0.975 1.195 0.987 0.637 0.960 1.010 0.876 +0 1.154 1.382 -0.452 0.377 0.483 0.676 1.339 0.692 2.173 1.237 1.262 -1.227 2.215 0.688 2.629 1.617 0.000 0.810 1.704 0.332 0.000 0.835 1.064 0.986 0.807 1.329 0.850 0.769 +1 0.446 1.683 -0.381 1.582 0.402 1.055 -0.768 -1.627 2.173 0.398 0.625 -1.115 0.000 0.677 -0.244 0.139 2.548 0.370 0.868 -0.611 0.000 0.241 0.862 0.980 0.662 1.086 1.116 0.851 +1 0.339 -1.053 -1.249 1.012 -0.330 0.980 0.415 0.655 0.000 0.912 0.959 -1.470 2.215 1.064 -0.396 -0.774 2.548 0.868 1.332 0.568 0.000 0.887 1.203 0.988 1.038 1.016 1.512 1.647 +0 1.419 0.396 1.002 0.443 1.688 0.693 -1.099 0.448 2.173 1.031 -0.404 -0.786 0.000 0.733 0.541 -0.626 0.000 0.566 -0.872 -1.317 0.000 0.771 0.992 0.992 0.480 0.906 0.833 0.763 +1 1.685 0.008 -0.919 0.370 0.504 1.191 -0.664 0.904 1.087 1.072 -1.301 -1.158 1.107 0.593 -1.043 -0.453 0.000 0.785 0.167 0.464 0.000 0.987 0.993 1.049 1.290 1.691 1.106 0.906 +0 1.706 -0.446 -1.112 1.030 -1.699 1.250 -1.446 0.834 1.087 0.717 -0.639 -0.508 2.215 0.650 -1.823 -0.634 0.000 0.683 -0.738 0.988 0.000 0.849 0.925 0.982 0.793 1.424 1.174 0.952 +0 0.887 0.542 0.166 1.002 1.644 1.452 -0.876 0.355 0.000 1.170 -0.545 1.523 1.107 1.629 0.125 -0.843 2.548 1.873 -0.212 -1.522 0.000 1.084 0.943 1.269 0.984 1.347 0.921 0.800 +0 0.692 0.228 -0.092 1.046 -0.932 0.627 0.242 0.412 2.173 1.021 0.254 1.010 0.000 1.121 1.245 -1.117 1.274 0.712 1.731 -1.717 0.000 1.351 1.053 0.990 1.024 1.197 0.878 0.913 +0 0.997 -0.819 0.668 1.295 0.156 0.870 1.141 -1.671 2.173 1.289 2.360 1.557 0.000 1.359 -0.955 -0.501 0.000 0.727 2.118 -0.660 0.000 1.150 0.814 0.987 2.225 0.567 1.392 2.070 +1 0.859 -0.088 0.119 0.486 -0.925 0.485 -0.552 1.280 2.173 1.235 -0.075 -1.038 2.215 1.226 -0.162 0.816 0.000 0.963 0.412 -0.248 0.000 1.064 0.831 0.988 0.833 1.026 0.810 0.736 +0 0.621 -0.297 1.723 0.984 0.918 0.357 -0.457 0.387 2.173 0.448 1.959 -0.750 0.000 0.841 0.707 -0.731 2.548 0.714 0.338 -1.247 0.000 0.733 0.923 0.993 1.089 0.732 0.746 0.865 +0 0.559 -0.091 1.469 1.146 -0.940 0.555 0.034 0.994 2.173 0.863 -0.753 -1.368 0.000 1.044 -0.623 0.524 2.548 0.687 -0.509 0.167 0.000 0.989 0.901 0.988 0.828 0.523 0.666 0.642 +0 1.381 -0.192 0.030 0.137 0.510 0.648 -0.101 1.474 0.000 0.990 -0.256 -1.426 2.215 0.688 -0.328 0.737 0.000 1.181 0.352 -0.608 3.102 0.757 0.893 0.997 1.001 0.740 0.702 0.712 +1 1.108 1.304 -1.228 1.017 0.073 0.640 1.869 -0.415 0.000 1.230 0.442 1.618 2.215 0.814 1.891 0.399 0.000 0.900 1.365 0.941 3.102 0.877 0.727 1.356 1.163 0.807 0.934 0.854 +0 1.145 0.081 1.327 0.797 -1.647 0.946 -0.539 -0.392 2.173 0.457 -2.442 0.534 0.000 0.522 0.194 0.844 0.000 0.766 1.240 -0.702 3.102 1.384 1.300 0.986 0.990 1.109 1.127 1.023 +1 1.500 -0.250 1.285 1.475 0.872 0.984 0.122 -0.981 2.173 1.180 -0.664 -0.203 1.107 0.500 0.735 -1.129 0.000 0.954 -0.147 0.382 0.000 0.923 0.840 0.981 1.380 1.216 1.183 0.990 +1 2.869 -0.817 0.610 1.640 0.326 0.691 -1.833 -1.465 0.000 1.638 -0.569 -0.547 2.215 1.452 1.392 -1.208 0.000 1.027 -0.498 1.445 3.102 0.836 1.051 0.980 1.613 1.141 1.161 1.527 +0 0.638 0.540 0.001 3.379 0.518 1.796 1.780 -0.807 0.000 2.169 1.574 1.612 0.000 0.583 0.766 -0.725 1.274 1.192 1.873 -1.184 0.000 0.900 0.739 0.983 0.708 0.744 1.059 1.459 +0 0.387 1.455 -1.237 0.747 0.654 0.741 0.562 -0.388 2.173 0.935 1.973 -1.633 0.000 0.686 -2.103 -1.635 0.000 1.414 -0.781 0.721 3.102 0.932 0.950 0.990 0.762 1.277 1.232 1.114 +0 1.403 -0.133 0.647 0.965 0.158 0.515 -0.962 1.481 2.173 0.597 1.034 -1.246 0.000 0.788 -1.263 -0.740 2.548 0.457 -0.087 -1.370 0.000 0.428 0.902 0.994 0.868 0.740 0.739 0.796 +0 0.584 -0.643 1.155 0.808 0.437 0.797 -2.921 -0.367 0.000 1.216 0.345 -0.388 2.215 2.678 -1.126 1.647 0.000 0.790 -0.499 0.405 3.102 3.595 2.129 0.991 1.449 0.727 2.035 1.555 +1 1.176 -0.121 -1.273 0.996 1.606 0.657 1.237 0.077 2.173 0.872 0.430 0.880 0.000 0.658 0.659 -0.303 0.000 0.535 0.304 -1.353 1.551 1.030 0.830 0.982 0.516 0.667 0.880 0.809 +1 0.940 0.677 -0.766 2.741 -1.152 1.446 -1.935 0.785 0.000 1.094 0.625 1.118 2.215 0.489 -0.967 1.518 0.000 1.649 1.111 -0.480 0.000 0.715 1.261 0.982 0.820 0.839 0.958 0.870 +1 0.716 0.262 -0.632 1.034 -1.058 0.578 0.171 1.317 2.173 0.553 1.230 -1.104 0.000 1.243 1.072 0.613 2.548 0.799 1.845 0.637 0.000 0.965 1.012 0.987 1.339 0.836 0.973 0.979 +1 0.686 -0.862 1.601 0.258 0.329 0.875 0.405 -1.639 2.173 0.734 -0.527 0.405 0.000 1.237 0.243 -0.112 0.000 1.931 -0.214 -1.028 3.102 0.895 1.074 0.978 0.742 0.860 0.947 0.796 +1 0.359 1.943 -0.187 0.358 -0.808 1.369 0.249 -1.609 2.173 0.865 -0.186 0.190 0.000 0.831 1.888 0.544 0.000 0.433 0.219 -0.694 3.102 0.967 1.318 0.979 0.471 0.599 0.766 0.690 +1 0.828 0.204 -0.552 0.837 0.855 1.019 0.757 -0.824 0.000 1.502 1.059 1.333 0.000 1.023 1.823 -0.517 0.000 1.689 0.214 0.787 1.551 1.312 1.110 1.101 0.708 0.645 0.980 0.861 +1 0.952 1.691 1.533 1.275 -1.169 0.574 -0.337 -0.211 0.000 0.917 0.997 1.107 2.215 1.085 0.298 0.353 2.548 1.069 1.065 -0.449 0.000 1.106 0.830 0.992 0.897 0.766 0.872 0.945 +0 1.037 -1.134 -0.484 0.816 -1.291 0.478 0.453 -1.333 0.000 0.798 -0.328 0.129 1.107 0.808 -0.005 1.184 0.000 0.636 -0.085 0.758 3.102 0.895 0.944 0.990 0.782 0.353 0.682 0.773 +1 0.723 -0.826 1.360 1.080 -0.727 1.647 -1.210 -0.030 2.173 1.139 -1.311 -1.482 0.000 0.978 -2.146 1.661 0.000 0.638 -0.551 0.577 1.551 0.932 0.771 1.166 1.177 0.643 0.962 0.859 +0 1.129 0.615 -0.014 2.421 -0.600 0.901 -1.331 1.108 0.000 1.449 2.416 1.395 0.000 1.495 1.003 0.149 2.548 0.971 1.255 -1.729 3.102 1.596 0.958 1.154 0.911 0.933 1.009 1.243 +1 1.216 0.012 -1.144 1.141 1.605 0.585 1.275 -0.460 2.173 0.630 -2.366 0.463 0.000 1.216 0.732 -1.658 0.000 1.848 0.667 0.462 3.102 0.988 1.028 1.005 1.073 0.856 0.927 0.834 +1 0.525 -0.779 -0.128 1.109 0.427 0.843 -0.123 -0.953 2.173 1.439 0.124 1.424 1.107 1.604 0.167 -0.436 0.000 0.706 -1.601 1.636 0.000 1.200 0.937 0.978 1.665 1.378 1.337 1.165 +0 0.584 -0.425 1.533 0.639 -0.687 0.739 -1.250 -0.559 0.000 0.972 1.363 1.668 2.215 1.033 -0.029 0.567 2.548 0.888 -0.344 0.151 0.000 0.915 0.960 0.990 1.545 1.219 1.242 1.085 +0 0.339 -0.395 -0.970 1.429 1.667 0.560 -1.462 1.586 1.087 0.536 1.957 -0.178 0.000 0.729 0.830 0.247 1.274 0.466 -0.353 0.215 0.000 1.031 0.639 0.989 0.539 1.406 1.117 1.238 +1 0.540 -0.714 1.278 1.993 0.808 1.149 -1.595 -0.912 0.000 0.471 -1.176 -0.793 2.215 0.975 -1.168 -0.161 0.000 1.317 -1.168 1.553 3.102 1.233 1.130 0.997 0.880 0.613 0.669 0.852 +0 0.936 -1.026 -1.696 0.772 0.939 0.640 -2.815 0.080 0.000 0.640 0.008 1.538 2.215 0.568 1.082 -1.334 0.000 1.524 1.045 -0.675 0.000 0.575 0.846 0.985 0.723 0.667 0.633 0.742 +1 0.368 -1.910 -0.446 1.105 1.065 0.854 -1.920 0.020 0.000 1.026 -1.816 1.484 0.000 0.910 -0.096 -1.508 2.548 0.798 -0.944 -1.322 0.000 0.811 0.919 0.989 1.895 0.571 1.364 1.124 +0 0.407 1.742 -1.394 1.689 -0.344 1.100 0.698 -0.819 1.087 1.026 0.421 1.552 0.000 1.708 0.316 0.689 2.548 1.422 -0.382 1.167 0.000 0.901 0.960 0.988 1.261 1.698 1.322 1.367 +1 0.656 -1.870 -1.284 0.374 1.317 1.010 -0.546 0.421 0.000 0.740 -0.816 -0.135 0.000 0.949 0.070 -1.086 1.274 1.694 -0.896 1.739 1.551 0.922 1.139 0.976 0.589 0.802 0.927 0.807 +1 2.363 1.058 0.907 1.071 0.330 1.278 0.820 -0.689 2.173 1.701 0.976 -1.556 0.000 0.351 -0.223 1.208 0.000 1.003 -0.170 0.377 3.102 0.802 0.896 1.093 0.852 1.176 1.130 0.883 +1 0.957 0.582 -0.121 0.996 -1.328 1.093 0.466 -1.608 0.000 2.118 0.394 0.607 2.215 0.551 1.062 0.125 2.548 1.057 0.858 -0.973 0.000 1.004 0.939 1.197 1.267 0.659 1.087 0.947 +1 1.178 -0.144 1.424 1.919 0.931 0.969 -0.214 -0.713 2.173 0.420 -0.360 -1.227 0.000 0.878 0.586 -0.288 0.000 0.873 -0.623 1.648 3.102 0.924 0.834 0.987 0.684 0.867 0.956 0.817 +0 1.270 0.146 0.332 0.048 -0.656 0.761 0.486 -1.297 0.000 0.711 1.044 0.939 2.215 0.653 -1.048 -0.368 2.548 0.870 -0.733 -1.309 0.000 0.921 0.911 0.838 0.763 1.189 0.859 0.780 +1 1.467 0.348 -0.666 0.540 1.315 0.473 0.122 0.813 0.000 0.428 0.754 0.237 0.000 0.677 -0.416 0.152 2.548 1.669 -0.869 1.669 3.102 0.571 1.018 1.205 0.757 0.832 0.774 0.714 +1 1.386 0.234 0.230 0.689 -1.693 0.693 -0.265 1.698 1.087 0.342 1.518 0.582 0.000 0.622 0.571 -1.556 2.548 0.666 -1.366 -0.962 0.000 0.885 0.853 1.336 0.781 0.414 0.665 0.662 +0 0.596 0.024 -0.552 0.841 1.461 0.959 0.848 0.867 2.173 0.751 -0.933 -1.607 0.000 1.535 -0.774 -0.380 2.548 0.672 -1.572 -0.840 0.000 0.738 0.886 0.987 0.996 1.992 1.270 1.000 +1 0.701 -0.496 -0.713 0.201 -1.618 1.329 0.720 0.893 2.173 1.316 0.586 -1.218 0.000 1.351 1.189 -0.913 0.000 1.919 0.304 -0.108 3.102 0.920 1.204 0.990 1.100 1.362 1.259 1.009 +1 1.151 -0.356 1.572 0.904 -1.504 3.212 -0.306 0.421 0.000 1.894 1.602 -1.731 0.000 2.083 -0.624 -1.119 2.548 2.679 -0.582 -0.523 3.102 0.306 0.738 0.989 0.847 0.924 0.865 0.735 +0 1.235 0.102 1.562 1.724 -1.559 0.805 2.612 -0.397 0.000 0.571 -1.552 0.863 0.000 0.714 -0.321 1.048 2.548 0.512 2.325 0.077 0.000 0.661 0.765 0.979 0.681 0.490 0.620 0.655 +0 0.749 -0.033 -0.142 0.891 1.739 0.790 1.264 0.164 0.000 1.345 0.721 -1.132 2.215 1.054 1.550 1.181 0.000 0.449 1.598 0.581 0.000 1.349 0.786 1.123 0.909 0.737 0.884 0.860 +1 0.298 -0.735 -1.386 2.426 -0.254 2.516 0.749 1.581 0.000 1.258 1.281 0.099 0.000 1.231 0.420 0.312 2.548 1.373 0.713 -0.372 3.102 1.718 1.174 1.005 1.011 0.607 0.867 1.309 +1 0.589 -1.402 -1.514 1.061 0.004 0.532 0.654 -0.865 0.000 0.543 -0.830 1.214 2.215 1.017 -1.216 -0.305 2.548 1.152 0.691 1.653 0.000 0.921 0.981 1.073 0.620 0.797 0.903 0.896 +0 0.650 0.044 -0.515 0.686 0.676 0.723 -1.105 0.599 2.173 0.845 -0.455 -1.709 2.215 0.594 -1.295 -1.464 0.000 0.522 -1.545 -0.455 0.000 0.503 0.735 0.987 0.778 1.073 0.690 0.616 +0 0.589 -0.031 -1.150 1.122 0.094 0.463 -0.845 1.099 1.087 0.622 -0.089 1.059 2.215 0.908 -1.505 -0.352 0.000 0.883 -0.364 1.561 0.000 1.167 0.947 1.015 0.788 0.309 0.621 0.656 +1 0.457 -1.114 0.142 0.965 1.086 0.912 -0.164 1.218 2.173 0.958 0.662 -0.897 0.000 0.855 0.063 -1.220 2.548 1.155 0.874 -0.226 0.000 0.855 1.150 0.985 1.183 0.900 1.056 1.114 +1 0.372 -0.862 -0.458 2.275 0.211 1.047 0.045 1.531 2.173 1.138 -0.356 -1.244 0.000 0.425 1.615 -0.462 0.000 0.621 0.871 -0.041 0.000 0.841 0.860 0.989 0.908 0.241 1.238 1.024 +1 0.997 0.520 0.208 1.357 0.153 0.889 0.184 -1.648 2.173 0.502 1.023 -1.149 1.107 0.713 -0.855 -1.088 0.000 0.698 0.508 1.409 0.000 0.899 0.743 0.997 1.448 0.613 0.970 0.903 +1 0.704 0.488 -1.193 1.092 -0.405 0.636 0.024 -0.872 2.173 0.736 0.030 1.026 0.000 0.801 1.306 0.758 0.000 0.802 -0.960 1.603 1.551 0.940 0.991 0.993 0.753 0.760 0.824 0.839 +0 1.702 -0.994 -1.217 1.793 -1.162 0.672 -0.966 0.724 0.000 0.583 1.687 1.444 0.000 0.447 2.194 -0.552 0.000 0.947 -0.848 -0.112 1.551 0.809 1.113 0.976 0.884 0.159 0.948 1.548 +1 0.717 0.047 -1.398 1.491 0.197 0.796 0.465 -0.469 2.173 0.987 0.465 1.340 2.215 1.292 0.850 0.098 0.000 0.571 -1.132 1.686 0.000 1.637 1.170 1.420 1.024 1.302 0.941 0.859 +0 1.272 0.353 0.351 0.752 1.714 0.853 -0.649 0.631 0.000 1.270 -0.698 -1.180 0.000 0.761 -0.043 1.244 2.548 0.441 -1.021 -0.807 1.551 2.208 1.188 1.277 0.709 0.508 0.784 0.809 +0 0.501 -0.358 0.657 0.832 -1.688 0.921 -0.852 1.544 2.173 1.066 -2.468 -0.167 0.000 0.871 -1.475 -0.965 2.548 0.988 -1.944 0.569 0.000 0.835 0.885 0.983 0.667 0.963 1.051 0.887 +1 0.342 -1.776 -1.059 1.905 -0.775 0.949 1.730 0.349 0.000 0.874 0.514 1.279 2.215 1.238 -0.848 1.304 1.274 0.868 -0.479 -0.136 0.000 0.886 1.028 0.979 1.062 0.882 0.962 0.810 +0 0.437 0.079 -1.091 1.127 1.415 2.370 -0.250 -1.716 0.000 2.256 0.743 -0.312 0.000 2.635 -0.835 0.307 0.000 1.244 1.795 1.353 0.000 0.780 0.879 0.985 0.598 1.084 0.865 0.904 +1 0.989 0.263 -0.368 0.472 -0.918 0.461 -1.168 -1.175 2.173 0.956 -1.218 0.640 0.000 1.167 0.640 -1.597 0.000 1.024 -0.172 0.894 0.000 0.764 0.958 0.993 0.635 0.738 0.620 0.664 +0 0.998 0.465 -0.585 0.287 1.674 0.640 0.889 1.272 2.173 0.409 -0.641 -0.562 0.000 1.037 0.433 -0.262 2.548 0.788 1.540 0.970 0.000 0.601 0.775 0.981 0.628 1.020 0.703 0.592 +0 0.903 0.598 -0.689 0.668 0.996 0.963 -0.817 -1.418 0.000 0.882 -0.159 0.696 2.215 0.427 -1.273 0.450 0.000 1.115 -0.359 -0.280 1.551 1.194 0.941 1.075 0.806 0.700 0.783 0.791 +1 1.000 -0.854 -0.659 0.608 -0.095 0.818 -0.640 0.920 0.000 0.616 -0.160 1.526 0.000 0.640 -0.712 1.367 2.548 0.591 2.341 -0.466 0.000 0.862 0.742 0.987 0.766 0.324 0.596 0.699 +0 2.606 1.568 1.167 2.059 0.908 0.504 0.626 -1.029 2.173 1.387 -0.060 -0.874 0.000 1.378 -0.407 -0.169 2.548 0.370 -1.562 -0.735 0.000 0.975 0.876 0.992 1.344 0.935 1.475 1.504 +0 0.477 0.580 0.763 1.469 -1.710 0.549 -0.116 -0.115 2.173 0.308 -0.202 0.390 0.000 0.622 0.538 -1.023 2.548 0.368 -1.732 0.830 0.000 0.502 0.670 0.985 1.035 0.593 0.700 0.707 +0 0.722 0.390 0.861 0.371 -0.438 1.100 -1.069 1.306 0.000 1.844 -0.050 -0.279 2.215 0.813 0.039 -1.238 2.548 0.920 -1.826 -1.439 0.000 1.309 1.203 0.989 1.134 0.991 1.347 1.307 +1 0.938 1.508 1.742 0.833 -0.096 1.016 1.127 0.323 2.173 1.185 0.825 -1.366 0.000 0.950 0.791 0.832 2.548 0.805 1.152 -0.668 0.000 0.824 0.958 1.221 0.966 0.566 0.875 0.793 +0 0.873 1.109 0.317 1.365 0.976 0.850 -0.829 -0.738 2.173 1.824 1.817 -0.914 0.000 1.140 -0.336 1.141 2.548 1.071 -1.269 1.291 0.000 0.904 0.925 0.988 1.217 1.251 1.442 1.372 +0 0.799 -1.630 1.368 1.027 -1.352 0.524 -0.533 1.183 0.000 0.898 0.037 -0.346 2.215 0.729 0.306 0.909 1.274 1.399 -1.357 -0.475 0.000 1.504 1.128 0.981 1.533 0.789 1.184 1.038 +0 1.166 0.661 -1.701 0.679 1.085 0.417 0.316 -0.202 0.000 0.569 1.256 -1.118 0.000 1.353 1.213 0.385 1.274 0.516 -0.145 -1.078 3.102 0.923 0.920 0.984 0.559 0.813 0.713 0.688 +0 1.042 -0.134 -0.324 1.332 0.445 1.496 -0.733 -1.186 1.087 1.112 -1.098 0.539 0.000 1.829 -0.594 1.058 2.548 1.125 0.945 -1.095 0.000 2.490 1.706 1.044 1.482 1.856 1.471 1.259 +1 0.807 1.024 1.355 0.223 -1.074 1.583 -1.823 -1.188 0.000 1.572 -0.524 0.804 2.215 2.125 -0.265 0.172 2.548 1.118 -0.055 -0.920 0.000 2.106 2.376 0.986 1.035 1.075 1.764 1.480 +0 2.082 0.110 -0.753 0.349 1.045 0.984 -0.538 -0.093 2.173 1.222 -0.790 1.517 2.215 0.488 -0.900 1.163 0.000 0.587 0.303 1.024 0.000 0.436 0.816 1.180 1.228 1.617 1.062 0.839 +1 0.385 2.114 -0.222 1.320 -0.943 0.505 -0.032 -0.258 0.000 0.615 0.257 0.907 2.215 1.518 -0.659 1.070 0.000 0.674 -0.031 -1.359 1.551 1.568 0.975 0.993 0.890 0.525 0.654 0.828 +0 0.624 0.610 -0.407 1.033 -1.363 1.284 1.568 -1.628 0.000 1.067 0.709 -0.006 2.215 1.332 1.619 -0.559 0.000 3.348 -0.055 0.925 1.551 0.919 1.133 0.982 0.904 1.448 0.984 0.841 +0 1.204 1.055 -0.376 0.485 0.107 0.671 -1.247 0.635 0.000 1.015 -0.367 -1.366 0.000 0.937 0.413 -1.146 2.548 1.157 -0.440 1.405 0.000 0.855 0.850 0.989 0.867 0.669 0.654 0.904 +0 1.209 1.787 0.916 0.984 0.267 1.026 0.488 0.415 2.173 1.843 0.445 -1.720 0.000 1.648 0.665 -0.462 0.000 1.846 1.211 -0.946 1.551 2.449 1.626 0.987 0.900 1.552 1.342 1.213 +0 0.643 -0.947 0.111 0.792 1.435 1.113 -1.151 -1.739 1.087 1.319 -0.241 -1.269 0.000 2.120 -0.236 0.150 2.548 0.583 -1.289 -1.189 0.000 0.801 0.932 0.989 0.848 2.096 1.192 0.963 +0 0.447 -0.922 1.561 1.690 0.295 0.590 -0.664 -0.955 2.173 0.640 -1.508 1.422 0.000 0.353 -2.031 0.757 0.000 0.764 0.573 -1.177 3.102 0.482 0.963 1.094 0.929 0.539 0.748 0.726 +1 0.823 -0.210 -0.051 0.418 -1.163 0.634 2.049 -0.784 0.000 0.644 -0.740 -0.344 0.000 1.065 -0.267 1.351 1.274 1.341 -0.681 0.841 0.000 1.061 0.886 0.988 0.982 0.701 0.775 0.731 +0 0.604 -1.149 0.898 1.823 1.197 1.452 0.298 -0.447 2.173 1.134 0.205 -1.338 2.215 0.682 1.002 0.175 0.000 0.543 0.166 0.448 0.000 0.353 0.886 0.988 1.674 1.361 1.245 0.983 +1 1.348 -1.798 0.277 0.431 -0.757 0.458 -1.518 0.618 0.000 0.631 -0.797 -0.928 0.000 0.720 -0.165 1.386 0.000 1.153 -0.825 -1.488 3.102 0.924 0.714 0.993 0.676 0.319 0.576 0.555 +0 0.472 -1.655 0.845 1.386 -0.059 1.278 -1.063 -0.363 2.173 1.992 0.607 1.244 2.215 1.023 -0.758 1.714 0.000 0.667 -1.694 -1.624 0.000 0.818 1.110 0.985 3.233 3.233 2.335 1.735 +1 0.752 1.159 1.100 0.849 -1.364 0.852 0.713 0.619 0.000 1.341 0.176 -1.342 2.215 1.688 0.237 -0.072 0.000 0.492 -1.046 1.260 3.102 1.355 1.093 0.989 0.777 0.777 1.044 0.903 +1 0.567 -0.559 1.176 0.601 -1.014 0.788 -0.428 -0.405 0.000 0.659 1.562 1.528 0.000 0.717 -0.530 0.299 2.548 1.212 0.182 1.268 3.102 0.730 1.112 0.989 0.603 0.619 0.741 0.670 +1 0.277 -2.072 0.073 1.109 -1.685 0.900 0.533 -1.107 2.173 0.830 0.032 0.303 0.000 0.866 -0.814 0.915 0.000 0.592 -0.027 -0.145 3.102 0.924 0.602 0.987 1.097 0.630 0.806 0.789 +1 0.524 0.750 -1.028 1.425 -1.514 3.720 -0.536 0.083 0.000 0.949 -0.860 0.530 1.107 2.287 -0.526 1.304 0.000 6.068 -0.601 -1.430 0.000 0.905 0.959 0.989 0.515 0.534 0.694 0.677 +0 0.604 0.470 -1.256 1.095 1.739 1.181 0.549 -0.149 0.000 0.863 -0.161 0.839 2.215 1.312 -0.160 -1.731 2.548 0.587 -0.912 -0.920 0.000 0.806 1.094 0.992 1.004 0.831 0.958 1.037 +1 0.899 0.177 -0.944 0.933 -1.535 0.500 0.840 -0.325 1.087 1.141 -0.699 0.223 2.215 0.777 0.252 1.594 0.000 0.532 -1.406 -1.697 0.000 0.824 1.003 0.985 1.368 1.098 0.963 0.879 +0 2.358 -1.304 -0.542 0.169 -0.327 1.299 -0.718 1.135 2.173 0.418 -0.869 -1.101 0.000 0.612 -0.593 0.554 0.000 0.474 0.162 -1.159 3.102 0.780 0.883 1.002 0.668 0.830 1.013 0.800 +0 1.097 -1.355 1.713 1.449 0.052 0.716 -0.459 1.129 2.173 0.447 -1.160 1.023 0.000 0.587 -1.532 -1.319 0.000 0.872 -0.868 -0.526 0.000 0.804 0.761 1.742 1.064 0.901 0.889 0.726 +1 0.472 -0.794 -1.435 1.257 0.745 0.704 1.635 -0.910 0.000 1.122 -0.053 1.183 0.000 1.429 -0.916 -0.480 2.548 1.840 0.229 -1.051 3.102 0.862 1.157 0.988 0.893 1.045 0.988 0.875 +0 1.287 0.556 0.852 1.862 0.693 1.452 0.483 -1.368 2.173 1.247 1.060 -0.966 0.000 1.322 1.239 0.187 0.000 0.551 0.093 0.101 1.551 1.718 1.028 1.011 1.760 0.935 1.104 1.210 +0 0.340 0.094 1.040 0.889 -0.434 0.844 1.227 -1.538 2.173 1.647 1.146 0.210 2.215 1.343 1.040 1.396 0.000 1.284 0.635 -0.656 0.000 1.420 1.011 0.987 0.789 1.736 1.060 0.868 +0 0.738 -0.560 0.650 0.798 -0.170 0.810 -0.864 -1.189 1.087 0.761 -0.816 -0.057 2.215 0.851 -0.155 1.021 0.000 0.817 -1.296 1.550 0.000 0.868 1.020 0.996 0.683 0.985 0.785 0.737 +0 0.366 -1.129 0.943 0.445 0.087 0.548 0.251 -1.447 0.000 0.893 0.511 1.528 2.215 0.607 -0.054 0.352 2.548 0.506 -0.877 -0.378 0.000 0.869 0.782 0.981 0.520 0.721 0.583 0.568 +0 0.999 0.954 -1.736 0.964 0.973 0.530 -1.735 -0.542 0.000 0.702 -0.494 0.778 2.215 0.818 1.118 -0.495 1.274 0.370 -0.154 -0.956 0.000 0.636 0.851 0.991 0.813 1.079 0.902 1.105 +0 0.640 -1.021 0.114 1.554 1.041 0.618 -0.985 -1.564 0.000 0.531 -2.373 0.469 0.000 0.353 0.182 -1.188 0.000 0.713 1.094 -0.941 3.102 0.601 0.827 1.025 0.959 0.332 0.850 0.773 +0 0.728 -0.686 1.522 1.698 -1.386 0.870 0.082 -0.410 0.000 1.020 0.036 0.395 0.000 0.873 0.070 1.276 2.548 0.664 0.821 -0.588 0.000 0.983 0.829 0.993 1.121 0.305 0.815 0.897 +1 0.906 -0.731 1.016 2.359 1.484 1.056 -1.052 -0.702 2.173 0.856 0.085 -0.046 2.215 0.436 -1.820 1.049 0.000 0.839 -0.602 -0.116 0.000 0.737 0.841 0.975 1.385 1.153 1.234 0.963 +0 0.841 0.048 0.915 0.687 -0.786 0.627 -0.686 -1.516 1.087 0.546 0.190 1.601 0.000 1.253 0.133 -0.135 2.548 0.980 -0.862 -0.036 0.000 0.725 0.879 1.053 0.780 1.150 0.707 0.633 +0 0.871 -1.221 1.366 0.826 -0.322 0.740 -0.558 0.125 1.087 0.492 -0.118 -1.691 2.215 0.799 -0.486 1.159 0.000 0.797 -0.151 -1.075 0.000 0.943 0.899 1.173 0.763 0.908 0.691 0.650 +0 0.710 -0.376 -1.609 1.226 -0.982 0.874 0.646 0.560 1.087 0.464 0.200 0.922 0.000 1.105 0.435 -1.278 2.548 1.163 -0.135 -0.093 0.000 0.779 0.846 0.985 1.144 1.224 0.831 0.740 +1 1.144 0.615 0.168 0.741 -0.292 0.571 1.603 -0.017 0.000 1.367 -0.318 1.642 0.000 1.089 -0.339 -1.521 2.548 0.757 -0.760 1.170 3.102 0.955 0.851 0.981 0.956 0.493 0.693 0.677 +1 1.552 -1.195 -0.462 0.730 -0.275 1.225 -1.226 1.309 1.087 0.665 -1.337 0.470 2.215 0.489 -0.937 -1.439 0.000 0.540 -2.067 -0.469 0.000 0.625 0.896 0.991 0.774 0.917 1.001 0.794 +1 0.289 2.404 -0.247 0.783 -0.856 0.584 0.219 -1.284 0.000 0.588 1.304 -0.253 0.000 0.876 0.693 1.134 2.548 1.444 -0.830 0.915 3.102 0.815 1.102 0.981 0.751 0.873 0.840 0.738 +1 0.945 -0.175 -0.909 1.056 -1.316 0.914 0.127 0.594 2.173 0.825 -0.505 1.575 0.000 0.441 -0.560 0.352 0.000 0.732 -1.076 -0.360 3.102 0.826 0.891 0.989 0.612 0.936 0.885 0.762 +0 0.605 -0.810 1.087 0.445 1.585 0.724 0.044 1.236 1.087 1.240 0.354 -0.262 0.000 1.193 0.775 -1.271 2.548 0.485 1.326 0.201 0.000 0.776 0.944 0.992 1.130 1.014 1.193 1.211 +0 1.070 -2.064 -1.551 1.149 -0.660 0.674 -1.434 0.305 0.000 0.771 -0.824 1.376 2.215 0.686 -0.842 -0.452 2.548 0.907 -0.866 0.961 0.000 0.709 0.767 1.105 0.769 0.771 0.756 0.752 +0 1.404 -0.626 0.868 0.964 -0.130 1.060 -0.165 1.631 2.173 1.078 -2.116 -0.607 0.000 1.289 -1.072 -0.195 2.548 1.164 -1.086 -1.324 0.000 0.964 0.878 1.262 1.195 1.642 1.011 0.915 +1 0.542 0.771 -1.067 1.028 0.667 0.983 -1.319 0.368 0.000 1.029 1.162 0.983 0.000 1.227 -1.078 -0.898 0.000 1.818 0.507 -0.653 3.102 0.793 1.018 1.034 0.750 0.671 0.715 0.655 +1 0.633 -0.076 -0.962 0.978 0.041 1.150 -0.247 1.287 2.173 0.853 1.036 -1.433 0.000 0.557 2.059 -0.307 0.000 0.755 -0.038 -0.103 3.102 0.985 0.749 0.987 1.116 0.942 0.819 0.820 +1 1.941 1.136 -1.738 1.119 -1.053 0.591 0.655 -0.053 2.173 0.462 0.061 -0.398 2.215 0.647 0.904 1.294 0.000 0.868 1.063 0.252 0.000 0.677 0.657 1.183 1.142 0.331 0.822 0.712 +1 1.022 -0.930 -0.240 0.656 0.456 0.552 0.537 -1.578 0.000 0.780 -1.063 -1.484 2.215 0.694 -1.731 0.097 0.000 0.950 -0.814 0.102 3.102 0.823 0.836 0.985 0.522 0.771 0.633 0.691 +0 1.195 -0.791 0.724 1.623 1.446 0.484 -1.318 -0.150 0.000 0.551 -0.934 -1.257 2.215 0.788 -0.109 -0.720 0.000 0.807 1.009 -0.369 1.551 0.899 0.948 1.170 0.843 0.901 0.885 0.842 +1 1.778 0.630 0.709 0.621 1.447 0.953 0.808 -1.552 2.173 0.930 0.680 -0.223 2.215 0.785 -0.309 -0.462 0.000 0.583 0.768 -0.729 0.000 0.533 0.907 0.987 0.940 1.292 0.923 0.805 +0 0.616 -0.057 0.362 0.979 -1.084 0.308 0.681 0.845 0.000 0.668 0.866 -1.300 2.215 0.864 -0.810 0.678 2.548 0.646 -1.162 -0.744 0.000 1.082 0.928 1.038 0.763 1.136 0.721 0.652 +1 0.876 -2.277 -1.225 1.021 -0.260 0.159 0.829 0.144 1.087 0.869 -0.527 1.572 2.215 0.452 -0.895 0.135 0.000 0.642 -1.931 0.637 0.000 0.500 0.793 1.002 1.162 0.666 0.989 0.788 +0 0.960 0.077 -1.037 1.884 -0.413 0.953 -1.034 1.483 2.173 0.486 -0.537 0.867 0.000 0.838 -0.668 -0.044 2.548 0.461 -1.138 1.136 0.000 0.298 0.492 0.995 1.523 1.106 1.038 0.830 +0 0.362 -1.252 -1.699 1.271 1.325 0.647 -0.045 -0.089 0.000 1.158 0.628 -1.167 2.215 0.940 -0.986 0.169 2.548 0.443 0.686 1.271 0.000 0.852 0.980 0.984 1.051 1.497 1.766 1.483 +0 0.988 1.156 -0.804 1.563 -0.898 0.675 1.670 0.875 2.173 0.569 0.234 1.046 2.215 1.132 1.047 0.297 0.000 0.611 1.972 -1.707 0.000 0.845 0.785 0.993 1.364 0.731 0.975 0.839 +0 1.114 0.636 0.369 0.891 1.045 0.615 0.896 -1.478 2.173 0.255 2.861 -1.620 0.000 0.920 0.727 -0.947 1.274 0.438 1.889 1.650 0.000 0.172 0.636 0.993 0.921 0.435 0.717 0.649 +0 0.608 0.001 -1.139 1.407 -0.218 0.751 0.348 0.361 2.173 0.901 2.202 1.446 0.000 1.207 2.709 -0.880 0.000 1.652 0.647 1.352 3.102 0.692 0.777 0.990 0.793 0.952 0.856 0.918 +0 0.319 -2.000 -1.007 1.956 1.396 0.791 0.162 0.194 0.000 0.818 -1.165 -0.106 2.215 1.659 -0.567 -1.506 2.548 0.699 1.101 -0.360 0.000 0.882 1.110 0.987 0.864 1.234 1.047 1.116 +0 1.888 -0.245 -0.586 0.377 1.255 0.460 -2.034 1.632 0.000 0.918 0.508 0.221 2.215 0.732 -0.214 -1.502 0.000 1.030 -0.537 1.203 0.000 0.896 1.058 1.164 0.919 0.392 0.965 0.905 +1 0.898 -1.507 -1.547 0.203 -1.193 0.926 -1.204 0.471 2.173 0.422 -0.929 1.465 0.000 0.940 0.404 -1.419 0.000 1.377 -1.012 -0.338 3.102 0.887 1.003 0.992 1.014 0.796 0.897 0.794 +0 0.372 0.722 1.225 1.699 -0.167 0.536 1.514 1.506 0.000 0.738 1.548 -1.257 0.000 1.387 -0.021 0.481 0.000 0.852 0.478 -0.803 3.102 0.809 0.724 1.047 0.889 0.474 0.599 0.673 +1 2.388 0.090 -0.653 0.571 -0.274 1.073 -0.345 0.862 2.173 0.974 0.617 -1.740 0.000 0.541 0.024 -1.742 2.548 0.522 0.480 -0.067 0.000 0.928 1.147 0.997 0.768 0.700 0.962 0.883 +0 0.606 -1.131 -0.208 0.916 1.695 1.448 -0.138 0.377 2.173 1.134 -0.811 -0.966 1.107 0.983 2.627 -1.353 0.000 1.046 -0.414 1.528 0.000 0.855 0.927 1.022 0.747 1.887 1.064 0.854 +1 0.517 -0.605 1.692 0.838 0.228 1.169 -0.564 0.791 0.000 1.458 -1.222 -1.159 1.107 0.800 -0.885 -0.478 0.000 0.658 -1.044 -0.029 3.102 1.623 0.949 0.986 0.924 0.753 0.986 0.817 +1 0.713 1.926 0.105 1.371 -1.300 1.313 0.357 0.282 2.173 0.828 0.980 -1.317 0.000 0.567 2.040 1.631 0.000 0.471 0.090 1.520 1.551 0.845 0.617 1.307 1.722 0.755 1.104 0.986 +1 1.039 1.036 -1.551 0.804 0.380 0.553 0.051 -0.253 0.000 0.549 -0.923 -0.542 2.215 1.023 -0.451 1.266 1.274 0.527 0.224 1.001 0.000 0.751 0.787 1.248 1.101 0.816 0.864 0.733 +1 0.893 -0.604 -1.067 1.040 0.013 1.538 -1.243 -1.427 0.000 1.124 0.752 0.269 0.000 1.736 -0.488 1.084 2.548 0.995 -1.841 -0.358 0.000 0.962 1.050 1.105 1.039 0.790 0.934 0.896 +0 0.361 -1.235 0.107 0.663 0.752 0.638 -1.422 0.914 0.000 0.790 -0.594 -1.056 2.215 0.277 -0.887 -1.634 0.000 1.193 0.448 -1.077 3.102 0.592 1.067 0.989 0.851 0.533 0.742 0.725 +0 1.852 -0.265 0.916 0.844 -1.361 0.605 1.741 -0.312 0.000 0.415 1.116 -0.888 0.000 0.673 -0.154 0.332 2.548 1.326 -0.120 -1.435 1.551 0.622 1.053 1.536 0.844 0.722 0.767 0.922 +0 0.476 -1.532 -1.726 0.861 -0.413 0.530 0.052 1.279 0.000 0.550 -0.218 -0.551 2.215 0.800 -0.868 0.064 2.548 0.416 -1.405 0.874 0.000 0.751 0.772 0.989 0.606 0.455 0.561 0.550 +0 0.526 0.968 -0.495 1.542 -1.350 0.557 1.246 0.001 0.000 0.362 1.400 -1.635 0.000 0.851 1.254 0.691 2.548 0.369 0.877 0.530 1.551 0.954 0.686 0.990 0.604 0.093 0.601 0.595 +0 0.824 0.771 1.669 0.945 1.050 1.218 0.604 0.084 0.000 0.406 1.079 0.690 0.000 1.020 2.035 -1.532 0.000 1.077 -0.825 -0.615 3.102 0.863 1.198 0.989 0.559 0.444 0.844 0.871 +1 1.464 -0.452 -0.601 0.579 -1.221 0.897 -0.776 1.256 2.173 1.296 -0.921 0.555 2.215 0.693 2.052 -1.517 0.000 0.654 -1.601 0.170 0.000 0.316 0.851 0.992 1.125 0.947 0.962 0.772 +1 0.688 0.310 -1.606 1.170 -0.765 2.223 0.764 0.626 2.173 1.834 2.557 1.091 0.000 3.284 0.738 -0.780 0.000 1.728 0.786 -1.080 1.551 5.418 3.061 0.985 1.817 2.081 2.528 2.135 +1 1.930 0.748 -0.663 0.374 0.176 0.874 -0.463 1.575 2.173 0.623 0.426 1.541 0.000 0.656 -1.210 0.274 2.548 0.940 0.003 0.326 0.000 0.913 0.852 0.988 1.204 0.965 1.121 0.920 +1 1.419 -1.257 -0.894 0.563 1.174 1.506 0.011 1.015 2.173 1.031 -1.231 -0.269 0.000 0.880 -0.225 0.233 2.548 1.040 -0.855 1.671 0.000 1.338 0.978 1.186 1.580 0.948 1.083 1.026 +1 1.704 0.155 1.731 0.736 -1.646 0.938 -1.133 -0.299 2.173 0.740 -0.422 0.405 2.215 0.616 -0.602 1.027 0.000 0.590 -1.297 -0.676 0.000 0.733 0.766 0.992 1.113 0.852 1.238 0.983 +1 1.794 0.282 0.078 0.419 -0.168 0.594 0.764 -1.648 0.000 0.879 -0.543 1.424 2.215 0.712 -1.105 -0.475 0.000 0.380 -0.256 1.144 3.102 1.701 0.932 0.993 1.217 0.146 0.758 0.838 +1 0.583 -0.581 1.146 0.634 -1.108 1.769 1.267 -0.515 0.000 0.967 -0.322 1.742 0.000 1.044 0.801 -1.583 0.000 2.199 0.416 1.061 1.551 0.881 0.860 0.987 0.762 0.940 0.698 0.635 +0 0.736 1.116 -1.708 1.329 1.621 1.207 -1.634 -0.363 0.000 0.695 -1.798 0.380 0.000 0.695 -0.567 -1.398 2.548 1.646 -0.464 0.841 3.102 1.222 1.159 0.977 1.825 0.738 1.346 2.309 +1 1.231 -1.082 -0.787 0.848 -0.418 1.273 1.535 1.076 0.000 0.624 -1.813 -1.176 0.000 0.742 1.125 -0.129 0.000 2.029 0.046 0.303 3.102 0.678 1.314 0.985 0.650 0.919 0.824 0.763 +1 0.398 -0.805 -1.342 0.958 1.518 0.485 -0.971 -0.350 0.000 0.591 -1.101 0.599 0.000 0.681 0.776 1.372 2.548 0.991 -0.159 -0.718 3.102 0.862 1.053 0.977 0.695 0.689 0.692 0.711 +0 0.886 -0.302 1.325 1.746 -1.665 1.058 0.481 -0.350 0.000 0.804 0.014 0.459 2.215 0.824 -1.342 1.540 0.000 0.831 -0.139 -0.580 3.102 0.895 0.897 0.991 0.817 0.598 0.780 0.975 +0 3.399 0.095 1.730 0.744 -1.024 2.037 -0.308 0.310 0.000 0.857 0.147 -1.204 2.215 0.946 -0.487 -0.438 2.548 0.420 -0.004 0.619 0.000 0.444 0.854 1.351 0.793 0.695 0.928 1.188 +1 0.664 -1.225 0.127 0.095 1.597 0.728 -1.023 0.717 0.000 0.898 -0.711 -0.629 2.215 0.818 -1.679 1.281 0.000 1.421 -0.176 1.716 3.102 0.889 0.985 0.984 0.703 0.914 0.856 0.753 +0 1.267 1.307 -1.730 0.879 -1.497 1.341 0.365 0.630 0.000 1.544 0.831 -0.947 2.215 1.718 0.656 0.206 2.548 1.125 1.254 -1.078 0.000 0.915 0.917 0.986 1.159 1.497 1.190 1.309 +0 0.990 -0.020 -0.828 1.176 0.791 0.667 -0.730 1.690 0.000 0.984 -0.325 0.586 1.107 0.734 0.152 -0.016 2.548 0.545 -1.848 -0.110 0.000 1.184 1.026 1.485 0.914 0.519 0.764 0.774 +0 0.875 -0.123 -0.007 0.826 1.533 0.484 -0.943 1.489 2.173 0.250 -0.037 -0.794 0.000 0.342 1.798 0.825 0.000 0.389 -1.262 -0.533 3.102 0.694 0.905 1.158 0.643 0.464 0.606 0.586 +1 0.998 -0.335 1.612 1.479 -1.110 0.665 1.011 0.463 2.173 0.403 0.937 -0.989 0.000 0.969 -0.171 -0.083 2.548 0.786 0.486 1.181 0.000 0.746 0.696 1.070 0.915 0.797 0.943 0.758 +1 1.387 0.206 1.244 0.159 -0.538 1.051 -2.343 -1.162 0.000 1.099 1.305 0.538 0.000 0.902 0.246 0.180 0.000 0.731 -0.470 1.453 3.102 0.888 0.921 0.988 0.529 0.220 0.610 0.620 +1 2.735 0.187 -1.566 0.360 -0.812 2.423 1.770 0.056 0.000 0.664 0.203 -1.164 0.000 1.491 0.473 0.631 2.548 1.331 -0.279 1.600 0.000 1.006 1.129 0.992 0.704 0.891 0.864 0.816 +0 0.336 0.941 0.226 0.291 1.219 1.203 -0.054 1.662 2.173 1.053 0.308 -0.318 2.215 1.213 -0.790 -0.281 0.000 0.434 -0.698 1.241 0.000 0.784 0.823 0.990 0.912 1.648 0.985 0.822 +0 0.900 0.318 1.416 0.455 0.801 0.916 0.734 -0.880 0.000 0.895 -0.189 0.525 2.215 0.878 -0.114 -0.627 0.000 0.738 0.990 1.250 3.102 0.807 0.885 0.982 0.690 0.707 0.822 0.798 +0 0.482 1.737 -1.519 0.486 -0.290 0.775 0.848 -0.525 0.000 0.691 0.563 0.012 2.215 1.810 0.613 1.517 2.548 0.711 2.030 1.298 0.000 1.504 1.033 0.983 0.823 1.162 0.947 0.786 +0 0.915 -0.392 -1.621 1.790 -1.036 0.661 0.162 0.284 0.000 0.788 -0.220 0.889 1.107 0.278 -0.542 1.165 0.000 0.596 1.403 0.584 0.000 0.840 0.766 0.980 0.733 0.819 0.756 0.791 +0 1.519 0.837 0.953 1.421 1.366 0.501 -2.876 1.470 0.000 1.043 0.545 -0.494 0.000 0.629 1.050 -0.097 2.548 0.836 -0.864 -0.492 3.102 4.465 2.349 0.995 0.828 0.761 1.648 1.965 +1 0.752 0.595 -0.077 2.233 0.518 1.511 -1.437 -1.427 0.000 1.145 -0.622 0.201 0.000 0.688 -0.854 1.665 1.274 1.026 0.368 -1.093 3.102 3.012 1.654 0.991 0.928 0.619 1.145 1.319 +0 3.405 0.177 -0.495 3.120 -0.598 3.668 -0.671 1.142 0.000 1.029 -0.760 -0.709 2.215 0.674 0.051 1.569 2.548 0.567 -1.222 1.353 0.000 0.984 0.921 0.962 0.878 0.872 1.253 1.931 +0 0.662 -1.343 1.504 1.003 -0.713 0.840 -2.135 0.066 0.000 1.028 -0.400 -1.564 2.215 1.313 -0.597 0.880 2.548 0.409 -0.852 -0.657 0.000 0.762 1.118 1.028 0.823 1.008 1.025 0.869 +1 1.314 0.194 0.837 0.813 -0.268 0.510 -1.054 -0.093 0.000 0.583 -0.897 -1.019 2.215 1.235 -1.004 1.657 2.548 1.278 1.457 -0.950 0.000 0.912 0.870 1.201 0.943 0.607 0.832 0.767 +1 0.411 0.641 1.160 1.004 -0.152 1.167 -0.001 -1.224 1.087 1.019 0.628 -0.430 0.000 2.239 -1.146 1.285 0.000 0.653 -1.928 0.531 0.000 0.860 1.151 0.987 0.522 1.034 0.801 0.726 +1 1.737 -0.307 -0.520 1.292 -1.310 1.885 1.265 0.975 0.000 1.485 0.795 -0.672 2.215 1.476 0.084 -1.228 2.548 0.816 -1.002 1.226 0.000 0.781 1.904 1.354 1.165 0.955 1.542 1.551 +0 1.297 0.338 1.480 1.147 0.897 1.318 1.038 -0.858 0.000 1.061 0.926 0.358 2.215 0.405 1.064 -1.409 0.000 0.526 -0.282 0.058 3.102 0.632 0.788 0.990 1.024 0.500 0.789 0.924 +0 0.818 0.367 -0.688 1.179 0.522 1.658 0.961 -1.725 2.173 1.074 0.676 0.211 0.000 1.268 0.212 -0.210 1.274 0.425 -0.222 1.503 0.000 0.921 0.717 1.207 1.429 1.896 1.134 0.970 +1 1.381 0.980 -1.538 1.033 1.460 0.503 -1.005 0.352 2.173 0.396 1.179 0.762 0.000 0.386 -0.942 -0.352 0.000 0.717 0.187 -0.606 0.000 0.969 0.837 0.981 0.775 0.742 0.874 0.755 +1 0.835 0.224 0.421 1.738 1.099 0.989 -0.050 -0.423 1.087 0.970 -1.097 -0.980 1.107 0.561 0.833 -1.479 0.000 0.623 -0.847 1.116 0.000 0.870 0.994 0.987 1.308 1.071 1.094 0.903 +1 0.289 -1.889 1.685 1.037 1.414 0.696 -0.652 1.083 2.173 0.651 -0.904 0.175 0.000 1.117 0.899 -0.945 0.000 1.924 -0.359 -0.554 3.102 1.829 1.209 0.992 0.632 1.227 1.006 0.892 +0 1.089 1.102 0.742 0.763 0.279 0.892 1.926 -1.022 0.000 0.605 0.113 0.873 0.000 1.059 0.706 -1.600 2.548 0.856 0.354 0.394 0.000 0.736 0.639 0.997 0.764 0.297 0.691 0.623 +0 0.499 2.010 -1.076 0.983 -1.033 1.110 0.789 -0.668 2.173 2.952 -0.292 0.898 2.215 0.573 -0.475 -0.829 0.000 0.498 -1.998 1.576 0.000 0.803 1.497 0.971 0.699 3.046 1.642 1.419 +1 1.564 -0.959 0.816 1.185 -0.668 0.416 -1.084 -0.424 0.000 1.101 0.202 -1.012 2.215 0.968 2.677 1.552 0.000 1.123 0.002 -0.014 3.102 0.808 0.943 1.834 1.414 0.793 0.964 1.165 +1 1.060 -0.270 1.452 0.472 -0.410 0.773 -0.366 0.482 0.000 0.648 0.454 1.623 2.215 0.464 0.877 -0.859 0.000 1.075 -1.284 -0.335 3.102 0.818 0.955 0.987 0.606 1.157 0.791 0.698 +0 1.876 0.736 1.723 0.744 1.296 0.965 0.891 0.400 2.173 0.675 1.451 -1.019 0.000 1.084 -0.054 -0.970 2.548 1.041 -0.162 0.584 0.000 0.669 0.964 0.983 1.243 1.360 0.997 0.967 +1 1.270 0.291 0.697 1.420 -1.208 0.501 -0.455 -1.257 0.000 0.777 -0.006 1.533 0.000 1.963 0.811 -0.298 2.548 0.655 -2.060 0.956 0.000 0.834 0.754 1.841 1.328 0.822 0.898 0.888 +0 0.630 0.241 -0.805 1.593 1.186 0.944 -0.239 -0.444 2.173 0.864 -0.897 1.203 1.107 0.487 0.423 0.020 0.000 0.749 -0.893 -1.140 0.000 0.806 0.814 1.353 1.198 1.402 0.980 0.798 +0 0.963 2.277 -1.672 0.846 -1.138 0.614 0.839 -0.358 1.087 1.223 0.209 0.622 1.107 0.269 0.097 -1.568 0.000 0.423 1.331 1.392 0.000 0.348 0.623 0.978 0.940 1.067 1.049 0.774 +0 0.800 0.949 0.174 1.257 0.968 0.432 0.320 -0.724 1.087 0.360 -0.926 0.264 0.000 0.788 -0.306 0.973 0.000 0.479 -0.987 1.441 3.102 0.550 0.736 0.984 0.950 0.599 0.755 0.722 +0 1.212 -1.496 1.480 0.614 0.562 1.167 -0.860 -0.715 1.087 1.128 -0.045 1.352 2.215 1.270 -2.696 -0.045 0.000 0.877 -1.125 0.070 0.000 0.793 0.878 0.988 1.191 1.768 1.062 0.843 +0 1.234 -1.096 -0.623 0.897 0.094 0.571 0.532 -1.720 0.000 0.874 0.100 1.168 1.107 0.992 -0.355 -0.933 2.548 0.792 -0.822 0.533 0.000 1.260 0.948 0.992 1.079 0.970 0.789 0.785 +0 1.300 0.825 0.617 1.870 0.057 1.122 -0.787 1.190 0.000 0.991 -1.147 1.536 0.000 2.030 2.350 -0.073 0.000 3.785 -0.034 -0.523 3.102 0.810 0.865 1.043 1.451 1.067 1.306 1.502 +0 2.315 1.083 -1.208 0.454 -0.964 1.399 0.743 -1.519 2.173 1.383 0.380 0.301 0.000 2.614 1.019 0.587 2.548 1.170 0.847 -0.066 0.000 0.753 0.907 0.987 0.908 2.298 1.373 1.305 +1 0.648 -0.362 -1.581 0.181 -0.801 1.511 -2.595 1.587 0.000 1.682 0.491 0.356 0.000 1.618 0.955 -0.790 2.548 1.033 -0.317 0.304 0.000 0.790 0.947 0.989 0.764 0.917 0.953 0.842 +0 0.710 -0.656 -0.163 0.577 1.274 0.603 -1.241 -1.196 0.000 1.109 0.345 1.624 2.215 2.525 -0.258 0.350 2.548 1.468 1.293 -1.192 0.000 0.966 1.012 0.988 0.805 1.722 1.185 0.955 +1 0.839 -1.630 0.660 0.761 -0.788 0.398 1.435 -1.140 2.173 0.283 -2.040 0.277 0.000 0.869 -0.486 1.380 2.548 0.655 0.164 0.561 0.000 0.843 1.005 1.068 1.592 1.020 1.074 1.092 +1 1.118 -0.287 0.737 1.336 0.133 1.924 -0.331 -1.474 2.173 1.520 1.021 0.587 0.000 0.445 1.104 -0.808 0.000 0.963 -0.506 -0.447 3.102 1.204 1.176 0.993 1.770 1.169 1.454 1.262 +1 0.756 0.428 -1.164 0.594 1.552 0.804 -0.922 -1.307 0.000 0.569 -1.601 0.145 0.000 0.756 -0.765 -0.497 2.548 1.691 -0.168 0.399 3.102 0.918 1.032 0.986 0.681 0.684 0.665 0.639 +0 1.973 0.057 -1.278 0.283 -1.287 1.494 0.749 0.736 0.000 2.003 -0.336 -0.829 1.107 1.482 -0.134 0.798 2.548 0.923 1.009 0.268 0.000 0.842 0.918 0.984 0.999 1.830 1.492 1.308 +0 0.487 -0.621 -0.212 1.328 1.615 1.087 -0.444 -0.598 2.173 0.588 0.057 -1.418 0.000 0.916 0.586 1.439 2.548 2.264 -0.145 0.354 0.000 0.776 0.868 1.111 1.032 1.395 0.923 0.844 +1 0.898 1.254 -1.200 0.366 0.031 0.662 -0.372 1.674 1.087 0.341 0.178 -0.334 0.000 0.583 1.195 0.813 0.000 1.579 -0.578 0.602 3.102 0.959 0.914 0.987 1.474 0.907 1.167 1.130 +0 0.356 -0.732 -0.492 1.340 0.052 1.112 0.301 -1.328 2.173 0.852 0.625 1.530 0.000 1.269 0.046 1.055 2.548 1.305 -1.135 -0.082 0.000 0.935 0.924 0.990 1.461 1.255 1.549 1.325 +1 0.513 -0.782 -1.207 1.613 0.194 1.398 1.928 -1.740 0.000 0.837 0.731 -0.250 2.215 1.164 -1.647 0.104 0.000 1.527 -0.782 1.631 0.000 1.587 0.923 1.201 1.043 0.535 1.004 0.869 +1 0.964 0.223 0.913 0.725 0.326 0.564 -0.462 -0.771 0.000 0.527 -1.180 -0.063 0.000 1.085 -0.212 -1.247 2.548 0.838 1.076 0.872 0.000 0.824 0.724 0.985 0.595 0.165 0.579 0.543 +0 0.968 1.482 0.538 1.529 0.021 1.084 -0.646 -1.345 0.000 0.951 1.131 1.323 2.215 0.592 -1.271 -0.631 0.000 0.730 0.022 0.299 3.102 1.025 0.940 0.982 1.090 0.751 1.073 1.546 +0 0.504 -1.328 0.924 0.770 -1.059 0.726 -0.445 1.436 2.173 1.073 0.095 -0.129 2.215 0.620 -0.711 -0.192 0.000 1.110 0.578 -1.634 0.000 1.149 0.947 0.990 1.022 1.332 1.084 0.979 +1 0.964 0.938 -1.532 0.869 -0.428 0.937 0.867 0.516 2.173 1.073 -0.085 1.250 0.000 1.162 0.422 -1.526 2.548 1.533 0.134 -0.636 0.000 0.658 1.034 1.064 0.698 1.282 0.858 0.773 +1 0.556 1.309 1.023 1.582 -1.642 0.849 0.337 -1.456 2.173 1.516 -1.483 0.226 0.000 0.806 0.760 -0.406 0.000 0.730 1.285 1.600 0.000 0.878 0.927 0.991 0.694 0.932 0.729 0.685 +0 1.115 -0.021 0.076 1.497 -0.153 0.791 -1.228 -0.995 2.173 0.596 -0.165 -1.304 0.000 1.064 -2.332 1.206 0.000 1.189 0.348 1.088 3.102 0.855 1.124 0.981 1.002 1.364 1.147 1.128 +0 1.929 0.412 -1.051 1.666 -0.692 0.875 0.392 1.457 2.173 0.904 0.454 0.779 0.000 0.744 -0.235 0.186 0.000 1.482 0.884 0.105 3.102 0.793 0.950 0.989 1.106 1.203 1.064 0.983 +1 1.393 0.238 -1.492 0.735 -0.652 0.789 0.509 0.871 1.087 0.740 0.327 0.177 2.215 0.433 -0.919 -1.370 0.000 0.817 -0.188 0.697 0.000 0.682 0.754 0.987 0.873 0.665 0.785 0.684 +1 1.163 0.604 1.308 0.456 0.120 1.343 -0.380 -1.554 0.000 0.885 0.210 0.200 0.000 0.779 1.709 0.008 0.000 1.091 0.954 0.243 0.000 1.203 0.730 0.985 0.569 0.259 0.459 0.532 +1 1.335 -1.804 1.014 0.617 -1.607 0.898 0.167 -0.415 2.173 0.480 0.125 0.376 0.000 0.858 0.236 1.408 0.000 0.816 -1.251 -0.760 3.102 0.792 0.969 0.991 0.730 0.893 1.180 1.071 +0 0.758 1.354 1.027 1.626 0.874 0.796 1.262 -0.916 2.173 0.815 0.569 -1.704 1.107 1.179 1.838 -0.625 0.000 0.772 1.072 0.636 0.000 1.033 1.081 0.993 1.294 0.877 0.922 0.914 +0 0.789 1.167 -1.095 0.533 -0.822 0.351 1.551 -0.504 0.000 0.819 1.442 0.458 0.000 0.279 -0.931 1.285 1.274 0.738 -0.009 1.214 1.551 0.869 0.927 0.978 0.656 0.184 0.630 0.655 +0 0.744 2.088 1.037 2.002 1.680 0.950 -0.851 -0.129 0.000 0.371 0.013 -0.708 1.107 0.818 0.480 1.540 1.274 0.941 -1.357 0.505 0.000 0.967 0.789 0.988 1.090 0.547 0.982 1.976 +0 0.692 -0.712 1.636 0.879 0.222 1.467 -0.444 0.380 0.000 0.960 -0.006 -1.307 0.000 0.498 1.256 -1.075 1.274 0.821 1.683 1.515 0.000 1.146 0.973 1.033 0.889 0.686 0.821 0.730 +1 1.003 -1.698 -1.084 0.919 -0.793 0.702 0.125 0.850 2.173 0.458 -1.539 1.632 0.000 0.821 0.232 -1.528 2.548 1.212 -1.080 0.578 0.000 0.858 0.947 0.984 0.844 0.797 0.896 0.769 +0 0.367 -1.548 1.143 1.634 -0.738 0.816 0.678 0.997 2.173 0.301 -0.355 1.292 1.107 0.318 1.078 0.578 0.000 0.419 -0.292 -0.617 0.000 0.491 0.584 1.065 0.739 0.444 1.109 0.862 +0 0.867 -0.113 -0.775 0.235 1.385 1.683 0.038 -1.569 2.173 1.237 -0.927 -0.123 0.000 1.755 0.024 0.517 1.274 1.271 -0.542 0.513 0.000 0.920 0.978 0.990 1.073 2.038 1.406 1.117 +1 0.655 1.488 -0.580 0.602 -0.301 0.512 -0.530 0.768 2.173 0.879 -0.992 -1.541 2.215 0.584 -1.491 1.344 0.000 0.460 0.299 -0.125 0.000 0.869 0.725 0.999 0.901 0.894 0.870 0.755 +1 0.353 1.279 -0.718 0.569 -0.442 0.670 -0.161 1.726 0.000 0.753 -0.412 1.017 0.000 1.097 1.175 0.267 2.548 1.128 1.081 -1.245 3.102 0.923 1.072 0.985 0.917 0.832 0.925 0.836 +1 1.937 -0.148 1.216 1.822 1.606 1.331 -1.234 0.913 2.173 1.825 1.178 -0.544 2.215 2.258 -0.339 -0.193 0.000 0.963 -0.907 -1.475 0.000 0.969 1.647 0.980 1.514 4.216 2.185 1.792 +0 0.789 0.965 0.502 2.311 0.839 1.527 -0.456 -0.941 0.000 0.611 0.297 -1.154 0.000 0.306 0.834 -0.488 2.548 0.489 -0.409 0.813 3.102 0.894 0.861 0.986 0.684 0.354 0.561 0.946 +0 0.606 -1.354 -1.573 1.494 -0.555 0.911 0.304 -1.586 2.173 1.039 -1.243 0.979 2.215 1.351 -1.269 -0.133 0.000 0.866 -1.702 -0.109 0.000 0.396 0.952 1.047 1.389 1.644 1.237 1.074 +0 1.967 -0.694 -1.601 0.818 1.603 1.037 -0.752 0.300 1.087 0.717 -0.177 -0.764 0.000 0.821 0.333 1.279 2.548 1.912 -1.018 -0.302 0.000 1.035 1.101 0.983 0.706 1.119 0.991 1.003 +1 0.411 0.204 -0.780 0.887 0.824 1.075 0.886 -0.505 2.173 0.862 -0.098 0.811 0.000 0.533 -0.535 -1.706 0.000 1.011 0.961 -1.544 3.102 0.837 0.857 0.989 1.240 0.900 0.915 0.852 +1 0.657 1.445 1.098 1.277 -0.945 0.938 0.757 -0.061 2.173 0.608 2.351 1.580 0.000 0.611 0.556 1.626 0.000 0.396 1.291 0.609 0.000 0.966 0.952 1.223 1.059 0.743 0.887 0.812 +1 3.063 0.272 0.964 2.048 1.233 3.061 1.445 -0.575 0.000 1.071 -0.001 1.490 2.215 0.438 -0.546 0.133 2.548 0.600 -0.758 -1.168 0.000 3.243 2.116 1.006 0.820 0.719 1.721 1.871 +1 1.723 0.612 0.471 0.785 0.990 0.761 0.785 -0.660 0.000 0.663 -0.481 -0.933 2.215 0.758 -0.394 -1.639 2.548 0.742 0.866 -1.045 0.000 0.795 0.692 0.996 1.015 0.449 0.888 0.744 +1 1.256 -1.245 1.041 1.088 -0.685 0.635 -0.940 -0.793 2.173 1.071 -1.157 -0.058 2.215 1.114 -0.769 1.437 0.000 0.380 2.459 1.510 0.000 0.836 0.966 1.619 0.992 0.761 0.783 0.757 +0 2.093 -0.848 -0.264 1.746 0.123 2.167 -0.456 -1.651 0.000 1.288 -1.061 1.637 0.000 2.372 -0.986 0.267 1.274 1.134 2.072 0.010 0.000 1.302 0.994 0.979 0.745 1.532 1.505 1.552 +1 0.734 0.737 1.555 1.508 -1.273 0.980 1.178 0.289 0.000 0.699 1.475 0.850 0.000 1.622 0.814 -0.869 2.548 0.386 -0.182 1.079 3.102 0.899 0.717 0.986 0.787 0.692 0.827 0.842 +1 1.094 -0.729 -0.742 0.727 0.108 1.036 -0.412 -1.246 2.173 0.511 0.570 1.476 1.107 1.209 -0.728 0.711 0.000 1.164 -0.553 -0.015 0.000 0.801 0.907 0.980 0.964 0.880 0.882 0.827 +1 0.513 -0.061 1.474 0.561 -0.268 0.646 -1.420 -1.207 0.000 0.918 -2.151 -0.662 0.000 1.337 -0.586 0.791 2.548 0.941 -1.164 1.435 3.102 1.012 0.884 0.989 0.714 0.575 0.901 0.769 +1 1.050 -1.102 -0.324 1.257 -0.088 0.881 -0.543 1.327 2.173 0.592 -1.146 0.756 0.000 0.858 -2.508 -0.138 0.000 2.901 -0.447 -1.405 3.102 1.236 1.467 0.996 1.539 1.062 1.275 1.182 +0 0.467 -0.884 0.882 2.474 -1.675 1.283 0.612 0.014 2.173 0.253 0.830 0.429 0.000 1.027 -0.653 -0.994 2.548 0.941 1.163 -1.564 0.000 0.644 0.956 1.107 0.818 1.526 1.384 1.157 +0 1.095 -0.492 0.518 1.169 -0.092 0.933 1.531 -1.238 0.000 0.490 0.407 0.589 2.215 0.646 1.153 1.588 0.000 0.550 1.114 1.053 3.102 0.804 0.979 0.988 0.954 0.293 0.667 1.096 +1 0.978 -0.225 1.629 1.119 -1.151 0.582 -1.567 0.461 2.173 0.842 -0.333 0.058 1.107 0.816 -0.070 -1.497 0.000 0.378 0.497 0.443 0.000 0.764 0.870 0.994 0.967 0.773 0.846 0.710 +1 0.277 -0.553 -1.277 1.442 0.502 0.713 1.311 1.685 2.173 1.561 0.387 -1.406 1.107 1.244 -0.143 0.047 0.000 1.353 0.133 0.970 0.000 0.992 1.192 0.991 1.005 0.919 0.938 0.871 +0 1.204 -0.223 -0.856 0.471 0.852 0.666 -0.365 -0.228 0.000 1.382 -0.142 0.975 2.215 1.153 -0.631 -1.474 2.548 0.891 1.858 -0.748 0.000 0.688 0.976 1.043 0.701 1.141 0.799 0.724 +1 1.416 -0.553 -1.506 1.680 -1.279 1.137 -0.304 0.921 0.000 0.656 2.054 -0.234 0.000 0.368 0.867 0.494 2.548 0.718 -1.423 -0.011 0.000 1.468 0.978 0.999 0.832 0.160 0.677 0.887 +1 0.641 -0.956 1.518 0.402 0.962 1.253 0.420 -0.128 2.173 1.319 -0.350 1.366 0.000 0.617 -0.577 -0.243 0.000 0.997 0.269 -0.940 1.551 0.867 0.904 0.987 1.116 0.793 0.976 0.954 +1 1.186 -0.091 -0.145 0.797 1.523 0.672 1.341 1.351 1.087 0.347 2.089 0.967 0.000 0.755 1.065 -0.553 2.548 1.399 1.169 -1.264 0.000 0.926 0.942 1.344 0.879 0.882 0.845 0.821 +0 1.300 2.183 1.344 1.412 0.793 0.995 1.235 1.051 2.173 1.692 2.285 -1.089 0.000 1.453 0.543 -0.260 0.000 1.873 1.396 -0.768 3.102 1.178 0.946 0.987 0.752 1.475 1.000 1.011 +0 0.641 -0.659 -0.537 0.619 0.808 1.646 -0.454 0.097 2.173 1.024 0.109 1.578 2.215 1.788 -0.616 -1.307 0.000 1.430 -0.852 -1.740 0.000 0.738 0.892 0.981 0.883 1.938 1.326 1.049 +0 0.566 -2.108 -0.472 1.119 -1.237 0.596 -0.318 1.229 0.000 0.725 -0.863 -1.263 2.215 0.579 -1.159 0.158 2.548 0.445 2.261 0.215 0.000 1.819 1.416 0.998 0.584 0.673 1.043 1.067 +1 0.778 -1.146 -0.244 1.537 -1.127 1.069 2.333 0.591 0.000 1.259 0.227 1.491 2.215 0.830 -1.291 0.290 0.000 1.072 -0.629 -1.151 3.102 0.853 0.811 1.081 1.396 0.903 1.051 0.944 +1 2.092 -0.227 -1.335 0.305 -0.052 1.122 0.108 0.402 2.173 0.519 0.408 -0.050 0.000 0.393 -1.188 1.155 0.000 0.752 -0.585 1.491 3.102 0.912 0.803 1.013 0.612 0.904 0.876 0.742 +0 0.409 1.107 1.142 0.696 0.010 1.167 0.217 1.550 0.000 0.683 0.689 -0.871 2.215 0.932 0.222 0.454 0.000 1.070 1.095 -0.316 0.000 0.937 0.796 0.992 0.561 0.200 0.583 0.638 +1 1.582 -0.668 1.466 1.026 -1.531 1.049 -0.172 -0.235 2.173 0.487 -0.007 -1.327 0.000 0.838 0.754 0.083 0.000 0.586 0.174 1.187 3.102 1.026 0.935 0.991 0.643 0.811 0.956 0.891 +0 0.734 -2.036 -0.436 0.074 -1.579 1.048 0.053 1.463 2.173 0.919 0.428 -1.313 2.215 1.462 -0.278 -0.261 0.000 1.998 -0.060 0.304 0.000 0.946 1.266 0.986 1.217 0.906 1.082 1.015 +1 0.627 -0.877 1.299 1.163 0.143 2.519 -0.384 -1.187 0.000 1.407 -0.261 0.525 2.215 1.373 0.590 0.685 1.274 0.853 -0.587 0.905 0.000 0.438 0.511 1.020 0.748 0.733 0.733 0.561 +1 0.903 0.291 -1.699 0.365 -0.039 1.402 0.932 0.683 2.173 0.841 0.950 -1.339 0.000 1.588 -0.216 -0.794 0.000 0.869 0.355 -0.173 3.102 1.014 0.755 0.982 0.990 0.870 1.080 0.901 +1 2.139 -0.406 -0.271 0.629 -0.757 0.906 -0.080 1.461 2.173 0.615 -0.422 0.231 0.000 0.783 -0.600 0.966 0.000 0.459 2.266 -1.577 0.000 0.885 1.061 0.989 0.777 0.464 0.877 0.797 +0 0.560 -2.215 1.475 0.350 -1.638 0.769 -0.346 -1.270 2.173 0.780 0.125 0.690 2.215 0.357 -2.373 0.318 0.000 0.423 0.856 -0.363 0.000 1.307 1.033 0.989 0.802 1.151 0.850 0.775 +1 0.861 -0.598 -0.461 0.844 0.583 0.923 -0.875 -1.358 0.000 0.987 -0.028 1.597 2.215 1.176 0.351 0.279 0.000 1.718 -1.169 -0.179 3.102 0.760 1.101 0.986 0.903 1.464 0.903 0.839 +1 1.157 -0.452 1.219 1.024 -1.381 1.070 -0.037 0.878 0.000 2.073 -0.052 -0.382 1.107 0.612 -1.241 -1.242 0.000 0.961 0.231 -1.103 1.551 1.720 1.187 1.081 1.413 0.798 1.119 1.008 +1 0.953 0.155 0.346 0.571 1.502 1.057 0.408 1.307 2.173 1.333 0.178 -0.325 0.000 1.189 -0.393 -1.554 2.548 0.668 1.165 -0.433 0.000 0.803 1.170 0.987 0.771 0.960 1.048 0.876 +1 0.653 1.237 1.537 1.887 -0.949 1.417 0.379 0.330 1.087 0.677 2.468 1.641 0.000 0.493 -0.718 -0.361 0.000 0.513 0.665 -0.949 0.000 0.939 0.741 1.207 1.635 0.851 1.054 1.010 +0 0.912 1.870 1.192 0.644 1.225 0.848 -0.026 -0.494 2.173 1.045 1.821 1.599 0.000 0.397 0.265 0.404 0.000 0.406 0.480 1.365 1.551 1.228 0.691 0.994 1.284 0.646 0.900 0.823 +0 0.855 -0.682 1.111 1.573 -0.102 0.522 -0.669 -0.405 2.173 0.525 -1.283 -1.651 0.000 1.113 -0.178 1.622 2.548 0.448 -1.790 0.200 0.000 0.764 0.961 1.426 0.814 0.947 0.772 0.789 +1 0.763 0.313 1.712 1.152 -1.238 0.858 0.549 0.342 2.173 0.455 -0.543 0.914 2.215 0.426 2.238 1.678 0.000 1.174 0.357 -0.737 0.000 0.743 0.822 0.981 0.919 0.703 0.863 0.763 +1 0.791 0.128 0.592 0.643 1.393 0.462 -1.042 -0.468 0.000 0.595 0.717 1.040 2.215 1.078 -0.163 -0.992 0.000 1.421 0.301 1.735 3.102 0.888 1.031 0.986 0.757 0.511 0.778 0.694 +1 0.849 1.071 -1.294 0.579 -0.059 0.499 0.358 -0.341 2.173 0.949 1.211 1.444 0.000 1.155 -0.614 0.582 2.548 0.729 0.600 -1.223 0.000 0.785 0.933 0.982 0.962 0.867 0.877 0.762 +1 0.884 -0.029 -0.007 1.037 -1.297 1.143 -0.421 -1.577 2.173 1.327 -0.283 0.391 0.000 0.753 0.585 0.742 2.548 0.943 2.095 -0.489 0.000 2.971 1.641 1.216 0.989 1.194 1.504 1.219 +1 1.098 0.457 -1.569 1.894 -1.483 1.406 0.019 0.125 2.173 0.592 -2.846 0.911 0.000 0.690 0.294 -0.978 2.548 0.745 1.786 0.291 0.000 5.453 2.993 0.976 1.980 1.046 2.047 2.094 +1 1.082 1.205 -0.401 1.359 -0.834 0.605 -0.565 1.500 1.087 0.467 0.751 0.964 2.215 0.622 0.181 0.116 0.000 0.875 -1.949 1.116 0.000 1.480 1.076 0.983 0.921 0.673 1.161 1.478 +1 1.653 0.394 0.442 0.905 0.344 1.101 0.438 -1.681 2.173 0.590 -0.059 -0.524 0.000 0.647 -0.896 -1.589 0.000 0.572 -0.769 -1.032 3.102 0.899 1.045 0.986 0.956 0.774 1.006 0.922 +0 0.774 -0.272 -0.899 2.040 -1.676 2.358 -0.758 0.050 2.173 0.726 -0.134 0.363 0.000 2.806 -0.368 1.624 1.274 1.769 -1.720 1.650 0.000 0.954 0.926 1.121 0.835 3.220 1.757 1.378 +1 1.008 -0.465 0.520 1.383 0.765 2.309 -1.253 1.461 0.000 1.450 -1.379 -0.419 2.215 2.145 -2.273 -0.193 0.000 1.031 -0.415 -0.495 0.000 0.756 0.769 0.988 0.970 0.929 0.934 0.795 +1 0.538 0.152 -0.995 1.099 0.298 0.560 2.908 -0.471 0.000 1.200 -1.545 1.416 0.000 1.377 -1.799 -0.896 0.000 2.433 -0.743 1.611 1.551 0.875 0.989 0.987 1.147 1.116 0.817 0.859 +0 1.545 0.627 1.545 1.826 -1.433 1.797 -0.206 -0.033 1.087 0.695 0.021 1.242 2.215 0.591 1.253 -0.300 0.000 0.585 -0.834 1.111 0.000 1.148 0.842 1.031 2.152 1.512 1.424 1.138 +1 1.100 -0.890 1.619 0.303 -0.686 0.931 -0.064 -0.935 2.173 1.167 -0.164 0.506 2.215 2.051 0.342 0.916 0.000 1.557 -0.761 -0.544 0.000 0.600 1.097 0.984 0.825 1.479 0.901 0.802 +1 0.869 0.410 0.823 0.869 -0.769 0.611 -1.339 1.129 0.000 0.927 -0.956 -1.736 2.215 1.476 0.342 -0.119 2.548 0.596 0.281 -1.138 0.000 1.198 0.840 1.192 0.787 1.535 0.965 0.890 +0 0.436 1.282 0.764 1.658 -0.173 1.575 -0.227 -1.585 0.000 1.109 -0.372 0.295 1.107 0.569 0.280 -0.249 1.274 0.563 0.443 1.425 0.000 0.818 0.952 0.985 1.634 0.496 1.033 1.310 +1 0.299 -1.278 -0.227 1.038 -1.736 1.601 0.163 0.979 2.173 1.294 -0.142 -0.088 0.000 1.035 0.352 -1.208 0.000 0.762 1.247 -0.945 0.000 0.617 0.545 0.990 1.027 0.889 0.893 0.775 +1 2.021 -0.876 0.247 0.713 0.519 0.950 -0.088 -1.467 2.173 0.568 0.272 0.969 2.215 0.922 -0.249 1.496 0.000 1.667 0.915 -0.654 0.000 0.832 0.745 0.998 1.421 0.899 0.969 0.841 +0 0.359 2.293 0.673 0.490 -0.554 0.700 1.846 -1.085 0.000 0.803 1.221 0.429 0.000 0.827 0.493 -1.616 2.548 0.662 0.209 -0.236 3.102 1.568 0.918 0.977 0.670 0.542 0.693 0.646 +1 0.407 -1.107 1.742 1.758 -0.039 1.561 2.138 -1.414 0.000 1.397 -0.644 0.032 2.215 1.487 -1.243 0.367 2.548 2.333 -0.920 1.484 0.000 1.032 1.046 1.172 0.768 0.714 0.949 0.847 +0 0.516 0.963 -1.547 1.652 0.920 0.386 0.933 0.266 0.000 0.815 0.976 -1.038 2.215 0.942 -0.860 0.284 2.548 0.978 -0.748 1.581 0.000 0.971 0.909 1.017 0.920 1.367 0.979 0.858 +1 0.594 0.899 0.975 0.617 0.334 0.466 1.160 -1.420 0.000 0.621 -0.586 -1.004 0.000 0.448 0.496 1.210 2.548 0.505 -2.133 0.645 0.000 1.107 0.910 0.988 0.477 0.125 0.592 0.597 +1 0.321 1.752 -1.487 0.733 -1.266 0.837 1.549 1.120 0.000 0.819 -0.829 0.795 2.215 1.285 2.335 -0.822 0.000 0.729 0.591 -0.286 3.102 2.076 1.330 0.993 0.953 0.834 1.575 1.283 +1 1.395 1.611 -0.320 1.482 0.462 0.592 1.975 -0.947 0.000 0.354 -2.337 1.185 0.000 1.308 1.023 -1.608 2.548 0.863 0.104 0.918 3.102 0.795 0.907 1.291 1.225 0.748 0.907 0.795 +0 0.639 -0.283 0.412 1.448 0.813 0.914 0.513 -1.089 1.087 0.606 0.221 -0.010 2.215 1.402 -1.311 1.596 0.000 1.134 1.274 -0.516 0.000 3.196 1.873 0.978 1.640 0.919 1.303 1.298 +1 1.236 -0.877 -0.832 1.648 0.440 1.171 -0.181 -1.393 2.173 0.389 -1.299 -1.074 0.000 1.279 -1.238 1.040 2.548 0.802 -0.754 0.308 0.000 0.878 0.974 1.802 1.156 1.562 1.182 0.941 +1 0.610 0.505 -1.070 0.959 0.733 1.376 -0.030 1.522 2.173 1.753 1.712 -0.276 0.000 0.640 -0.039 -1.336 2.548 0.973 -0.221 0.504 0.000 0.659 1.171 1.058 0.651 0.625 0.744 0.721 +1 0.658 -0.236 -1.561 1.060 1.141 0.768 0.150 -1.098 0.000 1.025 0.491 0.862 2.215 1.746 0.827 0.181 2.548 1.067 1.292 -0.813 0.000 1.105 1.302 0.987 1.006 0.868 1.067 1.110 +0 1.452 0.447 -0.655 0.779 1.571 0.531 0.251 -0.130 0.000 0.629 1.133 1.639 2.215 0.357 1.307 1.225 2.548 0.559 2.229 0.943 0.000 0.775 0.787 1.336 0.745 0.196 0.575 0.573 +1 0.670 -1.632 -1.241 0.149 0.602 0.741 -0.662 -0.916 1.087 0.907 0.393 1.103 0.000 1.128 -0.605 0.311 2.548 0.844 -1.330 -0.454 0.000 1.750 1.154 0.995 0.668 1.019 0.909 0.776 +1 0.772 0.504 1.460 1.306 -1.183 0.974 0.352 0.400 2.173 0.563 0.944 1.643 0.000 0.987 0.868 -0.298 2.548 0.897 -0.214 0.811 0.000 0.874 0.909 0.987 0.813 0.809 0.849 0.756 +0 2.074 1.036 -1.410 2.308 -0.938 1.880 -1.353 0.566 2.173 0.899 -1.993 1.739 0.000 0.437 -1.148 1.286 0.000 1.839 -1.365 0.154 0.000 0.863 0.836 1.253 0.772 2.725 2.680 2.205 +0 1.075 -1.129 0.553 0.754 -0.035 0.529 0.255 -1.424 0.000 0.976 0.292 -0.665 1.107 1.035 0.240 1.432 2.548 0.568 1.430 1.461 0.000 0.794 0.838 0.990 0.912 1.014 0.833 0.809 +0 0.443 1.680 0.666 3.283 1.198 1.754 0.431 -0.322 0.000 0.805 -0.602 -0.609 2.215 0.821 0.595 -0.815 0.000 2.246 0.867 1.629 3.102 0.957 0.982 0.989 1.296 1.572 1.904 1.927 +0 0.913 -2.064 -0.613 1.183 -0.363 1.050 0.660 0.674 2.173 0.741 -0.711 1.311 0.000 0.656 -2.497 -1.247 0.000 0.960 -1.580 -1.686 0.000 0.824 0.684 0.987 1.994 1.465 1.354 1.175 +1 1.570 -0.554 1.279 0.461 -1.541 0.761 -0.344 -0.007 2.173 0.800 -0.252 -0.827 0.000 0.525 -1.435 0.459 0.000 0.521 0.757 -1.275 3.102 1.151 0.893 0.988 0.638 0.753 0.726 0.724 +0 0.356 0.249 0.242 0.692 -1.534 0.458 1.636 -0.829 0.000 0.457 -0.081 1.358 2.215 1.449 1.335 1.000 1.274 1.439 0.585 -0.418 0.000 0.759 0.923 0.987 1.454 0.787 0.931 0.963 +1 0.771 1.091 -0.086 0.909 0.225 1.721 0.354 -0.228 0.000 1.906 0.733 1.259 0.000 2.556 -0.172 -1.486 0.000 1.311 0.310 0.197 3.102 1.242 1.119 0.985 0.510 0.370 0.906 0.889 +0 0.858 0.592 -1.577 0.595 0.822 0.964 0.425 1.079 1.087 1.775 0.817 -0.710 2.215 0.681 -0.053 1.275 0.000 1.006 -1.649 0.016 0.000 0.840 1.405 0.988 1.124 1.964 1.402 1.112 +0 1.190 -1.429 -1.655 0.443 -0.108 0.503 -0.361 -0.088 0.000 0.424 0.693 -0.873 0.000 0.664 0.227 0.582 2.548 1.219 0.404 1.064 3.102 0.845 0.833 0.990 0.866 0.300 0.745 0.733 +0 0.593 -0.108 1.009 0.315 -1.550 0.714 0.761 1.076 0.000 0.800 0.338 -1.453 2.215 1.395 0.365 -0.441 2.548 0.783 0.373 0.155 0.000 0.863 1.011 0.988 0.684 0.888 0.764 0.679 +1 0.557 -0.533 0.220 0.150 0.996 1.686 0.603 -0.903 2.173 1.502 -0.820 0.966 0.000 1.132 0.037 0.555 0.000 1.125 -0.432 -1.336 3.102 0.815 0.921 0.982 1.104 1.028 1.005 0.853 +0 1.185 0.601 0.929 0.752 0.020 0.838 0.909 -1.534 2.173 0.547 1.628 0.956 0.000 1.582 0.479 -0.727 2.548 1.075 -0.792 -0.239 0.000 1.076 0.939 0.987 0.994 0.996 0.861 0.827 +0 0.906 -0.234 -1.702 0.891 -1.146 1.017 1.583 -0.025 2.173 0.448 0.770 0.702 0.000 1.533 0.720 1.380 2.548 0.898 0.436 -0.827 0.000 0.819 0.899 0.988 0.799 1.618 1.079 0.859 +0 0.660 1.007 1.269 0.596 -0.484 0.966 0.636 1.733 1.087 1.300 0.737 -0.587 2.215 0.911 -0.400 -0.381 0.000 0.852 -0.419 1.295 0.000 0.934 0.936 0.987 0.781 1.433 0.925 0.771 +0 0.790 0.417 1.211 0.383 0.567 1.141 1.773 -1.095 0.000 1.720 0.108 0.569 1.107 0.805 2.065 -1.635 0.000 1.102 0.364 -0.761 3.102 0.906 1.021 0.977 0.802 1.175 1.479 1.326 +1 0.873 -1.555 -1.094 0.611 0.758 0.630 -0.135 -0.970 2.173 0.752 -1.669 1.394 0.000 0.957 -0.997 0.641 0.000 1.425 0.324 0.241 3.102 0.915 1.232 1.007 0.905 0.928 0.967 0.866 +0 0.647 -1.125 0.635 0.244 -0.148 0.821 0.364 -1.277 0.000 0.617 0.804 -0.772 0.000 1.103 0.459 0.886 2.548 0.681 0.463 0.640 3.102 0.752 1.062 0.978 0.537 0.147 0.677 0.669 +1 0.963 -0.376 0.842 1.288 -0.637 0.854 0.341 0.770 0.000 1.064 0.663 -1.226 2.215 0.731 -0.256 -0.510 2.548 0.644 -0.251 -1.236 0.000 1.161 0.907 1.499 1.162 0.733 0.777 0.799 +0 0.465 1.593 1.083 0.111 0.731 0.867 0.147 -1.092 2.173 0.828 -0.849 0.464 2.215 0.633 -0.719 -0.827 0.000 0.881 -0.122 0.774 0.000 0.894 1.006 0.976 0.831 1.397 0.836 0.751 +0 1.770 0.674 1.278 1.333 -1.545 0.956 0.469 0.423 0.000 0.791 0.058 -1.186 2.215 0.757 -0.257 -0.698 2.548 0.409 1.719 -0.875 0.000 1.218 1.016 1.199 0.909 0.378 0.780 0.865 +0 0.813 0.084 0.273 0.847 -0.276 0.781 -1.042 -0.750 2.173 0.693 -0.163 -1.120 0.000 0.870 -0.276 1.498 1.274 1.896 1.576 0.776 0.000 1.488 1.002 0.990 0.804 1.003 0.788 0.788 +0 0.774 0.918 0.674 1.565 0.013 0.850 -0.799 -1.462 2.173 0.643 0.005 1.394 2.215 0.568 0.007 -0.328 0.000 0.390 0.173 1.050 0.000 0.494 0.745 0.990 0.869 0.739 0.987 0.746 +0 1.528 0.589 -0.221 2.069 -0.068 1.517 0.016 -1.721 0.000 1.180 -0.085 1.343 0.000 1.063 0.425 -0.710 2.548 1.068 -0.962 1.472 3.102 0.924 1.027 0.999 1.705 1.051 1.141 1.101 +0 0.479 1.225 -1.623 1.920 -1.209 0.998 -1.466 0.313 2.173 0.477 -1.636 -1.690 0.000 1.197 -1.836 -0.687 0.000 2.006 1.145 0.677 0.000 0.930 1.153 0.980 0.883 0.539 1.209 1.159 +1 2.876 0.394 -1.413 0.380 -1.016 1.438 -0.797 0.169 0.000 1.441 -0.050 0.771 2.215 0.523 -0.281 -1.386 2.548 0.838 0.183 -0.518 0.000 1.323 1.043 0.983 1.472 0.867 0.929 1.135 +1 0.670 1.150 -1.180 0.738 0.148 1.007 -0.495 -1.572 2.173 0.645 0.500 0.214 2.215 0.653 1.461 0.537 0.000 0.816 -0.194 0.598 0.000 0.850 1.351 0.987 0.610 1.341 0.905 0.793 +0 0.704 -0.284 -0.879 0.942 -0.158 0.719 -0.294 1.236 2.173 0.904 -1.440 0.868 2.215 0.659 -0.906 -0.903 0.000 0.473 0.658 -0.849 0.000 0.627 0.979 0.987 1.021 0.840 0.993 0.825 +0 1.065 -1.802 -0.187 0.677 -0.865 1.512 -1.647 -0.575 2.173 1.240 -1.524 0.898 2.215 1.767 -0.792 1.199 0.000 1.361 -1.099 -1.691 0.000 0.957 0.911 0.982 0.833 1.958 1.307 1.163 +0 0.395 2.359 0.175 2.788 -1.684 1.351 0.927 -1.664 0.000 1.107 1.478 0.430 2.215 0.506 2.056 -1.137 0.000 0.842 0.507 -0.402 3.102 1.256 1.032 1.446 1.436 0.723 1.085 1.104 +1 1.650 0.515 -0.188 0.135 -0.695 0.470 -0.066 -1.011 0.000 1.012 0.424 1.230 0.000 0.655 0.577 1.018 2.548 0.803 -0.543 -1.740 1.551 1.024 0.699 0.985 0.731 0.510 0.662 0.655 +1 1.235 -0.734 1.509 0.977 1.384 0.949 -0.137 0.197 1.087 1.345 -0.653 -0.745 2.215 1.094 -1.704 -1.678 0.000 1.412 0.788 -0.302 0.000 1.033 0.946 0.987 1.251 1.327 1.092 0.956 +1 0.986 0.812 0.981 0.411 -1.195 1.009 -0.164 -0.675 2.173 0.422 1.960 0.975 0.000 0.685 0.228 0.236 2.548 0.366 1.489 -1.386 0.000 0.439 1.281 0.986 0.611 0.788 0.810 0.729 +0 0.281 1.588 -0.927 1.059 1.008 0.737 0.098 -0.055 0.000 0.717 0.869 -1.095 0.000 0.419 0.036 0.169 2.548 0.966 -0.287 1.665 3.102 0.920 0.791 0.980 0.545 0.484 0.479 0.508 +0 0.552 0.952 -1.360 1.402 -0.023 1.022 0.339 -0.861 2.173 0.690 -1.573 0.605 0.000 0.508 -0.077 1.489 0.000 0.564 -1.096 1.195 3.102 0.963 0.548 1.138 0.942 1.072 0.886 0.999 +0 0.673 -1.584 -0.298 0.478 1.329 0.864 0.699 -0.968 0.000 1.220 -0.049 0.079 2.215 2.528 -1.088 1.416 1.274 0.569 -1.734 0.125 0.000 2.214 1.576 0.991 0.932 2.076 1.538 1.191 +0 1.074 0.265 -0.145 0.548 -0.039 0.418 -0.650 0.986 0.000 0.721 -0.492 -1.508 1.107 0.507 -1.489 0.791 0.000 1.153 0.411 -1.229 1.551 0.447 0.897 1.001 0.874 0.475 0.681 0.680 +1 0.935 0.086 1.368 1.399 0.417 1.246 -1.042 -0.623 2.173 0.874 -0.874 0.947 2.215 1.468 -0.016 -1.673 0.000 0.454 -0.234 -0.908 0.000 0.808 0.871 1.198 1.547 1.522 1.132 0.975 +1 0.313 2.008 0.367 1.243 0.862 1.182 0.693 -0.693 0.000 0.873 0.666 -1.401 2.215 0.680 -0.922 0.750 1.274 0.700 1.725 1.627 0.000 0.886 1.013 0.999 0.904 1.094 0.778 0.715 +1 0.328 0.448 -1.494 0.815 0.483 1.276 -0.632 0.818 2.173 1.193 -0.508 -1.210 0.000 0.603 2.367 -0.722 0.000 0.688 -0.274 -0.509 3.102 3.016 1.672 0.983 0.763 0.935 1.588 1.266 +0 0.702 -0.046 -0.140 0.905 -1.532 0.504 -1.643 0.557 0.000 1.001 -1.322 -0.544 1.107 1.093 -1.144 1.486 2.548 0.543 -0.407 0.978 0.000 0.576 0.852 1.049 0.861 1.076 0.802 0.735 +1 2.589 -1.034 -0.139 0.604 -0.494 1.009 -0.204 0.984 2.173 0.521 -1.188 0.253 0.000 0.881 -0.653 0.778 2.548 2.100 1.486 -1.025 0.000 0.874 0.747 0.982 1.431 0.374 0.954 0.871 +1 1.594 -1.303 1.472 0.755 0.662 0.826 -0.344 -1.461 0.000 0.971 -0.424 0.194 2.215 1.174 -0.623 -0.392 0.000 1.729 -1.171 -0.646 3.102 0.871 0.918 1.013 1.029 0.996 0.888 0.754 +0 0.908 -1.333 1.595 0.847 -1.164 0.980 -0.655 0.533 2.173 0.735 -0.639 -0.637 0.000 0.767 -0.312 0.013 0.000 0.824 0.494 -1.681 3.102 0.663 0.908 0.989 1.110 1.075 1.047 0.920 +0 1.049 0.704 -1.448 0.787 -0.727 0.787 0.418 0.541 2.173 0.512 1.460 -0.809 0.000 0.622 1.101 1.272 0.000 0.443 0.393 0.116 1.551 0.835 0.942 0.993 0.577 0.234 0.649 0.639 +0 1.014 -1.148 1.527 1.452 -1.078 0.835 1.645 0.331 0.000 1.003 -0.204 -1.659 0.000 1.037 0.602 -0.179 2.548 1.253 0.239 0.258 1.551 2.825 1.659 1.201 1.361 0.373 1.042 1.378 +0 1.026 0.439 -0.752 1.796 1.443 0.702 -1.065 1.028 0.000 0.639 -0.577 -0.124 1.107 0.756 0.200 0.303 2.548 0.470 -1.285 -1.666 0.000 0.610 0.798 1.727 1.184 0.415 0.821 0.853 +1 1.305 -1.012 0.220 2.617 0.400 0.931 -1.340 -1.614 2.173 0.953 1.003 -1.547 0.000 1.133 -1.098 -0.557 2.548 0.654 -2.426 0.831 0.000 1.023 0.898 0.990 1.082 1.047 1.215 1.046 +0 2.769 0.632 -0.193 1.567 -0.817 0.969 -1.016 1.566 2.173 0.970 -0.794 1.177 0.000 0.706 1.402 1.516 0.000 1.569 1.134 -0.491 3.102 0.603 0.875 1.536 0.866 2.321 1.620 1.771 +0 1.914 1.213 0.736 0.769 0.105 1.447 -0.620 -1.227 2.173 0.268 -1.061 1.371 0.000 0.687 -0.243 -0.583 2.548 0.810 -0.350 0.862 0.000 0.339 0.928 0.983 1.067 0.718 1.561 1.196 +0 0.484 0.152 1.701 0.751 0.079 0.700 1.453 -0.666 0.000 0.761 1.414 0.601 0.000 0.851 1.150 -1.704 1.274 0.534 0.717 1.666 3.102 1.410 1.028 0.988 0.633 0.117 0.624 0.816 +0 0.726 1.854 -0.338 0.624 1.622 0.595 -0.003 1.248 2.173 0.484 0.644 0.108 0.000 0.661 -0.185 -1.301 0.000 0.920 -1.669 0.661 0.000 0.914 0.796 0.991 0.992 0.788 0.949 0.831 +0 0.539 -0.677 -0.536 1.206 0.324 1.560 -1.123 0.196 0.000 2.429 -2.388 -1.260 0.000 1.265 -0.495 -1.467 1.274 1.839 -0.022 0.960 3.102 4.963 3.114 0.992 0.777 0.999 2.212 1.884 +0 1.097 -1.459 -0.531 1.007 -0.455 0.753 0.812 -1.528 2.173 0.590 0.296 1.211 0.000 0.910 -0.305 0.209 2.548 0.748 0.529 0.615 0.000 0.627 0.794 0.978 2.478 1.208 1.626 1.516 +0 0.760 -0.844 -0.870 0.961 -0.358 0.733 -1.360 0.978 1.087 0.619 -0.898 0.394 0.000 0.589 -1.722 -0.644 0.000 1.105 -1.186 -1.356 0.000 0.954 0.859 0.989 0.675 0.232 0.772 0.686 +1 0.967 0.792 0.884 0.220 -1.367 0.604 1.037 -1.740 0.000 0.486 2.189 1.434 0.000 1.490 0.913 -0.460 2.548 1.720 0.580 0.129 1.551 0.803 1.131 0.991 0.938 0.647 0.869 0.810 +1 0.930 0.080 -0.163 1.073 -1.266 0.856 -0.346 -1.212 0.000 1.676 0.119 1.001 0.000 0.946 -0.351 -1.740 0.000 1.792 0.586 0.440 3.102 1.003 0.926 1.159 0.566 0.640 0.626 0.621 +1 1.121 0.747 -0.549 1.085 0.468 1.943 -2.429 1.209 0.000 0.863 1.271 -1.148 2.215 2.123 0.109 -0.995 2.548 1.356 0.517 0.351 0.000 1.017 0.964 1.212 1.137 0.927 0.878 0.801 +1 0.676 -1.613 -1.684 0.757 -0.463 0.797 -1.183 1.413 1.087 0.708 -0.875 0.584 2.215 1.363 -1.999 -0.339 0.000 0.843 -1.291 -1.081 0.000 0.829 0.993 0.986 0.800 0.770 0.859 0.745 +0 1.705 0.150 -0.771 1.067 1.240 0.691 0.503 1.208 0.000 1.254 -1.046 -0.439 1.107 1.388 -0.701 1.235 1.274 0.824 0.232 0.414 0.000 0.769 0.858 1.814 1.414 1.416 1.142 1.034 +1 0.514 1.000 1.110 0.961 -1.643 1.453 2.434 0.584 0.000 1.286 1.041 0.889 0.000 1.527 -0.388 -0.962 2.548 1.806 0.624 -1.339 3.102 0.989 1.772 0.987 0.856 0.895 1.873 1.559 +0 1.358 -1.770 -0.624 0.535 0.248 0.533 -0.266 1.692 0.000 0.568 -1.578 1.732 0.000 0.913 -1.349 1.044 1.274 1.162 -0.342 -0.148 1.551 0.855 0.944 0.988 0.819 0.821 0.675 0.726 +0 0.647 -0.509 -0.721 0.974 -1.703 0.790 0.474 -0.070 1.087 0.958 0.152 0.951 2.215 0.306 -1.270 -1.410 0.000 0.645 -1.180 -0.208 0.000 0.433 0.877 0.985 1.015 1.040 0.968 0.781 +1 1.299 -0.067 0.303 0.318 -0.513 0.745 -0.468 -1.709 2.173 0.719 -0.737 -1.089 0.000 1.253 0.836 0.745 0.000 1.017 0.448 -0.611 3.102 0.724 1.030 0.981 0.713 0.911 0.743 0.749 +0 1.014 -0.642 1.378 1.609 1.681 0.969 -0.424 -0.030 0.000 0.731 -0.778 -0.915 1.107 0.777 -1.281 0.029 2.548 0.627 -1.645 0.558 0.000 1.181 0.997 0.987 0.933 0.650 0.757 0.858 +1 0.453 1.202 -1.688 1.452 -0.966 0.658 0.229 0.969 0.000 0.620 -0.510 -1.477 2.215 1.336 0.245 0.367 0.000 0.959 -0.768 -0.407 3.102 0.873 0.963 0.981 0.650 0.589 0.753 0.775 +1 1.648 1.455 -1.610 0.988 -0.817 1.066 1.161 0.639 2.173 0.660 1.066 1.151 0.000 0.862 2.248 0.240 0.000 1.950 1.381 -1.030 0.000 1.206 0.963 1.160 0.843 0.693 0.919 0.856 +0 0.652 -0.137 -0.411 1.548 -1.261 0.654 0.519 0.081 0.000 0.745 -0.429 1.167 1.107 0.762 0.754 -1.451 2.548 0.446 1.113 0.059 0.000 0.345 0.868 0.987 0.721 0.784 0.680 0.729 +1 1.888 -0.987 1.687 3.028 -1.740 2.632 0.945 0.088 0.000 1.077 -0.858 -0.238 2.215 0.957 -0.412 1.288 0.000 1.734 -0.263 -1.317 1.551 3.329 2.522 1.003 1.674 1.079 1.785 2.442 +1 0.951 -0.103 0.628 0.777 -1.299 1.867 -0.150 1.624 2.173 2.080 0.072 -0.072 0.000 0.773 -0.310 -1.208 2.548 1.173 -0.773 -0.294 0.000 0.732 0.654 1.175 1.073 0.841 0.894 0.791 +1 0.662 -0.340 -1.026 0.637 -1.456 0.659 -0.218 -0.015 1.087 1.073 0.427 0.569 0.000 1.594 1.173 -1.608 0.000 0.409 0.853 0.666 3.102 2.052 1.083 0.985 0.976 0.482 0.912 1.114 +1 1.863 -0.563 -0.633 0.606 -0.943 1.305 -0.349 0.076 0.000 2.097 0.816 -1.631 0.000 1.191 -1.102 0.254 0.000 1.298 -0.671 1.483 3.102 0.869 0.891 0.999 1.107 0.531 0.829 0.802 +1 1.841 -1.141 -0.668 0.954 -1.159 0.850 -0.705 0.311 2.173 1.036 -0.197 1.212 2.215 0.582 -0.250 -1.578 0.000 1.211 -0.087 0.580 0.000 0.866 0.812 0.980 1.401 1.063 1.126 0.944 +1 1.801 -0.529 0.511 1.065 1.168 0.571 -0.146 0.143 0.000 2.300 2.695 -1.255 0.000 0.931 0.465 -0.210 2.548 1.257 0.712 1.203 3.102 0.676 0.783 1.072 0.992 0.804 0.808 0.686 +0 1.763 0.366 1.286 0.631 1.567 0.884 0.441 -0.152 1.087 0.960 0.834 -1.174 0.000 1.073 1.314 -0.535 0.000 1.797 0.954 0.858 3.102 0.965 1.059 0.981 0.925 1.156 0.982 1.023 +1 0.498 -0.823 1.189 0.520 -1.534 1.297 0.648 -1.559 0.000 1.436 0.223 0.384 0.000 1.556 0.823 -0.242 2.548 0.484 1.977 -0.366 0.000 1.557 0.861 0.988 0.947 0.576 0.667 0.710 +0 0.628 0.884 1.172 1.073 0.695 1.319 0.866 -1.599 0.000 0.893 -0.178 -0.519 2.215 1.829 -0.442 -0.011 2.548 1.531 -1.202 0.440 0.000 1.284 1.533 0.989 0.955 0.635 1.380 1.167 +0 1.933 0.882 0.964 0.429 -1.481 0.645 0.153 -0.768 2.173 0.567 1.335 -0.521 2.215 0.685 0.861 1.716 0.000 0.679 1.034 0.080 0.000 0.757 0.773 1.019 0.870 0.609 0.801 0.663 +1 0.571 -0.657 0.666 0.666 -1.569 1.017 0.035 0.142 2.173 0.479 -1.108 0.183 0.000 1.449 0.015 -1.371 2.548 0.704 -0.500 -0.474 0.000 0.849 0.925 0.983 0.968 1.480 1.020 0.866 +0 1.361 -1.447 -0.132 0.714 -1.628 0.490 0.052 -0.325 0.000 0.564 0.296 0.590 0.000 1.501 -1.267 -1.241 2.548 1.254 0.906 1.087 3.102 0.832 0.822 1.332 0.909 1.890 1.232 1.076 +0 0.721 0.543 1.158 0.935 -0.943 0.460 -0.761 1.605 0.000 0.498 -2.184 -1.636 0.000 1.078 -1.422 -0.498 2.548 1.569 -0.612 0.453 1.551 0.852 0.987 1.079 1.215 0.862 0.921 0.927 +1 0.370 -1.708 -0.548 1.023 -1.270 0.980 -0.143 0.552 2.173 0.324 0.994 -0.521 0.000 1.077 0.423 1.521 0.000 1.010 0.801 -0.131 3.102 0.910 1.055 0.991 0.853 0.862 0.831 0.773 +1 0.754 -1.504 1.562 0.583 0.535 1.184 2.054 -1.625 0.000 1.237 -0.130 -0.144 2.215 1.943 1.063 -0.812 0.000 2.263 -0.296 0.513 3.102 0.809 1.218 0.990 0.772 0.859 1.088 1.051 +0 0.645 0.838 -1.180 0.197 -0.660 0.942 1.233 -0.463 2.173 1.206 0.431 1.045 0.000 0.812 0.853 0.498 0.000 0.819 0.953 -1.665 3.102 0.812 0.736 0.999 1.032 0.822 0.863 0.826 +0 0.817 0.349 -0.034 0.427 -0.673 0.738 1.252 1.457 0.000 1.543 1.446 -0.199 1.107 1.076 0.460 1.571 0.000 0.724 -0.884 -0.007 0.000 0.802 0.982 0.981 1.368 0.940 0.932 1.016 +1 0.726 0.312 1.062 0.635 -0.028 0.843 -0.342 0.573 0.000 1.411 -0.555 -1.311 2.215 1.017 2.323 0.323 0.000 1.413 -1.110 -0.785 3.102 0.893 0.789 0.992 0.847 0.766 0.773 0.670 +1 1.640 -0.097 -1.259 0.795 -0.366 0.895 0.794 0.413 2.173 0.973 -0.371 0.845 2.215 0.944 0.822 1.444 0.000 0.425 0.082 -0.409 0.000 0.753 0.823 1.141 1.094 1.006 1.012 0.838 +1 0.546 -1.934 1.106 1.127 -1.560 0.915 -0.694 -0.675 0.000 1.544 -2.824 0.222 0.000 1.030 0.342 -1.680 2.548 1.582 -0.779 1.353 3.102 1.189 1.180 0.980 0.898 0.788 0.946 0.836 +0 0.423 1.479 -1.494 0.983 0.163 1.336 -0.295 -0.732 0.000 0.912 0.538 0.430 2.215 1.334 0.730 1.347 2.548 1.901 0.945 -1.617 0.000 0.432 0.960 0.987 0.777 0.875 0.654 0.636 +1 1.118 -0.323 -1.418 0.309 0.934 0.903 -0.377 1.415 0.000 0.815 -2.300 -0.434 0.000 1.237 -1.049 -0.047 2.548 1.005 -1.446 0.535 0.000 0.916 0.876 0.990 0.661 0.726 0.764 0.734 +1 0.997 -0.258 0.109 0.952 1.553 0.623 -0.068 0.737 0.000 1.018 -0.877 -1.039 2.215 0.859 -0.719 -1.646 2.548 0.653 0.936 -0.021 0.000 0.871 0.941 1.301 0.991 0.520 0.825 0.760 +1 1.575 -0.507 -1.086 0.544 -1.424 0.530 -0.810 0.657 1.087 0.763 -0.887 1.195 0.000 1.221 -0.717 -0.214 1.274 0.584 0.059 0.002 0.000 0.885 0.844 0.987 1.017 0.708 0.807 0.732 +0 0.737 -0.746 1.527 0.203 0.362 1.026 -2.822 1.143 0.000 1.038 1.160 -0.857 1.107 1.277 -1.017 -0.388 2.548 0.897 -0.012 1.272 0.000 1.006 0.907 0.984 0.931 1.821 1.014 0.851 +0 1.285 -0.713 0.693 0.961 1.451 0.980 0.358 -1.143 1.087 0.514 1.056 -0.453 0.000 0.542 -0.710 0.353 0.000 0.524 1.184 0.070 3.102 1.023 1.022 0.987 0.867 0.794 0.920 0.824 +0 0.381 0.818 -1.402 1.045 1.204 0.873 -0.583 -0.279 2.173 0.460 -0.397 0.357 0.000 1.017 0.888 -0.934 2.548 0.511 1.948 1.274 0.000 0.670 1.272 0.991 0.932 1.207 0.888 0.832 +0 1.991 0.236 0.088 2.083 -0.011 1.273 -0.230 -0.853 0.000 1.884 -0.706 1.456 2.215 1.184 0.454 1.370 0.000 1.039 0.377 0.596 3.102 0.698 0.747 0.977 2.340 1.184 1.462 1.268 +1 0.943 -1.469 -0.262 2.380 0.365 0.946 -1.298 -1.641 2.173 0.347 -0.529 1.081 2.215 0.449 0.370 -1.585 0.000 0.764 -0.699 1.583 0.000 0.466 0.635 1.112 0.791 0.629 0.959 0.840 +1 0.951 0.704 0.529 0.773 -0.663 0.538 -0.141 1.468 0.000 0.385 0.838 -0.922 0.000 1.002 1.326 1.224 0.000 1.540 0.606 -0.360 0.000 0.935 0.858 1.044 0.875 0.492 0.746 0.699 +0 0.557 -1.212 0.730 0.887 -0.990 0.920 -0.837 0.370 2.173 0.992 -1.676 1.732 1.107 0.645 0.425 0.241 0.000 0.621 -1.231 -1.000 0.000 1.010 0.890 0.988 0.768 1.469 0.916 0.773 +1 1.112 -1.749 1.455 0.377 -0.582 1.581 0.357 -1.000 0.000 1.063 -0.933 1.197 2.215 1.306 -1.758 -0.309 0.000 1.662 1.166 0.654 0.000 0.886 1.026 0.985 0.841 0.497 0.823 0.762 +1 0.325 -1.722 -0.097 2.580 -0.239 0.968 -0.508 -1.319 2.173 0.574 -1.304 -1.113 0.000 1.509 0.717 0.940 0.000 1.145 -1.184 1.011 1.551 0.903 1.055 0.990 1.252 1.095 1.002 1.061 +1 0.739 -0.843 -0.227 0.654 -1.399 0.933 0.762 -0.798 2.173 1.169 -0.365 1.264 0.000 0.402 -0.788 -1.713 0.000 0.992 0.857 0.793 1.551 0.976 0.943 0.987 1.143 1.017 1.099 0.920 +0 0.457 -1.336 0.185 0.880 -1.398 1.205 -0.049 -0.683 2.173 1.137 -1.101 1.055 0.000 1.685 -0.752 1.604 0.000 1.090 -0.568 0.548 3.102 1.061 0.865 0.987 0.886 1.154 1.174 0.937 +1 0.400 -0.015 0.684 2.768 1.514 0.875 -0.098 -0.048 2.173 0.336 2.011 0.382 0.000 0.857 -0.989 -0.933 2.548 0.785 0.298 -0.736 0.000 0.858 0.954 0.992 1.058 0.951 1.032 0.975 +1 0.942 0.718 1.649 1.613 -1.241 0.915 -0.168 -0.183 2.173 1.130 0.875 0.352 0.000 0.998 2.668 1.524 0.000 0.941 1.271 -0.906 0.000 0.886 1.064 0.991 0.719 0.711 0.836 0.746 +1 0.454 1.719 0.069 1.357 -1.684 0.891 1.239 -0.345 0.000 0.405 1.304 0.445 0.000 0.892 -0.215 1.583 2.548 1.047 0.921 1.360 3.102 0.836 0.884 1.088 1.050 0.554 0.799 0.788 +0 1.373 1.370 -1.718 0.494 -0.836 0.452 0.900 0.803 2.173 0.534 0.287 -1.585 0.000 0.685 0.285 0.519 2.548 1.938 0.074 -0.253 0.000 1.243 0.992 0.988 0.893 0.274 0.667 0.785 +0 1.141 0.176 -0.690 1.698 -0.070 1.000 0.238 -1.128 2.173 1.330 -2.274 0.764 0.000 1.125 0.747 1.210 0.000 1.172 -0.355 -1.709 3.102 4.425 2.469 1.024 1.022 0.692 1.888 1.681 +1 0.659 -0.289 1.108 1.131 0.118 1.022 -1.118 -1.720 2.173 0.879 -0.556 0.332 0.000 0.589 1.251 0.149 0.000 0.438 -1.994 -1.285 0.000 1.165 1.087 0.988 1.254 0.904 0.906 0.874 +1 0.930 -1.632 0.827 1.223 -0.824 0.970 -1.600 -0.958 1.087 1.078 -0.770 0.195 2.215 0.722 -1.708 -1.593 0.000 0.599 -1.252 1.184 0.000 0.815 1.020 1.473 1.072 1.445 0.937 0.845 +0 0.533 -1.483 1.324 0.552 -0.596 0.802 0.071 -0.131 2.173 0.483 -0.454 -1.535 0.000 1.310 -0.179 1.155 2.548 0.394 1.147 -0.594 0.000 0.737 0.809 0.989 0.739 1.183 0.744 0.659 +0 1.379 0.643 1.613 0.635 0.818 0.499 0.824 0.459 2.173 0.851 -0.110 -0.988 1.107 1.195 -1.419 -0.511 0.000 0.834 1.659 1.188 0.000 0.852 1.039 0.989 0.751 1.038 0.743 0.720 +0 0.509 1.660 0.101 0.905 1.166 0.827 0.414 0.497 2.173 0.897 -0.476 -0.531 0.000 1.845 0.247 -1.252 2.548 0.508 -0.715 1.664 0.000 0.824 0.897 0.991 1.349 1.543 1.362 1.333 +0 0.652 -0.025 -0.488 0.889 0.642 1.352 1.633 1.191 0.000 1.260 0.404 0.065 2.215 1.243 -1.543 -0.261 0.000 1.768 -0.762 -1.568 1.551 1.196 1.007 0.991 0.798 1.652 1.114 0.910 +0 0.744 -1.339 1.108 1.265 -0.117 1.061 0.033 -1.610 2.173 0.904 0.523 1.071 0.000 1.542 1.263 -0.760 1.274 2.149 0.066 0.350 0.000 0.937 1.030 1.200 2.102 1.604 1.573 1.275 +0 0.780 -0.209 -0.396 1.516 0.604 1.264 -1.444 -1.401 0.000 0.711 -0.710 0.040 0.000 0.966 0.808 0.210 2.548 0.982 0.071 -1.631 3.102 0.922 0.931 1.182 0.840 0.802 0.675 0.653 +0 0.515 -0.616 -0.983 1.371 1.470 0.556 -1.431 -1.266 0.000 0.796 -0.882 -0.335 2.215 1.650 0.404 0.910 1.274 1.796 -1.343 -0.171 0.000 1.280 0.881 0.984 0.879 1.411 1.204 1.042 +0 0.916 -0.774 -1.599 1.237 1.654 0.699 -1.346 -0.683 0.000 1.280 -2.726 0.108 0.000 0.919 0.543 -1.191 2.548 2.122 -1.351 0.795 3.102 2.057 1.613 0.984 0.694 1.779 1.698 1.634 +1 0.373 -1.421 -1.256 1.301 1.070 1.247 -0.256 -0.546 2.173 0.515 -1.144 -0.765 0.000 0.597 0.230 1.645 0.000 0.508 1.607 0.475 0.000 0.895 1.281 0.987 1.205 0.949 1.318 1.087 +0 0.934 0.845 0.735 1.438 1.326 1.287 0.649 -0.063 2.173 0.883 0.176 -1.548 0.000 0.700 1.337 -1.069 0.000 0.466 -0.748 -0.792 0.000 0.945 1.065 0.989 0.689 0.425 0.873 0.813 +1 0.925 -0.516 1.553 1.652 -1.320 1.381 -0.674 0.582 2.173 0.788 0.171 -0.914 0.000 0.747 -0.376 -0.320 2.548 0.874 0.493 -0.489 0.000 0.822 1.096 0.987 0.833 0.934 1.017 0.890 +0 2.976 0.647 1.331 1.349 0.977 1.716 1.199 -0.346 1.087 0.551 0.250 -1.300 2.215 0.603 0.406 -0.864 0.000 0.490 0.517 -0.446 0.000 0.227 0.585 0.987 0.948 1.295 1.467 1.083 +0 1.600 0.098 0.989 1.153 -0.323 0.770 0.652 -1.612 2.173 1.032 -0.993 -0.077 2.215 0.820 -1.548 -1.004 0.000 0.638 -1.462 -0.287 0.000 0.897 0.947 1.741 1.236 1.785 1.142 1.090 +1 0.469 -0.520 -0.197 0.784 0.493 0.706 1.172 -0.188 0.000 0.943 0.652 -1.704 2.215 0.339 0.820 -1.278 0.000 0.909 -0.942 1.443 1.551 0.973 1.006 0.983 0.882 0.895 0.847 0.759 +0 1.012 -1.405 -0.993 0.339 1.459 0.950 -0.743 0.933 2.173 0.684 0.085 -0.010 2.215 0.893 -1.307 0.068 0.000 1.705 -1.928 -1.273 0.000 0.956 0.933 0.989 0.963 1.027 0.798 0.733 +1 1.538 -0.911 -1.649 0.908 -1.338 1.176 -0.450 -0.022 0.000 0.686 0.431 1.465 1.107 1.427 -1.358 -0.508 0.000 1.544 -0.812 -1.018 0.000 0.918 0.881 0.980 1.128 0.338 0.805 0.776 +1 1.822 0.449 -0.602 1.204 1.549 1.469 1.241 0.911 2.173 1.035 -2.882 -0.626 0.000 0.778 0.724 -1.323 1.274 0.445 1.300 1.206 0.000 0.339 0.708 1.914 0.959 1.242 1.161 0.879 +0 1.310 -0.374 0.374 0.728 -0.391 0.820 1.373 1.643 2.173 0.502 0.478 -1.600 2.215 0.588 0.264 -0.007 0.000 0.615 0.397 -1.227 0.000 0.649 1.012 0.991 0.892 0.482 1.087 0.849 +1 0.658 -0.398 1.056 1.011 -1.420 0.639 -2.260 -0.564 0.000 0.838 -0.458 0.564 2.215 0.645 -1.067 -1.598 2.548 0.751 -2.263 -0.833 0.000 0.437 1.011 0.990 0.811 0.778 0.741 0.763 +0 1.411 0.549 0.986 1.593 1.617 1.176 1.928 -0.584 0.000 1.099 0.132 0.543 2.215 0.626 0.672 -0.394 0.000 1.125 0.383 -1.359 3.102 1.025 1.079 1.118 0.986 1.007 1.122 1.146 +0 1.715 -0.244 0.174 1.362 1.027 0.765 -0.958 -0.561 0.000 1.403 -0.976 -1.520 2.215 0.473 -0.121 -1.634 2.548 0.544 0.595 0.529 0.000 1.239 0.827 1.473 1.514 0.400 0.959 0.912 +0 0.402 0.329 0.974 4.086 0.837 1.679 -1.142 -0.863 0.000 1.498 -0.269 -1.014 2.215 2.589 -0.021 1.090 2.548 0.996 -0.507 -0.456 0.000 0.893 0.974 0.988 0.900 2.001 1.525 1.599 +0 0.561 -1.451 -0.368 1.163 -0.726 0.330 1.443 0.731 0.000 0.939 -0.073 1.592 2.215 0.493 0.098 0.709 0.000 0.468 -1.351 -1.458 0.000 0.714 0.641 0.993 0.814 0.450 1.122 0.907 +0 0.701 -1.620 0.066 0.967 -1.049 0.781 -0.442 1.067 2.173 0.496 -0.588 -0.870 0.000 0.508 -1.017 0.551 0.000 1.068 0.262 -1.041 3.102 0.834 0.863 0.987 1.047 0.988 0.956 0.777 +0 0.401 2.426 -0.606 1.490 1.552 1.029 -0.751 -0.573 2.173 0.850 -0.038 -1.131 0.000 1.216 -1.988 1.033 0.000 2.206 1.315 0.532 3.102 0.884 0.940 0.997 1.040 2.675 2.067 1.644 +0 0.831 0.442 -0.560 0.735 0.812 0.867 0.360 -1.449 2.173 0.837 0.873 0.571 2.215 0.767 0.808 -1.130 0.000 0.538 1.355 0.290 0.000 0.730 0.763 1.023 0.676 1.261 0.757 0.654 +1 0.875 1.742 -0.607 1.152 -0.394 1.357 0.734 1.317 2.173 0.925 0.277 -0.263 0.000 1.456 -0.111 0.236 2.548 2.737 0.248 -1.555 0.000 0.911 0.716 0.996 1.476 1.646 1.197 0.995 +1 1.970 -1.563 1.674 0.458 1.198 0.530 -0.770 -1.064 0.000 1.630 -1.088 0.186 2.215 1.104 -0.491 0.859 2.548 0.563 0.079 -0.921 0.000 0.507 1.061 0.997 1.033 0.915 1.077 0.958 +1 0.450 0.935 1.725 1.143 0.293 0.690 -0.337 -1.355 0.000 0.789 -0.795 0.828 2.215 0.809 -1.585 -1.150 0.000 1.681 0.337 0.323 1.551 1.050 1.103 0.989 0.589 0.815 0.972 0.912 +1 0.427 -1.574 -0.395 1.617 0.575 0.723 0.834 -1.380 2.173 0.503 0.861 -0.403 0.000 0.787 0.059 0.618 2.548 0.498 -0.488 -1.535 0.000 0.765 0.705 0.984 1.167 0.988 1.587 1.288 +1 0.828 1.317 -1.179 1.732 0.598 0.919 1.156 0.315 2.173 0.413 1.029 1.732 0.000 0.419 0.559 -1.103 0.000 1.244 -0.241 0.126 3.102 1.028 0.900 1.658 1.006 0.919 1.064 1.143 +0 0.313 1.929 -1.618 0.968 -1.034 1.050 -0.541 1.030 2.173 1.094 -0.941 -0.978 0.000 1.372 0.428 0.457 1.274 0.480 0.493 -1.428 0.000 0.895 1.294 0.992 1.216 1.084 1.036 0.953 +1 0.295 1.743 1.398 0.617 -0.558 1.201 1.148 -1.353 2.173 0.815 0.151 0.607 0.000 1.295 0.920 -0.017 0.000 0.994 0.020 1.345 3.102 1.091 0.916 0.996 0.943 1.023 1.029 0.851 +0 0.656 -1.342 -0.147 0.498 1.556 0.834 -0.052 0.529 0.000 1.124 -0.850 -1.564 0.000 0.457 -0.864 -1.026 2.548 0.964 -0.644 -0.552 0.000 1.074 0.898 0.987 0.534 0.469 0.535 0.522 +1 0.301 -1.398 -0.961 1.635 0.359 0.537 -0.360 0.980 0.000 0.863 -0.250 1.684 2.215 1.350 0.727 -1.105 2.548 0.750 0.585 -0.694 0.000 0.847 0.977 0.989 1.342 0.926 1.515 1.210 +1 0.809 -0.770 -1.438 0.414 -0.017 0.946 -0.015 -0.712 1.087 0.841 -0.102 0.161 2.215 1.645 -0.593 0.972 0.000 0.440 -1.367 1.629 0.000 0.838 0.846 0.983 0.984 0.931 0.893 0.852 +0 0.914 -1.331 0.121 0.891 -0.625 0.363 -0.929 -1.204 2.173 0.690 -1.310 0.843 0.000 0.481 0.398 1.660 2.548 0.373 -1.464 -0.438 0.000 0.681 0.792 0.985 0.727 0.476 0.719 0.664 +0 0.363 1.698 1.031 0.515 -0.515 1.489 0.552 -1.621 1.087 0.965 0.264 -0.365 2.215 1.642 1.183 0.953 0.000 1.235 1.580 0.271 0.000 1.024 1.343 0.992 0.982 1.617 1.300 1.031 +0 0.426 0.962 1.263 1.068 0.715 2.309 0.454 -1.300 0.000 2.321 -0.427 0.388 2.215 0.318 1.250 0.037 2.548 0.543 0.988 -0.961 0.000 0.818 0.900 0.990 0.889 0.987 1.616 1.306 +0 3.174 -0.834 0.584 1.252 0.590 2.229 -0.326 -1.073 0.000 0.854 0.454 1.403 2.215 0.773 -0.557 -0.325 2.548 0.408 0.580 0.001 0.000 0.470 0.481 0.995 1.280 0.992 0.953 0.724 +0 0.744 -1.858 -1.488 0.354 1.529 1.012 -1.097 0.649 1.087 0.902 -1.136 -1.482 2.215 0.897 -0.968 -0.771 0.000 0.704 0.571 -0.403 0.000 0.914 0.910 0.998 0.963 1.321 0.928 0.802 +1 0.427 1.935 -0.740 0.358 1.031 1.412 0.748 1.134 0.000 1.128 2.390 -0.585 0.000 0.619 1.224 1.696 0.000 0.764 -0.889 0.602 3.102 0.958 0.896 0.995 0.791 0.558 0.783 0.704 +0 1.099 0.926 -0.328 2.444 0.807 0.476 0.239 -0.867 0.000 0.679 -0.141 -1.348 2.215 0.875 -0.966 -1.420 2.548 0.731 0.614 0.341 0.000 0.830 0.880 1.938 1.396 0.392 1.207 0.964 +0 1.800 0.980 -0.200 1.139 -0.498 0.794 1.300 0.718 2.173 0.853 -0.486 -1.335 0.000 1.001 1.087 1.336 0.000 1.184 0.389 -1.270 3.102 0.838 1.329 0.975 0.829 1.096 0.896 0.921 +0 1.384 1.429 -1.160 1.575 -0.165 0.769 0.056 1.470 0.000 1.060 0.712 -0.133 2.215 1.068 -0.929 1.203 0.000 0.796 1.044 0.822 3.102 1.001 0.995 1.599 1.037 0.666 0.979 1.200 +0 0.752 0.023 0.513 1.425 -1.452 0.362 -0.547 -0.141 0.000 0.891 1.166 -1.497 0.000 1.164 -0.167 0.882 2.548 1.160 0.836 -0.651 0.000 0.924 1.213 1.406 0.750 0.341 0.730 0.741 +1 0.609 -0.545 -0.152 0.547 -0.941 1.281 0.395 0.938 2.173 0.745 0.074 -1.054 0.000 0.799 -0.626 0.332 2.548 0.858 1.365 -1.130 0.000 0.902 1.071 0.993 1.669 0.973 1.083 1.115 +1 1.732 -0.716 1.307 0.731 -1.693 1.781 1.419 -0.132 0.000 0.805 0.032 -0.962 2.215 0.834 0.187 0.742 2.548 0.593 -1.198 -0.814 0.000 3.242 1.956 0.992 1.065 0.873 1.301 1.588 +1 1.502 -2.000 1.067 0.893 -1.232 0.639 -0.366 0.566 2.173 0.700 -0.957 0.188 0.000 1.100 -0.502 -0.496 2.548 0.370 -1.676 -1.563 0.000 0.763 0.641 1.408 1.263 0.859 1.051 0.845 +0 1.205 0.393 -0.622 1.410 0.215 1.350 -0.152 0.096 2.173 2.229 -0.706 1.528 2.215 0.718 -1.895 -1.602 0.000 1.458 1.348 -1.107 0.000 0.917 0.969 1.238 0.913 2.563 1.530 1.327 +0 0.890 0.409 -1.370 0.294 -0.524 0.777 0.706 1.532 0.000 0.872 0.068 -0.060 2.215 1.042 0.108 0.413 2.548 0.678 0.598 -0.857 0.000 0.925 0.946 0.981 0.882 0.419 0.738 0.711 +0 1.150 1.215 -0.010 0.780 1.369 0.647 1.927 1.408 0.000 1.635 1.296 -0.668 2.215 1.026 -0.217 0.961 2.548 0.597 -1.350 1.513 0.000 0.772 1.152 1.243 0.986 1.819 1.104 0.941 +1 1.501 -1.538 1.725 0.678 -0.128 0.831 -0.384 0.409 2.173 0.634 0.088 -0.893 0.000 0.602 -1.332 -0.146 2.548 0.677 -1.051 1.463 0.000 0.957 1.002 1.391 0.793 0.652 0.827 0.781 +1 0.556 -1.234 -1.384 0.887 0.099 0.782 -2.655 -1.663 0.000 0.959 -1.007 -0.267 2.215 0.548 -0.658 1.458 2.548 0.519 -2.186 0.135 0.000 0.972 0.928 0.987 0.633 0.779 0.886 0.803 +1 1.273 -0.051 1.263 0.909 -0.898 1.198 0.057 0.474 2.173 0.936 -1.019 1.461 0.000 1.977 0.857 -0.510 2.548 0.889 -0.268 -1.109 0.000 0.979 1.402 1.386 1.271 1.725 1.332 1.127 +1 1.031 0.476 -0.487 1.201 -0.238 0.770 -0.599 1.447 2.173 0.449 -1.479 1.050 0.000 0.535 -0.929 -0.913 2.548 0.552 -1.722 -1.273 0.000 0.590 0.677 0.988 0.633 0.699 0.793 0.745 +1 0.652 1.815 -0.194 0.102 0.885 0.669 -0.181 -1.314 2.173 0.688 1.139 0.712 0.000 0.921 1.435 1.492 0.000 0.988 -0.154 -0.005 3.102 0.828 0.961 0.995 0.912 0.796 0.861 0.755 +0 0.619 -1.019 -0.097 1.186 0.560 0.939 1.433 -0.944 2.173 1.077 0.352 -1.623 0.000 1.423 0.230 1.209 2.548 1.101 2.302 -0.156 0.000 0.950 1.025 0.982 1.468 1.623 1.997 1.561 +0 0.682 -0.056 1.375 0.770 -0.795 0.975 -2.354 0.473 0.000 1.348 -0.297 0.206 0.000 1.796 -0.084 -1.317 2.548 1.304 0.643 1.559 0.000 1.110 0.840 0.987 0.499 0.144 0.482 0.508 +0 0.307 0.677 -1.475 0.890 1.507 0.831 1.435 0.616 0.000 0.733 -1.846 -1.350 0.000 1.236 0.466 -0.098 2.548 0.726 0.219 -0.916 0.000 0.618 0.828 0.987 1.078 0.215 0.852 0.775 +0 0.838 0.951 -1.299 1.376 -1.128 0.675 1.143 0.735 0.000 0.655 0.489 -0.250 2.215 1.055 -0.389 0.522 2.548 0.410 0.132 1.657 0.000 0.724 0.817 0.995 0.985 0.709 1.108 0.935 +0 0.363 -0.449 1.045 1.043 1.241 1.332 0.567 0.584 2.173 0.988 2.469 -1.077 0.000 1.982 0.075 -0.862 2.548 1.660 -0.020 1.080 0.000 3.182 2.456 0.988 1.220 2.016 1.828 1.417 +1 0.418 1.992 -0.009 0.434 -1.725 0.520 1.356 0.105 0.000 0.763 0.790 -0.511 0.000 1.242 -0.498 1.233 0.000 1.172 0.363 -1.128 3.102 0.800 0.786 0.983 0.518 0.236 0.520 0.515 +1 0.938 0.586 -0.409 0.308 -0.932 1.032 1.194 0.512 0.000 1.728 1.285 -0.960 2.215 1.771 1.465 0.979 0.000 1.029 0.760 1.330 3.102 1.108 0.830 0.992 0.763 1.090 1.169 0.983 +1 1.024 -0.546 -1.488 0.893 0.742 0.309 -1.944 -0.282 0.000 0.772 1.030 -1.205 2.215 0.605 -0.800 1.106 0.000 0.790 -1.433 0.177 0.000 0.860 0.678 1.199 1.076 0.528 0.838 0.771 +1 0.523 0.925 -0.158 0.718 1.169 0.941 0.426 -1.532 2.173 0.790 -0.219 0.180 0.000 0.708 -0.710 -1.583 0.000 0.990 -2.242 0.625 0.000 0.818 0.769 0.987 0.854 0.900 1.148 0.987 +1 1.260 1.007 0.192 0.505 -1.233 0.367 -1.224 -1.310 2.173 0.626 -0.337 1.075 2.215 0.548 0.249 1.167 0.000 0.751 -0.610 -0.981 0.000 0.758 0.602 1.060 0.886 0.673 0.833 0.692 +1 1.282 0.369 -0.090 0.149 -0.570 0.680 1.053 0.898 1.087 0.685 0.002 1.559 0.000 0.701 -0.793 -1.087 2.548 0.829 1.854 -1.072 0.000 0.944 1.090 0.989 0.709 1.281 0.825 0.759 +0 0.331 -2.005 1.520 0.485 0.069 0.711 -0.243 0.230 0.000 0.824 0.867 -0.716 2.215 1.827 0.362 1.604 2.548 0.387 -0.090 -0.773 0.000 0.631 0.844 0.977 0.973 1.177 0.873 0.786 +1 0.679 -0.432 -0.483 0.444 1.661 0.920 0.431 1.568 2.173 0.841 0.148 -0.191 0.000 1.240 -0.521 0.248 1.274 0.845 -0.812 -1.156 0.000 1.063 0.851 0.982 1.139 1.420 0.927 0.845 +0 0.676 0.091 -1.669 0.964 -0.792 1.991 1.877 1.189 0.000 1.520 -2.296 -0.471 0.000 1.190 -0.065 -0.881 2.548 1.850 1.437 0.521 0.000 0.656 1.024 0.989 0.663 0.348 0.906 0.798 +1 0.280 0.811 0.232 1.078 1.348 0.911 -0.233 -0.860 0.000 1.246 0.155 1.177 2.215 0.842 -0.316 0.228 2.548 1.320 -0.882 -0.546 0.000 0.851 0.870 0.984 0.702 0.868 0.965 0.837 +0 0.763 -0.043 -1.486 0.834 0.666 0.779 0.036 0.883 0.000 0.960 0.481 -0.325 2.215 0.952 -0.682 -0.671 2.548 0.976 -0.578 1.617 0.000 0.957 1.037 1.030 0.852 0.742 0.852 0.739 +0 1.628 -0.421 0.916 0.651 0.386 0.709 0.821 -0.917 0.000 1.225 -0.035 -0.886 1.107 0.658 0.547 1.452 1.274 0.674 -0.036 0.520 0.000 1.122 0.889 0.980 0.667 0.876 0.834 0.800 +1 1.197 1.292 0.632 0.115 -0.768 0.932 -0.352 -1.151 2.173 0.737 0.822 0.914 0.000 1.088 0.232 -0.366 2.548 0.676 -1.164 -1.573 0.000 1.117 1.151 0.992 1.230 0.904 1.176 0.980 +1 0.798 0.803 -0.317 1.451 -0.005 0.545 -0.634 -0.467 0.000 1.418 -0.172 1.628 2.215 0.937 -1.180 1.038 2.548 0.790 -0.053 -1.354 0.000 0.776 1.013 0.976 1.101 0.952 1.042 0.880 +1 0.549 0.720 -1.017 0.378 -0.784 0.672 1.032 1.002 0.000 0.969 0.218 -1.742 0.000 1.688 -0.908 -0.359 0.000 1.551 -0.430 0.175 3.102 0.901 0.881 0.984 0.746 0.584 0.850 0.818 +1 1.388 0.814 -0.929 0.752 1.169 0.966 -0.601 1.475 2.173 0.457 -0.532 0.197 0.000 1.272 -0.627 -0.421 2.548 1.003 0.789 0.530 0.000 0.768 0.867 1.344 1.323 1.369 1.105 0.944 +0 1.201 -0.727 0.120 1.121 0.664 0.489 1.227 0.423 2.173 0.789 0.193 -0.930 0.000 1.027 -1.081 -1.394 1.274 0.629 0.829 1.538 0.000 0.820 0.928 0.980 1.396 1.616 1.136 1.021 +1 2.302 0.967 -0.183 1.723 0.243 1.648 0.265 1.687 2.173 0.565 0.924 -1.578 0.000 0.755 0.772 0.361 2.548 1.112 0.327 -0.693 0.000 0.878 1.029 1.036 0.569 1.356 1.397 1.085 +0 0.772 0.975 -0.343 3.733 0.125 1.062 0.826 -1.451 2.173 0.862 1.219 1.657 2.215 0.917 1.118 0.745 0.000 1.025 0.629 1.344 0.000 0.921 1.197 0.993 1.792 0.556 1.316 1.276 +1 1.322 -0.057 -1.155 1.228 -0.795 0.668 0.883 0.198 2.173 0.497 -2.015 0.196 0.000 0.736 0.090 1.692 0.000 0.842 0.973 0.741 0.000 0.819 0.907 0.990 1.318 0.870 1.055 0.962 +1 1.209 -0.271 1.366 1.608 -1.540 0.841 -0.296 -0.142 2.173 0.672 1.054 0.725 1.107 0.781 -0.259 -0.684 0.000 0.973 1.708 -0.707 0.000 0.912 1.170 0.990 1.142 1.137 1.052 1.023 +1 1.679 0.866 -1.547 0.650 1.446 3.177 -0.994 -0.394 0.000 2.719 0.112 1.184 2.215 0.887 0.390 0.535 0.000 0.676 0.742 1.078 1.551 0.458 0.803 0.985 0.585 0.500 0.924 0.824 +0 0.672 -0.444 -0.749 1.491 0.160 0.503 -0.941 1.711 2.173 0.578 0.193 -1.446 1.107 0.739 0.482 1.240 0.000 0.609 0.990 -0.270 0.000 0.765 0.905 1.014 0.847 0.534 0.711 0.692 +1 0.393 0.298 -0.602 0.761 0.230 0.654 -0.862 -1.403 0.000 1.664 -0.382 -0.068 2.215 1.480 -0.873 1.501 0.000 2.229 0.488 1.183 1.551 0.888 1.376 0.984 0.752 1.802 1.315 1.081 +0 1.965 0.015 -1.078 1.834 -1.513 0.983 -1.160 0.475 0.000 1.370 -0.091 0.653 1.107 1.060 -0.790 -0.699 1.274 0.622 -0.375 0.100 0.000 0.591 0.896 1.002 1.568 1.305 1.136 1.132 +0 1.339 -1.146 1.691 1.009 -0.423 0.553 -1.650 -1.511 0.000 0.754 2.626 0.905 0.000 0.826 -1.249 -0.023 2.548 1.097 -0.354 -0.007 3.102 0.915 1.499 1.522 0.897 0.357 1.496 1.666 +0 0.875 -1.405 1.598 1.502 -1.077 1.243 -0.550 0.708 0.000 0.986 0.597 -0.376 0.000 0.904 -0.325 -0.235 2.548 1.526 -0.561 1.539 3.102 0.795 0.905 1.060 0.957 0.909 0.755 0.890 +0 0.941 -0.130 1.085 1.833 -1.473 0.588 -0.519 -0.682 2.173 0.390 -0.496 0.090 2.215 0.418 1.692 0.810 0.000 0.632 -2.095 -0.876 0.000 2.558 1.411 1.352 1.005 0.452 0.923 0.929 +1 0.410 1.530 -0.047 1.723 1.261 0.834 -0.022 -0.675 0.000 0.941 -0.304 -0.105 0.000 0.886 0.616 -1.442 2.548 1.286 0.535 0.933 3.102 0.965 1.028 1.077 0.689 0.687 0.825 0.985 +0 1.643 1.920 -0.834 0.596 0.819 0.314 0.953 1.240 2.173 0.542 0.136 0.539 0.000 0.657 2.699 -0.002 0.000 0.423 0.761 1.732 1.551 1.819 1.036 1.367 0.871 0.165 0.662 0.724 +1 1.263 -0.324 -1.330 0.879 -1.105 1.129 2.222 0.158 0.000 0.926 0.117 0.672 0.000 0.987 0.075 1.596 2.548 0.752 0.877 -0.111 0.000 0.951 1.655 0.989 0.601 0.866 1.950 1.868 +0 0.458 -0.679 1.010 1.061 0.027 0.459 0.645 -1.276 0.000 0.806 0.345 1.370 2.215 0.600 -0.233 -0.052 0.000 0.995 1.130 -0.823 1.551 0.952 0.829 0.988 1.497 0.854 1.135 0.958 +0 0.929 -1.495 1.261 0.941 0.527 0.755 -0.943 -0.948 2.173 0.302 -2.844 -0.602 0.000 0.651 0.367 -1.636 0.000 0.663 -0.897 0.543 3.102 1.819 1.062 0.991 1.016 0.731 0.770 0.761 +0 0.903 -0.404 0.577 1.613 1.362 0.788 1.083 -0.629 2.173 0.885 2.188 -0.619 0.000 1.406 0.600 1.561 2.548 1.121 1.697 0.121 0.000 0.814 0.865 1.087 0.924 1.240 1.091 1.320 +1 0.430 -0.517 -1.086 1.119 0.367 1.171 -0.970 -1.738 1.087 1.217 -1.147 -0.436 0.000 1.185 -1.075 1.072 2.548 0.724 -2.325 -0.278 0.000 1.109 1.218 0.987 1.173 0.851 1.083 1.027 +1 0.732 -1.361 1.083 1.131 -1.352 0.503 -0.085 -1.569 0.000 0.966 1.076 0.147 0.000 0.826 0.080 -0.802 1.274 0.573 1.918 1.178 0.000 0.991 0.998 1.024 0.679 0.228 0.665 1.072 +0 2.456 -1.429 -1.717 0.197 -1.586 0.648 -1.317 -0.489 0.000 1.775 1.145 0.564 2.215 0.738 -1.293 -0.882 2.548 1.338 0.948 -0.262 0.000 0.815 0.829 0.972 0.712 2.376 1.896 1.550 +1 0.574 -0.903 -0.179 0.218 0.638 0.885 -0.656 -1.050 0.000 1.333 -0.224 0.469 2.215 1.170 -0.151 1.125 0.000 0.396 0.239 -0.684 3.102 0.909 0.635 0.979 0.733 0.590 0.877 0.775 +1 1.100 0.125 1.370 0.797 -1.120 1.204 0.026 0.251 2.173 0.837 0.315 -0.619 0.000 1.156 -0.403 0.940 2.548 0.967 0.745 -1.074 0.000 0.577 1.079 1.016 1.161 0.923 0.901 0.842 +0 0.640 0.554 -0.854 0.517 0.768 0.653 0.651 -1.686 1.087 0.489 -0.892 0.321 0.000 1.065 -0.300 1.280 0.000 1.852 -0.659 -0.284 3.102 0.904 0.939 0.987 0.761 1.441 0.886 0.744 +1 0.898 -0.919 -1.230 1.775 -1.067 3.290 0.745 0.740 2.173 1.155 -1.214 -1.023 0.000 1.936 -0.402 -1.052 2.548 0.686 -1.478 -0.222 0.000 0.826 0.869 0.990 2.817 3.718 2.473 1.997 +0 0.898 -0.579 -0.895 0.481 0.266 1.002 -0.434 1.542 0.000 1.038 -0.381 -0.427 2.215 0.744 -1.161 0.279 2.548 0.731 -1.116 1.159 0.000 0.755 0.893 0.992 0.634 0.700 0.843 0.757 +1 0.976 0.562 -1.314 0.042 -0.888 0.345 -1.612 1.362 0.000 0.469 0.520 0.347 2.215 0.613 -0.417 -1.085 0.000 0.848 0.368 -0.092 0.000 0.840 0.875 0.823 0.626 0.306 0.600 0.583 +1 0.762 -0.230 0.902 1.662 0.148 0.395 -1.424 0.310 1.087 0.751 -1.582 1.687 2.215 0.685 0.651 -1.587 0.000 1.069 -0.819 -0.659 0.000 0.900 1.822 0.988 1.215 0.762 1.540 1.292 +0 0.526 1.027 0.306 0.760 1.101 1.431 0.558 0.021 2.173 1.661 -0.718 -1.312 0.000 1.528 -2.229 0.914 0.000 1.649 -1.052 -0.965 1.551 1.263 0.926 0.989 1.386 2.118 1.710 1.914 +1 0.966 0.863 -0.461 1.140 -1.530 1.987 1.369 -1.721 0.000 3.129 0.610 0.172 2.215 1.659 0.672 -1.583 0.000 1.784 0.367 0.617 3.102 0.796 0.980 1.193 1.560 0.857 1.176 1.078 +0 0.683 -1.500 0.194 0.300 1.120 0.733 -0.964 0.556 2.173 0.439 -1.639 1.018 0.000 1.123 -1.588 -1.356 0.000 1.811 -0.498 -1.045 3.102 0.907 1.008 0.999 0.817 1.231 0.833 0.743 +1 0.707 -0.267 -1.036 0.661 0.297 0.534 -1.130 -0.146 2.173 0.505 -0.033 0.486 0.000 0.866 0.308 1.710 0.000 0.824 -0.957 -1.393 3.102 0.925 0.986 0.982 0.588 0.633 0.667 0.603 +1 0.361 -2.075 -1.158 0.480 -0.690 0.877 -0.763 1.216 0.000 0.516 -0.280 -1.716 0.000 1.029 -1.319 0.420 0.000 2.681 0.307 -0.701 3.102 0.943 1.152 0.997 0.719 0.853 0.769 0.693 +0 0.287 -2.017 -0.482 0.669 1.712 0.784 -0.242 1.412 2.173 0.488 0.324 0.155 0.000 1.219 -0.538 -0.237 2.548 0.723 -0.701 -0.865 0.000 0.781 0.891 0.993 0.753 1.232 0.719 0.641 +0 2.353 -1.596 -1.284 0.329 -0.025 1.118 -0.854 0.258 2.173 0.809 -1.235 1.348 2.215 0.635 -0.373 -0.721 0.000 0.718 -0.569 0.925 0.000 0.749 0.796 1.105 0.885 1.199 1.055 0.854 +0 1.735 -0.152 0.244 0.577 -1.364 1.115 -0.228 -0.997 1.087 0.956 -0.462 0.673 2.215 1.146 -0.902 -1.348 0.000 1.246 -0.704 1.397 0.000 0.822 0.986 1.375 1.165 1.527 0.958 0.903 +0 1.749 0.203 1.214 0.333 -0.163 1.069 -0.108 -0.940 2.173 0.805 -0.412 0.185 2.215 0.483 0.026 -0.166 0.000 0.521 0.638 1.132 0.000 0.677 0.829 1.000 0.823 1.179 0.901 0.721 +0 0.536 -0.150 0.151 1.955 0.167 1.482 -0.858 -1.701 2.173 0.823 -0.418 -0.397 2.215 0.916 -1.344 -1.165 0.000 0.685 -1.110 0.971 0.000 0.822 0.879 0.985 0.790 1.542 1.198 0.963 +1 0.429 0.128 1.159 1.567 -1.045 1.043 1.148 0.606 0.000 0.744 -0.803 1.284 1.107 0.766 0.790 -0.881 0.000 0.944 -0.682 -0.517 3.102 1.583 1.391 1.040 0.891 0.755 1.093 0.961 +1 1.115 0.166 0.395 0.574 -0.059 0.457 -0.565 0.400 0.000 1.514 0.487 -1.270 2.215 0.978 0.689 -0.434 2.548 0.760 -1.484 1.367 0.000 0.899 1.150 0.993 1.318 0.900 1.048 0.941 +0 0.991 -0.327 -0.317 1.242 -1.314 0.777 -0.382 -1.323 2.173 1.058 0.118 0.965 0.000 0.306 -2.208 0.250 0.000 0.821 -0.428 0.725 3.102 1.531 0.844 1.202 0.742 0.815 0.799 0.790 +1 2.115 0.145 1.612 0.171 0.256 1.080 0.163 -0.565 2.173 1.030 0.344 -1.326 2.215 1.993 0.620 -0.872 0.000 2.771 -0.026 0.075 0.000 0.900 1.128 0.989 0.742 0.992 0.973 0.970 +0 0.720 0.215 -1.644 0.663 -1.244 0.996 -0.214 0.097 1.087 0.499 -0.871 0.820 2.215 0.586 -0.894 -0.483 0.000 0.806 -1.133 1.248 0.000 0.772 0.904 0.988 0.702 0.725 0.774 0.668 +0 0.650 1.213 -1.659 1.007 -0.191 0.565 1.235 -0.030 2.173 0.716 1.003 -1.194 0.000 1.297 0.308 0.750 2.548 1.051 0.154 1.728 0.000 0.739 0.968 1.087 0.664 0.852 0.767 0.708 +0 1.389 0.316 0.779 0.515 0.032 0.963 -0.185 -0.320 0.000 0.910 -0.266 -1.534 2.215 0.597 -0.585 -1.080 0.000 1.187 -0.204 1.166 3.102 0.917 0.988 0.985 0.945 0.611 0.749 0.751 +0 1.477 -1.232 -1.403 2.478 0.739 0.722 -2.632 0.550 0.000 1.191 -0.846 0.027 2.215 1.041 -1.295 -1.024 2.548 1.085 -0.912 0.903 0.000 1.241 1.229 2.480 1.551 1.016 1.132 1.103 +0 1.743 0.631 0.983 0.360 -1.295 0.761 -0.091 -1.714 2.173 0.982 0.344 0.303 1.107 1.331 -0.273 -0.559 0.000 0.728 1.061 -0.627 0.000 0.948 0.938 0.987 0.822 1.265 0.869 0.829 +1 0.955 0.720 -0.512 0.748 0.585 0.764 0.066 0.125 0.000 1.089 0.266 1.683 1.107 0.967 0.960 1.435 0.000 0.902 0.011 -1.177 3.102 0.987 0.763 0.988 0.665 0.493 0.649 0.616 +0 0.710 1.010 -0.547 1.962 -0.115 0.517 -1.508 1.463 0.000 0.353 0.763 1.118 1.107 0.569 -0.538 -1.186 0.000 1.116 -2.247 1.369 0.000 0.816 0.920 0.992 0.827 0.260 0.719 1.256 +1 0.791 1.625 -0.842 1.190 0.526 1.009 0.953 -0.496 0.000 0.630 1.433 0.253 0.000 2.080 0.518 -1.537 2.548 0.686 0.206 0.502 0.000 0.983 0.932 1.268 1.309 0.899 0.968 0.888 +0 1.053 1.508 -1.693 0.568 0.846 0.397 -0.074 0.312 0.000 0.471 -1.189 -0.713 0.000 0.663 -0.216 1.325 2.548 1.114 0.919 -0.502 3.102 0.920 0.926 0.982 0.966 0.808 0.751 0.937 +0 0.398 0.361 -1.595 1.257 -0.890 0.488 -0.037 0.201 2.173 0.495 0.920 -1.451 0.000 1.420 -0.910 0.913 2.548 0.773 -1.878 1.382 0.000 1.969 1.339 0.983 1.838 0.809 1.238 1.269 +0 1.436 0.754 1.461 0.679 0.937 1.147 0.907 0.728 2.173 1.259 2.236 -0.826 0.000 0.618 2.559 -0.588 0.000 1.200 0.783 -0.651 0.000 0.839 0.891 0.990 0.894 0.912 1.075 1.055 +1 1.971 0.244 1.418 0.300 0.855 1.172 -0.721 -0.027 2.173 1.213 -0.333 1.476 0.000 1.845 -1.755 -0.914 0.000 1.467 -0.459 0.744 3.102 1.927 1.609 0.987 1.600 0.896 1.170 1.393 +0 0.302 2.018 -1.277 0.597 1.259 1.128 0.103 1.665 2.173 1.425 0.883 0.157 0.000 0.998 1.104 -0.389 2.548 0.521 -0.086 -0.064 0.000 0.944 0.747 0.980 0.757 1.486 1.026 0.844 +0 0.576 -1.449 -0.841 1.060 0.461 0.406 -0.230 -0.552 0.000 0.727 0.336 -1.537 2.215 0.654 -0.839 0.407 0.000 0.528 -0.155 0.309 0.000 0.885 0.779 0.998 0.997 0.491 0.851 0.727 +0 0.689 -0.916 0.548 0.582 -0.777 1.086 -0.155 -0.016 2.173 0.534 -1.636 1.683 0.000 1.166 -0.829 -1.333 2.548 0.914 -0.271 1.172 0.000 0.780 0.686 0.986 0.736 1.408 0.917 0.770 +1 1.186 0.864 -1.263 0.128 -0.595 0.624 -0.351 -0.838 0.000 1.119 -0.320 0.439 1.107 1.216 -1.048 1.183 2.548 0.993 -0.835 -0.519 0.000 0.912 1.011 0.985 1.054 0.932 0.923 0.880 +1 0.971 -0.290 -0.067 0.518 -1.355 1.235 -0.582 1.097 2.173 0.454 0.731 -0.682 0.000 0.612 -2.476 -0.510 0.000 1.040 -1.711 -1.530 0.000 0.985 0.732 0.988 1.027 1.111 0.932 0.804 +0 1.612 -1.261 0.767 0.427 -0.792 1.001 0.579 1.631 2.173 0.632 0.206 -0.626 0.000 0.662 1.057 -0.479 0.000 0.536 -1.199 0.278 0.000 0.907 0.937 1.133 0.672 1.522 1.108 0.945 +0 0.977 -0.370 0.268 0.552 -0.557 0.660 0.655 -1.497 2.173 0.487 0.118 0.080 0.000 0.827 0.266 -1.264 2.548 1.086 -0.301 1.101 0.000 0.788 0.911 0.979 0.724 0.257 0.640 0.625 +0 1.102 0.438 0.216 2.674 -0.117 1.806 0.117 0.347 1.087 1.679 -0.558 1.620 0.000 1.705 -0.046 -1.712 0.000 2.180 -0.314 -1.167 3.102 0.774 0.970 1.008 1.229 2.121 1.557 1.657 +1 0.782 -1.167 0.637 1.254 0.318 0.591 -0.677 1.648 2.173 1.543 -0.682 -1.025 2.215 0.443 2.021 0.329 0.000 0.539 -0.013 -1.063 0.000 0.897 1.132 0.992 1.309 0.938 1.026 1.008 +0 0.410 1.046 -0.996 1.391 0.347 0.950 0.196 1.532 2.173 1.142 -0.547 -0.658 2.215 0.509 -1.036 0.232 0.000 0.367 -0.751 1.202 0.000 0.370 0.789 0.986 1.315 1.530 1.143 0.923 +0 0.479 0.381 -0.501 0.937 0.474 0.583 -0.236 -1.522 0.000 0.595 0.260 0.029 2.215 1.088 -0.392 1.074 2.548 0.984 0.463 -1.118 0.000 0.795 0.810 0.987 0.645 0.756 0.726 0.724 +0 0.560 0.745 -1.406 1.016 -0.085 1.266 1.348 0.797 0.000 2.000 -0.325 -1.488 0.000 1.941 0.705 -0.665 2.548 1.779 -0.105 0.245 0.000 1.021 1.017 0.987 0.704 1.061 1.038 0.952 +1 0.359 0.516 1.009 0.928 -1.661 1.737 -1.449 0.845 0.000 2.331 0.054 -1.149 1.107 0.668 -1.865 -0.914 0.000 0.645 -1.891 1.021 0.000 0.924 0.808 0.991 1.684 0.906 1.283 1.162 +0 0.583 1.670 0.232 1.047 -1.147 0.383 1.250 -0.708 1.087 0.861 0.839 -1.680 2.215 0.922 0.646 0.725 0.000 1.100 -0.605 0.498 0.000 0.897 0.983 1.024 0.790 0.670 0.778 0.800 +1 1.042 0.073 -1.709 0.733 -1.244 0.751 -1.236 0.407 0.000 0.987 -0.109 -1.042 1.107 1.164 0.851 -0.533 2.548 0.412 -0.746 -0.296 0.000 0.525 1.088 0.994 0.820 0.802 0.958 0.957 +0 1.561 0.156 1.161 0.981 0.477 0.919 0.613 -0.100 1.087 0.767 -0.225 -0.788 0.000 1.113 0.213 -1.480 0.000 0.697 0.890 1.516 3.102 0.892 1.152 0.990 0.634 0.863 0.765 0.819 +0 0.380 -1.728 -0.043 1.067 -1.011 0.663 -1.366 1.091 0.000 0.805 -0.549 0.209 2.215 1.101 -1.087 -0.932 2.548 0.528 -0.544 -1.575 0.000 0.695 0.843 0.981 0.755 0.915 0.695 0.658 +1 0.794 -1.267 -1.112 0.105 1.390 2.089 2.397 0.795 0.000 2.684 0.253 -1.161 0.000 1.144 -2.231 0.078 0.000 1.417 -0.103 0.286 3.102 0.684 0.801 0.977 0.765 0.809 0.865 0.769 +1 2.189 -1.398 0.462 0.401 0.213 0.883 -2.216 -1.411 0.000 0.710 -0.133 -1.077 0.000 0.711 -0.443 1.544 2.548 0.556 -0.851 0.821 0.000 0.893 0.687 0.998 0.967 0.720 0.954 0.850 +0 0.378 0.470 -0.426 1.218 1.485 0.638 0.128 1.089 0.000 0.902 -0.260 -0.009 2.215 0.872 1.224 -0.769 2.548 0.467 -0.762 -0.383 0.000 0.928 1.024 0.985 0.849 1.035 0.772 0.712 +0 0.822 -0.857 -1.585 2.376 1.716 1.383 1.061 0.435 2.173 0.729 -0.799 -0.180 0.000 0.823 0.693 -1.267 2.548 0.639 1.594 -0.003 0.000 1.639 1.146 0.987 2.053 1.344 1.349 1.245 +0 1.386 0.269 0.523 0.810 1.034 0.949 2.790 -1.579 0.000 1.040 -0.587 -0.154 2.215 0.908 0.943 -0.851 2.548 0.413 -1.365 0.921 0.000 4.451 2.470 0.990 1.141 1.124 2.102 1.673 +0 0.671 0.041 0.854 0.253 -0.983 0.611 -1.057 1.428 2.173 0.328 1.090 -1.242 0.000 0.560 -0.274 0.226 2.548 1.228 -1.112 0.238 0.000 1.522 0.877 0.988 0.657 0.705 0.721 0.634 +0 0.400 -0.065 0.333 1.495 -1.370 0.814 -0.855 -0.416 2.173 0.980 0.199 0.820 0.000 0.653 -1.121 0.945 0.000 0.456 1.466 1.137 0.000 0.897 1.103 1.071 0.519 0.764 0.697 0.679 +1 1.130 0.874 -0.450 1.158 -1.282 0.844 0.815 1.672 0.000 1.550 0.497 0.023 2.215 0.757 -2.651 -1.588 0.000 0.777 2.167 0.829 0.000 0.612 1.401 1.079 0.644 0.384 0.793 0.791 +1 0.869 1.335 -1.680 1.378 -1.025 1.077 1.231 0.802 1.087 0.902 0.928 -1.072 0.000 1.727 -0.573 0.491 0.000 1.029 0.306 1.705 3.102 0.888 0.710 0.991 1.263 0.953 0.933 0.845 +0 1.130 0.396 1.621 0.779 0.939 0.981 1.259 0.052 0.000 1.422 -0.275 -1.411 2.215 0.928 1.691 0.474 0.000 0.977 0.876 -0.842 3.102 0.815 0.844 0.989 0.883 0.925 1.264 1.130 +0 0.907 0.285 -1.367 0.727 -0.102 0.718 1.421 0.126 2.173 0.738 -1.752 -1.692 0.000 0.487 0.297 1.239 2.548 0.373 2.149 -0.085 0.000 3.096 1.674 1.023 0.926 0.756 1.421 1.116 +1 1.242 -0.631 0.797 0.402 1.067 0.727 1.354 -1.348 0.000 0.768 -1.334 0.655 0.000 1.586 0.488 -0.581 0.000 1.115 -0.354 -0.128 3.102 1.482 1.178 0.998 0.723 0.636 0.866 0.891 +1 0.994 -1.394 1.075 0.724 -0.228 0.775 -1.007 0.141 2.173 1.711 -1.670 -1.147 0.000 0.599 -1.570 0.832 0.000 0.687 -1.874 -1.445 0.000 0.655 0.830 1.084 0.669 0.921 0.623 0.570 +0 1.431 1.705 0.589 0.627 -0.072 1.273 0.069 -1.595 2.173 1.251 1.533 -0.424 0.000 0.685 -0.557 0.764 2.548 1.014 1.388 -0.945 0.000 0.980 1.322 0.980 1.655 1.068 1.196 1.134 +1 1.614 -1.055 -0.165 1.002 1.341 1.906 -0.172 1.364 0.000 1.846 0.055 -0.539 0.000 0.990 0.852 -1.227 2.548 0.645 0.378 -0.236 3.102 3.968 2.132 1.722 1.514 0.499 1.360 1.358 +1 1.113 1.125 1.698 1.183 0.996 0.923 0.040 0.400 2.173 1.709 0.299 -0.882 0.000 0.509 2.066 0.355 0.000 0.902 -0.714 -1.259 3.102 1.919 1.133 0.991 1.276 1.062 1.057 1.186 +1 0.971 1.015 0.913 0.381 0.209 0.907 0.309 -0.639 1.087 0.620 0.796 -1.188 2.215 0.941 0.448 1.723 0.000 0.526 0.093 -0.243 0.000 0.775 0.810 0.994 0.782 0.596 0.724 0.631 +1 1.596 -0.534 -0.244 0.993 0.482 1.555 -0.248 1.567 0.000 1.366 0.231 -0.813 2.215 0.703 -1.605 0.579 0.000 1.647 -0.339 0.589 0.000 0.868 0.655 1.062 1.123 0.725 0.901 0.815 +1 0.499 0.260 -1.622 0.717 1.398 1.161 -0.942 0.522 0.000 1.357 -1.229 -1.139 2.215 0.555 -1.240 -0.296 1.274 0.976 -1.948 -0.797 0.000 0.895 0.737 0.991 2.063 0.636 1.407 1.345 +1 0.338 -2.151 -1.013 1.110 1.079 0.672 0.569 1.450 2.173 0.972 -0.746 -0.986 0.000 1.012 -1.036 -0.251 0.000 0.892 0.044 0.272 3.102 0.977 0.858 0.987 1.081 0.748 0.902 0.850 +0 1.611 -1.225 -0.889 0.743 0.387 0.491 -0.905 -1.456 0.000 0.773 -2.126 -0.293 0.000 1.706 -0.399 1.026 2.548 0.788 -0.341 0.026 0.000 0.956 0.945 1.383 0.717 0.183 0.773 0.689 +1 1.185 0.360 -0.318 0.298 1.062 0.736 -0.466 -0.747 2.173 0.704 0.706 1.573 0.000 1.029 -0.302 1.039 0.000 1.242 1.958 1.368 0.000 0.943 1.178 0.989 0.711 0.877 0.925 0.792 +0 2.497 0.558 1.015 1.826 0.430 2.579 0.443 -0.915 0.000 0.255 0.725 -1.423 0.000 1.055 -0.227 1.333 2.548 0.695 1.208 -0.201 1.551 0.804 0.923 1.486 1.037 0.897 0.971 1.298 +0 0.698 -2.000 0.894 1.397 -0.337 1.332 -2.786 -0.959 0.000 0.481 0.291 1.501 2.215 1.006 -0.852 0.399 2.548 1.037 -1.562 -1.121 0.000 0.910 1.638 1.225 1.286 0.787 1.454 1.203 +1 0.337 -1.037 -0.039 0.893 0.340 1.412 1.135 0.583 0.000 3.551 0.863 -1.677 2.215 1.216 0.206 -0.585 2.548 2.482 0.665 0.137 0.000 0.926 1.034 0.996 1.696 1.984 1.661 1.336 +1 0.413 0.354 0.086 0.927 -1.095 0.817 0.410 0.710 0.000 0.815 0.191 1.278 0.000 1.081 -0.969 -0.486 2.548 0.940 0.627 -1.415 0.000 0.868 1.367 0.986 0.727 0.598 0.858 0.880 +1 0.773 0.328 -1.375 1.655 1.345 1.339 -0.134 -0.354 1.087 0.652 -0.895 0.946 2.215 0.978 0.154 0.256 0.000 1.306 1.670 -1.580 0.000 1.807 1.629 0.999 1.478 1.383 1.340 1.179 +1 0.665 -2.237 -1.046 0.826 -1.293 1.036 -0.730 0.685 2.173 0.334 -0.638 1.087 0.000 1.186 -0.374 -0.047 1.274 1.231 -0.859 -1.214 0.000 1.015 0.949 0.971 1.209 0.877 0.924 0.953 +0 0.286 0.656 0.666 0.731 0.745 0.713 -0.346 -1.022 0.000 0.625 -1.531 -1.727 2.215 0.778 -0.656 0.506 2.548 0.513 -1.869 -0.027 0.000 1.245 0.949 1.001 0.726 0.745 0.696 0.662 +1 0.882 -0.688 0.443 0.627 -1.538 0.817 0.408 1.045 0.000 1.656 0.301 -0.934 0.000 1.463 -0.310 1.069 2.548 1.254 -0.067 -0.445 0.000 0.882 0.649 1.007 0.696 0.766 0.872 0.788 +1 0.715 -0.257 1.054 0.861 -1.434 1.119 -0.832 -1.436 2.173 1.047 0.168 0.686 0.000 1.808 -0.025 0.028 0.000 0.400 -1.329 -0.973 0.000 1.096 0.589 0.987 0.731 0.615 0.819 0.738 +1 0.332 -0.322 0.340 0.585 -0.965 0.435 0.461 0.895 0.000 0.869 -0.835 0.379 2.215 1.393 -0.669 -1.228 2.548 0.757 1.083 1.704 0.000 0.693 1.041 0.983 1.068 1.163 0.962 0.859 +1 1.375 -0.400 -0.117 0.770 -0.626 0.940 -1.284 1.320 0.000 0.490 -1.242 -0.603 2.215 0.674 -1.940 0.813 0.000 0.396 -0.410 -1.394 3.102 0.883 0.934 0.981 0.575 0.306 0.578 0.840 +0 0.717 -0.219 -1.605 0.272 -0.213 0.711 2.248 0.563 0.000 0.788 1.019 -0.194 2.215 1.028 0.474 -1.402 2.548 0.643 1.488 1.594 0.000 0.864 0.951 0.993 0.590 0.885 0.847 0.782 +1 0.688 -0.868 -0.619 1.368 0.303 0.584 -1.894 -1.503 0.000 0.884 0.257 0.713 2.215 0.507 -0.246 1.642 1.274 1.206 -0.120 -0.619 0.000 1.580 0.974 0.994 0.903 0.562 0.913 0.838 +0 0.552 0.712 0.466 1.403 1.145 1.561 0.049 1.200 1.087 2.900 -0.894 -0.665 2.215 0.425 1.004 -0.154 0.000 0.556 -1.275 -0.995 0.000 0.999 1.215 0.985 0.708 3.489 1.732 1.328 +0 0.479 -0.536 -0.643 3.147 0.004 1.617 0.764 0.659 0.000 0.987 1.500 -0.739 1.107 2.318 -0.015 -1.730 0.000 3.221 -0.425 -1.623 3.102 1.129 1.480 0.985 1.733 2.273 1.810 1.721 +1 1.234 0.113 -1.238 1.294 -0.638 1.356 -0.155 -0.773 1.087 2.207 -0.615 -1.120 2.215 6.523 0.503 0.868 0.000 1.161 -0.997 1.248 0.000 0.818 1.234 0.992 0.801 0.992 1.110 0.912 +1 0.406 -1.686 -1.060 1.341 0.139 0.772 -1.264 1.619 2.173 1.449 -0.097 0.432 2.215 0.754 -2.615 -1.071 0.000 0.622 -0.822 -1.471 0.000 0.864 0.872 0.987 1.690 1.675 1.308 1.151 +0 0.601 1.261 -1.003 1.282 0.181 1.482 1.487 1.366 1.087 2.116 1.299 -0.495 1.107 1.137 0.870 1.645 0.000 0.895 1.411 0.516 0.000 1.036 0.892 1.066 0.862 2.600 1.296 1.072 +1 0.307 2.039 0.768 1.287 -0.088 2.655 1.139 1.135 0.000 2.068 0.645 -0.945 1.107 1.182 0.779 -0.269 0.000 2.898 0.008 -0.601 3.102 1.168 0.992 0.981 1.127 0.999 0.908 0.825 +1 1.739 -1.042 -0.513 0.496 1.343 1.236 -0.293 0.841 1.087 0.603 -2.822 -1.483 0.000 0.737 -1.904 -1.591 0.000 0.530 0.049 -0.903 3.102 0.439 0.932 1.280 1.350 0.870 1.239 1.083 +0 0.749 1.581 1.427 1.943 -1.168 0.874 1.595 -0.744 2.173 1.114 1.111 0.826 2.215 2.859 -0.080 0.539 0.000 1.621 1.114 -1.286 0.000 0.828 0.938 1.203 0.872 1.475 0.980 0.793 +1 0.988 -0.305 0.006 0.949 -1.014 0.819 -1.139 -1.625 2.173 0.920 0.339 0.741 0.000 0.528 0.575 -0.311 0.000 1.110 -0.927 -0.428 3.102 0.909 1.138 1.066 1.018 0.890 1.038 0.893 +0 1.306 0.494 -0.051 1.547 -0.598 1.329 0.836 -0.430 2.173 3.610 0.378 1.451 0.000 0.489 -0.807 -0.220 0.000 0.861 -0.777 1.103 3.102 2.464 1.519 0.991 0.596 1.595 1.640 1.453 +1 0.952 -1.626 1.588 1.204 -1.249 1.275 -1.147 0.069 2.173 0.416 -1.203 1.241 0.000 0.470 -2.106 -0.802 0.000 0.788 -0.304 0.657 3.102 0.757 0.985 0.990 0.965 0.688 1.045 0.830 +1 0.763 -0.093 1.647 1.519 0.864 0.889 0.751 -0.381 2.173 0.908 0.308 0.832 0.000 2.052 1.146 -1.000 2.548 0.375 1.043 0.737 0.000 0.374 0.951 0.986 1.619 0.987 1.238 1.011 +1 0.883 -1.128 -1.705 1.412 0.049 0.595 -0.553 1.025 0.000 1.737 -0.961 0.007 0.000 0.757 -0.635 1.495 0.000 1.126 -0.488 -0.748 1.551 0.949 1.185 1.546 0.881 0.473 0.807 0.789 +1 0.755 0.612 -0.190 0.410 -1.450 1.358 -0.040 -0.532 2.173 0.890 0.130 1.673 1.107 0.976 -1.832 0.616 0.000 1.683 0.531 1.223 0.000 0.807 1.105 0.987 0.891 1.486 0.971 0.812 +0 0.682 -0.171 -0.685 1.082 0.961 0.681 -0.546 -0.091 0.000 0.670 -0.725 -1.738 1.107 0.917 0.453 0.951 1.274 0.920 0.468 -1.346 0.000 0.881 0.932 1.185 0.717 0.778 0.710 0.658 +1 0.396 -0.764 -0.193 1.550 1.273 1.358 -0.155 1.647 0.000 1.739 1.096 -0.574 2.215 1.783 0.952 1.068 0.000 2.200 2.485 -0.509 0.000 0.893 0.860 1.052 1.885 1.400 1.270 1.040 +1 0.728 -2.210 0.418 0.481 -0.559 0.955 -1.134 -0.484 0.000 0.658 -1.216 -1.138 0.000 1.848 -1.375 1.376 0.000 1.122 -0.633 0.889 3.102 0.939 1.030 0.993 0.481 0.293 0.624 0.593 +0 0.701 0.605 -1.429 0.987 0.499 0.969 1.748 1.576 0.000 1.983 -0.745 0.034 2.215 1.189 -0.875 -1.154 2.548 0.826 -0.663 0.413 0.000 1.056 0.937 1.136 1.361 1.441 1.111 0.950 +1 0.734 -1.285 -0.627 0.787 -1.654 1.046 -0.965 0.156 2.173 0.882 -0.789 -1.494 0.000 1.117 -0.786 0.705 2.548 0.487 1.551 -1.147 0.000 1.536 1.307 0.982 1.089 0.643 1.097 1.059 +1 1.182 0.013 1.063 0.312 1.360 0.863 0.857 1.514 2.173 1.142 0.126 -0.658 0.000 1.063 0.777 -0.572 0.000 1.323 0.335 0.064 0.000 0.994 0.843 0.990 1.030 0.737 0.872 0.881 +1 0.653 0.439 0.083 0.428 0.052 0.960 -1.125 -1.130 0.000 1.129 -0.052 0.662 2.215 0.658 0.634 -1.529 2.548 0.702 -1.087 1.256 0.000 0.943 0.957 0.979 0.741 0.913 0.911 0.803 +1 2.221 -0.693 1.244 0.570 -1.156 0.899 -0.973 -0.331 2.173 0.356 0.207 0.120 0.000 0.465 0.923 -0.330 2.548 0.579 0.624 -1.335 0.000 0.593 0.855 1.294 1.034 0.944 0.980 0.800 +0 1.442 1.484 0.930 0.637 0.093 0.914 2.178 0.815 0.000 2.322 1.080 -0.996 1.107 0.922 0.957 -0.751 1.274 0.952 -1.445 0.403 0.000 0.846 1.047 0.987 1.490 0.344 1.129 1.017 +0 2.073 0.641 0.075 1.716 -0.228 4.010 -0.595 1.706 2.173 2.140 -0.629 -0.256 0.000 2.537 0.784 0.363 2.548 1.080 -2.189 -1.325 0.000 2.817 3.426 1.003 4.120 4.888 3.219 2.973 +1 0.768 0.407 -0.985 0.890 0.292 0.773 -0.445 -1.107 0.000 0.515 0.314 -0.521 0.000 0.894 1.221 0.926 2.548 1.352 -0.247 0.894 1.551 0.859 1.007 1.046 0.780 0.768 0.873 0.761 +1 1.125 1.229 -0.604 0.330 -1.455 0.987 0.847 0.456 0.000 1.506 1.097 1.573 2.215 0.639 1.191 -1.145 1.274 0.428 0.362 -0.484 0.000 0.773 0.829 0.991 1.065 0.671 0.820 0.803 +0 1.617 -0.783 0.262 0.372 0.937 0.547 -0.974 1.734 1.087 0.377 -1.260 -0.918 2.215 0.594 1.138 1.409 0.000 0.950 0.814 -0.630 0.000 0.807 0.939 0.992 0.899 0.467 0.767 0.872 +0 0.988 0.109 1.692 0.611 0.345 1.186 1.112 -0.674 2.173 1.218 -0.286 0.717 1.107 1.217 0.953 0.663 0.000 1.314 0.890 -1.584 0.000 1.253 1.287 1.008 1.162 2.163 1.243 1.017 +1 0.504 1.216 -0.733 1.923 -0.408 1.108 0.252 0.758 2.173 1.561 -1.496 -1.490 0.000 1.497 0.646 0.129 0.000 1.128 0.940 1.615 3.102 0.741 1.731 0.989 1.300 0.982 1.464 1.389 +1 0.720 1.102 1.415 0.355 0.036 1.241 2.773 1.585 0.000 1.353 -1.029 -0.807 2.215 1.136 -0.392 0.419 2.548 0.974 0.075 0.010 0.000 3.462 3.174 0.990 1.198 1.250 3.072 2.196 +1 1.290 -0.496 1.019 0.618 -0.358 1.181 -0.777 -0.856 0.000 1.017 -1.291 0.793 0.000 0.642 -0.080 0.202 2.548 0.505 -0.388 -1.636 1.551 0.857 0.762 1.170 0.612 0.441 0.506 0.536 +1 0.777 -0.634 1.117 0.893 -0.031 1.135 0.456 1.297 0.000 1.270 0.506 -0.685 2.215 0.991 -0.250 -1.297 2.548 1.181 -1.151 -0.463 0.000 2.574 1.556 0.991 1.083 0.793 1.192 1.022 +1 0.799 -1.564 -1.377 0.395 0.506 1.455 -0.782 0.798 2.173 0.783 -1.049 -1.210 0.000 0.980 -0.211 -0.403 0.000 1.204 1.335 -0.436 0.000 0.894 1.236 0.989 0.994 0.902 1.226 1.029 +1 0.839 0.838 1.258 0.783 -1.188 1.349 1.147 -1.164 0.000 1.859 -1.358 0.615 2.215 0.635 -0.652 0.662 0.000 1.431 -0.624 -0.850 3.102 0.532 0.890 0.988 1.115 1.506 1.590 1.203 +0 0.782 -1.153 -1.374 2.708 -0.599 0.736 0.639 1.362 0.000 1.258 -1.872 -1.618 0.000 2.063 -0.219 0.948 2.548 3.434 -0.880 0.170 0.000 0.767 0.942 1.297 0.669 0.536 1.079 1.095 +0 0.671 -0.423 0.105 0.436 1.464 0.904 0.539 0.102 1.087 1.300 0.066 -1.290 2.215 0.864 -1.040 0.974 0.000 0.419 0.137 0.740 0.000 0.735 1.047 0.992 1.140 1.564 1.053 0.906 +1 0.736 1.192 1.468 0.914 0.260 1.198 -0.367 -0.941 2.173 0.749 0.681 1.400 0.000 0.782 -0.596 0.473 2.548 0.541 0.070 0.746 0.000 0.530 1.208 1.007 0.897 1.167 1.056 0.877 +1 0.419 -2.247 1.488 0.852 0.832 1.935 -1.921 0.078 0.000 2.141 1.598 1.129 0.000 3.370 -0.016 -0.770 2.548 3.197 0.406 -1.536 3.102 13.098 7.392 0.996 1.578 1.721 4.543 3.278 +0 0.984 1.504 0.575 1.586 0.716 1.029 0.355 -0.866 0.000 0.523 -1.136 -0.922 0.000 1.502 -1.753 0.639 0.000 1.150 1.040 -0.702 0.000 1.030 0.862 0.982 1.255 0.506 0.976 1.216 +0 0.507 0.904 1.087 1.248 -0.508 0.881 0.095 1.054 2.173 0.516 0.596 -1.077 0.000 0.686 -0.733 -1.005 0.000 0.794 0.296 0.317 3.102 0.706 1.055 1.092 0.625 0.558 0.694 0.706 +1 0.770 -0.476 0.768 1.330 0.849 0.837 0.359 -1.081 0.000 0.827 0.098 -0.112 0.000 0.806 -2.542 0.034 0.000 1.521 0.757 -1.585 3.102 1.374 1.098 0.992 0.682 0.310 0.982 1.058 +0 0.802 0.631 0.901 1.141 0.431 0.531 1.633 -1.193 0.000 0.542 -0.211 1.625 2.215 0.485 -0.962 -1.619 0.000 0.542 -2.132 -0.100 0.000 0.724 0.729 0.973 0.870 0.576 0.749 1.024 +0 0.852 -1.002 0.740 0.304 -1.283 1.022 0.476 0.704 2.173 1.255 -1.051 -0.599 2.215 0.653 0.443 -1.406 0.000 1.022 -1.233 1.459 0.000 0.793 0.881 0.987 0.863 2.105 1.272 1.005 +1 0.714 0.081 1.160 0.892 0.012 0.915 -0.948 -1.475 0.000 0.804 -0.498 1.029 2.215 1.104 -1.006 -0.903 2.548 1.514 -0.682 0.047 0.000 1.767 1.106 0.987 0.646 1.032 0.857 0.791 +0 1.162 -0.039 0.307 0.826 -0.604 0.847 0.077 -0.270 0.000 0.916 -0.429 1.413 2.215 0.448 -0.511 -1.125 2.548 0.878 0.767 1.228 0.000 1.407 0.896 0.993 0.945 0.515 0.757 0.705 +1 0.609 2.089 1.548 1.898 -0.290 0.882 -0.102 0.620 0.000 0.690 -2.189 1.352 0.000 2.182 0.617 -0.222 2.548 0.731 0.948 -1.461 3.102 2.374 1.819 1.484 1.426 0.896 1.605 2.117 +1 1.925 0.683 -1.310 0.496 -0.611 0.627 -0.706 -0.532 2.173 0.454 -0.522 1.467 0.000 0.879 2.155 0.859 0.000 0.886 0.075 0.253 0.000 1.046 0.866 0.994 0.910 0.477 0.838 0.888 +0 0.667 -0.068 -1.425 0.427 0.836 0.870 -0.162 1.384 2.173 1.669 -0.115 -0.713 2.215 1.017 -0.715 0.575 0.000 0.512 0.129 0.466 0.000 0.778 0.908 0.995 1.032 1.683 0.961 0.858 +0 0.757 -0.232 -0.098 0.644 0.982 0.797 -0.924 -0.969 0.000 1.186 -0.578 0.456 2.215 0.763 -0.010 -1.511 0.000 1.142 -1.106 1.453 3.102 0.938 0.864 0.989 0.777 0.911 0.871 0.832 +1 0.533 1.404 -0.991 0.591 0.235 1.389 2.920 -1.354 0.000 1.257 0.468 -0.032 0.000 1.590 0.180 0.357 0.000 1.184 1.098 0.791 3.102 0.805 0.899 0.995 0.806 0.872 0.911 0.775 +1 0.618 -1.212 -0.702 0.560 0.389 1.122 -0.587 -1.710 1.087 0.747 0.146 -0.208 0.000 1.323 0.098 -1.490 0.000 1.415 0.123 0.460 3.102 1.088 0.802 0.990 0.983 1.335 1.060 0.893 +1 1.001 -0.516 -0.671 0.490 1.045 1.025 0.553 0.711 2.173 0.438 -1.570 -0.905 0.000 0.788 0.023 -0.496 0.000 0.731 -1.744 1.682 0.000 0.886 0.570 0.988 1.015 0.734 0.834 0.722 +0 0.403 0.667 -1.518 1.124 0.285 0.850 -1.029 -1.303 1.087 0.461 -1.096 0.392 0.000 1.110 0.427 0.452 0.000 0.652 -0.195 -1.016 0.000 0.969 1.233 0.987 0.614 0.968 0.799 0.730 +1 0.816 -0.544 -0.577 1.445 0.434 0.716 -0.382 1.544 2.173 0.381 0.094 -1.664 0.000 0.635 -1.875 -0.040 0.000 0.932 0.039 -0.979 3.102 1.155 1.022 1.189 0.791 0.685 0.744 0.721 +0 1.390 0.266 -0.533 1.271 -0.310 1.329 0.124 1.437 2.173 1.560 -0.585 -0.738 2.215 1.239 0.258 0.851 0.000 0.775 1.081 1.073 0.000 0.611 0.837 1.002 0.741 2.111 1.281 1.171 +1 1.262 0.316 -1.008 1.507 -1.171 2.164 1.238 0.913 0.000 1.485 -1.044 -0.227 2.215 0.633 -1.529 -1.602 0.000 1.104 -0.397 -0.919 3.102 4.601 2.788 1.008 2.010 0.760 2.286 1.947 +1 0.442 -1.143 1.198 0.812 -0.094 0.849 0.441 1.554 0.000 1.928 0.213 0.237 2.215 0.736 1.234 -1.428 2.548 1.047 -1.236 -1.596 0.000 1.672 1.310 0.980 1.828 1.473 1.563 1.447 +1 0.446 -1.308 -1.057 1.617 1.411 2.207 -0.844 -0.913 0.000 1.145 -0.271 0.804 2.215 2.078 -1.081 0.583 2.548 1.616 -0.967 0.108 0.000 0.911 0.883 0.989 1.027 0.836 0.812 0.740 +0 0.558 0.238 -1.479 1.509 0.812 0.752 0.121 0.833 2.173 0.563 0.451 -0.024 0.000 1.762 -0.504 -1.085 2.548 0.710 0.869 -0.637 0.000 0.496 0.963 1.119 0.649 1.500 0.923 0.809 +0 0.879 0.088 -0.737 0.743 -0.181 1.159 -0.258 1.321 0.000 1.509 0.371 0.229 2.215 1.999 -0.737 -1.512 2.548 0.760 -0.205 -1.688 0.000 0.938 0.975 0.982 0.880 2.183 1.243 0.998 +0 1.853 0.940 -0.872 0.647 1.591 0.955 0.257 0.644 1.087 0.548 -0.943 -0.240 0.000 0.816 0.476 1.296 2.548 0.456 0.478 -1.507 0.000 0.811 0.950 1.208 0.851 0.626 0.896 0.828 +0 0.749 -0.298 -0.212 0.459 1.728 0.790 0.136 -1.725 0.000 0.871 -1.631 1.473 0.000 1.865 0.798 -0.113 2.548 0.734 -1.265 -1.344 1.551 1.306 1.098 0.982 0.805 1.566 1.033 0.836 +0 0.748 -0.403 -0.542 0.774 -0.261 0.448 -1.752 0.466 0.000 0.665 -1.392 0.995 0.000 1.135 -1.160 -1.534 2.548 0.399 -0.942 -1.138 3.102 0.721 0.774 0.982 0.630 0.183 0.746 0.847 +0 0.333 0.384 0.592 1.948 -0.187 0.687 1.500 1.525 0.000 0.579 0.327 1.577 0.000 0.619 -0.220 -1.632 2.548 0.989 0.448 -0.595 3.102 0.861 0.755 0.986 0.806 0.537 0.615 0.621 +0 0.643 -1.776 -1.451 0.658 -1.041 0.694 -0.151 0.142 2.173 0.730 0.578 -0.964 2.215 0.912 0.373 1.172 0.000 0.581 1.708 0.857 0.000 0.769 0.987 0.995 0.855 0.966 0.796 0.868 +0 0.562 -0.537 -0.541 0.715 0.508 0.792 -0.274 -1.062 1.087 0.690 -1.489 0.374 0.000 0.642 0.188 1.457 2.548 1.459 -1.039 1.217 0.000 0.919 0.891 0.994 0.980 0.713 0.849 0.774 +0 0.799 -0.112 -0.026 0.556 1.105 0.728 1.610 1.536 0.000 0.566 0.829 -1.150 1.107 1.027 0.758 0.413 2.548 0.558 1.209 -0.753 0.000 0.862 0.910 0.987 0.703 0.800 0.640 0.644 +1 0.729 -0.072 0.129 0.760 0.674 0.773 0.324 -0.798 2.173 0.648 0.283 0.688 0.000 1.530 0.100 -1.487 2.548 0.553 1.118 -0.267 0.000 0.950 0.852 0.992 0.941 0.802 0.837 0.728 +0 1.111 0.975 -1.220 0.722 -0.156 1.408 0.437 -1.452 0.000 1.606 0.457 0.413 1.107 1.039 1.056 -0.441 2.548 0.881 -0.080 0.084 0.000 1.735 1.322 1.016 1.104 1.070 1.172 0.977 +1 1.372 -0.040 -0.325 0.336 -1.281 0.787 0.472 1.459 2.173 0.503 1.166 0.316 0.000 0.545 0.810 0.980 2.548 1.111 1.875 -0.726 0.000 0.951 0.733 0.985 0.995 0.382 0.734 0.766 +1 1.260 -0.415 -1.468 0.973 1.404 0.824 -0.539 -0.455 2.173 0.739 1.674 0.491 0.000 0.561 -1.421 0.723 0.000 1.077 0.906 -0.326 1.551 0.864 0.702 0.986 1.100 0.913 0.903 0.933 +1 0.726 -0.993 1.628 1.282 -0.936 1.181 0.242 0.924 1.087 0.798 -0.634 -1.144 0.000 0.988 -0.166 0.348 2.548 0.865 0.524 -1.041 0.000 0.891 0.888 0.988 1.444 0.728 1.006 0.921 +0 2.224 0.516 0.846 0.308 0.297 0.998 0.145 -1.248 2.173 0.898 -0.032 -0.134 0.000 0.940 1.017 -0.900 0.000 0.625 -0.646 -1.340 3.102 1.240 0.897 0.984 1.313 0.405 0.876 0.884 +0 1.402 0.815 -0.603 0.891 1.041 0.487 0.272 1.123 2.173 0.707 1.194 -1.526 1.107 0.492 -1.834 1.326 0.000 1.400 -0.249 -0.377 0.000 0.882 0.855 1.543 0.925 0.729 0.794 0.818 +1 0.881 0.421 0.029 0.684 1.615 1.138 0.455 -1.564 0.000 1.225 -0.489 0.139 2.215 0.658 0.119 -1.004 2.548 0.731 0.386 1.204 0.000 0.839 0.663 1.065 0.895 0.874 0.929 0.797 +1 0.851 -0.896 -0.661 0.450 0.932 0.357 1.549 1.027 0.000 0.931 -0.655 -1.499 2.215 0.561 -0.145 0.445 2.548 0.812 -1.033 -0.158 0.000 1.728 0.987 0.989 0.722 0.781 0.836 0.727 +0 1.292 -1.041 0.545 1.577 0.988 1.476 -0.377 0.391 1.087 0.977 0.325 -0.963 2.215 1.392 -1.271 -1.053 0.000 1.854 -0.357 -1.187 0.000 0.917 1.037 0.990 0.866 1.781 1.315 1.228 +0 1.349 -0.693 -0.284 1.086 -1.730 0.957 1.431 -0.865 0.000 0.836 0.023 1.665 1.107 1.368 -1.341 0.603 0.000 0.794 0.417 0.242 0.000 0.908 1.135 1.617 0.920 0.675 0.750 0.758 +0 0.621 1.075 -1.168 1.425 1.024 0.653 -0.049 1.297 2.173 1.368 0.077 -0.451 0.000 0.718 1.041 0.077 2.548 0.961 -0.170 -1.165 0.000 0.921 0.869 1.199 0.878 0.932 0.823 0.857 +1 0.870 0.786 1.607 0.205 -1.338 0.612 -2.319 -1.573 0.000 1.403 0.597 0.386 2.215 1.397 0.745 -0.492 2.548 0.705 0.320 -0.287 0.000 2.050 2.243 0.981 1.021 1.068 1.740 1.362 +0 1.551 0.529 -1.023 1.002 -1.610 0.806 -0.965 0.617 0.000 1.050 -0.901 0.086 0.000 1.118 -0.242 1.443 2.548 0.820 0.111 -0.401 3.102 0.902 0.849 0.990 0.830 0.744 0.767 0.952 +0 0.745 -1.345 1.399 1.516 0.432 0.833 -0.527 -0.350 2.173 0.890 -2.203 1.617 0.000 0.501 -2.448 1.262 0.000 0.463 0.599 -1.238 0.000 0.388 1.432 1.126 1.098 0.439 1.138 1.006 +1 0.686 0.142 -1.426 0.985 0.120 1.117 -0.531 0.171 0.000 0.981 -0.391 1.636 1.107 0.554 0.857 0.702 2.548 0.701 -1.958 -1.380 0.000 1.934 1.452 1.121 0.878 0.814 1.062 0.923 +1 0.900 -0.445 -0.014 1.414 0.748 0.893 1.252 1.002 0.000 1.715 -1.701 -0.508 0.000 1.062 2.145 -0.723 0.000 3.456 -0.317 -1.399 3.102 1.262 1.254 0.991 1.380 1.162 1.195 1.114 +0 1.143 0.593 0.060 0.792 1.341 0.800 1.178 -1.730 2.173 0.479 0.423 -1.523 0.000 0.962 -0.671 -0.248 1.274 0.518 -0.899 0.520 0.000 0.826 0.934 1.206 0.930 1.621 0.952 0.794 +1 0.406 1.032 -1.285 0.477 0.432 0.894 0.818 -0.251 2.173 1.279 0.633 1.697 0.000 0.689 -0.340 1.413 0.000 0.376 -0.736 0.161 3.102 0.853 0.752 0.981 0.863 0.631 0.866 0.744 +1 1.478 1.156 1.148 1.144 0.412 1.031 0.654 -0.999 2.173 0.614 0.264 1.500 2.215 0.610 1.644 -0.785 0.000 0.747 -0.548 -0.264 0.000 1.216 0.961 1.108 0.813 0.937 0.955 0.880 +1 0.547 -0.326 1.308 0.170 1.534 0.660 1.194 -0.126 0.000 1.052 1.071 1.685 2.215 0.600 0.464 -0.234 0.000 0.850 0.845 -0.551 0.000 0.962 1.064 0.999 0.530 0.531 0.681 0.615 +1 1.062 -0.076 0.594 0.903 -0.298 2.590 -1.528 -1.243 0.000 0.934 -0.878 -0.026 0.000 1.129 -1.416 0.418 0.000 2.018 -0.450 1.205 3.102 0.806 1.121 0.988 0.610 0.854 0.903 0.812 +0 0.860 0.224 -0.608 1.260 -1.455 0.506 0.371 -1.248 0.000 1.251 -0.004 0.534 2.215 1.548 -0.420 0.996 0.000 1.995 -0.679 -0.220 3.102 0.852 0.903 0.996 1.022 1.076 0.943 0.779 +1 1.231 -0.447 -0.724 0.658 -1.395 0.889 -0.337 0.081 2.173 0.314 -1.595 0.647 0.000 0.717 -0.215 -1.517 0.000 1.439 -0.769 1.551 3.102 0.874 0.940 0.990 0.861 1.214 0.847 0.735 +1 1.160 0.229 0.513 0.819 -0.530 1.210 0.110 -0.939 0.000 0.647 -1.320 0.616 2.215 0.615 0.573 0.924 2.548 0.811 0.386 -1.571 0.000 0.857 0.933 1.089 0.934 0.803 0.920 0.838 +1 0.389 -1.290 0.025 0.114 -0.786 0.735 -0.304 0.621 2.173 0.722 1.082 -1.237 0.000 0.596 0.263 1.477 0.000 0.728 -0.275 -0.345 3.102 0.774 1.114 0.987 0.526 0.592 0.716 0.637 +0 1.158 0.807 -0.364 1.093 -0.725 1.006 -0.601 0.594 2.173 0.396 0.157 -1.226 0.000 1.528 0.943 1.711 2.548 0.376 1.235 1.695 0.000 0.433 1.005 0.974 1.155 1.926 1.221 0.923 +0 0.728 -0.882 1.150 0.798 -0.694 0.965 -0.619 0.685 2.173 0.875 1.282 -1.109 0.000 0.909 -0.636 -0.962 2.548 0.723 0.992 0.906 0.000 0.899 0.940 1.051 0.862 1.164 1.047 0.918 +1 1.249 -0.167 -0.846 1.821 -0.240 0.921 -1.055 -1.730 2.173 0.570 -1.218 0.970 0.000 0.771 -0.058 0.913 2.548 0.530 0.713 0.520 0.000 0.946 1.006 1.086 0.940 0.906 1.017 0.885 +0 1.030 1.120 0.018 0.251 -0.492 0.886 1.534 -1.287 0.000 1.162 1.131 1.522 1.107 2.420 1.987 0.432 0.000 0.724 0.334 -0.221 0.000 0.983 0.866 0.990 0.528 0.504 0.661 0.620 +0 0.723 -1.223 0.403 0.461 -1.134 1.047 -0.519 1.136 2.173 0.831 -0.614 -0.524 0.000 0.532 1.003 -1.190 2.548 0.655 0.617 -0.087 0.000 0.802 0.738 0.991 1.092 1.164 1.010 0.929 +1 0.752 1.290 -0.907 0.710 0.893 0.790 0.244 1.034 2.173 1.095 0.933 -0.083 0.000 0.992 0.437 -0.627 0.000 1.266 -0.045 -1.463 3.102 0.847 1.014 1.011 0.881 0.838 0.890 0.796 +1 0.902 -0.291 0.639 1.229 1.317 0.647 -1.570 -0.516 0.000 0.524 0.033 -1.389 0.000 1.051 -0.009 -0.688 2.548 0.832 -0.020 0.844 1.551 0.610 0.614 0.986 0.537 0.702 0.666 0.613 +1 0.381 -0.758 -0.324 1.917 0.675 0.944 0.249 -0.471 0.000 1.031 0.076 -1.703 2.215 0.773 0.641 1.029 0.000 1.766 0.958 -1.263 1.551 0.933 0.854 0.989 1.861 0.831 1.324 1.096 +1 0.369 -1.145 -1.354 0.457 1.547 0.871 -0.258 0.216 2.173 0.870 -0.077 -1.248 0.000 0.874 0.857 -1.674 0.000 0.825 0.066 0.752 3.102 0.871 0.799 0.995 0.935 0.444 0.814 0.710 +0 0.799 1.230 1.526 0.741 0.595 0.418 1.845 -0.544 0.000 0.611 0.539 -1.294 2.215 0.637 0.798 0.204 0.000 0.471 -0.616 1.603 3.102 0.750 0.833 0.990 0.702 0.416 0.594 0.600 +1 0.605 0.386 1.666 0.609 -0.301 1.026 0.395 1.122 0.000 1.024 -0.581 -1.164 0.000 0.708 0.760 0.839 0.000 1.251 0.911 1.406 0.000 0.800 0.683 0.984 0.686 0.181 0.879 0.827 +0 1.482 -0.245 -0.208 0.538 0.254 1.036 -0.317 1.641 0.000 0.191 1.178 -1.043 0.000 0.317 -0.680 0.390 2.548 0.472 1.176 1.168 1.551 1.012 0.763 0.996 0.483 0.430 0.555 0.710 +1 1.334 0.459 0.112 1.735 -0.377 0.998 -0.007 1.465 0.000 0.803 1.274 -1.050 1.107 0.404 -0.387 -0.032 0.000 0.701 0.149 0.985 3.102 1.140 1.240 0.984 0.768 0.769 0.749 0.879 +0 0.973 -1.017 0.711 1.341 1.397 0.389 0.646 -0.905 0.000 0.977 0.066 0.133 1.107 1.509 -1.023 -1.142 0.000 0.463 -0.460 -1.021 0.000 0.420 0.705 0.987 0.731 0.212 0.781 0.747 +1 0.818 -0.461 -0.763 0.888 1.116 0.564 -0.732 1.623 1.087 1.077 -1.430 0.796 2.215 1.007 -0.612 -0.057 0.000 1.123 0.593 -0.544 0.000 0.978 0.927 1.172 0.953 0.890 0.892 0.778 +0 0.351 -1.774 0.847 2.170 0.511 0.725 1.059 -0.921 0.000 0.710 -0.490 1.682 2.215 0.868 1.167 -0.176 0.000 1.193 0.394 -1.417 3.102 0.901 0.788 1.001 0.923 0.509 0.821 1.047 +1 0.751 -1.273 -0.508 0.387 0.768 0.691 0.168 0.033 2.173 0.572 0.802 -1.273 2.215 0.655 0.220 0.876 0.000 1.172 -0.090 -1.498 0.000 0.832 0.879 0.989 0.821 0.908 0.681 0.643 +0 0.693 0.011 1.682 2.099 -1.084 0.926 -0.222 0.378 1.087 0.762 0.779 1.436 2.215 0.594 0.696 0.894 0.000 0.672 1.075 -0.402 0.000 0.667 0.829 1.011 0.903 1.206 1.007 0.838 +1 1.258 -1.820 0.777 0.822 -0.693 1.025 -0.102 -1.187 2.173 0.519 -1.171 0.231 0.000 0.566 -1.657 -1.288 0.000 0.927 -0.532 1.203 3.102 0.855 1.146 1.366 0.865 0.905 1.060 0.869 +1 0.360 -2.304 -1.175 0.378 0.944 0.488 0.859 0.762 2.173 0.436 -0.938 -0.106 2.215 0.600 -1.738 0.871 0.000 0.550 0.788 -1.391 0.000 1.367 0.876 0.977 0.966 0.859 0.768 0.703 +0 0.690 0.677 0.788 2.548 0.235 1.258 0.833 -1.272 1.087 1.138 -2.556 -0.296 0.000 2.626 0.184 1.602 2.548 0.572 -2.416 0.593 0.000 0.769 3.251 0.990 1.716 1.394 2.852 2.423 +0 0.560 -1.821 1.155 1.775 0.682 0.912 0.877 -0.712 2.173 0.666 -0.019 -0.892 0.000 0.680 -0.423 1.098 2.548 0.696 -0.354 -1.623 0.000 0.659 0.706 0.995 0.556 1.212 1.193 0.931 +0 1.835 0.947 -0.665 0.911 -1.381 0.848 -0.350 1.167 0.000 0.738 -1.441 0.961 0.000 0.734 -0.334 0.038 1.274 0.511 -0.821 1.245 3.102 1.049 0.938 1.076 0.955 0.440 0.758 1.053 +1 0.546 1.060 -0.017 1.378 1.673 1.224 0.542 -0.463 2.173 1.515 0.948 1.223 0.000 0.855 0.850 -1.399 2.548 0.798 0.050 0.293 0.000 1.263 0.988 1.201 1.193 0.984 1.040 0.908 +0 0.736 1.285 -0.513 1.678 -0.041 1.041 1.131 0.343 0.000 1.322 0.460 1.739 0.000 0.836 -0.461 -1.731 2.548 0.620 1.416 -1.357 0.000 0.871 0.793 0.979 1.391 0.113 1.231 1.095 +0 1.078 0.087 1.014 1.521 -1.548 0.636 -1.342 0.640 0.000 1.096 0.519 -0.489 2.215 0.464 0.249 -0.071 0.000 0.701 -1.286 -1.361 3.102 1.061 0.829 1.314 1.191 1.128 0.910 0.928 +0 0.812 0.041 -0.323 0.424 0.592 0.655 0.951 1.666 0.000 0.517 1.095 -1.114 2.215 0.879 0.109 -1.456 2.548 0.477 0.782 0.494 0.000 0.744 0.632 0.983 0.655 0.434 0.552 0.547 +1 0.888 -1.152 -1.011 0.659 1.355 0.484 1.141 0.198 2.173 0.441 0.479 -0.644 0.000 0.652 -0.309 -0.371 0.000 1.522 0.453 1.251 3.102 0.802 0.720 0.989 1.478 0.789 1.109 0.987 +0 0.526 1.430 -0.336 1.636 -1.386 0.733 0.339 0.297 0.000 1.049 1.336 1.171 0.000 1.212 0.340 -0.626 2.548 0.383 -0.817 1.328 1.551 1.658 1.090 1.042 0.876 0.637 0.873 0.899 +1 1.166 0.158 -0.711 0.606 -0.288 0.925 -0.444 0.664 2.173 1.069 -0.152 0.063 2.215 1.246 -0.179 -1.651 0.000 0.439 0.188 1.470 0.000 0.316 0.974 0.993 1.057 0.784 0.770 0.769 +1 0.903 0.728 1.547 0.458 -0.191 1.460 -2.134 -0.631 0.000 1.943 0.210 0.972 2.215 1.131 0.332 -0.648 0.000 1.487 -0.445 1.394 3.102 0.805 0.978 0.983 1.004 0.820 0.878 0.803 +1 0.405 0.985 -0.698 0.871 0.163 0.890 0.197 -1.579 0.000 0.436 -1.407 0.121 2.215 0.677 -1.085 0.683 0.000 1.104 1.076 1.707 1.551 0.848 1.029 0.994 0.795 1.309 1.240 1.078 +0 1.658 -1.042 0.132 1.080 0.533 0.930 -0.633 1.677 2.173 0.745 -0.272 -0.843 0.000 0.924 2.127 0.965 0.000 1.167 0.106 -0.481 3.102 0.805 1.192 0.990 1.386 1.113 1.460 1.905 +0 0.813 0.038 -1.396 1.185 -0.174 0.402 -1.269 1.534 0.000 0.679 0.800 0.271 0.000 0.801 0.843 1.737 0.000 0.369 -0.708 -1.556 3.102 0.770 0.539 1.213 0.628 0.148 0.466 0.489 +0 0.764 0.937 -0.993 0.867 -1.466 0.992 -0.363 0.954 0.000 0.681 -0.252 -0.136 1.107 0.571 -0.985 -0.113 0.000 0.771 0.195 -1.254 3.102 0.777 0.771 0.980 0.469 0.577 0.528 0.560 +1 1.421 1.077 0.298 0.253 -1.243 0.921 0.753 -1.492 0.000 0.582 0.817 0.956 2.215 1.224 -0.030 -0.327 2.548 1.091 -0.056 -1.669 0.000 1.121 1.074 0.988 0.611 0.915 0.758 0.715 +1 1.650 0.334 1.108 0.981 -1.428 0.508 -0.754 -0.133 0.000 0.452 0.047 1.361 0.000 0.732 -1.083 0.965 2.548 0.794 -0.610 -1.174 1.551 1.079 0.771 1.333 0.850 0.562 0.710 0.695 +0 0.792 -0.771 0.105 0.596 -1.237 0.569 -0.816 1.058 2.173 0.444 -0.335 -1.118 1.107 0.773 1.178 -0.979 0.000 1.512 0.520 0.901 0.000 0.663 1.066 0.985 0.607 0.706 0.689 0.726 +1 0.920 -1.089 0.682 0.129 -1.596 1.151 -0.931 -0.520 0.000 1.150 1.861 1.519 0.000 1.256 -2.119 1.399 0.000 2.006 -0.881 -0.930 0.000 0.849 1.114 0.987 0.459 0.387 0.705 0.728 +0 0.486 1.519 0.443 0.609 0.943 0.406 -0.679 -1.180 2.173 0.657 -0.385 1.420 2.215 0.595 0.767 -0.578 0.000 0.816 -1.699 -0.372 0.000 1.540 0.962 0.978 0.690 0.555 0.713 0.744 +1 4.314 0.390 0.801 1.470 0.748 2.193 -0.363 -1.123 0.000 1.204 0.238 -0.749 1.107 0.790 0.510 -0.224 0.000 0.906 1.321 -0.295 0.000 0.851 0.878 0.985 0.540 0.808 1.121 0.953 +0 1.768 0.606 1.739 0.554 -1.210 0.955 0.442 0.521 2.173 0.727 0.394 -0.031 0.000 1.274 -0.611 -1.166 2.548 1.205 -0.663 0.132 0.000 0.798 1.036 0.975 1.227 1.593 1.105 1.020 +0 0.695 0.554 0.615 1.008 1.378 1.564 0.975 -0.496 2.173 1.539 0.233 1.386 2.215 0.337 1.265 1.275 0.000 0.721 0.315 -0.542 0.000 0.614 0.799 0.988 1.356 2.426 1.294 0.959 +1 0.709 -1.443 0.093 0.644 -1.704 1.134 -0.541 1.388 2.173 1.116 -0.338 -0.110 0.000 1.148 -0.674 -0.594 0.000 0.948 -0.299 -1.056 3.102 0.810 0.679 0.988 1.077 0.891 0.945 0.895 +0 1.899 0.755 0.127 0.809 -1.464 0.684 -1.040 -1.266 2.173 0.720 0.311 1.343 2.215 0.429 -0.117 -1.605 0.000 0.772 0.787 1.571 0.000 0.397 0.775 1.700 1.065 1.059 1.106 0.845 +1 0.694 0.043 -1.538 1.484 -0.058 1.190 0.925 -0.583 2.173 1.096 -0.119 0.818 0.000 1.690 0.557 1.284 1.274 0.874 -0.465 1.652 0.000 0.914 0.853 1.367 1.105 1.779 1.178 1.035 +1 0.584 2.017 0.156 2.029 -0.060 1.297 -0.483 1.701 0.000 0.398 -1.163 0.748 2.215 0.647 0.392 -0.864 1.274 0.527 0.713 1.119 0.000 1.112 0.943 0.978 1.275 0.725 0.872 1.095 +0 0.829 -0.099 -1.673 1.868 1.312 0.837 -1.625 -0.796 0.000 1.136 -1.133 -0.370 0.000 0.816 0.485 0.435 2.548 0.857 -0.890 0.903 3.102 0.922 0.981 1.000 0.979 0.625 0.955 1.013 +1 2.427 1.329 0.795 0.241 0.633 0.456 1.495 -0.569 2.173 0.987 -0.001 -1.378 0.000 1.055 1.630 0.100 0.000 0.932 0.256 -0.970 3.102 0.726 1.123 0.977 1.048 0.503 0.807 1.021 +0 1.114 -0.469 0.602 1.864 0.224 0.863 0.940 -0.793 0.000 0.736 -0.597 1.589 2.215 1.486 0.721 -1.399 1.274 1.342 -1.072 0.781 0.000 1.134 0.921 0.990 1.023 0.975 1.229 1.491 +0 1.090 0.187 -0.191 1.284 0.515 0.434 0.015 0.575 2.173 0.295 1.378 1.592 0.000 0.927 0.320 -1.299 0.000 1.138 -0.272 -0.777 3.102 0.756 0.852 0.986 0.764 0.710 0.643 0.710 +1 0.583 1.109 -0.748 0.750 -1.632 1.035 0.691 0.472 2.173 0.808 -1.074 1.681 0.000 0.940 -0.294 1.581 0.000 1.407 0.523 -0.309 1.551 0.795 0.895 0.993 1.054 0.828 0.843 0.768 +1 0.489 2.130 0.147 0.337 0.594 0.985 0.543 0.902 1.087 1.478 0.877 -1.247 0.000 0.836 0.981 -0.725 0.000 0.738 0.507 -0.349 3.102 0.788 0.647 1.000 0.789 0.816 0.900 0.786 +1 0.595 -1.501 -0.727 0.688 -1.103 0.686 -0.497 0.814 0.000 2.037 -1.732 0.503 0.000 2.069 0.149 -1.063 2.548 1.034 -1.258 1.399 0.000 0.935 0.929 0.981 0.759 0.939 0.992 0.836 +1 0.996 0.855 1.263 0.860 1.213 1.590 0.046 0.575 2.173 1.027 0.250 -1.522 0.000 1.409 0.617 -0.551 0.000 1.287 1.615 -1.196 0.000 0.794 0.698 0.992 1.642 0.916 1.162 1.040 +1 0.651 -1.376 -1.337 1.135 0.084 1.097 -0.352 1.366 0.000 1.006 -1.606 0.956 0.000 1.028 -2.558 0.322 0.000 1.640 -0.790 -0.298 3.102 1.088 0.952 1.141 0.694 0.452 0.760 0.690 +1 1.295 0.962 -0.633 1.039 -1.417 0.515 0.094 -0.329 2.173 1.100 0.435 0.823 2.215 0.563 -1.429 1.205 0.000 0.715 -0.289 1.548 0.000 0.502 0.872 1.042 0.796 0.975 0.858 0.883 +1 2.256 -1.085 -0.494 0.836 0.817 0.310 -1.738 -1.237 0.000 0.973 -1.386 1.672 2.215 0.768 -0.341 0.264 0.000 2.076 0.320 1.484 3.102 0.849 0.897 1.760 1.276 1.345 1.247 1.025 +1 0.397 0.573 0.095 1.575 -0.081 0.792 0.096 1.580 2.173 0.635 -1.042 -1.284 0.000 0.392 -0.740 -0.007 0.000 0.689 -0.330 1.181 3.102 0.706 0.875 0.980 0.692 0.338 0.785 0.689 +1 0.857 1.579 0.283 0.963 -1.105 0.723 0.182 -1.533 2.173 0.403 -1.070 1.118 2.215 0.978 0.548 0.492 0.000 0.772 0.905 -0.667 0.000 0.861 0.965 1.194 1.270 0.770 0.976 0.834 +0 1.241 0.276 0.156 0.561 1.578 0.746 -0.745 -1.431 1.087 1.131 -0.028 0.857 2.215 1.143 -1.479 -0.830 0.000 0.431 -0.973 -0.427 0.000 0.328 1.246 1.108 1.041 1.292 0.895 0.879 +0 0.482 -0.944 -1.531 1.272 0.510 1.057 -0.565 0.843 2.173 0.390 -1.867 -1.346 0.000 0.789 -0.171 -0.664 2.548 0.750 -0.203 -1.365 0.000 0.867 1.062 1.046 0.768 1.133 0.760 0.689 +1 1.052 1.240 -0.065 0.590 -1.608 0.741 -0.949 0.587 0.000 0.721 -0.516 -1.676 2.215 0.743 0.758 -0.672 2.548 0.539 -0.618 -1.131 0.000 0.971 1.059 1.074 1.058 0.838 0.754 0.871 +1 0.601 1.661 -1.731 1.456 1.002 0.574 0.428 -1.618 0.000 1.079 0.804 -0.350 2.215 1.099 0.464 0.588 0.000 0.707 0.000 -0.279 0.000 0.934 0.918 0.989 1.738 0.959 1.314 1.109 +1 0.535 -1.733 0.749 0.914 0.930 0.968 0.829 -0.754 0.000 0.919 -0.899 1.250 0.000 1.037 -1.620 -0.714 0.000 0.864 -0.882 -1.486 3.102 0.828 0.719 0.993 0.686 0.434 0.515 0.592 +1 2.028 0.788 0.716 0.671 -1.629 0.749 -1.628 -1.024 0.000 0.884 0.458 0.483 0.000 1.351 -0.342 -0.729 1.274 0.704 -0.761 1.550 3.102 2.764 1.526 1.385 1.344 0.690 1.018 1.205 +0 0.655 1.474 1.227 0.755 0.011 1.058 0.477 -1.191 1.087 1.299 -0.137 0.182 2.215 0.499 0.329 0.699 0.000 1.531 0.624 1.732 0.000 0.795 0.883 0.987 1.458 1.719 1.286 1.038 +1 0.499 -0.978 -0.806 1.469 0.596 1.375 0.162 -1.437 0.000 1.343 0.114 -0.085 2.215 0.761 0.885 1.468 0.000 0.697 -0.160 0.875 3.102 1.194 0.926 1.131 1.044 0.679 1.000 1.027 +1 0.703 -0.292 -1.092 0.565 1.199 1.514 -0.152 -0.834 0.000 0.869 -0.740 0.903 0.000 1.210 0.372 0.993 2.548 1.710 -0.563 0.106 3.102 2.549 1.649 0.980 0.892 1.006 1.191 0.990 +1 0.421 -1.418 -0.657 0.738 -0.046 1.169 0.845 -1.250 1.087 1.037 -0.571 -0.523 0.000 1.819 1.881 0.808 0.000 0.873 0.133 0.754 3.102 0.813 0.733 0.997 1.161 1.106 0.914 0.803 +0 0.324 1.762 -1.683 3.700 1.443 1.385 -1.039 -0.163 2.173 0.604 -2.086 -0.456 0.000 0.393 0.910 -0.690 2.548 0.550 -1.662 0.734 0.000 0.662 0.888 0.995 1.124 1.191 4.075 4.044 +1 0.466 -0.675 1.055 0.589 0.649 1.094 -1.485 -1.269 0.000 1.218 -1.302 0.408 2.215 0.585 -1.523 1.478 0.000 0.554 0.032 -0.103 3.102 0.897 0.931 0.979 0.727 0.643 0.892 0.799 +1 1.134 -0.092 1.631 1.231 1.645 0.379 1.442 1.414 0.000 0.807 0.291 -0.507 2.215 1.290 0.711 0.516 2.548 0.923 0.757 -1.226 0.000 0.671 0.834 0.988 1.021 0.903 0.868 0.741 +1 1.243 0.198 1.111 0.266 -1.708 0.921 0.421 -1.010 2.173 0.751 0.037 -0.536 0.000 1.023 -0.241 1.326 0.000 1.324 -0.830 0.711 3.102 0.910 0.967 0.986 1.066 1.476 0.886 0.785 +0 0.728 0.876 1.365 0.600 -0.332 1.248 2.023 0.872 0.000 1.552 0.331 0.228 0.000 1.380 -0.547 -0.303 2.548 1.554 -0.454 -0.692 0.000 0.859 0.765 0.989 0.790 1.823 1.081 0.896 +1 1.128 -0.987 0.759 0.992 1.737 2.239 -0.375 -1.174 0.000 2.114 -0.444 0.340 1.107 1.549 0.324 0.144 0.000 1.091 0.301 1.299 3.102 1.149 0.899 1.131 1.207 1.191 0.911 0.874 +1 1.399 -1.384 -0.039 0.487 1.387 0.716 -1.093 0.494 0.000 1.575 -1.689 -1.513 0.000 0.790 -0.288 -1.513 2.548 1.040 0.287 0.236 0.000 1.083 1.013 1.098 0.590 0.485 0.631 0.653 +1 1.744 0.643 -1.368 0.874 1.555 1.065 -0.202 0.572 1.087 0.767 0.391 -0.994 2.215 0.555 1.587 -0.234 0.000 0.389 -1.086 0.285 0.000 1.144 0.862 0.986 1.381 1.376 1.001 0.894 +1 0.663 -0.902 0.581 0.602 -1.172 0.487 0.544 1.260 0.000 1.230 1.026 -1.195 2.215 1.008 0.839 0.570 0.000 0.396 1.375 -0.492 3.102 0.771 1.138 0.992 0.962 0.419 1.088 1.004 +0 0.878 -0.807 -0.795 1.466 -0.235 0.734 -1.203 1.449 2.173 0.609 -1.638 0.476 0.000 0.902 -0.079 1.294 2.548 0.876 -1.588 -1.315 0.000 0.957 0.982 0.987 1.224 0.619 0.895 0.858 +1 1.250 0.081 -0.122 1.090 -1.068 0.720 0.570 1.039 2.173 0.499 0.391 0.316 0.000 1.019 -0.487 1.674 2.548 0.481 1.577 -1.144 0.000 0.808 0.925 1.216 1.102 0.850 0.854 0.754 +1 0.889 0.439 0.395 1.363 -0.628 0.621 -1.143 0.793 2.173 0.796 -0.394 1.738 0.000 0.929 1.072 -0.647 0.000 0.831 0.230 1.122 0.000 1.069 0.854 1.214 1.242 0.719 0.853 0.828 +0 0.291 2.093 -0.176 1.875 1.128 0.868 -0.420 -0.879 1.087 0.959 -1.114 -1.022 0.000 1.406 0.287 0.530 2.548 0.727 0.767 -1.097 0.000 1.338 0.872 0.987 1.631 1.414 1.896 1.897 +0 0.911 -0.754 -0.992 0.476 0.530 0.781 -1.140 -0.581 0.000 0.933 1.246 1.098 0.000 0.619 0.889 1.727 2.548 0.819 0.157 1.081 3.102 0.889 0.646 0.984 0.899 0.372 0.636 0.780 +0 0.513 1.389 -1.573 1.377 -0.754 0.809 0.763 0.412 0.000 0.681 0.775 1.062 1.107 1.194 1.024 -1.137 2.548 1.043 2.431 0.404 0.000 0.769 0.990 0.982 0.853 0.892 0.673 0.692 +1 1.456 -1.233 0.352 1.908 -0.563 0.797 -0.550 0.645 0.000 1.924 -0.725 -1.349 2.215 0.821 -1.065 1.264 0.000 1.252 -0.837 -0.410 3.102 0.893 0.941 1.694 1.653 1.065 1.072 1.061 +1 0.859 -0.226 1.471 2.092 0.452 0.975 0.551 -0.916 2.173 0.646 0.195 1.593 0.000 0.641 0.349 0.436 2.548 0.493 -0.735 -0.856 0.000 0.725 0.845 1.475 0.749 0.929 0.975 0.813 +1 1.090 1.435 -1.715 1.789 1.430 1.230 2.009 -0.178 0.000 0.420 1.798 -0.999 0.000 0.538 1.217 -1.282 2.548 0.768 -0.670 0.356 3.102 1.039 0.834 1.006 1.094 0.800 1.017 1.085 +0 2.377 -0.255 -0.099 0.231 0.111 0.839 -2.473 -1.522 0.000 0.539 -1.224 0.859 2.215 0.583 -1.536 -0.902 0.000 1.523 -0.519 1.567 3.102 0.848 0.953 0.992 1.084 0.554 0.796 1.059 +1 0.358 -1.786 -0.012 1.796 1.139 0.732 0.191 -0.584 2.173 0.746 -0.212 1.715 2.215 0.483 -0.517 -1.010 0.000 0.442 -0.019 0.008 0.000 0.428 0.515 0.987 1.416 0.980 1.013 0.768 +1 0.688 -0.188 0.301 0.462 -0.683 0.886 -0.743 -0.924 0.000 0.408 -2.185 0.626 0.000 1.336 -0.958 0.897 2.548 1.015 0.169 1.699 3.102 1.639 1.236 0.986 0.750 0.838 0.917 0.785 +1 1.242 0.552 -1.537 0.830 -1.463 0.609 -0.039 0.033 2.173 0.933 -0.404 1.183 2.215 0.493 0.840 -0.752 0.000 0.507 1.557 0.412 0.000 0.926 0.889 0.999 1.023 0.977 0.819 0.753 +0 0.337 -0.619 -1.143 1.744 1.464 0.544 0.352 -0.219 0.000 0.869 0.814 0.827 2.215 0.570 1.887 -0.675 0.000 1.234 -0.436 -0.405 3.102 0.860 0.830 0.988 0.946 1.083 0.788 0.712 +0 0.699 -0.398 1.454 0.200 -1.529 0.949 0.788 0.915 2.173 1.224 -0.560 -0.439 1.107 0.840 -0.105 0.307 0.000 2.005 -0.003 -1.420 0.000 1.433 1.192 0.991 0.779 1.898 1.130 0.937 +1 0.581 -0.191 -1.465 0.269 1.169 0.890 -0.949 0.811 2.173 1.329 -0.473 -0.561 0.000 1.084 -1.007 -1.077 1.274 1.204 -0.025 0.368 0.000 0.797 1.236 0.982 0.636 1.217 0.848 0.742 +1 0.839 -0.255 1.497 0.199 1.436 0.656 0.208 1.725 2.173 0.641 -2.198 -0.113 0.000 0.516 0.045 -0.321 2.548 0.749 -1.311 0.318 0.000 0.466 0.750 0.982 0.808 0.700 0.911 0.794 +0 0.977 0.914 0.763 2.001 1.353 1.272 0.161 -0.900 2.173 1.525 0.182 -0.426 0.000 1.577 -0.112 0.774 2.548 0.458 0.872 0.195 0.000 0.752 0.887 0.988 1.011 1.779 1.294 1.161 +1 1.248 -0.435 1.554 1.281 0.872 0.485 -0.446 -1.234 0.000 0.731 -0.753 -0.713 2.215 1.425 -0.363 0.134 2.548 0.390 0.254 0.129 0.000 0.676 0.777 1.009 0.988 0.777 0.822 0.700 +1 1.029 -1.121 1.143 0.618 -0.826 0.717 -1.336 0.040 0.000 0.527 1.081 -1.696 2.215 0.941 -0.589 -1.010 2.548 0.892 -0.633 0.828 0.000 0.875 0.913 1.082 1.067 0.863 0.916 0.817 +1 0.895 0.360 0.185 1.693 -0.449 2.636 -0.732 1.317 0.000 1.383 0.347 -0.522 1.107 0.570 -1.009 -0.012 0.000 0.483 -1.614 -0.664 0.000 0.637 1.043 0.989 0.749 0.806 0.725 0.690 +0 1.240 0.246 -0.444 0.528 -0.670 0.964 0.909 0.843 1.087 0.497 2.192 0.467 0.000 1.350 1.225 -1.182 0.000 0.906 2.135 -1.213 0.000 0.883 0.869 0.987 1.353 0.588 0.920 1.072 +1 0.790 -1.255 -0.364 1.299 0.861 0.819 0.212 0.229 2.173 1.461 -0.883 -1.232 2.215 1.092 -0.177 -1.721 0.000 0.719 -0.462 0.198 0.000 0.981 0.987 1.254 1.192 1.826 1.147 0.953 +1 0.514 -0.878 -1.250 0.660 0.837 1.102 0.811 -0.895 2.173 0.800 0.710 1.141 0.000 0.597 -0.682 0.972 1.274 1.228 0.655 -0.050 0.000 1.136 0.881 0.983 1.017 1.333 0.910 0.792 +1 0.605 0.348 -0.325 1.493 -1.034 0.530 -0.563 0.267 2.173 1.157 0.207 1.023 2.215 0.695 0.288 -0.922 0.000 0.668 0.713 0.604 0.000 0.765 0.808 0.992 1.135 0.860 0.989 0.786 +0 0.338 0.647 0.201 1.775 1.315 1.107 -2.031 -1.308 0.000 0.823 -1.676 0.888 0.000 0.767 -1.404 0.007 0.000 1.569 -0.283 0.005 1.551 0.876 0.940 0.986 0.965 0.793 0.789 0.858 +0 0.629 0.294 1.035 1.534 0.191 0.883 -0.056 -1.298 0.000 0.713 -0.706 1.281 0.000 0.825 -1.063 -1.097 1.274 1.018 -0.400 0.083 3.102 1.357 0.947 0.987 0.543 0.660 0.720 0.782 +0 0.503 1.243 -0.503 1.248 0.411 0.515 0.988 1.673 0.000 0.594 -0.267 -1.114 2.215 0.320 0.022 0.782 0.000 0.659 -0.736 -1.474 3.102 0.884 0.922 0.979 0.793 0.251 0.656 0.679 +0 1.319 0.844 0.704 0.106 -0.559 0.870 -0.618 -0.898 0.000 0.740 -1.422 0.129 0.000 0.834 0.278 -1.569 2.548 0.651 -1.532 1.508 0.000 0.873 1.076 0.981 0.592 0.292 0.631 0.737 +0 0.761 -1.227 -0.603 3.297 -1.001 1.794 0.074 0.868 1.087 0.991 0.832 0.414 0.000 1.305 0.112 -0.716 2.548 0.523 2.436 0.415 0.000 0.926 1.128 0.988 2.435 1.888 1.640 1.612 +1 1.480 -0.774 0.402 0.765 0.841 0.276 -1.428 0.686 0.000 0.475 0.155 -0.617 2.215 1.989 -0.920 -1.268 2.548 0.677 0.401 1.439 0.000 0.845 1.010 0.986 0.785 0.858 0.937 0.786 +1 0.632 -0.704 -0.259 0.385 0.640 1.176 -1.250 -1.247 0.000 0.598 -1.697 -0.754 0.000 1.445 -1.318 0.803 2.548 0.624 -0.183 -0.347 3.102 0.883 0.805 0.979 0.717 0.784 0.871 0.766 +0 1.710 0.553 1.007 0.943 0.235 1.022 -0.675 -0.241 2.173 1.260 0.662 1.586 2.215 1.554 -0.704 -0.772 0.000 0.432 -1.096 -1.080 0.000 0.549 0.722 1.128 0.989 2.069 1.237 1.110 +0 0.937 -1.652 -0.302 1.216 0.524 0.600 -0.587 -1.675 2.173 0.506 0.758 1.730 2.215 0.799 -0.201 0.136 0.000 0.653 -1.808 1.351 0.000 1.150 0.947 1.002 1.378 0.604 1.015 0.876 +1 0.453 -1.869 1.212 1.513 0.292 0.750 -0.138 -1.448 2.173 0.669 0.474 1.043 2.215 0.643 0.593 -0.310 0.000 0.530 -1.153 -1.242 0.000 0.911 0.831 0.989 1.188 0.879 0.937 0.815 +1 0.892 -0.083 -0.819 0.682 1.029 0.705 -0.895 -0.672 2.173 0.786 0.109 1.161 0.000 1.009 -0.664 0.952 1.274 0.396 0.181 -1.524 0.000 1.034 1.102 1.076 0.743 1.048 0.743 0.689 +1 1.275 1.144 1.160 0.760 0.309 0.643 1.062 -0.762 2.173 0.247 0.942 0.023 0.000 0.452 1.742 0.727 0.000 0.524 -0.579 0.317 0.000 0.938 0.883 0.990 1.010 0.671 0.812 0.716 +1 0.552 -0.755 1.278 0.907 -0.688 0.574 -0.558 0.619 2.173 0.943 -0.374 -0.174 2.215 1.480 -0.800 -1.456 0.000 0.669 1.233 0.641 0.000 0.843 1.000 0.989 0.695 0.717 0.819 0.703 +1 0.427 -0.316 -1.412 2.032 1.559 1.207 1.522 0.239 2.173 0.545 1.243 -1.224 0.000 0.872 0.441 -0.619 2.548 0.416 -1.313 -0.188 0.000 1.199 0.811 0.988 1.572 1.137 1.107 0.982 +1 0.419 -0.195 0.841 1.880 0.141 0.836 0.628 1.421 2.173 0.634 2.623 -1.078 0.000 1.471 1.088 -1.273 2.548 0.901 0.792 0.048 0.000 1.285 1.050 0.991 1.075 0.985 0.966 0.998 +1 0.587 -0.784 1.290 0.148 -1.475 1.800 1.340 0.802 2.173 1.091 1.186 -1.070 2.215 2.017 1.338 -0.701 0.000 0.586 1.992 -0.384 0.000 0.664 0.665 0.979 1.263 2.052 1.291 1.141 +0 1.628 -2.105 0.660 0.399 1.496 0.659 -0.474 0.109 1.087 0.810 -0.595 -1.578 0.000 0.623 -1.486 -1.352 0.000 0.874 -0.073 -0.880 3.102 0.620 1.039 0.985 1.043 0.644 0.859 0.823 +0 1.072 -0.550 -1.394 0.138 -0.867 1.333 -1.024 1.610 2.173 1.381 0.818 -0.125 2.215 1.899 -0.917 0.360 0.000 0.445 -0.721 1.301 0.000 0.838 1.467 0.995 1.097 2.940 1.579 1.266 +1 1.637 0.528 1.738 0.246 0.256 1.435 0.134 -0.198 0.000 1.060 1.143 1.653 0.000 0.864 0.415 1.085 0.000 1.211 -0.597 -0.749 1.551 0.899 1.165 0.984 0.931 0.840 0.942 0.824 +0 2.520 0.563 0.730 0.628 -0.951 0.640 -0.497 1.302 0.000 0.999 -0.872 -0.734 2.215 1.196 0.446 -0.982 0.000 0.539 0.482 -0.556 3.102 1.610 0.946 1.740 1.602 0.545 1.000 0.973 +1 1.133 1.384 -1.226 1.025 -1.735 1.574 1.084 0.280 0.000 0.989 1.995 1.360 0.000 1.749 0.293 -1.220 2.548 0.905 -1.837 0.466 0.000 0.831 0.910 0.975 1.183 0.847 0.901 0.841 +0 1.372 -0.415 -0.373 1.040 -0.660 1.029 -1.197 1.186 2.173 0.402 -2.691 1.410 0.000 0.650 -1.294 0.540 0.000 0.536 0.327 -0.951 3.102 0.794 0.943 0.982 1.625 1.015 1.032 1.041 +0 0.418 1.038 1.293 3.230 -1.493 0.734 -0.423 0.147 2.173 0.663 0.959 -0.282 0.000 1.136 1.356 0.119 2.548 0.476 1.536 0.599 0.000 0.614 0.938 0.989 1.247 1.265 1.398 1.081 +1 0.516 -0.325 -1.148 1.427 0.533 2.421 1.287 -1.574 0.000 1.284 -0.794 0.019 2.215 2.020 -1.579 -0.187 0.000 1.739 -0.372 0.667 1.551 9.420 5.128 1.187 0.847 0.784 3.190 2.329 +1 0.726 0.348 0.200 1.322 0.989 1.293 -2.300 0.420 0.000 1.657 -0.726 0.378 2.215 5.502 -1.315 -1.158 2.548 1.610 0.121 1.334 0.000 0.760 1.174 0.991 3.316 3.354 2.412 1.831 +1 1.207 -0.169 0.687 2.252 1.136 1.195 0.715 -0.373 2.173 0.753 -0.144 -1.152 0.000 0.618 0.945 -1.268 2.548 0.489 -1.026 -1.624 0.000 0.556 1.166 0.997 0.964 0.791 1.136 0.989 +0 0.519 -1.206 1.470 1.417 -1.608 0.962 0.348 -0.085 0.000 0.944 0.401 0.487 2.215 1.218 0.919 1.560 2.548 1.216 0.932 -0.515 0.000 0.901 0.882 0.990 0.854 0.999 0.872 0.951 +0 1.024 -0.217 1.081 0.407 -0.941 1.281 -0.667 -1.727 2.173 0.884 -0.668 -0.488 2.215 0.576 1.281 -0.220 0.000 0.380 0.365 0.468 0.000 0.397 0.791 0.989 0.925 1.408 0.978 0.827 +1 0.288 -1.722 -1.172 1.034 0.304 0.592 1.169 1.720 1.087 0.655 0.689 0.407 2.215 0.655 1.235 -1.110 0.000 0.473 -0.772 -1.264 0.000 0.868 0.816 0.984 1.120 0.876 0.811 0.732 +1 1.556 -0.361 -0.947 0.857 1.688 0.934 0.078 0.418 1.087 0.514 0.107 1.197 0.000 0.419 -1.034 -0.558 1.274 0.469 -0.854 0.058 0.000 0.668 0.654 1.110 0.627 0.783 0.816 0.675 +0 0.379 0.950 -0.639 1.301 1.224 0.623 0.739 0.637 0.000 0.674 -0.074 -0.731 2.215 0.625 -2.111 -0.123 0.000 1.259 0.570 -1.408 3.102 0.865 0.927 0.989 0.688 0.575 0.671 0.653 +0 1.502 -1.716 0.053 0.998 -0.242 0.961 -1.457 -1.503 0.000 0.432 -0.393 -1.553 0.000 0.822 -1.166 1.162 2.548 0.743 -0.612 0.119 3.102 0.766 0.849 0.979 0.959 0.510 0.684 0.865 +1 1.895 -0.610 0.247 0.915 -0.219 1.240 -1.444 -1.644 2.173 0.503 -0.535 0.933 0.000 0.492 -0.796 -0.279 2.548 0.494 -1.647 -1.083 0.000 0.807 0.874 1.003 0.503 0.962 1.015 0.816 +0 2.285 0.628 -0.293 0.146 -1.137 0.682 0.480 1.685 0.000 0.583 0.136 0.635 0.000 0.841 -1.052 1.312 2.548 0.603 -0.308 -1.132 0.000 0.798 0.703 0.987 0.688 0.156 0.777 0.658 +1 1.184 0.663 0.361 1.183 -0.072 0.870 0.748 -1.226 2.173 0.703 -0.052 -0.820 0.000 0.913 0.522 1.246 2.548 0.992 1.913 1.055 0.000 1.926 1.227 0.998 1.203 0.884 0.898 0.924 +0 1.227 0.391 0.979 0.600 1.185 0.953 0.406 -1.619 2.173 1.089 -0.115 -0.110 1.107 0.813 1.145 0.062 0.000 0.805 -2.292 -0.715 0.000 0.845 1.246 1.001 0.965 1.520 1.235 1.310 +0 0.867 -1.036 0.005 0.575 -1.652 0.886 -0.850 -0.707 0.000 0.555 -0.611 -1.423 0.000 0.716 -0.277 0.969 2.548 1.418 -0.610 0.542 3.102 0.911 0.940 0.987 0.643 0.333 0.720 0.635 +1 0.596 0.239 1.590 2.467 0.959 0.912 -0.121 -0.468 0.000 1.432 0.733 -0.535 2.215 0.994 -1.281 1.264 0.000 0.772 1.204 1.424 0.000 0.734 0.830 0.983 0.875 1.028 1.040 0.820 +0 1.172 -0.194 1.462 0.682 -1.700 0.862 -1.113 -0.218 0.000 0.733 -0.965 0.687 2.215 1.442 -0.558 -1.538 2.548 1.584 0.645 -0.318 0.000 0.718 0.949 0.970 0.612 1.013 0.839 0.800 +1 2.157 -0.070 1.114 1.536 0.728 1.108 0.319 -0.610 2.173 0.541 -0.212 -1.125 0.000 0.637 -0.891 0.369 1.274 0.409 -0.925 -1.334 0.000 0.305 0.667 0.984 0.707 1.089 1.120 0.898 +0 0.754 -1.565 0.905 0.721 -0.372 1.041 -0.024 -0.154 2.173 0.517 -1.020 0.453 0.000 2.381 -0.399 -1.629 2.548 1.421 -1.774 1.503 0.000 0.919 1.229 0.991 1.348 1.948 1.312 1.125 +1 1.020 0.691 -0.298 0.197 0.663 1.419 0.295 -1.538 2.173 0.830 1.458 0.411 0.000 0.696 0.321 0.706 1.274 0.545 -0.440 -0.262 0.000 1.163 0.742 0.982 1.143 1.115 0.977 0.854 +0 0.708 0.328 -0.295 0.962 1.122 1.455 -0.163 -1.287 0.000 1.814 0.104 0.730 2.215 0.786 0.211 1.641 0.000 2.118 -0.832 -0.431 1.551 1.001 1.427 1.095 0.838 1.845 1.401 1.135 +0 0.923 0.125 -0.412 0.665 -1.093 0.740 1.071 -1.644 0.000 0.605 -0.104 0.910 1.107 0.865 0.469 0.075 2.548 0.694 1.310 0.711 0.000 0.970 0.929 0.991 0.851 0.579 0.685 0.685 +0 1.023 1.749 1.349 0.435 -1.271 0.828 0.965 0.292 2.173 0.848 -1.200 -1.581 1.107 0.717 0.078 -0.127 0.000 0.433 0.539 -1.556 0.000 0.616 0.869 0.976 0.933 2.060 1.266 0.958 +0 0.754 -0.272 0.435 0.955 1.450 0.702 -0.844 1.304 0.000 1.082 -0.608 -0.158 2.215 1.542 -0.370 -1.140 2.548 0.389 -1.329 -0.430 0.000 0.854 1.001 0.991 0.914 1.073 0.808 0.740 +0 1.133 -0.061 1.707 0.870 -0.792 1.336 0.803 -1.037 2.173 2.750 0.904 0.427 0.000 0.502 0.881 1.630 1.274 0.792 -0.645 1.273 0.000 2.274 1.400 1.070 0.973 0.690 1.395 1.204 +0 0.877 2.411 -0.894 0.696 -0.382 0.737 0.941 0.772 2.173 0.454 -0.624 -0.284 1.107 0.332 0.965 1.525 0.000 0.514 -0.086 1.732 0.000 0.293 0.530 0.997 1.134 1.022 0.948 0.742 +1 1.575 1.095 1.361 0.706 -0.377 0.536 0.462 -0.251 1.087 0.872 0.822 0.882 0.000 0.563 -1.025 -1.006 1.274 0.928 1.119 -0.931 0.000 1.206 0.937 1.461 1.193 0.742 0.874 0.814 +0 0.840 -0.246 0.692 0.613 -1.424 0.861 2.932 -1.688 0.000 0.935 2.431 -0.787 0.000 1.008 0.601 0.738 2.548 1.768 0.186 0.367 1.551 0.846 1.199 0.987 0.701 0.404 1.028 0.960 +0 0.353 -2.228 -0.435 1.961 -1.358 0.649 -0.505 0.852 0.000 0.899 0.346 0.309 1.107 0.575 -0.998 -1.591 0.000 0.997 -0.388 -0.252 3.102 0.945 0.971 0.986 0.858 0.549 1.025 0.899 +1 1.494 -0.034 -0.268 0.933 -0.910 1.080 -0.054 -1.469 1.087 1.102 1.338 0.683 2.215 0.428 2.136 1.068 0.000 0.780 1.228 -0.781 0.000 0.871 1.047 0.990 1.468 1.950 1.264 1.017 +1 0.602 2.366 0.556 0.896 -0.459 0.573 0.450 0.684 1.087 0.884 0.988 -1.200 0.000 1.278 0.443 1.408 0.000 1.413 1.252 -0.390 3.102 1.239 1.073 0.990 0.577 0.943 0.848 0.813 +1 1.368 1.271 -1.230 1.484 1.559 0.891 0.622 0.582 2.173 0.804 0.968 -0.943 0.000 1.171 0.872 0.025 0.000 0.646 0.210 -0.216 3.102 1.140 1.134 1.159 0.861 0.551 0.890 0.855 +1 0.639 0.517 1.404 0.908 0.294 2.246 2.203 -0.966 0.000 2.006 0.710 0.501 0.000 1.355 0.314 0.804 0.000 2.227 0.001 -1.711 3.102 0.840 0.642 0.984 0.847 0.858 0.937 0.788 +1 0.498 -0.293 1.398 1.341 -0.240 0.614 -1.077 0.734 2.173 0.657 0.592 0.813 2.215 0.604 -0.792 -0.735 0.000 0.414 1.812 -0.186 0.000 1.232 0.934 1.127 0.853 0.894 0.840 0.759 +0 0.993 1.122 1.419 1.040 0.934 0.697 0.393 -0.238 0.000 1.041 0.975 -0.779 1.107 1.335 0.008 1.438 0.000 0.780 1.732 -0.631 0.000 0.873 0.642 0.977 1.098 0.696 0.715 0.659 +0 1.246 0.479 0.982 0.769 -1.277 0.789 0.542 1.656 0.000 0.892 0.880 -0.492 2.215 0.712 -0.605 0.024 2.548 0.376 -0.284 -0.564 0.000 0.979 0.993 1.213 0.954 0.828 0.759 0.731 +1 0.950 -1.671 1.292 1.318 -0.641 1.461 -0.358 0.027 0.000 1.365 -1.512 -1.608 0.000 0.907 -2.258 -0.816 0.000 1.423 1.096 1.171 3.102 0.755 0.647 1.528 2.132 0.819 1.447 1.301 +0 2.325 -1.227 -0.936 0.493 1.374 1.102 -0.200 0.302 0.000 0.705 0.908 0.992 2.215 0.487 0.583 -0.256 0.000 1.079 -0.772 1.579 3.102 0.855 0.958 1.294 0.815 0.932 1.070 1.068 +0 0.704 -0.182 -1.558 2.905 -1.583 1.816 -0.965 0.199 2.173 0.953 -0.766 0.880 2.215 1.779 0.446 -1.028 0.000 0.708 -1.124 0.199 0.000 1.712 1.478 0.992 2.155 1.129 1.458 1.426 +1 2.573 -1.145 0.361 0.243 -0.714 0.894 -1.310 -1.093 0.000 0.585 -0.500 -1.421 1.107 0.721 -0.246 1.553 2.548 0.501 -1.783 -1.490 0.000 0.559 0.773 0.988 0.998 0.319 0.765 0.784 +1 0.338 -2.226 0.079 0.246 -0.567 0.789 -0.939 1.291 0.000 0.908 0.085 0.369 2.215 1.028 -0.358 -0.669 2.548 1.407 -0.946 -1.598 0.000 0.837 1.053 0.992 0.755 0.864 0.901 0.768 +1 0.469 0.928 1.710 0.463 -1.315 0.923 -0.718 0.246 0.000 1.047 -0.262 -0.965 2.215 1.676 1.617 -1.651 0.000 1.738 0.047 0.234 0.000 0.775 0.940 0.983 0.685 0.897 0.886 0.799 +1 3.112 -0.993 -0.505 1.488 0.976 1.218 -0.103 -1.348 2.173 1.569 -1.574 1.123 0.000 1.071 -0.204 -0.185 2.548 0.588 -1.095 0.635 0.000 0.567 1.250 2.899 2.070 1.236 1.386 1.359 +1 0.935 -0.311 -0.107 0.151 1.166 0.668 -0.345 -0.978 0.000 0.610 0.893 -1.120 0.000 1.156 0.676 -0.097 1.274 2.065 1.031 0.874 3.102 0.933 0.979 0.988 0.897 0.953 0.964 0.814 +1 1.105 -0.760 -1.537 0.295 -1.075 0.580 -0.919 1.019 0.000 0.443 -1.656 -0.193 0.000 0.978 -1.047 0.273 2.548 0.970 0.296 -0.881 3.102 0.955 0.932 0.996 0.843 0.889 0.734 0.680 +1 1.023 -0.940 0.622 0.471 1.292 1.300 -1.107 -1.070 0.000 1.186 0.057 0.600 1.107 0.625 0.078 -1.119 0.000 0.680 -0.806 -0.596 3.102 1.038 0.638 0.995 0.666 0.836 0.994 0.896 +1 0.716 -1.460 0.782 1.109 1.113 3.671 0.578 -1.698 0.000 2.633 -0.268 -0.142 2.215 3.606 1.040 0.156 0.000 0.606 0.581 0.469 1.551 1.450 1.329 1.003 1.477 0.830 2.172 1.816 +1 0.441 -1.170 0.457 2.301 1.309 1.019 -0.471 -0.205 2.173 0.459 -0.693 1.188 2.215 1.077 -1.339 -1.226 0.000 0.944 -0.569 0.381 0.000 1.192 1.114 0.985 0.471 0.964 0.886 0.825 +0 2.576 0.292 0.224 0.770 1.184 0.632 0.106 -1.176 2.173 1.292 -0.413 -1.737 2.215 0.526 -1.151 -0.523 0.000 0.979 0.997 -0.668 0.000 0.592 0.894 1.486 1.248 0.736 1.107 0.994 +0 1.594 0.209 -1.307 0.786 1.640 0.875 -0.356 0.631 0.000 1.094 0.130 -0.828 2.215 1.130 0.092 -0.127 0.000 1.161 -0.792 1.225 3.102 1.186 0.831 0.984 0.816 1.137 0.922 0.995 +1 0.385 0.138 -0.814 0.454 1.005 0.870 1.156 -1.282 2.173 0.509 1.020 0.558 2.215 0.491 -0.895 -0.576 0.000 0.518 -0.211 0.497 0.000 0.503 1.061 0.989 0.588 0.977 0.748 0.692 +0 1.470 -0.993 -0.651 0.936 1.639 0.930 -0.769 1.612 1.087 0.489 -1.514 -0.100 0.000 1.132 -0.835 0.706 2.548 1.140 -1.302 0.408 0.000 0.942 1.102 1.431 1.028 0.938 0.939 0.941 +1 0.640 1.352 1.370 2.096 0.592 0.917 1.661 -0.389 0.000 1.330 0.952 -1.393 2.215 0.667 1.158 -1.110 2.548 0.752 1.091 0.848 0.000 0.729 0.902 1.035 0.872 0.292 0.881 0.742 +1 0.305 -0.657 -1.369 0.448 0.308 0.949 -0.525 0.593 0.000 2.055 0.334 -0.989 2.215 1.284 0.892 0.638 0.000 1.266 -0.949 0.028 0.000 0.968 0.997 0.997 0.997 0.363 1.182 1.000 +0 0.785 1.286 1.552 1.035 1.643 0.690 1.215 0.065 0.000 0.650 0.534 -1.302 2.215 0.764 0.492 -0.759 0.000 0.869 0.186 0.719 0.000 0.903 0.949 0.977 0.929 0.571 0.770 0.817 +1 0.708 -1.985 -0.401 0.539 1.028 0.905 -0.720 0.048 2.173 1.192 -1.791 -1.481 0.000 0.585 0.206 -1.149 0.000 1.120 -0.532 -1.345 1.551 1.059 0.906 0.983 0.700 1.014 0.682 0.642 +0 0.681 -0.051 0.756 1.667 -0.187 0.564 0.538 1.109 2.173 0.650 1.936 -1.583 0.000 1.393 0.354 -0.661 2.548 0.763 -0.560 1.157 0.000 0.910 0.894 1.108 0.920 1.107 0.788 0.725 +0 0.661 -1.408 -0.363 0.853 -1.078 0.498 0.148 0.208 0.000 0.784 0.411 -0.819 2.215 1.295 -0.120 0.903 1.274 0.644 -1.618 1.477 0.000 1.335 0.960 0.983 1.541 1.112 1.276 1.095 +0 1.428 1.355 1.469 0.740 -0.524 0.360 0.929 0.247 0.000 0.495 0.599 1.685 0.000 0.889 0.261 0.679 2.548 1.863 0.324 -0.677 3.102 0.875 0.830 1.389 0.954 0.926 0.840 0.710 +0 0.703 1.104 0.869 0.851 -1.620 0.868 -0.077 -0.298 1.087 1.073 1.090 1.421 2.215 0.936 1.173 -1.077 0.000 0.754 -0.384 0.864 0.000 1.066 0.920 0.986 1.024 1.683 0.932 0.800 +1 0.691 -0.124 0.394 1.188 0.772 0.545 -1.023 -0.539 0.000 1.120 -1.478 -1.380 0.000 1.208 -0.368 -1.507 2.548 0.649 -0.020 0.010 1.551 1.211 0.985 0.989 0.585 0.675 0.733 1.046 +1 1.696 -0.505 0.798 0.339 -1.273 0.839 0.266 -0.975 2.173 0.590 -1.059 0.302 0.000 1.204 -1.156 -1.420 0.000 1.048 0.935 0.013 3.102 0.788 0.970 1.005 0.922 0.886 0.873 0.757 +1 0.499 -0.672 1.669 0.355 -0.133 1.201 0.166 0.594 2.173 1.938 -1.477 -1.473 0.000 1.369 0.218 -0.873 0.000 1.106 -0.356 -1.216 0.000 0.899 0.776 0.987 0.624 0.544 0.610 0.611 +1 1.029 -1.002 1.587 0.778 -1.162 0.531 -0.225 1.226 0.000 0.446 0.453 -1.391 0.000 1.184 0.124 -0.388 2.548 2.598 0.914 0.305 3.102 0.816 0.918 0.988 2.181 1.031 1.468 1.170 +1 2.857 -0.526 -0.384 0.111 -1.727 1.248 -0.406 1.522 2.173 0.793 0.392 0.319 2.215 0.963 0.187 -1.682 0.000 0.630 -0.585 0.378 0.000 0.918 0.825 0.987 0.907 1.432 1.152 0.937 +1 0.561 0.546 -0.005 0.544 -1.662 1.513 1.131 1.485 0.000 0.778 -0.757 0.529 2.215 1.928 -0.564 -0.511 2.548 0.960 0.472 -0.550 0.000 1.857 1.868 0.986 1.337 1.054 1.570 1.273 +0 0.456 1.699 1.131 0.593 -0.739 1.314 -0.026 -0.303 2.173 1.334 0.550 1.260 1.107 0.576 1.246 1.533 0.000 0.903 0.787 0.195 0.000 0.762 1.158 0.992 0.820 2.010 1.040 0.856 +0 0.493 1.481 0.711 3.382 0.233 1.099 0.050 0.032 2.173 2.022 -0.511 -1.601 2.215 1.078 0.870 -1.116 0.000 1.728 0.503 1.661 0.000 0.937 1.337 0.995 0.974 2.275 1.753 1.486 +1 0.696 1.069 1.236 0.820 -1.160 0.838 0.103 0.113 0.000 0.996 1.021 1.616 2.215 0.662 0.747 -0.845 2.548 0.629 2.011 0.688 0.000 1.008 0.886 0.985 0.625 0.696 0.607 0.579 +1 1.520 0.812 -1.610 1.616 -1.305 1.475 -0.149 0.501 0.000 0.928 0.079 0.071 2.215 1.127 0.827 -0.805 2.548 0.845 -0.102 -1.740 0.000 1.535 1.054 0.990 0.778 0.900 0.972 1.224 +0 1.147 0.341 -1.310 0.590 -0.148 0.650 -1.048 -1.580 2.173 0.469 -0.854 -0.026 0.000 0.923 -0.250 1.034 0.000 1.400 1.195 0.029 3.102 0.881 0.900 0.988 0.838 1.897 1.031 0.882 +1 1.053 0.253 0.526 1.596 -0.270 0.681 0.964 -1.266 2.173 0.848 -0.194 1.574 2.215 0.378 -1.302 -0.595 0.000 0.730 0.110 -1.711 0.000 0.700 0.854 1.180 1.103 0.928 0.941 0.791 +1 0.775 -0.102 0.184 1.164 1.569 1.916 0.282 -0.182 0.000 2.229 -1.929 1.587 0.000 0.489 0.991 -0.374 0.000 0.996 1.090 1.453 3.102 0.778 0.679 1.248 0.838 0.758 0.800 0.839 +0 0.705 0.104 -0.502 0.984 1.131 0.956 0.705 -0.107 2.173 1.185 -0.558 1.328 2.215 0.515 0.190 -1.385 0.000 0.925 -0.978 -1.505 0.000 0.705 0.943 1.148 0.859 1.853 0.978 0.810 +1 0.501 -1.232 -1.501 0.334 -1.063 0.879 1.023 0.309 0.000 0.722 0.082 1.652 2.215 0.537 -0.606 -0.749 0.000 0.698 0.676 1.171 1.551 0.378 0.788 0.996 0.622 0.359 0.593 0.556 +0 0.424 -1.379 -0.987 1.048 -0.640 0.723 0.165 1.731 0.000 1.075 -0.432 0.476 2.215 0.495 -0.569 -0.944 0.000 1.036 -0.967 0.667 3.102 0.840 0.904 1.000 0.957 0.385 0.743 0.740 +1 0.396 -1.560 0.925 1.587 -0.865 0.541 -0.119 1.456 0.000 0.562 1.441 -1.388 0.000 0.431 1.853 -0.087 0.000 1.343 0.044 0.445 0.000 0.868 0.656 1.098 0.696 0.219 0.661 0.661 +0 1.527 -0.559 -1.131 0.688 -0.239 1.129 -1.113 0.969 2.173 0.949 -0.744 1.740 0.000 1.487 -1.355 -0.482 2.548 1.200 1.896 -0.080 0.000 1.001 2.461 1.024 1.299 1.587 1.947 1.548 +0 1.099 0.339 -0.942 0.882 -0.040 0.492 1.737 -1.146 0.000 0.802 0.971 0.419 2.215 0.810 1.527 1.268 0.000 1.218 0.528 1.307 1.551 0.935 0.950 0.992 0.821 0.661 0.675 0.742 +1 1.330 0.713 -0.182 1.194 -0.991 1.348 0.420 1.030 2.173 0.603 -0.545 -1.287 0.000 0.519 -0.991 0.463 2.548 0.838 2.012 -1.362 0.000 1.946 1.484 1.161 1.478 1.005 1.181 1.105 +1 1.005 -0.134 0.804 0.900 -1.617 0.830 0.264 -1.310 0.000 1.414 -0.882 0.186 2.215 0.543 0.043 1.432 2.548 0.767 0.122 -0.656 0.000 0.683 0.599 1.080 1.093 0.958 0.901 0.810 +1 0.587 -1.723 -1.584 1.163 0.500 1.569 -1.331 -0.211 2.173 0.853 -0.989 1.045 0.000 0.491 -0.935 -0.911 0.000 0.473 -0.208 -1.208 3.102 0.975 1.290 1.091 0.701 0.885 0.797 0.761 +1 0.881 -0.756 0.638 0.825 0.596 0.863 0.120 0.465 2.173 1.191 0.818 1.738 0.000 1.096 -0.436 -0.389 2.548 1.870 0.667 -1.356 0.000 0.749 1.100 0.991 1.183 0.921 0.917 1.130 +0 0.574 -0.435 -0.208 0.921 0.753 0.507 -1.484 -1.268 0.000 0.766 -0.558 -0.815 2.215 0.702 -0.057 0.470 0.000 0.522 -1.215 1.562 3.102 1.361 0.936 0.983 0.559 0.546 0.586 0.605 +1 0.874 -0.231 1.279 0.469 -1.241 0.988 0.258 0.595 1.087 0.719 -1.052 -1.670 0.000 0.787 -1.047 -0.678 1.274 1.064 -2.121 -0.425 0.000 1.189 0.741 0.991 0.880 1.308 1.097 1.016 +0 1.773 1.594 -1.689 0.671 -0.863 1.122 -0.250 0.522 1.087 0.486 0.257 0.546 2.215 1.104 -0.209 -0.867 0.000 0.605 0.714 -1.381 0.000 0.725 0.692 1.025 1.940 0.284 1.240 1.003 +0 0.627 -0.171 0.273 1.031 -1.163 1.001 0.629 1.511 1.087 0.750 -0.820 0.742 0.000 0.777 0.718 -0.650 0.000 0.562 1.061 -0.234 0.000 0.942 0.589 1.071 1.010 0.326 0.767 0.720 +1 1.714 0.186 0.528 1.289 1.185 0.419 1.385 -0.250 0.000 0.817 1.379 -1.342 0.000 1.067 0.715 -0.607 2.548 0.632 1.038 -1.016 3.102 1.034 0.747 1.149 0.888 0.269 0.785 0.821 +1 0.880 0.268 -0.328 1.763 -1.128 1.348 0.849 1.488 0.000 1.335 -0.857 0.371 1.107 0.918 -2.090 -0.212 0.000 1.058 -1.066 -0.362 1.551 1.118 1.125 1.139 0.968 0.692 1.015 1.036 +0 1.091 -1.011 -0.472 0.587 -1.164 1.120 1.210 -1.544 2.173 1.256 0.181 1.483 0.000 1.440 0.441 0.437 0.000 1.543 0.854 0.271 0.000 0.937 0.691 0.988 2.388 1.576 1.567 1.310 +0 1.090 -1.603 -0.768 0.212 1.488 0.547 -0.992 -0.248 2.173 0.810 -1.672 1.398 0.000 0.568 -1.030 0.383 0.000 0.853 -0.164 1.645 3.102 0.880 0.889 0.981 0.676 0.776 0.643 0.617 +0 0.536 1.003 -0.547 1.158 1.736 1.231 1.996 0.237 0.000 1.488 1.500 1.442 2.215 1.273 -0.337 -1.143 2.548 0.850 -0.167 -0.292 0.000 0.626 0.768 0.987 0.881 1.939 1.036 0.849 +1 1.541 0.811 1.588 1.078 -1.276 0.845 -0.594 0.683 2.173 0.676 -0.490 -0.093 0.000 1.140 0.310 -0.113 2.548 0.771 -1.945 -0.948 0.000 0.667 0.697 0.984 1.380 1.001 1.048 0.932 +1 0.557 0.582 -1.081 1.370 0.459 1.026 0.333 -1.462 0.000 1.080 1.081 1.019 1.107 1.639 2.198 -0.534 0.000 0.601 -0.953 -0.653 0.000 0.833 1.087 1.190 0.730 0.715 0.732 0.688 +1 0.511 -1.812 -0.744 0.683 -0.679 0.837 -0.542 -1.488 0.000 1.183 0.102 0.801 2.215 0.674 1.938 0.955 0.000 1.212 0.594 -0.059 3.102 0.737 1.006 1.002 1.085 0.827 0.847 0.795 +1 0.773 0.120 1.103 1.161 -1.675 1.079 -2.118 -0.376 0.000 0.936 -1.601 1.130 0.000 1.121 -0.166 -1.252 2.548 1.931 -0.073 -0.086 3.102 0.796 1.149 0.983 1.086 0.978 0.949 1.045 +1 0.406 -1.893 0.722 1.207 1.209 1.436 2.323 -0.439 0.000 0.562 -2.204 -1.249 0.000 0.900 -0.975 1.266 2.548 0.973 -0.562 1.056 3.102 9.576 5.150 0.981 0.527 0.196 3.158 2.476 +0 1.250 -0.255 -0.634 0.954 -1.692 0.752 -1.231 -1.417 1.087 0.986 -0.765 0.945 0.000 1.334 -0.552 0.216 2.548 0.712 -0.114 -1.679 0.000 0.918 0.907 1.233 0.907 1.306 0.914 0.827 +0 0.719 -0.677 -1.742 1.184 0.783 0.459 0.459 -0.907 0.000 0.749 -0.658 -0.314 0.000 0.636 0.187 -1.236 2.548 0.494 -0.618 1.266 3.102 0.982 0.732 0.987 0.758 0.393 0.489 0.616 +1 1.326 -1.194 0.190 0.524 0.404 1.523 1.023 1.721 0.000 0.409 0.330 0.421 0.000 0.627 0.595 -0.091 0.000 0.886 -0.433 -1.110 0.000 0.914 0.763 0.992 0.827 0.386 0.632 0.671 +0 0.884 -1.211 0.814 0.539 0.800 1.025 0.215 -0.300 2.173 0.768 -1.103 -1.617 1.107 0.457 0.433 1.624 0.000 0.379 -2.467 1.372 0.000 1.245 0.829 0.977 1.095 1.540 1.018 0.897 +1 0.793 0.578 -1.437 1.190 1.452 1.517 -2.713 0.064 0.000 0.949 0.011 1.361 0.000 1.605 0.700 1.647 2.548 1.572 -0.838 1.631 3.102 1.060 0.861 0.987 0.606 1.226 0.945 0.936 +1 1.217 1.084 0.024 0.394 0.086 2.125 1.627 -1.580 0.000 2.598 0.250 0.139 2.215 0.899 -0.377 -1.339 0.000 0.779 0.503 1.586 1.551 1.274 0.771 0.995 0.769 1.259 0.935 0.799 +0 0.982 -1.470 1.681 0.499 -1.505 0.552 -0.975 -0.130 0.000 0.942 0.287 -1.059 2.215 0.689 0.648 1.574 0.000 1.465 -0.690 0.479 3.102 0.702 0.955 1.002 0.905 1.217 0.817 0.779 +0 1.270 -0.512 1.322 0.595 0.757 0.453 -2.820 0.081 0.000 1.641 -0.413 -0.654 2.215 0.971 2.571 1.152 0.000 1.293 -0.757 -1.655 3.102 10.180 5.401 0.980 1.295 1.081 3.300 2.413 +0 0.561 -0.002 -1.356 0.975 -0.001 0.691 0.555 0.704 0.000 0.780 2.101 -1.507 0.000 1.668 -0.561 -0.990 2.548 1.177 -0.808 0.438 3.102 1.986 1.896 0.990 0.761 1.046 1.521 1.207 +0 0.898 -0.888 1.496 0.852 0.488 0.786 -1.368 0.343 2.173 1.158 -1.175 -1.254 2.215 0.401 0.119 -1.475 0.000 0.499 -0.862 -0.404 0.000 0.507 0.750 0.988 0.949 1.397 0.856 0.685 +0 0.401 -1.310 1.347 1.524 -0.226 1.071 -0.651 -1.715 2.173 0.712 -2.171 -0.288 0.000 1.153 -0.911 1.069 0.000 1.555 -0.167 0.073 3.102 1.594 1.305 1.070 1.200 1.400 1.123 0.968 +0 0.753 1.034 1.414 0.999 0.420 1.665 0.426 -0.673 0.000 2.501 0.630 0.995 2.215 1.896 -0.072 -0.883 2.548 0.533 0.365 0.584 0.000 1.303 0.973 0.987 0.789 2.453 1.560 1.244 +0 1.460 -1.466 0.411 0.866 -0.507 1.207 -0.818 -1.229 0.000 0.803 -0.383 0.600 2.215 0.281 -0.974 1.283 0.000 1.262 0.329 -1.674 0.000 0.815 0.857 1.146 0.883 0.299 0.741 0.796 +0 1.057 0.392 -1.160 0.794 -0.089 0.859 0.138 0.084 2.173 1.851 -0.622 1.398 0.000 0.793 -1.359 0.919 0.000 0.574 1.664 -0.325 0.000 1.128 1.219 1.044 0.824 0.895 1.108 1.020 +1 0.854 0.541 0.634 1.425 -0.009 1.668 1.234 -1.529 2.173 0.823 2.188 -0.303 0.000 0.539 0.790 -0.559 0.000 1.796 1.467 0.970 0.000 0.800 0.690 0.996 1.605 0.253 0.986 0.923 +1 0.315 1.685 1.697 1.676 0.882 1.172 -0.317 -1.414 2.173 2.171 -0.269 -0.726 0.000 1.801 1.381 0.950 1.274 0.842 -0.339 0.570 0.000 0.839 1.211 0.987 0.781 2.447 1.690 1.416 +0 0.869 0.648 -1.549 0.999 1.491 0.399 0.568 -0.446 0.000 0.500 -0.269 0.387 2.215 0.351 2.252 -0.311 0.000 0.926 1.255 -0.188 3.102 0.743 0.802 0.988 0.783 0.694 0.745 0.675 +0 0.912 1.504 1.203 0.790 0.084 0.846 -1.057 1.581 0.000 1.488 0.111 -0.017 2.215 1.213 -0.412 -0.375 0.000 1.831 1.837 -1.681 0.000 1.875 1.209 0.995 1.179 0.934 1.061 1.188 +1 0.728 -0.063 -0.311 1.206 1.060 0.817 -0.510 0.935 0.000 1.118 -1.131 -0.131 2.215 1.036 0.179 1.304 0.000 1.702 -1.809 -0.738 0.000 0.973 0.938 1.226 1.061 0.930 1.104 0.941 +1 1.040 1.814 0.953 0.785 -0.364 0.974 2.446 -0.476 0.000 0.613 2.292 1.264 0.000 0.945 0.785 1.731 2.548 0.431 1.207 1.453 1.551 1.644 0.982 1.160 0.870 0.189 0.830 0.754 +1 1.215 -0.573 -0.050 0.248 0.289 1.277 -1.133 0.243 0.000 1.883 -1.086 -1.589 0.000 1.283 -0.705 -1.390 2.548 0.489 -1.173 -0.290 0.000 1.000 1.055 0.989 0.932 0.377 0.883 0.775 +0 1.924 -0.015 0.357 0.038 1.009 0.934 0.426 -1.028 1.087 1.252 1.215 -0.350 0.000 2.856 -1.044 1.313 0.000 1.395 1.008 1.049 0.000 0.758 1.239 0.829 1.130 1.309 1.349 1.200 +0 1.267 1.313 0.403 0.729 1.386 0.615 -0.538 0.839 1.087 0.480 0.230 -1.035 2.215 1.548 -0.878 -0.817 0.000 0.922 0.674 -1.456 0.000 1.509 0.876 1.032 1.125 0.856 0.841 1.014 +0 3.593 0.242 0.321 1.261 0.651 1.850 0.154 -1.486 2.173 0.817 2.302 -1.007 0.000 0.866 -0.558 0.698 0.000 1.041 0.931 -1.162 0.000 0.840 0.580 0.975 2.398 0.760 1.517 1.259 +0 0.873 -0.567 -1.218 1.008 1.333 1.187 2.130 -0.527 0.000 1.423 -0.476 1.142 2.215 1.407 0.418 0.258 2.548 1.354 1.086 -0.926 0.000 1.059 1.461 0.987 0.778 1.306 1.757 1.539 +0 0.874 1.474 0.182 1.018 -0.547 1.012 -0.139 1.026 2.173 0.931 0.858 -0.188 2.215 1.054 -0.586 1.578 0.000 0.965 -0.643 -0.908 0.000 0.875 1.002 0.987 0.548 1.481 0.996 0.992 +1 0.359 -0.913 0.872 1.107 1.432 1.509 -0.149 0.046 0.000 0.636 -1.672 -1.575 0.000 1.227 1.568 -1.287 0.000 1.343 0.713 1.602 0.000 0.915 1.014 0.984 0.803 0.840 0.919 0.806 +0 1.900 0.155 0.948 1.185 0.584 0.645 1.440 -1.265 0.000 0.661 -0.942 0.465 2.215 1.128 -0.421 -0.793 2.548 0.857 -0.803 -1.398 0.000 0.501 0.714 0.992 1.261 0.864 0.940 0.884 +0 3.131 -0.361 -0.598 0.745 1.556 1.909 -0.735 1.204 0.000 1.456 -1.008 -0.380 1.107 0.815 -0.377 1.355 1.274 1.005 -1.625 1.242 0.000 1.361 0.818 1.972 1.253 1.214 1.208 1.331 +0 0.673 -0.700 1.510 1.549 -1.554 0.476 -0.059 0.045 0.000 0.775 -0.708 0.666 0.000 0.830 0.599 -0.653 2.548 0.398 -1.126 0.317 0.000 0.813 0.910 0.985 0.818 0.715 0.705 0.764 +0 0.468 -0.783 -0.745 1.750 1.042 1.198 -1.845 -0.575 0.000 2.480 -0.125 1.375 2.215 1.703 0.085 -0.700 0.000 1.753 -0.175 0.264 3.102 0.925 0.872 1.253 1.112 1.587 1.165 1.029 +0 1.199 0.277 -0.242 0.308 0.591 1.072 -0.169 0.965 2.173 1.379 0.361 -0.795 2.215 0.593 -0.045 0.463 0.000 1.072 -0.074 -1.514 0.000 0.860 0.907 0.981 0.979 1.855 1.001 0.818 +1 2.513 -0.748 1.308 0.061 -1.302 0.892 -1.071 -0.890 2.173 0.806 -1.090 0.380 0.000 0.332 0.892 0.068 2.548 0.697 1.539 -0.204 0.000 0.241 1.604 0.982 0.814 0.977 0.940 1.034 +0 0.630 -1.672 -1.182 2.606 -1.578 0.731 -0.752 0.856 0.000 0.766 -0.702 -0.114 0.000 1.425 -0.196 -0.091 2.548 0.427 0.005 0.530 3.102 1.220 0.955 0.981 1.143 0.323 1.386 1.279 +1 1.101 -2.331 -0.601 0.580 0.611 0.571 -0.150 1.585 2.173 0.980 -1.958 0.169 0.000 0.576 -1.793 0.842 0.000 0.760 -0.723 -1.026 3.102 0.658 0.784 0.987 1.371 0.557 0.896 0.834 +1 1.929 0.444 -0.895 0.308 -0.778 1.688 0.644 0.965 2.173 1.353 2.194 -1.070 0.000 1.111 0.309 0.111 1.274 0.500 0.839 1.472 0.000 1.077 1.459 0.986 1.664 1.219 1.436 1.268 +1 0.846 1.102 0.846 0.465 1.693 0.538 0.672 1.645 0.000 2.005 -0.167 -0.178 2.215 1.088 -0.598 -1.039 2.548 0.719 0.229 1.040 0.000 0.531 0.869 0.987 1.835 1.165 1.405 1.128 +0 2.721 -0.080 0.877 0.396 1.161 0.743 -0.583 -0.843 0.000 0.671 0.159 -0.901 0.000 0.925 -0.990 -0.385 2.548 0.916 -0.248 1.211 3.102 0.628 0.721 1.000 1.096 0.753 0.717 0.776 +0 0.335 1.069 -1.529 0.156 0.552 0.669 1.616 0.856 0.000 1.524 -0.610 -1.003 2.215 0.754 0.358 1.021 2.548 0.867 2.003 0.016 0.000 0.923 0.874 0.978 0.826 1.259 1.472 1.191 +0 1.532 -0.071 -0.894 0.077 -0.853 1.843 0.233 -0.458 2.173 3.116 -1.174 1.737 1.107 2.168 1.211 0.455 0.000 2.108 0.421 0.732 0.000 1.158 2.167 0.998 1.946 4.263 2.984 2.207 +0 0.962 -0.519 0.792 1.389 1.306 1.237 2.711 -1.296 0.000 1.350 0.717 0.558 0.000 2.291 0.250 -0.034 1.274 0.435 -1.540 -1.657 0.000 0.848 0.819 0.992 1.262 0.544 1.587 1.654 +0 0.626 0.301 -1.678 0.230 1.094 1.020 -1.143 0.513 2.173 1.183 0.209 -0.928 2.215 0.635 -1.149 1.490 0.000 0.519 -1.270 -0.335 0.000 0.637 0.979 0.992 1.794 1.963 1.369 1.119 +0 0.589 -1.076 -1.285 1.041 0.270 1.329 -1.797 0.219 0.000 1.519 -1.480 1.702 0.000 0.869 -1.034 -1.048 2.548 1.106 -0.409 1.525 0.000 0.950 0.838 1.070 0.575 0.255 0.490 0.602 +1 0.725 1.494 -0.973 1.366 -0.197 1.281 0.573 1.407 2.173 0.505 0.758 -0.718 0.000 0.475 1.593 0.769 0.000 0.395 -1.058 -0.922 0.000 0.823 1.050 0.988 0.915 0.830 1.118 0.885 +0 0.459 0.027 -1.104 1.304 1.564 1.428 0.314 -0.528 2.173 1.706 0.791 1.011 2.215 0.991 0.540 0.130 0.000 0.668 0.114 -1.404 0.000 0.906 1.027 0.982 1.482 2.330 1.483 1.151 +0 0.665 -0.224 1.578 1.155 -0.342 1.073 0.488 -1.440 0.000 0.730 -0.015 0.094 2.215 0.810 -0.795 0.454 0.000 1.132 0.762 0.973 1.551 0.934 0.793 1.199 0.726 0.705 0.650 0.598 +0 0.464 -0.327 0.009 1.798 -1.317 1.031 1.264 0.290 0.000 0.802 0.971 1.131 2.215 0.720 1.188 -0.358 0.000 1.143 -1.065 -1.712 3.102 0.857 1.186 1.176 1.129 1.301 0.885 0.849 +1 0.473 1.141 0.371 1.032 -1.253 2.915 0.341 -1.518 0.000 1.908 0.474 0.491 0.000 1.836 -0.395 -0.234 1.274 2.080 -0.587 0.937 3.102 1.206 1.336 0.989 1.125 1.315 1.109 1.023 +0 0.968 -2.219 -1.049 1.000 -1.408 0.852 0.083 0.364 2.173 0.683 0.656 -1.737 0.000 0.546 0.680 -0.427 0.000 0.607 0.641 0.993 0.000 0.867 0.996 0.990 0.693 0.465 0.995 1.014 +0 0.776 -1.556 0.544 0.437 1.725 0.453 1.130 -1.011 1.087 1.080 0.530 1.614 1.107 0.730 -2.313 0.199 0.000 0.504 0.194 -0.626 0.000 1.334 1.753 0.986 1.042 0.785 1.328 1.082 +1 2.332 0.058 1.446 0.780 -1.460 1.884 -1.155 -0.807 0.000 1.186 -0.377 0.440 0.000 1.115 -0.906 0.453 0.000 0.806 -0.284 0.854 3.102 0.545 0.621 0.987 0.667 0.528 0.727 0.818 +1 0.714 -0.154 1.130 0.623 -0.824 0.558 0.569 -1.271 0.000 0.886 0.513 0.167 2.215 1.313 0.225 -0.860 0.000 0.660 2.012 0.703 0.000 0.790 0.900 0.985 0.683 0.642 0.610 0.584 +1 1.550 -0.157 0.227 1.559 1.558 2.453 1.147 1.137 0.000 2.864 -0.195 -0.223 0.000 2.075 0.607 -0.651 1.274 3.513 0.228 -1.307 0.000 3.564 1.964 2.007 1.600 0.946 1.328 1.332 +1 0.360 0.689 -0.639 2.863 -0.031 1.313 0.146 1.230 0.000 0.766 -0.042 -1.128 0.000 1.123 0.427 -1.631 1.274 0.972 1.530 -0.932 0.000 0.974 0.756 0.987 0.768 0.884 0.867 0.771 +1 0.919 -0.701 -1.249 0.960 0.500 0.800 -1.109 0.748 2.173 0.901 -0.355 0.083 2.215 1.237 -0.191 -1.228 0.000 0.730 1.832 -1.525 0.000 1.615 1.469 1.301 0.893 0.855 1.282 1.086 +1 0.789 0.664 1.558 1.660 1.138 1.195 0.883 -0.415 2.173 0.778 0.528 -1.229 0.000 0.544 -0.253 0.022 2.548 0.905 0.953 0.958 0.000 1.061 0.818 0.989 1.492 0.731 1.045 0.897 +1 0.615 0.017 1.704 0.716 -0.399 1.086 -0.430 -0.758 1.087 0.619 1.515 1.698 0.000 1.682 0.134 0.625 0.000 0.603 -0.568 1.230 3.102 1.761 1.087 0.987 0.738 0.842 1.118 0.927 +0 0.556 -1.548 -1.037 0.856 0.212 0.933 -0.905 -1.292 2.173 0.920 2.570 -0.033 0.000 1.296 -1.176 0.440 0.000 1.451 -0.343 1.508 3.102 0.877 0.872 0.985 1.062 0.779 0.898 0.764 +1 0.522 0.561 -0.392 0.367 1.553 0.621 -0.646 -0.865 0.000 1.155 0.931 1.220 2.215 1.188 0.769 0.298 2.548 0.467 0.950 -1.077 0.000 0.853 1.063 0.995 0.828 0.922 0.931 0.854 +1 1.723 1.079 -0.866 0.191 0.790 1.872 0.813 0.741 2.173 0.968 0.703 -1.259 0.000 0.779 0.582 1.517 0.000 0.502 -0.368 -0.832 3.102 0.978 0.606 0.992 1.554 1.227 1.058 0.999 +1 0.348 -0.112 1.501 1.172 0.418 1.730 1.434 -1.573 2.173 2.008 1.420 -0.417 2.215 1.016 0.881 0.895 0.000 0.473 -0.128 -0.415 0.000 0.929 0.968 0.988 2.752 2.367 2.321 1.720 +1 0.329 0.542 0.901 1.701 -0.135 1.082 0.772 -1.682 2.173 0.532 1.597 0.693 0.000 0.677 0.211 -1.084 2.548 0.470 0.270 -0.217 0.000 0.664 0.972 0.990 0.819 0.623 0.887 0.743 +0 0.591 0.231 0.615 1.102 1.663 0.662 -0.145 1.699 1.087 0.759 0.400 0.948 0.000 1.008 0.362 -0.639 0.000 1.500 -0.176 -0.259 3.102 1.111 1.159 0.987 0.834 1.035 0.763 0.740 +0 1.025 -1.817 -1.011 1.285 -1.580 0.775 -0.523 -0.007 2.173 0.773 -1.053 1.045 0.000 0.858 0.359 -0.669 0.000 0.700 1.317 0.708 0.000 0.971 1.044 0.982 1.333 0.876 1.207 1.392 +1 1.991 -1.558 0.725 0.671 0.446 1.110 -1.014 -1.522 2.173 1.018 -0.341 -0.765 2.215 0.853 -0.362 -0.197 0.000 0.393 0.363 1.272 0.000 0.677 1.025 1.007 1.313 1.124 1.161 0.941 +0 1.172 0.155 -0.552 0.611 -1.569 0.671 -0.833 0.405 0.000 1.070 0.594 -1.652 2.215 0.600 0.481 0.773 0.000 1.276 -0.019 0.191 3.102 0.921 1.279 0.987 0.715 1.104 0.831 0.773 +0 0.410 -0.956 0.737 1.888 0.359 1.378 0.823 -1.663 0.000 0.962 0.625 -0.776 1.107 1.362 -0.845 0.097 2.548 1.040 1.416 -1.534 0.000 0.847 1.096 0.989 0.899 1.363 1.482 2.122 +0 0.317 -0.989 0.432 2.230 -0.761 1.020 0.823 1.739 2.173 0.693 -0.309 1.131 0.000 0.773 -0.682 -0.648 2.548 0.991 -1.804 0.407 0.000 0.883 1.209 1.023 0.504 1.338 1.160 1.289 +1 0.376 1.678 0.710 1.470 -1.277 0.965 1.029 -1.140 2.173 0.986 0.719 0.360 0.000 0.534 -0.036 1.293 2.548 0.705 0.847 -0.260 0.000 0.595 1.128 1.005 0.818 0.885 0.758 0.756 +0 0.506 -0.067 1.275 0.365 -0.463 0.634 -0.824 -0.877 2.173 0.632 -0.171 1.044 0.000 0.409 -1.426 -0.997 0.000 0.910 -0.659 0.873 3.102 0.946 0.952 0.986 0.568 0.804 0.616 0.565 +1 0.894 1.433 -0.254 0.554 1.419 0.746 0.360 -1.308 0.000 1.064 1.248 0.602 2.215 0.490 -1.058 1.443 0.000 1.277 0.536 1.343 3.102 1.131 0.870 0.986 0.730 0.736 0.909 0.857 +0 0.723 -0.942 0.570 2.772 -1.632 0.752 -0.857 -0.366 0.000 1.700 0.281 0.108 2.215 1.312 -0.374 -1.733 1.274 0.731 0.974 0.117 0.000 1.443 1.160 1.797 0.964 1.679 1.391 1.254 +1 1.328 0.802 1.246 1.645 0.636 1.199 0.544 -1.621 0.000 0.422 0.546 -0.296 0.000 1.736 0.602 -0.867 2.548 0.481 0.961 -0.738 3.102 0.937 0.836 1.069 0.728 0.192 0.857 0.757 +1 0.911 -0.888 -0.319 2.009 -1.365 1.312 0.399 0.802 2.173 0.407 0.172 -0.126 0.000 0.300 -0.363 -1.432 0.000 0.552 0.590 -1.104 0.000 0.519 0.829 1.515 0.823 0.498 1.171 0.877 +1 0.470 1.532 -0.364 0.980 1.076 0.668 -0.660 0.864 1.087 0.968 0.331 -0.828 2.215 0.389 2.034 -1.359 0.000 0.455 -0.812 -0.635 0.000 0.977 1.134 0.989 0.874 1.335 0.883 0.786 +1 0.471 1.800 -0.519 0.221 0.342 0.858 1.238 1.570 2.173 0.480 1.323 -0.275 0.000 0.790 1.782 0.863 0.000 1.056 0.245 0.776 3.102 0.857 0.932 0.976 0.627 0.830 0.680 0.656 +0 1.039 -0.887 -0.598 2.530 -0.884 0.738 -1.702 0.992 0.000 1.022 -2.322 0.721 0.000 0.689 -1.284 0.515 2.548 1.090 -1.122 -1.727 3.102 0.774 0.875 0.996 1.074 0.597 0.840 1.191 +0 0.407 1.132 -0.697 1.627 1.534 0.949 -0.443 -1.406 2.173 1.238 0.047 -0.145 0.000 2.054 -0.081 0.526 2.548 0.810 -1.056 -0.412 0.000 0.973 1.073 1.020 1.224 1.742 1.210 1.162 +1 0.574 -0.320 -1.254 0.758 -0.309 1.311 -0.021 -1.688 2.173 1.141 0.145 0.914 0.000 1.425 -0.284 -0.260 0.000 0.893 0.577 -0.567 0.000 0.878 1.340 0.988 0.602 0.678 0.784 0.741 +1 0.755 0.421 -1.067 1.036 1.670 0.690 -0.169 -0.360 1.087 2.072 0.802 0.612 2.215 0.780 0.212 1.553 0.000 0.541 0.173 -1.474 0.000 0.920 1.203 0.989 0.882 1.631 1.147 1.048 +1 1.544 -0.667 1.683 0.460 -0.673 0.901 -0.688 0.382 2.173 0.597 -0.377 -1.029 0.000 0.847 0.262 -0.240 2.548 0.812 0.275 1.323 0.000 0.842 1.027 0.995 0.868 0.803 0.810 0.721 +1 0.703 0.975 -1.061 1.224 1.205 0.560 1.361 -1.363 0.000 1.806 0.808 0.167 2.215 0.868 2.033 1.453 0.000 0.842 0.068 -0.932 3.102 0.903 0.929 1.145 1.182 1.024 1.036 0.901 +1 1.020 -0.172 0.051 0.827 1.679 0.850 0.651 -0.794 0.000 1.380 -0.696 1.451 2.215 1.563 -0.844 0.068 0.000 1.259 0.749 -1.442 0.000 0.891 0.735 1.266 0.985 0.908 0.987 0.886 +0 0.631 0.317 -0.817 0.249 -0.317 0.541 -0.592 1.415 0.000 0.835 -1.181 0.111 2.215 1.423 1.171 -1.178 1.274 1.342 -1.150 0.793 0.000 0.869 0.892 0.976 1.174 2.141 1.345 1.076 +1 0.567 1.648 -0.361 0.958 1.051 0.952 0.926 -0.732 2.173 0.832 0.466 1.667 0.000 1.031 1.290 0.131 2.548 1.429 1.008 1.331 0.000 0.678 1.012 0.988 0.947 0.916 0.891 0.781 +0 1.134 -0.303 -0.806 1.270 -1.358 0.720 0.140 0.842 0.000 0.410 0.097 -0.101 0.000 0.310 1.538 1.343 0.000 0.576 1.156 -1.669 3.102 0.868 0.746 0.984 0.866 0.484 0.709 0.735 +1 0.758 0.682 -0.444 0.938 -1.632 1.179 -0.084 0.624 0.000 0.880 0.535 0.412 0.000 1.320 0.587 1.483 2.548 2.797 0.726 -1.090 1.551 0.934 0.869 1.024 0.698 1.089 0.735 0.650 +1 1.095 -0.733 0.372 0.939 0.191 1.186 0.014 -1.148 2.173 0.896 -0.208 1.243 0.000 0.647 -2.172 -0.622 0.000 0.612 0.423 1.064 3.102 1.923 1.222 1.002 1.687 0.853 1.124 1.103 +1 0.433 0.852 -1.542 1.366 0.726 1.085 -0.534 -1.507 0.000 0.814 -0.632 -1.026 2.215 1.652 -1.256 -0.104 0.000 1.415 0.489 0.883 3.102 0.897 0.972 0.990 0.604 1.151 0.947 1.158 +0 0.416 -1.352 0.839 0.854 1.269 0.978 -0.442 -1.513 2.173 0.898 2.369 0.484 0.000 0.764 0.045 -0.462 2.548 1.698 -0.947 -0.406 0.000 4.819 2.601 0.983 0.871 0.915 1.898 1.486 +0 0.535 1.359 1.126 1.322 -1.572 1.203 1.087 -1.301 1.087 1.973 -0.763 0.410 2.215 1.053 -0.945 -0.083 0.000 0.593 -2.137 -0.134 0.000 0.789 0.844 0.992 0.815 3.348 1.654 1.385 +0 1.719 0.561 1.517 0.784 -1.006 0.942 0.932 0.153 0.000 0.345 0.832 1.035 0.000 0.795 0.857 -1.473 2.548 0.811 0.179 -0.663 3.102 0.866 0.917 1.229 0.758 0.469 0.615 0.697 +1 1.040 -0.803 1.637 0.264 -0.814 1.819 0.055 0.353 0.000 1.541 -0.934 -0.811 2.215 1.701 -0.112 -1.387 0.000 1.762 -0.267 1.163 3.102 1.301 1.135 0.979 0.914 1.534 1.142 1.018 diff --git a/examples/binary_classification/binary.train.weight b/examples/binary_classification/binary.train.weight new file mode 100644 index 0000000..690e87e --- /dev/null +++ b/examples/binary_classification/binary.train.weight @@ -0,0 +1,7000 @@ +1.2 +1.1 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 +1.0 diff --git a/examples/binary_classification/forced_splits.json b/examples/binary_classification/forced_splits.json new file mode 100644 index 0000000..b09391a --- /dev/null +++ b/examples/binary_classification/forced_splits.json @@ -0,0 +1,12 @@ +{ + "feature": 25, + "threshold": 1.3, + "left": { + "feature": 26, + "threshold": 0.85 + }, + "right": { + "feature": 26, + "threshold": 0.85 + } +} diff --git a/examples/binary_classification/predict.conf b/examples/binary_classification/predict.conf new file mode 100644 index 0000000..f42a9be --- /dev/null +++ b/examples/binary_classification/predict.conf @@ -0,0 +1,5 @@ +task = predict + +data = binary.test + +input_model= LightGBM_model.txt diff --git a/examples/binary_classification/train.conf b/examples/binary_classification/train.conf new file mode 100644 index 0000000..66b3b4d --- /dev/null +++ b/examples/binary_classification/train.conf @@ -0,0 +1,114 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# alias: application, app +objective = binary + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = binary_logloss,auc + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# column in data to use as label +label_column = 0 + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "binary.train.weight" +# alias: train_data, train +data = binary.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "binary.test.weight" +# alias: valid, test, test_data, +valid_data = binary.test + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 63 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.8 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = LightGBM_model.txt + +# support continuous train from trained gbdt model +# input_model= trained_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + + +# number of machines in distributed training, alias: num_machine +num_machines = 1 + +# local listening port in distributed training, alias: local_port +local_listen_port = 12400 + +# machines list file for distributed training, alias: mlist +machine_list_file = mlist.txt + +# force splits +# forced_splits = forced_splits.json diff --git a/examples/binary_classification/train_linear.conf b/examples/binary_classification/train_linear.conf new file mode 100644 index 0000000..05ae61b --- /dev/null +++ b/examples/binary_classification/train_linear.conf @@ -0,0 +1,113 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# alias: application, app +objective = binary + +linear_tree = true + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = binary_logloss,auc + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "binary.train.weight" +# alias: train_data, train +data = binary.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "binary.test.weight" +# alias: valid, test, test_data, +valid_data = binary.test + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 63 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use each CPU. The default is set to CPU count. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.8 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = LightGBM_model.txt + +# support continuous train from trained gbdt model +# input_model= trained_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + + +# number of machines in distributed training, alias: num_machine +num_machines = 1 + +# local listening port in distributed training, alias: local_port +local_listen_port = 12400 + +# machines list file for distributed training, alias: mlist +machine_list_file = mlist.txt + +# force splits +# forced_splits = forced_splits.json diff --git a/examples/lambdarank/README.md b/examples/lambdarank/README.md new file mode 100644 index 0000000..2ec7afc --- /dev/null +++ b/examples/lambdarank/README.md @@ -0,0 +1,33 @@ +LambdaRank Example +================== + +Here is an example for LightGBM to run LambdaRank task. + +***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) +for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** + +Training +-------- + +Run the following command in this folder: + +```bash +"../../lightgbm" config=train.conf +``` + +Prediction +---------- + +You should finish training first. + +Run the following command in this folder: + +```bash +"../../lightgbm" config=predict.conf +``` + +Data Format +----------- + +To learn more about the query format used in this example, check out the +[query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data). diff --git a/examples/lambdarank/predict.conf b/examples/lambdarank/predict.conf new file mode 100644 index 0000000..4c781b4 --- /dev/null +++ b/examples/lambdarank/predict.conf @@ -0,0 +1,5 @@ +task = predict + +data = rank.test + +input_model= LightGBM_model.txt diff --git a/examples/lambdarank/rank.test b/examples/lambdarank/rank.test new file mode 100644 index 0000000..25a96ce --- /dev/null +++ b/examples/lambdarank/rank.test @@ -0,0 +1,768 @@ +2 1:0.74 6:0.87 8:0.75 9:0.80 11:0.88 12:0.37 17:0.66 20:0.97 21:0.30 27:0.15 28:0.95 30:0.54 32:0.80 34:0.21 36:0.62 37:0.40 39:0.43 41:0.88 43:0.90 60:0.87 66:0.47 69:0.47 70:0.62 74:0.97 77:0.96 78:0.96 81:0.84 83:0.85 85:0.98 91:0.48 96:0.86 98:0.73 100:0.91 101:0.85 104:0.95 106:0.81 108:0.36 111:0.94 114:0.86 117:0.86 120:0.77 122:0.57 123:0.35 124:0.66 126:0.54 127:0.68 129:0.81 133:0.67 135:0.78 140:0.45 144:0.85 145:0.67 146:0.95 147:0.96 149:0.97 150:0.89 151:0.94 152:0.97 153:0.80 154:0.89 155:0.67 158:0.92 159:0.82 161:0.88 162:0.78 164:0.70 165:0.84 167:0.47 169:0.86 172:0.86 173:0.25 176:0.73 177:0.38 179:0.24 181:0.89 186:0.96 187:0.39 189:0.90 192:0.59 195:0.92 201:0.74 202:0.59 206:0.81 208:0.64 212:0.39 215:0.72 216:0.48 222:0.76 232:0.97 235:0.42 238:0.83 241:0.12 242:0.51 243:0.59 245:0.94 247:0.67 248:0.84 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 261:0.94 265:0.74 266:0.80 267:0.65 268:0.64 271:0.21 276:0.88 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.99 300:0.70 +3 1:0.74 6:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.47 20:0.99 21:0.30 27:0.15 28:0.95 30:0.17 34:0.80 36:1.00 37:0.81 39:0.43 41:0.95 43:0.77 55:0.71 60:0.97 66:0.34 69:0.80 70:0.68 74:0.79 78:0.99 81:0.84 83:0.84 85:0.80 91:0.38 96:0.91 98:0.43 100:0.97 101:0.73 104:0.98 108:0.64 111:0.98 114:0.86 117:0.86 120:0.84 122:0.67 123:0.62 124:0.77 126:0.54 127:0.37 129:0.59 133:0.76 135:0.60 138:0.98 140:0.45 144:0.85 145:0.74 146:0.67 147:0.72 149:0.66 150:0.89 151:0.97 152:0.93 153:0.89 154:0.69 155:0.67 158:0.92 159:0.64 161:0.88 162:0.65 163:0.81 164:0.82 165:0.84 167:0.53 169:0.96 172:0.37 173:0.71 176:0.73 177:0.38 179:0.67 181:0.89 186:0.98 187:0.87 189:0.83 192:0.59 201:0.74 202:0.77 208:0.64 212:0.19 215:0.72 216:0.54 222:0.76 232:0.95 235:0.42 238:0.90 241:0.46 242:0.63 243:0.50 245:0.57 247:0.47 248:0.90 253:0.92 255:0.79 256:0.81 259:0.21 260:0.84 261:0.96 265:0.45 266:0.75 267:0.65 268:0.79 271:0.21 276:0.47 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43 +2 1:0.74 6:0.80 8:0.60 11:0.88 12:0.37 17:0.61 20:0.91 27:0.34 28:0.95 30:0.21 32:0.78 34:0.63 36:0.93 37:0.36 39:0.43 43:0.87 60:0.87 66:0.29 69:0.54 70:0.87 74:0.85 77:0.89 78:0.90 81:0.98 83:0.84 85:0.90 91:0.25 98:0.38 100:0.84 101:0.78 104:0.80 108:0.84 111:0.88 114:0.98 123:0.83 124:0.79 126:0.54 127:0.34 129:0.81 133:0.76 135:0.98 144:0.85 145:0.99 146:0.62 147:0.67 149:0.79 150:0.99 152:0.85 154:0.85 155:0.67 158:0.92 159:0.64 161:0.88 163:0.90 165:0.92 167:0.47 169:0.81 172:0.45 173:0.39 176:0.73 177:0.38 178:0.55 179:0.50 181:0.89 186:0.90 187:0.21 189:0.83 192:0.59 195:0.87 201:0.74 208:0.64 212:0.27 215:0.96 216:0.84 222:0.76 232:0.85 235:0.05 238:0.81 241:0.12 242:0.18 243:0.37 245:0.69 247:0.67 253:0.78 259:0.21 261:0.88 265:0.40 266:0.75 267:0.28 271:0.21 276:0.59 279:0.86 282:0.86 283:0.82 290:0.98 297:0.36 300:0.70 +0 1:0.74 6:0.84 7:0.81 8:0.66 9:0.80 11:0.88 12:0.37 17:0.84 20:0.92 21:0.05 27:0.27 28:0.95 30:0.73 32:0.80 34:0.29 36:0.72 37:0.35 39:0.43 41:0.82 43:0.96 60:0.87 66:0.27 69:0.10 70:0.40 74:0.96 77:0.70 78:0.86 81:0.95 83:0.85 85:0.98 91:0.15 96:0.77 98:0.51 100:0.83 101:0.85 104:0.79 106:0.81 108:0.90 111:0.84 114:0.95 117:0.86 120:0.65 121:0.99 122:0.57 123:0.09 124:0.71 126:0.54 127:0.71 129:0.81 133:0.67 135:0.63 140:0.45 144:0.85 145:0.70 146:0.80 147:0.83 149:0.97 150:0.98 151:0.77 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.78 161:0.88 162:0.86 164:0.57 165:0.90 167:0.48 169:0.81 172:0.73 173:0.10 176:0.73 177:0.38 179:0.33 181:0.89 186:0.86 187:0.21 189:0.86 192:0.59 195:0.90 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.73 215:0.91 216:0.70 220:0.62 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.82 241:0.21 242:0.57 243:0.46 245:0.91 247:0.55 248:0.71 253:0.85 255:0.79 256:0.45 259:0.21 260:0.66 261:0.87 265:0.44 266:0.80 267:0.84 268:0.46 271:0.21 276:0.76 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.83 9:0.80 11:0.88 12:0.37 17:0.43 20:0.83 21:0.22 27:0.42 28:0.95 30:0.76 32:0.80 34:0.30 36:0.83 37:0.55 39:0.43 41:0.82 43:0.86 60:0.99 66:0.93 69:0.60 70:0.33 74:0.96 77:0.89 78:0.91 81:0.88 83:0.88 85:0.97 91:0.35 96:0.73 98:0.76 100:0.81 101:0.86 104:0.92 106:0.81 108:0.45 111:0.88 114:0.90 117:0.86 120:0.60 121:0.90 122:0.57 123:0.44 126:0.54 127:0.25 129:0.05 135:0.78 140:0.45 144:0.85 145:0.74 146:0.79 147:0.82 149:0.97 150:0.93 151:0.63 152:0.79 153:0.48 154:0.83 155:0.67 158:0.92 159:0.35 161:0.88 162:0.86 164:0.57 165:0.86 167:0.48 169:0.79 172:0.82 173:0.60 176:0.73 177:0.38 179:0.27 181:0.89 186:0.91 187:0.39 189:0.92 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.92 215:0.79 216:0.66 220:0.82 222:0.76 230:0.87 232:0.94 233:0.76 235:0.31 238:0.82 241:0.21 242:0.63 243:0.94 247:0.39 248:0.60 253:0.81 255:0.79 256:0.45 259:0.21 260:0.60 261:0.84 265:0.76 266:0.27 267:0.65 268:0.46 271:0.21 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.97 300:0.43 +1 1:0.74 11:0.88 12:0.37 17:0.62 20:0.80 21:0.40 27:0.33 28:0.95 30:0.70 32:0.80 34:0.63 36:0.91 37:0.78 39:0.43 43:0.86 66:0.46 69:0.60 70:0.43 74:0.94 77:0.89 78:0.85 81:0.87 83:0.76 85:0.98 91:0.09 98:0.55 100:0.73 101:0.84 104:0.84 106:0.81 108:0.46 111:0.83 114:0.82 117:0.86 122:0.57 123:0.44 124:0.58 126:0.54 127:0.54 129:0.59 133:0.47 135:0.51 144:0.85 145:0.99 146:0.84 147:0.87 149:0.96 150:0.92 152:0.77 154:0.83 155:0.67 159:0.78 161:0.88 165:0.86 167:0.56 169:0.72 172:0.77 173:0.40 176:0.73 177:0.38 178:0.55 179:0.32 181:0.89 186:0.86 187:0.39 192:0.59 195:0.92 201:0.74 206:0.81 212:0.50 215:0.67 216:0.62 222:0.76 232:0.89 235:0.68 241:0.56 242:0.58 243:0.59 245:0.93 247:0.47 253:0.76 259:0.21 261:0.79 265:0.56 266:0.75 267:0.65 271:0.21 276:0.76 282:0.88 290:0.82 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.86 8:0.70 9:0.80 11:0.88 12:0.37 17:0.15 20:0.93 21:0.76 27:0.15 28:0.95 30:0.45 34:0.63 36:0.70 37:0.88 39:0.43 41:0.82 43:0.83 55:0.84 66:0.24 69:0.69 70:0.73 74:0.95 78:0.98 81:0.50 83:0.77 85:0.91 91:0.06 96:0.82 98:0.52 100:0.92 101:0.75 108:0.52 111:0.95 114:0.66 120:0.72 122:0.67 123:0.83 124:0.74 126:0.54 127:0.42 129:0.81 133:0.67 135:0.83 140:0.90 144:0.85 146:0.89 147:0.91 149:0.88 150:0.65 151:0.90 152:0.90 153:0.69 154:0.78 155:0.67 159:0.85 161:0.88 162:0.48 163:0.75 164:0.62 165:0.65 167:0.51 169:0.90 172:0.70 173:0.40 176:0.73 177:0.38 178:0.50 179:0.24 181:0.89 186:0.97 187:0.95 189:0.88 191:0.70 192:0.59 201:0.74 202:0.67 208:0.64 212:0.16 215:0.46 216:0.68 222:0.76 232:0.99 235:0.97 238:0.83 241:0.39 242:0.76 243:0.44 245:0.93 247:0.47 248:0.79 253:0.87 255:0.79 256:0.45 259:0.21 260:0.73 261:0.94 265:0.49 266:0.80 267:0.91 268:0.55 271:0.21 276:0.84 285:0.62 290:0.66 297:0.36 300:0.70 +0 1:0.74 6:0.86 7:0.81 8:0.68 9:0.80 11:0.88 12:0.37 17:0.73 20:0.95 21:0.30 27:0.25 28:0.95 30:0.57 34:0.37 36:0.55 37:0.55 39:0.43 41:0.88 43:0.85 60:0.97 66:0.93 69:0.61 70:0.64 74:0.94 78:0.95 81:0.84 83:0.87 85:0.95 91:0.38 96:0.82 98:0.88 100:0.88 101:0.85 108:0.47 111:0.92 114:0.86 120:0.72 121:0.97 122:0.57 123:0.45 126:0.54 127:0.41 129:0.05 135:0.65 140:0.45 144:0.85 146:0.85 147:0.88 149:0.94 150:0.89 151:0.87 152:0.88 153:0.48 154:0.82 155:0.67 158:0.87 159:0.42 161:0.88 162:0.86 164:0.67 165:0.84 167:0.47 169:0.86 172:0.81 173:0.64 176:0.73 177:0.38 179:0.40 181:0.89 186:0.94 187:0.39 189:0.87 192:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.60 215:0.72 216:0.38 222:0.76 230:0.87 233:0.76 235:0.42 238:0.82 241:0.15 242:0.44 243:0.93 247:0.67 248:0.79 253:0.88 255:0.79 256:0.58 259:0.21 260:0.73 261:0.92 265:0.88 266:0.54 267:0.23 268:0.59 271:0.21 276:0.70 279:0.86 283:0.71 285:0.62 290:0.86 297:0.36 300:0.55 +2 1:0.74 6:0.86 8:0.77 9:0.80 11:0.88 12:0.37 17:0.13 20:0.80 21:0.74 27:0.15 28:0.95 30:0.53 34:0.55 36:0.43 37:0.88 39:0.43 41:0.90 43:0.83 55:0.84 66:0.15 69:0.68 70:0.76 74:0.95 78:0.95 81:0.53 83:0.77 85:0.91 91:0.10 96:0.82 98:0.49 100:0.93 101:0.74 108:0.85 111:0.93 114:0.67 120:0.72 122:0.67 123:0.92 124:0.87 126:0.54 127:0.42 129:0.89 133:0.87 135:0.85 140:0.80 144:0.85 146:0.38 147:0.45 149:0.88 150:0.67 151:0.87 152:0.90 153:0.48 154:0.78 155:0.67 159:0.88 161:0.88 162:0.49 163:0.75 164:0.72 165:0.65 167:0.49 169:0.90 172:0.70 173:0.36 176:0.73 177:0.38 178:0.42 179:0.23 181:0.89 186:0.95 187:0.95 189:0.92 192:0.59 201:0.74 202:0.76 208:0.64 212:0.15 215:0.46 216:0.68 220:0.87 222:0.76 232:0.99 235:0.95 238:0.85 241:0.32 242:0.74 243:0.16 245:0.88 247:0.60 248:0.79 253:0.87 255:0.79 256:0.64 259:0.21 260:0.73 261:0.94 265:0.49 266:0.87 267:0.84 268:0.65 271:0.21 276:0.85 285:0.62 290:0.67 297:0.36 300:0.70 +1 1:0.74 11:0.88 12:0.37 17:0.43 21:0.54 27:0.45 28:0.95 30:0.95 34:0.81 36:0.19 37:0.50 39:0.43 43:0.82 66:0.54 69:0.69 74:0.43 81:0.70 83:0.74 85:0.72 91:0.12 98:0.09 101:0.72 108:0.52 114:0.77 123:0.50 126:0.54 127:0.06 129:0.05 135:0.60 144:0.85 145:0.49 146:0.13 147:0.18 149:0.46 150:0.80 154:0.78 155:0.67 159:0.08 161:0.88 165:0.79 167:0.47 172:0.10 173:0.67 176:0.73 177:0.38 178:0.55 179:0.08 181:0.89 187:0.68 192:0.59 201:0.74 215:0.58 216:0.77 222:0.76 235:0.68 241:0.15 243:0.63 253:0.59 259:0.21 265:0.09 267:0.28 271:0.21 290:0.77 297:0.36 300:0.08 +2 1:0.74 6:0.87 8:0.72 9:0.80 11:0.88 12:0.37 17:0.66 20:0.93 21:0.59 27:0.15 28:0.95 30:0.45 34:0.58 36:0.74 37:0.40 39:0.43 41:0.90 43:0.90 60:0.87 66:0.87 69:0.49 70:0.48 74:0.85 78:0.93 81:0.66 83:0.82 85:0.90 91:0.37 96:0.85 98:0.76 100:0.86 101:0.80 104:0.94 106:0.81 108:0.38 111:0.90 114:0.74 117:0.86 120:0.74 122:0.43 123:0.36 126:0.54 127:0.25 129:0.05 135:0.39 140:0.45 144:0.85 145:0.79 146:0.83 147:0.86 149:0.86 150:0.77 151:0.91 152:0.97 153:0.72 154:0.89 155:0.67 158:0.92 159:0.39 161:0.88 162:0.58 164:0.72 165:0.78 167:0.46 169:0.86 172:0.63 173:0.45 176:0.73 177:0.38 179:0.51 181:0.89 186:0.93 187:0.68 189:0.86 192:0.59 201:0.74 202:0.70 206:0.81 208:0.64 212:0.27 215:0.55 216:0.48 222:0.76 232:0.96 235:0.74 238:0.82 241:0.10 242:0.40 243:0.88 247:0.60 248:0.81 253:0.87 255:0.79 256:0.68 259:0.21 260:0.75 261:0.94 265:0.76 266:0.54 267:0.52 268:0.69 271:0.21 276:0.52 279:0.86 283:0.82 285:0.62 290:0.74 297:0.36 300:0.43 +1 11:0.88 12:0.37 17:0.47 21:0.78 27:0.45 28:0.95 30:0.95 34:0.69 36:0.23 37:0.50 39:0.43 43:0.79 66:0.54 69:0.77 74:0.42 85:0.72 91:0.07 98:0.09 101:0.72 108:0.60 123:0.58 127:0.06 129:0.05 135:0.51 144:0.85 145:0.56 146:0.13 147:0.18 149:0.43 154:0.72 159:0.08 161:0.88 167:0.47 172:0.10 173:0.75 177:0.38 178:0.55 179:0.08 181:0.89 187:0.68 216:0.77 222:0.76 235:0.98 241:0.12 243:0.63 253:0.37 259:0.21 265:0.09 267:0.36 271:0.21 300:0.08 +0 12:0.79 17:0.13 21:0.74 27:0.41 30:0.62 34:0.39 36:0.61 37:0.53 39:0.29 43:0.41 55:0.84 66:0.19 69:0.42 70:0.63 74:0.89 76:0.85 77:0.70 81:0.32 91:0.35 96:0.68 98:0.27 108:0.76 114:0.61 117:0.86 122:0.67 123:0.75 124:0.91 126:0.32 127:0.50 129:0.97 133:0.92 135:0.75 140:0.45 145:0.95 146:0.05 147:0.05 149:0.47 150:0.29 151:0.46 153:0.46 154:0.31 158:0.84 159:0.88 162:0.86 163:0.96 172:0.37 173:0.15 175:0.75 176:0.56 177:0.05 179:0.58 187:0.87 190:0.77 195:0.97 202:0.49 212:0.18 216:0.59 220:0.99 222:0.35 232:0.98 235:0.88 241:0.01 242:0.82 243:0.10 244:0.61 245:0.60 247:0.30 248:0.47 253:0.66 256:0.58 257:0.65 259:0.21 265:0.29 266:0.87 267:0.94 268:0.58 271:0.37 276:0.59 277:0.69 282:0.84 285:0.50 290:0.61 300:0.55 +1 12:0.79 17:0.40 21:0.78 27:0.45 30:0.76 34:0.74 36:0.18 37:0.50 39:0.29 43:0.28 55:0.96 66:0.13 69:0.74 70:0.15 74:0.48 91:0.61 98:0.06 108:0.57 122:0.57 123:0.54 124:0.75 127:0.20 129:0.81 133:0.67 135:0.90 145:0.45 146:0.05 147:0.05 149:0.19 154:0.21 159:0.53 163:0.99 172:0.05 173:0.58 175:0.75 177:0.05 178:0.55 179:0.95 187:0.68 212:0.95 216:0.77 222:0.35 235:0.80 241:0.43 242:0.82 243:0.34 244:0.61 245:0.37 247:0.12 253:0.40 257:0.65 259:0.21 265:0.06 266:0.12 267:0.82 271:0.37 276:0.18 277:0.69 300:0.13 +1 7:0.78 12:0.79 17:0.53 21:0.71 27:0.55 30:0.37 32:0.75 34:0.94 36:0.21 37:0.66 39:0.29 43:0.30 55:0.42 66:0.06 69:0.30 70:0.66 74:0.77 76:0.85 77:0.70 81:0.44 91:0.63 98:0.34 108:0.78 114:0.68 123:0.52 124:0.75 126:0.54 127:0.57 129:0.05 133:0.74 135:0.21 145:0.45 146:0.21 147:0.27 149:0.47 150:0.54 154:0.84 159:0.46 172:0.51 173:0.34 176:0.73 177:0.38 178:0.55 179:0.65 187:0.68 195:0.70 212:0.75 215:0.48 216:0.18 222:0.35 230:0.81 233:0.76 235:0.88 241:0.05 242:0.73 243:0.31 245:0.62 247:0.55 253:0.63 254:0.84 259:0.21 265:0.26 266:0.54 267:0.94 271:0.37 276:0.52 277:0.69 282:0.83 290:0.68 297:0.36 300:0.55 +2 7:0.78 12:0.79 17:0.77 21:0.47 25:0.88 27:0.72 30:0.21 34:0.74 36:0.66 39:0.29 43:0.45 55:0.98 66:0.16 69:0.33 70:0.86 74:0.63 76:0.85 81:0.48 91:0.29 98:0.33 104:0.54 108:0.26 123:0.69 124:0.84 126:0.32 127:0.98 129:0.93 133:0.84 135:0.60 145:0.56 146:0.26 147:0.32 149:0.47 150:0.38 154:0.34 159:0.76 172:0.21 173:0.21 175:0.75 177:0.05 178:0.55 179:0.88 187:0.39 202:0.36 212:0.42 215:0.63 216:0.02 222:0.35 230:0.81 233:0.76 235:0.60 241:0.01 242:0.45 243:0.44 244:0.61 245:0.47 247:0.47 253:0.49 257:0.65 259:0.21 265:0.16 266:0.38 267:0.87 271:0.37 276:0.34 277:0.69 297:1.00 300:0.55 +1 8:0.65 12:0.79 17:0.79 21:0.05 27:0.59 30:0.58 34:0.34 36:0.75 37:0.29 39:0.29 43:0.28 55:0.96 66:0.06 69:0.73 70:0.62 74:0.92 77:0.70 81:0.88 91:0.58 98:0.22 108:0.75 114:0.77 117:0.86 122:0.67 123:0.94 124:0.98 126:0.32 127:0.74 129:1.00 133:0.98 135:0.80 145:0.45 146:0.05 147:0.05 149:0.56 150:0.74 154:0.21 158:0.84 159:0.91 163:0.97 172:0.32 173:0.39 175:0.75 176:0.56 177:0.05 178:0.55 179:0.43 187:0.21 195:0.98 212:0.14 216:0.52 222:0.35 232:0.94 235:0.05 241:0.02 242:0.82 243:0.08 244:0.61 245:0.63 247:0.30 253:0.73 257:0.65 259:0.21 265:0.15 266:0.94 267:0.87 271:0.37 276:0.75 277:0.69 282:0.84 290:0.77 300:0.70 +0 12:0.79 17:0.61 21:0.59 27:0.72 30:0.45 34:0.66 36:0.94 37:0.32 39:0.29 43:0.44 55:0.84 66:0.29 69:0.21 70:0.57 74:0.40 81:0.38 91:0.44 98:0.53 104:0.82 106:0.81 108:0.89 123:0.88 124:0.79 126:0.32 127:0.62 129:0.89 133:0.78 135:0.83 146:0.39 147:0.46 149:0.48 150:0.34 154:0.34 159:0.78 163:0.94 172:0.37 173:0.13 175:0.75 177:0.05 178:0.55 179:0.71 187:0.21 206:0.81 212:0.37 215:0.55 216:0.37 222:0.35 235:0.68 241:0.03 242:0.78 243:0.27 244:0.61 245:0.62 247:0.39 253:0.35 257:0.65 259:0.21 265:0.54 266:0.80 267:0.76 271:0.37 276:0.52 277:0.69 283:0.71 297:0.36 300:0.43 +2 7:0.78 12:0.79 17:0.85 21:0.78 27:0.64 30:0.11 34:0.70 36:0.96 37:0.82 39:0.29 43:0.26 55:0.52 66:0.47 69:0.80 70:0.54 74:0.40 91:0.35 98:0.44 108:0.64 123:0.61 124:0.52 127:0.37 129:0.05 133:0.39 135:0.34 145:0.40 146:0.59 147:0.65 149:0.28 154:0.18 159:0.53 163:0.88 172:0.21 173:0.78 177:0.38 178:0.55 179:0.91 187:0.21 212:0.30 216:0.39 222:0.35 230:0.81 233:0.76 235:0.22 241:0.05 242:0.77 243:0.59 244:0.61 245:0.46 247:0.30 253:0.36 259:0.21 265:0.46 266:0.27 267:0.73 271:0.37 276:0.23 300:0.33 +0 8:0.90 12:0.79 17:0.08 21:0.78 27:0.41 30:0.95 34:0.34 36:0.39 37:0.53 39:0.29 43:0.37 55:1.00 66:0.20 69:0.52 74:0.30 91:0.18 98:0.05 108:0.40 123:0.39 124:0.61 127:0.57 129:0.59 133:0.47 135:0.78 145:0.95 146:0.05 147:0.05 149:0.15 154:0.28 159:0.92 172:0.05 173:0.17 175:0.75 177:0.05 178:0.55 179:1.00 187:0.87 191:0.76 202:0.54 216:0.59 222:0.35 235:0.60 241:0.03 243:0.40 244:0.61 245:0.36 253:0.27 257:0.65 259:0.21 265:0.05 267:0.97 271:0.37 277:0.69 300:0.08 +0 8:0.72 12:0.79 17:0.47 21:0.78 27:0.60 30:0.21 34:0.59 36:0.91 37:0.44 39:0.29 43:0.07 55:0.59 66:0.20 69:0.99 70:0.37 74:0.30 91:0.35 98:0.09 108:0.97 123:0.97 124:0.73 127:0.18 129:0.81 133:0.67 135:0.54 146:0.05 147:0.05 149:0.08 154:0.07 159:0.88 163:0.90 172:0.10 173:0.92 175:0.75 177:0.05 178:0.55 179:0.80 187:0.68 202:0.63 212:0.42 216:0.46 222:0.35 235:0.22 241:0.52 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.27 257:0.65 259:0.21 265:0.09 266:0.23 267:0.59 271:0.37 276:0.14 277:0.69 300:0.25 +0 12:0.79 17:0.45 21:0.78 27:0.30 30:0.95 34:0.64 36:0.76 37:0.64 39:0.29 43:0.36 55:0.99 66:0.20 69:0.56 74:0.34 91:0.27 98:0.07 108:0.43 123:0.41 124:0.61 127:0.58 129:0.59 133:0.47 135:0.98 145:0.65 146:0.05 147:0.05 149:0.17 154:0.27 159:0.67 172:0.05 173:0.44 175:0.75 177:0.05 178:0.55 179:1.00 187:0.95 202:0.53 216:0.75 222:0.35 235:0.95 241:0.01 243:0.40 244:0.61 245:0.36 253:0.31 257:0.65 259:0.21 265:0.07 267:0.73 271:0.37 277:0.69 300:0.08 +2 7:0.78 12:0.79 17:0.43 21:0.54 27:0.26 30:0.62 34:0.78 36:0.88 37:0.68 39:0.29 43:0.29 55:0.98 66:0.30 69:0.73 70:0.34 74:0.63 76:0.85 81:0.42 91:0.33 98:0.33 104:0.99 106:0.81 108:0.68 122:0.51 123:0.66 124:0.78 126:0.32 127:0.68 129:0.89 132:0.97 133:0.78 135:0.49 145:0.56 146:0.05 147:0.05 149:0.29 150:0.36 154:0.21 159:0.74 163:0.96 172:0.16 173:0.60 175:0.75 177:0.05 178:0.55 179:0.95 187:0.87 206:0.81 212:0.30 215:0.58 216:0.90 222:0.35 230:0.81 233:0.76 235:0.60 242:0.33 243:0.23 244:0.61 245:0.40 247:0.39 253:0.49 257:0.65 259:0.21 265:0.35 266:0.27 267:0.88 271:0.37 276:0.28 277:0.69 297:0.36 300:0.25 +1 12:0.79 17:0.57 21:0.78 27:0.16 30:0.55 32:0.75 34:0.39 36:0.21 37:0.55 39:0.29 43:0.36 55:0.92 66:0.64 69:0.94 70:0.53 74:0.63 91:0.58 98:0.64 104:0.89 108:0.88 122:0.67 123:0.88 124:0.55 127:0.72 129:0.05 133:0.74 135:0.42 145:0.42 146:0.90 147:0.05 149:0.37 154:0.27 159:0.79 163:0.86 172:0.54 173:0.93 177:0.05 178:0.55 179:0.75 187:0.87 195:0.90 212:0.18 216:0.94 222:0.35 235:0.42 241:0.02 242:0.71 243:0.13 244:0.61 245:0.51 247:0.47 253:0.49 257:0.65 259:0.21 265:0.65 266:0.75 267:0.59 271:0.37 276:0.49 300:0.43 +2 12:0.79 17:0.09 21:0.30 27:0.30 30:0.11 34:0.62 36:0.45 37:0.64 39:0.29 43:0.41 55:0.52 66:0.48 69:0.88 70:0.69 74:0.48 81:0.60 91:0.12 98:0.48 108:0.76 123:0.75 124:0.74 126:0.32 127:0.60 129:0.05 133:0.74 135:0.95 146:0.72 147:0.05 149:0.38 150:0.45 154:0.31 159:0.71 163:0.94 172:0.37 173:0.84 177:0.05 178:0.55 179:0.82 187:0.68 212:0.28 215:0.72 216:0.75 222:0.35 235:0.31 241:0.03 242:0.29 243:0.16 244:0.61 245:0.49 247:0.55 253:0.40 257:0.65 259:0.21 265:0.50 266:0.63 267:0.65 271:0.37 276:0.40 297:0.36 300:0.43 +1 7:0.78 12:0.79 17:0.49 21:0.78 27:0.42 30:0.32 32:0.75 34:0.26 36:0.99 37:0.31 39:0.29 43:0.40 55:0.71 66:0.10 69:0.77 70:0.75 74:0.46 91:0.35 98:0.29 104:0.93 106:0.81 108:0.30 123:0.88 124:0.89 127:0.76 129:0.59 133:0.86 135:0.82 145:0.45 146:0.40 147:0.45 149:0.57 154:0.46 159:0.70 172:0.27 173:0.69 177:0.05 178:0.55 179:0.67 187:0.68 195:0.86 206:0.81 212:0.21 216:0.75 222:0.35 230:0.81 233:0.76 235:0.22 241:0.43 242:0.74 243:0.34 245:0.62 247:0.39 253:0.40 254:0.90 257:0.65 259:0.21 265:0.26 266:0.87 267:0.79 271:0.37 276:0.57 277:0.69 283:0.71 300:0.55 +0 12:0.79 17:0.43 21:0.30 27:0.30 30:0.76 34:0.69 36:0.74 37:0.64 39:0.29 43:0.36 55:0.96 66:0.20 69:0.52 70:0.22 74:0.35 81:0.60 91:0.22 98:0.23 104:0.98 106:0.81 108:0.64 123:0.61 124:0.81 126:0.32 127:0.44 129:0.89 133:0.78 135:0.86 146:0.05 147:0.05 149:0.28 150:0.45 154:0.27 159:0.64 163:0.96 172:0.10 173:0.40 175:0.75 177:0.05 178:0.55 179:0.95 187:0.87 206:0.81 212:0.42 215:0.72 216:0.75 222:0.35 235:0.31 241:0.18 242:0.75 243:0.26 244:0.61 245:0.39 247:0.30 253:0.31 257:0.65 259:0.21 265:0.25 266:0.23 267:0.84 271:0.37 276:0.24 277:0.69 297:0.36 300:0.18 +0 12:0.79 17:0.08 21:0.78 27:0.30 30:0.62 34:0.71 36:0.71 37:0.64 39:0.29 43:0.36 55:0.96 66:0.30 69:0.52 70:0.34 74:0.45 91:0.38 98:0.34 108:0.84 123:0.84 124:0.78 127:0.49 129:0.89 133:0.78 135:0.76 146:0.05 147:0.05 149:0.29 154:0.27 159:0.62 163:0.97 172:0.16 173:0.42 175:0.75 177:0.05 178:0.55 179:0.93 187:0.68 202:0.76 212:0.30 216:0.75 222:0.35 235:0.22 241:0.44 242:0.33 243:0.23 244:0.61 245:0.40 247:0.39 253:0.39 257:0.65 259:0.21 265:0.36 266:0.27 267:0.76 271:0.37 276:0.27 277:0.69 300:0.25 +2 8:0.70 12:0.79 17:0.28 21:0.78 27:0.63 30:0.45 34:0.40 36:0.97 37:0.28 39:0.29 43:0.45 55:0.84 66:0.06 69:0.05 70:0.59 74:0.34 91:0.58 98:0.22 108:0.97 120:0.80 123:0.97 124:0.98 127:0.84 129:1.00 133:0.98 135:0.75 140:0.45 146:0.34 147:0.40 149:0.61 151:0.60 153:0.85 154:0.34 159:0.95 162:0.72 163:0.94 164:0.88 172:0.32 173:0.05 175:0.75 177:0.05 179:0.31 187:0.39 190:0.77 191:0.75 202:0.82 212:0.30 216:0.42 220:0.82 222:0.35 235:0.05 241:0.07 242:0.81 243:0.09 244:0.61 245:0.73 247:0.39 248:0.72 253:0.31 256:0.83 257:0.65 259:0.21 260:0.81 265:0.24 266:0.96 267:0.36 268:0.85 271:0.37 276:0.84 277:0.69 283:0.71 285:0.50 300:0.70 +2 12:0.79 17:0.62 21:0.78 27:0.19 30:0.41 34:0.36 36:0.46 37:0.43 39:0.29 43:0.43 55:0.84 66:0.20 69:0.05 70:0.72 74:0.39 91:0.14 98:0.21 104:0.98 106:0.81 108:0.05 123:0.77 124:0.84 127:0.88 129:0.93 133:0.84 135:0.85 146:0.50 147:0.56 149:0.51 154:0.33 159:0.90 163:0.86 172:0.27 173:0.06 175:0.75 177:0.05 178:0.55 179:0.79 187:0.68 206:0.81 212:0.30 216:0.88 222:0.35 241:0.05 242:0.77 243:0.40 244:0.61 245:0.55 247:0.39 253:0.35 257:0.65 259:0.21 265:0.22 266:0.71 267:0.65 271:0.37 276:0.33 277:0.69 283:0.71 300:0.55 +1 12:0.79 17:0.43 21:0.13 27:0.49 30:0.36 34:0.44 36:0.99 37:0.42 39:0.29 43:0.45 55:0.92 66:0.07 69:0.07 70:0.73 74:0.34 81:0.76 91:0.35 98:0.22 108:0.58 123:0.96 124:0.96 126:0.32 127:0.84 129:0.99 133:0.96 135:0.87 146:0.26 147:0.32 149:0.55 150:0.56 154:0.34 159:0.91 163:0.96 172:0.27 173:0.06 175:0.75 177:0.05 178:0.55 179:0.58 187:0.68 212:0.16 215:0.84 216:0.94 222:0.35 235:0.12 241:0.03 242:0.80 243:0.15 244:0.61 245:0.58 247:0.39 253:0.31 257:0.65 259:0.21 265:0.23 266:0.94 267:0.65 271:0.37 276:0.65 277:0.69 283:0.71 297:0.36 300:0.55 +1 6:0.95 7:0.81 8:0.81 12:0.37 17:0.40 20:0.78 21:0.47 27:0.21 28:0.83 30:0.09 34:0.25 36:0.91 37:0.74 43:0.52 55:0.45 66:0.13 69:0.15 70:0.95 78:0.76 81:0.88 91:0.63 98:0.32 100:0.80 104:0.84 106:0.81 108:0.87 111:0.76 120:0.83 123:0.86 124:0.97 126:0.54 127:0.84 129:0.99 133:0.97 135:0.76 140:0.45 146:0.26 147:0.32 149:0.68 150:0.77 151:0.74 152:0.97 153:0.81 154:0.41 159:0.93 161:0.72 162:0.59 164:0.88 167:0.59 169:0.83 172:0.59 173:0.07 177:0.05 179:0.29 181:0.66 186:0.76 187:0.39 191:0.76 202:0.85 206:0.81 212:0.15 215:0.63 216:0.95 220:0.93 230:0.87 233:0.76 235:0.52 241:0.18 242:0.82 243:0.09 245:0.78 247:0.12 248:0.75 256:0.85 259:0.21 260:0.84 261:0.87 265:0.35 266:0.96 267:0.79 268:0.85 271:0.47 276:0.85 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.89 8:0.72 12:0.37 17:0.34 20:0.88 21:0.47 27:0.36 28:0.83 30:0.60 34:0.19 36:0.99 37:0.64 43:0.33 55:0.71 66:0.16 69:0.67 70:0.58 78:0.75 81:0.88 91:0.68 98:0.33 100:0.79 104:0.89 106:0.81 108:0.67 111:0.75 120:0.91 123:0.64 124:0.93 126:0.54 127:0.40 129:0.98 133:0.93 135:0.97 140:0.45 146:0.38 147:0.44 149:0.63 150:0.77 151:0.82 152:0.83 153:0.94 154:0.25 159:0.88 161:0.72 162:0.83 164:0.86 167:0.59 169:0.77 172:0.51 173:0.33 177:0.05 179:0.33 181:0.66 186:0.76 187:0.39 189:0.91 202:0.76 206:0.81 212:0.16 215:0.63 216:0.72 235:0.52 238:0.92 241:0.21 242:0.82 243:0.11 245:0.74 247:0.12 248:0.88 256:0.81 259:0.21 260:0.92 261:0.78 265:0.35 266:0.95 267:0.84 268:0.82 271:0.47 276:0.76 283:0.82 285:0.62 297:0.36 300:0.55 +2 12:0.37 17:0.20 21:0.59 25:0.88 27:0.07 28:0.83 30:0.62 32:0.80 34:0.88 36:0.40 37:0.91 43:0.52 55:0.71 66:0.64 69:0.19 70:0.54 81:0.85 91:0.29 98:0.74 104:0.58 106:0.81 108:0.15 120:0.54 123:0.15 124:0.44 126:0.54 127:0.69 129:0.59 133:0.47 135:0.72 140:0.80 145:0.59 146:0.75 147:0.79 149:0.53 150:0.66 151:0.47 153:0.48 154:0.41 159:0.49 161:0.72 162:0.62 164:0.57 167:0.70 172:0.45 173:0.26 177:0.05 178:0.42 179:0.83 181:0.66 187:0.98 195:0.68 202:0.50 206:0.81 212:0.28 215:0.55 216:0.68 220:0.82 235:0.68 241:0.57 242:0.82 243:0.69 245:0.55 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 265:0.74 266:0.63 267:0.44 268:0.46 271:0.47 276:0.39 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.96 8:0.97 12:0.37 17:0.03 20:0.75 21:0.40 27:0.39 28:0.83 30:0.45 34:0.25 36:0.71 37:0.98 43:0.48 55:0.59 66:0.77 69:0.35 70:0.33 78:0.91 81:0.90 91:0.88 98:0.94 100:0.90 104:0.58 108:0.27 111:0.90 120:0.82 123:0.26 126:0.54 127:0.60 129:0.05 135:0.58 140:0.45 146:0.61 147:0.67 149:0.45 150:0.81 151:0.66 152:0.74 153:0.48 154:0.37 159:0.23 161:0.72 162:0.74 164:0.94 167:0.95 169:0.88 172:0.37 173:0.62 177:0.05 179:0.92 181:0.66 186:0.91 189:0.97 191:0.85 202:0.90 212:0.52 215:0.67 216:0.40 220:0.82 235:0.42 238:1.00 241:0.98 242:0.82 243:0.79 247:0.12 248:0.74 256:0.94 259:0.21 260:0.83 261:0.77 265:0.94 266:0.27 267:0.73 268:0.93 271:0.47 276:0.31 283:0.60 285:0.62 297:0.36 300:0.25 +2 7:0.81 8:0.96 12:0.37 17:0.70 20:0.81 21:0.47 27:0.38 28:0.83 30:0.91 32:0.80 34:0.74 36:0.62 37:0.92 43:0.51 55:0.96 66:0.10 69:0.19 70:0.13 78:0.74 81:0.88 91:0.88 98:0.09 100:0.73 108:0.15 111:0.73 120:0.54 123:0.15 124:0.82 126:0.54 127:0.93 129:0.89 133:0.78 135:0.68 140:0.80 145:0.74 146:0.05 147:0.05 149:0.30 150:0.77 151:0.47 152:0.78 153:0.48 154:0.40 159:0.38 161:0.72 162:0.55 163:0.89 164:0.72 167:0.93 169:0.72 172:0.05 173:0.35 177:0.05 178:0.42 179:0.98 181:0.66 186:0.75 191:0.75 195:0.58 202:0.69 212:0.61 215:0.63 216:0.11 220:0.82 230:0.65 233:0.63 235:0.52 241:0.88 242:0.82 243:0.29 245:0.37 247:0.12 248:0.47 256:0.64 259:0.21 260:0.54 261:0.74 265:0.09 266:0.17 267:0.36 268:0.65 271:0.47 276:0.23 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.93 7:0.81 8:0.86 12:0.37 17:0.78 20:0.97 21:0.05 27:0.19 28:0.83 30:0.85 32:0.80 34:0.53 36:0.66 37:0.73 43:0.42 55:0.45 66:0.96 69:0.48 70:0.28 78:0.81 81:0.95 91:0.40 98:0.90 100:0.87 104:0.80 106:0.81 108:0.37 111:0.82 120:0.69 123:0.36 126:0.54 127:0.47 129:0.05 135:0.85 140:0.45 145:0.80 146:0.82 147:0.85 149:0.85 150:0.93 151:0.66 152:0.92 153:0.48 154:0.32 159:0.38 161:0.72 162:0.86 164:0.57 167:0.75 169:0.85 172:0.89 173:0.55 177:0.05 179:0.29 181:0.66 186:0.82 187:0.39 189:0.94 195:0.63 202:0.36 206:0.81 212:0.93 215:0.91 216:0.81 230:0.65 233:0.63 235:0.05 238:0.93 241:0.68 242:0.82 243:0.96 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.88 265:0.90 266:0.27 267:0.76 268:0.46 271:0.47 276:0.81 285:0.62 297:0.36 300:0.33 +4 6:0.95 8:0.91 12:0.37 17:0.09 20:0.99 21:0.54 25:0.88 27:0.07 28:0.83 30:0.76 34:0.80 36:0.42 37:0.91 43:0.28 55:0.45 66:0.80 69:0.77 70:0.28 78:0.90 81:0.87 91:0.99 98:0.68 100:0.99 104:0.58 108:0.60 111:0.98 120:0.99 123:0.58 126:0.54 127:0.21 129:0.05 135:0.90 140:0.45 146:0.60 147:0.66 149:0.43 150:0.72 151:0.90 152:0.99 153:0.99 154:0.21 159:0.22 161:0.72 162:0.75 164:0.99 167:0.95 169:0.97 172:0.45 173:0.87 177:0.05 179:0.64 181:0.66 186:0.90 189:0.96 191:0.95 202:0.98 212:0.71 215:0.58 216:0.68 235:0.60 238:0.99 241:1.00 242:0.82 243:0.82 247:0.12 248:0.99 256:0.98 259:0.21 260:0.99 261:0.99 265:0.69 266:0.27 267:0.76 268:0.99 271:0.47 276:0.35 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.93 7:0.81 8:0.75 12:0.37 17:0.69 20:0.77 21:0.68 27:0.38 28:0.83 30:0.54 32:0.80 34:0.88 36:0.95 37:0.69 43:0.35 55:0.45 66:0.94 69:0.65 70:0.58 78:0.80 81:0.91 91:0.68 98:0.91 100:0.83 104:0.92 106:0.81 108:0.50 111:0.80 120:0.85 123:0.48 126:0.54 127:0.50 129:0.05 135:0.97 140:0.45 145:0.85 146:0.90 147:0.92 149:0.76 150:0.82 151:0.75 152:0.75 153:0.84 154:0.26 159:0.50 161:0.72 162:0.81 164:0.80 167:0.74 169:0.80 172:0.84 173:0.65 177:0.05 179:0.39 181:0.66 186:0.81 187:0.87 189:0.94 195:0.72 202:0.70 206:0.81 212:0.74 215:0.49 216:0.59 230:0.65 233:0.63 235:0.84 238:0.84 241:0.65 242:0.82 243:0.94 247:0.12 248:0.79 256:0.79 259:0.21 260:0.85 261:0.76 265:0.91 266:0.54 267:0.44 268:0.76 271:0.47 276:0.74 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 12:0.37 17:0.22 21:0.59 25:0.88 27:0.07 28:0.83 30:0.72 32:0.80 34:0.92 36:0.32 37:0.91 43:0.52 55:0.71 66:0.84 69:0.21 70:0.22 81:0.93 91:0.25 98:0.97 104:0.58 106:0.81 108:0.17 123:0.16 126:0.54 127:0.74 129:0.05 135:0.68 145:0.61 146:0.80 147:0.83 149:0.56 150:0.89 154:0.41 159:0.35 161:0.72 167:0.63 172:0.54 173:0.38 177:0.05 178:0.55 179:0.82 181:0.66 187:0.98 195:0.60 206:0.81 212:0.23 215:0.55 216:0.68 230:0.65 233:0.63 235:0.74 241:0.37 242:0.82 243:0.85 247:0.12 259:0.21 265:0.97 266:0.54 267:0.52 271:0.47 276:0.45 297:0.36 300:0.18 +2 7:0.81 12:0.37 17:0.30 21:0.22 25:0.88 27:0.07 28:0.83 30:0.61 32:0.80 34:0.81 36:0.43 37:0.91 43:0.52 55:0.71 66:0.78 69:0.10 70:0.53 81:0.98 91:0.46 98:0.85 104:0.58 106:0.81 108:0.09 123:0.09 124:0.39 126:0.54 127:0.76 129:0.59 133:0.47 135:0.65 145:0.67 146:0.86 147:0.89 149:0.70 150:0.97 154:0.41 159:0.53 161:0.72 167:0.65 172:0.73 173:0.18 177:0.05 178:0.55 179:0.58 181:0.66 187:0.99 195:0.70 206:0.81 212:0.58 215:0.79 216:0.68 230:0.87 233:0.76 235:0.31 241:0.43 242:0.82 243:0.80 245:0.67 247:0.12 259:0.21 265:0.85 266:0.63 267:0.44 271:0.47 276:0.63 283:0.60 297:0.36 300:0.43 +1 7:0.81 8:0.96 12:0.37 17:0.94 20:0.77 21:0.68 27:0.72 28:0.83 30:0.68 32:0.80 34:0.55 36:0.46 37:0.98 43:0.33 55:0.84 66:0.79 69:0.68 70:0.31 78:0.72 81:0.91 91:0.80 98:0.85 100:0.73 104:0.57 108:0.52 111:0.72 123:0.50 126:0.54 127:0.36 129:0.05 135:0.47 140:0.45 145:0.96 146:0.76 147:0.80 149:0.41 150:0.82 152:0.75 153:0.48 154:0.25 159:0.32 161:0.72 162:0.62 163:0.92 164:0.57 167:0.93 169:0.72 172:0.41 173:0.79 177:0.05 179:0.85 181:0.66 186:0.73 195:0.63 202:0.50 212:0.61 215:0.49 216:0.01 220:0.99 230:0.65 233:0.63 235:0.84 241:0.93 242:0.82 243:0.80 247:0.12 256:0.45 259:0.21 261:0.73 265:0.85 266:0.27 267:0.52 268:0.46 271:0.47 276:0.35 285:0.62 297:0.36 300:0.25 +2 6:0.95 7:0.81 8:0.86 12:0.37 17:0.12 20:0.91 21:0.54 27:0.24 28:0.83 30:0.28 32:0.80 34:0.75 36:0.60 37:0.80 43:0.32 55:0.71 66:0.60 69:0.69 70:0.76 78:0.76 81:0.87 91:0.73 98:0.76 100:0.80 104:0.80 106:0.81 108:0.53 111:0.76 120:0.86 123:0.51 124:0.51 126:0.54 127:0.38 129:0.59 133:0.47 135:0.56 140:0.45 145:0.67 146:0.88 147:0.90 149:0.71 150:0.72 151:0.76 152:0.91 153:0.87 154:0.24 159:0.57 161:0.72 162:0.81 164:0.89 167:0.69 169:0.78 172:0.73 173:0.62 177:0.05 179:0.39 181:0.66 186:0.77 187:0.21 189:0.96 195:0.81 202:0.81 206:0.81 212:0.70 215:0.58 216:0.67 220:0.62 230:0.87 233:0.76 235:0.60 238:0.93 241:0.53 242:0.82 243:0.67 245:0.86 247:0.12 248:0.81 256:0.86 259:0.21 260:0.87 261:0.81 265:0.76 266:0.75 267:0.23 268:0.86 271:0.47 276:0.70 283:0.82 285:0.62 297:0.36 300:0.70 +1 12:0.37 17:0.45 21:0.30 27:0.45 28:0.83 30:0.62 32:0.80 34:0.45 36:0.18 37:0.50 43:0.32 55:0.59 66:0.47 69:0.68 70:0.34 81:0.92 91:0.18 98:0.31 108:0.65 123:0.63 124:0.52 126:0.54 127:0.16 129:0.59 133:0.47 135:0.99 145:0.47 146:0.22 147:0.28 149:0.36 150:0.84 154:0.24 159:0.26 161:0.72 163:0.75 167:0.60 172:0.21 173:0.65 177:0.05 178:0.55 179:0.65 181:0.66 187:0.68 195:0.75 212:0.30 215:0.72 216:0.77 235:0.31 241:0.24 242:0.82 243:0.37 245:0.46 247:0.12 259:0.21 265:0.33 266:0.27 267:0.99 271:0.47 276:0.26 283:0.60 297:0.36 300:0.25 +1 6:0.92 8:0.93 12:0.37 17:0.30 20:0.85 21:0.78 25:0.88 27:0.72 28:0.83 34:0.52 36:0.22 37:0.99 78:0.75 91:0.90 100:0.79 104:0.58 111:0.75 120:0.69 135:0.78 140:0.45 151:0.66 152:0.81 153:0.48 161:0.72 162:0.86 164:0.57 167:0.93 169:0.77 175:0.75 181:0.66 186:0.75 189:0.94 202:0.36 216:0.09 238:0.92 241:0.90 244:0.61 248:0.63 256:0.45 257:0.65 259:0.21 260:0.70 261:0.77 267:0.44 268:0.46 271:0.47 277:0.69 285:0.62 +1 8:0.87 12:0.37 17:0.04 20:0.84 21:0.78 27:0.27 28:0.83 34:0.36 36:0.68 37:0.99 78:0.74 91:0.85 100:0.79 111:0.74 120:0.84 135:0.44 140:0.96 151:0.74 152:0.80 153:0.83 161:0.72 162:0.46 164:0.83 167:0.92 169:0.77 175:0.75 178:0.54 181:0.66 186:0.75 187:0.21 202:0.96 216:0.59 220:0.74 235:0.12 241:0.85 244:0.61 248:0.76 256:0.78 257:0.65 260:0.85 261:0.76 267:0.28 268:0.79 271:0.47 277:0.69 283:0.60 285:0.62 +3 6:0.95 7:0.81 8:0.87 12:0.37 17:0.38 20:0.99 21:0.30 27:0.21 28:0.83 30:0.54 34:0.39 36:0.90 37:0.74 43:0.52 55:0.52 66:0.09 69:0.10 70:0.65 78:0.79 81:0.92 91:0.77 98:0.52 100:0.87 104:0.84 106:0.81 108:0.96 111:0.81 120:0.95 123:0.76 124:0.91 126:0.54 127:0.83 129:0.97 133:0.92 135:0.32 140:0.45 146:0.82 147:0.85 149:0.91 150:0.84 151:0.83 152:0.97 153:0.96 154:0.41 159:0.94 161:0.72 162:0.70 164:0.93 167:0.71 169:0.83 172:0.95 173:0.06 177:0.05 179:0.11 181:0.66 186:0.79 187:0.39 189:0.96 191:0.75 202:0.89 206:0.81 212:0.71 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.59 242:0.82 243:0.19 245:0.98 247:0.12 248:0.93 256:0.91 259:0.21 260:0.95 261:0.87 265:0.49 266:0.92 267:0.96 268:0.92 271:0.47 276:0.98 283:0.82 285:0.62 297:0.36 300:0.70 +1 12:0.37 17:0.38 21:0.47 27:0.45 28:0.83 30:0.45 32:0.80 34:0.83 36:0.17 37:0.50 43:0.32 55:0.92 66:0.53 69:0.68 70:0.57 81:0.88 91:0.25 98:0.50 108:0.52 123:0.50 124:0.52 126:0.54 127:0.32 129:0.59 133:0.47 135:0.96 145:0.50 146:0.71 147:0.75 149:0.51 150:0.77 154:0.24 159:0.57 161:0.72 167:0.66 172:0.45 173:0.59 177:0.05 178:0.55 179:0.67 181:0.66 187:0.68 195:0.83 212:0.13 215:0.63 216:0.77 235:0.52 241:0.47 242:0.82 243:0.62 245:0.64 247:0.12 259:0.21 265:0.51 266:0.80 267:0.96 271:0.47 276:0.45 283:0.60 297:0.36 300:0.43 +2 12:0.37 17:0.28 21:0.47 27:0.36 28:0.83 30:0.26 34:0.42 36:0.98 37:0.64 43:0.27 55:0.71 66:0.11 69:0.79 70:0.88 81:0.88 91:0.06 98:0.36 104:0.89 106:0.81 108:0.91 120:0.91 123:0.91 124:0.93 126:0.54 127:0.38 129:0.98 133:0.93 135:0.80 140:0.45 146:0.78 147:0.81 149:0.78 150:0.77 151:0.82 153:0.94 154:0.20 159:0.90 161:0.72 162:0.83 164:0.86 167:0.54 172:0.70 173:0.43 177:0.05 179:0.15 181:0.66 187:0.39 202:0.76 206:0.81 212:0.42 215:0.63 216:0.72 235:0.52 241:0.03 242:0.82 243:0.21 245:0.93 247:0.12 248:0.88 256:0.81 259:0.21 260:0.92 265:0.39 266:0.91 267:0.69 268:0.82 271:0.47 276:0.92 283:0.82 285:0.62 297:0.36 300:0.84 +1 8:0.73 12:0.06 17:0.26 21:0.47 27:0.45 28:0.83 30:0.84 34:0.70 36:0.17 37:0.50 43:0.32 55:0.59 66:0.51 69:0.69 70:0.28 81:0.92 91:0.46 98:0.65 108:0.79 123:0.78 124:0.65 126:0.54 127:0.52 129:0.81 133:0.67 135:0.74 145:0.50 146:0.72 147:0.76 149:0.73 150:0.86 154:0.24 159:0.70 161:0.93 167:0.45 172:0.76 173:0.57 177:0.05 178:0.55 179:0.35 181:0.91 187:0.68 212:0.76 215:0.63 216:0.77 235:0.60 241:0.07 242:0.82 243:0.40 245:0.86 247:0.12 259:0.21 265:0.66 266:0.63 267:0.69 271:0.17 276:0.77 297:0.36 300:0.43 +1 12:0.06 17:0.38 21:0.30 27:0.45 28:0.83 30:0.11 34:0.75 36:0.18 37:0.50 43:0.32 55:0.59 66:0.16 69:0.68 70:0.54 81:0.95 91:0.20 98:0.30 108:0.82 123:0.65 124:0.71 126:0.54 127:0.37 129:0.81 133:0.67 135:0.81 145:0.47 146:0.05 147:0.05 149:0.30 150:0.92 154:0.24 159:0.50 161:0.93 163:0.96 167:0.46 172:0.16 173:0.66 177:0.05 178:0.55 179:0.91 181:0.91 187:0.68 212:0.30 215:0.72 216:0.77 235:0.42 241:0.18 242:0.82 243:0.23 245:0.43 247:0.12 259:0.21 265:0.24 266:0.27 267:0.44 271:0.17 276:0.26 297:0.36 300:0.33 +1 6:0.79 8:0.58 12:0.06 17:0.45 20:0.85 21:0.13 27:0.40 28:0.83 30:0.45 34:0.53 36:0.97 37:0.38 43:0.52 55:0.84 66:0.15 69:0.07 70:0.66 78:0.80 81:0.97 91:0.44 98:0.46 100:0.79 108:0.94 111:0.78 120:0.80 123:0.93 124:0.90 126:0.54 127:0.77 129:0.96 133:0.90 135:0.81 140:0.45 146:0.55 147:0.61 149:0.76 150:0.96 151:0.72 152:0.80 153:0.78 154:0.41 159:0.87 161:0.93 162:0.79 163:0.94 164:0.76 167:0.49 169:0.77 172:0.57 173:0.07 177:0.05 179:0.33 181:0.91 186:0.80 187:0.68 189:0.81 202:0.66 212:0.22 215:0.84 216:0.25 235:0.12 238:0.82 241:0.35 242:0.82 243:0.29 245:0.84 247:0.12 248:0.73 256:0.68 259:0.21 260:0.81 261:0.81 265:0.48 266:0.92 267:0.23 268:0.71 271:0.17 276:0.82 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.90 7:0.81 8:0.76 12:0.06 17:0.15 20:0.87 21:0.30 27:0.50 28:0.83 30:0.12 32:0.80 34:0.67 36:0.91 37:0.60 43:0.51 55:0.84 66:0.10 69:0.15 70:0.91 78:0.86 81:0.95 91:0.10 98:0.35 100:0.90 104:0.84 106:0.81 108:0.96 111:0.86 120:0.75 123:0.95 124:0.97 126:0.54 127:0.95 129:1.00 133:0.98 135:0.70 140:0.45 145:0.63 146:0.29 147:0.35 149:0.74 150:0.92 151:0.73 152:0.82 153:0.78 154:0.40 159:0.94 161:0.93 162:0.78 164:0.70 167:0.45 169:0.87 172:0.71 173:0.07 177:0.05 179:0.20 181:0.91 186:0.86 187:0.39 189:0.91 191:0.70 195:0.99 202:0.59 206:0.81 212:0.37 215:0.72 216:0.86 230:0.87 233:0.76 235:0.31 238:0.91 241:0.03 242:0.82 243:0.07 245:0.86 247:0.12 248:0.68 256:0.58 259:0.21 260:0.76 261:0.88 265:0.24 266:0.97 267:0.65 268:0.64 271:0.17 276:0.92 283:0.82 285:0.62 297:0.36 300:0.84 +2 6:0.86 7:0.81 8:0.76 12:0.06 17:0.45 20:0.95 21:0.13 25:0.88 27:0.45 28:0.83 30:0.56 32:0.80 34:0.18 36:0.61 37:0.38 43:0.48 55:0.71 66:0.12 69:0.30 70:0.42 78:0.85 81:0.97 91:0.83 98:0.25 100:0.86 104:0.58 108:0.78 111:0.84 120:0.88 123:0.88 124:0.88 126:0.54 127:0.50 129:0.95 132:0.97 133:0.87 135:0.81 140:0.45 145:0.56 146:0.28 147:0.35 149:0.45 150:0.96 151:0.73 152:0.94 153:0.78 154:0.37 159:0.71 161:0.93 162:0.65 163:0.98 164:0.92 167:0.79 169:0.84 172:0.21 173:0.19 177:0.05 179:0.78 181:0.91 186:0.85 189:0.88 191:0.88 195:0.88 202:0.89 212:0.23 215:0.84 216:0.41 220:0.62 230:0.87 233:0.76 235:0.12 238:0.86 241:0.87 242:0.82 243:0.25 245:0.49 247:0.12 248:0.83 256:0.89 259:0.21 260:0.89 261:0.91 265:0.23 266:0.71 267:0.52 268:0.90 271:0.17 276:0.44 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.84 7:0.81 8:0.64 12:0.06 17:0.55 20:0.80 21:0.13 25:0.88 27:0.33 28:0.83 30:0.66 34:0.77 36:0.72 37:0.59 43:0.52 55:0.71 66:0.93 69:0.08 70:0.48 78:0.83 81:1.00 91:0.33 98:0.97 100:0.88 104:0.58 108:0.07 111:0.84 123:0.07 126:0.54 127:0.73 129:0.05 135:0.76 145:0.93 146:0.93 147:0.94 149:0.74 150:1.00 152:0.94 154:0.41 159:0.55 161:0.93 167:0.49 169:0.90 172:0.80 173:0.16 177:0.05 178:0.55 179:0.50 181:0.91 186:0.83 187:0.21 189:0.83 212:0.71 215:0.84 216:0.46 230:0.87 233:0.76 235:0.22 238:0.90 241:0.39 242:0.82 243:0.93 247:0.12 259:0.21 261:0.93 265:0.97 266:0.54 267:0.52 271:0.17 276:0.70 297:0.36 300:0.43 +3 6:0.91 7:0.81 8:0.84 12:0.06 17:0.03 20:0.94 21:0.30 27:0.21 28:0.83 30:0.28 34:0.71 36:0.83 37:0.74 43:0.52 55:0.84 66:0.08 69:0.10 70:0.91 78:0.90 81:0.95 91:0.77 98:0.25 100:0.97 104:0.84 106:0.81 108:0.95 111:0.94 120:0.93 123:0.95 124:0.97 126:0.54 127:0.82 129:1.00 133:0.97 135:0.24 140:0.45 146:0.48 147:0.55 149:0.70 150:0.92 151:0.80 152:0.97 153:0.93 154:0.41 159:0.93 161:0.93 162:0.56 164:0.91 167:0.50 169:0.94 172:0.48 173:0.06 177:0.05 179:0.28 181:0.91 186:0.90 187:0.39 189:0.93 191:0.78 202:0.89 206:0.81 212:0.16 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.43 242:0.82 243:0.13 245:0.78 247:0.12 248:0.90 256:0.88 259:0.21 260:0.93 261:0.97 265:0.27 266:0.97 267:0.84 268:0.88 271:0.17 276:0.86 283:0.82 285:0.62 297:0.36 300:0.84 +2 6:0.86 8:0.75 12:0.06 17:0.11 20:0.95 21:0.78 27:0.35 28:0.83 30:0.89 34:0.76 36:0.95 37:0.40 43:0.26 55:0.84 66:0.47 69:0.81 70:0.20 78:0.84 91:0.62 98:0.39 100:0.85 108:0.65 111:0.83 120:0.79 123:0.63 124:0.52 127:0.21 129:0.59 133:0.47 135:0.69 140:0.87 146:0.51 147:0.57 149:0.31 151:0.66 152:0.88 153:0.48 154:0.19 159:0.35 161:0.93 162:0.58 163:0.87 164:0.77 167:0.59 169:0.83 172:0.21 173:0.80 177:0.05 178:0.48 179:0.80 181:0.91 186:0.84 187:0.68 189:0.87 191:0.76 202:0.73 212:0.30 216:0.58 220:0.87 235:0.42 238:0.84 241:0.64 242:0.82 243:0.59 245:0.46 247:0.12 248:0.72 256:0.71 259:0.21 260:0.80 261:0.88 265:0.41 266:0.27 267:0.52 268:0.72 271:0.17 276:0.27 285:0.62 300:0.18 +3 6:0.86 7:0.81 8:0.88 12:0.06 17:0.26 20:0.95 21:0.78 25:0.88 27:0.33 28:0.83 30:0.76 34:0.82 36:0.57 37:0.59 43:0.48 55:0.84 66:0.72 69:0.44 70:0.22 78:0.85 91:0.93 98:0.92 100:0.94 104:0.58 108:0.34 111:0.89 120:0.93 123:0.32 127:0.54 129:0.05 135:0.26 140:0.45 145:0.93 146:0.55 147:0.61 149:0.38 151:0.81 152:0.94 153:0.94 154:0.36 159:0.20 161:0.93 162:0.79 163:0.75 164:0.94 167:0.80 169:0.91 172:0.27 173:0.74 177:0.05 179:0.96 181:0.91 186:0.84 189:0.89 191:0.89 202:0.89 212:0.42 216:0.46 230:0.87 233:0.76 235:0.97 238:0.96 241:0.96 242:0.82 243:0.75 247:0.12 248:0.89 256:0.92 259:0.21 260:0.93 261:0.93 265:0.92 266:0.23 267:0.18 268:0.93 271:0.17 276:0.26 283:0.82 285:0.62 300:0.18 +2 7:0.81 12:0.06 17:0.83 21:0.22 27:0.58 28:0.83 30:0.08 32:0.80 34:0.74 36:0.35 37:0.39 43:0.51 55:0.45 66:0.36 69:0.12 70:0.89 78:0.83 81:0.96 91:0.42 98:0.54 104:0.92 106:0.81 108:0.66 111:0.83 120:0.89 123:0.64 124:0.77 126:0.54 127:0.79 129:0.89 133:0.78 135:0.81 140:0.45 145:0.45 146:0.18 147:0.23 149:0.55 150:0.94 151:0.74 153:0.84 154:0.40 159:0.56 161:0.93 162:0.83 164:0.82 167:0.54 169:0.83 172:0.41 173:0.19 177:0.05 179:0.76 181:0.91 187:0.68 195:0.73 202:0.72 206:0.81 212:0.18 215:0.79 216:0.79 230:0.87 233:0.76 235:0.22 241:0.54 242:0.82 243:0.21 245:0.59 247:0.12 248:0.84 256:0.80 260:0.89 265:0.56 266:0.63 267:0.23 268:0.78 271:0.17 276:0.49 283:0.82 285:0.62 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.79 17:0.69 21:0.05 27:0.65 30:0.62 34:0.33 36:0.38 37:0.46 39:0.41 43:0.91 60:0.87 66:0.29 69:0.41 70:0.54 74:0.78 81:0.95 83:0.86 85:0.88 91:0.46 98:0.43 101:0.80 104:0.84 106:0.81 108:0.70 114:0.95 117:0.86 122:0.57 123:0.68 124:0.72 126:0.54 127:0.53 129:0.81 133:0.67 135:0.54 146:0.13 147:0.18 149:0.77 150:0.98 154:0.90 155:0.67 158:0.92 159:0.46 165:0.90 172:0.27 173:0.43 176:0.73 177:0.38 178:0.55 179:0.81 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.14 215:0.91 216:0.54 222:0.25 232:0.91 235:0.12 242:0.45 243:0.26 245:0.56 247:0.47 253:0.76 259:0.21 265:0.45 266:0.71 267:0.52 271:0.11 276:0.36 279:0.86 283:0.82 290:0.95 297:0.36 300:0.43 +0 1:0.74 12:0.79 17:0.95 21:0.13 27:0.63 34:0.36 36:0.59 37:0.38 39:0.41 81:0.92 83:0.77 91:0.63 114:0.92 126:0.54 135:0.79 150:0.96 155:0.67 165:0.88 175:0.91 176:0.73 178:0.55 187:0.39 192:0.59 201:0.74 202:0.50 215:0.84 216:0.69 222:0.25 235:0.22 241:0.54 244:0.83 253:0.69 257:0.84 259:0.21 267:0.36 271:0.11 277:0.87 290:0.92 297:0.36 +0 8:0.92 12:0.79 17:0.84 20:0.94 21:0.78 27:0.50 34:0.33 36:0.21 37:0.66 39:0.41 60:0.87 74:0.47 78:0.92 83:0.73 91:0.82 96:0.70 100:0.73 104:0.54 111:0.89 120:0.82 135:0.49 140:0.45 151:0.63 152:0.92 153:0.45 155:0.67 158:0.92 162:0.74 164:0.85 169:0.72 175:0.91 186:0.92 190:0.77 191:0.87 192:0.59 202:0.78 208:0.64 216:0.13 220:0.74 222:0.25 235:0.02 241:0.80 244:0.83 248:0.74 253:0.44 256:0.83 257:0.84 259:0.21 260:0.83 261:0.90 267:0.19 268:0.82 271:0.11 277:0.87 279:0.86 283:0.82 285:0.62 +1 1:0.74 11:0.57 12:0.79 17:0.82 21:0.54 27:0.42 30:0.72 34:0.58 36:0.59 37:0.25 39:0.41 43:0.98 60:0.87 66:0.33 69:0.19 70:0.29 74:0.76 81:0.71 83:0.84 85:0.90 91:0.18 98:0.23 101:0.80 108:0.84 114:0.77 122:0.43 123:0.83 124:0.71 126:0.54 127:0.67 129:0.81 133:0.67 135:0.28 145:0.47 146:0.28 147:0.34 149:0.81 150:0.80 154:0.98 155:0.67 158:0.87 159:0.59 165:0.79 172:0.32 173:0.20 176:0.73 177:0.38 178:0.55 179:0.81 187:0.21 192:0.59 201:0.74 208:0.64 212:0.39 215:0.58 216:0.57 222:0.25 235:0.68 241:0.01 242:0.63 243:0.39 245:0.58 247:0.30 253:0.67 259:0.21 265:0.25 266:0.54 267:0.59 271:0.11 276:0.37 279:0.86 283:0.71 290:0.77 297:0.36 300:0.25 +2 1:0.74 11:0.57 12:0.79 17:0.73 21:0.22 27:0.70 30:0.66 34:0.61 36:0.52 37:0.25 39:0.41 43:0.90 60:0.95 66:0.85 69:0.44 70:0.33 74:0.88 81:0.88 83:0.89 85:0.90 91:0.48 98:0.61 101:0.85 104:0.83 106:0.81 108:0.34 114:0.90 117:0.86 122:0.57 123:0.33 126:0.54 127:0.18 129:0.05 135:0.30 146:0.36 147:0.42 149:0.88 150:0.93 154:0.89 155:0.67 158:0.92 159:0.14 165:0.86 172:0.57 173:0.72 176:0.73 177:0.38 178:0.55 179:0.41 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.92 215:0.79 216:0.48 222:0.25 232:0.91 235:0.31 241:0.18 242:0.52 243:0.86 247:0.47 253:0.76 259:0.21 265:0.62 266:0.17 267:0.23 271:0.11 276:0.46 279:0.86 283:0.82 290:0.90 297:0.36 300:0.25 +0 1:0.74 11:0.57 12:0.79 17:0.89 21:0.47 27:0.72 30:0.21 34:0.37 36:0.62 37:0.71 39:0.41 43:0.80 66:0.72 69:0.73 70:0.37 74:0.65 81:0.75 83:0.74 85:0.72 91:0.44 98:0.15 101:0.71 108:0.56 114:0.80 122:0.67 123:0.53 126:0.54 127:0.09 129:0.05 135:0.87 146:0.24 147:0.30 149:0.48 150:0.83 154:0.75 155:0.67 159:0.11 165:0.80 172:0.27 173:0.68 176:0.73 177:0.38 178:0.55 179:0.11 187:0.39 192:0.59 201:0.74 212:0.78 215:0.63 216:0.18 222:0.25 235:0.60 241:0.07 242:0.75 243:0.75 247:0.30 253:0.65 259:0.21 265:0.17 266:0.17 267:0.52 271:0.11 276:0.23 290:0.80 297:0.36 300:0.25 +3 1:0.74 6:1.00 8:1.00 9:0.80 11:0.57 12:0.79 17:0.70 20:0.94 27:0.72 30:0.15 39:0.41 41:0.96 43:0.94 55:0.52 60:0.99 66:0.24 69:0.23 70:0.68 74:0.72 78:0.99 81:0.98 83:0.89 85:0.72 91:0.69 96:0.96 98:0.18 100:0.99 101:0.69 104:0.54 108:0.88 111:1.00 114:0.98 120:0.93 122:0.67 123:0.87 124:0.80 126:0.54 127:0.69 129:0.05 133:0.74 140:0.45 146:0.13 147:0.18 149:0.58 150:0.99 151:0.98 152:0.94 153:0.92 154:0.94 155:0.67 158:0.92 159:0.82 162:0.79 163:0.80 164:0.87 165:0.92 169:0.99 172:0.21 173:0.12 176:0.73 177:0.38 179:0.86 186:0.99 189:1.00 191:0.80 192:0.59 201:0.74 202:0.80 208:0.64 212:0.30 215:0.96 216:0.11 222:0.25 232:0.76 235:0.05 238:0.97 241:0.81 242:0.80 243:0.28 245:0.50 247:0.30 248:0.96 253:0.96 255:0.79 256:0.85 259:0.21 260:0.94 261:0.96 265:0.19 266:0.54 268:0.85 271:0.11 276:0.33 277:0.69 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.43 +1 11:0.57 12:0.79 17:0.49 21:0.05 27:0.70 30:0.54 34:0.30 36:0.36 37:0.25 39:0.41 43:0.81 55:0.52 66:0.90 69:0.45 70:0.51 74:0.96 81:0.82 85:0.72 91:0.69 98:0.63 101:0.73 108:0.35 114:0.77 117:0.86 122:0.67 123:0.34 126:0.32 127:0.18 129:0.05 135:0.28 146:0.63 147:0.68 149:0.70 150:0.62 154:0.76 158:0.85 159:0.23 172:0.71 173:0.49 176:0.56 177:0.38 178:0.55 179:0.28 187:0.68 212:0.93 216:0.48 222:0.25 232:0.86 235:0.05 241:0.15 242:0.76 243:0.90 247:0.39 253:0.75 259:0.21 265:0.64 266:0.17 267:0.82 271:0.11 276:0.61 290:0.77 300:0.43 +1 1:0.74 11:0.57 12:0.79 17:0.53 20:0.94 21:0.30 27:0.42 30:0.87 34:0.66 36:0.51 37:0.25 39:0.41 43:0.98 60:0.97 66:0.79 69:0.12 70:0.20 74:0.78 78:0.95 81:0.84 83:0.83 85:0.88 91:0.56 98:0.40 100:0.73 101:0.81 108:0.10 111:0.92 114:0.86 120:0.54 122:0.43 123:0.10 126:0.54 127:0.13 129:0.05 135:0.21 140:0.45 145:0.47 146:0.37 147:0.44 149:0.85 150:0.90 151:0.47 152:0.97 153:0.46 154:0.98 155:0.67 158:0.87 159:0.14 162:0.86 164:0.65 165:0.84 169:0.72 172:0.41 173:0.28 176:0.73 177:0.38 179:0.34 186:0.95 187:0.21 190:0.77 192:0.59 201:0.74 202:0.49 208:0.64 212:0.95 215:0.72 216:0.57 220:0.99 222:0.25 235:0.42 241:0.05 242:0.63 243:0.80 247:0.30 248:0.47 253:0.72 256:0.58 259:0.21 260:0.54 261:0.90 265:0.42 266:0.12 267:0.44 268:0.58 271:0.11 276:0.31 279:0.86 283:0.71 285:0.50 290:0.86 297:0.36 300:0.18 +1 1:0.74 11:0.57 12:0.79 17:0.79 20:0.93 21:0.64 27:0.59 30:0.58 34:0.28 36:0.53 37:0.31 39:0.41 43:0.85 55:0.71 60:0.87 66:0.80 69:0.63 70:0.33 74:0.84 78:0.94 81:0.62 83:0.83 85:0.80 91:0.33 98:0.51 100:0.73 101:0.77 108:0.48 111:0.91 114:0.72 122:0.67 123:0.46 126:0.54 127:0.15 129:0.05 135:0.84 146:0.45 147:0.52 149:0.72 150:0.75 152:0.87 154:0.81 155:0.67 158:0.87 159:0.16 165:0.70 169:0.72 172:0.45 173:0.74 176:0.73 177:0.38 178:0.55 179:0.43 186:0.94 187:0.39 192:0.59 201:0.74 208:0.64 212:0.90 215:0.53 216:0.70 222:0.25 235:0.80 241:0.12 242:0.72 243:0.82 247:0.39 253:0.68 259:0.21 261:0.89 265:0.52 266:0.17 267:0.44 271:0.11 276:0.35 279:0.86 283:0.71 290:0.72 297:0.36 300:0.25 +0 1:0.74 8:0.78 11:0.57 12:0.79 17:0.79 27:0.39 30:0.21 34:0.75 36:0.42 37:0.64 39:0.41 43:0.98 66:0.24 69:0.37 70:0.67 74:0.74 76:0.94 77:0.89 81:0.98 83:0.80 85:0.80 91:0.27 98:0.19 101:0.69 108:0.97 114:0.98 122:0.43 123:0.97 124:0.85 126:0.54 127:0.85 129:0.81 133:0.83 135:0.89 145:0.96 146:0.13 147:0.18 149:0.72 150:0.99 154:0.98 155:0.67 159:0.93 163:0.86 165:0.93 172:0.21 173:0.10 176:0.73 177:0.38 178:0.55 179:0.89 187:0.39 192:0.59 195:0.99 201:0.74 212:0.19 215:0.96 216:0.19 222:0.25 235:0.12 241:0.61 242:0.45 243:0.28 245:0.48 247:0.47 253:0.77 259:0.21 265:0.21 266:0.47 267:0.76 271:0.11 276:0.29 282:0.84 290:0.98 297:1.00 300:0.43 +1 1:0.74 11:0.57 12:0.79 17:0.83 21:0.47 27:0.42 30:0.87 34:0.80 36:0.53 37:0.25 39:0.41 43:0.98 60:0.87 66:0.45 69:0.17 70:0.27 74:0.78 81:0.75 83:0.84 85:0.90 91:0.33 98:0.35 101:0.83 108:0.65 114:0.80 122:0.43 123:0.63 124:0.57 126:0.54 127:0.44 129:0.59 133:0.47 135:0.26 145:0.47 146:0.28 147:0.34 149:0.85 150:0.83 154:0.98 155:0.67 158:0.87 159:0.31 165:0.80 172:0.32 173:0.35 176:0.73 177:0.38 178:0.55 179:0.82 187:0.21 192:0.59 201:0.74 208:0.64 212:0.50 215:0.63 216:0.57 222:0.25 235:0.60 241:0.02 242:0.63 243:0.44 245:0.59 247:0.30 253:0.69 259:0.21 265:0.38 266:0.47 267:0.23 271:0.11 276:0.27 279:0.86 283:0.71 290:0.80 297:0.36 300:0.25 +1 1:0.74 8:0.59 11:0.57 12:0.79 17:0.75 20:0.94 21:0.13 27:0.68 30:0.76 34:0.89 36:0.37 37:0.32 39:0.41 43:0.81 66:0.72 69:0.71 70:0.22 74:0.60 78:0.99 81:0.92 83:0.77 85:0.80 91:0.42 98:0.43 100:0.94 101:0.79 108:0.54 111:0.97 114:0.92 122:0.43 123:0.52 126:0.54 127:0.14 129:0.05 135:0.81 145:0.42 146:0.31 147:0.38 149:0.66 150:0.96 152:0.87 154:0.76 155:0.67 159:0.13 165:0.88 169:0.92 172:0.27 173:0.90 176:0.73 177:0.38 178:0.55 179:0.57 186:0.99 187:0.21 192:0.59 201:0.74 212:0.78 215:0.84 216:0.63 222:0.25 235:0.22 242:0.45 243:0.75 247:0.35 253:0.71 259:0.21 261:0.94 265:0.45 266:0.17 267:0.28 271:0.11 276:0.22 290:0.92 297:0.36 300:0.18 +2 1:0.74 11:0.57 12:0.79 17:0.66 21:0.05 27:0.65 30:0.62 34:0.34 36:0.37 37:0.46 39:0.41 43:0.91 60:0.87 66:0.29 69:0.41 70:0.54 74:0.78 81:0.95 83:0.86 85:0.88 91:0.46 98:0.43 101:0.80 104:0.84 106:0.81 108:0.70 114:0.95 117:0.86 122:0.57 123:0.68 124:0.72 126:0.54 127:0.53 129:0.81 133:0.67 135:0.44 146:0.13 147:0.18 149:0.77 150:0.98 154:0.90 155:0.67 158:0.92 159:0.46 165:0.90 172:0.27 173:0.43 176:0.73 177:0.38 178:0.55 179:0.81 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.14 215:0.91 216:0.54 222:0.25 232:0.91 235:0.12 241:0.01 242:0.45 243:0.26 245:0.56 247:0.47 253:0.76 259:0.21 265:0.45 266:0.71 267:0.52 271:0.11 276:0.36 279:0.86 283:0.82 290:0.95 297:0.36 300:0.43 +1 1:0.74 11:0.57 12:0.79 17:0.97 20:0.93 21:0.13 27:0.63 30:0.68 32:0.80 34:0.53 36:0.47 37:0.38 39:0.41 43:0.92 66:0.79 69:0.38 70:0.21 74:0.81 77:0.70 78:0.98 81:0.92 83:0.77 85:0.85 91:0.46 98:0.45 100:0.73 101:0.83 108:0.30 111:0.95 114:0.92 122:0.43 123:0.28 126:0.54 127:0.14 129:0.05 135:0.83 145:0.41 146:0.26 147:0.32 149:0.79 150:0.96 152:0.87 154:0.91 155:0.67 159:0.11 165:0.88 169:0.72 172:0.41 173:0.75 176:0.73 177:0.38 178:0.55 179:0.39 186:0.98 187:0.39 192:0.59 195:0.53 201:0.74 212:0.95 215:0.84 216:0.69 222:0.25 235:0.22 241:0.12 242:0.45 243:0.80 247:0.39 253:0.75 259:0.21 261:0.92 265:0.47 266:0.12 267:0.19 271:0.11 276:0.35 282:0.88 290:0.92 297:0.36 299:0.88 300:0.18 +0 7:0.72 8:0.93 9:0.76 12:0.37 17:0.92 21:0.74 27:0.29 28:0.92 29:0.86 32:0.69 34:0.21 36:0.66 37:0.83 39:0.52 43:0.18 48:0.91 66:0.36 69:0.29 71:0.88 74:0.48 76:0.85 77:0.70 81:0.23 83:0.60 91:0.73 96:0.77 98:0.14 104:0.58 108:0.22 120:0.65 123:0.22 126:0.26 127:0.09 129:0.05 135:0.28 139:0.64 140:0.45 145:0.77 146:0.05 147:0.05 149:0.07 150:0.23 151:0.65 153:0.48 154:0.12 155:0.58 159:0.05 161:0.86 162:0.74 164:0.64 167:0.82 172:0.05 173:0.75 175:0.91 177:0.05 181:0.47 190:0.77 192:0.59 195:0.54 202:0.56 204:0.85 208:0.54 215:0.36 216:0.34 220:0.74 222:0.60 230:0.75 232:0.77 233:0.76 235:0.88 241:0.95 243:0.51 244:0.83 248:0.67 253:0.58 255:0.74 256:0.58 257:0.84 259:0.21 260:0.64 265:0.15 267:0.23 268:0.59 271:0.44 277:0.87 281:0.89 282:0.77 285:0.56 297:0.36 +2 1:0.74 6:0.92 8:0.86 11:0.64 12:0.37 17:0.88 18:0.69 20:0.85 21:0.68 27:0.35 28:0.92 29:0.86 30:0.74 32:0.69 34:0.88 36:0.21 37:0.73 39:0.52 43:0.71 45:0.96 48:0.91 64:0.77 66:0.19 69:0.39 70:0.80 71:0.88 74:0.73 77:0.70 78:0.98 81:0.41 83:0.60 86:0.67 91:0.29 98:0.22 99:0.83 100:0.94 101:0.52 104:0.88 108:0.95 111:0.95 114:0.56 122:0.51 123:0.95 124:0.95 126:0.54 127:0.93 129:0.59 133:0.95 135:0.26 139:0.69 145:0.70 146:0.28 147:0.35 149:0.83 150:0.65 152:0.81 154:0.61 155:0.67 159:0.94 161:0.86 165:0.70 167:0.45 169:0.92 172:0.63 173:0.10 176:0.73 177:0.70 178:0.55 179:0.35 181:0.47 186:0.97 187:0.39 189:0.93 191:0.77 192:0.87 195:0.99 201:0.93 208:0.64 212:0.30 215:0.37 216:0.53 222:0.60 232:0.91 235:0.88 238:0.86 242:0.58 243:0.12 244:0.61 245:0.79 247:0.55 253:0.43 259:0.21 261:0.90 265:0.24 266:0.91 267:0.59 271:0.44 276:0.80 277:0.69 281:0.91 282:0.77 290:0.56 297:0.36 300:0.98 +2 1:0.74 6:0.87 7:0.81 8:0.78 9:0.80 11:0.64 12:0.37 17:0.97 18:0.83 20:0.91 21:0.64 27:0.61 28:0.92 29:0.86 30:0.41 31:0.91 32:0.78 34:0.36 36:0.92 37:0.55 39:0.52 41:0.88 43:0.59 44:0.93 45:0.81 48:0.98 64:0.77 66:0.20 69:0.41 70:0.54 71:0.88 74:0.71 77:0.70 78:0.96 79:0.92 81:0.42 83:0.91 86:0.83 91:0.77 96:0.75 98:0.45 99:0.83 100:0.95 101:0.52 108:0.31 111:0.95 114:0.57 120:0.76 121:0.90 122:0.82 123:0.30 124:0.80 126:0.54 127:0.85 129:0.59 133:0.74 135:0.51 137:0.91 139:0.90 140:0.94 145:0.56 146:0.47 147:0.54 149:0.51 150:0.66 151:0.51 152:0.92 153:0.72 154:0.74 155:0.67 159:0.48 161:0.86 162:0.65 164:0.79 165:0.70 167:0.78 169:0.92 172:0.27 173:0.44 176:0.73 177:0.70 179:0.78 181:0.47 186:0.96 189:0.89 190:0.77 191:0.86 192:0.87 195:0.67 196:0.94 201:0.93 202:0.80 204:0.89 208:0.64 212:0.30 215:0.38 216:0.35 219:0.89 220:0.74 222:0.60 230:0.87 233:0.76 235:0.84 238:0.87 241:0.80 242:0.33 243:0.40 245:0.58 247:0.60 248:0.52 253:0.60 254:0.96 255:0.95 256:0.82 259:0.21 260:0.69 261:0.93 265:0.47 266:0.54 267:0.23 268:0.82 271:0.44 276:0.47 281:0.91 282:0.86 284:0.97 285:0.62 290:0.57 292:0.91 297:0.36 300:0.43 +1 1:0.69 6:0.84 8:0.89 11:0.64 12:0.37 17:0.36 18:0.62 20:0.87 21:0.30 27:0.05 28:0.92 29:0.86 30:0.72 34:0.93 36:0.74 37:0.95 39:0.52 43:0.71 45:0.83 66:0.33 69:0.83 70:0.48 71:0.88 74:0.61 78:0.89 81:0.48 83:0.60 86:0.72 91:0.60 98:0.46 99:0.83 100:0.89 101:0.52 104:0.58 108:0.25 111:0.88 114:0.63 122:0.51 123:0.66 124:0.61 126:0.44 127:0.50 129:0.05 133:0.43 135:0.93 139:0.69 146:0.41 147:0.39 149:0.68 150:0.57 152:0.99 154:0.63 155:0.58 159:0.36 161:0.86 165:0.70 167:0.66 169:0.91 172:0.32 173:0.94 176:0.66 177:0.38 178:0.55 179:0.78 181:0.47 186:0.89 187:0.39 189:0.84 191:0.83 192:0.59 201:0.68 202:0.46 208:0.54 212:0.23 215:0.44 216:0.72 222:0.60 232:0.77 235:0.42 238:0.87 241:0.68 242:0.55 243:0.50 245:0.64 247:0.47 253:0.48 254:0.92 257:0.65 259:0.21 261:0.94 265:0.35 266:0.54 267:0.52 271:0.44 276:0.35 290:0.63 297:0.36 300:0.43 +2 6:0.82 7:0.72 8:0.84 9:0.76 12:0.37 17:0.94 20:0.89 21:0.74 27:0.13 28:0.92 29:0.86 32:0.69 34:0.22 36:0.49 37:0.85 39:0.52 43:0.18 48:0.91 66:0.36 69:0.29 71:0.88 74:0.48 76:0.85 77:0.70 78:0.85 81:0.23 83:0.60 91:0.76 96:0.75 98:0.14 100:0.89 104:0.58 108:0.22 111:0.85 120:0.63 123:0.22 126:0.26 127:0.09 129:0.05 135:0.28 139:0.64 140:0.45 145:0.77 146:0.05 147:0.05 149:0.07 150:0.23 151:0.65 152:0.83 153:0.48 154:0.12 155:0.58 159:0.05 161:0.86 162:0.62 164:0.54 167:0.81 169:0.86 172:0.05 173:0.74 175:0.91 177:0.05 181:0.47 186:0.85 189:0.84 190:0.77 192:0.59 195:0.54 202:0.50 204:0.85 208:0.54 215:0.36 216:0.32 220:0.62 222:0.60 230:0.75 232:0.77 233:0.76 235:0.88 238:0.89 241:0.90 243:0.51 244:0.83 248:0.63 253:0.55 255:0.74 256:0.45 257:0.84 259:0.21 260:0.63 261:0.85 265:0.15 267:0.36 268:0.46 271:0.44 277:0.87 281:0.89 282:0.77 285:0.56 297:0.36 +2 1:0.74 6:0.81 7:0.81 8:0.62 11:0.85 12:0.37 17:0.99 18:0.92 20:0.82 21:0.59 27:0.28 28:0.92 29:0.86 30:0.28 32:0.78 34:0.87 36:0.54 37:0.37 39:0.52 43:0.61 44:0.93 45:0.92 64:0.77 66:0.68 69:0.27 70:0.87 71:0.88 74:0.89 77:0.70 78:0.96 81:0.50 83:0.91 86:0.93 91:0.38 98:0.72 99:0.83 100:0.96 101:0.87 104:0.96 106:0.81 108:0.21 111:0.95 114:0.58 117:0.86 121:0.90 122:0.51 123:0.20 124:0.48 126:0.54 127:0.75 129:0.05 133:0.60 135:0.88 139:0.95 145:0.87 146:0.86 147:0.88 149:0.82 150:0.72 152:0.83 154:0.86 155:0.67 159:0.67 161:0.86 165:0.93 167:0.45 169:0.91 172:0.85 173:0.22 176:0.73 177:0.70 178:0.55 179:0.35 181:0.47 186:0.96 187:0.21 189:0.85 192:0.87 195:0.81 201:0.93 204:0.89 206:0.81 208:0.64 212:0.71 215:0.39 216:0.51 222:0.60 230:0.87 232:0.97 233:0.76 235:0.88 238:0.89 241:0.01 242:0.27 243:0.72 245:0.84 247:0.88 253:0.48 254:0.92 259:0.21 261:0.92 265:0.73 266:0.75 267:0.23 271:0.44 276:0.80 281:0.91 282:0.86 290:0.58 297:0.36 300:0.84 +2 1:0.69 6:0.84 8:0.66 11:0.64 12:0.37 17:0.59 18:0.71 20:0.92 21:0.13 27:0.05 28:0.92 29:0.86 30:0.72 34:0.93 36:0.73 37:0.95 39:0.52 43:0.71 45:0.92 66:0.93 69:0.32 70:0.47 71:0.88 74:0.83 78:0.95 81:0.78 83:0.60 86:0.82 91:0.40 98:0.92 99:0.95 100:0.95 101:0.52 104:0.58 108:0.25 111:0.94 114:0.75 122:0.51 123:0.24 126:0.44 127:0.54 129:0.05 135:0.73 139:0.78 146:0.72 147:0.76 149:0.88 150:0.74 152:0.99 154:0.76 155:0.58 159:0.29 161:0.86 165:0.70 167:0.57 169:0.91 172:0.80 173:0.50 176:0.66 177:0.70 178:0.55 179:0.46 181:0.47 186:0.94 187:0.39 189:0.87 192:0.59 201:0.68 208:0.54 212:0.92 215:0.61 216:0.72 222:0.60 232:0.77 235:0.42 238:0.90 241:0.57 242:0.55 243:0.93 247:0.60 253:0.56 254:0.84 259:0.21 261:0.94 265:0.92 266:0.27 267:0.65 271:0.44 276:0.70 290:0.75 297:0.36 300:0.55 +0 1:0.69 11:0.64 12:0.37 17:0.36 18:0.63 21:0.13 27:0.45 28:0.92 29:0.86 30:0.60 34:0.73 36:0.17 37:0.50 39:0.52 43:0.64 45:0.83 66:0.34 69:0.68 70:0.79 71:0.88 74:0.54 81:0.64 83:0.60 86:0.64 91:0.14 98:0.37 99:0.83 101:0.52 108:0.52 114:0.75 122:0.51 123:0.49 124:0.68 126:0.44 127:0.46 129:0.05 133:0.60 135:0.89 139:0.62 145:0.49 146:0.53 147:0.59 149:0.64 150:0.62 154:0.54 155:0.58 159:0.59 161:0.86 165:0.70 167:0.51 172:0.37 173:0.62 176:0.66 177:0.70 178:0.55 179:0.71 181:0.47 187:0.68 192:0.59 201:0.68 208:0.54 212:0.42 215:0.61 216:0.77 222:0.60 235:0.22 241:0.41 242:0.45 243:0.50 244:0.61 245:0.61 247:0.55 253:0.54 259:0.21 265:0.39 266:0.71 267:0.52 271:0.44 276:0.42 290:0.75 297:0.36 300:0.70 +2 7:0.72 8:0.85 9:0.76 12:0.37 17:0.89 20:0.79 21:0.74 27:0.29 28:0.92 29:0.86 32:0.69 34:0.29 36:0.44 37:0.93 39:0.52 43:0.18 48:0.91 66:0.36 69:0.29 71:0.88 74:0.48 76:0.85 77:0.70 78:0.90 81:0.23 83:0.60 91:0.82 96:0.93 98:0.14 100:0.73 108:0.22 111:0.87 120:0.85 123:0.22 126:0.26 127:0.09 129:0.05 135:0.24 139:0.64 140:0.80 145:0.77 146:0.05 147:0.05 149:0.07 150:0.23 151:0.93 152:0.77 153:0.87 154:0.12 155:0.58 159:0.05 161:0.86 162:0.78 164:0.76 167:0.82 169:0.72 172:0.05 173:0.74 175:0.91 177:0.05 181:0.47 186:0.90 190:0.77 191:0.93 192:0.59 195:0.54 202:0.79 204:0.85 208:0.54 215:0.36 216:0.14 220:0.62 222:0.60 230:0.75 232:0.76 233:0.76 235:0.88 241:0.94 243:0.51 244:0.83 248:0.88 253:0.87 255:0.74 256:0.82 257:0.84 259:0.21 260:0.82 261:0.80 265:0.15 267:0.59 268:0.83 271:0.44 277:0.87 281:0.89 282:0.77 285:0.56 297:0.36 +3 1:0.69 6:0.88 7:0.81 8:0.73 9:0.76 11:0.64 12:0.37 17:0.95 20:0.86 21:0.13 27:0.61 28:0.92 29:0.86 30:0.76 32:0.69 34:0.86 36:0.84 37:0.55 39:0.52 43:0.72 45:0.90 66:0.90 69:0.08 70:0.37 71:0.88 74:0.84 77:0.70 78:0.91 81:0.64 83:0.60 91:0.57 96:0.69 97:0.89 98:0.94 99:0.83 100:0.90 101:0.52 104:0.86 106:0.81 108:0.07 111:0.90 114:0.75 117:0.86 120:0.55 121:0.90 122:0.51 123:0.07 126:0.44 127:0.63 129:0.05 135:0.60 139:0.74 140:0.45 145:0.45 146:0.68 147:0.72 149:0.87 150:0.62 151:0.51 152:0.92 153:0.48 154:0.62 155:0.67 158:0.78 159:0.26 161:0.86 162:0.86 164:0.54 165:0.70 167:0.47 169:0.92 172:0.71 173:0.35 175:0.75 176:0.66 177:0.38 179:0.61 181:0.47 186:0.91 187:0.21 189:0.87 190:0.77 191:0.76 192:0.87 195:0.56 201:0.68 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.61 216:0.35 220:0.87 222:0.60 230:0.87 232:0.90 233:0.76 235:0.22 238:0.88 241:0.18 242:0.62 243:0.90 244:0.61 247:0.39 248:0.50 253:0.57 255:0.74 256:0.45 257:0.65 259:0.21 260:0.55 261:0.93 265:0.94 266:0.27 267:0.36 268:0.46 271:0.44 276:0.59 277:0.69 279:0.82 281:0.91 282:0.77 283:0.78 285:0.56 290:0.75 297:0.36 300:0.33 +2 1:0.74 6:0.81 7:0.72 8:0.63 9:0.80 11:0.64 12:0.37 17:0.83 18:0.77 20:0.96 21:0.40 27:0.49 28:0.92 29:0.86 30:0.66 31:0.86 32:0.80 34:0.86 36:0.81 37:0.32 39:0.52 43:0.68 44:0.93 45:0.89 48:0.97 64:0.77 66:0.69 69:0.21 70:0.71 71:0.88 74:0.81 77:0.70 78:0.94 79:0.86 81:0.57 83:0.60 86:0.80 91:0.48 96:0.68 97:0.89 98:0.81 99:0.83 100:0.94 101:0.52 108:0.17 111:0.93 114:0.62 120:0.55 122:0.51 123:0.16 124:0.44 126:0.54 127:0.58 129:0.05 133:0.43 135:0.32 137:0.86 139:0.85 140:0.45 145:0.61 146:0.78 147:0.82 149:0.79 150:0.73 151:0.50 152:0.89 153:0.48 154:0.70 155:0.67 158:0.79 159:0.44 161:0.86 162:0.86 164:0.57 165:0.70 167:0.46 169:0.92 172:0.68 173:0.29 176:0.73 177:0.70 179:0.56 181:0.47 186:0.93 187:0.21 189:0.83 192:0.87 195:0.70 196:0.89 201:0.93 202:0.36 204:0.85 208:0.64 212:0.51 215:0.42 216:0.31 220:0.91 222:0.60 230:0.75 233:0.76 235:0.68 238:0.88 241:0.10 242:0.45 243:0.72 245:0.75 247:0.74 248:0.49 253:0.50 254:0.97 255:0.95 256:0.45 259:0.21 260:0.55 261:0.92 265:0.81 266:0.54 267:0.36 268:0.46 271:0.44 276:0.60 279:0.82 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.62 297:0.36 300:0.70 +2 1:0.74 6:0.89 8:0.83 9:0.80 11:0.64 12:0.37 17:0.75 18:0.79 20:0.83 21:0.74 27:0.34 28:0.92 29:0.86 30:0.60 31:0.90 32:0.78 34:0.66 36:0.92 37:0.57 39:0.52 43:0.58 44:0.93 45:0.88 64:0.77 66:0.51 69:0.61 70:0.54 71:0.88 74:0.75 77:0.70 78:0.96 79:0.91 81:0.40 83:0.60 86:0.87 91:0.15 96:0.68 98:0.62 99:0.83 100:0.91 101:0.52 108:0.47 111:0.93 114:0.54 120:0.54 122:0.51 123:0.45 124:0.74 126:0.54 127:0.36 129:0.59 133:0.77 135:0.97 137:0.90 139:0.88 140:0.45 145:0.96 146:0.81 147:0.84 149:0.59 150:0.65 151:0.48 152:0.85 153:0.48 154:0.80 155:0.67 159:0.60 161:0.86 162:0.86 163:0.75 164:0.57 165:0.70 167:0.53 169:0.87 172:0.59 173:0.50 176:0.73 177:0.70 179:0.50 181:0.47 186:0.95 187:0.21 189:0.90 190:0.89 192:0.87 195:0.82 196:0.92 201:0.93 202:0.50 208:0.64 212:0.18 215:0.36 216:0.76 220:0.93 222:0.60 235:0.97 238:0.85 241:0.46 242:0.24 243:0.61 245:0.67 247:0.74 248:0.48 253:0.43 254:0.84 255:0.95 256:0.58 259:0.21 260:0.54 261:0.90 265:0.63 266:0.75 267:0.99 268:0.59 271:0.44 276:0.61 282:0.86 284:0.91 285:0.62 290:0.54 297:0.36 300:0.43 +1 1:0.69 8:0.86 11:0.64 12:0.37 17:0.32 18:0.62 21:0.30 27:0.05 28:0.92 29:0.86 30:0.72 34:0.93 36:0.74 37:0.95 39:0.52 43:0.71 45:0.83 66:0.33 69:0.83 70:0.48 71:0.88 74:0.61 81:0.48 83:0.60 86:0.72 91:0.57 98:0.46 99:0.83 101:0.52 104:0.58 108:0.25 114:0.63 122:0.51 123:0.66 124:0.61 126:0.44 127:0.50 129:0.05 133:0.43 135:0.93 139:0.69 146:0.41 147:0.39 149:0.68 150:0.57 154:0.63 155:0.58 159:0.36 161:0.86 165:0.70 167:0.66 172:0.32 173:0.94 176:0.66 177:0.38 178:0.55 179:0.78 181:0.47 187:0.39 192:0.59 201:0.68 202:0.36 208:0.54 212:0.23 215:0.44 216:0.72 222:0.60 232:0.77 235:0.42 241:0.68 242:0.55 243:0.50 245:0.64 247:0.47 253:0.48 254:0.92 257:0.65 259:0.21 265:0.35 266:0.54 267:0.52 271:0.44 276:0.35 290:0.63 297:0.36 300:0.43 +1 1:0.74 6:0.89 8:0.69 11:0.64 12:0.37 17:0.53 18:0.80 20:0.89 21:0.64 27:0.45 28:0.92 29:0.86 30:0.42 32:0.80 34:0.81 36:0.18 37:0.50 39:0.52 43:0.60 44:0.93 45:0.83 64:0.77 66:0.54 69:0.71 70:0.72 71:0.88 74:0.76 77:0.70 78:0.93 81:0.42 83:0.60 86:0.86 91:0.15 97:0.89 98:0.62 99:0.83 100:0.89 101:0.52 108:0.54 111:0.91 114:0.57 122:0.51 123:0.52 124:0.51 126:0.54 127:0.36 129:0.59 133:0.46 135:0.73 139:0.91 145:0.49 146:0.74 147:0.78 149:0.41 150:0.66 152:0.92 154:0.75 155:0.67 158:0.91 159:0.50 161:0.86 163:0.81 165:0.70 167:0.47 169:0.91 172:0.54 173:0.68 176:0.73 177:0.70 178:0.55 179:0.60 181:0.47 186:0.93 187:0.68 189:0.87 192:0.87 195:0.75 201:0.93 208:0.64 212:0.35 215:0.38 216:0.77 219:0.95 222:0.60 235:0.84 238:0.84 241:0.15 242:0.24 243:0.63 245:0.71 247:0.60 253:0.44 254:0.74 259:0.21 261:0.93 265:0.63 266:0.63 267:0.52 271:0.44 276:0.49 279:0.86 282:0.88 283:0.77 290:0.57 292:0.91 297:0.36 300:0.55 +2 1:0.69 6:0.84 8:0.65 11:0.64 12:0.37 17:0.45 18:0.71 20:0.91 21:0.30 27:0.05 28:0.92 29:0.86 30:0.72 34:0.89 36:0.71 37:0.95 39:0.52 43:0.71 45:0.92 66:0.93 69:0.32 70:0.46 71:0.88 74:0.83 78:0.87 81:0.48 83:0.60 86:0.82 91:0.42 98:0.94 99:0.83 100:0.85 101:0.52 104:0.58 108:0.25 111:0.85 114:0.63 122:0.51 123:0.24 126:0.44 127:0.62 129:0.05 135:0.49 139:0.78 146:0.73 147:0.78 149:0.88 150:0.57 152:0.99 154:0.76 155:0.58 159:0.30 161:0.86 165:0.70 167:0.61 169:0.91 172:0.80 173:0.50 176:0.66 177:0.70 178:0.55 179:0.48 181:0.47 186:0.87 187:0.39 189:0.85 192:0.59 201:0.68 208:0.54 212:0.92 215:0.44 216:0.72 222:0.60 232:0.77 235:0.42 238:0.84 241:0.63 242:0.55 243:0.93 247:0.60 253:0.51 254:0.84 259:0.21 261:0.94 265:0.94 266:0.27 267:0.52 271:0.44 276:0.70 290:0.63 297:0.36 300:0.55 +0 1:0.69 8:0.84 11:0.64 12:0.06 17:0.45 18:0.95 21:0.47 27:0.07 29:0.62 30:0.27 34:1.00 36:0.26 37:0.63 39:0.74 43:0.68 44:0.90 45:0.66 48:0.91 55:0.59 64:0.77 66:0.23 69:0.89 70:0.92 71:0.90 74:0.79 76:0.94 77:0.70 81:0.33 86:0.94 91:0.07 96:0.85 98:0.55 101:0.52 108:0.90 114:0.59 117:0.86 122:0.77 123:0.89 124:0.87 126:0.44 127:0.80 129:0.59 133:0.86 135:0.99 139:0.94 140:0.45 145:0.45 146:0.70 147:0.41 149:0.54 150:0.48 151:0.60 153:0.48 154:0.56 155:0.58 158:0.74 159:0.79 162:0.81 172:0.73 173:0.81 176:0.66 177:0.38 179:0.27 187:0.68 190:0.89 191:0.70 192:0.51 195:0.91 201:0.68 202:0.70 208:0.54 212:0.67 216:0.95 220:0.74 222:0.76 232:0.82 235:0.60 241:0.18 242:0.44 243:0.25 245:0.89 247:0.93 248:0.61 253:0.80 254:0.84 256:0.73 257:0.65 259:0.21 265:0.56 266:0.80 267:0.82 268:0.75 271:0.50 276:0.86 281:0.89 282:0.77 285:0.47 290:0.59 300:0.84 +1 1:0.69 9:0.76 11:0.64 12:0.06 17:0.72 18:0.96 21:0.22 22:0.93 27:0.07 29:0.62 30:0.10 31:0.90 34:0.69 36:0.47 37:0.63 39:0.74 43:0.68 44:0.90 45:0.73 47:0.93 48:0.91 55:0.71 64:0.77 66:0.96 69:0.54 70:0.92 71:0.90 74:0.78 76:0.85 77:0.70 79:0.90 81:0.43 83:0.60 86:0.94 91:0.09 96:0.84 98:0.93 101:0.52 108:0.42 114:0.67 117:0.86 122:0.86 123:0.40 126:0.44 127:0.56 129:0.05 135:0.92 137:0.90 139:0.93 140:0.45 145:0.67 146:0.98 147:0.98 149:0.59 150:0.53 151:0.65 153:0.48 154:0.57 155:0.58 158:0.75 159:0.76 162:0.86 172:0.91 173:0.35 176:0.66 177:0.70 179:0.28 187:0.68 190:0.89 192:0.51 195:0.90 196:0.94 201:0.68 202:0.50 208:0.54 212:0.50 216:0.95 220:0.62 222:0.76 232:0.82 235:0.31 241:0.21 242:0.18 243:0.96 244:0.61 247:0.90 248:0.63 253:0.81 255:0.74 256:0.58 259:0.21 262:0.93 265:0.93 266:0.63 267:0.65 268:0.59 271:0.50 276:0.83 281:0.89 282:0.77 284:0.87 285:0.56 290:0.67 300:0.70 +1 1:0.69 8:0.68 9:0.76 11:0.64 12:0.06 17:0.55 18:0.96 21:0.76 22:0.93 27:0.07 29:0.62 30:0.13 31:0.90 34:1.00 36:0.22 37:0.63 39:0.74 43:0.68 44:0.90 45:0.66 47:0.93 48:0.91 55:0.59 64:0.77 66:0.47 69:0.87 70:0.97 71:0.90 74:0.80 76:0.85 77:0.70 79:0.90 81:0.28 86:0.94 91:0.53 96:0.78 98:0.63 101:0.52 108:0.74 114:0.53 117:0.86 120:0.59 122:0.77 123:0.72 124:0.88 126:0.44 127:0.91 129:0.05 133:0.91 135:1.00 137:0.90 139:0.95 140:0.45 145:0.65 146:0.92 147:0.41 149:0.51 150:0.47 151:0.60 153:0.48 154:0.45 155:0.58 158:0.78 159:0.84 162:0.76 164:0.65 172:0.83 173:0.74 176:0.66 177:0.38 179:0.30 187:0.68 190:0.89 192:0.51 195:0.93 196:0.94 201:0.68 202:0.76 204:0.85 208:0.54 212:0.58 216:0.95 219:0.92 220:0.99 222:0.76 232:0.84 235:0.97 241:0.12 242:0.30 243:0.26 245:0.83 247:0.93 248:0.62 253:0.72 254:0.95 255:0.74 256:0.79 257:0.65 259:0.21 260:0.58 262:0.93 265:0.64 266:0.84 267:0.89 268:0.80 271:0.50 276:0.85 279:0.82 281:0.88 282:0.77 284:0.96 285:0.62 290:0.53 292:0.91 300:0.84 +0 1:0.74 8:0.69 9:0.80 11:0.83 12:0.06 17:0.95 18:0.74 21:0.54 27:0.72 29:0.62 30:0.89 31:0.87 34:0.20 36:0.25 39:0.74 41:0.82 43:0.74 44:0.90 45:0.66 48:0.91 64:0.77 66:0.30 69:0.84 70:0.20 71:0.90 74:0.57 76:0.99 77:0.70 79:0.87 81:0.53 83:0.91 85:0.72 86:0.78 91:0.85 96:0.69 98:0.12 99:0.83 101:0.97 104:0.58 108:0.69 114:0.59 120:0.56 122:0.57 123:0.67 124:0.71 126:0.54 127:0.46 129:0.05 133:0.60 135:0.47 137:0.87 139:0.83 140:0.45 145:0.61 146:0.17 147:0.05 149:0.56 150:0.73 151:0.52 153:0.69 154:0.65 155:0.67 159:0.34 162:0.57 163:0.75 164:0.62 165:1.00 172:0.16 173:0.95 176:0.73 177:0.05 179:0.93 192:0.87 195:0.63 196:0.89 201:0.93 202:0.56 204:0.85 208:0.64 212:0.30 215:0.39 216:0.03 220:0.87 222:0.76 232:0.75 235:0.88 241:0.83 242:0.33 243:0.23 245:0.43 247:0.39 248:0.52 253:0.45 255:0.95 256:0.45 257:0.84 259:0.21 260:0.56 265:0.13 266:0.27 267:0.28 268:0.55 271:0.50 276:0.23 281:0.91 282:0.77 284:0.90 285:0.62 290:0.59 297:0.36 300:0.18 +1 12:0.06 17:0.91 21:0.47 27:0.36 29:0.62 32:0.69 34:0.70 36:0.25 37:0.92 39:0.74 43:0.15 44:0.90 48:0.91 64:0.77 66:0.36 69:0.53 71:0.90 81:0.33 91:0.51 98:0.08 108:0.41 117:0.86 123:0.39 126:0.44 127:0.06 129:0.05 135:0.66 139:0.71 145:0.65 146:0.05 147:0.05 149:0.07 150:0.27 154:0.11 159:0.05 172:0.05 173:0.55 175:0.91 177:0.05 178:0.55 187:0.39 192:0.51 195:0.67 201:0.68 215:0.41 216:0.23 222:0.76 232:0.88 235:0.60 241:0.30 243:0.51 244:0.83 253:0.20 257:0.84 259:0.21 265:0.08 267:0.73 271:0.50 277:0.87 281:0.91 297:0.36 +2 1:0.74 9:0.80 11:0.57 12:0.06 17:0.55 18:0.86 21:0.30 27:0.07 29:0.62 30:0.21 31:0.89 34:0.66 36:0.22 37:0.63 39:0.74 41:0.82 43:0.85 44:0.87 55:0.71 60:0.87 64:0.77 66:0.51 69:0.61 70:0.92 71:0.90 74:0.84 79:0.88 81:0.57 83:0.91 85:0.72 86:0.88 91:0.20 96:0.72 98:0.68 101:0.87 108:0.72 114:0.65 120:0.59 122:0.77 123:0.70 124:0.74 126:0.54 127:0.74 129:0.05 133:0.74 135:0.95 137:0.89 139:0.90 140:0.45 145:0.91 146:0.61 147:0.67 149:0.73 150:0.76 151:0.61 153:0.48 154:0.82 155:0.67 158:0.91 159:0.79 162:0.86 164:0.57 165:0.93 172:0.82 173:0.42 176:0.73 177:0.70 179:0.31 187:0.95 192:0.87 195:0.90 196:0.92 201:0.93 202:0.36 208:0.64 212:0.48 215:0.44 216:0.95 219:0.95 220:0.74 222:0.76 235:0.52 241:0.21 242:0.70 243:0.24 245:0.88 247:0.74 248:0.59 253:0.63 255:0.95 256:0.45 259:0.21 260:0.60 265:0.68 266:0.80 267:0.79 268:0.46 271:0.50 276:0.83 277:0.69 279:0.86 283:0.78 284:0.89 285:0.62 290:0.65 292:0.91 297:0.36 300:0.70 +1 1:0.74 12:0.06 17:0.83 18:0.75 21:0.30 27:0.36 29:0.62 30:0.56 32:0.80 34:0.74 36:0.24 37:0.92 39:0.74 43:0.15 44:0.93 48:0.91 55:0.84 64:0.77 66:0.91 69:0.54 70:0.39 71:0.90 74:0.78 76:0.85 77:0.89 81:0.60 83:0.60 86:0.74 91:0.54 98:0.88 99:0.95 108:0.41 114:0.65 117:0.86 122:0.77 123:0.39 126:0.54 127:0.42 129:0.05 135:0.96 139:0.82 145:0.69 146:0.89 147:0.91 149:0.31 150:0.75 154:0.54 155:0.67 158:0.75 159:0.48 165:0.70 172:0.74 173:0.52 176:0.73 177:0.70 178:0.55 179:0.52 187:0.39 192:0.87 195:0.72 201:0.93 208:0.64 212:0.42 215:0.44 216:0.23 222:0.76 232:0.88 235:0.68 241:0.41 242:0.73 243:0.91 244:0.61 247:0.55 253:0.51 254:0.84 259:0.21 265:0.88 266:0.63 267:0.36 271:0.50 276:0.63 281:0.91 282:0.88 290:0.65 297:0.36 300:0.33 +1 12:0.06 17:0.36 18:0.75 21:0.76 27:0.45 29:0.62 30:0.19 34:0.79 36:0.17 37:0.50 39:0.74 43:0.13 55:0.59 66:0.29 69:0.84 70:0.97 71:0.90 74:0.75 77:0.70 81:0.23 86:0.81 91:0.25 98:0.34 108:0.70 114:0.53 122:0.77 123:0.68 124:0.87 126:0.26 127:0.58 129:0.05 133:0.85 135:0.90 139:0.77 145:0.50 146:0.63 147:0.30 149:0.25 150:0.23 154:0.63 159:0.78 172:0.61 173:0.72 176:0.61 177:0.05 178:0.55 179:0.41 187:0.68 195:0.91 212:0.40 216:0.77 222:0.76 235:0.95 241:0.63 242:0.75 243:0.12 245:0.77 247:0.76 253:0.43 254:0.84 257:0.84 259:0.21 265:0.36 266:0.91 267:0.84 271:0.50 276:0.73 282:0.73 290:0.53 300:0.84 +0 1:0.74 9:0.80 11:0.64 12:0.06 17:0.24 18:0.76 21:0.47 27:0.06 29:0.62 30:0.33 31:0.87 34:0.61 36:0.95 37:0.82 39:0.74 43:0.29 44:0.90 45:0.66 48:0.91 55:0.71 64:0.77 66:0.63 69:0.93 70:0.98 71:0.90 74:0.60 76:0.85 77:0.70 79:0.87 81:0.44 86:0.80 91:0.27 96:0.69 98:0.48 101:0.52 108:0.86 114:0.60 120:0.55 122:0.51 123:0.85 124:0.51 126:0.54 127:0.28 129:0.05 133:0.60 135:0.83 137:0.87 139:0.82 140:0.45 145:0.77 146:0.78 147:0.05 149:0.24 150:0.63 151:0.49 153:0.87 154:0.46 155:0.67 159:0.64 162:0.67 164:0.78 172:0.65 173:0.91 176:0.73 177:0.05 179:0.43 187:0.39 192:0.87 195:0.90 196:0.89 201:0.93 202:0.71 208:0.64 212:0.61 215:0.41 216:0.92 220:0.93 222:0.76 235:0.68 241:0.44 242:0.13 243:0.10 245:0.67 247:0.86 248:0.51 253:0.46 254:0.86 255:0.95 256:0.68 257:0.84 259:0.21 260:0.55 265:0.50 266:0.80 267:0.73 268:0.73 271:0.50 276:0.57 281:0.91 282:0.77 284:0.96 285:0.62 290:0.60 297:0.36 300:0.94 +1 8:0.61 9:0.80 11:0.83 12:0.06 17:0.57 18:0.93 21:0.78 27:0.39 29:0.62 30:0.09 31:0.92 34:0.36 36:0.97 37:0.31 39:0.74 41:0.93 43:0.86 45:0.81 55:0.84 60:0.95 66:0.13 69:0.30 70:1.00 71:0.90 74:0.86 79:0.91 83:0.91 85:0.85 86:0.95 91:0.01 96:0.77 98:0.22 101:0.97 108:0.24 120:0.65 122:0.77 123:0.23 124:0.98 127:0.95 129:0.59 133:0.98 135:0.39 137:0.92 139:0.95 140:0.45 146:0.90 147:0.91 149:0.87 151:0.72 153:0.48 154:0.83 155:0.67 158:0.91 159:0.98 162:0.74 164:0.77 172:0.73 173:0.06 177:0.70 179:0.21 187:0.21 192:0.87 196:0.95 202:0.70 208:0.64 212:0.30 216:0.44 219:0.95 220:0.82 222:0.76 235:0.05 241:0.24 242:0.37 243:0.34 245:0.83 247:0.84 248:0.69 253:0.78 255:0.95 256:0.73 259:0.21 260:0.65 265:0.23 266:1.00 267:0.76 268:0.74 271:0.50 276:0.91 279:0.86 283:0.78 284:0.96 285:0.62 292:0.91 300:1.00 +0 1:0.69 9:0.76 11:0.64 12:0.06 17:0.77 18:0.73 21:0.13 27:0.72 29:0.62 30:0.33 34:0.78 36:0.41 37:0.83 39:0.74 43:0.71 45:0.77 55:0.45 66:0.46 69:0.08 70:0.75 71:0.90 74:0.95 76:0.99 77:0.70 81:0.55 83:0.60 86:0.81 87:0.98 91:0.49 96:0.77 97:0.89 98:0.58 101:0.52 108:0.07 114:0.75 120:0.65 122:0.77 123:0.07 124:0.69 126:0.44 127:0.62 129:0.81 132:0.97 133:0.67 135:0.74 139:0.77 140:0.45 145:0.80 146:0.66 147:0.71 149:0.58 150:0.57 151:0.65 153:0.48 154:0.76 155:0.58 158:0.78 159:0.54 162:0.86 164:0.64 172:0.79 173:0.16 176:0.66 177:0.70 179:0.30 187:0.39 190:0.77 192:0.59 195:0.73 201:0.68 202:0.50 208:0.54 212:0.91 215:0.61 216:0.28 220:0.62 222:0.76 232:0.72 235:0.22 241:0.53 242:0.52 243:0.58 245:0.91 247:0.76 248:0.67 253:0.80 254:0.90 255:0.74 256:0.58 259:0.21 260:0.64 265:0.59 266:0.54 267:0.87 268:0.59 271:0.50 276:0.80 279:0.82 282:0.73 283:0.78 285:0.56 290:0.75 297:0.36 300:0.70 +0 12:0.06 17:0.88 18:0.81 21:0.76 27:0.69 29:0.62 30:0.45 34:0.86 36:0.25 37:0.45 39:0.74 43:0.17 55:0.92 66:0.24 69:0.45 70:0.61 71:0.90 74:0.50 76:0.85 77:0.70 81:0.23 86:0.75 91:0.17 98:0.32 108:0.35 114:0.53 117:0.86 122:0.80 123:0.33 124:0.88 126:0.26 127:0.73 129:0.05 133:0.85 135:0.28 139:0.72 145:0.79 146:0.56 147:0.62 149:0.22 150:0.23 154:0.11 159:0.76 172:0.27 173:0.28 176:0.61 177:0.70 178:0.55 179:0.79 187:0.21 195:0.88 212:0.13 216:0.18 222:0.76 232:0.95 235:0.95 241:0.18 242:0.22 243:0.44 244:0.83 245:0.51 247:0.74 253:0.36 259:0.21 265:0.34 266:0.80 267:0.19 271:0.50 276:0.46 282:0.73 290:0.53 300:0.43 +1 8:0.95 12:0.06 17:0.02 18:0.93 21:0.64 27:0.03 29:0.62 30:0.86 31:0.99 34:0.82 36:0.91 37:0.94 39:0.74 43:0.16 44:0.87 48:1.00 55:0.59 64:0.77 66:0.49 69:0.48 70:0.32 71:0.90 79:0.99 81:0.24 86:0.87 91:1.00 98:0.49 108:0.37 120:0.89 122:0.86 123:0.35 124:0.72 126:0.26 127:0.37 129:0.89 133:0.78 135:0.65 137:0.99 139:0.88 140:0.99 145:0.82 146:0.65 147:0.70 149:0.26 150:0.24 151:0.65 153:0.93 154:0.11 159:0.54 162:0.47 164:0.93 172:0.45 173:0.40 175:0.91 177:0.05 178:0.53 179:0.67 190:0.77 191:0.98 192:0.51 195:0.82 196:0.99 202:1.00 212:0.19 216:0.84 219:0.89 220:0.91 222:0.76 235:0.80 241:0.98 242:0.77 243:0.60 244:0.83 245:0.55 247:0.39 248:0.81 253:0.20 255:0.74 256:0.99 257:0.84 259:0.21 260:0.85 265:0.50 266:0.71 267:0.59 268:0.98 271:0.50 276:0.47 277:0.87 281:0.91 283:0.78 284:1.00 285:0.56 292:0.91 300:0.33 +1 1:0.74 8:0.60 9:0.80 11:0.64 12:0.06 17:0.20 18:0.82 21:0.54 27:0.60 29:0.62 30:0.38 31:0.91 34:0.77 36:0.69 37:0.41 39:0.74 43:0.64 45:0.66 55:0.84 64:0.77 66:0.83 69:0.37 70:0.63 71:0.90 74:0.75 79:0.91 81:0.41 86:0.86 91:0.08 96:0.76 98:0.81 101:0.52 108:0.29 114:0.59 120:0.64 122:0.80 123:0.28 126:0.54 127:0.30 129:0.05 135:0.83 137:0.91 139:0.87 140:0.45 146:0.56 147:0.62 149:0.34 150:0.63 151:0.72 153:0.48 154:0.91 155:0.67 158:0.91 159:0.20 162:0.86 164:0.67 172:0.51 173:0.60 176:0.73 177:0.70 179:0.71 187:0.39 192:0.87 196:0.93 201:0.93 202:0.50 208:0.64 212:0.57 215:0.39 216:0.59 219:0.95 220:0.62 222:0.76 232:0.96 235:0.74 241:0.57 242:0.29 243:0.84 247:0.55 248:0.67 253:0.70 254:0.74 255:0.95 256:0.58 259:0.21 260:0.65 265:0.81 266:0.38 267:0.69 268:0.59 271:0.50 276:0.40 279:0.86 283:0.78 284:0.92 285:0.62 290:0.59 292:0.91 297:0.36 300:0.43 +1 1:0.74 9:0.80 11:0.64 12:0.06 17:0.47 18:0.95 21:0.54 27:0.55 29:0.62 30:0.33 31:0.86 34:0.40 36:0.28 37:0.62 39:0.74 43:0.61 45:0.83 55:0.71 64:0.77 66:0.93 69:0.33 70:0.62 71:0.90 74:0.79 79:0.86 81:0.41 86:0.96 91:0.20 96:0.68 98:0.93 101:0.52 104:0.95 108:0.26 114:0.59 120:0.53 122:0.80 123:0.25 126:0.54 127:0.57 129:0.05 135:0.91 137:0.86 139:0.95 140:0.45 146:0.84 147:0.87 149:0.66 150:0.63 151:0.47 153:0.48 154:0.90 155:0.67 158:0.78 159:0.40 162:0.86 164:0.57 172:0.82 173:0.40 176:0.73 177:0.70 179:0.43 187:0.68 192:0.87 196:0.88 201:0.93 202:0.36 208:0.64 212:0.88 215:0.39 216:0.54 219:0.92 220:0.96 222:0.76 232:0.96 235:0.74 241:0.27 242:0.15 243:0.94 247:0.84 248:0.47 253:0.46 254:0.80 255:0.95 256:0.45 259:0.21 260:0.54 265:0.93 266:0.27 267:0.82 268:0.46 271:0.50 276:0.73 279:0.82 284:0.89 285:0.62 290:0.59 292:0.91 297:0.36 300:0.43 +0 12:0.06 17:0.89 18:0.91 21:0.78 27:0.72 29:0.62 30:0.66 34:0.36 36:0.65 39:0.74 43:0.12 55:0.59 66:0.85 69:0.25 70:0.40 71:0.90 74:0.44 86:0.93 91:0.85 98:0.87 108:0.20 122:0.86 123:0.19 127:0.39 129:0.05 135:0.28 139:0.90 146:0.55 147:0.61 149:0.11 154:0.71 159:0.20 172:0.57 173:0.55 177:0.70 178:0.55 179:0.71 202:0.36 212:0.83 216:0.03 222:0.76 235:0.05 241:0.82 242:0.52 243:0.86 247:0.67 253:0.33 254:1.00 259:0.21 265:0.87 266:0.27 267:0.23 271:0.50 276:0.46 300:0.33 +2 1:0.74 11:0.64 12:0.06 17:0.22 18:0.74 21:0.59 27:0.45 29:0.62 30:0.45 34:0.82 36:0.18 37:0.50 39:0.74 43:0.63 45:0.73 55:0.59 64:0.77 66:0.61 69:0.71 70:0.78 71:0.90 74:0.74 81:0.43 83:0.60 86:0.80 91:0.35 97:0.89 98:0.55 99:0.83 101:0.52 108:0.54 114:0.58 122:0.77 123:0.51 124:0.50 126:0.54 127:0.34 129:0.05 133:0.43 135:0.56 139:0.84 145:0.45 146:0.67 147:0.72 149:0.41 150:0.66 154:0.76 155:0.67 158:0.91 159:0.48 165:0.70 172:0.59 173:0.68 176:0.73 177:0.70 178:0.55 179:0.54 187:0.68 192:0.87 201:0.93 208:0.64 212:0.39 215:0.39 216:0.77 219:0.95 222:0.76 235:0.80 241:0.43 242:0.74 243:0.67 245:0.74 247:0.60 253:0.45 254:0.74 259:0.21 265:0.56 266:0.75 267:0.59 271:0.50 276:0.56 279:0.86 283:0.77 290:0.58 292:0.91 297:0.36 300:0.70 +0 7:0.72 11:0.64 12:0.06 17:0.88 18:0.74 21:0.54 22:0.93 27:0.54 29:0.62 30:0.15 31:0.86 34:0.25 36:0.86 37:0.46 39:0.74 43:0.29 45:0.73 47:0.93 48:0.97 55:0.52 60:0.87 64:0.77 66:0.54 69:0.19 70:0.98 71:0.90 74:0.77 76:0.85 79:0.86 81:0.31 83:0.91 86:0.82 91:0.31 98:0.59 101:0.52 108:0.15 120:0.53 122:0.77 123:0.15 124:0.63 126:0.44 127:0.59 129:0.05 132:0.97 133:0.60 135:0.32 137:0.86 139:0.87 140:0.45 146:0.73 147:0.77 149:0.35 150:0.25 151:0.47 153:0.48 154:0.90 155:0.67 158:0.91 159:0.60 162:0.86 164:0.54 172:0.57 173:0.19 177:0.70 179:0.64 187:0.39 190:0.77 192:0.87 196:0.88 201:0.68 202:0.36 204:0.89 208:0.64 212:0.30 215:0.39 216:0.49 219:0.95 220:0.96 222:0.76 230:0.74 233:0.73 235:0.68 241:0.12 242:0.21 243:0.62 245:0.67 247:0.82 248:0.47 253:0.43 254:0.86 255:0.74 256:0.45 260:0.53 262:0.93 265:0.60 266:0.75 267:0.99 268:0.46 271:0.50 276:0.54 279:0.86 281:0.91 283:0.78 284:0.87 285:0.56 292:0.91 297:0.36 300:0.84 +1 1:0.69 7:0.72 8:0.62 9:0.76 11:0.64 12:0.06 17:0.91 18:0.90 21:0.05 22:0.93 27:0.54 29:0.62 30:0.70 31:0.91 34:0.98 36:0.90 37:0.29 39:0.74 43:0.68 44:0.90 45:0.81 47:0.93 48:0.97 55:0.71 64:0.77 66:0.92 69:0.19 70:0.56 71:0.90 74:0.84 76:0.97 77:0.70 79:0.91 81:0.61 83:0.60 86:0.92 87:0.98 91:0.54 96:0.76 98:0.95 99:0.83 101:0.52 108:0.15 114:0.85 122:0.80 123:0.15 126:0.44 127:0.66 129:0.05 135:0.56 137:0.91 139:0.93 140:0.45 145:0.63 146:0.79 147:0.82 149:0.64 150:0.61 151:0.70 153:0.69 154:0.59 155:0.58 158:0.78 159:0.35 162:0.86 165:0.70 172:0.77 173:0.36 176:0.66 177:0.70 179:0.54 187:0.21 190:0.77 192:0.59 195:0.64 196:0.94 201:0.68 202:0.46 208:0.54 212:0.92 216:0.39 219:0.92 220:0.62 222:0.76 230:0.75 232:0.72 233:0.76 235:0.12 241:0.53 242:0.33 243:0.92 244:0.61 247:0.76 248:0.65 253:0.76 255:0.74 256:0.45 259:0.21 262:0.93 265:0.95 266:0.23 267:0.59 268:0.55 271:0.50 276:0.66 279:0.82 281:0.91 282:0.77 284:0.87 285:0.56 290:0.85 292:0.95 300:0.55 +1 1:0.74 11:0.64 12:0.06 17:0.64 18:0.88 21:0.22 22:0.93 25:0.88 27:0.30 29:0.62 30:0.15 34:0.96 36:0.22 37:0.56 39:0.74 43:0.63 44:0.90 45:0.77 47:0.93 55:0.59 64:0.77 66:0.27 69:0.25 70:0.98 71:0.90 74:0.65 77:0.70 81:0.61 83:0.60 86:0.91 91:0.20 97:0.89 98:0.46 99:0.83 101:0.52 104:0.58 108:0.20 114:0.71 117:0.86 122:0.80 123:0.19 124:0.78 126:0.54 127:0.69 129:0.59 133:0.73 135:0.66 139:0.92 145:0.96 146:0.55 147:0.61 149:0.45 150:0.75 154:0.62 155:0.67 158:0.79 159:0.55 165:0.70 172:0.41 173:0.26 176:0.73 177:0.70 178:0.55 179:0.66 187:0.98 192:0.87 195:0.72 201:0.93 204:0.85 208:0.64 212:0.30 215:0.51 216:0.82 219:0.92 222:0.76 232:0.83 235:0.42 241:0.35 242:0.21 243:0.46 245:0.66 247:0.79 253:0.53 254:0.80 259:0.21 262:0.93 265:0.47 266:0.75 267:0.76 271:0.50 276:0.56 279:0.82 281:0.91 282:0.77 290:0.71 292:0.95 297:0.36 300:0.84 +1 1:0.69 7:0.81 11:0.64 12:0.06 17:0.81 18:0.84 21:0.22 27:0.31 29:0.62 30:0.60 32:0.80 34:0.94 36:0.97 37:0.26 39:0.74 43:0.69 44:0.93 45:0.66 48:0.99 55:0.59 64:0.77 66:0.95 69:0.50 70:0.55 71:0.90 74:0.90 76:1.00 77:0.96 81:0.51 86:0.85 91:0.71 96:0.72 98:0.92 101:0.52 108:0.38 114:0.67 117:0.86 121:0.90 122:0.77 123:0.37 126:0.44 127:0.53 129:0.05 135:0.93 139:0.91 140:0.45 145:0.63 146:0.92 147:0.94 149:0.59 150:0.55 151:0.51 153:0.48 154:0.59 155:0.67 159:0.54 162:0.86 172:0.86 173:0.46 175:0.75 176:0.66 177:0.38 179:0.36 187:0.21 190:0.89 192:0.87 195:0.75 201:0.68 202:0.36 204:0.89 208:0.64 212:0.61 216:0.38 219:0.89 220:0.74 222:0.76 230:0.87 232:0.79 233:0.76 235:0.42 241:0.24 242:0.73 243:0.95 244:0.61 247:0.55 248:0.51 253:0.60 256:0.45 257:0.65 259:0.21 265:0.92 266:0.38 267:0.73 268:0.46 271:0.50 276:0.77 277:0.69 281:0.91 282:0.88 285:0.47 290:0.67 292:0.91 300:0.43 +1 9:0.76 11:0.64 12:0.06 17:0.66 18:0.83 21:0.64 27:0.66 29:0.62 30:0.55 31:0.90 34:0.73 36:0.21 37:0.69 39:0.74 43:0.68 44:0.87 45:0.73 55:0.71 64:0.77 66:0.92 69:0.61 70:0.46 71:0.90 74:0.74 79:0.90 81:0.24 83:0.60 86:0.83 91:0.38 96:0.75 97:0.89 98:0.93 101:0.52 108:0.47 122:0.75 123:0.45 126:0.26 127:0.57 129:0.05 135:0.80 137:0.90 139:0.84 140:0.80 145:0.89 146:0.88 147:0.90 149:0.53 150:0.24 151:0.65 153:0.48 154:0.57 155:0.58 158:0.78 159:0.47 162:0.62 172:0.79 173:0.64 177:0.70 178:0.42 179:0.48 187:0.68 190:0.77 192:0.51 195:0.70 196:0.92 202:0.50 208:0.54 212:0.81 216:0.41 219:0.92 220:0.62 222:0.76 235:0.80 241:0.46 242:0.36 243:0.93 244:0.61 247:0.84 248:0.63 253:0.63 255:0.74 256:0.45 259:0.21 265:0.93 266:0.27 267:0.52 268:0.46 271:0.50 276:0.69 279:0.82 284:0.87 285:0.56 292:0.91 300:0.43 +1 12:0.92 17:0.51 21:0.78 27:0.45 28:0.93 34:0.76 36:0.19 37:0.50 43:0.28 66:0.36 69:0.79 91:0.22 98:0.07 108:0.63 123:0.60 127:0.06 129:0.05 135:0.30 145:0.47 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 161:0.76 167:0.54 172:0.05 173:0.79 177:0.05 178:0.55 181:0.82 187:0.68 216:0.77 235:0.97 241:0.05 243:0.51 259:0.21 265:0.08 267:0.92 +2 8:0.65 12:0.92 17:0.81 21:0.78 27:0.52 28:0.93 30:0.76 34:0.79 36:0.67 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.48 98:0.22 108:0.65 123:0.63 124:0.52 127:0.24 129:0.59 133:0.47 135:0.51 146:0.05 147:0.05 149:0.24 154:0.25 159:0.23 161:0.76 163:0.96 167:0.54 172:0.10 173:0.79 177:0.05 178:0.55 179:0.97 181:0.82 187:0.39 212:0.95 216:0.13 235:0.22 241:0.05 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.23 266:0.12 267:0.28 276:0.14 300:0.13 +1 12:0.92 17:0.81 21:0.78 27:0.52 28:0.93 30:0.76 34:0.22 36:0.73 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.54 98:0.18 108:0.73 123:0.72 124:0.52 127:0.28 129:0.59 133:0.47 135:0.70 146:0.05 147:0.05 149:0.22 154:0.25 159:0.33 161:0.76 163:0.96 167:0.61 172:0.10 173:0.71 177:0.05 178:0.55 179:0.98 181:0.82 187:0.39 202:0.59 212:0.95 216:0.13 235:0.12 241:0.35 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.20 266:0.12 267:0.44 276:0.14 300:0.13 +1 12:0.92 17:0.30 21:0.78 27:0.45 28:0.93 34:0.88 36:0.18 37:0.50 43:0.28 66:0.36 69:0.78 91:0.20 98:0.07 108:0.62 123:0.59 127:0.06 129:0.05 135:0.92 145:0.54 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 161:0.76 167:0.59 172:0.05 173:0.74 177:0.05 178:0.55 181:0.82 187:0.68 216:0.77 235:0.97 241:0.27 243:0.51 259:0.21 265:0.07 267:0.92 +1 12:0.92 17:0.70 20:0.85 21:0.30 27:0.34 28:0.93 30:0.62 34:0.56 36:0.73 37:0.28 43:0.40 55:0.96 66:0.30 69:0.54 70:0.34 78:0.83 81:0.69 91:0.48 98:0.23 100:0.73 106:0.81 108:0.41 111:0.81 123:0.39 124:0.78 126:0.54 127:0.23 129:0.89 133:0.78 135:0.61 145:0.88 146:0.38 147:0.45 149:0.36 150:0.50 152:0.81 154:0.30 159:0.48 161:0.76 163:0.89 167:0.57 169:0.72 172:0.16 173:0.40 177:0.05 178:0.55 179:0.83 181:0.82 186:0.83 187:0.21 206:0.81 212:0.30 215:0.72 216:0.94 235:0.31 241:0.18 242:0.82 243:0.48 245:0.40 247:0.12 259:0.21 261:0.81 265:0.25 266:0.27 267:0.69 276:0.28 297:0.36 300:0.25 +1 8:0.77 12:0.92 17:0.79 21:0.78 27:0.55 28:0.93 30:0.62 34:0.58 36:0.62 37:0.29 43:0.44 55:0.71 66:0.61 69:0.45 70:0.34 91:0.37 98:0.69 108:0.35 123:0.33 124:0.43 127:0.59 129:0.59 133:0.47 135:0.79 146:0.65 147:0.70 149:0.38 154:0.33 159:0.41 161:0.76 163:0.94 167:0.59 172:0.27 173:0.51 177:0.05 178:0.55 179:0.94 181:0.82 187:0.21 212:0.61 216:0.23 235:0.05 241:0.27 242:0.82 243:0.68 245:0.42 247:0.12 259:0.21 265:0.69 266:0.23 267:0.85 276:0.27 300:0.25 +2 12:0.92 17:0.82 21:0.30 25:0.88 27:0.59 28:0.93 34:0.68 36:0.38 37:0.43 43:0.14 66:0.36 69:0.95 81:0.69 91:0.46 98:0.06 108:0.91 120:0.53 123:0.91 126:0.54 127:0.05 129:0.05 135:0.73 140:0.45 145:0.52 146:0.05 147:0.05 149:0.07 150:0.50 151:0.46 153:0.48 154:0.10 159:0.05 161:0.76 162:0.86 164:0.57 167:0.55 172:0.05 173:1.00 177:0.05 181:0.82 187:0.21 202:0.36 215:0.72 216:0.27 220:0.87 235:0.31 241:0.07 243:0.51 248:0.46 256:0.45 259:0.21 260:0.53 265:0.07 267:0.69 268:0.46 285:0.62 297:0.36 +1 8:0.63 12:0.92 17:0.67 21:0.78 27:0.55 28:0.93 30:0.62 34:0.55 36:0.54 37:0.29 43:0.44 55:0.71 66:0.30 69:0.45 70:0.34 91:0.42 98:0.30 108:0.35 123:0.34 124:0.71 127:0.84 129:0.81 133:0.67 135:0.51 146:0.41 147:0.48 149:0.34 154:0.33 159:0.62 161:0.76 163:0.94 167:0.62 172:0.16 173:0.40 177:0.05 178:0.55 179:0.95 181:0.82 187:0.21 202:0.36 212:0.30 216:0.23 235:0.12 241:0.37 242:0.82 243:0.48 245:0.43 247:0.12 259:0.21 265:0.32 266:0.27 267:0.85 276:0.25 300:0.25 +2 8:0.60 12:0.92 17:0.59 20:0.82 21:0.78 27:0.55 28:0.93 30:0.89 34:0.66 36:0.67 37:0.29 43:0.44 55:0.71 66:0.61 69:0.45 70:0.20 78:0.86 91:0.29 98:0.61 100:0.73 108:0.35 111:0.84 123:0.33 124:0.43 127:0.56 129:0.59 133:0.47 135:0.82 146:0.54 147:0.60 149:0.39 152:0.98 154:0.33 159:0.37 161:0.76 163:0.79 167:0.66 169:0.82 172:0.27 173:0.54 177:0.05 178:0.55 179:0.94 181:0.82 186:0.87 187:0.21 212:0.30 216:0.23 235:0.05 241:0.50 242:0.82 243:0.68 245:0.42 247:0.12 259:0.21 261:0.90 265:0.62 266:0.27 267:0.95 276:0.25 300:0.18 +2 8:0.62 12:0.92 17:0.73 21:0.78 27:0.72 28:0.93 30:0.62 34:0.19 36:0.58 37:0.29 43:0.44 55:0.71 66:0.61 69:0.46 70:0.23 91:0.71 98:0.68 108:0.35 123:0.34 124:0.43 127:0.75 129:0.59 133:0.47 135:0.70 146:0.59 147:0.65 149:0.38 154:0.33 159:0.39 161:0.76 163:0.94 167:0.90 172:0.27 173:0.56 177:0.05 178:0.55 179:0.95 181:0.82 202:0.60 212:0.61 216:0.03 235:0.22 241:0.82 242:0.82 243:0.68 245:0.42 247:0.12 259:0.21 265:0.68 266:0.23 267:0.79 276:0.26 300:0.18 +2 12:0.92 17:0.75 21:0.64 25:0.88 27:0.59 28:0.93 30:0.95 32:0.80 34:0.29 36:0.33 37:0.43 43:0.50 55:0.99 66:0.20 69:0.32 81:0.55 91:0.35 98:0.06 108:0.25 123:0.24 124:0.61 126:0.54 127:0.92 129:0.59 133:0.47 135:0.68 140:0.45 145:1.00 146:0.05 147:0.05 149:0.17 150:0.42 153:0.48 154:0.39 159:0.78 161:0.76 162:0.86 164:0.57 167:0.58 172:0.05 173:0.19 177:0.05 179:1.00 181:0.82 187:0.68 195:0.89 202:0.36 215:0.53 216:0.27 220:0.97 235:0.74 241:0.21 243:0.40 245:0.36 256:0.45 259:0.21 265:0.06 267:0.28 268:0.46 285:0.62 297:0.36 300:0.08 +4 6:0.96 8:0.85 12:0.92 17:0.76 20:0.99 21:0.47 25:0.88 27:0.59 28:0.93 30:0.76 32:0.80 34:0.25 36:0.29 37:0.43 43:0.50 55:0.96 66:0.13 69:0.27 70:0.15 78:0.99 81:0.63 91:0.81 98:0.07 100:1.00 108:0.21 111:1.00 120:0.81 123:0.20 124:0.75 126:0.54 127:0.85 129:0.81 133:0.67 135:0.63 140:0.80 145:1.00 146:0.05 147:0.05 149:0.22 150:0.46 151:0.73 152:0.98 153:0.78 154:0.39 159:0.59 161:0.76 162:0.52 163:0.90 164:0.82 167:0.91 169:0.99 172:0.05 173:0.26 177:0.05 178:0.42 179:0.99 181:0.82 186:0.98 189:0.98 191:0.81 195:0.74 202:0.82 212:0.95 215:0.63 216:0.27 220:0.62 235:0.52 238:0.98 241:0.88 242:0.82 243:0.34 245:0.37 247:0.12 248:0.74 256:0.75 259:0.21 260:0.82 261:0.98 265:0.08 266:0.12 267:0.76 268:0.78 276:0.17 283:0.60 285:0.62 297:0.36 300:0.13 +2 12:0.92 17:0.84 21:0.59 25:0.88 27:0.59 28:0.93 30:0.95 32:0.80 34:0.21 36:0.42 37:0.43 43:0.50 55:0.99 66:0.20 69:0.30 81:0.58 91:0.40 98:0.06 108:0.24 123:0.23 124:0.61 126:0.54 127:0.86 129:0.59 133:0.47 135:0.65 140:0.45 145:1.00 146:0.05 147:0.05 149:0.17 150:0.43 153:0.69 154:0.39 159:0.81 161:0.76 162:0.86 164:0.62 167:0.58 172:0.05 173:0.16 177:0.05 179:1.00 181:0.82 187:0.68 195:0.92 202:0.46 215:0.55 216:0.27 220:0.97 235:0.68 241:0.21 243:0.40 245:0.36 256:0.45 259:0.21 265:0.06 267:0.69 268:0.55 285:0.62 297:0.36 300:0.08 +2 12:0.92 17:0.90 21:0.54 25:0.88 27:0.59 28:0.93 32:0.80 34:0.81 36:0.58 37:0.43 43:0.11 66:0.36 69:0.97 81:0.61 91:0.27 98:0.06 108:0.94 123:0.93 126:0.54 127:0.05 129:0.05 135:0.61 145:0.98 146:0.05 147:0.05 149:0.06 150:0.45 154:0.09 159:0.05 161:0.76 167:0.56 172:0.05 173:1.00 177:0.05 178:0.55 181:0.82 187:0.21 195:0.93 215:0.58 216:0.27 235:0.60 241:0.12 243:0.51 259:0.21 265:0.06 267:0.59 297:0.36 +2 8:0.71 12:0.92 17:0.79 20:0.97 21:0.78 27:0.52 28:0.93 30:0.76 34:0.26 36:0.65 37:0.39 43:0.34 55:0.59 66:0.64 69:0.64 70:0.15 78:0.83 91:0.68 98:0.43 100:0.73 108:0.49 111:0.81 123:0.47 127:0.14 129:0.05 135:0.49 146:0.30 147:0.37 149:0.26 152:0.97 154:0.25 159:0.13 161:0.76 163:0.90 167:0.90 169:0.72 172:0.16 173:0.86 177:0.05 178:0.55 179:0.80 181:0.82 186:0.83 191:0.75 202:0.65 212:0.95 216:0.13 241:0.82 242:0.82 243:0.69 247:0.12 259:0.21 261:0.86 265:0.45 266:0.12 267:0.82 276:0.19 300:0.13 +2 12:0.92 17:0.92 21:0.59 25:0.88 27:0.59 28:0.93 32:0.80 34:0.91 36:0.27 37:0.43 43:0.20 66:0.36 69:0.93 81:0.58 91:0.27 98:0.07 108:0.87 123:0.86 126:0.54 127:0.06 129:0.05 135:0.54 145:0.98 146:0.05 147:0.05 149:0.08 150:0.43 154:0.13 159:0.05 161:0.76 167:0.57 172:0.05 173:0.99 177:0.05 178:0.55 181:0.82 187:0.21 195:0.77 215:0.55 216:0.27 235:0.68 241:0.18 243:0.51 259:0.21 265:0.07 267:0.44 297:0.36 +0 8:0.66 12:0.92 17:1.00 21:0.78 27:0.54 28:0.93 34:0.92 36:0.59 37:0.33 43:0.21 66:0.36 69:0.93 91:0.05 98:0.05 108:0.85 123:0.84 127:0.05 129:0.05 135:0.54 145:0.39 146:0.05 147:0.05 149:0.08 154:0.14 159:0.05 161:0.76 167:0.59 172:0.05 173:0.71 177:0.05 178:0.55 181:0.82 187:0.21 202:0.59 216:0.31 235:0.31 241:0.27 243:0.51 259:0.21 265:0.05 267:0.98 +2 12:0.92 17:0.84 21:0.64 25:0.88 27:0.59 28:0.93 30:0.76 32:0.80 34:0.88 36:0.19 37:0.43 43:0.50 55:0.71 66:0.36 69:0.32 70:0.15 81:0.55 91:0.17 98:0.20 108:0.83 123:0.82 124:0.52 126:0.54 127:0.79 129:0.59 133:0.47 135:0.68 145:1.00 146:0.05 147:0.05 149:0.21 150:0.42 154:0.39 159:0.56 161:0.76 163:0.90 167:0.60 172:0.10 173:0.31 177:0.05 178:0.55 179:0.99 181:0.82 187:0.68 195:0.73 212:0.95 215:0.53 216:0.27 235:0.74 241:0.30 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.22 266:0.12 267:0.82 276:0.13 297:0.36 300:0.13 +1 12:0.92 17:0.85 21:0.78 27:0.52 28:0.93 30:0.76 34:0.18 36:0.59 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.44 98:0.15 108:0.85 123:0.84 124:0.52 127:0.44 129:0.59 133:0.47 135:0.78 146:0.05 147:0.05 149:0.20 154:0.25 159:0.56 161:0.76 163:0.97 167:0.58 172:0.10 173:0.60 177:0.05 178:0.55 179:0.99 181:0.82 187:0.39 202:0.46 212:0.95 216:0.13 235:0.12 241:0.21 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.17 266:0.12 267:0.28 276:0.13 300:0.13 +2 12:0.92 17:0.79 21:0.30 25:0.88 27:0.59 28:0.93 30:0.95 34:0.44 36:0.47 37:0.43 43:0.50 55:0.99 66:0.20 69:0.23 81:0.69 91:0.42 98:0.06 108:0.18 120:0.53 123:0.18 124:0.61 126:0.54 127:0.86 129:0.59 133:0.47 135:0.75 140:0.45 145:0.52 146:0.05 147:0.05 149:0.17 150:0.50 151:0.46 153:0.48 154:0.39 159:0.80 161:0.76 162:0.86 164:0.57 167:0.59 172:0.05 173:0.14 177:0.05 179:1.00 181:0.82 187:0.68 202:0.36 215:0.72 216:0.27 220:0.87 235:0.31 241:0.27 243:0.40 245:0.36 248:0.46 256:0.45 259:0.21 260:0.53 265:0.06 267:0.44 268:0.46 285:0.62 297:0.36 300:0.08 +2 8:0.60 12:0.92 17:0.70 21:0.78 27:0.55 28:0.93 30:0.62 34:0.90 36:0.65 37:0.29 43:0.44 55:0.71 66:0.30 69:0.45 70:0.34 91:0.48 98:0.26 108:0.35 123:0.34 124:0.71 127:0.78 129:0.81 133:0.67 135:0.78 146:0.41 147:0.48 149:0.33 154:0.33 159:0.71 161:0.76 163:0.94 167:0.58 172:0.16 173:0.33 177:0.05 178:0.55 179:0.95 181:0.82 187:0.21 202:0.36 212:0.30 216:0.23 235:0.12 241:0.21 242:0.82 243:0.48 245:0.43 247:0.12 259:0.21 265:0.28 266:0.27 267:0.84 276:0.23 300:0.25 +2 8:0.67 12:0.92 17:0.84 21:0.78 27:0.52 28:0.93 30:0.76 34:0.18 36:0.67 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.64 98:0.16 108:0.84 123:0.84 124:0.52 127:0.49 129:0.59 133:0.47 135:0.70 146:0.05 147:0.05 149:0.20 154:0.25 159:0.55 161:0.76 163:0.97 167:0.55 172:0.10 173:0.62 177:0.05 178:0.55 179:0.99 181:0.82 187:0.39 202:0.50 212:0.95 216:0.13 235:0.22 241:0.10 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.17 266:0.12 267:0.28 276:0.13 300:0.13 +2 8:0.63 12:0.92 17:0.78 21:0.78 27:0.55 28:0.93 30:0.62 34:0.90 36:0.53 37:0.29 43:0.44 55:0.71 66:0.30 69:0.45 70:0.34 91:0.53 98:0.31 108:0.35 123:0.34 124:0.71 127:0.67 129:0.81 133:0.67 135:0.71 146:0.41 147:0.48 149:0.35 154:0.33 159:0.57 161:0.76 163:0.94 167:0.58 172:0.16 173:0.41 177:0.05 178:0.55 179:0.95 181:0.82 187:0.21 212:0.30 216:0.23 235:0.12 241:0.21 242:0.82 243:0.48 245:0.43 247:0.12 259:0.21 265:0.34 266:0.27 267:0.84 276:0.26 300:0.25 +1 1:0.74 7:0.81 11:0.92 12:0.06 17:0.79 18:1.00 21:0.13 22:0.93 27:0.31 28:0.85 29:0.71 30:0.60 32:0.80 34:0.56 36:0.24 37:0.81 39:0.50 43:0.96 44:0.93 45:0.92 46:0.99 47:0.93 48:0.91 55:0.52 60:0.87 64:0.77 66:0.99 69:0.15 70:0.52 71:0.75 74:0.95 77:0.70 81:0.77 83:0.91 85:0.96 86:1.00 91:0.58 98:0.99 101:0.87 104:0.96 106:0.81 107:1.00 108:0.12 110:0.99 114:0.79 117:0.86 121:0.90 122:0.86 123:0.12 125:0.88 126:0.54 127:0.86 129:0.05 135:0.89 139:1.00 144:0.85 145:0.84 146:0.97 147:0.98 149:0.98 150:0.85 154:0.96 155:0.67 158:0.92 159:0.70 160:0.88 161:0.63 165:0.93 167:0.45 172:0.98 173:0.15 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 187:0.87 192:0.87 195:0.82 201:0.93 204:0.89 206:0.81 208:0.64 212:0.90 215:0.61 216:0.59 219:0.95 222:0.25 230:0.87 233:0.76 235:0.31 241:0.12 242:0.63 243:0.99 247:0.67 253:0.59 259:0.21 262:0.93 265:0.99 266:0.27 267:0.65 271:0.33 276:0.95 279:0.86 281:0.91 282:0.88 283:0.82 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.92 7:0.72 8:0.85 9:0.80 11:0.92 12:0.06 17:0.95 18:0.99 20:0.79 21:0.13 22:0.93 27:0.29 28:0.85 29:0.71 30:0.33 31:0.92 32:0.80 34:0.36 36:0.29 37:0.88 39:0.50 41:0.82 43:0.97 44:0.93 45:0.91 46:0.99 47:0.93 48:0.91 55:0.52 60:0.87 64:0.77 66:0.68 69:0.10 70:0.72 71:0.75 74:0.93 77:0.89 78:0.91 79:0.92 81:0.77 83:0.91 85:0.93 86:0.99 91:0.56 96:0.82 98:0.77 100:0.87 101:0.87 104:1.00 106:0.81 107:1.00 108:0.76 110:0.99 111:0.88 114:0.79 117:0.86 120:0.72 122:0.86 123:0.74 124:0.50 125:0.99 126:0.54 127:0.79 129:0.59 133:0.66 135:0.70 137:0.92 139:1.00 140:0.45 144:0.85 145:0.52 146:0.59 147:0.64 149:0.96 150:0.85 151:0.61 152:0.77 153:0.48 154:0.97 155:0.67 158:0.92 159:0.65 160:0.97 161:0.63 162:0.86 164:0.65 165:0.93 167:0.45 169:0.84 172:0.96 173:0.14 176:0.73 177:0.70 179:0.16 181:0.55 186:0.91 187:0.87 189:0.94 190:0.77 191:0.90 192:0.87 195:0.79 196:0.97 201:0.93 202:0.60 204:0.85 206:0.81 208:0.64 212:0.91 215:0.61 216:0.61 219:0.95 220:0.74 222:0.25 230:0.75 233:0.76 235:0.31 238:0.84 241:0.24 242:0.70 243:0.25 245:0.97 247:0.84 248:0.59 253:0.85 255:0.95 256:0.68 259:0.21 260:0.69 261:0.82 262:0.93 265:0.77 266:0.71 267:0.28 268:0.69 271:0.33 276:0.94 279:0.86 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.81 11:0.91 12:0.06 17:0.22 18:0.84 21:0.30 22:0.93 27:0.10 28:0.85 29:0.71 30:0.89 32:0.80 34:0.75 36:0.54 37:0.73 39:0.50 43:0.96 44:0.93 45:0.94 46:0.98 47:0.93 48:0.99 64:0.77 66:0.80 69:0.21 70:0.46 71:0.75 74:0.89 77:0.70 81:0.70 83:0.91 86:0.89 91:0.31 97:0.97 98:0.69 99:0.95 101:0.52 104:0.89 106:0.81 107:1.00 108:0.17 110:0.99 114:0.65 117:0.86 122:0.51 123:0.16 124:0.40 125:0.88 126:0.54 127:0.50 129:0.05 133:0.59 135:0.90 139:0.94 144:0.85 145:0.56 146:0.89 147:0.91 149:0.95 150:0.82 154:0.96 155:0.67 158:0.92 159:0.70 160:0.88 161:0.63 165:0.93 167:0.45 172:0.91 173:0.15 176:0.73 177:0.70 178:0.55 179:0.25 181:0.55 187:0.95 192:0.87 195:0.87 201:0.93 204:0.89 206:0.81 208:0.64 212:0.69 215:0.44 216:0.87 219:0.95 222:0.25 230:0.87 233:0.76 235:0.68 241:0.05 242:0.42 243:0.82 245:0.82 247:0.74 253:0.52 259:0.21 262:0.93 265:0.70 266:0.71 267:0.28 271:0.33 276:0.84 279:0.86 281:0.91 282:0.88 283:0.82 289:0.98 290:0.65 292:0.95 297:0.36 300:0.94 +2 1:0.74 6:0.95 7:0.63 8:0.88 9:0.80 11:0.92 12:0.06 17:0.62 18:0.99 20:0.78 21:0.13 27:0.34 28:0.85 29:0.71 30:0.72 31:0.89 32:0.80 34:0.73 36:0.18 37:0.71 39:0.50 43:0.94 44:0.93 45:0.92 46:0.99 48:0.91 60:0.87 64:0.77 66:0.31 69:0.12 70:0.62 71:0.75 74:0.95 77:0.70 78:0.91 79:0.88 81:0.76 83:0.91 85:0.98 86:1.00 91:0.62 96:0.85 97:0.99 98:0.57 99:0.83 100:0.89 101:0.87 104:0.93 106:0.81 107:1.00 108:0.56 110:0.99 111:0.90 114:0.79 117:0.86 120:0.76 122:0.57 123:0.54 124:0.84 125:1.00 126:0.54 127:0.86 129:0.93 133:0.84 135:0.49 137:0.89 139:1.00 140:0.80 144:0.85 145:0.49 146:0.31 147:0.37 149:0.99 150:0.83 151:0.61 152:0.76 153:0.77 154:0.94 155:0.67 158:0.92 159:0.78 160:0.97 161:0.63 162:0.86 164:0.66 165:0.70 167:0.45 169:0.87 172:0.90 173:0.11 176:0.73 177:0.70 179:0.16 181:0.55 186:0.92 187:0.68 189:0.96 190:0.77 191:0.70 192:0.87 195:0.88 196:0.94 201:0.93 202:0.54 206:0.81 208:0.64 212:0.85 215:0.61 216:0.55 219:0.95 222:0.25 230:0.64 233:0.76 235:0.31 238:0.87 241:0.03 242:0.24 243:0.19 245:0.97 247:0.82 248:0.59 253:0.88 255:0.95 256:0.58 259:0.21 260:0.73 261:0.82 265:0.58 266:0.84 267:0.36 268:0.63 271:0.33 276:0.94 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.66 8:0.94 9:0.80 11:0.93 12:0.06 17:0.59 18:0.99 20:0.76 21:0.30 22:0.93 27:0.26 28:0.85 29:0.71 30:0.84 31:0.94 32:0.80 34:0.55 36:0.28 37:0.86 39:0.50 43:0.95 44:0.93 45:0.99 46:1.00 47:0.93 48:0.97 64:0.77 66:0.23 69:0.21 70:0.45 71:0.75 74:0.96 77:0.89 78:0.85 79:0.93 81:0.66 83:0.91 86:0.99 91:0.48 96:0.80 97:0.97 98:0.60 99:0.95 100:0.88 101:0.87 104:0.96 106:0.81 107:1.00 108:0.85 110:0.99 111:0.84 114:0.65 117:0.86 120:0.69 122:0.51 123:0.16 124:0.80 125:0.99 126:0.54 127:0.91 129:0.81 133:0.78 135:0.90 137:0.94 139:0.99 140:0.45 144:0.85 145:0.74 146:0.83 147:0.86 149:0.99 150:0.80 151:0.86 152:0.88 153:0.48 154:0.95 155:0.67 158:0.92 159:0.76 160:0.96 161:0.63 162:0.86 164:0.57 165:0.93 167:0.45 169:0.95 172:0.91 173:0.15 176:0.73 177:0.70 179:0.14 181:0.55 186:0.88 187:0.87 192:0.87 195:0.88 196:0.98 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.89 215:0.44 216:0.57 219:0.95 222:0.25 230:0.69 233:0.73 235:0.60 241:0.15 242:0.36 243:0.43 245:0.98 247:0.86 248:0.77 253:0.87 255:0.95 256:0.45 260:0.70 261:0.95 262:0.93 265:0.47 266:0.84 267:0.69 268:0.46 271:0.33 276:0.95 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 300:0.84 +1 1:0.74 11:0.91 12:0.06 17:0.26 18:0.85 21:0.71 27:0.45 28:0.85 29:0.71 30:0.87 32:0.80 34:0.86 36:0.21 37:0.50 39:0.50 43:0.47 44:0.93 45:0.98 46:0.99 48:0.91 64:0.77 66:0.31 69:0.63 70:0.46 71:0.75 74:0.85 77:0.70 81:0.41 83:0.60 86:0.87 91:0.06 98:0.52 99:0.83 101:0.52 107:1.00 108:0.93 110:0.99 114:0.55 122:0.51 123:0.92 124:0.89 125:0.88 126:0.54 127:0.72 129:0.59 133:0.90 135:0.74 139:0.89 144:0.85 145:0.50 146:0.39 147:0.45 149:0.90 150:0.65 154:0.76 155:0.67 159:0.85 160:0.88 161:0.63 165:0.70 167:0.45 172:0.88 173:0.37 176:0.73 177:0.70 178:0.55 179:0.17 181:0.55 187:0.68 192:0.87 195:0.95 201:0.93 208:0.64 212:0.71 215:0.37 216:0.77 222:0.25 235:0.95 241:0.15 242:0.52 243:0.13 245:0.94 247:0.82 253:0.45 254:0.74 259:0.21 265:0.53 266:0.92 267:0.36 271:0.33 276:0.93 281:0.91 282:0.88 289:0.98 290:0.55 297:0.36 300:0.94 +2 1:0.74 6:0.95 7:0.72 8:0.94 9:0.80 11:0.92 12:0.06 17:0.51 18:0.99 20:0.89 21:0.30 22:0.93 27:0.26 28:0.85 29:0.71 30:0.53 31:0.98 32:0.80 34:0.68 36:0.25 37:0.86 39:0.50 41:0.94 43:0.96 44:0.93 45:0.91 46:0.98 47:0.93 48:0.97 55:0.45 60:1.00 64:0.77 66:0.43 69:0.19 70:0.73 71:0.75 74:0.90 77:0.70 78:0.97 79:0.98 81:0.63 83:0.91 85:0.90 86:0.99 91:0.60 96:0.93 97:0.89 98:0.61 100:0.97 101:0.87 104:0.96 106:0.81 107:1.00 108:0.15 110:0.99 111:0.97 114:0.65 117:0.86 120:0.87 122:0.86 123:0.15 124:0.85 125:0.99 126:0.54 127:0.90 129:0.81 133:0.86 135:0.91 137:0.98 139:0.99 140:0.45 144:0.85 145:0.72 146:0.83 147:0.86 149:0.94 150:0.78 151:0.95 152:0.88 153:0.83 154:0.96 155:0.67 158:0.92 159:0.75 160:0.99 161:0.63 162:0.81 164:0.80 165:0.93 167:0.45 169:0.95 172:0.89 173:0.15 176:0.73 177:0.70 179:0.20 181:0.55 186:0.96 187:0.87 189:0.97 191:0.82 192:0.87 195:0.87 196:0.99 201:0.93 202:0.71 204:0.85 206:0.81 208:0.64 212:0.83 215:0.44 216:0.57 219:0.95 222:0.25 230:0.75 233:0.76 235:0.52 238:0.95 241:0.18 242:0.63 243:0.57 245:0.93 247:0.79 248:0.92 253:0.95 255:0.95 256:0.78 260:0.88 261:0.95 262:0.93 265:0.62 266:0.75 267:0.59 268:0.77 271:0.33 276:0.91 279:0.86 281:0.91 282:0.88 283:0.82 284:0.97 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.81 8:0.85 9:0.80 11:0.92 12:0.06 17:0.89 18:0.89 20:0.85 21:0.22 22:0.93 27:0.42 28:0.85 29:0.71 30:0.86 31:0.88 32:0.80 34:0.96 36:0.27 37:0.77 39:0.50 41:0.82 43:0.95 44:0.93 45:0.95 46:1.00 47:0.93 48:0.98 60:0.87 64:0.77 66:0.96 69:0.19 70:0.45 71:0.75 74:0.89 77:0.89 78:0.79 79:0.88 81:0.70 83:0.91 85:0.80 86:0.95 91:0.46 96:0.84 97:0.94 98:0.96 100:0.82 101:0.87 104:0.97 106:0.81 107:1.00 108:0.15 110:0.99 111:0.79 114:0.71 117:0.86 120:0.74 122:0.51 123:0.15 125:0.99 126:0.54 127:0.71 129:0.05 135:0.73 137:0.88 139:0.96 140:0.80 144:0.85 145:0.54 146:0.91 147:0.93 149:0.93 150:0.82 151:0.56 152:0.80 153:0.72 154:0.95 155:0.67 158:0.91 159:0.52 160:0.97 161:0.63 162:0.86 164:0.68 165:0.93 167:0.44 169:0.80 172:0.89 173:0.24 176:0.73 177:0.70 179:0.33 181:0.55 186:0.80 187:0.68 189:0.83 190:0.77 191:0.70 192:0.87 195:0.70 196:0.92 201:0.93 202:0.55 206:0.81 208:0.64 212:0.72 215:0.51 216:0.51 219:0.95 222:0.25 232:0.98 235:0.42 238:0.87 241:0.01 242:0.50 243:0.96 247:0.67 248:0.55 253:0.85 255:0.95 256:0.58 259:0.21 260:0.70 261:0.80 262:0.93 265:0.96 266:0.38 267:0.19 268:0.63 271:0.33 276:0.81 279:0.86 281:0.91 282:0.88 283:0.82 284:0.90 285:0.62 289:0.98 290:0.71 292:0.91 297:0.36 299:0.97 300:0.70 +2 1:0.74 7:0.81 11:0.92 12:0.06 17:0.67 18:0.92 21:0.13 22:0.93 27:0.36 28:0.85 29:0.71 30:0.75 32:0.80 34:0.79 36:0.28 37:0.79 39:0.50 43:0.97 44:0.93 45:0.94 46:0.98 47:0.93 48:0.91 60:0.95 64:0.77 66:0.23 69:0.10 70:0.71 71:0.75 74:0.92 77:0.70 81:0.77 83:0.91 85:0.90 86:0.98 91:0.11 98:0.33 101:0.87 104:0.93 106:0.81 107:1.00 108:0.78 110:0.99 114:0.79 117:0.86 121:0.90 122:0.51 123:0.76 124:0.90 125:0.88 126:0.54 127:0.95 129:0.81 133:0.90 135:0.99 139:0.99 144:0.85 145:0.44 146:0.52 147:0.58 149:0.95 150:0.85 154:0.97 155:0.67 158:0.92 159:0.85 160:0.88 161:0.63 165:0.93 167:0.44 172:0.73 173:0.09 176:0.73 177:0.70 178:0.55 179:0.28 181:0.55 187:0.87 191:0.78 192:0.87 195:0.94 201:0.93 204:0.89 206:0.81 208:0.64 212:0.72 215:0.61 216:0.62 219:0.95 222:0.25 230:0.87 233:0.76 235:0.31 241:0.01 242:0.37 243:0.36 245:0.88 247:0.76 253:0.58 259:0.21 262:0.93 265:0.35 266:0.89 267:0.23 271:0.33 276:0.85 279:0.86 281:0.91 282:0.88 283:0.82 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.84 7:0.81 8:0.69 9:0.80 11:0.91 12:0.06 17:0.15 18:0.85 20:0.84 21:0.40 22:0.93 27:0.21 28:0.85 29:0.71 30:0.21 31:0.87 32:0.80 34:0.66 36:0.23 37:0.71 39:0.50 41:0.88 43:0.56 44:0.93 45:0.81 46:0.95 47:0.93 48:0.97 64:0.77 66:0.16 69:0.95 70:0.86 71:0.75 74:0.74 77:0.89 78:0.95 79:0.87 81:0.57 83:0.91 86:0.81 91:0.42 96:0.70 97:0.94 98:0.35 100:0.95 101:0.52 104:0.91 106:0.81 107:0.99 108:0.91 110:0.96 111:0.94 114:0.62 117:0.86 120:0.56 121:0.97 122:0.86 123:0.48 124:0.73 125:0.98 126:0.54 127:0.64 129:0.05 133:0.60 135:0.84 137:0.87 139:0.92 140:0.45 144:0.85 145:0.65 146:0.41 147:0.66 149:0.42 150:0.76 151:0.52 152:0.92 153:0.48 154:0.30 155:0.67 158:0.91 159:0.75 160:0.98 161:0.63 162:0.71 164:0.72 165:0.93 167:0.46 169:0.87 172:0.41 173:0.98 176:0.73 177:0.05 179:0.69 181:0.55 186:0.94 187:0.87 189:0.86 191:0.88 192:0.87 195:0.89 196:0.91 201:0.93 202:0.63 204:0.89 206:0.81 208:0.64 212:0.23 215:0.42 216:0.79 219:0.95 220:0.91 222:0.25 230:0.87 233:0.76 235:0.60 238:0.90 241:0.32 242:0.37 243:0.37 245:0.68 247:0.67 248:0.52 253:0.51 254:0.74 255:0.95 256:0.64 257:0.84 259:0.21 260:0.57 261:0.90 262:0.93 265:0.25 266:0.75 267:0.19 268:0.66 271:0.33 276:0.38 277:0.87 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 289:0.98 290:0.62 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.94 7:0.81 8:0.90 9:0.80 11:0.92 12:0.06 17:0.70 18:1.00 20:0.85 21:0.13 22:0.93 27:0.31 28:0.85 29:0.71 30:0.52 31:0.95 32:0.80 34:0.49 36:0.30 37:0.81 39:0.50 41:0.82 43:0.96 44:0.93 45:0.93 46:0.99 47:0.93 48:0.91 55:0.52 60:0.97 64:0.77 66:0.61 69:0.15 70:0.57 71:0.75 74:0.95 77:0.70 78:0.92 79:0.95 81:0.77 83:0.91 85:0.96 86:1.00 91:0.68 96:0.85 98:0.80 100:0.90 101:0.87 104:0.97 106:0.81 107:1.00 108:0.12 110:0.99 111:0.91 114:0.79 117:0.86 120:0.75 121:0.90 122:0.86 123:0.12 124:0.63 125:0.99 126:0.54 127:0.88 129:0.59 133:0.66 135:0.91 137:0.95 139:1.00 140:0.45 144:0.85 145:0.80 146:0.93 147:0.95 149:0.98 150:0.85 151:0.93 152:0.85 153:0.78 154:0.96 155:0.67 158:0.92 159:0.75 160:0.96 161:0.63 162:0.57 164:0.65 165:0.93 167:0.45 169:0.85 172:0.97 173:0.13 176:0.73 177:0.70 179:0.14 181:0.55 186:0.92 187:0.87 189:0.95 191:0.73 192:0.87 195:0.85 196:0.98 201:0.93 202:0.59 204:0.89 206:0.81 208:0.64 212:0.89 215:0.61 216:0.59 219:0.95 222:0.25 230:0.87 233:0.76 235:0.31 238:0.86 241:0.24 242:0.62 243:0.67 245:0.98 247:0.82 248:0.83 253:0.90 255:0.95 256:0.45 259:0.21 260:0.76 261:0.88 262:0.93 265:0.80 266:0.71 267:0.59 268:0.58 271:0.33 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.91 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +0 1:0.74 11:0.91 12:0.06 17:0.34 18:0.87 21:0.77 27:0.45 28:0.85 29:0.71 30:0.61 34:0.90 36:0.18 37:0.50 39:0.50 43:0.47 45:0.93 46:0.98 48:0.91 64:0.77 66:0.15 69:0.63 70:0.87 71:0.75 74:0.77 81:0.40 83:0.60 86:0.90 91:0.03 97:0.89 98:0.42 99:0.83 101:0.52 107:1.00 108:0.88 110:0.99 114:0.53 122:0.51 123:0.88 124:0.86 125:0.88 126:0.54 127:0.51 129:0.59 133:0.84 135:0.78 139:0.91 144:0.85 145:0.42 146:0.55 147:0.61 149:0.74 150:0.65 154:0.76 155:0.67 158:0.91 159:0.73 160:0.88 161:0.63 165:0.70 167:0.45 172:0.51 173:0.47 176:0.73 177:0.70 178:0.55 179:0.34 181:0.55 187:0.68 192:0.87 201:0.93 208:0.64 212:0.48 215:0.36 216:0.77 219:0.95 222:0.25 235:0.99 241:0.12 242:0.39 243:0.28 245:0.84 247:0.76 253:0.43 254:0.74 259:0.21 265:0.44 266:0.80 267:0.19 271:0.33 276:0.77 279:0.86 281:0.91 283:0.77 289:0.98 290:0.53 292:0.91 297:0.36 300:0.84 +2 1:0.74 6:0.85 7:0.81 8:0.74 9:0.80 11:0.92 12:0.06 17:0.77 18:0.98 20:0.84 21:0.30 27:0.31 28:0.85 29:0.71 30:0.71 31:0.87 32:0.80 34:0.55 36:0.38 37:0.84 39:0.50 41:0.82 43:0.96 44:0.93 45:0.98 46:1.00 48:0.91 60:0.87 64:0.77 66:0.60 69:0.19 70:0.67 71:0.75 74:0.96 77:0.70 78:0.94 79:0.87 81:0.63 83:0.91 85:0.91 86:0.99 91:0.54 96:0.88 97:0.99 98:0.83 100:0.90 101:0.87 104:0.95 106:0.81 107:1.00 108:0.79 110:0.99 111:0.92 114:0.65 117:0.86 120:0.80 121:0.90 122:0.86 123:0.77 124:0.64 125:0.99 126:0.54 127:0.85 129:0.59 133:0.66 135:0.93 137:0.87 139:0.99 140:0.80 144:0.85 145:0.65 146:0.68 147:0.73 149:0.98 150:0.78 151:0.52 152:0.82 153:0.72 154:0.96 155:0.67 158:0.92 159:0.79 160:0.98 161:0.63 162:0.81 164:0.70 165:0.93 167:0.45 169:0.85 172:0.97 173:0.13 176:0.73 177:0.70 179:0.14 181:0.55 186:0.94 187:0.68 189:0.87 190:0.77 191:0.75 192:0.87 195:0.89 196:0.91 201:0.93 202:0.61 204:0.89 206:0.81 208:0.64 212:0.86 215:0.44 216:0.28 219:0.95 222:0.25 230:0.87 232:0.96 233:0.76 235:0.52 238:0.85 241:0.15 242:0.27 243:0.23 245:0.98 247:0.79 248:0.51 253:0.90 255:0.95 256:0.64 259:0.21 260:0.77 261:0.88 265:0.83 266:0.63 267:0.36 268:0.66 271:0.33 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.88 7:0.81 8:0.92 9:0.80 11:0.92 12:0.06 17:0.64 18:0.92 20:0.77 21:0.13 27:0.18 28:0.85 29:0.71 30:0.62 31:0.89 32:0.80 34:0.39 36:0.20 37:0.69 39:0.50 41:0.82 43:0.97 44:0.93 45:0.92 46:0.98 48:0.99 64:0.77 66:0.26 69:0.10 70:0.74 71:0.75 74:0.85 77:0.70 78:0.95 79:0.88 81:0.77 83:0.91 85:0.80 86:0.95 91:0.42 96:0.72 98:0.47 100:0.90 101:0.87 107:1.00 108:0.80 110:0.99 111:0.93 114:0.79 120:0.59 121:0.90 122:0.51 123:0.78 124:0.84 125:0.99 126:0.54 127:0.84 129:0.05 133:0.81 135:0.99 137:0.89 139:0.96 140:0.45 144:0.85 145:0.77 146:0.63 147:0.68 149:0.90 150:0.85 151:0.61 152:0.82 153:0.48 154:0.97 155:0.67 159:0.74 160:0.96 161:0.63 162:0.86 164:0.57 165:0.93 167:0.45 169:0.82 172:0.67 173:0.11 176:0.73 177:0.70 179:0.36 181:0.55 186:0.96 187:0.39 189:0.97 192:0.87 195:0.86 196:0.93 201:0.93 202:0.36 204:0.89 208:0.64 212:0.51 215:0.61 216:0.47 220:0.74 222:0.25 230:0.87 233:0.76 235:0.31 238:0.86 241:0.27 242:0.33 243:0.40 245:0.85 247:0.79 248:0.59 253:0.68 255:0.95 256:0.45 259:0.21 260:0.60 261:0.84 265:0.48 266:0.91 267:0.44 268:0.46 271:0.33 276:0.79 277:0.69 281:0.91 282:0.88 284:0.89 285:0.62 289:0.98 290:0.79 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.88 7:0.81 8:0.66 9:0.76 11:0.92 12:0.06 17:0.61 18:0.92 20:0.87 21:0.22 27:0.18 28:0.85 29:0.71 30:0.76 32:0.80 34:0.24 36:0.30 37:0.69 39:0.50 43:0.97 44:0.93 45:0.95 46:0.99 48:0.99 64:0.77 66:0.69 69:0.12 70:0.57 71:0.75 74:0.90 77:0.70 78:0.81 81:0.70 83:0.91 85:0.80 86:0.95 91:0.31 96:0.80 98:0.75 100:0.83 101:0.87 107:1.00 108:0.10 110:0.99 111:0.80 114:0.71 120:0.69 121:0.90 122:0.51 123:0.10 124:0.48 125:1.00 126:0.54 127:0.86 129:0.59 133:0.61 135:0.99 139:0.96 140:0.45 144:0.85 145:0.80 146:0.88 147:0.90 149:0.95 150:0.82 151:0.81 152:0.82 153:0.48 154:0.97 155:0.67 159:0.68 160:0.96 161:0.63 162:0.86 164:0.54 165:0.93 167:0.45 169:0.82 172:0.89 173:0.14 176:0.73 177:0.70 179:0.30 181:0.55 186:0.81 187:0.39 189:0.84 190:0.77 192:0.87 195:0.81 201:0.93 202:0.36 204:0.89 208:0.64 212:0.60 215:0.51 216:0.47 222:0.25 230:0.87 233:0.76 235:0.42 238:0.87 241:0.30 242:0.50 243:0.73 245:0.88 247:0.67 248:0.73 253:0.83 255:0.74 256:0.45 259:0.21 260:0.68 261:0.84 265:0.76 266:0.63 267:0.44 268:0.46 271:0.33 276:0.84 281:0.91 282:0.88 285:0.56 289:0.98 290:0.71 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.96 7:0.72 8:0.61 9:0.80 11:0.92 12:0.06 17:0.47 18:0.99 20:0.80 21:0.30 22:0.93 27:0.26 28:0.85 29:0.71 30:0.54 31:0.94 32:0.80 34:0.73 36:0.21 37:0.86 39:0.50 41:0.82 43:0.96 44:0.93 45:0.95 46:1.00 47:0.93 48:0.97 55:0.45 60:0.87 64:0.77 66:0.99 69:0.19 70:0.57 71:0.75 74:0.93 77:0.70 78:0.82 79:0.93 81:0.63 83:0.91 85:0.90 86:0.99 91:0.58 96:0.80 98:0.99 100:0.87 101:0.87 104:0.96 106:0.81 107:1.00 108:0.15 110:0.99 111:0.81 114:0.65 117:0.86 120:0.69 122:0.86 123:0.15 125:1.00 126:0.54 127:0.88 129:0.05 135:0.90 137:0.94 139:0.99 140:0.45 144:0.85 145:0.74 146:0.95 147:0.96 149:0.96 150:0.78 151:0.87 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.62 160:0.96 161:0.63 162:0.86 164:0.57 165:0.93 167:0.45 169:0.98 172:0.97 173:0.20 176:0.73 177:0.70 179:0.18 181:0.55 186:0.73 187:0.87 192:0.87 195:0.77 196:0.98 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.88 215:0.44 216:0.57 219:0.95 222:0.25 230:0.75 233:0.76 235:0.52 241:0.21 242:0.57 243:0.99 247:0.67 248:0.77 253:0.86 255:0.95 256:0.45 260:0.70 261:0.95 262:0.93 265:0.99 266:0.38 267:0.59 268:0.46 271:0.33 276:0.93 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.79 7:0.72 8:0.58 9:0.80 11:0.91 12:0.06 17:0.09 18:0.88 20:0.90 21:0.30 22:0.93 27:0.11 28:0.85 29:0.71 30:0.76 31:0.87 32:0.80 34:0.66 36:0.57 37:0.82 39:0.50 41:0.82 43:0.60 44:0.93 45:0.95 46:0.99 47:0.93 48:0.91 64:0.77 66:0.96 69:0.56 70:0.53 71:0.75 74:0.86 77:0.70 78:0.86 79:0.87 81:0.67 83:0.91 86:0.88 91:0.56 96:0.70 97:0.94 98:0.93 100:0.86 101:0.52 104:0.92 106:0.81 107:1.00 108:0.43 110:0.99 111:0.84 114:0.65 117:0.86 120:0.56 122:0.51 123:0.42 125:0.97 126:0.54 127:0.56 129:0.05 135:0.86 137:0.87 139:0.91 140:0.45 144:0.85 145:0.74 146:0.95 147:0.96 149:0.86 150:0.80 151:0.53 152:0.85 153:0.72 154:0.70 155:0.67 158:0.79 159:0.62 160:0.96 161:0.63 162:0.76 164:0.64 165:0.93 167:0.45 169:0.83 172:0.90 173:0.48 176:0.73 177:0.70 179:0.30 181:0.55 186:0.86 187:0.95 189:0.81 192:0.87 195:0.80 196:0.90 201:0.93 202:0.58 204:0.85 206:0.81 208:0.64 212:0.60 215:0.44 216:0.74 219:0.89 220:0.87 222:0.25 230:0.75 233:0.76 235:0.60 238:0.87 241:0.30 242:0.45 243:0.96 247:0.67 248:0.52 253:0.54 254:0.74 255:0.95 256:0.58 259:0.21 260:0.56 261:0.87 262:0.93 265:0.93 266:0.63 267:0.23 268:0.62 271:0.33 276:0.82 279:0.82 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.86 9:0.80 11:0.92 12:0.06 17:0.66 18:0.98 20:0.94 21:0.13 22:0.93 27:0.19 28:0.85 29:0.71 30:0.84 31:0.98 32:0.80 34:0.69 36:0.25 37:0.93 39:0.50 41:0.96 43:0.97 44:0.93 45:0.99 46:0.99 47:0.93 48:0.98 60:1.00 64:0.77 66:0.27 69:0.10 70:0.50 71:0.75 74:0.95 77:0.89 78:0.98 79:0.98 81:0.77 83:0.91 85:0.91 86:0.99 91:0.66 96:0.94 97:0.89 98:0.36 100:0.98 101:0.87 104:0.82 106:0.81 107:1.00 108:0.83 110:0.99 111:0.98 114:0.79 117:0.86 120:0.89 121:0.97 122:0.86 123:0.83 124:0.87 125:0.99 126:0.54 127:0.93 129:0.89 133:0.87 135:0.89 137:0.98 139:0.99 140:0.45 144:0.85 145:0.80 146:0.42 147:0.49 149:0.99 150:0.85 151:0.96 152:0.94 153:0.86 154:0.97 155:0.67 158:0.92 159:0.88 160:0.99 161:0.63 162:0.82 164:0.86 165:0.93 167:0.44 169:0.97 172:0.94 173:0.08 176:0.73 177:0.70 179:0.13 181:0.55 186:0.98 187:0.21 189:0.94 191:0.91 192:0.87 195:0.96 196:0.99 201:0.93 202:0.77 204:0.89 206:0.81 208:0.64 212:0.90 215:0.61 216:0.61 219:0.95 220:0.74 222:0.25 230:0.87 233:0.76 235:0.31 238:0.95 241:0.02 242:0.39 243:0.12 245:0.98 247:0.82 248:0.92 253:0.97 255:0.95 256:0.83 259:0.21 260:0.89 261:0.97 262:0.93 265:0.38 266:0.87 267:0.79 268:0.83 271:0.33 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.97 300:0.98 +1 10:0.99 17:0.88 21:0.13 27:0.39 29:0.71 30:0.44 34:0.19 36:0.17 37:0.37 39:0.80 43:0.19 48:0.91 55:0.42 66:0.93 69:0.15 70:0.53 71:0.83 74:0.28 75:0.96 81:0.42 83:0.56 91:0.31 98:0.91 102:0.95 108:0.12 122:0.95 123:0.12 126:0.24 127:0.50 129:0.05 131:0.61 135:0.56 139:0.67 145:0.52 146:0.80 147:0.83 149:0.42 150:0.46 154:0.21 155:0.49 157:0.61 159:0.36 166:0.61 170:0.87 172:0.82 173:0.29 174:0.98 175:0.75 177:0.70 178:0.55 179:0.42 182:0.61 187:0.39 192:0.45 193:0.98 195:0.61 197:0.99 204:0.79 208:0.50 212:0.83 216:0.28 222:0.35 226:0.96 227:0.61 229:0.61 231:0.62 235:0.12 236:0.92 239:0.99 241:0.46 242:0.79 243:0.93 244:0.83 246:0.61 247:0.55 253:0.25 254:0.95 257:0.65 259:0.21 265:0.91 266:0.38 267:0.44 271:0.73 276:0.71 277:0.69 281:0.91 287:0.61 300:0.43 +0 8:0.68 10:0.98 11:0.46 17:0.62 18:0.63 21:0.78 23:0.98 27:0.63 29:0.71 30:0.27 34:0.92 36:0.48 37:0.26 39:0.80 43:0.57 45:0.66 55:0.52 62:0.98 66:0.84 69:0.12 70:0.72 71:0.83 74:0.29 86:0.66 91:0.69 98:0.76 101:0.47 108:0.10 122:0.95 123:0.10 124:0.37 127:0.47 128:0.96 129:0.05 131:0.61 133:0.36 135:0.82 139:0.65 140:0.92 146:0.83 147:0.86 149:0.41 151:0.53 153:0.46 154:0.45 157:0.61 159:0.52 162:0.50 166:0.61 168:0.96 170:0.87 172:0.74 173:0.17 174:0.96 177:0.88 178:0.51 179:0.52 182:0.61 187:0.39 190:0.95 197:0.98 202:0.79 205:0.98 212:0.73 216:0.44 220:0.87 222:0.35 227:0.61 229:0.61 231:0.63 235:0.12 239:0.97 241:0.37 242:0.72 243:0.85 244:0.83 245:0.58 246:0.61 247:0.76 248:0.57 253:0.26 256:0.73 259:0.21 265:0.76 266:0.47 267:0.82 268:0.72 271:0.73 276:0.61 285:0.46 287:0.61 300:0.55 +0 10:0.84 17:0.45 18:0.64 21:0.78 27:0.34 29:0.71 30:0.76 34:0.52 36:0.41 37:0.46 39:0.80 43:0.12 55:0.71 66:0.36 69:0.76 70:0.15 71:0.83 86:0.64 91:0.09 98:0.15 108:0.60 122:0.93 123:0.57 124:0.52 127:0.18 129:0.05 131:0.61 133:0.39 135:0.92 139:0.63 145:0.65 146:0.28 147:0.34 149:0.10 154:0.09 157:0.61 159:0.49 163:0.98 166:0.61 170:0.87 172:0.10 173:0.60 174:0.79 175:0.91 177:0.38 178:0.55 179:0.93 182:0.61 187:0.87 197:0.83 212:0.95 216:0.53 222:0.35 227:0.61 229:0.61 231:0.61 235:0.31 239:0.83 241:0.24 242:0.82 243:0.51 244:0.93 245:0.37 246:0.61 247:0.12 253:0.20 257:0.84 259:0.21 265:0.16 266:0.12 267:0.19 271:0.73 276:0.14 277:0.87 287:0.61 300:0.13 +0 8:0.65 10:0.98 17:0.69 18:0.68 21:0.78 23:0.98 27:0.49 29:0.71 30:0.33 34:0.47 36:0.88 37:0.35 39:0.80 43:0.19 55:0.92 62:0.99 66:0.05 69:0.35 70:0.70 71:0.83 74:0.27 75:0.96 86:0.67 91:0.17 98:0.19 108:0.99 120:0.78 122:0.95 123:0.99 124:1.00 127:0.98 128:0.99 129:0.93 131:0.80 133:1.00 135:0.18 139:0.65 140:0.80 146:0.85 147:0.88 149:0.43 151:0.70 153:0.72 154:0.08 157:0.61 159:0.99 162:0.51 163:0.94 164:0.66 166:0.61 168:0.99 170:0.87 172:0.57 173:0.06 174:1.00 175:0.75 177:0.70 178:0.42 179:0.12 182:0.61 187:0.68 190:0.95 191:0.70 197:0.99 202:0.82 205:0.98 212:0.15 216:0.56 220:0.74 222:0.35 226:0.96 227:0.82 229:0.61 231:0.62 235:0.05 239:0.98 241:0.46 242:0.62 243:0.11 244:0.83 245:0.89 246:0.61 247:0.95 248:0.79 253:0.25 254:0.74 256:0.73 257:0.65 259:0.21 260:0.78 265:0.21 266:0.98 267:0.84 268:0.76 271:0.73 276:0.97 277:0.87 285:0.50 287:0.61 300:0.84 +1 10:0.85 11:0.50 17:0.64 18:0.78 21:0.13 27:0.24 29:0.71 30:0.90 34:0.75 36:0.20 37:0.65 39:0.80 43:0.58 45:0.77 55:0.45 64:0.77 66:0.96 69:0.17 70:0.36 71:0.83 74:0.44 81:0.36 86:0.76 91:0.22 98:0.94 101:0.47 106:0.81 108:0.14 123:0.13 126:0.32 127:0.60 129:0.05 131:0.61 135:0.87 139:0.72 144:0.57 146:0.82 147:0.85 149:0.67 150:0.37 154:0.46 157:0.91 159:0.38 166:0.72 170:0.87 172:0.91 173:0.31 174:0.79 177:0.88 178:0.55 179:0.28 182:0.80 187:0.39 192:0.46 197:0.82 201:0.58 206:0.81 212:0.90 215:0.84 216:0.76 222:0.35 227:0.61 229:0.61 231:0.65 235:0.22 239:0.84 241:0.18 242:0.77 243:0.97 244:0.83 246:0.61 247:0.84 253:0.37 259:0.21 265:0.94 266:0.38 267:0.52 271:0.73 276:0.83 287:0.61 297:0.36 300:0.55 +0 8:0.73 10:0.87 17:0.08 18:0.63 21:0.13 27:0.11 29:0.71 30:0.91 31:0.90 34:0.30 36:0.91 37:0.84 39:0.80 43:0.07 44:0.88 48:0.91 55:0.92 64:0.77 66:0.43 69:0.96 70:0.22 71:0.83 74:0.26 79:0.90 81:0.30 86:0.67 91:0.18 98:0.62 102:0.96 108:0.86 123:0.85 124:0.76 126:0.24 127:0.31 129:0.05 131:0.61 133:0.74 135:0.96 137:0.90 139:0.72 140:0.80 145:0.65 146:0.63 147:0.27 149:0.12 150:0.34 151:0.57 153:0.48 154:0.17 157:0.61 159:0.70 162:0.62 163:0.94 166:0.61 170:0.87 172:0.48 173:0.97 174:0.88 175:0.75 177:0.38 178:0.42 179:0.53 182:0.61 187:0.95 190:0.95 193:0.97 195:0.92 196:0.90 197:0.85 202:0.50 212:0.61 216:0.68 220:0.74 222:0.35 227:0.61 229:0.61 231:0.63 235:0.12 239:0.90 241:0.12 242:0.33 243:0.25 244:0.83 245:0.63 246:0.61 247:0.76 248:0.56 253:0.24 254:0.80 256:0.45 257:0.84 259:0.21 265:0.63 266:0.54 267:0.18 268:0.46 271:0.73 276:0.57 277:0.87 281:0.86 284:0.88 285:0.46 287:0.61 300:0.25 +0 8:0.79 10:0.97 11:0.46 17:0.72 18:0.57 21:0.30 23:0.99 27:0.53 29:0.71 30:0.08 33:0.97 34:0.78 36:0.49 37:0.49 39:0.80 43:0.20 45:0.66 55:0.96 62:0.99 66:0.12 69:0.17 70:1.00 71:0.83 74:0.26 75:0.96 81:0.38 86:0.59 91:0.02 98:0.14 101:0.47 108:0.14 122:0.95 123:0.13 124:0.99 126:0.24 127:0.99 128:0.98 129:0.96 131:0.61 133:0.99 135:0.19 139:0.57 140:0.80 146:0.72 147:0.76 149:0.38 150:0.42 151:0.63 153:0.72 154:0.09 157:0.61 159:0.99 162:0.54 166:0.61 168:0.98 170:0.87 172:0.61 173:0.05 174:0.99 175:0.91 177:0.38 178:0.42 179:0.28 182:0.61 187:0.39 190:0.95 191:0.70 193:0.96 197:0.98 202:0.78 205:0.99 212:0.17 216:0.87 220:0.74 222:0.35 226:0.96 227:0.61 229:0.61 231:0.61 235:0.31 236:0.92 239:0.97 241:0.32 242:0.61 243:0.33 244:0.83 245:0.71 246:0.61 247:0.91 248:0.68 253:0.24 256:0.75 257:0.84 259:0.21 265:0.14 266:1.00 267:0.96 268:0.75 271:0.73 276:0.86 277:0.87 285:0.46 287:0.61 291:0.97 300:1.00 +0 10:0.87 17:0.12 18:0.59 21:0.78 27:0.45 29:0.71 30:0.11 34:0.79 36:0.31 37:0.50 39:0.80 43:0.09 55:0.52 66:0.47 69:0.93 70:0.54 71:0.83 74:0.26 86:0.63 91:0.15 98:0.21 108:0.85 122:0.80 123:0.84 124:0.65 127:0.19 129:0.05 131:0.61 133:0.59 135:0.89 139:0.61 145:0.40 146:0.38 147:0.05 149:0.08 154:0.16 157:0.61 159:0.47 163:0.75 166:0.61 170:0.87 172:0.21 173:0.90 174:0.86 175:0.75 177:0.05 178:0.55 179:0.75 182:0.61 187:0.68 197:0.85 212:0.30 216:0.77 222:0.35 227:0.61 229:0.61 231:0.61 235:0.42 239:0.85 241:0.30 242:0.33 243:0.23 244:0.61 245:0.42 246:0.61 247:0.39 253:0.24 254:0.99 257:0.93 259:0.21 265:0.23 266:0.27 267:0.73 271:0.73 276:0.25 277:0.69 287:0.61 300:0.33 +0 8:0.64 10:0.99 17:0.24 21:0.30 23:1.00 27:0.21 29:0.71 30:0.33 33:0.97 34:0.53 36:0.79 37:0.74 39:0.80 43:0.20 55:0.59 62:1.00 66:0.26 69:0.94 70:0.73 71:0.83 74:0.26 75:0.96 81:0.38 91:0.86 98:0.66 108:0.87 122:0.95 123:0.66 124:0.77 126:0.24 127:0.96 128:1.00 129:0.05 131:0.61 133:0.74 135:0.17 140:0.45 146:0.80 147:0.63 149:0.46 150:0.42 151:0.84 153:0.98 154:0.13 157:0.61 159:0.80 162:0.69 166:0.61 168:1.00 170:0.87 172:0.76 173:0.93 174:0.99 175:0.91 177:0.05 179:0.31 182:0.61 187:0.39 190:0.95 191:0.78 193:0.97 197:0.99 202:0.90 205:1.00 212:0.72 216:0.95 222:0.35 226:0.98 227:0.61 229:0.61 231:0.61 235:0.31 236:0.92 239:0.99 241:0.52 242:0.81 243:0.25 244:0.83 245:0.90 246:0.61 247:0.47 248:0.96 253:0.24 254:0.92 256:0.92 257:0.93 259:0.21 265:0.53 266:0.87 267:0.85 268:0.92 271:0.73 276:0.84 277:0.95 285:0.46 287:0.61 291:0.97 300:0.70 +0 10:0.85 17:0.36 18:0.68 21:0.78 27:0.45 29:0.71 30:0.91 34:0.88 36:0.20 37:0.50 39:0.80 43:0.11 55:0.71 66:0.69 69:0.79 70:0.13 71:0.83 74:0.27 86:0.69 91:0.12 98:0.32 108:0.62 122:0.93 123:0.60 127:0.12 129:0.05 131:0.61 135:0.54 139:0.66 145:0.50 146:0.40 147:0.47 149:0.08 154:0.18 157:0.61 159:0.15 163:0.81 166:0.61 170:0.87 172:0.21 173:0.80 174:0.79 175:0.75 177:0.70 178:0.55 179:0.50 182:0.61 187:0.68 197:0.81 212:0.61 216:0.77 222:0.35 227:0.61 229:0.61 231:0.61 235:0.84 239:0.83 241:0.10 242:0.63 243:0.73 244:0.83 246:0.61 247:0.30 253:0.25 254:0.98 257:0.65 259:0.21 265:0.35 266:0.17 267:0.36 271:0.73 276:0.23 277:0.69 283:0.67 287:0.61 300:0.13 +0 10:0.99 17:0.76 18:0.63 21:0.13 23:0.95 27:0.38 29:0.71 30:0.21 33:0.97 34:0.50 36:0.63 37:0.58 39:0.80 43:0.16 55:0.45 62:0.98 66:0.94 69:0.54 70:0.68 71:0.83 75:0.96 81:0.42 86:0.64 91:0.70 98:0.94 102:0.95 108:0.42 122:0.95 123:0.40 126:0.24 127:0.62 128:0.97 129:0.05 131:0.61 135:0.79 139:0.63 140:0.45 145:0.79 146:0.89 147:0.91 149:0.39 150:0.46 151:0.61 153:0.48 154:0.47 157:0.61 159:0.48 162:0.86 166:0.61 168:0.97 170:0.87 172:0.83 173:0.56 174:0.98 175:0.75 177:0.70 179:0.43 182:0.61 187:0.68 190:0.95 193:0.97 195:0.68 197:0.99 202:0.36 205:0.95 212:0.71 216:0.60 220:0.62 222:0.35 226:0.98 227:0.61 229:0.61 231:0.61 235:0.12 236:0.92 239:0.99 241:0.24 242:0.78 243:0.94 244:0.83 246:0.61 247:0.60 248:0.59 253:0.20 254:0.86 256:0.45 257:0.65 259:0.21 265:0.94 266:0.54 267:0.85 268:0.46 271:0.73 276:0.73 277:0.69 285:0.46 287:0.61 291:0.97 300:0.55 +1 10:0.85 11:0.50 17:0.61 18:0.79 21:0.13 27:0.24 29:0.71 30:0.94 34:0.61 36:0.22 37:0.65 39:0.80 43:0.60 45:0.73 55:0.45 64:0.77 66:0.96 69:0.17 70:0.27 71:0.83 74:0.41 81:0.36 86:0.77 91:0.27 98:0.95 101:0.47 106:0.81 108:0.14 123:0.13 126:0.32 127:0.67 129:0.05 131:0.61 135:0.71 139:0.72 144:0.57 146:0.79 147:0.82 149:0.65 150:0.37 154:0.49 157:0.87 159:0.35 166:0.72 170:0.87 172:0.90 173:0.34 174:0.79 177:0.88 178:0.55 179:0.31 182:0.77 187:0.39 192:0.46 197:0.82 201:0.58 206:0.81 212:0.92 215:0.84 216:0.76 222:0.35 227:0.61 229:0.61 231:0.65 235:0.22 239:0.84 241:0.18 242:0.78 243:0.96 244:0.83 246:0.61 247:0.82 253:0.36 259:0.21 265:0.95 266:0.27 267:0.65 271:0.73 276:0.82 287:0.61 297:0.36 300:0.55 +0 8:0.78 10:0.85 17:0.32 18:0.57 21:0.78 27:0.21 29:0.71 30:0.13 34:0.49 36:0.77 37:0.74 39:0.80 43:0.19 55:0.92 66:0.10 69:0.40 70:0.97 71:0.83 74:0.30 86:0.59 91:0.76 96:0.74 98:0.20 108:0.31 120:0.89 122:0.41 123:0.30 124:0.95 127:0.96 129:0.81 131:0.85 133:0.94 135:0.23 139:0.57 140:0.87 146:0.64 147:0.69 149:0.21 151:0.79 153:0.91 154:0.12 157:0.61 159:0.94 162:0.47 163:0.86 164:0.79 166:0.61 170:0.87 172:0.21 173:0.10 174:0.86 175:0.91 177:0.38 178:0.48 179:0.68 182:0.61 187:0.39 190:0.95 191:0.76 197:0.85 202:0.91 212:0.12 216:0.95 222:0.35 227:0.86 229:0.61 231:0.69 235:0.42 239:0.84 241:0.03 242:0.13 243:0.29 244:0.93 245:0.55 246:0.61 247:0.84 248:0.84 253:0.53 256:0.77 257:0.84 259:0.21 260:0.89 265:0.22 266:0.95 267:0.85 268:0.76 271:0.73 276:0.58 277:0.87 283:0.82 285:0.50 287:0.61 300:0.84 +0 8:0.86 10:0.90 17:0.72 18:0.79 21:0.13 27:0.24 29:0.71 30:0.19 31:0.93 34:0.84 36:0.19 37:0.65 39:0.80 43:0.19 55:0.71 64:0.77 66:0.08 69:0.15 70:0.63 71:0.83 74:0.27 79:0.93 81:0.30 86:0.76 91:0.61 98:0.23 108:0.12 122:0.95 123:0.87 124:0.92 126:0.24 127:0.94 129:0.89 131:0.61 133:0.91 135:0.89 137:0.93 139:0.72 140:0.80 145:0.80 146:0.36 147:0.43 149:0.21 150:0.34 151:0.61 153:0.48 154:0.23 157:0.61 159:0.82 162:0.55 166:0.61 170:0.87 172:0.16 173:0.10 174:0.93 175:0.91 177:0.38 178:0.42 179:0.71 182:0.61 187:0.39 190:0.95 196:0.91 197:0.91 202:0.70 212:0.12 216:0.76 220:0.74 222:0.35 227:0.61 229:0.61 231:0.62 235:0.12 239:0.89 241:0.24 242:0.33 243:0.25 244:0.83 245:0.56 246:0.61 247:0.79 248:0.62 253:0.25 254:0.94 256:0.64 257:0.84 259:0.21 265:0.21 266:0.92 267:0.59 268:0.67 271:0.73 276:0.54 277:0.95 284:0.94 285:0.46 287:0.61 300:0.43 +2 1:0.67 8:0.95 9:0.75 10:0.85 11:0.50 17:0.69 18:0.77 21:0.05 27:0.24 29:0.71 30:0.67 31:0.92 34:0.45 36:0.19 37:0.65 39:0.80 43:0.59 45:0.95 55:0.45 64:0.77 66:0.98 69:0.17 70:0.56 71:0.83 74:0.78 79:0.92 81:0.59 83:0.56 86:0.75 91:0.67 96:0.87 98:0.96 99:0.83 101:0.47 106:0.81 108:0.14 114:0.95 117:0.86 120:0.79 123:0.13 126:0.51 127:0.72 129:0.05 131:0.85 135:0.80 137:0.92 139:0.71 140:0.80 144:0.57 146:0.92 147:0.94 149:0.90 150:0.52 151:0.80 153:0.78 154:0.37 155:0.64 157:0.98 159:0.55 162:0.79 164:0.71 165:0.65 166:0.79 170:0.87 172:0.96 173:0.21 174:0.79 176:0.70 177:0.88 179:0.19 182:0.94 187:0.39 190:0.89 192:0.98 196:0.91 197:0.81 201:0.64 202:0.60 206:0.81 208:0.61 212:0.91 215:0.91 216:0.76 222:0.35 227:0.86 229:0.97 231:0.70 232:0.96 235:0.22 239:0.84 241:0.35 242:0.57 243:0.98 244:0.61 246:0.90 247:0.90 248:0.73 253:0.89 254:0.84 255:0.77 256:0.58 259:0.21 260:0.79 265:0.96 266:0.27 267:0.73 268:0.65 271:0.73 276:0.92 284:0.91 285:0.60 287:0.92 290:0.95 297:0.36 300:0.55 +0 8:0.83 10:0.79 17:0.55 18:0.57 21:0.78 27:0.52 29:0.71 30:0.76 34:0.33 36:0.62 37:0.43 39:0.80 43:0.06 55:0.99 66:0.09 69:0.98 70:0.22 71:0.83 74:0.25 86:0.59 91:0.02 98:0.05 108:0.96 122:0.77 123:0.96 124:0.86 127:0.43 129:0.93 131:0.61 133:0.84 135:0.81 139:0.58 145:0.56 146:0.05 147:0.05 149:0.05 154:0.06 157:0.61 159:0.97 163:1.00 166:0.61 170:0.87 172:0.05 173:0.80 174:0.79 175:0.97 177:0.05 178:0.55 179:0.95 182:0.61 187:0.68 197:0.79 202:0.36 212:0.42 216:0.79 222:0.35 227:0.61 229:0.61 231:0.61 235:0.60 239:0.79 241:0.05 242:0.45 243:0.26 244:0.93 245:0.38 246:0.61 247:0.35 253:0.22 257:0.93 259:0.21 265:0.05 266:0.23 267:0.44 271:0.73 276:0.24 277:0.95 287:0.61 300:0.18 +0 6:0.90 8:0.81 9:0.76 11:0.81 12:0.69 17:0.62 18:0.97 20:0.84 21:0.78 22:0.93 28:0.68 29:0.71 30:0.75 31:0.93 34:0.36 36:0.56 37:0.98 39:0.56 43:0.61 45:0.98 46:0.88 47:0.93 66:0.78 69:0.76 70:0.41 71:0.81 74:0.90 78:0.75 79:0.94 83:0.60 86:0.98 91:0.51 96:0.83 97:0.94 98:0.78 100:0.79 101:0.52 107:0.97 108:0.78 110:0.96 111:0.75 117:0.86 122:0.80 123:0.77 124:0.41 125:0.88 127:0.46 129:0.59 131:0.86 133:0.46 135:0.24 137:0.93 139:0.98 140:0.80 144:0.76 145:0.83 146:0.41 147:0.48 149:0.94 151:0.83 152:0.81 153:0.69 154:0.67 155:0.58 157:0.87 158:0.79 159:0.70 160:0.88 161:0.79 162:0.59 166:0.61 167:0.72 169:0.77 172:0.95 173:0.64 177:0.70 178:0.42 179:0.17 181:0.55 182:0.78 186:0.76 187:0.39 189:0.92 190:0.77 191:0.70 192:0.51 196:0.97 202:0.55 208:0.54 212:0.90 216:0.95 219:0.92 222:0.84 227:0.94 229:0.87 231:0.70 232:0.85 235:0.05 238:0.94 241:0.54 242:0.61 243:0.19 245:0.95 246:0.61 247:0.55 248:0.75 253:0.84 254:0.92 255:0.74 256:0.45 259:0.21 261:0.78 262:0.93 265:0.78 266:0.54 267:0.69 268:0.55 276:0.91 279:0.82 284:0.87 285:0.56 287:0.95 289:0.94 292:0.95 300:0.70 +1 1:0.74 6:0.87 7:0.66 8:0.67 9:0.80 11:0.87 12:0.69 17:0.98 18:0.81 20:0.91 21:0.40 27:0.31 28:0.68 29:0.71 30:0.33 31:0.98 32:0.80 34:0.97 36:0.78 37:0.32 39:0.56 41:0.96 43:0.95 44:0.93 45:0.73 46:0.95 48:0.91 60:0.87 64:0.77 66:0.45 69:0.19 70:0.89 71:0.81 74:0.80 76:0.85 77:0.70 78:0.79 79:0.98 81:0.61 83:0.91 85:0.80 86:0.92 91:0.69 96:0.93 98:0.34 100:0.85 101:0.97 104:0.88 106:0.81 107:0.97 108:0.15 110:0.96 111:0.79 114:0.62 120:0.95 123:0.15 124:0.67 125:0.97 126:0.54 127:0.85 129:0.59 131:0.86 133:0.61 135:0.72 137:0.98 139:0.94 140:0.93 144:0.76 145:0.87 146:0.42 147:0.49 149:0.81 150:0.78 151:0.96 152:0.86 153:0.48 154:0.95 155:0.67 157:0.81 158:0.87 159:0.57 160:0.99 161:0.79 162:0.79 164:0.91 165:0.93 166:0.80 167:0.64 169:0.82 172:0.61 173:0.23 176:0.73 177:0.70 179:0.57 181:0.55 182:0.78 186:0.79 187:0.21 189:0.89 192:0.87 195:0.73 196:0.99 201:0.93 202:0.89 206:0.81 208:0.64 212:0.78 215:0.42 216:0.47 219:0.95 220:0.62 222:0.84 227:0.94 229:0.88 230:0.69 231:0.71 233:0.76 235:0.68 238:0.94 241:0.21 242:0.58 243:0.58 245:0.77 246:0.93 247:0.55 248:0.90 253:0.94 255:0.95 256:0.93 259:0.21 260:0.93 261:0.85 265:0.36 266:0.63 267:0.52 268:0.92 276:0.59 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 287:0.95 289:0.96 290:0.62 292:0.91 297:0.36 299:0.88 300:0.84 +1 6:0.85 7:0.66 11:0.81 12:0.69 17:0.62 18:0.63 20:0.76 21:0.40 27:0.67 28:0.68 29:0.71 30:0.08 32:0.78 34:0.92 36:0.25 37:0.44 39:0.56 43:0.12 44:0.93 45:0.66 46:0.88 66:0.64 69:0.73 70:0.74 71:0.81 74:0.54 77:0.70 78:0.72 81:0.31 86:0.74 91:0.44 98:0.46 100:0.73 101:0.52 107:0.93 108:0.56 110:0.93 111:0.72 123:0.53 124:0.42 125:0.88 126:0.26 127:0.20 129:0.05 131:0.61 133:0.37 135:0.74 139:0.80 144:0.76 145:0.67 146:0.48 147:0.54 149:0.11 150:0.29 152:0.79 154:0.69 157:0.76 159:0.25 160:0.88 161:0.79 166:0.61 167:0.63 169:0.75 172:0.32 173:0.79 177:0.70 178:0.55 179:0.71 181:0.55 182:0.73 186:0.73 187:0.39 195:0.66 212:0.23 215:0.42 216:0.56 222:0.84 227:0.61 229:0.61 230:0.69 231:0.63 233:0.73 235:0.42 241:0.15 242:0.73 243:0.69 245:0.43 246:0.61 247:0.35 253:0.37 254:0.95 259:0.21 261:0.76 265:0.48 266:0.38 267:0.52 276:0.29 282:0.86 287:0.61 289:0.93 297:0.36 300:0.43 +1 1:0.74 7:0.81 9:0.80 11:0.87 12:0.69 17:0.98 18:0.84 21:0.54 22:0.93 27:0.28 28:0.68 29:0.71 30:0.62 31:0.87 32:0.80 34:0.52 36:0.33 37:0.37 39:0.56 41:0.82 43:0.97 44:0.93 45:0.73 46:0.96 47:0.93 60:0.87 64:0.77 66:0.79 69:0.27 70:0.53 71:0.81 74:0.85 77:0.70 79:0.87 81:0.54 83:0.91 85:0.88 86:0.93 91:0.31 96:0.69 98:0.62 101:0.97 107:0.97 108:0.21 110:0.96 114:0.59 117:0.86 120:0.55 121:0.99 123:0.20 124:0.39 125:0.98 126:0.54 127:0.30 129:0.05 131:0.86 133:0.43 135:0.51 137:0.87 139:0.96 140:0.45 144:0.76 145:0.88 146:0.75 147:0.79 149:0.91 150:0.75 151:0.52 153:0.48 154:0.97 155:0.67 157:0.82 158:0.92 159:0.47 160:0.96 161:0.79 162:0.86 164:0.57 165:0.93 166:0.79 167:0.56 172:0.75 173:0.24 176:0.73 177:0.70 179:0.39 181:0.55 182:0.78 187:0.21 192:0.87 195:0.78 196:0.91 201:0.93 202:0.36 204:0.89 208:0.64 212:0.82 215:0.39 216:0.51 219:0.95 220:0.87 222:0.84 227:0.94 229:0.87 230:0.86 231:0.70 232:0.98 233:0.73 235:0.88 241:0.01 242:0.45 243:0.80 245:0.68 246:0.93 247:0.67 248:0.51 253:0.50 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.63 266:0.47 267:0.52 268:0.46 276:0.60 279:0.86 281:0.89 282:0.88 283:0.82 284:0.89 285:0.62 287:0.95 289:0.96 290:0.59 292:0.95 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.86 7:0.66 8:0.70 9:0.80 11:0.81 12:0.69 17:0.98 18:0.78 20:0.89 27:0.20 28:0.68 29:0.71 30:0.41 31:0.94 32:0.80 34:0.42 36:0.43 37:0.70 39:0.56 41:0.82 43:0.58 44:0.93 45:0.81 46:0.88 60:0.87 64:0.77 66:0.86 69:0.75 70:0.36 71:0.81 74:0.78 77:0.70 78:0.84 79:0.93 81:0.90 83:0.91 86:0.88 91:0.56 96:0.80 98:0.71 100:0.91 101:0.52 104:0.91 106:0.81 107:0.96 108:0.58 110:0.96 111:0.86 114:0.98 120:0.69 123:0.56 125:1.00 126:0.54 127:0.22 129:0.05 131:0.86 135:0.47 137:0.94 139:0.93 140:0.45 144:0.76 145:0.70 146:0.54 147:0.60 149:0.27 150:0.94 151:0.87 152:0.85 153:0.48 154:0.72 155:0.67 157:0.81 158:0.92 159:0.19 160:0.96 161:0.79 162:0.86 164:0.57 165:0.93 166:0.80 167:0.55 169:0.88 172:0.59 173:0.91 176:0.73 177:0.70 179:0.50 181:0.55 182:0.78 186:0.84 187:0.21 189:0.88 192:0.87 195:0.56 196:0.96 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.78 215:0.96 216:0.03 219:0.95 222:0.84 227:0.94 229:0.87 230:0.69 231:0.70 232:0.79 233:0.76 235:0.12 238:0.94 242:0.33 243:0.86 246:0.93 247:0.67 248:0.77 253:0.85 254:0.80 255:0.95 256:0.45 259:0.21 260:0.70 261:0.90 265:0.72 266:0.38 267:0.69 268:0.46 276:0.43 279:0.86 281:0.89 282:0.88 283:0.82 284:0.89 285:0.62 287:0.95 289:0.96 290:0.98 292:0.95 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.94 7:0.66 8:0.89 9:0.80 11:0.87 12:0.69 17:0.98 18:0.75 20:0.85 21:0.22 22:0.93 27:0.35 28:0.68 29:0.71 30:0.62 31:0.95 32:0.75 34:0.79 36:0.46 37:0.60 39:0.56 41:0.88 43:0.80 44:0.93 45:0.73 46:0.94 47:0.93 64:0.77 66:0.36 69:0.41 70:0.54 71:0.81 74:0.69 77:0.70 78:0.77 79:0.95 81:0.70 83:0.91 85:0.72 86:0.85 91:0.64 96:0.85 98:0.45 100:0.82 101:0.97 104:0.98 106:0.81 107:0.96 108:0.56 110:0.95 111:0.77 114:0.71 117:0.86 120:0.78 122:0.57 123:0.54 124:0.70 125:0.99 126:0.54 127:0.51 129:0.59 131:0.86 133:0.66 135:0.51 137:0.95 139:0.88 140:0.80 144:0.76 145:0.70 146:0.22 147:0.27 149:0.62 150:0.82 151:0.93 152:0.80 153:0.77 154:0.81 155:0.67 157:0.80 159:0.36 160:0.97 161:0.79 162:0.86 164:0.72 165:0.93 166:0.80 167:0.59 169:0.79 172:0.32 173:0.50 176:0.73 177:0.70 179:0.80 181:0.55 182:0.78 186:0.78 187:0.39 189:0.95 192:0.87 195:0.60 196:0.96 201:0.93 202:0.60 204:0.85 206:0.81 208:0.64 212:0.42 215:0.51 216:0.17 220:0.62 222:0.84 227:0.94 229:0.87 230:0.69 231:0.70 232:0.75 233:0.76 235:0.42 238:0.93 241:0.05 242:0.45 243:0.34 245:0.55 246:0.93 247:0.60 248:0.82 253:0.84 255:0.95 256:0.64 259:0.21 260:0.78 261:0.80 262:0.93 265:0.47 266:0.47 267:0.89 268:0.69 276:0.37 281:0.89 282:0.83 283:0.82 284:0.93 285:0.62 287:0.95 289:0.96 290:0.71 297:0.36 300:0.43 +3 6:0.98 7:0.66 8:0.98 9:0.80 12:0.69 17:0.06 20:0.99 21:0.74 25:0.88 27:0.13 28:0.68 29:0.71 30:0.45 31:0.99 32:0.64 34:0.94 36:0.42 37:0.82 39:0.56 41:0.94 43:0.13 46:0.88 55:0.52 66:0.49 69:0.70 70:0.25 71:0.81 78:0.88 79:0.99 81:0.23 83:0.91 91:1.00 96:0.95 98:0.39 100:0.98 104:0.58 107:0.90 108:0.69 110:0.90 111:0.97 120:1.00 123:0.67 124:0.48 125:0.96 126:0.26 127:0.36 129:0.59 131:0.85 133:0.47 135:0.24 137:0.99 140:0.98 144:0.76 145:0.72 146:0.05 147:0.05 149:0.11 150:0.23 151:0.96 152:0.98 153:0.48 154:0.10 155:0.67 157:0.61 159:0.27 160:0.98 161:0.79 162:0.74 163:0.96 164:0.99 166:0.61 167:0.99 169:0.96 172:0.16 173:0.85 175:0.91 177:0.05 179:0.96 181:0.55 182:0.77 186:0.88 189:0.99 191:0.97 192:0.87 195:0.58 202:0.99 208:0.64 212:0.61 215:0.36 216:0.75 222:0.84 227:0.93 229:0.87 230:0.69 231:0.70 233:0.73 235:0.88 238:0.99 241:0.96 242:0.82 243:0.29 244:0.83 245:0.39 246:0.61 247:0.12 248:0.94 253:0.92 255:0.95 256:0.99 257:0.84 259:0.21 260:0.99 261:0.99 265:0.41 266:0.17 267:0.73 268:0.99 276:0.17 277:0.87 283:0.82 284:0.98 285:0.62 287:0.94 289:0.95 297:0.36 300:0.18 +0 11:0.81 12:0.69 17:0.69 18:0.95 21:0.78 22:0.93 28:0.68 29:0.71 30:0.75 34:0.61 36:0.68 37:0.98 39:0.56 43:0.61 45:0.97 46:0.88 47:0.93 66:0.35 69:0.76 70:0.48 71:0.81 74:0.85 83:0.60 86:0.96 91:0.22 97:0.89 98:0.66 101:0.52 107:0.97 108:0.85 110:0.96 117:0.86 122:0.80 123:0.84 124:0.71 125:0.88 127:0.48 129:0.59 131:0.61 133:0.66 135:0.28 139:0.96 144:0.76 145:0.83 146:0.41 147:0.48 149:0.90 154:0.67 155:0.58 157:0.86 158:0.79 159:0.72 160:0.88 161:0.79 166:0.61 167:0.63 172:0.80 173:0.63 177:0.70 178:0.55 179:0.23 181:0.55 182:0.77 187:0.39 192:0.51 208:0.54 212:0.75 216:0.95 219:0.92 222:0.84 227:0.61 229:0.61 231:0.69 232:0.85 235:0.42 241:0.15 242:0.59 243:0.25 245:0.94 246:0.61 247:0.55 253:0.45 254:0.92 259:0.21 262:0.93 265:0.67 266:0.63 267:0.28 276:0.85 277:0.69 279:0.82 287:0.61 289:0.93 292:0.95 300:0.70 +0 11:0.81 12:0.69 17:0.53 18:0.62 21:0.78 27:0.45 28:0.68 29:0.71 30:0.76 34:0.89 36:0.18 37:0.50 39:0.56 43:0.58 45:0.66 46:0.88 66:0.64 69:0.82 70:0.15 71:0.81 74:0.35 86:0.67 91:0.06 98:0.16 101:0.52 107:0.90 108:0.67 110:0.90 122:0.57 123:0.65 125:0.88 127:0.09 129:0.05 131:0.61 135:0.44 139:0.65 144:0.76 145:0.49 146:0.26 147:0.32 149:0.27 154:0.47 157:0.76 159:0.11 160:0.88 161:0.79 163:0.97 166:0.61 167:0.67 172:0.16 173:0.79 177:0.70 178:0.55 179:0.18 181:0.55 182:0.72 187:0.68 212:0.95 216:0.77 222:0.84 227:0.61 229:0.61 231:0.61 235:0.99 241:0.32 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.29 259:0.21 265:0.18 266:0.12 267:0.44 276:0.14 287:0.61 289:0.88 300:0.13 +0 11:0.81 12:0.69 17:0.34 18:0.62 21:0.78 27:0.45 28:0.68 29:0.71 30:0.76 34:0.86 36:0.17 37:0.50 39:0.56 43:0.75 45:0.66 46:0.88 66:0.64 69:0.80 70:0.15 71:0.81 74:0.40 86:0.72 91:0.11 98:0.12 101:0.52 107:0.89 108:0.64 110:0.89 122:0.80 123:0.61 125:0.88 127:0.08 129:0.05 131:0.61 135:0.90 139:0.69 144:0.76 145:0.50 146:0.22 147:0.27 149:0.48 154:0.65 157:0.75 159:0.10 160:0.88 161:0.79 163:0.97 166:0.61 167:0.70 172:0.16 173:0.66 177:0.70 178:0.55 179:0.10 181:0.55 182:0.72 187:0.68 212:0.95 216:0.77 222:0.84 227:0.61 229:0.61 231:0.61 235:0.68 241:0.47 242:0.82 243:0.69 246:0.61 247:0.12 253:0.31 254:0.93 259:0.21 265:0.13 266:0.12 267:0.44 276:0.15 287:0.61 289:0.88 300:0.13 +0 1:0.69 8:0.92 9:0.76 11:0.81 12:0.69 17:0.85 18:0.91 20:0.74 21:0.05 22:0.93 27:0.06 28:0.68 29:0.71 30:0.73 31:0.90 34:0.31 36:0.49 37:0.87 39:0.56 43:0.72 44:0.90 45:0.91 46:0.88 47:0.93 64:0.77 66:0.44 69:0.93 70:0.54 71:0.81 74:0.81 77:0.70 78:0.72 79:0.90 81:0.68 83:0.60 86:0.93 91:0.18 96:0.75 97:0.89 98:0.35 99:0.83 100:0.73 101:0.52 107:0.96 108:0.07 110:0.95 111:0.72 114:0.85 117:0.86 122:0.80 123:0.07 124:0.67 125:0.88 126:0.44 127:0.70 129:0.05 131:0.86 133:0.66 135:0.26 137:0.90 139:0.94 140:0.45 144:0.76 145:0.44 146:0.49 147:0.57 149:0.85 150:0.64 151:0.65 152:0.76 153:0.48 154:0.45 155:0.58 157:0.84 158:0.79 159:0.66 160:0.88 161:0.79 162:0.86 165:0.70 166:0.80 167:0.59 169:0.72 172:0.59 173:0.97 176:0.66 177:0.38 179:0.56 181:0.55 182:0.78 186:0.73 187:0.39 190:0.77 192:0.51 195:0.81 196:0.94 201:0.68 202:0.36 204:0.85 208:0.54 212:0.74 216:0.54 219:0.92 220:0.62 222:0.84 227:0.94 229:0.87 231:0.70 232:0.85 235:0.12 241:0.05 242:0.59 243:0.57 245:0.76 246:0.93 247:0.55 248:0.63 253:0.74 254:0.90 255:0.74 256:0.45 257:0.65 259:0.21 261:0.76 262:0.93 265:0.37 266:0.71 267:0.52 268:0.46 276:0.53 277:0.69 279:0.82 281:0.89 282:0.77 284:0.87 285:0.56 287:0.95 289:0.93 290:0.85 292:0.95 300:0.55 +1 6:0.98 7:0.66 8:0.92 9:0.80 12:0.69 17:0.47 20:0.76 21:0.54 25:0.88 27:0.13 28:0.68 29:0.71 30:0.32 31:0.86 32:0.64 34:0.98 36:0.66 37:0.82 39:0.56 41:0.82 43:0.12 46:0.88 55:0.84 66:0.13 69:0.71 70:0.68 71:0.81 78:0.80 79:0.86 81:0.27 83:0.91 91:0.40 96:0.68 98:0.41 100:0.84 104:0.58 107:0.91 108:0.91 110:0.91 111:0.80 120:0.82 123:0.70 124:0.85 125:0.97 126:0.26 127:0.67 129:0.93 131:0.86 133:0.84 135:0.63 137:0.86 140:0.90 144:0.76 145:0.70 146:0.48 147:0.54 149:0.24 150:0.26 151:0.47 152:0.98 153:0.48 154:0.10 155:0.67 157:0.61 159:0.76 160:0.96 161:0.79 162:0.86 163:0.92 164:0.75 166:0.61 167:0.74 169:0.96 172:0.32 173:0.57 175:0.91 177:0.05 179:0.66 181:0.55 182:0.78 186:0.80 187:0.39 190:0.89 192:0.87 195:0.89 202:0.68 208:0.64 212:0.21 215:0.39 216:0.75 222:0.84 227:0.94 229:0.87 230:0.69 231:0.70 233:0.73 235:0.60 241:0.59 242:0.82 243:0.34 244:0.83 245:0.64 246:0.61 247:0.12 248:0.47 253:0.31 255:0.95 256:0.75 257:0.84 259:0.21 260:0.76 261:0.99 265:0.29 266:0.84 267:0.82 268:0.76 276:0.56 277:0.87 284:0.89 285:0.62 287:0.95 289:0.96 297:0.36 300:0.43 +2 1:0.74 6:0.82 8:0.64 9:0.80 11:0.87 12:0.69 17:0.61 18:0.86 20:0.80 21:0.13 27:0.61 28:0.68 29:0.71 30:0.93 31:0.89 34:0.62 36:0.71 37:0.41 39:0.56 41:0.82 43:0.97 45:0.66 46:0.99 64:0.77 66:0.86 69:0.10 70:0.19 71:0.81 74:0.78 78:0.81 79:0.89 81:0.77 83:0.91 85:0.88 86:0.95 91:0.22 96:0.73 98:0.41 100:0.83 101:0.97 107:0.96 108:0.09 110:0.96 111:0.80 114:0.79 120:0.59 122:0.57 123:0.09 125:0.97 126:0.54 127:0.13 129:0.05 131:0.86 135:0.60 137:0.89 139:0.93 140:0.80 144:0.76 146:0.28 147:0.34 149:0.91 150:0.86 151:0.61 152:0.79 153:0.48 154:0.97 155:0.67 157:0.82 159:0.12 160:0.96 161:0.79 162:0.86 164:0.57 165:0.93 166:0.80 167:0.65 169:0.80 172:0.59 173:0.43 176:0.73 177:0.70 179:0.20 181:0.55 182:0.78 186:0.81 187:0.39 189:0.84 192:0.87 196:0.93 201:0.93 202:0.50 208:0.64 212:0.95 215:0.61 216:0.45 220:0.87 222:0.84 227:0.94 229:0.87 231:0.70 235:0.31 238:0.88 241:0.24 242:0.45 243:0.86 246:0.93 247:0.47 248:0.59 253:0.66 255:0.95 256:0.58 259:0.21 260:0.60 261:0.81 265:0.43 266:0.12 267:0.36 268:0.59 276:0.48 284:0.92 285:0.62 287:0.95 289:0.96 290:0.79 297:0.36 300:0.18 +2 6:0.84 7:0.66 8:0.65 11:0.81 12:0.69 17:0.47 18:0.62 20:0.79 21:0.40 27:0.31 28:0.68 29:0.71 30:0.21 32:0.75 34:0.97 36:0.26 37:0.55 39:0.56 43:0.59 44:0.93 45:0.66 46:0.88 66:0.72 69:0.75 70:0.37 71:0.81 74:0.56 77:0.70 78:0.78 81:0.31 86:0.67 91:0.15 98:0.53 100:0.81 101:0.52 104:0.95 106:0.81 107:0.92 108:0.58 110:0.92 111:0.77 122:0.41 123:0.56 125:0.88 126:0.26 127:0.16 129:0.05 131:0.61 135:0.88 139:0.80 144:0.76 145:0.63 146:0.54 147:0.60 149:0.30 150:0.29 152:0.88 154:0.48 155:0.58 157:0.76 159:0.19 160:0.88 161:0.79 163:0.81 166:0.61 167:0.65 169:0.77 172:0.27 173:0.82 177:0.70 178:0.55 179:0.70 181:0.55 182:0.73 186:0.78 187:0.39 189:0.85 192:0.51 195:0.67 204:0.85 206:0.81 208:0.54 212:0.42 215:0.42 216:0.85 222:0.84 227:0.61 229:0.61 230:0.69 231:0.63 233:0.76 235:0.42 238:0.89 241:0.24 242:0.45 243:0.75 244:0.61 246:0.61 247:0.35 253:0.37 259:0.21 261:0.80 265:0.54 266:0.23 267:0.23 276:0.21 281:0.91 282:0.84 283:0.82 287:0.61 289:0.93 297:0.36 300:0.25 +1 1:0.69 8:0.66 9:0.76 11:0.81 12:0.69 17:0.91 18:0.97 20:0.76 21:0.40 22:0.93 27:0.42 28:0.68 29:0.71 30:0.67 31:0.93 34:0.97 36:0.85 37:0.47 39:0.56 43:0.71 45:0.99 46:0.88 47:0.93 64:0.77 66:0.35 69:0.25 70:0.71 71:0.81 74:0.89 77:0.70 78:0.77 79:0.94 81:0.39 83:0.60 86:0.97 91:0.23 96:0.83 97:0.94 98:0.60 99:0.83 100:0.73 101:0.52 107:0.98 108:0.94 110:0.96 111:0.76 114:0.61 117:0.86 122:0.80 123:0.94 124:0.92 125:0.88 126:0.44 127:0.94 129:0.81 131:0.86 133:0.93 135:0.47 137:0.93 139:0.97 140:0.45 144:0.76 145:0.49 146:0.80 147:0.83 149:0.93 150:0.54 151:0.81 152:0.75 153:0.48 154:0.74 155:0.58 157:0.87 158:0.79 159:0.92 160:0.88 161:0.79 162:0.79 165:0.70 166:0.80 167:0.57 169:0.72 172:0.93 173:0.09 176:0.66 177:0.70 179:0.15 181:0.55 182:0.78 186:0.78 187:0.68 190:0.77 192:0.51 195:0.98 196:0.97 201:0.68 202:0.68 204:0.85 208:0.54 212:0.58 216:0.72 219:0.92 220:0.87 222:0.84 227:0.94 229:0.88 231:0.71 232:0.99 235:0.52 241:0.01 242:0.54 243:0.22 245:0.95 246:0.93 247:0.86 248:0.75 253:0.84 254:0.86 255:0.74 256:0.71 259:0.21 261:0.75 262:0.93 265:0.61 266:0.94 267:0.73 268:0.73 276:0.95 279:0.82 281:0.91 282:0.71 284:0.95 285:0.56 287:0.95 289:0.94 290:0.61 292:0.95 300:0.84 +2 1:0.74 6:0.82 8:0.65 11:0.87 12:0.69 17:0.70 18:0.86 20:0.80 21:0.13 27:0.61 28:0.68 29:0.71 30:0.93 34:0.62 36:0.66 37:0.41 39:0.56 43:0.97 45:0.66 46:0.99 64:0.77 66:0.86 69:0.10 70:0.19 71:0.81 74:0.78 78:0.78 81:0.77 83:0.91 85:0.88 86:0.95 91:0.10 98:0.43 100:0.80 101:0.97 107:0.96 108:0.09 110:0.96 111:0.77 114:0.79 122:0.57 123:0.09 125:0.88 126:0.54 127:0.14 129:0.05 131:0.61 135:0.54 139:0.93 144:0.76 146:0.28 147:0.35 149:0.91 150:0.86 152:0.79 154:0.97 155:0.67 157:0.82 159:0.12 160:0.88 161:0.79 165:0.93 166:0.80 167:0.62 169:0.80 172:0.59 173:0.43 176:0.73 177:0.70 178:0.55 179:0.22 181:0.55 182:0.77 186:0.79 187:0.39 189:0.85 192:0.87 201:0.93 208:0.64 212:0.95 215:0.61 216:0.45 222:0.84 227:0.61 229:0.61 231:0.70 235:0.31 238:0.87 241:0.12 242:0.45 243:0.86 246:0.93 247:0.47 253:0.57 259:0.21 261:0.81 265:0.45 266:0.12 267:0.36 276:0.48 287:0.61 289:0.95 290:0.79 297:0.36 300:0.18 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.64 12:0.20 17:0.34 20:0.86 21:0.22 27:0.16 28:0.68 30:0.85 32:0.80 34:0.96 36:0.22 37:0.78 39:0.44 41:0.94 43:0.98 60:0.95 66:0.54 69:0.10 70:0.40 74:0.91 77:0.70 78:0.87 81:0.84 83:0.88 85:0.95 91:0.54 96:0.93 98:0.62 100:0.89 101:0.84 104:0.82 108:0.56 111:0.87 114:0.90 120:0.87 121:0.97 122:0.43 123:0.54 124:0.68 126:0.54 127:0.85 129:0.89 131:0.99 133:0.78 135:0.98 140:0.45 144:0.57 145:0.45 146:0.13 147:0.18 149:0.92 150:0.90 151:0.97 152:0.81 153:0.89 154:0.98 155:0.67 157:0.98 158:0.92 159:0.57 161:0.48 162:0.79 164:0.82 165:0.86 166:0.97 167:0.62 169:0.87 172:0.59 173:0.17 176:0.73 177:0.38 179:0.66 181:0.82 182:0.96 186:0.87 187:0.39 189:0.95 191:0.86 192:0.59 195:0.71 201:0.74 202:0.74 204:0.89 208:0.64 212:0.19 215:0.79 216:0.60 222:0.48 227:0.84 229:0.98 230:0.84 231:0.96 232:0.86 233:0.69 235:0.31 238:0.91 241:0.51 242:0.63 243:0.18 245:0.63 246:0.84 247:0.30 248:0.92 253:0.95 255:0.79 256:0.77 259:0.21 260:0.88 261:0.86 265:0.63 266:0.63 267:0.91 268:0.79 271:0.60 276:0.56 279:0.86 282:0.88 283:0.82 285:0.62 287:0.87 290:0.90 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.82 7:0.81 8:0.63 9:0.80 11:0.64 12:0.20 17:0.43 20:0.85 21:0.30 27:0.21 28:0.68 30:0.76 34:0.66 36:0.93 37:0.74 39:0.44 41:0.94 43:0.98 60:0.95 66:0.59 69:0.12 70:0.56 74:0.96 78:0.82 81:0.80 83:0.88 85:0.98 91:0.62 96:0.93 98:0.68 100:0.84 101:0.87 104:0.84 106:0.81 108:0.69 111:0.82 114:0.86 117:0.86 120:0.88 121:0.97 122:0.43 123:0.67 124:0.52 126:0.54 127:0.41 129:0.59 131:0.99 133:0.47 135:0.20 140:0.45 144:0.57 146:0.63 147:0.68 149:0.98 150:0.86 151:0.97 152:0.81 153:0.90 154:0.98 155:0.67 157:0.99 158:0.92 159:0.52 161:0.48 162:0.68 164:0.83 165:0.84 166:0.97 167:0.56 169:0.82 172:0.81 173:0.17 176:0.73 177:0.38 179:0.29 181:0.82 182:0.96 186:0.82 187:0.39 189:0.84 191:0.75 192:0.59 201:0.74 202:0.76 204:0.89 206:0.81 208:0.64 212:0.74 215:0.72 216:0.95 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.88 233:0.76 235:0.42 238:0.87 241:0.32 242:0.63 243:0.39 245:0.92 246:0.84 247:0.39 248:0.93 253:0.95 255:0.79 256:0.77 259:0.21 260:0.88 261:0.81 265:0.69 266:0.54 267:0.89 268:0.78 271:0.60 276:0.79 279:0.86 283:0.82 285:0.62 287:0.87 290:0.86 297:0.36 300:0.70 +2 1:0.74 6:0.89 8:0.76 9:0.80 11:0.64 12:0.20 17:0.67 20:0.82 21:0.13 27:0.26 28:0.68 30:0.76 34:0.56 36:0.57 37:0.73 39:0.44 41:0.92 43:0.95 60:0.97 66:0.54 69:0.21 70:0.63 74:0.97 78:0.86 81:0.88 83:0.88 85:0.99 91:0.58 96:0.89 98:0.75 100:0.89 101:0.87 104:0.87 108:0.58 111:0.86 114:0.92 120:0.81 122:0.43 123:0.55 124:0.63 126:0.54 127:0.73 129:0.81 131:0.99 133:0.67 135:0.75 140:0.45 144:0.57 146:0.13 147:0.18 149:0.99 150:0.93 151:0.95 152:0.82 153:0.84 154:0.95 155:0.67 157:0.99 158:0.92 159:0.60 161:0.48 162:0.58 164:0.77 165:0.88 166:0.97 167:0.74 169:0.86 172:0.87 173:0.21 176:0.73 177:0.38 179:0.27 181:0.82 182:0.96 186:0.86 187:0.39 189:0.91 191:0.88 192:0.59 201:0.74 202:0.73 208:0.64 212:0.81 215:0.84 216:0.43 222:0.48 227:0.84 229:0.98 231:0.96 232:0.89 235:0.22 238:0.90 241:0.70 242:0.63 243:0.08 245:0.92 246:0.84 247:0.30 248:0.88 253:0.93 255:0.79 256:0.68 259:0.21 260:0.82 261:0.86 265:0.76 266:0.54 267:0.44 268:0.72 271:0.60 276:0.85 279:0.86 283:0.82 285:0.62 287:0.87 290:0.92 297:0.36 300:0.84 +2 1:0.74 6:0.93 8:0.77 9:0.80 11:0.64 12:0.20 17:0.09 20:0.93 21:0.30 27:0.04 28:0.68 30:0.74 32:0.80 34:0.96 36:0.27 37:0.88 39:0.44 41:0.93 43:0.83 60:0.87 66:0.49 69:0.67 70:0.36 74:0.83 77:0.89 78:0.93 81:0.80 83:0.84 85:0.93 91:0.63 96:0.84 98:0.48 100:0.95 101:0.83 104:0.95 108:0.63 111:0.93 114:0.86 120:0.74 122:0.43 123:0.61 124:0.66 126:0.54 127:0.27 129:0.81 131:0.99 133:0.67 135:0.99 140:0.45 144:0.57 145:0.76 146:0.13 147:0.18 149:0.87 150:0.86 151:0.81 152:1.00 153:0.72 154:0.78 155:0.67 157:0.98 158:0.87 159:0.37 161:0.48 162:0.77 164:0.79 165:0.84 166:0.97 167:0.73 169:0.94 172:0.48 173:0.69 176:0.73 177:0.38 179:0.54 181:0.82 182:0.96 186:0.93 187:0.39 189:0.88 191:0.90 192:0.59 195:0.68 201:0.74 202:0.70 208:0.64 212:0.48 215:0.72 216:0.55 220:0.87 222:0.48 227:0.84 229:0.98 231:0.96 232:0.96 235:0.42 238:0.92 241:0.69 242:0.58 243:0.20 245:0.64 246:0.84 247:0.47 248:0.81 253:0.86 255:0.79 256:0.71 259:0.21 260:0.75 261:0.98 265:0.49 266:0.38 267:0.69 268:0.74 271:0.60 276:0.50 279:0.86 282:0.88 283:0.71 285:0.62 287:0.87 290:0.86 297:0.36 299:0.97 300:0.33 +2 1:0.74 6:0.93 8:0.89 9:0.80 11:0.64 12:0.20 17:0.22 20:0.99 21:0.22 27:0.04 28:0.68 30:0.86 32:0.80 34:0.94 36:0.23 37:0.88 39:0.44 41:0.96 43:0.83 60:0.99 66:0.15 69:0.67 70:0.21 74:0.83 77:0.89 78:0.96 81:0.84 83:0.87 85:0.93 91:0.72 96:0.93 98:0.22 100:0.99 101:0.82 104:0.95 108:0.64 111:0.98 114:0.90 120:0.88 122:0.43 123:0.77 124:0.82 126:0.54 127:0.31 129:0.89 131:0.99 133:0.78 135:0.98 140:0.45 144:0.57 145:0.72 146:0.13 147:0.18 149:0.86 150:0.90 151:0.98 152:1.00 153:0.91 154:0.78 155:0.67 157:0.98 158:0.92 159:0.42 161:0.48 162:0.77 163:0.81 164:0.86 165:0.86 166:0.97 167:0.79 169:0.94 172:0.21 173:0.67 176:0.73 177:0.38 179:0.63 181:0.82 182:0.96 186:0.96 187:0.39 189:0.96 191:0.92 192:0.59 195:0.70 201:0.74 202:0.78 208:0.64 212:0.42 215:0.79 216:0.55 222:0.48 227:0.84 229:0.98 231:0.96 232:0.96 235:0.31 238:0.98 241:0.76 242:0.63 243:0.22 245:0.59 246:0.84 247:0.30 248:0.93 253:0.94 255:0.79 256:0.81 259:0.21 260:0.88 261:0.98 265:0.21 266:0.38 267:0.59 268:0.82 271:0.60 276:0.46 279:0.86 282:0.88 283:0.82 285:0.62 287:0.87 290:0.90 297:0.36 299:0.97 300:0.18 +1 1:0.74 11:0.64 12:0.20 17:0.64 21:0.68 27:0.45 28:0.68 30:0.84 32:0.80 34:0.98 36:0.28 37:0.50 39:0.44 43:0.81 66:0.64 69:0.71 70:0.44 74:0.93 77:0.70 81:0.55 83:0.73 85:0.97 91:0.06 98:0.53 101:0.86 108:0.54 114:0.70 122:0.43 123:0.51 124:0.47 126:0.54 127:0.40 129:0.59 131:0.61 133:0.47 135:0.66 144:0.57 145:0.49 146:0.68 147:0.73 149:0.96 150:0.69 154:0.77 155:0.67 157:0.99 159:0.54 161:0.48 165:0.70 166:0.97 167:0.52 172:0.77 173:0.66 176:0.73 177:0.38 178:0.55 179:0.36 181:0.82 182:0.96 187:0.68 192:0.59 195:0.77 201:0.74 212:0.88 215:0.49 216:0.77 222:0.48 227:0.61 229:0.61 231:0.96 235:0.84 241:0.10 242:0.63 243:0.69 245:0.86 246:0.84 247:0.30 253:0.72 259:0.21 265:0.55 266:0.54 267:0.28 271:0.60 276:0.61 282:0.88 287:0.61 290:0.70 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.89 8:0.75 9:0.80 11:0.64 12:0.20 17:0.64 20:0.84 21:0.22 27:0.26 28:0.68 30:0.72 34:0.55 36:0.84 37:0.73 39:0.44 41:0.95 43:0.94 60:0.95 66:0.61 69:0.25 70:0.66 74:0.96 78:0.85 81:0.84 83:0.89 85:0.99 91:0.60 96:0.92 98:0.84 100:0.87 101:0.87 104:0.87 108:0.20 111:0.84 114:0.90 120:0.86 122:0.43 123:0.19 124:0.50 126:0.54 127:0.69 129:0.59 131:0.99 133:0.47 135:0.93 140:0.45 144:0.57 146:0.90 147:0.92 149:0.98 150:0.90 151:0.87 152:0.82 153:0.48 154:0.94 155:0.67 157:0.99 158:0.87 159:0.60 161:0.48 162:0.57 164:0.83 165:0.86 166:0.97 167:0.72 169:0.86 172:0.87 173:0.24 176:0.73 177:0.38 179:0.28 181:0.82 182:0.96 186:0.85 187:0.39 189:0.90 191:0.91 192:0.59 201:0.74 202:0.80 208:0.64 212:0.68 215:0.79 216:0.43 220:0.62 222:0.48 227:0.84 229:0.98 231:0.96 232:0.89 235:0.31 238:0.90 241:0.68 242:0.62 243:0.68 245:0.94 246:0.84 247:0.47 248:0.91 253:0.95 255:0.79 256:0.79 259:0.21 260:0.86 261:0.86 265:0.84 266:0.54 267:0.65 268:0.79 271:0.60 276:0.84 279:0.86 283:0.71 285:0.62 287:0.87 290:0.90 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.20 17:0.43 21:0.30 27:0.45 28:0.68 30:0.85 32:0.80 34:0.85 36:0.23 37:0.50 39:0.44 43:0.82 66:0.54 69:0.68 70:0.42 74:0.94 77:0.70 81:0.80 83:0.75 85:0.98 91:0.22 98:0.55 101:0.86 108:0.52 114:0.86 122:0.57 123:0.50 124:0.52 126:0.54 127:0.36 129:0.59 131:0.61 133:0.47 135:0.69 144:0.57 145:0.47 146:0.68 147:0.73 149:0.97 150:0.86 154:0.77 155:0.67 157:0.99 159:0.50 161:0.48 165:0.84 166:0.97 167:0.52 172:0.76 173:0.66 176:0.73 177:0.38 178:0.55 179:0.32 181:0.82 182:0.96 187:0.68 192:0.59 195:0.76 201:0.74 212:0.76 215:0.72 216:0.77 222:0.48 227:0.61 229:0.61 231:0.96 235:0.42 241:0.10 242:0.63 243:0.63 245:0.89 246:0.84 247:0.39 253:0.77 259:0.21 265:0.57 266:0.54 267:0.44 271:0.60 276:0.67 282:0.88 287:0.61 290:0.86 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.87 7:0.81 8:0.68 9:0.80 11:0.64 12:0.20 17:0.45 20:0.86 21:0.40 27:0.10 28:0.68 30:0.86 32:0.80 34:0.99 36:0.36 37:0.84 39:0.44 41:0.95 43:0.98 60:0.87 66:0.27 69:0.15 70:0.32 74:0.89 77:0.89 78:0.84 81:0.75 83:0.88 85:0.93 91:0.73 96:0.92 98:0.40 100:0.85 101:0.84 104:0.96 108:0.64 111:0.84 114:0.82 120:0.85 121:1.00 122:0.43 123:0.62 124:0.74 126:0.54 127:0.31 129:0.81 131:0.99 133:0.67 135:0.63 138:0.97 140:0.45 144:0.57 145:0.79 146:0.20 147:0.25 149:0.90 150:0.83 151:0.96 152:0.82 153:0.86 154:0.98 155:0.67 157:0.98 158:0.92 159:0.32 161:0.48 162:0.77 164:0.82 165:0.83 166:0.97 167:0.80 169:0.84 172:0.32 173:0.27 176:0.73 177:0.38 179:0.63 181:0.82 182:0.96 186:0.84 187:0.87 189:0.89 191:0.90 192:0.59 195:0.66 201:0.74 202:0.74 204:0.89 208:0.64 212:0.42 215:0.67 216:0.71 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.97 233:0.76 235:0.52 238:0.89 241:0.76 242:0.63 243:0.36 245:0.63 246:0.84 247:0.30 248:0.92 253:0.94 255:0.79 256:0.75 259:0.21 260:0.86 261:0.84 265:0.42 266:0.38 267:0.89 268:0.78 271:0.60 276:0.43 279:0.86 282:0.88 283:0.82 285:0.62 287:0.87 290:0.82 297:0.36 299:0.97 300:0.33 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.20 17:0.53 21:0.40 27:0.10 28:0.68 30:0.86 32:0.80 34:0.99 36:0.35 37:0.84 39:0.44 41:0.88 43:0.98 66:0.27 69:0.15 70:0.32 74:0.87 77:0.89 81:0.75 83:0.80 85:0.93 91:0.63 96:0.88 98:0.40 101:0.84 104:0.96 108:0.64 114:0.82 120:0.80 121:1.00 122:0.43 123:0.62 124:0.74 126:0.54 127:0.31 129:0.81 131:0.99 133:0.67 135:0.61 140:0.45 144:0.57 145:0.79 146:0.20 147:0.25 149:0.90 150:0.83 151:0.93 153:0.77 154:0.98 155:0.67 157:0.98 159:0.32 161:0.48 162:0.86 164:0.69 165:0.83 166:0.97 167:0.75 172:0.32 173:0.27 176:0.73 177:0.38 179:0.63 181:0.82 182:0.96 187:0.95 192:0.59 195:0.66 201:0.74 202:0.54 204:0.89 208:0.64 212:0.42 215:0.67 216:0.71 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.97 233:0.76 235:0.52 241:0.72 242:0.63 243:0.36 245:0.63 246:0.84 247:0.30 248:0.87 253:0.91 255:0.79 256:0.58 259:0.21 260:0.81 265:0.42 266:0.38 267:0.65 268:0.63 271:0.60 276:0.43 282:0.88 285:0.62 287:0.87 290:0.82 297:0.36 299:0.97 300:0.33 +3 1:0.74 6:0.91 7:0.81 8:0.79 9:0.80 11:0.64 12:0.20 17:0.59 20:0.87 21:0.47 27:0.09 28:0.68 30:0.75 34:0.98 36:0.33 37:0.82 39:0.44 41:0.98 43:0.84 60:0.95 66:0.27 69:0.64 70:0.48 74:0.91 78:0.88 81:0.83 83:0.89 85:0.97 91:0.58 96:0.95 98:0.57 100:0.92 101:0.85 104:0.91 108:0.49 111:0.89 114:0.80 120:0.91 121:0.99 122:0.43 123:0.47 124:0.81 126:0.54 127:0.51 129:0.89 131:0.99 133:0.78 135:0.97 140:0.45 144:0.57 145:0.61 146:0.78 147:0.82 149:0.94 150:0.89 151:0.97 152:0.82 153:0.90 154:0.81 155:0.67 157:0.98 158:0.92 159:0.67 161:0.48 162:0.81 163:0.81 164:0.88 165:0.86 166:0.97 167:0.69 169:0.90 172:0.59 173:0.52 176:0.73 177:0.38 179:0.40 181:0.82 182:0.96 186:0.88 187:0.39 189:0.92 191:0.92 192:0.59 201:0.74 202:0.81 204:0.89 208:0.64 212:0.30 215:0.63 216:0.94 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.93 233:0.76 235:0.84 238:0.93 241:0.64 242:0.62 243:0.46 245:0.82 246:0.84 247:0.39 248:0.95 253:0.97 255:0.79 256:0.85 259:0.21 260:0.92 261:0.87 265:0.58 266:0.80 267:0.95 268:0.86 271:0.60 276:0.72 279:0.86 283:0.82 285:0.62 287:0.87 290:0.80 297:0.36 300:0.55 +0 12:0.98 17:0.57 21:0.78 27:0.44 28:0.57 30:0.95 34:0.64 36:0.65 37:0.43 43:0.27 55:0.52 66:0.54 69:0.79 91:0.27 98:0.14 108:0.63 123:0.60 127:0.09 129:0.05 135:0.42 145:0.39 146:0.20 147:0.25 149:0.18 154:0.20 159:0.10 161:0.69 167:0.49 172:0.10 173:0.86 177:0.05 178:0.55 179:0.20 181:0.87 187:0.39 202:0.50 216:0.73 235:0.12 241:0.03 243:0.63 259:0.21 265:0.15 267:0.87 300:0.08 +0 6:0.80 8:0.59 12:0.98 17:0.79 20:0.83 21:0.78 27:0.44 28:0.57 34:0.37 36:0.34 37:0.43 43:0.28 66:0.36 69:0.78 78:0.81 91:0.18 98:0.08 100:0.73 108:0.61 111:0.79 123:0.59 127:0.06 129:0.05 135:0.37 145:0.39 146:0.05 147:0.05 149:0.12 152:0.85 154:0.20 159:0.05 161:0.69 167:0.54 169:0.96 172:0.05 173:0.84 177:0.05 178:0.55 181:0.87 186:0.82 187:0.39 202:0.60 216:0.73 235:0.12 241:0.21 243:0.51 259:0.21 261:0.96 265:0.08 267:0.91 +0 12:0.98 17:0.76 21:0.78 27:0.72 28:0.57 34:0.95 36:0.17 43:0.30 66:0.36 69:0.74 91:0.31 98:0.10 108:0.58 123:0.55 127:0.07 129:0.05 135:0.21 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 161:0.69 167:0.55 172:0.05 173:0.94 177:0.05 178:0.55 181:0.87 187:0.68 216:0.03 235:0.60 241:0.27 243:0.51 259:0.21 265:0.11 267:0.23 +1 12:0.98 17:0.76 21:0.78 27:0.49 28:0.57 30:0.76 34:0.80 36:0.28 37:0.46 43:0.26 55:0.71 66:0.36 69:0.82 70:0.22 91:0.33 98:0.19 108:0.67 123:0.65 124:0.57 127:0.36 129:0.59 133:0.47 135:0.49 146:0.30 147:0.36 149:0.26 154:0.18 159:0.56 161:0.69 163:0.80 167:0.52 172:0.16 173:0.78 177:0.05 178:0.55 179:0.94 181:0.87 187:0.39 202:0.72 212:0.42 216:0.73 235:0.74 241:0.15 242:0.82 243:0.51 245:0.43 247:0.12 259:0.21 265:0.21 266:0.23 267:0.96 276:0.15 300:0.18 +1 12:0.98 17:0.53 21:0.78 27:0.49 28:0.57 30:0.45 34:0.70 36:0.28 37:0.46 43:0.29 55:0.59 66:0.49 69:0.76 70:0.25 91:0.35 98:0.28 108:0.81 123:0.80 124:0.48 127:0.27 129:0.59 133:0.47 135:0.63 146:0.05 147:0.05 149:0.23 154:0.21 159:0.46 161:0.69 163:0.98 167:0.54 172:0.16 173:0.73 177:0.05 178:0.55 179:0.95 181:0.87 187:0.39 202:0.74 212:0.61 216:0.73 235:0.68 241:0.21 242:0.82 243:0.29 245:0.39 247:0.12 259:0.21 265:0.31 266:0.17 267:0.97 276:0.16 300:0.18 +0 12:0.98 17:0.92 21:0.78 27:0.72 28:0.57 34:0.86 36:0.31 43:0.30 66:0.36 69:0.74 91:0.37 98:0.10 108:0.57 123:0.54 127:0.07 129:0.05 135:0.18 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 161:0.69 167:0.55 172:0.05 173:0.93 177:0.05 178:0.55 181:0.87 187:0.68 216:0.02 235:0.02 241:0.27 243:0.51 259:0.21 265:0.11 267:0.36 +2 7:0.78 8:0.59 9:0.80 12:0.86 17:0.16 21:0.78 25:0.88 27:0.03 28:0.47 30:0.17 32:0.77 34:0.58 36:0.87 37:0.66 39:0.95 41:0.82 43:0.35 55:0.45 66:0.91 69:0.07 70:0.85 74:0.82 77:0.70 83:0.76 91:0.88 96:0.86 98:0.92 104:0.58 108:0.06 120:0.96 123:0.06 127:0.53 129:0.05 135:0.51 140:0.99 145:0.69 146:0.87 147:0.89 149:0.39 151:0.87 153:0.90 154:0.77 155:0.67 159:0.44 161:0.65 162:0.56 164:0.95 167:0.90 172:0.74 173:0.17 177:0.38 179:0.55 181:0.86 191:0.91 192:0.59 195:0.66 202:0.94 208:0.64 212:0.42 216:0.47 220:0.62 222:0.60 230:0.81 233:0.76 235:0.05 241:0.89 242:0.24 243:0.91 247:0.60 248:0.81 253:0.87 254:0.74 255:0.79 256:0.94 259:0.21 260:0.96 265:0.92 266:0.54 267:0.44 268:0.94 271:0.29 276:0.62 282:0.85 283:0.71 285:0.62 300:0.55 +2 6:0.88 8:0.93 9:0.80 12:0.86 17:0.12 20:0.98 21:0.78 25:0.88 27:0.03 28:0.47 34:0.75 36:0.64 37:0.66 39:0.95 41:0.82 78:0.92 83:0.74 91:0.94 96:0.93 100:0.97 104:0.58 111:0.96 120:0.96 135:0.56 138:0.97 140:0.93 151:0.96 152:0.99 153:0.95 155:0.67 161:0.65 162:0.62 164:0.94 167:0.74 169:0.95 175:0.91 181:0.86 186:0.92 187:0.21 189:0.96 191:0.94 192:0.59 202:0.92 208:0.64 216:0.47 222:0.60 235:0.05 238:0.96 241:0.69 244:0.83 248:0.92 253:0.90 255:0.79 256:0.92 257:0.84 259:0.21 260:0.96 261:0.96 267:0.36 268:0.93 271:0.29 277:0.87 285:0.62 +2 8:0.88 9:0.80 12:0.86 17:0.85 21:0.05 27:0.72 28:0.47 32:0.75 34:0.28 36:0.41 37:0.61 39:0.95 41:0.82 74:0.45 77:0.70 81:0.84 83:0.73 91:0.93 96:0.93 120:0.95 126:0.32 135:0.51 138:0.96 140:0.80 145:0.70 150:0.64 151:0.98 153:0.94 155:0.67 161:0.65 162:0.72 164:0.94 167:0.91 175:0.91 181:0.86 191:0.89 192:0.59 195:0.52 202:0.90 208:0.64 215:0.91 216:0.12 222:0.60 235:0.05 241:0.94 244:0.83 248:0.93 253:0.89 255:0.79 256:0.92 257:0.84 259:0.21 260:0.95 267:0.36 268:0.92 271:0.29 277:0.87 282:0.83 285:0.62 297:0.36 +2 8:0.74 9:0.80 12:0.86 17:0.62 21:0.05 27:0.18 28:0.47 34:0.63 36:0.49 37:0.82 39:0.95 41:0.82 81:0.84 83:0.74 91:0.90 96:0.93 104:0.96 106:0.81 120:0.92 126:0.32 135:0.83 140:0.80 150:0.64 151:0.91 153:0.94 155:0.67 161:0.65 162:0.79 164:0.92 167:0.84 175:0.91 181:0.86 187:0.95 191:0.92 192:0.59 202:0.87 206:0.81 208:0.64 215:0.91 216:0.53 220:0.62 222:0.60 235:0.05 241:0.77 244:0.83 248:0.92 253:0.90 255:0.79 256:0.91 257:0.84 259:0.21 260:0.93 267:0.28 268:0.91 271:0.29 277:0.87 283:0.71 285:0.62 297:0.36 +2 7:0.78 8:0.63 12:0.86 17:0.66 21:0.05 25:0.88 27:0.03 28:0.47 30:0.76 32:0.77 34:0.36 36:0.47 37:0.66 39:0.95 43:0.36 55:0.52 66:0.80 69:0.07 70:0.28 74:0.77 77:0.70 78:0.99 81:0.45 91:0.76 96:0.83 98:0.75 104:0.58 108:0.06 111:0.98 114:0.77 120:0.67 123:0.06 126:0.32 127:0.24 129:0.05 135:0.37 140:0.80 145:0.69 146:0.36 147:0.43 149:0.27 150:0.36 151:0.64 153:0.78 154:0.87 158:0.84 159:0.14 161:0.65 162:0.56 164:0.65 167:0.65 169:0.96 172:0.45 173:0.47 176:0.56 177:0.38 178:0.42 179:0.71 181:0.86 187:0.21 190:0.77 191:0.78 195:0.53 202:0.73 212:0.95 216:0.47 220:0.62 222:0.60 230:0.81 232:0.84 233:0.76 235:0.05 238:0.90 241:0.53 242:0.58 243:0.82 247:0.47 248:0.69 253:0.84 254:0.74 256:0.71 259:0.21 260:0.67 265:0.75 266:0.12 267:0.36 268:0.71 271:0.29 276:0.38 282:0.85 285:0.62 290:0.77 300:0.25 +2 6:0.92 8:0.83 9:0.80 12:0.86 17:0.26 20:0.96 21:0.05 27:0.12 28:0.47 34:0.73 36:0.92 37:0.67 39:0.95 41:0.90 43:0.33 66:0.36 69:0.62 74:0.43 78:0.97 81:0.84 83:0.77 91:0.93 96:0.94 98:0.12 100:0.98 104:0.84 108:0.47 111:0.97 120:0.95 123:0.46 126:0.32 127:0.08 129:0.05 135:0.58 138:0.96 140:0.87 145:0.80 146:0.05 147:0.05 149:0.13 150:0.64 151:0.98 152:0.98 153:0.92 154:0.24 155:0.67 159:0.05 161:0.65 162:0.69 164:0.93 167:0.66 169:0.96 172:0.05 173:0.93 175:0.75 177:0.05 181:0.86 186:0.96 187:0.87 189:0.93 191:0.91 192:0.59 202:0.89 208:0.64 215:0.91 216:0.68 220:0.62 222:0.60 235:0.05 238:0.95 241:0.55 243:0.51 244:0.61 248:0.94 253:0.90 255:0.79 256:0.91 257:0.65 259:0.21 260:0.95 261:0.97 265:0.13 267:0.87 268:0.91 271:0.29 277:0.69 283:0.71 285:0.62 297:0.36 +2 6:0.92 8:0.73 9:0.80 12:0.86 17:0.28 20:0.96 21:0.05 27:0.12 28:0.47 30:0.95 34:0.70 36:0.89 37:0.67 39:0.95 41:0.90 43:0.73 55:0.59 66:0.54 69:0.62 74:0.41 78:0.95 81:0.84 83:0.78 91:0.95 96:0.94 98:0.30 100:0.94 104:0.84 108:0.47 111:0.93 120:0.95 123:0.46 126:0.32 127:0.12 129:0.05 135:0.51 138:0.97 140:0.90 145:0.80 146:0.24 147:0.30 149:0.36 150:0.64 151:0.96 152:0.98 153:0.89 154:0.63 155:0.67 159:0.11 161:0.65 162:0.67 164:0.92 167:0.69 169:0.96 172:0.10 173:0.87 177:0.38 179:0.80 181:0.86 186:0.94 187:0.87 189:0.93 191:0.91 192:0.59 202:0.89 208:0.64 215:0.91 216:0.68 220:0.62 222:0.60 235:0.05 238:0.89 241:0.60 243:0.63 248:0.94 253:0.91 254:0.98 255:0.79 256:0.91 259:0.21 260:0.95 261:0.97 265:0.32 267:0.28 268:0.91 271:0.29 283:0.71 285:0.62 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.86 17:0.43 21:0.59 27:0.45 28:0.47 30:0.76 34:0.80 36:0.18 37:0.50 39:0.95 43:0.82 55:0.59 66:0.20 69:0.70 70:0.22 74:0.48 81:0.55 83:0.73 85:0.72 91:0.20 98:0.14 101:0.70 108:0.53 114:0.74 122:0.41 123:0.89 124:0.57 126:0.54 127:0.47 129:0.59 133:0.47 135:0.49 145:0.49 146:0.23 147:0.29 149:0.48 150:0.71 154:0.77 155:0.67 159:0.68 161:0.65 163:0.80 165:0.78 167:0.63 172:0.16 173:0.58 176:0.73 177:0.38 178:0.55 179:0.95 181:0.86 187:0.68 192:0.59 201:0.74 212:0.42 215:0.55 216:0.77 222:0.60 235:0.74 241:0.49 242:0.45 243:0.51 245:0.43 247:0.35 253:0.58 259:0.21 265:0.15 266:0.23 267:0.44 271:0.29 276:0.13 290:0.74 297:0.36 300:0.18 +1 1:0.74 6:0.88 7:0.78 8:0.59 9:0.80 12:0.86 17:0.59 20:0.98 21:0.40 25:0.88 27:0.03 28:0.47 30:0.13 32:0.77 34:0.75 36:0.66 37:0.66 39:0.95 43:0.91 55:0.59 66:0.35 69:0.17 70:0.79 74:0.82 77:0.70 78:0.94 81:0.64 91:0.69 96:0.88 98:0.77 100:0.93 104:0.58 108:0.14 111:0.92 114:0.82 120:0.88 123:0.79 124:0.61 126:0.54 127:0.74 129:0.05 133:0.39 135:0.54 138:0.97 140:0.80 145:0.69 146:0.86 147:0.89 149:0.89 150:0.69 151:0.92 152:0.99 153:0.86 154:0.90 155:0.67 158:0.87 159:0.73 161:0.65 162:0.83 164:0.87 167:0.61 169:0.95 172:0.76 173:0.14 176:0.73 177:0.38 179:0.33 181:0.86 186:0.93 187:0.21 189:0.82 191:0.77 192:0.59 195:0.86 201:0.74 202:0.79 208:0.64 212:0.58 215:0.67 216:0.47 222:0.60 230:0.81 233:0.76 235:0.52 238:0.88 241:0.41 242:0.77 243:0.51 245:0.94 247:0.55 248:0.89 253:0.90 255:0.79 256:0.83 259:0.21 260:0.88 261:0.96 265:0.67 266:0.63 267:0.69 268:0.84 271:0.29 276:0.82 277:0.69 279:0.86 282:0.85 283:0.71 285:0.62 290:0.82 297:0.36 300:0.70 +1 1:0.74 11:0.57 12:0.86 17:0.57 21:0.47 27:0.45 28:0.47 30:0.21 32:0.78 34:0.83 36:0.18 37:0.50 39:0.95 43:0.82 55:0.59 66:0.61 69:0.69 70:0.82 74:0.79 77:0.70 81:0.62 83:0.74 85:0.72 91:0.14 98:0.28 101:0.71 108:0.53 114:0.80 123:0.50 124:0.47 126:0.54 127:0.42 129:0.05 133:0.39 135:0.82 145:0.44 146:0.41 147:0.47 149:0.53 150:0.76 154:0.77 155:0.67 159:0.57 161:0.65 165:0.80 167:0.53 172:0.48 173:0.64 176:0.73 177:0.38 178:0.55 179:0.72 181:0.86 187:0.68 192:0.59 195:0.79 201:0.74 212:0.76 215:0.63 216:0.77 222:0.60 235:0.60 241:0.10 242:0.22 243:0.68 245:0.62 247:0.60 253:0.70 259:0.21 265:0.30 266:0.38 267:0.23 271:0.29 276:0.25 282:0.86 290:0.80 297:0.36 300:0.55 +3 6:0.92 8:0.83 9:0.80 12:0.86 17:0.55 20:0.77 21:0.22 27:0.12 28:0.47 30:0.33 34:0.68 36:0.77 37:0.67 39:0.95 41:0.82 43:0.28 55:0.59 66:0.86 69:0.62 70:0.65 74:0.74 78:0.96 81:0.35 83:0.76 91:0.89 96:0.95 98:0.88 100:0.97 104:0.84 108:0.47 111:0.97 114:0.72 120:0.91 123:0.46 126:0.32 127:0.42 129:0.05 135:0.71 138:0.97 140:0.92 145:0.80 146:0.78 147:0.81 149:0.36 150:0.31 151:0.97 152:0.98 153:0.90 154:0.74 155:0.67 158:0.84 159:0.34 161:0.65 162:0.60 164:0.86 167:0.67 169:0.96 172:0.61 173:0.72 176:0.56 177:0.38 179:0.67 181:0.86 186:0.95 187:0.68 189:0.94 191:0.87 192:0.59 202:0.89 208:0.64 212:0.69 216:0.68 220:0.82 222:0.60 235:0.22 238:0.95 241:0.57 242:0.54 243:0.87 247:0.67 248:0.91 253:0.95 254:0.86 255:0.79 256:0.90 259:0.21 260:0.91 261:0.97 265:0.88 266:0.38 267:0.79 268:0.90 271:0.29 276:0.49 285:0.62 290:0.72 300:0.43 +2 1:0.74 6:0.83 8:0.74 9:0.80 12:0.86 17:0.38 20:0.91 21:0.47 27:0.10 28:0.47 30:0.54 34:0.56 36:0.90 37:0.69 39:0.95 41:0.93 43:0.41 55:0.71 66:0.36 69:0.36 70:0.71 74:0.52 78:0.86 81:0.59 83:0.78 91:0.79 96:0.95 98:0.45 100:0.84 108:0.81 111:0.84 114:0.80 120:0.95 123:0.80 124:0.78 126:0.54 127:0.87 129:0.89 133:0.78 135:0.82 138:0.99 140:0.80 146:0.44 147:0.51 149:0.59 150:0.66 151:0.93 152:0.85 153:0.81 154:0.31 155:0.67 158:0.87 159:0.78 161:0.65 162:0.64 164:0.95 167:0.71 169:0.82 172:0.51 173:0.21 175:0.75 176:0.73 177:0.05 179:0.65 181:0.86 186:0.86 187:0.39 189:0.85 191:0.89 192:0.59 201:0.74 202:0.93 208:0.64 212:0.35 215:0.63 216:0.66 220:0.62 222:0.60 235:0.60 238:0.83 241:0.64 242:0.81 243:0.28 244:0.61 245:0.68 247:0.35 248:0.95 253:0.93 255:0.79 256:0.94 257:0.65 259:0.21 260:0.95 261:0.86 265:0.47 266:0.80 267:0.28 268:0.95 271:0.29 276:0.56 277:0.69 279:0.86 283:0.71 285:0.62 290:0.80 297:0.36 300:0.55 +0 7:0.78 8:0.62 12:0.86 17:0.57 21:0.30 27:0.21 28:0.47 30:0.33 34:0.73 36:0.89 37:0.74 39:0.95 43:0.45 55:0.84 66:0.09 69:0.95 70:0.81 74:0.34 81:0.60 91:0.68 98:0.33 104:0.84 106:0.81 108:0.97 120:0.94 123:0.98 124:0.98 126:0.32 127:0.87 129:0.59 133:0.98 135:0.20 140:0.45 146:0.19 147:0.58 149:0.76 150:0.45 151:0.83 153:0.96 154:0.24 159:0.96 161:0.65 162:0.56 163:0.92 164:0.90 167:0.63 172:0.73 173:0.72 177:0.05 179:0.16 181:0.86 187:0.39 190:0.77 191:0.77 202:0.89 206:0.81 212:0.16 215:0.72 216:0.95 222:0.60 230:0.81 233:0.76 235:0.31 241:0.49 242:0.82 243:0.11 245:0.91 247:0.39 248:0.92 253:0.31 254:0.86 256:0.88 257:0.65 259:0.21 260:0.95 265:0.35 266:0.97 267:0.96 268:0.88 271:0.29 276:0.95 283:0.82 285:0.50 297:0.36 300:0.84 +2 6:0.97 8:0.85 11:0.57 12:0.01 17:0.76 20:0.79 21:0.78 27:0.07 28:0.66 30:0.76 34:0.40 36:0.71 37:0.96 39:0.33 43:0.94 66:0.86 69:0.12 70:0.40 74:0.96 78:0.84 85:0.98 89:0.98 91:0.22 98:0.55 100:0.85 101:0.86 108:0.10 111:0.83 122:0.43 123:0.10 124:0.37 127:0.86 129:0.59 133:0.47 135:0.78 141:0.91 146:0.77 147:0.81 149:0.98 152:0.96 154:0.94 159:0.73 161:0.52 167:0.56 169:0.98 172:0.86 173:0.13 177:0.38 178:0.55 179:0.39 181:0.89 186:0.85 187:0.39 189:0.92 202:0.36 212:0.94 216:0.41 222:0.60 223:0.92 225:0.96 232:0.75 234:0.91 238:0.84 240:0.95 241:0.18 242:0.62 243:0.86 245:0.75 247:0.39 253:0.68 259:0.21 261:0.98 265:0.56 266:0.23 267:0.28 271:0.79 274:0.91 276:0.59 300:0.43 +1 1:0.74 11:0.57 12:0.01 17:0.94 27:0.72 28:0.66 30:0.62 34:0.25 36:0.28 39:0.33 43:0.88 66:0.75 69:0.52 70:0.23 74:0.69 81:0.94 83:0.80 85:0.80 89:0.96 91:0.78 98:0.69 101:0.81 104:0.58 108:0.40 114:0.98 122:0.51 123:0.38 126:0.54 127:0.21 129:0.05 135:0.34 141:0.91 146:0.30 147:0.36 149:0.68 150:0.97 154:0.86 155:0.67 159:0.12 161:0.52 165:0.92 167:0.86 172:0.32 173:0.91 176:0.73 177:0.38 178:0.55 179:0.79 181:0.89 192:0.59 201:0.74 212:0.84 215:0.96 222:0.60 223:0.93 225:0.97 232:0.82 234:0.91 235:0.05 240:0.95 241:0.78 242:0.33 243:0.77 247:0.39 253:0.76 259:0.21 265:0.69 266:0.17 267:0.23 271:0.79 274:0.91 276:0.25 290:0.98 297:0.36 300:0.18 +4 1:0.74 7:0.81 8:0.95 9:0.80 11:0.57 12:0.01 17:0.53 27:0.27 28:0.66 30:0.91 32:0.80 34:0.18 36:0.56 37:0.90 39:0.33 41:0.98 43:0.90 66:0.27 69:0.44 70:0.13 74:0.70 76:0.85 77:0.89 81:0.94 83:0.89 85:0.80 89:0.96 91:0.93 96:0.97 98:0.11 101:0.77 104:0.58 108:0.34 114:0.98 120:0.93 121:0.90 122:0.43 123:0.33 124:0.61 126:0.54 127:0.78 129:0.59 133:0.47 135:0.77 140:0.45 141:0.97 145:0.96 146:0.13 147:0.18 149:0.62 150:0.97 151:0.99 153:0.96 154:0.89 155:0.67 159:0.43 161:0.52 162:0.58 163:0.88 164:0.90 165:0.92 167:0.89 172:0.10 173:0.52 176:0.73 177:0.38 179:0.98 181:0.89 191:0.89 192:0.59 195:0.63 201:0.74 202:0.88 204:0.89 208:0.64 212:0.61 215:0.96 216:0.39 222:0.60 223:0.93 225:0.97 230:0.87 232:0.82 233:0.76 234:0.98 235:0.05 240:0.99 241:0.85 242:0.63 243:0.46 245:0.40 247:0.30 248:0.97 253:0.97 255:0.79 256:0.87 259:0.21 260:0.93 265:0.11 266:0.17 267:0.52 268:0.88 271:0.79 274:0.99 276:0.12 282:0.88 285:0.62 290:0.98 297:0.36 299:0.97 300:0.13 +1 1:0.74 7:0.81 8:0.75 9:0.80 11:0.57 12:0.01 17:0.43 20:0.82 21:0.30 25:0.88 27:0.71 28:0.66 30:0.88 34:0.87 36:0.27 37:0.60 39:0.33 41:0.88 43:0.98 55:0.42 66:0.98 69:0.19 70:0.31 74:0.85 78:0.81 81:0.81 83:0.78 85:0.90 89:0.98 91:0.54 96:0.82 98:0.90 100:0.78 101:0.83 108:0.15 111:0.80 114:0.68 117:0.86 120:0.72 121:0.90 123:0.15 126:0.54 127:0.46 129:0.05 135:0.87 138:0.95 140:0.45 141:0.97 146:0.79 147:0.82 149:0.96 150:0.87 151:0.87 152:0.78 153:0.48 154:0.98 155:0.67 159:0.35 161:0.52 162:0.86 164:0.69 165:0.81 167:0.58 169:0.80 172:0.94 173:0.33 176:0.56 177:0.38 179:0.19 181:0.89 186:0.77 187:0.68 192:0.59 201:0.74 202:0.53 204:0.89 208:0.64 212:0.93 215:0.67 216:0.56 220:0.87 222:0.60 223:0.93 225:0.97 230:0.87 232:0.81 233:0.76 234:1.00 235:0.68 238:0.87 240:0.99 241:0.30 242:0.78 243:0.98 247:0.60 248:0.79 253:0.85 255:0.79 256:0.58 260:0.72 261:0.76 265:0.90 266:0.38 267:0.69 268:0.62 271:0.79 274:0.98 276:0.88 285:0.62 286:0.99 290:0.68 297:0.36 300:0.70 +1 1:0.74 11:0.57 12:0.01 17:0.79 21:0.13 27:0.40 28:0.66 30:0.87 34:0.80 36:0.53 37:0.69 39:0.33 43:0.93 66:0.54 69:0.33 70:0.27 74:0.69 81:0.86 83:0.77 85:0.88 89:0.97 91:0.23 98:0.62 101:0.83 108:0.26 114:0.92 122:0.43 123:0.25 124:0.48 126:0.54 127:0.47 129:0.59 133:0.47 135:0.44 141:0.91 146:0.41 147:0.48 149:0.83 150:0.91 154:0.92 155:0.67 159:0.24 161:0.52 163:0.75 165:0.88 167:0.61 172:0.32 173:0.56 176:0.73 177:0.38 178:0.55 179:0.88 181:0.89 187:0.68 192:0.59 201:0.74 212:0.42 215:0.84 216:0.19 222:0.60 223:0.93 225:0.97 232:0.77 234:0.91 235:0.22 240:0.95 241:0.39 242:0.63 243:0.63 245:0.50 247:0.30 253:0.73 259:0.21 265:0.63 266:0.27 267:0.28 271:0.79 274:0.91 276:0.29 290:0.92 297:0.36 300:0.25 +2 1:0.74 6:0.86 8:0.73 9:0.80 11:0.57 12:0.01 17:0.85 20:0.85 21:0.64 27:0.72 28:0.66 30:0.62 34:0.34 36:0.43 39:0.33 41:0.88 43:0.96 60:0.87 66:0.75 69:0.29 70:0.23 74:0.76 78:0.78 81:0.56 83:0.83 85:0.80 89:0.96 91:0.72 96:0.78 98:0.89 100:0.80 101:0.81 104:0.58 108:0.22 111:0.77 114:0.72 120:0.66 122:0.51 123:0.22 126:0.54 127:0.44 129:0.05 135:0.24 140:0.45 141:0.97 146:0.34 147:0.40 149:0.70 150:0.70 151:0.77 152:0.80 153:0.48 154:0.96 155:0.67 158:0.92 159:0.13 161:0.52 162:0.74 164:0.67 165:0.70 167:0.87 169:0.78 172:0.32 173:0.80 176:0.73 177:0.38 179:0.92 181:0.89 186:0.78 189:0.87 192:0.59 201:0.74 202:0.56 208:0.64 212:0.30 215:0.53 216:0.07 220:0.62 222:0.60 223:0.93 225:0.97 232:0.82 234:0.99 235:0.80 238:0.87 240:0.99 241:0.80 242:0.33 243:0.77 247:0.39 248:0.71 253:0.80 255:0.79 256:0.58 259:0.21 260:0.67 261:0.78 265:0.89 266:0.27 267:0.17 268:0.59 271:0.79 274:0.98 276:0.22 279:0.86 283:0.82 285:0.62 290:0.72 297:0.36 300:0.18 +1 11:0.57 12:0.01 17:0.45 21:0.78 27:0.45 28:0.66 30:0.68 34:0.76 36:0.19 37:0.50 39:0.33 43:0.78 66:0.36 69:0.78 70:0.61 74:0.73 85:0.91 89:0.97 91:0.14 98:0.26 101:0.77 108:0.70 122:0.43 123:0.68 124:0.83 127:0.53 129:0.81 133:0.83 135:0.73 141:0.91 145:0.47 146:0.13 147:0.18 149:0.82 154:0.71 159:0.80 161:0.52 163:0.88 167:0.63 172:0.41 173:0.60 177:0.38 178:0.55 179:0.71 181:0.89 187:0.68 212:0.29 216:0.77 222:0.60 223:0.92 225:0.96 234:0.91 235:0.88 240:0.95 241:0.46 242:0.54 243:0.21 245:0.56 247:0.39 253:0.54 259:0.21 265:0.29 266:0.71 267:0.88 271:0.79 274:0.91 276:0.45 300:0.55 +1 6:0.90 8:0.78 9:0.80 12:0.01 17:0.59 20:0.94 21:0.78 27:0.15 28:0.66 34:0.47 36:0.22 37:0.72 39:0.33 41:0.88 78:0.92 83:0.78 89:0.95 91:0.91 96:0.87 100:0.94 111:0.92 120:0.79 135:0.76 140:0.45 141:0.97 151:0.93 152:0.88 153:0.78 155:0.67 161:0.52 162:0.60 164:0.71 167:0.91 169:0.92 175:0.91 181:0.89 186:0.92 189:0.91 192:0.59 202:0.65 208:0.64 216:0.05 222:0.60 223:0.93 225:0.97 232:0.72 234:0.98 238:0.91 240:0.99 241:0.96 244:0.83 248:0.86 253:0.82 255:0.79 256:0.58 257:0.84 259:0.21 260:0.80 261:0.93 267:0.36 268:0.65 271:0.79 274:0.98 277:0.87 285:0.62 +0 1:0.74 9:0.80 11:0.57 12:0.01 17:0.83 27:0.27 28:0.66 30:0.89 34:0.30 36:0.36 37:0.90 39:0.33 41:0.82 43:0.78 66:0.75 69:0.77 70:0.20 74:0.58 81:0.94 83:0.81 85:0.85 89:0.97 91:0.57 96:0.80 98:0.52 101:0.81 104:0.58 108:0.60 114:0.98 120:0.69 122:0.43 123:0.58 126:0.54 127:0.16 129:0.05 135:0.47 140:0.80 141:0.97 146:0.40 147:0.46 149:0.71 150:0.97 151:0.87 153:0.48 154:0.71 155:0.67 159:0.15 161:0.52 162:0.54 164:0.57 165:0.92 167:0.63 172:0.32 173:0.93 176:0.73 177:0.38 178:0.42 179:0.61 181:0.89 187:0.39 192:0.59 201:0.74 202:0.56 208:0.64 212:0.30 215:0.96 216:0.39 222:0.60 223:0.93 225:0.97 232:0.82 234:0.99 235:0.05 240:0.99 241:0.47 242:0.63 243:0.77 247:0.30 248:0.78 253:0.83 255:0.79 256:0.45 259:0.21 260:0.70 265:0.54 266:0.27 267:0.98 268:0.46 271:0.79 274:0.97 276:0.22 285:0.62 290:0.98 297:0.36 300:0.18 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.01 17:0.76 21:0.05 27:0.72 28:0.66 30:0.85 32:0.80 34:0.70 36:0.69 39:0.33 41:0.90 43:0.93 66:0.49 69:0.32 70:0.28 74:0.88 76:0.94 77:0.70 81:0.90 83:0.80 85:0.94 89:0.97 91:0.72 96:0.80 98:0.70 101:0.85 104:0.54 108:0.25 114:0.95 120:0.69 121:0.99 122:0.43 123:0.24 124:0.56 126:0.54 127:0.82 129:0.59 133:0.47 135:0.42 140:0.45 141:0.97 145:0.88 146:0.59 147:0.65 149:0.92 150:0.94 151:0.81 153:0.72 154:0.92 155:0.67 159:0.38 161:0.52 162:0.83 164:0.72 165:0.90 167:0.87 172:0.48 173:0.44 176:0.73 177:0.38 179:0.74 181:0.89 192:0.59 195:0.62 201:0.74 202:0.59 204:0.89 208:0.64 212:0.48 215:0.91 216:0.02 220:0.62 222:0.60 223:0.93 225:0.97 230:0.87 232:0.78 233:0.76 234:0.99 235:0.12 240:0.99 241:0.79 242:0.63 243:0.60 245:0.71 247:0.30 248:0.77 253:0.87 255:0.79 256:0.64 259:0.21 260:0.70 265:0.70 266:0.38 267:0.23 268:0.66 271:0.79 274:0.98 276:0.50 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.25 +1 1:0.74 11:0.57 12:0.01 17:0.32 21:0.13 27:0.45 28:0.66 30:0.68 34:0.70 36:0.17 37:0.50 39:0.33 43:0.82 60:0.87 66:0.35 69:0.68 70:0.41 74:0.87 81:0.86 83:0.85 85:0.94 89:0.97 91:0.20 98:0.53 101:0.82 108:0.75 114:0.92 122:0.57 123:0.74 124:0.70 126:0.54 127:0.45 129:0.59 133:0.64 135:0.89 141:0.91 145:0.52 146:0.52 147:0.58 149:0.90 150:0.91 154:0.77 155:0.67 158:0.92 159:0.60 161:0.52 163:0.81 165:0.88 167:0.57 172:0.51 173:0.61 176:0.73 177:0.38 178:0.55 179:0.54 181:0.89 187:0.68 192:0.59 201:0.74 208:0.64 212:0.30 215:0.84 216:0.77 222:0.60 223:0.93 225:0.97 234:0.91 235:0.22 240:0.95 241:0.24 242:0.55 243:0.43 245:0.74 247:0.47 253:0.77 259:0.21 265:0.55 266:0.71 267:0.36 271:0.79 274:0.91 276:0.59 279:0.86 283:0.82 290:0.92 297:0.36 300:0.33 +0 1:0.74 6:0.97 8:0.96 9:0.80 11:0.57 12:0.01 17:0.24 20:0.97 27:0.07 28:0.66 30:0.95 34:0.42 36:0.79 37:0.96 39:0.33 41:0.98 43:0.88 60:0.87 66:0.54 69:0.50 74:0.57 78:0.95 81:0.94 83:0.90 85:0.72 89:0.96 91:0.94 96:0.98 98:0.17 100:0.99 101:0.77 108:0.38 111:0.99 114:0.98 120:0.96 123:0.37 126:0.54 127:0.10 129:0.05 135:0.32 140:0.45 141:0.97 146:0.13 147:0.18 149:0.50 150:0.97 151:0.99 152:0.96 153:0.97 154:0.87 155:0.67 158:0.92 159:0.08 161:0.52 162:0.66 164:0.92 165:0.92 167:0.90 169:0.98 172:0.10 173:0.89 176:0.73 177:0.38 179:0.20 181:0.89 186:0.94 189:0.98 191:0.86 192:0.59 201:0.74 202:0.89 208:0.64 215:0.96 216:0.41 222:0.60 223:0.93 225:0.97 232:0.75 234:0.98 235:0.05 238:0.98 240:0.99 241:0.89 243:0.63 248:0.98 253:0.97 255:0.79 256:0.90 259:0.21 260:0.96 261:0.98 265:0.18 267:0.96 268:0.90 271:0.79 274:1.00 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.01 17:0.90 21:0.71 27:0.72 28:0.66 30:0.91 34:0.34 36:0.50 39:0.33 43:0.87 55:0.52 66:0.69 69:0.53 70:0.13 74:0.63 81:0.50 83:0.73 85:0.72 89:0.96 91:0.79 98:0.51 101:0.77 104:0.54 108:0.41 114:0.68 122:0.57 123:0.39 126:0.54 127:0.15 129:0.05 135:0.26 141:0.91 146:0.22 147:0.28 149:0.50 150:0.66 154:0.85 155:0.67 159:0.11 161:0.52 165:0.69 167:0.82 172:0.21 173:0.98 176:0.73 177:0.38 178:0.55 179:0.75 181:0.89 187:0.21 192:0.59 201:0.74 212:0.61 215:0.48 222:0.60 223:0.92 225:0.96 232:0.78 234:0.91 235:0.88 240:0.95 241:0.76 242:0.63 243:0.73 247:0.30 253:0.58 259:0.21 265:0.52 266:0.17 267:0.17 271:0.79 274:0.91 276:0.21 290:0.68 297:0.36 300:0.13 +1 1:0.74 11:0.57 12:0.01 17:0.96 27:0.72 28:0.66 30:0.62 34:0.24 36:0.32 39:0.33 43:0.88 66:0.75 69:0.52 70:0.23 74:0.69 81:0.94 83:0.80 85:0.80 89:0.96 91:0.78 98:0.69 101:0.81 104:0.58 108:0.40 114:0.98 122:0.51 123:0.38 126:0.54 127:0.21 129:0.05 135:0.37 141:0.91 146:0.30 147:0.36 149:0.68 150:0.97 154:0.86 155:0.67 159:0.12 161:0.52 165:0.92 167:0.85 172:0.32 173:0.91 176:0.73 177:0.38 178:0.55 179:0.79 181:0.89 192:0.59 201:0.74 212:0.84 215:0.96 222:0.60 223:0.93 225:0.97 232:0.82 234:0.91 235:0.05 240:0.95 241:0.78 242:0.33 243:0.77 247:0.39 253:0.76 259:0.21 265:0.69 266:0.17 267:0.23 271:0.79 274:0.91 276:0.25 290:0.98 297:0.36 300:0.18 +1 1:0.74 9:0.80 11:0.57 12:0.01 17:0.57 21:0.05 27:0.40 28:0.66 30:0.91 34:0.67 36:0.54 37:0.69 39:0.33 41:0.88 43:0.92 66:0.69 69:0.37 70:0.13 74:0.56 81:0.90 83:0.80 85:0.80 89:0.96 91:0.54 96:0.85 98:0.87 101:0.80 108:0.29 114:0.95 120:0.76 122:0.43 123:0.28 126:0.54 127:0.39 129:0.05 135:0.34 140:0.45 141:0.97 146:0.44 147:0.51 149:0.68 150:0.94 151:0.87 153:0.48 154:0.91 155:0.67 159:0.16 161:0.52 162:0.86 163:0.75 164:0.67 165:0.90 167:0.74 172:0.21 173:0.74 176:0.73 177:0.38 179:0.97 181:0.89 187:0.68 192:0.59 201:0.74 202:0.50 208:0.64 212:0.61 215:0.91 216:0.19 220:0.62 222:0.60 223:0.93 225:0.97 232:0.77 234:1.00 235:0.12 240:0.99 241:0.69 242:0.63 243:0.73 247:0.30 248:0.83 253:0.84 255:0.79 256:0.58 259:0.21 260:0.77 265:0.87 266:0.17 267:0.73 268:0.59 271:0.79 274:0.98 276:0.13 285:0.62 290:0.95 297:0.36 300:0.13 +1 1:0.74 6:0.86 7:0.81 9:0.80 11:0.57 12:0.01 17:0.66 20:0.80 21:0.05 27:0.40 28:0.66 30:0.91 34:0.67 36:0.49 37:0.69 39:0.33 41:0.88 43:0.92 66:0.69 69:0.33 70:0.13 74:0.72 76:0.85 77:0.70 78:0.88 81:0.90 83:0.81 85:0.80 89:0.96 91:0.61 96:0.88 98:0.83 100:0.73 101:0.80 108:0.26 111:0.85 114:0.95 120:0.79 121:0.90 122:0.43 123:0.25 126:0.54 127:0.32 129:0.05 135:0.34 140:0.45 141:0.97 145:0.93 146:0.41 147:0.48 149:0.68 150:0.94 151:0.93 152:0.84 153:0.78 154:0.92 155:0.67 159:0.15 161:0.52 162:0.61 163:0.75 164:0.71 165:0.90 167:0.83 169:0.79 172:0.21 173:0.70 176:0.73 177:0.38 179:0.96 181:0.89 186:0.88 187:0.39 191:0.84 192:0.59 195:0.54 201:0.74 202:0.65 204:0.89 208:0.64 212:0.61 215:0.91 216:0.19 222:0.60 223:0.93 225:0.97 230:0.87 232:0.77 233:0.76 234:0.98 235:0.12 240:0.99 241:0.76 242:0.63 243:0.73 247:0.30 248:0.87 253:0.88 255:0.79 256:0.58 259:0.21 260:0.80 261:0.84 265:0.83 266:0.17 267:0.44 268:0.65 271:0.79 274:0.98 276:0.13 282:0.84 285:0.62 290:0.95 297:0.36 300:0.13 +1 1:0.74 6:0.88 8:0.74 9:0.80 11:0.57 12:0.01 17:0.88 20:0.94 21:0.71 27:0.33 28:0.66 30:0.76 32:0.80 34:0.45 36:0.41 37:0.70 39:0.33 41:0.90 43:0.86 66:0.36 69:0.60 70:0.22 74:0.74 76:0.99 77:0.98 78:0.81 81:0.50 83:0.76 85:0.80 89:0.96 91:0.73 96:0.87 98:0.32 100:0.85 101:0.78 104:0.58 108:0.65 111:0.81 114:0.68 120:0.73 122:0.51 123:0.63 124:0.57 126:0.54 127:0.41 129:0.59 133:0.47 135:0.30 140:0.80 141:0.97 145:0.85 146:0.13 147:0.18 149:0.65 150:0.66 151:0.81 152:0.91 153:0.72 154:0.84 155:0.67 159:0.26 161:0.52 162:0.84 163:0.88 164:0.78 165:0.69 167:0.89 169:0.83 172:0.16 173:0.78 176:0.73 177:0.38 179:0.95 181:0.89 186:0.82 189:0.89 192:0.59 195:0.58 201:0.74 202:0.73 208:0.64 212:0.42 215:0.48 216:0.22 220:0.82 222:0.60 223:0.93 225:0.97 232:0.82 234:0.98 235:0.88 238:0.90 240:0.99 241:0.86 242:0.45 243:0.40 245:0.43 247:0.35 248:0.80 253:0.87 255:0.79 256:0.77 259:0.21 260:0.74 261:0.85 265:0.34 266:0.23 267:0.28 268:0.79 271:0.79 274:0.98 276:0.16 282:0.88 285:0.62 290:0.68 297:0.36 299:0.88 300:0.18 +1 1:0.68 10:0.96 11:0.81 12:0.69 17:0.51 18:0.92 21:0.13 22:0.93 27:0.25 29:0.77 30:0.57 33:0.97 34:0.24 36:0.24 37:0.89 39:0.51 43:0.48 45:0.94 47:0.93 64:0.77 66:0.33 69:0.55 70:0.62 71:0.91 74:0.48 81:0.76 83:0.69 86:0.88 91:0.20 98:0.66 99:0.95 101:0.65 104:0.54 108:0.42 114:0.92 122:0.93 123:0.40 124:0.79 126:0.54 127:0.80 129:0.59 131:0.61 133:0.78 135:0.32 139:0.83 144:0.85 146:0.89 147:0.91 149:0.66 150:0.63 154:0.60 155:0.52 157:0.99 159:0.78 165:0.81 166:0.86 170:0.82 172:0.73 173:0.37 174:0.95 176:0.70 177:0.88 178:0.55 179:0.35 182:1.00 187:0.21 192:0.54 197:0.96 201:0.67 208:0.64 212:0.28 215:0.84 216:0.36 222:0.48 227:0.61 229:0.61 231:0.78 235:0.42 236:0.95 239:0.94 241:0.05 242:0.18 243:0.50 244:0.61 245:0.88 246:1.00 247:0.94 253:0.63 254:0.86 259:0.21 262:0.93 265:0.66 266:0.80 267:0.59 271:0.61 276:0.81 287:0.61 290:0.92 291:0.97 297:0.36 300:0.70 +2 1:0.61 8:0.77 9:0.72 10:0.94 11:0.55 12:0.69 17:0.15 18:0.95 21:0.54 23:0.99 27:0.14 29:0.77 30:0.45 31:0.92 34:0.96 36:0.71 37:0.82 39:0.51 43:0.27 44:0.88 45:0.93 48:0.91 55:0.52 62:0.98 64:0.77 66:0.48 69:0.39 70:0.73 71:0.91 74:0.33 75:0.97 76:0.85 79:0.92 81:0.53 83:0.56 86:0.87 91:0.70 96:0.89 97:0.89 98:0.45 99:0.83 101:0.47 102:0.95 104:0.89 108:0.87 114:0.67 120:0.61 122:0.93 123:0.86 124:0.83 126:0.51 127:0.72 128:0.97 129:0.05 131:0.90 133:0.86 135:0.20 137:0.92 138:0.98 139:0.87 140:0.87 144:0.85 145:0.56 146:0.43 147:0.49 149:0.51 150:0.45 151:0.56 153:0.72 154:0.48 155:0.65 157:0.86 158:0.73 159:0.76 162:0.73 164:0.54 165:0.65 166:0.77 168:0.97 170:0.82 172:0.82 173:0.23 174:0.95 176:0.59 177:0.88 179:0.29 182:0.79 187:0.68 190:0.95 192:0.87 193:0.97 195:0.88 196:0.94 197:0.94 201:0.60 202:0.83 204:0.80 205:0.98 208:0.64 212:0.80 216:0.84 219:0.91 220:0.82 222:0.48 226:0.95 227:0.95 229:0.88 231:0.78 235:0.74 236:0.94 239:0.95 241:0.63 242:0.29 243:0.27 244:0.61 245:0.85 246:0.92 247:0.92 248:0.58 253:0.84 254:0.99 255:0.71 256:0.85 259:0.21 260:0.62 265:0.47 266:0.84 267:0.94 268:0.87 271:0.61 276:0.83 277:0.69 279:0.78 281:0.91 284:0.97 285:0.62 287:0.94 290:0.67 292:0.86 300:0.84 +1 1:0.65 7:0.81 8:0.63 10:0.93 11:0.81 12:0.69 17:0.81 18:0.89 21:0.40 27:0.53 29:0.77 30:0.55 32:0.71 34:0.86 36:0.75 37:0.28 39:0.51 43:0.44 44:0.92 45:0.90 64:0.77 66:0.92 69:0.61 70:0.81 71:0.91 74:0.64 77:0.70 81:0.71 83:0.69 86:0.83 91:0.20 97:0.89 98:0.90 99:0.95 101:0.65 102:0.97 104:0.94 106:0.81 108:0.46 114:0.82 121:0.90 122:0.76 123:0.44 126:0.54 127:0.46 129:0.05 131:0.61 132:0.97 135:0.96 139:0.88 144:0.85 145:0.85 146:0.85 147:0.87 149:0.66 150:0.56 154:0.47 155:0.57 157:0.99 158:0.75 159:0.41 165:0.81 166:0.85 170:0.82 172:0.79 173:0.66 174:0.91 176:0.70 177:0.88 178:0.55 179:0.45 182:1.00 187:0.39 192:0.85 193:1.00 195:0.65 197:0.94 201:0.65 204:0.84 206:0.81 208:0.64 212:0.70 215:0.67 216:0.34 222:0.48 227:0.61 229:0.61 230:0.87 231:0.78 233:0.76 235:0.68 236:0.95 239:0.96 241:0.07 242:0.13 243:0.93 244:0.83 246:1.00 247:0.93 253:0.61 254:0.74 259:0.21 265:0.90 266:0.54 267:0.79 271:0.61 276:0.68 279:0.76 281:0.91 282:0.80 283:0.82 287:0.61 290:0.82 297:0.36 300:0.70 +2 7:0.69 8:0.88 9:0.75 10:0.88 11:0.81 12:0.69 17:0.24 18:0.73 21:0.47 23:0.97 25:0.88 27:0.25 29:0.77 30:0.75 31:0.93 32:0.65 34:0.63 36:0.71 37:0.55 39:0.51 43:0.26 45:0.90 62:0.96 66:0.68 69:0.80 70:0.41 71:0.91 74:0.60 77:0.70 79:0.93 81:0.30 83:0.69 86:0.72 87:0.98 91:0.95 96:0.99 97:1.00 98:0.70 101:0.65 104:0.58 108:0.64 120:0.98 122:0.55 123:0.62 124:0.44 126:0.24 127:0.34 128:0.96 129:0.05 131:0.90 133:0.39 135:0.65 137:0.93 139:0.69 140:0.45 144:0.85 145:0.72 146:0.72 147:0.77 149:0.56 150:0.33 151:0.99 153:0.97 154:0.50 155:0.58 157:0.99 158:0.76 159:0.40 162:0.68 164:0.97 166:0.74 168:0.96 170:0.82 172:0.67 173:0.85 174:0.83 177:0.88 179:0.48 182:1.00 190:0.95 191:0.94 192:0.86 195:0.68 196:0.90 197:0.86 202:0.95 204:0.80 205:0.97 208:0.64 212:0.39 215:0.63 216:0.49 219:0.87 220:0.62 222:0.48 227:0.95 229:1.00 230:0.71 231:0.78 232:0.80 233:0.76 235:0.52 239:0.87 241:0.91 242:0.42 243:0.72 244:0.61 245:0.74 246:0.61 247:0.76 248:0.99 253:0.98 254:0.88 255:0.73 256:0.96 259:0.21 260:0.98 265:0.71 266:0.47 267:0.82 268:0.96 271:0.61 276:0.60 279:0.82 282:0.74 283:0.82 284:0.95 285:0.62 287:1.00 292:0.88 297:0.36 300:0.43 +1 1:0.63 7:0.65 8:0.78 10:0.96 11:0.81 12:0.69 17:0.26 18:0.96 21:0.40 23:0.95 27:0.15 29:0.77 30:0.37 34:0.70 36:0.53 37:0.58 39:0.51 43:0.57 44:0.85 45:0.95 62:0.97 64:0.77 66:0.67 69:0.23 70:0.83 71:0.91 74:0.42 75:0.97 76:0.85 81:0.56 83:0.69 86:0.91 91:0.29 97:0.99 98:0.85 99:0.83 101:0.65 108:0.73 114:0.70 120:0.61 122:0.93 123:0.71 124:0.50 126:0.51 127:0.78 128:0.96 129:0.59 131:0.83 133:0.60 135:0.95 139:0.89 140:0.45 144:0.85 145:0.59 146:0.54 147:0.60 149:0.69 150:0.47 151:0.53 153:0.48 154:0.49 155:0.52 157:0.98 158:0.76 159:0.74 162:0.86 164:0.66 165:0.65 166:0.78 168:0.96 170:0.82 172:0.90 173:0.17 174:0.96 176:0.59 177:0.88 179:0.26 182:0.94 187:0.87 190:0.98 191:0.73 192:0.55 193:0.98 195:0.85 197:0.96 201:0.61 202:0.56 204:0.80 205:0.95 208:0.64 212:0.60 216:0.50 219:0.91 220:0.91 222:0.48 226:0.96 227:0.90 229:0.61 230:0.67 231:0.78 233:0.65 235:0.60 236:0.94 239:0.96 241:0.24 242:0.23 243:0.31 244:0.61 245:0.91 246:0.92 247:0.97 248:0.57 253:0.53 254:0.86 256:0.64 259:0.21 260:0.61 265:0.85 266:0.54 267:0.59 268:0.65 271:0.61 276:0.87 279:0.82 281:0.86 283:0.82 285:0.50 287:0.61 290:0.70 292:0.88 300:0.70 +1 10:0.87 11:0.81 12:0.69 17:0.47 18:0.94 21:0.22 27:0.45 29:0.77 30:0.10 34:0.75 36:0.18 37:0.50 39:0.51 43:0.26 45:0.87 55:0.92 64:0.77 66:0.51 69:0.79 70:0.95 71:0.91 74:0.48 81:0.28 86:0.74 91:0.06 98:0.46 101:0.65 108:0.63 122:0.92 123:0.61 124:0.91 126:0.24 127:0.67 129:0.05 131:0.61 133:0.94 135:0.89 139:0.71 144:0.85 145:0.54 146:0.86 147:0.05 149:0.44 150:0.31 154:0.50 157:0.96 159:0.87 166:0.72 170:0.82 172:0.78 173:0.56 174:0.83 177:0.05 178:0.55 179:0.36 182:0.88 187:0.68 197:0.83 212:0.47 216:0.77 222:0.48 227:0.61 229:0.61 231:0.70 235:0.22 239:0.86 241:0.32 242:0.60 243:0.07 244:0.61 245:0.70 246:0.61 247:0.88 253:0.40 254:0.86 257:0.93 259:0.21 265:0.48 266:0.89 267:0.73 271:0.61 276:0.78 287:0.61 300:0.84 +3 1:0.58 7:0.64 8:0.95 9:0.72 12:0.69 17:0.11 21:0.47 23:1.00 27:0.19 29:0.77 31:0.96 34:0.71 36:0.39 37:0.69 39:0.51 43:0.10 44:0.88 62:0.97 64:0.77 66:0.36 69:0.51 71:0.91 79:0.95 81:0.55 83:0.69 91:1.00 96:0.88 98:0.10 99:0.83 102:0.95 104:0.58 108:0.39 120:0.95 123:0.38 126:0.51 127:0.07 128:0.98 129:0.05 131:0.90 135:0.24 137:0.96 139:0.69 140:0.90 144:0.85 145:0.91 146:0.05 147:0.05 149:0.06 150:0.44 151:0.57 153:0.46 154:0.09 155:0.63 157:0.61 159:0.05 162:0.58 164:0.96 165:0.65 166:0.79 168:0.98 170:0.82 172:0.05 173:0.66 175:0.97 177:0.05 178:0.42 182:1.00 190:0.95 191:0.96 192:0.86 193:0.97 195:0.59 196:0.91 201:0.60 202:0.96 204:0.82 205:1.00 208:0.61 215:0.63 216:0.56 220:0.62 222:0.48 227:0.96 229:1.00 230:0.64 231:0.79 233:0.66 235:0.68 236:0.94 239:0.87 241:0.95 243:0.51 244:0.97 246:0.92 248:0.68 253:0.83 255:0.71 256:0.97 257:0.93 259:0.21 260:0.95 265:0.10 267:0.87 268:0.96 271:0.61 277:0.95 281:0.91 283:0.67 284:0.99 285:0.62 287:1.00 297:0.36 +3 1:0.59 8:0.91 9:0.75 11:0.55 12:0.69 17:0.49 18:0.71 21:0.40 23:0.97 27:0.25 29:0.77 30:0.45 31:0.93 34:0.67 36:0.42 37:0.89 39:0.51 41:0.82 43:0.56 45:0.77 66:0.45 69:0.43 70:0.47 71:0.91 74:0.48 79:0.92 81:0.50 83:0.69 86:0.60 91:0.94 96:0.98 97:0.97 98:0.26 99:0.83 101:0.47 104:0.54 108:0.33 114:0.76 120:0.97 122:0.55 123:0.32 124:0.67 126:0.32 127:0.54 128:0.97 129:0.05 131:0.90 133:0.62 135:0.49 137:0.93 139:0.59 140:0.99 144:0.85 145:0.54 146:0.28 147:0.35 149:0.47 150:0.44 151:0.72 153:0.93 154:0.45 155:0.65 157:0.87 158:0.75 159:0.38 162:0.68 164:0.95 165:0.65 166:0.80 168:0.97 170:0.82 172:0.32 173:0.51 175:0.91 176:0.62 177:0.38 179:0.84 182:1.00 190:0.95 191:0.95 192:0.98 196:0.87 201:0.56 202:0.93 205:0.98 208:0.64 212:0.50 215:0.67 216:0.36 220:0.62 222:0.48 227:0.96 229:1.00 231:0.79 235:0.52 241:0.83 242:0.53 243:0.58 244:0.93 245:0.52 246:0.96 247:0.47 248:0.68 253:0.97 255:0.74 256:0.94 257:0.84 259:0.21 260:0.97 265:0.28 266:0.47 267:0.73 268:0.94 271:0.61 276:0.33 277:0.87 279:0.79 283:0.82 284:0.94 285:0.62 287:1.00 290:0.76 297:0.36 300:0.33 +2 8:0.66 9:0.73 10:0.96 11:0.87 12:0.69 17:0.75 18:0.93 21:0.78 27:0.41 29:0.77 30:0.33 31:0.86 34:0.59 36:0.74 37:0.38 39:0.51 43:0.83 45:0.83 66:0.93 69:0.42 70:0.76 71:0.91 74:0.67 79:0.86 83:0.56 85:0.80 86:0.90 91:0.77 96:0.80 98:0.92 101:0.96 104:0.58 108:0.32 120:0.69 122:0.92 123:0.31 127:0.53 129:0.05 131:0.88 132:0.97 135:0.74 137:0.86 139:0.87 140:0.90 144:0.85 146:0.80 147:0.83 149:0.83 151:0.85 153:0.48 154:0.79 155:0.56 157:0.99 159:0.35 162:0.52 164:0.55 166:0.61 170:0.82 172:0.82 173:0.53 174:0.88 177:0.88 178:0.50 179:0.42 182:0.97 190:0.95 191:0.73 192:0.58 196:0.88 197:0.92 202:0.67 208:0.50 212:0.84 216:0.18 222:0.48 227:0.94 229:0.94 231:0.77 232:0.79 235:0.31 239:0.95 241:0.83 242:0.19 243:0.94 246:0.61 247:0.90 248:0.76 253:0.80 255:0.72 256:0.58 260:0.70 265:0.92 266:0.38 267:0.91 268:0.59 271:0.61 276:0.72 284:0.87 285:0.60 287:0.97 300:0.55 +2 10:0.97 11:0.81 12:0.69 17:0.57 18:0.97 21:0.40 22:0.93 27:0.15 29:0.77 30:0.55 31:0.87 32:0.66 34:0.52 36:0.54 37:0.58 39:0.51 43:0.57 44:0.86 45:0.95 47:0.93 48:0.91 64:0.77 66:0.97 69:0.19 70:0.62 71:0.91 74:0.61 76:0.85 77:0.70 79:0.87 81:0.27 83:0.69 86:0.93 91:0.53 97:0.89 98:0.95 101:0.65 104:0.87 108:0.15 122:0.93 123:0.15 126:0.24 127:0.67 129:0.05 131:0.80 135:0.65 137:0.87 139:0.90 140:0.80 144:0.85 145:0.92 146:0.91 147:0.93 149:0.72 150:0.30 151:0.50 153:0.48 154:0.53 155:0.50 157:0.99 158:0.75 159:0.52 162:0.57 166:0.72 170:0.82 172:0.92 173:0.24 174:0.96 177:0.88 178:0.42 179:0.27 182:0.96 187:0.68 190:0.98 192:0.46 193:0.97 195:0.70 196:0.91 197:0.97 202:0.54 204:0.80 208:0.61 212:0.89 216:0.50 220:0.91 222:0.48 227:0.83 229:0.61 231:0.75 232:0.91 235:0.42 239:0.96 241:0.30 242:0.27 243:0.97 244:0.61 246:0.61 247:0.97 248:0.50 253:0.47 254:0.80 256:0.45 259:0.21 262:0.93 265:0.95 266:0.27 267:0.85 268:0.46 271:0.61 276:0.85 279:0.76 281:0.91 282:0.75 283:0.82 284:0.87 285:0.46 287:0.61 300:0.55 +1 1:0.61 7:0.64 8:0.62 9:0.73 10:0.93 11:0.55 12:0.69 17:0.64 18:0.87 20:0.95 21:0.54 23:0.99 27:0.37 29:0.77 30:0.66 31:0.91 32:0.63 34:0.64 36:0.98 37:0.29 39:0.51 43:0.29 44:0.88 45:0.87 48:0.91 62:0.97 64:0.77 66:0.63 69:0.49 70:0.61 71:0.91 78:0.95 79:0.91 81:0.45 83:0.69 86:0.82 91:0.83 96:0.80 98:0.79 99:0.83 100:0.96 101:0.47 102:0.94 104:0.58 108:0.62 111:0.95 120:0.89 122:0.93 123:0.60 124:0.47 126:0.32 127:0.64 128:0.97 129:0.59 131:0.90 133:0.46 135:0.42 137:0.91 138:0.97 139:0.81 140:0.93 144:0.85 145:0.76 146:0.26 147:0.32 149:0.46 150:0.40 151:0.85 152:0.88 153:0.48 154:0.48 155:0.64 157:0.84 159:0.43 162:0.68 164:0.88 165:0.65 166:0.72 168:0.97 169:0.94 170:0.82 172:0.59 173:0.55 174:0.91 175:0.75 177:0.70 178:0.50 179:0.67 182:1.00 186:0.95 190:0.95 191:0.93 192:0.86 193:0.97 195:0.67 196:0.92 197:0.93 201:0.58 202:0.88 204:0.81 205:0.99 208:0.61 212:0.41 216:0.36 220:0.74 222:0.48 227:0.95 229:1.00 230:0.64 231:0.78 232:0.80 233:0.66 235:0.74 236:0.92 239:0.92 241:0.95 242:0.33 243:0.31 244:0.83 245:0.71 246:0.92 247:0.67 248:0.82 253:0.73 254:0.90 255:0.71 256:0.89 257:0.65 259:0.21 260:0.89 261:0.94 265:0.79 266:0.63 267:0.52 268:0.90 271:0.61 276:0.53 277:0.69 281:0.91 283:0.67 284:0.98 285:0.62 287:1.00 300:0.55 +0 1:0.67 9:0.75 10:0.93 11:0.81 12:0.69 17:0.88 18:0.91 21:0.22 22:0.93 23:0.98 27:0.72 29:0.77 30:0.38 31:0.91 33:0.97 34:0.34 36:0.56 39:0.51 43:0.40 45:0.89 47:0.93 62:0.97 64:0.77 66:0.36 69:0.17 70:0.78 71:0.91 74:0.51 75:0.98 79:0.91 81:0.74 83:0.69 86:0.84 91:0.73 96:0.87 97:0.94 98:0.59 99:0.95 101:0.65 104:0.95 106:0.81 108:0.84 114:0.88 117:0.86 120:0.79 122:0.93 123:0.83 124:0.77 126:0.54 127:0.86 128:0.97 129:0.59 131:0.90 133:0.74 135:0.70 137:0.91 139:0.83 140:0.45 144:0.85 146:0.43 147:0.50 149:0.42 150:0.60 151:0.65 153:0.83 154:0.66 155:0.65 157:0.97 158:0.81 159:0.68 162:0.83 164:0.75 165:0.81 166:0.85 168:0.97 170:0.82 172:0.63 173:0.16 174:0.93 176:0.70 177:0.88 179:0.50 182:1.00 187:0.39 190:0.95 192:0.98 196:0.93 197:0.93 201:0.66 202:0.66 205:0.97 206:0.81 208:0.64 212:0.51 215:0.79 216:0.23 219:0.94 220:0.82 222:0.48 226:0.95 227:0.96 229:1.00 231:0.79 232:0.97 235:0.52 236:0.95 239:0.94 241:0.30 242:0.32 243:0.31 244:0.61 245:0.78 246:1.00 247:0.76 248:0.67 253:0.84 254:0.84 255:0.78 256:0.68 259:0.21 260:0.79 262:0.93 265:0.60 266:0.75 267:0.65 268:0.72 271:0.61 276:0.69 279:0.79 283:0.66 284:0.94 285:0.62 287:1.00 290:0.88 291:0.97 292:0.87 297:0.36 300:0.70 +1 1:0.64 7:0.69 8:0.68 9:0.74 10:0.96 11:0.87 12:0.69 17:0.64 18:0.94 27:0.28 29:0.77 30:0.60 31:0.89 32:0.66 34:0.87 36:0.96 37:0.48 39:0.51 43:0.67 44:0.86 45:0.96 48:0.91 60:0.87 66:0.98 69:0.05 70:0.62 71:0.91 74:0.80 75:0.99 77:0.89 79:0.89 81:0.60 83:0.69 85:0.72 86:0.91 91:0.79 96:0.88 97:0.99 98:0.97 99:0.83 101:0.96 104:0.91 106:0.81 108:0.05 114:0.92 117:0.86 120:0.79 122:0.55 123:0.05 126:0.32 127:0.76 129:0.05 131:0.88 132:0.97 135:0.81 137:0.89 139:0.91 140:0.45 144:0.85 145:0.65 146:0.93 147:0.94 149:0.81 150:0.56 151:0.93 153:0.80 154:0.56 155:0.57 157:1.00 158:0.92 159:0.56 162:0.81 164:0.68 165:0.65 166:0.80 170:0.82 172:0.94 173:0.13 174:0.94 176:0.62 177:0.88 179:0.24 182:0.98 187:0.39 190:0.95 191:0.70 192:0.86 193:0.96 195:0.72 196:0.92 197:0.95 201:0.60 202:0.61 204:0.83 206:0.81 208:0.64 212:0.86 215:0.96 216:0.48 219:0.95 222:0.48 226:0.98 227:0.94 229:0.94 230:0.71 231:0.78 232:0.98 233:0.76 235:0.05 239:0.97 241:0.27 242:0.15 243:0.98 246:0.97 247:0.96 248:0.86 253:0.90 255:0.72 256:0.64 260:0.80 265:0.97 266:0.27 267:0.84 268:0.66 271:0.61 276:0.88 279:0.82 281:0.86 282:0.75 283:0.82 284:0.87 285:0.60 287:0.97 290:0.92 292:0.95 297:0.36 300:0.55 +1 1:0.60 7:0.69 8:0.81 9:0.71 10:0.96 11:0.81 12:0.69 17:0.30 18:0.97 21:0.22 27:0.15 29:0.77 30:0.45 31:0.89 32:0.66 34:0.53 36:0.50 37:0.58 39:0.51 43:0.57 44:0.86 45:0.95 48:0.91 64:0.77 66:0.97 69:0.17 70:0.76 71:0.91 74:0.59 77:0.70 79:0.88 81:0.37 83:0.69 86:0.91 91:0.54 96:0.73 97:0.97 98:0.96 101:0.65 104:0.97 108:0.14 114:0.69 120:0.58 122:0.93 123:0.13 126:0.32 127:0.69 129:0.05 131:0.88 135:0.88 137:0.89 138:0.96 139:0.89 140:0.80 144:0.85 145:0.92 146:0.92 147:0.94 149:0.73 150:0.42 151:0.63 153:0.72 154:0.47 155:0.63 157:0.99 158:0.76 159:0.55 162:0.59 164:0.54 166:0.75 170:0.82 172:0.92 173:0.21 174:0.95 176:0.56 177:0.88 178:0.42 179:0.27 182:0.98 187:0.87 190:0.95 191:0.70 192:0.86 193:0.97 195:0.72 196:0.91 197:0.97 201:0.57 202:0.62 204:0.82 208:0.64 212:0.78 216:0.50 219:0.87 220:0.82 222:0.48 227:0.94 229:0.94 230:0.71 231:0.78 233:0.76 235:0.31 239:0.95 241:0.51 242:0.27 243:0.97 244:0.61 246:0.61 247:0.97 248:0.62 253:0.60 254:0.84 255:0.70 256:0.58 259:0.21 260:0.58 265:0.96 266:0.38 267:0.96 268:0.61 271:0.61 276:0.86 279:0.81 281:0.91 282:0.75 283:0.82 284:0.90 285:0.60 286:0.99 287:0.97 290:0.69 292:0.88 300:0.55 +0 1:0.68 10:0.96 11:0.81 12:0.69 17:0.97 18:0.91 21:0.05 27:0.67 29:0.77 30:0.75 34:0.34 36:0.40 37:0.74 39:0.51 43:0.56 45:0.94 64:0.77 66:0.60 69:0.47 70:0.66 71:0.91 74:0.52 81:0.81 83:0.69 86:0.89 91:0.29 97:0.89 98:0.69 101:0.65 104:0.91 106:0.81 108:0.80 114:0.95 117:0.86 122:0.93 123:0.79 124:0.63 126:0.54 127:0.77 129:0.59 131:0.61 133:0.64 135:0.82 139:0.85 144:0.85 146:0.32 147:0.38 149:0.71 150:0.66 154:0.60 155:0.53 157:0.99 158:0.75 159:0.65 165:0.93 166:0.86 170:0.82 172:0.79 173:0.38 174:0.95 176:0.73 177:0.88 178:0.55 179:0.40 182:1.00 187:0.39 192:0.55 197:0.96 201:0.67 206:0.81 208:0.64 212:0.61 215:0.91 216:0.16 222:0.48 227:0.61 229:0.61 231:0.78 232:0.95 235:0.42 236:0.97 239:0.94 241:0.05 242:0.41 243:0.29 244:0.61 245:0.85 246:1.00 247:0.82 253:0.67 259:0.21 265:0.69 266:0.75 267:0.28 271:0.61 276:0.75 279:0.76 283:0.82 287:0.61 290:0.95 297:0.36 300:0.84 +1 1:0.69 10:0.93 11:0.55 12:0.69 17:0.40 18:0.94 22:0.93 27:0.25 29:0.77 30:0.38 33:0.97 34:0.69 36:0.25 37:0.89 39:0.51 43:0.43 45:0.88 47:0.93 64:0.77 66:0.80 69:0.61 70:0.65 71:0.91 75:0.96 81:0.82 83:0.69 86:0.84 91:0.37 97:0.89 98:0.83 101:0.47 104:0.54 108:0.46 114:0.98 122:0.93 123:0.44 124:0.39 126:0.54 127:0.55 129:0.05 131:0.61 133:0.43 135:0.86 139:0.81 144:0.85 146:0.88 147:0.90 149:0.56 150:0.71 154:0.26 155:0.53 157:0.85 159:0.54 165:0.93 166:0.86 170:0.82 172:0.77 173:0.58 174:0.92 175:0.75 176:0.73 177:0.70 178:0.55 179:0.48 182:1.00 187:0.21 192:0.55 197:0.94 201:0.68 208:0.64 212:0.29 215:0.96 216:0.36 219:0.90 222:0.48 226:0.98 227:0.61 229:0.61 231:0.78 235:0.31 236:0.97 239:0.93 241:0.15 242:0.38 243:0.82 244:0.83 245:0.70 246:1.00 247:0.79 253:0.69 254:0.86 257:0.65 259:0.21 262:0.93 265:0.83 266:0.54 267:0.52 271:0.61 276:0.68 277:0.69 279:0.76 287:0.61 290:0.98 291:0.97 292:0.95 297:0.36 300:0.43 +0 1:0.61 7:0.64 8:0.61 10:0.91 11:0.81 12:0.69 17:0.49 18:0.78 21:0.22 22:0.93 27:0.21 29:0.77 30:0.60 32:0.62 33:0.97 34:0.97 36:0.71 37:0.66 39:0.51 43:0.26 44:0.88 45:0.88 47:0.93 66:0.51 69:0.83 70:0.60 71:0.91 74:0.39 81:0.53 83:0.56 86:0.75 91:0.51 98:0.38 99:0.83 101:0.65 102:0.95 104:0.95 106:0.81 108:0.82 114:0.82 117:0.86 122:0.93 123:0.81 124:0.56 126:0.32 127:0.26 129:0.59 131:0.61 133:0.47 135:0.89 139:0.74 144:0.85 145:0.70 146:0.47 147:0.54 149:0.25 150:0.48 154:0.35 155:0.48 157:0.98 158:0.72 159:0.45 165:0.65 166:0.80 170:0.82 172:0.59 173:0.81 174:0.89 176:0.62 177:0.88 178:0.55 179:0.41 182:0.97 187:0.68 192:0.47 195:0.77 197:0.89 201:0.58 206:0.81 208:0.50 212:0.73 215:0.79 216:0.54 222:0.48 227:0.61 229:0.61 230:0.64 231:0.76 232:0.98 233:0.66 235:0.31 239:0.90 241:0.47 242:0.24 243:0.43 244:0.61 245:0.79 246:0.97 247:0.86 253:0.58 254:0.97 259:0.21 262:0.93 265:0.40 266:0.54 267:0.28 271:0.61 276:0.54 287:0.61 290:0.82 291:0.97 297:0.36 300:0.55 +1 7:0.66 8:0.86 9:0.70 10:0.95 11:0.81 12:0.69 17:0.34 18:0.97 21:0.59 23:0.96 27:0.15 29:0.77 30:0.44 31:0.87 32:0.63 34:0.56 36:0.62 37:0.58 39:0.51 43:0.57 44:0.88 45:0.95 48:0.91 62:0.96 64:0.77 66:0.45 69:0.25 70:0.75 71:0.91 74:0.49 75:0.98 76:0.94 79:0.87 81:0.26 83:0.69 86:0.89 91:0.44 96:0.69 97:0.94 98:0.58 101:0.65 102:0.96 108:0.85 122:0.93 123:0.84 124:0.69 126:0.24 127:0.73 128:0.95 129:0.59 131:0.84 133:0.67 135:0.86 137:0.87 139:0.90 140:0.45 144:0.85 145:0.58 146:0.78 147:0.82 149:0.74 150:0.28 151:0.51 153:0.69 154:0.22 155:0.57 157:0.99 158:0.75 159:0.72 162:0.86 166:0.72 168:0.95 170:0.82 172:0.84 173:0.18 174:0.95 177:0.88 179:0.26 182:0.96 187:0.87 190:0.89 192:0.58 193:0.96 195:0.84 196:0.90 197:0.96 202:0.46 204:0.85 205:0.95 208:0.64 212:0.72 216:0.50 219:0.94 220:0.96 222:0.48 226:0.95 227:0.91 229:0.88 230:0.68 231:0.76 233:0.65 235:0.68 239:0.96 241:0.01 242:0.22 243:0.36 244:0.61 245:0.94 246:0.61 247:0.96 248:0.50 253:0.41 254:0.84 255:0.69 256:0.45 259:0.21 265:0.60 266:0.63 267:0.82 268:0.55 271:0.61 276:0.86 279:0.81 281:0.86 283:0.82 284:0.90 285:0.60 287:0.94 292:0.86 300:0.55 +2 1:0.69 8:0.88 9:0.79 10:0.86 11:0.81 12:0.69 17:0.96 18:0.84 20:0.94 23:0.97 27:0.25 29:0.77 30:0.13 31:0.95 34:0.77 36:0.76 37:0.84 39:0.51 41:0.82 43:0.29 44:0.88 45:0.87 62:0.97 64:0.77 66:0.72 69:0.77 70:0.83 71:0.91 74:0.49 78:0.97 79:0.94 81:0.82 83:0.69 86:0.70 91:0.61 96:0.88 97:0.94 98:0.85 100:0.96 101:0.65 102:0.95 104:0.82 106:0.81 108:0.61 111:0.96 114:0.98 117:0.86 120:0.80 123:0.58 124:0.43 126:0.54 127:0.62 128:0.98 129:0.05 131:0.90 133:0.45 135:0.96 137:0.95 139:0.74 140:0.45 144:0.85 145:0.67 146:0.89 147:0.43 149:0.48 150:0.71 151:0.91 152:0.88 153:0.72 154:0.38 155:0.66 157:0.98 158:0.75 159:0.56 162:0.86 164:0.72 165:0.93 166:0.86 168:0.98 169:0.94 170:0.82 172:0.79 173:0.77 174:0.88 176:0.73 177:0.70 179:0.43 182:1.00 186:0.96 187:0.21 192:0.99 193:0.97 195:0.74 196:0.92 197:0.89 201:0.68 202:0.58 204:0.83 205:0.95 206:0.81 208:0.64 212:0.41 215:0.96 216:0.40 222:0.48 227:0.96 229:1.00 231:0.79 232:0.89 235:0.31 236:0.97 239:0.89 241:0.07 242:0.18 243:0.23 244:0.83 245:0.82 246:1.00 247:0.92 248:0.81 253:0.86 254:0.80 255:0.94 256:0.64 257:0.65 259:0.21 260:0.80 261:0.94 265:0.85 266:0.63 267:0.96 268:0.66 271:0.61 276:0.73 279:0.78 281:0.91 283:0.82 284:0.91 285:0.62 287:1.00 290:0.98 297:0.36 300:0.55 +2 8:0.89 9:0.74 11:0.55 12:0.69 17:0.20 18:0.79 21:0.40 25:0.88 27:0.25 29:0.77 30:0.62 31:0.92 34:0.85 36:0.57 37:0.55 39:0.51 43:0.25 45:0.77 55:0.52 64:0.77 66:0.36 69:0.65 70:0.40 71:0.91 74:0.37 79:0.92 81:0.27 83:0.56 86:0.63 87:0.98 91:0.77 96:0.90 98:0.35 101:0.47 104:0.58 108:0.73 120:0.86 122:0.55 123:0.71 124:0.60 126:0.24 127:0.47 129:0.59 131:0.88 133:0.47 135:0.61 137:0.92 139:0.62 140:0.93 144:0.76 146:0.33 147:0.40 149:0.33 150:0.30 151:0.85 153:0.48 154:0.21 155:0.56 157:0.88 159:0.35 162:0.67 164:0.80 166:0.72 170:0.82 172:0.32 173:0.76 175:0.91 177:0.38 179:0.80 182:0.89 187:0.39 190:0.95 191:0.86 192:0.58 196:0.88 202:0.79 208:0.50 212:0.42 216:0.49 219:0.87 220:0.91 222:0.48 227:0.94 229:0.94 231:0.74 235:0.42 241:0.74 242:0.29 243:0.48 244:0.93 245:0.62 246:0.61 247:0.55 248:0.89 253:0.84 255:0.73 256:0.81 257:0.84 259:0.21 260:0.86 265:0.37 266:0.54 267:0.73 268:0.82 271:0.61 276:0.30 277:0.87 283:0.67 284:0.96 285:0.60 287:0.97 292:0.88 300:0.33 +0 1:0.56 8:0.60 9:0.74 10:0.95 11:0.55 12:0.69 17:0.84 18:0.93 21:0.71 23:1.00 27:0.28 29:0.77 30:0.72 31:1.00 34:0.67 36:0.55 37:0.26 39:0.51 43:0.56 45:0.89 62:1.00 64:0.77 66:0.82 69:0.29 70:0.45 71:0.91 75:0.96 79:1.00 81:0.48 83:0.56 86:0.87 91:0.83 96:0.74 97:0.97 98:0.71 99:0.83 101:0.47 108:0.22 120:0.95 122:0.93 123:0.22 124:0.37 126:0.51 127:0.62 128:1.00 129:0.05 131:0.90 133:0.37 135:0.49 137:1.00 139:0.85 140:0.95 144:0.76 146:0.61 147:0.67 149:0.69 150:0.38 151:0.82 153:0.94 154:0.45 155:0.58 157:0.85 159:0.36 162:0.79 164:0.94 165:0.65 166:0.79 168:1.00 170:0.82 172:0.70 173:0.41 174:0.93 175:0.75 177:0.70 179:0.61 182:0.79 190:0.89 191:0.70 192:0.86 196:0.99 197:0.96 201:0.54 202:0.93 205:1.00 208:0.61 212:0.87 215:0.48 216:0.21 219:0.90 220:0.74 222:0.48 226:0.96 227:0.95 229:0.88 231:0.78 235:0.95 236:0.94 239:0.94 241:0.86 242:0.62 243:0.83 244:0.83 245:0.56 246:0.92 247:0.47 248:0.96 253:0.51 254:0.97 255:0.78 256:0.95 257:0.65 259:0.21 260:0.94 265:0.72 266:0.38 267:0.59 268:0.96 271:0.61 276:0.53 277:0.69 279:0.79 283:0.67 284:1.00 285:0.62 287:0.94 292:0.88 297:0.36 300:0.43 +1 8:0.74 10:0.92 11:0.49 17:0.32 18:0.69 21:0.54 23:0.95 27:0.41 29:0.71 30:0.09 33:0.97 34:0.68 36:0.73 37:0.44 39:0.74 43:0.12 44:0.85 45:0.90 55:0.71 62:0.97 66:0.86 69:0.55 70:0.95 71:0.65 74:0.43 75:0.95 81:0.25 86:0.64 91:0.49 98:0.86 101:0.52 102:0.94 108:0.42 122:0.98 123:0.41 124:0.38 126:0.22 127:0.63 128:0.97 129:0.05 133:0.43 135:0.80 139:0.63 140:0.45 145:0.92 146:0.96 147:0.97 149:0.17 150:0.27 151:0.75 153:0.48 154:0.15 159:0.74 162:0.62 168:0.97 170:0.90 172:0.93 173:0.39 174:0.98 177:0.99 179:0.22 187:0.68 190:0.99 192:0.46 193:0.95 195:0.90 197:0.98 202:0.50 205:0.95 212:0.78 216:0.75 220:0.62 222:0.25 226:0.96 232:0.95 235:0.60 236:0.91 239:0.91 241:0.03 242:0.14 243:0.86 244:0.83 245:0.88 247:0.97 248:0.69 253:0.37 254:0.97 255:0.70 256:0.45 259:0.21 264:0.90 265:0.86 266:0.75 267:0.69 268:0.46 271:0.95 275:0.91 276:0.88 285:0.47 291:0.97 294:0.93 300:0.84 +0 1:0.59 8:0.79 9:0.72 10:0.83 11:0.46 17:0.36 18:0.67 21:0.05 27:0.54 29:0.71 30:0.32 32:0.63 34:0.45 36:0.98 37:0.46 39:0.74 43:0.10 45:0.87 55:0.71 66:0.49 69:0.64 70:0.77 71:0.65 74:0.47 76:0.94 77:0.70 81:0.35 83:0.54 86:0.61 91:0.73 96:0.85 97:0.94 98:0.71 99:0.83 101:0.47 108:0.87 114:0.81 117:0.86 120:0.73 122:0.90 123:0.86 124:0.72 126:0.26 127:0.68 129:0.05 133:0.72 135:0.81 139:0.59 140:0.80 145:0.91 146:0.85 147:0.87 149:0.25 150:0.34 151:0.81 153:0.78 154:0.10 155:0.52 158:0.73 159:0.80 162:0.69 164:0.68 165:0.63 170:0.90 172:0.85 173:0.45 174:0.88 176:0.59 177:0.99 178:0.42 179:0.26 187:0.39 190:0.99 191:0.79 192:0.58 195:0.93 197:0.91 201:0.56 202:0.65 208:0.49 212:0.56 215:0.91 216:0.67 220:0.62 222:0.25 232:0.89 235:0.12 239:0.82 241:0.35 242:0.59 243:0.40 244:0.98 245:0.90 247:0.90 248:0.80 253:0.81 254:0.88 255:0.71 256:0.64 259:0.21 260:0.73 264:0.90 265:0.71 266:0.75 267:0.85 268:0.67 271:0.95 275:0.93 276:0.86 277:0.95 279:0.75 282:0.72 283:0.67 285:0.47 290:0.81 294:0.92 297:0.36 300:0.70 +0 8:0.81 10:0.91 11:0.49 17:0.93 18:0.64 21:0.13 27:0.53 29:0.71 30:0.31 34:0.92 36:0.87 37:0.82 39:0.74 43:0.13 45:0.83 55:0.59 66:0.48 69:0.08 70:0.92 71:0.65 74:0.44 75:0.95 76:0.94 77:0.89 81:0.27 86:0.60 87:0.98 91:0.53 98:0.55 101:0.52 102:0.94 108:0.07 122:0.92 123:0.07 124:0.80 126:0.22 127:0.89 129:0.05 133:0.81 135:0.90 139:0.59 145:0.72 146:0.79 147:0.82 149:0.19 150:0.30 154:0.15 159:0.76 170:0.90 172:0.81 173:0.10 174:0.97 177:0.99 178:0.55 179:0.33 187:0.21 192:0.49 193:0.95 195:0.87 197:0.98 212:0.69 216:0.32 222:0.25 226:0.96 232:0.72 235:0.12 236:0.91 239:0.90 241:0.05 242:0.14 243:0.60 244:0.97 245:0.86 247:0.94 253:0.37 254:0.94 259:0.21 264:0.90 265:0.56 266:0.80 267:0.82 271:0.95 275:0.91 276:0.82 282:0.72 294:0.93 300:0.84 +0 8:0.86 10:0.90 11:0.49 17:0.79 18:0.62 21:0.30 23:0.95 27:0.71 29:0.71 30:0.20 32:0.64 34:0.36 36:0.96 37:0.70 39:0.74 43:0.11 45:0.92 55:0.71 62:0.96 66:0.12 69:0.97 70:0.96 71:0.65 74:0.40 75:0.95 76:0.94 77:0.70 81:0.26 86:0.61 91:0.42 98:0.38 101:0.65 102:0.94 108:0.90 122:0.93 123:0.90 124:0.96 126:0.22 127:0.92 128:0.96 129:0.05 133:0.95 135:0.89 139:0.59 140:0.45 145:0.94 146:0.88 147:0.79 149:0.21 150:0.28 151:0.53 153:0.48 154:0.06 159:0.93 162:0.86 168:0.96 170:0.90 172:0.76 173:0.93 174:0.99 177:0.99 179:0.17 187:0.21 190:1.00 193:0.95 195:0.99 197:0.98 202:0.36 205:0.95 212:0.29 216:0.12 220:0.82 222:0.25 226:0.96 235:0.31 236:0.91 239:0.90 241:0.24 242:0.16 243:0.19 244:0.97 245:0.92 247:0.98 248:0.53 251:1.00 253:0.35 254:0.80 256:0.45 257:0.65 259:0.21 264:0.90 265:0.40 266:0.92 267:0.97 268:0.46 271:0.95 275:0.96 276:0.94 277:0.87 282:0.73 285:0.45 294:0.94 300:0.94 +0 8:0.83 9:0.71 10:0.80 11:0.46 17:0.18 18:0.58 21:0.30 27:0.21 29:0.71 30:0.16 31:0.89 34:0.20 36:0.76 37:0.74 39:0.74 43:0.09 45:0.95 55:0.84 66:0.08 69:0.85 70:0.99 71:0.65 74:0.51 76:0.94 79:0.89 81:0.26 83:0.56 86:0.58 91:0.87 96:0.99 98:0.33 101:0.47 108:0.71 114:0.71 117:0.86 120:0.79 122:0.90 123:0.69 124:0.99 126:0.22 127:0.95 129:0.59 133:0.99 135:0.23 137:0.89 139:0.56 140:0.97 146:0.97 147:0.36 149:0.26 150:0.28 151:0.93 153:0.98 154:0.05 155:0.54 158:0.73 159:0.98 162:0.55 164:0.68 170:0.90 172:0.84 173:0.33 174:0.89 175:0.97 176:0.59 177:0.05 179:0.11 187:0.39 190:0.99 191:0.87 192:0.87 196:0.87 197:0.80 202:0.95 208:0.50 212:0.27 216:0.95 222:0.25 232:0.85 235:0.31 239:0.79 241:0.64 242:0.75 243:0.06 244:0.98 245:0.95 247:0.96 248:0.85 253:0.98 254:0.80 255:0.70 256:0.95 257:0.99 259:0.21 260:0.78 264:0.90 265:0.35 266:0.98 267:1.00 268:0.95 271:0.95 275:0.96 276:0.98 277:0.95 284:0.91 285:0.55 290:0.71 294:0.93 300:0.94 +1 10:0.90 11:0.46 17:0.78 18:0.62 21:0.40 23:0.95 27:0.39 29:0.71 30:0.26 33:0.97 34:0.71 36:0.66 37:0.59 39:0.74 43:0.11 45:0.77 55:0.45 62:0.96 66:0.36 69:0.17 70:0.89 71:0.65 74:0.53 81:0.30 86:0.60 91:0.42 98:0.76 101:0.47 108:0.14 114:0.70 122:0.90 123:0.13 124:0.77 126:0.26 127:0.83 128:0.96 129:0.05 133:0.74 135:0.87 139:0.58 140:0.45 146:0.95 147:0.96 149:0.18 150:0.31 151:0.57 153:0.48 154:0.11 159:0.81 162:0.86 168:0.96 170:0.90 172:0.90 173:0.11 174:0.97 176:0.59 177:0.99 179:0.18 187:0.87 190:1.00 192:0.45 197:0.97 201:0.54 202:0.36 205:0.95 212:0.83 216:0.62 220:0.74 222:0.25 232:0.83 235:0.52 236:0.91 239:0.88 241:0.44 242:0.70 243:0.51 244:0.97 245:0.95 247:0.94 248:0.55 253:0.54 254:0.90 256:0.45 259:0.21 264:0.90 265:0.76 266:0.63 267:0.59 268:0.46 271:0.95 275:0.91 276:0.93 285:0.45 290:0.70 291:0.97 294:0.93 300:0.70 +1 8:0.78 9:0.73 10:0.92 11:0.42 17:0.40 18:0.63 21:0.54 23:0.95 27:0.41 29:0.71 30:0.27 33:0.97 34:0.87 36:0.22 37:0.44 39:0.74 43:0.12 45:0.66 55:0.59 62:0.97 66:0.63 69:0.47 70:0.97 71:0.65 74:0.38 75:0.95 77:0.89 81:0.25 83:0.60 86:0.60 91:0.33 98:0.76 101:0.45 102:0.94 108:0.36 122:0.98 123:0.34 124:0.65 126:0.22 127:0.87 128:0.97 129:0.05 133:0.73 135:0.47 139:0.59 140:0.45 143:0.99 145:0.89 146:0.96 147:0.97 149:0.16 150:0.27 151:0.75 153:0.48 154:0.13 155:0.58 159:0.85 162:0.86 168:0.97 170:0.90 172:0.91 173:0.23 174:0.99 175:0.75 177:0.99 179:0.24 187:0.68 190:0.98 192:0.58 193:0.95 195:0.94 197:0.99 202:0.36 205:0.95 208:0.54 212:0.48 216:0.75 220:0.62 222:0.25 226:0.96 235:0.60 236:0.91 239:0.91 241:0.01 242:0.27 243:0.69 244:0.98 245:0.91 247:0.84 248:0.70 253:0.34 254:0.94 255:0.73 256:0.45 257:0.65 259:0.21 264:0.90 265:0.76 266:0.87 267:0.52 268:0.46 271:0.95 275:0.91 276:0.88 277:0.69 282:0.72 285:0.55 291:0.97 294:0.93 300:0.94 +0 1:0.58 7:0.65 8:0.73 10:0.84 11:0.46 17:0.76 18:0.60 21:0.13 27:0.49 29:0.71 30:0.11 34:0.88 36:0.99 37:0.64 39:0.74 43:0.09 45:0.73 55:0.42 66:0.29 69:0.87 70:0.98 71:0.65 74:0.37 75:0.95 76:0.97 77:0.70 81:0.41 83:0.54 86:0.58 91:0.57 98:0.46 99:0.83 101:0.47 102:0.94 108:0.82 114:0.80 122:0.97 123:0.81 124:0.74 126:0.32 127:0.57 129:0.59 133:0.67 135:0.72 139:0.57 145:0.47 146:0.33 147:0.51 149:0.11 150:0.37 154:0.12 155:0.48 158:0.73 159:0.60 165:0.63 170:0.90 172:0.37 173:0.88 174:0.90 175:0.97 176:0.59 177:0.70 178:0.55 179:0.70 187:0.68 192:0.57 193:0.95 195:0.79 197:0.91 201:0.60 204:0.78 208:0.49 212:0.16 215:0.84 216:0.43 222:0.25 226:0.95 230:0.67 232:0.88 233:0.65 235:0.31 236:0.91 239:0.86 241:0.01 242:0.16 243:0.38 244:0.99 245:0.67 247:0.84 253:0.57 257:0.97 259:0.21 264:0.90 265:0.48 266:0.75 267:0.73 271:0.95 275:0.91 276:0.49 277:0.95 282:0.72 290:0.80 294:0.92 297:0.36 300:0.84 +0 8:0.75 10:0.91 11:0.46 17:0.53 18:0.58 21:0.40 23:0.98 27:0.44 29:0.71 30:0.10 34:0.59 36:0.42 37:0.62 39:0.74 43:0.06 45:0.91 55:0.71 62:0.97 66:0.09 69:0.71 70:0.99 71:0.65 74:0.36 75:0.95 76:0.85 81:0.30 86:0.57 91:0.53 96:0.80 98:0.25 101:0.47 102:0.94 108:0.54 122:0.98 123:0.99 124:0.99 126:0.26 127:0.87 128:0.98 129:0.05 133:0.99 135:0.81 139:0.56 140:0.45 145:0.42 146:0.89 147:0.91 149:0.15 150:0.32 151:0.85 153:0.48 154:0.09 158:0.73 159:0.97 162:0.59 168:0.98 170:0.90 172:0.78 173:0.19 174:0.99 175:0.91 177:0.96 179:0.15 187:0.21 190:0.99 191:0.76 192:0.57 193:0.95 195:1.00 197:0.99 201:0.54 202:0.73 205:0.98 212:0.18 216:0.44 220:0.91 222:0.25 226:0.98 232:0.80 235:0.52 236:0.91 239:0.90 241:0.21 242:0.24 243:0.31 244:0.98 245:0.88 247:0.98 248:0.82 253:0.72 254:0.93 255:0.71 256:0.71 257:0.84 259:0.21 264:0.90 265:0.26 266:0.98 267:0.69 268:0.73 271:0.95 275:0.97 276:0.95 277:0.98 285:0.50 294:0.93 300:0.94 +1 8:0.77 10:0.92 11:0.46 17:0.16 18:0.59 21:0.13 23:0.99 27:0.34 29:0.71 30:0.36 31:0.89 34:0.30 36:0.93 37:0.54 39:0.74 43:0.12 45:0.81 55:0.59 62:0.98 66:0.80 69:0.37 70:0.89 71:0.65 74:0.36 75:0.95 77:0.70 79:0.89 81:0.33 82:0.98 86:0.58 88:0.98 91:0.70 96:0.74 98:0.72 101:0.47 102:0.94 108:0.29 122:0.98 123:0.28 124:0.40 126:0.26 127:0.36 128:0.99 129:0.05 133:0.59 135:0.73 137:0.89 139:0.56 140:0.45 145:0.96 146:0.95 147:0.96 149:0.17 150:0.35 151:0.75 153:0.48 154:0.16 159:0.77 162:0.66 168:0.99 170:0.90 172:0.90 173:0.17 174:0.99 175:0.75 177:0.99 179:0.22 187:0.87 190:0.99 192:0.48 193:0.95 195:0.94 196:0.87 197:0.99 201:0.55 202:0.82 205:0.99 212:0.70 216:0.85 220:0.74 222:0.25 226:0.95 235:0.22 236:0.91 239:0.91 241:0.41 242:0.71 243:0.82 244:0.97 245:0.81 247:0.92 248:0.88 253:0.53 254:0.93 255:0.71 256:0.85 257:0.65 259:0.21 264:0.90 265:0.72 266:0.80 267:0.44 268:0.84 271:0.95 275:0.94 276:0.84 277:0.69 282:0.73 284:0.87 285:0.55 294:0.95 300:0.94 +0 8:0.84 10:0.93 17:0.62 18:0.72 21:0.05 23:0.98 27:0.72 29:0.71 30:0.54 33:0.97 34:0.81 36:0.74 37:0.79 39:0.74 43:0.06 55:0.71 62:0.98 66:0.20 69:0.84 70:0.61 71:0.65 74:0.26 75:0.95 81:0.28 86:0.63 91:0.87 98:0.58 108:0.70 122:0.98 123:0.68 124:0.85 126:0.22 127:0.91 128:0.98 129:0.05 133:0.81 135:0.79 139:0.61 140:0.45 146:0.92 147:0.72 149:0.20 150:0.31 151:0.64 153:0.82 154:0.09 159:0.87 162:0.83 168:0.98 170:0.90 172:0.82 173:0.66 174:1.00 175:0.99 177:0.38 179:0.19 187:0.21 190:1.00 197:1.00 202:0.64 205:0.98 212:0.71 216:0.22 220:0.62 222:0.25 226:0.96 235:0.05 236:0.91 239:0.92 241:0.21 242:0.80 243:0.21 244:0.99 245:0.96 247:0.60 248:0.66 253:0.24 254:0.80 256:0.68 257:0.99 259:0.21 264:0.90 265:0.59 266:0.54 267:0.52 268:0.70 271:0.95 276:0.93 277:0.98 285:0.45 291:0.97 300:0.55 +0 8:0.64 9:0.70 10:0.81 11:0.46 17:0.64 18:0.92 21:0.22 27:0.30 29:0.71 30:0.11 31:0.90 34:0.56 36:0.87 37:0.57 39:0.74 43:0.21 45:0.95 55:0.59 64:0.77 66:0.51 69:0.84 70:0.92 71:0.65 74:0.51 79:0.90 81:0.34 83:0.54 86:0.78 91:0.25 96:0.75 97:0.89 98:0.72 101:0.47 108:0.94 117:0.86 120:0.57 122:0.90 123:0.94 124:0.91 126:0.26 127:0.96 129:0.59 133:0.94 135:0.82 137:0.90 138:0.96 139:0.73 140:0.45 146:0.75 147:0.45 149:0.37 150:0.36 151:0.53 153:0.48 154:0.16 155:0.52 158:0.73 159:0.92 162:0.86 164:0.65 170:0.90 172:0.96 173:0.55 174:0.83 175:0.75 177:0.88 179:0.14 187:0.39 190:1.00 192:0.86 196:0.90 197:0.81 201:0.55 202:0.62 208:0.50 212:0.61 216:0.67 219:0.88 220:0.93 222:0.25 235:0.31 239:0.80 241:0.21 242:0.54 243:0.16 244:0.97 245:0.94 247:0.98 248:0.52 253:0.59 255:0.70 256:0.68 257:0.93 259:0.21 260:0.57 264:0.90 265:0.72 266:0.94 267:1.00 268:0.71 271:0.95 275:0.94 276:0.96 277:0.69 279:0.75 283:0.65 284:0.94 285:0.55 292:0.88 294:0.94 300:0.84 +0 10:0.80 11:0.42 17:0.69 18:0.66 21:0.78 27:0.72 29:0.71 30:0.43 34:0.31 36:0.85 39:0.74 43:0.06 45:0.87 66:0.11 69:0.99 70:0.64 71:0.65 74:0.27 86:0.60 91:0.49 98:0.19 101:0.45 108:0.91 122:0.83 123:0.90 124:0.94 127:0.68 129:0.05 133:0.94 135:0.76 139:0.58 146:0.38 147:0.05 149:0.10 154:0.06 159:0.95 163:0.88 170:0.90 172:0.27 173:0.99 174:0.83 175:0.99 177:0.05 178:0.55 179:0.59 187:0.21 197:0.82 212:0.16 216:0.18 222:0.25 235:0.05 239:0.80 241:0.24 242:0.24 243:0.10 244:0.99 245:0.60 247:0.88 253:0.24 257:0.99 259:0.21 264:0.90 265:0.20 266:0.92 267:0.44 271:0.95 275:0.96 276:0.62 277:0.99 294:0.92 300:0.55 +1 1:0.57 10:0.83 11:0.46 17:0.38 18:0.82 21:0.59 27:0.45 29:0.71 30:0.38 34:0.80 36:0.17 37:0.50 39:0.74 43:0.08 45:0.77 55:0.92 64:0.77 66:0.33 69:0.91 70:0.90 71:0.65 74:0.35 77:0.70 81:0.42 83:0.56 86:0.68 91:0.48 97:0.94 98:0.57 99:0.95 101:0.47 108:0.82 114:0.66 122:0.91 123:0.81 124:0.83 126:0.32 127:0.46 129:0.05 133:0.81 135:0.94 139:0.67 145:0.47 146:0.82 147:0.28 149:0.09 150:0.36 154:0.15 155:0.46 158:0.73 159:0.70 163:0.90 165:0.65 170:0.90 172:0.48 173:0.88 174:0.86 175:0.91 176:0.59 177:0.88 178:0.55 179:0.56 187:0.68 192:0.47 195:0.87 197:0.85 201:0.56 208:0.50 212:0.27 215:0.55 216:0.77 219:0.88 222:0.25 235:0.80 239:0.82 241:0.24 242:0.18 243:0.28 244:0.97 245:0.65 247:0.84 253:0.51 254:0.94 257:0.93 259:0.21 264:0.90 265:0.59 266:0.80 267:0.65 271:0.95 276:0.60 277:0.87 279:0.75 282:0.72 283:0.67 290:0.66 292:0.88 297:0.36 300:0.70 +0 10:0.80 11:0.42 17:0.47 18:0.61 21:0.78 27:0.34 29:0.71 30:0.45 34:0.75 36:0.91 37:0.46 39:0.74 43:0.11 45:0.73 66:0.64 69:0.83 70:0.33 71:0.65 74:0.27 86:0.61 91:0.12 98:0.37 101:0.45 108:0.68 122:0.80 123:0.66 124:0.42 127:0.16 129:0.05 133:0.36 135:0.92 139:0.59 145:0.59 146:0.49 147:0.05 149:0.09 154:0.10 159:0.26 170:0.90 172:0.32 173:0.83 174:0.79 175:0.97 177:0.05 178:0.55 179:0.59 187:0.87 197:0.83 212:0.52 216:0.53 222:0.25 235:0.22 239:0.80 241:0.21 242:0.24 243:0.21 244:0.98 245:0.43 247:0.47 253:0.25 254:0.92 257:0.99 259:0.21 264:0.90 265:0.39 266:0.27 267:0.23 271:0.95 275:0.93 276:0.28 277:0.95 294:0.93 300:0.25 +0 8:0.85 10:0.83 11:0.42 17:0.94 18:0.59 21:0.78 27:0.68 29:0.71 30:0.30 34:0.42 36:0.26 37:0.38 39:0.74 43:0.07 45:0.66 55:0.71 66:0.12 69:0.94 70:0.92 71:0.65 74:0.26 86:0.58 91:0.78 98:0.23 101:0.45 108:0.78 122:0.98 123:0.88 124:0.74 127:0.31 129:0.05 133:0.60 135:0.56 139:0.56 145:0.63 146:0.46 147:0.51 149:0.08 154:0.07 159:0.70 170:0.90 172:0.27 173:0.92 174:0.92 175:0.99 177:0.05 178:0.55 179:0.68 191:0.73 197:0.91 202:0.79 212:0.23 216:0.30 222:0.25 235:0.88 239:0.82 241:0.82 242:0.42 243:0.45 244:0.99 245:0.59 247:0.67 253:0.23 257:0.99 259:0.21 264:0.90 265:0.25 266:0.71 267:0.28 271:0.95 275:0.91 276:0.39 277:0.98 294:0.92 300:0.70 +0 10:0.80 11:0.42 17:0.79 18:0.65 21:0.78 27:0.72 29:0.71 30:0.33 34:0.47 36:0.92 39:0.74 43:0.10 45:0.81 66:0.19 69:0.95 70:0.76 71:0.65 74:0.28 86:0.60 91:0.60 98:0.19 101:0.45 108:0.89 122:0.97 123:0.89 124:0.88 127:0.38 129:0.05 133:0.86 135:0.88 139:0.59 145:0.67 146:0.47 147:0.05 149:0.10 154:0.06 159:0.86 163:0.97 170:0.90 172:0.27 173:0.85 174:0.79 175:0.97 177:0.05 178:0.55 179:0.65 197:0.81 202:0.72 212:0.18 216:0.09 222:0.25 235:0.88 239:0.79 241:0.78 242:0.18 243:0.13 244:0.98 245:0.55 247:0.79 253:0.25 254:0.96 257:0.99 264:0.90 265:0.20 266:0.80 267:0.94 271:0.95 275:0.95 276:0.46 277:0.95 294:0.93 300:0.55 +1 1:0.56 10:0.88 11:0.49 17:0.30 18:0.75 21:0.47 27:0.45 29:0.71 30:0.18 34:0.89 36:0.17 37:0.50 39:0.74 43:0.06 44:0.86 45:0.83 55:0.52 64:0.77 66:0.12 69:0.69 70:0.96 71:0.65 74:0.39 75:0.95 77:0.70 81:0.40 83:0.54 86:0.68 91:0.35 97:0.89 98:0.44 99:0.83 101:0.65 102:0.94 108:0.52 122:0.90 123:0.73 124:0.79 126:0.32 127:0.46 129:0.05 133:0.73 135:0.93 139:0.69 145:0.47 146:0.56 147:0.62 149:0.07 150:0.37 154:0.15 155:0.46 159:0.66 165:0.63 170:0.90 172:0.54 173:0.58 174:0.95 175:0.91 177:0.96 178:0.55 179:0.42 187:0.68 192:0.45 195:0.84 197:0.95 201:0.57 208:0.49 212:0.48 216:0.77 219:0.88 222:0.25 226:0.95 235:0.68 236:0.91 239:0.88 241:0.21 242:0.16 243:0.45 244:0.97 245:0.79 247:0.90 253:0.34 254:0.90 257:0.84 259:0.21 264:0.90 265:0.38 266:0.87 267:0.44 271:0.95 276:0.70 277:0.95 279:0.74 282:0.73 292:0.87 300:0.84 +1 8:0.78 10:0.89 11:0.46 17:0.08 18:0.61 21:0.71 23:0.99 27:0.02 29:0.71 30:0.26 34:0.89 36:0.95 37:0.91 39:0.74 43:0.10 45:0.83 55:0.71 62:0.97 66:0.95 69:0.67 70:0.90 71:0.65 74:0.37 75:0.95 77:0.70 81:0.27 86:0.59 91:0.22 96:0.77 98:0.85 101:0.47 108:0.51 114:0.63 122:0.98 123:0.49 126:0.26 127:0.36 128:0.97 129:0.05 135:0.79 139:0.57 140:0.45 145:0.49 146:0.93 147:0.94 149:0.13 150:0.27 151:0.63 153:0.45 154:0.11 158:0.73 159:0.56 162:0.65 168:0.97 170:0.90 172:0.86 173:0.60 174:0.97 176:0.59 177:0.99 179:0.30 187:0.99 190:0.99 192:0.56 193:0.95 195:0.82 197:0.97 201:0.53 202:0.76 205:0.99 212:0.61 216:0.93 220:0.93 222:0.25 226:0.95 232:0.98 235:0.88 236:0.91 239:0.89 241:0.44 242:0.18 243:0.95 244:0.97 247:0.92 248:0.71 253:0.64 254:0.86 255:0.70 256:0.78 259:0.21 264:0.90 265:0.85 266:0.54 267:0.69 268:0.77 271:0.95 275:0.91 276:0.77 282:0.72 285:0.50 290:0.63 294:0.93 300:0.70 +1 8:0.78 10:0.87 17:0.03 18:0.61 21:0.47 23:1.00 27:0.30 29:0.71 30:0.32 31:0.90 33:0.97 34:0.86 36:0.92 37:0.60 39:0.74 43:0.06 55:0.84 62:0.97 66:0.80 69:0.57 70:0.63 71:0.65 74:0.30 75:0.95 79:0.90 81:0.30 86:0.59 91:0.71 96:0.74 98:0.76 102:0.94 108:0.44 117:0.86 122:0.98 123:0.42 124:0.38 126:0.26 127:0.35 128:0.99 129:0.05 133:0.36 135:0.61 137:0.90 139:0.58 140:0.45 145:0.59 146:0.81 147:0.84 149:0.07 150:0.31 151:0.75 153:0.80 154:0.11 159:0.45 162:0.58 163:0.75 168:0.99 170:0.90 172:0.67 173:0.56 174:0.94 175:0.97 177:0.88 179:0.55 187:0.87 190:1.00 192:0.55 193:0.95 195:0.72 196:0.87 197:0.96 201:0.54 202:0.87 205:1.00 212:0.30 216:0.82 220:0.93 222:0.25 226:0.95 232:0.99 235:0.60 236:0.91 239:0.87 241:0.46 242:0.31 243:0.82 244:0.99 245:0.55 247:0.74 248:0.85 253:0.52 254:0.74 255:0.71 256:0.88 257:0.93 259:0.21 264:0.90 265:0.76 266:0.54 267:0.91 268:0.88 271:0.95 276:0.57 277:0.95 284:0.87 285:0.55 291:0.97 300:0.43 +1 8:0.79 10:0.80 11:0.42 12:0.51 17:0.55 18:0.99 21:0.54 22:0.93 27:0.72 29:0.52 30:0.31 31:0.93 34:0.86 36:0.19 37:0.33 39:0.91 43:0.06 45:0.73 47:0.93 55:0.84 64:0.77 66:0.10 69:0.99 70:0.72 71:0.73 74:0.25 79:0.93 81:0.26 86:0.79 91:0.03 98:0.38 101:0.45 108:0.96 122:0.95 123:0.96 124:0.98 126:0.22 127:0.97 129:0.05 131:0.61 133:0.98 135:0.73 137:0.93 139:0.76 140:0.45 146:0.83 147:0.94 149:0.17 150:0.28 151:0.65 153:0.48 154:0.05 157:0.61 159:0.98 162:0.86 163:0.94 166:0.61 170:0.97 172:0.86 173:0.98 174:0.95 175:0.97 177:0.70 179:0.12 182:0.61 187:0.95 190:1.00 196:0.92 197:0.89 202:0.36 212:0.21 216:0.16 219:0.87 222:0.35 227:0.61 229:0.61 231:0.62 235:0.60 239:0.79 241:0.02 242:0.73 243:0.19 244:0.98 245:0.94 246:0.61 247:0.94 248:0.62 253:0.22 254:0.90 256:0.45 257:0.97 259:0.21 262:0.93 264:0.95 265:0.40 266:0.98 267:0.44 268:0.46 271:0.22 275:0.93 276:0.97 277:0.98 284:0.87 285:0.45 287:0.61 292:0.88 294:0.91 300:0.84 +0 8:0.61 10:0.80 12:0.51 17:0.11 18:0.99 21:0.05 27:0.58 29:0.52 30:0.27 31:0.90 34:0.30 36:0.51 37:0.38 39:0.91 43:0.06 55:0.84 64:0.77 66:0.16 69:0.23 70:0.76 71:0.73 74:0.25 79:0.90 81:0.30 86:0.84 91:0.15 98:0.49 108:0.18 122:0.95 123:0.18 124:0.97 126:0.22 127:0.97 129:0.05 131:0.61 133:0.97 135:0.90 137:0.90 139:0.80 140:0.45 146:0.99 147:0.99 149:0.20 150:0.33 151:0.57 153:0.48 154:0.08 157:0.61 159:0.97 162:0.62 166:0.61 170:0.97 172:0.92 173:0.06 174:0.91 175:0.91 177:0.96 179:0.12 182:0.61 187:0.21 190:1.00 196:0.91 197:0.87 202:0.50 212:0.19 216:0.67 219:0.87 220:0.74 222:0.35 227:0.61 229:0.61 231:0.62 235:0.05 239:0.79 241:0.07 242:0.78 243:0.37 244:0.99 245:0.97 246:0.61 247:0.95 248:0.55 253:0.22 254:0.80 256:0.45 257:0.84 259:0.21 264:0.95 265:0.50 266:0.95 267:0.87 268:0.46 271:0.22 275:0.93 276:0.98 277:0.87 284:0.87 285:0.45 287:0.61 292:0.88 294:0.91 300:0.84 +1 8:0.86 10:0.79 11:0.55 12:0.51 17:0.72 18:0.97 21:0.74 27:0.41 29:0.52 30:0.17 31:0.86 34:0.28 36:0.33 37:0.50 39:0.91 43:0.07 45:0.88 55:0.71 64:0.77 66:0.26 69:0.98 70:0.95 71:0.73 74:0.28 79:0.86 81:0.24 86:0.76 91:0.20 98:0.48 101:0.65 108:0.95 122:0.95 123:0.95 124:0.95 126:0.22 127:0.75 129:0.59 131:0.61 133:0.95 135:0.86 137:0.86 139:0.72 140:0.45 144:0.76 146:0.98 147:0.54 149:0.18 150:0.26 151:0.47 153:0.48 154:0.06 157:0.84 159:0.95 162:0.86 166:0.61 170:0.97 172:0.92 173:0.87 174:0.92 175:0.75 177:0.38 179:0.13 182:0.76 187:0.39 190:1.00 196:0.88 197:0.82 202:0.36 212:0.55 216:0.88 220:0.97 222:0.35 227:0.61 229:0.61 231:0.64 235:0.88 239:0.79 241:0.10 242:0.63 243:0.07 244:0.98 245:0.95 246:0.61 247:0.99 248:0.47 253:0.26 256:0.45 257:0.99 259:0.21 264:0.95 265:0.49 266:0.96 267:0.69 268:0.46 271:0.22 275:0.93 276:0.96 277:0.69 284:0.87 285:0.45 287:0.61 294:0.91 300:0.94 +0 8:0.61 10:0.79 12:0.51 17:0.57 18:0.57 21:0.59 27:0.39 29:0.52 30:0.20 34:0.56 36:0.68 37:0.33 39:0.91 43:0.14 55:0.71 66:0.05 69:1.00 70:0.92 71:0.73 74:0.35 81:0.24 86:0.57 91:0.01 96:0.68 98:0.17 108:1.00 114:0.62 122:0.90 123:0.99 124:1.00 126:0.22 127:0.96 129:0.05 131:0.80 133:1.00 135:0.66 139:0.55 140:0.45 146:0.54 147:0.72 149:0.24 150:0.25 151:0.47 153:0.69 154:0.10 157:0.61 159:1.00 162:0.86 166:0.72 170:0.97 172:0.80 173:0.95 174:0.97 175:0.99 176:0.55 177:0.38 179:0.09 182:0.61 187:0.68 190:1.00 197:0.82 202:0.46 212:0.21 216:0.88 220:0.97 222:0.35 227:0.82 229:0.61 231:0.63 232:0.86 235:0.68 239:0.79 241:0.03 242:0.77 243:0.08 244:0.99 245:0.95 246:0.61 247:0.99 248:0.47 253:0.47 256:0.45 257:0.99 259:0.21 264:0.95 265:0.19 266:1.00 267:0.79 268:0.55 271:0.22 275:0.96 276:0.99 277:0.99 285:0.45 287:0.61 290:0.62 294:0.91 300:0.99 +0 8:0.88 10:0.80 11:0.49 12:0.51 17:0.70 18:0.97 21:0.78 27:0.54 29:0.52 30:0.10 31:0.86 34:0.44 36:0.30 37:0.71 39:0.91 43:0.06 45:0.81 55:0.84 66:0.05 69:0.88 70:0.97 71:0.73 74:0.25 79:0.86 86:0.74 91:0.12 98:0.16 101:0.47 108:0.76 122:0.95 123:0.74 124:1.00 127:0.95 129:0.59 131:0.61 133:1.00 135:0.71 137:0.86 139:0.71 140:0.94 144:0.57 145:0.59 146:0.78 147:0.72 149:0.14 151:0.47 153:0.48 154:0.05 157:0.77 159:0.99 162:0.47 163:0.94 166:0.61 170:0.97 172:0.48 173:0.30 174:0.95 175:0.97 177:0.38 178:0.53 179:0.13 182:0.72 187:0.21 190:1.00 191:0.82 196:0.87 197:0.88 202:0.75 212:0.12 216:0.58 220:0.99 222:0.35 227:0.61 229:0.61 231:0.64 235:0.98 239:0.80 241:0.05 242:0.57 243:0.10 244:0.98 245:0.87 246:0.61 247:0.99 248:0.47 253:0.22 254:0.96 256:0.45 257:0.99 259:0.21 264:0.95 265:0.17 266:0.99 267:0.84 268:0.46 271:0.22 275:0.97 276:0.97 277:0.95 284:0.87 285:0.45 287:0.61 294:0.91 300:0.98 +1 8:0.73 10:0.80 11:0.56 12:0.51 17:0.30 18:0.98 21:0.30 27:0.62 29:0.52 30:0.32 31:0.88 34:0.76 36:0.22 37:0.31 39:0.91 43:0.07 45:0.92 55:0.84 64:0.77 66:0.06 69:0.98 70:0.93 71:0.73 74:0.26 79:0.88 81:0.28 86:0.79 91:0.04 98:0.30 101:0.71 108:0.99 122:0.95 123:0.95 124:0.99 126:0.22 127:0.99 129:0.81 131:0.61 133:0.99 135:0.26 137:0.88 139:0.76 140:0.45 144:0.57 146:0.97 147:0.36 149:0.19 150:0.30 151:0.51 153:0.48 154:0.06 157:0.75 159:0.98 162:0.62 163:0.75 166:0.61 170:0.97 172:0.82 173:0.75 174:0.94 175:0.91 177:0.05 179:0.11 182:0.71 187:0.68 190:1.00 196:0.89 197:0.84 202:0.50 212:0.14 216:0.41 219:0.87 220:0.87 222:0.35 227:0.61 229:0.61 231:0.64 235:0.31 239:0.79 241:0.02 242:0.72 243:0.05 244:0.83 245:0.95 246:0.61 247:0.98 248:0.51 253:0.24 254:0.93 256:0.45 257:0.99 259:0.21 264:0.95 265:0.29 266:0.98 267:0.76 268:0.46 271:0.22 275:0.96 276:0.98 277:0.87 284:0.87 285:0.45 287:0.61 292:0.88 294:0.92 300:0.94 +1 8:0.83 10:0.79 11:0.42 12:0.51 17:0.12 18:0.97 21:0.71 27:0.62 29:0.52 30:0.27 34:0.21 36:0.52 37:0.45 39:0.91 43:0.06 45:0.73 55:0.84 64:0.77 66:0.06 69:0.41 70:0.89 71:0.73 74:0.26 81:0.25 86:0.75 91:0.06 98:0.29 101:0.45 108:0.99 122:0.95 123:0.95 124:0.99 126:0.22 127:0.97 129:0.59 131:0.61 133:0.99 135:0.66 139:0.73 146:0.90 147:0.92 149:0.16 150:0.26 154:0.09 157:0.61 159:0.97 163:0.94 166:0.61 170:0.97 172:0.74 173:0.07 174:0.83 175:0.97 177:0.88 178:0.55 179:0.13 182:0.61 187:0.87 197:0.82 202:0.36 212:0.18 216:0.62 219:0.87 222:0.35 227:0.61 229:0.61 231:0.62 235:0.84 239:0.79 241:0.01 242:0.60 243:0.15 244:0.99 245:0.92 246:0.61 247:0.98 253:0.24 257:0.93 259:0.21 264:0.95 265:0.26 266:0.98 267:0.85 271:0.22 275:0.91 276:0.97 277:0.95 287:0.61 292:0.88 294:0.91 300:0.94 +0 10:0.80 11:0.53 12:0.51 17:0.49 18:0.73 21:0.78 27:0.72 29:0.52 30:0.09 34:0.17 36:0.46 39:0.91 43:0.06 45:0.73 55:0.84 66:0.06 69:0.72 70:0.99 71:0.73 74:0.26 86:0.62 91:0.01 98:0.14 101:0.47 108:0.99 123:0.99 124:1.00 127:0.95 129:0.98 131:0.61 132:0.97 133:1.00 135:0.86 139:0.60 144:0.76 146:0.64 147:0.69 149:0.09 154:0.05 157:0.75 159:0.99 166:0.61 170:0.97 172:0.51 173:0.11 174:0.95 175:0.99 177:0.70 178:0.55 179:0.15 182:0.72 187:0.68 197:0.86 212:0.16 216:0.16 222:0.35 227:0.61 229:0.61 231:0.64 235:0.22 239:0.79 241:0.02 242:0.14 243:0.11 244:0.98 245:0.83 246:0.61 247:1.00 253:0.23 254:0.98 257:0.97 259:0.21 264:0.95 265:0.15 266:1.00 267:0.44 271:0.22 275:0.97 276:0.95 277:0.98 287:0.61 294:0.92 300:0.98 +1 8:0.82 10:0.80 11:0.49 12:0.51 17:0.36 18:0.99 21:0.40 27:0.62 29:0.52 30:0.37 34:0.30 36:0.20 37:0.69 39:0.91 43:0.06 45:0.89 55:0.84 64:0.77 66:0.16 69:0.95 70:0.76 71:0.73 74:0.28 81:0.27 86:0.83 91:0.29 98:0.65 101:0.47 108:0.90 122:0.95 123:0.95 124:0.74 126:0.22 127:0.92 129:0.05 131:0.61 133:0.67 135:0.71 139:0.79 144:0.57 146:0.97 147:0.98 149:0.19 150:0.30 154:0.08 157:0.61 159:0.92 166:0.61 170:0.97 172:0.91 173:0.82 174:0.83 177:0.96 178:0.55 179:0.15 182:0.61 187:0.39 197:0.80 212:0.30 216:0.73 222:0.35 227:0.61 229:0.61 231:0.64 235:0.42 239:0.79 241:0.02 242:0.55 243:0.40 244:0.93 245:0.99 246:0.61 247:0.98 253:0.26 254:0.99 257:0.84 259:0.21 264:0.95 265:0.63 266:0.63 267:0.44 271:0.22 275:0.91 276:0.95 277:0.87 287:0.61 294:0.92 300:0.70 +1 8:0.84 10:0.79 11:0.52 12:0.51 17:0.22 18:0.99 21:0.59 27:0.45 29:0.52 30:0.21 31:0.89 34:0.24 36:0.30 37:0.39 39:0.91 43:0.06 44:0.85 45:0.85 55:0.84 64:0.77 66:0.10 69:0.98 70:0.90 71:0.73 74:0.27 79:0.88 81:0.26 86:0.79 91:0.17 98:0.37 101:0.65 108:0.97 122:0.95 123:0.96 124:0.96 126:0.22 127:0.79 129:0.81 131:0.61 133:0.96 135:0.89 137:0.89 139:0.76 140:0.45 144:0.57 145:0.83 146:0.83 147:0.72 149:0.18 150:0.28 151:0.52 153:0.72 154:0.06 157:0.75 159:0.95 162:0.65 166:0.61 170:0.97 172:0.80 173:0.88 174:0.89 175:0.75 177:0.05 179:0.13 182:0.72 187:0.68 190:1.00 195:0.99 196:0.90 197:0.82 202:0.61 212:0.19 216:0.84 219:0.87 220:0.87 222:0.35 227:0.61 229:0.61 231:0.64 235:0.68 239:0.79 241:0.15 242:0.73 243:0.10 244:0.93 245:0.96 246:0.61 247:0.99 248:0.52 253:0.25 254:0.94 256:0.58 257:0.99 259:0.21 264:0.95 265:0.39 266:0.92 267:0.69 268:0.62 271:0.22 275:0.93 276:0.96 277:0.95 284:0.92 285:0.45 287:0.61 292:0.88 294:0.91 300:0.94 +2 10:0.81 11:0.52 12:0.51 17:0.62 18:0.68 21:0.78 27:0.45 29:0.52 30:0.56 34:0.86 36:0.20 37:0.50 39:0.91 43:0.14 45:0.77 66:0.74 69:0.73 70:0.42 71:0.73 74:0.27 86:0.66 91:0.25 98:0.35 101:0.52 108:0.56 123:0.54 124:0.39 127:0.20 129:0.05 131:0.61 133:0.36 135:0.51 139:0.63 144:0.57 145:0.52 146:0.45 147:0.51 149:0.10 154:0.14 157:0.81 159:0.33 166:0.61 170:0.97 172:0.51 173:0.72 174:0.79 177:0.99 178:0.55 179:0.52 182:0.74 187:0.68 197:0.84 212:0.87 216:0.77 222:0.35 227:0.61 229:0.61 231:0.63 235:0.68 239:0.81 241:0.12 242:0.24 243:0.77 244:0.97 245:0.49 246:0.61 247:0.74 253:0.24 254:0.95 259:0.21 264:0.95 265:0.37 266:0.23 267:0.28 271:0.22 275:0.91 276:0.33 287:0.61 294:0.92 300:0.33 +1 8:0.71 10:0.79 11:0.42 12:0.51 17:0.43 18:0.96 21:0.54 27:0.72 29:0.52 30:0.14 34:0.68 36:0.17 37:0.47 39:0.91 43:0.06 45:0.81 55:0.92 66:0.05 69:0.98 70:0.95 71:0.73 74:0.25 81:0.25 86:0.73 91:0.04 98:0.18 101:0.45 104:0.95 106:0.81 108:0.98 120:0.59 122:0.95 123:0.99 124:1.00 126:0.22 127:0.97 129:0.59 131:0.81 133:1.00 135:0.71 139:0.70 140:0.45 146:0.75 147:0.28 149:0.15 150:0.26 151:0.51 153:0.48 154:0.06 157:0.61 159:0.99 162:0.70 164:0.70 166:0.72 170:0.97 172:0.67 173:0.71 174:0.94 175:0.97 177:0.05 179:0.11 182:0.61 187:0.95 190:1.00 197:0.82 202:0.63 206:0.81 212:0.13 215:0.58 216:0.06 220:0.96 222:0.35 227:0.83 229:0.61 231:0.65 235:0.60 239:0.79 241:0.12 242:0.52 243:0.05 244:0.99 245:0.92 246:0.61 247:0.99 248:0.52 253:0.23 256:0.64 257:0.99 259:0.21 260:0.59 264:0.95 265:0.16 266:0.98 267:0.85 268:0.65 271:0.22 275:0.93 276:0.98 277:0.95 283:0.67 285:0.45 287:0.61 294:0.91 297:0.36 300:0.94 +1 8:0.86 10:0.80 11:0.55 12:0.51 17:0.22 18:0.99 21:0.59 27:0.62 29:0.52 30:0.53 34:0.25 36:0.21 37:0.69 39:0.91 43:0.08 45:0.77 55:0.84 64:0.77 66:0.19 69:0.32 70:0.69 71:0.73 74:0.28 81:0.26 86:0.81 91:0.22 98:0.65 101:0.52 108:0.25 122:0.95 123:0.93 124:0.84 126:0.22 127:0.93 129:0.05 131:0.61 133:0.80 135:0.30 139:0.77 144:0.76 146:0.97 147:0.97 149:0.19 150:0.28 154:0.09 157:0.76 159:0.93 163:0.94 166:0.61 170:0.97 172:0.86 173:0.09 174:0.83 177:0.99 178:0.55 179:0.17 182:0.72 187:0.39 197:0.85 212:0.26 216:0.73 222:0.35 227:0.61 229:0.61 231:0.63 235:0.68 239:0.80 241:0.05 242:0.76 243:0.43 244:0.97 245:0.96 246:0.61 247:0.95 253:0.25 254:0.92 259:0.21 264:0.95 265:0.58 266:0.80 267:0.52 271:0.22 275:0.93 276:0.94 277:0.99 287:0.61 294:0.92 300:0.84 +0 10:0.81 11:0.53 12:0.51 17:0.03 18:0.59 21:0.78 27:0.62 29:0.52 30:0.09 34:0.26 36:0.20 37:0.45 39:0.91 43:0.13 45:0.73 55:0.71 66:0.09 69:0.74 70:0.98 71:0.73 74:0.29 86:0.58 91:0.04 98:0.24 101:0.47 108:0.96 123:0.95 124:0.93 127:0.91 129:0.05 131:0.61 133:0.92 135:0.66 139:0.56 144:0.76 146:0.28 147:0.05 149:0.14 154:0.10 157:0.84 159:0.93 166:0.61 170:0.97 172:0.37 173:0.38 174:0.88 175:0.99 177:0.05 178:0.55 179:0.53 182:0.75 187:0.68 197:0.88 212:0.18 216:0.62 222:0.35 227:0.61 229:0.61 231:0.62 235:0.52 239:0.80 241:0.01 242:0.71 243:0.09 244:0.99 245:0.67 246:0.61 247:0.82 253:0.26 257:0.99 259:0.21 264:0.95 265:0.26 266:0.95 267:0.73 271:0.22 275:0.91 276:0.68 277:0.99 287:0.61 294:0.92 300:0.84 +0 8:0.88 10:0.80 11:0.48 12:0.51 17:0.28 18:0.97 21:0.59 22:0.93 27:0.44 29:0.52 30:0.37 31:0.86 34:0.31 36:0.29 37:0.57 39:0.91 43:0.06 45:0.73 47:0.93 55:0.84 64:0.77 66:0.07 69:0.82 70:0.77 71:0.73 74:0.25 79:0.86 81:0.26 86:0.76 91:0.31 98:0.31 101:0.45 108:0.67 122:0.95 123:0.95 124:0.98 126:0.22 127:0.93 129:0.81 131:0.61 133:0.98 135:0.98 137:0.86 139:0.73 140:0.45 144:0.57 145:0.38 146:0.90 147:0.26 149:0.17 150:0.28 151:0.48 153:0.77 154:0.05 157:0.78 159:0.96 162:0.66 163:0.94 166:0.61 170:0.97 172:0.70 173:0.37 174:0.91 175:0.97 177:0.05 179:0.15 182:0.73 187:0.87 190:1.00 191:0.77 196:0.88 197:0.84 202:0.55 212:0.23 216:0.68 219:0.87 220:0.97 222:0.35 227:0.61 229:0.61 231:0.63 235:0.68 239:0.80 241:0.03 242:0.63 243:0.06 244:0.98 245:0.91 246:0.61 247:0.97 248:0.47 253:0.23 254:0.88 256:0.45 257:0.99 259:0.21 262:0.93 264:0.95 265:0.30 266:0.96 267:0.94 268:0.57 271:0.22 275:0.96 276:0.95 277:0.95 284:0.91 285:0.45 287:0.61 292:0.88 294:0.92 300:0.84 +1 8:0.81 10:0.79 11:0.42 12:0.51 17:0.22 18:0.99 21:0.47 27:0.62 29:0.52 30:0.38 34:0.36 36:0.18 37:0.69 39:0.91 43:0.06 45:0.66 55:0.71 64:0.77 66:0.23 69:0.96 70:0.68 71:0.73 74:0.25 81:0.30 86:0.86 91:0.22 98:0.66 101:0.45 108:0.24 122:0.95 123:0.92 124:0.77 126:0.26 127:0.92 129:0.05 131:0.61 133:0.74 135:0.75 139:0.81 146:0.98 147:0.99 149:0.15 150:0.31 154:0.06 157:0.61 159:0.93 166:0.72 170:0.97 172:0.94 173:0.85 174:0.83 175:0.91 177:0.88 178:0.55 179:0.13 182:0.61 187:0.39 192:0.45 197:0.80 201:0.54 212:0.54 215:0.63 216:0.73 222:0.35 227:0.61 229:0.61 231:0.64 235:0.60 239:0.78 241:0.05 242:0.31 243:0.49 244:0.98 245:0.99 246:0.61 247:0.97 253:0.23 254:0.84 257:0.93 259:0.21 264:0.95 265:0.66 266:0.80 267:0.44 271:0.22 275:0.91 276:0.97 277:0.87 287:0.61 294:0.91 297:0.36 300:0.70 +2 1:0.67 7:0.70 10:0.99 11:0.92 12:0.51 17:0.69 18:0.89 21:0.05 22:0.93 27:0.64 29:0.86 30:0.94 33:0.97 34:0.97 36:0.46 37:0.36 39:0.23 43:0.84 44:0.92 45:0.85 47:0.93 48:0.91 64:0.77 66:0.93 69:0.12 70:0.19 71:0.64 74:0.81 76:0.85 77:0.89 81:0.75 83:0.91 85:0.90 86:0.97 91:0.42 98:0.88 99:0.95 101:1.00 102:0.97 108:0.10 114:0.82 117:0.86 122:0.89 123:0.10 126:0.51 127:0.41 129:0.05 135:0.61 139:0.97 144:0.85 145:0.59 146:0.63 147:0.68 149:0.95 150:0.67 154:0.80 155:0.55 159:0.23 165:0.81 170:0.82 172:0.81 173:0.39 174:0.92 176:0.59 177:0.88 178:0.55 179:0.40 187:0.68 192:0.55 193:0.99 195:0.58 197:0.95 201:0.64 204:0.82 208:0.64 212:0.87 216:0.17 222:0.60 230:0.73 232:0.87 233:0.76 235:0.22 236:0.95 239:0.99 241:0.41 242:0.39 243:0.93 247:0.60 253:0.67 259:0.21 262:0.93 265:0.88 266:0.27 267:0.28 271:0.46 276:0.68 281:0.91 282:0.73 290:0.82 291:0.97 300:0.33 +2 1:0.66 9:0.70 10:0.95 11:0.88 12:0.51 18:0.70 21:0.30 23:0.95 27:0.15 29:0.86 30:0.91 31:0.87 34:0.95 36:0.37 37:0.47 39:0.23 43:0.78 62:0.96 64:0.77 66:0.82 69:0.77 70:0.21 71:0.64 74:0.51 79:0.87 81:0.79 83:0.91 85:0.80 86:0.81 91:0.27 98:0.65 101:0.87 108:0.60 114:0.86 122:0.65 123:0.58 126:0.54 127:0.19 128:0.95 129:0.05 135:0.54 137:0.87 138:0.95 139:0.77 140:0.45 144:0.85 145:0.94 146:0.49 147:0.55 149:0.78 150:0.64 151:0.54 153:0.48 154:0.71 155:0.53 159:0.18 162:0.86 165:0.93 168:0.95 170:0.82 172:0.48 173:0.93 174:0.88 176:0.73 177:0.88 179:0.56 187:0.87 190:0.89 192:0.51 196:0.89 197:0.90 201:0.65 202:0.36 205:0.95 208:0.64 212:0.84 215:0.72 216:0.94 220:0.91 222:0.60 235:0.60 236:0.97 239:0.94 241:0.24 242:0.33 243:0.83 247:0.47 248:0.53 253:0.62 255:0.69 256:0.45 259:0.21 265:0.66 266:0.23 267:0.52 268:0.46 271:0.46 276:0.30 284:0.88 285:0.50 286:0.99 290:0.86 297:0.36 298:0.99 300:0.25 +2 10:0.94 11:0.92 12:0.51 17:0.43 18:0.88 21:0.78 23:0.96 27:0.70 29:0.86 30:0.30 33:0.97 34:0.44 36:1.00 37:0.62 39:0.23 43:0.85 45:0.73 62:0.96 66:0.61 69:0.12 70:0.76 71:0.64 74:0.58 75:0.96 85:0.72 86:0.94 91:0.04 98:0.34 101:0.87 108:0.10 122:0.82 123:0.10 124:0.65 127:0.83 128:0.96 129:0.05 133:0.72 135:0.74 139:0.89 140:0.45 144:0.85 146:0.43 147:0.49 149:0.71 151:0.52 153:0.69 154:0.81 159:0.57 162:0.86 168:0.96 170:0.82 172:0.65 173:0.18 174:0.89 177:0.88 179:0.61 187:0.99 190:0.95 197:0.91 202:0.46 205:0.95 212:0.82 216:0.33 220:0.87 222:0.60 226:0.96 235:0.05 239:0.93 241:0.55 242:0.13 243:0.68 245:0.65 247:0.91 248:0.52 253:0.45 256:0.45 259:0.21 265:0.37 266:0.54 267:0.59 268:0.55 271:0.46 276:0.55 285:0.46 291:0.97 300:0.70 +2 1:0.68 7:0.81 10:0.98 11:0.92 12:0.51 17:0.72 18:0.86 21:0.05 22:0.93 27:0.64 29:0.86 30:0.93 32:0.80 33:0.97 34:1.00 36:0.37 37:0.36 39:0.23 43:0.88 44:0.93 45:0.89 47:0.93 48:0.91 64:0.77 66:0.91 69:0.15 70:0.21 71:0.64 74:0.78 76:0.94 77:0.89 81:0.83 83:0.91 85:0.80 86:0.94 91:0.42 98:0.87 101:1.00 102:0.98 104:0.87 106:0.81 108:0.12 114:0.95 117:0.86 121:0.90 122:0.89 123:0.12 126:0.54 127:0.38 129:0.05 135:0.82 139:0.95 144:0.85 145:0.61 146:0.62 147:0.67 149:0.92 150:0.78 154:0.86 155:0.57 159:0.23 165:0.93 170:0.82 172:0.75 173:0.41 174:0.91 176:0.73 177:0.88 178:0.55 179:0.48 187:0.68 192:0.58 193:0.99 195:0.59 197:0.95 201:0.67 204:0.83 206:0.81 208:0.64 212:0.73 215:0.91 216:0.17 222:0.60 230:0.83 232:0.91 233:0.76 235:0.31 236:0.97 239:0.99 241:0.21 242:0.38 243:0.91 247:0.76 253:0.72 259:0.21 262:0.93 265:0.87 266:0.27 267:0.36 271:0.46 276:0.62 281:0.91 282:0.88 290:0.95 291:0.97 297:0.36 299:0.88 300:0.33 +0 1:0.64 10:0.93 11:0.87 12:0.51 17:0.78 18:0.84 21:0.30 22:0.93 27:0.72 29:0.86 30:0.75 33:0.97 34:0.36 36:0.19 39:0.23 43:0.38 45:0.95 47:0.93 64:0.77 66:0.69 69:0.58 70:0.59 71:0.64 74:0.73 81:0.69 83:0.69 86:0.91 91:0.18 97:0.94 98:0.76 99:0.95 101:0.65 104:0.89 106:0.81 108:0.79 114:0.84 117:0.86 122:0.65 123:0.78 124:0.47 126:0.51 127:0.69 129:0.59 133:0.60 135:0.96 139:0.89 144:0.85 146:0.25 147:0.31 149:0.76 150:0.58 154:0.73 155:0.49 158:0.81 159:0.68 165:0.81 170:0.82 172:0.84 173:0.47 174:0.90 176:0.70 177:0.88 178:0.55 179:0.36 187:0.87 192:0.49 197:0.90 201:0.62 202:0.36 206:0.81 208:0.61 212:0.57 215:0.72 216:0.08 219:0.94 222:0.60 232:0.93 235:0.52 239:0.91 241:0.12 242:0.24 243:0.25 245:0.82 247:0.92 253:0.65 254:0.90 259:0.21 262:0.93 265:0.76 266:0.80 267:0.85 271:0.46 276:0.77 279:0.78 283:0.67 290:0.84 291:0.97 292:0.88 297:0.36 300:0.70 +1 1:0.64 8:0.69 10:0.92 11:0.92 12:0.51 17:0.86 18:0.73 27:0.47 29:0.86 30:0.44 34:0.97 36:0.57 37:0.48 39:0.23 43:0.74 45:0.95 66:0.15 69:0.35 70:0.81 71:0.64 74:0.65 81:0.64 83:0.56 85:0.72 86:0.79 91:0.71 97:0.94 98:0.41 99:0.83 101:1.00 104:0.84 108:0.96 114:0.95 122:0.83 123:0.79 124:0.87 126:0.32 127:0.92 129:0.59 133:0.86 135:0.80 139:0.75 144:0.85 146:0.81 147:0.84 149:0.82 150:0.60 154:0.58 155:0.49 158:0.77 159:0.91 165:0.65 170:0.82 172:0.76 173:0.11 174:0.93 176:0.63 177:0.88 178:0.55 179:0.24 187:0.21 192:0.48 197:0.88 201:0.60 208:0.50 212:0.61 215:0.96 216:0.17 222:0.60 232:0.89 235:0.05 239:0.90 241:0.21 242:0.45 243:0.37 245:0.92 247:0.88 253:0.68 259:0.21 265:0.36 266:0.95 267:0.28 271:0.46 276:0.88 277:0.87 279:0.78 283:0.82 290:0.95 297:0.36 300:0.94 +1 7:0.75 10:0.85 11:0.87 12:0.51 17:0.85 18:0.68 21:0.78 27:0.70 29:0.86 30:0.75 32:0.72 34:0.77 36:0.66 37:0.34 39:0.23 43:0.67 44:0.92 45:0.90 66:0.68 69:0.56 70:0.45 71:0.64 74:0.73 77:0.70 83:0.69 86:0.73 91:0.09 97:0.94 98:0.43 101:0.65 104:0.88 108:0.43 122:0.82 123:0.41 124:0.47 127:0.67 129:0.05 133:0.60 135:0.39 139:0.85 144:0.85 145:0.47 146:0.58 147:0.63 149:0.72 154:0.56 155:0.54 158:0.82 159:0.62 170:0.82 172:0.67 173:0.49 174:0.83 177:0.88 178:0.55 179:0.60 187:0.39 192:0.56 195:0.77 197:0.86 204:0.83 208:0.61 212:0.77 216:0.50 219:0.94 222:0.60 230:0.77 232:0.91 233:0.66 235:0.05 239:0.85 241:0.43 242:0.42 243:0.72 244:0.61 245:0.65 247:0.76 253:0.52 259:0.21 265:0.45 266:0.54 267:0.44 271:0.46 276:0.50 279:0.78 281:0.86 282:0.80 283:0.82 292:0.95 300:0.55 +0 1:0.63 10:0.81 11:0.84 12:0.51 17:0.69 21:0.40 27:0.29 29:0.86 30:0.67 32:0.72 34:0.95 36:0.22 37:0.68 39:0.23 43:0.27 44:0.92 45:0.88 64:0.77 66:0.71 69:0.91 70:0.47 71:0.64 74:0.64 77:0.99 81:0.67 83:0.69 91:0.08 97:0.94 98:0.15 99:0.95 101:0.47 108:0.81 114:0.82 122:0.55 123:0.80 124:0.47 126:0.51 127:0.42 129:0.05 133:0.74 135:0.89 139:0.77 144:0.85 145:0.99 146:0.29 147:0.05 149:0.61 150:0.56 154:0.20 155:0.49 158:0.81 159:0.77 165:0.81 170:0.82 172:0.65 173:0.83 174:0.79 175:0.91 176:0.70 177:0.05 178:0.55 179:0.57 187:0.99 192:0.48 195:0.92 197:0.80 201:0.61 208:0.61 212:0.86 215:0.67 216:0.38 219:0.94 222:0.60 235:0.60 239:0.80 241:0.24 242:0.54 243:0.11 244:0.83 245:0.55 247:0.55 253:0.61 257:0.93 259:0.21 265:0.17 266:0.38 267:0.73 271:0.46 276:0.40 277:0.87 279:0.78 282:0.80 283:0.67 290:0.82 292:0.88 297:0.36 300:0.43 +1 1:0.61 10:0.98 11:0.87 12:0.51 17:0.55 18:0.75 21:0.22 27:0.34 29:0.86 30:0.52 34:0.91 36:0.21 37:0.50 39:0.23 43:0.57 45:0.96 66:0.94 69:0.71 70:0.64 71:0.64 74:0.82 81:0.57 83:0.56 86:0.80 91:0.51 97:0.89 98:0.88 99:0.83 101:0.65 108:0.54 114:0.85 122:0.67 123:0.52 124:0.36 126:0.32 127:0.59 129:0.05 133:0.36 135:0.39 139:0.76 144:0.85 145:0.50 146:0.95 147:0.96 149:0.72 150:0.52 154:0.44 155:0.48 158:0.77 159:0.68 165:0.65 170:0.82 172:0.94 173:0.61 174:0.97 176:0.63 177:0.88 178:0.55 179:0.22 187:0.87 192:0.47 197:0.97 201:0.58 208:0.50 212:0.80 215:0.79 216:0.37 222:0.60 235:0.31 239:0.97 241:0.49 242:0.18 243:0.94 245:0.74 247:0.93 253:0.68 254:0.98 259:0.21 265:0.88 266:0.54 267:0.44 271:0.46 276:0.88 279:0.76 283:0.82 290:0.85 297:0.36 300:0.70 +2 1:0.64 10:0.98 11:0.92 12:0.51 17:0.85 18:0.82 21:0.47 22:0.93 27:0.72 29:0.86 30:0.58 33:0.97 34:0.52 36:0.22 39:0.23 43:0.89 45:0.89 47:0.93 60:0.95 64:0.77 66:0.36 69:0.49 70:0.75 71:0.64 74:0.80 75:0.99 81:0.76 83:0.91 85:0.88 86:0.90 91:0.06 98:0.59 101:0.96 104:0.96 106:0.81 108:0.78 114:0.80 117:0.86 122:0.95 123:0.77 124:0.82 126:0.54 127:0.88 129:0.59 133:0.81 135:0.98 139:0.91 144:0.85 146:0.54 147:0.60 149:0.92 150:0.59 154:0.88 155:0.50 158:0.86 159:0.81 165:0.93 170:0.82 172:0.79 173:0.28 174:0.97 176:0.73 177:0.88 178:0.55 179:0.30 187:0.87 192:0.49 197:0.96 201:0.64 206:0.81 208:0.64 212:0.59 215:0.63 216:0.11 219:0.95 222:0.60 226:0.96 232:0.98 235:0.74 236:0.97 239:0.98 242:0.24 243:0.32 245:0.88 247:0.95 253:0.65 259:0.21 262:0.93 265:0.60 266:0.91 267:0.84 271:0.46 276:0.83 279:0.81 283:0.67 290:0.80 291:0.97 292:0.88 297:0.36 300:0.94 +2 7:0.81 10:0.96 11:0.92 12:0.51 17:0.72 18:0.86 21:0.78 27:0.42 29:0.86 30:0.45 32:0.80 34:0.78 36:0.87 37:0.48 39:0.23 43:0.77 44:0.93 45:0.77 60:0.87 66:0.16 69:0.80 70:0.73 71:0.64 74:0.79 75:0.99 77:0.70 83:0.91 85:0.80 86:0.90 91:0.11 98:0.53 101:0.87 102:0.98 108:0.63 121:0.90 122:0.57 123:0.74 124:0.73 127:0.42 129:0.05 133:0.73 135:0.99 139:0.94 144:0.85 145:0.95 146:0.49 147:0.55 149:0.80 154:0.70 155:0.56 158:0.86 159:0.61 170:0.82 172:0.68 173:0.74 174:0.89 177:0.88 178:0.55 179:0.40 187:0.39 192:0.57 193:0.99 195:0.83 197:0.90 204:0.84 208:0.64 212:0.70 216:0.24 219:0.95 222:0.60 226:0.96 230:0.83 233:0.66 235:0.88 239:0.98 241:0.47 242:0.12 243:0.60 245:0.76 247:0.95 253:0.55 259:0.21 265:0.34 266:0.80 267:0.85 271:0.46 276:0.70 277:0.69 279:0.80 281:0.86 282:0.88 283:0.67 292:0.88 299:0.88 300:0.55 +2 1:0.68 7:0.81 10:0.98 11:0.92 12:0.51 17:0.70 18:0.86 21:0.05 22:0.93 27:0.64 29:0.86 30:0.93 32:0.80 33:0.97 34:1.00 36:0.33 37:0.36 39:0.23 43:0.88 44:0.93 45:0.89 47:0.93 48:0.91 64:0.77 66:0.91 69:0.15 70:0.21 71:0.64 74:0.78 76:0.94 77:0.89 81:0.83 83:0.91 85:0.80 86:0.94 91:0.46 98:0.87 101:1.00 102:0.98 104:0.87 106:0.81 108:0.12 114:0.95 117:0.86 121:0.90 122:0.89 123:0.12 126:0.54 127:0.39 129:0.05 135:0.81 139:0.95 144:0.85 145:0.63 146:0.62 147:0.67 149:0.92 150:0.78 154:0.86 155:0.56 159:0.23 165:0.93 170:0.82 172:0.75 173:0.41 174:0.91 176:0.73 177:0.88 178:0.55 179:0.48 187:0.68 192:0.57 193:0.99 195:0.60 197:0.95 201:0.67 204:0.83 206:0.81 208:0.64 212:0.73 215:0.91 216:0.17 222:0.60 230:0.83 232:0.91 233:0.76 235:0.31 236:0.97 239:0.99 241:0.21 242:0.38 243:0.91 247:0.76 253:0.72 259:0.21 262:0.93 265:0.87 266:0.27 267:0.36 271:0.46 276:0.62 281:0.91 282:0.88 290:0.95 291:0.97 297:0.36 299:0.88 300:0.33 +2 1:0.65 7:0.75 10:0.87 11:0.88 12:0.51 17:0.70 18:0.72 21:0.22 27:0.13 29:0.86 30:0.62 32:0.67 34:0.98 36:0.28 37:0.85 39:0.23 43:0.59 45:0.85 64:0.77 66:0.72 69:0.88 70:0.75 71:0.64 74:0.59 77:0.89 81:0.71 83:0.69 86:0.80 91:0.38 98:0.49 99:0.95 101:0.96 108:0.75 114:0.88 122:0.82 123:0.74 124:0.43 126:0.51 127:0.24 129:0.05 133:0.59 135:0.87 139:0.81 144:0.85 145:0.77 146:0.64 147:0.69 149:0.43 150:0.60 154:0.51 155:0.54 159:0.42 165:0.81 170:0.82 172:0.59 173:0.88 174:0.83 176:0.70 177:0.88 178:0.55 179:0.49 187:0.39 192:0.56 195:0.82 197:0.83 201:0.63 204:0.82 208:0.61 212:0.61 215:0.79 216:0.34 222:0.60 230:0.77 233:0.66 235:0.42 239:0.86 241:0.53 242:0.16 243:0.75 245:0.53 247:0.82 253:0.63 254:0.86 259:0.21 265:0.51 266:0.54 267:0.44 271:0.46 276:0.49 281:0.86 282:0.75 290:0.88 297:0.36 300:0.70 +1 1:0.74 11:0.57 12:0.06 17:0.07 20:0.85 21:0.64 26:0.96 27:0.55 30:0.21 32:0.75 34:0.86 36:0.96 37:0.43 39:0.55 43:0.67 55:0.42 58:0.93 66:0.30 69:0.78 70:0.91 74:0.97 77:0.70 78:0.89 81:0.62 85:0.72 87:0.98 91:0.11 98:0.71 100:0.91 101:0.70 108:0.91 111:0.89 114:0.72 117:0.86 122:0.67 123:0.59 124:0.76 126:0.54 127:0.73 129:0.05 133:0.74 135:0.61 141:0.91 145:0.44 146:0.93 147:0.05 149:0.77 150:0.68 152:0.85 154:0.61 155:0.67 159:0.79 169:0.88 172:0.85 173:0.63 176:0.73 177:0.05 178:0.55 179:0.21 186:0.89 187:0.99 192:0.59 195:0.91 199:1.00 201:0.74 212:0.73 215:0.53 216:0.59 222:0.90 223:0.96 224:0.93 232:0.70 234:0.91 235:0.84 241:0.07 242:0.72 243:0.06 245:0.94 247:0.67 253:0.75 257:0.65 259:0.21 261:0.88 265:0.42 266:0.84 267:0.28 271:0.55 274:0.91 276:0.90 282:0.83 290:0.72 297:0.36 300:0.84 +1 12:0.06 17:0.59 21:0.54 26:0.96 27:0.06 30:0.76 34:0.33 36:0.59 37:0.90 39:0.55 43:0.38 55:0.84 58:0.93 66:0.45 69:0.90 70:0.43 74:0.81 77:0.70 81:0.38 91:0.14 98:0.50 108:0.80 114:0.65 117:0.86 122:0.67 123:0.79 124:0.81 126:0.32 127:0.44 129:0.59 133:0.82 135:0.60 141:0.91 145:0.94 146:0.84 147:0.05 149:0.40 150:0.33 154:0.52 159:0.78 163:0.94 172:0.48 173:0.81 176:0.56 177:0.05 178:0.55 179:0.63 187:0.87 195:0.94 199:0.97 212:0.23 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.41 242:0.70 243:0.12 245:0.58 247:0.60 253:0.63 254:0.80 257:0.65 259:0.21 265:0.52 266:0.80 267:0.82 271:0.55 274:0.91 276:0.54 282:0.84 290:0.65 300:0.43 +2 1:0.74 7:0.78 8:0.97 9:0.80 12:0.06 17:0.36 21:0.22 25:0.88 26:0.96 27:0.03 30:0.58 32:0.80 34:0.94 36:0.28 37:0.98 39:0.55 41:0.82 43:0.43 55:0.92 58:0.98 66:0.49 69:0.23 70:0.33 74:0.75 76:0.94 77:0.89 81:0.88 83:0.76 91:0.44 96:0.73 98:0.75 104:0.58 108:0.18 114:0.90 117:0.86 120:0.60 123:0.18 124:0.52 126:0.54 127:0.54 129:0.05 133:0.39 135:0.79 140:0.45 141:0.98 145:0.65 146:0.67 147:0.71 149:0.35 150:0.93 151:0.62 153:0.48 154:0.70 155:0.67 158:0.85 159:0.36 162:0.86 163:0.86 164:0.57 165:0.86 172:0.32 173:0.36 176:0.73 177:0.38 179:0.86 187:0.68 192:0.59 195:0.62 199:0.97 201:0.74 202:0.36 208:0.64 212:0.37 215:0.79 216:0.54 220:0.82 222:0.90 223:0.96 224:1.00 230:0.81 232:0.97 233:0.76 234:1.00 235:0.42 241:0.57 242:0.58 243:0.60 245:0.55 247:0.47 248:0.60 253:0.76 254:0.86 255:0.79 256:0.45 259:0.21 260:0.60 265:0.75 266:0.38 267:0.84 268:0.46 271:0.55 274:0.98 276:0.37 282:0.88 285:0.62 290:0.90 297:0.36 299:0.88 300:0.25 +1 8:0.87 12:0.06 17:0.55 21:0.59 26:0.96 27:0.06 30:0.89 34:0.67 36:0.79 37:0.90 39:0.55 43:0.44 55:0.96 58:0.98 66:0.16 69:0.25 70:0.20 74:0.84 76:0.85 77:0.70 81:0.35 91:0.18 96:0.69 98:0.13 108:0.20 114:0.64 117:0.86 122:0.67 123:0.19 124:0.86 126:0.32 127:0.20 129:0.93 133:0.84 135:0.58 140:0.45 141:0.98 145:0.74 146:0.24 147:0.30 149:0.35 150:0.31 151:0.48 153:0.72 154:0.33 158:0.84 159:0.54 162:0.67 163:0.79 172:0.10 173:0.14 175:0.75 176:0.56 177:0.05 179:0.79 187:0.95 190:0.77 195:0.91 199:0.97 202:0.53 212:0.30 216:0.76 220:0.97 222:0.90 223:0.96 224:0.98 232:0.95 234:0.98 235:0.68 241:0.52 242:0.63 243:0.37 244:0.61 245:0.40 247:0.35 248:0.48 253:0.65 256:0.45 257:0.65 259:0.21 265:0.14 266:0.27 267:0.59 268:0.57 271:0.55 274:0.97 276:0.29 277:0.69 282:0.84 285:0.50 290:0.64 300:0.18 +1 8:0.87 12:0.06 17:0.43 20:0.93 21:0.78 26:0.96 27:0.02 34:0.55 36:0.70 37:0.98 39:0.55 58:0.98 74:0.45 78:0.72 91:0.23 96:0.69 100:0.73 111:0.72 117:0.86 135:0.56 140:0.45 141:0.98 145:0.93 151:0.48 152:0.97 153:0.48 158:0.84 162:0.86 169:0.72 175:0.91 186:0.73 187:0.39 190:0.77 199:0.98 202:0.36 216:0.73 220:0.96 222:0.90 223:0.96 224:0.99 232:0.87 234:0.99 235:0.22 241:0.37 244:0.83 248:0.48 253:0.41 256:0.45 257:0.84 259:0.21 261:0.73 267:0.76 268:0.46 271:0.55 274:0.98 277:0.87 285:0.50 +1 12:0.06 17:0.20 21:0.64 26:0.96 27:0.61 30:0.44 32:0.75 34:0.55 36:0.94 37:0.37 39:0.55 43:0.69 55:0.42 58:0.93 66:0.12 69:0.76 70:0.78 74:0.97 81:0.52 91:0.12 98:0.54 108:0.90 114:0.72 117:0.86 122:0.67 123:0.57 124:0.78 126:0.54 127:0.73 129:0.59 133:0.82 135:0.86 141:0.91 145:0.42 146:0.81 147:0.05 149:0.77 150:0.56 154:0.62 159:0.76 172:0.86 173:0.63 176:0.73 177:0.05 178:0.55 179:0.26 187:0.99 195:0.89 199:0.99 212:0.74 215:0.53 216:0.87 222:0.90 223:0.96 224:0.93 232:0.84 234:0.91 235:0.80 241:0.10 242:0.78 243:0.06 245:0.88 247:0.67 253:0.74 257:0.65 259:0.21 265:0.46 266:0.87 267:0.36 271:0.55 274:0.91 276:0.86 290:0.72 297:0.36 300:0.84 +1 1:0.74 11:0.57 12:0.06 17:0.24 21:0.47 26:0.96 27:0.56 30:0.26 32:0.78 34:0.75 36:0.32 37:0.60 39:0.55 43:0.65 55:0.45 58:0.93 66:0.90 69:0.83 70:0.82 74:0.92 77:0.70 81:0.70 85:0.80 91:0.15 98:0.85 101:0.74 104:0.90 106:0.81 108:0.67 114:0.80 122:0.67 123:0.65 124:0.36 126:0.54 127:0.50 129:0.05 133:0.39 135:0.47 141:0.91 145:0.67 146:0.93 147:0.05 149:0.69 150:0.75 154:0.62 155:0.67 159:0.62 172:0.87 173:0.79 176:0.73 177:0.05 178:0.55 179:0.32 187:0.39 192:0.59 195:0.79 199:0.99 201:0.74 206:0.81 212:0.60 215:0.63 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.10 242:0.40 243:0.07 245:0.67 247:0.67 253:0.75 257:0.65 259:0.21 265:0.85 266:0.71 267:0.44 271:0.55 274:0.91 276:0.79 282:0.86 290:0.80 297:0.36 300:0.84 +2 6:0.98 7:0.78 8:0.92 12:0.06 17:0.38 20:0.85 21:0.40 25:0.88 26:0.95 27:0.03 30:0.45 32:0.75 34:0.92 36:0.55 37:0.98 39:0.55 43:0.43 55:0.84 58:0.93 66:0.82 69:0.23 70:0.47 74:0.57 78:0.92 81:0.62 91:0.40 98:0.95 100:0.98 104:0.58 106:0.81 108:0.18 111:0.97 123:0.18 126:0.32 127:0.65 129:0.05 135:0.92 141:0.91 145:0.77 146:0.79 147:0.82 149:0.47 150:0.45 152:1.00 154:0.33 159:0.34 163:0.75 169:0.97 172:0.48 173:0.39 177:0.38 178:0.55 179:0.85 186:0.92 187:0.68 195:0.59 199:0.95 206:0.81 212:0.30 215:0.67 216:0.54 222:0.90 223:0.93 224:0.93 230:0.81 233:0.76 234:0.91 235:0.42 241:0.50 242:0.53 243:0.83 244:0.61 247:0.55 253:0.46 259:0.21 261:0.98 265:0.95 266:0.47 267:0.79 271:0.55 274:0.91 276:0.40 297:0.36 300:0.33 +1 8:0.82 12:0.06 17:0.61 20:0.96 21:0.78 26:0.96 27:0.02 34:0.58 36:0.71 37:0.98 39:0.55 58:0.99 74:0.45 78:0.72 91:0.23 96:0.69 100:0.73 111:0.72 117:0.86 135:0.58 140:0.45 141:0.98 145:0.93 151:0.48 152:0.97 153:0.46 158:0.84 162:0.86 169:0.72 175:0.91 186:0.73 187:0.39 190:0.77 199:0.98 202:0.49 216:0.73 220:0.96 222:0.90 223:0.96 224:0.99 232:0.87 234:0.99 235:0.22 241:0.51 244:0.83 248:0.48 253:0.42 256:0.58 257:0.84 259:0.21 261:0.73 267:0.76 268:0.58 271:0.55 274:0.98 277:0.87 285:0.50 +1 7:0.78 8:0.98 12:0.06 17:0.40 21:0.78 25:0.88 26:0.94 27:0.03 30:0.58 32:0.75 34:0.88 36:0.81 37:0.98 39:0.55 43:0.43 55:0.92 58:0.93 66:0.32 69:0.82 70:0.54 74:0.44 91:0.46 98:0.60 104:0.58 106:0.81 108:0.78 123:0.77 124:0.73 127:0.87 129:0.59 133:0.64 135:0.92 141:0.91 145:0.52 146:0.59 147:0.62 149:0.45 154:0.59 159:0.57 163:0.86 172:0.41 173:0.84 177:0.05 178:0.55 179:0.72 187:0.68 191:0.78 195:0.71 199:0.94 202:0.46 206:0.81 212:0.23 216:0.54 222:0.90 223:0.92 224:0.93 230:0.81 233:0.69 234:0.91 235:0.22 241:0.27 242:0.73 243:0.32 245:0.68 247:0.39 253:0.38 254:0.86 257:0.65 259:0.21 265:0.61 266:0.63 267:0.87 271:0.55 274:0.91 276:0.55 300:0.43 +2 6:0.98 7:0.78 8:0.92 12:0.06 17:0.36 20:0.86 21:0.40 25:0.88 26:0.94 27:0.03 30:0.11 32:0.75 34:0.92 36:0.45 37:0.98 39:0.55 43:0.43 55:0.45 58:0.93 66:0.36 69:0.23 70:0.69 74:0.41 78:0.92 81:0.62 91:0.58 98:0.58 100:0.97 104:0.58 108:0.18 111:0.95 120:0.73 123:0.18 124:0.60 126:0.32 127:0.71 129:0.05 133:0.39 135:0.95 140:0.45 141:0.91 145:0.54 146:0.61 147:0.67 149:0.48 150:0.45 151:0.66 152:1.00 153:0.48 154:0.33 159:0.49 162:0.71 164:0.75 169:0.97 172:0.32 173:0.29 177:0.38 179:0.84 186:0.91 187:0.68 190:0.77 191:0.81 195:0.67 199:0.94 202:0.66 212:0.14 215:0.67 216:0.54 220:0.91 222:0.90 223:0.92 224:0.93 230:0.81 233:0.76 234:0.91 235:0.42 241:0.70 242:0.75 243:0.51 244:0.61 245:0.62 247:0.39 248:0.66 253:0.36 256:0.64 259:0.21 260:0.74 261:0.98 265:0.59 266:0.71 267:0.92 268:0.69 271:0.55 274:0.91 276:0.39 285:0.50 297:0.36 300:0.43 +1 1:0.74 7:0.81 12:0.06 17:0.69 21:0.40 26:0.96 27:0.11 30:0.62 32:0.75 34:0.88 36:0.71 37:0.94 39:0.55 43:0.31 55:0.52 58:0.93 66:0.75 69:0.49 70:0.23 74:0.65 77:0.99 81:0.76 91:0.27 98:0.74 108:0.38 114:0.82 121:0.97 123:0.36 126:0.54 127:0.24 129:0.05 135:0.30 141:0.91 145:0.98 146:0.56 147:0.62 149:0.24 150:0.79 154:0.79 155:0.67 159:0.20 163:0.75 172:0.32 173:0.66 176:0.73 177:0.38 178:0.55 179:0.84 187:0.39 192:0.59 195:0.58 199:0.97 201:0.74 204:0.89 208:0.64 212:0.30 215:0.67 216:0.40 222:0.90 223:0.96 224:0.93 230:0.87 232:0.72 233:0.76 234:0.91 235:0.52 241:0.27 242:0.63 243:0.77 247:0.35 253:0.67 254:0.86 259:0.21 265:0.74 266:0.27 267:0.69 271:0.55 274:0.91 276:0.27 282:0.84 290:0.82 297:0.36 300:0.18 +1 8:0.86 12:0.06 17:0.93 21:0.13 26:0.96 27:0.06 30:0.85 34:0.53 36:0.54 37:0.87 39:0.55 43:0.45 55:0.84 58:0.98 66:0.87 69:0.07 70:0.29 74:0.94 76:0.85 77:0.89 81:0.69 91:0.31 96:0.74 98:0.93 108:0.06 114:0.74 117:0.86 122:0.67 123:0.06 126:0.32 127:0.58 129:0.05 135:0.42 140:0.45 141:0.98 145:0.44 146:0.87 147:0.89 149:0.60 150:0.50 151:0.58 153:0.48 154:0.34 159:0.44 162:0.86 163:0.94 172:0.63 173:0.18 176:0.56 177:0.38 179:0.70 187:0.68 190:0.77 195:0.65 199:0.98 202:0.36 212:0.48 216:0.54 220:0.74 222:0.90 223:0.96 224:1.00 232:0.82 234:1.00 235:0.12 241:0.18 242:0.80 243:0.88 244:0.61 247:0.35 248:0.56 253:0.80 256:0.45 259:0.21 265:0.93 266:0.63 267:0.36 268:0.46 271:0.55 274:0.97 276:0.53 282:0.84 285:0.50 290:0.74 300:0.33 +1 12:0.06 17:0.49 21:0.64 26:0.96 27:0.24 30:0.90 34:0.61 36:0.34 37:0.80 39:0.55 43:0.29 55:0.52 58:0.93 66:0.63 69:0.95 70:0.29 74:0.89 76:0.85 77:0.89 81:0.33 91:0.22 98:0.36 108:0.91 114:0.63 117:0.86 122:0.67 123:0.91 124:0.64 126:0.32 127:0.70 129:0.05 133:0.74 135:0.26 141:0.91 145:0.74 146:0.50 147:0.05 149:0.50 150:0.30 154:0.21 159:0.77 172:0.75 173:0.95 176:0.56 177:0.05 178:0.55 179:0.47 187:0.68 195:0.89 199:0.99 212:0.81 216:0.67 222:0.90 223:0.96 224:0.93 232:0.90 234:0.91 235:0.74 241:0.07 242:0.77 243:0.08 244:0.61 245:0.73 247:0.47 253:0.67 257:0.65 259:0.21 265:0.38 266:0.47 267:0.19 271:0.55 274:0.91 276:0.59 277:0.69 282:0.84 290:0.63 300:0.43 +1 1:0.74 11:0.57 12:0.06 17:0.22 21:0.47 26:0.96 27:0.56 30:0.43 32:0.78 34:0.75 36:0.43 37:0.60 39:0.55 43:0.73 55:0.45 58:0.93 66:0.49 69:0.57 70:0.86 74:0.96 77:0.70 81:0.70 85:0.80 91:0.17 98:0.61 101:0.74 104:0.90 106:0.81 108:0.65 114:0.80 122:0.67 123:0.62 124:0.73 126:0.54 127:0.49 129:0.59 133:0.76 135:0.34 141:0.91 145:0.67 146:0.32 147:0.38 149:0.74 150:0.75 154:0.82 155:0.67 159:0.58 172:0.75 173:0.51 176:0.73 177:0.38 178:0.55 179:0.35 187:0.39 192:0.59 195:0.76 199:0.99 201:0.74 206:0.81 212:0.77 215:0.63 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.21 242:0.59 243:0.18 245:0.82 247:0.60 253:0.76 259:0.21 265:0.62 266:0.71 267:0.52 271:0.55 274:0.91 276:0.75 282:0.86 290:0.80 297:0.36 300:0.84 +1 1:0.74 12:0.06 17:0.30 21:0.47 26:0.96 27:0.56 30:0.41 32:0.78 34:0.70 36:0.31 37:0.60 39:0.55 43:0.29 55:0.45 58:0.93 66:0.89 69:0.83 70:0.77 74:0.94 77:0.70 81:0.70 91:0.10 98:0.83 104:0.90 106:0.81 108:0.67 114:0.80 122:0.67 123:0.65 124:0.36 126:0.54 127:0.46 129:0.05 133:0.39 135:0.56 141:0.91 145:0.67 146:0.92 147:0.05 149:0.57 150:0.75 154:0.62 155:0.67 159:0.61 172:0.86 173:0.79 176:0.73 177:0.05 178:0.55 179:0.34 187:0.39 192:0.59 195:0.79 199:0.99 201:0.74 206:0.81 212:0.61 215:0.63 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.15 242:0.60 243:0.07 245:0.66 247:0.67 253:0.75 254:0.74 257:0.65 259:0.21 265:0.83 266:0.71 267:0.44 271:0.55 274:0.91 276:0.77 282:0.86 290:0.80 297:0.36 300:0.70 +0 1:0.74 7:0.66 9:0.80 12:0.06 17:0.59 21:0.59 27:0.72 29:0.71 30:0.33 31:0.86 34:0.79 36:0.26 37:0.64 39:0.74 43:0.17 55:0.71 64:0.77 66:0.60 69:0.38 70:0.75 71:0.73 79:0.86 81:0.45 83:0.60 91:0.54 96:0.68 98:0.77 99:0.95 108:0.30 114:0.58 120:0.56 123:0.28 124:0.50 126:0.54 127:0.67 129:0.59 133:0.47 135:0.26 137:0.86 140:0.45 146:0.74 147:0.78 149:0.35 150:0.67 151:0.47 153:0.48 154:0.12 155:0.67 159:0.45 162:0.81 164:0.68 165:0.70 172:0.63 173:0.43 175:0.91 176:0.73 177:0.05 179:0.60 187:0.39 190:0.89 192:0.87 201:0.93 202:0.61 208:0.64 212:0.73 215:0.39 216:0.20 220:0.96 222:0.35 230:0.69 233:0.73 235:0.88 241:0.46 242:0.82 243:0.67 244:0.83 245:0.78 247:0.12 248:0.47 253:0.40 255:0.95 256:0.64 257:0.84 259:0.21 260:0.55 265:0.77 266:0.47 267:0.36 268:0.66 271:0.88 276:0.61 277:0.87 283:0.78 284:0.91 285:0.62 290:0.58 297:0.36 300:0.55 +1 12:0.06 17:0.89 21:0.78 27:0.66 29:0.71 30:0.30 34:0.55 36:0.29 37:0.35 39:0.74 43:0.12 55:0.92 66:0.12 69:0.92 70:0.60 71:0.73 74:0.32 91:0.22 98:0.25 104:0.89 106:0.81 108:0.83 120:0.53 123:0.82 124:0.89 127:0.49 129:0.05 133:0.87 135:0.61 140:0.45 145:0.87 146:0.47 147:0.33 149:0.18 151:0.46 153:0.48 154:0.19 159:0.75 162:0.86 163:0.96 164:0.53 172:0.16 173:0.87 175:0.75 177:0.05 179:0.77 187:0.21 190:0.89 202:0.36 206:0.81 212:0.23 216:0.19 220:0.97 222:0.35 235:0.97 241:0.21 242:0.76 243:0.25 244:0.61 245:0.50 247:0.39 248:0.46 253:0.27 254:0.88 256:0.45 257:0.84 259:0.21 260:0.53 265:0.27 266:0.63 267:0.59 268:0.46 271:0.88 276:0.44 277:0.69 285:0.47 300:0.43 +0 12:0.06 17:0.84 21:0.78 27:0.58 29:0.71 30:0.95 34:0.94 36:0.66 37:0.29 39:0.74 43:0.10 55:0.98 66:0.20 69:0.83 71:0.73 74:0.30 91:0.35 98:0.08 108:0.67 123:0.65 124:0.61 127:0.22 129:0.59 133:0.47 135:0.58 146:0.05 147:0.05 149:0.07 154:0.08 159:0.33 172:0.05 173:0.85 175:0.91 177:0.05 178:0.55 179:0.99 187:0.68 216:0.36 222:0.35 235:0.60 241:0.10 243:0.40 244:0.83 245:0.36 253:0.26 257:0.84 259:0.21 265:0.08 267:0.44 271:0.88 277:0.87 300:0.08 +1 1:0.69 7:0.66 11:0.83 12:0.06 17:0.72 18:0.61 21:0.68 27:0.57 29:0.71 30:0.43 32:0.64 34:0.24 36:0.48 37:0.61 39:0.74 43:0.68 45:0.91 55:0.71 66:0.93 69:0.88 70:0.66 71:0.73 74:0.86 76:0.85 77:0.70 81:0.29 85:0.72 86:0.68 91:0.09 98:0.82 101:0.87 104:0.87 106:0.81 108:0.77 114:0.54 117:0.86 122:0.75 123:0.75 124:0.36 126:0.44 127:0.52 129:0.05 133:0.43 135:0.63 139:0.66 145:0.77 146:0.97 147:0.18 149:0.85 150:0.47 154:0.37 155:0.58 159:0.80 172:0.96 173:0.77 176:0.61 177:0.38 178:0.55 179:0.17 187:0.21 192:0.59 195:0.93 201:0.68 204:0.85 206:0.81 208:0.54 212:0.90 215:0.37 216:0.86 222:0.35 230:0.69 232:0.83 233:0.73 235:0.84 241:0.30 242:0.33 243:0.07 245:0.85 247:0.82 253:0.46 257:0.65 265:0.82 266:0.75 267:0.82 271:0.88 276:0.92 282:0.73 290:0.54 297:0.36 300:0.70 +0 8:0.67 12:0.06 17:0.89 18:0.97 21:0.78 27:0.56 29:0.71 30:0.87 34:0.61 36:0.30 37:0.26 39:0.74 43:0.06 55:0.52 66:0.84 69:0.95 70:0.27 71:0.73 74:0.38 86:0.94 91:0.44 98:0.66 108:0.91 122:0.86 123:0.90 124:0.39 127:0.50 129:0.05 133:0.60 135:0.70 139:0.92 145:0.72 146:0.24 147:0.26 149:0.36 154:0.28 159:0.82 172:0.95 173:0.90 177:0.38 178:0.55 179:0.18 187:0.21 202:0.36 212:0.92 216:0.35 222:0.35 235:0.60 241:0.21 242:0.81 243:0.07 245:0.87 247:0.60 253:0.31 254:0.96 257:0.65 259:0.21 265:0.67 266:0.75 267:0.36 271:0.88 276:0.88 277:0.87 300:0.55 +0 12:0.06 17:0.59 18:0.64 21:0.78 27:0.45 29:0.71 30:0.95 34:0.83 36:0.17 37:0.50 39:0.74 43:0.11 55:0.71 66:0.54 69:0.78 71:0.73 86:0.64 91:0.25 98:0.19 108:0.61 123:0.59 127:0.10 129:0.05 135:0.90 139:0.62 145:0.59 146:0.29 147:0.35 149:0.08 154:0.09 159:0.12 172:0.10 173:0.74 175:0.75 177:0.38 178:0.55 179:0.49 187:0.68 216:0.77 222:0.35 235:1.00 241:0.21 243:0.63 244:0.83 253:0.20 257:0.65 259:0.21 265:0.21 267:0.88 271:0.88 277:0.69 300:0.08 +0 12:0.06 17:0.75 18:0.78 21:0.78 27:0.72 29:0.71 30:0.68 34:0.50 36:0.51 39:0.74 43:0.13 48:0.91 55:0.92 66:0.20 69:0.79 70:0.43 71:0.73 74:0.36 86:0.74 91:0.37 98:0.27 108:0.63 122:0.83 123:0.60 124:0.85 127:0.35 129:0.59 132:0.97 133:0.82 135:0.61 139:0.75 145:0.54 146:0.35 147:0.05 149:0.15 154:0.27 159:0.47 163:0.86 172:0.16 173:0.80 175:0.75 177:0.05 178:0.55 179:0.84 187:0.21 212:0.19 216:0.05 222:0.35 235:0.99 241:0.47 242:0.45 243:0.19 244:0.61 245:0.43 247:0.47 253:0.30 254:0.74 257:0.84 259:0.21 265:0.29 266:0.47 267:0.59 271:0.88 276:0.34 277:0.69 281:0.91 300:0.33 +1 11:0.57 12:0.06 17:0.66 18:0.81 21:0.05 25:0.88 27:0.63 29:0.71 30:0.53 34:0.47 36:0.63 37:0.63 39:0.74 43:0.75 66:0.69 69:0.82 70:0.68 71:0.73 74:0.61 81:0.40 85:0.88 86:0.92 91:0.17 98:0.75 101:0.87 104:0.54 108:0.67 123:0.64 124:0.43 126:0.26 127:0.78 129:0.05 133:0.37 135:0.85 139:0.89 146:0.74 147:0.35 149:0.84 150:0.34 154:0.67 159:0.47 172:0.70 173:0.89 177:0.05 178:0.55 179:0.59 187:0.95 212:0.61 215:0.78 216:0.21 222:0.35 235:0.05 241:0.10 242:0.45 243:0.25 245:0.75 247:0.67 253:0.39 257:0.84 259:0.21 265:0.76 266:0.63 267:0.52 271:0.88 276:0.59 297:0.36 300:0.55 +0 11:0.57 12:0.06 17:0.49 18:0.70 21:0.05 27:0.14 29:0.71 30:0.09 34:0.42 36:0.69 37:0.78 39:0.74 43:0.70 66:0.23 69:0.87 70:0.94 71:0.73 74:0.51 81:0.40 85:0.85 86:0.81 91:0.07 98:0.60 101:0.87 104:0.58 108:0.74 123:0.84 124:0.82 126:0.26 127:0.77 129:0.05 133:0.81 135:0.92 139:0.77 146:0.71 147:0.31 149:0.76 150:0.34 154:0.60 159:0.71 172:0.63 173:0.84 177:0.05 178:0.55 179:0.46 187:0.68 212:0.30 215:0.78 216:0.63 222:0.35 235:0.05 241:0.18 242:0.73 243:0.20 245:0.77 247:0.47 253:0.36 257:0.84 259:0.21 265:0.51 266:0.75 267:0.84 271:0.88 276:0.73 277:0.87 297:0.36 300:0.70 +0 1:0.74 7:0.66 12:0.06 17:0.53 21:0.59 27:0.72 29:0.71 30:0.45 34:0.99 36:0.42 37:0.64 39:0.74 43:0.17 55:0.84 64:0.77 66:0.43 69:0.38 70:0.57 71:0.73 81:0.45 83:0.60 91:0.17 98:0.65 99:0.95 108:0.72 114:0.58 120:0.53 123:0.71 124:0.59 126:0.54 127:0.59 129:0.59 133:0.47 135:0.56 140:0.45 146:0.61 147:0.66 149:0.28 150:0.67 151:0.46 153:0.46 154:0.12 155:0.67 159:0.50 162:0.62 164:0.53 165:0.70 172:0.45 173:0.38 175:0.91 176:0.73 177:0.05 179:0.70 187:0.39 190:0.89 191:0.70 192:0.87 201:0.93 202:0.49 208:0.64 212:0.37 215:0.39 216:0.20 220:0.97 222:0.35 230:0.69 233:0.73 235:0.88 241:0.32 242:0.82 243:0.47 244:0.83 245:0.72 247:0.12 248:0.46 253:0.40 256:0.45 257:0.84 259:0.21 260:0.53 265:0.66 266:0.63 267:0.95 268:0.45 271:0.88 276:0.51 277:0.87 283:0.78 285:0.47 290:0.58 297:0.36 300:0.43 +0 12:0.06 17:0.28 18:0.61 21:0.54 27:0.45 29:0.71 30:0.56 34:0.67 36:0.18 37:0.50 39:0.74 43:0.13 55:0.92 66:0.51 69:0.80 70:0.46 71:0.73 74:0.41 81:0.24 86:0.63 91:0.12 98:0.42 108:0.64 123:0.61 124:0.77 126:0.26 127:0.58 129:0.05 133:0.81 135:0.92 139:0.61 145:0.50 146:0.74 147:0.05 149:0.18 150:0.24 154:0.28 159:0.79 172:0.41 173:0.65 177:0.05 178:0.55 179:0.79 187:0.68 212:0.13 215:0.39 216:0.77 222:0.35 235:0.60 241:0.46 242:0.24 243:0.15 244:0.61 245:0.48 247:0.74 253:0.32 254:0.95 257:0.84 259:0.21 265:0.44 266:0.75 267:0.44 271:0.88 276:0.41 297:0.36 300:0.33 +1 11:0.57 12:0.06 17:0.84 18:0.95 21:0.68 22:0.93 27:0.57 29:0.71 30:0.09 32:0.80 34:0.80 36:0.32 37:0.38 39:0.74 43:0.68 44:0.93 47:0.93 64:0.77 66:0.97 69:0.44 70:0.94 71:0.73 74:0.73 77:0.70 81:0.29 85:0.95 86:0.97 91:0.08 98:0.93 101:0.87 104:0.90 106:0.81 108:0.34 123:0.33 124:0.36 126:0.44 127:0.68 129:0.05 133:0.37 135:0.97 139:0.97 145:0.97 146:0.99 147:0.99 149:0.91 150:0.24 154:0.57 159:0.82 172:0.98 173:0.22 177:0.70 178:0.55 179:0.15 187:0.21 192:0.51 195:0.93 201:0.68 206:0.81 212:0.72 215:0.37 216:0.72 222:0.35 235:0.84 242:0.45 243:0.97 245:0.80 247:0.82 253:0.42 259:0.21 262:0.93 265:0.93 266:0.63 267:0.59 271:0.88 276:0.94 282:0.88 297:0.36 299:0.88 300:0.70 +0 11:0.83 12:0.06 17:0.40 18:0.80 21:0.05 27:0.14 29:0.71 30:0.09 34:0.47 36:0.62 37:0.78 39:0.74 43:0.75 45:0.77 66:0.74 69:0.79 70:0.97 71:0.73 74:0.63 81:0.40 85:0.72 86:0.91 91:0.20 98:0.82 101:0.87 104:0.58 108:0.63 123:0.61 124:0.42 126:0.26 127:0.69 129:0.05 133:0.37 135:0.97 139:0.88 146:0.84 147:0.31 149:0.59 150:0.34 154:0.69 159:0.52 172:0.77 173:0.82 177:0.05 178:0.55 179:0.49 187:0.95 212:0.71 215:0.78 216:0.63 222:0.35 235:0.05 241:0.32 242:0.63 243:0.22 245:0.79 247:0.67 253:0.39 257:0.84 259:0.21 265:0.82 266:0.75 267:0.87 271:0.88 276:0.68 297:0.36 300:0.84 +0 1:0.74 7:0.66 11:0.64 12:0.06 17:0.95 18:0.82 21:0.59 27:0.72 29:0.71 30:0.60 34:0.68 36:0.28 37:0.64 39:0.74 43:0.17 45:0.66 55:0.59 64:0.77 66:0.67 69:0.38 70:0.79 71:0.73 74:0.39 81:0.45 83:0.60 86:0.81 91:0.27 98:0.66 99:0.95 101:0.52 108:0.30 114:0.58 123:0.28 124:0.58 126:0.54 127:0.75 129:0.05 133:0.73 135:0.44 139:0.77 146:0.82 147:0.85 149:0.42 150:0.67 154:0.63 155:0.67 159:0.68 165:0.70 172:0.80 173:0.28 176:0.73 177:0.70 178:0.55 179:0.41 187:0.21 192:0.87 201:0.93 208:0.64 212:0.68 215:0.39 216:0.20 222:0.35 230:0.69 233:0.73 235:0.88 241:0.44 242:0.73 243:0.72 245:0.76 247:0.76 253:0.40 254:0.80 259:0.21 265:0.66 266:0.71 267:0.59 271:0.88 276:0.75 290:0.58 297:0.36 300:0.84 +0 1:0.74 12:0.06 17:0.78 18:0.70 21:0.64 27:0.72 29:0.71 30:0.76 34:0.61 36:0.23 39:0.74 43:0.12 55:0.59 64:0.77 66:0.64 69:0.76 70:0.15 71:0.73 81:0.42 83:0.60 86:0.68 91:0.82 98:0.60 99:0.83 108:0.59 114:0.57 122:0.85 123:0.57 126:0.54 127:0.18 129:0.05 135:0.44 139:0.66 146:0.44 147:0.50 149:0.10 150:0.66 154:0.09 155:0.67 159:0.16 163:0.98 165:0.70 172:0.16 173:0.93 175:0.75 176:0.73 177:0.38 178:0.55 179:0.92 187:0.21 192:0.87 201:0.93 208:0.64 212:0.95 215:0.38 216:0.08 222:0.35 235:0.84 241:0.81 242:0.82 243:0.69 244:0.83 247:0.12 253:0.38 257:0.65 259:0.21 265:0.61 266:0.12 267:0.36 271:0.88 276:0.15 277:0.69 290:0.57 297:0.36 300:0.13 +0 1:0.74 7:0.66 9:0.80 12:0.06 17:0.64 21:0.59 27:0.72 29:0.71 30:0.55 31:0.86 34:0.98 36:0.19 37:0.64 39:0.74 43:0.17 55:0.71 64:0.77 66:0.25 69:0.38 70:0.57 71:0.73 79:0.86 81:0.45 83:0.60 91:0.37 96:0.67 98:0.63 99:0.95 108:0.71 114:0.58 120:0.64 123:0.28 124:0.57 126:0.54 127:0.63 129:0.59 133:0.47 135:0.56 137:0.86 140:0.80 146:0.62 147:0.67 149:0.27 150:0.67 151:0.47 153:0.48 154:0.12 155:0.67 159:0.45 162:0.86 164:0.65 165:0.70 172:0.45 173:0.43 175:0.91 176:0.73 177:0.05 179:0.73 187:0.39 190:0.89 192:0.87 201:0.93 202:0.50 208:0.64 212:0.59 215:0.39 216:0.20 220:0.62 222:0.35 230:0.69 233:0.73 235:0.88 241:0.41 242:0.82 243:0.45 244:0.83 245:0.70 247:0.12 248:0.46 253:0.40 255:0.95 256:0.58 257:0.84 259:0.21 260:0.61 265:0.63 266:0.54 267:0.36 268:0.59 271:0.88 276:0.50 277:0.87 284:0.89 285:0.62 290:0.58 297:0.36 300:0.43 +2 11:0.57 12:0.06 17:0.66 18:0.90 21:0.78 27:0.51 29:0.71 30:0.33 34:0.53 36:0.42 37:0.33 39:0.74 43:0.73 66:0.72 69:0.86 70:0.80 71:0.73 74:0.63 85:0.91 86:0.94 91:0.10 98:0.68 101:0.87 108:0.81 122:0.65 123:0.80 124:0.43 127:0.25 129:0.59 133:0.46 135:0.99 139:0.92 145:0.70 146:0.45 147:0.52 149:0.85 154:0.63 159:0.62 172:0.84 173:0.76 177:0.70 178:0.55 179:0.22 187:0.21 212:0.47 216:0.85 222:0.35 235:0.80 241:0.03 242:0.29 243:0.25 245:0.88 247:0.79 253:0.39 259:0.21 265:0.68 266:0.63 267:0.99 271:0.88 276:0.78 300:0.55 +0 11:0.57 12:0.06 17:0.49 18:0.74 21:0.05 27:0.14 29:0.71 30:0.09 34:0.49 36:0.77 37:0.78 39:0.74 43:0.79 55:0.71 66:0.59 69:0.08 70:0.91 71:0.73 74:0.57 81:0.40 85:0.85 86:0.85 91:0.06 98:0.79 101:0.87 104:0.58 108:0.07 123:0.07 124:0.52 126:0.26 127:0.87 129:0.05 133:0.37 135:0.92 139:0.81 146:0.92 147:0.93 149:0.83 150:0.34 154:0.72 159:0.72 172:0.78 173:0.11 177:0.70 178:0.55 179:0.42 187:0.68 212:0.30 215:0.78 216:0.63 222:0.35 235:0.05 241:0.18 242:0.75 243:0.67 245:0.90 247:0.47 253:0.38 259:0.21 265:0.79 266:0.75 267:0.79 271:0.88 276:0.76 297:0.36 300:0.70 +1 11:0.57 12:0.06 17:0.40 18:0.81 21:0.05 27:0.14 29:0.71 30:0.26 34:0.63 36:0.75 37:0.78 39:0.74 43:0.75 66:0.75 69:0.82 70:0.91 71:0.73 74:0.61 81:0.40 85:0.88 86:0.92 91:0.15 98:0.83 101:0.87 104:0.58 108:0.67 123:0.65 124:0.41 126:0.26 127:0.73 129:0.05 133:0.37 135:0.93 139:0.89 146:0.89 147:0.31 149:0.85 150:0.34 154:0.67 159:0.60 172:0.79 173:0.82 177:0.05 178:0.55 179:0.47 187:0.95 212:0.52 215:0.78 216:0.63 222:0.35 235:0.05 241:0.07 242:0.63 243:0.21 245:0.79 247:0.67 253:0.39 257:0.84 259:0.21 265:0.83 266:0.71 267:0.76 271:0.88 276:0.70 297:0.36 300:0.84 +0 1:0.74 8:0.59 12:0.06 17:0.78 18:0.70 21:0.64 27:0.53 29:0.71 30:0.76 34:0.61 36:0.24 37:0.24 39:0.74 43:0.12 55:0.59 64:0.77 66:0.64 69:0.76 70:0.15 71:0.73 81:0.42 83:0.60 86:0.68 91:0.86 98:0.60 99:0.83 108:0.59 114:0.57 122:0.85 123:0.57 126:0.54 127:0.18 129:0.05 135:0.49 139:0.66 146:0.44 147:0.50 149:0.10 150:0.66 154:0.09 155:0.67 159:0.16 163:0.98 165:0.70 172:0.16 173:0.93 175:0.75 176:0.73 177:0.38 178:0.55 179:0.92 191:0.73 192:0.87 201:0.93 208:0.64 212:0.95 215:0.38 216:0.14 222:0.35 235:0.84 241:0.94 242:0.82 243:0.69 244:0.83 247:0.12 253:0.38 257:0.65 259:0.21 265:0.61 266:0.12 267:0.59 271:0.88 276:0.15 277:0.69 290:0.57 297:0.36 300:0.13 +2 7:0.66 8:0.66 11:0.92 12:0.06 17:0.83 18:0.83 21:0.47 27:0.53 29:0.71 30:0.61 32:0.64 34:0.74 36:0.31 37:0.45 39:0.74 43:0.63 45:0.88 66:0.86 69:0.89 70:0.44 71:0.73 74:0.76 81:0.25 85:0.93 86:0.93 91:0.37 98:0.68 101:0.97 104:0.91 106:0.81 108:0.79 120:0.53 123:0.77 124:0.38 126:0.26 127:0.54 129:0.05 133:0.59 135:0.69 139:0.90 140:0.45 145:0.67 146:0.83 147:0.05 149:0.88 150:0.25 151:0.46 153:0.48 154:0.53 159:0.63 162:0.86 164:0.53 172:0.91 173:0.90 177:0.05 179:0.26 187:0.21 190:0.89 195:0.83 202:0.36 206:0.81 212:0.91 215:0.41 216:0.27 220:0.99 222:0.35 230:0.69 233:0.73 235:0.52 241:0.32 242:0.40 243:0.06 245:0.76 247:0.60 248:0.46 253:0.43 256:0.45 257:0.84 259:0.21 260:0.53 265:0.69 266:0.47 267:0.59 268:0.46 271:0.88 276:0.82 283:0.78 285:0.47 297:0.36 300:0.55 +1 1:0.74 11:0.88 12:0.37 17:0.38 21:0.59 27:0.45 28:0.92 30:0.58 32:0.80 34:0.70 36:0.19 37:0.50 39:0.53 43:0.82 60:0.87 66:0.51 69:0.70 70:0.62 74:0.92 77:0.70 81:0.60 83:0.84 85:0.96 91:0.05 98:0.64 101:0.82 108:0.66 114:0.74 122:0.57 123:0.64 124:0.78 126:0.54 127:0.56 129:0.89 133:0.83 135:0.86 144:0.85 145:0.49 146:0.24 147:0.30 149:0.92 150:0.74 154:0.77 155:0.67 158:0.87 159:0.77 161:0.85 165:0.78 167:0.45 172:0.71 173:0.53 176:0.73 177:0.38 178:0.55 179:0.43 181:0.64 187:0.68 192:0.59 195:0.91 201:0.74 208:0.64 212:0.16 215:0.55 216:0.77 222:0.76 235:0.74 241:0.24 242:0.54 243:0.20 245:0.75 247:0.47 253:0.73 259:0.21 265:0.65 266:0.91 267:0.59 271:0.38 276:0.72 279:0.86 282:0.88 283:0.71 290:0.74 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 8:0.90 9:0.80 11:0.88 12:0.37 17:0.15 21:0.59 27:0.20 28:0.92 30:0.26 32:0.80 34:0.58 36:0.85 37:0.80 39:0.53 41:0.82 43:0.75 55:0.71 60:0.87 66:0.34 69:0.78 70:0.81 74:0.97 76:0.97 77:0.99 81:0.60 83:0.75 85:0.99 91:0.27 96:0.70 98:0.61 101:0.83 108:0.62 114:0.74 120:0.54 121:0.90 122:0.57 123:0.59 124:0.78 126:0.54 127:0.65 129:0.89 133:0.78 135:0.44 140:0.90 144:0.85 145:1.00 146:0.96 147:0.97 149:0.95 150:0.74 151:0.49 153:0.72 154:0.67 155:0.67 158:0.87 159:0.90 161:0.85 162:0.67 164:0.57 165:0.78 167:0.46 172:0.90 173:0.48 176:0.73 177:0.38 179:0.16 181:0.64 187:0.68 190:0.77 192:0.59 195:0.98 201:0.74 202:0.65 204:0.89 208:0.64 212:0.67 215:0.55 216:0.65 220:0.96 222:0.76 230:0.87 233:0.76 235:0.74 241:0.37 242:0.36 243:0.50 245:0.97 247:0.67 248:0.49 253:0.76 255:0.79 256:0.64 259:0.21 260:0.55 265:0.62 266:0.89 267:0.91 268:0.66 271:0.38 276:0.93 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.97 300:0.94 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.37 17:0.30 21:0.59 27:0.16 28:0.92 30:0.52 32:0.80 34:0.39 36:0.71 37:0.83 39:0.53 41:0.82 43:0.79 60:0.87 66:0.98 69:0.75 70:0.54 74:0.98 76:0.97 77:0.89 81:0.64 83:0.81 85:0.99 91:0.25 96:0.69 98:0.84 101:0.86 104:0.78 108:0.58 114:0.74 120:0.55 121:0.90 122:0.57 123:0.56 126:0.54 127:0.34 129:0.05 135:0.44 140:0.45 144:0.85 145:0.86 146:0.95 147:0.96 149:0.97 150:0.76 151:0.50 153:0.72 154:0.72 155:0.67 158:0.92 159:0.62 161:0.85 162:0.67 164:0.64 165:0.78 167:0.53 172:0.94 173:0.65 176:0.73 177:0.38 179:0.17 181:0.64 187:0.39 192:0.59 195:0.86 201:0.74 202:0.53 204:0.89 208:0.64 212:0.84 215:0.55 216:0.61 220:0.97 222:0.76 230:0.87 232:0.83 233:0.76 235:0.80 241:0.61 242:0.45 243:0.98 247:0.60 248:0.50 253:0.76 255:0.79 256:0.45 259:0.21 260:0.55 265:0.84 266:0.38 267:0.19 268:0.57 271:0.38 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 290:0.74 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.94 7:0.76 8:0.83 9:0.80 11:0.88 12:0.37 17:0.64 20:0.91 21:0.59 27:0.32 28:0.92 30:0.29 32:0.80 34:0.73 36:0.85 37:0.70 39:0.53 41:0.93 43:0.76 55:0.84 60:0.97 66:0.54 69:0.81 70:0.73 74:0.93 77:0.70 78:0.81 81:0.60 83:0.84 85:0.96 91:0.11 96:0.84 98:0.68 100:0.86 101:0.81 108:0.66 111:0.81 114:0.74 117:0.86 120:0.75 122:0.57 123:0.64 124:0.55 126:0.54 127:0.40 129:0.59 133:0.47 135:0.65 140:0.45 144:0.85 145:0.92 146:0.93 147:0.94 149:0.90 150:0.74 151:0.87 152:0.93 153:0.48 154:0.68 155:0.67 158:0.87 159:0.76 161:0.85 162:0.86 164:0.78 165:0.78 167:0.45 169:0.87 172:0.84 173:0.66 176:0.73 177:0.38 179:0.24 181:0.64 186:0.81 187:0.98 189:0.94 191:0.70 192:0.59 195:0.93 201:0.74 202:0.65 208:0.64 212:0.30 215:0.55 216:0.55 220:0.96 222:0.76 230:0.78 232:1.00 233:0.69 235:0.74 238:0.93 241:0.15 242:0.31 243:0.62 245:0.94 247:0.67 248:0.82 253:0.88 255:0.79 256:0.73 259:0.21 260:0.76 261:0.92 265:0.69 266:0.80 267:0.79 268:0.73 271:0.38 276:0.83 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.88 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.37 17:0.53 21:0.47 27:0.27 28:0.92 30:0.53 32:0.80 34:0.47 36:0.65 37:0.73 39:0.53 41:0.82 43:0.95 60:0.97 66:0.74 69:0.33 70:0.56 74:0.98 76:0.97 77:0.99 81:0.79 83:0.84 85:0.98 91:0.40 96:0.85 98:0.83 101:0.86 104:0.80 106:0.81 108:0.26 114:0.80 117:0.86 120:0.69 121:0.99 122:0.57 123:0.25 124:0.43 126:0.54 127:0.65 129:0.59 133:0.47 135:0.24 140:0.45 144:0.85 145:0.74 146:0.93 147:0.95 149:0.98 150:0.86 151:0.87 153:0.48 154:0.95 155:0.67 158:0.92 159:0.69 161:0.85 162:0.86 164:0.57 165:0.84 167:0.46 172:0.91 173:0.23 176:0.73 177:0.38 179:0.26 181:0.64 187:0.39 192:0.59 195:0.84 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.81 215:0.63 216:0.54 222:0.76 230:0.87 232:0.86 233:0.76 235:0.80 241:0.32 242:0.52 243:0.77 245:0.92 247:0.60 248:0.78 253:0.90 255:0.79 256:0.58 259:0.21 260:0.70 265:0.83 266:0.54 267:0.44 268:0.59 271:0.38 276:0.86 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.99 300:0.43 +2 1:0.74 6:0.94 8:0.88 9:0.80 11:0.88 12:0.37 17:0.62 20:0.86 21:0.40 27:0.33 28:0.92 30:0.70 34:0.75 36:0.73 37:0.78 39:0.53 41:0.94 43:0.86 60:0.95 66:0.23 69:0.58 70:0.72 74:0.92 76:0.85 78:0.86 81:0.81 83:0.84 85:0.99 91:0.53 96:0.92 98:0.39 100:0.95 101:0.83 104:0.84 106:0.81 108:0.96 111:0.90 114:0.82 117:0.86 120:0.83 122:0.57 123:0.95 124:0.95 126:0.54 127:0.77 129:0.98 133:0.95 135:0.47 140:0.45 144:0.85 145:0.87 146:0.13 147:0.18 149:0.94 150:0.87 151:0.96 152:0.81 153:0.87 154:0.84 155:0.67 158:0.92 159:0.91 161:0.85 162:0.83 164:0.78 165:0.86 167:0.57 169:0.93 172:0.74 173:0.24 176:0.73 177:0.38 179:0.25 181:0.64 186:0.86 187:0.39 189:0.95 192:0.59 201:0.74 202:0.69 206:0.81 208:0.64 212:0.35 215:0.67 216:0.62 222:0.76 232:0.89 235:0.68 238:0.97 241:0.65 242:0.59 243:0.08 245:0.84 247:0.47 248:0.90 253:0.94 255:0.79 256:0.73 259:0.21 260:0.84 261:0.91 265:0.41 266:0.91 267:0.73 268:0.75 271:0.38 276:0.87 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.84 +2 1:0.74 7:0.81 8:0.95 11:0.88 12:0.37 17:0.28 21:0.40 27:0.13 28:0.92 30:0.58 32:0.80 34:0.62 36:0.64 37:0.81 39:0.53 43:0.85 60:0.87 66:0.75 69:0.61 70:0.55 74:0.98 77:0.70 78:0.97 81:0.72 83:0.87 85:0.99 91:0.03 98:0.82 101:0.86 104:0.99 106:0.81 108:0.46 111:0.99 114:0.82 117:0.86 121:0.90 122:0.57 123:0.44 124:0.42 126:0.54 127:0.41 129:0.59 133:0.47 135:0.44 144:0.85 145:0.40 146:0.96 147:0.97 149:0.98 150:0.82 154:0.83 155:0.67 158:0.92 159:0.71 161:0.85 165:0.83 167:0.44 169:1.00 172:0.92 173:0.43 176:0.73 177:0.38 178:0.55 179:0.20 181:0.64 187:0.87 192:0.59 195:0.89 201:0.74 204:0.89 206:0.81 208:0.64 212:0.54 215:0.67 216:0.80 222:0.76 230:0.87 232:0.99 233:0.76 235:0.52 238:0.97 241:0.01 242:0.52 243:0.77 245:0.93 247:0.47 253:0.77 259:0.21 265:0.82 266:0.63 267:0.59 271:0.38 276:0.88 279:0.86 282:0.88 283:0.82 290:0.82 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.90 7:0.81 8:0.80 9:0.80 11:0.88 12:0.37 17:0.78 20:0.97 21:0.05 27:0.18 28:0.92 30:0.53 32:0.80 34:0.40 36:0.58 37:0.49 39:0.53 41:0.88 43:0.95 60:0.97 66:0.35 69:0.15 70:0.80 74:0.99 77:0.96 78:0.88 81:0.91 83:0.86 85:0.98 91:0.35 96:0.87 98:0.69 100:0.92 101:0.86 104:0.79 106:0.81 108:0.12 111:0.89 114:0.95 117:0.86 120:0.79 121:0.90 122:0.57 123:0.12 124:0.66 126:0.54 127:0.74 129:0.81 133:0.67 135:0.19 140:0.45 144:0.85 145:0.88 146:0.89 147:0.91 149:0.98 150:0.95 151:0.92 152:0.91 153:0.72 154:0.95 155:0.67 158:0.92 159:0.74 161:0.85 162:0.86 164:0.73 165:0.90 167:0.48 169:0.90 172:0.84 173:0.13 176:0.73 177:0.38 179:0.23 181:0.64 186:0.87 187:0.21 189:0.92 192:0.59 195:0.86 201:0.74 202:0.59 204:0.89 206:0.81 208:0.64 212:0.70 215:0.91 216:0.63 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.93 241:0.47 242:0.42 243:0.51 245:0.95 247:0.60 248:0.85 253:0.92 255:0.79 256:0.64 259:0.21 260:0.80 261:0.94 265:0.70 266:0.63 267:0.65 268:0.68 271:0.38 276:0.88 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.97 300:0.84 +1 1:0.74 11:0.88 12:0.37 17:0.40 21:0.54 27:0.45 28:0.92 30:0.54 32:0.72 34:0.78 36:0.18 37:0.50 39:0.53 43:0.82 66:0.88 69:0.70 70:0.71 74:0.78 81:0.63 83:0.74 85:0.91 91:0.08 98:0.51 101:0.78 108:0.53 114:0.77 122:0.57 123:0.51 126:0.54 127:0.15 129:0.05 135:0.79 144:0.85 145:0.49 146:0.79 147:0.83 149:0.83 150:0.76 154:0.77 155:0.67 159:0.35 161:0.85 165:0.79 167:0.56 172:0.67 173:0.54 176:0.73 177:0.38 178:0.55 179:0.23 181:0.64 187:0.68 192:0.59 195:0.89 201:0.74 212:0.30 215:0.58 216:0.77 222:0.76 235:0.68 241:0.65 242:0.44 243:0.89 247:0.60 253:0.68 259:0.21 265:0.53 266:0.38 267:0.73 271:0.38 276:0.54 290:0.77 297:0.36 300:0.55 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.37 17:0.06 21:0.13 27:0.16 28:0.92 30:0.61 32:0.80 34:0.74 36:0.73 37:0.69 39:0.53 41:0.97 43:0.82 60:1.00 66:0.74 69:0.69 70:0.57 74:0.98 77:0.70 81:0.86 83:0.83 85:0.98 91:0.53 96:0.91 98:0.73 101:0.85 104:0.99 106:0.81 108:0.52 114:0.92 117:0.86 120:0.84 121:0.90 122:0.57 123:0.50 124:0.42 126:0.54 127:0.28 129:0.59 133:0.47 135:0.30 138:0.96 140:0.45 144:0.85 145:0.40 146:0.95 147:0.96 149:0.97 150:0.92 151:0.99 153:0.95 154:0.77 155:0.67 158:0.92 159:0.71 161:0.85 162:0.68 164:0.86 165:0.88 167:0.51 172:0.91 173:0.48 176:0.73 177:0.38 179:0.17 181:0.64 187:0.87 192:0.59 195:0.93 201:0.74 202:0.81 204:0.89 206:0.81 208:0.64 212:0.55 215:0.84 216:0.80 222:0.76 230:0.87 232:0.99 233:0.76 235:0.22 241:0.56 242:0.54 243:0.77 245:0.93 247:0.47 248:0.90 253:0.94 255:0.79 256:0.83 259:0.21 260:0.84 265:0.74 266:0.80 267:0.73 268:0.84 271:0.38 276:0.86 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.93 7:0.76 8:0.88 9:0.80 11:0.88 12:0.37 17:0.34 20:0.86 21:0.74 27:0.16 28:0.92 30:0.20 32:0.80 34:0.33 36:0.80 37:0.83 39:0.53 41:0.93 43:0.76 60:0.99 66:0.12 69:0.77 70:0.85 74:0.98 76:0.85 77:0.70 78:0.81 81:0.48 83:0.77 85:1.00 91:0.06 96:0.71 98:0.37 100:0.88 101:0.83 108:0.60 111:0.83 114:0.67 120:0.56 122:0.57 123:0.58 124:0.96 126:0.54 127:0.79 129:0.99 133:0.97 135:0.69 140:0.80 144:0.85 145:0.79 146:0.95 147:0.96 149:0.98 150:0.65 151:0.50 152:0.94 153:0.45 154:0.68 155:0.67 158:0.87 159:0.96 161:0.85 162:0.61 164:0.78 165:0.65 167:0.45 169:0.84 172:0.90 173:0.31 176:0.73 177:0.38 179:0.11 181:0.64 186:0.81 187:0.68 189:0.93 190:0.77 192:0.59 195:0.99 201:0.74 202:0.75 208:0.64 212:0.48 215:0.46 216:0.61 220:0.97 222:0.76 230:0.79 233:0.76 235:0.95 238:0.94 241:0.21 242:0.29 243:0.32 245:0.97 247:0.67 248:0.51 253:0.77 255:0.79 256:0.79 259:0.21 260:0.57 261:0.90 265:0.39 266:0.95 267:0.92 268:0.76 271:0.38 276:0.98 279:0.86 282:0.88 283:0.71 285:0.62 290:0.67 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.81 8:0.91 9:0.80 11:0.88 12:0.37 17:0.15 20:0.93 21:0.40 27:0.29 28:0.92 30:0.54 32:0.80 34:0.67 36:0.64 37:0.99 39:0.53 41:0.96 43:0.76 60:0.99 66:0.94 69:0.82 70:0.47 74:0.98 76:0.94 77:0.70 78:0.87 81:0.77 83:0.82 85:0.99 91:0.44 96:0.88 98:0.81 100:0.94 101:0.86 104:0.98 106:0.81 108:0.72 111:0.90 114:0.82 117:0.86 120:0.78 121:0.90 122:0.57 123:0.70 124:0.36 126:0.54 127:0.34 129:0.59 133:0.47 135:0.24 140:0.45 144:0.85 145:0.42 146:0.13 147:0.18 149:0.98 150:0.84 151:0.78 152:0.86 153:0.46 154:0.67 155:0.67 158:0.92 159:0.74 161:0.85 162:0.74 164:0.84 165:0.81 167:0.54 169:0.92 172:0.97 173:0.66 176:0.73 177:0.38 179:0.14 181:0.64 186:0.87 187:0.87 189:0.96 191:0.70 192:0.59 195:0.93 201:0.74 202:0.77 204:0.89 206:0.81 208:0.64 212:0.88 215:0.67 216:0.45 220:0.87 222:0.76 230:0.87 233:0.76 235:0.60 238:0.95 241:0.63 242:0.45 243:0.07 245:0.86 247:0.60 248:0.85 253:0.92 255:0.79 256:0.83 259:0.21 260:0.79 261:0.94 265:0.81 266:0.47 267:0.76 268:0.81 271:0.38 276:0.93 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 11:0.88 12:0.37 17:0.30 21:0.54 27:0.05 28:0.92 30:0.62 32:0.80 34:0.77 36:0.86 37:0.79 39:0.53 43:0.84 66:0.60 69:0.64 70:0.56 74:0.96 76:0.85 77:0.89 81:0.63 83:0.74 85:0.98 91:0.05 98:0.80 101:0.86 108:0.49 114:0.77 121:0.90 122:0.57 123:0.47 124:0.51 126:0.54 127:0.46 129:0.59 133:0.47 135:0.75 144:0.85 145:0.97 146:0.93 147:0.94 149:0.96 150:0.76 154:0.81 155:0.67 159:0.66 161:0.85 165:0.79 167:0.45 172:0.84 173:0.53 176:0.73 177:0.38 178:0.55 179:0.27 181:0.64 187:0.68 192:0.59 195:0.87 201:0.74 204:0.89 208:0.64 212:0.55 215:0.58 216:0.90 222:0.76 230:0.87 233:0.76 235:0.68 241:0.24 242:0.54 243:0.67 245:0.93 247:0.47 253:0.75 259:0.21 265:0.80 266:0.63 267:0.94 271:0.38 276:0.82 282:0.88 290:0.77 297:0.36 299:0.88 300:0.70 +2 11:0.88 12:0.37 17:0.61 21:0.47 27:0.21 28:0.92 30:0.33 34:0.68 36:0.73 37:0.74 39:0.53 43:0.83 66:0.98 69:0.17 70:0.74 74:0.98 76:0.85 81:0.42 85:0.98 91:0.23 96:0.79 98:0.94 101:0.85 108:0.14 114:0.55 117:0.86 122:0.57 123:0.13 126:0.32 127:0.61 129:0.05 135:0.17 140:0.45 144:0.85 146:0.98 147:0.99 149:0.97 150:0.35 151:0.61 153:0.48 154:0.79 158:0.82 159:0.79 161:0.85 162:0.69 167:0.47 172:0.95 173:0.11 176:0.53 177:0.38 179:0.20 181:0.64 187:0.39 190:0.77 202:0.67 212:0.50 216:0.95 220:0.93 222:0.76 232:0.90 235:0.52 241:0.43 242:0.41 243:0.98 247:0.60 248:0.61 253:0.84 256:0.68 259:0.21 265:0.94 266:0.54 267:0.44 268:0.70 271:0.38 276:0.90 285:0.50 290:0.55 300:0.70 +0 1:0.74 6:0.92 7:0.81 8:0.83 9:0.80 11:0.88 12:0.37 17:0.76 20:0.91 21:0.05 27:0.27 28:0.92 30:0.41 32:0.80 34:0.34 36:0.74 37:0.35 39:0.53 41:0.88 43:0.96 60:1.00 66:0.68 69:0.10 70:0.73 74:0.99 77:0.89 78:0.87 81:0.91 83:0.82 85:0.99 91:0.53 96:0.86 98:0.87 100:0.92 101:0.86 104:0.79 106:0.81 108:0.80 111:0.88 114:0.95 117:0.86 120:0.77 121:0.99 122:0.57 123:0.79 124:0.46 126:0.54 127:0.92 129:0.59 133:0.47 135:0.84 140:0.45 144:0.85 145:0.74 146:0.86 147:0.89 149:0.98 150:0.95 151:0.87 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.85 161:0.85 162:0.86 164:0.69 165:0.90 167:0.46 169:0.89 172:0.96 173:0.09 176:0.73 177:0.38 179:0.17 181:0.64 186:0.87 187:0.21 189:0.93 192:0.59 195:0.93 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.55 215:0.91 216:0.70 220:0.62 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.92 241:0.35 242:0.43 243:0.31 245:0.98 247:0.60 248:0.84 253:0.91 255:0.79 256:0.58 259:0.21 260:0.78 261:0.93 265:0.87 266:0.63 267:0.36 268:0.63 271:0.38 276:0.94 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.70 +0 8:0.66 9:0.73 10:0.83 12:0.20 17:0.32 21:0.78 23:0.98 26:0.98 27:0.03 29:0.91 30:0.91 31:0.93 34:0.80 36:0.91 37:0.90 39:0.42 43:0.12 55:0.99 58:0.98 62:0.95 66:0.27 69:0.92 70:0.13 71:0.66 79:0.93 83:0.56 89:0.96 91:0.17 98:0.22 108:0.84 122:0.77 123:0.83 124:0.72 127:0.31 128:0.98 129:0.05 133:0.62 135:0.61 137:0.93 140:0.96 141:0.99 145:0.42 146:0.46 147:0.05 149:0.09 151:0.85 153:0.48 154:0.09 155:0.55 159:0.75 162:0.48 163:0.99 168:0.98 170:0.77 172:0.10 173:0.83 174:0.83 175:0.91 177:0.05 178:0.53 179:0.96 187:0.39 190:0.89 192:0.49 197:0.82 199:0.94 202:0.78 205:0.98 208:0.50 212:0.61 216:0.88 220:0.97 222:0.76 223:0.98 224:0.98 225:0.99 234:0.97 235:0.80 239:0.83 240:0.99 241:0.39 242:0.63 243:0.29 244:0.93 245:0.38 247:0.30 248:0.77 253:0.20 255:0.71 256:0.64 257:0.93 259:0.21 265:0.24 266:0.17 267:0.28 268:0.64 271:0.35 274:0.97 276:0.21 277:0.87 284:0.88 285:0.50 300:0.13 +0 10:0.91 12:0.20 17:0.95 18:0.71 21:0.59 26:0.95 27:0.72 29:0.91 30:0.87 32:0.66 34:0.73 36:0.77 39:0.42 43:0.20 55:0.84 58:0.93 66:0.72 69:0.23 70:0.27 71:0.66 81:0.28 86:0.68 89:0.98 91:0.11 98:0.77 102:0.96 108:0.18 122:0.94 123:0.18 124:0.40 126:0.24 127:0.70 129:0.05 133:0.37 135:0.47 139:0.66 141:0.91 145:0.58 146:0.77 147:0.81 149:0.20 150:0.30 154:0.25 159:0.49 163:0.94 170:0.77 172:0.45 173:0.29 174:0.89 175:0.75 177:0.70 178:0.55 179:0.86 187:0.68 195:0.68 197:0.92 199:0.95 212:0.30 215:0.55 222:0.76 223:0.93 224:0.93 225:0.97 234:0.91 235:0.74 239:0.91 240:0.95 241:0.18 242:0.33 243:0.75 244:0.83 245:0.47 247:0.60 253:0.20 254:0.92 257:0.65 265:0.77 266:0.54 267:0.36 271:0.35 274:0.91 276:0.38 277:0.69 297:0.36 300:0.25 +0 10:0.89 12:0.20 17:0.93 18:0.68 21:0.78 26:0.94 27:0.72 29:0.91 30:0.33 32:0.65 34:0.70 36:0.73 39:0.42 43:0.20 55:0.71 58:0.93 66:0.54 69:0.80 70:0.49 71:0.66 86:0.66 89:0.98 91:0.10 98:0.53 108:0.64 123:0.61 124:0.55 127:0.39 129:0.05 133:0.62 135:0.54 139:0.65 141:0.91 145:0.70 146:0.59 147:0.05 149:0.16 154:0.19 159:0.44 170:0.77 172:0.32 173:0.83 174:0.88 175:0.91 177:0.05 178:0.55 179:0.86 187:0.68 193:0.96 195:0.72 197:0.89 199:0.94 212:0.19 222:0.76 223:0.92 224:0.93 225:0.96 234:0.91 235:0.52 239:0.89 240:0.95 241:0.18 242:0.19 243:0.19 244:0.83 245:0.45 247:0.55 253:0.20 254:0.94 257:0.93 265:0.55 266:0.47 267:0.44 271:0.35 274:0.91 276:0.33 277:0.87 300:0.33 +0 1:0.69 10:0.99 11:0.46 12:0.20 17:0.98 18:0.97 21:0.54 22:0.93 26:0.98 27:0.69 29:0.91 30:0.75 32:0.65 33:0.97 34:0.95 36:0.31 37:0.41 39:0.42 43:0.58 45:0.97 47:0.93 58:0.93 64:0.77 66:0.26 69:0.74 70:0.44 71:0.66 75:0.97 81:0.61 83:0.56 86:0.97 89:0.99 91:0.18 97:0.89 98:0.58 99:0.99 101:0.47 108:0.80 122:0.93 123:0.55 124:0.57 126:0.32 127:0.33 129:0.05 133:0.43 135:0.94 139:0.97 141:0.91 145:0.67 146:0.72 147:0.76 149:0.92 150:0.56 154:0.47 155:0.54 159:0.50 165:0.65 170:0.77 172:0.86 173:0.71 174:0.98 175:0.75 177:0.70 178:0.55 179:0.18 187:0.21 192:0.48 193:0.98 195:0.78 197:0.99 199:0.99 201:0.65 204:0.83 208:0.50 212:0.93 216:0.41 219:0.91 222:0.76 223:0.98 224:0.93 225:0.98 226:0.96 234:0.91 235:0.97 236:0.92 239:0.99 240:0.95 241:0.03 242:0.59 243:0.46 244:0.83 245:0.96 247:0.60 253:0.20 257:0.65 259:0.21 262:0.93 265:0.47 266:0.38 267:0.59 271:0.35 274:0.91 276:0.86 277:0.69 279:0.75 281:0.91 291:0.97 292:0.88 300:0.55 +0 12:0.20 17:0.22 18:0.88 21:0.68 26:0.98 27:0.69 29:0.91 30:0.14 34:0.93 36:0.83 37:0.77 39:0.42 43:0.15 48:0.98 55:0.92 58:0.93 64:0.77 66:0.31 69:0.63 70:0.82 71:0.66 81:0.27 83:0.56 86:0.84 89:0.95 91:0.37 98:0.42 108:0.48 122:0.92 123:0.46 124:0.84 126:0.24 127:0.67 129:0.59 133:0.82 135:0.56 139:0.83 141:0.91 145:0.38 146:0.62 147:0.67 149:0.22 150:0.29 154:0.21 155:0.50 159:0.67 170:0.77 172:0.37 173:0.53 175:0.91 177:0.38 178:0.55 179:0.74 187:0.68 192:0.45 193:0.98 199:0.98 202:0.36 204:0.79 208:0.50 212:0.12 216:0.37 222:0.76 223:0.98 224:0.93 225:0.95 228:0.99 234:0.91 235:0.80 239:0.86 240:0.95 241:0.12 242:0.43 243:0.49 244:0.83 245:0.56 247:0.60 253:0.20 254:0.86 257:0.84 259:0.21 265:0.44 266:0.87 267:0.59 271:0.35 274:0.91 276:0.50 277:0.87 281:0.91 300:0.55 +1 10:0.92 11:0.46 12:0.20 17:0.94 18:0.99 21:0.22 26:0.98 27:0.16 29:0.91 30:0.45 34:0.36 36:0.45 37:0.99 39:0.42 43:0.56 44:0.88 45:0.83 48:0.91 55:0.59 58:0.93 64:0.77 66:0.94 69:0.74 70:0.56 71:0.66 81:0.33 86:0.98 89:0.98 91:0.14 98:0.93 101:0.47 108:0.57 122:0.92 123:0.55 124:0.36 126:0.24 127:0.83 129:0.05 133:0.37 135:0.26 139:0.97 141:0.91 145:0.41 146:0.97 147:0.05 149:0.65 150:0.37 154:0.44 159:0.74 170:0.77 172:0.94 173:0.64 174:0.92 175:0.75 177:0.05 178:0.55 179:0.25 187:0.21 195:0.85 197:0.91 199:0.99 212:0.77 216:0.08 222:0.76 223:0.98 224:0.93 225:0.97 228:0.99 234:0.91 235:0.22 239:0.91 240:0.95 241:0.37 242:0.71 243:0.06 244:0.83 245:0.73 247:0.79 253:0.20 257:0.93 259:0.21 265:0.93 266:0.63 267:0.59 271:0.35 274:0.91 276:0.88 277:0.69 281:0.86 300:0.55 +0 10:0.93 12:0.20 17:0.92 18:0.68 21:0.59 26:0.94 27:0.72 29:0.91 30:0.56 32:0.66 34:0.90 36:0.77 39:0.42 43:0.20 55:0.59 58:0.93 66:0.84 69:0.21 70:0.30 71:0.66 74:0.33 81:0.26 86:0.66 89:0.98 91:0.07 98:0.85 102:0.96 108:0.17 122:0.95 123:0.16 126:0.24 127:0.36 129:0.05 135:0.42 139:0.65 141:0.91 145:0.82 146:0.79 147:0.82 149:0.17 150:0.28 154:0.27 159:0.35 170:0.77 172:0.54 173:0.32 174:0.90 177:0.88 178:0.55 179:0.72 187:0.68 195:0.64 197:0.93 199:0.94 212:0.23 222:0.76 223:0.92 224:0.93 225:0.98 234:0.91 235:0.68 236:0.92 239:0.92 240:0.95 241:0.12 242:0.24 243:0.85 244:0.83 247:0.67 253:0.30 254:0.74 265:0.85 266:0.54 267:0.36 271:0.35 274:0.91 276:0.44 300:0.25 +0 1:0.56 8:0.80 9:0.72 10:0.99 11:0.46 12:0.20 17:0.62 18:0.99 21:0.71 22:0.93 23:0.98 26:0.98 27:0.54 29:0.91 30:0.20 31:0.98 33:0.97 34:0.64 36:0.44 37:0.62 39:0.42 43:0.66 44:0.88 45:0.99 47:0.93 58:0.99 62:0.99 64:0.77 66:0.08 69:0.99 70:0.94 71:0.66 75:0.97 79:0.98 81:0.41 83:0.56 86:0.98 89:0.99 91:0.05 97:0.99 98:0.30 99:0.83 101:0.47 102:0.96 108:0.98 122:0.93 123:0.94 124:0.97 126:0.32 127:0.95 128:0.97 129:0.05 133:0.97 135:0.71 137:0.98 139:0.98 140:0.80 141:0.99 145:0.40 146:0.86 147:0.91 149:0.86 150:0.37 151:0.82 153:0.78 154:0.56 155:0.55 159:0.96 162:0.73 165:0.65 168:0.97 170:0.77 172:0.94 173:0.99 174:0.99 175:0.75 177:0.38 179:0.12 187:0.39 190:0.89 192:0.49 195:0.99 196:0.99 197:0.99 199:1.00 201:0.54 202:0.74 205:0.98 208:0.50 212:0.69 216:0.80 219:0.91 222:0.76 223:0.98 224:0.99 225:0.99 226:0.98 234:0.99 235:0.88 236:0.94 239:0.99 240:0.99 241:0.51 242:0.37 243:0.23 244:0.83 245:0.97 247:0.82 248:0.74 253:0.20 255:0.71 256:0.77 257:0.84 259:0.21 262:0.93 265:0.28 266:0.98 267:0.28 268:0.78 271:0.35 274:0.99 276:0.98 277:0.87 279:0.81 284:0.97 285:0.50 291:0.97 292:0.95 300:0.98 +0 7:0.69 10:0.92 12:0.20 17:0.91 18:0.68 21:0.64 26:0.94 27:0.72 29:0.91 30:0.94 34:0.53 36:0.83 39:0.42 43:0.20 55:0.84 58:0.93 66:0.80 69:0.23 70:0.18 71:0.66 74:0.41 77:0.70 81:0.26 86:0.66 89:0.98 91:0.05 98:0.66 102:0.96 108:0.18 122:0.95 123:0.18 126:0.24 127:0.20 129:0.05 135:0.54 139:0.65 141:0.91 145:0.82 146:0.82 147:0.85 149:0.19 150:0.28 154:0.20 159:0.38 163:0.94 170:0.77 172:0.45 173:0.19 174:0.89 177:0.88 178:0.55 179:0.61 187:0.68 195:0.81 197:0.92 199:0.94 212:0.55 222:0.76 223:0.92 224:0.93 225:0.98 230:0.71 233:0.76 234:0.91 235:0.74 236:0.92 239:0.91 240:0.95 241:0.21 242:0.40 243:0.82 244:0.83 247:0.55 253:0.36 254:0.96 265:0.67 266:0.27 267:0.28 271:0.35 274:0.91 276:0.38 282:0.75 300:0.18 +0 12:0.20 17:0.34 18:0.71 21:0.68 26:0.98 27:0.15 29:0.91 30:0.76 34:0.85 36:0.95 37:0.77 39:0.42 43:0.17 48:0.98 55:0.96 58:0.93 64:0.77 66:0.20 69:0.55 70:0.22 71:0.66 81:0.33 83:0.56 86:0.68 89:0.95 91:0.27 98:0.40 108:0.43 120:0.57 122:0.43 123:0.41 124:0.73 126:0.32 127:0.58 129:0.81 133:0.67 135:0.72 139:0.72 140:0.45 141:0.91 145:0.38 146:0.47 147:0.53 149:0.14 150:0.33 151:0.50 153:0.48 154:0.11 155:0.50 159:0.50 162:0.86 163:0.96 164:0.55 170:0.77 172:0.10 173:0.56 175:0.97 177:0.05 179:0.96 187:0.68 190:0.95 192:0.46 193:0.98 199:0.96 201:0.54 202:0.36 204:0.79 208:0.50 212:0.42 215:0.49 216:0.56 220:0.91 222:0.76 223:0.98 224:0.93 225:0.95 228:0.99 234:0.91 235:0.88 239:0.86 240:0.95 241:0.32 242:0.45 243:0.40 244:0.93 245:0.40 247:0.35 248:0.50 253:0.20 256:0.45 257:0.93 259:0.21 260:0.57 265:0.42 266:0.23 267:0.59 268:0.46 271:0.35 274:0.91 276:0.24 277:0.95 281:0.91 283:0.67 285:0.46 297:0.36 300:0.18 +2 1:0.74 6:0.91 7:0.78 8:0.85 9:0.80 12:0.20 17:0.38 20:0.77 21:0.30 27:0.14 28:0.59 30:0.45 34:0.79 36:0.37 37:0.67 39:0.59 41:0.82 43:0.34 55:0.45 66:0.36 69:0.59 70:0.33 74:0.78 78:0.95 81:0.79 83:0.75 91:0.72 96:0.73 98:0.45 100:0.93 108:0.45 111:0.94 114:0.86 120:0.60 122:0.67 123:0.43 124:0.58 126:0.54 127:0.33 129:0.59 133:0.47 135:0.75 140:0.80 145:0.58 146:0.35 147:0.42 149:0.40 150:0.86 151:0.62 152:0.91 153:0.48 154:0.26 155:0.67 159:0.24 161:0.88 162:0.58 163:0.93 164:0.57 165:0.83 167:0.66 169:0.91 172:0.21 173:0.75 175:0.75 176:0.73 177:0.05 178:0.42 179:0.87 181:0.86 186:0.95 187:0.68 191:0.76 192:0.59 201:0.74 202:0.53 208:0.64 212:0.52 215:0.72 216:0.53 220:0.82 222:0.35 230:0.81 233:0.76 235:0.68 241:0.64 242:0.82 243:0.51 244:0.61 245:0.50 247:0.12 248:0.60 253:0.76 255:0.79 256:0.45 257:0.65 260:0.60 261:0.92 265:0.47 266:0.27 267:0.76 268:0.46 271:0.35 276:0.29 277:0.69 285:0.62 290:0.86 297:0.36 300:0.25 +2 1:0.74 7:0.81 9:0.80 12:0.20 17:0.32 21:0.30 27:0.03 28:0.59 30:0.76 32:0.78 34:0.94 36:0.25 37:0.95 39:0.59 43:0.39 55:0.71 66:0.72 69:0.44 70:0.22 74:0.85 76:0.94 77:0.89 81:0.77 91:0.48 96:0.71 98:0.63 108:0.34 114:0.86 117:0.86 120:0.58 121:0.90 122:0.67 123:0.33 126:0.54 127:0.19 129:0.05 135:0.77 140:0.80 145:0.49 146:0.52 147:0.58 149:0.35 150:0.80 151:0.58 153:0.48 154:0.30 155:0.67 158:0.85 159:0.19 161:0.88 162:0.62 164:0.57 167:0.73 172:0.27 173:0.57 175:0.75 176:0.73 177:0.05 178:0.42 179:0.81 181:0.86 187:0.95 192:0.59 195:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.42 215:0.72 216:0.76 220:0.87 222:0.35 230:0.87 232:1.00 233:0.76 235:0.60 241:0.71 242:0.82 243:0.75 244:0.61 247:0.12 248:0.57 253:0.76 255:0.79 256:0.45 257:0.65 259:0.21 260:0.59 265:0.64 266:0.23 267:0.52 268:0.46 271:0.35 276:0.25 277:0.69 282:0.86 285:0.62 290:0.86 297:0.36 300:0.18 +2 6:0.94 7:0.81 8:0.86 9:0.80 12:0.20 17:0.88 20:0.82 21:0.64 27:0.48 28:0.59 30:0.95 32:0.75 34:0.19 36:0.25 37:0.84 39:0.59 41:0.82 43:0.39 55:0.71 66:0.54 69:0.45 74:0.66 76:0.94 78:0.97 81:0.35 83:0.74 91:0.73 96:0.88 98:0.21 100:0.97 108:0.35 111:0.96 117:0.86 120:0.65 121:0.97 123:0.33 126:0.32 127:0.11 129:0.05 135:0.63 140:0.80 145:0.84 146:0.28 147:0.34 149:0.22 150:0.31 151:0.73 152:0.79 153:0.48 154:0.30 155:0.67 158:0.85 159:0.12 161:0.88 162:0.82 164:0.76 167:0.69 169:0.95 172:0.10 173:0.44 177:0.38 179:0.59 181:0.86 186:0.97 187:0.21 189:0.95 190:0.77 191:0.82 192:0.59 195:0.72 202:0.70 204:0.89 208:0.64 215:0.53 216:0.21 220:0.96 222:0.35 230:0.87 232:0.83 233:0.76 235:0.80 238:0.92 241:0.67 243:0.63 244:0.61 248:0.68 253:0.85 255:0.79 256:0.75 259:0.21 260:0.65 261:0.89 265:0.23 267:0.76 268:0.76 271:0.35 285:0.62 297:0.36 300:0.08 +1 1:0.74 7:0.81 8:0.62 9:0.80 11:0.57 12:0.20 17:0.79 20:0.82 21:0.54 27:0.49 28:0.59 30:0.62 34:0.99 36:0.90 37:0.34 39:0.59 41:0.95 43:0.94 55:0.42 60:0.87 66:0.87 69:0.30 70:0.39 74:0.90 76:0.85 77:0.70 78:0.84 81:0.70 83:0.84 85:0.91 91:0.72 96:0.79 98:0.88 100:0.87 101:0.85 108:0.24 111:0.84 114:0.77 120:0.68 121:0.90 122:0.43 123:0.23 126:0.54 127:0.43 129:0.05 135:0.47 140:0.87 145:0.98 146:0.62 147:0.67 149:0.91 150:0.81 151:0.64 152:0.79 153:0.46 154:0.94 155:0.67 158:0.87 159:0.23 161:0.88 162:0.55 164:0.78 165:0.81 167:0.76 169:0.85 172:0.63 173:0.54 176:0.73 177:0.38 178:0.48 179:0.65 181:0.86 186:0.85 187:0.21 191:0.70 192:0.59 195:0.59 201:0.74 202:0.75 204:0.89 208:0.64 212:0.86 215:0.58 216:0.36 220:0.91 222:0.35 230:0.87 232:0.88 233:0.76 235:0.88 241:0.75 242:0.51 243:0.88 247:0.55 248:0.75 253:0.85 255:0.79 256:0.77 259:0.21 260:0.69 261:0.82 265:0.88 266:0.27 267:0.52 268:0.73 271:0.35 276:0.52 279:0.86 282:0.84 283:0.71 285:0.62 290:0.77 297:0.36 300:0.33 +2 1:0.74 6:0.86 7:0.81 8:0.66 9:0.80 11:0.57 12:0.20 17:0.89 20:0.82 21:0.40 27:0.44 28:0.59 30:0.66 32:0.80 34:0.87 36:0.76 37:0.55 39:0.59 41:0.88 43:0.94 55:0.42 66:0.88 69:0.19 70:0.33 74:0.91 77:0.70 78:0.93 81:0.70 83:0.75 85:0.93 91:0.70 96:0.72 98:0.95 100:0.87 101:0.84 106:0.81 108:0.15 111:0.91 114:0.82 117:0.86 120:0.59 121:0.97 122:0.57 123:0.15 126:0.54 127:0.65 129:0.05 135:0.68 140:0.45 145:0.89 146:0.87 147:0.90 149:0.88 150:0.81 151:0.56 152:0.84 153:0.77 154:0.94 155:0.67 158:0.84 159:0.45 161:0.88 162:0.86 163:0.81 164:0.69 165:0.81 167:0.49 169:0.90 172:0.67 173:0.28 176:0.73 177:0.38 179:0.67 181:0.86 186:0.93 187:0.21 189:0.86 191:0.77 192:0.59 195:0.67 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.41 215:0.67 216:0.28 220:0.91 222:0.35 230:0.87 232:0.92 233:0.76 235:0.60 238:0.83 241:0.10 242:0.59 243:0.89 247:0.39 248:0.58 253:0.78 255:0.79 256:0.58 259:0.21 260:0.59 261:0.92 265:0.95 266:0.54 267:0.44 268:0.63 271:0.35 276:0.56 282:0.83 285:0.62 290:0.82 297:0.36 299:0.88 300:0.33 +0 8:0.98 12:0.20 17:0.62 21:0.74 27:0.09 28:0.59 32:0.80 34:0.24 36:0.20 37:0.96 39:0.59 74:0.47 77:0.70 81:0.28 91:0.97 114:0.61 120:0.58 126:0.32 135:0.26 140:0.99 145:0.72 150:0.27 151:0.52 153:0.48 161:0.88 162:0.45 164:0.56 167:0.86 175:0.91 176:0.56 178:0.55 181:0.86 190:0.77 191:0.97 195:0.64 202:0.93 216:0.31 220:0.87 222:0.35 235:0.88 241:0.89 244:0.83 248:0.52 253:0.48 256:0.45 257:0.84 259:0.21 260:0.59 267:0.98 268:0.46 271:0.35 277:0.87 282:0.88 285:0.50 290:0.61 299:0.88 +2 1:0.74 6:0.83 7:0.78 8:0.65 9:0.80 11:0.57 12:0.20 17:0.82 20:0.85 21:0.05 27:0.70 28:0.59 30:0.32 34:0.37 36:0.32 37:0.37 39:0.59 41:0.88 43:0.94 55:0.45 60:0.87 66:0.89 69:0.23 70:0.88 74:0.96 76:0.98 77:0.70 78:0.93 81:0.88 83:0.87 85:0.80 91:0.56 96:0.84 98:0.89 100:0.88 101:0.78 108:0.18 111:0.91 114:0.95 120:0.77 122:0.67 123:0.18 126:0.54 127:0.45 129:0.05 135:0.32 140:0.45 145:0.56 146:0.70 147:0.74 149:0.81 150:0.93 151:0.83 152:0.80 153:0.78 154:0.94 155:0.67 158:0.92 159:0.27 161:0.88 162:0.81 164:0.73 165:0.90 167:0.63 169:0.86 172:0.68 173:0.43 176:0.73 177:0.38 179:0.60 181:0.86 186:0.93 187:0.39 189:0.85 192:0.59 195:0.59 201:0.74 202:0.62 208:0.64 212:0.77 215:0.91 216:0.37 220:0.62 222:0.35 230:0.81 233:0.76 235:0.22 238:0.83 241:0.59 242:0.74 243:0.89 247:0.55 248:0.81 253:0.89 255:0.79 256:0.64 259:0.21 260:0.78 261:0.87 265:0.89 266:0.27 267:0.52 268:0.67 271:0.35 276:0.55 279:0.86 282:0.84 283:0.82 285:0.62 290:0.95 297:0.36 300:0.70 +1 6:0.87 7:0.78 8:0.81 9:0.80 12:0.20 17:0.16 20:0.86 21:0.71 27:0.38 28:0.59 32:0.75 34:0.47 36:0.30 37:0.42 39:0.59 78:0.96 81:0.31 91:0.96 96:0.77 100:0.97 111:0.96 120:0.83 126:0.32 135:0.75 140:0.94 145:0.70 150:0.29 151:0.59 152:0.82 153:0.78 155:0.67 161:0.88 162:0.50 164:0.92 167:0.88 169:0.96 175:0.91 178:0.50 181:0.86 186:0.96 189:0.89 190:0.77 191:0.94 192:0.59 195:0.79 202:0.94 208:0.64 215:0.48 216:0.54 220:0.82 222:0.35 230:0.81 233:0.69 235:0.88 238:0.92 241:0.99 244:0.83 248:0.69 253:0.62 255:0.79 256:0.90 257:0.84 259:0.21 260:0.83 261:0.92 267:0.91 268:0.90 271:0.35 277:0.87 285:0.62 297:0.36 +2 1:0.74 6:0.91 7:0.78 8:0.82 9:0.80 12:0.20 17:0.38 20:0.92 21:0.54 25:0.88 27:0.63 28:0.59 30:0.95 34:0.68 36:0.32 37:0.56 39:0.59 41:0.94 43:0.25 55:0.92 66:0.54 69:0.82 74:0.73 76:0.85 77:0.70 78:0.95 81:0.61 83:0.78 87:0.98 91:0.72 96:0.87 98:0.33 100:0.93 108:0.66 111:0.93 114:0.77 120:0.81 123:0.64 126:0.54 127:0.12 129:0.05 135:0.49 140:0.87 145:0.91 146:0.45 147:0.51 149:0.17 150:0.68 151:0.91 152:0.88 153:0.72 154:0.18 155:0.67 158:0.84 159:0.16 161:0.88 162:0.75 164:0.84 167:0.85 169:0.91 172:0.10 173:0.81 175:0.75 176:0.73 177:0.05 178:0.48 179:0.86 181:0.86 186:0.94 189:0.92 191:0.83 192:0.59 195:0.80 201:0.74 202:0.76 208:0.64 215:0.58 216:0.43 222:0.35 230:0.81 232:0.79 233:0.76 235:0.68 238:0.86 241:0.85 243:0.63 244:0.61 248:0.85 253:0.87 255:0.79 256:0.80 257:0.65 259:0.21 260:0.82 261:0.93 265:0.35 267:0.23 268:0.80 271:0.35 277:0.69 282:0.84 285:0.62 290:0.77 297:0.36 300:0.08 +2 1:0.74 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.97 21:0.54 25:0.88 27:0.72 28:0.59 30:0.15 32:0.80 34:0.64 36:0.60 39:0.59 41:0.95 43:0.94 55:0.45 60:0.99 66:0.93 69:0.23 70:0.66 74:0.98 77:0.70 81:0.62 83:0.83 85:0.96 91:0.78 96:0.93 98:0.98 101:0.87 104:0.76 108:0.18 114:0.77 120:0.87 121:0.90 122:0.43 123:0.18 126:0.54 127:0.84 129:0.05 135:0.28 140:0.45 145:0.63 146:0.67 147:0.72 149:0.96 150:0.75 151:0.94 153:0.82 154:0.94 155:0.67 158:0.92 159:0.26 161:0.88 162:0.80 164:0.86 165:0.79 167:0.84 172:0.82 173:0.51 176:0.73 177:0.38 179:0.48 181:0.86 191:0.81 192:0.59 195:0.56 201:0.74 202:0.78 204:0.89 208:0.64 212:0.88 215:0.58 216:0.18 220:0.62 222:0.35 230:0.84 232:0.84 233:0.69 235:0.74 241:0.82 242:0.41 243:0.94 247:0.60 248:0.92 253:0.95 255:0.79 256:0.82 259:0.21 260:0.88 265:0.98 266:0.27 267:0.28 268:0.83 271:0.35 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.77 297:0.36 299:0.88 300:0.43 +1 1:0.74 6:0.86 7:0.81 8:0.71 9:0.80 11:0.57 12:0.20 17:0.90 20:0.87 21:0.40 27:0.44 28:0.59 30:0.21 32:0.80 34:0.47 36:0.93 37:0.55 39:0.59 41:0.95 43:0.94 55:0.42 66:0.10 69:0.19 70:0.50 74:0.78 77:0.70 78:0.96 81:0.70 83:0.77 85:0.80 91:0.85 96:0.80 98:0.42 100:0.93 101:0.76 108:0.79 111:0.94 114:0.82 120:0.78 121:0.97 123:0.60 124:0.73 126:0.54 127:0.85 129:0.81 133:0.67 135:0.68 140:0.87 145:0.89 146:0.38 147:0.45 149:0.68 150:0.81 151:0.69 152:0.84 153:0.84 154:0.94 155:0.67 159:0.50 161:0.88 162:0.55 163:0.75 164:0.87 165:0.81 167:0.84 169:0.90 172:0.21 173:0.27 176:0.73 177:0.38 178:0.48 179:0.89 181:0.86 186:0.96 189:0.89 191:0.89 192:0.59 195:0.68 201:0.74 202:0.85 204:0.89 208:0.64 212:0.16 215:0.67 216:0.28 220:0.91 222:0.35 230:0.87 233:0.76 235:0.60 238:0.86 241:0.84 242:0.40 243:0.39 245:0.51 247:0.55 248:0.73 253:0.84 255:0.79 256:0.83 259:0.21 260:0.78 261:0.92 265:0.35 266:0.54 267:0.69 268:0.84 271:0.35 276:0.36 282:0.83 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.57 12:0.20 17:0.49 21:0.13 27:0.45 28:0.59 30:0.68 34:0.80 36:0.18 37:0.50 39:0.59 43:0.81 55:0.59 66:0.32 69:0.68 70:0.24 74:0.51 81:0.84 83:0.77 85:0.80 91:0.22 98:0.21 101:0.75 108:0.81 114:0.92 123:0.80 124:0.72 126:0.54 127:0.34 129:0.59 133:0.64 135:0.65 145:0.47 146:0.17 147:0.22 149:0.64 150:0.90 154:0.76 155:0.67 159:0.47 161:0.88 163:0.88 165:0.88 167:0.56 172:0.21 173:0.67 176:0.73 177:0.38 178:0.55 179:0.84 181:0.86 187:0.68 192:0.59 201:0.74 212:0.42 215:0.84 216:0.77 222:0.35 235:0.31 241:0.41 242:0.45 243:0.32 245:0.48 247:0.39 253:0.70 259:0.21 265:0.23 266:0.38 267:0.28 271:0.35 276:0.29 277:0.69 290:0.92 297:0.36 300:0.18 +2 6:0.86 7:0.81 8:0.84 9:0.80 11:0.57 12:0.20 17:0.73 20:0.90 21:0.47 27:0.52 28:0.59 30:0.62 32:0.80 34:0.44 36:0.53 37:0.70 39:0.59 41:0.92 43:0.89 55:0.42 66:0.75 69:0.47 70:0.34 74:0.76 76:0.85 77:0.70 78:0.92 81:0.44 83:0.79 85:0.80 91:0.95 96:0.90 98:0.79 100:0.92 101:0.79 108:0.36 111:0.91 120:0.85 121:0.97 122:0.41 123:0.34 126:0.32 127:0.28 129:0.05 135:0.42 140:0.96 145:0.65 146:0.49 147:0.55 149:0.67 150:0.36 151:0.86 152:0.85 153:0.48 154:0.88 155:0.67 159:0.18 161:0.88 162:0.48 164:0.89 167:0.86 169:0.89 172:0.32 173:0.73 177:0.38 178:0.48 179:0.87 181:0.86 186:0.92 189:0.88 191:0.94 192:0.59 195:0.55 202:0.94 204:0.89 208:0.64 212:0.30 215:0.63 216:0.26 220:0.93 222:0.35 230:0.87 233:0.76 235:0.60 238:0.86 241:0.90 242:0.33 243:0.77 247:0.39 248:0.87 253:0.90 255:0.79 256:0.88 259:0.21 260:0.85 261:0.90 265:0.79 266:0.27 267:0.59 268:0.87 271:0.35 276:0.19 282:0.88 285:0.62 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.85 7:0.81 8:0.64 12:0.20 17:0.47 20:0.78 21:0.40 27:1.00 28:0.59 32:0.78 34:0.78 36:0.40 37:0.62 39:0.59 74:0.58 76:0.85 77:0.70 78:0.92 81:0.68 91:0.53 100:0.87 111:0.90 114:0.82 121:0.97 126:0.54 135:0.66 145:0.50 150:0.73 152:0.77 155:0.67 161:0.88 167:0.61 169:0.83 175:0.91 176:0.73 178:0.55 181:0.86 186:0.92 187:0.39 189:0.88 192:0.59 195:0.62 201:0.74 204:0.89 208:0.64 215:0.67 216:0.27 222:0.35 230:0.87 233:0.76 235:0.60 238:0.84 241:0.54 244:0.83 253:0.65 257:0.84 259:0.21 261:0.81 267:0.23 271:0.35 277:0.87 282:0.86 290:0.82 297:0.36 +2 1:0.74 6:0.89 7:0.78 8:0.84 9:0.80 12:0.20 17:0.28 20:0.96 21:0.47 27:0.14 28:0.59 30:0.95 34:0.19 36:0.76 37:0.67 39:0.59 41:0.97 43:0.35 55:0.45 66:0.64 69:0.57 74:0.63 78:0.93 81:0.66 83:0.77 91:0.94 96:0.85 98:0.13 100:0.94 108:0.44 111:0.93 114:0.80 120:0.86 122:0.67 123:0.42 126:0.54 127:0.08 129:0.05 135:0.44 140:0.93 145:0.58 146:0.21 147:0.26 149:0.27 150:0.78 151:0.59 152:0.89 153:0.48 154:0.26 155:0.67 159:0.10 161:0.88 162:0.49 163:0.90 164:0.93 165:0.80 167:0.86 169:0.92 172:0.16 173:0.49 175:0.75 176:0.73 177:0.05 178:0.52 179:0.11 181:0.86 186:0.93 189:0.90 190:0.77 191:0.96 192:0.59 201:0.74 202:0.96 208:0.64 212:0.95 215:0.63 216:0.53 220:0.91 222:0.35 230:0.81 233:0.76 235:0.68 238:0.89 241:0.91 242:0.82 243:0.69 244:0.61 247:0.12 248:0.74 253:0.84 255:0.79 256:0.94 257:0.65 260:0.86 261:0.93 265:0.14 266:0.12 267:0.95 268:0.93 271:0.35 276:0.19 277:0.69 285:0.62 290:0.80 297:0.36 300:0.08 +0 1:0.74 12:0.20 17:0.53 21:0.54 27:0.45 28:0.59 30:0.38 34:0.85 36:0.20 37:0.50 39:0.59 43:0.29 55:0.71 66:0.29 69:0.70 70:0.63 74:0.77 81:0.59 91:0.18 98:0.44 108:0.77 114:0.77 122:0.57 123:0.76 124:0.73 126:0.54 127:0.32 129:0.59 133:0.64 135:0.65 145:0.49 146:0.26 147:0.32 149:0.38 150:0.67 154:0.75 155:0.67 158:0.86 159:0.45 161:0.88 163:0.90 167:0.52 172:0.27 173:0.69 176:0.73 177:0.38 178:0.55 179:0.73 181:0.86 187:0.68 192:0.59 201:0.74 208:0.64 212:0.42 215:0.58 216:0.77 222:0.35 235:0.74 241:0.24 242:0.29 243:0.34 245:0.56 247:0.60 253:0.67 254:0.74 259:0.21 265:0.46 266:0.54 267:0.28 271:0.35 276:0.39 279:0.86 283:0.67 290:0.77 297:0.36 300:0.43 +0 1:0.74 8:0.58 9:0.80 12:0.20 17:0.38 20:0.79 21:0.13 27:0.35 28:0.59 30:0.45 34:0.34 36:0.93 37:0.49 39:0.59 43:0.29 55:0.45 66:0.77 69:0.63 70:0.33 74:0.73 78:0.86 81:0.86 91:0.25 96:0.74 98:0.30 100:0.73 104:0.90 106:0.81 108:0.48 111:0.83 114:0.92 117:0.86 120:0.76 122:0.41 123:0.46 126:0.54 127:0.12 129:0.05 135:0.78 140:0.80 146:0.52 147:0.58 149:0.19 150:0.88 151:0.69 152:0.77 153:0.77 154:0.79 155:0.67 158:0.87 159:0.18 161:0.88 162:0.86 164:0.69 167:0.50 169:0.72 172:0.37 173:0.48 176:0.73 177:0.38 179:0.25 181:0.86 186:0.86 187:0.87 190:0.77 192:0.59 201:0.74 202:0.54 206:0.81 208:0.64 212:0.52 215:0.84 216:0.84 222:0.35 232:0.98 235:0.22 241:0.15 242:0.24 243:0.79 247:0.47 248:0.65 253:0.79 254:0.74 255:0.79 256:0.58 259:0.21 260:0.77 261:0.80 265:0.32 266:0.27 267:0.44 268:0.63 271:0.35 276:0.30 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.25 +2 1:0.74 7:0.81 9:0.80 12:0.20 17:0.38 21:0.40 27:1.00 28:0.59 32:0.78 34:0.52 36:0.34 37:0.62 39:0.59 74:0.58 76:0.85 77:0.70 81:0.68 91:0.54 96:0.70 114:0.82 120:0.57 121:0.97 126:0.54 135:0.51 140:0.45 145:0.50 150:0.73 151:0.54 153:0.48 155:0.67 161:0.88 162:0.86 164:0.57 167:0.71 175:0.91 176:0.73 181:0.86 187:0.39 192:0.59 195:0.64 201:0.74 202:0.36 204:0.89 208:0.64 215:0.67 216:0.27 220:0.91 222:0.35 230:0.87 233:0.76 235:0.60 241:0.69 244:0.83 248:0.53 253:0.66 255:0.79 256:0.45 257:0.84 259:0.21 260:0.57 267:0.36 268:0.46 271:0.35 277:0.87 282:0.86 285:0.62 290:0.82 297:0.36 +0 1:0.74 9:0.80 11:0.57 12:0.20 17:0.76 21:0.13 27:0.44 28:0.59 30:0.70 32:0.78 34:0.76 36:0.39 37:0.86 39:0.59 43:0.84 55:0.45 66:0.83 69:0.63 70:0.62 74:0.96 77:0.99 81:0.84 83:0.77 85:0.85 91:0.60 96:0.89 98:0.86 101:0.76 106:0.81 108:0.67 114:0.92 117:0.86 120:0.82 122:0.57 123:0.64 124:0.39 126:0.54 127:0.60 129:0.59 133:0.47 135:0.79 140:0.45 145:0.76 146:0.31 147:0.38 149:0.76 150:0.90 151:0.75 153:0.84 154:0.80 155:0.67 159:0.66 161:0.88 162:0.83 164:0.83 165:0.88 167:0.53 172:0.90 173:0.54 176:0.73 177:0.38 179:0.27 181:0.86 187:0.21 192:0.59 195:0.84 201:0.74 202:0.75 206:0.81 208:0.64 212:0.59 215:0.84 216:0.62 220:0.74 222:0.35 232:0.87 235:0.31 241:0.27 242:0.54 243:0.14 245:0.86 247:0.60 248:0.88 253:0.93 255:0.79 256:0.79 259:0.21 260:0.82 265:0.86 266:0.63 267:0.69 268:0.81 271:0.35 276:0.84 282:0.86 285:0.62 290:0.92 297:0.36 300:0.84 +2 1:0.56 8:0.60 10:0.94 11:0.57 12:0.86 17:0.36 18:0.96 21:0.71 27:0.48 29:0.86 30:0.44 34:0.88 36:0.91 37:0.42 39:0.51 43:0.65 45:0.98 46:0.93 64:0.77 66:0.43 69:0.81 70:0.77 71:0.57 74:0.94 81:0.42 83:0.56 86:0.97 91:0.20 97:0.89 98:0.71 99:0.83 101:0.96 107:0.97 108:0.65 110:0.97 114:0.66 122:0.86 123:0.63 124:0.68 125:0.88 126:0.32 127:0.49 129:0.59 133:0.67 135:0.56 139:0.96 146:0.96 147:0.59 149:0.91 150:0.37 154:0.27 155:0.46 158:0.76 159:0.84 160:0.88 165:0.65 170:0.82 172:0.94 173:0.59 174:0.96 176:0.63 177:0.70 178:0.55 179:0.13 187:0.21 192:0.44 197:0.92 201:0.54 208:0.50 212:0.78 216:0.70 219:0.91 222:0.35 235:0.88 239:0.92 241:0.27 242:0.18 243:0.15 244:0.61 245:0.99 247:0.97 253:0.67 257:0.65 259:0.21 265:0.71 266:0.54 267:0.59 271:0.38 276:0.96 279:0.75 289:0.94 290:0.66 292:0.88 300:0.70 +4 1:0.64 7:0.69 9:0.70 10:0.98 11:0.52 12:0.86 17:0.40 18:0.73 21:0.47 23:0.96 27:0.72 29:0.86 30:0.40 31:0.87 33:0.97 34:0.71 36:0.85 39:0.51 43:0.58 45:0.87 46:0.96 62:0.96 64:0.77 66:0.35 69:0.25 70:0.81 71:0.57 74:0.61 75:0.96 76:0.85 79:0.87 81:0.71 83:0.69 86:0.84 91:0.35 96:0.69 98:0.62 99:0.95 101:0.65 102:0.95 107:0.95 108:0.75 110:0.94 114:0.80 120:0.56 122:0.95 123:0.73 124:0.71 125:0.97 126:0.54 127:0.74 128:0.95 129:0.59 133:0.67 135:0.56 137:0.87 139:0.79 140:0.45 145:0.49 146:0.34 147:0.40 149:0.39 150:0.56 151:0.52 153:0.48 154:0.83 155:0.55 159:0.52 160:0.96 162:0.86 164:0.57 165:0.81 168:0.95 170:0.82 172:0.54 173:0.28 174:0.94 176:0.73 177:0.88 179:0.58 187:0.21 192:0.57 195:0.70 196:0.89 197:0.96 201:0.64 202:0.36 205:0.95 208:0.64 212:0.49 215:0.63 216:0.09 220:0.93 222:0.35 226:0.96 230:0.71 233:0.76 235:0.74 236:0.97 239:0.97 241:0.24 242:0.22 243:0.37 245:0.77 247:0.76 248:0.52 253:0.61 254:0.86 255:0.70 256:0.45 259:0.21 260:0.56 265:0.63 266:0.71 267:0.36 268:0.46 271:0.38 276:0.62 284:0.89 285:0.62 289:0.95 290:0.80 291:0.97 297:0.36 300:0.70 +3 1:0.66 10:0.87 11:0.52 12:0.86 17:0.43 18:0.64 21:0.13 27:0.72 29:0.86 30:0.68 34:0.90 36:0.62 37:0.92 39:0.51 43:0.68 45:0.73 46:0.96 55:0.71 64:0.77 66:0.79 69:0.52 70:0.31 71:0.57 74:0.41 81:0.70 83:0.69 86:0.70 91:0.27 98:0.80 99:0.95 101:0.65 107:0.92 108:0.40 110:0.92 114:0.92 122:0.41 123:0.38 125:0.88 126:0.51 127:0.28 129:0.05 135:0.37 139:0.67 146:0.55 147:0.61 149:0.47 150:0.58 154:0.58 155:0.50 159:0.20 160:0.88 165:0.81 170:0.82 172:0.41 173:0.73 174:0.83 176:0.70 177:0.88 178:0.55 179:0.80 187:0.39 192:0.50 197:0.88 201:0.63 208:0.61 212:0.42 215:0.84 216:0.24 222:0.35 235:0.31 239:0.87 241:0.41 242:0.19 243:0.80 244:0.61 247:0.55 253:0.63 259:0.21 265:0.80 266:0.27 267:0.52 271:0.38 276:0.32 289:0.95 290:0.92 297:0.36 300:0.25 +2 1:0.66 9:0.74 10:0.89 11:0.52 12:0.86 17:0.73 18:0.69 21:0.13 27:0.72 29:0.86 30:0.72 31:0.92 34:0.37 36:0.40 39:0.51 43:0.69 45:0.83 46:0.96 64:0.77 66:0.60 69:0.75 70:0.37 71:0.57 74:0.50 79:0.91 81:0.70 83:0.69 86:0.75 91:0.83 96:0.77 98:0.79 99:0.95 101:0.65 107:0.94 108:0.59 110:0.94 114:0.92 120:0.65 122:0.65 123:0.56 124:0.48 125:0.99 126:0.51 127:0.87 129:0.05 133:0.43 135:0.26 137:0.92 139:0.72 140:0.45 146:0.66 147:0.42 149:0.65 150:0.58 151:0.76 153:0.48 154:0.58 155:0.64 159:0.36 160:0.96 162:0.86 164:0.56 165:0.81 170:0.82 172:0.45 173:0.89 174:0.83 175:0.75 176:0.70 177:0.38 179:0.83 190:0.77 192:0.97 196:0.90 197:0.88 201:0.63 202:0.36 208:0.61 212:0.23 215:0.84 216:0.10 220:0.62 222:0.35 235:0.31 239:0.87 241:0.86 242:0.24 243:0.33 244:0.61 245:0.60 247:0.74 248:0.70 253:0.75 255:0.73 256:0.45 257:0.84 260:0.66 265:0.79 266:0.38 267:0.23 268:0.46 271:0.38 276:0.42 277:0.69 284:0.88 285:0.60 289:0.95 290:0.92 297:0.36 300:0.33 +3 1:0.65 7:0.69 10:0.84 11:0.52 12:0.86 17:0.47 18:0.83 21:0.22 27:0.72 29:0.86 30:0.60 32:0.72 34:0.69 36:0.65 39:0.51 43:0.77 44:0.92 45:0.88 46:0.98 64:0.77 66:0.90 69:0.17 70:0.51 71:0.57 74:0.83 76:0.85 77:0.89 81:0.68 83:0.69 86:0.88 91:0.46 97:0.99 98:0.95 99:0.95 101:0.65 107:0.96 108:0.14 110:0.96 114:0.88 122:0.65 123:0.13 125:0.88 126:0.51 127:0.68 129:0.05 135:0.54 139:0.90 145:0.98 146:0.74 147:0.78 149:0.85 150:0.56 154:0.69 155:0.50 158:0.81 159:0.31 160:0.88 165:0.81 170:0.82 172:0.73 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.60 192:0.49 195:0.57 197:0.83 201:0.63 208:0.61 212:0.90 215:0.79 216:0.09 219:0.94 222:0.35 230:0.71 233:0.76 235:0.42 239:0.83 241:0.84 242:0.24 243:0.91 244:0.61 247:0.84 253:0.69 259:0.21 265:0.95 266:0.27 267:0.23 271:0.38 276:0.59 279:0.81 282:0.80 283:0.67 289:0.95 290:0.88 292:0.88 297:0.36 300:0.43 +1 10:0.97 11:0.52 12:0.86 17:0.38 18:0.70 21:0.78 27:0.54 29:0.86 30:0.13 34:0.49 36:0.64 37:0.72 39:0.51 43:0.19 45:0.81 46:0.93 55:0.52 66:0.12 69:0.73 70:0.98 71:0.57 74:0.66 86:0.82 91:0.44 98:0.25 101:0.65 107:0.94 108:0.56 110:0.94 122:0.94 123:0.84 124:0.94 125:0.88 127:0.75 129:0.05 133:0.94 135:0.81 139:0.78 146:0.28 147:0.05 149:0.19 154:0.71 159:0.84 160:0.88 170:0.82 172:0.48 173:0.52 174:0.94 177:0.05 178:0.55 179:0.44 187:0.39 197:0.94 212:0.42 216:0.32 222:0.35 235:0.12 239:0.96 241:0.07 242:0.14 243:0.08 245:0.69 247:0.90 253:0.49 254:0.74 257:0.93 259:0.21 265:0.15 266:0.92 267:0.44 271:0.38 276:0.73 277:0.87 289:0.91 300:0.94 +0 1:0.67 10:0.98 11:0.81 12:0.86 17:0.55 18:0.79 21:0.22 27:0.64 29:0.86 30:0.84 32:0.80 34:0.34 36:0.90 37:0.49 39:0.51 43:0.88 44:0.93 45:0.85 46:0.94 60:0.87 64:0.77 66:0.51 69:0.51 70:0.37 71:0.57 74:0.85 75:0.99 76:0.85 77:0.89 81:0.79 83:0.91 85:0.85 86:0.90 91:0.35 98:0.38 101:0.87 102:0.98 104:0.97 106:0.81 107:0.95 108:0.39 110:0.95 114:0.90 117:0.86 122:0.65 123:0.37 124:0.73 125:0.88 126:0.54 127:0.30 129:0.05 133:0.76 135:0.76 139:0.93 145:0.98 146:0.70 147:0.75 149:0.92 150:0.63 154:0.87 155:0.52 158:0.86 159:0.70 160:0.88 165:0.93 170:0.82 172:0.68 173:0.29 174:0.90 176:0.73 177:0.88 178:0.55 179:0.34 187:0.39 192:0.51 193:0.97 195:0.93 197:0.94 201:0.66 206:0.81 208:0.64 212:0.59 215:0.79 216:0.41 219:0.95 222:0.35 226:0.96 232:0.99 235:0.52 236:0.97 239:0.98 241:0.27 242:0.16 243:0.61 245:0.75 247:0.91 253:0.71 259:0.21 265:0.40 266:0.71 267:0.59 271:0.38 276:0.66 279:0.80 282:0.88 283:0.67 289:0.95 290:0.90 292:0.88 297:0.36 299:0.88 300:0.55 +2 1:0.66 10:0.87 11:0.52 12:0.86 17:0.59 18:0.79 21:0.13 27:0.72 29:0.86 30:0.74 34:0.49 36:0.95 37:0.92 39:0.51 43:0.73 45:0.87 46:0.97 64:0.77 66:0.87 69:0.65 70:0.47 71:0.57 74:0.58 81:0.70 83:0.69 86:0.84 91:0.35 98:0.85 99:0.95 101:0.65 107:0.95 108:0.50 110:0.95 114:0.92 122:0.65 123:0.48 125:0.88 126:0.51 127:0.36 129:0.05 135:0.56 139:0.81 146:0.75 147:0.79 149:0.72 150:0.58 154:0.63 155:0.50 159:0.31 160:0.88 163:0.90 165:0.81 170:0.82 172:0.63 173:0.76 174:0.83 176:0.70 177:0.88 178:0.55 179:0.62 187:0.21 192:0.50 197:0.86 201:0.63 202:0.36 208:0.61 212:0.37 215:0.84 216:0.24 222:0.35 235:0.31 239:0.85 241:0.63 242:0.40 243:0.88 244:0.61 247:0.74 253:0.65 259:0.21 265:0.85 266:0.47 267:0.52 271:0.38 276:0.50 289:0.95 290:0.92 297:0.36 300:0.43 +2 9:0.72 10:0.90 11:0.52 12:0.86 17:0.24 18:0.72 21:0.78 27:0.72 29:0.86 30:0.87 31:0.90 34:0.78 36:0.89 39:0.51 43:0.69 45:0.81 46:0.98 66:0.45 69:0.37 70:0.27 71:0.57 74:0.54 79:0.89 83:0.69 86:0.81 91:0.18 96:0.73 98:0.55 101:0.65 107:0.93 108:0.29 110:0.93 120:0.61 122:0.65 123:0.28 124:0.56 125:0.99 127:0.48 129:0.05 133:0.45 135:0.34 137:0.90 139:0.77 140:0.45 146:0.33 147:0.40 149:0.44 151:0.63 153:0.69 154:0.81 155:0.63 159:0.21 160:0.96 162:0.86 163:0.81 164:0.62 170:0.82 172:0.27 173:0.65 174:0.79 177:0.88 179:0.88 187:0.21 190:0.77 192:0.87 196:0.90 197:0.84 202:0.46 208:0.61 212:0.42 216:0.08 220:0.82 222:0.35 235:0.02 239:0.89 241:0.56 242:0.45 243:0.58 245:0.53 247:0.47 248:0.61 253:0.53 254:0.74 255:0.71 256:0.45 259:0.21 260:0.61 265:0.56 266:0.27 267:0.23 268:0.55 271:0.38 276:0.26 284:0.90 285:0.60 289:0.95 300:0.25 +1 1:0.66 10:0.88 11:0.52 12:0.86 17:0.51 18:0.74 21:0.13 27:0.72 29:0.86 30:0.57 34:0.66 36:0.93 37:0.92 39:0.51 43:0.61 45:0.87 46:0.96 64:0.77 66:0.89 69:0.69 70:0.62 71:0.57 74:0.56 81:0.70 83:0.69 86:0.80 91:0.27 98:0.90 99:0.95 101:0.65 107:0.95 108:0.53 110:0.95 114:0.92 122:0.65 123:0.51 125:0.88 126:0.51 127:0.46 129:0.05 135:0.21 139:0.76 146:0.88 147:0.91 149:0.64 150:0.58 154:0.50 155:0.50 159:0.47 160:0.88 163:0.90 165:0.81 170:0.82 172:0.70 173:0.71 174:0.88 176:0.70 177:0.88 178:0.55 179:0.58 187:0.39 192:0.50 197:0.89 201:0.63 202:0.36 208:0.61 212:0.28 215:0.84 216:0.24 222:0.35 235:0.31 239:0.87 241:0.57 242:0.29 243:0.90 244:0.61 247:0.84 253:0.64 259:0.21 265:0.90 266:0.63 267:0.79 271:0.38 276:0.58 289:0.95 290:0.92 297:0.36 300:0.55 +2 8:0.88 9:0.76 12:0.06 17:0.30 18:0.71 21:0.54 27:0.16 29:0.77 30:0.58 34:0.80 36:0.56 37:0.79 39:0.31 43:0.15 55:0.84 66:0.49 69:0.84 70:0.60 71:0.71 74:0.39 81:0.26 83:0.91 86:0.59 91:0.56 96:0.76 97:0.94 98:0.48 108:0.69 114:0.56 120:0.63 122:0.82 123:0.67 124:0.64 126:0.26 127:0.43 129:0.05 131:0.92 133:0.60 135:0.68 139:0.69 140:0.80 144:0.76 146:0.55 147:0.30 149:0.14 150:0.26 151:0.65 153:0.48 154:0.39 155:0.67 157:0.61 158:0.79 159:0.46 162:0.74 163:0.86 164:0.54 166:0.87 172:0.32 173:0.88 176:0.55 177:0.38 178:0.42 179:0.84 182:0.61 187:0.39 190:0.77 192:0.87 202:0.65 208:0.64 212:0.37 216:0.74 219:0.92 220:0.62 222:0.90 227:0.96 229:0.61 231:0.88 235:0.60 241:0.66 242:0.40 243:0.29 244:0.61 245:0.48 246:0.61 247:0.55 248:0.63 253:0.46 254:0.90 255:0.74 256:0.68 257:0.65 259:0.21 260:0.63 265:0.50 266:0.47 267:0.36 268:0.69 271:0.18 276:0.34 279:0.82 283:0.78 285:0.56 287:0.61 290:0.56 292:0.95 300:0.43 +1 8:0.97 12:0.06 17:0.28 21:0.78 27:0.26 29:0.77 30:0.72 32:0.77 34:0.89 36:0.30 37:0.90 39:0.31 43:0.16 44:0.93 55:0.52 66:0.84 69:0.47 70:0.22 71:0.71 74:0.52 77:0.70 91:0.33 98:0.79 104:0.86 108:0.36 120:0.65 123:0.34 127:0.28 129:0.05 131:0.61 135:0.30 139:0.78 140:0.45 144:0.76 145:0.92 146:0.55 147:0.61 149:0.27 151:0.55 153:0.48 154:0.11 155:0.58 157:0.61 159:0.20 162:0.86 164:0.56 166:0.61 172:0.54 173:0.68 175:0.91 177:0.05 179:0.65 182:0.75 187:0.39 190:0.89 192:0.51 195:0.56 202:0.50 204:0.85 208:0.54 212:0.92 216:0.47 220:0.62 222:0.90 227:0.61 229:0.61 231:0.69 235:0.31 241:0.52 242:0.82 243:0.85 244:0.83 246:0.61 247:0.12 248:0.56 253:0.36 256:0.58 257:0.84 259:0.21 260:0.62 265:0.79 266:0.17 267:0.19 268:0.59 271:0.18 276:0.45 277:0.87 281:0.89 282:0.85 285:0.47 287:0.61 300:0.18 +1 8:0.96 11:0.81 12:0.06 17:0.64 18:0.95 21:0.13 27:0.55 29:0.77 30:0.21 31:0.86 34:0.74 36:0.80 37:0.94 39:0.31 43:0.57 45:0.66 55:0.71 66:0.75 69:0.41 70:0.90 71:0.71 74:0.43 79:0.88 81:0.53 86:0.79 91:0.82 96:0.69 98:0.84 101:0.52 104:0.58 108:0.31 120:0.58 122:0.83 123:0.30 124:0.43 126:0.26 127:0.87 129:0.05 131:0.97 133:0.60 135:0.82 137:0.86 139:0.75 140:0.90 144:0.76 146:0.91 147:0.93 149:0.58 150:0.40 151:0.50 153:0.45 154:0.73 157:0.80 159:0.65 162:0.50 164:0.53 166:0.61 172:0.86 173:0.33 177:0.70 178:0.50 179:0.36 182:0.74 190:0.89 191:0.86 196:0.88 202:0.63 212:0.58 215:0.61 216:0.14 220:0.74 222:0.90 227:0.98 229:0.61 231:0.92 235:0.12 241:0.79 242:0.33 243:0.77 245:0.81 246:0.61 247:0.79 248:0.51 253:0.33 254:0.92 256:0.64 259:0.21 260:0.57 265:0.84 266:0.54 267:0.52 268:0.55 271:0.18 276:0.80 284:0.86 285:0.62 287:0.61 297:0.36 300:0.70 +2 11:0.81 12:0.06 17:0.61 18:0.77 21:0.74 27:0.49 29:0.77 30:0.41 34:0.94 36:0.68 37:0.86 39:0.31 43:0.16 45:0.73 55:0.59 66:0.77 69:0.50 70:0.60 71:0.71 74:0.46 77:0.70 81:0.23 83:0.60 86:0.76 91:0.31 97:0.89 98:0.78 101:0.52 108:0.39 114:0.53 123:0.37 124:0.39 126:0.26 127:0.49 129:0.05 131:0.61 133:0.37 135:0.49 139:0.73 144:0.76 145:0.77 146:0.69 147:0.73 149:0.18 150:0.23 154:0.82 155:0.58 157:0.61 158:0.72 159:0.35 166:0.87 172:0.57 173:0.60 176:0.55 177:0.70 178:0.55 179:0.72 182:0.61 187:0.21 192:0.59 195:0.59 208:0.54 212:0.42 216:0.27 222:0.90 227:0.61 229:0.61 231:0.88 235:0.88 241:0.41 242:0.19 243:0.79 245:0.51 246:0.61 247:0.74 253:0.35 254:0.86 259:0.21 265:0.78 266:0.27 267:0.28 271:0.18 276:0.47 279:0.82 282:0.71 283:0.78 287:0.61 290:0.53 300:0.43 +0 8:0.61 11:0.81 12:0.06 17:0.32 18:0.84 21:0.78 27:0.45 29:0.77 30:0.21 34:0.86 36:0.23 37:0.50 39:0.31 43:0.11 45:0.66 55:0.42 66:0.80 69:0.80 70:0.67 71:0.71 74:0.40 86:0.81 91:0.10 98:0.35 101:0.52 108:0.63 122:0.86 123:0.61 127:0.12 129:0.05 131:0.61 135:0.83 139:0.77 144:0.76 145:0.47 146:0.48 147:0.54 149:0.10 154:0.67 157:0.80 159:0.17 166:0.61 172:0.45 173:0.77 177:0.70 178:0.55 179:0.24 182:0.74 187:0.68 212:0.82 216:0.77 222:0.90 227:0.61 229:0.61 231:0.73 235:0.74 241:0.39 242:0.58 243:0.82 246:0.61 247:0.47 253:0.32 254:0.74 259:0.21 265:0.37 266:0.23 267:0.52 271:0.18 276:0.31 287:0.61 300:0.43 +0 12:0.06 17:0.59 18:0.69 21:0.78 27:0.45 29:0.77 30:0.58 34:0.80 36:0.21 37:0.50 39:0.31 43:0.10 55:0.59 66:0.61 69:0.86 70:0.60 71:0.71 74:0.26 86:0.59 91:0.15 98:0.19 108:0.73 123:0.71 124:0.51 127:0.23 129:0.05 131:0.61 133:0.62 135:0.89 139:0.58 145:0.47 146:0.39 147:0.05 149:0.14 154:0.22 157:0.61 159:0.66 166:0.61 172:0.37 173:0.71 175:0.75 177:0.05 178:0.55 179:0.70 182:0.61 187:0.68 212:0.71 216:0.77 222:0.90 227:0.61 229:0.61 231:0.67 235:0.98 241:0.59 242:0.40 243:0.18 244:0.61 245:0.45 246:0.61 247:0.55 253:0.23 254:0.98 257:0.84 259:0.21 265:0.21 266:0.27 267:0.36 271:0.18 276:0.23 277:0.69 287:0.61 300:0.43 +2 8:0.85 9:0.76 12:0.06 17:0.28 18:0.81 21:0.54 27:0.16 29:0.77 30:0.33 34:0.84 36:0.68 37:0.79 39:0.31 43:0.15 55:0.59 66:0.71 69:0.84 70:0.76 71:0.71 74:0.29 81:0.26 83:0.60 86:0.61 91:0.49 96:0.78 98:0.72 104:0.84 106:0.81 108:0.69 114:0.56 120:0.67 122:0.80 123:0.67 124:0.42 126:0.26 127:0.45 129:0.05 131:0.92 133:0.43 135:0.68 139:0.60 140:0.45 146:0.80 147:0.30 149:0.19 150:0.26 151:0.74 153:0.82 154:0.36 155:0.58 157:0.61 158:0.71 159:0.52 162:0.86 163:0.75 164:0.65 166:0.87 172:0.57 173:0.85 176:0.55 177:0.38 179:0.68 182:0.61 187:0.68 190:0.77 191:0.73 192:0.59 202:0.62 206:0.81 208:0.54 212:0.29 216:0.74 219:0.87 220:0.62 222:0.90 227:0.96 229:0.61 231:0.90 235:0.60 241:0.18 242:0.18 243:0.21 244:0.61 245:0.60 246:0.61 247:0.79 248:0.68 253:0.48 254:0.92 255:0.74 256:0.68 257:0.65 259:0.21 260:0.65 265:0.73 266:0.63 267:0.97 268:0.70 271:0.18 276:0.48 283:0.78 285:0.56 287:0.61 290:0.56 292:0.91 300:0.55 +0 11:0.81 12:0.06 17:0.45 18:0.87 21:0.30 27:0.26 29:0.77 30:0.90 32:0.77 34:0.70 36:0.28 37:0.90 39:0.31 43:0.63 44:0.93 45:0.66 55:0.52 66:0.69 69:0.47 70:0.28 71:0.71 74:0.56 77:0.70 81:0.36 86:0.70 91:0.53 98:0.64 101:0.52 104:0.86 108:0.36 120:0.57 122:0.86 123:0.34 124:0.42 126:0.26 127:0.35 129:0.05 131:0.61 133:0.37 135:0.84 139:0.82 140:0.45 144:0.76 145:0.92 146:0.56 147:0.62 149:0.38 150:0.32 151:0.48 153:0.77 154:0.53 155:0.58 157:0.81 159:0.31 162:0.86 164:0.62 166:0.61 172:0.54 173:0.56 177:0.70 179:0.66 182:0.78 187:0.39 190:0.89 192:0.51 195:0.62 202:0.54 204:0.85 208:0.54 212:0.61 215:0.44 216:0.47 220:0.87 222:0.90 227:0.61 229:0.61 231:0.80 235:0.31 241:0.57 242:0.45 243:0.73 244:0.61 245:0.59 246:0.61 247:0.67 248:0.49 253:0.38 256:0.58 259:0.21 260:0.56 265:0.65 266:0.47 267:0.36 268:0.63 271:0.18 276:0.43 281:0.89 282:0.85 285:0.47 287:0.61 297:0.36 300:0.33 +1 8:0.88 11:0.81 12:0.06 17:0.34 18:0.94 21:0.78 27:0.26 29:0.77 30:0.43 32:0.77 34:0.91 36:0.26 37:0.90 39:0.31 43:0.68 44:0.93 45:0.83 55:0.52 66:0.89 69:0.57 70:0.64 71:0.71 74:0.69 77:0.70 86:0.87 91:0.57 98:0.80 101:0.52 108:0.44 122:0.86 123:0.42 127:0.29 129:0.05 131:0.61 135:0.81 139:0.90 144:0.76 145:0.92 146:0.74 147:0.78 149:0.69 154:0.57 155:0.58 157:0.95 159:0.31 166:0.61 172:0.70 173:0.64 177:0.70 178:0.55 179:0.47 182:0.88 187:0.39 192:0.51 195:0.64 202:0.50 204:0.85 208:0.54 212:0.61 216:0.47 222:0.90 227:0.61 229:0.61 231:0.87 235:0.22 241:0.51 242:0.39 243:0.90 244:0.61 246:0.61 247:0.55 253:0.41 259:0.21 265:0.80 266:0.38 267:0.65 271:0.18 276:0.57 281:0.89 282:0.85 287:0.61 300:0.55 +1 11:0.81 12:0.06 17:0.38 18:0.84 21:0.30 27:0.26 29:0.77 30:0.91 32:0.77 34:0.69 36:0.31 37:0.90 39:0.31 43:0.63 44:0.93 45:0.66 55:0.52 66:0.68 69:0.47 70:0.25 71:0.71 74:0.56 77:0.70 81:0.36 86:0.70 91:0.42 98:0.61 101:0.52 104:0.86 106:0.81 108:0.36 120:0.54 122:0.86 123:0.34 124:0.43 126:0.26 127:0.33 129:0.05 131:0.61 133:0.37 135:0.83 139:0.82 140:0.45 144:0.76 145:0.92 146:0.55 147:0.61 149:0.36 150:0.32 151:0.47 153:0.46 154:0.53 155:0.58 157:0.81 159:0.31 162:0.86 164:0.54 166:0.61 172:0.51 173:0.56 177:0.70 179:0.68 182:0.78 187:0.68 190:0.89 192:0.51 195:0.62 202:0.49 204:0.85 206:0.81 208:0.54 212:0.58 215:0.44 216:0.47 220:0.91 222:0.90 227:0.61 229:0.61 231:0.80 235:0.31 241:0.51 242:0.38 243:0.72 244:0.61 245:0.58 246:0.61 247:0.67 248:0.47 253:0.38 256:0.58 259:0.21 260:0.54 265:0.62 266:0.47 267:0.52 268:0.58 271:0.18 276:0.41 281:0.89 282:0.85 285:0.47 287:0.61 297:0.36 300:0.33 +2 1:0.69 7:0.66 8:0.60 11:0.87 12:0.06 17:0.93 18:0.79 21:0.22 27:0.72 29:0.77 30:0.45 32:0.80 34:0.55 36:0.66 39:0.31 43:0.77 44:0.93 45:0.73 66:0.85 69:0.25 70:0.74 71:0.71 74:0.64 76:0.85 77:0.70 81:0.63 83:0.60 85:0.72 86:0.83 91:0.58 98:0.91 99:0.83 101:0.87 108:0.20 114:0.62 122:0.57 123:0.19 126:0.44 127:0.50 129:0.05 131:0.61 135:0.74 139:0.86 144:0.76 145:0.58 146:0.72 147:0.76 149:0.61 150:0.61 154:0.89 155:0.58 157:0.81 159:0.29 165:0.70 166:0.87 172:0.57 173:0.44 176:0.55 177:0.70 178:0.55 179:0.75 182:0.78 192:0.59 195:0.61 201:0.68 208:0.54 212:0.58 215:0.51 216:0.03 222:0.90 227:0.61 229:0.61 230:0.69 231:0.90 233:0.76 235:0.31 241:0.78 242:0.22 243:0.86 246:0.61 247:0.74 253:0.47 259:0.21 265:0.91 266:0.54 267:0.28 271:0.18 276:0.45 282:0.88 287:0.61 290:0.62 297:0.36 299:0.88 300:0.55 +2 11:0.81 12:0.06 17:0.62 18:0.95 21:0.78 27:0.55 29:0.77 30:0.44 31:0.88 34:0.81 36:0.72 37:0.94 39:0.31 43:0.17 45:0.66 55:0.71 66:0.29 69:0.30 70:0.85 71:0.71 74:0.44 79:0.90 86:0.79 91:0.20 98:0.56 101:0.52 104:0.58 108:0.70 122:0.86 123:0.68 124:0.89 127:0.90 129:0.05 131:0.83 133:0.90 135:0.97 137:0.88 139:0.75 140:0.45 144:0.76 146:0.61 147:0.66 149:0.35 151:0.55 153:0.48 154:0.79 157:0.77 159:0.90 162:0.86 166:0.61 172:0.79 173:0.11 177:0.70 179:0.25 182:0.72 187:0.39 190:0.89 196:0.89 202:0.36 212:0.26 216:0.14 220:0.62 222:0.90 227:0.89 229:0.61 231:0.73 235:0.02 241:0.15 242:0.24 243:0.20 245:0.89 246:0.61 247:0.76 248:0.54 253:0.33 254:0.93 256:0.45 259:0.21 265:0.57 266:0.95 267:0.52 268:0.46 271:0.18 276:0.88 277:0.69 284:0.86 285:0.47 287:0.61 300:0.84 +3 7:0.66 11:0.64 12:0.06 17:0.77 18:0.80 21:0.78 27:0.71 29:0.77 30:0.72 34:0.95 36:0.95 37:0.65 39:0.31 43:0.69 45:0.66 55:0.71 66:0.59 69:0.45 70:0.57 71:0.71 74:0.31 77:0.70 83:0.60 86:0.61 91:0.44 97:0.89 98:0.65 101:0.52 108:0.35 123:0.34 124:0.61 127:0.53 129:0.59 131:0.61 133:0.66 135:0.83 139:0.59 145:0.79 146:0.72 147:0.76 149:0.43 154:0.65 155:0.58 157:0.61 158:0.72 159:0.51 166:0.61 172:0.61 173:0.43 177:0.70 178:0.55 179:0.59 182:0.61 187:0.21 192:0.59 195:0.74 202:0.36 208:0.54 212:0.68 216:0.15 222:0.90 227:0.61 229:0.61 230:0.69 231:0.86 233:0.76 235:0.80 241:0.24 242:0.18 243:0.67 245:0.69 246:0.61 247:0.86 253:0.27 254:0.99 259:0.21 265:0.65 266:0.54 267:0.65 271:0.18 276:0.60 279:0.82 282:0.71 283:0.78 287:0.61 300:0.55 +3 6:0.93 7:0.81 8:0.91 12:0.20 17:0.01 20:0.94 21:0.64 27:0.04 28:0.73 32:0.80 34:0.47 36:0.30 37:0.88 43:0.27 66:0.36 69:0.80 78:0.82 81:0.61 91:0.98 98:0.09 100:0.88 108:0.64 111:0.83 120:0.87 123:0.62 126:0.54 127:0.07 129:0.05 135:0.18 140:0.94 145:0.69 146:0.05 147:0.05 149:0.11 150:0.45 151:0.77 152:0.97 153:0.88 154:0.19 159:0.05 161:0.60 162:0.46 164:0.97 167:0.84 169:0.94 172:0.05 173:0.93 177:0.05 178:0.53 181:0.89 186:0.82 189:0.91 191:0.99 195:0.61 202:0.99 215:0.53 216:0.82 220:0.99 230:0.87 233:0.76 235:0.74 238:0.93 241:0.99 243:0.51 248:0.82 256:0.96 259:0.21 260:0.88 261:0.97 265:0.09 267:0.36 268:0.96 271:0.94 285:0.62 297:0.36 +3 6:0.96 8:0.95 12:0.20 17:0.34 20:0.95 21:0.30 25:0.88 27:0.09 28:0.73 30:0.71 34:0.20 36:0.63 37:0.96 43:0.39 55:0.84 66:0.53 69:0.56 70:0.29 78:0.85 81:0.75 91:0.93 98:0.72 100:0.91 104:0.54 108:0.70 111:0.86 120:0.92 123:0.68 124:0.51 126:0.54 127:0.49 129:0.59 133:0.47 135:0.63 140:0.45 146:0.55 147:0.61 149:0.44 150:0.56 151:0.83 152:0.88 153:0.96 154:0.29 159:0.50 161:0.60 162:0.78 163:0.94 164:0.93 167:0.82 169:0.89 172:0.37 173:0.54 177:0.05 179:0.83 181:0.89 186:0.85 189:0.97 191:0.94 202:0.87 212:0.61 215:0.72 216:0.44 220:0.74 235:0.31 238:0.96 241:0.91 242:0.82 243:0.37 245:0.57 247:0.12 248:0.88 256:0.89 259:0.21 260:0.92 261:0.92 265:0.72 266:0.38 267:0.36 268:0.91 271:0.94 276:0.40 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.83 7:0.81 8:0.63 12:0.20 17:0.02 20:0.86 21:0.71 27:0.15 28:0.73 30:0.95 32:0.80 34:0.26 36:0.43 37:0.27 43:0.34 55:0.71 66:0.54 69:0.66 78:0.87 81:0.55 91:0.96 98:0.43 100:0.90 104:0.58 108:0.50 111:0.86 120:0.86 123:0.48 126:0.54 127:0.14 129:0.05 135:0.28 140:0.90 145:0.74 146:0.26 147:0.32 149:0.21 150:0.42 151:0.73 152:0.81 153:0.80 154:0.25 159:0.11 161:0.60 162:0.54 164:0.94 167:0.81 169:0.87 172:0.10 173:0.96 177:0.05 178:0.50 179:0.93 181:0.89 186:0.87 189:0.85 191:0.92 195:0.54 202:0.93 215:0.48 216:0.36 220:0.62 230:0.87 233:0.76 235:0.84 238:0.91 241:0.84 243:0.63 248:0.81 256:0.96 259:0.21 260:0.87 261:0.88 265:0.45 267:0.44 268:0.92 271:0.94 283:0.82 285:0.62 297:0.36 300:0.08 +1 7:0.81 12:0.20 17:0.51 21:0.54 27:0.15 28:0.73 30:0.11 32:0.80 34:0.75 36:0.30 37:0.79 43:0.37 55:0.52 66:0.49 69:0.61 70:0.85 81:0.67 91:0.22 98:0.45 104:0.88 106:0.81 108:0.81 123:0.80 124:0.78 126:0.54 127:0.34 129:0.93 133:0.84 135:0.99 145:0.86 146:0.38 147:0.44 149:0.52 150:0.49 154:0.28 159:0.58 161:0.60 167:0.48 172:0.48 173:0.50 177:0.05 178:0.55 179:0.60 181:0.89 187:0.68 195:0.83 206:0.81 212:0.48 215:0.58 216:0.92 230:0.65 233:0.63 235:0.60 241:0.21 242:0.82 243:0.27 245:0.55 247:0.12 259:0.21 265:0.47 266:0.75 267:0.97 271:0.94 276:0.52 283:0.82 297:0.36 300:0.55 +2 8:0.94 12:0.20 17:0.09 21:0.78 27:0.03 28:0.73 34:0.61 36:0.23 37:0.90 91:0.99 120:0.82 135:0.88 140:1.00 151:0.66 153:0.48 161:0.60 162:0.46 164:0.91 167:0.83 175:0.75 178:0.55 181:0.89 191:0.99 202:0.99 216:0.79 220:0.62 241:0.93 244:0.61 248:0.74 256:0.92 257:0.65 259:0.21 260:0.82 267:0.91 268:0.89 271:0.94 277:0.69 285:0.62 +0 7:0.81 8:0.90 12:0.20 17:0.40 20:0.74 21:0.64 27:0.65 28:0.73 30:0.73 32:0.80 37:0.33 43:0.27 55:0.52 66:0.44 69:0.80 70:0.37 78:0.76 81:0.76 91:0.93 98:0.34 100:0.80 108:0.64 111:0.76 120:0.97 123:0.61 124:0.58 126:0.54 127:0.26 129:0.59 133:0.47 140:0.45 145:0.59 146:0.45 147:0.51 149:0.50 150:0.57 151:0.84 152:0.74 153:0.98 154:0.19 159:0.41 161:0.60 162:0.60 164:0.94 167:0.81 169:0.78 172:0.41 173:0.80 177:0.05 179:0.57 181:0.89 186:0.77 191:0.88 195:0.75 202:0.92 212:0.72 215:0.53 216:0.34 230:0.87 233:0.76 235:0.80 241:0.86 242:0.82 243:0.57 245:0.68 247:0.12 248:0.96 256:0.92 260:0.97 261:0.74 265:0.37 266:0.47 268:0.93 271:0.94 276:0.34 283:0.82 285:0.62 297:0.36 300:0.33 +2 6:0.86 7:0.81 8:0.72 12:0.20 17:0.24 20:0.97 21:0.54 25:0.88 27:0.12 28:0.73 30:0.08 32:0.80 34:0.61 36:0.54 37:0.53 43:0.33 55:0.45 66:0.36 69:0.68 70:0.86 78:0.81 81:0.67 91:0.92 98:0.59 100:0.85 104:0.58 108:0.75 111:0.81 120:0.96 123:0.74 124:0.67 126:0.54 127:0.37 129:0.81 133:0.67 135:0.96 140:0.45 145:0.72 146:0.31 147:0.37 149:0.51 150:0.49 151:0.82 152:0.92 153:0.94 154:0.24 159:0.48 161:0.60 162:0.66 164:0.96 167:0.83 169:0.82 172:0.37 173:0.67 177:0.05 179:0.70 181:0.89 186:0.82 189:0.88 191:0.96 195:0.77 202:0.93 212:0.22 215:0.58 216:0.19 220:0.96 230:0.87 233:0.76 235:0.60 238:0.90 241:0.92 242:0.82 243:0.44 245:0.59 247:0.12 248:0.94 256:0.94 259:0.21 260:0.96 261:0.88 265:0.60 266:0.63 267:0.99 268:0.94 271:0.94 276:0.45 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.86 7:0.81 8:0.68 12:0.20 17:0.30 20:0.85 21:0.40 27:0.21 28:0.73 30:0.45 34:0.71 36:0.68 37:0.74 43:0.52 55:0.71 66:0.91 69:0.15 70:0.39 78:0.80 81:0.72 91:0.84 98:0.86 100:0.83 104:0.91 106:0.81 108:0.12 111:0.80 120:0.93 123:0.12 126:0.54 127:0.38 129:0.05 135:0.54 140:0.45 146:0.89 147:0.91 149:0.72 150:0.54 151:0.82 152:0.80 153:0.95 154:0.41 159:0.49 161:0.60 162:0.72 164:0.94 167:0.64 169:0.81 172:0.76 173:0.19 177:0.05 179:0.46 181:0.89 186:0.80 187:0.39 189:0.88 191:0.83 202:0.90 206:0.81 212:0.35 215:0.67 216:0.95 220:0.62 230:0.87 233:0.76 235:0.42 238:0.91 241:0.65 242:0.82 243:0.92 247:0.12 248:0.90 256:0.91 259:0.21 260:0.94 261:0.82 265:0.86 266:0.54 267:0.89 268:0.92 271:0.94 276:0.65 283:0.82 285:0.62 297:0.36 300:0.33 +1 8:0.86 12:0.20 17:0.30 21:0.22 27:0.45 28:0.73 30:0.38 34:0.86 36:0.24 37:0.50 43:0.32 55:0.52 66:0.43 69:0.69 70:0.53 81:0.78 91:0.14 98:0.65 108:0.52 123:0.50 124:0.70 126:0.54 127:0.47 129:0.81 133:0.67 135:0.78 145:0.50 146:0.82 147:0.85 149:0.68 150:0.58 154:0.24 159:0.63 161:0.60 167:0.48 172:0.61 173:0.60 177:0.05 178:0.55 179:0.46 181:0.89 187:0.68 212:0.51 215:0.79 216:0.77 235:0.22 241:0.21 242:0.82 243:0.57 245:0.80 247:0.12 259:0.21 265:0.66 266:0.75 267:0.59 271:0.94 276:0.68 283:0.60 297:0.36 300:0.43 +1 6:0.83 7:0.81 8:0.64 12:0.20 17:0.49 20:0.81 21:0.71 27:0.28 28:0.73 30:0.95 32:0.80 34:0.29 36:0.36 37:0.43 43:0.34 55:0.71 66:0.54 69:0.66 78:0.84 81:0.55 91:0.76 98:0.43 100:0.86 104:0.72 108:0.50 111:0.83 120:0.62 123:0.48 126:0.54 127:0.14 129:0.05 135:0.30 140:0.80 145:0.74 146:0.26 147:0.32 149:0.21 150:0.42 151:0.57 152:0.78 153:0.80 154:0.25 159:0.11 161:0.60 162:0.48 164:0.70 167:0.82 169:0.84 172:0.10 173:0.96 177:0.05 178:0.42 179:0.93 181:0.89 186:0.84 189:0.85 191:0.77 195:0.54 202:0.76 215:0.48 216:0.24 220:0.62 230:0.87 233:0.76 235:0.84 238:0.89 241:0.89 243:0.63 248:0.55 256:0.58 259:0.21 260:0.62 261:0.83 265:0.45 267:0.23 268:0.64 271:0.94 283:0.60 285:0.62 297:0.36 300:0.08 +1 8:0.65 12:0.20 17:0.67 21:0.30 27:0.45 28:0.73 30:0.42 32:0.80 34:0.58 36:0.28 37:0.50 43:0.32 55:0.59 66:0.62 69:0.69 70:0.68 81:0.75 91:0.27 98:0.70 108:0.52 120:0.56 123:0.50 124:0.50 126:0.54 127:0.48 129:0.59 133:0.47 135:0.79 140:0.45 145:0.39 146:0.88 147:0.90 149:0.79 150:0.56 151:0.50 153:0.69 154:0.24 159:0.66 161:0.60 162:0.86 164:0.62 167:0.52 172:0.83 173:0.58 177:0.05 179:0.30 181:0.89 187:0.68 195:0.84 202:0.46 212:0.71 215:0.72 216:0.77 220:0.74 235:0.31 241:0.41 242:0.82 243:0.68 245:0.91 247:0.12 248:0.49 256:0.45 259:0.21 260:0.57 265:0.70 266:0.75 267:0.82 268:0.55 271:0.94 276:0.80 285:0.62 297:0.36 300:0.55 +2 6:0.93 7:0.81 8:0.80 12:0.20 17:0.01 20:0.89 21:0.22 27:0.04 28:0.73 30:0.58 32:0.80 34:0.71 36:0.45 37:0.88 43:0.35 55:0.59 66:0.80 69:0.62 70:0.33 78:0.89 81:0.78 91:0.84 98:0.79 100:0.98 104:0.91 108:0.47 111:0.95 120:0.98 123:0.46 126:0.54 127:0.27 129:0.05 135:0.51 140:0.45 145:0.70 146:0.61 147:0.66 149:0.48 150:0.58 151:0.85 152:0.97 153:0.99 154:0.27 159:0.22 161:0.60 162:0.69 163:0.75 164:0.99 167:0.76 169:0.94 172:0.45 173:0.79 177:0.05 179:0.75 181:0.89 186:0.89 187:0.39 189:0.94 191:0.98 195:0.58 202:0.98 212:0.37 215:0.79 216:0.82 220:0.62 230:0.87 233:0.76 235:0.22 238:0.98 241:0.76 242:0.82 243:0.82 247:0.12 248:0.98 256:0.99 259:0.21 260:0.98 261:0.97 265:0.79 266:0.27 267:0.36 268:0.99 271:0.94 276:0.38 283:0.82 285:0.62 297:0.36 300:0.25 +1 7:0.81 8:0.92 12:0.20 17:0.45 21:0.47 27:0.34 28:0.73 30:0.44 32:0.80 34:0.86 36:0.54 37:0.57 43:0.23 55:0.52 66:0.97 69:0.87 70:0.72 81:0.83 91:0.78 98:0.85 108:0.74 120:0.96 123:0.73 126:0.54 127:0.35 129:0.05 135:0.26 140:0.45 145:0.79 146:0.95 147:0.96 149:0.77 150:0.63 151:0.84 153:0.97 154:0.16 159:0.64 161:0.60 162:0.79 164:0.93 167:0.66 172:0.92 173:0.81 177:0.05 179:0.21 181:0.89 187:0.21 191:0.92 195:0.88 202:0.87 212:0.86 215:0.63 216:0.64 230:0.87 233:0.76 235:0.60 241:0.67 242:0.82 243:0.97 247:0.12 248:0.95 256:0.89 259:0.21 260:0.97 265:0.85 266:0.47 267:0.52 268:0.91 271:0.94 276:0.85 283:0.60 285:0.62 297:0.36 300:0.70 +2 6:0.86 7:0.81 8:0.66 12:0.20 17:0.13 20:0.74 21:0.40 27:0.21 28:0.73 30:0.28 34:0.62 36:0.94 37:0.74 43:0.52 55:0.59 66:0.96 69:0.12 70:0.57 78:0.90 81:0.72 91:0.88 98:0.93 100:0.93 104:0.84 106:0.81 108:0.10 111:0.88 120:0.91 123:0.10 126:0.54 127:0.58 129:0.05 135:0.60 140:0.45 146:0.97 147:0.97 149:0.79 150:0.54 151:0.74 152:0.80 153:0.84 154:0.41 159:0.69 161:0.60 162:0.73 164:0.94 167:0.61 169:0.81 172:0.88 173:0.13 177:0.05 179:0.32 181:0.89 186:0.97 187:0.39 189:0.83 191:0.85 202:0.90 206:0.81 212:0.54 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.99 241:0.62 242:0.82 243:0.96 247:0.12 248:0.87 256:0.92 259:0.21 260:0.92 261:0.82 265:0.93 266:0.63 267:0.84 268:0.93 271:0.94 276:0.81 283:0.82 285:0.62 297:0.36 300:0.43 +0 7:0.81 8:0.81 12:0.20 17:0.38 20:0.74 21:0.54 27:0.39 28:0.73 30:0.76 32:0.80 37:0.29 43:0.30 55:0.52 66:0.80 69:0.73 70:0.21 78:0.72 81:0.81 91:0.88 98:0.51 100:0.73 108:0.56 111:0.72 120:0.94 123:0.54 126:0.54 127:0.15 129:0.05 140:0.87 145:0.63 146:0.32 147:0.39 149:0.47 150:0.61 151:0.80 152:0.73 153:0.93 154:0.22 159:0.13 161:0.60 162:0.52 164:0.90 167:0.79 169:0.72 172:0.45 173:0.96 177:0.05 178:0.48 179:0.43 181:0.89 186:0.73 191:0.86 195:0.57 202:0.90 212:0.90 215:0.58 216:0.18 230:0.87 233:0.76 235:0.68 241:0.81 242:0.82 243:0.82 247:0.12 248:0.91 256:0.86 259:0.21 260:0.94 261:0.73 265:0.53 266:0.17 268:0.87 271:0.94 276:0.38 283:0.82 285:0.62 297:0.36 300:0.18 +1 6:0.82 8:0.67 12:0.20 17:0.01 20:0.87 21:0.22 27:0.31 28:0.73 30:0.91 34:0.28 36:0.79 37:0.35 43:0.51 55:0.84 66:0.69 69:0.12 70:0.13 78:0.79 81:0.78 91:0.95 98:0.29 100:0.81 104:0.58 108:0.10 111:0.79 120:0.97 123:0.10 126:0.54 127:0.12 129:0.05 135:0.24 140:0.80 146:0.44 147:0.50 149:0.34 150:0.58 151:0.70 152:0.82 153:0.48 154:0.40 159:0.16 161:0.60 162:0.51 163:0.86 164:0.97 167:0.81 169:0.79 172:0.21 173:0.14 177:0.05 178:0.42 179:0.43 181:0.89 186:0.80 189:0.84 191:0.86 202:0.98 212:0.61 215:0.79 216:0.65 235:0.22 238:0.87 241:0.84 242:0.82 243:0.73 247:0.12 248:0.96 256:0.96 259:0.21 260:0.97 261:0.82 265:0.31 266:0.17 267:0.82 268:0.97 271:0.94 276:0.22 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.91 8:0.79 12:0.20 17:0.34 20:0.74 21:0.68 27:0.33 28:0.73 30:0.62 34:0.22 36:0.42 37:0.73 43:0.50 55:0.84 66:0.75 69:0.32 70:0.34 78:0.90 81:0.74 91:0.96 98:0.82 100:0.94 108:0.25 111:0.89 120:0.93 123:0.24 126:0.54 127:0.31 129:0.05 135:0.37 140:0.45 146:0.63 147:0.68 149:0.41 150:0.55 151:0.84 152:0.74 153:0.97 154:0.39 159:0.23 161:0.60 162:0.65 163:0.75 164:0.96 167:0.81 169:0.88 172:0.32 173:0.50 177:0.05 179:0.89 181:0.89 186:0.96 189:0.92 191:0.88 202:0.95 212:0.30 215:0.49 216:0.44 235:0.84 238:0.91 241:0.86 242:0.82 243:0.77 247:0.12 248:0.90 256:0.94 259:0.21 260:0.93 261:0.77 265:0.82 266:0.27 267:0.89 268:0.96 271:0.94 276:0.28 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.95 8:0.96 12:0.20 17:0.12 20:0.97 21:0.78 27:0.07 28:0.73 30:0.62 34:0.42 36:0.51 37:0.96 43:0.39 55:0.84 66:0.75 69:0.54 70:0.34 78:0.85 91:1.00 98:0.78 100:0.92 108:0.42 111:0.87 120:0.90 123:0.40 127:0.26 129:0.05 135:0.51 140:0.94 146:0.58 147:0.64 149:0.39 151:0.78 152:0.89 153:0.89 154:0.30 159:0.21 161:0.60 162:0.51 163:0.75 164:0.95 167:0.82 169:0.90 172:0.32 173:0.73 177:0.05 178:0.53 179:0.87 181:0.89 186:0.85 189:0.96 191:0.97 202:0.96 212:0.30 216:0.61 235:0.05 238:0.95 241:0.92 242:0.82 243:0.77 247:0.12 248:0.85 256:0.97 259:0.21 260:0.90 261:0.92 265:0.78 266:0.27 267:0.88 268:0.94 271:0.94 276:0.28 285:0.62 300:0.25 +0 12:0.37 17:0.49 18:0.63 21:0.78 27:0.45 28:0.98 29:0.91 30:0.76 34:0.84 36:0.19 37:0.50 39:0.28 43:0.10 55:0.42 66:0.64 69:0.80 70:0.15 71:0.63 74:0.35 86:0.67 91:0.25 98:0.18 108:0.64 122:0.67 123:0.61 127:0.10 129:0.05 135:0.86 139:0.65 145:0.49 146:0.28 147:0.35 149:0.06 154:0.47 159:0.12 161:0.99 163:0.97 167:0.50 172:0.16 173:0.74 177:0.70 178:0.55 179:0.23 181:0.64 187:0.68 212:0.95 216:0.77 222:0.60 235:0.60 241:0.21 242:0.82 243:0.69 244:0.61 247:0.12 253:0.29 254:0.74 259:0.21 265:0.19 266:0.12 267:0.44 271:0.88 276:0.14 300:0.13 +1 12:0.37 17:0.94 18:0.98 21:0.64 27:0.55 28:0.98 29:0.91 30:0.62 31:0.88 34:0.83 36:0.22 37:0.49 39:0.28 43:0.15 44:0.87 55:0.45 64:0.77 66:0.18 69:0.54 70:0.60 71:0.63 74:0.34 79:0.89 81:0.30 86:0.94 91:0.58 98:0.67 108:0.41 120:0.55 122:0.86 123:0.66 124:0.52 126:0.44 127:0.35 129:0.59 133:0.47 135:0.56 137:0.88 139:0.93 140:0.45 145:0.65 146:0.49 147:0.56 149:0.40 150:0.24 151:0.49 153:0.48 154:0.11 159:0.47 161:0.99 162:0.74 164:0.54 167:0.56 172:0.67 173:0.50 175:0.75 177:0.38 179:0.43 181:0.64 187:0.68 190:0.89 192:0.51 195:0.74 196:0.92 201:0.68 202:0.56 212:0.78 215:0.38 216:0.30 219:0.89 220:0.91 222:0.60 235:0.80 241:0.49 242:0.79 243:0.63 244:0.83 245:0.82 247:0.47 248:0.48 253:0.28 255:0.74 256:0.58 257:0.65 259:0.21 260:0.54 265:0.41 266:0.63 267:0.19 268:0.59 271:0.88 276:0.65 277:0.69 284:0.91 285:0.56 292:0.91 297:0.36 300:0.55 +1 8:0.68 11:0.64 12:0.37 17:0.79 18:0.79 20:0.87 21:0.77 27:0.59 28:0.98 29:0.91 30:0.21 34:0.74 36:0.63 37:0.27 39:0.28 43:0.64 44:0.90 45:0.66 55:0.42 66:0.16 69:0.71 70:0.76 71:0.63 74:0.75 76:0.98 77:0.98 78:0.89 81:0.23 86:0.80 91:0.85 96:0.68 98:0.45 100:0.73 101:0.52 108:0.70 111:0.86 114:0.53 122:0.80 123:0.52 124:0.75 126:0.26 127:0.80 129:0.59 133:0.76 135:0.39 139:0.80 140:0.96 145:0.98 146:0.48 147:0.05 149:0.44 150:0.23 151:0.46 152:0.82 153:0.48 154:0.53 159:0.49 161:0.99 162:0.47 167:0.83 169:0.72 172:0.48 173:0.76 175:0.75 176:0.61 177:0.05 178:0.54 179:0.71 181:0.64 186:0.89 190:0.89 195:0.70 202:0.85 212:0.68 216:0.24 220:0.99 222:0.60 235:0.97 241:0.84 242:0.37 243:0.12 244:0.61 245:0.60 247:0.47 248:0.46 253:0.43 256:0.68 257:0.84 259:0.21 261:0.86 265:0.25 266:0.54 267:0.44 268:0.69 271:0.88 276:0.50 277:0.69 282:0.77 285:0.47 290:0.53 300:0.55 +2 1:0.69 7:0.66 9:0.76 11:0.64 12:0.37 17:0.94 18:0.97 21:0.40 22:0.93 27:0.71 28:0.98 29:0.91 30:0.10 31:0.87 34:0.25 36:0.29 37:0.40 39:0.28 43:0.62 44:0.90 45:0.81 47:0.93 64:0.77 66:0.97 69:0.66 70:0.83 71:0.63 74:0.92 77:1.00 79:0.87 81:0.45 83:0.60 86:0.97 91:0.61 96:0.69 97:0.89 98:0.89 99:0.83 101:0.52 108:0.50 114:0.61 117:0.86 122:0.80 123:0.48 126:0.44 127:0.43 129:0.05 135:0.51 137:0.87 139:0.97 140:0.45 145:0.96 146:0.88 147:0.90 149:0.83 150:0.56 151:0.50 153:0.77 154:0.51 155:0.58 158:0.79 159:0.46 161:0.99 162:0.86 165:0.70 167:0.59 172:0.92 173:0.68 175:0.75 176:0.66 177:0.38 179:0.23 181:0.64 187:0.39 190:0.77 192:0.51 195:0.72 196:0.90 201:0.68 202:0.55 204:0.85 208:0.54 212:0.91 216:0.12 219:0.92 220:0.91 222:0.60 230:0.69 232:0.80 233:0.73 235:0.52 241:0.54 242:0.54 243:0.97 244:0.61 247:0.60 248:0.50 253:0.52 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.89 266:0.47 267:0.69 268:0.64 271:0.88 276:0.85 277:0.69 279:0.82 281:0.91 282:0.77 284:0.92 285:0.56 290:0.61 292:0.95 300:0.55 +1 8:0.65 12:0.37 17:0.69 18:0.63 20:0.87 21:0.30 27:0.12 28:0.98 29:0.91 30:0.72 34:0.85 36:0.69 37:0.82 39:0.28 43:0.16 55:0.59 66:0.84 69:0.40 70:0.29 71:0.63 74:0.64 76:0.85 77:0.70 78:0.90 81:0.36 86:0.64 87:0.98 91:0.29 98:0.89 100:0.92 108:0.31 111:0.90 114:0.63 117:0.86 122:0.77 123:0.30 126:0.26 127:0.44 129:0.05 135:0.61 139:0.62 145:0.58 146:0.78 147:0.82 149:0.26 150:0.32 152:0.82 154:0.11 159:0.34 161:0.99 167:0.53 169:0.89 172:0.54 173:0.49 176:0.61 177:0.70 178:0.55 179:0.76 181:0.64 186:0.90 187:0.39 195:0.66 212:0.81 216:0.54 222:0.60 232:0.72 235:0.31 241:0.37 242:0.76 243:0.85 244:0.83 247:0.39 253:0.48 259:0.21 261:0.90 265:0.89 266:0.27 267:0.65 271:0.88 276:0.44 282:0.73 290:0.63 300:0.25 +1 7:0.72 8:0.60 12:0.37 17:0.95 18:0.87 21:0.40 22:0.93 27:0.57 28:0.98 29:0.91 30:0.68 31:0.86 34:0.69 36:0.23 37:0.40 39:0.28 43:0.18 44:0.87 47:0.93 48:0.91 55:0.71 64:0.77 66:0.54 69:0.27 70:0.43 71:0.63 74:0.32 79:0.86 81:0.36 86:0.81 91:0.46 98:0.74 108:0.21 122:0.86 123:0.20 124:0.48 126:0.26 127:0.70 129:0.05 133:0.39 135:0.54 137:0.86 139:0.84 140:0.45 145:0.47 146:0.61 147:0.66 149:0.20 150:0.32 151:0.47 153:0.69 154:0.12 159:0.35 161:0.99 162:0.86 163:0.81 167:0.45 172:0.32 173:0.42 175:0.75 177:0.38 179:0.91 181:0.64 187:0.21 190:0.89 192:0.51 195:0.58 196:0.88 202:0.46 212:0.42 216:0.11 219:0.89 220:0.91 222:0.60 230:0.74 233:0.66 235:0.52 241:0.01 242:0.63 243:0.63 244:0.83 245:0.50 247:0.39 248:0.47 253:0.27 256:0.45 257:0.65 259:0.21 262:0.93 265:0.74 266:0.38 267:0.23 268:0.55 271:0.88 276:0.33 277:0.69 281:0.86 284:0.86 285:0.47 292:0.91 300:0.33 +1 8:0.71 11:0.64 12:0.37 17:0.85 18:0.61 21:0.05 27:0.34 28:0.98 29:0.91 30:0.09 32:0.64 34:0.49 36:0.35 37:0.37 39:0.28 43:0.48 44:0.90 45:0.66 55:0.45 66:0.77 69:0.86 70:0.95 71:0.63 74:0.77 76:1.00 77:1.00 81:0.62 86:0.66 91:0.57 98:0.67 101:0.52 108:0.73 114:0.81 117:0.86 122:0.77 123:0.71 124:0.39 126:0.26 127:0.30 129:0.05 133:0.39 135:0.68 139:0.77 145:1.00 146:0.76 147:0.23 149:0.35 150:0.45 154:0.37 155:0.58 158:0.74 159:0.44 161:0.99 167:0.53 172:0.71 173:0.89 175:0.75 176:0.61 177:0.05 178:0.55 179:0.43 181:0.64 187:0.39 192:0.51 195:0.80 204:0.85 208:0.54 212:0.75 216:0.28 222:0.60 232:0.95 235:0.05 241:0.35 242:0.80 243:0.16 244:0.61 245:0.67 247:0.39 253:0.57 257:0.84 259:0.21 265:0.67 266:0.54 267:0.36 271:0.88 276:0.60 277:0.69 281:0.91 282:0.77 290:0.81 300:0.84 +2 1:0.69 12:0.37 17:0.96 18:0.68 21:0.64 27:0.34 28:0.98 29:0.91 30:0.74 34:0.21 36:0.38 37:0.37 39:0.28 43:0.11 44:0.87 55:0.59 64:0.77 66:0.77 69:0.95 70:0.59 71:0.63 74:0.66 76:0.85 77:0.98 81:0.32 86:0.71 91:0.25 96:0.75 98:0.47 108:0.91 114:0.56 117:0.86 122:0.77 123:0.90 124:0.41 126:0.44 127:0.54 129:0.05 133:0.62 135:0.83 139:0.72 140:0.45 145:0.70 146:0.78 147:0.05 149:0.17 150:0.48 151:0.55 153:0.48 154:0.15 155:0.58 158:0.74 159:0.78 161:0.99 162:0.86 167:0.52 172:0.79 173:0.96 175:0.75 176:0.66 177:0.05 179:0.45 181:0.64 187:0.39 190:0.89 192:0.51 195:0.92 201:0.68 202:0.36 208:0.54 212:0.87 216:0.28 220:0.62 222:0.60 232:1.00 235:0.84 241:0.30 242:0.63 243:0.08 244:0.61 245:0.67 247:0.55 248:0.54 253:0.57 254:0.74 256:0.45 257:0.84 259:0.21 265:0.49 266:0.47 267:0.23 268:0.46 271:0.88 276:0.55 277:0.69 282:0.77 285:0.47 290:0.56 300:0.70 +1 8:0.67 12:0.37 17:0.64 18:0.63 21:0.13 27:0.12 28:0.98 29:0.91 30:0.56 34:0.83 36:0.68 37:0.82 39:0.28 43:0.13 55:0.59 66:0.74 69:0.64 70:0.30 71:0.63 74:0.62 76:0.85 77:0.70 81:0.52 86:0.64 87:0.98 91:0.31 98:0.78 108:0.49 114:0.72 122:0.77 123:0.46 124:0.39 126:0.26 127:0.40 129:0.05 133:0.37 135:0.66 139:0.62 145:0.58 146:0.72 147:0.77 149:0.25 150:0.40 154:0.10 159:0.36 161:0.99 167:0.55 172:0.51 173:0.71 176:0.61 177:0.70 178:0.55 179:0.74 181:0.64 187:0.39 195:0.68 212:0.73 216:0.54 222:0.60 232:0.72 235:0.12 241:0.46 242:0.76 243:0.77 244:0.83 245:0.49 247:0.39 253:0.53 259:0.21 265:0.78 266:0.38 267:0.59 271:0.88 276:0.44 282:0.73 290:0.72 300:0.25 +1 1:0.69 7:0.66 11:0.64 12:0.37 17:0.89 18:0.78 21:0.68 27:0.69 28:0.98 29:0.91 30:0.45 34:1.00 36:0.46 37:0.24 39:0.28 43:0.61 44:0.90 45:0.66 48:0.98 55:0.45 64:0.77 66:0.82 69:0.76 70:0.47 71:0.63 74:0.66 76:0.98 77:0.96 81:0.32 86:0.81 91:0.54 98:0.62 101:0.52 108:0.60 114:0.55 117:0.86 122:0.80 123:0.57 126:0.44 127:0.18 129:0.05 132:0.97 135:0.56 139:0.84 145:0.98 146:0.51 147:0.57 149:0.30 150:0.48 154:0.51 155:0.58 159:0.18 161:0.99 167:0.51 172:0.48 173:0.90 175:0.75 176:0.66 177:0.38 178:0.55 179:0.53 181:0.64 187:0.39 192:0.51 195:0.60 201:0.68 204:0.85 208:0.54 212:0.50 216:0.39 222:0.60 230:0.69 232:0.93 233:0.73 235:0.97 241:0.27 242:0.33 243:0.83 244:0.61 247:0.55 253:0.41 257:0.65 265:0.63 266:0.38 267:0.65 271:0.88 276:0.38 277:0.69 281:0.88 282:0.77 290:0.55 300:0.33 +1 12:0.37 17:0.89 18:0.61 21:0.64 27:0.34 28:0.98 29:0.91 30:0.87 32:0.64 34:0.39 36:0.64 37:0.37 39:0.28 43:0.11 55:0.59 66:0.86 69:0.95 70:0.36 71:0.63 74:0.63 76:0.85 77:0.96 81:0.26 86:0.66 91:0.40 96:0.80 98:0.46 108:0.90 114:0.56 117:0.86 122:0.77 123:0.89 124:0.37 126:0.26 127:0.30 129:0.05 133:0.39 135:0.82 139:0.64 140:0.45 145:1.00 146:0.85 147:0.05 149:0.18 150:0.25 151:0.60 153:0.48 154:0.15 158:0.75 159:0.78 161:0.99 162:0.86 167:0.49 172:0.78 173:0.89 175:0.75 176:0.61 177:0.05 179:0.37 181:0.64 187:0.39 190:0.89 195:0.95 202:0.36 212:0.89 216:0.28 222:0.60 232:0.98 235:0.80 241:0.18 242:0.77 243:0.09 244:0.61 245:0.60 247:0.47 248:0.59 253:0.69 254:0.74 256:0.45 257:0.84 259:0.21 265:0.47 266:0.38 267:0.19 268:0.46 271:0.88 276:0.52 277:0.69 282:0.73 285:0.47 290:0.56 300:0.55 +0 1:0.69 11:0.85 12:0.37 17:1.00 18:0.96 21:0.30 22:0.93 27:0.72 28:0.98 29:0.91 30:0.13 34:0.75 36:0.49 39:0.28 43:0.71 44:0.90 45:0.89 47:0.93 48:0.91 55:0.42 64:0.77 66:0.90 69:0.85 70:0.92 71:0.63 74:0.89 76:0.85 77:0.70 81:0.52 83:0.60 86:0.94 91:0.18 97:0.89 98:0.83 99:0.83 101:0.87 104:0.74 108:0.71 114:0.63 117:0.86 122:0.77 123:0.69 124:0.37 126:0.44 127:0.81 129:0.05 133:0.43 135:0.74 139:0.94 145:0.58 146:0.91 147:0.18 149:0.77 150:0.58 154:0.53 155:0.58 158:0.79 159:0.66 161:0.99 165:0.70 167:0.45 172:0.93 173:0.84 176:0.66 177:0.38 178:0.55 179:0.25 181:0.64 187:0.39 192:0.51 195:0.79 201:0.68 204:0.85 208:0.54 212:0.86 216:0.04 219:0.92 222:0.60 232:0.90 235:0.42 242:0.43 243:0.08 245:0.81 247:0.79 253:0.51 254:0.96 257:0.65 259:0.21 262:0.93 265:0.83 266:0.54 267:0.84 271:0.88 276:0.86 279:0.82 281:0.89 282:0.77 290:0.63 292:0.95 300:0.84 +2 12:0.37 17:0.93 18:0.58 21:0.78 27:0.34 28:0.98 29:0.91 30:0.21 34:0.21 36:0.41 37:0.37 39:0.28 43:0.11 55:0.42 66:0.20 69:0.95 70:0.37 71:0.63 74:0.39 76:0.85 86:0.63 91:0.11 98:0.09 108:0.90 122:0.55 123:0.90 124:0.73 127:0.28 129:0.05 133:0.62 135:0.84 139:0.61 145:0.89 146:0.19 147:0.05 149:0.08 154:0.15 159:0.71 161:0.99 163:0.81 167:0.47 172:0.10 173:0.93 175:0.75 177:0.05 178:0.55 179:0.91 181:0.64 187:0.39 212:0.42 216:0.28 222:0.60 235:0.97 241:0.07 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.31 254:0.84 257:0.84 259:0.21 265:0.08 266:0.23 267:0.23 271:0.88 276:0.15 277:0.69 300:0.25 +1 12:0.37 17:0.20 18:0.85 21:0.64 27:0.45 28:0.98 29:0.91 30:0.08 32:0.68 34:0.66 36:0.18 37:0.50 39:0.28 43:0.57 44:0.90 64:0.77 66:0.24 69:0.70 70:0.95 71:0.63 74:0.41 81:0.30 86:0.89 91:0.23 98:0.33 108:0.53 122:0.75 123:0.69 124:0.71 126:0.44 127:0.35 129:0.05 133:0.60 135:0.89 139:0.90 145:0.47 146:0.41 147:0.48 149:0.38 150:0.24 154:0.70 159:0.50 161:0.99 163:0.79 167:0.54 172:0.32 173:0.67 177:0.70 178:0.55 179:0.69 181:0.64 187:0.68 192:0.51 195:0.75 201:0.68 212:0.22 215:0.38 216:0.77 219:0.92 222:0.60 235:0.80 241:0.41 242:0.22 243:0.48 245:0.60 247:0.67 253:0.32 254:0.80 259:0.21 265:0.33 266:0.54 267:0.36 271:0.88 276:0.42 277:0.69 283:0.71 292:0.91 297:0.36 300:0.70 +0 1:0.69 7:0.81 12:0.37 17:0.90 18:0.92 21:0.40 22:0.93 27:0.55 28:0.98 29:0.91 30:0.55 34:0.97 36:0.33 37:0.32 39:0.28 43:0.09 44:0.90 47:0.93 48:0.99 55:0.42 64:0.77 66:0.92 69:0.78 70:0.51 71:0.63 74:0.82 76:0.98 77:0.89 81:0.42 86:0.93 91:0.25 98:0.60 108:0.61 114:0.61 117:0.86 122:0.85 123:0.59 126:0.44 127:0.17 129:0.05 135:0.95 139:0.95 145:0.83 146:0.68 147:0.72 149:0.08 150:0.52 154:0.49 155:0.67 158:0.78 159:0.26 161:0.99 167:0.47 172:0.77 173:0.79 175:0.75 176:0.66 177:0.38 178:0.55 179:0.21 181:0.64 187:0.39 192:0.87 195:0.73 201:0.68 204:0.89 208:0.64 212:0.89 216:0.81 219:0.92 222:0.60 230:0.86 232:0.83 233:0.73 235:0.52 241:0.03 242:0.45 243:0.92 244:0.61 247:0.67 253:0.49 254:0.74 257:0.65 259:0.21 262:0.93 265:0.61 266:0.38 267:0.65 271:0.88 276:0.66 277:0.69 279:0.82 281:0.89 282:0.77 290:0.61 292:0.91 300:0.43 +2 6:0.85 8:0.70 11:0.64 12:0.37 17:0.93 18:0.61 20:0.87 21:0.05 27:0.34 28:0.98 29:0.91 30:0.42 32:0.64 34:0.75 36:0.31 37:0.37 39:0.28 43:0.48 45:0.66 55:0.71 66:0.72 69:0.86 70:0.85 71:0.63 74:0.68 76:0.85 77:0.96 78:0.87 81:0.62 86:0.66 91:0.54 96:0.75 98:0.59 100:0.89 101:0.52 108:0.73 111:0.86 114:0.81 117:0.86 122:0.77 123:0.71 124:0.41 126:0.26 127:0.26 129:0.05 133:0.39 135:0.76 139:0.64 140:0.45 145:1.00 146:0.67 147:0.23 149:0.34 150:0.45 151:0.55 152:0.94 153:0.48 154:0.37 158:0.74 159:0.38 161:0.99 162:0.86 167:0.52 169:0.90 172:0.61 173:0.90 175:0.75 176:0.61 177:0.05 179:0.50 181:0.64 186:0.87 187:0.39 190:0.89 195:0.73 202:0.36 212:0.73 216:0.28 220:0.62 222:0.60 232:0.95 235:0.05 241:0.32 242:0.78 243:0.19 244:0.61 245:0.62 247:0.39 248:0.54 253:0.65 256:0.45 257:0.84 259:0.21 261:0.93 265:0.60 266:0.54 267:0.52 268:0.46 271:0.88 276:0.50 277:0.69 282:0.73 285:0.47 290:0.81 300:0.70 +0 12:0.37 17:0.98 18:0.98 21:0.54 22:0.93 27:0.55 28:0.98 29:0.91 30:0.72 34:0.75 36:0.30 37:0.49 39:0.28 43:0.14 44:0.87 47:0.93 48:0.91 55:0.45 64:0.77 66:0.13 69:0.83 70:0.52 71:0.63 74:0.31 81:0.40 86:0.94 91:0.15 98:0.58 108:0.46 122:0.86 123:0.66 124:0.50 126:0.44 127:0.29 129:0.05 133:0.39 135:0.34 139:0.93 145:0.54 146:0.66 147:0.51 149:0.38 150:0.32 154:0.10 159:0.40 161:0.99 167:0.55 172:0.65 173:0.86 175:0.75 177:0.05 178:0.55 179:0.42 181:0.64 187:0.68 192:0.51 195:0.71 201:0.68 212:0.82 215:0.39 216:0.30 222:0.60 235:0.88 241:0.46 242:0.80 243:0.68 244:0.83 245:0.78 247:0.39 253:0.27 257:0.84 259:0.21 262:0.93 265:0.38 266:0.47 267:0.36 271:0.88 276:0.60 277:0.69 281:0.89 297:0.36 300:0.55 +1 1:0.74 11:0.83 12:0.37 17:0.93 18:0.81 21:0.30 27:0.72 28:0.57 29:0.71 30:0.40 34:0.88 36:0.74 39:0.84 43:0.80 45:0.66 55:0.59 64:0.77 66:0.62 69:0.72 70:0.54 71:0.77 74:0.71 76:0.85 81:0.57 83:0.91 85:0.80 86:0.87 91:0.29 98:0.77 101:0.87 104:0.58 108:0.65 114:0.65 122:0.77 123:0.63 124:0.55 126:0.54 127:0.71 129:0.59 133:0.66 135:0.23 139:0.83 145:0.92 146:0.20 147:0.18 149:0.75 150:0.76 154:0.74 155:0.67 159:0.46 161:0.86 165:0.93 167:0.53 172:0.67 173:0.78 176:0.73 177:0.38 178:0.55 179:0.58 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.58 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.32 242:0.16 243:0.15 245:0.71 247:0.90 253:0.50 257:0.65 259:0.21 265:0.77 266:0.27 267:0.18 271:0.88 276:0.63 290:0.65 297:0.36 300:0.43 +1 1:0.74 8:0.78 9:0.80 11:0.92 12:0.37 17:0.76 18:0.95 21:0.05 22:0.93 27:0.54 28:0.57 29:0.71 30:0.68 31:0.91 32:0.79 34:0.50 36:0.94 37:0.65 39:0.84 41:0.82 43:0.78 44:0.93 45:0.83 47:0.93 60:0.95 64:0.77 66:0.95 69:0.78 70:0.45 71:0.77 74:0.97 76:0.85 77:0.96 78:0.93 79:0.90 81:0.76 83:0.91 85:0.97 86:0.99 91:0.48 96:0.75 97:0.94 98:0.80 101:0.97 106:0.81 108:0.62 111:0.96 114:0.89 117:0.86 120:0.64 122:0.57 123:0.60 124:0.36 126:0.54 127:0.34 129:0.05 133:0.37 135:0.87 137:0.91 139:0.99 140:0.45 145:0.91 146:0.92 147:0.05 149:0.98 150:0.85 151:0.72 153:0.48 154:0.70 155:0.67 158:0.92 159:0.59 161:0.86 162:0.86 164:0.66 165:0.93 167:0.56 169:0.95 172:0.96 173:0.71 176:0.73 177:0.05 179:0.15 181:0.74 187:0.87 191:0.75 192:0.87 195:0.85 196:0.96 201:0.93 202:0.56 206:0.81 208:0.64 212:0.93 215:0.78 216:0.62 219:0.95 220:0.62 222:0.48 232:0.96 235:0.22 238:0.95 241:0.44 242:0.27 243:0.06 245:0.76 247:0.90 248:0.67 253:0.81 255:0.95 256:0.64 257:0.84 260:0.65 262:0.93 265:0.80 266:0.38 267:0.98 268:0.65 271:0.88 276:0.91 279:0.86 282:0.87 283:0.82 284:0.89 285:0.62 290:0.89 292:0.95 297:0.36 300:0.55 +2 1:0.74 9:0.80 11:0.92 12:0.37 17:0.95 18:0.81 21:0.13 27:1.00 28:0.57 29:0.71 30:0.43 31:0.99 32:0.80 34:0.24 36:0.54 39:0.84 41:0.96 43:0.89 44:0.93 45:0.73 60:0.87 64:0.77 66:0.13 69:0.17 70:0.79 71:0.77 74:0.85 77:0.96 79:0.99 81:0.68 83:0.91 85:0.80 86:0.91 91:0.90 96:0.96 98:0.31 101:0.97 108:0.14 114:0.79 120:0.92 122:0.77 123:0.90 124:0.72 126:0.54 127:0.98 129:0.59 133:0.66 135:0.63 137:0.99 139:0.93 140:0.45 145:0.52 146:0.39 147:0.46 149:0.68 150:0.81 151:0.99 153:0.97 154:0.90 155:0.67 158:0.91 159:0.80 161:0.86 162:0.77 164:0.89 165:0.93 167:0.83 172:0.48 173:0.12 176:0.73 177:0.70 179:0.68 181:0.74 192:0.87 195:0.89 196:0.99 201:0.93 202:0.87 208:0.64 212:0.57 215:0.61 216:0.13 219:0.95 222:0.48 235:0.31 241:0.81 242:0.29 243:0.51 245:0.72 247:0.79 248:0.96 253:0.97 255:0.95 256:0.90 259:0.21 260:0.92 265:0.23 266:0.71 267:0.76 268:0.91 271:0.88 276:0.39 277:0.69 279:0.86 282:0.88 283:0.78 284:0.99 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.70 +2 6:0.96 7:0.72 8:0.90 9:0.76 12:0.37 17:0.96 20:0.91 21:0.40 27:0.09 28:0.57 29:0.71 30:0.86 32:0.77 34:0.61 36:0.80 37:0.86 39:0.84 43:0.15 44:0.93 55:0.45 66:0.68 69:0.52 70:0.32 71:0.77 74:0.70 76:0.99 77:0.70 78:0.86 81:0.27 83:0.60 91:0.77 96:0.77 98:0.43 100:0.94 104:0.58 108:0.85 111:0.89 120:0.68 123:0.85 124:0.64 126:0.26 127:0.59 129:0.59 133:0.82 135:0.75 139:0.74 140:0.80 145:0.77 146:0.36 147:0.43 149:0.38 150:0.26 151:0.65 152:0.96 153:0.48 154:0.18 155:0.58 158:0.75 159:0.66 161:0.86 162:0.69 164:0.86 167:0.55 169:0.94 172:0.86 173:0.41 175:0.75 177:0.38 178:0.42 179:0.31 181:0.74 186:0.86 187:0.39 189:0.97 190:0.77 191:0.91 192:0.59 195:0.84 202:0.85 204:0.85 208:0.54 212:0.86 215:0.42 216:0.67 220:0.82 222:0.48 230:0.75 232:0.90 233:0.76 235:0.42 238:0.95 241:0.39 242:0.77 243:0.18 244:0.61 245:0.79 247:0.60 248:0.67 253:0.67 254:0.84 255:0.74 256:0.88 257:0.65 259:0.21 260:0.66 261:0.96 265:0.45 266:0.80 267:0.44 268:0.88 271:0.88 276:0.79 277:0.69 282:0.85 283:0.78 285:0.56 297:0.36 300:0.55 +2 6:0.97 8:0.99 9:0.80 11:0.57 12:0.37 17:0.67 18:0.67 20:0.82 21:0.78 27:0.04 28:0.57 29:0.71 30:0.54 31:0.95 34:0.37 36:0.66 37:0.94 39:0.84 41:0.94 43:0.94 55:0.45 66:0.12 69:0.10 70:0.80 71:0.77 74:0.83 76:0.85 77:0.70 78:0.81 79:0.94 83:0.91 85:0.72 86:0.77 91:0.78 96:0.88 98:0.46 100:0.85 101:0.87 108:0.09 111:0.81 120:0.79 122:0.77 123:0.80 124:0.73 127:0.50 129:0.05 133:0.59 135:0.61 137:0.95 139:0.74 140:0.45 145:0.39 146:0.45 147:0.52 149:0.68 151:0.87 152:0.92 153:0.48 154:0.94 155:0.67 158:0.74 159:0.60 161:0.86 162:0.48 164:0.81 167:0.75 169:0.91 172:0.48 173:0.14 177:0.70 179:0.58 181:0.74 186:0.81 187:0.21 189:1.00 191:0.88 192:0.87 195:0.78 196:0.92 202:0.91 208:0.64 212:0.55 216:0.87 220:0.87 222:0.48 232:0.83 238:0.93 241:0.74 242:0.80 243:0.50 245:0.74 247:0.39 248:0.81 253:0.89 255:0.95 256:0.92 259:0.21 260:0.79 261:0.93 265:0.33 266:0.71 267:0.17 268:0.81 271:0.88 276:0.55 277:0.87 282:0.73 284:0.97 285:0.62 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.83 12:0.37 17:0.97 18:0.91 21:0.13 27:0.72 28:0.57 29:0.71 30:0.86 31:0.99 32:0.68 34:0.59 36:0.72 39:0.84 41:0.96 43:0.91 44:0.90 45:0.66 64:0.77 66:0.72 69:0.39 70:0.41 71:0.77 74:0.89 79:0.99 81:0.68 83:0.91 85:0.94 86:0.97 91:0.89 96:0.97 98:0.73 101:0.97 104:0.58 108:0.65 114:0.79 120:0.94 121:0.97 122:0.57 123:0.62 124:0.45 126:0.54 127:0.82 129:0.81 133:0.67 135:0.20 137:0.99 139:0.97 140:0.45 145:0.56 146:0.17 147:0.22 149:0.96 150:0.81 151:0.99 153:0.97 154:0.91 155:0.67 159:0.40 161:0.86 162:0.85 164:0.92 165:0.93 167:0.84 172:0.82 173:0.49 176:0.73 177:0.70 179:0.42 181:0.74 192:0.87 195:0.62 196:1.00 201:0.93 202:0.87 204:0.89 208:0.64 212:0.89 215:0.61 216:0.15 222:0.48 228:0.99 230:0.87 232:0.77 233:0.76 235:0.31 241:0.85 242:0.33 243:0.15 245:0.78 247:0.84 248:0.96 253:0.98 255:0.95 256:0.89 259:0.21 260:0.94 265:0.73 266:0.27 267:0.23 268:0.92 271:0.88 276:0.73 281:0.91 284:0.99 285:0.62 290:0.79 297:0.36 300:0.55 +1 1:0.74 7:0.72 8:0.88 9:0.80 12:0.37 17:0.83 18:0.69 20:0.75 21:0.59 27:0.71 28:0.57 29:0.71 30:0.45 31:0.97 32:0.79 34:0.59 36:0.47 37:0.77 39:0.84 41:0.82 43:0.17 44:0.93 55:0.52 64:0.77 66:0.49 69:0.83 70:0.25 71:0.77 74:0.61 76:0.99 77:0.70 78:0.89 79:0.97 81:0.47 83:0.91 86:0.70 91:0.83 96:0.96 98:0.23 100:0.92 108:0.67 111:0.87 114:0.58 120:0.88 122:0.80 123:0.65 124:0.48 126:0.54 127:0.22 129:0.05 133:0.39 135:0.61 137:0.97 139:0.78 140:0.94 145:0.76 146:0.30 147:0.05 149:0.09 150:0.71 151:0.96 152:0.74 153:0.89 154:0.45 155:0.67 158:0.74 159:0.32 161:0.86 162:0.79 163:0.88 164:0.82 165:0.93 167:0.83 169:0.81 172:0.16 173:0.87 175:0.75 176:0.73 177:0.05 179:0.92 181:0.74 186:0.99 191:0.70 192:0.87 195:0.72 196:0.95 201:0.93 202:0.88 204:0.85 208:0.64 212:0.61 215:0.39 216:0.22 220:0.93 222:0.48 230:0.75 232:0.82 233:0.76 235:0.88 241:0.81 242:0.63 243:0.29 244:0.61 245:0.39 247:0.30 248:0.86 253:0.93 254:0.90 255:0.95 256:0.89 257:0.84 259:0.21 260:0.87 261:0.78 265:0.25 266:0.17 267:0.23 268:0.92 271:0.88 276:0.15 277:0.69 282:0.87 284:0.96 285:0.62 290:0.58 297:0.36 300:0.18 +0 1:0.74 7:0.66 8:0.71 9:0.80 11:0.64 12:0.37 17:0.79 18:0.73 21:0.13 27:0.68 28:0.57 29:0.71 30:0.14 31:0.97 32:0.79 34:0.58 36:0.85 37:0.73 39:0.84 43:0.96 44:0.93 45:0.66 48:0.91 55:0.84 64:0.77 66:0.31 69:0.17 70:0.98 71:0.77 74:0.78 76:0.94 77:0.89 79:0.97 81:0.75 83:0.91 86:0.80 91:0.68 96:0.94 97:0.89 98:0.58 99:0.95 101:0.52 108:0.80 114:0.79 120:0.61 122:0.77 123:0.79 124:0.72 126:0.54 127:0.96 129:0.59 133:0.61 135:0.49 137:0.97 138:0.98 139:0.90 140:0.96 145:0.79 146:0.17 147:0.22 149:0.67 150:0.84 151:0.63 153:0.78 154:0.96 155:0.67 158:0.87 159:0.60 161:0.86 162:0.61 163:0.81 164:0.66 165:0.93 167:0.81 172:0.37 173:0.21 176:0.73 177:0.70 179:0.77 181:0.74 190:0.77 192:0.87 195:0.75 196:0.97 201:0.93 202:0.87 204:0.85 208:0.64 212:0.18 215:0.61 216:0.13 219:0.95 220:0.62 222:0.48 230:0.69 232:0.72 233:0.76 235:0.52 241:0.78 242:0.43 243:0.28 245:0.64 247:0.74 248:0.61 253:0.93 255:0.95 256:0.88 259:0.21 260:0.61 265:0.59 266:0.84 267:0.28 268:0.88 271:0.88 276:0.47 279:0.86 281:0.89 282:0.87 283:0.71 284:0.96 285:0.62 290:0.79 292:0.91 297:0.36 300:0.84 +1 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 12:0.37 17:0.66 20:0.76 21:0.47 27:0.04 28:0.57 29:0.71 30:0.14 31:0.91 32:0.80 34:0.61 36:0.39 37:0.94 39:0.84 41:1.00 43:0.14 44:0.93 55:0.59 64:0.77 66:0.71 69:0.63 70:0.82 71:0.77 74:0.79 76:0.99 77:0.96 78:0.90 79:0.90 81:0.52 83:0.91 91:0.82 96:0.79 98:0.60 100:0.92 108:0.48 111:0.91 114:0.60 120:0.73 121:0.90 122:0.77 123:0.46 124:0.43 126:0.54 127:0.39 129:0.81 133:0.67 135:0.54 137:0.91 139:0.82 140:0.45 145:0.89 146:0.70 147:0.75 149:0.28 150:0.74 151:0.60 152:0.92 153:0.45 154:0.10 155:0.67 158:0.75 159:0.49 161:0.86 162:0.57 164:0.84 165:0.93 167:0.73 169:0.91 172:0.57 173:0.61 175:0.91 176:0.73 177:0.05 179:0.66 181:0.74 186:0.88 187:0.68 191:0.92 192:0.87 195:0.75 196:0.92 201:0.93 202:0.86 204:0.89 208:0.64 212:0.29 215:0.41 216:0.87 220:0.87 222:0.48 230:0.87 232:0.85 233:0.76 235:0.80 238:0.93 241:0.72 242:0.82 243:0.75 244:0.83 245:0.52 247:0.12 248:0.64 253:0.76 255:0.95 256:1.00 257:0.84 259:0.21 260:0.68 261:0.93 265:0.61 266:0.54 267:0.65 268:0.85 271:0.88 276:0.48 277:0.87 281:0.91 282:0.88 284:0.97 285:0.62 290:0.60 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.83 12:0.37 17:0.97 18:0.79 21:0.30 27:0.72 28:0.57 29:0.71 30:0.28 34:0.89 36:0.92 39:0.84 43:0.80 45:0.66 64:0.77 66:0.54 69:0.72 70:0.76 71:0.77 74:0.67 76:0.85 81:0.57 83:0.91 85:0.80 86:0.85 91:0.14 98:0.80 101:0.87 104:0.58 108:0.65 114:0.65 122:0.57 123:0.63 124:0.63 126:0.54 127:0.83 129:0.59 133:0.66 135:0.47 139:0.81 145:0.97 146:0.20 147:0.18 149:0.75 150:0.76 154:0.74 155:0.67 159:0.47 161:0.86 165:0.93 167:0.53 172:0.59 173:0.78 176:0.73 177:0.38 178:0.55 179:0.66 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.42 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.32 242:0.13 243:0.18 245:0.68 247:0.86 253:0.50 257:0.65 259:0.21 265:0.80 266:0.54 267:0.18 271:0.88 276:0.58 290:0.65 297:0.36 300:0.55 +2 1:0.74 7:0.72 9:0.80 11:0.64 12:0.37 17:0.84 18:0.83 21:0.30 27:0.13 28:0.57 29:0.71 30:0.09 31:0.87 32:0.69 34:0.77 36:0.65 37:0.94 39:0.84 43:0.58 45:0.77 55:0.52 64:0.77 66:0.63 69:0.68 70:0.98 71:0.77 74:0.82 77:0.89 79:0.87 81:0.57 83:0.91 86:0.89 91:0.14 96:0.82 97:0.89 98:0.71 99:0.83 101:0.52 104:0.58 108:0.52 114:0.65 120:0.56 122:0.77 123:0.50 124:0.48 126:0.54 127:0.46 129:0.05 133:0.43 135:0.68 137:0.87 139:0.88 140:0.45 145:0.92 146:0.84 147:0.87 149:0.43 150:0.73 151:0.53 153:0.48 154:0.65 155:0.67 158:0.79 159:0.59 161:0.86 162:0.86 164:0.64 165:0.70 167:0.60 172:0.73 173:0.63 176:0.73 177:0.70 179:0.44 181:0.74 187:0.39 190:0.89 191:0.94 192:0.87 195:0.80 196:0.90 201:0.93 202:0.53 204:0.89 208:0.64 212:0.60 215:0.44 216:0.67 220:0.87 222:0.48 230:0.74 233:0.73 235:0.60 241:0.55 242:0.42 243:0.69 245:0.83 247:0.67 248:0.52 253:0.79 254:0.86 255:0.95 256:0.58 259:0.21 260:0.56 265:0.72 266:0.71 267:0.44 268:0.62 271:0.88 276:0.66 279:0.82 281:0.89 282:0.77 283:0.82 284:0.91 285:0.62 290:0.65 297:0.36 300:0.84 +2 1:0.74 7:0.66 11:0.64 12:0.37 17:0.97 18:0.67 27:0.72 28:0.57 29:0.71 30:0.45 32:0.80 34:0.42 36:0.54 39:0.84 43:0.71 44:0.93 45:0.66 55:0.84 60:0.87 64:0.77 66:0.52 69:0.27 70:0.50 71:0.77 74:0.73 76:0.98 77:0.96 81:0.83 83:0.91 86:0.70 91:0.54 97:0.97 98:0.29 101:0.52 104:0.58 108:0.21 114:0.98 122:0.77 123:0.20 124:0.50 126:0.54 127:0.95 129:0.05 133:0.39 135:0.37 139:0.87 145:0.58 146:0.42 147:0.49 149:0.40 150:0.89 154:0.61 155:0.67 158:0.92 159:0.68 161:0.86 165:0.93 167:0.79 172:0.27 173:0.22 175:0.75 176:0.73 177:0.38 178:0.55 179:0.94 181:0.74 192:0.87 195:0.82 201:0.93 204:0.85 208:0.64 212:0.23 215:0.96 216:0.01 219:0.95 222:0.48 230:0.69 232:0.77 233:0.73 235:0.12 241:0.77 242:0.73 243:0.61 244:0.61 245:0.48 247:0.35 253:0.67 257:0.65 259:0.21 265:0.31 266:0.38 267:0.44 271:0.88 276:0.17 277:0.69 279:0.86 281:0.91 282:0.88 283:0.82 290:0.98 292:0.95 297:0.36 299:0.88 300:0.33 +2 6:0.96 8:0.93 9:0.80 12:0.37 17:0.61 20:0.97 21:0.74 27:0.09 28:0.57 29:0.71 30:0.14 31:0.92 32:0.64 34:0.21 36:0.66 37:0.86 39:0.84 41:1.00 43:0.15 55:0.84 66:0.10 69:0.55 70:0.94 71:0.77 74:0.55 76:0.85 78:0.92 79:0.94 81:0.23 83:0.91 91:0.98 96:0.87 98:0.24 100:0.97 104:0.58 108:0.42 111:0.95 120:0.97 122:0.77 123:0.94 124:0.88 126:0.26 127:0.88 129:0.59 133:0.86 135:0.51 137:0.92 140:1.00 145:0.74 146:0.36 147:0.43 149:0.22 150:0.23 151:0.46 152:0.96 153:0.84 154:0.11 155:0.67 159:0.88 161:0.86 162:0.53 163:0.98 164:0.98 167:0.83 169:0.94 172:0.27 173:0.26 175:0.75 177:0.38 179:0.77 181:0.74 186:0.91 189:0.97 190:0.89 191:0.98 192:0.87 195:0.96 202:0.99 208:0.64 212:0.18 215:0.36 216:0.67 220:0.62 222:0.48 235:0.88 238:0.96 241:0.82 242:0.78 243:0.40 244:0.83 245:0.55 247:0.39 248:0.46 253:0.78 255:0.95 256:1.00 257:0.65 259:0.21 260:0.95 261:0.96 265:0.18 266:0.84 267:0.91 268:0.99 271:0.88 276:0.48 277:0.87 283:0.78 284:0.92 285:0.62 297:0.36 300:0.70 +2 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 12:0.37 17:0.78 20:0.96 21:0.47 27:0.04 28:0.57 29:0.71 30:0.28 31:0.91 32:0.80 34:0.76 36:0.65 37:0.94 39:0.84 41:0.92 43:0.14 44:0.93 55:0.45 64:0.77 66:0.79 69:0.61 70:0.65 71:0.77 74:0.79 76:0.99 77:0.96 78:0.89 79:0.91 81:0.52 83:0.91 91:0.65 96:0.78 98:0.77 100:0.93 108:0.46 111:0.90 114:0.60 120:0.59 121:0.90 122:0.77 123:0.45 124:0.38 126:0.54 127:0.35 129:0.59 133:0.47 135:0.65 137:0.91 139:0.82 140:0.45 145:0.89 146:0.81 147:0.85 149:0.29 150:0.74 151:0.56 152:0.92 153:0.48 154:0.10 155:0.67 158:0.75 159:0.45 161:0.86 162:0.60 163:0.81 164:0.78 165:0.93 167:0.77 169:0.91 172:0.61 173:0.60 175:0.91 176:0.73 177:0.05 179:0.61 181:0.74 186:0.89 187:0.39 189:0.98 190:0.77 191:0.76 192:0.87 195:0.74 196:0.92 201:0.93 202:0.77 204:0.89 208:0.64 212:0.37 215:0.41 216:0.87 220:0.87 222:0.48 230:0.87 232:0.85 233:0.76 235:0.80 238:0.92 241:0.76 242:0.82 243:0.80 244:0.83 245:0.53 247:0.12 248:0.58 253:0.73 255:0.95 256:0.75 257:0.84 259:0.21 260:0.59 261:0.93 265:0.77 266:0.47 267:0.82 268:0.77 271:0.88 276:0.51 277:0.87 281:0.91 282:0.88 284:0.96 285:0.62 290:0.60 297:0.36 299:0.88 300:0.43 +1 1:0.74 8:0.81 11:0.85 12:0.37 17:0.43 18:0.80 20:0.81 21:0.54 27:0.45 28:0.57 29:0.71 30:0.12 34:0.81 36:0.30 37:0.50 39:0.84 43:0.81 45:0.83 55:0.71 64:0.77 66:0.27 69:0.70 70:0.98 71:0.77 74:0.80 77:0.70 78:0.72 81:0.44 83:0.60 86:0.82 91:0.03 97:0.89 98:0.44 99:0.83 100:0.80 101:0.87 108:0.53 111:0.74 114:0.59 122:0.77 123:0.51 124:0.91 126:0.54 127:0.68 129:0.05 133:0.91 135:0.83 139:0.85 145:0.50 146:0.84 147:0.87 149:0.68 150:0.66 152:0.78 154:0.77 155:0.67 158:0.91 159:0.86 161:0.86 165:0.70 167:0.65 169:0.78 172:0.63 173:0.44 176:0.73 177:0.70 178:0.55 179:0.38 181:0.74 186:0.73 187:0.68 192:0.87 195:0.96 201:0.93 208:0.64 212:0.22 215:0.39 216:0.77 219:0.95 222:0.48 235:0.74 241:0.63 242:0.44 243:0.46 245:0.76 247:0.91 253:0.46 259:0.21 261:0.76 265:0.46 266:0.95 267:0.73 271:0.88 276:0.76 279:0.86 282:0.73 283:0.78 290:0.59 292:0.91 297:0.36 300:0.94 +2 1:0.74 9:0.80 11:0.57 12:0.37 17:0.96 18:0.85 25:0.88 27:0.72 28:0.57 29:0.71 30:0.66 31:0.99 32:0.69 34:0.21 36:0.55 39:0.84 41:0.94 43:0.97 60:0.95 64:0.77 66:0.10 69:0.07 70:0.70 71:0.77 74:0.84 77:0.98 79:0.98 81:0.83 83:0.91 85:0.90 86:0.94 87:0.98 91:0.70 96:0.94 98:0.31 101:0.87 104:0.54 108:0.06 114:0.98 120:0.90 122:0.57 123:0.90 124:0.72 126:0.54 127:0.99 129:0.59 133:0.66 135:0.75 137:0.99 139:0.94 140:0.45 145:0.49 146:0.37 147:0.43 149:0.90 150:0.89 151:0.97 153:0.90 154:0.97 155:0.67 158:0.91 159:0.80 161:0.86 162:0.86 164:0.80 165:0.93 167:0.81 172:0.54 173:0.09 176:0.73 177:0.70 179:0.60 181:0.74 192:0.87 195:0.89 196:0.99 201:0.93 202:0.71 208:0.64 212:0.74 215:0.96 216:0.08 219:0.95 222:0.48 228:0.99 232:0.74 235:0.12 241:0.79 242:0.28 243:0.50 245:0.78 247:0.74 248:0.94 253:0.96 255:0.95 256:0.79 259:0.21 260:0.90 265:0.22 266:0.71 267:0.79 268:0.78 271:0.88 276:0.44 277:0.69 279:0.86 282:0.77 283:0.78 284:0.97 285:0.62 290:0.98 292:0.91 297:0.36 300:0.70 +1 1:0.74 11:0.83 12:0.37 17:0.61 18:0.74 21:0.59 27:0.45 28:0.57 29:0.71 30:0.28 32:0.80 34:0.85 36:0.19 37:0.50 39:0.84 43:0.81 44:0.93 45:0.66 60:0.87 64:0.77 66:0.23 69:0.71 70:0.75 71:0.77 74:0.70 77:0.70 81:0.46 83:0.91 85:0.72 86:0.80 91:0.14 98:0.40 101:0.97 108:0.54 114:0.58 122:0.77 123:0.52 124:0.79 126:0.54 127:0.49 129:0.59 133:0.74 135:0.93 139:0.88 145:0.47 146:0.65 147:0.70 149:0.66 150:0.70 154:0.76 155:0.67 158:0.91 159:0.70 161:0.86 165:0.93 167:0.52 172:0.32 173:0.58 176:0.73 177:0.70 178:0.55 179:0.68 181:0.74 187:0.68 192:0.87 195:0.87 201:0.93 208:0.64 212:0.16 215:0.39 216:0.77 219:0.95 222:0.48 235:0.80 241:0.27 242:0.16 243:0.43 245:0.62 247:0.84 253:0.44 259:0.21 265:0.42 266:0.87 267:0.73 271:0.88 276:0.51 279:0.86 282:0.88 283:0.78 290:0.58 292:0.91 297:0.36 299:0.88 300:0.55 +0 9:0.80 11:0.83 12:0.37 17:0.90 18:0.92 20:0.76 21:0.78 25:0.88 27:0.72 28:0.57 29:0.71 30:0.85 31:0.98 34:0.68 36:0.30 39:0.84 41:0.94 43:0.96 45:0.83 66:0.46 69:0.05 70:0.43 71:0.77 74:0.86 78:0.72 79:0.97 83:0.91 85:0.72 86:0.95 87:0.98 91:0.80 96:0.92 98:0.34 100:0.87 101:0.97 104:0.73 108:0.05 111:0.79 120:0.87 122:0.76 123:0.05 124:0.76 127:0.99 129:0.59 133:0.74 135:0.91 137:0.98 138:0.98 139:0.94 140:0.45 146:0.59 147:0.65 149:0.92 151:0.98 152:0.75 153:0.94 154:0.96 155:0.67 158:0.78 159:0.78 161:0.86 162:0.82 164:0.85 167:0.84 169:0.84 172:0.67 173:0.06 177:0.70 179:0.52 181:0.74 186:0.73 192:0.87 196:0.99 202:0.81 208:0.64 212:0.73 216:0.07 219:0.92 222:0.48 228:0.99 232:0.70 241:0.83 242:0.16 243:0.59 245:0.77 247:0.88 248:0.93 253:0.94 255:0.95 256:0.85 259:0.21 260:0.86 261:0.75 265:0.37 266:0.75 267:0.87 268:0.86 271:0.88 276:0.61 279:0.86 283:0.78 284:0.98 285:0.62 292:0.91 300:0.55 +2 1:0.74 8:0.92 9:0.80 11:0.92 12:0.37 17:0.92 18:0.84 25:0.88 27:0.72 28:0.57 29:0.71 30:0.71 31:0.96 32:0.80 34:0.20 36:0.50 39:0.84 41:0.94 43:0.97 44:0.93 45:0.73 60:0.87 64:0.77 66:0.09 69:0.07 70:0.71 71:0.77 74:0.89 77:0.99 78:0.94 79:0.95 81:0.83 83:0.91 85:0.88 86:0.92 87:0.98 91:0.58 96:0.86 97:0.97 98:0.31 101:0.97 104:0.58 108:0.06 111:0.96 114:0.98 120:0.79 122:0.57 123:0.90 124:0.67 126:0.54 127:0.99 129:0.59 133:0.66 135:0.47 137:0.96 139:0.95 140:0.45 145:0.50 146:0.39 147:0.45 149:0.90 150:0.89 151:0.93 153:0.77 154:0.97 155:0.67 158:0.92 159:0.80 161:0.86 162:0.86 164:0.77 165:0.93 167:0.80 169:0.97 172:0.65 173:0.09 176:0.73 177:0.70 179:0.56 181:0.74 192:0.87 195:0.89 196:0.98 201:0.93 202:0.64 208:0.64 212:0.78 215:0.96 216:0.06 219:0.95 220:0.74 222:0.48 228:0.99 232:0.78 235:0.12 238:0.95 241:0.78 242:0.18 243:0.60 245:0.79 247:0.84 248:0.85 253:0.90 255:0.95 256:0.94 259:0.21 260:0.79 265:0.23 266:0.71 267:0.79 268:0.73 271:0.88 276:0.47 277:0.69 279:0.86 282:0.88 283:0.82 284:0.95 285:0.62 290:0.98 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.92 8:0.85 9:0.80 11:0.57 12:0.37 17:0.94 18:0.76 20:0.81 25:0.88 27:1.00 28:0.57 29:0.71 30:0.43 31:0.99 32:0.79 34:0.21 36:0.54 39:0.84 41:0.94 43:0.93 44:0.93 64:0.77 66:0.36 69:0.07 70:0.92 71:0.77 74:0.83 77:0.98 78:0.85 79:0.99 81:0.83 83:0.91 85:0.80 86:0.87 87:0.98 91:0.72 96:0.95 97:0.89 98:0.49 100:0.86 101:0.87 104:0.73 108:0.91 111:0.84 114:0.98 120:0.91 122:0.75 123:0.90 124:0.60 126:0.54 127:0.99 129:0.59 133:0.46 135:0.86 137:0.99 138:0.97 139:0.90 140:0.45 145:0.50 146:0.46 147:0.52 149:0.74 150:0.89 151:0.99 152:0.78 153:0.95 154:0.93 155:0.67 158:0.78 159:0.81 161:0.86 162:0.86 164:0.83 165:0.93 167:0.81 169:0.84 172:0.54 173:0.09 176:0.73 177:0.70 179:0.63 181:0.74 186:0.85 189:0.93 192:0.87 195:0.89 196:0.99 201:0.93 202:0.74 208:0.64 212:0.55 215:0.96 216:0.09 219:0.92 222:0.48 232:0.70 235:0.12 238:0.91 241:0.78 242:0.24 243:0.50 245:0.81 247:0.74 248:0.95 253:0.96 255:0.95 256:0.81 259:0.21 260:0.91 261:0.82 265:0.50 266:0.75 267:0.73 268:0.82 271:0.88 276:0.44 279:0.86 282:0.87 283:0.78 284:0.97 285:0.62 290:0.98 292:0.91 297:0.36 300:0.84 +1 1:0.74 11:0.83 12:0.37 17:0.96 18:0.77 21:0.30 27:0.72 28:0.57 29:0.71 30:0.33 34:0.87 36:0.76 39:0.84 43:0.80 45:0.66 55:0.71 64:0.77 66:0.63 69:0.72 70:0.78 71:0.77 74:0.71 76:0.94 81:0.57 83:0.91 85:0.80 86:0.83 91:0.20 98:0.80 101:0.87 104:0.58 108:0.65 114:0.65 122:0.77 123:0.63 124:0.52 126:0.54 127:0.74 129:0.59 133:0.66 135:0.51 139:0.79 145:0.90 146:0.20 147:0.18 149:0.76 150:0.76 154:0.74 155:0.67 159:0.50 161:0.86 165:0.93 167:0.55 172:0.70 173:0.76 176:0.73 177:0.38 178:0.55 179:0.55 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.54 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.39 242:0.45 243:0.15 245:0.72 247:0.88 253:0.50 257:0.65 259:0.21 265:0.80 266:0.54 267:0.19 271:0.88 276:0.65 290:0.65 297:0.36 300:0.55 +2 6:0.96 8:0.95 9:0.80 12:0.37 17:0.43 20:0.97 21:0.68 27:0.13 28:0.57 29:0.71 30:0.76 31:0.95 34:0.26 36:0.60 37:0.70 39:0.84 41:1.00 43:0.08 44:0.90 48:0.91 55:0.71 66:0.36 69:0.91 70:0.15 71:0.77 74:0.48 76:0.85 77:0.89 78:0.91 79:0.95 81:0.23 83:0.91 91:0.98 96:0.98 98:0.19 100:0.96 108:0.82 111:0.93 114:0.55 120:0.83 122:0.77 123:0.81 124:0.52 126:0.26 127:0.14 129:0.59 133:0.47 135:0.44 137:0.95 139:0.72 140:0.99 145:0.86 146:0.28 147:0.34 149:0.08 150:0.23 151:0.75 152:0.89 153:0.91 154:0.07 155:0.67 159:0.22 161:0.86 162:0.54 163:0.99 164:0.90 167:0.86 169:0.94 172:0.10 173:0.95 175:0.91 176:0.61 177:0.05 178:0.48 179:0.83 181:0.74 186:0.91 189:0.97 191:0.98 192:0.87 195:0.80 196:0.91 202:0.97 208:0.64 212:0.95 216:0.73 220:0.62 222:0.48 232:0.72 235:0.80 238:0.94 241:0.96 242:0.82 243:0.51 244:0.83 245:0.37 247:0.12 248:0.76 253:0.95 255:0.95 256:1.00 257:0.84 259:0.21 260:0.80 261:0.95 265:0.21 266:0.12 267:0.65 268:0.97 271:0.88 276:0.16 277:0.87 281:0.91 282:0.77 284:0.97 285:0.62 290:0.55 300:0.13 +1 1:0.74 11:0.83 12:0.37 17:0.96 18:0.81 21:0.30 27:0.72 28:0.57 29:0.71 30:0.14 34:0.84 36:0.88 39:0.84 43:0.80 45:0.66 64:0.77 66:0.62 69:0.72 70:0.84 71:0.77 74:0.68 81:0.57 83:0.91 85:0.80 86:0.86 91:0.33 98:0.80 101:0.87 104:0.58 108:0.65 114:0.65 122:0.77 123:0.63 124:0.55 126:0.54 127:0.67 129:0.59 133:0.66 135:0.26 139:0.82 145:0.95 146:0.20 147:0.18 149:0.75 150:0.76 154:0.74 155:0.67 159:0.51 161:0.86 165:0.93 167:0.56 172:0.67 173:0.75 176:0.73 177:0.38 178:0.55 179:0.57 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.49 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.43 242:0.16 243:0.15 245:0.71 247:0.90 253:0.50 257:0.65 259:0.21 265:0.80 266:0.54 267:0.28 271:0.88 276:0.63 290:0.65 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.37 17:0.72 21:0.13 27:0.45 28:0.85 30:0.19 34:0.64 36:0.19 37:0.50 39:0.74 43:0.82 66:0.35 69:0.68 70:0.80 74:0.79 81:0.80 83:0.77 85:0.85 91:0.14 98:0.39 101:0.76 108:0.82 114:0.92 122:0.57 123:0.81 124:0.72 126:0.54 127:0.41 129:0.81 133:0.67 135:0.83 145:0.49 146:0.31 147:0.38 149:0.75 150:0.87 154:0.77 155:0.67 159:0.55 161:0.54 165:0.88 167:0.53 172:0.45 173:0.64 176:0.73 177:0.38 178:0.55 179:0.60 181:0.82 187:0.68 192:0.59 201:0.74 212:0.57 215:0.84 216:0.77 222:0.48 235:0.22 241:0.24 242:0.33 243:0.36 245:0.69 247:0.60 253:0.75 259:0.21 265:0.41 266:0.63 267:0.36 271:0.88 276:0.51 290:0.92 297:0.36 300:0.55 +2 1:0.74 7:0.78 8:0.79 9:0.80 12:0.37 17:0.75 20:0.75 21:0.71 27:1.00 28:0.85 30:0.28 34:0.50 36:0.43 37:0.70 39:0.74 41:0.99 43:0.43 55:0.59 66:0.29 69:0.76 70:0.70 74:0.94 76:0.99 77:0.89 78:0.88 81:0.47 83:0.90 87:0.98 91:0.95 96:1.00 98:0.59 100:0.73 108:0.59 111:0.89 114:0.68 120:0.97 122:0.67 123:0.57 124:0.79 126:0.54 127:0.85 129:0.05 133:0.74 135:0.42 140:0.45 145:0.99 146:0.68 147:0.33 149:0.54 150:0.65 151:1.00 152:0.74 153:0.98 154:0.60 155:0.67 158:0.84 159:0.58 161:0.54 162:0.84 164:0.95 165:0.69 167:0.88 169:0.90 172:0.37 173:0.76 176:0.73 177:0.05 179:0.74 181:0.82 186:0.83 191:0.78 192:0.59 195:0.74 201:0.74 202:0.96 208:0.64 212:0.48 215:0.48 216:0.38 222:0.48 230:0.81 232:0.72 233:0.69 235:0.88 238:0.90 241:0.96 242:0.78 243:0.33 245:0.62 247:0.39 248:0.99 253:1.00 254:0.80 255:0.79 256:0.98 257:0.65 260:0.97 261:0.75 265:0.60 266:0.75 267:0.89 268:0.98 271:0.88 276:0.51 282:0.84 285:0.62 290:0.68 297:0.36 300:0.43 +0 1:0.74 8:0.63 11:0.57 12:0.37 17:0.62 20:0.76 21:0.54 27:0.45 28:0.85 30:0.09 32:0.80 34:0.91 36:0.28 37:0.50 39:0.74 43:0.82 55:0.71 60:0.87 66:0.53 69:0.69 70:0.99 74:0.95 77:0.70 78:0.77 81:0.57 83:0.84 85:0.80 91:0.14 98:0.46 100:0.73 101:0.72 108:0.53 111:0.76 114:0.77 122:0.67 123:0.50 124:0.84 126:0.54 127:0.56 129:0.81 133:0.89 135:0.88 145:0.44 146:0.77 147:0.80 149:0.77 150:0.73 152:0.75 154:0.77 155:0.67 158:0.87 159:0.77 161:0.54 165:0.79 167:0.53 169:0.75 172:0.68 173:0.52 176:0.73 177:0.38 178:0.55 179:0.47 181:0.82 186:0.78 187:0.68 192:0.59 195:0.90 201:0.74 208:0.64 212:0.50 215:0.58 216:0.77 222:0.48 235:0.68 241:0.21 242:0.74 243:0.62 245:0.65 247:0.67 253:0.75 259:0.21 261:0.75 265:0.48 266:0.92 267:0.82 271:0.88 276:0.68 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.94 +1 1:0.74 7:0.81 8:0.94 9:0.80 11:0.57 12:0.37 17:0.85 21:0.30 27:0.47 28:0.85 30:0.94 32:0.80 34:0.50 36:0.57 37:0.79 39:0.74 41:1.00 43:0.57 55:0.45 66:0.77 69:0.94 70:0.23 74:0.98 76:0.85 77:0.98 81:0.74 83:0.82 85:0.85 91:0.94 96:0.97 98:0.47 101:0.74 108:0.88 114:0.68 117:0.86 120:0.88 121:0.90 122:0.67 123:0.88 124:0.43 126:0.54 127:0.78 129:0.59 133:0.64 135:0.42 138:0.97 140:0.92 145:0.82 146:0.77 147:0.46 149:0.77 150:0.83 151:0.77 153:0.98 154:0.46 155:0.67 158:0.85 159:0.79 161:0.54 162:0.81 164:0.90 165:0.81 167:0.69 172:0.92 173:0.94 176:0.56 177:0.05 179:0.26 181:0.82 187:0.21 190:0.77 191:0.88 192:0.59 195:0.90 201:0.74 202:0.89 204:0.89 208:0.64 212:0.88 215:0.67 216:0.65 222:0.48 230:0.84 232:0.82 233:0.69 235:0.60 241:0.66 242:0.80 243:0.08 245:0.87 247:0.47 248:0.94 253:0.98 255:0.79 256:1.00 257:0.65 260:0.89 265:0.49 266:0.75 267:0.87 268:0.93 271:0.88 276:0.78 282:0.88 285:0.62 290:0.68 297:0.36 299:0.88 300:0.84 +3 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 11:0.57 12:0.37 17:0.96 20:0.85 21:0.30 27:0.05 28:0.85 30:0.94 34:0.25 36:0.45 37:0.94 39:0.74 41:1.00 43:0.31 55:0.45 66:0.06 69:0.96 70:0.22 74:0.98 76:0.94 77:0.70 78:0.86 81:0.74 83:0.82 85:0.72 91:0.73 96:0.97 98:0.38 100:0.89 101:0.69 108:0.92 111:0.86 114:0.86 117:0.86 120:0.88 121:0.90 122:0.67 123:0.77 124:0.51 126:0.54 127:0.67 129:0.05 133:0.82 135:0.37 140:0.87 145:0.67 146:0.69 147:0.33 149:0.71 150:0.83 151:0.69 152:0.93 153:0.91 154:0.22 155:0.67 158:0.85 159:0.81 161:0.54 162:0.77 164:0.89 165:0.84 167:0.61 169:0.87 172:0.92 173:0.97 176:0.73 177:0.05 179:0.24 181:0.82 186:0.86 187:0.21 189:0.98 190:0.77 191:0.85 192:0.59 195:0.93 201:0.74 202:0.86 204:0.89 208:0.64 212:0.86 215:0.72 216:0.72 222:0.48 230:0.87 232:0.83 233:0.76 235:0.52 238:0.90 241:0.52 242:0.81 243:0.08 245:0.83 247:0.47 248:0.93 253:0.98 255:0.79 256:1.00 257:0.65 259:0.21 260:0.88 261:0.91 265:0.39 266:0.87 267:0.79 268:0.90 271:0.88 276:0.84 277:0.69 282:0.84 285:0.62 290:0.86 297:0.36 300:0.84 +1 1:0.74 6:0.92 7:0.81 8:0.84 9:0.80 12:0.37 17:0.57 20:0.85 21:0.40 27:0.59 28:0.85 30:0.32 34:0.93 36:0.59 37:0.60 39:0.74 41:0.97 43:0.38 55:0.45 66:0.36 69:0.49 70:0.61 74:0.93 76:0.97 77:0.70 78:0.80 81:0.70 83:0.88 91:0.80 96:0.98 98:0.22 100:0.81 108:0.88 111:0.79 114:0.66 117:0.86 120:0.93 121:0.90 122:0.67 123:0.88 124:0.77 126:0.54 127:0.70 129:0.89 133:0.78 135:0.54 138:0.97 140:0.45 145:0.89 146:0.33 147:0.40 149:0.52 150:0.80 151:0.99 152:0.80 153:0.96 154:0.28 155:0.67 158:0.85 159:0.69 161:0.54 162:0.75 164:0.88 165:0.80 167:0.70 169:0.79 172:0.48 173:0.37 175:0.75 176:0.56 177:0.05 179:0.66 181:0.82 186:0.81 187:0.39 189:0.94 191:0.79 192:0.59 195:0.84 201:0.74 202:0.88 204:0.89 208:0.64 212:0.59 215:0.63 216:0.57 222:0.48 230:0.87 232:0.81 233:0.76 235:0.68 238:0.84 241:0.67 242:0.82 243:0.29 244:0.61 245:0.66 247:0.12 248:0.96 253:0.99 255:0.79 256:0.91 257:0.65 259:0.21 260:0.93 261:0.81 265:0.24 266:0.63 267:0.85 268:0.91 271:0.88 276:0.52 277:0.69 282:0.84 285:0.62 290:0.66 297:0.36 300:0.43 +2 6:0.97 8:0.96 9:0.80 12:0.37 17:0.36 20:0.88 21:0.78 27:0.05 28:0.85 34:0.52 36:0.28 37:0.94 39:0.74 41:1.00 78:0.88 83:0.82 91:0.99 96:0.97 100:0.91 111:0.88 120:0.95 135:0.86 138:0.99 140:0.95 151:0.78 152:0.93 153:0.78 155:0.67 161:0.54 162:0.49 164:0.98 167:0.87 169:0.87 175:0.91 178:0.53 181:0.82 186:0.88 189:0.98 191:0.98 192:0.59 202:0.99 208:0.64 216:0.72 220:0.96 222:0.48 238:0.91 241:0.89 244:0.83 248:0.95 253:0.95 255:0.79 256:1.00 257:0.84 259:0.21 260:0.96 261:0.91 267:0.95 268:0.98 271:0.88 277:0.87 285:0.62 +2 1:0.74 6:0.96 7:0.81 8:0.95 9:0.80 11:0.57 12:0.37 17:0.96 20:0.86 21:0.68 27:0.09 28:0.85 30:0.85 32:0.80 34:0.50 36:0.74 37:0.86 39:0.74 41:1.00 43:0.85 55:0.45 60:0.87 66:0.82 69:0.44 70:0.28 74:0.95 77:0.96 78:0.85 81:0.58 83:0.89 85:0.85 91:0.69 96:0.96 98:0.34 100:0.90 101:0.72 104:0.58 106:0.81 108:0.95 111:0.86 114:0.70 117:0.86 120:0.89 121:0.97 122:0.43 123:0.95 124:0.44 126:0.54 127:0.87 129:0.59 133:0.86 135:0.81 140:0.45 145:0.69 146:0.37 147:0.43 149:0.65 150:0.73 151:0.98 152:0.98 153:0.92 154:0.81 155:0.67 158:0.92 159:0.89 161:0.54 162:0.85 164:0.86 165:0.79 167:0.63 169:0.91 172:0.97 173:0.17 176:0.73 177:0.38 179:0.16 181:0.82 186:0.85 187:0.21 189:0.98 191:0.83 192:0.59 195:0.97 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.91 215:0.49 216:0.67 222:0.48 230:0.87 232:0.88 233:0.76 235:0.97 238:0.92 241:0.56 242:0.61 243:0.09 245:0.87 247:0.67 248:0.94 253:0.97 255:0.79 256:1.00 259:0.21 260:0.90 261:0.95 265:0.37 266:0.87 267:0.88 268:0.86 271:0.88 276:0.93 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.89 7:0.81 8:0.81 9:0.80 11:0.57 12:0.37 17:0.85 20:0.77 21:0.47 27:0.61 28:0.85 30:0.56 32:0.80 34:0.86 36:0.77 37:0.57 39:0.74 41:0.99 43:0.97 60:0.97 66:0.27 69:0.21 70:0.73 74:0.96 77:0.89 78:0.89 81:0.65 83:0.87 85:0.96 91:0.83 96:0.97 98:0.68 100:0.89 101:0.86 104:0.58 108:0.72 111:0.91 114:0.80 120:0.95 121:0.90 122:0.57 123:0.16 124:0.58 126:0.54 127:0.94 129:0.59 132:0.97 133:0.47 135:0.66 140:0.80 145:0.70 146:0.67 147:0.72 149:0.95 150:0.78 151:0.98 152:0.75 153:0.92 154:0.97 155:0.67 158:0.92 159:0.48 161:0.54 162:0.79 164:0.95 165:0.80 167:0.85 169:0.92 172:0.70 173:0.30 176:0.73 177:0.38 178:0.42 179:0.48 181:0.82 186:0.91 189:0.90 191:0.73 192:0.59 195:0.66 201:0.74 202:0.91 204:0.89 208:0.64 212:0.82 215:0.63 216:0.33 220:0.93 222:0.48 230:0.87 233:0.76 235:0.68 238:0.93 241:0.83 242:0.44 243:0.46 245:0.89 247:0.67 248:0.96 253:0.98 255:0.79 256:0.95 260:0.95 261:0.79 265:0.68 266:0.54 267:0.44 268:0.94 271:0.88 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.96 8:0.93 9:0.80 12:0.37 17:0.61 20:0.93 21:0.71 27:0.09 28:0.85 30:0.45 32:0.80 34:0.21 36:0.66 37:0.86 39:0.74 41:1.00 43:0.40 55:0.71 66:0.27 69:0.43 70:0.25 74:0.66 76:0.85 77:0.70 78:0.90 81:0.44 83:0.82 91:0.98 96:0.97 98:0.19 100:0.94 104:0.58 108:0.33 111:0.91 114:0.68 120:0.99 123:0.95 124:0.61 126:0.54 127:0.91 129:0.05 133:0.39 135:0.51 138:0.99 140:0.93 145:0.74 146:0.27 147:0.33 149:0.24 150:0.61 151:0.73 152:0.98 153:0.84 154:0.31 155:0.67 159:0.89 161:0.54 162:0.53 163:0.90 164:0.99 167:0.85 169:0.91 172:0.10 173:0.17 176:0.73 177:0.38 179:0.98 181:0.82 186:0.90 189:0.97 190:0.77 191:0.98 192:0.59 195:0.96 201:0.74 202:0.99 208:0.64 212:0.61 215:0.48 216:0.67 220:0.62 222:0.48 235:0.88 238:0.93 241:0.82 242:0.63 243:0.46 244:0.61 245:0.40 247:0.30 248:0.95 253:0.96 255:0.79 256:1.00 259:0.21 260:0.99 261:0.95 265:0.14 266:0.17 267:0.91 268:0.99 271:0.88 276:0.13 277:0.69 282:0.88 283:0.71 285:0.62 290:0.68 297:0.36 299:0.88 300:0.18 +3 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 12:0.37 17:0.78 20:0.90 21:0.59 27:0.04 28:0.85 30:0.88 32:0.80 34:0.76 36:0.65 37:0.94 39:0.74 41:0.92 43:0.38 55:0.45 66:0.67 69:0.50 70:0.36 74:0.97 77:0.70 78:0.86 81:0.58 83:0.74 91:0.65 96:0.87 98:0.55 100:0.88 108:0.66 111:0.85 114:0.74 117:0.86 120:0.59 121:0.99 122:0.67 123:0.63 124:0.55 126:0.54 127:0.61 129:0.89 133:0.78 135:0.65 140:0.45 145:0.89 146:0.05 147:0.05 149:0.65 150:0.72 151:0.56 152:0.85 153:0.69 154:0.29 155:0.67 158:0.85 159:0.53 161:0.54 162:0.59 164:0.78 165:0.78 167:0.78 169:0.85 172:0.70 173:0.48 175:0.75 176:0.73 177:0.05 179:0.54 181:0.82 186:0.86 187:0.39 189:0.98 190:0.77 191:0.76 192:0.59 195:0.74 201:0.74 202:0.77 204:0.89 208:0.64 212:0.74 215:0.55 216:0.87 220:0.93 222:0.48 230:0.87 232:0.85 233:0.76 235:0.80 238:0.89 241:0.76 242:0.82 243:0.09 244:0.61 245:0.64 247:0.12 248:0.58 253:0.91 255:0.79 256:0.75 257:0.65 259:0.21 260:0.60 261:0.88 265:0.57 266:0.71 267:0.82 268:0.77 271:0.88 276:0.63 277:0.69 282:0.88 285:0.62 290:0.74 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.89 7:0.81 8:0.84 9:0.80 11:0.57 12:0.37 17:0.97 20:0.80 21:0.22 27:0.63 28:0.85 30:0.41 32:0.80 34:0.26 36:0.77 37:0.85 39:0.74 41:0.98 43:0.93 60:0.95 66:0.54 69:0.32 70:0.60 74:0.93 77:0.89 78:0.78 81:0.74 83:0.89 85:0.88 91:0.88 96:0.96 98:0.53 100:0.80 101:0.81 104:0.58 108:0.25 111:0.78 114:0.90 120:0.93 121:0.90 122:0.67 123:0.24 124:0.50 126:0.54 127:0.86 129:0.59 133:0.47 135:0.21 140:0.45 145:0.76 146:0.50 147:0.56 149:0.81 150:0.84 151:0.99 152:0.77 153:0.96 154:0.93 155:0.67 158:0.92 159:0.43 161:0.54 162:0.84 164:0.90 165:0.86 167:0.86 169:0.79 172:0.48 173:0.40 176:0.73 177:0.38 179:0.78 181:0.82 186:0.79 189:0.90 191:0.78 192:0.59 195:0.61 201:0.74 202:0.83 204:0.89 208:0.64 212:0.78 215:0.79 216:0.11 222:0.48 230:0.87 232:0.77 233:0.76 235:0.31 238:0.88 241:0.86 242:0.45 243:0.63 245:0.66 247:0.47 248:0.96 253:0.97 255:0.79 256:0.86 259:0.21 260:0.93 261:0.78 265:0.55 266:0.38 267:0.17 268:0.88 271:0.88 276:0.40 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.43 +2 6:0.96 8:0.94 9:0.80 12:0.37 17:0.78 20:0.74 21:0.47 27:0.24 28:0.85 30:0.37 34:0.79 36:0.71 37:0.77 39:0.74 41:0.97 43:0.75 55:0.42 60:0.87 66:0.80 69:0.77 70:0.50 74:0.92 78:0.99 81:0.37 83:0.79 91:0.87 96:0.98 98:0.69 100:0.98 108:0.60 111:0.98 114:0.66 117:0.86 120:0.81 122:0.67 123:0.58 124:0.38 126:0.32 127:0.40 129:0.05 133:0.39 135:0.76 138:0.98 140:0.96 146:0.68 147:0.05 149:0.66 150:0.33 151:0.82 152:0.74 153:0.88 154:0.66 155:0.67 158:0.92 159:0.39 161:0.54 162:0.82 164:0.90 167:0.73 169:0.96 172:0.65 173:0.84 176:0.56 177:0.05 179:0.60 181:0.82 186:1.00 187:0.21 189:0.97 191:0.93 192:0.59 202:0.93 208:0.64 212:0.84 216:0.64 220:0.62 222:0.48 232:0.83 235:0.52 238:0.80 241:0.71 242:0.79 243:0.11 245:0.54 247:0.39 248:0.89 253:0.99 255:0.79 256:0.96 257:0.65 259:0.21 260:0.82 261:0.77 265:0.70 266:0.38 267:0.52 268:0.96 271:0.88 276:0.51 279:0.86 283:0.82 285:0.62 290:0.66 300:0.43 +2 1:0.74 6:0.95 8:0.93 9:0.80 12:0.37 17:0.51 20:0.81 21:0.59 27:0.07 28:0.85 34:0.39 36:0.79 37:0.88 39:0.74 41:1.00 43:0.41 66:0.36 69:0.39 74:0.65 76:0.85 77:0.70 78:0.85 81:0.51 83:0.82 91:0.98 96:0.98 98:0.06 100:0.86 108:0.30 111:0.84 114:0.74 120:0.96 123:0.29 126:0.54 127:0.05 129:0.05 135:0.58 138:0.99 140:0.90 145:0.77 146:0.05 147:0.05 149:0.14 150:0.62 151:0.92 152:0.80 153:0.83 154:0.31 155:0.67 159:0.05 161:0.54 162:0.49 164:0.98 167:0.85 169:0.83 172:0.05 173:0.15 175:0.75 176:0.73 177:0.05 178:0.50 181:0.82 186:0.85 189:0.96 191:0.98 192:0.59 195:0.96 201:0.74 202:0.99 208:0.64 215:0.55 216:0.81 220:0.93 222:0.48 235:0.74 238:0.88 241:0.83 243:0.51 244:0.61 248:0.95 253:0.97 255:0.79 256:1.00 257:0.65 259:0.21 260:0.96 261:0.83 265:0.06 267:0.82 268:0.98 271:0.88 277:0.69 282:0.84 285:0.62 290:0.74 297:0.36 +2 1:0.74 6:0.97 7:0.81 8:0.97 9:0.80 11:0.57 12:0.37 17:0.93 20:0.86 21:0.47 27:0.05 28:0.85 30:0.94 32:0.80 34:0.24 36:0.46 37:0.94 39:0.74 41:1.00 43:0.31 55:0.45 66:0.06 69:0.96 70:0.24 74:0.98 76:0.97 77:0.70 78:0.85 81:0.68 83:0.84 85:0.72 91:0.71 96:0.97 98:0.39 100:0.88 101:0.69 108:0.92 111:0.85 114:0.80 117:0.86 120:0.91 121:0.97 122:0.67 123:0.77 124:0.52 126:0.54 127:0.67 129:0.05 133:0.82 135:0.37 138:0.98 140:0.80 145:0.84 146:0.69 147:0.33 149:0.71 150:0.79 151:0.87 152:0.93 153:0.93 154:0.22 155:0.67 158:0.85 159:0.81 161:0.54 162:0.77 164:0.94 165:0.79 167:0.65 169:0.87 172:0.92 173:0.97 176:0.73 177:0.05 179:0.24 181:0.82 186:0.85 187:0.39 189:0.98 191:0.90 192:0.59 195:0.93 201:0.74 202:0.90 204:0.89 208:0.64 212:0.86 215:0.63 216:0.72 222:0.48 230:0.87 232:0.83 233:0.76 235:0.74 238:0.89 241:0.59 242:0.81 243:0.08 245:0.84 247:0.47 248:0.95 253:0.98 255:0.79 256:1.00 257:0.65 259:0.21 260:0.91 261:0.91 265:0.39 266:0.87 267:0.84 268:0.93 271:0.88 276:0.85 277:0.69 282:0.88 285:0.62 290:0.80 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.94 7:0.81 8:0.89 9:0.80 11:0.57 12:0.37 17:0.85 20:0.76 21:0.47 27:0.29 28:0.85 30:0.13 32:0.80 34:0.40 36:0.95 37:0.79 39:0.74 41:0.98 43:0.98 55:0.52 60:0.98 66:0.24 69:0.17 70:0.97 74:0.99 76:0.94 77:0.96 78:0.81 81:0.61 83:0.88 85:0.97 87:0.98 91:0.66 96:0.96 98:0.56 100:0.90 101:0.82 104:0.82 106:0.81 108:0.14 111:0.81 114:0.80 117:0.86 120:0.91 121:0.97 122:0.67 123:0.91 124:0.82 126:0.54 127:0.98 129:0.81 132:0.97 133:0.86 135:0.76 138:0.97 140:0.45 145:0.70 146:0.83 147:0.86 149:0.96 150:0.76 151:0.89 152:0.75 153:0.48 154:0.98 155:0.67 158:0.92 159:0.84 161:0.54 162:0.86 164:0.94 165:0.80 167:0.63 169:0.80 172:0.87 173:0.11 176:0.73 177:0.38 179:0.24 181:0.82 186:0.91 187:0.21 189:0.95 191:0.77 192:0.59 195:0.93 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.72 215:0.63 216:0.60 220:0.93 222:0.48 230:0.87 232:0.76 233:0.76 235:0.60 238:0.90 241:0.56 242:0.60 243:0.58 245:0.90 247:0.60 248:0.96 253:0.98 255:0.79 256:0.93 259:0.21 260:0.92 261:0.78 265:0.52 266:0.89 267:0.79 268:0.94 271:0.88 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.97 300:0.94 +3 6:0.96 7:0.81 8:0.95 9:0.80 12:0.37 17:0.84 20:0.94 21:0.78 27:0.06 28:0.85 30:0.37 32:0.75 34:0.68 36:0.81 37:0.91 39:0.74 41:0.94 43:0.40 55:0.71 66:0.35 69:0.42 70:0.94 74:0.81 78:0.90 83:0.74 91:0.97 96:0.89 98:0.18 100:0.93 108:0.97 111:0.90 120:0.88 121:0.90 122:0.67 123:0.96 124:0.89 127:0.93 129:0.96 133:0.90 135:0.87 140:0.99 145:0.72 146:0.25 147:0.31 149:0.39 151:0.69 152:0.88 153:0.48 154:0.31 155:0.67 159:0.92 161:0.54 162:0.48 164:0.93 167:0.88 169:0.91 172:0.45 173:0.13 175:0.75 177:0.05 178:0.53 179:0.71 181:0.82 186:0.90 189:0.97 190:0.77 191:0.98 192:0.59 195:0.98 202:0.96 204:0.89 208:0.64 212:0.50 216:0.71 220:0.97 222:0.48 230:0.87 233:0.76 235:0.84 238:0.91 241:0.95 242:0.82 243:0.19 244:0.61 245:0.56 247:0.12 248:0.74 253:0.90 255:0.79 256:0.93 257:0.65 259:0.21 260:0.88 261:0.93 265:0.20 266:0.80 267:0.76 268:0.92 271:0.88 276:0.46 277:0.69 283:0.71 285:0.62 300:0.84 +3 6:0.96 8:0.95 9:0.80 12:0.37 17:0.43 20:0.98 21:0.64 27:0.13 28:0.85 30:0.84 34:0.26 36:0.60 37:0.70 39:0.74 41:1.00 43:0.39 55:0.45 66:0.82 69:0.45 70:0.37 74:0.98 76:1.00 77:0.96 78:0.93 81:0.36 83:0.84 87:0.98 91:0.98 96:1.00 98:0.58 100:0.98 108:0.83 111:0.96 114:0.63 120:0.91 122:0.67 123:0.82 124:0.39 126:0.32 127:0.90 129:0.81 133:0.67 135:0.44 138:0.99 140:0.94 145:0.86 146:0.20 147:0.25 149:0.70 150:0.32 151:0.77 152:0.90 153:0.99 154:0.30 155:0.67 158:0.85 159:0.64 161:0.54 162:0.71 164:0.93 167:0.88 169:0.97 172:0.86 173:0.39 175:0.75 176:0.56 177:0.05 179:0.40 181:0.82 186:0.93 189:0.97 191:0.98 192:0.59 195:0.80 202:0.97 208:0.64 212:0.84 216:0.73 220:0.62 222:0.48 232:0.72 235:0.80 238:0.97 241:0.96 242:0.82 243:0.11 244:0.61 245:0.71 247:0.12 248:0.96 253:1.00 255:0.79 256:1.00 257:0.65 259:0.21 260:0.92 261:0.97 265:0.59 266:0.63 267:0.65 268:0.98 271:0.88 276:0.70 277:0.69 282:0.84 285:0.62 290:0.63 300:0.70 +2 1:0.74 6:0.96 7:0.81 8:0.90 9:0.80 11:0.57 12:0.37 17:0.96 20:0.91 21:0.30 27:0.09 28:0.85 30:0.44 32:0.80 34:0.61 36:0.80 37:0.86 39:0.74 41:0.96 43:0.60 55:0.45 60:0.87 66:0.30 69:0.37 70:0.52 74:0.99 76:0.94 77:0.70 78:0.86 81:0.69 83:0.81 85:0.72 91:0.77 96:0.93 98:0.60 100:0.91 101:0.71 104:0.58 108:0.85 111:0.87 114:0.86 120:0.82 121:0.99 122:0.67 123:0.67 124:0.65 126:0.54 127:0.63 129:0.59 133:0.64 135:0.75 138:0.97 140:0.80 145:0.77 146:0.27 147:0.33 149:0.84 150:0.81 151:0.77 152:0.98 153:0.48 154:0.49 155:0.67 158:0.92 159:0.67 161:0.54 162:0.81 164:0.90 165:0.84 167:0.56 169:0.91 172:0.90 173:0.27 176:0.73 177:0.38 179:0.18 181:0.82 186:0.86 187:0.39 189:0.96 190:0.77 191:0.91 192:0.59 195:0.84 201:0.74 202:0.85 204:0.89 208:0.64 212:0.93 215:0.72 216:0.67 220:0.62 222:0.48 230:0.87 232:0.90 233:0.76 235:0.42 238:0.92 241:0.39 242:0.58 243:0.07 245:0.97 247:0.67 248:0.89 253:0.96 255:0.79 256:0.89 259:0.21 260:0.83 261:0.95 265:0.45 266:0.54 267:0.44 268:0.90 271:0.88 276:0.91 277:0.69 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.78 8:0.98 9:0.80 12:0.37 17:0.34 21:0.54 27:0.04 28:0.85 30:0.93 34:0.55 36:0.38 37:0.94 39:0.74 41:1.00 43:0.38 55:0.45 66:0.79 69:0.49 70:0.27 74:0.99 76:0.85 78:0.92 81:0.54 83:0.82 91:0.99 96:0.98 98:0.69 108:0.78 111:0.92 114:0.77 120:0.94 122:0.67 123:0.77 124:0.41 126:0.54 127:0.84 129:0.81 133:0.67 135:0.32 138:1.00 140:0.93 145:0.70 146:0.05 147:0.05 149:0.80 150:0.64 151:0.77 153:0.80 154:0.29 155:0.67 159:0.60 161:0.54 162:0.47 164:0.96 167:0.89 169:0.90 172:0.91 173:0.44 175:0.75 176:0.73 177:0.05 178:0.52 179:0.29 181:0.82 190:0.77 191:0.99 192:0.59 201:0.74 202:0.99 208:0.64 212:0.89 215:0.58 216:0.87 220:0.96 222:0.48 230:0.81 233:0.69 235:0.68 241:1.00 242:0.82 243:0.06 244:0.61 245:0.83 247:0.12 248:0.95 253:0.99 255:0.79 256:1.00 257:0.65 259:0.21 260:0.94 265:0.70 266:0.63 267:0.59 268:0.97 271:0.88 276:0.82 277:0.69 285:0.62 290:0.77 297:0.36 300:0.70 +2 1:0.74 6:0.95 7:0.81 8:0.88 9:0.80 11:0.57 12:0.37 17:0.59 20:0.91 21:0.30 27:0.39 28:0.85 30:0.14 32:0.80 34:0.49 36:0.78 37:0.71 39:0.74 41:0.99 43:0.80 60:0.87 66:0.11 69:0.73 70:0.95 74:0.92 77:0.89 78:0.81 81:0.69 83:0.89 85:0.94 91:0.93 96:0.97 98:0.42 100:0.82 101:0.79 108:0.90 111:0.80 114:0.86 120:0.94 121:0.90 122:0.57 123:0.92 124:0.80 126:0.54 127:0.91 129:0.89 133:0.83 135:0.44 138:0.97 140:0.90 145:0.69 146:0.28 147:0.37 149:0.81 150:0.81 151:0.99 152:0.85 153:0.94 154:0.74 155:0.67 158:0.87 159:0.84 161:0.54 162:0.52 164:0.95 165:0.84 167:0.86 169:0.80 172:0.73 173:0.53 176:0.73 177:0.05 178:0.50 179:0.43 181:0.82 186:0.81 189:0.96 191:0.84 192:0.59 195:0.93 201:0.74 202:0.96 204:0.89 208:0.64 212:0.51 215:0.72 216:0.49 220:0.87 222:0.48 230:0.84 233:0.69 235:0.42 238:0.86 241:0.84 242:0.29 243:0.11 245:0.79 247:0.67 248:0.97 253:0.98 255:0.79 256:0.95 257:0.65 259:0.21 260:0.94 261:0.84 265:0.42 266:0.80 267:0.44 268:0.95 271:0.88 276:0.70 279:0.86 282:0.83 283:0.71 285:0.62 290:0.86 297:0.36 299:0.97 300:0.84 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.37 17:0.96 21:0.40 27:0.09 28:0.85 30:0.43 32:0.80 34:0.61 36:0.74 37:0.86 39:0.74 41:0.88 43:0.60 55:0.45 60:0.87 66:0.31 69:0.40 70:0.59 74:0.99 76:0.94 77:0.70 81:0.72 83:0.83 85:0.85 91:0.56 96:0.90 98:0.60 101:0.75 104:0.58 106:0.81 108:0.85 114:0.82 117:0.86 120:0.79 121:0.99 122:0.67 123:0.67 124:0.65 126:0.54 127:0.65 129:0.59 133:0.64 135:0.88 140:0.80 145:0.74 146:0.27 147:0.33 149:0.87 150:0.82 151:0.94 153:0.84 154:0.49 155:0.67 158:0.92 159:0.69 161:0.54 162:0.84 164:0.75 165:0.81 167:0.55 172:0.91 173:0.28 176:0.73 177:0.38 179:0.17 181:0.82 187:0.39 192:0.59 195:0.85 201:0.74 202:0.67 204:0.89 206:0.81 208:0.64 212:0.92 215:0.67 216:0.67 222:0.48 230:0.87 232:0.93 233:0.76 235:0.68 241:0.35 242:0.58 243:0.07 245:0.97 247:0.67 248:0.84 253:0.94 255:0.79 256:0.68 259:0.21 260:0.80 265:0.54 266:0.71 267:0.23 268:0.73 271:0.88 276:0.92 277:0.69 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.94 +2 1:0.74 8:0.97 9:0.80 11:0.57 12:0.37 17:0.84 20:0.74 21:0.30 27:0.67 28:0.85 30:0.62 32:0.80 34:0.52 36:0.66 37:0.63 39:0.74 41:1.00 43:0.96 55:0.45 66:0.67 69:0.19 70:0.62 74:0.95 76:0.94 77:0.98 78:0.90 81:0.78 83:0.86 85:0.80 91:0.88 96:0.97 98:0.51 100:0.73 101:0.78 108:0.15 111:0.87 114:0.86 120:0.92 122:0.67 123:0.15 124:0.45 126:0.54 127:0.90 129:0.59 133:0.47 135:0.47 140:0.45 145:0.47 146:0.42 147:0.49 149:0.79 150:0.85 151:0.99 152:0.74 153:0.97 154:0.96 155:0.67 159:0.37 161:0.54 162:0.66 164:0.90 165:0.83 167:0.84 169:0.72 172:0.57 173:0.36 176:0.73 177:0.38 179:0.75 181:0.82 186:0.96 191:0.73 192:0.59 195:0.60 201:0.74 202:0.89 208:0.64 212:0.71 215:0.72 216:0.39 222:0.48 232:0.72 235:0.68 241:0.80 242:0.74 243:0.72 245:0.66 247:0.39 248:0.96 253:0.98 255:0.79 256:1.00 259:0.21 260:0.92 261:0.75 265:0.53 266:0.54 267:0.95 268:0.91 271:0.88 276:0.42 282:0.88 285:0.62 290:0.86 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.97 7:0.81 8:0.97 9:0.80 12:0.37 17:0.93 20:0.76 21:0.30 27:0.02 28:0.85 30:0.95 32:0.80 34:0.26 36:0.62 37:0.98 39:0.74 41:1.00 43:0.32 55:0.45 66:0.72 69:0.65 70:0.15 74:0.90 76:0.94 77:0.89 78:0.92 81:0.74 83:0.81 91:0.73 96:0.96 98:0.21 100:0.90 108:0.95 111:0.91 114:0.68 117:0.86 120:0.86 121:0.90 122:0.67 123:0.95 124:0.47 126:0.54 127:0.89 129:0.89 133:0.78 135:0.69 138:0.95 140:0.45 145:0.80 146:0.19 147:0.24 149:0.45 150:0.83 151:0.87 152:0.75 153:0.77 154:0.23 155:0.67 158:0.85 159:0.87 161:0.54 162:0.82 164:0.87 165:0.81 167:0.69 169:0.88 172:0.80 173:0.38 175:0.75 176:0.56 177:0.05 179:0.46 181:0.82 186:0.92 187:0.21 189:0.97 191:0.85 192:0.59 195:0.96 201:0.74 202:0.85 204:0.89 208:0.64 212:0.88 215:0.67 216:0.81 220:0.87 222:0.48 230:0.87 232:0.84 233:0.76 235:0.60 238:0.89 241:0.65 242:0.82 243:0.12 244:0.61 245:0.70 247:0.12 248:0.93 253:0.97 255:0.79 256:1.00 257:0.65 259:0.21 260:0.87 261:0.80 265:0.23 266:0.54 267:0.92 268:0.89 271:0.88 276:0.63 277:0.69 282:0.88 285:0.62 286:0.99 290:0.68 297:0.36 299:0.88 300:0.43 +1 1:0.68 7:0.65 10:0.92 11:0.88 12:0.51 17:0.06 18:0.79 21:0.05 27:0.49 29:0.62 30:0.68 32:0.71 34:0.96 36:0.75 37:0.47 39:0.51 43:0.41 44:0.92 45:0.94 56:0.97 64:0.77 66:0.92 69:0.54 70:0.45 71:0.79 74:0.59 77:0.70 80:0.98 81:0.81 82:0.99 83:0.69 86:0.82 88:0.98 91:0.40 98:0.90 99:0.99 101:0.70 102:0.97 108:0.42 114:0.95 122:0.91 123:0.40 126:0.54 127:0.46 129:0.05 131:0.61 135:0.96 139:0.83 144:0.92 145:0.58 146:0.82 147:0.85 149:0.50 150:0.64 154:0.57 155:0.57 157:0.91 159:0.38 165:0.88 166:0.94 170:0.90 172:0.79 173:0.61 174:0.88 176:0.70 177:0.99 178:0.55 179:0.44 182:0.93 187:0.87 192:0.87 193:0.95 195:0.64 197:0.90 201:0.68 204:0.84 208:0.64 212:0.86 215:0.91 216:0.56 222:0.48 227:0.61 229:0.61 230:0.67 231:0.90 233:0.66 235:0.68 236:0.95 239:0.93 241:0.05 242:0.17 243:0.93 244:0.61 246:0.92 247:0.79 253:0.67 254:0.80 259:0.21 264:0.98 265:0.90 266:0.38 267:0.79 271:0.89 275:0.95 276:0.67 281:0.86 282:0.80 287:0.61 290:0.95 294:0.98 297:0.36 300:0.43 +1 1:0.61 8:0.59 9:0.76 10:0.89 11:0.88 12:0.51 17:0.32 18:0.83 20:0.91 21:0.54 23:0.98 27:0.68 29:0.62 30:0.53 31:0.96 34:0.30 36:0.94 37:0.27 39:0.51 43:0.23 45:0.95 56:0.97 62:0.97 64:0.77 66:0.36 69:0.47 70:0.84 71:0.79 74:0.49 75:0.97 78:0.99 79:0.95 81:0.69 83:0.69 86:0.74 91:0.40 96:0.86 97:0.98 98:0.61 99:0.99 100:0.73 101:0.97 108:0.36 111:0.95 114:0.74 122:0.91 123:0.34 124:0.79 126:0.54 127:0.45 128:0.98 129:0.05 131:0.97 133:0.80 135:0.99 137:0.96 139:0.75 140:0.87 143:0.99 144:0.92 145:0.83 146:0.89 147:0.91 149:0.31 150:0.48 151:0.85 152:0.85 153:0.48 154:0.61 155:0.65 157:0.91 158:0.77 159:0.76 162:0.55 165:0.88 166:0.94 168:0.98 169:0.72 170:0.90 172:0.75 173:0.26 174:0.86 176:0.63 177:0.99 178:0.48 179:0.29 182:0.94 186:0.98 187:0.87 190:0.89 191:0.70 192:0.99 196:0.93 197:0.88 201:0.63 202:0.69 205:0.98 208:0.64 212:0.55 216:0.42 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 231:0.91 235:0.95 236:0.94 239:0.90 241:0.21 242:0.18 243:0.51 244:0.61 245:0.84 246:0.92 247:0.93 248:0.83 253:0.82 254:0.90 255:0.94 256:0.64 259:0.21 261:0.90 264:0.98 265:0.62 266:0.80 267:0.69 268:0.65 271:0.89 275:0.95 276:0.79 279:0.79 284:0.94 285:0.61 287:0.94 290:0.74 292:0.95 294:0.97 295:0.99 300:0.84 +0 1:0.62 6:0.84 7:0.64 8:0.58 9:0.74 10:0.83 11:0.86 12:0.51 17:0.59 18:0.65 20:0.89 21:0.54 23:0.95 27:0.21 29:0.62 30:0.89 31:0.92 34:0.93 36:0.89 37:0.78 39:0.51 43:0.06 44:0.86 45:0.77 56:0.97 62:0.96 64:0.77 66:0.75 69:0.29 70:0.20 71:0.79 74:0.38 75:0.95 77:0.70 78:1.00 79:0.91 81:0.71 82:0.98 83:0.69 86:0.63 88:0.98 91:0.63 96:0.77 97:0.97 98:0.78 99:0.99 100:0.92 101:0.64 102:0.94 108:0.22 111:0.98 114:0.72 122:0.67 123:0.22 126:0.54 127:0.27 128:0.97 129:0.05 131:0.96 135:0.86 137:0.92 139:0.68 140:0.45 143:0.99 144:0.92 145:0.56 146:0.46 147:0.52 149:0.05 150:0.52 151:0.75 152:0.84 153:0.48 154:0.17 155:0.65 157:0.81 158:0.73 159:0.17 162:0.86 165:0.79 166:0.94 168:0.97 169:0.90 170:0.90 172:0.32 173:0.60 174:0.79 176:0.62 177:0.99 179:0.87 182:0.94 186:1.00 187:0.39 189:0.86 190:0.98 192:0.99 195:0.55 196:0.89 197:0.84 201:0.64 202:0.36 205:0.95 208:0.64 212:0.61 215:0.58 216:0.50 219:0.88 220:0.62 222:0.48 226:0.96 227:0.95 229:0.97 230:0.64 231:0.91 232:0.90 233:0.76 235:0.88 236:0.92 238:0.81 239:0.86 241:0.18 242:0.33 243:0.77 244:0.97 246:0.92 247:0.39 248:0.69 253:0.68 254:0.74 255:0.78 256:0.45 259:0.21 261:0.92 264:0.98 265:0.79 266:0.23 267:0.73 268:0.46 271:0.89 275:0.91 276:0.14 279:0.79 282:0.73 284:0.88 285:0.61 287:0.94 290:0.72 292:0.88 294:0.95 295:0.98 297:0.36 300:0.18 +1 1:0.66 8:0.61 10:0.94 11:0.88 12:0.51 17:0.16 18:0.90 21:0.13 27:0.49 29:0.62 30:0.85 32:0.71 34:0.98 36:0.99 37:0.47 39:0.51 43:0.57 44:0.92 45:0.98 56:0.97 64:0.77 66:0.52 69:0.54 70:0.44 71:0.79 74:0.71 75:0.97 77:0.70 80:0.98 81:0.75 82:0.99 83:0.69 86:0.87 88:0.98 91:0.31 97:0.99 98:0.71 99:0.99 101:0.70 102:0.97 108:0.42 114:0.88 122:0.91 123:0.40 124:0.55 126:0.54 127:0.66 129:0.59 131:0.61 133:0.46 135:0.97 139:0.88 144:0.92 145:0.61 146:0.75 147:0.79 149:0.83 150:0.58 154:0.57 155:0.55 157:0.96 158:0.77 159:0.51 165:0.88 166:0.94 170:0.90 172:0.82 173:0.55 174:0.91 176:0.63 177:0.99 178:0.55 179:0.31 182:0.93 187:0.39 192:0.58 193:0.95 195:0.69 197:0.93 201:0.66 204:0.82 208:0.64 212:0.84 216:0.56 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 231:0.91 235:0.68 236:0.94 239:0.95 241:0.05 242:0.17 243:0.62 244:0.61 245:0.94 246:0.92 247:0.82 253:0.66 254:0.84 259:0.21 264:0.98 265:0.71 266:0.63 267:0.79 271:0.89 275:0.96 276:0.80 279:0.81 281:0.86 282:0.79 287:0.61 290:0.88 292:0.95 294:0.99 295:0.99 300:0.70 +0 1:0.65 10:0.93 11:0.92 12:0.51 17:0.22 18:0.96 21:0.22 22:0.93 27:0.43 29:0.62 30:0.75 33:0.97 34:0.77 36:0.43 37:0.98 39:0.51 43:0.75 44:0.88 45:0.98 47:0.93 56:0.97 64:0.77 66:0.59 69:0.56 70:0.36 71:0.79 74:0.62 77:0.70 80:0.98 81:0.74 82:0.99 83:0.69 85:0.72 86:0.86 88:0.99 91:0.29 98:0.67 99:0.99 101:1.00 102:0.96 108:0.75 114:0.85 117:0.86 122:0.91 123:0.73 124:0.52 126:0.54 127:0.54 129:0.05 131:0.61 133:0.43 135:0.42 139:0.85 144:0.92 145:0.72 146:0.69 147:0.74 149:0.81 150:0.56 154:0.67 155:0.54 157:0.90 159:0.54 165:0.88 166:0.94 170:0.90 172:0.90 173:0.53 174:0.89 176:0.63 177:0.99 178:0.55 179:0.20 182:0.93 187:0.95 192:0.57 193:0.95 195:0.76 197:0.91 201:0.66 204:0.82 208:0.64 212:0.88 216:0.64 222:0.48 227:0.61 229:0.61 231:0.90 232:0.94 235:0.74 236:0.94 239:0.93 241:0.05 242:0.45 243:0.40 245:0.97 246:0.92 247:0.86 253:0.62 259:0.21 262:0.93 264:0.98 265:0.68 266:0.71 267:0.88 271:0.89 275:0.94 276:0.88 277:0.87 281:0.86 282:0.75 287:0.61 290:0.85 291:0.97 294:0.98 300:0.70 +1 1:0.68 6:0.84 7:0.65 8:0.63 10:0.92 11:0.88 12:0.51 17:0.04 18:0.79 20:0.93 21:0.05 27:0.49 29:0.62 30:0.68 32:0.71 34:0.97 36:0.75 37:0.47 39:0.51 43:0.41 44:0.92 45:0.94 56:0.97 64:0.77 66:0.92 69:0.54 70:0.45 71:0.79 74:0.63 75:0.97 77:0.70 78:1.00 80:0.98 81:0.81 82:0.99 83:0.69 86:0.82 88:0.98 91:0.37 97:0.98 98:0.90 99:0.99 100:0.98 101:0.70 102:0.97 108:0.42 111:0.99 114:0.95 122:0.91 123:0.40 126:0.54 127:0.46 129:0.05 131:0.61 135:0.97 139:0.85 144:0.92 145:0.58 146:0.82 147:0.85 149:0.50 150:0.64 152:0.97 154:0.57 155:0.57 157:0.91 158:0.77 159:0.38 165:0.88 166:0.94 169:0.94 170:0.90 172:0.79 173:0.61 174:0.88 176:0.70 177:0.99 178:0.55 179:0.44 182:0.93 186:1.00 187:0.68 189:0.86 192:0.87 193:0.95 195:0.64 197:0.90 201:0.68 204:0.84 208:0.64 212:0.86 215:0.91 216:0.56 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 230:0.67 231:0.90 233:0.66 235:0.68 236:0.95 238:0.85 239:0.94 241:0.05 242:0.17 243:0.93 244:0.61 246:0.92 247:0.79 253:0.68 254:0.80 259:0.21 261:0.96 264:0.98 265:0.90 266:0.38 267:0.76 271:0.89 275:0.95 276:0.67 279:0.79 281:0.86 282:0.80 283:0.82 287:0.61 290:0.95 292:0.95 294:0.98 295:0.99 297:0.36 300:0.43 +0 1:0.66 6:0.84 8:0.63 9:0.76 10:0.92 11:0.88 12:0.51 17:0.03 18:0.87 20:0.94 21:0.13 23:0.98 27:0.49 29:0.62 30:0.73 31:0.96 34:0.74 36:1.00 37:0.47 39:0.51 43:0.57 44:0.88 45:0.97 56:0.97 62:0.98 64:0.77 66:0.46 69:0.54 70:0.52 71:0.79 74:0.60 75:0.97 77:0.89 78:0.97 79:0.95 80:0.98 81:0.75 82:0.99 83:0.69 86:0.80 88:0.99 91:0.42 96:0.86 97:0.99 98:0.67 99:0.99 100:0.73 101:0.96 102:0.96 108:0.42 111:0.94 114:0.88 122:0.98 123:0.40 124:0.69 126:0.54 127:0.85 128:0.98 129:0.05 131:0.97 133:0.67 135:1.00 137:0.96 139:0.83 140:0.92 143:0.99 144:0.92 145:0.70 146:0.86 147:0.88 149:0.70 150:0.58 151:0.85 152:0.97 153:0.48 154:0.57 155:0.65 157:0.95 158:0.77 159:0.73 162:0.53 165:0.88 166:0.94 168:0.98 169:0.94 170:0.90 172:0.79 173:0.41 174:0.91 176:0.63 177:0.99 178:0.48 179:0.33 182:0.94 186:0.97 187:0.39 190:0.89 192:0.99 193:0.95 195:0.84 196:0.95 197:0.91 201:0.66 202:0.70 204:0.84 205:0.98 208:0.64 212:0.61 216:0.56 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 231:0.91 235:0.68 236:0.94 239:0.94 241:0.37 242:0.16 243:0.58 244:0.61 245:0.91 246:0.92 247:0.88 248:0.79 253:0.84 254:0.84 255:0.94 256:0.64 259:0.21 261:0.96 264:0.98 265:0.68 266:0.84 267:0.59 268:0.65 271:0.89 275:0.96 276:0.81 279:0.81 281:0.86 282:0.75 284:0.94 285:0.61 287:0.94 290:0.88 292:0.95 294:0.98 295:0.99 300:0.70 +0 1:0.67 7:0.64 8:0.58 9:0.73 10:0.89 11:0.88 12:0.51 17:0.34 18:0.79 21:0.05 22:0.93 23:0.96 27:0.58 29:0.62 30:0.87 31:0.92 32:0.71 33:0.97 34:0.86 36:0.96 37:0.36 39:0.51 43:0.57 44:0.92 45:0.93 47:0.93 56:0.97 62:0.97 64:0.77 66:0.83 69:0.79 70:0.36 71:0.79 74:0.56 75:0.97 77:0.70 79:0.92 80:0.98 81:0.76 82:0.99 83:0.69 86:0.73 88:0.98 91:0.44 96:0.78 97:0.99 98:0.62 99:0.99 101:0.70 102:0.97 108:0.63 114:0.92 117:0.86 122:0.91 123:0.60 124:0.37 126:0.54 127:0.30 128:0.97 129:0.05 131:0.61 133:0.36 135:0.75 137:0.92 139:0.81 140:0.45 144:0.92 145:0.76 146:0.59 147:0.05 149:0.65 150:0.60 151:0.77 153:0.69 154:0.46 155:0.58 157:0.87 158:0.77 159:0.31 162:0.86 165:0.88 166:0.94 168:0.97 170:0.90 172:0.71 173:0.88 174:0.89 175:0.75 176:0.63 177:0.05 179:0.45 182:0.93 187:0.39 190:0.99 192:0.87 193:0.95 195:0.66 196:0.93 197:0.91 201:0.67 202:0.46 204:0.81 205:0.95 208:0.64 212:0.84 216:0.63 219:0.91 220:0.62 222:0.48 226:0.98 227:0.61 229:0.61 230:0.64 231:0.91 232:0.83 233:0.76 235:0.60 236:0.94 239:0.93 241:0.32 242:0.33 243:0.10 244:0.83 245:0.57 246:0.92 247:0.76 248:0.71 253:0.77 255:0.72 256:0.45 257:0.99 259:0.21 262:0.93 264:0.98 265:0.63 266:0.47 267:0.44 268:0.55 271:0.89 275:0.93 276:0.55 277:0.69 279:0.81 281:0.86 282:0.80 284:0.90 285:0.50 287:0.61 290:0.92 291:0.97 292:0.95 294:0.98 295:0.99 300:0.43 +0 1:0.65 7:0.64 8:0.58 10:0.92 11:0.88 12:0.51 17:0.22 18:0.95 21:0.22 22:0.93 27:0.43 29:0.62 30:0.75 32:0.63 33:0.97 34:0.37 36:0.84 37:0.98 39:0.51 43:0.48 44:0.88 45:0.98 47:0.93 56:0.97 64:0.77 66:0.59 69:0.56 70:0.36 71:0.79 74:0.63 75:0.97 77:0.70 80:0.98 81:0.74 82:0.99 83:0.69 86:0.84 88:0.98 91:0.27 97:0.99 98:0.52 99:0.99 101:0.70 102:0.96 108:0.83 114:0.85 117:0.86 122:0.91 123:0.82 124:0.64 126:0.54 127:0.52 129:0.05 131:0.61 133:0.66 135:0.98 139:0.85 144:0.92 145:0.74 146:0.35 147:0.42 149:0.55 150:0.56 154:0.61 155:0.54 157:0.90 158:0.77 159:0.63 165:0.88 166:0.94 170:0.90 172:0.89 173:0.46 174:0.86 176:0.63 177:0.99 178:0.55 179:0.21 182:0.93 187:0.87 192:0.56 193:0.95 195:0.83 197:0.89 201:0.66 204:0.81 208:0.64 212:0.90 216:0.64 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 230:0.64 231:0.90 232:0.94 233:0.76 235:0.74 236:0.94 239:0.94 241:0.07 242:0.50 243:0.28 244:0.61 245:0.94 246:0.92 247:0.84 253:0.63 254:0.80 259:0.21 262:0.93 264:0.98 265:0.53 266:0.63 267:0.76 271:0.89 275:0.94 276:0.86 277:0.95 279:0.81 281:0.86 282:0.75 287:0.61 290:0.85 291:0.97 292:0.95 294:0.98 295:0.99 300:0.70 +0 1:0.64 7:0.64 10:0.89 11:0.86 12:0.51 17:0.77 18:0.81 21:0.47 27:0.25 29:0.62 30:0.87 32:0.65 34:0.88 36:0.41 37:0.83 39:0.51 43:0.05 44:0.88 45:0.93 56:0.97 64:0.77 66:0.92 69:0.64 70:0.32 71:0.79 74:0.47 77:0.70 80:0.98 81:0.73 82:0.98 83:0.69 86:0.73 88:0.98 91:0.20 98:0.85 99:0.99 101:0.64 102:0.95 104:0.76 108:0.49 114:0.74 122:0.67 123:0.46 126:0.54 127:0.36 129:0.05 131:0.61 135:0.56 139:0.74 144:0.92 145:0.52 146:0.75 147:0.79 149:0.05 150:0.54 154:0.17 155:0.57 157:0.95 159:0.31 165:0.79 166:0.94 170:0.90 172:0.79 173:0.74 174:0.89 176:0.62 177:0.99 178:0.55 179:0.41 182:0.93 187:0.68 192:0.87 193:0.95 195:0.61 197:0.92 201:0.66 204:0.84 208:0.64 212:0.69 215:0.63 216:0.40 222:0.48 227:0.61 229:0.61 230:0.64 231:0.90 232:0.86 233:0.66 235:0.84 236:0.92 239:0.90 241:0.21 242:0.24 243:0.92 244:0.93 246:0.92 247:0.60 253:0.55 254:0.93 259:0.21 264:0.98 265:0.85 266:0.38 267:0.52 271:0.89 275:0.96 276:0.66 281:0.86 282:0.74 287:0.61 290:0.74 294:0.97 297:0.36 300:0.43 +0 1:0.67 7:0.64 8:0.79 9:0.75 10:0.90 11:0.88 12:0.51 17:0.85 18:0.82 21:0.30 22:0.93 23:0.97 27:0.67 29:0.62 30:0.33 31:0.93 33:0.97 34:0.40 36:0.89 39:0.51 43:0.62 45:0.96 47:0.93 56:0.97 62:0.97 64:0.77 66:0.07 69:0.27 70:0.97 71:0.79 74:0.52 75:0.97 79:0.92 80:0.99 81:0.75 82:0.98 83:0.69 86:0.76 88:0.99 91:0.12 96:0.79 97:1.00 98:0.28 99:1.00 101:0.97 102:0.94 108:0.99 114:0.82 117:0.86 122:0.90 123:0.98 124:0.97 126:0.54 127:1.00 128:0.97 129:0.59 131:0.96 133:0.97 135:0.34 137:0.93 139:0.77 140:0.45 143:0.99 144:0.92 145:0.58 146:0.67 147:0.72 149:0.55 150:0.58 151:0.80 153:0.72 154:0.52 155:0.65 157:0.90 158:0.76 159:0.97 162:0.76 165:0.88 166:0.94 168:0.97 170:0.90 172:0.70 173:0.06 174:0.89 176:0.63 177:0.99 179:0.25 182:0.94 187:0.21 190:0.89 192:0.99 193:0.96 195:1.00 196:0.92 197:0.88 201:0.67 202:0.58 204:0.81 205:0.95 208:0.64 212:0.55 216:0.03 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 230:0.64 231:0.91 232:0.70 233:0.76 235:0.88 236:0.94 239:0.92 241:0.30 242:0.14 243:0.32 244:0.83 245:0.82 246:0.92 247:0.98 248:0.73 253:0.77 255:0.78 256:0.58 259:0.21 262:0.93 264:0.98 265:0.15 266:0.98 267:0.23 268:0.62 271:0.89 275:0.97 276:0.88 277:0.99 279:0.81 281:0.91 284:0.93 285:0.61 287:0.94 290:0.82 291:0.97 292:0.88 294:0.98 295:0.99 300:0.98 +0 1:0.66 8:0.64 9:0.74 10:0.92 11:0.88 12:0.51 17:0.22 18:0.81 21:0.13 23:0.95 27:0.35 29:0.62 30:0.75 31:0.92 34:0.26 36:0.84 37:0.43 39:0.51 43:0.28 44:0.88 45:0.99 56:0.97 62:0.97 64:0.77 66:0.23 69:0.89 70:0.56 71:0.79 74:0.58 75:0.97 77:0.70 79:0.91 80:0.98 81:0.75 82:0.99 83:0.69 86:0.79 88:0.99 91:0.44 96:0.77 97:0.99 98:0.34 99:0.99 101:0.97 102:0.96 108:0.78 114:0.88 122:0.91 123:0.76 124:0.90 126:0.54 127:0.62 128:0.97 129:0.59 131:0.96 133:0.88 135:1.00 137:0.92 139:0.82 140:0.80 143:0.99 144:0.92 145:0.83 146:0.69 147:0.05 149:0.73 150:0.58 151:0.75 153:0.48 154:0.22 155:0.65 157:0.96 158:0.77 159:0.83 162:0.49 165:0.88 166:0.94 168:0.97 170:0.90 172:0.78 173:0.77 174:0.95 176:0.63 177:0.05 178:0.42 179:0.21 182:0.94 187:0.68 190:0.98 191:0.73 192:0.99 193:0.95 195:0.94 196:0.93 197:0.92 201:0.66 202:0.64 204:0.82 205:0.95 208:0.64 212:0.68 216:0.70 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 231:0.91 235:0.68 236:0.94 239:0.94 241:0.41 242:0.21 243:0.06 244:0.61 245:0.91 246:0.92 247:0.90 248:0.69 253:0.76 254:0.96 255:0.78 256:0.45 257:0.99 259:0.21 264:0.98 265:0.36 266:0.80 267:0.59 268:0.46 271:0.89 275:0.97 276:0.88 279:0.81 281:0.86 282:0.75 284:0.88 285:0.61 287:0.94 290:0.88 292:0.95 294:0.98 295:0.99 300:0.84 +0 1:0.56 10:0.82 11:0.91 12:0.51 17:0.34 18:0.78 21:0.64 27:0.45 29:0.62 30:0.66 32:0.62 34:0.71 36:0.24 37:0.50 39:0.51 43:0.22 45:0.89 64:0.77 66:0.30 69:0.69 70:0.61 71:0.79 74:0.32 81:0.36 83:0.54 86:0.64 91:0.12 98:0.49 99:0.83 101:0.87 108:0.86 114:0.63 122:0.67 123:0.51 124:0.59 126:0.26 127:0.40 129:0.05 131:0.61 133:0.39 135:0.74 139:0.63 144:0.92 145:0.49 146:0.67 147:0.72 149:0.25 150:0.34 154:0.14 155:0.46 157:0.79 159:0.58 165:0.63 166:0.61 170:0.90 172:0.48 173:0.63 174:0.79 176:0.59 177:0.99 178:0.55 179:0.60 182:0.73 187:0.68 192:0.44 195:0.81 197:0.82 201:0.54 208:0.49 212:0.57 216:0.77 222:0.48 227:0.61 229:0.61 231:0.65 235:0.80 239:0.82 241:0.18 242:0.33 243:0.48 244:0.97 245:0.75 246:0.61 247:0.74 253:0.48 259:0.21 264:0.98 265:0.17 266:0.63 267:0.52 271:0.89 275:0.91 276:0.40 287:0.61 290:0.63 294:0.93 300:0.55 +0 1:0.66 10:0.88 11:0.88 12:0.51 17:0.55 18:0.71 21:0.13 27:0.54 29:0.62 30:0.60 34:0.97 36:0.76 37:0.41 39:0.51 43:0.09 45:0.85 56:0.97 64:0.77 66:0.69 69:0.51 70:0.67 71:0.79 74:0.46 75:0.97 81:0.75 83:0.69 86:0.72 91:0.31 97:0.98 98:0.69 99:0.99 101:0.87 108:0.39 114:0.88 122:0.41 123:0.38 124:0.42 126:0.54 127:0.43 129:0.05 131:0.61 133:0.36 135:0.96 139:0.73 144:0.92 145:0.59 146:0.61 147:0.66 149:0.11 150:0.58 154:0.60 155:0.51 157:0.87 158:0.77 159:0.34 165:0.88 166:0.94 170:0.90 172:0.54 173:0.61 174:0.79 176:0.63 177:0.99 178:0.55 179:0.70 182:0.93 187:0.87 192:0.55 197:0.84 201:0.66 208:0.64 212:0.42 216:0.55 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 231:0.90 235:0.68 236:0.94 239:0.89 241:0.32 242:0.12 243:0.73 244:0.61 245:0.59 246:0.92 247:0.82 253:0.62 254:0.90 259:0.21 264:0.98 265:0.69 266:0.38 267:0.88 271:0.89 275:0.93 276:0.45 279:0.79 287:0.61 290:0.88 292:0.95 294:0.97 295:0.99 300:0.55 +0 1:0.61 7:0.64 8:0.89 9:0.73 10:0.92 11:0.88 12:0.51 17:0.73 18:0.78 21:0.59 23:0.95 27:0.40 29:0.62 30:0.56 31:0.91 34:0.98 36:0.88 37:0.88 39:0.51 43:0.05 44:0.88 45:0.92 56:0.97 62:0.97 64:0.77 66:0.91 69:0.64 70:0.62 71:0.79 74:0.58 75:0.97 77:0.70 79:0.91 80:0.98 81:0.65 82:0.99 83:0.69 86:0.79 88:0.98 91:0.48 96:0.77 97:0.99 98:0.83 99:0.99 101:0.64 102:0.96 108:0.49 114:0.71 122:0.91 123:0.47 126:0.54 127:0.33 128:0.97 129:0.05 131:0.61 135:0.84 137:0.91 139:0.82 140:0.90 144:0.92 145:0.77 146:0.80 147:0.83 149:0.05 150:0.46 151:0.75 153:0.48 154:0.50 155:0.57 157:0.93 158:0.76 159:0.36 162:0.51 165:0.79 166:0.94 168:0.97 170:0.90 172:0.74 173:0.70 174:0.88 176:0.63 177:0.99 178:0.50 179:0.46 182:0.93 187:0.39 190:0.99 191:0.87 192:0.87 193:0.95 195:0.69 196:0.93 197:0.90 201:0.62 202:0.60 204:0.81 205:0.95 208:0.64 212:0.71 216:0.41 219:0.91 220:0.62 222:0.48 226:0.96 227:0.61 229:0.61 230:0.64 231:0.90 232:0.87 233:0.76 235:0.97 236:0.94 239:0.93 241:0.37 242:0.24 243:0.91 244:0.83 246:0.92 247:0.67 248:0.69 253:0.72 254:0.74 255:0.72 256:0.45 259:0.21 264:0.98 265:0.83 266:0.47 267:0.44 268:0.46 271:0.89 275:0.95 276:0.60 279:0.81 281:0.86 282:0.75 284:0.87 285:0.50 287:0.61 290:0.71 292:0.88 294:0.98 295:0.98 300:0.55 +0 1:0.63 8:0.61 10:0.87 11:0.86 12:0.51 17:0.24 18:0.95 20:0.92 21:0.47 27:0.20 29:0.62 30:0.70 34:0.36 36:0.70 37:0.68 39:0.51 43:0.08 44:0.86 45:0.97 56:0.97 64:0.77 66:0.97 69:0.69 70:0.55 71:0.79 74:0.51 75:0.95 77:0.89 78:0.96 80:0.98 81:0.67 82:0.98 83:0.69 86:0.76 88:0.98 91:0.23 97:0.97 98:0.86 99:0.99 100:0.73 101:0.64 102:0.94 108:0.52 111:0.93 114:0.72 122:0.91 123:0.50 126:0.54 127:0.37 129:0.05 131:0.61 135:0.85 139:0.77 144:0.92 145:0.72 146:0.89 147:0.91 149:0.32 150:0.50 152:0.95 154:0.16 155:0.57 157:0.94 158:0.73 159:0.48 165:0.79 166:0.94 169:0.72 170:0.90 172:0.93 173:0.67 174:0.88 176:0.60 177:0.99 178:0.55 179:0.19 182:0.93 186:0.96 187:0.87 192:0.86 193:0.95 195:0.75 197:0.90 201:0.64 202:0.36 204:0.85 208:0.64 212:0.85 216:0.83 219:0.88 222:0.48 226:0.95 227:0.61 229:0.61 231:0.90 232:0.91 235:0.80 236:0.92 239:0.89 241:0.03 242:0.28 243:0.97 244:0.97 246:0.92 247:0.86 253:0.55 254:0.74 259:0.21 261:0.91 264:0.98 265:0.86 266:0.54 267:0.82 271:0.89 275:0.96 276:0.87 279:0.79 281:0.91 282:0.73 287:0.61 290:0.72 292:0.87 294:0.97 295:0.98 300:0.70 +1 7:0.69 10:0.94 11:0.46 12:0.20 17:0.86 21:0.40 27:0.39 29:0.53 30:0.37 32:0.67 34:0.69 36:0.18 37:0.69 39:0.32 43:0.65 45:0.66 55:0.59 66:0.74 69:0.83 70:0.59 71:0.71 74:0.51 76:0.94 77:0.89 81:0.32 83:0.56 91:0.38 98:0.83 101:0.47 108:0.68 114:0.69 122:0.83 123:0.66 124:0.42 126:0.24 127:0.78 129:0.05 131:0.61 133:0.43 135:0.63 139:0.67 144:0.57 145:0.88 146:0.89 147:0.63 149:0.42 150:0.35 154:0.55 155:0.49 157:0.61 159:0.61 166:0.82 170:0.99 172:0.84 173:0.83 174:0.92 175:0.75 176:0.59 177:0.38 178:0.55 179:0.38 182:0.61 187:0.39 192:0.46 195:0.78 197:0.92 204:0.78 208:0.50 212:0.80 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.42 239:0.92 241:0.24 242:0.30 243:0.23 244:0.83 245:0.86 246:0.61 247:0.76 253:0.53 257:0.84 259:0.21 265:0.83 266:0.47 267:0.52 271:0.16 276:0.78 277:0.69 281:0.91 282:0.72 287:0.61 290:0.69 300:0.55 +0 8:0.85 10:0.91 11:0.55 12:0.20 17:0.67 18:0.62 21:0.71 27:0.39 29:0.53 30:0.13 32:0.67 34:1.00 36:0.18 37:0.69 39:0.32 43:0.65 45:0.81 66:0.71 69:0.41 70:0.84 71:0.71 74:0.49 76:0.94 77:0.89 81:0.30 86:0.65 91:0.40 98:0.80 101:0.65 108:0.32 114:0.62 117:0.86 122:0.57 123:0.31 124:0.43 126:0.24 127:0.64 129:0.05 131:0.61 133:0.39 135:0.98 139:0.63 144:0.57 145:0.74 146:0.79 147:0.83 149:0.57 150:0.33 154:0.50 157:0.78 158:0.72 159:0.48 166:0.82 170:0.99 172:0.71 173:0.43 174:0.86 176:0.59 177:0.88 178:0.55 179:0.55 182:0.73 187:0.68 195:0.70 197:0.87 212:0.68 216:0.22 222:0.60 227:0.61 229:0.61 231:0.79 232:0.72 235:0.88 239:0.89 241:0.24 242:0.21 243:0.75 244:0.61 245:0.76 246:0.61 247:0.86 253:0.48 254:0.74 259:0.21 265:0.80 266:0.63 267:0.59 271:0.16 276:0.63 282:0.72 287:0.61 290:0.62 300:0.70 +0 7:0.69 8:0.77 10:0.87 11:0.46 12:0.20 17:0.79 21:0.40 27:0.39 29:0.53 30:0.87 32:0.67 34:0.63 36:0.17 37:0.69 39:0.32 43:0.65 45:0.66 55:0.71 66:0.60 69:0.35 70:0.42 71:0.71 74:0.50 76:0.94 77:0.89 81:0.32 83:0.56 91:0.38 98:0.74 101:0.47 108:0.70 114:0.69 122:0.83 123:0.68 124:0.63 126:0.24 127:0.73 129:0.81 131:0.61 133:0.67 135:0.83 139:0.67 144:0.57 145:0.88 146:0.62 147:0.67 149:0.47 150:0.35 154:0.55 155:0.49 157:0.61 159:0.63 166:0.82 170:0.99 172:0.77 173:0.29 174:0.86 175:0.91 176:0.59 177:0.38 178:0.55 179:0.42 182:0.61 187:0.21 192:0.46 195:0.80 197:0.86 204:0.78 208:0.50 212:0.76 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.42 239:0.86 241:0.24 242:0.70 243:0.28 244:0.83 245:0.83 246:0.61 247:0.74 253:0.53 257:0.84 259:0.21 265:0.74 266:0.71 267:0.65 271:0.16 276:0.75 277:0.87 281:0.91 282:0.72 287:0.61 290:0.69 300:0.70 +2 1:0.65 7:0.69 8:0.79 9:0.72 10:0.84 11:0.55 12:0.20 17:0.93 18:0.93 21:0.13 27:0.59 29:0.53 30:0.40 32:0.67 34:0.31 36:0.48 37:0.74 39:0.32 43:0.66 45:0.99 55:0.84 66:1.00 69:0.10 70:0.58 71:0.71 74:0.86 76:0.85 77:0.98 81:0.66 83:0.56 86:0.93 91:0.49 96:0.75 97:0.94 98:1.00 99:0.83 101:0.87 104:0.54 108:0.09 114:0.78 120:0.63 122:0.83 123:0.09 126:0.32 127:0.99 129:0.05 131:0.88 135:0.34 139:0.91 140:0.45 144:0.57 145:0.96 146:1.00 147:1.00 149:0.94 150:0.61 151:0.70 153:0.69 154:0.22 155:0.55 157:0.94 158:0.73 159:0.93 162:0.86 163:0.81 164:0.62 165:0.65 166:0.83 170:0.99 172:0.99 173:0.07 174:0.83 176:0.59 177:0.88 179:0.11 182:0.84 187:0.21 190:0.89 192:0.58 195:0.98 197:0.82 201:0.61 202:0.46 208:0.50 212:0.39 215:0.84 216:0.01 220:0.74 222:0.60 227:0.96 229:0.61 230:0.71 231:0.87 232:0.74 233:0.76 235:0.31 239:0.84 241:0.64 242:0.50 243:1.00 244:0.61 246:0.61 247:0.95 248:0.65 253:0.79 255:0.71 256:0.45 259:0.21 260:0.64 265:1.00 266:0.54 267:0.18 268:0.55 271:0.16 276:0.98 279:0.78 282:0.72 283:0.67 285:0.50 287:0.61 290:0.78 297:0.36 300:0.55 +0 1:0.64 10:0.81 11:0.55 12:0.20 17:1.00 18:0.96 27:0.72 29:0.53 30:0.21 34:0.24 36:0.28 39:0.32 43:0.62 45:0.99 55:0.45 66:0.75 69:0.61 70:0.71 71:0.71 74:0.90 81:0.65 83:0.56 86:0.96 91:0.23 97:0.89 98:0.76 99:0.83 101:0.87 104:0.87 106:0.81 108:0.46 114:0.84 117:0.86 122:0.83 123:0.45 124:0.64 126:0.32 127:0.55 129:0.05 131:0.61 132:0.97 133:0.88 135:0.60 139:0.94 144:0.57 146:0.99 147:0.99 149:0.97 150:0.61 154:0.46 155:0.49 157:0.94 158:0.73 159:0.92 165:0.65 166:0.83 170:0.99 172:0.99 173:0.22 174:0.79 176:0.59 177:0.88 178:0.55 179:0.09 182:0.84 187:0.39 192:0.48 197:0.80 201:0.60 206:0.81 208:0.50 212:0.93 215:0.96 216:0.15 222:0.60 227:0.61 229:0.61 231:0.87 232:0.95 235:0.05 239:0.81 241:0.05 242:0.32 243:0.77 244:0.61 245:0.98 246:0.61 247:0.97 253:0.71 259:0.21 265:0.76 266:0.87 267:0.18 271:0.16 276:0.99 279:0.76 283:0.82 287:0.61 290:0.84 297:0.36 300:0.94 +0 7:0.69 10:0.87 11:0.46 12:0.20 17:0.82 21:0.59 27:0.39 29:0.53 30:0.86 32:0.65 34:0.83 36:0.17 37:0.69 39:0.32 43:0.65 45:0.66 55:0.71 66:0.77 69:0.38 70:0.40 71:0.71 74:0.50 76:0.94 77:0.70 81:0.29 83:0.56 91:0.42 98:0.91 101:0.47 108:0.30 114:0.64 122:0.83 123:0.28 124:0.40 126:0.24 127:0.77 129:0.05 131:0.61 133:0.43 135:0.85 139:0.67 144:0.57 145:0.74 146:0.93 147:0.95 149:0.48 150:0.32 154:0.55 155:0.49 157:0.61 159:0.63 166:0.82 170:0.99 172:0.83 173:0.31 174:0.86 175:0.75 176:0.59 177:0.70 178:0.55 179:0.42 182:0.61 187:0.39 192:0.46 195:0.80 197:0.86 204:0.78 208:0.50 212:0.75 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.68 239:0.86 241:0.15 242:0.60 243:0.79 244:0.83 245:0.82 246:0.61 247:0.76 253:0.50 257:0.65 259:0.21 265:0.91 266:0.47 267:0.65 271:0.16 276:0.76 277:0.69 281:0.91 282:0.72 287:0.61 290:0.64 300:0.55 +1 10:0.90 11:0.55 12:0.20 17:0.72 18:0.62 21:0.68 27:0.39 29:0.53 30:0.11 32:0.67 33:0.97 34:1.00 36:0.18 37:0.69 39:0.32 43:0.65 45:0.77 66:0.67 69:0.41 70:0.92 71:0.71 74:0.48 75:0.97 76:0.94 77:0.70 81:0.38 86:0.66 91:0.42 98:0.77 101:0.65 108:0.32 114:0.63 117:0.86 122:0.57 123:0.31 124:0.45 126:0.32 127:0.57 129:0.05 131:0.61 133:0.39 135:0.98 139:0.64 144:0.57 145:0.79 146:0.74 147:0.78 149:0.56 150:0.39 154:0.55 157:0.78 158:0.73 159:0.44 166:0.82 170:0.99 172:0.65 173:0.46 174:0.86 176:0.59 177:0.88 178:0.55 179:0.60 182:0.73 187:0.68 192:0.44 193:0.97 195:0.68 197:0.89 201:0.56 212:0.49 216:0.22 222:0.60 226:0.95 227:0.61 229:0.61 231:0.79 232:0.72 235:0.88 236:0.94 239:0.92 241:0.32 242:0.18 243:0.72 244:0.83 245:0.73 246:0.61 247:0.86 253:0.49 259:0.21 265:0.77 266:0.54 267:0.69 271:0.16 276:0.58 282:0.72 287:0.61 290:0.63 291:0.97 300:0.70 +2 10:0.89 11:0.55 12:0.20 17:0.96 18:0.76 21:0.40 27:0.64 29:0.53 30:0.55 34:0.66 36:0.24 39:0.32 43:0.60 45:0.81 55:0.71 66:0.95 69:0.35 70:0.64 71:0.71 74:0.57 76:1.00 77:0.96 81:0.37 86:0.77 91:0.22 98:0.94 101:0.65 108:0.27 114:0.69 117:0.86 122:0.83 123:0.26 126:0.24 127:0.60 129:0.05 131:0.61 135:0.56 139:0.74 144:0.57 145:0.91 146:0.84 147:0.87 149:0.67 150:0.42 154:0.50 157:0.83 158:0.73 159:0.41 166:0.82 170:0.99 172:0.86 173:0.42 174:0.83 176:0.59 177:0.88 178:0.55 179:0.37 182:0.75 187:0.39 192:0.46 193:0.98 195:0.65 197:0.86 212:0.93 216:0.27 222:0.60 227:0.61 229:0.61 231:0.83 232:0.95 235:0.52 239:0.90 241:0.18 242:0.70 243:0.95 244:0.61 246:0.61 247:0.84 253:0.54 254:0.80 259:0.21 265:0.94 266:0.27 267:0.44 271:0.16 276:0.77 282:0.72 287:0.61 290:0.69 300:0.55 +1 8:0.88 10:0.85 11:0.55 12:0.20 17:0.85 18:0.91 21:0.74 27:0.66 29:0.53 30:0.36 34:0.21 36:0.31 37:0.30 39:0.32 43:0.75 45:0.91 66:0.95 69:0.37 70:0.83 71:0.71 74:0.60 81:0.27 86:0.88 91:0.23 98:0.90 101:0.65 102:0.95 108:0.29 122:0.75 123:0.28 126:0.24 127:0.46 129:0.05 131:0.61 135:0.58 139:0.85 144:0.57 145:0.87 146:0.84 147:0.87 149:0.83 150:0.29 154:0.67 157:0.89 159:0.41 166:0.61 170:0.99 172:0.86 173:0.42 174:0.79 177:0.88 178:0.55 179:0.34 182:0.79 187:0.68 193:0.97 195:0.65 197:0.85 212:0.88 216:0.08 222:0.60 227:0.61 229:0.61 231:0.82 235:0.88 236:0.92 239:0.89 241:0.21 242:0.23 243:0.95 244:0.61 246:0.61 247:0.82 253:0.46 259:0.21 265:0.90 266:0.27 267:0.28 271:0.16 276:0.76 287:0.61 300:0.70 +2 1:0.66 7:0.69 8:0.79 9:0.73 10:0.82 11:0.50 12:0.20 17:0.92 18:0.77 21:0.05 27:0.27 29:0.53 30:0.95 32:0.67 34:0.17 36:0.18 37:0.57 39:0.32 43:0.58 45:0.81 55:0.71 66:0.80 69:0.44 70:0.15 71:0.71 74:0.55 76:0.94 77:0.89 81:0.66 83:0.56 86:0.77 91:0.67 96:0.85 98:0.81 99:0.83 101:0.47 108:0.34 114:0.76 117:0.86 120:0.65 122:0.83 123:0.32 124:0.39 126:0.32 127:0.56 129:0.05 131:0.88 133:0.62 135:0.34 138:0.95 139:0.74 140:0.45 144:0.57 145:0.40 146:0.89 147:0.91 149:0.62 150:0.62 151:0.82 153:0.78 154:0.46 155:0.56 157:0.87 158:0.72 159:0.60 162:0.86 164:0.69 165:0.65 166:0.83 170:0.99 172:0.76 173:0.36 174:0.79 175:0.91 176:0.59 177:0.38 179:0.50 182:0.77 187:0.39 190:0.89 191:0.77 192:0.58 195:0.77 197:0.81 201:0.61 202:0.62 208:0.50 212:0.89 215:0.84 216:0.35 220:0.62 222:0.60 227:0.96 229:0.61 230:0.71 231:0.84 232:0.95 233:0.76 235:0.22 239:0.82 241:0.39 242:0.63 243:0.82 244:0.83 245:0.60 246:0.61 247:0.60 248:0.77 253:0.82 255:0.72 256:0.68 257:0.84 259:0.21 260:0.66 265:0.81 266:0.38 267:0.23 268:0.70 271:0.16 276:0.67 277:0.87 282:0.72 285:0.50 286:0.99 287:0.61 290:0.76 297:0.36 298:0.99 300:0.18 +2 1:0.65 7:0.69 8:0.82 9:0.72 10:0.84 11:0.55 12:0.20 17:0.95 18:0.93 21:0.13 27:0.59 29:0.53 30:0.40 32:0.67 34:0.26 36:0.49 37:0.74 39:0.32 43:0.66 45:0.99 55:0.84 66:1.00 69:0.10 70:0.58 71:0.71 74:0.88 76:0.85 77:0.98 81:0.66 83:0.56 86:0.94 91:0.72 96:0.74 98:1.00 99:0.83 101:0.87 104:0.54 108:0.09 114:0.78 120:0.62 122:0.83 123:0.09 126:0.32 127:1.00 129:0.05 131:0.88 135:0.32 139:0.92 140:0.45 144:0.57 145:0.96 146:1.00 147:1.00 149:0.95 150:0.61 151:0.65 153:0.48 154:0.21 155:0.55 157:0.94 159:0.93 162:0.86 163:0.81 164:0.56 165:0.65 166:0.83 170:0.99 172:0.99 173:0.07 174:0.83 176:0.59 177:0.88 179:0.11 182:0.85 190:0.89 192:0.57 195:0.98 197:0.82 201:0.61 202:0.36 208:0.50 212:0.39 215:0.84 216:0.01 220:0.74 222:0.60 227:0.96 229:0.61 230:0.71 231:0.87 232:0.74 233:0.76 235:0.31 239:0.84 241:0.80 242:0.50 243:1.00 244:0.61 246:0.61 247:0.95 248:0.63 253:0.78 255:0.71 256:0.45 259:0.21 260:0.63 265:1.00 266:0.54 267:0.19 268:0.46 271:0.16 276:0.98 282:0.72 285:0.50 287:0.61 290:0.78 297:0.36 300:0.55 +1 8:0.67 10:0.95 11:0.55 12:0.20 17:0.75 18:0.61 21:0.68 27:0.39 29:0.53 30:0.20 32:0.71 33:0.97 34:1.00 36:0.17 37:0.69 39:0.32 43:0.65 45:0.77 55:0.52 66:0.72 69:0.41 70:0.94 71:0.71 74:0.51 75:0.97 76:0.94 77:0.89 81:0.38 86:0.68 91:0.42 98:0.79 101:0.65 102:0.97 108:0.32 114:0.63 117:0.86 122:0.90 123:0.31 124:0.43 126:0.32 127:0.70 129:0.05 131:0.61 133:0.39 135:0.98 139:0.65 144:0.57 145:0.76 146:0.77 147:0.80 149:0.36 150:0.39 154:0.49 157:0.78 158:0.73 159:0.46 166:0.82 170:0.99 172:0.73 173:0.46 174:0.90 176:0.59 177:0.88 178:0.55 179:0.54 182:0.73 187:0.68 192:0.44 193:0.97 195:0.68 197:0.92 201:0.56 212:0.61 216:0.22 222:0.60 226:0.95 227:0.61 229:0.61 231:0.79 232:0.72 235:0.88 236:0.94 239:0.96 241:0.30 242:0.27 243:0.75 244:0.61 245:0.76 246:0.61 247:0.88 253:0.50 254:0.86 259:0.21 265:0.79 266:0.54 267:0.59 271:0.16 276:0.64 282:0.72 287:0.61 290:0.63 291:0.97 300:0.84 +1 7:0.69 8:0.77 10:0.91 11:0.46 12:0.20 17:0.85 21:0.40 27:0.39 29:0.53 30:0.45 32:0.67 34:0.71 36:0.17 37:0.69 39:0.32 43:0.65 45:0.66 55:0.71 66:0.72 69:0.83 70:0.57 71:0.71 74:0.50 76:0.94 77:0.89 81:0.32 83:0.56 91:0.38 98:0.82 101:0.47 108:0.68 114:0.69 122:0.83 123:0.66 124:0.43 126:0.24 127:0.76 129:0.05 131:0.61 133:0.43 135:0.60 139:0.67 144:0.57 145:0.88 146:0.87 147:0.63 149:0.46 150:0.35 154:0.55 155:0.49 157:0.61 159:0.58 166:0.82 170:0.99 172:0.82 173:0.85 174:0.89 175:0.75 176:0.59 177:0.38 178:0.55 179:0.41 182:0.61 187:0.39 192:0.46 195:0.77 197:0.90 204:0.78 208:0.50 212:0.82 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.42 239:0.90 241:0.21 242:0.62 243:0.25 244:0.83 245:0.85 246:0.61 247:0.76 253:0.53 257:0.84 259:0.21 265:0.82 266:0.47 267:0.59 271:0.16 276:0.76 277:0.69 281:0.91 282:0.72 287:0.61 290:0.69 300:0.55 +1 1:0.74 6:0.94 8:0.90 9:0.80 11:0.78 12:0.69 17:0.47 20:0.98 21:0.47 26:0.95 27:0.12 28:0.78 30:0.57 32:0.80 34:0.96 36:0.47 37:0.78 39:0.41 41:0.99 43:0.77 58:1.00 60:0.99 66:0.20 69:0.80 70:0.76 74:0.94 77:0.70 78:0.78 81:0.65 83:0.89 85:0.97 91:0.75 96:0.98 98:0.50 100:0.83 101:0.84 108:0.83 111:0.78 114:0.80 120:0.94 122:0.57 123:0.62 124:0.65 126:0.54 127:0.41 129:0.81 131:0.90 133:0.67 135:0.20 140:0.45 141:0.97 144:0.76 145:0.54 146:0.75 147:0.79 149:0.93 150:0.78 151:0.99 152:0.90 153:0.96 154:0.69 155:0.67 157:0.90 158:0.92 159:0.67 161:0.72 162:0.73 164:0.92 165:0.80 166:0.84 167:0.75 169:0.81 172:0.79 173:0.71 176:0.73 177:0.38 179:0.28 181:0.55 182:0.82 186:0.79 187:0.68 189:0.95 191:0.93 192:0.59 195:0.86 199:1.00 201:0.74 202:0.90 208:0.64 212:0.78 215:0.63 216:0.81 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 231:0.77 234:0.98 235:0.60 238:0.93 241:0.64 242:0.51 243:0.40 245:0.89 246:0.93 247:0.60 248:0.97 253:0.99 255:0.79 256:0.93 259:0.21 260:0.95 261:0.83 265:0.42 266:0.75 267:0.91 268:0.93 271:0.87 274:1.00 276:0.78 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.80 297:0.36 299:0.88 300:0.84 +2 8:0.85 9:0.80 12:0.69 17:0.04 21:0.22 25:0.88 26:0.95 27:0.10 28:0.78 30:0.44 34:0.49 36:0.64 37:0.87 39:0.41 41:0.92 43:0.41 55:0.59 58:0.99 66:0.67 69:0.86 70:0.67 74:0.94 78:0.83 81:0.77 83:0.78 87:0.98 91:0.40 96:0.91 98:0.70 104:0.54 108:0.72 111:0.82 114:0.55 120:0.77 122:0.67 123:0.70 124:0.51 126:0.32 127:0.47 129:0.05 131:0.89 133:0.62 135:0.54 140:0.90 141:0.97 144:0.76 146:0.82 147:0.43 149:0.69 150:0.57 151:0.87 153:0.48 154:0.31 155:0.67 157:0.61 158:0.83 159:0.57 161:0.72 162:0.73 164:0.75 166:0.77 167:0.90 169:0.99 172:0.76 173:0.86 176:0.53 177:0.05 178:0.42 179:0.41 181:0.55 182:0.82 187:0.68 191:0.90 192:0.59 199:0.96 202:0.74 208:0.64 212:0.73 216:0.70 220:0.82 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.76 234:0.98 235:0.31 241:0.77 242:0.81 243:0.17 244:0.61 245:0.77 246:0.61 247:0.39 248:0.84 253:0.94 255:0.79 256:0.78 257:0.65 259:0.21 260:0.77 265:0.70 266:0.75 267:0.18 268:0.78 271:0.87 274:0.98 276:0.71 285:0.62 287:0.95 290:0.55 300:0.55 +2 1:0.74 6:0.98 8:0.93 9:0.80 11:0.78 12:0.69 17:0.13 20:0.77 21:0.54 26:0.95 27:0.30 28:0.78 30:0.33 34:0.97 36:0.59 37:0.93 39:0.41 41:0.96 43:0.58 55:0.71 58:0.99 66:0.67 69:0.83 70:0.88 74:0.96 78:0.82 81:0.68 83:0.82 85:0.80 87:0.98 91:0.53 96:0.94 98:0.79 100:0.91 101:0.71 108:0.68 111:0.85 114:0.77 120:0.87 122:0.67 123:0.66 124:0.57 126:0.54 127:0.74 129:0.05 131:0.89 133:0.74 135:0.47 140:0.45 141:0.97 144:0.76 146:0.98 147:0.44 149:0.75 150:0.79 151:0.97 152:0.78 153:0.90 154:0.47 155:0.67 157:0.78 158:0.82 159:0.86 161:0.72 162:0.81 164:0.85 165:0.79 166:0.84 167:0.91 169:0.90 172:0.89 173:0.63 176:0.73 177:0.05 179:0.27 181:0.55 182:0.82 186:0.83 187:0.87 189:0.99 191:0.92 192:0.59 199:0.98 201:0.74 202:0.78 208:0.64 212:0.54 215:0.58 216:0.46 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 231:0.77 232:0.70 234:0.99 235:0.74 238:0.96 241:0.78 242:0.78 243:0.11 245:0.87 246:0.93 247:0.39 248:0.93 253:0.96 255:0.79 256:0.83 257:0.65 259:0.21 260:0.88 261:0.84 265:0.79 266:0.89 267:0.79 268:0.83 271:0.87 274:0.99 276:0.85 285:0.62 287:0.95 290:0.77 297:0.36 300:0.84 +2 1:0.74 6:0.97 7:0.81 8:0.81 11:0.78 12:0.69 17:0.32 20:0.77 21:0.30 26:0.95 27:0.21 28:0.78 30:0.10 34:0.34 36:0.72 37:0.74 39:0.41 43:0.98 55:0.71 58:0.93 66:0.13 69:0.12 70:0.94 74:0.98 78:0.75 81:0.75 83:0.75 85:1.00 91:0.01 98:0.32 100:0.79 101:0.80 104:0.84 106:0.81 108:0.99 111:0.75 114:0.86 121:1.00 122:0.67 123:0.99 124:0.98 126:0.54 127:0.98 129:1.00 131:0.61 133:0.98 135:0.20 141:0.91 144:0.76 146:0.57 147:0.63 149:0.96 150:0.84 152:0.91 154:0.98 155:0.67 157:0.90 159:0.98 161:0.72 165:0.84 166:0.84 167:0.70 169:0.83 172:0.94 173:0.05 176:0.73 177:0.38 178:0.55 179:0.10 181:0.55 182:0.81 186:0.76 187:0.39 192:0.59 199:1.00 201:0.74 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 230:0.87 231:0.77 233:0.76 234:0.91 235:0.42 241:0.50 242:0.31 243:0.10 245:0.97 246:0.93 247:0.67 253:0.78 259:0.21 261:0.84 265:0.34 266:0.95 267:0.44 271:0.87 274:0.91 276:0.99 283:0.71 287:0.61 290:0.86 297:0.36 300:0.94 +0 1:0.74 7:0.76 11:0.78 12:0.69 17:0.43 21:0.13 26:0.95 27:0.45 28:0.78 30:0.27 32:0.80 34:0.67 36:0.18 37:0.50 39:0.41 43:0.82 55:0.71 58:0.93 60:0.87 66:0.32 69:0.61 70:0.84 74:0.86 77:0.70 81:0.85 83:0.87 85:0.88 91:0.10 98:0.59 101:0.78 108:0.46 114:0.92 123:0.78 124:0.69 126:0.54 127:0.47 129:0.05 131:0.61 133:0.62 135:0.72 141:0.91 144:0.76 145:0.39 146:0.59 147:0.64 149:0.85 150:0.91 154:0.77 155:0.67 157:0.84 158:0.92 159:0.59 161:0.72 165:0.88 166:0.85 167:0.69 172:0.51 173:0.53 176:0.73 177:0.38 178:0.55 179:0.52 181:0.55 182:0.81 187:0.68 192:0.59 195:0.79 199:0.98 201:0.74 208:0.64 212:0.49 215:0.84 216:0.77 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 230:0.79 231:0.77 233:0.76 234:0.91 235:0.22 241:0.46 242:0.57 243:0.49 245:0.76 246:0.93 247:0.67 253:0.76 259:0.21 265:0.44 266:0.71 267:0.23 271:0.87 274:0.91 276:0.63 277:0.69 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.70 +3 1:0.74 8:0.92 9:0.80 11:0.78 12:0.69 17:0.30 21:0.22 25:0.88 26:0.95 27:0.10 28:0.78 30:0.43 34:0.97 36:0.68 37:0.87 39:0.41 41:0.94 43:0.67 55:0.84 58:0.99 66:0.51 69:0.86 70:0.79 74:0.89 78:0.72 81:0.85 83:0.80 85:0.72 87:0.98 91:0.68 96:0.92 98:0.66 101:0.71 104:0.54 108:0.72 111:0.82 114:0.90 120:0.80 122:0.67 123:0.71 124:0.65 126:0.54 127:0.41 129:0.05 131:0.89 133:0.62 135:0.49 140:0.87 141:0.97 144:0.76 146:0.79 147:0.43 149:0.65 150:0.90 151:0.87 153:0.48 154:0.57 155:0.67 157:0.76 158:0.82 159:0.55 161:0.72 162:0.68 164:0.81 165:0.86 166:0.84 167:0.91 169:0.99 172:0.57 173:0.86 176:0.73 177:0.05 178:0.42 179:0.56 181:0.55 182:0.82 187:0.68 191:0.92 192:0.59 199:0.97 201:0.74 202:0.79 208:0.64 212:0.28 215:0.79 216:0.70 220:0.62 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.76 234:0.98 235:0.42 241:0.78 242:0.79 243:0.23 245:0.69 246:0.93 247:0.39 248:0.87 253:0.94 255:0.79 256:0.81 257:0.65 259:0.21 260:0.81 265:0.67 266:0.75 267:0.65 268:0.82 271:0.87 274:0.99 276:0.58 285:0.62 287:0.95 290:0.90 297:0.36 300:0.70 +2 1:0.74 6:0.97 7:0.81 8:0.89 9:0.80 11:0.78 12:0.69 17:0.16 20:0.98 21:0.30 26:0.95 27:0.21 28:0.78 30:0.11 34:0.30 36:0.74 37:0.74 39:0.41 41:0.98 43:0.98 55:0.71 58:1.00 60:0.95 66:0.13 69:0.12 70:0.94 74:0.98 78:0.77 81:0.75 83:0.90 85:1.00 91:0.72 96:0.98 98:0.32 100:0.85 101:0.80 104:0.84 106:0.81 108:0.99 111:0.79 114:0.86 117:0.86 120:0.96 121:1.00 122:0.57 123:0.99 124:0.98 126:0.54 127:0.98 129:1.00 131:0.89 133:0.98 135:0.20 140:0.45 141:0.97 144:0.76 146:0.56 147:0.62 149:0.96 150:0.84 151:1.00 152:0.91 153:0.98 154:0.98 155:0.67 157:0.90 158:0.92 159:0.98 161:0.72 162:0.65 164:0.93 165:0.84 166:0.84 167:0.74 169:0.83 172:0.94 173:0.05 176:0.73 177:0.38 179:0.10 181:0.55 182:0.82 186:0.78 187:0.39 189:0.97 191:0.73 192:0.59 199:1.00 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 230:0.87 231:0.77 232:0.91 233:0.76 234:0.98 235:0.42 238:0.97 241:0.61 242:0.31 243:0.10 245:0.97 246:0.93 247:0.67 248:0.98 253:0.99 255:0.79 256:0.92 259:0.21 260:0.96 261:0.84 265:0.34 266:0.95 267:0.65 268:0.92 271:0.87 274:1.00 276:0.99 279:0.86 283:0.82 285:0.62 287:0.95 290:0.86 297:0.36 300:0.94 +1 1:0.74 6:0.85 8:0.76 11:0.78 12:0.69 17:0.32 20:0.76 21:0.40 26:0.95 27:0.45 28:0.78 30:0.11 32:0.80 34:0.67 36:0.23 37:0.50 39:0.41 43:0.82 58:0.93 66:0.32 69:0.69 70:0.95 74:0.83 77:0.70 78:0.80 81:0.70 83:0.75 85:0.90 91:0.09 98:0.39 100:0.81 101:0.77 108:0.52 111:0.79 114:0.82 122:0.43 123:0.50 124:0.87 126:0.54 127:0.54 129:0.89 131:0.61 133:0.87 135:0.86 141:0.91 144:0.76 145:0.41 146:0.68 147:0.72 149:0.81 150:0.81 152:0.75 154:0.77 155:0.67 157:0.84 159:0.76 161:0.72 165:0.83 166:0.84 167:0.70 169:0.79 172:0.54 173:0.53 176:0.73 177:0.38 178:0.55 179:0.51 181:0.55 182:0.81 186:0.81 187:0.68 189:0.87 192:0.59 195:0.90 199:0.98 201:0.74 212:0.19 215:0.67 216:0.77 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 231:0.76 234:0.91 235:0.52 238:0.85 241:0.51 242:0.33 243:0.49 245:0.68 246:0.93 247:0.67 253:0.72 259:0.21 261:0.75 265:0.41 266:0.92 267:0.28 271:0.87 274:0.91 276:0.65 282:0.88 287:0.61 290:0.82 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.81 8:0.91 9:0.80 11:0.78 12:0.69 17:0.16 20:0.92 21:0.54 26:0.95 27:0.11 28:0.78 30:0.09 32:0.80 34:0.44 36:0.95 37:0.80 39:0.41 41:0.98 43:0.89 55:0.52 58:1.00 60:0.98 66:0.18 69:0.50 70:0.97 74:0.94 77:0.70 78:0.81 81:0.61 83:0.89 85:0.90 91:0.76 96:0.98 98:0.37 100:0.84 101:0.75 108:0.80 111:0.80 114:0.77 120:0.94 121:0.99 122:0.67 123:0.79 124:0.93 126:0.54 127:0.84 129:0.96 131:0.89 133:0.93 135:0.78 140:0.45 141:0.97 144:0.76 145:0.54 146:0.13 147:0.18 149:0.77 150:0.75 151:0.99 152:0.91 153:0.95 154:0.88 155:0.67 157:0.83 158:0.92 159:0.87 161:0.72 162:0.72 164:0.92 165:0.79 166:0.84 167:0.79 169:0.81 172:0.61 173:0.23 176:0.73 177:0.38 179:0.34 181:0.55 182:0.82 186:0.81 187:0.68 189:0.96 191:0.93 192:0.59 195:0.95 199:1.00 201:0.74 202:0.91 204:0.89 208:0.64 212:0.29 215:0.58 216:0.86 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 230:0.87 231:0.77 233:0.76 234:0.98 235:0.68 238:0.92 241:0.70 242:0.53 243:0.10 245:0.81 246:0.93 247:0.60 248:0.97 253:0.99 255:0.79 256:0.94 259:0.21 260:0.95 261:0.85 265:0.39 266:0.92 267:0.69 268:0.94 271:0.87 274:1.00 276:0.82 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.77 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.85 9:0.80 11:0.78 12:0.69 17:0.03 20:0.90 21:0.68 26:0.95 27:0.06 28:0.78 30:0.57 32:0.80 34:0.19 36:0.73 37:0.85 39:0.41 41:0.97 43:0.59 55:0.59 58:1.00 60:0.87 66:0.60 69:0.93 70:0.74 74:0.94 77:0.70 78:0.78 81:0.55 83:0.83 85:0.80 91:0.76 96:0.98 98:0.58 100:0.81 101:0.71 104:0.58 108:0.87 111:0.78 114:0.70 117:0.86 120:0.95 121:0.90 122:0.67 123:0.86 124:0.73 126:0.54 127:0.61 129:0.59 131:0.89 133:0.82 135:0.84 138:0.98 140:0.90 141:0.97 144:0.76 145:0.96 146:0.93 147:0.29 149:0.71 150:0.69 151:0.98 152:0.85 153:0.98 154:0.47 155:0.67 157:0.78 158:0.92 159:0.86 161:0.72 162:0.75 164:0.93 165:0.69 166:0.84 167:0.75 169:0.79 172:0.86 173:0.84 176:0.73 177:0.05 179:0.28 181:0.55 182:0.82 186:0.79 187:0.68 189:0.94 191:0.88 192:0.59 195:0.96 199:1.00 201:0.74 202:0.91 204:0.89 208:0.64 212:0.68 215:0.49 216:0.88 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 230:0.87 231:0.77 232:0.95 233:0.76 234:0.98 235:0.88 238:0.90 241:0.65 242:0.80 243:0.09 245:0.84 246:0.93 247:0.60 248:0.96 253:0.99 255:0.79 256:0.93 257:0.65 259:0.21 260:0.95 261:0.82 265:0.59 266:0.89 267:0.76 268:0.93 271:0.87 274:1.00 276:0.83 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.70 297:0.36 299:0.88 300:0.94 +4 6:0.98 8:0.97 9:0.80 12:0.69 20:0.93 21:0.78 26:0.95 27:0.03 28:0.78 34:0.77 36:0.26 37:0.97 39:0.41 41:1.00 58:1.00 60:0.87 74:0.47 78:0.94 83:0.90 87:0.98 91:0.98 96:1.00 100:0.99 111:0.99 120:0.98 131:0.89 135:0.81 138:0.99 140:0.45 141:0.97 144:0.76 151:1.00 152:0.86 153:0.99 155:0.67 157:0.61 158:0.92 161:0.72 162:0.68 164:0.97 166:0.61 167:0.97 169:1.00 175:0.91 181:0.55 182:0.82 186:0.94 189:0.99 191:0.97 192:0.59 199:1.00 202:0.97 208:0.64 216:0.54 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.72 234:0.98 238:0.99 241:0.95 244:0.83 246:0.61 248:0.99 253:0.99 255:0.79 256:0.98 257:0.84 259:0.21 260:0.98 261:0.98 267:0.91 268:0.98 271:0.87 274:1.00 277:0.87 279:0.86 283:0.82 285:0.62 287:0.95 +1 1:0.74 6:0.94 7:0.81 8:0.87 9:0.80 11:0.78 12:0.69 17:0.51 20:0.85 21:0.64 26:0.95 27:0.05 28:0.78 30:0.17 32:0.80 34:0.30 36:0.75 37:0.78 39:0.41 41:0.98 43:0.70 58:1.00 60:0.99 66:0.35 69:0.86 70:0.92 74:0.94 77:0.70 78:0.78 81:0.55 83:0.85 85:0.94 91:0.85 96:0.96 98:0.42 100:0.81 101:0.80 108:0.72 111:0.77 114:0.72 117:0.86 120:0.90 121:0.90 122:0.67 123:0.71 124:0.77 126:0.54 127:0.70 129:0.81 131:0.89 133:0.76 135:0.73 140:0.87 141:0.97 144:0.76 145:0.79 146:0.72 147:0.69 149:0.86 150:0.70 151:0.98 152:0.90 153:0.97 154:0.60 155:0.67 157:0.87 158:0.92 159:0.79 161:0.72 162:0.70 164:0.90 165:0.70 166:0.84 167:0.72 169:0.78 172:0.67 173:0.76 176:0.73 177:0.05 179:0.42 181:0.55 182:0.82 186:0.79 187:0.87 189:0.95 191:0.90 192:0.59 195:0.91 199:0.99 201:0.74 202:0.88 204:0.89 208:0.64 212:0.68 215:0.53 216:0.83 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 230:0.87 231:0.77 232:0.97 233:0.76 234:0.98 235:0.80 238:0.89 241:0.55 242:0.41 243:0.19 245:0.82 246:0.93 247:0.60 248:0.94 253:0.97 255:0.79 256:0.90 257:0.65 259:0.21 260:0.91 261:0.82 265:0.44 266:0.84 267:0.76 268:0.90 271:0.87 274:1.00 276:0.70 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.72 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.98 8:0.93 9:0.80 11:0.78 12:0.69 17:0.11 20:0.79 21:0.54 26:0.95 27:0.30 28:0.78 30:0.28 34:0.93 36:0.72 37:0.93 39:0.41 41:0.98 43:0.41 55:0.59 58:1.00 66:0.30 69:0.83 70:0.82 74:0.95 78:0.85 81:0.68 83:0.88 85:0.72 87:0.98 91:0.76 96:0.98 98:0.71 100:0.93 101:0.69 108:0.91 111:0.88 114:0.77 120:0.93 122:0.67 123:0.66 124:0.76 126:0.54 127:0.67 129:0.59 131:0.90 133:0.76 135:0.68 138:0.98 140:0.45 141:0.97 144:0.76 146:0.95 147:0.44 149:0.72 150:0.79 151:0.99 152:0.78 153:0.95 154:0.33 155:0.67 157:0.76 159:0.82 161:0.72 162:0.82 164:0.91 165:0.79 166:0.84 167:0.93 169:0.90 172:0.76 173:0.67 176:0.73 177:0.05 179:0.29 181:0.55 182:0.82 186:0.85 187:0.95 189:0.99 191:0.90 192:0.59 199:0.99 201:0.74 202:0.87 208:0.64 212:0.51 215:0.58 216:0.46 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 231:0.77 232:0.70 234:0.99 235:0.74 238:0.97 241:0.81 242:0.79 243:0.12 245:0.89 246:0.93 247:0.39 248:0.97 253:0.98 255:0.79 256:0.91 257:0.65 259:0.21 260:0.94 261:0.84 265:0.52 266:0.80 267:0.52 268:0.91 271:0.87 274:1.00 276:0.83 285:0.62 287:0.95 290:0.77 297:0.36 300:0.70 +2 1:0.74 7:0.81 8:0.95 9:0.80 12:0.69 21:0.71 26:0.95 27:0.06 28:0.78 30:0.19 32:0.80 34:0.21 36:0.93 37:0.85 39:0.41 41:0.96 43:0.41 55:0.71 58:1.00 66:0.35 69:0.97 70:0.74 74:0.74 77:0.70 81:0.47 83:0.81 91:0.89 96:0.95 98:0.33 104:0.58 108:0.94 114:0.54 120:0.97 121:0.90 123:0.94 124:0.90 126:0.54 127:0.81 129:0.05 131:0.89 133:0.91 135:0.73 140:0.95 141:0.97 144:0.76 145:0.67 146:0.71 147:0.05 149:0.42 150:0.61 151:0.96 153:0.88 154:0.31 155:0.67 157:0.61 158:0.82 159:0.86 161:0.72 162:0.55 164:0.97 166:0.84 167:0.75 172:0.45 173:0.98 176:0.53 177:0.05 178:0.48 179:0.70 181:0.55 182:0.80 187:0.21 191:0.95 192:0.59 195:0.95 199:1.00 201:0.74 202:0.97 204:0.89 208:0.64 212:0.22 215:0.48 216:0.88 220:0.62 222:0.35 223:0.93 224:0.98 227:0.94 229:0.89 230:0.87 231:0.77 233:0.76 234:0.97 235:0.88 241:0.63 242:0.14 243:0.11 244:0.61 245:0.54 246:0.61 247:0.67 248:0.91 253:0.95 255:0.79 256:0.97 257:0.65 259:0.21 260:0.97 265:0.36 266:0.84 267:0.88 268:0.97 271:0.87 274:1.00 276:0.55 282:0.83 283:0.71 285:0.62 287:0.94 290:0.54 297:0.36 299:0.88 300:0.55 +3 1:0.74 7:0.81 11:0.78 12:0.69 17:0.51 21:0.30 26:0.95 27:0.04 28:0.78 30:0.33 32:0.80 34:0.69 36:0.30 37:0.99 39:0.41 43:0.59 58:0.93 66:0.49 69:0.93 70:0.88 74:0.80 77:0.70 81:0.75 83:0.75 85:0.95 91:0.15 98:0.43 101:0.78 108:0.95 114:0.86 121:0.99 122:0.57 123:0.94 124:0.79 126:0.54 127:0.38 129:0.93 131:0.61 133:0.84 135:0.82 141:0.91 144:0.76 145:0.47 146:0.55 147:0.61 149:0.71 150:0.84 154:0.49 155:0.67 157:0.87 159:0.83 161:0.72 165:0.84 166:0.84 167:0.62 172:0.73 173:0.81 176:0.73 177:0.38 178:0.55 179:0.34 181:0.55 182:0.81 187:0.87 192:0.59 195:0.96 199:0.98 201:0.74 204:0.89 208:0.64 212:0.59 215:0.72 216:0.57 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 230:0.87 231:0.76 233:0.76 234:0.91 235:0.42 241:0.21 242:0.37 243:0.23 245:0.77 246:0.93 247:0.60 253:0.72 259:0.21 265:0.45 266:0.89 267:0.44 271:0.87 274:0.91 276:0.72 282:0.88 287:0.61 290:0.86 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.97 7:0.81 8:0.78 9:0.80 11:0.78 12:0.69 17:0.30 20:0.77 21:0.30 26:0.95 27:0.21 28:0.78 30:0.10 34:0.30 36:0.71 37:0.74 39:0.41 41:0.82 43:0.98 55:0.71 58:0.98 66:0.13 69:0.12 70:0.94 74:0.98 78:0.75 81:0.75 83:0.76 85:1.00 91:0.01 96:0.82 98:0.32 100:0.79 101:0.80 108:0.99 111:0.75 114:0.86 117:0.86 120:0.58 121:1.00 122:0.57 123:0.99 124:0.98 126:0.54 127:0.98 129:1.00 131:0.89 133:0.98 135:0.21 140:0.87 141:0.97 144:0.76 146:0.57 147:0.63 149:0.96 150:0.84 151:0.58 152:0.91 153:0.48 154:0.98 155:0.67 157:0.90 159:0.98 161:0.72 162:0.74 164:0.57 165:0.84 166:0.84 167:0.74 169:0.83 172:0.94 173:0.05 176:0.73 177:0.38 179:0.10 181:0.55 182:0.82 186:0.76 187:0.39 190:0.77 192:0.59 199:1.00 201:0.74 202:0.56 204:0.89 208:0.64 212:0.42 215:0.72 216:0.95 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 230:0.87 231:0.77 232:0.91 233:0.76 234:0.98 235:0.42 241:0.61 242:0.31 243:0.10 245:0.97 246:0.93 247:0.67 248:0.57 253:0.88 255:0.79 256:0.58 259:0.21 260:0.59 261:0.84 265:0.34 266:0.95 267:0.59 268:0.59 271:0.87 274:0.97 276:0.99 285:0.62 287:0.94 290:0.86 297:0.36 300:0.94 +4 1:0.74 6:0.99 8:0.96 9:0.80 11:0.51 12:0.69 17:0.02 20:0.81 21:0.54 25:0.88 26:0.95 27:0.10 28:0.78 30:0.19 34:0.96 36:0.68 37:0.87 39:0.41 41:1.00 43:0.41 55:0.59 58:1.00 60:0.97 66:0.07 69:0.95 70:0.88 74:0.94 78:0.94 81:0.68 83:0.90 87:0.98 91:0.95 96:1.00 98:0.33 100:0.99 104:0.54 108:0.91 111:0.99 114:0.77 120:0.99 122:0.67 123:0.66 124:0.90 126:0.54 127:0.77 129:0.05 131:0.90 133:0.89 135:0.49 138:0.99 140:0.45 141:0.97 144:0.76 146:0.66 147:0.44 149:0.68 150:0.79 151:1.00 152:0.86 153:0.99 154:0.21 155:0.67 157:0.61 158:0.92 159:0.86 161:0.72 162:0.67 164:0.98 165:0.79 166:0.84 167:0.97 169:0.94 172:0.57 173:0.92 176:0.73 177:0.05 179:0.34 181:0.55 182:0.82 186:0.94 187:0.39 189:0.99 191:0.98 192:0.59 199:1.00 201:0.74 202:0.97 208:0.64 212:0.42 215:0.58 216:0.70 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.76 234:0.98 235:0.74 238:0.99 241:0.94 242:0.81 243:0.13 245:0.83 246:0.93 247:0.39 248:1.00 253:1.00 254:0.86 255:0.79 256:0.98 257:0.65 259:0.21 260:0.99 261:0.93 265:0.32 266:0.94 267:0.82 268:0.98 271:0.87 274:1.00 276:0.81 277:0.69 279:0.86 283:0.82 285:0.62 287:0.95 290:0.77 297:0.36 300:0.70 +1 1:0.74 6:0.81 7:0.64 8:0.68 11:0.86 12:0.86 17:0.62 18:0.67 20:0.94 21:0.40 27:0.72 29:0.62 30:0.76 32:0.63 34:0.45 36:0.67 37:0.27 39:0.54 43:0.71 45:0.73 48:0.91 64:0.77 66:0.72 69:0.27 70:0.22 71:0.92 74:0.48 76:0.94 77:0.70 78:0.97 81:0.54 83:0.60 86:0.77 91:0.87 98:0.55 99:0.83 100:0.89 101:0.87 108:0.21 111:0.94 114:0.62 122:0.51 123:0.20 126:0.54 127:0.16 129:0.05 131:0.61 132:0.97 135:0.32 139:0.73 144:0.57 145:0.44 146:0.24 147:0.30 149:0.36 150:0.71 152:0.88 154:0.88 155:0.67 157:0.61 159:0.11 165:0.70 166:0.93 169:0.87 172:0.27 173:0.76 176:0.73 177:0.70 178:0.55 179:0.71 182:0.61 186:0.97 187:0.21 189:0.84 192:0.87 195:0.53 201:0.93 204:0.85 208:0.64 212:0.95 215:0.42 216:0.14 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 233:0.76 235:0.60 238:0.81 241:0.78 242:0.45 243:0.75 246:0.61 247:0.35 253:0.46 254:0.80 259:0.21 261:0.91 265:0.56 266:0.12 267:0.23 276:0.21 281:0.91 282:0.71 287:0.61 290:0.62 297:0.36 300:0.18 +1 7:0.63 8:0.61 11:0.75 12:0.86 17:0.28 18:0.97 21:0.59 27:0.61 29:0.62 30:0.53 34:0.53 36:0.25 37:0.27 39:0.54 43:0.18 44:0.85 45:0.96 48:0.91 55:0.59 66:0.98 69:0.19 70:0.37 71:0.92 74:0.89 76:0.85 77:0.70 81:0.24 86:0.99 91:0.46 96:0.67 98:0.97 101:0.52 108:0.15 114:0.54 122:0.80 123:0.15 126:0.26 127:0.76 129:0.05 131:0.83 135:0.21 139:0.99 140:0.45 144:0.57 145:0.79 146:0.88 147:0.90 149:0.07 150:0.24 151:0.46 153:0.48 154:0.78 155:0.58 157:0.61 159:0.47 162:0.86 166:0.75 172:0.94 173:0.28 176:0.54 177:0.70 179:0.23 182:0.61 187:0.21 190:0.89 192:0.51 195:0.67 202:0.36 204:0.85 208:0.54 212:0.94 216:0.26 219:0.86 220:0.97 222:0.76 227:0.84 229:0.61 230:0.63 231:0.89 233:0.73 235:0.68 241:0.32 242:0.44 243:0.98 246:0.61 247:0.76 248:0.46 253:0.46 254:0.84 256:0.45 259:0.21 265:0.97 266:0.27 267:0.28 268:0.46 276:0.89 281:0.89 282:0.71 285:0.47 287:0.61 290:0.54 292:0.91 300:0.43 +2 1:0.69 6:0.79 7:0.81 8:0.61 11:0.86 12:0.86 17:0.53 18:0.92 20:0.89 21:0.54 27:0.61 29:0.62 30:0.10 32:0.80 34:0.83 36:0.23 37:0.27 39:0.54 43:0.58 44:0.93 45:0.83 48:0.97 55:0.42 66:0.12 69:0.21 70:1.00 71:0.92 74:0.81 76:0.85 77:0.89 78:0.98 81:0.33 86:0.86 91:0.20 98:0.30 100:0.94 101:0.87 108:0.61 111:0.96 114:0.57 122:0.57 123:0.92 124:0.94 126:0.44 127:0.99 129:0.98 131:0.61 133:0.94 135:0.28 139:0.92 144:0.57 145:0.80 146:0.32 147:0.38 149:0.33 150:0.49 152:0.96 154:0.86 155:0.67 157:0.61 158:0.78 159:0.94 166:0.87 169:0.91 172:0.71 173:0.07 176:0.55 177:0.70 178:0.55 179:0.28 182:0.61 186:0.98 187:0.21 189:0.83 191:0.75 192:0.87 195:0.99 201:0.68 204:0.89 208:0.64 212:0.40 215:0.39 216:0.26 219:0.92 222:0.76 227:0.61 229:0.61 230:0.86 231:0.89 233:0.73 235:0.68 238:0.82 241:0.07 242:0.21 243:0.19 245:0.84 246:0.61 247:0.91 253:0.45 254:0.74 259:0.21 261:0.94 265:0.27 266:0.96 267:0.19 276:0.86 279:0.86 281:0.89 282:0.88 283:0.77 287:0.61 290:0.57 292:0.91 297:0.36 300:0.98 +1 1:0.74 11:0.85 12:0.86 17:0.72 18:0.95 21:0.13 22:0.93 27:0.62 29:0.62 30:0.60 34:0.98 36:0.35 39:0.54 43:0.80 45:0.73 47:0.93 64:0.77 66:0.96 69:0.46 70:0.50 71:0.92 74:0.82 81:0.80 83:0.91 85:0.94 86:0.97 87:0.98 91:0.11 98:0.94 101:0.97 106:0.81 108:0.35 114:0.79 117:0.86 122:0.57 123:0.34 126:0.54 127:0.63 129:0.05 131:0.61 135:0.65 139:0.96 144:0.57 146:0.92 147:0.93 149:0.95 150:0.87 154:0.74 155:0.67 157:0.99 159:0.54 165:0.93 166:0.93 172:0.91 173:0.44 176:0.73 177:0.70 178:0.55 179:0.29 182:0.98 187:0.39 192:0.87 201:0.93 206:0.81 208:0.64 212:0.81 215:0.61 216:0.18 222:0.76 227:0.61 228:0.99 229:0.61 231:0.90 232:0.70 235:0.42 241:0.39 242:0.21 243:0.96 246:0.93 247:0.91 253:0.57 259:0.21 262:0.93 265:0.94 266:0.54 267:0.44 276:0.83 287:0.61 290:0.79 297:0.36 300:0.43 +1 1:0.69 8:0.66 12:0.86 17:0.36 18:0.65 21:0.54 27:0.34 29:0.62 30:0.37 34:0.81 36:0.99 37:0.46 39:0.54 43:0.14 55:0.71 66:0.33 69:0.91 70:0.88 71:0.92 74:0.30 81:0.33 86:0.59 91:0.18 98:0.51 104:0.92 106:0.81 108:0.94 114:0.55 117:0.86 122:0.51 123:0.94 124:0.86 126:0.44 127:0.88 129:0.59 131:0.61 133:0.86 135:0.77 139:0.57 146:0.82 147:0.84 149:0.26 150:0.48 154:0.55 155:0.58 157:0.61 158:0.72 159:0.88 166:0.87 172:0.78 173:0.78 176:0.55 177:0.38 178:0.55 179:0.29 182:0.61 187:0.68 192:0.59 201:0.68 206:0.81 208:0.54 212:0.49 215:0.39 216:0.71 222:0.76 227:0.61 229:0.61 231:0.86 232:0.82 235:0.74 241:0.03 242:0.18 243:0.34 244:0.61 245:0.87 246:0.61 247:0.91 253:0.36 254:0.74 257:0.65 259:0.21 265:0.52 266:0.94 267:0.76 276:0.85 279:0.82 283:0.78 287:0.61 290:0.55 297:0.36 300:0.84 +1 11:0.75 12:0.86 17:0.36 18:0.87 21:0.78 22:0.93 27:0.67 29:0.62 30:0.38 31:0.90 32:0.62 34:0.95 36:0.68 37:0.26 39:0.54 43:0.72 44:0.85 45:0.66 47:0.93 66:0.83 69:0.29 70:0.58 71:0.92 74:0.46 77:0.70 79:0.93 86:0.77 91:0.66 98:0.78 101:0.52 104:0.87 106:0.81 108:0.22 120:0.71 122:0.75 123:0.22 127:0.26 129:0.05 131:0.90 135:0.54 137:0.90 139:0.74 140:0.45 144:0.57 145:0.63 146:0.53 147:0.59 149:0.38 151:0.81 153:0.48 154:0.86 157:0.61 159:0.19 162:0.74 164:0.53 166:0.61 172:0.51 173:0.53 177:0.70 179:0.67 182:0.61 187:0.21 190:0.77 191:0.77 192:0.51 195:0.55 196:0.90 202:0.56 206:0.81 212:0.86 216:0.28 219:0.87 220:0.87 222:0.76 227:0.88 229:0.61 231:0.86 235:0.95 241:0.54 242:0.29 243:0.84 246:0.61 247:0.60 248:0.74 253:0.34 254:0.84 255:0.74 256:0.58 259:0.21 260:0.61 262:0.93 265:0.78 266:0.23 267:0.28 268:0.59 276:0.39 282:0.71 283:0.82 284:0.86 285:0.56 287:0.61 292:0.95 300:0.43 +0 11:0.86 12:0.86 17:0.66 18:0.71 21:0.78 27:0.69 29:0.62 30:0.38 34:0.44 36:0.88 37:0.53 39:0.54 43:0.72 45:0.77 66:0.54 69:0.05 70:0.63 71:0.92 74:0.45 83:0.60 86:0.75 91:0.23 97:0.89 98:0.64 101:0.87 108:0.05 122:0.51 123:0.05 124:0.50 127:0.51 129:0.05 131:0.61 133:0.43 135:0.85 139:0.72 144:0.57 146:0.49 147:0.55 149:0.49 154:0.77 155:0.58 157:0.61 158:0.72 159:0.29 166:0.61 172:0.41 173:0.24 177:0.70 178:0.55 179:0.80 182:0.61 187:0.39 192:0.59 208:0.54 212:0.42 216:0.30 222:0.76 227:0.61 229:0.61 231:0.79 232:0.72 235:0.05 241:0.21 242:0.13 243:0.63 245:0.59 246:0.61 247:0.74 253:0.33 254:0.80 259:0.21 265:0.65 266:0.54 267:0.65 276:0.36 279:0.82 283:0.82 287:0.61 300:0.43 +1 1:0.74 6:0.83 7:0.81 8:0.66 9:0.80 11:0.85 12:0.86 17:0.78 18:0.99 20:0.92 21:0.22 27:0.66 29:0.62 30:0.26 31:0.89 32:0.80 34:0.90 36:0.63 37:0.55 39:0.54 41:0.88 43:0.78 44:0.93 45:0.66 48:0.97 55:0.52 64:0.77 66:0.97 69:0.30 70:0.73 71:0.92 74:0.65 77:0.89 78:0.99 79:0.88 81:0.75 83:0.91 85:0.72 86:0.84 91:0.33 96:0.72 98:0.84 100:0.95 101:0.97 104:0.87 108:0.24 111:0.98 114:0.71 120:0.59 121:0.90 122:0.86 123:0.23 126:0.54 127:0.34 129:0.05 131:0.96 135:0.34 137:0.89 139:0.90 140:0.45 144:0.57 145:0.63 146:0.92 147:0.93 149:0.70 150:0.84 151:0.56 152:0.87 153:0.48 154:0.70 155:0.67 157:0.84 159:0.53 162:0.86 164:0.67 165:0.93 166:0.93 169:0.93 172:0.93 173:0.24 176:0.73 177:0.70 179:0.18 182:0.99 186:0.99 187:0.21 189:0.84 192:0.87 195:0.80 196:0.92 201:0.93 202:0.50 204:0.89 208:0.64 212:0.94 215:0.51 216:0.14 219:0.87 220:0.82 222:0.76 227:0.92 229:0.99 230:0.87 231:0.90 233:0.76 235:0.60 238:0.84 241:0.05 242:0.21 243:0.97 246:0.93 247:0.92 248:0.58 253:0.58 255:0.95 256:0.58 259:0.21 260:0.59 261:0.94 265:0.84 266:0.27 267:0.69 268:0.59 276:0.87 281:0.91 282:0.88 283:0.78 284:0.92 285:0.62 287:0.95 290:0.71 292:0.91 297:0.36 299:0.88 300:0.55 +1 1:0.69 11:0.85 12:0.86 17:0.26 18:0.80 21:0.68 27:0.45 29:0.62 30:0.61 34:0.71 36:0.17 37:0.50 39:0.54 43:0.82 44:0.85 45:0.83 60:0.87 64:0.77 66:0.27 69:0.70 70:0.65 71:0.92 74:0.64 77:0.70 81:0.32 83:0.91 85:0.72 86:0.83 91:0.09 98:0.17 99:0.83 101:0.97 108:0.92 114:0.54 122:0.85 123:0.91 124:0.87 126:0.44 127:0.54 129:0.05 131:0.61 133:0.86 135:0.66 139:0.85 144:0.57 145:0.50 146:0.24 147:0.30 149:0.66 150:0.51 154:0.77 155:0.67 157:0.82 158:0.91 159:0.77 165:0.70 166:0.83 172:0.41 173:0.53 176:0.55 177:0.70 178:0.55 179:0.63 182:0.75 187:0.68 192:0.87 195:0.91 201:0.68 208:0.64 212:0.59 216:0.77 219:0.95 222:0.76 227:0.61 229:0.61 231:0.84 235:0.84 241:0.51 242:0.51 243:0.39 245:0.60 246:0.61 247:0.67 253:0.40 259:0.21 265:0.18 266:0.54 267:0.36 276:0.54 277:0.69 279:0.86 282:0.71 283:0.78 287:0.61 290:0.54 292:0.91 300:0.55 +1 1:0.69 6:0.82 8:0.63 9:0.76 11:0.75 12:0.86 17:0.24 18:0.93 20:0.88 27:0.51 29:0.62 30:0.10 31:0.91 34:0.21 36:0.87 37:0.29 39:0.54 43:0.68 44:0.85 45:0.73 64:0.77 66:0.53 69:0.32 70:0.94 71:0.92 74:0.59 76:0.85 77:0.70 78:0.99 79:0.96 81:0.77 83:0.60 86:0.91 91:0.80 96:0.87 97:0.94 98:0.91 99:0.83 100:0.87 101:0.52 108:0.45 111:0.96 114:0.78 122:0.75 123:0.43 124:0.52 126:0.44 127:0.81 129:0.59 131:0.88 132:0.97 133:0.46 135:0.37 137:0.91 139:0.88 140:0.80 144:0.57 145:0.39 146:0.21 147:0.27 149:0.38 150:0.74 151:0.81 152:0.83 153:0.48 154:0.94 155:0.58 157:0.61 158:0.72 159:0.31 162:0.70 165:0.70 166:0.83 169:0.85 172:0.54 173:0.50 176:0.55 177:0.70 178:0.42 179:0.67 182:0.61 186:0.99 189:0.84 190:0.77 191:0.70 192:0.51 195:0.62 196:0.93 201:0.68 202:0.65 208:0.54 212:0.61 216:0.08 219:0.87 220:0.74 222:0.76 227:0.87 229:0.61 231:0.84 232:0.72 235:0.12 238:0.82 241:0.82 242:0.16 243:0.40 245:0.74 246:0.61 247:0.74 248:0.83 253:0.68 254:0.74 255:0.74 256:0.68 261:0.90 265:0.91 266:0.38 267:0.79 268:0.68 276:0.53 279:0.82 282:0.71 284:0.86 285:0.56 287:0.61 290:0.78 292:0.91 300:0.70 +1 1:0.74 6:0.79 7:0.64 8:0.59 11:0.64 12:0.86 17:0.47 18:0.80 20:0.89 21:0.47 27:0.61 29:0.62 30:0.09 34:0.99 36:0.81 37:0.27 39:0.54 43:0.72 44:0.85 45:0.85 48:0.98 64:0.77 66:0.67 69:0.21 70:0.93 71:0.92 74:0.35 76:0.94 77:0.70 78:0.99 81:0.48 83:0.60 86:0.58 91:0.54 97:0.97 98:0.75 99:0.83 100:0.92 101:0.52 108:0.17 111:0.96 114:0.58 122:0.55 123:0.16 124:0.50 126:0.54 127:0.75 129:0.05 131:0.61 133:0.66 135:0.76 139:0.59 144:0.57 145:0.74 146:0.82 147:0.85 149:0.78 150:0.68 152:0.96 154:0.59 155:0.67 157:0.61 158:0.72 159:0.58 165:0.70 166:0.93 169:0.91 172:0.78 173:0.23 176:0.66 177:0.70 178:0.55 179:0.45 182:0.61 186:0.99 187:0.21 189:0.81 192:0.87 195:0.75 201:0.93 204:0.85 208:0.64 212:0.68 215:0.39 216:0.26 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 232:0.86 233:0.73 235:0.74 238:0.82 241:0.15 242:0.18 243:0.72 245:0.78 246:0.61 247:0.84 253:0.41 254:0.90 259:0.21 261:0.94 265:0.75 266:0.54 267:0.85 276:0.73 279:0.82 281:0.89 282:0.70 283:0.78 287:0.61 290:0.58 297:0.36 300:0.70 +1 1:0.74 6:0.79 7:0.81 8:0.59 9:0.80 11:0.85 12:0.86 17:0.70 18:0.93 20:0.91 21:0.47 27:0.61 29:0.62 30:0.21 31:0.87 32:0.62 34:0.98 36:0.59 37:0.27 39:0.54 43:0.58 44:0.85 45:0.81 48:0.91 55:0.71 64:0.77 66:0.33 69:0.19 70:0.90 71:0.92 74:0.50 76:0.94 77:0.89 78:0.99 79:0.87 81:0.50 83:0.60 86:0.62 91:0.60 96:0.70 98:0.48 99:0.83 100:0.93 101:0.87 108:0.15 111:0.96 114:0.60 120:0.56 122:0.86 123:0.15 124:0.87 126:0.54 127:0.81 129:0.59 131:0.96 133:0.86 135:0.63 137:0.87 139:0.76 140:0.80 144:0.57 145:0.79 146:0.76 147:0.80 149:0.50 150:0.68 151:0.52 152:0.96 153:0.48 154:0.60 155:0.67 157:0.61 159:0.78 162:0.80 164:0.72 165:0.70 166:0.93 169:0.90 172:0.68 173:0.13 176:0.73 177:0.70 178:0.42 179:0.40 182:0.61 186:0.98 187:0.21 189:0.81 191:0.70 192:0.87 195:0.90 196:0.89 201:0.93 202:0.60 204:0.89 208:0.64 212:0.37 215:0.41 216:0.26 219:0.87 220:0.87 222:0.76 227:0.92 229:0.61 230:0.86 231:0.90 233:0.73 235:0.68 238:0.83 241:0.27 242:0.27 243:0.50 244:0.61 245:0.80 246:0.61 247:0.93 248:0.52 253:0.46 255:0.95 256:0.64 259:0.21 260:0.57 261:0.94 265:0.49 266:0.87 267:0.69 268:0.65 276:0.77 281:0.89 282:0.71 283:0.78 284:0.94 285:0.62 287:0.61 290:0.60 292:0.91 297:0.36 300:0.70 +3 1:0.74 6:0.80 7:0.64 8:0.59 11:0.42 12:0.86 17:0.90 18:0.76 20:0.96 21:0.40 22:0.93 27:0.06 29:0.62 30:0.33 32:0.78 34:0.99 36:0.25 37:0.60 39:0.54 43:0.10 44:0.93 47:0.93 48:0.91 55:0.45 64:0.77 66:0.79 69:0.78 70:0.49 71:0.92 74:0.54 76:0.85 77:0.70 78:0.99 81:0.52 86:0.73 91:0.40 98:0.28 100:0.93 104:0.88 106:0.81 108:0.62 111:0.96 114:0.62 117:0.86 122:0.57 123:0.59 126:0.54 127:0.12 129:0.05 131:0.61 135:0.96 139:0.80 144:0.57 145:0.44 146:0.59 147:0.65 149:0.06 150:0.66 152:0.89 154:0.69 155:0.67 157:0.61 159:0.22 166:0.93 169:0.91 172:0.41 173:0.59 176:0.73 177:0.70 178:0.55 179:0.20 182:0.61 186:0.99 187:0.68 189:0.82 192:0.87 195:0.94 201:0.93 206:0.81 208:0.64 212:0.19 215:0.42 216:0.85 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 232:0.96 233:0.73 235:0.60 238:0.82 241:0.27 242:0.45 243:0.80 246:0.61 247:0.39 253:0.47 254:0.84 259:0.21 261:0.93 262:0.93 265:0.30 266:0.47 267:0.84 276:0.31 281:0.88 282:0.86 287:0.61 290:0.62 297:0.36 300:0.33 +3 1:0.74 7:0.64 8:0.64 9:0.80 11:0.75 12:0.86 17:0.53 18:0.81 21:0.64 27:0.54 29:0.62 30:0.53 31:0.88 32:0.79 34:0.99 36:0.18 37:0.35 39:0.54 43:0.18 44:0.93 45:0.66 48:0.91 55:0.42 64:0.77 66:0.60 69:0.80 70:0.95 71:0.92 74:0.74 76:0.85 77:0.89 79:0.87 81:0.39 86:0.82 91:0.04 96:0.70 98:0.27 101:0.52 108:0.64 114:0.57 120:0.57 122:0.77 123:0.61 124:0.82 126:0.54 127:0.94 129:0.05 131:0.96 133:0.89 135:0.70 137:0.88 139:0.89 140:0.80 144:0.57 145:1.00 146:0.76 147:0.05 149:0.25 150:0.62 151:0.54 153:0.46 154:0.63 155:0.67 157:0.61 158:0.87 159:0.93 162:0.57 164:0.54 166:0.93 172:0.85 173:0.45 176:0.73 177:0.05 178:0.42 179:0.33 182:0.61 187:0.21 192:0.87 195:0.99 196:0.91 201:0.93 202:0.49 208:0.64 212:0.84 215:0.38 216:0.55 219:0.95 220:0.82 222:0.76 227:0.92 229:0.61 230:0.64 231:0.90 232:0.72 233:0.73 235:0.84 241:0.15 242:0.51 243:0.07 245:0.80 246:0.61 247:0.90 248:0.53 253:0.49 254:0.80 255:0.95 256:0.45 257:0.84 259:0.21 260:0.57 265:0.29 266:0.80 267:0.59 268:0.45 276:0.80 279:0.86 281:0.88 282:0.87 283:0.71 284:0.87 285:0.62 287:0.61 290:0.57 292:0.91 297:0.36 300:0.99 +2 7:0.64 8:0.63 11:0.75 12:0.86 17:0.45 18:0.73 21:0.30 22:0.93 27:0.39 29:0.62 30:0.76 31:0.87 32:0.63 34:0.31 36:0.54 37:0.43 39:0.54 43:0.85 44:0.85 45:0.66 47:0.93 48:0.98 55:0.45 64:0.77 66:0.72 69:0.51 70:0.22 71:0.92 74:0.45 79:0.94 81:0.42 86:0.76 91:0.53 98:0.20 101:0.52 108:0.39 122:0.57 123:0.37 126:0.44 127:0.10 129:0.05 131:0.81 135:0.51 137:0.87 139:0.73 140:0.45 144:0.57 145:0.65 146:0.25 147:0.31 149:0.59 150:0.33 151:0.60 153:0.48 154:0.82 157:0.61 159:0.11 162:0.82 166:0.85 172:0.27 173:0.55 177:0.70 179:0.17 182:0.61 187:0.68 190:0.89 191:0.70 192:0.51 195:0.66 196:0.89 201:0.68 202:0.63 212:0.78 215:0.44 216:0.44 219:0.86 220:0.74 222:0.76 227:0.83 229:0.61 230:0.64 231:0.85 233:0.73 235:0.42 241:0.57 242:0.45 243:0.75 246:0.61 247:0.35 248:0.61 253:0.33 254:0.84 256:0.68 259:0.21 262:0.93 265:0.22 266:0.17 267:0.19 268:0.69 276:0.24 281:0.89 284:0.86 285:0.47 287:0.61 292:0.95 297:0.36 300:0.18 +1 1:0.69 7:0.63 8:0.77 9:0.76 11:0.64 12:0.86 17:0.04 18:0.88 21:0.05 22:0.93 27:0.66 29:0.62 30:0.55 31:0.91 32:0.78 34:0.56 36:0.69 37:0.31 39:0.54 43:0.56 44:0.93 45:0.73 47:0.93 55:0.59 64:0.77 66:0.25 69:0.08 70:0.60 71:0.92 74:0.48 77:0.70 79:0.95 81:0.68 83:0.60 86:0.61 91:0.74 96:0.88 98:0.50 99:0.83 101:0.52 108:0.61 114:0.70 122:0.85 123:0.59 124:0.74 126:0.44 127:0.72 129:0.81 131:0.88 132:0.97 133:0.67 135:0.60 137:0.91 139:0.75 140:0.45 144:0.57 145:0.58 146:0.43 147:0.50 149:0.40 150:0.64 151:0.81 153:0.48 154:0.56 155:0.58 157:0.61 159:0.39 162:0.82 165:0.70 166:0.83 172:0.32 173:0.24 175:0.75 176:0.55 177:0.38 179:0.75 182:0.61 187:0.21 190:0.77 192:0.51 195:0.61 196:0.91 201:0.68 202:0.70 204:0.85 208:0.54 212:0.59 216:0.44 219:0.86 220:0.62 222:0.76 227:0.87 229:0.61 230:0.63 231:0.82 232:0.78 233:0.73 235:0.12 241:0.59 242:0.31 243:0.40 244:0.61 245:0.65 246:0.61 247:0.67 248:0.81 253:0.62 255:0.74 256:0.75 257:0.65 262:0.93 265:0.52 266:0.47 267:0.36 268:0.76 276:0.49 277:0.69 281:0.91 282:0.86 284:0.86 285:0.56 287:0.61 290:0.70 292:0.95 300:0.43 +2 1:0.74 6:0.80 7:0.63 8:0.60 11:0.92 12:0.86 17:0.82 18:0.96 20:0.85 22:0.93 27:0.48 29:0.62 30:0.36 32:0.80 34:0.95 36:0.63 37:0.29 39:0.54 43:0.87 44:0.93 45:0.73 47:0.93 48:0.97 60:0.87 64:0.77 66:0.95 69:0.55 70:0.77 71:0.92 74:0.75 76:0.85 77:0.70 78:1.00 81:0.88 83:0.91 85:0.80 86:0.92 91:0.33 98:0.92 100:0.96 101:0.97 104:0.96 106:0.81 108:0.42 111:0.98 114:0.98 117:0.86 122:0.86 123:0.40 126:0.54 127:0.52 129:0.05 131:0.61 132:0.97 135:0.65 139:0.94 144:0.57 145:0.44 146:0.80 147:0.84 149:0.82 150:0.93 152:0.91 154:0.85 155:0.67 157:0.95 158:0.92 159:0.36 165:0.93 166:0.93 169:0.91 172:0.86 173:0.65 176:0.73 177:0.70 178:0.55 179:0.36 182:0.98 186:1.00 187:0.39 189:0.81 192:0.87 195:0.67 201:0.93 206:0.81 208:0.64 212:0.88 215:0.96 216:0.20 219:0.95 222:0.76 227:0.61 229:0.61 230:0.63 231:0.90 232:0.98 233:0.76 235:0.12 238:0.83 241:0.27 242:0.23 243:0.95 246:0.93 247:0.86 253:0.67 261:0.93 262:0.93 265:0.92 266:0.54 267:0.59 276:0.75 279:0.86 281:0.91 282:0.88 283:0.82 287:0.61 290:0.98 292:0.95 297:0.36 299:0.88 300:0.70 +0 11:0.85 12:0.86 17:0.51 18:0.75 21:0.78 27:0.45 29:0.62 30:0.76 34:0.79 36:0.17 37:0.50 39:0.54 43:0.80 45:0.66 66:0.25 69:0.73 70:0.29 71:0.92 74:0.43 85:0.72 86:0.74 91:0.14 98:0.13 101:0.97 108:0.56 122:0.82 123:0.82 124:0.71 127:0.33 129:0.05 131:0.61 133:0.60 135:0.68 139:0.71 144:0.57 145:0.49 146:0.17 147:0.22 149:0.61 154:0.75 157:0.84 159:0.48 163:0.97 166:0.61 172:0.16 173:0.71 177:0.70 178:0.55 179:0.87 182:0.76 187:0.68 212:0.52 216:0.77 222:0.76 227:0.61 229:0.61 231:0.71 235:0.88 241:0.27 242:0.55 243:0.45 245:0.45 246:0.61 247:0.39 253:0.33 259:0.21 265:0.10 266:0.27 267:0.28 276:0.24 277:0.69 287:0.61 300:0.25 +2 1:0.74 7:0.63 9:0.76 11:0.64 12:0.86 17:0.55 18:0.71 21:0.30 27:0.02 29:0.62 30:0.21 31:0.88 32:0.80 34:0.98 36:0.40 37:0.95 39:0.54 43:0.57 44:0.93 45:0.66 55:0.42 64:0.77 66:0.27 69:0.83 70:0.86 71:0.92 74:0.49 77:0.70 79:0.90 81:0.59 83:0.60 86:0.59 91:0.25 96:0.75 97:0.89 98:0.16 99:0.83 101:0.52 108:0.68 114:0.65 122:0.80 123:0.66 124:0.70 126:0.54 127:0.17 129:0.05 131:0.88 133:0.60 135:0.80 137:0.88 139:0.75 140:0.45 144:0.57 145:0.47 146:0.31 147:0.38 149:0.28 150:0.74 151:0.65 153:0.48 154:0.46 155:0.67 157:0.61 158:0.72 159:0.48 162:0.86 165:0.70 166:0.93 172:0.21 173:0.68 176:0.73 177:0.70 179:0.53 182:0.61 187:0.68 190:0.77 192:0.87 195:0.93 196:0.89 201:0.93 202:0.36 204:0.85 208:0.64 212:0.16 215:0.44 216:0.87 219:0.87 220:0.62 222:0.76 227:0.87 229:0.61 230:0.63 231:0.90 233:0.73 235:0.52 241:0.49 242:0.40 243:0.46 244:0.61 245:0.50 246:0.61 247:0.47 248:0.63 253:0.52 255:0.74 256:0.45 259:0.21 265:0.17 266:0.54 267:0.44 268:0.46 276:0.31 279:0.82 281:0.91 282:0.88 284:0.86 285:0.56 287:0.61 290:0.65 292:0.95 297:0.36 300:0.55 +3 1:0.69 7:0.64 8:0.79 11:0.86 12:0.86 17:0.86 18:0.88 21:0.05 27:0.54 29:0.62 30:0.13 32:0.63 34:0.74 36:0.31 37:0.35 39:0.54 43:0.42 45:0.87 55:0.59 66:0.26 69:0.79 70:0.95 71:0.92 74:0.52 76:0.94 77:0.70 81:0.73 83:0.60 86:0.72 91:0.27 96:0.73 97:0.89 98:0.69 99:0.83 101:0.87 108:0.63 114:0.70 122:0.77 123:0.85 124:0.64 126:0.44 127:0.92 129:0.05 131:0.83 133:0.60 135:0.65 139:0.70 140:0.45 144:0.57 145:0.39 146:0.81 147:0.05 149:0.66 150:0.69 151:0.53 153:0.72 154:0.63 155:0.58 157:0.61 158:0.72 159:0.76 162:0.66 165:0.70 166:0.88 172:0.88 173:0.69 176:0.55 177:0.05 179:0.25 182:0.61 187:0.21 190:0.89 191:0.82 192:0.59 195:0.86 201:0.68 202:0.61 204:0.85 208:0.54 212:0.86 215:0.78 216:0.55 220:0.74 222:0.76 227:0.84 229:0.61 230:0.64 231:0.89 232:0.72 233:0.76 235:0.12 241:0.64 242:0.51 243:0.06 245:0.94 246:0.61 247:0.82 248:0.52 253:0.52 254:0.84 256:0.58 257:0.84 259:0.21 265:0.58 266:0.80 267:0.36 268:0.62 276:0.88 277:0.69 279:0.82 282:0.71 283:0.78 285:0.47 287:0.61 290:0.70 297:0.36 300:0.94 +1 1:0.74 7:0.63 11:0.64 12:0.86 17:0.59 18:0.70 21:0.05 22:0.93 27:0.72 29:0.62 30:0.33 34:0.92 36:0.76 39:0.54 43:0.79 47:0.93 60:0.87 64:0.77 66:0.79 69:0.74 70:0.49 71:0.92 74:0.55 76:0.94 81:0.83 83:0.91 85:0.72 86:0.74 91:0.35 98:0.60 101:0.87 108:0.57 114:0.89 117:0.86 122:0.57 123:0.55 126:0.54 127:0.18 129:0.05 131:0.61 135:0.56 139:0.80 144:0.57 145:0.74 146:0.40 147:0.47 149:0.55 150:0.89 154:0.72 155:0.67 157:0.87 158:0.92 159:0.15 165:0.93 166:0.93 172:0.41 173:0.93 176:0.73 177:0.70 178:0.55 179:0.59 182:0.98 187:0.68 192:0.87 201:0.93 208:0.64 212:0.42 215:0.78 216:0.08 219:0.95 222:0.76 227:0.61 229:0.61 230:0.63 231:0.89 232:0.99 233:0.76 235:0.22 241:0.12 242:0.19 243:0.80 246:0.93 247:0.55 253:0.60 259:0.21 262:0.93 265:0.61 266:0.38 267:0.52 276:0.31 279:0.86 283:0.82 287:0.61 290:0.89 292:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.64 8:0.63 11:0.92 12:0.86 17:0.94 18:0.99 21:0.13 27:0.66 29:0.62 30:0.40 34:0.89 36:0.53 37:0.55 39:0.54 43:0.74 44:0.85 45:0.92 48:0.99 55:0.42 64:0.77 66:0.74 69:0.10 70:0.73 71:0.92 74:0.92 76:0.97 77:0.96 81:0.73 83:0.60 85:0.72 86:0.99 91:0.12 97:0.89 98:0.70 101:0.97 104:0.87 108:0.09 114:0.79 122:0.80 123:0.09 124:0.44 126:0.54 127:0.45 129:0.05 131:0.61 133:0.60 135:0.44 139:0.98 144:0.57 145:0.74 146:0.89 147:0.91 149:0.67 150:0.80 154:0.88 155:0.67 157:0.83 158:0.72 159:0.67 166:0.93 172:0.96 173:0.11 176:0.73 177:0.70 178:0.55 179:0.14 182:0.75 187:0.21 192:0.87 195:0.86 201:0.93 204:0.85 208:0.64 212:0.91 215:0.61 216:0.14 219:0.87 222:0.76 227:0.61 229:0.61 230:0.64 231:0.90 232:0.97 233:0.73 235:0.31 241:0.01 242:0.30 243:0.77 245:0.95 246:0.61 247:0.97 253:0.58 259:0.21 265:0.71 266:0.75 267:0.52 276:0.93 279:0.82 281:0.91 282:0.71 287:0.61 290:0.79 292:0.91 297:0.36 300:0.84 +2 8:0.71 11:0.85 12:0.86 17:0.59 18:0.86 21:0.13 27:0.49 29:0.62 30:0.14 32:0.62 34:0.61 36:0.43 37:0.29 39:0.54 43:0.18 45:0.73 55:0.52 66:0.64 69:0.10 70:0.94 71:0.92 74:0.29 81:0.45 86:0.61 91:0.72 96:0.68 98:0.50 101:0.87 108:0.09 120:0.61 122:0.57 123:0.09 124:0.55 126:0.26 127:0.60 129:0.05 131:0.91 133:0.73 135:0.42 139:0.59 140:0.45 145:0.54 146:0.58 147:0.64 149:0.18 150:0.37 151:0.53 153:0.82 154:0.81 157:0.61 158:0.71 159:0.53 162:0.65 164:0.52 166:0.77 172:0.54 173:0.17 177:0.70 179:0.73 182:0.61 187:0.39 190:0.89 191:0.76 195:0.72 202:0.63 212:0.69 215:0.61 216:0.31 220:0.74 222:0.76 227:0.89 229:0.61 231:0.88 235:0.12 241:0.62 242:0.18 243:0.69 245:0.51 246:0.61 247:0.82 248:0.52 253:0.28 254:0.92 256:0.58 259:0.21 260:0.55 265:0.51 266:0.47 267:0.52 268:0.64 276:0.47 283:0.78 285:0.56 287:0.61 297:0.36 300:0.70 +1 1:0.74 7:0.64 8:0.66 11:0.86 12:0.86 17:0.55 18:0.67 21:0.40 27:0.72 29:0.62 30:0.76 32:0.63 34:0.33 36:0.67 37:0.27 39:0.54 43:0.71 45:0.73 48:0.91 64:0.77 66:0.72 69:0.27 70:0.22 71:0.92 74:0.48 76:0.94 77:0.70 81:0.54 83:0.60 86:0.77 91:0.89 98:0.55 99:0.83 101:0.87 108:0.21 114:0.62 122:0.51 123:0.20 126:0.54 127:0.16 129:0.05 131:0.61 132:0.97 135:0.32 139:0.73 144:0.57 145:0.44 146:0.24 147:0.30 149:0.36 150:0.71 154:0.88 155:0.67 157:0.61 159:0.11 165:0.70 166:0.93 172:0.27 173:0.76 176:0.73 177:0.70 178:0.55 179:0.71 182:0.61 192:0.87 195:0.53 201:0.93 202:0.50 204:0.85 208:0.64 212:0.95 215:0.42 216:0.14 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 233:0.76 235:0.60 241:0.90 242:0.45 243:0.75 246:0.61 247:0.35 253:0.46 254:0.80 259:0.21 265:0.56 266:0.12 267:0.18 276:0.21 281:0.91 282:0.71 287:0.61 290:0.62 297:0.36 300:0.18 +2 1:0.74 6:0.96 7:0.81 8:0.94 9:0.80 11:0.64 12:0.86 17:0.92 20:0.84 21:0.40 27:0.41 28:0.93 30:0.30 32:0.80 34:0.18 36:0.23 37:0.92 39:0.56 41:0.94 43:0.59 55:0.59 66:0.67 69:0.93 70:0.80 74:0.67 77:0.89 78:0.94 81:0.68 83:0.82 85:0.80 91:0.92 96:0.93 98:0.32 100:0.95 101:0.74 104:0.58 108:0.85 111:0.93 114:0.82 120:0.92 121:0.90 123:0.84 124:0.46 126:0.54 127:0.21 129:0.05 131:0.89 133:0.62 135:0.44 140:0.45 144:0.57 145:0.79 146:0.44 147:0.05 149:0.48 150:0.80 151:0.98 152:0.80 153:0.93 154:0.48 155:0.67 157:0.81 159:0.36 161:0.77 162:0.79 164:0.87 165:0.83 166:0.84 167:0.81 169:0.93 172:0.48 173:0.98 176:0.73 177:0.05 179:0.53 181:0.60 182:0.86 186:0.94 189:0.97 191:0.94 192:0.59 195:0.76 201:0.74 202:0.80 204:0.89 208:0.64 212:0.81 215:0.67 216:0.20 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.52 238:0.90 241:0.84 242:0.63 243:0.15 245:0.49 246:0.91 247:0.47 248:0.93 253:0.92 255:0.79 256:0.88 257:0.65 259:0.21 260:0.92 261:0.89 265:0.35 266:0.27 267:0.69 268:0.84 271:0.78 276:0.40 282:0.88 285:0.62 287:0.93 290:0.82 297:0.36 299:0.97 300:0.55 +2 1:0.74 6:0.95 7:0.81 8:0.93 9:0.80 11:0.64 12:0.86 17:0.72 20:0.77 21:0.54 27:0.08 28:0.93 30:0.40 32:0.80 34:0.55 36:0.36 37:0.98 39:0.56 41:0.90 43:0.95 66:0.49 69:0.23 70:0.88 74:0.95 76:0.85 77:0.70 78:0.89 81:0.66 83:0.78 85:0.96 91:0.53 96:0.83 98:0.68 100:0.91 101:0.85 104:0.58 108:0.74 111:0.89 114:0.77 120:0.73 121:0.90 122:0.43 123:0.73 124:0.57 126:0.54 127:0.72 129:0.59 131:0.89 133:0.47 135:0.42 140:0.45 144:0.57 145:0.69 146:0.70 147:0.75 149:0.95 150:0.77 151:0.90 152:0.91 153:0.69 154:0.95 155:0.67 157:0.92 159:0.55 161:0.77 162:0.86 164:0.74 165:0.79 166:0.84 167:0.59 169:0.94 172:0.78 173:0.25 176:0.73 177:0.38 179:0.36 181:0.60 182:0.86 186:0.89 187:0.39 189:0.97 192:0.59 195:0.74 201:0.74 202:0.60 204:0.89 208:0.64 212:0.80 215:0.58 216:0.45 220:0.93 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.80 238:0.87 241:0.59 242:0.24 243:0.45 245:0.92 246:0.91 247:0.67 248:0.81 253:0.88 255:0.79 256:0.64 259:0.21 260:0.74 261:0.95 265:0.69 266:0.54 267:0.76 268:0.69 271:0.78 276:0.78 282:0.88 285:0.62 287:0.93 290:0.77 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.85 7:0.81 8:0.89 9:0.80 11:0.64 12:0.86 17:0.73 20:0.92 21:0.22 27:0.34 28:0.93 30:0.67 32:0.80 34:0.31 36:0.73 37:0.72 39:0.56 41:0.99 43:0.97 60:0.87 66:0.64 69:0.23 70:0.65 74:0.96 76:0.98 77:0.96 78:0.93 81:0.85 83:0.89 85:0.94 91:0.88 96:0.98 98:0.66 100:0.91 101:0.84 108:0.76 111:0.91 114:0.69 120:0.96 121:0.90 122:0.43 123:0.75 124:0.48 126:0.54 127:0.97 129:0.59 131:0.89 133:0.47 135:0.42 138:0.98 140:0.45 144:0.57 145:0.65 146:0.59 147:0.64 149:0.98 150:0.90 151:0.98 152:0.86 153:0.93 154:0.97 155:0.67 157:0.91 158:0.92 159:0.55 161:0.77 162:0.78 164:0.96 165:0.86 166:0.84 167:0.82 169:0.89 172:0.88 173:0.27 176:0.56 177:0.38 179:0.31 181:0.60 182:0.86 186:0.92 189:0.87 191:0.93 192:0.59 195:0.73 201:0.74 202:0.94 204:0.89 208:0.64 212:0.87 215:0.72 216:0.42 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.72 233:0.76 235:0.74 238:0.85 241:0.89 242:0.28 243:0.33 245:0.94 246:0.91 247:0.60 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.96 261:0.91 265:0.67 266:0.54 267:0.73 268:0.96 271:0.78 276:0.81 279:0.86 282:0.88 283:0.82 285:0.62 286:0.99 287:0.93 290:0.69 297:0.36 299:0.97 300:0.84 +0 1:0.74 7:0.78 8:0.61 9:0.80 12:0.86 17:0.73 21:0.47 27:0.72 28:0.93 32:0.80 34:0.49 36:0.32 39:0.56 41:0.82 74:0.57 76:0.94 77:0.96 81:0.67 83:0.74 91:0.84 96:0.70 104:0.58 114:0.80 120:0.56 126:0.54 131:0.89 135:0.77 140:0.45 144:0.57 145:0.70 150:0.79 151:0.52 153:0.48 155:0.67 157:0.61 161:0.77 162:0.86 164:0.57 165:0.80 166:0.84 167:0.81 175:0.91 176:0.73 181:0.60 182:0.86 192:0.59 195:0.57 201:0.74 202:0.36 204:0.89 208:0.64 215:0.63 216:0.33 220:0.93 222:0.60 227:0.92 229:0.92 230:0.81 231:0.77 232:0.81 233:0.69 235:0.68 241:0.86 244:0.83 246:0.91 248:0.52 253:0.64 255:0.79 256:0.45 257:0.84 259:0.21 260:0.56 267:0.44 268:0.46 271:0.78 277:0.87 282:0.88 285:0.62 287:0.93 290:0.80 297:0.36 299:0.99 +1 7:0.81 12:0.86 17:0.83 21:0.40 27:0.72 28:0.93 32:0.80 34:0.37 36:0.48 39:0.56 74:0.59 77:0.70 81:0.47 91:0.83 104:0.58 114:0.55 121:1.00 126:0.32 131:0.61 135:0.54 144:0.57 145:0.45 150:0.38 155:0.67 157:0.61 161:0.77 166:0.76 167:0.80 175:0.91 176:0.53 178:0.55 181:0.60 182:0.73 191:0.77 192:0.59 195:0.52 204:0.89 208:0.64 222:0.60 227:0.61 229:0.61 230:0.87 231:0.70 232:0.81 233:0.76 235:0.42 241:0.83 244:0.83 246:0.61 253:0.47 257:0.84 259:0.21 267:0.23 271:0.78 277:0.87 282:0.88 287:0.61 290:0.55 299:0.88 +1 1:0.74 11:0.64 12:0.86 17:0.36 21:0.77 27:0.45 28:0.93 30:0.11 34:0.73 36:0.18 37:0.50 39:0.56 43:0.82 66:0.29 69:0.71 70:0.93 74:0.77 81:0.42 83:0.72 85:0.85 91:0.14 98:0.35 101:0.76 108:0.66 114:0.64 122:0.67 123:0.64 124:0.70 126:0.54 127:0.43 129:0.81 131:0.61 133:0.67 135:0.92 144:0.57 145:0.49 146:0.13 147:0.18 149:0.68 150:0.62 154:0.77 155:0.67 157:0.84 159:0.59 161:0.77 165:0.63 166:0.84 167:0.53 172:0.37 173:0.64 176:0.73 177:0.38 178:0.55 179:0.65 181:0.60 182:0.84 187:0.68 192:0.59 201:0.74 212:0.37 215:0.44 216:0.77 222:0.60 227:0.61 229:0.61 231:0.76 235:0.98 241:0.44 242:0.51 243:0.20 245:0.65 246:0.90 247:0.47 253:0.61 259:0.21 265:0.37 266:0.71 267:0.85 271:0.78 276:0.42 287:0.61 290:0.64 297:0.36 300:0.70 +2 1:0.74 6:0.90 7:0.81 8:0.81 9:0.80 11:0.64 12:0.86 17:0.81 20:0.86 21:0.59 27:0.42 28:0.93 30:0.54 32:0.80 34:0.77 36:0.75 37:0.68 39:0.56 41:0.99 43:0.97 60:0.99 66:0.72 69:0.29 70:0.77 74:0.95 77:0.89 78:0.91 81:0.64 83:0.89 85:0.95 91:0.91 96:0.97 98:0.89 100:0.88 101:0.85 104:0.75 108:0.57 111:0.89 114:0.63 120:0.94 121:0.90 122:0.43 123:0.55 124:0.43 126:0.54 127:0.96 129:0.59 131:0.89 133:0.47 135:0.58 138:0.96 140:0.45 144:0.57 145:0.77 146:0.26 147:0.32 149:0.94 150:0.77 151:0.99 152:0.81 153:0.97 154:0.97 155:0.67 157:0.92 158:0.92 159:0.51 161:0.77 162:0.80 164:0.95 165:0.80 166:0.84 167:0.78 169:0.86 172:0.80 173:0.33 176:0.56 177:0.38 179:0.47 181:0.60 182:0.86 186:0.91 189:0.91 191:0.85 192:0.59 195:0.68 201:0.74 202:0.90 204:0.89 208:0.64 212:0.74 215:0.53 216:0.32 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.83 233:0.76 235:0.88 238:0.84 241:0.79 242:0.32 243:0.23 245:0.83 246:0.91 247:0.67 248:0.97 253:0.98 255:0.79 256:0.92 259:0.21 260:0.95 261:0.87 265:0.89 266:0.27 267:0.65 268:0.93 271:0.78 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.63 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.97 9:0.80 11:0.64 12:0.86 17:0.85 20:0.98 21:0.30 27:0.10 28:0.93 30:0.18 32:0.80 34:0.63 36:0.47 37:0.96 39:0.56 41:0.99 43:0.90 60:0.99 66:0.64 69:0.17 70:0.82 74:0.86 77:0.70 78:0.93 81:0.76 83:0.89 85:0.93 91:0.89 96:0.97 98:0.63 100:0.93 101:0.81 104:0.58 108:0.84 111:0.92 114:0.68 120:0.96 121:0.97 122:0.43 123:0.83 124:0.51 126:0.54 127:0.98 129:0.81 131:0.89 133:0.67 135:0.18 138:0.97 140:0.94 144:0.57 145:0.77 146:0.42 147:0.49 149:0.85 150:0.84 151:0.96 152:0.92 153:0.86 154:0.89 155:0.67 157:0.90 158:0.92 159:0.70 161:0.77 162:0.69 164:0.95 165:0.81 166:0.84 167:0.83 169:0.91 172:0.75 173:0.16 176:0.56 177:0.38 178:0.48 179:0.52 181:0.60 182:0.86 186:0.93 189:0.94 191:0.97 192:0.59 195:0.83 201:0.74 202:0.93 204:0.89 208:0.64 212:0.70 215:0.67 216:0.52 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.60 238:0.88 241:0.93 242:0.39 243:0.25 245:0.77 246:0.91 247:0.60 248:0.97 253:0.98 255:0.79 256:0.94 259:0.21 260:0.96 261:0.93 265:0.64 266:0.75 267:0.52 268:0.94 271:0.78 276:0.67 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.68 297:0.36 299:0.88 300:0.55 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.86 17:0.04 21:0.54 27:0.08 28:0.93 30:0.68 32:0.80 34:0.42 36:0.35 37:0.98 39:0.56 41:0.88 43:0.95 66:0.79 69:0.23 70:0.31 74:0.78 76:0.85 77:0.89 81:0.66 83:0.74 85:0.85 91:0.68 96:0.71 98:0.76 101:0.82 104:0.58 108:0.18 114:0.77 120:0.58 121:0.90 122:0.41 123:0.18 126:0.54 127:0.25 129:0.05 131:0.89 135:0.32 140:0.45 144:0.57 145:0.79 146:0.44 147:0.50 149:0.79 150:0.77 151:0.54 153:0.83 154:0.95 155:0.67 157:0.86 159:0.16 161:0.77 162:0.71 164:0.72 165:0.79 166:0.84 167:0.60 172:0.41 173:0.55 176:0.73 177:0.38 179:0.76 181:0.60 182:0.86 187:0.39 191:0.95 192:0.59 195:0.55 201:0.74 202:0.63 204:0.89 208:0.64 212:0.78 215:0.58 216:0.45 220:0.93 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.80 241:0.60 242:0.45 243:0.80 246:0.91 247:0.39 248:0.56 253:0.70 255:0.79 256:0.58 259:0.21 260:0.58 265:0.76 266:0.23 267:0.23 268:0.66 271:0.78 276:0.24 282:0.88 285:0.62 287:0.93 290:0.77 297:0.36 299:0.97 300:0.25 +3 1:0.74 6:0.91 7:0.76 8:0.82 9:0.80 11:0.64 12:0.86 17:0.92 20:0.95 21:0.68 27:0.19 28:0.93 30:0.28 32:0.80 34:0.85 36:0.40 37:0.74 39:0.56 41:0.98 43:0.80 60:0.95 66:0.79 69:0.51 70:0.80 74:0.95 76:0.94 77:0.89 78:0.96 81:0.53 83:0.89 85:0.94 91:0.88 96:0.96 98:0.81 100:0.97 101:0.84 104:0.58 108:0.39 111:0.96 114:0.70 120:0.94 122:0.43 123:0.38 124:0.39 126:0.54 127:0.46 129:0.05 131:0.89 133:0.39 135:0.34 140:0.45 144:0.57 145:0.88 146:0.84 147:0.86 149:0.92 150:0.68 151:0.99 152:0.91 153:0.96 154:0.75 155:0.67 157:0.91 158:0.92 159:0.48 161:0.77 162:0.80 164:0.94 165:0.69 166:0.84 167:0.81 169:0.95 172:0.82 173:0.50 176:0.73 177:0.38 179:0.38 181:0.60 182:0.86 186:0.96 189:0.92 191:0.92 192:0.59 195:0.73 201:0.74 202:0.89 208:0.64 212:0.67 215:0.49 216:0.33 222:0.60 227:0.92 229:0.92 230:0.79 231:0.77 232:0.81 233:0.76 235:0.88 238:0.93 241:0.87 242:0.43 243:0.80 245:0.78 246:0.91 247:0.67 248:0.96 253:0.98 255:0.79 256:0.91 259:0.21 260:0.94 261:0.95 265:0.81 266:0.38 267:0.28 268:0.92 271:0.78 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.70 297:0.36 299:0.97 300:0.55 +1 1:0.74 6:0.92 8:0.87 9:0.80 12:0.86 17:0.47 20:0.76 21:0.30 27:0.16 28:0.93 30:0.76 32:0.80 34:0.28 36:0.76 37:0.59 39:0.56 41:0.92 43:0.34 55:0.92 66:0.25 69:0.59 70:0.29 74:0.68 77:0.89 78:0.93 81:0.78 83:0.78 91:0.65 96:0.95 98:0.33 100:0.91 108:0.45 111:0.91 114:0.86 117:0.86 120:0.75 122:0.51 123:0.43 124:0.71 126:0.54 127:0.55 129:0.59 131:0.89 133:0.64 135:0.66 138:0.96 140:0.80 144:0.57 145:0.49 146:0.57 147:0.63 149:0.31 150:0.85 151:0.87 152:0.75 153:0.95 154:0.26 155:0.67 157:0.61 158:0.83 159:0.72 161:0.77 162:0.81 163:0.93 164:0.81 165:0.84 166:0.84 167:0.61 169:0.89 172:0.16 173:0.43 176:0.73 177:0.38 179:0.91 181:0.60 182:0.86 186:0.93 187:0.21 189:0.93 191:0.82 192:0.59 195:0.87 201:0.74 202:0.81 208:0.64 212:0.23 215:0.72 216:0.79 222:0.60 227:0.92 229:0.92 231:0.77 232:0.83 235:0.52 238:0.88 241:0.62 242:0.55 243:0.45 244:0.61 245:0.45 246:0.91 247:0.39 248:0.84 253:0.94 255:0.79 256:0.84 259:0.21 260:0.76 261:0.78 265:0.36 266:0.38 267:0.59 268:0.86 271:0.78 276:0.27 282:0.88 285:0.62 287:0.93 290:0.86 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.92 7:0.81 8:0.85 9:0.80 12:0.86 17:0.90 20:0.82 21:0.05 27:0.72 28:0.93 32:0.80 34:0.59 36:0.86 37:1.00 39:0.56 41:0.88 74:0.59 76:0.85 77:0.89 78:0.90 81:0.87 83:0.81 91:0.85 96:0.88 100:0.89 104:0.74 111:0.89 114:0.95 120:0.79 121:0.90 126:0.54 131:0.89 135:0.18 140:0.45 144:0.57 145:0.85 150:0.93 151:0.94 152:0.79 153:0.80 155:0.67 157:0.61 161:0.77 162:0.79 164:0.71 165:0.90 166:0.85 167:0.81 169:0.87 175:0.91 176:0.73 181:0.60 182:0.86 186:0.90 189:0.93 192:0.59 195:0.65 201:0.74 202:0.60 204:0.89 208:0.64 215:0.91 216:0.03 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.83 233:0.76 235:0.12 238:0.86 241:0.86 244:0.83 246:0.91 248:0.87 253:0.87 255:0.79 256:0.58 257:0.84 259:0.21 260:0.80 261:0.84 267:0.82 268:0.65 271:0.78 277:0.87 282:0.88 285:0.62 287:0.93 290:0.95 297:0.36 299:0.97 +2 1:0.74 6:0.87 7:0.81 8:0.78 9:0.80 11:0.64 12:0.86 17:0.72 20:0.90 21:0.40 27:0.44 28:0.93 30:0.45 32:0.80 34:0.49 36:0.93 37:0.70 39:0.56 41:0.95 43:0.80 60:0.87 66:0.94 69:0.64 70:0.53 74:0.93 77:0.70 78:0.88 81:0.71 83:0.85 85:0.95 91:0.92 96:0.94 98:0.88 100:0.86 101:0.86 108:0.49 111:0.86 114:0.82 120:0.93 121:0.97 122:0.43 123:0.46 126:0.54 127:0.42 129:0.05 131:0.89 135:0.37 140:0.87 144:0.57 145:0.76 146:0.72 147:0.76 149:0.94 150:0.81 151:0.99 152:0.84 153:0.95 154:0.75 155:0.67 157:0.92 158:0.92 159:0.29 161:0.77 162:0.77 164:0.92 165:0.81 166:0.84 167:0.80 169:0.83 172:0.83 173:0.78 176:0.73 177:0.38 179:0.37 181:0.60 182:0.86 186:0.88 189:0.89 191:0.84 192:0.59 195:0.61 201:0.74 202:0.88 204:0.89 208:0.64 212:0.93 215:0.67 216:0.29 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.72 233:0.76 235:0.60 238:0.83 241:0.83 242:0.44 243:0.94 246:0.91 247:0.47 248:0.93 253:0.96 255:0.79 256:0.89 259:0.21 260:0.93 261:0.87 265:0.88 266:0.23 267:0.59 268:0.91 271:0.78 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.82 297:0.36 299:0.88 300:0.43 +2 1:0.74 7:0.81 9:0.80 12:0.86 17:0.79 27:0.19 28:0.93 32:0.80 34:0.18 36:0.21 37:0.74 39:0.56 41:0.82 74:0.59 77:0.70 81:0.92 83:0.81 91:0.64 96:0.83 104:0.58 114:0.98 120:0.72 121:0.90 126:0.54 131:0.89 135:0.60 140:0.45 144:0.57 145:0.44 150:0.96 151:0.90 153:0.69 155:0.67 157:0.61 161:0.77 162:0.86 164:0.62 165:0.92 166:0.85 167:0.54 175:0.91 176:0.73 181:0.60 182:0.86 187:0.21 191:0.85 192:0.59 195:0.71 201:0.74 202:0.46 204:0.89 208:0.64 215:0.96 216:0.33 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.81 233:0.76 235:0.05 241:0.49 244:0.83 246:0.91 248:0.80 253:0.83 255:0.79 256:0.45 257:0.84 259:0.21 260:0.73 267:0.36 268:0.55 271:0.78 277:0.87 282:0.88 285:0.62 287:0.93 290:0.98 297:0.36 299:0.88 +1 1:0.74 6:0.97 7:0.81 8:0.97 9:0.80 11:0.64 12:0.86 17:0.87 20:0.75 21:0.54 27:0.27 28:0.93 30:0.14 32:0.80 34:0.21 36:0.23 37:0.95 39:0.56 41:0.93 43:0.94 66:0.30 69:0.30 70:0.94 74:0.91 76:0.85 77:0.70 78:1.00 81:0.63 83:0.77 85:0.95 91:0.87 96:0.82 98:0.26 100:0.97 101:0.77 104:0.58 108:0.24 111:0.99 114:0.77 120:0.81 121:0.97 123:0.23 124:0.95 126:0.54 127:0.99 129:0.81 131:0.89 133:0.96 135:0.24 140:0.45 144:0.57 145:0.70 146:0.67 147:0.72 149:0.93 150:0.76 151:0.87 152:0.74 153:0.48 154:0.94 155:0.67 157:0.89 159:0.91 161:0.77 162:0.85 164:0.85 165:0.79 166:0.84 167:0.80 169:0.95 172:0.71 173:0.10 176:0.73 177:0.38 179:0.36 181:0.60 182:0.86 186:1.00 189:0.97 191:0.94 192:0.59 195:0.97 201:0.74 202:0.76 204:0.89 208:0.64 212:0.58 215:0.58 216:0.26 220:0.96 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.74 238:0.90 241:0.82 242:0.45 243:0.48 245:0.74 246:0.91 247:0.60 248:0.79 253:0.86 255:0.79 256:0.81 259:0.21 260:0.81 261:0.80 265:0.28 266:0.92 267:0.69 268:0.82 271:0.78 276:0.81 282:0.88 283:0.71 285:0.62 287:0.93 290:0.77 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.81 8:0.97 9:0.80 11:0.64 12:0.86 17:0.43 20:0.95 21:0.54 27:0.08 28:0.93 30:0.42 32:0.80 34:0.34 36:0.48 37:0.98 39:0.56 41:0.99 43:0.95 66:0.51 69:0.23 70:0.85 74:0.95 76:0.85 77:0.70 78:0.95 81:0.66 83:0.83 85:0.96 91:0.93 96:0.96 98:0.72 100:0.96 101:0.86 104:0.58 108:0.72 111:0.95 114:0.77 120:0.96 121:0.90 122:0.43 123:0.70 124:0.56 126:0.54 127:0.81 129:0.59 131:0.89 133:0.47 135:0.34 138:0.98 140:0.87 144:0.57 145:0.69 146:0.63 147:0.69 149:0.96 150:0.77 151:0.94 152:0.91 153:0.81 154:0.95 155:0.67 157:0.92 159:0.52 161:0.77 162:0.76 164:0.96 165:0.79 166:0.84 167:0.82 169:0.94 172:0.78 173:0.28 176:0.73 177:0.38 179:0.38 181:0.60 182:0.86 186:0.95 189:0.96 191:0.97 192:0.59 195:0.71 201:0.74 202:0.93 204:0.89 208:0.64 212:0.82 215:0.58 216:0.45 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.80 238:0.92 241:0.89 242:0.24 243:0.44 245:0.92 246:0.91 247:0.67 248:0.95 253:0.97 255:0.79 256:0.95 259:0.21 260:0.96 261:0.95 265:0.72 266:0.54 267:0.59 268:0.95 271:0.78 276:0.78 282:0.88 283:0.71 285:0.62 287:0.93 290:0.77 297:0.36 299:0.88 300:0.84 +0 1:0.74 11:0.64 12:0.86 17:0.32 21:0.54 27:0.45 28:0.93 30:0.58 34:0.86 36:0.18 37:0.50 39:0.56 43:0.82 55:0.84 60:0.87 66:0.10 69:0.69 70:0.60 74:0.62 81:0.59 83:0.86 85:0.72 91:0.17 98:0.18 101:0.68 108:0.52 114:0.77 122:0.43 123:0.70 124:0.82 126:0.54 127:0.64 129:0.59 131:0.61 133:0.76 135:0.81 144:0.57 145:0.49 146:0.26 147:0.32 149:0.56 150:0.74 154:0.77 155:0.67 157:0.76 158:0.87 159:0.84 161:0.77 163:0.79 165:0.79 166:0.84 167:0.48 172:0.16 173:0.45 176:0.73 177:0.38 178:0.55 179:0.88 181:0.60 182:0.85 187:0.68 192:0.59 201:0.74 208:0.64 212:0.16 215:0.58 216:0.77 222:0.60 227:0.61 229:0.61 231:0.76 235:0.68 241:0.21 242:0.40 243:0.39 245:0.48 246:0.91 247:0.55 253:0.63 259:0.21 265:0.14 266:0.54 267:0.52 271:0.78 276:0.32 279:0.86 283:0.71 287:0.61 290:0.77 297:0.36 300:0.43 +0 12:0.86 17:0.47 18:0.69 21:0.78 27:0.45 29:0.53 30:0.45 34:0.67 36:0.19 37:0.50 39:0.37 43:0.12 55:0.45 66:0.27 69:0.81 70:0.25 71:0.95 74:0.26 86:0.67 91:0.22 98:0.10 108:0.66 122:0.57 123:0.63 124:0.72 127:0.17 129:0.05 133:0.62 135:0.54 139:0.65 145:0.47 146:0.19 147:0.05 149:0.08 154:0.47 159:0.36 163:0.93 172:0.10 173:0.73 175:0.75 177:0.05 178:0.55 179:0.84 187:0.68 212:0.61 216:0.77 222:0.60 235:0.74 241:0.47 242:0.63 243:0.29 244:0.61 245:0.38 247:0.30 253:0.23 254:0.84 257:0.84 259:0.21 265:0.11 266:0.17 267:0.76 271:0.43 276:0.19 277:0.69 300:0.18 +2 1:0.74 9:0.80 11:0.91 12:0.86 17:0.34 18:0.87 21:0.64 27:0.48 29:0.53 30:0.26 31:0.86 32:0.78 34:0.67 36:0.29 37:0.55 39:0.37 43:0.63 44:0.93 45:0.91 64:0.77 66:0.09 69:0.56 70:0.97 71:0.95 74:0.81 77:0.70 79:0.86 81:0.44 83:0.60 86:0.90 91:0.07 96:0.68 97:0.94 98:0.40 99:0.83 101:0.52 108:0.43 114:0.57 120:0.53 122:0.51 123:0.89 124:0.87 126:0.54 127:0.89 129:0.81 133:0.86 135:0.42 137:0.86 139:0.90 140:0.45 144:0.85 145:0.65 146:0.70 147:0.74 149:0.74 150:0.66 151:0.47 153:0.48 154:0.70 155:0.67 158:0.78 159:0.86 162:0.86 164:0.57 165:0.70 172:0.65 173:0.30 176:0.73 177:0.70 179:0.38 187:0.39 192:0.87 195:0.94 196:0.88 201:0.93 202:0.36 208:0.64 212:0.54 215:0.38 216:0.92 220:0.96 222:0.60 228:0.99 235:0.88 241:0.64 242:0.19 243:0.45 245:0.82 247:0.88 248:0.47 253:0.45 254:0.84 255:0.95 256:0.45 259:0.21 260:0.54 265:0.36 266:0.92 267:0.91 268:0.46 271:0.43 276:0.78 279:0.82 282:0.86 283:0.82 284:0.89 285:0.62 290:0.57 297:0.36 300:0.98 +0 1:0.69 7:0.72 11:0.91 12:0.86 17:0.98 18:0.77 27:0.53 29:0.53 30:0.31 32:0.80 34:0.50 36:0.64 37:0.27 39:0.37 43:0.63 44:0.93 45:0.92 55:0.52 66:0.54 69:0.69 70:0.87 71:0.95 74:0.88 77:0.99 81:0.83 83:0.60 86:0.82 91:0.27 97:0.89 98:0.72 99:0.83 101:0.52 104:0.86 106:0.81 108:0.68 114:0.95 117:0.86 123:0.66 124:0.64 126:0.44 127:0.37 129:0.59 133:0.61 135:0.54 139:0.85 144:0.85 145:0.97 146:0.40 147:0.46 149:0.85 150:0.80 154:0.75 155:0.58 158:0.79 159:0.70 165:0.70 172:0.89 173:0.53 176:0.66 177:0.70 178:0.55 179:0.18 187:0.21 192:0.59 195:0.90 201:0.68 204:0.85 206:0.81 208:0.54 212:0.80 215:0.96 216:0.30 222:0.60 230:0.75 232:0.92 233:0.76 235:0.05 241:0.01 242:0.53 243:0.36 245:0.94 247:0.88 253:0.65 254:0.74 259:0.21 265:0.72 266:0.47 267:0.28 271:0.43 276:0.89 279:0.82 282:0.88 283:0.82 290:0.95 297:0.36 300:0.70 +0 1:0.69 8:0.72 9:0.76 11:0.91 12:0.86 17:0.75 18:0.58 20:0.92 27:0.29 29:0.53 30:0.43 34:0.20 36:0.31 37:0.53 39:0.37 43:0.61 45:0.92 66:0.11 69:0.77 70:0.91 71:0.95 74:0.72 78:0.97 81:0.83 83:0.60 86:0.60 91:0.17 96:0.80 97:0.89 98:0.34 99:0.83 100:0.92 101:0.52 108:0.70 111:0.95 114:0.95 120:0.69 122:0.51 123:0.90 124:0.89 126:0.44 127:0.45 129:0.93 133:0.90 135:0.47 139:0.59 140:0.45 144:0.85 146:0.28 147:0.35 149:0.83 150:0.80 151:0.81 152:0.86 153:0.48 154:0.50 155:0.58 158:0.79 159:0.83 162:0.86 164:0.54 165:0.70 169:0.90 172:0.65 173:0.54 175:0.75 176:0.66 177:0.38 179:0.32 186:0.97 187:0.68 190:0.77 192:0.59 201:0.68 202:0.36 208:0.54 212:0.50 215:0.96 216:0.21 222:0.60 235:0.05 241:0.05 242:0.33 243:0.20 244:0.61 245:0.79 247:0.76 248:0.73 253:0.81 255:0.74 256:0.45 257:0.65 259:0.21 260:0.68 261:0.93 265:0.22 266:0.89 267:0.28 268:0.46 271:0.43 276:0.77 277:0.69 279:0.82 283:0.82 285:0.56 290:0.95 297:0.36 300:0.84 +0 1:0.69 7:0.72 9:0.76 11:0.91 12:0.86 17:0.79 18:0.69 21:0.05 27:0.24 29:0.53 30:0.45 34:0.73 36:0.51 37:0.61 39:0.37 43:0.69 45:0.66 66:0.27 69:0.54 70:0.25 71:0.95 74:0.53 81:0.75 83:0.60 86:0.67 91:0.53 96:0.80 97:0.89 98:0.19 99:0.83 101:0.52 108:0.41 114:0.85 120:0.69 122:0.57 123:0.65 124:0.61 126:0.44 127:0.17 129:0.05 133:0.39 135:0.89 139:0.65 140:0.45 144:0.85 146:0.13 147:0.18 149:0.36 150:0.71 151:0.81 153:0.48 154:0.58 155:0.58 158:0.79 159:0.29 162:0.86 163:0.88 164:0.54 165:0.70 172:0.10 173:0.48 175:0.75 176:0.66 177:0.38 179:0.85 187:0.39 190:0.77 192:0.59 201:0.68 202:0.36 204:0.85 208:0.54 212:0.61 215:0.78 216:0.61 222:0.60 230:0.75 233:0.76 235:0.12 241:0.30 242:0.63 243:0.46 244:0.61 245:0.40 247:0.30 248:0.73 253:0.76 255:0.74 256:0.45 257:0.65 259:0.21 260:0.68 265:0.09 266:0.17 267:0.95 268:0.46 271:0.43 276:0.15 277:0.87 279:0.82 283:0.82 285:0.56 290:0.85 297:0.36 300:0.18 +0 1:0.69 11:0.91 12:0.86 17:0.76 18:0.86 21:0.40 27:0.19 29:0.53 30:0.13 34:0.44 36:0.36 37:0.38 39:0.37 43:0.63 44:0.87 45:0.77 55:0.59 66:0.67 69:0.35 70:0.96 71:0.95 74:0.45 81:0.42 83:0.60 86:0.80 91:0.03 98:0.82 99:0.83 101:0.52 106:0.81 108:0.27 114:0.61 117:0.86 122:0.51 123:0.26 124:0.45 126:0.44 127:0.55 129:0.05 133:0.37 135:0.92 139:0.78 144:0.85 145:0.83 146:0.85 147:0.88 149:0.56 150:0.55 154:0.53 155:0.58 159:0.52 165:0.70 172:0.70 173:0.33 176:0.66 177:0.70 178:0.55 179:0.53 187:0.39 192:0.59 195:0.77 201:0.68 206:0.81 208:0.54 212:0.40 215:0.42 216:0.87 222:0.60 228:0.99 235:0.52 241:0.54 242:0.28 243:0.72 244:0.61 245:0.78 247:0.76 253:0.45 259:0.21 265:0.82 266:0.75 267:0.65 271:0.43 276:0.64 290:0.61 297:0.36 300:0.84 +0 1:0.69 7:0.72 9:0.76 11:0.91 12:0.86 17:0.66 18:0.66 20:0.94 21:0.22 27:0.72 29:0.53 30:0.76 32:0.79 34:0.47 36:0.26 39:0.37 43:0.72 44:0.93 45:0.83 66:0.48 69:0.12 70:0.36 71:0.95 74:0.74 77:0.70 78:0.86 81:0.57 83:0.60 86:0.76 91:0.37 96:0.71 97:0.89 98:0.23 99:0.83 100:0.73 101:0.52 104:0.88 106:0.81 108:0.10 111:0.83 114:0.67 117:0.86 120:0.57 122:0.51 123:0.10 124:0.56 126:0.44 127:0.16 129:0.05 132:0.97 133:0.43 135:0.72 139:0.81 140:0.45 144:0.85 145:1.00 146:0.33 147:0.40 149:0.62 150:0.59 151:0.53 152:0.87 153:0.48 154:0.84 155:0.58 158:0.78 159:0.27 162:0.86 164:0.54 165:0.70 169:0.72 172:0.37 173:0.16 176:0.66 177:0.70 179:0.40 186:0.86 187:0.68 190:0.77 192:0.59 195:0.77 201:0.68 202:0.36 204:0.85 206:0.81 208:0.54 212:0.78 215:0.51 216:0.20 220:0.82 222:0.60 230:0.75 232:0.93 233:0.76 235:0.31 241:0.07 242:0.45 243:0.60 245:0.61 247:0.47 248:0.53 253:0.55 254:0.80 255:0.74 256:0.45 259:0.21 260:0.57 261:0.85 265:0.25 266:0.27 267:0.36 268:0.46 271:0.43 276:0.29 279:0.82 282:0.87 283:0.78 285:0.56 290:0.67 297:0.36 300:0.33 +0 1:0.69 8:0.62 9:0.76 11:0.91 12:0.86 17:0.64 18:0.63 21:0.47 27:0.57 29:0.53 30:0.28 34:0.58 36:0.74 37:0.27 39:0.37 43:0.71 45:0.81 66:0.29 69:0.29 70:0.97 71:0.95 74:0.56 81:0.38 83:0.60 86:0.64 91:0.54 96:0.86 97:0.99 98:0.35 99:0.83 101:0.52 104:0.86 106:0.81 108:0.56 114:0.59 117:0.86 120:0.77 123:0.53 124:0.84 126:0.44 127:0.75 129:0.89 133:0.83 135:0.34 138:0.97 139:0.62 140:0.45 144:0.85 146:0.13 147:0.18 149:0.61 150:0.54 151:0.84 153:0.72 154:0.61 155:0.58 158:0.79 159:0.74 162:0.84 163:0.88 164:0.71 165:0.70 172:0.37 173:0.18 175:0.75 176:0.66 177:0.38 179:0.73 187:0.39 190:0.77 191:0.80 192:0.59 201:0.68 202:0.66 206:0.81 208:0.54 212:0.27 215:0.41 216:0.17 220:0.93 222:0.60 232:0.92 235:0.60 241:0.27 242:0.40 243:0.20 244:0.61 245:0.58 247:0.74 248:0.83 253:0.78 255:0.74 256:0.71 257:0.65 259:0.21 260:0.73 265:0.37 266:0.71 267:0.28 268:0.73 271:0.43 276:0.49 277:0.69 279:0.82 283:0.82 285:0.56 290:0.59 297:0.36 300:0.84 +0 1:0.69 11:0.91 12:0.86 17:0.49 21:0.13 27:0.25 29:0.53 30:0.95 32:0.75 34:0.24 36:0.56 37:0.90 39:0.37 43:0.60 44:0.93 45:0.66 66:0.54 69:0.79 71:0.95 74:0.50 77:0.70 81:0.65 83:0.60 91:0.42 98:0.12 99:0.83 101:0.52 104:0.86 106:0.81 108:0.63 114:0.75 117:0.86 123:0.61 126:0.44 127:0.08 129:0.05 135:0.39 139:0.73 144:0.85 145:0.61 146:0.13 147:0.18 149:0.28 150:0.63 154:0.49 155:0.58 159:0.08 165:0.70 172:0.10 173:0.95 175:0.75 176:0.66 177:0.38 178:0.55 179:0.10 187:0.39 192:0.59 195:0.56 201:0.68 206:0.81 208:0.54 215:0.61 216:0.87 222:0.60 232:0.92 235:0.22 241:0.05 243:0.63 244:0.61 253:0.54 257:0.65 259:0.21 265:0.12 267:0.82 271:0.43 277:0.69 282:0.83 290:0.75 297:0.36 300:0.08 +1 1:0.74 9:0.80 11:0.91 12:0.86 17:0.49 18:0.89 21:0.59 27:0.48 29:0.53 30:0.40 31:0.86 32:0.79 34:0.40 36:0.21 37:0.55 39:0.37 43:0.63 44:0.93 45:0.91 64:0.77 66:0.45 69:0.25 70:0.95 71:0.95 74:0.78 77:0.70 79:0.86 81:0.46 83:0.60 86:0.90 91:0.15 96:0.68 98:0.56 99:0.83 101:0.52 108:0.20 114:0.58 120:0.54 122:0.51 123:0.19 124:0.81 126:0.54 127:0.90 129:0.05 133:0.81 135:0.61 137:0.86 139:0.90 140:0.45 144:0.85 145:0.59 146:0.79 147:0.83 149:0.77 150:0.67 151:0.48 153:0.48 154:0.71 155:0.67 159:0.75 162:0.86 164:0.57 165:0.70 172:0.71 173:0.17 176:0.73 177:0.70 179:0.44 187:0.39 192:0.87 195:0.85 196:0.88 201:0.93 202:0.36 208:0.64 212:0.37 215:0.39 216:0.92 220:0.93 222:0.60 228:0.99 235:0.84 241:0.56 242:0.36 243:0.58 245:0.79 247:0.79 248:0.48 253:0.46 254:0.80 255:0.95 256:0.45 259:0.21 260:0.54 265:0.58 266:0.75 267:0.36 268:0.46 271:0.43 276:0.73 282:0.87 283:0.82 284:0.89 285:0.62 290:0.58 297:0.36 300:0.94 +0 8:0.66 12:0.79 17:0.55 21:0.59 27:0.45 28:0.49 30:0.17 32:0.80 34:0.79 36:0.20 37:0.50 43:0.32 55:0.59 66:0.47 69:0.70 70:0.73 81:0.82 91:0.09 98:0.71 108:0.82 123:0.81 124:0.65 126:0.54 127:0.58 129:0.81 133:0.67 135:0.82 145:0.47 146:0.84 147:0.87 149:0.70 150:0.62 154:0.24 159:0.78 161:0.55 167:0.64 172:0.71 173:0.52 177:0.05 178:0.55 179:0.40 181:0.68 187:0.68 195:0.91 212:0.21 215:0.55 216:0.77 235:0.68 241:0.24 242:0.82 243:0.45 245:0.84 247:0.12 259:0.21 265:0.71 266:0.71 267:0.69 271:0.83 276:0.75 283:0.60 297:0.36 300:0.70 +1 6:0.86 7:0.81 8:0.78 12:0.79 17:0.91 20:0.75 21:0.77 27:0.56 28:0.49 30:0.95 32:0.80 34:0.97 36:0.81 37:0.57 43:0.45 55:0.99 66:0.20 69:0.51 78:0.83 81:0.61 91:0.97 98:0.07 100:0.81 108:0.39 111:0.82 120:0.96 123:0.37 124:0.61 126:0.54 127:0.32 129:0.59 133:0.47 135:0.73 140:0.90 145:0.79 146:0.05 147:0.05 149:0.18 150:0.45 151:0.83 152:0.74 153:0.95 154:0.34 159:0.53 161:0.55 162:0.49 164:0.97 167:0.96 169:0.83 172:0.05 173:0.41 177:0.05 178:0.50 179:1.00 181:0.68 186:0.80 191:0.94 195:0.82 202:0.98 215:0.43 216:0.56 230:0.65 233:0.63 235:0.98 238:0.90 241:0.87 243:0.40 245:0.36 248:0.94 256:0.98 259:0.21 260:0.96 261:0.75 265:0.07 267:1.00 268:0.96 271:0.83 285:0.62 297:0.36 300:0.08 +0 12:0.79 17:0.53 20:0.75 21:0.78 27:0.15 28:0.49 34:0.71 36:0.34 37:0.85 78:0.72 91:0.56 100:0.73 104:0.81 106:0.81 111:0.72 120:0.60 135:0.60 140:0.45 151:0.54 152:0.74 153:0.48 161:0.55 162:0.86 164:0.57 167:0.85 169:0.72 175:0.75 181:0.68 186:0.73 187:0.21 202:0.36 206:0.81 216:0.79 220:0.62 235:0.68 241:0.74 244:0.61 248:0.54 256:0.45 257:0.65 259:0.21 260:0.61 261:0.73 267:0.89 268:0.46 271:0.83 277:0.69 285:0.62 +4 6:0.96 8:0.98 12:0.79 17:0.16 20:0.86 21:0.68 27:0.06 28:0.49 32:0.80 34:0.89 36:0.38 37:0.92 78:0.85 81:0.77 91:0.90 100:0.90 111:0.86 120:0.96 126:0.54 135:0.32 140:0.45 145:0.76 150:0.57 151:0.84 152:0.84 153:0.98 161:0.55 162:0.56 164:0.96 167:0.92 169:0.90 175:0.75 181:0.68 186:0.85 187:0.68 189:0.99 191:0.97 195:0.62 202:0.95 215:0.49 216:0.75 235:0.80 238:0.95 241:0.78 244:0.61 248:0.94 256:0.94 257:0.65 259:0.21 260:0.96 261:0.89 267:0.23 268:0.94 271:0.83 277:0.69 283:0.82 285:0.62 297:0.36 +2 8:0.95 12:0.79 17:0.13 21:0.78 27:0.72 28:0.49 34:0.64 36:0.27 37:0.94 91:0.88 104:0.58 120:0.75 135:0.78 140:0.45 151:0.71 153:0.72 161:0.55 162:0.67 164:0.75 167:0.96 175:0.75 181:0.68 202:0.68 216:0.32 241:0.88 244:0.61 248:0.68 256:0.68 257:0.65 259:0.21 260:0.76 267:0.44 268:0.70 271:0.83 277:0.69 283:0.60 285:0.62 +4 6:0.96 7:0.81 8:0.96 12:0.79 17:0.24 20:0.75 21:0.68 27:0.06 28:0.49 30:0.95 32:0.80 34:0.67 36:0.71 37:0.92 43:0.50 55:0.96 66:0.20 69:0.30 78:0.94 81:0.77 91:0.96 98:0.11 100:0.97 108:0.24 111:0.98 120:0.99 123:0.23 124:0.61 126:0.54 127:0.44 129:0.59 133:0.47 135:0.23 140:0.45 145:0.63 146:0.05 147:0.05 149:0.22 150:0.57 151:0.90 152:0.84 153:0.99 154:0.39 159:0.20 161:0.55 162:0.58 164:0.99 167:0.99 169:0.90 172:0.05 173:0.61 177:0.05 179:1.00 181:0.68 186:0.92 189:0.97 191:0.98 195:0.55 202:0.99 215:0.49 216:0.75 230:0.87 233:0.76 235:0.80 238:0.98 241:1.00 243:0.40 245:0.36 248:0.98 256:0.99 259:0.21 260:0.99 261:0.89 265:0.12 267:0.82 268:0.99 271:0.83 283:0.82 285:0.62 297:0.36 300:0.08 +1 6:0.91 8:0.86 12:0.79 17:0.24 20:0.90 21:0.78 27:0.37 28:0.49 34:0.55 36:0.25 37:0.90 78:0.76 91:0.91 100:0.79 104:0.58 111:0.75 120:0.76 135:0.72 140:0.80 151:0.66 152:0.84 153:0.48 161:0.55 162:0.55 164:0.79 167:0.97 169:0.76 175:0.75 178:0.42 181:0.68 186:0.76 189:0.92 191:0.90 202:0.77 216:0.29 220:0.62 238:0.90 241:0.89 244:0.61 248:0.68 256:0.73 257:0.65 259:0.21 260:0.77 261:0.78 267:0.44 268:0.74 271:0.83 277:0.69 285:0.62 +2 12:0.79 17:0.47 21:0.78 27:0.21 28:0.49 30:0.21 34:0.42 36:0.73 37:0.74 43:0.42 55:0.71 66:0.15 69:0.51 70:0.82 91:0.29 98:0.36 108:0.92 120:0.76 123:0.92 124:0.95 127:0.60 129:0.99 133:0.95 135:0.26 140:0.90 146:0.05 147:0.05 149:0.73 151:0.73 153:0.80 154:0.32 159:0.93 161:0.55 162:0.49 164:0.79 167:0.69 172:0.70 173:0.14 177:0.05 178:0.50 179:0.21 181:0.68 187:0.39 202:0.84 212:0.19 216:0.95 235:0.42 241:0.46 242:0.82 243:0.06 245:0.86 247:0.12 248:0.68 256:0.78 259:0.21 260:0.77 265:0.38 266:0.95 267:0.19 268:0.74 271:0.83 276:0.89 283:0.82 285:0.62 300:0.70 +2 6:0.81 8:0.62 12:0.79 17:0.43 20:0.96 21:0.78 27:0.21 28:0.49 30:0.28 34:0.45 36:0.74 37:0.74 43:0.48 55:0.71 66:0.15 69:0.36 70:0.81 78:0.75 91:0.56 98:0.36 100:0.78 104:0.84 106:0.81 108:0.92 111:0.75 120:0.92 123:0.92 124:0.95 127:0.61 129:0.99 133:0.95 135:0.24 140:0.45 146:0.28 147:0.34 149:0.74 151:0.82 152:0.90 153:0.95 154:0.36 159:0.93 161:0.55 162:0.58 164:0.89 167:0.70 169:0.76 172:0.70 173:0.09 177:0.05 179:0.21 181:0.68 186:0.76 187:0.39 189:0.83 191:0.73 202:0.86 206:0.81 212:0.21 216:0.95 235:0.42 238:0.87 241:0.47 242:0.82 243:0.08 245:0.87 247:0.12 248:0.88 256:0.85 259:0.21 260:0.92 261:0.78 265:0.38 266:0.95 267:0.44 268:0.86 271:0.83 276:0.89 283:0.82 285:0.62 300:0.70 +0 12:0.79 17:0.66 21:0.78 27:0.10 28:0.49 34:0.33 36:0.52 37:0.28 91:0.86 104:0.58 106:0.81 120:0.90 135:0.49 140:0.45 151:0.80 153:0.93 161:0.55 162:0.63 164:0.91 167:0.87 175:0.75 181:0.68 187:0.68 202:0.87 206:0.81 216:0.23 235:0.05 241:0.75 244:0.61 248:0.86 256:0.88 257:0.65 259:0.21 260:0.91 267:0.23 268:0.89 271:0.83 277:0.69 285:0.62 +0 8:0.75 12:0.79 17:0.69 21:0.47 27:0.45 28:0.49 30:0.20 32:0.80 34:0.80 36:0.20 37:0.50 43:0.32 55:0.42 66:0.72 69:0.69 70:0.92 81:0.86 91:0.11 98:0.55 108:0.52 123:0.50 124:0.45 126:0.54 127:0.49 129:0.81 133:0.67 135:0.79 145:0.41 146:0.77 147:0.80 149:0.69 150:0.72 154:0.24 159:0.67 161:0.55 167:0.69 172:0.73 173:0.59 177:0.05 178:0.55 179:0.49 181:0.68 187:0.68 195:0.84 212:0.72 215:0.63 216:0.77 235:0.52 241:0.44 242:0.82 243:0.75 245:0.68 247:0.12 259:0.21 265:0.56 266:0.71 267:0.23 271:0.83 276:0.61 297:0.36 300:0.84 +1 8:1.00 12:0.79 17:0.53 20:0.74 21:0.78 27:0.42 28:0.49 34:0.62 36:0.24 37:1.00 78:0.83 91:0.90 100:0.86 104:0.58 111:0.83 120:0.60 135:0.72 140:0.87 151:0.54 152:0.73 153:0.48 161:0.55 162:0.54 164:0.57 167:0.95 169:0.84 175:0.75 178:0.48 181:0.68 186:0.83 191:0.89 202:0.56 216:0.17 220:0.62 241:0.84 244:0.61 248:0.54 256:0.45 257:0.65 259:0.21 260:0.61 261:0.74 267:0.59 268:0.46 271:0.83 277:0.69 285:0.62 +1 6:0.96 8:0.97 12:0.79 17:0.38 20:0.73 21:0.78 27:0.18 28:0.49 34:0.62 36:0.51 37:0.96 78:0.82 91:0.33 100:0.91 111:0.83 120:0.86 135:0.69 140:0.45 151:0.76 152:0.76 153:0.87 161:0.55 162:0.61 164:0.89 167:0.84 169:0.76 175:0.75 181:0.68 186:0.86 187:0.87 191:0.89 202:0.84 216:0.52 235:0.12 241:0.73 244:0.61 248:0.81 256:0.85 257:0.65 259:0.21 260:0.87 261:0.75 267:0.65 268:0.86 271:0.83 277:0.69 283:0.60 285:0.62 +2 6:0.96 8:0.93 12:0.79 17:0.38 20:0.74 21:0.71 27:0.06 28:0.49 32:0.80 34:0.71 36:0.63 37:0.92 78:0.81 81:0.74 91:0.82 100:0.80 111:0.80 120:0.82 126:0.54 135:0.65 140:0.94 145:0.69 150:0.55 151:0.75 152:0.84 153:0.85 161:0.55 162:0.49 164:0.81 167:0.91 169:0.90 175:0.75 178:0.52 181:0.68 186:0.82 187:0.68 191:0.95 195:0.62 202:0.85 215:0.48 216:0.75 235:0.84 241:0.77 244:0.61 248:0.74 256:0.75 257:0.65 259:0.21 260:0.83 261:0.89 267:0.19 268:0.76 271:0.83 277:0.69 285:0.62 297:0.36 +1 6:0.96 8:0.95 12:0.79 17:0.32 20:0.79 21:0.78 27:0.18 28:0.49 34:0.33 36:0.61 37:0.96 78:0.75 91:0.78 100:0.79 111:0.75 120:0.59 135:0.61 140:0.93 151:0.51 152:0.76 153:0.45 161:0.55 162:0.51 164:0.84 167:0.98 169:0.76 175:0.75 178:0.52 181:0.68 186:0.76 189:0.98 191:0.90 202:0.84 216:0.52 220:0.91 235:0.12 238:0.91 241:0.94 244:0.61 248:0.53 256:0.83 257:0.65 259:0.21 260:0.60 261:0.75 267:0.73 268:0.80 271:0.83 277:0.69 285:0.62 +3 6:0.96 7:0.81 8:0.95 12:0.79 17:0.45 20:0.83 21:0.68 27:0.06 28:0.49 30:0.95 32:0.80 34:0.85 36:0.71 37:0.92 43:0.50 55:0.96 66:0.20 69:0.30 78:0.88 81:0.77 91:0.94 98:0.11 100:0.94 108:0.24 111:0.90 120:0.97 123:0.23 124:0.61 126:0.54 127:0.28 129:0.59 133:0.47 135:0.26 140:0.80 145:0.63 146:0.05 147:0.05 149:0.22 150:0.57 151:0.84 152:0.84 153:0.98 154:0.39 159:0.20 161:0.55 162:0.55 164:0.97 167:0.96 169:0.90 172:0.05 173:0.54 177:0.05 178:0.42 179:0.99 181:0.68 186:0.88 187:0.21 189:0.96 191:0.97 195:0.57 202:0.96 215:0.49 216:0.75 230:0.87 233:0.76 235:0.80 238:0.95 241:0.86 243:0.40 245:0.36 248:0.95 256:0.95 259:0.21 260:0.97 261:0.89 265:0.11 267:0.76 268:0.96 271:0.83 283:0.82 285:0.62 297:0.36 300:0.08 +2 7:0.81 8:0.93 12:0.79 17:0.24 21:0.59 27:0.06 28:0.49 32:0.80 34:0.78 36:0.35 37:0.92 81:0.82 91:0.84 120:0.84 126:0.54 135:0.37 140:0.90 145:0.72 150:0.62 151:0.74 153:0.83 161:0.55 162:0.52 164:0.89 167:0.89 175:0.75 178:0.50 181:0.68 187:0.39 191:0.93 195:0.62 202:0.89 215:0.55 216:0.75 220:0.97 230:0.65 233:0.63 235:0.68 241:0.76 244:0.61 248:0.76 256:0.86 257:0.65 259:0.21 260:0.85 267:0.18 268:0.86 271:0.83 277:0.69 285:0.62 297:0.36 +0 8:0.96 12:0.51 17:0.15 21:0.71 25:0.88 27:0.12 28:0.66 30:0.21 34:0.30 36:0.83 37:0.79 39:0.92 43:0.24 55:0.52 66:0.36 69:0.30 70:0.50 74:0.63 76:0.85 81:0.38 91:0.94 98:0.31 104:0.58 108:0.24 120:0.97 123:0.23 124:0.82 126:0.32 127:0.54 129:0.05 133:0.82 135:0.34 140:0.45 145:0.77 146:0.44 147:0.51 149:0.20 150:0.34 151:0.80 153:0.94 154:0.81 159:0.61 161:0.68 162:0.80 164:0.96 167:0.99 172:0.27 173:0.24 177:0.38 179:0.86 181:0.56 190:0.77 191:0.95 202:0.92 212:0.16 215:0.48 216:0.53 220:0.62 222:0.48 235:0.84 241:0.90 242:0.40 243:0.51 245:0.45 247:0.47 248:0.96 253:0.49 254:0.86 256:0.94 259:0.21 260:0.97 265:0.33 266:0.54 267:0.76 268:0.95 276:0.36 283:0.71 285:0.50 297:0.36 300:0.33 +4 1:0.74 6:0.99 7:0.81 8:0.98 9:0.80 11:0.57 12:0.51 17:0.11 20:0.99 21:0.64 25:0.88 27:0.09 28:0.66 30:0.89 32:0.80 34:0.28 36:0.39 37:0.95 39:0.92 41:1.00 43:0.46 60:1.00 66:0.16 69:0.95 70:0.20 74:0.70 77:0.70 78:0.98 81:0.70 83:0.90 85:0.85 87:0.98 91:0.96 96:0.99 98:0.06 100:1.00 101:0.72 104:0.58 108:0.90 111:1.00 114:0.72 120:0.97 121:0.90 122:0.43 123:0.90 124:0.75 126:0.54 127:0.29 129:0.81 133:0.67 135:0.23 140:0.45 145:0.90 146:0.13 147:0.18 149:0.36 150:0.80 151:0.99 152:1.00 153:0.94 154:0.35 155:0.67 158:0.92 159:0.81 161:0.68 162:0.75 163:0.88 164:0.97 165:0.80 167:0.99 169:0.99 172:0.10 173:0.87 176:0.73 177:0.38 179:0.89 181:0.56 186:0.97 189:0.99 191:0.92 192:0.59 195:0.97 201:0.74 202:0.94 204:0.89 208:0.64 212:0.30 215:0.53 216:0.68 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.88 238:1.00 241:0.93 242:0.63 243:0.37 245:0.43 247:0.30 248:0.99 253:0.99 255:0.79 256:0.96 259:0.21 260:0.97 261:1.00 265:0.06 266:0.27 267:0.76 268:0.96 276:0.22 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.88 300:0.18 +2 1:0.74 6:0.99 7:0.81 8:0.94 9:0.80 11:0.57 12:0.51 17:0.04 20:0.78 21:0.40 25:0.88 27:0.09 28:0.66 30:0.87 32:0.80 34:0.33 36:0.93 37:0.95 39:0.92 41:0.95 43:0.57 60:0.87 66:0.54 69:0.94 70:0.27 74:0.74 77:0.70 78:0.84 81:0.85 83:0.83 85:0.88 87:0.98 91:0.15 96:0.90 98:0.21 100:0.95 101:0.76 104:0.58 108:0.89 111:0.90 114:0.82 120:0.83 121:0.90 122:0.43 123:0.88 124:0.48 126:0.54 127:0.17 129:0.59 133:0.47 135:0.58 140:0.90 145:0.90 146:0.38 147:0.45 149:0.53 150:0.91 151:0.85 152:1.00 153:0.46 154:0.45 155:0.67 158:0.87 159:0.42 161:0.68 162:0.53 164:0.81 165:0.86 167:0.76 169:0.99 172:0.32 173:0.94 176:0.73 177:0.38 178:0.50 179:0.58 181:0.56 186:0.84 187:0.87 191:0.89 192:0.59 195:0.90 201:0.74 202:0.79 204:0.89 208:0.64 212:0.19 215:0.67 216:0.68 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.68 241:0.62 242:0.63 243:0.63 245:0.50 247:0.30 248:0.89 253:0.90 255:0.79 256:0.77 259:0.21 260:0.83 261:1.00 265:0.23 266:0.47 267:0.69 268:0.76 276:0.20 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.25 +1 1:0.74 8:0.95 9:0.80 11:0.57 12:0.51 17:0.79 21:0.59 25:0.88 27:0.72 28:0.66 30:0.91 34:0.73 36:0.69 37:0.95 39:0.92 41:0.96 43:0.88 66:0.27 69:0.54 70:0.13 74:0.51 81:0.74 83:0.80 85:0.80 87:0.98 91:0.56 96:0.90 98:0.10 101:0.76 104:0.54 108:0.42 114:0.74 120:0.83 122:0.43 123:0.40 124:0.61 126:0.54 127:0.70 129:0.59 133:0.47 135:0.69 140:0.87 145:0.77 146:0.13 147:0.18 149:0.61 150:0.83 151:0.87 153:0.48 154:0.87 155:0.67 159:0.46 161:0.68 162:0.64 163:0.88 164:0.83 165:0.81 167:0.79 172:0.10 173:0.59 176:0.73 177:0.38 178:0.48 179:0.98 181:0.56 187:0.39 192:0.59 201:0.74 202:0.77 208:0.64 212:0.61 215:0.55 216:0.21 220:0.93 222:0.48 232:0.76 235:0.84 241:0.68 242:0.63 243:0.46 245:0.40 247:0.30 248:0.89 253:0.87 255:0.79 256:0.78 259:0.21 260:0.84 265:0.11 266:0.17 267:0.19 268:0.78 276:0.12 285:0.62 290:0.74 297:0.36 300:0.13 +0 1:0.74 12:0.51 17:0.53 21:0.78 27:0.45 28:0.66 30:0.21 34:0.73 36:0.36 37:0.50 39:0.92 43:0.22 55:0.45 66:0.36 69:0.72 70:0.37 74:0.42 81:0.42 91:0.20 98:0.32 108:0.79 114:0.63 123:0.78 124:0.57 126:0.54 127:0.23 129:0.05 133:0.39 135:0.83 145:0.50 146:0.18 147:0.23 149:0.17 150:0.60 154:0.73 155:0.67 159:0.47 161:0.68 163:0.89 167:0.65 172:0.16 173:0.63 176:0.73 177:0.38 178:0.55 179:0.88 181:0.56 187:0.68 192:0.59 201:0.74 212:0.42 215:0.43 216:0.77 222:0.48 235:1.00 241:0.21 242:0.45 243:0.40 245:0.43 247:0.35 253:0.50 254:0.80 259:0.21 265:0.34 266:0.23 267:0.94 276:0.19 277:0.69 290:0.63 297:0.36 300:0.25 +1 1:0.74 7:0.78 8:0.92 9:0.80 12:0.51 17:0.51 20:0.82 27:0.51 28:0.66 30:0.91 32:0.75 34:0.29 36:0.80 37:0.70 39:0.92 43:0.87 66:0.69 69:0.55 70:0.13 74:0.76 76:0.97 77:0.89 78:0.72 81:0.96 91:0.82 96:0.93 98:0.34 100:0.80 104:0.76 108:0.43 111:0.75 114:0.77 120:0.88 122:0.43 123:0.41 126:0.54 127:0.12 129:0.05 135:0.18 138:0.95 140:0.45 145:0.74 146:0.36 147:0.43 149:0.67 150:0.98 151:0.98 152:0.79 153:0.94 154:0.84 155:0.67 158:0.84 159:0.14 161:0.68 162:0.72 163:0.81 164:0.88 167:0.97 169:0.78 172:0.21 173:0.61 176:0.56 177:0.38 179:0.52 181:0.56 186:0.73 191:0.76 192:0.59 195:0.72 201:0.74 202:0.82 204:0.89 208:0.64 212:0.61 215:0.91 216:0.29 222:0.48 230:0.81 232:0.72 233:0.69 235:0.05 241:0.83 242:0.63 243:0.73 247:0.30 248:0.96 253:0.93 255:0.79 256:0.84 259:0.21 260:0.89 261:0.76 265:0.36 266:0.17 267:0.59 268:0.85 276:0.13 279:0.86 282:0.83 283:0.71 285:0.62 286:0.99 290:0.77 297:0.36 298:0.99 300:0.13 +2 1:0.74 7:0.81 8:0.86 9:0.80 11:0.57 12:0.51 17:0.67 20:0.83 21:0.13 25:0.88 27:0.68 28:0.66 30:0.89 34:0.67 36:0.74 37:0.56 39:0.92 41:0.82 43:0.48 66:0.75 69:0.95 70:0.20 74:0.55 78:0.79 81:0.90 83:0.79 85:0.85 87:0.98 91:0.04 96:0.80 98:0.29 100:0.73 101:0.75 104:0.58 108:0.90 111:0.77 114:0.92 120:0.76 121:0.90 122:0.43 123:0.90 126:0.54 127:0.12 129:0.05 135:0.30 140:0.80 146:0.49 147:0.55 149:0.44 150:0.95 151:0.87 152:0.79 153:0.48 154:0.37 155:0.67 159:0.18 161:0.68 162:0.86 164:0.67 165:0.88 167:0.70 169:0.72 172:0.32 173:0.98 176:0.73 177:0.38 179:0.27 181:0.56 186:0.80 187:0.39 191:0.70 192:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.30 215:0.84 216:0.37 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.22 241:0.43 242:0.63 243:0.77 247:0.30 248:0.78 253:0.82 255:0.79 256:0.58 259:0.21 260:0.76 261:0.78 265:0.31 266:0.27 267:0.87 268:0.59 276:0.19 285:0.62 290:0.92 297:0.36 300:0.18 +0 12:0.51 17:0.34 21:0.59 27:0.45 28:0.66 30:0.62 34:0.86 36:0.19 37:0.50 39:0.92 43:0.30 55:0.98 66:0.30 69:0.87 70:0.34 74:0.44 81:0.52 91:0.14 98:0.24 108:0.74 122:0.43 123:0.72 124:0.71 126:0.32 127:0.28 129:0.59 133:0.64 135:0.65 145:0.49 146:0.37 147:0.05 149:0.24 150:0.40 154:0.48 159:0.52 161:0.68 163:0.88 167:0.62 172:0.16 173:0.84 177:0.05 178:0.55 179:0.88 181:0.56 187:0.68 212:0.30 215:0.55 216:0.77 222:0.48 235:0.68 241:0.10 242:0.63 243:0.23 245:0.43 247:0.35 253:0.38 254:0.84 257:0.65 259:0.21 265:0.26 266:0.27 267:0.95 276:0.26 283:0.71 297:0.36 300:0.25 +2 1:0.74 8:0.96 9:0.80 11:0.57 12:0.51 17:0.83 20:0.82 21:0.59 25:0.88 27:0.72 28:0.66 30:0.91 34:0.73 36:0.69 37:0.95 39:0.92 41:0.94 43:0.88 66:0.27 69:0.54 70:0.13 74:0.51 78:0.72 81:0.74 83:0.79 85:0.80 87:0.98 91:0.54 96:0.86 98:0.10 100:0.73 101:0.76 104:0.54 108:0.42 111:0.72 114:0.74 120:0.77 122:0.43 123:0.40 124:0.61 126:0.54 127:0.70 129:0.59 133:0.47 135:0.69 140:0.87 145:0.77 146:0.13 147:0.18 149:0.61 150:0.83 151:0.87 152:0.79 153:0.48 154:0.87 155:0.67 159:0.46 161:0.68 162:0.57 163:0.88 164:0.78 165:0.81 167:0.80 169:0.72 172:0.10 173:0.59 176:0.73 177:0.38 178:0.48 179:0.98 181:0.56 186:0.73 187:0.21 191:0.87 192:0.59 201:0.74 202:0.73 208:0.64 212:0.61 215:0.55 216:0.21 220:0.93 222:0.48 232:0.76 235:0.84 241:0.68 242:0.63 243:0.46 245:0.40 247:0.30 248:0.85 253:0.83 255:0.79 256:0.71 259:0.21 260:0.78 261:0.73 265:0.11 266:0.17 267:0.65 268:0.72 276:0.12 285:0.62 290:0.74 297:0.36 300:0.13 +2 1:0.74 7:0.81 8:0.63 9:0.80 11:0.57 12:0.51 17:0.16 20:0.94 21:0.40 27:0.21 28:0.66 30:0.57 34:0.47 36:0.87 37:0.74 39:0.92 41:0.92 43:0.97 60:0.95 66:0.82 69:0.17 70:0.64 74:0.97 78:0.75 81:0.78 83:0.82 85:0.97 91:0.27 96:0.85 98:0.82 100:0.73 101:0.85 104:0.93 106:0.81 108:0.14 111:0.74 114:0.82 117:0.86 120:0.76 121:0.90 122:0.57 123:0.13 124:0.39 126:0.54 127:0.54 129:0.05 133:0.39 135:0.17 140:0.45 146:0.91 147:0.92 149:0.97 150:0.85 151:0.86 152:0.87 153:0.86 154:0.97 155:0.67 158:0.87 159:0.61 161:0.68 162:0.73 164:0.78 165:0.83 167:0.70 169:0.72 172:0.86 173:0.17 176:0.73 177:0.38 179:0.34 181:0.56 186:0.75 187:0.39 192:0.59 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.70 215:0.67 216:0.95 220:0.62 222:0.48 230:0.84 232:0.95 233:0.69 235:0.52 241:0.41 242:0.53 243:0.83 245:0.80 247:0.47 248:0.83 253:0.90 255:0.79 256:0.68 259:0.21 260:0.77 261:0.77 265:0.82 266:0.63 267:0.69 268:0.73 276:0.78 279:0.86 283:0.71 285:0.62 290:0.82 297:0.36 300:0.55 +0 8:0.96 9:0.80 12:0.51 17:0.53 21:0.05 27:0.17 28:0.66 34:0.29 36:0.87 37:0.92 39:0.92 41:0.82 81:0.94 83:0.73 91:0.62 96:0.74 104:0.58 120:0.88 126:0.32 135:0.34 140:0.97 150:0.89 151:0.69 153:0.92 155:0.67 161:0.68 162:0.68 164:0.88 167:0.89 175:0.91 181:0.56 187:0.39 190:0.77 191:0.92 192:0.59 202:0.83 208:0.64 215:0.91 216:0.43 220:0.62 222:0.48 235:0.05 241:0.75 244:0.83 248:0.65 253:0.55 255:0.79 256:0.86 257:0.84 259:0.21 260:0.88 267:0.19 268:0.85 277:0.87 285:0.62 297:0.36 +2 1:0.74 6:0.99 7:0.81 8:0.80 11:0.57 12:0.51 17:0.11 20:0.79 21:0.22 25:0.88 27:0.09 28:0.66 30:0.76 32:0.80 34:0.39 36:0.87 37:0.95 39:0.92 43:0.85 66:0.10 69:0.64 70:0.44 74:0.97 77:0.70 78:0.72 81:0.91 83:0.78 85:1.00 87:0.98 91:0.18 98:0.38 100:0.82 101:0.86 104:0.58 108:0.95 111:0.76 114:0.90 121:0.90 122:0.57 123:0.95 124:0.95 126:0.54 127:0.92 129:0.99 133:0.95 135:0.78 145:0.79 146:0.78 147:0.82 149:0.99 150:0.95 152:1.00 154:0.81 155:0.67 159:0.92 161:0.68 163:0.81 165:0.90 167:0.75 169:0.99 172:0.73 173:0.29 176:0.73 177:0.38 178:0.55 179:0.18 181:0.56 186:0.73 187:0.21 192:0.59 195:0.98 201:0.74 202:0.54 204:0.89 208:0.64 212:0.37 215:0.79 216:0.68 222:0.48 230:0.84 232:0.80 233:0.69 235:0.52 241:0.61 242:0.63 243:0.28 245:0.92 247:0.39 253:0.79 259:0.21 261:1.00 265:0.40 266:0.80 267:0.73 276:0.93 282:0.88 290:0.90 297:0.36 299:0.88 300:0.55 +0 1:0.74 8:0.90 9:0.80 12:0.51 17:0.64 21:0.22 27:0.72 28:0.66 34:0.63 36:0.90 37:0.86 39:0.92 81:0.86 91:0.80 96:0.73 104:0.73 114:0.90 120:0.77 126:0.54 135:0.20 140:0.87 150:0.88 151:0.62 153:0.48 155:0.67 161:0.68 162:0.86 164:0.71 167:0.99 175:0.91 176:0.73 181:0.56 190:0.77 192:0.59 201:0.74 202:0.56 208:0.64 215:0.79 216:0.10 222:0.48 235:0.31 241:0.90 244:0.83 248:0.60 253:0.70 255:0.79 256:0.64 257:0.84 259:0.21 260:0.77 267:0.17 268:0.65 277:0.87 285:0.62 290:0.90 297:0.36 +2 1:0.74 6:0.91 8:0.84 9:0.80 12:0.51 17:0.69 20:0.99 21:0.30 25:0.88 27:0.55 28:0.66 34:0.47 36:0.51 37:0.35 39:0.92 41:0.95 78:0.79 81:0.82 83:0.81 87:0.98 91:0.72 96:0.91 100:0.83 104:0.58 111:0.79 114:0.86 120:0.85 126:0.54 135:0.37 140:0.45 150:0.88 151:0.92 152:0.96 153:0.72 155:0.67 161:0.68 162:0.86 164:0.82 165:0.84 167:0.97 169:0.79 175:0.91 176:0.73 181:0.56 186:0.79 189:0.93 192:0.59 201:0.74 202:0.69 208:0.64 215:0.72 216:0.43 220:0.87 222:0.48 232:0.80 235:0.42 238:0.92 241:0.83 244:0.83 248:0.91 253:0.88 255:0.79 256:0.78 257:0.84 259:0.21 260:0.85 261:0.83 267:0.23 268:0.77 277:0.87 285:0.62 290:0.86 297:0.36 +2 1:0.74 6:0.99 7:0.81 8:0.92 9:0.80 11:0.57 12:0.51 17:0.16 20:0.81 21:0.30 25:0.88 27:0.09 28:0.66 30:0.87 32:0.80 34:0.44 36:0.73 37:0.95 39:0.92 41:0.94 43:0.68 60:0.87 66:0.24 69:0.89 70:0.27 74:0.77 77:0.70 78:0.85 81:0.89 83:0.83 85:0.90 87:0.98 91:0.23 96:0.90 98:0.17 100:0.94 101:0.78 104:0.58 108:0.87 111:0.89 114:0.86 120:0.82 121:0.90 122:0.43 123:0.87 124:0.81 126:0.54 127:0.30 129:0.89 133:0.78 135:0.30 140:0.87 145:0.90 146:0.13 147:0.18 149:0.67 150:0.93 151:0.87 152:1.00 153:0.48 154:0.58 155:0.67 158:0.87 159:0.57 161:0.68 162:0.61 163:0.88 164:0.80 165:0.88 167:0.75 169:0.99 172:0.21 173:0.86 176:0.73 177:0.38 178:0.48 179:0.74 181:0.56 186:0.85 187:0.87 189:0.97 191:0.79 192:0.59 195:0.85 201:0.74 202:0.74 204:0.89 208:0.64 212:0.50 215:0.72 216:0.68 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.60 238:0.97 241:0.61 242:0.63 243:0.28 245:0.50 247:0.30 248:0.89 253:0.90 255:0.79 256:0.73 259:0.21 260:0.83 261:1.00 265:0.18 266:0.38 267:0.91 268:0.75 276:0.36 279:0.86 282:0.88 283:0.71 285:0.62 290:0.86 297:0.36 299:0.88 300:0.25 +1 1:0.60 7:0.70 8:0.76 10:0.86 11:0.75 12:0.20 17:0.62 18:0.78 21:0.22 27:0.59 29:0.71 30:0.21 32:0.67 34:0.56 36:0.23 37:0.27 39:0.38 43:0.66 45:0.87 46:0.88 55:0.52 66:0.94 69:0.12 70:0.88 71:0.78 74:0.61 77:0.70 81:0.51 86:0.68 91:0.33 98:0.93 99:0.83 101:0.87 107:0.91 108:0.10 110:0.93 114:0.85 122:0.83 123:0.10 125:0.88 126:0.32 127:0.58 129:0.05 131:0.61 135:0.26 139:0.66 144:0.76 145:0.44 146:0.81 147:0.84 149:0.58 150:0.45 154:0.55 155:0.51 157:0.95 158:0.73 159:0.37 160:0.88 165:0.65 166:0.86 170:0.90 172:0.85 173:0.28 174:0.89 176:0.63 177:0.88 178:0.55 179:0.38 182:1.00 187:0.21 192:0.51 195:0.60 197:0.92 201:0.57 204:0.81 208:0.50 212:0.88 215:0.79 216:0.40 222:0.76 227:0.61 229:0.61 230:0.73 231:0.86 233:0.76 235:0.42 239:0.85 241:0.24 242:0.39 243:0.95 244:0.83 246:0.84 247:0.82 253:0.62 259:0.21 265:0.93 266:0.27 267:0.23 271:0.57 276:0.75 279:0.75 282:0.75 283:0.66 287:0.61 289:0.91 290:0.85 297:0.36 300:0.70 +0 1:0.59 10:0.88 11:0.75 12:0.20 17:0.43 18:0.70 21:0.64 27:0.34 29:0.71 30:0.43 34:0.91 36:0.53 37:0.48 39:0.38 43:0.37 45:0.87 46:0.88 66:0.89 69:0.86 70:0.84 71:0.78 74:0.38 81:0.47 86:0.71 91:0.10 98:0.61 99:0.83 101:0.87 107:0.90 108:0.73 110:0.92 114:0.69 122:0.83 123:0.71 125:0.88 126:0.32 127:0.18 129:0.05 131:0.61 135:0.24 139:0.69 144:0.76 146:0.79 147:0.82 149:0.56 150:0.40 154:0.28 155:0.47 157:0.97 159:0.35 160:0.88 165:0.65 166:0.85 170:0.90 172:0.70 173:0.83 174:0.88 176:0.63 177:0.88 178:0.55 179:0.27 182:1.00 187:0.68 192:0.46 197:0.88 201:0.56 208:0.50 212:0.61 215:0.53 216:0.88 222:0.76 227:0.61 229:0.61 231:0.84 235:0.88 239:0.87 241:0.01 242:0.19 243:0.90 244:0.83 246:0.84 247:0.67 253:0.52 259:0.21 265:0.62 266:0.63 267:0.97 271:0.57 276:0.57 287:0.61 289:0.91 290:0.69 297:0.36 300:0.70 +0 10:0.85 11:0.49 12:0.20 17:0.67 18:0.62 21:0.54 27:0.52 29:0.71 30:0.33 34:0.36 36:0.48 37:0.43 39:0.38 43:0.09 45:0.66 46:0.88 55:0.52 66:0.60 69:0.65 70:0.83 71:0.78 74:0.36 76:0.99 77:0.89 81:0.29 86:0.67 91:0.29 96:0.74 98:0.53 101:0.47 107:0.90 108:0.50 110:0.91 114:0.64 122:0.83 123:0.48 124:0.58 125:0.88 126:0.24 127:0.46 129:0.05 131:0.85 133:0.60 135:0.76 139:0.64 140:0.45 144:0.57 145:0.70 146:0.78 147:0.82 149:0.18 150:0.32 151:0.57 153:0.48 154:0.36 157:0.76 158:0.72 159:0.70 160:0.88 162:0.86 166:0.78 170:0.90 172:0.63 173:0.51 174:0.79 176:0.56 177:0.88 179:0.55 182:0.72 187:0.87 190:0.98 195:0.88 197:0.82 202:0.36 212:0.30 216:0.79 220:0.74 222:0.76 227:0.82 229:0.61 231:0.79 235:0.60 239:0.84 241:0.10 242:0.55 243:0.67 244:0.83 245:0.70 246:0.61 247:0.79 248:0.56 253:0.58 254:0.88 256:0.45 259:0.21 265:0.54 266:0.84 267:0.17 268:0.46 271:0.57 276:0.59 282:0.71 285:0.46 287:0.61 289:0.89 290:0.64 300:0.70 +1 1:0.61 10:0.94 11:0.82 12:0.20 17:0.38 18:0.73 21:0.13 27:0.63 29:0.71 30:0.45 34:0.68 36:0.70 37:0.66 39:0.38 43:0.92 45:0.81 46:0.95 66:0.19 69:0.12 70:0.54 71:0.78 74:0.63 81:0.53 85:0.88 86:0.84 91:0.53 97:0.89 98:0.25 99:0.83 101:0.87 104:0.93 107:0.91 108:0.91 110:0.93 114:0.88 122:0.55 123:0.90 124:0.82 125:0.88 126:0.32 127:0.61 129:0.89 131:0.61 133:0.78 135:0.83 139:0.80 144:0.76 146:0.24 147:0.30 149:0.84 150:0.46 154:0.91 155:0.48 157:1.00 158:0.77 159:0.77 160:0.88 165:0.65 166:0.86 170:0.90 172:0.41 173:0.11 174:0.88 176:0.63 177:0.88 178:0.55 179:0.54 182:1.00 187:0.39 192:0.47 197:0.88 201:0.58 208:0.50 212:0.56 215:0.84 216:0.29 222:0.76 227:0.61 229:0.61 231:0.85 232:0.95 235:0.31 239:0.93 241:0.24 242:0.21 243:0.33 245:0.75 246:0.84 247:0.67 253:0.64 259:0.21 265:0.27 266:0.84 267:0.82 271:0.57 276:0.52 279:0.76 283:0.82 287:0.61 289:0.91 290:0.88 297:0.36 300:0.55 +0 10:0.85 11:0.52 12:0.20 17:0.32 18:0.64 21:0.78 27:0.45 29:0.71 30:0.40 34:0.64 36:0.27 37:0.50 39:0.38 43:0.31 45:0.73 46:0.88 55:0.59 66:0.35 69:0.76 70:0.79 71:0.78 74:0.31 86:0.66 91:0.04 98:0.42 101:0.47 107:0.89 108:0.59 110:0.90 123:0.57 124:0.83 125:0.88 127:0.75 129:0.59 131:0.61 133:0.85 135:0.66 139:0.65 144:0.76 145:0.44 146:0.93 147:0.94 149:0.52 154:0.23 157:0.77 159:0.93 160:0.88 166:0.61 170:0.90 172:0.90 173:0.39 174:0.83 177:0.88 178:0.55 179:0.18 182:0.72 187:0.68 197:0.84 212:0.81 216:0.77 222:0.76 227:0.61 229:0.61 231:0.71 235:0.68 239:0.84 241:0.07 242:0.80 243:0.51 244:0.83 245:0.94 246:0.61 247:0.76 253:0.28 259:0.21 265:0.44 266:0.92 267:0.52 271:0.57 276:0.92 287:0.61 289:0.89 300:0.98 +0 8:0.86 10:0.89 11:0.49 12:0.20 17:0.72 18:0.70 21:0.78 27:0.10 29:0.71 30:0.21 34:0.45 36:0.54 37:0.86 39:0.38 43:0.25 45:0.73 46:0.88 55:0.59 66:0.88 69:0.50 70:0.77 71:0.78 74:0.27 86:0.70 91:0.40 98:0.80 101:0.47 107:0.90 108:0.38 110:0.91 122:0.94 123:0.37 125:0.88 127:0.29 129:0.05 131:0.61 135:0.66 139:0.68 144:0.57 145:0.54 146:0.78 147:0.81 149:0.24 154:0.49 157:0.80 159:0.34 160:0.88 166:0.61 170:0.90 172:0.65 173:0.54 174:0.94 177:0.88 178:0.55 179:0.53 182:0.74 187:0.39 191:0.70 197:0.96 202:0.53 212:0.59 216:0.55 222:0.76 227:0.61 229:0.61 231:0.71 235:0.22 239:0.88 241:0.51 242:0.15 243:0.88 244:0.83 246:0.61 247:0.84 253:0.25 254:0.80 259:0.21 265:0.80 266:0.47 267:0.65 271:0.57 276:0.54 287:0.61 289:0.89 300:0.55 +0 8:0.72 10:0.83 11:0.49 12:0.20 17:0.92 18:0.62 21:0.78 27:0.72 29:0.71 30:0.68 34:0.37 36:0.57 37:0.23 39:0.38 43:0.26 45:0.66 46:0.88 55:0.71 66:0.20 69:0.84 70:0.43 71:0.78 74:0.28 86:0.64 91:0.48 98:0.20 101:0.47 107:0.89 108:0.69 110:0.90 122:0.77 123:0.87 124:0.74 125:0.88 127:0.48 129:0.05 131:0.61 133:0.60 135:0.20 139:0.62 144:0.57 145:0.65 146:0.25 147:0.26 149:0.22 154:0.19 157:0.76 159:0.64 160:0.88 163:0.80 166:0.61 170:0.90 172:0.16 173:0.79 174:0.83 175:0.75 177:0.05 178:0.55 179:0.88 182:0.72 197:0.84 202:0.36 212:0.19 216:0.06 222:0.76 227:0.61 229:0.61 231:0.70 235:0.80 239:0.83 241:0.77 242:0.19 243:0.32 244:0.93 245:0.49 246:0.61 247:0.55 253:0.26 257:0.93 259:0.21 265:0.17 266:0.47 267:0.19 271:0.57 276:0.24 277:0.87 287:0.61 289:0.89 300:0.33 +0 1:0.56 10:0.90 11:0.52 12:0.20 17:0.62 18:0.74 21:0.74 27:0.63 29:0.71 30:0.36 34:0.52 36:0.87 37:0.79 39:0.38 43:0.10 45:0.73 46:0.88 55:0.45 64:0.77 66:0.36 69:0.45 70:0.91 71:0.78 74:0.36 81:0.48 83:0.56 86:0.76 91:0.12 98:0.66 99:0.83 101:0.47 107:0.91 108:0.35 110:0.92 123:0.33 124:0.68 125:0.88 126:0.51 127:0.66 129:0.59 131:0.61 133:0.60 135:0.58 139:0.72 144:0.76 146:0.75 147:0.79 149:0.16 150:0.36 154:0.52 155:0.46 157:0.80 159:0.56 160:0.88 163:0.81 165:0.65 166:0.80 170:0.90 172:0.57 173:0.41 174:0.86 177:0.88 178:0.55 179:0.55 182:0.81 187:0.39 192:0.44 197:0.89 201:0.56 208:0.50 212:0.27 215:0.46 216:0.41 222:0.76 227:0.61 229:0.61 231:0.82 235:0.97 236:0.94 239:0.89 241:0.10 242:0.37 243:0.51 244:0.83 245:0.76 246:0.91 247:0.79 253:0.32 254:0.80 259:0.21 265:0.67 266:0.75 267:0.36 271:0.57 276:0.64 287:0.61 289:0.91 297:0.36 300:0.84 +0 1:0.62 10:0.90 11:0.75 12:0.20 17:0.16 18:0.74 21:0.47 27:0.47 29:0.71 30:0.68 34:0.82 36:0.19 37:0.66 39:0.38 43:0.61 44:0.88 45:0.85 46:0.88 66:0.36 69:0.40 70:0.52 71:0.78 74:0.49 77:0.96 81:0.51 86:0.75 91:0.18 97:0.97 98:0.31 99:0.83 101:0.96 102:0.96 104:0.90 106:0.81 107:0.90 108:0.63 110:0.92 114:0.76 117:0.86 122:0.85 123:0.61 124:0.60 125:0.88 126:0.32 127:0.17 129:0.05 131:0.61 133:0.45 135:0.51 139:0.76 144:0.76 145:1.00 146:0.47 147:0.53 149:0.62 150:0.45 154:0.50 155:0.48 157:0.97 158:0.76 159:0.32 160:0.88 165:0.65 166:0.85 170:0.90 172:0.41 173:0.28 174:0.86 176:0.63 177:0.88 178:0.55 179:0.31 182:1.00 187:0.39 192:0.47 195:0.88 197:0.88 201:0.58 206:0.81 208:0.50 212:0.69 215:0.63 216:0.82 222:0.76 227:0.61 229:0.61 231:0.85 232:0.84 235:0.74 239:0.91 241:0.05 242:0.31 243:0.49 244:0.83 245:0.71 246:0.84 247:0.60 253:0.56 259:0.21 265:0.33 266:0.47 267:0.59 271:0.57 276:0.48 277:0.69 279:0.79 282:0.72 283:0.67 287:0.61 289:0.91 290:0.76 297:0.36 300:0.43 +1 1:0.60 8:0.59 10:0.85 11:0.75 12:0.20 17:0.57 18:0.72 21:0.30 27:0.59 29:0.71 30:0.26 34:0.80 36:0.47 37:0.27 39:0.38 43:0.66 45:0.81 46:0.88 55:0.59 66:0.67 69:0.15 70:0.75 71:0.78 74:0.52 81:0.50 86:0.65 91:0.08 97:0.89 98:0.86 99:0.83 101:0.87 107:0.92 108:0.12 110:0.93 114:0.82 122:0.83 123:0.12 124:0.47 125:0.88 126:0.32 127:0.65 129:0.05 131:0.61 133:0.45 135:0.47 139:0.64 144:0.76 146:0.90 147:0.92 149:0.55 150:0.43 154:0.55 155:0.47 157:0.98 158:0.77 159:0.59 160:0.88 165:0.65 166:0.85 170:0.90 172:0.79 173:0.18 174:0.86 176:0.63 177:0.88 178:0.55 179:0.41 182:1.00 187:0.21 192:0.46 197:0.89 201:0.57 208:0.50 212:0.68 215:0.72 216:0.40 222:0.76 227:0.61 229:0.61 231:0.85 235:0.52 239:0.84 241:0.30 242:0.41 243:0.71 244:0.83 245:0.87 246:0.84 247:0.94 253:0.59 259:0.21 265:0.86 266:0.63 267:0.28 271:0.57 276:0.75 279:0.76 283:0.82 287:0.61 289:0.91 290:0.82 297:0.36 300:0.55 +2 1:0.63 8:0.67 10:0.91 11:0.75 12:0.20 17:0.47 18:0.82 22:0.93 27:0.27 29:0.71 30:0.17 33:0.97 34:0.73 36:0.76 37:0.69 39:0.38 43:0.57 45:0.87 46:0.88 47:0.93 66:0.92 69:0.37 70:0.91 71:0.78 74:0.42 75:0.96 81:0.57 83:0.56 86:0.78 87:0.98 91:0.15 97:0.89 98:0.94 99:0.83 101:0.96 107:0.92 108:0.29 110:0.93 114:0.95 122:0.89 123:0.28 125:0.88 126:0.32 127:0.63 129:0.05 131:0.61 135:0.56 139:0.77 144:0.76 146:0.87 147:0.90 149:0.62 150:0.51 154:0.46 155:0.49 157:0.99 159:0.45 160:0.88 165:0.65 166:0.86 170:0.90 172:0.78 173:0.41 174:0.90 176:0.63 177:0.88 178:0.55 179:0.52 182:1.00 187:0.68 192:0.48 197:0.93 201:0.60 208:0.61 212:0.57 215:0.96 216:0.44 219:0.90 222:0.76 226:0.96 227:0.61 229:0.61 231:0.86 232:0.75 235:0.12 239:0.91 241:0.39 242:0.14 243:0.92 244:0.83 246:0.84 247:0.92 253:0.65 259:0.21 262:0.93 265:0.94 266:0.38 267:0.36 271:0.57 276:0.66 279:0.75 287:0.61 289:0.91 290:0.95 291:0.97 292:0.88 297:0.36 300:0.70 +1 1:0.63 10:0.86 11:0.64 12:0.20 17:0.34 18:0.62 22:0.93 27:0.27 29:0.71 30:0.45 33:0.97 34:0.79 36:0.68 37:0.69 39:0.38 43:0.59 45:0.73 46:0.88 47:0.93 55:0.52 66:0.82 69:0.37 70:0.61 71:0.78 74:0.44 75:0.96 81:0.57 83:0.56 86:0.67 87:0.98 91:0.08 97:0.94 98:0.83 99:0.83 101:0.65 107:0.90 108:0.29 110:0.91 114:0.95 122:0.41 123:0.28 125:0.88 126:0.32 127:0.33 129:0.05 131:0.61 135:0.66 139:0.68 144:0.76 146:0.51 147:0.57 149:0.27 150:0.51 154:0.56 155:0.49 157:0.96 159:0.18 160:0.88 165:0.65 166:0.86 170:0.90 172:0.48 173:0.65 174:0.79 176:0.63 177:0.88 178:0.55 179:0.76 182:1.00 187:0.68 192:0.48 197:0.82 201:0.60 208:0.61 212:0.75 215:0.96 216:0.44 219:0.90 222:0.76 226:0.96 227:0.61 229:0.61 231:0.83 232:0.75 235:0.12 239:0.87 241:0.46 242:0.33 243:0.83 246:0.84 247:0.60 253:0.65 254:0.99 259:0.21 262:0.93 265:0.83 266:0.27 267:0.23 271:0.57 276:0.31 279:0.78 287:0.61 289:0.90 290:0.95 291:0.97 292:0.88 297:0.36 300:0.43 +0 1:0.63 8:0.79 10:0.92 11:0.64 12:0.20 17:0.34 18:0.70 27:0.27 29:0.71 30:0.14 34:0.75 36:0.78 37:0.69 39:0.38 43:0.58 45:0.83 46:0.88 55:0.52 66:0.69 69:0.08 70:0.97 71:0.78 74:0.56 81:0.57 86:0.78 87:0.98 91:0.37 98:0.85 99:0.83 101:0.65 107:0.91 108:0.07 110:0.93 114:0.95 117:0.86 122:0.55 123:0.07 124:0.44 125:0.88 126:0.32 127:0.71 129:0.05 131:0.61 133:0.39 135:0.66 139:0.73 144:0.76 146:0.84 147:0.87 149:0.38 150:0.51 154:0.63 155:0.49 157:1.00 158:0.72 159:0.49 160:0.88 165:0.65 166:0.86 170:0.90 172:0.75 173:0.18 174:0.88 176:0.63 177:0.88 178:0.55 179:0.51 182:1.00 187:0.39 192:0.48 197:0.90 201:0.60 208:0.50 212:0.57 215:0.96 216:0.44 222:0.76 227:0.61 229:0.61 231:0.85 232:0.75 235:0.12 239:0.89 241:0.41 242:0.13 243:0.73 245:0.80 246:0.84 247:0.90 253:0.67 254:0.95 259:0.21 265:0.85 266:0.63 267:0.59 271:0.57 276:0.68 287:0.61 289:0.91 290:0.95 297:0.36 300:0.84 +0 1:0.64 8:0.61 10:0.96 11:0.49 12:0.20 17:0.24 18:0.96 22:0.93 27:0.27 29:0.71 30:0.43 33:0.97 34:0.37 36:0.96 37:0.31 39:0.38 43:0.57 45:0.95 46:0.88 47:0.93 64:0.77 66:0.25 69:0.15 70:0.85 71:0.78 74:0.25 81:0.55 83:0.56 86:0.90 91:0.37 98:0.70 99:0.83 101:0.47 107:0.91 108:0.74 110:0.92 122:0.93 123:0.72 124:0.84 125:0.88 126:0.32 127:0.95 129:0.89 131:0.61 133:0.83 135:0.69 139:0.87 144:0.57 146:0.75 147:0.79 149:0.76 150:0.49 154:0.49 155:0.49 157:0.90 159:0.82 160:0.88 165:0.65 166:0.74 170:0.90 172:0.76 173:0.11 174:0.97 175:0.75 177:0.70 178:0.55 179:0.27 182:0.82 187:0.39 192:0.45 197:0.98 201:0.60 208:0.50 212:0.52 216:0.53 219:0.87 222:0.76 227:0.61 229:0.61 231:0.76 235:0.05 236:0.92 239:0.95 241:0.15 242:0.16 243:0.40 244:0.83 245:0.91 246:0.91 247:0.97 253:0.23 257:0.65 259:0.21 262:0.93 265:0.70 266:0.80 267:0.73 271:0.57 276:0.87 277:0.69 287:0.61 289:0.90 291:0.97 292:0.88 300:0.84 +0 10:0.83 11:0.49 12:0.20 17:0.88 18:0.62 21:0.78 27:0.72 29:0.71 30:0.21 34:0.75 36:0.91 37:0.23 39:0.38 43:0.26 45:0.66 46:0.88 66:0.36 69:0.84 70:0.37 71:0.78 74:0.28 86:0.64 91:0.07 98:0.16 101:0.47 107:0.89 108:0.69 110:0.89 122:0.85 123:0.67 124:0.57 125:0.88 127:0.14 129:0.05 131:0.61 133:0.37 135:0.37 139:0.62 144:0.57 145:0.59 146:0.25 147:0.26 149:0.21 154:0.19 157:0.76 159:0.23 160:0.88 163:0.88 166:0.61 170:0.90 172:0.16 173:0.81 174:0.79 175:0.75 177:0.05 178:0.55 179:0.61 182:0.72 187:0.21 197:0.82 212:0.42 216:0.06 222:0.76 227:0.61 229:0.61 231:0.67 235:0.31 239:0.83 241:0.05 242:0.45 243:0.40 244:0.93 245:0.43 246:0.61 247:0.35 253:0.26 257:0.93 259:0.21 265:0.18 266:0.23 267:0.18 271:0.57 276:0.17 277:0.69 287:0.61 289:0.88 300:0.25 +1 1:0.63 8:0.59 10:0.92 11:0.64 12:0.20 17:0.72 18:0.75 27:0.27 29:0.71 30:0.17 34:0.39 36:0.82 37:0.69 39:0.38 43:0.24 45:0.85 46:0.88 55:0.59 66:0.96 69:0.37 70:0.88 71:0.78 74:0.49 81:0.57 86:0.79 87:0.98 91:0.09 98:0.97 99:0.83 101:0.65 107:0.92 108:0.29 110:0.94 114:0.95 117:0.86 122:0.41 123:0.28 125:0.88 126:0.32 127:0.79 129:0.05 131:0.61 135:0.56 139:0.73 144:0.76 146:0.95 147:0.96 149:0.37 150:0.51 154:0.47 155:0.49 157:0.98 158:0.72 159:0.61 160:0.88 165:0.65 166:0.86 170:0.90 172:0.91 173:0.32 174:0.94 176:0.63 177:0.88 178:0.55 179:0.31 182:1.00 187:0.39 192:0.48 197:0.95 201:0.60 208:0.50 212:0.80 215:0.96 216:0.44 222:0.76 227:0.61 229:0.61 231:0.87 232:0.75 235:0.12 239:0.90 241:0.37 242:0.16 243:0.96 244:0.61 246:0.84 247:0.98 253:0.66 254:0.98 259:0.21 265:0.97 266:0.54 267:0.28 271:0.57 276:0.83 287:0.61 289:0.92 290:0.95 297:0.36 300:0.70 +0 8:0.68 10:0.86 11:0.49 12:0.20 17:0.64 18:0.60 21:0.78 27:0.45 29:0.71 30:0.76 34:0.79 36:0.22 37:0.50 39:0.38 43:0.08 45:0.66 46:0.88 55:0.59 66:0.52 69:0.83 70:0.29 71:0.78 74:0.27 86:0.68 91:0.09 98:0.19 101:0.47 107:0.89 108:0.68 110:0.89 122:0.43 123:0.66 124:0.50 125:0.88 127:0.33 129:0.05 131:0.61 133:0.36 135:0.77 139:0.64 144:0.57 145:0.69 146:0.33 147:0.40 149:0.07 154:0.28 157:0.76 159:0.64 160:0.88 166:0.61 170:0.90 172:0.27 173:0.74 174:0.79 177:0.88 178:0.55 179:0.87 182:0.72 187:0.68 197:0.81 202:0.36 212:0.52 216:0.77 222:0.76 227:0.61 229:0.61 231:0.67 235:1.00 239:0.84 241:0.39 242:0.24 243:0.61 244:0.61 245:0.48 246:0.61 247:0.47 253:0.25 254:0.99 259:0.21 265:0.21 266:0.27 267:0.28 271:0.57 276:0.15 287:0.61 289:0.88 300:0.25 +0 1:0.63 10:0.91 11:0.49 12:0.20 17:0.38 18:0.78 21:0.05 22:0.93 27:0.43 29:0.71 30:0.28 33:0.97 34:0.69 36:0.48 37:0.48 39:0.38 43:0.57 45:0.81 46:0.88 47:0.93 64:0.77 66:0.54 69:0.79 70:0.89 71:0.78 74:0.27 81:0.53 83:0.56 86:0.76 91:0.35 98:0.42 99:0.83 101:0.47 104:0.77 107:0.90 108:0.63 110:0.91 122:0.93 123:0.61 124:0.64 125:0.88 126:0.32 127:0.75 129:0.05 131:0.61 133:0.60 135:0.58 139:0.72 144:0.57 146:0.49 147:0.30 149:0.29 150:0.47 154:0.45 155:0.49 157:0.84 159:0.53 160:0.88 165:0.65 166:0.74 170:0.90 172:0.51 173:0.83 174:0.88 177:0.70 178:0.55 179:0.73 182:0.82 187:0.68 192:0.45 197:0.89 201:0.59 208:0.50 212:0.71 216:0.40 222:0.76 227:0.61 229:0.61 231:0.76 235:0.12 236:0.92 239:0.89 241:0.24 242:0.28 243:0.27 244:0.83 245:0.62 246:0.91 247:0.79 253:0.24 254:0.88 257:0.65 259:0.21 262:0.93 265:0.44 266:0.47 267:0.36 271:0.57 276:0.45 287:0.61 289:0.90 291:0.97 300:0.70 +1 7:0.66 10:0.91 11:0.49 12:0.20 17:0.49 18:0.87 21:0.68 26:0.99 27:0.45 28:0.70 29:0.71 30:0.21 34:0.80 36:0.17 37:0.50 39:0.61 43:0.34 45:0.94 58:0.93 66:0.26 69:0.63 70:0.90 71:0.87 74:0.61 81:0.27 86:0.90 89:0.98 91:0.14 98:0.43 101:0.65 108:0.48 123:0.46 124:0.90 126:0.24 127:0.75 129:0.81 133:0.89 135:0.78 139:0.84 141:0.91 145:0.47 146:0.84 147:0.87 149:0.57 150:0.29 154:0.47 159:0.88 161:0.68 167:0.63 170:0.77 172:0.84 173:0.34 174:0.91 177:0.88 178:0.55 179:0.19 181:0.64 187:0.68 197:0.90 199:0.97 212:0.60 215:0.49 216:0.77 222:0.21 223:0.99 224:0.93 225:0.97 230:0.68 233:0.66 234:0.91 235:0.84 239:0.89 240:0.95 241:0.12 242:0.42 243:0.46 244:0.61 245:0.93 247:0.91 253:0.46 254:0.94 259:0.21 265:0.45 266:0.87 267:0.28 271:0.57 274:0.91 276:0.92 297:0.36 300:0.84 +1 8:0.96 9:0.80 12:0.20 17:0.02 20:0.84 21:0.13 23:0.98 26:1.00 27:0.13 28:0.70 29:0.71 31:0.99 34:0.62 36:0.57 37:0.94 39:0.61 41:0.88 58:1.00 60:0.87 62:0.98 71:0.87 74:0.47 75:0.99 78:0.72 79:0.99 81:0.30 83:0.69 89:0.95 91:0.99 96:0.97 100:0.73 104:0.58 111:0.72 120:0.96 126:0.24 128:0.99 135:0.44 137:0.99 139:0.74 140:0.99 141:1.00 150:0.33 151:0.86 152:0.82 153:0.78 155:0.66 158:0.92 161:0.68 162:0.73 164:0.94 167:1.00 168:0.99 169:0.72 170:0.77 175:0.99 181:0.64 186:0.73 191:0.86 192:0.99 196:0.96 199:0.99 202:0.91 205:0.98 208:0.64 215:0.84 216:0.52 219:0.95 220:0.62 222:0.21 223:0.99 224:0.98 225:0.99 226:0.98 234:0.97 235:0.12 239:0.90 240:1.00 241:0.99 244:0.98 248:0.84 253:0.95 255:0.95 256:0.93 257:0.97 259:0.21 260:0.96 261:0.73 267:0.69 268:0.93 271:0.57 274:1.00 277:0.98 279:0.80 283:0.82 284:0.99 285:0.62 292:0.95 297:0.36 +2 1:0.67 6:0.97 8:0.89 9:0.80 10:0.89 11:0.57 12:0.20 17:0.34 18:0.76 20:0.89 21:0.05 23:0.97 26:1.00 27:0.38 28:0.70 29:0.71 30:0.15 31:0.99 34:0.28 36:0.77 37:0.72 39:0.61 41:0.82 43:0.58 45:0.88 58:1.00 62:0.97 64:0.77 66:0.92 69:0.56 70:0.88 71:0.87 74:0.61 78:0.93 79:0.99 81:0.69 83:0.69 86:0.84 89:0.97 91:0.72 96:0.96 97:0.94 98:0.91 99:0.95 100:0.99 101:0.96 108:0.43 111:0.98 114:0.95 120:0.90 123:0.41 126:0.51 127:0.52 128:0.98 129:0.05 135:0.56 137:0.99 139:0.83 140:0.95 141:1.00 146:0.86 147:0.88 149:0.39 150:0.56 151:0.91 152:0.99 153:0.85 154:0.54 155:0.66 158:0.81 159:0.43 161:0.68 162:0.83 164:0.87 165:0.81 167:0.82 168:0.98 169:0.99 170:0.77 172:0.79 173:0.61 174:0.83 176:0.70 177:0.88 179:0.48 181:0.64 186:0.92 187:0.39 189:0.98 191:0.88 192:0.99 196:0.98 197:0.85 199:0.99 201:0.64 202:0.83 205:0.97 208:0.64 212:0.59 215:0.91 216:0.65 219:0.94 220:0.62 222:0.21 223:0.99 224:0.99 225:0.99 234:0.99 235:0.31 238:0.98 239:0.87 240:1.00 241:0.71 242:0.38 243:0.92 247:0.76 248:0.81 251:1.00 253:0.96 254:0.90 255:0.95 256:0.87 259:0.21 260:0.91 261:0.99 265:0.91 266:0.54 267:0.23 268:0.88 271:0.57 274:0.99 276:0.68 279:0.78 283:0.67 284:0.99 285:0.62 290:0.95 292:0.88 297:0.36 300:0.70 +1 1:0.68 8:0.64 9:0.76 10:0.87 11:0.49 12:0.20 18:0.62 20:0.89 21:0.05 23:0.97 26:1.00 27:0.13 28:0.70 29:0.71 30:0.91 31:0.94 34:0.37 36:0.52 37:0.94 39:0.61 41:0.82 43:0.63 45:0.73 58:0.98 62:0.96 64:0.77 66:0.69 69:0.79 70:0.13 71:0.87 74:0.36 78:0.72 79:0.94 81:0.80 83:0.69 86:0.69 89:0.96 91:0.62 96:0.82 98:0.18 100:0.73 101:0.65 104:0.58 108:0.63 111:0.72 114:0.95 120:0.81 122:0.89 123:0.61 126:0.54 127:0.10 128:0.97 129:0.05 135:0.42 137:0.94 139:0.66 140:0.45 141:1.00 146:0.20 147:0.25 149:0.45 150:0.64 151:0.84 152:0.87 153:0.82 154:0.52 155:0.66 159:0.10 161:0.68 162:0.73 164:0.80 165:0.93 167:0.80 168:0.97 169:0.72 170:0.77 172:0.21 173:0.99 174:0.79 176:0.73 177:0.88 179:0.16 181:0.64 186:0.73 187:0.21 191:0.70 192:0.99 196:0.89 197:0.82 199:0.95 201:0.67 202:0.72 205:0.95 208:0.64 212:0.61 215:0.91 216:0.52 220:0.62 222:0.21 223:0.99 224:0.98 225:0.99 234:0.97 235:0.42 236:0.97 239:0.85 240:1.00 241:0.68 242:0.63 243:0.73 244:0.61 247:0.30 248:0.75 253:0.81 254:0.84 255:0.79 256:0.75 259:0.21 260:0.81 261:0.73 265:0.20 266:0.17 267:0.28 268:0.76 271:0.57 274:0.98 276:0.20 284:0.93 285:0.62 290:0.95 297:0.36 300:0.13 +2 1:0.60 8:0.71 9:0.79 10:0.98 11:0.57 12:0.20 17:0.57 18:0.99 21:0.30 22:0.93 23:0.98 26:1.00 27:0.50 28:0.70 29:0.71 30:0.60 31:0.98 32:0.71 33:0.97 34:0.66 36:0.81 37:0.60 39:0.61 43:0.39 44:0.92 45:1.00 47:0.93 58:0.99 62:0.99 64:0.77 66:0.15 69:0.53 70:0.75 71:0.87 74:0.95 75:0.98 77:0.89 79:0.98 81:0.49 83:0.69 86:0.99 89:1.00 91:0.18 96:0.93 97:1.00 98:0.56 99:0.83 101:0.96 102:0.97 108:0.99 114:0.80 117:0.86 122:0.91 123:0.99 124:0.94 126:0.32 127:0.97 128:0.99 129:0.96 133:0.94 135:0.61 137:0.98 139:0.99 140:0.90 141:1.00 145:0.70 146:0.70 147:0.74 149:0.92 150:0.43 151:0.91 153:0.85 154:0.19 155:0.64 158:0.82 159:0.98 161:0.68 162:0.81 165:0.65 167:0.54 168:0.99 170:0.77 172:0.99 173:0.09 174:0.99 176:0.62 177:0.88 179:0.09 181:0.64 187:0.39 190:0.89 192:0.58 195:1.00 196:0.99 197:0.97 199:1.00 201:0.57 202:0.75 204:0.81 205:0.98 208:0.64 212:0.60 216:0.86 219:0.94 222:0.21 223:0.99 224:0.99 225:0.99 226:0.98 234:0.99 235:0.42 239:0.98 240:1.00 242:0.27 243:0.10 245:1.00 247:0.99 248:0.87 253:0.96 254:0.84 255:0.78 256:0.80 259:0.21 262:0.93 265:0.57 266:0.87 267:0.82 268:0.81 271:0.57 274:0.99 276:1.00 279:0.82 281:0.91 282:0.79 283:0.82 284:0.98 285:0.60 290:0.80 291:0.97 292:0.95 300:0.84 +1 1:0.69 9:0.79 10:0.87 11:0.49 12:0.20 17:0.08 18:0.93 23:0.96 26:1.00 27:0.31 28:0.70 29:0.71 30:0.73 31:0.95 34:0.75 36:0.45 37:0.70 39:0.61 41:0.82 43:0.56 45:0.92 58:0.98 62:0.96 64:0.77 66:0.92 69:0.25 70:0.56 71:0.87 74:0.69 79:0.94 81:0.81 83:0.69 86:0.91 89:0.96 91:0.56 96:0.83 98:0.96 101:0.65 108:0.20 114:0.98 120:0.78 122:0.86 123:0.19 126:0.54 127:0.71 128:0.98 129:0.05 135:0.68 137:0.95 139:0.87 140:0.80 141:1.00 146:0.69 147:0.73 149:0.66 150:0.68 151:0.90 153:0.69 154:0.54 155:0.66 159:0.27 161:0.68 162:0.86 164:0.70 165:0.93 167:0.81 168:0.98 170:0.77 172:0.78 173:0.50 174:0.79 176:0.73 177:0.88 179:0.53 181:0.64 187:0.68 192:0.99 196:0.95 197:0.83 199:0.97 201:0.68 202:0.55 205:0.95 208:0.64 212:0.89 215:0.96 216:0.62 220:0.62 222:0.21 223:0.99 224:0.99 225:0.99 234:0.98 235:0.31 236:0.97 239:0.86 240:1.00 241:0.69 242:0.45 243:0.92 244:0.61 247:0.79 248:0.80 253:0.85 254:0.93 255:0.94 256:0.58 259:0.21 260:0.78 265:0.96 266:0.38 267:0.23 268:0.64 271:0.57 274:0.97 276:0.64 283:0.67 284:0.90 285:0.62 290:0.98 297:0.36 300:0.55 +2 1:0.64 6:0.95 8:0.70 9:0.79 10:0.97 11:0.57 12:0.20 17:0.64 18:1.00 20:0.82 21:0.30 22:0.93 23:0.99 26:1.00 27:0.21 28:0.70 29:0.71 30:0.45 31:1.00 34:0.25 36:0.66 37:0.74 39:0.61 41:0.82 43:0.74 45:1.00 47:0.93 58:1.00 62:0.99 64:0.77 66:0.17 69:0.15 70:0.82 71:0.87 74:0.96 75:0.97 78:0.72 79:0.99 81:0.65 83:0.69 86:1.00 89:0.99 91:0.70 96:0.98 97:1.00 98:0.59 99:0.95 100:0.90 101:0.96 108:0.98 111:0.82 114:0.82 117:0.86 120:0.72 122:0.86 123:0.98 124:0.95 126:0.51 127:1.00 128:0.99 129:0.97 133:0.96 135:0.51 137:1.00 139:0.99 140:0.98 141:1.00 146:0.91 147:0.93 149:0.96 150:0.52 151:0.69 152:0.87 153:0.96 154:0.12 155:0.65 158:0.77 159:0.98 161:0.68 162:0.81 164:0.70 165:0.81 167:0.71 168:0.99 169:0.84 170:0.77 172:0.99 173:0.05 174:0.99 176:0.63 177:0.88 179:0.08 181:0.64 186:0.73 187:0.39 190:0.89 191:0.73 192:0.98 193:0.96 196:1.00 197:0.97 199:1.00 201:0.62 202:0.85 204:0.80 205:0.99 208:0.64 212:0.54 216:0.95 219:0.91 222:0.21 223:1.00 224:0.99 225:0.99 226:0.98 234:0.99 235:0.52 236:0.94 239:0.97 240:1.00 241:0.44 242:0.32 243:0.15 245:1.00 247:0.99 248:0.64 253:0.99 254:0.86 255:0.79 256:0.89 259:0.21 260:0.72 261:0.84 262:0.93 265:0.60 266:0.89 267:0.28 268:0.89 271:0.57 274:1.00 276:1.00 279:0.82 281:0.91 284:0.99 285:0.62 290:0.82 292:0.95 300:0.84 +2 1:0.64 6:0.95 8:0.84 9:0.80 10:0.98 11:0.57 12:0.20 17:0.55 18:1.00 20:0.89 21:0.30 22:0.93 23:1.00 26:1.00 27:0.21 28:0.70 29:0.71 30:0.45 31:1.00 33:0.97 34:0.28 36:0.72 37:0.74 39:0.61 43:0.73 45:1.00 47:0.93 58:1.00 62:1.00 64:0.77 66:0.17 69:0.15 70:0.82 71:0.87 74:0.96 75:0.97 78:0.81 79:1.00 81:0.65 83:0.69 86:0.99 89:0.99 91:0.75 96:0.99 97:1.00 98:0.60 99:0.95 100:0.83 101:0.96 108:0.98 111:0.81 114:0.82 117:0.86 120:0.86 122:0.86 123:0.98 124:0.96 126:0.51 127:0.98 128:1.00 129:0.98 133:0.97 135:0.49 137:1.00 139:0.99 140:0.98 141:1.00 146:0.93 147:0.95 149:0.96 150:0.52 151:0.90 152:0.87 153:0.98 154:0.11 155:0.66 158:0.82 159:0.98 161:0.68 162:0.79 164:0.81 165:0.81 167:0.65 168:1.00 169:0.83 170:0.77 172:0.99 173:0.05 174:0.99 176:0.63 177:0.88 179:0.08 181:0.64 186:0.82 187:0.39 190:0.77 191:0.80 192:0.99 193:0.96 196:1.00 197:0.97 199:1.00 201:0.62 202:0.93 204:0.80 205:1.00 208:0.64 212:0.54 216:0.95 219:0.94 222:0.21 223:1.00 224:0.99 225:0.99 226:0.98 234:0.99 235:0.52 236:0.94 239:0.97 240:1.00 241:0.21 242:0.32 243:0.16 245:1.00 247:0.99 248:0.85 253:1.00 254:0.86 255:0.94 256:0.96 259:0.21 260:0.87 261:0.84 262:0.93 265:0.61 266:0.89 267:0.82 268:0.96 271:0.57 274:1.00 276:1.00 279:0.82 281:0.91 283:0.82 284:1.00 285:0.62 290:0.82 291:0.97 292:0.95 300:0.84 +3 1:0.64 6:0.97 8:0.84 9:0.80 10:0.84 11:0.54 12:0.20 17:0.26 18:0.88 20:0.99 21:0.59 23:1.00 26:1.00 27:0.38 28:0.70 29:0.71 30:0.58 31:1.00 32:0.80 34:0.19 36:0.71 37:0.72 39:0.61 41:0.97 43:0.55 44:0.93 45:0.93 58:1.00 62:1.00 64:0.77 66:0.94 69:0.62 70:0.71 71:0.87 74:0.74 76:0.85 77:1.00 78:0.92 79:1.00 81:0.64 83:0.69 86:0.86 89:0.96 91:0.95 96:1.00 97:1.00 98:0.95 99:0.99 100:0.99 101:0.87 102:0.98 108:0.47 111:0.98 114:0.73 120:0.98 122:0.86 123:0.46 126:0.51 127:0.64 128:1.00 129:0.05 135:0.49 137:1.00 139:0.91 140:0.98 141:1.00 145:0.84 146:0.89 147:0.91 149:0.67 150:0.49 151:0.94 152:0.99 153:0.97 154:0.44 155:0.67 158:0.82 159:0.49 161:0.68 162:0.84 164:0.97 165:0.81 167:0.97 168:1.00 169:0.99 170:0.77 172:0.84 173:0.65 174:0.79 176:0.70 177:0.88 179:0.41 181:0.64 186:0.92 189:0.98 191:0.93 192:0.99 193:0.98 195:0.70 196:1.00 197:0.81 199:1.00 201:0.62 202:0.96 204:0.85 205:1.00 208:0.64 212:0.73 215:0.55 216:0.65 219:0.94 222:0.21 223:1.00 224:0.99 225:0.99 234:0.99 235:0.98 238:0.98 239:0.92 240:1.00 241:0.85 242:0.41 243:0.94 244:0.61 247:0.76 248:0.95 251:1.00 253:1.00 255:1.00 256:0.98 259:0.21 260:0.98 261:0.99 265:0.95 266:0.54 267:0.59 268:0.98 271:0.57 274:1.00 276:0.74 279:0.82 281:0.91 282:0.88 283:0.82 284:1.00 285:0.62 290:0.73 292:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.67 8:0.96 9:0.79 10:0.85 11:0.45 12:0.20 17:0.11 18:0.81 21:0.05 23:0.99 26:1.00 27:0.28 28:0.70 29:0.71 30:0.76 31:1.00 34:0.49 36:0.56 37:0.84 39:0.61 43:0.33 45:0.83 58:1.00 62:0.96 64:0.77 66:0.83 69:0.73 70:0.36 71:0.87 74:0.50 79:1.00 81:0.64 83:0.69 86:0.77 87:0.98 89:0.96 91:0.94 96:0.99 97:0.98 98:0.74 99:0.83 101:0.47 108:0.56 114:0.92 120:0.72 122:0.86 123:0.54 126:0.51 127:0.24 128:1.00 129:0.05 135:0.21 137:1.00 139:0.76 140:0.99 141:1.00 146:0.54 147:0.60 149:0.44 150:0.58 151:0.86 153:0.91 154:0.25 155:0.66 158:0.75 159:0.20 161:0.68 162:0.83 164:0.65 165:0.65 167:0.99 168:1.00 170:0.77 172:0.51 173:0.90 174:0.79 175:0.75 176:0.63 177:0.70 179:0.63 181:0.64 190:0.77 191:0.89 192:0.98 196:0.98 197:0.82 199:1.00 201:0.64 202:0.92 205:0.99 208:0.64 212:0.57 216:0.55 219:0.90 220:0.62 222:0.21 223:1.00 224:0.99 225:0.99 228:0.99 234:0.99 235:0.22 236:0.94 239:0.83 240:1.00 241:0.91 242:0.60 243:0.84 244:0.83 247:0.35 248:0.77 251:1.00 253:0.98 254:0.90 255:0.79 256:0.94 257:0.65 259:0.21 260:0.73 265:0.75 266:0.38 267:0.59 268:0.95 271:0.57 274:1.00 276:0.39 277:0.69 279:0.80 284:1.00 285:0.62 290:0.92 292:0.88 300:0.33 +1 1:0.64 6:0.92 8:0.72 9:0.79 10:0.91 11:0.81 12:0.20 17:0.22 18:0.88 20:0.90 21:0.30 23:0.97 26:1.00 27:0.50 28:0.70 29:0.71 30:0.40 31:0.99 34:0.70 36:0.88 37:0.46 39:0.61 41:0.82 43:0.77 45:0.93 58:1.00 62:0.97 64:0.77 66:0.94 69:0.23 70:0.78 71:0.87 74:0.78 78:0.79 79:0.98 81:0.64 83:0.69 85:0.72 86:0.93 89:0.98 91:0.51 96:0.94 97:0.98 98:0.94 99:0.95 100:0.84 101:0.96 108:0.18 111:0.79 114:0.84 120:0.87 122:0.65 123:0.18 126:0.51 127:0.63 128:0.98 129:0.05 135:0.66 137:0.99 139:0.93 140:0.95 141:1.00 146:0.82 147:0.85 149:0.90 150:0.49 151:0.62 152:0.91 153:0.78 154:0.62 155:0.66 158:0.81 159:0.38 161:0.68 162:0.86 164:0.87 165:0.81 167:0.79 168:0.98 169:0.81 170:0.77 172:0.83 173:0.36 174:0.88 176:0.70 177:0.88 179:0.43 181:0.64 186:0.80 187:0.39 190:0.89 192:0.99 196:0.99 197:0.88 199:0.99 201:0.62 202:0.80 204:0.81 205:0.97 208:0.64 212:0.82 215:0.72 216:0.47 219:0.94 220:0.87 222:0.21 223:0.99 224:0.99 225:0.99 234:0.98 235:0.60 239:0.89 240:1.00 241:0.68 242:0.29 243:0.94 247:0.84 248:0.60 253:0.95 255:0.94 256:0.86 259:0.21 260:0.87 261:0.83 265:0.94 266:0.27 267:0.23 268:0.86 271:0.57 274:0.99 276:0.72 279:0.81 281:0.91 283:0.67 284:0.98 285:0.62 290:0.84 292:0.88 297:0.36 300:0.70 +2 1:0.62 6:0.84 7:0.70 8:0.77 9:0.80 10:0.86 11:0.49 12:0.20 17:0.66 18:0.91 20:0.92 21:0.47 23:0.99 26:1.00 27:0.55 28:0.70 29:0.71 30:0.76 31:1.00 34:0.61 36:0.41 37:0.69 39:0.61 43:0.63 44:0.88 45:0.94 58:1.00 62:0.96 64:0.77 66:0.32 69:0.19 70:0.61 71:0.87 74:0.70 77:0.96 78:0.83 79:0.99 81:0.54 83:0.69 86:0.88 89:0.96 91:0.93 96:0.98 97:0.94 98:0.46 99:0.83 100:0.87 101:0.65 104:0.75 108:0.77 111:0.83 114:0.76 120:0.90 122:0.86 123:0.76 124:0.73 126:0.51 127:0.63 128:0.99 129:0.05 133:0.65 135:0.75 137:1.00 139:0.89 140:0.96 141:1.00 145:0.89 146:0.31 147:0.37 149:0.70 150:0.46 151:0.93 152:0.86 153:0.88 154:0.54 155:0.66 158:0.76 159:0.63 161:0.68 162:0.82 164:0.88 165:0.65 167:0.99 168:0.99 169:0.85 170:0.77 172:0.61 173:0.18 174:0.79 176:0.63 177:0.88 179:0.44 181:0.64 186:0.84 189:0.86 190:0.77 191:0.79 192:0.99 195:0.82 196:1.00 197:0.85 199:1.00 201:0.60 202:0.89 204:0.83 205:0.99 208:0.64 212:0.70 215:0.63 216:0.51 219:0.91 220:0.93 222:0.21 223:1.00 224:0.99 225:0.99 230:0.73 233:0.66 234:0.98 235:0.68 238:0.91 239:0.86 240:1.00 241:0.89 242:0.51 243:0.33 244:0.83 245:0.84 247:0.67 248:0.89 253:0.98 255:0.94 256:0.92 259:0.21 260:0.91 261:0.86 265:0.47 266:0.63 267:0.44 268:0.93 271:0.57 274:1.00 276:0.66 277:0.69 279:0.80 281:0.91 282:0.74 283:0.67 284:0.99 285:0.62 290:0.76 292:0.95 297:0.36 300:0.84 +3 1:0.64 6:0.94 8:0.75 9:0.80 10:0.98 11:0.57 12:0.20 17:0.62 18:1.00 20:0.83 21:0.30 22:0.93 23:0.99 26:1.00 27:0.21 28:0.70 29:0.71 30:0.45 31:0.99 33:0.97 34:0.21 36:0.70 37:0.74 39:0.61 43:0.72 45:1.00 47:0.93 58:1.00 62:1.00 64:0.77 66:0.17 69:0.15 70:0.82 71:0.87 74:0.96 75:0.97 78:0.79 79:0.99 81:0.65 83:0.69 86:0.99 89:0.99 91:0.35 96:0.97 97:1.00 98:0.60 99:0.95 100:0.89 101:0.96 108:0.98 111:0.82 114:0.82 117:0.86 120:0.77 122:0.86 123:0.98 124:0.96 126:0.51 127:0.99 128:1.00 129:0.98 133:0.97 135:0.54 137:0.99 139:0.99 140:0.97 141:1.00 146:0.93 147:0.95 149:0.96 150:0.52 151:0.66 152:0.91 153:0.92 154:0.11 155:0.66 158:0.82 159:0.98 161:0.68 162:0.77 164:0.71 165:0.81 167:0.67 168:1.00 169:0.84 170:0.77 172:0.99 173:0.05 174:0.99 176:0.63 177:0.88 179:0.08 181:0.64 186:0.80 187:0.39 190:0.89 192:0.99 193:0.96 196:1.00 197:0.97 199:1.00 201:0.62 202:0.82 204:0.80 205:0.99 208:0.64 212:0.54 216:0.95 219:0.94 222:0.21 223:0.99 224:0.99 225:0.99 226:0.98 234:0.99 235:0.52 236:0.94 239:0.97 240:1.00 241:0.30 242:0.32 243:0.16 245:1.00 247:0.99 248:0.63 253:0.99 254:0.86 255:0.94 256:0.85 259:0.21 260:0.78 261:0.85 262:0.93 265:0.61 266:0.89 267:0.73 268:0.87 271:0.57 274:0.99 276:1.00 279:0.82 281:0.91 283:0.82 284:0.99 285:0.62 290:0.82 291:0.97 292:0.95 300:0.84 +2 1:0.67 6:0.98 8:0.94 9:0.80 10:0.89 11:0.57 12:0.20 17:0.34 18:0.76 20:0.90 21:0.05 23:0.99 26:1.00 27:0.38 28:0.70 29:0.71 30:0.15 31:1.00 34:0.28 36:0.77 37:0.72 39:0.61 41:0.88 43:0.58 45:0.88 58:1.00 62:0.97 64:0.77 66:0.92 69:0.56 70:0.88 71:0.87 74:0.61 78:0.92 79:1.00 81:0.69 83:0.69 86:0.84 89:0.97 91:0.74 96:0.98 97:0.99 98:0.91 99:0.95 100:0.98 101:0.96 108:0.43 111:0.97 114:0.95 120:0.94 123:0.41 126:0.51 127:0.52 128:0.99 129:0.05 135:0.51 137:1.00 139:0.83 140:0.96 141:1.00 146:0.86 147:0.88 149:0.39 150:0.56 151:0.91 152:0.99 153:0.88 154:0.54 155:0.67 158:0.81 159:0.43 161:0.68 162:0.85 164:0.91 165:0.81 167:0.80 168:0.99 169:0.99 170:0.77 172:0.79 173:0.61 174:0.83 176:0.70 177:0.88 179:0.48 181:0.64 186:0.92 187:0.21 189:0.98 191:0.88 192:0.99 196:0.99 197:0.85 199:0.99 201:0.64 202:0.87 205:0.99 208:0.64 212:0.59 215:0.91 216:0.65 219:0.94 220:0.62 222:0.21 223:1.00 224:0.99 225:0.99 234:0.99 235:0.31 238:0.98 239:0.87 240:1.00 241:0.69 242:0.38 243:0.92 247:0.76 248:0.85 251:1.00 253:0.98 254:0.90 255:1.00 256:0.91 259:0.21 260:0.94 261:0.98 265:0.91 266:0.54 267:0.23 268:0.92 271:0.57 274:1.00 276:0.68 279:0.81 283:0.67 284:0.99 285:0.62 290:0.95 292:0.88 297:0.36 300:0.70 +1 1:0.69 7:0.70 9:0.75 10:0.98 11:0.78 12:0.20 17:0.78 18:0.93 21:0.47 23:0.96 26:1.00 27:0.52 28:0.70 29:0.71 30:0.88 31:0.92 32:0.71 34:0.22 36:0.23 37:0.43 39:0.61 41:0.82 43:0.61 44:0.92 45:0.98 58:0.99 62:0.97 64:0.77 66:0.84 69:0.82 70:0.40 71:0.87 74:0.87 77:0.89 79:0.92 81:0.83 83:0.69 85:0.72 86:0.97 89:1.00 91:0.31 96:0.78 97:0.94 98:0.79 99:0.99 101:1.00 102:0.97 108:0.67 114:0.80 120:0.66 122:0.85 123:0.65 124:0.40 126:0.54 127:0.57 128:0.95 129:0.05 133:0.73 135:0.66 137:0.92 139:0.97 140:0.90 141:1.00 145:0.82 146:0.94 147:0.22 149:0.67 150:0.65 151:0.58 153:0.48 154:0.47 155:0.65 158:0.77 159:0.73 161:0.68 162:0.62 164:0.67 165:1.00 167:0.65 168:0.95 170:0.77 172:0.95 173:0.73 174:0.97 176:0.73 177:0.70 178:0.48 179:0.19 181:0.64 187:0.39 190:0.89 192:0.98 193:0.98 195:0.88 196:0.96 197:0.95 199:0.99 201:0.68 202:0.60 204:0.85 205:0.95 208:0.64 212:0.77 215:0.63 216:0.79 219:0.91 220:0.62 222:0.21 223:0.99 224:0.99 225:0.99 230:0.73 233:0.66 234:0.98 235:0.99 236:0.97 239:0.97 240:1.00 241:0.21 242:0.17 243:0.09 245:0.83 247:0.93 248:0.56 253:0.83 255:0.77 256:0.58 257:0.65 259:0.21 260:0.67 265:0.79 266:0.87 267:0.17 268:0.59 271:0.57 274:0.98 276:0.90 279:0.81 281:0.86 282:0.80 283:0.82 284:0.92 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 +2 1:0.65 6:0.96 8:0.93 9:0.79 10:0.86 11:0.49 12:0.20 17:0.38 18:0.88 20:0.90 21:0.22 23:1.00 26:1.00 27:0.31 28:0.70 29:0.71 30:0.88 31:1.00 34:0.44 36:0.79 37:0.70 39:0.61 43:0.65 44:0.88 45:0.87 58:1.00 62:1.00 64:0.77 66:0.85 69:0.15 70:0.21 71:0.87 74:0.67 75:0.97 76:0.85 77:0.89 78:0.83 79:1.00 81:0.66 83:0.91 86:0.85 89:0.96 91:0.95 96:1.00 97:0.99 98:0.89 99:0.95 100:0.91 101:0.65 102:0.96 108:0.12 111:0.86 114:0.85 120:0.89 122:0.86 123:0.12 126:0.51 127:0.45 128:1.00 129:0.05 135:0.63 137:1.00 138:1.00 139:0.88 140:0.97 141:1.00 145:0.76 146:0.36 147:0.42 149:0.64 150:0.54 151:0.94 152:0.92 153:0.98 154:0.55 155:0.66 158:0.77 159:0.14 161:0.68 162:0.80 164:0.83 165:0.81 167:0.99 168:1.00 169:0.87 170:0.77 172:0.57 173:0.68 174:0.79 175:0.75 176:0.63 177:0.70 179:0.73 181:0.64 186:0.84 189:0.97 190:0.77 191:0.88 192:0.98 193:0.98 195:0.54 196:1.00 197:0.85 199:1.00 201:0.63 202:0.97 204:0.85 205:1.00 208:0.64 212:0.95 216:0.62 219:0.91 220:0.62 222:0.21 223:1.00 224:0.99 225:0.99 226:0.98 234:0.99 235:0.42 236:0.94 238:0.94 239:0.93 240:1.00 241:0.92 242:0.52 243:0.86 244:0.83 247:0.47 248:0.84 253:1.00 255:0.79 256:0.98 257:0.65 259:0.21 260:0.89 261:0.87 265:0.89 266:0.12 267:0.94 268:0.98 271:0.57 274:1.00 276:0.46 277:0.69 279:0.81 281:0.91 282:0.75 284:1.00 285:0.62 290:0.85 292:0.95 300:0.18 +1 12:0.99 17:0.87 21:0.40 27:0.44 28:0.57 34:0.49 36:0.17 37:0.46 81:0.66 91:0.17 106:0.81 126:0.54 135:0.81 150:0.48 161:0.98 167:0.61 175:0.75 178:0.55 181:0.98 187:0.39 206:0.81 215:0.67 216:0.55 235:0.42 241:0.59 244:0.61 257:0.65 259:0.21 267:0.44 277:0.69 297:0.36 +0 12:0.99 17:0.99 21:0.78 27:0.67 28:0.57 30:0.95 34:0.24 36:0.42 37:0.29 43:0.05 55:0.99 66:0.20 69:1.00 98:0.05 108:1.00 123:1.00 124:0.61 127:0.10 129:0.59 133:0.47 135:0.26 146:0.05 147:0.05 149:0.05 154:0.05 159:0.47 161:0.98 167:0.46 172:0.05 173:0.98 177:0.05 178:0.55 179:0.48 181:0.98 187:0.68 216:0.60 235:0.12 241:0.01 243:0.40 245:0.36 259:0.21 265:0.05 267:0.76 300:0.08 +0 12:0.99 17:1.00 21:0.78 27:0.56 28:0.57 34:0.33 36:0.74 37:0.44 43:0.06 66:0.36 69:0.99 98:0.05 108:0.99 123:0.99 127:0.05 129:0.05 135:0.20 146:0.05 147:0.05 149:0.05 154:0.06 159:0.05 161:0.98 167:0.45 172:0.05 173:0.88 177:0.05 178:0.55 181:0.98 187:0.21 216:0.16 235:0.12 243:0.51 259:0.21 265:0.05 267:0.82 +1 12:0.99 17:0.76 21:0.78 27:0.51 28:0.57 30:0.95 34:0.94 36:0.73 37:0.62 43:0.26 55:0.59 66:0.64 69:0.80 91:0.37 98:0.20 108:0.64 123:0.62 127:0.10 129:0.05 135:0.28 145:0.45 146:0.27 147:0.33 149:0.23 154:0.19 159:0.12 161:0.98 163:0.97 167:0.48 172:0.16 173:0.84 177:0.05 178:0.55 179:0.31 181:0.98 187:0.39 212:0.95 216:0.28 235:0.05 241:0.12 242:0.82 243:0.69 247:0.12 259:0.21 265:0.22 266:0.12 267:0.69 276:0.19 300:0.08 +0 12:0.99 17:0.89 21:0.78 27:0.52 28:0.57 30:0.95 34:0.88 36:0.75 37:0.48 43:0.28 55:0.98 66:0.20 69:0.78 91:0.12 98:0.08 108:0.61 123:0.59 124:0.61 127:0.22 129:0.59 133:0.47 135:0.47 146:0.05 147:0.05 149:0.17 154:0.20 159:0.29 161:0.98 167:0.47 172:0.05 173:0.83 177:0.05 178:0.55 179:0.99 181:0.98 187:0.68 216:0.20 235:0.31 241:0.07 243:0.40 245:0.36 259:0.21 265:0.08 267:0.28 300:0.08 +0 12:0.99 17:0.98 20:0.95 21:0.78 27:0.50 28:0.57 34:0.50 36:0.68 37:0.42 43:0.34 66:0.36 69:0.65 78:0.77 91:0.02 98:0.06 100:0.80 108:0.49 111:0.76 123:0.47 127:0.05 129:0.05 135:0.34 146:0.05 147:0.05 149:0.13 152:0.97 154:0.26 159:0.05 161:0.98 167:0.45 169:0.78 172:0.05 173:0.49 177:0.05 178:0.55 181:0.98 186:0.77 187:0.68 216:0.92 235:0.68 243:0.51 259:0.21 261:0.86 265:0.06 267:0.82 +0 12:0.99 17:0.22 21:0.78 27:0.72 28:0.57 34:0.29 36:0.62 37:0.79 43:0.29 66:0.36 69:0.75 91:0.46 98:0.06 108:0.58 123:0.55 127:0.05 129:0.05 135:0.30 146:0.05 147:0.05 149:0.12 154:0.21 159:0.05 161:0.98 167:0.64 172:0.05 173:0.47 177:0.05 178:0.55 181:0.98 187:0.87 216:0.46 235:0.05 241:0.64 243:0.51 265:0.06 267:0.36 +0 12:0.99 17:1.00 20:0.95 21:0.78 27:0.50 28:0.57 34:0.42 36:0.64 37:0.42 43:0.23 66:0.36 69:0.88 78:0.72 91:0.02 98:0.06 100:0.80 108:0.77 111:0.74 123:0.75 127:0.05 129:0.05 135:0.54 146:0.05 147:0.05 149:0.09 152:0.97 154:0.16 159:0.05 161:0.98 167:0.45 169:0.78 172:0.05 173:0.82 177:0.05 178:0.55 181:0.98 186:0.73 187:0.68 216:0.92 235:0.02 243:0.51 259:0.21 261:0.86 265:0.06 267:0.79 +0 8:0.58 12:0.99 17:0.88 20:0.96 21:0.78 27:0.52 28:0.57 34:0.45 36:0.44 37:0.30 78:0.77 91:0.54 100:0.85 111:0.79 120:0.65 135:0.58 140:0.45 151:0.56 152:0.89 153:0.72 161:0.98 162:0.71 164:0.73 167:0.57 169:0.83 175:0.75 181:0.98 186:0.78 187:0.21 202:0.63 216:0.65 220:0.62 235:0.05 241:0.53 244:0.61 248:0.59 256:0.64 257:0.65 259:0.21 260:0.66 261:0.89 267:0.76 268:0.66 277:0.69 285:0.62 +3 1:0.57 8:0.95 9:0.75 10:0.87 11:0.86 12:0.37 17:0.01 18:0.62 21:0.40 23:0.98 27:0.24 29:0.77 30:0.43 31:1.00 34:0.30 36:0.70 37:0.72 39:0.82 43:0.21 45:0.87 46:0.97 62:0.96 66:0.91 69:0.25 70:0.53 71:0.80 74:0.44 79:1.00 81:0.35 83:0.69 86:0.62 91:0.97 96:1.00 97:1.00 98:0.99 99:0.83 101:0.52 104:0.76 107:0.94 108:0.20 110:0.93 114:0.72 120:0.99 122:0.90 123:0.19 125:0.97 126:0.26 127:0.90 128:0.98 129:0.05 131:0.94 135:0.28 137:1.00 138:0.99 139:0.60 140:1.00 143:1.00 144:0.92 146:0.79 147:0.83 149:0.29 150:0.34 151:0.83 153:0.99 154:0.52 155:0.64 157:0.61 158:0.73 159:0.35 160:1.00 162:0.81 164:0.98 165:0.63 166:0.61 168:0.98 170:0.90 172:0.74 173:0.42 174:0.86 175:0.75 176:0.59 177:0.99 179:0.62 182:0.86 190:0.89 191:0.94 192:1.00 196:0.89 197:0.90 201:0.54 202:0.96 205:0.98 208:0.64 212:0.60 215:0.67 216:0.58 222:0.60 227:0.97 229:0.92 231:0.86 232:0.82 235:0.52 239:0.86 241:0.96 242:0.32 243:0.91 244:0.83 246:0.61 247:0.86 248:0.75 253:0.99 254:0.84 255:0.78 256:0.97 257:0.65 259:0.21 260:0.99 264:0.96 265:0.99 266:0.47 267:0.65 268:0.98 271:0.62 275:0.93 276:0.62 277:0.69 279:0.80 283:0.82 284:0.99 285:0.62 287:0.95 289:0.93 290:0.72 294:0.95 297:0.36 298:0.99 300:0.43 +0 1:0.60 7:0.65 8:0.89 9:0.72 10:0.96 11:0.84 12:0.37 17:0.97 18:0.68 21:0.22 23:0.97 27:0.68 29:0.77 30:0.54 32:0.66 34:0.88 36:0.62 37:0.89 39:0.82 43:0.15 45:0.98 46:0.99 56:0.97 62:0.98 66:0.60 69:0.82 70:0.75 71:0.80 74:0.61 75:0.96 77:0.70 80:0.98 81:0.54 82:0.99 83:0.56 86:0.61 88:0.98 91:0.15 96:0.78 97:0.94 98:0.85 99:0.95 101:0.47 102:0.96 104:0.95 106:0.81 107:0.97 108:0.67 110:0.96 114:0.82 120:0.67 122:0.93 123:0.65 124:0.52 125:0.99 126:0.43 127:0.74 128:0.97 129:0.05 131:0.61 133:0.43 135:0.37 139:0.60 140:0.45 143:0.99 144:0.85 145:0.69 146:0.92 147:0.66 149:0.33 150:0.43 151:0.75 153:0.48 154:0.37 155:0.55 157:0.61 158:0.75 159:0.65 160:0.97 162:0.86 164:0.66 165:0.65 166:0.61 168:0.97 170:0.90 172:0.94 173:0.80 174:0.96 175:0.75 176:0.62 177:0.88 179:0.18 182:0.61 187:0.21 190:0.95 192:0.99 193:0.95 195:0.80 197:0.98 201:0.62 202:0.50 204:0.80 205:0.97 206:0.81 208:0.50 212:0.90 215:0.79 216:0.34 220:0.62 222:0.60 226:0.95 227:0.61 229:0.61 230:0.68 231:0.73 233:0.76 235:0.52 236:0.92 239:0.96 241:0.24 242:0.12 243:0.39 244:0.93 245:0.98 246:0.61 247:0.99 248:0.72 253:0.78 254:0.74 255:0.74 256:0.58 257:0.93 259:0.21 260:0.67 264:0.96 265:0.85 266:0.47 267:0.36 268:0.59 271:0.62 275:0.99 276:0.93 277:0.69 279:0.75 282:0.75 283:0.66 285:0.55 287:0.61 289:0.94 290:0.82 294:0.99 295:0.98 297:0.36 300:0.70 +1 1:0.66 2:0.99 7:0.69 8:0.64 9:0.74 10:0.95 11:0.92 12:0.37 17:0.98 18:0.86 27:0.58 29:0.77 30:0.55 31:0.93 32:0.64 34:0.87 36:0.66 37:0.50 39:0.82 43:0.21 44:0.88 45:0.98 46:0.97 56:0.97 64:0.77 66:0.35 69:0.15 70:0.76 71:0.80 74:0.64 77:0.89 79:0.92 80:0.99 81:0.69 83:0.69 86:0.86 91:0.75 96:0.79 97:0.94 98:0.81 99:0.95 101:0.65 107:0.97 108:0.12 110:0.95 114:0.89 120:0.67 122:0.41 123:0.80 124:0.60 125:0.96 126:0.51 127:0.91 129:0.59 131:0.84 133:0.47 135:0.30 137:0.93 138:0.95 139:0.83 140:0.45 143:0.99 144:0.92 145:0.93 146:0.88 147:0.90 149:0.42 150:0.56 151:0.92 153:0.77 154:0.48 155:0.63 157:0.91 159:0.79 160:0.96 162:0.86 164:0.64 165:0.65 166:0.77 170:0.90 172:0.94 173:0.12 174:0.95 176:0.62 177:0.99 179:0.15 182:0.80 190:0.89 191:0.70 192:1.00 193:0.97 195:0.90 196:0.94 197:0.94 201:0.67 202:0.54 204:0.82 208:0.64 212:0.88 215:0.91 216:0.08 219:0.88 222:0.60 227:0.89 229:0.61 230:0.71 231:0.84 233:0.76 235:0.42 239:0.93 241:0.79 242:0.12 243:0.57 244:0.61 245:0.99 246:0.61 247:1.00 248:0.82 253:0.80 254:0.74 255:0.78 256:0.58 259:0.21 260:0.68 264:0.96 265:0.65 266:0.47 267:0.59 268:0.63 271:0.62 275:0.99 276:0.95 279:0.78 281:0.91 282:0.73 284:0.91 285:0.60 286:0.99 287:0.61 289:0.93 290:0.89 292:0.88 294:0.99 295:0.98 297:0.36 298:0.99 300:0.70 +2 1:0.63 7:0.66 8:0.72 9:0.73 10:0.81 11:0.86 12:0.37 17:0.91 18:0.92 21:0.13 27:0.49 29:0.77 30:0.57 31:0.95 32:0.65 34:0.67 36:0.75 37:0.78 39:0.82 43:0.57 44:0.88 45:0.92 46:0.98 48:0.91 56:0.97 64:0.77 66:0.54 69:0.86 70:0.59 71:0.80 74:0.57 77:0.70 79:0.95 80:0.99 81:0.63 82:0.98 83:0.56 86:0.83 88:0.98 91:0.23 96:0.80 97:0.89 98:0.72 99:0.95 101:0.52 104:0.81 106:0.81 107:0.95 108:0.12 110:0.94 114:0.82 117:0.86 120:0.68 122:0.57 123:0.12 124:0.52 125:0.97 126:0.51 127:0.56 129:0.05 131:0.84 133:0.45 135:0.60 137:0.95 138:0.95 139:0.80 140:0.45 143:0.99 144:0.85 145:0.49 146:0.44 147:0.75 149:0.59 150:0.49 151:0.70 153:0.46 154:0.19 155:0.58 157:0.61 158:0.73 159:0.44 160:0.96 162:0.86 164:0.66 165:0.65 166:0.77 170:0.90 172:0.86 173:0.95 174:0.79 175:0.75 176:0.62 177:0.96 179:0.26 182:0.61 187:0.39 190:0.98 191:0.82 192:1.00 193:0.95 195:0.65 196:0.94 197:0.81 201:0.65 202:0.56 204:0.82 206:0.81 208:0.61 212:0.93 215:0.79 216:0.30 220:0.74 222:0.60 227:0.89 229:0.61 230:0.68 231:0.67 232:0.87 233:0.65 235:0.52 239:0.82 241:0.02 242:0.16 243:0.63 244:0.83 245:0.95 246:0.61 247:0.98 248:0.65 253:0.79 254:0.92 255:0.74 256:0.64 257:0.84 259:0.21 260:0.69 264:0.96 265:0.72 266:0.27 267:0.36 268:0.64 271:0.62 275:0.97 276:0.83 277:0.87 279:0.74 281:0.86 282:0.74 283:0.82 284:0.93 285:0.60 286:0.99 287:0.61 289:0.93 290:0.82 294:0.98 297:0.36 298:0.99 300:0.55 +1 1:0.66 7:0.65 8:0.83 9:0.74 10:0.96 11:0.87 12:0.37 17:0.97 18:0.93 21:0.13 23:0.97 27:0.68 29:0.77 30:0.62 31:0.91 32:0.71 34:0.75 36:0.68 37:0.89 39:0.82 43:0.23 44:0.92 45:0.99 46:0.99 48:0.91 56:0.97 62:0.98 64:0.77 66:0.99 69:0.15 70:0.64 71:0.80 74:0.71 75:0.97 77:0.70 79:0.90 80:0.99 81:0.66 82:0.99 83:0.69 86:0.89 88:0.98 91:0.11 96:0.79 97:1.00 98:0.98 99:0.99 101:0.96 102:0.97 104:0.95 106:0.81 107:0.97 108:0.12 110:0.96 114:0.88 120:0.68 122:0.86 123:0.12 125:0.98 126:0.51 127:0.83 128:0.97 129:0.05 131:0.61 135:0.26 137:0.91 139:0.89 140:0.87 143:0.99 144:0.85 145:0.69 146:0.97 147:0.97 149:0.46 150:0.51 151:0.65 153:0.48 154:0.53 155:0.64 157:0.61 158:0.76 159:0.69 160:0.97 162:0.86 164:0.66 165:0.70 166:0.61 168:0.97 170:0.90 172:0.98 173:0.15 174:0.95 176:0.63 177:0.99 179:0.15 182:0.61 187:0.21 190:0.95 191:0.85 192:1.00 193:0.95 195:0.83 196:0.93 197:0.97 201:0.66 202:0.58 204:0.81 205:0.97 206:0.81 208:0.61 212:0.91 215:0.84 216:0.34 219:0.91 220:0.91 222:0.60 226:0.96 227:0.61 229:0.61 230:0.68 231:0.75 233:0.76 235:0.52 236:0.94 239:0.96 241:0.15 242:0.12 243:0.99 244:0.83 246:0.61 247:1.00 248:0.63 253:0.82 255:0.78 256:0.64 259:0.21 260:0.68 264:0.96 265:0.98 266:0.54 267:0.28 268:0.66 271:0.62 275:0.99 276:0.95 279:0.81 281:0.86 282:0.79 283:0.82 284:0.93 285:0.60 287:0.61 289:0.94 290:0.88 292:0.88 294:0.99 295:0.98 297:0.36 300:0.55 +0 1:0.60 7:0.65 8:0.59 9:0.72 10:0.96 11:0.84 12:0.37 17:0.95 18:0.68 21:0.22 23:0.97 27:0.68 29:0.77 30:0.53 32:0.66 34:0.90 36:0.55 37:0.89 39:0.82 43:0.17 45:0.97 46:0.99 56:0.97 62:0.98 66:0.98 69:0.15 70:0.72 71:0.80 74:0.59 75:0.96 77:0.70 80:0.98 81:0.54 82:0.99 83:0.56 86:0.61 88:0.98 91:0.22 96:0.79 97:0.97 98:0.97 99:0.95 101:0.47 102:0.96 104:0.95 106:0.81 107:0.97 108:0.12 110:0.96 114:0.82 120:0.67 122:0.93 123:0.12 125:0.99 126:0.43 127:0.78 128:0.97 129:0.05 131:0.61 135:0.49 139:0.60 140:0.45 143:0.99 144:0.85 145:0.69 146:0.95 147:0.96 149:0.34 150:0.43 151:0.75 153:0.48 154:0.37 155:0.55 157:0.61 158:0.75 159:0.61 160:0.97 162:0.86 164:0.68 165:0.65 166:0.61 168:0.97 170:0.90 172:0.96 173:0.18 174:0.96 175:0.75 176:0.62 177:0.99 179:0.19 182:0.61 187:0.21 190:0.95 192:0.99 193:0.95 195:0.77 197:0.98 201:0.62 202:0.53 204:0.80 205:0.97 206:0.81 208:0.50 212:0.89 215:0.79 216:0.34 220:0.82 222:0.60 226:0.95 227:0.61 229:0.61 230:0.68 231:0.73 233:0.76 235:0.52 236:0.92 239:0.96 241:0.35 242:0.13 243:0.98 244:0.93 246:0.61 247:0.99 248:0.73 253:0.78 254:0.74 255:0.74 256:0.58 257:0.65 259:0.21 260:0.68 264:0.96 265:0.97 266:0.47 267:0.36 268:0.62 271:0.62 275:0.99 276:0.92 277:0.69 279:0.77 282:0.75 283:0.82 285:0.55 287:0.61 289:0.94 290:0.82 294:0.99 295:0.98 297:0.36 300:0.55 +0 1:0.63 8:0.91 9:0.72 10:0.95 11:0.92 12:0.37 17:0.95 18:0.94 21:0.22 23:0.97 27:0.68 29:0.77 30:0.26 31:0.89 34:0.80 36:0.57 37:0.89 39:0.82 43:0.20 44:0.88 45:0.96 46:0.97 48:0.91 56:0.97 62:0.98 64:0.77 66:0.99 69:0.17 70:0.75 71:0.80 74:0.42 75:0.96 76:0.85 77:0.70 79:0.89 80:0.99 81:0.54 82:0.99 83:0.60 86:0.86 88:0.98 91:0.15 96:0.76 97:0.97 98:0.98 99:0.95 101:0.96 102:0.96 107:0.96 108:0.14 110:0.94 114:0.73 122:0.99 123:0.13 125:0.97 126:0.43 127:0.80 128:0.97 129:0.05 131:0.61 135:0.19 137:0.89 139:0.85 140:0.80 143:0.99 144:0.92 145:0.69 146:0.96 147:0.97 149:0.29 150:0.44 151:0.61 153:0.48 154:0.31 155:0.57 157:0.78 158:0.72 159:0.67 160:0.96 162:0.86 165:0.65 166:0.61 168:0.97 170:0.90 172:0.97 173:0.16 174:0.95 176:0.59 177:0.99 179:0.17 182:0.73 187:0.21 190:0.98 191:0.79 192:0.98 193:0.95 195:0.82 196:0.92 197:0.97 201:0.62 202:0.50 204:0.80 205:0.97 208:0.54 212:0.91 216:0.34 219:0.90 220:0.74 222:0.60 226:0.96 227:0.61 229:0.61 231:0.77 235:0.52 236:0.92 239:0.95 241:0.10 242:0.12 243:0.99 244:0.83 246:0.61 247:1.00 248:0.59 253:0.65 254:0.94 255:0.72 256:0.58 259:0.21 264:0.96 265:0.98 266:0.54 267:0.28 268:0.59 271:0.62 275:0.99 276:0.93 279:0.77 281:0.86 282:0.72 284:0.88 285:0.55 287:0.61 289:0.92 290:0.73 292:0.88 294:0.99 295:0.98 300:0.55 +2 1:0.58 2:1.00 8:0.96 9:0.74 10:0.82 11:0.84 12:0.37 17:0.30 18:0.63 21:0.05 27:0.24 29:0.77 30:0.60 31:0.88 34:0.76 36:0.81 37:0.72 39:0.82 43:0.23 45:0.81 46:0.96 56:0.97 66:0.86 69:0.42 70:0.67 71:0.80 74:0.36 79:0.87 81:0.34 83:0.56 86:0.62 91:0.73 96:0.88 98:0.88 101:0.47 104:0.76 107:0.92 108:0.32 110:0.92 120:0.79 122:0.83 123:0.31 125:0.97 126:0.26 127:0.43 129:0.05 131:0.95 135:0.30 137:0.88 138:0.96 139:0.61 140:0.87 143:0.99 144:0.92 146:0.64 147:0.69 149:0.24 150:0.38 151:0.75 153:0.48 154:0.22 155:0.63 157:0.61 159:0.24 160:0.98 162:0.76 164:0.78 166:0.77 170:0.90 172:0.59 173:0.64 174:0.79 175:0.75 177:0.99 178:0.42 179:0.70 182:0.88 187:0.95 190:0.98 191:0.84 192:0.99 196:0.87 197:0.82 201:0.55 202:0.74 208:0.61 212:0.72 216:0.58 220:0.74 222:0.60 227:0.97 229:0.94 231:0.88 232:0.82 235:0.12 239:0.81 241:0.70 242:0.19 243:0.86 244:0.93 246:0.61 247:0.79 248:0.74 253:0.82 254:0.95 255:0.78 256:0.79 257:0.65 259:0.21 260:0.79 264:0.96 265:0.88 266:0.38 267:0.36 268:0.78 271:0.62 275:0.93 276:0.44 277:0.69 279:0.77 284:0.88 285:0.62 287:0.96 289:0.93 294:0.95 295:0.98 300:0.55 +0 1:0.60 7:0.65 9:0.72 10:0.95 11:0.91 12:0.37 17:0.96 18:0.68 21:0.22 23:0.97 27:0.68 29:0.77 30:0.61 32:0.66 34:0.75 36:0.55 37:0.89 39:0.82 43:0.17 45:0.98 46:0.99 56:0.97 62:0.98 66:0.99 69:0.15 70:0.68 71:0.80 74:0.64 75:0.96 77:0.70 80:0.98 81:0.54 82:0.99 83:0.56 86:0.61 88:0.98 91:0.11 96:0.78 97:0.97 98:0.98 99:0.95 101:0.52 102:0.96 104:0.95 106:0.81 107:0.97 108:0.12 110:0.96 114:0.82 120:0.67 122:0.86 123:0.12 125:0.99 126:0.43 127:0.83 128:0.97 129:0.05 131:0.61 135:0.30 139:0.60 140:0.45 143:0.99 144:0.92 145:0.69 146:0.97 147:0.98 149:0.40 150:0.43 151:0.75 153:0.48 154:0.14 155:0.55 157:0.85 158:0.75 159:0.71 160:0.97 162:0.86 164:0.66 165:0.65 166:0.61 168:0.97 170:0.90 172:0.97 173:0.14 174:0.95 176:0.62 177:0.99 179:0.16 182:0.76 187:0.21 190:0.95 191:0.76 192:0.99 193:0.95 195:0.85 197:0.97 201:0.62 202:0.50 204:0.80 205:0.97 206:0.81 208:0.50 212:0.88 215:0.79 216:0.34 220:0.62 222:0.60 226:0.95 227:0.61 229:0.61 230:0.68 231:0.80 233:0.76 235:0.52 236:0.92 239:0.95 241:0.18 242:0.17 243:0.99 244:0.83 246:0.61 247:0.99 248:0.72 253:0.79 254:0.93 255:0.74 256:0.58 259:0.21 260:0.67 264:0.96 265:0.98 266:0.54 267:0.28 268:0.59 271:0.62 275:0.99 276:0.94 279:0.77 282:0.75 283:0.82 285:0.55 287:0.61 289:0.94 290:0.82 294:0.99 295:0.98 297:0.36 300:0.70 +1 1:0.65 10:0.99 11:0.64 12:0.95 17:0.77 18:0.71 21:0.30 26:0.94 27:0.15 29:0.62 30:0.62 34:0.89 36:0.34 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.98 69:0.19 70:0.61 74:0.53 75:0.95 80:0.99 81:0.45 82:0.98 83:0.54 86:0.76 88:0.99 91:0.11 98:0.99 99:0.95 101:0.69 102:0.95 108:0.15 122:0.99 123:0.15 126:0.31 127:0.89 129:0.05 131:0.61 135:0.23 139:0.72 141:0.91 144:0.76 145:0.50 146:0.94 147:0.95 149:0.81 150:0.42 154:0.58 155:0.51 157:0.86 159:0.60 165:0.63 166:0.75 170:0.79 172:0.95 173:0.22 174:0.99 177:0.96 178:0.55 179:0.22 182:0.77 187:0.39 192:0.46 193:0.97 195:0.73 197:0.99 199:0.94 201:0.61 204:0.79 208:0.49 212:0.90 216:0.38 222:0.20 223:0.92 224:0.93 226:0.96 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.92 239:0.99 241:0.01 242:0.31 243:0.98 244:0.61 246:0.90 247:0.95 251:1.00 253:0.42 254:0.94 259:0.21 264:0.93 265:0.99 266:0.54 267:0.65 271:0.24 274:0.91 275:0.99 276:0.90 287:0.61 294:1.00 300:0.70 +1 1:0.67 8:0.93 10:1.00 11:0.55 12:0.95 17:0.89 18:0.64 21:0.30 26:0.94 27:0.15 29:0.62 30:0.95 33:0.97 34:0.74 36:0.46 37:0.64 39:0.97 43:0.57 45:0.94 55:0.52 56:0.97 58:0.93 66:1.00 69:0.21 70:0.19 74:0.49 75:0.96 80:0.99 81:0.48 82:0.98 83:0.54 86:0.72 88:0.99 91:0.04 97:0.89 98:1.00 99:0.95 101:0.46 102:0.95 108:0.17 122:0.98 123:0.16 126:0.31 127:0.96 129:0.05 131:0.61 135:0.24 139:0.68 141:0.91 144:0.76 145:0.52 146:1.00 147:1.00 149:0.87 150:0.44 154:0.58 155:0.52 157:0.84 159:0.91 165:0.63 166:0.75 170:0.79 172:1.00 173:0.08 174:1.00 177:0.96 178:0.55 179:0.09 182:0.77 187:0.39 192:0.47 193:0.96 195:0.98 197:1.00 199:0.94 201:0.62 204:0.82 208:0.49 212:0.94 216:0.38 222:0.20 223:0.92 224:0.93 226:0.96 227:0.61 229:0.61 231:0.64 234:0.91 235:0.68 236:0.92 239:0.99 241:0.02 242:0.80 243:1.00 244:0.61 246:0.90 247:0.93 251:1.00 253:0.40 254:0.94 259:0.21 264:0.93 265:1.00 266:0.38 267:0.19 271:0.24 274:0.91 275:0.98 276:1.00 279:0.75 287:0.61 291:0.97 294:0.99 295:0.98 300:0.55 +0 7:0.66 10:0.97 11:0.55 12:0.95 17:0.95 18:0.58 21:0.47 26:0.99 27:0.69 29:0.62 30:0.21 32:0.64 34:0.19 36:0.78 37:0.63 39:0.97 43:0.15 45:0.95 55:0.52 58:0.98 66:0.59 69:0.83 70:0.78 74:0.37 81:0.29 86:0.59 91:0.29 98:0.80 101:0.46 104:0.76 108:0.68 120:0.56 123:0.65 124:0.60 126:0.23 127:0.79 129:0.05 131:0.61 133:0.65 135:0.97 139:0.58 140:0.45 141:0.99 144:0.76 145:0.61 146:0.95 147:0.50 149:0.23 150:0.32 151:0.49 153:0.48 154:0.28 157:0.85 159:0.78 162:0.62 164:0.55 166:0.61 170:0.79 172:0.93 173:0.72 174:0.97 175:0.75 177:0.38 179:0.18 182:0.76 187:0.39 190:0.98 195:0.90 197:0.97 199:0.99 202:0.50 212:0.89 215:0.63 216:0.10 220:0.93 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 230:0.68 231:0.63 233:0.76 234:0.98 235:0.52 239:0.95 241:0.10 242:0.36 243:0.07 244:0.61 245:0.96 246:0.61 247:0.88 248:0.48 253:0.33 254:0.93 256:0.45 257:0.93 259:0.21 260:0.56 264:0.93 265:0.80 266:0.71 267:0.28 268:0.46 271:0.24 274:0.97 275:0.99 276:0.92 277:0.69 285:0.45 287:0.61 294:0.99 297:0.36 300:0.84 +1 1:0.58 10:0.96 11:0.64 12:0.95 17:0.88 18:0.76 21:0.30 26:0.94 27:0.28 29:0.62 30:0.87 34:0.92 36:0.27 37:0.24 39:0.97 43:0.57 45:0.91 56:0.97 58:0.93 66:0.84 69:0.21 70:0.41 74:0.43 80:0.99 81:0.39 82:0.98 83:0.54 86:0.84 88:0.99 91:0.25 98:0.79 99:0.83 101:0.69 102:0.95 108:0.17 122:0.99 123:0.16 124:0.37 126:0.31 127:0.57 129:0.05 131:0.61 133:0.36 135:0.56 139:0.78 141:0.91 144:0.76 145:0.77 146:0.76 147:0.80 149:0.44 150:0.37 154:0.56 155:0.52 157:0.83 159:0.44 165:0.63 166:0.75 170:0.79 172:0.74 173:0.29 174:0.93 175:0.75 177:0.88 178:0.55 179:0.55 182:0.77 187:0.87 192:0.46 193:0.96 195:0.66 197:0.96 199:0.94 201:0.56 204:0.82 208:0.49 212:0.58 216:0.34 222:0.20 223:0.92 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 236:0.92 239:0.95 241:0.30 242:0.30 243:0.85 244:0.61 245:0.58 246:0.89 247:0.79 253:0.37 254:0.84 257:0.65 259:0.21 264:0.93 265:0.79 266:0.47 267:0.19 271:0.24 274:0.91 275:0.96 276:0.60 277:0.69 287:0.61 294:0.99 300:0.55 +0 1:0.66 10:0.99 11:0.64 12:0.95 17:0.77 18:0.71 21:0.22 26:0.98 27:0.15 29:0.62 30:0.60 32:0.67 34:0.79 36:0.56 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.33 69:0.19 70:0.59 74:0.52 75:0.95 80:0.99 81:0.58 82:0.99 83:0.54 86:0.77 88:0.98 91:0.17 98:0.71 99:0.95 101:0.69 102:0.96 104:0.89 106:0.81 108:0.15 122:0.99 123:0.74 124:0.60 126:0.43 127:0.91 129:0.59 131:0.61 133:0.47 135:0.21 139:0.72 141:0.91 144:0.76 145:0.50 146:0.76 147:0.80 149:0.82 150:0.50 154:0.58 155:0.51 157:0.86 159:0.57 165:0.63 166:0.75 170:0.79 172:0.89 173:0.23 174:0.99 177:0.96 178:0.55 179:0.21 182:0.77 187:0.39 192:0.49 193:0.97 195:0.71 197:0.99 199:0.97 201:0.65 204:0.79 206:0.81 208:0.49 212:0.93 215:0.79 216:0.38 222:0.20 223:0.98 224:0.93 226:0.95 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.94 239:0.99 242:0.31 243:0.57 244:0.61 245:0.98 246:0.90 247:0.97 251:1.00 253:0.42 254:0.94 259:0.21 264:0.93 265:0.70 266:0.54 267:0.73 271:0.24 274:0.91 275:0.99 276:0.91 283:0.82 287:0.61 294:1.00 297:0.36 300:0.84 +0 7:0.66 10:0.97 11:0.55 12:0.95 17:0.91 18:0.61 21:0.47 26:0.99 27:0.69 29:0.62 30:0.14 32:0.64 34:0.40 36:0.39 37:0.63 39:0.97 43:0.20 45:0.95 58:0.98 66:0.93 69:0.82 70:0.83 74:0.34 81:0.34 86:0.64 91:0.37 98:0.89 101:0.46 104:0.76 108:0.67 120:0.65 123:0.65 124:0.36 126:0.23 127:0.81 129:0.05 131:0.61 133:0.38 135:0.60 139:0.61 140:0.45 141:0.99 144:0.76 145:0.41 146:0.96 147:0.50 149:0.25 150:0.38 151:0.60 153:0.48 154:0.33 157:0.85 159:0.72 162:0.86 164:0.55 166:0.61 170:0.79 172:0.96 173:0.76 174:0.97 177:0.38 179:0.19 182:0.76 187:0.21 190:0.98 195:0.84 197:0.96 199:0.99 202:0.36 212:0.92 215:0.63 216:0.10 220:0.62 222:0.20 223:0.99 224:1.00 227:0.61 229:0.61 230:0.68 231:0.63 233:0.76 234:1.00 235:0.60 239:0.95 241:0.18 242:0.36 243:0.07 244:0.61 245:0.85 246:0.61 247:0.88 248:0.59 253:0.30 254:0.93 256:0.45 257:0.93 259:0.21 260:0.66 264:0.93 265:0.89 266:0.71 267:0.19 268:0.46 271:0.24 274:0.97 275:0.98 276:0.92 283:0.67 285:0.45 287:0.61 294:0.99 297:0.36 300:0.84 +0 10:0.86 11:0.64 12:0.95 17:0.16 18:0.71 21:0.78 26:0.97 27:0.20 29:0.62 30:0.66 33:0.97 34:0.75 36:0.61 37:0.66 39:0.97 43:0.64 45:0.73 55:0.59 58:0.93 66:0.85 69:0.47 70:0.53 74:0.40 82:0.98 86:0.77 88:0.99 91:0.20 98:0.84 101:0.52 102:0.95 108:0.36 123:0.35 127:0.35 129:0.05 131:0.61 135:0.34 139:0.72 141:0.91 144:0.76 145:0.54 146:0.60 147:0.65 149:0.35 154:0.53 157:0.76 159:0.22 166:0.61 170:0.79 172:0.57 173:0.69 174:0.79 177:0.96 178:0.55 179:0.68 182:0.73 187:0.68 193:0.94 195:0.57 197:0.85 199:0.95 212:0.88 216:0.60 222:0.20 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.12 239:0.88 241:0.27 242:0.22 243:0.86 244:0.61 246:0.61 247:0.74 253:0.35 254:0.97 259:0.21 264:0.93 265:0.84 266:0.23 267:0.65 271:0.24 274:0.91 275:0.91 276:0.44 287:0.61 291:0.97 294:0.96 300:0.43 +0 10:0.91 11:0.64 12:0.95 17:0.61 18:0.77 21:0.78 26:0.97 27:0.02 29:0.62 30:0.61 33:0.97 34:0.53 36:0.51 37:0.95 39:0.97 43:0.62 45:0.85 58:0.93 66:0.10 69:0.60 70:0.48 74:0.32 86:0.75 91:0.14 98:0.31 101:0.69 108:0.46 122:0.57 123:0.79 124:0.69 127:0.20 129:0.05 131:0.61 133:0.66 135:0.70 139:0.72 141:0.91 144:0.76 145:0.41 146:0.53 147:0.59 149:0.63 154:0.51 157:0.80 159:0.54 166:0.61 170:0.79 172:0.51 173:0.39 174:0.88 175:0.91 177:0.70 178:0.55 179:0.35 182:0.74 187:0.68 197:0.91 199:0.96 202:0.36 212:0.59 216:0.87 222:0.20 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.31 239:0.89 241:0.57 242:0.14 243:0.58 244:0.83 245:0.70 246:0.61 247:0.79 253:0.29 257:0.84 259:0.21 264:0.93 265:0.29 266:0.75 267:0.28 271:0.24 274:0.91 275:0.95 276:0.54 277:0.95 287:0.61 291:0.97 294:0.97 300:0.43 +1 1:0.67 8:0.92 9:0.72 10:1.00 11:0.55 12:0.95 17:0.89 18:0.64 21:0.30 23:0.96 26:0.94 27:0.15 29:0.62 30:0.95 33:0.97 34:0.44 36:0.50 37:0.64 39:0.97 43:0.57 45:0.94 55:0.52 56:0.97 58:0.93 62:0.97 66:1.00 69:0.21 70:0.19 74:0.49 75:0.95 80:0.99 81:0.48 82:0.98 83:0.54 86:0.72 88:0.99 91:0.14 98:1.00 99:0.95 101:0.46 102:0.95 108:0.17 122:0.98 123:0.16 126:0.31 127:0.95 128:0.96 129:0.05 131:0.82 135:0.34 139:0.68 140:0.45 141:0.91 143:0.99 144:0.76 145:0.52 146:1.00 147:1.00 149:0.87 150:0.44 151:0.58 153:0.69 154:0.58 155:0.54 157:0.84 159:0.91 162:0.86 165:0.63 166:0.75 168:0.96 170:0.79 172:1.00 173:0.08 174:1.00 177:0.96 179:0.09 182:0.77 187:0.39 190:0.95 191:0.76 192:0.48 193:0.96 195:0.98 197:1.00 199:0.94 201:0.62 202:0.46 204:0.82 205:0.95 208:0.49 212:0.94 216:0.38 220:0.87 222:0.20 223:0.92 224:0.93 226:0.96 227:0.87 229:0.87 231:0.64 234:0.91 235:0.68 236:0.92 239:0.99 241:0.15 242:0.80 243:1.00 244:0.61 246:0.90 247:0.93 248:0.57 251:1.00 253:0.40 254:0.94 255:0.70 256:0.45 259:0.21 264:0.93 265:1.00 266:0.38 267:0.19 268:0.55 271:0.24 274:0.91 275:0.98 276:1.00 285:0.49 287:0.92 291:0.97 294:0.99 300:0.55 +1 7:0.66 8:0.74 10:0.88 11:0.55 12:0.95 17:0.13 18:0.63 21:0.40 26:0.99 27:0.21 29:0.62 30:0.15 34:0.56 36:0.77 37:0.74 39:0.97 43:0.57 45:0.73 55:0.52 58:0.99 66:0.54 69:0.12 70:0.90 74:0.39 80:0.98 81:0.30 86:0.65 91:0.44 98:0.50 101:0.46 104:0.84 106:0.81 108:0.10 120:0.74 123:0.10 124:0.52 126:0.23 127:0.44 129:0.05 131:0.61 133:0.45 135:0.19 139:0.63 140:0.80 141:0.99 144:0.76 146:0.65 147:0.70 149:0.37 150:0.33 151:0.65 153:0.48 154:0.47 157:0.78 159:0.56 162:0.70 164:0.71 166:0.61 170:0.79 172:0.57 173:0.16 174:0.83 175:0.75 177:0.88 178:0.42 179:0.59 182:0.73 187:0.39 190:0.98 197:0.87 199:0.98 202:0.63 206:0.81 212:0.59 215:0.67 216:0.95 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 230:0.68 231:0.62 233:0.76 234:0.99 235:0.42 239:0.87 241:0.30 242:0.12 243:0.62 244:0.83 245:0.75 246:0.61 247:0.91 248:0.66 253:0.34 256:0.64 257:0.65 259:0.21 260:0.74 264:0.93 265:0.52 266:0.63 267:0.69 268:0.65 271:0.24 274:0.98 275:0.95 276:0.52 277:0.69 283:0.82 285:0.45 287:0.61 294:0.96 297:0.36 300:0.70 +0 1:0.58 10:0.95 11:0.64 12:0.95 17:0.76 18:0.78 21:0.30 26:0.99 27:0.43 29:0.62 30:0.40 34:0.69 36:0.42 37:0.29 39:0.97 43:0.61 45:0.92 56:0.97 58:0.98 66:0.63 69:0.48 70:0.96 74:0.35 80:0.99 81:0.39 82:0.98 83:0.54 86:0.78 88:0.99 91:0.22 98:0.67 99:0.83 101:0.69 102:0.95 108:0.37 120:0.65 122:0.99 123:0.36 124:0.64 126:0.31 127:0.84 129:0.05 131:0.61 133:0.72 135:0.20 139:0.74 140:0.45 141:0.99 144:0.76 145:0.89 146:0.90 147:0.92 149:0.75 150:0.37 151:0.60 153:0.48 154:0.51 155:0.51 157:0.83 159:0.79 162:0.86 164:0.55 165:0.63 166:0.75 170:0.79 172:0.82 173:0.30 174:0.94 177:0.96 179:0.38 182:0.77 187:0.39 190:0.98 192:0.46 193:0.97 195:0.90 197:0.96 199:0.95 201:0.56 202:0.36 204:0.81 208:0.49 212:0.54 216:0.46 220:0.62 222:0.20 223:0.99 224:1.00 227:0.61 229:0.61 231:0.64 234:1.00 235:0.42 236:0.92 239:0.95 241:0.41 242:0.16 243:0.69 244:0.83 245:0.81 246:0.89 247:0.96 248:0.59 253:0.31 256:0.45 259:0.21 260:0.66 264:0.93 265:0.68 266:0.87 267:0.52 268:0.46 271:0.24 274:0.97 275:0.98 276:0.78 285:0.45 287:0.61 294:0.99 300:0.94 +0 10:0.92 11:0.64 12:0.95 17:0.51 18:0.63 21:0.59 26:0.98 27:0.67 29:0.62 30:0.68 32:0.64 34:0.78 36:0.45 37:0.57 39:0.97 43:0.39 45:0.77 58:0.93 66:0.86 69:0.37 70:0.21 74:0.36 81:0.28 86:0.72 91:0.38 98:0.81 101:0.69 104:0.97 106:0.81 108:0.29 123:0.28 126:0.23 127:0.29 129:0.05 131:0.61 135:0.68 139:0.67 141:0.91 144:0.76 145:0.56 146:0.56 147:0.62 149:0.29 150:0.30 154:0.56 157:0.78 159:0.20 166:0.61 170:0.79 172:0.61 173:0.60 174:0.88 177:0.96 178:0.55 179:0.59 182:0.73 187:0.87 195:0.55 197:0.90 199:0.97 206:0.81 212:0.95 215:0.55 216:0.36 222:0.20 223:0.98 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.68 239:0.89 241:0.03 242:0.31 243:0.87 244:0.61 246:0.61 247:0.76 253:0.32 254:0.95 259:0.21 264:0.93 265:0.81 266:0.12 267:0.28 271:0.24 274:0.91 275:0.93 276:0.50 283:0.82 287:0.61 294:0.96 297:0.36 300:0.18 +0 10:0.89 11:0.55 12:0.95 17:0.32 18:0.75 21:0.78 26:0.96 27:0.21 29:0.62 30:0.17 34:0.62 36:0.85 37:0.74 39:0.97 43:0.26 45:0.77 55:0.45 58:0.93 66:0.48 69:0.74 70:0.90 74:0.44 86:0.68 91:0.23 98:0.46 101:0.46 108:0.57 122:0.95 123:0.54 124:0.57 127:0.35 129:0.05 131:0.61 133:0.45 135:0.20 139:0.66 141:0.91 144:0.76 146:0.64 147:0.69 149:0.31 154:0.46 157:0.79 159:0.55 166:0.61 170:0.79 172:0.59 173:0.67 174:0.88 175:0.75 177:0.88 178:0.55 179:0.47 182:0.73 187:0.39 197:0.89 199:0.95 202:0.50 212:0.60 216:0.95 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.52 239:0.88 241:0.18 242:0.24 243:0.60 244:0.61 245:0.81 246:0.61 247:0.88 253:0.37 254:0.93 257:0.65 259:0.21 264:0.93 265:0.48 266:0.75 267:0.36 271:0.24 274:0.91 275:0.95 276:0.57 277:0.69 287:0.61 294:0.96 300:0.84 +0 1:0.66 10:0.99 11:0.64 12:0.95 17:0.81 18:0.72 21:0.22 26:0.98 27:0.15 29:0.62 30:0.58 32:0.67 34:0.89 36:0.55 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.98 69:0.19 70:0.59 74:0.54 75:0.95 80:0.99 81:0.58 82:0.99 83:0.54 86:0.77 88:0.98 91:0.25 98:0.99 99:0.95 101:0.69 102:0.96 104:0.89 106:0.81 108:0.15 122:0.99 123:0.15 126:0.43 127:0.89 129:0.05 131:0.61 135:0.23 139:0.72 141:0.91 144:0.76 145:0.50 146:0.92 147:0.94 149:0.83 150:0.50 154:0.58 155:0.51 157:0.86 159:0.54 165:0.63 166:0.75 170:0.79 172:0.96 173:0.24 174:0.99 177:0.96 178:0.55 179:0.20 182:0.77 187:0.39 192:0.49 193:0.97 195:0.69 197:1.00 199:0.97 201:0.65 204:0.79 206:0.81 208:0.49 212:0.92 215:0.79 216:0.38 222:0.20 223:0.98 224:0.93 226:0.95 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.94 239:0.99 242:0.30 243:0.98 244:0.61 246:0.90 247:0.98 251:1.00 253:0.43 254:0.94 259:0.21 264:0.93 265:0.99 266:0.38 267:0.69 271:0.24 274:0.91 275:0.99 276:0.91 283:0.82 287:0.61 294:1.00 297:0.36 300:0.70 +1 1:0.58 10:0.95 11:0.64 12:0.95 17:0.81 18:0.78 21:0.30 26:0.96 27:0.43 29:0.62 30:0.27 34:0.81 36:0.39 37:0.29 39:0.97 43:0.62 45:0.92 56:0.97 58:0.93 66:0.53 69:0.48 70:0.95 74:0.40 80:0.99 81:0.39 82:0.98 83:0.54 86:0.78 88:0.99 91:0.15 98:0.66 99:0.83 101:0.69 102:0.95 108:0.37 122:0.90 123:0.36 124:0.64 126:0.31 127:0.82 129:0.05 131:0.61 133:0.60 135:0.21 139:0.74 141:0.91 144:0.76 145:0.89 146:0.90 147:0.92 149:0.75 150:0.37 154:0.51 155:0.51 157:0.83 159:0.79 165:0.63 166:0.75 170:0.79 172:0.82 173:0.29 174:0.94 177:0.96 178:0.55 179:0.33 182:0.77 187:0.39 192:0.46 193:0.97 195:0.91 197:0.96 199:0.95 201:0.56 204:0.81 208:0.49 212:0.54 216:0.46 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 236:0.92 239:0.95 241:0.15 242:0.16 243:0.62 244:0.83 245:0.90 246:0.89 247:0.97 253:0.35 259:0.21 264:0.93 265:0.67 266:0.84 267:0.52 271:0.24 274:0.91 275:0.98 276:0.82 287:0.61 294:0.99 300:0.84 +1 1:0.58 10:0.92 11:0.64 12:0.95 17:0.03 18:0.65 21:0.40 26:0.96 27:0.26 29:0.62 30:0.66 34:0.67 36:0.36 37:0.58 39:0.97 43:0.60 45:0.83 55:0.71 56:0.97 58:0.93 66:0.92 69:0.23 70:0.49 74:0.35 81:0.38 83:0.54 86:0.67 91:0.12 98:0.94 99:0.83 101:0.69 108:0.18 122:0.98 123:0.18 126:0.31 127:0.62 129:0.05 131:0.61 135:0.30 139:0.66 141:0.91 144:0.76 146:0.92 147:0.93 149:0.50 150:0.36 154:0.52 155:0.47 157:0.81 159:0.53 165:0.63 166:0.75 170:0.79 172:0.79 173:0.25 174:0.95 177:0.96 178:0.55 179:0.50 182:0.77 187:0.87 192:0.44 197:0.97 199:0.95 201:0.55 208:0.49 212:0.48 216:0.90 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.52 236:0.92 239:0.90 241:0.07 242:0.31 243:0.92 244:0.83 246:0.89 247:0.94 253:0.32 259:0.21 264:0.93 265:0.94 266:0.47 267:0.28 271:0.24 274:0.91 275:0.96 276:0.68 287:0.61 294:0.97 300:0.43 +1 1:0.62 10:0.95 11:0.64 12:0.95 17:0.87 18:0.76 21:0.30 26:0.94 27:0.28 29:0.62 30:0.87 34:0.96 36:0.33 37:0.24 39:0.97 43:0.56 44:0.88 45:0.90 56:0.97 58:0.93 64:0.77 66:0.83 69:0.44 70:0.36 74:0.43 80:1.00 81:0.61 82:0.99 83:0.60 86:0.84 88:0.99 91:0.23 98:0.78 99:0.95 101:0.69 102:0.96 108:0.34 122:0.97 123:0.33 124:0.37 126:0.43 127:0.64 129:0.05 131:0.61 133:0.36 135:0.44 139:0.84 141:0.91 144:0.76 145:0.79 146:0.76 147:0.80 149:0.34 150:0.50 154:0.55 155:0.55 157:0.82 159:0.45 165:0.70 166:0.75 170:0.79 172:0.71 173:0.48 174:0.93 175:0.75 177:0.88 178:0.55 179:0.59 182:0.77 187:0.87 192:0.51 193:0.98 195:0.66 197:0.95 199:0.94 201:0.61 204:0.84 208:0.54 212:0.61 216:0.34 222:0.20 223:0.92 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.52 236:0.94 239:0.95 241:0.46 242:0.24 243:0.84 244:0.61 245:0.57 246:0.89 247:0.79 253:0.37 254:0.86 257:0.65 259:0.21 264:0.93 265:0.78 266:0.63 267:0.44 271:0.24 274:0.91 275:0.96 276:0.57 277:0.69 281:0.86 287:0.61 294:0.99 300:0.43 +0 1:0.58 10:0.87 11:0.64 12:0.95 17:0.26 18:0.66 21:0.40 26:0.99 27:0.20 29:0.62 30:0.58 34:0.69 36:0.51 37:0.66 39:0.97 43:0.63 45:0.77 56:0.97 58:0.98 66:0.80 69:0.52 70:0.44 74:0.38 80:0.99 81:0.38 82:0.98 83:0.54 86:0.68 88:0.99 91:0.14 98:0.78 99:0.83 101:0.69 102:0.95 108:0.40 120:0.56 122:0.90 123:0.38 126:0.31 127:0.27 129:0.05 131:0.61 135:0.42 139:0.66 140:0.45 141:0.99 144:0.76 145:0.41 146:0.60 147:0.66 149:0.41 150:0.36 151:0.49 153:0.48 154:0.53 155:0.49 157:0.78 159:0.22 162:0.86 164:0.55 165:0.63 166:0.75 170:0.79 172:0.45 173:0.69 174:0.83 175:0.75 177:0.88 179:0.75 182:0.77 187:0.68 190:0.98 192:0.45 193:0.97 195:0.58 197:0.88 199:0.94 201:0.55 202:0.36 204:0.79 208:0.49 212:0.55 216:0.60 220:0.93 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 231:0.64 234:0.98 235:0.52 236:0.92 239:0.90 241:0.52 242:0.40 243:0.82 244:0.83 246:0.89 247:0.55 248:0.48 253:0.34 256:0.45 257:0.65 259:0.21 260:0.56 264:0.93 265:0.79 266:0.27 267:0.65 268:0.46 271:0.24 274:0.97 275:0.93 276:0.33 277:0.69 285:0.45 287:0.61 294:0.97 300:0.33 +1 1:0.58 10:0.95 11:0.64 12:0.95 17:0.78 18:0.78 21:0.30 26:0.96 27:0.43 29:0.62 30:0.38 34:0.83 36:0.30 37:0.29 39:0.97 43:0.62 45:0.92 56:0.97 58:0.93 66:0.62 69:0.48 70:0.93 74:0.36 80:0.99 81:0.39 82:0.98 83:0.54 86:0.78 88:0.99 91:0.18 98:0.72 99:0.83 101:0.69 102:0.95 108:0.37 122:0.99 123:0.36 124:0.65 126:0.31 127:0.87 129:0.05 131:0.61 133:0.72 135:0.18 139:0.74 141:0.91 144:0.76 145:0.89 146:0.90 147:0.92 149:0.75 150:0.37 154:0.51 155:0.51 157:0.83 159:0.74 165:0.63 166:0.75 170:0.79 172:0.82 173:0.34 174:0.94 177:0.96 178:0.55 179:0.37 182:0.77 187:0.39 192:0.46 193:0.97 195:0.87 197:0.96 199:0.95 201:0.56 204:0.81 208:0.49 212:0.59 216:0.46 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 236:0.92 239:0.95 241:0.15 242:0.16 243:0.68 244:0.83 245:0.82 246:0.89 247:0.97 253:0.32 259:0.21 264:0.93 265:0.73 266:0.84 267:0.52 271:0.24 274:0.91 275:0.98 276:0.79 287:0.61 294:0.99 300:0.84 +0 1:0.58 10:0.91 11:0.64 12:0.95 17:0.36 18:0.66 21:0.40 26:0.99 27:0.20 29:0.62 30:0.38 34:0.70 36:0.50 37:0.66 39:0.97 43:0.63 45:0.77 56:0.97 58:0.98 66:0.83 69:0.52 70:0.63 74:0.40 80:0.99 81:0.38 82:0.98 83:0.54 86:0.68 88:0.99 91:0.14 98:0.88 99:0.83 101:0.69 102:0.95 108:0.40 120:0.56 122:0.90 123:0.38 126:0.31 127:0.41 129:0.05 131:0.61 135:0.42 139:0.66 140:0.45 141:0.99 144:0.76 145:0.41 146:0.68 147:0.73 149:0.42 150:0.36 151:0.49 153:0.48 154:0.52 155:0.49 157:0.78 159:0.27 162:0.86 164:0.55 165:0.63 166:0.75 170:0.79 172:0.51 173:0.69 174:0.83 177:0.96 179:0.77 182:0.77 187:0.68 190:0.98 192:0.45 193:0.97 195:0.58 197:0.87 199:0.94 201:0.55 202:0.36 204:0.79 208:0.49 212:0.42 216:0.60 220:0.93 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 231:0.64 234:0.98 235:0.52 236:0.92 239:0.91 241:0.52 242:0.45 243:0.84 244:0.61 246:0.89 247:0.60 248:0.48 253:0.35 254:0.97 256:0.45 259:0.21 260:0.56 264:0.93 265:0.88 266:0.27 267:0.65 268:0.46 271:0.24 274:0.97 275:0.93 276:0.41 285:0.45 287:0.61 294:0.97 300:0.43 +0 1:0.66 10:0.99 11:0.64 12:0.95 17:0.78 18:0.72 21:0.22 26:0.98 27:0.15 29:0.62 30:0.60 32:0.67 34:0.89 36:0.61 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.43 69:0.19 70:0.58 74:0.52 75:0.95 80:0.99 81:0.58 82:0.99 83:0.54 86:0.77 88:0.98 91:0.18 98:0.69 99:0.95 101:0.69 102:0.96 104:0.89 106:0.81 108:0.15 122:0.99 123:0.15 124:0.60 126:0.43 127:0.71 129:0.59 131:0.61 133:0.47 135:0.20 139:0.72 141:0.91 144:0.76 145:0.50 146:0.76 147:0.80 149:0.83 150:0.50 154:0.58 155:0.51 157:0.86 159:0.55 165:0.63 166:0.75 170:0.79 172:0.89 173:0.23 174:0.99 177:0.96 178:0.55 179:0.19 182:0.77 187:0.39 192:0.49 193:0.97 195:0.71 197:1.00 199:0.97 201:0.65 204:0.79 206:0.81 208:0.49 212:0.93 215:0.79 216:0.38 222:0.20 223:0.98 224:0.93 226:0.95 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.94 239:0.99 242:0.31 243:0.57 244:0.61 245:0.98 246:0.90 247:0.97 251:1.00 253:0.42 254:0.94 259:0.21 264:0.93 265:0.70 266:0.54 267:0.69 271:0.24 274:0.91 275:0.99 276:0.91 283:0.82 287:0.61 294:1.00 297:0.36 300:0.70 +3 6:0.90 8:0.97 12:0.37 20:0.79 21:0.78 27:0.03 28:0.96 29:0.62 34:0.75 36:0.98 37:0.93 39:0.98 71:0.70 78:0.82 91:1.00 96:0.76 100:0.86 111:0.82 135:0.26 140:1.00 145:0.76 151:0.53 152:0.97 153:0.72 161:0.79 162:0.45 167:0.86 169:0.96 175:0.97 178:0.55 181:0.59 186:0.82 190:0.89 191:0.99 202:0.99 216:0.76 220:0.74 222:0.21 235:0.97 241:0.98 244:0.93 248:0.55 253:0.53 256:0.75 257:0.93 259:0.21 261:0.98 267:0.91 268:0.69 271:0.48 277:0.95 285:0.47 +4 1:0.74 6:0.90 7:0.66 8:0.92 9:0.80 11:0.57 12:0.37 18:0.72 20:0.84 21:0.13 27:0.03 28:0.96 29:0.62 30:0.95 31:0.99 34:0.88 36:0.79 37:0.93 39:0.98 41:0.96 43:0.83 48:0.91 64:0.77 66:0.75 69:0.65 70:0.13 71:0.70 74:0.54 78:0.97 79:0.99 81:0.65 83:0.91 85:0.80 86:0.83 91:0.79 96:0.96 98:0.21 100:0.98 101:0.87 104:0.93 108:0.50 111:0.98 114:0.79 120:0.92 122:0.57 123:0.48 126:0.54 127:0.10 129:0.05 135:0.63 137:0.99 139:0.81 140:0.45 146:0.23 147:0.29 149:0.76 150:0.79 151:0.97 152:0.97 153:0.89 154:0.79 155:0.67 159:0.11 161:0.79 162:0.69 164:0.86 165:0.93 167:0.79 169:0.96 172:0.32 173:0.79 176:0.73 177:0.70 179:0.15 181:0.59 186:0.97 187:0.68 189:0.98 191:0.97 192:0.87 196:0.98 201:0.93 202:0.80 208:0.64 212:0.84 215:0.61 216:0.76 222:0.21 230:0.69 233:0.76 235:0.31 238:0.97 241:0.77 242:0.63 243:0.77 247:0.35 248:0.96 253:0.94 255:0.95 256:0.83 259:0.21 260:0.92 261:0.98 265:0.23 266:0.17 267:0.59 268:0.83 271:0.48 276:0.23 281:0.91 284:0.98 285:0.62 290:0.79 297:0.36 300:0.13 +1 1:0.74 11:0.57 12:0.37 17:0.61 18:0.71 21:0.68 27:0.45 28:0.96 29:0.62 30:0.95 32:0.80 34:0.85 36:0.20 37:0.50 39:0.98 43:0.81 44:0.93 64:0.77 66:0.75 69:0.71 70:0.13 71:0.70 74:0.63 77:0.70 81:0.44 83:0.91 85:0.80 86:0.82 91:0.23 98:0.16 101:0.87 108:0.54 114:0.56 122:0.57 123:0.52 126:0.54 127:0.09 129:0.05 135:0.78 139:0.85 145:0.49 146:0.22 147:0.28 149:0.75 150:0.69 154:0.76 155:0.67 159:0.11 161:0.79 165:0.93 167:0.50 172:0.32 173:0.72 176:0.73 177:0.70 178:0.55 179:0.10 181:0.59 187:0.68 192:0.87 195:0.72 201:0.93 208:0.64 212:0.84 215:0.37 216:0.77 222:0.21 235:0.88 241:0.18 242:0.63 243:0.77 247:0.35 253:0.41 259:0.21 265:0.17 266:0.17 267:0.36 271:0.48 276:0.24 282:0.88 290:0.56 297:0.36 299:0.88 300:0.13 +3 1:0.74 8:0.68 9:0.80 11:0.57 12:0.37 17:0.64 18:0.96 20:0.80 21:0.68 28:0.96 29:0.62 30:0.91 31:0.91 32:0.80 34:0.61 36:0.29 37:0.97 39:0.98 41:0.82 43:0.80 44:0.93 60:0.87 64:0.77 66:0.26 69:0.73 70:0.30 71:0.70 74:0.96 77:0.70 78:0.84 79:0.90 81:0.44 83:0.91 85:0.99 86:0.99 91:0.22 96:0.75 98:0.57 100:0.73 101:0.87 104:0.80 108:0.57 111:0.82 114:0.56 120:0.63 122:0.57 123:0.93 124:0.81 126:0.54 127:0.75 129:0.93 133:0.84 135:0.54 137:0.91 139:0.99 140:0.80 145:0.65 146:0.81 147:0.84 149:0.99 150:0.69 151:0.72 152:0.77 153:0.48 154:0.74 155:0.67 158:0.91 159:0.89 161:0.79 162:0.53 164:0.57 165:0.93 167:0.46 169:0.72 172:0.90 173:0.44 176:0.73 177:0.70 178:0.42 179:0.17 181:0.59 186:0.84 187:0.21 192:0.87 195:0.97 196:0.96 201:0.93 202:0.58 208:0.64 212:0.49 215:0.37 216:0.98 219:0.95 220:0.62 222:0.21 235:0.88 241:0.01 242:0.45 243:0.50 245:0.96 247:0.55 248:0.67 253:0.79 255:0.95 256:0.45 259:0.21 260:0.64 261:0.79 265:0.39 266:0.75 267:0.92 268:0.46 271:0.48 276:0.91 279:0.86 282:0.88 283:0.78 284:0.89 285:0.62 290:0.56 292:0.91 297:0.36 299:0.88 300:0.94 +3 1:0.74 6:0.87 7:0.81 8:0.69 9:0.80 11:0.57 12:0.37 17:0.06 18:0.95 20:0.93 21:0.13 22:0.93 27:0.18 28:0.96 29:0.62 30:0.91 31:0.92 32:0.80 34:0.87 36:0.36 37:0.46 39:0.98 41:0.88 43:0.93 44:0.93 47:0.93 60:0.98 64:0.77 66:0.48 69:0.32 70:0.33 71:0.70 74:0.95 77:0.89 78:0.87 79:0.91 81:0.65 83:0.91 85:0.99 86:0.99 91:0.17 96:0.77 98:0.57 100:0.88 101:0.87 104:0.77 106:0.81 108:0.76 111:0.86 114:0.79 117:0.86 120:0.65 121:1.00 122:0.57 123:0.74 124:0.76 126:0.54 127:0.64 129:0.89 133:0.78 135:0.94 137:0.92 139:0.99 140:0.45 145:0.80 146:0.57 147:0.63 149:0.99 150:0.79 151:0.72 152:0.87 153:0.48 154:0.93 155:0.67 158:0.92 159:0.77 161:0.79 162:0.74 164:0.67 165:0.93 167:0.47 169:0.86 172:0.89 173:0.17 176:0.73 177:0.70 179:0.20 181:0.59 186:0.87 187:0.87 189:0.88 191:0.84 192:0.87 195:0.91 196:0.96 201:0.93 202:0.56 204:0.89 206:0.81 208:0.64 212:0.77 215:0.61 216:0.75 219:0.95 220:0.62 222:0.21 230:0.87 233:0.76 235:0.31 238:0.88 241:0.03 242:0.44 243:0.19 245:0.94 247:0.74 248:0.70 253:0.82 255:0.95 256:0.58 259:0.21 260:0.66 261:0.88 262:0.93 265:0.58 266:0.71 267:0.65 268:0.59 271:0.48 276:0.89 279:0.86 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 290:0.79 292:0.95 297:0.36 299:0.97 300:0.94 +3 1:0.74 11:0.57 12:0.37 17:0.55 18:0.96 21:0.68 27:0.48 28:0.96 29:0.62 30:0.91 32:0.80 34:0.45 36:0.28 37:0.55 39:0.98 43:0.80 44:0.93 60:0.99 64:0.77 66:0.26 69:0.73 70:0.30 71:0.70 74:0.96 77:0.70 81:0.44 83:0.91 85:0.99 86:0.99 91:0.09 97:0.89 98:0.57 101:0.87 108:0.57 114:0.56 122:0.57 123:0.93 124:0.81 126:0.54 127:0.75 129:0.93 133:0.84 135:0.44 139:0.99 145:0.65 146:0.81 147:0.84 149:0.99 150:0.69 154:0.74 155:0.67 158:0.92 159:0.89 161:0.79 165:0.93 167:0.55 172:0.90 173:0.44 176:0.73 177:0.70 178:0.55 179:0.17 181:0.59 187:0.39 192:0.87 195:0.97 201:0.93 202:0.36 208:0.64 212:0.49 215:0.37 216:0.92 219:0.95 222:0.21 235:0.88 241:0.41 242:0.45 243:0.50 245:0.96 247:0.55 253:0.48 259:0.21 265:0.39 266:0.75 267:0.76 271:0.48 276:0.91 279:0.86 282:0.88 283:0.82 290:0.56 292:0.95 297:0.36 299:0.88 300:0.94 +0 1:0.74 11:0.57 12:0.37 17:0.45 18:0.72 21:0.13 27:0.45 28:0.96 29:0.62 30:0.91 32:0.80 34:0.61 36:0.17 37:0.50 39:0.98 43:0.82 44:0.93 64:0.77 66:0.64 69:0.68 70:0.19 71:0.70 74:0.63 77:0.70 81:0.65 83:0.91 85:0.80 86:0.83 91:0.14 98:0.15 101:0.87 108:0.52 114:0.79 122:0.57 123:0.50 124:0.42 126:0.54 127:0.33 129:0.05 133:0.37 135:0.58 139:0.85 145:0.41 146:0.22 147:0.27 149:0.75 150:0.79 154:0.77 155:0.67 159:0.41 161:0.79 165:0.93 167:0.54 172:0.32 173:0.70 176:0.73 177:0.70 178:0.55 179:0.87 181:0.59 187:0.68 192:0.87 195:0.69 201:0.93 208:0.64 212:0.73 215:0.61 216:0.77 222:0.21 235:0.31 241:0.37 242:0.55 243:0.69 245:0.43 247:0.35 253:0.56 259:0.21 265:0.16 266:0.23 267:0.28 271:0.48 276:0.13 282:0.88 290:0.79 297:0.36 299:0.88 300:0.18 +3 1:0.74 6:0.94 7:0.81 8:0.85 9:0.80 11:0.57 12:0.37 17:0.32 18:0.90 20:0.96 21:0.30 22:0.93 27:0.21 28:0.96 29:0.62 30:0.73 31:0.98 34:0.50 36:0.64 37:0.74 39:0.98 41:0.95 43:0.97 47:0.93 48:0.91 60:0.95 64:0.77 66:0.17 69:0.15 70:0.63 71:0.70 74:0.83 78:0.91 79:0.98 81:0.54 83:0.91 85:0.94 86:0.96 91:0.31 96:0.93 98:0.22 100:0.97 101:0.87 104:0.84 106:0.81 108:0.12 111:0.94 114:0.65 117:0.86 120:0.87 121:0.90 122:0.57 123:0.12 124:0.95 126:0.54 127:0.77 129:0.96 133:0.94 135:0.24 137:0.98 139:0.97 140:0.45 146:0.60 147:0.65 149:0.92 150:0.75 151:0.97 152:0.92 153:0.90 154:0.97 155:0.67 158:0.92 159:0.91 161:0.79 162:0.63 164:0.83 165:0.93 167:0.54 169:0.95 172:0.54 173:0.07 176:0.73 177:0.70 179:0.40 181:0.59 186:0.91 187:0.39 189:0.95 191:0.75 192:0.87 196:0.99 201:0.93 202:0.78 204:0.89 206:0.81 208:0.64 212:0.22 215:0.44 216:0.95 219:0.95 222:0.21 230:0.87 233:0.76 235:0.52 238:0.96 241:0.37 242:0.32 243:0.38 245:0.73 247:0.74 248:0.92 253:0.94 255:0.95 256:0.79 259:0.21 260:0.88 261:0.94 262:0.93 265:0.24 266:0.92 267:0.52 268:0.80 271:0.48 276:0.75 279:0.86 281:0.91 283:0.82 284:0.97 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:1.00 7:0.66 8:0.93 9:0.80 11:0.57 12:0.37 17:0.34 18:0.96 20:0.76 21:0.13 27:0.02 28:0.96 29:0.62 30:0.93 31:0.91 32:0.80 34:0.47 36:0.20 37:0.92 39:0.98 41:0.82 43:0.89 44:0.93 48:0.91 60:0.87 64:0.77 66:0.97 69:0.50 70:0.20 71:0.70 74:0.96 77:0.70 78:0.95 79:0.90 81:0.65 83:0.91 85:0.98 86:0.99 91:0.42 96:0.75 98:0.69 100:0.98 101:0.87 108:0.39 111:0.97 114:0.79 120:0.63 122:0.57 123:0.37 126:0.54 127:0.21 129:0.05 135:0.85 137:0.91 139:0.99 140:0.87 145:0.41 146:0.90 147:0.92 149:0.99 150:0.79 151:0.72 152:0.82 153:0.48 154:0.87 155:0.67 158:0.91 159:0.50 161:0.79 162:0.50 164:0.57 165:0.93 167:0.60 169:0.89 172:0.93 173:0.33 176:0.73 177:0.70 178:0.48 179:0.13 181:0.59 186:0.94 187:0.39 191:0.77 192:0.87 195:0.88 196:0.96 201:0.93 202:0.61 208:0.64 212:0.87 215:0.61 216:0.99 219:0.95 220:0.62 222:0.21 230:0.69 233:0.76 235:0.31 241:0.55 242:0.50 243:0.97 247:0.35 248:0.67 253:0.81 255:0.95 256:0.45 259:0.21 260:0.64 261:0.85 265:0.69 266:0.27 267:0.89 268:0.46 271:0.48 276:0.87 279:0.86 281:0.91 282:0.88 283:0.78 284:0.89 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.43 +1 1:0.74 6:1.00 7:0.66 8:0.96 9:0.80 11:0.57 12:0.37 17:0.13 18:0.89 20:0.87 21:0.22 27:0.02 28:0.96 29:0.62 30:0.91 31:0.95 34:0.49 36:0.30 37:0.92 39:0.98 41:0.88 43:0.82 44:0.87 48:0.91 60:0.87 64:0.77 66:0.79 69:0.68 70:0.22 71:0.70 74:0.85 78:0.82 79:0.94 81:0.59 83:0.91 85:0.98 86:0.96 91:0.33 96:0.83 98:0.63 100:0.88 101:0.87 108:0.86 111:0.83 114:0.71 120:0.73 122:0.57 123:0.86 124:0.41 126:0.54 127:0.54 129:0.81 133:0.67 135:0.92 137:0.95 139:0.97 140:0.45 145:0.44 146:0.17 147:0.22 149:0.95 150:0.77 151:0.87 152:0.82 153:0.48 154:0.78 155:0.67 158:0.91 159:0.75 161:0.79 162:0.55 164:0.69 165:0.93 167:0.67 169:0.88 172:0.91 173:0.53 176:0.73 177:0.70 179:0.25 181:0.59 186:0.83 187:0.39 189:1.00 191:0.73 192:0.87 195:0.89 196:0.97 201:0.93 202:0.65 208:0.64 212:0.75 215:0.51 216:0.99 219:0.95 220:0.74 222:0.21 230:0.69 233:0.76 235:0.42 238:0.94 241:0.66 242:0.45 243:0.11 245:0.84 247:0.55 248:0.80 253:0.86 255:0.95 256:0.58 259:0.21 260:0.74 261:0.85 265:0.63 266:0.75 267:0.65 268:0.62 271:0.48 276:0.81 279:0.86 281:0.91 283:0.78 284:0.93 285:0.62 290:0.71 292:0.91 297:0.36 300:0.43 +2 1:0.74 9:0.80 11:0.57 12:0.37 17:0.04 18:0.77 21:0.05 27:0.33 28:0.96 29:0.62 30:0.94 31:0.94 34:0.50 36:0.23 37:0.41 39:0.98 41:0.82 43:0.86 60:0.97 64:0.77 66:0.33 69:0.57 70:0.19 71:0.70 74:0.70 79:0.93 81:0.72 83:0.91 85:0.88 86:0.88 91:0.40 96:0.80 98:0.21 101:0.87 108:0.68 114:0.89 120:0.69 122:0.57 123:0.65 124:0.72 126:0.54 127:0.39 129:0.81 133:0.67 135:0.99 137:0.94 139:0.89 140:0.45 145:0.93 146:0.17 147:0.22 149:0.84 150:0.83 151:0.87 153:0.48 154:0.84 155:0.67 158:0.92 159:0.60 161:0.79 162:0.86 163:0.90 164:0.57 165:0.93 167:0.48 172:0.32 173:0.47 176:0.73 177:0.70 179:0.74 181:0.59 187:0.39 192:0.87 196:0.95 201:0.93 202:0.36 208:0.64 212:0.23 215:0.78 216:0.88 219:0.95 222:0.21 235:0.22 241:0.07 242:0.55 243:0.33 245:0.58 247:0.35 248:0.77 253:0.82 255:0.95 256:0.45 259:0.21 260:0.70 265:0.23 266:0.71 267:0.82 268:0.46 271:0.48 276:0.33 279:0.86 283:0.82 284:0.89 285:0.62 290:0.89 292:0.95 297:0.36 300:0.25 +1 1:0.74 6:0.89 7:0.81 8:0.69 9:0.80 11:0.57 12:0.37 17:0.20 18:0.78 20:0.93 21:0.54 22:0.93 27:0.07 28:0.96 29:0.62 30:0.89 31:0.97 32:0.80 34:0.97 36:0.21 37:0.63 39:0.98 41:0.96 43:0.77 44:0.93 47:0.93 60:1.00 64:0.77 66:0.20 69:0.80 70:0.29 71:0.70 74:0.77 77:0.70 78:0.82 79:0.96 81:0.48 83:0.91 85:0.85 86:0.89 91:0.53 96:0.88 98:0.14 100:0.86 101:0.87 104:0.79 106:0.81 108:0.63 111:0.82 114:0.59 120:0.80 121:0.90 122:0.57 123:0.61 124:0.74 126:0.54 127:0.28 129:0.81 133:0.67 135:0.97 137:0.97 139:0.94 140:0.80 145:0.50 146:0.22 147:0.28 149:0.74 150:0.72 151:0.87 152:0.86 153:0.48 154:0.70 155:0.67 158:0.92 159:0.44 161:0.79 162:0.61 163:0.97 164:0.84 165:0.93 167:0.61 169:0.84 172:0.21 173:0.79 176:0.73 177:0.70 178:0.42 179:0.69 181:0.59 186:0.82 187:0.68 189:0.91 192:0.87 195:0.78 196:0.98 201:0.93 202:0.79 204:0.89 206:0.81 208:0.64 212:0.42 215:0.39 216:0.95 219:0.95 220:0.82 222:0.21 230:0.86 233:0.73 235:0.80 238:0.90 241:0.57 242:0.45 243:0.40 245:0.56 247:0.39 248:0.87 253:0.89 255:0.95 256:0.80 259:0.21 260:0.81 261:0.85 262:0.93 265:0.15 266:0.27 267:0.52 268:0.79 271:0.48 276:0.33 279:0.86 281:0.89 282:0.88 283:0.82 284:0.97 285:0.62 290:0.59 292:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.90 7:0.66 8:0.93 9:0.80 12:0.37 20:0.81 21:0.22 27:0.03 28:0.96 29:0.62 31:0.96 34:0.80 36:0.88 37:0.93 39:0.98 41:0.94 48:0.91 64:0.77 71:0.70 78:0.93 79:0.96 81:0.59 83:0.91 91:0.76 96:0.87 100:0.97 104:0.91 111:0.95 114:0.71 120:0.78 126:0.54 135:0.71 137:0.96 139:0.64 140:0.45 145:0.59 150:0.77 151:0.87 152:0.97 153:0.48 155:0.67 161:0.79 162:0.72 164:0.79 165:0.93 167:0.76 169:0.96 175:0.97 176:0.73 181:0.59 186:0.93 187:0.87 189:0.97 191:0.96 192:0.87 196:0.89 201:0.93 202:0.71 208:0.64 215:0.51 216:0.76 220:0.82 222:0.21 230:0.69 233:0.76 235:0.42 238:0.97 241:0.75 244:0.93 248:0.85 253:0.82 255:0.95 256:0.73 257:0.93 259:0.21 260:0.79 261:0.98 267:0.59 268:0.74 271:0.48 277:0.95 281:0.91 284:0.96 285:0.62 290:0.71 297:0.36 +1 1:0.74 11:0.57 12:0.37 17:0.78 18:0.92 27:0.39 28:0.96 29:0.62 30:0.90 34:0.45 36:0.78 37:0.27 39:0.98 43:0.85 60:0.95 64:0.77 66:0.59 69:0.61 70:0.28 71:0.70 74:0.86 81:0.79 83:0.91 85:0.94 86:0.97 91:0.03 98:0.53 101:0.87 108:0.47 114:0.98 122:0.57 123:0.45 124:0.63 126:0.54 127:0.42 129:0.81 133:0.67 135:0.97 139:0.97 146:0.57 147:0.63 149:0.96 150:0.86 154:0.82 155:0.67 158:0.92 159:0.43 161:0.79 165:0.93 167:0.50 172:0.71 173:0.64 176:0.73 177:0.70 178:0.55 179:0.42 181:0.59 187:0.39 192:0.87 201:0.93 208:0.64 212:0.84 215:0.96 216:0.42 219:0.95 222:0.21 235:0.12 241:0.18 242:0.45 243:0.67 245:0.79 247:0.55 253:0.68 259:0.21 265:0.55 266:0.54 267:0.69 271:0.48 276:0.66 279:0.86 283:0.82 290:0.98 292:0.95 297:0.36 300:0.55 +0 8:0.80 10:0.82 12:0.37 17:0.88 18:0.68 21:0.78 27:0.63 29:0.98 30:0.94 34:0.50 36:0.61 37:0.46 39:0.89 43:0.20 46:0.88 55:0.84 66:0.80 69:0.93 70:0.19 71:0.57 74:0.29 86:0.57 91:0.20 98:0.71 107:0.93 108:0.85 110:0.92 122:0.83 123:0.84 124:0.39 125:0.88 127:0.54 129:0.05 131:0.61 133:0.62 135:0.30 139:0.56 145:0.59 146:0.94 147:0.05 149:0.37 154:0.13 157:0.61 159:0.80 160:0.88 163:0.94 166:0.61 170:0.97 172:0.77 173:0.86 174:0.79 175:0.91 177:0.05 178:0.55 179:0.48 182:0.61 187:0.39 197:0.82 212:0.51 216:0.42 222:0.48 227:0.61 229:0.61 231:0.75 235:0.52 239:0.82 241:0.07 242:0.58 243:0.09 244:0.93 245:0.60 246:0.61 247:0.60 253:0.26 257:0.93 259:0.21 265:0.71 266:0.63 267:0.19 271:0.46 276:0.66 277:0.87 287:0.61 289:0.90 300:0.18 +1 10:0.96 12:0.37 17:0.95 21:0.78 27:0.36 29:0.98 30:0.14 34:0.59 36:0.37 37:0.82 39:0.89 43:0.10 46:0.88 48:0.91 55:0.84 66:0.11 69:0.99 70:0.82 71:0.57 74:0.25 91:0.05 98:0.30 107:0.89 108:0.98 110:0.89 122:0.95 123:0.98 124:0.98 125:0.88 127:0.95 129:0.05 131:0.61 133:0.98 135:0.44 139:0.56 145:0.50 146:0.89 147:0.05 149:0.30 154:0.08 157:0.61 159:0.95 160:0.88 163:0.86 166:0.61 170:0.97 172:0.63 173:0.99 174:0.99 175:0.75 177:0.05 178:0.55 179:0.24 182:0.61 187:0.68 197:0.99 202:0.50 212:0.13 216:0.13 222:0.48 227:0.61 229:0.61 231:0.62 235:0.42 239:0.95 241:0.46 242:0.76 243:0.06 244:0.93 245:0.80 246:0.61 247:0.74 253:0.23 257:0.93 259:0.21 265:0.33 266:0.98 267:0.36 271:0.46 276:0.89 277:0.69 281:0.91 287:0.61 289:0.88 300:0.70 +0 7:0.64 8:0.60 10:0.82 11:0.45 12:0.37 17:0.98 18:0.97 21:0.78 27:0.63 29:0.98 30:0.86 34:0.59 36:0.71 37:0.28 39:0.89 43:0.10 45:0.77 46:0.88 55:0.59 66:0.29 69:0.89 70:0.41 71:0.57 74:0.31 83:0.56 86:0.62 91:0.15 98:0.70 101:0.47 107:0.96 108:0.78 110:0.97 122:0.92 123:0.42 124:0.58 125:0.88 127:0.61 129:0.05 131:0.61 133:0.45 135:0.21 139:0.61 146:0.76 147:0.83 149:0.23 154:0.36 155:0.50 157:0.61 159:0.58 160:0.88 166:0.61 170:0.97 172:0.73 173:0.92 174:0.79 177:0.70 178:0.55 179:0.39 182:0.61 187:0.21 192:0.49 197:0.80 204:0.80 208:0.50 212:0.61 216:0.38 222:0.48 227:0.61 229:0.61 230:0.65 231:0.91 233:0.76 235:0.22 239:0.81 241:0.03 242:0.70 243:0.47 244:0.83 245:0.90 246:0.61 247:0.55 253:0.28 254:0.88 257:0.65 259:0.21 265:0.65 266:0.63 267:0.44 271:0.46 276:0.76 277:0.69 287:0.61 289:0.95 300:0.55 +0 8:0.65 10:0.82 12:0.37 17:1.00 18:0.61 21:0.78 27:0.71 29:0.98 30:0.33 34:0.33 36:0.76 37:0.27 39:0.89 43:0.07 46:0.88 55:1.00 66:0.12 69:0.87 70:0.69 71:0.57 74:0.25 86:0.57 91:0.01 98:0.08 107:0.88 108:0.99 110:0.88 122:0.95 123:0.99 124:0.91 125:0.88 127:0.85 129:0.96 131:0.61 133:0.90 135:0.75 139:0.55 146:0.05 147:0.05 149:0.07 154:0.07 157:0.61 159:0.99 160:0.88 163:0.90 166:0.61 170:0.97 172:0.10 173:0.31 174:0.89 175:0.97 177:0.05 178:0.55 179:0.92 182:0.61 187:0.87 197:0.82 202:0.53 212:0.19 216:0.17 222:0.48 227:0.61 229:0.61 231:0.61 235:0.42 239:0.81 241:0.02 242:0.63 243:0.19 244:0.97 245:0.42 246:0.61 247:0.39 253:0.22 257:0.93 259:0.21 265:0.08 266:0.47 267:0.98 271:0.46 276:0.33 277:0.95 287:0.61 289:0.88 300:0.43 +0 8:0.81 10:0.81 11:0.45 12:0.37 17:1.00 18:0.57 21:0.78 27:1.00 29:0.98 30:0.45 34:0.96 36:0.47 39:0.89 43:0.07 45:0.66 46:0.88 55:1.00 66:0.13 69:0.99 70:0.50 71:0.57 74:0.25 86:0.56 91:0.03 98:0.05 101:0.47 107:0.88 108:0.98 110:0.89 122:0.55 123:0.98 124:0.89 125:0.88 127:0.43 129:0.05 131:0.61 133:0.87 135:0.89 139:0.55 146:0.13 147:0.05 149:0.07 154:0.06 157:0.61 159:0.97 160:0.88 163:0.90 166:0.61 170:0.97 172:0.10 173:0.90 174:0.83 175:0.91 177:0.05 178:0.55 179:0.90 182:0.61 187:0.39 197:0.81 202:0.36 212:0.23 216:0.05 222:0.48 227:0.61 229:0.61 231:0.62 235:0.05 239:0.81 241:0.01 242:0.24 243:0.21 244:0.93 245:0.40 246:0.61 247:0.47 253:0.22 257:0.93 259:0.21 265:0.05 266:0.38 267:0.99 271:0.46 276:0.30 277:0.87 287:0.61 289:0.88 300:0.33 +1 8:0.81 10:0.85 11:0.45 12:0.37 17:0.88 18:0.64 21:0.78 27:0.54 29:0.98 30:0.45 34:0.68 36:0.92 37:0.49 39:0.89 43:0.10 45:0.66 46:0.88 55:0.92 66:0.09 69:0.76 70:0.72 71:0.57 74:0.25 86:0.57 91:0.01 98:0.29 101:0.47 107:0.92 108:0.96 110:0.94 123:0.95 124:0.97 125:0.88 127:0.84 129:0.59 131:0.61 133:0.97 135:0.71 139:0.56 146:0.45 147:0.05 149:0.21 154:0.08 157:0.61 159:0.90 160:0.88 163:0.94 166:0.61 170:0.97 172:0.32 173:0.47 174:0.89 175:0.75 177:0.05 178:0.55 179:0.47 182:0.61 187:0.21 191:0.90 197:0.86 212:0.13 216:0.22 222:0.48 227:0.61 229:0.61 231:0.79 239:0.84 241:0.10 242:0.57 243:0.08 244:0.83 245:0.64 246:0.61 247:0.84 253:0.23 254:0.98 257:0.93 259:0.21 265:0.31 266:0.96 267:0.82 271:0.46 276:0.73 277:0.87 287:0.61 289:0.91 300:0.70 +0 7:0.64 8:0.66 10:0.83 11:0.45 12:0.37 17:0.87 18:0.72 21:0.78 27:0.72 29:0.98 30:0.76 34:0.45 36:0.21 39:0.89 43:0.10 44:0.85 45:0.66 46:0.88 48:0.91 55:0.84 66:0.72 69:0.27 70:0.22 71:0.57 74:0.30 76:0.94 83:0.56 86:0.58 91:0.86 98:0.62 101:0.47 107:0.91 108:0.21 110:0.93 122:0.65 123:0.20 125:0.88 127:0.18 129:0.05 131:0.61 135:0.44 139:0.57 145:0.50 146:0.49 147:0.55 149:0.08 154:0.45 155:0.49 157:0.61 159:0.18 160:0.88 166:0.61 170:0.97 172:0.27 173:0.44 174:0.79 177:0.88 178:0.55 179:0.80 182:0.61 192:0.48 193:0.96 195:0.60 197:0.84 204:0.79 208:0.50 212:0.42 216:0.02 222:0.48 227:0.61 229:0.61 230:0.65 231:0.79 233:0.76 235:0.88 239:0.85 241:0.84 242:0.45 243:0.75 244:0.83 246:0.61 247:0.35 253:0.27 254:0.97 259:0.21 265:0.63 266:0.23 267:0.36 271:0.46 276:0.24 281:0.91 287:0.61 289:0.91 300:0.18 +0 8:0.80 10:0.82 12:0.37 17:0.88 18:0.68 21:0.78 27:0.63 29:0.98 30:0.90 34:0.56 36:0.52 37:0.46 39:0.89 43:0.21 46:0.88 55:0.84 66:0.82 69:0.93 70:0.19 71:0.57 74:0.30 86:0.57 91:0.20 98:0.70 107:0.93 108:0.85 110:0.93 122:0.82 123:0.84 124:0.39 125:0.88 127:0.52 129:0.05 131:0.61 133:0.62 135:0.28 139:0.56 145:0.59 146:0.94 147:0.05 149:0.47 154:0.14 157:0.61 159:0.80 160:0.88 163:0.92 166:0.61 170:0.97 172:0.80 173:0.86 174:0.79 175:0.91 177:0.05 178:0.55 179:0.43 182:0.61 187:0.39 197:0.82 212:0.35 216:0.42 222:0.48 227:0.61 229:0.61 231:0.76 235:0.52 239:0.82 241:0.07 242:0.42 243:0.08 244:0.93 245:0.62 246:0.61 247:0.60 253:0.27 257:0.93 259:0.21 265:0.71 266:0.63 267:0.19 271:0.46 276:0.69 277:0.87 287:0.61 289:0.90 300:0.18 +0 9:0.73 10:0.92 11:0.64 12:0.37 17:0.85 18:0.98 21:0.68 23:0.95 27:0.72 29:0.98 30:0.45 31:0.93 34:0.64 36:0.47 39:0.89 43:0.58 44:0.85 45:0.77 46:0.94 62:0.97 64:0.77 66:0.94 69:0.27 70:0.53 71:0.57 74:0.37 75:0.96 79:0.93 81:0.39 83:0.56 86:0.88 91:0.20 98:0.83 101:0.87 107:0.94 108:0.21 110:0.96 122:0.92 123:0.20 125:0.88 126:0.24 127:0.32 128:0.97 129:0.05 131:0.92 132:0.97 135:0.69 137:0.93 139:0.83 140:0.45 144:0.57 145:0.45 146:0.77 147:0.80 149:0.53 150:0.44 151:0.85 153:0.48 154:0.55 155:0.56 157:0.86 159:0.33 160:0.88 162:0.86 166:0.88 168:0.97 170:0.97 172:0.85 173:0.36 174:0.89 177:0.88 179:0.29 182:0.76 187:0.87 190:0.95 192:0.49 195:0.62 196:0.94 197:0.92 202:0.36 205:0.95 208:0.50 212:0.84 216:0.12 219:0.86 222:0.48 226:0.98 227:0.87 229:0.61 231:0.89 235:0.88 239:0.92 241:0.01 242:0.72 243:0.95 246:0.61 247:0.74 248:0.76 253:0.33 254:0.97 255:0.72 256:0.45 259:0.21 265:0.83 266:0.38 267:0.19 268:0.46 271:0.46 276:0.74 279:0.76 284:0.87 285:0.50 287:0.61 289:0.94 292:0.95 300:0.55 +1 10:0.84 11:0.45 12:0.37 17:0.62 18:0.59 21:0.78 27:1.00 29:0.98 30:0.36 34:0.30 36:0.62 37:0.27 39:0.89 43:0.28 45:0.83 46:0.88 55:0.71 66:0.30 69:0.95 70:0.92 71:0.57 74:0.30 86:0.57 91:0.20 98:0.34 101:0.47 107:0.92 108:0.94 110:0.92 122:0.83 123:0.94 124:0.91 125:0.88 127:0.64 129:0.05 131:0.61 133:0.91 135:0.93 139:0.55 146:0.65 147:0.18 149:0.35 154:0.20 157:0.61 159:0.90 160:0.88 166:0.61 170:0.97 172:0.65 173:0.85 174:0.89 175:0.75 177:0.38 178:0.55 179:0.39 182:0.61 187:0.68 197:0.86 212:0.36 216:0.37 222:0.48 227:0.61 229:0.61 231:0.73 235:0.52 239:0.84 241:0.05 242:0.59 243:0.11 244:0.93 245:0.75 246:0.61 247:0.84 253:0.27 257:0.84 259:0.21 265:0.36 266:0.96 267:0.76 271:0.46 276:0.75 277:0.87 287:0.61 289:0.90 300:0.84 +0 10:0.81 11:0.49 12:0.37 17:0.98 18:0.95 21:0.54 22:0.93 27:0.61 29:0.98 30:0.21 34:0.95 36:0.60 37:0.42 39:0.89 43:0.11 45:0.73 46:0.88 47:0.93 55:0.92 64:0.77 66:0.06 69:0.98 70:0.93 71:0.57 74:0.26 81:0.33 86:0.61 91:0.01 98:0.21 101:0.65 107:0.94 108:0.97 110:0.96 122:0.92 123:0.83 124:0.98 125:0.88 126:0.24 127:0.89 129:0.05 131:0.61 133:0.98 135:0.85 139:0.59 146:0.51 147:0.05 149:0.24 150:0.37 154:0.08 157:0.61 159:0.95 160:0.88 163:0.90 166:0.88 170:0.97 172:0.37 173:0.94 174:0.79 177:0.05 178:0.55 179:0.32 182:0.61 187:0.98 197:0.79 212:0.13 216:0.49 219:0.86 222:0.48 227:0.61 229:0.61 231:0.88 235:0.60 239:0.80 241:0.01 242:0.75 243:0.06 244:0.61 245:0.71 246:0.61 247:0.82 253:0.23 254:0.97 257:0.93 259:0.21 262:0.93 265:0.17 266:0.97 267:0.99 271:0.46 276:0.84 277:0.87 287:0.61 289:0.94 292:0.88 300:0.84 +0 10:0.81 12:0.37 17:0.66 18:0.57 21:0.78 27:0.66 29:0.98 30:0.33 34:0.64 36:0.77 37:0.49 39:0.89 43:0.05 46:0.88 55:0.99 66:0.12 69:0.97 70:0.69 71:0.57 74:0.24 86:0.56 91:0.01 98:0.06 107:0.88 108:0.96 110:0.89 123:0.96 124:0.91 125:0.88 127:0.23 129:0.96 131:0.61 133:0.90 135:0.58 139:0.55 146:0.05 147:0.05 149:0.06 154:0.05 157:0.61 159:0.95 160:0.88 163:0.90 166:0.61 170:0.97 172:0.10 173:0.69 174:0.83 175:0.97 177:0.05 178:0.55 179:0.73 182:0.61 187:0.39 197:0.80 212:0.19 216:0.21 222:0.48 227:0.61 229:0.61 231:0.62 235:0.98 239:0.80 241:0.05 242:0.19 243:0.19 244:0.97 245:0.42 246:0.61 247:0.55 253:0.20 257:0.93 259:0.21 265:0.06 266:0.47 267:0.23 271:0.46 276:0.33 277:0.95 287:0.61 289:0.88 300:0.43 +2 10:0.83 12:0.37 17:0.59 18:0.60 21:0.78 27:0.24 29:0.98 30:0.91 34:0.39 36:0.37 37:0.80 39:0.89 43:0.09 46:0.88 55:0.99 66:0.10 69:0.72 70:0.13 71:0.57 74:0.24 86:0.57 91:0.03 98:0.06 107:0.89 108:0.55 110:0.90 122:0.91 123:0.52 124:0.82 125:0.88 127:0.41 129:0.89 131:0.61 133:0.78 135:0.23 139:0.55 145:0.80 146:0.05 147:0.05 149:0.08 154:0.08 157:0.61 159:0.81 160:0.88 163:0.99 166:0.61 170:0.97 172:0.05 173:0.47 174:0.79 175:0.97 177:0.05 178:0.55 179:0.97 182:0.61 187:0.68 197:0.84 212:0.61 216:0.67 222:0.48 227:0.61 229:0.61 231:0.65 235:0.84 239:0.82 241:0.10 242:0.63 243:0.29 244:0.97 245:0.37 246:0.61 247:0.30 253:0.22 257:0.93 259:0.21 265:0.06 266:0.17 267:0.19 271:0.46 276:0.23 277:0.95 287:0.61 289:0.88 300:0.13 +0 1:0.74 7:0.81 8:0.69 11:0.64 12:0.86 17:0.36 18:0.92 21:0.13 26:0.96 27:0.40 29:0.77 30:0.27 32:0.79 34:0.31 36:0.21 37:0.74 43:0.69 44:0.93 45:0.73 48:0.91 55:0.71 58:0.93 64:0.77 66:0.19 69:0.54 70:0.91 71:0.55 74:0.74 77:0.70 81:0.68 83:0.60 86:0.90 91:0.44 97:0.89 98:0.45 99:0.83 101:0.52 104:0.95 106:0.81 108:0.41 114:0.79 122:0.86 123:0.80 124:0.80 126:0.54 127:0.61 129:0.59 133:0.77 135:1.00 139:0.93 141:0.91 145:0.67 146:0.58 147:0.64 149:0.45 150:0.79 154:0.70 155:0.67 158:0.78 159:0.67 165:0.70 172:0.51 173:0.42 176:0.73 177:0.70 178:0.55 179:0.50 187:0.87 192:0.87 195:0.82 199:0.97 201:0.93 204:0.89 206:0.81 208:0.64 212:0.48 215:0.61 216:0.36 222:0.60 223:0.96 224:0.93 230:0.86 233:0.73 234:0.91 235:0.31 241:0.07 242:0.43 243:0.45 245:0.77 247:0.86 253:0.57 254:0.74 259:0.21 265:0.41 266:0.84 267:0.36 271:0.64 274:0.91 276:0.67 279:0.82 281:0.88 282:0.87 283:0.78 290:0.79 297:0.36 300:0.70 +1 1:0.69 7:0.72 8:0.68 11:0.64 12:0.86 17:0.53 18:0.84 21:0.30 26:0.96 27:0.06 29:0.77 30:0.43 32:0.69 34:0.95 36:0.17 37:0.86 43:0.63 45:0.92 58:0.93 66:0.43 69:0.57 70:0.81 71:0.55 74:0.82 77:0.70 81:0.41 83:0.60 86:0.87 91:0.23 97:0.89 98:0.66 99:0.83 101:0.52 104:0.91 106:0.81 108:0.44 114:0.63 117:0.86 122:0.51 123:0.42 124:0.60 126:0.44 127:0.46 129:0.59 133:0.46 135:1.00 139:0.83 141:0.91 145:0.61 146:0.88 147:0.90 149:0.80 150:0.55 154:0.81 155:0.58 158:0.78 159:0.70 163:0.81 165:0.70 172:0.73 173:0.41 176:0.66 177:0.70 178:0.55 179:0.32 187:0.87 192:0.59 195:0.88 199:0.99 201:0.68 204:0.85 206:0.81 208:0.54 212:0.37 215:0.44 216:0.76 222:0.60 223:0.96 224:0.93 230:0.74 233:0.73 234:0.91 235:0.42 241:0.56 242:0.39 243:0.57 245:0.92 247:0.84 253:0.51 254:0.84 259:0.21 265:0.66 266:0.54 267:0.88 271:0.64 274:0.91 276:0.78 279:0.82 282:0.77 283:0.78 290:0.63 297:0.36 300:0.70 +2 1:0.74 8:0.91 9:0.76 11:0.64 12:0.86 17:0.85 18:0.72 21:0.30 22:0.93 26:0.96 27:0.60 29:0.77 30:0.09 34:0.18 36:0.50 37:0.59 43:0.87 45:0.83 47:0.93 55:0.71 58:0.98 64:0.77 66:0.11 69:0.56 70:0.97 71:0.55 74:0.87 81:0.55 83:0.60 86:0.80 91:0.17 96:0.75 97:0.97 98:0.31 99:0.83 101:0.52 104:0.96 106:0.81 108:0.43 114:0.65 117:0.86 120:0.63 122:0.77 123:0.84 124:0.92 126:0.54 127:0.54 129:0.81 133:0.91 135:0.98 139:0.84 140:0.45 141:0.98 146:0.61 147:0.66 149:0.76 150:0.72 151:0.65 153:0.48 154:0.85 155:0.67 158:0.91 159:0.82 162:0.86 164:0.54 165:0.70 172:0.59 173:0.31 176:0.73 177:0.70 179:0.29 187:0.95 190:0.77 192:0.87 199:0.98 201:0.93 202:0.36 206:0.81 208:0.64 212:0.59 215:0.44 216:0.20 219:0.95 220:0.62 222:0.60 223:0.96 224:1.00 234:1.00 235:0.52 241:0.01 242:0.45 243:0.36 245:0.83 247:0.76 248:0.63 253:0.72 255:0.74 256:0.45 259:0.21 260:0.63 262:0.93 265:0.31 266:0.89 267:0.23 268:0.46 271:0.64 274:0.97 276:0.82 277:0.69 279:0.86 283:0.78 285:0.56 290:0.65 292:0.91 297:0.36 300:0.84 +2 1:0.74 8:0.78 9:0.80 11:0.64 12:0.86 17:0.40 18:0.80 21:0.47 22:0.93 26:0.96 27:0.56 29:0.77 30:0.10 31:0.89 34:0.42 36:0.88 37:0.71 43:0.61 45:0.90 47:0.93 55:0.59 58:0.99 64:0.77 66:0.23 69:0.47 70:0.96 71:0.55 74:0.87 79:0.88 81:0.47 83:0.60 86:0.88 91:0.38 96:0.72 97:0.97 98:0.59 99:0.83 101:0.52 104:0.93 106:0.81 108:0.90 114:0.60 117:0.86 120:0.59 122:0.75 123:0.89 124:0.88 126:0.54 127:0.57 129:0.93 133:0.87 135:1.00 137:0.89 139:0.89 140:0.45 141:0.98 145:0.41 146:0.26 147:0.32 149:0.63 150:0.67 151:0.59 153:0.84 154:0.86 155:0.67 158:0.87 159:0.84 162:0.66 164:0.74 165:0.70 172:0.74 173:0.21 176:0.73 177:0.70 179:0.24 187:0.68 192:0.87 196:0.92 199:0.99 201:0.93 202:0.66 206:0.81 208:0.64 212:0.30 215:0.41 216:0.41 219:0.95 220:0.82 222:0.60 223:0.96 224:0.98 234:0.98 235:0.68 241:0.15 242:0.24 243:0.15 245:0.90 247:0.92 248:0.59 253:0.62 254:0.80 255:0.95 256:0.64 259:0.21 260:0.60 262:0.93 265:0.60 266:0.91 267:0.73 268:0.68 271:0.64 274:0.98 276:0.87 279:0.86 283:0.71 284:0.95 285:0.62 290:0.60 292:0.91 297:0.36 300:0.84 +0 11:0.64 12:0.86 17:0.22 21:0.78 26:0.95 27:0.45 29:0.77 30:0.30 34:0.76 36:0.19 37:0.50 43:0.63 45:0.77 58:0.93 66:0.19 69:0.72 70:0.80 71:0.55 74:0.48 91:0.15 98:0.23 101:0.52 108:0.92 122:0.51 123:0.53 124:0.67 127:0.59 129:0.59 133:0.64 135:0.87 141:0.91 145:0.49 146:0.44 147:0.51 149:0.42 154:0.53 159:0.78 172:0.37 173:0.54 175:0.75 177:0.38 178:0.55 179:0.80 187:0.68 199:0.96 212:0.52 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.31 241:0.27 242:0.24 243:0.39 244:0.61 245:0.56 247:0.67 253:0.34 257:0.65 259:0.21 265:0.19 266:0.54 267:0.65 271:0.64 274:0.91 276:0.36 277:0.69 300:0.55 +1 1:0.69 7:0.72 11:0.64 12:0.86 17:0.67 18:0.86 21:0.30 26:0.96 27:0.06 29:0.77 30:0.33 32:0.69 34:0.95 36:0.17 37:0.86 43:0.63 45:0.91 58:0.93 66:0.44 69:0.69 70:0.85 71:0.55 74:0.79 77:0.70 81:0.41 83:0.60 86:0.87 91:0.14 98:0.58 99:0.83 101:0.52 104:0.91 106:0.81 108:0.52 114:0.63 117:0.86 122:0.51 123:0.50 124:0.68 126:0.44 127:0.35 129:0.59 133:0.66 135:0.99 139:0.83 141:0.91 145:0.61 146:0.85 147:0.87 149:0.77 150:0.55 154:0.75 155:0.58 159:0.68 165:0.70 172:0.73 173:0.53 176:0.66 177:0.70 178:0.55 179:0.29 187:0.95 192:0.59 195:0.90 199:0.98 201:0.68 204:0.85 206:0.81 208:0.54 212:0.39 215:0.44 216:0.76 222:0.60 223:0.96 224:0.93 230:0.74 233:0.73 234:0.91 235:0.42 241:0.64 242:0.32 243:0.57 245:0.87 247:0.84 253:0.50 254:0.84 259:0.21 265:0.60 266:0.54 267:0.89 271:0.64 274:0.91 276:0.77 282:0.77 290:0.63 297:0.36 300:0.70 +2 1:0.74 7:0.72 8:0.70 11:0.64 12:0.86 17:0.82 18:0.70 21:0.30 22:0.93 26:0.96 27:0.26 29:0.77 30:0.53 32:0.69 34:0.63 36:0.89 37:0.84 43:0.68 45:0.73 47:0.93 48:0.97 55:0.84 58:0.93 64:0.77 66:0.91 69:0.58 70:0.68 71:0.55 74:0.77 76:0.85 77:0.70 81:0.55 83:0.60 86:0.70 91:0.44 98:0.85 99:0.83 101:0.52 108:0.44 114:0.65 122:0.77 123:0.42 126:0.54 127:0.35 129:0.05 135:1.00 139:0.71 141:0.91 145:0.61 146:0.92 147:0.93 149:0.49 150:0.72 154:0.57 155:0.67 159:0.54 165:0.70 172:0.75 173:0.50 176:0.73 177:0.70 178:0.55 179:0.46 187:0.87 192:0.87 195:0.80 199:0.96 201:0.93 204:0.85 208:0.64 212:0.29 215:0.44 216:0.42 222:0.60 223:0.96 224:0.93 230:0.74 233:0.73 234:0.91 235:0.52 241:0.10 242:0.30 243:0.91 244:0.61 247:0.74 253:0.51 259:0.21 262:0.93 265:0.85 266:0.63 267:0.52 271:0.64 274:0.91 276:0.64 281:0.89 282:0.77 290:0.65 297:0.36 300:0.55 +2 1:0.74 8:0.83 9:0.80 11:0.85 12:0.86 17:0.51 18:0.75 21:0.30 22:0.93 26:0.96 27:0.56 29:0.77 30:0.15 31:0.87 34:0.18 36:0.61 37:0.71 43:0.14 45:0.85 47:0.93 55:0.71 58:0.98 64:0.77 66:0.13 69:0.47 70:0.98 71:0.55 74:0.87 79:0.87 81:0.55 83:0.91 86:0.83 91:0.38 96:0.69 97:0.97 98:0.31 99:0.83 101:0.87 104:0.93 106:0.81 108:0.36 114:0.65 117:0.86 120:0.55 122:0.77 123:0.34 124:0.97 126:0.54 127:0.83 129:0.95 133:0.97 135:1.00 137:0.87 139:0.85 140:0.45 141:0.98 145:0.41 146:0.90 147:0.92 149:0.39 150:0.72 151:0.52 153:0.48 154:0.88 155:0.67 158:0.91 159:0.95 162:0.86 164:0.57 165:0.70 172:0.79 173:0.10 176:0.73 177:0.70 179:0.16 187:0.68 192:0.87 196:0.89 199:0.99 201:0.93 202:0.36 206:0.81 208:0.64 212:0.22 215:0.44 216:0.41 219:0.95 220:0.87 222:0.60 223:0.96 224:0.99 234:0.98 235:0.52 241:0.15 242:0.24 243:0.34 245:0.90 247:0.96 248:0.51 253:0.54 254:0.74 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.33 266:0.98 267:0.97 268:0.46 271:0.64 274:0.97 276:0.94 279:0.86 283:0.78 284:0.89 285:0.62 290:0.65 292:0.95 297:0.36 300:0.94 +2 1:0.74 6:0.91 8:0.89 11:0.85 12:0.86 17:0.51 18:0.75 20:0.94 21:0.30 22:0.93 26:0.96 27:0.56 29:0.77 30:0.15 31:0.87 34:0.17 36:0.63 37:0.71 43:0.14 45:0.85 47:0.93 55:0.71 58:0.93 64:0.77 66:0.13 69:0.45 70:0.98 71:0.55 74:0.86 78:0.97 79:0.87 81:0.55 83:0.60 86:0.83 91:0.33 97:0.94 98:0.31 99:0.83 100:0.94 101:0.87 104:0.84 106:0.81 108:0.35 111:0.95 114:0.65 117:0.86 122:0.77 123:0.34 124:0.97 126:0.54 127:0.81 129:0.95 133:0.97 135:1.00 137:0.87 139:0.79 140:0.80 141:0.91 145:0.41 146:0.90 147:0.92 149:0.39 150:0.72 151:0.48 152:0.95 153:0.48 154:0.88 155:0.67 158:0.79 159:0.95 162:0.62 165:0.70 169:0.91 172:0.79 173:0.10 176:0.73 177:0.70 178:0.42 179:0.16 186:0.97 187:0.39 189:0.93 190:0.89 192:0.87 196:0.88 199:0.99 201:0.93 202:0.50 206:0.81 208:0.64 212:0.22 215:0.44 216:0.41 220:0.87 222:0.60 223:0.96 224:0.93 234:0.91 235:0.52 238:0.88 241:0.05 242:0.24 243:0.34 245:0.90 247:0.96 248:0.48 253:0.52 254:0.74 256:0.45 259:0.21 261:0.94 262:0.93 265:0.33 266:0.98 267:0.97 268:0.46 271:0.64 274:0.91 276:0.94 279:0.82 283:0.82 284:0.86 285:0.47 290:0.65 297:0.36 300:0.94 +2 1:0.74 6:0.87 7:0.81 8:0.78 11:0.64 12:0.86 17:0.40 18:0.95 20:0.93 26:0.96 27:0.40 29:0.77 30:0.45 32:0.80 34:0.36 36:0.18 37:0.74 43:0.69 44:0.93 45:0.83 55:0.71 58:0.93 64:0.77 66:0.44 69:0.52 70:0.83 71:0.55 74:0.83 77:0.70 78:0.99 81:0.84 83:0.91 86:0.94 91:0.48 97:0.94 98:0.70 100:0.99 101:0.52 104:0.92 106:0.81 108:0.77 111:0.99 114:0.98 121:0.90 122:0.86 123:0.75 124:0.67 126:0.54 127:0.62 129:0.59 133:0.61 135:0.99 139:0.95 141:0.91 145:0.63 146:0.79 147:0.82 149:0.47 150:0.89 152:0.96 154:0.69 155:0.67 158:0.79 159:0.71 165:0.93 169:0.97 172:0.75 173:0.38 176:0.73 177:0.70 178:0.55 179:0.35 186:0.98 187:0.87 189:0.89 192:0.87 195:0.86 199:0.98 201:0.93 204:0.89 206:0.81 208:0.64 212:0.55 215:0.96 216:0.36 222:0.60 223:0.96 224:0.93 230:0.87 233:0.76 234:0.91 235:0.12 238:0.96 241:0.01 242:0.37 243:0.45 245:0.88 247:0.84 253:0.68 254:0.74 259:0.21 261:0.97 265:0.70 266:0.75 267:0.44 271:0.64 274:0.91 276:0.79 279:0.82 281:0.91 282:0.88 283:0.82 290:0.98 297:0.36 299:0.88 300:0.70 +2 1:0.74 9:0.80 11:0.64 12:0.86 17:0.81 18:0.76 21:0.68 26:0.96 27:0.55 29:0.77 30:0.08 31:0.91 34:0.25 36:0.76 37:0.62 43:0.48 45:0.77 55:0.59 58:0.98 64:0.77 66:0.36 69:0.55 70:0.98 71:0.55 74:0.69 79:0.90 81:0.41 83:0.60 86:0.82 91:0.27 96:0.75 97:0.89 98:0.59 99:0.83 101:0.52 108:0.81 114:0.56 120:0.63 122:0.51 123:0.80 124:0.70 126:0.54 127:0.42 129:0.59 133:0.66 135:1.00 137:0.91 139:0.84 140:0.45 141:0.98 145:0.96 146:0.67 147:0.72 149:0.50 150:0.65 151:0.72 153:0.48 154:0.72 155:0.67 158:0.91 159:0.64 162:0.86 164:0.57 165:0.70 172:0.63 173:0.42 176:0.73 177:0.70 179:0.40 187:0.39 192:0.87 196:0.93 199:0.98 201:0.93 202:0.36 208:0.64 212:0.48 215:0.37 216:0.19 219:0.95 220:0.62 222:0.60 223:0.96 224:1.00 234:0.99 235:0.88 241:0.61 242:0.21 243:0.43 245:0.82 247:0.91 248:0.67 253:0.67 254:0.74 255:0.95 256:0.45 259:0.21 260:0.64 265:0.60 266:0.63 267:0.99 268:0.46 271:0.64 274:0.97 276:0.70 279:0.86 283:0.78 284:0.89 285:0.62 290:0.56 292:0.91 297:0.36 300:0.84 +2 1:0.74 8:0.89 9:0.80 11:0.64 12:0.86 17:0.61 18:0.76 21:0.40 22:0.93 26:0.96 27:0.56 29:0.77 30:0.20 31:0.90 34:0.21 36:0.91 37:0.71 43:0.57 45:0.85 47:0.93 55:0.59 58:0.98 64:0.77 66:0.25 69:0.46 70:0.94 71:0.55 74:0.86 79:0.89 81:0.51 83:0.60 86:0.85 91:0.38 96:0.73 97:0.94 98:0.41 99:0.83 101:0.52 104:0.93 106:0.81 108:0.35 114:0.62 117:0.86 120:0.61 122:0.77 123:0.34 124:0.95 126:0.54 127:0.69 129:0.81 133:0.95 135:1.00 137:0.90 139:0.86 140:0.45 141:0.98 145:0.41 146:0.87 147:0.89 149:0.35 150:0.69 151:0.63 153:0.72 154:0.87 155:0.67 158:0.91 159:0.89 162:0.86 164:0.69 165:0.70 172:0.75 173:0.17 176:0.73 177:0.70 179:0.26 187:0.68 192:0.87 196:0.92 199:0.99 201:0.93 202:0.53 206:0.81 208:0.64 212:0.39 215:0.42 216:0.41 219:0.95 220:0.74 222:0.60 223:0.96 224:0.99 234:0.98 235:0.60 241:0.01 242:0.17 243:0.44 245:0.83 247:0.91 248:0.61 253:0.65 254:0.80 255:0.95 256:0.58 259:0.21 260:0.61 262:0.93 265:0.43 266:0.96 267:0.59 268:0.62 271:0.64 274:0.98 276:0.86 279:0.86 283:0.77 284:0.93 285:0.62 290:0.62 292:0.91 297:0.36 300:0.70 +0 1:0.69 11:0.64 12:0.86 17:0.61 21:0.64 26:0.96 27:0.45 29:0.77 30:0.21 32:0.69 34:0.75 36:0.20 37:0.50 43:0.65 45:0.81 58:0.93 66:0.09 69:0.69 70:0.79 71:0.55 74:0.62 77:0.70 81:0.33 83:0.60 91:0.20 98:0.29 99:0.83 101:0.52 108:0.87 114:0.56 122:0.55 123:0.68 124:0.79 126:0.44 127:0.46 129:0.81 133:0.76 135:0.89 141:0.91 145:0.47 146:0.32 147:0.38 149:0.58 150:0.51 154:0.54 155:0.58 159:0.62 165:0.70 172:0.37 173:0.61 175:0.75 176:0.66 177:0.38 178:0.55 179:0.64 187:0.68 192:0.59 195:0.82 199:0.97 201:0.68 208:0.54 212:0.59 215:0.38 216:0.77 222:0.60 223:0.95 224:0.93 234:0.91 235:0.84 241:0.18 242:0.24 243:0.40 244:0.61 245:0.63 247:0.60 253:0.41 257:0.65 259:0.21 265:0.26 266:0.71 267:1.00 271:0.64 274:0.91 276:0.51 277:0.69 282:0.77 290:0.56 297:0.36 300:0.55 +0 1:0.74 11:0.78 12:0.86 17:0.51 18:0.72 21:0.54 26:0.96 27:0.45 28:0.73 29:0.77 30:0.91 32:0.80 34:0.52 36:0.22 37:0.50 39:0.66 43:0.82 44:0.93 58:0.93 64:0.77 66:0.36 69:0.69 70:0.19 74:0.60 77:0.70 81:0.52 83:0.91 85:0.80 86:0.81 91:0.17 98:0.24 101:0.87 108:0.79 114:0.59 122:0.57 123:0.78 124:0.58 126:0.54 127:0.33 129:0.59 131:0.61 133:0.46 135:0.84 139:0.84 141:0.91 144:0.76 145:0.45 146:0.17 147:0.22 149:0.69 150:0.73 154:0.77 155:0.67 157:0.81 159:0.44 161:0.58 163:0.90 165:0.93 166:0.85 167:0.61 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.87 181:0.64 182:0.84 187:0.68 192:0.87 195:0.71 199:0.95 201:0.93 208:0.64 212:0.52 215:0.39 216:0.77 222:0.48 223:0.95 224:0.93 227:0.61 229:0.61 231:0.77 234:0.91 235:0.74 241:0.12 242:0.55 243:0.45 245:0.50 246:0.96 247:0.39 253:0.43 259:0.21 265:0.26 266:0.27 267:0.28 271:0.64 274:0.91 276:0.16 282:0.88 287:0.61 290:0.59 297:0.36 299:0.88 300:0.18 +3 1:0.69 6:0.93 7:0.81 8:0.78 9:0.80 11:0.81 12:0.86 17:0.20 18:0.83 20:0.99 21:0.64 26:0.96 27:0.09 28:0.73 29:0.77 30:0.45 31:0.98 32:0.80 34:0.97 36:0.77 37:0.86 39:0.66 41:0.92 43:0.71 44:0.93 45:0.87 58:0.99 64:0.77 66:0.43 69:0.39 70:0.66 74:0.63 77:0.70 78:0.84 79:0.98 81:0.34 83:0.91 86:0.89 91:0.67 96:0.93 98:0.48 99:0.83 100:0.86 101:0.52 108:0.82 111:0.83 114:0.55 120:0.83 121:0.90 122:0.80 123:0.81 124:0.77 126:0.44 127:0.75 129:0.81 131:0.90 133:0.74 135:0.76 137:0.98 139:0.93 140:0.45 141:0.98 144:0.76 145:0.65 146:0.37 147:0.44 149:0.59 150:0.52 151:0.97 152:0.99 153:0.89 154:0.76 155:0.67 157:0.61 159:0.60 161:0.58 162:0.85 164:0.81 165:0.70 166:0.75 167:0.90 169:0.92 172:0.54 173:0.34 176:0.55 177:0.70 179:0.62 181:0.64 182:0.85 186:0.84 187:0.21 189:0.90 191:0.78 192:0.87 195:0.78 196:0.99 199:0.98 201:0.68 202:0.75 204:0.89 208:0.64 212:0.52 216:0.45 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.87 231:0.78 232:0.72 233:0.76 234:0.98 235:0.80 238:0.89 241:0.77 242:0.33 243:0.31 245:0.69 246:0.61 247:0.76 248:0.90 253:0.90 254:0.80 255:0.95 256:0.81 259:0.21 260:0.84 261:0.94 265:0.50 266:0.80 267:0.73 268:0.82 271:0.64 274:0.99 276:0.60 281:0.91 282:0.88 284:0.98 285:0.62 287:0.97 290:0.55 299:0.88 300:0.55 +3 1:0.74 8:0.77 9:0.80 11:0.64 12:0.86 17:0.15 18:0.79 21:0.30 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.14 31:0.98 34:0.42 36:0.97 37:0.80 39:0.66 41:0.90 43:0.66 45:0.87 55:0.71 58:0.99 64:0.77 66:0.19 69:0.34 70:0.94 74:0.31 79:0.98 81:0.66 83:0.91 86:0.83 91:0.53 96:0.94 97:0.99 98:0.41 99:0.83 101:0.52 104:0.58 108:0.94 114:0.65 120:0.83 123:0.93 124:0.91 126:0.54 127:0.94 129:0.81 131:0.90 133:0.91 135:0.56 137:0.98 139:0.83 140:0.45 141:0.98 144:0.76 146:0.68 147:0.73 149:0.65 150:0.77 151:0.97 153:0.90 154:0.22 155:0.67 157:0.61 158:0.72 159:0.87 161:0.58 162:0.69 163:0.81 164:0.79 165:0.70 166:0.85 167:0.63 172:0.59 173:0.14 176:0.73 177:0.70 179:0.39 181:0.64 182:0.85 187:0.39 191:0.76 192:0.87 196:0.97 199:0.99 201:0.93 202:0.80 208:0.64 212:0.21 215:0.44 216:0.82 219:0.92 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 231:0.78 232:0.86 234:0.98 235:0.52 241:0.21 242:0.71 243:0.31 245:0.79 246:0.61 247:0.67 248:0.90 253:0.87 254:0.88 255:0.95 256:0.79 259:0.21 260:0.84 265:0.43 266:0.95 267:0.69 268:0.82 271:0.64 274:0.99 276:0.78 279:0.82 284:0.98 285:0.62 287:0.97 290:0.65 292:0.91 297:0.36 300:0.84 +3 1:0.74 6:0.98 7:0.81 8:0.97 9:0.80 11:0.87 12:0.86 17:0.20 18:0.88 20:0.85 21:0.22 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.29 31:1.00 32:0.78 34:0.45 36:0.96 37:0.80 39:0.66 41:0.99 43:0.82 44:0.93 45:0.92 58:1.00 64:0.77 66:0.54 69:0.35 70:0.81 74:0.75 77:0.70 78:0.94 79:1.00 81:0.80 83:0.91 85:0.72 86:0.94 91:0.91 96:0.99 97:0.99 98:0.77 100:0.98 101:0.97 104:0.58 108:0.27 111:0.97 114:0.71 120:0.97 121:0.90 123:0.26 124:0.70 126:0.54 127:0.92 129:0.89 131:0.90 133:0.78 135:0.85 137:1.00 138:0.99 139:0.96 140:0.45 141:0.98 144:0.76 145:0.74 146:0.96 147:0.97 149:0.78 150:0.87 151:1.00 152:1.00 153:0.98 154:0.80 155:0.67 157:0.76 158:0.72 159:0.83 161:0.58 162:0.79 164:0.95 165:0.93 166:0.85 167:0.76 169:0.98 172:0.85 173:0.17 176:0.73 177:0.70 179:0.31 181:0.64 182:0.85 186:0.94 187:0.21 189:0.99 191:0.95 192:0.87 195:0.92 196:1.00 199:1.00 201:0.93 202:0.93 204:0.89 208:0.64 212:0.23 215:0.51 216:0.82 219:0.92 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.87 231:0.78 232:0.83 233:0.76 234:0.98 235:0.60 238:0.98 241:0.65 242:0.32 243:0.63 245:0.89 246:0.96 247:0.67 248:0.99 253:0.99 255:0.95 256:0.95 259:0.21 260:0.96 261:0.98 265:0.77 266:0.87 267:0.73 268:0.95 271:0.64 274:1.00 276:0.84 279:0.82 281:0.91 282:0.86 284:1.00 285:0.62 287:0.97 290:0.71 292:0.95 297:0.36 300:0.70 +3 1:0.74 6:0.94 8:0.83 9:0.80 11:0.64 12:0.86 17:0.40 18:0.79 20:0.82 21:0.30 25:0.88 26:0.96 27:0.61 28:0.73 29:0.77 30:0.28 31:0.96 34:0.21 36:0.93 37:0.44 39:0.66 41:0.82 43:0.24 45:0.97 58:0.99 64:0.77 66:0.12 69:0.95 70:0.90 74:0.48 78:0.78 79:0.95 81:0.66 83:0.91 86:0.83 91:0.25 96:0.86 97:0.99 98:0.32 99:0.83 100:0.81 101:0.52 104:0.72 108:0.98 111:0.77 114:0.65 120:0.82 123:0.98 124:0.99 126:0.54 127:0.94 129:0.98 131:0.90 133:0.99 135:0.92 137:0.96 139:0.85 140:0.45 141:0.98 144:0.76 146:0.71 147:0.05 149:0.64 150:0.77 151:0.87 152:0.98 153:0.48 154:0.17 155:0.67 157:0.61 158:0.86 159:0.97 161:0.58 162:0.84 163:0.81 164:0.73 165:0.70 166:0.85 167:0.64 169:0.76 172:0.78 173:0.64 176:0.73 177:0.05 179:0.16 181:0.64 182:0.84 186:0.78 187:0.21 189:0.98 192:0.87 196:0.96 199:1.00 201:0.93 202:0.74 208:0.64 212:0.14 215:0.44 216:0.64 219:0.95 220:0.87 222:0.48 223:0.96 224:0.99 227:0.96 229:0.91 231:0.78 232:0.84 234:0.99 235:0.52 238:0.90 241:0.27 242:0.21 243:0.05 245:0.87 246:0.61 247:0.88 248:0.79 253:0.78 254:0.86 255:0.95 256:0.80 257:0.84 259:0.21 260:0.74 261:0.79 265:0.34 266:0.99 267:0.85 268:0.80 271:0.64 274:0.99 276:0.94 279:0.86 283:0.78 284:0.96 285:0.62 287:0.97 290:0.65 292:0.87 297:0.36 300:0.84 +2 1:0.74 6:0.94 8:0.75 9:0.80 11:0.64 12:0.86 17:0.11 18:0.76 20:0.87 21:0.59 25:0.88 26:0.96 27:0.61 28:0.73 29:0.77 30:0.08 31:0.98 34:0.62 36:0.23 37:0.44 39:0.66 41:0.96 43:0.58 45:0.81 58:1.00 64:0.77 66:0.16 69:0.77 70:1.00 74:0.30 78:0.75 79:0.98 81:0.46 83:0.91 86:0.82 91:0.53 96:0.94 98:0.16 99:0.83 100:0.78 101:0.52 104:0.72 108:0.60 111:0.74 114:0.58 120:0.87 123:0.57 124:0.96 126:0.54 127:0.92 129:0.05 131:0.90 133:0.95 135:0.88 137:0.98 139:0.78 140:0.80 141:0.98 144:0.76 146:0.50 147:0.56 149:0.50 150:0.67 151:0.96 152:0.98 153:0.86 154:0.68 155:0.67 157:0.61 159:0.95 161:0.58 162:0.51 164:0.86 165:0.70 166:0.84 167:0.64 169:0.76 172:0.41 173:0.34 176:0.73 177:0.70 178:0.42 179:0.56 181:0.64 182:0.85 186:0.76 187:0.87 189:0.91 191:0.70 192:0.87 196:0.96 199:0.99 201:0.93 202:0.90 208:0.64 212:0.12 215:0.39 216:0.64 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 231:0.78 232:0.84 234:0.97 235:0.80 238:0.85 241:0.27 242:0.71 243:0.37 245:0.60 246:0.61 247:0.60 248:0.91 253:0.88 254:0.88 255:0.95 256:0.87 259:0.21 260:0.86 261:0.79 265:0.17 266:0.96 267:0.69 268:0.87 271:0.64 274:0.99 276:0.66 284:0.98 285:0.62 287:0.97 290:0.58 297:0.36 300:0.98 +2 6:0.96 8:0.82 9:0.80 11:0.64 12:0.86 17:0.28 18:0.61 20:0.99 21:0.13 25:0.88 26:0.96 27:0.31 28:0.73 29:0.77 30:0.15 31:1.00 34:0.21 36:0.81 37:0.54 39:0.66 41:0.99 43:0.15 45:0.66 55:0.59 58:1.00 60:0.95 66:0.33 69:0.84 70:0.84 74:0.48 78:0.77 79:1.00 81:0.39 83:0.91 86:0.70 91:0.65 96:0.98 98:0.56 100:0.81 101:0.52 104:0.74 108:0.38 111:0.77 120:0.95 123:0.67 124:0.61 126:0.26 127:0.63 129:0.05 131:0.90 133:0.37 135:0.47 137:1.00 139:0.78 140:0.45 141:0.98 144:0.76 146:0.50 147:0.52 149:0.19 150:0.34 151:0.99 152:0.90 153:0.96 154:0.58 155:0.67 157:0.61 158:0.91 159:0.38 161:0.58 162:0.50 163:0.90 164:0.95 166:0.75 167:0.94 169:0.79 172:0.27 173:0.95 177:0.05 179:0.85 181:0.64 182:0.85 186:0.78 189:0.97 191:0.90 192:0.87 196:0.98 199:1.00 202:0.98 208:0.64 212:0.30 215:0.61 216:0.17 219:0.95 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 231:0.78 234:0.97 235:0.12 238:0.93 241:0.82 242:0.73 243:0.50 245:0.60 246:0.61 247:0.39 248:0.97 253:0.95 254:0.88 255:0.95 256:0.98 257:0.84 260:0.94 261:0.81 265:0.52 266:0.54 267:0.44 268:0.97 271:0.64 274:1.00 276:0.37 279:0.86 283:0.78 284:1.00 285:0.62 287:0.97 292:0.91 297:0.36 300:0.55 +1 1:0.69 6:0.84 8:0.65 9:0.80 11:0.87 12:0.86 17:0.59 18:0.98 20:0.95 21:0.47 22:0.93 26:0.96 27:0.21 28:0.73 29:0.77 30:0.66 31:0.97 34:0.76 36:0.86 37:0.74 39:0.66 41:0.82 43:0.92 45:0.99 47:0.93 58:0.98 60:0.87 64:0.77 66:0.27 69:0.19 70:0.66 74:0.75 78:0.76 79:0.97 81:0.41 83:0.91 85:0.88 86:0.99 91:0.25 96:0.91 97:0.94 98:0.55 99:0.83 100:0.78 101:0.97 104:0.84 106:0.81 108:0.95 111:0.75 114:0.57 117:0.86 120:0.80 122:0.85 123:0.95 124:0.87 126:0.44 127:0.83 129:0.89 131:0.90 133:0.87 135:0.23 137:0.97 139:0.99 140:0.45 141:0.98 144:0.76 146:0.90 147:0.92 149:0.95 150:0.55 151:0.96 152:0.91 153:0.87 154:0.91 155:0.67 157:0.82 158:0.92 159:0.91 161:0.58 162:0.74 164:0.68 165:0.70 166:0.75 167:0.61 169:0.76 172:0.92 173:0.08 176:0.55 177:0.70 179:0.14 181:0.64 182:0.85 186:0.77 187:0.39 189:0.86 191:0.70 192:0.87 196:0.99 199:0.99 201:0.68 202:0.72 204:0.85 206:0.81 208:0.64 212:0.67 216:0.95 219:0.95 222:0.48 223:0.96 224:0.98 227:0.96 229:0.91 231:0.78 232:0.92 234:0.97 235:0.60 238:0.86 241:0.15 242:0.36 243:0.38 245:0.98 246:0.61 247:0.82 248:0.85 253:0.88 255:0.95 256:0.73 259:0.21 260:0.79 261:0.79 262:0.93 265:0.56 266:0.89 267:0.44 268:0.76 271:0.64 274:0.98 276:0.96 279:0.86 281:0.89 283:0.82 284:0.96 285:0.62 287:0.97 290:0.57 292:0.95 300:0.94 +2 1:0.74 6:0.93 7:0.63 8:0.91 9:0.80 11:0.64 12:0.86 17:0.55 18:0.73 20:0.99 21:0.30 26:0.96 27:0.09 28:0.73 29:0.77 30:0.62 31:1.00 32:0.80 34:0.76 36:0.87 37:0.86 39:0.66 41:0.98 43:0.17 44:0.93 45:0.73 58:1.00 64:0.77 66:0.75 69:0.30 70:0.34 74:0.49 77:0.70 78:0.91 79:1.00 81:0.67 83:0.91 86:0.85 91:0.91 96:0.98 97:0.89 98:0.75 100:0.96 101:0.52 108:0.24 111:0.93 114:0.65 120:0.95 122:0.57 123:0.23 126:0.54 127:0.24 129:0.05 131:0.90 135:0.23 137:1.00 139:0.90 140:0.45 141:0.98 144:0.76 145:0.56 146:0.31 147:0.38 149:0.07 150:0.80 151:0.99 152:0.99 153:0.97 154:0.82 155:0.67 157:0.61 158:0.72 159:0.13 161:0.58 162:0.78 164:0.92 165:0.93 166:0.85 167:0.97 169:0.92 172:0.32 173:0.76 176:0.73 177:0.70 179:0.84 181:0.64 182:0.85 186:0.91 189:0.95 191:0.94 192:0.87 195:0.53 196:1.00 199:0.99 201:0.93 202:0.90 204:0.85 208:0.64 212:0.95 215:0.44 216:0.45 219:0.92 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 230:0.63 231:0.78 232:0.72 233:0.73 234:0.98 235:0.52 238:0.95 241:0.95 242:0.63 243:0.77 246:0.96 247:0.35 248:0.97 253:0.96 254:0.90 255:0.95 256:0.93 259:0.21 260:0.95 261:0.94 265:0.75 266:0.12 267:0.79 268:0.93 271:0.64 274:1.00 276:0.27 279:0.82 281:0.91 282:0.88 284:0.99 285:0.62 287:0.97 290:0.65 292:0.91 297:0.36 300:0.25 +2 6:0.98 7:0.63 8:0.97 9:0.80 11:0.64 12:0.86 17:0.09 18:0.80 20:0.99 21:0.59 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.13 31:1.00 32:0.62 34:0.52 36:0.64 37:0.80 39:0.66 41:0.99 43:0.62 45:0.90 55:0.59 58:1.00 60:0.97 66:0.32 69:0.51 70:0.96 74:0.49 78:0.92 79:1.00 81:0.26 83:0.91 86:0.86 91:0.99 96:0.99 97:0.94 98:0.55 100:0.98 101:0.52 104:0.58 108:0.94 111:0.96 120:0.99 123:0.94 124:0.92 126:0.26 127:0.93 129:0.81 131:0.90 133:0.92 135:0.65 137:1.00 138:0.99 139:0.88 140:0.45 141:0.98 144:0.76 145:0.89 146:0.55 147:0.61 149:0.60 150:0.25 151:1.00 152:1.00 153:0.99 154:0.80 155:0.67 157:0.61 158:0.92 159:0.88 161:0.58 162:0.79 164:0.97 166:0.75 167:0.95 169:0.98 172:0.77 173:0.23 177:0.70 179:0.30 181:0.64 182:0.85 186:0.92 189:0.99 191:0.98 192:0.87 195:0.96 196:1.00 199:1.00 202:0.98 208:0.64 212:0.21 215:0.39 216:0.82 219:0.95 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.63 231:0.78 233:0.73 234:0.99 235:0.74 238:0.98 241:0.85 242:0.63 243:0.23 245:0.83 246:0.61 247:0.60 248:0.99 253:0.99 254:0.92 255:0.95 256:0.99 259:0.21 260:0.98 261:0.98 265:0.56 266:0.92 267:0.65 268:0.99 271:0.64 274:1.00 276:0.85 279:0.86 283:0.82 284:1.00 285:0.62 287:0.97 292:0.95 297:0.36 300:0.94 +2 9:0.80 11:0.81 12:0.86 17:0.07 18:0.88 21:0.30 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.74 31:0.95 34:0.50 36:0.86 37:0.80 39:0.66 41:0.82 43:0.72 45:0.87 58:0.98 66:0.87 69:0.17 70:0.44 74:0.44 79:0.94 81:0.31 83:0.91 86:0.91 91:0.31 96:0.84 98:0.94 101:0.52 104:0.58 108:0.14 120:0.81 122:0.80 123:0.13 126:0.26 127:0.62 129:0.05 131:0.90 135:0.61 137:0.95 139:0.88 140:0.80 141:0.98 144:0.76 146:0.74 147:0.78 149:0.78 150:0.29 151:0.92 153:0.72 154:0.68 155:0.67 157:0.61 159:0.30 161:0.58 162:0.78 164:0.65 166:0.75 167:0.74 172:0.63 173:0.38 177:0.70 179:0.71 181:0.64 182:0.85 187:0.87 192:0.87 196:0.96 199:0.96 202:0.59 208:0.64 212:0.48 215:0.44 216:0.82 222:0.48 223:0.96 224:1.00 227:0.96 229:0.92 231:0.77 232:0.86 234:1.00 235:0.31 241:0.60 242:0.58 243:0.88 246:0.61 247:0.39 248:0.81 253:0.78 254:0.80 255:0.95 256:0.58 259:0.21 260:0.76 265:0.94 266:0.38 267:0.69 268:0.64 271:0.64 274:0.98 276:0.49 284:0.91 285:0.62 287:0.97 297:0.36 300:0.43 +0 1:0.74 7:0.63 11:0.78 12:0.86 17:0.40 18:0.72 21:0.68 26:0.96 27:0.45 28:0.73 29:0.77 30:0.32 34:0.71 36:0.19 37:0.50 39:0.66 43:0.82 55:0.59 58:0.93 60:0.87 64:0.77 66:0.64 69:0.63 70:0.72 74:0.60 81:0.45 83:0.91 85:0.80 86:0.80 91:0.08 98:0.69 101:0.87 108:0.71 114:0.56 123:0.69 124:0.46 126:0.54 127:0.41 129:0.59 131:0.61 133:0.46 135:0.79 139:0.84 141:0.91 144:0.76 145:0.49 146:0.33 147:0.39 149:0.71 150:0.70 154:0.77 155:0.67 157:0.81 158:0.91 159:0.50 161:0.58 165:0.93 166:0.84 167:0.61 172:0.61 173:0.60 176:0.73 177:0.70 178:0.55 179:0.58 181:0.64 182:0.84 187:0.68 192:0.87 199:0.98 201:0.93 208:0.64 212:0.30 215:0.37 216:0.77 219:0.95 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 230:0.64 231:0.76 233:0.76 234:0.91 235:0.88 241:0.15 242:0.57 243:0.29 245:0.71 246:0.96 247:0.67 253:0.41 259:0.21 265:0.69 266:0.71 267:0.28 271:0.64 274:0.91 276:0.54 279:0.86 283:0.78 287:0.61 290:0.56 292:0.91 297:0.36 300:0.55 +2 1:0.69 6:0.93 8:0.95 9:0.80 11:0.64 12:0.86 17:0.24 18:0.90 20:0.74 21:0.13 26:0.96 27:0.09 28:0.73 29:0.77 30:0.67 31:0.96 34:0.92 36:0.89 37:0.86 39:0.66 41:0.88 43:0.71 44:0.90 45:0.91 58:0.98 64:0.77 66:0.47 69:0.32 70:0.63 74:0.35 77:0.70 78:0.72 79:0.95 81:0.74 83:0.91 86:0.96 91:0.38 96:0.86 97:0.89 98:0.44 99:0.83 100:0.73 101:0.52 108:0.25 111:0.72 114:0.64 120:0.75 122:0.80 123:0.24 124:0.66 126:0.44 127:0.77 129:0.59 131:0.90 133:0.66 135:0.54 137:0.96 139:0.96 140:0.45 141:0.98 144:0.76 145:0.79 146:0.61 147:0.66 149:0.62 150:0.70 151:0.92 152:0.99 153:0.72 154:0.80 155:0.67 157:0.61 158:0.72 159:0.65 161:0.58 162:0.86 164:0.70 165:0.70 166:0.75 167:0.75 169:0.92 172:0.63 173:0.25 176:0.55 177:0.70 179:0.54 181:0.64 182:0.85 186:0.73 187:0.39 192:0.87 195:0.80 196:0.98 199:0.96 201:0.68 202:0.59 208:0.64 212:0.57 216:0.45 219:0.92 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 231:0.78 232:0.72 234:0.99 235:0.22 241:0.64 242:0.32 243:0.59 245:0.78 246:0.61 247:0.60 248:0.83 253:0.80 254:0.86 255:0.95 256:0.64 259:0.21 260:0.76 261:0.94 265:0.46 266:0.75 267:0.59 268:0.68 271:0.64 274:0.98 276:0.61 279:0.82 282:0.71 284:0.94 285:0.62 287:0.97 290:0.64 292:0.91 300:0.70 +2 6:0.94 7:0.76 8:0.86 9:0.80 12:0.86 17:0.53 20:0.91 21:0.54 25:0.88 27:0.57 28:0.73 30:0.68 32:0.72 34:0.49 36:0.37 37:0.62 39:0.73 41:0.90 43:0.45 55:0.59 66:0.31 69:0.17 70:0.57 78:0.88 81:0.37 83:0.77 91:0.86 96:0.84 98:0.44 100:0.92 104:0.54 108:0.78 111:0.89 120:0.93 123:0.77 124:0.72 126:0.32 127:0.75 129:0.81 133:0.67 135:0.94 140:0.97 144:0.85 145:0.87 146:0.30 147:0.36 149:0.49 150:0.33 151:0.87 152:0.85 153:0.90 154:0.34 155:0.67 159:0.63 161:0.74 162:0.82 164:0.91 167:0.87 169:0.90 172:0.37 173:0.18 175:0.75 177:0.05 179:0.75 181:0.91 186:0.88 189:0.95 191:0.79 192:0.59 195:0.81 202:0.85 208:0.64 212:0.18 215:0.58 216:0.49 222:0.35 230:0.79 233:0.76 235:0.60 238:0.91 241:0.88 242:0.82 243:0.28 244:0.61 245:0.64 247:0.12 248:0.82 253:0.78 255:0.79 256:0.89 257:0.65 259:0.21 260:0.93 261:0.90 265:0.46 266:0.71 267:0.65 268:0.90 271:0.64 276:0.44 277:0.69 283:0.82 285:0.62 297:0.36 300:0.55 +1 1:0.74 7:0.76 11:0.88 12:0.86 17:0.30 21:0.47 27:0.45 28:0.73 30:0.31 32:0.80 34:0.88 36:0.19 37:0.50 39:0.73 43:0.82 55:0.59 60:0.87 66:0.24 69:0.61 70:0.81 74:0.79 77:0.70 81:0.63 83:0.84 85:0.80 91:0.17 98:0.57 101:0.72 108:0.90 114:0.80 123:0.45 124:0.78 126:0.54 127:0.60 129:0.81 133:0.76 135:0.87 144:0.85 145:0.42 146:0.84 147:0.87 149:0.79 150:0.77 154:0.77 155:0.67 158:0.87 159:0.77 161:0.74 165:0.80 167:0.59 172:0.63 173:0.43 176:0.73 177:0.38 178:0.55 179:0.36 181:0.91 187:0.68 192:0.59 195:0.90 201:0.74 208:0.64 212:0.51 215:0.63 216:0.77 222:0.35 230:0.79 233:0.76 235:0.60 241:0.44 242:0.57 243:0.44 245:0.85 247:0.67 253:0.69 259:0.21 265:0.46 266:0.87 267:0.36 271:0.64 276:0.78 279:0.86 282:0.88 283:0.71 290:0.80 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.97 7:0.81 8:0.93 9:0.80 12:0.86 17:0.32 20:0.98 21:0.40 27:0.29 28:0.73 30:0.66 32:0.80 34:0.49 36:0.85 37:0.81 39:0.73 41:0.95 43:0.42 55:0.59 60:0.87 66:0.68 69:0.73 70:0.64 74:0.70 76:0.98 77:0.70 78:0.92 81:0.67 83:0.81 91:0.92 96:0.88 98:0.84 100:0.96 104:0.79 108:0.56 111:0.93 114:0.82 120:0.95 121:0.90 123:0.54 124:0.43 126:0.54 127:0.78 129:0.05 133:0.39 135:0.54 138:0.99 140:0.80 144:0.85 145:0.63 146:0.75 147:0.33 149:0.57 150:0.80 151:0.92 152:0.90 153:0.90 154:0.62 155:0.67 158:0.87 159:0.41 161:0.74 162:0.76 164:0.95 165:0.83 167:0.88 169:0.94 172:0.61 173:0.83 176:0.73 177:0.05 179:0.69 181:0.91 186:0.92 189:0.98 191:0.87 192:0.59 195:0.63 201:0.74 202:0.91 204:0.89 208:0.64 212:0.61 215:0.67 216:0.50 220:0.62 222:0.35 230:0.87 233:0.76 235:0.52 238:0.94 241:0.90 242:0.79 243:0.25 245:0.68 247:0.39 248:0.89 253:0.88 254:0.84 255:0.79 256:0.93 257:0.65 259:0.21 260:0.95 261:0.94 265:0.84 266:0.38 267:0.65 268:0.94 271:0.64 276:0.55 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.82 8:0.87 9:0.80 11:0.88 12:0.86 17:0.18 20:0.87 21:0.22 27:0.38 28:0.73 30:0.11 34:0.34 36:0.64 37:0.90 39:0.73 41:0.95 43:0.67 55:0.71 60:0.87 66:0.13 69:0.54 70:0.84 74:0.69 78:0.77 81:0.77 83:0.81 85:0.72 91:0.85 96:0.93 98:0.43 100:0.80 101:0.72 108:0.78 111:0.76 114:0.90 120:0.93 122:0.43 123:0.66 124:0.73 126:0.54 127:0.51 129:0.81 133:0.67 135:0.23 140:0.93 144:0.85 146:0.30 147:0.36 149:0.46 150:0.85 151:0.87 152:0.83 153:0.84 154:0.63 155:0.67 158:0.87 159:0.46 161:0.74 162:0.79 164:0.92 165:0.86 167:0.75 169:0.78 172:0.27 173:0.56 176:0.73 177:0.38 179:0.81 181:0.91 186:0.78 187:0.39 189:0.84 191:0.89 192:0.59 201:0.74 202:0.87 208:0.64 212:0.28 215:0.79 216:0.79 220:0.62 222:0.35 235:0.31 238:0.87 241:0.72 242:0.45 243:0.34 245:0.56 247:0.47 248:0.88 253:0.93 255:0.79 256:0.91 259:0.21 260:0.92 261:0.79 265:0.25 266:0.63 267:0.84 268:0.91 271:0.64 276:0.39 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.55 +3 6:0.89 7:0.81 8:0.86 9:0.80 11:0.88 12:0.86 17:0.05 20:0.98 21:0.13 25:0.88 27:0.24 28:0.73 30:0.21 32:0.80 34:0.30 36:0.79 37:0.71 39:0.73 41:0.97 43:0.60 55:0.71 66:0.44 69:0.90 70:0.86 74:0.65 76:0.94 77:0.70 78:0.95 81:0.67 83:0.83 85:0.72 91:0.92 96:0.95 98:0.69 100:0.98 101:0.71 104:0.76 108:0.79 111:0.97 120:0.97 121:0.90 123:0.78 124:0.65 126:0.32 127:0.94 129:0.05 133:0.62 135:0.17 140:0.90 144:0.85 145:0.47 146:0.79 147:0.05 149:0.74 150:0.48 151:0.98 152:0.90 153:0.97 154:0.51 155:0.67 159:0.63 161:0.74 162:0.77 164:0.97 167:0.89 169:0.97 172:0.74 173:0.93 177:0.05 179:0.40 181:0.91 186:0.95 189:0.91 191:0.91 192:0.59 195:0.76 202:0.94 204:0.89 208:0.64 212:0.77 215:0.84 216:0.57 220:0.74 222:0.35 230:0.87 233:0.76 235:0.12 238:0.95 241:0.97 242:0.80 243:0.07 245:0.87 247:0.39 248:0.94 253:0.94 255:0.79 256:0.96 257:0.65 259:0.21 260:0.97 261:0.96 265:0.69 266:0.54 267:0.59 268:0.96 271:0.64 276:0.78 282:0.88 283:0.71 285:0.62 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.93 7:0.76 8:0.84 9:0.80 11:0.88 12:0.86 17:0.43 20:0.99 21:0.30 27:0.21 28:0.73 30:0.14 34:0.28 36:0.88 37:0.74 39:0.73 41:0.99 43:0.98 55:0.71 60:0.97 66:0.13 69:0.12 70:0.88 74:0.90 76:0.97 78:0.88 81:0.72 83:0.90 85:0.96 91:0.72 96:0.98 98:0.45 100:0.96 101:0.73 104:0.84 106:0.81 108:0.97 111:0.92 114:0.86 117:0.86 120:0.98 123:0.97 124:0.96 126:0.54 127:0.99 129:0.99 133:0.96 135:0.19 138:0.98 140:0.45 144:0.85 146:0.49 147:0.55 149:0.91 150:0.82 151:1.00 152:0.91 153:0.98 154:0.98 155:0.67 158:0.92 159:0.98 161:0.74 162:0.73 164:0.95 165:0.84 167:0.67 169:0.94 172:0.95 173:0.05 176:0.73 177:0.38 179:0.10 181:0.91 186:0.88 187:0.39 189:0.94 191:0.80 192:0.59 201:0.74 202:0.92 206:0.81 208:0.64 212:0.49 215:0.72 216:0.95 222:0.35 230:0.79 232:0.92 233:0.76 235:0.42 238:0.96 241:0.62 242:0.73 243:0.08 245:0.99 247:0.60 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.93 265:0.47 266:0.94 267:0.36 268:0.94 271:0.64 276:0.99 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94 +2 6:0.88 8:0.73 9:0.80 12:0.86 17:0.02 20:0.97 21:0.30 25:0.88 27:0.39 28:0.73 30:0.76 34:0.30 36:0.89 37:0.45 39:0.73 41:0.97 43:0.43 55:0.59 66:0.72 69:0.35 70:0.22 74:0.40 78:0.85 81:0.52 83:0.84 91:0.85 96:0.96 98:0.92 100:0.87 104:0.58 108:0.27 111:0.85 120:0.97 122:0.41 123:0.26 126:0.32 127:0.54 129:0.05 135:0.28 140:0.87 144:0.85 146:0.44 147:0.51 149:0.27 150:0.40 151:0.98 152:0.89 153:0.97 154:0.72 155:0.67 159:0.16 161:0.74 162:0.83 164:0.96 167:0.88 169:0.85 172:0.27 173:0.75 177:0.38 179:0.96 181:0.91 186:0.85 189:0.90 191:0.77 192:0.59 202:0.92 208:0.64 212:0.42 215:0.72 216:0.46 222:0.35 235:0.31 238:0.88 241:0.93 242:0.45 243:0.75 247:0.35 248:0.95 253:0.93 254:0.94 255:0.79 256:0.95 259:0.21 260:0.97 261:0.88 265:0.92 266:0.23 267:0.19 268:0.95 271:0.64 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +1 1:0.74 7:0.76 11:0.88 12:0.86 17:0.47 21:0.40 27:0.45 28:0.73 30:0.15 34:0.86 36:0.28 37:0.50 39:0.73 43:0.82 66:0.43 69:0.61 70:0.87 74:0.71 81:0.67 83:0.75 85:0.90 91:0.06 98:0.71 101:0.78 108:0.47 114:0.82 122:0.41 123:0.45 124:0.60 126:0.54 127:0.53 129:0.59 133:0.47 135:0.93 144:0.85 145:0.50 146:0.88 147:0.90 149:0.81 150:0.80 154:0.77 155:0.67 159:0.68 161:0.74 165:0.83 167:0.54 172:0.61 173:0.49 176:0.73 177:0.38 178:0.55 179:0.48 181:0.91 187:0.68 192:0.59 201:0.74 212:0.29 215:0.67 216:0.77 222:0.35 230:0.79 233:0.76 235:0.52 241:0.27 242:0.31 243:0.57 245:0.85 247:0.60 253:0.68 259:0.21 265:0.71 266:0.75 267:0.28 271:0.64 276:0.67 290:0.82 297:0.36 300:0.70 +2 1:0.74 7:0.76 8:0.75 9:0.80 11:0.88 12:0.86 17:0.40 21:0.40 27:0.21 28:0.73 30:0.18 34:0.61 36:0.93 37:0.74 39:0.73 41:0.92 43:0.97 55:0.71 60:0.87 66:0.16 69:0.17 70:0.96 74:0.89 76:0.85 81:0.67 83:0.81 85:0.93 91:0.23 96:0.85 98:0.41 101:0.75 104:0.89 106:0.81 108:0.95 114:0.82 117:0.86 120:0.82 123:0.93 124:0.91 126:0.54 127:0.76 129:0.93 133:0.91 135:0.54 140:0.80 144:0.85 146:0.38 147:0.45 149:0.89 150:0.80 151:0.87 153:0.91 154:0.97 155:0.67 158:0.87 159:0.90 161:0.74 162:0.67 164:0.80 165:0.83 167:0.68 172:0.77 173:0.08 176:0.73 177:0.38 179:0.19 181:0.91 187:0.39 192:0.59 201:0.74 202:0.74 206:0.81 208:0.64 212:0.67 215:0.67 216:0.95 220:0.62 222:0.35 230:0.78 232:0.95 233:0.69 235:0.52 241:0.64 242:0.71 243:0.16 245:0.92 247:0.55 248:0.84 253:0.89 255:0.79 256:0.71 259:0.21 260:0.82 265:0.37 266:0.94 267:0.36 268:0.76 271:0.64 276:0.91 279:0.86 283:0.71 285:0.62 290:0.82 297:0.36 300:0.98 +1 6:0.92 7:0.76 8:0.97 9:0.80 11:0.82 12:0.86 17:0.09 20:0.98 21:0.59 25:0.88 27:0.08 28:0.73 30:0.17 32:0.72 34:0.52 36:0.64 37:0.80 39:0.73 41:0.98 43:0.36 55:0.84 66:0.16 69:0.55 70:0.86 74:0.89 76:0.94 77:0.70 78:0.92 81:0.42 83:0.87 91:0.99 96:0.97 98:0.59 100:0.95 104:0.58 108:0.42 111:0.93 120:0.99 122:0.43 123:0.93 124:0.88 126:0.32 127:0.92 129:0.93 133:0.90 135:0.65 138:0.99 140:0.95 144:0.85 145:0.89 146:0.93 147:0.95 149:0.68 150:0.35 151:0.98 152:0.93 153:0.98 154:0.63 155:0.67 159:0.88 161:0.74 162:0.73 164:0.99 167:0.87 169:0.92 172:0.80 173:0.26 177:0.38 179:0.27 181:0.91 186:0.92 189:0.93 191:0.98 192:0.59 195:0.96 202:0.98 208:0.64 212:0.22 215:0.55 216:0.82 220:0.74 222:0.35 230:0.78 233:0.69 235:0.74 238:0.93 241:0.85 242:0.21 243:0.50 245:0.88 247:0.67 248:0.97 253:0.98 254:0.74 255:0.79 256:0.98 259:0.21 260:0.99 261:0.94 265:0.60 266:0.87 267:0.65 268:0.99 271:0.64 276:0.87 282:0.79 283:0.71 285:0.62 297:0.36 300:0.84 +1 12:0.06 17:0.40 21:0.64 27:0.45 28:0.64 30:0.58 32:0.80 34:0.78 36:0.18 37:0.50 43:0.32 55:0.52 66:0.80 69:0.70 70:0.33 81:0.81 91:0.12 98:0.48 108:0.53 123:0.51 126:0.54 127:0.15 129:0.05 135:0.39 145:0.47 146:0.59 147:0.64 149:0.46 150:0.60 154:0.24 159:0.21 161:0.68 167:0.48 172:0.45 173:0.69 177:0.05 178:0.55 179:0.40 181:0.64 187:0.68 195:0.73 212:0.71 215:0.53 216:0.77 235:0.80 241:0.12 242:0.82 243:0.82 247:0.12 259:0.21 265:0.50 266:0.27 267:0.44 271:0.63 276:0.36 283:0.60 297:0.36 300:0.25 +1 6:0.83 7:0.81 8:0.63 12:0.06 17:0.26 20:0.97 21:0.47 27:0.07 28:0.64 30:0.33 32:0.80 34:0.25 36:0.89 37:0.32 43:0.35 55:0.52 66:0.12 69:0.63 70:0.49 78:0.92 81:0.87 91:0.95 98:0.27 100:0.93 108:0.48 111:0.91 120:0.84 123:0.71 124:0.66 126:0.54 127:0.41 129:0.81 133:0.67 135:0.65 140:0.87 145:0.45 146:0.31 147:0.37 149:0.43 150:0.73 151:0.73 152:0.89 153:0.81 154:0.27 159:0.46 161:0.68 162:0.49 164:0.94 167:0.82 169:0.90 172:0.27 173:0.63 177:0.05 178:0.48 179:0.86 181:0.64 186:0.91 189:0.85 191:0.91 195:0.71 202:0.96 212:0.42 215:0.63 216:0.52 220:0.93 230:0.87 233:0.76 235:0.52 238:0.88 241:0.81 242:0.82 243:0.58 245:0.47 247:0.12 248:0.76 256:0.93 259:0.21 260:0.85 261:0.92 265:0.26 266:0.38 267:0.91 268:0.92 271:0.63 276:0.30 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.97 8:0.95 12:0.06 17:0.06 20:0.96 21:0.78 27:0.12 28:0.64 34:0.52 36:0.37 37:0.88 78:0.95 91:0.89 100:0.98 104:0.89 106:0.81 111:0.97 120:0.97 135:0.68 140:0.87 151:0.81 152:0.89 153:0.94 161:0.68 162:0.55 164:0.99 167:0.74 169:0.97 175:0.75 178:0.48 181:0.64 186:0.95 187:0.68 189:0.98 191:0.98 202:0.99 206:0.81 216:0.91 220:0.62 238:0.97 241:0.74 244:0.61 248:0.95 256:0.99 257:0.65 259:0.21 260:0.97 261:0.96 267:0.79 268:0.99 271:0.63 277:0.69 283:0.82 285:0.62 +2 6:0.83 7:0.81 8:0.62 12:0.06 17:0.01 20:0.98 21:0.47 27:0.04 28:0.64 30:0.95 32:0.80 34:0.28 36:0.74 37:0.60 43:0.47 55:0.84 66:0.64 69:0.39 78:0.91 81:0.87 91:0.98 98:0.89 100:0.89 104:0.58 108:0.30 111:0.90 120:0.97 123:0.29 126:0.54 127:0.44 129:0.05 135:0.28 140:0.45 145:0.94 146:0.50 147:0.56 149:0.27 150:0.73 151:0.79 152:0.91 153:0.91 154:0.36 159:0.18 161:0.68 162:0.71 163:0.86 164:0.98 167:0.85 169:0.86 172:0.16 173:0.72 177:0.05 179:0.99 181:0.64 186:0.91 189:0.85 191:0.87 195:0.54 202:0.97 212:0.95 215:0.63 216:0.61 220:0.62 230:0.87 233:0.76 235:0.52 238:0.84 241:0.96 242:0.82 243:0.69 247:0.12 248:0.95 256:0.98 259:0.21 260:0.97 261:0.91 265:0.89 266:0.12 267:0.79 268:0.98 271:0.63 276:0.19 283:0.60 285:0.62 297:0.36 300:0.08 +2 6:0.83 7:0.81 8:0.63 12:0.06 17:0.38 20:0.90 21:0.30 27:0.21 28:0.64 30:0.52 34:0.47 36:0.93 37:0.74 43:0.52 55:0.71 66:0.44 69:0.12 70:0.63 78:0.82 81:0.90 91:0.76 98:0.65 100:0.81 106:0.81 108:0.77 111:0.81 120:0.93 123:0.75 124:0.85 126:0.54 127:0.76 129:0.95 133:0.87 135:0.28 140:0.45 146:0.80 147:0.84 149:0.83 150:0.82 151:0.82 152:0.84 153:0.95 154:0.41 159:0.88 161:0.68 162:0.75 164:0.90 167:0.59 169:0.80 172:0.87 173:0.08 177:0.05 179:0.22 181:0.64 186:0.81 187:0.39 189:0.85 191:0.76 202:0.83 206:0.81 212:0.49 215:0.72 216:0.95 230:0.87 233:0.76 235:0.42 238:0.84 241:0.55 242:0.82 243:0.29 245:0.91 247:0.12 248:0.89 256:0.86 259:0.21 260:0.93 261:0.82 265:0.66 266:0.91 267:0.76 268:0.87 271:0.63 276:0.90 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.86 7:0.81 8:0.77 12:0.06 17:0.01 20:0.82 21:0.30 27:0.30 28:0.64 30:0.21 32:0.80 34:0.67 36:0.53 37:0.32 43:0.41 55:0.52 66:0.80 69:0.52 70:0.50 78:0.89 81:0.97 91:0.92 98:0.81 100:0.87 104:0.91 106:0.81 108:0.40 111:0.87 120:0.96 123:0.39 126:0.54 127:0.30 129:0.05 135:0.65 140:0.45 145:0.52 146:0.68 147:0.72 149:0.47 150:0.95 151:0.80 152:0.86 153:0.93 154:0.31 159:0.26 161:0.68 162:0.78 163:0.90 164:0.99 167:0.74 169:0.83 172:0.45 173:0.66 177:0.05 179:0.78 181:0.64 186:0.88 187:0.21 189:0.88 191:0.88 195:0.61 202:0.97 206:0.81 212:0.37 215:0.72 216:0.64 220:0.82 230:0.87 233:0.76 235:0.52 238:0.84 241:0.74 242:0.82 243:0.82 247:0.12 248:0.94 256:0.99 259:0.21 260:0.96 261:0.87 265:0.81 266:0.38 267:0.65 268:0.99 271:0.63 276:0.36 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.06 17:0.30 21:0.40 27:0.45 28:0.64 30:0.55 32:0.80 34:0.69 36:0.19 37:0.50 43:0.32 55:0.59 66:0.52 69:0.70 70:0.43 81:0.89 91:0.25 98:0.57 108:0.53 123:0.51 124:0.65 126:0.54 127:0.41 129:0.81 133:0.67 135:0.92 145:0.52 146:0.76 147:0.80 149:0.53 150:0.78 154:0.24 159:0.61 161:0.68 163:0.86 167:0.55 172:0.48 173:0.62 177:0.05 178:0.55 179:0.67 181:0.64 187:0.68 195:0.82 212:0.18 215:0.67 216:0.77 235:0.52 241:0.44 242:0.82 243:0.61 245:0.61 247:0.12 259:0.21 265:0.58 266:0.80 267:0.59 271:0.63 276:0.49 297:0.36 300:0.33 +1 6:0.81 7:0.81 8:0.61 12:0.06 17:0.02 20:0.95 21:0.59 27:0.06 28:0.64 30:0.76 32:0.80 34:0.68 36:0.43 37:0.26 43:0.44 55:0.71 66:0.72 69:0.49 70:0.22 78:0.92 81:0.83 91:0.96 98:0.66 100:0.89 104:0.74 108:0.38 111:0.90 120:0.90 123:0.36 126:0.54 127:0.19 129:0.05 135:0.39 140:0.87 145:0.72 146:0.55 147:0.61 149:0.36 150:0.63 151:0.74 152:0.88 153:0.83 154:0.33 159:0.20 161:0.68 162:0.64 163:0.81 164:0.95 167:0.82 169:0.86 172:0.27 173:0.61 177:0.05 178:0.48 179:0.83 181:0.64 186:0.92 189:0.83 191:0.83 195:0.59 202:0.93 212:0.42 215:0.55 216:0.39 220:0.62 230:0.65 233:0.63 235:0.74 238:0.83 241:0.83 242:0.82 243:0.75 247:0.12 248:0.86 256:0.94 259:0.21 260:0.91 261:0.92 265:0.66 266:0.23 267:0.95 268:0.94 271:0.63 276:0.24 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.90 7:0.81 8:0.76 12:0.06 17:0.07 20:0.98 21:0.13 27:0.07 28:0.64 30:0.45 32:0.80 34:0.21 36:0.49 37:0.68 43:0.49 55:0.52 66:0.85 69:0.21 70:0.69 78:0.91 81:0.93 91:0.98 98:0.90 100:0.91 104:0.76 108:0.17 111:0.90 120:0.97 123:0.16 126:0.54 127:0.47 129:0.05 135:0.47 140:0.45 145:0.77 146:0.78 147:0.81 149:0.61 150:0.88 151:0.80 152:0.90 153:0.93 154:0.38 159:0.34 161:0.68 162:0.75 164:0.99 167:0.83 169:0.89 172:0.57 173:0.35 177:0.05 179:0.74 181:0.64 186:0.91 189:0.91 191:0.90 195:0.65 202:0.98 212:0.49 215:0.84 216:0.64 220:0.74 230:0.87 233:0.76 235:0.12 238:0.86 241:0.85 242:0.82 243:0.86 247:0.12 248:0.96 256:0.99 259:0.21 260:0.97 261:0.92 265:0.90 266:0.54 267:0.98 268:0.99 271:0.63 276:0.45 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.85 7:0.81 8:0.67 12:0.06 20:0.98 21:0.64 25:0.88 27:0.13 28:0.64 30:0.76 32:0.80 34:0.37 36:0.55 37:0.29 43:0.46 55:0.71 66:0.80 69:0.47 70:0.37 78:0.93 81:0.91 91:0.99 98:0.90 100:0.93 104:0.58 108:0.36 111:0.93 120:0.99 123:0.35 126:0.54 127:0.48 129:0.05 135:0.26 140:0.45 145:0.94 146:0.71 147:0.75 149:0.48 150:0.83 151:0.84 152:0.93 153:0.98 154:0.35 159:0.28 161:0.68 162:0.65 164:0.99 167:0.85 169:0.92 172:0.45 173:0.64 177:0.05 179:0.85 181:0.64 186:0.92 189:0.87 191:0.95 195:0.57 202:0.98 212:0.55 215:0.53 216:0.72 220:0.62 230:0.65 233:0.63 235:0.80 238:0.90 241:0.96 242:0.82 243:0.82 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.93 265:0.90 266:0.38 267:0.65 268:0.99 271:0.63 276:0.37 283:0.82 285:0.62 297:0.36 300:0.33 +0 12:0.20 17:0.93 21:0.78 27:0.58 30:0.95 37:0.35 39:0.40 43:0.06 55:1.00 66:0.20 69:0.99 74:0.26 91:0.01 98:0.05 108:0.99 123:0.99 124:0.61 127:0.23 129:0.59 133:0.47 146:0.05 147:0.05 149:0.05 154:0.06 159:0.99 172:0.05 173:0.78 175:0.75 177:0.05 178:0.55 179:0.99 187:0.21 202:0.36 216:0.11 222:0.25 235:0.60 243:0.40 244:0.61 245:0.36 253:0.23 257:0.65 259:0.21 265:0.05 277:0.69 300:0.08 +0 12:0.20 17:0.83 21:0.78 27:0.61 30:0.95 37:0.28 39:0.40 43:0.06 55:1.00 66:0.20 69:0.99 74:0.28 98:0.05 108:0.98 123:0.98 124:0.61 127:0.29 129:0.59 133:0.47 146:0.05 147:0.05 149:0.05 154:0.06 159:0.97 172:0.05 173:0.87 175:0.75 177:0.05 178:0.55 179:0.99 187:0.68 216:0.64 222:0.25 241:0.03 243:0.40 244:0.61 245:0.36 253:0.25 257:0.65 265:0.05 277:0.69 300:0.08 +0 12:0.20 17:0.89 21:0.78 27:0.63 30:0.95 34:0.36 36:0.42 37:0.30 39:0.40 43:0.05 55:1.00 66:0.20 69:1.00 74:0.26 98:0.05 108:1.00 123:1.00 124:0.61 127:0.23 129:0.59 133:0.47 135:0.70 146:0.05 147:0.05 149:0.05 154:0.05 159:0.99 172:0.05 173:0.87 175:0.75 177:0.05 178:0.55 179:0.99 187:0.68 202:0.36 216:0.59 222:0.25 243:0.40 244:0.61 245:0.36 253:0.24 257:0.65 259:0.21 265:0.05 267:0.44 277:0.69 300:0.08 +0 12:0.20 17:0.72 21:0.78 27:0.72 30:0.76 34:0.31 36:0.33 39:0.40 43:0.20 55:0.59 66:0.36 69:0.72 70:0.15 74:0.41 91:0.06 98:0.15 108:0.55 122:0.57 123:0.52 124:0.52 127:0.47 129:0.05 133:0.39 135:0.30 146:0.24 147:0.30 149:0.08 154:0.58 159:0.59 163:0.98 172:0.10 173:0.66 177:0.38 178:0.55 179:0.99 187:0.21 212:0.95 216:0.04 222:0.25 235:0.12 241:0.24 242:0.82 243:0.51 245:0.37 247:0.12 253:0.36 254:0.98 259:0.21 265:0.16 266:0.12 267:0.23 276:0.13 300:0.13 +1 12:0.20 17:0.77 21:0.78 27:0.58 30:0.76 34:0.31 36:0.46 37:0.24 39:0.40 43:0.27 55:0.92 66:0.36 69:0.76 70:0.15 74:0.40 91:0.38 98:0.19 108:0.59 122:0.57 123:0.57 124:0.52 127:0.65 129:0.05 133:0.39 135:0.78 146:0.46 147:0.53 149:0.18 154:0.20 159:0.89 163:0.98 172:0.10 173:0.48 177:0.38 178:0.55 179:0.99 187:0.39 212:0.95 216:0.40 222:0.25 235:0.60 241:0.07 242:0.82 243:0.51 244:0.61 245:0.37 247:0.12 253:0.36 259:0.21 265:0.21 266:0.12 267:0.59 276:0.13 300:0.13 +0 12:0.20 17:0.69 21:0.78 27:0.71 30:0.95 34:0.40 36:0.68 37:0.25 39:0.40 43:0.44 55:1.00 66:0.20 69:0.05 74:0.42 96:0.67 98:0.05 108:0.05 123:0.05 124:0.61 127:0.93 129:0.59 133:0.47 135:0.66 140:0.45 146:0.05 147:0.05 149:0.15 151:0.46 153:0.48 154:0.33 159:1.00 162:0.86 172:0.05 173:0.05 175:0.75 177:0.05 179:1.00 187:0.95 190:0.77 202:0.36 216:0.16 220:1.00 222:0.25 241:0.05 243:0.40 244:0.61 245:0.36 248:0.46 253:0.37 256:0.45 257:0.65 259:0.21 265:0.05 267:0.18 268:0.46 277:0.69 285:0.50 300:0.08 diff --git a/examples/lambdarank/rank.test.query b/examples/lambdarank/rank.test.query new file mode 100644 index 0000000..f924152 --- /dev/null +++ b/examples/lambdarank/rank.test.query @@ -0,0 +1,50 @@ +12 +19 +18 +10 +15 +15 +22 +23 +18 +16 +16 +11 +6 +13 +17 +21 +20 +16 +13 +16 +21 +15 +10 +19 +10 +13 +18 +17 +23 +24 +16 +13 +17 +24 +17 +10 +17 +15 +18 +16 +9 +9 +21 +14 +13 +13 +13 +10 +10 +6 diff --git a/examples/lambdarank/rank.train b/examples/lambdarank/rank.train new file mode 100644 index 0000000..6312ba3 --- /dev/null +++ b/examples/lambdarank/rank.train @@ -0,0 +1,3005 @@ +0 10:0.89 11:0.75 12:0.01 17:0.45 18:0.91 21:0.78 27:0.72 29:0.77 30:0.76 39:0.65 43:0.79 44:0.88 45:0.88 66:0.64 69:0.25 70:0.41 71:0.83 74:0.79 77:0.70 83:0.56 85:0.72 86:0.93 91:0.35 98:0.70 101:0.96 108:0.20 122:0.86 123:0.19 124:0.47 127:0.43 129:0.05 133:0.45 139:0.92 145:0.47 146:0.58 147:0.63 149:0.84 154:0.73 155:0.50 159:0.31 170:0.90 172:0.67 173:0.40 174:0.79 177:0.88 178:0.55 179:0.52 187:0.21 192:0.48 195:0.59 197:0.83 204:0.80 208:0.50 212:0.82 216:0.29 222:0.48 235:0.22 239:0.88 241:0.21 242:0.40 243:0.69 245:0.77 247:0.76 253:0.55 265:0.70 266:0.27 271:0.34 276:0.58 281:0.91 282:0.75 300:0.43 +1 1:0.69 11:0.64 12:0.51 17:0.53 18:0.75 21:0.77 27:0.45 29:0.71 30:0.45 34:0.81 36:0.21 37:0.50 39:0.48 43:0.10 45:0.66 55:0.52 64:0.77 66:0.52 69:0.71 70:0.33 71:0.97 74:0.50 81:0.37 83:0.60 86:0.80 91:0.08 98:0.24 99:0.83 101:0.52 108:0.54 114:0.53 122:0.80 123:0.52 124:0.50 126:0.54 127:0.17 129:0.05 133:0.43 135:0.56 139:0.77 145:0.49 146:0.31 147:0.38 149:0.08 150:0.58 154:0.72 155:0.58 159:0.25 165:0.70 172:0.27 173:0.72 176:0.73 177:0.70 178:0.55 179:0.61 187:0.68 192:0.59 201:0.74 208:0.64 212:0.52 215:0.36 216:0.77 222:0.48 235:0.99 241:0.21 242:0.55 243:0.61 245:0.48 247:0.39 253:0.36 254:0.90 259:0.21 265:0.26 266:0.27 267:0.69 271:0.60 276:0.25 290:0.53 297:0.36 300:0.25 +0 1:0.69 11:0.64 12:0.51 17:0.34 18:0.85 21:0.13 27:0.18 29:0.71 30:0.17 34:0.47 36:0.30 37:0.85 39:0.48 43:0.65 45:0.77 66:0.20 69:0.10 70:0.68 71:0.97 74:0.59 81:0.62 83:0.60 86:0.85 91:0.31 97:0.89 98:0.50 99:0.83 101:0.52 104:0.84 108:0.09 114:0.75 122:0.82 123:0.70 124:0.67 126:0.44 127:0.52 129:0.59 133:0.61 135:0.34 139:0.81 146:0.59 147:0.65 149:0.46 150:0.61 154:0.83 155:0.58 158:0.79 159:0.56 165:0.70 172:0.41 173:0.15 176:0.66 177:0.70 178:0.55 179:0.73 187:0.21 192:0.59 201:0.68 208:0.54 212:0.19 215:0.61 216:0.82 222:0.48 235:0.22 241:0.50 242:0.33 243:0.57 245:0.60 247:0.67 253:0.54 254:0.90 259:0.21 265:0.48 266:0.71 267:0.19 271:0.60 276:0.47 279:0.82 283:0.82 290:0.75 297:0.36 300:0.43 +1 11:0.64 12:0.51 17:0.11 18:0.82 21:0.78 27:0.45 29:0.71 30:0.45 34:0.82 36:0.27 37:0.50 39:0.48 43:0.12 45:0.66 55:0.59 66:0.63 69:0.72 70:0.61 71:0.97 74:0.41 86:0.83 91:0.17 98:0.47 101:0.52 108:0.56 122:0.51 123:0.53 124:0.45 127:0.29 129:0.05 133:0.37 135:0.39 139:0.79 145:0.50 146:0.57 147:0.63 149:0.11 154:0.66 159:0.43 172:0.41 173:0.71 177:0.70 178:0.55 179:0.74 187:0.68 202:0.36 212:0.50 216:0.77 222:0.48 235:0.68 241:0.32 242:0.14 243:0.68 245:0.54 247:0.67 253:0.32 254:0.96 259:0.21 265:0.49 266:0.47 267:0.44 271:0.60 276:0.32 300:0.43 +0 1:0.69 7:0.72 11:0.64 12:0.51 17:0.76 18:0.61 21:0.47 27:0.72 29:0.71 30:0.74 32:0.69 34:0.55 36:0.78 39:0.48 43:0.72 45:0.87 66:0.43 69:0.81 70:0.37 71:0.97 74:0.75 77:0.98 81:0.37 83:0.60 86:0.71 91:0.72 98:0.63 99:0.83 101:0.52 104:0.58 108:0.66 114:0.59 122:0.75 123:0.63 124:0.59 126:0.44 127:0.86 129:0.05 133:0.43 135:0.32 139:0.68 145:1.00 146:0.53 147:0.48 149:0.75 150:0.54 154:0.58 155:0.58 158:0.74 159:0.37 165:0.70 172:0.45 173:0.94 176:0.66 177:0.38 178:0.55 179:0.74 192:0.59 195:0.59 201:0.68 204:0.85 208:0.54 212:0.37 215:0.41 216:0.03 222:0.48 230:0.74 232:0.78 233:0.73 235:0.60 241:0.79 242:0.51 243:0.47 245:0.72 247:0.60 253:0.46 254:0.95 257:0.65 259:0.21 265:0.64 266:0.63 267:0.36 271:0.60 276:0.48 282:0.77 290:0.59 297:0.36 300:0.33 +1 1:0.69 7:0.72 11:0.64 12:0.51 17:0.06 18:0.91 21:0.22 22:0.93 27:0.11 29:0.71 30:0.21 34:0.92 36:0.26 37:0.91 39:0.48 43:0.60 45:0.73 47:0.93 55:0.59 66:0.75 69:0.10 70:0.64 71:0.97 74:0.59 81:0.54 83:0.60 86:0.88 91:0.53 97:0.89 98:0.81 99:0.83 101:0.52 108:0.09 114:0.67 122:0.86 123:0.09 124:0.39 126:0.44 127:0.57 129:0.05 133:0.37 135:0.94 139:0.84 145:0.59 146:0.68 147:0.72 149:0.37 150:0.58 154:0.70 155:0.58 158:0.79 159:0.33 165:0.70 172:0.54 173:0.29 176:0.66 177:0.70 178:0.55 179:0.77 187:0.39 192:0.59 201:0.68 204:0.85 208:0.54 212:0.49 215:0.51 216:0.75 222:0.48 230:0.75 233:0.76 235:0.31 241:0.37 242:0.52 243:0.77 245:0.50 247:0.47 253:0.51 254:0.97 259:0.21 262:0.93 265:0.81 266:0.47 267:0.44 271:0.60 276:0.46 279:0.82 283:0.82 290:0.67 297:0.36 300:0.43 +0 1:0.69 12:0.51 17:0.69 18:0.60 21:0.05 25:0.88 27:0.72 29:0.71 30:0.76 34:0.29 36:0.55 39:0.48 43:0.10 55:0.84 66:0.36 69:0.90 70:0.15 71:0.97 74:0.34 81:0.79 83:0.60 86:0.62 87:0.98 91:0.76 98:0.26 99:0.83 104:0.58 108:0.80 114:0.85 122:0.80 123:0.79 124:0.52 126:0.44 127:0.23 129:0.05 133:0.39 135:0.51 139:0.60 146:0.32 147:0.05 149:0.08 150:0.75 154:0.08 155:0.58 159:0.33 163:0.98 165:0.70 172:0.10 173:0.97 175:0.75 176:0.66 177:0.05 178:0.55 179:0.96 192:0.59 201:0.68 208:0.54 212:0.95 215:0.78 216:0.01 222:0.48 232:0.79 235:0.12 241:0.81 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.58 257:0.84 259:0.21 265:0.28 266:0.12 267:0.52 271:0.60 276:0.15 277:0.69 290:0.85 297:0.36 300:0.13 +1 1:0.69 11:0.64 12:0.51 17:0.86 18:0.64 21:0.05 22:0.93 27:0.72 29:0.71 30:0.76 34:0.33 36:0.37 39:0.48 43:0.71 45:0.81 47:0.93 66:0.80 69:0.32 70:0.28 71:0.97 74:0.59 81:0.71 83:0.60 86:0.75 91:0.62 98:0.79 99:0.83 101:0.52 104:0.54 108:0.25 114:0.85 122:0.51 123:0.24 126:0.44 127:0.28 129:0.05 135:0.34 139:0.71 145:0.45 146:0.48 147:0.54 149:0.60 150:0.67 154:0.72 155:0.58 159:0.17 165:0.70 172:0.45 173:0.61 176:0.66 177:0.70 178:0.55 179:0.76 187:0.21 192:0.59 201:0.68 208:0.54 212:0.71 215:0.78 216:0.11 222:0.48 232:0.74 235:0.12 241:0.49 242:0.40 243:0.82 247:0.47 253:0.58 254:0.97 259:0.21 262:0.93 265:0.79 266:0.27 267:0.23 271:0.60 276:0.33 290:0.85 297:0.36 300:0.25 +1 1:0.69 9:0.76 11:0.64 12:0.51 17:0.47 18:0.90 21:0.05 27:0.02 29:0.71 30:0.37 32:0.69 34:0.75 36:0.22 37:0.92 39:0.48 43:0.69 45:0.77 55:0.59 66:0.48 69:0.38 70:0.85 71:0.97 74:0.61 77:0.70 81:0.71 83:0.60 86:0.83 91:0.38 96:0.76 97:0.89 98:0.46 99:0.83 101:0.52 108:0.69 114:0.85 120:0.63 122:0.86 123:0.67 124:0.57 126:0.44 127:0.23 129:0.05 133:0.37 135:0.88 139:0.82 140:0.80 145:0.56 146:0.49 147:0.55 149:0.63 150:0.67 151:0.65 153:0.48 154:0.59 155:0.58 158:0.79 159:0.44 162:0.86 164:0.54 165:0.70 172:0.51 173:0.29 176:0.66 177:0.70 179:0.43 187:0.39 190:0.77 192:0.59 195:0.81 201:0.68 202:0.50 208:0.54 212:0.50 215:0.78 216:0.99 219:0.89 220:0.96 222:0.48 235:0.12 241:0.53 242:0.71 243:0.44 244:0.61 245:0.75 247:0.55 248:0.63 253:0.68 255:0.74 256:0.58 259:0.21 260:0.63 265:0.48 266:0.71 267:0.79 268:0.59 271:0.60 276:0.53 277:0.87 279:0.82 282:0.77 283:0.82 285:0.56 290:0.85 292:0.95 297:0.36 300:0.70 +0 1:0.69 7:0.72 8:0.67 9:0.76 11:0.64 12:0.51 17:0.59 18:0.61 21:0.47 27:0.72 29:0.71 30:0.74 32:0.69 34:0.49 36:0.81 39:0.48 43:0.72 45:0.87 66:0.43 69:0.81 70:0.37 71:0.97 74:0.75 77:0.98 81:0.37 83:0.60 86:0.71 91:0.76 96:0.78 98:0.63 99:0.83 101:0.52 104:0.58 108:0.66 114:0.59 120:0.66 122:0.75 123:0.63 124:0.59 126:0.44 127:0.86 129:0.05 133:0.43 135:0.30 139:0.68 140:0.45 145:1.00 146:0.53 147:0.48 149:0.75 150:0.54 151:0.65 153:0.48 154:0.58 155:0.58 158:0.74 159:0.37 162:0.86 164:0.65 165:0.70 172:0.45 173:0.94 176:0.66 177:0.38 179:0.74 190:0.77 192:0.59 195:0.60 201:0.68 202:0.53 204:0.85 208:0.54 212:0.37 215:0.41 216:0.02 220:0.74 222:0.48 230:0.74 232:0.78 233:0.73 235:0.60 241:0.82 242:0.51 243:0.47 245:0.72 247:0.60 248:0.68 253:0.71 254:0.95 255:0.74 256:0.58 257:0.65 259:0.21 260:0.65 265:0.64 266:0.63 267:0.36 268:0.62 271:0.60 276:0.48 282:0.77 285:0.56 290:0.59 297:0.36 300:0.33 +1 1:0.74 11:0.64 12:0.51 17:0.62 18:0.87 21:0.05 27:0.53 29:0.71 30:0.76 32:0.75 34:0.42 36:0.25 37:0.86 39:0.48 43:0.82 44:0.93 45:0.73 48:0.91 55:0.52 64:0.77 66:0.61 69:0.47 70:0.36 71:0.97 74:0.68 77:0.70 81:0.79 83:0.91 86:0.96 91:0.27 98:0.38 101:0.52 104:0.91 106:0.81 108:0.36 114:0.89 117:0.86 122:0.82 123:0.35 124:0.47 126:0.54 127:0.49 129:0.59 133:0.46 135:0.75 139:0.96 145:0.65 146:0.38 147:0.45 149:0.57 150:0.87 154:0.81 155:0.67 159:0.39 165:0.93 172:0.48 173:0.54 176:0.73 177:0.70 178:0.55 179:0.75 187:0.39 192:0.87 195:0.63 201:0.93 206:0.81 208:0.64 212:0.76 215:0.78 216:0.65 219:0.89 222:0.48 232:0.94 235:0.22 241:0.10 242:0.38 243:0.68 245:0.62 247:0.47 253:0.61 254:0.86 259:0.21 265:0.40 266:0.38 267:0.65 271:0.60 276:0.28 281:0.91 282:0.84 290:0.89 292:0.91 297:0.36 300:0.33 +0 7:0.66 11:0.64 12:0.51 17:0.36 18:0.94 21:0.54 27:0.20 29:0.71 30:0.13 34:0.88 36:0.37 37:0.60 39:0.48 43:0.14 45:0.66 55:0.59 64:0.77 66:0.59 69:0.49 70:0.98 71:0.97 74:0.56 76:0.85 81:0.31 86:0.92 91:0.29 98:0.45 101:0.52 104:0.94 106:0.81 108:0.38 120:0.54 122:0.86 123:0.36 124:0.78 126:0.44 127:0.94 129:0.05 133:0.86 135:0.80 139:0.90 140:0.45 145:0.61 146:0.84 147:0.86 149:0.29 150:0.25 151:0.46 153:0.48 154:0.76 159:0.87 162:0.86 164:0.53 172:0.71 173:0.23 177:0.70 179:0.52 187:0.68 190:0.89 192:0.51 201:0.68 202:0.36 206:0.81 212:0.30 215:0.39 216:0.84 219:0.89 220:0.93 222:0.48 230:0.69 233:0.76 235:0.68 241:0.41 242:0.51 243:0.67 245:0.67 247:0.90 248:0.46 253:0.37 254:0.96 256:0.45 259:0.21 260:0.54 265:0.47 266:0.92 267:0.44 268:0.46 271:0.60 276:0.66 283:0.78 285:0.47 292:0.91 297:0.36 300:0.94 +1 1:0.74 7:0.66 11:0.64 12:0.51 17:0.49 18:0.97 21:0.30 22:0.93 27:0.20 29:0.71 30:0.42 32:0.80 34:0.95 36:0.32 37:0.60 39:0.48 43:0.14 44:0.93 45:0.77 47:0.93 55:0.71 64:0.77 66:0.49 69:0.49 70:0.85 71:0.97 74:0.72 76:0.85 77:0.70 81:0.59 83:0.60 86:0.96 91:0.25 98:0.76 99:0.83 101:0.52 104:0.99 106:0.81 108:0.38 114:0.65 117:0.86 122:0.86 123:0.36 124:0.65 126:0.54 127:0.88 129:0.05 133:0.66 135:0.70 139:0.96 145:0.88 146:0.91 147:0.92 149:0.34 150:0.74 154:0.85 155:0.67 159:0.73 165:0.70 172:0.77 173:0.35 176:0.73 177:0.70 178:0.55 179:0.39 187:0.68 192:0.87 195:0.86 201:0.93 206:0.81 208:0.64 212:0.54 215:0.44 216:0.84 222:0.48 230:0.69 232:0.99 233:0.76 235:0.60 241:0.21 242:0.70 243:0.60 245:0.87 247:0.84 253:0.50 254:0.80 259:0.21 262:0.93 265:0.76 266:0.80 267:0.87 271:0.60 276:0.79 282:0.88 290:0.65 297:0.36 300:0.70 +1 1:0.74 7:0.66 9:0.76 11:0.64 12:0.51 17:0.34 18:0.96 21:0.22 27:0.20 29:0.71 30:0.40 32:0.75 34:0.94 36:0.19 37:0.60 39:0.48 43:0.69 44:0.93 45:0.81 55:0.71 64:0.77 66:0.10 69:0.48 70:0.86 71:0.97 74:0.75 76:0.85 77:0.70 81:0.63 83:0.60 86:0.94 91:0.37 96:0.80 97:0.99 98:0.43 99:0.83 101:0.52 104:0.98 106:0.81 108:0.37 114:0.71 117:0.86 120:0.69 122:0.86 123:0.82 124:0.90 126:0.54 127:0.80 129:0.59 133:0.89 135:0.69 139:0.94 140:0.45 145:0.70 146:0.59 147:0.65 149:0.63 150:0.76 151:0.81 153:0.48 154:0.79 155:0.67 158:0.78 159:0.77 162:0.86 164:0.54 165:0.70 172:0.63 173:0.31 176:0.73 177:0.70 179:0.37 187:0.68 190:0.77 192:0.87 195:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.48 215:0.51 216:0.84 219:0.89 222:0.48 230:0.69 232:0.99 233:0.76 235:0.42 241:0.41 242:0.61 243:0.44 245:0.81 247:0.91 248:0.73 253:0.80 254:0.93 255:0.74 256:0.45 259:0.21 260:0.68 265:0.36 266:0.80 267:0.44 268:0.46 271:0.60 276:0.79 277:0.69 279:0.82 282:0.83 283:0.78 285:0.56 290:0.71 292:0.91 297:0.36 300:0.70 +1 11:0.83 12:0.51 17:0.32 18:0.70 21:0.78 27:0.28 29:0.86 30:0.85 34:0.73 36:0.90 37:0.65 39:0.55 43:0.57 45:0.77 66:0.80 69:0.94 70:0.28 71:0.79 74:0.50 85:0.85 86:0.77 91:0.02 98:0.32 101:0.87 108:0.88 122:0.82 123:0.87 124:0.38 127:0.25 129:0.05 133:0.37 135:0.90 139:0.74 146:0.68 147:0.05 149:0.66 154:0.46 159:0.71 163:0.81 172:0.65 173:0.87 177:0.05 178:0.55 179:0.47 187:0.87 212:0.30 216:0.44 222:0.48 241:0.10 242:0.33 243:0.11 245:0.54 247:0.60 253:0.35 257:0.84 259:0.21 265:0.35 266:0.63 267:0.23 271:0.56 276:0.34 300:0.33 +1 11:0.57 12:0.51 17:0.28 18:0.60 21:0.78 27:0.28 29:0.86 30:0.91 34:0.50 36:0.79 37:0.65 39:0.55 43:0.65 66:0.49 69:0.90 70:0.13 71:0.79 74:0.37 85:0.72 86:0.69 91:0.02 98:0.08 101:0.87 108:0.80 122:0.41 123:0.79 124:0.48 127:0.17 129:0.05 133:0.37 135:0.71 139:0.67 146:0.17 147:0.05 149:0.41 154:0.54 159:0.46 163:0.97 172:0.16 173:0.81 177:0.05 178:0.55 179:0.83 187:0.87 212:0.61 216:0.44 222:0.48 241:0.12 242:0.63 243:0.29 245:0.39 247:0.30 253:0.30 257:0.84 259:0.21 265:0.08 266:0.17 267:0.23 271:0.56 276:0.12 300:0.13 +1 11:0.57 12:0.51 17:0.40 18:0.66 21:0.78 27:0.66 29:0.86 30:0.95 34:0.85 36:0.88 37:0.57 39:0.55 43:0.85 66:0.64 69:0.61 71:0.79 74:0.44 85:0.72 86:0.75 91:0.22 98:0.17 101:0.87 108:0.46 122:0.57 123:0.45 127:0.10 129:0.05 135:0.66 139:0.72 146:0.17 147:0.22 149:0.60 154:0.82 159:0.09 163:0.90 172:0.16 173:0.92 177:0.70 178:0.55 179:0.16 187:0.98 212:0.95 216:0.23 222:0.48 235:0.22 241:0.44 242:0.82 243:0.69 247:0.12 253:0.33 259:0.21 265:0.19 266:0.12 267:0.52 271:0.56 276:0.19 300:0.08 +1 11:0.57 12:0.51 17:0.47 18:0.74 21:0.78 27:0.28 29:0.86 30:0.87 34:0.44 36:0.79 37:0.65 39:0.55 43:0.56 66:0.79 69:0.95 70:0.24 71:0.79 74:0.55 85:0.93 86:0.84 91:0.02 98:0.32 101:0.87 108:0.89 122:0.57 123:0.89 124:0.40 127:0.21 129:0.05 133:0.59 135:0.89 139:0.81 146:0.80 147:0.05 149:0.77 154:0.45 159:0.77 163:0.75 172:0.75 173:0.82 177:0.05 178:0.55 179:0.27 187:0.87 212:0.39 216:0.44 222:0.48 241:0.15 242:0.41 243:0.09 245:0.59 247:0.60 253:0.37 257:0.84 259:0.21 265:0.34 266:0.54 267:0.23 271:0.56 276:0.53 300:0.33 +1 8:0.60 11:0.83 12:0.51 17:0.49 18:0.60 21:0.78 27:0.28 29:0.86 30:0.57 34:0.23 36:0.81 37:0.65 39:0.55 43:0.31 45:0.93 66:0.07 69:0.99 70:0.80 71:0.79 74:0.53 85:0.80 86:0.64 91:0.08 98:0.15 101:0.87 108:0.98 122:0.51 123:0.98 124:0.98 127:0.84 129:0.81 133:0.98 135:0.19 139:0.63 146:0.34 147:0.05 149:0.66 154:0.12 159:0.97 163:0.88 172:0.37 173:0.94 177:0.05 178:0.55 179:0.32 187:0.39 212:0.12 216:0.44 222:0.48 235:0.31 241:0.10 242:0.31 243:0.07 245:0.71 247:0.86 253:0.36 257:0.84 259:0.21 265:0.16 266:0.98 267:0.82 271:0.56 276:0.83 277:0.69 300:0.84 +1 11:0.57 12:0.37 17:0.83 21:0.78 27:0.29 30:0.95 34:0.52 36:0.21 37:0.26 43:0.79 66:0.54 69:0.74 74:0.42 85:0.72 91:0.12 98:0.07 101:0.68 108:0.57 123:0.55 127:0.05 129:0.05 135:0.90 146:0.13 147:0.18 149:0.44 154:0.73 159:0.08 172:0.10 173:0.53 177:0.38 178:0.55 179:0.08 187:0.21 216:0.26 222:0.84 241:0.24 243:0.63 253:0.37 259:0.21 265:0.07 267:0.23 271:0.42 300:0.08 +1 11:0.57 12:0.37 17:0.22 21:0.78 27:0.32 30:0.20 34:0.44 36:0.57 37:0.66 43:0.64 55:0.71 66:0.12 69:0.57 70:1.00 74:0.65 85:0.80 91:0.02 98:0.12 101:0.47 108:0.97 122:0.67 123:0.97 124:0.94 127:0.15 129:0.89 133:0.93 135:0.73 146:0.17 147:0.22 149:0.44 154:0.82 159:0.95 163:0.87 172:0.37 173:0.06 177:0.38 178:0.55 179:0.14 187:0.68 212:0.12 216:0.80 222:0.84 235:0.42 241:0.07 242:0.72 243:0.13 245:0.67 247:0.55 253:0.50 259:0.21 265:0.12 266:0.97 267:0.82 271:0.42 276:0.65 277:0.69 300:0.99 +0 11:0.57 12:0.37 17:0.69 21:0.78 27:0.64 30:0.95 34:0.64 36:0.97 37:0.39 43:0.90 66:0.54 69:0.45 74:0.45 85:0.72 91:0.25 98:0.13 101:0.75 108:0.35 123:0.33 127:0.08 129:0.05 135:0.77 146:0.13 147:0.18 149:0.51 154:0.89 159:0.08 172:0.10 173:0.72 177:0.38 178:0.55 179:0.12 187:0.68 216:0.19 222:0.84 235:0.05 241:0.41 243:0.63 253:0.39 259:0.21 265:0.14 267:0.17 271:0.42 300:0.08 +1 11:0.57 12:0.37 21:0.78 27:0.32 30:0.95 34:0.69 36:0.98 37:0.66 43:0.76 66:0.54 69:0.81 74:0.41 85:0.72 91:0.08 98:0.10 101:0.73 108:0.65 123:0.63 127:0.07 129:0.05 135:0.79 146:0.13 147:0.18 149:0.40 154:0.68 159:0.08 172:0.10 173:0.90 177:0.38 178:0.55 179:0.09 187:0.68 216:0.80 222:0.84 235:0.52 241:0.05 243:0.63 253:0.36 259:0.21 265:0.11 267:0.36 271:0.42 300:0.08 +1 11:0.57 12:0.37 17:0.40 21:0.78 27:0.72 30:0.95 34:0.90 36:0.95 37:0.45 43:0.76 66:0.54 69:0.81 74:0.41 85:0.72 91:0.07 98:0.11 101:0.74 108:0.65 123:0.63 127:0.08 129:0.05 135:0.61 146:0.13 147:0.18 149:0.41 154:0.68 159:0.08 172:0.10 173:0.94 177:0.38 178:0.55 179:0.10 187:0.21 216:0.12 222:0.84 235:0.68 241:0.43 243:0.63 253:0.36 259:0.21 265:0.12 267:0.23 271:0.42 300:0.08 +1 11:0.57 12:0.37 17:0.67 21:0.78 27:0.64 30:0.76 34:0.31 36:0.96 37:0.39 43:0.87 66:0.64 69:0.53 70:0.15 74:0.45 85:0.72 91:0.18 98:0.13 101:0.71 108:0.41 122:0.43 123:0.39 127:0.08 129:0.05 135:0.87 146:0.20 147:0.26 149:0.50 154:0.85 159:0.10 163:0.90 172:0.16 173:0.46 177:0.38 178:0.55 179:0.11 187:0.68 212:0.95 216:0.19 222:0.84 235:0.05 241:0.37 242:0.82 243:0.69 247:0.12 253:0.39 259:0.21 265:0.14 266:0.12 267:0.18 271:0.42 276:0.15 300:0.13 +1 11:0.57 12:0.37 17:0.30 21:0.78 27:0.32 30:0.27 34:0.30 36:0.47 37:0.66 43:0.71 55:0.71 66:0.13 69:0.87 70:0.98 74:0.59 85:0.80 98:0.12 101:0.47 108:0.97 122:0.67 123:0.97 124:0.93 127:0.14 129:0.81 133:0.92 135:0.85 146:0.31 147:0.37 149:0.48 154:0.61 159:0.94 163:0.87 172:0.37 173:0.19 177:0.38 178:0.55 179:0.14 187:0.68 212:0.12 216:0.80 222:0.84 241:0.07 242:0.73 243:0.19 245:0.67 247:0.55 253:0.47 259:0.21 265:0.12 266:0.97 267:0.82 271:0.42 276:0.62 277:0.69 300:0.98 +2 11:0.57 12:0.37 17:0.51 21:0.78 27:0.64 30:0.76 34:0.61 36:1.00 37:0.39 43:0.81 66:0.64 69:0.59 70:0.15 74:0.54 85:0.72 91:0.15 98:0.61 101:0.75 108:0.45 122:0.57 123:0.43 127:0.18 129:0.05 135:0.74 146:0.36 147:0.42 149:0.49 154:0.76 159:0.14 163:0.93 172:0.16 173:0.84 177:0.38 178:0.55 179:0.92 187:0.68 212:0.95 216:0.19 222:0.84 235:0.05 241:0.43 242:0.82 243:0.69 247:0.12 253:0.44 259:0.21 265:0.62 266:0.12 267:0.28 271:0.42 276:0.13 300:0.13 +0 7:0.78 8:0.90 9:0.80 12:0.20 17:0.26 21:0.40 27:0.06 28:0.56 30:0.58 32:0.75 34:0.59 36:0.34 37:0.92 39:0.50 41:0.82 43:0.29 55:0.52 66:0.27 69:0.71 70:0.44 81:0.50 83:0.76 91:0.64 96:0.80 98:0.57 108:0.74 120:0.79 123:0.52 124:0.59 126:0.32 127:0.32 129:0.59 133:0.47 135:0.28 140:0.92 145:0.56 146:0.54 147:0.60 149:0.41 150:0.39 151:0.87 153:0.48 154:0.21 155:0.67 159:0.32 161:0.74 162:0.56 164:0.72 167:0.85 172:0.27 173:0.79 175:0.75 177:0.05 178:0.48 179:0.79 181:0.72 187:0.39 191:0.85 192:0.59 195:0.66 202:0.69 208:0.64 212:0.37 215:0.67 216:0.75 220:0.91 222:0.84 230:0.81 233:0.76 235:0.42 241:0.75 242:0.82 243:0.46 244:0.61 245:0.56 247:0.12 248:0.78 253:0.74 255:0.79 256:0.64 257:0.65 259:0.21 260:0.79 265:0.25 266:0.47 267:0.59 268:0.66 276:0.34 277:0.69 283:0.71 285:0.62 297:0.36 300:0.33 +1 11:0.57 12:0.20 17:0.67 21:0.78 27:0.45 28:0.56 30:0.58 34:0.96 36:0.21 37:0.50 39:0.50 43:0.76 66:0.36 69:0.76 70:0.24 74:0.53 85:0.85 91:0.17 98:0.37 101:0.76 108:0.59 122:0.41 123:0.57 124:0.59 127:0.18 129:0.59 133:0.47 135:0.73 145:0.49 146:0.55 147:0.61 149:0.68 154:0.67 159:0.35 161:0.74 163:0.81 167:0.63 172:0.27 173:0.69 177:0.38 178:0.55 179:0.55 181:0.72 187:0.68 212:0.37 216:0.77 222:0.84 235:0.98 241:0.24 242:0.40 243:0.51 245:0.56 247:0.47 253:0.44 259:0.21 265:0.39 266:0.47 267:0.89 276:0.35 300:0.18 +4 6:0.99 7:0.78 8:0.96 9:0.80 12:0.20 17:0.24 20:0.89 21:0.68 27:0.06 28:0.56 30:0.58 32:0.75 34:0.67 36:0.71 37:0.92 39:0.50 41:0.95 43:0.44 55:0.52 66:0.80 69:0.27 70:0.60 78:0.94 81:0.32 83:0.82 91:0.96 96:0.94 98:0.98 100:0.99 108:0.21 111:0.99 120:0.99 123:0.20 126:0.32 127:0.81 129:0.05 135:0.23 140:0.95 145:0.63 146:0.61 147:0.66 149:0.48 150:0.30 151:0.98 152:0.92 153:0.48 154:0.33 155:0.67 159:0.22 161:0.74 162:0.51 164:0.98 167:0.98 169:0.98 172:0.45 173:0.58 175:0.75 177:0.05 178:0.48 179:0.89 181:0.72 186:0.93 189:0.99 191:0.98 192:0.59 195:0.56 202:0.99 208:0.64 212:0.37 215:0.49 216:0.75 222:0.84 230:0.81 233:0.76 235:0.80 238:0.99 241:1.00 242:0.82 243:0.82 244:0.61 247:0.12 248:0.93 253:0.92 255:0.79 256:0.98 257:0.65 259:0.21 260:0.99 261:0.97 265:0.98 266:0.27 267:0.82 268:0.98 276:0.35 277:0.69 283:0.82 285:0.62 297:0.36 300:0.43 +1 7:0.78 11:0.57 12:0.20 17:0.47 21:0.47 27:0.21 28:0.56 30:0.26 34:0.58 36:0.56 37:0.74 39:0.50 43:0.89 55:0.59 66:0.64 69:0.15 70:0.81 74:0.73 81:0.44 85:0.94 91:0.33 98:0.84 101:0.79 106:0.81 108:0.78 120:0.73 123:0.77 124:0.48 126:0.32 127:0.68 129:0.59 133:0.47 135:0.17 140:0.45 146:0.83 147:0.86 149:0.93 150:0.36 151:0.63 153:0.72 154:0.87 159:0.82 161:0.74 162:0.66 164:0.79 167:0.68 172:0.94 173:0.10 177:0.38 179:0.18 181:0.72 187:0.39 190:0.77 202:0.73 206:0.81 212:0.71 215:0.63 216:0.95 220:0.93 222:0.84 230:0.81 233:0.76 235:0.52 241:0.43 242:0.77 243:0.33 245:0.98 247:0.67 248:0.66 253:0.54 256:0.71 259:0.21 260:0.74 265:0.84 266:0.63 267:0.36 268:0.75 276:0.93 283:0.71 285:0.50 297:0.36 300:0.70 +1 1:0.74 9:0.80 11:0.57 12:0.20 17:0.55 20:0.87 21:0.59 27:0.35 28:0.56 30:0.21 34:0.47 36:0.96 37:0.48 39:0.50 41:0.82 43:0.84 55:0.52 60:0.87 66:0.23 69:0.63 70:0.93 74:0.81 78:0.74 81:0.60 83:0.83 85:0.93 91:0.11 96:0.83 98:0.30 100:0.77 101:0.77 108:0.88 111:0.74 114:0.74 117:0.86 120:0.72 123:0.88 124:0.91 126:0.54 127:0.47 129:0.89 133:0.91 135:0.89 140:0.45 146:0.67 147:0.72 149:0.88 150:0.74 151:0.90 152:0.82 153:0.69 154:0.81 155:0.67 158:0.87 159:0.84 161:0.74 162:0.86 164:0.62 165:0.78 167:0.59 169:0.75 172:0.63 173:0.36 176:0.73 177:0.38 179:0.31 181:0.72 186:0.75 187:0.87 192:0.59 201:0.74 202:0.46 208:0.64 212:0.67 215:0.55 216:0.64 222:0.84 232:1.00 235:0.74 241:0.10 242:0.55 243:0.37 245:0.79 247:0.55 248:0.80 253:0.85 255:0.79 256:0.45 259:0.21 260:0.73 261:0.76 265:0.32 266:0.89 267:0.23 268:0.55 276:0.79 279:0.86 283:0.71 285:0.62 290:0.74 297:0.36 300:0.84 +4 6:0.99 8:0.95 9:0.80 11:0.57 12:0.20 17:0.22 20:0.82 21:0.68 27:0.06 28:0.56 30:0.33 32:0.75 34:0.86 36:0.39 37:0.92 39:0.50 41:0.82 43:0.75 66:0.68 69:0.83 70:0.49 74:0.40 78:0.84 81:0.32 83:0.77 85:0.72 91:0.85 96:0.85 98:0.51 100:0.94 101:0.73 108:0.68 111:0.88 120:0.90 123:0.66 124:0.41 126:0.32 127:0.45 129:0.05 133:0.39 135:0.44 140:0.93 145:0.76 146:0.46 147:0.05 149:0.53 150:0.30 151:0.93 152:0.92 153:0.48 154:0.66 155:0.67 159:0.34 161:0.74 162:0.60 164:0.87 167:0.88 169:0.98 172:0.37 173:0.94 177:0.05 179:0.87 181:0.72 186:0.84 187:0.87 189:0.99 191:0.96 192:0.59 195:0.62 202:0.84 208:0.64 212:0.42 215:0.49 216:0.75 222:0.84 235:0.80 238:0.97 241:0.76 242:0.63 243:0.19 245:0.45 247:0.39 248:0.83 253:0.80 255:0.79 256:0.83 257:0.65 259:0.21 260:0.90 261:0.97 265:0.53 266:0.38 267:0.23 268:0.84 276:0.25 283:0.71 285:0.62 297:0.36 300:0.33 +1 1:0.74 11:0.57 12:0.20 17:0.77 21:0.71 27:0.37 28:0.56 30:0.76 32:0.80 34:0.90 36:0.34 37:0.58 39:0.50 43:0.78 66:0.80 69:0.79 70:0.28 74:0.71 77:0.70 81:0.51 83:0.73 85:0.88 91:0.37 98:0.41 101:0.81 108:0.63 114:0.68 117:0.86 122:0.43 123:0.60 126:0.54 127:0.13 129:0.05 135:0.49 145:0.47 146:0.38 147:0.44 149:0.77 150:0.67 154:0.70 155:0.67 159:0.15 161:0.74 165:0.69 167:0.56 172:0.45 173:0.90 176:0.73 177:0.38 178:0.55 179:0.31 181:0.72 187:0.68 192:0.59 195:0.59 201:0.74 212:0.90 215:0.48 216:0.35 222:0.84 232:0.95 235:0.88 241:0.02 242:0.72 243:0.82 247:0.30 253:0.61 259:0.21 265:0.43 266:0.17 267:0.76 276:0.34 282:0.88 290:0.68 297:0.36 299:0.88 300:0.25 +0 1:0.74 6:0.89 7:0.81 8:0.62 9:0.80 11:0.57 12:0.20 17:0.43 20:0.82 21:0.30 27:0.21 28:0.56 30:0.19 34:0.45 36:0.74 37:0.74 39:0.50 41:0.92 43:0.98 55:0.71 60:0.87 66:0.29 69:0.12 70:0.88 74:0.95 78:0.79 81:0.77 83:0.82 85:0.99 91:0.56 96:0.82 98:0.65 100:0.89 101:0.82 108:0.83 111:0.82 114:0.86 120:0.77 121:0.97 123:0.82 124:0.89 126:0.54 127:0.88 129:0.93 133:0.90 135:0.24 140:0.80 146:0.91 147:0.92 149:0.98 150:0.85 151:0.82 152:0.95 153:0.46 154:0.98 155:0.67 158:0.92 159:0.94 161:0.74 162:0.50 164:0.80 165:0.84 167:0.69 169:0.83 172:0.96 173:0.06 176:0.73 177:0.38 178:0.42 179:0.12 181:0.72 186:0.80 187:0.39 189:0.92 191:0.73 192:0.59 201:0.74 202:0.86 204:0.89 208:0.64 212:0.55 215:0.72 216:0.95 220:0.87 222:0.84 230:0.87 233:0.76 235:0.42 238:0.96 241:0.47 242:0.71 243:0.27 245:0.99 247:0.67 248:0.79 253:0.88 255:0.79 256:0.79 259:0.21 260:0.78 261:0.87 265:0.66 266:0.80 267:0.44 268:0.79 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +3 6:0.99 7:0.78 8:0.95 9:0.80 12:0.20 17:0.45 20:0.83 21:0.68 27:0.06 28:0.56 30:0.58 32:0.75 34:0.85 36:0.71 37:0.92 39:0.50 41:0.94 43:0.44 55:0.52 66:0.80 69:0.27 70:0.60 78:0.86 81:0.32 83:0.82 91:0.94 96:0.94 98:0.87 100:0.93 108:0.21 111:0.88 120:0.98 123:0.20 126:0.32 127:0.39 129:0.05 135:0.26 140:0.95 145:0.63 146:0.61 147:0.66 149:0.48 150:0.30 151:0.98 152:0.92 153:0.48 154:0.33 155:0.67 159:0.22 161:0.74 162:0.58 164:0.97 167:0.95 169:0.98 172:0.45 173:0.52 175:0.75 177:0.05 179:0.82 181:0.72 186:0.86 187:0.21 189:0.97 191:0.97 192:0.59 195:0.58 202:0.96 208:0.64 212:0.37 215:0.49 216:0.75 222:0.84 230:0.81 233:0.76 235:0.80 238:0.95 241:0.86 242:0.82 243:0.82 244:0.61 247:0.12 248:0.93 253:0.91 255:0.79 256:0.96 257:0.65 259:0.21 260:0.98 261:0.97 265:0.87 266:0.27 267:0.76 268:0.96 276:0.35 277:0.69 283:0.82 285:0.62 297:0.36 300:0.43 +1 8:0.62 11:0.57 12:0.20 17:0.64 21:0.78 27:0.45 28:0.56 30:0.40 34:0.81 36:0.18 37:0.50 39:0.50 43:0.74 55:0.59 66:0.43 69:0.85 70:0.96 74:0.56 85:0.85 91:0.25 98:0.40 101:0.75 108:0.71 123:0.69 124:0.66 127:0.37 129:0.59 133:0.64 135:0.94 145:0.47 146:0.65 147:0.70 149:0.72 154:0.65 159:0.66 161:0.74 167:0.66 172:0.57 173:0.77 177:0.38 178:0.55 179:0.47 181:0.72 187:0.68 212:0.73 216:0.77 222:0.84 235:1.00 241:0.37 242:0.63 243:0.57 245:0.75 247:0.39 253:0.45 259:0.21 265:0.42 266:0.63 267:0.91 276:0.56 300:0.94 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.20 17:0.24 21:0.54 27:0.56 28:0.56 30:0.16 34:0.30 36:0.33 37:0.53 39:0.50 41:0.82 43:0.98 60:0.87 66:0.05 69:0.19 70:0.98 74:0.87 81:0.63 83:0.84 85:0.99 91:0.04 96:0.79 98:0.17 101:0.78 106:0.81 108:0.99 114:0.77 117:0.86 120:0.68 121:0.99 123:0.97 124:0.99 126:0.54 127:0.93 129:0.99 133:0.99 135:0.32 140:0.45 146:0.64 147:0.70 149:0.93 150:0.77 151:0.83 153:0.80 154:0.98 155:0.67 158:0.87 159:0.98 161:0.74 162:0.78 164:0.70 165:0.79 167:0.57 172:0.59 173:0.05 176:0.73 177:0.38 179:0.14 181:0.72 187:0.39 192:0.59 201:0.74 202:0.59 204:0.89 206:0.81 208:0.64 212:0.26 215:0.58 216:0.87 220:0.62 222:0.84 230:0.84 232:0.88 233:0.69 235:0.68 241:0.03 242:0.63 243:0.10 245:0.91 247:0.47 248:0.75 253:0.84 255:0.79 256:0.58 259:0.21 260:0.69 265:0.18 266:0.99 267:0.99 268:0.64 276:0.96 279:0.86 283:0.71 285:0.62 290:0.77 297:0.36 300:0.94 +1 7:0.78 8:0.73 11:0.57 12:0.20 17:0.43 21:0.47 27:0.21 28:0.56 30:0.26 34:0.52 36:0.55 37:0.74 39:0.50 43:0.88 55:0.59 66:0.64 69:0.15 70:0.81 74:0.73 81:0.44 85:0.94 91:0.35 98:0.84 101:0.79 108:0.78 120:0.56 123:0.77 124:0.48 126:0.32 127:0.69 129:0.59 133:0.47 135:0.17 140:0.80 146:0.83 147:0.86 149:0.93 150:0.36 151:0.49 153:0.48 154:0.86 159:0.82 161:0.74 162:0.49 164:0.56 167:0.68 172:0.94 173:0.10 177:0.38 178:0.42 179:0.18 181:0.72 187:0.39 190:0.77 202:0.66 212:0.71 215:0.63 216:0.95 220:0.93 222:0.84 230:0.81 233:0.76 235:0.52 241:0.43 242:0.77 243:0.33 245:0.98 247:0.67 248:0.49 253:0.54 256:0.45 259:0.21 260:0.56 265:0.84 266:0.63 267:0.28 268:0.46 276:0.93 285:0.50 297:0.36 300:0.70 +1 7:0.78 8:0.92 9:0.80 11:0.57 12:0.20 17:0.03 21:0.68 27:0.06 28:0.56 30:0.17 32:0.75 34:0.93 36:0.92 37:0.92 39:0.50 41:0.90 43:0.61 55:0.52 66:0.49 69:0.92 70:0.90 74:0.36 81:0.38 83:0.74 85:0.72 91:0.93 96:0.77 98:0.39 101:0.71 108:0.83 120:0.97 123:0.82 124:0.73 126:0.32 127:0.67 129:0.05 133:0.74 135:0.81 140:1.00 145:0.63 146:0.51 147:0.05 149:0.50 150:0.34 151:0.69 153:0.48 154:0.50 155:0.67 159:0.59 161:0.74 162:0.56 164:0.98 167:0.82 172:0.45 173:0.97 177:0.05 179:0.76 181:0.72 187:0.68 190:0.77 191:0.95 192:0.59 195:0.76 202:0.97 208:0.64 212:0.30 215:0.49 216:0.75 222:0.84 230:0.81 233:0.76 235:0.84 241:0.72 242:0.77 243:0.13 245:0.55 247:0.39 248:0.70 253:0.62 255:0.79 256:0.97 257:0.65 259:0.21 260:0.98 265:0.41 266:0.63 267:0.28 268:0.97 276:0.44 283:0.71 285:0.62 297:0.36 300:0.70 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.55 21:0.22 27:0.51 28:0.56 30:0.74 34:0.86 36:0.41 37:0.47 39:0.50 43:0.87 66:0.77 69:0.55 70:0.51 74:0.93 81:0.82 83:0.76 85:0.97 91:0.29 98:0.75 101:0.86 106:0.81 108:0.62 114:0.90 117:0.86 121:0.90 122:0.43 123:0.60 124:0.41 126:0.54 127:0.35 129:0.59 133:0.47 135:0.60 146:0.20 147:0.25 149:0.96 150:0.88 154:0.85 155:0.67 159:0.49 161:0.74 165:0.86 167:0.68 172:0.82 173:0.49 176:0.73 177:0.38 178:0.55 179:0.31 181:0.72 187:0.68 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.69 215:0.79 216:0.74 222:0.84 230:0.84 232:0.85 233:0.69 235:0.31 241:0.44 242:0.58 243:0.19 245:0.81 247:0.55 253:0.77 259:0.21 265:0.75 266:0.63 267:0.44 276:0.74 290:0.90 297:0.36 300:0.55 +0 6:0.97 8:0.95 9:0.80 12:0.20 17:0.09 20:0.89 21:0.05 27:0.32 28:0.56 30:0.28 32:0.75 34:0.37 36:0.25 37:0.97 39:0.50 41:0.90 43:0.44 55:0.71 66:0.87 69:0.08 70:0.79 78:0.77 81:0.81 83:0.79 91:0.89 96:0.90 98:0.98 100:0.84 108:0.07 111:0.78 120:0.94 123:0.07 126:0.32 127:0.84 129:0.05 135:0.39 140:0.94 145:0.74 146:0.84 147:0.86 149:0.59 150:0.60 151:0.96 152:0.84 153:0.48 154:0.33 155:0.67 159:0.40 161:0.74 162:0.51 164:0.92 167:0.94 169:0.82 172:0.63 173:0.24 175:0.75 177:0.05 179:0.74 181:0.72 186:0.77 189:0.98 191:0.95 192:0.59 195:0.59 202:0.93 208:0.64 212:0.48 215:0.91 216:0.37 222:0.84 235:0.05 238:0.96 241:0.84 242:0.82 243:0.88 244:0.61 247:0.12 248:0.87 253:0.85 255:0.79 256:0.89 257:0.65 259:0.21 260:0.94 261:0.82 265:0.98 266:0.54 267:0.87 268:0.90 276:0.52 277:0.69 283:0.82 285:0.62 297:0.36 300:0.55 +0 7:0.81 8:0.98 9:0.80 12:0.20 17:0.51 21:0.74 27:0.06 28:0.56 30:0.45 32:0.75 34:0.86 36:0.51 37:0.92 39:0.50 41:0.92 43:0.37 55:0.52 66:0.24 69:0.55 70:0.41 74:0.47 81:0.34 83:0.75 91:0.89 96:0.79 98:0.54 108:0.76 120:0.93 121:0.90 123:0.61 124:0.71 126:0.32 127:0.65 129:0.81 133:0.67 135:0.39 140:0.98 145:0.61 146:0.39 147:0.46 149:0.50 150:0.31 151:0.80 153:0.48 154:0.28 155:0.67 159:0.41 161:0.74 162:0.54 164:0.94 167:0.86 172:0.32 173:0.62 175:0.75 177:0.05 178:0.42 179:0.78 181:0.72 187:0.21 190:0.77 191:0.94 192:0.59 195:0.65 202:0.93 204:0.89 208:0.64 212:0.36 215:0.46 216:0.75 220:1.00 222:0.84 230:0.87 233:0.76 235:0.95 241:0.75 242:0.82 243:0.23 244:0.61 245:0.60 247:0.12 248:0.74 253:0.69 255:0.79 256:0.92 257:0.65 259:0.21 260:0.93 265:0.26 266:0.54 267:0.87 268:0.92 276:0.45 277:0.69 283:0.71 285:0.62 297:0.36 300:0.33 +1 7:0.78 8:0.97 9:0.80 12:0.20 17:0.08 21:0.59 27:0.04 28:0.56 30:0.58 34:0.39 36:0.68 37:0.94 39:0.50 41:0.90 43:0.39 55:0.52 66:0.61 69:0.46 70:0.44 81:0.36 83:0.79 91:0.98 96:0.92 98:0.28 108:0.75 120:1.00 123:0.73 124:0.47 126:0.32 127:0.80 129:0.59 133:0.47 135:0.18 140:0.99 145:0.59 146:0.21 147:0.27 149:0.41 150:0.32 151:0.97 153:0.48 154:0.29 155:0.67 159:0.41 161:0.74 162:0.54 164:1.00 167:0.96 172:0.37 173:0.55 175:0.75 177:0.05 179:0.89 181:0.72 191:0.99 192:0.59 202:1.00 208:0.64 212:0.71 215:0.55 216:0.87 222:0.84 230:0.81 233:0.76 235:0.68 241:0.93 242:0.82 243:0.29 244:0.61 245:0.52 247:0.12 248:0.88 253:0.88 255:0.79 256:0.99 257:0.65 259:0.21 260:1.00 265:0.30 266:0.27 267:0.79 268:0.99 276:0.17 277:0.69 283:0.82 285:0.62 297:0.36 300:0.33 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.20 17:0.47 21:0.30 27:0.21 28:0.56 30:0.19 34:0.42 36:0.73 37:0.74 39:0.50 41:0.94 43:0.98 55:0.71 60:0.87 66:0.30 69:0.12 70:0.88 74:0.95 81:0.77 83:0.83 85:0.99 91:0.29 96:0.94 98:0.65 101:0.82 106:0.81 108:0.83 114:0.86 117:0.86 120:0.92 121:0.97 123:0.82 124:0.89 126:0.54 127:0.87 129:0.93 133:0.90 135:0.26 140:0.45 146:0.91 147:0.92 149:0.98 150:0.85 151:0.98 153:0.93 154:0.98 155:0.67 158:0.92 159:0.94 161:0.74 162:0.59 164:0.86 165:0.84 167:0.68 172:0.96 173:0.06 176:0.73 177:0.38 179:0.12 181:0.72 187:0.39 192:0.59 201:0.74 202:0.84 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.95 222:0.84 230:0.87 232:0.88 233:0.76 235:0.42 241:0.46 242:0.71 243:0.27 245:0.99 247:0.67 248:0.93 253:0.96 255:0.79 256:0.84 259:0.21 260:0.92 265:0.66 266:0.80 267:0.19 268:0.84 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.47 21:0.05 27:0.61 28:0.56 30:0.21 37:0.58 39:0.50 43:0.98 66:0.85 69:0.07 70:0.64 74:0.71 81:0.91 83:0.79 85:0.85 91:0.23 98:0.72 101:0.78 106:0.81 108:0.06 114:0.95 117:0.86 121:0.97 123:0.06 126:0.54 127:0.22 129:0.05 146:0.76 147:0.80 149:0.80 150:0.95 154:0.98 155:0.67 159:0.32 161:0.74 165:0.90 167:0.61 172:0.57 173:0.15 176:0.73 177:0.38 178:0.55 179:0.54 181:0.72 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.49 215:0.91 216:0.92 222:0.84 230:0.87 232:0.88 233:0.76 235:0.12 241:0.18 242:0.52 243:0.86 247:0.47 253:0.75 265:0.72 266:0.54 276:0.46 290:0.95 297:0.36 300:0.43 +1 1:0.69 8:0.86 11:0.64 12:0.96 17:0.47 18:0.63 21:0.54 26:0.94 27:0.72 29:0.86 30:0.62 34:0.89 36:0.49 37:0.23 39:0.68 43:0.69 45:0.66 58:0.93 66:0.61 69:0.50 70:0.34 71:0.86 74:0.52 77:0.70 81:0.36 83:0.60 86:0.64 91:0.38 96:0.72 98:0.24 99:0.83 101:0.52 108:0.38 114:0.57 117:0.86 122:0.57 123:0.37 124:0.43 126:0.44 127:0.45 129:0.05 133:0.39 135:0.37 139:0.62 140:0.45 141:0.91 145:0.50 146:0.24 147:0.30 149:0.44 150:0.53 151:0.51 153:0.48 154:0.58 155:0.58 159:0.27 162:0.62 165:0.70 172:0.27 173:0.67 175:0.75 176:0.61 177:0.38 179:0.93 187:0.39 190:0.89 192:0.59 195:0.56 199:0.94 201:0.68 202:0.50 208:0.54 212:0.61 215:0.39 216:0.07 220:0.74 222:0.48 223:0.92 224:0.93 232:0.92 234:0.91 235:0.88 241:0.61 242:0.33 243:0.68 244:0.61 245:0.42 247:0.39 248:0.51 253:0.47 256:0.45 257:0.65 259:0.21 265:0.26 266:0.23 267:0.23 268:0.46 271:0.51 274:0.91 276:0.15 277:0.69 282:0.73 285:0.47 290:0.57 297:0.36 300:0.25 +1 11:0.64 12:0.96 17:0.43 18:0.84 21:0.30 26:0.96 27:1.00 29:0.86 30:0.08 31:0.87 34:0.87 36:0.47 37:0.40 39:0.68 43:0.68 45:0.88 55:0.71 58:0.98 64:0.77 66:0.31 69:0.50 70:1.00 71:0.86 74:0.70 79:0.87 81:0.30 86:0.82 91:0.03 98:0.21 101:0.52 104:0.58 108:0.39 122:0.86 123:0.37 124:0.97 126:0.26 127:0.99 129:0.05 133:0.98 135:0.26 137:0.87 139:0.80 140:0.45 141:0.98 146:0.83 147:0.86 149:0.84 150:0.28 151:0.48 153:0.48 154:0.71 159:0.97 162:0.86 172:0.81 173:0.09 177:0.70 179:0.26 187:0.87 190:0.89 196:0.88 199:0.99 202:0.36 212:0.39 216:0.06 219:0.89 220:0.87 222:0.48 223:0.95 224:0.99 234:0.99 235:0.31 241:0.15 242:0.24 243:0.49 245:0.78 247:0.88 248:0.48 253:0.41 254:0.99 256:0.45 259:0.21 265:0.23 266:0.99 267:0.23 268:0.46 271:0.51 274:0.97 276:0.88 284:0.86 285:0.47 292:0.91 300:0.98 +0 7:0.66 11:0.64 12:0.96 17:0.64 18:0.99 21:0.40 22:0.93 26:0.96 27:0.39 29:0.86 30:0.31 32:0.64 34:0.75 36:0.35 37:0.67 39:0.68 43:0.30 45:0.73 47:0.93 48:0.91 55:0.59 58:0.93 64:0.77 66:0.71 69:0.52 70:0.72 71:0.86 74:0.35 81:0.36 86:0.98 91:0.22 98:0.81 101:0.52 104:1.00 106:0.81 108:0.40 120:0.59 122:0.86 123:0.38 124:0.47 126:0.44 127:0.62 129:0.59 133:0.61 135:0.92 139:0.98 140:0.45 141:0.91 145:0.82 146:0.97 147:0.97 149:0.59 150:0.29 151:0.51 153:0.48 154:0.56 159:0.80 162:0.86 164:0.53 172:0.94 173:0.30 177:0.70 179:0.19 187:0.21 190:0.89 192:0.51 195:0.92 199:1.00 201:0.68 202:0.36 206:0.81 212:0.88 215:0.42 216:0.89 220:0.74 222:0.48 223:0.95 224:0.93 230:0.69 233:0.73 234:0.91 235:0.52 241:0.05 242:0.73 243:0.75 244:0.61 245:0.93 247:0.79 248:0.51 253:0.29 256:0.45 259:0.21 260:0.58 262:0.93 265:0.81 266:0.75 267:0.28 268:0.46 271:0.51 274:0.91 276:0.91 281:0.91 283:0.78 285:0.47 297:0.36 300:0.84 +2 1:0.69 7:0.72 9:0.76 11:0.64 12:0.96 17:0.89 18:0.69 21:0.22 26:0.94 27:1.00 29:0.86 30:0.44 32:0.69 34:0.96 36:0.64 37:0.40 39:0.68 43:0.72 45:0.95 58:0.93 66:0.51 69:0.15 70:0.84 71:0.86 74:0.91 76:0.85 77:0.70 81:0.47 83:0.60 86:0.67 91:0.70 96:0.76 97:0.89 98:0.89 99:0.83 101:0.52 104:0.58 106:0.81 108:0.65 114:0.63 117:0.86 120:0.64 122:0.51 123:0.62 124:0.56 126:0.44 127:0.90 129:0.59 133:0.46 135:0.58 139:0.65 140:0.45 141:0.91 145:0.54 146:0.41 147:0.47 149:0.94 150:0.56 151:0.70 153:0.69 154:0.62 155:0.58 158:0.78 159:0.67 162:0.86 164:0.55 165:0.70 172:0.85 173:0.16 176:0.61 177:0.70 179:0.29 187:0.21 190:0.77 192:0.59 195:0.80 199:0.94 201:0.68 202:0.46 204:0.85 206:0.81 208:0.54 212:0.77 215:0.44 216:0.06 220:0.62 222:0.48 223:0.92 224:0.93 230:0.75 233:0.76 234:0.91 235:0.42 241:0.50 242:0.41 243:0.45 244:0.61 245:0.95 247:0.82 248:0.65 253:0.75 255:0.74 256:0.45 259:0.21 260:0.64 265:0.89 266:0.71 267:0.65 268:0.55 271:0.51 274:0.91 276:0.85 279:0.82 282:0.77 283:0.78 285:0.56 290:0.63 297:0.36 300:0.84 +2 11:0.64 12:0.96 17:0.83 18:0.69 21:0.22 26:0.94 27:1.00 29:0.86 30:0.57 34:0.96 36:0.18 37:0.40 39:0.68 43:0.72 45:0.94 58:0.93 66:0.36 69:0.10 70:0.64 71:0.86 74:0.88 81:0.32 86:0.67 91:0.54 98:0.82 101:0.52 104:0.58 108:0.65 122:0.75 123:0.62 124:0.61 126:0.26 127:0.71 129:0.59 133:0.46 135:0.66 139:0.66 141:0.91 146:0.41 147:0.47 149:0.93 150:0.29 154:0.62 159:0.55 172:0.75 173:0.17 177:0.70 178:0.55 179:0.34 187:0.21 199:0.94 212:0.83 215:0.51 216:0.06 222:0.48 223:0.92 224:0.93 234:0.91 235:0.22 241:0.55 242:0.51 243:0.51 244:0.61 245:0.93 247:0.76 253:0.46 259:0.21 265:0.82 266:0.71 267:0.28 271:0.51 274:0.91 276:0.80 297:0.36 300:0.70 +2 1:0.69 7:0.72 8:0.62 9:0.76 11:0.64 12:0.96 17:0.87 18:0.69 21:0.22 26:0.94 27:1.00 29:0.86 30:0.52 32:0.69 34:0.90 36:0.48 37:0.40 39:0.68 43:0.72 45:0.97 58:0.93 66:0.61 69:0.17 70:0.80 71:0.86 74:0.93 77:0.70 81:0.51 83:0.60 86:0.67 91:0.85 96:0.91 97:0.89 98:0.92 99:0.83 101:0.52 104:0.58 108:0.65 114:0.63 120:0.90 122:0.51 123:0.62 124:0.50 126:0.44 127:0.98 129:0.59 133:0.46 135:0.51 138:0.97 139:0.65 140:0.45 141:0.91 145:0.92 146:0.41 147:0.47 149:0.95 150:0.57 151:0.95 153:0.92 154:0.62 155:0.58 158:0.79 159:0.73 162:0.83 164:0.84 165:0.70 172:0.92 173:0.15 176:0.61 177:0.70 179:0.23 190:0.77 191:0.70 192:0.59 195:0.84 199:0.94 201:0.68 202:0.81 204:0.85 208:0.54 212:0.78 215:0.44 216:0.06 222:0.48 223:0.92 224:0.93 230:0.75 233:0.76 234:0.91 235:0.52 241:0.86 242:0.41 243:0.37 244:0.61 245:0.97 247:0.79 248:0.87 253:0.93 255:0.74 256:0.84 259:0.21 260:0.87 265:0.92 266:0.75 267:0.28 268:0.86 271:0.51 274:0.91 276:0.90 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +1 1:0.69 7:0.72 11:0.64 12:0.96 17:0.88 18:0.79 21:0.22 26:0.95 27:1.00 29:0.86 30:0.52 32:0.69 34:0.98 36:0.55 37:0.40 39:0.68 43:0.72 45:0.95 58:0.93 66:0.80 69:0.15 70:0.66 71:0.86 74:0.92 77:0.70 81:0.47 83:0.60 86:0.86 91:0.51 97:0.89 98:0.86 99:0.83 101:0.52 104:0.58 106:0.81 108:0.12 114:0.63 117:0.86 122:0.51 123:0.12 124:0.39 126:0.44 127:0.69 129:0.05 133:0.43 135:0.63 139:0.81 141:0.91 145:0.58 146:0.88 147:0.90 149:0.94 150:0.56 154:0.69 155:0.58 158:0.78 159:0.55 165:0.70 172:0.90 173:0.20 176:0.61 177:0.70 178:0.55 179:0.28 187:0.21 192:0.59 195:0.73 199:0.95 201:0.68 204:0.85 206:0.81 208:0.54 212:0.83 215:0.44 216:0.06 222:0.48 223:0.93 224:0.93 230:0.75 233:0.76 234:0.91 235:0.42 241:0.39 242:0.38 243:0.82 245:0.88 247:0.91 253:0.51 254:0.88 259:0.21 265:0.86 266:0.47 267:0.28 271:0.51 274:0.91 276:0.84 279:0.82 282:0.77 283:0.78 290:0.63 297:0.36 300:0.55 +0 7:0.72 11:0.64 12:0.96 17:0.47 18:0.79 21:0.71 22:0.93 26:0.96 27:0.59 29:0.86 30:0.15 31:0.86 32:0.64 34:0.80 36:0.65 37:0.34 39:0.68 43:0.12 45:0.66 47:0.93 55:0.42 58:0.98 64:0.77 66:0.45 69:0.85 70:0.73 71:0.86 74:0.55 76:0.85 79:0.86 81:0.29 86:0.82 91:0.23 98:0.46 101:0.52 104:0.96 106:0.81 108:0.71 120:0.53 123:0.69 124:0.58 126:0.44 127:0.21 129:0.05 133:0.43 135:0.60 137:0.86 139:0.81 140:0.45 141:0.98 145:0.93 146:0.57 147:0.46 149:0.17 150:0.23 151:0.46 153:0.48 154:0.58 155:0.58 159:0.34 162:0.86 164:0.54 172:0.51 173:0.87 177:0.38 179:0.37 187:0.87 190:0.77 192:0.59 195:0.77 196:0.87 199:0.96 201:0.68 202:0.36 204:0.85 206:0.81 208:0.54 212:0.77 215:0.37 216:0.87 219:0.92 220:1.00 222:0.48 223:0.95 224:0.98 230:0.75 233:0.76 234:0.97 235:0.88 241:0.05 242:0.21 243:0.46 245:0.76 247:0.82 248:0.46 253:0.37 254:0.74 255:0.74 256:0.45 257:0.65 259:0.21 260:0.53 262:0.93 265:0.48 266:0.47 267:0.52 268:0.46 271:0.51 274:0.97 276:0.56 283:0.66 284:0.87 285:0.56 292:0.87 297:0.36 300:0.55 +0 7:0.66 11:0.64 12:0.96 17:0.92 18:0.92 21:0.05 26:0.96 27:0.72 29:0.86 30:0.72 32:0.64 34:0.63 36:0.54 39:0.68 43:0.71 45:0.66 55:0.71 58:0.93 64:0.77 66:0.49 69:0.29 70:0.65 71:0.86 74:0.37 81:0.72 86:0.89 91:0.10 98:0.68 101:0.52 108:0.83 120:0.69 123:0.82 124:0.75 126:0.44 127:0.75 129:0.05 133:0.73 135:0.44 139:0.85 140:0.45 141:0.91 145:0.49 146:0.53 147:0.59 149:0.61 150:0.50 151:0.60 153:0.48 154:0.61 159:0.72 162:0.86 164:0.53 172:0.79 173:0.20 177:0.70 179:0.34 187:0.68 190:0.89 192:0.51 195:0.85 199:0.98 201:0.68 202:0.36 212:0.81 215:0.78 216:0.01 222:0.48 223:0.95 224:0.93 228:0.99 230:0.69 233:0.73 234:0.91 235:0.22 242:0.45 243:0.31 244:0.61 245:0.86 247:0.76 248:0.59 253:0.30 256:0.45 259:0.21 260:0.66 265:0.68 266:0.71 267:0.52 268:0.46 271:0.51 274:0.91 276:0.81 277:0.69 283:0.82 285:0.47 297:0.36 300:0.84 +2 11:0.64 12:0.96 17:0.86 18:0.84 21:0.05 22:0.93 26:0.96 27:1.00 29:0.86 30:0.55 34:0.99 36:0.37 37:0.40 39:0.68 43:0.72 45:0.95 47:0.93 58:0.93 64:0.77 66:0.77 69:0.07 70:0.74 71:0.86 74:0.89 81:0.47 86:0.85 91:0.61 98:0.82 101:0.52 104:0.58 106:0.81 108:0.06 117:0.86 122:0.51 123:0.06 124:0.42 126:0.26 127:0.83 129:0.05 133:0.60 135:0.97 139:0.83 141:0.91 146:0.86 147:0.88 149:0.93 150:0.37 154:0.66 159:0.57 172:0.88 173:0.14 177:0.70 178:0.55 179:0.33 187:0.39 199:0.97 206:0.81 212:0.76 216:0.06 219:0.89 222:0.48 223:0.95 224:0.93 234:0.91 235:0.05 241:0.32 242:0.40 243:0.79 245:0.82 247:0.90 253:0.46 254:0.74 259:0.21 262:0.93 265:0.82 266:0.54 267:0.36 271:0.51 274:0.91 276:0.82 292:0.91 300:0.70 +1 7:0.72 11:0.64 12:0.96 17:0.76 18:0.78 21:0.40 22:0.93 26:0.96 27:0.18 29:0.86 30:0.62 32:0.68 34:0.85 36:0.31 37:0.93 39:0.68 43:0.10 44:0.90 45:0.73 47:0.93 55:0.42 58:0.93 64:0.77 66:0.72 69:0.49 70:0.54 71:0.86 74:0.68 81:0.36 86:0.89 91:0.53 98:0.34 101:0.52 104:0.80 106:0.81 108:0.38 122:0.51 123:0.36 124:0.40 126:0.44 127:0.32 129:0.05 133:0.37 135:0.37 139:0.89 141:0.91 145:0.94 146:0.29 147:0.36 149:0.06 150:0.29 154:0.85 155:0.58 159:0.24 172:0.48 173:0.66 177:0.70 178:0.55 179:0.73 187:0.21 192:0.59 195:0.60 199:0.96 201:0.68 204:0.85 206:0.81 208:0.54 212:0.86 215:0.42 216:0.43 219:0.89 222:0.48 223:0.95 224:0.93 230:0.75 233:0.76 234:0.91 235:0.52 241:0.30 242:0.29 243:0.75 245:0.48 247:0.60 253:0.41 254:0.74 259:0.21 262:0.93 265:0.36 266:0.23 267:0.65 271:0.51 274:0.91 276:0.25 292:0.95 297:0.36 300:0.43 +0 7:0.66 11:0.64 12:0.96 17:0.49 18:0.92 21:0.22 26:0.96 27:0.37 29:0.86 30:0.76 32:0.64 34:0.99 36:0.35 37:0.89 39:0.68 43:0.64 45:0.66 55:0.59 58:0.93 64:0.77 66:0.91 69:0.54 70:0.30 71:0.86 74:0.41 76:0.94 81:0.34 86:0.87 91:0.35 98:0.94 101:0.52 104:0.58 108:0.41 122:0.86 123:0.39 126:0.26 127:0.63 129:0.05 135:0.34 139:0.84 141:0.91 145:0.65 146:0.89 147:0.91 149:0.47 150:0.31 154:0.54 159:0.48 172:0.76 173:0.56 177:0.70 178:0.55 179:0.54 187:0.68 195:0.69 199:0.97 212:0.68 216:0.24 219:0.89 222:0.48 223:0.95 224:0.93 230:0.69 233:0.76 234:0.91 235:0.22 241:0.12 242:0.55 243:0.92 244:0.61 247:0.74 253:0.32 259:0.21 265:0.94 266:0.54 267:0.36 271:0.51 274:0.91 276:0.65 292:0.91 300:0.33 +0 1:0.69 7:0.81 10:1.00 11:0.83 12:0.51 17:0.97 18:0.99 22:0.93 27:0.70 29:0.91 30:0.87 32:0.80 33:0.97 34:0.75 36:0.80 37:0.35 39:0.90 43:0.96 44:0.93 45:0.99 47:0.93 48:0.91 60:0.95 64:0.77 66:0.99 69:0.08 70:0.39 71:0.92 74:0.98 75:0.99 77:0.98 81:0.79 83:0.91 85:0.94 86:1.00 91:0.60 98:0.97 101:0.96 102:0.98 104:0.82 106:0.81 108:0.07 114:0.98 117:0.86 121:0.90 122:0.65 123:0.07 126:0.54 127:0.74 129:0.05 131:0.61 135:0.34 139:1.00 144:0.57 145:0.74 146:0.90 147:0.92 149:0.99 150:0.62 154:0.96 155:0.58 157:0.91 158:0.92 159:0.50 165:0.93 166:0.74 170:0.82 172:0.98 173:0.18 174:0.98 176:0.73 177:0.88 178:0.55 179:0.14 182:0.82 187:0.21 192:0.86 193:1.00 195:0.70 197:0.99 201:0.68 204:0.84 206:0.81 208:0.64 212:0.94 215:0.96 216:0.35 219:0.95 222:0.48 226:0.98 227:0.61 229:0.61 230:0.87 231:0.63 232:0.88 233:0.76 235:0.22 236:0.97 239:1.00 241:0.15 242:0.21 243:0.99 246:0.89 247:0.97 253:0.78 259:0.21 262:0.93 265:0.97 266:0.27 267:0.65 271:0.42 276:0.96 279:0.82 281:0.91 282:0.88 283:0.82 287:0.61 290:0.98 291:0.97 292:0.95 297:0.36 299:0.97 300:0.55 +0 1:0.67 8:0.63 10:0.96 11:0.64 12:0.51 17:0.75 18:0.87 21:0.47 27:0.54 29:0.91 30:0.87 34:0.97 36:0.34 37:0.60 39:0.90 43:0.61 45:0.91 64:0.77 66:0.90 69:0.32 70:0.30 71:0.92 74:0.73 81:0.73 83:0.91 86:0.96 91:0.06 98:0.71 99:0.99 101:0.87 108:0.25 114:0.80 122:0.65 123:0.24 126:0.54 127:0.22 129:0.05 131:0.61 135:0.37 139:0.94 144:0.57 146:0.53 147:0.59 149:0.43 150:0.53 154:0.87 155:0.52 157:0.83 159:0.19 165:0.93 166:0.74 170:0.82 172:0.71 173:0.50 174:0.86 176:0.73 177:0.88 178:0.55 179:0.35 182:0.81 187:0.68 192:0.50 197:0.90 201:0.66 208:0.64 212:0.80 215:0.63 216:0.64 222:0.48 227:0.61 229:0.61 231:0.63 235:0.80 236:0.97 239:0.95 241:0.05 242:0.18 243:0.90 246:0.89 247:0.79 253:0.64 254:0.80 259:0.21 265:0.71 266:0.27 267:0.65 271:0.42 276:0.57 287:0.61 290:0.80 297:0.36 300:0.33 +0 1:0.58 8:0.70 10:0.96 11:0.64 12:0.51 17:0.62 18:0.95 20:0.88 21:0.77 27:0.45 29:0.91 30:0.36 34:0.85 36:0.39 37:0.50 39:0.90 43:0.58 44:0.88 45:0.91 48:0.91 64:0.77 66:0.49 69:0.63 70:0.64 71:0.92 74:0.62 78:0.98 81:0.62 83:0.69 86:0.92 91:0.08 98:0.61 99:0.95 100:0.73 101:0.87 102:0.96 108:0.48 111:0.95 114:0.64 122:0.92 123:0.46 124:0.56 126:0.54 127:0.41 129:0.05 131:0.61 133:0.39 135:0.42 139:0.90 144:0.57 145:0.49 146:0.71 147:0.75 149:0.48 150:0.43 152:0.85 154:0.73 155:0.47 157:0.86 159:0.50 165:0.81 166:0.74 169:0.72 170:0.82 172:0.75 173:0.60 174:0.93 176:0.73 177:0.88 178:0.55 179:0.33 182:0.81 186:0.98 187:0.68 192:0.45 195:0.73 197:0.94 201:0.58 208:0.64 212:0.73 215:0.44 216:0.77 222:0.48 227:0.61 229:0.61 231:0.63 235:1.00 236:0.97 239:0.95 241:0.21 242:0.42 243:0.60 245:0.91 246:0.89 247:0.82 253:0.53 254:0.80 259:0.21 261:0.91 265:0.62 266:0.54 267:0.28 271:0.42 276:0.76 281:0.86 287:0.61 290:0.64 297:0.36 300:0.55 +0 8:0.58 10:0.81 11:0.52 12:0.51 17:0.45 18:0.74 21:0.13 25:0.88 27:1.00 29:0.91 30:0.33 34:0.78 36:0.84 37:0.27 39:0.90 43:0.75 45:0.73 55:0.71 66:0.86 69:0.37 70:0.65 71:0.92 74:0.44 81:0.34 86:0.64 91:0.61 98:0.91 101:0.65 104:0.58 108:0.29 120:0.62 123:0.28 126:0.24 127:0.51 129:0.05 131:0.61 135:0.44 139:0.62 140:0.45 146:0.83 147:0.86 149:0.56 150:0.37 151:0.57 153:0.48 154:0.67 157:0.61 159:0.39 162:0.86 164:0.55 166:0.61 170:0.82 172:0.61 173:0.44 174:0.83 177:0.88 179:0.70 182:0.61 187:0.21 190:0.95 197:0.85 202:0.36 212:0.59 215:0.84 216:0.23 220:0.74 222:0.48 227:0.61 229:0.61 231:0.62 235:0.12 239:0.80 241:0.56 242:0.31 243:0.87 244:0.61 246:0.61 247:0.79 248:0.56 253:0.38 256:0.45 259:0.21 260:0.63 265:0.91 266:0.47 267:0.28 268:0.46 271:0.42 276:0.50 285:0.46 287:0.61 297:0.36 300:0.43 +0 10:0.80 11:0.46 12:0.51 17:0.83 18:0.69 21:0.78 27:0.34 29:0.91 30:0.55 34:0.56 36:0.54 37:0.46 39:0.90 43:0.21 45:0.66 55:0.99 66:0.07 69:0.96 70:0.60 71:0.92 74:0.36 86:0.61 91:0.12 98:0.13 101:0.47 108:0.91 122:0.93 123:0.98 124:0.96 127:0.86 129:0.59 131:0.61 133:0.95 135:0.51 139:0.59 145:0.54 146:0.13 147:0.05 149:0.19 154:0.14 157:0.61 159:0.96 163:0.90 166:0.61 170:0.82 172:0.10 173:0.75 174:0.86 175:0.91 177:0.05 178:0.55 179:0.76 182:0.61 187:0.95 197:0.80 212:0.18 216:0.53 222:0.48 227:0.61 229:0.61 231:0.61 235:0.60 239:0.79 241:0.18 242:0.31 243:0.13 244:0.83 245:0.48 246:0.61 247:0.76 253:0.32 257:0.93 259:0.21 265:0.06 266:0.84 267:0.87 271:0.42 276:0.50 277:0.87 287:0.61 300:0.55 +0 1:0.59 7:0.70 8:0.70 9:0.73 10:0.84 11:0.50 12:0.51 17:0.84 18:0.95 21:0.40 27:0.49 29:0.91 30:0.10 31:0.91 32:0.72 34:0.97 36:0.35 37:0.52 39:0.90 43:0.66 44:0.86 45:0.77 48:0.99 55:0.71 66:0.59 69:0.15 70:0.92 71:0.92 74:0.63 77:0.89 79:0.90 81:0.46 83:0.69 86:0.74 91:0.42 96:0.75 97:0.97 98:0.74 99:0.83 101:0.47 108:0.12 114:0.78 120:0.63 122:0.92 123:0.12 124:0.63 126:0.32 127:0.69 129:0.05 131:0.80 133:0.60 135:0.66 137:0.91 139:0.73 140:0.45 144:0.57 145:0.77 146:0.77 147:0.81 149:0.55 150:0.41 151:0.70 153:0.69 154:0.48 155:0.64 157:0.61 158:0.76 159:0.52 162:0.86 164:0.66 165:0.65 166:0.61 170:0.82 172:0.68 173:0.22 174:0.79 175:0.75 176:0.63 177:0.70 179:0.53 182:0.61 187:0.39 190:0.77 192:0.87 195:0.72 196:0.90 197:0.81 201:0.56 202:0.50 204:0.79 208:0.61 212:0.52 215:0.67 216:0.44 220:0.74 222:0.48 227:0.82 229:0.61 230:0.73 231:0.62 233:0.76 235:0.52 239:0.83 241:0.55 242:0.52 243:0.67 244:0.61 245:0.76 246:0.61 247:0.86 248:0.66 253:0.72 254:0.96 255:0.72 256:0.58 257:0.65 259:0.21 260:0.64 265:0.74 266:0.54 267:0.96 268:0.59 271:0.42 276:0.67 277:0.69 279:0.79 281:0.86 282:0.80 283:0.67 284:0.90 285:0.60 287:0.61 290:0.78 297:0.36 300:0.70 +1 1:0.61 6:0.83 7:0.70 8:0.65 10:0.91 11:0.64 12:0.51 17:0.94 18:0.93 20:0.93 21:0.22 27:0.64 29:0.91 30:0.45 32:0.67 34:0.98 36:0.25 37:0.27 39:0.90 43:0.61 44:0.86 45:0.96 66:0.97 69:0.17 70:0.72 71:0.92 74:0.85 76:0.97 77:0.89 78:0.99 81:0.50 83:0.91 86:0.84 91:0.51 97:0.94 98:0.96 99:0.83 100:0.89 101:0.87 102:0.94 104:0.93 106:0.81 108:0.14 111:0.96 114:0.85 117:0.86 122:0.91 123:0.13 126:0.32 127:0.71 129:0.05 131:0.61 135:0.96 139:0.82 144:0.57 145:0.98 146:0.96 147:0.97 149:0.81 150:0.44 152:0.94 154:0.58 155:0.64 157:0.80 158:0.77 159:0.66 165:0.65 166:0.61 169:0.95 170:0.82 172:0.93 173:0.16 174:0.89 176:0.63 177:0.88 178:0.55 179:0.25 182:0.74 186:0.99 187:0.68 189:0.84 192:0.87 193:0.98 195:0.80 197:0.90 201:0.58 204:0.85 206:0.81 208:0.64 212:0.68 215:0.79 216:0.18 222:0.48 227:0.61 229:0.61 230:0.72 231:0.63 232:0.95 233:0.65 235:0.31 238:0.82 239:0.91 241:0.07 242:0.16 243:0.97 246:0.61 247:0.97 253:0.69 254:0.80 259:0.21 261:0.95 265:0.96 266:0.38 267:0.93 271:0.42 276:0.87 279:0.78 281:0.86 282:0.76 283:0.82 287:0.61 290:0.85 297:0.36 300:0.70 +2 1:0.56 10:0.92 11:0.55 12:0.51 17:0.49 18:0.95 21:0.78 27:0.45 29:0.91 30:0.21 34:0.85 36:0.18 37:0.50 39:0.90 43:0.60 45:0.88 48:0.91 55:0.71 66:0.27 69:0.63 70:0.93 71:0.92 74:0.41 81:0.38 83:0.56 86:0.85 91:0.20 98:0.55 99:0.83 101:0.65 108:0.87 114:0.62 122:0.93 123:0.46 124:0.77 126:0.32 127:0.59 129:0.05 131:0.61 133:0.74 135:0.79 139:0.82 144:0.57 145:0.50 146:0.78 147:0.81 149:0.66 150:0.33 154:0.44 155:0.46 157:0.86 159:0.70 165:0.65 166:0.61 170:0.82 172:0.68 173:0.51 174:0.93 176:0.63 177:0.88 178:0.55 179:0.33 182:0.76 187:0.68 192:0.44 197:0.92 201:0.53 208:0.50 212:0.74 215:0.43 216:0.77 222:0.48 227:0.61 229:0.61 231:0.63 235:1.00 239:0.91 241:0.43 242:0.19 243:0.46 244:0.61 245:0.86 246:0.61 247:0.93 253:0.47 254:0.97 259:0.21 265:0.49 266:0.80 267:0.79 271:0.42 276:0.79 281:0.86 287:0.61 290:0.62 297:0.36 300:0.84 +2 1:0.67 6:0.83 7:0.70 8:0.61 10:1.00 11:0.82 12:0.51 17:0.95 18:0.98 20:0.89 21:0.22 22:0.93 27:0.64 29:0.91 30:0.72 33:0.97 34:0.98 36:0.33 37:0.27 39:0.90 43:0.61 44:0.88 45:0.99 47:0.93 64:0.77 66:0.99 69:0.15 70:0.53 71:0.92 74:0.86 75:0.99 76:0.97 78:0.99 81:0.71 83:0.91 85:0.72 86:0.99 91:0.42 97:0.97 98:0.99 99:0.95 100:0.98 101:0.96 102:0.96 104:0.96 106:0.81 108:0.12 111:0.98 114:0.90 117:0.86 122:0.93 123:0.12 126:0.54 127:0.88 129:0.05 131:0.61 135:0.98 139:0.99 144:0.57 145:0.97 146:0.97 147:0.98 149:0.89 150:0.56 152:0.94 154:0.86 155:0.64 157:0.91 158:0.84 159:0.70 165:0.81 166:0.74 169:0.95 170:0.82 172:0.97 173:0.15 174:0.99 176:0.73 177:0.88 178:0.55 179:0.17 182:0.82 186:0.99 187:0.68 189:0.87 191:0.70 192:0.87 193:0.98 195:0.82 197:0.99 201:0.66 204:0.85 206:0.81 208:0.64 212:0.87 215:0.79 216:0.18 219:0.95 222:0.48 226:0.95 227:0.61 229:0.61 230:0.72 231:0.63 232:0.98 233:0.65 235:0.52 236:0.97 238:0.91 239:1.00 241:0.12 242:0.24 243:0.99 246:0.89 247:0.94 253:0.71 259:0.21 261:0.95 262:0.93 265:0.99 266:0.38 267:0.44 271:0.42 276:0.94 279:0.81 281:0.86 283:0.82 287:0.61 290:0.90 291:0.97 292:0.87 297:0.36 300:0.55 +1 1:0.67 8:0.63 10:0.96 11:0.64 12:0.51 17:0.76 18:0.87 21:0.47 27:0.54 29:0.91 30:0.87 34:0.97 36:0.37 37:0.60 39:0.90 43:0.61 45:0.91 64:0.77 66:0.90 69:0.32 70:0.30 71:0.92 74:0.73 81:0.73 83:0.91 86:0.96 91:0.06 98:0.71 99:0.99 101:0.87 108:0.25 114:0.80 122:0.65 123:0.24 126:0.54 127:0.22 129:0.05 131:0.61 135:0.37 139:0.94 144:0.57 146:0.53 147:0.59 149:0.43 150:0.53 154:0.87 155:0.52 157:0.83 159:0.19 165:0.93 166:0.74 170:0.82 172:0.71 173:0.50 174:0.86 176:0.73 177:0.88 178:0.55 179:0.35 182:0.81 187:0.68 192:0.50 197:0.90 201:0.66 208:0.64 212:0.80 215:0.63 216:0.64 222:0.48 227:0.61 229:0.61 231:0.63 235:0.80 236:0.97 239:0.95 241:0.05 242:0.18 243:0.90 246:0.89 247:0.79 253:0.64 254:0.80 259:0.21 265:0.71 266:0.27 267:0.65 271:0.42 276:0.57 287:0.61 290:0.80 297:0.36 300:0.33 +0 7:0.69 8:0.89 10:0.82 11:0.55 12:0.51 17:0.16 18:0.73 21:0.47 27:0.28 29:0.91 30:0.29 32:0.65 34:0.55 36:0.95 37:0.72 39:0.90 43:0.61 45:0.91 55:0.59 66:0.53 69:0.84 70:0.84 71:0.92 74:0.73 81:0.29 86:0.65 91:0.46 98:0.75 101:0.65 104:0.82 106:0.81 108:0.69 120:0.56 123:0.67 124:0.74 126:0.24 127:0.81 129:0.05 131:0.61 133:0.76 135:0.70 139:0.63 140:0.45 144:0.57 145:0.44 146:0.96 147:0.67 149:0.77 150:0.32 151:0.49 153:0.72 154:0.22 157:0.76 159:0.85 162:0.56 164:0.63 166:0.61 170:0.82 172:0.92 173:0.67 174:0.79 177:0.38 179:0.19 182:0.72 187:0.39 190:0.95 195:0.95 197:0.80 202:0.58 206:0.81 212:0.59 215:0.63 216:0.82 220:0.93 222:0.48 227:0.61 229:0.61 230:0.71 231:0.61 232:0.88 233:0.76 235:0.52 239:0.82 241:0.15 242:0.28 243:0.22 244:0.61 245:0.95 246:0.61 247:0.96 248:0.49 253:0.52 254:0.99 256:0.45 257:0.84 259:0.21 260:0.56 265:0.75 266:0.63 267:0.94 268:0.57 271:0.42 276:0.92 285:0.46 287:0.61 297:0.36 300:0.70 +1 1:0.58 7:0.70 10:0.85 11:0.50 12:0.51 17:0.87 18:0.96 21:0.47 27:0.49 29:0.91 30:0.45 32:0.72 34:0.89 36:0.62 37:0.52 39:0.90 43:0.66 44:0.86 45:0.97 48:0.99 66:0.67 69:0.17 70:0.72 71:0.92 74:0.84 77:0.89 81:0.45 83:0.56 86:0.76 91:0.18 97:0.94 98:0.80 99:0.83 101:0.47 108:0.79 114:0.76 122:0.82 123:0.78 124:0.47 126:0.32 127:0.82 129:0.05 131:0.61 133:0.45 135:0.82 139:0.75 144:0.57 145:0.76 146:0.78 147:0.82 149:0.86 150:0.40 154:0.47 155:0.50 157:0.61 158:0.77 159:0.75 165:0.65 166:0.61 170:0.82 172:0.94 173:0.14 174:0.79 176:0.63 177:0.88 178:0.55 179:0.20 182:0.61 187:0.39 192:0.49 195:0.88 197:0.82 201:0.55 204:0.79 208:0.50 212:0.82 215:0.63 216:0.44 222:0.48 227:0.61 229:0.61 230:0.73 231:0.61 233:0.76 235:0.60 239:0.84 241:0.10 242:0.41 243:0.32 244:0.61 245:0.97 246:0.61 247:0.91 253:0.65 254:0.97 259:0.21 265:0.80 266:0.63 267:0.98 271:0.42 276:0.91 277:0.69 279:0.78 281:0.86 282:0.80 283:0.82 287:0.61 290:0.76 297:0.36 300:0.84 +0 1:0.58 7:0.70 10:0.88 11:0.55 12:0.51 17:0.78 18:0.90 21:0.71 27:0.32 29:0.91 30:0.41 32:0.67 34:0.97 36:0.96 37:0.85 39:0.90 43:0.60 44:0.88 45:0.81 48:0.99 55:0.71 64:0.77 66:0.92 69:0.33 70:0.68 71:0.92 74:0.69 77:0.98 81:0.57 83:0.69 86:0.78 91:0.29 97:0.89 98:0.92 99:0.95 101:0.65 102:0.96 108:0.26 114:0.67 122:0.92 123:0.25 126:0.51 127:0.55 129:0.05 131:0.61 135:0.71 139:0.77 144:0.57 145:1.00 146:0.88 147:0.90 149:0.46 150:0.41 154:0.61 155:0.50 157:0.80 158:0.76 159:0.46 165:0.81 166:0.72 170:0.82 172:0.78 173:0.36 174:0.83 176:0.70 177:0.88 178:0.55 179:0.50 182:0.75 187:0.21 192:0.49 195:0.69 197:0.85 201:0.57 204:0.79 208:0.61 212:0.68 215:0.48 216:0.55 222:0.48 227:0.61 229:0.61 230:0.73 231:0.62 233:0.66 235:0.95 239:0.89 241:0.32 242:0.19 243:0.92 244:0.61 246:0.61 247:0.92 253:0.57 254:0.92 259:0.21 265:0.92 266:0.54 267:0.94 271:0.42 276:0.67 279:0.75 281:0.86 282:0.75 283:0.67 287:0.61 290:0.67 297:0.36 300:0.55 +2 1:0.56 10:0.90 11:0.55 12:0.51 17:0.62 18:0.96 21:0.77 27:0.45 29:0.91 30:0.53 32:0.71 34:0.90 36:0.24 37:0.50 39:0.90 43:0.57 44:0.86 45:0.81 48:0.91 55:0.71 64:0.77 66:0.45 69:0.63 70:0.75 71:0.92 74:0.49 77:0.70 81:0.45 83:0.56 86:0.81 91:0.10 98:0.68 99:0.83 101:0.65 108:0.48 114:0.63 122:0.92 123:0.46 124:0.59 126:0.51 127:0.44 129:0.05 131:0.61 133:0.39 135:0.47 139:0.79 144:0.57 145:0.61 146:0.76 147:0.80 149:0.56 150:0.37 154:0.60 155:0.46 157:0.82 159:0.51 165:0.65 166:0.72 170:0.82 172:0.65 173:0.60 174:0.88 176:0.70 177:0.88 178:0.55 179:0.42 182:0.75 187:0.68 192:0.45 195:0.73 197:0.89 201:0.55 208:0.61 212:0.69 215:0.43 216:0.77 222:0.48 227:0.61 229:0.61 231:0.62 235:1.00 239:0.89 241:0.21 242:0.62 243:0.58 244:0.61 245:0.87 246:0.61 247:0.74 253:0.49 254:0.74 259:0.21 265:0.69 266:0.54 267:0.52 271:0.42 276:0.70 281:0.86 282:0.80 287:0.61 290:0.63 297:0.36 300:0.70 +1 1:0.69 7:0.81 8:0.60 10:1.00 11:0.83 12:0.51 17:0.94 18:0.99 22:0.93 27:0.71 29:0.91 30:0.90 32:0.77 33:0.97 34:0.86 36:0.59 37:0.27 39:0.90 43:0.96 44:0.93 45:0.98 47:0.93 60:0.87 64:0.77 66:0.99 69:0.08 70:0.37 71:0.92 74:0.98 75:0.99 77:0.70 81:0.79 83:0.91 85:0.97 86:0.99 91:0.23 97:0.89 98:0.98 101:0.96 102:0.98 104:0.93 106:0.81 108:0.07 114:0.98 117:0.86 121:0.90 122:0.65 123:0.07 126:0.54 127:0.85 129:0.05 131:0.61 132:0.97 135:0.65 139:0.99 144:0.57 145:0.45 146:0.95 147:0.96 149:1.00 150:0.62 154:0.96 155:0.58 157:0.91 158:0.92 159:0.62 165:0.93 166:0.74 170:0.82 172:0.98 173:0.14 174:0.97 176:0.73 177:0.88 178:0.55 179:0.15 182:0.82 187:0.21 192:0.86 193:1.00 195:0.75 197:0.98 201:0.68 204:0.84 206:0.81 208:0.64 212:0.90 215:0.96 216:0.16 219:0.95 222:0.48 226:0.98 227:0.61 229:0.61 230:0.87 231:0.63 232:0.95 233:0.76 235:0.22 236:0.97 239:1.00 241:0.12 242:0.30 243:0.99 246:0.89 247:0.91 253:0.78 262:0.93 265:0.98 266:0.47 267:0.69 271:0.42 276:0.95 279:0.81 281:0.91 282:0.85 283:0.82 287:0.61 290:0.98 291:0.97 292:0.95 297:0.36 300:0.70 +1 1:0.62 8:0.86 9:0.71 10:0.91 11:0.55 12:0.51 17:0.64 18:0.92 20:0.87 21:0.47 22:0.93 23:0.96 27:0.42 29:0.91 30:0.09 31:0.89 33:0.97 34:0.56 36:0.90 37:0.57 39:0.90 43:0.21 44:0.92 45:0.93 47:0.93 48:0.91 55:0.71 62:0.97 64:0.77 66:0.06 69:0.32 70:0.99 71:0.92 74:0.69 75:0.98 76:0.85 77:0.96 78:0.94 79:0.88 81:0.50 83:0.69 86:0.81 91:0.17 96:0.72 97:0.97 98:0.22 99:0.83 100:0.92 101:0.65 102:0.97 108:0.98 111:0.92 114:0.78 117:0.86 122:0.92 123:0.95 124:0.98 126:0.51 127:0.94 128:0.96 129:0.96 131:0.82 133:0.98 135:0.74 137:0.89 139:0.85 140:0.45 144:0.57 145:0.80 146:0.29 147:0.35 149:0.44 150:0.42 151:0.59 152:0.82 153:0.69 154:0.64 155:0.57 157:0.84 158:0.81 159:0.96 162:0.86 165:0.65 166:0.74 168:0.96 169:0.90 170:0.82 172:0.70 173:0.07 174:0.96 175:0.75 176:0.70 177:0.70 179:0.19 182:0.82 186:0.94 187:0.21 190:0.77 192:0.56 193:0.95 195:0.99 196:0.91 197:0.89 201:0.60 202:0.46 205:0.95 208:0.61 212:0.30 216:0.56 219:0.94 220:0.87 222:0.48 226:0.96 227:0.85 229:0.90 231:0.63 232:0.88 235:0.68 236:0.95 239:0.94 241:0.01 242:0.14 243:0.13 244:0.61 245:0.86 246:0.89 247:0.99 248:0.57 253:0.65 254:0.84 255:0.70 256:0.45 257:0.65 259:0.21 261:0.87 262:0.93 265:0.18 266:0.99 267:0.19 268:0.55 271:0.42 276:0.92 277:0.69 279:0.81 281:0.91 282:0.80 284:0.90 285:0.60 287:0.91 290:0.78 291:0.97 292:0.88 300:0.98 +0 1:0.58 6:0.81 8:0.74 10:0.96 11:0.50 12:0.51 17:0.43 18:0.94 20:0.94 21:0.47 27:0.33 29:0.91 30:0.10 34:0.71 36:0.79 37:0.88 39:0.90 43:0.50 45:0.97 55:0.71 64:0.77 66:0.06 69:0.27 70:0.97 71:0.92 74:0.62 75:0.97 78:0.87 81:0.53 83:0.56 86:0.90 91:0.18 97:0.94 98:0.22 99:0.83 100:0.92 101:0.47 108:0.99 111:0.88 122:0.83 123:0.99 124:0.99 126:0.51 127:1.00 129:0.98 131:0.61 133:0.99 135:0.28 139:0.88 144:0.57 146:0.85 147:0.88 149:0.70 150:0.41 152:0.97 154:0.64 155:0.47 157:0.88 159:0.98 165:0.65 166:0.74 169:0.90 170:0.82 172:0.77 173:0.06 174:0.99 177:0.88 178:0.55 179:0.11 182:0.81 186:0.87 187:0.39 192:0.46 197:0.96 201:0.60 208:0.50 212:0.30 215:0.63 216:0.49 219:0.91 222:0.48 226:0.96 227:0.61 229:0.61 231:0.63 235:0.68 236:0.95 239:0.96 241:0.10 242:0.16 243:0.17 244:0.61 245:0.95 246:0.89 247:1.00 253:0.47 254:0.86 259:0.21 261:0.93 265:0.24 266:0.98 267:0.36 271:0.42 276:0.98 277:0.69 279:0.78 287:0.61 292:0.88 297:0.36 300:0.98 +2 1:0.57 7:0.69 10:0.80 11:0.52 12:0.51 17:0.89 18:0.96 21:0.64 27:0.72 29:0.91 30:0.62 32:0.67 34:0.88 36:0.55 39:0.90 43:0.62 45:0.99 48:0.91 66:0.43 69:0.94 70:0.56 71:0.92 74:0.90 76:0.85 77:0.70 81:0.42 83:0.56 86:0.78 91:0.71 97:0.89 98:0.68 99:0.83 101:0.65 104:0.78 108:0.88 114:0.69 122:0.65 123:0.87 124:0.91 126:0.32 127:0.89 129:0.05 131:0.61 133:0.93 135:0.96 139:0.74 145:0.74 146:0.98 147:0.94 149:0.94 150:0.37 154:0.57 155:0.46 157:0.61 158:0.76 159:0.92 165:0.65 166:0.61 170:0.82 172:0.96 173:0.79 174:0.79 176:0.63 177:0.70 178:0.55 179:0.13 182:0.61 192:0.45 195:0.98 197:0.80 201:0.54 208:0.50 212:0.50 215:0.53 216:0.09 222:0.48 227:0.61 229:0.61 230:0.71 231:0.61 232:0.84 233:0.76 235:0.80 239:0.79 241:0.78 242:0.21 243:0.34 244:0.61 245:0.97 246:0.61 247:0.95 253:0.65 257:0.65 259:0.21 265:0.68 266:0.87 267:0.94 271:0.42 276:0.97 279:0.75 281:0.91 282:0.75 283:0.67 287:0.61 290:0.69 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.20 17:0.70 21:0.30 30:0.85 32:0.80 34:0.94 36:0.19 37:0.97 43:0.92 66:0.87 69:0.37 70:0.40 74:0.88 77:0.99 81:0.65 83:0.75 85:0.94 91:0.05 98:0.78 101:0.86 104:0.87 106:0.81 108:0.29 114:0.86 117:0.86 122:0.43 123:0.28 126:0.54 127:0.26 129:0.05 135:0.47 145:0.99 146:0.54 147:0.60 149:0.94 150:0.79 154:0.92 155:0.67 159:0.19 165:0.84 172:0.63 173:0.59 176:0.73 177:0.38 178:0.55 179:0.53 187:0.68 192:0.59 195:0.54 201:0.74 206:0.81 212:0.86 215:0.72 216:0.94 222:0.90 232:0.90 235:0.42 241:0.44 242:0.63 243:0.88 247:0.30 253:0.75 259:0.21 265:0.78 266:0.27 267:0.44 271:0.68 276:0.51 282:0.88 290:0.86 297:0.36 299:1.00 300:0.43 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.38 21:0.40 27:0.39 30:0.68 32:0.80 34:0.97 36:0.48 37:0.55 43:0.91 66:0.79 69:0.41 70:0.43 74:0.81 77:0.70 81:0.61 83:0.75 85:0.85 91:0.20 98:0.48 101:0.80 104:0.80 106:0.81 108:0.32 114:0.82 117:0.86 121:0.99 122:0.43 123:0.31 126:0.54 127:0.15 129:0.05 135:0.42 145:0.72 146:0.43 147:0.50 149:0.78 150:0.76 154:0.91 155:0.67 158:0.84 159:0.16 165:0.83 172:0.41 173:0.51 176:0.73 177:0.38 178:0.55 179:0.44 187:0.21 192:0.59 195:0.62 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.67 216:0.59 222:0.90 230:0.87 232:0.85 233:0.76 235:0.52 241:0.24 242:0.45 243:0.80 247:0.47 253:0.71 259:0.21 265:0.50 266:0.27 267:0.84 271:0.68 276:0.32 279:0.86 282:0.88 283:0.67 290:0.82 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.57 12:0.20 17:0.86 21:0.05 27:0.44 30:0.76 32:0.80 34:0.63 36:0.34 37:0.28 43:0.98 60:0.87 66:0.85 69:0.07 70:0.43 74:0.86 77:0.89 81:0.81 83:0.88 85:0.91 91:0.23 98:0.71 101:0.84 108:0.06 114:0.95 122:0.43 123:0.06 126:0.54 127:0.22 129:0.05 135:0.56 145:0.84 146:0.59 147:0.64 149:0.89 150:0.88 154:0.98 155:0.67 158:0.92 159:0.21 165:0.90 172:0.57 173:0.24 176:0.73 177:0.38 178:0.55 179:0.52 187:0.87 192:0.59 195:0.57 201:0.74 208:0.64 212:0.58 215:0.91 216:0.91 222:0.90 235:0.12 241:0.18 242:0.61 243:0.86 247:0.39 253:0.78 259:0.21 265:0.71 266:0.27 267:0.52 271:0.68 276:0.43 279:0.86 282:0.88 283:0.82 290:0.95 297:0.36 299:0.97 300:0.43 +1 11:0.57 12:0.20 17:0.87 21:0.78 27:0.44 30:0.95 34:0.55 36:0.26 37:0.28 43:0.87 55:0.45 66:0.90 69:0.52 70:0.13 74:0.97 85:0.72 91:0.02 98:0.27 101:0.72 108:0.40 122:0.67 123:0.38 127:0.11 129:0.05 135:0.56 146:0.36 147:0.43 149:0.74 154:0.85 159:0.14 172:0.71 173:0.48 177:0.38 178:0.55 179:0.10 187:0.95 212:0.93 216:0.91 222:0.90 235:0.12 241:0.15 242:0.80 243:0.90 247:0.39 253:0.69 259:0.21 265:0.30 266:0.17 267:0.36 271:0.68 276:0.61 300:0.18 +0 11:0.57 12:0.20 17:0.45 21:0.78 30:0.76 34:0.73 36:0.30 37:0.99 43:0.61 66:0.64 69:0.92 70:0.15 74:0.36 85:0.72 91:0.23 98:0.15 101:0.70 108:0.84 122:0.41 123:0.83 127:0.09 129:0.05 132:0.97 135:0.85 146:0.26 147:0.32 149:0.30 154:0.50 159:0.11 163:0.99 172:0.16 173:0.90 177:0.38 178:0.55 179:0.14 187:0.68 202:0.65 212:0.95 216:1.00 222:0.90 235:0.31 241:0.41 242:0.82 243:0.69 247:0.12 253:0.32 259:0.21 265:0.16 266:0.12 267:0.88 271:0.68 276:0.13 300:0.13 +1 11:0.57 12:0.51 17:0.78 21:0.78 27:0.63 30:0.95 34:0.85 36:0.59 37:0.23 39:0.26 43:0.86 66:0.54 69:0.59 74:0.44 85:0.72 91:0.70 98:0.13 101:0.75 104:0.89 106:0.81 108:0.45 123:0.43 127:0.08 129:0.05 135:0.47 146:0.13 147:0.18 149:0.49 154:0.83 159:0.08 172:0.10 173:0.84 177:0.38 178:0.55 179:0.12 187:0.68 206:0.81 216:0.11 222:0.35 235:0.12 241:0.61 243:0.63 253:0.39 259:0.21 265:0.14 267:0.59 271:0.11 300:0.08 +0 11:0.57 12:0.51 17:0.49 21:0.78 27:0.21 30:0.95 34:0.71 36:0.70 37:0.74 39:0.26 43:0.09 66:0.54 69:1.00 74:0.27 85:0.72 91:0.02 98:0.05 101:0.47 108:0.99 123:0.99 127:0.05 129:0.05 135:0.18 146:0.13 147:0.18 149:0.07 154:0.08 159:0.08 172:0.10 173:0.96 177:0.38 178:0.55 179:0.08 187:0.39 216:0.95 222:0.35 232:0.85 235:0.42 241:0.70 243:0.63 253:0.25 259:0.21 265:0.05 267:0.52 271:0.11 300:0.08 +2 1:0.74 11:0.57 12:0.51 17:0.69 21:0.22 27:0.51 30:0.87 34:0.97 36:0.32 37:0.46 39:0.26 43:0.40 66:0.82 69:0.95 70:0.20 74:0.48 81:0.93 83:0.76 85:0.90 91:0.17 98:0.32 101:0.76 106:0.81 108:0.91 114:0.90 117:0.86 122:0.43 123:0.90 126:0.54 127:0.12 129:0.05 135:0.66 145:0.82 146:0.66 147:0.71 149:0.55 150:0.96 154:0.31 155:0.67 159:0.25 163:0.81 165:0.86 172:0.48 173:0.94 176:0.73 177:0.38 178:0.55 179:0.20 187:0.39 192:0.59 201:0.74 206:0.81 212:0.61 215:0.79 216:0.12 222:0.35 235:0.31 241:0.37 242:0.63 243:0.83 247:0.30 253:0.67 259:0.21 265:0.35 266:0.23 267:0.44 271:0.11 276:0.37 290:0.90 297:0.36 300:0.18 +2 11:0.57 12:0.51 17:0.51 21:0.78 27:0.51 30:0.95 34:0.70 36:0.80 37:0.29 39:0.26 43:0.94 66:0.54 69:0.40 74:0.47 85:0.72 91:0.88 98:0.16 101:0.76 108:0.31 123:0.30 127:0.09 129:0.05 135:0.34 145:0.61 146:0.13 147:0.18 149:0.52 154:0.94 159:0.08 172:0.10 173:0.78 177:0.38 178:0.55 179:0.18 187:0.21 202:0.36 216:0.16 222:0.35 235:0.99 241:0.74 243:0.63 253:0.40 259:0.21 265:0.17 267:0.79 271:0.11 300:0.08 +2 1:0.74 7:0.78 8:0.58 12:0.51 17:0.69 20:0.99 21:0.30 27:0.38 30:0.95 32:0.75 34:0.97 36:0.25 37:0.24 39:0.26 43:0.61 66:0.54 69:0.92 74:0.68 76:0.85 77:0.89 78:0.99 81:0.92 83:0.74 91:0.44 98:0.08 100:0.92 108:0.84 111:0.97 114:0.61 123:0.83 126:0.54 127:0.06 129:0.05 135:0.71 145:0.59 146:0.13 147:0.18 149:0.29 150:0.95 152:0.90 154:0.51 155:0.67 158:0.84 159:0.08 165:0.80 169:0.90 172:0.10 173:0.95 176:0.54 177:0.38 178:0.55 179:0.08 186:0.99 187:0.39 192:0.59 195:0.80 201:0.74 204:0.89 208:0.64 215:0.63 216:0.48 222:0.35 230:0.81 233:0.69 235:0.60 241:0.21 243:0.63 253:0.55 259:0.21 261:0.93 265:0.09 267:0.36 271:0.11 279:0.86 282:0.83 283:0.71 290:0.61 297:0.36 300:0.08 +2 1:0.74 6:0.81 7:0.81 9:0.80 11:0.57 12:0.51 17:0.84 20:0.94 21:0.22 27:0.55 30:0.95 34:0.93 36:0.65 37:0.24 39:0.26 41:0.82 43:0.81 66:0.54 69:0.69 74:0.56 78:1.00 81:0.93 83:0.77 85:0.72 91:0.64 96:0.73 98:0.14 100:0.73 101:0.75 108:0.53 111:0.97 114:0.90 120:0.60 121:0.90 123:0.51 126:0.54 127:0.09 129:0.05 135:0.23 140:0.45 145:0.87 146:0.13 147:0.18 149:0.46 150:0.96 151:0.63 152:0.94 153:0.48 154:0.77 155:0.67 159:0.08 162:0.86 164:0.57 165:0.86 169:0.72 172:0.10 173:0.94 176:0.73 177:0.38 179:0.13 186:0.99 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 208:0.64 215:0.79 216:0.07 220:0.82 222:0.35 230:0.87 233:0.76 235:0.31 241:0.59 243:0.63 248:0.60 253:0.72 255:0.79 256:0.45 259:0.21 260:0.60 261:0.93 265:0.14 267:0.44 268:0.46 271:0.11 285:0.62 290:0.90 297:0.36 300:0.08 +1 11:0.57 12:0.51 17:0.77 21:0.78 27:0.72 30:0.91 34:0.90 36:0.49 37:0.93 39:0.26 43:0.84 66:0.27 69:0.65 70:0.13 74:0.52 85:0.80 91:0.44 98:0.11 101:0.77 108:0.49 122:0.43 123:0.47 124:0.61 127:0.27 129:0.59 133:0.47 135:0.34 146:0.13 147:0.18 149:0.62 154:0.80 159:0.27 163:0.88 172:0.10 173:0.75 177:0.38 178:0.55 179:0.94 187:0.39 212:0.61 216:0.07 222:0.35 235:0.12 241:0.24 242:0.63 243:0.46 245:0.40 247:0.30 253:0.43 259:0.21 265:0.11 266:0.17 267:0.79 271:0.11 276:0.12 300:0.13 +2 11:0.57 12:0.51 17:0.57 21:0.78 27:0.51 30:0.95 34:0.75 36:0.87 37:0.29 39:0.26 43:0.89 66:0.54 69:0.46 74:0.45 85:0.72 91:0.77 98:0.15 101:0.76 108:0.35 120:0.56 123:0.34 127:0.09 129:0.05 135:0.28 140:0.45 145:0.61 146:0.13 147:0.18 149:0.50 151:0.49 153:0.48 154:0.87 159:0.08 162:0.86 164:0.56 172:0.10 173:0.80 177:0.38 179:0.15 187:0.39 190:0.77 202:0.36 216:0.16 220:0.93 222:0.35 241:0.59 243:0.63 248:0.49 253:0.39 256:0.45 259:0.21 260:0.56 265:0.16 267:0.79 268:0.46 271:0.11 285:0.50 300:0.08 +1 8:0.63 11:0.57 12:0.51 17:0.36 21:0.78 27:0.21 30:0.95 34:0.66 36:0.83 37:0.74 39:0.26 43:0.09 66:0.54 69:1.00 74:0.27 85:0.72 91:0.15 98:0.05 101:0.47 108:0.99 123:0.99 127:0.05 129:0.05 135:0.18 146:0.13 147:0.18 149:0.07 154:0.08 159:0.08 172:0.10 173:0.96 177:0.38 178:0.55 179:0.08 187:0.39 202:0.76 216:0.95 222:0.35 232:0.85 235:0.42 241:0.64 243:0.63 253:0.25 259:0.21 265:0.05 267:0.59 271:0.11 300:0.08 +2 9:0.80 11:0.57 12:0.51 17:0.77 21:0.78 27:0.63 30:0.95 34:0.85 36:0.54 37:0.23 39:0.26 41:0.82 43:0.86 66:0.54 69:0.59 74:0.44 83:0.76 85:0.72 91:0.76 96:0.80 98:0.13 101:0.75 104:0.89 106:0.81 108:0.45 120:0.69 123:0.43 127:0.08 129:0.05 135:0.47 140:0.45 146:0.13 147:0.18 149:0.49 151:0.87 153:0.48 154:0.83 155:0.67 159:0.08 162:0.86 164:0.57 172:0.10 173:0.84 177:0.38 179:0.12 187:0.68 192:0.59 202:0.36 206:0.81 208:0.64 216:0.11 222:0.35 235:0.12 241:0.67 243:0.63 248:0.78 253:0.75 255:0.79 256:0.45 259:0.21 260:0.70 265:0.14 267:0.59 268:0.46 271:0.11 285:0.62 300:0.08 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.90 21:0.22 27:0.55 30:0.95 34:0.96 36:0.63 37:0.24 39:0.26 43:0.82 66:0.54 69:0.69 74:0.56 81:0.93 83:0.76 85:0.72 91:0.58 98:0.14 101:0.75 108:0.53 114:0.90 121:0.90 123:0.50 126:0.54 127:0.09 129:0.05 135:0.28 145:0.87 146:0.13 147:0.18 149:0.46 150:0.96 154:0.77 155:0.67 159:0.08 165:0.86 172:0.10 173:0.94 176:0.73 177:0.38 178:0.55 179:0.13 187:0.39 192:0.59 201:0.74 204:0.89 208:0.64 215:0.79 216:0.07 222:0.35 230:0.87 233:0.76 235:0.31 241:0.39 243:0.63 253:0.68 259:0.21 265:0.14 267:0.44 271:0.11 290:0.90 297:0.36 300:0.08 +2 1:0.74 11:0.57 12:0.51 17:0.78 21:0.30 27:0.16 30:0.62 34:0.93 36:0.54 37:0.37 39:0.26 43:0.73 66:0.75 69:0.86 70:0.23 74:0.48 81:0.90 83:0.75 85:0.80 91:0.60 98:0.41 101:0.75 106:0.81 108:0.73 114:0.86 117:0.86 122:0.43 123:0.71 126:0.54 127:0.13 129:0.05 135:0.26 146:0.50 147:0.56 149:0.57 150:0.94 154:0.63 155:0.67 159:0.18 165:0.84 172:0.32 173:0.90 176:0.73 177:0.38 178:0.55 179:0.47 187:0.39 192:0.59 201:0.74 206:0.81 212:0.30 215:0.72 216:0.06 222:0.35 235:0.42 241:0.15 242:0.63 243:0.77 247:0.35 253:0.65 265:0.43 266:0.27 267:0.28 271:0.11 276:0.25 290:0.86 297:0.36 300:0.18 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.83 21:0.47 27:0.37 30:0.95 32:0.80 34:0.99 36:0.65 37:0.32 39:0.26 43:0.81 66:0.54 69:0.72 74:0.65 77:0.70 81:0.82 83:0.74 85:0.72 91:0.64 98:0.13 101:0.75 108:0.55 114:0.80 121:0.97 123:0.52 126:0.54 127:0.08 129:0.05 135:0.34 145:0.76 146:0.13 147:0.18 149:0.45 150:0.88 154:0.75 155:0.67 159:0.08 165:0.80 172:0.10 173:0.91 176:0.73 177:0.38 178:0.55 179:0.11 187:0.39 192:0.59 195:0.59 201:0.74 204:0.89 208:0.64 215:0.63 216:0.30 222:0.35 230:0.87 233:0.76 235:0.60 241:0.10 243:0.63 253:0.65 259:0.21 265:0.13 267:0.28 271:0.11 282:0.88 290:0.80 297:0.36 299:0.88 300:0.08 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.77 21:0.13 27:0.55 30:0.95 34:0.95 36:0.59 37:0.24 39:0.26 43:0.82 66:0.54 69:0.67 74:0.56 81:0.96 83:0.77 85:0.72 91:0.44 98:0.14 101:0.75 108:0.51 114:0.92 121:0.90 123:0.49 126:0.54 127:0.09 129:0.05 135:0.21 145:0.87 146:0.13 147:0.18 149:0.46 150:0.98 154:0.78 155:0.67 159:0.08 165:0.88 172:0.10 173:0.92 176:0.73 177:0.38 178:0.55 179:0.13 187:0.39 192:0.59 201:0.74 204:0.89 208:0.64 215:0.84 216:0.07 222:0.35 230:0.87 233:0.76 235:0.22 241:0.41 243:0.63 253:0.70 259:0.21 265:0.14 267:0.65 271:0.11 290:0.92 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.51 17:0.77 18:0.78 21:0.47 27:0.63 29:0.56 30:0.94 32:0.80 34:1.00 36:0.32 37:0.29 39:0.82 43:0.87 44:0.93 60:0.87 64:0.77 66:0.80 69:0.55 70:0.18 71:0.92 74:0.77 77:0.70 81:0.49 83:0.91 85:0.85 86:0.89 91:0.68 98:0.67 101:0.87 108:0.42 114:0.60 122:0.57 123:0.41 126:0.54 127:0.20 129:0.05 135:0.58 139:0.92 145:0.58 146:0.38 147:0.45 149:0.85 150:0.72 154:0.86 155:0.67 158:0.92 159:0.15 165:0.93 172:0.45 173:0.82 176:0.73 177:0.70 178:0.55 179:0.62 187:0.21 192:0.87 195:0.54 201:0.93 208:0.64 212:0.55 215:0.41 216:0.25 219:0.95 222:0.25 235:0.68 241:0.50 242:0.58 243:0.82 247:0.35 253:0.47 259:0.21 265:0.67 266:0.27 267:0.44 271:0.47 276:0.26 279:0.86 282:0.88 283:0.82 290:0.60 292:0.95 297:0.36 299:0.88 300:0.18 +0 1:0.74 9:0.80 12:0.51 17:0.69 18:0.67 21:0.05 27:0.69 29:0.56 31:0.91 34:0.26 36:0.80 37:0.37 39:0.82 41:0.82 43:0.17 48:0.98 64:0.77 66:0.36 69:0.33 71:0.92 74:0.39 76:0.94 79:0.90 81:0.74 83:0.91 86:0.66 91:0.78 96:0.75 98:0.15 108:0.26 114:0.89 120:0.63 123:0.25 126:0.54 127:0.09 129:0.05 135:0.54 137:0.91 139:0.75 140:0.45 146:0.05 147:0.05 149:0.07 150:0.84 151:0.72 153:0.48 154:0.11 155:0.67 159:0.05 162:0.62 164:0.57 165:0.93 172:0.05 173:0.84 175:0.91 176:0.73 177:0.05 187:0.39 191:0.82 192:0.87 196:0.91 201:0.93 202:0.50 204:0.85 208:0.64 215:0.78 216:0.18 219:0.89 220:0.62 222:0.25 232:0.87 235:0.22 241:0.30 243:0.51 244:0.83 248:0.67 253:0.70 255:0.95 256:0.45 257:0.84 259:0.21 260:0.64 265:0.16 267:0.28 268:0.46 271:0.47 277:0.87 281:0.91 284:0.89 285:0.62 290:0.89 292:0.91 297:0.36 +2 1:0.69 8:0.68 9:0.80 11:0.64 12:0.51 17:0.61 18:0.98 21:0.22 22:0.93 27:0.30 29:0.56 30:0.09 31:0.88 34:0.55 36:0.94 37:0.57 39:0.82 41:0.82 43:0.64 45:0.85 47:0.93 55:0.59 64:0.77 66:0.36 69:0.10 70:0.98 71:0.92 74:0.75 79:0.88 81:0.44 83:0.91 86:0.97 91:0.67 96:0.71 97:0.89 98:0.54 101:0.52 108:0.95 114:0.67 120:0.56 122:0.86 123:0.95 124:0.88 126:0.44 127:0.93 129:0.59 133:0.89 135:0.75 137:0.88 139:0.97 140:0.45 146:0.79 147:0.82 149:0.65 150:0.53 151:0.53 153:0.48 154:0.84 155:0.67 158:0.86 159:0.90 162:0.86 164:0.70 172:0.89 173:0.07 176:0.66 177:0.70 179:0.20 187:0.39 190:0.77 192:0.87 196:0.92 201:0.68 202:0.59 208:0.64 212:0.49 216:0.67 219:0.95 220:0.87 222:0.25 232:0.86 235:0.31 241:0.21 242:0.72 243:0.25 245:0.93 247:0.76 248:0.52 253:0.56 254:0.84 255:0.95 256:0.64 259:0.21 260:0.57 262:0.93 265:0.55 266:0.92 267:0.99 268:0.67 271:0.47 276:0.92 279:0.86 283:0.67 284:0.94 285:0.62 290:0.67 292:0.91 300:0.94 +1 1:0.74 11:0.92 12:0.51 17:0.28 18:0.92 21:0.40 27:0.45 29:0.56 30:0.44 32:0.80 34:0.78 36:0.22 37:0.50 39:0.82 43:0.82 44:0.93 45:0.85 64:0.77 66:0.20 69:0.69 70:0.86 71:0.92 74:0.78 77:0.70 81:0.52 83:0.91 85:0.90 86:0.94 91:0.06 98:0.51 101:0.97 108:0.90 114:0.62 122:0.86 123:0.89 124:0.90 126:0.54 127:0.68 129:0.81 133:0.89 135:0.63 139:0.94 145:0.50 146:0.45 147:0.52 149:0.90 150:0.73 154:0.77 155:0.67 159:0.84 165:0.93 172:0.65 173:0.45 176:0.73 177:0.70 178:0.55 179:0.31 187:0.68 192:0.87 195:0.94 201:0.93 208:0.64 212:0.35 215:0.42 216:0.77 222:0.25 235:0.60 241:0.30 242:0.30 243:0.27 245:0.84 247:0.88 253:0.49 259:0.21 265:0.52 266:0.89 267:0.36 271:0.47 276:0.82 282:0.88 290:0.62 297:0.36 299:0.88 300:0.84 +1 1:0.69 9:0.80 11:0.64 12:0.51 17:0.75 18:0.68 21:0.13 27:0.54 29:0.56 30:0.95 31:0.88 34:0.69 36:0.68 37:0.79 39:0.82 41:0.82 43:0.72 45:0.66 64:0.77 66:0.54 69:0.15 71:0.92 74:0.39 79:0.88 81:0.54 83:0.91 86:0.70 91:0.65 96:0.71 98:0.15 99:0.83 101:0.52 108:0.12 114:0.75 120:0.57 123:0.12 126:0.44 127:0.09 129:0.05 135:0.44 137:0.88 139:0.68 140:0.45 146:0.13 147:0.18 149:0.36 150:0.58 151:0.56 153:0.48 154:0.62 155:0.67 159:0.08 162:0.86 164:0.57 165:0.70 172:0.10 173:0.57 175:0.75 176:0.66 177:0.38 179:0.16 187:0.21 192:0.87 196:0.88 201:0.68 202:0.36 208:0.64 216:0.41 220:0.82 222:0.25 232:0.84 235:0.22 241:0.35 243:0.63 244:0.61 248:0.55 253:0.55 255:0.95 256:0.45 257:0.65 259:0.21 260:0.58 265:0.16 267:0.96 268:0.46 271:0.47 277:0.69 284:0.89 285:0.62 290:0.75 300:0.08 +0 8:0.61 9:0.80 12:0.51 17:0.24 18:0.85 21:0.74 27:0.38 29:0.56 30:0.87 31:0.89 34:1.00 36:0.40 37:0.57 39:0.82 41:0.82 43:0.11 48:0.91 55:0.52 64:0.77 66:0.79 69:0.71 70:0.20 71:0.92 79:0.88 81:0.23 83:0.91 86:0.84 91:0.66 96:0.71 98:0.72 108:0.54 120:0.57 122:0.86 123:0.52 126:0.26 127:0.22 129:0.05 135:0.37 137:0.89 139:0.83 140:0.90 146:0.67 147:0.72 149:0.11 150:0.23 151:0.56 153:0.48 154:0.50 155:0.67 159:0.26 162:0.58 164:0.57 172:0.41 173:0.79 175:0.75 177:0.38 178:0.42 179:0.73 187:0.68 192:0.87 196:0.91 202:0.69 208:0.64 212:0.19 216:0.56 219:0.89 220:0.99 222:0.25 235:0.95 241:0.69 242:0.75 243:0.80 244:0.61 247:0.35 248:0.55 253:0.43 254:0.90 255:0.95 256:0.68 257:0.65 259:0.21 260:0.58 265:0.73 266:0.47 267:0.44 268:0.68 271:0.47 276:0.32 277:0.69 281:0.89 283:0.78 284:0.93 285:0.62 292:0.91 300:0.18 +1 1:0.69 11:0.57 12:0.51 17:0.36 18:0.96 22:0.93 27:0.64 29:0.56 30:0.21 34:0.75 36:0.86 37:0.29 39:0.82 43:0.94 47:0.93 55:0.45 64:0.77 66:0.91 69:0.19 70:0.59 71:0.92 74:0.59 81:0.71 83:0.60 85:0.80 86:0.97 91:0.77 97:0.89 98:0.93 99:0.83 101:0.87 108:0.15 114:0.95 117:0.86 122:0.86 123:0.15 126:0.44 127:0.58 129:0.05 135:0.51 139:0.96 146:0.78 147:0.81 149:0.77 150:0.67 154:0.94 155:0.58 158:0.79 159:0.34 165:0.70 172:0.76 173:0.36 176:0.66 177:0.70 178:0.55 179:0.53 187:0.39 192:0.51 201:0.68 208:0.54 212:0.91 216:0.12 219:0.92 222:0.25 232:0.88 235:0.05 241:0.24 242:0.74 243:0.92 247:0.60 253:0.64 259:0.21 262:0.93 265:0.93 266:0.27 267:0.44 271:0.47 276:0.64 279:0.82 290:0.95 292:0.95 300:0.43 +0 11:0.83 12:0.51 17:0.28 18:0.72 21:0.78 27:0.45 29:0.56 30:0.45 34:0.71 36:0.17 37:0.50 39:0.82 43:0.74 45:0.66 66:0.72 69:0.84 70:0.74 71:0.92 74:0.71 85:0.80 86:0.83 91:0.11 98:0.29 101:0.97 108:0.69 123:0.67 124:0.43 127:0.24 129:0.05 133:0.59 135:0.88 139:0.79 145:0.50 146:0.50 147:0.05 149:0.74 154:0.64 159:0.53 172:0.68 173:0.76 177:0.05 178:0.55 179:0.37 187:0.68 212:0.87 216:0.77 222:0.25 235:0.22 241:0.15 242:0.17 243:0.10 245:0.62 247:0.86 253:0.42 257:0.84 259:0.21 265:0.31 266:0.38 267:0.44 271:0.47 276:0.52 300:0.70 +2 1:0.74 11:0.83 12:0.51 17:0.32 18:0.84 21:0.13 27:0.39 29:0.56 30:0.55 34:0.31 36:0.23 39:0.82 43:0.94 45:0.77 60:0.97 64:0.77 66:0.12 69:0.25 70:0.60 71:0.92 74:0.77 81:0.67 83:0.91 85:0.80 86:0.91 91:0.54 98:0.47 101:0.87 108:0.75 114:0.79 122:0.57 123:0.19 124:0.69 126:0.54 127:0.55 129:0.59 133:0.66 135:0.42 139:0.91 146:0.51 147:0.57 149:0.78 150:0.80 154:0.94 155:0.67 158:0.92 159:0.46 165:0.93 172:0.61 173:0.30 176:0.73 177:0.70 178:0.55 179:0.51 187:0.39 192:0.87 201:0.93 202:0.36 208:0.64 212:0.75 215:0.61 216:0.36 219:0.95 222:0.25 235:0.31 241:0.39 242:0.19 243:0.32 245:0.78 247:0.90 253:0.57 259:0.21 265:0.43 266:0.54 267:0.52 271:0.47 276:0.64 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.57 12:0.51 18:0.74 21:0.05 22:0.93 27:0.30 29:0.56 30:0.95 31:0.89 34:0.79 36:0.76 37:0.31 39:0.82 41:0.82 43:0.95 47:0.93 60:0.87 64:0.77 66:0.75 69:0.17 70:0.13 71:0.92 74:0.67 79:0.88 81:0.74 83:0.91 85:0.80 86:0.85 91:0.66 96:0.72 98:0.65 101:0.87 104:0.96 106:0.81 108:0.14 114:0.89 117:0.86 120:0.59 122:0.57 123:0.13 126:0.54 127:0.19 129:0.05 135:0.79 137:0.89 139:0.87 140:0.80 146:0.26 147:0.32 149:0.80 150:0.84 151:0.61 153:0.48 154:0.95 155:0.67 158:0.91 159:0.11 162:0.62 164:0.57 165:0.93 172:0.32 173:0.73 176:0.73 177:0.70 178:0.42 179:0.74 187:0.87 192:0.87 196:0.92 201:0.93 202:0.50 206:0.81 208:0.64 212:0.95 215:0.78 216:0.80 219:0.95 220:0.74 222:0.25 232:0.98 235:0.22 241:0.49 242:0.63 243:0.77 247:0.35 248:0.59 253:0.66 255:0.95 256:0.45 259:0.21 260:0.60 262:0.93 265:0.65 266:0.12 267:0.79 268:0.46 271:0.47 276:0.20 279:0.86 283:0.78 284:0.89 285:0.62 290:0.89 292:0.91 297:0.36 300:0.13 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.69 18:0.77 21:0.68 22:0.93 27:0.58 29:0.56 30:0.33 31:0.86 34:0.20 36:0.58 37:0.47 39:0.82 41:0.82 43:0.89 47:0.93 60:0.95 64:0.77 66:0.20 69:0.53 70:0.49 71:0.92 74:0.57 79:0.86 81:0.44 83:0.91 85:0.72 86:0.81 91:0.06 96:0.68 98:0.20 101:0.87 104:1.00 106:0.81 108:0.41 114:0.56 117:0.86 120:0.53 122:0.57 123:0.89 124:0.70 126:0.54 127:0.67 129:0.05 133:0.59 135:0.72 137:0.86 139:0.84 140:0.45 146:0.31 147:0.38 149:0.63 150:0.70 151:0.47 153:0.48 154:0.87 155:0.67 158:0.91 159:0.73 162:0.62 163:0.75 164:0.57 165:0.93 172:0.21 173:0.37 176:0.73 177:0.70 179:0.90 187:0.95 192:0.87 196:0.88 201:0.93 202:0.50 206:0.81 208:0.64 212:0.19 215:0.37 216:0.45 219:0.95 220:0.96 222:0.25 232:1.00 235:0.88 242:0.19 243:0.49 245:0.48 247:0.55 248:0.47 253:0.40 255:0.95 256:0.45 259:0.21 260:0.54 262:0.93 265:0.20 266:0.47 267:0.88 268:0.46 271:0.47 276:0.23 277:0.87 279:0.86 283:0.78 284:0.89 285:0.62 290:0.56 292:0.91 297:0.36 300:0.33 +0 1:0.69 8:0.77 9:0.80 11:0.64 12:0.51 17:0.06 18:0.73 21:0.22 27:0.14 29:0.56 30:0.21 31:0.88 34:0.45 36:0.25 37:0.67 39:0.82 41:0.82 43:0.68 45:0.81 66:0.26 69:0.93 70:0.60 71:0.92 74:0.62 79:0.88 81:0.44 83:0.91 86:0.73 91:0.72 96:0.80 97:0.94 98:0.38 99:0.83 101:0.52 108:0.86 114:0.67 120:0.73 122:0.80 123:0.86 124:0.84 126:0.44 127:0.45 129:0.05 133:0.84 135:0.97 137:0.88 139:0.71 140:0.98 145:0.96 146:0.61 147:0.18 149:0.57 150:0.56 151:0.58 153:0.46 154:0.57 155:0.67 158:0.78 159:0.67 162:0.51 163:0.88 164:0.79 165:0.70 172:0.37 173:0.94 176:0.66 177:0.38 179:0.64 187:0.39 190:0.89 192:0.87 196:0.89 201:0.68 202:0.82 208:0.64 212:0.35 215:0.51 216:0.93 220:0.74 222:0.25 235:0.31 241:0.39 242:0.24 243:0.19 244:0.61 245:0.60 247:0.74 248:0.56 253:0.76 255:0.95 256:0.77 257:0.65 259:0.21 260:0.69 265:0.40 266:0.63 267:0.88 268:0.78 271:0.47 276:0.52 279:0.82 283:0.78 284:0.86 285:0.62 290:0.67 297:0.36 300:0.43 +1 1:0.74 11:0.83 12:0.51 17:0.49 18:0.95 21:0.13 27:0.58 29:0.56 30:0.54 34:0.37 36:0.84 37:0.27 39:0.82 43:0.92 45:0.66 55:0.59 60:0.87 64:0.77 66:0.88 69:0.38 70:0.44 71:0.92 74:0.66 81:0.76 83:0.91 85:0.72 86:0.94 91:0.44 97:0.89 98:0.82 99:0.83 101:0.97 108:0.30 114:0.79 122:0.86 123:0.28 126:0.54 127:0.31 129:0.05 135:0.70 139:0.94 146:0.79 147:0.83 149:0.78 150:0.84 154:0.91 155:0.67 158:0.92 159:0.35 165:1.00 172:0.67 173:0.42 176:0.73 177:0.70 178:0.55 179:0.54 187:0.39 192:0.87 201:0.93 208:0.64 212:0.61 215:0.61 216:0.39 219:0.95 222:0.25 235:0.52 241:0.07 242:0.77 243:0.89 247:0.47 253:0.56 259:0.21 265:0.82 266:0.47 267:0.28 271:0.47 276:0.55 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.33 +1 12:0.86 17:0.92 21:0.78 27:0.72 43:0.08 66:0.36 69:0.98 91:0.07 98:0.05 108:0.97 123:0.97 127:0.05 129:0.05 145:0.52 146:0.05 147:0.05 149:0.05 154:0.07 159:0.05 172:0.05 173:0.91 177:0.05 178:0.55 187:0.39 216:0.07 235:0.95 241:0.44 243:0.51 265:0.05 +1 12:0.86 17:0.16 21:0.78 27:0.72 30:0.62 34:0.36 36:0.25 43:0.08 55:0.99 66:0.07 69:0.98 70:0.34 91:0.01 98:0.05 108:0.96 123:0.96 124:0.89 127:0.20 129:0.95 133:0.87 135:0.78 146:0.05 147:0.05 149:0.10 154:0.07 159:0.92 163:0.99 172:0.05 173:0.84 177:0.05 178:0.55 179:0.78 187:0.68 212:0.30 216:0.04 241:0.03 242:0.82 243:0.23 245:0.39 247:0.12 259:0.21 265:0.05 266:0.27 267:0.76 276:0.29 300:0.25 +1 12:0.86 17:0.77 21:0.78 27:0.72 34:0.50 36:0.21 37:0.78 43:0.31 66:0.36 69:0.72 91:0.27 98:0.09 108:0.55 123:0.53 127:0.07 129:0.05 135:0.70 145:0.41 146:0.05 147:0.05 149:0.13 154:0.23 159:0.05 172:0.05 173:0.85 177:0.05 178:0.55 187:0.68 216:0.19 235:0.31 241:0.21 243:0.51 259:0.21 265:0.10 267:0.52 +1 12:0.86 17:0.40 21:0.78 27:0.21 34:0.25 36:0.80 37:0.59 43:0.06 66:0.36 69:0.99 91:0.02 98:0.05 108:0.99 123:0.99 127:0.05 129:0.05 135:0.61 146:0.05 147:0.05 149:0.05 154:0.06 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.87 216:0.67 235:0.12 241:0.03 243:0.51 259:0.21 265:0.05 267:0.23 +0 12:0.86 17:0.75 21:0.78 27:0.72 43:0.27 66:0.36 69:0.80 91:0.22 98:0.06 108:0.64 123:0.61 127:0.05 129:0.05 146:0.05 147:0.05 149:0.11 154:0.19 159:0.05 172:0.05 173:0.71 177:0.05 178:0.55 187:0.87 216:0.11 235:0.52 241:0.05 243:0.51 265:0.07 +0 12:0.86 17:0.64 21:0.78 27:0.72 43:0.27 66:0.36 69:0.81 91:0.37 98:0.06 108:0.65 123:0.62 127:0.05 129:0.05 146:0.05 147:0.05 149:0.11 154:0.19 159:0.05 172:0.05 173:0.72 177:0.05 178:0.55 187:0.87 216:0.11 235:0.74 241:0.07 243:0.51 265:0.07 +0 12:0.86 17:0.47 21:0.78 27:0.72 34:0.69 36:0.18 37:0.78 43:0.28 66:0.36 69:0.78 91:0.48 98:0.07 108:0.61 123:0.59 127:0.06 129:0.05 135:0.75 145:0.40 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 172:0.05 173:0.76 177:0.05 178:0.55 187:0.39 216:0.19 235:0.22 241:0.05 243:0.51 259:0.21 265:0.07 267:0.28 +1 12:0.86 17:0.57 21:0.78 27:0.72 34:0.58 36:0.83 43:0.21 66:0.36 69:0.92 91:0.44 98:0.07 108:0.83 123:0.82 127:0.05 129:0.05 135:0.80 146:0.05 147:0.05 149:0.08 154:0.14 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.39 216:0.42 235:0.68 241:0.15 243:0.51 259:0.21 265:0.07 267:0.59 +1 8:0.86 12:0.86 17:0.83 21:0.78 27:0.72 34:0.40 36:0.68 37:0.81 43:0.32 66:0.36 69:0.70 91:0.91 98:0.06 108:0.53 123:0.51 127:0.05 129:0.05 135:0.39 145:0.59 146:0.05 147:0.05 149:0.13 154:0.24 159:0.05 172:0.05 173:0.56 177:0.05 178:0.55 191:0.89 202:0.90 216:0.45 235:0.60 241:0.83 243:0.51 259:0.21 265:0.06 267:0.69 +1 12:0.86 17:0.94 21:0.78 27:0.57 30:0.95 34:0.82 36:0.57 37:0.44 43:0.24 55:0.59 66:0.54 69:0.85 91:0.42 98:0.16 108:0.72 123:0.70 127:0.09 129:0.05 135:0.51 145:0.59 146:0.23 147:0.29 149:0.16 154:0.17 159:0.11 172:0.10 173:0.90 177:0.05 178:0.55 179:0.33 187:0.68 216:0.22 235:0.22 241:0.07 243:0.63 259:0.21 265:0.18 267:0.28 300:0.08 +0 12:0.86 17:0.53 21:0.78 27:0.53 34:0.29 36:0.61 37:0.38 43:0.19 66:0.36 69:0.94 91:0.22 98:0.06 108:0.89 123:0.88 127:0.05 129:0.05 135:0.82 145:0.49 146:0.05 147:0.05 149:0.08 154:0.13 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.39 216:0.80 235:0.31 241:0.05 243:0.51 259:0.21 265:0.06 267:0.36 +2 7:0.81 12:0.86 17:0.72 21:0.05 27:0.55 30:0.45 32:0.80 34:0.29 36:0.23 37:0.31 43:0.48 55:0.45 66:0.27 69:0.29 70:0.25 81:0.77 91:0.25 98:0.19 106:0.81 108:0.22 123:0.22 124:0.61 126:0.54 127:0.52 129:0.59 133:0.47 135:0.61 145:0.41 146:0.24 147:0.30 149:0.29 150:0.57 154:0.37 159:0.43 163:0.79 172:0.10 173:0.34 177:0.05 178:0.55 179:0.98 187:0.39 195:0.65 206:0.81 212:0.61 215:0.91 216:0.58 230:0.87 233:0.76 235:0.05 241:0.05 242:0.82 243:0.46 245:0.40 247:0.12 265:0.21 266:0.17 267:0.52 276:0.14 283:0.60 297:0.36 300:0.18 +1 8:0.81 12:0.86 17:0.92 21:0.78 27:0.72 34:0.31 36:0.69 37:0.81 43:0.32 66:0.36 69:0.70 91:0.58 98:0.06 108:0.53 123:0.51 127:0.05 129:0.05 135:0.42 145:0.59 146:0.05 147:0.05 149:0.13 154:0.24 159:0.05 172:0.05 173:0.56 177:0.05 178:0.55 187:0.21 202:0.46 216:0.45 235:0.60 241:0.80 243:0.51 259:0.21 265:0.06 267:0.52 +0 12:0.86 17:0.91 21:0.78 27:0.43 34:0.56 36:0.43 37:0.28 43:0.28 66:0.36 69:0.78 91:0.11 98:0.08 108:0.61 123:0.59 127:0.06 129:0.05 135:0.30 145:0.40 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 172:0.05 173:0.86 177:0.05 178:0.55 187:0.39 216:0.18 235:0.31 241:0.12 243:0.51 259:0.21 265:0.09 267:0.65 +1 12:0.86 17:0.82 21:0.78 27:0.54 34:0.64 36:0.28 37:0.48 43:0.23 66:0.36 69:0.87 91:0.62 98:0.08 108:0.74 123:0.73 127:0.06 129:0.05 135:0.56 146:0.05 147:0.05 149:0.10 154:0.16 159:0.05 172:0.05 173:0.95 177:0.05 178:0.55 187:0.68 202:0.46 216:0.23 235:0.60 241:0.62 243:0.51 259:0.21 265:0.08 267:0.36 +0 12:0.86 17:0.96 21:0.78 27:0.72 34:0.49 36:0.50 43:0.20 66:0.36 69:0.94 91:0.23 98:0.06 108:0.88 123:0.87 127:0.05 129:0.05 135:0.54 145:0.44 146:0.05 147:0.05 149:0.08 154:0.13 159:0.05 172:0.05 173:0.95 177:0.05 178:0.55 187:0.68 216:0.14 235:0.52 243:0.51 259:0.21 265:0.06 267:0.28 +2 12:0.86 17:0.90 21:0.78 27:0.72 30:0.95 34:0.96 36:0.47 43:0.28 55:0.59 66:0.54 69:0.78 91:0.53 98:0.16 108:0.62 123:0.60 127:0.09 129:0.05 135:0.42 145:0.49 146:0.24 147:0.30 149:0.18 154:0.20 159:0.11 172:0.10 173:0.79 177:0.05 178:0.55 179:0.32 187:0.39 216:0.30 235:0.84 241:0.05 243:0.63 259:0.21 265:0.17 267:0.85 300:0.08 +2 1:0.74 6:0.87 8:0.75 9:0.80 11:0.92 12:0.20 17:0.90 18:1.00 20:0.99 21:0.71 22:0.93 27:0.57 28:0.57 29:0.94 30:0.40 31:0.86 32:0.80 34:0.95 36:0.25 37:0.38 39:0.47 41:0.82 43:0.85 44:0.93 45:0.96 47:0.93 60:0.95 64:0.77 66:0.81 69:0.44 70:0.77 71:0.71 74:0.98 77:0.98 78:0.96 79:0.86 81:0.44 83:0.91 85:0.88 86:1.00 91:0.15 96:0.68 98:0.90 100:0.97 101:0.97 106:0.81 108:0.34 111:0.96 114:0.55 117:0.86 120:0.54 122:0.85 123:0.33 124:0.40 126:0.54 127:0.77 129:0.59 133:0.46 135:0.96 137:0.86 139:1.00 140:0.45 145:0.94 146:0.98 147:0.98 149:0.96 150:0.70 151:0.48 152:0.91 153:0.48 154:0.90 155:0.67 158:0.92 159:0.80 161:0.97 162:0.86 164:0.57 165:0.93 167:0.48 169:0.95 172:0.98 173:0.25 176:0.73 177:0.70 179:0.14 181:0.45 186:0.95 187:0.39 189:0.89 192:0.87 195:0.91 196:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.80 215:0.37 216:0.72 219:0.95 220:0.93 222:0.35 232:0.84 235:0.95 238:0.92 241:0.21 242:0.28 243:0.83 245:0.98 247:0.92 248:0.48 253:0.49 255:0.95 256:0.45 259:0.21 260:0.54 261:0.94 262:0.93 265:0.90 266:0.54 267:0.99 268:0.46 271:0.38 276:0.96 279:0.86 282:0.88 283:0.82 284:0.89 285:0.62 290:0.55 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 8:0.60 11:0.64 12:0.20 17:0.78 18:0.86 21:0.22 27:0.43 28:0.57 29:0.94 30:0.53 32:0.80 34:0.99 36:0.17 37:0.25 39:0.47 43:0.79 44:0.93 45:0.81 48:0.91 55:0.42 64:0.77 66:0.91 69:0.76 70:0.48 71:0.71 74:0.86 77:0.89 81:0.70 83:0.60 86:0.94 91:0.48 97:0.89 98:0.75 99:0.83 101:0.52 108:0.59 114:0.71 122:0.57 123:0.57 126:0.54 127:0.24 129:0.05 135:0.87 139:0.97 145:0.96 146:0.70 147:0.74 149:0.55 150:0.80 154:0.72 155:0.67 158:0.87 159:0.28 161:0.97 165:0.70 167:0.49 172:0.75 173:0.85 176:0.73 177:0.70 178:0.55 179:0.35 181:0.45 187:0.21 192:0.87 195:0.65 201:0.93 204:0.89 208:0.64 212:0.76 215:0.51 216:0.33 219:0.95 222:0.35 230:0.86 233:0.76 235:0.52 241:0.27 242:0.30 243:0.91 247:0.67 253:0.54 259:0.21 265:0.75 266:0.54 267:0.52 271:0.38 276:0.60 279:0.86 281:0.88 282:0.88 283:0.71 290:0.71 292:0.91 297:0.36 300:0.43 +3 1:0.74 6:0.85 7:0.81 8:0.67 11:0.92 12:0.20 17:0.79 18:0.95 20:0.87 21:0.40 27:0.31 28:0.57 29:0.94 30:0.10 32:0.80 34:0.97 36:0.25 37:0.71 39:0.47 43:0.94 44:0.93 45:0.77 48:0.99 55:0.71 64:0.77 66:0.74 69:0.35 70:0.94 71:0.71 74:0.86 77:1.00 78:0.97 81:0.66 83:0.91 85:0.80 86:0.97 91:0.20 98:0.77 99:0.83 100:0.91 101:0.97 106:0.81 108:0.58 111:0.94 114:0.62 120:0.64 121:0.97 122:0.85 123:0.55 124:0.44 126:0.54 127:0.70 129:0.59 133:0.61 135:0.86 139:0.97 140:0.45 145:0.96 146:0.17 147:0.22 149:0.80 150:0.80 151:0.56 152:0.83 153:0.69 154:0.94 155:0.67 159:0.62 161:0.97 162:0.86 164:0.53 165:0.93 167:0.51 169:0.88 172:0.84 173:0.29 176:0.73 177:0.70 179:0.37 181:0.45 186:0.97 187:0.39 189:0.86 190:0.89 192:0.87 195:0.78 201:0.93 202:0.46 204:0.89 206:0.81 208:0.64 212:0.73 215:0.42 216:0.28 220:0.62 222:0.35 230:0.87 232:0.92 233:0.76 235:0.84 238:0.82 241:0.39 242:0.42 243:0.14 245:0.80 247:0.84 248:0.55 253:0.50 256:0.45 259:0.21 260:0.62 261:0.90 265:0.77 266:0.71 267:0.96 268:0.55 271:0.38 276:0.77 281:0.91 282:0.88 283:0.78 285:0.47 290:0.62 297:0.36 299:0.99 300:0.84 +2 1:0.74 7:0.81 11:0.83 12:0.20 17:0.57 18:0.96 21:0.68 22:0.93 27:0.57 28:0.57 29:0.94 30:0.75 32:0.80 34:0.45 36:0.37 37:0.61 39:0.47 43:0.86 44:0.93 45:0.97 47:0.93 64:0.77 66:0.90 69:0.60 70:0.46 71:0.71 74:0.96 77:0.70 81:0.45 83:0.91 85:0.94 86:0.99 91:0.06 98:0.82 101:0.87 106:0.81 108:0.72 114:0.56 117:0.86 121:0.90 122:0.57 123:0.70 124:0.37 126:0.54 127:0.49 129:0.59 133:0.61 135:0.44 139:0.99 145:0.77 146:0.17 147:0.22 149:0.96 150:0.70 154:0.84 155:0.67 159:0.73 161:0.97 165:0.93 167:0.50 172:0.97 173:0.42 176:0.73 177:0.70 178:0.55 179:0.15 181:0.45 187:0.21 192:0.87 195:0.89 201:0.93 204:0.89 206:0.81 208:0.64 212:0.90 215:0.37 216:0.86 222:0.35 230:0.87 232:0.85 233:0.76 235:0.88 241:0.32 242:0.39 243:0.08 245:0.86 247:0.74 253:0.49 262:0.93 265:0.82 266:0.63 267:0.94 271:0.38 276:0.93 281:0.91 282:0.88 290:0.56 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.86 7:0.72 8:0.70 11:0.64 12:0.20 17:0.73 18:0.95 20:0.82 21:0.22 22:0.93 27:0.43 28:0.57 29:0.94 30:0.33 32:0.80 34:0.99 36:0.64 37:0.38 39:0.47 43:0.09 44:0.93 45:0.83 47:0.93 48:0.91 55:0.71 64:0.77 66:0.68 69:0.65 70:0.89 71:0.71 74:0.83 77:0.70 78:0.99 81:0.67 83:0.60 86:0.96 91:0.25 97:0.89 98:0.79 99:0.83 100:0.93 101:0.52 106:0.81 108:0.63 111:0.96 114:0.71 117:0.86 122:0.86 123:0.61 124:0.45 126:0.54 127:0.35 129:0.59 133:0.46 135:0.98 139:0.97 145:0.42 146:0.29 147:0.35 149:0.23 150:0.78 152:0.83 154:0.79 155:0.67 158:0.87 159:0.61 161:0.97 165:0.70 167:0.51 169:0.88 172:0.79 173:0.53 176:0.73 177:0.70 178:0.55 179:0.32 181:0.45 186:0.98 187:0.39 192:0.87 195:0.85 201:0.93 204:0.85 206:0.81 208:0.64 212:0.59 215:0.51 216:0.41 219:0.95 222:0.35 230:0.75 232:0.93 233:0.76 235:0.42 241:0.39 242:0.60 243:0.29 245:0.86 247:0.76 253:0.54 254:0.74 259:0.21 261:0.90 262:0.93 265:0.80 266:0.47 267:0.97 271:0.38 276:0.74 279:0.86 281:0.89 282:0.88 283:0.71 290:0.71 292:0.91 297:0.36 300:0.70 +2 1:0.74 7:0.81 11:0.83 12:0.20 17:0.53 18:0.96 21:0.47 22:0.93 27:0.48 28:0.57 29:0.94 30:0.84 32:0.80 34:0.88 36:0.85 37:0.42 39:0.47 43:0.87 44:0.93 45:0.73 47:0.93 60:0.87 64:0.77 66:0.95 69:0.58 70:0.53 71:0.71 74:0.93 77:0.70 81:0.58 83:0.91 85:0.95 86:0.99 91:0.17 98:0.88 101:0.87 106:0.81 108:0.44 114:0.60 117:0.86 121:0.90 122:0.57 123:0.43 126:0.54 127:0.43 129:0.05 135:0.56 139:0.99 145:0.44 146:0.86 147:0.88 149:0.96 150:0.76 154:0.85 155:0.67 158:0.92 159:0.43 161:0.97 165:0.93 167:0.47 172:0.88 173:0.61 176:0.73 177:0.70 178:0.55 179:0.30 181:0.45 187:0.68 192:0.87 195:0.67 201:0.93 204:0.89 206:0.81 208:0.64 212:0.80 215:0.41 216:0.51 219:0.95 222:0.35 230:0.87 232:0.97 233:0.76 235:0.84 241:0.18 242:0.37 243:0.95 247:0.67 253:0.50 259:0.21 262:0.93 265:0.88 266:0.27 267:0.73 271:0.38 276:0.78 279:0.86 281:0.91 282:0.88 283:0.82 290:0.60 292:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.20 17:0.92 18:0.83 21:0.22 27:0.17 28:0.57 29:0.94 30:0.19 31:0.91 34:0.94 36:0.28 37:0.81 39:0.47 43:0.08 44:0.87 45:0.77 48:0.98 55:0.42 64:0.77 66:0.53 69:0.86 70:0.96 71:0.71 74:0.65 79:0.90 81:0.67 83:0.60 86:0.89 91:0.27 96:0.75 98:0.48 99:0.83 101:0.52 108:0.72 114:0.71 120:0.63 122:0.57 123:0.71 124:0.52 126:0.54 127:0.23 129:0.05 133:0.43 135:0.96 137:0.91 139:0.90 140:0.45 145:0.84 146:0.68 147:0.72 149:0.08 150:0.78 151:0.72 153:0.48 154:0.63 155:0.67 159:0.45 161:0.97 162:0.86 164:0.57 165:0.70 167:0.52 172:0.54 173:0.83 176:0.73 177:0.70 179:0.42 181:0.45 187:0.68 192:0.87 195:0.82 196:0.94 201:0.93 202:0.36 204:0.89 208:0.64 212:0.30 215:0.51 216:0.38 220:0.62 222:0.35 230:0.86 233:0.76 235:0.42 241:0.44 242:0.23 243:0.62 245:0.74 247:0.60 248:0.67 253:0.69 254:0.74 255:0.95 256:0.45 259:0.21 260:0.64 265:0.50 266:0.63 267:0.52 268:0.46 271:0.38 276:0.52 281:0.91 284:0.89 285:0.62 290:0.71 297:0.36 300:0.84 +2 1:0.74 8:0.83 11:0.92 12:0.20 17:0.38 18:0.88 20:0.82 21:0.47 27:0.45 28:0.57 29:0.94 30:0.37 34:0.84 36:0.19 37:0.50 39:0.47 43:0.82 45:0.73 55:0.84 64:0.77 66:0.48 69:0.69 70:0.90 71:0.71 74:0.56 78:0.98 81:0.52 83:0.91 85:0.72 86:0.89 91:0.17 98:0.43 100:0.73 101:0.97 108:0.52 111:0.94 114:0.60 122:0.86 123:0.50 124:0.73 126:0.54 127:0.46 129:0.05 133:0.73 135:0.89 139:0.86 145:0.50 146:0.68 147:0.72 149:0.66 150:0.74 152:0.79 154:0.77 155:0.67 159:0.69 161:0.97 163:0.81 165:0.93 167:0.48 169:0.72 172:0.51 173:0.56 176:0.73 177:0.70 178:0.55 179:0.62 181:0.45 186:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.22 215:0.41 216:0.77 222:0.35 235:0.68 241:0.24 242:0.63 243:0.59 245:0.62 247:0.47 253:0.45 259:0.21 261:0.86 265:0.45 266:0.80 267:0.52 271:0.38 276:0.54 290:0.60 297:0.36 300:0.84 +2 1:0.74 7:0.81 11:0.64 12:0.20 17:0.84 18:0.90 21:0.22 22:0.93 27:0.17 28:0.57 29:0.94 30:0.33 34:0.98 36:0.18 37:0.81 39:0.47 43:0.09 44:0.87 45:0.77 47:0.93 48:0.98 55:0.42 64:0.77 66:0.31 69:0.62 70:0.84 71:0.71 74:0.74 81:0.67 83:0.60 86:0.94 91:0.17 98:0.44 99:0.83 101:0.52 106:0.81 108:0.75 114:0.71 117:0.86 122:0.85 123:0.73 124:0.73 126:0.54 127:0.42 129:0.59 133:0.66 135:0.96 139:0.94 145:0.84 146:0.42 147:0.49 149:0.08 150:0.78 154:0.80 155:0.67 159:0.43 161:0.97 165:0.70 167:0.56 172:0.48 173:0.65 176:0.73 177:0.70 178:0.55 179:0.53 181:0.45 187:0.68 192:0.87 195:0.68 201:0.93 204:0.89 206:0.81 208:0.64 212:0.52 215:0.51 216:0.38 222:0.35 230:0.86 232:0.99 233:0.76 235:0.42 241:0.54 242:0.24 243:0.36 245:0.75 247:0.67 253:0.53 254:0.74 259:0.21 262:0.93 265:0.46 266:0.38 267:0.79 271:0.38 276:0.59 277:0.69 281:0.91 290:0.71 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.20 17:0.30 18:0.83 21:0.64 27:0.45 28:0.57 29:0.94 30:0.15 34:0.83 36:0.20 37:0.50 39:0.47 43:0.09 45:0.66 55:0.71 64:0.77 66:0.33 69:0.70 70:0.84 71:0.71 74:0.56 81:0.42 83:0.60 86:0.83 91:0.15 97:0.89 98:0.33 99:0.83 101:0.52 108:0.81 114:0.57 122:0.86 123:0.80 124:0.71 126:0.54 127:0.27 129:0.05 133:0.59 135:0.83 139:0.85 145:0.49 146:0.18 147:0.23 149:0.13 150:0.66 154:0.76 155:0.67 158:0.91 159:0.50 161:0.97 163:0.88 165:0.70 167:0.49 172:0.27 173:0.62 176:0.73 177:0.70 178:0.55 179:0.71 181:0.45 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.38 216:0.77 219:0.95 222:0.35 235:0.84 241:0.30 242:0.77 243:0.37 245:0.53 247:0.35 253:0.41 254:0.74 259:0.21 265:0.35 266:0.47 267:0.44 271:0.38 276:0.36 277:0.87 279:0.86 283:0.77 290:0.57 292:0.91 297:0.36 300:0.55 +2 1:0.74 6:0.84 8:0.62 9:0.80 11:0.64 12:0.20 17:0.13 18:0.82 20:0.87 21:0.71 27:0.17 28:0.57 29:0.94 30:0.45 31:0.91 34:1.00 36:0.42 37:0.65 39:0.47 43:0.92 44:0.87 45:0.66 64:0.77 66:0.36 69:0.45 70:0.25 71:0.71 74:0.58 78:0.96 79:0.90 81:0.41 83:0.60 86:0.88 91:0.54 96:0.75 97:0.89 98:0.16 99:0.83 100:0.90 101:0.52 108:0.35 111:0.94 114:0.55 120:0.63 122:0.86 123:0.33 124:0.69 126:0.54 127:0.39 129:0.05 133:0.59 135:0.89 137:0.91 139:0.90 140:0.80 145:0.82 146:0.21 147:0.27 149:0.64 150:0.65 151:0.72 152:0.94 153:0.48 154:0.91 155:0.67 158:0.91 159:0.39 161:0.97 162:0.49 164:0.57 165:0.70 167:0.59 169:0.88 172:0.21 173:0.49 176:0.73 177:0.70 178:0.42 179:0.89 181:0.45 186:0.96 187:0.95 189:0.83 191:0.85 192:0.87 195:0.65 196:0.94 201:0.93 202:0.64 208:0.64 212:0.23 215:0.37 216:0.59 219:0.95 220:0.62 222:0.35 235:0.95 238:0.82 241:0.61 242:0.55 243:0.51 245:0.45 247:0.39 248:0.67 253:0.62 255:0.95 256:0.45 259:0.21 260:0.64 261:0.93 265:0.17 266:0.38 267:0.96 268:0.46 271:0.38 276:0.26 279:0.86 283:0.78 284:0.89 285:0.62 290:0.55 292:0.91 297:0.36 300:0.18 +1 7:0.81 12:0.20 17:0.73 18:0.79 21:0.78 27:0.49 28:0.57 29:0.94 30:0.76 31:0.90 34:0.31 36:0.33 37:0.43 39:0.47 43:0.13 48:0.99 55:0.71 66:0.72 69:0.64 70:0.22 71:0.71 74:0.51 79:0.90 86:0.76 91:0.69 98:0.72 108:0.49 121:0.90 122:0.85 123:0.46 127:0.22 129:0.05 135:0.42 137:0.90 139:0.81 140:0.45 145:0.40 146:0.55 147:0.61 149:0.11 151:0.55 153:0.48 154:0.48 155:0.67 159:0.20 161:0.97 162:0.86 167:0.66 172:0.27 173:0.80 175:0.75 177:0.38 179:0.87 181:0.45 187:0.39 190:0.89 192:0.87 196:0.91 202:0.36 204:0.89 208:0.64 212:0.42 216:0.12 220:0.62 222:0.35 230:0.87 233:0.76 235:0.02 241:0.68 242:0.45 243:0.75 244:0.61 247:0.35 248:0.54 253:0.36 254:0.80 256:0.45 257:0.65 259:0.21 265:0.72 266:0.23 267:0.19 268:0.46 271:0.38 276:0.23 277:0.69 281:0.91 284:0.86 285:0.47 300:0.18 +2 1:0.74 11:0.83 12:0.20 17:0.77 18:0.92 21:0.59 27:0.54 28:0.57 29:0.94 30:0.21 34:0.99 36:0.19 37:0.54 39:0.47 43:0.79 44:0.90 45:0.77 48:0.99 55:0.59 64:0.77 66:0.94 69:0.75 70:0.77 71:0.71 74:0.75 77:0.70 81:0.46 83:0.60 85:0.80 86:0.97 91:0.29 98:0.87 99:0.83 101:0.87 108:0.58 114:0.58 122:0.82 123:0.56 126:0.54 127:0.40 129:0.05 135:0.81 139:0.97 145:0.45 146:0.90 147:0.92 149:0.76 150:0.67 154:0.73 155:0.67 159:0.49 161:0.97 165:0.70 167:0.46 172:0.83 173:0.75 176:0.73 177:0.70 178:0.55 179:0.36 181:0.45 187:0.39 192:0.87 195:0.74 201:0.93 208:0.64 212:0.68 215:0.39 216:0.37 222:0.35 235:0.84 241:0.10 242:0.18 243:0.94 247:0.88 253:0.45 259:0.21 265:0.87 266:0.47 267:0.44 271:0.38 276:0.72 281:0.89 282:0.77 290:0.58 297:0.36 300:0.55 +2 1:0.74 7:0.72 8:0.59 11:0.64 12:0.20 17:0.84 18:0.89 21:0.22 22:0.93 27:0.57 28:0.57 29:0.94 30:0.43 32:0.80 34:0.99 36:0.27 37:0.26 39:0.47 43:0.88 44:0.93 45:0.83 47:0.93 48:0.99 64:0.77 66:0.89 69:0.23 70:0.57 71:0.71 74:0.87 77:0.70 81:0.67 83:0.60 86:0.95 91:0.44 97:0.89 98:0.68 99:0.83 101:0.52 108:0.18 114:0.71 122:0.57 123:0.18 126:0.54 127:0.20 129:0.05 135:0.84 139:0.96 145:0.91 146:0.63 147:0.69 149:0.67 150:0.78 154:0.93 155:0.67 158:0.91 159:0.24 161:0.97 165:0.70 167:0.54 172:0.70 173:0.33 176:0.73 177:0.70 178:0.55 179:0.33 181:0.45 187:0.21 192:0.87 195:0.64 201:0.93 204:0.85 208:0.64 212:0.78 215:0.51 216:0.46 219:0.95 222:0.35 230:0.74 233:0.73 235:0.42 241:0.51 242:0.29 243:0.90 247:0.76 253:0.54 259:0.21 262:0.93 265:0.68 266:0.27 267:0.44 271:0.38 276:0.57 279:0.86 281:0.89 282:0.88 283:0.77 290:0.71 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.81 11:0.92 12:0.20 17:0.67 18:0.81 21:0.05 27:0.49 28:0.57 29:0.94 30:0.62 32:0.80 34:0.99 36:0.38 37:0.43 39:0.47 43:0.84 44:0.93 45:0.73 48:0.98 60:0.87 64:0.77 66:0.29 69:0.64 70:0.40 71:0.71 74:0.79 77:0.70 81:0.83 83:0.91 85:0.72 86:0.87 91:0.46 98:0.62 101:0.97 108:0.49 114:0.89 121:0.90 122:0.57 123:0.61 124:0.60 126:0.54 127:0.30 129:0.59 133:0.46 135:0.91 139:0.94 145:0.39 146:0.22 147:0.28 149:0.65 150:0.89 154:0.80 155:0.67 158:0.92 159:0.32 161:0.97 165:0.93 167:0.57 172:0.32 173:0.72 176:0.73 177:0.70 178:0.55 179:0.71 181:0.45 187:0.39 192:0.87 195:0.64 201:0.93 204:0.89 208:0.64 212:0.42 215:0.78 216:0.12 219:0.95 222:0.35 230:0.87 233:0.76 235:0.22 241:0.57 242:0.45 243:0.51 245:0.62 247:0.55 253:0.61 259:0.21 265:0.19 266:0.47 267:0.23 271:0.38 276:0.39 279:0.86 281:0.91 282:0.88 283:0.82 290:0.89 292:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 7:0.81 11:0.64 12:0.20 17:0.93 18:0.86 21:0.22 22:0.93 27:0.17 28:0.57 29:0.94 30:0.33 34:0.98 36:0.20 37:0.81 39:0.47 43:0.08 44:0.87 45:0.77 47:0.93 48:0.98 55:0.42 64:0.77 66:0.31 69:0.84 70:0.92 71:0.71 74:0.69 81:0.67 83:0.60 86:0.91 91:0.18 98:0.38 99:0.83 101:0.52 106:0.81 108:0.83 114:0.71 117:0.86 122:0.85 123:0.82 124:0.73 126:0.54 127:0.29 129:0.59 133:0.66 135:0.93 139:0.92 145:0.84 146:0.42 147:0.49 149:0.07 150:0.78 154:0.64 155:0.67 159:0.43 161:0.97 165:0.70 167:0.52 172:0.48 173:0.86 176:0.73 177:0.70 178:0.55 179:0.44 181:0.45 187:0.68 192:0.87 195:0.74 201:0.93 204:0.89 206:0.81 208:0.64 212:0.52 215:0.51 216:0.38 222:0.35 230:0.86 232:0.99 233:0.76 235:0.42 241:0.44 242:0.24 243:0.36 245:0.75 247:0.67 253:0.53 254:0.74 259:0.21 262:0.93 265:0.40 266:0.63 267:0.65 271:0.38 276:0.59 277:0.69 281:0.91 290:0.71 297:0.36 300:0.84 +1 1:0.64 10:0.89 11:0.57 12:0.20 17:0.77 18:0.92 26:0.94 27:0.41 28:0.76 29:0.53 30:0.70 34:0.47 36:0.35 37:0.29 39:0.58 43:0.75 45:0.95 58:0.93 66:0.97 69:0.15 70:0.50 71:0.83 74:0.87 81:0.60 83:0.56 86:0.94 91:0.33 98:0.97 99:0.83 101:0.96 108:0.12 114:0.95 122:0.86 123:0.12 126:0.32 127:0.76 129:0.05 135:0.49 139:0.92 141:0.91 146:0.90 147:0.92 149:0.91 150:0.56 154:0.66 155:0.49 159:0.50 161:0.67 165:0.65 167:0.63 170:0.94 172:0.92 173:0.23 174:0.83 176:0.63 177:0.88 178:0.55 179:0.29 181:0.68 187:0.39 192:0.48 197:0.87 199:0.94 201:0.60 208:0.50 212:0.83 215:0.96 216:0.46 222:0.35 223:0.92 224:0.93 234:0.91 235:0.05 239:0.88 241:0.27 242:0.22 243:0.97 247:0.95 253:0.73 259:0.21 265:0.97 266:0.27 267:0.28 271:0.13 274:0.91 276:0.85 290:0.95 297:0.36 300:0.55 +2 1:0.60 7:0.69 8:0.58 10:0.90 12:0.20 17:0.67 18:0.69 20:0.96 21:0.59 22:0.93 26:0.96 27:0.31 28:0.76 29:0.53 30:0.21 34:0.98 36:0.30 37:0.55 39:0.58 43:0.10 44:0.92 47:0.93 48:0.91 55:0.45 58:0.93 64:0.77 66:0.69 69:0.74 70:0.67 71:0.83 74:0.57 76:0.85 77:0.70 78:0.80 81:0.41 86:0.78 91:0.06 98:0.46 100:0.80 102:0.97 106:0.81 108:0.58 111:0.79 114:0.73 117:0.86 122:0.57 123:0.55 124:0.41 126:0.51 127:0.24 129:0.05 133:0.36 135:0.84 139:0.80 141:0.91 145:0.76 146:0.50 147:0.56 149:0.09 150:0.43 152:0.91 154:0.57 155:0.51 159:0.31 161:0.67 167:0.63 169:0.78 170:0.94 172:0.41 173:0.79 174:0.83 176:0.70 177:0.88 178:0.55 179:0.71 181:0.68 186:0.81 187:0.39 192:0.48 193:0.97 195:0.69 197:0.85 199:0.96 201:0.59 204:0.79 206:0.81 208:0.61 212:0.55 216:0.85 222:0.35 223:0.95 224:0.93 230:0.71 232:0.94 233:0.66 234:0.91 235:0.80 236:0.95 239:0.92 241:0.27 242:0.16 243:0.73 245:0.46 247:0.60 253:0.56 254:0.97 259:0.21 261:0.83 262:0.93 265:0.48 266:0.38 267:0.28 271:0.13 274:0.91 276:0.30 281:0.86 282:0.79 290:0.73 300:0.43 +0 10:0.89 11:0.52 12:0.20 17:0.43 18:0.64 20:0.87 21:0.78 26:0.94 27:0.45 28:0.76 29:0.53 30:0.91 34:0.63 36:0.18 37:0.50 39:0.58 43:0.78 45:0.73 58:0.93 66:0.69 69:0.74 70:0.13 71:0.83 74:0.42 78:0.76 86:0.74 91:0.18 98:0.12 100:0.73 101:0.65 108:0.57 111:0.75 122:0.65 123:0.55 127:0.08 129:0.05 135:0.82 139:0.71 141:0.91 145:0.50 146:0.19 147:0.25 149:0.61 152:0.82 154:0.71 159:0.10 161:0.67 167:0.63 169:0.72 170:0.94 172:0.21 173:0.72 174:0.79 177:0.88 178:0.55 179:0.09 181:0.68 186:0.77 187:0.68 197:0.83 199:0.94 212:0.61 216:0.77 222:0.35 223:0.91 224:0.93 234:0.91 235:0.68 239:0.88 241:0.27 242:0.63 243:0.73 247:0.30 253:0.36 254:0.80 259:0.21 261:0.77 265:0.13 266:0.17 267:0.23 271:0.13 274:0.91 276:0.21 300:0.13 +0 10:0.91 11:0.52 12:0.20 17:0.24 18:0.78 21:0.78 26:0.94 27:0.48 28:0.76 29:0.53 30:0.29 34:0.52 36:0.30 37:0.55 39:0.58 43:0.21 45:0.87 58:0.93 66:0.36 69:0.92 70:0.89 71:0.83 74:0.50 86:0.80 91:0.12 98:0.34 101:0.65 108:0.90 122:0.86 123:0.89 124:0.82 127:0.18 129:0.05 133:0.81 135:0.84 139:0.76 141:0.91 145:0.89 146:0.25 147:0.31 149:0.35 154:0.50 159:0.72 161:0.67 167:0.69 170:0.94 172:0.59 173:0.72 174:0.86 177:0.88 178:0.55 179:0.22 181:0.68 187:0.39 197:0.85 199:0.95 202:0.58 212:0.37 216:0.92 222:0.35 223:0.92 224:0.93 234:0.91 235:0.95 239:0.89 241:0.47 242:0.32 243:0.31 245:0.71 247:0.84 253:0.41 254:0.80 259:0.21 265:0.37 266:0.91 267:0.28 271:0.13 274:0.91 276:0.66 277:0.69 300:0.84 +4 1:0.57 6:0.99 8:0.98 9:0.74 11:0.46 12:0.20 17:0.73 18:0.66 20:0.99 21:0.59 23:0.98 26:0.96 27:0.15 28:0.76 29:0.53 30:0.28 31:0.96 34:0.94 36:0.30 37:0.99 39:0.58 43:0.35 45:0.77 55:0.71 58:0.99 62:0.96 64:0.77 66:0.67 69:0.87 70:0.95 71:0.83 74:0.46 78:0.96 79:0.96 81:0.43 83:0.69 86:0.68 91:0.67 96:0.90 98:0.36 99:0.83 100:1.00 101:0.47 108:0.93 111:1.00 114:0.71 122:0.83 123:0.93 124:0.45 126:0.32 127:0.47 128:0.97 129:0.59 133:0.47 135:0.49 137:0.96 139:0.66 140:0.96 141:0.98 146:0.22 147:0.28 149:0.31 150:0.38 151:0.62 152:0.92 153:0.48 154:0.26 155:0.63 159:0.79 161:0.67 162:0.74 165:0.65 167:0.89 168:0.97 169:1.00 170:0.94 172:0.57 173:0.74 175:0.91 176:0.63 177:0.38 179:0.67 181:0.68 186:0.96 187:0.21 189:0.99 190:0.89 191:0.90 192:0.57 193:0.96 196:0.90 199:0.96 201:0.55 202:0.79 205:0.98 208:0.61 212:0.55 216:0.22 220:0.97 222:0.35 223:0.95 224:0.98 232:0.76 234:0.97 235:0.74 238:0.99 239:0.84 241:0.77 242:0.72 243:0.27 244:0.83 245:0.66 247:0.47 248:0.61 253:0.86 255:0.73 256:0.84 257:0.84 259:0.21 261:0.99 265:0.38 266:0.63 267:0.44 268:0.83 271:0.13 274:0.99 276:0.29 277:0.87 284:0.98 285:0.60 290:0.71 300:0.84 +0 1:0.60 8:0.75 9:0.73 12:0.20 17:0.49 20:0.79 21:0.30 26:0.93 27:0.72 28:0.76 29:0.53 30:0.95 31:0.97 34:0.42 36:0.90 37:0.29 39:0.58 43:0.16 55:0.92 58:0.93 64:0.77 66:0.20 69:0.54 71:0.83 74:0.41 78:0.72 79:0.97 81:0.49 83:0.56 91:0.83 96:0.88 97:0.94 98:0.11 99:0.83 100:0.73 108:0.42 111:0.72 114:0.82 123:0.40 124:0.61 126:0.32 127:0.29 129:0.59 133:0.47 135:0.44 137:0.97 139:0.67 140:0.87 141:0.91 146:0.05 147:0.05 149:0.09 150:0.44 151:0.85 152:0.77 153:0.48 154:0.11 155:0.56 158:0.76 159:0.17 161:0.67 162:0.57 165:0.65 167:0.96 169:0.72 170:0.94 172:0.05 173:0.82 175:0.97 176:0.63 177:0.05 179:0.99 181:0.68 186:0.73 190:0.89 192:0.55 196:0.90 199:0.93 201:0.57 202:0.78 208:0.50 216:0.14 219:0.91 220:0.62 222:0.35 223:0.91 224:0.93 232:0.72 234:0.91 235:0.42 241:0.93 243:0.40 244:0.93 245:0.36 248:0.82 253:0.84 255:0.72 256:0.77 257:0.93 259:0.21 261:0.73 265:0.12 267:0.23 268:0.77 271:0.13 274:0.91 277:0.95 279:0.79 284:0.96 285:0.50 290:0.82 292:0.88 300:0.08 +1 1:0.67 6:0.79 8:0.58 10:0.96 11:0.57 12:0.20 17:0.84 18:0.95 20:0.92 26:0.95 27:0.41 28:0.76 29:0.53 30:0.58 34:0.64 36:0.47 37:0.29 39:0.58 43:0.75 45:0.95 58:0.93 64:0.77 66:0.80 69:0.47 70:0.72 71:0.83 74:0.90 78:0.87 81:0.74 83:0.69 86:0.97 91:0.29 97:0.94 98:0.83 99:0.95 100:0.85 101:1.00 108:0.36 111:0.85 114:0.97 122:0.86 123:0.35 124:0.40 126:0.51 127:0.57 129:0.05 133:0.45 135:0.39 139:0.96 141:0.91 146:0.86 147:0.89 149:0.89 150:0.64 152:0.95 154:0.57 155:0.52 158:0.82 159:0.53 161:0.67 165:0.81 167:0.64 169:0.81 170:0.94 172:0.92 173:0.45 174:0.91 176:0.70 177:0.88 178:0.55 179:0.24 181:0.68 186:0.87 187:0.39 189:0.81 192:0.51 197:0.91 199:0.96 201:0.65 208:0.61 212:0.85 215:0.96 216:0.46 219:0.94 222:0.35 223:0.93 224:0.93 234:0.91 235:0.12 238:0.86 239:0.95 241:0.30 242:0.24 243:0.82 245:0.90 247:0.93 253:0.75 259:0.21 261:0.89 265:0.83 266:0.63 267:0.28 271:0.13 274:0.91 276:0.85 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.84 +0 1:0.66 10:0.93 11:0.46 12:0.20 17:0.08 18:0.72 21:0.30 22:0.93 26:0.96 27:0.60 28:0.76 29:0.53 30:0.68 33:0.97 34:0.82 36:0.89 37:0.45 39:0.58 43:0.64 45:0.77 47:0.93 58:0.93 64:0.77 66:0.71 69:0.78 70:0.48 71:0.83 74:0.52 81:0.69 83:0.56 86:0.83 91:0.23 98:0.69 99:0.83 101:0.47 106:0.81 108:0.61 114:0.86 117:0.86 122:0.85 123:0.59 124:0.42 126:0.54 127:0.43 129:0.05 133:0.36 135:0.39 139:0.78 141:0.91 145:0.70 146:0.64 147:0.41 149:0.44 150:0.59 154:0.64 155:0.51 159:0.37 161:0.67 165:0.65 167:0.60 170:0.94 172:0.57 173:0.86 174:0.90 176:0.73 177:0.05 178:0.55 179:0.68 181:0.68 187:0.68 192:0.50 197:0.93 199:0.97 201:0.65 206:0.81 208:0.64 212:0.69 215:0.72 216:0.59 222:0.35 223:0.95 224:0.93 232:0.99 234:0.91 235:0.60 236:0.97 239:0.92 241:0.15 242:0.31 243:0.21 245:0.60 247:0.74 251:1.00 253:0.62 254:0.84 257:0.93 259:0.21 262:0.93 265:0.69 266:0.54 267:0.28 271:0.13 274:0.91 276:0.47 290:0.86 291:0.97 297:0.36 300:0.43 +0 10:0.94 11:0.52 12:0.20 17:0.11 18:0.86 21:0.47 26:0.96 27:0.56 28:0.76 29:0.53 30:0.62 33:0.97 34:0.64 36:0.27 37:0.60 39:0.58 43:0.59 45:0.81 55:0.45 58:0.93 66:0.49 69:0.82 70:0.63 71:0.83 74:0.43 81:0.28 86:0.84 91:0.07 98:0.60 101:0.87 102:0.95 108:0.67 122:0.95 123:0.65 124:0.56 126:0.24 127:0.27 129:0.05 133:0.36 135:0.44 139:0.80 141:0.91 145:0.65 146:0.70 147:0.75 149:0.58 150:0.30 154:0.48 159:0.41 161:0.67 167:0.59 170:0.94 172:0.67 173:0.84 174:0.96 177:0.88 178:0.55 179:0.33 181:0.68 187:0.39 195:0.72 197:0.95 199:0.98 212:0.70 216:0.76 222:0.35 223:0.95 224:0.93 234:0.91 235:0.52 236:0.92 239:0.94 241:0.10 242:0.63 243:0.60 244:0.61 245:0.86 247:0.82 253:0.37 259:0.21 265:0.61 266:0.54 267:0.44 271:0.13 274:0.91 276:0.68 291:0.97 300:0.55 +1 1:0.64 6:0.79 8:0.58 10:0.98 11:0.82 12:0.20 17:0.84 18:0.92 20:0.92 26:0.95 27:0.41 28:0.76 29:0.53 30:0.75 34:0.37 36:0.24 37:0.29 39:0.58 43:0.73 45:0.95 58:0.93 66:0.97 69:0.47 70:0.46 71:0.83 74:0.90 78:0.84 81:0.60 83:0.56 85:0.85 86:0.97 91:0.33 97:0.89 98:0.95 99:0.83 100:0.81 101:1.00 108:0.36 111:0.82 114:0.95 122:0.65 123:0.34 126:0.32 127:0.63 129:0.05 135:0.47 139:0.95 141:0.91 146:0.89 147:0.91 149:0.94 150:0.56 152:0.95 154:0.63 155:0.49 158:0.77 159:0.48 161:0.67 165:0.65 167:0.65 169:0.81 170:0.94 172:0.92 173:0.48 174:0.92 176:0.63 177:0.88 178:0.55 179:0.26 181:0.68 186:0.84 187:0.39 189:0.81 192:0.48 197:0.92 199:0.96 201:0.60 208:0.50 212:0.90 215:0.96 216:0.46 222:0.35 223:0.93 224:0.93 234:0.91 235:0.05 238:0.84 239:0.96 241:0.32 242:0.15 243:0.97 247:0.96 253:0.74 259:0.21 261:0.89 265:0.95 266:0.38 267:0.28 271:0.13 274:0.91 276:0.86 279:0.76 283:0.82 290:0.95 297:0.36 300:0.55 +4 1:0.57 8:0.95 9:0.75 11:0.46 12:0.20 17:0.81 18:0.66 20:0.80 21:0.59 23:0.99 26:0.96 27:0.18 28:0.76 29:0.53 30:0.28 31:0.99 34:0.47 36:0.23 37:0.98 39:0.58 43:0.35 45:0.77 55:0.71 58:0.99 62:0.96 64:0.77 66:0.67 69:0.87 70:0.95 71:0.83 74:0.46 78:0.85 79:0.98 81:0.43 83:0.69 86:0.68 91:0.93 96:0.92 98:0.36 99:0.83 100:0.88 101:0.47 108:0.93 111:0.85 114:0.71 122:0.83 123:0.93 124:0.45 126:0.32 127:0.48 128:0.99 129:0.59 133:0.47 135:0.37 137:0.99 138:0.98 139:0.70 140:0.98 141:0.98 146:0.22 147:0.28 149:0.31 150:0.38 151:0.60 152:0.77 153:0.82 154:0.26 155:0.64 159:0.79 161:0.67 162:0.76 165:0.65 167:0.97 168:0.99 169:0.86 170:0.94 172:0.57 173:0.74 175:0.91 176:0.63 177:0.38 178:0.48 179:0.67 181:0.68 186:0.85 190:0.89 191:0.83 192:0.58 193:0.96 196:0.93 199:0.96 201:0.55 202:0.86 205:0.99 208:0.61 212:0.55 216:0.27 219:0.90 220:0.82 222:0.35 223:0.95 224:0.98 234:0.97 235:0.74 239:0.84 241:0.98 242:0.72 243:0.27 244:0.83 245:0.66 247:0.47 248:0.58 253:0.89 255:0.74 256:0.89 257:0.84 259:0.21 261:0.80 265:0.38 266:0.63 267:0.28 268:0.90 271:0.13 274:0.99 276:0.29 277:0.87 284:0.99 285:0.60 290:0.71 292:0.88 300:0.84 +1 10:0.96 11:0.54 12:0.06 17:0.09 18:0.91 21:0.78 26:0.96 27:0.28 29:0.56 30:0.76 34:0.63 36:0.70 37:0.77 39:0.51 43:0.56 45:0.96 58:0.93 66:0.77 69:0.62 70:0.45 71:0.66 74:0.75 86:0.94 89:0.97 91:0.20 98:0.76 101:0.87 108:0.60 122:0.67 123:0.57 124:0.41 127:0.35 129:0.59 133:0.47 135:0.32 139:0.90 141:0.91 146:0.19 147:0.25 149:0.57 154:0.79 159:0.42 170:0.96 172:0.86 173:0.63 174:0.90 177:0.96 178:0.55 179:0.26 187:0.68 197:0.92 199:0.96 212:0.81 216:0.56 222:0.76 223:0.97 224:0.93 225:0.96 234:0.91 235:0.31 239:0.94 240:0.95 241:0.10 242:0.19 243:0.19 245:0.86 247:0.93 253:0.53 254:0.74 259:0.21 264:0.90 265:0.76 266:0.27 267:0.28 271:0.13 274:0.91 275:0.93 276:0.79 294:0.98 300:0.55 +1 8:0.83 10:0.93 11:0.54 12:0.06 17:0.04 18:0.70 21:0.22 26:0.95 27:0.28 29:0.56 30:0.54 34:0.62 36:0.85 37:0.77 39:0.51 43:0.10 45:0.85 56:0.97 58:0.93 66:0.60 69:0.48 70:0.51 71:0.66 74:0.51 81:0.28 86:0.81 89:0.99 91:0.57 98:0.74 101:0.69 108:0.37 122:0.99 123:0.36 124:0.50 126:0.23 127:0.41 129:0.59 133:0.47 135:0.58 139:0.77 140:0.45 141:0.91 143:1.00 146:0.75 147:0.79 149:0.16 150:0.31 151:0.72 153:0.78 154:0.84 159:0.43 162:0.70 170:0.96 172:0.57 173:0.50 174:0.83 177:0.96 179:0.60 187:0.68 190:0.98 191:0.75 197:0.87 199:0.94 202:0.69 212:0.30 216:0.56 220:0.82 222:0.76 223:0.94 224:0.93 225:1.00 234:0.91 235:0.22 239:0.91 240:1.00 241:0.64 242:0.59 243:0.67 245:0.73 247:0.60 248:0.70 253:0.41 254:0.74 256:0.68 259:0.21 264:0.90 265:0.74 266:0.75 267:0.76 268:0.72 271:0.13 274:0.91 275:0.97 276:0.53 285:0.45 294:0.98 300:0.43 +1 8:0.98 10:0.93 11:0.54 12:0.06 17:0.20 18:0.71 21:0.78 26:0.96 27:0.28 29:0.56 30:0.74 34:0.76 36:0.86 37:0.77 39:0.51 43:0.09 45:0.89 58:0.93 66:0.07 69:0.63 70:0.59 71:0.66 74:0.52 86:0.82 89:0.99 91:0.38 98:0.31 101:0.69 108:0.84 122:0.67 123:0.58 124:0.78 127:0.46 129:0.59 133:0.73 135:0.30 139:0.78 141:0.91 146:0.19 147:0.25 149:0.10 154:0.79 159:0.57 170:0.96 172:0.48 173:0.57 174:0.88 177:0.96 178:0.55 179:0.58 187:0.39 197:0.88 199:0.95 202:0.36 212:0.61 216:0.56 222:0.76 223:0.95 224:0.93 225:0.97 234:0.91 235:0.31 239:0.92 240:0.95 241:0.03 242:0.19 243:0.33 245:0.67 247:0.76 253:0.42 254:0.74 259:0.21 264:0.90 265:0.24 266:0.75 267:0.36 271:0.13 274:0.91 275:0.96 276:0.54 277:0.98 294:0.98 300:0.70 +1 2:0.99 10:0.94 11:0.54 12:0.06 18:0.72 21:0.78 26:0.95 27:0.52 29:0.56 30:0.73 32:0.74 34:0.89 36:0.49 37:0.57 39:0.51 43:0.09 44:0.93 45:0.85 58:0.93 66:0.86 69:0.19 70:0.47 71:0.66 74:0.63 77:0.70 82:0.99 86:0.84 88:0.98 89:0.99 91:0.49 98:0.87 101:0.69 102:0.98 108:0.15 122:0.67 123:0.15 127:0.40 129:0.05 135:0.39 139:0.85 140:0.80 141:0.91 143:0.99 145:0.96 146:0.62 147:0.68 149:0.07 151:0.65 153:0.48 154:0.85 159:0.23 162:0.54 170:0.96 172:0.59 173:0.45 174:0.83 177:0.96 178:0.42 179:0.69 187:0.87 190:0.98 195:0.56 197:0.87 199:0.95 202:0.65 212:0.72 216:0.57 222:0.76 223:0.96 224:0.93 225:1.00 234:0.91 235:0.31 239:0.95 240:1.00 241:0.64 242:0.19 243:0.86 247:0.60 248:0.65 253:0.47 254:0.80 256:0.58 259:0.21 264:0.90 265:0.87 266:0.27 267:0.52 268:0.59 271:0.13 274:0.91 275:0.96 276:0.43 282:0.83 285:0.45 294:0.99 295:0.98 300:0.43 +1 10:0.97 11:0.54 12:0.06 17:0.02 18:0.90 21:0.78 26:0.96 27:0.29 29:0.56 30:0.73 32:0.71 34:0.70 36:0.63 37:0.78 39:0.51 43:0.59 44:0.92 45:0.94 58:0.93 66:0.94 69:0.27 70:0.51 71:0.66 74:0.76 77:0.98 82:0.99 86:0.93 88:0.98 89:0.99 91:0.40 98:0.93 101:0.87 108:0.21 122:0.67 123:0.20 127:0.58 129:0.05 135:0.32 139:0.91 141:0.91 145:0.97 146:0.81 147:0.84 149:0.67 154:0.85 159:0.37 170:0.96 172:0.84 173:0.38 174:0.89 177:0.96 178:0.55 179:0.40 187:0.87 195:0.60 197:0.92 199:0.95 202:0.46 212:0.85 216:0.82 222:0.76 223:0.97 224:0.93 225:0.97 234:0.91 235:0.52 239:0.95 240:0.95 241:0.39 242:0.17 243:0.94 247:0.96 253:0.53 254:0.74 259:0.21 264:0.90 265:0.93 266:0.27 267:0.28 271:0.13 274:0.91 275:0.96 276:0.74 282:0.79 294:0.99 300:0.55 +2 1:0.66 7:0.70 10:0.96 11:0.54 12:0.06 17:0.69 18:0.69 21:0.30 22:0.93 26:0.97 29:0.56 30:0.41 32:0.80 33:0.97 34:0.95 36:0.74 37:0.98 39:0.51 43:0.08 44:0.93 45:0.85 47:0.93 55:0.52 56:0.97 58:0.93 64:0.77 66:0.27 69:0.81 70:0.60 71:0.66 74:0.64 77:0.70 80:1.00 81:0.79 82:0.99 83:0.91 86:0.80 88:0.99 89:0.98 91:0.05 98:0.40 101:0.69 102:0.98 106:0.81 108:0.65 114:0.86 117:0.86 122:0.98 123:0.75 124:0.58 126:0.54 127:0.22 129:0.05 133:0.38 135:0.32 139:0.86 141:0.91 145:0.65 146:0.42 147:0.49 149:0.12 150:0.60 154:0.66 155:0.57 159:0.36 165:0.93 170:0.96 172:0.63 173:0.82 174:0.96 176:0.73 177:0.96 178:0.55 179:0.29 187:0.68 192:0.86 193:0.98 195:0.75 197:0.96 199:0.98 201:0.66 204:0.84 206:0.81 208:0.64 212:0.80 215:0.72 216:0.95 222:0.76 223:0.98 224:0.93 225:0.99 230:0.73 232:0.99 233:0.76 234:0.91 235:0.68 236:0.97 239:0.97 240:0.95 241:0.53 242:0.39 243:0.59 245:0.84 247:0.74 253:0.64 254:0.74 259:0.21 262:0.93 264:0.90 265:0.35 266:0.63 267:0.96 271:0.13 274:0.91 275:0.96 276:0.61 277:0.95 281:0.91 282:0.88 290:0.86 291:0.97 294:0.99 297:0.36 299:0.88 300:0.55 +1 10:0.96 11:0.54 12:0.06 17:0.03 18:0.91 21:0.78 26:0.96 27:0.28 29:0.56 30:0.76 34:0.89 36:0.49 37:0.77 39:0.51 43:0.57 45:0.96 58:0.93 66:0.95 69:0.59 70:0.48 71:0.66 74:0.75 86:0.94 89:0.97 91:0.29 98:0.87 101:0.87 108:0.45 122:0.67 123:0.43 127:0.39 129:0.05 135:0.51 139:0.91 141:0.91 146:0.85 147:0.87 149:0.58 154:0.81 159:0.42 170:0.96 172:0.88 173:0.61 174:0.90 177:0.96 178:0.55 179:0.28 187:0.39 197:0.92 199:0.96 212:0.81 216:0.56 222:0.76 223:0.97 224:0.93 225:0.96 234:0.91 235:0.31 239:0.94 240:0.95 241:0.10 242:0.19 243:0.96 247:0.93 253:0.53 254:0.74 259:0.21 264:0.90 265:0.87 266:0.27 267:0.23 271:0.13 274:0.91 275:0.93 276:0.79 294:0.98 300:0.70 +1 2:0.99 8:0.78 10:0.97 11:0.54 12:0.06 17:0.70 18:0.80 26:0.97 27:0.61 29:0.56 30:0.66 34:0.33 36:0.76 37:0.82 39:0.51 43:0.11 45:0.98 56:0.97 58:0.93 66:0.07 69:0.76 70:0.71 71:0.66 74:0.68 81:0.31 86:0.92 89:1.00 91:0.15 98:0.21 101:0.69 108:0.79 122:0.99 123:0.77 124:0.96 126:0.23 127:0.93 129:0.99 133:0.96 135:0.28 139:0.88 141:0.91 146:0.51 147:0.47 149:0.22 150:0.35 154:0.65 159:0.91 170:0.96 172:0.54 173:0.45 174:0.95 177:0.05 178:0.55 179:0.20 187:0.39 197:0.92 199:0.97 202:0.36 212:0.52 216:0.46 222:0.76 223:0.97 224:0.93 225:0.99 234:0.91 235:0.02 239:0.96 240:0.95 241:0.07 242:0.22 243:0.09 245:0.89 247:0.76 253:0.50 254:0.84 257:0.97 259:0.21 264:0.90 265:0.23 266:0.94 267:0.79 271:0.13 274:0.91 275:0.99 276:0.91 294:1.00 295:0.98 300:0.84 +2 10:0.95 11:0.57 12:0.06 17:0.11 18:0.89 21:0.78 26:0.96 27:0.28 29:0.56 30:0.85 34:0.80 36:0.86 37:0.77 39:0.51 43:0.69 45:0.93 58:0.93 66:0.92 69:0.58 70:0.45 71:0.66 74:0.70 75:0.98 83:0.72 86:0.91 89:0.96 91:0.27 97:0.97 98:0.80 101:0.96 108:0.45 122:0.67 123:0.43 127:0.29 129:0.05 135:0.51 139:0.89 141:0.91 146:0.80 147:0.83 149:0.77 154:0.81 158:0.81 159:0.36 170:0.96 172:0.79 173:0.61 174:0.89 177:0.96 178:0.55 179:0.35 187:0.68 197:0.92 199:0.96 208:0.64 212:0.61 216:0.56 219:0.94 222:0.76 223:0.97 224:0.93 225:0.96 226:0.96 234:0.91 235:0.22 239:0.95 240:0.95 241:0.12 242:0.14 243:0.92 247:0.92 253:0.51 254:0.74 259:0.21 264:0.90 265:0.80 266:0.27 267:0.28 271:0.13 274:0.91 275:0.91 276:0.67 279:0.79 283:0.67 292:0.88 294:0.96 300:0.55 +2 10:0.95 11:0.54 12:0.06 17:0.04 18:0.72 21:0.78 26:0.96 27:0.29 29:0.56 30:0.73 32:0.80 34:0.50 36:0.39 37:0.78 39:0.51 43:0.11 44:0.93 45:0.85 58:0.93 66:0.44 69:0.17 70:0.68 71:0.66 74:0.63 77:0.70 82:0.99 86:0.83 88:0.99 89:0.99 91:0.23 98:0.57 101:0.69 102:0.98 108:0.59 122:0.67 123:0.56 124:0.58 127:0.70 129:0.59 133:0.47 135:0.24 139:0.85 141:0.91 145:0.87 146:0.29 147:0.35 149:0.09 154:0.82 159:0.27 170:0.96 172:0.41 173:0.43 174:0.86 177:0.96 178:0.55 179:0.76 187:0.87 195:0.55 197:0.89 199:0.95 212:0.72 216:0.82 222:0.76 223:0.96 224:0.93 225:0.97 234:0.91 235:0.05 239:0.96 240:0.95 241:0.24 242:0.19 243:0.46 245:0.68 247:0.67 253:0.48 254:0.74 259:0.21 264:0.90 265:0.58 266:0.47 267:0.65 271:0.13 274:0.91 275:0.96 276:0.40 282:0.88 294:0.99 299:0.88 300:0.70 +1 10:1.00 11:0.54 12:0.06 17:0.34 18:0.95 21:0.78 26:0.97 27:0.28 29:0.56 30:0.84 34:0.55 36:0.79 37:0.77 39:0.51 43:0.23 45:1.00 58:0.93 66:0.11 69:0.62 70:0.52 71:0.66 74:0.92 86:0.99 89:1.00 91:0.05 98:0.43 101:0.87 108:0.91 122:0.97 123:0.90 124:0.95 127:0.89 129:0.97 133:0.95 135:0.58 139:0.98 141:0.91 146:0.19 147:0.25 149:0.73 154:0.79 159:0.96 170:0.96 172:0.95 173:0.17 174:1.00 177:0.96 178:0.55 179:0.09 187:0.39 197:0.99 199:1.00 202:0.36 212:0.68 216:0.56 222:0.76 223:0.98 224:0.93 225:0.99 234:0.91 235:0.42 239:1.00 240:0.95 241:0.01 242:0.16 243:0.06 245:0.99 247:0.99 253:0.61 254:0.74 259:0.21 264:0.90 265:0.45 266:0.91 267:0.76 271:0.13 274:0.91 275:1.00 276:0.99 294:1.00 300:0.94 +1 10:0.98 11:0.81 12:0.06 17:0.01 18:0.88 21:0.78 26:0.97 27:0.52 29:0.56 30:0.73 34:0.71 36:0.37 37:0.57 39:0.51 43:0.84 45:0.88 58:0.93 60:0.87 66:0.94 69:0.21 70:0.47 71:0.66 74:0.75 75:0.99 83:0.91 85:0.80 86:0.92 89:0.99 91:0.25 98:0.93 101:0.97 108:0.17 122:0.91 123:0.16 127:0.58 129:0.05 135:0.42 139:0.91 141:0.91 145:0.50 146:0.81 147:0.84 149:0.86 154:0.85 158:0.86 159:0.37 170:0.96 172:0.84 173:0.34 174:0.92 177:0.96 178:0.55 179:0.40 187:0.87 197:0.95 199:0.96 202:0.36 208:0.64 212:0.85 216:0.57 219:0.95 222:0.76 223:0.97 224:0.93 225:0.98 226:0.96 234:0.91 235:0.42 239:0.98 240:0.95 241:0.47 242:0.17 243:0.94 247:0.94 253:0.53 259:0.21 264:0.90 265:0.93 266:0.27 267:0.44 271:0.13 274:0.91 275:0.97 276:0.74 279:0.80 283:0.67 292:0.88 294:1.00 295:0.98 300:0.43 +3 1:0.66 10:0.97 11:0.81 12:0.06 17:0.64 18:0.77 21:0.30 22:0.93 26:0.97 29:0.56 30:0.85 33:0.97 34:0.73 36:0.61 37:0.98 39:0.51 43:0.61 45:0.96 47:0.93 56:0.97 58:0.93 64:0.77 66:0.63 69:0.90 70:0.40 71:0.66 74:0.62 81:0.79 83:0.91 85:0.72 86:0.89 89:0.99 91:0.14 98:0.57 101:0.96 106:0.81 108:0.86 114:0.86 117:0.86 122:0.67 123:0.85 124:0.64 126:0.54 127:0.38 129:0.59 133:0.72 135:0.20 139:0.85 141:0.91 145:0.83 146:0.60 147:0.29 149:0.54 150:0.60 154:0.50 155:0.51 159:0.72 165:0.93 170:0.96 172:0.87 173:0.84 174:0.96 176:0.73 177:0.05 178:0.55 179:0.23 187:0.39 192:0.55 197:0.95 199:0.98 201:0.66 202:0.36 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.76 223:0.98 224:0.93 225:0.99 234:0.91 235:0.68 236:0.97 239:0.96 240:0.95 241:0.39 242:0.19 243:0.09 245:0.86 247:0.82 253:0.64 257:0.97 259:0.21 262:0.93 264:0.90 265:0.59 266:0.63 267:0.97 271:0.13 274:0.91 275:0.98 276:0.83 290:0.86 291:0.97 294:0.99 297:0.36 300:0.55 +3 10:0.96 11:0.54 12:0.06 17:0.16 18:0.84 21:0.78 26:0.97 27:0.72 29:0.56 30:0.53 32:0.80 34:0.49 36:0.95 39:0.51 43:0.24 44:0.93 45:0.92 58:0.93 66:0.45 69:0.58 70:0.89 71:0.66 74:0.72 77:0.70 82:0.99 86:0.90 88:0.99 89:0.99 91:0.20 98:0.42 101:0.87 102:0.98 108:0.72 122:0.91 123:0.71 124:0.80 127:0.78 129:0.59 133:0.80 135:0.76 139:0.90 141:0.91 145:0.94 146:0.39 147:0.45 149:0.32 154:0.79 159:0.77 170:0.96 172:0.73 173:0.41 174:0.93 177:0.96 178:0.55 179:0.41 187:0.87 195:0.88 197:0.93 199:0.96 212:0.71 216:0.49 222:0.76 223:0.97 224:0.93 225:0.98 234:0.91 235:0.22 239:0.97 240:0.95 241:0.24 242:0.16 243:0.37 245:0.80 247:0.96 253:0.52 254:0.74 259:0.21 264:0.90 265:0.44 266:0.80 267:0.52 271:0.13 274:0.91 275:0.97 276:0.74 282:0.88 294:0.99 299:0.88 300:0.84 +1 2:0.99 8:0.83 10:0.98 11:0.81 12:0.06 17:0.64 18:0.85 21:0.13 26:0.97 27:0.59 29:0.56 30:0.91 34:0.71 36:0.66 37:0.42 39:0.51 43:0.86 45:0.83 56:0.97 58:0.93 66:0.95 69:0.53 70:0.21 71:0.66 74:0.75 80:0.99 81:0.29 82:0.98 85:0.90 86:0.94 88:0.99 89:1.00 91:0.35 98:0.83 101:0.96 108:0.41 122:0.67 123:0.39 126:0.23 127:0.33 129:0.05 135:0.34 139:0.92 140:0.45 141:0.91 143:0.99 145:0.45 146:0.65 147:0.70 149:0.96 150:0.32 151:0.65 153:0.48 154:0.83 159:0.25 162:0.86 170:0.96 172:0.86 173:0.70 174:0.92 177:0.96 179:0.29 187:0.39 190:0.98 195:0.61 197:0.95 199:0.96 202:0.36 212:0.94 216:0.33 222:0.76 223:0.97 224:0.93 225:1.00 234:0.91 235:0.12 239:0.98 240:1.00 241:0.02 242:0.19 243:0.95 247:0.82 248:0.62 253:0.53 256:0.45 259:0.21 264:0.90 265:0.83 266:0.17 267:0.28 268:0.46 271:0.13 274:0.91 275:0.98 276:0.76 285:0.45 294:1.00 295:0.99 300:0.25 +2 1:0.66 7:0.70 10:0.96 11:0.54 12:0.06 17:0.67 18:0.69 21:0.30 22:0.93 26:0.97 29:0.56 30:0.41 32:0.80 33:0.97 34:0.94 36:0.78 37:0.98 39:0.51 43:0.08 44:0.93 45:0.85 47:0.93 55:0.52 56:0.97 58:0.93 64:0.77 66:0.47 69:0.81 70:0.60 71:0.66 74:0.64 77:0.70 80:1.00 81:0.79 82:0.99 83:0.91 86:0.80 88:0.99 89:0.98 91:0.03 98:0.33 101:0.69 102:0.98 106:0.81 108:0.65 114:0.86 117:0.86 122:0.98 123:0.63 124:0.65 126:0.54 127:0.23 129:0.05 133:0.59 135:0.28 139:0.86 141:0.91 145:0.65 146:0.42 147:0.49 149:0.12 150:0.60 154:0.66 155:0.57 159:0.37 165:0.93 170:0.96 172:0.63 173:0.82 174:0.96 176:0.73 177:0.96 178:0.55 179:0.30 187:0.68 192:0.86 193:0.98 195:0.75 197:0.97 199:0.98 201:0.66 204:0.84 206:0.81 208:0.64 212:0.80 215:0.72 216:0.95 222:0.76 223:0.98 224:0.93 225:0.99 230:0.73 232:0.99 233:0.76 234:0.91 235:0.68 236:0.97 239:0.97 240:0.95 241:0.58 242:0.55 243:0.59 245:0.77 247:0.74 253:0.64 254:0.74 259:0.21 262:0.93 264:0.90 265:0.35 266:0.63 267:0.84 271:0.13 274:0.91 275:0.95 276:0.62 281:0.91 282:0.88 290:0.86 291:0.97 294:0.99 297:0.36 299:0.88 300:0.55 +1 10:0.94 11:0.54 12:0.06 17:0.01 18:0.72 21:0.40 26:0.95 27:0.52 29:0.56 30:0.68 32:0.71 34:0.58 36:0.55 37:0.57 39:0.51 43:0.10 44:0.92 45:0.85 56:0.97 58:0.93 66:0.86 69:0.21 70:0.52 71:0.66 74:0.60 77:0.70 80:0.99 81:0.27 82:0.99 86:0.84 88:0.99 89:0.99 91:0.20 98:0.94 101:0.69 108:0.17 122:0.67 123:0.16 126:0.23 127:0.62 129:0.05 135:0.32 139:0.83 140:0.45 141:0.91 143:0.99 145:0.94 146:0.59 147:0.65 149:0.09 150:0.30 151:0.65 153:0.48 154:0.85 159:0.22 162:0.86 170:0.96 172:0.61 173:0.53 174:0.83 177:0.96 179:0.73 187:0.87 190:0.98 195:0.54 197:0.88 199:0.94 202:0.36 212:0.75 216:0.57 222:0.76 223:0.96 224:0.93 225:0.99 234:0.91 235:0.42 239:0.92 240:1.00 241:0.59 242:0.18 243:0.87 247:0.60 248:0.62 253:0.46 254:0.80 256:0.45 259:0.21 264:0.90 265:0.94 266:0.27 267:0.52 268:0.46 271:0.13 274:0.91 275:0.96 276:0.48 282:0.79 285:0.45 294:0.99 300:0.43 +1 10:0.95 11:0.81 12:0.06 17:0.05 18:0.75 21:0.78 26:0.95 27:0.29 29:0.56 30:0.88 34:0.76 36:0.39 37:0.78 39:0.51 43:0.75 45:0.77 58:0.93 66:0.20 69:0.21 70:0.28 71:0.66 74:0.58 85:0.80 86:0.86 89:0.99 91:0.42 98:0.56 101:0.96 108:0.58 122:0.67 123:0.55 124:0.75 127:0.74 129:0.81 133:0.67 135:0.23 139:0.82 140:0.45 141:0.91 143:0.99 145:0.50 146:0.29 147:0.35 149:0.80 151:0.70 153:0.72 154:0.82 159:0.40 162:0.57 170:0.96 172:0.37 173:0.34 174:0.86 177:0.96 179:0.65 187:0.87 190:0.98 197:0.89 199:0.95 202:0.66 212:0.75 216:0.82 220:0.74 222:0.76 223:0.96 224:0.93 225:1.00 234:0.91 235:0.22 239:0.93 240:1.00 241:0.64 242:0.19 243:0.37 245:0.73 247:0.67 248:0.68 253:0.45 256:0.58 259:0.21 264:0.90 265:0.58 266:0.47 267:0.44 268:0.65 271:0.13 274:0.91 275:0.97 276:0.55 285:0.45 294:0.99 300:0.33 +1 1:0.65 9:0.70 10:0.96 11:0.82 12:0.06 17:0.67 18:0.77 21:0.54 26:0.97 27:0.29 29:0.56 30:0.66 34:0.73 36:0.62 37:0.78 39:0.51 43:0.64 45:0.85 56:0.97 58:0.93 64:0.77 66:0.18 69:0.45 70:0.85 71:0.66 74:0.62 81:0.76 83:0.91 85:0.80 86:0.88 89:1.00 91:0.09 96:0.71 98:0.31 101:0.96 108:0.35 114:0.77 120:0.58 122:0.99 123:0.86 124:0.87 126:0.54 127:0.50 129:0.81 133:0.85 135:0.44 139:0.84 140:0.45 141:0.98 146:0.35 147:0.42 149:0.68 150:0.56 151:0.57 153:0.48 154:0.83 155:0.53 159:0.74 162:0.86 164:0.55 165:0.93 170:0.96 172:0.54 173:0.27 174:0.88 176:0.73 177:0.96 179:0.39 187:0.95 190:0.95 192:0.57 197:0.90 199:0.95 201:0.66 202:0.36 208:0.64 212:0.76 215:0.58 216:0.82 220:0.87 222:0.76 223:0.98 224:0.93 225:0.99 234:0.91 235:0.88 236:0.97 239:0.94 240:0.95 241:0.21 242:0.43 243:0.40 245:0.76 247:0.60 248:0.56 253:0.62 255:0.69 256:0.45 259:0.21 260:0.58 264:0.90 265:0.21 266:0.75 267:0.36 268:0.46 271:0.13 274:0.91 275:0.98 276:0.70 285:0.49 290:0.77 294:1.00 297:0.36 300:0.94 +1 8:0.84 10:0.97 11:0.54 12:0.06 17:0.66 18:0.80 26:0.97 27:0.61 29:0.56 30:0.66 34:0.40 36:0.83 37:0.82 39:0.51 43:0.11 45:0.98 56:1.00 58:0.93 66:0.07 69:0.84 70:0.71 71:0.66 74:0.68 81:0.38 86:0.92 89:1.00 91:0.23 98:0.21 101:0.69 108:0.82 122:0.99 123:0.81 124:0.97 126:0.23 127:0.94 129:0.99 133:0.96 135:0.28 139:0.88 140:0.45 141:0.91 143:0.99 146:0.51 147:0.05 149:0.24 150:0.43 151:0.73 153:0.83 154:0.65 159:0.91 162:0.64 170:0.96 172:0.54 173:0.56 174:0.95 177:0.05 179:0.20 187:0.39 190:0.98 197:0.92 199:0.97 202:0.63 212:0.52 216:0.46 222:0.76 223:0.97 224:0.93 225:1.00 234:0.91 235:0.12 239:0.96 240:1.00 241:0.41 242:0.22 243:0.06 245:0.89 247:0.76 248:0.69 253:0.50 254:0.84 256:0.58 257:0.97 259:0.21 264:0.90 265:0.23 266:0.94 267:0.88 268:0.64 271:0.13 274:0.91 275:0.99 276:0.91 285:0.45 294:1.00 300:0.84 +1 2:0.99 10:0.97 11:0.81 12:0.06 18:0.75 21:0.30 26:0.96 27:0.29 29:0.56 30:0.73 34:0.82 36:0.70 37:0.78 39:0.51 43:0.75 45:0.77 56:0.97 58:0.93 66:0.91 69:0.23 70:0.45 71:0.66 74:0.60 81:0.28 82:0.98 85:0.80 86:0.87 88:0.99 89:1.00 91:0.17 98:0.93 101:0.96 108:0.18 122:0.99 123:0.18 126:0.23 127:0.59 129:0.05 135:0.42 139:0.83 141:0.91 145:0.97 146:0.78 147:0.81 149:0.81 150:0.30 154:0.85 159:0.34 170:0.96 172:0.76 173:0.39 174:0.88 177:0.96 178:0.55 179:0.53 187:0.87 195:0.57 197:0.91 199:0.95 212:0.88 216:0.82 222:0.76 223:0.96 224:0.93 225:0.99 234:0.91 235:0.31 239:0.96 240:0.95 241:0.27 242:0.21 243:0.92 247:0.74 253:0.46 259:0.21 264:0.90 265:0.93 266:0.38 267:0.52 271:0.13 274:0.91 275:0.98 276:0.63 294:0.99 295:0.98 300:0.43 +1 1:0.64 10:0.86 11:0.52 12:0.20 17:0.87 18:0.90 21:0.30 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.51 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.48 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.68 83:0.69 86:0.90 91:0.27 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.84 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.69 146:0.82 147:0.85 149:0.73 150:0.56 154:0.55 155:0.49 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.49 195:0.80 197:0.82 201:0.62 206:0.81 208:0.61 212:0.50 215:0.72 216:0.17 219:0.94 222:0.35 232:0.87 235:0.52 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.65 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.84 292:0.88 297:0.36 300:0.84 +1 1:0.58 10:0.91 11:0.46 12:0.20 17:0.30 18:0.98 21:0.47 27:0.52 29:0.91 30:0.70 34:0.62 36:0.51 37:0.56 39:0.69 43:0.61 45:0.99 64:0.77 66:0.78 69:0.64 70:0.62 71:0.57 74:0.88 81:0.49 83:0.56 86:0.98 91:0.12 98:0.81 99:0.83 101:0.47 108:0.81 114:0.76 122:0.91 123:0.80 124:0.44 126:0.32 127:0.72 129:0.59 133:0.77 135:0.84 139:0.98 146:0.62 147:0.68 149:0.94 150:0.44 154:0.19 155:0.51 159:0.89 165:0.65 170:0.87 172:0.98 173:0.33 174:0.90 175:0.75 176:0.63 177:0.70 178:0.55 179:0.12 187:0.21 192:0.48 197:0.93 201:0.55 204:0.81 208:0.50 212:0.58 216:0.48 219:0.90 222:0.35 232:0.98 235:0.60 239:0.90 241:0.10 242:0.52 243:0.11 244:0.61 245:0.96 247:0.91 253:0.67 257:0.65 259:0.21 265:0.81 266:0.84 267:0.28 271:0.23 276:0.97 277:0.69 281:0.91 290:0.76 292:0.95 300:0.94 +0 10:0.91 11:0.52 12:0.20 17:0.28 18:0.59 21:0.54 27:0.52 29:0.91 30:0.33 34:0.44 36:0.40 37:0.56 39:0.69 43:0.25 45:0.90 55:0.52 66:0.75 69:0.90 70:0.74 71:0.57 74:0.84 76:0.85 81:0.27 86:0.63 91:0.23 96:0.71 98:0.83 101:0.65 108:0.80 114:0.72 122:0.83 123:0.79 124:0.47 126:0.24 127:0.71 129:0.05 133:0.77 135:0.91 139:0.61 140:0.45 146:0.99 147:0.68 149:0.67 150:0.29 151:0.52 153:0.48 154:0.13 159:0.88 162:0.86 170:0.87 172:0.96 173:0.75 174:0.90 175:0.75 176:0.62 177:0.38 179:0.16 187:0.21 190:0.95 197:0.93 202:0.36 212:0.58 216:0.48 220:0.87 222:0.35 232:0.98 235:0.60 239:0.90 241:0.35 242:0.63 243:0.13 244:0.61 245:0.93 247:0.91 248:0.51 253:0.69 254:0.99 256:0.45 257:0.84 259:0.21 265:0.83 266:0.87 267:0.52 268:0.46 271:0.23 276:0.94 277:0.69 285:0.46 290:0.72 300:0.70 +0 1:0.63 10:0.85 11:0.52 12:0.20 17:0.89 18:0.90 21:0.40 22:0.93 27:0.70 29:0.91 30:0.41 32:0.72 34:1.00 36:0.42 37:0.26 39:0.69 43:0.66 44:0.92 45:0.88 47:0.93 64:0.77 66:0.68 69:0.48 70:0.94 71:0.57 74:0.75 76:0.85 77:0.89 81:0.66 83:0.69 86:0.90 91:0.29 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.82 117:0.86 122:0.86 123:0.36 124:0.47 126:0.51 127:0.65 129:0.05 133:0.62 135:0.58 139:0.91 145:0.72 146:0.85 147:0.88 149:0.74 150:0.54 154:0.55 155:0.49 158:0.81 159:0.70 165:0.81 170:0.87 172:0.73 173:0.35 174:0.79 176:0.70 177:0.88 178:0.55 179:0.52 187:0.21 192:0.48 195:0.85 197:0.82 201:0.61 206:0.81 208:0.61 212:0.52 215:0.67 216:0.17 219:0.94 222:0.35 232:0.87 235:0.60 239:0.85 241:0.24 242:0.19 243:0.72 244:0.61 245:0.71 247:0.86 253:0.64 259:0.21 262:0.93 265:0.67 266:0.87 267:0.36 271:0.23 276:0.65 279:0.78 282:0.80 283:0.67 290:0.82 292:0.88 297:0.36 300:0.94 +0 1:0.64 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.30 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.50 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.48 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.68 83:0.69 86:0.90 91:0.27 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.84 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.69 146:0.82 147:0.85 149:0.73 150:0.56 154:0.55 155:0.49 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.49 195:0.80 197:0.82 201:0.62 206:0.81 208:0.61 212:0.50 215:0.72 216:0.17 219:0.94 222:0.35 232:0.87 235:0.52 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.65 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.84 292:0.88 297:0.36 300:0.84 +0 1:0.64 8:0.71 10:0.90 11:0.46 12:0.20 17:0.85 18:0.72 21:0.30 22:0.93 27:0.43 29:0.91 30:0.19 32:0.72 34:0.99 36:0.40 37:0.55 39:0.69 43:0.76 44:0.92 45:0.73 47:0.93 64:0.77 66:0.88 69:0.19 70:0.59 71:0.57 74:0.68 77:0.70 81:0.62 83:0.56 86:0.83 91:0.42 98:0.91 99:0.83 101:0.47 104:0.96 106:0.81 108:0.15 114:0.84 117:0.86 123:0.15 126:0.51 127:0.51 129:0.05 135:0.88 139:0.82 145:0.40 146:0.91 147:0.92 149:0.57 150:0.56 154:0.69 155:0.49 159:0.51 165:0.65 170:0.87 172:0.67 173:0.23 174:0.79 176:0.70 177:0.88 178:0.55 179:0.64 187:0.21 192:0.49 195:0.71 197:0.83 201:0.62 206:0.81 208:0.61 212:0.57 215:0.72 216:0.34 222:0.35 232:0.88 235:0.52 239:0.88 241:0.49 242:0.44 243:0.89 247:0.79 253:0.64 254:0.96 259:0.21 262:0.93 265:0.91 266:0.47 267:0.44 271:0.23 276:0.56 282:0.80 290:0.84 297:0.36 300:0.43 +0 1:0.66 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.13 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:0.99 36:0.52 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.47 70:0.82 71:0.57 74:0.74 76:0.85 77:0.89 81:0.72 83:0.69 86:0.90 91:0.27 97:0.94 98:0.68 99:0.95 101:0.87 104:0.96 106:0.81 108:0.36 114:0.92 117:0.86 122:0.65 123:0.34 124:0.51 126:0.51 127:0.60 129:0.05 133:0.60 135:0.69 139:0.91 145:0.61 146:0.82 147:0.85 149:0.73 150:0.61 154:0.55 155:0.50 158:0.81 159:0.62 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.52 187:0.21 192:0.50 195:0.79 197:0.82 201:0.63 206:0.81 208:0.61 212:0.50 215:0.84 216:0.17 219:0.94 222:0.35 232:0.87 235:0.31 239:0.85 241:0.27 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.68 259:0.21 262:0.93 265:0.68 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.92 292:0.88 297:0.36 300:0.70 +2 1:0.61 7:0.75 8:0.59 10:0.84 11:0.46 12:0.20 17:0.81 18:0.80 21:0.54 27:0.53 29:0.91 30:0.45 32:0.71 34:0.89 36:0.61 37:0.25 39:0.69 43:0.12 44:0.92 45:0.77 48:0.91 55:0.42 64:0.77 66:0.87 69:0.57 70:0.71 71:0.57 74:0.75 76:0.85 77:0.70 81:0.56 83:0.56 86:0.87 91:0.74 98:0.81 99:0.83 101:0.47 108:0.44 114:0.76 122:0.65 123:0.42 126:0.51 127:0.29 129:0.05 135:0.42 139:0.88 145:0.69 146:0.62 147:0.67 149:0.12 150:0.49 154:0.64 155:0.53 159:0.23 165:0.65 170:0.87 172:0.63 173:0.74 174:0.79 176:0.70 177:0.88 178:0.55 179:0.56 187:0.21 192:0.56 195:0.59 197:0.83 201:0.60 204:0.82 208:0.61 212:0.77 215:0.58 216:0.34 222:0.35 230:0.77 233:0.65 235:0.74 239:0.83 241:0.67 242:0.28 243:0.88 244:0.61 247:0.74 253:0.62 254:0.74 259:0.21 265:0.81 266:0.38 267:0.65 271:0.23 276:0.50 281:0.86 282:0.80 290:0.76 297:0.36 300:0.55 +0 10:0.82 11:0.52 12:0.20 17:0.75 18:0.58 21:0.78 27:0.58 29:0.91 30:0.76 34:0.17 36:0.35 37:0.57 39:0.69 43:0.23 45:0.73 66:0.54 69:0.96 70:0.22 71:0.57 74:0.31 86:0.62 91:0.27 98:0.14 101:0.65 108:0.93 122:0.65 123:0.93 124:0.45 127:0.12 129:0.05 133:0.36 135:0.34 139:0.61 146:0.28 147:0.05 149:0.20 154:0.16 159:0.27 170:0.87 172:0.21 173:0.97 174:0.83 177:0.05 178:0.55 179:0.42 187:0.39 197:0.81 212:0.42 216:0.59 222:0.35 235:0.42 239:0.82 241:0.05 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.28 257:0.93 259:0.21 265:0.15 266:0.23 267:0.52 271:0.23 276:0.16 300:0.18 +1 1:0.65 7:0.75 10:0.92 11:0.52 12:0.20 17:0.16 18:0.76 21:0.22 27:0.52 29:0.91 30:0.66 34:0.91 36:0.22 37:0.56 39:0.69 43:0.62 45:0.89 64:0.77 66:0.33 69:0.70 70:0.58 71:0.57 74:0.61 81:0.70 83:0.69 86:0.82 91:0.17 98:0.60 99:0.95 101:0.65 104:1.00 108:0.54 114:0.88 122:0.65 123:0.51 124:0.78 126:0.51 127:0.44 129:0.05 133:0.74 135:0.97 139:0.83 146:0.77 147:0.81 149:0.74 150:0.58 154:0.52 155:0.55 159:0.60 163:0.75 165:0.81 170:0.87 172:0.54 173:0.63 174:0.90 176:0.70 177:0.88 178:0.55 179:0.49 187:0.21 192:0.57 197:0.93 201:0.63 204:0.83 208:0.61 212:0.27 215:0.79 216:0.48 222:0.35 230:0.77 232:0.97 233:0.76 235:0.42 239:0.90 241:0.30 242:0.28 243:0.50 244:0.61 245:0.73 247:0.79 253:0.64 259:0.21 265:0.61 266:0.71 267:0.52 271:0.23 276:0.64 281:0.91 290:0.88 297:0.36 300:0.55 +0 1:0.65 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.22 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.53 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.47 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.70 83:0.69 86:0.90 91:0.25 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.36 114:0.88 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.65 146:0.82 147:0.85 149:0.73 150:0.58 154:0.55 155:0.50 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.37 174:0.79 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.49 195:0.80 197:0.82 201:0.63 206:0.81 208:0.61 212:0.50 215:0.79 216:0.17 219:0.94 222:0.35 232:0.87 235:0.42 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.67 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.88 292:0.88 297:0.36 300:0.84 +0 1:0.58 10:0.91 11:0.52 12:0.20 17:0.38 18:0.98 21:0.47 27:0.52 29:0.91 30:0.68 34:0.78 36:0.32 37:0.56 39:0.69 43:0.61 45:0.99 48:0.91 64:0.77 66:0.79 69:0.63 70:0.59 71:0.57 74:0.86 76:0.85 81:0.37 86:0.98 91:0.09 98:0.81 101:0.87 108:0.81 114:0.72 122:0.91 123:0.80 124:0.43 126:0.32 127:0.72 129:0.59 133:0.77 135:0.92 139:0.97 146:0.65 147:0.70 149:0.93 150:0.41 154:0.16 155:0.51 159:0.88 170:0.87 172:0.98 173:0.34 174:0.90 175:0.75 176:0.62 177:0.70 178:0.55 179:0.13 187:0.21 192:0.48 197:0.93 201:0.55 204:0.81 208:0.50 212:0.37 216:0.48 222:0.35 232:1.00 235:0.60 239:0.90 241:0.24 242:0.54 243:0.12 244:0.61 245:0.95 247:0.91 253:0.65 257:0.65 259:0.21 265:0.81 266:0.84 267:0.28 271:0.23 276:0.96 277:0.69 281:0.86 290:0.72 300:0.94 +0 1:0.64 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.30 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.51 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.48 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.68 83:0.69 86:0.90 91:0.27 97:0.94 98:0.67 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.84 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.69 146:0.82 147:0.85 149:0.73 150:0.56 154:0.55 155:0.49 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.51 187:0.21 192:0.49 195:0.80 197:0.82 201:0.62 206:0.81 208:0.61 212:0.50 215:0.72 216:0.17 219:0.94 222:0.35 232:0.87 235:0.52 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.65 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.84 292:0.88 297:0.36 300:0.84 +1 1:0.65 7:0.75 10:0.92 11:0.52 12:0.20 17:0.18 18:0.76 21:0.22 27:0.52 29:0.91 30:0.70 34:0.91 36:0.21 37:0.56 39:0.69 43:0.62 45:0.89 64:0.77 66:0.35 69:0.70 70:0.53 71:0.57 74:0.60 81:0.70 83:0.69 86:0.82 91:0.18 98:0.60 99:0.95 101:0.65 104:1.00 108:0.54 114:0.88 122:0.65 123:0.51 124:0.77 126:0.51 127:0.42 129:0.05 133:0.74 135:0.97 139:0.83 146:0.77 147:0.81 149:0.74 150:0.58 154:0.52 155:0.55 159:0.59 163:0.75 165:0.81 170:0.87 172:0.54 173:0.63 174:0.90 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.57 197:0.93 201:0.63 204:0.83 208:0.61 212:0.29 215:0.79 216:0.48 222:0.35 230:0.77 232:0.97 233:0.76 235:0.42 239:0.90 241:0.32 242:0.30 243:0.51 244:0.61 245:0.72 247:0.76 253:0.63 259:0.21 265:0.61 266:0.71 267:0.59 271:0.23 276:0.63 281:0.91 290:0.88 297:0.36 300:0.55 +1 1:0.74 6:0.81 7:0.81 8:0.61 11:0.83 12:0.20 17:0.64 18:0.94 20:0.83 21:0.40 27:0.52 29:0.71 30:0.30 32:0.80 34:0.71 36:0.39 37:0.37 39:0.44 43:0.92 44:0.93 45:0.87 48:0.97 64:0.77 66:0.53 69:0.40 70:0.71 71:0.84 74:0.84 77:0.70 78:0.97 81:0.62 83:0.91 85:0.72 86:0.96 91:0.29 98:0.85 100:0.95 101:0.97 104:0.98 106:0.81 108:0.60 111:0.96 114:0.62 121:0.99 122:0.80 123:0.58 124:0.55 126:0.54 127:0.76 129:0.59 133:0.46 135:0.95 139:0.97 145:0.69 146:0.37 147:0.43 149:0.85 150:0.78 152:0.86 154:0.91 155:0.67 159:0.50 165:0.93 169:0.91 172:0.76 173:0.42 176:0.73 177:0.70 178:0.55 179:0.41 186:0.97 187:0.95 192:0.87 195:0.69 201:0.93 204:0.89 206:0.81 208:0.64 212:0.77 215:0.42 216:0.37 222:0.48 230:0.87 233:0.76 235:0.80 241:0.41 242:0.16 243:0.40 245:0.90 247:0.92 253:0.50 259:0.21 261:0.94 265:0.85 266:0.54 267:0.52 271:0.27 276:0.75 281:0.91 282:0.88 283:0.82 290:0.62 297:0.36 299:0.88 300:0.55 +1 1:0.69 8:0.65 9:0.76 11:0.64 12:0.20 17:0.62 18:0.89 20:0.92 22:0.93 27:1.00 29:0.71 30:0.93 31:0.93 34:0.84 36:0.48 37:0.27 39:0.44 43:0.68 45:0.85 47:0.93 55:0.59 64:0.77 66:0.99 69:0.52 70:0.27 71:0.84 74:0.97 78:0.83 79:0.93 81:0.81 83:0.60 86:0.93 91:0.53 96:0.80 98:0.96 99:0.83 100:0.73 101:0.52 108:0.40 111:0.81 114:0.95 117:0.86 122:0.77 123:0.38 126:0.44 127:0.70 129:0.05 135:0.68 137:0.93 139:0.90 140:0.45 146:0.98 147:0.98 149:0.72 150:0.78 151:0.81 152:0.86 153:0.48 154:0.85 155:0.58 159:0.74 162:0.86 165:0.70 169:0.72 172:0.97 173:0.36 176:0.66 177:0.70 179:0.16 186:0.84 187:0.68 190:0.77 192:0.51 196:0.95 201:0.68 202:0.36 208:0.54 212:0.95 216:0.24 222:0.48 232:0.90 235:0.05 241:0.27 242:0.73 243:0.99 247:0.86 248:0.73 253:0.86 254:0.74 255:0.74 256:0.45 259:0.21 261:0.86 262:0.93 265:0.96 266:0.17 267:0.23 268:0.46 271:0.27 276:0.94 284:0.87 285:0.56 290:0.95 300:0.43 +1 1:0.69 7:0.72 8:0.64 9:0.76 11:0.85 12:0.20 17:0.32 18:0.90 21:0.22 27:0.58 29:0.71 30:0.56 32:0.69 34:0.97 36:0.44 37:0.35 39:0.44 43:0.65 45:0.88 66:0.64 69:0.56 70:0.70 71:0.84 74:0.87 76:0.85 77:0.70 81:0.52 83:0.60 86:0.94 91:0.58 96:0.71 97:0.89 98:0.61 99:0.83 101:0.87 104:0.88 106:0.81 108:0.43 114:0.67 117:0.86 120:0.57 122:0.80 123:0.41 124:0.50 126:0.44 127:0.41 129:0.59 133:0.61 135:0.70 139:0.91 140:0.45 145:0.47 146:0.61 147:0.66 149:0.36 150:0.58 151:0.53 153:0.48 154:0.79 155:0.58 158:0.78 159:0.40 162:0.86 164:0.54 165:0.70 172:0.67 173:0.60 176:0.66 177:0.70 179:0.51 187:0.68 190:0.77 192:0.59 195:0.65 201:0.68 202:0.36 204:0.85 206:0.81 208:0.54 212:0.82 215:0.51 216:0.48 220:0.82 222:0.48 230:0.74 232:0.90 233:0.73 235:0.31 241:0.21 242:0.16 243:0.69 245:0.68 247:0.86 248:0.53 253:0.57 254:0.90 255:0.74 256:0.45 259:0.21 260:0.57 265:0.62 266:0.54 267:0.59 268:0.46 271:0.27 276:0.60 279:0.82 282:0.77 283:0.78 285:0.56 290:0.67 297:0.36 300:0.70 +2 1:0.69 8:0.87 9:0.76 11:0.64 12:0.20 17:0.98 18:0.91 21:0.71 27:1.00 29:0.71 30:0.09 31:0.91 34:0.20 36:0.47 37:0.84 39:0.44 43:0.71 44:0.90 45:0.81 55:0.71 64:0.77 66:0.13 69:0.30 70:0.98 71:0.84 74:0.74 76:0.85 77:0.70 79:0.91 81:0.32 83:0.60 86:0.92 87:0.98 91:0.65 96:0.77 97:0.89 98:0.33 99:0.83 101:0.52 108:0.90 114:0.54 122:0.80 123:0.89 124:0.91 126:0.44 127:0.97 129:0.05 133:0.89 135:0.83 137:0.91 139:0.92 140:0.45 145:0.99 146:0.32 147:0.38 149:0.70 150:0.51 151:0.70 153:0.72 154:0.61 155:0.58 158:0.78 159:0.75 162:0.86 165:0.70 172:0.37 173:0.20 176:0.66 177:0.70 179:0.54 190:0.77 191:0.70 192:0.51 195:0.87 196:0.94 201:0.68 202:0.63 208:0.54 212:0.39 216:0.07 219:0.92 220:0.96 222:0.48 232:0.72 235:0.95 241:0.79 242:0.17 243:0.29 244:0.61 245:0.71 247:0.79 248:0.67 253:0.67 255:0.74 256:0.68 259:0.21 265:0.35 266:0.95 267:0.36 268:0.71 271:0.27 276:0.69 277:0.69 279:0.82 282:0.77 284:0.94 285:0.56 290:0.54 292:0.91 300:0.84 +1 1:0.74 7:0.81 9:0.76 11:0.85 12:0.20 17:0.70 18:0.83 21:0.30 27:0.48 29:0.71 30:0.33 32:0.69 34:0.95 36:0.92 37:0.48 39:0.44 43:0.70 45:0.81 55:0.59 64:0.77 66:0.31 69:0.42 70:0.89 71:0.84 74:0.81 76:0.85 77:0.70 81:0.59 83:0.60 86:0.81 91:0.44 96:0.71 97:0.89 98:0.54 99:0.83 101:0.87 104:0.94 106:0.81 108:0.32 114:0.65 117:0.86 120:0.57 121:0.90 122:0.80 123:0.31 124:0.70 126:0.54 127:0.55 129:0.05 133:0.66 135:0.93 139:0.84 140:0.45 145:0.83 146:0.63 147:0.68 149:0.63 150:0.74 151:0.53 153:0.48 154:0.60 155:0.67 158:0.78 159:0.52 162:0.52 164:0.54 165:0.70 172:0.48 173:0.40 176:0.73 177:0.70 179:0.58 187:0.68 190:0.77 192:0.87 195:0.74 201:0.93 202:0.58 204:0.89 206:0.81 208:0.64 212:0.52 215:0.44 216:0.40 220:0.82 222:0.48 230:0.87 232:0.95 233:0.76 235:0.52 241:0.53 242:0.24 243:0.49 244:0.61 245:0.74 247:0.79 248:0.53 253:0.54 255:0.74 256:0.45 259:0.21 260:0.57 265:0.56 266:0.63 267:0.96 268:0.46 271:0.27 276:0.60 279:0.82 281:0.91 282:0.77 283:0.71 285:0.56 290:0.65 297:0.36 300:0.70 +1 1:0.74 9:0.80 11:0.64 12:0.20 17:0.13 18:0.91 21:0.30 27:0.12 29:0.71 30:0.84 31:0.87 34:0.93 36:0.67 37:0.95 39:0.44 43:0.11 45:0.89 55:0.45 64:0.77 66:0.93 69:0.54 70:0.28 71:0.84 74:0.85 79:0.87 81:0.59 83:0.60 86:0.98 91:0.27 96:0.69 98:0.64 99:0.83 101:0.52 108:0.42 114:0.65 120:0.55 122:0.57 123:0.40 126:0.54 127:0.19 129:0.05 135:0.24 137:0.87 139:0.96 140:0.45 146:0.63 147:0.69 149:0.08 150:0.74 151:0.52 153:0.48 154:0.84 155:0.67 159:0.24 162:0.86 164:0.57 165:0.70 172:0.80 173:0.59 176:0.73 177:0.70 179:0.21 187:0.68 192:0.87 196:0.91 201:0.93 202:0.36 208:0.64 212:0.84 215:0.44 216:0.61 220:0.87 222:0.48 235:0.52 241:0.32 242:0.45 243:0.93 247:0.60 248:0.51 253:0.54 254:0.80 255:0.95 256:0.45 259:0.21 260:0.56 265:0.65 266:0.27 267:0.44 268:0.46 271:0.27 276:0.68 284:0.89 285:0.62 290:0.65 297:0.36 300:0.33 +1 1:0.74 11:0.85 12:0.20 17:0.30 18:0.80 21:0.30 27:0.45 29:0.71 30:0.08 34:0.88 36:0.18 37:0.50 39:0.44 43:0.26 44:0.87 45:0.73 64:0.77 66:0.30 69:0.69 70:1.00 71:0.84 74:0.63 81:0.59 83:0.60 86:0.86 91:0.14 97:0.89 98:0.27 99:0.83 101:0.87 108:0.86 114:0.65 122:0.77 123:0.85 124:0.77 126:0.54 127:0.33 129:0.05 133:0.73 135:0.85 139:0.88 145:0.52 146:0.41 147:0.48 149:0.22 150:0.74 154:0.76 155:0.67 158:0.91 159:0.61 165:0.70 172:0.32 173:0.57 176:0.73 177:0.70 178:0.55 179:0.67 187:0.68 192:0.87 195:0.86 201:0.93 208:0.64 212:0.22 215:0.44 216:0.77 219:0.95 222:0.48 235:0.52 241:0.21 242:0.38 243:0.44 245:0.55 247:0.67 253:0.50 254:0.74 259:0.21 265:0.29 266:0.71 267:0.65 271:0.27 276:0.43 277:0.69 279:0.86 283:0.77 290:0.65 292:0.91 297:0.36 300:0.94 +1 1:0.74 7:0.66 11:0.57 12:0.20 17:0.13 18:0.78 21:0.77 27:0.36 29:0.71 30:0.94 32:0.80 34:0.98 36:0.51 37:0.57 39:0.44 43:0.88 44:0.93 64:0.77 66:0.80 69:0.58 70:0.13 71:0.84 74:0.75 77:0.70 81:0.43 83:0.91 85:0.85 86:0.89 91:0.48 98:0.45 101:0.87 108:0.45 114:0.53 122:0.57 123:0.43 126:0.54 127:0.14 129:0.05 135:0.74 139:0.91 145:0.88 146:0.28 147:0.34 149:0.86 150:0.69 154:0.86 155:0.67 159:0.12 165:0.93 172:0.45 173:0.86 176:0.73 177:0.70 178:0.55 179:0.35 187:0.39 192:0.87 195:0.60 201:0.93 204:0.85 208:0.64 212:0.90 215:0.36 216:0.59 222:0.48 230:0.69 233:0.73 235:1.00 241:0.57 242:0.58 243:0.82 247:0.35 253:0.43 259:0.21 265:0.47 266:0.17 267:0.52 271:0.27 276:0.34 281:0.91 282:0.88 290:0.53 297:0.36 299:0.88 300:0.13 +1 1:0.74 11:0.83 12:0.20 17:0.89 18:0.95 21:0.22 22:0.93 27:0.62 29:0.71 30:0.55 32:0.80 34:0.89 36:0.43 37:0.27 39:0.44 43:0.76 44:0.93 45:0.98 47:0.93 55:0.42 64:0.77 66:0.99 69:0.78 70:0.29 71:0.84 74:0.94 77:0.70 81:0.67 83:0.91 85:0.72 86:0.99 91:0.17 98:0.94 101:0.97 104:0.82 106:0.81 108:0.62 114:0.71 117:0.86 122:0.57 123:0.59 126:0.54 127:0.60 129:0.05 135:0.74 139:0.99 145:0.50 146:0.99 147:1.00 149:0.51 150:0.80 154:0.67 155:0.67 159:0.89 163:0.90 165:0.93 172:0.98 173:0.49 176:0.73 177:0.70 178:0.55 179:0.14 187:0.21 192:0.87 195:0.97 201:0.93 206:0.81 208:0.64 212:0.47 215:0.51 216:0.60 222:0.48 232:0.87 235:0.42 241:0.10 242:0.50 243:0.99 247:0.35 253:0.55 259:0.21 262:0.93 265:0.94 266:0.27 267:0.52 271:0.27 276:0.95 282:0.88 290:0.71 297:0.36 299:0.88 300:0.33 +0 1:0.69 8:0.65 9:0.76 11:0.85 12:0.20 17:0.57 18:0.99 21:0.47 27:0.14 29:0.71 30:0.70 31:0.86 34:0.90 36:0.53 37:0.92 39:0.44 43:0.58 44:0.90 45:0.90 48:0.91 55:0.52 64:0.77 66:0.99 69:0.48 70:0.58 71:0.84 74:0.92 77:0.70 79:0.86 81:0.35 86:0.98 91:0.46 96:0.68 98:0.98 101:0.87 108:0.37 114:0.59 122:0.77 123:0.36 126:0.44 127:0.80 129:0.05 135:0.83 137:0.86 139:0.98 140:0.45 145:0.70 146:0.96 147:0.97 149:0.82 150:0.49 151:0.48 153:0.69 154:0.47 155:0.58 159:0.67 162:0.86 172:0.96 173:0.38 176:0.66 177:0.70 179:0.19 187:0.21 190:0.77 192:0.51 195:0.84 196:0.89 201:0.68 202:0.46 204:0.85 208:0.54 212:0.89 216:0.36 220:0.93 222:0.48 232:0.83 235:0.60 241:0.35 242:0.52 243:0.99 244:0.61 247:0.82 248:0.48 253:0.50 255:0.74 256:0.45 259:0.21 265:0.98 266:0.27 267:0.17 268:0.55 271:0.27 276:0.92 281:0.89 282:0.77 284:0.87 285:0.56 290:0.59 300:0.70 +1 1:0.69 8:0.63 11:0.64 12:0.20 17:0.91 18:0.97 21:0.71 27:0.70 29:0.71 30:0.12 34:0.91 36:0.75 37:0.38 39:0.44 43:0.15 44:0.87 45:0.93 48:0.91 55:0.42 64:0.77 66:0.98 69:0.39 70:0.78 71:0.84 74:0.95 81:0.29 86:0.99 91:0.42 98:0.89 101:0.52 108:0.30 114:0.54 122:0.80 123:0.29 126:0.44 127:0.43 129:0.05 135:0.44 139:0.99 145:0.72 146:0.96 147:0.97 149:0.08 150:0.47 154:0.58 155:0.58 159:0.68 172:0.95 173:0.25 176:0.66 177:0.70 178:0.55 179:0.17 187:0.87 192:0.51 195:0.88 201:0.68 208:0.54 212:0.58 216:0.13 222:0.48 235:0.88 241:0.15 242:0.21 243:0.98 247:0.88 253:0.48 254:0.74 259:0.21 265:0.89 266:0.27 267:0.36 271:0.27 276:0.91 281:0.91 290:0.54 300:0.55 +0 1:0.69 7:0.72 11:0.83 12:0.20 17:0.97 18:0.63 21:0.40 22:0.93 27:0.55 29:0.71 30:0.86 32:0.69 34:0.96 36:0.47 37:0.32 39:0.44 43:0.78 45:0.83 47:0.93 66:0.77 69:0.78 70:0.30 71:0.84 74:0.73 77:0.89 81:0.39 83:0.60 85:0.72 86:0.73 91:0.10 98:0.34 99:0.83 101:0.87 104:0.81 106:0.81 108:0.61 114:0.61 117:0.86 122:0.51 123:0.59 124:0.39 126:0.44 127:0.18 129:0.05 133:0.37 135:0.94 139:0.70 145:0.83 146:0.40 147:0.47 149:0.71 150:0.54 154:0.70 155:0.58 159:0.25 165:0.70 172:0.57 173:0.81 176:0.66 177:0.70 178:0.55 179:0.37 187:0.39 192:0.59 195:0.72 201:0.68 204:0.85 206:0.81 208:0.54 212:0.89 215:0.42 216:0.81 222:0.48 230:0.74 232:0.86 233:0.73 235:0.52 241:0.10 242:0.58 243:0.79 245:0.51 247:0.47 253:0.48 259:0.21 262:0.93 265:0.36 266:0.23 267:0.65 271:0.27 276:0.39 282:0.77 290:0.61 297:0.36 300:0.33 +1 1:0.69 8:0.64 9:0.76 11:0.64 12:0.20 17:0.40 18:0.86 20:0.89 21:0.05 27:0.48 29:0.71 30:0.15 31:0.90 34:0.95 36:0.89 37:0.49 39:0.44 43:0.13 45:0.66 64:0.77 66:0.63 69:0.51 70:0.68 71:0.84 74:0.61 78:0.72 79:0.90 81:0.71 83:0.60 86:0.88 91:0.58 96:0.75 97:0.89 98:0.46 100:0.73 101:0.52 108:0.39 111:0.72 114:0.85 122:0.86 123:0.38 124:0.45 126:0.44 127:0.26 129:0.05 133:0.37 135:0.54 137:0.90 139:0.87 140:0.45 146:0.41 147:0.48 149:0.12 150:0.64 151:0.65 152:0.83 153:0.48 154:0.84 155:0.58 158:0.78 159:0.27 162:0.86 169:0.72 172:0.41 173:0.61 176:0.66 177:0.70 179:0.70 186:0.73 187:0.39 190:0.77 192:0.51 196:0.93 201:0.68 202:0.36 208:0.54 212:0.50 216:0.36 219:0.92 220:0.62 222:0.48 232:0.91 235:0.12 241:0.41 242:0.53 243:0.68 245:0.54 247:0.55 248:0.63 253:0.68 254:0.80 255:0.74 256:0.45 259:0.21 261:0.73 265:0.47 266:0.38 267:0.52 268:0.46 271:0.27 276:0.34 279:0.82 284:0.87 285:0.56 290:0.85 292:0.87 300:0.43 +1 1:0.74 6:0.81 7:0.72 8:0.64 9:0.80 11:0.85 12:0.20 17:0.64 18:0.96 20:0.90 21:0.30 27:0.52 29:0.71 30:0.32 31:0.87 32:0.80 34:0.61 36:0.89 37:0.37 39:0.44 43:0.71 44:0.93 45:0.83 55:0.71 64:0.77 66:0.53 69:0.35 70:0.83 71:0.84 74:0.80 76:0.85 77:0.70 78:0.93 79:0.87 81:0.63 83:0.60 86:0.95 91:0.27 96:0.71 97:0.89 98:0.79 99:0.83 100:0.92 101:0.87 104:0.98 106:0.81 108:0.81 111:0.92 114:0.65 117:0.86 120:0.58 122:0.86 123:0.80 124:0.64 126:0.54 127:0.85 129:0.05 133:0.66 135:0.97 137:0.87 139:0.95 140:0.45 145:0.69 146:0.76 147:0.80 149:0.57 150:0.76 151:0.53 152:0.86 153:0.48 154:0.80 155:0.67 158:0.78 159:0.77 162:0.86 164:0.67 165:0.70 169:0.91 172:0.81 173:0.21 176:0.73 177:0.70 179:0.35 186:0.93 187:0.87 190:0.77 192:0.87 195:0.89 196:0.91 201:0.93 202:0.53 204:0.89 206:0.81 208:0.64 212:0.60 215:0.44 216:0.37 220:0.87 222:0.48 230:0.75 232:0.94 233:0.76 235:0.60 241:0.27 242:0.43 243:0.37 245:0.89 247:0.79 248:0.52 253:0.56 254:0.95 255:0.95 256:0.58 259:0.21 260:0.58 261:0.94 265:0.79 266:0.75 267:0.76 268:0.62 271:0.27 276:0.81 277:0.69 279:0.82 281:0.91 282:0.88 283:0.78 284:0.91 285:0.62 290:0.65 297:0.36 300:0.70 +1 8:0.82 11:0.85 12:0.20 17:0.88 18:1.00 20:0.84 21:0.68 27:0.14 29:0.71 30:0.73 34:0.97 36:0.31 37:0.92 39:0.44 43:0.58 44:0.87 45:0.92 48:1.00 55:0.52 64:0.77 66:0.48 69:0.51 70:0.42 71:0.84 74:0.96 76:0.85 78:0.92 81:0.25 86:1.00 91:0.23 98:0.72 100:0.94 101:0.87 108:0.93 111:0.92 122:0.86 123:0.93 124:0.85 126:0.26 127:0.89 129:0.59 133:0.88 135:0.70 139:1.00 145:0.93 146:0.89 147:0.91 149:0.82 150:0.25 152:0.85 154:0.17 155:0.58 159:0.90 169:0.89 172:0.99 173:0.20 177:0.70 178:0.55 179:0.10 186:0.92 187:0.68 191:0.70 192:0.51 195:0.97 202:0.36 204:0.85 208:0.54 212:0.93 216:0.36 222:0.48 235:0.95 241:0.51 242:0.42 243:0.22 245:0.99 247:0.94 253:0.48 254:0.74 259:0.21 261:0.92 265:0.72 266:0.54 267:0.44 271:0.27 276:0.99 281:0.89 300:0.70 +1 1:0.74 11:0.92 12:0.20 17:0.40 18:0.77 21:0.30 27:0.45 29:0.71 30:0.62 32:0.69 34:0.86 36:0.17 37:0.50 39:0.44 43:0.82 45:0.73 60:0.87 64:0.77 66:0.20 69:0.69 70:0.54 71:0.84 74:0.67 77:0.70 81:0.61 83:0.91 85:0.72 86:0.81 91:0.18 98:0.16 101:0.97 108:0.52 114:0.65 122:0.57 123:0.84 124:0.78 126:0.54 127:0.39 129:0.05 133:0.73 135:0.86 139:0.84 145:0.52 146:0.25 147:0.31 149:0.65 150:0.77 154:0.77 155:0.67 158:0.91 159:0.56 165:0.93 172:0.27 173:0.63 176:0.73 177:0.70 178:0.55 179:0.77 187:0.68 192:0.87 195:0.79 201:0.93 208:0.64 212:0.42 215:0.44 216:0.77 219:0.95 222:0.48 235:0.52 241:0.21 242:0.29 243:0.48 245:0.51 247:0.60 253:0.50 259:0.21 265:0.17 266:0.54 267:0.28 271:0.27 276:0.36 277:0.69 279:0.86 282:0.77 283:0.78 290:0.65 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.66 8:0.88 11:0.57 12:0.20 17:0.28 18:0.98 20:0.93 21:0.68 27:0.36 29:0.71 30:0.93 32:0.80 34:0.96 36:0.51 37:0.57 39:0.44 43:0.87 44:0.93 60:0.87 64:0.77 66:0.97 69:0.58 70:0.18 71:0.84 74:0.98 77:0.70 78:0.88 81:0.44 83:0.91 85:0.98 86:1.00 91:0.42 98:0.82 100:0.92 101:0.87 108:0.44 111:0.89 114:0.56 122:0.57 123:0.43 126:0.54 127:0.31 129:0.05 135:0.75 139:1.00 145:0.83 146:0.63 147:0.68 149:1.00 150:0.70 152:0.87 154:0.85 155:0.67 158:0.92 159:0.24 165:0.93 169:0.90 172:0.94 173:0.75 176:0.73 177:0.70 178:0.55 179:0.17 186:0.88 187:0.39 192:0.87 195:0.65 201:0.93 204:0.85 208:0.64 212:0.94 215:0.37 216:0.59 219:0.95 222:0.48 230:0.69 233:0.76 235:0.88 241:0.49 242:0.50 243:0.98 247:0.35 253:0.49 259:0.21 261:0.92 265:0.82 266:0.17 267:0.44 271:0.27 276:0.88 279:0.86 281:0.91 282:0.88 283:0.82 290:0.56 292:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.69 8:0.74 11:0.64 12:0.20 17:0.85 18:0.99 21:0.71 22:0.93 27:0.70 29:0.71 30:0.42 34:0.94 36:0.91 37:0.38 39:0.44 43:0.63 44:0.90 45:0.91 47:0.93 55:0.59 64:0.77 66:0.98 69:0.39 70:0.67 71:0.84 74:0.90 76:0.85 77:0.70 81:0.29 86:0.99 91:0.46 98:0.88 101:0.52 108:0.30 114:0.54 117:0.86 122:0.86 123:0.29 126:0.44 127:0.42 129:0.05 135:0.30 139:0.99 145:0.69 146:0.97 147:0.98 149:0.79 150:0.47 154:0.76 155:0.58 159:0.71 172:0.96 173:0.23 176:0.66 177:0.70 178:0.55 179:0.17 187:0.87 192:0.51 195:0.89 201:0.68 208:0.54 212:0.75 216:0.13 222:0.48 232:0.93 235:0.88 241:0.10 242:0.43 243:0.98 247:0.86 253:0.47 254:0.92 259:0.21 262:0.93 265:0.88 266:0.27 267:0.65 271:0.27 276:0.91 282:0.77 290:0.54 300:0.55 +1 1:0.74 7:0.81 11:0.64 12:0.20 17:0.93 18:0.90 21:0.59 27:0.14 29:0.71 30:0.60 32:0.80 34:0.94 36:0.56 37:0.92 39:0.44 43:0.69 44:0.93 45:0.95 48:0.99 64:0.77 66:0.96 69:0.32 70:0.39 71:0.84 74:0.95 76:0.85 77:0.98 81:0.48 83:0.60 86:0.95 91:0.20 97:0.89 98:0.93 99:0.95 101:0.52 104:0.94 106:0.81 108:0.25 114:0.58 117:0.86 122:0.75 123:0.24 126:0.54 127:0.56 129:0.05 135:0.56 139:0.96 145:0.84 146:0.80 147:0.84 149:0.87 150:0.68 154:0.82 155:0.67 158:0.78 159:0.36 165:0.70 172:0.90 173:0.42 176:0.73 177:0.70 178:0.55 179:0.29 187:0.68 192:0.87 195:0.63 201:0.93 204:0.89 206:0.81 208:0.64 212:0.95 215:0.39 216:0.36 222:0.48 230:0.86 232:0.95 233:0.73 235:0.97 241:0.10 242:0.43 243:0.96 247:0.82 253:0.49 254:0.80 259:0.21 265:0.93 266:0.12 267:0.52 271:0.27 276:0.83 279:0.82 281:0.89 282:0.88 283:0.78 290:0.58 297:0.36 300:0.33 +1 1:0.74 7:0.81 11:0.92 12:0.20 17:0.72 18:0.99 21:0.59 27:0.57 29:0.71 30:0.36 32:0.80 34:0.82 36:0.32 37:0.61 39:0.44 43:0.84 44:0.93 45:0.97 64:0.77 66:0.91 69:0.64 70:0.67 71:0.84 74:0.98 77:0.70 81:0.47 83:0.91 85:0.98 86:1.00 91:0.27 98:0.87 101:0.97 104:0.91 108:0.49 114:0.58 121:0.90 122:0.57 123:0.47 124:0.37 126:0.54 127:0.56 129:0.05 133:0.43 135:0.76 139:1.00 145:0.74 146:0.99 147:0.99 149:0.97 150:0.71 154:0.81 155:0.67 159:0.86 165:0.93 172:0.99 173:0.36 176:0.73 177:0.70 178:0.55 179:0.11 187:0.39 192:0.87 195:0.96 201:0.93 204:0.89 208:0.64 212:0.93 215:0.39 216:0.65 222:0.48 230:0.87 232:0.94 233:0.76 235:0.80 241:0.37 242:0.27 243:0.91 245:0.98 247:0.92 253:0.50 265:0.87 266:0.54 267:0.36 271:0.27 276:0.98 281:0.91 282:0.88 290:0.58 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.66 9:0.76 11:0.57 12:0.20 17:0.45 18:0.97 21:0.68 27:0.32 29:0.71 30:0.93 32:0.80 34:0.95 36:0.52 37:0.91 39:0.44 43:0.81 44:0.93 60:0.87 64:0.77 66:0.97 69:0.72 70:0.18 71:0.84 74:0.98 77:0.70 81:0.44 83:0.91 85:0.98 86:1.00 91:0.42 96:0.68 98:0.72 101:0.87 108:0.55 114:0.56 120:0.54 122:0.57 123:0.53 126:0.54 127:0.22 129:0.05 135:0.61 139:1.00 140:0.45 145:0.49 146:0.63 147:0.68 149:1.00 150:0.70 151:0.48 153:0.48 154:0.75 155:0.67 158:0.92 159:0.24 162:0.86 164:0.54 165:0.93 172:0.94 173:0.83 176:0.73 177:0.70 179:0.14 187:0.39 190:0.77 192:0.87 195:0.65 201:0.93 202:0.36 204:0.85 208:0.64 212:0.94 215:0.37 216:0.43 219:0.95 220:0.93 222:0.48 230:0.69 233:0.76 235:0.88 241:0.58 242:0.50 243:0.98 247:0.35 248:0.48 253:0.49 255:0.74 256:0.45 259:0.21 260:0.54 265:0.72 266:0.17 267:0.91 268:0.46 271:0.27 276:0.88 279:0.86 281:0.91 282:0.88 283:0.82 285:0.56 290:0.56 292:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.63 10:0.83 11:0.45 12:0.20 17:0.82 18:0.76 21:0.22 27:0.44 29:0.56 30:0.76 34:0.33 36:0.33 37:0.41 39:0.56 43:0.23 45:0.73 55:0.71 64:0.77 66:0.59 69:0.90 70:0.43 71:0.66 74:0.53 81:0.55 83:0.54 86:0.73 91:0.27 98:0.60 99:0.83 101:0.46 108:0.80 114:0.85 122:0.90 123:0.79 124:0.67 126:0.43 127:0.62 129:0.05 133:0.73 135:0.85 139:0.69 146:0.88 147:0.53 149:0.24 150:0.49 154:0.48 155:0.49 158:0.73 159:0.80 165:0.63 170:0.82 172:0.68 173:0.82 174:0.83 175:0.75 176:0.63 177:0.70 178:0.55 179:0.52 187:0.68 192:0.50 197:0.82 201:0.61 208:0.54 212:0.30 215:0.79 216:0.93 222:0.60 235:0.42 239:0.82 241:0.05 242:0.52 243:0.23 244:0.83 245:0.71 247:0.88 253:0.61 254:0.93 257:0.84 259:0.21 264:0.98 265:0.61 266:0.80 267:0.44 271:0.29 276:0.65 277:0.69 290:0.85 297:0.36 300:0.43 +0 10:0.80 11:0.49 12:0.20 17:0.57 18:0.88 21:0.78 27:0.64 29:0.56 30:0.26 34:0.28 36:0.31 37:0.46 39:0.56 43:0.58 45:0.90 48:0.91 66:0.15 69:0.25 70:1.00 71:0.66 74:0.63 76:0.85 86:0.82 91:0.40 98:0.22 101:0.52 108:0.20 122:0.90 123:0.19 124:0.95 127:0.79 129:0.95 133:0.95 135:0.86 139:0.80 146:0.61 147:0.66 149:0.67 154:0.47 159:0.92 170:0.82 172:0.57 173:0.08 174:0.79 175:0.91 177:0.70 178:0.55 179:0.34 187:0.68 197:0.79 212:0.37 216:0.41 222:0.60 235:0.68 239:0.80 242:0.24 243:0.36 244:0.83 245:0.75 247:0.91 253:0.47 257:0.84 259:0.21 264:0.98 265:0.24 266:0.97 267:0.59 271:0.29 276:0.80 277:0.87 281:0.91 300:0.98 +1 8:0.70 11:0.49 12:0.20 17:0.89 18:0.97 21:0.78 27:0.47 29:0.56 30:0.29 34:0.30 36:0.41 37:0.55 39:0.56 43:0.30 45:0.85 55:0.59 66:0.71 69:0.21 70:0.88 71:0.66 74:0.76 76:0.85 86:0.94 91:0.10 98:0.90 101:0.52 108:0.17 117:0.86 122:0.91 123:0.16 124:0.45 127:0.85 129:0.59 133:0.46 135:0.70 139:0.90 145:0.52 146:0.98 147:0.99 149:0.44 154:0.22 159:0.82 170:0.82 172:0.95 173:0.12 175:0.91 177:0.70 178:0.55 179:0.18 187:0.39 212:0.73 216:0.26 222:0.60 232:0.83 235:0.60 241:0.07 242:0.14 243:0.75 244:0.83 245:0.97 247:0.96 253:0.53 254:0.86 257:0.84 259:0.21 264:0.98 265:0.90 266:0.80 267:0.28 271:0.29 276:0.93 277:0.87 300:0.84 +1 1:0.58 10:0.80 11:0.49 12:0.20 17:0.66 18:0.85 21:0.74 27:0.55 29:0.56 30:0.08 34:0.63 36:0.66 37:0.40 39:0.56 43:0.23 45:0.77 48:0.91 55:0.42 64:0.77 66:0.30 69:0.58 70:1.00 71:0.66 74:0.54 77:0.70 81:0.43 83:0.54 86:0.80 91:0.02 98:0.25 99:0.83 101:0.52 108:0.97 114:0.64 117:0.86 122:0.95 123:0.97 124:0.83 126:0.43 127:0.96 129:0.05 133:0.83 135:0.95 139:0.77 145:0.45 146:0.49 147:0.55 149:0.23 150:0.39 154:0.50 155:0.47 159:0.93 165:0.63 170:0.82 172:0.59 173:0.22 174:0.79 175:0.91 176:0.63 177:0.70 178:0.55 179:0.50 187:1.00 192:0.46 195:0.98 197:0.79 201:0.56 208:0.54 212:0.68 215:0.46 216:0.61 222:0.60 232:0.99 235:0.98 239:0.79 241:0.05 242:0.37 243:0.40 244:0.83 245:0.76 247:0.94 253:0.51 254:0.74 257:0.84 259:0.21 264:0.98 265:0.27 266:0.87 267:0.18 271:0.29 276:0.65 277:0.95 281:0.91 282:0.73 290:0.64 297:0.36 300:0.98 +1 10:0.87 11:0.45 12:0.20 17:0.91 18:0.93 21:0.54 22:0.93 27:0.55 29:0.56 30:0.40 34:0.52 36:0.46 37:0.35 39:0.56 43:0.38 45:0.66 47:0.93 48:0.91 55:0.71 64:0.77 66:0.67 69:0.50 70:0.49 71:0.66 74:0.45 81:0.27 86:0.89 91:0.33 98:0.64 101:0.46 108:0.38 122:0.94 123:0.37 124:0.46 126:0.23 127:0.39 129:0.05 133:0.37 135:0.58 139:0.85 146:0.67 147:0.72 149:0.28 150:0.30 154:0.44 159:0.42 170:0.82 172:0.68 173:0.52 174:0.89 175:0.91 177:0.70 178:0.55 179:0.48 187:0.39 197:0.90 212:0.86 216:0.29 222:0.60 235:0.60 239:0.86 241:0.21 242:0.16 243:0.71 244:0.83 245:0.77 247:0.86 253:0.38 254:0.96 257:0.84 259:0.21 262:0.93 264:0.98 265:0.65 266:0.27 267:0.36 271:0.29 276:0.63 277:0.87 281:0.86 300:0.43 +0 8:0.59 10:0.81 11:0.49 12:0.20 17:0.78 18:0.88 21:0.78 27:0.60 29:0.56 30:0.43 34:0.74 36:0.42 37:0.24 39:0.56 43:0.56 45:0.77 55:0.92 66:0.13 69:0.74 70:0.91 71:0.66 74:0.55 86:0.82 91:0.37 98:0.44 101:0.69 108:0.58 122:0.91 123:0.94 124:0.91 127:0.73 129:0.59 133:0.91 135:0.87 139:0.78 146:0.85 147:0.87 149:0.42 154:0.45 159:0.87 163:0.92 170:0.82 172:0.61 173:0.49 174:0.79 175:0.91 177:0.70 178:0.55 179:0.36 187:0.21 197:0.81 212:0.16 216:0.37 222:0.60 232:0.72 235:0.22 239:0.81 241:0.05 242:0.27 243:0.40 244:0.83 245:0.80 247:0.86 253:0.44 257:0.84 259:0.21 264:0.98 265:0.46 266:0.91 267:0.19 271:0.29 276:0.80 277:0.95 300:0.94 +1 8:0.86 10:0.83 12:0.20 17:0.97 18:0.63 21:0.78 27:0.60 29:0.56 30:0.41 34:0.18 36:0.59 37:0.88 39:0.56 43:0.11 48:0.91 55:0.59 66:0.27 69:0.74 70:0.65 71:0.66 74:0.52 76:0.94 77:0.89 86:0.65 87:0.98 91:0.62 96:0.75 98:0.22 108:0.57 122:0.90 123:0.55 124:0.79 127:0.99 129:0.05 133:0.74 135:0.65 139:0.66 140:0.80 145:0.85 146:0.49 147:0.35 149:0.17 151:0.59 153:0.72 154:0.18 158:0.74 159:0.88 162:0.55 170:0.82 172:0.32 173:0.49 174:0.79 175:0.91 177:0.38 178:0.42 179:0.80 190:0.98 191:0.70 195:0.96 197:0.82 202:0.59 212:0.19 216:0.07 220:0.74 222:0.60 232:0.72 235:0.95 239:0.83 241:0.78 242:0.33 243:0.29 244:0.93 245:0.58 247:0.74 248:0.57 253:0.59 254:0.84 256:0.45 257:0.93 259:0.21 264:0.98 265:0.24 266:0.63 267:0.65 268:0.57 271:0.29 275:0.91 276:0.34 277:0.87 281:0.91 282:0.73 285:0.45 294:0.93 300:0.55 +1 8:0.76 9:0.71 10:0.84 11:0.45 12:0.20 17:0.47 18:0.67 21:0.40 27:0.21 29:0.56 30:0.13 34:0.49 36:0.78 37:0.74 39:0.56 43:0.22 45:0.66 55:0.45 66:0.13 69:0.12 70:0.94 71:0.66 74:0.83 76:0.94 81:0.28 83:0.54 86:0.66 91:0.64 96:0.94 98:0.33 101:0.46 108:0.97 114:0.74 117:0.86 120:0.65 122:0.90 123:0.97 124:0.95 126:0.23 127:0.88 129:0.96 133:0.95 135:0.65 139:0.64 140:0.97 146:0.83 147:0.85 149:0.45 150:0.30 151:0.75 153:0.90 154:0.15 155:0.53 158:0.73 159:0.94 162:0.66 164:0.55 170:0.82 172:0.84 173:0.06 174:0.93 175:0.97 176:0.60 177:0.38 179:0.14 187:0.39 190:0.95 191:0.75 192:0.57 197:0.84 202:0.83 208:0.49 212:0.60 216:0.95 220:0.62 222:0.60 232:0.85 235:0.42 239:0.83 241:0.35 242:0.77 243:0.23 244:0.93 245:0.95 247:0.82 248:0.70 253:0.95 255:0.70 256:0.84 257:0.93 259:0.21 260:0.66 264:0.98 265:0.35 266:0.96 267:0.28 268:0.85 271:0.29 276:0.95 277:0.95 285:0.49 290:0.74 300:0.94 +1 7:0.66 10:0.92 11:0.49 12:0.20 17:0.87 18:0.96 21:0.77 27:0.55 29:0.56 30:0.12 32:0.64 34:1.00 36:0.28 37:0.40 39:0.56 43:0.30 45:0.73 48:0.91 55:0.52 66:0.23 69:0.55 70:0.95 71:0.66 74:0.32 81:0.24 86:0.92 91:0.25 98:0.61 101:0.52 108:0.43 122:0.95 123:0.75 124:0.73 126:0.23 127:0.75 129:0.05 133:0.73 135:0.49 139:0.88 145:1.00 146:0.76 147:0.80 149:0.39 150:0.25 154:0.22 159:0.70 170:0.82 172:0.74 173:0.44 174:0.93 175:0.75 177:0.88 178:0.55 179:0.40 187:0.95 195:0.83 197:0.94 212:0.68 215:0.43 216:0.35 222:0.60 230:0.68 233:0.66 235:0.98 239:0.90 241:0.52 242:0.32 243:0.58 244:0.93 245:0.82 247:0.92 253:0.29 257:0.65 259:0.21 264:0.98 265:0.57 266:0.80 267:0.36 271:0.29 276:0.77 277:0.87 281:0.86 283:0.67 297:0.36 300:0.84 +2 7:0.81 12:0.79 17:0.88 20:0.89 27:0.52 28:0.51 30:0.95 32:0.80 34:0.73 36:0.26 37:0.32 43:0.38 55:0.59 66:0.96 69:0.57 70:0.13 78:0.81 81:1.00 91:0.56 98:0.77 100:0.80 106:0.81 108:0.86 111:0.80 120:0.69 123:0.85 124:0.36 126:0.54 127:0.57 129:0.59 133:0.47 135:0.78 140:0.45 145:0.50 146:0.05 147:0.05 149:0.80 150:0.99 151:0.66 152:0.93 153:0.48 154:0.28 159:0.85 161:0.87 162:0.86 164:0.57 167:0.68 169:0.76 172:0.97 173:0.30 177:0.05 179:0.16 181:0.64 186:0.81 187:0.21 195:0.95 202:0.36 206:0.81 212:0.94 215:0.96 216:0.32 230:0.87 233:0.76 235:0.02 241:0.56 242:0.82 243:0.05 245:0.78 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.83 265:0.77 266:0.23 267:0.28 268:0.46 271:0.15 276:0.91 283:0.82 285:0.62 297:0.36 300:0.18 +1 6:0.95 7:0.81 8:0.83 12:0.79 17:0.81 20:0.97 21:0.13 25:0.88 27:0.51 28:0.51 30:0.45 32:0.80 34:0.49 36:0.27 37:0.29 43:0.28 55:0.59 66:0.49 69:0.78 70:0.25 78:0.88 81:0.99 91:0.33 98:0.29 100:0.95 108:0.86 111:0.91 123:0.85 124:0.48 126:0.54 127:0.46 129:0.59 133:0.47 135:0.82 145:0.61 146:0.05 147:0.05 149:0.21 150:0.99 152:0.95 154:0.20 159:0.57 161:0.87 163:0.99 167:0.67 169:0.97 172:0.16 173:0.75 177:0.05 178:0.55 179:0.97 181:0.64 186:0.88 187:0.39 189:0.95 195:0.78 212:0.61 215:0.84 216:0.25 230:0.87 233:0.76 235:0.12 238:0.97 241:0.55 242:0.82 243:0.29 245:0.39 247:0.12 259:0.21 261:0.98 265:0.31 266:0.17 267:0.36 271:0.15 276:0.15 297:0.36 300:0.18 +1 12:0.79 17:0.40 21:0.13 27:0.29 28:0.51 30:0.74 34:0.75 36:0.24 37:0.92 43:0.33 55:0.84 66:0.87 69:0.66 70:0.28 81:0.99 91:0.29 98:0.92 106:0.81 108:0.50 120:0.72 123:0.48 126:0.54 127:0.53 129:0.05 135:0.74 140:0.45 146:0.95 147:0.96 149:0.53 150:0.99 151:0.70 153:0.69 154:0.25 159:0.61 161:0.87 162:0.86 163:0.94 164:0.62 167:0.67 172:0.63 173:0.60 177:0.05 179:0.69 181:0.64 187:0.21 202:0.46 206:0.81 212:0.61 215:0.84 216:0.93 235:0.12 241:0.54 242:0.82 243:0.88 247:0.12 248:0.66 256:0.45 259:0.21 260:0.73 265:0.92 266:0.38 267:0.85 268:0.55 271:0.15 276:0.54 285:0.62 297:0.36 300:0.25 +1 12:0.79 17:0.90 21:0.30 27:0.48 28:0.51 30:0.76 34:1.00 36:0.38 37:0.43 43:0.30 66:0.64 69:0.72 70:0.15 81:0.98 91:0.51 98:0.16 108:0.56 120:0.53 123:0.53 126:0.54 127:0.09 129:0.05 135:0.66 140:0.45 145:0.54 146:0.21 147:0.27 149:0.25 150:0.97 151:0.46 153:0.48 154:0.22 159:0.10 161:0.87 162:0.86 163:0.97 164:0.57 167:0.62 172:0.16 173:0.80 177:0.05 179:0.17 181:0.64 187:0.39 202:0.36 212:0.95 215:0.72 216:0.32 220:0.87 235:0.31 241:0.39 242:0.82 243:0.69 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.18 266:0.12 267:0.44 268:0.46 271:0.15 276:0.15 285:0.62 297:0.36 300:0.13 +1 12:0.79 17:0.45 21:0.59 25:0.88 27:0.72 28:0.51 30:0.95 32:0.80 34:0.91 36:0.57 43:0.52 55:0.59 66:0.64 69:0.19 81:0.93 91:0.71 98:0.78 108:0.15 123:0.15 126:0.54 127:0.26 129:0.05 135:0.44 145:0.47 146:0.28 147:0.34 149:0.30 150:0.88 154:0.41 159:0.12 161:0.87 167:0.57 172:0.16 173:0.77 177:0.05 178:0.55 179:0.97 181:0.64 187:0.21 195:0.53 212:0.95 215:0.55 216:0.04 235:0.68 241:0.18 242:0.82 243:0.69 247:0.12 259:0.21 265:0.78 266:0.12 267:0.23 271:0.15 276:0.19 297:0.36 300:0.08 +1 12:0.79 17:0.67 21:0.78 27:0.45 28:0.51 34:0.86 36:0.18 37:0.50 43:0.28 66:0.36 69:0.78 91:0.22 98:0.07 108:0.62 123:0.59 127:0.05 129:0.05 135:0.87 145:0.47 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 161:0.87 167:0.58 172:0.05 173:0.72 177:0.05 178:0.55 181:0.64 187:0.68 216:0.77 235:0.95 241:0.24 243:0.51 259:0.21 265:0.07 267:0.28 271:0.15 +2 12:0.79 17:0.47 20:0.83 21:0.13 25:0.88 27:0.72 28:0.51 30:0.91 32:0.80 34:0.29 36:0.50 43:0.52 55:0.52 66:0.69 69:0.07 70:0.13 78:0.77 81:0.99 91:0.82 98:0.93 100:0.82 108:0.06 111:0.78 120:0.56 123:0.06 126:0.54 127:0.56 129:0.05 135:0.37 140:0.45 145:0.39 146:0.43 147:0.49 149:0.35 150:0.99 151:0.50 152:0.80 153:0.72 154:0.41 159:0.16 161:0.87 162:0.67 164:0.64 167:0.89 169:0.82 172:0.21 173:0.52 177:0.05 179:0.98 181:0.64 186:0.78 195:0.53 202:0.53 212:0.61 215:0.84 216:0.04 220:0.74 235:0.12 241:0.82 242:0.82 243:0.73 247:0.12 248:0.50 256:0.45 259:0.21 260:0.57 261:0.80 265:0.93 266:0.17 267:0.17 268:0.57 271:0.15 276:0.15 283:0.60 285:0.62 297:0.36 300:0.13 +2 12:0.79 17:0.92 21:0.40 25:0.88 27:0.51 28:0.51 30:0.76 32:0.80 34:0.52 36:0.40 37:0.29 43:0.20 55:0.59 66:0.36 69:0.93 70:0.15 81:0.97 91:0.57 98:0.13 108:0.92 120:0.73 123:0.92 124:0.52 126:0.54 127:0.23 129:0.59 133:0.47 135:0.78 140:0.45 145:0.42 146:0.05 147:0.05 149:0.12 150:0.95 151:0.66 153:0.48 154:0.13 159:0.58 161:0.87 162:0.86 163:1.00 164:0.67 167:0.64 172:0.10 173:0.90 177:0.05 179:0.96 181:0.64 187:0.21 195:0.90 202:0.50 212:0.95 215:0.67 216:0.25 220:0.62 235:0.42 241:0.47 242:0.82 243:0.34 245:0.37 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 265:0.14 266:0.12 267:0.28 268:0.59 271:0.15 276:0.13 285:0.62 297:0.36 300:0.13 +2 7:0.81 12:0.79 17:0.95 20:0.81 27:0.58 28:0.51 30:0.95 32:0.80 34:0.47 36:0.33 37:0.59 43:0.40 55:0.99 66:0.20 69:0.52 78:0.72 81:1.00 91:0.27 98:0.06 100:0.73 106:0.81 108:0.40 111:0.72 123:0.38 124:0.61 126:0.54 127:0.23 129:0.59 133:0.47 135:0.82 145:0.50 146:0.05 147:0.05 149:0.17 150:0.99 152:0.82 154:0.30 159:0.64 161:0.87 167:0.59 169:0.72 172:0.05 173:0.28 177:0.05 178:0.55 179:0.99 181:0.64 186:0.73 187:0.21 195:0.94 206:0.81 215:0.96 216:0.24 230:0.87 233:0.76 235:0.02 241:0.27 243:0.40 245:0.36 259:0.21 261:0.78 265:0.06 267:0.28 271:0.15 283:0.82 297:0.36 300:0.08 +4 6:0.95 7:0.81 8:0.90 12:0.79 17:0.86 20:0.90 21:0.74 25:0.88 27:0.51 28:0.51 30:0.76 32:0.80 34:0.67 36:0.80 37:0.29 43:0.46 55:0.99 66:0.20 69:0.48 70:0.22 78:0.97 81:0.86 91:0.90 98:0.13 100:0.99 108:0.94 111:0.99 120:0.86 123:0.93 124:0.81 126:0.54 127:0.95 129:0.89 133:0.78 135:0.94 140:0.45 145:0.58 146:0.05 147:0.05 149:0.24 150:0.70 151:0.80 152:0.95 153:0.92 154:0.35 159:0.84 161:0.87 162:0.63 163:0.97 164:0.90 167:0.90 169:0.97 172:0.10 173:0.26 177:0.05 179:0.97 181:0.64 186:0.97 189:0.96 191:0.89 195:0.93 202:0.86 212:0.42 215:0.46 216:0.25 230:0.87 233:0.76 235:0.88 238:0.99 241:0.86 242:0.82 243:0.26 245:0.39 247:0.12 248:0.80 256:0.85 259:0.21 260:0.86 261:0.98 265:0.14 266:0.23 267:0.87 268:0.87 271:0.15 276:0.25 283:0.82 285:0.62 297:0.36 300:0.18 +1 7:0.81 12:0.79 17:0.96 21:0.40 27:0.58 28:0.51 30:0.45 32:0.80 34:1.00 36:0.38 37:0.59 43:0.40 55:0.71 66:0.49 69:0.54 70:0.25 81:0.97 91:0.33 98:0.31 108:0.87 123:0.87 124:0.48 126:0.54 127:0.61 129:0.59 133:0.47 135:0.75 145:0.50 146:0.05 147:0.05 149:0.22 150:0.95 154:0.30 159:0.68 161:0.87 163:0.97 167:0.57 172:0.16 173:0.41 177:0.05 178:0.55 179:0.98 181:0.64 187:0.21 195:0.84 212:0.61 215:0.67 216:0.24 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.29 245:0.39 247:0.12 259:0.21 265:0.33 266:0.17 267:0.28 271:0.15 276:0.15 297:0.36 300:0.18 +1 6:0.94 8:0.83 12:0.79 17:0.81 20:0.95 21:0.78 27:0.59 28:0.51 30:0.95 34:0.39 36:0.80 37:0.41 43:0.23 55:0.99 66:0.13 69:0.89 78:0.88 91:0.93 98:0.06 100:0.96 108:0.78 111:0.92 120:0.67 123:0.76 124:0.75 127:0.45 129:0.81 133:0.67 135:0.87 140:0.96 145:0.54 146:0.05 147:0.05 149:0.14 151:0.64 152:0.95 153:0.46 154:0.15 159:0.78 161:0.87 162:0.46 163:0.99 164:0.66 167:0.90 169:0.92 172:0.05 173:0.78 177:0.05 178:0.54 179:0.99 181:0.64 186:0.88 189:0.95 191:0.91 202:0.86 212:0.95 216:0.32 220:0.74 235:0.88 238:0.98 241:0.85 242:0.82 243:0.34 245:0.37 247:0.12 248:0.62 256:0.58 259:0.21 260:0.68 261:0.94 265:0.06 266:0.12 267:0.82 268:0.58 271:0.15 276:0.19 285:0.62 300:0.08 +3 6:0.92 8:0.83 12:0.79 17:0.24 20:0.85 21:0.05 25:0.88 27:0.64 28:0.51 30:0.45 34:0.22 36:0.32 37:0.43 43:0.52 55:0.45 66:0.27 69:0.05 70:0.25 78:0.85 81:0.99 91:0.84 98:0.16 100:0.89 108:0.05 111:0.85 120:0.76 123:0.83 124:0.61 126:0.54 127:0.96 129:0.59 133:0.47 135:0.83 140:0.45 146:0.19 147:0.24 149:0.28 150:0.99 151:0.66 152:0.82 153:0.48 154:0.41 159:0.59 161:0.87 162:0.75 163:0.80 164:0.87 167:0.88 169:0.86 172:0.10 173:0.12 177:0.05 179:0.98 181:0.64 186:0.85 189:0.94 202:0.80 212:0.61 215:0.91 216:0.23 220:0.74 235:0.05 238:0.96 241:0.80 242:0.82 243:0.46 245:0.40 247:0.12 248:0.68 256:0.81 259:0.21 260:0.77 261:0.86 265:0.13 266:0.17 267:0.73 268:0.83 271:0.15 276:0.13 283:0.60 285:0.62 297:0.36 300:0.18 +1 12:0.79 17:0.81 21:0.78 25:0.88 27:0.51 28:0.51 30:0.76 32:0.80 34:0.50 36:0.28 37:0.29 43:0.28 55:0.59 66:0.36 69:0.78 70:0.15 91:0.58 98:0.16 108:0.86 120:0.69 123:0.86 124:0.52 127:0.44 129:0.59 133:0.47 135:0.77 140:0.45 145:0.63 146:0.05 147:0.05 149:0.18 151:0.66 153:0.48 154:0.20 159:0.56 161:0.87 162:0.57 163:0.99 164:0.57 167:0.74 172:0.10 173:0.75 177:0.05 179:0.99 181:0.64 187:0.39 195:0.78 202:0.55 212:0.95 216:0.25 235:0.22 241:0.68 242:0.82 243:0.34 245:0.37 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.17 266:0.12 267:0.36 268:0.46 271:0.15 276:0.13 285:0.62 300:0.13 +1 1:0.63 10:0.85 11:0.86 12:0.20 17:0.47 18:0.78 21:0.54 22:0.93 27:0.02 29:0.77 30:0.43 34:0.91 36:0.54 37:0.99 39:0.69 43:0.23 45:0.85 46:0.96 47:0.93 56:0.97 64:0.77 66:0.89 69:0.71 70:0.87 71:0.71 74:0.42 81:0.58 83:0.54 86:0.74 91:0.31 98:0.79 99:0.95 101:0.70 104:0.88 106:0.81 107:0.93 108:0.54 110:0.90 114:0.71 117:0.86 123:0.51 125:0.88 126:0.54 127:0.27 129:0.05 135:0.98 139:0.70 144:0.85 146:0.65 147:0.70 149:0.29 150:0.46 154:0.38 155:0.49 159:0.25 160:0.88 165:0.63 170:0.82 172:0.70 173:0.84 174:0.83 176:0.63 177:0.99 178:0.55 179:0.46 187:0.68 192:0.55 197:0.86 201:0.65 206:0.81 208:0.64 212:0.78 215:0.55 216:0.85 222:0.35 232:0.91 235:0.95 236:0.94 239:0.84 241:0.64 242:0.19 243:0.90 244:0.83 247:0.84 253:0.53 254:0.93 259:0.21 262:0.93 264:0.93 265:0.79 266:0.38 267:0.52 271:0.40 275:0.93 276:0.56 283:0.82 289:0.92 290:0.71 294:0.94 297:0.36 300:0.70 +2 1:0.65 7:0.69 9:0.72 11:0.48 12:0.20 17:0.72 18:0.96 21:0.47 22:0.93 23:0.96 27:0.32 29:0.77 30:0.90 31:0.88 32:0.67 34:0.90 36:0.95 37:0.62 39:0.69 43:0.48 44:0.88 45:0.98 46:0.88 47:0.93 48:0.98 56:0.97 64:0.77 66:0.97 69:0.57 70:0.28 71:0.71 74:0.76 77:0.70 79:0.88 81:0.75 83:0.72 86:0.93 91:0.44 96:0.71 97:0.94 98:0.89 99:0.99 101:0.64 104:0.87 106:0.81 107:0.88 108:0.44 110:0.88 114:0.80 117:0.86 120:0.58 122:0.67 123:0.42 125:0.97 126:0.54 127:0.44 128:0.95 129:0.05 135:0.95 137:0.88 139:0.93 140:0.45 143:0.99 144:0.85 145:0.83 146:0.81 147:0.84 149:0.84 150:0.53 151:0.57 153:0.48 154:0.47 155:0.64 158:0.75 159:0.37 160:0.96 162:0.86 164:0.57 165:0.88 168:0.95 170:0.82 172:0.92 173:0.65 175:0.91 176:0.73 177:0.88 179:0.23 187:0.98 192:1.00 195:0.65 196:0.92 201:0.66 202:0.36 204:0.83 205:0.95 206:0.81 208:0.64 212:0.93 215:0.63 216:0.69 219:0.90 220:0.87 222:0.35 230:0.71 232:0.90 233:0.76 235:0.88 236:0.97 241:0.57 242:0.30 243:0.97 244:0.83 247:0.76 248:0.56 253:0.68 254:0.86 255:0.72 256:0.45 257:0.84 259:0.21 260:0.58 262:0.93 264:0.93 265:0.89 266:0.27 267:0.59 268:0.46 271:0.40 276:0.85 277:0.87 279:0.77 281:0.91 282:0.75 283:0.82 284:0.89 285:0.62 289:0.92 290:0.80 292:0.95 297:0.36 300:0.43 +2 1:0.64 7:0.69 10:0.90 11:0.87 12:0.20 17:0.34 18:0.83 21:0.05 27:0.06 29:0.77 30:0.85 32:0.71 34:0.94 36:0.71 37:0.90 39:0.69 43:0.57 44:0.92 45:0.89 46:0.95 64:0.77 66:0.43 69:0.89 70:0.40 71:0.71 74:0.56 77:0.70 81:0.57 82:0.98 83:0.60 86:0.81 88:0.98 91:0.11 98:0.51 99:0.95 101:0.87 104:0.98 106:0.81 107:0.92 108:0.07 110:0.90 114:0.89 122:0.67 123:0.07 124:0.68 125:0.88 126:0.32 127:0.43 129:0.05 133:0.62 135:0.98 139:0.82 144:0.85 145:0.80 146:0.31 147:0.65 149:0.42 150:0.48 154:0.52 155:0.52 159:0.49 160:0.88 165:0.69 170:0.82 172:0.51 173:0.95 174:0.83 176:0.62 177:0.96 178:0.55 179:0.57 187:0.87 192:0.57 195:0.74 197:0.85 201:0.62 204:0.81 206:0.81 208:0.54 212:0.61 215:0.91 216:0.76 222:0.35 230:0.71 233:0.66 235:0.22 239:0.87 241:0.46 242:0.13 243:0.57 244:0.61 245:0.71 247:0.88 253:0.63 254:0.88 257:0.65 259:0.21 264:0.93 265:0.53 266:0.71 267:0.69 271:0.40 275:0.93 276:0.55 277:0.69 281:0.86 282:0.79 289:0.89 290:0.89 294:0.96 297:0.36 300:0.43 +0 8:0.66 10:0.83 11:0.87 12:0.20 17:0.43 18:0.65 21:0.78 27:0.45 29:0.77 30:0.91 32:0.65 34:0.80 36:0.17 37:0.50 39:0.69 43:0.35 44:0.88 45:0.81 46:0.93 66:0.25 69:0.71 70:0.19 71:0.71 74:0.39 77:0.70 86:0.65 91:0.20 98:0.08 101:0.70 107:0.89 108:0.54 110:0.89 122:0.67 123:0.52 124:0.71 125:0.88 127:0.37 129:0.59 133:0.61 135:0.91 139:0.68 144:0.85 145:0.49 146:0.17 147:0.22 149:0.37 154:0.26 159:0.76 160:0.88 163:0.97 170:0.82 172:0.16 173:0.51 174:0.79 175:0.97 177:0.70 178:0.55 179:0.88 187:0.68 195:0.93 197:0.82 212:0.52 216:0.77 222:0.35 235:0.74 239:0.82 241:0.49 242:0.24 243:0.45 244:0.93 245:0.45 247:0.47 253:0.34 257:0.93 259:0.21 264:0.93 265:0.08 266:0.27 267:0.91 271:0.40 275:0.91 276:0.21 277:0.95 282:0.74 289:0.88 294:0.93 300:0.18 +1 1:0.67 7:0.69 10:0.89 11:0.87 12:0.20 17:0.24 18:0.84 21:0.22 22:0.93 27:0.24 29:0.77 30:0.86 32:0.74 33:0.97 34:0.96 36:0.47 37:0.80 39:0.69 43:0.41 44:0.93 45:0.95 46:0.95 47:0.93 56:0.97 64:0.77 66:0.12 69:0.63 70:0.45 71:0.71 74:0.61 77:0.70 80:0.99 81:0.77 82:0.99 83:0.72 86:0.76 88:0.98 91:0.25 98:0.32 99:0.99 101:0.87 102:0.98 107:0.95 108:0.85 110:0.92 114:0.90 117:0.86 122:0.91 123:0.46 124:0.80 125:0.88 126:0.54 127:0.34 129:0.59 133:0.74 135:0.42 139:0.83 144:0.85 145:0.50 146:0.52 147:0.58 149:0.71 150:0.57 154:0.31 155:0.56 159:0.60 160:0.88 165:0.88 170:0.82 172:0.61 173:0.51 174:0.88 176:0.73 177:0.99 178:0.55 179:0.29 187:0.39 192:0.87 193:0.96 195:0.85 197:0.90 201:0.67 204:0.83 208:0.64 212:0.60 215:0.79 216:0.67 222:0.35 230:0.71 232:0.88 233:0.76 235:0.68 236:0.97 239:0.93 241:0.10 242:0.18 243:0.32 244:0.93 245:0.85 247:0.90 253:0.65 259:0.21 262:0.93 264:0.93 265:0.31 266:0.71 267:0.18 271:0.40 275:0.95 276:0.75 281:0.91 282:0.83 289:0.92 290:0.90 291:0.97 294:0.98 297:0.36 300:0.70 +1 10:0.87 11:0.86 12:0.20 17:0.47 18:0.69 21:0.78 27:0.45 29:0.77 30:0.71 34:0.71 36:0.28 37:0.50 39:0.69 43:0.23 45:0.88 46:0.94 66:0.89 69:0.80 70:0.45 71:0.71 74:0.40 86:0.73 91:0.05 98:0.57 101:0.64 107:0.92 108:0.64 110:0.90 123:0.61 125:0.88 127:0.17 129:0.05 135:0.88 139:0.70 144:0.85 145:0.49 146:0.76 147:0.80 149:0.22 154:0.35 159:0.32 160:0.88 170:0.82 172:0.68 173:0.74 174:0.83 177:0.99 178:0.55 179:0.26 187:0.68 197:0.83 212:0.77 216:0.77 222:0.35 235:0.31 239:0.85 241:0.30 242:0.31 243:0.89 244:0.61 247:0.76 253:0.35 254:0.90 259:0.21 264:0.93 265:0.59 266:0.54 267:0.44 271:0.40 275:0.93 276:0.56 289:0.89 294:0.95 300:0.43 +1 1:0.62 7:0.69 10:0.88 11:0.86 12:0.20 17:0.53 18:0.69 21:0.22 22:0.93 27:0.18 29:0.77 30:0.87 32:0.71 34:0.96 36:0.18 37:0.79 39:0.69 43:0.23 44:0.92 45:0.88 46:0.95 47:0.93 48:0.91 64:0.77 66:0.36 69:0.90 70:0.30 71:0.71 74:0.51 76:0.85 77:0.70 80:0.99 81:0.53 82:0.99 83:0.69 86:0.71 88:0.98 91:0.27 98:0.51 99:0.95 101:0.70 102:0.97 104:0.97 106:0.81 107:0.94 108:0.80 110:0.91 114:0.82 117:0.86 122:0.67 123:0.79 124:0.69 125:0.88 126:0.32 127:0.30 129:0.05 133:0.62 135:0.95 139:0.78 144:0.85 145:0.69 146:0.62 147:0.29 149:0.27 150:0.44 154:0.46 155:0.52 159:0.44 160:0.88 165:0.69 170:0.82 172:0.41 173:0.95 174:0.86 176:0.62 177:0.96 178:0.55 179:0.59 187:0.87 192:0.57 193:0.95 195:0.77 197:0.86 201:0.60 204:0.81 206:0.81 208:0.61 212:0.18 215:0.79 216:0.72 222:0.35 230:0.71 232:0.99 233:0.76 235:0.42 239:0.91 241:0.67 242:0.18 243:0.40 244:0.61 245:0.63 247:0.74 253:0.59 254:0.95 257:0.65 259:0.21 262:0.93 264:0.93 265:0.52 266:0.71 267:0.65 271:0.40 275:0.94 276:0.47 281:0.91 282:0.80 289:0.90 290:0.82 294:0.97 297:0.36 300:0.33 +1 1:0.64 8:0.92 10:0.85 11:0.86 12:0.20 17:0.53 18:0.77 21:0.47 22:0.93 27:0.02 29:0.77 30:0.43 34:0.92 36:0.51 37:0.99 39:0.69 43:0.23 45:0.85 46:0.96 47:0.93 56:0.97 64:0.77 66:0.89 69:0.70 70:0.87 71:0.71 74:0.42 81:0.59 83:0.54 86:0.76 91:0.33 98:0.79 99:0.95 101:0.70 104:0.88 106:0.81 107:0.93 108:0.53 110:0.90 114:0.74 117:0.86 123:0.51 125:0.88 126:0.54 127:0.27 129:0.05 135:0.97 139:0.71 144:0.85 146:0.65 147:0.70 149:0.29 150:0.47 154:0.38 155:0.49 159:0.25 160:0.88 165:0.63 170:0.82 172:0.70 173:0.84 174:0.83 176:0.63 177:0.99 178:0.55 179:0.46 187:0.68 192:0.55 197:0.86 201:0.65 206:0.81 208:0.64 212:0.78 215:0.58 216:0.85 222:0.35 232:0.91 235:0.88 236:0.94 239:0.84 241:0.65 242:0.19 243:0.90 244:0.83 247:0.84 253:0.54 254:0.93 259:0.21 262:0.93 264:0.93 265:0.79 266:0.27 267:0.52 271:0.40 275:0.93 276:0.56 289:0.92 290:0.74 294:0.94 297:0.36 300:0.70 +2 1:0.62 7:0.69 10:0.96 11:0.86 12:0.20 17:0.36 18:0.64 21:0.22 22:0.93 27:0.18 29:0.77 30:0.56 32:0.65 34:0.94 36:0.18 37:0.79 39:0.69 43:0.12 44:0.88 45:0.97 46:0.99 47:0.93 48:0.91 64:0.77 66:0.98 69:0.82 70:0.37 71:0.71 74:0.45 76:0.85 77:0.70 80:0.99 81:0.53 83:0.69 86:0.69 91:0.31 98:0.87 99:0.95 101:0.70 104:0.97 106:0.81 107:0.98 108:0.66 110:0.94 114:0.82 117:0.86 122:0.98 123:0.64 125:0.88 126:0.32 127:0.39 129:0.05 135:0.90 139:0.73 144:0.85 145:0.63 146:0.95 147:0.96 149:0.09 150:0.44 154:0.46 155:0.52 159:0.63 160:0.88 165:0.69 170:0.82 172:0.94 173:0.75 174:0.97 176:0.62 177:0.99 178:0.55 179:0.18 187:0.87 192:0.57 193:0.95 195:0.87 197:0.97 201:0.60 204:0.81 206:0.81 208:0.61 212:0.91 215:0.79 216:0.72 222:0.35 230:0.71 232:0.99 233:0.76 235:0.42 239:0.95 241:0.63 242:0.44 243:0.98 244:0.61 247:0.67 253:0.59 254:0.95 259:0.21 262:0.93 264:0.93 265:0.87 266:0.47 267:0.36 271:0.40 275:0.99 276:0.89 281:0.91 282:0.74 289:0.91 290:0.82 294:0.99 297:0.36 300:0.55 +1 1:0.61 7:0.69 10:0.95 11:0.87 12:0.20 17:0.47 18:0.80 21:0.30 22:0.93 27:0.18 29:0.77 30:0.89 32:0.74 34:0.95 36:0.19 37:0.79 39:0.69 43:0.21 44:0.93 45:0.95 46:0.98 47:0.93 48:0.91 64:0.77 66:0.93 69:0.82 70:0.29 71:0.71 74:0.67 76:0.85 77:0.70 80:0.99 81:0.52 82:0.99 83:0.69 86:0.88 88:0.98 91:0.20 98:0.74 99:0.95 101:0.87 102:0.98 104:0.97 106:0.81 107:0.96 108:0.66 110:0.93 114:0.80 117:0.86 122:0.91 123:0.64 125:0.88 126:0.32 127:0.24 129:0.05 135:0.93 139:0.88 144:0.85 145:0.69 146:0.75 147:0.79 149:0.11 150:0.43 154:0.51 155:0.52 159:0.31 160:0.88 165:0.69 170:0.82 172:0.82 173:0.87 174:0.90 176:0.62 177:0.99 178:0.55 179:0.26 187:0.87 192:0.57 193:0.95 195:0.71 197:0.90 201:0.59 204:0.81 206:0.81 208:0.61 212:0.82 215:0.72 216:0.72 222:0.35 230:0.71 232:0.99 233:0.76 235:0.52 239:0.95 241:0.67 242:0.24 243:0.94 247:0.79 253:0.61 254:0.94 259:0.21 262:0.93 264:0.93 265:0.74 266:0.47 267:0.28 271:0.40 275:0.96 276:0.70 281:0.86 282:0.83 289:0.90 290:0.80 294:0.99 297:0.36 300:0.43 +1 1:0.67 7:0.69 10:0.89 11:0.87 12:0.20 17:0.20 18:0.84 21:0.22 22:0.93 27:0.24 29:0.77 30:0.86 32:0.74 33:0.97 34:0.96 36:0.55 37:0.80 39:0.69 43:0.41 44:0.93 45:0.95 46:0.95 47:0.93 56:0.97 64:0.77 66:0.12 69:0.63 70:0.43 71:0.71 74:0.61 77:0.70 80:0.99 81:0.77 82:0.99 83:0.72 86:0.76 88:0.98 91:0.20 98:0.32 99:0.99 101:0.87 102:0.98 107:0.95 108:0.85 110:0.92 114:0.90 117:0.86 122:0.91 123:0.46 124:0.80 125:0.88 126:0.54 127:0.34 129:0.59 133:0.74 135:0.44 139:0.83 144:0.85 145:0.50 146:0.52 147:0.58 149:0.71 150:0.57 154:0.31 155:0.56 159:0.60 160:0.88 165:0.88 170:0.82 172:0.61 173:0.51 174:0.88 176:0.73 177:0.99 178:0.55 179:0.29 187:0.39 192:0.87 193:0.96 195:0.85 197:0.90 201:0.67 204:0.83 208:0.64 212:0.60 215:0.79 216:0.67 222:0.35 230:0.71 232:0.88 233:0.76 235:0.68 236:0.97 239:0.93 241:0.10 242:0.18 243:0.32 244:0.93 245:0.85 247:0.90 253:0.65 259:0.21 262:0.93 264:0.93 265:0.32 266:0.71 267:0.18 271:0.40 275:0.95 276:0.75 281:0.91 282:0.83 289:0.92 290:0.90 291:0.97 294:0.98 297:0.36 300:0.70 +0 10:0.81 11:0.84 12:0.20 17:0.36 18:0.81 21:0.05 27:0.43 29:0.77 30:0.14 34:0.70 36:0.97 37:0.87 39:0.38 43:0.58 45:0.87 55:0.92 66:0.15 69:0.43 70:0.87 71:0.97 74:0.54 81:0.33 86:0.83 91:0.05 98:0.42 101:0.47 104:0.93 106:0.81 108:0.57 122:0.83 123:0.93 124:0.91 126:0.24 127:0.91 129:0.89 133:0.91 135:0.82 139:0.78 144:0.85 146:0.28 147:0.34 149:0.56 150:0.37 154:0.56 159:0.88 163:0.94 170:0.96 172:0.57 173:0.17 174:0.79 175:0.75 177:0.70 178:0.55 179:0.43 187:0.39 197:0.80 206:0.81 212:0.16 215:0.91 216:0.28 222:0.35 232:0.74 235:0.05 239:0.80 241:0.07 242:0.33 243:0.19 244:0.61 245:0.76 247:0.90 253:0.43 254:0.95 257:0.65 259:0.21 265:0.39 266:0.91 267:0.82 271:0.43 276:0.74 277:0.69 283:0.67 297:0.36 300:0.84 +2 1:0.65 10:0.97 11:0.84 12:0.20 17:0.69 18:0.78 21:0.22 27:0.72 29:0.77 30:0.17 33:0.97 34:0.85 36:0.62 37:0.47 39:0.38 43:0.65 45:0.77 55:0.71 64:0.77 66:0.33 69:0.77 70:0.96 71:0.97 74:0.49 75:0.96 81:0.63 83:0.56 86:0.79 91:0.15 98:0.52 99:0.83 101:0.47 102:0.95 108:0.61 114:0.88 122:0.95 123:0.58 124:0.83 126:0.51 127:0.70 129:0.05 133:0.80 135:0.34 139:0.75 144:0.85 145:0.41 146:0.76 147:0.46 149:0.61 150:0.57 154:0.45 155:0.50 159:0.73 165:0.65 170:0.96 172:0.65 173:0.68 174:0.97 176:0.70 177:0.70 178:0.55 179:0.42 187:0.68 192:0.47 195:0.85 197:0.97 201:0.63 208:0.61 212:0.71 216:0.17 222:0.35 226:0.98 232:0.80 235:0.42 236:0.95 239:0.96 241:0.12 242:0.57 243:0.23 245:0.79 247:0.86 253:0.62 254:0.99 257:0.65 259:0.21 265:0.53 266:0.80 267:0.44 271:0.43 276:0.74 290:0.88 291:0.97 300:0.84 +0 1:0.61 8:0.90 9:0.73 10:0.88 11:0.84 12:0.20 17:0.22 18:0.67 21:0.22 27:0.08 29:0.77 30:0.13 31:0.94 34:0.36 36:0.75 37:0.87 39:0.38 43:0.41 44:0.88 45:0.73 55:0.52 64:0.77 66:0.77 69:0.85 70:0.84 71:0.97 74:0.54 76:0.85 77:0.70 79:0.94 81:0.51 83:0.56 86:0.69 91:0.72 96:0.92 97:0.97 98:0.76 99:0.83 101:0.47 108:0.71 114:0.85 117:0.86 122:0.83 123:0.69 124:0.42 126:0.32 127:0.73 129:0.05 133:0.60 135:0.54 137:0.94 139:0.76 140:0.87 144:0.85 145:0.65 146:0.92 147:0.31 149:0.44 150:0.45 151:0.80 153:0.93 154:0.31 155:0.55 158:0.76 159:0.73 162:0.83 165:0.65 170:0.96 172:0.81 173:0.78 174:0.89 176:0.63 177:0.38 179:0.44 187:0.95 190:0.95 191:0.73 192:0.55 195:0.88 196:0.93 197:0.87 201:0.58 202:0.75 208:0.50 212:0.42 216:0.83 219:0.91 222:0.35 232:0.92 235:0.31 239:0.86 241:0.39 242:0.45 243:0.12 244:0.83 245:0.72 247:0.92 248:0.76 253:0.90 255:0.71 256:0.79 257:0.84 259:0.21 265:0.76 266:0.84 267:0.97 268:0.81 271:0.43 276:0.73 279:0.79 282:0.75 284:0.95 285:0.50 290:0.85 292:0.88 300:0.84 +0 10:0.87 11:0.84 12:0.20 17:0.30 18:0.62 21:0.59 27:0.08 29:0.77 30:0.13 34:0.75 36:0.74 37:0.87 39:0.38 43:0.13 45:0.73 55:0.59 66:0.71 69:0.92 70:0.94 71:0.97 74:0.43 81:0.28 86:0.70 91:0.02 98:0.49 101:0.47 104:0.96 106:0.81 108:0.85 114:0.64 122:0.83 123:0.84 124:0.56 126:0.24 127:0.62 129:0.05 133:0.80 135:0.26 139:0.68 144:0.85 145:0.94 146:0.73 147:0.05 149:0.19 150:0.31 154:0.46 158:0.72 159:0.71 170:0.96 172:0.76 173:0.92 174:0.86 176:0.59 177:0.05 178:0.55 179:0.48 187:1.00 193:0.97 197:0.85 206:0.81 212:0.77 216:0.83 222:0.35 232:0.99 235:0.68 239:0.88 241:0.12 242:0.16 243:0.08 245:0.64 247:0.93 253:0.50 254:0.84 257:0.93 259:0.21 265:0.50 266:0.75 267:0.96 271:0.43 276:0.68 290:0.64 300:0.70 +1 10:0.90 11:0.84 12:0.20 17:0.40 18:0.69 21:0.54 27:0.08 29:0.77 30:0.33 34:0.80 36:0.82 37:0.87 39:0.38 43:0.27 44:0.88 45:0.77 55:0.59 66:0.54 69:0.93 70:0.98 71:0.97 74:0.49 77:0.70 81:0.37 86:0.72 91:0.06 98:0.41 101:0.47 102:0.95 104:0.99 106:0.81 108:0.87 114:0.67 123:0.86 124:0.67 126:0.32 127:0.29 129:0.05 133:0.73 135:0.75 139:0.74 144:0.85 145:0.93 146:0.76 147:0.05 149:0.28 150:0.39 154:0.34 159:0.71 170:0.96 172:0.70 173:0.88 174:0.89 176:0.59 177:0.05 178:0.55 179:0.35 187:1.00 192:0.44 193:0.97 195:0.94 197:0.89 201:0.55 206:0.81 212:0.68 216:0.83 222:0.35 235:0.68 236:0.94 239:0.91 241:0.10 242:0.23 243:0.09 245:0.73 247:0.93 253:0.52 254:0.84 257:0.93 259:0.21 265:0.43 266:0.84 267:0.97 271:0.43 276:0.67 282:0.75 290:0.67 300:0.94 +2 8:0.72 9:0.73 10:0.92 11:0.84 12:0.20 17:0.55 18:0.63 21:0.40 23:0.98 27:0.72 29:0.77 30:0.21 31:0.91 34:0.47 36:0.67 39:0.38 43:0.58 45:0.66 62:0.97 66:0.80 69:0.19 70:0.50 71:0.97 74:0.38 79:0.90 81:0.32 83:0.56 86:0.72 91:0.79 96:0.75 98:0.93 101:0.47 102:0.95 108:0.15 122:0.77 123:0.15 126:0.24 127:0.58 128:0.97 129:0.05 135:0.21 137:0.91 139:0.67 140:0.80 144:0.85 145:0.45 146:0.76 147:0.80 149:0.30 150:0.36 151:0.71 153:0.72 154:0.55 155:0.64 159:0.32 162:0.53 163:0.81 168:0.97 170:0.96 172:0.45 173:0.37 174:0.86 177:0.88 178:0.42 179:0.87 190:0.77 192:0.57 193:0.97 195:0.58 196:0.89 197:0.89 202:0.71 205:0.98 208:0.61 212:0.55 216:0.08 220:0.74 222:0.35 235:0.42 236:0.92 239:0.92 241:0.86 242:0.16 243:0.82 244:0.61 247:0.60 248:0.67 251:1.00 253:0.57 254:0.97 255:0.73 256:0.64 259:0.21 265:0.93 266:0.27 267:0.19 268:0.66 271:0.43 276:0.36 284:0.91 285:0.60 300:0.33 +1 10:0.88 11:0.84 12:0.20 17:0.22 18:0.69 21:0.64 27:0.08 29:0.77 30:0.08 34:0.75 36:0.83 37:0.87 39:0.38 43:0.25 45:0.77 55:0.52 66:0.68 69:0.92 70:0.98 71:0.97 74:0.42 81:0.27 86:0.72 91:0.03 96:0.68 98:0.57 101:0.47 104:0.96 106:0.81 108:0.84 114:0.63 123:0.83 124:0.66 126:0.24 127:0.61 129:0.05 133:0.85 135:0.32 139:0.69 140:0.45 144:0.85 145:0.92 146:0.82 147:0.05 149:0.28 150:0.30 151:0.46 153:0.48 154:0.35 159:0.74 162:0.86 170:0.96 172:0.77 173:0.90 174:0.88 176:0.59 177:0.05 179:0.45 187:0.99 190:0.95 193:0.97 197:0.86 202:0.36 206:0.81 212:0.73 216:0.83 220:1.00 222:0.35 232:0.99 235:0.74 239:0.89 241:0.10 242:0.15 243:0.08 245:0.64 247:0.93 248:0.46 253:0.49 254:0.84 256:0.45 257:0.93 259:0.21 265:0.58 266:0.84 267:0.96 268:0.46 271:0.43 276:0.70 285:0.46 290:0.63 300:0.84 +2 1:0.60 8:0.88 10:0.88 11:0.84 12:0.20 17:0.07 18:0.75 21:0.30 27:0.43 29:0.77 30:0.21 34:0.75 36:0.82 37:0.87 39:0.38 43:0.58 45:0.73 64:0.77 66:0.80 69:0.66 70:0.50 71:0.97 74:0.41 75:0.96 81:0.49 83:0.56 86:0.75 91:0.25 98:0.74 99:0.83 101:0.47 108:0.50 114:0.82 122:0.91 123:0.48 126:0.32 127:0.24 129:0.05 135:0.39 139:0.71 144:0.85 146:0.66 147:0.71 149:0.31 150:0.44 154:0.49 155:0.47 159:0.25 165:0.65 170:0.96 172:0.45 173:0.77 174:0.83 176:0.63 177:0.88 178:0.55 179:0.71 187:0.21 192:0.45 197:0.86 201:0.57 208:0.50 212:0.55 216:0.28 222:0.35 226:0.96 232:0.74 235:0.42 239:0.89 241:0.47 242:0.40 243:0.82 244:0.61 247:0.55 253:0.58 254:0.94 259:0.21 265:0.75 266:0.38 267:0.28 271:0.43 276:0.35 290:0.82 300:0.33 +2 10:0.95 11:0.87 12:0.20 17:0.85 18:0.77 21:0.40 27:0.66 29:0.77 30:0.30 34:0.53 36:0.95 37:0.78 39:0.38 43:0.61 45:0.85 55:0.59 66:0.23 69:0.12 70:0.89 71:0.97 74:0.55 81:0.31 86:0.82 91:0.18 98:0.57 101:0.65 108:0.77 114:0.69 122:0.83 123:0.10 124:0.72 126:0.24 127:0.75 129:0.59 133:0.67 135:0.74 139:0.77 144:0.85 146:0.61 147:0.66 149:0.59 150:0.34 154:0.32 158:0.72 159:0.50 170:0.96 172:0.63 173:0.21 174:0.92 176:0.59 177:0.88 178:0.55 179:0.45 187:0.99 197:0.93 212:0.78 216:0.20 222:0.35 235:0.42 239:0.93 241:0.18 242:0.13 243:0.43 245:0.85 247:0.96 253:0.54 254:0.99 259:0.21 265:0.48 266:0.63 267:0.52 271:0.43 276:0.72 290:0.69 300:0.84 +0 10:1.00 11:0.87 12:0.20 17:0.62 18:0.74 21:0.47 23:0.95 27:0.53 29:0.77 30:0.60 33:0.97 34:0.70 36:0.55 37:0.38 39:0.38 43:0.39 45:0.81 55:0.52 62:0.97 66:0.90 69:0.96 70:0.62 71:0.97 74:0.34 75:0.97 81:0.38 86:0.69 91:0.07 98:0.85 101:0.87 102:0.95 104:1.00 106:0.81 108:0.92 120:0.56 122:0.94 123:0.92 124:0.39 126:0.32 127:0.71 128:0.95 129:0.05 133:0.85 135:0.72 139:0.66 140:0.45 144:0.85 145:0.47 146:1.00 147:0.18 149:0.74 150:0.39 151:0.52 153:0.48 154:0.18 159:0.93 162:0.86 164:0.56 168:0.95 170:0.96 172:1.00 173:0.83 174:1.00 177:0.38 179:0.09 187:0.68 190:0.89 192:0.45 193:0.96 195:0.99 197:1.00 201:0.55 202:0.36 205:0.95 206:0.81 212:0.93 215:0.63 216:0.80 220:0.93 222:0.35 226:0.96 235:0.60 236:0.94 239:1.00 241:0.07 242:0.24 243:0.05 245:0.97 247:0.98 248:0.51 253:0.30 254:0.74 255:0.69 256:0.45 257:0.84 259:0.21 260:0.56 265:0.85 266:0.84 267:0.96 268:0.46 271:0.43 276:0.99 283:0.67 285:0.50 291:0.97 297:0.36 300:0.94 +0 8:0.69 10:0.86 11:0.84 12:0.20 17:0.36 18:0.74 21:0.22 23:0.95 27:0.16 29:0.77 30:0.38 33:0.97 34:0.42 36:0.54 37:0.87 39:0.38 43:0.57 45:0.77 62:0.95 66:0.83 69:0.53 70:0.33 71:0.97 74:0.44 75:0.96 81:0.35 86:0.75 91:0.33 98:0.84 101:0.47 108:0.41 122:0.86 123:0.39 126:0.24 127:0.34 128:0.95 129:0.05 135:0.32 139:0.72 140:0.80 144:0.85 146:0.66 147:0.71 149:0.52 150:0.39 151:0.50 153:0.48 154:0.46 159:0.25 162:0.62 168:0.95 170:0.96 172:0.51 173:0.70 174:0.83 177:0.88 178:0.42 179:0.74 187:0.68 190:0.95 191:0.70 197:0.87 202:0.50 205:0.95 212:0.42 216:0.50 220:0.91 222:0.35 226:0.98 232:0.72 235:0.22 236:0.92 239:0.88 241:0.50 242:0.29 243:0.84 244:0.83 247:0.60 248:0.50 253:0.37 256:0.45 259:0.21 265:0.84 266:0.47 267:0.52 268:0.46 271:0.43 276:0.42 285:0.46 291:0.97 300:0.25 +2 1:0.62 9:0.70 10:0.89 11:0.84 12:0.20 17:0.34 18:0.63 21:0.47 23:0.96 27:0.08 29:0.77 30:0.21 31:0.86 33:0.97 34:0.61 36:0.87 37:0.87 39:0.38 43:0.56 45:0.73 55:0.59 62:0.96 64:0.77 66:0.63 69:0.90 70:0.96 71:0.97 74:0.50 75:0.98 79:0.86 81:0.57 83:0.56 86:0.69 91:0.06 96:0.68 97:0.89 98:0.38 99:0.83 101:0.47 104:0.99 106:0.81 108:0.89 114:0.78 122:0.83 123:0.88 124:0.67 126:0.51 127:0.32 128:0.95 129:0.59 133:0.80 135:0.65 137:0.86 139:0.75 140:0.45 144:0.85 145:0.94 146:0.29 147:0.05 149:0.38 150:0.50 151:0.49 153:0.69 154:0.17 155:0.50 158:0.81 159:0.75 162:0.86 165:0.65 168:0.95 170:0.96 172:0.73 173:0.80 174:0.89 176:0.70 177:0.05 179:0.37 187:1.00 190:0.77 192:0.48 193:0.97 196:0.88 197:0.88 201:0.60 202:0.46 205:0.95 206:0.81 208:0.61 212:0.73 216:0.83 219:0.94 220:0.97 222:0.35 226:0.96 235:0.68 236:0.95 239:0.92 241:0.05 242:0.17 243:0.09 245:0.67 247:0.93 248:0.49 253:0.57 254:0.99 255:0.69 256:0.45 257:0.93 259:0.21 265:0.40 266:0.80 267:0.96 268:0.55 271:0.43 276:0.66 279:0.78 284:0.90 285:0.60 290:0.78 291:0.97 292:0.88 300:0.84 +0 1:0.62 8:0.96 10:0.89 11:0.84 12:0.20 17:0.11 18:0.64 21:0.13 27:0.16 29:0.77 30:0.58 34:0.29 36:0.83 37:0.87 39:0.38 43:0.13 45:0.66 55:0.52 64:0.77 66:0.69 69:0.84 70:0.44 71:0.97 74:0.34 81:0.53 83:0.56 86:0.67 91:0.62 98:0.65 99:0.83 101:0.47 108:0.70 114:0.88 122:0.95 123:0.68 124:0.41 126:0.32 127:0.36 129:0.05 133:0.36 135:0.84 139:0.65 144:0.85 146:0.65 147:0.05 149:0.13 150:0.47 154:0.44 155:0.48 159:0.38 165:0.65 170:0.96 172:0.41 173:0.92 174:0.88 176:0.63 177:0.05 178:0.55 179:0.82 187:0.68 192:0.46 197:0.90 201:0.58 202:0.74 208:0.50 212:0.16 216:0.50 222:0.35 232:0.72 235:0.22 239:0.88 241:0.63 242:0.40 243:0.18 244:0.61 245:0.46 247:0.55 253:0.61 254:0.84 257:0.93 259:0.21 265:0.66 266:0.54 267:0.19 271:0.43 276:0.33 290:0.88 300:0.33 +1 10:0.93 11:0.84 12:0.20 17:0.13 18:0.95 21:0.78 27:0.43 29:0.77 30:0.60 34:0.75 36:0.50 37:0.87 39:0.38 43:0.29 45:0.91 55:0.59 66:0.35 69:0.93 70:0.77 71:0.97 74:0.51 75:0.97 86:0.84 91:0.31 98:0.61 101:0.47 104:0.93 108:0.91 122:0.92 123:0.90 124:0.85 127:0.96 129:0.05 133:0.86 135:0.37 139:0.80 144:0.85 146:0.76 147:0.60 149:0.66 154:0.49 159:0.83 170:0.96 172:0.83 173:0.89 174:0.91 177:0.70 178:0.55 179:0.26 187:0.39 197:0.93 212:0.71 216:0.28 222:0.35 226:0.95 232:0.74 235:0.31 239:0.93 241:0.51 242:0.58 243:0.22 244:0.83 245:0.90 247:0.94 253:0.41 257:0.65 259:0.21 265:0.62 266:0.80 267:0.28 271:0.43 276:0.88 277:0.87 283:0.65 300:0.70 +0 10:0.88 11:0.87 12:0.20 17:0.38 18:0.87 21:0.47 27:0.67 29:0.77 30:0.62 34:0.69 36:0.88 37:0.46 39:0.38 43:0.61 45:0.88 66:0.90 69:0.56 70:0.50 71:0.97 74:0.73 81:0.28 83:0.69 86:0.89 91:0.33 98:0.90 101:0.87 108:0.43 122:0.86 123:0.41 126:0.24 127:0.48 129:0.05 135:0.65 139:0.88 144:0.85 145:0.83 146:0.79 147:0.83 149:0.67 150:0.31 154:0.60 155:0.54 159:0.35 170:0.96 172:0.71 173:0.66 174:0.79 177:0.88 178:0.55 179:0.57 187:0.68 192:0.50 193:0.99 197:0.84 202:0.36 204:0.83 208:0.61 212:0.35 215:0.63 216:0.39 222:0.35 232:0.72 235:0.52 239:0.91 241:0.44 242:0.44 243:0.90 244:0.61 247:0.76 253:0.52 259:0.21 265:0.90 266:0.54 267:0.36 271:0.43 276:0.59 281:0.91 297:0.36 300:0.43 +0 1:0.59 8:0.88 10:0.84 11:0.84 12:0.20 17:0.51 18:0.92 21:0.40 22:0.93 27:0.42 29:0.77 30:0.45 34:0.37 36:0.50 37:0.49 39:0.38 43:0.27 44:0.88 45:0.89 47:0.93 55:0.42 64:0.77 66:0.88 69:0.90 70:0.62 71:0.97 74:0.63 77:0.70 81:0.48 83:0.56 86:0.85 91:0.07 97:0.89 98:0.72 99:0.83 101:0.47 104:0.94 106:0.81 108:0.80 114:0.78 117:0.86 122:0.92 123:0.79 124:0.37 126:0.32 127:0.38 129:0.05 133:0.59 135:0.71 139:0.87 144:0.85 145:0.76 146:0.95 147:0.05 149:0.38 150:0.42 154:0.31 155:0.51 158:0.77 159:0.77 165:0.65 170:0.96 172:0.91 173:0.81 174:0.79 175:0.75 176:0.63 177:0.05 178:0.55 179:0.23 187:0.95 192:0.48 195:0.93 197:0.83 201:0.56 202:0.36 204:0.80 206:0.81 208:0.50 212:0.54 216:0.37 219:0.91 222:0.35 232:0.97 235:0.52 239:0.83 241:0.05 242:0.39 243:0.06 244:0.61 245:0.70 247:0.88 253:0.60 254:0.86 257:0.93 259:0.21 262:0.93 265:0.73 266:0.75 267:0.96 271:0.43 276:0.83 277:0.69 279:0.76 281:0.91 282:0.75 290:0.78 292:0.95 300:0.84 +1 10:0.94 11:0.87 12:0.20 17:0.83 18:0.84 21:0.13 27:0.62 29:0.77 30:0.32 34:0.36 36:0.95 37:0.81 39:0.38 43:0.55 45:0.88 66:0.26 69:0.29 70:0.92 71:0.97 74:0.58 81:0.36 86:0.86 91:0.15 98:0.57 101:0.65 102:0.95 108:0.22 122:0.86 123:0.79 124:0.73 126:0.24 127:0.72 129:0.59 133:0.64 135:0.49 139:0.82 144:0.85 145:0.47 146:0.43 147:0.50 149:0.64 150:0.41 154:0.74 159:0.60 170:0.96 172:0.54 173:0.26 174:0.90 177:0.88 178:0.55 179:0.49 187:0.39 193:0.97 195:0.75 197:0.91 202:0.36 212:0.69 216:0.14 222:0.35 235:0.12 236:0.92 239:0.94 241:0.30 242:0.16 243:0.46 245:0.82 247:0.91 253:0.45 254:0.88 259:0.21 265:0.34 266:0.71 267:0.23 271:0.43 276:0.67 300:0.84 +1 8:0.88 10:0.89 12:0.20 17:0.40 21:0.78 23:0.97 27:0.35 29:0.77 30:0.62 34:0.83 36:0.56 37:0.82 39:0.38 43:0.20 55:0.59 62:0.97 66:0.61 69:0.80 70:0.34 71:0.97 74:0.37 83:0.56 91:0.37 98:0.46 102:0.95 108:0.63 123:0.61 124:0.43 127:0.45 128:0.97 129:0.05 133:0.39 135:0.37 139:0.67 140:0.80 144:0.85 145:0.40 146:0.38 147:0.05 149:0.11 151:0.57 153:0.48 154:0.29 155:0.50 159:0.31 162:0.54 168:0.97 170:0.96 172:0.27 173:0.93 174:0.86 175:0.91 177:0.05 178:0.42 179:0.93 187:0.39 190:0.95 191:0.78 192:0.47 193:0.97 195:0.60 197:0.89 202:0.65 204:0.80 205:0.97 208:0.50 212:0.30 216:0.74 220:0.82 222:0.35 235:0.74 239:0.91 241:0.54 242:0.33 243:0.23 244:0.83 245:0.42 247:0.39 248:0.58 253:0.33 254:0.84 256:0.58 257:0.93 259:0.21 265:0.48 266:0.27 267:0.44 268:0.59 271:0.43 276:0.21 277:0.87 281:0.91 285:0.46 300:0.25 +0 1:0.59 10:0.89 11:0.84 12:0.20 17:0.67 18:0.81 21:0.68 27:0.31 29:0.77 30:0.32 32:0.65 34:0.95 36:0.76 37:0.83 39:0.38 43:0.62 45:0.77 55:0.52 64:0.77 66:0.27 69:0.60 70:0.95 71:0.97 74:0.48 76:0.85 81:0.52 83:0.56 86:0.74 91:0.27 98:0.39 99:0.83 101:0.47 102:0.95 108:0.85 114:0.69 122:0.92 123:0.44 124:0.67 126:0.51 127:0.31 129:0.05 133:0.60 135:0.42 139:0.76 144:0.85 145:0.76 146:0.64 147:0.69 149:0.45 150:0.44 154:0.52 155:0.53 159:0.61 165:0.65 170:0.96 172:0.48 173:0.45 174:0.86 176:0.70 177:0.88 178:0.55 179:0.51 187:0.68 192:0.50 195:0.86 197:0.89 201:0.57 204:0.82 208:0.61 212:0.54 216:0.16 222:0.35 232:0.84 235:0.88 236:0.95 239:0.89 241:0.27 242:0.21 243:0.46 244:0.83 245:0.69 247:0.82 253:0.53 259:0.21 265:0.28 266:0.71 267:0.44 271:0.43 276:0.51 281:0.91 290:0.69 300:0.84 +0 10:0.88 11:0.87 12:0.20 17:0.36 18:0.87 21:0.47 27:0.67 29:0.77 30:0.62 34:0.75 36:0.89 37:0.46 39:0.38 43:0.61 45:0.88 66:0.90 69:0.56 70:0.50 71:0.97 74:0.73 81:0.28 83:0.69 86:0.89 91:0.42 98:0.90 101:0.87 108:0.43 122:0.86 123:0.41 126:0.24 127:0.48 129:0.05 135:0.54 139:0.88 144:0.85 145:0.83 146:0.79 147:0.83 149:0.67 150:0.31 154:0.60 155:0.54 159:0.35 170:0.96 172:0.71 173:0.66 174:0.79 177:0.88 178:0.55 179:0.57 187:0.68 192:0.50 193:0.99 197:0.84 202:0.46 204:0.83 208:0.61 212:0.35 215:0.63 216:0.39 222:0.35 232:0.72 235:0.52 239:0.91 241:0.49 242:0.44 243:0.90 244:0.61 247:0.76 253:0.52 259:0.21 265:0.90 266:0.54 267:0.36 271:0.43 276:0.59 281:0.91 297:0.36 300:0.43 +2 8:0.93 9:0.72 10:0.90 11:0.54 12:0.20 17:0.26 18:0.73 21:0.22 23:0.97 27:0.67 28:0.83 29:0.62 30:0.28 34:0.45 36:0.42 37:0.44 39:0.60 43:0.34 45:0.81 62:0.97 66:0.67 69:0.68 70:0.95 71:0.88 74:0.39 75:0.96 81:0.29 83:0.54 86:0.71 91:0.53 97:0.97 98:0.38 101:0.69 108:0.51 123:0.49 124:0.45 126:0.23 127:0.69 128:0.98 129:0.05 133:0.36 135:0.47 139:0.68 140:0.90 143:0.99 146:0.66 147:0.71 149:0.32 150:0.32 151:0.85 153:0.48 154:0.48 155:0.54 159:0.77 161:0.87 162:0.60 167:0.60 168:0.98 170:0.87 172:0.57 173:0.52 174:0.86 177:0.96 179:0.72 181:0.91 187:0.39 190:0.95 192:0.48 197:0.89 202:0.63 205:0.97 208:0.49 212:0.55 215:0.79 216:0.39 220:0.91 222:0.76 226:0.98 232:0.84 235:0.22 239:0.90 241:0.60 242:0.12 243:0.72 244:0.83 245:0.66 247:0.86 248:0.76 253:0.34 254:0.74 255:0.71 256:0.58 259:0.21 264:0.95 265:0.41 266:0.54 267:0.69 268:0.62 271:0.43 275:0.93 276:0.31 279:0.78 285:0.49 294:0.96 295:0.99 297:0.36 300:0.84 +0 6:0.95 7:0.66 8:0.89 9:0.73 10:0.95 11:0.54 12:0.20 17:0.28 18:0.81 20:0.90 21:0.59 23:0.99 27:0.21 28:0.83 29:0.62 30:0.32 31:0.89 33:0.97 34:0.45 36:0.75 37:0.74 39:0.60 43:0.10 45:0.85 55:0.59 62:0.99 66:0.29 69:0.25 70:0.99 71:0.88 74:0.66 75:0.97 76:0.85 78:0.80 79:0.89 81:0.32 83:0.60 86:0.72 91:0.63 96:0.86 97:0.99 98:0.24 100:0.83 101:0.69 108:0.20 111:0.80 114:0.69 117:0.86 120:0.58 122:0.90 123:0.19 124:0.89 126:0.31 127:0.91 128:0.99 129:0.05 133:0.89 135:0.28 137:0.89 139:0.70 140:0.97 143:1.00 146:0.82 147:0.85 149:0.22 150:0.32 151:0.57 152:0.97 153:0.72 154:0.50 155:0.58 158:0.76 159:0.96 161:0.87 162:0.56 164:0.56 167:0.62 168:0.99 169:0.86 170:0.87 172:0.74 173:0.07 174:0.96 176:0.62 177:0.96 179:0.32 181:0.91 186:0.81 187:0.39 190:0.89 191:0.70 192:0.97 193:0.96 196:0.89 197:0.94 201:0.54 202:0.86 205:0.99 208:0.64 212:0.68 216:0.95 220:0.82 222:0.76 226:0.98 230:0.68 232:0.91 233:0.76 235:0.74 236:0.92 239:0.94 241:0.63 242:0.13 243:0.47 244:0.61 245:0.85 247:0.98 248:0.56 253:0.85 254:0.99 255:0.74 256:0.84 259:0.21 260:0.58 261:0.90 264:0.95 265:0.26 266:0.94 267:0.65 268:0.85 271:0.43 275:0.95 276:0.71 279:0.79 284:0.86 285:0.62 290:0.69 291:0.97 294:0.97 295:0.99 300:0.99 +1 1:0.57 7:0.66 8:0.88 10:0.82 11:0.45 12:0.20 17:0.53 18:0.76 21:0.68 27:0.12 28:0.83 29:0.62 30:0.11 32:0.64 34:0.75 36:0.41 37:0.80 39:0.60 43:0.29 45:0.66 55:0.52 56:0.97 66:0.36 69:0.93 70:0.88 71:0.88 74:0.49 76:0.85 77:0.96 80:0.99 81:0.52 82:0.99 83:0.54 86:0.72 88:0.99 91:0.56 98:0.41 99:0.83 101:0.46 102:0.96 108:0.85 114:0.69 122:0.86 123:0.84 124:0.70 126:0.53 127:0.47 129:0.05 133:0.65 135:0.32 139:0.68 145:0.91 146:0.61 147:0.39 149:0.24 150:0.41 154:0.15 155:0.49 159:0.64 161:0.87 165:0.63 167:0.68 170:0.87 172:0.51 173:0.94 174:0.79 175:0.75 176:0.70 177:0.70 178:0.55 179:0.57 181:0.91 187:0.87 192:0.47 193:0.97 195:0.85 197:0.80 201:0.59 202:0.73 204:0.78 208:0.54 212:0.55 215:0.49 216:0.75 222:0.76 230:0.68 232:0.75 233:0.66 235:0.95 236:0.95 239:0.88 241:0.69 242:0.13 243:0.37 244:0.83 245:0.73 247:0.86 253:0.53 254:0.93 257:0.84 259:0.21 264:0.95 265:0.43 266:0.63 267:0.44 271:0.43 276:0.51 277:0.69 282:0.75 290:0.69 294:0.96 297:0.36 300:0.70 +1 1:0.63 8:0.71 10:0.99 11:0.57 12:0.20 17:0.69 18:0.97 21:0.22 27:0.24 28:0.83 29:0.62 30:0.33 32:0.71 33:0.97 34:0.44 36:0.73 37:0.45 39:0.60 43:0.58 44:0.88 45:0.99 56:0.97 66:0.92 69:0.52 70:0.81 71:0.88 74:0.86 75:0.98 77:0.70 81:0.64 82:0.99 83:0.54 86:0.96 88:0.98 91:0.15 97:0.89 98:0.95 99:0.83 101:0.97 102:0.97 104:0.82 106:0.81 108:0.63 114:0.88 117:0.86 122:0.97 123:0.61 124:0.37 126:0.53 127:0.96 129:0.59 133:0.73 135:0.34 139:0.93 145:0.88 146:0.36 147:0.42 149:0.83 150:0.54 154:0.67 155:0.49 158:0.81 159:0.94 161:0.87 165:0.63 167:0.45 170:0.87 172:0.99 173:0.15 174:0.99 176:0.70 177:0.96 178:0.55 179:0.11 181:0.91 187:0.39 192:0.50 195:0.99 197:0.98 201:0.65 206:0.81 208:0.54 212:0.75 215:0.79 216:0.53 222:0.76 226:0.96 232:0.91 235:0.52 236:0.95 239:0.99 241:0.02 242:0.12 243:0.07 245:0.95 247:1.00 253:0.70 254:0.80 259:0.21 264:0.95 265:0.95 266:0.80 267:0.36 271:0.43 275:0.99 276:0.98 279:0.77 282:0.80 283:0.67 290:0.88 291:0.97 294:1.00 295:0.98 297:0.36 300:0.84 +1 1:0.56 7:0.66 8:0.84 9:0.73 10:0.87 11:0.45 12:0.20 17:0.13 18:0.74 21:0.68 23:0.97 27:0.12 28:0.83 29:0.62 30:0.11 31:0.93 32:0.68 34:0.69 36:0.48 37:0.80 39:0.60 43:0.19 45:0.66 55:0.45 56:0.97 62:0.98 66:0.33 69:0.95 70:0.96 71:0.88 74:0.43 75:0.96 76:0.85 77:0.70 79:0.93 80:0.99 81:0.44 82:0.99 83:0.60 86:0.70 88:0.98 91:0.46 97:0.89 98:0.33 99:0.83 101:0.46 102:0.97 108:0.90 120:0.57 122:0.95 123:0.90 124:0.72 126:0.43 127:0.59 128:0.98 129:0.05 133:0.65 135:0.47 137:0.93 139:0.67 140:0.45 143:0.99 145:0.96 146:0.60 147:0.57 149:0.16 150:0.37 151:0.85 153:0.48 154:0.13 155:0.57 159:0.77 161:0.87 162:0.86 163:0.75 164:0.55 165:0.63 167:0.60 168:0.98 170:0.87 172:0.48 173:0.96 174:0.89 175:0.75 177:0.70 179:0.60 181:0.91 187:0.68 190:0.95 192:0.56 193:0.97 195:0.91 196:0.90 197:0.87 201:0.55 202:0.56 204:0.78 205:0.97 208:0.54 212:0.27 215:0.49 216:0.75 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.92 241:0.60 242:0.13 243:0.40 244:0.83 245:0.73 247:0.92 248:0.80 253:0.37 254:0.93 255:0.72 256:0.64 257:0.84 259:0.21 260:0.57 264:0.95 265:0.36 266:0.75 267:0.59 268:0.65 271:0.43 276:0.47 277:0.69 279:0.75 282:0.77 283:0.67 284:0.88 285:0.61 294:0.97 295:0.98 297:0.36 300:0.84 +3 1:0.65 7:0.75 8:0.86 9:0.79 10:1.00 11:0.54 12:0.20 17:0.91 18:0.91 20:0.85 21:0.05 23:0.99 27:0.18 28:0.83 29:0.62 30:0.45 31:0.93 32:0.72 33:0.97 34:0.55 36:0.30 37:0.49 39:0.60 43:0.77 45:0.99 55:0.45 56:0.97 62:1.00 66:0.08 69:0.21 70:0.82 71:0.88 74:0.87 75:0.98 76:1.00 77:0.89 78:0.80 79:0.93 80:1.00 81:0.68 82:0.99 83:0.91 86:0.92 88:0.99 91:0.33 96:0.93 97:0.99 98:0.34 99:0.83 100:0.90 101:0.87 102:0.97 104:0.94 106:0.81 108:0.17 111:0.84 114:0.95 117:0.86 120:0.86 122:0.98 123:0.96 124:0.97 126:0.53 127:0.98 128:0.99 129:0.05 133:0.97 135:0.17 137:0.93 139:0.88 140:0.45 143:1.00 145:0.87 146:0.80 147:0.83 149:0.90 150:0.59 151:0.95 152:0.85 153:0.86 154:0.48 155:0.66 158:0.82 159:0.95 161:0.87 162:0.86 164:0.79 165:0.63 167:0.45 168:0.99 169:0.86 170:0.87 172:0.91 173:0.07 174:1.00 176:0.70 177:0.96 179:0.11 181:0.91 186:0.81 187:0.21 190:0.77 192:1.00 193:0.99 195:0.99 196:0.95 197:0.99 201:0.66 202:0.70 204:0.79 205:0.99 206:0.81 208:0.64 212:0.70 215:0.91 216:0.63 219:0.90 220:0.62 222:0.76 226:0.98 230:0.77 232:0.82 233:0.76 235:0.31 236:0.95 239:1.00 242:0.19 243:0.33 245:0.98 247:1.00 248:0.91 253:0.95 254:1.00 255:0.95 256:0.78 259:0.21 260:0.86 261:0.85 264:0.95 265:0.29 266:0.75 267:0.94 268:0.78 271:0.43 275:0.97 276:0.98 277:0.87 279:0.81 282:0.80 283:0.82 284:0.92 285:0.62 290:0.95 291:0.97 292:0.95 294:1.00 295:0.99 297:0.36 300:0.94 +1 1:0.63 8:0.63 10:0.94 11:0.49 12:0.20 17:0.64 18:0.70 20:0.90 21:0.22 27:0.24 28:0.83 29:0.62 30:0.21 32:0.72 33:0.97 34:0.63 36:0.62 37:0.45 39:0.60 43:0.75 45:0.85 56:0.97 66:0.23 69:0.52 70:0.80 71:0.88 74:0.68 75:0.98 77:0.89 78:0.83 81:0.68 82:0.99 83:0.60 86:0.66 88:0.99 91:0.17 97:0.94 98:0.46 99:0.95 100:0.73 101:0.52 102:0.97 104:0.82 106:0.81 108:0.40 111:0.81 114:0.88 117:0.86 122:0.41 123:0.71 124:0.89 126:0.53 127:0.87 129:0.59 133:0.88 135:0.42 139:0.64 145:0.86 146:0.71 147:0.75 149:0.77 150:0.55 152:0.85 154:0.66 155:0.49 158:0.82 159:0.83 161:0.87 165:0.70 167:0.50 169:0.72 170:0.87 172:0.57 173:0.31 174:0.89 176:0.70 177:0.96 178:0.55 179:0.44 181:0.91 186:0.83 187:0.39 192:0.50 195:0.92 197:0.91 201:0.65 206:0.81 208:0.54 212:0.29 215:0.79 216:0.53 222:0.76 226:0.98 232:0.87 235:0.52 236:0.95 239:0.96 241:0.27 242:0.12 243:0.43 244:0.61 245:0.76 247:0.97 253:0.65 259:0.21 261:0.84 264:0.95 265:0.40 266:0.87 267:0.36 271:0.43 275:0.95 276:0.75 279:0.77 282:0.80 283:0.82 290:0.88 291:0.97 294:0.99 295:0.99 297:0.36 300:0.70 +1 1:0.62 10:0.95 11:0.57 12:0.20 17:0.30 18:0.87 21:0.30 27:0.45 28:0.83 29:0.62 30:0.28 32:0.65 34:0.87 36:0.22 37:0.50 39:0.60 43:0.23 45:0.89 56:0.97 64:0.77 66:0.35 69:0.64 70:0.89 71:0.88 74:0.50 75:0.97 77:0.70 81:0.53 83:0.54 86:0.89 91:0.17 97:0.89 98:0.45 99:0.83 101:0.96 104:0.95 108:0.49 122:0.90 123:0.47 124:0.79 126:0.43 127:0.57 129:0.59 133:0.74 135:0.88 139:0.86 145:0.50 146:0.69 147:0.73 149:0.39 150:0.47 154:0.67 155:0.48 159:0.70 161:0.87 165:0.63 167:0.54 170:0.87 172:0.68 173:0.51 174:0.93 177:0.96 178:0.55 179:0.37 181:0.91 187:0.68 192:0.46 195:0.86 197:0.94 201:0.61 208:0.54 212:0.56 216:0.77 219:0.91 222:0.76 226:0.95 235:0.52 236:0.94 239:0.94 241:0.46 242:0.12 243:0.51 245:0.84 247:0.98 253:0.41 254:0.92 259:0.21 264:0.95 265:0.47 266:0.84 267:0.44 271:0.43 275:0.97 276:0.76 279:0.77 282:0.74 292:0.87 294:0.99 295:0.98 300:0.84 +0 8:0.65 9:0.72 10:0.98 11:0.54 12:0.20 17:0.24 18:0.86 21:0.59 23:0.98 27:0.21 28:0.83 29:0.62 30:0.33 33:0.97 34:0.69 36:0.66 37:0.74 39:0.60 43:0.26 45:0.89 55:0.59 62:0.98 66:0.51 69:0.25 70:0.78 71:0.88 74:0.57 75:0.96 81:0.27 83:0.54 86:0.86 91:0.12 96:0.77 97:0.89 98:0.54 101:0.87 108:0.20 122:0.57 123:0.19 124:0.64 126:0.23 127:0.88 128:0.97 129:0.59 133:0.60 135:0.30 139:0.80 140:0.45 143:0.99 146:0.87 147:0.90 149:0.41 150:0.29 151:0.76 153:0.48 154:0.46 155:0.54 159:0.84 161:0.87 162:0.53 167:0.56 168:0.97 170:0.87 172:0.85 173:0.12 174:0.97 177:0.96 179:0.29 181:0.91 187:0.39 190:0.89 192:0.56 193:0.95 197:0.98 202:0.69 205:0.98 208:0.49 212:0.83 216:0.95 220:0.62 222:0.76 226:0.96 232:0.93 235:0.68 236:0.92 239:0.97 241:0.51 242:0.12 243:0.61 244:0.61 245:0.92 247:1.00 248:0.70 253:0.68 254:0.80 255:0.73 256:0.64 259:0.21 264:0.95 265:0.56 266:0.75 267:0.28 268:0.64 271:0.43 275:0.94 276:0.78 279:0.75 285:0.55 291:0.97 294:0.96 295:0.98 300:0.70 +2 7:0.69 8:0.94 9:0.71 10:0.98 11:0.49 12:0.20 17:0.47 18:0.90 21:0.54 23:0.98 27:0.21 28:0.83 29:0.62 30:0.43 31:0.89 33:0.97 34:0.67 36:0.64 37:0.74 39:0.60 43:0.34 45:0.85 55:0.52 62:0.99 66:0.59 69:0.19 70:0.75 71:0.88 74:0.57 75:0.96 76:0.85 79:0.89 81:0.33 83:0.54 86:0.90 91:0.53 98:0.63 101:0.69 104:0.84 106:0.81 108:0.94 120:0.67 123:0.93 124:0.63 126:0.31 127:0.79 128:0.97 129:0.05 133:0.66 135:0.20 137:0.89 139:0.83 140:0.97 143:0.99 146:0.92 147:0.94 149:0.42 150:0.34 151:0.59 153:0.46 154:0.53 155:0.53 159:0.91 161:0.87 162:0.53 164:0.75 167:0.55 168:0.97 170:0.87 172:0.96 173:0.08 174:0.99 177:0.96 178:0.42 179:0.14 181:0.91 187:0.39 190:0.98 192:0.55 193:0.96 196:0.91 197:0.99 201:0.54 202:0.79 205:0.99 206:0.81 208:0.49 212:0.85 215:0.58 216:0.95 220:0.82 222:0.76 226:0.96 230:0.68 232:0.93 233:0.65 235:0.68 236:0.92 239:0.98 241:0.50 242:0.13 243:0.39 244:0.61 245:0.98 247:1.00 248:0.57 253:0.44 254:0.90 255:0.70 256:0.81 259:0.21 260:0.67 264:0.95 265:0.64 266:0.87 267:0.65 268:0.76 271:0.43 275:0.94 276:0.95 277:0.69 283:0.82 284:0.86 285:0.61 291:0.97 294:0.96 297:0.36 300:0.94 +2 1:0.58 7:0.66 8:0.98 9:0.73 10:0.85 12:0.20 17:0.45 18:0.63 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.60 31:0.89 32:0.64 34:0.34 36:0.62 37:0.80 39:0.60 43:0.08 55:0.45 62:0.98 66:0.34 69:0.79 70:0.46 71:0.88 74:0.39 75:0.96 76:0.85 77:0.70 79:0.89 81:0.39 83:0.60 86:0.62 91:0.80 96:0.86 98:0.21 104:0.89 108:0.95 114:0.69 120:0.77 123:0.94 124:0.72 126:0.43 127:0.69 128:0.99 129:0.59 133:0.66 135:0.61 137:0.89 139:0.61 140:0.87 143:1.00 145:0.76 146:0.28 147:0.34 149:0.08 150:0.39 151:0.64 153:0.83 154:0.20 155:0.57 159:0.87 161:0.87 162:0.63 164:0.85 167:0.66 168:0.99 170:0.87 172:0.37 173:0.55 174:0.86 175:0.91 176:0.63 177:0.70 179:0.76 181:0.91 187:0.68 190:0.95 191:0.70 192:0.87 193:0.95 195:0.96 196:0.87 197:0.85 201:0.59 202:0.88 205:1.00 208:0.54 212:0.19 215:0.53 216:0.75 220:0.91 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.88 241:0.67 242:0.12 243:0.36 244:0.93 245:0.62 247:0.82 248:0.68 253:0.81 254:0.80 255:0.73 256:0.89 257:0.84 259:0.21 260:0.77 264:0.95 265:0.23 266:0.63 267:0.23 268:0.89 271:0.43 276:0.23 277:0.87 282:0.73 283:0.67 284:0.88 285:0.62 290:0.69 297:0.36 300:0.43 +3 1:0.56 7:0.69 8:0.96 9:0.74 10:0.84 11:0.45 12:0.20 17:0.13 18:0.58 21:0.64 23:1.00 27:0.56 28:0.83 29:0.62 30:0.45 31:0.98 32:0.65 34:0.71 36:0.44 39:0.60 43:0.57 45:0.73 55:0.52 62:0.99 66:0.36 69:0.98 70:0.74 71:0.88 74:0.44 75:0.97 77:0.70 79:0.98 81:0.45 83:0.91 86:0.60 91:0.94 96:0.98 97:0.97 98:0.20 99:0.83 101:0.46 104:0.79 108:0.96 114:0.69 120:0.93 123:0.96 124:0.70 126:0.43 127:0.97 128:1.00 129:0.05 133:0.60 135:0.39 137:0.98 138:0.99 139:0.68 140:1.00 143:1.00 145:0.47 146:0.48 147:0.25 149:0.28 150:0.38 151:0.76 153:0.91 154:0.45 155:0.65 159:0.90 161:0.87 162:0.54 164:0.95 165:0.63 167:0.74 168:1.00 170:0.87 172:0.37 173:0.99 174:0.86 175:0.91 176:0.63 177:0.38 179:0.82 181:0.91 187:0.98 190:0.89 192:0.99 195:0.97 196:0.92 197:0.86 201:0.54 202:0.98 204:0.79 205:1.00 208:0.64 212:0.22 215:0.53 216:0.72 219:0.91 220:0.74 222:0.76 226:0.98 230:0.71 233:0.66 235:0.84 236:0.94 239:0.88 241:0.74 242:0.22 243:0.31 244:0.93 245:0.60 247:0.76 248:0.77 253:0.96 255:0.78 256:0.98 257:0.93 259:0.21 260:0.93 264:0.95 265:0.21 266:0.75 267:0.99 268:0.98 271:0.43 276:0.29 277:0.87 279:0.79 282:0.74 284:0.99 285:0.62 290:0.69 292:0.95 294:0.95 295:0.99 297:0.36 300:0.55 +2 1:0.65 6:0.96 7:0.75 8:0.84 9:0.75 10:0.97 11:0.49 12:0.20 17:0.57 18:0.74 20:0.94 21:0.05 23:0.99 27:0.37 28:0.83 29:0.62 30:0.45 32:0.67 33:0.97 34:0.81 36:0.43 37:0.94 39:0.60 43:0.65 45:0.85 56:0.97 62:1.00 66:0.86 69:0.45 70:0.66 71:0.88 74:0.77 75:0.98 76:0.94 78:0.89 80:1.00 81:0.72 82:0.99 83:0.72 86:0.73 88:0.99 91:0.67 96:0.91 97:1.00 98:0.50 99:0.95 100:0.94 101:0.52 102:0.96 104:0.95 106:0.81 108:0.35 111:0.90 114:0.95 117:0.86 120:0.84 122:0.90 123:0.33 124:0.37 126:0.53 127:0.82 128:0.99 129:0.05 133:0.36 135:0.72 139:0.73 140:0.45 143:1.00 145:0.59 146:0.70 147:0.74 149:0.64 150:0.59 151:0.84 152:0.88 153:0.72 154:0.59 155:0.58 158:0.81 159:0.70 161:0.87 162:0.72 164:0.79 165:0.70 167:0.54 168:0.99 169:0.92 170:0.87 172:0.78 173:0.34 174:0.90 176:0.70 177:0.96 179:0.53 181:0.91 186:0.89 187:0.68 189:0.97 190:0.77 192:0.98 193:0.99 195:0.83 197:0.93 201:0.66 202:0.79 204:0.85 205:0.99 206:0.81 208:0.64 212:0.83 215:0.91 216:0.25 219:0.91 220:0.62 222:0.76 226:0.96 230:0.77 232:0.96 233:0.76 235:0.31 236:0.95 238:0.93 239:0.97 241:0.46 242:0.18 243:0.86 245:0.60 247:0.92 248:0.87 253:0.92 254:1.00 255:0.94 256:0.82 259:0.21 260:0.84 261:0.92 264:0.95 265:0.52 266:0.47 267:0.79 268:0.82 271:0.43 275:0.95 276:0.45 279:0.82 283:0.67 285:0.61 290:0.95 291:0.97 292:0.88 294:0.99 295:0.98 297:0.36 300:0.70 +3 1:0.58 7:0.66 8:0.98 9:0.73 10:0.85 12:0.20 17:0.45 18:0.64 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.45 31:0.89 32:0.64 34:0.31 36:0.62 37:0.80 39:0.60 43:0.08 55:0.45 62:0.98 66:0.34 69:0.97 70:0.65 71:0.88 74:0.39 75:0.96 76:0.85 77:0.70 79:0.89 81:0.39 83:0.60 86:0.63 91:0.78 96:0.86 98:0.23 104:0.89 108:0.95 114:0.69 120:0.77 123:0.94 124:0.72 126:0.43 127:0.74 128:0.99 129:0.05 133:0.65 135:0.54 137:0.89 139:0.61 140:0.87 143:1.00 145:0.76 146:0.53 147:0.34 149:0.08 150:0.39 151:0.64 153:0.83 154:0.19 155:0.57 159:0.87 161:0.87 162:0.64 164:0.85 167:0.66 168:0.99 170:0.87 172:0.41 173:0.98 174:0.86 175:0.75 176:0.63 177:0.70 179:0.73 181:0.91 187:0.68 190:0.95 191:0.70 192:0.87 193:0.95 195:0.96 196:0.87 197:0.85 201:0.59 202:0.88 205:1.00 208:0.54 212:0.27 215:0.53 216:0.75 220:0.91 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.88 241:0.67 242:0.12 243:0.33 244:0.93 245:0.66 247:0.86 248:0.68 253:0.81 254:0.80 255:0.73 256:0.89 257:0.84 259:0.21 260:0.77 264:0.95 265:0.25 266:0.63 267:0.23 268:0.89 271:0.43 276:0.27 277:0.69 282:0.73 283:0.67 284:0.88 285:0.62 290:0.69 297:0.36 300:0.55 +3 1:0.56 6:0.99 7:0.66 8:0.97 9:0.73 10:0.87 12:0.20 17:0.43 18:0.71 20:0.98 21:0.68 23:1.00 27:0.12 28:0.83 29:0.62 30:0.45 31:0.90 32:0.66 34:0.34 36:0.65 37:0.80 39:0.60 43:0.08 55:0.71 62:0.99 66:0.18 69:0.80 70:0.54 71:0.88 74:0.49 75:0.97 76:0.85 77:0.70 78:0.94 79:0.90 81:0.36 83:0.54 86:0.66 91:0.91 96:0.97 97:0.97 98:0.26 100:0.99 102:0.96 108:0.92 111:0.98 114:0.67 120:0.68 122:0.98 123:0.91 124:0.86 126:0.43 127:0.60 128:1.00 129:0.05 133:0.84 135:0.58 137:0.90 139:0.64 140:0.92 143:1.00 145:0.92 146:0.26 147:0.32 149:0.15 150:0.36 151:0.94 152:0.94 153:0.97 154:0.19 155:0.55 158:0.77 159:0.79 161:0.87 162:0.61 163:0.75 164:0.80 167:0.74 168:1.00 169:0.97 170:0.87 172:0.32 173:0.65 174:0.89 175:0.91 176:0.63 177:0.70 179:0.64 181:0.91 186:0.94 187:0.39 189:0.99 190:0.89 192:0.86 193:0.95 195:0.92 196:0.88 197:0.90 201:0.56 202:0.92 205:1.00 208:0.54 212:0.21 215:0.49 216:0.75 222:0.76 226:0.98 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 238:0.97 239:0.92 241:0.75 242:0.21 243:0.24 244:0.93 245:0.64 247:0.84 248:0.84 253:0.95 254:0.80 255:0.77 256:0.92 257:0.84 259:0.21 260:0.68 261:0.97 264:0.95 265:0.28 266:0.80 267:0.69 268:0.93 271:0.43 276:0.50 277:0.95 279:0.78 282:0.75 284:0.94 285:0.62 290:0.67 294:0.95 295:0.99 297:0.36 300:0.43 +2 6:0.95 7:0.69 8:0.89 9:0.74 10:0.98 11:0.54 12:0.20 17:0.47 18:0.87 20:0.91 21:0.54 23:0.99 27:0.21 28:0.83 29:0.62 30:0.44 33:0.97 34:0.68 36:0.77 37:0.74 39:0.60 43:0.33 45:0.88 55:0.52 62:0.99 66:0.51 69:0.19 70:0.76 71:0.88 74:0.58 75:0.97 76:0.85 78:0.82 81:0.33 83:0.60 86:0.85 91:0.49 96:0.85 97:0.94 98:0.52 100:0.93 101:0.87 104:0.84 106:0.81 108:0.93 111:0.87 120:0.74 122:0.98 123:0.93 124:0.69 126:0.31 127:0.90 128:0.99 129:0.05 133:0.74 135:0.18 139:0.79 140:0.97 143:1.00 146:0.90 147:0.92 149:0.41 150:0.34 151:0.80 152:0.97 153:0.46 154:0.52 155:0.58 159:0.90 161:0.87 162:0.56 164:0.75 167:0.51 168:0.99 169:0.86 170:0.87 172:0.94 173:0.09 174:0.99 177:0.96 179:0.16 181:0.91 186:0.83 187:0.39 190:0.77 192:0.98 193:0.96 197:0.98 201:0.54 202:0.76 205:0.99 206:0.81 208:0.54 212:0.87 215:0.58 216:0.95 220:0.74 222:0.76 226:0.98 230:0.68 232:0.93 233:0.65 235:0.68 236:0.92 239:0.98 241:0.37 242:0.12 243:0.43 244:0.61 245:0.97 247:1.00 248:0.75 253:0.82 254:0.90 255:0.78 256:0.79 259:0.21 260:0.74 261:0.90 264:0.95 265:0.53 266:0.87 267:0.73 268:0.75 271:0.43 275:0.95 276:0.92 277:0.69 279:0.77 283:0.66 285:0.61 291:0.97 294:0.97 295:0.99 297:0.36 300:0.98 +3 1:0.56 8:0.89 9:0.73 10:0.87 11:0.45 12:0.20 17:0.13 18:0.61 20:0.81 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.30 31:0.95 34:0.80 36:0.42 37:0.78 39:0.60 43:0.27 45:0.77 56:0.97 62:0.99 66:0.26 69:0.83 70:0.75 71:0.88 74:0.30 75:0.97 78:0.72 79:0.94 81:0.38 83:0.60 86:0.66 91:0.85 96:0.93 97:0.97 98:0.19 99:0.83 100:0.73 101:0.46 108:0.67 111:0.72 120:0.75 122:0.57 123:0.93 124:0.72 126:0.31 127:0.60 128:1.00 129:0.05 133:0.66 135:0.24 137:0.95 139:0.70 140:0.94 143:1.00 145:0.49 146:0.24 147:0.30 149:0.17 150:0.36 151:0.91 152:0.78 153:0.86 154:0.27 155:0.54 159:0.80 161:0.87 162:0.57 164:0.73 165:0.63 167:0.63 168:1.00 169:0.72 170:0.87 172:0.27 173:0.68 174:0.86 175:0.91 177:0.70 178:0.42 179:0.80 181:0.91 186:0.73 187:0.68 190:0.89 192:0.85 196:0.91 197:0.87 201:0.54 202:0.93 205:1.00 208:0.54 212:0.39 216:0.81 219:0.91 220:0.99 222:0.76 226:0.98 232:0.83 235:0.80 236:0.92 239:0.89 241:0.64 242:0.13 243:0.45 244:0.83 245:0.58 247:0.76 248:0.91 253:0.90 254:0.86 255:0.77 256:0.94 257:0.84 259:0.21 260:0.76 261:0.80 264:0.95 265:0.14 266:0.54 267:0.76 268:0.93 271:0.43 275:0.94 276:0.30 277:0.95 279:0.79 284:0.95 285:0.62 292:0.95 294:0.97 295:0.99 300:0.55 +2 8:0.92 9:0.74 10:0.86 11:0.45 12:0.20 17:0.12 18:0.63 21:0.68 23:0.99 27:0.05 28:0.83 29:0.62 30:0.15 31:0.87 34:0.24 36:0.43 37:0.78 39:0.60 43:0.14 45:0.66 55:0.45 62:0.98 66:0.33 69:0.96 70:0.84 71:0.88 74:0.37 75:0.97 79:0.87 81:0.25 83:0.60 86:0.62 91:0.86 96:0.92 97:0.94 98:0.33 101:0.46 104:0.94 106:0.81 108:0.92 120:0.73 122:0.75 123:0.92 124:0.71 126:0.23 127:0.45 128:1.00 129:0.05 133:0.65 135:0.23 137:0.87 139:0.61 140:0.98 143:1.00 146:0.63 147:0.24 149:0.11 150:0.27 151:0.76 153:0.72 154:0.16 155:0.58 158:0.77 159:0.76 161:0.87 162:0.57 163:0.80 164:0.83 167:0.56 168:1.00 170:0.87 172:0.27 173:0.98 174:0.86 175:0.75 177:0.38 179:0.82 181:0.91 187:0.87 190:0.89 192:0.98 196:0.87 197:0.86 202:0.88 205:0.99 206:0.81 208:0.54 212:0.15 215:0.49 216:0.83 220:0.62 222:0.76 226:0.98 232:0.88 235:0.80 239:0.88 241:0.53 242:0.14 243:0.28 244:0.93 245:0.53 247:0.67 248:0.70 253:0.87 255:0.78 256:0.88 257:0.93 259:0.21 260:0.74 264:0.95 265:0.35 266:0.63 267:0.36 268:0.88 271:0.43 275:0.91 276:0.29 277:0.69 279:0.77 283:0.66 284:0.86 285:0.62 294:0.95 295:0.99 297:0.36 300:0.55 +1 1:0.66 7:0.75 10:0.93 11:0.82 17:0.89 18:0.86 21:0.13 22:0.93 27:0.69 29:0.71 30:0.68 32:0.72 34:1.00 36:0.46 37:0.43 39:0.40 43:0.65 44:0.92 45:0.85 47:0.93 64:0.77 66:0.63 69:0.81 70:0.79 71:0.67 74:0.68 77:0.70 81:0.68 83:0.69 85:0.72 86:0.90 91:0.38 97:0.94 98:0.61 99:0.95 101:0.96 106:0.81 108:0.65 114:0.92 117:0.86 122:0.93 123:0.63 124:0.47 126:0.51 127:0.26 129:0.59 133:0.47 135:0.97 139:0.93 145:0.41 146:0.71 147:0.76 149:0.76 150:0.56 154:0.52 155:0.55 158:0.81 159:0.40 165:0.81 170:0.90 172:0.65 173:0.81 174:0.90 176:0.70 177:0.88 178:0.55 179:0.41 187:0.21 192:0.57 195:0.74 197:0.91 201:0.63 204:0.83 206:0.81 208:0.61 212:0.58 215:0.84 216:0.13 219:0.94 222:0.35 230:0.77 232:0.99 233:0.76 235:0.31 239:0.92 241:0.15 242:0.24 243:0.69 245:0.76 247:0.79 253:0.67 259:0.21 262:0.93 265:0.62 266:0.54 267:0.73 271:0.89 276:0.58 279:0.78 281:0.91 282:0.80 283:0.67 290:0.92 292:0.88 297:0.36 300:0.84 +1 1:0.60 8:0.81 9:0.70 10:0.98 11:0.52 17:0.12 18:0.82 21:0.74 23:0.96 27:0.45 29:0.71 30:0.09 31:0.86 32:0.66 34:0.59 36:0.19 37:0.50 39:0.40 43:0.24 44:0.88 45:0.81 55:0.45 62:0.96 64:0.77 66:0.29 69:0.71 70:0.94 71:0.67 74:0.76 75:0.98 79:0.86 81:0.52 83:0.56 86:0.88 91:0.23 97:0.94 98:0.46 99:0.95 101:0.65 102:0.96 108:0.54 120:0.54 122:0.83 123:0.85 124:0.82 126:0.51 127:0.55 128:0.95 129:0.59 133:0.80 135:0.89 137:0.86 139:0.87 140:0.45 145:0.54 146:0.72 147:0.76 149:0.27 150:0.41 151:0.47 153:0.48 154:0.60 155:0.48 159:0.77 162:0.86 164:0.56 165:0.65 168:0.95 170:0.90 172:0.75 173:0.54 174:0.94 177:0.88 179:0.27 187:0.68 190:0.77 192:0.46 195:0.90 196:0.88 197:0.95 201:0.59 202:0.36 205:0.95 208:0.50 212:0.71 215:0.46 216:0.77 219:0.94 220:0.99 222:0.35 226:0.95 235:0.99 236:0.95 239:0.97 241:0.35 242:0.23 243:0.48 245:0.87 247:0.97 248:0.47 253:0.53 254:0.90 255:0.68 256:0.45 259:0.21 260:0.54 265:0.43 266:0.87 267:0.76 268:0.46 271:0.89 276:0.83 279:0.79 283:0.65 284:0.88 285:0.60 292:0.86 297:0.36 300:0.70 +2 1:0.58 7:0.70 8:0.75 10:0.99 11:0.46 17:0.18 18:0.92 21:0.47 27:0.39 29:0.71 30:0.16 32:0.69 34:0.45 36:0.44 37:0.65 39:0.40 43:0.61 44:0.92 45:0.73 48:0.91 55:0.42 64:0.77 66:0.63 69:0.57 70:0.84 71:0.67 74:0.53 75:0.98 81:0.44 86:0.95 91:0.46 98:0.72 101:0.47 102:0.97 108:0.44 122:0.77 123:0.42 124:0.55 126:0.51 127:0.55 129:0.05 133:0.65 135:0.94 139:0.95 145:0.84 146:0.85 147:0.87 149:0.39 150:0.42 154:0.50 155:0.47 159:0.61 170:0.90 172:0.88 173:0.49 174:0.97 177:0.88 178:0.55 179:0.25 187:0.87 192:0.47 193:0.98 195:0.81 197:0.98 201:0.60 202:0.36 208:0.50 212:0.86 215:0.63 216:0.31 219:0.94 222:0.35 226:0.95 230:0.73 233:0.66 235:0.68 236:0.95 239:0.99 241:0.10 242:0.12 243:0.69 245:0.90 247:0.99 253:0.42 254:0.99 259:0.21 265:0.72 266:0.54 267:0.91 271:0.89 276:0.84 279:0.75 281:0.86 283:0.65 292:0.88 297:0.36 300:0.84 +2 10:0.97 11:0.82 17:0.69 18:0.94 21:0.78 27:0.72 29:0.71 30:0.76 34:1.00 36:0.37 37:0.37 39:0.40 43:0.77 45:0.89 66:0.47 69:0.32 70:0.59 71:0.67 74:0.62 83:0.69 85:0.72 86:0.96 91:0.60 97:0.94 98:0.55 101:1.00 108:0.25 122:0.65 123:0.24 124:0.68 127:0.87 129:0.59 133:0.67 135:0.65 139:0.95 145:0.98 146:0.70 147:0.74 149:0.85 154:0.61 158:0.81 159:0.64 170:0.90 172:0.65 173:0.26 174:0.92 177:0.88 178:0.55 179:0.53 187:0.68 197:0.95 208:0.61 212:0.68 216:0.27 219:0.94 222:0.35 235:0.88 239:0.96 241:0.61 242:0.13 243:0.59 245:0.80 247:0.90 253:0.47 259:0.21 265:0.57 266:0.71 267:0.52 271:0.89 276:0.60 279:0.79 283:0.67 292:0.88 300:0.84 +1 1:0.68 8:0.77 10:0.98 11:0.52 17:0.79 18:0.96 21:0.30 27:0.54 29:0.71 30:0.62 34:0.88 36:0.43 37:0.60 39:0.40 43:0.14 45:0.94 64:0.77 66:0.98 69:0.29 70:0.75 71:0.67 74:0.94 81:0.76 83:0.69 86:0.98 91:0.23 98:0.91 99:0.95 101:0.65 108:0.22 114:0.86 122:0.67 123:0.22 126:0.54 127:0.51 129:0.05 135:0.97 139:0.97 146:0.94 147:0.95 149:0.14 150:0.62 154:0.90 155:0.53 159:0.58 165:0.81 170:0.90 172:0.94 173:0.24 174:0.92 176:0.73 177:0.88 178:0.55 179:0.21 187:0.68 192:0.55 197:0.93 201:0.67 202:0.36 208:0.64 212:0.81 215:0.72 216:0.64 222:0.35 235:0.68 236:0.97 239:0.97 241:0.02 242:0.14 243:0.98 247:0.90 253:0.73 254:0.74 259:0.21 265:0.91 266:0.27 267:0.87 271:0.89 276:0.88 290:0.86 297:0.36 300:0.70 +1 1:0.64 7:0.75 10:0.88 11:0.75 17:0.34 18:0.68 21:0.54 27:0.02 29:0.71 30:0.87 32:0.80 34:0.99 36:0.21 37:0.91 39:0.40 43:0.75 44:0.93 45:0.73 48:0.91 64:0.77 66:0.82 69:0.83 70:0.32 71:0.67 74:0.67 77:0.70 81:0.73 83:0.91 85:0.72 86:0.77 91:0.14 98:0.39 101:0.87 102:0.98 108:0.68 114:0.77 122:0.65 123:0.66 126:0.54 127:0.13 129:0.05 135:0.92 139:0.85 145:0.49 146:0.45 147:0.51 149:0.58 150:0.54 154:0.66 155:0.55 159:0.16 165:0.93 170:0.90 172:0.48 173:0.87 174:0.79 176:0.73 177:0.88 178:0.55 179:0.26 187:0.68 192:0.57 193:0.96 195:0.72 197:0.82 201:0.63 204:0.83 208:0.64 212:0.61 215:0.58 216:0.81 222:0.35 230:0.77 233:0.66 235:0.80 236:0.97 239:0.93 241:0.57 242:0.33 243:0.83 247:0.60 253:0.60 259:0.21 265:0.41 266:0.38 267:0.96 271:0.89 276:0.34 281:0.91 282:0.88 290:0.77 297:0.36 299:0.88 300:0.33 +0 10:0.83 17:0.91 21:0.76 27:0.71 29:0.71 30:0.76 34:0.86 36:0.22 37:0.48 39:0.40 43:0.09 44:0.88 48:0.91 55:0.98 64:0.77 66:0.13 69:0.87 70:0.15 71:0.67 74:0.30 81:0.25 91:0.09 98:0.06 108:0.74 122:0.94 123:0.73 124:0.75 126:0.24 127:0.37 129:0.81 133:0.67 135:0.32 139:0.69 145:0.82 146:0.05 147:0.05 149:0.08 150:0.27 154:0.08 159:0.68 163:0.99 170:0.90 172:0.05 173:0.80 174:0.79 175:0.97 177:0.05 178:0.55 179:0.98 187:0.21 195:0.89 197:0.82 212:0.95 216:0.13 222:0.35 235:0.95 239:0.82 241:0.15 242:0.82 243:0.34 244:0.93 245:0.37 247:0.12 253:0.27 257:0.93 259:0.21 265:0.06 266:0.12 267:0.52 271:0.89 276:0.17 277:0.95 281:0.86 300:0.13 +2 8:0.80 9:0.72 10:0.87 11:0.57 17:0.75 18:0.84 21:0.22 23:0.95 27:0.53 29:0.71 30:0.33 31:0.92 32:0.67 34:0.55 36:0.98 37:0.77 39:0.40 43:0.58 44:0.88 45:0.81 48:0.91 55:0.59 62:0.96 64:0.77 66:0.90 69:0.60 70:0.63 71:0.67 74:0.70 75:0.97 79:0.91 81:0.32 83:0.56 86:0.89 91:0.51 96:0.77 97:0.89 98:0.75 101:0.87 108:0.46 122:0.83 123:0.44 126:0.24 127:0.24 128:0.96 129:0.05 135:0.80 137:0.92 139:0.88 140:0.87 145:0.67 146:0.63 147:0.68 149:0.30 150:0.36 151:0.76 153:0.48 154:0.52 155:0.55 158:0.76 159:0.23 162:0.54 168:0.96 170:0.90 172:0.73 173:0.73 174:0.79 177:0.88 178:0.48 179:0.38 187:0.39 190:0.89 191:0.70 192:0.55 195:0.64 196:0.94 197:0.82 202:0.64 205:0.95 208:0.61 212:0.94 216:0.33 219:0.91 220:0.62 222:0.35 226:0.98 235:0.22 239:0.89 241:0.41 242:0.24 243:0.91 244:0.61 247:0.84 248:0.70 253:0.75 254:0.86 255:0.71 256:0.58 259:0.21 265:0.75 266:0.17 267:0.82 268:0.59 271:0.89 276:0.62 279:0.78 281:0.91 284:0.88 285:0.60 292:0.95 300:0.43 +2 1:0.58 8:0.76 10:0.98 11:0.57 17:0.22 18:0.94 21:0.47 22:0.93 27:0.38 29:0.71 30:0.31 33:0.97 34:0.96 36:0.59 37:0.39 39:0.40 43:0.60 44:0.88 45:0.91 47:0.93 55:0.52 64:0.77 66:0.42 69:0.68 70:0.89 71:0.67 74:0.71 75:0.97 81:0.48 83:0.56 86:0.96 91:0.38 97:0.98 98:0.77 99:0.83 101:0.96 102:0.96 108:0.52 122:0.94 123:0.50 124:0.60 126:0.32 127:0.57 129:0.59 133:0.47 135:0.83 139:0.95 145:0.83 146:0.91 147:0.92 149:0.51 150:0.43 154:0.47 155:0.47 158:0.76 159:0.67 165:0.65 170:0.90 172:0.85 173:0.59 174:0.96 177:0.88 178:0.55 179:0.21 187:0.39 192:0.44 195:0.83 197:0.96 201:0.55 208:0.61 212:0.78 216:0.62 219:0.91 222:0.35 226:0.96 232:0.92 235:0.60 236:0.94 239:0.98 241:0.10 242:0.12 243:0.57 245:0.97 247:1.00 253:0.51 254:0.84 259:0.21 262:0.93 265:0.77 266:0.63 267:0.91 271:0.89 276:0.89 279:0.81 291:0.97 292:0.88 300:0.84 +0 8:0.80 10:0.89 11:0.52 17:0.76 18:0.99 21:0.30 27:0.42 29:0.71 30:0.15 31:0.90 34:0.44 36:0.40 37:0.55 39:0.40 43:0.14 44:0.88 45:0.73 55:0.71 64:0.77 66:0.23 69:0.98 70:0.90 71:0.67 74:0.56 77:0.70 79:0.89 81:0.31 86:0.98 91:0.54 98:0.47 101:0.65 108:0.97 122:0.92 123:0.97 124:0.97 126:0.24 127:0.94 129:0.89 133:0.97 135:0.84 137:0.90 139:0.97 140:0.45 145:0.42 146:0.62 147:0.56 149:0.47 150:0.35 151:0.53 153:0.77 154:0.10 159:0.97 162:0.86 170:0.90 172:0.96 173:0.88 174:0.97 177:0.38 179:0.10 187:0.39 190:0.95 195:1.00 196:0.94 197:0.88 202:0.54 212:0.48 216:0.34 219:0.90 220:0.87 222:0.35 232:0.80 235:0.31 239:0.88 241:0.03 242:0.55 243:0.06 244:0.61 245:0.98 247:1.00 248:0.54 253:0.44 254:0.74 256:0.58 257:0.84 259:0.21 265:0.49 266:0.95 267:0.65 268:0.63 271:0.89 276:0.99 282:0.75 284:0.93 285:0.46 292:0.88 300:0.94 +0 1:0.58 8:0.80 9:0.72 10:0.86 11:0.52 17:0.57 18:0.96 21:0.54 22:0.93 27:0.56 29:0.71 30:0.20 31:0.92 33:0.97 34:0.67 36:0.87 37:0.55 39:0.40 43:0.34 44:0.88 45:0.83 47:0.93 55:0.59 64:0.77 66:0.45 69:0.63 70:0.94 71:0.67 74:0.80 77:0.70 79:0.92 81:0.36 86:0.96 91:0.60 96:0.78 98:0.57 101:0.87 108:0.84 114:0.74 117:0.86 122:0.92 123:0.83 124:0.80 126:0.32 127:0.67 129:0.59 133:0.81 135:0.98 137:0.92 138:0.96 139:0.95 140:0.45 145:0.54 146:0.66 147:0.71 149:0.49 150:0.41 151:0.82 153:0.46 154:0.63 155:0.55 158:0.76 159:0.81 162:0.82 170:0.90 172:0.88 173:0.42 174:0.79 175:0.75 176:0.63 177:0.70 179:0.21 187:0.39 190:0.89 192:0.55 195:0.92 196:0.96 197:0.84 201:0.55 202:0.58 208:0.50 212:0.75 216:0.16 219:0.91 220:0.74 222:0.35 235:0.68 239:0.85 241:0.46 242:0.16 243:0.27 244:0.61 245:0.93 247:0.86 248:0.76 253:0.81 255:0.71 256:0.64 257:0.65 259:0.21 262:0.93 265:0.58 266:0.87 267:0.19 268:0.64 271:0.89 276:0.90 277:0.69 279:0.79 282:0.75 284:0.94 285:0.50 290:0.74 291:0.97 292:0.87 300:0.84 +1 1:0.59 10:0.92 11:0.46 17:0.24 18:0.87 21:0.68 27:0.45 29:0.71 30:0.36 34:0.81 36:0.21 37:0.50 39:0.40 43:0.39 44:0.88 45:0.73 55:0.52 64:0.77 66:0.46 69:0.71 70:0.87 71:0.67 74:0.83 81:0.38 86:0.91 91:0.17 98:0.63 101:0.47 108:0.54 114:0.69 122:0.83 123:0.52 124:0.73 126:0.51 127:0.53 129:0.59 133:0.73 135:0.86 139:0.89 145:0.49 146:0.85 147:0.88 149:0.37 150:0.40 154:0.65 155:0.47 158:0.81 159:0.71 170:0.90 172:0.82 173:0.58 174:0.88 176:0.70 177:0.88 178:0.55 179:0.26 187:0.68 192:0.46 195:0.87 197:0.87 201:0.57 208:0.61 212:0.73 215:0.49 216:0.77 219:0.94 222:0.35 235:0.88 239:0.90 241:0.27 242:0.19 243:0.58 245:0.89 247:0.95 253:0.62 254:0.93 259:0.21 265:0.64 266:0.80 267:0.97 271:0.89 276:0.84 279:0.78 283:0.65 290:0.69 292:0.86 297:0.36 300:0.84 +2 1:0.68 7:0.75 10:0.88 11:0.75 17:0.40 18:0.69 21:0.13 27:0.02 29:0.71 30:0.87 32:0.80 34:0.99 36:0.20 37:0.91 39:0.40 43:0.76 44:0.93 45:0.73 48:0.91 64:0.77 66:0.82 69:0.81 70:0.27 71:0.67 74:0.67 76:0.85 77:0.70 81:0.80 83:0.91 85:0.72 86:0.77 91:0.42 98:0.38 101:0.87 102:0.98 108:0.65 114:0.92 122:0.65 123:0.63 126:0.54 127:0.13 129:0.05 135:0.89 139:0.85 145:0.42 146:0.43 147:0.49 149:0.59 150:0.64 154:0.68 155:0.56 159:0.16 165:0.93 170:0.90 172:0.48 173:0.86 174:0.79 176:0.73 177:0.88 178:0.55 179:0.25 187:0.68 192:0.57 193:0.96 195:0.70 197:0.83 201:0.67 204:0.83 208:0.64 212:0.61 215:0.84 216:0.81 222:0.35 230:0.77 233:0.66 235:0.42 236:0.97 239:0.93 241:0.61 242:0.33 243:0.83 247:0.60 253:0.68 259:0.21 265:0.40 266:0.38 267:0.91 271:0.89 276:0.34 281:0.91 282:0.88 290:0.92 297:0.36 299:0.88 300:0.25 +0 1:0.61 7:0.70 9:0.74 11:0.52 12:0.01 17:0.11 18:0.76 21:0.54 27:0.05 29:0.56 30:0.87 31:0.92 32:0.72 34:0.78 36:0.75 37:0.79 39:0.93 43:0.74 44:0.92 45:0.83 64:0.77 66:0.45 69:0.62 70:0.20 71:0.90 74:0.70 77:0.70 79:0.92 81:0.59 83:0.69 86:0.82 91:0.20 96:0.78 97:0.94 98:0.38 99:0.95 101:0.87 108:0.59 114:0.76 120:0.66 122:0.65 123:0.57 124:0.67 126:0.51 127:0.21 129:0.59 133:0.61 135:0.92 137:0.92 139:0.86 140:0.45 145:0.45 146:0.17 147:0.22 149:0.65 150:0.44 151:0.76 153:0.48 154:0.64 155:0.64 158:0.81 159:0.34 162:0.86 164:0.72 165:0.81 170:0.94 172:0.32 173:0.59 175:0.75 176:0.70 177:0.70 179:0.61 187:0.68 190:0.77 192:0.97 193:0.97 195:0.77 196:0.94 201:0.60 202:0.58 204:0.79 208:0.61 212:0.30 215:0.58 216:0.90 219:0.94 220:0.96 222:0.35 230:0.73 233:0.66 235:0.74 239:0.84 241:0.30 242:0.33 243:0.37 244:0.61 245:0.52 247:0.55 248:0.71 253:0.79 255:0.73 256:0.64 257:0.65 259:0.21 260:0.67 265:0.40 266:0.38 267:0.94 268:0.66 271:0.22 276:0.36 277:0.69 279:0.78 282:0.80 283:0.67 284:0.94 285:0.60 290:0.76 292:0.88 297:0.36 300:0.18 +1 1:0.66 10:0.92 11:0.75 12:0.01 17:0.51 18:0.79 21:0.30 27:0.69 29:0.56 30:0.68 34:0.73 36:0.84 37:0.27 39:0.93 43:0.83 45:0.77 64:0.77 66:0.86 69:0.67 70:0.48 71:0.90 74:0.60 81:0.76 83:0.91 85:0.72 86:0.86 91:0.12 98:0.79 101:0.96 102:0.95 108:0.51 114:0.86 122:0.86 123:0.49 126:0.54 127:0.27 129:0.05 132:0.97 135:0.85 139:0.81 145:0.58 146:0.60 147:0.66 149:0.70 150:0.57 154:0.78 155:0.51 159:0.22 165:0.93 170:0.94 172:0.61 173:0.84 174:0.83 176:0.73 177:0.88 178:0.55 179:0.56 187:0.39 192:0.50 195:0.56 197:0.87 201:0.65 208:0.64 212:0.81 215:0.72 216:0.59 222:0.35 235:0.60 236:0.97 239:0.92 241:0.43 242:0.18 243:0.87 247:0.79 253:0.63 265:0.79 266:0.27 267:0.85 271:0.22 276:0.48 290:0.86 297:0.36 300:0.43 +1 8:0.89 9:0.79 12:0.01 17:0.36 20:0.99 21:0.78 23:0.96 27:0.21 29:0.56 31:0.94 34:0.40 36:0.88 37:0.74 39:0.93 41:0.82 43:0.14 66:0.36 69:0.65 71:0.90 78:0.83 79:0.93 83:0.91 91:0.61 96:0.80 98:0.06 100:0.73 108:0.49 111:0.81 120:0.69 123:0.47 127:0.05 128:0.98 129:0.05 135:0.20 137:0.94 140:0.97 146:0.05 147:0.05 149:0.07 151:0.86 152:0.90 153:0.48 154:0.10 155:0.66 159:0.05 162:0.46 164:0.57 168:0.98 169:0.72 170:0.94 172:0.05 173:0.46 175:0.97 177:0.05 178:0.54 186:0.84 187:0.39 192:0.99 202:0.78 205:0.95 208:0.64 216:0.95 222:0.35 235:0.42 241:0.32 243:0.51 244:0.93 248:0.77 253:0.74 255:0.94 256:0.45 257:0.93 259:0.21 260:0.70 261:0.85 265:0.06 267:0.65 268:0.46 271:0.22 277:0.95 284:0.89 285:0.62 +1 1:0.66 7:0.75 8:0.61 9:0.74 10:0.82 11:0.52 12:0.01 17:0.24 18:0.98 21:0.59 22:0.93 27:0.41 29:0.56 30:0.91 31:0.92 32:0.72 34:0.53 36:0.65 37:0.38 39:0.93 43:0.75 44:0.92 45:0.99 47:0.93 48:0.91 64:0.77 66:0.78 69:0.54 70:0.27 71:0.90 74:0.96 77:0.99 79:0.91 81:0.63 83:0.69 86:0.99 91:0.35 96:0.77 97:1.00 98:0.78 99:0.99 101:0.65 106:0.81 108:0.61 114:0.73 117:0.86 120:0.65 122:0.65 123:0.59 124:0.44 126:0.51 127:0.64 129:0.81 133:0.74 135:0.89 137:0.92 139:0.99 140:0.45 145:0.96 146:0.25 147:0.31 149:0.99 150:0.48 151:0.68 153:0.48 154:0.66 155:0.64 158:0.81 159:0.67 162:0.84 164:0.78 165:0.81 170:0.94 172:0.96 173:0.44 174:0.79 175:0.75 176:0.70 177:0.70 179:0.16 187:0.68 190:0.77 192:0.97 195:0.82 196:0.96 197:0.80 201:0.64 202:0.66 204:0.85 206:0.81 208:0.61 212:0.89 215:0.55 216:0.56 219:0.94 220:0.93 222:0.35 230:0.77 232:0.87 233:0.66 235:0.95 239:0.81 241:0.01 242:0.50 243:0.15 244:0.61 245:0.92 247:0.55 248:0.70 253:0.84 255:0.73 256:0.71 257:0.65 259:0.21 260:0.66 262:0.93 265:0.78 266:0.71 267:0.44 268:0.73 271:0.22 276:0.93 277:0.69 279:0.82 281:0.86 282:0.80 283:0.67 284:0.96 285:0.60 290:0.73 292:0.88 297:0.36 300:0.55 +0 1:0.62 7:0.75 10:0.90 11:0.52 12:0.01 17:0.79 18:0.82 21:0.47 27:0.65 29:0.56 30:0.90 32:0.72 34:0.95 36:0.55 37:0.27 39:0.93 43:0.75 44:0.92 45:0.90 48:0.91 64:0.77 66:0.45 69:0.50 70:0.23 71:0.90 74:0.75 76:0.85 77:0.70 81:0.60 83:0.69 86:0.88 91:0.40 97:0.98 98:0.59 99:0.95 101:0.65 108:0.64 114:0.78 122:0.65 123:0.62 124:0.58 126:0.51 127:0.42 129:0.59 133:0.47 135:0.96 139:0.92 145:0.99 146:0.29 147:0.36 149:0.80 150:0.45 154:0.54 155:0.53 158:0.81 159:0.33 165:0.81 170:0.94 172:0.48 173:0.60 174:0.83 176:0.70 177:0.88 178:0.55 179:0.63 187:0.39 192:0.56 195:0.62 197:0.87 201:0.60 204:0.82 208:0.61 212:0.42 215:0.63 216:0.26 219:0.94 222:0.35 230:0.77 232:0.95 233:0.76 235:0.68 239:0.88 241:0.21 242:0.45 243:0.46 245:0.74 247:0.47 253:0.63 254:0.94 259:0.21 265:0.60 266:0.54 267:0.59 271:0.22 276:0.47 279:0.81 281:0.91 282:0.80 283:0.67 290:0.78 292:0.88 297:0.36 300:0.25 +0 1:0.60 11:0.52 12:0.01 17:0.13 18:0.77 21:0.59 27:0.05 29:0.56 30:0.94 34:0.79 36:0.66 37:0.79 39:0.93 43:0.74 45:0.85 64:0.77 66:0.80 69:0.60 70:0.18 71:0.90 74:0.54 81:0.59 83:0.69 86:0.83 91:0.15 98:0.44 99:0.95 101:0.65 108:0.46 114:0.73 122:0.65 123:0.44 126:0.51 127:0.14 129:0.05 135:0.79 139:0.79 145:0.42 146:0.40 147:0.46 149:0.76 150:0.43 154:0.65 155:0.48 159:0.15 165:0.81 170:0.94 172:0.45 173:0.71 175:0.75 176:0.70 177:0.70 178:0.55 179:0.35 187:0.68 192:0.47 193:0.97 201:0.59 202:0.36 208:0.61 212:0.55 215:0.55 216:0.90 222:0.35 235:0.80 239:0.84 241:0.32 242:0.58 243:0.82 244:0.61 247:0.35 253:0.56 257:0.65 259:0.21 265:0.46 266:0.27 267:0.94 271:0.22 276:0.25 277:0.69 290:0.73 297:0.36 300:0.18 +1 7:0.75 10:0.88 11:0.81 12:0.01 17:0.66 18:0.74 21:0.78 27:0.16 29:0.56 30:0.85 34:0.68 36:0.55 37:0.69 39:0.93 43:0.62 45:0.88 66:0.12 69:0.96 70:0.41 71:0.90 74:0.56 83:0.69 85:0.72 86:0.79 91:0.08 98:0.14 101:0.87 108:0.93 122:0.65 123:0.93 124:0.93 127:0.58 129:0.05 133:0.92 135:0.89 139:0.80 145:0.74 146:0.18 147:0.05 149:0.68 154:0.26 155:0.54 159:0.87 163:0.97 170:0.94 172:0.27 173:0.90 174:0.88 177:0.05 178:0.55 179:0.58 187:0.87 192:0.56 197:0.87 202:0.36 204:0.83 208:0.61 212:0.23 216:0.74 222:0.35 230:0.77 233:0.66 235:0.22 239:0.87 242:0.33 243:0.10 245:0.60 247:0.82 253:0.44 257:0.93 259:0.21 265:0.14 266:0.80 267:0.69 271:0.22 276:0.59 277:0.69 281:0.86 300:0.43 +1 8:0.83 10:0.90 11:0.52 12:0.01 17:0.09 18:0.64 21:0.78 27:0.11 29:0.56 30:0.62 32:0.78 34:0.89 36:0.72 37:0.79 39:0.93 43:0.10 44:0.93 45:0.73 66:0.16 69:0.71 70:0.34 71:0.90 74:0.57 77:0.70 86:0.74 91:0.25 98:0.17 101:0.65 102:0.98 108:0.54 122:0.65 123:0.64 124:0.52 127:0.15 129:0.05 133:0.39 135:0.56 139:0.80 145:0.96 146:0.19 147:0.24 149:0.08 154:0.74 159:0.22 170:0.94 172:0.21 173:0.71 174:0.83 177:0.88 178:0.55 179:0.60 187:0.95 195:0.71 197:0.87 202:0.65 212:0.30 216:0.85 222:0.35 235:0.52 239:0.93 241:0.27 242:0.33 243:0.59 245:0.46 247:0.39 253:0.45 254:0.74 259:0.21 265:0.13 266:0.27 267:0.69 271:0.22 276:0.17 277:0.87 282:0.86 300:0.25 +1 1:0.64 10:0.91 11:0.75 12:0.01 17:0.85 18:0.76 21:0.47 27:0.12 29:0.56 30:0.62 34:0.67 36:0.83 37:0.55 39:0.93 43:0.78 45:0.77 64:0.77 66:0.87 69:0.75 70:0.62 71:0.90 74:0.58 81:0.73 83:0.91 85:0.72 86:0.84 91:0.08 98:0.57 101:0.96 108:0.58 114:0.80 122:0.86 123:0.55 126:0.54 127:0.17 129:0.05 135:0.51 139:0.79 145:0.45 146:0.67 147:0.72 149:0.66 150:0.54 154:0.70 155:0.50 159:0.26 165:0.93 170:0.94 172:0.63 173:0.74 174:0.83 176:0.73 177:0.88 178:0.55 179:0.31 187:0.39 192:0.49 197:0.86 201:0.64 208:0.64 212:0.77 215:0.63 216:0.51 222:0.35 235:0.74 236:0.97 239:0.90 241:0.24 242:0.16 243:0.88 247:0.82 253:0.60 259:0.21 265:0.58 266:0.38 267:0.59 271:0.22 276:0.50 290:0.80 297:0.36 300:0.55 +1 1:0.64 10:0.91 11:0.52 12:0.01 17:0.93 18:0.66 21:0.30 22:0.93 27:0.72 29:0.56 30:0.91 34:0.83 36:0.51 39:0.93 43:0.87 45:0.73 47:0.93 64:0.77 66:0.69 69:0.27 70:0.13 71:0.90 74:0.53 81:0.62 83:0.69 86:0.77 91:0.17 97:0.94 98:0.25 99:0.95 101:0.65 106:0.81 108:0.21 114:0.84 117:0.86 122:0.65 123:0.20 126:0.51 127:0.11 129:0.05 135:0.54 139:0.79 146:0.20 147:0.25 149:0.68 150:0.48 154:0.84 155:0.49 158:0.81 159:0.10 165:0.81 170:0.94 172:0.21 173:0.67 174:0.79 176:0.70 177:0.88 178:0.55 179:0.29 187:0.68 192:0.49 197:0.85 201:0.62 202:0.36 206:0.81 208:0.61 212:0.95 215:0.72 216:0.13 219:0.94 222:0.35 232:0.95 235:0.52 239:0.89 241:0.24 242:0.63 243:0.73 247:0.30 253:0.61 254:0.84 259:0.21 262:0.93 265:0.27 266:0.12 267:0.28 271:0.22 276:0.20 279:0.78 283:0.67 290:0.84 292:0.88 297:0.36 300:0.13 +0 8:0.77 10:0.80 11:0.46 12:0.01 17:0.66 18:0.58 21:0.78 27:0.25 29:0.56 30:0.09 34:0.17 36:0.31 37:0.60 39:0.93 43:0.13 45:0.66 55:0.45 66:0.32 69:0.97 70:1.00 71:0.90 74:0.44 86:0.61 91:0.75 96:0.74 98:0.15 101:0.47 108:0.96 120:0.67 122:0.83 123:0.96 124:0.92 127:0.54 129:0.05 133:0.93 135:0.61 139:0.59 140:0.98 145:0.77 146:0.44 147:0.05 149:0.18 151:0.61 153:0.48 154:0.10 159:0.95 162:0.45 164:0.64 170:0.94 172:0.51 173:0.85 174:0.79 175:0.91 177:0.05 178:0.54 179:0.54 190:0.95 191:0.94 197:0.79 202:0.93 212:0.49 216:0.34 220:0.62 222:0.35 235:0.60 239:0.79 241:0.79 242:0.74 243:0.10 244:0.83 245:0.60 247:0.67 248:0.62 253:0.53 256:0.73 257:0.93 259:0.21 260:0.68 265:0.16 266:0.91 267:0.52 268:0.62 271:0.22 276:0.59 277:0.95 285:0.50 300:0.98 +0 10:0.82 12:0.01 17:0.81 18:0.76 21:0.78 27:0.69 29:0.56 30:0.38 34:0.96 36:0.33 37:0.45 39:0.93 43:0.08 55:0.92 66:0.13 69:0.91 70:0.50 71:0.90 86:0.72 91:0.10 98:0.29 108:0.81 122:0.92 123:0.80 124:0.82 127:0.23 129:0.05 133:0.74 135:0.42 139:0.70 145:0.74 146:0.53 147:0.59 149:0.15 154:0.07 159:0.55 163:0.79 170:0.94 172:0.16 173:0.86 174:0.79 175:0.91 177:0.38 178:0.55 179:0.61 187:0.21 197:0.81 212:0.14 216:0.18 222:0.35 235:0.80 239:0.82 241:0.18 242:0.75 243:0.34 244:0.93 245:0.53 247:0.39 253:0.20 257:0.84 259:0.21 265:0.31 266:0.71 267:0.36 271:0.22 276:0.42 277:0.87 300:0.33 +1 1:0.65 7:0.75 8:0.66 10:0.82 11:0.52 12:0.01 17:0.09 18:0.93 21:0.54 27:0.41 29:0.56 30:0.91 32:0.72 34:0.64 36:0.40 37:0.38 39:0.93 43:0.75 44:0.92 45:0.98 48:0.91 64:0.77 66:0.42 69:0.58 70:0.28 71:0.90 74:0.88 77:0.70 81:0.64 83:0.69 86:0.96 91:0.11 97:0.99 98:0.33 99:0.99 101:0.65 108:0.79 114:0.76 122:0.65 123:0.77 124:0.77 126:0.51 127:0.57 129:0.81 133:0.74 135:0.92 139:0.97 145:0.76 146:0.41 147:0.48 149:0.96 150:0.50 154:0.66 155:0.57 158:0.81 159:0.62 165:0.81 170:0.94 172:0.78 173:0.50 174:0.79 175:0.75 176:0.70 177:0.70 178:0.55 179:0.29 187:0.87 192:0.86 195:0.79 197:0.81 201:0.63 202:0.36 204:0.85 208:0.61 212:0.89 215:0.58 216:0.56 219:0.94 222:0.35 230:0.77 232:0.87 233:0.66 235:0.88 239:0.82 241:0.01 242:0.50 243:0.37 244:0.61 245:0.89 247:0.55 253:0.67 257:0.65 259:0.21 265:0.35 266:0.54 267:0.36 271:0.22 276:0.77 277:0.69 279:0.81 281:0.86 282:0.80 283:0.67 290:0.76 292:0.88 297:0.36 300:0.70 +1 1:0.58 10:0.84 11:0.52 12:0.01 17:0.76 18:0.64 21:0.76 27:0.67 29:0.56 30:0.76 34:0.96 36:0.56 37:0.27 39:0.93 43:0.69 45:0.73 64:0.77 66:0.54 69:0.69 70:0.22 71:0.90 74:0.38 81:0.68 83:0.91 86:0.70 91:0.25 98:0.33 101:0.65 108:0.52 114:0.66 122:0.65 123:0.50 124:0.45 126:0.54 127:0.31 129:0.05 133:0.36 135:0.51 139:0.68 145:1.00 146:0.37 147:0.44 149:0.45 150:0.45 154:0.58 155:0.47 159:0.37 163:0.79 165:0.93 170:0.94 172:0.21 173:0.73 174:0.79 176:0.73 177:0.88 178:0.55 179:0.93 187:0.39 192:0.46 197:0.84 201:0.59 208:0.64 212:0.42 215:0.46 216:0.48 222:0.35 235:0.99 236:0.97 239:0.84 241:0.24 242:0.45 243:0.63 244:0.61 245:0.40 247:0.35 253:0.50 259:0.21 265:0.35 266:0.23 267:0.52 271:0.22 276:0.17 290:0.66 297:0.36 300:0.18 +0 8:0.61 10:0.90 11:0.52 12:0.01 17:0.13 18:0.72 21:0.40 27:0.13 29:0.56 30:0.45 32:0.78 34:0.73 36:0.19 37:0.79 39:0.93 43:0.14 44:0.93 45:0.73 66:0.13 69:0.80 70:0.50 71:0.90 74:0.54 75:0.96 77:0.70 81:0.30 86:0.76 91:0.10 98:0.32 101:0.65 102:0.98 108:0.64 122:0.65 123:0.69 124:0.69 126:0.24 127:0.28 129:0.05 133:0.59 135:0.95 139:0.81 145:0.61 146:0.19 147:0.05 149:0.10 150:0.33 154:0.68 159:0.33 170:0.94 172:0.21 173:0.87 174:0.83 177:0.05 178:0.55 179:0.83 187:0.68 193:0.97 195:0.64 197:0.87 212:0.23 216:0.83 222:0.35 226:0.96 235:0.42 236:0.92 239:0.94 241:0.15 242:0.24 243:0.21 245:0.45 247:0.47 253:0.43 254:0.74 257:0.93 259:0.21 265:0.14 266:0.38 267:0.69 271:0.22 276:0.29 277:0.95 282:0.86 300:0.33 +1 7:0.69 10:0.96 11:0.75 12:0.01 17:0.81 18:0.69 21:0.54 27:0.59 29:0.56 30:0.32 32:0.66 33:0.97 34:0.56 36:0.56 37:0.49 39:0.93 43:0.78 45:0.66 55:0.59 66:0.89 69:0.54 70:0.61 71:0.90 74:0.49 81:0.28 85:0.72 86:0.80 91:0.23 98:0.66 101:0.96 102:0.96 108:0.41 122:0.77 123:0.40 126:0.24 127:0.20 129:0.05 135:0.66 139:0.76 145:0.94 146:0.73 147:0.77 149:0.62 150:0.31 154:0.70 159:0.30 170:0.94 172:0.68 173:0.52 174:0.91 177:0.88 178:0.55 179:0.34 187:0.87 193:0.97 195:0.75 197:0.93 212:0.59 216:0.40 222:0.35 230:0.71 232:1.00 233:0.76 235:0.60 236:0.92 239:0.96 241:0.15 242:0.21 243:0.89 247:0.79 253:0.40 259:0.21 265:0.67 266:0.47 267:0.44 271:0.22 276:0.57 291:0.97 300:0.43 +1 8:0.70 10:0.90 11:0.81 12:0.01 17:0.69 18:0.76 21:0.78 27:0.09 29:0.56 30:0.30 32:0.80 34:0.20 36:0.47 37:0.82 39:0.93 43:0.79 44:0.93 45:0.77 66:0.09 69:0.75 70:0.93 71:0.90 74:0.62 77:0.89 85:0.72 86:0.82 91:0.33 98:0.19 101:0.96 102:0.98 108:0.58 122:0.65 123:0.93 124:0.93 127:0.45 129:0.05 133:0.92 135:0.77 139:0.84 145:1.00 146:0.25 147:0.31 149:0.68 154:0.73 159:0.87 170:0.94 172:0.27 173:0.45 174:0.86 177:0.88 178:0.55 179:0.52 187:0.68 195:0.98 197:0.85 202:0.54 212:0.23 216:0.56 222:0.35 235:0.42 239:0.93 241:0.46 242:0.16 243:0.31 245:0.61 247:0.88 253:0.47 259:0.21 265:0.12 266:0.89 267:0.28 271:0.22 276:0.61 277:0.69 282:0.88 299:0.88 300:0.84 +1 10:0.96 11:0.52 12:0.01 17:0.11 18:0.73 21:0.68 27:0.45 29:0.56 30:0.11 32:0.72 34:0.91 36:0.18 37:0.50 39:0.93 43:0.11 44:0.92 45:0.73 55:0.92 66:0.23 69:0.70 70:0.85 71:0.90 74:0.51 75:0.96 77:0.70 81:0.26 86:0.78 91:0.12 98:0.40 101:0.65 108:0.80 122:0.95 123:0.78 124:0.87 126:0.24 127:0.49 129:0.05 133:0.85 135:0.84 139:0.79 145:0.52 146:0.24 147:0.30 149:0.16 150:0.29 154:0.70 159:0.67 170:0.94 172:0.32 173:0.60 174:0.93 177:0.88 178:0.55 179:0.68 187:0.68 195:0.84 197:0.93 212:0.16 216:0.77 222:0.35 226:0.96 235:0.80 236:0.92 239:0.94 241:0.27 242:0.28 243:0.38 245:0.56 247:0.79 253:0.41 254:0.86 259:0.21 265:0.42 266:0.80 267:0.69 271:0.22 276:0.52 277:0.87 282:0.80 300:0.55 +0 10:0.90 11:0.75 12:0.01 17:0.20 18:0.81 20:0.89 21:0.78 27:0.45 29:0.56 30:0.36 32:0.72 34:0.78 36:0.17 37:0.50 39:0.93 43:0.80 44:0.92 45:0.81 66:0.19 69:0.73 70:0.56 71:0.90 74:0.69 77:0.70 78:0.86 85:0.72 86:0.84 91:0.11 98:0.58 100:0.73 101:0.96 108:0.56 111:0.84 122:0.83 123:0.84 124:0.73 127:0.37 129:0.59 133:0.73 135:0.87 139:0.85 145:0.52 146:0.72 147:0.76 149:0.77 152:0.87 154:0.75 159:0.69 169:0.72 170:0.94 172:0.61 173:0.58 174:0.83 177:0.88 178:0.55 179:0.46 186:0.86 187:0.68 195:0.89 197:0.86 212:0.27 216:0.77 222:0.35 235:0.95 239:0.89 241:0.24 242:0.15 243:0.59 245:0.71 247:0.90 253:0.50 259:0.21 261:0.85 265:0.45 266:0.80 267:0.44 271:0.22 276:0.65 282:0.80 300:0.43 +2 1:0.65 6:0.83 8:0.63 9:0.73 10:0.96 11:0.75 12:0.01 17:0.43 18:0.83 20:0.93 21:0.40 22:0.93 23:0.97 27:0.38 29:0.56 30:0.76 31:0.90 32:0.80 33:0.97 34:0.61 36:0.91 37:0.28 39:0.93 41:0.88 43:0.93 44:0.93 45:0.77 47:0.93 60:0.95 62:0.98 64:0.77 66:0.89 69:0.33 70:0.47 71:0.90 74:0.80 75:0.99 77:0.70 78:0.98 79:0.89 81:0.75 83:0.91 85:0.80 86:0.91 91:0.10 96:0.73 98:0.74 100:0.98 101:0.96 102:0.98 106:0.81 108:0.26 111:0.98 114:0.82 117:0.86 120:0.61 122:0.65 123:0.25 126:0.54 127:0.23 128:0.96 129:0.05 132:0.97 135:0.98 137:0.90 139:0.93 140:0.45 145:0.72 146:0.72 147:0.76 149:0.86 150:0.56 151:0.62 152:0.87 153:0.48 154:0.93 155:0.65 158:0.86 159:0.29 162:0.86 164:0.67 165:0.93 168:0.96 169:0.98 170:0.94 172:0.68 173:0.38 174:0.86 176:0.73 177:0.88 179:0.41 186:0.98 187:0.68 189:0.85 192:0.98 195:0.68 196:0.93 197:0.90 201:0.65 202:0.50 205:0.97 206:0.81 208:0.64 212:0.77 215:0.67 216:0.67 219:0.95 220:0.91 222:0.35 226:0.96 232:0.98 235:0.68 236:0.97 238:0.95 239:0.97 241:0.15 242:0.14 243:0.89 247:0.84 248:0.61 253:0.75 255:0.73 256:0.58 260:0.61 261:0.96 262:0.93 265:0.74 266:0.27 267:0.65 268:0.59 271:0.22 276:0.54 279:0.81 282:0.88 283:0.67 284:0.92 285:0.62 290:0.82 291:0.97 292:0.88 297:0.36 299:0.88 300:0.55 +1 1:0.66 7:0.81 8:0.67 10:0.98 11:0.75 12:0.20 17:0.73 18:0.87 21:0.30 22:0.93 27:0.43 29:0.94 30:0.40 32:0.80 33:0.97 34:0.89 36:0.44 37:0.44 39:0.36 43:0.78 44:0.93 45:0.90 47:0.93 48:0.91 64:0.77 66:0.54 69:0.79 70:0.94 71:0.86 74:0.86 75:0.99 76:0.85 77:0.70 81:0.77 83:0.69 86:0.92 91:0.12 97:0.94 98:0.41 99:0.95 101:0.65 102:0.98 104:0.96 106:0.81 108:0.62 114:0.86 117:0.86 122:0.95 123:0.60 124:0.83 126:0.54 127:0.46 129:0.05 131:0.61 133:0.88 135:0.91 139:0.96 144:0.76 145:0.41 146:0.80 147:0.83 149:0.91 150:0.67 154:0.70 155:0.57 157:0.87 158:0.86 159:0.83 165:0.81 166:0.96 170:0.90 172:0.84 173:0.57 174:0.98 176:0.73 177:0.88 178:0.55 179:0.26 182:0.87 187:0.39 192:0.58 193:0.99 195:0.95 197:0.98 201:0.65 202:0.49 204:0.83 206:0.81 208:0.64 212:0.60 215:0.72 216:0.48 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 230:0.83 231:0.94 232:0.97 233:0.76 235:0.60 236:0.97 239:0.99 241:0.07 242:0.21 243:0.63 245:0.81 246:0.96 247:0.93 253:0.70 259:0.21 262:0.93 265:0.43 266:0.92 267:0.18 271:0.32 276:0.83 279:0.80 281:0.91 282:0.88 283:0.67 287:0.61 290:0.86 291:0.97 292:0.88 297:0.36 300:0.94 +1 1:0.66 10:0.98 11:0.75 12:0.20 17:0.66 18:0.97 21:0.30 22:0.93 27:0.24 29:0.94 30:0.45 32:0.80 33:0.97 34:0.97 36:0.38 37:0.80 39:0.36 43:0.79 44:0.93 45:0.96 47:0.93 64:0.77 66:0.63 69:0.76 70:0.59 71:0.86 74:0.76 77:0.70 81:0.77 83:0.69 86:0.96 91:0.15 98:0.62 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.89 114:0.86 117:0.86 122:0.92 123:0.89 124:0.49 126:0.54 127:0.59 129:0.59 131:0.61 133:0.47 135:0.94 139:0.96 144:0.76 145:0.52 146:0.71 147:0.76 149:0.93 150:0.67 154:0.72 155:0.51 157:0.94 159:0.78 165:0.81 166:0.96 170:0.90 172:0.94 173:0.59 174:0.97 176:0.73 177:0.88 178:0.55 179:0.17 182:0.87 187:0.39 192:0.50 195:0.91 197:0.96 201:0.65 206:0.81 208:0.64 212:0.88 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.98 241:0.10 242:0.52 243:0.36 245:0.98 246:0.96 247:0.91 253:0.67 259:0.21 262:0.93 265:0.63 266:0.63 267:0.92 271:0.32 276:0.89 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.70 +2 1:0.67 8:0.67 10:0.98 11:0.75 12:0.20 17:0.55 18:0.95 21:0.22 22:0.93 27:0.24 29:0.94 30:0.60 32:0.80 33:0.97 34:0.89 36:0.47 37:0.80 39:0.36 43:0.79 44:0.93 45:0.97 47:0.93 64:0.77 66:0.68 69:0.75 70:0.56 71:0.86 74:0.80 75:0.99 77:0.70 81:0.79 83:0.69 86:0.95 91:0.31 97:0.94 98:0.41 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.96 114:0.90 117:0.86 122:0.92 123:0.95 124:0.50 126:0.54 127:0.76 129:0.59 131:0.61 133:0.60 135:0.98 139:0.96 144:0.76 145:0.69 146:0.66 147:0.71 149:0.92 150:0.72 154:0.72 155:0.52 157:0.93 158:0.86 159:0.90 165:0.81 166:0.96 170:0.90 172:0.95 173:0.45 174:0.97 176:0.73 177:0.88 178:0.55 179:0.17 182:0.87 187:0.21 192:0.51 195:0.98 197:0.94 201:0.66 206:0.81 208:0.64 212:0.89 215:0.79 216:0.67 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 231:0.94 232:0.95 235:0.52 236:0.97 239:0.98 241:0.46 242:0.51 243:0.31 245:0.96 246:0.96 247:0.90 253:0.70 259:0.21 262:0.93 265:0.43 266:0.63 267:0.94 271:0.32 276:0.82 279:0.80 282:0.88 283:0.67 287:0.61 290:0.90 291:0.97 292:0.88 297:0.36 300:0.70 +2 1:0.64 10:0.98 11:0.75 12:0.20 17:0.95 18:0.89 21:0.30 22:0.93 27:0.72 29:0.94 30:0.33 33:0.97 34:0.96 36:0.85 37:0.95 39:0.36 43:0.62 45:0.93 47:0.93 64:0.77 66:0.35 69:0.40 70:0.93 71:0.86 74:0.70 81:0.59 83:0.56 86:0.93 91:0.27 98:0.42 99:0.83 101:0.65 108:0.31 122:0.95 123:0.30 124:0.82 126:0.32 127:0.81 129:0.59 131:0.61 133:0.81 135:0.98 139:0.90 144:0.76 146:0.77 147:0.81 149:0.58 150:0.55 154:0.87 155:0.49 157:0.90 159:0.83 165:0.65 166:0.96 170:0.90 172:0.77 173:0.19 174:0.97 177:0.88 178:0.55 179:0.31 182:0.87 187:0.87 192:0.45 197:0.98 201:0.60 208:0.50 212:0.82 216:0.06 222:0.97 227:0.61 229:0.61 231:0.94 235:0.52 236:0.94 239:0.97 241:0.41 242:0.28 243:0.51 245:0.87 246:0.96 247:0.86 253:0.51 254:0.74 259:0.21 262:0.93 265:0.44 266:0.89 267:0.79 271:0.32 276:0.81 287:0.61 291:0.97 300:0.98 +2 1:0.64 7:0.81 10:0.98 11:0.75 12:0.20 17:0.61 18:0.95 21:0.54 22:0.93 27:0.24 29:0.94 30:0.68 32:0.80 33:0.97 34:0.97 36:0.69 37:0.80 39:0.36 43:0.81 44:0.93 45:0.98 47:0.93 64:0.77 66:0.35 69:0.70 70:0.59 71:0.86 74:0.78 77:0.70 81:0.72 83:0.69 86:0.96 91:0.20 98:0.47 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.92 114:0.77 117:0.86 122:0.92 123:0.91 124:0.69 126:0.54 127:0.68 129:0.81 131:0.61 133:0.67 135:0.90 139:0.97 144:0.76 145:0.70 146:0.26 147:0.32 149:0.91 150:0.58 154:0.75 155:0.64 157:0.95 159:0.86 165:0.81 166:0.96 170:0.90 172:0.90 173:0.45 174:0.98 176:0.73 177:0.88 178:0.55 179:0.17 182:0.87 187:0.39 192:0.87 193:0.99 195:0.95 197:0.96 201:0.63 204:0.85 206:0.81 208:0.64 212:0.90 215:0.58 216:0.67 222:0.97 227:0.61 229:0.61 230:0.83 231:0.94 232:0.95 233:0.66 235:0.80 236:0.97 239:0.99 241:0.05 242:0.27 243:0.16 245:0.98 246:0.96 247:0.82 253:0.64 259:0.21 262:0.93 265:0.49 266:0.75 267:0.52 271:0.32 276:0.88 281:0.86 282:0.88 287:0.61 290:0.77 291:0.97 297:0.36 300:0.94 +2 1:0.64 7:0.75 10:0.93 11:0.75 12:0.20 17:0.45 18:0.76 21:0.47 22:0.93 27:0.41 29:0.94 30:0.86 32:0.78 33:0.97 34:0.97 36:0.40 37:0.68 39:0.36 43:0.29 44:0.93 45:0.87 47:0.93 64:0.77 66:0.44 69:0.76 70:0.30 71:0.86 74:0.57 76:0.85 77:0.70 81:0.74 83:0.69 86:0.82 91:0.15 98:0.19 99:0.95 101:0.65 102:0.98 108:0.79 114:0.80 122:0.93 123:0.78 124:0.69 126:0.54 127:0.42 129:0.05 131:0.61 133:0.62 135:0.93 139:0.87 144:0.76 145:0.54 146:0.22 147:0.28 149:0.49 150:0.60 154:0.62 155:0.53 157:0.90 159:0.45 165:0.81 166:0.96 170:0.90 172:0.41 173:0.79 174:0.89 176:0.73 177:0.88 178:0.55 179:0.70 182:0.87 187:0.68 192:0.55 193:0.99 195:0.69 197:0.90 201:0.64 204:0.80 208:0.64 212:0.61 215:0.63 216:0.59 222:0.97 227:0.61 229:0.61 230:0.77 231:0.94 233:0.65 235:0.74 236:0.97 239:0.95 241:0.10 242:0.33 243:0.36 245:0.61 246:0.96 247:0.55 253:0.60 254:0.94 259:0.21 262:0.93 265:0.21 266:0.47 267:0.91 271:0.32 276:0.35 277:0.87 281:0.86 282:0.86 287:0.61 290:0.80 291:0.97 297:0.36 300:0.33 +1 1:0.60 8:0.87 9:0.74 10:0.98 11:0.75 12:0.20 17:0.59 18:0.94 21:0.30 22:0.93 23:0.98 27:0.19 29:0.94 30:0.75 31:0.96 33:0.97 34:0.67 36:0.26 37:0.73 39:0.36 43:0.63 44:0.88 45:0.94 47:0.93 62:0.99 64:0.77 66:0.09 69:0.51 70:0.31 71:0.86 74:0.51 75:0.99 79:0.95 81:0.55 83:0.69 86:0.94 91:0.64 97:0.99 98:0.49 99:0.83 101:0.65 102:0.96 108:0.66 122:0.93 123:0.38 124:0.44 126:0.32 127:0.34 128:0.99 129:0.05 131:0.98 133:0.39 135:0.92 137:0.96 139:0.95 140:0.45 144:0.76 145:0.47 146:0.44 147:0.50 149:0.87 150:0.50 151:0.91 153:0.72 154:0.52 155:0.56 157:0.94 158:0.86 159:0.31 162:0.86 165:0.65 166:0.96 168:0.99 170:0.90 172:0.79 173:0.61 174:0.96 177:0.88 179:0.33 182:0.88 187:0.87 190:0.89 191:0.81 192:0.49 193:0.98 195:0.60 196:0.98 197:0.98 201:0.57 202:0.61 204:0.81 205:0.98 208:0.64 212:0.88 216:0.81 219:0.95 222:0.97 226:0.98 227:0.99 229:0.94 231:0.95 235:0.42 236:0.94 239:0.99 241:0.61 242:0.59 243:0.27 244:0.83 245:0.84 246:0.96 247:0.55 248:0.85 253:0.41 255:0.72 256:0.68 259:0.21 262:0.93 265:0.44 266:0.38 267:0.84 268:0.70 271:0.32 276:0.67 279:0.82 281:0.86 283:0.67 284:0.94 285:0.50 287:0.97 291:0.97 292:0.95 300:0.33 +2 1:0.66 10:0.97 11:0.75 12:0.20 17:0.67 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.45 32:0.80 33:0.97 34:0.97 36:0.20 37:0.80 39:0.36 43:0.79 44:0.93 45:0.92 47:0.93 55:0.45 64:0.77 66:0.97 69:0.76 70:0.48 71:0.86 74:0.73 77:0.70 81:0.77 83:0.69 86:0.95 91:0.20 98:0.84 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.59 114:0.86 117:0.86 122:0.92 123:0.56 126:0.54 127:0.34 129:0.05 131:0.61 135:0.87 139:0.95 144:0.76 145:0.54 146:0.88 147:0.90 149:0.91 150:0.67 154:0.72 155:0.51 157:0.92 159:0.46 165:0.81 166:0.96 170:0.90 172:0.93 173:0.75 174:0.93 176:0.73 177:0.88 178:0.55 179:0.19 182:0.87 187:0.21 192:0.50 195:0.74 197:0.94 201:0.65 206:0.81 208:0.64 212:0.88 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.98 241:0.18 242:0.63 243:0.97 246:0.96 247:0.82 253:0.66 259:0.21 262:0.93 265:0.84 266:0.54 267:0.23 271:0.32 276:0.86 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.55 +1 1:0.66 10:0.99 11:0.75 12:0.20 17:0.70 18:0.99 21:0.30 22:0.93 27:0.24 29:0.94 30:0.45 32:0.80 33:0.97 34:0.97 36:0.21 37:0.80 39:0.36 43:0.79 44:0.93 45:0.96 47:0.93 64:0.77 66:0.63 69:0.76 70:0.56 71:0.86 74:0.77 77:0.70 81:0.77 83:0.69 86:0.97 91:0.14 98:0.72 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.79 114:0.86 117:0.86 122:0.92 123:0.77 124:0.50 126:0.54 127:0.44 129:0.59 131:0.61 133:0.47 135:0.93 139:0.97 144:0.76 145:0.63 146:0.71 147:0.76 149:0.94 150:0.67 154:0.72 155:0.51 157:0.94 159:0.62 165:0.81 166:0.96 170:0.90 172:0.94 173:0.69 174:0.96 176:0.73 177:0.88 178:0.55 179:0.16 182:0.87 187:0.21 192:0.50 195:0.82 197:0.97 201:0.65 206:0.81 208:0.64 212:0.89 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.99 241:0.07 242:0.53 243:0.36 245:0.98 246:0.96 247:0.82 253:0.67 259:0.21 262:0.93 265:0.72 266:0.38 267:0.36 271:0.32 276:0.92 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.55 +2 1:0.66 10:0.98 11:0.75 12:0.20 17:0.62 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.44 32:0.80 33:0.97 34:0.98 36:0.21 37:0.80 39:0.36 43:0.79 44:0.93 45:0.95 47:0.93 64:0.77 66:0.98 69:0.76 70:0.56 71:0.86 74:0.80 77:0.70 81:0.77 83:0.69 86:0.97 91:0.11 97:0.89 98:0.89 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.59 114:0.86 117:0.86 122:0.92 123:0.56 126:0.54 127:0.44 129:0.05 131:0.61 135:0.93 139:0.97 144:0.76 145:0.63 146:0.94 147:0.95 149:0.94 150:0.67 154:0.72 155:0.51 157:0.94 158:0.77 159:0.60 165:0.81 166:0.96 170:0.90 172:0.96 173:0.70 174:0.96 176:0.73 177:0.88 178:0.55 179:0.16 182:0.87 187:0.21 192:0.50 195:0.80 197:0.96 201:0.65 206:0.81 208:0.64 212:0.88 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.99 241:0.10 242:0.55 243:0.98 246:0.96 247:0.86 253:0.68 259:0.21 262:0.93 265:0.89 266:0.63 267:0.87 271:0.32 276:0.91 279:0.76 282:0.88 283:0.82 287:0.61 290:0.86 291:0.97 297:0.36 300:0.55 +1 1:0.67 8:0.63 10:0.98 11:0.75 12:0.20 17:0.59 18:0.94 21:0.22 22:0.93 27:0.24 29:0.94 30:0.60 32:0.80 33:0.97 34:0.87 36:0.47 37:0.80 39:0.36 43:0.79 44:0.93 45:0.97 47:0.93 64:0.77 66:0.67 69:0.75 70:0.55 71:0.86 74:0.75 77:0.70 81:0.79 83:0.69 86:0.95 91:0.25 98:0.42 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.96 114:0.90 117:0.86 122:0.92 123:0.95 124:0.50 126:0.54 127:0.78 129:0.59 131:0.61 133:0.60 135:0.99 139:0.94 144:0.76 145:0.69 146:0.66 147:0.71 149:0.92 150:0.72 154:0.72 155:0.52 157:0.93 159:0.90 165:0.81 166:0.96 170:0.90 172:0.95 173:0.45 174:0.96 176:0.73 177:0.88 178:0.55 179:0.18 182:0.87 187:0.39 192:0.51 195:0.98 197:0.94 201:0.66 206:0.81 208:0.64 212:0.89 215:0.79 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.52 236:0.97 239:0.98 241:0.46 242:0.52 243:0.31 245:0.96 246:0.96 247:0.90 253:0.68 259:0.21 262:0.93 265:0.44 266:0.71 267:0.94 271:0.32 276:0.82 282:0.88 287:0.61 290:0.90 291:0.97 297:0.36 300:0.70 +2 1:0.66 7:0.81 10:0.99 11:0.75 12:0.20 17:0.64 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.61 32:0.80 33:0.97 34:0.95 36:0.32 37:0.80 39:0.36 43:0.81 44:0.93 45:0.97 47:0.93 48:0.91 64:0.77 66:0.33 69:0.69 70:0.56 71:0.86 74:0.83 77:0.70 81:0.77 83:0.69 86:0.98 91:0.29 98:0.63 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.68 114:0.86 117:0.86 122:0.92 123:0.66 124:0.69 126:0.54 127:0.47 129:0.81 131:0.61 133:0.67 135:0.77 139:0.98 144:0.76 145:0.63 146:0.24 147:0.30 149:0.96 150:0.67 154:0.75 155:0.63 157:0.95 159:0.61 165:0.81 166:0.96 170:0.90 172:0.87 173:0.62 174:0.97 176:0.73 177:0.88 178:0.55 179:0.16 182:0.88 187:0.39 192:0.86 193:0.99 195:0.81 197:0.98 201:0.65 204:0.85 206:0.81 208:0.64 212:0.92 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 230:0.83 231:0.94 232:0.95 233:0.66 235:0.60 236:0.97 239:0.99 241:0.07 242:0.33 243:0.18 245:0.97 246:0.96 247:0.82 253:0.69 259:0.21 262:0.93 265:0.64 266:0.54 267:0.28 271:0.32 276:0.91 281:0.86 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.70 +2 1:0.68 10:0.91 11:0.75 12:0.20 17:0.06 18:0.67 21:0.13 22:0.93 27:0.36 29:0.94 30:0.95 33:0.97 34:0.47 36:0.39 37:0.64 39:0.36 43:0.91 45:0.73 47:0.93 64:0.77 66:0.69 69:0.41 71:0.86 74:0.57 75:0.99 81:0.81 83:0.69 86:0.77 91:0.53 97:0.94 98:0.17 99:0.95 101:0.65 104:0.96 106:0.81 108:0.31 114:0.92 117:0.86 122:0.93 123:0.30 126:0.54 127:0.10 129:0.05 131:0.61 135:0.73 139:0.82 144:0.76 146:0.18 147:0.23 149:0.70 150:0.77 154:0.90 155:0.52 157:0.79 158:0.86 159:0.09 165:0.81 166:0.96 170:0.90 172:0.21 173:0.69 174:0.79 176:0.73 177:0.88 178:0.55 179:0.13 182:0.87 187:0.68 192:0.54 197:0.85 201:0.67 206:0.81 208:0.64 212:0.95 215:0.84 216:0.72 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 231:0.94 232:0.97 235:0.42 236:0.97 239:0.93 241:0.27 242:0.63 243:0.73 246:0.96 247:0.30 253:0.66 259:0.21 262:0.93 265:0.18 266:0.12 267:0.23 271:0.32 276:0.23 279:0.80 283:0.67 287:0.61 290:0.92 291:0.97 292:0.88 297:0.36 300:0.08 +1 1:0.68 8:0.76 10:0.99 11:0.75 12:0.20 17:0.85 18:0.92 21:0.05 22:0.93 27:0.31 29:0.94 30:0.09 33:0.97 34:0.40 36:0.95 37:0.68 39:0.36 43:0.88 45:0.88 47:0.93 64:0.77 66:0.86 69:0.35 70:0.95 71:0.86 74:0.87 75:0.99 77:0.70 81:0.83 83:0.69 86:0.95 91:0.25 97:0.94 98:0.89 99:0.95 101:0.65 102:0.94 104:0.98 106:0.81 108:0.27 114:0.95 117:0.86 122:0.94 123:0.26 124:0.38 126:0.54 127:0.82 129:0.05 131:0.61 133:0.62 135:0.97 139:0.94 144:0.76 145:0.65 146:0.99 147:0.99 149:0.85 150:0.82 154:0.87 155:0.53 157:0.87 158:0.86 159:0.87 165:0.81 166:0.96 170:0.90 172:0.96 173:0.14 174:0.99 176:0.73 177:0.88 178:0.55 179:0.19 182:0.87 187:0.39 192:0.55 195:0.95 197:0.99 201:0.67 202:0.46 206:0.81 208:0.64 212:0.60 215:0.91 216:0.57 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 231:0.94 232:0.99 235:0.31 236:0.97 239:0.99 241:0.43 242:0.18 243:0.87 245:0.87 246:0.96 247:0.98 253:0.74 262:0.93 265:0.89 266:0.75 267:0.91 271:0.32 276:0.92 279:0.80 282:0.75 283:0.67 287:0.61 290:0.95 291:0.97 292:0.88 297:0.36 300:0.84 +2 1:0.68 7:0.70 8:0.71 9:0.74 10:0.97 11:0.75 12:0.20 17:0.06 18:0.83 21:0.13 23:0.96 27:0.19 29:0.94 30:0.74 31:0.90 34:0.79 36:0.75 37:0.73 39:0.36 43:0.96 45:0.90 62:0.98 64:0.77 66:0.77 69:0.15 70:0.53 71:0.86 74:0.85 75:0.99 79:0.90 81:0.81 83:0.69 86:0.92 91:0.17 96:0.74 97:0.94 98:0.76 99:0.95 101:0.65 102:0.94 108:0.59 114:0.92 120:0.62 122:0.94 123:0.57 124:0.41 126:0.54 127:0.56 128:0.96 129:0.59 131:0.98 133:0.47 135:0.70 137:0.90 139:0.93 140:0.80 144:0.76 145:0.67 146:0.18 147:0.23 149:0.92 150:0.77 151:0.69 153:0.48 154:0.96 155:0.65 157:0.89 158:0.86 159:0.40 162:0.51 164:0.57 165:0.81 166:0.96 168:0.96 170:0.90 172:0.81 173:0.27 174:0.94 176:0.73 177:0.88 178:0.42 179:0.41 182:0.88 187:0.87 192:0.98 193:0.98 195:0.63 196:0.94 197:0.96 201:0.67 202:0.60 204:0.83 205:0.95 208:0.64 212:0.90 215:0.84 216:0.81 219:0.95 220:0.74 222:0.97 226:0.96 227:0.99 229:0.93 230:0.73 231:0.94 233:0.66 235:0.42 236:0.97 239:0.98 241:0.12 242:0.18 243:0.19 245:0.81 246:0.96 247:0.94 248:0.64 253:0.80 255:0.74 256:0.45 259:0.21 260:0.63 265:0.76 266:0.47 267:0.18 268:0.46 271:0.32 276:0.70 279:0.80 281:0.86 283:0.67 284:0.89 285:0.62 287:0.97 290:0.92 292:0.88 297:0.36 300:0.70 +1 1:0.58 10:0.94 11:0.75 12:0.20 17:0.67 18:0.84 21:0.47 22:0.93 27:0.19 29:0.94 30:0.45 33:0.97 34:0.90 36:0.43 37:0.73 39:0.36 43:0.11 45:0.81 47:0.93 48:0.91 64:0.77 66:0.30 69:0.37 70:0.46 71:0.86 74:0.57 81:0.51 83:0.69 86:0.87 91:0.40 97:0.89 98:0.19 99:0.83 101:0.65 108:0.29 122:0.65 123:0.28 124:0.78 126:0.32 127:0.47 129:0.59 131:0.61 133:0.73 135:0.90 139:0.84 144:0.76 145:0.61 146:0.25 147:0.31 149:0.09 150:0.46 154:0.74 155:0.52 157:0.83 158:0.77 159:0.45 165:0.65 166:0.96 170:0.90 172:0.32 173:0.39 174:0.86 177:0.88 178:0.55 179:0.74 182:0.87 187:0.98 192:0.46 193:0.98 197:0.89 201:0.55 204:0.82 208:0.61 212:0.58 216:0.81 222:0.97 227:0.61 229:0.61 231:0.94 235:0.60 236:0.94 239:0.93 241:0.27 242:0.22 243:0.48 245:0.56 246:0.96 247:0.67 253:0.45 254:0.74 259:0.21 262:0.93 265:0.20 266:0.47 267:0.69 271:0.32 276:0.40 279:0.76 281:0.91 283:0.82 287:0.61 291:0.97 300:0.33 +2 1:0.66 7:0.81 8:0.77 10:0.98 11:0.75 12:0.20 17:0.92 18:0.93 21:0.30 22:0.93 27:0.34 29:0.94 30:0.36 32:0.80 33:0.97 34:0.76 36:0.99 37:0.64 39:0.36 43:0.83 44:0.93 45:0.92 47:0.93 48:0.91 64:0.77 66:0.60 69:0.37 70:0.85 71:0.86 74:0.83 75:0.99 77:0.89 81:0.77 83:0.69 86:0.93 91:0.18 97:0.99 98:0.85 99:0.95 101:0.65 102:0.98 104:0.99 106:0.81 108:0.29 114:0.86 117:0.86 122:0.94 123:0.28 124:0.51 126:0.54 127:0.46 129:0.59 131:0.61 133:0.47 135:0.93 139:0.96 144:0.76 145:0.89 146:0.95 147:0.96 149:0.85 150:0.67 154:0.79 155:0.57 157:0.92 158:0.86 159:0.69 165:0.81 166:0.96 170:0.90 172:0.87 173:0.23 174:0.98 176:0.73 177:0.88 178:0.55 179:0.23 182:0.87 187:0.68 192:0.58 193:0.99 195:0.88 197:0.98 201:0.65 202:0.36 204:0.84 206:0.81 208:0.64 212:0.74 215:0.72 216:0.51 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 230:0.83 231:0.94 232:0.99 233:0.66 235:0.60 236:0.97 239:0.99 241:0.03 242:0.27 243:0.67 245:0.95 246:0.96 247:0.94 253:0.69 259:0.21 262:0.93 265:0.85 266:0.63 267:0.59 271:0.32 276:0.85 279:0.82 281:0.91 282:0.88 283:0.67 287:0.61 290:0.86 291:0.97 292:0.95 297:0.36 300:0.70 +2 1:0.66 7:0.81 10:0.98 11:0.75 12:0.20 17:0.61 18:0.96 21:0.30 22:0.93 27:0.24 29:0.94 30:0.60 32:0.80 33:0.97 34:0.94 36:0.41 37:0.80 39:0.36 43:0.81 44:0.93 45:0.97 47:0.93 64:0.77 66:0.32 69:0.69 70:0.59 71:0.86 74:0.79 77:0.70 81:0.77 83:0.69 86:0.96 91:0.20 98:0.48 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.88 114:0.86 117:0.86 122:0.92 123:0.87 124:0.69 126:0.54 127:0.63 129:0.81 131:0.61 133:0.67 135:0.89 139:0.97 144:0.76 145:0.63 146:0.25 147:0.31 149:0.91 150:0.67 154:0.75 155:0.63 157:0.94 159:0.79 165:0.81 166:0.96 170:0.90 172:0.87 173:0.52 174:0.97 176:0.73 177:0.88 178:0.55 179:0.18 182:0.87 187:0.39 192:0.86 193:0.99 195:0.91 197:0.96 201:0.65 204:0.85 206:0.81 208:0.64 212:0.91 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 230:0.83 231:0.94 232:0.95 233:0.66 235:0.60 236:0.97 239:0.99 241:0.07 242:0.36 243:0.18 245:0.97 246:0.96 247:0.84 253:0.68 259:0.21 262:0.93 265:0.50 266:0.75 267:0.36 271:0.32 276:0.87 281:0.86 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.84 +2 1:0.64 7:0.75 10:0.96 11:0.75 12:0.20 17:0.84 18:0.82 21:0.47 22:0.93 27:0.58 29:0.94 30:0.74 32:0.78 33:0.97 34:0.67 36:0.46 37:0.71 39:0.36 43:0.19 44:0.93 45:0.95 47:0.93 64:0.77 66:0.23 69:0.84 70:0.39 71:0.86 74:0.74 76:0.85 77:0.70 81:0.74 83:0.69 86:0.89 91:0.15 97:0.89 98:0.39 99:0.95 101:0.65 102:0.98 104:0.99 106:0.81 108:0.89 114:0.80 117:0.86 122:0.93 123:0.89 124:0.87 126:0.54 127:0.68 129:0.93 131:0.61 133:0.87 135:0.21 139:0.91 144:0.76 145:0.77 146:0.47 147:0.05 149:0.09 150:0.60 154:0.52 155:0.53 157:0.92 158:0.76 159:0.84 165:0.81 166:0.96 170:0.90 172:0.63 173:0.66 174:0.94 176:0.73 177:0.05 178:0.55 179:0.34 182:0.87 187:0.68 192:0.55 193:0.99 195:0.94 197:0.91 201:0.64 204:0.80 206:0.81 208:0.64 212:0.30 215:0.63 216:0.49 222:0.97 227:0.61 229:0.61 230:0.77 231:0.94 232:0.99 233:0.65 235:0.74 236:0.97 239:0.97 241:0.52 242:0.33 243:0.07 245:0.83 246:0.96 247:0.55 253:0.64 254:0.74 257:0.93 259:0.21 262:0.93 265:0.41 266:0.84 267:0.44 271:0.32 276:0.78 279:0.75 281:0.86 282:0.86 283:0.67 287:0.61 290:0.80 291:0.97 297:0.36 300:0.55 +0 1:0.64 9:0.75 10:0.93 11:0.75 12:0.20 17:0.81 18:0.96 21:0.47 22:0.93 23:0.98 27:0.61 29:0.94 30:0.29 31:0.93 33:0.97 37:0.41 39:0.36 43:0.74 44:0.85 45:0.87 47:0.93 55:0.71 62:0.98 64:0.77 66:0.68 69:0.84 70:0.74 71:0.86 74:0.62 75:0.99 79:0.92 81:0.74 83:0.69 86:0.85 91:0.23 96:0.79 97:0.94 98:0.74 99:0.95 101:0.65 102:0.94 104:0.96 106:0.81 108:0.80 114:0.80 117:0.86 120:0.67 122:0.92 123:0.78 124:0.48 126:0.54 127:0.34 128:0.97 129:0.59 131:0.98 133:0.60 137:0.93 139:0.88 140:0.45 144:0.76 145:0.87 146:0.58 147:0.63 149:0.76 150:0.60 151:0.77 153:0.48 154:0.65 155:0.66 157:0.83 158:0.86 159:0.80 162:0.79 164:0.71 165:0.81 166:0.96 168:0.97 170:0.90 172:0.91 173:0.64 174:0.93 176:0.73 177:0.88 179:0.18 182:0.88 187:0.39 192:0.99 193:0.98 195:0.95 196:0.95 197:0.93 201:0.64 202:0.60 204:0.81 205:0.98 206:0.81 208:0.64 212:0.61 215:0.63 216:0.40 219:0.95 220:0.87 222:0.97 226:0.96 227:0.99 229:0.94 231:0.95 232:0.97 235:0.74 236:0.97 239:0.95 241:0.37 242:0.74 243:0.29 245:0.91 246:0.96 247:0.91 248:0.74 253:0.79 255:0.78 256:0.64 260:0.68 262:0.93 265:0.75 266:0.75 268:0.65 271:0.32 276:0.88 279:0.80 281:0.91 283:0.67 284:0.94 285:0.62 287:0.97 290:0.80 291:0.97 292:0.88 297:0.36 300:0.70 +2 1:0.66 10:0.98 11:0.75 12:0.20 17:0.66 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.40 32:0.80 33:0.97 34:0.95 36:0.23 37:0.80 39:0.36 43:0.79 44:0.93 45:0.95 47:0.93 64:0.77 66:0.59 69:0.76 70:0.59 71:0.86 74:0.75 77:0.70 81:0.77 83:0.69 86:0.96 91:0.12 98:0.60 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.85 114:0.86 117:0.86 122:0.92 123:0.84 124:0.59 126:0.54 127:0.48 129:0.59 131:0.61 133:0.60 135:0.95 139:0.95 144:0.76 145:0.54 146:0.78 147:0.82 149:0.93 150:0.67 154:0.72 155:0.51 157:0.93 159:0.68 165:0.81 166:0.96 170:0.90 172:0.92 173:0.66 174:0.94 176:0.73 177:0.88 178:0.55 179:0.18 182:0.87 187:0.21 192:0.50 195:0.86 197:0.94 201:0.65 206:0.81 208:0.64 212:0.86 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.98 241:0.15 242:0.63 243:0.39 245:0.95 246:0.96 247:0.82 253:0.67 259:0.21 262:0.93 265:0.61 266:0.71 267:0.93 271:0.32 276:0.90 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.70 +2 9:0.75 10:0.91 11:0.75 12:0.20 17:0.51 18:0.67 21:0.78 22:0.93 23:0.96 27:0.36 29:0.94 30:0.95 31:0.92 33:0.97 34:0.37 36:0.66 37:0.64 39:0.36 43:0.91 45:0.73 47:0.93 62:0.97 66:0.69 69:0.35 71:0.86 74:0.57 75:0.99 79:0.91 83:0.69 86:0.77 91:0.48 96:0.77 97:0.94 98:0.19 101:0.65 104:0.96 106:0.81 108:0.27 117:0.86 120:0.65 122:0.93 123:0.26 127:0.10 128:0.97 129:0.05 131:0.98 135:0.79 137:0.92 139:0.82 140:0.45 144:0.76 146:0.18 147:0.23 149:0.70 151:0.77 153:0.48 154:0.90 155:0.65 157:0.79 158:0.86 159:0.09 162:0.86 164:0.57 166:0.61 168:0.97 170:0.90 172:0.21 173:0.72 174:0.79 177:0.88 179:0.16 182:0.88 187:0.39 192:0.98 196:0.93 197:0.85 202:0.36 205:0.95 206:0.81 208:0.64 212:0.95 216:0.72 219:0.95 220:0.62 222:0.97 226:0.96 227:0.99 229:0.93 231:0.94 232:0.97 235:0.02 239:0.93 241:0.63 242:0.63 243:0.73 246:0.61 247:0.30 248:0.70 253:0.69 255:0.78 256:0.45 259:0.21 260:0.66 262:0.93 265:0.21 266:0.12 267:0.19 268:0.46 271:0.32 276:0.23 279:0.80 283:0.67 284:0.89 285:0.62 287:0.97 291:0.97 292:0.88 300:0.08 +0 11:0.82 17:0.22 18:0.64 21:0.78 27:0.45 29:0.77 30:0.76 32:0.69 34:0.77 36:0.21 37:0.50 39:0.98 43:0.60 55:0.42 66:0.77 69:0.70 70:0.29 71:0.89 74:0.57 77:0.70 86:0.74 91:0.10 98:0.39 108:0.53 122:0.51 123:0.51 127:0.13 129:0.05 135:0.61 139:0.71 144:0.85 145:0.52 146:0.48 147:0.54 149:0.42 154:0.72 159:0.17 163:0.90 172:0.37 173:0.70 177:0.70 178:0.55 179:0.37 187:0.68 195:0.71 212:0.52 216:0.77 222:0.20 235:0.22 241:0.10 242:0.24 243:0.79 247:0.47 253:0.38 254:0.92 259:0.21 265:0.41 266:0.27 267:0.44 271:0.47 276:0.22 282:0.77 300:0.25 +0 11:0.91 17:0.57 18:0.74 21:0.78 27:0.62 29:0.77 30:0.13 34:0.99 36:0.68 37:0.70 39:0.98 43:0.67 45:0.66 55:0.84 66:0.71 69:0.37 70:0.84 71:0.89 74:0.36 86:0.73 91:0.44 98:0.74 101:0.52 108:0.62 122:0.77 123:0.59 124:0.43 127:0.47 129:0.59 133:0.47 135:0.47 139:0.70 144:0.85 145:0.91 146:0.38 147:0.45 149:0.48 154:0.56 159:0.41 172:0.65 173:0.42 175:0.75 177:0.38 178:0.55 179:0.59 187:0.39 202:0.46 212:0.57 216:0.32 222:0.20 235:0.74 241:0.21 242:0.45 243:0.23 244:0.61 245:0.69 247:0.60 253:0.29 257:0.65 259:0.21 265:0.74 266:0.54 267:0.28 271:0.47 276:0.58 277:0.69 300:0.55 +0 1:0.69 11:0.91 17:0.81 18:0.73 21:0.74 27:0.52 29:0.77 30:0.71 32:0.78 34:0.97 36:0.26 37:0.34 39:0.98 43:0.64 44:0.93 45:0.81 66:0.63 69:0.33 70:0.40 71:0.89 74:0.73 77:0.70 81:0.31 83:0.60 86:0.85 91:0.58 97:0.98 98:0.36 99:0.83 101:0.52 108:0.26 114:0.54 122:0.51 123:0.25 124:0.45 126:0.44 127:0.27 129:0.05 133:0.43 135:0.42 139:0.86 144:0.85 145:0.50 146:0.51 147:0.57 149:0.40 150:0.51 154:0.85 155:0.58 158:0.78 159:0.47 165:0.70 172:0.41 173:0.26 176:0.66 177:0.70 178:0.55 179:0.72 187:0.68 192:0.59 195:0.82 201:0.68 208:0.54 212:0.61 215:0.36 216:0.40 222:0.20 235:0.95 241:0.10 242:0.33 243:0.68 245:0.54 247:0.55 253:0.42 254:0.92 259:0.21 265:0.38 266:0.38 267:0.65 271:0.47 276:0.26 279:0.82 282:0.86 283:0.78 290:0.54 297:0.36 300:0.33 +0 8:0.66 11:0.91 17:0.18 18:0.88 21:0.59 22:0.93 27:0.21 29:0.77 30:0.40 31:0.90 34:0.42 36:0.47 37:0.74 39:0.98 43:0.34 45:0.81 47:0.93 48:0.91 55:0.59 64:0.77 66:0.60 69:0.27 70:0.84 71:0.89 74:0.66 79:0.91 81:0.24 86:0.84 91:0.49 98:0.75 101:0.52 108:0.93 122:0.77 123:0.93 124:0.66 126:0.26 127:0.95 129:0.59 133:0.74 135:0.24 137:0.90 139:0.83 140:0.45 144:0.85 146:0.95 147:0.96 149:0.74 150:0.24 151:0.55 153:0.48 154:0.38 159:0.92 162:0.62 172:0.99 173:0.09 177:0.70 179:0.11 187:0.39 190:0.89 196:0.92 202:0.67 212:0.91 216:0.95 219:0.89 220:0.87 222:0.20 235:0.68 241:0.37 242:0.58 243:0.37 244:0.61 245:0.99 247:0.92 248:0.56 253:0.40 256:0.68 259:0.21 262:0.93 265:0.76 266:0.80 267:0.99 268:0.68 271:0.47 276:0.98 281:0.89 284:0.92 285:0.47 292:0.91 300:0.94 +0 11:0.91 17:0.15 18:0.72 21:0.13 22:0.93 27:0.43 29:0.77 30:0.21 34:0.95 36:0.55 37:0.66 39:0.98 43:0.63 45:0.81 47:0.93 55:0.45 64:0.77 66:0.96 69:0.65 70:0.77 71:0.89 74:0.65 81:0.36 86:0.69 91:0.35 98:0.89 101:0.52 108:0.49 122:0.75 123:0.47 126:0.26 127:0.43 129:0.05 135:0.68 139:0.67 144:0.85 146:0.84 147:0.87 149:0.68 150:0.32 154:0.53 159:0.41 172:0.90 173:0.70 177:0.70 178:0.55 179:0.27 187:0.68 212:0.90 216:0.68 222:0.20 235:0.12 241:0.37 242:0.30 243:0.96 244:0.61 247:0.74 253:0.40 259:0.21 262:0.93 265:0.89 266:0.27 267:0.36 271:0.47 276:0.82 300:0.55 +0 1:0.69 8:0.62 9:0.80 11:0.91 17:0.24 21:0.47 27:0.28 29:0.77 30:0.28 31:0.87 34:0.59 36:0.47 37:0.49 39:0.98 43:0.72 45:0.66 55:0.71 66:0.29 69:0.19 70:0.95 71:0.89 74:0.48 79:0.87 81:0.34 83:0.60 91:0.48 96:0.76 97:0.89 98:0.47 99:0.83 101:0.52 108:0.67 114:0.59 117:0.86 120:0.74 122:0.77 123:0.65 124:0.70 126:0.44 127:0.73 129:0.59 133:0.64 135:0.83 137:0.87 139:0.68 140:0.80 144:0.85 146:0.13 147:0.18 149:0.44 150:0.52 151:0.65 153:0.48 154:0.62 155:0.67 158:0.79 159:0.58 162:0.86 164:0.55 165:0.70 172:0.37 173:0.21 175:0.75 176:0.66 177:0.38 179:0.73 187:0.99 190:0.89 192:0.87 196:0.87 201:0.68 202:0.56 208:0.64 212:0.27 215:0.41 216:0.67 219:0.92 222:0.20 235:0.60 241:0.21 242:0.58 243:0.20 244:0.61 245:0.65 247:0.55 248:0.64 253:0.57 255:0.79 256:0.64 257:0.65 259:0.21 260:0.64 265:0.49 266:0.71 267:0.44 268:0.65 271:0.47 276:0.49 277:0.69 279:0.86 283:0.82 284:0.87 285:0.62 290:0.59 292:0.87 297:0.36 300:0.84 +1 8:0.75 11:0.91 17:0.82 18:0.93 21:0.47 22:0.93 27:0.40 29:0.77 30:0.70 31:0.93 34:0.44 36:0.45 37:0.31 39:0.98 43:0.57 44:0.87 45:0.77 47:0.93 48:0.91 55:0.45 64:0.77 66:0.43 69:0.25 70:0.57 71:0.89 74:0.46 79:0.95 81:0.26 86:0.88 91:0.78 98:0.56 101:0.52 108:0.81 123:0.80 124:0.76 126:0.26 127:0.90 129:0.05 133:0.74 135:0.78 137:0.93 139:0.88 140:0.80 144:0.85 145:0.90 146:0.82 147:0.85 149:0.66 150:0.25 151:0.60 153:0.48 154:0.67 159:0.81 162:0.56 172:0.92 173:0.14 177:0.70 178:0.42 179:0.17 187:0.21 190:0.89 191:0.82 195:0.90 196:0.95 202:0.72 212:0.88 216:0.36 219:0.89 220:0.93 222:0.20 235:0.52 241:0.49 242:0.38 243:0.36 245:0.97 247:0.88 248:0.61 253:0.34 254:0.99 256:0.68 259:0.21 262:0.93 265:0.57 266:0.75 267:0.28 268:0.70 271:0.47 276:0.94 277:0.69 281:0.89 284:0.93 285:0.47 292:0.95 300:0.70 +0 7:0.63 11:0.91 17:0.73 18:0.82 21:0.30 27:0.72 29:0.77 30:0.87 32:0.63 34:0.34 36:0.28 37:0.23 39:0.98 43:0.57 44:0.90 45:0.73 48:0.97 55:0.59 64:0.77 66:0.84 69:0.62 70:0.39 71:0.89 74:0.39 81:0.39 86:0.79 91:0.33 98:0.86 101:0.52 108:0.47 123:0.46 124:0.38 126:0.44 127:0.61 129:0.05 132:0.97 133:0.37 135:0.69 139:0.81 144:0.85 145:0.79 146:0.91 147:0.92 149:0.51 150:0.32 154:0.53 159:0.58 172:0.83 173:0.58 177:0.70 178:0.55 179:0.41 187:0.39 192:0.51 195:0.81 201:0.68 212:0.80 215:0.44 216:0.16 222:0.20 230:0.63 233:0.73 235:0.42 241:0.49 242:0.72 243:0.85 244:0.61 245:0.73 247:0.79 253:0.31 259:0.21 265:0.86 266:0.54 267:0.87 271:0.47 276:0.74 281:0.91 297:0.36 300:0.55 +0 8:0.66 11:0.91 17:0.49 18:0.94 21:0.13 27:0.65 29:0.77 30:0.14 31:0.89 34:0.64 36:0.94 37:0.36 39:0.98 43:0.62 45:0.66 55:0.84 64:0.77 66:0.16 69:0.12 70:0.99 71:0.89 74:0.38 79:0.89 81:0.36 86:0.92 91:0.31 98:0.24 101:0.52 108:0.10 122:0.86 123:0.10 124:0.97 126:0.26 127:0.96 129:0.05 133:0.97 135:0.88 137:0.89 139:0.91 140:0.45 144:0.85 146:0.78 147:0.82 149:0.36 150:0.32 151:0.51 153:0.48 154:0.67 159:0.95 162:0.86 172:0.70 173:0.06 177:0.70 179:0.26 187:0.95 190:0.89 192:0.51 196:0.92 202:0.50 212:0.30 216:0.27 219:0.92 220:0.74 222:0.20 235:0.12 241:0.07 242:0.41 243:0.37 245:0.82 247:0.96 248:0.52 253:0.30 254:0.97 256:0.58 259:0.21 265:0.26 266:0.98 267:0.89 268:0.59 271:0.47 276:0.87 283:0.78 284:0.88 285:0.47 292:0.91 300:0.99 +0 8:0.81 11:0.82 17:0.78 18:0.64 21:0.78 27:0.52 29:0.77 30:0.76 34:0.24 36:0.24 39:0.98 43:0.12 55:0.42 66:0.64 69:0.73 70:0.15 71:0.89 74:0.36 86:0.65 91:0.72 98:0.20 108:0.56 122:0.83 123:0.54 127:0.10 129:0.05 135:0.49 139:0.63 144:0.85 145:0.87 146:0.33 147:0.40 149:0.06 154:0.50 159:0.13 163:0.98 172:0.16 173:0.64 177:0.70 178:0.55 179:0.32 202:0.80 212:0.95 216:0.18 222:0.20 235:0.84 241:0.85 242:0.82 243:0.69 244:0.61 247:0.12 253:0.29 254:0.74 259:0.21 265:0.22 266:0.12 267:0.76 271:0.47 276:0.13 300:0.13 +0 9:0.76 17:0.04 21:0.78 27:0.16 29:0.77 30:0.95 31:0.90 34:0.80 36:0.71 37:0.79 39:0.98 43:0.64 55:0.59 66:0.54 69:0.48 71:0.89 74:0.26 79:0.91 91:0.73 96:0.78 98:0.24 108:0.37 120:0.68 123:0.35 127:0.11 129:0.05 135:0.30 137:0.90 138:0.98 140:0.90 144:0.85 146:0.21 147:0.27 149:0.31 151:0.81 153:0.48 154:0.53 155:0.58 159:0.10 162:0.53 164:0.76 172:0.10 173:0.76 175:0.75 177:0.38 178:0.50 179:0.66 187:0.39 190:0.77 192:0.59 202:0.86 208:0.54 216:0.69 220:0.82 222:0.20 235:0.80 241:0.64 243:0.63 244:0.61 248:0.76 253:0.55 254:0.93 255:0.79 256:0.85 257:0.65 259:0.21 260:0.64 265:0.26 267:0.44 268:0.83 271:0.47 277:0.69 284:0.91 285:0.62 300:0.08 +0 11:0.91 17:0.20 18:0.90 21:0.78 27:0.12 29:0.77 30:0.54 34:0.96 36:0.46 37:0.87 39:0.98 43:0.15 45:0.83 55:0.71 66:0.90 69:0.56 70:0.32 71:0.89 74:0.70 86:0.95 91:0.29 98:0.87 101:0.52 108:0.43 122:0.57 123:0.41 127:0.41 129:0.05 135:0.60 139:0.92 144:0.85 146:0.68 147:0.73 149:0.17 154:0.74 159:0.26 172:0.71 173:0.73 177:0.70 178:0.55 179:0.54 187:0.68 212:0.93 216:0.52 222:0.20 235:0.60 241:0.24 242:0.37 243:0.90 247:0.55 253:0.41 254:0.93 259:0.21 265:0.87 266:0.17 267:0.36 271:0.47 276:0.61 300:0.25 +0 11:0.91 17:0.36 18:0.59 21:0.13 22:0.93 27:0.27 29:0.77 30:0.11 34:0.37 36:0.76 37:0.35 39:0.98 43:0.59 45:0.66 47:0.93 55:0.42 64:0.77 66:0.67 69:0.92 70:0.93 71:0.89 74:0.49 81:0.55 86:0.68 91:0.15 98:0.58 101:0.52 106:0.81 108:0.34 117:0.86 122:0.75 123:0.33 124:0.45 126:0.44 127:0.65 129:0.05 133:0.43 135:0.73 139:0.65 144:0.85 146:0.23 147:0.73 149:0.30 150:0.39 154:0.47 158:0.71 159:0.57 172:0.57 173:0.98 177:0.38 178:0.55 179:0.71 187:0.95 192:0.51 201:0.68 206:0.81 212:0.27 215:0.61 216:0.75 222:0.20 235:0.22 241:0.15 242:0.28 243:0.72 245:0.66 247:0.67 253:0.35 254:0.96 257:0.65 259:0.21 262:0.93 265:0.59 266:0.63 267:0.59 271:0.47 276:0.40 277:0.69 297:0.36 300:0.70 +0 1:0.74 11:0.91 17:0.55 18:0.95 21:0.54 27:0.52 29:0.77 30:0.27 34:0.97 36:0.65 37:0.35 39:0.98 43:0.57 44:0.87 45:0.66 48:0.97 55:0.52 64:0.77 66:0.29 69:0.83 70:0.79 71:0.89 74:0.43 81:0.44 83:0.60 86:0.90 91:0.22 97:0.89 98:0.61 99:0.83 101:0.52 108:0.21 114:0.59 122:0.86 123:0.66 124:0.71 126:0.54 127:0.80 129:0.59 133:0.66 135:0.74 139:0.89 144:0.85 145:0.92 146:0.70 147:0.54 149:0.44 150:0.66 154:0.57 155:0.67 158:0.78 159:0.59 165:0.70 172:0.54 173:0.84 176:0.73 177:0.38 178:0.55 179:0.53 187:0.68 192:0.87 195:0.76 201:0.93 202:0.36 208:0.64 212:0.59 215:0.39 216:0.34 222:0.20 235:0.74 241:0.37 242:0.54 243:0.48 244:0.61 245:0.80 247:0.84 253:0.42 257:0.65 259:0.21 265:0.39 266:0.71 267:0.79 271:0.47 276:0.67 279:0.82 281:0.89 283:0.66 290:0.59 297:0.36 300:0.70 +0 11:0.82 17:0.30 18:0.83 21:0.78 27:0.45 29:0.77 30:0.43 34:0.87 36:0.20 37:0.50 39:0.98 43:0.49 55:0.52 66:0.20 69:0.85 70:0.88 71:0.89 74:0.44 86:0.87 91:0.12 98:0.45 108:0.71 122:0.77 123:0.79 124:0.70 127:0.27 129:0.59 133:0.66 135:0.88 139:0.83 144:0.85 145:0.47 146:0.51 147:0.40 149:0.30 154:0.48 159:0.52 172:0.68 173:0.81 177:0.38 178:0.55 179:0.27 187:0.68 212:0.80 216:0.77 222:0.20 235:0.42 241:0.47 242:0.31 243:0.23 245:0.85 247:0.82 253:0.33 254:0.88 257:0.65 259:0.21 265:0.35 266:0.54 267:0.44 271:0.47 276:0.73 300:0.84 +0 7:0.63 8:0.63 11:0.82 17:0.95 18:0.64 21:0.78 27:0.66 29:0.77 30:0.72 32:0.62 34:0.26 36:0.61 37:0.27 39:0.98 43:0.22 55:0.59 66:0.08 69:0.39 70:0.56 71:0.89 74:0.29 86:0.64 91:0.62 98:0.26 108:0.30 123:0.29 124:0.95 127:0.81 129:0.81 133:0.94 135:0.60 139:0.63 144:0.85 145:0.58 146:0.70 147:0.75 149:0.34 154:0.15 159:0.92 172:0.32 173:0.12 175:0.75 177:0.38 178:0.55 179:0.41 195:0.98 202:0.74 212:0.27 216:0.18 222:0.20 230:0.63 233:0.73 235:0.98 241:0.78 242:0.73 243:0.24 244:0.61 245:0.73 247:0.79 253:0.25 257:0.65 259:0.21 265:0.28 266:0.95 267:0.23 271:0.47 276:0.75 277:0.69 300:0.70 +1 8:0.62 11:0.91 17:0.45 18:0.83 21:0.05 22:0.93 27:0.26 29:0.77 30:0.14 31:0.90 34:0.89 36:0.73 37:0.84 39:0.98 43:0.56 45:0.73 47:0.93 55:0.71 64:0.77 66:0.53 69:0.61 70:0.95 71:0.89 74:0.39 79:0.90 81:0.43 86:0.77 91:0.58 98:0.64 101:0.52 108:0.47 122:0.75 123:0.45 124:0.65 126:0.26 127:0.54 129:0.05 133:0.66 135:0.54 137:0.90 139:0.77 140:0.45 144:0.85 145:0.38 146:0.77 147:0.81 149:0.45 150:0.36 151:0.55 153:0.48 154:0.45 159:0.59 162:0.86 172:0.63 173:0.55 177:0.70 179:0.54 187:0.39 190:0.89 196:0.90 202:0.36 212:0.29 216:0.58 219:0.89 220:0.62 222:0.20 235:0.05 241:0.32 242:0.16 243:0.62 244:0.61 245:0.74 247:0.90 248:0.54 253:0.31 256:0.45 259:0.21 262:0.93 265:0.64 266:0.75 267:0.36 268:0.46 271:0.47 276:0.62 284:0.86 285:0.47 292:0.91 300:0.84 +1 8:0.82 11:0.91 17:0.84 18:0.93 21:0.47 22:0.93 27:0.40 29:0.77 30:0.65 31:0.91 34:0.50 36:0.49 37:0.31 39:0.98 43:0.61 44:0.87 45:0.73 47:0.93 48:0.91 55:0.45 64:0.77 66:0.35 69:0.25 70:0.58 71:0.89 74:0.50 79:0.92 81:0.26 86:0.88 91:0.78 98:0.54 101:0.52 108:0.81 122:0.77 123:0.80 124:0.77 126:0.26 127:0.71 129:0.05 133:0.74 135:0.84 137:0.91 139:0.88 140:0.45 144:0.85 145:0.89 146:0.82 147:0.85 149:0.71 150:0.25 151:0.58 153:0.80 154:0.67 159:0.81 162:0.86 172:0.92 173:0.14 177:0.70 179:0.15 187:0.21 190:0.89 191:0.82 195:0.91 196:0.93 202:0.61 212:0.90 216:0.36 219:0.89 220:0.62 222:0.20 235:0.52 241:0.43 242:0.38 243:0.40 245:0.98 247:0.88 248:0.56 253:0.35 254:0.99 256:0.64 259:0.21 262:0.93 265:0.55 266:0.75 267:0.36 268:0.69 271:0.47 276:0.94 277:0.69 281:0.89 284:0.92 285:0.47 292:0.91 300:0.70 +0 11:0.82 17:0.97 18:0.67 21:0.78 27:0.72 29:0.77 30:0.45 32:0.62 34:0.86 36:0.33 39:0.98 43:0.16 55:0.71 66:0.16 69:0.86 70:0.61 71:0.89 74:0.33 77:0.89 86:0.66 91:0.66 98:0.20 108:0.84 122:0.75 123:0.71 124:0.84 127:0.80 129:0.59 133:0.82 135:0.81 139:0.64 144:0.85 145:1.00 146:0.31 147:0.05 149:0.13 154:0.20 159:0.68 163:0.97 172:0.21 173:0.84 175:0.75 177:0.05 178:0.55 179:0.87 195:0.83 202:0.36 212:0.30 222:0.20 235:0.12 241:0.78 242:0.33 243:0.17 244:0.61 245:0.47 247:0.60 253:0.28 254:0.74 257:0.84 259:0.21 265:0.21 266:0.54 267:0.18 271:0.47 276:0.37 277:0.69 282:0.71 300:0.43 +0 1:0.69 9:0.76 17:0.61 18:0.71 21:0.59 27:0.66 29:0.77 30:0.56 32:0.64 34:0.47 36:0.99 37:0.73 39:0.98 43:0.16 55:0.59 66:0.68 69:0.45 70:0.59 71:0.89 74:0.50 76:0.85 77:0.70 81:0.29 86:0.72 91:0.57 96:0.75 98:0.68 106:0.81 108:0.35 114:0.56 117:0.86 120:0.63 122:0.77 123:0.33 124:0.48 126:0.44 127:0.61 129:0.05 133:0.60 135:0.86 138:0.95 139:0.69 140:0.45 144:0.85 145:0.52 146:0.80 147:0.83 149:0.43 150:0.47 151:0.73 153:0.80 154:0.69 155:0.58 158:0.74 159:0.60 162:0.78 164:0.63 172:0.80 173:0.38 176:0.61 177:0.70 179:0.40 187:0.87 190:0.77 191:0.70 192:0.59 195:0.79 201:0.68 202:0.59 206:0.81 208:0.54 212:0.78 215:0.38 216:0.32 220:0.62 222:0.20 235:0.74 241:0.27 242:0.76 243:0.72 245:0.79 247:0.74 248:0.73 253:0.52 254:0.96 255:0.74 256:0.58 259:0.21 260:0.61 265:0.68 266:0.71 267:0.73 268:0.64 271:0.47 276:0.74 279:0.82 282:0.73 283:0.77 285:0.56 286:0.99 290:0.56 297:0.36 298:0.99 300:0.70 +0 8:0.68 11:0.91 17:0.73 18:0.95 21:0.64 22:0.93 27:0.40 29:0.77 30:0.55 31:0.94 34:0.67 36:0.48 37:0.31 39:0.98 43:0.57 44:0.87 45:0.81 47:0.93 48:0.98 55:0.45 64:0.77 66:0.48 69:0.32 70:0.81 71:0.89 74:0.45 79:0.96 81:0.24 86:0.91 91:0.65 98:0.43 101:0.52 108:0.86 122:0.86 123:0.86 124:0.88 126:0.26 127:0.95 129:0.59 133:0.91 135:0.81 137:0.94 139:0.90 140:0.45 144:0.85 145:0.88 146:0.80 147:0.83 149:0.63 150:0.24 151:0.64 153:0.83 154:0.46 159:0.89 162:0.75 172:0.92 173:0.12 177:0.70 179:0.18 187:0.21 190:0.89 191:0.89 195:0.96 196:0.96 202:0.71 212:0.77 216:0.36 219:0.89 222:0.20 235:0.80 241:0.32 242:0.28 243:0.25 244:0.61 245:0.92 247:0.91 248:0.65 253:0.33 256:0.79 259:0.21 262:0.93 265:0.45 266:0.89 267:0.52 268:0.75 271:0.47 276:0.93 277:0.69 281:0.89 284:0.95 285:0.47 292:0.95 300:0.84 +1 11:0.91 17:0.16 18:0.88 21:0.05 22:0.93 27:0.72 29:0.77 30:0.26 31:0.90 34:0.49 36:0.47 39:0.98 43:0.24 45:0.83 47:0.93 55:0.59 64:0.77 66:0.98 69:0.07 70:0.76 71:0.89 74:0.61 79:0.91 81:0.43 86:0.91 91:0.75 98:0.99 101:0.52 108:0.06 122:0.77 123:0.06 126:0.26 127:0.89 129:0.05 135:0.23 137:0.90 139:0.88 140:0.45 144:0.85 146:0.99 147:0.99 149:0.42 150:0.36 151:0.55 153:0.48 154:0.66 159:0.83 162:0.86 172:0.96 173:0.08 177:0.70 179:0.20 187:0.21 190:0.89 196:0.93 202:0.50 212:0.51 216:0.23 219:0.89 220:0.82 222:0.20 235:0.05 241:0.62 242:0.32 243:0.98 247:0.91 248:0.55 253:0.39 254:0.90 256:0.58 259:0.21 262:0.93 265:0.99 266:0.63 267:0.52 268:0.59 271:0.47 276:0.92 284:0.88 285:0.47 292:0.91 300:0.55 +0 8:0.59 11:0.57 12:0.01 17:0.34 21:0.78 27:0.45 28:0.96 30:0.84 34:0.84 36:0.21 37:0.50 39:0.83 43:0.78 66:0.95 69:0.77 70:0.21 74:0.92 85:0.98 91:0.25 98:0.74 101:0.85 108:0.61 122:0.43 123:0.58 127:0.23 129:0.05 135:0.69 145:0.50 146:0.93 147:0.94 149:0.96 154:0.71 159:0.56 161:0.48 163:0.81 167:0.65 172:0.88 173:0.64 177:0.38 178:0.55 179:0.20 181:0.92 187:0.68 212:0.48 216:0.77 222:0.76 235:0.31 241:0.37 242:0.63 243:0.95 247:0.30 253:0.65 259:0.21 265:0.74 266:0.47 267:0.44 271:0.42 276:0.79 300:0.18 +2 1:0.74 9:0.80 11:0.57 12:0.01 17:0.02 21:0.05 27:0.33 28:0.96 30:0.75 34:0.47 36:0.23 37:0.41 39:0.83 41:0.90 43:0.86 60:0.99 66:0.30 69:0.57 70:0.36 74:0.85 81:0.80 83:0.85 85:0.97 91:0.62 96:0.90 98:0.41 101:0.83 108:0.86 114:0.95 120:0.81 122:0.43 123:0.85 124:0.87 126:0.54 127:0.56 129:0.95 133:0.87 135:0.99 140:0.45 145:0.86 146:0.13 147:0.18 149:0.91 150:0.88 151:0.95 153:0.84 154:0.84 155:0.67 158:0.92 159:0.76 161:0.48 162:0.84 163:0.81 164:0.75 165:0.90 167:0.58 172:0.59 173:0.38 176:0.73 177:0.38 179:0.44 181:0.92 187:0.39 192:0.59 201:0.74 202:0.65 208:0.64 212:0.35 215:0.91 216:0.88 222:0.76 235:0.12 241:0.10 242:0.63 243:0.13 245:0.74 247:0.39 248:0.88 253:0.92 255:0.79 256:0.68 259:0.21 260:0.82 265:0.43 266:0.71 267:0.88 268:0.71 271:0.42 276:0.71 279:0.86 283:0.82 285:0.62 290:0.95 297:0.36 300:0.33 +2 6:0.98 8:0.89 9:0.80 11:0.57 12:0.01 17:0.05 20:0.98 21:0.78 27:0.09 28:0.96 30:0.89 32:0.80 34:0.49 36:0.68 37:0.78 39:0.83 41:0.99 43:0.92 60:0.99 66:0.23 69:0.41 70:0.28 74:0.85 77:0.70 78:0.84 83:0.90 85:0.94 91:0.63 96:0.98 98:0.20 100:0.90 101:0.79 108:0.32 111:0.86 120:0.95 122:0.43 123:0.31 124:0.79 127:0.65 129:0.89 133:0.78 135:0.92 138:0.99 140:0.45 145:0.74 146:0.37 147:0.44 149:0.88 151:0.97 152:0.99 153:0.90 154:0.91 155:0.67 158:0.92 159:0.79 161:0.48 162:0.69 164:0.94 167:0.67 169:0.98 172:0.32 173:0.22 177:0.38 179:0.71 181:0.92 186:0.85 187:0.68 189:0.95 191:0.73 192:0.59 195:0.92 202:0.90 208:0.64 212:0.61 216:0.89 220:0.62 222:0.76 235:0.74 238:0.94 241:0.46 242:0.63 243:0.43 245:0.62 247:0.30 248:0.98 253:0.98 255:0.79 256:0.92 259:0.21 260:0.96 261:0.99 265:0.21 266:0.47 267:0.59 268:0.92 271:0.42 276:0.45 279:0.86 282:0.88 283:0.82 285:0.62 299:0.88 300:0.33 +2 1:0.74 7:0.81 8:0.79 9:0.80 11:0.57 12:0.01 17:0.13 20:0.79 21:0.30 27:0.11 28:0.96 30:0.86 34:0.52 36:0.80 37:0.79 39:0.83 41:0.92 43:0.87 60:0.87 66:0.33 69:0.56 70:0.40 74:0.74 78:0.76 81:0.64 83:0.86 85:0.91 91:0.25 96:0.88 98:0.17 100:0.80 101:0.77 106:0.81 108:0.92 111:0.75 114:0.86 117:0.86 120:0.80 121:0.90 122:0.43 123:0.92 124:0.78 126:0.54 127:0.55 129:0.89 133:0.78 135:0.89 140:0.45 145:0.52 146:0.13 147:0.18 149:0.71 150:0.78 151:0.95 152:0.77 153:0.84 154:0.85 155:0.67 158:0.92 159:0.80 161:0.48 162:0.84 164:0.77 165:0.84 167:0.56 169:0.77 172:0.32 173:0.34 176:0.73 177:0.38 179:0.79 181:0.92 186:0.76 187:0.39 192:0.59 201:0.74 202:0.65 204:0.89 206:0.81 208:0.64 212:0.39 215:0.72 216:0.85 222:0.76 230:0.87 232:0.91 233:0.76 235:0.42 241:0.03 242:0.63 243:0.25 245:0.54 247:0.30 248:0.87 253:0.89 255:0.79 256:0.68 259:0.21 260:0.81 261:0.76 265:0.18 266:0.54 267:0.87 268:0.72 271:0.42 276:0.36 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43 +3 1:0.74 8:0.93 9:0.80 11:0.57 12:0.01 17:0.28 21:0.22 27:0.09 28:0.96 30:0.84 34:0.76 36:0.97 37:0.78 39:0.83 41:0.98 43:0.92 60:0.98 66:0.16 69:0.37 70:0.53 74:0.94 81:0.69 83:0.89 85:0.99 91:0.51 96:0.96 98:0.35 101:0.84 108:0.76 114:0.90 120:0.92 122:0.43 123:0.75 124:0.94 126:0.54 127:0.75 129:0.98 133:0.94 135:0.72 140:0.45 146:0.31 147:0.38 149:0.97 150:0.81 151:0.98 153:0.91 154:0.91 155:0.67 158:0.92 159:0.89 161:0.48 162:0.72 164:0.90 165:0.86 167:0.59 172:0.67 173:0.13 176:0.73 177:0.38 179:0.26 181:0.92 187:0.98 191:0.70 192:0.59 201:0.74 202:0.84 208:0.64 212:0.54 215:0.79 216:0.89 220:0.62 222:0.76 235:0.31 241:0.12 242:0.63 243:0.13 245:0.85 247:0.30 248:0.96 253:0.97 255:0.79 256:0.87 259:0.21 260:0.92 265:0.37 266:0.80 267:0.88 268:0.87 271:0.42 276:0.86 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.70 +1 1:0.74 7:0.81 8:0.93 9:0.80 11:0.57 12:0.01 17:0.02 21:0.77 27:0.09 28:0.96 30:0.73 34:0.97 36:0.65 37:0.78 39:0.83 41:0.98 43:0.92 60:0.95 66:0.27 69:0.45 70:0.47 74:0.83 78:0.81 81:0.40 83:0.88 85:0.91 91:0.60 96:0.96 98:0.19 101:0.77 108:0.35 111:0.83 114:0.64 120:0.92 121:0.90 122:0.43 123:0.34 124:0.84 126:0.54 127:0.67 129:0.81 133:0.83 135:0.97 140:0.45 145:0.89 146:0.37 147:0.44 149:0.85 150:0.62 151:0.97 153:0.91 154:0.91 155:0.67 158:0.92 159:0.82 161:0.48 162:0.55 164:0.92 165:0.63 167:0.69 169:0.98 172:0.32 173:0.23 176:0.73 177:0.38 179:0.76 181:0.92 187:0.98 191:0.76 192:0.59 201:0.74 202:0.91 204:0.89 208:0.64 212:0.42 215:0.44 216:0.89 222:0.76 230:0.87 232:0.94 233:0.76 235:0.98 241:0.51 242:0.63 243:0.46 245:0.55 247:0.30 248:0.96 253:0.96 255:0.79 256:0.90 259:0.21 260:0.92 265:0.20 266:0.63 267:0.76 268:0.90 271:0.42 276:0.42 279:0.86 283:0.82 285:0.62 290:0.64 297:0.36 300:0.43 +4 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.57 12:0.01 17:0.01 20:0.99 21:0.47 27:0.09 28:0.96 30:0.85 32:0.80 34:0.66 36:0.86 37:0.78 39:0.83 41:1.00 43:0.92 60:1.00 66:0.35 69:0.40 70:0.41 74:0.90 76:0.85 77:0.70 78:0.92 81:0.57 83:0.91 85:0.96 91:0.89 96:1.00 98:0.44 100:0.99 101:0.82 108:0.85 111:0.99 114:0.80 120:0.99 121:0.97 122:0.43 123:0.84 124:0.83 126:0.54 127:0.62 129:0.93 133:0.84 135:0.89 138:1.00 140:0.45 145:0.67 146:0.37 147:0.44 149:0.91 150:0.74 151:1.00 152:0.99 153:0.99 154:0.91 155:0.67 158:0.92 159:0.79 161:0.48 162:0.66 164:0.98 165:0.80 167:0.79 169:0.98 172:0.54 173:0.21 176:0.73 177:0.38 179:0.56 181:0.92 186:0.92 187:0.68 189:0.99 191:0.92 192:0.59 195:0.91 201:0.74 202:0.97 204:0.89 208:0.64 212:0.42 215:0.63 216:0.89 222:0.76 230:0.87 232:0.88 233:0.76 235:0.60 238:0.99 241:0.71 242:0.63 243:0.33 245:0.68 247:0.30 248:1.00 253:1.00 255:0.79 256:0.98 259:0.21 260:0.99 261:0.99 265:0.46 266:0.63 267:0.84 268:0.98 271:0.42 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.85 8:0.73 9:0.80 11:0.57 12:0.01 17:0.08 20:0.85 21:0.05 27:0.14 28:0.96 30:0.76 34:0.56 36:0.19 37:0.67 39:0.83 41:0.97 43:0.86 60:0.99 66:0.31 69:0.57 70:0.41 74:0.86 78:0.76 81:0.80 83:0.90 85:0.97 91:0.73 96:0.97 98:0.42 100:0.79 101:0.84 108:0.85 111:0.75 114:0.95 120:0.94 122:0.43 123:0.84 124:0.87 126:0.54 127:0.47 129:0.95 133:0.87 135:0.98 140:0.45 145:0.86 146:0.34 147:0.41 149:0.92 150:0.88 151:0.98 152:0.89 153:0.92 154:0.84 155:0.67 158:0.92 159:0.73 161:0.48 162:0.77 163:0.81 164:0.88 165:0.90 167:0.59 169:0.77 172:0.59 173:0.39 176:0.73 177:0.38 179:0.42 181:0.92 186:0.76 187:0.39 189:0.88 192:0.59 201:0.74 202:0.81 208:0.64 212:0.30 215:0.91 216:0.93 222:0.76 235:0.12 238:0.87 241:0.12 242:0.63 243:0.21 245:0.73 247:0.39 248:0.97 253:0.97 255:0.79 256:0.86 259:0.21 260:0.94 261:0.79 265:0.44 266:0.75 267:0.84 268:0.86 271:0.42 276:0.70 279:0.86 283:0.82 285:0.62 290:0.95 297:0.36 300:0.43 +1 7:0.81 9:0.80 11:0.57 12:0.01 17:0.36 21:0.13 27:0.43 28:0.96 30:0.85 34:0.19 36:0.98 37:0.84 39:0.83 41:0.97 43:0.85 60:0.95 66:0.46 69:0.62 70:0.44 74:0.90 78:0.91 81:0.56 83:0.89 85:0.96 91:0.78 96:0.97 98:0.29 101:0.82 108:0.47 111:0.97 114:0.74 120:0.93 121:0.90 122:0.43 123:0.45 124:0.67 126:0.32 127:0.65 129:0.81 133:0.67 135:0.85 140:0.45 145:0.39 146:0.53 147:0.59 149:0.93 150:0.42 151:0.98 153:0.93 154:0.81 155:0.67 158:0.92 159:0.77 161:0.48 162:0.81 164:0.89 167:0.78 169:0.97 172:0.59 173:0.44 176:0.56 177:0.38 179:0.56 181:0.92 187:0.21 191:0.77 192:0.59 202:0.83 204:0.89 208:0.64 212:0.76 216:0.50 222:0.76 230:0.87 232:0.74 233:0.76 235:0.12 238:0.98 241:0.70 242:0.63 243:0.58 245:0.75 247:0.30 248:0.96 253:0.98 255:0.79 256:0.88 260:0.94 265:0.31 266:0.71 267:0.36 268:0.87 271:0.42 276:0.44 279:0.86 283:0.82 285:0.62 290:0.74 300:0.70 +3 1:0.74 6:0.96 8:0.94 9:0.80 11:0.57 12:0.01 17:0.32 20:0.99 21:0.30 27:0.34 28:0.96 30:0.76 32:0.80 34:0.33 36:0.38 37:0.85 39:0.83 41:0.98 43:0.89 60:1.00 66:0.36 69:0.49 70:0.28 74:0.79 77:0.89 78:0.83 81:0.64 83:0.88 85:0.88 91:0.93 96:0.96 98:0.29 100:0.92 101:0.77 108:0.38 111:0.86 114:0.86 120:0.92 122:0.57 123:0.36 124:0.59 126:0.54 127:0.61 129:0.59 133:0.47 135:0.17 138:0.98 140:0.45 145:0.90 146:0.47 147:0.53 149:0.75 150:0.78 151:0.99 152:0.90 153:0.95 154:0.88 155:0.67 158:0.92 159:0.69 161:0.48 162:0.69 163:0.81 164:0.91 165:0.84 167:0.95 169:0.90 172:0.27 173:0.36 176:0.73 177:0.38 179:0.87 181:0.92 186:0.83 189:0.97 191:0.94 192:0.59 195:0.83 201:0.74 202:0.87 208:0.64 212:0.37 215:0.72 216:0.58 222:0.76 232:0.71 235:0.42 238:0.96 241:0.94 242:0.58 243:0.51 245:0.56 247:0.39 248:0.96 253:0.96 255:0.79 256:0.89 259:0.21 260:0.93 261:0.90 265:0.31 266:0.47 267:0.89 268:0.89 271:0.42 276:0.20 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.25 +3 1:0.74 6:0.93 7:0.81 8:0.83 9:0.80 11:0.57 12:0.01 17:0.22 20:0.99 21:0.30 27:0.21 28:0.96 30:0.75 34:0.49 36:0.80 37:0.74 39:0.83 41:0.99 43:0.98 60:0.99 66:0.11 69:0.12 70:0.54 74:0.98 78:0.79 81:0.64 83:0.90 85:1.00 91:0.70 96:0.99 98:0.44 100:0.86 101:0.86 106:0.81 108:0.97 111:0.81 114:0.86 117:0.86 120:0.97 121:0.90 122:0.57 123:0.86 124:0.95 126:0.54 127:0.83 129:0.99 133:0.96 135:0.30 138:0.99 140:0.45 146:0.59 147:0.65 149:0.99 150:0.78 151:0.99 152:0.96 153:0.97 154:0.98 155:0.67 158:0.92 159:0.94 161:0.48 162:0.77 164:0.94 165:0.84 167:0.65 169:0.82 172:0.92 173:0.06 176:0.73 177:0.38 179:0.13 181:0.92 186:0.79 187:0.39 189:0.94 191:0.70 192:0.59 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.76 230:0.87 232:0.87 233:0.76 235:0.42 238:0.95 241:0.39 242:0.62 243:0.13 245:0.96 247:0.60 248:0.99 253:0.99 255:0.79 256:0.92 259:0.21 260:0.97 261:0.85 265:0.45 266:0.91 267:0.59 268:0.92 271:0.42 276:0.96 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.01 17:0.81 21:0.22 27:0.08 28:0.96 30:0.74 32:0.80 34:0.37 36:0.33 37:0.81 39:0.83 41:0.93 43:0.92 66:0.97 69:0.35 70:0.20 74:1.00 77:0.70 81:0.69 83:0.81 85:1.00 91:0.57 96:0.90 98:0.93 101:0.87 108:0.65 114:0.90 120:0.82 121:0.90 122:0.43 123:0.63 124:0.36 126:0.54 127:0.84 129:0.89 133:0.78 135:0.85 140:0.45 145:0.42 146:0.26 147:0.32 149:1.00 150:0.81 151:0.92 153:0.72 154:0.92 155:0.67 159:0.92 161:0.48 162:0.86 164:0.77 165:0.86 167:0.72 172:1.00 173:0.11 176:0.73 177:0.38 179:0.08 181:0.92 187:0.68 192:0.59 195:0.98 201:0.74 202:0.64 204:0.89 208:0.64 212:0.94 215:0.79 216:0.79 220:0.82 222:0.76 230:0.84 233:0.69 235:0.31 241:0.59 242:0.63 243:0.05 245:0.99 247:0.30 248:0.89 253:0.94 255:0.79 256:0.71 259:0.21 260:0.83 265:0.93 266:0.63 267:0.59 268:0.72 271:0.42 276:1.00 282:0.88 285:0.62 290:0.90 297:0.36 299:0.88 300:0.55 +0 11:0.57 12:0.01 17:0.45 21:0.78 27:0.45 28:0.96 30:0.10 34:0.87 36:0.20 37:0.50 39:0.83 43:0.74 55:0.45 66:0.34 69:0.79 70:0.96 74:0.94 85:0.90 91:0.12 98:0.35 101:0.77 108:0.62 122:0.67 123:0.60 124:0.86 127:0.50 129:0.81 133:0.86 135:0.89 145:0.41 146:0.64 147:0.69 149:0.78 154:0.65 159:0.76 161:0.48 167:0.66 172:0.63 173:0.64 177:0.38 178:0.55 179:0.41 181:0.92 187:0.68 212:0.72 216:0.77 222:0.76 235:0.97 241:0.41 242:0.72 243:0.51 245:0.74 247:0.47 253:0.66 259:0.21 265:0.37 266:0.87 267:0.69 271:0.42 276:0.71 300:0.84 +1 1:0.60 10:0.92 11:0.49 17:0.24 21:0.22 27:0.65 29:0.62 30:0.17 34:0.47 36:0.72 37:0.29 39:0.94 43:0.07 45:0.99 55:0.71 56:0.97 66:0.07 69:0.99 70:0.94 71:0.78 74:0.24 81:0.46 83:0.56 91:0.10 98:0.30 99:0.95 101:0.65 108:0.99 122:0.98 123:0.99 124:0.99 126:0.32 127:1.00 129:0.05 131:0.61 133:0.99 135:0.30 146:0.98 147:0.62 149:0.26 150:0.39 154:0.06 155:0.48 157:0.61 159:0.99 165:0.65 166:0.61 170:0.90 172:0.94 173:0.89 174:1.00 175:0.75 177:0.38 178:0.55 179:0.09 182:0.61 187:0.39 192:0.45 197:1.00 201:0.59 208:0.50 212:0.56 216:0.43 222:0.35 227:0.61 229:0.61 231:0.61 235:0.42 239:0.91 241:0.03 242:0.41 243:0.05 244:0.97 245:0.99 246:0.61 247:1.00 253:0.20 254:0.92 257:0.99 259:0.21 264:0.96 265:0.32 266:0.97 267:0.52 271:0.75 275:1.00 276:1.00 277:0.69 287:0.61 294:0.95 300:0.94 +0 8:0.86 9:0.70 10:0.80 11:0.42 17:0.38 18:0.85 21:0.78 22:0.93 27:0.11 29:0.62 30:0.27 31:0.91 34:0.82 36:0.76 37:0.89 39:0.94 43:0.05 47:0.93 55:0.71 66:0.62 69:0.83 70:0.58 71:0.78 74:0.30 79:0.91 86:0.61 91:0.20 96:0.72 98:0.56 108:0.67 117:0.86 122:0.67 123:0.65 124:0.64 127:0.32 129:0.05 131:0.85 133:0.72 135:0.70 137:0.91 139:0.61 140:0.45 144:0.57 146:0.88 147:0.26 149:0.07 151:0.58 153:0.48 154:0.09 155:0.50 157:0.61 158:0.72 159:0.74 162:0.83 163:0.75 166:0.61 170:0.90 172:0.67 173:0.66 174:0.83 175:0.91 177:0.70 179:0.43 182:0.61 187:0.87 190:1.00 191:0.84 192:0.54 196:0.87 197:0.84 202:0.63 208:0.49 212:0.22 216:0.90 219:0.87 220:0.87 222:0.35 227:0.85 229:0.61 231:0.68 232:0.97 239:0.80 241:0.18 242:0.12 243:0.21 244:0.99 245:0.66 246:0.61 247:0.92 248:0.57 253:0.46 254:0.90 255:0.69 256:0.68 257:0.97 259:0.21 262:0.93 264:0.96 265:0.57 266:0.75 267:0.17 268:0.70 271:0.75 275:0.96 276:0.61 277:0.87 279:0.75 284:0.95 285:0.47 287:0.61 292:0.87 294:0.93 300:0.43 +0 1:0.56 11:0.42 17:0.38 18:0.68 21:0.54 27:0.12 29:0.62 30:0.30 34:0.88 36:0.55 37:0.81 39:0.94 43:0.05 55:0.71 56:0.97 66:0.51 69:0.60 70:0.80 71:0.78 74:0.28 81:0.31 86:0.59 91:0.11 98:0.38 106:0.81 108:0.46 114:0.63 122:0.55 123:0.44 124:0.64 126:0.32 127:0.29 129:0.05 131:0.61 133:0.60 135:0.80 139:0.58 144:0.57 146:0.47 147:0.53 149:0.05 150:0.31 154:0.09 155:0.46 157:0.61 159:0.41 166:0.72 170:0.90 172:0.41 173:0.59 175:0.97 176:0.55 177:0.88 178:0.55 179:0.67 182:0.61 187:0.98 192:0.47 201:0.57 202:0.36 206:0.81 208:0.49 212:0.39 215:0.58 216:0.90 222:0.35 227:0.61 229:0.61 231:0.64 235:0.74 241:0.12 242:0.13 243:0.61 244:0.99 245:0.55 246:0.61 247:0.76 253:0.48 254:0.88 257:0.93 259:0.21 264:0.96 265:0.40 266:0.54 267:0.91 271:0.75 275:0.93 276:0.40 277:0.95 287:0.61 290:0.63 294:0.93 297:0.36 300:0.55 +1 1:0.56 8:0.72 9:0.70 10:0.92 11:0.48 17:0.22 18:0.60 21:0.68 27:0.63 29:0.62 30:0.18 34:0.63 36:0.85 37:0.72 39:0.94 43:0.12 45:0.97 55:0.59 66:0.08 69:0.33 70:0.95 71:0.78 74:0.25 81:0.33 83:0.54 86:0.58 91:0.06 97:0.89 98:0.37 99:0.83 101:0.47 108:0.26 122:0.90 123:0.98 124:0.98 126:0.26 127:0.75 129:0.05 131:0.61 133:0.98 135:0.70 139:0.56 140:0.45 146:0.96 147:0.97 149:0.27 150:0.31 151:0.57 153:0.48 154:0.13 155:0.50 157:0.61 159:0.97 162:0.86 165:0.63 166:0.61 170:0.90 172:0.94 173:0.06 174:1.00 177:0.99 179:0.09 182:0.61 187:0.39 190:1.00 192:0.45 197:0.99 201:0.54 202:0.36 208:0.49 212:0.73 216:0.34 220:0.87 222:0.35 227:0.61 229:0.61 231:0.61 235:0.88 239:0.89 241:0.05 242:0.17 243:0.29 244:0.93 245:0.99 246:0.61 247:1.00 248:0.56 253:0.23 254:0.97 255:0.69 256:0.45 259:0.21 264:0.96 265:0.33 266:0.96 267:0.36 268:0.46 271:0.75 275:0.99 276:0.99 277:0.87 279:0.74 285:0.47 287:0.61 294:0.95 300:0.94 +1 1:0.56 8:0.64 9:0.70 10:0.92 11:0.48 17:0.11 18:0.60 21:0.68 27:0.63 29:0.62 30:0.17 34:0.45 36:0.91 37:0.72 39:0.94 43:0.12 45:0.97 55:0.71 66:0.24 69:0.33 70:0.97 71:0.78 74:0.25 81:0.33 83:0.54 86:0.58 91:0.11 97:0.89 98:0.40 99:0.83 101:0.47 108:0.26 122:0.90 123:0.25 124:0.98 126:0.26 127:1.00 129:0.05 131:0.61 133:0.98 135:0.56 139:0.56 140:0.45 146:0.98 147:0.99 149:0.30 150:0.31 151:0.53 153:0.48 154:0.13 155:0.49 157:0.61 159:0.98 162:0.86 165:0.63 166:0.61 170:0.90 172:0.98 173:0.06 174:0.99 177:0.99 179:0.10 182:0.61 187:0.39 190:1.00 192:0.45 197:0.99 201:0.54 202:0.36 208:0.49 212:0.69 216:0.34 220:0.91 222:0.35 227:0.61 229:0.61 231:0.61 235:0.88 239:0.89 241:0.03 242:0.16 243:0.44 244:0.93 245:0.98 246:0.61 247:1.00 248:0.53 253:0.23 254:0.97 255:0.69 256:0.45 259:0.21 264:0.96 265:0.42 266:0.99 267:0.44 268:0.46 271:0.75 275:0.99 276:0.99 279:0.74 285:0.47 287:0.61 294:0.95 300:0.98 +0 8:0.81 9:0.71 10:0.83 11:0.42 17:0.38 18:0.86 21:0.68 23:0.95 27:0.12 29:0.62 30:0.16 31:0.91 34:0.96 36:0.85 37:0.81 39:0.94 43:0.05 55:0.45 62:0.95 66:0.26 69:0.89 70:0.79 71:0.78 74:0.34 79:0.90 81:0.23 86:0.66 91:0.38 96:0.80 98:0.54 108:0.46 114:0.60 122:0.91 123:0.76 124:0.73 126:0.22 127:0.38 128:0.95 129:0.05 131:0.85 133:0.67 135:0.68 137:0.91 139:0.64 140:0.80 144:0.57 146:0.82 147:0.81 149:0.07 150:0.24 151:0.70 153:0.48 154:0.09 155:0.51 157:0.61 159:0.71 162:0.55 166:0.72 168:0.95 170:0.90 172:0.65 173:0.82 174:0.91 175:0.91 176:0.55 177:0.88 178:0.42 179:0.30 182:0.61 187:0.98 190:1.00 191:0.73 192:0.56 196:0.88 197:0.91 202:0.74 205:0.95 208:0.49 212:0.37 216:0.90 220:0.74 222:0.35 227:0.85 229:0.61 231:0.68 235:0.80 239:0.82 241:0.46 242:0.22 243:0.47 244:0.99 245:0.88 246:0.61 247:0.97 248:0.65 253:0.71 254:0.93 255:0.70 256:0.71 257:0.93 259:0.21 264:0.96 265:0.49 266:0.54 267:0.85 268:0.72 271:0.75 275:0.91 276:0.78 277:0.87 284:0.90 285:0.50 287:0.61 290:0.60 294:0.92 300:0.55 +0 1:0.56 8:0.98 9:0.70 10:0.80 11:0.42 17:0.24 18:0.78 21:0.47 27:0.13 29:0.62 30:0.33 31:0.89 34:0.63 36:0.99 37:0.79 39:0.94 43:0.05 55:0.84 64:0.77 66:0.64 69:0.82 70:0.46 71:0.78 74:0.29 79:0.89 81:0.28 86:0.60 91:0.35 96:0.73 98:0.68 108:0.66 114:0.67 122:0.99 123:0.64 124:0.49 126:0.26 127:0.29 129:0.05 131:0.85 133:0.59 135:0.96 137:0.89 139:0.60 140:0.80 144:0.57 145:0.56 146:0.86 147:0.23 149:0.06 150:0.31 151:0.58 153:0.71 154:0.08 155:0.51 157:0.61 158:0.72 159:0.55 162:0.56 163:0.86 166:0.78 170:0.90 172:0.54 173:0.76 174:0.83 175:0.91 176:0.59 177:0.38 178:0.42 179:0.59 182:0.61 187:0.87 190:1.00 191:0.84 192:0.55 196:0.87 197:0.84 201:0.54 202:0.69 208:0.49 212:0.29 216:0.91 219:0.87 220:0.87 222:0.35 227:0.85 229:0.61 231:0.68 235:0.60 239:0.80 241:0.44 242:0.18 243:0.21 244:1.00 245:0.56 246:0.61 247:0.82 248:0.59 253:0.56 254:0.80 255:0.69 256:0.64 257:0.99 259:0.21 264:0.96 265:0.69 266:0.54 267:0.91 268:0.66 271:0.75 275:0.96 276:0.51 277:0.87 279:0.74 284:0.94 285:0.47 287:0.61 290:0.67 292:0.87 294:0.93 300:0.33 +1 8:0.89 9:0.71 10:0.81 11:0.46 17:0.47 18:0.79 21:0.47 27:0.12 29:0.62 30:0.41 31:0.91 34:0.63 36:0.77 37:0.81 39:0.94 43:0.08 45:0.66 55:0.71 66:0.68 69:0.83 70:0.72 71:0.78 74:0.30 76:0.98 79:0.91 81:0.24 86:0.60 91:0.58 96:0.79 98:0.74 101:0.45 108:0.68 114:0.63 117:0.86 122:0.90 123:0.65 124:0.45 126:0.22 127:0.35 129:0.05 131:0.85 133:0.37 135:0.81 137:0.91 139:0.59 140:0.80 144:0.57 145:0.45 146:0.88 147:0.49 149:0.09 150:0.25 151:0.74 153:0.48 154:0.07 155:0.52 157:0.77 158:0.71 159:0.58 162:0.61 166:0.72 170:0.90 172:0.73 173:0.78 174:0.86 175:0.75 176:0.55 177:0.38 179:0.42 182:0.73 187:0.68 190:1.00 192:0.56 196:0.87 197:0.86 202:0.68 208:0.49 212:0.49 216:0.90 220:0.82 222:0.35 227:0.85 229:0.61 231:0.68 232:0.82 235:0.52 239:0.80 241:0.44 242:0.19 243:0.28 244:1.00 245:0.79 246:0.61 247:0.93 248:0.69 253:0.69 255:0.70 256:0.64 257:0.99 259:0.21 264:0.96 265:0.75 266:0.75 267:0.79 268:0.68 271:0.75 275:0.91 276:0.67 277:0.69 284:0.87 285:0.47 287:0.61 290:0.63 294:0.91 300:0.70 +0 1:0.59 8:0.73 10:0.80 11:0.49 17:0.69 18:0.96 21:0.05 27:0.49 29:0.62 30:0.09 34:0.44 36:0.88 37:0.47 39:0.94 43:0.05 45:0.66 55:0.42 64:0.77 66:0.06 69:0.99 70:0.98 71:0.78 74:0.38 81:0.31 86:0.72 91:0.03 98:0.25 101:0.45 108:0.97 114:0.78 117:0.86 123:0.97 124:1.00 126:0.26 127:0.98 129:0.05 131:0.61 133:1.00 135:0.39 139:0.69 144:0.57 146:0.96 147:0.39 149:0.10 150:0.35 154:0.08 155:0.47 157:0.61 159:0.99 166:0.78 170:0.90 172:0.82 173:0.81 174:0.94 175:0.91 176:0.59 177:0.38 178:0.55 179:0.10 182:0.61 187:0.39 192:0.47 197:0.83 201:0.56 202:0.60 208:0.49 212:0.29 216:0.61 222:0.35 227:0.61 229:0.61 231:0.68 232:0.89 235:0.12 239:0.80 241:0.35 242:0.24 243:0.06 244:0.99 245:0.96 246:0.61 247:1.00 253:0.57 257:0.99 259:0.21 264:0.96 265:0.27 266:0.98 267:0.52 271:0.75 275:1.00 276:0.99 277:0.87 287:0.61 290:0.78 294:0.95 300:0.94 +2 1:0.56 2:0.99 10:0.87 11:0.45 17:0.49 21:0.47 27:0.24 29:0.62 30:0.17 33:0.97 34:0.66 36:0.96 37:0.79 39:0.94 43:0.08 45:0.83 55:0.71 56:0.97 66:0.85 69:0.87 70:0.93 71:0.78 74:0.28 75:0.95 81:0.40 83:0.54 91:0.17 98:0.80 101:0.52 108:0.75 114:0.64 122:0.98 123:0.73 124:0.38 126:0.43 127:0.61 129:0.05 131:0.61 133:0.43 135:0.97 145:0.89 146:0.95 147:0.24 149:0.12 150:0.38 154:0.10 155:0.46 157:0.61 158:0.72 159:0.76 166:0.72 170:0.90 172:0.90 173:0.79 174:0.96 175:0.75 176:0.55 177:0.05 178:0.55 179:0.29 182:0.61 187:1.00 192:0.47 195:0.90 197:0.95 201:0.60 208:0.49 212:0.73 216:0.65 222:0.35 226:0.95 227:0.61 229:0.61 231:0.61 235:0.74 236:0.91 239:0.86 241:0.03 242:0.12 243:0.09 244:0.98 245:0.75 246:0.61 247:0.99 253:0.49 254:0.80 257:0.99 259:0.21 264:0.96 265:0.80 266:0.80 267:0.73 271:0.75 275:0.97 276:0.82 277:0.69 279:0.74 287:0.61 290:0.64 291:0.97 294:0.95 295:0.98 300:0.84 +0 1:0.64 8:0.78 9:0.70 10:0.79 11:0.42 17:0.61 18:0.98 21:0.13 22:0.93 27:0.49 29:0.62 30:0.28 31:0.87 34:0.52 36:0.86 37:0.47 39:0.94 43:0.05 47:0.93 55:0.42 64:0.77 66:0.07 69:1.00 70:0.96 71:0.78 74:0.41 79:0.87 81:0.34 86:0.75 91:0.04 96:0.70 98:0.21 108:0.39 114:0.72 117:0.86 122:0.94 123:0.94 124:0.99 126:0.26 127:0.96 129:0.05 131:0.85 133:0.99 135:0.37 137:0.87 139:0.72 140:0.45 144:0.57 146:0.71 147:0.73 149:0.07 150:0.38 151:0.52 153:0.72 154:0.05 155:0.49 157:0.61 159:0.98 162:0.67 166:0.78 170:0.90 172:0.80 173:1.00 174:0.86 175:0.75 176:0.56 177:0.70 179:0.12 182:0.61 187:0.39 190:1.00 192:0.51 196:0.88 197:0.80 201:0.60 202:0.53 208:0.49 212:0.30 216:0.61 220:0.93 222:0.35 227:0.85 229:0.61 231:0.68 232:0.89 235:0.42 239:0.79 241:0.24 242:0.17 243:0.23 244:0.98 245:0.92 246:0.61 247:1.00 248:0.52 253:0.54 254:0.97 255:0.69 256:0.45 257:0.97 259:0.21 262:0.93 264:0.96 265:0.17 266:0.97 267:0.19 268:0.57 271:0.75 275:1.00 276:0.98 277:0.95 284:0.90 285:0.47 287:0.61 290:0.72 294:0.94 300:0.94 +1 1:0.56 2:0.99 9:0.70 10:0.87 11:0.45 17:0.47 21:0.47 23:0.95 27:0.06 29:0.62 30:0.19 33:0.97 34:0.44 36:1.00 37:0.90 39:0.94 43:0.08 45:0.83 55:0.71 56:0.97 62:0.96 66:0.84 69:0.87 70:0.90 71:0.78 74:0.28 75:0.95 81:0.40 83:0.54 91:0.18 96:0.72 98:0.70 101:0.52 108:0.74 114:0.64 122:0.98 123:0.73 124:0.39 126:0.43 127:0.39 128:0.96 129:0.05 131:0.80 133:0.59 135:0.98 140:0.45 143:0.99 145:0.89 146:0.91 147:0.93 149:0.12 150:0.38 151:0.59 153:0.46 154:0.10 155:0.50 157:0.61 158:0.72 159:0.70 162:0.62 166:0.72 168:0.96 170:0.90 172:0.88 173:0.79 174:0.96 175:0.91 176:0.55 177:0.96 179:0.26 182:0.61 187:1.00 190:0.99 192:0.58 195:0.90 197:0.95 201:0.60 202:0.49 205:0.95 208:0.49 212:0.77 216:0.73 220:0.82 222:0.35 226:0.95 227:0.82 229:0.61 231:0.61 235:0.74 236:0.91 239:0.86 241:0.03 242:0.12 243:0.85 244:0.98 245:0.73 246:0.61 247:0.98 248:0.57 253:0.53 254:0.80 255:0.71 256:0.45 257:0.84 259:0.21 264:0.96 265:0.71 266:0.63 267:0.84 268:0.45 271:0.75 275:0.97 276:0.80 277:0.87 279:0.74 285:0.55 287:0.61 290:0.64 291:0.97 294:0.95 295:0.98 300:0.84 +0 8:0.86 9:0.70 10:0.82 11:0.42 17:0.53 18:0.83 21:0.77 27:0.12 29:0.62 30:0.10 31:0.89 34:0.94 36:0.91 37:0.81 39:0.94 43:0.05 55:0.92 66:0.17 69:0.85 70:0.90 71:0.78 74:0.34 79:0.88 81:0.23 86:0.66 91:0.51 96:0.75 98:0.41 108:0.71 114:0.58 120:0.59 122:0.90 123:0.69 124:0.90 126:0.22 127:0.60 129:0.59 131:0.85 133:0.89 135:0.66 137:0.89 139:0.63 140:0.95 144:0.57 146:0.79 147:0.63 149:0.08 150:0.24 151:0.57 153:0.48 154:0.08 155:0.52 157:0.61 159:0.84 162:0.58 163:0.81 164:0.66 166:0.72 170:0.90 172:0.59 173:0.68 174:0.89 175:0.97 176:0.55 177:0.70 178:0.50 179:0.32 182:0.61 187:0.98 190:1.00 191:0.73 192:0.86 196:0.87 197:0.89 202:0.78 208:0.50 212:0.30 216:0.90 220:0.99 222:0.35 227:0.85 229:0.61 231:0.68 235:0.97 239:0.81 241:0.27 242:0.24 243:0.33 244:0.99 245:0.83 246:0.61 247:0.96 248:0.56 253:0.56 254:0.93 255:0.69 256:0.78 257:0.97 259:0.21 260:0.59 264:0.96 265:0.43 266:0.84 267:0.93 268:0.78 271:0.75 275:0.91 276:0.81 277:0.95 284:0.92 285:0.50 287:0.61 290:0.58 294:0.92 300:0.70 +1 1:0.56 8:0.65 9:0.70 10:0.93 11:0.48 17:0.34 18:0.70 21:0.74 27:0.63 29:0.62 30:0.18 34:0.80 36:0.87 37:0.72 39:0.94 43:0.12 45:0.98 55:0.59 66:0.24 69:0.35 70:0.96 71:0.78 74:0.25 81:0.32 83:0.54 86:0.59 91:0.07 97:0.89 98:0.43 99:0.83 101:0.47 108:0.27 122:0.90 123:0.26 124:0.98 126:0.26 127:0.88 129:0.05 131:0.61 133:0.98 135:0.66 139:0.57 140:0.45 146:0.99 147:0.99 149:0.27 150:0.30 151:0.60 153:0.48 154:0.14 155:0.50 157:0.61 159:0.97 162:0.86 165:0.63 166:0.61 170:0.90 172:0.98 173:0.06 174:1.00 177:0.99 179:0.09 182:0.61 187:0.39 190:1.00 192:0.46 197:0.99 201:0.53 202:0.50 208:0.49 212:0.77 216:0.34 220:0.82 222:0.35 227:0.61 229:0.61 231:0.62 235:0.97 239:0.90 241:0.03 242:0.16 243:0.44 244:0.93 245:0.99 246:0.61 247:1.00 248:0.58 253:0.23 254:0.97 255:0.69 256:0.58 259:0.21 264:0.96 265:0.45 266:0.98 267:0.28 268:0.59 271:0.75 275:0.99 276:0.99 279:0.74 285:0.47 287:0.61 294:0.95 300:0.98 +1 1:0.56 9:0.70 10:0.86 11:0.45 17:0.49 21:0.47 23:0.95 27:0.06 29:0.62 30:0.28 33:0.97 34:0.55 36:0.98 37:0.90 39:0.94 43:0.08 45:0.83 55:0.71 56:0.97 62:0.95 66:0.84 69:0.87 70:0.85 71:0.78 74:0.27 81:0.40 83:0.54 91:0.18 96:0.69 98:0.79 101:0.52 108:0.75 114:0.64 122:0.98 123:0.73 124:0.38 126:0.43 127:0.63 128:0.95 129:0.05 131:0.80 133:0.43 135:0.98 140:0.80 143:0.98 145:0.89 146:0.95 147:0.24 149:0.13 150:0.38 151:0.50 153:0.46 154:0.13 155:0.47 157:0.61 159:0.75 162:0.56 166:0.72 168:0.95 170:0.90 172:0.89 173:0.80 174:0.96 175:0.75 176:0.55 177:0.05 178:0.42 179:0.31 182:0.61 187:1.00 190:0.99 192:0.50 195:0.90 197:0.95 201:0.60 202:0.50 205:0.95 208:0.49 212:0.75 216:0.73 220:0.96 222:0.35 227:0.82 229:0.61 231:0.61 235:0.74 236:0.91 239:0.85 241:0.12 242:0.12 243:0.10 244:0.97 245:0.74 246:0.61 247:0.98 248:0.49 253:0.50 254:0.90 255:0.69 256:0.45 257:0.99 259:0.21 264:0.96 265:0.79 266:0.80 267:0.89 268:0.45 271:0.75 275:0.97 276:0.81 277:0.69 285:0.55 287:0.61 290:0.64 291:0.97 294:0.94 300:0.70 +1 8:0.77 9:0.71 10:0.83 11:0.42 17:0.32 18:0.86 21:0.64 27:0.12 29:0.62 30:0.12 31:0.92 34:0.96 36:0.83 37:0.81 39:0.94 43:0.05 55:0.45 66:0.68 69:0.60 70:0.79 71:0.78 74:0.34 79:0.92 81:0.23 83:0.54 86:0.66 91:0.40 96:0.79 98:0.75 108:0.46 114:0.61 122:0.90 123:0.44 124:0.49 126:0.22 127:0.36 129:0.05 131:0.85 133:0.60 135:0.65 137:0.92 139:0.64 140:0.87 144:0.57 146:0.94 147:0.95 149:0.06 150:0.24 151:0.65 153:0.48 154:0.09 155:0.51 157:0.61 159:0.70 162:0.63 166:0.72 170:0.90 172:0.82 173:0.42 174:0.89 175:0.75 176:0.55 177:0.99 178:0.48 179:0.30 182:0.88 187:0.98 190:1.00 192:0.56 196:0.88 197:0.90 202:0.73 208:0.49 212:0.27 216:0.90 220:0.91 222:0.35 227:0.85 229:0.93 231:0.69 235:0.74 239:0.82 241:0.62 242:0.18 243:0.72 244:0.99 245:0.82 246:0.61 247:0.92 248:0.70 253:0.67 254:0.93 255:0.70 256:0.75 257:0.65 259:0.21 264:0.96 265:0.76 266:0.54 267:0.91 268:0.74 271:0.75 275:0.91 276:0.77 277:0.69 284:0.95 285:0.47 287:0.89 290:0.61 294:0.92 300:0.55 +0 8:0.94 9:0.70 10:0.79 11:0.42 17:0.34 18:0.80 21:0.54 27:0.12 29:0.62 30:0.42 31:0.91 34:0.96 36:0.74 37:0.81 39:0.94 43:0.05 55:0.84 66:0.20 69:0.92 70:0.54 71:0.78 74:0.31 76:0.97 77:0.70 79:0.91 81:0.24 86:0.62 91:0.82 96:0.92 98:0.57 108:0.46 114:0.62 122:0.90 123:0.83 124:0.75 126:0.22 127:0.44 129:0.05 131:0.85 133:0.64 135:0.80 137:0.91 139:0.60 140:0.99 144:0.57 145:0.83 146:0.81 147:0.75 149:0.08 150:0.25 151:0.59 153:0.72 154:0.06 155:0.49 157:0.61 158:0.71 159:0.68 162:0.73 166:0.72 170:0.90 172:0.51 173:0.91 174:0.79 175:0.91 176:0.55 177:0.88 179:0.39 182:0.61 187:0.87 190:1.00 191:0.83 192:0.51 195:0.88 196:0.87 197:0.80 202:0.92 208:0.49 212:0.19 216:0.90 219:0.86 220:0.82 222:0.35 227:0.85 229:0.61 231:0.68 235:0.60 239:0.79 241:0.62 242:0.41 243:0.40 244:0.99 245:0.85 246:0.61 247:0.84 248:0.62 253:0.86 254:0.97 255:0.69 256:0.94 257:0.93 259:0.21 264:0.96 265:0.47 266:0.63 267:0.99 268:0.94 271:0.75 275:0.91 276:0.73 277:0.87 279:0.74 282:0.71 284:0.96 285:0.47 287:0.61 290:0.62 292:0.86 294:0.92 300:0.43 +0 8:0.76 10:0.89 12:0.01 17:0.90 18:0.92 21:0.78 27:0.47 29:0.52 30:0.43 34:0.47 36:0.22 37:0.55 39:0.56 43:0.56 48:0.91 55:0.84 66:0.35 69:0.71 70:0.73 71:0.61 74:0.45 76:0.85 86:0.89 91:0.12 98:0.68 108:0.54 117:0.86 122:0.92 123:0.52 124:0.76 127:0.83 129:0.05 133:0.74 135:0.78 139:0.86 145:0.56 146:0.92 147:0.23 149:0.48 154:0.45 159:0.80 163:0.92 170:0.82 172:0.67 173:0.54 174:0.90 177:0.05 178:0.55 179:0.44 187:0.39 197:0.90 212:0.21 216:0.26 222:0.60 232:0.83 235:0.68 239:0.88 241:0.07 242:0.21 243:0.12 244:0.83 245:0.81 247:0.88 253:0.38 257:0.93 259:0.21 265:0.69 266:0.80 267:0.69 271:0.23 276:0.75 281:0.91 300:0.70 +0 1:0.59 8:0.69 10:0.88 11:0.52 12:0.01 17:0.86 18:0.98 21:0.40 27:0.54 29:0.52 30:0.42 34:0.36 36:0.20 37:0.54 39:0.56 43:0.31 45:0.73 48:0.91 55:0.71 64:0.77 66:0.71 69:0.23 70:0.66 71:0.61 74:0.65 76:1.00 81:0.40 86:0.97 91:0.31 98:0.88 101:0.65 108:0.18 114:0.78 122:0.92 123:0.18 124:0.44 126:0.32 127:0.83 129:0.59 133:0.47 135:0.54 139:0.96 145:0.38 146:0.96 147:0.97 149:0.46 150:0.44 154:0.21 155:0.49 158:0.76 159:0.75 170:0.82 172:0.90 173:0.16 174:0.88 176:0.63 177:0.88 178:0.55 179:0.28 187:0.87 192:0.47 197:0.87 201:0.56 204:0.78 208:0.50 212:0.58 216:0.19 219:0.91 222:0.60 235:0.52 239:0.87 241:0.24 242:0.42 243:0.75 244:0.61 245:0.93 247:0.96 253:0.60 254:0.97 259:0.21 265:0.88 266:0.63 267:0.69 271:0.23 276:0.86 279:0.75 281:0.86 290:0.78 292:0.87 300:0.70 +1 8:0.67 10:0.92 11:0.57 12:0.01 17:0.90 18:0.99 21:0.68 27:0.69 29:0.52 30:0.40 34:0.39 36:0.42 37:0.42 39:0.56 43:0.60 45:0.93 55:0.52 66:0.99 69:0.29 70:0.58 71:0.61 74:0.93 76:0.85 77:0.70 81:0.28 86:0.99 91:0.12 98:0.99 101:0.87 108:0.22 114:0.66 122:0.83 123:0.22 126:0.24 127:0.92 129:0.05 135:0.61 139:0.98 145:0.42 146:0.99 147:0.99 149:0.71 150:0.31 154:0.55 158:0.75 159:0.82 170:0.82 172:0.99 173:0.15 174:0.88 176:0.62 177:0.88 178:0.55 179:0.13 187:0.87 195:0.91 197:0.86 212:0.92 216:0.25 222:0.60 235:0.84 239:0.89 241:0.15 242:0.14 243:0.99 247:0.98 253:0.66 254:0.98 259:0.21 265:0.99 266:0.38 267:0.44 271:0.23 276:0.96 282:0.74 290:0.66 300:0.55 +0 8:0.81 10:0.88 11:0.52 12:0.01 17:0.87 18:0.91 21:0.78 27:0.47 29:0.52 30:0.17 34:0.47 36:0.19 37:0.55 39:0.56 43:0.23 45:0.73 48:0.91 55:0.92 66:0.53 69:0.71 70:0.94 71:0.61 74:0.54 76:0.85 86:0.89 91:0.15 98:0.76 101:0.65 108:0.85 117:0.86 122:0.92 123:0.84 124:0.76 127:0.86 129:0.59 133:0.80 135:0.71 139:0.86 145:0.56 146:0.77 147:0.23 149:0.34 154:0.53 159:0.80 170:0.82 172:0.77 173:0.55 174:0.89 177:0.05 178:0.55 179:0.41 187:0.39 197:0.88 212:0.18 216:0.26 222:0.60 232:0.83 235:0.68 239:0.87 241:0.15 242:0.15 243:0.11 244:0.61 245:0.78 247:0.96 253:0.43 254:0.74 257:0.93 259:0.21 265:0.76 266:0.80 267:0.79 271:0.23 276:0.77 281:0.91 300:0.84 +0 8:0.70 10:0.85 11:0.52 12:0.01 17:0.89 18:1.00 21:0.78 27:0.47 29:0.52 30:0.53 34:0.30 36:0.41 37:0.55 39:0.56 43:0.57 45:0.93 48:0.91 55:0.52 66:0.99 69:0.21 70:0.56 71:0.61 74:0.87 76:0.85 86:1.00 91:0.10 98:0.99 101:0.65 108:0.17 117:0.86 122:0.92 123:0.16 127:0.86 129:0.05 135:0.70 139:0.99 145:0.52 146:0.99 147:0.99 149:0.76 154:0.18 159:0.83 170:0.82 172:0.99 173:0.12 174:0.83 177:0.88 178:0.55 179:0.13 187:0.39 197:0.81 212:0.93 216:0.26 222:0.60 232:0.83 235:0.60 239:0.84 241:0.07 242:0.41 243:0.99 247:0.93 253:0.59 254:0.99 259:0.21 265:0.99 266:0.27 267:0.28 271:0.23 276:0.97 281:0.91 300:0.55 +1 8:0.63 10:0.97 11:0.57 12:0.01 17:0.53 18:0.99 21:0.30 22:0.93 27:0.32 29:0.52 30:0.53 34:0.55 36:0.39 37:0.46 39:0.56 43:0.38 44:0.88 45:0.97 47:0.93 48:0.91 55:0.59 64:0.77 66:0.99 69:0.77 70:0.72 71:0.61 74:0.66 81:0.34 86:0.99 91:0.64 98:0.91 101:0.96 108:0.61 122:0.92 123:0.58 126:0.24 127:0.51 129:0.05 135:0.23 139:0.99 145:0.63 146:0.99 147:0.99 149:0.79 150:0.37 154:0.35 159:0.87 170:0.82 172:0.99 173:0.51 174:0.97 177:0.88 178:0.55 179:0.12 187:0.68 195:0.97 197:0.96 212:0.80 216:0.73 219:0.90 222:0.60 235:0.31 239:0.96 241:0.10 242:0.40 243:0.99 247:0.97 253:0.49 254:0.94 259:0.21 262:0.93 265:0.91 266:0.71 267:0.52 271:0.23 276:0.96 281:0.91 292:0.95 300:0.70 +0 8:0.71 10:0.87 11:0.46 12:0.01 17:0.91 18:0.91 21:0.78 27:0.47 29:0.52 30:0.14 34:0.45 36:0.20 37:0.55 39:0.56 43:0.20 45:0.66 48:0.91 55:0.92 66:0.12 69:0.94 70:0.90 71:0.61 74:0.51 76:0.85 86:0.87 91:0.15 98:0.38 101:0.47 108:0.90 117:0.86 122:0.92 123:0.82 124:0.92 127:0.91 129:0.59 133:0.91 135:0.88 139:0.84 145:0.56 146:0.54 147:0.23 149:0.31 154:0.15 159:0.80 163:0.75 170:0.82 172:0.48 173:0.93 174:0.88 177:0.05 178:0.55 179:0.43 187:0.39 197:0.86 212:0.23 216:0.26 222:0.60 232:0.83 235:0.68 239:0.86 241:0.12 242:0.39 243:0.11 244:0.61 245:0.76 247:0.97 253:0.41 254:0.92 257:0.93 259:0.21 265:0.37 266:0.75 267:0.91 271:0.23 276:0.76 277:0.87 281:0.91 300:0.84 +1 8:0.71 9:0.73 10:0.90 11:0.52 12:0.01 17:0.76 18:0.99 21:0.30 27:0.65 29:0.52 30:0.17 31:0.97 34:0.34 36:0.40 37:0.52 39:0.56 43:0.66 45:0.92 55:0.71 64:0.77 66:0.35 69:0.10 70:0.83 71:0.61 74:0.69 79:0.97 81:0.34 83:0.56 86:0.98 91:0.54 96:0.84 97:0.89 98:0.75 101:0.65 108:0.90 122:0.92 123:0.09 124:0.60 126:0.24 127:0.92 129:0.59 133:0.47 135:0.76 137:0.97 139:0.98 140:0.92 146:0.97 147:0.97 149:0.81 150:0.37 151:0.85 153:0.48 154:0.16 155:0.56 158:0.76 159:0.86 162:0.86 170:0.82 172:0.91 173:0.08 174:0.92 177:0.88 179:0.18 187:0.39 190:0.89 191:0.73 192:0.55 196:0.99 197:0.88 202:0.73 208:0.50 212:0.47 216:0.17 219:0.91 220:0.62 222:0.60 235:0.31 239:0.88 241:0.53 242:0.54 243:0.51 244:0.61 245:0.98 247:0.91 248:0.82 253:0.83 255:0.72 256:0.81 259:0.21 265:0.74 266:0.63 267:0.84 268:0.81 271:0.23 276:0.93 279:0.75 284:0.98 285:0.50 292:0.88 300:0.55 +1 1:0.63 7:0.70 10:0.85 12:0.01 17:0.78 18:0.80 21:0.40 22:0.93 27:0.67 29:0.52 30:0.19 32:0.67 34:0.99 36:0.57 37:0.36 39:0.56 43:0.59 44:0.88 47:0.93 48:0.98 64:0.77 66:0.35 69:0.56 70:0.86 71:0.61 74:0.52 81:0.51 86:0.82 91:0.29 98:0.43 106:0.81 108:0.43 114:0.82 117:0.86 122:0.83 123:0.41 124:0.67 126:0.51 127:0.54 129:0.05 133:0.60 135:0.94 139:0.86 145:0.59 146:0.55 147:0.61 149:0.48 150:0.50 154:0.65 155:0.49 159:0.57 170:0.82 172:0.45 173:0.51 174:0.79 176:0.70 177:0.88 178:0.55 179:0.65 187:0.95 192:0.55 195:0.75 197:0.82 201:0.61 206:0.81 208:0.61 212:0.50 215:0.67 216:0.14 219:0.91 222:0.60 230:0.73 232:0.93 233:0.66 235:0.60 239:0.84 241:0.18 242:0.23 243:0.51 244:0.61 245:0.67 247:0.79 253:0.59 254:0.74 259:0.21 262:0.93 265:0.45 266:0.63 267:0.52 271:0.23 276:0.50 281:0.86 283:0.67 290:0.82 292:0.88 297:0.36 300:0.70 +1 1:0.58 8:0.63 10:0.87 11:0.46 12:0.01 17:0.94 18:0.96 21:0.47 22:0.93 27:0.55 29:0.52 30:0.30 34:0.97 36:0.57 37:0.50 39:0.56 43:0.58 44:0.88 45:0.94 47:0.93 64:0.77 66:0.97 69:0.69 70:0.79 71:0.61 74:0.84 77:0.70 81:0.49 83:0.56 86:0.97 91:0.29 97:0.89 98:0.88 99:0.83 101:0.47 108:0.52 114:0.76 117:0.86 122:0.91 123:0.50 126:0.32 127:0.42 129:0.05 135:0.93 139:0.97 145:0.96 146:0.97 147:0.98 149:0.74 150:0.44 154:0.47 155:0.51 158:0.76 159:0.70 165:0.65 170:0.82 172:0.93 173:0.54 174:0.83 176:0.63 177:0.88 178:0.55 179:0.20 187:0.21 192:0.48 195:0.90 197:0.87 201:0.55 204:0.80 208:0.50 212:0.57 216:0.16 219:0.91 222:0.60 232:0.88 235:0.60 239:0.86 242:0.33 243:0.97 244:0.61 247:0.82 253:0.65 254:0.86 259:0.21 262:0.93 265:0.88 266:0.38 267:0.59 271:0.23 276:0.87 279:0.75 281:0.86 282:0.75 290:0.76 292:0.88 300:0.55 +2 6:1.00 8:1.00 12:0.20 17:0.59 20:0.82 21:0.74 25:0.88 27:0.05 28:0.78 30:0.45 32:0.80 34:0.21 36:0.55 43:0.52 55:0.45 66:0.36 69:0.27 70:0.53 78:0.79 81:0.77 91:0.89 98:0.75 100:0.89 104:0.73 108:0.67 111:0.82 120:0.84 123:0.64 124:0.60 126:0.54 127:0.95 129:0.59 133:0.47 135:0.54 140:0.90 145:0.92 146:0.51 147:0.57 149:0.68 150:0.57 151:0.76 152:0.78 153:0.87 154:0.41 159:0.44 161:0.69 162:0.59 164:0.92 167:0.94 169:0.87 172:0.48 173:0.36 177:0.05 178:0.50 179:0.69 181:0.72 186:0.80 189:1.00 191:0.95 195:0.65 202:0.89 212:0.68 215:0.46 216:0.16 220:0.62 235:0.88 238:0.98 241:0.99 242:0.82 243:0.50 245:0.77 247:0.12 248:0.76 256:0.88 259:0.21 260:0.84 261:0.82 265:0.75 266:0.63 267:0.92 268:0.90 271:0.50 276:0.55 283:0.60 285:0.62 297:0.36 300:0.43 +1 12:0.20 17:0.53 21:0.59 27:0.45 28:0.78 30:0.11 32:0.80 34:0.91 36:0.18 37:0.50 43:0.32 55:0.59 66:0.30 69:0.70 70:0.54 81:0.87 91:0.27 98:0.30 108:0.53 123:0.51 124:0.61 126:0.54 127:0.21 129:0.59 133:0.47 135:0.75 145:0.44 146:0.48 147:0.54 149:0.33 150:0.72 154:0.24 159:0.44 161:0.69 163:0.88 167:0.68 172:0.16 173:0.60 177:0.05 178:0.55 179:0.80 181:0.72 187:0.68 195:0.83 212:0.30 215:0.55 216:0.77 235:0.68 241:0.53 242:0.82 243:0.48 245:0.47 247:0.12 259:0.21 265:0.32 266:0.27 267:0.28 271:0.50 276:0.25 283:0.60 297:0.36 300:0.33 +3 6:0.96 7:0.81 8:0.92 12:0.20 17:0.47 20:0.99 21:0.30 27:0.21 28:0.78 30:0.27 34:0.66 36:0.87 37:0.74 43:0.52 55:0.71 66:0.44 69:0.10 70:0.80 78:0.79 81:0.93 91:0.80 98:0.75 100:0.85 104:0.84 106:0.81 108:0.87 111:0.80 120:0.92 123:0.86 124:0.67 126:0.54 127:0.66 129:0.81 133:0.67 135:0.20 140:0.45 146:0.92 147:0.93 149:0.85 150:0.88 151:0.82 152:0.90 153:0.95 154:0.41 159:0.84 161:0.69 162:0.61 164:0.90 167:0.72 169:0.82 172:0.89 173:0.08 177:0.05 179:0.19 181:0.72 186:0.79 187:0.39 189:0.97 191:0.70 202:0.86 206:0.81 212:0.60 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.94 241:0.62 242:0.82 243:0.45 245:0.96 247:0.12 248:0.88 256:0.89 259:0.21 260:0.92 261:0.85 265:0.75 266:0.63 267:0.44 268:0.88 271:0.50 276:0.91 283:0.82 285:0.62 297:0.36 300:0.70 +1 12:0.20 17:0.64 21:0.54 27:0.45 28:0.78 30:0.76 34:0.80 36:0.17 37:0.50 43:0.32 55:0.92 66:0.36 69:0.69 70:0.15 81:0.89 91:0.35 98:0.34 108:0.53 123:0.50 124:0.52 126:0.54 127:0.19 129:0.59 133:0.47 135:0.56 145:0.52 146:0.46 147:0.52 149:0.23 150:0.77 154:0.24 159:0.32 161:0.69 163:0.96 167:0.56 172:0.10 173:0.66 177:0.05 178:0.55 179:0.94 181:0.72 187:0.68 212:0.95 215:0.58 216:0.77 235:0.60 241:0.12 242:0.82 243:0.51 245:0.37 247:0.12 259:0.21 265:0.36 266:0.12 267:0.28 271:0.50 276:0.18 297:0.36 300:0.13 +1 8:0.82 12:0.20 17:0.66 21:0.40 27:0.26 28:0.78 30:0.33 32:0.80 34:0.71 36:0.62 37:0.91 43:0.52 55:0.52 66:0.36 69:0.12 70:0.61 81:0.92 91:0.65 98:0.66 104:0.73 108:0.63 120:0.73 123:0.61 124:0.60 126:0.54 127:0.58 129:0.59 133:0.47 135:0.54 140:0.87 145:0.52 146:0.47 147:0.53 149:0.62 150:0.85 151:0.66 153:0.48 154:0.41 159:0.37 161:0.69 162:0.61 164:0.75 167:0.78 172:0.41 173:0.28 177:0.05 178:0.48 179:0.72 181:0.72 187:0.21 191:0.75 195:0.64 202:0.68 212:0.59 215:0.67 216:0.45 220:0.82 235:0.42 241:0.72 242:0.82 243:0.49 245:0.71 247:0.12 248:0.66 256:0.68 259:0.21 260:0.74 265:0.66 266:0.63 267:0.65 268:0.69 271:0.50 276:0.48 285:0.62 297:0.36 300:0.43 +4 6:0.98 8:0.97 12:0.20 17:0.06 20:0.99 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.50 36:0.74 37:0.82 43:0.47 55:0.59 66:0.33 69:0.46 70:0.81 78:0.92 81:0.77 91:1.00 98:0.56 100:0.99 104:0.58 108:0.81 111:0.98 120:0.99 123:0.80 124:0.78 126:0.54 127:0.90 129:0.89 133:0.78 135:0.32 140:0.45 145:0.97 146:0.25 147:0.31 149:0.67 150:0.57 151:0.91 152:1.00 153:1.00 154:0.35 159:0.59 161:0.69 162:0.69 164:0.99 167:0.94 169:0.96 172:0.54 173:0.42 177:0.05 179:0.59 181:0.72 186:0.91 189:0.99 191:0.97 195:0.76 202:0.99 212:0.40 215:0.46 216:0.87 235:0.88 238:0.99 241:0.98 242:0.82 243:0.29 245:0.73 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.57 266:0.63 267:0.79 268:0.99 271:0.50 276:0.64 283:0.82 285:0.62 297:0.36 300:0.55 +3 6:0.96 7:0.81 8:0.97 12:0.20 17:0.12 20:0.85 21:0.78 25:0.88 27:0.44 28:0.78 30:0.45 32:0.80 34:0.55 36:0.63 43:0.47 55:0.45 66:0.43 69:0.46 70:0.57 78:0.77 91:0.88 98:0.39 100:0.82 104:0.76 108:0.86 111:0.77 120:0.94 123:0.85 124:0.66 127:0.93 129:0.81 133:0.67 135:0.65 140:0.45 145:0.85 146:0.27 147:0.33 149:0.56 151:0.83 152:0.81 153:0.96 154:0.35 159:0.67 161:0.69 162:0.55 164:0.99 167:0.92 169:0.80 172:0.45 173:0.37 177:0.05 179:0.75 181:0.72 186:0.77 189:0.97 191:0.92 195:0.81 202:0.98 212:0.55 216:0.49 220:0.62 230:0.65 233:0.63 235:0.88 238:0.96 241:0.92 242:0.82 243:0.43 245:0.64 247:0.12 248:0.91 256:0.98 259:0.21 260:0.94 261:0.80 265:0.41 266:0.63 267:0.44 268:0.98 271:0.50 276:0.39 283:0.60 285:0.62 300:0.55 +1 12:0.20 17:0.18 21:0.78 25:0.88 27:0.13 28:0.78 30:0.21 34:0.37 36:0.92 37:0.82 43:0.52 55:0.52 66:0.63 69:0.05 70:0.80 91:0.58 98:0.80 104:0.58 108:0.05 120:0.80 123:0.05 124:0.52 127:0.86 129:0.81 133:0.67 135:0.34 140:0.45 146:0.87 147:0.90 149:0.75 151:0.74 153:0.83 154:0.41 159:0.62 161:0.69 162:0.85 164:0.82 167:0.86 172:0.73 173:0.10 177:0.05 179:0.53 181:0.72 187:0.21 202:0.70 212:0.54 216:0.87 235:0.02 241:0.77 242:0.82 243:0.69 245:0.75 247:0.12 248:0.72 256:0.77 259:0.21 260:0.81 265:0.80 266:0.80 267:0.44 268:0.78 271:0.50 276:0.68 283:0.60 285:0.62 300:0.55 +3 6:0.98 8:0.95 12:0.20 17:0.34 20:0.81 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.52 36:0.93 37:0.82 43:0.52 55:0.52 66:0.27 69:0.27 70:0.73 78:0.81 81:0.77 91:0.78 98:0.75 100:0.90 104:0.58 108:0.81 111:0.83 120:0.79 123:0.20 124:0.58 126:0.54 127:0.78 129:0.59 133:0.47 135:0.81 140:0.45 145:0.97 146:0.85 147:0.88 149:0.76 150:0.57 151:0.75 152:1.00 153:0.86 154:0.41 159:0.63 161:0.69 162:0.84 164:0.81 167:0.82 169:0.96 172:0.73 173:0.24 177:0.05 179:0.43 181:0.72 186:0.81 187:0.21 189:0.98 195:0.80 202:0.69 212:0.58 215:0.46 216:0.87 235:0.88 238:0.97 241:0.75 242:0.82 243:0.46 245:0.90 247:0.12 248:0.71 256:0.73 259:0.21 260:0.79 261:0.98 265:0.62 266:0.80 267:0.65 268:0.76 271:0.50 276:0.75 285:0.62 297:0.36 300:0.55 +1 7:0.81 8:0.77 12:0.20 17:0.40 21:0.54 25:0.88 27:0.44 28:0.78 30:0.38 32:0.80 34:0.80 36:0.56 43:0.52 55:0.45 66:0.19 69:0.17 70:0.71 81:0.89 91:0.68 98:0.74 104:0.76 106:0.81 108:0.14 120:0.53 123:0.55 124:0.75 126:0.54 127:0.84 129:0.81 133:0.67 135:0.39 140:0.87 145:0.72 146:0.27 147:0.33 149:0.67 150:0.77 151:0.46 153:0.48 154:0.41 159:0.59 161:0.69 162:0.54 164:0.57 167:0.67 172:0.37 173:0.20 177:0.05 178:0.48 179:0.64 181:0.72 187:0.87 195:0.76 202:0.56 206:0.81 212:0.60 215:0.58 216:0.49 220:0.87 230:0.87 233:0.76 235:0.60 241:0.51 242:0.82 243:0.40 245:0.74 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.20 266:0.71 267:0.91 268:0.46 271:0.50 276:0.59 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.98 8:0.93 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.94 98:0.71 100:0.85 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.86 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.79 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.98 8:0.97 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.87 98:0.71 100:0.82 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.75 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.28 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +3 6:1.00 7:0.81 8:1.00 12:0.20 17:0.11 20:0.99 21:0.13 27:0.07 28:0.78 30:0.45 32:0.80 34:0.67 36:0.82 37:0.84 43:0.47 55:0.45 66:0.33 69:0.37 70:0.42 78:0.83 81:0.96 91:0.96 98:0.55 100:0.93 104:0.58 108:0.68 111:0.88 120:0.94 123:0.66 124:0.71 126:0.54 127:0.88 129:0.81 133:0.67 135:0.30 140:0.45 145:0.86 146:0.19 147:0.25 149:0.47 150:0.93 151:0.81 152:0.95 153:0.94 154:0.36 159:0.51 161:0.69 162:0.58 164:0.97 167:0.92 169:0.89 172:0.27 173:0.39 177:0.05 179:0.87 181:0.72 186:0.84 189:1.00 191:0.94 195:0.70 202:0.96 212:0.15 215:0.84 216:0.63 220:0.74 230:0.87 233:0.76 235:0.12 238:0.98 241:0.91 242:0.82 243:0.37 245:0.53 247:0.12 248:0.92 256:0.95 259:0.21 260:0.95 261:0.92 265:0.56 266:0.63 267:0.99 268:0.96 271:0.50 276:0.36 283:0.60 285:0.62 297:0.36 300:0.33 +1 1:0.74 11:0.64 12:0.51 17:0.61 21:0.74 27:0.45 28:0.70 30:0.21 32:0.80 34:0.92 36:0.18 37:0.50 39:0.50 43:0.81 55:0.52 66:0.27 69:0.71 70:0.97 74:0.91 77:0.89 81:0.51 83:0.72 85:0.91 91:0.20 98:0.56 101:0.79 108:0.77 114:0.67 122:0.67 123:0.75 124:0.79 126:0.54 127:0.46 129:0.81 131:0.61 133:0.76 135:0.85 144:0.57 145:0.54 146:0.61 147:0.66 149:0.85 150:0.66 154:0.77 155:0.67 157:0.85 159:0.66 161:0.45 165:0.65 166:0.83 167:0.64 172:0.59 173:0.61 176:0.73 177:0.38 178:0.55 179:0.39 181:0.94 182:0.80 187:0.68 192:0.59 195:0.84 201:0.74 212:0.54 215:0.46 216:0.77 222:0.76 227:0.61 229:0.61 231:0.75 235:0.97 241:0.18 242:0.29 243:0.40 245:0.81 246:0.89 247:0.67 253:0.69 259:0.21 265:0.58 266:0.71 267:0.28 271:0.69 276:0.72 282:0.88 287:0.61 290:0.67 297:0.36 299:0.88 300:0.84 +3 1:0.74 7:0.81 8:0.83 11:0.64 12:0.51 17:0.77 21:0.30 27:0.21 28:0.70 30:0.12 34:0.56 36:0.49 37:0.74 39:0.50 43:0.98 66:0.48 69:0.12 70:0.95 74:0.99 81:0.75 83:0.75 85:1.00 91:0.03 98:0.64 101:0.85 104:0.84 106:0.81 108:0.96 114:0.86 120:0.65 121:0.97 122:0.67 123:0.96 124:0.87 126:0.54 127:0.87 129:0.96 131:0.81 133:0.90 135:0.24 140:0.45 144:0.57 146:0.56 147:0.62 149:0.99 150:0.84 151:0.61 153:0.48 154:0.98 155:0.67 157:0.91 159:0.94 161:0.45 162:0.86 164:0.55 165:0.84 166:0.84 167:0.56 172:0.99 173:0.06 176:0.73 177:0.38 179:0.10 181:0.94 182:0.81 187:0.39 190:0.77 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.84 215:0.72 216:0.95 220:0.62 222:0.76 227:0.85 229:0.61 230:0.87 231:0.76 233:0.76 235:0.42 242:0.58 243:0.08 245:0.99 246:0.89 247:0.67 248:0.59 253:0.79 256:0.45 259:0.21 260:0.65 265:0.65 266:0.89 267:0.44 268:0.46 271:0.69 276:0.99 283:0.71 285:0.50 287:0.61 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:1.00 8:0.97 9:0.80 11:0.64 12:0.51 17:0.55 20:0.93 21:0.22 27:0.03 28:0.70 30:0.95 34:0.53 36:0.40 37:1.00 39:0.50 41:0.93 43:0.79 66:0.54 69:0.76 74:0.42 78:0.83 81:0.80 83:0.81 85:0.72 91:0.83 96:0.92 98:0.10 100:0.92 101:0.73 104:0.58 108:0.59 111:0.86 114:0.90 120:0.86 123:0.56 126:0.54 127:0.07 129:0.05 131:0.89 135:0.42 140:0.45 144:0.57 146:0.13 147:0.18 149:0.43 150:0.87 151:0.98 152:1.00 153:0.92 154:0.72 155:0.67 157:0.77 159:0.08 161:0.45 162:0.57 164:0.84 165:0.86 166:0.84 167:0.86 169:0.97 172:0.10 173:0.84 176:0.73 177:0.38 179:0.09 181:0.94 182:0.81 186:0.83 187:0.68 189:0.99 191:0.95 192:0.59 201:0.74 202:0.81 208:0.64 215:0.79 216:0.76 222:0.76 227:0.92 229:0.89 231:0.76 235:0.31 238:0.97 241:0.74 243:0.63 246:0.89 248:0.92 253:0.89 255:0.79 256:0.77 259:0.21 260:0.87 261:0.99 265:0.11 267:0.91 268:0.81 271:0.69 285:0.62 287:0.92 290:0.90 297:0.36 300:0.08 +1 1:0.74 11:0.64 12:0.51 17:0.57 21:0.40 27:0.01 28:0.70 30:0.74 32:0.80 34:0.90 36:0.68 37:0.97 39:0.50 43:0.87 66:0.68 69:0.37 70:0.41 74:0.92 77:0.70 81:0.76 83:0.76 85:0.97 91:0.25 98:0.75 101:0.86 104:0.84 106:0.81 108:0.29 114:0.66 117:0.86 122:0.43 123:0.28 124:0.45 126:0.54 127:0.60 129:0.59 131:0.61 133:0.47 135:0.81 144:0.57 145:0.84 146:0.76 147:0.80 149:0.97 150:0.85 154:0.85 155:0.67 157:0.89 159:0.47 161:0.45 165:0.86 166:0.84 167:0.69 172:0.76 173:0.39 176:0.56 177:0.38 178:0.55 179:0.46 181:0.94 182:0.81 187:0.68 192:0.59 195:0.66 201:0.74 206:0.81 212:0.68 215:0.63 216:0.85 222:0.76 227:0.61 229:0.61 231:0.75 232:0.90 235:0.68 241:0.39 242:0.60 243:0.72 245:0.83 246:0.90 247:0.39 253:0.70 259:0.21 265:0.75 266:0.63 267:0.52 271:0.69 276:0.66 282:0.88 287:0.61 290:0.66 297:0.36 299:0.88 300:0.55 +4 1:0.74 6:1.00 8:0.99 9:0.80 11:0.64 12:0.51 17:0.28 20:0.97 21:0.13 27:0.03 28:0.70 30:0.95 34:0.52 36:0.61 37:1.00 39:0.50 41:1.00 43:0.78 60:0.99 66:0.54 69:0.77 74:0.55 78:0.96 81:0.84 83:0.91 85:0.72 91:1.00 96:1.00 98:0.09 100:0.99 101:0.71 104:0.58 108:0.60 111:0.99 114:0.92 120:1.00 123:0.58 126:0.54 127:0.06 129:0.05 131:0.89 135:0.66 138:1.00 140:0.45 144:0.57 146:0.13 147:0.18 149:0.43 150:0.91 151:1.00 152:1.00 153:1.00 154:0.71 155:0.67 157:0.76 158:0.92 159:0.08 161:0.45 162:0.69 164:0.99 165:0.88 166:0.84 167:1.00 169:0.97 172:0.10 173:0.75 176:0.73 177:0.38 179:0.08 181:0.94 182:0.81 186:0.95 189:1.00 191:0.97 192:0.59 201:0.74 202:0.98 208:0.64 215:0.84 216:0.76 222:0.76 227:0.92 229:0.89 231:0.76 235:0.22 238:1.00 241:1.00 243:0.63 246:0.90 248:1.00 253:1.00 255:0.79 256:0.99 259:0.21 260:1.00 261:0.99 265:0.09 267:0.94 268:0.99 271:0.69 279:0.86 283:0.82 285:0.62 287:0.92 290:0.92 297:0.36 300:0.08 +2 1:0.74 6:0.94 7:0.81 8:0.87 9:0.80 11:0.64 12:0.51 17:0.85 20:0.85 27:0.30 28:0.70 30:0.56 32:0.80 34:0.47 36:0.71 37:0.72 39:0.50 41:0.88 43:0.69 60:0.87 66:0.06 69:0.86 70:0.75 74:0.99 77:0.70 78:0.78 81:0.93 83:0.88 85:1.00 91:0.18 96:0.87 98:0.29 100:0.83 101:0.86 104:0.92 106:0.81 108:0.84 111:0.78 114:0.98 117:0.86 120:0.82 121:0.90 122:0.43 123:0.98 124:0.99 126:0.54 127:0.90 129:1.00 131:0.89 133:0.99 135:0.63 140:0.45 144:0.57 145:0.67 146:0.69 147:0.74 149:0.99 150:0.97 151:0.95 152:0.86 153:0.83 154:0.59 155:0.67 157:0.91 158:0.92 159:0.98 161:0.45 162:0.84 164:0.80 165:0.92 166:0.84 167:0.62 169:0.80 172:0.92 173:0.35 176:0.73 177:0.38 179:0.09 181:0.94 182:0.81 186:0.79 187:0.39 189:0.96 191:0.70 192:0.59 195:1.00 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.59 215:0.96 216:0.32 222:0.76 227:0.92 229:0.89 230:0.87 231:0.76 232:0.85 233:0.76 235:0.05 238:0.94 241:0.10 242:0.51 243:0.08 245:0.99 246:0.90 247:0.67 248:0.85 253:0.92 255:0.79 256:0.81 259:0.21 260:0.82 261:0.82 265:0.30 266:0.91 267:0.85 268:0.75 271:0.69 276:0.99 279:0.86 282:0.88 283:0.82 285:0.62 287:0.92 290:0.98 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 8:0.68 11:0.64 12:0.51 17:0.38 21:0.40 27:0.21 28:0.70 30:0.21 34:0.63 36:0.42 37:0.74 39:0.50 43:0.82 55:0.59 66:0.31 69:0.15 70:0.95 74:0.95 76:0.85 81:0.67 85:0.80 91:0.06 98:0.54 101:0.71 104:0.84 106:0.81 108:0.88 114:0.82 117:0.86 121:0.90 122:0.57 123:0.87 124:0.86 126:0.54 127:0.75 129:0.89 131:0.61 133:0.87 135:0.30 144:0.57 146:0.72 147:0.76 149:0.80 150:0.73 154:0.92 155:0.67 157:0.78 158:0.87 159:0.84 161:0.45 166:0.84 167:0.65 172:0.79 173:0.10 176:0.73 177:0.38 178:0.55 179:0.26 181:0.94 182:0.73 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.73 215:0.67 216:0.95 222:0.76 227:0.61 229:0.61 230:0.87 231:0.76 232:0.94 233:0.76 235:0.52 241:0.24 242:0.30 243:0.40 245:0.88 246:0.61 247:0.67 253:0.77 259:0.21 265:0.55 266:0.91 267:0.36 271:0.69 276:0.86 279:0.86 283:0.71 287:0.61 290:0.82 297:0.36 300:0.84 +1 1:0.74 11:0.64 12:0.51 17:0.57 21:0.59 27:0.45 28:0.70 30:0.17 32:0.80 34:0.90 36:0.21 37:0.50 39:0.50 43:0.82 66:0.30 69:0.70 70:0.92 74:0.86 77:0.89 81:0.63 83:0.73 85:0.91 91:0.18 98:0.49 101:0.80 108:0.53 114:0.74 122:0.57 123:0.51 124:0.77 126:0.54 127:0.42 129:0.81 131:0.61 133:0.76 135:0.81 144:0.57 145:0.47 146:0.67 147:0.72 149:0.85 150:0.76 154:0.77 155:0.67 157:0.85 159:0.59 161:0.45 165:0.78 166:0.83 167:0.65 172:0.54 173:0.63 176:0.73 177:0.38 178:0.55 179:0.45 181:0.94 182:0.80 187:0.68 192:0.59 195:0.80 201:0.74 212:0.49 215:0.55 216:0.77 222:0.76 227:0.61 229:0.61 231:0.75 235:0.80 241:0.21 242:0.39 243:0.48 245:0.75 246:0.89 247:0.60 253:0.70 259:0.21 265:0.51 266:0.71 267:0.23 271:0.69 276:0.66 282:0.88 287:0.61 290:0.74 297:0.36 299:0.88 300:0.70 +2 6:0.93 8:0.98 9:0.80 12:0.51 17:0.08 20:0.81 21:0.78 27:0.20 28:0.70 34:0.49 36:0.27 37:0.94 39:0.50 41:0.93 78:0.75 83:0.81 91:0.88 96:0.91 100:0.78 104:0.58 111:0.74 120:0.87 131:0.89 135:0.88 140:0.45 144:0.57 151:0.92 152:0.92 153:0.72 155:0.67 157:0.61 161:0.45 162:0.66 164:0.82 166:0.61 167:0.97 169:0.76 175:0.91 181:0.94 182:0.81 186:0.75 189:0.87 191:0.94 192:0.59 202:0.76 208:0.64 216:0.38 220:0.74 222:0.76 227:0.92 229:0.89 231:0.76 238:0.90 241:0.87 244:0.83 246:0.61 248:0.91 253:0.87 255:0.79 256:0.78 257:0.84 259:0.21 260:0.87 261:0.78 267:0.44 268:0.78 271:0.69 277:0.87 283:0.71 285:0.62 287:0.92 +3 1:0.74 6:0.88 7:0.81 9:0.80 11:0.64 12:0.51 17:0.75 20:0.73 21:0.30 27:0.21 28:0.70 30:0.13 34:0.62 36:0.50 37:0.74 39:0.50 41:0.82 43:0.98 60:0.87 66:0.48 69:0.12 70:0.95 74:1.00 78:0.72 81:0.75 83:0.84 85:1.00 91:0.03 96:0.77 98:0.64 100:0.73 101:0.86 104:0.84 106:0.81 108:0.96 111:0.72 114:0.86 117:0.86 120:0.65 121:0.97 122:0.67 123:0.96 124:0.87 126:0.54 127:0.87 129:0.96 131:0.89 133:0.90 135:0.23 140:0.45 144:0.57 146:0.56 147:0.62 149:0.99 150:0.84 151:0.77 152:0.91 153:0.48 154:0.98 155:0.67 157:0.91 158:0.87 159:0.94 161:0.45 162:0.86 164:0.57 165:0.84 166:0.84 167:0.54 169:0.78 172:0.99 173:0.06 176:0.73 177:0.38 179:0.10 181:0.94 182:0.81 186:0.73 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.84 215:0.72 216:0.95 220:0.62 222:0.76 227:0.92 229:0.89 230:0.87 231:0.76 232:0.90 233:0.76 235:0.42 242:0.58 243:0.08 245:0.99 246:0.89 247:0.67 248:0.71 253:0.85 255:0.79 256:0.45 259:0.21 260:0.66 261:0.81 265:0.65 266:0.89 267:0.44 268:0.46 271:0.69 276:0.99 279:0.86 283:0.71 285:0.62 287:0.91 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:1.00 8:0.95 9:0.80 11:0.64 12:0.51 17:0.55 20:0.75 21:0.22 27:0.03 28:0.70 30:0.58 34:0.74 36:0.66 37:1.00 39:0.50 41:0.98 43:0.79 66:0.27 69:0.74 70:0.44 74:0.55 78:0.83 81:0.80 83:0.88 85:0.85 91:0.81 96:0.96 98:0.40 100:0.91 101:0.78 104:0.58 108:0.71 111:0.84 114:0.90 120:0.94 122:0.43 123:0.69 124:0.73 126:0.54 127:0.32 129:0.81 131:0.89 133:0.67 135:0.39 140:0.80 144:0.57 146:0.13 147:0.18 149:0.69 150:0.87 151:0.98 152:1.00 153:0.91 154:0.73 155:0.67 157:0.82 159:0.38 161:0.45 162:0.80 163:0.86 164:0.91 165:0.86 166:0.84 167:0.90 169:0.97 172:0.21 173:0.79 176:0.73 177:0.38 178:0.42 179:0.80 181:0.94 182:0.81 186:0.84 187:0.21 189:0.93 191:0.89 192:0.59 201:0.74 202:0.85 208:0.64 212:0.16 215:0.79 216:0.76 222:0.76 227:0.92 229:0.89 231:0.76 235:0.31 238:0.95 241:0.76 242:0.58 243:0.29 245:0.51 246:0.89 247:0.47 248:0.96 253:0.95 255:0.79 256:0.88 259:0.21 260:0.94 261:0.99 265:0.42 266:0.54 267:0.96 268:0.89 271:0.69 276:0.34 285:0.62 287:0.92 290:0.90 297:0.36 300:0.33 +3 1:0.74 6:0.88 7:0.81 8:0.75 9:0.80 11:0.64 12:0.51 17:0.61 20:0.98 21:0.30 27:0.21 28:0.70 30:0.13 34:0.50 36:0.57 37:0.74 39:0.50 41:0.94 43:0.98 60:0.99 66:0.49 69:0.12 70:0.94 74:0.99 76:0.85 78:0.77 81:0.75 83:0.85 85:1.00 91:0.40 96:0.94 98:0.63 100:0.81 101:0.86 104:0.84 106:0.81 108:0.95 111:0.77 114:0.86 117:0.86 120:0.90 121:0.90 122:0.67 123:0.95 124:0.83 126:0.54 127:0.84 129:0.95 131:0.89 133:0.87 135:0.21 140:0.45 144:0.57 146:0.29 147:0.36 149:0.99 150:0.84 151:0.99 152:0.91 153:0.95 154:0.98 155:0.67 157:0.91 158:0.92 159:0.93 161:0.45 162:0.56 164:0.85 165:0.84 166:0.84 167:0.67 169:0.79 172:0.99 173:0.07 176:0.73 177:0.38 179:0.10 181:0.94 182:0.81 186:0.77 187:0.39 189:0.90 192:0.59 201:0.74 202:0.82 204:0.89 206:0.81 208:0.64 212:0.85 215:0.72 216:0.95 222:0.76 227:0.92 229:0.89 230:0.87 231:0.76 232:0.90 233:0.76 235:0.42 238:0.93 241:0.30 242:0.57 243:0.06 245:0.99 246:0.89 247:0.67 248:0.94 253:0.97 255:0.79 256:0.81 259:0.21 260:0.91 261:0.81 265:0.64 266:0.80 267:0.28 268:0.81 271:0.69 276:0.99 279:0.86 283:0.82 285:0.62 287:0.92 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:0.91 7:0.81 8:0.81 9:0.80 11:0.88 12:0.06 17:0.05 18:0.92 20:0.94 21:0.22 22:0.93 27:0.19 28:0.87 29:0.86 30:0.93 31:0.97 32:0.80 34:0.39 36:0.56 37:0.66 39:0.90 41:0.95 43:0.81 44:0.93 47:0.93 60:0.99 64:0.77 66:0.49 69:0.70 70:0.28 71:0.71 74:0.93 77:0.99 78:0.80 79:0.96 81:0.60 83:0.91 85:0.97 86:0.98 91:0.67 96:0.88 98:0.59 100:0.83 101:0.87 106:0.81 108:0.75 111:0.80 114:0.71 117:0.86 120:0.80 121:0.97 122:0.57 123:0.73 124:0.67 126:0.54 127:0.33 129:0.81 133:0.67 135:0.60 137:0.97 139:0.99 140:0.45 144:0.85 145:0.91 146:0.21 147:0.27 149:0.98 150:0.77 151:0.82 152:0.92 153:0.46 154:0.76 155:0.67 158:0.92 159:0.48 161:0.80 162:0.79 164:0.82 165:0.93 167:0.65 169:0.81 172:0.78 173:0.67 176:0.73 177:0.70 179:0.25 181:0.96 186:0.81 187:0.68 189:0.92 191:0.70 192:0.87 195:0.75 196:0.99 201:0.93 202:0.74 204:0.89 206:0.81 208:0.64 212:0.76 215:0.51 216:0.73 219:0.95 220:0.62 222:0.84 230:0.86 232:0.90 233:0.73 235:0.42 238:0.89 241:0.57 242:0.51 243:0.22 245:0.89 247:0.35 248:0.87 253:0.92 255:0.95 256:0.78 259:0.21 260:0.81 261:0.85 262:0.93 265:0.60 266:0.54 267:0.89 268:0.79 271:0.81 276:0.77 279:0.86 281:0.89 282:0.88 283:0.82 284:0.97 285:0.62 290:0.71 292:0.95 297:0.36 299:1.00 300:0.55 +1 1:0.74 11:0.88 12:0.06 17:0.34 18:0.85 21:0.54 27:0.45 28:0.87 29:0.86 30:0.90 34:0.79 36:0.25 37:0.50 39:0.90 43:0.81 64:0.77 66:0.51 69:0.70 70:0.29 71:0.71 74:0.69 81:0.47 83:0.91 85:0.91 86:0.92 91:0.08 98:0.41 101:0.87 108:0.53 114:0.59 122:0.57 123:0.51 124:0.72 126:0.54 127:0.46 129:0.81 133:0.74 135:0.78 139:0.90 144:0.85 145:0.50 146:0.64 147:0.69 149:0.89 150:0.71 154:0.77 155:0.67 159:0.66 161:0.80 165:0.93 167:0.59 172:0.57 173:0.60 176:0.73 177:0.70 178:0.55 179:0.58 181:0.96 187:0.68 192:0.87 201:0.93 208:0.64 212:0.28 215:0.39 216:0.77 222:0.84 235:0.74 241:0.43 242:0.45 243:0.61 245:0.64 247:0.55 253:0.45 259:0.21 265:0.43 266:0.71 267:0.28 271:0.81 276:0.53 290:0.59 297:0.36 300:0.43 +2 1:0.74 6:0.90 7:0.81 8:0.75 9:0.80 11:0.92 12:0.06 17:0.15 18:0.89 20:0.93 21:0.71 22:0.93 27:0.10 28:0.87 29:0.86 30:0.91 31:0.97 32:0.80 34:0.66 36:0.98 37:0.73 39:0.90 41:0.95 43:0.96 44:0.93 45:0.66 47:0.93 60:0.99 64:0.77 66:0.75 69:0.30 70:0.27 71:0.71 74:0.90 77:0.70 78:0.82 79:0.97 81:0.43 83:0.91 85:0.94 86:0.96 91:0.62 96:0.89 98:0.62 100:0.85 101:0.87 106:0.81 108:0.24 111:0.82 114:0.55 117:0.86 120:0.82 121:0.90 122:0.57 123:0.23 124:0.41 126:0.54 127:0.22 129:0.59 133:0.46 135:0.83 137:0.97 139:0.97 140:0.45 144:0.85 145:0.69 146:0.85 147:0.87 149:0.96 150:0.69 151:0.96 152:0.91 153:0.87 154:0.96 155:0.67 158:0.92 159:0.49 161:0.80 162:0.68 164:0.83 165:0.93 167:0.54 169:0.83 172:0.75 173:0.19 176:0.73 177:0.70 179:0.28 181:0.96 186:0.83 187:0.87 189:0.91 191:0.70 192:0.87 195:0.87 196:0.99 201:0.93 202:0.76 204:0.89 206:0.81 208:0.64 212:0.37 215:0.37 216:0.87 219:0.95 222:0.84 230:0.86 232:0.90 233:0.73 235:0.95 238:0.89 241:0.24 242:0.45 243:0.77 245:0.74 247:0.60 248:0.88 253:0.92 255:0.95 256:0.77 259:0.21 260:0.82 261:0.87 262:0.93 265:0.63 266:0.75 267:0.52 268:0.78 271:0.81 276:0.65 279:0.86 281:0.89 282:0.88 283:0.82 284:0.97 285:0.62 290:0.55 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 7:0.81 8:0.94 9:0.80 11:0.88 12:0.06 17:0.73 18:0.91 21:0.54 22:0.93 27:0.15 28:0.87 29:0.86 30:0.93 31:0.96 32:0.80 34:0.49 36:0.31 37:0.83 39:0.90 41:0.93 43:0.91 44:0.93 47:0.93 60:0.97 64:0.77 66:0.79 69:0.44 70:0.23 71:0.71 74:0.93 77:0.70 79:0.95 81:0.47 83:0.91 85:0.95 86:0.98 91:0.63 96:0.86 98:0.42 101:0.87 106:0.81 108:0.34 114:0.59 117:0.86 120:0.77 121:0.90 122:0.57 123:0.32 124:0.40 126:0.54 127:0.75 129:0.59 132:0.97 133:0.46 135:0.93 137:0.96 139:0.98 140:0.45 144:0.85 145:0.74 146:0.69 147:0.74 149:0.97 150:0.71 151:0.87 153:0.48 154:0.90 155:0.67 158:0.92 159:0.77 161:0.80 162:0.78 164:0.77 165:0.93 167:0.52 172:0.80 173:0.27 176:0.73 177:0.70 179:0.46 181:0.96 187:0.39 191:0.73 192:0.87 195:0.89 196:0.98 201:0.93 202:0.67 204:0.89 206:0.81 208:0.64 212:0.86 215:0.39 216:0.62 219:0.95 220:0.62 222:0.84 230:0.87 232:0.92 233:0.76 235:0.74 241:0.12 242:0.51 243:0.80 245:0.77 247:0.35 248:0.84 253:0.90 255:0.95 256:0.71 260:0.78 262:0.93 265:0.44 266:0.54 267:0.82 268:0.72 271:0.81 276:0.43 279:0.86 281:0.91 282:0.88 283:0.82 284:0.96 285:0.62 290:0.59 292:0.95 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.92 12:0.06 17:0.32 18:0.95 20:0.94 21:0.40 22:0.93 27:0.21 28:0.87 29:0.86 30:0.87 31:0.99 34:0.61 36:0.66 37:0.74 39:0.90 41:0.98 43:0.96 45:0.66 47:0.93 60:0.97 64:0.77 66:0.06 69:0.19 70:0.46 71:0.71 74:0.91 78:0.88 79:0.99 81:0.52 83:0.91 85:0.98 86:0.98 91:0.56 96:0.96 98:0.34 100:0.96 101:0.87 106:0.81 108:0.94 111:0.93 114:0.62 117:0.86 120:0.92 121:0.97 122:0.57 123:0.81 124:0.93 126:0.54 127:0.72 129:0.97 133:0.93 135:0.21 137:0.99 139:0.99 140:0.45 144:0.85 146:0.52 147:0.58 149:0.97 150:0.73 151:0.94 152:0.93 153:0.81 154:0.96 155:0.67 158:0.92 159:0.87 161:0.80 162:0.60 164:0.90 165:0.93 167:0.61 169:0.94 172:0.77 173:0.09 176:0.73 177:0.70 179:0.20 181:0.96 186:0.88 187:0.39 189:0.96 191:0.73 192:0.87 196:1.00 201:0.93 202:0.87 204:0.89 206:0.81 208:0.64 212:0.58 215:0.42 216:0.95 219:0.95 220:0.62 222:0.84 230:0.86 232:0.91 233:0.73 235:0.60 238:0.96 241:0.49 242:0.39 243:0.19 245:0.91 247:0.82 248:0.96 253:0.97 255:0.95 256:0.87 259:0.21 260:0.92 261:0.95 262:0.93 265:0.33 266:0.87 267:0.23 268:0.88 271:0.81 276:0.91 279:0.86 281:0.89 283:0.82 284:0.99 285:0.62 290:0.62 292:0.95 297:0.36 300:0.70 +3 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.92 12:0.06 17:0.59 18:0.95 20:0.84 21:0.40 22:0.93 27:0.21 28:0.87 29:0.86 30:0.87 31:0.98 34:0.49 36:0.67 37:0.74 39:0.90 41:0.93 43:0.96 45:0.66 47:0.93 60:0.87 64:0.77 66:0.06 69:0.19 70:0.48 71:0.71 74:0.92 78:0.86 79:0.97 81:0.52 83:0.91 85:0.98 86:0.99 91:0.18 96:0.91 98:0.33 100:0.95 101:0.87 106:0.81 108:0.94 111:0.90 114:0.62 117:0.86 120:0.84 121:0.97 122:0.57 123:0.82 124:0.93 126:0.54 127:0.72 129:0.97 133:0.93 135:0.20 137:0.98 139:0.99 140:0.45 144:0.85 146:0.54 147:0.60 149:0.98 150:0.73 151:0.98 152:0.93 153:0.91 154:0.96 155:0.67 158:0.92 159:0.87 161:0.80 162:0.76 164:0.79 165:0.93 167:0.54 169:0.94 172:0.77 173:0.09 176:0.73 177:0.70 179:0.20 181:0.96 186:0.86 187:0.39 189:0.95 192:0.87 196:0.99 201:0.93 202:0.70 204:0.89 206:0.81 208:0.64 212:0.59 215:0.42 216:0.95 219:0.95 222:0.84 230:0.86 232:0.88 233:0.73 235:0.60 238:0.96 241:0.21 242:0.39 243:0.23 245:0.91 247:0.82 248:0.90 253:0.94 255:0.95 256:0.71 259:0.21 260:0.85 261:0.95 262:0.93 265:0.33 266:0.87 267:0.59 268:0.74 271:0.81 276:0.90 279:0.86 281:0.89 283:0.82 284:0.96 285:0.62 290:0.62 292:0.95 297:0.36 300:0.84 +1 1:0.74 9:0.80 11:0.92 12:0.06 17:0.26 18:0.97 21:0.22 22:0.93 27:0.18 28:0.87 29:0.86 30:0.91 31:0.94 34:0.87 36:0.49 37:0.85 39:0.90 41:0.82 43:0.89 45:0.73 47:0.93 60:0.99 64:0.77 66:0.97 69:0.47 70:0.27 71:0.71 74:0.95 79:0.93 81:0.60 83:0.91 85:0.98 86:0.99 91:0.29 96:0.80 98:0.90 101:0.97 108:0.36 114:0.71 120:0.69 122:0.57 123:0.35 126:0.54 127:0.48 129:0.05 135:0.86 137:0.94 139:0.99 140:0.45 144:0.85 146:0.91 147:0.93 149:0.99 150:0.77 151:0.87 153:0.48 154:0.88 155:0.67 158:0.92 159:0.53 161:0.80 162:0.86 164:0.57 165:0.93 167:0.55 172:0.93 173:0.43 176:0.73 177:0.70 179:0.21 181:0.96 187:0.39 192:0.87 196:0.98 201:0.93 202:0.36 208:0.64 212:0.78 215:0.51 216:0.82 219:0.95 222:0.84 232:0.92 235:0.42 241:0.27 242:0.45 243:0.97 247:0.60 248:0.77 253:0.87 255:0.95 256:0.45 259:0.21 260:0.70 262:0.93 265:0.90 266:0.54 267:0.36 268:0.46 271:0.81 276:0.87 279:0.86 283:0.82 284:0.89 285:0.62 290:0.71 292:0.95 297:0.36 300:0.43 +3 1:0.74 6:0.94 8:0.87 9:0.80 11:0.88 12:0.06 17:0.15 18:0.88 20:0.96 21:0.68 28:0.87 29:0.86 30:0.91 31:0.99 32:0.80 34:0.47 36:0.23 37:0.97 39:0.90 41:0.99 43:0.80 44:0.93 60:1.00 64:0.77 66:0.48 69:0.74 70:0.30 71:0.71 74:0.87 77:0.70 78:0.88 79:0.99 81:0.44 83:0.91 85:0.96 86:0.96 91:0.88 96:0.97 98:0.28 100:0.94 101:0.87 108:0.81 111:0.90 114:0.56 120:0.94 122:0.57 123:0.80 124:0.75 126:0.54 127:0.30 129:0.89 133:0.78 135:0.51 137:0.99 139:0.97 140:0.45 144:0.85 145:0.67 146:0.36 147:0.42 149:0.95 150:0.70 151:0.98 152:0.89 153:0.91 154:0.74 155:0.67 158:0.92 159:0.86 161:0.80 162:0.56 164:0.93 165:0.93 167:0.58 169:0.92 172:0.73 173:0.39 176:0.73 177:0.70 179:0.29 181:0.96 186:0.88 187:0.21 189:0.95 191:0.86 192:0.87 195:0.98 196:1.00 201:0.93 202:0.92 208:0.64 212:0.47 215:0.37 216:0.98 219:0.95 222:0.84 232:0.84 235:0.88 238:0.94 241:0.39 242:0.50 243:0.33 245:0.81 247:0.47 248:0.97 253:0.98 255:0.95 256:0.92 259:0.21 260:0.94 261:0.93 265:0.30 266:0.84 267:0.94 268:0.92 271:0.81 276:0.60 279:0.86 282:0.88 283:0.82 284:0.99 285:0.62 290:0.56 292:0.95 297:0.36 299:0.88 300:0.70 +4 1:0.74 6:0.88 8:0.75 9:0.80 11:0.88 12:0.06 17:0.02 18:0.84 20:0.96 21:0.22 27:0.15 28:0.87 29:0.86 30:0.91 31:1.00 34:0.44 36:0.67 37:0.69 39:0.90 41:1.00 43:0.96 60:0.99 64:0.77 66:0.61 69:0.15 70:0.21 71:0.71 74:0.76 78:0.94 79:1.00 81:0.60 83:0.91 85:0.88 86:0.92 91:0.97 96:0.99 98:0.66 100:0.98 101:0.87 108:0.12 111:0.96 114:0.71 120:0.98 122:0.57 123:0.12 124:0.47 126:0.54 127:0.45 129:0.59 133:0.46 135:0.78 137:1.00 139:0.92 140:0.45 144:0.85 146:0.50 147:0.56 149:0.90 150:0.77 151:0.99 152:0.97 153:0.97 154:0.96 155:0.67 158:0.92 159:0.28 161:0.80 162:0.65 164:0.97 165:0.93 167:0.87 169:0.95 172:0.48 173:0.36 176:0.73 177:0.70 179:0.73 181:0.96 186:0.93 189:0.89 191:0.87 192:0.87 196:1.00 201:0.93 202:0.96 208:0.64 212:0.22 215:0.51 216:0.46 219:0.95 220:0.62 222:0.84 232:0.75 235:0.42 238:0.96 241:0.86 242:0.52 243:0.68 245:0.62 247:0.47 248:0.99 253:0.99 255:0.95 256:0.97 259:0.21 260:0.98 261:0.97 265:0.66 266:0.54 267:0.82 268:0.97 271:0.81 276:0.40 279:0.86 283:0.82 284:1.00 285:0.62 290:0.71 292:0.95 297:0.36 300:0.25 +1 1:0.74 11:0.88 12:0.06 17:0.30 18:0.76 21:0.40 27:0.45 28:0.87 29:0.86 30:0.94 32:0.80 34:0.75 36:0.20 37:0.50 39:0.90 43:0.81 44:0.93 64:0.77 66:0.80 69:0.70 70:0.18 71:0.71 74:0.68 77:0.70 81:0.52 83:0.91 85:0.85 86:0.87 91:0.12 98:0.39 101:0.87 108:0.53 114:0.62 122:0.57 123:0.51 126:0.54 127:0.13 129:0.05 135:0.65 139:0.88 144:0.85 145:0.47 146:0.50 147:0.56 149:0.82 150:0.73 154:0.77 155:0.67 159:0.18 161:0.80 165:0.93 167:0.51 172:0.45 173:0.68 176:0.73 177:0.70 178:0.55 179:0.30 181:0.96 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.55 215:0.42 216:0.77 222:0.84 235:0.60 241:0.10 242:0.58 243:0.82 247:0.35 253:0.48 259:0.21 265:0.41 266:0.27 267:0.36 271:0.81 276:0.20 282:0.88 290:0.62 297:0.36 299:0.88 300:0.18 +3 1:0.74 9:0.80 11:0.88 12:0.06 17:0.15 18:0.92 20:0.76 21:0.47 22:0.93 27:0.49 28:0.87 29:0.86 30:0.90 31:0.96 32:0.80 34:0.47 36:0.40 37:0.38 39:0.90 41:0.88 43:0.85 44:0.93 47:0.93 60:0.87 64:0.77 66:0.45 69:0.63 70:0.39 71:0.71 74:0.90 77:0.99 78:0.72 79:0.95 81:0.49 83:0.91 85:0.98 86:0.98 91:0.23 96:0.86 98:0.44 100:0.73 101:0.87 108:0.91 111:0.72 114:0.60 117:0.86 120:0.77 122:0.57 123:0.90 124:0.76 126:0.54 127:0.58 129:0.81 133:0.74 135:0.79 137:0.96 139:0.98 140:0.45 144:0.85 145:0.99 146:0.61 147:0.66 149:0.97 150:0.72 151:0.87 152:0.77 153:0.48 154:0.82 155:0.67 158:0.92 159:0.79 161:0.80 162:0.77 164:0.66 165:0.93 167:0.53 169:0.81 172:0.84 173:0.42 176:0.73 177:0.70 179:0.24 181:0.96 186:0.73 187:0.39 192:0.87 195:0.91 196:0.98 201:0.93 202:0.55 208:0.64 212:0.73 215:0.41 216:0.88 219:0.95 222:0.84 232:0.85 235:0.68 241:0.15 242:0.43 243:0.31 245:0.91 247:0.74 248:0.84 253:0.89 255:0.95 256:0.58 260:0.78 261:0.78 262:0.93 265:0.46 266:0.84 267:0.84 268:0.58 271:0.81 276:0.83 279:0.86 282:0.88 283:0.82 284:0.92 285:0.62 290:0.60 292:0.95 297:0.36 299:1.00 300:0.84 +3 1:0.74 6:0.91 7:0.81 8:0.80 9:0.80 11:0.88 12:0.06 17:0.12 18:0.87 20:0.82 21:0.30 22:0.93 27:0.21 28:0.87 29:0.86 30:0.93 31:0.96 34:0.66 36:0.31 37:0.71 39:0.90 41:0.93 43:0.76 47:0.93 60:0.99 64:0.77 66:0.93 69:0.81 70:0.24 71:0.71 74:0.87 78:0.82 79:0.95 81:0.55 83:0.91 85:0.95 86:0.95 91:0.49 96:0.87 97:0.89 98:0.76 100:0.88 101:0.87 106:0.81 108:0.65 111:0.83 114:0.65 117:0.86 120:0.78 121:0.90 122:0.57 123:0.62 126:0.54 127:0.25 129:0.05 135:0.92 137:0.96 139:0.97 140:0.45 144:0.85 146:0.81 147:0.84 149:0.95 150:0.75 151:0.87 152:0.81 153:0.48 154:0.68 155:0.67 158:0.92 159:0.37 161:0.80 162:0.86 164:0.78 165:0.93 167:0.59 169:0.84 172:0.82 173:0.83 176:0.73 177:0.70 179:0.27 181:0.96 186:0.82 187:0.21 189:0.94 191:0.70 192:0.87 196:0.98 201:0.93 202:0.65 204:0.89 206:0.81 208:0.64 212:0.60 215:0.44 216:0.79 219:0.95 220:0.62 222:0.84 230:0.86 232:0.94 233:0.73 235:0.52 238:0.91 241:0.44 242:0.51 243:0.94 247:0.35 248:0.85 253:0.90 255:0.95 256:0.73 259:0.21 260:0.79 261:0.83 262:0.93 265:0.76 266:0.38 267:0.65 268:0.74 271:0.81 276:0.70 279:0.86 281:0.89 283:0.82 284:0.96 285:0.62 290:0.65 292:0.95 297:0.36 300:0.43 +1 1:0.74 7:0.81 8:0.64 9:0.80 11:0.92 12:0.06 17:0.18 18:0.93 20:0.84 21:0.13 27:0.55 28:0.87 29:0.86 30:0.93 31:0.97 32:0.80 34:0.45 36:0.59 37:0.25 39:0.90 41:0.92 43:0.91 44:0.93 45:0.66 60:0.87 64:0.77 66:0.94 69:0.41 70:0.28 71:0.71 74:0.95 77:0.89 78:0.77 79:0.97 81:0.66 83:0.91 85:0.96 86:0.98 91:0.51 96:0.90 98:0.83 100:0.78 101:0.87 108:0.31 111:0.76 114:0.79 120:0.82 121:0.90 122:0.57 123:0.30 126:0.54 127:0.33 129:0.05 135:0.81 137:0.97 139:0.99 140:0.45 144:0.85 145:0.67 146:0.70 147:0.74 149:0.98 150:0.80 151:0.95 152:0.80 153:0.83 154:0.90 155:0.67 158:0.92 159:0.27 161:0.80 162:0.69 164:0.75 165:0.93 167:0.65 169:0.76 172:0.85 173:0.54 176:0.73 177:0.70 179:0.29 181:0.96 186:0.77 187:0.39 192:0.87 195:0.59 196:0.99 201:0.93 202:0.67 204:0.89 208:0.64 212:0.89 215:0.61 216:0.50 219:0.95 222:0.84 230:0.87 232:0.88 233:0.76 235:0.31 241:0.57 242:0.45 243:0.95 247:0.47 248:0.89 253:0.94 255:0.95 256:0.68 259:0.21 260:0.83 261:0.78 265:0.83 266:0.27 267:0.44 268:0.70 271:0.81 276:0.75 279:0.86 281:0.91 282:0.88 283:0.82 284:0.95 285:0.62 290:0.79 292:0.95 297:0.36 299:0.97 300:0.55 +0 8:0.97 10:0.82 12:0.69 17:0.70 18:0.64 21:0.78 26:0.94 27:0.69 29:0.62 30:0.67 34:0.40 36:0.42 37:0.62 39:0.85 43:0.10 46:0.88 55:0.92 58:0.93 66:0.20 69:0.93 70:0.52 71:0.72 74:0.27 86:0.57 89:0.98 91:0.07 98:0.32 107:0.92 108:0.86 110:0.91 122:0.90 123:0.85 124:0.96 125:0.88 127:0.78 129:0.05 131:0.61 133:0.95 135:0.26 139:0.56 141:0.91 145:0.56 146:0.71 147:0.05 149:0.16 154:0.09 157:0.61 159:0.87 160:0.88 163:0.96 166:0.61 170:0.79 172:0.37 173:0.83 174:0.79 175:0.91 177:0.05 178:0.55 179:0.65 182:0.61 187:0.39 197:0.80 199:0.94 202:0.56 212:0.19 216:0.17 222:0.76 223:0.91 224:0.93 225:0.97 227:0.61 229:0.61 231:0.63 234:0.91 235:0.98 239:0.81 240:0.95 241:0.43 242:0.63 243:0.11 244:0.93 245:0.52 246:0.61 247:0.74 253:0.24 254:0.95 257:0.97 259:0.21 264:0.90 265:0.34 266:0.87 267:0.96 271:0.24 274:0.91 275:0.95 276:0.59 277:0.87 287:0.61 289:0.89 294:0.94 300:0.55 +1 1:0.60 8:0.84 10:0.86 11:0.56 12:0.69 17:0.61 18:0.91 21:0.13 26:0.98 27:0.42 29:0.62 30:0.38 34:0.76 36:0.51 37:0.65 39:0.85 43:0.65 45:0.88 46:0.98 56:0.97 58:0.93 66:0.92 69:0.41 70:0.56 71:0.72 74:0.49 81:0.50 83:0.54 86:0.86 89:0.95 91:0.46 98:0.90 99:0.83 101:0.87 104:0.58 107:0.95 108:0.32 110:0.96 123:0.31 125:0.88 126:0.31 127:0.47 129:0.05 131:0.61 135:0.70 139:0.82 141:0.91 144:0.57 145:0.38 146:0.70 147:0.75 149:0.73 150:0.46 154:0.55 155:0.47 157:0.97 159:0.28 160:0.88 165:0.63 166:0.61 170:0.79 172:0.79 173:0.58 174:0.79 175:0.75 177:0.88 178:0.55 179:0.46 182:0.91 187:0.39 192:0.44 197:0.83 199:0.96 201:0.57 208:0.49 212:0.85 216:0.30 222:0.76 223:0.98 224:0.93 225:1.00 227:0.61 229:0.61 231:0.86 234:0.91 235:0.22 236:0.92 239:0.86 240:0.95 241:0.64 242:0.14 243:0.92 244:0.61 246:0.61 247:0.92 253:0.40 257:0.65 259:0.21 264:0.90 265:0.90 266:0.27 267:0.19 271:0.24 274:0.91 276:0.67 277:0.69 287:0.61 289:0.93 300:0.43 +2 1:0.60 11:0.53 12:0.69 17:0.59 18:0.94 21:0.13 26:0.98 27:0.72 29:0.62 30:0.86 32:0.67 34:0.59 36:0.88 39:0.85 43:0.62 44:0.88 45:0.95 46:1.00 56:0.97 58:0.93 66:0.94 69:0.41 70:0.41 71:0.72 74:0.68 77:0.70 81:0.50 83:0.72 86:0.87 89:0.96 91:0.35 97:0.94 98:0.86 99:0.83 101:0.69 104:0.57 107:0.96 108:0.32 110:0.96 122:0.91 123:0.31 125:0.88 126:0.31 127:0.37 129:0.05 131:0.61 135:0.70 139:0.88 141:0.91 144:0.57 145:0.39 146:0.76 147:0.80 149:0.85 150:0.46 154:0.52 155:0.47 157:0.99 158:0.77 159:0.32 160:0.88 165:0.63 166:0.61 170:0.79 172:0.83 173:0.51 175:0.75 177:0.88 178:0.55 179:0.35 182:0.95 187:0.39 192:0.44 195:0.59 199:0.95 201:0.57 208:0.64 212:0.82 216:0.09 219:0.91 222:0.76 223:0.98 224:0.93 225:1.00 227:0.61 229:0.61 231:0.86 234:0.91 235:0.22 236:0.92 240:0.95 241:0.58 242:0.33 243:0.94 244:0.83 246:0.61 247:0.67 253:0.50 257:0.65 259:0.21 264:0.90 265:0.86 266:0.27 267:0.28 271:0.24 274:0.91 275:0.91 276:0.71 277:0.69 279:0.77 282:0.75 283:0.82 287:0.61 289:0.94 292:0.95 294:0.93 300:0.55 +2 10:0.94 11:0.53 12:0.69 17:0.76 18:0.68 21:0.54 26:0.97 27:0.28 29:0.62 30:0.44 34:0.40 36:0.90 37:0.82 39:0.85 43:0.10 45:0.73 46:0.94 55:0.71 56:0.97 58:0.93 66:0.93 69:0.32 70:0.66 71:0.72 74:0.37 80:0.99 81:0.29 82:0.98 86:0.69 88:0.99 89:1.00 91:0.07 98:0.95 101:0.52 107:0.92 108:0.25 110:0.92 123:0.24 125:0.88 126:0.23 127:0.67 129:0.05 131:0.61 135:0.82 139:0.66 141:0.91 144:0.57 145:0.45 146:0.88 147:0.90 149:0.18 150:0.32 154:0.55 157:0.86 159:0.45 160:0.88 166:0.61 170:0.79 172:0.82 173:0.37 174:0.88 177:0.96 178:0.55 179:0.46 182:0.76 187:0.68 195:0.70 197:0.90 199:0.96 212:0.85 216:0.54 222:0.76 223:0.97 224:0.93 225:1.00 227:0.61 229:0.61 231:0.82 234:0.91 235:0.68 239:0.90 240:0.95 241:0.53 242:0.15 243:0.93 246:0.61 247:0.94 253:0.33 254:0.99 259:0.21 264:0.90 265:0.95 266:0.27 267:0.73 271:0.24 274:0.91 275:0.96 276:0.72 287:0.61 289:0.90 294:0.98 300:0.55 +1 1:0.60 9:0.73 10:0.85 11:0.56 12:0.69 17:0.06 18:0.63 21:0.13 23:0.99 26:0.99 27:0.42 29:0.62 30:0.68 34:0.40 36:0.23 37:0.65 39:0.85 43:0.57 45:0.77 46:0.95 55:0.59 56:0.97 58:1.00 62:0.97 66:0.86 69:0.63 70:0.29 71:0.72 74:0.35 75:0.96 81:0.50 83:0.54 86:0.66 89:0.98 91:0.95 97:0.89 98:0.85 99:0.83 101:0.69 104:0.58 107:0.89 108:0.48 110:0.90 120:0.92 123:0.46 125:0.88 126:0.31 127:0.36 128:0.99 129:0.05 131:0.86 135:0.42 139:0.65 140:0.87 141:0.99 143:1.00 144:0.57 146:0.62 147:0.67 149:0.37 150:0.46 151:0.96 153:0.95 154:0.47 155:0.54 157:0.87 159:0.23 160:0.88 162:0.84 164:0.86 165:0.63 166:0.61 168:0.99 170:0.79 172:0.61 173:0.82 174:0.79 177:0.96 179:0.64 182:0.77 190:0.95 192:0.48 197:0.83 199:0.98 201:0.57 202:0.85 205:0.99 208:0.49 212:0.81 216:0.30 222:0.76 223:0.99 224:0.99 225:1.00 226:0.96 227:0.86 229:0.61 231:0.80 234:0.98 235:0.22 236:0.92 239:0.87 240:1.00 241:0.96 242:0.18 243:0.87 244:0.83 246:0.61 247:0.79 248:0.94 253:0.31 255:0.72 256:0.88 259:0.21 260:0.91 264:0.90 265:0.85 266:0.27 267:0.19 268:0.90 271:0.24 274:0.99 275:0.93 276:0.51 279:0.75 285:0.55 287:0.61 289:0.89 294:0.96 295:0.98 300:0.25 +1 1:0.58 7:0.63 8:0.72 9:0.73 10:0.94 11:0.56 12:0.69 17:0.32 18:0.64 21:0.30 23:0.99 26:0.99 27:0.21 29:0.62 30:0.13 33:0.97 34:0.66 36:0.95 37:0.74 39:0.85 43:0.57 45:0.94 46:0.93 56:0.97 58:0.99 62:0.99 66:0.19 69:0.92 70:0.91 71:0.72 74:0.33 75:0.96 80:0.99 81:0.46 83:0.54 86:0.66 89:1.00 91:0.48 97:0.89 98:0.56 99:0.83 101:0.69 104:0.84 107:0.93 108:0.84 110:0.93 120:0.78 123:0.83 124:0.90 125:0.88 126:0.31 127:0.76 128:0.99 129:0.05 131:0.86 133:0.88 135:0.24 139:0.64 140:0.80 141:0.99 143:1.00 144:0.57 146:0.91 147:0.82 149:0.68 150:0.43 151:0.96 153:0.87 154:0.13 155:0.54 157:0.89 159:0.86 160:0.88 162:0.67 164:0.65 165:0.63 166:0.61 168:0.99 170:0.79 172:0.78 173:0.82 174:0.95 177:0.88 179:0.20 182:0.79 187:0.39 190:0.95 192:0.48 197:0.95 199:0.99 201:0.56 202:0.75 205:0.98 208:0.49 212:0.68 216:0.95 222:0.76 223:0.99 224:0.98 225:1.00 226:0.98 227:0.86 229:0.61 230:0.63 231:0.85 233:0.76 234:0.98 235:0.42 236:0.92 239:0.93 240:1.00 241:0.35 242:0.16 243:0.31 244:0.61 245:0.92 246:0.61 247:0.99 248:0.91 253:0.30 254:0.99 255:0.72 256:0.73 257:0.65 259:0.21 260:0.77 264:0.90 265:0.57 266:0.80 267:0.79 268:0.78 271:0.24 274:0.98 275:0.99 276:0.91 279:0.75 283:0.82 285:0.55 287:0.61 289:0.91 291:0.97 294:0.99 295:0.99 300:0.84 +0 1:0.57 8:0.61 9:0.71 10:0.81 11:0.45 12:0.69 17:0.67 18:0.92 21:0.54 26:0.94 27:0.52 29:0.62 30:0.72 31:0.90 34:0.37 36:0.51 37:0.43 39:0.85 43:0.39 44:0.85 45:0.93 46:0.98 58:0.93 64:0.77 66:0.63 69:0.93 70:0.56 71:0.72 74:0.39 77:0.89 79:0.90 81:0.38 83:0.54 86:0.68 89:0.98 91:0.23 96:0.75 97:0.89 98:0.52 99:0.83 101:0.46 107:0.96 108:0.50 110:0.96 114:0.66 122:0.91 123:0.48 124:0.64 125:0.99 126:0.31 127:0.52 129:0.05 131:0.86 133:0.73 135:0.73 137:0.90 139:0.69 140:0.45 141:0.91 145:0.74 146:0.49 147:0.81 149:0.74 150:0.36 151:0.70 153:0.69 154:0.29 155:0.53 157:0.61 158:0.72 159:0.72 160:0.96 162:0.86 165:0.63 166:0.80 170:0.79 172:0.75 173:0.91 174:0.79 175:0.91 176:0.59 177:0.38 179:0.43 182:0.61 187:0.87 190:0.95 192:0.56 195:0.88 196:0.89 197:0.81 199:0.94 201:0.54 202:0.46 204:0.83 208:0.49 212:0.49 216:0.79 219:0.87 220:0.74 222:0.76 223:0.91 224:0.93 225:0.96 227:0.86 229:0.61 231:0.71 234:0.91 235:0.68 239:0.81 240:0.95 241:0.10 242:0.55 243:0.68 244:0.93 245:0.73 246:0.61 247:0.67 248:0.65 253:0.60 255:0.70 256:0.45 257:0.93 259:0.21 264:0.90 265:0.53 266:0.84 267:0.17 268:0.55 271:0.24 274:0.91 275:0.94 276:0.69 277:0.95 279:0.75 281:0.86 282:0.72 284:0.89 285:0.49 287:0.61 289:0.95 290:0.66 292:0.88 294:0.93 300:0.70 +0 8:0.78 12:0.69 17:0.96 18:0.94 21:0.78 26:0.94 27:0.42 29:0.62 30:0.09 34:0.42 36:0.30 37:0.35 39:0.85 43:0.06 46:0.88 55:0.92 58:0.93 66:0.19 69:0.94 70:1.00 71:0.72 74:0.25 86:0.60 89:0.97 91:0.35 98:0.21 107:0.94 108:0.96 110:0.95 122:0.95 123:0.96 124:0.99 125:0.88 127:0.68 129:0.97 131:0.61 133:0.99 135:0.26 139:0.59 141:0.91 145:0.70 146:0.45 147:0.05 149:0.24 154:0.06 157:0.61 159:0.97 160:0.88 166:0.61 170:0.79 172:0.84 173:0.60 175:0.97 177:0.05 178:0.55 179:0.16 182:0.61 191:0.78 199:0.94 202:0.69 212:0.37 216:0.10 222:0.76 223:0.91 224:0.93 225:0.96 227:0.61 229:0.61 231:0.76 234:0.91 235:0.05 240:0.95 241:0.85 242:0.76 243:0.05 244:0.93 245:0.85 246:0.61 247:0.93 253:0.23 254:0.94 257:0.97 259:0.21 264:0.90 265:0.23 266:0.99 267:0.23 271:0.24 274:0.91 275:0.96 276:0.94 277:0.98 287:0.61 289:0.92 294:0.93 300:0.98 +1 7:0.63 10:0.93 11:0.56 12:0.69 17:0.53 18:0.64 21:0.30 26:0.98 27:0.50 29:0.62 30:0.13 33:0.97 34:0.42 36:0.90 37:0.60 39:0.85 43:0.55 45:0.93 46:0.93 55:0.59 58:0.98 66:0.36 69:0.93 70:0.93 71:0.72 74:0.31 75:0.96 81:0.31 82:0.98 83:0.54 86:0.64 88:0.99 89:1.00 91:0.10 97:0.94 98:0.64 101:0.87 102:0.95 104:0.84 107:0.93 108:0.86 110:0.93 120:0.58 123:0.86 124:0.85 125:0.88 126:0.23 127:0.84 129:0.05 131:0.86 133:0.85 135:0.84 139:0.62 140:0.45 141:0.97 144:0.57 145:0.70 146:0.95 147:0.35 149:0.63 150:0.35 151:0.52 153:0.48 154:0.13 157:0.81 159:0.87 160:0.88 162:0.86 164:0.55 166:0.81 170:0.79 172:0.88 173:0.84 174:0.95 177:0.70 179:0.20 182:0.74 187:0.39 190:0.98 195:0.95 197:0.94 199:0.99 202:0.36 208:0.49 212:0.60 215:0.72 216:0.86 220:0.87 222:0.76 223:0.98 224:0.98 225:0.99 226:0.98 227:0.86 229:0.61 230:0.63 231:0.85 233:0.76 234:0.98 235:0.31 239:0.93 240:0.95 241:0.03 242:0.22 243:0.08 244:0.61 245:0.93 246:0.61 247:0.99 248:0.51 253:0.28 254:0.99 256:0.45 257:0.84 259:0.21 260:0.58 264:0.90 265:0.65 266:0.87 267:0.85 268:0.46 271:0.24 274:0.97 275:0.98 276:0.92 279:0.77 283:0.82 285:0.45 287:0.61 289:0.91 291:0.97 294:0.99 295:0.99 297:0.36 300:0.84 +1 1:0.66 7:0.70 10:0.89 11:0.56 12:0.69 17:0.34 18:0.69 21:0.30 22:0.93 26:0.99 27:0.31 29:0.62 30:0.87 32:0.75 34:0.98 36:0.28 37:0.55 39:0.85 43:0.58 44:0.93 45:0.83 46:0.95 47:0.93 56:0.97 58:0.93 64:0.77 66:0.72 69:0.75 70:0.32 71:0.72 74:0.60 77:0.70 81:0.77 82:0.99 83:0.72 86:0.77 88:0.98 89:0.97 91:0.08 98:0.21 99:0.99 101:0.69 102:0.98 106:0.81 107:0.92 108:0.59 110:0.92 114:0.86 117:0.86 122:0.67 123:0.56 124:0.40 125:0.88 126:0.54 127:0.27 129:0.05 131:0.61 133:0.36 135:0.72 139:0.83 141:0.91 144:0.57 145:0.67 146:0.27 147:0.33 149:0.34 150:0.60 154:0.63 155:0.54 157:0.94 159:0.35 160:0.88 165:0.86 166:0.93 170:0.79 172:0.45 173:0.80 174:0.79 176:0.73 177:0.96 178:0.55 179:0.72 182:0.97 187:0.39 192:0.58 195:0.69 197:0.82 199:0.96 201:0.66 204:0.81 206:0.81 208:0.64 212:0.84 215:0.72 216:0.85 222:0.76 223:0.98 224:0.93 225:1.00 227:0.61 229:0.61 230:0.73 231:0.90 232:0.97 233:0.76 234:0.91 235:0.68 236:0.97 239:0.92 240:0.95 241:0.24 242:0.33 243:0.75 245:0.47 246:0.93 247:0.60 253:0.63 254:0.93 259:0.21 262:0.93 264:0.90 265:0.23 266:0.23 267:0.28 271:0.24 274:0.91 275:0.93 276:0.17 281:0.86 282:0.83 287:0.61 289:0.94 290:0.86 294:0.98 297:0.36 300:0.33 +1 7:0.63 10:0.90 11:0.56 12:0.69 17:0.61 18:0.91 21:0.13 26:0.98 27:0.68 29:0.62 30:0.27 32:0.62 34:0.50 36:0.73 37:0.40 39:0.85 43:0.56 45:0.97 46:0.97 58:0.93 66:0.42 69:0.93 70:0.88 71:0.72 74:0.54 81:0.34 86:0.83 89:0.97 91:0.20 98:0.61 101:0.87 104:0.58 107:0.96 108:0.86 110:0.97 122:0.91 123:0.86 124:0.86 125:0.88 126:0.23 127:0.98 129:0.05 131:0.61 133:0.86 135:0.37 139:0.79 141:0.91 144:0.57 145:1.00 146:0.92 147:0.49 149:0.76 150:0.37 154:0.13 157:0.99 159:0.86 160:0.88 166:0.81 170:0.79 172:0.91 173:0.86 174:0.94 177:0.05 178:0.55 179:0.18 182:0.95 187:0.39 195:0.94 197:0.90 199:0.99 212:0.73 215:0.84 216:0.12 222:0.76 223:0.98 224:0.93 225:0.96 227:0.61 229:0.61 230:0.63 231:0.89 233:0.66 234:0.91 235:0.12 239:0.88 240:0.95 241:0.68 242:0.14 243:0.12 244:0.61 245:0.95 246:0.61 247:0.99 253:0.43 254:0.99 257:0.97 259:0.21 264:0.90 265:0.62 266:0.80 267:0.73 271:0.24 274:0.91 275:0.93 276:0.93 287:0.61 289:0.94 294:0.93 297:0.36 300:0.84 +1 7:0.63 10:0.90 11:0.56 12:0.69 17:0.61 18:0.91 21:0.13 26:0.98 27:0.68 29:0.62 30:0.27 32:0.62 34:0.50 36:0.73 37:0.40 39:0.85 43:0.56 45:0.97 46:0.97 58:0.93 66:0.42 69:0.93 70:0.88 71:0.72 74:0.54 81:0.34 86:0.83 89:0.97 91:0.18 98:0.61 101:0.87 104:0.58 107:0.96 108:0.86 110:0.97 122:0.91 123:0.86 124:0.86 125:0.88 126:0.23 127:0.98 129:0.05 131:0.61 133:0.86 135:0.37 139:0.79 141:0.91 144:0.57 145:1.00 146:0.92 147:0.49 149:0.76 150:0.37 154:0.13 157:0.99 159:0.86 160:0.88 166:0.81 170:0.79 172:0.91 173:0.86 174:0.94 177:0.05 178:0.55 179:0.18 182:0.95 187:0.39 195:0.94 197:0.90 199:0.99 212:0.73 215:0.84 216:0.12 222:0.76 223:0.98 224:0.93 225:0.96 227:0.61 229:0.61 230:0.63 231:0.89 233:0.66 234:0.91 235:0.12 239:0.88 240:0.95 241:0.68 242:0.14 243:0.12 244:0.61 245:0.95 246:0.61 247:0.99 253:0.43 254:0.99 257:0.97 259:0.21 264:0.90 265:0.62 266:0.80 267:0.73 271:0.24 274:0.91 275:0.93 276:0.93 287:0.61 289:0.94 294:0.93 297:0.36 300:0.84 +1 7:0.63 8:0.76 10:0.90 11:0.64 12:0.69 17:0.67 18:0.70 21:0.22 26:0.98 27:0.72 29:0.62 30:0.44 32:0.62 34:0.67 36:0.22 37:0.40 39:0.85 43:0.43 44:0.85 45:0.85 46:0.94 55:0.71 58:0.93 66:0.85 69:0.15 70:0.75 71:0.72 74:0.38 80:0.99 81:0.32 82:0.98 83:0.54 86:0.66 88:0.99 89:0.98 91:0.74 98:0.89 101:0.87 102:0.95 104:0.54 107:0.91 108:0.12 110:0.92 123:0.12 124:0.37 125:0.88 126:0.23 127:0.95 129:0.05 131:0.61 133:0.36 135:0.18 139:0.64 141:0.91 144:0.57 145:1.00 146:0.83 147:0.86 149:0.57 150:0.36 154:0.33 155:0.49 157:0.86 159:0.48 160:0.88 166:0.81 170:0.79 172:0.76 173:0.25 174:0.88 177:0.96 178:0.55 179:0.58 182:0.76 192:0.45 193:0.96 195:0.65 197:0.90 199:0.97 204:0.79 208:0.49 212:0.84 215:0.79 216:0.04 222:0.76 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 230:0.63 231:0.84 233:0.66 234:0.91 235:0.22 239:0.91 240:0.95 241:0.89 242:0.41 243:0.86 244:0.83 245:0.60 246:0.61 247:0.86 253:0.33 259:0.21 264:0.90 265:0.89 266:0.47 267:0.36 271:0.24 274:0.91 275:0.94 276:0.65 283:0.82 287:0.61 289:0.90 294:0.97 297:0.36 300:0.70 +0 7:0.81 12:0.51 17:0.59 25:0.88 27:0.72 28:0.46 30:0.76 43:0.23 55:0.52 66:0.36 69:0.88 70:0.15 81:0.90 91:0.57 98:0.13 104:0.58 108:0.85 120:0.80 123:0.85 124:0.52 126:0.54 127:0.24 129:0.59 133:0.47 140:0.45 145:0.69 146:0.05 147:0.05 149:0.17 150:0.81 151:0.73 153:0.78 154:0.16 159:0.39 161:0.77 162:0.78 163:0.98 164:0.70 167:0.89 172:0.10 173:0.91 177:0.05 179:0.97 181:0.54 202:0.59 212:0.95 215:0.96 216:0.06 230:0.87 233:0.76 235:0.02 241:0.85 242:0.82 243:0.34 245:0.37 247:0.12 248:0.73 256:0.58 260:0.81 265:0.14 266:0.12 268:0.64 271:0.58 276:0.13 283:0.82 285:0.62 297:0.36 300:0.13 +2 7:0.81 12:0.51 17:0.40 21:0.05 27:0.53 28:0.46 30:0.58 34:0.88 36:0.46 37:0.49 43:0.52 55:0.84 66:0.80 69:0.05 70:0.60 81:0.89 91:0.15 98:0.82 104:0.86 106:0.81 108:0.05 120:0.69 123:0.05 126:0.54 127:0.30 129:0.05 135:0.21 140:0.45 146:0.76 147:0.80 149:0.50 150:0.78 151:0.66 153:0.48 154:0.41 159:0.32 161:0.77 162:0.86 163:0.81 164:0.57 167:0.57 172:0.45 173:0.17 177:0.05 179:0.78 181:0.54 187:0.39 202:0.36 206:0.81 212:0.37 215:0.91 216:0.87 230:0.87 233:0.76 235:0.05 241:0.24 242:0.82 243:0.82 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.82 266:0.38 267:0.91 268:0.46 271:0.58 276:0.37 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.90 8:0.97 12:0.51 17:0.45 20:0.93 21:0.54 27:0.10 28:0.46 30:0.76 32:0.80 34:0.58 36:0.56 37:0.90 43:0.34 55:0.84 66:0.36 69:0.65 70:0.15 78:0.76 81:0.76 91:0.93 98:0.37 100:0.80 104:0.77 108:0.50 111:0.76 120:0.74 123:0.48 124:0.52 126:0.54 127:0.42 129:0.59 133:0.47 135:0.54 140:0.45 145:0.49 146:0.31 147:0.38 149:0.25 150:0.57 151:0.60 152:0.86 153:0.93 154:0.25 159:0.28 161:0.77 162:0.67 163:0.96 164:0.90 167:0.91 169:0.78 172:0.10 173:0.81 177:0.05 179:0.99 181:0.54 186:0.77 189:0.91 191:0.96 195:0.60 202:0.85 212:0.95 215:0.58 216:0.38 220:0.62 235:0.60 238:0.93 241:0.93 242:0.82 243:0.51 245:0.37 247:0.12 248:0.67 256:0.90 259:0.21 260:0.75 261:0.81 265:0.39 266:0.12 267:0.36 268:0.87 271:0.58 276:0.16 283:0.60 285:0.62 297:0.36 300:0.13 +1 6:0.90 7:0.81 8:0.93 12:0.51 17:0.20 20:0.92 21:0.78 27:0.13 28:0.46 30:0.45 34:0.90 36:0.45 37:0.70 43:0.33 55:0.84 66:0.77 69:0.68 70:0.33 78:0.75 91:0.99 98:0.88 100:0.79 104:0.78 108:0.52 111:0.75 120:0.83 123:0.50 127:0.43 129:0.05 135:0.26 140:0.45 145:0.83 146:0.75 147:0.79 149:0.39 151:0.56 152:0.85 153:0.72 154:0.25 159:0.32 161:0.77 162:0.53 163:0.86 164:0.98 167:0.92 169:0.77 172:0.37 173:0.81 177:0.05 179:0.90 181:0.54 186:0.76 189:0.92 191:0.96 202:0.97 212:0.52 216:0.65 220:0.74 230:0.87 233:0.76 235:0.88 238:0.92 241:0.99 242:0.82 243:0.79 247:0.12 248:0.75 256:0.97 259:0.21 260:0.83 261:0.79 265:0.88 266:0.23 267:0.88 268:0.97 271:0.58 276:0.32 283:0.60 285:0.62 300:0.25 +1 12:0.51 17:0.38 21:0.68 27:0.45 28:0.46 30:0.30 32:0.80 34:0.81 36:0.23 37:0.50 43:0.32 55:0.84 66:0.51 69:0.70 70:0.80 81:0.67 91:0.35 98:0.41 108:0.54 123:0.51 124:0.65 126:0.54 127:0.40 129:0.81 133:0.67 135:0.83 145:0.47 146:0.57 147:0.63 149:0.49 150:0.49 154:0.24 159:0.56 161:0.77 163:0.90 167:0.57 172:0.41 173:0.65 177:0.05 178:0.55 179:0.74 181:0.54 187:0.68 195:0.79 212:0.23 215:0.49 216:0.77 235:0.80 241:0.27 242:0.82 243:0.61 245:0.55 247:0.12 259:0.21 265:0.43 266:0.71 267:0.23 271:0.58 276:0.41 297:0.36 300:0.55 +1 8:0.93 12:0.51 17:0.12 21:0.13 27:0.38 28:0.46 34:0.31 36:0.59 37:0.90 81:0.87 91:0.89 104:0.76 120:0.85 126:0.54 135:0.49 140:0.45 150:0.74 151:0.76 153:0.87 161:0.77 162:0.74 164:0.86 167:0.91 175:0.75 181:0.54 191:0.81 202:0.78 215:0.84 216:0.19 235:0.12 241:0.98 244:0.61 248:0.79 256:0.81 257:0.65 259:0.21 260:0.86 267:0.19 268:0.82 271:0.58 277:0.69 285:0.62 297:0.36 +0 12:0.51 17:0.34 21:0.22 25:0.88 27:0.72 28:0.46 43:0.44 66:0.36 69:0.46 81:0.85 91:0.85 98:0.16 104:0.58 108:0.35 120:0.80 123:0.34 126:0.54 127:0.09 129:0.05 140:0.80 146:0.05 147:0.05 149:0.15 150:0.68 151:0.66 153:0.48 154:0.33 159:0.05 161:0.77 162:0.58 164:0.83 167:0.90 172:0.05 173:0.94 177:0.05 178:0.42 181:0.54 202:0.79 215:0.79 216:0.21 220:0.82 235:0.22 241:0.88 243:0.51 248:0.72 256:0.79 260:0.80 265:0.17 268:0.79 271:0.58 283:0.82 285:0.62 297:0.36 +2 7:0.81 12:0.51 17:0.75 21:0.40 25:0.88 27:0.57 28:0.46 30:0.45 32:0.80 34:0.21 36:0.47 37:0.44 43:0.24 55:0.52 66:0.77 69:0.86 70:0.33 81:0.81 91:0.61 98:0.57 104:0.54 108:0.73 120:0.53 123:0.71 126:0.54 127:0.17 129:0.05 135:0.20 140:0.45 145:0.58 146:0.55 147:0.61 149:0.32 150:0.61 151:0.46 153:0.48 154:0.17 159:0.20 161:0.77 162:0.86 164:0.57 167:0.88 172:0.37 173:0.96 177:0.05 179:0.61 181:0.54 187:0.21 195:0.64 202:0.36 212:0.23 215:0.67 216:0.55 220:0.91 230:0.65 233:0.63 235:0.42 241:0.81 242:0.82 243:0.79 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.58 266:0.38 267:0.82 268:0.46 271:0.58 276:0.32 285:0.62 297:0.36 300:0.25 +1 8:0.69 12:0.51 17:0.15 20:0.77 21:0.78 27:0.33 28:0.46 30:0.38 32:0.80 34:0.66 36:0.92 37:0.46 43:0.51 55:0.96 66:0.20 69:0.27 70:0.63 78:0.75 91:0.92 98:0.32 100:0.73 104:0.73 108:0.21 111:0.74 120:0.76 123:0.20 124:0.85 127:0.94 129:0.93 133:0.84 135:0.61 140:0.45 145:0.80 146:0.41 147:0.48 149:0.49 151:0.60 152:0.75 153:0.92 154:0.40 159:0.60 161:0.77 162:0.57 163:0.81 164:0.94 167:0.91 169:0.72 172:0.21 173:0.26 177:0.05 179:0.86 181:0.54 186:0.76 191:0.82 195:0.77 202:0.92 212:0.28 216:0.38 220:0.82 235:0.80 241:0.92 242:0.82 243:0.40 245:0.50 247:0.12 248:0.68 256:0.91 259:0.21 260:0.77 261:0.74 265:0.35 266:0.63 267:0.96 268:0.92 271:0.58 276:0.42 283:0.60 285:0.62 300:0.43 +3 6:0.98 8:0.97 12:0.51 17:0.28 20:0.97 21:0.78 25:0.88 27:0.16 28:0.46 34:0.34 36:0.38 37:0.77 78:0.89 91:0.94 100:0.99 104:0.58 111:0.98 120:0.97 135:0.20 140:0.45 151:0.84 152:0.98 153:0.97 161:0.77 162:0.71 164:0.95 167:0.90 169:0.97 175:0.75 181:0.54 186:0.89 189:0.99 191:0.91 202:0.92 216:0.52 235:0.05 238:1.00 241:0.88 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.97 261:1.00 267:0.79 268:0.94 271:0.58 277:0.69 283:0.60 285:0.62 +2 6:0.83 8:0.71 12:0.51 17:0.84 20:0.81 21:0.13 25:0.88 27:0.72 28:0.46 30:0.91 34:0.40 36:0.88 43:0.52 55:0.71 66:0.77 69:0.07 70:0.19 78:0.75 81:0.87 91:0.86 98:0.98 100:0.79 104:0.58 108:0.06 111:0.75 120:0.77 123:0.06 126:0.54 127:0.82 129:0.05 135:0.49 140:0.45 146:0.59 147:0.65 149:0.44 150:0.74 151:0.73 152:0.78 153:0.78 154:0.41 159:0.22 161:0.77 162:0.85 164:0.82 167:0.90 169:0.77 172:0.37 173:0.40 177:0.05 179:0.93 181:0.54 186:0.76 189:0.85 202:0.70 212:0.73 215:0.84 216:0.15 220:0.74 235:0.12 238:0.90 241:0.89 242:0.82 243:0.79 247:0.12 248:0.69 256:0.77 259:0.21 260:0.78 261:0.77 265:0.98 266:0.23 267:0.92 268:0.77 271:0.58 276:0.32 283:0.82 285:0.62 297:0.36 300:0.18 +2 6:0.88 7:0.81 8:0.77 12:0.51 17:0.22 20:0.95 21:0.30 27:0.21 28:0.46 30:0.45 34:0.75 36:0.68 37:0.74 43:0.52 55:0.52 66:0.82 69:0.10 70:0.33 78:0.75 81:0.84 91:0.65 98:0.72 100:0.80 104:0.84 106:0.81 108:0.09 111:0.75 120:0.84 123:0.09 126:0.54 127:0.22 129:0.05 135:0.21 140:0.45 146:0.63 147:0.68 149:0.56 150:0.64 151:0.79 152:0.94 153:0.90 154:0.41 159:0.24 161:0.77 162:0.59 164:0.85 167:0.74 169:0.78 172:0.48 173:0.26 177:0.05 179:0.63 181:0.54 186:0.75 187:0.39 189:0.90 202:0.80 206:0.81 212:0.91 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.69 242:0.82 243:0.83 247:0.12 248:0.76 256:0.79 259:0.21 260:0.85 261:0.81 265:0.72 266:0.17 267:0.79 268:0.81 271:0.58 276:0.38 283:0.82 285:0.62 297:0.36 300:0.25 +2 7:0.81 12:0.51 17:0.40 20:0.75 21:0.74 27:0.56 28:0.46 30:0.91 32:0.80 34:0.68 36:0.32 37:0.60 43:0.39 55:0.45 66:0.52 69:0.59 70:0.27 78:0.72 81:0.78 91:0.38 98:0.22 100:0.73 104:0.82 106:0.81 108:0.81 111:0.72 120:0.53 123:0.80 124:0.64 126:0.54 127:0.48 129:0.81 133:0.67 135:0.76 140:0.45 145:0.82 146:0.05 147:0.05 149:0.48 150:0.58 151:0.46 152:0.74 153:0.72 154:0.29 159:0.50 161:0.77 162:0.86 164:0.69 167:0.60 169:0.72 172:0.48 173:0.58 177:0.05 179:0.70 181:0.54 186:0.73 187:0.39 195:0.74 202:0.53 206:0.81 212:0.75 215:0.46 216:0.76 220:0.93 230:0.87 233:0.76 235:0.95 241:0.37 242:0.82 243:0.13 245:0.60 247:0.12 248:0.46 256:0.58 259:0.21 260:0.53 261:0.73 265:0.24 266:0.38 267:0.65 268:0.62 271:0.58 276:0.38 283:0.82 285:0.62 297:0.36 300:0.33 +1 7:0.81 12:0.51 17:0.70 21:0.40 25:0.88 27:0.72 28:0.46 30:0.45 32:0.80 34:0.21 36:0.44 43:0.24 55:0.52 66:0.77 69:0.86 70:0.33 81:0.81 91:0.58 98:0.57 104:0.58 108:0.73 123:0.71 126:0.54 127:0.17 129:0.05 135:0.20 145:0.58 146:0.55 147:0.61 149:0.32 150:0.61 154:0.17 159:0.20 161:0.77 167:0.88 172:0.37 173:0.96 177:0.05 178:0.55 179:0.61 181:0.54 187:0.21 195:0.64 202:0.50 212:0.23 215:0.67 216:0.50 230:0.65 233:0.63 235:0.42 241:0.82 242:0.82 243:0.79 247:0.12 259:0.21 265:0.58 266:0.38 267:0.79 271:0.58 276:0.32 297:0.36 300:0.25 +1 8:0.89 12:0.51 17:0.05 20:0.80 21:0.47 27:0.72 28:0.46 32:0.80 34:0.89 36:0.60 37:0.77 78:0.74 81:0.79 91:0.85 100:0.73 104:0.57 111:0.73 120:0.60 126:0.54 135:0.70 140:0.45 145:0.87 150:0.59 151:0.54 152:0.77 153:0.48 161:0.77 162:0.69 164:0.73 167:0.90 169:0.72 175:0.75 181:0.54 186:0.75 195:0.54 202:0.64 215:0.63 216:0.04 220:0.93 235:0.52 241:0.91 244:0.61 248:0.54 256:0.64 257:0.65 259:0.21 260:0.61 261:0.74 267:0.28 268:0.66 271:0.58 277:0.69 285:0.62 297:0.36 +2 8:0.91 12:0.51 17:0.69 21:0.22 27:0.52 28:0.46 30:0.21 34:0.90 36:0.53 37:0.73 43:0.46 55:0.71 66:0.77 69:0.44 70:0.89 81:0.85 91:0.44 98:0.57 104:0.93 106:0.81 108:0.34 120:0.84 123:0.32 124:0.41 126:0.54 127:0.34 129:0.81 133:0.67 135:0.66 140:0.45 145:0.61 146:0.81 147:0.84 149:0.68 150:0.68 151:0.74 153:0.82 154:0.35 159:0.64 161:0.77 162:0.67 163:0.90 164:0.78 167:0.67 172:0.68 173:0.28 177:0.05 179:0.50 181:0.54 187:0.39 202:0.70 206:0.81 212:0.35 215:0.79 216:0.34 235:0.22 241:0.56 242:0.82 243:0.79 245:0.56 247:0.12 248:0.76 256:0.71 259:0.21 260:0.85 265:0.58 266:0.47 267:0.65 268:0.73 271:0.58 276:0.56 283:0.82 285:0.62 297:0.36 300:0.70 +0 12:0.51 17:0.45 21:0.78 25:0.88 27:0.72 28:0.46 30:0.76 43:0.23 55:0.45 66:0.72 69:0.88 70:0.22 78:0.78 91:0.58 98:0.44 104:0.73 108:0.77 111:0.78 120:0.78 123:0.75 127:0.14 129:0.05 140:0.96 145:0.45 146:0.46 147:0.53 149:0.25 151:0.66 153:0.48 154:0.16 159:0.17 161:0.77 162:0.48 163:0.75 164:0.72 167:0.89 169:0.81 172:0.27 173:0.97 177:0.05 178:0.54 179:0.59 181:0.54 202:0.78 212:0.42 216:0.12 235:0.31 241:0.86 242:0.82 243:0.75 247:0.12 248:0.71 256:0.64 260:0.79 265:0.46 266:0.23 268:0.65 271:0.58 276:0.17 285:0.62 300:0.18 +1 12:0.51 17:0.49 27:0.03 28:0.46 30:0.11 34:0.44 36:0.19 37:0.91 43:0.40 55:0.92 66:0.16 69:0.52 70:0.97 81:0.96 91:0.48 98:0.40 104:0.78 106:0.81 108:0.40 120:0.61 123:0.80 124:0.84 126:0.54 127:0.46 129:0.95 133:0.87 135:0.60 140:0.45 146:0.60 147:0.66 149:0.63 150:0.93 151:0.56 153:0.72 154:0.30 159:0.79 161:0.77 162:0.56 164:0.64 167:0.69 172:0.51 173:0.29 177:0.05 179:0.56 181:0.54 187:0.21 202:0.58 206:0.81 212:0.18 215:0.96 216:0.86 220:0.62 235:0.05 241:0.61 242:0.82 243:0.51 245:0.61 247:0.12 248:0.55 256:0.45 259:0.21 260:0.62 265:0.32 266:0.87 267:0.52 268:0.57 271:0.58 276:0.59 285:0.62 297:0.36 300:0.84 +2 6:0.98 8:0.97 12:0.51 17:0.28 20:0.97 21:0.78 25:0.88 27:0.16 28:0.46 34:0.34 36:0.38 37:0.77 78:0.89 91:0.94 100:0.99 104:0.58 111:0.98 120:0.97 135:0.20 140:0.45 151:0.84 152:0.98 153:0.97 161:0.77 162:0.71 164:0.95 167:0.90 169:0.97 175:0.75 181:0.54 186:0.89 189:0.99 191:0.91 202:0.92 216:0.52 235:0.05 238:1.00 241:0.88 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.97 261:1.00 267:0.79 268:0.94 271:0.58 277:0.69 283:0.60 285:0.62 +3 7:0.81 12:0.51 17:0.36 21:0.74 25:0.88 27:0.16 28:0.46 30:0.45 32:0.80 34:0.95 36:0.45 37:0.77 43:0.51 55:0.45 66:0.49 69:0.30 70:0.25 81:0.61 91:0.87 98:0.32 104:0.58 108:0.53 120:0.92 123:0.51 124:0.48 126:0.54 127:0.27 129:0.59 133:0.47 135:0.34 140:0.45 145:0.59 146:0.05 147:0.05 149:0.32 150:0.45 151:0.81 153:0.94 154:0.40 159:0.19 161:0.77 162:0.78 163:0.81 164:0.90 167:0.78 172:0.16 173:0.54 177:0.05 179:0.94 181:0.54 187:0.98 195:0.58 202:0.84 212:0.61 215:0.46 216:0.52 230:0.87 233:0.76 235:0.88 241:0.73 242:0.82 243:0.29 245:0.39 247:0.12 248:0.89 256:0.87 259:0.21 260:0.92 265:0.34 266:0.17 267:0.23 268:0.88 271:0.58 276:0.16 285:0.62 297:0.36 300:0.18 +1 7:0.81 8:0.95 12:0.51 17:0.26 20:0.86 21:0.47 25:0.88 27:0.24 28:0.46 34:0.62 36:0.44 37:0.88 43:0.28 66:0.36 69:0.78 78:0.74 81:0.79 91:0.98 98:0.08 100:0.78 104:0.76 108:0.62 111:0.74 120:0.84 123:0.60 126:0.54 127:0.06 129:0.05 135:0.34 140:0.45 145:0.52 146:0.05 147:0.05 149:0.12 150:0.59 151:0.63 152:0.81 153:0.99 154:0.20 159:0.05 161:0.77 162:0.59 164:0.97 167:0.90 169:0.75 172:0.05 173:0.87 177:0.05 181:0.54 186:0.75 191:0.86 202:0.95 215:0.63 216:0.50 220:0.62 230:0.87 233:0.76 235:0.52 241:0.91 243:0.51 248:0.76 256:0.96 259:0.21 260:0.85 261:0.76 265:0.09 267:0.73 268:0.96 271:0.58 283:0.60 285:0.62 297:0.36 +2 8:0.88 12:0.51 17:0.72 20:0.74 21:0.78 27:0.43 28:0.46 34:0.58 36:0.22 78:0.72 91:0.89 100:0.92 104:0.58 111:0.84 120:0.76 135:0.78 140:0.45 151:0.72 152:0.74 153:0.77 161:0.77 162:0.86 164:0.69 167:0.89 169:0.90 175:0.75 181:0.54 186:0.73 202:0.54 216:0.18 241:0.86 244:0.61 248:0.68 256:0.58 257:0.65 259:0.21 260:0.77 261:0.75 267:0.52 268:0.63 271:0.58 277:0.69 285:0.62 +1 7:0.81 8:0.95 12:0.51 17:0.45 21:0.54 27:0.67 28:0.46 32:0.80 34:0.40 36:0.87 37:0.96 43:0.52 66:0.36 69:0.17 81:0.76 91:0.84 98:0.14 104:0.58 108:0.14 120:0.77 123:0.13 126:0.54 127:0.09 129:0.05 135:0.58 140:0.45 145:0.56 146:0.05 147:0.05 149:0.16 150:0.57 151:0.71 153:0.72 154:0.41 159:0.05 161:0.77 162:0.81 164:0.83 167:0.89 172:0.05 173:0.66 177:0.05 181:0.54 191:0.76 195:0.54 202:0.73 215:0.58 216:0.13 220:0.62 230:0.87 233:0.76 235:0.60 241:0.85 243:0.51 248:0.69 256:0.78 259:0.21 260:0.78 265:0.15 267:0.23 268:0.78 271:0.58 285:0.62 297:0.36 +0 12:0.06 17:0.69 21:0.78 27:0.69 30:0.58 34:0.33 36:0.63 37:0.64 39:0.31 43:0.07 55:0.98 66:0.10 69:0.98 70:0.33 74:0.26 91:0.01 98:0.08 108:0.97 123:0.97 124:0.91 127:0.32 129:0.96 131:0.61 133:0.90 135:0.23 145:0.52 146:0.28 147:0.34 149:0.11 154:0.07 157:0.61 159:0.98 163:0.87 166:0.61 172:0.10 173:0.75 175:0.75 177:0.05 178:0.55 179:0.79 182:0.61 187:0.68 202:0.36 212:0.16 216:0.24 222:0.35 227:0.61 229:0.61 231:0.76 235:0.12 241:0.01 242:0.80 243:0.29 244:0.61 245:0.43 246:0.61 247:0.30 253:0.24 257:0.65 259:0.21 265:0.08 266:0.54 267:0.52 276:0.31 277:0.69 287:0.61 300:0.25 +0 12:0.06 17:0.43 21:0.78 27:0.06 30:0.95 34:0.74 36:0.43 37:0.85 39:0.31 43:0.36 55:0.99 66:0.20 69:0.49 74:0.41 98:0.05 108:0.38 123:0.36 124:0.61 127:0.12 129:0.59 131:0.61 133:0.47 135:0.88 146:0.05 147:0.05 149:0.16 154:0.27 157:0.61 159:0.76 166:0.61 172:0.05 173:0.07 175:0.75 177:0.05 178:0.55 179:0.84 182:0.61 187:0.68 216:0.84 222:0.35 227:0.61 229:0.61 231:0.62 243:0.40 244:0.61 245:0.36 246:0.61 253:0.36 257:0.65 259:0.21 265:0.05 267:0.36 277:0.69 287:0.61 300:0.08 +0 8:0.59 12:0.06 17:0.59 21:0.78 27:0.14 30:0.91 34:0.37 36:0.60 37:0.71 39:0.31 43:0.07 55:1.00 66:0.10 69:0.99 70:0.13 74:0.30 98:0.05 108:0.98 122:0.51 123:0.98 124:0.82 127:0.21 129:0.89 131:0.61 133:0.78 135:0.69 146:0.05 147:0.05 149:0.06 154:0.06 157:0.61 159:0.99 163:1.00 166:0.61 172:0.05 173:0.65 175:0.75 177:0.05 178:0.55 179:0.91 182:0.61 187:0.68 191:0.73 202:0.71 212:0.95 216:0.68 222:0.35 227:0.61 229:0.61 231:0.62 241:0.27 242:0.63 243:0.29 244:0.61 245:0.37 246:0.61 247:0.30 253:0.27 257:0.65 259:0.21 265:0.05 266:0.12 267:0.28 276:0.22 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.22 21:0.78 27:0.42 30:0.95 34:0.61 36:0.72 37:0.60 39:0.31 43:0.08 55:1.00 66:0.20 69:0.98 74:0.26 98:0.05 108:0.96 123:0.96 124:0.61 127:0.52 129:0.59 131:0.61 133:0.47 135:0.37 146:0.05 147:0.05 149:0.06 154:0.07 157:0.61 159:0.98 166:0.61 172:0.05 173:0.73 175:0.75 177:0.05 178:0.55 179:1.00 182:0.61 187:0.68 216:0.58 222:0.35 227:0.61 229:0.61 231:0.62 241:0.03 243:0.40 244:0.61 245:0.36 246:0.61 253:0.24 257:0.65 259:0.21 265:0.05 267:0.73 277:0.69 287:0.61 300:0.08 +0 8:0.64 12:0.06 17:0.43 21:0.78 27:0.16 30:0.76 34:0.34 36:0.60 37:0.84 39:0.31 43:0.05 55:0.42 66:0.36 69:1.00 70:0.15 74:0.27 91:0.06 98:0.05 108:1.00 122:0.51 123:1.00 124:0.52 127:0.18 129:0.59 131:0.61 133:0.47 135:0.54 146:0.05 147:0.05 149:0.05 154:0.05 157:0.61 159:0.96 163:1.00 166:0.61 172:0.10 173:0.92 175:0.75 177:0.05 178:0.55 179:0.93 182:0.61 187:0.87 202:0.46 212:0.95 216:0.62 222:0.35 227:0.61 229:0.61 231:0.63 235:0.05 241:0.05 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.25 257:0.65 259:0.21 265:0.05 266:0.12 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.28 21:0.78 27:0.69 30:0.95 34:0.56 36:0.58 37:0.27 39:0.31 43:0.10 55:1.00 66:0.20 69:0.97 74:0.28 91:0.11 98:0.05 108:0.94 123:0.93 124:0.61 127:0.17 129:0.59 131:0.61 133:0.47 135:0.19 146:0.05 147:0.05 149:0.07 154:0.09 157:0.61 159:0.91 166:0.61 172:0.05 173:0.67 175:0.75 177:0.05 178:0.55 179:0.97 182:0.61 187:0.39 202:0.62 216:0.51 222:0.35 227:0.61 229:0.61 231:0.63 235:0.22 241:0.02 243:0.40 244:0.61 245:0.36 246:0.61 253:0.26 257:0.65 259:0.21 265:0.05 267:0.65 277:0.69 287:0.61 300:0.08 +0 12:0.06 17:0.62 21:0.40 27:0.39 30:0.62 34:0.42 36:0.53 37:0.26 39:0.31 43:0.30 55:1.00 66:0.16 69:0.68 70:0.34 74:0.49 81:0.58 91:0.01 98:0.07 108:0.52 114:0.55 117:0.86 123:0.50 124:0.81 126:0.32 127:0.48 129:0.89 131:0.61 132:0.97 133:0.78 135:0.83 145:0.47 146:0.27 147:0.33 149:0.20 150:0.43 154:0.22 157:0.61 159:0.99 163:0.90 166:0.94 172:0.10 173:0.08 175:0.75 176:0.53 177:0.05 178:0.55 179:0.93 182:0.61 187:0.95 212:0.30 216:0.73 222:0.35 227:0.61 229:0.61 231:0.95 235:0.42 242:0.63 243:0.37 244:0.61 245:0.42 246:0.61 247:0.35 253:0.42 257:0.65 265:0.07 266:0.27 267:0.19 276:0.24 277:0.69 287:0.61 290:0.55 300:0.25 +0 12:0.06 17:0.83 21:0.78 27:0.66 30:0.58 34:0.89 36:0.44 37:0.32 39:0.31 43:0.10 55:0.52 66:0.18 69:0.97 70:0.44 74:0.30 91:0.03 98:0.09 108:0.95 123:0.93 124:0.68 127:0.12 129:0.81 131:0.61 133:0.67 135:0.66 146:0.30 147:0.36 149:0.18 154:0.09 157:0.61 159:0.68 166:0.61 172:0.27 173:0.69 175:0.75 177:0.05 178:0.55 179:0.19 182:0.61 187:0.21 212:0.37 216:0.40 222:0.35 227:0.61 229:0.61 231:0.82 235:0.97 241:0.05 242:0.80 243:0.39 244:0.61 245:0.50 246:0.61 247:0.30 253:0.27 257:0.65 259:0.21 265:0.09 266:0.47 267:0.23 276:0.30 277:0.69 287:0.61 300:0.33 +1 12:0.06 17:0.49 21:0.78 27:0.55 30:0.95 34:0.28 36:0.63 37:0.73 39:0.31 43:0.10 55:0.84 66:1.00 69:0.97 70:0.13 74:0.25 98:0.99 108:0.94 123:0.94 127:0.86 129:0.05 131:0.61 135:0.21 146:1.00 147:1.00 149:0.77 154:0.08 157:0.61 159:1.00 163:0.94 166:0.61 172:1.00 173:0.43 177:0.38 178:0.55 179:0.08 182:0.61 187:0.39 212:0.94 216:0.41 222:0.35 227:0.61 229:0.61 231:0.95 241:0.21 242:0.82 243:1.00 244:0.61 246:0.61 247:0.39 253:0.23 265:0.99 266:0.47 267:0.23 276:1.00 287:0.61 300:0.13 +0 12:0.06 17:0.59 21:0.78 27:0.06 30:0.76 34:0.74 36:0.44 37:0.85 39:0.31 43:0.24 55:0.52 66:0.36 69:0.84 70:0.15 74:0.37 98:0.06 108:0.92 122:0.55 123:0.91 124:0.52 127:0.11 129:0.59 131:0.61 133:0.47 135:0.88 146:0.05 147:0.05 149:0.15 154:0.17 157:0.61 159:0.72 163:0.99 166:0.61 172:0.10 173:0.25 175:0.75 177:0.05 178:0.55 179:0.53 182:0.61 187:0.68 212:0.95 216:0.84 222:0.35 227:0.61 229:0.61 231:0.64 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.33 257:0.65 259:0.21 265:0.06 266:0.12 267:0.36 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.47 21:0.78 27:0.06 30:0.76 34:0.74 36:0.44 37:0.85 39:0.31 43:0.21 55:0.52 66:0.36 69:0.91 70:0.15 74:0.34 98:0.06 108:0.92 122:0.55 123:0.92 124:0.52 127:0.11 129:0.59 131:0.61 133:0.47 135:0.84 146:0.05 147:0.05 149:0.13 154:0.14 157:0.61 159:0.64 163:1.00 166:0.61 172:0.10 173:0.39 175:0.75 177:0.05 178:0.55 179:0.44 182:0.61 187:0.68 212:0.95 216:0.84 222:0.35 227:0.61 229:0.61 231:0.64 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.31 257:0.65 259:0.21 265:0.06 266:0.12 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.64 21:0.78 27:0.55 30:0.87 34:0.97 36:0.45 37:0.53 39:0.31 43:0.22 55:0.59 66:0.20 69:0.89 70:0.27 74:0.32 91:0.14 98:0.20 108:0.89 123:0.76 124:0.70 127:0.30 129:0.81 131:0.61 133:0.67 135:0.69 145:0.91 146:0.34 147:0.41 149:0.27 154:0.15 157:0.61 159:0.62 166:0.61 172:0.21 173:0.84 175:0.75 177:0.05 178:0.55 179:0.81 182:0.61 187:0.95 212:0.19 216:0.30 222:0.35 227:0.61 229:0.61 231:0.95 235:0.88 241:0.07 242:0.79 243:0.40 244:0.61 245:0.48 246:0.61 247:0.30 253:0.29 257:0.65 265:0.20 266:0.47 267:0.23 276:0.28 277:0.69 287:0.61 300:0.25 +0 12:0.06 17:0.51 21:0.78 27:0.06 30:0.95 34:0.74 36:0.42 37:0.85 39:0.31 43:0.13 55:0.71 66:0.54 69:0.95 74:0.32 98:0.08 108:0.91 123:0.90 127:0.06 129:0.05 131:0.61 135:0.88 146:0.28 147:0.34 149:0.09 154:0.10 157:0.61 159:0.12 166:0.61 172:0.10 173:0.58 177:0.38 178:0.55 179:0.09 182:0.61 187:0.68 216:0.84 222:0.35 227:0.61 229:0.61 231:0.63 243:0.63 244:0.61 246:0.61 253:0.29 259:0.21 265:0.09 267:0.36 287:0.61 300:0.08 +0 12:0.06 17:0.81 21:0.78 27:0.17 30:0.95 34:0.63 36:0.52 37:0.95 39:0.31 43:0.29 55:1.00 66:0.20 69:0.71 74:0.40 91:0.01 98:0.05 108:0.54 123:0.52 124:0.61 127:0.13 129:0.59 131:0.61 133:0.47 135:0.70 146:0.05 147:0.05 149:0.14 154:0.21 157:0.61 159:0.87 166:0.61 172:0.05 173:0.11 175:0.75 177:0.05 178:0.55 179:0.89 182:0.61 187:0.39 202:0.53 216:0.42 222:0.35 227:0.61 229:0.61 231:0.62 235:0.05 241:0.56 243:0.40 244:0.61 245:0.36 246:0.61 253:0.35 257:0.65 259:0.21 265:0.05 267:0.36 277:0.69 287:0.61 300:0.08 +0 12:0.06 17:0.51 21:0.78 27:0.06 30:0.76 34:0.74 36:0.47 37:0.85 39:0.31 43:0.24 55:0.52 66:0.36 69:0.83 70:0.15 74:0.37 98:0.06 108:0.92 122:0.55 123:0.91 124:0.52 127:0.12 129:0.59 131:0.61 133:0.47 135:0.88 146:0.05 147:0.05 149:0.15 154:0.17 157:0.61 159:0.73 163:0.99 166:0.61 172:0.10 173:0.23 175:0.75 177:0.05 178:0.55 179:0.55 182:0.61 187:0.68 212:0.95 216:0.84 222:0.35 227:0.61 229:0.61 231:0.64 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.34 257:0.65 259:0.21 265:0.06 266:0.12 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 8:0.63 12:0.06 17:0.82 21:0.78 27:0.66 30:0.58 34:0.89 36:0.48 37:0.32 39:0.31 43:0.10 55:0.52 66:0.18 69:0.97 70:0.44 74:0.30 91:0.04 98:0.09 108:0.95 123:0.93 124:0.68 127:0.12 129:0.81 131:0.61 133:0.67 135:0.66 146:0.30 147:0.36 149:0.18 154:0.09 157:0.61 159:0.68 166:0.61 172:0.27 173:0.69 175:0.75 177:0.05 178:0.55 179:0.19 182:0.61 187:0.21 202:0.36 212:0.37 216:0.40 222:0.35 227:0.61 229:0.61 231:0.82 235:0.98 241:0.10 242:0.80 243:0.39 244:0.61 245:0.50 246:0.61 247:0.30 253:0.27 257:0.65 259:0.21 265:0.09 266:0.47 267:0.28 276:0.30 277:0.69 287:0.61 300:0.33 +0 12:0.06 17:0.53 21:0.78 27:0.06 30:0.91 34:0.74 36:0.46 37:0.85 39:0.31 43:0.12 55:0.52 66:0.49 69:0.96 70:0.13 74:0.29 98:0.06 108:0.92 123:0.91 124:0.48 127:0.15 129:0.59 131:0.61 133:0.47 135:0.84 146:0.22 147:0.28 149:0.12 154:0.10 157:0.61 159:0.90 163:0.81 166:0.61 172:0.16 173:0.58 175:0.75 177:0.05 178:0.55 179:0.73 182:0.61 187:0.68 212:0.61 216:0.84 222:0.35 227:0.61 229:0.61 231:0.67 242:0.63 243:0.60 244:0.61 245:0.39 246:0.61 247:0.30 253:0.26 257:0.65 259:0.21 265:0.07 266:0.17 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.11 21:0.78 27:0.72 30:0.95 34:0.53 36:0.88 39:0.31 43:0.32 55:0.96 66:0.20 69:0.65 74:0.37 91:0.35 98:0.10 108:0.49 123:0.47 124:0.61 127:0.33 129:0.59 131:0.61 133:0.47 135:0.58 145:0.77 146:0.05 147:0.05 149:0.18 154:0.23 157:0.61 159:0.25 166:0.61 172:0.05 173:0.81 175:0.75 177:0.05 178:0.55 179:1.00 182:0.61 187:0.95 216:0.57 222:0.35 227:0.61 229:0.61 231:0.86 235:0.42 241:0.32 243:0.40 244:0.61 245:0.36 246:0.61 253:0.34 257:0.65 265:0.10 267:0.52 277:0.69 287:0.61 300:0.08 +3 1:0.74 8:0.67 9:0.80 11:0.88 12:0.51 17:0.02 27:0.33 28:0.91 30:0.61 34:0.33 36:0.32 37:0.41 39:0.75 41:0.92 43:0.86 66:0.09 69:0.57 70:0.75 74:0.89 81:0.94 83:0.83 85:0.98 91:0.49 96:0.93 98:0.21 101:0.83 104:0.84 106:0.81 108:0.78 114:0.98 117:0.86 120:0.87 122:0.43 123:0.94 124:0.94 126:0.54 127:0.75 129:0.97 133:0.94 135:0.89 138:0.96 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:0.98 153:0.91 154:0.84 155:0.67 159:0.86 161:0.77 162:0.73 164:0.79 165:0.92 167:0.64 172:0.61 173:0.30 176:0.73 177:0.38 179:0.33 181:0.80 187:0.39 192:0.59 201:0.74 202:0.71 206:0.81 208:0.64 212:0.52 215:0.96 216:0.88 222:0.25 232:0.83 235:0.05 241:0.61 242:0.51 243:0.15 245:0.80 247:0.67 248:0.93 253:0.95 255:0.79 256:0.71 259:0.21 260:0.88 265:0.19 266:0.92 267:0.59 268:0.74 271:0.32 276:0.80 285:0.62 290:0.98 297:0.36 300:0.84 +1 1:0.74 11:0.88 12:0.51 17:0.30 21:0.54 27:0.45 28:0.91 30:0.21 32:0.80 34:0.75 36:0.17 37:0.50 39:0.75 43:0.82 60:0.87 66:0.51 69:0.70 70:0.83 74:0.93 77:0.70 81:0.62 83:0.86 85:0.95 91:0.06 98:0.63 101:0.78 108:0.53 114:0.77 122:0.67 123:0.51 124:0.87 126:0.54 127:0.70 129:0.93 133:0.91 135:0.72 144:0.85 145:0.44 146:0.95 147:0.96 149:0.90 150:0.76 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 165:0.79 167:0.58 172:0.77 173:0.41 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 195:0.96 201:0.74 208:0.64 212:0.15 215:0.58 216:0.77 222:0.25 235:0.68 241:0.50 242:0.45 243:0.61 245:0.73 247:0.67 253:0.74 259:0.21 265:0.63 266:0.94 267:0.44 271:0.32 276:0.77 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.93 8:0.76 9:0.80 11:0.88 12:0.51 20:0.76 27:0.14 28:0.91 30:0.62 34:0.53 36:0.34 37:0.67 39:0.75 41:0.99 43:0.86 60:0.95 66:0.06 69:0.59 70:0.76 74:0.91 78:0.91 81:0.94 83:0.90 85:0.98 91:0.90 96:0.99 98:0.27 100:0.90 101:0.83 108:0.78 111:0.95 114:0.98 120:0.98 122:0.43 123:0.62 124:0.93 126:0.54 127:0.75 129:0.96 133:0.93 135:0.83 138:0.99 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:1.00 152:0.98 153:0.98 154:0.83 155:0.67 158:0.92 159:0.86 161:0.77 162:0.68 164:0.96 165:0.92 167:0.57 169:0.90 172:0.61 173:0.31 176:0.73 177:0.38 179:0.32 181:0.80 186:0.79 187:0.39 189:0.92 191:0.79 192:0.59 201:0.74 202:0.94 208:0.64 212:0.51 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.46 242:0.52 243:0.15 245:0.82 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.94 267:0.36 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +2 6:0.97 8:0.95 9:0.80 12:0.51 17:0.38 20:0.89 21:0.78 27:0.05 28:0.91 34:0.85 36:0.58 37:0.81 39:0.75 41:0.97 60:0.95 74:0.46 78:0.85 83:0.88 91:0.67 96:0.92 100:0.95 111:0.90 120:0.86 135:0.70 138:0.98 140:0.45 144:0.85 151:0.93 152:0.84 153:0.77 155:0.67 158:0.87 161:0.77 162:0.67 164:0.87 167:0.64 169:0.93 175:0.91 181:0.80 186:0.85 187:0.98 189:0.98 192:0.59 202:0.81 208:0.64 216:0.94 220:0.99 222:0.25 232:0.97 235:0.42 238:0.97 241:0.60 244:0.83 248:0.92 253:0.88 255:0.79 256:0.83 257:0.84 259:0.21 260:0.86 261:0.94 267:0.23 268:0.83 271:0.32 277:0.87 279:0.86 283:0.71 285:0.62 +1 1:0.74 8:0.66 9:0.80 11:0.88 12:0.51 17:0.04 21:0.22 27:0.14 28:0.91 30:0.71 32:0.80 34:0.63 36:0.30 37:0.67 39:0.75 41:0.98 43:0.86 60:0.99 66:0.52 69:0.58 70:0.66 74:0.86 77:0.70 78:0.80 81:0.81 83:0.88 85:0.96 91:0.77 96:0.95 98:0.53 101:0.83 108:0.86 111:0.79 114:0.90 120:0.91 122:0.43 123:0.85 124:0.73 126:0.54 127:0.47 129:0.89 133:0.78 135:0.98 140:0.45 144:0.85 145:0.94 146:0.13 147:0.18 149:0.89 150:0.87 151:0.91 153:0.72 154:0.84 155:0.67 158:0.92 159:0.71 161:0.77 162:0.72 164:0.91 165:0.86 167:0.58 169:0.95 172:0.67 173:0.42 176:0.73 177:0.38 179:0.46 181:0.80 187:0.39 191:0.75 192:0.59 195:0.88 201:0.74 202:0.85 208:0.64 212:0.36 215:0.79 216:0.93 220:0.74 222:0.25 235:0.31 238:0.86 241:0.47 242:0.61 243:0.14 245:0.74 247:0.39 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.91 265:0.54 266:0.71 267:0.87 268:0.88 271:0.32 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.88 12:0.51 17:0.26 20:0.97 21:0.30 27:0.25 28:0.91 30:0.75 32:0.80 34:0.31 36:0.90 37:0.43 39:0.75 41:0.97 43:0.83 60:0.87 66:0.60 69:0.65 70:0.59 74:0.90 77:0.89 78:0.78 81:0.76 83:0.89 85:0.97 91:0.40 96:0.94 98:0.32 100:0.83 101:0.83 104:0.93 106:0.81 108:0.90 111:0.79 114:0.86 117:0.86 120:0.90 121:0.99 122:0.43 123:0.90 124:0.58 126:0.54 127:0.44 129:0.81 133:0.67 135:0.93 140:0.45 144:0.85 145:0.72 146:0.54 147:0.60 149:0.90 150:0.84 151:0.96 152:0.90 153:0.85 154:0.80 155:0.67 158:0.92 159:0.75 161:0.77 162:0.85 164:0.89 165:0.84 167:0.55 169:0.81 172:0.74 173:0.46 176:0.73 177:0.38 179:0.40 181:0.80 186:0.79 187:0.39 189:0.94 192:0.59 195:0.92 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.73 215:0.72 216:0.80 220:0.74 222:0.25 230:0.87 232:0.95 233:0.76 235:0.42 238:0.92 241:0.39 242:0.59 243:0.31 245:0.79 247:0.55 248:0.94 253:0.96 255:0.79 256:0.83 259:0.21 260:0.90 261:0.87 265:0.35 266:0.71 267:0.65 268:0.86 271:0.32 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.84 +3 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.51 17:0.01 20:0.98 27:0.14 28:0.91 30:0.65 34:0.67 36:0.29 37:0.67 39:0.75 41:0.99 43:0.86 60:0.98 66:0.06 69:0.57 70:0.74 74:0.93 78:0.85 81:0.94 83:0.90 85:0.98 91:0.63 96:0.99 98:0.25 100:0.95 101:0.83 108:0.78 111:0.90 114:0.98 120:0.98 122:0.43 123:0.86 124:0.93 126:0.54 127:0.74 129:0.96 133:0.93 135:0.85 138:0.99 140:0.45 144:0.85 145:0.82 146:0.22 147:0.28 149:0.96 150:0.97 151:1.00 152:0.98 153:0.98 154:0.84 155:0.67 158:0.92 159:0.86 161:0.77 162:0.66 164:0.96 165:0.92 167:0.56 169:0.90 172:0.61 173:0.30 176:0.73 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.94 192:0.59 201:0.74 202:0.94 208:0.64 212:0.50 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.41 242:0.53 243:0.15 245:0.83 247:0.67 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.92 267:0.28 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +3 1:0.74 7:0.81 11:0.88 12:0.51 17:0.51 21:0.30 27:0.21 28:0.91 30:0.62 34:0.66 36:0.59 37:0.74 39:0.75 43:0.98 66:0.12 69:0.12 70:0.72 74:0.99 81:0.76 83:0.75 85:0.99 91:0.05 98:0.55 101:0.86 108:0.93 114:0.86 121:0.97 122:0.57 123:0.82 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.18 144:0.85 146:0.72 147:0.77 149:0.99 150:0.84 154:0.98 155:0.67 159:0.86 161:0.77 165:0.84 167:0.57 172:0.92 173:0.08 176:0.73 177:0.38 178:0.55 179:0.15 181:0.80 187:0.68 192:0.59 201:0.74 202:0.46 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 233:0.76 235:0.42 241:0.44 242:0.52 243:0.28 245:0.97 247:0.67 253:0.78 259:0.21 265:0.48 266:0.87 267:0.59 271:0.32 276:0.94 290:0.86 297:0.36 300:0.94 +1 1:0.74 11:0.88 12:0.51 17:0.26 21:0.40 27:0.45 28:0.91 30:0.31 34:0.68 36:0.17 37:0.50 39:0.75 43:0.82 55:0.71 60:0.87 66:0.10 69:0.69 70:0.81 74:0.91 81:0.71 83:0.84 85:0.93 91:0.04 98:0.32 101:0.76 108:0.82 114:0.82 122:0.67 123:0.63 124:0.93 126:0.54 127:0.71 129:0.96 133:0.93 135:0.78 144:0.85 145:0.49 146:0.13 147:0.18 149:0.83 150:0.81 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 163:0.81 165:0.83 167:0.52 172:0.54 173:0.40 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 201:0.74 208:0.64 212:0.18 215:0.67 216:0.77 222:0.25 235:0.52 241:0.27 242:0.61 243:0.11 245:0.76 247:0.60 253:0.75 259:0.21 265:0.34 266:0.94 267:0.65 271:0.32 276:0.77 279:0.86 283:0.71 290:0.82 297:0.36 300:0.70 +3 1:0.74 7:0.81 9:0.80 11:0.88 12:0.51 17:0.06 21:0.13 27:0.13 28:0.91 30:0.86 32:0.80 34:0.44 36:0.44 37:0.79 39:0.75 41:0.97 43:0.81 60:0.87 66:0.84 69:0.70 70:0.28 74:0.86 77:0.70 81:0.85 83:0.89 85:0.91 91:0.75 96:0.95 98:0.63 101:0.83 108:0.53 114:0.92 120:0.90 121:0.90 122:0.43 123:0.51 126:0.54 127:0.19 129:0.05 135:0.89 140:0.45 144:0.85 145:0.54 146:0.60 147:0.65 149:0.86 150:0.91 151:0.93 153:0.78 154:0.76 155:0.67 158:0.92 159:0.22 161:0.77 162:0.83 164:0.89 165:0.88 167:0.72 172:0.54 173:0.78 176:0.73 177:0.38 179:0.47 181:0.80 187:0.39 192:0.59 195:0.63 201:0.74 202:0.81 204:0.89 208:0.64 212:0.52 215:0.84 216:0.83 220:0.74 222:0.25 230:0.87 233:0.76 235:0.22 241:0.70 242:0.63 243:0.85 247:0.30 248:0.95 253:0.96 255:0.79 256:0.84 259:0.21 260:0.91 265:0.64 266:0.38 267:0.52 268:0.87 271:0.32 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.88 12:0.51 17:0.22 20:0.98 21:0.30 27:0.21 28:0.91 30:0.60 34:0.66 36:0.70 37:0.74 39:0.75 41:0.99 43:0.98 60:0.87 66:0.10 69:0.12 70:0.68 74:0.99 78:0.85 81:0.76 83:0.90 85:0.99 91:0.81 96:0.98 98:0.56 100:0.95 101:0.86 104:0.84 106:0.81 108:0.93 111:0.91 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.82 124:0.71 126:0.54 127:0.61 129:0.81 133:0.67 135:0.17 140:0.45 144:0.85 146:0.90 147:0.92 149:0.99 150:0.84 151:0.99 152:0.97 153:0.97 154:0.98 155:0.67 158:0.92 159:0.85 161:0.77 162:0.64 164:0.94 165:0.84 167:0.61 169:0.91 172:0.91 173:0.08 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.39 189:0.97 191:0.75 192:0.59 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.56 242:0.51 243:0.38 245:0.98 247:0.67 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.96 265:0.48 266:0.80 267:0.59 268:0.92 271:0.32 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +2 6:0.83 8:0.63 9:0.80 11:0.88 12:0.51 17:0.08 20:0.80 21:0.78 27:0.31 28:0.91 30:0.95 34:0.25 36:0.85 37:0.30 39:0.75 41:0.97 43:0.86 66:0.54 69:0.59 74:0.44 78:0.79 83:0.83 85:0.72 91:0.92 96:0.94 98:0.16 100:0.83 101:0.76 104:0.58 108:0.45 111:0.79 120:0.91 123:0.43 127:0.09 129:0.05 135:0.20 140:0.45 144:0.85 146:0.13 147:0.18 149:0.49 151:0.96 152:0.77 153:0.86 154:0.83 155:0.67 159:0.08 161:0.77 162:0.65 164:0.89 167:0.83 169:0.81 172:0.10 173:0.93 177:0.38 179:0.18 181:0.80 186:0.79 189:0.85 191:0.81 192:0.59 202:0.85 208:0.64 216:0.19 220:0.74 222:0.25 232:0.78 235:0.22 238:0.92 241:0.82 243:0.63 248:0.94 253:0.92 255:0.79 256:0.86 259:0.21 260:0.91 261:0.81 265:0.17 267:0.23 268:0.86 271:0.32 285:0.62 300:0.08 +1 1:0.74 6:0.95 8:0.86 9:0.80 11:0.88 12:0.51 17:0.45 20:0.74 21:0.59 27:0.06 28:0.91 30:0.38 32:0.80 34:0.75 36:0.75 37:0.82 39:0.75 41:0.95 43:0.76 55:0.71 60:0.97 66:0.13 69:0.81 70:0.80 74:0.94 77:0.96 78:0.99 81:0.58 83:0.86 85:0.94 91:0.48 96:0.87 98:0.38 100:0.96 101:0.79 108:0.90 111:0.98 114:0.74 120:0.78 122:0.67 123:0.77 124:0.88 126:0.54 127:0.38 129:0.93 133:0.87 135:0.77 140:0.45 144:0.85 145:0.90 146:0.36 147:0.43 149:0.86 150:0.73 151:0.87 152:0.74 153:0.48 154:0.67 155:0.67 158:0.87 159:0.79 161:0.77 162:0.81 164:0.85 165:0.78 167:0.57 169:0.92 172:0.61 173:0.61 176:0.73 177:0.38 179:0.26 181:0.80 186:0.99 187:0.39 189:0.96 192:0.59 195:0.94 201:0.74 202:0.78 208:0.64 212:0.37 215:0.55 216:0.92 220:0.91 222:0.25 235:0.74 238:0.80 241:0.44 242:0.55 243:0.21 245:0.85 247:0.47 248:0.86 253:0.91 255:0.79 256:0.81 259:0.21 260:0.79 261:0.78 265:0.38 266:0.80 267:0.52 268:0.83 271:0.32 276:0.81 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.79 8:0.59 12:0.51 17:0.83 20:0.87 27:0.64 28:0.59 30:0.94 34:0.90 36:0.47 37:0.33 39:0.35 43:0.41 55:0.59 66:0.72 69:0.29 70:0.13 74:0.69 78:0.98 81:0.92 83:0.80 91:0.48 98:0.62 100:0.73 104:0.95 106:0.81 108:0.22 111:0.95 114:0.98 122:0.51 123:0.22 126:0.54 127:0.18 129:0.05 135:0.34 146:0.31 147:0.38 149:0.22 150:0.96 152:0.82 154:0.67 155:0.67 159:0.13 161:0.96 165:0.92 167:0.45 169:0.72 172:0.27 173:0.67 176:0.73 177:0.38 178:0.55 179:0.79 181:0.46 186:0.98 187:0.68 189:0.82 192:0.59 201:0.74 206:0.81 212:0.95 215:0.96 216:0.33 222:0.76 235:0.05 238:0.81 241:0.12 242:0.45 243:0.75 247:0.35 253:0.76 254:0.97 259:0.21 261:0.87 265:0.63 266:0.12 267:0.19 276:0.26 283:0.82 290:0.98 297:0.36 300:0.13 +1 1:0.74 7:0.78 9:0.80 12:0.51 17:0.88 21:0.05 27:0.64 28:0.59 30:0.87 34:0.18 36:0.49 37:0.72 39:0.35 43:0.91 66:0.32 69:0.40 70:0.27 74:0.64 76:0.94 81:0.86 91:0.05 96:0.77 98:0.16 104:0.99 106:0.81 108:0.92 114:0.74 117:0.86 120:0.65 122:0.43 123:0.92 124:0.72 126:0.54 127:0.70 129:0.81 133:0.67 135:0.23 138:0.95 140:0.45 146:0.13 147:0.18 149:0.70 150:0.88 151:0.87 153:0.48 154:0.90 155:0.67 159:0.79 161:0.96 162:0.86 164:0.56 167:0.48 172:0.21 173:0.22 176:0.56 177:0.38 179:0.91 181:0.46 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.19 215:0.84 216:0.23 222:0.76 230:0.80 232:0.98 233:0.66 235:0.12 241:0.41 242:0.63 243:0.32 245:0.48 247:0.30 248:0.78 253:0.76 255:0.79 256:0.45 259:0.21 260:0.66 265:0.17 266:0.47 267:0.28 268:0.46 276:0.24 285:0.62 286:0.99 290:0.74 297:0.36 298:0.99 300:0.25 +2 1:0.74 7:0.81 8:0.59 9:0.80 11:0.57 12:0.51 17:0.43 21:0.05 27:0.58 28:0.59 30:0.73 34:0.55 36:0.18 37:0.29 39:0.35 41:0.82 43:0.81 55:0.42 60:0.87 66:0.61 69:0.61 70:0.51 74:0.98 76:0.85 77:0.70 81:0.87 83:0.81 85:0.88 91:0.62 96:0.80 98:0.73 101:0.79 104:0.94 106:0.81 108:0.71 114:0.95 117:0.86 120:0.69 122:0.43 123:0.69 124:0.51 126:0.54 127:0.56 129:0.59 132:0.97 133:0.47 135:0.85 140:0.45 145:0.41 146:0.60 147:0.65 149:0.93 150:0.89 151:0.87 153:0.48 154:0.80 155:0.67 158:0.92 159:0.50 161:0.96 162:0.86 164:0.57 167:0.45 172:0.88 173:0.61 176:0.73 177:0.38 179:0.24 181:0.46 187:0.68 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.91 216:0.33 222:0.76 230:0.84 232:0.88 233:0.69 235:0.12 241:0.07 242:0.37 243:0.37 245:0.95 247:0.67 248:0.78 253:0.88 255:0.79 256:0.45 259:0.21 260:0.70 265:0.74 266:0.27 267:0.23 268:0.46 276:0.85 279:0.86 282:0.84 283:0.82 285:0.62 290:0.95 297:0.36 300:0.70 +2 6:0.86 8:0.70 9:0.80 12:0.51 17:0.83 20:0.89 21:0.71 27:0.54 28:0.59 30:0.91 37:0.30 39:0.35 41:0.90 43:0.89 55:0.42 60:0.95 66:0.69 69:0.51 70:0.13 74:0.65 78:1.00 81:0.38 83:0.80 91:0.84 96:0.90 98:0.31 100:0.97 104:0.76 108:0.39 111:0.98 114:0.68 120:0.91 122:0.43 123:0.38 126:0.54 127:0.12 129:0.05 138:0.97 140:0.90 145:0.61 146:0.19 147:0.25 149:0.50 150:0.52 151:0.96 152:0.83 153:0.86 154:0.87 155:0.67 158:0.92 159:0.10 161:0.96 162:0.79 164:0.87 167:0.77 169:0.95 172:0.21 173:0.95 176:0.73 177:0.38 179:0.42 181:0.46 186:1.00 189:0.88 191:0.70 192:0.59 202:0.81 208:0.64 212:0.61 215:0.48 216:0.09 220:0.62 222:0.76 235:0.88 238:0.85 241:0.88 242:0.63 243:0.73 247:0.30 248:0.90 253:0.89 255:0.79 256:0.82 259:0.21 260:0.91 261:0.92 265:0.33 266:0.17 268:0.86 276:0.23 279:0.86 283:0.82 285:0.62 290:0.68 297:0.36 300:0.13 +1 1:0.74 7:0.81 8:0.69 9:0.80 11:0.57 12:0.51 17:0.78 21:0.54 27:0.57 28:0.59 30:0.68 32:0.80 34:0.69 36:0.87 37:0.48 39:0.35 41:0.88 43:0.96 55:0.59 60:0.87 66:0.35 69:0.30 70:0.66 74:0.93 76:0.94 77:0.89 81:0.67 83:0.80 85:0.85 91:0.70 96:0.88 98:0.60 101:0.78 104:0.87 106:0.81 108:0.68 114:0.64 120:0.88 121:0.90 123:0.66 124:0.67 126:0.54 127:0.75 129:0.59 132:0.97 133:0.64 135:0.42 140:0.90 145:0.86 146:0.44 147:0.50 149:0.81 150:0.77 151:0.92 153:0.72 154:0.96 155:0.67 158:0.92 159:0.49 161:0.96 162:0.86 164:0.80 165:0.70 167:0.45 172:0.51 173:0.34 176:0.56 177:0.38 179:0.62 181:0.46 187:0.39 192:0.59 195:0.72 201:0.74 202:0.67 204:0.89 206:0.81 208:0.64 212:0.81 215:0.55 216:0.35 220:0.62 222:0.76 230:0.87 233:0.76 235:0.88 241:0.07 242:0.24 243:0.49 245:0.73 247:0.67 248:0.87 253:0.91 255:0.79 256:0.73 259:0.21 260:0.88 265:0.61 266:0.54 267:0.28 268:0.75 276:0.59 279:0.86 282:0.88 283:0.82 285:0.62 290:0.64 297:0.36 299:0.88 300:0.70 +0 11:0.57 12:0.51 17:0.34 21:0.78 27:0.06 28:0.59 30:0.41 32:0.75 34:0.58 36:0.31 37:0.95 39:0.35 43:0.80 66:0.86 69:0.72 70:0.60 74:0.84 77:0.89 85:0.85 91:0.46 98:0.55 101:0.80 104:0.87 106:0.81 108:0.55 122:0.57 123:0.52 127:0.16 129:0.05 135:0.81 145:0.76 146:0.45 147:0.52 149:0.79 154:0.74 159:0.17 161:0.96 167:0.45 172:0.59 173:0.85 177:0.38 178:0.55 179:0.32 181:0.46 187:0.21 195:0.64 206:0.81 212:0.92 216:0.50 222:0.76 235:0.12 241:0.10 242:0.45 243:0.86 247:0.47 253:0.60 259:0.21 265:0.56 266:0.17 267:0.36 276:0.48 282:0.83 300:0.43 +2 7:0.81 8:0.70 12:0.51 17:0.79 21:0.68 27:0.53 28:0.59 30:0.87 34:0.83 36:0.31 37:0.55 39:0.35 43:0.41 55:0.42 66:0.20 69:0.39 70:0.19 74:1.00 76:0.98 81:0.34 91:0.77 96:0.78 98:0.63 104:0.93 108:0.72 114:0.62 117:0.86 122:0.67 123:0.29 124:0.52 126:0.32 127:0.60 129:0.05 132:0.97 133:0.39 135:0.51 140:0.45 146:0.59 147:0.65 149:0.87 150:0.31 151:0.63 153:0.69 154:0.71 155:0.67 158:0.84 159:0.41 161:0.96 162:0.86 167:0.46 172:0.86 173:0.46 176:0.56 177:0.38 179:0.27 181:0.46 187:0.39 190:0.77 192:0.59 202:0.46 204:0.89 208:0.64 212:0.92 216:0.27 220:0.62 222:0.76 230:0.83 232:0.96 233:0.66 235:0.80 241:0.27 242:0.82 243:0.40 245:0.95 247:0.39 248:0.61 253:0.85 254:0.84 256:0.45 265:0.47 266:0.54 267:0.19 268:0.55 276:0.81 285:0.50 290:0.62 300:0.33 +2 6:0.81 7:0.81 8:0.62 12:0.51 17:0.67 20:0.88 21:0.47 27:0.35 28:0.59 30:0.73 32:0.80 34:0.83 36:0.38 37:0.32 39:0.35 43:0.39 55:0.42 66:0.25 69:0.45 70:0.26 74:1.00 77:0.70 78:0.92 81:0.49 91:0.57 98:0.69 100:0.83 104:0.91 108:0.69 111:0.89 114:0.66 117:0.86 121:0.90 122:0.67 123:0.33 124:0.56 126:0.32 127:0.50 129:0.05 132:0.97 133:0.39 135:0.44 145:0.70 146:0.61 147:0.67 149:0.85 150:0.38 152:0.84 154:0.29 155:0.67 158:0.84 159:0.36 161:0.96 167:0.45 169:0.83 172:0.79 173:0.55 176:0.56 177:0.38 178:0.55 179:0.30 181:0.46 186:0.92 187:0.39 189:0.85 192:0.59 195:0.60 204:0.89 208:0.64 212:0.88 216:0.25 222:0.76 230:0.84 232:0.78 233:0.69 235:0.52 238:0.81 241:0.12 242:0.81 243:0.45 244:0.61 245:0.93 247:0.39 253:0.75 261:0.86 265:0.43 266:0.38 267:0.19 276:0.78 282:0.88 290:0.66 299:0.88 300:0.43 +1 1:0.74 7:0.78 12:0.51 17:0.85 21:0.05 27:0.72 28:0.59 30:0.44 34:0.62 36:0.39 39:0.35 43:0.76 55:0.42 66:0.27 69:0.07 70:0.71 74:0.96 76:0.85 77:0.96 81:0.87 91:0.53 98:0.30 106:0.81 108:0.63 114:0.95 117:0.86 122:0.67 123:0.61 124:0.79 126:0.54 127:0.72 129:0.81 132:0.97 133:0.76 135:0.49 145:0.47 146:0.27 147:0.33 149:0.86 150:0.89 154:0.87 155:0.67 159:0.55 161:0.96 167:0.45 172:0.54 173:0.14 176:0.73 177:0.38 178:0.55 179:0.51 181:0.46 187:0.68 192:0.59 195:0.73 201:0.74 206:0.81 212:0.84 215:0.91 216:0.21 222:0.76 230:0.81 232:0.75 233:0.76 235:0.12 241:0.18 242:0.57 243:0.39 245:0.77 247:0.60 253:0.80 259:0.21 265:0.33 266:0.54 267:0.18 276:0.63 282:0.84 283:0.71 290:0.95 297:0.36 300:0.70 +2 1:0.74 6:0.93 7:0.78 8:0.85 12:0.51 17:0.85 20:0.86 21:0.13 27:0.50 28:0.59 30:0.75 34:0.47 36:0.41 37:0.70 39:0.35 43:0.43 55:0.52 66:0.89 69:0.21 70:0.42 74:0.81 76:0.85 77:0.89 78:1.00 81:0.80 91:0.68 96:0.74 98:0.81 100:0.97 104:0.92 106:0.81 108:0.17 111:0.98 114:0.72 117:0.86 120:0.69 123:0.16 124:0.36 126:0.54 127:0.44 129:0.05 132:0.97 133:0.39 135:0.21 140:0.45 145:0.70 146:0.86 147:0.89 149:0.71 150:0.83 151:0.66 152:0.83 153:0.48 154:0.59 155:0.67 159:0.51 161:0.96 162:0.58 164:0.56 167:0.51 169:0.95 172:0.86 173:0.23 176:0.56 177:0.38 179:0.33 181:0.46 186:0.99 187:0.39 189:0.95 190:0.77 191:0.80 192:0.59 195:0.74 201:0.74 202:0.63 206:0.81 212:0.91 215:0.79 216:0.62 220:0.82 222:0.76 230:0.81 232:0.86 233:0.76 235:0.22 238:0.89 241:0.52 242:0.78 243:0.90 245:0.66 247:0.55 248:0.66 253:0.74 254:0.84 256:0.58 259:0.21 260:0.70 261:0.91 265:0.81 266:0.47 267:0.23 268:0.62 276:0.76 282:0.84 283:0.82 285:0.62 290:0.72 297:0.36 300:0.55 +2 8:0.64 12:0.51 17:0.90 21:0.13 27:0.61 28:0.59 30:0.45 34:0.18 36:0.59 37:0.39 39:0.35 43:0.42 55:0.52 66:0.79 69:0.78 70:0.56 74:0.88 81:0.77 87:0.98 91:0.89 98:0.77 108:0.61 114:0.74 122:0.67 123:0.59 124:0.38 126:0.32 127:0.73 129:0.05 133:0.39 135:0.23 146:0.68 147:0.05 149:0.51 150:0.57 154:0.55 159:0.40 161:0.96 167:0.76 172:0.61 173:0.88 176:0.56 177:0.05 178:0.55 179:0.73 181:0.46 187:0.21 212:0.55 216:0.29 222:0.76 232:0.72 235:0.12 241:0.82 242:0.72 243:0.12 245:0.53 247:0.39 253:0.71 254:0.94 257:0.65 259:0.21 265:0.77 266:0.47 267:0.18 276:0.48 290:0.74 300:0.43 +1 8:0.71 12:0.51 17:0.87 20:0.81 21:0.05 27:0.67 28:0.59 34:0.52 36:0.31 37:0.66 39:0.35 43:0.37 66:0.36 69:0.49 74:0.57 76:0.85 77:0.70 78:0.96 81:0.84 91:0.83 98:0.07 100:0.73 108:0.38 111:0.93 114:0.77 123:0.36 126:0.32 127:0.06 129:0.05 135:0.39 145:0.42 146:0.05 147:0.05 149:0.14 150:0.65 152:0.78 154:0.28 159:0.05 161:0.96 167:0.77 169:0.72 172:0.05 173:0.47 175:0.75 176:0.56 177:0.05 178:0.55 181:0.46 186:0.96 191:0.73 195:0.74 202:0.46 216:0.02 222:0.76 232:0.76 235:0.05 241:0.85 243:0.51 244:0.61 253:0.61 257:0.65 259:0.21 261:0.82 265:0.08 267:0.23 277:0.69 282:0.84 290:0.77 +2 7:0.78 11:0.57 12:0.51 17:0.94 21:0.78 27:0.72 28:0.59 30:0.60 32:0.78 34:0.64 36:0.65 39:0.35 43:0.95 55:0.52 66:0.86 69:0.05 70:0.56 74:0.93 76:0.85 77:0.70 85:0.80 91:0.60 98:0.79 101:0.79 104:0.82 106:0.81 108:0.05 122:0.67 123:0.05 127:0.27 129:0.05 132:0.97 135:0.49 145:0.47 146:0.55 147:0.61 149:0.77 154:0.95 155:0.67 158:0.87 159:0.20 161:0.96 167:0.45 172:0.59 173:0.25 177:0.38 178:0.55 179:0.59 181:0.46 187:0.39 192:0.59 195:0.56 206:0.81 208:0.64 212:0.84 216:0.14 222:0.76 230:0.81 232:0.70 233:0.76 241:0.05 242:0.72 243:0.86 247:0.55 253:0.66 259:0.21 265:0.79 266:0.27 267:0.23 276:0.47 279:0.86 282:0.86 283:0.82 300:0.43 +1 11:0.57 12:0.51 17:0.67 21:0.78 27:0.55 28:0.59 30:0.15 34:0.56 36:0.52 37:0.32 39:0.35 43:0.74 55:0.71 66:0.80 69:0.48 70:0.86 74:0.94 76:0.85 85:0.72 91:0.44 98:0.84 101:0.72 108:0.37 122:0.67 123:0.35 124:0.39 127:0.61 129:0.05 133:0.39 135:0.74 145:0.41 146:0.85 147:0.88 149:0.69 154:0.79 159:0.51 161:0.96 167:0.45 172:0.77 173:0.47 177:0.38 178:0.55 179:0.50 181:0.46 187:0.39 212:0.55 216:0.44 222:0.76 232:0.82 235:0.22 241:0.15 242:0.31 243:0.82 245:0.70 247:0.67 253:0.67 259:0.21 265:0.84 266:0.71 267:0.59 276:0.67 300:0.70 +1 12:0.51 17:0.51 21:0.78 27:0.45 28:0.59 30:0.91 34:0.85 36:0.17 37:0.50 39:0.35 43:0.26 55:0.98 66:0.27 69:0.81 70:0.13 74:0.45 91:0.40 98:0.19 108:0.65 122:0.51 123:0.62 124:0.72 127:0.38 129:0.05 133:0.62 135:0.82 145:0.50 146:0.30 147:0.36 149:0.22 154:0.18 159:0.59 161:0.96 163:0.97 167:0.46 172:0.10 173:0.75 177:0.38 178:0.55 179:0.97 181:0.46 187:0.68 212:0.61 216:0.77 222:0.76 235:0.97 241:0.21 242:0.63 243:0.46 244:0.61 245:0.38 247:0.30 253:0.39 259:0.21 265:0.20 266:0.17 267:0.59 276:0.21 300:0.13 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.95 21:0.54 27:0.59 28:0.59 30:0.67 32:0.80 34:0.50 36:0.35 37:0.23 39:0.35 43:0.73 55:0.52 66:0.80 69:0.70 70:0.46 74:1.00 76:0.99 77:0.99 81:0.75 85:0.72 91:0.42 98:0.84 101:0.70 104:0.94 106:0.81 108:0.53 114:0.77 121:0.90 122:0.67 123:0.51 124:0.42 126:0.54 127:0.86 129:0.05 132:0.97 133:0.74 135:0.30 145:0.74 146:0.96 147:0.23 149:0.88 150:0.78 154:0.63 155:0.67 159:0.77 161:0.96 167:0.45 172:0.94 173:0.57 176:0.73 177:0.05 178:0.55 179:0.22 181:0.46 187:0.39 192:0.59 195:0.88 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.58 216:0.22 222:0.76 230:0.87 232:0.77 233:0.76 235:0.88 241:0.02 242:0.81 243:0.07 245:0.86 247:0.39 253:0.77 257:0.65 259:0.21 265:0.84 266:0.54 267:0.17 276:0.90 282:0.88 290:0.77 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.78 12:0.51 17:0.32 21:0.13 27:0.55 28:0.59 30:0.72 32:0.78 34:0.80 36:0.70 37:0.32 39:0.35 43:0.37 55:0.71 66:0.67 69:0.48 70:0.48 74:0.82 77:0.70 81:0.82 91:0.77 98:0.73 108:0.37 114:0.92 123:0.35 124:0.43 126:0.54 127:0.49 129:0.05 133:0.39 135:0.70 145:0.41 146:0.65 147:0.70 149:0.45 150:0.84 154:0.81 155:0.67 159:0.36 161:0.96 167:0.45 172:0.48 173:0.57 176:0.73 177:0.38 178:0.55 179:0.77 181:0.46 187:0.68 192:0.59 195:0.60 201:0.74 212:0.39 215:0.84 216:0.44 222:0.76 230:0.81 232:0.82 233:0.69 235:0.22 241:0.10 242:0.42 243:0.72 245:0.56 247:0.55 253:0.76 254:0.80 259:0.21 265:0.73 266:0.54 267:0.76 276:0.43 282:0.86 290:0.92 297:0.36 300:0.43 +1 9:0.80 12:0.51 17:0.96 21:0.64 27:0.72 28:0.59 30:0.45 34:0.25 36:0.80 37:0.24 39:0.35 41:0.82 43:0.27 55:0.84 66:0.82 69:0.75 70:0.47 74:0.82 76:1.00 81:0.59 83:0.76 91:0.88 96:0.80 98:0.74 104:0.74 108:0.58 114:0.63 120:0.77 122:0.67 123:0.55 126:0.32 127:0.24 129:0.05 135:0.60 140:0.80 145:0.77 146:0.63 147:0.68 149:0.39 150:0.44 151:0.87 153:0.48 154:0.65 155:0.67 159:0.24 161:0.96 162:0.78 164:0.70 167:0.77 172:0.48 173:0.87 176:0.56 177:0.38 179:0.67 181:0.46 192:0.59 202:0.59 208:0.64 212:0.30 216:0.22 220:0.62 222:0.76 235:0.88 241:0.87 242:0.77 243:0.83 247:0.35 248:0.78 253:0.84 254:0.92 255:0.79 256:0.58 259:0.21 260:0.78 265:0.75 266:0.47 267:0.82 268:0.64 276:0.39 283:0.71 285:0.62 290:0.63 300:0.33 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.95 21:0.54 27:0.59 28:0.59 30:0.66 32:0.80 34:0.59 36:0.33 37:0.23 39:0.35 43:0.74 55:0.52 66:0.83 69:0.29 70:0.48 74:1.00 76:0.99 77:0.99 81:0.75 85:0.72 91:0.42 98:0.88 101:0.70 104:0.94 106:0.81 108:0.22 114:0.77 121:0.90 122:0.67 123:0.22 124:0.39 126:0.54 127:0.89 129:0.05 132:0.97 133:0.62 135:0.30 145:0.70 146:0.96 147:0.97 149:0.88 150:0.78 154:0.64 155:0.67 159:0.76 161:0.96 167:0.45 172:0.94 173:0.18 176:0.73 177:0.38 178:0.55 179:0.23 181:0.46 187:0.39 192:0.59 195:0.88 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.58 216:0.22 222:0.76 230:0.87 232:0.77 233:0.76 235:0.88 241:0.01 242:0.81 243:0.84 245:0.87 247:0.39 253:0.77 259:0.21 265:0.88 266:0.63 267:0.17 276:0.90 282:0.88 290:0.77 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.82 7:0.78 8:0.62 11:0.57 12:0.51 17:0.89 20:0.87 21:0.47 27:0.49 28:0.59 30:0.62 32:0.75 34:0.90 36:0.70 37:0.27 39:0.35 43:0.88 55:0.52 66:0.75 69:0.23 70:0.34 74:0.76 76:0.85 77:0.70 78:0.99 81:0.73 83:0.75 85:0.72 91:0.68 98:0.67 100:0.94 101:0.76 104:0.95 106:0.81 108:0.18 111:0.98 114:0.80 117:0.86 122:0.41 123:0.18 126:0.54 127:0.20 129:0.05 135:0.49 145:0.69 146:0.32 147:0.39 149:0.57 150:0.83 152:0.83 154:0.86 155:0.67 159:0.13 161:0.96 165:0.84 167:0.46 169:0.91 172:0.32 173:0.65 176:0.73 177:0.38 178:0.55 179:0.77 181:0.46 186:0.99 187:0.39 189:0.84 192:0.59 195:0.54 201:0.74 206:0.81 212:0.95 215:0.63 216:0.36 222:0.76 230:0.81 232:0.80 233:0.76 235:0.74 238:0.82 241:0.24 242:0.33 243:0.77 247:0.39 253:0.69 259:0.21 261:0.91 265:0.68 266:0.12 267:0.44 276:0.29 282:0.83 283:0.82 290:0.80 297:0.36 300:0.25 +0 1:0.56 7:0.70 10:0.81 11:0.84 12:0.06 17:0.75 18:0.64 21:0.77 27:0.12 29:0.62 30:0.45 32:0.67 34:0.98 36:0.58 37:0.85 43:0.25 45:0.81 66:0.16 69:0.94 70:0.61 71:0.95 74:0.53 77:0.70 81:0.39 83:0.56 86:0.61 91:0.12 98:0.31 99:0.83 101:0.47 108:0.83 114:0.63 122:0.65 123:0.82 124:0.82 126:0.32 127:0.28 129:0.05 131:0.61 133:0.77 135:0.96 139:0.59 144:0.85 145:0.70 146:0.38 147:0.25 149:0.32 150:0.34 154:0.46 155:0.50 157:0.61 159:0.58 163:0.89 165:0.65 166:0.61 170:0.87 172:0.21 173:0.95 174:0.86 176:0.63 177:0.70 178:0.55 179:0.62 182:0.61 187:0.87 192:0.50 195:0.87 197:0.85 201:0.53 204:0.80 208:0.50 212:0.22 215:0.44 216:0.69 222:0.35 227:0.61 229:0.61 230:0.73 231:0.62 233:0.76 235:0.98 239:0.80 241:0.46 242:0.22 243:0.31 244:0.61 245:0.57 246:0.61 247:0.74 253:0.50 254:0.84 257:0.65 259:0.21 265:0.33 266:0.75 267:0.94 271:0.27 276:0.44 277:0.69 282:0.75 287:0.61 290:0.63 297:0.36 300:0.43 +0 10:0.86 11:0.86 12:0.06 17:0.76 18:0.80 21:0.59 27:0.57 29:0.62 30:0.10 32:0.63 34:0.84 36:0.32 37:0.38 43:0.65 45:0.85 48:0.91 55:0.59 66:0.20 69:0.90 70:0.95 71:0.95 74:0.49 81:0.33 86:0.67 91:0.11 98:0.51 101:0.47 104:0.79 106:0.81 108:0.79 120:0.69 123:0.90 124:0.83 126:0.32 127:0.85 129:0.59 131:0.61 133:0.82 135:0.97 139:0.66 140:0.45 144:0.92 145:0.88 146:0.76 147:0.32 149:0.68 150:0.33 151:0.65 153:0.48 154:0.18 157:0.61 159:0.81 162:0.86 164:0.55 166:0.73 170:0.87 172:0.78 173:0.82 174:0.95 177:0.38 179:0.27 182:0.61 187:0.39 190:0.95 193:0.95 195:0.91 197:0.93 202:0.36 206:0.81 212:0.72 215:0.55 216:0.72 222:0.35 227:0.61 229:0.61 231:0.65 235:0.74 236:0.91 239:0.85 241:0.24 242:0.24 243:0.23 244:0.61 245:0.90 246:0.61 247:0.97 248:0.63 253:0.40 254:0.99 256:0.45 257:0.84 259:0.21 260:0.69 265:0.46 266:0.92 267:0.99 268:0.46 271:0.27 276:0.86 281:0.91 283:0.82 285:0.46 287:0.61 297:0.36 300:0.84 +2 1:0.62 7:0.70 8:0.75 10:0.90 11:0.88 12:0.06 17:0.55 18:0.75 21:0.13 27:0.55 29:0.62 30:0.66 32:0.67 34:0.95 36:0.48 37:0.84 43:0.66 45:0.89 66:0.61 69:0.84 70:0.70 71:0.95 74:0.62 77:0.70 81:0.52 83:0.56 86:0.75 91:0.38 97:0.89 98:0.52 99:0.83 101:0.65 104:0.98 106:0.81 108:0.81 114:0.88 117:0.86 122:0.65 123:0.80 124:0.56 126:0.32 127:0.56 129:0.59 131:0.61 132:0.97 133:0.64 135:1.00 139:0.72 144:0.92 145:0.72 146:0.29 147:0.18 149:0.59 150:0.46 154:0.25 155:0.53 157:0.88 158:0.76 159:0.62 165:0.65 166:0.61 170:0.87 172:0.65 173:0.82 174:0.89 176:0.63 177:0.38 178:0.55 179:0.56 182:0.77 187:0.39 192:0.55 195:0.79 197:0.88 201:0.58 204:0.82 206:0.81 208:0.50 212:0.42 215:0.84 216:0.49 222:0.35 227:0.61 229:0.61 230:0.73 231:0.63 233:0.76 235:0.22 239:0.89 241:0.01 242:0.16 243:0.16 244:0.61 245:0.70 246:0.61 247:0.86 253:0.64 254:0.86 257:0.84 265:0.54 266:0.63 267:0.44 271:0.27 276:0.57 279:0.75 282:0.75 283:0.67 287:0.61 290:0.88 297:0.36 300:0.70 +1 1:0.59 7:0.70 8:0.84 9:0.74 10:0.81 11:0.84 12:0.06 17:0.57 18:0.63 21:0.40 27:0.14 29:0.62 30:0.74 34:0.93 36:0.79 37:0.91 43:0.65 45:0.89 66:0.47 69:0.38 70:0.70 71:0.95 74:0.70 76:0.85 81:0.47 83:0.56 86:0.61 91:0.63 96:0.89 97:0.89 98:0.35 99:0.83 101:0.47 104:0.99 106:0.81 108:0.30 114:0.78 117:0.86 120:0.79 122:0.55 123:0.28 124:0.67 126:0.32 127:0.35 129:0.05 131:0.61 133:0.62 135:0.96 139:0.60 140:0.45 144:0.85 145:0.52 146:0.63 147:0.68 149:0.72 150:0.42 151:0.90 153:0.69 154:0.56 155:0.56 157:0.61 158:0.77 159:0.69 162:0.55 164:0.77 165:0.65 166:0.61 170:0.87 172:0.54 173:0.21 174:0.79 176:0.63 177:0.88 179:0.53 182:0.61 187:0.87 190:0.89 191:0.75 192:0.58 197:0.82 201:0.56 202:0.77 204:0.79 206:0.81 208:0.50 212:0.28 215:0.67 216:0.57 220:0.62 222:0.35 227:0.61 229:0.61 230:0.73 231:0.61 233:0.76 235:0.52 239:0.80 241:0.44 242:0.45 243:0.59 244:0.61 245:0.70 246:0.61 247:0.67 248:0.86 253:0.89 254:0.74 255:0.72 256:0.73 259:0.21 260:0.80 265:0.37 266:0.75 267:0.95 268:0.75 271:0.27 276:0.51 279:0.76 283:0.82 285:0.50 287:0.61 290:0.78 297:0.36 300:0.84 +1 1:0.62 7:0.70 8:0.71 10:0.82 11:0.84 12:0.06 17:0.70 18:0.76 21:0.13 27:0.24 29:0.62 30:0.45 32:0.67 34:1.00 36:0.38 37:0.60 43:0.25 45:0.81 55:0.71 66:0.61 69:0.71 70:0.65 71:0.95 74:0.62 77:0.70 81:0.52 83:0.56 86:0.65 91:0.42 97:0.89 98:0.45 99:0.83 101:0.47 104:0.95 108:0.54 114:0.88 122:0.65 123:0.52 124:0.55 126:0.32 127:0.49 129:0.05 131:0.61 133:0.60 135:0.74 139:0.63 144:0.85 145:0.87 146:0.62 147:0.67 149:0.24 150:0.46 154:0.52 155:0.53 157:0.61 158:0.76 159:0.60 165:0.65 166:0.61 170:0.87 172:0.54 173:0.66 174:0.88 176:0.63 177:0.88 178:0.55 179:0.68 182:0.61 187:0.68 192:0.55 195:0.80 197:0.89 201:0.58 204:0.83 208:0.50 212:0.55 215:0.84 216:0.55 222:0.35 227:0.61 229:0.61 230:0.73 231:0.63 233:0.76 235:0.22 239:0.82 241:0.27 242:0.28 243:0.68 244:0.61 245:0.60 246:0.61 247:0.74 253:0.64 254:0.88 259:0.21 265:0.47 266:0.54 267:0.52 271:0.27 276:0.43 279:0.75 282:0.75 283:0.67 287:0.61 290:0.88 297:0.36 300:0.55 +2 1:0.60 7:0.70 10:0.82 11:0.84 12:0.06 17:0.11 21:0.30 27:0.34 29:0.62 30:0.21 32:0.74 34:0.92 36:0.18 37:0.44 43:0.59 44:0.86 45:0.66 66:0.72 69:0.73 70:0.37 71:0.95 74:0.54 77:0.70 81:0.48 83:0.69 91:0.18 98:0.46 99:0.83 101:0.47 102:0.94 108:0.56 114:0.82 122:0.67 123:0.53 126:0.32 127:0.14 129:0.05 131:0.61 135:0.83 139:0.69 144:0.92 145:0.65 146:0.52 147:0.58 149:0.30 150:0.43 154:0.48 155:0.54 157:0.61 159:0.19 163:0.81 165:0.65 166:0.61 170:0.87 172:0.27 173:0.75 174:0.86 175:0.75 176:0.63 177:0.70 178:0.55 179:0.61 182:0.61 187:0.39 192:0.51 193:0.98 195:0.71 197:0.88 201:0.57 204:0.82 208:0.61 212:0.42 215:0.72 216:0.71 222:0.35 227:0.61 229:0.61 230:0.73 231:0.62 233:0.66 235:0.42 239:0.87 241:0.55 242:0.45 243:0.75 244:0.83 246:0.61 247:0.35 253:0.59 257:0.65 259:0.21 265:0.47 266:0.23 267:0.94 271:0.27 276:0.21 277:0.69 281:0.91 282:0.82 287:0.61 290:0.82 297:0.36 300:0.25 +2 1:0.57 8:0.71 10:0.88 11:0.84 12:0.06 17:0.85 18:0.67 21:0.59 27:0.57 29:0.62 30:0.40 32:0.67 34:0.84 36:0.30 37:0.38 43:0.65 45:0.92 48:0.91 66:0.95 69:0.43 70:0.88 71:0.95 74:0.77 77:0.98 81:0.50 83:0.56 86:0.71 91:0.10 97:0.89 98:0.93 99:0.83 101:0.47 104:0.79 106:0.81 108:0.33 114:0.73 117:0.86 122:0.55 123:0.32 126:0.51 127:0.58 129:0.05 131:0.61 135:0.94 139:0.67 144:0.85 145:0.74 146:0.92 147:0.94 149:0.81 150:0.40 154:0.55 155:0.46 157:0.61 158:0.77 159:0.55 165:0.65 166:0.73 170:0.87 172:0.88 173:0.39 174:0.91 176:0.70 177:0.88 178:0.55 179:0.34 182:0.61 187:0.21 192:0.45 193:0.95 195:0.75 197:0.93 201:0.55 206:0.81 208:0.50 212:0.71 215:0.55 216:0.72 222:0.35 227:0.61 229:0.61 231:0.63 235:0.80 236:0.91 239:0.87 241:0.27 242:0.19 243:0.95 246:0.61 247:0.92 253:0.62 254:0.99 259:0.21 265:0.93 266:0.54 267:0.99 271:0.27 276:0.79 279:0.76 281:0.91 282:0.75 283:0.82 287:0.61 290:0.73 297:0.36 300:0.70 +2 1:0.57 10:0.86 11:0.88 12:0.06 17:0.81 18:0.80 21:0.59 27:0.70 29:0.62 30:0.33 32:0.71 34:0.97 36:0.26 37:0.44 43:0.34 44:0.86 45:0.85 66:0.54 69:0.75 70:0.77 71:0.95 74:0.60 77:0.70 81:0.43 83:0.56 86:0.71 91:0.12 98:0.59 99:0.83 101:0.65 108:0.58 114:0.71 122:0.65 123:0.55 124:0.63 126:0.32 127:0.38 129:0.05 131:0.61 133:0.60 135:0.88 139:0.69 144:0.92 145:0.97 146:0.73 147:0.77 149:0.24 150:0.38 154:0.53 155:0.46 157:0.78 159:0.53 165:0.65 166:0.61 170:0.87 172:0.61 173:0.71 174:0.89 176:0.63 177:0.88 178:0.55 179:0.51 182:0.73 187:0.68 192:0.45 195:0.79 197:0.90 201:0.55 208:0.50 212:0.58 215:0.55 216:0.38 222:0.35 227:0.61 229:0.61 231:0.63 235:0.74 239:0.85 241:0.18 242:0.24 243:0.63 245:0.70 246:0.61 247:0.84 253:0.56 254:0.99 259:0.21 265:0.60 266:0.71 267:0.98 271:0.27 276:0.59 282:0.79 287:0.61 290:0.71 297:0.36 300:0.55 +1 1:0.59 10:0.86 11:0.88 12:0.06 17:0.67 18:0.79 21:0.68 27:0.45 29:0.62 30:0.33 34:0.94 36:0.25 37:0.50 43:0.60 45:0.73 64:0.77 66:0.12 69:0.71 70:0.49 71:0.95 74:0.33 81:0.40 83:0.56 86:0.74 91:0.14 98:0.12 99:0.83 101:0.65 108:0.54 122:0.65 123:0.81 124:0.81 126:0.32 127:0.24 129:0.59 131:0.61 133:0.76 135:0.83 139:0.71 144:0.92 145:0.50 146:0.19 147:0.24 149:0.39 150:0.35 154:0.49 155:0.47 157:0.77 159:0.47 163:0.75 165:0.65 166:0.76 170:0.87 172:0.16 173:0.63 174:0.79 175:0.91 177:0.38 178:0.55 179:0.75 182:0.86 187:0.68 192:0.44 197:0.84 201:0.56 208:0.50 212:0.19 216:0.77 222:0.35 227:0.61 229:0.61 231:0.65 235:0.88 236:0.94 239:0.85 241:0.15 242:0.19 243:0.40 244:0.83 245:0.46 246:0.85 247:0.55 253:0.29 257:0.84 259:0.21 265:0.11 266:0.47 267:0.88 271:0.27 276:0.32 277:0.95 287:0.61 300:0.33 +1 10:0.82 11:0.84 12:0.06 17:0.62 18:0.69 21:0.76 27:0.45 29:0.62 30:0.15 32:0.67 34:0.90 36:0.20 37:0.50 43:0.25 45:0.73 64:0.77 66:0.53 69:0.70 70:0.68 71:0.95 74:0.47 77:0.70 81:0.23 86:0.62 91:0.14 98:0.37 101:0.47 108:0.53 122:0.67 123:0.51 124:0.63 126:0.24 127:0.38 129:0.05 131:0.61 133:0.60 135:0.80 139:0.61 144:0.85 145:0.49 146:0.52 147:0.58 149:0.21 150:0.24 154:0.48 157:0.61 159:0.55 166:0.72 170:0.87 172:0.37 173:0.65 174:0.79 177:0.88 178:0.55 179:0.79 182:0.61 187:0.68 195:0.79 197:0.82 212:0.15 216:0.77 219:0.87 222:0.35 227:0.61 229:0.61 231:0.62 235:0.95 239:0.81 241:0.21 242:0.14 243:0.62 244:0.61 245:0.50 246:0.61 247:0.67 253:0.39 254:0.97 259:0.21 265:0.39 266:0.63 267:0.36 271:0.27 276:0.32 282:0.75 287:0.61 292:0.88 300:0.43 +1 1:0.56 7:0.70 10:0.82 11:0.84 12:0.06 17:0.76 18:0.66 21:0.77 27:0.72 29:0.62 30:0.70 34:1.00 36:0.49 43:0.39 45:0.87 48:0.91 66:0.72 69:0.82 70:0.56 71:0.95 74:0.61 81:0.39 83:0.56 86:0.63 91:0.18 97:0.94 98:0.51 99:0.83 101:0.47 108:0.66 114:0.63 122:0.55 123:0.64 124:0.41 126:0.32 127:0.23 129:0.05 131:0.61 132:0.97 133:0.39 135:0.92 139:0.62 144:0.85 146:0.54 147:0.60 149:0.41 150:0.34 154:0.58 155:0.52 157:0.61 158:0.77 159:0.31 165:0.65 166:0.61 170:0.87 172:0.61 173:0.87 174:0.79 176:0.63 177:0.88 178:0.55 179:0.45 182:0.61 187:0.68 192:0.54 197:0.82 201:0.53 204:0.82 208:0.50 212:0.73 215:0.44 216:0.15 222:0.35 227:0.61 229:0.61 230:0.73 231:0.61 233:0.66 235:0.98 239:0.81 241:0.07 242:0.24 243:0.75 245:0.62 246:0.61 247:0.67 253:0.52 254:0.93 259:0.21 265:0.52 266:0.54 267:0.44 271:0.27 276:0.47 279:0.78 281:0.91 283:0.82 287:0.61 290:0.63 297:0.36 300:0.55 +3 1:0.57 7:0.70 8:0.64 10:0.86 11:0.88 12:0.06 17:0.77 18:0.60 21:0.64 27:0.56 29:0.62 30:0.62 32:0.67 34:1.00 36:0.38 37:0.52 43:0.27 45:0.92 66:0.10 69:0.97 70:0.75 71:0.95 74:0.60 77:0.70 81:0.42 83:0.56 86:0.67 91:0.03 98:0.21 99:0.83 101:0.65 104:0.97 106:0.81 108:0.82 114:0.69 117:0.86 122:0.55 123:0.85 124:0.88 126:0.32 127:0.32 129:0.05 131:0.61 133:0.86 135:0.92 139:0.64 144:0.92 145:0.72 146:0.34 147:0.52 149:0.56 150:0.37 154:0.16 155:0.53 157:0.80 159:0.78 165:0.65 166:0.61 170:0.87 172:0.41 173:0.98 174:0.92 176:0.63 177:0.38 178:0.55 179:0.35 182:0.74 187:0.68 192:0.55 195:0.95 197:0.89 201:0.54 204:0.84 206:0.81 208:0.50 212:0.37 215:0.53 216:0.70 222:0.35 227:0.61 229:0.61 230:0.73 231:0.62 233:0.66 235:0.80 239:0.85 241:0.27 242:0.32 243:0.34 245:0.75 246:0.61 247:0.86 253:0.55 254:0.94 257:0.84 259:0.21 265:0.22 266:0.91 267:0.95 271:0.27 276:0.68 277:0.87 282:0.75 287:0.61 290:0.69 297:0.36 300:0.70 +1 1:0.58 10:0.87 11:0.64 12:0.79 17:0.67 18:0.79 21:0.30 26:0.96 27:0.67 28:0.70 29:0.71 30:0.76 34:0.95 36:0.24 37:0.83 39:0.38 43:0.60 45:0.83 58:0.93 66:0.83 69:0.64 70:0.28 71:0.79 74:0.38 81:0.48 83:0.54 86:0.69 91:0.49 98:0.43 99:0.83 101:0.69 104:0.58 108:0.49 114:0.80 122:0.57 123:0.47 126:0.31 127:0.14 129:0.05 131:0.61 135:0.63 139:0.67 141:0.91 144:0.76 146:0.40 147:0.46 149:0.37 150:0.45 154:0.63 155:0.47 157:0.97 159:0.15 161:0.79 165:0.63 166:0.78 167:0.72 170:0.82 172:0.51 173:0.74 174:0.83 176:0.62 177:0.96 178:0.55 179:0.28 181:0.50 182:0.96 187:0.21 192:0.46 197:0.87 199:0.94 201:0.56 208:0.49 212:0.91 215:0.72 216:0.29 222:0.60 223:0.95 224:0.93 227:0.61 229:0.61 231:0.76 232:0.80 234:0.91 235:0.42 239:0.86 241:0.51 242:0.13 243:0.84 246:0.96 247:0.74 253:0.57 254:0.97 259:0.21 264:0.90 265:0.45 266:0.17 267:0.82 271:0.56 274:0.91 275:0.91 276:0.40 287:0.61 290:0.80 294:0.95 297:0.36 300:0.25 +1 10:0.96 11:0.64 12:0.79 17:0.62 18:0.93 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.84 34:0.82 36:0.84 37:0.90 39:0.38 43:0.60 45:0.98 58:0.93 66:0.71 69:0.07 70:0.47 71:0.79 74:0.63 86:0.89 91:0.04 98:0.86 101:0.69 104:0.58 108:0.06 122:0.67 123:0.06 124:0.47 127:0.93 129:0.05 131:0.61 133:0.60 135:0.82 139:0.84 141:0.91 144:0.76 146:0.97 147:0.98 149:0.87 154:0.72 157:1.00 159:0.81 161:0.79 166:0.61 167:0.71 170:0.82 172:0.93 173:0.08 174:0.95 177:0.96 178:0.55 179:0.23 181:0.50 182:1.00 187:0.68 197:0.95 199:0.97 212:0.56 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.12 239:0.94 241:0.46 242:0.14 243:0.75 245:0.92 246:0.61 247:0.94 253:0.47 254:0.93 259:0.21 264:0.90 265:0.86 266:0.80 267:0.52 271:0.56 274:0.91 275:0.97 276:0.89 287:0.61 294:0.98 300:0.70 +1 1:0.57 10:0.92 11:0.64 12:0.79 17:0.91 18:0.90 21:0.54 26:0.97 27:0.67 28:0.70 29:0.71 30:0.30 34:0.63 36:0.49 37:0.83 39:0.38 43:0.34 45:0.95 58:0.93 66:0.84 69:0.73 70:0.57 71:0.79 74:0.42 81:0.42 83:0.54 86:0.81 91:0.44 98:0.82 99:0.83 101:0.87 104:0.58 108:0.56 114:0.72 122:0.99 123:0.53 124:0.38 126:0.31 127:0.48 129:0.05 131:0.61 133:0.38 135:0.30 139:0.77 141:0.91 144:0.76 146:0.85 147:0.88 149:0.81 150:0.40 154:0.25 155:0.46 157:1.00 159:0.50 161:0.79 165:0.63 166:0.78 167:0.72 170:0.82 172:0.95 173:0.73 174:0.97 176:0.62 177:0.96 178:0.55 179:0.17 181:0.50 182:1.00 187:0.68 192:0.45 197:0.98 199:0.96 201:0.54 208:0.49 212:0.94 215:0.58 216:0.29 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.82 232:0.80 234:0.91 235:0.68 239:0.91 241:0.50 242:0.45 243:0.85 244:0.83 245:0.93 246:0.96 247:0.98 253:0.53 259:0.21 264:0.90 265:0.82 266:0.38 267:0.28 271:0.56 274:0.91 275:0.99 276:0.91 287:0.61 290:0.72 294:0.97 297:0.36 300:0.55 +2 1:0.56 6:0.99 8:0.86 9:0.70 10:0.96 11:0.64 12:0.79 17:0.64 18:0.94 20:0.88 21:0.68 26:0.97 27:0.20 28:0.70 29:0.71 30:0.87 32:0.74 34:0.81 36:0.44 37:0.84 39:0.38 43:0.60 44:0.93 45:0.95 48:0.98 58:0.93 66:0.95 69:0.30 70:0.27 71:0.79 74:0.50 77:0.70 78:0.87 81:0.39 82:0.99 83:0.60 86:0.89 88:0.98 91:0.46 96:0.68 98:0.97 99:0.83 100:0.93 101:0.69 102:0.98 108:0.24 111:0.88 114:0.66 120:0.54 122:0.97 123:0.23 126:0.31 127:0.74 129:0.05 131:0.84 135:0.49 139:0.90 140:0.80 141:0.91 144:0.76 145:0.89 146:0.86 147:0.88 149:0.87 150:0.37 151:0.48 152:0.95 153:0.48 154:0.49 155:0.51 157:1.00 159:0.43 161:0.79 162:0.58 164:0.55 165:0.63 166:0.78 167:0.70 169:1.00 170:0.82 172:0.86 173:0.38 174:0.93 176:0.62 177:0.96 178:0.42 179:0.39 181:0.50 182:1.00 186:0.87 187:0.21 189:0.93 190:0.95 191:0.75 192:0.49 193:0.95 195:0.65 197:0.96 199:0.97 201:0.54 202:0.53 204:0.80 208:0.54 212:0.60 215:0.49 216:0.45 220:0.99 222:0.60 223:0.97 224:0.93 227:0.94 229:0.88 231:0.82 234:0.91 235:0.84 238:0.95 239:0.96 241:0.43 242:0.32 243:0.95 244:0.83 246:0.96 247:0.88 248:0.48 253:0.51 255:0.68 256:0.45 259:0.21 260:0.54 261:0.98 264:0.90 265:0.97 266:0.38 267:0.17 268:0.46 271:0.56 274:0.91 275:0.97 276:0.77 281:0.91 282:0.82 285:0.49 287:0.97 290:0.66 294:0.99 297:0.36 300:0.33 +2 1:0.65 10:0.95 11:0.64 12:0.79 17:0.62 18:0.89 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.73 32:0.78 33:0.97 34:0.77 36:0.23 37:0.60 39:0.38 43:0.36 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.88 69:0.83 70:0.56 71:0.79 74:0.77 77:0.89 81:0.75 82:0.99 83:0.72 86:0.86 88:0.98 91:0.06 98:0.89 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.68 114:0.82 117:0.86 122:0.57 123:0.66 124:0.37 126:0.54 127:0.75 129:0.05 131:0.61 133:0.38 135:0.56 139:0.86 141:0.91 144:0.76 145:0.72 146:0.96 147:0.47 149:0.78 150:0.57 154:0.58 155:0.50 157:1.00 159:0.73 161:0.79 165:0.86 166:0.88 167:0.61 170:0.82 172:0.96 173:0.75 174:0.97 176:0.73 177:0.38 178:0.55 179:0.17 181:0.50 182:1.00 187:0.39 192:0.51 195:0.85 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.69 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.95 241:0.07 242:0.15 243:0.11 245:0.93 246:1.00 247:0.96 253:0.66 254:0.84 257:0.93 259:0.21 262:0.93 264:0.90 265:0.89 266:0.75 267:0.44 271:0.56 274:0.91 275:0.98 276:0.93 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +1 1:0.65 10:0.98 11:0.64 12:0.79 17:0.55 18:0.92 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.84 32:0.78 33:0.97 34:0.93 36:0.26 37:0.60 39:0.38 43:0.38 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.79 69:0.81 70:0.52 71:0.79 74:0.85 77:0.89 81:0.75 82:0.99 83:0.72 86:0.94 88:0.98 91:0.05 98:0.86 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.65 114:0.82 117:0.86 122:0.57 123:0.63 124:0.40 126:0.54 127:0.64 129:0.05 131:0.61 133:0.45 135:0.17 139:0.92 141:0.91 144:0.76 145:0.72 146:0.94 147:0.46 149:0.75 150:0.57 154:0.60 155:0.50 157:1.00 159:0.67 161:0.79 165:0.86 166:0.88 167:0.65 170:0.82 172:0.96 173:0.75 174:0.97 176:0.73 177:0.88 178:0.55 179:0.17 181:0.50 182:1.00 187:0.39 192:0.51 195:0.81 197:0.98 199:0.98 201:0.66 206:0.81 208:0.64 212:0.78 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.98 241:0.24 242:0.14 243:0.19 245:0.96 246:1.00 247:0.94 253:0.69 254:0.90 257:0.65 259:0.21 262:0.93 264:0.90 265:0.86 266:0.75 267:0.44 271:0.56 274:0.91 275:0.97 276:0.93 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +2 1:0.60 8:0.72 9:0.72 10:0.95 11:0.64 12:0.79 17:0.81 18:0.89 20:0.82 21:0.47 23:0.97 26:0.98 27:0.67 28:0.70 29:0.71 30:0.91 31:0.90 32:0.74 34:0.64 36:0.78 37:0.42 39:0.38 43:0.65 44:0.93 45:0.93 56:0.97 58:0.98 62:0.97 64:0.77 66:0.91 69:0.33 70:0.23 71:0.79 74:0.51 77:0.70 78:0.72 79:0.89 80:1.00 81:0.54 82:0.98 83:0.72 86:0.92 88:0.98 91:0.72 96:0.74 98:0.84 99:0.95 100:0.73 101:0.69 102:0.94 108:0.26 111:0.72 120:0.62 122:0.97 123:0.25 126:0.43 127:0.34 128:0.96 129:0.05 131:0.90 135:0.61 137:0.90 139:0.91 140:0.80 141:0.99 144:0.76 145:0.72 146:0.54 147:0.60 149:0.73 150:0.42 151:0.61 152:0.81 153:0.48 154:0.65 155:0.63 157:1.00 159:0.19 161:0.79 162:0.54 164:0.70 165:0.70 166:0.76 167:0.98 168:0.96 169:0.72 170:0.82 172:0.74 173:0.60 174:0.92 177:0.96 178:0.42 179:0.47 181:0.50 182:1.00 186:0.73 190:0.95 192:0.87 193:0.98 195:0.55 196:0.93 197:0.95 199:0.98 201:0.59 202:0.72 204:0.84 205:0.97 208:0.64 212:0.95 216:0.22 220:0.87 222:0.60 223:0.98 224:0.98 227:0.95 229:0.89 231:0.83 234:0.97 235:0.68 236:0.94 239:0.94 241:0.88 242:0.32 243:0.91 246:0.95 247:0.74 248:0.69 253:0.56 254:0.94 255:0.70 256:0.68 259:0.21 260:0.62 261:0.73 264:0.90 265:0.84 266:0.12 267:0.18 268:0.69 271:0.56 274:0.98 275:0.96 276:0.61 281:0.86 282:0.82 284:0.91 285:0.61 287:0.97 294:0.98 300:0.25 +1 10:0.99 11:0.64 12:0.79 17:0.32 18:0.89 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.67 34:0.53 36:0.25 37:0.90 39:0.38 43:0.57 45:0.97 58:0.93 66:0.69 69:0.07 70:0.58 71:0.79 74:0.86 86:0.97 91:0.15 98:0.71 101:0.69 104:0.58 108:0.06 122:0.97 123:0.06 124:0.48 127:0.79 129:0.05 131:0.61 133:0.60 135:0.77 139:0.95 141:0.91 144:0.76 146:0.84 147:0.87 149:0.47 154:0.72 157:0.99 159:0.66 161:0.79 166:0.61 167:0.66 170:0.82 172:0.92 173:0.12 174:0.95 177:0.96 178:0.55 179:0.24 181:0.50 182:0.95 187:0.68 197:0.96 199:0.97 212:0.87 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.12 239:0.99 241:0.27 242:0.18 243:0.73 245:0.92 246:0.61 247:0.82 253:0.58 254:0.90 259:0.21 264:0.90 265:0.71 266:0.71 267:0.28 271:0.56 274:0.91 275:0.98 276:0.88 287:0.61 294:1.00 300:0.70 +2 1:0.61 6:0.99 8:0.93 9:0.74 10:0.91 11:0.64 12:0.79 17:0.59 18:0.88 20:0.96 21:0.64 23:0.99 26:0.98 27:0.20 28:0.70 29:0.71 30:0.87 31:0.92 32:0.74 34:0.44 36:0.37 37:0.84 39:0.38 43:0.57 44:0.93 45:0.88 48:0.98 56:0.97 58:0.99 62:0.97 64:0.77 66:0.36 69:0.73 70:0.41 71:0.79 74:0.53 75:0.95 77:0.70 78:0.94 79:0.92 81:0.70 82:0.99 83:0.72 86:0.86 88:0.98 91:0.85 96:0.93 97:0.89 98:0.36 99:0.99 100:1.00 101:0.69 102:0.98 108:0.56 111:0.99 114:0.72 120:0.88 122:0.97 123:0.54 124:0.70 126:0.54 127:0.76 128:0.97 129:0.59 131:0.93 133:0.62 135:0.54 137:0.92 139:0.86 140:0.80 141:0.99 143:0.98 144:0.76 145:0.89 146:0.31 147:0.23 149:0.58 150:0.50 151:0.54 152:0.95 153:0.82 154:0.63 155:0.64 157:0.99 159:0.34 161:0.79 162:0.64 164:0.87 165:0.86 166:0.88 167:0.98 168:0.97 169:1.00 170:0.82 172:0.51 173:0.87 174:0.89 176:0.73 177:0.38 178:0.42 179:0.63 181:0.50 182:1.00 186:0.94 189:0.99 190:0.95 191:0.92 192:0.99 193:0.95 195:0.60 196:0.94 197:0.92 199:0.97 201:0.62 202:0.84 204:0.80 205:0.98 208:0.64 212:0.80 215:0.53 216:0.45 219:0.87 220:0.93 222:0.60 223:0.98 224:0.98 226:0.96 227:0.96 229:1.00 231:0.83 234:0.97 235:0.95 236:0.97 238:0.99 239:0.93 241:0.89 242:0.13 243:0.23 245:0.73 246:1.00 247:0.88 248:0.55 253:0.91 254:0.95 255:0.73 256:0.85 257:0.93 259:0.21 260:0.88 261:0.98 264:0.90 265:0.38 266:0.54 267:0.65 268:0.86 271:0.56 274:0.99 275:0.95 276:0.53 279:0.75 281:0.91 282:0.82 284:0.97 285:0.62 287:1.00 290:0.72 292:0.88 294:0.98 297:0.36 300:0.55 +2 1:0.65 10:0.97 11:0.64 12:0.79 17:0.66 18:0.93 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.86 32:0.80 33:0.97 34:0.94 36:0.19 37:0.60 39:0.38 43:0.40 44:0.93 45:0.97 47:0.93 56:0.97 58:0.93 64:0.77 66:0.71 69:0.54 70:0.39 71:0.79 74:0.68 77:0.70 81:0.78 82:0.99 83:0.91 86:0.93 88:0.99 91:0.08 98:0.59 101:0.69 102:0.98 104:0.91 106:0.81 108:0.73 114:0.82 117:0.86 122:0.98 123:0.71 124:0.45 126:0.54 127:0.45 129:0.59 131:0.61 133:0.47 135:0.37 139:0.92 141:0.91 144:0.76 145:0.70 146:0.57 147:0.62 149:0.72 150:0.58 154:0.63 155:0.50 157:1.00 159:0.46 161:0.79 165:0.93 166:0.88 167:0.57 170:0.82 172:0.89 173:0.55 174:0.97 176:0.73 177:0.96 178:0.55 179:0.23 181:0.50 182:1.00 187:0.39 192:0.51 195:0.68 197:0.98 199:0.98 201:0.66 206:0.81 208:0.64 212:0.89 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.82 232:0.95 234:0.91 235:0.74 236:0.97 239:0.97 241:0.01 242:0.27 243:0.28 245:0.93 246:1.00 247:0.90 253:0.63 254:0.74 259:0.21 262:0.93 264:0.90 265:0.60 266:0.54 267:0.36 271:0.56 274:0.91 275:0.96 276:0.83 282:0.88 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 299:0.88 300:0.70 +1 1:0.67 7:0.69 8:0.58 10:0.92 11:0.64 12:0.79 17:0.66 18:0.67 20:0.85 21:0.22 26:0.98 27:0.67 28:0.70 29:0.71 30:0.76 32:0.69 34:0.89 36:0.24 37:0.44 39:0.38 43:0.32 44:0.86 45:0.87 56:0.97 58:0.93 64:0.77 66:0.77 69:0.74 70:0.42 71:0.79 74:0.58 77:0.70 78:0.79 81:0.80 83:0.91 86:0.80 91:0.29 98:0.56 100:0.73 101:0.69 102:0.94 108:0.58 111:0.78 114:0.90 122:0.67 123:0.55 124:0.38 126:0.54 127:0.27 129:0.05 131:0.61 133:0.36 135:0.78 139:0.76 141:0.91 144:0.76 145:0.70 146:0.54 147:0.05 149:0.24 150:0.62 152:0.81 154:0.64 155:0.55 157:0.99 159:0.30 161:0.79 165:0.93 166:0.89 167:0.63 169:0.72 170:0.82 172:0.59 173:0.83 174:0.89 176:0.73 177:0.05 178:0.55 179:0.56 181:0.50 182:1.00 186:0.80 187:0.39 192:0.58 193:0.95 195:0.64 197:0.92 199:0.95 201:0.67 204:0.82 208:0.64 212:0.75 215:0.79 216:0.56 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 230:0.71 231:0.82 233:0.76 234:0.91 235:0.60 236:0.97 239:0.90 241:0.15 242:0.18 243:0.13 245:0.52 246:1.00 247:0.74 253:0.64 254:0.92 257:0.97 259:0.21 261:0.79 264:0.90 265:0.58 266:0.47 267:0.36 271:0.56 274:0.91 275:0.93 276:0.44 281:0.91 282:0.77 287:0.61 290:0.90 294:0.97 297:0.36 300:0.43 +2 1:0.65 10:0.96 11:0.64 12:0.79 17:0.45 18:0.91 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.74 32:0.78 33:0.97 34:0.63 36:0.26 37:0.60 39:0.38 43:0.35 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.84 69:0.83 70:0.57 71:0.79 74:0.78 77:0.89 81:0.75 82:0.99 83:0.72 86:0.90 88:0.98 91:0.05 98:0.85 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.68 114:0.82 117:0.86 122:0.98 123:0.66 124:0.38 126:0.54 127:0.62 129:0.05 131:0.61 133:0.38 135:0.74 139:0.89 141:0.91 144:0.76 145:0.72 146:0.94 147:0.54 149:0.68 150:0.57 154:0.58 155:0.50 157:1.00 159:0.68 161:0.79 165:0.86 166:0.88 167:0.62 170:0.82 172:0.95 173:0.78 174:0.97 176:0.73 177:0.38 178:0.55 179:0.19 181:0.50 182:1.00 187:0.39 192:0.51 195:0.82 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.60 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.96 241:0.10 242:0.15 243:0.14 245:0.93 246:1.00 247:0.96 253:0.66 254:0.90 257:0.93 259:0.21 262:0.93 264:0.90 265:0.85 266:0.63 267:0.44 271:0.56 274:0.91 275:0.97 276:0.91 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +0 1:0.56 10:0.98 11:0.64 12:0.79 17:0.72 18:0.95 21:0.78 26:0.98 27:0.45 28:0.70 29:0.71 30:0.87 34:0.89 36:0.18 37:0.50 39:0.38 43:0.60 45:0.98 56:0.97 58:0.93 64:0.77 66:0.82 69:0.83 70:0.41 71:0.79 74:0.55 81:0.48 83:0.60 86:0.96 91:0.06 98:0.66 99:0.95 101:0.69 108:0.68 122:0.97 123:0.66 124:0.41 126:0.43 127:0.44 129:0.05 131:0.61 133:0.72 135:0.71 139:0.93 141:0.91 144:0.76 145:0.49 146:0.84 147:0.22 149:0.83 150:0.36 154:0.60 155:0.46 157:1.00 159:0.63 161:0.79 165:0.70 166:0.76 167:0.65 170:0.82 172:0.94 173:0.78 174:0.97 177:0.70 178:0.55 179:0.19 181:0.50 182:1.00 187:0.68 192:0.44 197:0.98 199:0.98 201:0.54 208:0.54 212:0.86 216:0.77 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.77 234:0.91 235:1.00 236:0.94 239:0.97 241:0.21 242:0.24 243:0.10 245:0.82 246:0.95 247:0.84 253:0.43 254:0.86 257:0.84 259:0.21 264:0.90 265:0.67 266:0.71 267:0.36 271:0.56 274:0.91 275:0.98 276:0.88 287:0.61 294:0.99 300:0.70 +1 1:0.65 10:0.97 11:0.64 12:0.79 17:0.47 18:0.91 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.76 32:0.78 33:0.97 34:0.84 36:0.19 37:0.60 39:0.38 43:0.36 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.75 69:0.83 70:0.50 71:0.79 74:0.81 77:0.89 81:0.75 82:0.99 83:0.72 86:0.91 88:0.98 91:0.04 98:0.84 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.67 114:0.82 117:0.86 122:0.98 123:0.65 124:0.42 126:0.54 127:0.59 129:0.05 131:0.61 133:0.45 135:0.47 139:0.89 141:0.91 144:0.76 145:0.72 146:0.94 147:0.53 149:0.69 150:0.57 154:0.58 155:0.50 157:1.00 159:0.68 161:0.79 165:0.86 166:0.88 167:0.67 170:0.82 172:0.95 173:0.77 174:0.97 176:0.73 177:0.88 178:0.55 179:0.19 181:0.50 182:1.00 187:0.39 192:0.51 195:0.82 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.60 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.97 241:0.30 242:0.15 243:0.23 245:0.96 246:1.00 247:0.93 253:0.67 254:0.88 257:0.65 259:0.21 262:0.93 264:0.90 265:0.84 266:0.75 267:0.36 271:0.56 274:0.91 275:0.97 276:0.91 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +1 1:0.63 10:0.97 11:0.64 12:0.79 17:0.69 18:0.93 21:0.54 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.87 32:0.80 33:0.97 34:0.91 36:0.18 37:0.60 39:0.38 43:0.60 44:0.93 45:0.97 47:0.93 56:0.97 58:0.93 64:0.77 66:0.16 69:0.48 70:0.42 71:0.79 74:0.66 77:0.70 81:0.75 82:0.99 83:0.91 86:0.91 88:0.99 91:0.10 98:0.62 101:0.69 102:0.98 104:0.97 106:0.81 108:0.73 114:0.77 117:0.86 122:0.98 123:0.35 124:0.50 126:0.54 127:0.50 129:0.59 131:0.61 133:0.47 135:0.68 139:0.90 141:0.91 144:0.76 145:0.74 146:0.67 147:0.72 149:0.84 150:0.54 154:0.61 155:0.49 157:1.00 159:0.48 161:0.79 165:0.93 166:0.88 167:0.65 170:0.82 172:0.87 173:0.48 174:0.97 176:0.73 177:0.96 178:0.55 179:0.24 181:0.50 182:1.00 187:0.21 192:0.50 195:0.68 197:0.98 199:0.98 201:0.64 206:0.81 208:0.64 212:0.87 215:0.58 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.82 232:0.99 234:0.91 235:0.84 236:0.97 239:0.97 241:0.24 242:0.24 243:0.37 245:0.95 246:1.00 247:0.88 253:0.60 254:0.74 259:0.21 262:0.93 264:0.90 265:0.61 266:0.75 267:0.84 271:0.56 274:0.91 275:0.96 276:0.84 282:0.88 287:0.61 290:0.77 291:0.97 294:0.99 297:0.36 299:0.88 300:0.84 +0 1:0.65 10:0.93 11:0.64 12:0.79 17:0.57 18:0.87 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.73 32:0.78 33:0.97 34:0.93 36:0.29 37:0.60 39:0.38 43:0.27 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.81 69:0.81 70:0.59 71:0.79 74:0.80 77:0.89 81:0.75 82:0.99 83:0.72 86:0.82 88:0.98 91:0.05 98:0.86 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.65 114:0.82 117:0.86 122:0.57 123:0.63 124:0.39 126:0.54 127:0.63 129:0.05 131:0.61 133:0.45 135:0.17 139:0.83 141:0.91 144:0.76 145:0.72 146:0.94 147:0.38 149:0.73 150:0.57 154:0.49 155:0.50 157:1.00 159:0.67 161:0.79 165:0.86 166:0.88 167:0.65 170:0.82 172:0.95 173:0.76 174:0.96 176:0.73 177:0.88 178:0.55 179:0.19 181:0.50 182:1.00 187:0.39 192:0.51 195:0.81 197:0.96 199:0.98 201:0.66 206:0.81 208:0.64 212:0.75 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.94 241:0.24 242:0.17 243:0.17 245:0.94 246:1.00 247:0.94 253:0.67 254:0.90 257:0.65 259:0.21 262:0.93 264:0.90 265:0.86 266:0.75 267:0.44 271:0.56 274:0.91 275:0.97 276:0.91 282:0.86 287:0.61 290:0.82 291:0.97 294:0.98 297:0.36 300:0.84 +2 1:0.66 10:0.99 11:0.83 12:0.79 17:0.94 18:0.93 21:0.30 26:0.98 27:0.63 28:0.70 29:0.71 30:0.87 32:0.78 34:0.31 36:0.21 37:0.61 39:0.38 43:0.63 44:0.93 45:0.99 56:0.97 58:0.93 64:0.77 66:0.77 69:0.54 70:0.44 71:0.79 74:0.91 77:0.70 81:0.76 82:0.99 83:0.72 85:0.72 86:0.97 88:0.98 91:0.05 98:0.84 99:0.99 101:0.97 102:0.98 104:0.94 106:0.81 108:0.41 114:0.86 117:0.86 122:0.97 123:0.40 124:0.43 126:0.54 127:0.79 129:0.05 131:0.61 133:0.62 135:0.96 139:0.97 141:0.91 144:0.76 145:0.67 146:0.98 147:0.98 149:0.84 150:0.59 154:0.77 155:0.51 157:1.00 159:0.83 161:0.79 165:0.86 166:0.89 167:0.61 170:0.82 172:0.97 173:0.31 174:0.97 176:0.73 177:0.96 178:0.55 179:0.15 181:0.50 182:1.00 187:0.39 192:0.55 195:0.92 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.68 215:0.72 216:0.55 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.97 234:0.91 235:0.68 236:0.97 239:0.99 241:0.07 242:0.18 243:0.79 245:0.96 246:1.00 247:0.91 253:0.72 259:0.21 264:0.90 265:0.84 266:0.75 267:0.87 271:0.56 274:0.91 275:0.99 276:0.95 282:0.86 287:0.61 290:0.86 294:1.00 297:0.36 300:0.84 +2 1:0.65 10:0.98 11:0.64 12:0.79 17:0.45 18:0.93 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.85 32:0.78 33:0.97 34:0.68 36:0.24 37:0.60 39:0.38 43:0.36 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.85 69:0.83 70:0.48 71:0.79 74:0.80 77:0.89 81:0.75 82:0.99 83:0.72 86:0.94 88:0.98 91:0.08 98:0.86 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.68 114:0.82 117:0.86 122:0.98 123:0.66 124:0.38 126:0.54 127:0.68 129:0.05 131:0.61 133:0.38 135:0.34 139:0.93 141:0.91 144:0.76 145:0.72 146:0.94 147:0.52 149:0.73 150:0.57 154:0.58 155:0.50 157:1.00 159:0.67 161:0.79 165:0.86 166:0.88 167:0.63 170:0.82 172:0.96 173:0.79 174:0.98 176:0.73 177:0.38 178:0.55 179:0.18 181:0.50 182:1.00 187:0.39 192:0.51 195:0.81 197:0.98 199:0.98 201:0.66 206:0.81 208:0.64 212:0.68 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.98 241:0.15 242:0.15 243:0.13 245:0.93 246:1.00 247:0.95 253:0.67 254:0.88 257:0.93 259:0.21 262:0.93 264:0.90 265:0.86 266:0.63 267:0.44 271:0.56 274:0.91 275:0.97 276:0.92 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +2 1:0.65 8:0.67 9:0.75 10:0.98 11:0.64 12:0.79 17:0.91 18:0.97 20:0.90 21:0.59 23:0.99 26:0.98 27:0.62 28:0.70 29:0.71 30:0.87 31:0.95 34:0.29 36:0.48 37:0.27 39:0.38 43:0.62 44:0.88 45:0.98 58:1.00 62:0.99 64:0.77 66:0.67 69:0.56 70:0.42 71:0.79 74:0.60 75:0.97 78:0.84 79:0.95 80:0.98 81:0.44 82:0.99 83:0.91 86:0.95 88:0.99 91:0.73 96:0.85 97:0.97 98:0.85 99:0.99 100:0.82 101:0.69 102:0.96 108:0.43 111:0.82 120:0.76 122:0.97 123:0.41 124:0.47 126:0.31 127:0.66 128:0.98 129:0.59 131:0.93 133:0.47 135:0.28 137:0.95 139:0.94 140:0.97 141:0.99 143:0.99 144:0.76 145:0.82 146:0.93 147:0.94 149:0.87 150:0.41 151:0.66 152:0.84 153:0.78 154:0.70 155:0.65 157:1.00 159:0.66 161:0.79 162:0.75 164:0.81 165:0.63 166:0.73 167:0.97 168:0.98 169:0.80 170:0.82 172:0.93 173:0.46 174:0.98 177:0.96 178:0.48 179:0.20 181:0.50 182:1.00 186:0.84 190:0.77 192:1.00 193:0.95 195:0.83 196:0.97 197:0.99 199:0.99 201:0.61 202:0.81 204:0.81 205:0.99 208:0.64 212:0.77 216:0.23 219:0.91 220:0.74 222:0.60 223:0.98 224:0.98 226:0.96 227:0.96 229:1.00 231:0.83 234:0.98 235:0.95 236:0.91 239:0.98 241:0.85 242:0.28 243:0.72 245:0.97 246:0.61 247:0.74 248:0.63 253:0.82 254:0.86 255:0.78 256:0.85 259:0.21 260:0.77 261:0.85 264:0.90 265:0.85 266:0.38 267:0.23 268:0.85 271:0.56 274:0.99 275:0.97 276:0.90 279:0.79 281:0.91 284:0.97 285:0.62 287:1.00 292:0.88 294:0.99 295:0.98 300:0.70 +1 10:0.99 11:0.64 12:0.79 17:0.36 18:0.89 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.67 34:0.53 36:0.22 37:0.90 39:0.38 43:0.57 45:0.97 58:0.93 66:0.69 69:0.07 70:0.58 71:0.79 74:0.85 86:0.97 91:0.20 98:0.71 101:0.69 104:0.58 108:0.06 122:0.97 123:0.06 124:0.48 127:0.81 129:0.05 131:0.61 133:0.60 135:0.77 139:0.95 141:0.91 144:0.76 146:0.84 147:0.87 149:0.47 154:0.72 157:0.99 159:0.66 161:0.79 166:0.61 167:0.67 170:0.82 172:0.92 173:0.12 174:0.95 177:0.96 178:0.55 179:0.24 181:0.50 182:0.95 187:0.68 197:0.96 199:0.97 212:0.87 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.12 239:0.99 241:0.32 242:0.18 243:0.73 245:0.92 246:0.61 247:0.82 253:0.58 254:0.90 259:0.21 264:0.90 265:0.72 266:0.71 267:0.36 271:0.56 274:0.91 275:0.98 276:0.88 287:0.61 294:1.00 300:0.70 +1 10:0.97 11:0.64 12:0.79 17:0.47 18:0.92 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.85 34:0.77 36:0.66 37:0.90 39:0.38 43:0.59 45:0.98 58:0.93 66:0.71 69:0.08 70:0.46 71:0.79 74:0.66 86:0.94 91:0.12 98:0.80 101:0.69 104:0.58 108:0.07 122:0.97 123:0.07 124:0.47 127:0.87 129:0.05 131:0.61 133:0.60 135:0.83 139:0.87 141:0.91 144:0.76 146:0.93 147:0.95 149:0.85 154:0.71 157:1.00 159:0.74 161:0.79 166:0.61 167:0.71 170:0.82 172:0.93 173:0.10 174:0.94 177:0.96 178:0.55 179:0.23 181:0.50 182:1.00 187:0.68 197:0.96 199:0.97 212:0.73 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.22 239:0.96 241:0.46 242:0.17 243:0.75 245:0.92 246:0.61 247:0.88 253:0.49 254:0.93 259:0.21 264:0.90 265:0.80 266:0.75 267:0.44 271:0.56 274:0.91 275:0.98 276:0.89 287:0.61 294:0.99 300:0.70 +1 1:0.67 7:0.69 10:0.89 11:0.64 12:0.79 17:0.66 18:0.62 21:0.22 26:0.98 27:0.31 28:0.70 29:0.71 30:0.76 32:0.77 34:0.98 36:0.29 37:0.55 39:0.38 43:0.31 44:0.93 45:0.83 56:0.97 58:0.93 64:0.77 66:0.64 69:0.60 70:0.36 71:0.79 74:0.60 77:0.70 80:0.98 81:0.80 82:0.99 83:0.91 86:0.73 88:0.98 91:0.09 98:0.52 101:0.69 102:0.98 104:0.95 106:0.81 108:0.45 114:0.90 117:0.86 122:0.57 123:0.44 124:0.44 126:0.54 127:0.23 129:0.05 131:0.61 133:0.38 135:0.87 139:0.79 141:0.91 144:0.76 145:0.72 146:0.53 147:0.59 149:0.31 150:0.62 154:0.64 155:0.54 157:0.98 159:0.28 161:0.79 165:0.93 166:0.89 167:0.65 170:0.82 172:0.45 173:0.64 174:0.86 176:0.73 177:0.96 178:0.55 179:0.61 181:0.50 182:1.00 187:0.39 192:0.57 193:0.95 195:0.67 197:0.89 199:0.95 201:0.67 204:0.81 206:0.81 208:0.64 212:0.57 215:0.79 216:0.85 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 230:0.71 231:0.82 232:0.97 233:0.76 234:0.91 235:0.60 236:0.97 239:0.92 241:0.24 242:0.13 243:0.69 245:0.55 246:1.00 247:0.74 253:0.65 254:0.92 259:0.21 264:0.90 265:0.54 266:0.47 267:0.28 271:0.56 274:0.91 275:0.91 276:0.37 281:0.91 282:0.85 287:0.61 290:0.90 294:0.98 297:0.36 300:0.33 +1 6:0.84 7:0.81 8:0.70 12:0.06 17:0.03 20:0.85 21:0.30 27:0.50 28:0.53 30:0.45 34:0.56 36:0.80 37:0.25 43:0.52 55:0.59 66:0.94 69:0.10 70:0.49 78:0.76 81:0.91 91:0.87 98:0.95 100:0.79 104:0.79 106:0.81 108:0.09 111:0.76 120:0.98 123:0.09 126:0.54 127:0.64 129:0.05 135:0.37 140:0.45 146:0.91 147:0.92 149:0.76 150:0.83 151:0.89 152:0.90 153:0.99 154:0.41 159:0.51 161:0.68 162:0.79 164:0.98 167:0.78 169:0.77 172:0.83 173:0.18 177:0.05 179:0.43 181:0.64 186:0.76 187:0.21 189:0.87 191:0.82 202:0.96 206:0.81 212:0.75 215:0.72 216:0.75 230:0.87 233:0.76 235:0.31 238:0.92 241:0.73 242:0.82 243:0.94 247:0.12 248:0.98 256:0.97 259:0.21 260:0.98 261:0.80 265:0.95 266:0.47 267:0.94 268:0.97 271:0.52 276:0.73 283:0.82 285:0.62 297:0.36 300:0.33 +0 8:0.77 12:0.06 17:0.09 20:0.77 21:0.78 27:0.58 28:0.53 34:0.53 36:0.29 37:0.45 78:0.75 91:0.96 100:0.78 104:0.58 111:0.74 120:0.90 135:0.80 140:0.87 151:0.78 152:0.75 153:0.89 161:0.68 162:0.57 164:0.93 167:0.91 169:0.76 175:0.75 178:0.48 181:0.64 186:0.76 191:0.80 202:0.91 216:0.50 241:0.90 244:0.61 248:0.86 256:0.91 257:0.65 259:0.21 260:0.91 261:0.75 267:0.44 268:0.91 271:0.52 277:0.69 285:0.62 +1 6:0.79 8:0.58 12:0.06 17:0.06 20:0.98 21:0.78 27:0.07 28:0.53 34:0.80 36:0.28 37:0.24 78:0.82 91:0.99 100:0.88 104:0.58 111:0.83 120:0.99 135:0.81 140:0.45 151:0.90 152:0.93 153:0.99 161:0.68 162:0.74 164:0.99 167:0.91 169:0.85 175:0.75 181:0.64 186:0.82 189:0.82 191:0.81 202:0.98 216:0.76 238:0.93 241:0.89 244:0.61 248:0.98 256:0.98 257:0.65 259:0.21 260:0.99 261:0.89 267:0.87 268:0.99 271:0.52 277:0.69 285:0.62 +1 12:0.06 17:0.40 21:0.30 27:0.45 28:0.53 30:0.33 32:0.80 34:0.71 36:0.17 37:0.50 43:0.32 55:0.59 66:0.46 69:0.68 70:0.80 81:0.91 91:0.42 98:0.32 108:0.82 123:0.81 124:0.74 126:0.54 127:0.27 129:0.89 133:0.78 135:0.92 145:0.39 146:0.49 147:0.56 149:0.50 150:0.83 154:0.24 159:0.54 161:0.68 167:0.64 172:0.45 173:0.58 177:0.05 178:0.55 179:0.56 181:0.64 187:0.68 195:0.84 212:0.51 215:0.72 216:0.77 235:0.31 241:0.47 242:0.82 243:0.34 245:0.57 247:0.12 259:0.21 265:0.35 266:0.71 267:0.28 271:0.52 276:0.49 283:0.60 297:0.36 300:0.55 +1 6:0.82 8:0.64 12:0.06 17:0.06 20:0.83 21:0.05 27:0.63 28:0.53 30:0.33 34:0.92 36:0.39 37:0.31 43:0.51 55:0.52 66:0.86 69:0.10 70:0.72 78:0.74 81:0.95 91:0.80 98:0.90 100:0.78 104:0.78 106:0.81 108:0.09 111:0.74 120:0.95 123:0.09 126:0.54 127:0.46 129:0.05 135:0.69 140:0.87 146:0.70 147:0.75 149:0.63 150:0.91 151:0.80 152:0.79 153:0.93 154:0.40 159:0.28 161:0.68 162:0.59 164:0.95 167:0.74 169:0.76 172:0.61 173:0.32 177:0.05 178:0.48 179:0.69 181:0.64 186:0.75 187:0.21 189:0.84 191:0.76 202:0.94 206:0.81 212:0.69 215:0.91 216:0.58 235:0.05 238:0.89 241:0.67 242:0.82 243:0.87 247:0.12 248:0.92 256:0.94 259:0.21 260:0.95 261:0.76 265:0.90 266:0.27 267:0.59 268:0.94 271:0.52 276:0.48 283:0.60 285:0.62 297:0.36 300:0.55 +1 6:0.88 7:0.81 8:0.73 12:0.06 17:0.22 20:0.99 21:0.78 27:0.21 28:0.53 30:0.36 34:0.67 36:0.95 37:0.74 43:0.38 55:0.59 66:0.26 69:0.58 70:0.75 78:0.75 91:0.87 98:0.62 100:0.79 108:0.93 111:0.75 120:0.97 123:0.82 124:0.83 127:0.63 129:0.93 133:0.84 135:0.49 140:0.45 146:0.78 147:0.81 149:0.78 151:0.85 152:0.98 153:0.98 154:0.29 159:0.86 161:0.68 162:0.64 164:0.95 167:0.59 169:0.77 172:0.80 173:0.29 177:0.05 179:0.22 181:0.64 186:0.76 187:0.39 189:0.88 191:0.82 202:0.93 212:0.42 216:0.95 230:0.65 233:0.63 235:0.52 238:0.93 241:0.32 242:0.82 243:0.21 245:0.92 247:0.12 248:0.96 256:0.93 259:0.21 260:0.98 261:0.82 265:0.52 266:0.91 267:0.85 268:0.94 271:0.52 276:0.89 283:0.82 285:0.62 300:0.70 +1 6:0.82 7:0.81 8:0.74 12:0.06 17:0.01 20:0.92 21:0.78 27:0.48 28:0.53 30:0.33 34:0.49 36:0.85 37:0.80 43:0.46 55:0.52 66:0.72 69:0.40 70:0.64 78:0.74 91:0.91 98:0.68 100:0.77 104:0.74 108:0.68 111:0.73 120:0.93 123:0.66 124:0.42 127:0.34 129:0.59 133:0.47 135:0.71 140:0.96 146:0.50 147:0.56 149:0.62 151:0.74 152:0.86 153:0.84 154:0.35 159:0.53 161:0.68 162:0.51 164:0.95 167:0.89 169:0.75 172:0.68 173:0.31 177:0.05 178:0.54 179:0.48 181:0.64 186:0.75 189:0.84 191:0.82 202:0.96 212:0.58 216:0.62 220:0.62 230:0.87 233:0.76 235:0.02 238:0.88 241:0.82 242:0.82 243:0.22 245:0.71 247:0.12 248:0.90 256:0.93 259:0.21 260:0.93 261:0.76 265:0.68 266:0.75 267:0.89 268:0.94 271:0.52 276:0.60 283:0.60 285:0.62 300:0.55 +2 7:0.81 8:0.71 12:0.06 17:0.64 21:0.40 27:0.21 28:0.53 30:0.66 34:0.37 36:0.73 37:0.74 43:0.52 55:0.71 66:0.63 69:0.12 70:0.51 78:0.80 81:0.89 91:0.37 98:0.80 104:0.84 106:0.81 108:0.10 111:0.83 120:0.82 123:0.10 124:0.64 126:0.54 127:0.83 129:0.89 133:0.78 135:0.24 140:0.45 146:0.99 147:0.99 149:0.88 150:0.79 151:0.73 153:0.78 154:0.41 159:0.92 161:0.68 162:0.82 164:0.85 167:0.56 169:0.87 172:0.96 173:0.07 177:0.05 179:0.16 181:0.64 187:0.68 202:0.76 206:0.81 212:0.55 215:0.67 216:0.95 220:0.91 230:0.65 233:0.63 235:0.42 241:0.18 242:0.82 243:0.69 245:0.96 247:0.12 248:0.75 256:0.81 259:0.21 260:0.83 265:0.80 266:0.84 267:0.19 268:0.81 271:0.52 276:0.94 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.97 8:0.96 12:0.06 17:0.13 20:0.96 21:0.59 25:0.88 27:0.04 28:0.53 30:0.45 32:0.80 34:0.21 36:0.23 37:0.94 43:0.25 55:0.71 66:0.13 69:0.84 70:0.50 78:0.88 81:0.84 91:0.99 98:0.40 100:0.99 104:0.58 108:0.79 111:0.97 120:0.98 123:0.67 124:0.50 126:0.54 127:0.25 129:0.59 133:0.47 135:0.24 140:0.45 145:0.79 146:0.43 147:0.50 149:0.31 150:0.64 151:0.89 152:0.94 153:0.99 154:0.18 159:0.31 161:0.68 162:0.76 164:0.99 167:0.92 169:0.97 172:0.27 173:0.90 177:0.05 179:0.81 181:0.64 186:0.88 189:0.98 191:0.98 195:0.66 202:0.97 212:0.52 215:0.55 216:0.72 235:0.68 238:0.99 241:0.96 242:0.82 243:0.34 245:0.48 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:0.98 265:0.37 266:0.27 267:0.73 268:0.98 271:0.52 276:0.29 283:0.82 285:0.62 297:0.36 300:0.33 +1 7:0.81 8:0.60 12:0.06 17:0.30 21:0.30 25:0.88 27:0.72 28:0.53 30:0.45 34:0.53 36:0.80 43:0.52 55:0.59 66:0.94 69:0.10 70:0.49 81:0.91 91:0.54 98:0.95 104:0.54 108:0.09 120:0.93 123:0.09 126:0.54 127:0.67 129:0.05 135:0.32 140:0.45 146:0.91 147:0.92 149:0.76 150:0.83 151:0.80 153:0.92 154:0.41 159:0.51 161:0.68 162:0.85 164:0.88 167:0.90 172:0.83 173:0.19 177:0.05 179:0.44 181:0.64 202:0.78 212:0.75 215:0.72 216:0.34 230:0.87 233:0.76 235:0.31 241:0.87 242:0.82 243:0.94 247:0.12 248:0.89 256:0.84 259:0.21 260:0.93 265:0.95 266:0.47 267:0.88 268:0.85 271:0.52 276:0.73 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.06 17:0.34 21:0.30 27:0.45 28:0.53 30:0.55 32:0.80 34:0.71 36:0.24 37:0.50 43:0.32 55:0.84 66:0.52 69:0.68 70:0.51 81:0.91 91:0.14 98:0.70 108:0.78 123:0.77 124:0.64 126:0.54 127:0.51 129:0.81 133:0.67 135:0.95 145:0.47 146:0.71 147:0.75 149:0.63 150:0.83 154:0.24 159:0.73 161:0.68 163:0.81 167:0.62 172:0.65 173:0.54 177:0.05 178:0.55 179:0.50 181:0.64 187:0.68 195:0.88 212:0.19 215:0.72 216:0.77 235:0.31 241:0.43 242:0.82 243:0.39 245:0.75 247:0.12 259:0.21 265:0.70 266:0.84 267:0.65 271:0.52 276:0.66 283:0.60 297:0.36 300:0.43 +1 8:0.77 12:0.06 17:0.09 20:0.77 21:0.78 27:0.58 28:0.53 34:0.53 36:0.29 37:0.45 78:0.75 91:0.96 100:0.78 104:0.58 111:0.74 120:0.90 135:0.80 140:0.87 151:0.78 152:0.75 153:0.89 161:0.68 162:0.57 164:0.93 167:0.91 169:0.76 175:0.75 178:0.48 181:0.64 186:0.76 191:0.80 202:0.91 216:0.50 241:0.90 244:0.61 248:0.86 256:0.91 257:0.65 259:0.21 260:0.91 261:0.75 267:0.44 268:0.91 271:0.52 277:0.69 285:0.62 +1 12:0.06 17:0.26 20:0.81 21:0.13 25:0.88 27:0.72 28:0.53 34:0.26 36:0.61 37:0.24 43:0.52 66:0.36 69:0.07 78:0.74 81:0.93 91:0.89 98:0.09 100:0.73 104:0.54 108:0.06 111:0.73 120:0.90 123:0.06 126:0.54 127:0.06 129:0.05 135:0.37 140:0.45 146:0.05 147:0.05 149:0.16 150:0.89 151:0.73 152:0.78 153:0.80 154:0.41 159:0.05 161:0.68 162:0.83 164:0.93 167:0.90 169:0.72 172:0.05 173:0.21 177:0.05 181:0.64 186:0.75 202:0.86 215:0.84 216:0.62 220:0.74 235:0.12 241:0.85 243:0.51 248:0.85 256:0.90 259:0.21 260:0.90 261:0.74 265:0.09 267:0.69 268:0.91 271:0.52 283:0.60 285:0.62 297:0.36 +2 6:0.98 8:0.97 12:0.06 17:0.78 20:0.82 21:0.13 25:0.88 27:0.20 28:0.53 30:0.45 34:0.89 36:0.50 37:0.94 43:0.35 55:0.59 66:0.64 69:0.63 70:0.33 78:0.76 81:0.93 91:0.91 98:0.64 100:0.84 104:0.54 108:0.61 111:0.78 120:0.84 123:0.59 124:0.42 126:0.54 127:0.34 129:0.59 133:0.47 135:0.47 140:0.45 146:0.05 147:0.05 149:0.38 150:0.89 151:0.78 152:0.78 153:0.89 154:0.26 159:0.28 161:0.68 162:0.80 164:0.91 167:0.89 169:0.82 172:0.32 173:0.77 177:0.05 179:0.87 181:0.64 186:0.77 187:0.21 189:0.99 191:0.97 202:0.84 212:0.23 215:0.84 216:0.26 220:0.74 235:0.12 238:0.98 241:0.83 242:0.82 243:0.21 245:0.43 247:0.12 248:0.76 256:0.88 259:0.21 260:0.85 261:0.79 265:0.64 266:0.38 267:0.28 268:0.88 271:0.52 276:0.29 285:0.62 297:0.36 300:0.25 +2 1:0.65 8:0.64 10:0.96 11:0.77 12:0.69 17:0.95 18:0.86 21:0.47 26:0.97 27:0.64 29:0.77 30:0.85 34:0.75 36:0.61 37:0.34 39:0.72 43:0.40 45:0.99 56:0.97 58:0.93 64:0.77 66:0.61 69:0.29 70:0.56 71:0.88 74:0.44 77:0.70 80:0.99 81:0.77 83:0.73 86:0.73 89:0.98 91:0.09 97:0.94 98:0.66 99:1.00 101:0.87 108:0.22 114:0.80 122:0.98 123:0.22 124:0.72 126:0.54 127:0.90 129:0.05 131:0.61 133:0.81 135:0.32 139:0.72 141:0.91 144:0.76 145:0.54 146:0.89 147:0.91 149:0.83 150:0.54 154:0.37 155:0.53 157:0.87 158:0.73 159:0.79 165:0.88 166:0.81 170:0.77 172:0.93 173:0.17 174:0.96 176:0.73 177:0.99 178:0.55 179:0.20 182:0.80 187:0.68 192:0.58 193:0.96 195:0.89 197:0.98 199:0.98 201:0.65 204:0.80 208:0.64 212:0.71 215:0.63 216:0.15 219:0.88 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.71 234:0.91 235:0.95 236:0.97 239:0.95 240:0.95 241:0.18 242:0.23 243:0.68 244:0.83 245:0.93 246:0.91 247:0.97 253:0.58 254:0.93 259:0.21 264:0.96 265:0.66 266:0.91 267:0.23 271:0.44 274:0.91 275:0.98 276:0.92 279:0.75 282:0.71 283:0.67 287:0.61 290:0.80 292:0.88 294:0.99 297:0.36 300:0.84 +2 1:0.61 8:0.75 10:0.91 11:0.77 12:0.69 17:0.98 18:0.72 21:0.40 26:0.97 27:0.72 29:0.77 30:0.54 32:0.78 34:0.47 36:0.83 39:0.72 43:0.06 44:0.93 45:0.85 48:0.98 56:0.97 58:0.93 66:0.88 69:0.25 70:0.54 71:0.88 74:0.60 76:0.97 77:0.89 80:1.00 81:0.62 82:0.99 83:0.60 86:0.79 88:0.98 89:0.98 91:0.76 98:0.95 99:0.99 101:0.87 102:0.98 108:0.20 114:0.70 122:0.41 123:0.19 126:0.51 127:0.66 129:0.05 131:0.61 135:0.30 139:0.83 141:0.91 144:0.76 145:0.69 146:0.54 147:0.60 149:0.05 150:0.45 154:0.84 155:0.57 157:0.80 159:0.20 165:0.70 166:0.73 170:0.77 172:0.67 173:0.61 174:0.79 176:0.59 177:0.99 178:0.55 179:0.67 182:0.76 192:0.98 193:0.99 195:0.53 197:0.85 199:0.96 201:0.63 204:0.85 208:0.64 212:0.88 216:0.08 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.69 234:0.91 235:0.74 236:0.94 239:0.95 240:0.95 241:0.81 242:0.14 243:0.89 246:0.61 247:0.86 253:0.56 254:0.84 259:0.21 264:0.96 265:0.95 266:0.27 267:0.28 271:0.44 274:0.91 275:0.91 276:0.55 281:0.86 282:0.86 287:0.61 290:0.70 294:0.99 300:0.43 +1 10:0.92 11:0.77 12:0.69 17:0.32 18:0.70 21:0.78 26:0.95 27:0.28 29:0.77 30:0.91 34:0.77 36:0.65 37:0.93 39:0.72 43:0.28 45:0.92 58:0.93 66:0.64 69:0.87 70:0.24 71:0.88 74:0.43 86:0.77 89:0.97 91:0.23 98:0.40 101:0.71 104:0.58 108:0.74 122:0.98 123:0.73 124:0.50 127:0.79 129:0.05 131:0.61 133:0.59 135:0.34 139:0.72 141:0.91 144:0.76 146:0.40 147:0.22 149:0.25 154:0.53 157:0.80 159:0.44 166:0.61 170:0.77 172:0.61 173:0.97 174:0.86 177:0.70 178:0.55 179:0.67 182:0.74 187:0.39 197:0.88 199:0.95 212:0.85 216:0.25 222:0.84 223:0.94 224:0.93 225:0.96 227:0.61 229:0.61 231:0.64 232:0.79 234:0.91 235:0.05 239:0.89 240:0.95 241:0.21 242:0.21 243:0.24 245:0.63 246:0.61 247:0.79 253:0.37 254:0.90 257:0.97 259:0.21 264:0.96 265:0.42 266:0.38 267:0.36 271:0.44 274:0.91 275:0.94 276:0.46 287:0.61 294:0.97 300:0.33 +0 8:0.75 10:0.89 11:0.75 12:0.69 17:0.81 18:0.82 21:0.78 26:0.96 27:0.28 29:0.77 30:0.85 34:0.67 36:0.83 37:0.93 39:0.72 43:0.26 45:0.96 58:0.93 66:0.33 69:0.55 70:0.44 71:0.88 74:0.41 86:0.72 89:0.95 91:0.03 98:0.64 101:0.65 104:0.58 108:0.70 122:0.41 123:0.68 124:0.70 127:0.50 129:0.59 131:0.61 133:0.67 135:0.51 139:0.70 141:0.91 144:0.76 146:0.59 147:0.65 149:0.66 154:0.17 157:0.85 159:0.60 166:0.61 170:0.77 172:0.74 173:0.47 174:0.91 175:0.75 177:0.99 178:0.55 179:0.29 182:0.76 187:0.21 197:0.93 199:0.95 212:0.58 216:0.25 222:0.84 223:0.95 224:0.93 225:0.95 227:0.61 229:0.61 231:0.68 232:0.79 234:0.91 235:0.05 239:0.88 240:0.95 241:0.10 242:0.17 243:0.46 244:0.93 245:0.91 246:0.61 247:0.92 253:0.36 254:0.95 257:0.65 259:0.21 264:0.96 265:0.65 266:0.71 267:0.93 271:0.44 274:0.91 275:0.96 276:0.81 277:0.69 287:0.61 294:0.96 300:0.55 +1 1:0.58 10:0.94 11:0.77 12:0.69 17:0.79 18:0.68 21:0.13 26:0.97 27:0.72 29:0.77 30:0.85 34:0.75 36:0.99 37:0.26 39:0.72 43:0.28 45:0.97 56:0.97 58:0.93 66:0.54 69:0.82 70:0.51 71:0.88 74:0.48 80:0.99 81:0.38 82:0.98 83:0.56 86:0.80 88:0.99 89:0.98 91:0.27 98:0.75 99:0.83 101:0.87 102:0.95 108:0.67 122:0.98 123:0.65 124:0.52 126:0.26 127:0.62 129:0.05 131:0.61 133:0.43 135:0.26 139:0.75 141:0.91 144:0.76 145:0.45 146:0.75 147:0.64 149:0.57 150:0.37 154:0.51 155:0.53 157:0.82 159:0.48 165:0.63 166:0.61 170:0.77 172:0.83 173:0.87 174:0.93 177:0.88 178:0.55 179:0.31 182:0.75 187:0.21 192:0.55 193:0.96 195:0.68 197:0.95 199:0.97 201:0.55 204:0.83 208:0.50 212:0.82 216:0.21 222:0.84 223:0.97 224:0.93 225:0.97 227:0.61 229:0.61 231:0.65 234:0.91 235:0.22 236:0.91 239:0.94 240:0.95 241:0.35 242:0.27 243:0.40 245:0.93 246:0.61 247:0.93 253:0.39 254:0.88 257:0.93 259:0.21 264:0.96 265:0.75 266:0.47 267:0.19 271:0.44 274:0.91 275:0.97 276:0.81 287:0.61 294:0.98 300:0.70 +1 1:0.63 10:0.90 11:0.77 12:0.69 17:0.38 18:0.77 26:0.95 27:0.72 29:0.77 30:0.91 34:0.87 36:0.81 37:0.26 39:0.72 43:0.75 45:0.89 58:0.93 64:0.77 66:0.86 69:0.07 70:0.21 71:0.88 74:0.47 81:0.55 83:0.56 86:0.74 89:0.95 91:0.25 97:0.94 98:0.82 99:0.95 101:0.96 104:0.94 106:0.81 108:0.06 114:0.89 122:0.67 123:0.06 126:0.32 127:0.31 129:0.05 131:0.61 135:0.58 139:0.74 141:0.91 144:0.76 146:0.41 147:0.47 149:0.72 150:0.48 154:0.66 155:0.49 157:0.80 158:0.73 159:0.15 165:0.65 166:0.81 170:0.77 172:0.61 173:0.46 174:0.83 175:0.75 176:0.60 177:0.99 178:0.55 179:0.60 182:0.81 187:0.68 192:0.55 197:0.88 199:0.94 201:0.62 206:0.81 208:0.50 212:0.95 215:0.96 216:0.39 219:0.88 222:0.84 223:0.93 224:0.93 225:0.95 227:0.61 229:0.61 231:0.72 234:0.91 235:0.12 239:0.89 240:0.95 241:0.27 242:0.18 243:0.87 244:0.61 246:0.92 247:0.79 253:0.62 257:0.65 259:0.21 264:0.96 265:0.82 266:0.12 267:0.88 271:0.44 274:0.91 275:0.93 276:0.49 277:0.69 279:0.75 283:0.67 287:0.61 290:0.89 292:0.88 294:0.96 297:0.36 300:0.25 +1 1:0.65 10:0.96 11:0.77 12:0.69 17:0.55 18:0.92 21:0.40 22:0.93 26:0.97 27:0.56 29:0.77 30:0.93 32:0.80 33:0.97 34:0.92 36:0.23 37:0.60 39:0.72 43:0.36 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.97 69:0.58 70:0.28 71:0.88 74:0.72 77:0.70 81:0.79 82:0.99 83:0.91 86:0.90 88:0.99 89:0.98 91:0.03 97:0.94 98:0.82 101:0.87 102:0.98 104:0.91 106:0.81 108:0.44 114:0.82 117:0.86 122:0.67 123:0.43 126:0.54 127:0.31 129:0.05 131:0.61 135:0.75 139:0.90 141:0.91 144:0.76 145:0.74 146:0.83 147:0.86 149:0.63 150:0.55 154:0.58 155:0.50 157:0.88 158:0.74 159:0.39 165:0.93 166:0.81 170:0.77 172:0.92 173:0.59 174:0.91 176:0.73 177:0.99 178:0.55 179:0.20 182:0.80 187:0.39 192:0.57 195:0.68 197:0.93 199:0.96 201:0.67 206:0.81 208:0.64 212:0.82 215:0.67 216:0.76 219:0.88 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.72 232:0.95 234:0.91 235:0.84 236:0.97 239:0.96 240:0.95 241:0.02 242:0.15 243:0.97 246:0.91 247:0.90 253:0.64 254:0.74 259:0.21 262:0.93 264:0.96 265:0.82 266:0.27 267:0.79 271:0.44 274:0.91 275:0.96 276:0.83 279:0.76 282:0.88 283:0.82 287:0.61 290:0.82 291:0.97 292:0.95 294:0.99 297:0.36 299:0.88 300:0.70 +1 1:0.64 10:0.98 11:0.77 12:0.69 17:0.34 18:0.92 21:0.40 22:0.93 26:0.97 27:0.56 29:0.77 30:0.90 33:0.97 34:0.66 36:0.26 37:0.60 39:0.72 43:0.33 44:0.92 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.47 69:0.87 70:0.39 71:0.88 74:0.62 77:0.89 81:0.71 82:0.99 83:0.69 86:0.85 88:0.98 89:0.98 91:0.02 98:0.76 99:0.99 101:0.87 102:0.97 108:0.75 114:0.82 117:0.86 122:0.98 123:0.73 124:0.69 126:0.54 127:0.60 129:0.05 131:0.61 133:0.64 135:0.28 139:0.84 141:0.91 144:0.76 145:0.74 146:0.92 147:0.70 149:0.76 150:0.50 154:0.51 155:0.49 157:0.89 159:0.72 165:0.81 166:0.81 170:0.77 172:0.94 173:0.82 174:0.97 176:0.70 177:0.99 178:0.55 179:0.14 182:0.80 187:0.39 192:0.55 195:0.86 197:0.97 199:0.98 201:0.65 208:0.64 212:0.81 216:0.76 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.72 232:0.98 234:0.91 235:0.80 236:0.95 239:0.97 240:0.95 241:0.35 242:0.24 243:0.29 245:0.98 246:0.91 247:0.96 253:0.61 254:0.93 257:0.65 259:0.21 262:0.93 264:0.96 265:0.76 266:0.63 267:0.23 271:0.44 274:0.91 275:0.99 276:0.94 282:0.80 287:0.61 290:0.82 291:0.97 294:0.99 300:0.84 +1 1:0.60 10:0.92 11:0.77 12:0.69 17:0.70 18:0.92 21:0.22 22:0.93 26:0.95 27:0.72 29:0.77 30:0.88 34:0.80 36:0.54 39:0.72 43:0.27 45:0.96 47:0.93 58:0.93 64:0.77 66:0.45 69:0.12 70:0.41 71:0.88 74:0.50 81:0.50 83:0.56 86:0.82 89:0.97 91:0.27 98:0.76 99:0.95 101:0.87 104:0.80 106:0.81 108:0.73 114:0.80 117:0.86 122:0.97 123:0.72 124:0.59 126:0.32 127:0.73 129:0.59 131:0.61 133:0.47 135:0.97 139:0.77 141:0.91 144:0.76 146:0.68 147:0.73 149:0.55 150:0.43 154:0.56 155:0.48 157:0.87 159:0.60 165:0.65 166:0.81 170:0.77 172:0.78 173:0.17 174:0.89 176:0.60 177:0.99 178:0.55 179:0.34 182:0.80 187:0.39 192:0.50 197:0.90 199:0.95 201:0.59 206:0.81 208:0.50 212:0.60 215:0.79 216:0.18 222:0.84 223:0.95 224:0.93 225:0.96 227:0.61 229:0.61 231:0.72 232:0.87 234:0.91 235:0.42 239:0.90 240:0.95 241:0.05 242:0.13 243:0.48 244:0.61 245:0.94 246:0.92 247:0.94 253:0.58 254:0.92 259:0.21 262:0.93 264:0.96 265:0.76 266:0.75 267:0.73 271:0.44 274:0.91 275:0.96 276:0.80 287:0.61 290:0.80 294:0.97 297:0.36 300:0.70 +2 1:0.63 10:0.82 11:0.64 12:0.69 17:0.38 18:0.67 26:0.94 27:0.28 29:0.77 30:0.45 34:0.42 36:0.63 37:0.93 39:0.72 43:0.28 45:0.77 58:0.93 64:0.77 66:0.82 69:0.37 70:0.61 71:0.88 74:0.32 81:0.55 83:0.56 86:0.64 89:0.96 91:0.57 98:0.85 99:0.95 101:0.52 104:0.58 108:0.29 114:0.89 123:0.28 126:0.32 127:0.35 129:0.05 131:0.61 135:0.69 139:0.62 141:0.91 144:0.76 146:0.60 147:0.65 149:0.29 150:0.48 154:0.20 155:0.49 157:0.77 159:0.22 165:0.65 166:0.81 170:0.77 172:0.48 173:0.59 174:0.79 176:0.60 177:0.99 178:0.55 179:0.78 182:0.81 187:0.39 192:0.55 197:0.83 199:0.93 201:0.62 208:0.50 212:0.50 215:0.96 216:0.25 222:0.84 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.72 232:0.79 234:0.91 235:0.12 239:0.81 240:0.95 241:0.24 242:0.33 243:0.83 244:0.97 246:0.92 247:0.60 253:0.62 259:0.21 264:0.96 265:0.85 266:0.27 267:0.36 271:0.44 274:0.91 275:0.91 276:0.37 287:0.61 290:0.89 294:0.93 297:0.36 300:0.43 +1 1:0.65 10:0.97 11:0.77 12:0.69 17:0.53 18:0.86 21:0.40 22:0.93 26:0.97 27:0.56 29:0.77 30:0.93 32:0.80 33:0.97 34:0.93 36:0.28 37:0.60 39:0.72 43:0.36 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.20 69:0.58 70:0.28 71:0.88 74:0.65 77:0.70 81:0.79 82:0.99 83:0.91 86:0.83 88:0.99 89:0.97 91:0.05 97:0.94 98:0.60 101:0.87 102:0.98 104:0.91 106:0.81 108:0.73 114:0.82 117:0.86 122:0.98 123:0.43 124:0.52 126:0.54 127:0.45 129:0.59 131:0.61 133:0.47 135:0.74 139:0.86 141:0.91 144:0.76 145:0.74 146:0.65 147:0.70 149:0.78 150:0.55 154:0.58 155:0.50 157:0.86 158:0.74 159:0.46 165:0.93 166:0.81 170:0.77 172:0.89 173:0.59 174:0.96 176:0.73 177:0.99 178:0.55 179:0.20 182:0.80 187:0.21 192:0.57 195:0.68 197:0.97 199:0.98 201:0.67 206:0.81 208:0.64 212:0.86 215:0.67 216:0.76 219:0.88 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.71 232:0.95 234:0.91 235:0.84 236:0.97 239:0.97 240:0.95 241:0.18 242:0.30 243:0.40 245:0.97 246:0.91 247:0.93 253:0.63 254:0.74 259:0.21 262:0.93 264:0.96 265:0.61 266:0.27 267:0.52 271:0.44 274:0.91 275:0.98 276:0.87 279:0.76 282:0.88 283:0.82 287:0.61 290:0.82 291:0.97 292:0.95 294:0.99 297:0.36 299:0.88 300:0.70 +2 1:0.69 8:1.00 9:0.80 10:0.90 11:0.75 12:0.69 17:0.75 18:0.75 23:0.98 26:0.97 27:0.28 29:0.77 30:0.75 31:0.99 34:0.61 36:0.99 37:0.93 39:0.72 43:0.24 44:0.92 45:0.91 56:0.97 58:0.99 62:0.99 64:0.77 66:0.92 69:0.59 70:0.50 71:0.88 74:0.60 76:0.94 77:0.89 79:0.98 80:0.99 81:0.83 82:0.99 83:0.91 86:0.83 88:0.98 89:0.97 91:0.86 96:0.94 97:0.94 98:0.84 99:1.00 101:0.71 102:0.97 104:0.58 108:0.45 114:0.98 120:0.90 122:0.67 123:0.43 126:0.54 127:0.33 128:0.99 129:0.05 131:0.87 135:0.74 137:0.99 139:0.83 140:0.80 141:0.99 143:1.00 144:0.76 145:0.59 146:0.74 147:0.78 149:0.20 150:0.66 151:0.97 153:0.88 154:0.57 155:0.67 157:0.84 158:0.73 159:0.30 162:0.79 164:0.82 165:0.88 166:0.81 168:0.99 170:0.77 172:0.79 173:0.70 174:0.83 176:0.73 177:0.99 179:0.39 182:0.81 191:0.87 192:1.00 193:0.96 195:0.64 196:0.98 197:0.86 199:0.96 201:0.68 202:0.76 204:0.79 205:0.98 208:0.64 212:0.72 215:0.96 216:0.25 219:0.88 222:0.84 223:0.97 224:0.98 225:0.99 227:0.92 229:0.89 231:0.72 232:0.79 234:0.97 235:0.52 236:0.97 239:0.92 240:0.99 241:0.85 242:0.18 243:0.92 246:0.92 247:0.88 248:0.90 253:0.93 254:0.92 255:1.00 256:0.78 259:0.21 260:0.90 264:0.96 265:0.84 266:0.38 267:0.19 268:0.80 271:0.44 274:0.98 275:0.93 276:0.66 279:0.75 282:0.80 283:0.67 284:0.97 285:0.62 287:0.94 290:0.98 292:0.88 294:0.98 297:0.36 300:0.55 +0 1:0.59 8:0.87 9:0.76 10:0.91 11:0.75 12:0.69 17:0.79 18:0.63 21:0.40 23:0.97 26:0.97 27:0.44 29:0.77 30:0.42 31:0.94 34:0.75 36:0.96 37:0.69 39:0.72 43:0.23 45:0.90 56:0.97 58:0.98 62:0.97 66:0.93 69:0.41 70:0.62 71:0.88 74:0.32 79:0.93 80:0.99 81:0.53 82:0.98 83:0.73 86:0.62 88:0.99 89:0.98 91:0.73 96:0.80 98:0.94 99:0.95 101:0.65 102:0.95 104:0.78 108:0.31 120:0.76 123:0.30 126:0.43 127:0.59 128:0.98 129:0.05 131:0.87 135:0.58 137:0.94 139:0.61 140:0.94 141:0.99 143:0.99 144:0.76 145:0.70 146:0.79 147:0.82 149:0.21 150:0.42 151:0.86 153:0.48 154:0.45 155:0.66 157:0.77 159:0.34 162:0.79 164:0.81 165:0.65 166:0.61 168:0.98 170:0.77 172:0.82 173:0.53 174:0.88 177:0.99 179:0.43 182:0.80 191:0.80 192:1.00 193:0.96 195:0.62 196:0.88 197:0.90 199:0.96 201:0.61 202:0.72 204:0.79 205:0.97 208:0.64 212:0.89 215:0.67 216:0.26 220:0.82 222:0.84 223:0.97 224:0.98 225:0.98 227:0.92 229:0.89 231:0.72 234:0.97 235:0.68 236:0.92 239:0.91 240:0.99 241:0.81 242:0.15 243:0.94 244:0.83 246:0.61 247:0.88 248:0.77 253:0.74 254:0.93 255:1.00 256:0.77 259:0.21 260:0.77 264:0.96 265:0.94 266:0.27 267:0.36 268:0.77 271:0.44 274:0.98 275:0.95 276:0.72 283:0.67 284:0.89 285:0.62 287:0.93 294:0.97 297:0.36 300:0.55 +2 8:0.94 12:0.20 17:0.53 20:0.77 21:0.13 25:0.88 27:0.42 28:0.92 30:0.95 34:0.58 36:0.70 37:0.47 43:0.21 55:0.52 66:0.54 69:0.93 78:0.78 81:0.92 91:0.69 98:0.12 100:0.83 104:0.74 108:0.86 111:0.79 120:0.84 123:0.85 126:0.54 127:0.08 129:0.05 135:0.23 140:0.80 146:0.20 147:0.25 149:0.11 150:0.85 151:0.77 152:0.75 153:0.88 154:0.13 159:0.10 161:0.73 162:0.65 164:0.91 167:0.76 169:0.82 172:0.10 173:0.99 177:0.05 178:0.42 179:0.14 181:0.73 186:0.79 187:0.68 191:0.91 202:0.87 215:0.84 216:0.66 220:0.74 235:0.12 241:0.72 243:0.63 248:0.76 256:0.88 259:0.21 260:0.85 261:0.76 265:0.13 267:0.91 268:0.88 271:0.35 283:0.60 285:0.62 297:0.36 300:0.08 +2 6:0.91 7:0.81 8:0.79 12:0.20 17:0.38 20:0.95 21:0.30 27:0.21 28:0.92 30:0.16 34:0.61 36:0.90 37:0.74 43:0.52 55:0.59 66:0.23 69:0.10 70:0.85 78:0.80 81:0.89 91:0.90 98:0.51 100:0.90 104:0.84 106:0.81 108:0.98 111:0.84 120:0.95 123:0.98 124:0.95 126:0.54 127:0.88 129:0.99 133:0.95 135:0.32 140:0.45 146:0.83 147:0.86 149:0.90 150:0.79 151:0.83 152:0.97 153:0.96 154:0.41 159:0.96 161:0.73 162:0.68 164:0.95 167:0.69 169:0.86 172:0.95 173:0.06 177:0.05 179:0.11 181:0.73 186:0.81 187:0.39 189:0.93 191:0.78 202:0.93 206:0.81 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.62 242:0.82 243:0.19 245:0.98 247:0.12 248:0.93 256:0.93 259:0.21 260:0.96 261:0.88 265:0.53 266:0.89 267:0.99 268:0.94 271:0.35 276:0.98 283:0.82 285:0.62 297:0.36 300:0.94 +1 12:0.20 17:0.11 21:0.78 27:0.04 28:0.92 30:0.68 34:0.47 36:0.57 37:0.91 43:0.50 55:0.52 66:0.54 69:0.15 70:0.31 91:0.51 98:0.53 108:0.12 120:0.53 123:0.12 124:0.48 127:0.24 129:0.59 133:0.47 135:0.65 140:0.45 146:0.51 147:0.57 149:0.48 151:0.46 153:0.48 154:0.39 159:0.27 161:0.73 162:0.62 164:0.57 167:0.70 172:0.32 173:0.27 177:0.05 179:0.75 181:0.73 187:0.68 202:0.50 212:0.42 216:0.73 220:0.91 235:0.05 241:0.64 242:0.82 243:0.63 245:0.50 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.54 266:0.27 267:0.98 268:0.46 271:0.35 276:0.32 285:0.62 300:0.25 +3 7:0.81 12:0.20 17:0.53 21:0.30 27:0.21 28:0.92 30:0.21 34:0.55 36:0.86 37:0.74 43:0.52 55:0.71 66:0.12 69:0.10 70:0.85 81:0.89 91:0.44 98:0.50 104:0.87 106:0.81 108:0.85 123:0.84 124:0.95 126:0.54 127:0.88 129:0.99 133:0.95 135:0.42 146:0.83 147:0.86 149:0.90 150:0.79 154:0.41 159:0.96 161:0.73 167:0.61 172:0.90 173:0.06 177:0.05 178:0.55 179:0.11 181:0.73 187:0.39 206:0.81 212:0.52 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 241:0.44 242:0.82 243:0.19 245:0.98 247:0.12 259:0.21 265:0.52 266:0.92 267:0.97 271:0.35 276:0.98 297:0.36 300:0.94 +2 7:0.81 12:0.20 17:0.59 21:0.30 27:0.21 28:0.92 30:0.16 34:0.69 36:0.86 37:0.74 43:0.52 55:0.59 66:0.23 69:0.10 70:0.85 81:0.89 91:0.44 98:0.51 108:0.98 123:0.98 124:0.95 126:0.54 127:0.88 129:0.99 133:0.95 135:0.37 146:0.83 147:0.86 149:0.90 150:0.79 154:0.41 159:0.96 161:0.73 167:0.56 172:0.95 173:0.06 177:0.05 178:0.55 179:0.11 181:0.73 187:0.39 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 241:0.24 242:0.82 243:0.19 245:0.98 247:0.12 259:0.21 265:0.53 266:0.89 267:0.96 271:0.35 276:0.98 297:0.36 300:0.94 +1 8:0.79 12:0.20 17:0.08 21:0.13 25:0.88 27:0.42 28:0.92 30:0.45 34:0.22 36:0.59 37:0.47 43:0.44 55:0.52 66:0.69 69:0.45 70:0.25 81:0.92 91:0.86 98:0.89 104:0.74 108:0.35 120:0.81 123:0.34 126:0.54 127:0.43 129:0.05 135:0.61 140:0.45 146:0.56 147:0.62 149:0.31 150:0.85 151:0.78 153:0.89 154:0.33 159:0.20 161:0.73 162:0.73 163:0.93 164:0.86 167:0.89 172:0.21 173:0.73 177:0.05 179:0.97 181:0.73 191:0.86 202:0.79 212:0.61 215:0.84 216:0.66 235:0.12 241:0.90 242:0.82 243:0.73 247:0.12 248:0.73 256:0.81 259:0.21 260:0.82 265:0.89 266:0.17 267:0.36 268:0.83 271:0.35 276:0.20 283:0.82 285:0.62 297:0.36 300:0.18 +2 8:0.96 12:0.20 17:0.57 21:0.13 25:0.88 27:0.07 28:0.92 30:0.95 34:0.59 36:0.71 37:0.98 43:0.21 55:0.52 66:0.54 69:0.93 81:0.92 91:0.17 98:0.12 104:0.58 108:0.86 120:0.86 123:0.85 126:0.54 127:0.08 129:0.05 135:0.37 140:0.45 146:0.20 147:0.25 149:0.11 150:0.85 151:0.76 153:0.87 154:0.13 159:0.10 161:0.73 162:0.82 164:0.86 167:0.80 172:0.10 173:1.00 177:0.05 179:0.14 181:0.73 187:0.21 202:0.77 215:0.84 216:0.67 235:0.12 241:0.75 243:0.63 248:0.80 256:0.81 259:0.21 260:0.86 265:0.13 267:0.65 268:0.82 271:0.35 285:0.62 297:0.36 300:0.08 +1 6:0.95 8:0.95 12:0.20 17:0.11 20:0.77 21:0.78 27:0.04 28:0.92 30:0.68 34:0.53 36:0.60 37:0.91 43:0.50 55:0.52 66:0.54 69:0.15 70:0.31 78:0.91 91:0.38 98:0.53 100:0.99 104:0.90 106:0.81 108:0.12 111:0.98 123:0.12 124:0.48 127:0.24 129:0.59 133:0.47 135:0.63 146:0.51 147:0.57 149:0.48 152:0.79 154:0.39 159:0.27 161:0.73 167:0.60 169:0.94 172:0.32 173:0.27 177:0.05 178:0.55 179:0.75 181:0.73 186:0.91 187:0.95 189:0.99 206:0.81 212:0.42 216:0.73 235:0.05 238:0.99 241:0.41 242:0.82 243:0.63 245:0.50 247:0.12 259:0.21 261:0.88 265:0.54 266:0.27 267:0.97 271:0.35 276:0.32 300:0.25 +1 12:0.20 17:0.22 21:0.40 27:0.45 28:0.92 30:0.62 32:0.80 34:0.53 36:0.28 37:0.50 43:0.32 55:0.84 66:0.33 69:0.69 70:0.74 81:0.88 91:0.25 98:0.62 108:0.52 123:0.50 124:0.79 126:0.54 127:0.50 129:0.89 133:0.78 135:0.83 145:0.40 146:0.84 147:0.87 149:0.57 150:0.74 154:0.24 159:0.70 161:0.73 163:0.94 167:0.60 172:0.48 173:0.56 177:0.05 178:0.55 179:0.58 181:0.73 187:0.68 195:0.86 212:0.27 215:0.67 216:0.77 235:0.42 241:0.43 242:0.82 243:0.50 245:0.69 247:0.12 259:0.21 265:0.62 266:0.80 267:0.69 271:0.35 276:0.60 297:0.36 300:0.70 +2 8:0.74 12:0.20 17:0.08 21:0.78 27:0.72 28:0.92 34:0.70 36:0.77 37:0.84 91:0.84 104:0.58 120:0.92 135:0.28 140:0.80 151:0.79 153:0.91 161:0.73 162:0.54 164:0.93 167:0.90 175:0.75 178:0.42 181:0.73 191:0.75 202:0.93 216:0.36 235:0.52 241:0.94 244:0.61 248:0.88 256:0.91 257:0.65 259:0.21 260:0.92 267:0.19 268:0.91 271:0.35 277:0.69 283:0.82 285:0.62 +3 6:0.98 8:0.98 12:0.20 17:0.07 20:0.94 21:0.47 25:0.88 27:0.07 28:0.92 30:0.21 32:0.80 34:0.31 36:0.81 37:0.98 43:0.23 55:0.59 66:0.27 69:0.87 70:0.50 78:0.92 81:0.86 91:1.00 98:0.44 100:0.99 104:0.58 108:0.82 111:0.98 120:0.98 123:0.74 124:0.59 126:0.54 127:0.21 129:0.59 133:0.47 135:0.19 140:0.45 145:0.91 146:0.51 147:0.57 149:0.34 150:0.69 151:0.90 152:0.97 153:0.99 154:0.16 159:0.30 161:0.73 162:0.75 164:0.99 167:0.91 169:0.97 172:0.27 173:0.93 177:0.05 179:0.64 181:0.73 186:0.93 189:0.99 191:0.97 195:0.68 202:0.98 212:0.37 215:0.63 216:0.67 220:0.62 235:0.52 238:1.00 241:0.97 242:0.82 243:0.46 245:0.56 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.28 266:0.38 267:0.84 268:0.99 271:0.35 276:0.35 283:0.82 285:0.62 297:0.36 300:0.33 +1 8:0.60 12:0.20 17:0.34 21:0.13 25:0.88 27:0.69 28:0.92 34:0.26 36:0.33 37:0.29 81:0.92 91:0.85 104:0.58 120:0.86 126:0.54 135:0.30 140:0.80 150:0.85 151:0.75 153:0.86 161:0.73 162:0.58 164:0.88 167:0.90 175:0.75 178:0.42 181:0.73 202:0.85 215:0.84 216:0.51 235:0.12 241:0.92 244:0.61 248:0.81 256:0.86 257:0.65 259:0.21 260:0.87 267:0.65 268:0.85 271:0.35 277:0.69 283:0.82 285:0.62 297:0.36 +1 12:0.20 17:0.16 25:0.88 27:0.72 28:0.92 30:0.62 34:0.71 36:0.58 43:0.47 55:0.92 66:0.61 69:0.33 70:0.34 78:0.80 81:0.95 91:0.84 98:0.77 104:0.58 108:0.54 111:0.80 120:0.89 123:0.52 124:0.43 126:0.54 127:0.55 129:0.59 133:0.47 135:0.47 140:0.45 146:0.05 147:0.05 149:0.37 150:0.91 151:0.79 153:0.90 154:0.36 159:0.32 161:0.73 162:0.82 163:0.86 164:0.87 167:0.90 169:0.82 172:0.27 173:0.48 177:0.05 179:0.94 181:0.73 202:0.78 212:0.30 215:0.96 216:0.18 235:0.02 241:0.92 242:0.82 243:0.23 245:0.42 247:0.12 248:0.84 256:0.84 260:0.89 265:0.77 266:0.27 267:0.85 268:0.84 271:0.35 276:0.28 285:0.62 297:0.36 300:0.25 +2 6:0.87 8:0.81 12:0.20 17:0.26 20:0.81 21:0.05 25:0.88 27:0.72 28:0.92 30:0.66 32:0.80 34:0.82 36:0.55 43:0.51 55:0.59 66:0.46 69:0.32 70:0.42 78:0.77 81:0.99 91:0.66 98:0.73 100:0.81 104:0.76 108:0.49 111:0.77 120:0.82 123:0.47 124:0.74 126:0.54 127:0.65 129:0.89 133:0.78 135:0.70 140:0.45 145:0.96 146:0.05 147:0.05 149:0.62 150:0.99 151:0.75 152:0.78 153:0.86 154:0.40 159:0.41 161:0.73 162:0.69 164:0.85 167:0.80 169:0.79 172:0.45 173:0.40 177:0.05 179:0.72 181:0.73 186:0.78 187:0.21 189:0.88 191:0.96 195:0.61 202:0.78 212:0.36 215:0.91 216:0.34 220:0.93 235:0.52 238:0.91 241:0.76 242:0.82 243:0.13 245:0.57 247:0.12 248:0.74 256:0.80 259:0.21 260:0.83 261:0.78 265:0.74 266:0.54 267:0.99 268:0.81 271:0.35 276:0.46 285:0.62 297:1.00 300:0.33 +1 12:0.20 17:0.32 25:0.88 27:0.07 28:0.92 30:0.91 32:0.80 34:0.34 36:0.97 37:0.98 43:0.50 55:0.59 66:0.69 69:0.30 70:0.13 81:0.97 91:0.58 98:0.69 104:0.58 108:0.24 120:0.53 123:0.23 126:0.54 127:0.21 129:0.05 135:0.61 140:0.45 145:0.91 146:0.32 147:0.39 149:0.35 150:0.96 151:0.46 153:0.48 154:0.39 159:0.13 161:0.73 162:0.62 164:0.57 167:0.82 172:0.21 173:0.70 177:0.05 179:0.90 181:0.73 187:0.21 195:0.52 202:0.50 212:0.95 215:0.96 216:0.67 220:0.87 235:0.52 241:0.76 242:0.82 243:0.73 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.69 266:0.12 267:0.23 268:0.46 271:0.35 276:0.23 285:0.62 297:0.99 300:0.13 +1 6:0.84 8:0.74 12:0.20 17:0.24 20:0.94 25:0.88 27:0.32 28:0.92 34:0.53 36:0.48 37:0.60 78:0.80 81:0.95 91:0.96 100:0.83 104:0.58 111:0.80 120:0.98 126:0.54 135:0.61 140:0.45 150:0.91 151:0.84 152:0.87 153:0.98 161:0.73 162:0.56 164:0.98 167:0.90 169:0.82 175:0.75 181:0.73 186:0.80 189:0.86 191:0.85 202:0.97 215:0.96 216:0.50 235:0.02 238:0.91 241:0.95 244:0.61 248:0.97 256:0.97 257:0.65 260:0.98 261:0.84 267:0.82 268:0.97 271:0.35 277:0.69 283:0.82 285:0.62 297:0.36 +1 6:0.98 8:0.60 12:0.20 17:0.12 20:0.94 25:0.88 27:0.07 28:0.92 34:0.69 36:0.75 37:0.98 78:0.84 81:0.95 91:0.74 100:0.89 104:0.58 111:0.84 120:0.84 126:0.54 135:0.65 140:0.45 150:0.91 151:0.78 152:0.97 153:0.90 161:0.73 162:0.49 164:0.82 167:0.82 169:0.97 175:0.75 181:0.73 186:0.84 187:0.21 189:0.83 202:0.85 215:0.96 216:0.67 235:0.05 238:0.92 241:0.76 244:0.61 248:0.76 256:0.78 257:0.65 259:0.21 260:0.85 261:0.98 267:0.18 268:0.77 271:0.35 277:0.69 285:0.62 297:0.36 +2 6:0.84 7:0.81 8:0.73 12:0.20 17:0.55 20:0.83 21:0.30 27:0.50 28:0.92 30:0.21 32:0.80 34:0.67 36:0.88 37:0.60 43:0.40 55:0.71 66:0.19 69:0.53 70:0.81 78:0.77 81:0.89 91:0.37 98:0.50 100:0.79 104:0.84 106:0.81 108:0.99 111:0.76 120:0.80 123:0.99 124:0.97 126:0.54 127:0.97 129:0.99 133:0.97 135:0.74 140:0.45 145:0.70 146:0.97 147:0.98 149:0.91 150:0.79 151:0.73 152:0.79 153:0.78 154:0.31 159:0.97 161:0.73 162:0.72 164:0.79 167:0.55 169:0.76 172:0.97 173:0.09 177:0.05 179:0.10 181:0.73 186:0.77 187:0.39 189:0.86 195:1.00 202:0.71 206:0.81 212:0.54 215:0.72 216:0.86 220:0.87 230:0.87 233:0.76 235:0.31 238:0.85 241:0.21 242:0.82 243:0.24 245:0.99 247:0.12 248:0.73 256:0.71 259:0.21 260:0.81 261:0.77 265:0.52 266:0.94 267:0.96 268:0.75 271:0.35 276:0.99 283:0.82 285:0.62 297:0.36 300:0.94 +1 12:0.20 17:0.28 21:0.71 27:0.45 28:0.92 30:0.30 32:0.80 34:0.64 36:0.35 37:0.50 43:0.32 55:0.84 66:0.31 69:0.70 70:0.97 81:0.73 91:0.17 98:0.29 108:0.97 123:0.97 124:0.89 126:0.54 127:0.63 129:0.96 133:0.90 135:0.89 145:0.49 146:0.66 147:0.71 149:0.57 150:0.54 154:0.24 159:0.93 161:0.73 163:0.90 167:0.54 172:0.65 173:0.28 177:0.05 178:0.55 179:0.39 181:0.73 187:0.68 195:0.99 212:0.16 215:0.48 216:0.77 235:0.84 241:0.15 242:0.82 243:0.33 245:0.76 247:0.12 259:0.21 265:0.31 266:0.96 267:0.44 271:0.35 276:0.67 297:0.36 300:0.98 +2 7:0.81 8:0.92 12:0.20 17:0.92 21:0.05 27:0.72 28:0.92 30:0.62 34:0.45 36:0.77 37:0.84 43:0.49 55:0.84 66:0.83 69:0.21 70:0.34 81:0.93 91:0.81 98:0.95 104:0.54 108:0.17 120:0.80 123:0.16 126:0.54 127:0.64 129:0.05 135:0.51 140:0.45 146:0.83 147:0.86 149:0.52 150:0.88 151:0.70 153:0.71 154:0.38 159:0.40 161:0.73 162:0.66 164:0.74 167:0.89 172:0.51 173:0.33 177:0.05 179:0.83 181:0.73 191:0.83 202:0.66 212:0.28 215:0.91 216:0.29 220:0.62 230:0.87 233:0.76 235:0.05 241:0.91 242:0.82 243:0.84 247:0.12 248:0.72 256:0.64 259:0.21 260:0.81 265:0.95 266:0.47 267:0.69 268:0.68 271:0.35 276:0.43 285:0.62 297:0.36 300:0.25 +0 7:0.81 12:0.20 17:0.30 20:0.75 21:0.54 27:0.61 28:0.92 30:0.30 32:0.80 37:0.28 43:0.50 55:0.84 66:0.26 69:0.25 70:0.60 78:0.89 81:0.84 91:0.87 98:0.25 100:0.73 104:0.90 106:0.81 108:0.68 111:0.96 120:0.96 123:0.66 124:0.87 126:0.54 127:0.51 129:0.95 133:0.87 140:0.45 145:0.49 146:0.05 147:0.05 149:0.42 150:0.64 151:0.84 152:0.74 153:0.98 154:0.39 159:0.86 161:0.73 162:0.75 163:0.86 164:0.97 167:0.61 169:0.95 172:0.27 173:0.10 177:0.05 179:0.78 181:0.73 186:0.73 187:0.39 195:0.97 202:0.94 206:0.81 212:0.13 215:0.58 216:0.75 230:0.87 233:0.76 235:0.60 238:0.98 241:0.44 242:0.82 243:0.15 245:0.49 247:0.12 248:0.94 256:0.96 260:0.96 261:0.73 265:0.27 266:0.75 268:0.96 271:0.35 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 12:0.20 17:0.94 21:0.59 27:0.72 28:0.92 30:0.43 32:0.80 34:0.86 36:0.56 43:0.50 55:0.59 66:0.99 69:0.34 70:0.41 81:0.97 91:0.82 98:1.00 104:0.57 108:0.27 123:0.26 126:0.54 127:0.98 129:0.05 135:0.49 145:0.82 146:0.98 147:0.99 149:0.96 150:0.96 154:0.39 159:0.78 161:0.73 167:0.85 172:0.98 173:0.21 177:0.05 178:0.55 179:0.14 181:0.73 195:0.90 212:0.94 215:0.55 216:0.04 230:0.87 233:0.76 235:0.84 241:0.78 242:0.82 243:0.99 247:0.12 259:0.21 265:1.00 266:0.38 267:0.18 271:0.35 276:0.96 297:0.36 300:0.43 +1 6:0.80 8:0.59 12:0.20 17:0.34 20:0.94 27:0.35 28:0.92 30:0.53 34:0.67 36:0.76 37:0.24 43:0.52 55:0.84 66:0.54 69:0.05 70:0.64 78:0.83 81:0.95 91:0.79 98:0.67 100:0.87 104:0.89 106:0.81 108:0.05 111:0.83 120:0.87 123:0.05 124:0.69 126:0.54 127:0.75 129:0.89 133:0.78 135:0.79 140:0.45 146:0.91 147:0.93 149:0.79 150:0.91 151:0.79 152:0.88 153:0.91 154:0.41 159:0.80 161:0.73 162:0.77 164:0.92 167:0.64 169:0.84 172:0.80 173:0.07 177:0.05 179:0.36 181:0.73 186:0.83 187:0.39 189:0.82 202:0.86 206:0.81 212:0.37 215:0.96 216:0.51 220:0.74 235:0.02 238:0.90 241:0.53 242:0.82 243:0.63 245:0.84 247:0.12 248:0.81 256:0.87 259:0.21 260:0.87 261:0.87 265:0.68 266:0.89 267:0.52 268:0.89 271:0.35 276:0.79 283:0.82 285:0.62 297:0.36 300:0.70 +2 1:0.74 8:0.95 9:0.80 11:0.64 12:0.37 17:0.24 20:0.78 21:0.40 27:0.61 28:0.66 30:0.38 34:0.26 36:0.60 37:0.89 39:0.96 41:0.98 43:0.82 46:0.96 66:0.49 69:0.29 70:0.83 74:0.87 78:0.76 81:0.56 83:0.86 85:0.91 91:0.65 96:0.96 98:0.40 100:0.80 101:0.80 104:0.54 107:0.98 108:0.22 110:0.90 111:0.76 114:0.82 120:0.92 122:0.57 123:0.22 124:0.65 125:0.99 126:0.54 127:0.96 129:0.59 131:0.81 133:0.64 135:0.74 140:0.45 144:0.57 146:0.57 147:0.62 149:0.85 150:0.74 151:0.97 152:0.76 153:0.90 154:0.77 155:0.67 157:0.83 159:0.68 160:0.99 161:0.68 162:0.73 164:0.88 165:0.83 166:0.73 167:0.90 169:0.77 172:0.57 173:0.23 176:0.73 177:0.38 179:0.66 181:0.76 182:0.79 186:0.77 191:0.92 192:0.59 201:0.74 202:0.83 208:0.64 212:0.55 215:0.67 216:0.36 222:0.35 227:0.84 229:0.88 231:0.62 232:0.74 235:0.52 241:0.89 242:0.52 243:0.60 245:0.70 246:0.87 247:0.47 248:0.96 253:0.97 255:0.79 256:0.87 259:0.21 260:0.92 261:0.75 265:0.42 266:0.63 267:0.28 268:0.86 271:0.87 276:0.53 285:0.62 287:0.90 289:0.89 290:0.82 297:0.36 300:0.70 +3 1:0.74 6:0.98 7:0.81 8:0.99 9:0.80 11:0.64 12:0.37 17:0.09 20:0.85 21:0.47 27:0.09 28:0.66 30:0.33 32:0.80 34:0.44 36:0.83 37:0.83 39:0.96 41:0.99 43:0.91 46:0.95 60:0.99 66:0.18 69:0.43 70:0.94 74:0.91 76:0.85 77:0.89 78:0.77 81:0.54 83:0.90 85:0.93 91:0.80 96:0.99 98:0.26 100:0.86 101:0.77 104:0.79 107:0.98 108:0.94 110:0.90 111:0.79 114:0.80 120:0.98 121:0.90 122:0.67 123:0.94 124:0.88 125:0.99 126:0.54 127:0.92 129:0.93 131:0.81 133:0.87 135:0.39 138:0.99 140:0.45 144:0.57 145:0.59 146:0.54 147:0.60 149:0.77 150:0.72 151:0.99 152:0.90 153:0.97 154:0.91 155:0.67 157:0.82 158:0.92 159:0.86 160:1.00 161:0.68 162:0.75 164:0.96 165:0.80 166:0.73 167:0.84 169:0.85 172:0.41 173:0.20 176:0.73 177:0.38 179:0.57 181:0.76 182:0.79 186:0.78 187:0.21 189:1.00 191:0.93 192:0.59 195:0.94 201:0.74 202:0.94 204:0.89 208:0.64 212:0.19 215:0.63 216:0.84 220:0.62 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.84 233:0.69 235:0.68 238:0.97 241:0.77 242:0.45 243:0.32 245:0.70 246:0.87 247:0.47 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.98 261:0.85 265:0.28 266:0.91 267:0.28 268:0.96 271:0.87 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 287:0.90 289:0.89 290:0.80 297:0.36 299:0.88 300:0.94 +1 1:0.74 11:0.64 12:0.37 17:0.43 21:0.59 27:0.45 28:0.66 30:0.45 32:0.80 34:0.83 36:0.18 37:0.50 39:0.96 43:0.82 46:0.94 66:0.30 69:0.70 70:0.61 74:0.76 77:0.70 81:0.48 83:0.73 85:0.88 91:0.06 98:0.24 101:0.75 107:0.97 108:0.53 110:0.90 114:0.74 122:0.43 123:0.51 124:0.71 125:0.88 126:0.54 127:0.26 129:0.81 131:0.61 133:0.67 135:0.68 144:0.57 145:0.45 146:0.48 147:0.54 149:0.81 150:0.67 154:0.77 155:0.67 157:0.80 159:0.66 160:0.88 161:0.68 163:0.81 165:0.78 166:0.73 167:0.66 172:0.32 173:0.50 176:0.73 177:0.38 178:0.55 179:0.60 181:0.76 182:0.78 187:0.68 192:0.59 195:0.92 201:0.74 212:0.36 215:0.55 216:0.77 222:0.35 227:0.61 229:0.61 231:0.62 235:0.74 241:0.54 242:0.52 243:0.48 245:0.60 246:0.87 247:0.55 253:0.66 259:0.21 265:0.26 266:0.54 267:0.36 271:0.87 276:0.37 282:0.88 287:0.61 289:0.89 290:0.74 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.64 12:0.37 17:0.09 20:0.97 21:0.47 27:0.11 28:0.66 30:0.56 34:0.36 36:0.83 37:0.79 39:0.96 41:1.00 43:0.39 46:0.93 55:0.92 60:0.98 66:0.19 69:0.96 70:0.46 74:0.81 76:0.85 77:0.89 78:0.93 81:0.53 83:0.91 85:0.80 91:0.96 96:1.00 98:0.38 100:0.99 101:0.72 104:0.73 107:0.96 108:0.92 110:0.90 111:0.98 114:0.80 120:1.00 121:0.90 122:0.43 123:0.93 124:0.85 125:0.96 126:0.54 127:0.86 129:0.59 131:0.81 133:0.82 135:0.21 138:1.00 140:0.45 144:0.57 145:0.82 146:0.23 147:0.05 149:0.48 150:0.71 151:1.00 152:0.95 153:0.99 154:0.24 155:0.67 157:0.77 158:0.87 159:0.84 160:1.00 161:0.68 162:0.49 164:1.00 165:0.80 166:0.73 167:0.90 169:0.98 172:0.21 173:0.96 176:0.73 177:0.05 179:0.83 181:0.76 182:0.78 186:0.93 189:0.99 191:0.98 192:0.59 195:0.94 201:0.74 202:1.00 204:0.89 208:0.64 212:0.13 215:0.63 216:0.85 220:0.74 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.79 233:0.69 235:0.60 238:0.99 241:0.86 242:0.24 243:0.15 245:0.52 246:0.87 247:0.60 248:1.00 253:1.00 255:0.79 256:1.00 257:0.65 259:0.21 260:1.00 261:0.98 265:0.13 266:0.75 267:0.97 268:1.00 271:0.87 276:0.37 279:0.86 282:0.81 283:0.71 285:0.62 287:0.90 289:0.89 290:0.80 297:0.36 300:0.33 +3 1:0.74 6:0.98 7:0.81 8:0.81 9:0.80 11:0.64 12:0.37 17:0.01 20:0.88 21:0.64 27:0.11 28:0.66 30:0.15 34:0.29 36:0.97 37:0.79 39:0.96 41:1.00 43:0.73 46:0.93 55:0.71 60:0.95 66:0.33 69:0.86 70:0.84 74:0.83 76:0.85 77:0.70 78:0.77 81:0.48 83:0.90 85:0.72 91:0.82 96:0.99 98:0.48 100:0.81 101:0.71 104:0.73 107:0.95 108:0.86 110:0.89 111:0.77 114:0.72 117:0.86 120:0.97 121:0.90 122:0.51 123:0.85 124:0.71 125:0.96 126:0.54 127:0.71 129:0.59 131:0.81 133:0.64 135:0.84 138:1.00 140:0.45 144:0.57 145:0.86 146:0.13 147:0.41 149:0.54 150:0.66 151:0.99 152:0.95 153:0.97 154:0.63 155:0.67 157:0.76 158:0.87 159:0.70 160:1.00 161:0.68 162:0.48 163:0.75 164:0.98 165:0.70 166:0.73 167:0.82 169:0.98 172:0.27 173:0.82 176:0.73 177:0.05 179:0.86 181:0.76 182:0.78 186:0.78 187:0.39 189:0.97 191:0.91 192:0.59 195:0.83 201:0.74 202:0.99 204:0.89 208:0.64 212:0.15 215:0.53 216:0.85 220:0.74 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.79 233:0.69 235:0.88 238:0.91 241:0.76 242:0.33 243:0.37 245:0.53 246:0.87 247:0.39 248:0.99 253:0.99 255:0.79 256:0.99 257:0.65 259:0.21 260:0.97 261:0.98 265:0.50 266:0.63 267:0.94 268:0.98 271:0.87 276:0.36 279:0.86 282:0.81 283:0.71 285:0.62 287:0.90 289:0.89 290:0.72 297:0.36 300:0.55 +3 1:0.74 6:0.93 8:0.93 9:0.80 11:0.64 12:0.37 17:0.26 20:0.82 21:0.05 27:0.12 28:0.66 30:0.37 34:0.19 36:0.62 37:0.88 39:0.96 41:0.95 43:0.73 46:0.95 66:0.15 69:0.85 70:0.87 74:0.78 77:0.70 78:0.81 81:0.74 83:0.82 85:0.90 91:0.80 96:0.95 98:0.27 100:0.92 101:0.78 104:0.58 107:0.98 108:0.91 110:0.90 111:0.86 114:0.95 117:0.86 120:0.85 122:0.57 123:0.69 124:0.76 125:0.98 126:0.54 127:0.82 129:0.81 131:0.81 133:0.76 135:0.19 140:0.80 144:0.57 145:0.38 146:0.46 147:0.05 149:0.70 150:0.84 151:0.86 152:0.92 153:0.95 154:0.64 155:0.67 157:0.82 158:0.83 159:0.75 160:0.99 161:0.68 162:0.84 164:0.82 165:0.90 166:0.73 167:0.81 169:0.96 172:0.48 173:0.78 176:0.73 177:0.05 179:0.70 181:0.76 182:0.78 186:0.82 187:0.21 189:0.99 191:0.82 192:0.59 195:0.86 201:0.74 202:0.78 208:0.64 212:0.57 215:0.91 216:0.72 222:0.35 227:0.84 229:0.88 231:0.62 232:0.77 235:0.22 238:0.98 241:0.75 242:0.44 243:0.11 245:0.63 246:0.87 247:0.60 248:0.91 253:0.95 255:0.79 256:0.83 257:0.65 259:0.21 260:0.85 261:0.96 265:0.21 266:0.71 267:0.59 268:0.84 271:0.87 276:0.50 282:0.81 285:0.62 287:0.90 289:0.89 290:0.95 297:0.36 300:0.70 +3 1:0.74 6:0.83 7:0.76 8:0.93 9:0.80 11:0.64 12:0.37 17:0.16 20:0.89 27:0.13 28:0.66 30:0.68 32:0.80 34:0.25 36:0.92 37:0.86 39:0.96 41:0.98 43:0.93 46:0.96 60:0.97 66:0.32 69:0.33 70:0.24 74:0.79 77:0.70 78:0.77 81:0.82 83:0.90 85:0.85 91:0.89 96:0.97 98:0.17 100:0.80 101:0.78 104:0.58 107:0.96 108:0.26 110:0.90 111:0.77 114:0.98 120:0.93 122:0.43 123:0.25 124:0.72 125:0.98 126:0.54 127:0.64 129:0.59 131:0.81 133:0.64 135:0.24 138:0.98 140:0.45 144:0.57 145:0.86 146:0.24 147:0.30 149:0.77 150:0.89 151:0.98 152:0.84 153:0.93 154:0.93 155:0.67 157:0.80 158:0.92 159:0.52 160:1.00 161:0.68 162:0.80 164:0.92 165:0.93 166:0.73 167:0.92 169:0.78 172:0.21 173:0.33 176:0.73 177:0.38 179:0.90 181:0.76 182:0.79 186:0.78 189:0.85 191:0.91 192:0.59 195:0.73 201:0.74 202:0.88 208:0.64 212:0.42 215:0.96 216:0.35 220:0.74 222:0.35 227:0.84 229:0.88 230:0.78 231:0.62 232:0.77 233:0.69 235:0.05 238:0.89 241:0.96 242:0.45 243:0.49 245:0.48 246:0.87 247:0.39 248:0.97 253:0.97 255:0.79 256:0.91 259:0.21 260:0.94 261:0.80 265:0.19 266:0.27 267:0.44 268:0.92 271:0.87 276:0.28 279:0.86 282:0.88 283:0.82 285:0.62 287:0.90 289:0.89 290:0.98 297:0.99 299:0.88 300:0.18 +2 1:0.74 8:0.98 9:0.80 11:0.64 12:0.37 17:0.34 27:0.55 28:0.66 30:0.56 32:0.71 34:0.40 36:0.95 37:0.83 39:0.96 41:0.99 43:0.68 46:0.95 60:1.00 66:0.44 69:0.89 70:0.58 74:0.82 76:0.85 77:0.70 78:0.88 81:0.80 83:0.90 85:0.88 91:0.90 96:0.99 98:0.35 101:0.77 104:0.58 107:0.97 108:0.78 110:0.90 111:0.91 114:0.98 120:0.97 122:0.57 123:0.76 124:0.68 125:0.99 126:0.54 127:0.92 129:0.59 131:0.81 133:0.64 135:0.30 140:0.45 144:0.57 145:0.61 146:0.54 147:0.32 149:0.68 150:0.88 151:0.99 153:0.97 154:0.57 155:0.67 157:0.81 158:0.92 159:0.71 160:1.00 161:0.68 162:0.77 164:0.94 165:0.92 166:0.73 167:0.91 169:0.93 172:0.37 173:0.87 176:0.73 177:0.05 179:0.83 181:0.76 182:0.79 191:0.93 192:0.59 195:0.82 201:0.74 202:0.89 208:0.64 212:0.23 215:0.96 216:0.45 222:0.35 227:0.84 229:0.88 231:0.62 232:0.77 235:0.05 238:0.95 241:0.93 242:0.55 243:0.25 245:0.57 246:0.87 247:0.39 248:0.99 253:0.99 255:0.79 256:0.92 257:0.65 260:0.97 265:0.37 266:0.47 267:0.52 268:0.92 271:0.87 276:0.38 279:0.86 282:0.79 283:0.82 285:0.62 287:0.90 289:0.89 290:0.98 297:0.36 300:0.43 +1 1:0.74 8:0.95 9:0.80 11:0.64 12:0.37 17:0.24 20:0.78 21:0.40 27:0.61 28:0.66 30:0.38 34:0.26 36:0.60 37:0.89 39:0.96 41:0.98 43:0.82 46:0.96 66:0.49 69:0.29 70:0.83 74:0.87 78:0.76 81:0.56 83:0.86 85:0.91 91:0.65 96:0.96 98:0.40 100:0.80 101:0.80 104:0.54 107:0.98 108:0.22 110:0.90 111:0.76 114:0.82 120:0.92 122:0.57 123:0.22 124:0.65 125:0.99 126:0.54 127:0.96 129:0.59 131:0.81 133:0.64 135:0.74 140:0.45 144:0.57 146:0.57 147:0.62 149:0.85 150:0.74 151:0.97 152:0.76 153:0.90 154:0.77 155:0.67 157:0.83 159:0.68 160:0.99 161:0.68 162:0.73 164:0.88 165:0.83 166:0.73 167:0.90 169:0.77 172:0.57 173:0.23 176:0.73 177:0.38 179:0.66 181:0.76 182:0.79 186:0.77 191:0.92 192:0.59 201:0.74 202:0.83 208:0.64 212:0.55 215:0.67 216:0.36 222:0.35 227:0.84 229:0.88 231:0.62 232:0.74 235:0.52 241:0.89 242:0.52 243:0.60 245:0.70 246:0.87 247:0.47 248:0.96 253:0.97 255:0.79 256:0.87 259:0.21 260:0.92 261:0.75 265:0.42 266:0.63 267:0.28 268:0.86 271:0.87 276:0.53 285:0.62 287:0.90 289:0.89 290:0.82 297:0.36 300:0.70 +0 1:0.74 8:0.95 9:0.80 11:0.64 12:0.37 17:0.22 21:0.13 27:0.13 28:0.66 30:0.30 34:0.56 36:0.57 37:0.86 39:0.96 41:0.99 43:0.77 46:0.94 55:0.45 60:0.98 66:0.26 69:0.78 70:0.80 74:0.85 81:0.68 83:0.90 85:0.80 91:0.94 96:0.98 98:0.25 101:0.73 107:0.96 108:0.32 110:0.89 114:0.92 120:0.95 122:0.67 123:0.31 124:0.79 125:0.98 126:0.54 127:0.89 129:0.59 131:0.81 133:0.76 135:0.63 138:0.99 140:0.45 144:0.57 146:0.28 147:0.46 149:0.66 150:0.81 151:0.97 153:0.88 154:0.70 155:0.67 157:0.78 158:0.87 159:0.71 160:1.00 161:0.68 162:0.74 164:0.94 165:0.88 166:0.73 167:0.89 172:0.27 173:0.71 176:0.73 177:0.05 179:0.83 181:0.76 182:0.79 191:0.91 192:0.59 201:0.74 202:0.90 208:0.64 212:0.39 215:0.84 216:0.55 220:0.62 222:0.35 227:0.84 229:0.88 231:0.62 232:0.74 235:0.22 241:0.82 242:0.63 243:0.45 245:0.54 246:0.87 247:0.39 248:0.98 253:0.98 255:0.79 256:0.92 257:0.65 259:0.21 260:0.95 265:0.27 266:0.54 267:0.73 268:0.93 271:0.87 276:0.32 277:0.69 279:0.86 283:0.71 285:0.62 287:0.90 289:0.89 290:0.92 297:0.36 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.91 9:0.80 12:0.37 17:0.15 20:0.93 21:0.54 27:0.27 28:0.66 30:0.45 34:0.55 36:0.87 37:0.68 39:0.96 41:1.00 43:0.43 46:0.88 55:0.45 60:1.00 66:0.49 69:0.32 70:0.25 74:0.76 76:0.94 78:0.80 81:0.52 83:0.90 91:0.94 96:1.00 98:0.28 100:0.86 104:0.58 107:0.90 108:0.25 110:0.88 111:0.81 114:0.77 120:0.99 121:0.90 122:0.67 123:0.24 124:0.48 125:0.99 126:0.54 127:0.55 129:0.59 131:0.81 133:0.47 135:0.42 140:0.45 144:0.57 146:0.36 147:0.42 149:0.30 150:0.69 151:1.00 152:0.87 153:0.99 154:0.32 155:0.67 157:0.61 158:0.92 159:0.53 160:1.00 161:0.68 162:0.79 163:0.75 164:0.97 165:0.79 166:0.73 167:0.90 169:0.85 172:0.16 173:0.30 175:0.75 176:0.73 177:0.05 179:0.98 181:0.76 182:0.79 186:0.80 189:0.92 191:0.97 192:0.59 201:0.74 202:0.95 204:0.89 208:0.64 212:0.61 215:0.58 216:0.60 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.77 233:0.69 235:0.74 238:0.95 241:0.87 242:0.82 243:0.60 244:0.61 245:0.39 246:0.87 247:0.12 248:1.00 253:0.99 255:0.79 256:0.97 257:0.65 259:0.21 260:0.99 261:0.85 265:0.30 266:0.17 267:0.69 268:0.97 271:0.87 276:0.15 277:0.69 279:0.86 283:0.82 285:0.62 287:0.90 289:0.89 290:0.77 297:0.36 300:0.18 +3 1:0.74 6:0.93 8:0.92 9:0.80 11:0.64 12:0.37 17:0.16 20:0.98 21:0.13 27:0.12 28:0.66 30:0.28 34:0.22 36:0.84 37:0.88 39:0.96 41:1.00 43:0.67 46:0.95 60:1.00 66:0.07 69:0.89 70:0.97 74:0.75 78:0.91 81:0.68 83:0.91 85:0.90 91:0.99 96:1.00 98:0.21 100:0.98 101:0.76 104:0.58 107:0.98 108:0.94 110:0.90 111:0.95 114:0.92 120:0.99 122:0.57 123:0.83 124:0.88 125:0.98 126:0.54 127:0.89 129:0.89 131:0.81 133:0.89 135:0.20 138:1.00 140:0.45 144:0.57 146:0.13 147:0.05 149:0.65 150:0.81 151:1.00 152:0.92 153:0.98 154:0.57 155:0.67 157:0.81 158:0.92 159:0.83 160:1.00 161:0.68 162:0.73 164:0.98 165:0.88 166:0.73 167:0.90 169:0.96 172:0.48 173:0.79 176:0.73 177:0.05 179:0.67 181:0.76 182:0.79 186:0.91 189:0.94 191:0.96 192:0.59 201:0.74 202:0.97 208:0.64 212:0.51 215:0.84 216:0.72 220:0.74 222:0.35 227:0.84 229:0.88 231:0.62 232:0.77 235:0.22 238:0.98 241:0.89 242:0.39 243:0.11 245:0.59 246:0.87 247:0.67 248:1.00 253:1.00 255:0.79 256:0.98 257:0.65 259:0.21 260:0.99 261:0.96 265:0.18 266:0.80 267:0.76 268:0.98 271:0.87 276:0.55 279:0.86 283:0.82 285:0.62 287:0.90 289:0.89 290:0.92 297:0.36 300:0.84 +1 1:0.74 11:0.64 12:0.37 17:0.34 21:0.59 27:0.45 28:0.66 30:0.45 34:0.61 36:0.20 37:0.50 39:0.96 43:0.82 46:0.94 55:0.84 60:0.87 66:0.47 69:0.61 70:0.61 74:0.76 81:0.48 83:0.86 85:0.72 91:0.17 98:0.59 101:0.72 107:0.97 108:0.46 110:0.90 114:0.74 122:0.51 123:0.44 124:0.56 125:0.88 126:0.54 127:0.45 129:0.59 131:0.61 133:0.47 135:0.78 144:0.57 145:0.50 146:0.69 147:0.73 149:0.61 150:0.67 154:0.77 155:0.67 157:0.76 158:0.87 159:0.51 160:0.88 161:0.68 163:0.90 165:0.78 166:0.73 167:0.55 172:0.41 173:0.58 176:0.73 177:0.38 178:0.55 179:0.73 181:0.76 182:0.78 187:0.68 192:0.59 201:0.74 208:0.64 212:0.36 215:0.55 216:0.77 222:0.35 227:0.61 229:0.61 231:0.62 235:0.74 241:0.12 242:0.22 243:0.59 245:0.66 246:0.87 247:0.60 253:0.66 259:0.21 265:0.60 266:0.63 267:0.23 271:0.87 276:0.42 279:0.86 283:0.71 287:0.61 289:0.89 290:0.74 297:0.36 300:0.43 +1 11:0.88 12:0.01 17:0.43 18:0.63 21:0.78 27:0.45 29:0.77 30:0.95 34:0.84 36:0.18 37:0.50 39:0.78 43:0.77 66:0.64 69:0.80 71:0.75 74:0.41 85:0.72 86:0.73 91:0.18 98:0.10 101:0.87 108:0.63 122:0.57 123:0.61 127:0.07 129:0.05 135:0.87 139:0.70 144:0.85 145:0.49 146:0.17 147:0.22 149:0.52 154:0.69 159:0.09 163:0.97 172:0.16 173:0.76 177:0.70 178:0.55 179:0.08 187:0.68 212:0.95 216:0.77 222:0.60 235:0.60 241:0.10 242:0.82 243:0.69 247:0.12 253:0.32 259:0.21 265:0.10 266:0.12 267:0.23 271:0.66 276:0.19 300:0.08 +1 9:0.80 11:0.91 12:0.01 17:0.49 18:0.93 20:0.91 21:0.78 27:0.38 29:0.77 30:0.58 31:0.92 34:0.29 36:0.95 37:0.55 39:0.78 43:0.66 45:0.77 55:0.92 66:0.33 69:0.65 70:0.62 71:0.75 74:0.46 78:0.72 79:0.92 83:0.60 86:0.78 91:0.22 96:0.78 98:0.67 100:0.92 101:0.52 108:0.49 111:0.85 120:0.65 122:0.86 123:0.47 124:0.71 127:0.65 129:0.59 133:0.64 135:0.92 137:0.92 139:0.75 140:0.45 144:0.85 145:0.65 146:0.89 147:0.91 149:0.57 151:0.75 152:0.85 153:0.72 154:0.55 155:0.67 159:0.74 162:0.72 163:0.94 164:0.64 169:0.90 172:0.54 173:0.51 175:0.75 177:0.38 179:0.55 186:0.73 187:0.68 192:0.87 196:0.91 202:0.56 208:0.64 212:0.27 216:0.58 220:0.62 222:0.60 235:0.31 241:0.21 242:0.74 243:0.50 244:0.61 245:0.78 247:0.47 248:0.69 253:0.66 255:0.95 256:0.58 257:0.65 259:0.21 260:0.65 261:0.91 265:0.68 266:0.80 267:0.59 268:0.59 271:0.66 276:0.65 277:0.69 284:0.92 285:0.62 300:0.55 +1 11:0.88 12:0.01 17:0.16 18:0.60 21:0.78 27:0.45 29:0.77 30:0.95 34:0.66 36:0.25 37:0.50 39:0.78 43:0.64 66:0.64 69:0.91 71:0.75 74:0.37 85:0.72 86:0.69 91:0.07 98:0.07 101:0.87 108:0.81 122:0.57 123:0.80 127:0.06 129:0.05 135:0.88 139:0.67 144:0.85 145:0.45 146:0.17 147:0.22 149:0.40 154:0.54 159:0.09 163:0.99 172:0.16 173:0.78 177:0.70 178:0.55 179:0.08 187:0.68 212:0.95 216:0.77 222:0.60 235:0.52 241:0.27 242:0.82 243:0.69 247:0.12 253:0.30 259:0.21 265:0.08 266:0.12 267:0.36 271:0.66 276:0.19 300:0.08 +1 1:0.74 11:0.91 12:0.01 17:0.07 18:0.65 21:0.13 22:0.93 27:0.16 29:0.77 30:0.76 34:0.75 36:0.67 37:0.85 39:0.78 43:0.82 45:0.66 47:0.93 64:0.77 66:0.64 69:0.50 70:0.15 71:0.75 74:0.44 81:0.71 83:0.91 86:0.76 91:0.12 98:0.34 101:0.52 108:0.38 114:0.79 122:0.57 123:0.37 126:0.54 127:0.12 129:0.05 135:0.68 139:0.72 144:0.85 146:0.22 147:0.28 149:0.57 150:0.82 154:0.77 155:0.67 159:0.11 163:0.90 165:0.93 172:0.16 173:0.84 176:0.73 177:0.70 178:0.55 179:0.65 187:0.95 192:0.87 201:0.93 208:0.64 212:0.95 215:0.61 216:0.54 222:0.60 235:0.31 241:0.12 242:0.82 243:0.69 247:0.12 253:0.55 254:0.95 259:0.21 262:0.93 265:0.36 266:0.12 267:0.36 271:0.66 276:0.14 290:0.79 297:0.36 300:0.13 +0 1:0.74 11:0.91 12:0.01 17:0.01 18:0.82 21:0.13 27:0.47 29:0.77 30:0.76 34:0.62 36:0.45 37:0.58 39:0.78 43:0.68 45:0.66 48:0.91 55:0.71 64:0.77 66:0.72 69:0.42 70:0.22 71:0.75 74:0.46 81:0.82 83:0.60 86:0.70 91:0.18 98:0.52 99:0.83 101:0.52 108:0.32 114:0.79 122:0.82 123:0.31 126:0.54 127:0.16 129:0.05 135:0.42 139:0.74 144:0.85 145:0.63 146:0.47 147:0.53 149:0.37 150:0.87 154:0.57 155:0.67 159:0.17 165:0.70 172:0.27 173:0.52 175:0.75 176:0.73 177:0.38 178:0.55 179:0.69 187:0.68 192:0.87 201:0.93 204:0.85 208:0.64 212:0.78 215:0.61 216:0.62 219:0.87 222:0.60 235:0.68 241:0.27 242:0.45 243:0.75 244:0.61 247:0.35 253:0.55 257:0.65 259:0.21 265:0.53 266:0.17 267:0.23 271:0.66 276:0.24 277:0.69 281:0.89 290:0.79 292:0.91 297:0.36 300:0.18 +1 1:0.69 8:0.92 9:0.76 11:0.82 12:0.01 17:0.45 18:0.97 21:0.40 22:0.93 27:0.21 29:0.77 30:0.36 31:0.95 34:0.37 36:0.89 37:0.74 39:0.78 43:0.60 47:0.93 48:0.91 64:0.77 66:0.52 69:0.92 70:0.64 71:0.75 74:0.61 76:0.85 79:0.96 81:0.36 83:0.60 86:0.83 91:0.37 96:0.87 98:0.71 108:0.83 114:0.61 117:0.86 122:0.86 123:0.82 124:0.65 126:0.44 127:0.52 129:0.05 133:0.66 135:0.87 137:0.95 139:0.85 140:0.45 144:0.85 146:0.94 147:0.36 149:0.81 150:0.50 151:0.86 153:0.78 154:0.61 155:0.58 158:0.78 159:0.78 162:0.74 172:0.83 173:0.86 176:0.66 177:0.38 179:0.27 187:0.39 190:0.77 192:0.51 196:0.95 201:0.68 202:0.69 204:0.85 208:0.54 212:0.59 216:0.95 219:0.92 222:0.60 232:0.85 235:0.52 241:0.37 242:0.39 243:0.17 244:0.61 245:0.91 247:0.74 248:0.82 253:0.82 255:0.74 256:0.71 257:0.65 259:0.21 262:0.93 265:0.71 266:0.75 267:0.36 268:0.73 271:0.66 276:0.83 279:0.82 281:0.89 284:0.94 285:0.56 290:0.61 292:0.91 300:0.55 +1 1:0.74 11:0.92 12:0.01 17:0.61 18:0.86 21:0.47 22:0.93 27:0.62 29:0.77 30:0.87 32:0.80 34:0.66 36:0.28 37:0.40 39:0.78 43:0.82 44:0.93 45:0.66 47:0.93 60:0.87 64:0.77 66:0.88 69:0.69 70:0.37 71:0.75 74:0.77 77:0.70 81:0.54 83:0.91 85:0.85 86:0.92 91:0.25 97:0.89 98:0.79 101:0.97 106:0.81 108:0.53 114:0.60 117:0.86 122:0.57 123:0.50 126:0.54 127:0.28 129:0.05 135:0.98 139:0.94 144:0.85 145:0.87 146:0.66 147:0.71 149:0.85 150:0.74 154:0.77 155:0.67 158:0.91 159:0.25 165:0.93 172:0.65 173:0.83 176:0.73 177:0.70 178:0.55 179:0.52 187:1.00 192:0.87 195:0.58 201:0.93 206:0.81 208:0.64 212:0.59 215:0.41 216:0.14 219:0.95 222:0.60 235:0.80 241:0.12 242:0.24 243:0.88 247:0.67 253:0.47 259:0.21 262:0.93 265:0.79 266:0.47 267:0.23 271:0.66 276:0.49 279:0.86 282:0.88 283:0.78 290:0.60 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 7:0.81 11:0.88 12:0.01 17:0.64 18:0.87 21:0.40 22:0.93 27:0.65 29:0.77 30:0.94 32:0.80 34:0.82 36:0.54 37:0.52 39:0.78 43:0.90 44:0.93 47:0.93 48:0.91 60:0.95 64:0.77 66:0.88 69:0.47 70:0.19 71:0.75 74:0.89 76:0.85 77:0.70 81:0.54 83:0.91 85:0.91 86:0.95 91:0.38 98:0.78 101:0.87 108:0.36 114:0.62 117:0.86 121:0.90 122:0.57 123:0.34 126:0.54 127:0.27 129:0.05 135:0.30 139:0.97 144:0.85 145:0.72 146:0.54 147:0.60 149:0.94 150:0.74 154:0.89 155:0.67 158:0.92 159:0.19 165:0.93 172:0.67 173:0.68 176:0.73 177:0.70 178:0.55 179:0.48 187:0.87 192:0.87 195:0.57 201:0.93 204:0.89 208:0.64 212:0.93 215:0.42 216:0.47 219:0.95 222:0.60 230:0.87 233:0.76 235:0.60 241:0.43 242:0.53 243:0.89 247:0.35 253:0.51 259:0.21 262:0.93 265:0.78 266:0.17 267:0.23 271:0.66 276:0.53 279:0.86 281:0.91 282:0.88 283:0.82 290:0.62 292:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.69 11:0.93 12:0.01 17:0.55 18:0.88 22:0.93 27:0.72 29:0.77 30:0.45 34:0.89 36:0.83 39:0.78 43:0.64 44:0.90 45:0.73 47:0.93 64:0.77 66:0.87 69:0.45 70:0.61 71:0.75 74:0.74 77:0.70 81:0.76 83:0.60 86:0.81 91:0.57 97:0.89 98:0.83 99:0.83 101:0.87 108:0.35 114:0.95 117:0.86 122:0.83 123:0.33 126:0.44 127:0.33 129:0.05 135:0.37 139:0.84 144:0.85 145:0.38 146:0.70 147:0.75 149:0.62 150:0.73 154:0.85 155:0.58 158:0.78 159:0.28 165:0.70 172:0.63 173:0.58 176:0.66 177:0.70 178:0.55 179:0.60 187:0.68 192:0.51 195:0.57 201:0.68 208:0.54 212:0.48 216:0.39 219:0.92 222:0.60 232:0.97 235:0.05 241:0.47 242:0.28 243:0.88 247:0.67 253:0.64 254:0.74 259:0.21 262:0.93 265:0.83 266:0.38 267:0.19 271:0.66 276:0.49 279:0.82 282:0.77 290:0.95 292:0.91 300:0.43 +2 11:0.88 12:0.01 17:0.18 18:0.93 21:0.05 27:0.71 29:0.77 30:0.76 34:0.69 36:0.88 37:0.25 39:0.78 43:0.97 64:0.77 66:0.64 69:0.07 70:0.44 71:0.75 74:0.47 81:0.65 85:0.72 86:0.80 91:0.57 98:0.81 101:0.87 108:0.06 122:0.83 123:0.06 124:0.47 126:0.44 127:0.58 129:0.05 133:0.43 135:0.72 139:0.77 144:0.85 146:0.83 147:0.86 149:0.89 150:0.45 154:0.97 159:0.50 172:0.71 173:0.15 177:0.70 178:0.55 179:0.51 187:0.39 192:0.51 201:0.68 212:0.42 215:0.78 216:0.32 219:0.87 222:0.60 232:0.88 235:0.12 241:0.18 242:0.63 243:0.69 245:0.81 247:0.39 253:0.34 259:0.21 265:0.81 266:0.38 267:0.52 271:0.66 276:0.66 283:0.78 292:0.91 297:0.36 300:0.55 +1 1:0.74 7:0.81 11:0.92 12:0.01 17:0.22 18:0.90 21:0.74 22:0.93 27:0.62 29:0.77 30:0.89 32:0.80 34:0.96 36:0.70 37:0.59 39:0.78 43:0.89 44:0.93 45:0.66 47:0.93 48:0.91 60:0.87 64:0.77 66:0.52 69:0.52 70:0.31 71:0.75 74:0.83 77:0.70 81:0.40 83:0.91 85:0.85 86:0.88 91:0.31 97:0.89 98:0.63 99:0.83 101:0.97 106:0.81 108:0.40 114:0.54 117:0.86 122:0.83 123:0.39 124:0.55 126:0.54 127:0.55 129:0.05 133:0.43 135:0.75 139:0.94 144:0.85 145:0.72 146:0.59 147:0.64 149:0.90 150:0.65 154:0.88 155:0.67 158:0.91 159:0.40 165:0.70 172:0.61 173:0.60 176:0.73 177:0.70 178:0.55 179:0.56 187:0.95 192:0.87 195:0.66 201:0.93 204:0.89 206:0.81 208:0.64 212:0.37 215:0.36 216:0.67 219:0.95 222:0.60 230:0.86 233:0.73 235:0.97 241:0.55 242:0.40 243:0.61 245:0.80 247:0.60 253:0.45 259:0.21 262:0.93 265:0.64 266:0.63 267:0.28 271:0.66 276:0.59 279:0.86 281:0.89 282:0.88 283:0.78 290:0.54 292:0.91 297:0.36 300:0.43 +1 1:0.74 11:0.88 12:0.01 17:0.08 18:0.68 20:0.99 21:0.13 22:0.93 27:0.16 29:0.77 30:0.95 34:0.66 36:0.61 37:0.85 39:0.78 43:0.97 47:0.93 60:0.87 64:0.77 66:0.64 69:0.10 71:0.75 74:0.58 78:0.92 81:0.71 83:0.91 85:0.72 86:0.77 91:0.10 97:0.89 98:0.17 100:0.91 101:0.87 108:0.09 111:0.91 114:0.79 122:0.57 123:0.09 126:0.54 127:0.10 129:0.05 135:0.60 139:0.82 144:0.85 146:0.17 147:0.22 149:0.64 150:0.82 152:0.94 154:0.97 155:0.67 158:0.92 159:0.09 165:0.93 169:0.87 172:0.16 173:0.52 176:0.73 177:0.70 178:0.55 179:0.16 186:0.92 187:0.87 192:0.87 201:0.93 208:0.64 212:0.95 215:0.61 216:0.54 219:0.95 222:0.60 235:0.31 241:0.37 242:0.82 243:0.69 247:0.12 253:0.56 259:0.21 261:0.96 262:0.93 265:0.19 266:0.12 267:0.36 271:0.66 276:0.19 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.08 +1 1:0.74 7:0.81 11:0.88 12:0.01 17:0.13 18:0.67 21:0.54 22:0.93 27:0.29 29:0.77 30:0.95 32:0.80 34:0.59 36:0.75 37:0.88 39:0.78 43:0.89 44:0.93 47:0.93 64:0.77 66:0.64 69:0.52 71:0.75 74:0.70 77:0.70 81:0.48 83:0.91 85:0.72 86:0.76 91:0.33 97:0.89 98:0.18 101:0.87 108:0.40 114:0.59 121:0.90 122:0.57 123:0.38 126:0.54 127:0.10 129:0.05 132:0.97 135:0.37 139:0.89 144:0.85 145:0.49 146:0.17 147:0.22 149:0.62 150:0.71 154:0.87 155:0.67 158:0.79 159:0.09 163:0.90 165:0.93 172:0.16 173:0.86 176:0.73 177:0.70 178:0.55 179:0.17 187:0.39 192:0.87 195:0.53 201:0.93 204:0.89 208:0.64 212:0.95 215:0.39 216:0.61 219:0.92 222:0.60 230:0.87 233:0.76 235:0.74 241:0.32 242:0.82 243:0.69 247:0.12 253:0.45 262:0.93 265:0.19 266:0.12 267:0.23 271:0.66 276:0.19 279:0.82 281:0.91 282:0.88 290:0.59 292:0.95 297:0.36 299:0.88 300:0.08 +1 1:0.74 11:0.92 12:0.01 17:0.70 18:0.86 21:0.47 27:0.62 29:0.77 30:0.87 32:0.80 34:0.62 36:0.26 37:0.40 39:0.78 43:0.81 44:0.93 45:0.66 64:0.77 66:0.88 69:0.71 70:0.37 71:0.75 74:0.74 77:0.70 81:0.54 83:0.91 85:0.85 86:0.92 91:0.18 98:0.79 101:0.97 108:0.54 114:0.60 122:0.57 123:0.52 126:0.54 127:0.28 129:0.05 135:0.98 139:0.93 144:0.85 145:0.85 146:0.66 147:0.71 149:0.85 150:0.74 154:0.76 155:0.67 158:0.78 159:0.25 165:0.93 172:0.65 173:0.84 176:0.73 177:0.70 178:0.55 179:0.52 187:1.00 192:0.87 195:0.58 201:0.93 208:0.64 212:0.59 215:0.41 216:0.14 219:0.92 222:0.60 235:0.80 241:0.15 242:0.24 243:0.88 247:0.67 253:0.47 259:0.21 265:0.79 266:0.54 267:0.23 271:0.66 276:0.49 279:0.82 282:0.88 290:0.60 292:0.91 297:0.36 299:0.88 300:0.43 +2 1:0.62 7:0.66 9:0.74 10:0.96 11:0.64 12:0.51 17:0.73 18:0.92 21:0.30 22:0.93 27:0.21 29:0.94 30:0.14 31:0.93 34:0.47 36:0.63 37:0.74 39:0.89 43:0.61 45:0.98 47:0.93 55:0.42 64:0.77 66:0.25 69:0.17 70:0.98 71:0.59 74:0.67 79:0.93 81:0.56 83:0.54 86:0.87 91:0.57 96:0.80 97:0.94 98:0.54 99:0.95 101:1.00 104:0.84 106:0.81 108:0.90 114:0.76 117:0.86 120:0.69 123:0.90 124:0.96 126:0.43 127:0.96 129:0.89 131:0.95 133:0.96 135:0.23 137:0.93 139:0.84 140:0.45 144:0.57 146:0.52 147:0.58 149:0.77 150:0.44 151:0.85 153:0.48 154:0.54 155:0.58 157:0.99 158:0.74 159:0.94 162:0.86 164:0.55 165:0.70 166:0.92 170:0.94 172:0.94 173:0.07 174:0.99 176:0.60 177:0.96 179:0.12 182:0.98 187:0.39 190:0.98 192:0.98 193:0.95 196:0.94 197:0.98 201:0.60 202:0.36 204:0.81 206:0.81 208:0.54 212:0.61 215:0.72 216:0.95 219:0.88 222:0.48 227:0.91 229:0.99 230:0.68 231:0.88 232:0.91 233:0.76 235:0.60 239:0.94 241:0.02 242:0.16 243:0.19 244:0.61 245:0.97 246:0.92 247:1.00 248:0.76 253:0.81 255:0.73 256:0.45 259:0.21 260:0.70 262:0.93 264:0.93 265:0.56 266:0.96 267:0.65 268:0.46 271:0.26 275:0.98 276:0.97 279:0.77 281:0.91 283:0.82 284:0.88 285:0.55 287:0.94 290:0.76 292:0.95 294:0.97 297:0.36 300:0.94 +2 1:0.63 7:0.64 8:0.77 9:0.75 10:0.94 11:0.52 12:0.51 17:0.61 18:0.86 21:0.54 23:0.98 27:0.70 29:0.94 30:0.14 31:0.94 32:0.67 34:0.40 36:0.96 37:0.54 39:0.89 43:0.26 44:0.88 45:0.93 55:0.71 56:0.97 62:0.97 64:0.77 66:0.98 69:0.44 70:0.91 71:0.59 74:0.60 77:0.70 79:0.94 81:0.67 82:0.99 83:0.54 86:0.83 88:0.98 91:0.65 96:0.83 97:0.94 98:0.98 99:0.95 101:0.69 102:0.96 108:0.34 114:0.74 120:0.73 122:0.98 123:0.32 126:0.54 127:0.83 128:0.96 129:0.05 131:0.95 135:0.92 137:0.94 139:0.83 140:0.45 143:0.99 144:0.57 145:0.65 146:0.97 147:0.98 149:0.40 150:0.49 151:0.57 153:0.48 154:0.51 155:0.65 157:0.99 158:0.74 159:0.73 162:0.68 164:0.77 165:0.70 166:0.91 168:0.96 170:0.94 172:0.94 173:0.30 174:0.97 176:0.63 177:0.96 179:0.24 182:0.99 187:0.39 190:0.98 192:1.00 193:0.95 195:0.86 196:0.94 197:0.98 201:0.64 202:0.70 204:0.78 205:0.98 208:0.64 212:0.68 215:0.58 216:0.26 219:0.88 220:0.91 222:0.48 227:0.91 229:0.99 230:0.65 231:0.88 233:0.76 235:0.88 236:0.94 239:0.93 241:0.44 242:0.19 243:0.98 244:0.83 246:0.92 247:0.97 248:0.61 253:0.81 254:0.80 255:0.78 256:0.71 259:0.21 260:0.73 264:0.93 265:0.98 266:0.63 267:0.18 268:0.72 271:0.26 275:0.95 276:0.88 279:0.77 281:0.91 282:0.75 283:0.82 284:0.96 285:0.62 287:0.94 290:0.74 292:0.95 294:0.98 297:0.36 300:0.70 +1 1:0.57 7:0.64 9:0.79 10:0.88 11:0.46 12:0.51 17:0.12 21:0.47 23:0.97 27:0.72 29:0.94 30:0.10 31:0.94 34:0.77 36:0.62 39:0.89 41:0.82 43:0.17 45:0.77 55:0.52 60:0.87 62:0.98 64:0.77 66:0.23 69:0.85 70:0.88 71:0.59 74:0.47 75:0.99 79:0.93 81:0.39 83:0.60 91:0.62 96:0.80 97:0.97 98:0.52 101:0.46 102:0.94 104:0.84 106:0.81 108:0.80 120:0.71 122:0.97 123:0.68 124:0.65 126:0.43 127:0.36 128:0.98 129:0.59 131:0.95 133:0.61 135:0.58 137:0.94 139:0.74 140:0.87 143:0.99 144:0.57 145:0.56 146:0.60 147:0.05 149:0.16 150:0.40 151:0.86 153:0.48 154:0.10 155:0.66 157:0.61 158:0.92 159:0.45 162:0.62 164:0.66 166:0.82 168:0.98 170:0.94 172:0.57 173:0.88 174:0.95 175:0.91 177:0.05 178:0.42 179:0.50 182:0.98 187:0.39 192:1.00 193:0.95 195:0.74 196:0.92 197:0.95 201:0.59 202:0.60 205:0.97 206:0.81 208:0.64 212:0.58 215:0.63 216:0.54 219:0.95 220:0.93 222:0.48 226:0.98 227:0.91 229:0.98 230:0.64 231:0.88 233:0.66 235:0.68 236:0.91 239:0.92 241:0.64 242:0.24 243:0.10 244:0.97 245:0.72 246:0.61 247:0.79 248:0.77 253:0.75 254:0.86 255:0.95 256:0.58 257:0.97 259:0.21 260:0.72 264:0.93 265:0.43 266:0.63 267:0.82 268:0.59 271:0.26 275:0.94 276:0.60 277:0.87 279:0.81 283:0.82 284:0.92 285:0.62 287:0.94 292:0.95 294:0.97 295:0.99 297:0.36 300:0.55 +2 1:0.63 8:0.86 9:0.75 10:0.93 11:0.83 12:0.51 17:0.26 18:0.78 21:0.22 27:0.17 29:0.94 30:0.17 31:0.96 34:0.52 36:0.47 37:0.97 39:0.89 43:0.75 45:0.90 55:0.42 64:0.77 66:0.33 69:0.48 70:0.99 71:0.59 74:0.53 79:0.95 81:0.58 83:0.54 85:0.72 86:0.82 91:0.63 96:0.86 97:0.99 98:0.51 99:0.95 101:1.00 108:0.37 114:0.80 120:0.78 123:0.36 124:0.83 126:0.43 127:0.72 129:0.59 131:0.95 133:0.81 135:0.66 137:0.96 139:0.79 140:0.45 144:0.57 146:0.81 147:0.84 149:0.70 150:0.45 151:0.85 153:0.48 154:0.66 155:0.58 157:0.98 158:0.74 159:0.79 162:0.52 164:0.74 165:0.70 166:0.92 170:0.94 172:0.74 173:0.29 174:0.93 176:0.60 177:0.96 179:0.33 182:0.99 187:0.39 190:0.98 191:0.76 192:0.98 196:0.94 197:0.94 201:0.61 202:0.74 208:0.54 212:0.47 215:0.79 216:0.68 219:0.88 220:0.82 222:0.48 227:0.91 229:0.99 231:0.88 235:0.52 239:0.91 241:0.51 242:0.21 243:0.50 245:0.86 246:0.92 247:0.98 248:0.84 253:0.83 255:0.74 256:0.64 259:0.21 260:0.78 264:0.93 265:0.53 266:0.84 267:0.84 268:0.69 271:0.26 275:0.96 276:0.81 279:0.81 283:0.82 284:0.95 285:0.55 287:0.94 290:0.80 292:0.95 294:0.97 297:0.36 300:0.94 +1 1:0.61 2:0.99 7:0.66 8:0.80 9:0.76 10:0.83 11:0.52 12:0.51 17:0.13 18:0.69 21:0.40 23:0.97 27:0.04 29:0.94 30:0.33 31:0.97 34:0.64 36:0.53 37:0.98 39:0.89 43:0.22 45:0.85 55:0.92 62:0.97 64:0.77 66:0.25 69:0.95 70:0.82 71:0.59 74:0.48 75:0.98 79:0.97 81:0.55 83:0.54 86:0.67 91:0.77 96:0.91 97:0.99 98:0.37 99:0.95 101:0.52 108:0.89 114:0.74 120:0.85 122:0.67 123:0.89 124:0.88 126:0.43 127:0.42 128:0.98 129:0.05 131:0.95 133:0.85 135:0.81 137:0.97 139:0.75 140:0.98 143:0.99 144:0.57 146:0.65 147:0.23 149:0.26 150:0.43 151:0.61 153:0.81 154:0.14 155:0.65 157:0.96 158:0.81 159:0.72 162:0.52 164:0.88 165:0.70 166:0.92 168:0.98 170:0.94 172:0.48 173:0.95 174:0.86 176:0.60 177:0.70 178:0.42 179:0.47 182:0.99 187:0.68 190:0.89 191:0.80 192:1.00 196:0.94 197:0.83 201:0.60 202:0.88 204:0.82 205:0.97 208:0.64 212:0.15 215:0.67 216:0.74 219:0.94 220:0.91 222:0.48 226:0.95 227:0.91 229:0.99 230:0.68 231:0.88 233:0.76 235:0.68 239:0.88 241:0.66 242:0.14 243:0.19 244:0.83 245:0.69 246:0.92 247:0.95 248:0.59 253:0.87 254:0.90 255:0.78 256:0.84 257:0.84 259:0.21 260:0.85 264:0.93 265:0.39 266:0.84 267:0.52 268:0.85 271:0.26 275:0.94 276:0.65 279:0.81 281:0.91 283:0.82 284:0.98 285:0.62 287:0.94 290:0.74 292:0.95 294:0.96 295:0.98 297:0.36 300:0.70 +2 1:0.66 7:0.64 8:0.77 9:0.80 10:0.89 11:0.64 12:0.51 17:0.38 18:0.75 21:0.30 23:0.99 27:0.04 29:0.94 30:0.53 31:0.98 34:0.87 36:0.91 37:0.98 39:0.89 41:0.88 43:0.62 44:0.92 45:0.83 55:0.59 56:0.97 62:0.99 64:0.77 66:0.54 69:0.84 70:0.57 71:0.59 74:0.59 75:0.98 77:0.70 79:0.97 81:0.74 82:0.99 83:0.60 86:0.71 88:0.99 91:0.74 96:0.91 97:0.97 98:0.67 99:0.99 101:0.87 102:0.97 108:0.70 114:0.84 120:0.85 122:0.97 123:0.68 124:0.52 126:0.54 127:0.29 128:0.99 129:0.05 131:0.95 133:0.45 135:0.65 137:0.98 139:0.82 140:0.95 143:1.00 144:0.57 145:0.95 146:0.86 147:0.88 149:0.52 150:0.55 151:0.86 153:0.48 154:0.51 155:0.66 157:0.92 158:0.81 159:0.56 162:0.51 164:0.84 165:0.86 166:0.91 168:0.99 170:0.94 172:0.77 173:0.78 174:0.96 176:0.70 177:0.96 178:0.52 179:0.27 182:0.99 187:0.87 191:0.80 192:1.00 195:0.85 196:0.96 197:0.95 201:0.66 202:0.85 204:0.79 205:0.99 208:0.64 212:0.85 215:0.72 216:0.74 219:0.94 220:0.62 222:0.48 226:0.96 227:0.91 229:0.99 230:0.64 231:0.88 233:0.66 235:0.80 236:0.95 239:0.93 241:0.65 242:0.16 243:0.62 244:0.61 245:0.90 246:0.61 247:0.91 248:0.81 253:0.90 255:1.00 256:0.81 259:0.21 260:0.85 264:0.93 265:0.67 266:0.47 267:0.36 268:0.81 271:0.26 275:0.91 276:0.76 279:0.80 281:0.91 282:0.80 283:0.67 284:0.98 285:0.62 287:0.94 290:0.84 292:0.88 294:0.98 295:0.98 297:0.36 300:0.55 +2 1:0.62 7:0.66 8:0.78 9:0.79 10:0.96 11:0.64 12:0.51 17:0.61 18:0.90 21:0.30 22:0.93 23:0.97 27:0.21 29:0.94 30:0.13 31:0.99 34:0.55 36:0.68 37:0.74 39:0.89 43:0.61 45:0.98 47:0.93 55:0.42 62:0.99 64:0.77 66:0.25 69:0.17 70:0.98 71:0.59 74:0.68 75:0.97 79:0.99 81:0.56 83:0.60 86:0.86 91:0.87 96:0.97 97:0.99 98:0.54 99:0.95 101:1.00 104:0.84 106:0.81 108:0.90 114:0.76 117:0.86 120:0.95 123:0.90 124:0.96 126:0.43 127:0.95 128:0.99 129:0.89 131:0.95 133:0.96 135:0.19 137:0.99 139:0.85 140:0.93 144:0.57 146:0.51 147:0.57 149:0.75 150:0.44 151:0.95 153:0.94 154:0.55 155:0.65 157:0.99 158:0.77 159:0.94 162:0.60 164:0.92 165:0.70 166:0.92 168:0.99 170:0.94 172:0.94 173:0.07 174:0.99 176:0.60 177:0.96 179:0.12 182:0.99 187:0.39 190:0.89 191:0.75 192:0.99 193:0.95 196:0.99 197:0.98 201:0.60 202:0.90 204:0.81 205:0.97 206:0.81 208:0.64 212:0.61 215:0.72 216:0.95 219:0.91 222:0.48 226:0.98 227:0.91 229:1.00 230:0.68 231:0.88 232:0.91 233:0.76 235:0.60 239:0.95 241:0.21 242:0.16 243:0.17 244:0.61 245:0.97 246:0.92 247:1.00 248:0.84 253:0.97 255:0.94 256:0.90 259:0.21 260:0.95 262:0.93 264:0.93 265:0.56 266:0.96 267:0.69 268:0.91 271:0.26 275:0.98 276:0.97 279:0.81 281:0.91 283:0.82 284:0.99 285:0.61 287:0.94 290:0.76 292:0.95 294:0.97 297:0.36 300:0.94 +2 1:0.62 2:1.00 7:0.66 8:0.60 9:0.75 10:0.99 11:0.64 12:0.51 17:0.24 18:0.96 21:0.59 23:0.98 27:0.64 29:0.94 30:0.54 31:0.95 32:0.75 34:0.50 36:0.87 37:0.28 39:0.89 43:0.61 44:0.92 45:0.99 55:0.42 56:0.97 60:0.87 62:0.98 64:0.77 66:0.86 69:0.36 70:0.70 71:0.59 74:0.88 75:0.99 77:0.89 79:0.94 80:0.98 81:0.70 82:0.99 83:0.54 86:0.96 88:0.98 91:0.61 96:0.83 97:1.00 98:0.90 99:0.99 101:1.00 102:0.96 108:0.62 114:0.73 120:0.73 122:0.67 123:0.60 124:0.39 126:0.54 127:0.93 128:0.96 129:0.59 131:0.95 133:0.60 135:0.82 137:0.95 139:0.96 140:0.45 143:0.99 144:0.57 145:0.63 146:0.45 147:0.52 149:0.72 150:0.47 151:0.59 153:0.48 154:0.66 155:0.65 157:1.00 158:0.86 159:0.78 162:0.84 164:0.77 165:0.86 166:0.91 168:0.96 170:0.94 172:0.98 173:0.22 174:0.98 176:0.70 177:0.96 179:0.13 182:0.99 187:0.21 190:0.98 192:1.00 193:0.95 195:0.88 196:0.97 197:0.98 201:0.62 202:0.65 204:0.84 205:0.98 208:0.64 212:0.89 215:0.55 216:0.61 219:0.95 220:0.87 222:0.48 226:0.96 227:0.91 229:0.99 230:0.68 231:0.88 233:0.76 235:0.97 236:0.95 239:0.99 241:0.50 242:0.24 243:0.12 244:0.61 245:0.95 246:0.92 247:0.99 248:0.61 253:0.87 255:0.78 256:0.71 259:0.21 260:0.73 264:0.93 265:0.90 266:0.47 267:0.69 268:0.72 271:0.26 275:0.99 276:0.96 279:0.82 281:0.91 282:0.83 283:0.67 284:0.96 285:0.62 287:0.94 290:0.73 292:0.88 294:1.00 295:0.98 297:0.36 300:0.70 +2 1:0.62 7:0.66 8:0.77 9:0.79 10:0.96 11:0.83 12:0.51 17:0.67 18:0.91 21:0.30 23:0.97 27:0.21 29:0.94 30:0.15 31:0.98 34:0.70 36:0.67 37:0.74 39:0.89 43:0.82 45:0.98 55:0.42 62:0.98 64:0.77 66:0.23 69:0.17 70:0.98 71:0.59 74:0.71 75:0.97 79:0.98 81:0.56 83:0.60 85:0.72 86:0.88 91:0.72 96:0.93 97:0.99 98:0.53 99:0.95 101:1.00 108:0.90 114:0.76 120:0.88 123:0.90 124:0.97 126:0.43 127:0.97 128:0.98 129:0.93 131:0.95 133:0.97 135:0.21 137:0.98 139:0.88 140:0.95 144:0.57 146:0.55 147:0.61 149:0.88 150:0.44 151:0.85 153:0.83 154:0.78 155:0.65 157:0.99 158:0.77 159:0.95 162:0.55 164:0.84 165:0.70 166:0.92 168:0.98 170:0.94 172:0.95 173:0.06 174:0.99 176:0.60 177:0.96 178:0.42 179:0.12 182:0.99 187:0.39 190:0.89 192:0.99 193:0.95 196:0.98 197:0.98 201:0.60 202:0.83 204:0.81 205:0.97 208:0.64 212:0.59 215:0.72 216:0.95 219:0.91 220:0.87 222:0.48 226:0.98 227:0.91 229:1.00 230:0.68 231:0.88 233:0.76 235:0.60 239:0.96 241:0.44 242:0.15 243:0.17 245:0.97 246:0.92 247:1.00 248:0.76 253:0.93 255:0.79 256:0.81 259:0.21 260:0.88 264:0.93 265:0.54 266:0.97 267:0.65 268:0.81 271:0.26 275:0.98 276:0.98 279:0.81 281:0.91 283:0.82 284:0.97 285:0.61 287:0.94 290:0.76 292:0.95 294:0.98 297:0.36 300:0.94 +1 10:0.93 11:0.83 12:0.51 17:0.62 18:0.74 21:0.78 27:0.45 29:0.94 30:0.74 34:0.76 36:0.32 37:0.50 39:0.89 43:0.78 45:0.81 55:0.42 66:0.67 69:0.76 70:0.36 71:0.59 74:0.52 85:0.72 86:0.82 91:0.27 98:0.26 101:1.00 108:0.60 122:0.67 123:0.57 124:0.45 127:0.26 129:0.05 131:0.61 133:0.36 135:0.60 139:0.77 144:0.57 145:0.50 146:0.34 147:0.40 149:0.67 154:0.71 157:0.97 159:0.39 166:0.61 170:0.94 172:0.57 173:0.78 174:0.83 177:0.96 178:0.55 179:0.53 182:0.90 187:0.68 197:0.86 212:0.82 216:0.77 222:0.48 227:0.61 229:0.61 231:0.74 235:0.52 239:0.91 241:0.15 242:0.16 243:0.72 245:0.66 246:0.61 247:0.79 253:0.42 259:0.21 264:0.93 265:0.28 266:0.27 267:0.36 271:0.26 275:0.94 276:0.29 287:0.61 294:0.97 300:0.33 +1 1:0.62 7:0.66 8:0.69 9:0.75 10:0.97 11:0.64 12:0.51 17:0.64 18:0.95 21:0.30 22:0.93 23:0.95 27:0.50 29:0.94 30:0.26 31:0.95 32:0.64 34:0.68 36:0.91 37:0.60 39:0.89 43:0.60 44:0.86 45:0.99 47:0.93 55:0.42 62:0.98 64:0.77 66:0.43 69:0.21 70:0.95 71:0.59 74:0.79 75:0.97 77:0.70 79:0.94 81:0.56 83:0.54 86:0.93 91:0.37 96:0.84 97:0.99 98:0.58 99:0.95 101:1.00 104:0.84 106:0.81 108:0.17 114:0.76 117:0.86 120:0.78 122:0.67 123:0.16 124:0.87 126:0.43 127:0.95 128:0.98 129:0.59 131:0.95 133:0.88 135:0.60 137:0.95 139:0.92 140:0.87 144:0.57 145:0.70 146:0.98 147:0.98 149:0.81 150:0.44 151:0.85 153:0.48 154:0.64 155:0.65 157:1.00 158:0.77 159:0.94 162:0.84 164:0.76 165:0.70 166:0.92 168:0.98 170:0.94 172:0.98 173:0.07 174:0.99 176:0.60 177:0.96 179:0.11 182:0.99 187:0.39 190:0.89 192:0.99 195:0.99 196:0.97 197:0.98 201:0.60 202:0.65 204:0.82 205:0.95 206:0.81 208:0.64 212:0.73 215:0.72 216:0.86 219:0.91 220:0.87 222:0.48 226:0.98 227:0.91 229:0.99 230:0.68 231:0.88 232:0.91 233:0.76 235:0.60 239:0.96 242:0.14 243:0.57 245:0.99 246:0.92 247:1.00 248:0.76 253:0.86 254:0.84 255:0.78 256:0.71 259:0.21 260:0.78 262:0.93 264:0.93 265:0.60 266:0.94 267:0.36 268:0.72 271:0.26 275:0.98 276:0.98 279:0.81 281:0.91 282:0.73 283:0.82 284:0.94 285:0.61 287:0.94 290:0.76 292:0.95 294:0.98 297:0.36 300:0.94 +1 1:0.59 7:0.64 8:0.78 10:0.86 11:0.52 12:0.51 17:0.86 18:0.77 21:0.59 27:0.72 29:0.94 30:0.32 32:0.71 34:0.93 36:0.58 37:0.42 39:0.89 43:0.21 44:0.93 45:0.87 55:0.71 64:0.77 66:0.71 69:0.82 70:0.79 71:0.59 74:0.52 77:0.89 81:0.44 82:0.99 86:0.71 88:0.98 91:0.29 98:0.58 99:0.83 101:0.69 102:0.98 104:0.89 106:0.81 108:0.67 114:0.67 122:0.67 123:0.65 124:0.46 126:0.43 127:0.37 129:0.05 131:0.61 133:0.59 135:0.49 139:0.77 144:0.57 145:0.82 146:0.68 147:0.40 149:0.26 150:0.38 154:0.19 155:0.47 157:0.98 159:0.48 165:0.63 166:0.91 170:0.94 172:0.76 173:0.83 174:0.89 176:0.60 177:0.38 178:0.55 179:0.39 182:0.97 187:0.21 192:0.47 195:0.74 197:0.90 201:0.57 206:0.81 208:0.54 212:0.86 215:0.55 216:0.10 222:0.48 227:0.61 229:0.61 230:0.64 231:0.87 233:0.66 235:0.84 239:0.90 241:0.12 242:0.22 243:0.18 244:0.83 245:0.73 246:0.92 247:0.91 253:0.53 254:0.97 257:0.93 259:0.21 264:0.93 265:0.59 266:0.54 267:0.28 271:0.26 275:0.91 276:0.68 282:0.83 283:0.67 287:0.61 290:0.67 294:0.97 297:0.36 300:0.70 +1 10:0.92 11:0.83 12:0.51 17:0.36 18:0.84 21:0.78 27:0.45 29:0.94 30:0.38 34:0.88 36:0.21 37:0.50 39:0.89 43:0.66 45:0.93 55:0.42 66:0.48 69:0.77 70:0.90 71:0.59 74:0.61 85:0.72 86:0.87 91:0.33 98:0.42 101:1.00 108:0.60 122:0.67 123:0.57 124:0.76 127:0.44 129:0.59 131:0.61 133:0.74 135:0.85 139:0.83 144:0.57 145:0.42 146:0.67 147:0.71 149:0.61 154:0.69 157:0.99 159:0.68 166:0.61 170:0.94 172:0.80 173:0.66 174:0.92 177:0.96 178:0.55 179:0.27 182:0.95 187:0.68 197:0.92 212:0.78 216:0.77 222:0.48 227:0.61 229:0.61 231:0.81 235:0.84 239:0.90 241:0.43 242:0.16 243:0.59 245:0.88 246:0.61 247:0.97 253:0.47 259:0.21 264:0.93 265:0.44 266:0.71 267:0.59 271:0.26 275:0.96 276:0.80 287:0.61 294:0.97 300:0.94 +1 1:0.60 9:0.79 10:0.92 11:0.64 12:0.51 17:0.28 18:0.84 21:0.47 23:0.96 27:0.27 29:0.94 30:0.55 31:0.94 34:0.36 36:0.70 37:0.36 39:0.89 41:0.82 43:0.28 45:0.92 55:0.42 62:0.98 64:0.77 66:0.85 69:0.27 70:0.67 71:0.59 74:0.66 75:0.99 79:0.93 81:0.54 83:0.54 86:0.83 91:0.35 96:0.80 97:0.97 98:0.81 99:0.95 101:0.96 108:0.21 114:0.72 120:0.69 122:0.67 123:0.20 124:0.37 126:0.43 127:0.57 128:0.98 129:0.05 131:0.95 133:0.36 135:0.70 137:0.94 139:0.84 140:0.45 143:0.99 144:0.57 146:0.71 147:0.76 149:0.36 150:0.41 151:0.86 153:0.48 154:0.63 155:0.66 157:0.99 158:0.84 159:0.37 162:0.86 164:0.57 165:0.70 166:0.92 168:0.98 170:0.94 172:0.76 173:0.39 174:0.88 176:0.60 177:0.96 179:0.52 182:0.98 187:0.95 192:1.00 196:0.95 197:0.91 201:0.59 202:0.36 205:0.95 208:0.64 212:0.78 215:0.63 216:0.89 219:0.95 222:0.48 226:0.95 227:0.91 229:0.99 231:0.88 235:0.74 239:0.94 241:0.37 242:0.27 243:0.86 244:0.61 245:0.60 246:0.92 247:0.84 248:0.77 253:0.81 254:0.86 255:0.95 256:0.45 259:0.21 260:0.70 264:0.93 265:0.81 266:0.47 267:0.23 268:0.46 271:0.26 275:0.96 276:0.63 279:0.79 283:0.66 284:0.89 285:0.62 287:0.94 290:0.72 292:0.87 294:0.98 295:0.98 297:0.36 300:0.55 +0 12:0.20 17:1.00 18:0.57 21:0.78 27:0.72 29:0.86 30:0.45 34:0.31 36:0.51 39:0.94 43:0.05 46:0.88 55:0.52 66:0.27 69:0.98 70:0.25 71:0.63 74:0.25 86:0.57 91:0.01 98:0.06 107:0.89 108:0.99 110:0.89 122:0.82 123:0.99 124:0.72 125:0.88 127:0.33 129:0.81 131:0.61 133:0.67 135:0.49 139:0.55 146:0.05 147:0.05 149:0.05 154:0.05 157:0.61 159:0.97 160:0.88 163:1.00 166:0.61 172:0.10 173:0.78 175:0.91 177:0.05 178:0.55 179:0.96 182:0.61 187:0.21 202:0.60 212:0.61 216:0.13 222:0.21 227:0.61 229:0.61 231:0.61 241:0.41 242:0.63 243:0.29 244:0.83 245:0.38 246:0.61 247:0.30 253:0.22 257:0.84 265:0.06 266:0.17 267:0.97 276:0.13 277:0.87 287:0.61 289:0.88 300:0.18 +0 12:0.20 17:0.99 18:0.56 21:0.78 27:0.69 29:0.86 30:0.87 37:0.27 39:0.94 43:0.05 46:0.88 55:0.59 66:0.20 69:1.00 70:0.27 71:0.63 74:0.25 86:0.56 98:0.08 107:0.89 108:0.99 110:0.89 122:0.77 123:0.99 124:0.74 125:0.88 127:0.18 129:0.05 131:0.61 133:0.62 139:0.55 146:0.21 147:0.34 149:0.05 154:0.06 157:0.61 159:0.93 160:0.88 163:0.80 166:0.61 172:0.16 173:0.99 175:0.75 177:0.05 178:0.55 179:0.61 182:0.61 187:0.68 202:0.63 212:0.19 216:0.85 222:0.21 227:0.61 229:0.61 231:0.61 235:0.12 241:0.05 242:0.75 243:0.40 244:0.61 245:0.49 246:0.61 247:0.35 253:0.22 254:0.92 257:0.84 265:0.08 266:0.47 276:0.19 277:0.87 287:0.61 289:0.88 300:0.25 +0 12:0.20 17:0.99 18:0.57 21:0.78 27:0.72 29:0.86 30:0.76 34:0.84 36:0.32 39:0.94 43:0.05 46:0.88 55:0.45 66:0.36 69:0.99 70:0.15 71:0.63 74:0.25 86:0.56 91:0.20 98:0.06 107:0.89 108:0.98 110:0.88 122:0.57 123:0.98 124:0.52 125:0.88 127:0.21 129:0.05 131:0.61 133:0.39 135:0.30 139:0.55 146:0.18 147:0.23 149:0.05 154:0.08 157:0.61 159:0.93 160:0.88 163:1.00 166:0.61 172:0.10 173:0.89 175:0.75 177:0.38 178:0.55 179:0.95 182:0.61 187:0.21 212:0.95 222:0.21 227:0.61 229:0.61 231:0.61 235:0.52 241:0.39 242:0.82 243:0.51 244:0.61 245:0.37 246:0.61 247:0.12 253:0.22 254:0.80 257:0.65 265:0.06 266:0.12 267:0.36 276:0.12 277:0.69 287:0.61 289:0.88 300:0.13 +0 11:0.75 12:0.20 17:0.83 18:0.60 21:0.78 27:0.72 29:0.86 30:0.76 34:0.62 36:0.96 39:0.94 43:0.32 45:0.66 46:0.93 66:0.36 69:0.91 70:0.15 71:0.63 74:0.33 86:0.65 91:0.03 98:0.07 101:0.52 107:0.91 108:0.82 110:0.90 122:0.80 123:0.81 124:0.52 125:0.88 127:0.18 129:0.05 131:0.61 133:0.39 135:0.65 139:0.63 144:0.57 146:0.13 147:0.18 149:0.21 154:0.23 157:0.78 159:0.48 160:0.88 163:0.99 166:0.61 172:0.10 173:0.83 175:0.75 177:0.38 178:0.55 179:0.92 182:0.73 187:0.39 212:0.95 216:0.12 222:0.21 227:0.61 229:0.61 231:0.63 235:0.31 241:0.18 242:0.82 243:0.51 244:0.61 245:0.37 246:0.61 247:0.12 253:0.28 257:0.65 259:0.21 265:0.07 266:0.12 267:0.36 276:0.12 277:0.69 287:0.61 289:0.88 300:0.13 +0 12:0.20 17:1.00 18:0.73 21:0.78 27:0.72 29:0.86 30:0.62 34:0.30 36:0.47 39:0.94 43:0.15 46:0.88 55:0.52 66:0.16 69:0.50 70:0.23 71:0.63 74:0.24 86:0.59 91:0.02 98:0.06 107:0.89 108:0.38 110:0.89 122:0.41 123:0.37 124:0.81 125:0.88 127:0.49 129:0.89 131:0.61 133:0.78 135:0.71 139:0.58 146:0.20 147:0.26 149:0.10 154:0.11 157:0.61 159:0.98 160:0.88 163:0.90 166:0.61 172:0.10 173:0.07 175:0.91 177:0.05 178:0.55 179:0.93 182:0.61 187:0.21 202:0.63 212:0.30 216:0.09 222:0.21 227:0.61 229:0.61 231:0.62 241:0.43 242:0.33 243:0.37 244:0.83 245:0.42 246:0.61 247:0.39 253:0.20 257:0.84 265:0.06 266:0.27 267:0.99 276:0.26 277:0.87 287:0.61 289:0.88 300:0.18 +2 6:0.95 7:0.63 8:0.93 9:0.80 11:0.87 12:0.69 17:0.24 18:0.90 20:0.91 21:0.78 27:0.10 28:0.56 29:0.62 30:0.60 31:0.99 34:0.95 36:0.92 37:0.92 39:0.80 41:0.98 43:0.79 44:0.85 45:0.73 48:0.91 55:0.45 66:0.27 69:0.71 70:0.44 71:0.86 74:0.43 76:0.94 78:0.82 79:1.00 83:0.91 85:0.72 86:0.73 91:0.98 96:0.99 97:0.94 98:0.42 100:0.86 101:0.97 108:0.86 111:0.83 120:0.96 122:0.86 123:0.92 124:0.73 127:0.69 129:0.05 131:0.91 133:0.60 135:0.96 137:0.99 138:1.00 139:0.71 140:0.98 144:0.76 145:0.70 146:0.46 147:0.53 149:0.63 151:0.97 152:0.95 153:0.96 154:0.72 155:0.67 157:0.82 158:0.72 159:0.81 161:0.77 162:0.48 164:0.94 166:0.61 167:0.72 169:0.86 172:0.51 173:0.51 177:0.70 178:0.53 179:0.53 181:0.64 182:0.97 186:0.82 187:0.39 189:0.98 191:0.98 192:0.87 195:0.92 196:0.96 202:0.99 204:0.85 208:0.64 212:0.49 216:0.88 219:0.87 222:0.84 227:0.95 229:0.98 230:0.63 231:0.80 233:0.73 235:0.31 238:0.92 241:0.69 242:0.63 243:0.28 245:0.80 246:0.61 247:0.76 248:0.96 253:0.96 255:0.95 256:0.99 259:0.21 260:0.94 261:0.89 265:0.17 266:0.84 267:0.84 268:0.99 271:0.48 276:0.60 277:0.69 279:0.82 281:0.89 284:1.00 285:0.62 287:0.99 292:0.95 300:0.33 +2 1:0.69 6:0.86 8:0.66 9:0.80 11:0.92 12:0.69 17:0.30 18:0.99 20:0.86 21:0.30 22:0.93 27:0.21 28:0.56 29:0.62 30:0.67 31:0.98 34:0.40 36:0.84 37:0.74 39:0.80 41:0.82 43:0.80 45:0.99 47:0.93 64:0.77 66:0.34 69:0.12 70:0.70 71:0.86 74:0.84 78:0.81 79:1.00 81:0.40 83:0.91 85:0.80 86:0.97 91:0.82 96:0.99 97:0.99 98:0.71 99:0.83 100:0.84 101:0.97 108:0.83 111:0.80 114:0.60 117:0.86 120:0.85 122:0.85 123:0.82 124:0.82 126:0.44 127:0.72 129:0.89 131:0.91 133:0.84 135:0.95 137:0.98 138:0.97 139:0.96 140:0.99 144:0.76 146:0.86 147:0.88 149:0.97 150:0.55 151:0.73 152:0.83 153:0.96 154:0.82 155:0.67 157:0.88 158:0.72 159:0.88 161:0.77 162:0.76 164:0.74 165:0.70 166:0.78 167:0.54 169:0.81 172:0.94 173:0.08 176:0.55 177:0.70 179:0.13 181:0.64 182:0.97 186:0.81 187:0.39 189:0.88 191:0.84 192:0.87 196:0.99 201:0.68 202:0.92 204:0.85 208:0.64 212:0.78 216:0.95 219:0.87 222:0.84 227:0.95 229:0.98 231:0.80 232:0.85 235:0.42 238:0.86 241:0.24 242:0.37 243:0.40 245:0.98 246:0.61 247:0.93 248:0.68 253:0.95 255:0.95 256:0.94 259:0.21 260:0.69 261:0.82 262:0.93 265:0.71 266:0.87 267:0.96 268:0.94 271:0.48 276:0.96 279:0.86 281:0.91 283:0.82 284:0.97 285:0.62 287:0.99 290:0.60 292:0.95 300:0.84 +1 1:0.69 6:0.84 8:0.87 9:0.80 11:0.81 12:0.69 17:0.34 18:0.68 20:0.80 21:0.05 27:0.05 28:0.56 29:0.62 30:0.71 31:0.91 34:0.77 36:0.26 37:0.98 39:0.80 43:0.27 45:0.81 60:0.87 66:0.33 69:0.69 70:0.40 71:0.86 74:0.65 78:0.86 79:0.96 81:0.64 83:0.91 86:0.78 91:0.71 96:0.94 97:0.94 98:0.12 99:0.83 100:0.87 101:0.52 104:0.96 106:0.81 108:0.53 111:0.85 114:0.70 117:0.86 120:0.85 122:0.51 123:0.51 124:0.71 126:0.44 127:0.64 129:0.59 131:0.91 133:0.66 135:0.78 137:0.91 139:0.82 140:0.45 144:0.76 146:0.20 147:0.26 149:0.18 150:0.62 151:0.84 152:0.77 153:0.72 154:0.75 155:0.67 157:0.61 158:0.92 159:0.69 161:0.77 162:0.58 164:0.66 165:0.70 166:0.80 167:0.66 169:0.86 172:0.27 173:0.60 176:0.55 177:0.70 179:0.85 181:0.64 182:0.61 186:0.85 187:0.68 189:0.86 190:0.77 191:0.97 192:0.87 196:0.93 201:0.68 202:0.84 206:0.81 208:0.64 212:0.30 215:0.78 216:0.63 219:0.95 220:0.62 222:0.84 227:0.95 229:0.61 231:0.80 232:0.98 235:0.12 238:0.87 241:0.59 242:0.33 243:0.50 245:0.53 246:0.61 247:0.47 248:0.92 253:0.81 254:0.74 255:0.79 256:0.83 260:0.67 261:0.81 265:0.13 266:0.54 267:0.52 268:0.84 271:0.48 276:0.28 279:0.86 283:0.82 284:0.87 285:0.62 287:0.61 290:0.70 292:0.95 297:0.36 300:0.33 +2 1:0.69 6:0.91 7:0.63 8:0.82 9:0.80 12:0.69 17:0.18 20:0.96 27:0.06 28:0.56 29:0.62 31:1.00 34:0.59 36:0.30 37:0.81 39:0.80 41:0.98 44:0.85 64:0.77 71:0.86 74:0.48 77:0.70 78:0.96 79:1.00 81:0.70 83:0.91 91:0.99 96:1.00 97:0.99 99:0.83 100:0.98 111:0.97 114:0.78 120:0.95 126:0.44 131:0.91 135:0.78 137:1.00 138:1.00 139:0.75 140:0.94 144:0.76 145:0.65 150:0.66 151:0.99 152:0.92 153:0.99 155:0.67 157:0.61 158:0.87 161:0.77 162:0.61 164:0.94 165:0.70 166:0.79 167:0.88 169:0.97 175:0.97 176:0.55 181:0.64 182:0.97 186:0.96 189:0.92 191:0.96 192:0.87 195:0.75 196:0.98 201:0.68 202:0.99 204:0.85 208:0.64 216:0.80 219:0.95 222:0.84 227:0.95 229:0.98 230:0.63 231:0.80 233:0.76 235:0.05 238:0.96 241:0.90 244:0.93 246:0.61 248:0.97 253:0.98 255:0.95 256:0.99 257:0.93 259:0.21 260:0.94 261:0.97 267:0.95 268:0.99 271:0.48 277:0.95 279:0.86 281:0.91 282:0.71 283:0.71 284:1.00 285:0.62 287:0.99 290:0.78 292:0.95 +1 6:0.90 8:0.84 9:0.80 11:0.64 12:0.69 17:0.38 18:0.81 20:0.77 21:0.78 27:0.30 28:0.56 29:0.62 30:0.33 31:0.97 34:0.80 36:0.28 37:0.68 39:0.80 41:0.92 43:0.71 45:0.66 55:0.52 66:0.54 69:0.17 70:0.49 71:0.86 74:0.29 78:0.93 79:0.98 83:0.91 86:0.59 91:0.64 96:0.96 97:0.94 98:0.34 100:0.97 101:0.52 108:0.82 111:0.96 120:0.87 122:0.86 123:0.81 124:0.48 127:0.80 129:0.05 131:0.91 133:0.39 135:0.98 137:0.97 139:0.59 140:0.95 144:0.76 146:0.13 147:0.18 149:0.39 151:0.87 152:0.76 153:0.90 154:0.61 155:0.67 157:0.61 158:0.72 159:0.57 161:0.77 162:0.69 164:0.74 166:0.61 167:0.66 169:0.95 172:0.32 173:0.21 175:0.75 177:0.38 179:0.91 181:0.64 182:0.99 186:0.93 187:0.68 189:0.94 191:0.95 192:0.87 196:0.87 202:0.85 208:0.64 212:0.42 216:0.60 219:0.87 222:0.84 227:0.95 229:0.99 231:0.80 238:0.95 241:0.60 242:0.79 243:0.32 244:0.61 245:0.50 246:0.61 247:0.30 248:0.85 253:0.86 255:0.95 256:0.87 257:0.65 259:0.21 260:0.81 261:0.82 265:0.36 266:0.38 267:0.19 268:0.87 271:0.48 276:0.18 277:0.87 279:0.82 283:0.78 284:0.96 285:0.62 287:0.99 292:0.91 300:0.33 +2 1:0.69 6:0.96 8:0.92 9:0.80 11:0.85 12:0.69 17:0.11 18:0.77 20:0.96 21:0.54 27:0.04 28:0.56 29:0.62 30:0.45 31:0.99 34:0.90 36:0.69 37:0.96 39:0.80 41:0.98 43:0.30 45:0.77 66:0.53 69:0.92 70:0.78 71:0.86 74:0.48 76:0.85 78:0.87 79:1.00 81:0.34 83:0.91 86:0.58 91:0.99 96:1.00 97:0.98 98:0.40 99:0.83 100:0.92 101:0.87 108:0.83 111:0.88 114:0.57 120:0.95 122:0.41 123:0.82 124:0.63 126:0.44 127:0.58 129:0.05 131:0.91 133:0.59 135:0.83 137:0.99 138:1.00 139:0.75 140:0.99 144:0.76 145:0.52 146:0.54 147:0.05 149:0.32 150:0.52 151:0.95 152:0.97 153:0.97 154:0.47 155:0.67 157:0.61 158:0.91 159:0.59 161:0.77 162:0.49 163:0.81 164:0.93 165:0.70 166:0.80 167:0.87 169:0.93 172:0.37 173:0.96 176:0.55 177:0.05 178:0.52 179:0.84 181:0.64 182:0.96 186:0.88 189:0.94 191:0.99 192:0.87 196:0.97 201:0.68 202:1.00 204:0.85 208:0.64 212:0.30 215:0.39 216:0.92 219:0.95 220:0.62 222:0.84 227:0.95 229:0.98 231:0.80 235:0.68 238:0.92 241:0.89 242:0.14 243:0.17 245:0.50 246:0.61 247:0.67 248:0.94 253:0.96 254:0.96 255:0.95 256:0.99 257:0.84 259:0.21 260:0.91 261:0.95 265:0.42 266:0.54 267:0.82 268:0.99 271:0.48 276:0.33 279:0.86 281:0.91 283:0.78 284:1.00 285:0.62 287:0.99 290:0.57 292:0.95 297:0.36 300:0.55 +2 6:0.98 7:0.64 8:0.98 9:0.80 12:0.69 17:0.36 18:0.58 20:0.81 21:0.78 27:0.11 28:0.56 29:0.62 30:0.95 31:0.99 34:0.52 36:0.18 37:0.89 39:0.80 41:0.97 43:0.22 44:0.85 55:0.42 66:0.54 69:0.96 71:0.86 74:0.30 77:0.70 78:0.90 79:1.00 83:0.91 86:0.57 91:0.95 96:0.99 97:0.94 98:0.08 100:0.94 108:0.92 111:0.90 120:0.90 123:0.92 127:0.06 129:0.05 131:0.91 135:0.63 137:0.99 138:1.00 139:0.60 140:0.99 144:0.76 145:0.54 146:0.17 147:0.22 149:0.13 151:0.87 152:0.84 153:0.92 154:0.14 155:0.67 157:0.61 158:0.72 159:0.09 161:0.77 162:0.50 164:0.91 166:0.61 167:0.75 169:0.92 172:0.10 173:0.98 175:0.75 177:0.38 178:0.53 179:0.08 181:0.64 182:0.97 186:0.89 187:0.21 189:0.99 191:0.98 192:0.87 195:0.91 196:0.88 202:0.99 204:0.85 208:0.64 216:0.90 219:0.87 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.73 235:0.22 238:0.93 241:0.73 243:0.63 244:0.61 246:0.61 248:0.92 253:0.92 254:0.74 255:0.95 256:0.98 257:0.65 259:0.21 260:0.87 261:0.90 265:0.08 267:0.92 268:0.98 271:0.48 277:0.69 279:0.82 282:0.71 284:0.99 285:0.62 287:0.99 292:0.95 300:0.08 +2 6:0.96 7:0.64 8:0.97 9:0.80 11:0.92 12:0.69 17:0.13 18:0.70 20:0.85 21:0.78 27:0.04 28:0.56 29:0.62 30:0.76 31:1.00 34:0.93 36:0.93 37:0.96 39:0.80 41:0.98 43:0.52 45:0.73 66:0.36 69:0.95 70:0.28 71:0.86 74:0.36 78:0.91 79:1.00 83:0.91 85:0.72 86:0.66 91:0.98 96:1.00 97:0.89 98:0.26 100:0.96 101:0.97 108:0.90 111:0.93 120:0.96 122:0.41 123:0.90 124:0.68 127:0.66 129:0.59 131:0.91 133:0.61 135:0.56 137:1.00 138:1.00 139:0.66 140:0.99 144:0.76 145:0.52 146:0.17 147:0.05 149:0.37 151:0.97 152:0.97 153:0.98 154:0.41 155:0.67 157:0.83 158:0.72 159:0.76 161:0.77 162:0.48 163:0.75 164:0.93 166:0.61 167:0.76 169:0.93 172:0.27 173:0.96 177:0.05 178:0.53 179:0.88 181:0.64 182:0.96 186:0.91 187:0.21 189:0.98 191:0.98 192:0.87 196:0.93 202:1.00 204:0.89 208:0.64 212:0.37 216:0.92 219:0.87 220:0.62 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.76 235:0.22 238:0.96 241:0.74 242:0.16 243:0.18 245:0.50 246:0.61 247:0.60 248:0.96 253:0.97 255:0.95 256:0.99 257:0.84 259:0.21 260:0.93 261:0.95 265:0.28 266:0.38 267:0.76 268:0.99 271:0.48 276:0.21 279:0.82 281:0.89 284:1.00 285:0.62 287:0.98 292:0.95 300:0.25 +2 1:0.69 6:0.86 8:0.67 9:0.80 11:0.92 12:0.69 17:0.45 18:0.99 20:0.79 21:0.30 22:0.93 27:0.21 28:0.56 29:0.62 30:0.68 31:0.96 34:0.47 36:0.82 37:0.74 39:0.80 41:0.88 43:0.92 45:0.99 47:0.93 60:0.95 64:0.77 66:0.34 69:0.12 70:0.69 71:0.86 74:0.89 78:0.81 79:0.96 81:0.40 83:0.91 85:0.85 86:0.98 91:0.53 96:0.87 98:0.71 99:0.83 100:0.84 101:0.97 104:0.84 106:0.81 108:0.83 111:0.81 114:0.60 117:0.86 120:0.78 122:0.85 123:0.82 124:0.82 126:0.44 127:0.73 129:0.89 131:0.91 133:0.84 135:0.96 137:0.96 139:0.97 140:0.80 144:0.76 146:0.86 147:0.88 149:0.97 150:0.55 151:0.87 152:0.83 153:0.48 154:0.92 155:0.67 157:0.93 158:0.92 159:0.88 161:0.77 162:0.86 164:0.67 165:0.70 166:0.78 167:0.48 169:0.81 172:0.95 173:0.08 176:0.55 177:0.70 179:0.13 181:0.64 182:0.99 186:0.82 187:0.39 189:0.87 192:0.87 196:0.98 201:0.68 202:0.59 204:0.85 206:0.81 208:0.64 212:0.79 216:0.95 219:0.95 220:0.87 222:0.84 227:0.95 229:0.99 231:0.80 232:0.91 235:0.42 238:0.87 241:0.01 242:0.37 243:0.43 245:0.98 246:0.61 247:0.93 248:0.86 253:0.90 255:0.95 256:0.64 259:0.21 260:0.79 261:0.82 262:0.93 265:0.71 266:0.87 267:0.97 268:0.67 271:0.48 276:0.96 279:0.86 281:0.91 283:0.82 284:0.93 285:0.62 287:0.99 290:0.60 292:0.95 300:0.84 +1 1:0.69 6:0.95 7:0.63 8:0.92 9:0.80 11:0.88 12:0.69 17:0.11 18:0.72 20:0.92 21:0.54 27:0.10 28:0.56 29:0.62 30:0.33 31:0.99 32:0.80 34:0.76 36:0.82 37:0.92 39:0.80 41:0.98 43:0.61 44:0.93 45:0.85 66:0.68 69:0.67 70:0.54 71:0.86 74:0.58 76:0.85 77:0.70 78:0.83 79:1.00 81:0.34 83:0.91 86:0.70 91:0.97 96:0.99 97:0.89 98:0.67 99:0.83 100:0.90 101:0.87 108:0.71 111:0.85 114:0.57 120:0.96 122:0.75 123:0.69 124:0.44 126:0.44 127:0.39 129:0.59 131:0.91 133:0.46 135:0.79 137:0.99 138:1.00 139:0.78 140:0.98 144:0.76 145:0.61 146:0.41 147:0.48 149:0.58 150:0.52 151:0.97 152:0.95 153:0.97 154:0.55 155:0.67 157:0.61 158:0.72 159:0.45 161:0.77 162:0.50 164:0.95 165:0.70 166:0.80 167:0.87 169:0.86 172:0.67 173:0.69 176:0.55 177:0.70 178:0.52 179:0.52 181:0.64 182:0.97 186:0.84 189:0.95 191:0.97 192:0.87 195:0.70 196:0.98 201:0.68 202:0.99 204:0.85 208:0.64 212:0.70 215:0.39 216:0.88 222:0.84 227:0.95 229:0.98 230:0.63 231:0.80 233:0.73 235:0.68 238:0.93 241:0.86 242:0.42 243:0.27 245:0.74 246:0.61 247:0.76 248:0.96 253:0.97 254:0.84 255:0.95 256:0.99 259:0.21 260:0.93 261:0.89 265:0.68 266:0.75 267:0.95 268:0.99 271:0.48 276:0.59 279:0.82 281:0.88 282:0.88 283:0.78 284:1.00 285:0.62 287:0.99 290:0.57 297:0.36 299:0.88 300:0.43 +2 1:0.69 7:0.64 8:0.94 9:0.80 11:0.85 12:0.69 17:0.57 18:0.91 21:0.22 27:0.10 28:0.56 29:0.62 30:0.71 31:0.99 34:0.85 36:0.84 37:0.92 39:0.80 41:0.95 43:0.60 44:0.85 45:0.85 55:0.45 64:0.77 66:0.45 69:0.66 70:0.48 71:0.86 74:0.34 77:0.70 79:0.99 81:0.46 83:0.91 86:0.63 91:0.93 96:0.98 97:0.89 98:0.29 99:0.83 101:0.87 108:0.51 114:0.62 120:0.89 122:0.86 123:0.48 124:0.58 126:0.44 127:0.65 129:0.05 131:0.91 133:0.43 135:0.98 137:0.99 138:1.00 139:0.63 140:0.97 144:0.76 145:0.52 146:0.55 147:0.61 149:0.57 150:0.56 151:0.92 153:0.89 154:0.68 155:0.67 157:0.61 158:0.72 159:0.78 161:0.77 162:0.55 164:0.89 165:0.70 166:0.78 167:0.75 172:0.63 173:0.49 176:0.55 177:0.70 178:0.42 179:0.51 181:0.64 182:0.97 187:0.39 191:0.95 192:0.87 195:0.91 196:0.90 201:0.68 202:0.97 204:0.89 208:0.64 212:0.88 216:0.88 219:0.87 220:0.82 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.73 235:0.31 241:0.72 242:0.50 243:0.58 245:0.85 246:0.61 247:0.76 248:0.92 253:0.92 254:0.93 255:0.95 256:0.97 259:0.21 260:0.87 265:0.32 266:0.47 267:0.79 268:0.97 271:0.48 276:0.37 279:0.82 281:0.89 282:0.71 284:0.99 285:0.62 287:0.99 290:0.62 292:0.91 300:0.55 +2 6:0.98 7:0.64 8:0.97 9:0.80 12:0.69 17:0.11 20:0.80 21:0.78 27:0.11 28:0.56 29:0.62 31:0.99 34:0.53 36:0.26 37:0.89 39:0.80 41:0.92 44:0.85 71:0.86 74:0.30 77:0.70 78:0.87 79:1.00 83:0.91 91:0.96 96:0.99 97:0.94 100:0.93 111:0.89 120:0.89 131:0.91 135:0.65 137:0.99 139:0.59 140:0.99 144:0.76 145:0.54 151:0.93 152:0.84 153:0.94 155:0.67 157:0.61 158:0.72 161:0.77 162:0.49 164:0.81 166:0.61 167:0.75 169:0.91 175:0.97 178:0.52 181:0.64 182:0.96 186:0.87 187:0.39 189:0.98 191:0.96 192:0.87 195:0.91 196:0.88 202:0.98 204:0.85 208:0.64 216:0.90 219:0.87 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.73 235:0.22 238:0.94 241:0.72 244:0.93 246:0.61 248:0.88 253:0.91 255:0.95 256:0.96 257:0.93 259:0.21 260:0.84 261:0.90 267:0.65 268:0.97 271:0.48 277:0.95 279:0.82 282:0.71 284:0.99 285:0.62 287:0.99 292:0.95 +1 1:0.74 11:0.87 12:0.69 17:0.22 18:0.91 21:0.40 27:0.45 28:0.56 29:0.62 30:0.74 34:0.68 36:0.23 37:0.50 39:0.80 43:0.82 45:0.89 64:0.77 66:0.61 69:0.69 70:0.62 71:0.86 74:0.54 81:0.53 83:0.91 85:0.85 86:0.83 91:0.29 98:0.56 101:0.97 108:0.52 114:0.62 122:0.80 123:0.50 124:0.57 126:0.54 127:0.53 129:0.59 131:0.61 133:0.66 135:0.86 139:0.79 144:0.76 145:0.44 146:0.80 147:0.83 149:0.88 150:0.74 154:0.77 155:0.67 157:0.96 159:0.71 161:0.77 165:0.93 166:0.86 167:0.59 172:0.75 173:0.56 176:0.73 177:0.70 178:0.55 179:0.42 181:0.64 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.42 215:0.42 216:0.77 222:0.84 227:0.61 229:0.61 231:0.79 235:0.60 241:0.44 242:0.41 243:0.68 245:0.80 246:0.99 247:0.82 253:0.47 259:0.21 265:0.57 266:0.71 267:0.44 271:0.48 276:0.69 287:0.61 290:0.62 297:0.36 300:0.70 +0 12:0.95 17:0.83 21:0.78 27:0.35 29:0.53 34:0.99 36:0.17 37:0.59 39:0.94 43:0.06 66:0.36 69:0.94 71:0.86 91:0.12 98:0.06 108:0.89 123:0.88 127:0.05 129:0.05 135:0.99 145:0.70 146:0.05 147:0.05 149:0.05 154:0.06 159:0.05 170:0.79 172:0.05 173:0.93 175:0.99 177:0.05 178:0.55 187:0.68 202:0.36 216:0.39 222:0.25 235:0.68 241:0.32 243:0.51 244:0.97 253:0.20 257:0.97 259:0.21 264:0.93 265:0.06 267:0.28 271:0.13 277:0.98 +0 1:0.63 7:0.66 8:0.73 9:0.73 10:0.92 11:0.49 12:0.95 17:0.32 18:0.80 21:0.47 22:0.93 23:0.97 25:0.88 27:0.32 29:0.53 30:0.86 31:0.91 32:0.64 33:0.97 34:0.34 36:0.40 37:0.36 39:0.94 43:0.65 45:0.92 47:0.93 62:0.97 64:0.77 66:0.06 69:0.40 70:0.47 71:0.86 74:0.51 79:0.90 81:0.60 83:0.60 86:0.81 91:0.12 96:0.75 98:0.14 99:0.95 101:0.52 104:0.58 108:0.31 114:0.78 117:0.86 120:0.68 122:0.91 123:0.96 124:0.92 126:0.53 127:0.96 128:0.96 129:0.93 133:0.91 135:0.71 137:0.91 139:0.77 140:0.45 145:0.69 146:0.24 147:0.30 149:0.71 150:0.46 151:0.66 153:0.48 154:0.54 155:0.64 159:0.92 162:0.66 164:0.71 165:0.70 168:0.96 170:0.79 172:0.32 173:0.12 174:0.90 175:0.91 176:0.70 177:0.70 179:0.62 187:0.68 190:0.98 192:0.99 195:0.98 196:0.91 197:0.92 201:0.61 202:0.64 205:0.97 208:0.64 212:0.23 215:0.63 216:0.43 220:0.93 222:0.25 230:0.68 233:0.66 235:0.74 236:0.95 239:0.91 241:0.41 242:0.40 243:0.34 244:0.83 245:0.63 247:0.67 248:0.65 253:0.67 255:0.73 256:0.64 257:0.84 259:0.21 260:0.68 262:0.93 264:0.93 265:0.11 266:0.80 267:0.91 268:0.65 271:0.13 275:0.93 276:0.60 277:0.98 284:0.92 285:0.61 290:0.78 291:0.97 294:0.92 297:0.36 300:0.70 +1 7:0.66 8:0.91 10:0.99 11:0.57 12:0.95 17:0.89 18:0.87 21:0.22 27:0.65 29:0.53 30:0.88 32:0.64 34:0.78 36:0.18 37:0.69 39:0.94 43:0.76 66:0.48 69:0.71 70:0.48 71:0.86 74:0.79 81:0.34 85:0.94 86:0.95 91:0.40 98:0.33 101:0.87 104:0.96 106:0.81 108:0.54 123:0.52 124:0.82 126:0.23 127:0.70 129:0.59 133:0.85 135:0.58 139:0.94 145:0.42 146:0.62 147:0.67 149:0.98 150:0.38 154:0.68 159:0.79 170:0.79 172:0.85 173:0.54 174:0.93 177:0.96 178:0.55 179:0.26 187:0.87 195:0.92 197:0.94 202:0.36 206:0.81 212:0.86 215:0.79 216:0.34 222:0.25 230:0.68 233:0.76 235:0.22 239:0.98 241:0.15 242:0.22 243:0.59 245:0.86 247:0.79 253:0.55 259:0.21 264:0.93 265:0.36 266:0.80 267:0.36 271:0.13 275:0.98 276:0.84 283:0.82 294:1.00 297:0.36 300:0.94 +0 12:0.95 17:0.88 21:0.54 27:0.37 29:0.53 34:0.99 36:0.58 37:0.60 39:0.94 43:0.09 66:0.36 69:0.68 71:0.86 81:0.29 91:0.23 98:0.09 108:0.52 123:0.49 126:0.23 127:0.06 129:0.05 135:0.49 145:0.91 146:0.05 147:0.05 149:0.06 150:0.32 154:0.08 159:0.05 170:0.79 172:0.05 173:0.79 175:0.99 177:0.05 178:0.55 187:0.39 215:0.58 216:0.47 222:0.25 235:0.60 241:0.21 243:0.51 244:0.97 253:0.20 257:0.97 259:0.21 264:0.93 265:0.09 267:0.91 271:0.13 277:0.98 283:0.67 297:0.36 +0 7:0.66 8:0.72 12:0.95 17:0.62 21:0.30 27:0.72 29:0.53 30:0.62 32:0.64 34:0.92 36:0.47 37:0.73 39:0.94 43:0.11 55:0.71 66:0.75 69:0.10 70:0.34 71:0.86 81:0.33 91:0.90 98:0.96 104:0.58 108:0.09 120:0.85 123:0.09 126:0.23 127:0.69 129:0.05 135:0.19 140:0.45 145:0.47 146:0.63 147:0.68 149:0.14 150:0.36 151:0.65 153:0.48 154:0.09 159:0.23 162:0.80 163:0.75 164:0.82 170:0.79 172:0.32 173:0.42 175:0.97 177:0.38 179:0.95 190:0.98 195:0.56 202:0.73 212:0.61 215:0.72 216:0.15 220:0.62 222:0.25 230:0.68 233:0.76 235:0.31 241:0.86 242:0.63 243:0.77 244:0.97 247:0.35 248:0.77 253:0.20 256:0.78 257:0.93 259:0.21 260:0.85 264:0.93 265:0.96 266:0.23 267:0.23 268:0.79 271:0.13 275:0.91 276:0.29 277:0.95 285:0.45 294:0.94 297:0.36 300:0.25 +0 1:0.61 7:0.66 10:0.86 11:0.49 12:0.95 17:0.36 18:0.67 21:0.59 22:0.93 25:0.88 27:0.32 29:0.53 30:0.33 32:0.64 33:0.97 34:0.36 36:0.63 37:0.36 39:0.94 43:0.65 45:0.73 47:0.93 64:0.77 66:0.12 69:0.41 70:0.69 71:0.86 74:0.37 81:0.58 83:0.60 86:0.69 91:0.17 98:0.20 99:0.95 101:0.52 104:0.58 108:0.32 114:0.73 117:0.86 122:0.85 123:0.88 124:0.81 126:0.53 127:0.93 129:0.05 133:0.73 135:0.37 139:0.67 145:0.69 146:0.17 147:0.22 149:0.41 150:0.44 154:0.54 155:0.48 159:0.77 165:0.70 170:0.79 172:0.16 173:0.26 174:0.79 175:0.91 176:0.70 177:0.70 178:0.55 179:0.92 187:0.68 192:0.48 195:0.88 197:0.85 201:0.60 202:0.36 208:0.64 212:0.19 215:0.55 216:0.43 222:0.25 230:0.68 233:0.66 235:0.84 236:0.95 239:0.86 241:0.35 242:0.19 243:0.40 244:0.83 245:0.46 247:0.55 253:0.54 257:0.84 259:0.21 262:0.93 264:0.93 265:0.09 266:0.47 267:0.73 271:0.13 275:0.93 276:0.28 277:0.98 290:0.73 291:0.97 294:0.93 297:0.36 300:0.43 +0 7:0.66 12:0.95 17:0.28 21:0.64 27:0.34 29:0.53 30:0.76 32:0.64 34:0.80 36:0.32 37:0.46 39:0.94 43:0.10 55:0.84 66:0.72 69:0.52 70:0.22 71:0.86 81:0.28 91:0.27 98:0.60 104:0.86 106:0.81 108:0.40 123:0.38 126:0.23 127:0.18 129:0.05 135:0.58 145:0.63 146:0.63 147:0.68 149:0.12 150:0.30 154:0.09 159:0.24 163:0.86 170:0.79 172:0.27 173:0.54 175:0.97 177:0.38 178:0.55 179:0.78 187:0.87 195:0.68 206:0.81 212:0.42 215:0.53 216:0.53 222:0.25 230:0.68 233:0.66 235:0.74 241:0.24 242:0.75 243:0.75 244:0.97 247:0.30 253:0.20 257:0.93 259:0.21 264:0.93 265:0.61 266:0.23 267:0.36 271:0.13 275:0.91 276:0.26 277:0.95 283:0.67 294:0.94 297:0.36 300:0.18 +0 8:0.77 12:0.95 17:0.89 21:0.78 27:0.72 29:0.53 34:0.91 36:0.18 37:0.59 39:0.94 43:0.09 66:0.36 69:0.72 71:0.86 82:0.98 88:0.99 91:0.58 98:0.09 108:0.55 123:0.52 127:0.07 129:0.05 135:0.56 145:0.85 146:0.05 147:0.05 149:0.06 154:0.08 159:0.05 170:0.79 172:0.05 173:0.85 175:0.99 177:0.05 178:0.55 187:0.21 195:0.65 216:0.04 222:0.25 235:0.31 241:0.46 243:0.51 244:0.97 253:0.20 257:0.97 259:0.21 264:0.93 265:0.10 267:0.69 271:0.13 277:0.98 294:0.94 +2 10:0.93 11:0.54 12:0.95 17:0.75 18:0.70 21:0.54 27:0.18 29:0.53 30:0.54 34:0.81 36:0.66 37:0.84 39:0.94 43:0.10 45:0.85 66:0.88 69:0.49 70:0.39 71:0.86 74:0.51 81:0.29 86:0.81 91:0.51 98:0.78 101:0.69 104:0.95 106:0.81 108:0.38 123:0.36 126:0.23 127:0.27 129:0.05 135:0.80 139:0.78 145:0.59 146:0.69 147:0.74 149:0.12 150:0.32 154:0.71 159:0.27 170:0.79 172:0.67 173:0.59 174:0.83 177:0.96 178:0.55 179:0.49 187:0.39 197:0.87 206:0.81 212:0.75 215:0.58 216:0.62 222:0.25 235:0.60 239:0.91 241:0.21 242:0.14 243:0.89 247:0.76 253:0.42 254:0.74 259:0.21 264:0.93 265:0.78 266:0.38 267:0.36 271:0.13 275:0.96 276:0.51 294:0.98 297:0.36 300:0.33 +1 10:0.98 11:0.57 12:0.95 17:0.45 18:0.86 21:0.78 27:0.45 29:0.53 30:0.94 34:0.82 36:0.22 37:0.50 39:0.94 43:0.75 66:0.60 69:0.77 70:0.21 71:0.86 74:0.78 85:0.94 86:0.95 91:0.07 98:0.44 101:0.87 108:0.61 122:0.67 123:0.58 124:0.51 127:0.17 129:0.59 133:0.47 135:0.60 139:0.93 145:0.52 146:0.60 147:0.66 149:0.98 154:0.65 159:0.31 170:0.79 172:0.84 173:0.73 174:0.93 177:0.96 178:0.55 179:0.13 187:0.68 197:0.94 212:0.90 216:0.77 222:0.25 235:0.52 239:0.98 241:0.32 242:0.23 243:0.67 245:0.93 247:0.74 253:0.54 259:0.21 264:0.93 265:0.46 266:0.38 267:0.19 271:0.13 275:0.98 276:0.79 294:1.00 300:0.55 +2 7:0.66 8:0.89 10:0.96 11:0.57 12:0.95 17:0.16 18:0.78 21:0.13 27:0.02 29:0.53 30:0.87 34:0.45 36:0.21 37:0.92 39:0.94 43:0.57 66:0.42 69:0.93 70:0.32 71:0.86 74:0.65 81:0.35 85:0.95 86:0.89 91:0.15 98:0.58 101:0.87 108:0.89 120:0.75 123:0.89 124:0.65 126:0.23 127:0.58 129:0.59 133:0.60 135:0.87 139:0.86 140:0.45 145:0.77 146:0.60 147:0.05 149:0.93 150:0.39 151:0.63 153:0.72 154:0.46 159:0.79 162:0.81 164:0.73 170:0.79 172:0.85 173:0.89 174:0.93 177:0.05 179:0.22 187:0.39 190:0.98 191:0.73 197:0.91 202:0.62 212:0.86 215:0.84 216:0.99 220:0.74 222:0.25 230:0.68 233:0.76 235:0.12 239:0.95 241:0.56 242:0.28 243:0.06 245:0.94 247:0.79 248:0.67 253:0.48 256:0.64 257:0.97 259:0.21 260:0.76 264:0.93 265:0.59 266:0.47 267:0.59 268:0.68 271:0.13 275:0.98 276:0.84 283:0.67 285:0.45 294:0.99 297:0.36 300:0.55 +1 10:0.98 11:0.57 12:0.95 17:0.40 18:0.86 21:0.78 27:0.45 29:0.53 30:0.94 34:0.81 36:0.21 37:0.50 39:0.94 43:0.75 66:0.59 69:0.78 70:0.19 71:0.86 74:0.78 85:0.94 86:0.95 91:0.14 98:0.43 101:0.87 108:0.61 122:0.67 123:0.59 124:0.52 127:0.17 129:0.59 133:0.47 135:0.69 139:0.93 145:0.50 146:0.59 147:0.65 149:0.98 154:0.65 159:0.30 170:0.79 172:0.83 173:0.74 174:0.93 177:0.96 178:0.55 179:0.13 187:0.68 197:0.94 212:0.90 216:0.77 222:0.25 235:0.31 239:0.98 241:0.27 242:0.24 243:0.67 245:0.93 247:0.67 253:0.54 259:0.21 264:0.93 265:0.45 266:0.38 267:0.23 271:0.13 275:0.98 276:0.78 294:1.00 300:0.43 +0 10:0.89 11:0.57 12:0.95 17:0.51 18:0.64 21:0.78 27:0.03 29:0.53 30:0.95 32:0.80 34:0.20 36:0.35 37:0.95 39:0.94 43:0.81 44:0.93 66:0.72 69:0.71 71:0.86 74:0.56 77:0.70 82:0.99 85:0.72 86:0.74 88:0.99 91:0.33 98:0.12 101:0.87 102:0.98 108:0.54 122:0.67 123:0.52 127:0.08 129:0.05 135:0.80 139:0.80 145:0.77 146:0.19 147:0.24 149:0.70 154:0.75 159:0.10 170:0.79 172:0.27 173:0.71 174:0.79 177:0.96 178:0.55 179:0.08 187:0.21 195:0.75 197:0.84 212:0.78 216:0.65 222:0.25 235:0.12 239:0.93 241:0.35 242:0.45 243:0.75 247:0.35 253:0.44 259:0.21 264:0.93 265:0.13 266:0.17 267:0.84 271:0.13 275:0.91 276:0.27 282:0.88 294:0.98 299:0.88 300:0.08 +1 7:0.66 8:0.93 10:0.99 11:0.57 12:0.95 17:0.32 18:0.87 21:0.13 27:0.29 29:0.53 30:0.87 34:0.59 36:0.21 37:0.92 39:0.94 43:0.75 66:0.36 69:0.50 70:0.32 71:0.86 74:0.81 81:0.35 85:0.95 86:0.96 91:0.33 98:0.60 101:0.87 108:0.38 120:0.68 123:0.73 124:0.61 126:0.23 127:0.26 129:0.59 133:0.47 135:0.82 139:0.94 140:0.45 145:0.77 146:0.75 147:0.79 149:0.98 150:0.39 151:0.63 153:0.72 154:0.66 159:0.64 162:0.86 164:0.68 170:0.79 172:0.84 173:0.29 174:0.93 177:0.96 179:0.15 187:0.39 190:0.98 197:0.95 202:0.55 212:0.84 215:0.84 216:0.93 220:0.62 222:0.25 230:0.68 233:0.76 235:0.12 239:0.98 241:0.68 242:0.28 243:0.51 245:0.97 247:0.79 248:0.62 253:0.55 256:0.58 259:0.21 260:0.69 264:0.93 265:0.46 266:0.54 267:0.73 268:0.63 271:0.13 275:0.98 276:0.88 285:0.45 294:1.00 297:0.36 300:0.43 +0 12:0.95 17:0.92 21:0.78 27:0.72 29:0.53 30:0.76 34:0.63 36:0.55 37:0.49 39:0.94 43:0.08 55:0.52 66:0.64 69:0.79 70:0.15 71:0.86 91:0.40 98:0.39 108:0.63 122:0.85 123:0.60 127:0.13 129:0.05 135:0.74 145:0.80 146:0.34 147:0.40 149:0.08 154:0.08 159:0.14 163:0.98 170:0.79 172:0.16 173:0.92 175:0.97 177:0.38 178:0.55 179:0.75 187:0.68 212:0.95 216:0.08 222:0.25 235:0.95 241:0.39 242:0.82 243:0.69 244:0.97 247:0.12 253:0.20 257:0.93 259:0.21 264:0.93 265:0.41 266:0.12 267:0.28 271:0.13 275:0.93 276:0.15 277:0.95 294:0.94 300:0.13 +0 7:0.69 10:0.85 11:0.46 12:0.51 17:0.77 18:0.79 21:0.78 26:0.97 27:0.55 29:0.56 30:0.76 34:0.80 36:0.42 37:0.64 39:0.70 43:0.61 45:0.73 48:0.91 58:0.93 66:0.85 69:0.46 70:0.27 71:0.84 74:0.41 86:0.80 89:0.97 91:0.15 98:0.74 101:0.47 108:0.35 122:0.75 123:0.34 127:0.23 129:0.05 135:0.34 139:0.78 141:0.91 146:0.61 147:0.67 149:0.70 154:0.50 159:0.23 170:0.82 172:0.57 173:0.59 174:0.79 177:0.88 178:0.55 179:0.56 187:0.95 197:0.85 199:0.96 212:0.76 216:0.27 222:0.21 223:0.97 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.05 239:0.84 240:0.95 241:0.27 242:0.38 243:0.86 244:0.83 247:0.60 253:0.35 259:0.21 265:0.74 266:0.38 267:0.76 271:0.31 274:0.91 276:0.44 281:0.91 300:0.25 +1 7:0.69 8:0.85 10:0.88 12:0.51 17:0.47 18:0.88 21:0.40 26:0.98 27:0.55 29:0.56 30:0.13 32:0.65 34:0.64 36:0.31 37:0.64 39:0.70 43:0.62 48:0.91 58:0.93 64:0.77 66:0.06 69:0.49 70:0.97 71:0.84 74:0.29 81:0.42 86:0.88 89:0.96 91:0.04 98:0.19 108:0.98 123:0.98 124:0.99 126:0.32 127:0.95 129:0.59 133:0.99 135:0.95 139:0.88 141:0.91 145:0.76 146:0.67 147:0.72 149:0.67 150:0.43 154:0.65 159:0.97 170:0.82 172:0.57 173:0.09 174:0.79 175:0.75 177:0.70 178:0.55 179:0.15 187:0.95 192:0.45 195:1.00 197:0.85 199:1.00 201:0.56 202:0.50 212:0.22 215:0.67 216:0.27 219:0.91 222:0.21 223:0.98 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.52 239:0.87 240:0.95 241:0.10 242:0.38 243:0.17 244:0.61 245:0.89 247:0.86 253:0.26 254:0.74 257:0.65 259:0.21 265:0.20 266:0.99 267:0.36 271:0.31 274:0.91 276:0.95 277:0.87 281:0.91 283:0.67 292:0.88 297:0.36 300:0.99 +0 7:0.69 10:0.88 12:0.51 17:0.40 18:0.88 21:0.40 26:0.99 27:0.55 29:0.56 30:0.13 31:0.86 32:0.65 34:0.68 36:0.30 37:0.64 39:0.70 43:0.62 48:0.91 58:0.98 64:0.77 66:0.06 69:0.49 70:0.97 71:0.84 74:0.29 79:0.86 81:0.42 86:0.88 89:0.96 91:0.04 98:0.19 108:0.98 120:0.54 123:0.98 124:0.99 126:0.32 127:0.95 129:0.59 133:0.99 135:0.92 137:0.86 139:0.88 140:0.45 141:0.99 145:0.76 146:0.67 147:0.72 149:0.67 150:0.43 151:0.48 153:0.48 154:0.65 159:0.97 162:0.86 164:0.56 170:0.82 172:0.57 173:0.09 174:0.79 175:0.75 177:0.70 179:0.15 187:0.95 190:0.89 192:0.45 195:1.00 196:0.88 197:0.85 199:1.00 201:0.56 202:0.36 212:0.22 215:0.67 216:0.27 219:0.91 220:0.99 222:0.21 223:0.99 224:0.98 225:0.96 230:0.71 233:0.76 234:0.98 235:0.52 239:0.87 240:0.95 241:0.07 242:0.38 243:0.17 244:0.61 245:0.89 247:0.86 248:0.48 253:0.26 254:0.74 255:0.68 256:0.45 257:0.65 259:0.21 260:0.54 265:0.20 266:0.99 267:0.28 268:0.46 271:0.31 274:0.97 276:0.95 277:0.87 281:0.91 283:0.67 284:0.88 285:0.50 292:0.88 297:0.36 300:0.99 +1 7:0.69 10:0.85 11:0.46 12:0.51 17:0.75 18:0.70 21:0.78 26:0.98 27:0.55 29:0.56 30:0.74 34:0.62 36:0.47 37:0.64 39:0.70 43:0.71 45:0.83 58:0.93 66:0.88 69:0.46 70:0.42 71:0.84 74:0.55 86:0.76 89:0.98 91:0.23 98:0.81 101:0.47 108:0.35 122:0.55 123:0.34 127:0.30 129:0.05 135:0.37 139:0.72 141:0.91 146:0.79 147:0.82 149:0.64 154:0.61 159:0.34 170:0.82 172:0.67 173:0.50 174:0.79 177:0.88 178:0.55 179:0.52 187:0.95 197:0.85 199:0.97 212:0.41 216:0.27 222:0.21 223:0.97 224:0.93 225:0.97 230:0.71 233:0.76 234:0.91 235:0.05 239:0.84 240:0.95 241:0.18 242:0.33 243:0.89 244:0.61 247:0.60 253:0.44 259:0.21 265:0.81 266:0.38 267:0.52 271:0.31 274:0.91 276:0.53 300:0.43 +0 7:0.69 10:0.85 11:0.46 12:0.51 17:0.76 18:0.76 21:0.78 26:0.97 27:0.55 29:0.56 30:0.76 34:0.80 36:0.33 37:0.64 39:0.70 43:0.61 45:0.73 48:0.91 58:0.93 66:0.83 69:0.46 70:0.20 71:0.84 74:0.41 86:0.76 89:0.97 91:0.17 98:0.72 101:0.47 108:0.35 122:0.55 123:0.34 127:0.22 129:0.05 135:0.42 139:0.76 141:0.91 146:0.60 147:0.65 149:0.66 154:0.50 159:0.22 170:0.82 172:0.51 173:0.59 174:0.79 177:0.88 178:0.55 179:0.60 187:0.95 197:0.85 199:0.96 212:0.86 216:0.27 222:0.21 223:0.97 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.05 239:0.84 240:0.95 241:0.18 242:0.29 243:0.84 244:0.83 247:0.60 253:0.35 259:0.21 265:0.72 266:0.23 267:0.79 271:0.31 274:0.91 276:0.40 281:0.91 300:0.18 +1 1:0.64 10:0.85 12:0.51 17:0.81 18:0.67 21:0.22 26:0.98 27:0.43 29:0.56 30:0.76 34:0.50 36:0.33 37:0.73 39:0.70 43:0.77 58:0.93 64:0.77 66:0.54 69:0.12 70:0.22 71:0.84 74:0.40 81:0.55 86:0.69 89:0.97 91:0.18 98:0.47 108:0.10 114:0.82 122:0.75 123:0.10 124:0.45 126:0.51 127:0.32 129:0.05 133:0.36 135:0.49 139:0.72 141:0.91 146:0.33 147:0.40 149:0.54 150:0.55 154:0.69 155:0.49 159:0.21 170:0.82 172:0.21 173:0.39 174:0.79 176:0.63 177:0.88 178:0.55 179:0.93 187:0.39 192:0.49 197:0.85 199:0.95 201:0.62 208:0.61 212:0.42 215:0.72 216:0.23 219:0.91 222:0.21 223:0.98 224:0.93 225:0.98 234:0.91 235:0.42 239:0.84 240:0.95 241:0.32 242:0.45 243:0.63 244:0.61 245:0.40 247:0.35 253:0.58 259:0.21 265:0.49 266:0.23 267:0.23 271:0.31 274:0.91 276:0.20 283:0.67 290:0.82 292:0.88 297:0.36 300:0.18 +0 7:0.69 10:0.86 11:0.46 12:0.51 17:0.55 21:0.30 26:0.99 27:0.50 29:0.56 30:0.18 32:0.67 34:0.55 36:0.86 37:0.60 39:0.70 43:0.63 45:0.91 55:0.59 58:0.93 66:0.52 69:0.78 70:0.93 71:0.84 74:0.70 77:0.70 81:0.33 83:0.56 89:0.99 91:0.12 97:0.89 98:0.64 101:0.47 104:0.84 106:0.81 108:0.90 117:0.86 123:0.89 124:0.78 126:0.24 127:0.82 129:0.05 133:0.83 135:0.47 141:0.91 145:0.67 146:0.47 147:0.27 149:0.73 150:0.37 154:0.52 158:0.77 159:0.82 170:0.82 172:0.86 173:0.62 174:0.83 175:0.75 177:0.38 178:0.55 179:0.28 187:0.39 195:0.92 197:0.86 199:0.99 206:0.81 208:0.50 212:0.60 215:0.72 216:0.86 222:0.21 223:0.98 224:0.93 225:0.97 230:0.71 232:0.90 233:0.76 234:0.91 235:0.31 239:0.85 240:0.95 242:0.45 243:0.11 244:0.83 245:0.88 247:0.79 253:0.51 257:0.84 259:0.21 265:0.64 266:0.87 267:0.65 271:0.31 274:0.91 276:0.85 277:0.87 279:0.76 282:0.75 283:0.82 297:0.36 300:0.84 +1 1:0.63 10:0.87 11:0.46 12:0.51 17:0.66 18:0.63 21:0.40 26:0.98 27:0.69 29:0.56 30:0.41 32:0.65 34:0.74 36:0.66 37:0.25 39:0.70 43:0.64 45:0.66 58:0.93 66:0.86 69:0.30 70:0.77 71:0.84 74:0.43 81:0.55 83:0.56 86:0.66 89:0.97 91:0.10 98:0.91 99:0.83 101:0.47 104:0.93 106:0.81 108:0.24 114:0.78 117:0.86 123:0.23 126:0.32 127:0.49 129:0.05 135:0.79 139:0.64 141:0.91 145:0.67 146:0.76 147:0.80 149:0.46 150:0.50 154:0.67 155:0.49 159:0.32 165:0.65 170:0.82 172:0.59 173:0.44 174:0.79 176:0.63 177:0.88 178:0.55 179:0.72 187:0.95 192:0.47 195:0.58 197:0.85 199:0.98 201:0.59 206:0.81 208:0.50 212:0.54 215:0.67 216:0.62 222:0.21 223:0.98 224:0.93 225:0.98 232:0.88 234:0.91 235:0.60 239:0.86 240:0.95 241:0.10 242:0.45 243:0.86 244:0.61 247:0.67 253:0.57 254:0.74 259:0.21 265:0.91 266:0.47 267:0.36 271:0.31 274:0.91 276:0.47 290:0.78 297:0.36 300:0.55 +1 1:0.56 10:0.86 11:0.46 12:0.51 17:0.61 18:0.62 21:0.68 26:0.98 27:0.30 29:0.56 30:0.45 32:0.67 34:0.97 36:0.44 37:0.80 39:0.70 43:0.58 45:0.81 58:0.93 66:0.29 69:0.84 70:0.81 71:0.84 74:0.52 77:0.70 81:0.42 83:0.56 86:0.66 89:0.98 91:0.10 98:0.28 99:0.83 101:0.47 104:0.89 106:0.81 108:0.80 114:0.67 117:0.86 122:0.55 123:0.79 124:0.84 126:0.32 127:0.59 129:0.05 133:0.83 135:0.23 139:0.64 141:0.91 145:0.80 146:0.32 147:0.05 149:0.56 150:0.38 154:0.47 155:0.46 159:0.58 163:0.88 165:0.65 170:0.82 172:0.37 173:0.85 174:0.83 175:0.75 176:0.63 177:0.05 178:0.55 179:0.70 187:0.95 192:0.45 195:0.76 197:0.87 199:0.97 201:0.54 206:0.81 208:0.50 212:0.16 215:0.49 216:0.74 222:0.21 223:0.98 224:0.93 225:0.98 232:0.85 234:0.91 235:0.84 239:0.86 240:0.95 241:0.35 242:0.16 243:0.12 244:0.83 245:0.58 247:0.74 253:0.53 257:0.93 259:0.21 265:0.31 266:0.84 267:0.23 271:0.31 274:0.91 276:0.50 277:0.87 282:0.75 290:0.67 297:0.36 300:0.70 +0 7:0.69 10:0.85 11:0.46 12:0.51 17:0.62 18:0.66 21:0.78 26:0.96 27:0.55 29:0.56 30:0.62 34:0.80 36:0.71 37:0.64 39:0.70 43:0.61 45:0.66 48:0.91 58:0.93 66:0.30 69:0.47 70:0.34 71:0.84 74:0.35 86:0.68 89:0.96 91:0.14 98:0.32 101:0.47 108:0.36 122:0.41 123:0.35 124:0.61 127:0.19 129:0.05 133:0.43 135:0.68 139:0.70 141:0.91 145:0.40 146:0.33 147:0.40 149:0.42 154:0.51 159:0.23 163:0.81 170:0.82 172:0.16 173:0.54 174:0.79 175:0.75 177:0.70 178:0.55 179:0.76 187:0.95 197:0.85 199:0.95 202:0.46 212:0.30 216:0.27 222:0.21 223:0.96 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.22 239:0.84 240:0.95 241:0.27 242:0.33 243:0.48 244:0.83 245:0.47 247:0.39 253:0.31 257:0.65 259:0.21 265:0.34 266:0.27 267:0.23 271:0.31 274:0.91 276:0.23 277:0.69 281:0.91 300:0.25 +0 1:0.69 7:0.75 9:0.75 10:0.81 11:0.46 12:0.51 17:0.94 18:0.96 22:0.93 26:0.99 27:0.72 29:0.56 30:0.94 31:0.94 32:0.72 34:0.53 36:0.88 39:0.70 43:0.74 44:0.92 45:0.98 47:0.93 48:0.91 58:0.98 64:0.77 66:0.20 69:0.97 70:0.21 71:0.84 74:0.92 77:0.70 79:0.94 81:0.77 83:0.56 86:0.98 89:0.99 91:0.68 96:0.83 97:0.89 98:0.57 99:0.83 101:0.47 106:0.81 108:0.81 114:0.97 117:0.86 120:0.72 122:0.75 123:0.80 124:0.85 126:0.51 127:0.90 129:0.05 133:0.84 135:0.84 137:0.94 139:0.98 140:0.45 141:0.99 145:0.40 146:0.83 147:0.90 149:0.99 150:0.78 151:0.90 153:0.69 154:0.65 155:0.64 158:0.82 159:0.88 162:0.86 164:0.62 165:0.65 170:0.82 172:0.91 173:0.97 174:0.79 176:0.70 177:0.70 179:0.13 187:0.87 190:0.77 192:0.98 195:0.96 196:0.98 197:0.79 199:1.00 201:0.67 202:0.46 204:0.84 206:0.81 208:0.61 212:0.49 215:0.96 216:0.16 219:0.94 222:0.21 223:0.99 224:1.00 225:0.99 230:0.77 233:0.76 234:1.00 235:0.22 239:0.80 240:0.99 241:0.03 242:0.32 243:0.33 244:0.61 245:0.98 247:0.76 248:0.80 253:0.89 255:0.78 256:0.45 257:0.65 260:0.73 262:0.93 265:0.59 266:0.71 267:0.59 268:0.55 271:0.31 274:0.98 276:0.96 277:0.69 279:0.80 281:0.91 282:0.80 283:0.82 284:0.90 285:0.60 290:0.97 292:0.95 297:0.36 300:0.55 +0 12:0.51 17:0.59 18:0.87 21:0.78 26:0.94 27:0.15 29:0.56 30:0.45 34:0.56 36:0.44 37:0.90 39:0.70 43:0.16 44:0.88 48:0.91 55:0.59 58:0.93 66:0.63 69:0.78 70:0.61 71:0.84 74:0.40 76:0.85 86:0.83 89:0.96 91:0.11 98:0.70 108:0.62 122:0.92 123:0.59 124:0.45 127:0.48 129:0.05 133:0.37 135:0.23 139:0.83 141:0.91 145:0.42 146:0.57 147:0.28 149:0.18 154:0.46 159:0.32 170:0.82 172:0.41 173:0.91 175:0.75 177:0.05 178:0.55 179:0.82 187:0.39 193:0.97 195:0.59 199:0.94 212:0.61 216:0.35 222:0.21 223:0.92 224:0.93 225:0.96 234:0.91 235:0.52 239:0.84 240:0.95 241:0.43 242:0.63 243:0.28 244:0.61 245:0.54 247:0.47 253:0.35 254:0.93 257:0.93 259:0.21 265:0.70 266:0.38 267:0.69 271:0.31 274:0.91 276:0.37 277:0.69 281:0.91 300:0.43 +0 8:0.93 10:0.84 12:0.51 17:0.73 18:0.90 21:0.78 26:0.96 27:0.20 29:0.56 30:0.71 34:0.50 36:0.68 37:0.97 39:0.70 43:0.56 55:0.45 58:0.93 66:0.75 69:0.56 70:0.56 71:0.84 74:0.39 86:0.89 89:0.97 91:0.62 98:0.75 108:0.43 122:0.92 123:0.41 124:0.40 127:0.43 129:0.05 133:0.39 135:0.28 139:0.84 141:0.91 146:0.73 147:0.77 149:0.35 154:0.45 159:0.40 170:0.82 172:0.65 173:0.61 174:0.79 177:0.88 178:0.55 179:0.59 187:0.21 197:0.83 199:0.95 212:0.77 216:0.39 222:0.21 223:0.96 224:0.93 225:0.96 234:0.91 235:0.22 239:0.83 240:0.95 241:0.21 242:0.51 243:0.77 244:0.83 245:0.64 247:0.79 253:0.34 259:0.21 265:0.75 266:0.47 267:0.59 271:0.31 274:0.91 276:0.56 300:0.55 +0 1:0.64 10:0.87 11:0.46 12:0.51 17:0.45 18:0.62 21:0.30 26:0.98 27:0.69 29:0.56 30:0.14 32:0.65 34:0.94 36:0.18 37:0.25 39:0.70 43:0.64 45:0.66 55:0.71 58:0.93 66:0.62 69:0.29 70:0.90 71:0.84 74:0.43 81:0.57 83:0.56 86:0.66 89:0.97 91:0.23 98:0.75 99:0.83 101:0.47 104:0.93 106:0.81 108:0.22 114:0.82 117:0.86 123:0.22 124:0.48 126:0.32 127:0.79 129:0.05 133:0.36 135:0.96 139:0.64 141:0.91 145:0.52 146:0.78 147:0.82 149:0.50 150:0.52 154:0.67 155:0.49 159:0.53 165:0.65 170:0.82 172:0.67 173:0.30 174:0.79 176:0.63 177:0.88 178:0.55 179:0.59 187:0.87 192:0.48 195:0.68 197:0.85 199:0.99 201:0.60 206:0.81 208:0.50 212:0.54 215:0.72 216:0.62 222:0.21 223:0.98 224:0.93 225:0.98 232:0.88 234:0.91 235:0.52 239:0.86 240:0.95 241:0.37 242:0.74 243:0.68 244:0.61 245:0.79 247:0.67 253:0.58 254:0.74 259:0.21 265:0.75 266:0.75 267:0.28 271:0.31 274:0.91 276:0.63 290:0.82 297:0.36 300:0.70 +1 1:0.69 11:0.75 12:0.37 17:0.26 18:0.90 21:0.13 27:0.47 29:0.62 30:0.62 32:0.63 34:0.42 36:0.23 37:0.66 39:0.51 43:0.67 44:0.85 45:0.92 46:0.88 64:0.77 66:0.74 69:0.79 70:0.49 71:0.74 74:0.43 77:0.98 81:0.60 83:0.60 86:0.72 91:0.27 98:0.73 99:0.83 101:0.52 107:0.93 108:0.62 110:0.95 114:0.64 122:0.80 123:0.60 124:0.42 125:0.88 126:0.44 127:0.29 129:0.05 131:0.61 133:0.43 135:0.94 139:0.71 144:0.57 145:0.90 146:0.87 147:0.28 149:0.81 150:0.60 154:0.55 155:0.58 157:0.61 159:0.51 160:0.88 165:0.70 166:0.84 172:0.78 173:0.73 176:0.55 177:0.38 178:0.55 179:0.32 182:0.61 187:0.39 192:0.51 195:0.82 201:0.68 204:0.85 208:0.54 212:0.60 216:0.82 222:0.35 227:0.61 229:0.61 231:0.90 232:0.96 235:0.22 241:0.49 242:0.54 243:0.21 245:0.79 246:0.61 247:0.67 253:0.48 254:0.86 257:0.65 259:0.21 265:0.73 266:0.38 267:0.36 276:0.70 281:0.91 282:0.71 287:0.61 289:0.94 290:0.64 300:0.43 +1 11:0.64 12:0.37 17:0.45 18:0.68 21:0.68 27:0.56 29:0.62 30:0.15 32:0.62 34:0.95 36:0.18 37:0.60 39:0.51 43:0.13 45:0.66 46:0.88 66:0.53 69:0.84 70:0.52 71:0.74 74:0.27 81:0.24 86:0.56 91:0.20 98:0.50 101:0.52 104:0.91 106:0.81 107:0.94 108:0.69 110:0.96 123:0.67 124:0.51 125:0.88 126:0.26 127:0.28 129:0.05 131:0.61 133:0.37 135:0.69 139:0.55 145:0.72 146:0.53 147:0.53 149:0.14 150:0.24 154:0.56 157:0.61 159:0.36 160:0.88 166:0.86 172:0.37 173:0.90 177:0.05 178:0.55 179:0.73 182:0.61 187:0.21 195:0.66 206:0.81 212:0.30 215:0.37 216:0.76 222:0.35 227:0.61 229:0.61 231:0.90 232:0.97 235:0.80 241:0.01 242:0.14 243:0.37 245:0.57 246:0.61 247:0.67 253:0.24 254:0.95 257:0.84 259:0.21 265:0.51 266:0.47 267:0.36 276:0.38 283:0.78 287:0.61 289:0.95 297:0.36 300:0.33 +0 12:0.37 17:0.93 18:0.88 21:0.78 27:0.72 29:0.62 30:0.28 34:0.63 36:0.94 37:0.66 39:0.51 43:0.18 46:0.88 55:0.71 66:0.87 69:0.05 70:0.47 71:0.74 74:0.25 86:0.59 91:0.33 98:0.95 107:0.95 108:0.05 110:0.97 123:0.05 125:0.88 127:0.65 129:0.05 131:0.61 135:0.75 139:0.57 146:0.79 147:0.83 149:0.26 154:0.60 157:0.61 159:0.35 160:0.88 166:0.61 172:0.63 173:0.13 177:0.70 178:0.55 179:0.71 182:0.61 187:0.39 202:0.36 212:0.61 216:0.09 222:0.35 227:0.61 229:0.61 231:0.90 241:0.21 242:0.16 243:0.88 246:0.61 247:0.79 253:0.22 254:1.00 259:0.21 265:0.95 266:0.54 267:0.44 276:0.53 287:0.61 289:0.95 300:0.33 +1 8:0.84 11:0.64 12:0.37 17:0.67 18:0.83 21:0.54 27:0.52 29:0.62 30:0.36 34:0.33 36:0.94 37:0.47 39:0.51 43:0.13 45:0.66 46:0.88 55:0.84 66:0.48 69:0.54 70:0.64 71:0.74 74:0.26 81:0.25 86:0.58 91:0.38 98:0.75 101:0.52 107:0.96 108:0.78 110:0.98 114:0.55 122:0.41 123:0.77 124:0.67 125:0.88 126:0.26 127:0.67 129:0.59 131:0.61 133:0.66 135:0.89 139:0.56 146:0.36 147:0.43 149:0.25 150:0.25 154:0.40 157:0.61 159:0.67 160:0.88 163:0.94 166:0.76 172:0.61 173:0.43 176:0.54 177:0.70 178:0.55 179:0.55 182:0.61 187:0.39 212:0.21 216:0.39 222:0.35 227:0.61 229:0.61 231:0.93 232:0.93 235:0.60 241:0.24 242:0.15 243:0.25 245:0.76 246:0.61 247:0.84 253:0.36 254:0.92 259:0.21 265:0.75 266:0.80 267:0.87 276:0.65 287:0.61 289:0.96 290:0.55 300:0.55 +2 8:0.65 11:0.64 12:0.37 17:0.94 18:0.65 21:0.74 27:0.69 29:0.62 30:0.37 32:0.62 34:0.93 36:0.52 37:0.25 39:0.51 43:0.34 45:0.73 46:0.88 55:0.59 66:0.88 69:0.86 70:0.87 71:0.74 74:0.28 76:0.85 81:0.23 86:0.59 91:0.15 98:0.70 101:0.52 107:0.95 108:0.73 110:0.97 123:0.71 125:0.88 126:0.26 127:0.21 129:0.05 131:0.61 135:0.47 139:0.57 145:0.41 146:0.78 147:0.81 149:0.38 150:0.23 154:0.25 157:0.61 159:0.34 160:0.88 166:0.86 172:0.67 173:0.88 177:0.70 178:0.55 179:0.39 182:0.61 187:0.68 195:0.76 212:0.57 215:0.36 216:0.55 222:0.35 227:0.61 229:0.61 231:0.93 235:0.88 241:0.15 242:0.14 243:0.89 244:0.61 246:0.61 247:0.82 253:0.25 259:0.21 265:0.70 266:0.47 267:0.23 276:0.55 283:0.78 287:0.61 289:0.96 297:0.36 300:0.70 +1 8:0.62 11:0.64 12:0.37 17:0.34 18:0.63 21:0.30 27:0.58 29:0.62 30:0.30 34:0.47 36:0.69 37:0.42 39:0.51 43:0.59 45:0.66 46:0.88 55:0.84 66:0.45 69:0.43 70:0.76 71:0.74 74:0.28 81:0.32 86:0.59 91:0.23 96:0.77 98:0.67 101:0.52 107:0.92 108:0.33 110:0.93 114:0.57 117:0.86 122:0.77 123:0.32 124:0.81 125:0.88 126:0.26 127:0.79 129:0.05 131:0.83 133:0.81 135:0.24 139:0.57 140:0.45 146:0.89 147:0.91 149:0.47 150:0.29 151:0.55 153:0.48 154:0.48 157:0.61 158:0.71 159:0.77 160:0.88 162:0.55 166:0.76 172:0.68 173:0.26 176:0.54 177:0.70 179:0.46 182:0.61 187:0.68 190:0.89 202:0.70 212:0.18 216:0.88 220:0.87 222:0.35 227:0.61 229:0.61 231:0.78 232:0.88 235:0.31 241:0.30 242:0.79 243:0.58 244:0.61 245:0.77 246:0.61 247:0.60 248:0.56 253:0.42 256:0.64 259:0.21 265:0.67 266:0.80 267:0.73 268:0.68 276:0.73 285:0.47 287:0.61 289:0.91 290:0.57 300:0.70 +1 11:0.64 12:0.37 17:0.75 18:0.62 21:0.47 27:0.61 29:0.62 30:0.33 34:0.58 36:0.47 37:0.61 39:0.51 43:0.59 45:0.66 46:0.88 55:0.84 66:0.46 69:0.58 70:0.77 71:0.74 74:0.28 76:0.85 77:0.70 81:0.27 86:0.58 91:0.08 98:0.66 101:0.52 107:0.92 108:0.44 110:0.93 114:0.56 117:0.86 122:0.77 123:0.43 124:0.76 125:0.88 126:0.26 127:0.63 129:0.05 131:0.61 133:0.73 135:0.44 139:0.57 145:0.47 146:0.89 147:0.91 149:0.45 150:0.26 154:0.47 157:0.61 158:0.71 159:0.75 160:0.88 166:0.76 172:0.68 173:0.41 176:0.54 177:0.70 178:0.55 179:0.44 182:0.61 187:0.95 195:0.90 212:0.19 216:0.44 222:0.35 227:0.61 229:0.61 231:0.78 232:0.97 235:0.52 241:0.02 242:0.79 243:0.58 244:0.61 245:0.79 246:0.61 247:0.60 253:0.37 259:0.21 265:0.67 266:0.80 267:0.19 276:0.72 282:0.70 287:0.61 289:0.91 290:0.56 300:0.70 +0 12:0.37 17:0.61 18:0.67 21:0.78 27:0.32 29:0.62 30:0.76 32:0.62 34:0.68 36:0.32 37:0.89 39:0.51 43:0.18 46:0.88 55:0.71 66:0.64 69:0.08 70:0.15 71:0.74 74:0.24 76:0.85 86:0.57 91:0.31 98:0.82 104:0.58 106:0.81 107:0.89 108:0.07 110:0.91 122:0.65 123:0.07 125:0.88 127:0.31 129:0.05 131:0.61 135:0.94 139:0.55 145:0.89 146:0.41 147:0.47 149:0.12 154:0.12 157:0.61 159:0.15 160:0.88 163:0.75 166:0.61 172:0.16 173:0.50 175:0.75 177:0.38 178:0.55 179:0.98 182:0.61 187:0.87 195:0.53 206:0.81 212:0.95 216:0.32 222:0.35 227:0.61 229:0.61 231:0.74 235:0.22 241:0.46 242:0.82 243:0.69 244:0.83 246:0.61 247:0.12 253:0.20 257:0.65 259:0.21 265:0.82 266:0.12 267:0.44 276:0.17 277:0.69 287:0.61 289:0.90 300:0.13 +1 11:0.64 12:0.37 17:0.18 18:0.65 21:0.78 27:0.56 29:0.62 30:0.55 34:0.80 36:0.23 37:0.60 39:0.51 43:0.41 45:0.66 46:0.88 55:0.71 66:0.36 69:0.88 70:0.71 71:0.74 74:0.27 86:0.58 91:0.07 98:0.51 101:0.52 107:0.94 108:0.83 110:0.96 122:0.77 123:0.82 124:0.76 125:0.88 127:0.48 129:0.59 131:0.61 133:0.76 135:0.60 139:0.56 145:0.65 146:0.41 147:0.05 149:0.30 154:0.31 157:0.61 158:0.71 159:0.71 160:0.88 163:0.94 166:0.61 172:0.41 173:0.83 175:0.75 177:0.05 178:0.55 179:0.69 182:0.61 187:0.87 212:0.18 216:0.76 222:0.35 227:0.61 229:0.61 231:0.87 232:0.95 235:0.52 241:0.49 242:0.31 243:0.13 244:0.61 245:0.58 246:0.61 247:0.74 253:0.24 257:0.84 259:0.21 265:0.52 266:0.80 267:0.36 276:0.49 277:0.69 287:0.61 289:0.93 300:0.55 +1 11:0.86 12:0.37 17:0.55 18:0.69 21:0.78 27:0.47 29:0.62 30:0.76 32:0.62 34:0.20 36:0.19 37:0.66 39:0.51 43:0.10 44:0.85 45:0.73 46:0.88 66:0.54 69:0.85 70:0.22 71:0.74 74:0.49 77:0.70 86:0.71 91:0.42 98:0.13 101:0.87 107:0.91 108:0.71 110:0.92 122:0.55 123:0.70 124:0.45 125:0.88 127:0.14 129:0.05 131:0.61 133:0.37 135:0.74 139:0.69 144:0.57 145:0.82 146:0.20 147:0.05 149:0.06 154:0.62 155:0.58 157:0.61 159:0.20 160:0.88 166:0.61 172:0.21 173:0.89 177:0.05 178:0.55 179:0.62 182:0.61 187:0.39 192:0.51 195:0.74 202:0.50 204:0.85 208:0.54 212:0.42 216:0.82 222:0.35 227:0.61 229:0.61 231:0.83 235:0.12 241:0.65 242:0.45 243:0.26 245:0.40 246:0.61 247:0.35 253:0.35 254:0.84 257:0.84 259:0.21 265:0.14 266:0.23 267:0.52 276:0.14 281:0.91 282:0.71 287:0.61 289:0.92 300:0.18 +1 9:0.76 11:0.64 12:0.37 17:0.62 18:0.92 21:0.78 27:0.47 29:0.62 30:0.72 31:0.87 32:0.62 34:0.50 36:0.21 37:0.66 39:0.51 43:0.65 44:0.85 45:0.92 46:0.88 66:0.93 69:0.66 70:0.39 71:0.74 74:0.36 77:0.70 79:0.88 83:0.60 86:0.66 91:0.42 96:0.72 97:0.89 98:0.75 101:0.52 107:0.94 108:0.50 110:0.95 122:0.80 123:0.48 125:0.88 127:0.24 129:0.05 131:0.89 135:0.93 137:0.87 139:0.66 140:0.45 145:0.76 146:0.80 147:0.84 149:0.83 151:0.59 153:0.48 154:0.65 155:0.58 157:0.61 158:0.72 159:0.36 160:0.88 162:0.86 166:0.61 172:0.80 173:0.66 177:0.70 179:0.29 182:0.61 187:0.68 190:0.77 192:0.51 195:0.77 196:0.87 202:0.36 208:0.54 212:0.80 216:0.82 219:0.87 220:0.74 222:0.35 227:0.61 229:0.61 231:0.90 232:0.92 235:0.22 241:0.18 242:0.55 243:0.93 246:0.61 247:0.60 248:0.58 253:0.40 254:0.90 255:0.74 256:0.45 259:0.21 265:0.75 266:0.38 267:0.28 268:0.46 276:0.70 279:0.82 282:0.71 284:0.86 285:0.56 287:0.61 289:0.95 292:0.95 300:0.33 +1 11:0.64 12:0.37 17:0.88 18:0.66 21:0.47 27:0.69 29:0.62 30:0.38 34:0.37 36:0.70 37:0.27 39:0.51 43:0.59 45:0.66 46:0.88 55:0.71 66:0.49 69:0.86 70:0.77 71:0.74 74:0.28 81:0.27 86:0.59 91:0.14 98:0.77 101:0.52 107:0.92 108:0.73 110:0.94 114:0.56 117:0.86 122:0.77 123:0.72 124:0.67 125:0.88 126:0.26 127:0.66 129:0.05 131:0.61 133:0.60 135:0.60 139:0.57 146:0.95 147:0.78 149:0.47 150:0.26 154:0.49 157:0.61 158:0.71 159:0.79 160:0.88 166:0.76 172:0.74 173:0.76 176:0.54 177:0.38 178:0.55 179:0.40 182:0.61 187:0.39 212:0.28 216:0.21 222:0.35 227:0.61 229:0.61 231:0.79 232:0.92 235:0.52 241:0.27 242:0.77 243:0.33 244:0.61 245:0.86 246:0.61 247:0.60 253:0.37 257:0.65 259:0.21 265:0.77 266:0.80 267:0.36 276:0.76 287:0.61 289:0.91 290:0.56 300:0.84 +0 8:0.69 12:0.37 17:0.61 18:0.66 21:0.22 27:0.37 29:0.62 30:0.44 34:0.81 36:0.99 37:0.37 39:0.51 43:0.14 46:0.88 55:0.92 66:0.20 69:0.58 70:0.66 71:0.74 74:0.24 81:0.49 86:0.57 91:0.56 98:0.48 104:0.89 107:0.92 108:0.85 110:0.95 120:0.80 123:0.85 124:0.93 125:0.88 126:0.26 127:0.72 129:0.59 131:0.91 133:0.92 135:0.89 139:0.55 140:0.45 145:0.52 146:0.51 147:0.57 149:0.26 150:0.38 151:0.60 153:0.46 154:0.10 157:0.61 159:0.86 160:0.88 162:0.69 163:0.94 164:0.53 166:0.87 172:0.45 173:0.31 175:0.75 177:0.38 179:0.55 182:0.61 187:0.39 190:0.89 202:0.76 212:0.30 215:0.51 216:0.60 220:0.62 222:0.35 227:0.61 229:0.61 231:0.87 235:0.22 241:0.41 242:0.80 243:0.28 244:0.83 245:0.64 246:0.61 247:0.39 248:0.65 253:0.20 256:0.78 257:0.65 259:0.21 260:0.58 265:0.50 266:0.92 267:0.65 268:0.79 276:0.66 277:0.87 283:0.82 285:0.47 287:0.61 289:0.93 297:0.36 300:0.55 +1 9:0.76 11:0.75 12:0.37 17:0.55 18:0.91 21:0.78 27:0.47 29:0.62 30:0.76 31:0.88 34:0.59 36:0.18 37:0.66 39:0.51 43:0.65 44:0.85 45:0.92 46:0.88 66:0.91 69:0.65 70:0.30 71:0.74 74:0.43 77:0.70 79:0.90 83:0.60 86:0.74 91:0.31 96:0.75 97:0.89 98:0.75 101:0.52 107:0.93 108:0.50 110:0.95 122:0.80 123:0.48 125:0.88 127:0.24 129:0.05 131:0.89 135:0.97 137:0.88 139:0.72 140:0.45 144:0.57 145:0.79 146:0.82 147:0.85 149:0.83 151:0.65 153:0.48 154:0.59 155:0.58 157:0.61 158:0.72 159:0.38 160:0.88 162:0.86 166:0.61 172:0.76 173:0.64 177:0.70 179:0.33 182:0.61 187:0.68 190:0.77 192:0.51 195:0.79 196:0.89 202:0.36 208:0.54 212:0.71 216:0.82 219:0.87 220:0.62 222:0.35 227:0.61 229:0.61 231:0.86 232:0.87 235:0.12 241:0.02 242:0.59 243:0.92 246:0.61 247:0.47 248:0.63 253:0.44 254:0.86 255:0.74 256:0.45 259:0.21 265:0.75 266:0.38 267:0.28 268:0.46 276:0.64 279:0.82 282:0.71 284:0.86 285:0.56 287:0.61 289:0.93 292:0.95 300:0.33 +1 11:0.42 12:0.01 17:0.51 21:0.78 27:0.66 29:0.56 30:0.43 34:0.77 36:0.72 37:0.47 39:0.65 43:0.17 55:0.84 66:0.85 69:0.23 70:0.56 71:0.58 74:0.39 91:0.05 98:0.85 108:0.18 123:0.18 124:0.38 127:0.44 129:0.05 131:0.61 133:0.39 135:0.61 139:0.64 144:0.57 146:0.97 147:0.97 149:0.30 154:0.51 157:0.61 159:0.72 166:0.61 172:0.85 173:0.14 175:0.75 177:0.38 178:0.55 179:0.34 182:0.61 187:0.39 212:0.27 216:0.23 219:0.89 222:0.35 227:0.61 229:0.61 231:0.80 241:0.05 242:0.70 243:0.86 244:0.61 245:0.74 246:0.61 247:0.47 253:0.31 254:0.74 257:0.65 259:0.21 265:0.85 266:0.63 267:0.44 271:0.56 276:0.77 277:0.69 287:0.61 292:0.91 300:0.55 +1 7:0.63 8:0.68 11:0.75 12:0.01 17:0.24 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.14 34:0.44 36:0.91 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.45 69:0.07 70:0.92 71:0.58 74:0.38 81:0.41 86:0.63 91:0.25 98:0.48 101:0.52 108:0.06 123:0.06 124:0.87 126:0.26 127:0.89 129:0.05 131:0.61 133:0.88 135:0.34 139:0.61 144:0.57 145:0.42 146:0.82 147:0.85 149:0.61 150:0.35 154:0.59 157:0.77 159:0.84 166:0.79 172:0.82 173:0.08 177:0.70 178:0.55 179:0.30 182:0.72 187:0.98 212:0.57 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.73 233:0.76 235:0.12 241:0.32 242:0.81 243:0.58 244:0.61 245:0.84 246:0.61 247:0.60 253:0.30 259:0.21 265:0.49 266:0.84 267:0.28 271:0.56 276:0.84 287:0.61 297:0.36 300:0.84 +1 11:0.75 12:0.01 17:0.28 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.11 34:0.44 36:0.95 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.61 69:0.07 70:0.91 71:0.58 74:0.38 81:0.41 86:0.66 91:0.22 98:0.60 101:0.52 108:0.06 123:0.06 124:0.85 126:0.26 127:0.88 129:0.05 131:0.61 133:0.92 135:0.49 139:0.64 144:0.57 146:0.91 147:0.93 149:0.61 150:0.35 154:0.59 157:0.77 159:0.85 166:0.79 172:0.87 173:0.08 177:0.70 178:0.55 179:0.30 182:0.72 187:0.98 212:0.58 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 231:0.73 235:0.12 241:0.24 242:0.81 243:0.68 244:0.61 245:0.77 246:0.61 247:0.60 253:0.30 259:0.21 265:0.61 266:0.89 267:0.44 271:0.56 276:0.84 287:0.61 297:0.36 300:0.70 +0 11:0.86 12:0.01 17:0.72 18:0.98 21:0.13 27:0.53 29:0.56 30:0.32 34:0.44 36:0.26 37:0.38 39:0.65 43:0.09 45:0.73 55:0.59 66:0.42 69:0.96 70:0.75 71:0.58 74:0.35 81:0.38 86:0.96 91:0.11 98:0.64 101:0.87 108:0.95 114:0.63 117:0.86 122:0.86 123:0.95 124:0.92 126:0.26 127:0.84 129:0.89 131:0.61 133:0.93 135:0.75 139:0.95 144:0.57 145:0.70 146:0.97 147:0.05 149:0.46 150:0.34 154:0.22 157:0.75 158:0.71 159:0.96 166:0.77 172:0.97 173:0.74 176:0.55 177:0.05 178:0.55 179:0.11 182:0.72 187:0.68 212:0.52 216:0.80 222:0.35 227:0.61 229:0.61 231:0.79 232:0.87 235:0.12 241:0.24 242:0.74 243:0.05 245:0.98 246:0.61 247:0.96 253:0.47 254:0.80 257:0.84 259:0.21 265:0.65 266:0.95 267:0.91 271:0.56 276:0.98 287:0.61 290:0.63 300:0.94 +0 8:0.61 12:0.01 17:0.79 18:0.96 21:0.59 27:0.68 29:0.56 30:0.37 34:0.97 36:0.88 37:0.44 39:0.65 43:0.12 44:0.87 48:0.91 55:0.84 64:0.77 66:0.26 69:0.95 70:0.79 71:0.58 74:0.25 81:0.27 86:0.91 91:0.48 98:0.55 108:0.80 122:0.86 123:0.79 124:0.84 126:0.26 127:0.72 129:0.59 131:0.61 133:0.82 135:0.82 139:0.91 145:0.85 146:0.36 147:0.57 149:0.33 150:0.26 154:0.09 157:0.61 159:0.80 163:0.94 166:0.61 172:0.59 173:0.93 175:0.75 177:0.05 178:0.55 179:0.44 182:0.61 187:0.39 195:0.92 212:0.22 216:0.27 219:0.89 222:0.35 227:0.61 229:0.61 231:0.64 235:0.74 241:0.05 242:0.78 243:0.32 244:0.83 245:0.80 246:0.61 247:0.67 253:0.22 257:0.84 259:0.21 265:0.56 266:0.87 267:0.36 271:0.56 276:0.74 277:0.87 281:0.89 287:0.61 292:0.91 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.16 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.14 34:0.44 36:0.95 37:0.89 39:0.65 43:0.69 45:0.66 55:0.45 66:0.67 69:0.07 70:0.90 71:0.58 74:0.38 81:0.41 86:0.63 91:0.33 98:0.68 101:0.52 108:0.06 123:0.06 124:0.76 126:0.26 127:0.92 129:0.05 131:0.61 133:0.88 135:0.34 139:0.61 144:0.57 145:0.42 146:0.91 147:0.92 149:0.61 150:0.35 154:0.59 157:0.77 159:0.80 166:0.79 172:0.86 173:0.09 177:0.70 178:0.55 179:0.33 182:0.73 187:0.98 212:0.60 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.73 233:0.76 235:0.12 241:0.41 242:0.80 243:0.71 244:0.61 245:0.77 246:0.61 247:0.60 253:0.30 259:0.21 265:0.68 266:0.91 267:0.44 271:0.56 276:0.83 287:0.61 297:0.36 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.69 18:0.61 21:0.05 25:0.88 27:0.09 29:0.56 30:0.21 32:0.62 34:0.29 36:0.81 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.92 69:0.05 70:0.72 71:0.58 74:0.38 81:0.50 86:0.63 91:0.33 98:0.95 101:0.52 108:0.05 120:0.63 123:0.05 126:0.26 127:0.66 129:0.05 131:0.85 135:0.65 139:0.61 140:0.45 144:0.57 145:0.56 146:0.89 147:0.91 149:0.54 150:0.39 151:0.55 153:0.48 154:0.59 157:0.79 159:0.49 162:0.86 164:0.52 166:0.79 172:0.79 173:0.15 177:0.70 179:0.50 182:0.73 187:0.39 190:0.89 195:0.67 202:0.36 212:0.68 215:0.78 216:0.85 220:0.62 222:0.35 227:0.84 229:0.61 230:0.64 231:0.74 233:0.76 235:0.05 241:0.69 242:0.78 243:0.93 244:0.61 246:0.61 247:0.60 248:0.54 253:0.30 256:0.45 259:0.21 260:0.57 265:0.95 266:0.63 267:0.36 268:0.46 271:0.56 276:0.69 285:0.47 287:0.61 297:0.36 300:0.55 +0 11:0.42 12:0.01 21:0.05 27:0.69 29:0.56 30:0.76 34:0.68 36:0.90 37:0.25 39:0.65 43:0.69 55:0.45 64:0.77 66:0.64 69:0.38 70:0.15 71:0.58 74:0.38 81:0.58 91:0.33 98:0.35 108:0.30 122:0.51 123:0.28 126:0.26 127:0.12 129:0.05 131:0.61 135:0.94 144:0.57 146:0.23 147:0.29 149:0.34 150:0.43 154:0.58 157:0.61 159:0.11 166:0.61 172:0.16 173:0.74 175:0.75 177:0.38 178:0.55 179:0.67 182:0.61 187:0.87 212:0.95 216:0.52 222:0.35 227:0.61 229:0.61 231:0.67 235:0.05 241:0.32 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.30 254:0.80 257:0.65 259:0.21 265:0.37 266:0.12 267:0.28 271:0.56 276:0.17 277:0.69 287:0.61 300:0.13 +0 8:0.81 12:0.01 17:0.77 18:0.93 21:0.68 27:0.68 29:0.56 30:0.30 34:0.98 36:0.92 37:0.44 39:0.65 43:0.12 44:0.87 48:0.91 55:0.71 64:0.77 66:0.44 69:0.93 70:0.81 71:0.58 74:0.25 81:0.24 86:0.87 91:0.23 98:0.52 108:0.57 122:0.86 123:0.55 124:0.82 126:0.26 127:0.63 129:0.05 131:0.61 133:0.82 135:0.80 139:0.88 145:0.85 146:0.35 147:0.89 149:0.27 150:0.24 154:0.09 157:0.61 159:0.82 163:0.94 166:0.61 172:0.57 173:0.86 175:0.75 177:0.05 178:0.55 179:0.58 182:0.61 187:0.39 195:0.94 212:0.23 216:0.27 219:0.89 222:0.35 227:0.61 229:0.61 231:0.64 235:0.80 241:0.10 242:0.78 243:0.58 244:0.83 245:0.66 246:0.61 247:0.55 253:0.22 257:0.84 259:0.21 265:0.54 266:0.92 267:0.59 271:0.56 276:0.61 277:0.87 281:0.89 287:0.61 292:0.91 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.20 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.10 34:0.45 36:0.91 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.61 69:0.07 70:0.89 71:0.58 74:0.38 81:0.41 86:0.63 91:0.20 98:0.69 101:0.52 108:0.06 123:0.06 124:0.80 126:0.26 127:0.92 129:0.05 131:0.61 133:0.89 135:0.54 139:0.61 144:0.57 145:0.42 146:0.95 147:0.96 149:0.62 150:0.35 154:0.59 157:0.77 159:0.85 166:0.79 172:0.88 173:0.08 177:0.70 178:0.55 179:0.28 182:0.72 187:0.98 212:0.51 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.77 233:0.76 235:0.12 241:0.32 242:0.79 243:0.68 244:0.61 245:0.83 246:0.61 247:0.60 253:0.31 259:0.21 265:0.70 266:0.89 267:0.44 271:0.56 276:0.87 287:0.61 297:0.36 300:0.70 +1 7:0.63 12:0.01 17:0.62 18:0.66 21:0.78 27:0.16 29:0.56 30:0.21 34:0.42 36:0.95 37:0.70 39:0.65 43:0.12 55:0.92 66:0.16 69:0.94 70:0.70 71:0.58 74:0.30 86:0.67 91:0.14 98:0.38 108:0.89 122:0.77 123:0.88 124:0.94 127:0.70 129:0.59 131:0.61 133:0.93 135:0.87 139:0.65 145:0.56 146:0.79 147:0.32 149:0.23 154:0.13 157:0.61 159:0.87 163:0.94 166:0.61 172:0.37 173:0.87 177:0.05 178:0.55 179:0.56 182:0.61 187:0.87 202:0.36 212:0.27 216:0.66 222:0.35 227:0.61 229:0.61 230:0.64 231:0.76 232:0.72 233:0.76 235:0.88 241:0.32 242:0.28 243:0.20 244:0.61 245:0.62 246:0.61 247:0.86 253:0.26 254:0.90 257:0.84 259:0.21 265:0.41 266:0.87 267:0.19 271:0.56 276:0.65 287:0.61 300:0.55 +1 12:0.01 17:0.77 21:0.78 27:0.58 29:0.56 30:0.21 34:0.52 36:0.97 37:0.26 39:0.65 43:0.18 55:0.92 66:0.16 69:0.05 70:0.92 71:0.58 74:0.32 83:0.60 91:0.05 97:0.89 98:0.36 108:0.05 117:0.86 122:0.77 123:0.05 124:0.85 127:0.82 129:0.59 131:0.61 133:0.82 135:0.81 139:0.68 146:0.53 147:0.59 149:0.24 154:0.12 155:0.58 157:0.61 158:0.72 159:0.68 166:0.61 172:0.21 173:0.06 175:0.75 177:0.38 178:0.55 179:0.80 182:0.61 187:0.87 192:0.51 208:0.54 212:0.22 216:0.67 219:0.92 222:0.35 227:0.61 229:0.61 231:0.73 232:0.94 241:0.07 242:0.71 243:0.37 244:0.83 245:0.54 246:0.61 247:0.39 253:0.27 257:0.65 259:0.21 265:0.38 266:0.75 267:0.84 271:0.56 276:0.46 277:0.69 279:0.82 287:0.61 292:0.95 300:0.70 +1 12:0.01 17:0.70 18:0.62 21:0.47 27:0.63 29:0.56 30:0.90 34:0.91 36:0.28 37:0.41 39:0.65 43:0.14 55:0.59 66:0.89 69:0.63 70:0.21 71:0.58 74:0.28 76:0.85 77:0.70 81:0.26 86:0.67 91:0.60 98:0.86 106:0.81 108:0.48 114:0.57 123:0.46 126:0.26 127:0.38 129:0.05 131:0.61 135:0.30 139:0.65 145:0.70 146:0.72 147:0.77 149:0.32 150:0.25 154:0.45 157:0.61 159:0.29 166:0.77 172:0.70 173:0.77 176:0.55 177:0.70 178:0.55 179:0.55 182:0.61 187:0.39 195:0.61 206:0.81 212:0.93 216:0.18 222:0.35 227:0.61 229:0.61 231:0.83 235:0.52 241:0.41 242:0.80 243:0.90 244:0.61 246:0.61 247:0.35 253:0.39 254:0.84 259:0.21 265:0.86 266:0.17 267:0.23 271:0.56 276:0.59 282:0.71 287:0.61 290:0.57 300:0.25 +1 7:0.63 8:0.63 11:0.75 12:0.01 17:0.32 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.10 34:0.39 36:0.99 37:0.89 39:0.65 43:0.69 45:0.66 55:0.71 66:0.48 69:0.07 70:0.87 71:0.58 74:0.38 81:0.41 86:0.63 91:0.27 98:0.61 101:0.52 108:0.06 123:0.06 124:0.80 126:0.26 127:0.88 129:0.05 131:0.61 133:0.81 135:0.65 139:0.61 144:0.57 145:0.42 146:0.91 147:0.93 149:0.62 150:0.35 154:0.59 157:0.77 159:0.85 166:0.79 172:0.85 173:0.08 177:0.70 178:0.55 179:0.27 182:0.72 187:0.98 212:0.51 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.73 233:0.76 235:0.12 241:0.15 242:0.81 243:0.59 244:0.61 245:0.90 246:0.61 247:0.60 253:0.30 259:0.21 265:0.62 266:0.89 267:0.44 271:0.56 276:0.87 287:0.61 297:0.36 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.28 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.10 34:0.44 36:0.93 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.49 69:0.07 70:0.84 71:0.58 74:0.38 81:0.41 86:0.66 91:0.18 98:0.64 101:0.52 108:0.06 123:0.06 124:0.84 126:0.26 127:0.91 129:0.05 131:0.61 133:0.89 135:0.37 139:0.64 144:0.57 145:0.42 146:0.94 147:0.95 149:0.62 150:0.35 154:0.59 157:0.77 159:0.86 166:0.79 172:0.87 173:0.07 177:0.70 178:0.55 179:0.26 182:0.72 187:0.98 212:0.55 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.78 233:0.76 235:0.12 241:0.27 242:0.78 243:0.60 244:0.61 245:0.86 246:0.61 247:0.60 253:0.31 259:0.21 265:0.65 266:0.80 267:0.36 271:0.56 276:0.87 283:0.78 287:0.61 297:0.36 300:0.70 +0 7:0.63 8:0.66 11:0.75 12:0.01 17:0.66 18:0.57 21:0.30 27:0.56 29:0.56 30:0.10 32:0.62 34:0.29 36:0.75 37:0.45 39:0.65 43:0.11 45:0.66 55:0.71 66:0.05 69:0.19 70:0.95 71:0.58 74:0.31 76:0.98 77:0.70 81:0.32 86:0.59 91:0.03 98:0.17 101:0.52 108:1.00 120:0.55 123:0.96 124:1.00 126:0.26 127:1.00 129:0.96 131:0.85 133:1.00 135:0.92 139:0.57 140:0.87 144:0.57 145:0.79 146:0.31 147:0.37 149:0.33 150:0.29 151:0.48 153:0.48 154:0.55 157:0.75 159:0.98 162:0.49 164:0.52 166:0.79 172:0.67 173:0.05 175:0.75 177:0.38 178:0.48 179:0.14 182:0.71 187:0.87 190:0.89 195:1.00 202:0.63 212:0.21 215:0.44 216:0.56 220:0.87 222:0.35 227:0.84 229:0.61 230:0.63 231:0.80 233:0.73 235:0.31 241:0.15 242:0.79 243:0.07 244:0.61 245:0.87 246:0.61 247:0.79 248:0.48 253:0.27 256:0.45 257:0.65 259:0.21 260:0.54 265:0.15 266:0.99 267:0.79 268:0.46 271:0.56 276:0.96 277:0.87 282:0.71 285:0.47 287:0.61 297:0.36 300:0.94 +2 7:0.81 12:0.69 17:0.72 20:0.80 27:0.58 28:0.46 30:0.94 32:0.80 34:0.96 36:0.49 37:0.30 43:0.52 55:0.59 66:0.72 69:0.05 70:0.13 78:0.75 81:0.99 91:0.33 98:0.58 100:0.79 104:0.89 106:0.81 108:0.05 111:0.75 120:0.69 123:0.05 126:0.54 127:0.17 129:0.05 135:0.49 140:0.45 145:0.38 146:0.52 147:0.58 149:0.37 150:0.99 151:0.66 152:0.77 153:0.48 154:0.41 159:0.19 161:0.94 162:0.86 164:0.57 167:0.52 169:0.77 172:0.27 173:0.17 177:0.05 179:0.76 181:0.52 186:0.76 187:0.39 195:0.60 202:0.36 206:0.81 212:0.42 215:0.96 216:0.33 230:0.87 233:0.76 235:0.02 241:0.02 242:0.82 243:0.75 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.76 265:0.59 266:0.23 267:0.36 268:0.46 276:0.22 283:0.82 285:0.62 297:0.36 300:0.13 +2 8:0.85 12:0.69 17:0.77 21:0.78 25:0.88 27:0.30 28:0.46 30:0.87 32:0.80 34:0.97 36:0.51 37:0.70 43:0.28 55:0.71 66:0.82 69:0.77 70:0.27 91:0.64 98:0.81 104:0.54 108:0.60 120:0.60 123:0.58 127:0.30 129:0.05 135:0.89 140:0.45 145:0.69 146:0.78 147:0.82 149:0.44 151:0.54 153:0.48 154:0.21 159:0.34 161:0.94 162:0.74 163:0.81 164:0.67 167:0.65 172:0.48 173:0.83 177:0.05 179:0.74 181:0.52 187:0.21 191:0.75 195:0.67 202:0.56 212:0.30 216:0.38 220:0.87 235:0.68 241:0.50 242:0.82 243:0.83 247:0.12 248:0.54 256:0.58 259:0.21 260:0.61 265:0.81 266:0.47 267:0.65 268:0.59 276:0.40 285:0.62 300:0.25 +2 6:0.97 8:0.95 12:0.69 17:0.70 20:0.86 21:0.47 25:0.88 27:0.27 28:0.46 34:0.50 36:0.83 37:0.88 43:0.44 66:0.36 69:0.47 78:0.76 81:0.95 91:0.33 98:0.16 100:0.83 104:0.58 108:0.36 111:0.77 120:0.60 123:0.35 126:0.54 127:0.09 129:0.05 135:0.37 140:0.45 146:0.05 147:0.05 149:0.15 150:0.92 151:0.54 152:1.00 153:0.48 154:0.33 159:0.05 161:0.94 162:0.86 164:0.57 167:0.73 169:0.88 172:0.05 173:0.95 177:0.05 181:0.52 186:0.77 187:0.21 189:0.99 202:0.36 215:0.63 216:0.28 220:0.62 235:0.52 238:0.96 241:0.66 243:0.51 248:0.54 256:0.45 259:0.21 260:0.61 261:0.92 265:0.17 267:0.44 268:0.46 285:0.62 297:0.36 +1 12:0.69 17:0.40 21:0.78 27:0.45 28:0.46 30:0.95 34:0.80 36:0.18 37:0.50 43:0.26 55:0.84 66:0.54 69:0.82 91:0.18 98:0.22 108:0.66 123:0.64 127:0.11 129:0.05 135:0.87 145:0.49 146:0.32 147:0.39 149:0.17 154:0.19 159:0.13 161:0.94 167:0.54 172:0.10 173:0.80 177:0.05 178:0.55 179:0.62 181:0.52 187:0.68 216:0.77 235:0.97 241:0.07 243:0.63 259:0.21 265:0.24 267:0.23 300:0.08 +3 8:0.95 12:0.69 17:0.66 21:0.76 25:0.88 27:0.30 28:0.46 30:0.76 32:0.80 34:0.79 36:0.62 37:0.70 43:0.36 55:0.71 66:0.72 69:0.64 70:0.22 81:0.83 91:0.90 98:0.80 104:0.54 108:0.49 120:0.85 123:0.46 126:0.54 127:0.29 129:0.05 135:0.47 140:0.45 145:0.72 146:0.50 147:0.56 149:0.35 150:0.63 151:0.72 153:0.48 154:0.27 159:0.18 161:0.94 162:0.81 164:0.85 167:0.91 172:0.27 173:0.88 177:0.05 179:0.92 181:0.52 187:0.21 191:0.87 195:0.56 202:0.77 212:0.42 215:0.46 216:0.38 235:0.95 241:0.90 242:0.82 243:0.75 247:0.12 248:0.78 256:0.82 259:0.21 260:0.85 265:0.80 266:0.23 267:0.36 268:0.82 276:0.25 283:0.60 285:0.62 297:0.36 300:0.18 +1 8:0.94 12:0.69 17:0.77 20:0.93 21:0.59 25:0.88 27:0.72 28:0.46 32:0.80 34:0.47 36:0.75 78:0.74 81:0.92 91:0.86 100:0.79 104:0.58 111:0.74 120:0.66 126:0.54 135:0.37 140:0.45 145:0.92 150:0.86 151:0.56 152:0.87 153:0.72 161:0.94 162:0.86 164:0.84 167:0.92 169:0.77 175:0.75 181:0.52 186:0.75 195:0.52 202:0.72 215:0.55 216:0.09 220:0.87 235:0.68 241:0.97 244:0.61 248:0.61 256:0.79 257:0.65 259:0.21 260:0.67 261:0.79 267:0.59 268:0.80 277:0.69 285:0.62 297:0.36 +2 8:0.80 12:0.69 17:0.88 21:0.40 25:0.88 27:0.27 28:0.46 30:0.76 34:0.90 36:0.87 37:0.88 43:0.52 55:0.71 66:0.36 69:0.12 70:0.15 81:0.96 91:0.53 98:0.21 104:0.58 108:0.10 120:0.53 123:0.10 124:0.52 126:0.54 127:0.16 129:0.59 133:0.47 135:0.71 140:0.45 146:0.27 147:0.33 149:0.28 150:0.94 151:0.46 153:0.48 154:0.41 159:0.20 161:0.94 162:0.74 163:0.75 164:0.67 167:0.77 172:0.10 173:0.22 177:0.05 179:0.88 181:0.52 187:0.21 202:0.56 212:0.95 215:0.67 216:0.28 220:0.91 235:0.42 241:0.72 242:0.82 243:0.51 245:0.37 247:0.12 248:0.46 256:0.58 259:0.21 260:0.53 265:0.23 266:0.12 267:0.65 268:0.59 276:0.16 285:0.62 297:0.36 300:0.13 +3 6:0.98 8:0.95 12:0.69 17:0.38 20:0.99 21:0.76 25:0.88 27:0.30 28:0.46 30:0.76 32:0.80 34:0.80 36:0.64 37:0.70 43:0.36 55:0.71 66:0.72 69:0.64 70:0.22 78:0.89 81:0.83 91:0.95 98:0.80 100:0.99 104:0.54 108:0.49 111:0.98 120:0.93 123:0.46 126:0.54 127:0.29 129:0.05 135:0.42 140:0.45 145:0.72 146:0.50 147:0.56 149:0.35 150:0.63 151:0.84 152:0.91 153:0.97 154:0.27 159:0.18 161:0.94 162:0.79 164:0.94 167:0.91 169:1.00 172:0.27 173:0.88 177:0.05 179:0.92 181:0.52 186:0.89 189:0.98 191:0.92 195:0.56 202:0.89 212:0.42 215:0.46 216:0.38 235:0.95 238:1.00 241:0.89 242:0.82 243:0.75 247:0.12 248:0.90 256:0.91 259:0.21 260:0.93 261:0.98 265:0.80 266:0.23 267:0.28 268:0.92 276:0.25 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.96 8:0.91 12:0.69 17:0.90 20:0.85 21:0.22 25:0.88 27:0.72 28:0.46 30:0.86 32:0.80 34:0.58 36:0.36 43:0.24 55:0.59 66:0.84 69:0.85 70:0.21 78:0.77 81:0.98 91:0.69 98:0.64 100:0.82 104:0.74 108:0.71 111:0.77 120:0.54 123:0.69 126:0.54 127:0.19 129:0.05 135:0.78 140:0.45 145:0.72 146:0.60 147:0.66 149:0.46 150:0.97 151:0.47 152:0.81 153:0.48 154:0.17 159:0.22 161:0.94 162:0.86 164:0.57 167:0.88 169:0.79 172:0.54 173:0.94 177:0.05 179:0.47 181:0.52 186:0.78 189:0.98 195:0.66 202:0.36 212:0.81 215:0.79 216:0.02 220:0.82 235:0.22 238:0.95 241:0.80 242:0.82 243:0.85 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 261:0.80 265:0.64 266:0.27 267:0.36 268:0.46 276:0.44 285:0.62 297:0.36 300:0.18 +1 12:0.69 17:0.49 21:0.78 27:0.72 28:0.46 34:0.82 36:0.23 91:0.90 104:0.58 120:0.63 135:0.81 140:0.45 151:0.54 153:0.48 161:0.94 162:0.84 164:0.77 167:0.90 175:0.75 181:0.52 191:0.70 202:0.65 216:0.11 220:0.93 241:0.86 244:0.61 248:0.57 256:0.68 257:0.65 259:0.21 260:0.64 267:0.44 268:0.71 277:0.69 285:0.62 +2 12:0.69 17:0.62 21:0.22 25:0.88 27:0.27 28:0.46 30:0.62 34:0.93 36:0.67 37:0.88 43:0.52 55:0.92 66:0.47 69:0.08 70:0.34 81:0.98 91:0.33 98:0.66 104:0.58 108:0.56 123:0.53 124:0.52 126:0.54 127:0.50 129:0.59 133:0.47 135:0.73 146:0.21 147:0.27 149:0.39 150:0.97 154:0.41 159:0.28 161:0.94 163:0.75 167:0.63 172:0.21 173:0.30 177:0.05 178:0.55 179:0.93 181:0.52 187:0.39 212:0.30 215:0.79 216:0.28 235:0.22 241:0.44 242:0.82 243:0.37 245:0.46 247:0.12 259:0.21 265:0.67 266:0.27 267:0.44 276:0.26 297:0.36 300:0.25 +1 12:0.69 17:0.05 20:0.76 21:0.78 27:0.72 28:0.46 34:0.33 36:0.54 43:0.30 66:0.36 69:0.74 78:0.72 91:0.82 98:0.12 100:0.73 104:0.58 108:0.58 111:0.72 120:0.74 123:0.55 127:0.08 129:0.05 132:0.97 135:0.60 140:0.45 146:0.05 147:0.05 149:0.12 151:0.66 152:0.74 153:0.48 154:0.22 159:0.05 161:0.94 162:0.66 164:0.79 167:0.91 169:0.72 172:0.05 173:1.00 177:0.05 181:0.52 186:0.73 202:0.72 216:0.12 235:0.05 241:0.89 243:0.51 248:0.67 256:0.75 260:0.75 261:0.73 265:0.13 267:0.28 268:0.74 283:0.82 285:0.62 +1 12:0.69 17:0.30 21:0.30 25:0.88 27:0.27 28:0.46 34:0.85 36:0.71 37:0.88 43:0.34 66:0.36 69:0.64 81:0.97 91:0.38 98:0.13 104:0.58 108:0.49 123:0.47 126:0.54 127:0.08 129:0.05 135:0.34 146:0.05 147:0.05 149:0.13 150:0.96 154:0.26 159:0.05 161:0.94 167:0.67 172:0.05 173:0.96 177:0.05 178:0.55 181:0.52 187:0.39 215:0.72 216:0.28 235:0.31 241:0.53 243:0.51 259:0.21 265:0.13 267:0.52 297:0.36 +2 8:0.88 12:0.69 17:0.75 21:0.47 25:0.88 27:0.30 28:0.46 30:0.76 34:0.89 36:0.47 37:0.70 43:0.25 55:0.71 66:0.64 69:0.84 70:0.15 81:0.95 91:0.68 98:0.49 104:0.54 108:0.69 120:0.53 123:0.67 126:0.54 127:0.15 129:0.05 135:0.81 140:0.45 145:0.45 146:0.44 147:0.50 149:0.21 150:0.92 151:0.46 153:0.69 154:0.18 159:0.16 161:0.94 162:0.86 163:0.98 164:0.62 167:0.62 172:0.16 173:0.95 177:0.05 179:0.85 181:0.52 187:0.21 191:0.70 202:0.46 212:0.95 215:0.63 216:0.38 220:0.93 235:0.52 241:0.41 242:0.82 243:0.69 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.50 266:0.12 267:0.44 268:0.55 276:0.18 285:0.62 297:0.36 300:0.13 +2 6:0.98 8:0.97 12:0.69 17:0.28 20:0.78 21:0.47 27:0.47 28:0.46 34:0.58 36:0.89 37:0.90 78:0.78 81:0.95 91:0.88 100:0.95 104:0.58 111:0.89 120:0.85 126:0.54 135:0.21 140:0.45 150:0.92 151:0.66 152:0.77 153:0.48 161:0.94 162:0.78 164:0.94 167:0.91 169:0.90 175:0.75 181:0.52 186:0.79 189:0.98 191:0.87 202:0.90 215:0.63 216:0.29 220:0.62 235:0.52 238:0.99 241:0.93 244:0.61 248:0.79 256:0.92 257:0.65 259:0.21 260:0.86 261:0.83 267:0.19 268:0.93 277:0.69 283:0.60 285:0.62 297:0.36 +2 8:0.94 12:0.69 17:0.77 20:0.93 21:0.59 25:0.88 27:0.72 28:0.46 32:0.80 34:0.47 36:0.75 78:0.74 81:0.92 91:0.86 100:0.79 104:0.58 111:0.74 120:0.66 126:0.54 135:0.37 140:0.45 145:0.92 150:0.86 151:0.56 152:0.87 153:0.72 161:0.94 162:0.86 164:0.84 167:0.92 169:0.77 175:0.75 181:0.52 186:0.75 195:0.52 202:0.72 215:0.55 216:0.09 220:0.87 235:0.68 241:0.97 244:0.61 248:0.61 256:0.79 257:0.65 259:0.21 260:0.67 261:0.79 267:0.59 268:0.80 277:0.69 285:0.62 297:0.36 +2 1:0.65 7:0.81 10:0.97 11:0.84 12:0.37 17:0.83 18:0.85 21:0.22 27:0.53 29:0.62 30:0.36 32:0.68 34:0.62 36:0.25 37:0.86 39:0.47 43:0.36 44:0.92 45:0.87 48:0.91 64:0.77 66:0.43 69:0.39 70:0.93 71:0.91 74:0.75 77:0.70 81:0.60 83:0.56 86:0.90 91:0.17 98:0.76 99:0.83 101:0.47 104:0.87 106:0.81 108:0.30 114:0.88 117:0.86 122:0.65 123:0.29 124:0.60 126:0.51 127:0.73 129:0.59 133:0.47 135:0.74 139:0.91 144:0.85 145:0.98 146:0.81 147:0.84 149:0.28 150:0.52 154:0.54 155:0.56 159:0.55 165:0.65 170:0.87 172:0.71 173:0.37 174:0.94 176:0.70 177:0.88 178:0.55 179:0.40 187:0.39 192:0.57 193:0.99 195:0.73 197:0.94 201:0.63 204:0.83 206:0.81 208:0.64 212:0.69 215:0.79 216:0.65 222:0.60 230:0.83 232:0.91 233:0.66 235:0.42 239:0.97 241:0.41 242:0.16 243:0.57 245:0.91 247:0.92 253:0.67 254:0.74 259:0.21 265:0.76 266:0.63 267:0.96 271:0.44 276:0.75 281:0.86 282:0.77 290:0.88 297:0.36 300:0.84 +0 8:0.62 10:0.83 11:0.84 12:0.37 17:0.22 18:0.69 21:0.78 27:0.45 29:0.62 30:0.76 34:0.84 36:0.18 37:0.50 39:0.47 43:0.32 45:0.66 55:0.96 66:0.36 69:0.87 70:0.22 71:0.91 74:0.33 86:0.67 91:0.23 98:0.21 101:0.47 108:0.74 122:0.65 123:0.72 124:0.67 127:0.18 129:0.05 133:0.59 135:0.91 139:0.65 144:0.85 145:0.47 146:0.38 147:0.05 149:0.24 154:0.24 159:0.46 163:0.93 170:0.87 172:0.16 173:0.77 174:0.79 175:0.75 177:0.05 178:0.55 179:0.80 187:0.68 197:0.82 212:0.42 216:0.77 222:0.60 235:0.22 239:0.82 241:0.24 242:0.45 243:0.26 244:0.83 245:0.40 247:0.35 253:0.29 257:0.93 259:0.21 265:0.23 266:0.23 267:0.44 271:0.44 276:0.24 277:0.69 300:0.18 +0 1:0.63 7:0.64 9:0.73 10:0.93 11:0.87 12:0.37 17:0.28 18:0.71 21:0.05 27:0.02 29:0.62 30:0.30 32:0.71 34:0.56 36:0.48 37:0.92 39:0.47 43:0.46 44:0.92 45:0.85 48:0.91 66:0.44 69:0.91 70:0.81 71:0.91 74:0.58 76:0.85 77:0.70 81:0.57 83:0.56 86:0.77 91:0.33 96:0.80 98:0.53 99:0.83 101:0.65 108:0.81 114:0.92 120:0.69 122:0.95 123:0.80 124:0.69 126:0.32 127:0.58 129:0.05 133:0.67 135:0.92 139:0.81 140:0.45 144:0.85 145:0.56 146:0.68 147:0.49 149:0.42 150:0.51 151:0.85 153:0.48 154:0.52 155:0.56 159:0.60 162:0.86 164:0.56 165:0.65 170:0.87 172:0.57 173:0.94 174:0.92 176:0.63 177:0.70 179:0.57 187:0.39 190:0.89 192:0.58 195:0.80 197:0.93 201:0.59 202:0.36 208:0.50 212:0.37 215:0.91 216:0.99 222:0.60 230:0.65 233:0.76 235:0.12 239:0.91 241:0.62 242:0.16 243:0.31 244:0.61 245:0.75 247:0.88 248:0.77 253:0.81 255:0.72 256:0.45 257:0.65 259:0.21 260:0.70 265:0.54 266:0.75 267:0.69 268:0.46 271:0.44 276:0.58 281:0.91 282:0.80 285:0.50 290:0.92 297:0.36 300:0.70 +1 10:0.96 11:0.84 12:0.37 17:0.82 18:0.65 27:0.27 29:0.62 30:0.38 34:0.25 36:0.41 37:0.94 39:0.47 43:0.65 45:0.81 66:0.90 69:0.05 70:0.73 71:0.91 74:0.56 76:0.85 77:0.70 81:0.59 86:0.71 91:0.38 98:0.87 101:0.47 102:0.96 108:0.05 114:0.84 117:0.86 122:0.95 123:0.05 126:0.32 127:0.38 129:0.05 135:0.75 139:0.68 144:0.85 145:0.38 146:0.73 147:0.77 149:0.71 150:0.58 154:0.63 159:0.29 170:0.87 172:0.71 173:0.21 174:0.93 176:0.59 177:0.88 178:0.55 179:0.53 187:0.39 192:0.45 193:0.97 195:0.58 197:0.96 201:0.60 212:0.80 216:0.59 222:0.60 232:0.82 235:0.05 236:0.94 239:0.96 241:0.21 242:0.27 243:0.90 244:0.61 247:0.84 253:0.61 254:0.74 259:0.21 265:0.87 266:0.27 267:0.44 271:0.44 276:0.58 282:0.72 290:0.84 300:0.55 +0 1:0.62 7:0.64 9:0.70 10:0.87 11:0.84 12:0.37 17:0.08 18:0.80 21:0.47 22:0.93 27:0.20 29:0.62 30:0.55 31:0.87 32:0.71 34:0.88 36:0.57 37:0.60 39:0.47 43:0.75 44:0.92 45:0.81 47:0.93 48:0.91 64:0.77 66:0.31 69:0.27 70:0.67 71:0.91 74:0.69 76:0.85 77:0.70 79:0.86 81:0.55 83:0.56 86:0.86 91:0.25 96:0.69 97:0.97 98:0.17 99:0.83 101:0.47 104:0.99 106:0.81 108:0.21 114:0.78 117:0.86 120:0.55 122:0.91 123:0.20 124:0.76 126:0.51 127:0.60 129:0.05 133:0.73 135:0.69 137:0.87 139:0.89 140:0.45 144:0.85 145:0.70 146:0.32 147:0.38 149:0.51 150:0.46 151:0.50 153:0.48 154:0.66 155:0.51 158:0.81 159:0.81 162:0.86 164:0.56 165:0.65 170:0.87 172:0.37 173:0.14 174:0.86 176:0.70 177:0.88 179:0.73 187:0.68 190:0.77 192:0.51 195:0.93 196:0.89 197:0.88 201:0.60 202:0.36 206:0.81 208:0.61 212:0.75 215:0.63 216:0.84 219:0.94 220:0.96 222:0.60 230:0.65 232:0.99 233:0.76 235:0.68 239:0.86 241:0.21 242:0.43 243:0.49 244:0.61 245:0.58 247:0.55 248:0.50 253:0.62 255:0.69 256:0.45 259:0.21 260:0.55 262:0.93 265:0.18 266:0.47 267:0.69 268:0.46 271:0.44 276:0.31 279:0.81 281:0.91 282:0.80 283:0.66 284:0.88 285:0.60 290:0.78 292:0.87 297:0.36 300:0.55 +2 1:0.62 10:0.91 11:0.87 12:0.37 17:0.70 18:0.73 21:0.05 27:0.20 29:0.62 30:0.57 34:0.92 36:0.65 37:0.60 39:0.47 43:0.67 45:0.90 66:0.77 69:0.17 70:0.49 71:0.91 74:0.70 81:0.71 83:0.56 86:0.79 91:0.12 98:0.71 99:0.83 101:0.65 102:0.95 104:0.84 106:0.81 108:0.14 114:0.95 117:0.86 122:0.55 123:0.13 124:0.40 126:0.51 127:0.36 129:0.05 133:0.45 135:0.60 139:0.76 144:0.85 145:0.59 146:0.77 147:0.81 149:0.73 150:0.64 154:0.62 155:0.48 159:0.45 165:0.65 170:0.87 172:0.76 173:0.21 174:0.86 176:0.70 177:0.88 178:0.55 179:0.41 187:0.39 192:0.49 193:0.97 195:0.73 197:0.89 201:0.64 206:0.81 208:0.50 212:0.72 215:0.91 216:0.84 222:0.60 232:0.89 235:0.22 236:0.95 239:0.92 241:0.21 242:0.24 243:0.79 244:0.61 245:0.74 247:0.79 253:0.69 259:0.21 265:0.71 266:0.38 267:0.69 271:0.44 276:0.67 290:0.95 297:0.36 300:0.43 +3 1:0.63 7:0.70 8:0.70 9:0.69 10:0.88 11:0.84 12:0.37 17:0.30 18:0.67 21:0.05 27:0.36 29:0.62 30:0.32 32:0.67 34:0.29 36:0.71 37:0.88 39:0.47 43:0.58 45:0.93 66:0.69 69:0.52 70:0.87 71:0.91 74:0.77 77:0.70 81:0.57 83:0.56 86:0.69 91:0.40 96:0.77 97:0.94 98:0.85 99:0.83 101:0.47 104:0.87 108:0.76 114:0.92 120:0.54 123:0.74 124:0.52 126:0.32 127:0.74 129:0.59 133:0.77 135:0.81 139:0.66 140:0.80 144:0.85 145:0.72 146:0.20 147:0.26 149:0.80 150:0.51 151:0.47 153:0.48 154:0.61 155:0.51 158:0.77 159:0.77 162:0.62 164:0.56 165:0.65 170:0.87 172:0.89 173:0.34 174:0.79 175:0.75 176:0.63 177:0.70 179:0.28 187:0.39 190:0.95 192:0.51 195:0.89 197:0.84 201:0.59 202:0.60 204:0.80 208:0.50 212:0.42 215:0.91 216:0.50 220:0.62 222:0.60 230:0.73 232:0.90 233:0.76 235:0.12 239:0.87 241:0.41 242:0.30 243:0.11 244:0.61 245:0.85 247:0.86 248:0.47 253:0.81 255:0.68 256:0.58 257:0.65 259:0.21 260:0.54 265:0.85 266:0.80 267:0.65 268:0.59 271:0.44 276:0.85 277:0.69 279:0.78 282:0.75 283:0.82 285:0.50 290:0.92 297:0.36 300:0.84 +1 8:0.59 10:0.96 11:0.84 12:0.37 17:0.75 18:0.65 27:0.27 29:0.62 30:0.38 34:0.24 36:0.42 37:0.94 39:0.47 43:0.65 45:0.81 66:0.90 69:0.05 70:0.73 71:0.91 74:0.56 76:0.85 77:0.70 81:0.59 86:0.71 91:0.44 96:0.80 98:0.87 101:0.47 102:0.96 108:0.05 114:0.84 117:0.86 122:0.95 123:0.05 126:0.32 127:0.38 129:0.05 135:0.75 139:0.68 140:0.45 144:0.85 145:0.38 146:0.73 147:0.77 149:0.71 150:0.58 151:0.65 153:0.48 154:0.63 158:0.73 159:0.29 162:0.86 170:0.87 172:0.71 173:0.21 174:0.93 176:0.59 177:0.88 179:0.53 187:0.39 190:0.95 192:0.45 193:0.97 195:0.58 197:0.96 201:0.60 202:0.36 212:0.80 216:0.59 222:0.60 232:0.82 235:0.05 236:0.94 239:0.96 241:0.32 242:0.27 243:0.90 244:0.61 247:0.84 248:0.63 253:0.80 254:0.74 256:0.45 259:0.21 265:0.87 266:0.27 267:0.44 268:0.46 271:0.44 276:0.58 282:0.72 285:0.46 290:0.84 300:0.55 +0 1:0.63 7:0.64 10:0.94 11:0.84 12:0.37 17:0.32 18:0.69 21:0.05 27:0.02 29:0.62 30:0.60 32:0.71 34:0.39 36:0.40 37:0.92 39:0.47 43:0.30 44:0.92 45:0.95 66:0.79 69:0.88 70:0.48 71:0.91 74:0.75 76:0.85 77:0.70 81:0.57 83:0.56 86:0.75 91:0.11 98:0.79 99:0.83 101:0.47 108:0.75 114:0.92 122:0.55 123:0.74 124:0.40 126:0.32 127:0.69 129:0.05 133:0.45 135:0.75 139:0.78 144:0.85 145:0.54 146:0.91 147:0.49 149:0.75 150:0.51 154:0.52 155:0.49 159:0.68 165:0.65 170:0.87 172:0.89 173:0.86 174:0.93 176:0.63 177:0.70 178:0.55 179:0.30 187:0.39 192:0.47 195:0.81 197:0.93 201:0.59 208:0.50 212:0.80 215:0.91 216:0.99 222:0.60 230:0.65 233:0.76 235:0.12 239:0.92 241:0.46 242:0.41 243:0.17 244:0.61 245:0.87 247:0.88 253:0.69 254:0.74 257:0.65 259:0.21 265:0.79 266:0.71 267:0.52 271:0.44 276:0.81 282:0.80 290:0.92 297:0.36 300:0.55 +1 1:0.59 7:0.70 8:0.74 9:0.75 10:0.89 11:0.84 12:0.37 17:0.09 18:0.82 21:0.40 27:0.20 29:0.62 30:0.66 31:0.90 32:0.71 34:0.44 36:0.73 37:0.57 39:0.47 43:0.62 44:0.92 45:0.95 66:0.96 69:0.57 70:0.52 71:0.91 74:0.86 77:0.70 79:0.90 81:0.49 83:0.56 86:0.89 91:0.71 96:0.96 97:0.94 98:0.83 99:0.83 101:0.47 108:0.44 114:0.78 120:0.93 122:0.55 123:0.42 126:0.32 127:0.33 129:0.05 135:0.42 137:0.90 138:0.98 139:0.86 140:0.97 144:0.85 145:0.92 146:0.80 147:0.83 149:0.80 150:0.44 151:0.66 153:0.91 154:0.58 155:0.64 158:0.76 159:0.36 162:0.81 164:0.92 165:0.65 170:0.87 172:0.90 173:0.62 174:0.79 176:0.63 177:0.88 179:0.23 187:0.39 190:0.89 191:0.76 192:0.87 195:0.69 196:0.93 197:0.82 201:0.56 202:0.86 204:0.80 208:0.61 212:0.91 215:0.67 216:0.73 222:0.60 230:0.73 233:0.76 235:0.52 239:0.86 241:0.68 242:0.38 243:0.96 247:0.90 248:0.63 253:0.97 254:0.97 255:0.73 256:0.89 259:0.21 260:0.93 265:0.83 266:0.38 267:0.76 268:0.90 271:0.44 276:0.82 279:0.78 282:0.79 283:0.67 284:0.88 285:0.60 290:0.78 297:0.36 300:0.43 +2 1:0.65 7:0.81 10:0.97 11:0.84 12:0.37 17:0.70 18:0.80 21:0.22 22:0.93 27:0.53 29:0.62 30:0.42 32:0.68 34:0.77 36:0.34 37:0.86 39:0.47 43:0.58 44:0.92 45:0.83 47:0.93 48:0.91 64:0.77 66:0.93 69:0.33 70:0.83 71:0.91 74:0.75 77:0.70 81:0.60 83:0.56 86:0.87 91:0.18 97:0.94 98:0.95 99:0.83 101:0.47 104:0.87 106:0.81 108:0.26 114:0.88 117:0.86 122:0.94 123:0.25 126:0.51 127:0.67 129:0.05 135:0.93 139:0.91 144:0.85 145:0.98 146:0.88 147:0.90 149:0.31 150:0.52 154:0.59 155:0.56 158:0.81 159:0.45 165:0.65 170:0.87 172:0.80 173:0.38 174:0.94 176:0.70 177:0.88 178:0.55 179:0.49 187:0.39 192:0.57 193:0.99 195:0.67 197:0.95 201:0.63 204:0.83 206:0.81 208:0.64 212:0.69 215:0.79 216:0.65 219:0.94 222:0.60 230:0.83 232:0.91 233:0.66 235:0.42 239:0.97 241:0.15 242:0.16 243:0.93 247:0.90 253:0.67 254:0.74 259:0.21 262:0.93 265:0.95 266:0.54 267:0.95 271:0.44 276:0.69 279:0.79 281:0.86 282:0.77 283:0.82 290:0.88 292:0.87 297:0.36 300:0.70 +2 1:0.62 10:0.97 11:0.84 12:0.37 17:0.55 18:0.75 21:0.47 22:0.93 27:0.04 29:0.62 30:0.13 32:0.67 34:0.94 36:0.33 37:0.94 39:0.47 43:0.56 45:0.81 47:0.93 64:0.77 66:0.54 69:0.35 70:0.93 71:0.91 74:0.62 77:0.70 81:0.55 83:0.56 86:0.85 91:0.31 98:0.74 99:0.83 101:0.47 104:0.99 106:0.81 108:0.60 114:0.78 117:0.86 122:0.95 123:0.58 124:0.52 126:0.51 127:0.50 129:0.59 133:0.47 135:0.60 139:0.80 144:0.85 145:0.49 146:0.34 147:0.41 149:0.27 150:0.46 154:0.84 155:0.48 159:0.39 165:0.65 170:0.87 172:0.65 173:0.42 174:0.94 176:0.70 177:0.88 178:0.55 179:0.51 187:0.87 192:0.48 193:0.96 195:0.64 197:0.96 201:0.60 206:0.81 208:0.61 212:0.81 215:0.63 216:0.74 222:0.60 232:0.99 235:0.68 239:0.96 241:0.32 242:0.21 243:0.39 245:0.82 247:0.86 253:0.59 254:0.84 259:0.21 262:0.93 265:0.74 266:0.47 267:0.92 271:0.44 276:0.63 282:0.75 290:0.78 297:0.36 300:0.70 +0 1:0.57 7:0.70 8:0.76 9:0.75 11:0.84 12:0.37 17:0.51 18:0.80 21:0.59 22:0.93 27:0.51 29:0.62 30:0.17 31:0.95 34:0.53 36:0.64 37:0.40 39:0.47 43:0.63 45:0.87 47:0.93 55:0.71 66:0.68 69:0.23 70:0.92 71:0.91 74:0.69 79:0.95 81:0.45 83:0.69 86:0.83 91:0.58 96:0.94 97:0.89 98:0.90 99:0.83 101:0.47 102:0.95 108:0.77 114:0.71 117:0.86 120:0.90 123:0.75 124:0.46 126:0.32 127:0.95 129:0.59 133:0.46 135:0.82 137:0.95 138:0.98 139:0.83 140:0.80 144:0.85 145:0.74 146:0.78 147:0.81 149:0.65 150:0.40 151:0.78 153:0.71 154:0.68 155:0.64 158:0.81 159:0.85 162:0.83 164:0.90 165:0.65 170:0.87 172:0.92 173:0.12 175:0.75 176:0.63 177:0.70 179:0.24 187:0.39 190:0.89 192:0.98 195:0.94 196:0.95 201:0.55 202:0.82 204:0.84 208:0.61 212:0.41 215:0.55 216:0.32 219:0.94 220:0.82 222:0.60 230:0.73 232:0.85 233:0.76 235:0.74 239:0.84 241:0.57 242:0.54 243:0.31 244:0.61 245:0.96 247:0.90 248:0.82 253:0.94 254:0.74 255:0.78 256:0.87 257:0.65 259:0.21 260:0.90 262:0.93 265:0.90 266:0.80 267:0.59 268:0.87 271:0.44 276:0.89 277:0.69 279:0.78 283:0.66 284:0.97 285:0.60 290:0.71 292:0.87 297:0.36 300:0.84 +0 10:0.82 11:0.84 12:0.37 17:0.45 18:0.82 21:0.78 27:0.45 29:0.62 30:0.38 34:0.71 36:0.19 37:0.50 39:0.47 43:0.25 45:0.66 55:0.84 66:0.48 69:0.89 70:0.45 71:0.91 74:0.31 86:0.77 91:0.12 98:0.42 101:0.47 108:0.78 122:0.92 123:0.77 124:0.78 127:0.27 129:0.05 133:0.80 135:0.86 139:0.73 144:0.85 145:0.47 146:0.79 147:0.05 149:0.25 154:0.18 159:0.71 163:0.94 170:0.87 172:0.37 173:0.77 174:0.79 177:0.05 178:0.55 179:0.68 187:0.68 197:0.81 212:0.28 216:0.77 222:0.60 235:1.00 239:0.82 241:0.21 242:0.60 243:0.16 244:0.83 245:0.47 247:0.55 253:0.28 257:0.93 259:0.21 265:0.44 266:0.63 267:0.73 271:0.44 276:0.42 300:0.33 +2 1:0.63 7:0.64 8:0.98 9:0.74 10:0.92 11:0.84 12:0.37 17:0.30 18:0.61 21:0.05 27:0.02 29:0.62 30:0.56 31:0.93 32:0.67 34:0.39 36:0.31 37:0.92 39:0.47 43:0.57 45:0.94 66:0.77 69:0.46 70:0.77 71:0.91 74:0.82 76:0.85 77:0.70 79:0.93 81:0.57 83:0.56 86:0.67 91:0.46 96:0.92 97:0.89 98:0.72 99:0.83 101:0.47 108:0.35 114:0.92 120:0.86 122:0.82 123:0.34 124:0.43 126:0.32 127:0.74 129:0.05 133:0.73 135:0.44 137:0.93 139:0.65 140:0.45 144:0.85 145:0.49 146:0.91 147:0.93 149:0.78 150:0.51 151:0.95 153:0.84 154:0.61 155:0.56 158:0.77 159:0.76 162:0.68 164:0.77 165:0.65 170:0.87 172:0.86 173:0.30 174:0.89 176:0.63 177:0.88 179:0.36 187:0.39 190:0.89 191:0.70 192:0.58 195:0.89 196:0.89 197:0.90 201:0.59 202:0.71 208:0.50 212:0.60 215:0.91 216:0.99 222:0.60 230:0.65 233:0.76 235:0.12 239:0.90 241:0.68 242:0.51 243:0.79 244:0.61 245:0.73 247:0.82 248:0.92 253:0.94 254:0.74 255:0.73 256:0.71 259:0.21 260:0.87 265:0.72 266:0.71 267:0.89 268:0.74 271:0.44 276:0.78 279:0.76 282:0.75 283:0.82 284:0.88 285:0.60 290:0.92 297:0.36 300:0.94 +2 6:0.99 8:0.97 9:0.80 11:0.83 12:0.20 17:0.73 18:0.81 20:0.85 21:0.05 27:0.01 28:0.93 29:0.71 30:0.44 31:0.95 34:0.89 36:0.61 37:0.98 39:0.47 41:0.90 43:0.59 45:0.88 46:0.97 64:0.77 66:0.60 69:0.90 70:0.85 71:0.89 74:0.55 78:0.83 79:0.96 81:0.41 83:0.91 85:0.72 86:0.85 91:0.72 96:0.88 98:0.50 100:0.95 101:0.87 104:0.58 107:0.96 108:0.85 110:0.94 111:0.90 120:0.81 122:0.51 123:0.84 124:0.58 125:0.99 126:0.26 127:0.45 129:0.59 133:0.61 135:0.39 137:0.95 139:0.82 140:0.87 146:0.34 147:0.23 149:0.66 150:0.35 151:0.72 152:0.96 153:0.48 154:0.49 155:0.67 159:0.60 160:0.99 161:0.80 162:0.86 164:0.79 167:0.93 169:0.98 172:0.74 173:0.91 177:0.05 179:0.41 181:0.69 186:0.83 187:0.21 189:0.99 190:0.77 191:0.91 192:0.87 196:0.95 202:0.72 208:0.64 212:0.71 216:0.82 219:0.89 220:0.62 222:0.48 232:0.76 235:0.05 238:0.98 241:0.78 242:0.24 243:0.13 245:0.79 247:0.79 248:0.74 253:0.83 255:0.95 256:0.79 257:0.84 259:0.21 260:0.79 261:0.99 265:0.52 266:0.71 267:0.52 268:0.80 271:0.94 276:0.66 284:0.96 285:0.62 289:0.93 292:0.91 300:0.94 +1 8:0.95 9:0.76 12:0.20 17:0.53 20:0.77 21:0.78 27:0.37 28:0.93 29:0.71 34:0.34 36:0.23 37:0.98 39:0.47 46:0.88 71:0.89 78:0.80 83:0.60 91:0.77 96:0.86 100:0.84 104:0.54 107:0.88 110:0.88 111:0.80 120:0.77 125:0.98 135:0.79 140:0.45 151:0.90 152:0.75 153:0.83 155:0.58 160:0.96 161:0.80 162:0.53 164:0.62 167:0.96 169:0.82 175:0.97 181:0.69 186:0.80 190:0.77 191:0.82 192:0.59 202:0.63 208:0.54 216:0.38 222:0.48 232:0.72 241:0.81 244:0.93 248:0.80 253:0.76 255:0.74 256:0.45 257:0.93 259:0.21 260:0.73 261:0.76 267:0.69 268:0.58 271:0.94 277:0.95 285:0.56 289:0.93 +1 1:0.69 6:0.94 8:0.86 9:0.76 11:0.64 12:0.20 17:0.51 18:0.82 20:0.76 21:0.13 27:0.08 28:0.93 29:0.71 30:0.65 31:0.89 34:0.91 36:0.59 37:0.95 39:0.47 43:0.69 45:0.94 46:0.99 66:0.74 69:0.48 70:0.82 71:0.89 74:0.75 78:0.72 79:0.89 81:0.52 83:0.60 86:0.85 91:0.64 96:0.74 98:0.79 99:0.83 100:0.83 101:0.52 104:0.75 107:0.97 108:0.74 110:0.95 111:0.76 114:0.75 120:0.61 122:0.51 123:0.72 124:0.42 125:0.98 126:0.44 127:0.72 129:0.59 133:0.46 135:0.39 137:0.89 139:0.81 140:0.45 146:0.62 147:0.68 149:0.82 150:0.58 151:0.62 152:0.94 153:0.80 154:0.54 155:0.58 159:0.63 160:0.96 161:0.80 162:0.79 164:0.62 165:0.70 167:0.89 169:0.82 172:0.85 173:0.41 176:0.66 177:0.70 179:0.37 181:0.69 186:0.73 187:0.21 190:0.77 192:0.59 196:0.90 201:0.68 202:0.60 208:0.54 212:0.57 215:0.61 216:0.47 220:0.74 222:0.48 232:0.80 235:0.22 241:0.75 242:0.38 243:0.23 245:0.87 247:0.79 248:0.60 253:0.64 254:0.74 255:0.74 256:0.58 259:0.21 260:0.60 261:0.85 265:0.79 266:0.63 267:0.69 268:0.65 271:0.94 276:0.78 284:0.86 285:0.62 289:0.93 290:0.75 297:0.36 300:0.84 +2 1:0.69 6:0.83 7:0.72 8:0.77 9:0.76 11:0.83 12:0.20 17:0.49 18:0.80 20:0.89 21:0.40 27:0.21 28:0.93 29:0.71 30:0.72 34:0.62 36:0.62 37:0.74 39:0.47 43:0.72 45:1.00 46:1.00 66:0.43 69:0.17 70:0.60 71:0.89 74:0.97 78:0.74 81:0.37 83:0.60 85:0.90 86:0.90 91:0.72 96:0.97 97:0.99 98:0.76 99:0.83 100:0.78 101:0.87 104:0.84 106:0.81 107:0.98 108:0.86 110:0.95 111:0.74 114:0.61 117:0.86 120:0.93 122:0.51 123:0.85 124:0.81 125:0.99 126:0.44 127:0.78 129:0.59 133:0.82 135:0.17 139:0.87 140:0.45 146:0.94 147:0.95 149:0.99 150:0.53 151:0.96 152:0.85 153:0.95 154:0.55 155:0.58 158:0.79 159:0.92 160:0.99 161:0.80 162:0.61 164:0.86 165:0.70 167:0.74 169:0.76 172:0.98 173:0.07 176:0.66 177:0.70 179:0.10 181:0.69 186:0.75 187:0.39 189:0.86 190:0.77 191:0.70 192:0.59 201:0.68 202:0.85 204:0.85 206:0.81 208:0.54 212:0.77 215:0.42 216:0.95 222:0.48 230:0.74 232:0.88 233:0.73 235:0.52 238:0.91 241:0.56 242:0.55 243:0.31 245:0.99 247:0.84 248:0.96 253:0.98 255:0.74 256:0.86 259:0.21 260:0.92 261:0.77 265:0.76 266:0.47 267:0.18 268:0.86 271:0.94 276:0.98 279:0.82 283:0.82 285:0.56 289:0.93 290:0.61 297:0.36 300:0.84 +2 6:0.99 8:0.97 9:0.80 11:0.83 12:0.20 17:0.73 18:0.81 20:0.85 21:0.05 27:0.01 28:0.93 29:0.71 30:0.44 31:0.95 34:0.89 36:0.60 37:0.98 39:0.47 41:0.90 43:0.59 45:0.88 46:0.97 64:0.77 66:0.60 69:0.90 70:0.85 71:0.89 74:0.55 78:0.83 79:0.96 81:0.41 83:0.91 85:0.72 86:0.85 91:0.72 96:0.88 98:0.50 100:0.95 101:0.87 104:0.58 107:0.96 108:0.85 110:0.94 111:0.90 120:0.81 122:0.51 123:0.84 124:0.58 125:0.99 126:0.26 127:0.45 129:0.59 133:0.61 135:0.39 137:0.95 139:0.82 140:0.87 146:0.34 147:0.23 149:0.66 150:0.35 151:0.72 152:0.96 153:0.48 154:0.49 155:0.67 159:0.60 160:0.99 161:0.80 162:0.86 164:0.79 167:0.93 169:0.98 172:0.74 173:0.91 177:0.05 179:0.41 181:0.69 186:0.83 187:0.21 189:0.99 190:0.77 191:0.91 192:0.87 196:0.95 202:0.72 208:0.64 212:0.71 216:0.82 219:0.89 220:0.62 222:0.48 232:0.76 235:0.05 238:0.98 241:0.78 242:0.24 243:0.13 245:0.79 247:0.79 248:0.74 253:0.83 255:0.95 256:0.79 257:0.84 259:0.21 260:0.79 261:0.99 265:0.52 266:0.71 267:0.52 268:0.80 271:0.94 276:0.66 284:0.96 285:0.62 289:0.93 292:0.91 300:0.94 +1 1:0.69 6:0.88 7:0.72 8:0.87 9:0.80 11:0.64 12:0.20 17:0.51 18:0.81 20:0.86 21:0.40 27:0.43 28:0.93 29:0.71 30:0.28 31:0.89 32:0.80 34:0.59 36:0.85 37:0.79 39:0.47 43:0.67 44:0.93 45:0.83 46:0.99 66:0.89 69:0.17 70:0.81 71:0.89 74:0.78 77:0.70 78:0.75 79:0.88 81:0.37 83:0.60 86:0.89 91:0.84 96:0.94 97:0.89 98:0.99 99:0.83 100:0.80 101:0.52 104:0.58 107:0.96 108:0.14 110:0.93 111:0.76 114:0.61 120:0.90 122:0.51 123:0.13 125:0.98 126:0.44 127:0.93 129:0.05 135:0.30 137:0.89 138:0.98 139:0.90 140:0.97 145:0.70 146:0.81 147:0.84 149:0.55 150:0.54 151:0.55 152:0.81 153:0.88 154:0.85 155:0.67 158:0.78 159:0.37 160:1.00 161:0.80 162:0.71 164:0.90 165:0.70 167:0.95 169:0.78 172:0.70 173:0.35 176:0.66 177:0.70 179:0.67 181:0.69 186:0.76 189:0.90 190:0.77 191:0.90 192:0.87 195:0.61 196:0.92 201:0.68 202:0.87 204:0.85 208:0.64 212:0.61 215:0.42 216:0.49 220:0.91 222:0.48 230:0.75 232:0.76 233:0.76 235:0.60 238:0.94 241:0.79 242:0.19 243:0.90 247:0.76 248:0.57 253:0.93 254:0.84 255:0.95 256:0.89 259:0.21 260:0.87 261:0.78 265:0.99 266:0.38 267:0.97 268:0.89 271:0.94 276:0.57 279:0.82 282:0.88 283:0.78 284:0.93 285:0.62 289:0.93 290:0.61 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.64 12:0.20 17:0.77 18:0.85 21:0.47 22:0.93 27:0.08 28:0.93 29:0.71 30:0.54 31:0.96 34:0.75 36:0.49 37:0.90 39:0.47 41:0.90 43:0.56 45:0.85 46:0.98 47:0.93 64:0.77 66:0.77 69:0.81 70:0.52 71:0.89 74:0.56 79:0.97 81:0.47 83:0.91 86:0.83 91:0.72 96:0.90 98:0.77 99:0.83 101:0.52 104:0.58 107:0.96 108:0.65 110:0.93 114:0.60 120:0.82 122:0.51 123:0.62 124:0.40 125:0.98 126:0.54 127:0.52 129:0.05 133:0.43 135:0.60 137:0.96 139:0.81 140:0.93 146:0.75 147:0.44 149:0.62 150:0.67 151:0.75 153:0.48 154:0.66 155:0.67 159:0.43 160:0.99 161:0.80 162:0.79 163:0.81 164:0.83 165:0.70 167:0.93 172:0.68 173:0.87 176:0.73 177:0.38 179:0.58 181:0.69 187:0.98 190:0.77 192:0.87 196:0.95 201:0.93 202:0.81 208:0.64 212:0.35 215:0.41 216:0.64 219:0.89 220:0.87 222:0.48 232:0.76 235:0.80 241:0.78 242:0.37 243:0.17 245:0.65 247:0.74 248:0.76 253:0.85 254:0.88 255:0.95 256:0.85 257:0.65 260:0.81 262:0.93 265:0.77 266:0.54 267:0.82 268:0.85 271:0.94 276:0.59 284:0.97 285:0.62 289:0.93 290:0.60 292:0.91 297:0.36 300:0.43 +1 1:0.69 7:0.72 8:0.91 9:0.76 11:0.64 12:0.20 17:0.26 18:0.78 20:0.77 21:0.54 27:0.18 28:0.93 29:0.71 30:0.67 34:0.49 36:0.98 37:0.97 39:0.47 43:0.72 45:0.95 46:0.98 48:0.91 66:0.19 69:0.67 70:0.57 71:0.89 74:0.82 76:0.85 78:0.78 81:0.34 83:0.60 86:0.81 91:0.75 96:0.83 97:0.94 98:0.53 99:0.83 100:0.73 101:0.52 104:0.57 107:0.97 108:0.83 110:0.95 111:0.76 114:0.58 120:0.73 122:0.51 123:0.82 124:0.85 125:0.98 126:0.44 127:0.86 129:0.81 133:0.84 135:0.86 139:0.80 140:0.45 146:0.78 147:0.18 149:0.85 150:0.52 151:0.81 152:0.76 153:0.48 154:0.67 155:0.67 158:0.78 159:0.76 160:0.99 161:0.80 162:0.73 163:0.81 164:0.78 165:0.70 167:0.97 169:0.72 172:0.61 173:0.54 176:0.66 177:0.38 179:0.36 181:0.69 186:0.78 190:0.77 192:0.87 201:0.68 202:0.73 204:0.89 208:0.64 212:0.36 215:0.39 216:0.10 220:0.96 222:0.48 230:0.74 232:0.75 233:0.73 235:0.68 241:0.83 242:0.52 243:0.10 245:0.85 247:0.76 248:0.75 253:0.82 254:0.84 255:0.74 256:0.80 257:0.65 259:0.21 260:0.69 261:0.75 265:0.55 266:0.75 267:0.17 268:0.77 271:0.94 276:0.80 277:0.69 279:0.82 281:0.86 283:0.78 285:0.56 289:0.93 290:0.58 297:0.36 300:0.70 +1 1:0.69 8:0.66 9:0.80 11:0.64 12:0.20 17:0.78 18:0.73 20:0.80 21:0.40 27:0.67 28:0.93 29:0.71 30:0.58 31:0.89 34:0.84 36:0.69 37:0.79 39:0.47 43:0.69 45:0.77 46:1.00 66:0.80 69:0.17 70:0.44 71:0.89 74:0.56 78:0.75 79:0.88 81:0.37 83:0.60 86:0.82 91:0.56 96:0.79 98:0.86 99:0.83 100:0.79 101:0.52 104:0.54 107:0.94 108:0.14 110:0.92 111:0.75 114:0.61 120:0.68 122:0.51 123:0.13 125:0.98 126:0.44 127:0.38 129:0.05 135:0.69 137:0.89 139:0.77 140:0.80 146:0.43 147:0.49 149:0.49 150:0.54 151:0.61 152:0.80 153:0.46 154:0.85 155:0.67 159:0.16 160:0.98 161:0.80 162:0.66 164:0.71 165:0.70 167:0.86 169:0.78 172:0.45 173:0.60 176:0.66 177:0.70 179:0.82 181:0.69 186:0.76 187:0.39 190:0.77 191:0.86 192:0.87 196:0.90 201:0.68 202:0.67 208:0.64 212:0.82 215:0.42 216:0.11 220:0.91 222:0.48 232:0.72 235:0.60 241:0.73 242:0.40 243:0.82 247:0.55 248:0.59 253:0.71 254:0.93 255:0.95 256:0.68 259:0.21 260:0.67 261:0.78 265:0.86 266:0.23 267:0.18 268:0.68 271:0.94 276:0.34 284:0.89 285:0.62 289:0.93 290:0.61 297:0.36 300:0.33 +2 1:0.69 6:0.97 7:0.72 8:0.88 9:0.76 11:0.83 12:0.20 17:0.81 18:0.82 20:0.79 27:0.17 28:0.93 29:0.71 30:0.45 32:0.69 34:0.50 36:0.47 37:0.80 39:0.47 43:0.94 45:0.83 46:0.98 66:0.60 69:0.19 70:0.65 71:0.89 74:0.69 77:0.70 78:0.79 81:0.69 83:0.60 85:0.72 86:0.86 91:0.73 96:0.83 98:0.72 99:0.83 100:0.88 101:0.87 104:0.87 107:0.96 108:0.70 110:0.93 111:0.81 114:0.95 120:0.72 122:0.51 123:0.68 124:0.50 125:1.00 126:0.44 127:0.75 129:0.59 133:0.46 135:0.72 139:0.82 140:0.45 145:0.54 146:0.64 147:0.69 149:0.77 150:0.65 151:0.83 152:0.77 153:0.69 154:0.94 155:0.58 159:0.50 160:0.96 161:0.80 162:0.86 164:0.55 165:0.70 167:0.91 169:0.85 172:0.63 173:0.26 176:0.66 177:0.70 179:0.61 181:0.69 186:0.79 187:0.21 189:0.98 190:0.77 192:0.59 195:0.73 201:0.68 202:0.46 204:0.85 208:0.54 212:0.30 215:0.96 216:0.34 222:0.48 230:0.75 232:0.89 233:0.76 235:0.05 238:0.96 241:0.77 242:0.17 243:0.36 245:0.78 247:0.82 248:0.75 253:0.81 255:0.74 256:0.45 259:0.21 260:0.69 261:0.79 265:0.73 266:0.54 267:0.28 268:0.55 271:0.94 276:0.60 282:0.77 285:0.56 289:0.93 290:0.95 297:0.36 300:0.55 +2 1:0.69 6:0.83 7:0.72 8:0.70 9:0.76 11:0.83 12:0.20 17:0.61 18:0.84 20:0.79 21:0.40 27:0.21 28:0.93 29:0.71 30:0.71 34:0.66 36:0.62 37:0.74 39:0.47 43:0.72 45:1.00 46:1.00 66:0.43 69:0.90 70:0.62 71:0.89 74:0.97 78:0.75 81:0.37 83:0.60 85:0.90 86:0.91 91:0.22 96:0.84 98:0.75 99:0.83 100:0.78 101:0.87 104:0.84 107:0.98 108:0.88 110:0.95 111:0.75 114:0.61 120:0.75 122:0.51 123:0.88 124:0.85 125:0.99 126:0.44 127:0.78 129:0.59 133:0.87 135:0.18 139:0.88 140:0.45 146:0.81 147:0.87 149:0.98 150:0.53 151:0.81 152:0.85 153:0.48 154:0.54 155:0.58 159:0.92 160:0.98 161:0.80 162:0.76 164:0.73 165:0.70 167:0.69 169:0.76 172:0.98 173:0.66 176:0.66 177:0.38 179:0.10 181:0.69 186:0.76 187:0.39 189:0.84 190:0.77 192:0.59 201:0.68 202:0.67 204:0.85 208:0.54 212:0.76 215:0.42 216:0.95 220:0.91 222:0.48 230:0.74 232:0.88 233:0.73 235:0.52 238:0.90 241:0.37 242:0.54 243:0.19 245:0.99 247:0.86 248:0.77 253:0.87 255:0.74 256:0.68 257:0.65 259:0.21 260:0.70 261:0.77 265:0.75 266:0.71 267:0.44 268:0.71 271:0.94 276:0.99 285:0.56 289:0.93 290:0.61 297:0.36 300:0.84 +1 1:0.74 6:0.84 8:0.63 9:0.80 11:0.83 12:0.20 17:0.24 18:0.74 20:0.87 21:0.59 27:0.45 28:0.93 29:0.71 30:0.33 31:0.86 34:0.66 36:0.20 37:0.50 39:0.47 41:0.82 43:0.82 45:0.81 46:0.96 60:0.87 64:0.77 66:0.46 69:0.60 70:0.95 71:0.89 74:0.71 78:0.74 79:0.86 81:0.46 83:0.91 85:0.72 86:0.85 91:0.25 96:0.68 98:0.43 100:0.77 101:0.87 107:0.96 108:0.46 110:0.93 111:0.74 114:0.58 120:0.54 122:0.41 123:0.44 124:0.76 125:0.98 126:0.54 127:0.40 129:0.59 133:0.73 135:0.83 137:0.86 139:0.87 140:0.45 145:0.52 146:0.63 147:0.68 149:0.71 150:0.70 151:0.48 152:0.82 153:0.48 154:0.77 155:0.67 158:0.91 159:0.60 160:0.96 161:0.80 162:0.86 163:0.90 164:0.57 165:0.93 167:0.73 169:0.76 172:0.57 173:0.50 176:0.73 177:0.70 179:0.52 181:0.69 186:0.75 187:0.68 189:0.84 192:0.87 196:0.88 201:0.93 202:0.36 208:0.64 212:0.30 215:0.39 216:0.77 219:0.95 220:0.93 222:0.48 235:0.80 238:0.90 241:0.54 242:0.17 243:0.59 245:0.68 247:0.74 248:0.48 253:0.44 255:0.95 256:0.45 259:0.21 260:0.54 261:0.77 265:0.45 266:0.71 267:0.69 268:0.46 271:0.94 276:0.59 279:0.86 283:0.78 284:0.89 285:0.62 289:0.93 290:0.58 292:0.91 297:0.36 300:0.84 +3 1:0.69 6:0.96 7:0.72 8:0.95 9:0.80 11:0.64 12:0.20 17:0.34 18:0.66 20:0.93 21:0.40 27:0.08 28:0.93 29:0.71 30:0.62 31:0.95 32:0.64 34:0.28 36:0.45 37:0.90 39:0.47 41:0.82 43:0.59 45:0.85 46:0.98 66:0.49 69:0.60 70:0.56 71:0.89 74:0.65 78:0.76 79:0.96 81:0.37 83:0.91 86:0.65 91:0.98 96:0.99 97:0.99 98:0.54 99:0.83 100:0.82 101:0.52 104:0.58 107:0.96 108:0.75 110:0.94 111:0.78 114:0.61 120:0.98 122:0.51 123:0.73 124:0.66 125:0.99 126:0.44 127:0.66 129:0.81 133:0.67 135:0.44 137:0.95 138:0.99 139:0.63 140:0.99 145:0.59 146:0.38 147:0.44 149:0.65 150:0.54 151:0.87 152:0.98 153:0.98 154:0.48 155:0.67 158:0.79 159:0.44 160:1.00 161:0.80 162:0.76 163:0.81 164:0.96 165:0.70 167:0.99 169:0.89 172:0.48 173:0.66 175:0.75 176:0.66 177:0.38 179:0.71 181:0.69 186:0.77 189:0.96 191:0.94 192:0.87 195:0.68 196:0.89 201:0.68 202:0.94 204:0.85 208:0.64 212:0.27 215:0.42 216:0.64 222:0.48 230:0.74 232:0.75 233:0.73 235:0.68 238:0.97 241:0.95 242:0.58 243:0.20 244:0.61 245:0.64 247:0.39 248:0.77 253:0.98 255:0.95 256:0.96 257:0.65 260:0.97 261:0.91 265:0.56 266:0.75 267:0.97 268:0.96 271:0.94 276:0.50 277:0.69 279:0.82 283:0.82 284:0.93 285:0.62 289:0.93 290:0.61 297:0.36 300:0.55 +2 8:0.87 9:0.76 11:0.83 12:0.20 17:0.85 18:0.81 21:0.05 27:0.01 28:0.93 29:0.71 30:0.44 31:0.91 34:0.91 36:0.59 37:0.98 39:0.47 43:0.59 45:0.88 46:0.97 64:0.77 66:0.60 69:0.90 70:0.85 71:0.89 74:0.55 79:0.92 81:0.41 83:0.60 85:0.72 86:0.85 91:0.51 96:0.68 98:0.50 101:0.87 104:0.58 107:0.96 108:0.85 110:0.94 120:0.54 122:0.51 123:0.84 124:0.58 125:0.96 126:0.26 127:0.45 129:0.59 133:0.61 135:0.34 137:0.91 139:0.82 140:0.87 146:0.34 147:0.23 149:0.66 150:0.35 151:0.48 153:0.83 154:0.49 155:0.58 159:0.60 160:0.96 161:0.80 162:0.56 164:0.54 167:0.89 172:0.74 173:0.91 177:0.05 179:0.41 181:0.69 187:0.21 190:0.89 191:0.93 192:0.59 196:0.92 202:0.66 208:0.54 212:0.71 216:0.82 219:0.89 220:0.62 222:0.48 232:0.76 235:0.05 241:0.75 242:0.24 243:0.13 245:0.79 247:0.79 248:0.48 253:0.38 255:0.74 256:0.58 257:0.84 259:0.21 260:0.54 265:0.52 266:0.71 267:0.69 268:0.64 271:0.94 276:0.66 284:0.87 285:0.62 289:0.93 292:0.91 300:0.94 +1 1:0.69 6:0.96 8:0.96 9:0.76 12:0.20 17:0.30 20:0.89 21:0.05 27:0.08 28:0.93 29:0.71 31:0.87 34:0.36 36:0.76 37:0.95 39:0.47 46:0.88 71:0.89 74:0.39 78:0.77 79:0.87 81:0.60 83:0.60 91:0.88 96:0.95 97:0.97 99:0.83 100:0.84 104:0.75 107:0.88 110:0.88 111:0.78 114:0.85 120:0.91 125:0.98 126:0.44 135:0.26 137:0.87 138:0.98 139:0.64 140:0.80 150:0.60 151:0.96 152:0.94 153:0.95 155:0.58 158:0.78 160:0.99 161:0.80 162:0.61 164:0.89 165:0.70 167:0.99 169:0.80 175:0.97 176:0.66 178:0.42 181:0.69 186:0.77 189:0.98 190:0.77 191:0.97 192:0.59 196:0.87 201:0.68 202:0.88 208:0.54 215:0.78 216:0.47 219:0.89 222:0.48 232:0.79 235:0.12 238:0.97 241:0.92 244:0.93 248:0.93 253:0.91 255:0.74 256:0.88 257:0.93 259:0.21 260:0.89 261:0.83 267:0.17 268:0.89 271:0.94 277:0.95 279:0.82 283:0.78 284:0.86 285:0.62 289:0.93 290:0.85 292:0.91 297:0.36 +1 1:0.69 6:0.96 7:0.72 8:0.95 9:0.80 11:0.64 12:0.20 17:0.34 18:0.66 20:0.93 21:0.40 27:0.08 28:0.93 29:0.71 30:0.62 31:0.95 32:0.64 34:0.28 36:0.45 37:0.90 39:0.47 41:0.82 43:0.59 45:0.85 46:0.98 66:0.49 69:0.60 70:0.56 71:0.89 74:0.65 78:0.76 79:0.96 81:0.37 83:0.91 86:0.65 91:0.98 96:0.99 97:0.99 98:0.54 99:0.83 100:0.82 101:0.52 104:0.58 107:0.96 108:0.75 110:0.94 111:0.78 114:0.61 120:0.98 122:0.51 123:0.73 124:0.66 125:0.99 126:0.44 127:0.66 129:0.81 133:0.67 135:0.44 137:0.95 138:0.99 139:0.63 140:0.99 145:0.59 146:0.38 147:0.44 149:0.65 150:0.54 151:0.87 152:0.98 153:0.98 154:0.48 155:0.67 158:0.79 159:0.44 160:1.00 161:0.80 162:0.76 163:0.81 164:0.96 165:0.70 167:0.99 169:0.89 172:0.48 173:0.66 175:0.75 176:0.66 177:0.38 179:0.71 181:0.69 186:0.77 189:0.96 191:0.94 192:0.87 195:0.68 196:0.89 201:0.68 202:0.94 204:0.85 208:0.64 212:0.27 215:0.42 216:0.64 222:0.48 230:0.74 232:0.75 233:0.73 235:0.68 238:0.97 241:0.95 242:0.58 243:0.20 244:0.61 245:0.64 247:0.39 248:0.77 253:0.98 255:0.95 256:0.96 257:0.65 260:0.97 261:0.91 265:0.56 266:0.75 267:0.97 268:0.96 271:0.94 276:0.50 277:0.69 279:0.82 283:0.82 284:0.93 285:0.62 289:0.93 290:0.61 297:0.36 300:0.55 +1 1:0.69 11:0.83 12:0.20 17:0.34 18:0.85 21:0.71 27:0.45 28:0.93 29:0.71 30:0.60 34:0.63 36:0.17 37:0.50 39:0.47 43:0.66 45:0.96 46:0.98 66:0.26 69:0.61 70:0.70 71:0.89 74:0.77 81:0.32 83:0.60 85:0.72 86:0.86 91:0.17 98:0.47 99:0.83 101:0.87 107:0.97 108:0.46 110:0.95 114:0.54 122:0.51 123:0.45 124:0.93 125:0.88 126:0.44 127:0.72 129:0.81 133:0.93 135:0.49 139:0.82 145:0.49 146:0.88 147:0.90 149:0.88 150:0.51 154:0.63 155:0.58 159:0.88 160:0.88 161:0.80 163:0.81 165:0.70 167:0.73 172:0.79 173:0.31 176:0.66 177:0.70 178:0.55 179:0.23 181:0.69 187:0.68 192:0.59 201:0.68 208:0.54 212:0.29 215:0.37 216:0.77 222:0.48 235:0.88 241:0.52 242:0.42 243:0.45 245:0.87 247:0.86 253:0.44 259:0.21 265:0.49 266:0.89 267:0.89 271:0.94 276:0.88 289:0.92 290:0.54 297:0.36 300:0.70 +1 1:0.69 9:0.76 11:0.83 12:0.20 17:0.38 18:0.85 21:0.54 27:0.08 28:0.93 29:0.71 30:0.76 34:0.76 36:0.47 37:0.90 39:0.47 43:0.75 45:0.92 46:0.99 66:0.18 69:0.81 70:0.52 71:0.89 74:0.83 81:0.34 83:0.60 85:0.90 86:0.93 91:0.38 96:0.82 98:0.57 99:0.83 101:0.87 104:0.58 107:0.97 108:0.65 110:0.95 114:0.58 120:0.71 122:0.51 123:0.79 124:0.64 125:0.99 126:0.44 127:0.64 129:0.05 133:0.66 135:0.82 139:0.90 140:0.80 146:0.67 147:0.44 149:0.93 150:0.52 151:0.81 153:0.48 154:0.66 155:0.58 159:0.56 160:0.97 161:0.80 162:0.67 164:0.64 165:0.70 167:0.83 172:0.79 173:0.81 176:0.66 177:0.38 178:0.42 179:0.34 181:0.69 187:0.98 190:0.77 192:0.59 201:0.68 202:0.59 208:0.54 212:0.60 215:0.39 216:0.64 222:0.48 232:0.76 235:0.80 241:0.71 242:0.31 243:0.10 245:0.87 247:0.76 248:0.74 253:0.81 255:0.74 256:0.58 257:0.65 260:0.68 265:0.58 266:0.54 267:0.99 268:0.59 271:0.94 276:0.79 277:0.69 285:0.56 289:0.93 290:0.58 297:0.36 300:0.70 +2 1:0.69 6:0.88 7:0.72 8:0.87 9:0.80 11:0.64 12:0.20 17:0.53 18:0.84 20:0.86 21:0.40 27:0.43 28:0.93 29:0.71 30:0.21 31:0.89 32:0.80 34:0.49 36:0.85 37:0.79 39:0.47 43:0.67 44:0.93 45:0.83 46:0.99 66:0.90 69:0.17 70:0.68 71:0.89 74:0.79 77:0.70 78:0.75 79:0.88 81:0.37 83:0.60 86:0.90 91:0.83 96:0.93 97:0.89 98:0.99 99:0.83 100:0.80 101:0.52 104:0.58 107:0.96 108:0.14 110:0.93 111:0.76 114:0.61 120:0.89 122:0.51 123:0.13 125:0.98 126:0.44 127:0.91 129:0.05 135:0.32 137:0.89 138:0.98 139:0.90 140:0.97 145:0.70 146:0.78 147:0.82 149:0.57 150:0.54 151:0.55 152:0.81 153:0.88 154:0.85 155:0.67 158:0.78 159:0.34 160:0.99 161:0.80 162:0.70 164:0.89 165:0.70 167:0.95 169:0.78 172:0.71 173:0.37 176:0.66 177:0.70 179:0.65 181:0.69 186:0.76 189:0.90 190:0.77 191:0.90 192:0.87 195:0.60 196:0.92 201:0.68 202:0.87 204:0.85 208:0.64 212:0.72 215:0.42 216:0.49 220:0.91 222:0.48 230:0.75 232:0.76 233:0.76 235:0.60 238:0.94 241:0.80 242:0.18 243:0.90 247:0.82 248:0.57 253:0.93 254:0.84 255:0.95 256:0.89 259:0.21 260:0.86 261:0.78 265:0.99 266:0.54 267:0.96 268:0.89 271:0.94 276:0.60 279:0.82 282:0.88 283:0.78 284:0.94 285:0.62 289:0.93 290:0.61 297:0.36 300:0.43 +4 6:0.99 7:0.72 8:0.97 9:0.80 11:0.64 12:0.20 17:0.06 18:0.64 20:0.99 21:0.78 27:0.01 28:0.93 29:0.71 30:0.45 31:0.99 32:0.69 34:0.53 36:0.64 37:0.98 39:0.47 41:0.97 43:0.25 45:0.87 46:0.97 66:0.54 69:0.93 70:0.89 71:0.89 74:0.63 77:0.70 78:0.90 79:0.99 83:0.91 86:0.67 91:0.99 96:1.00 97:0.99 98:0.42 100:0.99 101:0.52 104:0.58 107:0.96 108:0.86 110:0.94 111:0.99 120:1.00 122:0.51 123:0.86 124:0.69 125:0.98 127:0.51 129:0.05 133:0.73 135:0.32 137:0.99 138:0.99 139:0.65 140:0.99 145:0.74 146:0.60 147:0.23 149:0.56 151:0.92 152:0.96 153:1.00 154:0.21 155:0.67 158:0.79 159:0.62 160:1.00 161:0.80 162:0.68 164:0.99 167:1.00 169:0.98 172:0.61 173:0.98 177:0.05 179:0.56 181:0.69 186:0.90 189:0.99 191:0.95 192:0.87 195:0.84 196:0.91 202:0.99 204:0.85 208:0.64 212:0.52 216:0.82 222:0.48 230:0.75 232:0.75 233:0.76 235:0.88 238:0.99 241:1.00 242:0.42 243:0.16 244:0.61 245:0.66 247:0.60 248:0.94 253:1.00 255:0.95 256:0.99 257:0.84 259:0.21 260:0.99 261:0.99 265:0.44 266:0.80 267:0.95 268:0.99 271:0.94 276:0.56 279:0.82 282:0.77 283:0.82 284:0.99 285:0.62 289:0.93 300:0.84 +1 1:0.69 8:1.00 11:0.83 12:0.20 17:0.69 18:0.92 21:0.13 27:0.08 28:0.93 29:0.71 30:0.43 34:0.95 36:0.70 37:0.95 39:0.47 43:0.69 45:0.95 46:0.98 66:0.09 69:0.48 70:0.92 71:0.89 74:0.70 81:0.52 83:0.60 85:0.72 86:0.91 91:0.58 98:0.39 99:0.83 101:0.87 104:0.75 107:0.97 108:0.94 110:0.95 114:0.75 122:0.86 123:0.84 124:0.88 125:0.88 126:0.44 127:0.95 129:0.93 133:0.87 135:0.44 139:0.87 146:0.62 147:0.68 149:0.86 150:0.58 154:0.55 155:0.58 159:0.86 160:0.88 161:0.80 165:0.70 167:0.78 172:0.71 173:0.23 176:0.66 177:0.70 178:0.55 179:0.26 181:0.69 187:0.21 191:0.84 192:0.59 201:0.68 202:0.50 208:0.54 212:0.40 215:0.61 216:0.47 222:0.48 232:0.80 235:0.22 241:0.66 242:0.36 243:0.16 245:0.91 247:0.79 253:0.55 259:0.21 265:0.34 266:0.94 267:0.73 271:0.94 276:0.87 289:0.93 290:0.75 297:0.36 300:0.94 +1 7:0.81 8:0.79 9:0.80 11:0.83 12:0.20 17:0.96 18:0.97 21:0.13 22:0.93 27:0.60 29:0.53 30:0.15 31:0.96 32:0.69 34:0.67 36:0.39 37:0.80 39:0.99 41:0.82 43:0.88 44:0.90 45:0.73 47:0.93 48:1.00 55:0.42 64:0.77 66:0.96 69:0.12 70:0.84 71:0.82 74:0.68 78:0.97 79:0.97 81:0.67 83:0.91 85:0.72 86:0.99 89:0.99 91:0.58 96:0.78 98:0.98 101:0.87 108:0.10 111:0.94 120:0.73 121:0.90 122:0.83 123:0.10 126:0.44 127:0.80 129:0.05 135:0.66 137:0.96 139:0.99 140:0.80 141:0.97 145:0.61 146:0.90 147:0.92 149:0.78 150:0.46 151:0.78 153:0.77 154:0.87 155:0.67 159:0.50 162:0.85 164:0.71 169:0.90 172:0.90 173:0.22 177:0.70 179:0.33 187:0.39 190:0.89 191:0.81 192:0.87 195:0.68 196:0.98 201:0.68 202:0.68 204:0.89 208:0.64 212:0.85 215:0.61 216:0.21 219:0.92 220:0.62 222:0.21 223:0.94 225:0.99 230:0.86 233:0.73 234:1.00 235:0.42 240:0.99 241:0.44 242:0.19 243:0.96 247:0.84 248:0.71 253:0.72 255:0.95 256:0.71 259:0.21 260:0.72 262:0.93 265:0.98 266:0.27 267:0.85 268:0.75 271:0.86 274:0.99 276:0.82 281:0.89 283:0.78 284:0.95 285:0.62 292:0.95 297:0.36 300:0.70 +2 7:0.72 8:0.89 9:0.80 12:0.20 17:0.85 18:0.69 21:0.47 27:0.64 29:0.53 30:0.21 31:0.97 32:0.69 34:0.53 36:0.94 37:0.84 39:0.99 41:0.82 43:0.13 44:0.90 48:0.97 55:0.52 60:0.87 64:0.77 66:0.30 69:0.67 70:0.77 71:0.82 74:0.47 78:0.91 79:0.97 81:0.38 83:0.91 86:0.70 89:0.97 91:0.87 96:0.79 97:0.89 98:0.61 104:0.80 108:0.76 111:0.89 120:0.87 123:0.49 124:0.60 126:0.44 127:0.57 129:0.05 133:0.39 135:0.49 137:0.97 139:0.85 140:0.98 141:0.97 145:0.70 146:0.61 147:0.67 149:0.20 150:0.31 151:0.77 153:0.86 154:0.46 155:0.67 158:0.91 159:0.44 162:0.73 164:0.91 169:0.88 172:0.37 173:0.73 175:0.75 177:0.38 178:0.42 179:0.77 191:0.81 192:0.87 195:0.68 196:0.96 201:0.68 202:0.91 208:0.64 212:0.22 215:0.41 216:0.39 219:0.95 220:0.87 222:0.21 223:0.94 225:0.99 230:0.74 233:0.76 234:0.98 235:0.80 240:0.99 241:0.82 242:0.74 243:0.48 244:0.61 245:0.67 247:0.47 248:0.70 253:0.68 254:0.88 255:0.95 256:0.92 257:0.65 259:0.21 260:0.84 265:0.52 266:0.71 267:0.69 268:0.93 271:0.86 274:1.00 276:0.44 277:0.69 279:0.86 281:0.91 283:0.78 284:0.99 285:0.62 292:0.91 297:0.36 300:0.55 +1 1:0.74 6:0.91 7:0.81 8:0.92 9:0.80 11:0.83 12:0.20 17:0.84 18:0.84 20:0.80 21:0.47 27:0.41 29:0.53 30:0.62 31:0.91 32:0.80 34:0.71 36:0.50 37:0.80 39:0.99 41:0.90 43:0.76 44:0.93 45:0.95 48:0.97 60:0.87 64:0.77 66:0.89 69:0.78 70:0.48 71:0.82 74:0.92 77:0.70 78:0.91 79:0.91 81:0.53 83:0.91 85:0.72 86:0.90 89:0.98 91:0.62 96:0.76 98:0.84 100:0.91 101:0.87 104:0.74 108:0.61 111:0.90 114:0.60 120:0.73 121:0.97 122:0.51 123:0.59 124:0.37 126:0.54 127:0.78 129:0.05 133:0.37 135:0.56 137:0.91 139:0.95 140:0.80 141:0.97 145:0.92 146:0.83 147:0.25 149:0.92 150:0.74 151:0.72 152:0.90 153:0.88 154:0.68 155:0.67 158:0.91 159:0.50 162:0.77 164:0.77 165:0.93 169:0.87 172:0.92 173:0.83 176:0.73 177:0.05 179:0.28 186:0.91 187:0.39 191:0.70 192:0.87 195:0.71 196:0.95 201:0.93 202:0.70 204:0.89 208:0.64 212:0.90 215:0.41 216:0.28 219:0.95 220:0.62 222:0.21 223:0.94 225:0.99 230:0.87 233:0.76 234:0.98 235:0.84 240:0.99 241:0.74 242:0.44 243:0.09 245:0.80 247:0.82 248:0.68 253:0.79 255:0.95 256:0.71 257:0.84 259:0.21 260:0.69 261:0.90 265:0.84 266:0.54 267:0.19 268:0.74 271:0.86 274:0.98 276:0.84 279:0.86 281:0.91 282:0.88 283:0.78 284:0.95 285:0.62 290:0.60 292:0.91 297:0.36 299:0.88 300:0.70 +2 7:0.81 8:0.83 9:0.80 12:0.20 17:0.62 18:0.91 20:0.90 21:0.76 27:0.25 29:0.53 30:0.66 31:0.91 32:0.68 34:0.95 36:0.52 37:0.91 39:0.99 43:0.31 44:0.90 48:0.97 55:0.45 64:0.77 66:0.83 69:0.88 70:0.54 71:0.82 74:0.47 78:0.84 79:0.91 81:0.25 83:0.60 86:0.88 89:0.99 91:0.40 96:0.72 98:0.73 100:0.81 104:0.78 108:0.76 111:0.82 120:0.67 121:0.90 123:0.75 124:0.39 126:0.44 127:0.83 129:0.05 133:0.62 135:0.47 137:0.91 139:0.91 140:0.94 141:0.97 145:0.63 146:0.80 147:0.25 149:0.39 150:0.23 151:0.59 152:0.92 153:0.72 154:0.23 155:0.67 159:0.59 162:0.66 164:0.65 169:0.81 172:0.87 173:0.92 175:0.75 177:0.05 179:0.37 186:0.84 187:0.87 190:0.89 191:0.90 192:0.87 195:0.80 196:0.94 202:0.66 204:0.89 208:0.64 212:0.85 215:0.36 216:0.45 219:0.89 220:0.62 222:0.21 223:0.94 225:0.98 230:0.86 233:0.76 234:0.98 235:0.97 240:0.99 241:0.61 242:0.60 243:0.10 244:0.61 245:0.72 247:0.67 248:0.58 253:0.48 255:0.95 256:0.68 257:0.84 259:0.21 260:0.64 261:0.86 265:0.73 266:0.63 267:0.18 268:0.68 271:0.86 274:0.98 276:0.77 277:0.69 281:0.89 284:0.91 285:0.62 292:0.91 297:0.36 300:0.70 +1 7:0.72 8:0.95 9:0.80 12:0.20 17:0.89 18:0.88 20:0.86 21:0.54 22:0.93 27:0.12 29:0.53 30:0.11 31:0.97 32:0.68 34:0.66 36:0.62 37:0.91 39:0.99 41:0.82 43:0.64 44:0.90 47:0.93 48:0.99 55:0.45 64:0.77 66:0.36 69:0.67 70:0.84 71:0.82 78:0.88 79:0.97 81:0.33 83:0.91 86:0.88 89:0.99 91:0.73 96:0.79 98:0.48 100:0.90 108:0.70 111:0.87 120:0.68 122:0.86 123:0.68 124:0.60 126:0.44 127:0.39 129:0.59 133:0.47 135:0.66 137:0.97 139:0.90 140:0.96 141:0.98 145:0.61 146:0.23 147:0.29 149:0.34 150:0.27 151:0.63 152:0.81 153:0.86 154:0.54 155:0.67 159:0.32 162:0.86 164:0.79 169:0.87 172:0.32 173:0.79 175:0.75 177:0.38 179:0.77 186:0.88 187:0.21 191:0.79 192:0.87 195:0.61 196:0.97 201:0.68 202:0.78 208:0.64 212:0.28 215:0.39 216:0.60 219:0.89 222:0.21 223:0.94 225:0.99 230:0.74 233:0.73 234:0.99 235:0.74 240:0.99 241:0.75 242:0.45 243:0.48 244:0.61 245:0.62 247:0.39 248:0.74 253:0.68 255:0.95 256:0.89 257:0.65 259:0.21 260:0.69 261:0.86 262:0.93 265:0.49 266:0.54 267:0.36 268:0.85 271:0.86 274:1.00 276:0.32 277:0.69 281:0.89 284:0.98 285:0.62 292:0.95 297:0.36 300:0.55 +1 7:0.66 8:0.95 9:0.76 11:0.83 12:0.20 17:0.77 18:0.88 21:0.64 22:0.93 27:0.47 29:0.53 30:0.29 31:0.93 32:0.68 34:0.79 36:0.33 37:0.74 39:0.99 43:0.71 44:0.90 45:0.66 47:0.93 48:0.91 55:0.45 60:0.87 64:0.77 66:0.47 69:0.82 70:0.86 71:0.82 74:0.59 79:0.95 81:0.30 83:0.91 85:0.72 86:0.91 89:0.99 91:0.70 96:0.80 97:0.89 98:0.41 101:0.87 108:0.66 120:0.73 122:0.86 123:0.64 124:0.87 126:0.44 127:0.66 129:0.05 133:0.88 135:0.18 137:0.93 139:0.93 140:0.80 141:0.97 145:0.61 146:0.66 147:0.46 149:0.52 150:0.25 151:0.81 153:0.48 154:0.61 155:0.67 158:0.92 159:0.73 162:0.84 164:0.71 172:0.63 173:0.73 177:0.05 179:0.52 187:0.39 190:0.77 192:0.87 195:0.88 196:0.96 201:0.68 202:0.71 208:0.64 212:0.49 215:0.38 216:0.71 219:0.95 220:0.74 222:0.21 223:0.94 225:0.99 230:0.69 233:0.76 234:0.99 235:0.84 240:0.99 241:0.47 242:0.39 243:0.19 245:0.65 247:0.82 248:0.75 253:0.73 255:0.79 256:0.75 257:0.84 259:0.21 260:0.69 262:0.93 265:0.43 266:0.80 267:0.17 268:0.78 271:0.86 274:0.99 276:0.65 279:0.86 281:0.89 283:0.82 284:0.95 285:0.62 292:0.95 297:0.36 300:0.70 +3 7:0.81 8:0.97 12:0.20 17:0.49 18:0.74 21:0.76 27:0.49 29:0.53 30:0.91 31:0.91 34:0.44 36:0.67 37:0.91 39:0.99 43:0.11 48:0.99 55:0.92 64:0.77 66:0.13 69:0.84 70:0.19 71:0.82 74:0.47 79:0.92 81:0.23 86:0.70 89:0.98 91:0.79 98:0.34 108:0.60 120:0.64 121:0.90 122:0.43 123:0.68 124:0.82 126:0.26 127:0.40 129:0.05 133:0.74 135:0.18 137:0.91 139:0.78 140:0.94 141:0.97 146:0.39 147:0.29 149:0.15 150:0.23 151:0.65 153:0.48 154:0.09 155:0.67 159:0.42 162:0.53 163:0.94 164:0.63 172:0.10 173:0.90 175:0.75 177:0.05 178:0.50 179:0.89 190:0.77 191:0.87 192:0.87 196:0.92 202:0.76 204:0.89 208:0.64 212:0.52 216:0.21 220:0.97 222:0.21 223:0.94 225:0.99 230:0.86 233:0.73 234:0.97 235:0.95 240:0.99 241:0.96 242:0.24 243:0.34 244:0.83 245:0.43 247:0.47 248:0.63 253:0.34 255:0.74 256:0.71 257:0.84 259:0.21 260:0.63 265:0.19 266:0.27 267:0.44 268:0.72 271:0.86 274:0.98 276:0.32 277:0.69 281:0.91 284:0.93 285:0.56 300:0.18 +1 1:0.74 11:0.64 12:0.20 17:0.59 18:0.75 21:0.22 27:0.45 29:0.53 30:0.11 34:0.87 36:0.17 37:0.50 39:0.99 43:0.12 45:0.66 64:0.77 66:0.47 69:0.69 70:0.54 71:0.82 74:0.43 81:0.57 83:0.60 86:0.79 89:0.97 91:0.12 98:0.15 99:0.83 101:0.52 108:0.53 114:0.71 122:0.86 123:0.50 124:0.52 126:0.54 127:0.46 129:0.05 133:0.37 135:0.83 139:0.75 141:0.91 145:0.47 146:0.25 147:0.31 149:0.08 150:0.72 154:0.76 155:0.67 159:0.68 165:0.70 172:0.21 173:0.58 176:0.73 177:0.70 178:0.55 179:0.93 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.51 216:0.77 222:0.21 223:0.94 225:0.98 234:0.91 235:0.42 240:0.95 241:0.21 242:0.63 243:0.59 245:0.46 247:0.35 253:0.52 254:0.74 259:0.21 265:0.16 266:0.27 267:0.28 271:0.86 274:0.91 276:0.13 290:0.71 297:0.36 300:0.33 +1 1:0.74 7:0.72 8:0.83 12:0.20 17:0.64 18:0.73 20:0.78 21:0.74 27:0.72 29:0.53 30:0.89 31:0.89 32:0.79 34:0.55 36:0.90 37:0.95 39:0.99 43:0.18 44:0.93 48:0.91 55:0.71 64:0.77 66:0.75 69:0.35 70:0.20 71:0.82 74:0.53 77:0.70 78:0.72 79:0.89 81:0.40 83:0.60 86:0.70 89:0.97 91:0.83 98:0.92 99:0.83 100:0.73 104:0.74 108:0.27 111:0.72 114:0.54 120:0.72 122:0.43 123:0.26 126:0.54 127:0.52 129:0.05 135:0.37 137:0.89 139:0.81 140:0.80 141:0.97 145:0.56 146:0.47 147:0.53 149:0.19 150:0.65 151:0.60 152:0.76 153:0.85 154:0.12 155:0.67 159:0.17 162:0.65 164:0.72 165:0.70 169:0.72 172:0.32 173:0.73 175:0.75 176:0.73 177:0.38 179:0.93 186:0.73 190:0.89 192:0.87 195:0.53 196:0.91 201:0.93 202:0.71 204:0.85 208:0.64 212:0.95 215:0.36 216:0.23 220:0.62 222:0.21 223:0.94 225:0.98 230:0.74 233:0.73 234:0.97 235:0.97 240:0.99 241:0.95 242:0.33 243:0.77 244:0.83 247:0.39 248:0.59 253:0.38 255:0.74 256:0.71 257:0.65 259:0.21 260:0.67 261:0.73 265:0.92 266:0.12 267:0.84 268:0.72 271:0.86 274:0.98 276:0.29 277:0.69 281:0.86 282:0.87 284:0.91 285:0.56 290:0.54 297:0.36 300:0.18 +1 6:0.93 7:0.72 8:0.81 11:0.57 12:0.20 17:0.93 18:0.98 20:0.93 21:0.13 27:0.38 29:0.53 30:0.75 31:0.97 32:0.69 34:0.64 36:0.34 37:0.57 39:0.99 43:0.81 44:0.90 48:1.00 55:0.45 64:1.00 66:0.83 69:0.59 70:0.48 71:0.82 74:0.43 78:0.95 79:0.98 81:0.43 85:0.72 86:0.98 89:0.99 91:0.70 98:0.44 100:0.97 101:0.87 108:0.45 111:0.95 120:0.82 123:0.43 124:0.39 126:0.44 127:0.71 129:0.05 133:0.60 135:0.21 137:0.97 139:0.98 140:0.45 141:0.97 145:0.99 146:0.75 147:0.79 149:0.89 150:0.33 151:0.94 152:0.86 153:0.90 154:0.76 159:0.79 162:0.73 164:0.74 169:0.95 172:0.93 173:0.40 177:0.70 179:0.21 186:0.95 189:0.94 190:0.77 192:0.51 195:0.92 196:0.99 201:0.68 202:0.75 212:0.92 215:0.41 216:0.05 219:0.92 222:0.21 223:0.94 225:0.99 230:0.75 233:0.76 234:0.98 235:0.97 238:0.92 240:0.99 241:0.79 242:0.58 243:0.84 245:0.84 247:0.79 248:0.85 253:0.33 255:0.74 256:0.80 259:0.21 260:0.79 261:0.95 265:0.46 266:0.47 267:0.17 268:0.79 271:0.86 274:0.99 276:0.71 281:0.91 283:0.78 284:0.96 285:0.56 292:0.91 297:0.99 300:0.70 +1 7:0.81 8:0.74 12:0.20 17:0.78 18:0.98 21:0.22 22:0.93 27:0.21 29:0.53 30:0.88 32:0.80 34:0.28 36:0.28 37:0.89 39:0.99 43:0.12 44:0.93 47:0.93 55:0.71 64:0.77 66:0.97 69:0.44 70:0.21 71:0.82 74:0.58 77:0.70 81:0.46 86:0.98 89:0.99 91:0.23 98:0.90 104:0.83 108:0.34 121:0.90 122:0.83 123:0.32 126:0.44 127:0.49 129:0.05 135:0.69 139:0.98 141:0.91 145:0.63 146:0.97 147:0.98 149:0.20 150:0.34 154:0.57 155:0.67 159:0.70 172:0.92 173:0.28 175:0.75 177:0.38 178:0.55 179:0.23 187:0.87 191:0.73 192:0.87 195:0.88 201:0.68 204:0.89 208:0.64 212:0.93 215:0.51 216:0.56 219:0.89 222:0.21 223:0.94 225:0.98 230:0.86 233:0.73 234:0.91 235:0.31 240:0.95 241:0.30 242:0.51 243:0.97 244:0.61 247:0.60 253:0.38 254:0.74 257:0.65 259:0.21 262:0.93 265:0.90 266:0.27 267:0.28 271:0.86 274:0.91 276:0.86 277:0.69 281:0.89 282:0.88 292:0.95 297:0.36 299:0.88 300:0.43 +2 7:0.66 8:0.95 9:0.80 11:0.57 12:0.20 17:0.76 18:0.89 21:0.68 27:0.25 29:0.53 30:0.66 31:0.92 32:0.64 34:0.36 36:0.83 37:0.91 39:0.99 41:0.88 43:0.79 55:0.45 66:0.92 69:0.76 70:0.56 71:0.82 74:0.42 79:0.92 81:0.24 83:0.91 85:0.72 86:0.89 89:0.99 91:0.92 96:0.71 98:0.87 101:0.87 104:0.78 108:0.59 120:0.88 123:0.57 124:0.36 126:0.26 127:0.71 129:0.05 133:0.37 135:0.60 137:0.92 139:0.87 140:0.98 141:0.97 145:0.98 146:0.86 147:0.05 149:0.71 150:0.24 151:0.52 153:0.98 154:0.72 155:0.67 159:0.50 162:0.64 164:0.90 172:0.91 173:0.80 177:0.05 179:0.29 190:0.77 191:0.91 192:0.87 195:0.70 196:0.94 202:0.91 208:0.64 212:0.87 215:0.37 216:0.45 219:0.89 220:0.62 222:0.21 223:0.94 225:0.99 230:0.69 233:0.73 234:0.97 235:0.88 240:0.99 241:0.88 242:0.74 243:0.06 245:0.70 247:0.79 248:0.54 253:0.43 255:0.95 256:0.92 257:0.84 259:0.21 260:0.83 265:0.87 266:0.38 267:0.19 268:0.92 271:0.86 274:0.99 276:0.83 283:0.78 284:0.97 285:0.62 292:0.95 297:0.36 300:0.70 +1 8:0.59 11:0.64 12:0.20 17:0.76 18:0.96 21:0.77 27:0.45 29:0.53 30:0.43 34:0.87 36:0.28 37:0.50 39:0.99 43:0.13 44:0.87 45:0.77 55:0.59 64:0.77 66:0.86 69:0.71 70:0.83 71:0.82 74:0.53 81:0.22 86:0.97 89:0.99 91:0.04 98:0.75 101:0.52 108:0.54 122:0.86 123:0.52 124:0.37 126:0.26 127:0.53 129:0.05 133:0.37 135:0.83 139:0.96 141:0.91 145:0.58 146:0.93 147:0.95 149:0.19 150:0.22 154:0.65 159:0.74 172:0.86 173:0.56 177:0.70 178:0.55 179:0.34 187:0.68 195:0.89 212:0.77 216:0.77 222:0.21 223:0.94 225:0.98 234:0.91 235:0.98 240:0.95 241:0.18 242:0.33 243:0.86 245:0.75 247:0.67 253:0.36 254:0.74 259:0.21 265:0.75 266:0.80 267:0.36 271:0.86 274:0.91 276:0.75 300:0.70 +2 1:0.74 7:0.72 8:0.84 9:0.80 11:0.64 12:0.20 17:0.57 18:0.94 20:0.91 21:0.54 27:0.19 29:0.53 30:0.70 31:0.88 32:0.68 34:0.50 36:0.59 37:0.90 39:0.99 43:0.67 44:0.90 45:0.95 55:0.71 64:0.77 66:0.98 69:0.65 70:0.42 71:0.82 74:0.93 78:0.93 79:0.88 81:0.46 83:0.60 86:0.99 89:0.99 91:0.44 96:0.68 97:0.89 98:0.95 99:0.83 100:0.93 101:0.52 104:0.76 108:0.49 111:0.92 114:0.59 120:0.76 122:0.57 123:0.47 126:0.54 127:0.67 129:0.05 135:0.77 137:0.88 139:0.98 140:0.90 141:0.97 145:0.70 146:0.97 147:0.98 149:0.39 150:0.67 151:0.48 152:0.94 153:0.72 154:0.75 155:0.67 158:0.78 159:0.70 162:0.81 164:0.76 165:0.70 169:0.91 172:0.94 173:0.54 176:0.73 177:0.70 179:0.22 186:0.93 187:0.21 190:0.89 191:0.89 192:0.87 195:0.86 196:0.93 201:0.93 202:0.69 204:0.85 208:0.64 212:0.91 215:0.39 216:0.59 220:0.62 222:0.21 223:0.94 225:0.99 230:0.74 233:0.73 234:0.97 235:0.84 240:0.99 241:0.59 242:0.36 243:0.98 247:0.60 248:0.49 253:0.50 254:0.74 255:0.95 256:0.75 259:0.21 260:0.69 261:0.93 265:0.95 266:0.38 267:0.19 268:0.75 271:0.86 274:0.98 276:0.89 279:0.82 283:0.78 284:0.93 285:0.62 290:0.59 297:0.36 300:0.55 +1 1:0.74 7:0.66 8:0.84 9:0.80 11:0.64 12:0.20 17:0.78 18:0.91 20:0.79 21:0.13 22:0.93 27:0.35 29:0.53 30:0.70 31:0.90 32:0.74 34:0.40 36:0.76 37:0.79 39:0.99 43:0.26 44:0.90 45:0.89 47:0.93 48:0.91 55:0.52 64:0.77 66:0.99 69:0.33 70:0.55 71:0.82 74:0.74 77:0.70 78:0.83 79:0.90 81:0.63 83:0.60 86:0.95 89:0.99 91:0.46 96:0.74 98:0.98 99:0.83 100:0.89 101:0.52 104:0.80 106:0.81 108:0.26 111:0.84 114:0.79 120:0.68 123:0.25 126:0.54 127:0.83 129:0.05 135:0.49 137:0.90 139:0.94 140:0.90 141:0.97 145:0.76 146:0.98 147:0.98 149:0.71 150:0.76 151:0.63 152:0.77 153:0.48 154:0.91 155:0.67 159:0.74 162:0.85 164:0.74 165:0.70 169:0.86 172:0.98 173:0.21 176:0.73 177:0.70 179:0.15 186:0.83 187:0.39 190:0.89 191:0.78 192:0.87 195:0.86 196:0.94 201:0.93 202:0.71 206:0.81 208:0.64 212:0.88 215:0.61 216:0.70 220:0.74 222:0.21 223:0.94 225:0.99 230:0.69 233:0.73 234:0.98 235:0.31 240:0.99 241:0.62 242:0.74 243:0.99 247:0.90 248:0.62 253:0.67 254:0.74 255:0.95 256:0.75 259:0.21 260:0.66 261:0.80 262:0.93 265:0.98 266:0.54 267:0.17 268:0.78 271:0.86 274:0.99 276:0.95 281:0.89 282:0.77 283:0.78 284:0.95 285:0.62 290:0.79 297:0.36 300:0.70 +2 1:0.74 12:0.20 17:0.66 18:0.98 21:0.47 22:0.93 27:0.21 29:0.53 30:0.87 31:0.86 34:0.24 36:0.35 37:0.89 39:0.99 43:0.13 47:0.93 55:0.71 64:0.77 66:0.94 69:0.47 70:0.19 71:0.82 79:0.86 81:0.52 83:0.91 86:0.98 89:0.99 91:0.23 98:0.91 104:0.83 108:0.36 114:0.60 120:0.53 122:0.83 123:0.34 124:0.36 126:0.54 127:0.75 129:0.05 133:0.39 135:0.68 137:0.86 139:0.98 140:0.45 141:0.97 146:0.97 147:0.98 149:0.18 150:0.73 151:0.47 153:0.48 154:0.59 155:0.67 159:0.76 162:0.52 164:0.54 165:0.93 172:0.94 173:0.30 175:0.75 176:0.73 177:0.38 179:0.23 187:0.87 190:0.77 192:0.87 196:0.88 201:0.93 202:0.58 208:0.64 212:0.94 215:0.41 216:0.56 219:0.89 220:0.96 222:0.21 223:0.94 225:0.98 234:0.97 235:0.80 240:0.99 241:0.69 242:0.57 243:0.94 244:0.61 245:0.74 247:0.47 248:0.47 253:0.44 254:0.74 255:0.74 256:0.45 257:0.65 259:0.21 260:0.53 262:0.93 265:0.91 266:0.23 267:0.28 268:0.46 271:0.86 274:0.97 276:0.88 277:0.69 284:0.87 285:0.56 290:0.60 292:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.72 11:0.64 12:0.20 17:0.81 18:1.00 21:0.47 27:0.62 29:0.53 30:0.94 32:0.68 34:0.49 36:0.42 37:0.66 39:0.99 43:0.13 44:0.90 45:0.81 48:0.99 55:0.52 64:0.77 66:0.99 69:0.35 70:0.19 71:0.82 74:0.71 81:0.48 83:0.91 86:1.00 89:1.00 91:0.37 98:0.99 101:0.52 108:0.27 114:0.60 122:0.86 123:0.26 126:0.54 127:0.86 129:0.05 135:0.60 139:1.00 141:0.91 145:0.93 146:0.97 147:0.98 149:0.70 150:0.72 154:0.88 155:0.67 159:0.73 165:0.93 172:0.99 173:0.24 176:0.73 177:0.70 178:0.55 179:0.13 187:0.21 192:0.87 195:0.85 201:0.93 208:0.64 212:0.95 215:0.41 216:0.05 222:0.21 223:0.94 225:0.98 230:0.74 233:0.73 234:0.91 235:0.68 240:0.95 241:0.02 242:0.80 243:0.99 247:0.67 253:0.46 254:0.80 259:0.21 265:0.99 266:0.12 267:0.18 271:0.86 274:0.91 276:0.96 281:0.89 290:0.60 297:0.36 300:0.33 +2 1:0.74 6:0.91 7:0.81 8:0.91 9:0.80 11:0.83 12:0.20 17:0.77 18:0.84 20:0.93 21:0.47 27:0.41 29:0.53 30:0.62 31:0.96 32:0.80 34:0.31 36:0.45 37:0.80 39:0.99 41:0.92 43:0.76 44:0.93 45:0.95 48:0.97 64:0.77 66:0.89 69:0.78 70:0.48 71:0.82 74:0.90 77:0.70 78:0.90 79:0.96 81:0.53 83:0.91 85:0.72 86:0.90 89:0.98 91:0.86 96:0.85 98:0.86 100:0.88 101:0.87 104:0.74 108:0.61 111:0.88 114:0.60 120:0.87 121:0.97 122:0.51 123:0.59 124:0.37 126:0.54 127:0.92 129:0.05 133:0.37 135:0.39 137:0.96 139:0.93 140:0.45 141:0.97 145:0.92 146:0.83 147:0.25 149:0.92 150:0.74 151:0.90 152:0.90 153:0.96 154:0.68 155:0.67 159:0.50 162:0.79 164:0.86 165:0.93 169:0.86 172:0.92 173:0.83 176:0.73 177:0.05 179:0.30 186:0.90 189:0.92 191:0.75 192:0.87 195:0.70 196:0.97 201:0.93 202:0.85 204:0.89 208:0.64 212:0.90 215:0.41 216:0.28 219:0.89 220:0.62 222:0.21 223:0.94 225:0.99 230:0.87 233:0.76 234:0.98 235:0.84 238:0.85 240:0.99 241:0.95 242:0.44 243:0.09 245:0.80 247:0.82 248:0.83 253:0.88 255:0.95 256:0.87 257:0.84 259:0.21 260:0.83 261:0.90 265:0.86 266:0.54 267:0.19 268:0.89 271:0.86 274:0.99 276:0.84 281:0.91 282:0.88 283:0.78 284:0.97 285:0.62 290:0.60 292:0.91 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.66 8:0.85 9:0.80 11:0.64 12:0.20 17:0.85 18:0.92 21:0.13 22:0.93 27:0.35 29:0.53 30:0.70 31:0.89 32:0.74 34:0.39 36:0.75 37:0.79 39:0.99 43:0.26 44:0.90 45:0.92 47:0.93 48:0.91 55:0.52 64:0.77 66:0.35 69:0.33 70:0.56 71:0.82 74:0.81 77:0.70 79:0.89 81:0.63 83:0.60 86:0.97 89:0.99 91:0.49 96:0.72 98:0.73 99:0.83 101:0.52 104:0.80 106:0.81 108:0.26 114:0.79 120:0.64 123:0.82 124:0.60 126:0.54 127:0.81 129:0.59 133:0.46 135:0.39 137:0.89 139:0.96 140:0.92 141:0.97 145:0.76 146:0.90 147:0.92 149:0.71 150:0.76 151:0.59 153:0.90 154:0.91 155:0.67 159:0.75 162:0.85 164:0.74 165:0.70 172:0.94 173:0.21 176:0.73 177:0.70 179:0.15 187:0.39 191:0.70 192:0.87 195:0.87 196:0.94 201:0.93 202:0.69 206:0.81 208:0.64 212:0.88 215:0.61 216:0.70 219:0.89 220:0.74 222:0.21 223:0.94 225:0.99 230:0.69 233:0.73 234:0.98 235:0.31 240:0.99 241:0.61 242:0.73 243:0.57 245:0.99 247:0.90 248:0.58 253:0.65 254:0.74 255:0.95 256:0.73 259:0.21 260:0.63 262:0.93 265:0.72 266:0.47 267:0.17 268:0.76 271:0.86 274:0.99 276:0.95 281:0.89 282:0.77 283:0.78 284:0.95 285:0.62 290:0.79 292:0.91 297:0.36 300:0.70 +3 1:0.74 6:0.91 7:0.72 8:0.80 9:0.80 12:0.20 17:0.83 18:0.95 20:0.97 21:0.22 22:0.93 25:0.88 27:0.32 29:0.53 30:0.13 31:0.88 32:0.69 34:0.36 36:0.35 37:0.74 39:0.99 41:0.82 43:0.70 44:0.90 47:0.93 48:0.97 55:0.52 64:0.77 66:0.35 69:0.19 70:0.88 71:0.82 78:0.93 79:0.88 81:0.58 83:0.91 86:0.94 89:0.99 91:0.29 96:0.71 98:0.70 100:0.94 104:0.58 106:0.81 108:0.67 111:0.92 114:0.71 120:0.57 122:0.86 123:0.65 124:0.71 126:0.54 127:0.77 129:0.81 133:0.67 135:0.30 137:0.88 139:0.94 140:0.45 141:0.97 145:0.70 146:0.26 147:0.32 149:0.54 150:0.76 151:0.56 152:0.89 153:0.48 154:0.60 155:0.67 159:0.67 162:0.86 164:0.57 165:0.93 169:0.92 172:0.68 173:0.17 175:0.75 176:0.73 177:0.38 179:0.41 186:0.93 187:0.68 189:0.93 192:0.87 195:0.81 196:0.92 201:0.93 202:0.36 206:0.81 208:0.64 212:0.69 215:0.51 216:0.81 219:0.92 220:0.82 222:0.21 223:0.94 225:0.98 230:0.74 233:0.73 234:0.99 235:0.42 238:0.90 240:0.99 241:0.44 242:0.45 243:0.24 244:0.61 245:0.87 247:0.67 248:0.55 253:0.53 255:0.95 256:0.45 257:0.65 259:0.21 260:0.58 261:0.93 262:0.93 265:0.71 266:0.84 267:0.17 268:0.46 271:0.86 274:0.97 276:0.75 277:0.69 281:0.89 283:0.82 284:0.89 285:0.62 290:0.71 292:0.95 297:0.36 300:0.70 +2 6:0.93 7:0.66 8:0.89 11:0.57 12:0.20 17:0.81 18:0.99 20:0.98 21:0.40 22:0.93 27:0.53 29:0.53 30:0.53 31:0.93 32:0.68 34:0.21 36:0.70 37:0.70 39:0.99 43:0.95 44:0.90 47:0.93 55:0.45 60:0.98 64:0.77 66:0.79 69:0.25 70:0.52 71:0.82 74:0.71 78:0.92 79:0.95 81:0.36 83:0.91 85:0.85 86:1.00 89:1.00 91:0.17 98:0.82 100:0.93 101:0.87 104:0.74 108:0.75 111:0.92 120:0.64 123:0.74 124:0.41 126:0.44 127:0.66 129:0.05 133:0.43 135:0.32 137:0.93 139:1.00 140:0.87 141:0.97 145:0.44 146:0.65 147:0.70 149:0.91 150:0.29 151:0.65 152:0.90 153:0.48 154:0.95 155:0.67 158:0.91 159:0.75 162:0.86 164:0.68 169:0.91 172:0.99 173:0.16 177:0.70 179:0.11 186:0.92 187:0.39 189:0.94 190:0.89 192:0.87 195:0.87 196:0.97 201:0.68 202:0.63 208:0.64 212:0.94 215:0.42 216:0.59 219:0.95 222:0.21 223:0.94 225:0.99 230:0.69 233:0.76 234:0.99 235:0.52 238:0.89 240:0.99 241:0.59 242:0.50 243:0.19 245:0.99 247:0.82 248:0.63 253:0.42 255:0.74 256:0.71 259:0.21 260:0.63 261:0.93 262:0.93 265:0.82 266:0.27 267:0.17 268:0.71 271:0.86 274:0.99 276:0.97 277:0.69 279:0.86 283:0.78 284:0.94 285:0.56 292:0.95 297:0.36 300:0.55 +1 7:0.72 8:0.84 9:0.80 11:0.83 12:0.20 17:0.95 18:0.97 20:0.79 21:0.40 22:0.93 27:0.54 29:0.53 30:0.74 31:0.93 32:0.69 34:0.56 36:0.80 37:0.62 39:0.99 41:0.82 43:0.88 44:0.90 45:1.00 47:0.93 48:0.99 55:0.52 60:0.95 64:0.77 66:0.74 69:0.45 70:0.26 71:0.82 74:0.99 78:0.96 79:0.95 81:0.38 83:0.91 85:0.91 86:0.99 89:0.99 91:0.44 96:0.68 98:0.79 100:0.95 101:0.87 104:0.88 106:0.81 108:0.72 111:0.95 120:0.65 122:0.51 123:0.70 124:0.45 126:0.44 127:0.91 129:0.81 133:0.67 135:0.47 137:0.93 139:0.99 140:0.45 141:0.97 145:0.79 146:0.57 147:0.63 149:1.00 150:0.31 151:0.47 152:0.77 153:0.48 154:0.86 155:0.67 158:0.91 159:0.81 162:0.86 164:0.70 169:0.93 172:0.99 173:0.26 177:0.70 179:0.10 186:0.96 187:0.39 190:0.89 192:0.87 195:0.91 196:0.97 201:0.68 202:0.63 206:0.81 208:0.64 212:0.94 215:0.42 216:0.37 219:0.95 220:0.97 222:0.21 223:0.94 225:0.99 230:0.74 233:0.73 234:0.99 235:0.60 240:0.99 241:0.59 242:0.54 243:0.17 245:1.00 247:0.88 248:0.47 253:0.49 255:0.95 256:0.71 259:0.21 260:0.64 261:0.86 262:0.93 265:0.79 266:0.71 267:0.87 268:0.72 271:0.86 274:0.99 276:0.99 279:0.86 281:0.89 283:0.78 284:0.94 285:0.62 292:0.95 297:0.36 300:0.84 +1 10:0.90 11:0.88 12:0.69 17:0.77 18:0.56 21:0.78 27:0.72 29:0.94 30:0.15 34:0.21 36:0.48 39:0.54 43:0.09 55:0.98 66:0.05 69:1.00 70:0.99 74:0.29 85:0.72 86:0.59 91:0.17 98:0.12 101:0.87 108:0.99 122:0.95 123:0.98 124:0.98 127:0.58 129:0.05 133:0.98 135:0.39 139:0.58 144:0.85 145:0.47 146:0.18 147:0.25 149:0.17 154:0.08 159:0.98 163:0.79 170:0.79 172:0.21 173:0.97 174:0.98 177:0.38 178:0.55 179:0.39 187:0.21 197:0.90 212:0.12 216:0.15 222:0.84 235:0.74 239:0.89 241:0.12 242:0.61 243:0.11 245:0.61 247:0.82 253:0.26 257:0.84 259:0.21 265:0.06 266:0.98 267:0.23 271:0.27 276:0.75 277:0.95 300:0.98 +3 10:0.88 11:0.87 12:0.69 17:0.47 18:0.89 21:0.40 27:0.24 29:0.94 30:0.40 34:0.42 36:0.56 37:0.66 39:0.54 43:0.77 45:0.81 66:0.32 69:0.12 70:0.88 74:0.53 81:0.33 86:0.90 91:0.03 98:0.56 101:0.87 108:0.80 122:0.91 123:0.79 124:0.77 126:0.24 127:0.90 129:0.05 133:0.74 135:0.86 139:0.88 144:0.85 146:0.55 147:0.61 149:0.75 150:0.36 154:0.69 159:0.69 170:0.79 172:0.51 173:0.14 174:0.86 177:0.88 178:0.55 179:0.60 187:0.68 197:0.89 212:0.22 216:0.56 219:0.90 222:0.84 235:0.42 236:0.92 239:0.87 242:0.22 243:0.44 244:0.61 245:0.72 247:0.84 253:0.42 259:0.21 265:0.57 266:0.75 267:0.44 271:0.27 276:0.62 277:0.69 292:0.95 300:0.84 +2 11:0.84 12:0.69 17:0.40 18:0.59 21:0.78 27:0.43 29:0.94 30:0.95 34:0.63 36:0.44 37:0.53 39:0.54 43:0.33 45:0.66 66:0.64 69:0.93 74:0.33 86:0.65 91:0.04 98:0.08 101:0.47 108:0.86 122:0.91 123:0.85 127:0.06 129:0.05 135:0.19 139:0.63 144:0.85 145:0.69 146:0.17 147:0.22 149:0.26 154:0.25 159:0.09 163:0.99 170:0.79 172:0.16 173:0.89 175:0.75 177:0.70 178:0.55 179:0.08 187:0.87 193:0.97 212:0.95 216:0.81 222:0.84 235:0.68 239:0.84 241:0.07 242:0.82 243:0.69 244:0.61 247:0.12 253:0.30 257:0.65 259:0.21 265:0.08 266:0.12 267:0.28 271:0.27 276:0.19 277:0.69 300:0.08 +1 11:0.87 12:0.69 17:0.15 18:0.64 21:0.78 27:0.72 29:0.94 30:0.95 34:0.94 36:0.89 39:0.54 43:0.71 45:0.73 66:0.64 69:0.72 74:0.38 86:0.70 91:0.22 98:0.13 101:0.65 108:0.55 122:0.65 123:0.52 127:0.08 129:0.05 135:0.60 139:0.68 144:0.85 146:0.17 147:0.22 149:0.44 154:0.60 159:0.09 163:0.97 170:0.79 172:0.16 173:0.85 175:0.75 177:0.70 178:0.55 179:0.10 187:0.21 212:0.95 216:0.10 222:0.84 235:0.31 241:0.32 242:0.82 243:0.69 244:0.61 247:0.12 253:0.34 257:0.65 259:0.21 265:0.13 266:0.12 267:0.19 271:0.27 276:0.19 277:0.69 300:0.08 +1 10:0.93 11:0.87 12:0.69 17:0.45 18:0.76 21:0.22 27:0.64 29:0.94 30:0.09 34:0.58 36:0.93 37:0.34 39:0.54 43:0.75 45:0.73 66:0.51 69:0.35 70:0.85 74:0.40 81:0.35 86:0.81 91:0.06 98:0.62 101:0.65 108:0.27 122:0.94 123:0.26 124:0.64 126:0.24 127:0.69 129:0.05 133:0.60 135:0.86 139:0.76 144:0.85 146:0.59 147:0.64 149:0.55 150:0.39 154:0.66 159:0.43 170:0.79 172:0.41 173:0.42 174:0.91 177:0.88 178:0.55 179:0.81 187:0.39 197:0.94 212:0.39 216:0.45 222:0.84 235:0.22 236:0.92 239:0.91 241:0.18 242:0.24 243:0.61 244:0.61 245:0.55 247:0.67 253:0.35 259:0.21 265:0.63 266:0.63 267:0.52 271:0.27 276:0.43 300:0.55 +2 1:0.63 7:0.75 10:0.88 11:0.87 12:0.69 17:0.26 18:0.92 21:0.40 27:0.37 29:0.94 30:0.61 34:0.45 36:0.94 37:0.62 39:0.54 43:0.77 44:0.88 45:0.89 60:0.87 64:0.77 66:0.20 69:0.17 70:0.72 74:0.75 75:0.99 81:0.63 83:0.91 86:0.94 91:0.09 98:0.55 99:0.95 101:0.65 108:0.14 114:0.82 122:0.91 123:0.78 124:0.76 126:0.51 127:0.74 129:0.05 133:0.74 135:0.76 139:0.95 144:0.85 145:0.40 146:0.68 147:0.73 149:0.87 150:0.49 154:0.69 155:0.54 158:0.86 159:0.67 165:0.81 170:0.79 172:0.65 173:0.16 174:0.86 176:0.70 177:0.88 178:0.55 179:0.47 187:0.21 192:0.57 195:0.81 197:0.87 201:0.61 204:0.83 208:0.64 212:0.52 215:0.67 216:0.60 219:0.95 222:0.84 226:0.96 230:0.77 233:0.76 235:0.60 239:0.92 241:0.30 242:0.19 243:0.57 244:0.61 245:0.78 247:0.88 253:0.64 259:0.21 265:0.52 266:0.75 267:0.36 271:0.27 276:0.70 277:0.69 279:0.80 281:0.91 283:0.67 290:0.82 292:0.88 297:0.36 300:0.70 +1 10:0.95 11:0.87 12:0.69 17:0.32 18:0.60 21:0.78 27:0.52 29:0.94 30:0.28 34:0.22 36:0.68 37:0.49 39:0.54 43:0.22 45:0.73 55:0.92 66:0.13 69:0.97 70:0.98 74:0.30 86:0.66 91:0.25 98:0.25 101:0.65 108:0.94 122:0.95 123:0.94 124:0.96 127:0.82 129:0.05 133:0.96 135:0.56 139:0.64 144:0.85 146:0.70 147:0.65 149:0.31 154:0.15 159:0.92 163:0.98 170:0.79 172:0.45 173:0.91 174:0.98 177:0.05 178:0.55 179:0.45 187:0.21 197:0.96 202:0.36 212:0.15 216:0.49 222:0.84 235:0.31 239:0.93 241:0.15 242:0.78 243:0.23 244:0.61 245:0.67 247:0.74 253:0.27 257:0.93 259:0.21 265:0.28 266:0.96 267:0.79 271:0.27 276:0.74 300:0.98 +2 9:0.75 10:0.89 11:0.87 12:0.69 17:0.22 18:0.89 21:0.22 27:0.52 29:0.94 30:0.53 31:0.94 34:0.42 36:0.59 37:0.49 39:0.54 43:0.77 45:0.83 64:0.77 66:0.32 69:0.10 70:0.84 74:0.53 79:0.93 81:0.37 83:0.69 86:0.90 91:0.06 96:0.80 98:0.56 101:0.65 108:0.80 120:0.69 122:0.91 123:0.78 124:0.77 126:0.32 127:0.92 129:0.05 133:0.74 135:0.87 137:0.94 139:0.88 140:0.45 144:0.85 146:0.53 147:0.59 149:0.78 150:0.38 151:0.86 153:0.48 154:0.69 155:0.64 159:0.67 162:0.86 164:0.56 170:0.79 172:0.51 173:0.14 174:0.86 177:0.88 179:0.60 187:0.68 190:0.77 192:0.98 196:0.95 197:0.88 201:0.58 202:0.36 208:0.61 212:0.22 215:0.79 216:0.49 219:0.91 222:0.84 235:0.31 239:0.87 242:0.22 243:0.44 244:0.61 245:0.72 247:0.84 248:0.77 253:0.77 255:0.77 256:0.45 259:0.21 260:0.70 265:0.57 266:0.75 267:0.52 268:0.46 271:0.27 276:0.62 277:0.69 283:0.67 284:0.88 285:0.60 292:0.88 297:0.36 300:0.84 +1 10:0.91 11:0.87 12:0.69 17:0.15 18:0.61 21:0.05 27:0.68 29:0.94 30:0.58 33:0.97 34:0.71 36:0.44 37:0.46 39:0.54 43:0.60 45:0.73 55:0.96 66:0.27 69:0.85 70:0.33 74:0.35 81:0.38 86:0.67 91:0.04 98:0.25 101:0.65 108:0.70 122:0.95 123:0.69 124:0.84 126:0.24 127:0.63 129:0.05 132:0.97 133:0.80 135:0.34 139:0.66 144:0.85 146:0.35 147:0.05 149:0.41 150:0.43 154:0.49 159:0.59 163:0.79 170:0.79 172:0.21 173:0.85 174:0.90 177:0.05 178:0.55 179:0.88 187:0.68 197:0.93 212:0.16 216:0.20 222:0.84 235:0.05 236:0.92 239:0.90 241:0.21 242:0.40 243:0.18 244:0.61 245:0.45 247:0.55 253:0.32 257:0.93 259:0.21 265:0.27 266:0.54 267:0.23 271:0.27 276:0.36 291:0.97 300:0.25 +1 10:0.85 11:0.87 12:0.69 17:0.24 18:0.77 21:0.78 27:0.52 29:0.94 30:0.37 34:0.52 36:0.41 37:0.49 39:0.54 43:0.22 45:0.73 55:0.71 66:0.63 69:0.97 70:0.73 74:0.30 86:0.77 91:0.23 98:0.23 101:0.65 108:0.94 122:0.92 123:0.94 124:0.63 127:0.83 129:0.05 133:0.73 135:0.51 139:0.74 144:0.85 146:0.65 147:0.05 149:0.29 154:0.15 159:0.92 170:0.79 172:0.59 173:0.90 174:0.79 175:0.75 177:0.05 178:0.55 179:0.70 187:0.21 197:0.85 202:0.36 212:0.61 216:0.49 222:0.84 235:0.12 239:0.84 241:0.18 242:0.76 243:0.11 244:0.61 245:0.57 247:0.55 253:0.27 257:0.93 259:0.21 265:0.25 266:0.71 267:0.17 271:0.27 276:0.49 277:0.69 300:0.55 +2 1:0.66 7:0.70 8:0.72 10:0.95 11:0.56 12:0.37 17:0.77 18:0.92 21:0.40 27:0.58 29:0.97 30:0.42 32:0.67 34:0.97 36:0.54 37:0.49 39:0.86 43:0.17 44:0.86 45:0.66 48:0.91 55:0.45 64:0.77 66:0.27 69:0.90 70:0.88 71:0.61 74:0.38 81:0.56 86:0.75 91:0.54 98:0.30 101:0.47 104:0.81 108:0.95 114:0.76 122:0.75 123:0.95 124:0.91 126:0.51 127:0.93 129:0.59 131:0.61 133:0.91 135:0.75 139:0.74 144:0.76 145:0.86 146:0.67 147:0.62 149:0.22 150:0.55 154:0.30 155:0.51 157:0.61 159:0.88 166:0.87 170:0.82 172:0.86 173:0.74 174:0.94 176:0.63 177:0.70 178:0.55 179:0.19 182:0.61 187:0.21 192:0.55 195:0.96 197:0.92 201:0.65 208:0.61 212:0.75 215:0.63 216:0.49 219:0.87 222:0.25 227:0.61 229:0.61 230:0.73 231:0.80 233:0.66 235:0.74 239:0.92 241:0.32 242:0.41 243:0.17 245:0.93 246:0.61 247:0.98 253:0.55 254:0.88 257:0.65 259:0.21 265:0.32 266:0.84 267:0.44 271:0.82 276:0.92 281:0.86 283:0.67 287:0.61 290:0.76 292:0.88 297:0.36 300:0.94 +2 1:0.66 7:0.70 8:0.62 9:0.73 10:0.95 11:0.51 12:0.37 17:0.86 18:0.94 21:0.13 22:0.93 27:0.67 29:0.97 30:0.45 31:0.90 32:0.71 34:0.83 36:0.90 37:0.34 39:0.86 43:0.59 44:0.92 47:0.93 48:1.00 64:0.77 66:0.95 69:0.64 70:0.72 71:0.61 74:0.52 77:0.70 79:0.90 81:0.56 86:0.82 91:0.48 96:0.74 98:0.84 104:0.88 106:0.81 108:0.49 114:0.92 120:0.62 122:0.75 123:0.47 126:0.51 127:0.34 129:0.05 131:0.91 135:0.51 137:0.90 139:0.83 140:0.45 144:0.76 145:0.41 146:0.78 147:0.82 149:0.69 150:0.56 151:0.66 153:0.48 154:0.55 155:0.64 157:0.61 159:0.34 162:0.86 164:0.56 166:0.87 170:0.82 172:0.88 173:0.72 174:0.90 176:0.70 177:0.88 179:0.27 182:0.61 187:0.21 190:0.77 192:0.87 195:0.64 196:0.92 197:0.91 201:0.63 202:0.36 206:0.81 208:0.61 212:0.91 215:0.84 216:0.27 219:0.87 220:0.74 222:0.25 227:0.94 229:0.61 230:0.73 231:0.80 232:0.74 233:0.76 235:0.31 239:0.92 241:0.03 242:0.32 243:0.95 244:0.61 246:0.61 247:0.94 248:0.63 253:0.71 254:0.80 255:0.72 256:0.45 259:0.21 260:0.63 262:0.93 265:0.84 266:0.38 267:0.44 268:0.46 271:0.82 276:0.79 281:0.91 282:0.80 283:0.82 284:0.88 285:0.60 287:0.61 290:0.92 292:0.95 297:0.36 300:0.55 +2 1:0.59 7:0.75 10:1.00 11:0.56 12:0.37 17:0.69 18:0.99 21:0.64 22:0.93 27:0.51 29:0.97 30:0.54 32:0.71 34:0.45 36:0.46 37:0.33 39:0.86 43:0.12 44:0.92 45:0.97 47:0.93 48:0.91 55:0.45 64:0.77 66:1.00 69:0.63 70:0.64 71:0.61 74:0.96 77:0.70 81:0.51 83:0.56 86:1.00 91:0.20 98:0.95 99:0.83 101:0.47 104:0.99 106:0.81 108:0.48 114:0.70 117:0.86 122:0.65 123:0.46 126:0.51 127:0.65 129:0.05 131:0.61 135:0.96 139:1.00 144:0.76 145:0.72 146:0.99 147:0.99 149:0.30 150:0.44 154:0.59 155:0.54 157:0.61 158:0.76 159:0.82 165:0.65 166:0.86 170:0.82 172:0.99 173:0.41 174:0.99 176:0.70 177:0.88 178:0.55 179:0.10 182:0.61 187:0.21 192:0.56 195:0.93 197:0.99 201:0.58 204:0.83 206:0.81 208:0.61 212:0.93 215:0.53 216:0.85 219:0.91 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.66 235:0.84 239:1.00 242:0.13 243:1.00 246:0.61 247:0.99 253:0.70 254:0.88 259:0.21 262:0.93 265:0.95 266:0.47 267:0.98 271:0.82 276:0.99 279:0.75 281:0.86 282:0.80 283:0.82 287:0.61 290:0.70 292:0.88 297:0.36 300:0.70 +1 8:0.78 9:0.71 10:1.00 12:0.37 17:0.78 21:0.68 23:0.99 27:0.70 29:0.97 30:0.93 31:0.89 34:0.96 36:0.72 37:0.61 39:0.86 43:0.32 48:0.91 55:0.45 62:0.99 66:0.97 69:0.50 70:0.24 71:0.61 74:0.48 75:0.96 76:0.98 77:0.89 79:0.88 81:0.30 91:0.82 96:0.93 98:0.98 102:0.96 108:0.38 114:0.63 120:0.59 122:0.95 123:0.37 126:0.32 127:0.79 128:0.97 129:0.05 131:0.91 135:0.34 137:0.89 139:0.72 140:0.98 144:0.76 145:0.83 146:0.87 147:0.89 149:0.58 150:0.29 151:0.61 153:0.48 154:0.52 155:0.56 157:0.61 158:0.76 159:0.45 162:0.77 164:0.56 166:0.73 168:0.97 170:0.82 172:0.93 173:0.56 174:0.99 175:0.75 176:0.59 177:0.70 179:0.27 182:0.61 190:0.95 191:0.87 192:0.86 193:0.98 195:0.66 196:0.89 197:1.00 201:0.54 202:0.84 204:0.78 205:0.99 208:0.61 212:0.89 216:0.23 219:0.91 220:0.62 222:0.25 226:0.96 227:0.94 229:0.61 231:0.80 232:0.74 235:0.84 236:0.94 239:1.00 241:0.83 242:0.78 243:0.97 244:0.83 246:0.61 247:0.82 248:0.59 253:0.90 255:0.71 256:0.87 257:0.65 259:0.21 260:0.60 265:0.98 266:0.38 267:0.94 268:0.88 271:0.82 276:0.86 277:0.69 279:0.79 281:0.86 282:0.72 284:0.93 285:0.62 287:0.61 290:0.63 292:0.88 300:0.55 +0 1:0.59 7:0.75 10:0.94 11:0.56 12:0.37 17:0.77 18:0.88 21:0.68 22:0.93 27:0.51 29:0.97 30:0.43 32:0.71 34:0.44 36:0.35 37:0.33 39:0.86 43:0.11 44:0.92 45:0.89 47:0.93 48:0.91 55:0.45 64:0.77 66:0.98 69:0.69 70:0.76 71:0.61 74:0.80 76:0.85 77:0.70 81:0.39 86:0.92 91:0.05 98:0.90 101:0.47 104:1.00 106:0.81 108:0.52 114:0.69 117:0.86 122:0.65 123:0.50 126:0.51 127:0.47 129:0.05 131:0.61 135:0.94 139:0.92 144:0.76 145:0.74 146:0.95 147:0.96 149:0.28 150:0.41 154:0.61 155:0.54 157:0.61 159:0.63 166:0.86 170:0.82 172:0.96 173:0.61 174:0.89 176:0.70 177:0.88 178:0.55 179:0.17 182:0.61 187:0.21 192:0.56 195:0.82 197:0.88 201:0.57 204:0.83 206:0.81 208:0.61 212:0.91 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.65 235:0.88 239:0.92 241:0.07 242:0.13 243:0.98 246:0.61 247:0.97 253:0.61 254:0.88 259:0.21 262:0.93 265:0.90 266:0.27 267:0.69 271:0.82 276:0.92 281:0.86 282:0.80 287:0.61 290:0.69 297:0.36 300:0.70 +0 1:0.59 7:0.75 10:0.84 11:0.51 12:0.37 17:0.55 18:0.62 21:0.68 22:0.93 27:0.51 29:0.97 30:0.62 32:0.71 34:0.44 36:0.41 37:0.33 39:0.86 43:0.13 44:0.92 47:0.93 48:0.91 55:0.42 64:0.77 66:0.75 69:0.70 70:0.23 71:0.61 74:0.54 76:0.85 77:0.70 81:0.39 86:0.69 91:0.09 98:0.63 104:1.00 106:0.81 108:0.53 114:0.69 117:0.86 122:0.41 123:0.51 126:0.51 127:0.18 129:0.05 131:0.61 135:0.86 139:0.79 144:0.76 145:0.74 146:0.51 147:0.57 149:0.11 150:0.41 154:0.53 155:0.54 157:0.61 159:0.18 163:0.86 166:0.86 170:0.82 172:0.32 173:0.84 174:0.79 176:0.70 177:0.88 178:0.55 179:0.74 182:0.61 187:0.21 192:0.56 195:0.59 197:0.84 201:0.57 204:0.83 206:0.81 208:0.61 212:0.30 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.79 232:0.92 233:0.65 235:0.88 239:0.83 241:0.18 242:0.33 243:0.77 244:0.61 246:0.61 247:0.39 253:0.54 254:0.80 259:0.21 262:0.93 265:0.64 266:0.27 267:0.69 271:0.82 276:0.28 281:0.86 282:0.80 283:0.82 287:0.61 290:0.69 297:0.36 300:0.18 +0 1:0.61 10:0.93 11:0.56 12:0.37 17:0.12 18:0.77 21:0.64 27:0.08 29:0.97 30:0.37 32:0.65 34:0.99 36:0.27 37:0.94 39:0.86 43:0.38 45:0.77 55:0.59 64:0.77 66:0.53 69:0.77 70:0.70 71:0.61 74:0.53 81:0.59 83:0.56 86:0.84 91:0.44 98:0.59 99:0.83 101:0.47 108:0.70 114:0.72 122:0.86 123:0.68 124:0.64 126:0.54 127:0.43 129:0.05 131:0.61 133:0.60 135:0.39 139:0.78 144:0.76 145:0.41 146:0.24 147:0.05 149:0.25 150:0.48 154:0.66 155:0.48 157:0.84 159:0.43 165:0.65 166:0.86 170:0.82 172:0.54 173:0.81 174:0.88 176:0.73 177:0.05 178:0.55 179:0.61 182:0.84 187:0.68 192:0.47 195:0.66 197:0.90 201:0.61 208:0.64 212:0.75 215:0.53 216:0.67 222:0.25 227:0.61 229:0.61 231:0.79 235:0.88 236:0.97 239:0.91 241:0.54 242:0.33 243:0.11 245:0.65 246:0.93 247:0.82 253:0.55 254:0.88 257:0.93 259:0.21 265:0.60 266:0.47 267:0.59 271:0.82 276:0.52 277:0.69 283:0.67 287:0.61 290:0.72 297:0.36 300:0.55 +2 1:0.65 7:0.70 8:0.69 9:0.74 10:0.95 11:0.51 12:0.37 17:0.85 18:0.93 21:0.22 22:0.93 27:0.67 29:0.97 30:0.32 31:0.92 32:0.71 34:0.91 36:0.86 37:0.34 39:0.86 43:0.57 44:0.92 47:0.93 48:1.00 55:0.45 64:0.77 66:0.95 69:0.65 70:0.74 71:0.61 74:0.51 77:0.70 79:0.92 81:0.53 83:0.56 86:0.82 91:0.54 96:0.78 98:0.85 104:0.88 106:0.81 108:0.49 114:0.88 120:0.67 122:0.75 123:0.47 126:0.51 127:0.36 129:0.05 131:0.91 135:0.58 137:0.92 139:0.82 140:0.45 144:0.76 145:0.42 146:0.79 147:0.82 149:0.68 150:0.53 151:0.76 153:0.48 154:0.53 155:0.64 157:0.61 159:0.35 162:0.86 164:0.66 166:0.87 170:0.82 172:0.88 173:0.72 174:0.90 176:0.70 177:0.88 179:0.27 182:0.61 187:0.21 190:0.77 192:0.97 195:0.64 196:0.93 197:0.91 201:0.63 202:0.50 206:0.81 208:0.61 212:0.91 215:0.79 216:0.27 220:0.82 222:0.25 227:0.95 229:0.61 230:0.73 231:0.81 232:0.74 233:0.76 235:0.42 239:0.92 241:0.10 242:0.32 243:0.95 244:0.61 246:0.61 247:0.94 248:0.73 253:0.77 254:0.84 255:0.74 256:0.58 259:0.21 260:0.67 262:0.93 265:0.85 266:0.38 267:0.52 268:0.59 271:0.82 276:0.79 281:0.91 282:0.80 284:0.92 285:0.60 287:0.61 290:0.88 297:0.36 300:0.55 +0 1:0.58 8:0.78 11:0.56 12:0.37 17:0.61 18:0.93 21:0.47 22:0.93 27:0.12 29:0.97 30:0.52 33:0.97 34:0.58 36:0.75 37:0.82 39:0.86 43:0.62 44:0.88 45:0.95 47:0.93 64:0.77 66:0.96 69:0.59 70:0.47 71:0.61 74:0.80 77:0.70 81:0.46 83:0.56 86:0.95 91:0.23 98:0.90 99:0.83 101:0.47 108:0.45 114:0.76 117:0.86 122:0.86 123:0.43 126:0.32 127:0.49 129:0.05 131:0.61 135:0.49 139:0.94 144:0.76 145:0.41 146:0.93 147:0.94 149:0.68 150:0.41 154:0.51 155:0.52 157:0.93 159:0.55 165:0.65 166:0.87 170:0.82 172:0.91 173:0.54 175:0.75 176:0.63 177:0.70 178:0.55 179:0.26 182:0.85 187:0.98 192:0.50 195:0.77 201:0.55 204:0.82 208:0.50 212:0.72 216:0.79 222:0.25 227:0.61 229:0.61 231:0.80 232:0.98 235:0.60 241:0.35 242:0.24 243:0.97 244:0.61 246:0.93 247:0.60 253:0.64 254:0.88 257:0.65 259:0.21 262:0.93 265:0.90 266:0.27 267:0.23 271:0.82 276:0.84 277:0.69 281:0.91 282:0.75 287:0.61 290:0.76 291:0.97 300:0.33 +2 1:0.59 7:0.75 10:1.00 11:0.56 12:0.37 17:0.75 18:0.99 21:0.68 22:0.93 27:0.51 29:0.97 30:0.52 32:0.71 34:0.40 36:0.43 37:0.33 39:0.86 43:0.12 44:0.92 45:0.97 47:0.93 48:0.91 55:0.45 64:0.77 66:1.00 69:0.63 70:0.64 71:0.61 74:0.96 77:0.70 81:0.50 83:0.56 86:1.00 91:0.20 98:0.95 99:0.83 101:0.47 104:0.99 106:0.81 108:0.48 114:0.69 117:0.86 122:0.65 123:0.46 126:0.51 127:0.65 129:0.05 131:0.61 135:0.96 139:1.00 144:0.76 145:0.76 146:0.99 147:0.99 149:0.27 150:0.42 154:0.60 155:0.54 157:0.61 159:0.82 165:0.65 166:0.86 170:0.82 172:0.99 173:0.41 174:0.99 176:0.70 177:0.88 178:0.55 179:0.10 182:0.61 187:0.21 192:0.56 195:0.93 197:0.99 201:0.57 204:0.83 206:0.81 208:0.61 212:0.94 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.65 235:0.88 239:1.00 241:0.01 242:0.13 243:1.00 246:0.61 247:0.99 253:0.69 254:0.88 259:0.21 262:0.93 265:0.95 266:0.27 267:0.98 271:0.82 276:0.99 281:0.86 282:0.80 287:0.61 290:0.69 297:0.36 300:0.70 +0 1:0.56 10:0.84 11:0.56 12:0.37 17:0.45 18:0.80 21:0.77 27:0.45 29:0.97 30:0.68 34:0.80 36:0.28 37:0.50 39:0.86 43:0.29 44:0.88 45:0.85 64:0.77 66:0.36 69:0.72 70:0.41 71:0.61 74:0.62 77:0.89 81:0.46 83:0.56 86:0.84 91:0.23 98:0.22 99:0.83 101:0.47 108:0.79 114:0.63 122:0.86 123:0.78 124:0.60 126:0.51 127:0.32 129:0.05 131:0.61 133:0.39 135:0.65 139:0.82 144:0.76 145:0.47 146:0.29 147:0.35 149:0.44 150:0.39 154:0.58 155:0.46 157:0.89 159:0.42 165:0.65 166:0.86 170:0.82 172:0.41 173:0.73 174:0.79 176:0.70 177:0.88 178:0.55 179:0.61 182:0.84 187:0.68 192:0.45 195:0.71 197:0.83 201:0.55 208:0.61 212:0.81 215:0.43 216:0.77 222:0.25 227:0.61 229:0.61 231:0.79 235:1.00 239:0.83 241:0.10 242:0.43 243:0.49 244:0.61 245:0.71 246:0.92 247:0.67 253:0.52 254:0.84 259:0.21 265:0.24 266:0.27 267:0.36 271:0.82 276:0.26 277:0.87 282:0.75 287:0.61 290:0.63 297:0.36 300:0.33 +1 1:0.61 7:0.70 8:0.76 9:0.71 10:0.93 11:0.51 12:0.37 17:0.81 18:0.93 21:0.13 22:0.93 23:0.96 27:0.62 29:0.97 30:0.45 31:0.93 32:0.71 34:0.96 36:0.51 37:0.36 39:0.86 43:0.59 44:0.86 47:0.93 48:1.00 62:0.97 64:0.77 66:0.96 69:0.62 70:0.77 71:0.61 74:0.38 75:0.98 79:0.92 81:0.56 86:0.77 91:0.57 98:0.94 102:0.97 104:0.87 106:0.81 108:0.47 120:0.68 122:0.75 123:0.46 126:0.51 127:0.61 128:0.96 129:0.05 131:0.87 135:0.60 137:0.93 139:0.76 140:0.80 144:0.76 145:0.63 146:0.87 147:0.89 149:0.75 150:0.53 151:0.66 153:0.48 154:0.49 155:0.55 157:0.61 159:0.45 162:0.86 164:0.66 166:0.81 168:0.96 170:0.82 172:0.89 173:0.67 174:0.90 177:0.88 179:0.32 182:0.61 187:0.21 190:0.89 192:0.57 195:0.66 196:0.92 197:0.91 201:0.63 202:0.50 205:0.95 206:0.81 208:0.50 212:0.88 215:0.84 216:0.36 219:0.88 220:0.62 222:0.25 226:0.95 227:0.92 229:0.61 230:0.73 231:0.77 233:0.66 235:0.31 236:0.95 239:0.95 241:0.18 242:0.37 243:0.96 244:0.61 246:0.61 247:0.91 248:0.63 253:0.33 254:0.80 255:0.73 256:0.58 259:0.21 260:0.68 262:0.93 265:0.94 266:0.54 267:0.36 268:0.59 271:0.82 276:0.80 279:0.75 281:0.86 283:0.82 284:0.92 285:0.60 287:0.61 292:0.95 297:0.36 300:0.70 +0 8:0.97 10:0.84 11:0.56 12:0.37 17:0.88 18:0.70 21:0.78 23:0.95 27:0.53 29:0.97 30:0.62 34:0.64 36:0.34 37:0.64 39:0.86 43:0.64 45:0.73 62:0.95 66:0.30 69:0.45 70:0.23 71:0.61 74:0.40 86:0.71 91:0.81 98:0.44 101:0.47 108:0.35 122:0.65 123:0.33 124:0.71 127:0.76 128:0.96 129:0.05 131:0.61 133:0.60 135:0.30 139:0.69 140:0.98 144:0.76 145:0.45 146:0.47 147:0.53 149:0.40 151:0.52 153:0.45 154:0.54 157:0.82 159:0.48 162:0.45 163:0.87 166:0.61 168:0.96 170:0.82 172:0.16 173:0.48 174:0.79 175:0.75 177:0.70 178:0.54 179:0.95 182:0.74 190:0.95 191:0.87 197:0.84 202:0.85 205:0.98 212:0.30 216:0.28 220:0.62 222:0.25 227:0.61 229:0.61 231:0.67 235:0.74 239:0.84 241:0.87 242:0.33 243:0.48 244:0.83 245:0.43 246:0.61 247:0.39 248:0.52 253:0.35 256:0.68 257:0.65 259:0.21 265:0.46 266:0.27 267:0.28 268:0.45 271:0.82 276:0.27 277:0.69 285:0.46 287:0.61 300:0.18 +1 1:0.67 7:0.75 10:1.00 11:0.56 12:0.37 17:0.88 18:0.80 21:0.54 27:0.64 29:0.97 30:0.20 32:0.75 34:0.99 36:0.60 37:0.55 39:0.86 43:0.66 44:0.93 45:0.66 48:0.97 64:0.77 66:0.93 69:0.29 70:0.61 71:0.61 74:0.79 76:0.85 77:0.89 81:0.62 83:0.56 86:0.92 91:0.33 98:0.87 99:0.83 101:0.47 102:0.98 104:0.58 106:0.81 108:0.22 114:0.76 122:0.77 123:0.22 126:0.51 127:0.40 129:0.05 131:0.61 135:0.51 139:0.94 144:0.76 145:0.98 146:0.53 147:0.59 149:0.32 150:0.55 154:0.76 155:0.57 157:0.61 158:0.81 159:0.19 165:0.65 166:0.86 170:0.82 172:0.80 173:0.60 174:0.94 176:0.70 177:0.88 178:0.55 179:0.40 182:0.61 187:0.21 192:0.85 195:0.53 197:0.96 201:0.64 204:0.84 206:0.81 208:0.61 212:0.95 215:0.58 216:0.16 219:0.94 222:0.25 227:0.61 229:0.61 230:0.77 231:0.79 233:0.66 235:0.88 239:0.99 241:0.12 242:0.28 243:0.93 246:0.61 247:0.79 253:0.63 254:0.84 259:0.21 265:0.87 266:0.12 267:0.28 271:0.82 276:0.70 279:0.78 281:0.86 282:0.83 283:0.65 287:0.61 290:0.76 292:0.86 297:0.36 300:0.43 +1 1:0.62 7:0.70 8:0.72 10:0.93 11:0.75 12:0.37 17:0.83 18:0.82 21:0.59 27:0.70 29:0.97 30:0.20 32:0.67 34:0.26 36:0.83 37:0.70 39:0.86 43:0.13 45:0.73 55:0.52 64:0.77 66:0.43 69:0.62 70:0.99 71:0.61 74:0.35 76:0.85 77:0.70 81:0.55 83:0.56 86:0.67 91:0.31 98:0.40 101:0.65 102:0.95 104:0.92 106:0.81 108:0.95 114:0.66 123:0.94 124:0.89 126:0.54 127:0.86 129:0.59 131:0.61 133:0.90 135:0.49 139:0.64 144:0.76 145:0.87 146:0.62 147:0.67 149:0.25 150:0.50 154:0.57 155:0.52 157:0.61 159:0.88 166:0.86 170:0.82 172:0.78 173:0.33 174:0.93 176:0.59 177:0.88 178:0.55 179:0.33 182:0.61 187:0.39 192:0.54 193:0.98 195:0.96 197:0.89 201:0.63 204:0.80 206:0.81 208:0.64 212:0.54 215:0.55 216:0.21 222:0.25 227:0.61 229:0.61 230:0.73 231:0.79 232:0.72 233:0.76 235:0.88 236:0.97 239:0.93 241:0.32 242:0.33 243:0.27 244:0.61 245:0.81 246:0.61 247:0.96 253:0.51 254:0.84 259:0.21 265:0.42 266:0.91 267:0.23 271:0.82 276:0.81 282:0.72 283:0.82 287:0.61 290:0.66 297:0.36 300:0.98 +1 1:0.60 10:0.91 11:0.56 12:0.37 17:0.59 18:0.87 21:0.59 27:0.45 29:0.97 30:0.54 32:0.71 34:0.82 36:0.21 37:0.50 39:0.86 43:0.28 44:0.92 45:0.87 64:0.77 66:0.33 69:0.70 70:0.64 71:0.61 74:0.62 77:0.70 81:0.52 83:0.56 86:0.87 91:0.27 98:0.52 99:0.83 101:0.47 108:0.53 114:0.73 122:0.86 123:0.51 124:0.67 126:0.51 127:0.36 129:0.05 131:0.61 133:0.60 135:0.79 139:0.85 144:0.76 145:0.47 146:0.60 147:0.66 149:0.52 150:0.44 154:0.63 155:0.48 157:0.90 159:0.45 165:0.65 166:0.87 170:0.82 172:0.48 173:0.70 174:0.83 176:0.70 177:0.88 178:0.55 179:0.52 182:0.84 187:0.68 192:0.45 195:0.71 197:0.86 201:0.59 208:0.61 212:0.76 216:0.77 222:0.25 227:0.61 229:0.61 231:0.80 235:0.80 236:0.95 239:0.88 241:0.15 242:0.27 243:0.50 245:0.71 246:0.93 247:0.84 253:0.57 254:0.96 259:0.21 265:0.54 266:0.63 267:0.65 271:0.82 276:0.57 282:0.79 287:0.61 290:0.73 300:0.55 +2 1:0.68 10:0.98 11:0.56 12:0.37 17:0.91 18:0.86 21:0.13 27:0.51 29:0.97 30:0.31 32:0.77 34:0.33 36:0.92 37:0.37 39:0.86 43:0.20 44:0.93 45:0.66 55:0.45 64:0.77 66:0.98 69:0.17 70:0.63 71:0.61 74:0.61 77:0.70 81:0.71 83:0.56 86:0.85 91:0.12 98:0.98 99:0.83 101:0.47 102:0.98 104:0.91 108:0.14 114:0.92 123:0.13 126:0.54 127:0.82 129:0.05 131:0.61 135:0.90 139:0.85 144:0.76 145:0.42 146:0.95 147:0.96 149:0.38 150:0.61 154:0.58 155:0.52 157:0.61 159:0.64 165:0.65 166:0.87 170:0.82 172:0.95 173:0.18 174:0.97 176:0.73 177:0.88 178:0.55 179:0.21 182:0.61 187:0.68 192:0.54 195:0.76 197:0.98 201:0.67 208:0.64 212:0.91 215:0.84 216:0.42 222:0.25 227:0.61 229:0.61 231:0.80 232:0.85 235:0.42 236:0.97 239:0.98 241:0.03 242:0.44 243:0.98 246:0.61 247:0.93 253:0.67 254:0.80 259:0.21 265:0.98 266:0.54 267:0.59 271:0.82 276:0.91 282:0.85 283:0.82 287:0.61 290:0.92 297:0.36 300:0.70 +1 1:0.57 8:0.77 9:0.70 10:0.97 11:0.56 12:0.37 17:0.94 18:0.97 21:0.59 27:0.33 29:0.97 30:0.56 31:0.90 34:0.99 36:0.87 37:0.57 39:0.86 43:0.18 44:0.92 45:0.66 48:0.97 55:0.52 64:0.77 66:0.83 69:0.86 70:0.57 71:0.61 74:0.82 76:0.94 77:0.70 79:0.89 81:0.34 86:0.96 91:0.58 96:0.68 98:0.83 101:0.47 102:0.97 108:0.72 114:0.71 122:0.91 123:0.70 124:0.39 126:0.32 127:0.80 129:0.05 131:0.91 133:0.60 135:0.60 137:0.90 139:0.95 140:0.80 144:0.76 145:0.85 146:0.91 147:0.48 149:0.26 150:0.37 151:0.49 153:0.77 154:0.49 155:0.52 157:0.61 159:0.65 162:0.86 166:0.87 170:0.82 172:0.94 173:0.84 174:0.96 176:0.63 177:0.38 179:0.22 182:0.61 187:0.39 190:0.95 192:0.49 195:0.80 196:0.94 197:0.97 201:0.55 202:0.54 204:0.82 208:0.50 212:0.88 216:0.38 219:0.87 220:0.82 222:0.25 227:0.95 229:0.61 231:0.81 235:0.74 239:0.96 241:0.52 242:0.41 243:0.13 244:0.61 245:0.87 246:0.61 247:0.92 248:0.49 253:0.63 254:0.74 255:0.68 256:0.58 257:0.84 259:0.21 265:0.83 266:0.63 267:0.52 268:0.63 271:0.82 276:0.90 281:0.86 282:0.77 284:0.93 285:0.50 287:0.61 290:0.71 292:0.88 300:0.84 +2 1:0.58 7:0.69 8:0.82 10:0.97 12:0.37 17:0.73 18:0.93 21:0.47 22:0.93 27:0.55 29:0.97 30:0.52 32:0.65 34:0.98 36:0.50 37:0.63 39:0.86 43:0.19 44:0.85 47:0.93 48:0.91 55:0.45 64:0.77 66:0.98 69:0.39 70:0.72 71:0.61 74:0.34 77:0.70 81:0.46 86:0.77 91:0.54 98:0.98 104:0.91 106:0.81 108:0.30 122:0.75 123:0.29 126:0.51 127:0.81 129:0.05 131:0.61 135:0.76 139:0.74 145:0.96 146:0.95 147:0.96 149:0.26 150:0.44 154:0.45 155:0.47 157:0.61 159:0.64 166:0.81 170:0.82 172:0.96 173:0.32 174:0.95 177:0.88 178:0.55 179:0.20 182:0.61 187:0.39 192:0.46 195:0.77 197:0.95 201:0.60 206:0.81 208:0.50 212:0.92 215:0.63 216:0.42 219:0.87 222:0.25 227:0.61 229:0.61 230:0.71 231:0.78 233:0.66 235:0.68 236:0.95 239:0.96 241:0.46 242:0.28 243:0.98 244:0.61 246:0.61 247:0.96 253:0.30 254:0.80 259:0.21 262:0.93 265:0.98 266:0.54 267:0.44 271:0.82 276:0.91 281:0.86 282:0.72 283:0.67 287:0.61 292:0.88 297:0.36 300:0.70 +2 1:0.59 7:0.75 10:1.00 11:0.56 12:0.37 17:0.64 18:0.99 21:0.68 22:0.93 27:0.51 29:0.97 30:0.33 32:0.71 34:0.40 36:0.43 37:0.33 39:0.86 43:0.12 44:0.92 45:0.88 47:0.93 48:0.91 55:0.45 64:0.77 66:1.00 69:0.63 70:0.53 71:0.61 74:0.96 76:0.85 77:0.70 81:0.39 86:1.00 91:0.20 98:0.95 101:0.47 104:1.00 106:0.81 108:0.48 114:0.69 117:0.86 122:0.65 123:0.46 126:0.51 127:0.68 129:0.05 131:0.61 135:0.97 139:1.00 144:0.76 145:0.74 146:0.99 147:0.99 149:0.15 150:0.41 154:0.61 155:0.54 157:0.61 159:0.81 166:0.86 170:0.82 172:0.99 173:0.42 174:0.99 176:0.70 177:0.88 178:0.55 179:0.10 182:0.61 187:0.21 192:0.56 195:0.92 197:0.99 201:0.57 204:0.83 206:0.81 208:0.61 212:0.94 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.65 235:0.88 239:1.00 242:0.13 243:1.00 246:0.61 247:0.99 253:0.69 254:0.88 259:0.21 262:0.93 265:0.95 266:0.27 267:0.96 271:0.82 276:0.98 281:0.86 282:0.80 287:0.61 290:0.69 297:0.36 300:0.70 +0 10:0.84 11:0.56 12:0.37 17:0.72 18:0.78 21:0.78 22:0.93 27:0.62 29:0.97 30:0.32 33:0.97 34:0.42 36:0.21 37:0.66 39:0.86 43:0.58 45:0.83 47:0.93 66:0.89 69:0.61 70:0.86 71:0.61 74:0.56 86:0.84 91:0.42 98:0.85 101:0.47 104:0.99 106:0.81 108:0.47 122:0.86 123:0.45 127:0.35 129:0.05 131:0.61 135:0.74 139:0.78 144:0.76 146:0.79 147:0.82 149:0.39 154:0.50 157:0.88 159:0.35 166:0.61 170:0.82 172:0.68 173:0.68 174:0.79 177:0.88 178:0.55 179:0.55 182:0.78 187:0.87 197:0.84 206:0.81 212:0.73 216:0.13 222:0.25 227:0.61 229:0.61 231:0.74 239:0.84 241:0.15 242:0.21 243:0.89 244:0.61 246:0.61 247:0.84 253:0.44 254:0.92 262:0.93 265:0.85 266:0.47 267:0.36 271:0.82 276:0.55 287:0.61 291:0.97 300:0.70 +0 1:0.60 7:0.70 8:0.85 10:0.93 11:0.51 12:0.37 17:0.79 18:0.93 21:0.30 22:0.93 27:0.62 29:0.97 30:0.58 32:0.71 34:0.98 36:0.85 37:0.36 39:0.86 43:0.36 44:0.86 47:0.93 48:0.98 55:0.45 64:0.77 66:0.61 69:0.63 70:0.73 71:0.61 74:0.37 75:0.98 81:0.51 86:0.77 91:0.31 98:0.80 102:0.97 104:0.87 106:0.81 108:0.48 122:0.75 123:0.46 124:0.50 126:0.51 127:0.53 129:0.59 131:0.61 133:0.47 135:0.68 139:0.76 144:0.76 145:0.80 146:0.80 147:0.83 149:0.67 150:0.48 154:0.49 155:0.47 157:0.61 159:0.46 166:0.81 170:0.82 172:0.84 173:0.66 174:0.89 177:0.88 178:0.55 179:0.30 182:0.61 187:0.21 192:0.51 195:0.68 197:0.89 201:0.62 206:0.81 208:0.50 212:0.89 215:0.72 216:0.36 219:0.88 222:0.25 226:0.95 227:0.61 229:0.61 230:0.73 231:0.77 233:0.76 235:0.52 236:0.95 239:0.94 241:0.18 242:0.36 243:0.67 245:0.93 246:0.61 247:0.91 253:0.33 254:0.99 259:0.21 262:0.93 265:0.80 266:0.54 267:0.82 271:0.82 276:0.80 279:0.75 281:0.91 283:0.66 287:0.61 292:0.88 297:0.36 300:0.70 +1 1:0.74 11:0.42 12:0.69 17:0.47 21:0.59 26:0.95 27:0.45 28:0.68 30:0.68 34:0.66 36:0.17 37:0.50 39:0.35 43:0.29 55:0.71 58:0.93 66:0.54 69:0.69 70:0.43 74:0.62 81:0.57 91:0.11 98:0.47 108:0.53 114:0.74 122:0.51 123:0.51 124:0.48 126:0.54 127:0.31 129:0.05 131:0.61 133:0.39 135:0.75 141:0.91 144:0.57 145:0.52 146:0.60 147:0.65 149:0.32 150:0.65 154:0.75 155:0.67 157:0.61 159:0.47 161:0.95 166:0.95 167:0.49 172:0.32 173:0.67 176:0.73 177:0.38 178:0.55 179:0.82 181:0.96 182:0.61 187:0.68 192:0.59 199:0.95 201:0.74 212:0.42 215:0.55 216:0.77 222:0.76 223:0.94 224:0.93 227:0.61 229:0.61 231:0.92 234:0.91 235:0.74 241:0.15 242:0.45 243:0.63 245:0.50 246:0.61 247:0.47 253:0.61 254:0.74 259:0.21 265:0.49 266:0.38 267:0.36 271:0.84 274:0.91 276:0.27 287:0.61 290:0.74 297:0.36 300:0.33 +1 1:0.74 7:0.76 8:0.81 9:0.80 11:0.42 12:0.69 17:0.75 20:0.90 21:0.22 26:0.95 27:0.72 28:0.68 30:0.76 32:0.72 34:0.61 36:0.97 37:0.49 39:0.35 41:0.82 43:0.74 55:0.59 58:0.99 66:0.72 69:0.84 70:0.22 74:0.62 78:0.82 81:0.81 83:0.77 91:0.82 96:0.90 98:0.24 100:0.84 104:0.76 108:0.69 111:0.82 114:0.90 120:0.86 122:0.51 123:0.67 126:0.54 127:0.11 129:0.05 131:0.97 135:0.61 140:0.45 141:0.98 144:0.57 145:0.59 146:0.28 147:0.34 149:0.38 150:0.83 151:0.94 152:0.84 153:0.80 154:0.64 155:0.67 157:0.61 158:0.86 159:0.12 161:0.95 162:0.66 164:0.84 166:0.95 167:0.83 169:0.82 172:0.27 173:0.93 176:0.73 177:0.38 179:0.24 181:0.96 182:0.94 186:0.83 191:0.73 192:0.59 195:0.68 199:0.97 201:0.74 202:0.78 208:0.64 212:0.78 215:0.79 216:0.09 220:0.74 222:0.76 223:0.95 224:0.99 227:0.92 229:0.97 230:0.79 231:0.93 233:0.76 234:0.98 235:0.31 241:0.85 242:0.45 243:0.75 246:0.61 247:0.35 248:0.89 253:0.89 254:0.74 255:0.79 256:0.77 259:0.21 260:0.87 261:0.84 265:0.26 266:0.17 267:0.23 268:0.80 271:0.84 274:0.99 276:0.25 279:0.86 283:0.67 285:0.62 287:0.90 290:0.90 297:0.36 300:0.18 +2 1:0.74 7:0.76 8:0.93 9:0.80 11:0.42 12:0.69 17:0.55 21:0.22 26:0.95 27:0.31 28:0.68 30:0.72 32:0.80 34:0.53 36:0.55 37:0.55 39:0.35 43:0.25 55:0.92 58:0.98 66:0.35 69:0.74 70:0.54 74:0.78 76:0.85 77:0.98 81:0.81 91:0.14 96:0.82 98:0.62 104:0.82 106:0.81 108:0.90 114:0.90 117:0.86 120:0.55 123:0.89 124:0.86 126:0.54 127:0.80 129:0.59 131:0.97 133:0.86 135:0.88 140:0.45 141:0.98 144:0.57 145:0.92 146:0.61 147:0.66 149:0.56 150:0.83 151:0.51 153:0.48 154:0.70 155:0.67 157:0.61 158:0.83 159:0.88 161:0.95 162:0.86 163:0.94 164:0.64 166:0.95 167:0.53 172:0.70 173:0.48 176:0.73 177:0.38 179:0.39 181:0.96 182:0.75 187:0.39 190:0.77 192:0.59 195:0.96 199:0.99 201:0.74 202:0.53 206:0.81 208:0.64 212:0.40 215:0.79 216:0.85 220:0.96 222:0.76 223:0.95 224:0.98 227:0.92 229:0.61 230:0.79 231:0.93 232:0.91 233:0.76 234:0.98 235:0.31 241:0.35 242:0.74 243:0.26 245:0.80 246:0.61 247:0.60 248:0.51 253:0.84 254:0.80 255:0.79 256:0.58 259:0.21 260:0.56 265:0.63 266:0.71 267:0.19 268:0.62 271:0.84 274:0.97 276:0.77 282:0.88 285:0.62 287:0.61 290:0.90 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 8:0.81 9:0.80 11:0.64 12:0.69 17:0.88 20:0.79 26:0.95 27:0.06 28:0.68 30:0.36 32:0.80 34:0.42 36:0.44 37:0.87 39:0.35 41:0.82 43:0.98 58:0.98 66:0.91 69:0.05 70:0.70 74:0.91 76:0.85 77:0.70 78:0.85 81:0.95 83:0.81 85:0.90 91:0.17 96:0.80 98:0.89 100:0.91 101:0.83 104:0.79 106:0.81 108:0.05 111:0.86 114:0.98 117:0.86 120:0.69 121:0.90 123:0.05 126:0.54 127:0.44 129:0.05 131:0.97 135:0.23 140:0.45 141:0.98 144:0.57 145:0.42 146:0.75 147:0.79 149:0.92 150:0.98 151:0.87 152:0.85 153:0.48 154:0.98 155:0.67 157:0.96 159:0.31 161:0.95 162:0.86 164:0.57 165:0.92 166:0.95 167:0.47 169:0.86 172:0.76 173:0.21 176:0.73 177:0.38 179:0.49 181:0.96 182:0.95 186:0.86 187:0.68 192:0.59 195:0.59 199:0.98 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.03 242:0.28 243:0.92 246:0.88 247:0.60 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 261:0.88 265:0.89 266:0.27 267:0.36 268:0.46 271:0.84 274:0.97 276:0.65 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.92 7:0.76 8:0.84 9:0.80 11:0.64 12:0.69 17:0.57 20:0.99 21:0.40 26:0.95 27:0.47 28:0.68 30:0.20 32:0.80 34:0.36 36:0.54 37:0.57 39:0.35 41:0.90 43:0.98 55:0.45 58:0.99 60:0.87 66:0.27 69:0.15 70:0.92 74:0.89 76:0.94 77:0.70 78:0.96 81:0.73 83:0.83 85:0.80 91:0.85 96:0.87 98:0.74 100:0.98 101:0.77 104:0.79 108:0.12 111:0.97 114:0.82 120:0.78 123:0.60 124:0.58 126:0.54 127:0.80 129:0.59 131:0.97 133:0.47 135:0.32 140:0.45 141:0.98 144:0.57 145:0.50 146:0.38 147:0.44 149:0.81 150:0.82 151:0.92 152:0.93 153:0.72 154:0.98 155:0.67 157:0.89 158:0.87 159:0.40 161:0.95 162:0.76 164:0.77 165:0.83 166:0.95 167:0.83 169:0.97 172:0.57 173:0.30 176:0.73 177:0.38 179:0.62 181:0.96 182:0.95 186:0.96 189:0.93 192:0.59 195:0.63 199:0.98 201:0.74 202:0.69 208:0.64 212:0.70 215:0.67 216:0.23 220:0.74 222:0.76 223:0.95 224:0.98 227:0.92 229:0.97 230:0.79 231:0.93 232:0.72 233:0.76 234:0.98 235:0.52 238:0.95 241:0.86 242:0.63 243:0.59 245:0.80 246:0.88 247:0.60 248:0.85 253:0.90 255:0.79 256:0.68 259:0.21 260:0.79 261:0.97 265:0.43 266:0.71 267:0.69 268:0.73 271:0.84 274:0.98 276:0.59 279:0.86 282:0.88 283:0.71 285:0.62 287:0.90 290:0.82 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.69 17:0.99 21:0.05 26:0.95 27:0.72 28:0.68 30:0.40 32:0.78 34:0.34 36:0.49 39:0.35 41:0.82 43:0.98 55:0.59 58:0.98 60:0.87 66:0.91 69:0.07 70:0.67 74:0.93 76:0.94 77:0.89 81:0.91 83:0.85 85:0.85 91:0.83 96:0.78 98:0.99 101:0.81 104:0.58 108:0.06 114:0.95 120:0.66 121:0.90 122:0.41 123:0.06 126:0.54 127:0.90 129:0.05 131:0.97 135:0.77 140:0.45 141:0.98 144:0.57 145:0.69 146:0.73 147:0.77 149:0.81 150:0.95 151:0.79 153:0.69 154:0.98 155:0.67 157:0.93 158:0.87 159:0.30 161:0.95 162:0.86 164:0.62 165:0.90 166:0.95 167:0.80 172:0.75 173:0.30 176:0.73 177:0.38 179:0.60 181:0.96 182:0.95 192:0.59 195:0.57 199:0.98 201:0.74 202:0.46 204:0.89 208:0.64 212:0.90 215:0.91 216:0.03 220:0.62 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.87 231:0.93 233:0.76 234:1.00 235:0.12 241:0.79 242:0.22 243:0.91 246:0.88 247:0.67 248:0.72 253:0.85 255:0.79 256:0.45 259:0.21 260:0.67 265:0.99 266:0.27 267:0.17 268:0.55 271:0.84 274:0.97 276:0.64 279:0.86 282:0.86 283:0.71 285:0.62 287:0.90 290:0.95 297:0.36 300:0.55 +0 7:0.76 8:0.89 12:0.69 17:0.26 20:0.89 21:0.54 25:0.88 26:0.95 27:0.51 28:0.68 30:0.19 32:0.72 34:0.67 36:0.78 37:0.72 39:0.35 43:0.45 55:0.45 58:1.00 66:0.79 69:0.17 70:0.82 78:0.85 81:0.36 91:0.94 98:0.77 100:0.84 104:0.54 108:0.80 111:0.83 120:0.97 123:0.79 124:0.39 126:0.32 127:0.86 129:0.59 131:0.85 133:0.47 135:0.78 140:0.45 141:0.98 145:0.87 146:0.31 147:0.37 149:0.65 150:0.32 151:0.80 152:0.84 153:0.93 154:0.34 157:0.61 159:0.72 161:0.95 162:0.77 164:0.96 166:0.78 167:0.84 169:0.81 172:0.82 173:0.15 175:0.75 177:0.05 179:0.46 181:0.96 182:0.61 186:0.85 190:0.77 191:0.90 195:0.86 199:1.00 202:0.93 212:0.69 215:0.58 216:0.59 220:0.62 222:0.76 223:0.95 224:0.99 227:0.84 229:0.61 230:0.79 231:0.68 233:0.76 234:0.99 235:0.60 241:0.89 242:0.82 243:0.16 244:0.61 245:0.78 246:0.61 247:0.12 248:0.95 253:0.20 256:0.95 257:0.65 259:0.21 260:0.96 261:0.85 265:0.77 266:0.71 267:0.69 268:0.95 271:0.84 274:1.00 276:0.69 277:0.69 283:0.82 285:0.50 287:0.61 297:0.36 300:0.70 +1 7:0.78 11:0.64 12:0.69 17:0.90 21:0.76 26:0.95 27:0.72 28:0.68 30:0.08 32:0.77 34:0.70 36:0.52 39:0.35 43:0.98 58:0.98 66:0.45 69:0.29 70:0.89 74:0.73 76:0.94 77:0.70 81:0.27 85:0.72 91:0.67 98:0.69 101:0.74 104:0.58 108:0.60 120:0.54 122:0.43 123:0.58 124:0.56 126:0.32 127:0.65 129:0.59 131:0.84 133:0.47 135:0.24 140:0.45 141:0.98 144:0.57 145:0.59 146:0.24 147:0.30 149:0.61 150:0.26 151:0.46 153:0.69 154:0.98 155:0.67 157:0.81 159:0.33 161:0.95 162:0.86 164:0.62 166:0.78 167:0.82 172:0.27 173:0.45 177:0.38 179:0.90 181:0.96 182:0.74 190:0.77 192:0.59 195:0.64 199:0.97 202:0.46 204:0.89 208:0.64 212:0.19 215:0.46 216:0.01 220:1.00 222:0.76 223:0.95 224:0.98 227:0.84 229:0.61 230:0.81 231:0.88 233:0.76 234:0.98 235:0.95 241:0.83 242:0.45 243:0.40 245:0.53 246:0.61 247:0.47 248:0.46 253:0.54 256:0.45 259:0.21 260:0.54 265:0.69 266:0.47 267:0.19 268:0.55 271:0.84 274:0.97 276:0.31 282:0.85 285:0.50 287:0.61 297:0.36 300:0.55 +2 1:0.74 11:0.64 12:0.69 17:0.95 21:0.40 26:0.95 27:0.72 28:0.68 30:0.15 34:0.73 36:0.86 39:0.35 43:0.87 55:0.45 58:0.93 60:0.87 66:0.63 69:0.32 70:0.52 74:0.57 81:0.78 83:0.86 85:0.72 91:0.72 98:0.47 101:0.74 104:0.57 108:0.25 114:0.58 123:0.24 124:0.49 126:0.54 127:0.47 129:0.05 131:0.61 133:0.62 135:0.56 141:0.91 144:0.57 146:0.39 147:0.46 149:0.66 150:0.80 154:0.85 155:0.67 157:0.81 158:0.87 159:0.31 161:0.95 166:0.95 167:0.81 172:0.41 173:0.46 176:0.54 177:0.38 178:0.55 179:0.82 181:0.96 182:0.74 192:0.59 199:0.97 201:0.74 208:0.64 212:0.84 215:0.55 216:0.06 222:0.76 223:0.94 224:0.93 227:0.61 229:0.61 231:0.92 232:0.78 234:0.91 235:0.88 241:0.80 242:0.73 243:0.68 245:0.47 246:0.61 247:0.39 253:0.48 259:0.21 265:0.49 266:0.23 267:0.18 271:0.84 274:0.91 276:0.36 279:0.86 283:0.71 287:0.61 290:0.58 297:0.36 300:0.33 +2 1:0.74 6:0.94 7:0.81 8:0.77 9:0.80 11:0.64 12:0.69 17:0.89 20:0.80 26:0.95 27:0.06 28:0.68 30:0.86 32:0.80 34:0.26 36:0.47 37:0.87 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.96 69:0.05 70:0.42 74:0.90 76:0.85 77:0.70 78:0.90 81:0.95 83:0.81 85:0.90 91:0.11 96:0.80 98:0.96 100:0.92 101:0.79 104:0.79 106:0.81 108:0.05 111:0.90 114:0.98 117:0.86 120:0.69 121:0.90 123:0.05 126:0.54 127:0.72 129:0.05 131:0.97 135:0.24 140:0.45 141:0.98 144:0.57 145:0.42 146:0.96 147:0.97 149:0.93 150:0.98 151:0.87 152:0.84 153:0.48 154:0.98 155:0.67 157:0.95 159:0.67 161:0.95 162:0.86 164:0.57 165:0.92 166:0.95 167:0.46 169:0.90 172:0.89 173:0.10 176:0.73 177:0.38 179:0.33 181:0.96 182:0.95 186:0.90 187:0.68 192:0.59 195:0.80 199:0.99 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.72 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.02 242:0.63 243:0.96 246:0.88 247:0.60 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 261:0.90 265:0.96 266:0.38 267:0.36 268:0.46 271:0.84 274:0.97 276:0.82 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.86 7:0.81 8:0.73 9:0.80 11:0.42 12:0.69 17:0.93 20:0.92 21:0.54 26:0.95 27:0.31 28:0.68 30:0.85 32:0.78 34:0.66 36:0.66 37:0.62 39:0.35 41:0.90 43:0.71 55:0.59 58:0.99 66:0.91 69:0.30 70:0.41 74:0.84 77:0.70 78:0.94 81:0.68 83:0.77 91:0.08 96:0.86 98:0.90 100:0.92 106:0.81 108:0.24 111:0.92 114:0.77 117:0.86 120:0.77 121:0.90 123:0.23 126:0.54 127:0.46 129:0.05 131:0.97 135:0.68 138:0.96 140:0.45 141:0.98 144:0.57 145:0.44 146:0.87 147:0.89 149:0.73 150:0.74 151:0.87 152:0.86 153:0.48 154:0.83 155:0.67 157:0.61 159:0.44 161:0.95 162:0.86 164:0.75 166:0.95 167:0.59 169:0.90 172:0.75 173:0.33 176:0.73 177:0.38 179:0.52 181:0.96 182:0.95 186:0.93 187:0.98 189:0.88 192:0.59 195:0.67 199:0.98 201:0.74 202:0.61 204:0.89 206:0.81 208:0.64 212:0.73 215:0.58 216:0.38 220:0.96 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.87 231:0.93 232:0.85 233:0.76 234:0.99 235:0.74 238:0.86 241:0.54 242:0.63 243:0.91 246:0.61 247:0.55 248:0.86 253:0.88 255:0.79 256:0.68 259:0.21 260:0.77 261:0.92 265:0.90 266:0.27 267:0.59 268:0.70 271:0.84 274:0.99 276:0.64 282:0.86 283:0.82 285:0.62 287:0.90 290:0.77 297:0.36 300:0.43 +1 1:0.74 11:0.42 12:0.69 17:0.61 21:0.78 26:0.95 27:0.45 28:0.68 30:0.62 34:0.83 36:0.18 37:0.50 39:0.35 43:0.27 55:0.84 58:0.93 66:0.47 69:0.72 70:0.34 74:0.54 81:0.40 91:0.23 98:0.35 108:0.56 114:0.63 122:0.41 123:0.53 124:0.52 126:0.54 127:0.18 129:0.59 131:0.61 133:0.47 135:0.76 141:0.91 144:0.57 145:0.49 146:0.50 147:0.56 149:0.25 150:0.60 154:0.74 155:0.67 157:0.61 159:0.33 161:0.95 163:0.86 166:0.95 167:0.55 172:0.21 173:0.67 176:0.73 177:0.38 178:0.55 179:0.73 181:0.96 182:0.61 187:0.68 192:0.59 199:0.95 201:0.74 212:0.30 215:0.43 216:0.77 222:0.76 223:0.94 224:0.93 227:0.61 229:0.61 231:0.91 234:0.91 235:1.00 241:0.43 242:0.33 243:0.59 245:0.46 246:0.61 247:0.39 253:0.52 254:0.74 259:0.21 265:0.38 266:0.27 267:0.36 271:0.84 274:0.91 276:0.27 287:0.61 290:0.63 297:0.36 300:0.25 +2 1:0.74 6:0.85 7:0.81 8:0.69 9:0.80 11:0.42 12:0.69 17:0.57 20:0.95 21:0.22 26:0.95 27:0.72 28:0.68 30:0.68 32:0.78 34:0.50 36:0.25 37:0.37 39:0.35 41:0.82 43:0.32 55:0.42 58:0.99 60:0.87 66:0.45 69:0.58 70:0.31 74:0.79 76:0.94 77:0.89 78:0.88 81:0.87 83:0.83 91:0.84 96:0.90 98:0.34 100:0.90 104:0.54 108:0.64 111:0.88 114:0.69 120:0.83 122:0.43 123:0.61 124:0.56 126:0.54 127:0.43 129:0.59 131:0.97 133:0.47 135:0.32 138:0.96 140:0.45 141:0.98 144:0.57 145:0.42 146:0.27 147:0.33 149:0.13 150:0.89 151:0.95 152:0.88 153:0.84 154:0.82 155:0.67 157:0.61 158:0.92 159:0.24 161:0.95 162:0.83 164:0.82 166:0.95 167:0.82 169:0.87 172:0.27 173:0.78 176:0.56 177:0.38 179:0.87 181:0.96 182:0.95 186:0.88 189:0.87 192:0.59 195:0.56 199:0.98 201:0.74 202:0.71 204:0.89 208:0.64 212:0.42 215:0.72 216:0.15 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.81 233:0.69 234:0.99 235:0.60 238:0.87 241:0.83 242:0.63 243:0.40 245:0.53 246:0.61 247:0.39 248:0.92 253:0.91 254:0.74 255:0.79 256:0.73 259:0.21 260:0.84 261:0.90 265:0.37 266:0.38 267:0.23 268:0.77 271:0.84 274:0.99 276:0.25 279:0.86 282:0.86 283:0.82 285:0.62 287:0.90 290:0.69 297:0.36 300:0.25 +2 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.64 12:0.69 17:0.88 20:0.80 26:0.95 27:0.06 28:0.68 30:0.86 32:0.80 34:0.25 36:0.52 37:0.87 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.95 69:0.05 70:0.44 74:0.85 76:0.85 77:0.70 78:0.90 81:0.95 83:0.81 85:0.88 91:0.09 96:0.80 98:0.96 100:0.87 101:0.77 104:0.79 106:0.81 108:0.05 111:0.88 114:0.98 117:0.86 120:0.69 121:0.90 123:0.05 126:0.54 127:0.70 129:0.05 131:0.97 135:0.26 140:0.45 141:0.98 144:0.57 145:0.42 146:0.96 147:0.97 149:0.86 150:0.98 151:0.87 152:0.85 153:0.48 154:0.98 155:0.67 157:0.93 159:0.68 161:0.95 162:0.86 164:0.57 165:0.92 166:0.95 167:0.46 169:0.88 172:0.88 173:0.10 176:0.73 177:0.38 179:0.36 181:0.96 182:0.95 186:0.90 187:0.39 192:0.59 195:0.82 199:0.99 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.51 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.02 242:0.74 243:0.95 246:0.88 247:0.60 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 261:0.89 265:0.96 266:0.54 267:0.19 268:0.46 271:0.84 274:0.97 276:0.80 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.83 7:0.81 8:0.76 9:0.80 11:0.64 12:0.69 17:0.78 20:0.93 21:0.54 26:0.95 27:0.52 28:0.68 30:0.28 32:0.80 34:0.58 36:0.78 37:0.47 39:0.35 41:0.95 43:0.81 58:1.00 60:0.87 66:0.29 69:0.23 70:0.65 74:0.82 76:0.94 77:0.89 78:0.88 81:0.67 83:0.83 85:0.88 91:0.80 96:0.92 98:0.67 100:0.86 101:0.81 108:0.18 111:0.86 114:0.77 120:0.87 121:0.90 122:0.41 123:0.65 124:0.59 126:0.54 127:0.80 129:0.59 131:0.97 133:0.47 135:0.54 140:0.87 141:0.98 144:0.57 145:0.88 146:0.60 147:0.66 149:0.81 150:0.78 151:0.90 152:0.86 153:0.69 154:0.75 155:0.67 157:0.95 158:0.84 159:0.41 161:0.95 162:0.73 164:0.91 165:0.79 166:0.95 167:0.84 169:0.84 172:0.45 173:0.35 176:0.73 177:0.38 179:0.73 181:0.96 182:0.95 186:0.88 189:0.86 192:0.59 195:0.65 199:0.99 201:0.74 202:0.86 204:0.89 208:0.64 212:0.27 215:0.58 216:0.25 220:0.93 222:0.76 223:0.95 224:0.99 227:0.92 229:0.97 230:0.87 231:0.93 233:0.76 234:0.98 235:0.74 238:0.85 241:0.89 242:0.40 243:0.57 245:0.72 246:0.88 247:0.55 248:0.92 253:0.93 255:0.79 256:0.89 259:0.21 260:0.87 261:0.89 265:0.67 266:0.54 267:0.69 268:0.90 271:0.84 274:1.00 276:0.52 279:0.86 282:0.88 283:0.71 285:0.62 287:0.90 290:0.77 297:0.36 299:0.88 300:0.43 +2 1:0.74 7:0.81 8:0.84 9:0.80 11:0.64 12:0.69 17:0.85 20:0.80 26:0.95 27:0.06 28:0.68 30:0.88 32:0.80 34:0.29 36:0.46 37:0.87 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.95 69:0.05 70:0.36 74:0.84 76:0.85 77:0.70 78:0.91 81:0.95 83:0.81 85:0.88 91:0.33 96:0.83 98:0.96 100:0.89 101:0.78 104:0.79 106:0.81 108:0.05 111:0.90 114:0.98 117:0.86 120:0.72 121:0.90 123:0.05 126:0.54 127:0.69 129:0.05 131:0.97 135:0.32 140:0.45 141:0.98 144:0.57 145:0.42 146:0.96 147:0.97 149:0.87 150:0.98 151:0.90 152:0.85 153:0.69 154:0.98 155:0.67 157:0.93 159:0.66 161:0.95 162:0.86 164:0.62 165:0.92 166:0.95 167:0.46 169:0.86 172:0.87 173:0.10 176:0.73 177:0.38 179:0.37 181:0.96 182:0.95 186:0.91 187:0.68 192:0.59 195:0.80 199:0.99 201:0.74 202:0.46 204:0.89 206:0.81 208:0.64 212:0.61 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.02 242:0.77 243:0.95 246:0.88 247:0.60 248:0.80 253:0.87 255:0.79 256:0.45 259:0.21 260:0.73 261:0.88 265:0.96 266:0.54 267:0.28 268:0.55 271:0.84 274:0.97 276:0.79 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.84 7:0.81 8:0.70 9:0.80 11:0.64 12:0.69 17:0.88 20:0.93 21:0.22 26:0.95 27:0.72 28:0.68 30:0.68 32:0.80 34:0.81 36:0.48 37:0.41 39:0.35 41:0.94 43:0.85 58:0.99 60:0.95 66:0.71 69:0.29 70:0.43 74:0.89 76:0.85 77:0.70 78:0.90 81:0.82 83:0.87 85:0.91 91:0.88 96:0.89 98:0.76 100:0.90 101:0.84 104:0.58 108:0.22 111:0.89 114:0.90 120:0.80 121:0.90 122:0.43 123:0.22 124:0.42 126:0.54 127:0.84 129:0.59 131:0.97 133:0.47 135:0.26 140:0.45 141:0.98 144:0.57 145:0.47 146:0.63 147:0.68 149:0.88 150:0.89 151:0.95 152:0.87 153:0.84 154:0.87 155:0.67 157:0.96 158:0.92 159:0.37 161:0.95 162:0.84 164:0.83 165:0.86 166:0.95 167:0.83 169:0.88 172:0.57 173:0.43 176:0.73 177:0.38 179:0.76 181:0.96 182:0.95 186:0.90 189:0.86 191:0.80 192:0.59 195:0.59 199:0.98 201:0.74 202:0.75 204:0.89 208:0.64 212:0.75 215:0.79 216:0.23 220:0.87 222:0.76 223:0.95 224:0.99 227:0.92 229:0.97 230:0.87 231:0.93 232:0.79 233:0.76 234:0.98 235:0.31 238:0.87 241:0.86 242:0.54 243:0.75 245:0.60 246:0.88 247:0.47 248:0.87 253:0.92 255:0.79 256:0.78 259:0.21 260:0.81 261:0.90 265:0.76 266:0.47 267:0.23 268:0.81 271:0.84 274:0.99 276:0.45 279:0.86 282:0.88 283:0.82 285:0.62 287:0.90 290:0.90 297:0.36 299:0.88 300:0.43 +2 1:0.74 8:0.83 9:0.80 11:0.64 12:0.69 17:0.78 21:0.05 25:0.88 26:0.95 27:0.49 28:0.68 30:0.87 34:0.18 36:0.56 37:0.48 39:0.35 41:0.82 43:0.89 55:0.59 58:0.98 66:0.97 69:0.50 70:0.27 74:0.95 81:0.90 83:0.78 85:0.98 91:0.27 96:0.75 98:0.94 101:0.87 104:0.58 108:0.38 114:0.74 117:0.86 120:0.63 122:0.43 123:0.37 126:0.54 127:0.62 129:0.05 131:0.97 135:0.18 138:0.95 140:0.45 141:0.98 144:0.57 146:0.91 147:0.92 149:0.98 150:0.94 151:0.79 153:0.69 154:0.87 155:0.67 157:0.98 158:0.83 159:0.51 161:0.95 162:0.86 164:0.62 165:0.88 166:0.95 167:0.48 172:0.93 173:0.49 176:0.56 177:0.38 179:0.24 181:0.96 182:0.95 187:0.39 192:0.59 199:0.99 201:0.74 202:0.46 208:0.64 212:0.93 215:0.84 216:0.44 220:0.62 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 231:0.93 232:0.83 234:1.00 235:0.22 241:0.10 242:0.44 243:0.97 246:0.88 247:0.60 248:0.72 253:0.81 255:0.79 256:0.45 259:0.21 260:0.64 265:0.94 266:0.27 267:0.23 268:0.55 271:0.84 274:0.98 276:0.87 283:0.71 285:0.62 286:0.99 287:0.90 290:0.74 297:0.36 298:0.99 300:0.43 +1 10:0.85 11:0.64 12:0.51 17:0.57 18:0.70 21:0.78 27:0.43 29:0.52 30:0.28 34:0.90 36:0.19 37:0.61 39:0.52 43:0.23 45:0.81 66:0.96 69:0.47 70:0.71 71:0.93 74:0.34 85:0.80 86:0.66 91:0.46 98:0.98 101:0.96 108:0.36 122:0.98 123:0.35 127:0.81 129:0.05 135:0.66 139:0.64 146:0.98 147:0.98 149:0.54 154:0.16 159:0.76 170:0.77 172:0.91 173:0.31 174:0.97 177:1.00 178:0.55 179:0.31 187:0.39 197:0.98 212:0.55 216:0.72 222:0.48 235:0.60 239:0.84 241:0.02 242:0.21 243:0.97 244:0.93 247:0.94 253:0.30 259:0.21 264:0.93 265:0.98 266:0.63 267:0.73 271:0.88 275:0.94 276:0.84 294:0.94 300:0.55 +0 1:0.56 7:0.63 10:0.80 11:0.42 12:0.51 17:0.22 18:0.77 21:0.64 25:0.88 27:0.49 29:0.52 30:0.26 32:0.62 34:0.73 36:0.43 37:0.33 39:0.52 43:0.05 45:0.92 64:0.77 66:0.07 69:0.86 70:0.95 71:0.93 74:0.25 75:0.95 81:0.32 83:0.53 86:0.58 91:0.38 97:0.89 98:0.21 99:0.83 101:0.45 104:0.75 108:0.73 120:0.55 122:0.97 123:0.98 124:0.97 126:0.26 127:0.88 129:0.81 133:0.97 135:0.65 139:0.57 140:0.80 145:0.59 146:0.60 147:0.44 149:0.05 150:0.28 151:0.47 153:0.46 154:0.05 155:0.46 159:0.95 162:0.62 163:0.75 164:0.53 165:0.63 170:0.77 172:0.41 173:0.51 174:0.94 175:1.00 177:0.70 178:0.42 179:0.35 187:0.68 190:1.00 192:0.46 195:0.99 197:0.90 201:0.55 202:0.49 208:0.48 212:0.13 215:0.53 216:0.74 219:0.86 220:0.96 222:0.48 226:0.96 230:0.63 233:0.76 235:0.84 236:0.91 239:0.79 241:0.18 242:0.15 243:0.16 244:1.00 245:0.73 247:0.96 248:0.47 253:0.22 254:1.00 256:0.45 257:0.99 259:0.21 260:0.55 264:0.93 265:0.20 266:0.98 267:0.65 268:0.45 271:0.88 275:0.96 276:0.81 277:0.99 279:0.74 285:0.45 292:0.88 294:0.91 297:0.36 300:0.94 +2 10:0.84 11:0.64 12:0.51 17:0.59 18:0.78 21:0.78 27:0.71 29:0.52 30:0.21 32:0.62 34:0.47 36:0.33 37:0.49 39:0.52 43:0.31 44:0.85 45:0.85 66:0.08 69:0.71 70:0.90 71:0.93 74:0.33 77:0.70 82:0.98 85:0.72 86:0.65 88:0.99 91:0.29 98:0.29 101:0.96 102:0.94 108:0.54 122:0.98 123:0.95 124:0.97 127:0.85 129:0.05 133:0.97 135:0.79 139:0.64 145:0.83 146:0.36 147:0.42 149:0.49 154:0.23 159:0.91 170:0.77 172:0.51 173:0.37 174:0.96 177:1.00 178:0.55 179:0.34 187:0.68 195:0.98 197:0.94 202:0.63 212:0.23 216:0.29 222:0.48 235:0.88 239:0.84 241:0.12 242:0.12 243:0.32 244:0.93 245:0.74 247:0.98 253:0.30 259:0.21 264:0.93 265:0.16 266:0.96 267:0.73 271:0.88 275:0.96 276:0.81 277:0.99 282:0.71 294:0.94 300:0.84 +1 10:0.84 11:0.64 12:0.51 17:0.34 18:0.73 21:0.78 27:0.48 29:0.52 30:0.15 34:0.25 36:0.19 37:0.55 39:0.52 43:0.32 45:0.81 55:0.59 66:0.23 69:0.74 70:0.94 71:0.93 74:0.33 85:0.72 86:0.65 91:0.11 98:0.48 101:0.96 108:0.82 122:0.98 123:0.81 124:0.87 127:0.88 129:0.05 133:0.85 135:0.42 139:0.63 145:0.58 146:0.39 147:0.32 149:0.49 154:0.23 159:0.85 170:0.77 172:0.68 173:0.53 174:0.98 177:0.05 178:0.55 179:0.31 187:0.39 197:0.96 212:0.54 216:0.92 222:0.48 235:0.95 239:0.84 241:0.15 242:0.14 243:0.09 244:0.93 245:0.87 247:0.95 253:0.29 257:1.00 259:0.21 264:0.93 265:0.50 266:0.91 267:0.23 271:0.88 275:0.97 276:0.83 277:1.00 294:0.94 300:0.84 +0 10:0.79 11:0.42 12:0.51 17:0.45 18:0.66 21:0.78 27:0.52 29:0.52 30:0.21 34:0.68 36:0.39 37:0.43 39:0.52 43:0.05 45:0.81 55:0.71 66:0.54 69:0.90 70:0.89 71:0.93 74:0.25 76:0.85 86:0.57 91:0.18 98:0.57 101:0.45 108:0.81 122:0.55 123:0.79 124:0.64 127:0.28 129:0.05 133:0.59 135:0.65 139:0.56 145:0.69 146:0.92 147:0.93 149:0.05 154:0.05 159:0.75 170:0.77 172:0.73 173:0.78 174:0.88 175:0.75 177:1.00 178:0.55 179:0.30 187:0.68 197:0.85 212:0.51 216:0.79 222:0.48 235:0.80 239:0.78 241:0.10 242:0.12 243:0.62 244:1.00 245:0.81 247:0.97 253:0.23 254:0.80 257:0.65 259:0.21 264:0.93 265:0.58 266:0.84 267:0.44 271:0.88 275:0.97 276:0.72 277:0.69 294:0.91 300:0.70 +0 10:0.84 11:0.54 12:0.51 17:0.87 18:0.65 21:0.78 27:0.72 29:0.52 30:0.56 34:0.25 36:0.30 39:0.52 43:0.05 45:0.87 66:0.74 69:0.30 70:0.39 71:0.93 74:0.33 86:0.65 91:0.68 98:0.47 101:0.72 108:0.24 122:0.67 123:0.23 124:0.39 127:0.28 129:0.05 133:0.36 135:0.94 139:0.63 145:0.91 146:0.41 147:0.47 149:0.05 154:0.29 159:0.26 170:0.77 172:0.51 173:0.43 174:0.79 177:1.00 178:0.55 179:0.66 197:0.84 212:0.81 216:0.02 222:0.48 235:0.12 239:0.84 241:0.78 242:0.24 243:0.77 244:0.93 245:0.49 247:0.67 253:0.30 254:0.74 259:0.21 264:0.93 265:0.49 266:0.27 267:0.44 271:0.88 275:0.91 276:0.33 294:0.94 300:0.33 +0 1:0.57 8:0.63 9:0.71 10:0.79 11:0.42 12:0.51 17:0.89 21:0.13 27:0.72 29:0.52 30:0.53 34:0.50 36:0.39 39:0.52 43:0.05 45:0.97 66:0.11 69:0.94 70:0.82 71:0.93 74:0.28 81:0.34 83:0.53 91:0.78 96:0.77 98:0.32 99:0.83 101:0.46 108:0.43 114:0.66 120:0.65 122:0.86 123:0.42 124:0.94 126:0.24 127:0.85 129:0.59 133:0.94 135:0.96 140:0.45 146:0.70 147:0.80 149:0.08 150:0.34 151:0.68 153:0.72 154:0.05 155:0.50 159:0.90 162:0.86 163:0.81 164:0.70 165:0.63 170:0.77 172:0.68 173:0.83 174:0.79 175:0.99 176:0.55 177:0.88 179:0.20 190:1.00 192:0.57 197:0.80 201:0.55 202:0.58 208:0.48 212:0.26 215:0.84 216:0.02 220:0.74 222:0.48 232:0.84 235:0.22 239:0.78 241:0.79 242:0.30 243:0.31 244:1.00 245:0.91 247:0.98 248:0.68 253:0.62 254:0.92 255:0.70 256:0.64 257:0.99 259:0.21 260:0.65 264:0.93 265:0.34 266:0.84 267:0.52 268:0.66 271:0.88 275:0.94 276:0.91 277:0.99 285:0.46 290:0.66 294:0.91 297:0.36 300:0.84 +0 10:0.82 11:0.64 12:0.51 17:0.66 18:0.84 21:0.78 27:0.72 29:0.52 30:0.70 34:0.39 36:0.21 39:0.52 43:0.21 45:0.91 66:0.94 69:0.88 70:0.52 71:0.93 74:0.30 85:0.72 86:0.62 91:0.63 98:0.84 101:0.96 108:0.75 122:0.98 123:0.74 127:0.35 129:0.05 135:0.54 139:0.60 145:0.72 146:0.95 147:0.96 149:0.31 154:0.14 159:0.63 170:0.77 172:0.85 173:0.83 174:0.96 177:1.00 178:0.55 179:0.31 197:0.95 212:0.42 216:0.02 222:0.48 235:0.12 239:0.82 241:0.80 242:0.24 243:0.95 244:0.93 247:0.92 253:0.27 259:0.21 264:0.93 265:0.84 266:0.63 267:0.36 271:0.88 275:0.94 276:0.76 294:0.93 300:0.43 +0 1:0.56 10:0.80 11:0.42 12:0.51 17:0.32 18:0.87 21:0.74 27:0.45 29:0.52 30:0.26 34:0.91 36:0.18 37:0.50 39:0.52 43:0.05 44:0.85 45:0.89 64:0.77 66:0.18 69:0.71 70:0.78 71:0.93 75:0.95 81:0.29 83:0.53 86:0.59 91:0.29 97:0.89 98:0.62 99:0.83 101:0.45 102:0.94 108:0.54 122:0.97 123:0.84 124:0.76 126:0.24 127:0.44 129:0.59 133:0.74 135:0.89 139:0.58 145:0.50 146:0.66 147:0.71 149:0.05 150:0.27 154:0.05 155:0.46 159:0.71 165:0.63 170:0.77 172:0.70 173:0.56 174:0.93 175:1.00 177:0.88 178:0.55 179:0.36 187:0.68 192:0.44 195:0.88 197:0.94 201:0.53 208:0.48 212:0.54 216:0.77 219:0.86 222:0.48 226:0.96 235:0.95 236:0.91 239:0.80 241:0.41 242:0.14 243:0.58 244:1.00 245:0.81 247:0.93 253:0.20 254:0.80 257:0.99 259:0.21 264:0.93 265:0.41 266:0.84 267:0.65 271:0.88 275:0.97 276:0.74 277:0.99 279:0.74 292:0.88 294:0.91 300:0.70 +0 1:0.57 2:0.99 10:0.80 11:0.42 12:0.51 17:0.61 18:0.78 21:0.47 27:0.69 29:0.52 30:0.08 33:0.97 34:0.17 36:0.24 37:0.73 39:0.52 43:0.05 45:0.77 55:0.71 56:0.97 64:0.77 66:0.17 69:0.39 70:0.99 71:0.93 75:0.95 81:0.33 83:0.53 86:0.58 91:0.20 97:0.89 98:0.38 99:0.83 101:0.45 108:0.76 123:0.74 124:0.93 126:0.26 127:0.69 129:0.59 133:0.92 135:0.34 139:0.57 146:0.52 147:0.58 149:0.05 150:0.31 154:0.05 155:0.46 159:0.86 165:0.63 170:0.77 172:0.51 173:0.16 174:0.94 175:1.00 177:0.88 178:0.55 179:0.41 187:0.68 192:0.47 197:0.94 201:0.56 202:0.46 208:0.49 212:0.21 216:0.21 219:0.86 222:0.48 226:0.95 235:0.68 236:0.91 239:0.80 241:0.32 242:0.14 243:0.33 244:1.00 245:0.73 247:0.98 253:0.20 254:0.74 257:0.99 259:0.21 264:0.93 265:0.40 266:0.95 267:0.96 271:0.88 275:0.97 276:0.75 277:1.00 279:0.75 291:0.97 292:0.87 294:0.92 295:0.98 300:0.94 +1 1:0.57 7:0.63 10:0.84 11:0.64 12:0.51 17:0.89 18:0.64 21:0.13 27:0.72 29:0.52 30:0.86 32:0.62 34:0.99 36:0.19 39:0.52 43:0.33 45:0.66 66:0.84 69:0.58 70:0.27 71:0.93 74:0.33 77:0.70 81:0.34 83:0.53 85:0.72 86:0.65 91:0.53 98:0.67 99:0.83 101:0.87 106:0.81 108:0.45 114:0.66 117:0.86 122:0.67 123:0.43 126:0.24 127:0.20 129:0.05 135:0.73 139:0.63 145:0.94 146:0.45 147:0.51 149:0.50 150:0.34 154:0.25 155:0.48 159:0.16 165:0.63 170:0.77 172:0.54 173:0.79 174:0.79 176:0.55 177:1.00 178:0.55 179:0.51 187:0.39 192:0.51 195:0.55 197:0.84 201:0.55 204:0.78 206:0.81 208:0.48 212:0.81 215:0.84 216:0.12 222:0.48 230:0.63 232:0.87 233:0.66 235:0.22 239:0.83 241:0.35 242:0.24 243:0.85 244:0.93 247:0.67 253:0.50 259:0.21 264:0.93 265:0.67 266:0.27 267:0.73 271:0.88 275:0.91 276:0.30 282:0.71 290:0.66 294:0.94 297:0.36 300:0.25 +0 8:0.63 10:0.79 11:0.42 12:0.51 17:0.18 18:0.57 21:0.40 27:0.55 29:0.52 30:0.10 34:0.74 36:0.30 37:0.54 39:0.52 43:0.05 45:0.83 55:0.84 66:0.10 69:0.99 70:0.94 71:0.93 81:0.27 86:0.56 91:0.51 98:0.31 101:0.46 108:0.99 120:0.57 123:0.99 124:0.99 126:0.24 127:0.99 129:0.05 133:0.99 135:0.79 139:0.55 140:0.45 146:0.88 147:0.91 149:0.05 150:0.28 151:0.54 153:0.72 154:0.05 159:0.98 162:0.86 164:0.57 170:0.77 172:0.90 173:0.93 174:0.83 175:0.99 177:0.38 179:0.11 187:0.21 190:1.00 192:0.45 197:0.79 201:0.54 202:0.53 212:0.27 215:0.67 216:0.54 220:0.91 222:0.48 235:0.52 239:0.78 241:0.24 242:0.41 243:0.18 244:1.00 245:0.95 247:1.00 248:0.55 253:0.20 254:0.96 255:0.69 256:0.58 257:1.00 259:0.21 260:0.57 264:0.93 265:0.33 266:0.97 267:0.99 268:0.62 271:0.88 275:0.98 276:0.98 277:0.99 283:0.67 285:0.47 294:0.91 297:0.36 300:0.94 +0 8:0.87 10:0.80 11:0.42 12:0.51 17:0.64 18:0.90 21:0.78 27:0.51 29:0.52 30:0.72 34:0.83 36:0.25 37:0.80 39:0.52 43:0.05 45:0.98 66:0.09 69:0.98 70:0.66 71:0.93 86:0.59 91:0.65 98:0.25 101:0.45 108:0.96 122:0.97 123:0.96 124:0.98 127:0.81 129:0.05 133:0.98 135:0.91 139:0.57 145:0.79 146:0.86 147:0.41 149:0.06 154:0.05 159:0.96 170:0.77 172:0.63 173:0.88 174:0.99 175:1.00 177:0.38 178:0.55 179:0.20 187:0.21 191:0.70 197:0.96 212:0.18 216:0.10 222:0.48 235:0.88 239:0.80 241:0.02 242:0.52 243:0.10 244:1.00 245:0.83 247:0.82 253:0.20 257:1.00 259:0.21 264:0.93 265:0.28 266:0.97 267:0.18 271:0.88 275:0.95 276:0.91 277:0.99 294:0.91 300:0.84 +0 8:0.70 10:0.79 11:0.42 12:0.51 17:0.70 18:0.62 21:0.78 27:0.69 29:0.52 30:0.09 34:0.77 36:0.93 37:0.50 39:0.52 43:0.05 45:0.66 55:0.59 66:0.29 69:0.10 70:0.97 71:0.93 74:0.27 86:0.57 91:0.31 98:0.45 101:0.45 108:0.09 122:0.90 123:0.09 124:0.92 127:0.94 129:0.05 133:0.92 135:0.75 139:0.55 146:0.85 147:0.87 149:0.05 154:0.05 158:0.71 159:0.87 163:0.81 170:0.77 172:0.65 173:0.08 174:0.79 175:0.97 177:0.99 178:0.55 179:0.42 187:0.39 197:0.82 212:0.27 216:0.35 222:0.48 232:0.93 235:0.05 239:0.78 241:0.12 242:0.23 243:0.48 244:1.00 245:0.74 247:0.98 253:0.25 254:0.99 257:0.93 259:0.21 264:0.93 265:0.47 266:0.94 267:0.44 271:0.88 275:0.96 276:0.77 277:0.95 294:0.91 300:0.84 +0 7:0.63 8:0.92 10:0.79 11:0.42 12:0.51 17:0.13 18:0.57 21:0.40 27:0.35 29:0.52 30:0.10 32:0.62 34:0.80 36:0.50 37:0.67 39:0.52 43:0.05 45:0.66 55:0.71 66:0.13 69:0.66 70:0.96 71:0.93 74:0.25 81:0.28 86:0.56 91:0.60 98:0.34 101:0.45 104:0.95 106:0.81 108:0.50 120:0.79 123:0.48 124:0.96 126:0.24 127:0.83 129:0.05 133:0.96 135:0.56 139:0.55 140:0.45 145:0.47 146:0.84 147:0.86 149:0.05 150:0.28 151:0.70 153:0.81 154:0.05 159:0.92 162:0.70 164:0.84 170:0.77 172:0.68 173:0.30 174:0.79 175:0.99 177:0.96 179:0.22 187:0.21 190:1.00 191:0.70 192:0.51 195:0.98 197:0.79 201:0.54 202:0.80 206:0.81 212:0.26 215:0.67 216:0.75 220:0.87 222:0.48 230:0.63 233:0.65 235:0.52 239:0.78 241:0.39 242:0.50 243:0.33 244:1.00 245:0.86 247:0.98 248:0.75 253:0.22 254:1.00 255:0.70 256:0.81 257:0.97 259:0.21 260:0.77 264:0.93 265:0.36 266:0.95 267:0.99 268:0.83 271:0.88 275:0.96 276:0.90 277:0.98 283:0.82 285:0.46 294:0.91 297:0.36 300:0.84 +1 8:0.97 10:0.80 11:0.42 12:0.51 17:0.83 18:0.84 21:0.78 27:0.52 29:0.52 30:0.08 34:0.61 36:0.36 37:0.84 39:0.52 43:0.05 45:0.87 55:0.45 66:0.19 69:0.94 70:0.99 71:0.93 74:0.26 86:0.58 91:0.86 98:0.54 101:0.45 108:0.88 123:0.88 124:0.91 127:0.74 129:0.05 133:0.90 135:0.17 139:0.57 140:0.97 145:0.63 146:0.90 147:0.67 149:0.06 151:0.48 153:0.46 154:0.05 159:0.86 162:0.46 170:0.77 172:0.73 173:0.87 174:0.93 175:0.97 177:0.96 178:0.54 179:0.25 190:1.00 191:0.88 197:0.94 202:0.82 212:0.58 216:0.17 220:0.93 222:0.48 235:0.88 239:0.79 241:0.83 242:0.24 243:0.31 244:1.00 245:0.88 247:0.98 248:0.48 253:0.23 256:0.45 257:0.97 259:0.21 264:0.93 265:0.55 266:0.95 267:0.59 268:0.45 271:0.88 275:0.91 276:0.87 277:0.95 285:0.45 294:0.91 300:0.94 +0 1:0.57 8:0.77 9:0.71 10:0.79 11:0.42 12:0.51 17:0.38 18:0.57 21:0.05 27:0.19 29:0.52 30:0.14 34:0.34 36:0.68 37:0.62 39:0.52 43:0.05 45:0.83 55:0.71 66:0.05 69:0.88 70:0.97 71:0.93 74:0.27 81:0.35 83:0.53 86:0.56 91:0.15 96:0.82 97:0.89 98:0.17 99:0.83 101:0.46 108:0.77 114:0.67 117:0.86 120:0.72 123:0.75 124:1.00 126:0.24 127:0.98 129:0.59 133:1.00 135:0.78 139:0.55 140:0.45 146:0.90 147:0.75 149:0.05 150:0.35 151:0.79 153:0.80 154:0.05 155:0.50 158:0.71 159:1.00 162:0.86 164:0.71 165:0.63 170:0.77 172:0.67 173:0.24 174:0.88 175:0.99 176:0.55 177:0.70 179:0.09 187:0.39 190:1.00 191:0.82 192:0.58 197:0.79 201:0.55 202:0.59 208:0.48 212:0.16 215:0.91 216:0.73 220:0.62 222:0.48 232:0.95 235:0.12 239:0.78 241:0.01 242:0.39 243:0.07 244:1.00 245:0.95 247:1.00 248:0.75 253:0.73 254:0.88 255:0.70 256:0.64 257:0.99 259:0.21 260:0.69 264:0.93 265:0.18 266:0.99 267:0.99 268:0.67 271:0.88 275:1.00 276:0.99 277:0.98 279:0.75 283:0.67 285:0.46 290:0.67 294:0.92 297:0.36 300:0.98 +0 8:0.83 10:0.79 11:0.42 12:0.51 17:0.87 18:0.59 21:0.78 27:1.00 29:0.52 30:0.36 34:0.37 36:0.65 37:0.49 39:0.52 43:0.05 45:0.73 55:0.98 66:0.08 69:0.96 70:0.78 71:0.93 74:0.24 86:0.57 91:0.78 98:0.21 101:0.45 108:0.96 123:0.96 124:0.96 127:0.57 129:0.81 133:0.95 135:0.61 139:0.55 145:0.72 146:0.43 147:0.05 149:0.05 154:0.05 159:0.93 163:0.98 170:0.77 172:0.21 173:0.82 174:0.83 175:1.00 177:0.05 178:0.55 179:0.53 191:0.75 197:0.80 202:0.88 212:0.16 216:0.22 222:0.48 235:0.42 239:0.78 241:0.82 242:0.21 243:0.09 244:1.00 245:0.59 247:0.88 253:0.20 257:1.00 259:0.21 264:0.93 265:0.22 266:0.92 267:0.23 271:0.88 275:0.93 276:0.65 277:1.00 294:0.91 300:0.70 +0 1:0.57 8:0.77 9:0.71 10:0.79 11:0.42 12:0.51 17:0.38 18:0.57 21:0.05 27:0.19 29:0.52 30:0.14 34:0.34 36:0.68 37:0.62 39:0.52 43:0.05 45:0.83 55:0.71 66:0.05 69:0.88 70:0.97 71:0.93 74:0.27 81:0.35 83:0.53 86:0.56 91:0.15 96:0.82 97:0.89 98:0.17 99:0.83 101:0.46 108:0.77 114:0.67 117:0.86 120:0.72 123:0.75 124:1.00 126:0.24 127:0.98 129:0.59 133:1.00 135:0.78 139:0.55 140:0.45 146:0.90 147:0.75 149:0.05 150:0.35 151:0.79 153:0.80 154:0.05 155:0.50 158:0.71 159:1.00 162:0.86 164:0.71 165:0.63 170:0.77 172:0.67 173:0.24 174:0.88 175:0.99 176:0.55 177:0.70 179:0.09 187:0.39 190:1.00 191:0.82 192:0.58 197:0.79 201:0.55 202:0.59 208:0.48 212:0.16 215:0.91 216:0.73 220:0.62 222:0.48 232:0.95 235:0.12 239:0.78 241:0.01 242:0.39 243:0.07 244:1.00 245:0.95 247:1.00 248:0.75 253:0.73 254:0.88 255:0.70 256:0.64 257:0.99 259:0.21 260:0.69 264:0.93 265:0.18 266:0.99 267:0.99 268:0.67 271:0.88 275:1.00 276:0.99 277:0.98 279:0.75 283:0.67 285:0.46 290:0.67 294:0.92 297:0.36 300:0.98 +1 8:0.93 9:0.70 10:0.80 11:0.64 12:0.51 17:0.07 18:0.63 21:0.78 27:0.14 29:0.52 30:0.21 34:0.49 36:0.24 37:0.67 39:0.52 43:0.08 45:0.77 66:0.19 69:0.98 70:0.91 71:0.93 74:0.27 83:0.53 85:0.72 86:0.59 91:0.78 96:0.74 98:0.16 101:0.96 108:0.96 120:0.62 122:0.67 123:0.96 124:0.97 127:0.62 129:0.05 133:0.96 135:0.91 139:0.58 140:1.00 145:0.47 146:0.50 147:0.52 149:0.15 154:0.07 155:0.50 159:0.94 162:0.45 164:0.68 170:0.77 172:0.51 173:0.94 174:0.89 177:0.38 178:0.55 179:0.43 187:0.68 190:1.00 191:0.79 192:0.57 197:0.84 202:0.94 208:0.48 212:0.37 216:0.93 220:0.74 222:0.48 235:0.31 239:0.80 241:0.37 242:0.12 243:0.12 244:0.93 245:0.63 247:0.97 253:0.52 255:0.69 256:0.68 257:1.00 259:0.21 260:0.62 264:0.93 265:0.18 266:0.96 267:0.73 268:0.64 271:0.88 275:0.96 276:0.73 285:0.61 294:0.92 300:0.84 +0 8:0.75 10:0.79 11:0.42 12:0.51 17:0.51 18:0.61 21:0.78 27:0.28 29:0.52 30:0.12 34:0.62 36:0.92 37:0.72 39:0.52 43:0.05 45:0.83 55:0.71 66:0.10 69:0.82 70:0.99 71:0.93 74:0.27 76:0.99 86:0.57 91:0.49 96:0.77 98:0.25 101:0.46 108:0.96 120:0.57 122:0.90 123:0.65 124:0.95 127:0.84 129:0.81 132:0.97 133:0.94 135:0.88 139:0.55 140:0.45 145:0.54 146:0.64 147:0.42 149:0.05 151:0.53 153:0.48 154:0.05 159:0.91 162:0.53 164:0.65 170:0.77 172:0.51 173:0.55 174:0.88 175:1.00 177:0.38 179:0.34 187:0.39 190:1.00 191:0.79 192:0.44 197:0.81 202:0.70 212:0.30 216:0.67 220:0.93 222:0.48 235:0.84 239:0.78 241:0.21 242:0.17 243:0.18 244:1.00 245:0.79 247:0.98 248:0.53 253:0.59 254:0.96 255:0.69 256:0.58 257:1.00 259:0.21 260:0.57 264:0.93 265:0.26 266:0.95 267:1.00 268:0.66 271:0.88 275:0.95 276:0.81 277:0.99 283:0.67 285:0.47 294:0.91 300:0.98 +0 10:0.79 11:0.42 12:0.51 17:0.24 18:0.60 21:0.78 27:0.34 29:0.52 30:0.17 34:0.59 36:0.65 37:0.46 39:0.52 43:0.05 45:0.77 55:0.96 66:0.08 69:0.98 70:0.96 71:0.93 74:0.24 86:0.57 91:0.03 98:0.22 101:0.45 104:0.80 106:0.81 108:0.97 123:0.96 124:0.95 127:0.62 129:0.05 133:0.94 135:0.76 139:0.55 145:0.63 146:0.71 147:0.05 149:0.05 154:0.05 159:0.94 170:0.77 172:0.45 173:0.94 174:0.86 175:1.00 177:0.05 178:0.55 179:0.45 187:0.87 191:0.70 197:0.81 206:0.81 212:0.13 216:0.53 222:0.48 235:0.22 239:0.78 241:0.21 242:0.31 243:0.08 244:1.00 245:0.67 247:0.96 253:0.22 257:1.00 259:0.21 264:0.93 265:0.23 266:0.97 267:0.44 271:0.88 275:0.94 276:0.69 277:0.99 294:0.91 300:0.94 +0 10:0.79 11:0.42 12:0.51 17:0.28 18:0.61 21:0.74 27:0.45 29:0.52 30:0.37 34:0.89 36:0.17 37:0.50 39:0.52 43:0.05 45:0.73 55:0.71 66:0.48 69:0.70 70:0.83 71:0.93 74:0.25 81:0.23 86:0.57 91:0.31 98:0.64 101:0.46 108:0.53 123:0.51 124:0.66 126:0.22 127:0.44 129:0.05 133:0.59 135:0.93 139:0.55 145:0.49 146:0.84 147:0.87 149:0.05 150:0.24 154:0.05 159:0.67 170:0.77 172:0.71 173:0.59 174:0.79 175:0.75 177:1.00 178:0.55 179:0.37 187:0.68 197:0.82 212:0.49 215:0.46 216:0.77 222:0.48 235:0.95 239:0.78 241:0.44 242:0.22 243:0.60 244:1.00 245:0.83 247:0.96 253:0.22 257:0.65 259:0.21 264:0.93 265:0.64 266:0.63 267:0.44 271:0.88 275:0.97 276:0.74 277:0.69 294:0.91 297:0.36 300:0.70 +0 8:0.69 10:0.79 11:0.42 12:0.51 17:0.49 18:0.65 21:0.78 27:0.72 29:0.52 30:0.09 34:0.26 36:0.54 39:0.52 43:0.05 45:0.85 55:0.84 66:0.08 69:0.87 70:0.99 71:0.93 74:0.25 86:0.57 91:0.31 98:0.19 101:0.45 108:0.94 123:0.94 124:0.97 127:0.67 129:0.59 133:0.97 135:0.44 139:0.55 146:0.48 147:0.54 149:0.05 154:0.05 159:0.94 163:0.75 170:0.77 172:0.37 173:0.53 174:0.91 175:0.99 177:0.96 178:0.55 179:0.35 197:0.86 202:0.62 212:0.13 216:0.11 222:0.48 235:0.31 239:0.78 241:0.81 242:0.14 243:0.19 244:1.00 245:0.72 247:0.98 253:0.22 254:0.84 257:0.97 259:0.21 264:0.93 265:0.21 266:0.98 267:0.36 271:0.88 275:0.97 276:0.79 277:0.99 294:0.91 300:0.94 +0 10:0.84 11:0.64 12:0.51 17:0.49 18:0.63 21:0.78 27:0.31 29:0.52 30:0.09 34:0.53 36:0.88 37:0.60 39:0.52 43:0.28 45:0.66 55:0.71 66:0.08 69:0.75 70:0.99 71:0.93 74:0.32 85:0.72 86:0.64 91:0.04 98:0.27 101:0.96 108:0.96 123:0.96 124:0.97 127:0.86 129:0.05 133:0.97 135:0.87 139:0.62 146:0.47 147:0.53 149:0.46 154:0.20 159:0.95 170:0.77 172:0.59 173:0.31 174:0.79 177:1.00 178:0.55 179:0.19 187:0.68 197:0.83 202:0.36 212:0.26 216:0.71 222:0.48 235:0.80 239:0.83 241:0.32 242:0.24 243:0.23 244:0.93 245:0.88 247:0.99 253:0.29 259:0.21 264:0.93 265:0.29 266:0.99 267:0.96 271:0.88 275:0.97 276:0.92 277:1.00 294:0.94 300:0.94 +2 8:0.81 9:0.76 11:0.85 12:0.06 17:0.57 18:0.99 20:0.94 21:0.30 22:0.93 27:0.21 29:0.91 30:0.13 31:0.97 34:0.74 36:0.85 37:0.74 39:0.34 43:0.21 45:0.90 47:0.93 48:0.91 55:0.71 64:0.77 66:0.44 69:0.10 70:0.97 71:0.64 74:0.53 78:0.72 79:0.99 81:0.35 83:0.60 85:0.72 86:0.81 91:0.69 96:0.71 97:0.89 98:0.59 100:0.89 101:0.97 108:0.96 111:0.81 120:0.86 122:0.86 123:0.96 124:0.87 126:0.26 127:0.87 129:0.59 131:0.99 133:0.90 135:0.37 137:0.97 139:0.81 140:0.94 144:0.57 146:0.93 147:0.94 149:0.59 150:0.31 151:0.90 152:0.88 153:0.94 154:0.81 155:0.58 157:0.97 158:0.79 159:0.93 162:0.69 164:0.76 166:0.89 169:0.87 172:0.96 173:0.06 177:0.70 179:0.13 182:0.96 186:0.73 187:0.39 190:0.77 191:0.75 192:0.59 196:0.96 202:0.88 208:0.54 212:0.60 216:0.95 219:0.92 222:0.60 227:1.00 229:0.98 231:0.98 235:0.31 241:0.50 242:0.37 243:0.28 245:0.98 246:0.61 247:0.98 248:0.88 253:0.43 255:0.79 256:0.90 259:0.21 260:0.83 261:0.86 262:0.93 265:0.60 266:0.89 267:0.84 268:0.91 271:0.49 276:0.97 279:0.82 281:0.91 283:0.82 284:0.96 285:0.62 287:1.00 292:0.95 300:0.94 +2 6:0.95 8:0.95 9:0.80 12:0.06 17:0.01 20:0.86 21:0.13 27:0.10 29:0.91 30:0.76 31:0.98 34:0.93 36:0.44 37:0.97 39:0.34 41:0.82 43:0.12 44:0.85 48:0.99 55:0.59 66:0.64 69:0.76 70:0.15 71:0.64 74:0.30 76:0.94 77:0.70 78:0.88 79:0.99 81:0.49 83:0.91 87:0.98 91:0.97 96:1.00 98:0.44 100:0.98 108:0.59 111:0.96 114:0.63 120:0.91 122:0.77 123:0.56 126:0.26 127:0.14 129:0.05 131:0.99 135:0.44 137:0.98 138:0.99 139:0.59 140:0.99 144:0.57 145:0.65 146:0.32 147:0.38 149:0.10 150:0.38 151:0.87 152:0.95 153:0.99 154:0.09 155:0.67 157:0.61 158:0.72 159:0.13 162:0.74 163:0.97 164:0.86 166:0.89 169:0.97 172:0.16 173:0.95 175:0.91 176:0.55 177:0.05 179:0.81 182:0.96 186:0.88 187:0.39 191:0.97 192:0.87 195:0.58 196:0.87 202:0.96 208:0.64 212:0.95 216:0.82 219:0.87 222:0.60 227:1.00 229:0.98 231:0.98 232:0.72 235:0.12 241:0.85 242:0.82 243:0.69 244:0.83 246:0.61 247:0.12 248:0.87 253:0.95 255:0.95 256:0.97 257:0.84 259:0.21 260:0.89 261:0.97 265:0.46 266:0.12 267:0.79 268:0.97 271:0.49 276:0.19 277:0.87 279:0.82 281:0.91 282:0.71 284:0.98 285:0.62 287:1.00 290:0.63 292:0.95 300:0.13 +2 8:0.78 11:0.85 12:0.06 17:0.69 18:0.99 21:0.30 22:0.93 27:0.50 29:0.91 30:0.10 31:0.90 32:0.77 34:0.80 36:0.90 37:0.60 39:0.34 43:0.20 44:0.93 45:0.92 47:0.93 48:0.91 55:0.71 64:0.77 66:0.45 69:0.53 70:0.97 71:0.64 74:0.70 77:0.89 79:0.94 81:0.35 83:0.60 85:0.72 86:0.88 91:0.27 97:0.89 98:0.63 101:0.97 108:0.96 120:0.63 122:0.86 123:0.96 124:0.87 126:0.26 127:0.96 129:0.81 131:0.93 133:0.89 135:0.73 137:0.90 139:0.90 140:0.45 144:0.57 145:0.72 146:0.95 147:0.96 149:0.64 150:0.31 151:0.65 153:0.48 154:0.76 155:0.58 157:0.97 158:0.79 159:0.94 162:0.74 164:0.54 166:0.89 172:0.97 173:0.15 177:0.70 179:0.12 182:0.91 187:0.39 190:0.89 192:0.59 195:0.99 196:0.93 202:0.56 208:0.54 212:0.67 216:0.86 219:0.92 220:0.62 222:0.60 227:0.98 229:0.61 231:0.97 235:0.31 241:0.05 242:0.38 243:0.29 245:0.98 246:0.61 247:0.98 248:0.63 253:0.41 255:0.74 256:0.58 259:0.21 260:0.63 262:0.93 265:0.64 266:0.91 267:0.65 268:0.59 271:0.49 276:0.98 279:0.82 281:0.91 282:0.85 283:0.82 284:0.86 285:0.56 287:0.61 292:0.95 300:0.94 +1 1:0.69 9:0.80 11:0.42 12:0.06 17:0.36 18:0.91 21:0.22 22:0.93 25:0.88 27:0.54 29:0.91 30:0.21 31:0.97 37:0.67 39:0.34 41:0.82 43:0.13 44:0.85 47:0.93 48:1.00 55:0.59 64:0.77 66:0.10 69:0.56 70:0.77 71:0.64 74:0.47 77:0.70 79:0.98 81:0.59 83:0.91 86:0.71 91:0.54 96:0.91 97:0.89 98:0.40 104:0.74 108:0.43 114:0.67 120:0.87 122:0.86 123:0.90 124:0.85 126:0.44 127:0.72 129:0.05 131:0.99 133:0.82 137:0.97 138:0.98 139:0.75 140:0.97 144:0.57 145:0.58 146:0.68 147:0.73 149:0.23 150:0.58 151:0.87 153:0.78 154:0.55 155:0.67 157:0.61 158:0.78 159:0.82 162:0.72 164:0.74 166:0.99 172:0.45 173:0.34 175:0.75 176:0.66 177:0.38 179:0.56 182:0.96 187:0.39 192:0.87 195:0.93 196:0.94 201:0.68 202:0.80 208:0.64 212:0.21 216:0.74 219:0.92 222:0.60 227:1.00 229:0.98 231:0.98 235:0.31 241:0.64 242:0.73 243:0.43 244:0.61 245:0.71 246:0.61 247:0.60 248:0.77 253:0.85 254:0.84 255:0.95 256:0.83 257:0.65 259:0.21 260:0.85 262:0.93 265:0.38 266:0.91 268:0.83 271:0.49 276:0.64 277:0.87 279:0.82 281:0.91 282:0.71 284:0.96 285:0.62 287:1.00 290:0.67 292:0.91 300:0.55 +1 6:0.95 8:0.94 9:0.80 12:0.06 17:0.20 20:0.96 21:0.78 27:0.10 29:0.91 31:0.99 34:0.39 36:0.24 37:0.97 39:0.34 41:0.88 71:0.64 74:0.26 78:0.91 79:0.99 83:0.91 87:0.98 91:0.99 96:1.00 100:0.98 111:0.96 120:0.96 131:0.99 135:0.92 137:0.99 138:0.99 140:0.99 144:0.57 151:0.92 152:0.95 153:0.99 155:0.67 157:0.61 158:0.72 162:0.64 164:0.91 166:0.61 169:0.97 175:0.97 182:0.96 186:0.91 189:0.95 191:0.98 192:0.87 202:0.98 208:0.64 216:0.82 222:0.60 227:1.00 229:0.98 231:0.98 232:0.72 238:0.98 241:0.95 244:0.93 246:0.61 248:0.94 253:0.97 255:0.95 256:0.99 257:0.93 259:0.21 260:0.95 261:0.97 267:0.87 268:0.99 271:0.49 277:0.95 283:0.82 284:0.98 285:0.62 287:1.00 +1 1:0.74 11:0.42 12:0.06 17:0.43 18:0.72 21:0.47 27:0.45 29:0.91 30:0.21 32:0.64 34:0.78 36:0.21 37:0.50 39:0.34 43:0.82 64:0.77 66:0.27 69:0.69 70:0.50 71:0.64 74:0.37 81:0.45 86:0.69 91:0.27 98:0.27 108:0.53 114:0.58 122:0.82 123:0.78 124:0.70 126:0.54 127:0.35 129:0.05 131:0.61 133:0.60 135:0.49 139:0.67 144:0.57 145:0.52 146:0.24 147:0.30 149:0.59 150:0.64 154:0.77 155:0.67 157:0.61 159:0.45 166:0.98 172:0.21 173:0.70 176:0.66 177:0.70 178:0.55 179:0.81 182:0.61 187:0.68 192:0.87 195:0.72 201:0.93 208:0.64 212:0.37 215:0.39 216:0.77 222:0.60 227:0.61 229:0.61 231:0.97 235:0.68 241:0.03 242:0.40 243:0.46 245:0.50 246:0.61 247:0.55 253:0.41 259:0.21 265:0.18 266:0.47 267:0.73 271:0.49 276:0.29 277:0.69 287:0.61 290:0.58 297:0.36 300:0.33 +1 1:0.69 8:0.84 9:0.76 11:0.75 12:0.06 17:0.67 18:0.92 21:0.05 22:0.93 25:0.88 27:0.54 29:0.91 30:0.20 31:0.93 34:0.49 36:0.96 37:0.67 39:0.34 43:0.22 44:0.85 45:0.66 47:0.93 48:0.97 55:0.71 64:0.77 66:0.11 69:0.54 70:0.96 71:0.64 74:0.44 77:0.70 79:0.97 81:0.79 83:0.60 86:0.75 91:0.51 96:0.76 98:0.34 99:0.83 101:0.52 104:0.74 108:0.91 114:0.85 120:0.89 122:0.86 123:0.91 124:0.92 126:0.44 127:0.85 129:0.95 131:0.99 133:0.91 135:0.95 137:0.93 139:0.72 140:0.45 144:0.57 145:0.58 146:0.39 147:0.45 149:0.29 150:0.76 151:0.81 153:0.48 154:0.54 155:0.58 157:0.78 159:0.88 162:0.85 164:0.76 165:0.70 166:0.99 172:0.45 173:0.24 175:0.75 176:0.66 177:0.38 179:0.38 182:0.96 187:0.68 190:0.77 192:0.59 195:0.97 196:0.91 201:0.68 202:0.76 208:0.54 212:0.36 216:0.74 219:0.87 220:0.62 222:0.60 227:1.00 229:0.98 231:0.98 235:0.12 241:0.47 242:0.57 243:0.23 244:0.61 245:0.80 246:1.00 247:0.67 248:0.84 253:0.67 255:0.79 256:0.82 257:0.65 259:0.21 260:0.85 262:0.93 265:0.36 266:0.94 267:0.28 268:0.83 271:0.49 276:0.78 277:0.69 281:0.89 282:0.71 284:0.93 285:0.62 287:1.00 290:0.85 292:0.95 300:0.98 +1 9:0.80 11:0.42 12:0.06 17:0.05 18:0.62 21:0.30 25:0.88 27:0.64 29:0.91 30:0.30 31:0.92 34:0.80 36:0.31 37:0.82 39:0.34 43:0.16 55:0.52 66:0.74 69:0.76 70:0.48 71:0.64 74:0.37 79:0.97 81:0.35 83:0.91 86:0.67 91:0.90 96:0.98 98:0.66 104:0.75 108:0.60 114:0.59 117:0.86 120:0.94 122:0.77 123:0.57 124:0.39 126:0.26 127:0.47 129:0.05 131:0.99 133:0.37 135:0.34 137:0.92 138:0.99 139:0.65 140:0.94 144:0.57 145:0.38 146:0.54 147:0.05 149:0.16 150:0.31 151:0.90 153:0.90 154:0.46 155:0.67 157:0.61 158:0.71 159:0.32 162:0.71 164:0.90 166:0.89 172:0.51 173:0.89 176:0.55 177:0.05 179:0.77 182:0.95 187:0.95 190:0.77 192:0.87 196:0.88 202:0.93 208:0.64 212:0.87 216:0.66 220:0.87 222:0.60 227:1.00 229:0.97 231:0.98 232:0.99 235:0.31 241:0.68 242:0.55 243:0.15 244:0.61 245:0.49 246:0.61 247:0.60 248:0.95 253:0.86 254:0.80 255:0.95 256:0.95 257:0.84 259:0.21 260:0.92 265:0.66 266:0.23 267:0.88 268:0.95 271:0.49 276:0.39 284:0.94 285:0.62 287:1.00 290:0.59 300:0.33 +1 7:0.66 8:0.93 9:0.80 12:0.06 17:0.01 21:0.13 25:0.88 27:0.28 29:0.91 30:0.15 31:0.97 32:0.64 34:0.24 36:0.25 37:0.55 39:0.34 43:0.16 55:0.59 66:0.89 69:0.42 70:0.70 71:0.64 74:0.27 79:0.99 81:0.40 83:0.91 91:0.99 96:0.97 98:0.97 104:0.58 108:0.32 120:1.00 123:0.31 126:0.26 127:0.78 129:0.05 131:0.99 135:0.24 137:0.97 138:1.00 140:1.00 144:0.57 145:0.38 146:0.87 147:0.89 149:0.29 150:0.34 151:0.90 153:1.00 154:0.44 155:0.67 157:0.61 159:0.44 162:0.72 164:1.00 166:0.61 172:0.68 173:0.48 175:0.75 177:0.38 179:0.67 182:0.72 190:0.77 191:0.96 192:0.87 195:0.62 202:1.00 208:0.64 212:0.48 215:0.61 216:0.92 222:0.60 227:1.00 229:0.86 230:0.69 231:0.97 233:0.73 235:0.12 241:0.93 242:0.79 243:0.89 244:0.61 246:0.61 247:0.39 248:0.97 253:0.84 254:0.96 255:0.95 256:1.00 257:0.65 259:0.21 260:1.00 265:0.97 266:0.54 267:0.73 268:1.00 271:0.49 276:0.57 277:0.69 283:0.82 284:0.95 285:0.62 287:0.88 297:0.36 300:0.43 +1 1:0.69 8:0.88 9:0.80 11:0.75 12:0.06 17:0.67 18:0.92 20:0.92 21:0.05 22:0.93 25:0.88 27:0.54 29:0.91 30:0.20 31:0.98 34:0.53 36:0.96 37:0.67 39:0.34 41:0.88 43:0.22 44:0.85 45:0.66 47:0.93 48:0.97 55:0.71 64:0.77 66:0.11 69:0.54 70:0.96 71:0.64 74:0.44 77:0.70 78:0.89 79:0.99 81:0.79 83:0.91 86:0.75 91:0.57 96:0.92 98:0.34 99:0.83 100:0.95 101:0.52 104:0.74 108:0.91 111:0.92 114:0.85 120:0.92 122:0.86 123:0.91 124:0.92 126:0.44 127:0.85 129:0.95 131:0.99 133:0.91 135:0.95 137:0.98 138:0.98 139:0.72 140:0.95 144:0.57 145:0.58 146:0.39 147:0.45 149:0.29 150:0.76 151:0.87 152:0.94 153:0.82 154:0.54 155:0.67 157:0.78 159:0.88 162:0.79 164:0.82 165:0.70 166:0.99 169:0.92 172:0.45 173:0.24 175:0.75 176:0.66 177:0.38 179:0.38 182:0.96 186:0.89 187:0.39 191:0.80 192:0.87 195:0.97 196:0.94 201:0.68 202:0.84 208:0.64 212:0.36 216:0.74 219:0.87 220:0.62 222:0.60 227:1.00 229:0.98 231:0.98 235:0.12 241:0.54 242:0.57 243:0.23 244:0.61 245:0.80 246:1.00 247:0.67 248:0.89 253:0.88 255:0.95 256:0.88 257:0.65 259:0.21 260:0.89 261:0.93 262:0.93 265:0.36 266:0.94 267:0.36 268:0.88 271:0.49 276:0.78 277:0.69 281:0.89 282:0.71 284:0.97 285:0.62 287:1.00 290:0.85 292:0.95 300:0.98 +1 1:0.69 8:0.95 9:0.80 11:0.42 12:0.06 17:0.04 18:0.80 21:0.40 25:0.88 27:0.64 29:0.91 30:0.21 31:0.93 34:0.69 36:0.21 37:0.82 39:0.34 43:0.16 44:0.85 48:0.98 55:0.52 64:0.77 66:0.11 69:0.88 70:0.82 71:0.64 74:0.35 79:0.98 81:0.41 83:0.60 86:0.66 91:0.84 96:0.91 98:0.48 104:0.75 108:0.76 114:0.58 120:0.91 122:0.77 123:0.30 124:0.64 126:0.44 127:0.54 129:0.05 131:0.99 133:0.60 135:0.49 137:0.93 139:0.65 140:0.87 144:0.57 145:0.40 146:0.44 147:0.58 149:0.18 150:0.52 151:0.81 153:0.72 154:0.26 155:0.67 157:0.61 158:0.71 159:0.46 162:0.82 164:0.85 166:0.98 172:0.45 173:0.95 176:0.55 177:0.38 179:0.76 182:0.61 187:0.95 190:0.77 191:0.85 192:0.87 195:0.72 196:0.89 201:0.68 202:0.88 208:0.64 212:0.58 216:0.66 219:0.87 220:0.96 222:0.60 227:1.00 229:0.61 231:0.98 235:0.52 241:0.69 242:0.22 243:0.31 244:0.61 245:0.56 246:0.61 247:0.74 248:0.90 253:0.63 254:0.80 255:0.95 256:0.92 257:0.65 259:0.21 260:0.88 265:0.42 266:0.54 267:0.36 268:0.92 271:0.49 276:0.44 277:0.69 279:0.82 281:0.91 284:0.95 285:0.62 287:0.61 290:0.58 292:0.95 300:0.55 +1 1:0.69 8:0.95 9:0.80 11:0.42 12:0.06 17:0.04 18:0.80 21:0.40 25:0.88 27:0.64 29:0.91 30:0.21 31:0.92 34:0.69 36:0.21 37:0.82 39:0.34 43:0.16 44:0.85 48:0.98 55:0.52 64:0.77 66:0.11 69:0.88 70:0.82 71:0.64 74:0.34 79:0.97 81:0.41 83:0.60 86:0.66 91:0.84 96:0.86 98:0.48 104:0.75 108:0.76 114:0.58 120:0.84 122:0.77 123:0.30 124:0.64 126:0.44 127:0.54 129:0.05 131:0.99 133:0.60 135:0.49 137:0.92 138:0.96 139:0.65 140:0.80 144:0.57 145:0.40 146:0.44 147:0.58 149:0.18 150:0.52 151:0.84 153:0.72 154:0.26 155:0.67 157:0.61 159:0.46 162:0.76 164:0.81 166:0.98 172:0.45 173:0.95 176:0.55 177:0.38 179:0.76 182:0.96 187:0.87 190:0.77 191:0.77 192:0.87 195:0.72 196:0.89 201:0.68 202:0.85 208:0.64 212:0.58 216:0.66 219:0.87 220:0.62 222:0.60 227:1.00 229:0.98 231:0.98 235:0.52 241:0.73 242:0.22 243:0.31 244:0.61 245:0.56 246:0.61 247:0.74 248:0.79 253:0.56 254:0.80 255:0.95 256:0.89 257:0.65 259:0.21 260:0.78 265:0.42 266:0.54 267:0.23 268:0.89 271:0.49 276:0.44 277:0.69 281:0.91 284:0.94 285:0.62 287:1.00 290:0.58 292:0.95 300:0.55 +1 7:0.66 8:0.95 9:0.80 11:0.42 12:0.06 17:0.32 18:0.63 21:0.30 25:0.88 27:0.64 29:0.91 30:0.21 31:0.88 34:0.59 36:0.38 37:0.82 39:0.34 43:0.16 55:0.45 66:0.69 69:0.72 70:0.50 71:0.64 74:0.37 79:0.90 81:0.35 83:0.60 86:0.67 91:0.86 96:0.96 98:0.62 104:0.75 108:0.55 114:0.59 120:0.91 122:0.77 123:0.53 124:0.41 126:0.26 127:0.37 129:0.05 131:0.99 133:0.39 135:0.60 137:0.88 138:0.97 139:0.65 140:0.95 144:0.57 145:0.39 146:0.47 147:0.05 149:0.17 150:0.31 151:0.84 153:0.91 154:0.48 155:0.67 157:0.61 159:0.26 162:0.65 164:0.82 166:0.89 172:0.41 173:0.88 175:0.75 176:0.55 177:0.05 179:0.82 182:0.61 187:0.87 190:0.77 191:0.92 192:0.87 196:0.88 202:0.89 208:0.64 212:0.55 216:0.66 222:0.60 227:1.00 229:0.61 230:0.69 231:0.98 232:0.95 233:0.73 235:0.31 241:0.74 242:0.72 243:0.18 244:0.61 245:0.46 246:0.61 247:0.39 248:0.87 253:0.78 254:0.80 255:0.79 256:0.91 257:0.84 259:0.21 260:0.87 265:0.63 266:0.27 267:0.44 268:0.91 271:0.49 276:0.33 277:0.69 284:0.90 285:0.62 287:0.61 290:0.59 300:0.33 +2 6:0.95 8:0.93 9:0.80 12:0.06 17:0.01 20:0.83 21:0.13 27:0.10 29:0.91 30:0.76 31:0.98 34:0.93 36:0.44 37:0.97 39:0.34 41:0.82 43:0.12 44:0.85 48:0.99 55:0.59 66:0.64 69:0.76 70:0.15 71:0.64 74:0.30 76:0.94 77:0.70 78:0.91 79:0.99 81:0.49 83:0.91 87:0.98 91:0.93 96:1.00 98:0.44 100:0.98 108:0.59 111:0.96 114:0.63 120:0.91 122:0.77 123:0.56 126:0.26 127:0.14 129:0.05 131:0.99 135:0.44 137:0.98 138:0.99 139:0.59 140:0.99 144:0.57 145:0.65 146:0.32 147:0.38 149:0.10 150:0.38 151:0.87 152:0.95 153:0.99 154:0.09 155:0.67 157:0.61 158:0.72 159:0.13 162:0.74 163:0.97 164:0.86 166:0.89 169:0.97 172:0.16 173:0.95 175:0.91 176:0.55 177:0.05 179:0.81 182:0.96 186:0.91 187:0.39 191:0.90 192:0.87 195:0.58 196:0.87 202:0.96 208:0.64 212:0.95 216:0.82 219:0.87 222:0.60 227:1.00 229:0.98 231:0.98 232:0.72 235:0.12 241:0.85 242:0.82 243:0.69 244:0.83 246:0.61 247:0.12 248:0.87 253:0.95 255:0.95 256:0.97 257:0.84 259:0.21 260:0.89 261:0.97 265:0.46 266:0.12 267:0.65 268:0.97 271:0.49 276:0.19 277:0.87 279:0.82 281:0.91 282:0.71 284:0.98 285:0.62 287:1.00 290:0.63 292:0.95 300:0.13 +1 1:0.74 11:0.64 12:0.06 17:0.51 18:0.82 21:0.22 27:0.45 29:0.91 30:0.76 32:0.64 34:0.67 36:0.22 37:0.50 39:0.34 43:0.82 60:0.87 64:0.77 66:0.31 69:0.69 70:0.41 71:0.64 74:0.62 81:0.72 83:0.91 85:0.72 86:0.83 91:0.11 98:0.29 101:0.87 108:0.52 114:0.71 122:0.83 123:0.75 124:0.60 126:0.54 127:0.31 129:0.05 131:0.61 133:0.43 135:0.24 139:0.85 144:0.57 145:0.47 146:0.26 147:0.32 149:0.79 150:0.83 154:0.77 155:0.67 157:0.82 158:0.91 159:0.40 165:0.93 166:0.99 172:0.41 173:0.71 176:0.73 177:0.70 178:0.55 179:0.60 182:0.96 187:0.68 192:0.87 195:0.70 201:0.93 208:0.64 212:0.81 215:0.51 216:0.77 219:0.95 222:0.60 227:0.61 229:0.61 231:0.98 235:0.42 241:0.12 242:0.18 243:0.51 245:0.71 246:1.00 247:0.82 253:0.52 259:0.21 265:0.21 266:0.38 267:0.59 271:0.49 276:0.30 277:0.69 279:0.86 283:0.78 287:0.61 290:0.71 292:0.91 297:0.36 300:0.43 +1 1:0.69 7:0.72 8:0.82 9:0.80 12:0.06 17:0.34 18:0.63 20:0.85 21:0.22 25:0.88 27:0.28 29:0.91 30:0.42 31:0.97 32:0.69 34:0.67 36:0.94 37:0.55 39:0.34 41:0.82 43:0.13 55:0.59 66:0.93 69:0.44 70:0.59 71:0.64 74:0.36 76:0.85 77:0.70 78:0.85 79:0.99 81:0.55 83:0.91 86:0.58 91:0.66 96:0.94 97:0.89 98:0.90 100:0.91 104:0.58 106:0.81 108:0.34 111:0.86 114:0.62 120:0.95 122:0.51 123:0.32 126:0.44 127:0.47 129:0.05 131:0.99 135:0.47 137:0.97 139:0.58 140:0.99 144:0.57 145:0.40 146:0.79 147:0.83 149:0.20 150:0.57 151:0.82 152:0.81 153:0.90 154:0.59 155:0.67 157:0.61 158:0.72 159:0.35 162:0.80 164:0.91 166:0.89 169:0.88 172:0.82 173:0.53 176:0.55 177:0.70 179:0.40 182:0.95 186:0.85 187:0.87 191:0.75 192:0.87 195:0.60 196:0.87 201:0.68 202:0.89 204:0.85 206:0.81 208:0.64 212:0.86 215:0.51 216:0.92 219:0.87 222:0.60 227:1.00 229:0.98 230:0.74 231:0.98 233:0.73 235:0.31 241:0.63 242:0.30 243:0.94 244:0.61 246:0.61 247:0.79 248:0.74 253:0.80 254:0.74 255:0.95 256:0.92 259:0.21 260:0.94 261:0.86 265:0.90 266:0.27 267:0.44 268:0.93 271:0.49 276:0.73 279:0.82 282:0.71 283:0.82 284:0.96 285:0.62 287:1.00 290:0.62 292:0.95 297:0.36 300:0.43 +3 1:0.74 6:0.92 8:0.84 9:0.80 11:0.64 12:0.37 17:0.51 20:0.83 21:0.74 28:0.76 30:0.17 32:0.80 34:0.55 36:0.24 37:0.97 39:0.27 41:0.96 43:0.80 55:0.71 66:0.07 69:0.74 70:0.83 74:0.89 77:0.70 78:0.86 81:0.55 83:0.81 85:0.97 91:0.58 96:0.91 98:0.41 100:0.86 101:0.80 104:0.80 108:0.92 111:0.85 114:0.67 120:0.86 123:0.95 124:0.94 126:0.54 127:0.78 129:0.95 131:1.00 133:0.94 135:0.77 140:0.80 144:0.57 145:0.63 146:0.65 147:0.70 149:0.95 150:0.68 151:0.93 152:0.79 153:0.78 154:0.74 155:0.67 157:0.96 159:0.92 161:0.76 162:0.71 164:0.86 165:0.65 166:0.99 167:0.65 169:0.84 172:0.65 173:0.39 176:0.73 177:0.38 178:0.42 179:0.22 181:0.67 182:0.93 186:0.86 187:0.21 189:0.93 191:0.75 192:0.59 195:0.98 201:0.74 202:0.80 208:0.64 212:0.39 215:0.46 216:0.98 222:0.60 227:1.00 229:0.96 231:0.99 232:0.85 235:0.95 238:0.87 241:0.50 242:0.57 243:0.28 245:0.89 246:1.00 247:0.47 248:0.91 253:0.93 255:0.79 256:0.83 259:0.21 260:0.86 261:0.83 265:0.27 266:0.91 267:0.88 268:0.83 271:0.26 276:0.90 277:0.69 282:0.88 285:0.62 287:1.00 290:0.67 297:0.36 299:0.88 300:0.84 +2 1:0.74 8:0.77 11:0.64 12:0.37 17:0.26 27:0.13 28:0.76 30:0.76 34:0.87 36:0.36 37:0.68 39:0.27 43:0.77 66:0.64 69:0.79 70:0.15 74:0.41 81:0.99 83:0.80 85:0.72 91:0.51 98:0.28 101:0.75 104:0.58 108:0.62 114:0.98 120:0.65 122:0.41 123:0.60 126:0.54 127:0.11 129:0.05 131:0.95 135:0.23 140:0.80 144:0.57 146:0.24 147:0.30 149:0.44 150:1.00 151:0.61 153:0.48 154:0.69 155:0.67 157:0.80 159:0.11 161:0.76 162:0.50 163:0.97 164:0.55 165:0.92 166:0.99 167:0.75 172:0.16 173:0.98 176:0.73 177:0.38 178:0.42 179:0.52 181:0.67 182:0.92 187:0.21 190:0.77 191:0.70 192:0.59 201:0.74 202:0.63 212:0.95 215:0.96 216:0.39 220:0.62 222:0.60 227:1.00 229:0.61 231:0.99 232:0.77 235:0.05 241:0.69 242:0.82 243:0.69 246:1.00 247:0.12 248:0.59 253:0.73 256:0.45 259:0.21 260:0.65 265:0.30 266:0.12 267:0.89 268:0.46 271:0.26 276:0.14 285:0.50 287:0.61 290:0.98 297:0.36 300:0.13 +2 1:0.74 6:0.96 7:0.81 8:0.91 9:0.80 11:0.64 12:0.37 17:0.26 20:0.98 21:0.30 27:0.21 28:0.76 30:0.26 34:0.66 36:0.50 37:0.74 39:0.27 41:0.98 43:0.98 60:0.99 66:0.23 69:0.12 70:0.89 74:0.95 78:0.93 81:0.87 83:0.90 85:0.97 91:0.77 96:0.98 98:0.59 100:0.96 101:0.81 104:0.84 106:0.81 108:0.95 111:0.94 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.60 124:0.77 126:0.54 127:0.60 129:0.81 131:1.00 133:0.76 135:0.17 140:0.45 144:0.57 146:0.45 147:0.52 149:0.89 150:0.92 151:0.99 152:0.91 153:0.94 154:0.98 155:0.67 157:0.96 158:0.92 159:0.88 161:0.76 162:0.71 164:0.92 165:0.84 166:0.99 167:0.63 169:0.95 172:0.80 173:0.08 176:0.73 177:0.38 179:0.25 181:0.67 182:0.93 186:0.93 187:0.39 189:0.97 191:0.76 192:0.59 201:0.74 202:0.88 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.95 220:0.62 222:0.60 227:1.00 229:0.96 230:0.87 231:0.99 232:0.89 233:0.76 235:0.42 238:0.94 241:0.46 242:0.37 243:0.19 245:0.92 246:1.00 247:0.60 248:0.98 253:0.99 255:0.79 256:0.89 259:0.21 260:0.96 261:0.95 265:0.33 266:0.89 267:0.23 268:0.90 271:0.26 276:0.84 279:0.86 283:0.82 285:0.62 287:1.00 290:0.86 297:0.36 300:0.94 +3 1:0.74 6:0.93 8:0.73 9:0.80 11:0.64 12:0.37 17:0.26 20:0.85 27:0.13 28:0.76 30:0.54 34:0.52 36:0.23 37:0.68 39:0.27 41:0.82 43:0.79 66:0.97 69:0.76 70:0.48 74:0.91 78:0.84 81:0.99 83:0.81 85:0.98 91:0.46 96:0.84 98:0.91 100:0.83 101:0.86 104:0.58 108:0.59 111:0.82 114:0.98 120:0.75 122:0.43 123:0.56 126:0.54 127:0.50 129:0.05 131:0.99 135:0.51 140:0.45 144:0.57 146:0.94 147:0.95 149:0.96 150:1.00 151:0.93 152:0.97 153:0.77 154:0.72 155:0.67 157:0.97 159:0.58 161:0.76 162:0.68 164:0.64 165:0.92 166:0.99 167:0.68 169:0.96 172:0.92 173:0.72 176:0.73 177:0.38 179:0.24 181:0.67 182:0.93 186:0.84 187:0.21 189:0.86 192:0.59 201:0.74 202:0.54 208:0.64 212:0.85 215:0.96 216:0.39 222:0.60 227:1.00 229:0.96 231:0.99 232:0.77 235:0.05 238:0.84 241:0.57 242:0.51 243:0.97 246:1.00 247:0.47 248:0.82 253:0.89 255:0.79 256:0.45 259:0.21 260:0.75 261:0.97 265:0.91 266:0.38 267:0.52 268:0.57 271:0.26 276:0.86 285:0.62 287:1.00 290:0.98 297:0.36 300:0.55 +1 11:0.64 12:0.37 17:0.24 21:0.78 27:0.45 28:0.76 30:0.71 32:0.80 34:0.80 36:0.18 37:0.50 39:0.27 43:0.81 66:0.24 69:0.71 70:0.40 74:0.69 77:0.70 85:0.88 91:0.09 98:0.20 101:0.76 108:0.83 122:0.43 123:0.82 124:0.81 127:0.23 129:0.89 131:0.61 133:0.78 135:0.77 144:0.57 145:0.49 146:0.13 147:0.18 149:0.74 154:0.76 157:0.90 159:0.52 161:0.76 163:0.81 166:0.61 167:0.60 172:0.21 173:0.58 177:0.38 178:0.55 179:0.65 181:0.67 182:0.82 187:0.68 195:0.87 212:0.50 216:0.77 222:0.60 227:0.61 229:0.61 231:0.96 235:0.80 241:0.35 242:0.63 243:0.28 245:0.50 246:0.61 247:0.35 253:0.52 259:0.21 265:0.22 266:0.38 267:0.28 271:0.26 276:0.34 282:0.88 287:0.61 299:0.88 300:0.33 +2 1:0.74 6:0.93 8:0.64 9:0.80 12:0.37 17:0.09 20:0.88 27:0.13 28:0.76 30:0.95 34:0.68 36:0.23 37:0.68 39:0.27 41:0.88 43:0.35 55:0.71 66:0.54 69:0.54 78:0.85 81:0.99 83:0.81 91:0.80 96:0.87 98:0.55 100:0.84 104:0.58 108:0.42 111:0.84 114:0.98 120:0.78 123:0.40 126:0.54 127:0.16 129:0.05 131:1.00 135:0.39 140:0.80 144:0.57 146:0.28 147:0.35 149:0.21 150:1.00 151:0.87 152:0.97 153:0.48 154:0.26 155:0.67 157:0.61 159:0.12 161:0.76 162:0.56 164:0.67 165:0.92 166:0.99 167:0.77 169:0.96 172:0.10 173:0.88 175:0.75 176:0.73 177:0.05 178:0.42 179:0.97 181:0.67 182:0.93 186:0.86 187:0.21 189:0.85 192:0.59 201:0.74 202:0.63 208:0.64 215:0.96 216:0.39 222:0.60 227:1.00 229:0.96 231:0.99 232:0.77 235:0.05 238:0.84 241:0.71 243:0.63 244:0.61 246:1.00 248:0.86 253:0.85 255:0.79 256:0.58 257:0.65 259:0.21 260:0.79 261:0.97 265:0.56 267:0.44 268:0.59 271:0.26 277:0.69 285:0.62 287:1.00 290:0.98 297:0.36 300:0.08 +2 1:0.74 6:0.91 7:0.81 8:0.84 9:0.80 11:0.64 12:0.37 17:0.20 20:0.97 21:0.77 25:0.88 27:0.38 28:0.76 30:0.21 32:0.80 34:0.24 36:0.45 37:0.73 39:0.27 41:0.96 43:0.82 55:0.71 66:0.36 69:0.42 70:0.62 74:0.69 76:0.85 77:0.70 78:0.90 81:0.47 83:0.81 85:0.80 91:0.77 96:0.93 98:0.63 100:0.92 101:0.73 104:0.58 108:0.32 111:0.90 114:0.63 120:0.93 121:0.97 123:0.31 124:0.83 126:0.54 127:0.96 129:0.81 131:1.00 133:0.83 135:0.42 140:0.87 144:0.57 145:0.80 146:0.84 147:0.87 149:0.72 150:0.63 151:0.96 152:0.90 153:0.85 154:0.77 155:0.67 157:0.84 159:0.75 161:0.76 162:0.72 163:0.86 164:0.91 165:0.63 166:0.99 167:0.89 169:0.90 172:0.51 173:0.28 176:0.73 177:0.38 179:0.66 181:0.67 182:0.93 186:0.90 189:0.92 191:0.87 192:0.59 195:0.88 201:0.74 202:0.87 204:0.89 208:0.64 212:0.18 215:0.43 216:0.26 220:0.74 222:0.60 227:1.00 229:0.96 230:0.87 231:0.99 233:0.76 235:0.99 238:0.91 241:0.83 242:0.74 243:0.51 245:0.65 246:1.00 247:0.47 248:0.92 253:0.92 255:0.79 256:0.89 259:0.21 260:0.93 261:0.92 265:0.64 266:0.87 267:0.44 268:0.90 271:0.26 276:0.60 282:0.88 283:0.71 285:0.62 287:1.00 290:0.63 297:0.36 299:0.88 300:0.43 +1 11:0.64 12:0.37 17:0.18 21:0.78 27:0.45 28:0.76 30:0.84 34:0.92 36:0.18 37:0.50 39:0.27 43:0.73 66:0.48 69:0.87 70:0.39 74:0.89 85:0.98 91:0.09 98:0.33 101:0.85 108:0.74 122:0.43 123:0.72 124:0.67 127:0.24 129:0.81 131:0.61 133:0.67 135:0.84 144:0.57 145:0.50 146:0.57 147:0.63 149:0.95 154:0.62 157:0.97 159:0.54 161:0.76 166:0.61 167:0.56 172:0.75 173:0.80 177:0.38 178:0.55 179:0.22 181:0.67 182:0.90 187:0.68 212:0.87 216:0.77 222:0.60 227:0.61 229:0.61 231:0.98 235:0.80 241:0.18 242:0.63 243:0.60 245:0.86 246:0.61 247:0.30 253:0.63 259:0.21 265:0.36 266:0.47 267:0.19 271:0.26 276:0.74 287:0.61 300:0.43 +3 1:0.74 6:0.94 8:0.83 9:0.80 11:0.64 12:0.37 20:0.76 27:0.14 28:0.76 30:0.76 34:0.75 36:0.19 37:0.67 39:0.27 41:0.98 43:0.86 60:0.87 66:0.11 69:0.59 70:0.43 74:0.74 78:0.90 81:0.99 83:0.90 85:0.91 91:0.78 96:0.98 98:0.24 100:0.92 101:0.78 108:0.90 111:0.89 114:0.98 120:0.96 122:0.43 123:0.62 124:0.73 126:0.54 127:0.59 129:0.81 131:1.00 133:0.67 135:0.70 140:0.45 144:0.57 145:0.56 146:0.29 147:0.36 149:0.81 150:1.00 151:0.99 152:0.91 153:0.94 154:0.83 155:0.67 157:0.93 158:0.92 159:0.75 161:0.76 162:0.72 164:0.92 165:0.92 166:0.99 167:0.62 169:0.91 172:0.32 173:0.42 176:0.73 177:0.38 179:0.77 181:0.67 182:0.93 186:0.91 187:0.39 191:0.70 192:0.59 201:0.74 202:0.88 208:0.64 212:0.22 215:0.96 216:0.93 220:0.62 222:0.60 227:1.00 229:0.96 231:0.99 235:0.05 238:0.89 241:0.41 242:0.61 243:0.37 245:0.60 246:1.00 247:0.39 248:0.98 253:0.98 255:0.79 256:0.89 259:0.21 260:0.96 261:0.92 265:0.26 266:0.71 267:0.23 268:0.90 271:0.26 276:0.32 279:0.86 283:0.82 285:0.62 287:1.00 290:0.98 297:0.36 300:0.43 +2 1:0.74 6:0.94 8:0.88 9:0.80 11:0.64 12:0.37 17:0.01 20:0.97 27:0.14 28:0.76 30:0.86 34:0.83 36:0.18 37:0.67 39:0.27 41:0.98 43:0.86 60:0.97 66:0.33 69:0.57 70:0.32 74:0.75 78:0.90 81:0.99 83:0.90 85:0.91 91:0.54 96:0.98 98:0.25 100:0.93 101:0.78 108:0.90 111:0.90 114:0.98 120:0.96 122:0.43 123:0.90 124:0.71 126:0.54 127:0.57 129:0.81 131:1.00 133:0.67 135:0.65 140:0.45 144:0.57 145:0.56 146:0.22 147:0.28 149:0.81 150:1.00 151:0.99 152:0.91 153:0.94 154:0.84 155:0.67 157:0.93 158:0.92 159:0.74 161:0.76 162:0.70 164:0.93 165:0.92 166:0.99 167:0.63 169:0.91 172:0.32 173:0.40 176:0.73 177:0.38 179:0.79 181:0.67 182:0.93 186:0.90 187:0.39 189:0.95 192:0.59 201:0.74 202:0.88 208:0.64 212:0.23 215:0.96 216:0.93 220:0.62 222:0.60 227:1.00 229:0.96 231:0.99 235:0.05 238:0.91 241:0.44 242:0.63 243:0.39 245:0.58 246:1.00 247:0.30 248:0.98 253:0.98 255:0.79 256:0.90 259:0.21 260:0.96 261:0.92 265:0.27 266:0.54 267:0.19 268:0.91 271:0.26 276:0.26 279:0.86 283:0.82 285:0.62 287:1.00 290:0.98 297:0.36 300:0.33 +4 1:0.74 6:0.93 8:0.88 9:0.80 11:0.64 12:0.37 20:0.98 27:0.13 28:0.76 30:0.41 34:0.53 36:0.40 37:0.68 39:0.27 41:0.99 43:0.80 55:0.92 60:0.95 66:0.49 69:0.48 70:0.60 74:0.61 78:0.97 81:0.99 83:0.90 85:0.80 91:0.92 96:0.99 98:0.65 100:0.98 101:0.75 104:0.58 108:0.37 111:0.98 114:0.98 120:0.97 123:0.36 124:0.78 126:0.54 127:0.83 129:0.05 131:1.00 133:0.82 135:0.66 138:0.99 140:0.45 144:0.57 146:0.76 147:0.79 149:0.70 150:1.00 151:0.99 152:0.97 153:0.97 154:0.74 155:0.67 157:0.85 158:0.92 159:0.61 161:0.76 162:0.66 163:0.81 164:0.95 165:0.92 166:0.99 167:0.91 169:0.96 172:0.45 173:0.43 176:0.73 177:0.38 179:0.78 181:0.67 182:0.93 186:0.97 189:0.95 191:0.87 192:0.59 201:0.74 202:0.92 208:0.64 212:0.19 215:0.96 216:0.39 222:0.60 227:1.00 229:0.96 231:0.99 232:0.77 235:0.05 238:0.96 241:0.91 242:0.63 243:0.60 245:0.52 246:1.00 247:0.39 248:0.99 253:0.98 255:0.79 256:0.94 259:0.21 260:0.97 261:0.97 265:0.66 266:0.71 267:0.88 268:0.94 271:0.26 276:0.49 279:0.86 283:0.82 285:0.62 287:1.00 290:0.98 297:0.36 300:0.43 +2 1:0.74 6:0.91 7:0.81 8:0.81 9:0.80 11:0.64 12:0.37 17:0.26 20:0.90 21:0.13 27:0.13 28:0.76 30:0.76 32:0.80 34:0.86 36:0.36 37:0.79 39:0.27 41:0.93 43:0.81 60:0.87 66:0.86 69:0.70 70:0.29 74:0.88 77:0.70 78:0.80 81:0.94 83:0.87 85:0.93 91:0.42 96:0.91 98:0.69 100:0.83 101:0.84 108:0.53 111:0.79 114:0.92 120:0.84 121:0.90 122:0.57 123:0.51 126:0.54 127:0.21 129:0.05 131:1.00 135:0.76 140:0.45 144:0.57 145:0.54 146:0.62 147:0.67 149:0.88 150:0.97 151:0.93 152:0.85 153:0.78 154:0.76 155:0.67 157:0.95 158:0.92 159:0.23 161:0.76 162:0.84 164:0.78 165:0.88 166:0.99 167:0.63 169:0.80 172:0.61 173:0.80 176:0.73 177:0.38 179:0.45 181:0.67 182:0.93 186:0.80 187:0.68 189:0.93 192:0.59 195:0.61 201:0.74 202:0.66 204:0.89 208:0.64 212:0.69 215:0.84 216:0.83 222:0.60 227:1.00 229:0.96 230:0.87 231:0.99 233:0.76 235:0.22 238:0.87 241:0.44 242:0.61 243:0.87 246:1.00 247:0.39 248:0.90 253:0.93 255:0.79 256:0.71 259:0.21 260:0.85 261:0.83 265:0.70 266:0.38 267:0.59 268:0.73 271:0.26 276:0.49 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.92 297:0.36 299:0.88 300:0.25 +0 1:0.74 7:0.66 11:0.87 12:0.79 17:0.76 18:0.87 21:0.54 27:1.00 29:0.77 30:0.66 32:0.80 34:0.75 36:0.68 37:0.29 39:0.40 43:0.96 44:0.93 45:0.83 46:0.96 64:0.77 66:0.52 69:0.27 70:0.49 71:0.77 74:0.85 77:0.96 81:0.55 83:0.91 85:0.80 86:0.94 91:0.18 97:0.89 98:0.36 99:0.83 101:0.97 107:0.95 108:0.65 110:0.96 114:0.59 123:0.63 124:0.65 125:0.88 126:0.54 127:0.44 129:0.59 131:0.61 133:0.66 135:0.68 139:0.96 144:0.76 145:0.77 146:0.24 147:0.30 149:0.84 150:0.74 154:0.96 155:0.67 157:0.90 158:0.79 159:0.30 160:0.88 165:1.00 166:0.90 172:0.61 173:0.42 176:0.73 177:0.70 178:0.55 179:0.52 182:0.85 187:0.39 192:0.87 195:0.61 201:0.93 204:0.85 208:0.64 212:0.85 215:0.39 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.76 235:0.84 241:0.39 242:0.24 243:0.34 245:0.73 246:0.97 247:0.67 253:0.47 259:0.21 265:0.38 266:0.38 267:0.52 271:0.79 276:0.55 279:0.82 281:0.91 282:0.88 287:0.61 289:0.95 290:0.59 292:0.95 297:0.36 299:0.99 300:0.43 +1 1:0.74 11:0.81 12:0.79 17:0.49 18:0.94 21:0.54 27:0.26 29:0.77 30:0.38 32:0.79 34:0.30 36:0.62 37:0.49 39:0.40 43:0.66 44:0.93 45:0.87 46:0.88 48:0.91 55:0.52 64:0.77 66:0.93 69:0.25 70:0.52 71:0.77 74:0.86 77:0.70 81:0.47 83:0.60 86:0.96 91:0.42 97:0.89 98:0.88 99:0.83 101:0.52 107:0.96 108:0.20 110:0.96 114:0.59 122:0.85 123:0.19 125:0.88 126:0.54 127:0.42 129:0.05 131:0.61 135:0.88 139:0.97 144:0.76 145:0.86 146:0.73 147:0.78 149:0.51 150:0.67 154:0.85 155:0.67 157:0.90 158:0.78 159:0.30 160:0.88 165:0.70 166:0.90 172:0.81 173:0.41 176:0.73 177:0.70 178:0.55 179:0.40 182:0.84 187:0.68 192:0.87 195:0.58 201:0.93 204:0.85 208:0.64 212:0.93 215:0.39 216:0.35 219:0.92 222:0.90 227:0.61 228:0.99 229:0.61 231:0.84 235:0.74 241:0.35 242:0.16 243:0.93 246:0.97 247:0.88 253:0.48 254:0.80 259:0.21 265:0.88 266:0.23 267:0.23 271:0.79 276:0.71 279:0.82 281:0.89 282:0.87 287:0.61 289:0.95 290:0.59 292:0.91 297:0.36 300:0.43 +0 11:0.81 12:0.79 17:0.59 18:0.79 21:0.74 27:0.45 29:0.77 30:0.21 32:0.69 34:0.71 36:0.18 37:0.50 39:0.40 43:0.08 44:0.85 45:0.66 46:0.88 55:0.45 64:0.77 66:0.47 69:0.72 70:0.89 71:0.77 74:0.41 81:0.29 86:0.74 91:0.17 98:0.36 101:0.52 107:0.92 108:0.55 110:0.93 123:0.52 124:0.56 125:0.88 126:0.44 127:0.33 129:0.05 131:0.61 133:0.37 135:0.51 139:0.72 144:0.76 145:0.52 146:0.44 147:0.51 149:0.10 150:0.23 154:0.71 157:0.77 159:0.43 160:0.88 166:0.76 172:0.41 173:0.72 177:0.70 178:0.55 179:0.67 182:0.72 187:0.68 192:0.51 195:0.72 201:0.68 212:0.58 215:0.36 216:0.77 222:0.90 227:0.61 229:0.61 231:0.72 235:0.95 241:0.15 242:0.38 243:0.59 245:0.66 246:0.61 247:0.60 253:0.32 254:0.80 259:0.21 265:0.38 266:0.54 267:0.82 271:0.79 276:0.32 287:0.61 289:0.91 297:0.36 300:0.70 +0 11:0.81 12:0.79 17:0.62 18:0.62 21:0.78 27:0.45 29:0.77 30:0.76 34:0.80 36:0.26 37:0.50 39:0.40 43:0.74 45:0.66 46:0.88 66:0.64 69:0.78 70:0.15 71:0.77 74:0.40 86:0.73 91:0.06 98:0.16 101:0.52 107:0.89 108:0.62 110:0.90 122:0.57 123:0.59 125:0.88 127:0.09 129:0.05 131:0.61 135:0.83 139:0.69 144:0.76 145:0.44 146:0.24 147:0.30 149:0.47 154:0.64 157:0.77 159:0.11 160:0.88 163:0.97 166:0.61 172:0.16 173:0.78 177:0.70 178:0.55 179:0.18 182:0.72 187:0.68 212:0.95 216:0.77 222:0.90 227:0.61 229:0.61 231:0.64 235:0.99 241:0.43 242:0.82 243:0.69 246:0.61 247:0.12 253:0.31 254:0.97 259:0.21 265:0.18 266:0.12 267:0.28 271:0.79 276:0.14 287:0.61 289:0.88 300:0.13 +2 1:0.74 7:0.66 11:0.87 12:0.79 17:0.78 18:0.84 21:0.64 27:1.00 29:0.77 30:0.54 32:0.80 34:0.68 36:0.38 37:0.29 39:0.40 43:0.95 44:0.93 45:0.77 46:0.97 64:0.77 66:0.90 69:0.32 70:0.67 71:0.77 74:0.84 77:0.96 81:0.47 83:0.91 85:0.80 86:0.93 91:0.18 97:0.89 98:0.90 101:0.97 107:0.95 108:0.25 110:0.96 114:0.57 123:0.24 125:0.88 126:0.54 127:0.47 129:0.05 131:0.61 135:0.56 139:0.95 144:0.76 145:0.85 146:0.70 147:0.74 149:0.83 150:0.71 154:0.95 155:0.67 157:0.89 158:0.79 159:0.27 160:0.88 165:0.93 166:0.90 172:0.71 173:0.50 176:0.73 177:0.70 178:0.55 179:0.57 182:0.84 187:0.39 192:0.87 195:0.59 201:0.93 204:0.85 208:0.64 212:0.80 215:0.38 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.73 235:0.88 241:0.30 242:0.27 243:0.90 246:0.97 247:0.74 253:0.46 259:0.21 265:0.90 266:0.38 267:0.19 271:0.79 276:0.56 279:0.82 281:0.91 282:0.88 287:0.61 289:0.94 290:0.57 292:0.95 297:0.36 299:0.99 300:0.55 +2 1:0.74 7:0.66 11:0.87 12:0.79 17:0.79 18:0.87 21:0.54 27:1.00 29:0.77 30:0.60 32:0.80 34:0.64 36:0.55 37:0.29 39:0.40 43:0.96 44:0.93 45:0.81 46:0.97 64:0.77 66:0.23 69:0.27 70:0.53 71:0.77 74:0.83 77:0.96 81:0.55 83:0.91 85:0.80 86:0.95 91:0.15 97:0.89 98:0.56 99:0.83 101:0.97 107:0.95 108:0.61 110:0.96 114:0.59 123:0.20 124:0.56 125:0.88 126:0.54 127:0.47 129:0.59 131:0.61 133:0.46 135:0.61 139:0.96 144:0.76 145:0.77 146:0.42 147:0.48 149:0.85 150:0.74 154:0.96 155:0.67 157:0.90 158:0.79 159:0.28 160:0.88 165:1.00 166:0.90 172:0.59 173:0.46 176:0.73 177:0.70 178:0.55 179:0.55 182:0.85 187:0.39 192:0.87 195:0.59 201:0.93 204:0.85 208:0.64 212:0.84 215:0.39 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.76 235:0.84 241:0.30 242:0.24 243:0.43 245:0.79 246:0.97 247:0.76 253:0.47 259:0.21 265:0.47 266:0.38 267:0.52 271:0.79 276:0.55 279:0.82 281:0.91 282:0.88 287:0.61 289:0.95 290:0.59 292:0.95 297:0.36 299:0.99 300:0.55 +2 1:0.74 7:0.81 8:0.74 11:0.81 12:0.79 17:0.47 18:0.88 20:0.89 21:0.47 22:0.93 27:0.21 29:0.77 30:0.28 34:0.66 36:0.49 37:0.74 39:0.40 43:0.63 45:0.83 46:0.88 47:0.93 64:0.77 66:0.89 69:0.21 70:0.74 71:0.77 74:0.80 78:0.92 81:0.50 83:0.60 86:0.92 91:0.25 97:0.94 98:0.77 99:0.83 100:0.96 101:0.52 104:0.99 106:0.81 107:0.95 108:0.17 110:0.95 111:0.93 114:0.60 117:0.86 122:0.80 123:0.16 125:0.88 126:0.54 127:0.26 129:0.05 131:0.61 135:0.17 139:0.94 144:0.76 146:0.68 147:0.73 149:0.49 150:0.68 152:0.99 154:0.94 155:0.67 157:0.88 158:0.91 159:0.27 160:0.88 165:0.70 166:0.90 169:0.91 172:0.70 173:0.35 176:0.73 177:0.70 178:0.55 179:0.44 182:0.84 186:0.91 187:0.39 192:0.87 201:0.93 204:0.89 206:0.81 208:0.64 212:0.86 215:0.41 216:0.95 219:0.95 222:0.90 227:0.61 229:0.61 230:0.86 231:0.84 232:0.91 233:0.73 235:0.68 241:0.21 242:0.19 243:0.90 246:0.97 247:0.84 253:0.48 254:0.74 259:0.21 261:0.93 262:0.93 265:0.78 266:0.38 267:0.52 271:0.79 276:0.58 279:0.86 281:0.88 283:0.78 287:0.61 289:0.95 290:0.60 292:0.91 297:0.36 300:0.55 +2 1:0.74 8:0.59 11:0.81 12:0.79 17:0.26 18:0.69 21:0.64 22:0.93 27:0.42 29:0.77 30:0.45 32:0.79 34:0.50 36:0.46 37:0.63 39:0.40 43:0.09 44:0.93 45:0.66 46:0.88 47:0.93 64:0.77 66:0.49 69:0.76 70:0.25 71:0.77 74:0.55 77:0.70 81:0.42 83:0.60 86:0.73 91:0.22 98:0.13 99:0.83 101:0.52 104:0.98 106:0.81 107:0.90 108:0.59 110:0.90 114:0.57 117:0.86 122:0.80 123:0.57 124:0.48 125:0.88 126:0.54 127:0.18 129:0.05 131:0.61 133:0.37 135:0.23 139:0.80 144:0.76 145:0.61 146:0.19 147:0.24 149:0.06 150:0.66 154:0.71 155:0.67 157:0.77 159:0.23 160:0.88 163:0.79 165:0.70 166:0.90 172:0.16 173:0.83 176:0.73 177:0.70 178:0.55 179:0.86 182:0.84 187:0.87 192:0.87 195:0.64 201:0.93 206:0.81 208:0.64 212:0.61 215:0.38 216:0.68 222:0.90 227:0.61 229:0.61 231:0.84 232:0.89 235:0.84 241:0.50 242:0.63 243:0.60 245:0.39 246:0.97 247:0.30 253:0.40 254:0.80 259:0.21 262:0.93 265:0.14 266:0.17 267:0.36 271:0.79 276:0.13 282:0.87 287:0.61 289:0.94 290:0.57 297:0.36 300:0.18 +1 1:0.69 8:0.81 9:0.76 11:0.81 12:0.79 17:0.70 18:1.00 20:0.85 21:0.68 22:0.93 27:0.21 29:0.77 30:0.44 31:0.93 34:0.42 36:0.64 37:0.74 39:0.40 43:0.72 45:1.00 46:0.88 47:0.93 48:0.97 64:0.77 66:0.54 69:0.29 70:0.66 71:0.77 74:0.98 78:0.72 79:0.94 81:0.32 83:0.60 86:1.00 91:0.61 96:0.83 97:0.99 98:0.81 99:0.83 100:0.95 101:0.52 107:0.97 108:0.61 110:0.97 111:0.88 114:0.55 117:0.86 122:0.85 123:0.59 124:0.70 125:0.88 126:0.44 127:0.98 129:0.59 131:0.94 133:0.77 135:0.26 137:0.93 139:1.00 140:0.45 144:0.76 146:0.42 147:0.48 149:0.99 150:0.51 151:0.81 152:0.99 153:0.48 154:0.20 155:0.58 157:0.94 158:0.78 159:0.95 160:0.88 162:0.76 165:0.70 166:0.90 169:0.91 172:1.00 173:0.07 176:0.66 177:0.70 179:0.09 182:0.86 186:0.73 187:0.39 190:0.77 192:0.51 196:0.98 201:0.68 202:0.79 204:0.85 208:0.54 212:0.80 216:0.95 219:0.92 220:0.93 222:0.90 227:0.98 229:0.92 231:0.85 232:0.91 235:0.84 241:0.56 242:0.45 243:0.06 245:1.00 246:0.97 247:0.93 248:0.75 253:0.86 254:0.74 255:0.74 256:0.83 259:0.21 261:0.93 262:0.93 265:0.81 266:0.75 267:0.73 268:0.83 271:0.79 276:0.99 279:0.82 281:0.89 284:0.97 285:0.56 287:0.98 289:0.95 290:0.55 292:0.91 300:0.84 +1 7:0.66 8:0.77 11:0.81 12:0.79 17:0.51 18:0.97 20:0.90 22:0.93 27:0.69 29:0.77 30:0.32 31:0.90 34:0.28 36:0.91 37:0.47 39:0.40 43:0.69 45:0.77 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.09 69:0.97 70:0.87 71:0.77 74:0.35 76:0.85 78:0.87 79:0.94 81:0.77 86:0.72 91:0.20 98:0.31 100:0.89 101:0.52 104:0.83 106:0.81 107:0.95 108:0.96 110:0.95 111:0.86 120:0.72 122:0.86 123:0.96 124:0.96 125:0.88 126:0.44 127:0.89 129:0.59 131:0.84 133:0.95 135:0.39 137:0.90 139:0.70 140:0.45 144:0.76 146:0.75 147:0.44 149:0.59 150:0.55 151:0.81 152:0.84 153:0.48 154:0.59 157:0.80 159:0.91 160:0.88 162:0.86 163:0.81 164:0.64 166:0.77 169:0.87 172:0.54 173:0.95 177:0.38 179:0.27 182:0.74 186:0.87 187:0.21 190:0.77 192:0.51 196:0.89 201:0.68 202:0.50 206:0.81 212:0.14 215:0.96 216:0.30 220:0.74 222:0.90 227:0.92 229:0.61 230:0.69 231:0.78 233:0.76 235:0.05 241:0.21 242:0.70 243:0.17 244:0.61 245:0.84 246:0.61 247:0.82 248:0.75 253:0.29 255:0.74 256:0.58 257:0.65 259:0.21 260:0.69 261:0.87 262:0.93 265:0.33 266:0.95 267:0.23 268:0.59 271:0.79 276:0.87 281:0.91 283:0.82 284:0.86 285:0.56 287:0.61 289:0.93 297:0.36 300:0.94 +2 1:0.74 7:0.66 11:0.87 12:0.79 17:0.73 18:0.86 21:0.54 27:1.00 29:0.77 30:0.62 32:0.80 34:0.62 36:0.50 37:0.29 39:0.40 43:0.96 44:0.93 45:0.81 46:0.97 64:0.77 66:0.90 69:0.27 70:0.47 71:0.77 74:0.84 77:0.96 81:0.55 83:0.91 85:0.80 86:0.94 91:0.17 97:0.89 98:0.87 99:0.83 101:0.97 107:0.95 108:0.21 110:0.96 114:0.59 123:0.20 125:0.88 126:0.54 127:0.39 129:0.05 131:0.61 135:0.65 139:0.96 144:0.76 145:0.77 146:0.67 147:0.72 149:0.83 150:0.74 154:0.96 155:0.67 157:0.90 158:0.79 159:0.26 160:0.88 165:1.00 166:0.90 172:0.71 173:0.46 176:0.73 177:0.70 178:0.55 179:0.53 182:0.85 187:0.39 192:0.87 195:0.59 201:0.93 204:0.85 208:0.64 212:0.83 215:0.39 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.76 235:0.84 241:0.37 242:0.27 243:0.90 246:0.97 247:0.67 253:0.47 259:0.21 265:0.87 266:0.38 267:0.23 271:0.79 276:0.53 279:0.82 281:0.91 282:0.88 287:0.61 289:0.95 290:0.59 292:0.95 297:0.36 299:0.99 300:0.43 +2 6:1.00 7:0.66 8:0.98 9:0.80 12:0.79 17:0.22 20:0.99 21:0.78 27:0.13 29:0.77 31:0.98 34:0.49 36:0.78 37:0.97 39:0.40 43:0.13 44:0.85 46:0.88 48:0.91 66:0.36 69:0.64 71:0.77 78:0.94 79:0.99 83:0.91 91:0.96 96:0.94 98:0.09 100:0.99 107:0.88 108:0.49 110:0.88 111:0.99 120:0.63 123:0.47 125:0.88 127:0.07 129:0.05 131:0.94 135:0.42 137:0.98 139:0.59 140:0.95 144:0.76 145:0.89 146:0.05 147:0.05 149:0.07 151:0.52 152:0.90 153:0.72 154:0.10 155:0.67 157:0.61 159:0.05 160:0.88 162:0.52 164:0.85 166:0.61 169:1.00 172:0.05 173:0.78 175:0.91 177:0.05 178:0.42 182:0.86 186:0.94 189:1.00 190:0.77 191:0.84 192:0.87 195:0.61 196:0.87 202:0.93 208:0.64 216:0.53 219:0.87 220:0.74 222:0.90 227:0.98 229:0.92 230:0.69 231:0.85 233:0.76 235:0.12 238:0.99 241:0.87 243:0.51 244:0.83 246:0.61 248:0.54 253:0.88 255:0.95 256:0.91 257:0.84 259:0.21 260:0.62 261:0.98 265:0.10 267:0.44 268:0.92 271:0.79 277:0.87 281:0.89 284:0.99 285:0.62 287:0.98 289:0.95 292:0.91 +0 11:0.64 12:0.96 17:0.36 21:0.47 26:0.95 27:0.45 28:0.93 30:0.55 34:0.55 36:0.41 37:0.50 39:0.35 43:0.78 55:0.84 58:0.93 66:0.18 69:0.69 70:0.84 74:0.79 81:0.49 85:0.72 91:0.06 98:0.35 101:0.69 108:0.52 114:0.55 122:0.67 123:0.50 124:0.89 126:0.32 127:0.64 129:0.81 131:0.61 133:0.86 135:0.94 141:0.91 144:0.57 145:0.38 146:0.70 147:0.74 149:0.64 150:0.38 154:0.71 157:0.76 159:0.83 161:0.65 166:0.80 167:0.65 172:0.45 173:0.47 176:0.53 177:0.38 178:0.55 179:0.49 181:0.70 182:0.72 187:0.68 199:0.96 212:0.14 216:0.77 222:0.48 223:0.95 224:0.93 227:0.61 229:0.61 231:0.83 234:0.91 235:0.52 241:0.27 242:0.57 243:0.39 245:0.73 246:0.61 247:0.67 253:0.57 259:0.21 265:0.37 266:0.95 267:0.69 271:0.60 274:0.91 276:0.68 287:0.61 290:0.55 300:0.84 +2 1:0.74 7:0.81 11:0.64 12:0.96 17:0.64 21:0.30 26:0.97 27:0.04 28:0.93 30:0.14 32:0.80 34:0.75 36:0.27 37:0.99 39:0.35 43:0.59 58:0.93 66:0.19 69:0.93 70:0.97 74:0.72 77:0.70 81:0.79 83:0.75 85:0.85 91:0.20 98:0.21 101:0.72 108:0.86 114:0.86 121:0.99 122:0.67 123:0.85 124:0.85 126:0.54 127:0.33 129:0.81 131:0.61 133:0.83 135:0.79 141:0.91 144:0.57 145:0.49 146:0.49 147:0.55 149:0.50 150:0.86 154:0.48 155:0.67 157:0.81 159:0.80 161:0.65 163:0.81 165:0.84 166:0.91 167:0.60 172:0.27 173:0.83 176:0.73 177:0.38 178:0.55 179:0.62 181:0.70 182:0.84 187:0.87 192:0.59 195:0.96 199:0.97 201:0.74 204:0.89 208:0.64 212:0.18 215:0.72 216:0.57 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 230:0.87 231:0.85 233:0.76 234:0.91 235:0.42 241:0.07 242:0.31 243:0.40 245:0.58 246:0.97 247:0.60 253:0.70 259:0.21 265:0.23 266:0.84 267:0.44 271:0.60 274:0.91 276:0.47 282:0.88 287:0.61 290:0.86 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.81 8:0.78 9:0.80 11:0.64 12:0.96 17:0.49 21:0.47 26:0.97 27:0.21 28:0.93 30:0.21 34:0.61 36:0.75 37:0.74 39:0.35 41:0.88 43:0.97 55:0.84 58:0.98 60:0.95 66:0.52 69:0.19 70:0.94 74:0.96 81:0.69 83:0.82 85:0.96 91:0.17 96:0.85 98:0.69 101:0.77 104:0.89 106:0.81 108:0.94 114:0.80 117:0.86 120:0.76 121:1.00 122:0.67 123:0.94 124:0.87 126:0.54 127:0.87 129:0.96 131:0.94 133:0.91 135:0.17 140:0.45 141:0.99 144:0.57 146:0.68 147:0.73 149:0.90 150:0.80 151:0.94 153:0.80 154:0.97 155:0.67 157:0.89 158:0.92 159:0.93 161:0.65 162:0.79 164:0.71 165:0.80 166:0.91 167:0.72 172:0.92 173:0.07 176:0.73 177:0.38 179:0.19 181:0.70 182:0.85 187:0.39 192:0.59 199:0.99 201:0.74 202:0.60 204:0.89 206:0.81 208:0.64 212:0.30 215:0.63 216:0.95 222:0.48 223:0.96 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 232:0.95 233:0.76 234:0.99 235:0.60 241:0.55 242:0.38 243:0.19 245:0.91 246:0.97 247:0.67 248:0.83 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 265:0.69 266:0.95 267:0.76 268:0.65 271:0.60 274:0.98 276:0.92 279:0.86 283:0.82 285:0.62 287:0.98 290:0.80 297:0.36 300:0.94 +0 1:0.74 11:0.64 12:0.96 17:0.45 21:0.71 26:0.96 27:0.45 28:0.93 30:0.21 32:0.80 34:0.59 36:0.25 37:0.50 39:0.35 43:0.82 55:0.59 58:0.93 66:0.08 69:0.70 70:0.98 74:0.90 77:0.70 81:0.52 83:0.73 85:0.88 91:0.06 98:0.33 101:0.74 108:0.94 114:0.68 122:0.67 123:0.86 124:0.91 126:0.54 127:0.68 129:0.81 131:0.61 133:0.89 135:0.65 141:0.91 144:0.57 145:0.50 146:0.43 147:0.50 149:0.74 150:0.67 154:0.77 155:0.67 157:0.84 159:0.84 161:0.65 165:0.69 166:0.90 167:0.75 172:0.48 173:0.46 176:0.73 177:0.38 178:0.55 179:0.40 181:0.70 182:0.83 187:0.68 192:0.59 195:0.94 199:0.96 201:0.74 212:0.37 215:0.48 216:0.77 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 231:0.85 234:0.91 235:0.88 241:0.63 242:0.73 243:0.22 245:0.78 246:0.97 247:0.47 253:0.70 259:0.21 265:0.18 266:0.95 267:0.69 271:0.60 274:0.91 276:0.75 282:0.88 287:0.61 290:0.68 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.91 8:0.81 9:0.80 11:0.64 12:0.96 17:0.26 20:0.90 21:0.13 26:0.97 27:0.04 28:0.93 30:0.14 34:0.37 36:0.99 37:0.95 39:0.35 41:0.97 43:0.87 58:1.00 60:0.87 66:0.33 69:0.56 70:0.88 74:0.87 78:0.84 81:0.88 83:0.89 85:0.95 91:0.65 96:0.96 98:0.45 100:0.86 101:0.78 108:0.86 111:0.83 114:0.92 120:0.95 122:0.57 123:0.85 124:0.93 126:0.54 127:0.85 129:0.97 131:0.94 133:0.94 135:0.86 140:0.80 141:0.99 144:0.57 146:0.13 147:0.18 149:0.85 150:0.93 151:0.97 152:0.94 153:0.95 154:0.85 155:0.67 157:0.89 158:0.92 159:0.90 161:0.65 162:0.74 164:0.91 165:0.88 166:0.91 167:0.78 169:0.83 172:0.73 173:0.24 176:0.73 177:0.38 179:0.36 181:0.70 182:0.85 186:0.84 187:0.68 189:0.91 192:0.59 199:0.99 201:0.74 202:0.88 208:0.64 212:0.16 215:0.84 216:0.83 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 231:0.86 234:0.99 235:0.22 238:0.89 241:0.69 242:0.32 243:0.10 245:0.77 246:0.97 247:0.60 248:0.95 253:0.97 255:0.79 256:0.90 259:0.21 260:0.95 261:0.87 265:0.47 266:0.94 267:0.85 268:0.90 271:0.60 274:1.00 276:0.80 279:0.86 283:0.82 285:0.62 287:0.98 290:0.92 297:0.36 300:0.70 +0 1:0.74 6:0.98 8:0.92 9:0.80 11:0.64 12:0.96 17:0.24 20:0.90 21:0.40 26:0.97 27:0.08 28:0.93 30:0.86 34:0.20 36:0.62 37:0.91 39:0.35 41:0.92 43:0.62 55:0.45 58:0.99 66:0.49 69:0.91 70:0.41 74:0.95 78:0.88 81:0.74 83:0.80 85:0.88 87:0.98 91:0.75 96:0.95 98:0.43 100:0.95 101:0.79 108:0.41 111:0.91 114:0.82 120:0.84 122:0.67 123:0.40 124:0.56 126:0.54 127:0.65 129:0.05 131:0.94 133:0.39 135:0.44 140:0.87 141:0.99 144:0.57 146:0.55 147:0.62 149:0.81 150:0.83 151:0.95 152:0.99 153:0.86 154:0.51 155:0.67 157:0.86 158:0.82 159:0.59 161:0.65 162:0.64 164:0.79 165:0.83 166:0.91 167:0.92 169:0.98 172:0.67 173:0.96 176:0.73 177:0.05 179:0.49 181:0.70 182:0.85 186:0.88 187:0.39 189:0.95 191:0.84 192:0.59 199:0.97 201:0.74 202:0.84 208:0.64 212:0.81 215:0.67 216:0.63 220:0.62 222:0.48 223:0.96 224:0.98 227:0.98 229:0.92 231:0.86 232:0.72 234:0.97 235:0.52 238:0.96 241:0.78 242:0.72 243:0.60 245:0.86 246:0.97 247:0.60 248:0.88 253:0.96 255:0.79 256:0.85 257:0.65 259:0.21 260:0.84 261:0.98 265:0.45 266:0.71 267:0.44 268:0.86 271:0.60 274:0.99 276:0.57 277:0.69 285:0.62 287:0.98 290:0.82 297:0.36 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.82 9:0.80 11:0.64 12:0.96 17:0.34 20:0.99 21:0.54 26:0.97 27:0.11 28:0.93 30:0.33 32:0.80 34:0.49 36:0.98 37:0.80 39:0.35 41:0.98 43:0.89 55:0.71 58:1.00 60:0.98 66:0.31 69:0.50 70:0.66 74:0.93 77:0.70 78:0.83 81:0.65 83:0.89 85:0.93 91:0.86 96:0.98 98:0.61 100:0.83 101:0.79 108:0.67 111:0.82 114:0.77 120:0.96 121:0.99 122:0.67 123:0.65 124:0.83 126:0.54 127:0.77 129:0.93 131:0.94 133:0.84 135:0.37 140:0.45 141:0.99 144:0.57 145:0.54 146:0.13 147:0.18 149:0.85 150:0.77 151:0.99 152:0.91 153:0.97 154:0.88 155:0.67 157:0.88 158:0.92 159:0.80 161:0.65 162:0.79 164:0.93 165:0.79 166:0.90 167:0.81 169:0.81 172:0.68 173:0.30 176:0.73 177:0.38 179:0.37 181:0.70 182:0.85 186:0.83 187:0.68 189:0.92 191:0.91 192:0.59 195:0.91 199:1.00 201:0.74 202:0.90 204:0.89 208:0.64 212:0.27 215:0.58 216:0.86 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 233:0.76 234:0.98 235:0.68 238:0.86 241:0.71 242:0.38 243:0.10 245:0.83 246:0.97 247:0.67 248:0.97 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.86 265:0.62 266:0.84 267:0.28 268:0.93 271:0.60 274:1.00 276:0.79 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.77 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.95 7:0.81 8:0.83 9:0.80 11:0.64 12:0.96 17:0.55 20:0.99 21:0.30 26:0.97 27:0.21 28:0.93 30:0.10 34:0.63 36:0.83 37:0.74 39:0.35 41:0.98 43:0.98 58:1.00 60:0.98 66:0.07 69:0.12 70:0.94 74:0.99 78:0.78 81:0.79 83:0.89 85:0.99 91:0.78 96:0.98 98:0.36 100:0.86 101:0.81 104:0.84 106:0.81 108:0.98 111:0.80 114:0.86 117:0.86 120:0.95 121:0.90 122:0.67 123:0.88 124:0.98 126:0.54 127:0.89 129:0.99 131:0.94 133:0.98 135:0.19 140:0.45 141:0.99 144:0.57 146:0.61 147:0.67 149:0.96 150:0.86 151:0.99 152:0.91 153:0.97 154:0.98 155:0.67 157:0.92 158:0.92 159:0.96 161:0.65 162:0.73 164:0.92 165:0.84 166:0.91 167:0.65 169:0.84 172:0.84 173:0.06 176:0.73 177:0.38 179:0.12 181:0.70 182:0.85 186:0.78 187:0.39 189:0.96 191:0.73 192:0.59 199:1.00 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.36 215:0.72 216:0.95 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 232:0.93 233:0.76 234:0.98 235:0.42 238:0.97 241:0.27 242:0.45 243:0.11 245:0.96 246:0.97 247:0.67 248:0.97 253:0.99 255:0.79 256:0.91 259:0.21 260:0.95 261:0.85 265:0.28 266:0.96 267:0.28 268:0.92 271:0.60 274:1.00 276:0.97 279:0.86 283:0.82 285:0.62 287:0.98 290:0.86 297:0.36 300:0.94 +2 1:0.74 6:0.95 7:0.81 8:0.84 9:0.80 11:0.64 12:0.96 17:0.51 20:0.74 21:0.54 26:0.97 27:0.21 28:0.93 30:0.27 34:0.42 36:0.80 37:0.74 39:0.35 41:0.88 43:0.96 55:0.71 58:0.99 60:0.97 66:0.09 69:0.25 70:0.81 74:0.99 78:0.95 81:0.65 83:0.77 85:0.97 91:0.14 96:0.89 98:0.40 100:0.96 101:0.74 104:0.95 106:0.81 108:0.97 111:0.94 114:0.77 117:0.86 120:0.64 121:0.90 122:0.67 123:0.97 124:0.98 126:0.54 127:0.96 129:1.00 131:0.94 133:0.98 135:0.32 140:0.80 141:0.99 144:0.57 146:0.65 147:0.70 149:0.92 150:0.77 151:0.63 152:0.91 153:0.48 154:0.96 155:0.67 157:0.87 158:0.92 159:0.98 161:0.65 162:0.70 164:0.73 165:0.79 166:0.90 167:0.71 169:0.84 172:0.89 173:0.06 176:0.73 177:0.38 179:0.10 181:0.70 182:0.85 186:0.94 187:0.39 190:0.77 192:0.59 199:1.00 201:0.74 202:0.74 204:0.89 206:0.81 208:0.64 212:0.29 215:0.58 216:0.95 220:0.62 222:0.48 223:0.96 224:0.98 227:0.98 229:0.92 230:0.84 231:0.86 232:0.98 233:0.69 234:0.97 235:0.68 241:0.51 242:0.71 243:0.10 245:0.97 246:0.97 247:0.67 248:0.61 253:0.93 255:0.79 256:0.75 259:0.21 260:0.65 261:0.85 265:0.42 266:0.96 267:0.23 268:0.77 271:0.60 274:0.98 276:0.99 279:0.86 283:0.82 285:0.62 287:0.98 290:0.77 297:0.36 300:0.84 +4 1:0.74 6:0.98 7:0.76 8:0.95 9:0.80 12:0.96 17:0.01 20:0.99 21:0.40 26:0.97 27:0.08 28:0.93 30:0.62 32:0.80 34:0.21 36:0.53 37:0.91 39:0.35 41:1.00 43:0.35 55:0.45 58:1.00 60:0.99 66:0.67 69:0.56 70:0.45 74:0.97 76:1.00 77:1.00 78:0.93 81:0.74 83:0.91 87:0.98 91:0.99 96:1.00 98:0.77 100:0.99 108:0.43 111:0.99 114:0.82 120:0.99 122:0.67 123:0.41 124:0.46 126:0.54 127:0.63 129:0.59 131:0.94 133:0.47 135:0.34 138:0.99 140:0.45 141:0.99 144:0.57 145:0.76 146:0.86 147:0.89 149:0.76 150:0.83 151:1.00 152:0.99 153:0.99 154:0.26 155:0.67 157:0.61 158:0.92 159:0.60 161:0.65 162:0.73 164:0.98 165:0.83 166:0.91 167:0.98 169:0.98 172:0.78 173:0.49 176:0.73 177:0.38 179:0.43 181:0.70 182:0.85 186:0.93 189:0.99 191:0.96 192:0.59 195:0.80 199:1.00 201:0.74 202:0.97 208:0.64 212:0.59 215:0.67 216:0.63 222:0.48 223:0.97 224:0.98 227:0.98 229:0.92 230:0.79 231:0.86 232:0.72 233:0.76 234:0.98 235:0.52 238:0.99 241:0.96 242:0.77 243:0.72 244:0.61 245:0.85 246:0.97 247:0.47 248:1.00 253:1.00 255:0.79 256:0.98 259:0.21 260:0.99 261:0.98 265:0.77 266:0.63 267:0.82 268:0.98 271:0.60 274:1.00 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.82 297:0.36 299:0.88 300:0.43 +1 1:0.74 8:0.85 9:0.80 12:0.96 17:0.18 21:0.40 26:0.97 27:0.08 28:0.93 30:0.94 34:0.34 36:0.76 37:0.91 39:0.35 41:0.88 43:0.36 55:0.45 58:0.99 66:0.80 69:0.54 70:0.21 74:0.93 81:0.74 83:0.77 87:0.98 91:0.81 96:0.91 98:0.47 108:0.41 114:0.82 120:0.76 122:0.67 123:0.40 124:0.38 126:0.54 127:0.61 129:0.59 131:0.94 133:0.47 135:0.61 140:0.87 141:0.99 144:0.57 146:0.56 147:0.62 149:0.66 150:0.83 151:0.82 153:0.83 154:0.27 155:0.67 157:0.61 158:0.82 159:0.53 161:0.65 162:0.57 164:0.73 165:0.83 166:0.91 167:0.93 172:0.67 173:0.52 175:0.75 176:0.73 177:0.05 179:0.64 181:0.70 182:0.85 187:0.39 191:0.78 192:0.59 199:0.95 201:0.74 202:0.80 208:0.64 212:0.88 215:0.67 216:0.63 220:0.74 222:0.48 223:0.96 224:0.98 227:0.98 229:0.91 231:0.86 232:0.72 234:0.97 235:0.52 241:0.79 242:0.82 243:0.82 244:0.61 245:0.55 246:0.97 247:0.12 248:0.75 253:0.93 255:0.79 256:0.78 257:0.65 259:0.21 260:0.76 265:0.49 266:0.27 267:0.59 268:0.79 271:0.60 274:0.98 276:0.36 277:0.69 285:0.62 287:0.98 290:0.82 297:0.36 300:0.33 +2 1:0.74 6:0.90 8:0.77 9:0.80 11:0.64 12:0.96 17:0.47 20:0.99 21:0.47 26:0.97 27:0.12 28:0.93 30:0.36 32:0.80 34:0.96 36:0.30 37:0.78 39:0.35 41:0.97 43:0.76 58:1.00 60:0.99 66:0.61 69:0.81 70:0.89 74:0.94 77:0.70 78:0.78 81:0.69 83:0.88 85:0.96 91:0.72 96:0.97 98:0.63 100:0.81 101:0.82 108:0.65 111:0.77 114:0.80 120:0.95 122:0.57 123:0.62 124:0.65 126:0.54 127:0.44 129:0.81 131:0.94 133:0.76 135:0.23 140:0.45 141:0.99 144:0.57 145:0.54 146:0.90 147:0.91 149:0.91 150:0.80 151:0.99 152:0.90 153:0.97 154:0.68 155:0.67 157:0.91 158:0.92 159:0.75 161:0.65 162:0.76 164:0.91 165:0.80 166:0.91 167:0.75 169:0.79 172:0.82 173:0.66 176:0.73 177:0.38 179:0.29 181:0.70 182:0.85 186:0.78 187:0.68 189:0.92 191:0.89 192:0.59 195:0.91 199:0.99 201:0.74 202:0.88 208:0.64 212:0.68 215:0.63 216:0.81 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 231:0.86 234:0.98 235:0.60 238:0.89 241:0.63 242:0.38 243:0.68 245:0.83 246:0.97 247:0.60 248:0.96 253:0.98 255:0.79 256:0.90 259:0.21 260:0.95 261:0.82 265:0.64 266:0.89 267:0.23 268:0.91 271:0.60 274:1.00 276:0.79 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.80 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.84 9:0.80 11:0.64 12:0.96 17:0.55 20:0.88 21:0.64 26:0.97 27:0.05 28:0.93 30:0.45 32:0.72 34:0.45 36:0.86 37:0.78 39:0.35 41:0.97 43:0.52 55:0.59 58:1.00 60:0.98 66:0.30 69:0.59 70:0.82 74:0.91 78:0.79 81:0.58 83:0.84 85:0.88 91:0.82 96:0.97 98:0.59 100:0.81 101:0.76 108:0.90 111:0.79 114:0.72 117:0.86 120:0.92 121:0.90 122:0.67 123:0.43 124:0.59 126:0.54 127:0.60 129:0.59 131:0.94 133:0.47 135:0.51 140:0.80 141:0.99 144:0.57 145:0.77 146:0.84 147:0.87 149:0.67 150:0.72 151:0.98 152:0.90 153:0.97 154:0.66 155:0.67 157:0.85 158:0.92 159:0.75 161:0.65 162:0.76 164:0.90 165:0.70 166:0.90 167:0.81 169:0.79 172:0.68 173:0.42 176:0.73 177:0.38 179:0.42 181:0.70 182:0.85 186:0.80 187:0.87 189:0.95 191:0.76 192:0.59 195:0.89 199:0.99 201:0.74 202:0.87 204:0.89 208:0.64 212:0.73 215:0.53 216:0.83 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 232:0.97 233:0.76 234:0.98 235:0.80 238:0.88 241:0.71 242:0.44 243:0.48 245:0.89 246:0.97 247:0.47 248:0.95 253:0.98 255:0.79 256:0.89 259:0.21 260:0.92 261:0.84 265:0.37 266:0.80 267:0.19 268:0.90 271:0.60 274:0.99 276:0.65 279:0.86 283:0.82 285:0.62 287:0.98 290:0.72 297:0.36 300:0.70 +2 1:0.74 6:0.91 8:0.82 9:0.80 11:0.64 12:0.96 17:0.34 20:0.86 21:0.13 26:0.97 27:0.04 28:0.93 30:0.20 34:0.58 36:0.86 37:0.95 39:0.35 41:0.96 43:0.87 58:1.00 60:0.87 66:0.24 69:0.56 70:0.75 74:0.86 78:0.80 81:0.88 83:0.88 85:0.93 91:0.72 96:0.97 98:0.40 100:0.86 101:0.77 104:0.81 108:0.84 111:0.81 114:0.92 120:0.94 122:0.67 123:0.83 124:0.91 126:0.54 127:0.81 129:0.96 131:0.94 133:0.91 135:0.28 138:0.98 140:0.45 141:0.99 144:0.57 146:0.13 147:0.18 149:0.83 150:0.93 151:0.99 152:0.94 153:0.97 154:0.85 155:0.67 157:0.88 158:0.92 159:0.85 161:0.65 162:0.67 164:0.88 165:0.88 166:0.91 167:0.76 169:0.83 172:0.51 173:0.31 176:0.73 177:0.38 179:0.51 181:0.70 182:0.85 186:0.81 187:0.87 189:0.95 191:0.87 192:0.59 199:0.99 201:0.74 202:0.84 208:0.64 212:0.21 215:0.84 216:0.83 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 231:0.86 232:0.91 234:0.99 235:0.22 238:0.94 241:0.66 242:0.51 243:0.13 245:0.69 246:0.97 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 259:0.21 260:0.94 261:0.87 265:0.42 266:0.87 267:0.85 268:0.87 271:0.60 274:0.99 276:0.69 279:0.86 283:0.82 285:0.62 287:0.98 290:0.92 297:0.36 300:0.55 +2 1:0.74 7:0.81 8:0.73 9:0.80 11:0.64 12:0.96 17:0.69 21:0.30 26:0.97 27:0.21 28:0.93 30:0.18 34:0.40 36:0.74 37:0.74 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.07 69:0.12 70:0.87 74:0.99 81:0.79 83:0.79 85:0.99 91:0.06 96:0.91 98:0.37 101:0.76 104:0.84 106:0.81 108:0.97 114:0.86 117:0.86 120:0.75 121:0.90 122:0.67 123:0.97 124:0.99 126:0.54 127:0.99 129:1.00 131:0.94 133:0.99 135:0.32 140:0.45 141:0.99 144:0.57 146:0.64 147:0.69 149:0.96 150:0.86 151:0.93 153:0.77 154:0.98 155:0.67 157:0.90 158:0.84 159:0.98 161:0.65 162:0.61 164:0.64 165:0.84 166:0.91 167:0.73 172:0.89 173:0.05 176:0.73 177:0.38 179:0.10 181:0.70 182:0.85 187:0.39 192:0.59 199:1.00 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.30 215:0.72 216:0.95 222:0.48 223:0.96 224:0.98 227:0.98 229:0.91 230:0.87 231:0.86 232:0.93 233:0.76 234:0.97 235:0.42 241:0.57 242:0.70 243:0.10 245:0.98 246:0.97 247:0.67 248:0.82 253:0.94 255:0.79 256:0.68 259:0.21 260:0.75 265:0.39 266:0.96 267:0.69 268:0.69 271:0.60 274:0.97 276:0.99 279:0.86 283:0.71 285:0.62 287:0.98 290:0.86 297:0.36 300:0.94 +1 1:0.74 6:0.97 8:0.93 9:0.80 11:0.64 12:0.96 17:0.18 20:0.97 21:0.40 27:0.55 28:0.73 30:0.62 34:0.67 36:0.95 37:0.32 39:0.27 41:0.93 43:0.80 55:0.71 60:0.87 66:0.95 69:0.73 70:0.65 74:0.93 78:0.83 81:0.81 83:0.87 85:0.80 91:0.48 96:0.90 98:0.90 100:0.88 101:0.73 108:0.56 111:0.84 114:0.82 120:0.82 122:0.67 123:0.54 126:0.54 127:0.47 129:0.05 131:0.99 135:0.76 140:0.45 144:0.57 146:0.97 147:0.98 149:0.77 150:0.87 151:0.93 152:0.89 153:0.77 154:0.74 155:0.67 157:0.86 158:0.92 159:0.70 161:0.89 162:0.51 164:0.79 165:0.83 166:0.99 167:0.60 169:0.86 172:0.87 173:0.60 176:0.73 177:0.38 179:0.33 181:0.91 182:0.95 186:0.83 187:0.68 189:0.97 192:0.59 201:0.74 202:0.80 208:0.64 212:0.42 215:0.67 216:0.43 220:0.91 222:0.48 227:1.00 229:0.97 231:0.98 232:0.88 235:0.52 238:0.91 241:0.63 242:0.59 243:0.95 246:1.00 247:0.55 248:0.89 253:0.93 255:0.79 256:0.75 259:0.21 260:0.83 261:0.92 265:0.90 266:0.71 267:0.65 268:0.75 271:0.13 276:0.78 279:0.86 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 300:0.70 +1 9:0.80 11:0.64 12:0.96 17:0.18 21:0.78 27:0.33 28:0.73 30:0.76 34:0.39 36:0.61 37:0.55 39:0.27 41:0.95 43:0.64 60:0.97 66:0.77 69:0.91 70:0.29 74:0.62 83:0.88 85:0.85 91:0.49 96:0.95 98:0.28 101:0.75 108:0.81 120:0.91 122:0.57 123:0.80 127:0.12 129:0.05 131:0.99 135:0.73 140:0.45 144:0.57 145:0.67 146:0.47 147:0.53 149:0.57 151:0.97 153:0.90 154:0.54 155:0.67 157:0.90 158:0.92 159:0.17 161:0.89 162:0.53 164:0.85 166:0.61 167:0.48 172:0.37 173:0.88 177:0.38 179:0.22 181:0.91 182:0.95 187:0.68 192:0.59 202:0.85 208:0.64 212:0.23 216:0.81 222:0.48 227:1.00 229:0.97 231:0.98 235:0.60 241:0.24 242:0.55 243:0.79 246:0.61 247:0.39 248:0.95 253:0.93 255:0.79 256:0.81 259:0.21 260:0.91 265:0.30 266:0.38 267:0.76 268:0.82 271:0.13 276:0.27 279:0.86 283:0.82 285:0.62 287:1.00 300:0.25 +2 1:0.74 6:0.91 7:0.81 8:0.64 9:0.80 11:0.64 12:0.96 17:0.38 20:0.75 21:0.40 27:0.21 28:0.73 30:0.30 34:0.50 36:0.81 37:0.74 39:0.27 41:0.90 43:0.92 55:0.71 60:0.87 66:0.92 69:0.15 70:0.78 74:0.96 76:0.85 78:0.96 81:0.80 83:0.86 85:0.94 91:0.42 96:0.91 98:0.90 100:0.97 101:0.81 104:0.84 106:0.81 108:0.12 111:0.96 114:0.82 117:0.86 120:0.84 122:0.43 123:0.12 124:0.36 126:0.54 127:0.59 129:0.05 131:0.99 133:0.39 135:0.24 140:0.45 144:0.57 146:0.97 147:0.97 149:0.88 150:0.82 151:0.93 152:0.98 153:0.78 154:0.92 155:0.67 157:0.97 158:0.92 159:0.72 161:0.89 162:0.83 164:0.76 166:0.99 167:0.48 169:0.90 172:0.91 173:0.13 176:0.73 177:0.38 179:0.27 181:0.91 182:0.95 186:0.96 187:0.39 189:0.85 192:0.59 201:0.74 202:0.64 204:0.89 206:0.81 208:0.64 212:0.70 215:0.67 216:0.95 222:0.48 227:1.00 229:0.97 230:0.84 231:0.98 232:0.93 233:0.69 235:0.52 238:0.92 241:0.27 242:0.29 243:0.92 245:0.70 246:0.61 247:0.67 248:0.90 253:0.94 255:0.79 256:0.68 259:0.21 260:0.85 261:0.96 265:0.90 266:0.54 267:0.73 268:0.70 271:0.13 276:0.84 279:0.86 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 300:0.55 +1 1:0.74 6:0.91 7:0.81 8:0.80 9:0.80 11:0.64 12:0.96 17:0.22 20:0.85 21:0.40 27:0.21 28:0.73 30:0.29 34:0.44 36:0.83 37:0.74 39:0.27 41:0.82 43:0.75 55:0.71 60:0.87 66:0.92 69:0.15 70:0.78 74:0.96 76:0.94 78:0.84 81:0.80 83:0.79 85:0.94 91:0.49 96:0.88 98:0.89 100:0.91 101:0.81 104:0.92 106:0.81 108:0.12 111:0.86 114:0.82 117:0.86 120:0.78 122:0.43 123:0.12 124:0.36 126:0.54 127:0.55 129:0.05 131:0.99 133:0.39 135:0.17 140:0.45 144:0.57 146:0.97 147:0.97 149:0.85 150:0.82 151:0.90 152:0.98 153:0.69 154:0.92 155:0.67 157:0.97 158:0.92 159:0.71 161:0.89 162:0.58 164:0.75 166:0.99 167:0.46 169:0.90 172:0.90 173:0.13 176:0.73 177:0.38 179:0.28 181:0.91 182:0.95 186:0.84 187:0.39 189:0.92 192:0.59 201:0.74 202:0.71 204:0.89 206:0.81 208:0.64 212:0.68 215:0.67 216:0.95 220:0.62 222:0.48 227:1.00 229:0.97 230:0.84 231:0.98 232:0.85 233:0.69 235:0.52 238:0.94 241:0.12 242:0.39 243:0.92 245:0.70 246:0.61 247:0.67 248:0.86 253:0.92 255:0.79 256:0.68 259:0.21 260:0.79 261:0.96 265:0.89 266:0.54 267:0.97 268:0.70 271:0.13 276:0.83 279:0.86 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 300:0.55 +0 1:0.74 11:0.64 12:0.96 17:0.28 21:0.54 27:0.45 28:0.73 30:0.68 34:0.77 36:0.17 37:0.50 39:0.27 43:0.82 55:0.84 60:0.87 66:0.45 69:0.69 70:0.31 74:0.74 81:0.71 83:0.86 85:0.72 91:0.18 98:0.44 101:0.70 108:0.64 114:0.77 122:0.67 123:0.61 124:0.66 126:0.54 127:0.29 129:0.05 131:0.61 133:0.62 135:0.91 144:0.57 145:0.47 146:0.13 147:0.18 149:0.57 150:0.81 154:0.77 155:0.67 157:0.79 158:0.87 159:0.59 161:0.89 163:0.94 165:0.79 166:0.99 167:0.51 172:0.27 173:0.57 176:0.73 177:0.38 178:0.55 179:0.81 181:0.91 182:0.94 187:0.68 192:0.59 201:0.74 208:0.64 212:0.61 215:0.58 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.68 241:0.41 242:0.79 243:0.32 245:0.47 246:1.00 247:0.30 253:0.66 259:0.21 265:0.45 266:0.27 267:0.28 271:0.13 276:0.31 277:0.69 279:0.86 283:0.71 287:0.61 290:0.77 297:0.36 300:0.25 +1 1:0.74 6:0.94 8:0.90 9:0.80 11:0.64 12:0.96 17:0.20 20:0.95 21:0.54 27:0.06 28:0.73 30:0.21 32:0.78 34:0.78 36:0.98 37:0.82 39:0.27 41:0.92 43:0.75 55:0.96 60:0.97 66:0.09 69:0.82 70:0.91 74:0.83 77:0.70 78:0.78 81:0.70 83:0.84 85:0.80 91:0.57 96:0.91 98:0.22 100:0.81 101:0.72 108:0.67 111:0.78 114:0.77 120:0.86 122:0.67 123:0.84 124:0.88 126:0.54 127:0.28 129:0.59 131:0.99 133:0.86 135:0.74 140:0.45 144:0.57 145:0.69 146:0.32 147:0.39 149:0.63 150:0.75 151:0.92 152:0.97 153:0.76 154:0.66 155:0.67 157:0.85 158:0.92 159:0.68 161:0.89 162:0.60 164:0.88 166:0.99 167:0.54 169:0.80 172:0.32 173:0.68 176:0.73 177:0.38 179:0.53 181:0.91 182:0.95 186:0.78 187:0.39 189:0.96 192:0.59 195:0.92 201:0.74 202:0.85 208:0.64 212:0.35 215:0.58 216:0.92 220:0.96 222:0.48 227:1.00 229:0.97 231:0.98 235:0.68 238:0.89 241:0.51 242:0.55 243:0.40 245:0.58 246:0.61 247:0.60 248:0.89 253:0.92 255:0.79 256:0.86 259:0.21 260:0.86 261:0.90 265:0.19 266:0.75 267:0.44 268:0.86 271:0.13 276:0.53 277:0.69 279:0.86 282:0.86 283:0.82 285:0.62 287:1.00 290:0.77 297:0.36 300:0.70 +2 1:0.74 9:0.80 11:0.64 12:0.96 17:0.32 21:0.13 27:0.33 28:0.73 30:0.76 32:0.80 34:0.33 36:0.42 37:0.55 39:0.27 41:0.96 43:0.75 60:0.99 66:0.77 69:0.82 70:0.29 74:0.74 77:0.70 81:0.92 83:0.88 85:0.85 91:0.54 96:0.95 98:0.44 101:0.79 104:0.95 106:0.81 108:0.67 114:0.92 117:0.86 120:0.91 122:0.57 123:0.65 126:0.54 127:0.14 129:0.05 131:1.00 135:0.69 140:0.45 144:0.57 145:0.69 146:0.46 147:0.52 149:0.68 150:0.96 151:0.97 153:0.89 154:0.67 155:0.67 157:0.92 158:0.92 159:0.17 161:0.89 162:0.83 164:0.86 165:0.88 166:0.99 167:0.53 172:0.37 173:0.89 176:0.73 177:0.38 179:0.45 181:0.91 182:0.95 187:0.68 192:0.59 195:0.72 201:0.74 202:0.76 206:0.81 208:0.64 212:0.23 215:0.84 216:0.81 222:0.48 227:1.00 229:0.97 231:0.98 232:0.98 235:0.22 241:0.49 242:0.55 243:0.79 246:1.00 247:0.39 248:0.95 253:0.95 255:0.79 256:0.82 259:0.21 260:0.91 265:0.46 266:0.38 267:0.76 268:0.82 271:0.13 276:0.28 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.92 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.95 7:0.81 8:0.91 9:0.80 11:0.64 12:0.96 17:0.05 20:0.99 21:0.22 27:0.35 28:0.73 30:0.74 32:0.80 34:0.52 36:0.91 37:0.80 39:0.27 41:0.98 43:0.76 60:0.98 66:0.94 69:0.81 70:0.47 74:0.94 77:0.70 78:0.86 81:0.89 83:0.89 85:0.97 91:0.76 96:0.96 98:0.80 100:0.94 101:0.85 104:0.89 106:0.81 108:0.66 111:0.90 114:0.90 117:0.86 120:0.93 121:0.90 122:0.43 123:0.63 126:0.54 127:0.28 129:0.05 131:1.00 135:0.93 140:0.45 144:0.57 145:0.56 146:0.90 147:0.92 149:0.95 150:0.93 151:0.96 152:0.90 153:0.88 154:0.67 155:0.67 157:0.98 158:0.92 159:0.50 161:0.89 162:0.58 164:0.91 165:0.86 166:0.99 167:0.65 169:0.92 172:0.85 173:0.77 176:0.73 177:0.38 179:0.27 181:0.91 182:0.95 186:0.86 187:0.21 189:0.96 191:0.75 192:0.59 195:0.80 201:0.74 202:0.90 204:0.89 206:0.81 208:0.64 212:0.60 215:0.79 216:0.72 220:0.74 222:0.48 227:1.00 229:0.97 230:0.87 231:0.98 232:0.95 233:0.76 235:0.31 238:0.96 241:0.68 242:0.58 243:0.94 246:1.00 247:0.60 248:0.96 253:0.98 255:0.79 256:0.90 259:0.21 260:0.93 261:0.96 265:0.80 266:0.63 267:0.59 268:0.90 271:0.13 276:0.74 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.90 297:0.36 299:0.88 300:0.43 +1 6:0.91 8:0.92 9:0.80 11:0.64 12:0.96 17:0.43 20:0.90 21:0.78 27:0.21 28:0.73 30:0.21 34:0.62 36:0.75 37:0.74 39:0.27 41:0.82 43:0.92 60:0.95 66:0.27 69:0.41 70:1.00 74:0.90 78:0.78 83:0.88 85:0.97 91:0.05 96:0.85 98:0.19 100:0.83 101:0.73 104:0.84 106:0.81 108:0.96 111:0.78 117:0.86 120:0.76 122:0.57 123:0.96 124:0.91 127:0.62 129:0.95 131:0.99 133:0.93 135:0.30 140:0.45 144:0.57 146:0.51 147:0.57 149:0.88 151:0.94 152:0.98 153:0.80 154:0.91 155:0.67 157:0.93 158:0.92 159:0.98 161:0.89 162:0.56 164:0.65 166:0.61 167:0.46 169:0.90 172:0.76 173:0.07 177:0.38 179:0.25 181:0.91 182:0.95 186:0.78 187:0.39 189:0.97 192:0.59 202:0.60 206:0.81 208:0.64 212:0.54 216:0.95 222:0.48 227:1.00 229:0.97 231:0.98 232:0.93 235:0.68 238:0.93 241:0.12 242:0.28 243:0.17 245:0.84 246:0.61 247:0.60 248:0.83 253:0.88 255:0.79 256:0.45 259:0.21 260:0.77 261:0.96 265:0.21 266:0.95 267:0.84 268:0.58 271:0.13 276:0.83 279:0.86 283:0.82 285:0.62 287:1.00 300:1.00 +1 1:0.74 6:0.90 7:0.81 8:0.79 9:0.80 11:0.64 12:0.96 17:0.12 20:0.97 21:0.40 27:0.26 28:0.73 30:0.17 32:0.80 34:0.96 36:0.85 37:0.83 39:0.27 41:0.90 43:0.76 55:0.84 60:0.87 66:0.15 69:0.47 70:0.68 74:0.88 77:0.70 78:0.83 81:0.80 83:0.82 85:0.80 91:0.65 96:0.90 98:0.25 100:0.89 101:0.73 104:0.95 106:0.81 108:0.59 111:0.84 114:0.82 117:0.86 120:0.84 121:0.90 122:0.57 123:0.57 124:0.86 126:0.54 127:0.25 129:0.89 131:0.99 133:0.83 135:0.79 140:0.80 144:0.57 145:0.54 146:0.33 147:0.39 149:0.60 150:0.82 151:0.92 152:0.89 153:0.72 154:0.83 155:0.67 157:0.86 158:0.92 159:0.57 161:0.89 162:0.53 163:0.89 164:0.86 166:0.99 167:0.53 169:0.86 172:0.21 173:0.31 176:0.73 177:0.38 178:0.42 179:0.57 181:0.91 182:0.95 186:0.84 187:0.39 189:0.91 192:0.59 195:0.88 201:0.74 202:0.84 204:0.89 206:0.81 208:0.64 212:0.19 215:0.67 216:0.74 220:0.87 222:0.48 227:1.00 229:0.97 230:0.87 231:0.98 232:0.98 233:0.76 235:0.52 238:0.91 241:0.49 242:0.45 243:0.29 245:0.56 246:0.61 247:0.39 248:0.89 253:0.92 255:0.79 256:0.82 259:0.21 260:0.85 261:0.93 265:0.27 266:0.75 267:0.65 268:0.82 271:0.13 276:0.48 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.94 8:0.73 9:0.80 11:0.64 12:0.96 17:0.22 20:0.75 21:0.54 27:0.06 28:0.73 30:0.21 32:0.78 34:0.53 36:0.94 37:0.82 39:0.27 41:0.92 43:0.76 55:0.96 60:0.97 66:0.07 69:0.80 70:0.86 74:0.86 77:0.70 78:0.91 81:0.70 83:0.83 85:0.88 91:0.22 96:0.91 98:0.23 100:0.89 101:0.75 108:0.63 111:0.89 114:0.77 120:0.86 122:0.67 123:0.84 124:0.90 126:0.54 127:0.42 129:0.81 131:0.99 133:0.89 135:0.83 140:0.45 144:0.57 145:0.69 146:0.39 147:0.46 149:0.75 150:0.75 151:0.92 152:0.97 153:0.76 154:0.68 155:0.67 157:0.92 158:0.92 159:0.74 161:0.89 162:0.60 164:0.88 166:0.99 167:0.52 169:0.80 172:0.41 173:0.65 176:0.73 177:0.38 179:0.55 181:0.91 182:0.95 186:0.91 187:0.39 189:0.86 192:0.59 195:0.91 201:0.74 202:0.86 208:0.64 212:0.41 215:0.58 216:0.92 220:0.96 222:0.48 227:1.00 229:0.97 231:0.98 235:0.68 238:0.85 241:0.46 242:0.44 243:0.44 245:0.61 246:0.61 247:0.60 248:0.89 253:0.92 255:0.79 256:0.86 259:0.21 260:0.86 261:0.90 265:0.23 266:0.84 267:0.44 268:0.86 271:0.13 276:0.59 277:0.69 279:0.86 282:0.86 283:0.82 285:0.62 287:1.00 290:0.77 297:0.36 300:0.70 +2 1:0.74 11:0.64 12:0.96 17:0.92 27:0.72 28:0.73 30:0.68 32:0.80 34:0.47 36:0.85 39:0.27 43:0.93 66:0.79 69:0.07 70:0.21 74:0.79 77:0.70 81:0.98 83:0.80 85:0.85 91:0.82 98:0.97 101:0.83 104:0.54 108:0.06 114:0.98 122:0.57 123:0.06 126:0.54 127:0.77 129:0.05 131:0.61 135:0.44 144:0.57 145:0.44 146:0.48 147:0.54 149:0.80 150:0.99 154:0.92 155:0.67 157:0.93 159:0.17 161:0.89 165:0.92 166:0.99 167:0.76 172:0.41 173:0.50 176:0.73 177:0.38 178:0.55 179:0.91 181:0.91 182:0.95 192:0.59 195:0.52 201:0.74 212:0.89 215:0.96 222:0.48 227:0.61 229:0.61 231:0.98 232:0.80 235:0.05 241:0.77 242:0.63 243:0.80 246:1.00 247:0.35 253:0.77 265:0.97 266:0.17 267:0.36 271:0.13 276:0.29 282:0.88 287:0.61 290:0.98 297:0.36 299:0.88 300:0.18 +1 11:0.64 12:0.96 17:0.20 21:0.22 27:0.45 28:0.73 30:0.45 34:0.61 36:0.18 37:0.50 39:0.27 43:0.78 66:0.29 69:0.68 70:0.71 74:0.77 77:0.70 81:0.75 85:0.90 91:0.18 98:0.36 101:0.78 108:0.52 114:0.55 122:0.43 123:0.49 124:0.73 126:0.32 127:0.30 129:0.81 131:0.61 133:0.67 135:0.82 144:0.57 145:0.52 146:0.61 147:0.67 149:0.78 150:0.55 154:0.71 157:0.94 159:0.62 161:0.89 163:0.81 166:0.91 167:0.51 172:0.37 173:0.54 176:0.53 177:0.38 178:0.55 179:0.57 181:0.91 182:0.85 187:0.68 195:0.87 212:0.27 216:0.77 222:0.48 227:0.61 229:0.61 231:0.97 235:0.22 241:0.41 242:0.28 243:0.47 245:0.66 246:0.61 247:0.60 253:0.57 259:0.21 265:0.38 266:0.71 267:0.52 271:0.13 276:0.50 282:0.81 287:0.61 290:0.55 300:0.55 +1 6:0.91 7:0.76 8:0.81 9:0.80 11:0.64 12:0.96 17:0.36 20:0.97 21:0.30 27:0.21 28:0.73 30:0.27 34:0.61 36:0.78 37:0.74 39:0.27 41:0.82 43:0.58 55:0.84 66:0.06 69:0.91 70:0.96 74:0.65 78:0.84 81:0.67 83:0.73 85:0.94 91:0.73 96:0.79 98:0.29 100:0.94 101:0.75 104:0.84 106:0.81 108:0.90 111:0.89 120:0.90 123:0.96 124:0.97 126:0.32 127:0.85 129:0.98 131:0.99 133:0.96 135:0.17 140:0.94 144:0.57 146:0.55 147:0.73 149:0.81 150:0.48 151:0.70 152:0.98 153:0.93 154:0.50 155:0.67 157:0.94 159:0.94 161:0.89 162:0.59 164:0.86 166:0.90 167:0.48 169:0.90 172:0.54 173:0.65 177:0.05 179:0.18 181:0.91 182:0.94 186:0.84 187:0.39 189:0.92 191:0.77 192:0.59 202:0.83 206:0.81 208:0.64 212:0.22 215:0.72 216:0.95 222:0.48 227:1.00 229:0.97 230:0.79 231:0.98 233:0.76 235:0.31 238:0.97 241:0.24 242:0.70 243:0.10 245:0.90 246:0.61 247:0.67 248:0.74 253:0.76 255:0.79 256:0.84 257:0.65 259:0.21 260:0.90 261:0.96 265:0.28 266:0.95 267:0.91 268:0.84 271:0.13 276:0.93 283:0.82 285:0.62 287:1.00 297:0.36 300:0.94 +1 1:0.74 6:0.94 8:0.86 9:0.80 11:0.64 12:0.96 17:0.06 20:0.95 21:0.59 27:0.06 28:0.73 30:0.21 32:0.78 34:0.75 36:0.96 37:0.82 39:0.27 41:0.97 43:0.75 55:0.92 66:0.63 69:0.82 70:0.76 74:0.78 77:0.70 78:0.80 81:0.67 83:0.80 85:0.80 91:0.65 96:0.92 98:0.68 100:0.83 101:0.72 108:0.80 111:0.80 114:0.74 120:0.87 122:0.41 123:0.78 124:0.52 126:0.54 127:0.35 129:0.81 131:1.00 133:0.67 135:0.87 140:0.45 144:0.57 145:0.69 146:0.40 147:0.46 149:0.65 150:0.78 151:0.87 152:0.97 153:0.48 154:0.67 155:0.67 157:0.84 158:0.87 159:0.75 161:0.89 162:0.60 163:0.75 164:0.91 165:0.78 166:0.99 167:0.54 169:0.80 172:0.68 173:0.66 176:0.73 177:0.38 179:0.44 181:0.91 182:0.95 186:0.80 187:0.39 189:0.95 192:0.59 195:0.93 201:0.74 202:0.88 208:0.64 212:0.16 215:0.55 216:0.92 220:0.99 222:0.48 227:1.00 229:0.97 231:0.98 235:0.74 238:0.88 241:0.53 242:0.21 243:0.20 245:0.72 246:1.00 247:0.67 248:0.91 253:0.93 255:0.79 256:0.89 259:0.21 260:0.87 261:0.90 265:0.68 266:0.89 267:0.69 268:0.89 271:0.13 276:0.65 279:0.86 282:0.86 283:0.71 285:0.62 287:1.00 290:0.74 297:0.36 300:0.55 +1 1:0.68 7:0.66 9:0.75 12:0.51 17:0.98 18:0.65 21:0.59 27:0.72 28:0.89 29:0.71 30:0.72 31:0.96 32:0.67 34:0.45 36:0.34 39:0.51 43:0.21 44:0.88 48:0.91 55:0.45 64:0.77 66:0.44 69:0.92 70:0.41 71:0.94 74:0.78 76:0.98 77:0.99 79:0.96 81:0.64 83:0.91 86:0.68 91:0.42 96:0.79 98:0.75 104:0.91 106:0.81 108:0.84 114:0.71 120:0.88 123:0.84 124:0.60 126:0.54 127:0.89 129:0.05 133:0.43 135:0.51 137:0.96 139:0.74 140:0.97 145:0.89 146:0.93 147:0.87 149:0.59 150:0.62 151:0.82 153:0.46 154:0.14 155:0.65 159:0.78 161:0.96 162:0.85 164:0.86 167:0.59 172:0.94 173:0.90 176:0.63 177:0.38 179:0.15 181:0.48 187:0.21 190:0.89 192:0.87 195:0.89 196:0.93 201:0.67 202:0.78 206:0.81 208:0.64 212:0.92 215:0.55 216:0.18 220:0.62 222:0.35 230:0.68 233:0.66 235:0.97 241:0.53 242:0.70 243:0.50 244:0.83 245:0.99 247:0.86 248:0.74 253:0.82 255:0.78 256:0.85 257:0.65 259:0.21 260:0.88 265:0.75 266:0.71 267:0.44 268:0.84 271:0.54 276:0.95 281:0.91 282:0.75 283:0.67 284:0.96 285:0.62 290:0.71 297:0.36 300:0.84 +1 1:0.69 7:0.70 8:0.79 9:0.75 11:0.75 12:0.51 17:0.97 18:0.81 21:0.40 27:0.67 28:0.89 29:0.71 30:0.74 31:0.89 32:0.67 34:0.85 36:0.66 37:0.52 39:0.51 43:0.93 44:0.88 45:0.97 48:0.91 55:0.52 64:0.77 66:0.89 69:0.30 70:0.42 71:0.94 74:0.92 76:0.85 77:0.96 79:0.89 81:0.83 83:0.91 85:0.72 86:0.81 91:0.44 96:0.72 98:0.97 99:0.99 101:0.87 104:0.95 106:0.81 108:0.73 114:0.78 117:0.86 120:0.59 123:0.71 124:0.37 126:0.54 127:0.97 129:0.59 133:0.46 135:0.79 137:0.89 139:0.83 140:0.45 145:0.58 146:0.53 147:0.59 149:0.91 150:0.88 151:0.61 153:0.48 154:0.93 155:0.66 159:0.95 161:0.96 162:0.86 164:0.56 165:0.70 167:0.54 172:1.00 173:0.08 176:0.63 177:0.70 179:0.08 181:0.48 187:0.39 190:0.89 191:0.76 192:0.87 195:0.99 196:0.91 201:0.68 202:0.36 204:0.85 206:0.81 208:0.64 212:0.92 215:0.67 216:0.51 220:0.82 222:0.35 230:0.73 232:0.85 233:0.76 235:0.95 241:0.37 242:0.74 243:0.11 245:1.00 247:0.91 248:0.59 253:0.77 255:0.78 256:0.45 259:0.21 260:0.60 265:0.97 266:0.75 267:0.28 268:0.46 271:0.54 276:1.00 281:0.91 282:0.75 283:0.67 284:0.88 285:0.62 290:0.78 297:0.36 300:0.84 +1 1:0.67 8:0.85 9:0.75 12:0.51 17:0.86 18:0.60 21:0.47 27:0.72 28:0.89 29:0.71 30:0.91 31:0.96 34:0.18 36:0.43 39:0.51 43:0.10 44:0.86 48:0.91 55:0.42 64:0.77 66:0.17 69:0.82 70:0.30 71:0.94 74:0.57 76:0.98 77:0.99 79:0.96 81:0.45 83:0.60 86:0.65 91:0.68 96:0.87 97:0.89 98:0.64 108:0.40 114:0.66 123:0.64 124:0.51 126:0.44 127:0.55 129:0.05 133:0.37 135:0.54 137:0.96 138:0.97 139:0.76 140:0.87 145:0.82 146:0.57 147:0.55 149:0.37 150:0.48 151:0.91 153:0.72 154:0.18 155:0.58 158:0.81 159:0.37 161:0.96 162:0.69 167:0.84 172:0.77 173:0.92 176:0.59 177:0.05 179:0.38 181:0.48 190:0.77 192:0.51 195:0.64 196:0.93 201:0.62 202:0.73 208:0.54 212:0.86 216:0.09 219:0.94 220:0.62 222:0.35 232:0.75 235:0.74 241:0.85 242:0.76 243:0.67 244:0.83 245:0.89 247:0.74 248:0.81 253:0.84 254:0.96 255:0.73 256:0.71 257:0.84 259:0.21 265:0.56 266:0.38 267:0.36 268:0.75 271:0.54 276:0.73 279:0.82 281:0.86 282:0.73 284:0.96 285:0.56 286:0.99 290:0.66 292:0.88 300:0.55 +1 1:0.66 6:0.84 7:0.69 8:0.82 9:0.76 11:0.45 12:0.51 17:0.90 20:0.90 21:0.54 27:0.18 28:0.89 29:0.71 30:0.75 31:0.98 32:0.65 34:0.33 36:0.23 37:0.63 39:0.51 43:0.56 45:0.96 55:0.52 66:0.74 69:0.38 70:0.28 71:0.94 74:0.86 76:0.97 77:0.96 78:0.95 79:0.98 81:0.65 83:0.60 91:0.96 96:1.00 98:0.70 99:0.83 100:0.94 101:0.52 108:0.30 111:0.93 114:0.72 120:0.98 122:0.51 123:0.28 124:0.43 126:0.44 127:0.65 129:0.59 133:0.47 135:0.24 137:0.98 140:0.80 145:0.85 146:0.67 147:0.72 149:0.91 150:0.58 151:0.99 152:0.93 153:0.97 154:0.45 155:0.58 159:0.44 161:0.96 162:0.64 164:0.96 165:0.70 167:0.83 169:0.89 172:0.92 173:0.43 175:0.75 176:0.62 177:0.38 178:0.42 179:0.24 181:0.48 186:0.95 189:0.89 190:0.95 191:0.94 192:0.59 195:0.68 201:0.61 202:0.97 204:0.81 208:0.54 212:0.92 215:0.58 216:0.42 222:0.35 230:0.71 233:0.76 235:0.80 238:0.85 241:0.83 242:0.53 243:0.77 244:0.93 245:0.94 247:0.67 248:0.99 253:1.00 255:0.74 256:0.98 257:0.65 259:0.21 260:0.98 261:0.92 265:0.70 266:0.54 267:0.59 268:0.98 271:0.54 276:0.86 277:0.69 282:0.74 283:0.67 284:0.97 285:0.62 290:0.72 297:0.36 300:0.55 +1 7:0.69 8:0.94 9:0.79 11:0.45 12:0.51 17:0.88 21:0.78 27:0.72 28:0.89 29:0.71 30:0.55 31:0.95 32:0.74 34:0.61 36:0.68 37:0.93 39:0.51 43:0.56 44:0.88 45:0.73 55:0.45 66:0.52 69:0.19 70:0.71 71:0.94 74:0.62 76:0.85 77:0.96 79:0.94 83:0.60 91:0.88 96:0.84 98:0.56 101:0.52 108:0.60 120:0.74 123:0.58 124:0.52 127:0.46 129:0.59 133:0.47 135:0.47 137:0.95 139:0.67 140:0.45 145:0.85 146:0.32 147:0.39 149:0.38 151:0.91 153:0.72 154:0.45 155:0.66 159:0.30 161:0.96 162:0.65 164:0.64 167:0.85 172:0.48 173:0.37 175:0.75 177:0.38 179:0.69 181:0.48 190:0.89 191:0.89 192:0.87 195:0.61 196:0.90 202:0.55 204:0.81 208:0.64 212:0.69 216:0.04 222:0.35 230:0.71 233:0.66 241:0.89 242:0.31 243:0.40 244:0.93 245:0.69 247:0.60 248:0.81 253:0.81 255:0.79 256:0.45 257:0.65 259:0.21 260:0.74 265:0.58 266:0.54 267:0.28 268:0.57 271:0.54 276:0.45 277:0.69 282:0.86 284:0.91 285:0.62 300:0.55 +1 1:0.65 7:0.70 8:0.81 9:0.73 11:0.45 12:0.51 17:0.92 18:0.67 21:0.40 27:0.72 28:0.89 29:0.71 30:0.87 31:0.89 32:0.67 34:0.18 36:0.75 39:0.51 41:0.82 43:0.37 44:0.88 45:0.77 48:0.91 55:0.45 66:0.54 69:0.93 70:0.39 71:0.94 74:0.73 77:0.70 79:0.89 81:0.67 83:0.60 86:0.64 91:0.72 96:0.74 98:0.62 99:0.83 101:0.52 104:0.75 108:0.38 114:0.76 120:0.61 123:0.36 124:0.70 126:0.44 127:0.89 129:0.05 133:0.77 135:0.30 137:0.89 139:0.74 140:0.80 145:0.70 146:0.67 147:0.88 149:0.63 150:0.60 151:0.62 153:0.48 154:0.28 155:0.65 159:0.77 161:0.96 162:0.86 164:0.71 165:0.70 167:0.84 172:0.93 173:0.92 176:0.62 177:0.38 179:0.18 181:0.48 192:0.86 195:0.87 196:0.90 201:0.61 202:0.56 204:0.85 208:0.64 212:0.86 215:0.67 216:0.01 220:0.93 222:0.35 230:0.73 233:0.76 235:0.60 241:0.85 242:0.63 243:0.62 244:0.93 245:0.96 247:0.84 248:0.60 253:0.70 255:0.73 256:0.64 257:0.65 259:0.21 260:0.62 265:0.63 266:0.84 267:0.18 268:0.65 271:0.54 276:0.93 277:0.69 281:0.91 282:0.75 284:0.89 285:0.62 290:0.76 297:0.36 300:0.84 +1 1:0.57 8:0.84 12:0.51 17:0.85 21:0.74 27:0.72 28:0.89 29:0.71 30:0.45 34:0.25 36:0.57 37:0.70 39:0.51 43:0.11 44:0.88 48:0.91 55:0.59 64:0.77 66:0.69 69:0.48 70:0.25 71:0.94 74:0.48 76:0.98 77:0.70 81:0.33 91:0.88 96:0.72 98:0.82 108:0.37 114:0.63 122:0.77 123:0.35 126:0.44 127:0.31 129:0.05 135:0.37 139:0.66 140:0.90 145:0.70 146:0.47 147:0.53 149:0.11 150:0.37 151:0.51 153:0.71 154:0.09 155:0.46 159:0.17 161:0.96 162:0.56 163:0.75 167:0.86 172:0.21 173:0.78 175:0.91 176:0.62 177:0.05 178:0.50 179:0.96 181:0.48 190:0.98 191:0.87 192:0.44 195:0.55 201:0.54 202:0.73 208:0.54 212:0.61 216:0.02 220:0.93 222:0.35 235:0.95 241:0.94 242:0.82 243:0.73 244:0.97 247:0.12 248:0.52 253:0.53 256:0.68 257:0.84 259:0.21 265:0.82 266:0.17 267:0.65 268:0.72 271:0.54 276:0.21 277:0.87 281:0.86 282:0.73 285:0.47 290:0.63 300:0.18 +1 1:0.69 7:0.69 11:0.75 12:0.51 17:0.97 18:0.75 21:0.59 27:0.67 28:0.89 29:0.71 30:0.53 32:0.67 34:0.89 36:0.39 37:0.35 39:0.51 43:0.74 44:0.92 45:0.92 48:0.97 55:0.59 64:0.77 66:0.98 69:0.36 70:0.54 71:0.94 74:0.86 76:0.94 77:1.00 81:0.76 83:0.91 85:0.72 86:0.73 91:0.22 97:0.98 98:0.97 99:0.99 101:0.87 104:0.86 106:0.81 108:0.28 114:0.71 117:0.86 122:0.51 123:0.27 126:0.54 127:0.73 129:0.05 135:0.65 139:0.79 145:0.93 146:0.94 147:0.95 149:0.78 150:0.74 154:0.65 155:0.58 158:0.75 159:0.60 161:0.96 165:0.70 167:0.47 172:0.95 173:0.31 176:0.63 177:0.70 178:0.55 179:0.21 181:0.48 187:0.21 191:0.70 192:0.57 195:0.78 201:0.67 204:0.85 206:0.81 208:0.64 212:0.89 215:0.55 216:0.18 222:0.35 228:0.99 230:0.71 232:0.91 233:0.76 235:0.98 241:0.03 242:0.36 243:0.98 247:0.82 253:0.64 259:0.21 265:0.97 266:0.27 267:0.36 271:0.54 276:0.90 279:0.81 281:0.86 282:0.75 283:0.82 290:0.71 297:0.36 300:0.43 +1 1:0.64 6:0.84 7:0.69 8:0.75 9:0.76 11:0.45 12:0.51 17:0.91 20:0.89 21:0.47 27:0.18 28:0.89 29:0.71 30:0.75 31:0.99 32:0.65 34:0.31 36:0.30 37:0.63 39:0.51 43:0.56 45:0.99 55:0.52 66:0.61 69:0.36 70:0.29 71:0.94 74:0.95 76:0.85 77:0.89 78:0.93 79:0.99 81:0.65 83:0.60 91:0.92 96:0.99 97:0.89 98:0.85 99:0.83 100:0.73 101:0.52 104:0.82 106:0.81 108:0.72 111:0.89 114:0.74 117:0.86 120:0.99 122:0.51 123:0.70 124:0.51 126:0.44 127:0.76 129:0.59 133:0.47 135:0.47 137:0.99 140:0.45 145:0.79 146:0.67 147:0.72 149:0.97 150:0.57 151:0.98 152:0.93 153:0.48 154:0.45 155:0.58 158:0.75 159:0.72 161:0.96 162:0.69 164:0.97 165:0.70 167:0.70 169:0.89 172:0.98 173:0.24 175:0.75 176:0.62 177:0.38 179:0.12 181:0.48 186:0.92 187:0.21 189:0.81 190:0.95 191:0.90 192:0.59 195:0.86 201:0.60 202:0.96 204:0.84 206:0.81 208:0.54 212:0.93 215:0.63 216:0.42 220:0.62 222:0.35 230:0.71 232:0.88 233:0.76 235:0.68 238:0.81 241:0.69 242:0.53 243:0.38 244:0.93 245:1.00 247:0.67 248:0.99 253:1.00 255:0.74 256:0.97 257:0.65 259:0.21 260:0.99 261:0.92 265:0.85 266:0.54 267:0.69 268:0.97 271:0.54 276:0.97 277:0.69 279:0.77 282:0.74 283:0.67 284:0.99 285:0.62 290:0.74 297:0.36 300:0.84 +2 1:0.68 6:0.89 7:0.69 8:0.79 9:0.73 11:0.57 12:0.51 17:0.97 18:0.87 20:0.95 21:0.47 27:0.48 28:0.89 29:0.71 30:0.54 31:0.89 32:0.67 34:0.20 36:0.52 37:0.56 39:0.51 43:0.75 44:0.88 45:0.77 55:0.45 64:0.77 66:0.97 69:0.32 70:0.53 71:0.94 74:0.85 76:0.98 77:0.96 78:0.98 79:0.88 81:0.70 83:0.60 86:0.91 91:0.73 96:0.72 98:0.92 99:0.83 100:0.91 101:0.87 108:0.25 111:0.95 114:0.76 120:0.59 123:0.24 126:0.54 127:0.54 129:0.05 135:0.42 137:0.89 139:0.88 140:0.45 145:0.84 146:0.88 147:0.90 149:0.63 150:0.61 151:0.57 152:0.94 153:0.48 154:0.66 155:0.65 159:0.46 161:0.96 162:0.81 164:0.73 165:0.70 167:0.84 169:0.88 172:0.92 173:0.35 176:0.63 177:0.70 179:0.24 181:0.48 186:0.97 189:0.92 190:0.89 191:0.89 192:0.86 195:0.70 196:0.92 201:0.65 202:0.62 204:0.80 208:0.64 212:0.92 215:0.63 216:0.07 220:0.93 222:0.35 230:0.71 232:0.72 233:0.76 235:0.74 238:0.83 241:0.85 242:0.36 243:0.97 244:0.61 247:0.94 248:0.58 253:0.72 255:0.72 256:0.64 259:0.21 260:0.60 261:0.94 265:0.92 266:0.47 267:0.28 268:0.67 271:0.54 276:0.86 282:0.75 284:0.94 285:0.62 290:0.76 297:0.36 300:0.55 +2 1:0.62 6:0.85 7:0.70 8:0.78 9:0.70 11:0.45 12:0.51 17:0.88 18:0.78 20:0.85 21:0.71 27:0.42 28:0.89 29:0.71 30:0.76 31:0.86 32:0.64 34:0.22 36:0.69 37:0.62 39:0.51 43:0.63 44:0.88 45:0.73 48:0.97 55:0.52 64:0.77 66:0.54 69:0.48 70:0.45 71:0.94 74:0.73 76:0.85 77:0.89 78:0.94 79:0.86 81:0.63 83:0.60 86:0.78 87:0.98 91:0.91 96:0.78 98:0.59 99:0.83 100:0.89 101:0.52 108:0.37 111:0.92 114:0.66 120:0.54 123:0.35 124:0.64 126:0.54 127:0.79 129:0.05 133:0.60 135:0.26 137:0.86 139:0.80 140:0.45 145:0.76 146:0.78 147:0.81 149:0.63 150:0.52 151:0.48 152:0.82 153:0.48 154:0.53 155:0.57 159:0.70 161:0.96 162:0.56 164:0.74 165:0.70 167:0.86 169:0.85 172:0.90 173:0.36 176:0.63 177:0.70 179:0.22 181:0.48 186:0.94 190:0.98 191:0.90 192:0.56 195:0.85 196:0.88 201:0.61 202:0.74 204:0.84 208:0.64 212:0.91 215:0.48 216:0.10 220:1.00 222:0.35 230:0.73 232:0.79 233:0.76 235:0.97 241:0.95 242:0.76 243:0.62 244:0.83 245:0.95 247:0.84 248:0.49 253:0.78 255:0.69 256:0.73 259:0.21 260:0.55 261:0.87 265:0.60 266:0.71 267:0.19 268:0.73 271:0.54 276:0.88 281:0.91 282:0.73 284:0.95 285:0.62 290:0.66 297:0.36 300:0.94 +0 12:0.51 17:0.62 18:0.67 21:0.78 27:0.45 28:0.89 29:0.71 30:0.76 34:0.89 36:0.25 37:0.50 39:0.51 43:0.30 55:0.42 66:0.97 69:0.77 70:0.27 71:0.94 74:0.79 86:0.70 91:0.09 98:0.55 108:0.60 122:0.51 123:0.58 127:0.16 129:0.05 135:0.30 139:0.67 145:0.50 146:0.67 147:0.72 149:0.85 154:0.25 159:0.26 161:0.96 167:0.49 172:0.92 173:0.75 177:0.70 178:0.55 179:0.11 181:0.48 187:0.68 212:0.94 216:0.77 222:0.35 235:0.88 241:0.12 242:0.61 243:0.97 244:0.83 247:0.47 253:0.55 254:0.86 259:0.21 265:0.56 266:0.17 267:0.79 271:0.54 276:0.86 300:0.25 +0 1:0.64 7:0.70 8:0.78 9:0.71 11:0.45 12:0.51 17:0.88 18:0.91 20:0.81 21:0.76 27:0.63 28:0.89 29:0.71 30:0.61 31:0.87 34:0.71 36:0.41 37:0.70 39:0.51 43:0.16 45:0.66 48:0.97 55:0.45 64:0.77 66:0.59 69:0.39 70:0.66 71:0.94 74:0.79 76:0.97 78:0.92 79:0.86 81:0.61 83:0.91 86:0.87 91:0.27 96:0.69 98:0.46 99:0.83 100:0.73 101:0.52 104:0.86 106:0.81 108:0.30 111:0.89 114:0.63 117:0.86 120:0.55 123:0.29 124:0.69 126:0.54 127:0.90 129:0.05 133:0.74 135:0.39 137:0.87 139:0.85 140:0.45 145:0.40 146:0.74 147:0.78 149:0.41 150:0.51 151:0.50 152:0.78 153:0.48 154:0.50 155:0.58 159:0.78 161:0.96 162:0.62 164:0.56 165:0.70 167:0.52 169:0.72 172:0.94 173:0.23 176:0.63 177:0.70 179:0.18 181:0.48 186:0.92 187:0.39 190:0.89 192:0.56 196:0.89 201:0.62 202:0.50 204:0.83 206:0.81 208:0.64 212:0.91 215:0.46 216:0.28 220:0.96 222:0.35 230:0.73 232:0.91 233:0.76 235:1.00 241:0.30 242:0.55 243:0.67 244:0.83 245:0.95 247:0.93 248:0.50 253:0.59 254:0.80 255:0.70 256:0.45 259:0.21 260:0.55 261:0.84 265:0.48 266:0.54 267:0.69 268:0.46 271:0.54 276:0.90 281:0.91 284:0.88 285:0.62 290:0.63 297:0.36 300:0.84 +1 1:0.69 7:0.69 8:0.74 11:0.75 12:0.51 17:0.95 18:0.83 21:0.54 22:0.93 27:0.43 28:0.89 29:0.71 30:0.88 32:0.66 34:0.92 36:0.36 37:0.40 39:0.51 43:0.85 44:0.88 45:0.83 47:0.93 48:0.91 55:0.52 64:0.77 66:0.98 69:0.32 70:0.28 71:0.94 74:0.78 77:0.96 81:0.75 83:0.60 85:0.72 86:0.83 91:0.02 97:0.99 98:0.99 99:0.95 101:0.87 104:0.91 106:0.81 108:0.25 114:0.69 117:0.86 123:0.24 126:0.54 127:0.87 129:0.05 135:0.32 139:0.85 145:0.94 146:0.92 147:0.93 149:0.83 150:0.68 154:0.82 155:0.56 158:0.76 159:0.54 161:0.96 165:0.70 167:0.47 172:0.95 173:0.33 176:0.62 177:0.70 178:0.55 179:0.23 181:0.48 187:0.21 192:0.55 195:0.70 201:0.67 204:0.81 206:0.81 208:0.64 212:0.89 215:0.55 216:0.36 219:0.91 222:0.35 230:0.71 232:0.94 233:0.76 235:0.95 241:0.03 242:0.58 243:0.98 247:0.91 253:0.61 262:0.93 265:0.99 266:0.27 267:0.52 271:0.54 276:0.90 279:0.82 281:0.86 282:0.75 283:0.67 290:0.69 292:0.88 297:0.36 300:0.33 +1 1:0.59 6:0.82 7:0.66 8:0.75 9:0.72 11:0.45 12:0.51 17:0.98 18:0.73 20:0.84 21:0.77 27:0.52 28:0.89 29:0.71 30:0.27 31:0.87 32:0.64 34:0.28 36:0.36 39:0.51 43:0.57 45:0.73 48:0.91 55:0.52 64:0.77 66:0.77 69:0.73 70:0.83 71:0.94 74:0.67 76:0.85 77:0.98 78:0.95 79:0.87 81:0.59 83:0.91 86:0.70 91:0.63 96:0.70 98:0.91 99:0.83 100:0.88 101:0.52 104:0.79 108:0.56 111:0.92 114:0.63 120:0.56 123:0.54 124:0.41 126:0.54 127:0.94 129:0.05 133:0.43 135:0.28 137:0.87 138:0.95 139:0.69 140:0.45 145:0.67 146:0.88 147:0.31 149:0.47 150:0.47 151:0.55 152:0.84 153:0.72 154:0.15 155:0.64 159:0.53 161:0.96 162:0.86 164:0.63 165:0.70 167:0.82 169:0.82 172:0.85 173:0.76 176:0.63 177:0.38 179:0.39 181:0.48 186:0.95 190:0.89 191:0.76 192:0.86 195:0.69 196:0.88 201:0.58 202:0.49 208:0.64 212:0.82 215:0.44 216:0.40 220:0.91 222:0.35 228:0.99 230:0.68 233:0.76 235:1.00 241:0.80 242:0.38 243:0.20 244:0.83 245:0.85 247:0.88 248:0.54 253:0.55 254:0.97 255:0.71 256:0.45 257:0.65 259:0.21 260:0.56 261:0.89 265:0.91 266:0.27 267:0.52 268:0.57 271:0.54 276:0.78 281:0.91 282:0.73 284:0.91 285:0.62 286:0.99 290:0.63 297:0.36 298:0.99 300:0.70 +2 1:0.68 6:0.84 7:0.70 8:0.72 9:0.74 12:0.51 17:0.77 20:0.89 21:0.22 25:0.88 27:0.37 28:0.89 29:0.71 30:0.91 31:0.90 32:0.65 34:0.17 36:0.41 37:0.62 39:0.51 41:0.82 43:0.09 44:0.86 48:0.99 55:0.52 64:0.77 66:0.26 69:0.65 70:0.28 71:0.94 74:0.60 76:0.94 77:0.89 78:0.98 79:0.90 81:0.80 83:0.60 91:0.86 96:0.74 98:0.34 100:0.96 104:0.76 108:0.49 111:0.96 114:0.90 120:0.62 123:0.72 124:0.74 126:0.54 127:0.79 129:0.89 133:0.78 135:0.37 137:0.90 139:0.69 140:0.45 145:0.98 146:0.54 147:0.60 149:0.39 150:0.68 151:0.64 152:0.84 153:0.77 154:0.08 155:0.65 159:0.76 161:0.96 162:0.86 164:0.69 165:0.93 167:0.85 169:0.94 172:0.74 173:0.51 175:0.91 176:0.73 177:0.05 179:0.38 181:0.48 186:0.97 189:0.86 191:0.87 192:0.87 195:0.89 196:0.89 201:0.66 202:0.54 204:0.84 208:0.64 212:0.85 215:0.79 216:0.03 220:0.82 222:0.35 228:0.99 230:0.73 233:0.76 235:0.68 238:0.86 241:0.89 242:0.70 243:0.57 244:0.97 245:0.84 247:0.39 248:0.61 253:0.71 255:0.73 256:0.58 257:0.84 259:0.21 260:0.63 261:0.93 265:0.33 266:0.75 267:0.23 268:0.63 271:0.54 276:0.70 277:0.87 281:0.91 282:0.74 284:0.93 285:0.62 290:0.90 297:0.36 300:0.70 +0 1:0.61 11:0.45 12:0.51 17:0.57 18:0.62 21:0.68 27:0.45 28:0.89 29:0.71 30:0.45 34:0.84 36:0.19 37:0.50 39:0.51 43:0.35 45:0.77 55:0.52 66:0.61 69:0.70 70:0.59 71:0.94 74:0.59 77:0.70 81:0.56 83:0.60 86:0.64 91:0.10 98:0.45 99:0.83 101:0.52 108:0.53 114:0.66 122:0.77 123:0.51 124:0.50 126:0.44 127:0.39 129:0.05 133:0.43 135:0.70 139:0.62 145:0.50 146:0.62 147:0.67 149:0.33 150:0.46 154:0.27 155:0.48 159:0.56 161:0.96 165:0.70 167:0.60 172:0.59 173:0.64 176:0.62 177:0.70 178:0.55 179:0.57 181:0.48 187:0.68 192:0.46 195:0.79 201:0.57 208:0.54 212:0.73 215:0.49 216:0.77 222:0.35 235:0.88 241:0.55 242:0.41 243:0.67 244:0.93 245:0.74 247:0.67 253:0.53 259:0.21 265:0.47 266:0.54 267:0.36 271:0.54 276:0.41 282:0.73 290:0.66 297:0.36 300:0.55 +1 1:0.63 8:0.79 9:0.70 11:0.54 12:0.51 17:0.93 18:0.75 21:0.59 27:0.57 28:0.89 29:0.71 30:0.72 31:0.86 34:0.80 36:0.55 37:0.68 39:0.51 43:0.33 44:0.92 45:0.73 55:0.45 64:0.77 66:0.46 69:0.43 70:0.56 71:0.94 74:0.72 76:0.85 77:0.89 79:0.86 81:0.64 83:0.60 86:0.74 87:0.98 91:0.44 96:0.68 98:0.61 99:0.83 101:0.52 108:0.79 114:0.71 120:0.54 123:0.77 124:0.75 126:0.54 127:0.72 129:0.59 133:0.73 135:0.44 137:0.86 139:0.82 140:0.87 145:0.85 146:0.50 147:0.56 149:0.57 150:0.53 151:0.49 153:0.48 154:0.25 155:0.55 159:0.70 161:0.96 162:0.50 164:0.56 165:0.70 167:0.54 172:0.86 173:0.31 176:0.63 177:0.70 178:0.48 179:0.24 181:0.48 187:0.21 190:0.89 191:0.85 192:0.50 195:0.84 196:0.88 201:0.61 202:0.61 204:0.82 208:0.64 212:0.88 215:0.55 216:0.14 220:0.97 222:0.35 232:0.77 235:0.80 241:0.37 242:0.72 243:0.37 244:0.61 245:0.92 247:0.86 248:0.49 253:0.60 255:0.69 256:0.45 260:0.55 265:0.62 266:0.75 267:0.23 268:0.46 271:0.54 276:0.87 281:0.86 282:0.80 284:0.88 285:0.62 290:0.71 297:0.36 300:0.70 +1 1:0.65 8:0.84 11:0.54 12:0.51 17:0.96 18:0.79 21:0.47 27:0.57 28:0.89 29:0.71 30:0.62 34:0.70 36:0.60 37:0.68 39:0.51 43:0.11 44:0.88 45:0.73 48:0.98 55:0.45 64:0.77 66:0.59 69:0.41 70:0.51 71:0.94 74:0.72 76:0.98 77:0.98 81:0.67 83:0.60 86:0.73 87:0.98 91:0.23 98:0.74 99:0.83 101:0.52 108:0.79 114:0.76 123:0.77 124:0.64 126:0.54 127:0.77 129:0.59 133:0.61 135:0.47 139:0.76 145:0.72 146:0.50 147:0.56 149:0.48 150:0.57 154:0.17 155:0.56 159:0.67 161:0.96 165:0.70 167:0.50 172:0.92 173:0.32 176:0.63 177:0.70 178:0.55 179:0.20 181:0.48 187:0.21 192:0.50 195:0.81 201:0.62 202:0.50 204:0.84 208:0.64 212:0.90 215:0.63 216:0.14 222:0.35 232:0.77 235:0.68 241:0.18 242:0.60 243:0.32 244:0.93 245:0.96 247:0.86 253:0.61 254:0.74 265:0.74 266:0.75 267:0.19 271:0.54 276:0.91 281:0.86 282:0.74 290:0.76 297:0.36 300:0.70 +2 1:0.68 7:0.69 8:0.80 9:0.72 11:0.75 12:0.51 17:0.79 18:0.74 21:0.40 27:0.71 28:0.89 29:0.71 30:0.55 32:0.67 34:0.78 36:0.61 37:0.56 39:0.51 43:0.90 44:0.88 45:0.93 55:0.52 66:0.99 69:0.19 70:0.55 71:0.94 74:0.89 76:0.94 77:0.99 81:0.59 83:0.60 85:0.72 86:0.74 91:0.37 96:0.73 97:0.94 98:0.96 101:0.87 104:0.79 106:0.81 108:0.15 114:0.72 117:0.86 120:0.61 123:0.15 126:0.44 127:0.73 129:0.05 135:0.49 138:0.95 139:0.76 140:0.45 145:0.77 146:0.92 147:0.94 149:0.88 150:0.60 151:0.65 153:0.48 154:0.89 155:0.58 158:0.75 159:0.55 161:0.96 162:0.86 164:0.68 167:0.50 172:0.97 173:0.23 176:0.60 177:0.70 179:0.17 181:0.48 187:0.21 190:0.95 192:0.57 195:0.72 201:0.63 202:0.53 204:0.85 206:0.81 208:0.54 212:0.93 215:0.63 216:0.05 220:0.91 222:0.35 230:0.71 232:0.70 233:0.66 235:0.68 241:0.15 242:0.70 243:0.99 247:0.76 248:0.65 253:0.75 255:0.71 256:0.58 259:0.21 260:0.61 265:0.96 266:0.27 267:0.18 268:0.62 271:0.54 276:0.93 279:0.81 282:0.75 283:0.82 285:0.56 286:0.99 290:0.72 297:0.36 298:0.99 300:0.70 +0 1:0.69 7:0.69 8:0.72 11:0.45 12:0.51 17:0.90 18:0.74 21:0.13 27:0.32 28:0.89 29:0.71 30:0.76 32:0.67 34:0.28 36:0.34 37:0.64 39:0.51 43:0.40 44:0.88 45:0.83 55:0.52 64:0.77 66:0.93 69:0.15 70:0.42 71:0.94 74:0.77 76:0.94 77:0.99 81:0.83 83:0.60 86:0.75 91:0.37 98:0.93 99:0.83 101:0.52 108:0.12 114:0.88 117:0.86 122:0.77 123:0.12 126:0.54 127:0.59 129:0.05 135:0.51 139:0.76 145:0.97 146:0.73 147:0.77 149:0.42 150:0.86 154:0.54 155:0.56 159:0.29 161:0.96 165:0.70 167:0.52 172:0.82 173:0.37 176:0.63 177:0.70 178:0.55 179:0.43 181:0.48 187:0.21 191:0.70 192:0.55 195:0.60 201:0.68 204:0.80 208:0.64 212:0.88 215:0.84 216:0.16 222:0.35 230:0.71 232:0.86 233:0.76 235:0.52 241:0.27 242:0.70 243:0.94 244:0.83 247:0.79 253:0.68 254:0.74 259:0.21 265:0.93 266:0.27 267:0.23 271:0.54 276:0.72 282:0.75 290:0.88 297:0.36 300:0.55 +1 1:0.59 6:0.82 7:0.66 8:0.70 11:0.45 12:0.51 17:0.98 18:0.73 20:0.86 21:0.77 27:0.52 28:0.89 29:0.71 30:0.27 32:0.64 34:0.42 36:0.32 39:0.51 43:0.57 45:0.73 48:0.91 55:0.52 64:0.77 66:0.77 69:0.73 70:0.83 71:0.94 74:0.67 76:0.85 77:0.98 78:0.95 81:0.59 83:0.60 86:0.70 91:0.51 98:0.91 99:0.83 100:0.73 101:0.52 104:0.79 108:0.56 111:0.91 114:0.63 123:0.54 124:0.41 126:0.54 127:0.94 129:0.05 133:0.43 135:0.30 139:0.69 145:0.67 146:0.88 147:0.31 149:0.47 150:0.47 152:0.84 154:0.15 155:0.47 159:0.53 161:0.96 165:0.70 167:0.75 169:0.82 172:0.85 173:0.76 176:0.63 177:0.38 178:0.55 179:0.39 181:0.48 186:0.94 187:0.21 191:0.80 192:0.45 195:0.69 201:0.58 208:0.64 212:0.82 215:0.44 216:0.40 222:0.35 228:0.99 230:0.68 233:0.76 235:1.00 241:0.74 242:0.38 243:0.20 244:0.83 245:0.85 247:0.88 253:0.54 254:0.97 257:0.65 259:0.21 261:0.89 265:0.91 266:0.27 267:0.44 271:0.54 276:0.78 281:0.91 282:0.73 290:0.63 297:0.36 300:0.70 +1 1:0.58 6:0.82 7:0.69 8:0.71 9:0.71 11:0.45 12:0.51 17:0.88 18:0.64 20:0.89 21:0.64 27:0.51 28:0.89 29:0.71 30:0.16 32:0.65 34:0.78 36:0.56 37:0.33 39:0.51 43:0.39 45:0.92 55:0.45 66:0.88 69:0.83 70:0.88 71:0.94 74:0.85 76:0.85 77:0.70 78:0.87 81:0.38 86:0.67 91:0.18 96:0.73 98:0.90 100:0.90 101:0.52 104:0.98 106:0.81 108:0.67 111:0.87 114:0.67 117:0.86 120:0.61 122:0.51 123:0.65 124:0.38 126:0.44 127:0.63 129:0.05 133:0.43 135:0.97 139:0.65 140:0.80 145:0.70 146:0.98 147:0.37 149:0.65 150:0.43 151:0.61 152:0.90 153:0.48 154:0.25 155:0.57 158:0.75 159:0.80 161:0.96 162:0.86 164:0.76 167:0.48 169:0.90 172:0.98 173:0.69 176:0.62 177:0.38 179:0.13 181:0.48 186:0.87 187:0.21 190:0.95 192:0.56 195:0.91 201:0.55 202:0.63 204:0.82 206:0.81 208:0.54 212:0.88 215:0.53 216:0.85 220:0.97 222:0.35 230:0.71 232:0.92 233:0.66 235:0.80 241:0.10 242:0.39 243:0.11 244:0.83 245:0.97 247:0.79 248:0.60 253:0.72 254:0.84 255:0.70 256:0.68 257:0.65 259:0.21 260:0.61 261:0.88 265:0.90 266:0.54 267:1.00 268:0.71 271:0.54 276:0.96 279:0.82 282:0.74 283:0.82 285:0.56 290:0.67 297:0.36 300:0.84 +1 1:0.69 6:0.89 7:0.69 8:0.75 9:0.76 11:0.45 12:0.51 17:0.82 18:0.91 20:0.90 21:0.13 27:0.32 28:0.89 29:0.71 30:0.11 31:0.94 32:0.67 34:0.18 36:0.96 37:0.64 39:0.51 43:0.40 44:0.88 45:0.99 55:0.71 64:0.77 66:0.10 69:0.15 70:0.93 71:0.94 74:0.89 76:0.94 77:0.99 78:0.98 79:0.94 81:0.83 83:0.60 86:0.90 91:0.79 96:0.92 98:0.51 99:0.83 100:0.96 101:0.52 108:0.94 111:0.96 114:0.88 120:0.87 123:0.99 124:0.94 126:0.54 127:1.00 129:0.89 133:0.93 135:0.34 137:0.94 138:0.98 139:0.89 140:0.95 145:0.97 146:0.68 147:0.73 149:0.86 150:0.86 151:0.72 152:0.93 153:0.48 154:0.54 155:0.65 159:0.98 161:0.96 162:0.81 164:0.90 165:0.70 167:0.81 169:0.92 172:0.98 173:0.05 176:0.63 177:0.70 179:0.09 181:0.48 186:0.98 189:0.91 190:0.95 191:0.90 192:0.87 195:1.00 196:0.96 201:0.68 202:0.84 204:0.80 208:0.64 212:0.56 215:0.84 216:0.16 219:0.88 220:0.99 222:0.35 230:0.71 233:0.76 235:0.52 238:0.86 241:0.80 242:0.70 243:0.10 244:0.83 245:1.00 247:0.93 248:0.71 253:0.95 254:0.74 255:0.78 256:0.88 259:0.21 260:0.88 261:0.94 265:0.52 266:0.89 267:0.28 268:0.89 271:0.54 276:1.00 277:0.69 282:0.75 284:0.98 285:0.62 290:0.88 292:0.88 297:0.36 300:0.84 +2 1:0.69 7:0.70 11:0.75 12:0.51 17:0.82 18:0.85 21:0.54 27:0.59 28:0.89 29:0.71 30:0.88 32:0.67 34:0.37 36:0.22 37:0.47 39:0.51 43:0.91 44:0.88 45:0.87 48:0.91 55:0.52 64:0.77 66:0.98 69:0.44 70:0.29 71:0.94 74:0.85 76:0.85 77:0.99 81:0.76 83:0.91 85:0.72 86:0.85 91:0.04 97:0.89 98:0.95 99:0.95 101:0.87 108:0.34 114:0.69 123:0.33 126:0.54 127:0.64 129:0.05 135:0.26 139:0.88 145:1.00 146:0.93 147:0.94 149:0.87 150:0.73 154:0.91 155:0.64 158:0.77 159:0.56 161:0.96 165:0.70 167:0.57 172:0.96 173:0.40 176:0.62 177:0.70 178:0.55 179:0.18 181:0.48 187:0.39 192:0.58 195:0.73 201:0.68 204:0.85 208:0.64 212:0.93 215:0.55 216:0.36 219:0.91 222:0.35 230:0.73 233:0.66 235:0.97 241:0.47 242:0.53 243:0.99 247:0.88 253:0.64 265:0.95 266:0.27 267:0.19 271:0.54 276:0.92 279:0.80 281:0.86 282:0.75 283:0.82 290:0.69 292:0.95 297:0.36 300:0.70 +0 12:0.51 17:0.38 21:0.22 27:0.45 28:0.83 30:0.87 32:0.72 34:0.68 36:0.17 37:0.50 39:0.66 43:0.30 55:0.71 66:0.86 69:0.68 70:0.26 74:0.44 81:0.60 91:0.18 98:0.88 108:0.52 123:0.50 124:0.38 126:0.32 127:0.49 129:0.05 133:0.39 135:0.61 145:0.52 146:0.99 147:0.99 149:0.83 150:0.44 154:0.53 159:0.86 161:0.82 167:0.53 172:0.97 173:0.40 177:0.38 178:0.55 179:0.15 181:0.81 187:0.68 195:0.96 212:0.89 215:0.79 216:0.77 222:0.35 235:0.22 241:0.15 242:0.80 243:0.87 245:0.94 247:0.39 253:0.38 254:0.80 259:0.21 265:0.88 266:0.54 267:0.28 271:0.66 276:0.93 297:0.36 300:0.43 +3 1:0.74 6:0.95 7:0.81 8:0.92 9:0.80 12:0.51 17:0.64 20:0.96 27:0.25 28:0.83 32:0.80 34:0.21 36:0.38 37:0.92 39:0.66 41:0.94 74:0.59 76:0.94 77:0.70 78:0.95 81:0.92 83:0.84 91:0.88 96:0.93 100:0.97 104:0.58 111:0.96 114:0.98 120:0.88 121:0.90 126:0.54 135:0.32 140:0.45 144:0.85 145:0.40 150:0.96 151:0.96 152:0.89 153:0.85 155:0.67 161:0.82 162:0.61 164:0.80 165:0.92 167:0.88 169:0.96 175:0.91 176:0.73 181:0.81 186:0.95 189:0.96 191:0.90 192:0.59 195:0.52 201:0.74 202:0.74 204:0.89 208:0.64 215:0.96 216:0.09 222:0.35 230:0.87 232:0.78 233:0.76 235:0.05 238:0.95 241:0.87 244:0.83 248:0.93 253:0.92 255:0.79 256:0.75 257:0.84 259:0.21 260:0.88 261:0.96 267:0.44 268:0.75 271:0.66 277:0.87 282:0.88 285:0.62 290:0.98 297:0.36 299:0.88 +2 1:0.74 6:0.90 7:0.81 8:0.73 9:0.80 11:0.88 12:0.51 17:0.36 20:0.97 21:0.30 27:0.21 28:0.83 30:0.10 34:0.80 36:0.86 37:0.74 39:0.66 41:0.93 43:0.98 55:0.71 60:0.87 66:0.16 69:0.12 70:0.94 74:0.91 78:0.93 81:0.74 83:0.83 85:0.95 91:0.35 96:0.91 98:0.48 100:0.97 101:0.78 104:0.84 106:0.81 108:0.90 111:0.95 114:0.86 117:0.86 120:0.85 121:0.90 123:0.89 124:0.94 126:0.54 127:0.76 129:0.95 133:0.94 135:0.30 140:0.45 144:0.85 146:0.29 147:0.36 149:0.93 150:0.83 151:0.96 152:0.95 153:0.87 154:0.98 155:0.67 158:0.92 159:0.89 161:0.82 162:0.65 164:0.80 165:0.84 167:0.58 169:0.94 172:0.70 173:0.08 176:0.73 177:0.38 179:0.23 181:0.81 186:0.93 187:0.39 189:0.92 191:0.73 192:0.59 201:0.74 202:0.73 204:0.89 206:0.81 208:0.64 212:0.39 215:0.72 216:0.95 222:0.35 230:0.87 232:0.90 233:0.76 235:0.42 238:0.95 241:0.37 242:0.62 243:0.16 245:0.87 247:0.67 248:0.91 253:0.93 255:0.79 256:0.73 259:0.21 260:0.85 261:0.96 265:0.50 266:0.95 267:0.52 268:0.75 271:0.66 276:0.89 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +2 1:0.74 6:0.82 7:0.81 8:0.63 9:0.80 11:0.88 12:0.51 17:0.20 20:0.96 21:0.13 27:0.18 28:0.83 30:0.52 32:0.80 34:0.62 36:0.37 37:0.46 39:0.66 41:0.90 43:0.93 60:0.87 66:0.49 69:0.30 70:0.87 74:0.94 77:0.89 78:0.89 81:0.84 83:0.85 85:0.98 91:0.54 96:0.82 98:0.62 100:0.88 101:0.85 104:0.77 106:0.81 108:0.70 111:0.87 114:0.92 117:0.86 120:0.72 121:1.00 122:0.43 123:0.68 124:0.79 126:0.54 127:0.61 129:0.93 133:0.84 135:0.86 140:0.45 144:0.85 145:0.77 146:0.43 147:0.50 149:0.96 150:0.90 151:0.73 152:0.93 153:0.46 154:0.93 155:0.67 158:0.92 159:0.74 161:0.82 162:0.86 164:0.70 165:0.88 167:0.53 169:0.85 172:0.82 173:0.18 176:0.73 177:0.38 179:0.29 181:0.81 186:0.88 187:0.87 189:0.85 191:0.70 192:0.59 195:0.88 201:0.74 202:0.56 204:0.89 206:0.81 208:0.64 212:0.59 215:0.84 216:0.75 220:0.74 222:0.35 230:0.84 232:0.83 233:0.76 235:0.22 238:0.85 241:0.12 242:0.38 243:0.15 245:0.86 247:0.67 248:0.79 253:0.88 255:0.79 256:0.64 259:0.21 260:0.73 261:0.91 265:0.63 266:0.63 267:0.79 268:0.64 271:0.66 276:0.83 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.97 300:0.84 +0 12:0.51 17:0.38 21:0.74 27:0.45 28:0.83 30:0.72 32:0.72 34:0.69 36:0.17 37:0.50 39:0.66 43:0.30 55:0.71 66:0.86 69:0.71 70:0.36 74:0.38 81:0.28 91:0.12 98:0.84 108:0.54 123:0.51 124:0.38 126:0.32 127:0.47 129:0.05 133:0.39 135:0.84 145:0.50 146:0.98 147:0.98 149:0.83 150:0.27 154:0.48 159:0.78 161:0.82 167:0.54 172:0.94 173:0.51 177:0.38 178:0.55 179:0.19 181:0.81 187:0.68 195:0.93 212:0.90 215:0.46 216:0.77 222:0.35 235:0.88 241:0.18 242:0.81 243:0.87 245:0.89 247:0.39 253:0.34 254:0.80 259:0.21 265:0.84 266:0.71 267:0.28 271:0.66 276:0.89 297:0.36 300:0.84 +3 1:0.74 6:0.93 7:0.81 8:0.90 9:0.80 11:0.88 12:0.51 17:0.06 20:0.84 21:0.59 27:0.03 28:0.83 30:0.91 32:0.80 34:0.75 36:0.83 37:0.93 39:0.66 41:0.88 43:0.86 60:0.87 66:0.69 69:0.61 70:0.13 74:0.77 76:0.85 77:0.70 78:0.87 81:0.57 83:0.86 85:0.80 91:0.70 96:0.84 98:0.62 100:0.90 101:0.78 104:0.93 108:0.46 111:0.87 114:0.74 120:0.75 121:0.97 122:0.43 123:0.44 126:0.54 127:0.18 129:0.05 135:0.51 140:0.45 144:0.85 145:0.59 146:0.46 147:0.53 149:0.65 150:0.72 151:0.93 152:0.80 153:0.77 154:0.83 155:0.67 158:0.92 159:0.17 161:0.82 162:0.86 163:0.81 164:0.69 165:0.78 167:0.78 169:0.87 172:0.21 173:0.77 176:0.73 177:0.38 179:0.86 181:0.81 186:0.87 187:0.68 189:0.94 191:0.98 192:0.59 195:0.61 201:0.74 202:0.54 204:0.89 208:0.64 212:0.61 215:0.55 216:0.76 222:0.35 230:0.87 232:0.95 233:0.76 235:0.74 238:0.90 241:0.74 242:0.63 243:0.73 247:0.30 248:0.82 253:0.85 255:0.79 256:0.58 259:0.21 260:0.76 261:0.85 265:0.63 266:0.17 267:0.91 268:0.63 271:0.66 276:0.13 279:0.86 282:0.88 283:0.82 285:0.62 290:0.74 297:0.36 299:0.88 300:0.13 +2 1:0.74 6:0.80 8:0.61 9:0.80 11:0.88 12:0.51 17:0.57 20:0.88 21:0.22 27:0.09 28:0.83 30:0.33 32:0.80 34:0.61 36:0.95 37:0.36 39:0.66 41:0.92 43:0.79 55:0.59 60:0.87 66:0.98 69:0.74 70:0.66 74:0.89 77:0.70 78:0.83 81:0.79 83:0.87 85:0.95 91:0.60 96:0.86 98:0.90 100:0.80 101:0.84 108:0.58 111:0.81 114:0.90 120:0.77 123:0.55 126:0.54 127:0.47 129:0.05 135:0.42 140:0.80 144:0.85 145:0.82 146:0.93 147:0.94 149:0.95 150:0.86 151:0.87 152:0.83 153:0.48 154:0.73 155:0.67 158:0.92 159:0.57 161:0.82 162:0.68 164:0.75 165:0.86 167:0.63 169:0.78 172:0.96 173:0.71 176:0.73 177:0.38 178:0.42 179:0.17 181:0.81 186:0.83 187:0.21 189:0.82 192:0.59 195:0.80 201:0.74 202:0.67 208:0.64 212:0.93 215:0.79 216:0.46 222:0.35 232:0.82 235:0.31 238:0.82 241:0.52 242:0.76 243:0.98 247:0.67 248:0.84 253:0.89 255:0.79 256:0.68 259:0.21 260:0.78 261:0.83 265:0.90 266:0.38 267:0.28 268:0.69 271:0.66 276:0.92 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.90 7:0.81 8:0.68 9:0.80 11:0.88 12:0.51 17:0.51 20:0.89 21:0.54 27:0.21 28:0.83 30:0.45 34:0.62 36:0.67 37:0.74 39:0.66 41:0.88 43:0.96 55:0.71 60:0.87 66:0.62 69:0.23 70:0.79 74:0.83 78:0.86 81:0.60 83:0.81 85:0.93 91:0.08 96:0.71 98:0.85 100:0.85 101:0.75 104:0.91 106:0.81 108:0.18 111:0.85 114:0.77 117:0.86 120:0.58 121:0.90 123:0.18 124:0.50 126:0.54 127:0.79 129:0.59 133:0.47 135:0.21 140:0.45 144:0.85 146:0.99 147:0.99 149:0.95 150:0.75 151:0.54 152:0.95 153:0.48 154:0.96 155:0.67 158:0.87 159:0.91 161:0.82 162:0.86 164:0.67 165:0.79 167:0.55 169:0.94 172:0.98 173:0.09 176:0.73 177:0.38 179:0.13 181:0.81 186:0.86 187:0.39 189:0.85 191:0.70 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.73 215:0.58 216:0.95 220:0.96 222:0.35 230:0.84 232:0.94 233:0.69 235:0.68 238:0.84 241:0.21 242:0.79 243:0.68 245:0.99 247:0.47 248:0.55 253:0.72 255:0.79 256:0.58 259:0.21 260:0.58 261:0.96 265:0.85 266:0.80 267:0.85 268:0.59 271:0.66 276:0.97 279:0.86 283:0.71 285:0.62 290:0.77 297:0.36 300:0.84 +1 1:0.74 6:0.98 7:0.76 8:0.90 9:0.80 11:0.88 12:0.51 17:0.30 20:0.76 21:0.54 27:0.02 28:0.83 30:0.68 32:0.72 34:0.85 36:0.47 37:0.92 39:0.66 41:0.82 43:0.88 66:0.88 69:0.53 70:0.53 74:0.92 76:0.85 78:0.93 81:0.65 83:0.74 85:0.97 91:0.40 96:0.71 98:0.67 100:0.97 101:0.84 108:0.41 111:0.96 114:0.77 120:0.58 122:0.43 123:0.39 124:0.37 126:0.54 127:0.39 129:0.05 133:0.39 135:0.69 140:0.45 144:0.85 145:0.83 146:0.88 147:0.91 149:0.96 150:0.77 151:0.58 152:0.78 153:0.48 154:0.87 155:0.67 159:0.68 161:0.82 162:0.54 164:0.57 165:0.79 167:0.62 169:0.88 172:0.84 173:0.37 176:0.73 177:0.38 179:0.34 181:0.81 186:0.93 187:0.39 191:0.90 192:0.59 195:0.88 201:0.74 202:0.56 208:0.64 212:0.73 215:0.58 216:0.99 220:0.87 222:0.35 230:0.79 233:0.76 235:0.74 241:0.50 242:0.51 243:0.89 245:0.64 247:0.60 248:0.57 253:0.76 255:0.79 256:0.45 259:0.21 260:0.59 261:0.83 265:0.68 266:0.38 267:0.79 268:0.46 271:0.66 276:0.69 285:0.62 290:0.77 297:0.36 300:0.70 +3 1:0.74 8:0.59 9:0.80 11:0.88 12:0.51 17:0.04 21:0.05 27:0.33 28:0.83 30:0.53 32:0.80 34:0.59 36:0.26 37:0.41 39:0.66 41:0.82 43:0.86 60:0.97 66:0.71 69:0.57 70:0.90 74:0.90 77:0.89 81:0.88 83:0.87 85:0.94 91:0.38 96:0.80 98:0.68 101:0.81 108:0.67 114:0.95 120:0.69 122:0.57 123:0.65 124:0.46 126:0.54 127:0.42 129:0.81 133:0.67 135:0.98 140:0.45 144:0.85 145:0.96 146:0.13 147:0.18 149:0.89 150:0.93 151:0.87 153:0.48 154:0.84 155:0.67 158:0.92 159:0.68 161:0.82 162:0.86 164:0.57 165:0.90 167:0.53 172:0.76 173:0.42 176:0.73 177:0.38 179:0.42 181:0.81 187:0.39 192:0.59 195:0.87 201:0.74 202:0.36 208:0.64 212:0.27 215:0.91 216:0.88 222:0.35 235:0.12 241:0.15 242:0.33 243:0.13 245:0.73 247:0.67 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 265:0.68 266:0.75 267:0.82 268:0.46 271:0.66 276:0.68 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.80 8:0.58 9:0.80 11:0.88 12:0.51 17:0.11 20:0.96 21:0.05 27:0.14 28:0.83 30:0.28 32:0.80 34:0.50 36:0.18 37:0.67 39:0.66 41:0.90 43:0.86 60:0.97 66:0.60 69:0.57 70:0.95 74:0.91 77:0.89 78:0.91 81:0.88 83:0.86 85:0.93 91:0.49 96:0.88 98:0.69 100:0.90 101:0.80 108:0.68 111:0.90 114:0.95 120:0.79 122:0.67 123:0.66 124:0.63 126:0.54 127:0.46 129:0.81 133:0.67 135:0.98 140:0.45 144:0.85 145:0.96 146:0.34 147:0.41 149:0.88 150:0.93 151:0.87 152:0.92 153:0.48 154:0.84 155:0.67 158:0.92 159:0.69 161:0.82 162:0.54 164:0.73 165:0.90 167:0.55 169:0.88 172:0.73 173:0.42 176:0.73 177:0.38 179:0.42 181:0.81 186:0.91 187:0.39 189:0.82 191:0.70 192:0.59 195:0.87 201:0.74 202:0.70 208:0.64 212:0.26 215:0.91 216:0.93 220:0.62 222:0.35 235:0.12 238:0.86 241:0.21 242:0.44 243:0.21 245:0.79 247:0.67 248:0.86 253:0.91 255:0.79 256:0.64 259:0.21 260:0.80 261:0.92 265:0.69 266:0.80 267:0.88 268:0.66 271:0.66 276:0.70 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.79 7:0.81 8:0.58 9:0.80 11:0.88 12:0.51 17:0.28 20:0.85 21:0.13 27:0.39 28:0.83 30:0.41 32:0.80 34:0.28 36:0.91 37:0.30 39:0.66 41:0.82 43:0.80 60:0.87 66:0.86 69:0.73 70:0.45 74:0.81 76:0.94 77:0.70 78:0.84 81:0.84 83:0.84 85:0.88 91:0.62 96:0.84 98:0.73 100:0.78 101:0.78 104:0.95 106:0.81 108:0.56 111:0.82 114:0.92 117:0.86 120:0.74 121:0.90 122:0.43 123:0.53 126:0.54 127:0.23 129:0.05 135:1.00 140:0.45 144:0.85 145:0.61 146:0.83 147:0.85 149:0.80 150:0.90 151:0.92 152:0.81 153:0.72 154:0.74 155:0.67 158:0.92 159:0.39 161:0.82 162:0.67 163:0.75 164:0.64 165:0.88 167:0.56 169:0.77 172:0.59 173:0.70 176:0.73 177:0.38 179:0.52 181:0.81 186:0.84 187:0.39 189:0.81 192:0.59 195:0.77 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.19 215:0.84 216:0.65 222:0.35 230:0.87 232:0.96 233:0.76 235:0.22 238:0.81 241:0.30 242:0.45 243:0.86 247:0.47 248:0.81 253:0.86 255:0.79 256:0.45 259:0.21 260:0.75 261:0.82 265:0.73 266:0.63 267:0.73 268:0.57 271:0.66 276:0.49 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 6:0.81 8:0.61 9:0.80 11:0.88 12:0.51 17:0.07 20:0.97 21:0.30 27:0.11 28:0.83 30:0.60 34:0.24 36:0.47 37:0.83 39:0.66 41:0.88 43:0.95 60:0.95 66:0.86 69:0.23 70:0.53 74:0.97 78:0.87 81:0.74 83:0.84 85:0.99 91:0.37 96:0.73 98:0.95 100:0.85 101:0.86 104:0.93 108:0.61 111:0.85 114:0.86 120:0.61 122:0.43 123:0.59 124:0.38 126:0.54 127:0.89 129:0.59 133:0.47 135:0.98 140:0.45 144:0.85 146:0.40 147:0.46 149:0.99 150:0.83 151:0.60 152:0.89 153:0.72 154:0.95 155:0.67 158:0.92 159:0.83 161:0.82 162:0.76 164:0.69 165:0.84 167:0.55 169:0.82 172:0.96 173:0.13 176:0.73 177:0.38 179:0.19 181:0.81 186:0.86 187:0.87 189:0.84 191:0.73 192:0.59 201:0.74 202:0.58 208:0.64 212:0.57 215:0.72 216:0.83 220:0.87 222:0.35 232:0.95 235:0.42 238:0.83 241:0.24 242:0.45 243:0.13 245:0.93 247:0.67 248:0.61 253:0.81 255:0.79 256:0.58 259:0.21 260:0.61 261:0.88 265:0.95 266:0.38 267:0.59 268:0.62 271:0.66 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.55 +2 1:0.74 6:0.93 8:0.87 9:0.80 11:0.88 12:0.37 17:0.02 20:0.98 27:0.14 28:0.70 30:0.70 32:0.80 34:0.78 36:0.28 37:0.67 39:0.42 41:0.99 43:0.86 60:0.95 66:0.12 69:0.57 70:0.74 74:0.88 77:0.70 78:0.86 81:0.95 83:0.90 85:0.95 91:0.75 96:0.99 98:0.26 100:0.93 101:0.81 108:0.93 111:0.88 114:0.98 120:0.99 122:0.43 123:0.58 124:0.89 126:0.54 127:0.68 129:0.95 133:0.87 135:0.39 138:0.99 140:0.45 144:0.85 145:0.80 146:0.22 147:0.28 149:0.89 150:0.98 151:1.00 152:0.94 153:0.99 154:0.84 155:0.67 158:0.92 159:0.82 161:0.75 162:0.65 163:0.81 164:0.97 165:0.92 167:0.60 169:0.89 172:0.37 173:0.34 176:0.73 177:0.38 179:0.54 181:0.94 186:0.86 187:0.39 189:0.95 192:0.59 195:0.93 201:0.74 202:0.95 208:0.64 212:0.15 215:0.96 216:0.93 222:0.60 235:0.05 238:0.94 241:0.44 242:0.45 243:0.24 245:0.70 247:0.55 248:1.00 253:1.00 255:0.79 256:0.96 259:0.21 260:0.99 261:0.93 265:0.22 266:0.92 267:0.23 268:0.96 271:0.60 276:0.63 279:0.86 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.84 +3 1:0.74 6:0.85 7:0.81 8:0.68 9:0.80 11:0.88 12:0.37 17:0.18 20:0.99 21:0.40 27:0.09 28:0.70 30:0.55 34:0.19 36:0.52 37:0.32 39:0.42 41:0.99 43:0.92 55:0.71 60:0.95 66:0.92 69:0.37 70:0.60 74:0.77 78:0.88 81:0.73 83:0.90 85:0.88 91:0.90 96:0.99 98:0.98 100:0.90 101:0.80 104:0.54 108:0.29 111:0.87 114:0.82 120:0.98 121:0.90 123:0.28 126:0.54 127:0.84 129:0.05 135:0.49 138:0.98 140:0.45 144:0.85 146:0.91 147:0.93 149:0.85 150:0.83 151:0.98 152:0.90 153:0.93 154:0.92 155:0.67 158:0.92 159:0.53 161:0.75 162:0.73 164:0.96 165:0.83 167:0.88 169:0.88 172:0.77 173:0.38 176:0.73 177:0.38 179:0.56 181:0.94 186:0.88 189:0.87 191:0.77 192:0.59 201:0.74 202:0.93 204:0.89 208:0.64 212:0.30 215:0.67 216:0.23 222:0.60 230:0.87 232:0.74 233:0.76 235:0.52 238:0.88 241:0.85 242:0.75 243:0.92 247:0.47 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.92 265:0.98 266:0.63 267:0.28 268:0.95 271:0.60 276:0.66 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.55 +2 1:0.74 6:0.86 7:0.76 8:0.68 9:0.80 11:0.88 12:0.37 17:0.11 20:0.99 21:0.05 27:0.08 28:0.70 30:0.33 32:0.80 34:0.40 36:0.86 37:0.28 39:0.42 41:0.99 43:0.84 60:0.95 66:0.86 69:0.44 70:0.58 74:0.81 76:0.85 77:0.70 78:0.91 81:0.91 83:0.90 85:0.88 91:0.94 96:0.99 98:0.95 100:0.96 101:0.82 104:0.58 108:0.34 111:0.93 114:0.95 120:0.97 123:0.32 126:0.54 127:0.65 129:0.05 135:0.63 138:0.98 140:0.45 144:0.85 145:0.76 146:0.73 147:0.77 149:0.82 150:0.96 151:0.98 152:0.91 153:0.92 154:0.80 155:0.67 158:0.92 159:0.29 161:0.75 162:0.70 164:0.95 165:0.90 167:0.87 169:0.94 172:0.61 173:0.62 176:0.73 177:0.38 179:0.73 181:0.94 186:0.92 189:0.87 191:0.83 192:0.59 195:0.62 201:0.74 202:0.92 208:0.64 212:0.69 215:0.91 216:0.31 220:0.62 222:0.60 230:0.79 232:0.77 233:0.76 235:0.12 238:0.94 241:0.83 242:0.43 243:0.87 247:0.47 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.97 261:0.96 265:0.95 266:0.47 267:0.65 268:0.94 271:0.60 276:0.50 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.89 7:0.81 8:0.76 9:0.80 11:0.88 12:0.37 17:0.28 20:0.99 21:0.54 25:0.88 27:0.08 28:0.70 30:0.30 32:0.80 34:0.18 36:0.45 37:0.54 39:0.42 41:0.99 43:0.78 55:0.92 60:0.99 66:0.47 69:0.72 70:0.73 74:0.80 76:0.85 77:0.70 78:0.85 81:0.69 83:0.90 85:0.90 87:0.98 91:0.84 96:0.98 98:0.67 100:0.88 101:0.78 104:0.58 108:0.86 111:0.85 114:0.77 120:0.96 121:0.90 123:0.85 124:0.75 126:0.54 127:0.94 129:0.81 133:0.76 135:0.21 138:0.98 140:0.45 144:0.85 145:0.52 146:0.86 147:0.26 149:0.82 150:0.79 151:0.97 152:0.91 153:0.91 154:0.70 155:0.67 158:0.92 159:0.75 161:0.75 162:0.75 164:0.94 165:0.79 167:0.87 169:0.85 172:0.70 173:0.61 176:0.73 177:0.05 179:0.48 181:0.94 186:0.85 189:0.90 191:0.84 192:0.59 195:0.87 201:0.74 202:0.89 204:0.89 208:0.64 212:0.30 215:0.58 216:0.18 220:0.62 222:0.60 230:0.87 232:0.78 233:0.76 235:0.74 238:0.89 241:0.81 242:0.74 243:0.12 245:0.79 247:0.47 248:0.99 253:0.98 255:0.79 256:0.91 257:0.65 259:0.21 260:0.97 261:0.90 265:0.68 266:0.80 267:0.59 268:0.92 271:0.60 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 290:0.77 297:0.36 299:0.88 300:0.55 +3 1:0.74 6:0.93 8:0.78 9:0.80 11:0.88 12:0.37 17:0.02 20:0.75 27:0.14 28:0.70 30:0.70 32:0.80 34:0.66 36:0.34 37:0.67 39:0.42 41:0.99 43:0.86 60:0.87 66:0.08 69:0.57 70:0.66 74:0.86 77:0.70 78:0.85 81:0.95 83:0.90 85:0.95 91:0.92 96:0.99 98:0.22 100:0.95 101:0.80 108:0.93 111:0.87 114:0.98 120:0.98 122:0.43 123:0.91 124:0.89 126:0.54 127:0.67 129:0.95 133:0.87 135:0.51 138:0.98 140:0.45 144:0.85 145:0.80 146:0.29 147:0.36 149:0.89 150:0.98 151:1.00 152:0.94 153:0.99 154:0.84 155:0.67 158:0.92 159:0.83 161:0.75 162:0.65 164:0.96 165:0.92 167:0.60 169:0.89 172:0.41 173:0.33 176:0.73 177:0.38 179:0.54 181:0.94 186:0.88 187:0.39 189:0.94 191:0.83 192:0.59 195:0.93 201:0.74 202:0.95 208:0.64 212:0.19 215:0.96 216:0.93 222:0.60 235:0.05 238:0.94 241:0.43 242:0.53 243:0.24 245:0.70 247:0.60 248:1.00 253:1.00 255:0.79 256:0.95 259:0.21 260:0.99 261:0.93 265:0.24 266:0.84 267:0.65 268:0.96 271:0.60 276:0.63 279:0.86 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.88 12:0.37 17:0.18 20:0.99 21:0.30 27:0.21 28:0.70 30:0.21 34:0.49 36:0.54 37:0.74 39:0.42 41:0.99 43:0.98 55:0.71 60:0.98 66:0.08 69:0.12 70:0.89 74:0.98 78:0.82 81:0.78 83:0.90 85:0.99 91:0.84 96:0.99 98:0.34 100:0.93 101:0.80 104:0.84 106:0.81 108:0.97 111:0.86 114:0.86 117:0.86 120:0.97 121:0.90 122:0.67 123:0.96 124:0.97 126:0.54 127:0.75 129:0.99 133:0.97 135:0.28 140:0.45 144:0.85 146:0.90 147:0.92 149:0.94 150:0.86 151:0.99 152:0.91 153:0.97 154:0.98 155:0.67 158:0.92 159:0.96 161:0.75 162:0.68 164:0.95 165:0.84 167:0.59 169:0.90 172:0.80 173:0.06 176:0.73 177:0.38 179:0.12 181:0.94 186:0.83 187:0.39 189:0.96 191:0.82 192:0.59 201:0.74 202:0.92 204:0.89 206:0.81 208:0.64 212:0.35 215:0.72 216:0.95 222:0.60 230:0.87 232:0.89 233:0.76 235:0.42 238:0.96 241:0.41 242:0.45 243:0.19 245:0.96 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.97 261:0.92 265:0.29 266:0.95 267:0.28 268:0.94 271:0.60 276:0.97 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94 +3 1:0.74 7:0.81 11:0.88 12:0.37 17:0.43 21:0.30 27:0.21 28:0.70 30:0.26 34:0.55 36:0.47 37:0.74 39:0.42 43:0.98 55:0.71 66:0.08 69:0.12 70:0.88 74:0.98 81:0.78 83:0.75 85:0.99 91:0.04 98:0.35 101:0.80 108:0.96 114:0.86 121:0.90 122:0.67 123:0.96 124:0.97 126:0.54 127:0.86 129:0.99 133:0.97 135:0.37 144:0.85 146:0.90 147:0.92 149:0.93 150:0.86 154:0.98 155:0.67 159:0.96 161:0.75 165:0.84 167:0.66 172:0.79 173:0.06 176:0.73 177:0.38 178:0.55 179:0.12 181:0.94 187:0.68 192:0.59 201:0.74 202:0.36 204:0.89 208:0.64 212:0.35 215:0.72 216:0.95 222:0.60 230:0.87 233:0.76 235:0.42 241:0.57 242:0.52 243:0.19 245:0.96 247:0.67 253:0.78 259:0.21 265:0.37 266:0.95 267:0.44 271:0.60 276:0.97 290:0.86 297:0.36 300:0.94 +1 1:0.74 9:0.80 11:0.88 12:0.37 17:0.13 21:0.64 27:0.45 28:0.70 30:0.73 34:0.73 36:0.23 37:0.50 39:0.42 41:0.82 43:0.82 66:0.20 69:0.69 70:0.57 74:0.70 81:0.57 83:0.73 85:0.91 91:0.17 96:0.69 98:0.37 101:0.81 108:0.65 114:0.72 120:0.55 122:0.43 123:0.63 124:0.74 126:0.54 127:0.38 129:0.81 133:0.67 135:0.72 140:0.45 144:0.85 145:0.49 146:0.25 147:0.31 149:0.83 150:0.71 151:0.51 153:0.48 154:0.77 155:0.67 159:0.53 161:0.75 162:0.86 164:0.57 165:0.70 167:0.56 172:0.27 173:0.65 176:0.73 177:0.38 179:0.68 181:0.94 187:0.68 192:0.59 201:0.74 202:0.36 208:0.64 212:0.30 215:0.53 216:0.77 220:0.96 222:0.60 235:0.80 241:0.27 242:0.58 243:0.36 245:0.63 247:0.39 248:0.50 253:0.63 255:0.79 256:0.45 259:0.21 260:0.55 265:0.39 266:0.75 267:0.69 268:0.46 271:0.60 276:0.40 285:0.62 290:0.72 297:0.36 300:0.55 +3 1:0.74 6:0.85 7:0.81 8:0.63 9:0.80 11:0.88 12:0.37 17:0.79 20:0.98 27:0.05 28:0.70 30:0.44 32:0.80 34:0.17 36:0.48 37:0.28 39:0.42 41:0.97 43:0.56 55:0.71 66:0.36 69:0.94 70:0.65 74:0.67 77:0.70 78:0.90 81:0.95 83:0.88 85:0.85 91:0.83 96:0.96 98:0.69 100:0.91 101:0.75 104:0.58 108:0.45 111:0.89 114:0.98 120:0.92 121:0.90 123:0.44 124:0.69 126:0.54 127:0.79 129:0.05 133:0.62 135:0.19 140:0.45 144:0.85 145:0.70 146:0.78 147:0.90 149:0.72 150:0.98 151:0.98 152:0.90 153:0.92 154:0.45 155:0.67 159:0.73 161:0.75 162:0.56 163:0.75 164:0.87 165:0.92 167:0.90 169:0.89 172:0.61 173:0.96 176:0.73 177:0.05 179:0.52 181:0.94 186:0.90 189:0.86 191:0.84 192:0.59 195:0.86 201:0.74 202:0.84 204:0.89 208:0.64 212:0.18 215:0.96 216:0.15 222:0.60 230:0.87 232:0.77 233:0.76 235:0.05 238:0.89 241:0.95 242:0.77 243:0.51 245:0.80 247:0.55 248:0.96 253:0.96 255:0.79 256:0.83 257:0.65 259:0.21 260:0.93 261:0.94 265:0.69 266:0.75 267:0.28 268:0.84 271:0.60 276:0.69 277:0.69 282:0.88 285:0.62 290:0.98 297:0.36 299:0.88 300:0.70 +0 12:0.37 17:0.26 21:0.13 27:0.33 30:0.76 34:0.84 36:0.26 37:0.89 43:0.42 55:0.45 66:0.64 69:0.49 70:0.15 81:1.00 91:0.11 98:0.60 108:0.38 123:0.36 126:0.54 127:0.18 129:0.05 135:0.61 145:0.65 146:0.40 147:0.47 149:0.28 150:1.00 154:0.32 159:0.15 163:0.92 172:0.16 173:0.70 177:0.05 178:0.55 179:0.92 187:0.98 212:0.95 215:0.84 216:0.83 235:0.12 241:0.50 242:0.82 243:0.69 247:0.12 259:0.21 265:0.61 266:0.12 267:0.36 276:0.14 297:0.36 300:0.13 +1 8:0.79 12:0.37 17:0.28 21:0.78 27:0.45 30:0.95 34:0.76 36:0.26 37:0.50 43:0.25 55:0.84 66:0.54 69:0.84 91:0.17 98:0.19 108:0.70 123:0.68 127:0.10 129:0.05 135:0.86 145:0.45 146:0.35 147:0.41 149:0.17 154:0.18 159:0.14 172:0.10 173:0.74 177:0.05 178:0.55 179:0.51 187:0.68 216:0.77 235:0.60 241:0.18 243:0.63 259:0.21 265:0.21 267:0.69 300:0.08 +2 6:0.89 8:0.76 12:0.37 17:0.47 20:0.87 21:0.47 27:0.72 30:0.21 32:0.80 34:0.49 36:0.37 43:0.43 55:0.59 66:0.20 69:0.49 70:0.37 78:0.99 81:0.98 91:0.29 98:0.20 100:0.98 104:0.92 106:0.81 108:0.38 111:0.98 120:0.69 123:0.72 124:0.73 126:0.54 127:0.33 129:0.81 133:0.67 135:0.24 140:0.45 145:0.76 146:0.24 147:0.30 149:0.33 150:0.98 151:0.66 152:0.82 153:0.48 154:0.32 159:0.39 162:0.86 163:0.87 164:0.57 169:0.97 172:0.10 173:0.51 177:0.05 179:0.93 186:0.98 187:0.87 189:0.90 195:0.66 202:0.36 206:0.81 212:0.42 215:0.63 216:0.08 235:0.52 238:0.92 241:0.30 242:0.82 243:0.40 245:0.40 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.94 265:0.19 266:0.23 267:0.59 268:0.46 276:0.23 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.80 8:0.61 12:0.37 17:0.20 20:0.85 21:0.13 27:0.13 30:0.76 34:0.96 36:0.25 37:0.82 43:0.44 55:0.45 66:0.64 69:0.44 70:0.15 78:0.93 81:1.00 91:0.22 98:0.53 100:0.97 108:0.34 111:0.95 123:0.33 126:0.54 127:0.16 129:0.05 135:0.69 145:0.61 146:0.40 147:0.47 149:0.28 150:1.00 152:0.94 154:0.34 159:0.15 163:0.92 169:0.90 172:0.16 173:0.61 177:0.05 178:0.55 179:0.89 186:0.93 187:0.68 212:0.95 215:0.84 216:0.91 235:0.12 241:0.32 242:0.82 243:0.69 247:0.12 259:0.21 261:0.92 265:0.55 266:0.12 267:0.44 276:0.14 283:0.82 297:0.36 300:0.13 +2 6:0.90 8:0.83 12:0.37 17:0.49 20:0.91 21:0.78 27:0.72 34:0.30 36:0.83 37:0.56 78:0.88 91:0.86 100:0.93 111:0.89 120:0.65 135:0.32 140:0.98 145:0.70 151:0.60 152:0.85 153:0.46 162:0.45 164:0.53 169:0.91 175:0.75 178:0.55 186:0.88 189:0.92 191:0.75 202:0.87 216:0.26 235:0.12 238:0.93 241:0.86 244:0.61 248:0.58 256:0.45 257:0.65 259:0.21 260:0.65 261:0.89 267:0.28 268:0.45 277:0.69 285:0.62 +2 8:0.64 12:0.37 17:0.28 20:0.95 21:0.78 27:0.26 30:0.21 34:0.44 36:0.97 37:0.40 43:0.41 66:0.20 69:0.51 70:0.37 78:0.92 91:0.31 98:0.07 100:0.73 108:0.39 111:0.89 123:0.37 124:0.81 127:0.44 129:0.89 133:0.78 135:0.90 146:0.13 147:0.18 149:0.32 152:0.88 154:0.31 159:0.81 163:0.87 169:0.72 172:0.10 173:0.25 177:0.05 178:0.55 179:0.95 186:0.92 187:0.68 212:0.42 216:0.83 235:0.05 241:0.02 242:0.82 243:0.40 245:0.39 247:0.12 259:0.21 261:0.89 265:0.07 266:0.23 267:0.65 276:0.18 300:0.25 +1 12:0.37 17:0.16 21:0.05 27:0.13 30:0.76 34:0.95 36:0.29 37:0.82 43:0.40 55:0.45 66:0.64 69:0.52 70:0.15 81:1.00 91:0.20 98:0.53 108:0.40 123:0.38 126:0.54 127:0.16 129:0.05 135:0.58 145:0.61 146:0.40 147:0.47 149:0.27 150:1.00 154:0.30 159:0.15 163:0.92 172:0.16 173:0.68 177:0.05 178:0.55 179:0.88 187:0.98 212:0.95 215:0.91 216:0.91 235:0.05 241:0.49 242:0.82 243:0.69 247:0.12 259:0.21 265:0.55 266:0.12 267:0.52 276:0.14 297:0.36 300:0.13 +0 8:0.68 12:0.37 17:0.66 20:0.90 21:0.78 27:0.51 30:0.95 34:0.58 36:0.95 37:0.24 43:0.41 55:0.96 66:0.20 69:0.51 78:0.96 91:0.31 98:0.08 100:0.97 108:0.39 111:0.96 123:0.37 124:0.61 127:0.18 129:0.59 133:0.47 135:0.75 146:0.05 147:0.05 149:0.20 152:0.84 154:0.31 159:0.26 169:0.96 172:0.05 173:0.50 177:0.05 178:0.55 179:0.98 186:0.95 187:0.68 202:0.36 216:0.63 235:0.05 241:0.18 243:0.40 245:0.36 259:0.21 261:0.96 265:0.08 267:0.76 300:0.08 +1 8:0.64 12:0.37 20:0.83 21:0.78 27:0.66 34:0.73 36:0.27 37:0.25 78:0.92 91:0.96 100:0.90 111:0.90 120:0.73 135:0.77 140:0.99 151:0.66 152:0.79 153:0.48 162:0.45 164:0.67 169:0.88 175:0.75 178:0.55 186:0.92 191:0.85 202:0.94 216:0.62 220:0.62 241:0.94 244:0.61 248:0.66 256:0.58 257:0.65 259:0.21 260:0.74 261:0.85 267:0.69 268:0.59 277:0.69 285:0.62 +1 6:0.82 7:0.81 12:0.37 17:0.30 20:0.82 21:0.40 27:0.44 30:0.95 32:0.80 34:0.79 36:0.39 37:0.46 43:0.35 55:0.96 66:0.20 69:0.63 78:0.98 81:0.99 91:0.40 98:0.10 100:0.91 104:0.98 106:0.81 108:0.48 111:0.95 123:0.46 124:0.61 126:0.54 127:0.23 129:0.59 133:0.47 135:0.44 145:0.56 146:0.05 147:0.05 149:0.20 150:0.98 152:0.84 154:0.27 159:0.20 169:0.91 172:0.05 173:0.79 177:0.05 178:0.55 179:0.99 186:0.98 187:0.87 195:0.56 206:0.81 215:0.67 216:0.83 230:0.87 233:0.76 235:0.42 241:0.30 243:0.40 245:0.36 259:0.21 261:0.91 265:0.10 267:0.59 297:0.36 300:0.08 +1 6:0.80 8:0.67 12:0.37 20:0.91 21:0.78 27:0.13 34:0.98 36:0.32 37:0.82 78:0.89 91:0.86 100:0.87 111:0.87 120:0.87 135:0.56 140:0.98 145:0.47 151:0.75 152:0.93 153:0.85 162:0.45 164:0.82 169:0.90 175:0.75 178:0.55 186:0.89 187:0.39 191:0.92 202:0.99 216:0.91 235:0.88 241:0.72 244:0.61 248:0.81 256:0.78 257:0.65 259:0.21 260:0.87 261:0.92 267:0.88 268:0.78 277:0.69 285:0.62 +0 12:0.37 17:0.36 21:0.78 27:0.45 30:0.95 34:0.83 36:0.18 37:0.50 43:0.26 55:0.71 66:0.54 69:0.82 91:0.27 98:0.17 108:0.66 123:0.64 127:0.10 129:0.05 135:0.92 145:0.45 146:0.29 147:0.36 149:0.17 154:0.19 159:0.12 172:0.10 173:0.73 177:0.05 178:0.55 179:0.39 187:0.68 216:0.77 235:0.95 241:0.50 243:0.63 259:0.21 265:0.19 267:0.65 300:0.08 +0 1:0.74 11:0.64 12:0.95 17:0.57 21:0.30 27:0.55 30:0.15 34:0.84 36:0.49 37:0.57 39:0.37 43:0.92 55:0.59 66:0.54 69:0.12 70:0.89 74:0.99 81:0.73 83:0.75 85:0.85 91:0.40 98:0.68 101:0.71 106:0.81 108:0.75 114:0.86 117:0.86 122:0.67 123:0.73 124:0.91 126:0.54 127:0.98 129:0.89 131:0.61 133:0.95 135:0.71 144:0.57 146:0.67 147:0.72 149:0.91 150:0.83 154:0.91 155:0.67 157:0.91 159:0.93 165:0.84 166:1.00 172:0.97 173:0.07 176:0.73 177:0.38 178:0.55 179:0.14 182:0.97 187:0.68 192:0.59 201:0.74 206:0.81 212:0.68 215:0.72 216:0.23 222:0.21 227:0.61 229:0.61 231:1.00 235:0.42 241:0.05 242:0.50 243:0.25 245:0.94 246:0.61 247:0.67 253:0.78 259:0.21 265:0.68 266:0.95 267:0.17 276:0.96 287:0.61 290:0.86 297:0.36 300:0.84 +2 1:0.74 7:0.81 11:0.64 12:0.95 17:0.78 21:0.13 27:0.72 30:0.18 32:0.80 34:0.99 36:0.45 39:0.37 43:0.82 60:0.87 66:0.46 69:0.64 70:0.85 74:0.91 77:0.70 81:0.83 83:0.87 85:0.93 91:0.60 98:0.52 101:0.80 108:0.85 114:0.92 121:0.90 122:0.51 123:0.84 124:0.67 126:0.54 127:0.45 129:0.81 131:0.61 133:0.67 135:0.94 144:0.57 145:0.72 146:0.59 147:0.65 149:0.86 150:0.89 154:0.77 155:0.67 157:0.99 158:0.92 159:0.67 165:0.88 166:1.00 172:0.67 173:0.52 176:0.73 177:0.38 178:0.55 179:0.42 182:0.98 187:0.21 192:0.59 195:0.85 201:0.74 204:0.89 208:0.64 212:0.54 215:0.84 216:0.10 222:0.21 227:0.61 229:0.61 230:0.84 231:1.00 233:0.69 235:0.22 241:0.21 242:0.21 243:0.40 245:0.81 246:0.61 247:0.60 253:0.78 265:0.53 266:0.80 267:0.76 276:0.68 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.70 +1 1:0.74 9:0.80 11:0.64 12:0.95 17:0.76 21:0.22 27:0.72 30:0.53 34:0.80 36:0.77 39:0.37 41:0.82 43:0.92 55:0.71 66:0.32 69:0.10 70:0.62 74:0.99 81:0.78 83:0.77 85:0.85 91:0.40 96:0.74 98:0.71 101:0.71 106:0.81 108:0.95 114:0.90 117:0.86 120:0.61 122:0.67 123:0.94 124:0.83 126:0.54 127:0.97 129:0.89 131:1.00 133:0.83 135:0.88 140:0.45 144:0.57 146:0.91 147:0.93 149:0.90 150:0.86 151:0.64 153:0.46 154:0.92 155:0.67 157:0.91 159:0.92 162:0.62 164:0.54 165:0.86 166:1.00 172:0.94 173:0.07 176:0.73 177:0.38 179:0.14 182:0.98 187:0.87 192:0.59 201:0.74 202:0.49 206:0.81 208:0.64 212:0.67 215:0.79 216:0.17 220:0.74 222:0.21 227:0.61 229:0.99 231:1.00 235:0.31 241:0.03 242:0.70 243:0.38 245:0.98 246:0.61 247:0.67 248:0.62 253:0.82 255:0.79 256:0.45 259:0.21 260:0.62 265:0.71 266:0.89 267:0.44 268:0.45 276:0.96 285:0.62 287:0.61 290:0.90 297:0.36 300:0.84 +4 1:0.74 9:0.80 11:0.64 12:0.95 17:0.70 21:0.05 25:0.88 27:0.72 30:0.21 34:0.97 36:0.44 37:0.73 39:0.37 41:0.94 43:0.88 66:0.69 69:0.34 70:0.67 74:0.69 81:0.88 83:0.83 85:0.80 91:0.74 96:0.94 98:0.43 101:0.76 108:0.27 114:0.95 120:0.88 122:0.51 123:0.26 124:0.41 126:0.54 127:0.60 129:0.05 131:1.00 133:0.39 135:0.94 140:0.45 144:0.57 146:0.45 147:0.52 149:0.70 150:0.93 151:0.93 153:0.78 154:0.86 155:0.67 157:0.94 159:0.44 162:0.75 164:0.80 165:0.90 166:1.00 172:0.41 173:0.38 176:0.73 177:0.38 179:0.87 182:0.99 187:0.21 192:0.59 201:0.74 202:0.73 208:0.64 212:0.55 215:0.91 216:0.27 222:0.21 227:0.61 229:1.00 231:1.00 235:0.12 241:0.61 242:0.40 243:0.73 245:0.46 246:0.61 247:0.47 248:0.93 253:0.93 255:0.79 256:0.77 259:0.21 260:0.89 265:0.45 266:0.38 267:0.36 268:0.77 276:0.23 285:0.62 287:0.61 290:0.95 297:0.36 300:0.43 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.95 17:0.73 21:0.13 27:0.55 30:0.27 32:0.80 34:0.98 36:0.59 37:0.57 39:0.37 41:0.82 43:0.62 55:0.59 60:0.87 66:0.45 69:0.59 70:0.81 74:0.81 77:0.70 81:0.83 83:0.84 85:0.80 91:0.57 96:0.78 98:0.62 101:0.74 108:0.76 114:0.92 120:0.62 121:0.90 123:0.74 124:0.58 126:0.54 127:0.40 129:0.59 131:1.00 133:0.47 135:0.99 140:0.80 144:0.57 145:0.40 146:0.65 147:0.70 149:0.71 150:0.89 151:0.69 153:0.48 154:0.75 155:0.67 157:0.93 158:0.86 159:0.54 162:0.74 164:0.57 165:0.88 166:1.00 172:0.63 173:0.52 176:0.73 177:0.38 178:0.42 179:0.43 182:0.98 187:0.39 192:0.59 195:0.77 201:0.74 202:0.56 204:0.89 208:0.64 212:0.69 215:0.84 216:0.23 220:0.74 222:0.21 227:0.61 229:0.99 230:0.84 231:1.00 233:0.69 235:0.22 241:0.27 242:0.72 243:0.48 245:0.85 246:0.61 247:0.60 248:0.65 253:0.82 255:0.79 256:0.58 259:0.21 260:0.63 265:0.63 266:0.63 267:0.36 268:0.59 276:0.67 279:0.86 282:0.88 283:0.66 285:0.62 287:0.61 290:0.92 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:1.00 8:0.95 9:0.80 11:0.64 12:0.95 17:0.72 20:0.99 21:0.05 25:0.88 27:0.72 30:0.21 34:0.53 36:0.51 37:0.73 39:0.37 41:0.92 43:0.98 60:0.87 66:0.80 69:0.07 70:0.50 74:0.75 78:0.98 81:0.88 83:0.87 85:0.80 91:0.88 96:0.91 98:0.99 100:0.99 101:0.79 108:0.06 111:0.99 114:0.95 120:0.93 122:0.51 123:0.06 126:0.54 127:0.88 129:0.05 131:1.00 135:0.71 140:0.80 144:0.57 146:0.71 147:0.75 149:0.72 150:0.93 151:0.94 152:0.99 153:0.86 154:0.98 155:0.67 157:0.95 158:0.87 159:0.28 162:0.75 164:0.88 165:0.90 166:1.00 169:0.98 172:0.45 173:0.32 176:0.73 177:0.38 179:0.89 182:0.99 186:0.97 189:1.00 192:0.59 201:0.74 202:0.83 208:0.64 212:0.55 215:0.91 216:0.27 222:0.21 227:0.61 229:0.99 231:1.00 235:0.12 238:0.98 241:0.89 242:0.40 243:0.82 246:0.61 247:0.39 248:0.88 253:0.92 255:0.79 256:0.86 259:0.21 260:0.93 261:0.97 265:0.99 266:0.27 267:0.19 268:0.86 276:0.35 279:0.86 283:0.71 285:0.62 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.81 11:0.64 12:0.95 17:0.81 21:0.59 27:0.72 30:0.19 34:0.50 36:0.30 39:0.37 43:0.90 55:0.84 66:0.27 69:0.48 70:0.82 74:0.96 76:0.85 81:0.57 83:0.73 85:0.96 91:0.33 98:0.55 101:0.79 106:0.81 108:0.69 114:0.74 117:0.86 121:0.90 122:0.67 123:0.67 124:0.91 126:0.54 127:0.93 129:0.95 131:0.61 133:0.91 135:0.98 144:0.57 145:0.74 146:0.58 147:0.64 149:0.93 150:0.72 154:0.89 155:0.67 157:0.99 159:0.89 165:0.78 166:1.00 172:0.77 173:0.19 176:0.73 177:0.38 178:0.55 179:0.27 182:0.97 187:0.21 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.23 215:0.55 222:0.21 227:0.61 229:0.61 230:0.87 231:1.00 233:0.76 235:0.74 241:0.05 242:0.44 243:0.29 245:0.87 246:0.61 247:0.60 253:0.75 265:0.56 266:0.94 267:0.69 276:0.86 287:0.61 290:0.74 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.95 17:0.64 20:0.99 21:0.47 27:0.72 30:0.21 34:0.30 36:0.83 37:0.68 39:0.37 43:0.92 55:0.71 66:0.19 69:0.41 70:0.96 74:0.97 78:0.87 81:0.64 83:0.74 85:0.90 91:0.12 98:0.31 100:0.73 101:0.65 108:0.93 111:0.85 114:0.80 122:0.67 123:0.93 124:0.98 126:0.54 127:1.00 129:0.89 131:0.61 133:0.99 135:0.90 144:0.57 146:0.61 147:0.67 149:0.88 150:0.77 152:0.92 154:0.91 155:0.67 157:0.84 159:0.99 165:0.80 166:1.00 169:0.72 172:0.92 173:0.06 176:0.73 177:0.38 178:0.55 179:0.12 182:0.97 186:0.87 187:0.39 192:0.59 201:0.74 202:0.50 212:0.30 215:0.63 216:0.26 222:0.21 227:0.61 229:0.61 231:1.00 235:0.60 241:0.03 242:0.80 243:0.11 245:0.94 246:0.61 247:0.60 253:0.77 259:0.21 261:0.88 265:0.33 266:1.00 267:0.36 276:0.97 287:0.61 290:0.80 297:0.36 300:0.99 +2 7:0.78 12:0.06 17:0.72 27:0.70 30:0.56 32:0.78 34:0.33 36:0.87 37:0.32 39:0.96 43:0.32 55:0.84 66:0.35 69:0.07 70:0.76 74:0.89 76:0.85 77:0.70 81:0.87 91:0.54 98:0.73 104:0.89 106:0.81 108:0.93 117:0.86 123:0.93 124:0.88 126:0.32 127:0.92 129:0.59 133:0.89 135:0.71 145:0.50 146:0.80 147:0.83 149:0.79 150:0.73 154:0.86 159:0.91 172:0.89 173:0.06 177:0.38 178:0.55 179:0.19 187:0.39 195:0.98 206:0.81 212:0.27 215:0.96 216:0.38 222:0.97 230:0.81 232:0.82 233:0.76 235:0.02 241:0.21 242:0.70 243:0.36 245:0.93 247:0.67 253:0.63 254:0.74 259:0.21 265:0.74 266:0.92 267:0.76 271:0.22 276:0.92 282:0.86 283:0.82 297:0.36 300:0.70 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.06 17:0.47 21:0.13 27:0.72 30:0.28 34:0.93 36:0.48 37:0.55 39:0.96 43:0.83 60:0.87 66:0.79 69:0.65 70:0.83 74:0.90 81:0.78 83:0.85 85:0.88 91:0.61 96:0.75 98:0.75 101:0.79 104:0.80 106:0.81 108:0.49 114:0.92 117:0.86 120:0.63 121:0.90 122:0.51 123:0.47 124:0.38 126:0.54 127:0.37 129:0.05 133:0.39 135:0.54 140:0.45 145:0.97 146:0.78 147:0.82 149:0.81 150:0.86 151:0.70 153:0.69 154:0.80 155:0.67 158:0.92 159:0.43 162:0.86 164:0.62 165:0.88 172:0.61 173:0.66 176:0.73 177:0.38 179:0.62 187:0.21 192:0.59 201:0.74 202:0.46 204:0.89 206:0.81 208:0.64 212:0.27 215:0.84 216:0.16 220:0.74 222:0.97 230:0.87 232:0.84 233:0.76 235:0.22 241:0.24 242:0.40 243:0.80 245:0.53 247:0.47 248:0.66 253:0.82 255:0.79 256:0.45 259:0.21 260:0.64 265:0.75 266:0.47 267:0.52 268:0.55 271:0.22 276:0.51 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.55 +1 7:0.78 8:0.62 11:0.57 12:0.06 17:0.06 21:0.30 27:0.71 30:0.17 32:0.75 34:0.70 36:0.80 37:0.39 39:0.96 43:0.44 55:0.84 66:0.08 69:0.91 70:0.99 74:0.60 81:0.56 85:0.88 91:0.61 98:0.26 101:0.69 104:0.87 106:0.81 108:0.96 120:0.90 123:0.96 124:0.99 126:0.32 127:0.89 129:0.95 133:0.99 135:0.23 140:0.80 145:0.41 146:0.85 147:0.05 149:0.78 150:0.42 151:0.75 153:0.87 154:0.46 159:0.97 162:0.65 164:0.90 172:0.67 173:0.54 177:0.05 178:0.42 179:0.17 187:0.21 190:0.77 195:1.00 202:0.86 206:0.81 212:0.16 215:0.72 216:0.30 220:0.87 222:0.97 230:0.81 233:0.76 235:0.31 241:0.03 242:0.76 243:0.05 245:0.86 247:0.67 248:0.85 253:0.47 256:0.87 257:0.65 259:0.21 260:0.90 265:0.28 266:0.99 267:0.28 268:0.88 271:0.22 276:0.94 283:0.71 285:0.50 297:0.36 300:0.98 +1 1:0.74 7:0.78 8:0.71 9:0.80 12:0.06 17:0.53 21:0.05 27:0.64 30:0.20 34:0.53 36:0.74 37:0.32 39:0.96 41:0.82 43:0.44 55:0.92 66:0.07 69:0.97 70:0.95 74:0.59 81:0.83 83:0.79 91:0.54 96:0.77 98:0.24 108:0.10 114:0.95 120:0.65 123:0.94 124:0.98 126:0.54 127:0.92 129:0.81 133:0.97 135:0.78 140:0.45 146:0.38 147:0.44 149:0.72 150:0.90 151:0.77 153:0.48 154:0.17 155:0.67 159:0.95 162:0.86 163:0.94 164:0.57 165:0.90 172:0.45 173:0.83 176:0.73 177:0.05 179:0.30 187:0.21 192:0.59 201:0.74 202:0.36 208:0.64 212:0.16 215:0.91 216:0.13 220:0.62 222:0.97 230:0.81 233:0.69 235:0.12 241:0.21 242:0.73 243:0.24 245:0.77 247:0.60 248:0.71 253:0.80 254:0.74 255:0.79 256:0.45 257:0.65 259:0.21 260:0.66 265:0.14 266:0.98 267:0.84 268:0.46 271:0.22 276:0.85 283:0.71 285:0.62 290:0.95 297:0.36 300:0.94 +2 8:0.73 11:0.57 12:0.06 17:0.12 21:0.13 27:0.65 30:0.33 34:0.59 36:0.93 37:0.40 39:0.96 43:0.79 55:0.59 66:0.68 69:0.70 70:0.66 74:0.57 81:0.71 85:0.72 91:0.78 98:0.69 101:0.72 104:0.82 106:0.81 108:0.53 120:0.98 123:0.51 124:0.48 126:0.32 127:0.56 129:0.05 133:0.62 135:0.51 140:0.45 146:0.78 147:0.05 149:0.74 150:0.52 151:0.83 153:0.97 154:0.72 159:0.55 162:0.81 164:0.97 172:0.71 173:0.69 177:0.05 179:0.52 187:0.21 190:0.77 191:0.73 202:0.94 206:0.81 212:0.75 215:0.84 216:0.66 222:0.97 235:0.12 241:0.67 242:0.75 243:0.09 245:0.70 247:0.39 248:0.97 253:0.46 256:0.96 257:0.65 259:0.21 260:0.98 265:0.69 266:0.54 267:0.94 268:0.97 271:0.22 276:0.66 283:0.82 285:0.50 297:0.36 300:0.55 +1 7:0.78 8:0.65 11:0.57 12:0.06 17:0.16 21:0.13 27:0.71 30:0.42 32:0.75 34:0.71 36:0.83 37:0.27 39:0.96 43:0.48 55:0.71 66:0.16 69:0.08 70:0.85 74:0.60 81:0.71 85:0.72 91:0.70 98:0.47 101:0.65 104:0.97 106:0.81 108:0.07 120:0.74 123:0.07 124:0.94 126:0.32 127:0.95 129:0.93 133:0.93 135:0.24 140:0.45 145:0.52 146:0.91 147:0.93 149:0.81 150:0.52 151:0.66 153:0.48 154:0.60 159:0.91 162:0.71 164:0.69 172:0.75 173:0.07 177:0.38 179:0.21 187:0.87 190:0.77 195:0.97 202:0.60 206:0.81 212:0.40 215:0.84 216:0.37 220:0.74 222:0.97 230:0.81 233:0.69 235:0.12 241:0.03 242:0.75 243:0.37 245:0.90 247:0.60 248:0.67 253:0.48 256:0.58 259:0.21 260:0.75 265:0.49 266:0.94 267:0.85 268:0.63 271:0.22 276:0.91 283:0.82 285:0.50 297:0.36 300:0.84 +0 1:0.74 8:0.78 9:0.80 12:0.06 17:0.57 21:0.05 27:0.35 30:0.95 34:0.44 36:0.92 37:0.57 39:0.96 41:0.88 43:0.37 55:0.99 66:0.20 69:0.49 74:0.49 81:0.83 83:0.80 91:0.61 96:0.85 98:0.06 108:0.38 114:0.95 120:0.75 123:0.36 124:0.61 126:0.54 127:0.79 129:0.59 133:0.47 135:0.98 140:0.45 146:0.05 147:0.05 149:0.17 150:0.90 151:0.85 153:0.84 154:0.28 155:0.67 159:0.74 162:0.60 164:0.71 165:0.90 172:0.05 173:0.35 175:0.75 176:0.73 177:0.05 179:1.00 187:0.39 192:0.59 201:0.74 202:0.64 208:0.64 215:0.91 216:0.41 220:0.62 222:0.97 235:0.12 241:0.39 243:0.40 244:0.61 245:0.36 248:0.82 253:0.83 255:0.79 256:0.58 257:0.65 259:0.21 260:0.76 265:0.07 267:0.91 268:0.65 271:0.22 277:0.69 285:0.62 290:0.95 297:0.36 300:0.08 +0 1:0.74 8:0.96 9:0.80 12:0.06 17:0.30 21:0.30 27:0.27 30:0.76 34:0.71 36:0.89 37:0.86 39:0.96 43:0.38 55:0.45 66:0.64 69:0.15 70:0.15 74:0.65 81:0.65 91:0.70 96:0.80 98:0.44 108:0.12 114:0.86 117:0.86 120:0.61 122:0.57 123:0.12 126:0.54 127:0.14 129:0.05 135:0.69 140:0.80 146:0.28 147:0.35 149:0.14 150:0.70 151:0.60 153:0.84 154:0.88 155:0.67 158:0.84 159:0.12 162:0.52 164:0.71 172:0.16 173:0.48 176:0.73 177:0.38 179:0.80 187:0.39 190:0.77 191:0.96 192:0.59 201:0.74 202:0.78 208:0.64 212:0.95 215:0.72 216:0.45 220:0.74 222:0.97 232:0.85 235:0.42 241:0.65 242:0.82 243:0.69 247:0.12 248:0.62 253:0.81 254:0.80 255:0.79 256:0.77 259:0.21 260:0.62 265:0.46 266:0.12 267:0.73 268:0.74 271:0.22 276:0.15 285:0.62 290:0.86 297:0.36 300:0.13 +0 12:0.06 17:0.13 21:0.78 27:0.45 30:0.55 34:0.80 36:0.24 37:0.50 39:0.96 43:0.27 55:0.71 66:0.24 69:0.78 70:0.56 74:0.97 91:0.31 98:0.48 108:0.62 122:0.67 123:0.80 124:0.79 127:0.30 129:0.05 133:0.74 135:0.65 145:0.49 146:0.77 147:0.80 149:0.70 154:0.55 159:0.74 172:0.65 173:0.58 177:0.38 178:0.55 179:0.25 187:0.68 202:0.36 212:0.54 216:0.77 222:0.97 235:0.84 241:0.21 242:0.81 243:0.46 245:0.86 247:0.39 253:0.69 254:0.84 259:0.21 265:0.42 266:0.71 267:0.73 271:0.22 276:0.79 277:0.69 300:0.55 +1 1:0.74 11:0.57 12:0.06 17:0.34 21:0.59 27:0.41 30:0.26 32:0.78 34:0.91 36:0.86 37:0.72 39:0.96 43:0.89 55:0.71 66:0.15 69:0.53 70:0.87 74:0.88 77:0.70 81:0.53 83:0.73 85:0.80 91:0.38 98:0.45 101:0.72 108:0.85 114:0.74 123:0.85 124:0.86 126:0.54 127:0.64 129:0.93 133:0.84 135:0.65 145:0.87 146:0.68 147:0.73 149:0.78 150:0.70 154:0.87 155:0.67 159:0.82 165:0.78 172:0.59 173:0.30 176:0.73 177:0.38 178:0.55 179:0.30 187:0.39 192:0.59 195:0.93 201:0.74 212:0.37 215:0.55 216:0.94 222:0.97 235:0.74 241:0.27 242:0.63 243:0.31 245:0.88 247:0.67 253:0.71 259:0.21 265:0.47 266:0.94 267:0.95 271:0.22 276:0.83 282:0.86 290:0.74 297:0.36 300:0.70 +1 1:0.74 9:0.80 11:0.57 12:0.06 17:0.07 27:0.37 30:0.38 34:0.58 36:0.82 37:0.39 39:0.96 41:0.82 43:0.93 55:0.59 60:0.87 66:0.24 69:0.27 70:0.93 74:0.93 81:0.88 83:0.85 85:0.88 91:0.51 96:0.80 98:0.40 101:0.74 104:1.00 108:0.21 114:0.98 120:0.69 123:0.20 124:0.90 126:0.54 127:0.88 129:0.93 133:0.90 135:0.51 140:0.45 146:0.77 147:0.81 149:0.86 150:0.93 151:0.87 153:0.48 154:0.93 155:0.67 158:0.92 159:0.85 162:0.86 164:0.57 165:0.92 172:0.75 173:0.13 176:0.73 177:0.38 179:0.27 187:0.99 192:0.59 201:0.74 202:0.36 208:0.64 212:0.57 215:0.96 216:0.87 222:0.97 235:0.05 241:0.05 242:0.55 243:0.44 245:0.88 247:0.67 248:0.78 253:0.88 255:0.79 256:0.45 259:0.21 260:0.70 265:0.42 266:0.94 267:0.69 268:0.46 271:0.22 276:0.87 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +0 12:0.06 17:0.40 21:0.78 27:0.45 30:0.62 34:0.83 36:0.23 37:0.50 39:0.96 43:0.24 55:0.71 66:0.68 69:0.84 70:0.47 74:0.91 91:0.23 98:0.60 108:0.70 122:0.67 123:0.68 124:0.45 127:0.23 129:0.05 133:0.39 135:0.63 145:0.50 146:0.88 147:0.90 149:0.55 154:0.55 159:0.59 172:0.71 173:0.72 177:0.38 178:0.55 179:0.30 187:0.68 212:0.39 216:0.77 222:0.97 235:1.00 241:0.10 242:0.80 243:0.72 245:0.79 247:0.39 253:0.64 254:0.84 259:0.21 265:0.61 266:0.54 267:0.36 271:0.22 276:0.66 300:0.55 +0 1:0.74 8:0.89 9:0.80 12:0.06 17:0.01 21:0.40 27:0.43 30:0.45 32:0.78 34:0.47 36:0.40 37:0.72 39:0.96 43:0.21 55:0.42 66:0.49 69:0.75 70:0.25 74:0.71 76:0.85 77:0.70 81:0.60 91:0.51 96:0.80 98:0.14 108:0.58 114:0.82 120:0.68 122:0.51 123:0.56 124:0.48 126:0.54 127:0.31 129:0.05 133:0.39 135:0.28 140:0.45 145:0.44 146:0.21 147:0.27 149:0.09 150:0.67 151:0.79 153:0.69 154:0.71 155:0.67 159:0.42 162:0.84 163:0.79 164:0.72 172:0.16 173:0.76 176:0.73 177:0.38 179:0.96 187:0.87 192:0.59 195:0.72 201:0.74 202:0.60 208:0.64 212:0.61 215:0.67 216:0.66 220:0.62 222:0.97 235:0.52 241:0.65 242:0.63 243:0.60 245:0.39 247:0.30 248:0.76 253:0.83 254:0.74 255:0.79 256:0.64 259:0.21 260:0.70 265:0.15 266:0.17 267:0.93 268:0.66 271:0.22 276:0.13 282:0.86 285:0.62 290:0.82 297:0.36 300:0.18 +2 1:0.74 9:0.80 11:0.57 12:0.06 17:0.40 21:0.47 27:0.64 30:0.32 32:0.77 34:0.52 36:0.49 37:0.45 39:0.96 43:0.65 55:0.59 66:0.48 69:0.87 70:0.98 74:0.78 77:0.89 81:0.56 85:0.80 91:0.09 96:0.83 98:0.50 101:0.71 108:0.80 114:0.80 120:0.72 123:0.79 124:0.83 126:0.54 127:0.56 129:0.81 133:0.86 135:0.44 140:0.45 145:0.89 146:0.32 147:0.39 149:0.63 150:0.65 151:0.77 153:0.48 154:0.59 155:0.67 158:0.86 159:0.86 162:0.64 164:0.73 172:0.73 173:0.69 176:0.73 177:0.38 179:0.39 187:0.98 192:0.59 195:0.96 201:0.74 202:0.65 208:0.64 212:0.52 215:0.63 216:0.87 220:0.74 222:0.97 235:0.60 241:0.01 242:0.58 243:0.25 245:0.76 247:0.67 248:0.80 253:0.85 255:0.79 256:0.64 259:0.21 260:0.73 265:0.52 266:0.89 267:0.59 268:0.66 271:0.22 276:0.73 279:0.86 282:0.85 283:0.67 285:0.62 290:0.80 297:0.36 300:0.94 +2 1:0.74 8:0.82 11:0.57 12:0.06 17:0.15 21:0.30 27:0.58 30:0.21 34:0.33 36:0.64 37:0.45 39:0.96 43:0.96 55:0.71 60:0.95 66:0.09 69:0.15 70:0.75 74:0.89 81:0.75 83:0.87 85:0.88 91:0.51 98:0.50 101:0.78 108:0.12 114:0.86 122:0.67 123:0.83 124:0.80 126:0.54 127:0.89 129:0.81 133:0.76 135:0.51 146:0.35 147:0.41 149:0.84 150:0.84 154:0.96 155:0.67 158:0.86 159:0.65 165:0.84 172:0.45 173:0.17 176:0.73 177:0.38 178:0.55 179:0.65 187:0.68 192:0.59 201:0.74 202:0.46 208:0.64 212:0.55 215:0.72 216:0.50 222:0.97 235:0.52 241:0.10 242:0.44 243:0.47 245:0.70 247:0.60 253:0.75 259:0.21 265:0.26 266:0.75 267:0.84 271:0.22 276:0.58 277:0.69 279:0.86 283:0.66 290:0.86 297:0.36 300:0.55 +1 8:0.65 9:0.80 12:0.06 17:0.26 21:0.40 25:0.88 27:0.56 30:0.45 34:0.28 36:0.71 37:0.35 39:0.96 41:0.90 43:0.30 55:0.52 66:0.64 69:0.82 70:0.50 74:0.48 81:0.49 83:0.77 91:0.84 96:0.84 98:0.54 104:0.78 108:0.67 120:0.93 122:0.43 123:0.65 124:0.42 126:0.32 127:0.24 129:0.05 133:0.39 135:0.34 140:0.92 146:0.59 147:0.05 149:0.21 150:0.39 151:0.83 153:0.92 154:0.60 155:0.67 159:0.33 162:0.79 164:0.93 172:0.32 173:0.87 177:0.05 179:0.79 191:0.75 192:0.59 202:0.88 208:0.64 212:0.23 215:0.67 216:0.44 220:0.74 222:0.97 235:0.42 241:0.79 242:0.55 243:0.21 245:0.43 247:0.39 248:0.82 253:0.79 254:0.88 255:0.79 256:0.92 257:0.65 259:0.21 260:0.93 265:0.56 266:0.38 267:0.23 268:0.91 271:0.22 276:0.29 283:0.71 285:0.62 297:0.36 300:0.33 +2 6:0.87 7:0.81 8:0.86 12:0.06 17:0.43 20:0.91 21:0.40 25:0.88 27:0.05 28:0.46 34:0.86 36:0.66 37:0.78 78:0.79 81:0.84 91:0.91 100:0.83 104:0.72 111:0.79 120:0.92 126:0.54 135:0.51 140:0.90 150:0.64 151:0.74 152:0.85 153:0.82 161:0.54 162:0.53 164:0.93 167:0.79 169:0.83 175:0.75 178:0.50 181:0.62 186:0.79 187:0.39 189:0.89 191:0.99 202:0.93 215:0.67 216:0.67 220:0.62 230:0.65 233:0.63 235:0.42 238:0.93 241:0.71 244:0.61 248:0.88 256:0.91 257:0.65 259:0.21 260:0.92 261:0.82 267:0.91 268:0.91 271:0.94 277:0.69 283:0.60 285:0.62 297:0.36 +1 7:0.81 8:0.98 12:0.06 17:0.07 20:0.74 21:0.05 25:0.88 27:0.08 28:0.46 30:0.91 32:0.80 37:0.99 43:0.21 55:0.96 66:0.27 69:0.91 70:0.13 78:0.99 81:0.91 91:0.98 98:0.18 100:0.81 104:0.57 108:0.88 111:1.00 120:0.99 123:0.88 124:0.72 126:0.54 127:0.21 129:0.81 133:0.67 140:0.45 145:0.50 146:0.05 147:0.05 149:0.18 150:0.82 151:0.91 152:0.74 153:1.00 154:0.14 159:0.41 161:0.54 162:0.70 163:0.98 164:0.99 167:0.94 169:1.00 172:0.10 173:0.93 177:0.05 179:0.91 181:0.62 186:0.75 191:0.99 195:0.81 202:0.99 212:0.61 215:0.91 216:0.66 230:0.87 233:0.76 235:0.05 238:0.99 241:0.90 242:0.82 243:0.29 245:0.38 247:0.12 248:0.99 256:0.99 260:0.99 261:0.74 265:0.20 266:0.17 268:0.99 271:0.94 276:0.21 283:0.82 285:0.62 297:0.36 300:0.13 +1 6:0.91 8:0.74 12:0.06 17:0.20 20:0.75 21:0.78 25:0.88 27:0.32 28:0.46 34:0.71 36:0.51 37:0.73 43:0.51 66:0.36 69:0.15 78:0.77 91:0.94 98:0.10 100:0.78 104:0.74 108:0.12 111:0.76 120:0.98 123:0.12 127:0.07 129:0.05 135:0.56 140:0.45 146:0.05 147:0.05 149:0.16 151:0.85 152:0.77 153:0.98 154:0.40 159:0.05 161:0.54 162:0.72 164:0.99 167:0.81 169:0.76 172:0.05 173:0.41 177:0.05 181:0.62 186:0.76 187:0.39 189:0.83 191:0.93 202:0.98 216:0.88 235:0.22 238:0.83 241:0.72 243:0.51 248:0.98 256:0.98 259:0.21 260:0.98 261:0.75 265:0.11 267:0.98 268:0.99 271:0.94 283:0.82 285:0.62 +1 6:0.94 7:0.81 8:0.89 12:0.06 17:0.67 20:0.78 21:0.74 25:0.88 27:0.34 28:0.46 30:0.55 32:0.80 34:0.39 36:0.73 37:0.99 43:0.48 55:0.45 66:0.46 69:0.42 70:0.52 78:0.74 81:0.64 91:0.42 98:0.29 100:0.77 104:0.58 108:0.32 111:0.73 123:0.31 124:0.76 126:0.54 127:0.82 129:0.89 133:0.78 135:0.20 140:0.80 145:0.69 146:0.43 147:0.50 149:0.60 150:0.47 152:0.77 153:0.48 154:0.36 159:0.67 161:0.54 162:0.58 164:0.57 167:0.78 169:0.75 172:0.45 173:0.33 177:0.05 178:0.42 179:0.76 181:0.62 186:0.75 187:0.39 189:0.95 195:0.83 202:0.53 212:0.40 215:0.46 216:0.47 220:1.00 230:0.87 233:0.76 235:0.88 238:0.93 241:0.70 242:0.82 243:0.59 245:0.58 247:0.12 256:0.45 259:0.21 261:0.74 265:0.31 266:0.63 267:0.65 268:0.46 271:0.94 276:0.48 285:0.62 297:0.36 300:0.43 +2 12:0.06 17:0.43 21:0.78 27:0.07 28:0.46 34:0.21 36:0.36 37:0.87 43:0.51 66:0.36 69:0.10 91:0.92 98:0.13 108:0.09 120:0.94 123:0.09 127:0.08 129:0.05 135:0.23 140:0.87 145:0.50 146:0.05 147:0.05 149:0.16 151:0.80 153:0.93 154:0.40 159:0.05 161:0.54 162:0.52 164:0.94 167:0.89 172:0.05 173:0.52 177:0.05 178:0.48 181:0.62 187:0.39 202:0.94 216:0.88 235:0.05 241:0.77 243:0.51 248:0.91 256:0.92 259:0.21 260:0.94 265:0.13 267:0.44 268:0.92 271:0.94 283:0.82 285:0.62 +1 8:0.95 12:0.06 17:0.57 21:0.78 25:0.88 27:0.34 28:0.46 34:0.58 36:0.22 37:0.91 78:0.78 91:0.96 104:0.73 111:0.78 120:0.93 135:0.70 140:0.80 151:0.77 153:0.88 161:0.54 162:0.56 164:0.96 167:0.94 169:0.82 175:0.75 178:0.42 181:0.62 191:0.97 202:0.95 216:0.66 220:0.87 241:0.89 244:0.61 248:0.89 256:0.95 257:0.65 259:0.21 260:0.93 267:0.76 268:0.95 271:0.94 277:0.69 285:0.62 +4 8:0.90 12:0.06 17:0.38 21:0.13 25:0.88 27:0.64 28:0.46 30:0.21 32:0.80 34:0.75 36:0.63 37:0.85 43:0.42 55:0.52 66:0.24 69:0.48 70:0.87 81:0.89 91:0.82 98:0.45 104:0.54 108:0.87 120:0.84 123:0.87 124:0.84 126:0.54 127:0.87 129:0.93 133:0.84 135:0.18 140:0.45 145:0.85 146:0.54 147:0.60 149:0.59 150:0.79 151:0.78 153:0.89 154:0.32 159:0.71 161:0.54 162:0.68 163:0.92 164:0.84 167:0.94 172:0.41 173:0.36 177:0.05 179:0.65 181:0.62 191:0.98 195:0.81 202:0.77 212:0.18 215:0.84 216:0.18 235:0.12 241:0.88 242:0.82 243:0.37 245:0.66 247:0.12 248:0.76 256:0.78 259:0.21 260:0.84 265:0.47 266:0.87 267:0.18 268:0.80 271:0.94 276:0.59 283:0.60 285:0.62 297:0.36 300:0.70 +2 6:0.93 8:0.97 12:0.06 17:0.38 20:0.84 21:0.30 25:0.88 27:0.28 28:0.46 30:0.68 34:0.23 36:0.30 37:0.50 43:0.52 55:0.52 66:0.71 69:0.10 70:0.52 78:0.75 81:0.86 91:0.99 98:0.79 100:0.78 104:0.74 108:0.56 111:0.74 120:0.98 123:0.54 124:0.42 126:0.54 127:0.94 129:0.59 133:0.47 135:0.30 140:0.80 146:0.18 147:0.23 149:0.59 150:0.69 151:0.83 152:0.80 153:0.96 154:0.41 159:0.34 161:0.54 162:0.57 164:0.99 167:0.92 169:0.76 172:0.57 173:0.32 177:0.05 178:0.42 179:0.77 181:0.62 186:0.75 189:0.94 191:0.97 202:0.98 212:0.81 215:0.72 216:0.89 220:0.62 235:0.31 238:0.88 241:0.83 242:0.82 243:0.21 245:0.60 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.75 265:0.79 266:0.38 267:0.59 268:0.99 271:0.94 276:0.47 285:0.62 297:0.36 300:0.43 +1 6:0.93 7:0.81 8:0.97 12:0.06 17:0.07 20:0.81 21:0.74 25:0.88 27:0.15 28:0.46 30:0.15 32:0.80 34:0.25 36:0.52 37:0.93 43:0.48 55:0.45 66:0.53 69:0.42 70:0.68 78:0.75 81:0.64 91:0.88 98:0.34 100:0.78 104:0.57 108:0.32 111:0.75 120:0.93 123:0.31 124:0.68 126:0.54 127:0.93 129:0.89 133:0.78 135:0.19 140:0.45 145:0.69 146:0.51 147:0.57 149:0.50 150:0.47 151:0.80 152:0.78 153:0.93 154:0.36 159:0.69 161:0.54 162:0.65 164:0.95 167:0.95 169:0.77 172:0.37 173:0.32 177:0.05 179:0.88 181:0.62 186:0.75 189:0.94 191:0.99 195:0.84 202:0.93 212:0.15 215:0.46 216:0.49 220:0.62 230:0.87 233:0.76 235:0.88 238:0.94 241:0.91 242:0.82 243:0.62 245:0.46 247:0.12 248:0.90 256:0.94 259:0.21 260:0.93 261:0.75 265:0.37 266:0.63 267:0.97 268:0.94 271:0.94 276:0.36 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.91 7:0.81 8:0.81 12:0.06 17:0.02 20:0.87 21:0.05 25:0.88 27:0.30 28:0.46 30:0.30 32:0.80 34:0.20 36:0.58 37:0.32 43:0.47 55:0.71 66:0.51 69:0.37 70:0.60 78:0.75 81:0.91 91:0.98 98:0.76 100:0.80 104:0.79 108:0.67 111:0.75 120:0.98 123:0.65 124:0.52 126:0.54 127:0.75 129:0.59 133:0.47 135:0.37 140:0.45 145:0.38 146:0.49 147:0.56 149:0.51 150:0.82 151:0.83 152:0.82 153:0.96 154:0.35 159:0.48 161:0.54 162:0.52 163:0.81 164:0.99 167:0.94 169:0.77 172:0.41 173:0.41 177:0.05 179:0.82 181:0.62 186:0.76 189:0.92 191:0.95 195:0.64 202:0.99 212:0.23 215:0.91 216:0.65 220:0.62 230:0.65 233:0.63 235:0.05 238:0.94 241:0.90 242:0.82 243:0.39 245:0.63 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.78 265:0.76 266:0.71 267:0.96 268:0.99 271:0.94 276:0.44 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.93 8:0.88 12:0.06 17:0.20 20:0.83 21:0.05 25:0.88 27:0.42 28:0.46 30:0.62 32:0.80 34:0.47 36:0.55 37:0.62 43:0.44 55:0.45 66:0.30 69:0.44 70:0.34 78:0.75 81:0.91 91:0.99 98:0.22 100:0.79 104:0.73 108:0.73 111:0.75 120:0.97 123:0.71 124:0.71 126:0.54 127:0.63 129:0.81 133:0.67 135:0.30 140:0.45 145:0.76 146:0.05 147:0.05 149:0.34 150:0.82 151:0.81 152:0.79 153:0.94 154:0.34 159:0.37 161:0.54 162:0.69 163:0.81 164:0.99 167:0.95 169:0.76 172:0.16 173:0.54 177:0.05 179:0.94 181:0.62 186:0.76 189:0.95 191:0.94 195:0.60 202:0.97 212:0.30 215:0.91 216:0.82 220:0.62 235:0.05 238:0.91 241:0.92 242:0.82 243:0.23 245:0.43 247:0.12 248:0.96 256:0.98 259:0.21 260:0.97 261:0.76 265:0.24 266:0.27 267:0.79 268:0.98 271:0.94 276:0.23 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.97 8:0.91 12:0.06 17:0.55 20:0.75 21:0.05 25:0.88 27:0.64 28:0.46 30:0.62 34:0.23 36:0.36 37:0.77 43:0.37 55:0.92 66:0.30 69:0.59 70:0.34 78:0.77 81:0.91 91:0.87 98:0.27 100:0.79 104:0.57 108:0.45 111:0.77 120:0.87 123:0.61 124:0.61 126:0.54 127:0.15 129:0.59 133:0.47 135:0.30 140:0.45 146:0.38 147:0.44 149:0.37 150:0.82 151:0.74 152:0.74 153:0.84 154:0.28 159:0.28 161:0.54 162:0.66 163:0.93 164:0.90 167:0.95 169:0.79 172:0.16 173:0.47 177:0.05 179:0.59 181:0.62 186:0.76 189:0.98 191:0.98 202:0.85 212:0.61 215:0.91 216:0.38 220:0.62 235:0.05 238:0.95 241:0.96 242:0.82 243:0.48 245:0.47 247:0.12 248:0.81 256:0.86 259:0.21 260:0.87 261:0.74 265:0.26 266:0.23 267:0.28 268:0.87 271:0.94 276:0.28 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.94 8:0.96 12:0.06 17:0.05 20:0.80 21:0.78 28:0.46 34:0.53 36:0.25 37:0.96 78:0.82 91:0.99 100:0.82 111:0.84 120:0.98 135:0.78 140:0.95 151:0.83 152:0.77 153:0.95 161:0.54 162:0.46 164:0.99 167:0.95 169:0.87 175:0.75 178:0.53 181:0.62 186:0.77 189:0.95 191:1.00 202:1.00 216:0.88 220:0.62 238:0.97 241:0.95 244:0.61 248:0.97 256:0.99 257:0.65 259:0.21 260:0.98 261:0.77 267:0.91 268:0.99 271:0.94 277:0.69 285:0.62 +4 6:0.99 8:0.99 12:0.06 17:0.11 20:0.96 21:0.05 25:0.88 28:0.46 30:0.45 32:0.80 34:0.31 36:0.56 37:0.98 43:0.46 55:0.52 66:0.11 69:0.39 70:0.86 78:0.97 81:0.91 91:0.98 98:0.24 100:0.99 104:0.58 108:0.89 111:0.98 120:1.00 123:0.29 124:0.77 126:0.54 127:0.90 129:0.89 133:0.78 135:0.21 140:0.45 145:0.85 146:0.38 147:0.45 149:0.44 150:0.82 151:0.93 152:0.92 153:1.00 154:0.35 159:0.73 161:0.54 162:0.53 164:1.00 167:0.95 169:0.99 172:0.37 173:0.27 177:0.05 179:0.81 181:0.62 186:0.98 189:0.99 191:1.00 195:0.83 202:1.00 212:0.49 215:0.91 216:0.90 235:0.05 238:1.00 241:0.95 242:0.82 243:0.31 245:0.55 247:0.12 248:1.00 256:1.00 259:0.21 260:1.00 261:0.99 265:0.25 266:0.63 267:0.84 268:1.00 271:0.94 276:0.41 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.87 8:0.66 12:0.06 17:0.32 20:0.75 21:0.78 27:0.21 28:0.46 30:0.11 34:0.31 36:0.91 37:0.74 43:0.47 55:0.92 66:0.23 69:0.38 70:0.99 78:0.75 91:0.73 98:0.26 100:0.80 104:0.84 106:0.81 108:0.93 111:0.75 120:0.93 123:0.92 124:0.94 127:0.86 129:0.98 133:0.94 135:0.17 140:0.45 146:0.76 147:0.79 149:0.55 151:0.80 152:0.83 153:0.92 154:0.36 159:0.95 161:0.54 162:0.66 163:0.92 164:0.94 167:0.70 169:0.76 172:0.51 173:0.09 177:0.05 179:0.50 181:0.62 186:0.79 187:0.39 189:0.86 191:0.80 202:0.90 206:0.81 212:0.26 216:0.95 220:0.62 235:0.52 238:0.88 241:0.55 242:0.82 243:0.28 245:0.67 247:0.12 248:0.90 256:0.92 259:0.21 260:0.93 261:0.76 265:0.28 266:0.95 267:0.65 268:0.92 271:0.94 276:0.67 283:0.82 285:0.62 300:0.94 +1 6:0.98 7:0.81 8:0.98 12:0.06 17:0.05 20:0.75 21:0.74 25:0.88 27:0.11 28:0.46 30:0.55 32:0.80 34:0.21 36:0.96 37:0.98 43:0.43 55:0.98 66:0.10 69:0.51 70:0.71 78:0.77 81:0.64 91:0.93 98:0.23 100:0.91 104:0.57 108:0.94 111:0.78 120:0.98 123:0.92 124:0.92 126:0.54 127:0.96 129:0.97 132:0.97 133:0.92 135:0.34 140:0.45 145:0.52 146:0.05 147:0.05 149:0.37 150:0.47 151:0.90 152:0.74 153:0.99 154:0.33 159:0.87 161:0.54 162:0.64 163:0.99 164:0.99 167:0.93 169:0.81 172:0.27 173:0.24 177:0.05 179:0.77 181:0.62 186:0.81 189:0.99 191:1.00 195:0.95 202:0.98 212:0.29 215:0.46 216:0.55 230:0.65 233:0.63 235:0.88 238:0.96 241:0.86 242:0.82 243:0.13 245:0.51 247:0.12 248:0.97 256:0.98 260:0.98 261:0.75 265:0.17 266:0.75 267:0.76 268:0.98 271:0.94 276:0.51 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.99 8:0.93 12:0.06 17:0.43 20:0.74 21:0.05 25:0.88 28:0.46 30:0.38 34:0.58 36:0.44 37:0.98 43:0.46 55:0.42 66:0.54 69:0.41 70:0.58 78:0.84 81:0.91 91:0.81 98:0.63 100:0.88 104:0.58 108:0.32 111:0.84 120:0.89 123:0.31 124:0.50 126:0.54 127:0.64 129:0.59 133:0.47 135:0.20 140:0.45 146:0.52 147:0.58 149:0.54 150:0.82 151:0.75 152:0.92 153:0.85 154:0.35 159:0.34 161:0.54 162:0.79 164:0.92 167:0.84 169:0.99 172:0.41 173:0.54 177:0.05 179:0.83 181:0.62 186:0.84 187:0.21 189:0.90 191:0.96 202:0.86 212:0.42 215:0.91 216:0.90 220:0.62 235:0.05 238:0.91 241:0.75 242:0.82 243:0.63 245:0.59 247:0.12 248:0.85 256:0.89 259:0.21 260:0.90 261:0.99 265:0.64 266:0.54 267:0.79 268:0.90 271:0.94 276:0.38 283:0.60 285:0.62 297:0.36 300:0.43 +1 7:0.81 12:0.06 17:0.66 21:0.74 25:0.88 27:0.15 28:0.46 30:0.55 32:0.80 34:0.29 36:0.75 37:0.93 43:0.48 55:0.45 66:0.46 69:0.42 70:0.52 81:0.64 91:0.49 98:0.29 104:0.57 108:0.32 123:0.31 124:0.76 126:0.54 127:0.82 129:0.89 133:0.78 135:0.21 145:0.69 146:0.43 147:0.50 149:0.60 150:0.47 154:0.36 159:0.67 161:0.54 167:0.78 172:0.45 173:0.33 177:0.05 178:0.55 179:0.76 181:0.62 187:0.39 191:0.84 195:0.83 202:0.55 212:0.40 215:0.46 216:0.49 230:0.87 233:0.76 235:0.88 241:0.70 242:0.82 243:0.59 245:0.58 247:0.12 259:0.21 265:0.31 266:0.63 267:0.44 271:0.94 276:0.48 297:0.36 300:0.43 +2 6:0.87 7:0.81 8:0.73 12:0.06 17:0.28 20:0.88 21:0.30 27:0.21 28:0.46 30:0.43 34:0.75 36:0.96 37:0.74 43:0.52 55:0.59 66:0.93 69:0.10 70:0.59 78:0.74 81:0.86 91:0.77 98:0.93 100:0.78 104:0.84 106:0.81 108:0.09 111:0.74 120:0.96 123:0.09 124:0.37 126:0.54 127:0.75 129:0.81 133:0.67 135:0.17 140:0.45 146:1.00 147:1.00 149:0.91 150:0.69 151:0.84 152:0.83 153:0.97 154:0.41 159:0.91 161:0.54 162:0.71 164:0.94 167:0.73 169:0.76 172:0.98 173:0.07 177:0.05 179:0.13 181:0.62 186:0.75 187:0.39 189:0.88 191:0.73 202:0.90 206:0.81 212:0.75 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.91 241:0.62 242:0.82 243:0.93 245:0.89 247:0.12 248:0.94 256:0.92 259:0.21 260:0.96 261:0.76 265:0.93 266:0.75 267:0.82 268:0.92 271:0.94 276:0.96 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.97 8:0.94 12:0.06 17:0.30 20:0.91 21:0.54 25:0.88 27:0.16 28:0.46 30:0.38 32:0.80 34:0.66 36:0.30 37:0.85 43:0.52 55:0.92 66:0.29 69:0.17 70:0.78 78:0.74 81:0.79 91:0.96 98:0.31 100:0.79 104:0.78 108:0.14 111:0.74 120:0.95 123:0.13 124:0.83 126:0.54 127:0.79 129:0.93 133:0.84 135:0.88 140:0.80 145:0.82 146:0.49 147:0.55 149:0.49 150:0.59 151:0.80 152:0.91 153:0.92 154:0.41 159:0.71 161:0.54 162:0.58 164:0.99 167:0.77 169:0.77 172:0.27 173:0.15 177:0.05 178:0.42 179:0.84 181:0.62 186:0.75 187:0.39 189:0.98 191:0.98 195:0.84 202:0.98 212:0.28 215:0.58 216:0.81 220:0.62 235:0.60 238:0.97 241:0.69 242:0.82 243:0.48 245:0.49 247:0.12 248:0.93 256:0.98 259:0.21 260:0.95 261:0.79 265:0.33 266:0.63 267:1.00 268:0.98 271:0.94 276:0.39 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.93 8:0.90 12:0.06 17:0.11 20:0.81 21:0.78 27:0.07 28:0.46 34:0.19 36:0.33 37:0.87 43:0.51 66:0.36 69:0.10 78:0.86 91:0.99 98:0.13 100:0.79 108:0.09 111:0.85 120:0.99 123:0.09 127:0.08 129:0.05 135:0.24 140:0.95 145:0.50 146:0.05 147:0.05 149:0.16 151:0.86 152:0.85 153:0.99 154:0.40 159:0.05 161:0.54 162:0.51 164:1.00 167:0.94 169:0.82 172:0.05 173:0.54 177:0.05 178:0.53 181:0.62 186:0.76 189:0.96 191:1.00 202:1.00 216:0.88 220:0.62 235:0.05 238:0.88 241:0.91 243:0.51 248:0.99 256:0.99 259:0.21 260:0.99 261:0.79 265:0.14 267:0.44 268:1.00 271:0.94 285:0.62 +1 6:0.94 7:0.81 8:0.82 12:0.06 17:0.59 20:0.76 21:0.74 25:0.88 27:0.34 28:0.46 30:0.55 32:0.80 34:0.36 36:0.76 37:0.99 43:0.48 55:0.45 66:0.52 69:0.42 70:0.52 78:0.74 81:0.64 91:0.46 98:0.34 100:0.77 104:0.58 108:0.32 111:0.73 123:0.31 124:0.71 126:0.54 127:0.78 129:0.89 133:0.78 135:0.39 145:0.69 146:0.51 147:0.57 149:0.61 150:0.47 152:0.77 154:0.36 159:0.69 161:0.54 167:0.82 169:0.75 172:0.48 173:0.31 177:0.05 178:0.55 179:0.75 181:0.62 186:0.75 187:0.39 189:0.89 195:0.85 202:0.61 212:0.40 215:0.46 216:0.47 230:0.87 233:0.76 235:0.88 238:0.94 241:0.73 242:0.82 243:0.61 245:0.56 247:0.12 259:0.21 261:0.74 265:0.36 266:0.71 267:0.36 271:0.94 276:0.47 297:0.36 300:0.43 +1 7:0.81 12:0.06 17:0.49 21:0.22 27:0.45 28:0.46 30:0.14 32:0.80 34:0.77 36:0.28 37:0.50 43:0.36 55:0.52 66:0.43 69:0.60 70:0.96 81:0.88 91:0.06 98:0.44 108:0.83 123:0.82 124:0.76 126:0.54 127:0.62 129:0.89 133:0.78 135:0.66 145:0.52 146:0.58 147:0.63 149:0.67 150:0.74 154:0.27 159:0.80 161:0.54 167:0.59 172:0.70 173:0.39 177:0.05 178:0.55 179:0.39 181:0.62 187:0.68 195:0.92 212:0.54 215:0.79 216:0.77 230:0.87 233:0.76 235:0.22 241:0.15 242:0.82 243:0.22 245:0.82 247:0.12 259:0.21 265:0.46 266:0.87 267:0.69 271:0.94 276:0.74 283:0.60 297:0.36 300:0.94 +2 6:0.89 8:0.84 12:0.06 17:0.02 20:0.85 21:0.13 25:0.88 27:0.26 28:0.46 30:0.56 34:0.33 36:0.50 37:0.54 43:0.47 55:0.71 66:0.67 69:0.38 70:0.53 78:0.75 81:0.89 91:0.98 98:0.61 100:0.79 104:0.73 108:0.30 111:0.75 120:0.97 123:0.28 124:0.43 126:0.54 127:0.32 129:0.59 133:0.47 135:0.32 140:0.45 146:0.76 147:0.80 149:0.54 150:0.79 151:0.83 152:0.81 153:0.96 154:0.35 159:0.52 161:0.54 162:0.60 164:0.99 167:0.95 169:0.77 172:0.48 173:0.30 177:0.05 179:0.70 181:0.62 186:0.76 189:0.91 191:0.95 202:0.98 212:0.23 215:0.84 216:0.73 220:0.62 235:0.12 238:0.92 241:0.92 242:0.82 243:0.72 245:0.56 247:0.12 248:0.95 256:0.98 259:0.21 260:0.97 261:0.77 265:0.62 266:0.54 267:0.95 268:0.98 271:0.94 276:0.41 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.96 7:0.81 8:0.91 12:0.06 17:0.15 20:0.88 21:0.30 25:0.88 27:0.09 28:0.46 32:0.80 34:0.42 36:0.45 37:0.85 43:0.25 66:0.36 69:0.83 78:0.75 81:0.86 91:0.99 98:0.10 100:0.80 104:0.72 108:0.67 111:0.76 120:0.96 123:0.65 126:0.54 127:0.07 129:0.05 135:0.34 140:0.45 145:0.89 146:0.05 147:0.05 149:0.11 150:0.69 151:0.80 152:0.83 153:0.92 154:0.18 159:0.05 161:0.54 162:0.68 164:0.98 167:0.96 169:0.78 172:0.05 173:1.00 177:0.05 181:0.62 186:0.76 189:0.97 191:0.97 195:0.58 202:0.97 215:0.72 216:0.67 220:0.74 230:0.87 233:0.76 235:0.31 238:0.96 241:0.99 243:0.51 248:0.95 256:0.98 259:0.21 260:0.96 261:0.78 265:0.10 267:0.89 268:0.98 271:0.94 283:0.60 285:0.62 297:0.36 +1 1:0.74 11:0.57 12:0.69 17:0.49 21:0.68 27:0.45 28:0.73 30:0.45 32:0.75 34:0.83 36:0.17 37:0.50 43:0.81 55:0.92 60:0.87 66:0.36 69:0.71 70:0.50 74:0.56 81:0.48 83:0.85 85:0.72 91:0.25 98:0.30 101:0.71 108:0.54 114:0.70 123:0.52 124:0.76 126:0.54 127:0.41 129:0.05 133:0.74 135:0.85 145:0.50 146:0.46 147:0.52 149:0.53 150:0.66 154:0.76 155:0.67 158:0.87 159:0.61 161:0.57 163:0.75 165:0.69 167:0.47 172:0.21 173:0.63 176:0.73 177:0.38 178:0.55 179:0.89 181:0.51 187:0.68 192:0.59 195:0.82 201:0.74 208:0.64 212:0.23 215:0.49 216:0.77 222:0.25 235:0.88 241:0.18 242:0.73 243:0.51 245:0.42 247:0.35 253:0.57 259:0.21 265:0.32 266:0.38 267:0.59 271:0.97 276:0.30 279:0.86 283:0.71 290:0.70 297:0.36 300:0.33 +2 1:0.74 6:0.94 7:0.78 8:0.80 9:0.80 12:0.69 17:0.34 20:0.99 21:0.47 27:0.03 28:0.73 30:0.45 32:0.80 34:0.19 36:0.62 37:0.69 43:0.37 55:0.52 66:0.36 69:0.52 70:0.74 74:0.57 77:0.70 78:0.98 81:0.57 91:0.95 96:0.97 98:0.30 100:0.99 108:0.83 111:0.99 114:0.80 120:0.96 123:0.82 124:0.70 126:0.54 127:0.64 129:0.81 133:0.67 135:0.32 140:0.92 145:0.49 146:0.33 147:0.39 149:0.48 150:0.65 151:0.71 152:0.92 153:0.69 154:0.28 155:0.67 158:0.84 159:0.56 161:0.57 162:0.59 164:0.95 167:0.79 169:0.99 172:0.37 173:0.49 175:0.75 176:0.73 177:0.05 179:0.78 181:0.51 186:0.98 189:0.95 190:0.77 191:0.99 192:0.59 195:0.74 201:0.74 202:0.95 208:0.64 212:0.36 215:0.63 216:0.53 220:0.62 222:0.25 230:0.81 232:0.76 233:0.69 235:0.68 238:0.96 241:0.83 242:0.82 243:0.37 244:0.61 245:0.60 247:0.12 248:0.81 253:0.96 255:0.79 256:0.98 257:0.65 259:0.21 260:0.96 261:0.97 265:0.33 266:0.63 267:0.76 268:0.95 271:0.97 276:0.39 277:0.69 282:0.88 283:0.71 285:0.62 290:0.80 297:0.36 300:0.55 +3 6:0.89 7:0.78 8:0.70 9:0.80 11:0.57 12:0.69 17:0.89 20:0.87 21:0.54 27:0.68 28:0.73 30:0.60 32:0.75 34:0.19 36:0.68 37:0.64 41:0.82 43:0.60 55:0.45 66:0.99 69:0.43 70:0.50 74:0.36 78:0.95 81:0.68 83:0.75 85:0.72 91:0.83 96:0.79 98:0.99 100:0.95 101:0.71 104:0.58 108:0.33 111:0.94 120:0.94 123:0.32 126:0.32 127:0.90 129:0.05 135:0.37 140:0.98 145:0.70 146:0.95 147:0.96 149:0.92 150:0.49 151:0.82 152:0.82 153:0.46 154:0.49 155:0.67 159:0.62 161:0.57 162:0.80 164:0.92 167:0.78 169:0.93 172:0.97 173:0.37 177:0.38 179:0.18 181:0.51 186:0.94 189:0.91 191:0.86 192:0.59 195:0.77 202:0.86 208:0.64 212:0.93 215:0.58 216:0.19 220:0.62 222:0.25 230:0.81 233:0.69 235:0.88 238:0.90 241:0.79 242:0.82 243:0.99 247:0.39 248:0.74 253:0.68 255:0.79 256:0.91 259:0.21 260:0.94 261:0.90 265:0.99 266:0.47 267:1.00 268:0.90 271:0.97 276:0.93 283:0.71 285:0.62 297:0.36 300:0.70 +2 6:0.96 7:0.78 8:0.95 9:0.80 12:0.69 17:0.16 20:0.91 21:0.64 27:0.13 28:0.73 30:0.72 32:0.75 34:0.30 36:0.90 37:0.82 41:0.82 43:0.40 55:0.45 66:0.24 69:0.41 70:0.67 78:0.90 81:0.34 83:0.74 91:0.96 96:0.77 98:0.29 100:0.91 108:0.76 111:0.90 120:0.97 123:0.30 124:0.80 126:0.32 127:0.95 129:0.93 133:0.84 135:0.32 140:1.00 145:0.85 146:0.61 147:0.67 149:0.72 150:0.31 151:0.77 152:0.85 153:0.48 154:0.31 155:0.67 159:0.85 161:0.57 162:0.52 164:0.97 167:0.80 169:0.89 172:0.63 173:0.19 175:0.75 177:0.05 178:0.48 179:0.50 181:0.51 186:0.90 189:0.97 190:0.77 191:0.98 192:0.59 195:0.94 202:0.97 208:0.64 212:0.71 215:0.53 216:0.63 220:0.62 222:0.25 230:0.81 233:0.76 235:0.74 238:0.88 241:0.83 242:0.82 243:0.44 244:0.61 245:0.75 247:0.12 248:0.71 253:0.63 255:0.79 256:0.97 257:0.65 259:0.21 260:0.97 261:0.89 265:0.30 266:0.75 267:0.69 268:0.96 271:0.97 276:0.66 277:0.69 283:0.71 285:0.62 297:0.36 300:0.84 +1 6:0.83 7:0.78 8:0.71 12:0.69 17:0.75 20:0.85 21:0.59 27:0.18 28:0.73 30:0.95 34:0.31 36:0.39 37:0.37 43:0.37 55:0.84 66:0.54 69:0.53 74:0.44 78:0.88 81:0.28 91:0.94 96:0.72 98:0.35 100:0.86 108:0.41 111:0.86 114:0.64 120:0.76 123:0.39 126:0.32 127:0.12 129:0.05 135:0.24 140:0.45 145:0.47 146:0.32 147:0.38 149:0.21 150:0.27 151:0.62 152:0.80 153:0.45 154:0.28 159:0.13 161:0.57 162:0.50 164:0.72 167:0.78 169:0.84 172:0.10 173:0.66 176:0.56 177:0.38 179:0.87 181:0.51 186:0.88 189:0.85 190:0.77 191:0.97 202:0.86 216:0.35 220:0.97 222:0.25 230:0.81 233:0.69 235:0.74 238:0.84 241:0.80 243:0.63 244:0.61 248:0.69 253:0.55 256:0.91 259:0.21 260:0.77 261:0.83 265:0.37 267:0.88 268:0.79 271:0.97 283:0.71 285:0.62 290:0.64 300:0.08 +1 1:0.74 6:0.85 7:0.78 8:0.63 12:0.69 17:0.89 20:0.91 21:0.47 27:0.21 28:0.73 30:0.21 32:0.75 34:0.20 36:0.19 37:0.25 43:0.45 55:0.45 66:0.75 69:0.25 70:0.64 74:0.44 78:0.93 81:0.72 91:0.85 96:0.74 98:0.58 100:0.92 108:0.20 111:0.92 114:0.80 120:0.80 123:0.19 124:0.39 126:0.54 127:0.43 129:0.05 132:0.97 133:0.39 135:0.24 140:0.45 145:0.83 146:0.56 147:0.62 149:0.43 150:0.76 151:0.70 152:0.87 153:0.91 154:0.78 155:0.67 159:0.38 161:0.57 162:0.75 164:0.80 167:0.79 169:0.89 172:0.54 173:0.33 176:0.73 177:0.38 179:0.73 181:0.51 186:0.93 189:0.87 190:0.77 191:0.95 192:0.59 195:0.66 201:0.74 202:0.76 212:0.83 215:0.63 216:0.14 220:0.62 222:0.25 230:0.81 233:0.76 235:0.84 238:0.86 241:0.81 242:0.77 243:0.77 245:0.50 247:0.39 248:0.73 253:0.67 254:0.88 256:0.81 260:0.81 261:0.91 265:0.59 266:0.27 267:0.19 268:0.80 271:0.97 276:0.37 285:0.62 290:0.80 297:0.36 300:0.43 +2 8:0.62 12:0.69 17:0.67 21:0.64 27:0.45 28:0.73 30:0.76 34:0.83 36:0.18 37:0.50 43:0.74 55:0.42 66:0.77 69:0.70 70:0.53 74:0.49 81:0.34 91:0.17 98:0.59 108:0.53 123:0.51 124:0.41 126:0.32 127:0.44 129:0.05 133:0.39 135:0.76 145:0.49 146:0.77 147:0.81 149:0.88 150:0.31 154:0.65 159:0.62 161:0.57 167:0.48 172:0.89 173:0.62 177:0.38 178:0.55 179:0.24 181:0.51 187:0.68 212:0.86 215:0.53 216:0.77 222:0.25 235:0.74 241:0.21 242:0.81 243:0.79 245:0.90 247:0.47 253:0.41 259:0.21 265:0.60 266:0.75 267:0.52 271:0.97 276:0.80 297:0.36 300:0.70 +2 7:0.78 8:0.70 12:0.69 17:0.81 21:0.30 27:0.48 28:0.73 30:0.94 32:0.80 34:0.34 36:0.36 37:0.31 43:0.36 55:0.71 66:0.72 69:0.53 70:0.20 74:0.58 76:0.94 77:0.70 81:0.68 91:0.56 98:0.82 104:0.88 108:0.41 120:0.59 123:0.39 124:0.43 126:0.32 127:0.70 129:0.59 133:0.47 135:0.37 140:0.45 145:0.49 146:0.92 147:0.94 149:0.77 150:0.49 151:0.53 153:0.77 154:0.27 159:0.68 161:0.57 162:0.68 164:0.64 167:0.47 172:0.84 173:0.42 175:0.75 177:0.05 179:0.38 181:0.51 187:0.39 190:0.77 191:0.96 195:0.83 202:0.54 212:0.85 215:0.72 216:0.23 220:0.87 222:0.25 230:0.81 233:0.76 235:0.42 241:0.18 242:0.82 243:0.75 244:0.61 245:0.86 247:0.12 248:0.52 253:0.46 256:0.45 257:0.65 259:0.21 260:0.59 265:0.82 266:0.63 267:0.18 268:0.57 271:0.97 276:0.76 277:0.69 282:0.88 283:0.71 285:0.50 297:0.36 300:0.43 +2 6:0.93 7:0.78 8:0.88 12:0.69 17:0.13 20:0.87 21:0.30 27:0.09 28:0.73 30:0.31 32:0.75 34:0.18 36:0.49 37:0.54 43:0.45 55:0.59 66:0.20 69:0.17 70:0.82 78:0.95 81:0.77 91:0.97 98:0.62 100:0.95 108:0.92 111:0.94 120:0.98 123:0.92 124:0.91 126:0.32 127:0.99 129:0.97 133:0.92 135:0.34 140:0.45 145:0.91 146:0.33 147:0.39 149:0.93 150:0.57 151:0.78 152:0.82 153:0.78 154:0.34 159:0.97 161:0.57 162:0.57 164:0.98 167:0.77 169:0.93 172:0.98 173:0.06 175:0.75 177:0.05 179:0.09 181:0.51 186:0.95 189:0.94 190:0.77 191:0.98 195:1.00 202:0.97 212:0.69 215:0.72 216:0.57 220:0.62 222:0.25 230:0.81 233:0.69 235:0.60 238:0.90 241:0.79 242:0.82 243:0.05 244:0.61 245:1.00 247:0.12 248:0.97 253:0.20 256:0.99 257:0.65 259:0.21 260:0.98 261:0.90 265:0.63 266:0.91 267:0.98 268:0.97 271:0.97 276:0.99 277:0.69 283:0.71 285:0.50 297:0.36 300:0.94 +0 12:0.69 17:0.64 21:0.76 25:0.88 28:0.73 30:0.09 32:0.75 34:0.42 36:0.25 37:0.97 43:0.36 55:0.42 66:0.08 69:0.56 70:0.95 74:0.32 81:0.32 91:0.33 98:0.16 104:0.58 108:0.95 123:0.42 124:0.90 126:0.32 127:0.94 129:0.96 133:0.90 135:0.26 145:0.67 146:0.33 147:0.40 149:0.40 150:0.30 154:0.27 159:0.88 161:0.57 167:0.56 172:0.21 173:0.27 175:0.75 177:0.05 178:0.55 179:0.84 181:0.51 187:0.39 195:0.96 212:0.13 215:0.46 216:0.98 222:0.25 235:0.97 241:0.55 242:0.76 243:0.25 244:0.61 245:0.48 247:0.39 253:0.30 257:0.65 259:0.21 265:0.11 266:0.75 267:0.23 271:0.97 276:0.41 277:0.69 297:0.36 300:0.70 +2 6:0.82 7:0.78 8:0.69 9:0.80 12:0.69 17:0.83 20:0.80 21:0.30 27:0.48 28:0.73 30:0.95 32:0.80 34:0.44 36:0.28 37:0.31 43:0.36 55:0.71 66:0.72 69:0.53 70:0.19 74:0.58 76:0.94 77:0.70 78:0.90 81:0.68 91:0.65 96:0.72 98:0.82 100:0.91 104:0.88 108:0.41 111:0.89 120:0.74 123:0.39 124:0.43 126:0.32 127:0.61 129:0.59 133:0.47 135:0.70 140:0.94 145:0.49 146:0.91 147:0.92 149:0.78 150:0.49 151:0.60 152:0.78 153:0.48 154:0.27 155:0.67 159:0.63 161:0.57 162:0.83 164:0.81 167:0.51 169:0.89 172:0.84 173:0.44 175:0.75 177:0.05 179:0.36 181:0.51 186:0.90 187:0.39 189:0.85 190:0.77 191:0.97 192:0.59 195:0.81 202:0.71 208:0.64 212:0.92 215:0.72 216:0.23 220:0.87 222:0.25 230:0.81 233:0.76 235:0.42 238:0.90 241:0.39 242:0.82 243:0.75 244:0.61 245:0.86 247:0.12 248:0.58 253:0.53 255:0.79 256:0.82 257:0.65 259:0.21 260:0.75 261:0.83 265:0.82 266:0.38 267:0.18 268:0.77 271:0.97 276:0.77 277:0.69 282:0.88 283:0.71 285:0.62 297:0.36 300:0.33 +0 6:0.83 8:0.98 12:0.69 17:0.06 20:0.85 25:0.88 27:0.37 28:0.73 30:0.30 34:0.26 36:0.29 37:0.97 43:0.42 55:0.84 66:0.26 69:0.21 70:0.66 78:0.83 81:0.87 91:1.00 98:0.67 100:0.83 104:0.54 108:0.65 111:0.81 120:1.00 123:0.16 124:0.58 126:0.32 127:0.84 129:0.59 133:0.47 135:0.39 140:0.45 146:0.53 147:0.59 149:0.53 150:0.73 151:0.91 152:0.80 153:1.00 154:0.32 159:0.35 161:0.57 162:0.53 163:0.75 164:0.99 167:0.81 169:0.81 172:0.37 173:0.39 175:0.75 177:0.05 179:0.83 181:0.51 186:0.83 189:0.85 190:0.77 191:1.00 202:0.99 212:0.23 215:0.96 216:0.92 222:0.25 235:0.02 238:0.85 241:0.87 242:0.82 243:0.45 244:0.61 245:0.64 247:0.12 248:1.00 253:0.20 256:0.99 257:0.65 259:0.21 260:1.00 261:0.81 265:0.59 266:0.54 267:0.93 268:0.99 271:0.97 276:0.43 277:0.69 283:0.82 285:0.50 297:0.36 300:0.43 +2 1:0.74 6:0.85 7:0.78 8:0.63 12:0.69 17:0.89 20:0.91 21:0.47 27:0.21 28:0.73 30:0.21 32:0.75 34:0.20 36:0.19 37:0.25 43:0.45 55:0.45 66:0.75 69:0.25 70:0.64 74:0.44 78:0.93 81:0.72 91:0.85 96:0.74 98:0.58 100:0.92 108:0.20 111:0.92 114:0.80 120:0.80 123:0.19 124:0.39 126:0.54 127:0.43 129:0.05 132:0.97 133:0.39 135:0.24 140:0.45 145:0.83 146:0.56 147:0.62 149:0.43 150:0.76 151:0.70 152:0.87 153:0.91 154:0.78 155:0.67 159:0.38 161:0.57 162:0.75 164:0.80 167:0.79 169:0.89 172:0.54 173:0.33 176:0.73 177:0.38 179:0.73 181:0.51 186:0.93 189:0.87 190:0.77 191:0.95 192:0.59 195:0.66 201:0.74 202:0.76 212:0.83 215:0.63 216:0.14 220:0.62 222:0.25 230:0.81 233:0.76 235:0.84 238:0.86 241:0.81 242:0.77 243:0.77 245:0.50 247:0.39 248:0.73 253:0.67 254:0.88 256:0.81 260:0.81 261:0.91 265:0.59 266:0.27 267:0.19 268:0.80 271:0.97 276:0.37 285:0.62 290:0.80 297:0.36 300:0.43 +2 6:0.93 7:0.78 8:0.79 12:0.69 17:0.73 20:0.85 21:0.71 27:0.10 28:0.73 30:0.66 32:0.75 34:0.17 36:0.55 37:0.72 43:0.39 55:0.45 66:0.63 69:0.49 70:0.52 78:0.94 81:0.40 91:0.96 98:0.67 100:0.91 108:0.74 111:0.92 120:0.98 123:0.72 124:0.64 126:0.32 127:0.93 129:0.89 133:0.78 135:0.34 140:0.45 145:0.93 146:0.28 147:0.34 149:0.86 150:0.34 151:0.75 152:0.80 153:0.48 154:0.29 159:0.71 161:0.57 162:0.72 164:0.97 167:0.81 169:0.88 172:0.92 173:0.37 175:0.75 177:0.05 179:0.23 181:0.51 186:0.94 189:0.94 190:0.77 191:0.97 195:0.84 202:0.95 212:0.89 215:0.48 216:0.50 220:0.62 222:0.25 230:0.81 233:0.69 235:0.95 238:0.85 241:0.88 242:0.82 243:0.11 244:0.61 245:0.92 247:0.12 248:0.97 253:0.20 256:0.97 257:0.65 259:0.21 260:0.98 261:0.87 265:0.67 266:0.75 267:0.88 268:0.97 271:0.97 276:0.88 277:0.69 283:0.71 285:0.62 297:0.36 300:0.84 +3 6:0.79 7:0.78 8:0.58 12:0.69 17:0.89 20:0.79 21:0.78 27:0.57 28:0.73 30:0.38 32:0.75 34:0.33 36:0.33 37:0.31 43:0.42 55:0.59 66:0.64 69:0.29 70:0.45 74:0.46 78:0.93 91:0.84 98:0.48 100:0.84 104:0.58 108:0.22 111:0.90 123:0.22 124:0.44 127:0.94 129:0.59 133:0.47 135:0.39 145:0.52 146:0.56 147:0.62 149:0.53 152:0.77 154:0.32 158:0.85 159:0.57 161:0.57 167:0.81 169:0.82 172:0.45 173:0.29 175:0.75 177:0.05 178:0.55 179:0.86 181:0.51 186:0.93 189:0.81 191:0.79 195:0.71 202:0.60 212:0.57 216:0.04 222:0.25 230:0.81 233:0.69 235:0.42 238:0.82 241:0.89 242:0.82 243:0.69 244:0.61 245:0.55 247:0.12 253:0.39 257:0.65 259:0.21 261:0.82 265:0.50 266:0.47 267:0.19 271:0.97 276:0.27 277:0.69 283:0.71 300:0.33 +3 6:0.82 7:0.78 8:0.70 12:0.69 17:0.93 20:0.83 21:0.54 27:0.67 28:0.73 30:0.45 32:0.75 34:0.20 36:0.25 37:0.41 43:0.34 55:0.59 66:0.69 69:0.62 70:0.25 78:0.90 81:0.58 91:0.89 98:0.84 100:0.87 104:0.58 108:0.47 111:0.88 120:0.90 123:0.45 126:0.32 127:0.34 129:0.05 135:0.44 140:0.45 145:0.70 146:0.53 147:0.59 149:0.29 150:0.43 151:0.64 152:0.79 153:0.78 154:0.25 159:0.19 161:0.57 162:0.85 163:0.90 164:0.91 167:0.78 169:0.84 172:0.21 173:0.86 175:0.75 177:0.05 179:0.96 181:0.51 186:0.90 189:0.84 190:0.77 191:0.87 195:0.54 202:0.84 212:0.61 215:0.58 216:0.21 220:0.74 222:0.25 230:0.81 233:0.76 235:0.74 238:0.84 241:0.79 242:0.82 243:0.73 244:0.61 247:0.12 248:0.85 253:0.20 256:0.90 257:0.65 259:0.21 260:0.90 261:0.83 265:0.84 266:0.17 267:0.23 268:0.89 271:0.97 276:0.20 277:0.69 283:0.71 285:0.50 297:0.36 300:0.18 +1 1:0.74 8:0.58 9:0.80 12:0.69 17:0.90 21:0.30 27:0.72 28:0.73 30:0.76 32:0.74 34:0.37 36:0.80 37:0.24 43:0.34 55:0.59 66:0.77 69:0.55 70:0.29 74:0.61 77:0.70 81:0.73 91:0.85 96:0.84 98:0.84 108:0.42 114:0.86 120:0.77 123:0.41 126:0.54 127:0.34 129:0.05 135:0.49 140:0.90 145:0.52 146:0.51 147:0.57 149:0.26 150:0.77 151:0.58 153:0.72 154:0.74 155:0.67 159:0.18 161:0.57 162:0.84 163:0.90 164:0.76 167:0.79 172:0.37 173:0.83 176:0.73 177:0.38 179:0.87 181:0.51 190:0.77 192:0.59 195:0.53 201:0.74 202:0.67 208:0.64 212:0.52 215:0.72 216:0.05 220:0.74 222:0.25 235:0.60 241:0.82 242:0.55 243:0.79 247:0.39 248:0.57 253:0.83 254:0.88 255:0.79 256:0.71 259:0.21 260:0.78 265:0.84 266:0.27 267:0.23 268:0.74 271:0.97 276:0.31 282:0.83 283:0.71 285:0.62 290:0.86 297:0.36 300:0.25 +0 6:0.88 8:0.69 12:0.69 17:0.62 20:0.85 21:0.78 27:0.72 28:0.73 37:0.83 74:0.45 78:0.86 91:0.80 96:0.88 100:0.88 111:0.86 120:0.79 140:0.45 151:0.69 152:0.81 153:0.88 158:0.84 161:0.57 162:0.69 164:0.80 167:0.80 169:0.86 175:0.91 181:0.51 186:0.86 189:0.89 190:0.77 191:0.89 202:0.78 216:0.17 220:0.62 222:0.25 238:0.88 241:0.85 244:0.83 248:0.79 253:0.83 256:0.82 257:0.84 259:0.21 260:0.79 261:0.83 268:0.80 271:0.97 277:0.87 285:0.62 +3 7:0.78 8:0.79 12:0.69 17:0.40 21:0.59 27:0.04 28:0.73 30:0.30 32:0.75 34:0.20 36:0.60 37:0.62 43:0.39 55:0.45 66:0.12 69:0.45 70:0.75 81:0.53 91:0.89 98:0.43 104:0.58 108:0.69 120:0.94 123:0.77 124:0.64 126:0.32 127:0.88 129:0.81 133:0.67 135:0.44 140:0.45 145:0.82 146:0.05 147:0.05 149:0.46 150:0.40 151:0.74 154:0.30 159:0.51 161:0.57 162:0.60 164:0.93 167:0.82 172:0.41 173:0.47 175:0.75 177:0.05 179:0.83 181:0.51 190:0.77 191:0.95 195:0.68 202:0.91 212:0.39 215:0.55 216:0.34 220:0.62 222:0.25 230:0.81 233:0.69 235:0.80 241:0.93 242:0.82 243:0.15 244:0.61 245:0.55 247:0.12 248:0.91 253:0.20 256:0.93 257:0.65 259:0.21 260:0.94 265:0.41 266:0.63 267:0.79 268:0.91 271:0.97 276:0.40 277:0.69 283:0.71 285:0.50 297:0.36 300:0.55 +1 12:0.69 17:0.30 21:0.40 27:0.45 28:0.73 30:0.32 32:0.80 34:0.70 36:0.17 37:0.50 43:0.32 55:0.84 66:0.52 69:0.69 70:0.75 81:0.66 91:0.10 98:0.59 108:0.68 123:0.66 124:0.78 126:0.54 127:0.68 129:0.93 133:0.84 135:0.75 145:0.40 146:0.36 147:0.43 149:0.72 150:0.48 154:0.24 159:0.83 161:0.45 167:0.64 172:0.79 173:0.47 177:0.05 178:0.55 179:0.35 181:0.87 187:0.68 195:0.94 212:0.42 215:0.67 216:0.77 235:0.42 241:0.18 242:0.82 243:0.13 245:0.82 247:0.12 259:0.21 265:0.60 266:0.91 267:0.28 276:0.79 297:0.36 300:0.70 +0 6:0.84 8:0.67 12:0.69 17:0.12 20:0.85 21:0.74 27:0.08 28:0.73 30:0.71 32:0.80 34:0.26 36:0.28 37:0.59 43:0.46 55:0.71 66:0.82 69:0.48 70:0.52 78:0.75 81:0.47 91:0.99 98:0.89 100:0.77 108:0.37 111:0.74 120:0.98 123:0.35 126:0.54 127:0.44 129:0.05 135:0.54 140:0.45 145:0.63 146:0.71 147:0.75 149:0.53 150:0.38 151:0.84 152:0.87 153:0.97 154:0.35 159:0.28 161:0.45 162:0.61 164:0.98 167:0.97 169:0.75 172:0.48 173:0.64 177:0.05 179:0.81 181:0.87 186:0.76 189:0.84 191:0.97 195:0.59 202:0.98 212:0.61 215:0.46 216:0.52 235:0.88 238:0.87 241:0.86 242:0.82 243:0.83 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.77 265:0.89 266:0.38 267:0.88 268:0.98 276:0.39 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.87 8:0.81 12:0.69 17:0.18 20:0.75 21:0.05 25:0.88 27:0.19 28:0.73 34:0.36 36:0.21 37:0.64 78:0.80 81:0.77 91:0.94 100:0.82 104:0.74 111:0.79 120:0.94 126:0.54 135:0.37 140:0.45 150:0.57 151:0.83 152:0.74 153:0.95 161:0.45 162:0.76 164:0.94 167:0.95 169:0.79 175:0.75 181:0.87 186:0.81 189:0.89 191:0.93 202:0.90 215:0.91 216:0.27 235:0.05 238:0.87 241:0.81 244:0.61 248:0.92 256:0.92 257:0.65 259:0.21 260:0.95 261:0.74 267:0.85 268:0.93 277:0.69 283:0.82 285:0.62 297:0.36 +2 6:0.81 7:0.81 8:0.61 12:0.69 17:0.09 20:0.98 21:0.30 27:0.21 28:0.73 30:0.56 34:0.62 36:0.88 37:0.74 43:0.52 55:0.71 66:0.15 69:0.10 70:0.61 78:0.76 81:0.69 91:0.91 98:0.57 100:0.78 104:0.84 106:0.81 108:0.98 111:0.75 120:0.98 123:0.90 124:0.91 126:0.54 127:0.90 129:0.97 133:0.92 135:0.44 140:0.45 146:0.97 147:0.98 149:0.91 150:0.50 151:0.86 152:0.90 153:0.99 154:0.41 159:0.97 161:0.45 162:0.66 164:0.97 167:0.83 169:0.76 172:0.96 173:0.05 177:0.05 179:0.10 181:0.87 186:0.76 187:0.39 189:0.83 191:0.89 202:0.96 206:0.81 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.86 241:0.71 242:0.82 243:0.23 245:0.99 247:0.12 248:0.97 256:0.96 259:0.21 260:0.98 261:0.78 265:0.54 266:0.80 267:0.97 268:0.97 276:0.99 283:0.82 285:0.62 297:0.36 300:0.70 +3 7:0.81 8:0.74 12:0.69 17:0.28 21:0.54 25:0.88 28:0.73 30:0.21 32:0.80 34:0.29 36:0.98 37:0.97 43:0.48 55:0.42 66:0.18 69:0.36 70:0.50 81:0.61 91:0.94 98:0.32 104:0.58 108:0.83 120:0.87 123:0.27 124:0.68 126:0.54 127:0.43 129:0.81 133:0.67 135:0.94 140:0.80 145:0.97 146:0.44 147:0.51 149:0.41 150:0.45 151:0.73 153:0.81 154:0.37 159:0.55 161:0.45 162:0.56 164:0.93 167:0.87 172:0.27 173:0.30 177:0.05 178:0.42 179:0.84 181:0.87 187:0.39 191:0.94 195:0.84 202:0.92 212:0.37 215:0.58 216:0.98 220:0.96 230:0.65 233:0.63 235:0.60 241:0.74 242:0.82 243:0.39 245:0.50 247:0.12 248:0.81 256:0.91 259:0.21 260:0.87 265:0.21 266:0.47 267:0.76 268:0.92 276:0.32 285:0.62 297:0.36 300:0.33 +1 6:0.80 7:0.81 8:0.59 12:0.69 17:0.15 20:0.76 27:0.13 28:0.73 30:0.11 32:0.80 34:0.21 36:0.44 37:0.31 43:0.38 55:0.71 66:0.19 69:0.57 70:0.88 78:0.80 81:0.79 91:0.72 98:0.42 100:0.79 104:0.96 106:0.81 108:0.44 111:0.78 120:0.95 123:0.42 124:0.96 126:0.54 127:0.87 129:0.99 133:0.97 135:0.97 140:0.45 145:0.79 146:0.90 147:0.92 149:0.77 150:0.59 151:0.84 152:0.75 153:0.97 154:0.28 159:0.92 161:0.45 162:0.79 164:0.92 167:0.72 169:0.77 172:0.74 173:0.22 177:0.05 179:0.25 181:0.87 186:0.80 187:0.87 189:0.82 191:0.79 195:0.98 202:0.85 206:0.81 212:0.21 215:0.96 216:0.81 230:0.87 233:0.76 235:0.02 238:0.83 241:0.52 242:0.82 243:0.40 245:0.83 247:0.12 248:0.93 256:0.89 259:0.21 260:0.95 261:0.75 265:0.44 266:0.96 267:0.65 268:0.89 276:0.88 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.94 8:0.87 12:0.69 17:0.13 20:0.82 21:0.64 25:0.88 27:0.48 28:0.73 30:0.45 32:0.80 34:0.24 36:0.34 37:0.55 43:0.48 55:0.84 66:0.30 69:0.38 70:0.61 78:0.79 81:0.55 91:0.96 98:0.44 100:0.82 104:0.75 108:0.68 111:0.78 120:0.98 123:0.66 124:0.73 126:0.54 127:0.77 129:0.81 133:0.67 135:0.39 140:0.45 145:0.85 146:0.41 147:0.47 149:0.51 150:0.42 151:0.83 152:0.78 153:0.96 154:0.37 159:0.56 161:0.45 162:0.65 164:0.98 167:0.91 169:0.80 172:0.32 173:0.36 177:0.05 179:0.80 181:0.87 186:0.79 187:0.39 189:0.95 191:0.97 195:0.71 202:0.97 212:0.13 215:0.53 216:0.92 220:0.62 235:0.74 238:0.95 241:0.76 242:0.82 243:0.31 245:0.60 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:0.78 265:0.46 266:0.80 267:0.84 268:0.98 276:0.42 285:0.62 297:0.36 300:0.43 +0 6:0.99 8:0.91 12:0.69 17:0.40 20:0.75 21:0.64 25:0.88 28:0.73 30:0.21 32:0.80 34:0.25 36:0.24 37:0.97 43:0.40 55:0.42 66:0.27 69:0.55 70:0.67 78:0.87 81:0.70 91:0.95 98:0.23 100:0.73 104:0.58 108:0.42 111:0.84 120:0.97 123:0.41 124:0.70 126:0.54 127:0.94 129:0.81 133:0.67 135:0.37 140:0.80 145:0.61 146:0.57 147:0.62 149:0.35 150:0.52 151:0.81 152:0.97 153:0.94 154:0.31 159:0.89 161:0.45 162:0.59 164:0.96 167:0.78 169:1.00 172:0.21 173:0.25 177:0.05 178:0.42 179:0.90 181:0.87 186:0.87 187:0.21 191:0.79 195:0.97 202:0.94 212:0.16 215:0.53 216:0.98 235:0.80 241:0.67 242:0.82 243:0.46 245:0.50 247:0.12 248:0.95 256:0.95 259:0.21 260:0.97 261:1.00 265:0.25 266:0.54 267:0.95 268:0.95 276:0.27 283:0.82 285:0.62 297:0.36 300:0.43 +3 8:0.75 12:0.69 17:0.12 21:0.64 25:0.88 27:0.03 28:0.73 30:0.41 32:0.80 34:0.30 36:0.39 37:0.46 43:0.52 55:0.59 66:0.20 69:0.21 70:0.95 78:0.76 81:0.55 91:0.95 98:0.38 104:0.73 108:0.17 111:0.75 120:0.94 123:0.16 124:0.74 126:0.54 127:0.58 129:0.81 133:0.67 135:0.58 140:0.87 145:0.85 146:0.51 147:0.57 149:0.54 150:0.42 151:0.81 153:0.94 154:0.41 159:0.57 161:0.45 162:0.64 164:0.96 167:0.93 169:0.80 172:0.27 173:0.22 177:0.05 178:0.48 179:0.74 181:0.87 187:0.39 191:0.90 195:0.75 202:0.93 212:0.19 215:0.53 216:0.66 220:0.62 235:0.74 241:0.78 242:0.82 243:0.40 245:0.63 247:0.12 248:0.92 256:0.94 259:0.21 260:0.95 265:0.41 266:0.71 267:0.82 268:0.94 276:0.44 283:0.60 285:0.62 297:0.36 300:0.84 +3 6:1.00 8:0.86 12:0.69 17:0.13 20:0.79 21:0.64 25:0.88 28:0.73 30:0.45 32:0.80 34:0.24 36:0.36 37:0.97 43:0.48 55:0.84 66:0.30 69:0.38 70:0.61 78:0.78 81:0.55 91:0.96 98:0.44 100:0.81 104:0.58 108:0.68 111:0.78 120:0.98 123:0.66 124:0.73 126:0.54 127:0.77 129:0.81 133:0.67 135:0.32 140:0.45 145:0.85 146:0.41 147:0.47 149:0.51 150:0.42 151:0.84 152:0.95 153:0.97 154:0.37 159:0.56 161:0.45 162:0.64 164:0.99 167:0.90 169:0.98 172:0.32 173:0.36 177:0.05 179:0.80 181:0.87 186:0.79 187:0.39 189:0.92 191:0.94 195:0.71 202:0.98 212:0.13 215:0.53 216:0.98 220:0.62 235:0.74 238:0.93 241:0.76 242:0.82 243:0.31 245:0.60 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:1.00 265:0.46 266:0.80 267:0.84 268:0.98 276:0.42 285:0.62 297:0.36 300:0.43 +1 8:0.89 12:0.69 17:0.32 21:0.64 25:0.88 28:0.73 30:0.21 32:0.80 34:0.75 36:0.25 37:0.97 43:0.52 55:0.52 66:0.05 69:0.21 70:0.99 81:0.55 91:0.85 98:0.18 104:0.58 108:0.76 120:0.92 123:0.85 124:0.92 126:0.54 127:0.99 129:0.97 133:0.92 135:0.51 140:0.45 145:0.72 146:0.38 147:0.45 149:0.61 150:0.42 151:0.79 153:0.90 154:0.41 159:0.89 161:0.45 162:0.73 164:0.93 167:0.75 172:0.45 173:0.09 177:0.05 179:0.60 181:0.87 187:0.21 191:0.89 195:0.96 202:0.89 212:0.40 215:0.53 216:0.98 235:0.74 241:0.59 242:0.82 243:0.20 245:0.64 247:0.12 248:0.89 256:0.91 259:0.21 260:0.93 265:0.17 266:0.92 267:0.65 268:0.92 276:0.60 283:0.60 285:0.62 297:0.36 300:0.94 +1 12:0.69 17:0.20 21:0.59 25:0.88 27:0.19 28:0.73 30:0.15 32:0.80 34:0.53 36:0.23 37:0.38 43:0.52 55:0.59 66:0.24 69:0.19 70:0.84 81:0.58 91:0.76 98:0.35 104:0.72 108:0.84 120:0.61 123:0.15 124:0.69 126:0.54 127:0.58 129:0.81 133:0.67 135:0.87 140:0.92 145:0.79 146:0.48 147:0.54 149:0.45 150:0.43 151:0.54 153:0.48 154:0.41 159:0.60 161:0.45 162:0.46 164:0.69 167:0.89 172:0.27 173:0.19 177:0.05 178:0.51 179:0.84 181:0.87 187:0.39 195:0.79 202:0.85 212:0.30 215:0.55 216:0.87 220:0.82 235:0.68 241:0.75 242:0.82 243:0.44 245:0.53 247:0.12 248:0.54 256:0.58 259:0.21 260:0.61 265:0.22 266:0.54 267:0.92 268:0.62 276:0.30 285:0.62 297:0.36 300:0.55 +1 6:0.87 8:0.82 12:0.69 17:0.36 20:0.75 25:0.88 27:0.14 28:0.73 30:0.58 34:0.24 36:0.30 37:0.59 43:0.48 55:0.84 66:0.36 69:0.27 70:0.44 78:0.86 81:0.79 91:0.84 98:0.73 100:0.88 104:0.74 108:0.21 111:0.85 120:0.83 123:0.20 124:0.59 126:0.54 127:0.77 129:0.59 133:0.47 135:0.54 140:0.80 146:0.53 147:0.59 149:0.48 150:0.59 151:0.80 152:0.75 153:0.92 154:0.37 159:0.29 161:0.45 162:0.59 163:0.86 164:0.84 167:0.98 169:0.83 172:0.27 173:0.48 177:0.05 178:0.42 179:0.89 181:0.87 186:0.88 189:0.89 191:0.92 202:0.80 212:0.37 215:0.96 216:0.58 235:0.02 238:0.92 241:0.89 242:0.82 243:0.51 245:0.56 247:0.12 248:0.75 256:0.81 259:0.21 260:0.84 261:0.76 265:0.74 266:0.38 267:0.44 268:0.80 276:0.34 283:0.82 285:0.62 297:0.36 300:0.33 +4 6:1.00 8:0.93 12:0.69 17:0.09 20:0.76 25:0.88 28:0.73 30:0.41 34:0.24 36:0.35 37:0.97 43:0.52 55:0.59 66:0.86 69:0.05 70:0.42 78:0.85 81:0.79 91:0.98 98:0.85 100:0.90 104:0.58 108:0.05 111:0.86 120:0.97 123:0.05 126:0.54 127:0.36 129:0.05 135:0.34 140:0.87 146:0.76 147:0.80 149:0.60 150:0.59 151:0.83 152:0.95 153:0.96 154:0.41 159:0.32 161:0.45 162:0.52 164:0.97 167:0.93 169:0.99 172:0.59 173:0.17 177:0.05 178:0.48 179:0.67 181:0.87 186:0.85 187:0.39 189:0.99 191:0.99 202:0.98 212:0.42 215:0.96 216:0.98 220:0.62 235:0.02 238:0.96 241:0.78 242:0.82 243:0.86 247:0.12 248:0.95 256:0.97 259:0.21 260:0.97 261:1.00 265:0.85 266:0.54 267:0.76 268:0.97 276:0.49 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.91 8:0.82 12:0.69 17:0.49 20:0.74 25:0.88 27:0.03 28:0.73 30:0.62 34:0.22 36:0.28 37:0.46 43:0.48 55:0.42 66:0.16 69:0.27 70:0.34 78:0.79 81:0.79 91:0.73 98:0.20 100:0.81 104:0.73 108:0.89 111:0.78 120:0.88 123:0.20 124:0.52 126:0.54 127:0.84 129:0.59 133:0.47 135:0.37 140:0.45 146:0.31 147:0.37 149:0.30 150:0.59 151:0.79 152:0.84 153:0.91 154:0.37 159:0.70 161:0.45 162:0.72 164:0.83 167:0.80 169:0.78 172:0.21 173:0.20 177:0.05 179:0.95 181:0.87 186:0.80 187:0.39 189:0.89 191:0.95 202:0.75 212:0.30 215:0.96 216:0.66 235:0.02 238:0.95 241:0.69 242:0.82 243:0.37 245:0.46 247:0.12 248:0.83 256:0.77 259:0.21 260:0.89 261:0.80 265:0.14 266:0.27 267:0.79 268:0.79 276:0.14 285:0.62 297:0.36 300:0.25 +4 6:1.00 8:0.98 12:0.69 17:0.06 20:0.74 25:0.88 27:0.37 28:0.73 30:0.30 34:0.26 36:0.29 37:0.97 43:0.49 55:0.84 66:0.26 69:0.21 70:0.66 78:0.93 81:0.79 91:1.00 98:0.67 100:0.99 104:0.54 108:0.65 111:0.95 120:0.99 123:0.16 124:0.58 126:0.54 127:0.84 129:0.59 133:0.47 135:0.39 140:0.45 146:0.53 147:0.59 149:0.54 150:0.59 151:0.91 152:0.73 153:1.00 154:0.38 159:0.35 161:0.45 162:0.53 163:0.75 164:0.99 167:0.98 169:0.95 172:0.37 173:0.39 177:0.05 179:0.83 181:0.87 186:1.00 189:1.00 191:1.00 202:0.99 212:0.23 215:0.96 216:0.92 235:0.02 238:0.99 241:0.87 242:0.82 243:0.45 245:0.64 247:0.12 248:0.99 256:0.99 259:0.21 260:1.00 261:0.76 265:0.59 266:0.54 267:0.93 268:0.99 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +3 6:0.91 8:0.86 12:0.69 17:0.61 20:0.93 21:0.78 25:0.88 27:0.19 28:0.73 30:0.45 32:0.80 34:0.40 36:0.30 37:0.38 43:0.48 55:0.42 66:0.13 69:0.41 70:0.50 78:0.76 91:0.99 98:0.24 100:0.79 104:0.72 108:0.83 111:0.75 120:0.97 123:0.31 124:0.69 127:0.91 129:0.81 133:0.67 135:0.78 140:0.90 145:0.83 146:0.31 147:0.37 149:0.36 151:0.82 152:0.87 153:0.95 154:0.37 159:0.55 161:0.45 162:0.56 164:0.97 167:0.98 169:0.77 172:0.21 173:0.41 177:0.05 178:0.50 179:0.94 181:0.87 186:0.76 189:0.92 191:0.98 195:0.75 202:0.96 212:0.23 216:0.87 220:0.62 235:0.95 238:0.91 241:0.88 242:0.82 243:0.34 245:0.45 247:0.12 248:0.96 256:0.96 259:0.21 260:0.97 261:0.78 265:0.16 266:0.38 267:0.73 268:0.96 276:0.25 283:0.60 285:0.62 300:0.33 +1 6:0.81 7:0.81 8:0.91 12:0.69 17:0.43 20:0.73 21:0.68 25:0.88 27:0.68 28:0.73 30:0.21 32:0.80 34:0.19 36:0.27 43:0.52 55:0.42 66:0.36 69:0.23 70:0.37 78:0.80 81:0.52 91:0.99 98:0.12 100:0.81 104:0.73 108:0.18 111:0.79 120:0.88 123:0.18 124:0.67 126:0.54 127:0.99 129:0.81 133:0.67 135:0.51 140:0.94 145:0.85 146:0.22 147:0.27 149:0.32 150:0.40 151:0.74 152:0.73 153:0.84 154:0.41 159:0.82 161:0.45 162:0.48 164:0.92 167:0.98 169:0.78 172:0.16 173:0.13 177:0.05 178:0.53 179:0.97 181:0.87 186:0.81 189:0.84 191:0.95 195:0.92 202:0.95 212:0.42 215:0.49 216:0.55 220:0.62 230:0.65 233:0.63 235:0.80 238:0.80 241:0.89 242:0.82 243:0.51 245:0.40 247:0.12 248:0.83 256:0.89 259:0.21 260:0.89 261:0.74 265:0.12 266:0.23 267:0.85 268:0.89 276:0.16 283:0.60 285:0.62 297:0.36 300:0.25 +1 7:0.81 12:0.69 17:0.66 21:0.54 25:0.88 27:0.72 28:0.73 30:0.66 32:0.80 34:0.17 36:0.47 43:0.40 55:0.71 66:0.19 69:0.54 70:0.59 81:0.61 91:0.80 98:0.62 104:0.54 108:0.84 120:0.61 123:0.40 124:0.52 126:0.54 127:0.78 129:0.59 133:0.47 135:0.75 140:0.45 145:0.74 146:0.80 147:0.83 149:0.64 150:0.45 151:0.56 153:0.72 154:0.30 159:0.69 161:0.45 162:0.79 164:0.71 167:0.97 172:0.68 173:0.43 177:0.05 179:0.53 181:0.87 191:0.82 195:0.84 202:0.60 212:0.48 215:0.58 216:0.06 220:0.96 230:0.87 233:0.76 235:0.60 241:0.86 242:0.82 243:0.40 245:0.84 247:0.12 248:0.55 256:0.58 259:0.21 260:0.62 265:0.62 266:0.75 267:0.65 268:0.65 276:0.66 285:0.62 297:0.36 300:0.55 +1 7:0.81 12:0.69 17:0.36 21:0.30 27:0.45 28:0.73 30:0.66 34:0.83 36:0.18 37:0.50 43:0.36 55:0.71 66:0.54 69:0.61 70:0.41 81:0.69 91:0.14 98:0.67 108:0.46 123:0.44 124:0.52 126:0.54 127:0.51 129:0.59 133:0.47 135:0.92 145:0.39 146:0.86 147:0.88 149:0.69 150:0.50 154:0.27 159:0.67 161:0.45 167:0.67 172:0.70 173:0.48 177:0.05 178:0.55 179:0.45 181:0.87 187:0.68 212:0.74 215:0.72 216:0.77 230:0.87 233:0.76 235:0.31 241:0.30 242:0.82 243:0.62 245:0.86 247:0.12 259:0.21 265:0.68 266:0.75 267:0.23 276:0.67 297:0.36 300:0.43 +0 6:1.00 8:0.78 12:0.69 17:0.38 20:0.76 21:0.64 25:0.88 28:0.73 30:0.21 32:0.80 34:0.36 36:0.21 37:0.97 43:0.40 55:0.99 66:0.16 69:0.55 70:0.69 78:0.77 81:0.70 91:0.96 98:0.23 100:0.80 104:0.58 108:0.42 111:0.77 120:0.98 123:0.41 124:0.90 126:0.54 127:0.94 129:0.96 133:0.90 135:0.21 140:0.80 145:0.63 146:0.57 147:0.63 149:0.39 150:0.52 151:0.83 152:0.95 153:0.96 154:0.31 159:0.90 161:0.45 162:0.57 164:0.98 167:0.74 169:0.99 172:0.21 173:0.24 177:0.05 178:0.42 179:0.81 181:0.87 186:0.78 187:0.21 189:0.86 191:0.80 195:0.97 202:0.97 212:0.13 215:0.53 216:0.98 235:0.80 238:0.89 241:0.59 242:0.82 243:0.37 245:0.49 247:0.12 248:0.97 256:0.97 259:0.21 260:0.98 261:1.00 265:0.25 266:0.80 267:0.88 268:0.97 276:0.44 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.87 8:0.78 12:0.69 17:0.70 20:0.80 25:0.88 27:0.40 28:0.73 30:0.62 34:0.22 36:0.30 37:0.36 43:0.48 55:0.42 66:0.16 69:0.27 70:0.34 78:0.74 81:0.79 91:0.96 98:0.36 100:0.77 104:0.74 108:0.69 111:0.74 120:0.90 123:0.20 124:0.52 126:0.54 127:0.80 129:0.59 133:0.47 135:0.65 140:0.80 146:0.31 147:0.37 149:0.37 150:0.59 151:0.80 152:0.77 153:0.92 154:0.37 159:0.33 161:0.45 162:0.54 164:0.91 167:0.95 169:0.75 172:0.21 173:0.45 177:0.05 178:0.42 179:0.95 181:0.87 186:0.75 189:0.89 191:0.95 202:0.90 212:0.30 215:0.96 216:0.79 220:0.62 235:0.02 238:0.90 241:0.81 242:0.82 243:0.37 245:0.46 247:0.12 248:0.86 256:0.88 259:0.21 260:0.90 261:0.75 265:0.21 266:0.27 267:0.73 268:0.88 276:0.17 285:0.62 297:0.36 300:0.25 +1 7:0.81 12:0.69 17:0.24 21:0.71 25:0.88 27:0.19 28:0.73 30:0.55 32:0.80 34:0.69 36:0.41 37:0.38 43:0.52 55:0.84 66:0.46 69:0.27 70:0.71 81:0.65 91:0.27 98:0.54 104:0.72 108:0.21 120:0.58 123:0.20 124:0.66 126:0.54 127:0.69 129:0.81 133:0.67 135:0.83 140:0.45 145:0.85 146:0.77 147:0.81 149:0.55 150:0.47 151:0.50 153:0.72 154:0.41 159:0.72 161:0.45 162:0.76 163:0.94 164:0.69 167:0.82 172:0.45 173:0.18 177:0.05 179:0.74 181:0.87 187:0.39 195:0.87 202:0.58 212:0.29 215:0.48 216:0.87 220:0.74 230:0.87 233:0.76 235:0.88 241:0.70 242:0.82 243:0.59 245:0.62 247:0.12 248:0.51 256:0.58 259:0.21 260:0.58 265:0.56 266:0.75 267:0.95 268:0.62 276:0.46 283:0.60 285:0.62 297:0.36 300:0.55 +1 6:0.92 8:0.88 12:0.69 17:0.38 20:0.88 25:0.88 27:0.06 28:0.73 30:0.76 34:0.22 36:0.49 37:0.87 43:0.48 55:0.84 66:0.80 69:0.27 70:0.28 78:0.75 81:0.79 91:0.88 98:0.89 100:0.79 104:0.72 108:0.21 111:0.75 120:0.90 123:0.20 126:0.54 127:0.43 129:0.05 135:0.32 140:0.45 146:0.64 147:0.69 149:0.49 150:0.59 151:0.82 152:0.83 153:0.94 154:0.37 159:0.24 161:0.45 162:0.71 163:0.86 164:0.91 167:0.88 169:0.76 172:0.45 173:0.51 177:0.05 179:0.84 181:0.87 186:0.76 187:0.39 189:0.93 191:0.96 202:0.86 212:0.37 215:0.96 216:0.31 220:0.62 235:0.02 238:0.92 241:0.75 242:0.82 243:0.82 247:0.12 248:0.86 256:0.87 259:0.21 260:0.91 261:0.77 265:0.89 266:0.38 267:0.36 268:0.88 276:0.37 283:0.82 285:0.62 297:0.36 300:0.25 +0 1:0.74 11:0.83 12:0.37 17:0.40 18:0.75 21:0.40 27:0.45 28:0.91 29:0.86 30:0.85 32:0.64 34:0.75 36:0.19 37:0.50 39:0.95 43:0.82 45:0.73 60:0.87 64:0.77 66:0.49 69:0.68 70:0.40 71:0.56 74:0.71 81:0.58 83:0.91 85:0.85 86:0.86 91:0.06 98:0.40 101:0.87 108:0.52 114:0.62 122:0.57 123:0.50 124:0.66 126:0.54 127:0.36 129:0.05 133:0.66 135:0.61 139:0.88 145:0.50 146:0.51 147:0.57 149:0.84 150:0.76 154:0.77 155:0.67 158:0.91 159:0.48 161:0.78 165:0.93 167:0.50 172:0.48 173:0.67 176:0.73 177:0.70 178:0.55 179:0.62 181:0.97 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.48 215:0.42 216:0.77 219:0.95 222:0.25 235:0.60 241:0.18 242:0.28 243:0.60 245:0.64 247:0.67 253:0.48 259:0.21 265:0.42 266:0.63 267:0.28 271:0.34 276:0.46 279:0.86 283:0.78 290:0.62 292:0.91 297:0.36 300:0.43 +3 1:0.74 6:0.96 7:0.81 8:0.91 9:0.80 11:0.92 12:0.37 17:0.43 18:0.97 20:0.97 21:0.30 27:0.21 28:0.91 29:0.86 30:0.72 31:0.97 34:0.58 36:0.75 37:0.74 39:0.95 41:0.93 43:0.97 45:0.98 60:0.87 64:0.77 66:0.12 69:0.15 70:0.59 71:0.56 74:0.96 78:0.87 79:0.97 81:0.64 83:0.91 85:0.95 86:0.99 91:0.51 96:0.94 97:0.89 98:0.55 100:0.94 101:0.97 104:0.84 106:0.81 108:0.94 111:0.90 114:0.65 117:0.86 120:0.86 121:0.90 122:0.80 123:0.61 124:0.81 126:0.54 127:0.69 129:0.89 133:0.78 135:0.24 137:0.97 139:0.99 140:0.45 146:0.34 147:0.40 149:0.97 150:0.79 151:0.92 152:0.91 153:0.88 154:0.97 155:0.67 158:0.92 159:0.88 161:0.78 162:0.68 164:0.83 165:0.93 167:0.53 169:0.92 172:0.89 173:0.08 176:0.73 177:0.70 179:0.14 181:0.97 186:0.87 187:0.39 189:0.97 192:0.87 196:0.99 201:0.93 202:0.79 204:0.89 206:0.81 208:0.64 212:0.68 215:0.44 216:0.95 219:0.95 220:0.87 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 238:0.95 241:0.32 242:0.27 243:0.19 245:0.98 247:0.93 248:0.86 253:0.96 255:0.95 256:0.81 259:0.21 260:0.85 261:0.94 265:0.51 266:0.80 267:0.23 268:0.81 271:0.34 276:0.95 279:0.86 281:0.91 283:0.82 284:0.96 285:0.62 290:0.65 292:0.95 297:0.36 300:0.94 +3 1:0.74 6:0.93 7:0.81 8:0.81 9:0.80 11:0.92 12:0.37 17:0.85 18:0.94 20:0.88 21:0.68 27:0.12 28:0.91 29:0.86 30:0.73 31:0.89 32:0.80 34:0.83 36:0.99 37:0.80 39:0.95 41:0.90 43:0.77 44:0.93 45:0.96 64:0.77 66:0.71 69:0.80 70:0.59 71:0.56 74:0.90 77:0.89 78:0.81 79:0.89 81:0.45 83:0.91 85:0.90 86:0.97 91:0.27 96:0.73 98:0.63 100:0.82 101:0.97 108:0.77 111:0.80 114:0.56 120:0.60 121:0.90 122:0.80 123:0.75 124:0.47 126:0.54 127:0.58 129:0.59 133:0.66 135:0.78 137:0.89 139:0.97 140:0.45 145:0.92 146:0.56 147:0.62 149:0.93 150:0.70 151:0.61 152:0.89 153:0.48 154:0.70 155:0.67 159:0.89 161:0.78 162:0.86 164:0.73 165:0.93 167:0.51 169:0.79 172:0.92 173:0.52 176:0.73 177:0.70 179:0.22 181:0.97 186:0.81 187:0.68 189:0.95 192:0.87 195:0.97 196:0.94 201:0.93 202:0.58 204:0.89 208:0.64 212:0.51 215:0.37 216:0.75 220:0.96 222:0.25 230:0.87 233:0.76 235:0.88 238:0.86 241:0.21 242:0.42 243:0.23 245:0.91 247:0.88 248:0.60 253:0.63 255:0.95 256:0.64 259:0.21 260:0.60 261:0.85 265:0.64 266:0.80 267:0.28 268:0.66 271:0.34 276:0.84 281:0.91 282:0.88 284:0.94 285:0.62 290:0.56 297:0.36 299:0.97 300:0.70 +2 1:0.69 6:0.93 8:0.84 9:0.80 11:0.83 12:0.37 17:0.67 18:0.93 20:0.98 21:0.22 27:0.18 28:0.91 29:0.86 30:0.56 31:0.95 34:0.25 36:0.89 37:0.63 39:0.95 41:0.94 43:0.76 45:0.97 66:0.23 69:0.83 70:0.67 71:0.56 74:0.87 78:0.88 79:0.95 81:0.57 83:0.91 85:0.97 86:0.97 91:0.90 96:0.86 97:0.89 98:0.57 99:0.99 100:0.92 101:0.97 104:0.58 108:0.87 111:0.89 114:0.67 120:0.80 122:0.57 123:0.86 124:0.96 126:0.44 127:0.98 129:0.99 133:0.96 135:0.76 137:0.95 139:0.96 140:0.80 146:0.42 147:0.44 149:0.94 150:0.59 151:0.87 152:0.91 153:0.48 154:0.68 155:0.67 158:0.78 159:0.94 161:0.78 162:0.70 164:0.83 165:0.70 167:0.84 169:0.90 172:0.92 173:0.48 176:0.66 177:0.05 179:0.13 181:0.97 186:0.88 189:0.94 192:0.87 196:0.98 201:0.68 202:0.79 208:0.64 212:0.42 215:0.51 216:0.18 220:0.82 222:0.25 235:0.52 238:0.92 241:0.85 242:0.22 243:0.06 245:0.96 247:0.90 248:0.80 253:0.88 255:0.95 256:0.81 257:0.84 259:0.21 260:0.78 261:0.94 265:0.59 266:0.94 267:0.52 268:0.82 271:0.34 276:0.96 279:0.82 283:0.78 284:0.97 285:0.62 290:0.67 297:1.00 300:0.84 +2 1:0.74 9:0.80 11:0.83 12:0.37 17:0.57 18:0.93 21:0.30 22:0.93 27:0.06 28:0.91 29:0.86 30:0.90 31:0.95 34:0.47 36:0.54 37:0.84 39:0.95 41:0.88 43:0.71 45:0.66 47:0.93 60:0.87 64:0.77 66:0.86 69:0.87 70:0.27 71:0.56 74:0.93 79:0.94 81:0.64 83:0.91 85:0.99 86:0.98 91:0.15 96:0.84 98:0.81 101:0.87 104:0.84 106:0.81 108:0.74 114:0.65 117:0.86 120:0.74 122:0.57 123:0.72 124:0.38 126:0.54 127:0.43 129:0.05 133:0.37 135:0.34 137:0.95 139:0.98 140:0.45 145:0.92 146:0.95 147:0.96 149:0.98 150:0.79 151:0.92 153:0.72 154:0.61 155:0.67 158:0.92 159:0.72 161:0.78 162:0.86 164:0.69 165:0.93 167:0.58 172:0.95 173:0.79 176:0.73 177:0.70 179:0.17 181:0.97 187:0.39 192:0.87 196:0.98 201:0.93 202:0.53 206:0.81 208:0.64 212:0.54 215:0.44 216:0.70 219:0.95 222:0.25 232:0.89 235:0.52 241:0.52 242:0.44 243:0.86 245:0.91 247:0.67 248:0.81 253:0.88 255:0.95 256:0.58 259:0.21 260:0.75 262:0.93 265:0.81 266:0.54 267:0.28 268:0.62 271:0.34 276:0.90 279:0.86 283:0.82 284:0.93 285:0.62 290:0.65 292:0.95 297:0.36 300:0.43 +3 1:0.74 6:0.96 7:0.81 8:0.83 9:0.80 11:0.92 12:0.37 17:0.47 18:0.97 20:0.78 21:0.30 22:0.93 27:0.21 28:0.91 29:0.86 30:0.73 31:0.99 34:0.68 36:0.69 37:0.74 39:0.95 41:0.95 43:0.97 45:0.98 47:0.93 60:0.87 64:0.77 66:0.16 69:0.15 70:0.58 71:0.56 74:0.96 78:0.90 79:0.99 81:0.64 83:0.91 85:0.96 86:0.99 91:0.65 96:0.96 97:0.94 98:0.54 100:0.95 101:0.97 104:0.84 106:0.81 108:0.95 111:0.92 114:0.65 117:0.86 120:0.91 121:0.90 122:0.80 123:0.87 124:0.90 126:0.54 127:0.76 129:0.95 133:0.90 135:0.18 137:0.99 139:0.99 140:0.45 146:0.32 147:0.38 149:0.97 150:0.79 151:0.98 152:0.91 153:0.94 154:0.97 155:0.67 158:0.92 159:0.90 161:0.78 162:0.70 164:0.85 165:0.93 167:0.47 169:0.92 172:0.89 173:0.08 176:0.73 177:0.70 179:0.14 181:0.97 186:0.90 187:0.39 189:0.94 191:0.73 192:0.87 196:1.00 201:0.93 202:0.82 204:0.89 206:0.81 208:0.64 212:0.59 215:0.44 216:0.95 219:0.95 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 238:0.94 241:0.02 242:0.31 243:0.16 245:0.97 247:0.93 248:0.93 253:0.98 255:0.95 256:0.84 259:0.21 260:0.90 261:0.94 262:0.93 265:0.50 266:0.84 267:0.17 268:0.85 271:0.34 276:0.96 279:0.86 281:0.91 283:0.82 284:0.98 285:0.62 290:0.65 292:0.95 297:0.36 300:0.84 +1 1:0.69 11:0.92 12:0.37 17:0.38 18:0.92 21:0.47 27:0.45 28:0.91 29:0.86 30:0.72 32:0.69 34:0.79 36:0.43 37:0.50 39:0.95 43:0.77 45:0.95 66:0.63 69:0.69 70:0.71 71:0.56 74:0.88 77:0.70 81:0.36 83:0.60 85:0.88 86:0.95 91:0.02 97:0.89 98:0.67 99:0.83 101:0.97 108:0.74 114:0.59 122:0.80 123:0.72 124:0.65 126:0.44 127:0.59 129:0.59 133:0.77 135:0.81 139:0.93 145:0.42 146:0.62 147:0.67 149:0.92 150:0.53 154:0.70 155:0.58 158:0.78 159:0.78 161:0.78 165:0.70 167:0.56 172:0.88 173:0.51 176:0.66 177:0.70 178:0.55 179:0.25 181:0.97 187:0.68 192:0.59 195:0.91 201:0.68 208:0.54 212:0.54 215:0.41 216:0.77 222:0.25 235:0.60 241:0.44 242:0.36 243:0.25 245:0.88 247:0.90 253:0.49 259:0.21 265:0.68 266:0.63 267:0.36 271:0.34 276:0.85 279:0.82 282:0.77 283:0.78 290:0.59 297:0.36 300:0.94 +4 1:0.69 6:0.92 7:0.72 8:0.81 9:0.80 11:0.92 12:0.37 17:0.57 18:0.81 20:0.99 27:0.17 28:0.91 29:0.86 30:0.53 31:0.99 32:0.69 34:0.52 36:0.92 37:0.69 39:0.95 41:0.96 43:0.82 45:0.88 66:0.54 69:0.12 70:0.56 71:0.56 74:0.80 76:0.85 77:0.70 78:0.92 79:0.99 81:0.75 83:0.91 85:0.85 86:0.89 91:0.80 96:0.97 98:0.77 99:0.83 100:0.97 101:0.97 104:0.58 108:0.10 111:0.95 114:0.95 120:0.95 123:0.10 124:0.52 126:0.44 127:0.55 129:0.59 133:0.46 135:0.24 137:0.99 138:0.98 139:0.85 140:0.80 145:0.82 146:0.88 147:0.90 149:0.90 150:0.71 151:0.97 152:0.91 153:0.93 154:0.77 155:0.67 159:0.62 161:0.78 162:0.77 164:0.90 165:0.70 167:0.85 169:0.95 172:0.79 173:0.15 176:0.66 177:0.70 179:0.35 181:0.97 186:0.91 189:0.93 191:0.80 192:0.87 195:0.81 196:0.98 201:0.68 202:0.86 204:0.85 208:0.64 212:0.55 215:0.96 216:0.29 222:0.25 230:0.75 232:0.77 233:0.76 235:0.05 238:0.96 241:0.89 242:0.33 243:0.63 245:0.91 247:0.84 248:0.95 253:0.98 255:0.95 256:0.89 259:0.21 260:0.94 261:0.97 265:0.77 266:0.75 267:0.17 268:0.89 271:0.34 276:0.78 282:0.77 284:0.98 285:0.62 290:0.95 297:0.36 300:0.55 +2 1:0.74 7:0.81 9:0.80 11:0.92 12:0.37 17:0.51 18:0.98 21:0.30 22:0.93 27:0.50 28:0.91 29:0.86 30:0.75 31:0.87 32:0.80 34:0.55 36:0.74 37:0.60 39:0.95 41:0.82 43:0.88 44:0.93 45:0.98 47:0.93 60:0.87 64:0.77 66:0.12 69:0.54 70:0.55 71:0.56 74:0.98 77:0.70 79:0.87 81:0.64 83:0.91 85:0.98 86:0.99 91:0.08 96:0.69 97:0.94 98:0.53 101:0.97 104:0.84 106:0.81 108:0.95 114:0.65 117:0.86 120:0.55 121:0.90 122:0.80 123:0.91 124:0.90 126:0.54 127:0.83 129:0.95 133:0.90 135:0.32 137:0.87 139:1.00 140:0.45 145:0.74 146:0.53 147:0.59 149:0.99 150:0.79 151:0.52 153:0.48 154:0.86 155:0.67 158:0.92 159:0.91 161:0.78 162:0.86 164:0.57 165:0.93 167:0.45 172:0.94 173:0.21 176:0.73 177:0.70 179:0.12 181:0.97 187:0.39 192:0.87 195:0.98 196:0.91 201:0.93 202:0.36 204:0.89 206:0.81 208:0.64 212:0.71 215:0.44 216:0.86 219:0.95 220:0.87 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 242:0.27 243:0.16 245:0.98 247:0.94 248:0.51 253:0.56 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.51 266:0.80 267:0.19 268:0.46 271:0.34 276:0.97 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.65 292:0.95 297:0.36 299:0.88 300:0.84 +0 1:0.74 6:0.89 7:0.81 8:0.67 9:0.80 11:0.92 12:0.37 17:0.76 18:0.97 20:0.75 21:0.05 22:0.93 27:0.27 28:0.91 29:0.86 30:0.90 31:0.95 32:0.80 34:0.24 36:0.68 37:0.35 39:0.95 41:0.88 43:0.95 44:0.93 45:0.91 47:0.93 60:1.00 64:0.77 66:0.43 69:0.19 70:0.30 71:0.56 74:0.98 77:0.70 78:0.83 79:0.95 81:0.85 83:0.91 85:0.99 86:0.99 91:0.27 96:0.85 98:0.76 100:0.83 101:0.97 104:0.79 106:0.81 108:0.82 111:0.81 114:0.89 117:0.86 120:0.75 121:0.99 122:0.57 123:0.81 124:0.71 126:0.54 127:0.88 129:0.81 133:0.67 135:0.65 137:0.95 139:1.00 140:0.45 145:0.70 146:0.89 147:0.91 149:0.99 150:0.91 151:0.87 152:0.74 153:0.48 154:0.95 155:0.67 158:0.92 159:0.84 161:0.78 162:0.86 164:0.69 165:0.93 167:0.48 169:0.83 172:0.95 173:0.11 176:0.73 177:0.70 179:0.14 181:0.97 186:0.83 187:0.21 192:0.87 195:0.93 196:0.98 201:0.93 202:0.53 204:0.89 206:0.81 208:0.64 212:0.58 215:0.78 216:0.70 219:0.95 220:0.62 222:0.25 230:0.87 232:0.85 233:0.76 235:0.22 241:0.10 242:0.36 243:0.31 245:0.99 247:0.91 248:0.83 253:0.90 255:0.95 256:0.58 259:0.21 260:0.76 261:0.77 262:0.93 265:0.76 266:0.54 267:0.18 268:0.62 271:0.34 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 290:0.89 292:0.95 297:0.36 299:0.88 300:0.70 +3 1:0.74 7:0.81 9:0.80 11:0.92 12:0.37 17:0.64 18:0.97 21:0.30 22:0.93 27:0.21 28:0.91 29:0.86 30:0.73 31:0.94 34:0.58 36:0.68 37:0.74 39:0.95 41:0.82 43:0.97 45:0.98 47:0.93 64:0.77 66:0.16 69:0.15 70:0.57 71:0.56 74:0.96 79:0.93 81:0.64 83:0.91 85:0.96 86:0.99 91:0.18 96:0.87 98:0.54 101:0.97 104:0.84 106:0.81 108:0.95 114:0.65 117:0.86 120:0.78 121:0.90 122:0.80 123:0.87 124:0.90 126:0.54 127:0.76 129:0.95 133:0.90 135:0.18 137:0.94 139:0.99 140:0.80 146:0.34 147:0.40 149:0.98 150:0.79 151:0.87 153:0.48 154:0.97 155:0.67 159:0.90 161:0.78 162:0.86 164:0.65 165:0.93 167:0.46 172:0.89 173:0.08 176:0.73 177:0.70 179:0.14 181:0.97 187:0.39 192:0.87 196:0.98 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.60 215:0.44 216:0.95 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 241:0.01 242:0.30 243:0.19 245:0.97 247:0.93 248:0.77 253:0.91 255:0.95 256:0.58 259:0.21 260:0.78 262:0.93 265:0.50 266:0.84 267:0.18 268:0.59 271:0.34 276:0.96 281:0.91 284:0.89 285:0.62 290:0.65 297:0.36 300:0.84 +3 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.92 12:0.37 17:0.81 18:0.94 20:0.88 21:0.68 27:0.12 28:0.91 29:0.86 30:0.73 31:0.98 32:0.80 34:0.67 36:0.96 37:0.80 39:0.95 41:0.94 43:0.78 44:0.93 45:0.96 60:0.95 64:0.77 66:0.71 69:0.79 70:0.59 71:0.56 74:0.92 77:0.89 78:0.77 79:0.98 81:0.45 83:0.91 85:0.90 86:0.97 91:0.42 96:0.93 97:0.97 98:0.63 100:0.80 101:0.97 108:0.76 111:0.77 114:0.56 120:0.88 121:0.90 122:0.80 123:0.75 124:0.47 126:0.54 127:0.60 129:0.59 133:0.66 135:0.61 137:0.98 139:0.98 140:0.45 145:0.92 146:0.56 147:0.62 149:0.94 150:0.70 151:0.97 152:0.89 153:0.90 154:0.71 155:0.67 158:0.92 159:0.89 161:0.78 162:0.84 164:0.84 165:0.93 167:0.53 169:0.79 172:0.92 173:0.50 176:0.73 177:0.70 179:0.22 181:0.97 186:0.78 187:0.87 189:0.95 191:0.73 192:0.87 195:0.97 196:0.99 201:0.93 202:0.74 204:0.89 208:0.64 212:0.51 215:0.37 216:0.75 219:0.95 222:0.25 230:0.87 233:0.76 235:0.88 238:0.88 241:0.32 242:0.42 243:0.23 245:0.91 247:0.88 248:0.92 253:0.96 255:0.95 256:0.79 259:0.21 260:0.88 261:0.85 265:0.64 266:0.80 267:0.23 268:0.80 271:0.34 276:0.83 279:0.86 281:0.91 282:0.88 283:0.82 284:0.97 285:0.62 290:0.56 292:0.95 297:0.36 299:0.97 300:0.70 +0 6:0.98 8:0.98 12:0.06 17:0.04 20:0.90 21:0.78 27:0.06 28:0.64 30:0.45 34:0.40 36:0.85 37:0.82 43:0.32 55:0.98 66:0.13 69:0.71 70:0.33 78:0.80 91:0.98 98:0.13 100:0.86 104:0.73 108:0.94 111:0.81 120:0.98 123:0.94 124:0.89 127:0.48 129:0.95 133:0.87 135:0.24 140:0.94 145:0.76 146:0.05 147:0.05 149:0.24 151:0.84 152:0.84 153:0.98 154:0.23 159:0.86 161:0.50 162:0.46 163:0.98 164:0.99 167:0.94 169:0.84 172:0.10 173:0.42 177:0.05 178:0.53 179:0.91 181:0.82 186:0.81 189:0.98 191:0.99 202:1.00 212:0.23 216:0.92 220:0.62 235:0.88 238:0.96 241:0.82 242:0.82 243:0.21 245:0.40 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.84 265:0.14 266:0.38 267:0.59 268:0.99 271:0.67 276:0.32 285:0.62 300:0.25 +1 6:0.99 8:1.00 12:0.06 17:0.02 20:0.75 21:0.76 27:0.12 28:0.64 30:0.91 32:0.80 34:0.34 36:0.86 37:0.80 43:0.19 55:0.71 66:0.49 69:0.94 70:0.13 78:0.84 81:0.69 91:0.99 98:0.19 100:0.93 104:0.58 108:0.92 111:0.88 120:0.98 123:0.91 124:0.48 126:0.54 127:0.18 129:0.59 133:0.47 135:0.44 140:0.80 145:0.92 146:0.05 147:0.05 149:0.14 150:0.50 151:0.84 152:0.74 153:0.98 154:0.13 159:0.44 161:0.50 162:0.59 163:0.97 164:0.99 167:0.98 169:0.91 172:0.16 173:0.95 177:0.05 178:0.42 179:0.86 181:0.82 186:0.84 189:0.99 191:0.95 195:0.90 202:0.99 212:0.61 215:0.46 216:0.75 220:0.74 235:0.95 238:0.97 241:0.96 242:0.82 243:0.29 245:0.39 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.76 265:0.21 266:0.17 267:0.82 268:0.99 271:0.67 276:0.15 283:0.82 285:0.62 297:0.36 300:0.13 +2 7:0.81 12:0.06 17:0.22 21:0.74 25:0.88 27:0.03 28:0.64 30:0.21 32:0.80 34:0.52 36:0.96 37:0.95 43:0.52 55:0.84 66:0.43 69:0.27 70:0.82 81:0.72 91:0.75 98:0.52 104:0.58 108:0.88 120:0.94 123:0.87 124:0.87 126:0.54 127:0.95 129:0.96 133:0.90 135:0.81 140:0.45 145:0.83 146:0.05 147:0.05 149:0.63 150:0.53 151:0.84 153:0.97 154:0.41 159:0.87 161:0.50 162:0.70 163:0.88 164:0.96 167:0.80 172:0.70 173:0.12 177:0.05 179:0.45 181:0.82 187:0.21 195:0.95 202:0.93 212:0.14 215:0.46 216:0.86 220:0.62 230:0.87 233:0.76 235:0.88 241:0.70 242:0.82 243:0.08 245:0.75 247:0.12 248:0.92 256:0.94 259:0.21 260:0.95 265:0.54 266:0.92 267:0.36 268:0.95 271:0.67 276:0.74 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.96 7:0.81 8:0.93 12:0.06 17:0.01 20:0.98 21:0.74 25:0.88 27:0.03 28:0.64 30:0.17 32:0.80 34:0.28 36:0.90 37:0.95 43:0.52 55:0.71 66:0.34 69:0.27 70:0.98 78:0.94 81:0.72 91:0.99 98:0.37 100:1.00 104:0.58 108:0.78 111:0.99 120:1.00 123:0.77 124:0.77 126:0.54 127:0.99 129:0.89 133:0.78 135:0.82 140:0.45 145:0.83 146:0.05 147:0.05 149:0.45 150:0.53 151:0.91 152:0.99 153:1.00 154:0.41 159:0.79 161:0.50 162:0.68 163:0.88 164:1.00 167:0.98 169:0.99 172:0.37 173:0.16 177:0.05 179:0.80 181:0.82 186:0.94 189:0.97 191:0.98 195:0.90 202:0.99 212:0.19 215:0.46 216:0.86 230:0.87 233:0.76 235:0.88 238:0.99 241:0.94 242:0.82 243:0.13 245:0.57 247:0.12 248:0.99 256:0.99 259:0.21 260:1.00 261:1.00 265:0.39 266:0.80 267:0.91 268:1.00 271:0.67 276:0.42 283:0.82 285:0.62 297:0.36 300:0.84 +2 6:0.96 7:0.81 8:0.81 12:0.06 17:0.07 20:0.74 21:0.74 25:0.88 27:0.03 28:0.64 30:0.09 32:0.80 34:0.77 36:0.74 37:0.95 43:0.52 55:0.52 66:0.25 69:0.27 70:0.94 78:0.83 81:0.72 91:0.94 98:0.33 100:0.89 104:0.58 108:0.76 111:0.86 120:0.97 123:0.74 124:0.88 126:0.54 127:0.95 129:0.95 133:0.87 135:0.83 140:0.45 145:0.83 146:0.05 147:0.05 149:0.51 150:0.53 151:0.84 152:0.99 153:0.97 154:0.41 159:0.79 161:0.50 162:0.58 163:0.88 164:0.98 167:0.86 169:0.99 172:0.37 173:0.16 177:0.05 179:0.71 181:0.82 186:0.80 187:0.21 191:0.94 195:0.90 202:0.98 212:0.22 215:0.46 216:0.86 220:0.62 230:0.87 233:0.76 235:0.88 238:0.94 241:0.75 242:0.82 243:0.11 245:0.60 247:0.12 248:0.95 256:0.98 259:0.21 260:0.97 261:1.00 265:0.35 266:0.84 267:0.82 268:0.98 271:0.67 276:0.54 283:0.60 285:0.62 297:0.36 300:0.70 +1 8:0.72 12:0.06 17:0.36 21:0.76 27:0.45 28:0.64 30:0.62 34:0.66 36:0.46 37:0.50 43:0.32 55:0.84 66:0.52 69:0.71 70:0.64 81:0.69 91:0.04 98:0.61 108:0.54 123:0.52 124:0.65 126:0.54 127:0.38 129:0.81 133:0.67 135:0.87 145:0.45 146:0.88 147:0.90 149:0.65 150:0.50 154:0.23 159:0.72 161:0.50 167:0.69 172:0.65 173:0.54 177:0.05 178:0.55 179:0.45 181:0.82 187:0.68 212:0.19 215:0.46 216:0.77 235:0.95 241:0.46 242:0.82 243:0.62 245:0.76 247:0.12 259:0.21 265:0.62 266:0.84 267:0.28 271:0.67 276:0.66 283:0.60 297:0.36 300:0.55 +1 6:0.96 7:0.81 8:0.96 12:0.06 20:0.81 21:0.77 25:0.88 27:0.03 28:0.64 30:0.30 32:0.80 34:0.49 36:0.72 37:0.95 43:0.52 55:0.92 66:0.08 69:0.30 70:0.80 78:0.84 81:0.65 91:0.97 98:0.20 100:0.93 104:0.58 108:0.84 111:0.87 120:0.93 123:0.90 124:0.90 126:0.54 127:0.93 129:0.96 133:0.90 135:0.37 140:0.94 145:0.80 146:0.05 147:0.05 149:0.42 150:0.48 151:0.80 152:0.99 153:0.92 154:0.41 159:0.76 161:0.50 162:0.50 164:0.98 167:0.95 169:0.99 172:0.21 173:0.19 177:0.05 178:0.52 179:0.83 181:0.82 186:0.84 187:0.21 189:0.95 191:0.94 195:0.87 202:0.98 212:0.13 215:0.44 216:0.86 220:0.74 230:0.87 233:0.76 235:0.97 238:0.97 241:0.85 242:0.82 243:0.15 245:0.48 247:0.12 248:0.90 256:0.97 259:0.21 260:0.94 261:1.00 265:0.19 266:0.75 267:0.82 268:0.97 271:0.67 276:0.43 283:0.82 285:0.62 297:0.36 300:0.55 +0 6:0.92 7:0.81 8:0.95 12:0.06 17:0.28 20:0.95 21:0.74 27:0.13 28:0.64 30:0.62 32:0.80 34:0.22 36:0.96 37:0.67 43:0.23 55:0.99 66:0.30 69:0.88 70:0.34 78:0.76 81:0.72 91:0.95 98:0.14 100:0.81 104:0.58 108:0.93 111:0.76 120:0.90 123:0.93 124:0.71 126:0.54 127:0.44 129:0.81 133:0.67 135:0.63 140:0.94 145:0.82 146:0.19 147:0.24 149:0.22 150:0.53 151:0.76 152:0.88 153:0.87 154:0.16 159:0.77 161:0.50 162:0.51 163:0.90 164:0.94 167:0.96 169:0.79 172:0.16 173:0.78 177:0.05 178:0.53 179:0.93 181:0.82 186:0.77 189:0.93 191:0.93 195:0.93 202:0.95 212:0.30 215:0.46 216:0.61 230:0.87 233:0.76 235:0.88 238:0.94 241:0.86 242:0.82 243:0.37 245:0.43 247:0.12 248:0.85 256:0.92 259:0.21 260:0.90 261:0.81 265:0.15 266:0.27 267:0.98 268:0.92 271:0.67 276:0.24 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.92 7:0.81 8:0.81 12:0.06 17:0.32 20:0.99 21:0.30 27:0.21 28:0.64 30:0.20 34:0.33 36:0.86 37:0.74 43:0.52 55:0.71 66:0.19 69:0.10 70:0.83 78:0.77 81:0.90 91:0.88 98:0.46 100:0.82 104:0.84 106:0.81 108:0.98 111:0.77 120:0.98 123:0.98 124:0.97 126:0.54 127:0.95 129:0.99 133:0.97 135:0.24 140:0.45 146:0.87 147:0.89 149:0.89 150:0.82 151:0.85 152:0.92 153:0.99 154:0.41 159:0.97 161:0.50 162:0.77 164:0.96 167:0.76 169:0.80 172:0.96 173:0.05 177:0.05 179:0.10 181:0.82 186:0.77 187:0.39 189:0.93 191:0.82 202:0.92 206:0.81 212:0.39 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.95 241:0.65 242:0.82 243:0.15 245:0.98 247:0.12 248:0.97 256:0.94 259:0.21 260:0.98 261:0.81 265:0.48 266:0.96 267:0.44 268:0.94 271:0.67 276:0.99 283:0.82 285:0.62 297:0.36 300:0.84 +1 12:0.06 17:0.28 21:0.71 27:0.72 28:0.64 30:0.21 32:0.80 34:0.37 36:0.77 43:0.52 55:0.45 66:0.36 69:0.25 70:0.37 81:0.75 91:0.75 98:0.16 104:0.58 108:0.84 120:0.80 123:0.83 124:0.67 126:0.54 127:0.94 129:0.81 133:0.67 135:0.19 140:0.95 145:0.77 146:0.05 147:0.05 149:0.29 150:0.56 151:0.71 153:0.72 154:0.41 159:0.59 161:0.50 162:0.48 163:0.88 164:0.90 167:0.98 172:0.16 173:0.26 177:0.05 178:0.53 179:0.97 181:0.82 195:0.74 202:0.94 212:0.42 215:0.48 216:0.08 220:0.62 235:0.84 241:0.95 242:0.82 243:0.26 245:0.40 247:0.12 248:0.72 256:0.87 259:0.21 260:0.81 265:0.18 266:0.23 267:0.18 268:0.87 271:0.67 276:0.17 283:0.60 285:0.62 297:0.36 300:0.25 +1 12:0.06 17:0.08 21:0.74 27:0.72 28:0.64 30:0.21 32:0.80 34:0.25 36:0.79 43:0.52 55:0.45 66:0.36 69:0.27 70:0.37 81:0.72 91:0.76 98:0.16 104:0.58 108:0.85 120:0.93 123:0.84 124:0.67 126:0.54 127:0.94 129:0.81 133:0.67 135:0.34 140:0.92 145:0.77 146:0.05 147:0.05 149:0.29 150:0.53 151:0.80 153:0.93 154:0.41 159:0.59 161:0.50 162:0.49 163:0.88 164:0.97 167:0.98 172:0.16 173:0.27 177:0.05 178:0.51 179:0.97 181:0.82 195:0.75 202:0.98 212:0.42 215:0.46 216:0.16 235:0.88 241:0.97 242:0.82 243:0.26 245:0.40 247:0.12 248:0.90 256:0.96 259:0.21 260:0.93 265:0.18 266:0.23 267:0.28 268:0.96 271:0.67 276:0.17 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.96 8:0.92 12:0.06 17:0.06 20:0.84 21:0.76 25:0.88 27:0.03 28:0.64 30:0.21 32:0.80 34:0.56 36:0.58 37:0.95 43:0.52 55:0.71 66:0.24 69:0.29 70:0.92 78:0.77 81:0.69 91:0.94 98:0.33 100:0.82 104:0.58 108:0.78 111:0.77 120:0.94 123:0.77 124:0.84 126:0.54 127:0.96 129:0.93 133:0.84 135:0.66 140:0.80 145:0.91 146:0.05 147:0.05 149:0.45 150:0.50 151:0.79 152:0.99 153:0.91 154:0.41 159:0.65 161:0.50 162:0.52 163:0.88 164:0.96 167:0.93 169:0.99 172:0.27 173:0.25 177:0.05 178:0.42 179:0.81 181:0.82 186:0.77 187:0.21 189:0.93 191:0.94 195:0.80 202:0.96 212:0.22 215:0.46 216:0.86 235:0.95 238:0.95 241:0.79 242:0.82 243:0.14 245:0.53 247:0.12 248:0.92 256:0.95 259:0.21 260:0.94 261:1.00 265:0.36 266:0.71 267:0.76 268:0.95 271:0.67 276:0.45 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.92 7:0.81 8:0.96 12:0.06 17:0.78 20:0.77 21:0.78 27:0.10 28:0.64 34:0.55 36:0.42 37:0.74 78:0.76 91:0.98 100:0.80 104:0.75 111:0.76 120:0.89 135:0.23 140:0.80 145:0.87 151:0.71 152:0.75 153:0.72 161:0.50 162:0.51 164:0.95 167:0.98 169:0.78 175:0.75 178:0.42 181:0.82 186:0.77 189:0.93 191:0.97 202:0.95 216:0.55 220:0.62 230:0.65 233:0.63 235:0.74 238:0.91 241:0.97 244:0.61 248:0.85 256:0.93 257:0.65 259:0.21 260:0.90 261:0.75 267:0.52 268:0.93 271:0.67 277:0.69 285:0.62 +2 8:0.73 12:0.06 17:0.34 21:0.74 25:0.88 27:0.03 28:0.64 30:0.10 34:0.40 36:0.37 37:0.95 43:0.52 55:0.71 66:0.45 69:0.27 70:0.94 81:0.72 91:0.82 98:0.48 104:0.58 108:0.76 120:0.94 123:0.74 124:0.80 126:0.54 127:0.87 129:0.93 133:0.84 135:0.60 140:0.80 146:0.05 147:0.05 149:0.52 150:0.53 151:0.81 153:0.94 154:0.41 159:0.79 161:0.50 162:0.64 163:0.81 164:0.95 167:0.87 172:0.48 173:0.16 177:0.05 178:0.42 179:0.72 181:0.82 187:0.21 191:0.89 202:0.93 212:0.23 215:0.46 216:0.86 235:0.88 241:0.75 242:0.82 243:0.12 245:0.58 247:0.12 248:0.91 256:0.94 259:0.21 260:0.94 265:0.49 266:0.84 267:0.52 268:0.94 271:0.67 276:0.52 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.96 7:0.81 8:0.85 12:0.06 17:0.36 20:0.78 21:0.74 25:0.88 27:0.03 28:0.64 30:0.17 32:0.80 34:0.31 36:0.30 37:0.95 43:0.52 55:0.71 66:0.34 69:0.27 70:0.84 78:0.83 81:0.72 91:0.70 98:0.50 100:0.92 104:0.58 108:0.76 111:0.87 120:0.90 123:0.74 124:0.77 126:0.54 127:0.77 129:0.89 133:0.78 135:0.34 140:0.45 145:0.83 146:0.05 147:0.05 149:0.49 150:0.53 151:0.77 152:0.99 153:0.88 154:0.41 159:0.58 161:0.50 162:0.75 163:0.81 164:0.95 167:0.90 169:0.99 172:0.37 173:0.26 177:0.05 179:0.77 181:0.82 186:0.84 187:0.21 189:0.93 191:0.76 195:0.75 202:0.91 212:0.30 215:0.46 216:0.86 220:0.62 230:0.87 233:0.76 235:0.88 238:0.96 241:0.77 242:0.82 243:0.13 245:0.57 247:0.12 248:0.85 256:0.93 259:0.21 260:0.90 261:1.00 265:0.52 266:0.71 267:0.18 268:0.93 271:0.67 276:0.47 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.06 17:0.24 21:0.30 27:0.45 28:0.64 30:0.68 34:0.68 36:0.17 37:0.50 43:0.32 55:0.59 66:0.31 69:0.68 70:0.37 81:0.90 91:0.17 98:0.25 108:0.68 123:0.65 124:0.84 126:0.54 127:0.34 129:0.93 133:0.84 135:0.86 145:0.45 146:0.23 147:0.29 149:0.51 150:0.82 154:0.24 159:0.66 161:0.50 167:0.65 172:0.37 173:0.54 177:0.05 178:0.55 179:0.63 181:0.82 187:0.68 212:0.40 215:0.72 216:0.77 235:0.31 241:0.27 242:0.82 243:0.21 245:0.56 247:0.12 259:0.21 265:0.28 266:0.54 267:0.52 271:0.67 276:0.49 283:0.60 297:0.36 300:0.33 +2 12:0.06 17:0.73 18:0.80 21:0.64 22:0.93 27:0.69 29:0.71 30:0.66 34:1.00 36:0.42 37:0.53 43:0.18 44:0.87 47:0.93 55:0.71 64:0.77 66:0.94 69:0.23 70:0.41 71:0.77 74:0.33 81:0.25 86:0.78 91:0.27 98:0.90 108:0.18 123:0.18 126:0.26 127:0.46 129:0.05 135:0.74 139:0.79 145:0.77 146:0.95 147:0.96 149:0.41 150:0.25 154:0.47 159:0.63 172:0.85 173:0.19 177:0.70 178:0.55 179:0.36 187:0.68 195:0.81 212:0.73 216:0.33 219:0.89 222:0.76 235:0.80 241:0.39 242:0.77 243:0.94 244:0.61 247:0.67 253:0.28 254:0.97 259:0.21 262:0.93 265:0.90 266:0.54 267:0.73 271:0.22 276:0.76 292:0.91 300:0.43 +0 12:0.06 17:0.72 18:0.85 21:0.13 22:0.93 27:0.72 29:0.71 30:0.58 37:0.65 43:0.14 44:0.87 47:0.93 48:0.91 55:0.52 64:0.77 66:0.80 69:0.60 70:0.60 71:0.77 74:0.51 81:0.68 86:0.89 91:0.64 98:0.86 104:0.97 106:0.81 108:0.46 122:0.67 123:0.44 126:0.44 127:0.37 129:0.05 139:0.88 145:0.70 146:0.61 147:0.66 149:0.09 150:0.47 154:0.70 159:0.23 172:0.45 173:0.81 177:0.70 178:0.55 179:0.82 187:0.68 192:0.51 195:0.60 201:0.68 206:0.81 212:0.71 215:0.61 216:0.29 222:0.76 235:0.22 241:0.07 242:0.16 243:0.82 247:0.60 253:0.36 254:0.96 262:0.93 265:0.86 266:0.27 271:0.22 276:0.32 281:0.91 283:0.82 297:0.36 300:0.43 +1 1:0.69 7:0.66 12:0.06 17:0.66 18:0.76 21:0.74 27:0.63 29:0.71 30:0.56 32:0.68 34:0.96 36:0.24 37:0.27 43:0.09 55:0.59 66:0.17 69:0.70 70:0.66 71:0.77 74:0.60 76:0.85 77:0.70 81:0.29 86:0.71 91:0.08 98:0.25 104:0.87 106:0.81 108:0.75 114:0.54 117:0.86 122:0.86 123:0.74 124:0.85 126:0.44 127:0.69 129:0.81 133:0.83 135:0.24 139:0.69 145:0.74 146:0.19 147:0.25 149:0.13 150:0.47 154:0.51 155:0.58 158:0.78 159:0.87 172:0.37 173:0.43 175:0.75 176:0.66 177:0.38 178:0.55 179:0.59 187:0.39 192:0.59 195:0.96 201:0.68 206:0.81 208:0.54 212:0.55 215:0.36 216:0.84 222:0.76 230:0.69 233:0.73 235:0.95 241:0.03 242:0.62 243:0.16 244:0.61 245:0.69 247:0.74 253:0.39 254:0.84 257:0.65 259:0.21 265:0.27 266:0.71 267:0.88 271:0.22 276:0.56 277:0.69 279:0.82 282:0.77 283:0.67 290:0.54 297:0.36 300:0.70 +1 12:0.06 17:0.95 18:0.98 21:0.54 22:0.93 27:0.51 29:0.71 30:0.38 34:0.90 36:0.34 37:0.60 43:0.14 44:0.87 47:0.93 48:0.91 55:0.52 64:0.77 66:0.49 69:0.58 70:0.75 71:0.77 74:0.30 81:0.25 86:0.96 91:0.33 98:0.91 108:0.44 122:0.86 123:0.43 124:0.58 126:0.26 127:0.90 129:0.05 133:0.37 135:0.68 139:0.96 145:0.56 146:0.99 147:0.99 149:0.40 150:0.25 154:0.50 159:0.88 172:0.93 173:0.30 177:0.70 178:0.55 179:0.18 187:0.39 195:0.96 212:0.52 216:0.53 219:0.89 222:0.76 235:0.60 241:0.27 242:0.59 243:0.60 244:0.61 245:0.99 247:0.76 253:0.26 254:0.74 259:0.21 262:0.93 265:0.91 266:0.63 267:0.82 271:0.22 276:0.93 281:0.89 292:0.91 300:0.55 +1 1:0.69 12:0.06 17:0.34 18:0.80 21:0.77 27:0.66 29:0.71 30:0.58 34:0.99 36:0.45 37:0.26 43:0.08 44:0.90 48:0.97 55:0.45 64:0.77 66:0.20 69:0.93 70:0.88 71:0.77 74:0.54 76:0.85 77:0.70 81:0.28 86:0.82 91:0.11 98:0.26 108:0.86 114:0.53 122:0.85 123:0.85 124:0.81 126:0.44 127:0.73 129:0.05 132:0.97 133:0.77 135:0.91 139:0.83 145:0.72 146:0.57 147:0.28 149:0.10 150:0.47 154:0.51 155:0.58 159:0.85 172:0.32 173:0.85 176:0.66 177:0.38 178:0.55 179:0.71 187:0.68 192:0.51 195:0.95 201:0.68 208:0.54 212:0.16 216:0.73 222:0.76 235:0.99 241:0.10 242:0.24 243:0.26 244:0.61 245:0.64 247:0.76 253:0.37 254:0.84 257:0.65 265:0.28 266:0.87 267:0.99 271:0.22 276:0.46 281:0.91 282:0.77 290:0.53 300:0.84 +1 12:0.06 17:0.84 18:0.98 21:0.54 27:0.34 29:0.71 30:0.65 34:0.68 36:0.44 37:0.41 43:0.15 44:0.87 55:0.52 64:0.77 66:0.94 69:0.77 70:0.48 71:0.77 74:0.33 81:0.25 86:0.98 91:0.18 98:0.83 108:0.60 122:0.86 123:0.58 124:0.36 126:0.26 127:0.52 129:0.05 133:0.37 135:0.63 139:0.98 145:0.45 146:0.86 147:0.05 149:0.28 150:0.25 154:0.52 159:0.51 172:0.94 173:0.78 177:0.05 178:0.55 179:0.21 187:0.95 195:0.70 212:0.94 216:0.28 219:0.89 222:0.76 235:0.60 241:0.18 242:0.51 243:0.06 245:0.73 247:0.76 253:0.27 254:0.88 257:0.84 259:0.21 265:0.83 266:0.23 267:0.52 271:0.22 276:0.88 292:0.95 300:0.55 +2 7:0.72 12:0.06 17:0.66 18:0.86 21:0.40 27:0.51 29:0.71 30:0.57 32:0.77 34:1.00 36:0.53 37:0.60 43:0.12 44:0.93 48:0.91 55:0.59 64:0.77 66:0.89 69:0.58 70:0.52 71:0.77 74:0.51 77:0.70 81:0.41 86:0.84 91:0.33 98:0.84 104:0.99 108:0.44 123:0.42 126:0.44 127:0.34 129:0.05 135:0.54 139:0.90 145:0.50 146:0.85 147:0.88 149:0.24 150:0.32 154:0.56 159:0.42 172:0.70 173:0.58 177:0.70 178:0.55 179:0.52 187:0.39 192:0.51 195:0.69 201:0.68 212:0.57 215:0.42 216:0.53 219:0.92 222:0.76 230:0.74 233:0.73 235:0.52 241:0.27 242:0.45 243:0.90 244:0.61 247:0.76 253:0.36 254:0.74 259:0.21 265:0.84 266:0.47 267:0.52 271:0.22 276:0.59 281:0.88 282:0.85 283:0.77 292:0.91 297:0.36 300:0.43 +1 12:0.06 17:0.98 18:0.87 21:0.22 22:0.93 27:0.57 29:0.71 30:0.42 34:0.94 36:0.24 37:0.32 43:0.17 47:0.93 48:0.91 55:0.52 64:0.77 66:0.83 69:0.87 70:0.52 71:0.77 74:0.35 81:0.36 86:0.89 91:0.37 98:0.70 108:0.75 122:0.65 123:0.74 124:0.39 126:0.26 127:0.62 129:0.05 133:0.59 135:0.66 139:0.86 145:0.50 146:0.80 147:0.05 149:0.12 150:0.32 154:0.48 159:0.59 172:0.81 173:0.89 177:0.05 178:0.55 179:0.44 187:0.87 212:0.91 216:0.29 222:0.76 235:0.22 241:0.01 242:0.41 243:0.08 245:0.63 247:0.74 253:0.29 254:0.93 257:0.84 259:0.21 262:0.93 265:0.70 266:0.38 267:0.85 271:0.22 276:0.70 281:0.91 300:0.43 +1 7:0.72 12:0.06 17:0.57 18:0.86 21:0.40 27:0.51 29:0.71 30:0.43 32:0.78 34:1.00 36:0.55 37:0.60 43:0.11 44:0.93 48:0.91 55:0.71 64:0.77 66:0.89 69:0.58 70:0.59 71:0.77 74:0.51 77:0.70 81:0.41 86:0.85 91:0.35 98:0.83 104:0.98 108:0.44 123:0.42 126:0.44 127:0.33 129:0.05 135:0.68 139:0.90 145:0.45 146:0.84 147:0.87 149:0.23 150:0.32 154:0.72 159:0.41 172:0.70 173:0.58 177:0.70 178:0.55 179:0.51 187:0.39 192:0.51 195:0.69 201:0.68 212:0.51 215:0.42 216:0.53 219:0.92 222:0.76 230:0.74 233:0.73 235:0.52 241:0.30 242:0.54 243:0.90 247:0.76 253:0.36 254:0.98 259:0.21 265:0.83 266:0.63 267:0.59 271:0.22 276:0.59 281:0.88 282:0.86 283:0.77 292:0.91 297:0.36 300:0.55 +1 1:0.69 7:0.66 11:0.64 12:0.06 17:0.91 18:0.75 21:0.74 27:0.64 29:0.71 30:0.14 32:0.69 34:1.00 36:0.33 37:0.33 43:0.66 45:0.77 66:0.25 69:0.57 70:0.91 71:0.77 74:0.64 76:0.85 77:0.70 81:0.32 83:0.60 86:0.71 91:0.08 98:0.20 99:0.83 101:0.52 104:0.92 106:0.81 108:0.44 114:0.54 117:0.86 122:0.86 123:0.42 124:0.84 126:0.44 127:0.47 129:0.59 133:0.82 135:0.95 139:0.68 145:0.50 146:0.39 147:0.45 149:0.53 150:0.51 154:0.56 155:0.58 158:0.78 159:0.78 165:0.70 172:0.32 173:0.35 175:0.75 176:0.66 177:0.38 178:0.55 179:0.69 187:0.87 192:0.59 195:0.93 201:0.68 206:0.81 208:0.54 212:0.18 215:0.36 216:0.39 222:0.76 230:0.69 233:0.76 235:0.97 241:0.02 242:0.54 243:0.45 244:0.61 245:0.57 247:0.60 253:0.40 257:0.65 259:0.21 265:0.21 266:0.84 267:0.36 271:0.22 276:0.46 277:0.69 282:0.77 283:0.66 290:0.54 297:0.36 300:0.70 +0 1:0.58 8:0.72 9:0.73 10:0.79 11:0.45 12:0.20 17:0.98 18:0.72 21:0.40 22:0.93 26:0.98 27:0.66 29:0.53 30:0.89 31:0.87 34:0.30 36:0.93 37:0.57 39:0.95 43:0.20 44:0.86 45:1.00 46:0.88 47:0.93 48:0.99 58:0.98 64:0.77 66:0.72 69:0.34 70:0.31 71:0.83 74:0.33 79:0.87 81:0.49 83:0.56 86:0.64 89:0.98 91:0.11 98:0.85 99:0.95 101:0.47 107:0.98 108:0.27 110:0.89 122:0.67 123:0.26 124:0.51 125:0.88 126:0.32 127:0.83 129:0.05 131:0.61 133:0.74 135:0.70 137:0.87 139:0.66 140:0.45 141:0.99 145:0.63 146:0.99 147:0.99 149:0.67 150:0.39 151:0.53 153:0.48 154:0.09 155:0.55 157:0.61 159:0.88 160:0.88 162:0.86 165:0.65 166:0.61 170:0.79 172:0.99 173:0.13 174:0.79 177:1.00 179:0.12 182:0.61 187:0.68 190:0.99 192:0.56 195:0.96 196:0.88 197:0.80 199:1.00 201:0.60 202:0.50 204:0.82 208:0.50 212:0.82 216:0.61 220:0.91 222:0.35 223:0.98 224:0.99 225:0.99 227:0.61 229:0.61 231:0.62 234:0.99 235:0.68 239:0.79 240:0.99 241:0.03 242:0.39 243:0.75 244:0.97 245:0.98 246:0.61 247:0.96 248:0.53 253:0.30 254:0.90 255:0.72 256:0.58 259:0.21 262:0.93 264:0.93 265:0.85 266:0.75 267:0.99 268:0.59 271:0.60 274:0.98 275:0.91 276:0.97 281:0.86 284:0.87 285:0.50 287:0.61 289:0.88 294:0.91 300:0.70 +0 10:0.82 11:0.53 12:0.20 17:0.30 18:0.77 21:0.47 26:0.94 27:0.40 29:0.53 30:0.15 34:0.76 36:0.51 37:0.72 39:0.95 43:0.09 45:0.85 46:0.94 58:0.93 66:0.92 69:0.67 70:0.78 71:0.83 74:0.29 81:0.26 86:0.64 89:0.97 91:0.33 98:0.92 101:0.64 107:0.94 108:0.51 110:0.89 122:0.97 123:0.49 125:0.88 126:0.22 127:0.55 129:0.05 131:0.61 135:0.74 139:0.61 141:0.91 144:0.57 146:0.91 147:0.93 149:0.11 150:0.28 154:0.09 157:0.79 159:0.52 160:0.88 166:0.80 170:0.79 172:0.79 173:0.68 174:0.93 177:1.00 178:0.55 179:0.49 182:0.73 187:0.68 197:0.93 199:0.94 212:0.69 216:0.66 222:0.35 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.82 234:0.91 235:0.52 236:0.91 239:0.81 240:0.95 241:0.51 242:0.14 243:0.92 244:0.98 246:0.61 247:0.96 253:0.26 254:0.97 259:0.21 264:0.93 265:0.92 266:0.54 267:0.79 271:0.60 274:0.91 275:0.96 276:0.68 287:0.61 289:0.88 294:0.93 300:0.55 +0 1:0.56 10:0.79 11:0.45 12:0.20 17:0.69 18:0.93 21:0.47 26:0.98 27:0.72 29:0.53 30:0.14 31:0.88 34:0.42 36:0.77 37:0.44 39:0.95 43:0.06 44:0.85 45:0.94 46:0.88 48:0.97 55:0.92 58:0.93 64:0.77 66:0.13 69:0.99 70:0.96 71:0.83 74:0.26 79:0.88 81:0.37 83:0.54 86:0.67 89:0.98 91:0.09 98:0.31 99:0.83 101:0.47 107:0.91 108:0.98 110:0.88 122:0.95 123:0.98 124:0.99 125:0.88 126:0.31 127:0.99 129:0.05 131:0.61 133:0.99 135:0.65 137:0.88 139:0.66 140:0.80 141:0.91 145:0.99 146:0.94 147:0.05 149:0.13 150:0.34 151:0.51 153:0.46 154:0.05 155:0.47 157:0.61 159:0.97 160:0.88 162:0.54 165:0.63 166:0.61 170:0.79 172:0.79 173:0.94 174:0.91 175:0.75 177:0.05 178:0.42 179:0.16 182:0.61 187:0.39 190:1.00 192:0.46 195:1.00 196:0.88 197:0.86 199:0.98 201:0.57 202:0.49 204:0.78 208:0.48 212:0.14 216:0.13 220:0.82 222:0.35 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.70 234:0.91 235:0.68 239:0.79 240:0.98 241:0.01 242:0.36 243:0.05 244:0.98 245:0.87 246:0.61 247:0.99 248:0.51 253:0.24 254:0.90 256:0.45 257:1.00 259:0.21 264:0.93 265:0.33 266:0.98 267:0.19 268:0.45 271:0.60 274:0.91 275:0.96 276:0.95 277:0.69 281:0.91 284:0.86 285:0.45 287:0.61 289:0.88 294:0.91 300:0.94 +0 8:0.77 10:0.82 11:0.51 12:0.20 17:0.59 18:0.98 21:0.59 22:0.93 26:0.96 27:0.67 29:0.53 30:0.66 31:0.86 34:0.77 36:0.25 37:0.35 39:0.95 43:0.09 44:0.85 45:0.91 46:0.88 47:0.93 48:0.97 58:0.93 64:0.77 66:0.78 69:0.85 70:0.39 71:0.83 74:0.28 79:0.86 81:0.24 86:0.83 89:0.99 91:0.53 98:0.75 101:0.52 107:0.95 108:0.71 110:0.89 122:0.95 123:0.69 124:0.41 125:0.88 126:0.22 127:0.53 129:0.05 131:0.61 133:0.39 135:0.63 137:0.86 139:0.79 140:0.45 141:0.91 144:0.57 145:0.41 146:0.80 147:0.64 149:0.12 150:0.26 151:0.47 153:0.48 154:0.12 157:0.88 159:0.51 160:0.88 162:0.62 166:0.61 170:0.79 172:0.95 173:0.88 174:0.86 177:0.88 179:0.18 182:0.78 187:0.21 190:1.00 195:0.71 196:0.88 197:0.88 199:0.95 202:0.50 212:0.93 216:0.34 219:0.87 220:0.97 222:0.35 223:0.96 224:0.93 225:0.97 227:0.61 229:0.61 231:0.78 234:0.91 235:0.68 239:0.82 240:0.98 241:0.60 242:0.23 243:0.19 244:0.97 245:0.95 246:0.61 247:0.94 248:0.47 253:0.25 254:0.84 256:0.45 257:0.97 259:0.21 262:0.93 264:0.93 265:0.75 266:0.38 267:0.28 268:0.46 271:0.60 274:0.91 275:0.99 276:0.91 281:0.91 284:0.87 285:0.45 287:0.61 289:0.88 292:0.88 294:0.94 300:0.55 +0 1:0.57 10:0.81 11:0.49 12:0.20 17:0.24 18:0.69 21:0.30 26:0.95 27:0.45 29:0.53 30:0.21 34:0.80 36:0.17 37:0.50 39:0.95 43:0.05 45:0.73 46:0.88 55:0.52 58:0.93 66:0.36 69:0.79 70:0.91 71:0.83 74:0.30 81:0.27 86:0.60 89:0.96 91:0.14 98:0.27 101:0.47 107:0.94 108:0.63 110:0.89 114:0.71 122:0.41 123:0.61 124:0.82 125:0.88 126:0.24 127:0.78 129:0.05 131:0.61 133:0.80 135:0.66 139:0.58 141:0.91 144:0.57 145:0.47 146:0.42 147:0.49 149:0.06 150:0.29 154:0.09 155:0.46 157:0.78 159:0.69 160:0.88 166:0.61 170:0.79 172:0.45 173:0.73 174:0.79 175:0.91 176:0.59 177:0.38 178:0.55 179:0.71 182:0.73 187:0.68 192:0.46 197:0.82 199:0.94 201:0.54 208:0.48 212:0.52 215:0.72 216:0.77 222:0.35 223:0.93 224:0.93 225:0.96 227:0.61 229:0.61 231:0.65 234:0.91 235:0.42 239:0.80 240:0.95 241:0.21 242:0.12 243:0.26 244:0.99 245:0.59 246:0.61 247:0.88 253:0.53 254:0.86 257:0.99 259:0.21 264:0.93 265:0.30 266:0.71 267:0.23 271:0.60 274:0.91 275:0.93 276:0.50 277:0.87 287:0.61 289:0.89 290:0.71 294:0.92 297:0.36 300:0.70 +0 1:0.56 10:0.79 11:0.45 12:0.20 17:0.40 18:0.80 21:0.71 26:0.98 29:0.53 30:0.54 34:0.37 36:0.28 37:0.97 39:0.95 43:0.13 45:0.91 46:0.88 58:0.93 66:0.13 69:0.94 70:0.70 71:0.83 74:0.25 81:0.38 83:0.56 86:0.63 89:0.98 91:0.31 98:0.36 99:0.95 101:0.47 107:0.89 108:0.82 110:0.88 122:0.67 123:0.87 124:0.88 125:0.88 126:0.31 127:0.31 129:0.05 131:0.61 133:0.86 135:0.60 139:0.61 141:0.91 145:0.65 146:0.82 147:0.22 149:0.21 150:0.33 154:0.08 155:0.46 157:0.61 159:0.84 160:0.88 165:0.65 166:0.61 170:0.79 172:0.45 173:0.81 174:0.79 175:0.91 177:0.70 178:0.55 179:0.33 182:0.61 187:0.21 192:0.44 197:0.80 199:0.96 201:0.54 202:0.67 208:0.50 212:0.30 216:0.98 222:0.35 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.62 234:0.91 235:0.95 239:0.78 240:0.95 241:0.61 242:0.24 243:0.17 244:0.98 245:0.75 246:0.61 247:0.86 253:0.23 254:0.93 257:0.99 259:0.21 264:0.93 265:0.23 266:0.94 267:0.76 271:0.60 274:0.91 275:0.91 276:0.70 277:0.87 287:0.61 289:0.88 294:0.91 300:0.55 +0 8:0.83 10:0.79 11:0.45 12:0.20 17:0.45 18:1.00 21:0.40 26:0.97 27:0.58 29:0.53 30:0.84 31:0.87 32:0.63 34:0.30 36:0.62 37:0.30 39:0.95 43:0.12 44:0.86 45:0.97 46:0.88 48:0.91 55:0.71 58:0.93 64:0.77 66:1.00 69:0.78 70:0.27 71:0.83 74:0.30 77:0.70 79:0.87 81:0.25 86:0.86 89:0.99 91:0.02 98:1.00 101:0.47 107:0.88 108:0.62 110:0.88 122:0.95 123:0.60 124:0.36 125:0.88 126:0.22 127:1.00 129:0.05 131:0.61 133:0.36 135:0.44 137:0.87 139:0.83 140:0.45 141:0.91 145:0.90 146:1.00 147:0.05 149:0.33 150:0.27 151:0.50 153:0.48 154:0.09 157:0.61 159:0.97 160:0.88 162:0.86 166:0.61 170:0.79 172:1.00 173:0.27 174:0.79 175:0.91 177:0.05 179:0.09 182:0.61 187:0.39 190:1.00 195:1.00 196:0.90 197:0.79 199:0.97 202:0.36 212:0.94 216:0.36 219:0.87 220:0.91 222:0.35 223:0.97 224:0.93 225:0.97 227:0.61 229:0.61 231:0.61 234:0.91 235:0.42 239:0.78 240:0.98 241:0.07 242:0.77 243:0.05 244:0.98 245:0.94 246:0.61 247:0.92 248:0.50 253:0.27 254:0.97 256:0.45 257:1.00 259:0.21 264:0.93 265:1.00 266:0.27 267:0.36 268:0.46 271:0.60 274:0.91 276:1.00 277:0.87 281:0.86 282:0.72 284:0.87 285:0.45 287:0.61 289:0.88 292:0.95 300:0.70 +1 10:0.81 11:0.53 12:0.20 17:0.15 18:0.70 21:0.59 26:0.97 27:0.09 29:0.53 30:0.45 34:0.79 36:0.83 37:0.95 39:0.95 43:0.09 45:0.93 46:0.88 48:0.91 56:0.97 58:0.93 66:0.11 69:0.92 70:0.81 71:0.83 74:0.27 81:0.25 83:0.54 86:0.61 89:0.97 91:0.12 98:0.40 101:0.64 107:0.92 108:0.84 110:0.88 122:0.86 123:0.49 124:0.92 125:0.88 126:0.22 127:0.76 129:0.05 131:0.61 133:0.91 135:0.92 139:0.60 141:0.91 144:0.57 145:0.45 146:0.59 147:0.76 149:0.18 150:0.26 154:0.07 155:0.47 157:0.82 159:0.80 160:0.88 166:0.77 170:0.79 172:0.59 173:0.87 174:0.88 175:0.91 177:0.88 178:0.55 179:0.37 182:0.75 187:0.87 192:0.45 195:0.91 197:0.86 199:0.97 202:0.36 204:0.78 208:0.48 212:0.42 216:0.74 222:0.35 223:0.97 224:0.93 225:0.96 227:0.61 229:0.61 231:0.79 234:0.91 235:0.68 239:0.81 240:0.95 241:0.21 242:0.17 243:0.31 244:0.99 245:0.79 246:0.61 247:0.98 253:0.24 257:0.97 259:0.21 264:0.93 265:0.33 266:0.87 267:0.28 271:0.60 274:0.91 275:0.97 276:0.78 277:0.98 281:0.91 287:0.61 289:0.88 294:0.93 300:0.84 +0 8:0.85 10:0.79 11:0.45 12:0.20 17:0.55 18:0.67 21:0.78 26:0.94 27:0.52 29:0.53 30:0.14 34:0.26 36:0.48 37:0.43 39:0.95 43:0.05 45:0.73 46:0.93 55:0.96 58:0.93 66:0.05 69:0.99 70:0.99 71:0.83 74:0.28 86:0.59 89:0.96 91:0.01 98:0.14 101:0.47 107:0.97 108:0.99 110:0.89 122:0.41 123:0.91 124:0.98 125:0.88 127:0.96 129:0.05 131:0.61 133:0.98 135:0.79 139:0.58 141:0.91 145:0.54 146:0.35 147:0.05 149:0.06 154:0.05 157:0.61 159:0.98 160:0.88 163:0.99 166:0.61 170:0.79 172:0.32 173:0.99 174:0.79 175:1.00 177:0.05 178:0.55 179:0.44 182:0.61 187:0.68 197:0.79 199:0.94 202:0.56 212:0.14 216:0.79 222:0.35 223:0.92 224:0.93 225:0.95 227:0.61 229:0.61 231:0.61 234:0.91 235:0.52 239:0.78 240:0.95 241:0.12 242:0.19 243:0.08 244:1.00 245:0.61 246:0.61 247:0.92 253:0.25 257:1.00 259:0.21 264:0.93 265:0.12 266:0.98 267:0.52 271:0.60 274:0.91 275:0.93 276:0.76 277:1.00 287:0.61 289:0.88 294:0.91 300:0.94 +0 8:0.63 10:0.79 11:0.45 12:0.20 17:0.30 18:0.90 21:0.78 26:0.94 27:0.37 29:0.53 30:0.17 34:0.37 36:0.82 37:0.94 39:0.95 43:0.05 45:0.77 46:0.88 55:0.71 58:0.93 66:0.09 69:0.85 70:0.90 71:0.83 74:0.30 86:0.67 89:0.97 91:0.08 98:0.33 101:0.47 107:0.96 108:0.99 110:0.89 122:0.99 123:0.99 124:0.99 125:0.88 127:0.97 129:0.59 131:0.83 133:0.99 135:0.56 139:0.64 140:0.45 141:0.91 143:0.99 146:0.91 147:0.38 149:0.13 151:0.60 153:0.48 154:0.05 157:0.61 159:0.98 160:0.88 162:0.86 166:0.61 170:0.79 172:0.93 173:0.31 174:0.90 175:0.97 177:0.70 179:0.10 182:0.61 187:0.39 190:1.00 197:0.86 199:0.94 202:0.36 212:0.48 216:0.28 220:0.62 222:0.35 223:0.92 224:0.93 225:0.96 227:0.86 229:0.61 231:0.75 234:0.91 235:0.12 239:0.79 240:0.95 241:0.05 242:0.51 243:0.06 244:0.97 245:0.98 246:0.61 247:1.00 248:0.58 253:0.27 254:0.86 256:0.45 257:0.99 259:0.21 264:0.93 265:0.35 266:0.98 267:0.87 268:0.46 271:0.60 274:0.91 275:1.00 276:0.99 277:0.95 285:0.45 287:0.61 289:0.88 294:0.94 300:0.98 +0 1:0.60 10:0.80 11:0.51 12:0.20 17:0.78 18:0.92 21:0.22 26:0.98 27:0.72 29:0.53 30:0.68 34:0.77 36:0.72 39:0.95 43:0.20 45:0.87 46:0.88 55:0.84 58:0.93 66:0.92 69:0.19 70:0.37 71:0.83 74:0.26 81:0.45 83:0.56 86:0.67 89:0.98 91:0.25 97:0.94 98:0.94 99:0.95 101:0.52 107:0.91 108:0.15 110:0.88 122:0.95 123:0.15 125:0.88 126:0.31 127:0.59 129:0.05 131:0.61 135:0.97 139:0.65 141:0.91 144:0.57 146:0.94 147:0.95 149:0.21 150:0.39 154:0.13 155:0.47 157:0.79 159:0.58 160:0.88 163:0.75 165:0.65 166:0.61 170:0.79 172:0.79 173:0.20 174:0.79 177:1.00 178:0.55 179:0.48 182:0.73 187:0.39 192:0.45 197:0.81 199:0.96 201:0.59 208:0.50 212:0.22 216:0.32 222:0.35 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 239:0.79 240:0.95 241:0.07 242:0.36 243:0.93 244:0.98 246:0.61 247:0.82 253:0.24 254:0.98 259:0.21 264:0.93 265:0.94 266:0.63 267:0.28 271:0.60 274:0.91 275:0.91 276:0.69 279:0.75 287:0.61 289:0.88 294:0.92 300:0.33 +0 1:0.58 10:0.79 11:0.48 12:0.20 17:0.98 18:0.62 21:0.47 26:0.98 27:0.72 29:0.53 30:0.89 34:0.39 36:0.18 39:0.95 43:0.12 45:0.99 46:0.88 48:0.91 58:0.93 66:0.80 69:0.40 70:0.24 71:0.83 74:0.31 81:0.41 83:0.56 86:0.61 89:0.98 91:0.72 98:0.90 99:0.95 101:0.52 107:0.94 108:0.31 110:0.89 122:0.67 123:0.30 124:0.40 125:0.88 126:0.31 127:0.88 129:0.05 131:0.61 133:0.36 135:0.21 139:0.60 141:0.91 145:0.85 146:0.97 147:0.97 149:0.53 150:0.36 154:0.09 155:0.48 157:0.61 159:0.76 160:0.88 165:0.65 166:0.61 170:0.79 172:0.95 173:0.25 174:0.79 177:1.00 178:0.55 179:0.20 182:0.61 192:0.46 195:0.88 197:0.80 199:0.99 201:0.57 204:0.78 208:0.50 212:0.88 222:0.35 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.63 234:0.91 235:0.68 239:0.79 240:0.95 241:0.77 242:0.36 243:0.82 244:0.97 245:0.95 246:0.61 247:0.92 253:0.28 254:0.74 259:0.21 264:0.93 265:0.90 266:0.54 267:0.85 271:0.60 274:0.91 275:0.91 276:0.91 281:0.91 287:0.61 289:0.88 294:0.91 300:0.70 +1 10:0.83 11:0.53 12:0.20 17:0.43 18:0.78 21:0.47 26:0.94 27:0.40 29:0.53 30:0.20 34:0.77 36:0.49 37:0.72 39:0.95 43:0.09 45:0.87 46:0.94 58:0.93 66:0.93 69:0.68 70:0.80 71:0.83 74:0.29 81:0.26 86:0.64 89:0.97 91:0.29 98:0.92 101:0.64 107:0.93 108:0.52 110:0.89 122:0.97 123:0.49 125:0.88 126:0.22 127:0.53 129:0.05 131:0.61 135:0.69 139:0.61 141:0.91 144:0.57 146:0.89 147:0.91 149:0.12 150:0.28 154:0.09 157:0.84 159:0.49 160:0.88 166:0.80 170:0.79 172:0.80 173:0.70 174:0.94 177:1.00 178:0.55 179:0.45 182:0.76 187:0.68 197:0.94 199:0.94 212:0.71 216:0.66 222:0.35 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.83 234:0.91 235:0.52 236:0.91 239:0.82 240:0.95 241:0.32 242:0.16 243:0.93 244:0.98 246:0.61 247:0.93 253:0.26 254:0.97 259:0.21 264:0.93 265:0.92 266:0.54 267:0.82 271:0.60 274:0.91 275:0.96 276:0.70 287:0.61 289:0.88 294:0.93 300:0.55 +0 8:0.64 10:0.79 11:0.42 12:0.20 17:0.73 18:0.99 21:0.64 22:0.93 26:0.97 27:0.38 29:0.53 30:0.36 31:0.94 34:0.40 36:0.99 37:0.41 39:0.95 43:0.06 45:0.91 46:0.88 47:0.93 48:0.91 55:0.71 58:0.93 64:0.77 66:0.26 69:0.98 70:0.81 71:0.83 74:0.25 79:0.94 81:0.25 86:0.75 89:0.97 91:0.12 98:0.66 101:0.45 107:0.90 108:0.90 110:0.88 122:0.95 123:0.89 124:0.90 125:0.88 126:0.22 127:0.99 129:0.05 131:0.61 133:0.89 135:0.84 137:0.94 139:0.73 140:0.45 141:0.91 145:0.45 146:0.93 147:0.91 149:0.18 150:0.27 151:0.70 153:0.72 154:0.05 157:0.61 159:0.94 160:0.88 162:0.76 166:0.61 170:0.79 172:0.92 173:0.93 174:0.79 175:0.99 177:0.70 179:0.14 182:0.61 187:0.68 190:1.00 196:0.92 197:0.80 199:0.97 202:0.58 212:0.41 216:0.50 219:0.87 222:0.35 223:0.97 224:0.93 225:0.96 227:0.61 229:0.61 231:0.62 234:0.91 235:0.80 239:0.78 240:0.98 241:0.35 242:0.74 243:0.18 244:0.99 245:0.98 246:0.61 247:0.97 248:0.65 253:0.22 254:0.88 256:0.58 257:0.99 259:0.21 262:0.93 264:0.93 265:0.67 266:0.89 267:0.69 268:0.62 271:0.60 274:0.91 275:0.91 276:0.96 277:0.99 281:0.91 284:0.92 285:0.45 287:0.61 289:0.88 292:0.95 294:0.91 300:0.94 +0 8:0.72 10:0.79 11:0.45 12:0.20 17:0.66 18:0.79 21:0.78 26:0.94 27:0.51 29:0.53 30:0.40 34:0.39 36:0.99 37:0.49 39:0.95 43:0.14 45:0.73 46:0.88 55:0.71 58:0.93 66:0.60 69:0.71 70:0.74 71:0.83 74:0.27 86:0.62 89:0.97 91:0.40 98:0.79 101:0.47 107:0.95 108:0.79 110:0.89 122:0.99 123:0.78 124:0.58 125:0.88 127:0.56 129:0.05 131:0.61 133:0.60 135:0.88 139:0.61 141:0.91 146:0.67 147:0.72 149:0.14 154:0.10 157:0.61 159:0.72 160:0.88 166:0.61 170:0.79 172:0.75 173:0.58 174:0.83 175:0.91 177:0.99 178:0.55 179:0.42 182:0.61 187:0.21 197:0.83 199:0.94 212:0.22 216:0.38 219:0.87 222:0.35 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.74 234:0.91 235:0.05 239:0.78 240:0.95 241:0.03 242:0.14 243:0.36 244:0.99 245:0.81 246:0.61 247:0.96 253:0.25 257:0.84 259:0.21 264:0.93 265:0.79 266:0.71 267:0.88 271:0.60 274:0.91 275:0.98 276:0.73 277:0.95 287:0.61 289:0.88 292:0.88 294:0.92 300:0.70 +0 10:0.80 11:0.51 12:0.20 17:0.69 18:0.92 21:0.78 26:0.96 27:1.00 29:0.53 30:0.70 34:0.79 36:0.91 37:0.53 39:0.95 43:0.09 44:0.86 45:0.83 46:0.88 48:0.97 55:0.84 58:0.93 66:0.91 69:0.30 70:0.39 71:0.83 74:0.26 83:0.54 86:0.66 89:0.97 91:0.20 98:0.96 101:0.52 107:0.91 108:0.24 110:0.88 122:0.95 123:0.23 125:0.88 127:0.70 129:0.05 131:0.61 135:0.96 139:0.66 141:0.91 144:0.57 145:0.85 146:0.92 147:0.94 149:0.16 154:0.07 155:0.47 157:0.79 159:0.55 160:0.88 163:0.90 166:0.61 170:0.79 172:0.75 173:0.29 174:0.79 177:1.00 178:0.55 179:0.57 182:0.73 187:0.87 192:0.45 195:0.76 197:0.83 199:0.95 204:0.78 208:0.48 212:0.29 216:0.28 222:0.35 223:0.96 224:0.93 225:0.96 227:0.61 229:0.61 231:0.64 234:0.91 235:0.02 239:0.80 240:0.95 241:0.03 242:0.38 243:0.91 244:0.99 246:0.61 247:0.86 253:0.24 259:0.21 264:0.93 265:0.96 266:0.54 267:0.73 271:0.60 274:0.91 275:0.91 276:0.64 281:0.91 287:0.61 289:0.88 294:0.92 300:0.33 +0 11:0.88 12:0.51 17:0.18 21:0.78 27:0.55 30:0.95 34:0.62 36:0.88 37:0.32 39:0.31 43:0.61 66:0.54 69:0.92 74:0.36 85:0.72 91:0.01 98:0.08 101:0.71 108:0.83 123:0.82 127:0.06 129:0.05 135:0.61 144:0.85 146:0.13 147:0.18 149:0.29 154:0.51 159:0.08 172:0.10 173:0.94 177:0.38 178:0.55 179:0.08 187:0.87 216:0.58 222:0.48 241:0.03 243:0.63 253:0.32 259:0.21 265:0.09 267:0.36 271:0.69 300:0.08 +1 11:0.88 12:0.51 17:0.45 21:0.78 27:0.17 30:0.91 34:0.63 36:0.70 37:0.84 39:0.31 43:0.78 66:0.69 69:0.78 70:0.13 74:0.51 85:0.80 91:0.02 98:0.15 101:0.75 108:0.61 122:0.43 123:0.59 127:0.09 129:0.05 135:0.37 144:0.85 146:0.22 147:0.28 149:0.61 154:0.71 159:0.10 172:0.21 173:0.79 177:0.38 178:0.55 179:0.12 187:0.95 212:0.61 216:0.45 222:0.48 235:0.22 241:0.21 242:0.63 243:0.73 247:0.30 253:0.42 259:0.21 265:0.16 266:0.17 267:0.36 271:0.69 276:0.17 300:0.13 +1 11:0.88 12:0.51 17:0.62 21:0.78 27:0.49 30:0.95 34:0.42 36:0.41 37:0.42 39:0.31 43:0.71 66:0.54 69:0.87 74:0.38 85:0.72 91:0.03 98:0.11 101:0.73 108:0.74 123:0.72 127:0.07 129:0.05 135:0.63 144:0.85 146:0.13 147:0.18 149:0.36 154:0.61 159:0.08 172:0.10 173:0.99 177:0.38 178:0.55 179:0.09 187:0.39 202:0.36 216:0.94 222:0.48 235:0.05 241:0.32 243:0.63 253:0.35 259:0.21 265:0.11 267:0.17 271:0.69 300:0.08 +0 1:0.74 7:0.81 11:0.88 12:0.51 17:0.94 21:0.71 27:0.72 30:0.95 39:0.31 43:0.95 66:0.54 69:0.34 74:0.58 81:0.53 83:0.73 85:0.72 91:0.72 98:0.39 101:0.78 108:0.27 114:0.68 121:0.90 123:0.26 126:0.54 127:0.13 129:0.05 144:0.85 145:0.58 146:0.13 147:0.18 149:0.53 150:0.67 154:0.95 155:0.67 159:0.08 165:0.69 172:0.10 173:1.00 176:0.73 177:0.38 178:0.55 179:0.76 192:0.59 201:0.74 204:0.89 208:0.64 215:0.48 216:0.04 222:0.48 230:0.87 233:0.76 235:0.88 241:0.78 243:0.63 253:0.57 265:0.41 271:0.69 290:0.68 297:0.36 300:0.08 +0 12:0.51 17:0.49 21:0.78 27:0.45 30:0.76 34:0.59 36:0.17 37:0.50 39:0.31 43:0.24 55:0.92 66:0.13 69:0.84 70:0.15 74:0.37 91:0.03 98:0.06 108:0.69 122:0.55 123:0.67 124:0.75 127:0.16 129:0.81 133:0.67 135:0.75 145:0.49 146:0.05 147:0.05 149:0.18 154:0.17 159:0.54 163:0.98 172:0.05 173:0.61 175:0.75 177:0.05 178:0.55 179:0.88 187:0.68 212:0.95 216:0.77 222:0.48 235:0.60 241:0.24 242:0.82 243:0.34 244:0.61 245:0.37 247:0.12 253:0.33 257:0.65 259:0.21 265:0.06 266:0.12 267:0.28 271:0.69 276:0.16 277:0.69 300:0.13 +0 11:0.82 12:0.51 17:0.94 21:0.78 30:0.62 34:0.74 36:0.30 37:0.97 39:0.31 43:0.59 55:0.52 66:0.33 69:0.93 70:0.54 74:0.93 91:0.02 98:0.25 108:0.97 122:0.67 123:0.97 124:0.96 127:0.66 129:0.81 133:0.96 135:0.47 144:0.85 145:0.94 146:0.30 147:0.05 149:0.70 154:0.48 159:0.95 172:0.92 173:0.63 177:0.05 178:0.55 179:0.14 187:0.68 212:0.84 216:0.94 222:0.48 235:0.52 241:0.10 242:0.80 243:0.05 245:0.92 247:0.55 253:0.66 257:0.65 259:0.21 265:0.27 266:0.97 267:0.36 271:0.69 276:0.95 300:0.94 +1 11:0.88 12:0.51 17:0.12 21:0.78 27:0.28 30:0.62 34:0.44 36:0.90 37:0.50 39:0.31 43:0.88 55:0.71 66:0.75 69:0.52 70:0.23 74:0.68 85:0.72 91:0.02 98:0.71 101:0.74 108:0.40 122:0.67 123:0.38 127:0.22 129:0.05 135:0.71 144:0.85 146:0.59 147:0.64 149:0.59 154:0.86 159:0.21 163:0.75 172:0.32 173:0.64 177:0.38 178:0.55 179:0.81 187:0.87 212:0.30 216:0.67 222:0.48 235:0.12 241:0.18 242:0.77 243:0.77 247:0.30 253:0.51 259:0.21 265:0.71 266:0.27 267:0.65 271:0.69 276:0.29 300:0.18 +1 11:0.88 12:0.51 17:0.72 21:0.78 27:0.72 30:0.68 39:0.31 43:0.92 66:0.32 69:0.34 70:0.43 74:0.65 85:0.80 91:0.31 98:0.20 101:0.77 108:0.27 122:0.43 123:0.69 124:0.61 127:0.47 129:0.59 133:0.47 144:0.85 145:0.83 146:0.19 147:0.24 149:0.71 154:0.91 159:0.35 172:0.21 173:0.44 177:0.38 178:0.55 179:0.88 187:0.21 212:0.61 216:0.05 222:0.48 235:0.22 241:0.43 242:0.45 243:0.49 245:0.54 247:0.39 253:0.50 265:0.15 266:0.27 271:0.69 276:0.15 300:0.33 +0 11:0.88 12:0.51 17:0.61 21:0.78 27:0.43 30:0.62 34:0.93 36:0.74 37:0.71 39:0.31 43:0.68 55:0.71 66:0.61 69:0.88 70:0.34 74:0.54 85:0.72 91:0.20 98:0.26 101:0.72 108:0.76 122:0.57 123:0.75 124:0.43 127:0.21 129:0.05 133:0.39 135:0.44 144:0.85 145:0.84 146:0.34 147:0.05 149:0.41 154:0.57 159:0.33 172:0.27 173:0.92 177:0.05 178:0.55 179:0.79 187:0.68 212:0.61 216:0.61 222:0.48 235:0.88 241:0.01 242:0.63 243:0.23 245:0.42 247:0.35 253:0.44 257:0.65 259:0.21 265:0.28 266:0.23 267:0.36 271:0.69 276:0.18 300:0.25 +0 12:0.06 17:0.43 21:0.78 27:0.45 30:0.95 34:0.81 36:0.20 37:0.50 43:0.27 55:0.84 66:0.54 69:0.79 91:0.18 98:0.24 108:0.63 123:0.61 127:0.11 129:0.05 135:0.81 145:0.50 146:0.34 147:0.40 149:0.18 154:0.20 159:0.13 172:0.10 173:0.78 177:0.05 178:0.55 179:0.69 187:0.68 216:0.77 235:0.95 241:0.12 243:0.63 259:0.21 265:0.26 267:0.44 300:0.08 +0 7:0.81 12:0.06 17:0.49 21:0.30 27:0.50 30:0.43 32:0.80 34:0.83 36:0.97 37:0.60 43:0.40 55:0.71 66:0.52 69:0.53 70:0.64 81:0.69 91:0.54 98:0.78 104:0.84 106:0.81 108:0.82 120:0.53 123:0.81 124:0.65 126:0.54 127:0.78 129:0.81 133:0.67 135:0.92 140:0.80 145:0.84 146:0.87 147:0.89 149:0.76 150:0.50 151:0.46 153:0.48 154:0.31 159:0.83 162:0.80 163:0.92 164:0.72 172:0.82 173:0.30 177:0.05 178:0.42 179:0.32 187:0.39 195:0.93 202:0.60 206:0.81 212:0.22 215:0.72 216:0.86 220:1.00 230:0.87 233:0.76 235:0.31 241:0.07 242:0.82 243:0.39 245:0.90 247:0.12 248:0.46 256:0.64 259:0.21 260:0.53 265:0.78 266:0.87 267:0.59 268:0.65 276:0.83 283:0.82 285:0.62 297:0.36 300:0.55 +1 8:0.77 12:0.06 17:0.02 20:0.89 21:0.40 25:0.88 27:0.68 30:0.21 34:0.70 36:0.50 37:0.36 43:0.44 55:0.84 66:0.30 69:0.47 70:0.69 78:0.74 81:0.66 91:0.71 98:0.52 100:0.78 104:0.58 108:0.69 111:0.74 120:0.84 123:0.35 124:0.60 126:0.54 127:0.53 129:0.59 133:0.47 135:0.18 140:0.80 145:0.85 146:0.48 147:0.54 149:0.55 150:0.48 151:0.71 152:0.85 153:0.72 154:0.33 159:0.38 162:0.51 164:0.88 169:0.76 172:0.37 173:0.56 177:0.05 178:0.42 179:0.76 186:0.75 202:0.88 212:0.36 215:0.67 216:0.49 220:0.91 235:0.42 241:0.89 242:0.82 243:0.48 245:0.67 247:0.12 248:0.76 256:0.84 259:0.21 260:0.84 261:0.77 265:0.52 266:0.63 267:0.18 268:0.85 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 7:0.81 8:0.92 12:0.06 17:0.43 21:0.59 27:0.72 30:0.95 32:0.80 34:0.61 36:0.55 37:0.68 43:0.49 55:0.92 66:0.54 69:0.34 81:0.58 91:0.80 98:0.89 104:0.58 108:0.27 120:0.73 123:0.26 126:0.54 127:0.45 129:0.05 135:0.28 140:0.80 145:0.58 146:0.39 147:0.45 149:0.22 150:0.43 151:0.66 153:0.48 154:0.38 159:0.15 162:0.59 164:0.80 172:0.10 173:0.78 177:0.05 178:0.42 179:1.00 191:0.75 195:0.53 202:0.75 215:0.55 216:0.34 220:0.74 230:0.87 233:0.76 235:0.68 241:0.90 243:0.63 248:0.66 256:0.75 259:0.21 260:0.74 265:0.89 267:0.94 268:0.75 285:0.62 297:0.36 300:0.08 +0 12:0.06 17:0.49 21:0.78 27:0.45 30:0.76 34:0.50 36:0.46 37:0.50 43:0.26 55:0.71 66:0.52 69:0.82 70:0.29 91:0.20 98:0.40 108:0.67 123:0.65 124:0.50 127:0.18 129:0.59 133:0.47 135:0.85 145:0.47 146:0.58 147:0.64 149:0.33 154:0.18 159:0.34 163:0.86 172:0.27 173:0.78 177:0.05 178:0.55 179:0.67 187:0.68 212:0.23 216:0.77 235:0.74 241:0.27 242:0.82 243:0.61 245:0.48 247:0.12 259:0.21 265:0.42 266:0.38 267:0.65 276:0.31 300:0.25 +0 12:0.06 17:0.49 25:0.88 27:0.72 30:0.76 34:0.40 36:0.39 43:0.35 55:0.84 66:0.36 69:0.62 70:0.15 81:0.79 91:0.72 98:0.32 104:0.54 108:0.47 120:0.69 123:0.45 124:0.52 126:0.54 127:0.46 129:0.59 133:0.47 135:0.24 140:0.45 145:0.41 146:0.30 147:0.36 149:0.25 150:0.59 151:0.66 153:0.48 154:0.26 159:0.30 162:0.86 163:0.96 164:0.57 172:0.10 173:0.76 177:0.05 179:0.99 187:0.21 202:0.36 212:0.95 215:0.96 216:0.14 235:0.02 241:0.84 242:0.82 243:0.51 245:0.37 247:0.12 248:0.63 256:0.45 260:0.70 265:0.35 266:0.12 267:0.89 268:0.46 276:0.15 285:0.62 297:0.36 300:0.13 +0 6:0.84 7:0.81 8:0.79 12:0.06 17:0.62 20:0.80 21:0.30 25:0.88 27:0.72 30:0.45 32:0.80 34:0.44 36:0.41 37:0.82 43:0.34 55:0.52 66:0.27 69:0.65 70:0.25 78:0.81 81:0.82 91:0.78 98:0.26 100:0.87 104:0.54 108:0.50 111:0.82 120:0.61 123:0.48 124:0.61 126:0.54 127:0.31 129:0.59 133:0.47 135:0.34 140:0.45 145:0.74 146:0.23 147:0.29 149:0.30 150:0.61 151:0.54 152:0.79 153:0.48 154:0.25 159:0.21 162:0.76 163:0.81 164:0.80 169:0.85 172:0.10 173:0.87 177:0.05 179:0.96 186:0.81 189:0.85 191:0.85 195:0.57 202:0.71 212:0.61 215:0.72 216:0.30 220:0.87 230:0.87 233:0.76 235:0.42 238:0.96 241:0.85 242:0.82 243:0.46 245:0.40 247:0.12 248:0.55 256:0.75 259:0.21 260:0.62 261:0.81 265:0.28 266:0.17 267:0.52 268:0.76 276:0.16 285:0.62 297:0.36 300:0.18 +4 6:0.97 8:0.93 12:0.06 17:0.02 20:0.97 27:0.11 30:0.45 34:0.18 36:0.25 37:0.87 43:0.49 55:0.84 66:0.27 69:0.21 70:0.25 78:0.92 81:0.79 91:0.95 98:0.36 100:1.00 108:0.77 111:0.99 120:0.95 123:0.75 124:0.72 126:0.54 127:0.85 129:0.81 133:0.67 135:0.17 140:0.45 146:0.05 147:0.05 149:0.28 150:0.59 151:0.82 152:0.91 153:0.48 154:0.38 159:0.47 162:0.58 163:0.89 164:0.96 169:1.00 172:0.10 173:0.30 177:0.05 179:0.98 186:0.92 189:0.97 191:0.86 202:0.94 212:0.61 215:0.96 216:0.49 235:0.02 238:0.99 241:0.88 242:0.82 243:0.29 245:0.38 247:0.12 248:0.93 256:0.95 259:0.21 260:0.95 261:0.99 265:0.38 266:0.17 267:0.69 268:0.95 276:0.21 283:0.82 285:0.62 297:0.36 300:0.18 +1 12:0.06 17:0.84 20:0.82 21:0.05 25:0.88 27:0.72 30:0.68 34:0.45 36:0.46 43:0.49 55:0.71 66:0.79 69:0.23 70:0.31 78:0.72 81:0.77 91:0.81 98:0.84 100:0.73 104:0.58 108:0.18 111:0.72 120:0.61 123:0.18 126:0.54 127:0.35 129:0.05 135:0.70 140:0.45 146:0.62 147:0.68 149:0.47 150:0.57 151:0.56 152:0.78 153:0.77 154:0.38 159:0.23 162:0.68 164:0.64 169:0.72 172:0.41 173:0.46 177:0.05 179:0.84 186:0.73 202:0.54 212:0.42 215:0.91 216:0.10 220:0.62 235:0.05 241:0.84 242:0.82 243:0.80 247:0.12 248:0.55 256:0.45 259:0.21 260:0.62 261:0.73 265:0.84 266:0.27 267:0.73 268:0.57 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +0 6:0.79 8:0.63 12:0.06 17:0.02 20:0.89 21:0.78 25:0.88 27:0.72 34:0.56 36:0.23 78:0.77 91:0.93 100:0.79 104:0.58 111:0.76 120:0.75 135:0.77 140:0.87 151:0.70 152:0.84 153:0.69 162:0.50 164:0.79 169:0.77 175:0.75 178:0.48 186:0.78 189:0.81 202:0.81 216:0.25 220:0.62 238:0.87 241:0.94 244:0.61 248:0.68 256:0.73 257:0.65 259:0.21 260:0.76 261:0.80 267:0.65 268:0.74 277:0.69 285:0.62 +0 6:0.84 7:0.81 8:0.66 12:0.06 17:0.09 20:0.96 21:0.30 27:0.21 30:0.38 34:0.64 36:0.87 37:0.74 43:0.52 55:0.59 66:0.63 69:0.10 70:0.55 78:0.86 81:0.69 91:0.71 98:0.77 100:0.94 104:0.84 106:0.81 108:0.67 111:0.89 120:0.88 123:0.65 124:0.48 126:0.54 127:0.45 129:0.59 133:0.47 135:0.19 140:0.45 146:0.54 147:0.60 149:0.63 150:0.50 151:0.80 152:0.95 153:0.93 154:0.41 159:0.61 162:0.58 164:0.83 169:0.90 172:0.63 173:0.13 177:0.05 179:0.56 186:0.87 187:0.39 189:0.86 202:0.79 206:0.81 212:0.18 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.95 241:0.62 242:0.82 243:0.32 245:0.75 247:0.12 248:0.82 256:0.77 259:0.21 260:0.88 261:0.92 265:0.77 266:0.75 267:0.88 268:0.79 276:0.60 283:0.82 285:0.62 297:0.36 300:0.43 +0 7:0.81 8:0.78 12:0.06 17:0.72 20:0.83 21:0.22 27:0.11 30:0.62 32:0.80 34:0.61 36:0.75 37:0.86 43:0.40 55:0.52 66:0.25 69:0.53 70:0.65 78:0.76 81:0.72 91:0.53 98:0.69 100:0.81 104:0.93 106:0.81 108:0.41 111:0.76 120:0.74 123:0.76 124:0.79 126:0.54 127:0.71 129:0.93 133:0.84 135:0.61 140:0.45 145:0.63 146:0.64 147:0.69 149:0.86 150:0.53 151:0.71 152:0.79 153:0.72 154:0.30 159:0.74 162:0.83 164:0.77 169:0.79 172:0.79 173:0.37 177:0.05 179:0.29 186:0.77 187:0.39 191:0.83 195:0.88 202:0.66 206:0.81 212:0.69 215:0.79 216:0.84 230:0.87 233:0.76 235:0.22 241:0.52 242:0.82 243:0.57 245:0.87 247:0.12 248:0.67 256:0.71 259:0.21 260:0.75 261:0.77 265:0.41 266:0.80 267:0.82 268:0.72 276:0.84 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.96 7:0.81 8:0.86 12:0.06 17:0.38 20:0.83 21:0.54 27:0.40 30:0.62 34:0.19 36:0.77 37:0.81 43:0.28 55:0.96 66:0.20 69:0.77 70:0.54 78:0.81 81:0.61 91:0.73 98:0.27 100:0.89 104:0.58 108:0.88 111:0.83 120:0.81 123:0.87 124:0.85 126:0.54 127:0.37 129:0.93 133:0.84 135:0.42 140:0.87 145:0.44 146:0.43 147:0.50 149:0.40 150:0.45 151:0.71 152:0.79 153:0.48 154:0.21 159:0.65 162:0.51 163:0.98 164:0.84 169:0.86 172:0.21 173:0.66 177:0.05 178:0.48 179:0.76 186:0.82 189:0.97 191:0.73 202:0.85 212:0.28 215:0.58 216:0.33 220:0.96 230:0.87 233:0.76 235:0.60 238:0.96 241:0.79 242:0.82 243:0.34 245:0.49 247:0.12 248:0.73 256:0.80 259:0.21 260:0.81 261:0.82 265:0.30 266:0.63 267:1.00 268:0.80 276:0.42 285:0.62 297:0.36 300:0.43 +1 8:0.95 12:0.06 17:0.34 20:0.86 21:0.78 25:0.88 27:0.60 30:0.76 34:0.34 36:0.42 37:0.78 43:0.47 55:0.59 66:0.36 69:0.37 70:0.15 78:0.74 91:0.90 98:0.25 100:0.78 104:0.58 108:0.29 111:0.74 120:0.59 123:0.28 124:0.52 127:0.56 129:0.59 133:0.47 135:0.17 140:0.96 146:0.24 147:0.30 149:0.27 151:0.53 152:0.82 153:0.46 154:0.36 159:0.28 162:0.48 163:0.79 164:0.72 169:0.76 172:0.10 173:0.57 177:0.05 178:0.54 179:0.99 186:0.75 202:0.80 212:0.95 216:0.16 220:0.87 235:0.22 241:0.96 242:0.82 243:0.51 245:0.37 247:0.12 248:0.52 256:0.68 259:0.21 260:0.60 261:0.76 265:0.27 266:0.12 267:0.69 268:0.66 276:0.14 285:0.62 300:0.13 +1 8:0.70 12:0.06 17:0.03 20:0.95 21:0.78 25:0.88 27:0.63 30:0.95 34:0.53 36:1.00 37:0.32 43:0.46 55:0.96 66:0.13 69:0.44 78:0.74 91:0.81 98:0.08 100:0.78 104:0.58 108:0.34 111:0.74 120:0.94 123:0.32 124:0.75 127:0.34 129:0.81 133:0.67 135:0.37 140:0.87 146:0.05 147:0.05 149:0.24 151:0.80 152:0.90 153:0.92 154:0.35 159:0.38 162:0.55 163:0.97 164:0.94 169:0.76 172:0.05 173:0.46 177:0.05 178:0.48 179:0.98 186:0.75 202:0.93 212:0.95 216:0.62 235:0.52 238:0.89 241:0.88 242:0.82 243:0.34 245:0.37 247:0.12 248:0.91 256:0.92 259:0.21 260:0.94 261:0.77 265:0.08 266:0.12 267:0.65 268:0.92 276:0.19 283:0.82 285:0.62 300:0.08 +0 6:0.81 8:0.80 12:0.06 17:0.83 20:0.87 21:0.30 25:0.88 27:0.72 30:0.21 34:0.45 36:0.62 43:0.46 55:0.59 66:0.80 69:0.41 70:0.67 78:0.75 81:0.69 91:0.73 98:0.94 100:0.78 104:0.58 108:0.32 111:0.75 120:0.71 123:0.31 126:0.54 127:0.61 129:0.05 135:0.28 140:0.45 146:0.67 147:0.72 149:0.49 150:0.50 151:0.66 152:0.84 153:0.48 154:0.35 159:0.26 162:0.86 164:0.67 169:0.76 172:0.45 173:0.64 177:0.05 179:0.87 186:0.76 187:0.21 202:0.50 212:0.55 215:0.72 216:0.08 220:0.87 235:0.31 241:0.86 242:0.82 243:0.82 247:0.12 248:0.64 256:0.58 259:0.21 260:0.72 261:0.77 265:0.94 266:0.27 267:0.44 268:0.59 276:0.36 285:0.62 297:0.36 300:0.43 +2 6:0.87 8:0.76 12:0.06 17:0.28 20:0.79 21:0.78 27:0.21 30:0.62 34:0.80 36:0.92 37:0.74 43:0.39 55:0.92 66:0.23 69:0.56 70:0.65 78:0.87 91:0.46 98:0.27 100:0.90 108:0.91 111:0.87 120:0.76 123:0.90 124:0.88 127:0.51 129:0.95 133:0.87 135:0.58 140:0.80 146:0.45 147:0.52 149:0.47 151:0.66 152:0.92 153:0.48 154:0.30 159:0.77 162:0.48 163:0.94 164:0.70 169:0.90 172:0.32 173:0.35 177:0.05 178:0.42 179:0.68 186:0.87 187:0.39 189:1.00 202:0.77 212:0.27 216:0.95 235:0.52 238:0.90 241:0.32 242:0.82 243:0.27 245:0.56 247:0.12 248:0.69 256:0.64 259:0.21 260:0.77 261:0.91 265:0.29 266:0.84 267:0.44 268:0.63 276:0.52 283:0.82 285:0.62 300:0.55 +1 11:0.88 12:0.51 17:0.26 21:0.78 27:0.45 30:0.91 34:0.80 36:0.19 37:0.50 43:0.76 66:0.27 69:0.82 70:0.13 74:0.49 85:0.80 91:0.05 98:0.08 101:0.73 108:0.66 122:0.43 123:0.64 124:0.61 127:0.13 129:0.59 133:0.47 135:0.60 144:0.85 145:0.49 146:0.13 147:0.18 149:0.55 154:0.68 159:0.25 163:0.88 172:0.10 173:0.73 177:0.38 178:0.55 179:0.66 187:0.68 212:0.61 216:0.77 222:0.99 235:0.80 241:0.52 242:0.63 243:0.46 245:0.40 247:0.30 253:0.41 259:0.21 265:0.08 266:0.17 267:0.28 271:0.19 276:0.12 300:0.13 +1 8:0.61 11:0.88 12:0.51 17:0.03 20:0.95 21:0.54 27:0.55 30:0.45 32:0.72 34:0.86 36:0.27 37:0.32 43:0.80 55:0.84 66:0.67 69:0.41 70:0.57 74:0.43 78:0.97 81:0.49 85:0.72 91:0.35 98:0.75 100:0.73 101:0.72 104:0.93 106:0.81 108:0.31 111:0.94 123:0.30 124:0.47 126:0.32 127:0.49 129:0.05 133:0.62 135:0.81 144:0.85 145:0.69 146:0.82 147:0.85 149:0.67 150:0.39 152:0.88 154:0.74 159:0.53 163:0.75 169:0.72 172:0.57 173:0.37 177:0.38 178:0.55 179:0.67 186:0.97 187:0.87 195:0.72 206:0.81 212:0.16 215:0.58 216:0.62 222:0.99 235:0.60 241:0.01 242:0.78 243:0.72 245:0.57 247:0.39 253:0.37 259:0.21 261:0.92 265:0.75 266:0.63 267:0.52 271:0.19 276:0.52 283:0.71 297:0.36 300:0.43 +1 11:0.88 12:0.51 17:0.77 21:0.78 27:0.26 30:0.71 34:0.90 36:0.88 37:0.64 43:0.67 55:0.92 66:0.82 69:0.75 70:0.24 74:0.44 85:0.80 91:0.64 98:0.79 101:0.75 106:0.81 108:0.59 123:0.56 127:0.28 129:0.05 135:0.80 144:0.85 145:0.42 146:0.85 147:0.88 149:0.59 154:0.56 159:0.42 163:0.94 172:0.48 173:0.75 177:0.38 178:0.55 179:0.72 187:0.68 206:0.81 212:0.61 216:0.40 222:0.99 235:0.42 241:0.03 242:0.73 243:0.83 247:0.39 253:0.38 259:0.21 265:0.79 266:0.38 267:0.44 271:0.19 276:0.40 300:0.18 +1 11:0.88 12:0.51 17:0.84 21:0.78 27:0.43 30:0.85 34:0.58 36:0.88 37:0.42 43:0.77 66:0.89 69:0.79 70:0.27 74:0.77 85:0.95 91:0.60 98:0.80 101:0.84 104:0.99 106:0.81 108:0.63 122:0.43 123:0.60 127:0.28 129:0.05 135:0.65 144:0.85 145:0.42 146:0.88 147:0.90 149:0.88 154:0.70 159:0.47 163:0.81 172:0.70 173:0.77 177:0.38 178:0.55 179:0.47 187:0.68 206:0.81 212:0.57 216:0.41 222:0.99 235:0.42 241:0.03 242:0.63 243:0.90 247:0.30 253:0.56 259:0.21 265:0.80 266:0.47 267:0.36 271:0.19 276:0.58 300:0.25 +1 8:0.59 12:0.51 17:0.47 21:0.74 27:0.12 30:0.76 34:0.73 36:0.38 37:0.78 43:0.25 55:0.96 66:0.13 69:0.83 70:0.29 81:0.33 91:0.12 98:0.16 104:0.95 106:0.81 108:0.93 117:0.86 120:0.55 123:0.92 124:0.89 126:0.32 127:0.37 129:0.95 133:0.87 135:0.20 140:0.45 145:0.49 146:0.05 147:0.05 149:0.23 150:0.30 151:0.48 153:0.48 154:0.18 159:0.82 162:0.86 163:0.97 164:0.55 172:0.10 173:0.61 175:0.75 177:0.05 179:0.88 187:0.95 190:0.77 202:0.36 206:0.81 212:0.23 215:0.46 216:0.81 220:0.96 222:0.99 232:0.97 235:0.88 241:0.30 242:0.82 243:0.21 244:0.61 245:0.40 247:0.12 248:0.48 253:0.20 256:0.45 257:0.65 259:0.21 260:0.55 265:0.18 266:0.38 267:0.44 268:0.46 271:0.19 276:0.31 277:0.69 285:0.50 297:0.36 300:0.25 +1 1:0.74 11:0.88 12:0.51 17:0.51 21:0.22 27:0.57 30:0.95 34:0.47 36:0.35 37:0.31 43:0.96 60:0.87 66:0.54 69:0.15 74:0.58 81:0.82 83:0.85 85:0.72 91:0.20 98:0.15 101:0.76 104:0.98 108:0.12 114:0.90 123:0.12 126:0.54 127:0.09 129:0.05 135:0.58 144:0.85 145:0.38 146:0.13 147:0.18 149:0.53 150:0.89 154:0.96 155:0.67 158:0.87 159:0.08 165:0.86 172:0.10 173:0.57 176:0.73 177:0.38 178:0.55 179:0.16 187:0.95 192:0.59 201:0.74 208:0.64 215:0.79 216:0.90 222:0.99 235:0.31 241:0.12 243:0.63 253:0.69 259:0.21 265:0.16 267:0.23 271:0.19 279:0.86 283:0.71 290:0.90 297:0.36 300:0.08 +1 11:0.88 12:0.51 17:0.06 21:0.13 27:0.33 30:0.62 32:0.72 34:0.82 36:0.36 37:0.41 43:0.76 55:0.84 66:0.47 69:0.79 70:0.34 74:0.40 81:0.83 85:0.72 91:0.44 98:0.37 101:0.73 108:0.63 123:0.61 124:0.52 126:0.32 127:0.21 129:0.05 133:0.39 135:0.95 144:0.85 145:0.47 146:0.39 147:0.34 149:0.52 150:0.62 154:0.68 159:0.27 163:0.89 172:0.21 173:0.86 177:0.05 178:0.55 179:0.81 187:0.68 195:0.65 212:0.30 215:0.84 216:0.88 222:0.99 235:0.12 241:0.05 242:0.63 243:0.37 245:0.46 247:0.35 253:0.36 257:0.65 259:0.21 265:0.39 266:0.27 267:0.91 271:0.19 276:0.26 297:0.36 300:0.25 +2 11:0.88 12:0.51 17:0.22 21:0.78 27:0.33 30:0.76 34:1.00 36:0.43 37:0.69 43:0.85 66:0.64 69:0.61 70:0.29 74:0.61 85:0.85 91:0.15 98:0.56 101:0.79 108:0.47 122:0.43 123:0.45 124:0.42 127:0.29 129:0.05 133:0.39 135:0.51 144:0.85 145:0.61 146:0.54 147:0.60 149:0.75 154:0.82 159:0.31 163:0.81 172:0.32 173:0.68 177:0.38 178:0.55 179:0.84 187:0.68 212:0.52 216:0.76 222:0.99 235:0.52 241:0.27 242:0.73 243:0.69 245:0.43 247:0.30 253:0.48 259:0.21 265:0.58 266:0.23 267:0.65 271:0.19 276:0.28 300:0.25 +1 11:0.88 12:0.51 17:0.08 21:0.13 27:0.69 30:0.62 32:0.72 34:0.82 36:0.37 37:0.44 43:0.76 55:0.84 66:0.47 69:0.79 70:0.34 74:0.40 81:0.83 85:0.72 91:0.48 98:0.37 101:0.73 108:0.63 123:0.61 124:0.52 126:0.32 127:0.21 129:0.05 133:0.39 135:0.94 144:0.85 145:0.47 146:0.39 147:0.34 149:0.52 150:0.62 154:0.68 159:0.27 163:0.89 172:0.21 173:0.86 177:0.05 178:0.55 179:0.81 187:0.68 195:0.65 212:0.30 215:0.84 216:0.25 222:0.99 235:0.12 241:0.05 242:0.63 243:0.37 245:0.46 247:0.35 253:0.36 257:0.65 259:0.21 265:0.39 266:0.27 267:0.88 271:0.19 276:0.26 297:0.36 300:0.25 +2 11:0.88 12:0.51 17:0.82 21:0.78 27:1.00 30:0.76 34:0.50 36:0.60 37:0.40 43:0.82 60:0.87 66:0.72 69:0.68 70:0.22 74:0.63 83:0.83 85:0.80 91:0.31 98:0.17 101:0.75 108:0.52 122:0.41 123:0.50 127:0.10 129:0.05 135:0.20 144:0.85 145:0.82 146:0.24 147:0.30 149:0.65 154:0.77 155:0.67 158:0.87 159:0.11 172:0.27 173:0.71 177:0.38 178:0.55 179:0.13 187:0.39 192:0.59 208:0.64 212:0.78 216:0.10 222:0.99 235:0.52 241:0.61 242:0.45 243:0.75 247:0.35 253:0.49 259:0.21 265:0.19 266:0.17 267:0.23 271:0.19 276:0.23 279:0.86 283:0.71 300:0.18 +1 8:0.60 11:0.88 12:0.51 17:0.32 20:0.99 21:0.78 27:0.14 30:0.95 34:0.28 36:0.35 37:0.67 43:0.82 55:0.59 66:0.99 69:0.60 70:0.15 74:0.43 78:0.86 85:0.72 91:0.42 98:0.97 100:0.73 101:0.69 104:0.82 106:0.81 108:0.46 111:0.83 120:0.58 123:0.44 127:0.75 129:0.05 135:0.78 140:0.45 144:0.85 145:0.52 146:0.99 147:0.99 149:0.94 151:0.52 152:0.90 153:0.48 154:0.77 159:0.82 162:0.86 164:0.55 169:0.72 172:0.98 173:0.38 177:0.38 179:0.13 186:0.86 187:0.39 190:0.77 202:0.36 206:0.81 212:0.94 216:0.93 220:0.87 222:0.99 235:0.12 241:0.53 242:0.82 243:0.99 247:0.39 248:0.52 253:0.38 256:0.45 259:0.21 260:0.58 261:0.84 265:0.97 266:0.23 267:0.97 268:0.46 271:0.19 276:0.96 285:0.50 300:0.43 +1 11:0.88 12:0.51 17:0.40 21:0.78 27:0.43 30:0.76 34:0.88 36:0.19 37:0.35 43:0.21 66:0.20 69:0.98 70:0.22 74:0.32 85:0.80 91:0.01 98:0.05 101:0.64 108:0.97 122:0.41 123:0.97 124:0.73 127:0.11 129:0.59 133:0.64 135:0.76 144:0.85 145:0.44 146:0.13 147:0.05 149:0.19 154:0.14 159:0.46 163:0.88 172:0.10 173:0.86 177:0.05 178:0.55 179:0.23 187:0.95 212:0.42 216:0.82 222:0.99 235:0.42 242:0.45 243:0.26 245:0.40 247:0.35 253:0.29 257:0.65 259:0.21 265:0.05 266:0.23 267:0.36 271:0.19 276:0.18 300:0.18 +1 8:0.82 11:0.88 12:0.51 17:0.93 21:0.78 27:0.44 30:0.86 34:0.78 36:0.67 37:0.45 43:0.58 55:0.52 66:0.97 69:0.84 70:0.23 74:0.42 85:0.80 91:0.46 98:0.50 101:0.69 108:0.69 123:0.67 124:0.36 127:0.26 129:0.05 133:0.39 135:0.68 144:0.85 145:0.56 146:0.95 147:0.96 149:0.91 154:0.47 159:0.86 172:0.97 173:0.52 177:0.38 178:0.55 179:0.12 187:0.21 212:0.94 216:0.41 222:0.99 235:0.95 242:0.82 243:0.97 245:0.80 247:0.47 253:0.37 259:0.21 265:0.51 266:0.23 267:0.69 271:0.19 276:0.89 300:0.55 +2 11:0.88 12:0.51 17:0.05 21:0.13 27:0.33 30:0.62 32:0.72 34:0.76 36:0.36 37:0.41 43:0.76 55:0.84 66:0.47 69:0.79 70:0.34 74:0.40 81:0.83 85:0.72 91:0.44 98:0.37 101:0.73 108:0.63 123:0.61 124:0.52 126:0.32 127:0.21 129:0.05 133:0.39 135:0.95 144:0.85 145:0.47 146:0.40 147:0.34 149:0.52 150:0.62 154:0.67 159:0.27 163:0.89 172:0.21 173:0.86 177:0.05 178:0.55 179:0.81 187:0.68 195:0.65 212:0.30 215:0.84 216:0.88 222:0.99 235:0.12 241:0.12 242:0.63 243:0.37 245:0.46 247:0.35 253:0.36 257:0.65 259:0.21 265:0.39 266:0.27 267:0.91 271:0.19 276:0.26 297:0.36 300:0.25 +2 6:0.83 7:0.76 8:0.85 11:0.88 12:0.51 17:0.36 20:0.99 21:0.40 27:0.21 30:0.33 34:0.59 36:0.79 37:0.74 43:0.90 66:0.12 69:0.12 70:0.69 74:0.53 78:1.00 81:0.61 85:0.80 91:0.37 98:0.21 100:0.98 101:0.71 104:0.84 106:0.81 108:0.10 111:0.99 120:0.81 122:0.43 123:0.92 124:0.72 126:0.32 127:0.40 129:0.59 133:0.64 135:0.24 140:0.45 144:0.85 146:0.29 147:0.35 149:0.67 150:0.45 151:0.70 152:0.90 153:0.91 154:0.89 159:0.81 162:0.57 164:0.79 169:0.98 172:0.21 173:0.09 177:0.38 179:0.86 186:1.00 187:0.39 189:0.85 190:0.77 202:0.76 206:0.81 212:0.42 215:0.67 216:0.95 220:0.62 222:0.99 230:0.78 233:0.69 235:0.42 238:0.89 241:0.47 242:0.45 243:0.49 245:0.48 247:0.47 248:0.73 253:0.43 256:0.73 259:0.21 260:0.81 261:0.96 265:0.15 266:0.38 267:0.59 268:0.75 271:0.19 276:0.24 277:0.69 283:0.71 285:0.50 297:0.36 300:0.43 +2 6:0.96 8:0.85 9:0.80 11:0.57 12:0.69 17:0.03 20:0.99 21:0.78 27:0.59 28:0.66 30:0.89 32:0.80 34:0.53 36:0.57 37:0.31 39:0.79 41:0.98 43:0.76 55:0.92 60:0.95 66:0.61 69:0.76 70:0.15 74:0.77 77:0.70 78:0.88 83:0.89 85:0.80 91:0.81 96:0.97 98:0.73 100:0.92 101:0.78 108:0.60 111:0.88 120:0.93 122:0.57 123:0.57 124:0.43 127:0.48 129:0.05 133:0.39 135:0.61 140:0.45 145:0.91 146:0.58 147:0.05 149:0.61 151:0.98 152:0.92 153:0.93 154:0.67 155:0.67 158:0.87 159:0.30 161:0.82 162:0.52 163:0.81 164:0.92 167:0.99 169:0.89 172:0.27 173:0.91 177:0.05 179:0.93 181:0.56 186:0.88 189:0.97 191:0.85 192:0.59 195:0.58 202:0.92 208:0.64 212:0.30 216:0.52 222:0.48 235:0.60 238:0.93 241:0.94 242:0.63 243:0.23 245:0.42 247:0.35 248:0.97 253:0.97 255:0.79 256:0.90 257:0.65 259:0.21 260:0.94 261:0.90 265:0.73 266:0.27 267:0.79 268:0.90 276:0.27 279:0.86 282:0.88 283:0.71 285:0.62 299:0.88 300:0.13 +2 1:0.74 9:0.80 11:0.57 12:0.69 17:0.08 21:0.40 27:0.28 28:0.66 30:0.62 34:0.29 36:0.86 37:0.55 39:0.79 41:0.90 43:0.98 66:0.61 69:0.15 70:0.34 74:0.58 81:0.60 83:0.78 85:0.80 91:0.53 96:0.85 98:0.26 101:0.76 108:0.12 114:0.82 120:0.76 122:0.43 123:0.12 124:0.43 126:0.54 127:0.39 129:0.05 133:0.39 135:0.39 140:0.45 146:0.30 147:0.36 149:0.72 150:0.76 151:0.92 153:0.72 154:0.98 155:0.67 159:0.39 161:0.82 162:0.59 164:0.73 165:0.83 167:0.87 172:0.27 173:0.25 176:0.73 177:0.38 179:0.92 181:0.56 187:0.21 192:0.59 201:0.74 202:0.68 208:0.64 212:0.84 215:0.67 216:0.67 222:0.48 235:0.52 241:0.74 242:0.63 243:0.68 245:0.42 247:0.35 248:0.83 253:0.83 255:0.79 256:0.64 259:0.21 260:0.76 265:0.28 266:0.17 267:0.28 268:0.68 276:0.15 285:0.62 290:0.82 297:0.36 300:0.25 +2 1:0.74 6:0.96 8:0.92 9:0.80 11:0.57 12:0.69 17:0.02 20:0.80 21:0.13 27:0.28 28:0.66 30:0.76 34:0.28 36:0.41 37:0.55 39:0.79 41:0.90 43:0.83 66:0.36 69:0.65 70:0.22 74:0.50 78:0.81 81:0.75 83:0.81 85:0.80 91:0.42 96:0.89 98:0.29 100:0.87 101:0.75 108:0.80 111:0.82 114:0.92 120:0.89 122:0.41 123:0.79 124:0.57 126:0.54 127:0.50 129:0.59 133:0.47 135:0.56 140:0.80 146:0.13 147:0.18 149:0.59 150:0.84 151:0.93 152:0.98 153:0.78 154:0.80 155:0.67 159:0.48 161:0.82 162:0.59 163:0.89 164:0.87 165:0.88 167:0.76 169:0.99 172:0.16 173:0.66 176:0.73 177:0.38 179:0.96 181:0.56 186:0.82 187:0.39 192:0.59 201:0.74 202:0.84 208:0.64 212:0.42 215:0.84 216:0.67 220:0.74 222:0.48 235:0.22 241:0.63 242:0.45 243:0.40 245:0.43 247:0.35 248:0.88 253:0.87 255:0.79 256:0.84 259:0.21 260:0.89 261:0.98 265:0.31 266:0.23 267:0.44 268:0.84 276:0.15 283:0.82 285:0.62 290:0.92 297:0.36 300:0.18 +1 8:0.65 11:0.57 12:0.69 17:0.24 21:0.78 27:0.45 28:0.66 30:0.20 34:0.66 36:0.25 37:0.50 39:0.79 43:0.76 55:0.71 66:0.24 69:0.76 70:0.97 74:0.61 85:0.72 91:0.08 98:0.31 101:0.69 108:0.60 123:0.57 124:0.89 127:0.38 129:0.05 133:0.89 135:0.78 145:0.42 146:0.61 147:0.67 149:0.69 154:0.68 159:0.76 161:0.82 167:0.68 172:0.51 173:0.58 177:0.38 178:0.55 179:0.40 181:0.56 187:0.68 212:0.48 216:0.77 222:0.48 235:0.60 241:0.35 242:0.78 243:0.44 245:0.70 247:0.55 253:0.48 259:0.21 265:0.33 266:0.87 267:0.44 276:0.68 300:0.94 +2 1:0.74 6:0.96 7:0.81 8:0.86 9:0.80 11:0.57 12:0.69 17:0.32 20:0.99 21:0.30 27:0.21 28:0.66 30:0.17 34:0.34 36:0.84 37:0.74 39:0.79 41:0.92 43:0.98 55:0.71 60:0.95 66:0.35 69:0.12 70:0.90 74:0.88 78:0.87 81:0.65 83:0.82 85:0.91 91:0.62 96:0.92 98:0.62 100:0.89 101:0.77 104:0.84 106:0.81 108:0.10 111:0.86 114:0.86 117:0.86 120:0.85 121:0.90 123:0.10 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.30 140:0.45 146:0.91 147:0.92 149:0.85 150:0.79 151:0.96 152:1.00 153:0.86 154:0.98 155:0.67 158:0.92 159:0.81 161:0.82 162:0.73 164:0.80 165:0.84 167:0.63 169:0.84 172:0.75 173:0.09 176:0.73 177:0.38 179:0.32 181:0.56 186:0.87 187:0.39 189:0.99 192:0.59 201:0.74 202:0.72 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.48 230:0.87 232:0.91 233:0.76 235:0.42 238:0.91 241:0.12 242:0.57 243:0.51 245:0.85 247:0.60 248:0.91 253:0.93 255:0.79 256:0.73 259:0.21 260:0.86 261:0.89 265:0.63 266:0.84 267:0.87 268:0.76 276:0.81 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +1 1:0.74 6:0.96 7:0.81 8:0.62 9:0.80 11:0.57 12:0.69 17:0.38 20:0.99 21:0.47 27:0.21 28:0.66 30:0.13 34:0.53 36:0.87 37:0.74 39:0.79 43:0.79 55:0.42 66:0.20 69:0.17 70:0.83 74:0.91 76:0.85 78:0.80 81:0.54 85:0.80 91:0.22 96:0.76 98:0.50 100:0.82 101:0.73 104:0.92 106:0.81 108:0.14 111:0.79 114:0.80 117:0.86 120:0.65 122:0.43 123:0.83 124:0.79 126:0.54 127:0.57 129:0.89 133:0.78 135:0.20 140:0.45 146:0.66 147:0.71 149:0.72 150:0.64 151:0.69 152:1.00 153:0.48 154:0.92 155:0.67 158:0.86 159:0.67 161:0.82 162:0.65 164:0.69 167:0.65 169:0.84 172:0.61 173:0.15 176:0.73 177:0.38 179:0.41 181:0.56 186:0.81 187:0.39 192:0.59 201:0.74 202:0.61 204:0.89 206:0.81 208:0.64 212:0.40 215:0.63 216:0.95 220:0.82 222:0.48 230:0.83 232:0.85 233:0.66 235:0.60 241:0.21 242:0.43 243:0.48 245:0.82 247:0.55 248:0.69 253:0.82 255:0.79 256:0.58 259:0.21 260:0.65 261:0.89 265:0.46 266:0.63 267:0.52 268:0.62 276:0.74 279:0.86 283:0.71 285:0.62 290:0.80 297:0.36 300:0.55 +3 1:0.74 6:0.96 8:0.93 9:0.80 11:0.57 12:0.69 20:0.98 21:0.05 27:0.28 28:0.66 30:0.58 34:0.44 36:0.60 37:0.55 39:0.79 41:1.00 43:0.83 60:0.95 66:0.18 69:0.65 70:0.60 74:0.64 78:0.97 81:0.81 83:0.90 85:0.85 91:0.97 96:0.99 98:0.17 100:1.00 101:0.72 108:0.95 111:1.00 114:0.95 120:0.98 122:0.41 123:0.95 124:0.82 126:0.54 127:0.83 129:0.81 133:0.76 135:0.42 138:0.99 140:0.45 146:0.13 147:0.18 149:0.58 150:0.88 151:1.00 152:0.98 153:0.99 154:0.80 155:0.67 158:0.92 159:0.87 161:0.82 162:0.65 163:0.90 164:0.98 165:0.90 167:0.98 169:0.99 172:0.16 173:0.38 176:0.73 177:0.38 179:0.89 181:0.56 186:0.96 189:0.97 191:0.89 192:0.59 201:0.74 202:0.96 208:0.64 212:0.37 215:0.91 216:0.67 222:0.48 235:0.12 238:0.98 241:0.90 242:0.16 243:0.29 245:0.48 247:0.60 248:0.99 253:0.99 255:0.79 256:0.97 259:0.21 260:0.98 261:0.98 265:0.18 266:0.47 267:0.92 268:0.97 276:0.32 279:0.86 283:0.82 285:0.62 290:0.95 297:0.36 300:0.43 +1 1:0.68 7:0.70 8:0.75 9:0.73 10:0.88 11:0.86 12:0.20 17:0.85 18:0.98 21:0.54 22:0.93 27:0.51 29:0.91 30:0.75 31:0.93 32:0.72 34:0.42 36:0.66 37:0.46 39:0.55 43:0.32 44:0.92 45:0.98 47:0.93 64:0.77 66:0.98 69:0.75 70:0.32 71:0.63 74:0.84 77:0.89 79:0.93 81:0.63 83:0.69 86:0.96 91:0.37 96:0.80 98:0.91 99:0.95 101:0.65 108:0.59 114:0.74 117:0.86 122:0.86 123:0.56 126:0.51 127:0.51 129:0.05 135:0.28 137:0.93 139:0.96 140:0.45 144:0.85 145:0.83 146:0.96 147:0.97 149:0.88 150:0.57 151:0.85 153:0.48 154:0.18 155:0.58 159:0.67 162:0.86 165:0.65 170:0.82 172:0.96 173:0.66 174:0.88 176:0.63 177:0.88 179:0.17 187:0.39 190:0.95 192:0.86 195:0.85 196:0.97 197:0.86 201:0.65 202:0.36 204:0.85 208:0.61 212:0.92 215:0.58 216:0.39 222:0.60 230:0.73 232:0.88 233:0.76 235:0.88 239:0.87 241:0.50 242:0.57 243:0.99 244:0.83 247:0.74 248:0.76 253:0.85 255:0.72 256:0.45 259:0.21 262:0.93 265:0.91 266:0.47 267:0.28 268:0.46 271:0.78 276:0.92 281:0.91 282:0.80 284:0.88 285:0.50 290:0.74 297:0.36 300:0.43 +2 1:0.66 10:0.84 11:0.84 12:0.20 17:0.15 18:0.69 21:0.30 22:0.93 27:0.38 29:0.91 30:0.33 32:0.67 34:0.64 36:0.17 37:0.66 39:0.55 43:0.30 44:0.88 45:0.73 47:0.93 64:0.77 66:0.45 69:0.85 70:0.36 71:0.63 74:0.44 77:0.89 81:0.63 83:0.56 86:0.69 91:0.54 98:0.16 99:0.83 101:0.47 108:0.71 114:0.82 117:0.86 122:0.85 123:0.69 124:0.66 126:0.51 127:0.24 129:0.05 133:0.60 135:0.96 139:0.72 144:0.85 145:0.47 146:0.23 147:0.18 149:0.20 150:0.56 154:0.23 155:0.51 158:0.72 159:0.37 165:0.65 170:0.82 172:0.27 173:0.87 174:0.79 175:0.75 176:0.63 177:0.38 178:0.55 179:0.75 187:0.87 192:0.51 195:0.72 197:0.82 201:0.64 208:0.61 212:0.42 215:0.72 216:0.66 222:0.60 232:0.86 235:0.60 239:0.83 241:0.18 242:0.19 243:0.32 244:0.83 245:0.47 247:0.55 253:0.58 254:0.88 257:0.84 259:0.21 262:0.93 265:0.17 266:0.38 267:0.28 271:0.78 276:0.28 277:0.69 282:0.75 290:0.82 297:0.36 300:0.25 +0 1:0.59 10:0.82 11:0.84 12:0.20 17:0.08 18:0.90 21:0.40 22:0.93 27:0.42 29:0.91 30:0.14 34:0.83 36:0.55 37:0.72 39:0.55 43:0.56 45:0.83 47:0.93 64:0.77 66:0.53 69:0.44 70:0.96 71:0.63 74:0.52 81:0.50 83:0.56 86:0.80 91:0.23 97:0.94 98:0.71 99:0.83 101:0.47 108:0.34 114:0.76 117:0.86 123:0.32 124:0.52 126:0.32 127:0.78 129:0.05 133:0.36 135:0.93 139:0.79 144:0.85 146:0.77 147:0.81 149:0.60 150:0.44 154:0.45 155:0.47 158:0.75 159:0.56 165:0.65 170:0.82 172:0.63 173:0.41 174:0.79 176:0.62 177:0.88 178:0.55 179:0.59 187:0.87 192:0.45 197:0.82 201:0.56 208:0.50 212:0.80 216:0.55 219:0.90 222:0.60 232:0.85 235:0.52 239:0.82 241:0.02 242:0.38 243:0.62 244:0.93 245:0.81 247:0.79 253:0.57 259:0.21 262:0.93 265:0.72 266:0.63 267:0.28 271:0.78 276:0.57 279:0.78 290:0.76 292:0.95 300:0.84 +1 1:0.66 10:0.84 11:0.84 12:0.20 17:0.20 18:0.72 21:0.30 22:0.93 27:0.38 29:0.91 30:0.58 32:0.67 34:0.75 36:0.17 37:0.66 39:0.55 43:0.31 44:0.88 45:0.77 47:0.93 64:0.77 66:0.61 69:0.85 70:0.44 71:0.63 74:0.48 77:0.89 81:0.63 83:0.56 86:0.71 91:0.37 98:0.26 99:0.83 101:0.47 108:0.71 114:0.82 117:0.86 122:0.86 123:0.69 124:0.47 126:0.51 127:0.16 129:0.05 133:0.43 135:0.93 139:0.73 144:0.85 145:0.47 146:0.38 147:0.18 149:0.28 150:0.56 154:0.23 155:0.51 158:0.72 159:0.26 165:0.65 170:0.82 172:0.37 173:0.83 174:0.79 175:0.75 176:0.63 177:0.38 178:0.55 179:0.44 187:0.87 192:0.51 195:0.78 197:0.82 201:0.64 208:0.61 212:0.37 215:0.72 216:0.66 222:0.60 232:0.86 235:0.60 239:0.83 241:0.02 242:0.40 243:0.29 244:0.83 245:0.52 247:0.55 253:0.59 254:0.88 257:0.84 259:0.21 262:0.93 265:0.28 266:0.47 267:0.44 271:0.78 276:0.27 277:0.69 282:0.75 290:0.82 297:0.36 300:0.33 +0 1:0.64 8:0.60 9:0.73 10:0.82 11:0.84 12:0.20 17:0.67 18:0.94 27:0.72 29:0.91 30:0.54 31:0.93 34:0.36 36:1.00 37:0.42 39:0.55 43:0.57 45:0.83 55:0.71 64:0.77 66:0.90 69:0.05 70:0.43 71:0.63 74:0.52 79:0.93 81:0.60 83:0.56 86:0.84 91:0.54 96:0.80 97:0.89 98:0.94 99:0.83 101:0.47 108:0.05 114:0.92 122:0.92 123:0.05 126:0.32 127:0.63 129:0.05 135:0.94 137:0.93 139:0.81 140:0.45 144:0.85 146:0.82 147:0.85 149:0.49 150:0.55 151:0.85 153:0.48 154:0.51 155:0.56 158:0.75 159:0.38 162:0.86 165:0.65 170:0.82 172:0.71 173:0.19 174:0.79 176:0.62 177:0.88 179:0.61 187:0.39 190:0.95 192:0.55 196:0.94 197:0.82 201:0.60 202:0.36 208:0.50 212:0.72 216:0.04 219:0.90 222:0.60 232:0.92 235:0.05 239:0.82 241:0.15 242:0.44 243:0.90 244:0.83 247:0.79 248:0.76 253:0.80 254:0.80 255:0.72 256:0.45 259:0.21 265:0.94 266:0.47 267:0.52 268:0.46 271:0.78 276:0.60 279:0.76 284:0.88 285:0.50 290:0.92 292:0.95 300:0.33 +0 1:0.60 8:0.66 9:0.72 10:0.84 11:0.84 12:0.20 17:0.47 18:0.98 21:0.59 22:0.93 27:0.38 29:0.91 30:0.70 31:0.90 32:0.66 34:0.75 36:0.28 37:0.66 39:0.55 43:0.31 44:0.88 45:0.98 47:0.93 64:0.77 66:0.68 69:0.77 70:0.72 71:0.63 74:0.75 77:0.70 79:0.90 81:0.53 83:0.56 86:0.92 91:0.33 96:0.74 97:0.89 98:0.76 99:0.83 101:0.47 108:0.60 114:0.71 117:0.86 122:0.91 123:0.57 124:0.46 126:0.51 127:0.58 129:0.05 133:0.45 135:0.98 137:0.90 139:0.91 140:0.45 144:0.85 145:0.52 146:0.96 147:0.96 149:0.84 150:0.45 151:0.65 153:0.48 154:0.16 155:0.56 158:0.75 159:0.80 162:0.86 165:0.65 170:0.82 172:0.94 173:0.59 174:0.86 176:0.63 177:0.88 179:0.18 187:0.39 190:0.95 192:0.56 195:0.92 196:0.94 197:0.86 201:0.59 202:0.36 208:0.61 212:0.71 215:0.55 216:0.66 219:0.90 220:0.74 222:0.60 232:0.86 235:0.80 239:0.83 241:0.02 242:0.54 243:0.72 244:0.83 245:0.97 247:0.88 248:0.63 253:0.72 255:0.71 256:0.45 259:0.21 262:0.93 265:0.76 266:0.80 267:0.59 268:0.46 271:0.78 276:0.92 279:0.76 282:0.75 284:0.88 285:0.50 290:0.71 292:0.95 297:0.36 300:0.84 +0 1:0.57 7:0.70 10:0.81 11:0.84 12:0.20 17:0.61 18:0.86 21:0.59 22:0.93 27:0.34 29:0.91 30:0.76 34:0.66 36:0.79 37:0.46 39:0.55 43:0.40 44:0.88 45:0.90 47:0.93 64:0.77 66:0.09 69:0.65 70:0.46 71:0.63 74:0.62 77:0.70 81:0.45 83:0.56 86:0.81 91:0.20 97:0.97 98:0.20 99:0.83 101:0.47 108:0.49 114:0.69 117:0.86 122:0.86 123:0.85 124:0.91 126:0.32 127:0.45 129:0.95 133:0.90 135:0.32 139:0.85 144:0.85 145:0.59 146:0.33 147:0.39 149:0.61 150:0.40 154:0.30 155:0.56 158:0.77 159:0.81 163:0.81 165:0.65 170:0.82 172:0.32 173:0.40 174:0.79 175:0.91 176:0.62 177:0.38 178:0.55 179:0.56 187:0.87 192:0.57 195:0.95 197:0.80 201:0.55 204:0.85 208:0.61 212:0.27 216:0.53 219:0.91 222:0.60 230:0.72 232:0.86 233:0.65 235:0.74 239:0.80 241:0.24 242:0.58 243:0.37 244:0.93 245:0.62 247:0.47 253:0.56 257:0.84 259:0.21 262:0.93 265:0.18 266:0.80 267:0.65 271:0.78 276:0.58 277:0.87 279:0.81 281:0.86 282:0.74 283:0.82 290:0.69 292:0.95 300:0.55 +0 1:0.63 8:0.77 9:0.73 10:0.83 11:0.84 12:0.20 17:0.83 18:0.93 21:0.05 27:0.55 29:0.91 30:0.72 31:0.95 34:0.20 36:0.94 37:0.44 39:0.55 43:0.48 44:0.88 45:0.94 64:0.77 66:0.45 69:0.54 70:0.39 71:0.63 74:0.65 77:0.70 79:0.95 81:0.57 83:0.56 86:0.87 91:0.25 96:0.85 98:0.68 99:0.83 101:0.47 108:0.41 114:0.89 122:0.91 123:0.40 124:0.84 126:0.32 127:0.69 129:0.59 133:0.87 135:0.95 137:0.95 139:0.86 140:0.45 144:0.85 145:0.65 146:0.92 147:0.93 149:0.74 150:0.53 151:0.85 153:0.48 154:0.37 155:0.56 159:0.80 162:0.86 163:0.81 165:0.65 170:0.82 172:0.73 173:0.33 174:0.83 175:0.75 176:0.62 177:0.70 179:0.40 187:0.39 190:0.95 191:0.80 192:0.56 195:0.91 196:0.96 197:0.84 201:0.59 202:0.50 204:0.83 208:0.50 212:0.30 216:0.37 222:0.60 232:0.87 235:0.12 239:0.82 241:0.12 242:0.54 243:0.58 244:0.93 245:0.77 247:0.76 248:0.82 253:0.84 255:0.72 256:0.58 257:0.65 259:0.21 265:0.68 266:0.84 267:0.44 268:0.59 271:0.78 276:0.76 277:0.69 281:0.91 282:0.74 284:0.92 285:0.50 290:0.89 300:0.33 +1 1:0.61 10:0.87 11:0.84 12:0.20 17:0.24 18:0.70 21:0.22 27:0.13 29:0.91 30:0.62 34:0.50 36:0.22 37:0.79 39:0.55 43:0.37 44:0.88 45:0.73 64:0.77 66:0.75 69:0.67 70:0.34 71:0.63 74:0.49 77:0.70 81:0.53 83:0.56 86:0.73 91:0.35 98:0.53 99:0.83 101:0.47 108:0.51 114:0.82 122:0.86 123:0.49 126:0.32 127:0.16 129:0.05 135:0.87 139:0.76 144:0.85 145:0.54 146:0.40 147:0.47 149:0.22 150:0.48 154:0.50 155:0.51 159:0.15 165:0.65 170:0.82 172:0.32 173:0.84 174:0.79 176:0.62 177:0.88 178:0.55 179:0.63 187:0.95 192:0.49 195:0.57 197:0.82 201:0.58 204:0.81 208:0.50 212:0.30 216:0.83 222:0.60 235:0.31 239:0.85 241:0.12 242:0.33 243:0.77 244:0.61 247:0.39 253:0.59 254:0.93 259:0.21 265:0.55 266:0.27 267:0.44 271:0.78 276:0.18 281:0.91 282:0.74 290:0.82 300:0.25 +1 8:0.66 12:0.20 17:0.51 18:0.62 21:0.78 27:0.25 29:0.98 30:0.72 34:0.47 36:0.97 37:0.56 39:0.86 43:0.12 55:0.52 66:0.61 69:0.84 70:0.56 74:0.41 86:0.67 91:0.10 98:0.31 108:0.84 123:0.83 124:0.55 127:0.21 129:0.59 133:0.64 135:0.91 139:0.65 146:0.23 147:0.05 149:0.21 154:0.46 159:0.59 172:0.65 173:0.69 175:0.75 177:0.05 178:0.55 179:0.31 187:0.95 212:0.75 216:0.87 222:0.84 235:0.05 241:0.07 242:0.72 243:0.10 244:0.61 245:0.70 247:0.60 253:0.32 254:0.74 257:0.84 259:0.21 265:0.33 266:0.63 267:0.52 276:0.53 277:0.69 300:0.70 +0 12:0.20 17:0.53 18:0.87 21:0.74 27:0.40 29:0.98 30:0.21 34:0.93 36:0.72 37:0.83 39:0.86 43:0.14 55:0.52 66:0.25 69:0.60 70:0.88 74:0.59 81:0.23 86:0.81 91:0.10 98:0.55 108:0.80 114:0.54 122:0.77 123:0.79 124:0.83 126:0.26 127:0.52 129:0.59 133:0.82 135:0.88 139:0.77 146:0.29 147:0.35 149:0.32 150:0.23 154:0.14 159:0.76 172:0.57 173:0.41 175:0.75 176:0.61 177:0.38 178:0.55 179:0.41 187:0.68 212:0.41 216:0.30 222:0.84 235:0.88 241:0.10 242:0.72 243:0.16 244:0.61 245:0.78 247:0.79 253:0.39 254:0.94 257:0.65 259:0.21 265:0.56 266:0.84 267:0.36 276:0.72 277:0.87 290:0.54 300:0.70 +2 8:0.79 12:0.20 17:0.30 18:0.66 21:0.78 27:0.48 29:0.98 30:0.11 34:0.76 36:0.74 37:0.47 39:0.86 43:0.12 55:0.59 66:0.09 69:1.00 70:0.98 74:0.48 86:0.67 91:0.02 98:0.18 108:0.96 123:0.96 124:0.99 127:0.86 129:0.05 133:0.99 135:0.37 139:0.65 146:0.63 147:0.63 149:0.35 154:0.10 159:0.99 172:0.75 173:0.96 177:0.05 178:0.55 179:0.15 187:0.95 212:0.26 216:0.74 222:0.84 235:0.68 241:0.03 242:0.55 243:0.16 244:0.61 245:0.87 247:0.94 253:0.34 254:0.92 257:0.84 259:0.21 265:0.20 266:1.00 267:0.23 276:0.95 277:0.87 300:0.94 +0 12:0.20 17:0.78 18:0.87 21:0.78 27:0.72 29:0.98 30:0.32 34:0.69 36:0.52 37:0.70 39:0.86 43:0.07 55:0.59 66:0.12 69:0.93 70:0.94 74:0.40 86:0.86 91:0.11 98:0.20 108:0.93 122:0.86 123:0.86 124:0.92 127:0.19 129:0.59 133:0.91 135:0.30 139:0.81 146:0.72 147:0.76 149:0.15 154:0.20 159:0.87 172:0.57 173:0.64 177:0.70 178:0.55 179:0.16 187:0.39 212:0.27 216:0.11 222:0.84 235:0.12 241:0.03 242:0.32 243:0.32 245:0.79 247:0.84 253:0.31 254:0.97 259:0.21 265:0.22 266:0.91 267:0.23 276:0.78 300:0.94 +0 12:0.20 17:0.49 18:0.89 21:0.78 27:0.40 29:0.98 30:0.33 34:0.94 36:0.61 37:0.83 39:0.86 43:0.14 55:0.52 66:0.13 69:0.60 70:0.86 74:0.33 86:0.82 91:0.14 98:0.25 108:0.66 122:0.86 123:0.89 124:0.85 127:0.50 129:0.05 133:0.82 135:0.92 139:0.79 146:0.29 147:0.35 149:0.24 154:0.10 159:0.71 172:0.27 173:0.44 175:0.75 177:0.38 178:0.55 179:0.70 187:0.68 212:0.18 216:0.30 222:0.84 235:0.84 241:0.10 242:0.54 243:0.28 244:0.83 245:0.58 247:0.74 253:0.28 257:0.65 259:0.21 265:0.17 266:0.75 267:0.44 276:0.47 277:0.87 300:0.70 +0 12:0.20 17:0.38 18:0.75 21:0.78 27:0.43 29:0.98 30:0.17 34:0.55 36:0.73 37:0.53 39:0.86 43:0.12 55:0.92 66:0.08 69:0.73 70:0.71 74:0.70 86:0.71 91:0.03 98:0.34 108:0.93 117:0.86 122:0.77 123:0.85 124:0.96 127:0.80 129:0.93 133:0.96 135:0.88 139:0.69 146:0.62 147:0.67 149:0.35 154:0.09 159:0.92 172:0.61 173:0.39 175:0.75 177:0.38 178:0.55 179:0.27 187:0.68 212:0.23 216:0.81 222:0.84 232:0.85 241:0.07 242:0.77 243:0.17 244:0.61 245:0.82 247:0.74 253:0.41 254:0.97 257:0.65 259:0.21 265:0.30 266:0.96 267:0.44 276:0.86 277:0.69 300:0.43 +0 12:0.20 17:0.26 18:0.65 21:0.78 27:0.67 29:0.98 30:0.76 34:1.00 36:0.43 37:0.23 39:0.86 43:0.13 55:0.84 66:0.64 69:0.68 70:0.15 74:0.32 86:0.65 91:0.23 98:0.57 104:0.87 106:0.81 108:0.52 122:0.83 123:0.50 127:0.17 129:0.05 135:0.51 139:0.63 146:0.54 147:0.60 149:0.10 154:0.10 159:0.19 163:0.94 172:0.16 173:0.77 177:0.70 178:0.55 179:0.91 187:0.39 206:0.81 212:0.95 216:0.42 222:0.84 235:0.68 241:0.41 242:0.82 243:0.69 244:0.83 247:0.12 253:0.27 259:0.21 265:0.58 266:0.12 267:0.44 276:0.18 283:0.78 300:0.13 +0 11:0.64 12:0.20 17:0.70 18:0.65 21:0.78 27:0.72 29:0.98 30:0.45 34:0.74 36:0.55 39:0.86 43:0.59 45:0.66 55:0.45 66:0.82 69:0.69 70:0.61 74:0.46 86:0.65 91:0.23 98:0.72 101:0.52 108:0.52 122:0.41 123:0.50 127:0.22 129:0.05 135:0.34 139:0.63 146:0.55 147:0.61 149:0.29 154:0.50 159:0.20 172:0.48 173:0.85 177:0.70 178:0.55 179:0.64 187:0.21 212:0.50 216:0.08 222:0.84 235:0.02 241:0.50 242:0.33 243:0.83 244:0.61 247:0.55 253:0.34 259:0.21 265:0.72 266:0.38 267:0.44 276:0.38 300:0.43 +1 12:0.20 17:0.75 18:0.85 21:0.78 27:0.72 29:0.98 30:0.28 34:0.31 36:0.74 39:0.86 43:0.16 55:0.52 66:0.87 69:0.36 70:0.54 74:0.60 86:0.90 91:0.76 98:0.91 108:0.28 122:0.80 123:0.27 127:0.51 129:0.05 135:0.18 139:0.85 146:0.80 147:0.83 149:0.15 154:0.61 159:0.36 172:0.63 173:0.46 177:0.70 178:0.55 179:0.68 187:0.21 212:0.55 216:0.01 222:0.84 235:0.05 241:0.77 242:0.40 243:0.88 247:0.67 253:0.38 254:0.99 259:0.21 265:0.91 266:0.54 267:0.36 276:0.52 300:0.43 +0 12:0.20 17:0.30 18:0.98 21:0.78 22:0.93 27:0.60 29:0.98 30:0.21 34:0.61 36:0.82 37:0.33 39:0.86 43:0.13 47:0.93 55:0.71 66:0.19 69:0.97 70:0.80 74:0.43 86:0.96 91:0.03 98:0.52 108:0.96 122:0.86 123:0.96 124:0.95 127:0.94 129:0.05 133:0.95 135:0.88 139:0.94 146:0.93 147:0.05 149:0.41 154:0.16 159:0.94 172:0.88 173:0.88 177:0.05 178:0.55 179:0.15 187:0.87 212:0.30 216:0.29 222:0.84 241:0.12 242:0.78 243:0.05 245:0.95 247:0.79 253:0.33 254:0.97 257:0.84 259:0.21 262:0.93 265:0.54 266:0.94 267:0.79 276:0.95 277:0.87 300:0.70 +0 12:0.20 17:0.49 18:0.87 21:0.74 27:0.40 29:0.98 30:0.21 34:0.92 36:0.73 37:0.83 39:0.86 43:0.14 55:0.52 66:0.25 69:0.60 70:0.88 74:0.59 81:0.23 86:0.81 91:0.09 98:0.55 108:0.80 114:0.54 122:0.77 123:0.79 124:0.83 126:0.26 127:0.52 129:0.59 133:0.82 135:0.88 139:0.77 146:0.29 147:0.35 149:0.32 150:0.23 154:0.14 159:0.76 172:0.57 173:0.41 175:0.75 176:0.61 177:0.38 178:0.55 179:0.41 187:0.68 212:0.41 216:0.30 222:0.84 235:0.88 241:0.10 242:0.72 243:0.16 244:0.61 245:0.78 247:0.79 253:0.39 254:0.94 257:0.65 259:0.21 265:0.56 266:0.84 267:0.36 276:0.72 277:0.87 290:0.54 300:0.70 +0 8:0.64 11:0.64 12:0.20 17:0.94 18:0.92 21:0.78 27:0.70 29:0.98 30:0.11 34:0.34 36:0.47 37:0.54 39:0.86 43:0.16 44:0.87 45:0.66 48:0.97 55:0.52 66:0.35 69:0.41 70:0.99 74:0.63 86:0.87 91:0.15 98:0.27 101:0.52 108:0.97 122:0.86 123:0.97 124:0.97 127:0.97 129:0.81 133:0.97 135:0.72 139:0.86 145:0.94 146:0.58 147:0.64 149:0.39 154:0.09 159:0.95 172:0.90 173:0.09 175:0.75 177:0.38 178:0.55 179:0.18 187:0.39 195:0.99 212:0.59 216:0.14 222:0.84 235:0.12 241:0.03 242:0.74 243:0.12 244:0.61 245:0.87 247:0.79 253:0.39 257:0.65 259:0.21 265:0.30 266:0.98 267:0.36 276:0.93 277:0.69 281:0.89 300:0.98 +0 7:0.66 12:0.20 17:0.36 18:0.69 21:0.64 27:0.42 29:0.98 30:0.76 32:0.64 34:0.95 36:0.81 37:0.47 39:0.86 43:0.17 55:0.92 66:0.25 69:0.39 70:0.21 74:0.34 81:0.26 86:0.71 91:0.15 98:0.45 104:0.99 106:0.81 108:0.30 123:0.58 124:0.71 126:0.26 127:0.29 129:0.05 133:0.59 135:0.71 139:0.68 145:0.49 146:0.39 147:0.45 149:0.14 150:0.25 154:0.49 159:0.39 163:0.94 172:0.16 173:0.39 177:0.70 178:0.55 179:0.85 187:0.99 195:0.70 206:0.81 212:0.52 215:0.38 216:0.66 222:0.84 230:0.69 233:0.73 235:0.74 241:0.01 242:0.24 243:0.45 244:0.61 245:0.45 247:0.47 253:0.28 254:0.96 259:0.21 265:0.34 266:0.27 267:0.59 276:0.32 277:0.87 297:0.36 300:0.18 +0 8:0.64 12:0.20 17:0.26 18:0.99 21:0.71 27:0.52 29:0.98 30:0.15 31:0.86 34:0.64 36:0.55 37:0.40 39:0.86 43:0.15 55:0.84 64:0.77 66:0.16 69:0.87 70:0.90 74:0.36 79:0.86 81:0.24 86:0.97 91:0.18 98:0.44 108:0.75 122:0.86 123:0.73 124:0.96 126:0.26 127:0.93 129:0.05 133:0.96 135:0.20 137:0.86 139:0.96 140:0.80 146:0.96 147:0.76 149:0.46 150:0.24 151:0.46 153:0.46 154:0.20 159:0.95 162:0.69 172:0.83 173:0.52 177:0.05 178:0.42 179:0.16 187:0.87 190:0.89 196:0.87 202:0.55 212:0.18 216:0.82 219:0.89 220:0.99 222:0.84 235:0.84 241:0.12 242:0.81 243:0.12 244:0.61 245:0.92 247:0.67 248:0.46 253:0.29 254:0.84 256:0.58 257:0.84 259:0.21 265:0.46 266:0.97 267:0.23 268:0.58 276:0.94 284:0.87 285:0.47 292:0.91 300:0.84 +1 12:0.20 17:0.72 18:0.81 21:0.78 27:0.72 29:0.98 30:0.45 34:0.50 36:0.84 39:0.86 43:0.16 55:0.52 66:0.85 69:0.36 70:0.57 74:0.58 86:0.89 91:0.77 98:0.89 108:0.28 122:0.80 123:0.27 127:0.44 129:0.05 135:0.26 139:0.84 146:0.73 147:0.77 149:0.13 154:0.65 159:0.29 172:0.57 173:0.51 177:0.70 178:0.55 179:0.73 202:0.36 212:0.69 216:0.01 222:0.84 235:0.05 241:0.83 242:0.22 243:0.86 247:0.74 253:0.38 254:0.99 259:0.21 265:0.89 266:0.38 267:0.44 276:0.46 300:0.43 +1 1:0.74 7:0.66 11:0.57 12:0.37 17:0.36 18:0.91 21:0.68 27:0.02 28:0.87 29:0.56 30:0.68 32:0.80 34:0.92 36:0.54 37:0.92 39:0.79 43:0.88 44:0.93 48:0.97 64:0.77 66:0.60 69:0.55 70:0.56 71:0.62 74:0.82 76:0.85 77:0.70 81:0.44 83:0.91 85:0.93 86:0.96 91:0.31 98:0.59 101:0.87 108:0.42 114:0.56 122:0.57 123:0.41 124:0.67 126:0.54 127:0.66 129:0.59 133:0.73 135:0.69 139:0.96 145:0.82 146:0.86 147:0.88 149:0.93 150:0.70 154:0.86 155:0.67 159:0.78 161:0.85 163:0.90 165:0.93 167:0.58 172:0.75 173:0.36 176:0.73 177:0.70 178:0.55 179:0.44 181:0.60 187:0.39 192:0.87 195:0.92 201:0.93 208:0.64 212:0.30 215:0.37 216:0.99 222:0.35 230:0.69 233:0.76 235:0.88 241:0.24 242:0.29 243:0.67 245:0.76 247:0.82 253:0.45 259:0.21 265:0.60 266:0.71 267:0.89 271:0.31 276:0.70 281:0.91 282:0.88 290:0.56 297:0.36 299:0.88 300:0.70 +4 6:0.84 8:0.84 9:0.80 12:0.37 17:0.51 18:0.96 20:0.88 21:0.30 22:0.93 27:0.72 28:0.87 29:0.56 30:0.11 31:0.99 34:0.92 36:0.49 39:0.79 41:0.95 43:0.17 47:0.93 55:0.52 64:0.77 66:0.61 69:0.30 70:0.76 71:0.62 78:0.91 79:1.00 81:0.33 83:0.91 86:0.92 91:0.79 96:0.92 98:0.77 100:0.95 108:0.60 111:0.91 120:0.86 122:0.86 123:0.57 124:0.66 126:0.26 127:0.74 129:0.89 133:0.78 135:0.30 137:0.99 139:0.89 140:0.97 146:0.24 147:0.30 149:0.33 150:0.30 151:0.91 152:0.95 153:0.97 154:0.12 155:0.67 159:0.68 161:0.85 162:0.74 164:0.78 167:0.75 169:0.94 172:0.68 173:0.22 175:0.91 177:0.05 179:0.55 181:0.60 186:0.93 187:0.21 189:0.95 191:0.84 192:0.87 196:0.99 202:0.92 208:0.64 212:0.30 216:0.32 222:0.35 235:0.31 238:0.91 241:0.69 242:0.82 243:0.19 244:0.83 245:0.69 247:0.12 248:0.92 253:0.89 255:0.95 256:0.96 257:0.84 260:0.87 261:0.94 262:0.93 265:0.77 266:0.75 267:0.91 268:0.95 271:0.31 276:0.66 277:0.87 284:0.99 285:0.62 300:0.55 +4 9:0.80 12:0.37 17:0.13 18:0.97 21:0.40 22:0.93 27:0.72 28:0.87 29:0.56 30:0.26 31:0.98 39:0.79 41:0.82 43:0.17 47:0.93 55:0.71 64:0.77 66:0.63 69:0.33 70:0.73 71:0.62 78:0.95 79:0.99 81:0.30 83:0.91 86:0.93 91:0.78 96:0.76 98:0.68 108:0.79 111:0.97 120:0.65 122:0.86 123:0.78 124:0.52 126:0.26 127:0.38 129:0.81 133:0.67 137:0.98 139:0.91 140:0.94 146:0.65 147:0.70 149:0.34 150:0.28 151:0.75 153:0.95 154:0.12 155:0.67 159:0.72 161:0.85 162:0.70 164:0.64 167:0.68 169:0.93 172:0.75 173:0.18 175:0.91 177:0.05 179:0.37 181:0.60 187:0.39 190:0.89 191:0.86 192:0.87 196:0.98 202:0.85 208:0.64 212:0.23 216:0.32 219:0.89 222:0.35 235:0.42 238:0.96 241:0.55 242:0.82 243:0.31 244:0.83 245:0.78 247:0.12 248:0.69 253:0.61 255:0.95 256:0.91 257:0.84 260:0.65 262:0.93 265:0.69 266:0.80 268:0.88 271:0.31 276:0.71 277:0.87 284:0.98 285:0.62 292:0.91 300:0.55 +2 1:0.74 6:0.80 7:0.81 8:0.60 11:0.57 12:0.37 17:0.51 18:0.90 20:0.88 21:0.59 22:0.93 27:0.72 28:0.87 29:0.56 30:0.76 32:0.80 34:0.92 36:0.25 39:0.79 43:0.89 44:0.93 47:0.93 60:0.99 64:0.77 66:0.79 69:0.52 70:0.37 71:0.62 74:0.83 77:0.70 78:0.87 81:0.47 83:0.91 85:0.88 86:0.93 91:0.11 98:0.68 100:0.88 101:0.87 104:0.89 106:0.81 108:0.40 111:0.85 114:0.58 117:0.86 121:0.90 122:0.57 123:0.38 124:0.38 126:0.54 127:0.32 129:0.05 133:0.37 135:0.39 139:0.96 145:0.85 146:0.70 147:0.74 149:0.89 150:0.71 152:0.93 154:0.87 155:0.67 158:0.91 159:0.38 161:0.85 165:0.93 167:0.58 169:0.80 172:0.63 173:0.54 176:0.73 177:0.70 178:0.55 179:0.57 181:0.60 186:0.90 187:0.87 192:0.87 195:0.68 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.59 215:0.39 216:0.28 219:0.95 222:0.35 230:0.87 232:0.92 233:0.76 235:0.80 238:0.83 241:0.21 242:0.37 243:0.80 245:0.54 247:0.55 253:0.46 261:0.89 262:0.93 265:0.68 266:0.38 267:0.73 271:0.31 276:0.51 279:0.86 281:0.91 282:0.88 283:0.78 290:0.58 292:0.91 297:0.36 299:0.88 300:0.43 +2 1:0.74 11:0.64 12:0.37 17:0.30 18:0.89 21:0.54 27:0.54 28:0.87 29:0.56 30:0.21 31:0.92 34:0.44 36:0.23 37:0.58 39:0.79 43:0.90 45:0.77 64:0.77 66:0.49 69:0.47 70:0.80 71:0.62 74:0.65 79:0.93 81:0.46 83:0.60 86:0.92 91:0.46 98:0.42 99:0.83 101:0.52 108:0.36 114:0.59 122:0.82 123:0.34 124:0.79 126:0.54 127:0.62 129:0.05 133:0.81 135:0.84 137:0.92 139:0.89 140:0.45 146:0.68 147:0.73 149:0.79 150:0.67 151:0.60 153:0.48 154:0.89 155:0.67 159:0.74 161:0.85 162:0.62 165:0.70 167:0.56 172:0.57 173:0.30 176:0.73 177:0.70 179:0.61 181:0.60 187:0.68 190:0.89 192:0.87 196:0.94 201:0.93 202:0.50 208:0.64 212:0.35 215:0.39 216:0.41 222:0.35 235:0.74 241:0.15 242:0.18 243:0.60 245:0.63 247:0.82 248:0.59 253:0.44 256:0.45 259:0.21 265:0.44 266:0.75 267:0.73 268:0.46 271:0.31 276:0.58 284:0.86 285:0.47 290:0.59 297:0.36 300:0.70 +1 1:0.74 7:0.66 11:0.83 12:0.37 17:0.24 18:0.91 21:0.13 27:0.02 28:0.87 29:0.56 30:0.53 34:0.83 36:0.22 37:0.92 39:0.79 43:0.89 45:0.66 48:0.91 60:0.95 64:0.77 66:0.72 69:0.50 70:0.66 71:0.62 74:0.85 76:0.85 81:0.75 83:0.91 85:0.94 86:0.96 91:0.11 98:0.68 101:0.87 108:0.56 114:0.79 122:0.57 123:0.53 124:0.49 126:0.54 127:0.68 129:0.81 133:0.78 135:0.89 139:0.96 145:0.77 146:0.17 147:0.22 149:0.92 150:0.84 154:0.87 155:0.67 158:0.91 159:0.82 161:0.85 165:0.93 167:0.67 172:0.87 173:0.27 176:0.73 177:0.70 178:0.55 179:0.32 181:0.60 187:0.39 192:0.87 201:0.93 202:0.50 208:0.64 212:0.54 215:0.61 216:0.99 219:0.95 222:0.35 230:0.69 233:0.76 235:0.31 241:0.53 242:0.23 243:0.12 245:0.80 247:0.90 253:0.57 259:0.21 265:0.69 266:0.87 267:0.91 271:0.31 276:0.80 279:0.86 281:0.91 283:0.78 290:0.79 292:0.91 297:0.36 300:0.94 +2 1:0.74 6:0.93 7:0.81 8:0.88 9:0.80 11:0.57 12:0.37 17:0.75 18:0.96 20:0.92 21:0.13 27:0.56 28:0.87 29:0.56 30:0.58 31:0.98 34:0.37 36:0.54 39:0.79 41:0.90 43:0.93 44:0.87 48:0.97 55:0.71 64:0.77 66:0.88 69:0.29 70:0.40 71:0.62 74:0.58 78:0.95 79:0.98 81:0.75 83:0.91 85:0.72 86:0.94 91:0.54 96:0.91 98:0.96 100:0.99 101:0.87 108:0.22 111:0.96 114:0.79 120:0.84 121:0.90 122:0.86 123:0.22 126:0.54 127:0.72 129:0.05 135:0.39 137:0.98 139:0.95 140:0.87 145:0.42 146:0.66 147:0.71 149:0.68 150:0.84 151:0.90 152:0.94 153:0.78 154:0.93 155:0.67 159:0.25 161:0.85 162:0.58 164:0.73 165:0.93 167:0.89 169:0.95 172:0.65 173:0.55 176:0.73 177:0.70 179:0.70 181:0.60 186:0.98 189:0.95 192:0.87 195:0.56 196:0.99 201:0.93 202:0.78 204:0.89 208:0.64 212:0.87 215:0.61 216:0.10 219:0.89 222:0.35 228:0.99 230:0.87 233:0.76 235:0.31 238:0.95 241:0.82 242:0.80 243:0.88 247:0.35 248:0.90 253:0.89 255:0.95 256:0.80 259:0.21 260:0.85 261:0.97 265:0.96 266:0.27 267:0.69 268:0.78 271:0.31 276:0.55 281:0.91 284:0.96 285:0.62 290:0.79 292:0.95 297:0.36 300:0.33 +2 1:0.74 9:0.80 11:0.57 12:0.37 17:0.62 18:0.67 20:0.83 21:0.68 22:0.93 27:0.36 28:0.87 29:0.56 30:0.95 31:0.89 34:0.98 36:0.32 37:0.64 39:0.79 41:0.82 43:0.89 47:0.93 64:0.77 66:0.64 69:0.51 71:0.62 74:0.45 78:0.85 79:0.89 81:0.44 83:0.91 85:0.72 86:0.76 91:0.33 96:0.73 98:0.12 100:0.87 101:0.87 104:0.97 106:0.81 108:0.39 111:0.84 114:0.56 117:0.86 120:0.60 122:0.57 123:0.38 126:0.54 127:0.08 129:0.05 135:0.58 137:0.89 139:0.73 140:0.45 145:0.67 146:0.17 147:0.22 149:0.62 150:0.70 151:0.63 152:0.79 153:0.69 154:0.88 155:0.67 159:0.09 161:0.85 162:0.86 163:0.90 164:0.62 165:0.93 167:0.68 169:0.85 172:0.16 173:0.61 176:0.73 177:0.70 179:0.09 181:0.60 186:0.85 187:0.87 192:0.87 196:0.90 201:0.93 202:0.46 206:0.81 208:0.64 212:0.95 215:0.37 216:0.74 220:0.74 222:0.35 232:0.98 235:0.88 241:0.56 242:0.82 243:0.69 247:0.12 248:0.61 253:0.51 255:0.95 256:0.45 259:0.21 260:0.61 261:0.81 262:0.93 265:0.13 266:0.12 267:0.52 268:0.55 271:0.31 276:0.19 284:0.90 285:0.62 290:0.56 297:0.36 300:0.08 +2 1:0.74 6:0.93 7:0.81 8:0.94 9:0.80 11:0.57 12:0.37 17:0.75 18:0.96 20:0.84 21:0.13 22:0.93 27:0.56 28:0.87 29:0.56 30:0.58 31:0.99 34:0.56 36:0.55 39:0.79 41:0.94 43:0.93 44:0.87 47:0.93 48:0.97 55:0.71 64:0.77 66:0.88 69:0.29 70:0.40 71:0.62 74:0.58 78:0.96 79:0.99 81:0.75 83:0.91 85:0.72 86:0.94 91:0.61 96:0.92 98:0.92 100:0.98 101:0.87 108:0.22 111:0.96 114:0.79 120:0.87 121:0.90 122:0.86 123:0.22 126:0.54 127:0.53 129:0.05 135:0.39 137:0.99 139:0.95 140:0.45 145:0.42 146:0.66 147:0.71 149:0.68 150:0.84 151:0.94 152:0.94 153:0.82 154:0.93 155:0.67 159:0.25 161:0.85 162:0.76 164:0.82 165:0.93 167:0.75 169:0.96 172:0.65 173:0.52 176:0.73 177:0.70 179:0.66 181:0.60 186:0.98 187:0.21 189:0.97 191:0.77 192:0.87 195:0.57 196:0.99 201:0.93 202:0.83 204:0.89 208:0.64 212:0.87 215:0.61 216:0.10 219:0.89 222:0.35 228:0.99 230:0.87 233:0.76 235:0.31 238:0.93 241:0.68 242:0.80 243:0.88 247:0.35 248:0.91 253:0.90 255:0.95 256:0.87 259:0.21 260:0.87 261:0.97 262:0.93 265:0.92 266:0.27 267:0.76 268:0.86 271:0.31 276:0.55 281:0.91 284:0.98 285:0.62 290:0.79 292:0.91 297:0.36 300:0.33 +4 6:0.84 8:0.67 9:0.80 12:0.37 17:0.38 18:0.96 20:0.87 21:0.30 22:0.93 27:0.72 28:0.87 29:0.56 30:0.11 31:1.00 34:0.93 36:0.49 39:0.79 41:0.96 43:0.17 47:0.93 55:0.52 64:0.77 66:0.61 69:0.30 70:0.76 71:0.62 78:0.95 79:1.00 81:0.33 83:0.91 86:0.92 91:0.69 96:0.95 98:0.77 100:0.98 108:0.60 111:0.96 120:0.90 122:0.86 123:0.57 124:0.66 126:0.26 127:0.74 129:0.89 133:0.78 135:0.28 137:1.00 139:0.90 140:0.95 146:0.24 147:0.30 149:0.33 150:0.30 151:0.94 152:0.95 153:0.97 154:0.12 155:0.67 159:0.68 161:0.85 162:0.74 164:0.84 167:0.69 169:0.94 172:0.68 173:0.22 175:0.91 177:0.05 179:0.55 181:0.60 186:0.97 187:0.39 189:0.83 191:0.82 192:0.87 196:1.00 202:0.93 208:0.64 212:0.30 216:0.32 219:0.89 222:0.35 235:0.31 238:0.96 241:0.58 242:0.82 243:0.19 244:0.83 245:0.69 247:0.12 248:0.95 253:0.92 255:0.95 256:0.97 257:0.84 260:0.91 261:0.94 262:0.93 265:0.77 266:0.75 267:0.44 268:0.95 271:0.31 276:0.66 277:0.87 284:0.99 285:0.62 292:0.95 300:0.55 +3 1:0.74 7:0.66 9:0.80 11:0.57 12:0.37 17:0.36 18:0.95 21:0.76 22:0.93 27:0.72 28:0.87 29:0.56 30:0.15 31:0.92 39:0.79 41:0.88 43:0.88 47:0.93 48:0.97 53:1.00 60:0.95 64:0.77 66:0.17 69:0.56 70:0.94 71:0.62 74:0.67 76:0.85 79:0.91 81:0.43 83:0.91 85:0.88 86:0.94 91:0.37 96:0.77 98:0.34 101:0.87 108:0.90 114:0.54 120:0.65 122:0.86 123:0.90 124:0.90 126:0.54 127:0.73 129:0.81 133:0.89 137:0.92 139:0.94 140:0.45 145:0.40 146:0.70 147:0.74 149:0.80 150:0.69 151:0.75 153:0.72 154:0.86 155:0.67 158:0.91 159:0.88 161:0.85 162:0.86 164:0.69 165:0.93 167:0.60 172:0.51 173:0.27 176:0.73 177:0.70 179:0.42 181:0.60 187:0.21 192:0.87 196:0.95 201:0.93 202:0.58 208:0.64 212:0.35 215:0.36 216:0.10 219:0.95 220:0.62 222:0.35 230:0.69 233:0.76 235:0.98 241:0.32 242:0.71 243:0.36 245:0.77 247:0.67 248:0.70 253:0.70 255:0.95 256:0.64 260:0.66 262:0.93 265:0.36 266:0.94 268:0.66 271:0.31 276:0.74 279:0.86 281:0.91 283:0.78 284:0.94 285:0.62 290:0.54 292:0.91 297:0.36 300:0.84 +1 1:0.74 11:0.57 12:0.37 17:0.90 18:0.88 21:0.22 22:0.93 27:0.72 28:0.87 29:0.56 30:0.88 34:0.79 36:0.28 39:0.79 43:0.93 47:0.93 60:0.95 64:0.77 66:0.85 69:0.33 70:0.29 71:0.62 74:0.70 81:0.67 83:0.91 85:0.85 86:0.93 91:0.37 98:0.63 101:0.87 104:0.95 106:0.81 108:0.26 114:0.71 117:0.86 122:0.57 123:0.25 126:0.54 127:0.18 129:0.05 135:0.34 139:0.93 146:0.63 147:0.68 149:0.86 150:0.80 154:0.93 155:0.67 158:0.92 159:0.23 161:0.85 165:0.93 167:0.53 172:0.57 173:0.37 176:0.73 177:0.70 178:0.55 179:0.43 181:0.60 187:0.87 192:0.87 201:0.93 206:0.81 208:0.64 212:0.69 215:0.51 216:0.05 219:0.95 222:0.35 232:0.96 235:0.42 241:0.03 242:0.38 243:0.86 247:0.47 253:0.53 262:0.93 265:0.64 266:0.38 267:0.44 271:0.31 276:0.43 279:0.86 283:0.82 290:0.71 292:0.95 297:0.36 300:0.33 +1 1:0.74 11:0.83 12:0.37 17:0.36 18:0.65 21:0.30 27:0.45 28:0.87 29:0.56 30:0.89 34:0.82 36:0.18 37:0.50 39:0.79 43:0.82 45:0.66 64:0.77 66:0.30 69:0.69 70:0.20 71:0.62 74:0.49 81:0.61 83:0.91 85:0.72 86:0.74 91:0.22 98:0.12 101:0.87 108:0.52 114:0.65 122:0.55 123:0.81 124:0.61 126:0.54 127:0.33 129:0.05 133:0.43 135:0.80 139:0.72 145:0.52 146:0.17 147:0.22 149:0.61 150:0.77 154:0.77 155:0.67 159:0.48 161:0.85 163:0.80 165:0.93 167:0.54 172:0.16 173:0.66 176:0.73 177:0.70 178:0.55 179:0.90 181:0.60 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.44 216:0.77 222:0.35 235:0.52 241:0.07 242:0.33 243:0.48 245:0.47 247:0.39 253:0.49 259:0.21 265:0.10 266:0.27 267:0.36 271:0.31 276:0.13 277:0.69 290:0.65 297:0.36 300:0.18 +2 1:0.74 6:0.93 8:0.58 9:0.80 11:0.57 12:0.37 17:0.06 18:0.79 20:0.84 21:0.22 27:0.56 28:0.87 29:0.56 30:0.76 31:0.86 34:0.21 36:0.51 39:0.79 43:0.94 64:0.77 66:0.72 69:0.25 70:0.22 71:0.62 74:0.47 78:0.92 79:0.86 81:0.67 83:0.91 85:0.72 86:0.87 91:0.56 96:0.68 98:0.67 100:0.86 101:0.87 108:0.20 111:0.90 114:0.71 120:0.55 122:0.57 123:0.19 126:0.54 127:0.20 129:0.05 135:0.42 137:0.86 139:0.83 140:0.45 146:0.39 147:0.45 149:0.64 150:0.80 151:0.50 152:0.94 153:0.48 154:0.94 155:0.67 159:0.15 161:0.85 162:0.86 164:0.57 165:0.93 167:0.60 169:0.96 172:0.27 173:0.56 176:0.73 177:0.70 179:0.83 181:0.60 186:0.94 187:0.21 192:0.87 196:0.89 201:0.93 202:0.36 208:0.64 212:0.78 215:0.51 216:0.10 220:0.91 222:0.35 228:0.99 235:0.42 241:0.30 242:0.45 243:0.75 247:0.35 248:0.49 253:0.52 255:0.95 256:0.45 259:0.21 260:0.55 261:0.97 265:0.67 266:0.17 267:0.23 268:0.46 271:0.31 276:0.17 284:0.89 285:0.62 290:0.71 297:0.36 300:0.18 +1 11:0.78 12:0.79 17:0.12 20:0.95 21:0.30 27:0.31 30:0.13 34:0.31 36:0.90 37:0.46 39:0.51 43:0.77 55:0.71 66:0.12 69:0.19 70:0.98 74:0.83 78:0.86 81:0.60 85:0.80 91:0.11 98:0.24 100:0.73 101:0.52 108:0.95 111:0.83 120:0.58 123:0.95 124:0.99 126:0.32 127:0.95 129:0.99 131:0.91 133:0.99 135:0.65 140:0.45 144:0.76 146:0.83 147:0.86 149:0.74 150:0.44 151:0.52 152:0.88 153:0.48 154:0.70 157:0.76 159:0.98 162:0.86 164:0.55 166:0.87 169:0.72 172:0.76 173:0.05 177:0.38 179:0.17 182:0.72 186:0.86 187:0.39 190:0.77 202:0.36 212:0.22 215:0.72 216:0.60 220:0.87 222:0.76 227:0.96 229:0.61 231:0.94 235:0.31 241:0.07 242:0.27 243:0.25 245:0.84 246:0.61 247:0.67 248:0.52 253:0.59 256:0.45 259:0.21 260:0.58 261:0.84 265:0.26 266:1.00 267:0.52 268:0.46 271:0.17 276:0.94 285:0.50 287:0.61 297:0.36 300:0.98 +2 6:0.85 8:0.75 9:0.80 12:0.79 17:0.15 20:0.91 21:0.78 27:0.32 30:0.56 34:0.24 36:0.91 37:0.42 39:0.51 41:0.82 43:0.27 55:0.96 66:0.19 69:0.78 70:0.42 74:0.63 78:0.94 83:0.76 91:0.72 96:0.80 98:0.23 100:0.92 108:0.83 111:0.92 120:0.69 122:0.67 123:0.82 124:0.90 127:0.37 129:0.05 131:0.98 133:0.89 135:0.79 140:0.92 144:0.76 146:0.36 147:0.43 149:0.40 151:0.87 152:0.85 153:0.48 154:0.19 155:0.67 157:0.61 159:0.73 162:0.49 163:0.93 164:0.57 166:0.61 169:0.90 172:0.21 173:0.62 177:0.38 178:0.51 179:0.73 182:0.96 186:0.93 187:0.39 189:0.86 192:0.59 202:0.66 208:0.64 212:0.23 216:0.55 222:0.76 227:0.99 229:0.98 231:0.95 235:0.22 238:0.86 241:0.37 242:0.63 243:0.33 244:0.61 245:0.48 246:0.61 247:0.39 248:0.78 253:0.79 255:0.79 256:0.45 259:0.21 260:0.70 261:0.90 265:0.25 266:0.71 267:0.88 268:0.46 271:0.17 276:0.44 277:0.69 285:0.62 287:0.99 300:0.33 +2 8:0.76 11:0.78 12:0.79 17:0.24 20:0.86 21:0.78 27:0.56 30:0.37 34:0.49 36:0.83 37:0.53 39:0.51 43:0.50 55:0.71 66:0.27 69:0.80 70:0.96 74:0.92 78:0.93 85:0.72 91:0.56 98:0.36 100:0.73 101:0.64 108:0.64 111:0.90 122:0.67 123:0.61 124:0.94 127:0.75 129:0.59 131:0.61 133:0.94 135:0.23 144:0.76 146:0.90 147:0.92 149:0.68 152:0.81 154:0.54 157:0.77 159:0.93 166:0.61 169:0.72 172:0.71 173:0.43 177:0.38 178:0.55 179:0.32 182:0.72 186:0.93 187:0.68 191:0.70 202:0.63 212:0.19 216:0.87 222:0.76 227:0.61 229:0.61 231:0.86 235:0.74 241:0.15 242:0.79 243:0.46 245:0.79 246:0.61 247:0.39 253:0.65 259:0.21 261:0.87 265:0.38 266:0.95 267:0.94 271:0.17 276:0.81 287:0.61 300:0.99 +1 11:0.78 12:0.79 17:0.26 21:0.78 27:0.45 30:0.33 34:0.76 36:0.67 37:0.50 39:0.51 43:0.70 66:0.79 69:0.84 70:0.36 74:0.63 85:0.80 91:0.22 98:0.47 101:0.73 108:0.69 122:0.57 123:0.67 127:0.14 129:0.05 131:0.61 135:0.70 144:0.76 145:0.41 146:0.69 147:0.74 149:0.58 154:0.60 157:0.89 159:0.27 166:0.61 172:0.41 173:0.78 177:0.38 178:0.55 179:0.43 182:0.78 187:0.68 212:0.42 216:0.77 222:0.76 227:0.61 229:0.61 231:0.84 235:0.52 241:0.12 242:0.45 243:0.80 246:0.61 247:0.47 253:0.49 259:0.21 265:0.48 266:0.38 267:0.36 271:0.17 276:0.32 287:0.61 300:0.25 +1 11:0.78 12:0.79 17:0.22 21:0.78 27:0.66 30:0.12 34:0.79 36:0.91 37:0.36 39:0.51 43:0.90 55:0.71 66:0.06 69:0.44 70:0.97 74:0.86 85:0.98 91:0.11 98:0.22 101:0.75 108:0.72 123:0.96 124:0.99 127:0.97 129:0.99 131:0.61 133:0.99 135:0.51 144:0.76 146:0.22 147:0.27 149:0.95 154:0.89 157:0.97 159:0.98 166:0.61 172:0.63 173:0.07 177:0.38 178:0.55 179:0.18 182:0.90 187:0.68 212:0.21 216:0.32 222:0.76 227:0.61 229:0.61 231:0.93 235:0.02 241:0.05 242:0.60 243:0.09 245:0.85 246:0.61 247:0.60 253:0.61 259:0.21 265:0.19 266:1.00 267:0.36 271:0.17 276:0.93 287:0.61 300:0.99 +2 1:0.74 8:0.66 11:0.78 12:0.79 17:0.06 27:0.63 30:0.11 34:0.77 36:0.53 37:0.32 39:0.51 43:0.98 66:0.23 69:0.05 70:0.68 74:0.86 81:0.95 83:0.80 85:0.85 91:0.25 98:0.70 101:0.80 108:0.05 114:0.98 122:0.67 123:0.57 124:0.56 126:0.54 127:0.61 129:0.59 131:0.61 133:0.47 135:0.77 144:0.76 146:0.40 147:0.47 149:0.81 150:0.98 154:0.98 155:0.67 157:0.95 159:0.34 165:0.92 166:0.97 172:0.48 173:0.21 176:0.73 177:0.38 178:0.55 179:0.70 182:0.96 187:0.39 192:0.59 201:0.74 212:0.71 215:0.96 216:0.46 222:0.76 227:0.61 229:0.61 231:0.95 235:0.05 241:0.12 242:0.51 243:0.60 245:0.71 246:0.99 247:0.60 253:0.79 259:0.21 265:0.50 266:0.27 267:0.65 271:0.17 276:0.51 283:0.82 287:0.61 290:0.98 297:0.36 300:0.43 +1 8:0.72 11:0.78 12:0.79 17:0.26 21:0.47 27:0.21 30:0.19 34:0.55 36:0.87 37:0.74 39:0.51 43:0.69 55:0.71 66:0.23 69:0.19 70:0.91 74:0.95 76:0.94 81:0.42 85:0.93 91:0.72 98:0.49 101:0.79 108:0.15 114:0.55 122:0.67 123:0.15 124:0.90 126:0.32 127:0.59 129:0.89 131:0.61 133:0.89 135:0.34 144:0.76 146:0.79 147:0.82 149:0.87 150:0.36 154:0.59 157:0.97 159:0.77 166:0.83 172:0.59 173:0.13 176:0.53 177:0.38 178:0.55 179:0.38 182:0.91 187:0.39 202:0.75 212:0.48 216:0.95 222:0.76 227:0.61 229:0.61 231:0.93 235:0.52 241:0.50 242:0.63 243:0.43 245:0.78 246:0.61 247:0.47 253:0.68 259:0.21 265:0.51 266:0.87 267:0.94 271:0.17 276:0.76 287:0.61 290:0.55 300:0.70 +1 11:0.78 12:0.79 17:0.47 21:0.30 27:0.28 30:0.17 34:0.94 36:0.74 37:0.72 39:0.51 43:0.58 55:0.71 66:0.68 69:0.91 70:0.97 74:0.93 77:0.70 81:0.55 85:0.93 91:0.42 98:0.55 101:0.77 108:0.83 114:0.55 117:0.86 122:0.67 123:0.82 124:0.64 126:0.32 127:0.50 129:0.05 131:0.61 133:0.82 135:0.71 144:0.76 145:0.70 146:0.89 147:0.58 149:0.81 150:0.41 154:0.47 157:0.97 158:0.82 159:0.82 166:0.83 172:0.84 173:0.82 176:0.53 177:0.05 178:0.55 179:0.32 182:0.90 187:0.68 195:0.94 212:0.55 216:0.54 222:0.76 227:0.61 229:0.61 231:0.93 232:0.99 235:0.31 241:0.30 242:0.72 243:0.13 245:0.77 246:0.61 247:0.60 253:0.66 257:0.65 259:0.21 265:0.57 266:0.84 267:0.65 271:0.17 276:0.78 282:0.81 287:0.61 290:0.55 300:0.94 +1 1:0.74 11:0.51 12:0.79 17:0.30 21:0.22 27:0.72 30:0.76 34:0.94 36:0.66 37:0.44 39:0.51 43:0.92 55:0.42 66:0.85 69:0.15 70:0.43 74:0.84 81:0.82 91:0.29 98:0.82 108:0.12 114:0.90 122:0.43 123:0.12 126:0.54 127:0.31 129:0.05 131:0.61 135:0.32 144:0.76 146:0.57 147:0.63 149:0.90 150:0.84 154:0.91 155:0.67 157:0.61 158:0.87 159:0.21 166:0.97 172:0.57 173:0.41 176:0.73 177:0.38 178:0.55 179:0.65 182:0.61 187:0.39 192:0.59 201:0.74 208:0.64 212:0.69 215:0.79 216:0.15 222:0.76 227:0.61 229:0.61 231:0.95 235:0.31 241:0.46 242:0.61 243:0.86 246:0.61 247:0.39 253:0.75 254:0.74 259:0.21 265:0.82 266:0.27 267:0.36 271:0.17 276:0.44 279:0.86 283:0.71 287:0.61 290:0.90 297:0.36 300:0.43 +2 6:0.83 8:0.67 11:0.78 12:0.79 17:0.20 20:0.85 27:0.63 30:0.60 32:0.72 34:0.62 36:0.62 37:0.32 39:0.51 43:0.84 55:0.71 66:0.20 69:0.05 70:0.56 74:0.81 78:0.99 81:0.90 85:0.80 91:0.18 98:0.65 100:0.98 101:0.78 108:0.05 111:0.98 122:0.67 123:0.59 124:0.55 126:0.32 127:0.60 129:0.59 131:0.61 133:0.47 135:0.51 144:0.76 145:0.49 146:0.46 147:0.53 149:0.72 150:0.80 152:0.83 154:0.83 157:0.91 159:0.34 166:0.87 169:0.96 172:0.45 173:0.19 177:0.38 178:0.55 179:0.75 182:0.81 186:0.99 187:0.68 189:0.85 195:0.57 212:0.72 215:0.96 216:0.46 222:0.76 227:0.61 229:0.61 231:0.94 235:0.02 238:0.87 241:0.60 242:0.45 243:0.60 245:0.68 246:0.61 247:0.60 253:0.58 259:0.21 261:0.94 265:0.57 266:0.38 267:0.52 271:0.17 276:0.47 283:0.82 287:0.61 297:0.36 300:0.43 +2 1:0.74 7:0.81 11:0.78 12:0.79 17:0.12 21:0.22 27:0.63 30:0.43 34:0.83 36:0.60 37:0.49 39:0.51 43:0.91 60:0.87 66:0.31 69:0.42 70:0.87 74:0.95 81:0.83 83:0.85 85:0.97 91:0.07 98:0.59 101:0.84 108:0.71 114:0.90 121:0.90 122:0.43 123:0.69 124:0.79 126:0.54 127:0.63 129:0.89 131:0.61 133:0.78 135:0.54 144:0.76 145:0.70 146:0.66 147:0.71 149:0.95 150:0.89 154:0.90 155:0.67 157:0.99 158:0.87 159:0.73 165:0.86 166:0.97 172:0.70 173:0.27 176:0.73 177:0.38 178:0.55 179:0.34 182:0.96 187:0.95 192:0.59 201:0.74 204:0.89 208:0.64 212:0.40 215:0.79 216:0.64 222:0.76 227:0.61 229:0.61 230:0.84 231:0.95 233:0.69 235:0.31 241:0.21 242:0.62 243:0.39 245:0.87 246:0.99 247:0.55 253:0.78 265:0.60 266:0.63 267:0.23 271:0.17 276:0.79 279:0.86 283:0.71 287:0.61 290:0.90 297:0.36 300:0.84 +2 1:0.74 6:0.83 7:0.81 11:0.51 12:0.79 17:0.36 20:0.89 21:0.47 27:0.21 30:0.62 34:0.53 36:0.51 37:0.74 39:0.51 43:0.30 55:0.42 66:0.75 69:0.19 70:0.34 74:0.79 76:0.85 78:0.88 81:0.67 91:0.22 98:0.70 100:0.73 104:0.91 106:0.81 108:0.15 111:0.86 114:0.80 117:0.86 122:0.43 123:0.15 126:0.54 127:0.21 129:0.05 131:0.61 135:0.21 144:0.76 146:0.53 147:0.59 149:0.19 150:0.72 152:0.92 154:0.92 155:0.67 157:0.61 158:0.86 159:0.19 166:0.97 169:0.92 172:0.32 173:0.40 176:0.73 177:0.38 178:0.55 179:0.81 182:0.61 186:0.88 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.61 215:0.63 216:0.95 222:0.76 227:0.61 229:0.61 230:0.83 231:0.95 232:0.96 233:0.66 235:0.60 241:0.35 242:0.33 243:0.77 246:0.61 247:0.39 253:0.69 254:0.74 259:0.21 261:0.92 265:0.71 266:0.23 267:0.44 271:0.17 276:0.22 279:0.86 283:0.67 287:0.61 290:0.80 297:0.36 300:0.25 +0 8:0.78 11:0.78 12:0.79 17:0.20 21:0.78 27:0.56 30:0.18 34:0.61 36:0.96 37:0.43 39:0.51 43:0.86 55:0.96 66:0.06 69:0.12 70:0.96 74:0.65 85:0.93 91:0.06 98:0.19 101:0.71 108:0.10 123:0.10 124:0.99 127:0.97 129:0.95 131:0.61 133:0.99 135:0.79 144:0.76 146:0.72 147:0.77 149:0.84 154:0.84 157:0.91 159:0.97 163:0.81 166:0.61 172:0.37 173:0.05 177:0.38 178:0.55 179:0.26 182:0.81 187:0.87 202:0.36 212:0.12 216:0.89 222:0.76 227:0.61 229:0.61 231:0.91 235:0.31 241:0.01 242:0.74 243:0.18 245:0.75 246:0.61 247:0.47 253:0.50 259:0.21 265:0.20 266:0.99 267:0.59 271:0.17 276:0.88 283:0.71 287:0.61 300:0.94 +2 8:0.69 11:0.78 12:0.79 17:0.30 21:0.47 27:0.21 30:0.17 34:0.42 36:0.94 37:0.74 39:0.51 43:0.69 55:0.71 66:0.25 69:0.19 70:0.91 74:0.96 76:0.85 81:0.42 85:0.93 91:0.70 96:0.75 98:0.50 101:0.79 108:0.15 114:0.55 117:0.86 122:0.67 123:0.15 124:0.90 126:0.32 127:0.59 129:0.89 131:0.88 133:0.89 135:0.26 140:0.45 144:0.76 146:0.79 147:0.82 149:0.87 150:0.36 151:0.59 153:0.69 154:0.59 157:0.97 158:0.82 159:0.77 162:0.86 166:0.83 172:0.61 173:0.13 176:0.53 177:0.38 179:0.37 182:0.91 187:0.39 190:0.77 202:0.46 212:0.47 216:0.95 220:0.74 222:0.76 227:0.95 229:0.61 231:0.93 232:0.91 235:0.52 241:0.12 242:0.63 243:0.44 245:0.78 246:0.61 247:0.47 248:0.57 253:0.79 256:0.45 259:0.21 265:0.51 266:0.87 267:0.92 268:0.55 271:0.17 276:0.77 285:0.50 287:0.61 290:0.55 300:0.70 +1 8:0.68 11:0.78 12:0.79 17:0.24 21:0.78 27:0.45 30:0.28 34:0.64 36:0.27 37:0.50 39:0.51 43:0.78 55:0.52 66:0.30 69:0.75 70:0.75 74:0.89 85:0.72 91:0.20 98:0.45 101:0.71 108:0.59 122:0.67 123:0.71 124:0.60 127:0.19 129:0.05 131:0.61 133:0.39 135:0.89 144:0.76 145:0.42 146:0.51 147:0.57 149:0.65 154:0.71 157:0.81 159:0.41 166:0.61 172:0.51 173:0.66 177:0.38 178:0.55 179:0.31 182:0.74 187:0.68 212:0.70 216:0.77 222:0.76 227:0.61 229:0.61 231:0.83 235:0.60 241:0.44 242:0.79 243:0.57 245:0.78 246:0.61 247:0.39 253:0.63 259:0.21 265:0.34 266:0.54 267:0.59 271:0.17 276:0.58 277:0.69 287:0.61 300:0.70 +1 8:0.73 11:0.78 12:0.79 17:0.28 21:0.47 27:0.21 30:0.19 34:0.44 36:0.88 37:0.74 39:0.51 43:0.69 55:0.71 66:0.23 69:0.19 70:0.91 74:0.96 76:0.85 81:0.42 85:0.93 91:0.58 98:0.49 101:0.79 108:0.15 114:0.55 117:0.86 122:0.67 123:0.15 124:0.90 126:0.32 127:0.58 129:0.89 131:0.61 133:0.89 135:0.30 144:0.76 146:0.78 147:0.82 149:0.87 150:0.36 154:0.59 157:0.97 158:0.82 159:0.77 166:0.83 172:0.59 173:0.13 176:0.53 177:0.38 178:0.55 179:0.38 182:0.91 187:0.39 212:0.48 216:0.95 222:0.76 227:0.61 229:0.61 231:0.93 232:0.91 235:0.52 241:0.07 242:0.63 243:0.43 245:0.78 246:0.61 247:0.47 253:0.68 259:0.21 265:0.51 266:0.87 267:0.94 271:0.17 276:0.76 287:0.61 290:0.55 300:0.70 +1 11:0.88 12:0.79 17:0.20 21:0.64 27:0.45 28:0.70 30:0.11 34:0.89 36:0.18 37:0.50 39:0.55 43:0.78 55:0.84 66:0.29 69:0.70 70:0.85 74:0.75 81:0.37 85:0.80 91:0.06 98:0.27 101:0.72 108:0.53 122:0.67 123:0.51 124:0.84 126:0.32 127:0.34 129:0.05 133:0.82 135:0.83 144:0.85 145:0.47 146:0.53 147:0.59 149:0.70 150:0.33 154:0.70 159:0.71 161:0.79 167:0.60 172:0.37 173:0.52 177:0.38 178:0.55 179:0.60 181:0.98 187:0.68 212:0.27 215:0.53 216:0.77 222:0.35 235:0.74 241:0.61 242:0.72 243:0.47 245:0.58 247:0.47 253:0.55 259:0.21 265:0.30 266:0.80 267:0.28 271:0.34 276:0.51 283:0.71 297:0.36 300:0.55 +2 1:0.74 6:0.93 7:0.81 8:0.87 9:0.80 11:0.88 12:0.79 17:0.12 20:0.87 21:0.13 27:0.13 28:0.70 30:0.67 32:0.80 34:0.78 36:0.43 37:0.79 39:0.55 41:0.94 43:0.81 60:0.97 66:0.89 69:0.70 70:0.43 74:0.91 77:0.70 78:0.79 81:0.88 83:0.89 85:0.94 91:0.48 96:0.94 98:0.75 100:0.85 101:0.85 108:0.53 111:0.80 114:0.92 120:0.89 121:0.90 122:0.43 123:0.51 126:0.54 127:0.24 129:0.05 135:0.80 140:0.45 144:0.85 145:0.54 146:0.70 147:0.75 149:0.91 150:0.93 151:0.96 152:0.90 153:0.87 154:0.76 155:0.67 158:0.92 159:0.28 161:0.79 162:0.81 164:0.83 165:0.88 167:0.56 169:0.82 172:0.70 173:0.78 176:0.73 177:0.38 179:0.41 181:0.98 186:0.79 187:0.68 189:0.95 191:0.76 192:0.59 195:0.63 201:0.74 202:0.74 204:0.89 208:0.64 212:0.70 215:0.84 216:0.83 222:0.35 230:0.87 233:0.76 235:0.22 238:0.94 241:0.51 242:0.54 243:0.90 247:0.55 248:0.94 253:0.96 255:0.79 256:0.77 259:0.21 260:0.89 261:0.88 265:0.75 266:0.38 267:0.59 268:0.79 271:0.34 276:0.58 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.33 +3 1:0.74 6:0.93 8:0.82 9:0.80 11:0.88 12:0.79 17:0.05 20:0.76 27:0.14 28:0.70 30:0.75 34:0.70 36:0.28 37:0.67 39:0.55 41:0.99 43:0.86 60:0.97 66:0.24 69:0.59 70:0.69 74:0.90 78:0.88 81:0.96 83:0.91 85:0.98 91:0.88 96:1.00 98:0.23 100:0.96 101:0.80 108:0.64 111:0.89 114:0.98 120:0.99 122:0.43 123:0.62 124:0.93 126:0.54 127:0.83 129:0.97 133:0.93 135:0.60 138:0.99 140:0.45 144:0.85 145:0.49 146:0.29 147:0.36 149:0.95 150:0.98 151:0.99 152:0.96 153:0.97 154:0.83 155:0.67 158:0.92 159:0.92 161:0.79 162:0.76 164:0.97 165:0.92 167:0.56 169:0.91 172:0.61 173:0.23 176:0.73 177:0.38 179:0.40 181:0.98 186:0.84 187:0.39 189:0.94 191:0.83 192:0.59 201:0.74 202:0.95 208:0.64 212:0.35 215:0.96 216:0.93 222:0.35 235:0.05 238:0.93 241:0.52 242:0.59 243:0.18 245:0.76 247:0.47 248:1.00 253:1.00 255:0.79 256:0.96 259:0.21 260:0.99 261:0.96 265:0.26 266:0.94 267:0.95 268:0.96 271:0.34 276:0.75 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +3 1:0.74 7:0.81 11:0.88 12:0.79 17:0.66 21:0.30 27:0.21 28:0.70 30:0.44 34:0.63 36:0.92 37:0.74 39:0.55 43:0.98 66:0.16 69:0.12 70:0.75 74:0.95 81:0.80 83:0.75 85:0.97 91:0.10 98:0.44 101:0.81 108:0.95 114:0.86 121:0.90 122:0.57 123:0.78 124:0.84 126:0.54 127:0.68 129:0.81 133:0.83 135:0.30 144:0.85 146:0.63 147:0.68 149:0.95 150:0.87 154:0.98 155:0.67 159:0.89 161:0.79 165:0.84 167:0.47 172:0.67 173:0.08 176:0.73 177:0.38 178:0.55 179:0.31 181:0.98 187:0.68 192:0.59 201:0.74 202:0.36 204:0.89 208:0.64 212:0.35 215:0.72 216:0.95 222:0.35 230:0.87 233:0.76 235:0.42 241:0.10 242:0.45 243:0.34 245:0.87 247:0.47 253:0.77 259:0.21 265:0.34 266:0.87 267:0.52 271:0.34 276:0.78 290:0.86 297:0.36 300:0.84 +0 1:0.74 6:0.83 8:0.61 9:0.80 11:0.88 12:0.79 17:0.92 20:0.79 21:0.13 25:0.88 27:0.18 28:0.70 30:0.56 34:0.92 36:0.43 37:0.26 39:0.55 41:0.99 43:0.94 60:0.95 66:0.64 69:0.23 70:0.64 74:0.92 78:0.90 81:0.88 83:0.90 85:0.94 87:0.98 91:0.87 96:0.99 98:0.68 100:0.93 101:0.84 104:0.58 108:0.18 111:0.89 114:0.92 120:0.98 122:0.57 123:0.18 124:0.47 126:0.54 127:0.30 129:0.59 133:0.47 135:0.54 138:0.98 140:0.45 144:0.85 146:0.67 147:0.72 149:0.93 150:0.93 151:0.99 152:0.78 153:0.95 154:0.94 155:0.67 158:0.92 159:0.35 161:0.79 162:0.79 164:0.96 165:0.88 167:0.81 169:0.89 172:0.67 173:0.30 176:0.73 177:0.38 179:0.43 181:0.98 186:0.90 189:0.83 191:0.78 192:0.59 201:0.74 202:0.92 208:0.64 212:0.82 215:0.84 216:0.22 222:0.35 232:0.81 235:0.22 238:0.90 241:0.85 242:0.40 243:0.69 245:0.77 247:0.60 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.89 265:0.68 266:0.27 267:0.73 268:0.95 271:0.34 276:0.61 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.55 +3 1:0.74 6:0.87 7:0.81 8:0.73 9:0.80 11:0.88 12:0.79 17:0.51 20:0.75 21:0.68 25:0.88 27:0.72 28:0.70 30:0.33 32:0.80 34:0.19 36:0.93 39:0.55 41:0.99 43:0.75 60:0.97 66:0.12 69:0.83 70:0.62 74:0.82 76:0.94 77:0.89 78:0.86 81:0.65 83:0.90 85:0.91 87:0.98 91:0.77 96:0.99 98:0.34 100:0.91 101:0.78 104:0.58 108:0.86 111:0.85 114:0.70 120:0.97 121:1.00 122:0.43 123:0.86 124:0.91 126:0.54 127:0.98 129:0.95 133:0.90 135:0.58 138:0.99 140:0.45 144:0.85 145:0.79 146:0.13 147:0.59 149:0.77 150:0.75 151:0.99 152:0.74 153:0.96 154:0.66 155:0.67 158:0.92 159:0.83 161:0.79 162:0.83 163:0.86 164:0.94 165:0.69 167:0.80 169:0.84 172:0.27 173:0.70 176:0.73 177:0.05 179:0.64 181:0.98 186:0.89 189:0.88 191:0.82 192:0.59 195:0.93 201:0.74 202:0.89 204:0.89 208:0.64 212:0.13 215:0.49 216:0.24 222:0.35 230:0.87 232:0.81 233:0.76 235:0.95 238:0.88 241:0.82 242:0.24 243:0.22 245:0.64 247:0.60 248:0.99 253:0.99 255:0.79 256:0.92 257:0.65 259:0.21 260:0.97 261:0.77 265:0.36 266:0.89 267:0.84 268:0.93 271:0.34 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.97 300:0.43 +0 1:0.74 7:0.81 8:0.72 9:0.80 11:0.88 12:0.79 17:0.62 20:0.75 21:0.30 27:0.52 28:0.70 30:0.70 37:0.58 39:0.55 41:0.97 43:0.98 60:0.87 66:0.15 69:0.12 70:0.84 74:0.93 78:0.82 81:0.80 83:0.87 85:0.98 91:0.74 96:0.96 98:0.25 100:0.82 101:0.80 108:0.10 111:0.85 114:0.86 117:0.86 120:0.93 121:0.90 122:0.43 123:0.10 124:0.94 126:0.54 127:0.77 129:0.97 133:0.94 138:0.98 140:0.45 144:0.85 146:0.70 147:0.74 149:0.95 150:0.87 151:0.99 152:0.74 153:0.96 154:0.98 155:0.67 158:0.92 159:0.91 161:0.79 162:0.69 164:0.87 165:0.84 167:0.54 169:0.89 172:0.54 173:0.07 176:0.73 177:0.38 179:0.36 181:0.98 186:0.81 187:0.39 192:0.59 201:0.74 202:0.83 204:0.89 208:0.64 212:0.22 215:0.72 216:0.90 222:0.35 230:0.87 232:0.92 233:0.76 235:0.42 238:0.95 241:0.47 242:0.53 243:0.36 245:0.78 247:0.60 248:0.96 253:0.97 255:0.79 256:0.86 259:0.21 260:0.93 261:0.75 265:0.28 266:0.95 268:0.85 271:0.34 276:0.77 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.98 +2 1:0.74 7:0.81 8:0.70 9:0.80 11:0.88 12:0.79 17:0.18 21:0.13 27:0.03 28:0.70 30:0.62 32:0.80 34:0.96 36:0.62 37:0.92 39:0.55 41:0.82 43:0.90 60:0.87 66:0.30 69:0.44 70:0.59 74:0.98 77:0.70 81:0.88 83:0.86 85:0.98 91:0.22 96:0.74 98:0.69 101:0.86 104:0.84 106:0.81 108:0.34 114:0.92 117:0.86 120:0.62 121:0.90 122:0.43 123:0.75 124:0.58 126:0.54 127:0.43 129:0.59 133:0.47 135:0.92 140:0.45 144:0.85 145:0.59 146:0.78 147:0.81 149:0.99 150:0.93 151:0.69 153:0.48 154:0.89 155:0.67 158:0.92 159:0.65 161:0.79 162:0.62 164:0.57 165:0.88 167:0.49 172:0.82 173:0.30 176:0.73 177:0.38 179:0.24 181:0.98 187:0.68 192:0.59 195:0.85 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.57 215:0.84 216:0.79 220:0.74 222:0.35 230:0.87 232:0.91 233:0.76 235:0.22 241:0.21 242:0.51 243:0.58 245:0.95 247:0.60 248:0.65 253:0.83 255:0.79 256:0.45 259:0.21 260:0.63 265:0.57 266:0.38 267:0.36 268:0.46 271:0.34 276:0.84 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.88 12:0.79 17:0.45 21:0.71 27:0.45 28:0.70 30:0.45 32:0.80 34:0.80 36:0.18 37:0.50 39:0.55 43:0.81 55:0.84 66:0.47 69:0.71 70:0.86 74:0.56 77:0.70 81:0.52 83:0.73 85:0.72 91:0.04 98:0.36 101:0.70 108:0.64 114:0.68 123:0.61 124:0.79 126:0.54 127:0.58 129:0.05 133:0.82 135:0.70 144:0.85 145:0.50 146:0.13 147:0.18 149:0.63 150:0.67 154:0.77 155:0.67 159:0.76 161:0.79 163:0.75 165:0.69 167:0.56 172:0.41 173:0.55 176:0.73 177:0.38 178:0.55 179:0.77 181:0.98 187:0.68 192:0.59 195:0.90 201:0.74 212:0.13 215:0.48 216:0.77 222:0.35 235:0.88 241:0.53 242:0.79 243:0.23 245:0.51 247:0.35 253:0.56 259:0.21 265:0.38 266:0.80 267:0.59 271:0.34 276:0.40 277:0.69 282:0.88 290:0.68 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.79 17:0.04 20:0.96 27:0.14 28:0.70 30:0.75 34:0.75 36:0.28 37:0.67 39:0.55 41:1.00 43:0.85 60:0.99 66:0.23 69:0.57 70:0.60 74:0.87 78:0.84 81:0.96 83:0.91 85:0.97 91:0.67 96:1.00 98:0.22 100:0.95 101:0.79 108:0.61 111:0.89 114:0.98 120:0.99 122:0.43 123:0.58 124:0.93 126:0.54 127:0.79 129:0.97 133:0.93 135:0.61 138:0.99 140:0.45 144:0.85 145:0.47 146:0.22 147:0.28 149:0.93 150:0.98 151:0.99 152:0.96 153:0.97 154:0.82 155:0.67 158:0.92 159:0.92 161:0.79 162:0.75 164:0.97 165:0.92 167:0.53 169:0.91 172:0.57 173:0.22 176:0.73 177:0.38 179:0.44 181:0.98 186:0.84 187:0.39 189:0.94 191:0.75 192:0.59 201:0.74 202:0.95 208:0.64 212:0.30 215:0.96 216:0.93 222:0.35 235:0.05 238:0.97 241:0.44 242:0.60 243:0.16 245:0.73 247:0.47 248:1.00 253:1.00 255:0.79 256:0.96 259:0.21 260:0.99 261:0.96 265:0.24 266:0.92 267:0.87 268:0.97 271:0.34 276:0.71 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.94 +2 1:0.74 6:0.93 7:0.81 8:0.85 9:0.80 11:0.88 12:0.79 17:0.28 20:0.99 21:0.30 27:0.21 28:0.70 30:0.42 34:0.80 36:0.94 37:0.74 39:0.55 41:0.99 43:0.98 60:0.98 66:0.20 69:0.12 70:0.81 74:0.97 78:0.82 81:0.80 83:0.90 85:0.97 91:0.78 96:0.98 98:0.58 100:0.93 101:0.82 108:0.78 111:0.87 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.77 124:0.75 126:0.54 127:0.69 129:0.81 133:0.67 135:0.26 138:0.99 140:0.45 144:0.85 146:0.83 147:0.85 149:0.95 150:0.87 151:0.98 152:0.91 153:0.94 154:0.98 155:0.67 158:0.92 159:0.86 161:0.79 162:0.79 164:0.93 165:0.84 167:0.50 169:0.91 172:0.68 173:0.08 176:0.73 177:0.38 179:0.29 181:0.98 186:0.82 187:0.39 189:0.94 192:0.59 201:0.74 202:0.89 204:0.89 208:0.64 212:0.51 215:0.72 216:0.95 222:0.35 230:0.87 232:0.92 233:0.76 235:0.42 238:0.97 241:0.30 242:0.44 243:0.40 245:0.93 247:0.47 248:0.99 253:0.99 255:0.79 256:0.92 259:0.21 260:0.97 261:0.95 265:0.59 266:0.80 267:0.36 268:0.92 271:0.34 276:0.81 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +2 1:0.74 6:0.94 7:0.81 8:0.88 9:0.80 11:0.88 12:0.79 17:0.08 20:0.97 21:0.13 27:0.03 28:0.70 30:0.62 32:0.80 34:0.95 36:0.63 37:0.92 39:0.55 41:0.95 43:0.90 60:0.98 66:0.30 69:0.44 70:0.59 74:0.98 77:0.70 78:0.81 81:0.88 83:0.87 85:0.98 91:0.54 96:0.92 98:0.69 100:0.88 101:0.86 104:0.84 106:0.81 108:0.34 111:0.83 114:0.92 117:0.86 120:0.87 121:0.90 122:0.43 123:0.75 124:0.58 126:0.54 127:0.43 129:0.59 133:0.47 135:0.94 140:0.45 144:0.85 145:0.59 146:0.78 147:0.81 149:0.99 150:0.93 151:0.94 152:0.93 153:0.81 154:0.89 155:0.67 158:0.92 159:0.65 161:0.79 162:0.86 164:0.83 165:0.88 167:0.52 169:0.85 172:0.82 173:0.30 176:0.73 177:0.38 179:0.24 181:0.98 186:0.81 187:0.39 189:0.95 192:0.59 195:0.85 201:0.74 202:0.72 204:0.89 206:0.81 208:0.64 212:0.57 215:0.84 216:0.79 220:0.74 222:0.35 230:0.87 232:0.91 233:0.76 235:0.22 238:0.94 241:0.39 242:0.51 243:0.58 245:0.95 247:0.60 248:0.92 253:0.95 255:0.79 256:0.78 259:0.21 260:0.88 261:0.92 265:0.57 266:0.38 267:0.36 268:0.79 271:0.34 276:0.84 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +2 1:0.74 8:0.61 9:0.80 11:0.88 12:0.79 17:0.07 20:0.77 21:0.22 27:0.17 28:0.70 30:0.76 34:0.71 36:0.52 37:0.97 39:0.55 41:0.97 43:0.76 60:0.98 66:0.36 69:0.81 70:0.37 74:0.71 78:0.72 81:0.89 83:0.89 85:0.88 91:0.31 96:0.95 98:0.24 100:0.73 101:0.78 104:0.90 106:0.81 108:0.65 111:0.72 114:0.90 117:0.86 120:0.91 122:0.57 123:0.63 124:0.70 126:0.54 127:0.19 129:0.81 133:0.67 135:0.94 140:0.45 144:0.85 146:0.36 147:0.42 149:0.73 150:0.94 151:0.96 152:0.82 153:0.86 154:0.68 155:0.67 158:0.92 159:0.35 161:0.79 162:0.86 163:0.81 164:0.84 165:0.90 167:0.68 169:0.79 172:0.27 173:0.78 176:0.73 177:0.38 179:0.60 181:0.98 186:0.73 187:0.87 192:0.59 201:0.74 202:0.72 206:0.81 208:0.64 212:0.37 215:0.79 216:0.71 222:0.35 232:0.94 235:0.52 241:0.69 242:0.58 243:0.51 245:0.50 247:0.39 248:0.95 253:0.95 255:0.79 256:0.84 259:0.21 260:0.92 261:0.82 265:0.26 266:0.38 267:0.36 268:0.80 271:0.34 276:0.32 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.33 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.79 17:0.40 21:0.13 27:0.16 28:0.70 30:0.76 32:0.80 34:0.52 36:0.70 37:0.69 39:0.55 41:0.98 43:0.80 60:0.98 66:0.93 69:0.73 70:0.46 74:1.00 77:0.89 81:0.88 83:0.90 85:1.00 91:0.37 96:0.97 98:0.77 101:0.87 104:0.96 106:0.81 108:0.70 114:0.92 117:0.86 120:0.94 121:0.90 122:0.43 123:0.68 124:0.37 126:0.54 127:0.34 129:0.81 133:0.76 135:0.87 140:0.45 144:0.85 145:0.74 146:0.32 147:0.38 149:1.00 150:0.93 151:0.98 153:0.92 154:0.74 155:0.67 158:0.92 159:0.85 161:0.79 162:0.81 164:0.93 165:0.88 167:0.46 172:0.99 173:0.43 176:0.73 177:0.38 179:0.10 181:0.98 187:0.87 192:0.59 195:0.97 201:0.74 202:0.87 204:0.89 206:0.81 208:0.64 212:0.86 215:0.84 216:0.74 220:0.74 222:0.35 230:0.87 232:0.98 233:0.76 235:0.22 241:0.05 242:0.63 243:0.06 245:0.93 247:0.47 248:0.97 253:0.99 255:0.79 256:0.90 259:0.21 260:0.95 265:0.77 266:0.80 267:0.69 268:0.91 271:0.34 276:0.98 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.97 300:0.84 +2 1:0.74 7:0.81 11:0.88 12:0.79 17:0.53 21:0.30 27:0.21 28:0.70 30:0.43 34:0.76 36:0.92 37:0.74 39:0.55 43:0.98 66:0.20 69:0.12 70:0.80 74:0.97 81:0.80 83:0.75 85:0.98 91:0.08 98:0.57 101:0.82 104:0.91 106:0.81 108:0.10 114:0.86 117:0.86 121:0.90 122:0.57 123:0.77 124:0.75 126:0.54 127:0.65 129:0.81 133:0.67 135:0.34 144:0.85 146:0.83 147:0.86 149:0.96 150:0.87 154:0.98 155:0.67 159:0.86 161:0.79 165:0.84 167:0.49 172:0.70 173:0.08 176:0.73 177:0.38 178:0.55 179:0.27 181:0.98 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.54 215:0.72 216:0.95 222:0.35 230:0.87 232:0.95 233:0.76 235:0.42 241:0.24 242:0.45 243:0.43 245:0.93 247:0.47 253:0.78 259:0.21 265:0.44 266:0.80 267:0.36 271:0.34 276:0.82 290:0.86 297:0.36 300:0.84 +2 1:0.74 6:0.93 8:0.75 9:0.80 11:0.88 12:0.79 17:0.07 20:0.87 21:0.05 27:0.14 28:0.70 30:0.20 32:0.80 34:0.45 36:0.74 37:0.67 39:0.55 41:0.90 43:0.85 55:0.92 66:0.18 69:0.57 70:0.88 74:0.86 77:0.70 78:0.77 81:0.92 83:0.81 85:0.88 91:0.44 96:0.89 98:0.50 100:0.81 101:0.75 108:0.73 111:0.77 114:0.95 120:0.81 122:0.57 123:0.71 124:0.90 126:0.54 127:0.63 129:0.93 133:0.90 135:0.91 140:0.45 144:0.85 145:0.47 146:0.22 147:0.28 149:0.83 150:0.96 151:0.92 152:0.96 153:0.72 154:0.82 155:0.67 159:0.82 161:0.79 162:0.58 163:0.92 164:0.75 165:0.90 167:0.50 169:0.91 172:0.41 173:0.34 176:0.73 177:0.38 179:0.53 181:0.98 186:0.78 187:0.68 189:0.90 191:0.70 192:0.59 195:0.93 201:0.74 202:0.72 208:0.64 212:0.19 215:0.91 216:0.93 220:0.62 222:0.35 235:0.12 238:0.90 241:0.30 242:0.41 243:0.19 245:0.68 247:0.55 248:0.88 253:0.92 255:0.79 256:0.68 259:0.21 260:0.82 261:0.96 265:0.52 266:0.91 267:0.65 268:0.71 271:0.34 276:0.66 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 8:0.88 9:0.80 11:0.88 12:0.79 17:0.62 21:0.05 27:0.15 28:0.70 30:0.76 32:0.80 34:0.73 36:0.88 37:0.88 39:0.55 41:0.90 43:0.74 60:0.97 66:0.43 69:0.85 70:0.41 74:0.97 77:0.70 81:0.92 83:0.85 85:1.00 91:0.11 96:0.89 98:0.65 101:0.85 108:0.70 114:0.95 120:0.82 121:0.90 122:0.57 123:0.68 124:0.85 126:0.54 127:0.59 129:0.95 133:0.87 135:0.83 140:0.45 144:0.85 145:0.40 146:0.98 147:0.98 149:0.98 150:0.96 151:0.87 153:0.48 154:0.64 155:0.67 158:0.92 159:0.91 161:0.79 162:0.81 163:0.81 164:0.73 165:0.90 167:0.49 172:0.91 173:0.56 176:0.73 177:0.38 179:0.16 181:0.98 187:0.68 192:0.59 195:0.98 201:0.74 202:0.62 204:0.89 208:0.64 212:0.48 215:0.91 216:0.80 220:0.62 222:0.35 230:0.84 232:0.88 233:0.69 235:0.12 241:0.21 242:0.63 243:0.57 245:0.95 247:0.39 248:0.88 253:0.93 255:0.79 256:0.64 259:0.21 260:0.82 265:0.66 266:0.71 267:0.44 268:0.67 271:0.34 276:0.93 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.57 8:0.62 10:0.93 11:0.75 12:0.37 17:0.55 18:0.72 21:0.64 27:0.68 29:0.86 30:0.45 33:0.97 34:0.78 36:0.46 37:0.60 39:0.25 43:0.61 44:0.88 45:0.77 48:0.91 64:0.77 66:0.63 69:0.53 70:0.78 71:0.95 74:0.43 75:0.95 81:0.33 86:0.79 91:0.31 98:0.46 101:0.65 102:0.96 108:0.41 122:0.94 123:0.39 124:0.45 126:0.32 127:0.39 129:0.05 131:0.61 133:0.36 135:0.23 139:0.79 144:0.76 145:0.86 146:0.42 147:0.48 149:0.33 150:0.37 154:0.75 155:0.46 157:0.80 159:0.32 166:0.85 170:0.82 172:0.41 173:0.63 174:0.90 177:0.88 178:0.55 179:0.80 182:0.75 187:0.39 192:0.44 195:0.68 197:0.93 201:0.54 208:0.50 212:0.61 216:0.31 222:0.84 226:0.98 227:0.61 229:0.61 231:0.78 235:0.80 236:0.91 239:0.93 241:0.32 242:0.33 243:0.68 245:0.54 246:0.61 247:0.60 253:0.37 254:0.74 259:0.21 265:0.48 266:0.38 267:0.94 271:0.27 276:0.35 281:0.91 287:0.61 291:0.97 300:0.55 +1 10:0.95 11:0.75 12:0.37 17:0.78 18:0.82 21:0.74 27:0.65 29:0.86 30:0.76 34:0.68 36:0.80 37:0.57 39:0.25 43:0.61 44:0.85 45:0.81 48:0.97 64:0.77 66:0.80 69:0.36 70:0.28 71:0.95 74:0.45 75:0.95 81:0.25 86:0.88 91:0.17 98:0.81 101:0.65 108:0.28 122:0.65 123:0.27 126:0.24 127:0.30 129:0.05 131:0.61 135:0.24 139:0.84 144:0.76 145:0.52 146:0.39 147:0.45 149:0.29 150:0.26 154:0.84 157:0.83 159:0.15 166:0.74 170:0.82 172:0.45 173:0.75 174:0.86 177:0.88 178:0.55 179:0.77 182:0.75 187:0.21 193:0.95 195:0.55 197:0.90 212:0.82 216:0.29 222:0.84 226:0.98 227:0.61 229:0.61 231:0.73 235:0.88 239:0.94 241:0.41 242:0.16 243:0.82 246:0.61 247:0.60 253:0.38 254:0.80 259:0.21 265:0.81 266:0.23 267:0.52 271:0.27 276:0.27 281:0.86 287:0.61 300:0.25 +1 8:0.89 10:0.92 11:0.75 12:0.37 17:0.97 18:0.65 21:0.78 27:0.65 29:0.86 30:0.33 34:0.50 36:0.57 37:0.39 39:0.25 43:0.17 45:0.73 55:0.59 66:0.86 69:0.49 70:0.80 71:0.95 74:0.43 83:0.56 86:0.75 91:0.14 98:0.82 101:0.65 102:0.94 108:0.38 122:0.95 123:0.36 127:0.31 129:0.05 131:0.61 132:0.97 135:0.49 139:0.76 144:0.76 145:0.69 146:0.76 147:0.80 149:0.22 154:0.76 155:0.50 157:0.77 159:0.32 166:0.61 170:0.82 172:0.61 173:0.56 174:0.94 177:0.88 178:0.55 179:0.60 182:0.72 187:0.21 192:0.45 193:0.98 195:0.66 197:0.97 204:0.80 208:0.50 212:0.51 216:0.21 222:0.84 227:0.61 229:0.61 231:0.70 235:0.60 239:0.92 241:0.01 242:0.73 243:0.87 246:0.61 247:0.55 253:0.37 254:0.74 259:0.21 265:0.82 266:0.47 267:0.23 271:0.27 276:0.50 281:0.91 287:0.61 300:0.55 +2 10:0.90 11:0.75 12:0.37 17:0.64 18:0.65 21:0.78 27:0.72 29:0.86 30:0.76 34:0.85 36:0.95 39:0.25 43:0.83 45:0.73 48:0.97 66:0.72 69:0.60 70:0.22 71:0.95 74:0.44 86:0.75 91:0.17 98:0.22 101:0.65 108:0.46 122:0.65 123:0.44 127:0.11 129:0.05 131:0.61 135:0.54 139:0.73 144:0.76 145:0.72 146:0.20 147:0.26 149:0.66 154:0.79 157:0.77 159:0.10 166:0.61 170:0.82 172:0.27 173:0.86 174:0.79 177:0.88 178:0.55 179:0.18 182:0.72 187:0.21 197:0.84 212:0.78 216:0.09 222:0.84 227:0.61 229:0.61 231:0.63 235:0.68 239:0.89 241:0.57 242:0.45 243:0.75 246:0.61 247:0.35 253:0.37 254:0.80 259:0.21 265:0.24 266:0.17 267:0.87 271:0.27 276:0.26 281:0.91 287:0.61 300:0.18 +1 10:0.94 11:0.75 12:0.37 17:0.69 18:0.91 21:0.22 27:0.39 29:0.86 30:0.41 34:0.87 36:0.79 37:0.83 39:0.25 43:0.37 45:0.81 64:0.77 66:0.69 69:0.12 70:0.88 71:0.95 74:0.43 81:0.29 86:0.86 91:0.35 98:0.61 101:0.65 108:0.10 122:0.92 123:0.10 124:0.42 126:0.24 127:0.58 129:0.05 131:0.61 133:0.39 135:0.61 139:0.82 144:0.76 146:0.54 147:0.60 149:0.31 150:0.32 154:0.76 157:0.83 159:0.37 166:0.74 170:0.82 172:0.54 173:0.28 174:0.86 177:0.88 178:0.55 179:0.74 182:0.75 187:0.21 197:0.89 212:0.72 216:0.29 222:0.84 227:0.61 229:0.61 231:0.72 235:0.22 239:0.93 241:0.18 242:0.45 243:0.73 245:0.59 246:0.61 247:0.67 253:0.37 254:0.74 259:0.21 265:0.62 266:0.47 267:0.36 271:0.27 276:0.38 287:0.61 300:0.70 +4 1:0.61 8:0.82 9:0.75 10:0.96 11:0.75 12:0.37 17:0.38 18:0.80 21:0.13 23:0.99 27:0.53 29:0.86 30:0.66 31:0.95 34:0.74 36:0.58 37:0.85 39:0.25 43:0.63 45:0.83 48:0.91 62:0.99 64:0.77 66:0.85 69:0.15 70:0.53 71:0.95 74:0.55 75:0.99 79:0.95 81:0.66 83:0.69 86:0.88 91:0.82 96:0.82 97:0.97 98:0.93 99:0.83 101:0.65 102:0.94 104:0.87 108:0.12 120:0.78 122:0.76 123:0.12 126:0.51 127:0.58 128:0.98 129:0.05 131:0.90 135:0.28 137:0.95 139:0.88 140:0.90 144:0.76 145:0.38 146:0.47 147:0.53 149:0.32 150:0.58 151:0.68 153:0.46 154:0.74 155:0.65 157:0.85 158:0.85 159:0.17 162:0.70 164:0.78 165:0.65 166:0.86 168:0.98 170:0.82 172:0.57 173:0.58 174:0.88 177:0.88 179:0.77 182:0.82 190:0.89 192:0.98 193:0.95 195:0.53 196:0.96 197:0.91 201:0.63 202:0.71 205:0.98 208:0.64 212:0.83 215:0.84 216:0.16 219:0.95 220:0.74 222:0.84 226:0.98 227:0.94 229:0.90 231:0.79 235:0.31 236:0.95 239:0.96 241:0.84 242:0.22 243:0.86 246:0.92 247:0.60 248:0.66 253:0.78 254:0.84 255:0.78 256:0.73 259:0.21 260:0.78 265:0.93 266:0.27 267:0.23 268:0.74 271:0.27 276:0.45 279:0.81 281:0.91 283:0.66 284:0.95 285:0.62 287:0.94 292:0.95 297:0.36 300:0.43 +2 10:0.92 11:0.75 12:0.37 17:0.85 18:0.75 21:0.78 27:0.52 29:0.86 30:0.58 34:0.45 36:0.65 37:0.94 39:0.25 43:0.60 45:0.77 66:0.69 69:0.67 70:0.33 71:0.95 74:0.42 86:0.79 91:0.05 98:0.36 101:0.65 108:0.51 122:0.41 123:0.49 124:0.41 127:0.21 129:0.05 131:0.61 133:0.36 135:0.30 139:0.75 144:0.76 146:0.32 147:0.39 149:0.30 154:0.71 157:0.80 159:0.21 166:0.61 170:0.82 172:0.41 173:0.81 174:0.86 177:0.88 178:0.55 179:0.65 182:0.74 187:0.21 197:0.90 212:0.82 216:0.27 222:0.84 227:0.61 229:0.61 231:0.69 235:0.02 239:0.90 241:0.39 242:0.16 243:0.73 245:0.46 246:0.61 247:0.60 253:0.36 254:0.80 259:0.21 265:0.38 266:0.23 267:0.59 271:0.27 276:0.28 287:0.61 300:0.25 +1 10:0.94 11:0.75 12:0.37 17:0.64 18:0.88 21:0.78 27:0.39 29:0.86 30:0.45 34:0.88 36:0.71 37:0.83 39:0.25 43:0.33 45:0.81 66:0.68 69:0.62 70:0.69 71:0.95 74:0.42 86:0.85 91:0.20 98:0.58 101:0.65 108:0.47 122:0.92 123:0.45 124:0.43 127:0.47 129:0.05 131:0.61 133:0.39 135:0.47 139:0.80 144:0.76 146:0.54 147:0.60 149:0.28 154:0.72 157:0.83 159:0.38 166:0.61 170:0.82 172:0.51 173:0.70 174:0.86 177:0.88 178:0.55 179:0.74 182:0.75 187:0.21 197:0.88 212:0.58 216:0.29 222:0.84 227:0.61 229:0.61 231:0.71 235:1.00 239:0.92 241:0.18 242:0.38 243:0.72 245:0.58 246:0.61 247:0.67 253:0.36 254:0.74 259:0.21 265:0.59 266:0.54 267:0.36 271:0.27 276:0.36 287:0.61 300:0.55 +2 1:0.74 6:0.92 8:0.68 9:0.80 11:0.57 12:0.95 17:0.64 20:0.89 21:0.05 27:0.09 30:0.10 34:0.84 36:0.84 37:0.79 39:0.24 41:0.82 43:0.86 55:0.59 60:0.87 66:0.13 69:0.07 70:0.96 74:1.00 78:0.98 81:0.95 83:0.87 85:0.99 91:0.44 96:0.78 98:0.34 100:0.96 101:0.80 108:0.06 111:0.97 114:0.95 120:0.66 122:0.67 123:0.06 124:0.99 126:0.54 127:0.97 129:1.00 133:0.99 135:0.72 140:0.45 146:0.96 147:0.97 149:0.97 150:0.98 151:0.79 152:0.94 153:0.69 154:0.83 155:0.67 158:0.87 159:0.97 162:0.86 164:0.62 165:0.90 169:0.94 172:0.90 173:0.05 176:0.73 177:0.38 179:0.12 186:0.98 187:0.39 189:0.93 192:0.59 201:0.74 202:0.46 208:0.64 212:0.39 215:0.91 216:0.75 220:0.62 222:0.21 235:0.12 238:0.90 241:0.27 242:0.54 243:0.33 245:0.95 247:0.67 248:0.72 253:0.87 255:0.79 256:0.45 259:0.21 260:0.67 261:0.95 265:0.36 266:0.98 267:0.96 268:0.55 271:0.53 276:0.98 279:0.86 283:0.71 285:0.62 290:0.95 297:0.36 300:0.94 +2 1:0.74 11:0.57 12:0.95 17:0.32 21:0.30 27:0.45 30:0.08 34:0.68 36:0.21 37:0.50 39:0.24 43:0.71 55:0.42 66:0.10 69:0.69 70:0.99 74:0.82 81:0.82 85:0.72 91:0.40 98:0.34 101:0.71 108:0.71 114:0.86 122:0.67 123:0.79 124:0.67 126:0.54 127:0.26 129:0.59 133:0.64 135:0.85 145:0.50 146:0.24 147:0.30 149:0.45 150:0.84 154:0.75 155:0.67 158:0.86 159:0.51 172:0.45 173:0.59 176:0.73 177:0.38 178:0.55 179:0.54 187:0.68 192:0.59 201:0.74 208:0.64 212:0.40 215:0.72 216:0.77 222:0.21 235:0.42 241:0.24 242:0.43 243:0.28 245:0.63 247:0.60 253:0.73 259:0.21 265:0.32 266:0.63 267:0.44 271:0.53 276:0.48 277:0.69 279:0.86 283:0.67 290:0.86 297:0.36 300:0.84 +2 1:0.74 8:0.68 11:0.57 12:0.95 17:0.57 21:0.22 27:0.49 30:0.26 32:0.80 34:0.52 36:0.90 37:0.42 39:0.24 43:0.97 66:0.44 69:0.15 70:0.88 74:0.99 76:0.94 77:0.70 81:0.85 83:0.76 85:0.99 91:0.25 98:0.77 101:0.84 108:0.80 114:0.90 122:0.57 123:0.79 124:0.69 126:0.54 127:0.89 129:0.81 133:0.67 135:0.61 145:0.41 146:0.84 147:0.87 149:0.98 150:0.91 154:0.97 155:0.67 159:0.87 165:0.86 172:0.91 173:0.09 176:0.73 177:0.38 178:0.55 179:0.19 187:0.39 192:0.59 195:0.95 201:0.74 212:0.50 215:0.79 216:0.94 222:0.21 235:0.42 241:0.01 242:0.36 243:0.40 245:0.97 247:0.60 253:0.79 259:0.21 265:0.77 266:0.75 267:0.93 271:0.53 276:0.92 282:0.88 290:0.90 297:0.36 299:0.88 300:0.84 +1 8:0.87 11:0.57 12:0.95 17:0.13 20:0.87 21:0.71 27:0.11 30:0.32 34:0.40 36:0.88 37:0.80 39:0.24 43:0.34 55:0.84 66:0.09 69:0.96 70:0.78 74:1.00 76:1.00 78:0.90 81:0.35 85:0.72 91:0.79 96:0.96 98:0.38 100:0.73 101:0.52 108:0.93 111:0.87 114:0.61 117:0.86 122:0.67 123:0.97 124:0.98 126:0.32 127:0.93 129:0.89 133:0.98 135:0.39 140:0.45 146:0.97 147:0.31 149:0.89 150:0.31 151:0.80 152:0.82 153:0.93 154:0.16 158:0.85 159:0.98 162:0.71 169:0.72 172:0.90 173:0.69 176:0.56 177:0.05 179:0.11 186:0.90 187:0.68 190:0.77 191:0.80 202:0.87 212:0.29 216:0.81 222:0.21 235:0.88 241:0.05 242:0.81 243:0.06 245:0.97 247:0.67 248:0.88 253:0.97 256:0.88 257:0.65 259:0.21 261:0.85 265:0.36 266:0.97 267:0.95 268:0.89 271:0.53 276:0.98 285:0.50 290:0.61 300:0.94 +0 8:0.80 9:0.80 11:0.57 12:0.95 17:0.18 20:0.86 21:0.05 27:0.18 30:0.29 34:0.53 36:0.45 37:0.71 39:0.24 43:0.59 55:0.59 66:0.75 69:0.90 70:0.68 74:1.00 76:0.85 77:0.70 78:0.94 81:0.87 85:0.72 91:0.81 96:0.98 98:0.83 100:0.92 101:0.70 108:0.79 111:0.92 114:0.77 120:0.61 122:0.67 123:0.78 124:0.43 126:0.32 127:0.66 129:0.05 133:0.62 135:0.19 140:0.99 145:0.38 146:0.96 147:0.05 149:0.85 150:0.73 151:0.64 152:0.81 153:0.96 154:0.48 155:0.67 158:0.85 159:0.78 162:0.67 164:0.54 169:0.90 172:0.95 173:0.83 176:0.56 177:0.05 179:0.18 186:0.93 187:0.39 190:0.77 191:0.84 192:0.59 195:0.89 202:0.91 208:0.64 212:0.86 216:0.90 222:0.21 235:0.12 241:0.54 242:0.81 243:0.06 245:0.93 247:0.39 248:0.62 253:0.99 255:0.79 256:0.92 257:0.65 259:0.21 260:0.62 261:0.88 265:0.83 266:0.54 267:0.73 268:0.93 271:0.53 276:0.92 282:0.84 285:0.62 290:0.77 300:0.70 +1 7:0.81 8:0.89 11:0.57 12:0.95 17:0.36 21:0.05 27:0.29 30:0.13 32:0.77 34:0.44 36:0.93 37:0.70 39:0.24 43:0.73 55:0.71 66:0.79 69:0.48 70:0.92 74:0.98 76:0.99 77:0.96 81:0.87 85:0.85 91:0.57 96:0.86 98:0.81 101:0.76 108:0.37 114:0.77 121:0.90 122:0.67 123:0.35 124:0.39 126:0.32 127:0.54 129:0.05 133:0.39 135:0.95 140:0.45 145:0.89 146:0.90 147:0.92 149:0.81 150:0.73 151:0.70 153:0.69 154:0.75 155:0.67 158:0.85 159:0.61 162:0.81 172:0.82 173:0.39 176:0.56 177:0.38 179:0.40 187:0.39 190:0.77 192:0.59 195:0.80 202:0.58 204:0.89 208:0.64 212:0.60 216:0.41 220:0.62 222:0.21 230:0.84 233:0.69 235:0.12 241:0.39 242:0.73 243:0.80 245:0.78 247:0.55 248:0.69 253:0.91 256:0.58 259:0.21 265:0.81 266:0.80 267:0.79 268:0.64 271:0.53 276:0.73 282:0.85 285:0.50 290:0.77 300:0.70 +2 7:0.78 8:0.90 11:0.57 12:0.95 17:0.30 21:0.30 27:0.09 30:0.19 34:0.49 36:0.97 37:0.79 39:0.24 43:0.66 55:0.52 60:0.87 66:0.96 69:0.59 70:0.87 74:0.98 77:0.70 81:0.81 83:0.75 85:0.85 91:0.72 96:0.94 98:0.94 101:0.76 108:0.45 114:0.69 117:0.86 120:0.65 122:0.67 123:0.43 126:0.32 127:0.61 129:0.05 135:0.87 140:0.45 145:0.72 146:0.96 147:0.96 149:0.83 150:0.60 151:0.76 153:0.88 154:0.55 155:0.67 158:0.87 159:0.65 162:0.84 164:0.56 172:0.89 173:0.50 176:0.56 177:0.38 179:0.32 187:0.68 190:0.77 191:0.78 192:0.59 195:0.81 202:0.77 208:0.64 212:0.58 216:0.75 220:0.74 222:0.21 230:0.81 233:0.69 235:0.52 241:0.66 242:0.75 243:0.96 247:0.47 248:0.84 253:0.96 256:0.81 259:0.21 260:0.66 265:0.94 266:0.54 267:0.91 268:0.83 271:0.53 276:0.81 279:0.86 282:0.84 283:0.71 285:0.62 290:0.69 300:0.70 +2 6:0.99 8:0.82 9:0.80 11:0.57 12:0.95 17:0.18 20:0.89 21:0.22 27:0.06 30:0.42 34:0.64 36:0.62 37:0.85 39:0.24 41:0.82 43:0.66 55:0.52 66:0.61 69:0.76 70:0.84 74:0.99 76:0.85 77:0.70 78:0.95 81:0.75 83:0.74 85:0.85 91:0.57 96:0.78 98:0.74 100:0.94 101:0.73 108:0.59 111:0.93 114:0.72 117:0.86 120:0.65 122:0.67 123:0.57 124:0.57 126:0.32 127:0.49 129:0.05 133:0.62 135:0.39 140:0.80 145:0.45 146:0.96 147:0.96 149:0.87 150:0.55 151:0.77 152:0.83 153:0.48 154:0.55 155:0.67 158:0.84 159:0.80 162:0.86 164:0.57 169:0.92 172:0.93 173:0.57 176:0.56 177:0.38 179:0.17 186:0.95 187:0.68 189:1.00 191:0.81 192:0.59 195:0.93 202:0.50 208:0.64 212:0.73 216:0.87 220:0.82 222:0.21 235:0.31 238:0.89 241:0.64 242:0.79 243:0.68 245:0.96 247:0.60 248:0.71 253:0.86 255:0.79 256:0.58 259:0.21 260:0.66 261:0.91 265:0.74 266:0.75 267:0.93 268:0.59 271:0.53 276:0.91 282:0.84 285:0.62 290:0.72 300:0.84 +1 8:0.82 11:0.57 12:0.95 17:0.32 21:0.30 27:0.50 30:0.26 34:0.52 36:0.80 37:0.60 39:0.24 43:0.58 55:0.71 66:0.18 69:0.53 70:0.81 74:1.00 76:0.85 77:0.89 81:0.68 85:0.95 91:0.35 96:0.89 98:0.53 101:0.71 108:0.41 114:0.69 117:0.86 122:0.67 123:0.39 124:0.98 126:0.32 127:0.97 129:0.98 133:0.98 135:0.30 140:0.45 145:0.59 146:1.00 147:1.00 149:0.91 150:0.49 151:0.74 153:0.83 154:0.50 158:0.85 159:0.98 162:0.82 172:0.97 173:0.08 176:0.56 177:0.38 179:0.10 187:0.39 190:0.77 191:0.70 195:1.00 202:0.63 212:0.30 216:0.86 222:0.21 235:0.42 241:0.02 242:0.78 243:0.38 245:0.98 247:0.67 248:0.73 253:0.93 256:0.64 259:0.21 265:0.54 266:0.96 267:0.95 268:0.69 271:0.53 276:0.99 282:0.84 285:0.50 290:0.69 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.95 17:0.43 21:0.40 27:0.09 30:0.38 32:0.80 34:0.77 36:0.92 37:0.79 39:0.24 41:0.82 43:0.79 60:0.87 66:0.95 69:0.60 70:0.85 74:0.97 77:0.89 81:0.85 83:0.83 85:0.96 91:0.51 96:0.73 98:0.94 101:0.85 106:0.81 108:0.45 114:0.82 117:0.86 120:0.60 121:0.99 122:0.43 123:0.44 126:0.54 127:0.61 129:0.05 135:0.73 140:0.45 145:0.76 146:0.95 147:0.96 149:0.94 150:0.90 151:0.63 153:0.48 154:0.73 155:0.67 158:0.87 159:0.63 162:0.86 164:0.57 165:0.81 172:0.88 173:0.52 176:0.73 177:0.38 179:0.34 187:0.87 192:0.59 195:0.79 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.74 215:0.67 216:0.75 220:0.82 222:0.21 230:0.87 233:0.76 235:0.60 241:0.46 242:0.27 243:0.96 247:0.67 248:0.60 253:0.80 255:0.79 256:0.45 259:0.21 260:0.60 265:0.94 266:0.54 267:0.69 268:0.46 271:0.53 276:0.80 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.90 7:0.81 8:0.88 9:0.80 11:0.57 12:0.95 17:0.22 20:0.91 21:0.13 27:0.21 30:0.17 32:0.80 34:0.67 36:0.93 37:0.60 39:0.24 41:0.90 43:0.97 60:0.99 66:0.91 69:0.17 70:0.76 74:0.95 76:0.94 77:0.89 78:0.93 81:0.91 83:0.83 85:0.90 91:0.49 96:0.80 98:0.96 100:0.92 101:0.83 104:0.93 108:0.14 111:0.92 114:0.92 120:0.69 121:0.90 122:0.57 123:0.13 126:0.54 127:0.71 129:0.05 132:0.97 135:0.73 140:0.80 145:0.44 146:0.79 147:0.83 149:0.86 150:0.95 151:0.80 152:0.96 153:0.71 154:0.97 155:0.67 158:0.87 159:0.35 162:0.74 164:0.71 165:0.86 169:0.92 172:0.74 173:0.35 176:0.73 177:0.38 178:0.42 179:0.59 186:0.93 187:0.21 189:0.89 192:0.59 195:0.58 201:0.74 202:0.61 204:0.89 208:0.64 212:0.75 215:0.84 216:0.46 220:0.62 222:0.21 230:0.84 233:0.69 235:0.52 238:0.87 241:0.39 242:0.45 243:0.91 247:0.47 248:0.77 253:0.88 255:0.79 256:0.64 259:0.21 260:0.70 261:0.95 265:0.96 266:0.63 267:0.85 268:0.65 271:0.53 276:0.61 279:0.86 282:0.88 283:0.71 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +1 8:0.79 11:0.57 12:0.95 17:0.53 20:0.87 27:0.59 30:0.21 34:0.40 36:0.92 37:0.52 39:0.24 43:0.79 55:0.84 66:0.06 69:0.05 70:0.95 74:1.00 78:0.93 81:0.92 85:0.98 91:0.42 96:0.74 98:0.26 100:0.94 101:0.74 108:0.05 111:0.92 114:0.80 122:0.67 123:0.96 124:0.99 126:0.32 127:1.00 129:0.99 133:0.99 135:0.44 140:0.45 146:0.96 147:0.97 149:0.94 150:0.85 151:0.58 152:0.82 153:0.48 154:0.72 159:0.99 162:0.62 169:0.92 172:0.89 173:0.05 176:0.56 177:0.38 179:0.10 186:0.92 187:0.87 190:0.77 202:0.50 212:0.19 216:0.59 220:0.74 222:0.21 235:0.05 241:0.05 242:0.76 243:0.24 245:0.97 247:0.67 248:0.56 253:0.82 256:0.45 259:0.21 261:0.89 265:0.28 266:0.98 267:0.76 268:0.46 271:0.53 276:0.99 277:0.69 285:0.50 290:0.80 300:0.94 +1 6:0.98 8:0.95 9:0.80 11:0.57 12:0.95 17:0.15 20:0.96 21:0.30 27:0.21 30:0.56 34:0.30 36:0.78 37:0.74 39:0.24 41:0.82 43:0.72 55:0.71 66:0.11 69:0.12 70:0.66 74:1.00 76:0.85 78:0.93 81:0.68 83:0.74 85:0.91 91:0.93 96:0.99 98:0.45 100:0.97 101:0.70 108:0.98 111:0.95 114:0.69 117:0.86 120:0.65 122:0.67 123:0.98 124:0.97 126:0.32 127:1.00 129:0.98 133:0.97 135:0.20 140:0.98 146:0.82 147:0.85 149:0.88 150:0.49 151:0.77 152:0.92 153:0.98 154:0.62 155:0.67 158:0.85 159:0.97 162:0.66 164:0.57 169:0.95 172:0.92 173:0.05 176:0.56 177:0.38 179:0.10 186:0.93 187:0.39 189:0.99 190:0.77 191:0.87 192:0.59 202:0.92 208:0.64 212:0.36 216:0.95 222:0.21 235:0.42 238:0.95 241:0.24 242:0.78 243:0.11 245:0.98 247:0.67 248:0.71 253:0.99 255:0.79 256:0.94 259:0.21 260:0.66 261:0.94 265:0.47 266:0.96 267:0.95 268:0.94 271:0.53 276:0.99 285:0.62 290:0.69 300:0.94 +1 8:0.83 11:0.57 12:0.95 17:0.32 21:0.47 27:0.21 30:0.43 34:0.44 36:0.79 37:0.74 39:0.24 43:0.82 55:0.84 66:0.17 69:0.21 70:0.83 74:0.99 76:0.97 81:0.56 85:0.96 91:0.48 96:0.79 98:0.57 101:0.74 108:0.98 114:0.66 117:0.86 122:0.67 123:0.98 124:0.97 126:0.32 127:0.98 129:0.95 133:0.97 135:0.24 140:0.45 146:0.82 147:0.85 149:0.93 150:0.42 151:0.61 153:0.86 154:0.77 158:0.84 159:0.96 162:0.58 172:0.91 173:0.06 176:0.56 177:0.38 179:0.12 187:0.39 190:0.77 191:0.70 202:0.72 212:0.21 216:0.95 220:0.74 222:0.21 235:0.60 241:0.43 242:0.76 243:0.21 245:0.96 247:0.67 248:0.62 253:0.86 256:0.68 259:0.21 265:0.58 266:0.98 267:0.84 268:0.72 271:0.53 276:0.97 285:0.50 290:0.66 300:0.94 +2 1:0.74 6:0.90 7:0.81 8:0.74 9:0.80 11:0.57 12:0.95 17:0.15 20:0.90 21:0.13 27:0.21 30:0.27 32:0.80 34:0.68 36:0.74 37:0.60 39:0.24 41:0.88 43:0.97 55:0.52 60:0.95 66:0.91 69:0.17 70:0.83 74:0.95 76:0.94 77:0.89 78:0.96 81:0.91 83:0.84 85:0.90 91:0.46 96:0.83 98:0.92 100:0.95 101:0.84 104:0.91 108:0.14 111:0.95 114:0.92 120:0.73 121:0.90 122:0.43 123:0.13 126:0.54 127:0.52 129:0.05 132:0.97 135:0.65 140:0.45 145:0.44 146:0.72 147:0.76 149:0.88 150:0.95 151:0.81 152:0.96 153:0.72 154:0.97 155:0.67 158:0.87 159:0.29 162:0.76 164:0.69 165:0.86 169:0.92 172:0.75 173:0.38 176:0.73 177:0.38 179:0.53 186:0.96 187:0.21 189:0.93 191:0.70 192:0.59 195:0.56 201:0.74 202:0.58 204:0.89 208:0.64 212:0.88 215:0.84 216:0.46 220:0.62 222:0.21 230:0.84 233:0.69 235:0.52 238:0.89 241:0.44 242:0.30 243:0.91 247:0.60 248:0.80 253:0.89 255:0.79 256:0.58 259:0.21 260:0.74 261:0.95 265:0.92 266:0.38 267:0.36 268:0.62 271:0.53 276:0.62 279:0.86 282:0.88 283:0.71 285:0.62 290:0.92 297:0.36 299:0.88 300:0.70 +0 8:0.86 11:0.57 12:0.95 17:0.59 21:0.74 27:0.45 30:0.26 34:0.66 36:0.39 37:0.50 39:0.24 43:0.65 55:0.59 66:0.61 69:0.71 70:0.86 74:0.98 77:0.70 81:0.33 85:0.80 91:0.29 98:0.63 101:0.71 108:0.54 114:0.61 122:0.67 123:0.52 124:0.71 126:0.32 127:0.37 129:0.05 133:0.82 135:0.85 145:0.52 146:0.92 147:0.94 149:0.76 150:0.30 154:0.54 158:0.84 159:0.77 172:0.82 173:0.49 176:0.56 177:0.38 178:0.55 179:0.28 187:0.68 195:0.94 212:0.30 216:0.77 222:0.21 235:0.95 241:0.30 242:0.78 243:0.68 245:0.79 247:0.39 253:0.71 259:0.21 265:0.64 266:0.75 267:0.89 271:0.53 276:0.79 282:0.84 290:0.61 300:0.84 +2 1:0.74 7:0.81 11:0.57 12:0.95 17:0.32 21:0.22 27:0.09 30:0.21 32:0.80 34:0.59 36:0.92 37:0.79 39:0.24 43:0.79 55:0.71 66:0.53 69:0.59 70:0.94 74:0.95 76:0.94 77:0.99 81:0.87 85:0.85 91:0.57 98:0.72 101:0.75 106:0.81 108:0.45 114:0.90 117:0.86 120:0.67 121:0.97 122:0.65 123:0.43 124:0.64 126:0.54 127:0.69 129:0.59 133:0.64 135:0.82 140:0.45 145:0.76 146:0.90 147:0.92 149:0.82 150:0.88 151:0.63 153:0.72 154:0.73 155:0.67 159:0.74 162:0.67 164:0.64 172:0.76 173:0.44 176:0.73 177:0.38 179:0.40 187:0.87 190:0.77 192:0.59 195:0.88 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.49 215:0.79 216:0.75 220:0.62 222:0.21 230:0.84 233:0.76 235:0.31 241:0.50 242:0.24 243:0.62 245:0.84 247:0.67 248:0.61 253:0.78 256:0.45 259:0.21 260:0.68 265:0.72 266:0.75 267:0.87 268:0.57 271:0.53 276:0.75 282:0.88 283:0.71 285:0.50 290:0.90 297:0.36 299:0.97 300:0.84 +0 8:0.78 10:0.79 12:0.69 17:0.99 18:0.57 21:0.78 27:0.72 29:0.62 30:0.28 34:0.37 36:0.44 39:0.58 43:0.05 55:0.45 66:0.07 69:0.98 70:0.75 71:0.67 74:0.25 86:0.57 91:0.14 98:0.13 108:0.98 122:0.98 123:0.98 124:0.91 127:0.43 129:0.59 131:0.61 133:0.91 135:0.32 139:0.56 145:0.40 146:0.17 147:0.05 149:0.06 154:0.06 157:0.61 159:0.96 166:0.61 170:0.90 172:0.37 173:0.86 174:0.93 175:0.99 177:0.05 178:0.55 179:0.65 182:0.61 187:0.95 197:0.82 202:0.61 212:0.37 216:0.18 222:0.21 227:0.61 229:0.61 231:0.68 235:0.22 239:0.79 241:0.41 242:0.51 243:0.12 244:0.97 245:0.52 246:0.61 247:0.76 253:0.23 254:0.74 257:0.99 264:0.96 265:0.10 266:0.80 267:0.99 271:0.17 275:0.94 276:0.51 277:0.98 287:0.61 294:0.91 300:0.55 +0 10:0.80 12:0.69 17:0.99 18:0.57 21:0.78 27:0.72 29:0.62 30:0.62 34:0.67 36:0.25 37:0.24 39:0.58 43:0.07 55:0.92 66:0.16 69:0.97 70:0.23 71:0.67 74:0.26 86:0.58 91:0.01 98:0.17 108:0.94 122:0.67 123:0.93 124:0.81 127:0.71 129:0.59 131:0.61 133:0.76 135:0.58 139:0.56 146:0.44 147:0.05 149:0.06 154:0.07 157:0.61 159:0.91 163:0.97 166:0.61 170:0.90 172:0.10 173:0.90 174:0.79 175:0.99 177:0.05 178:0.55 179:0.95 182:0.61 187:0.39 197:0.84 212:0.30 216:0.09 222:0.21 227:0.61 229:0.61 231:0.62 239:0.79 241:0.12 242:0.33 243:0.23 244:0.98 245:0.42 246:0.61 247:0.39 253:0.24 257:0.99 259:0.21 264:0.96 265:0.18 266:0.27 267:0.23 271:0.17 275:0.91 276:0.25 277:0.98 287:0.61 294:0.91 300:0.18 +0 8:0.59 10:0.80 11:0.54 12:0.69 17:0.96 18:0.58 21:0.78 27:1.00 29:0.62 30:0.38 34:0.86 36:0.48 39:0.58 43:0.05 45:0.66 55:0.99 66:0.13 69:0.98 70:0.63 71:0.67 74:0.24 86:0.58 98:0.05 101:0.45 108:0.97 122:0.95 123:0.97 124:0.93 127:0.68 129:0.05 131:0.61 133:0.92 135:0.44 139:0.57 144:0.76 146:0.19 147:0.25 149:0.05 154:0.07 157:0.75 159:1.00 163:1.00 166:0.61 170:0.90 172:0.16 173:0.60 174:0.79 175:0.97 177:0.70 178:0.55 179:0.83 182:0.71 187:0.21 197:0.79 212:0.28 216:0.01 222:0.21 227:0.61 229:0.61 231:0.61 239:0.80 241:0.12 242:0.60 243:0.34 244:0.93 245:0.45 246:0.61 247:0.55 253:0.22 254:0.84 257:0.93 259:0.21 264:0.96 265:0.05 266:0.47 267:0.84 271:0.17 275:0.91 276:0.41 277:0.95 287:0.61 294:0.92 300:0.43 +0 10:0.82 11:0.54 12:0.69 17:0.97 18:0.63 21:0.78 27:0.65 29:0.62 30:0.45 34:0.53 36:0.43 37:0.31 39:0.58 43:0.21 45:0.66 55:0.92 66:0.52 69:0.82 70:0.50 71:0.67 74:0.30 86:0.61 91:0.01 98:0.22 101:0.45 108:0.66 123:0.64 124:0.63 127:0.18 129:0.05 131:0.61 133:0.59 135:0.94 139:0.59 144:0.76 146:0.62 147:0.05 149:0.16 154:0.14 157:0.76 159:0.72 163:0.75 166:0.61 170:0.90 172:0.27 173:0.51 174:0.79 175:0.75 177:0.05 178:0.55 179:0.65 182:0.72 187:0.95 197:0.82 212:0.23 216:0.63 222:0.21 227:0.61 229:0.61 231:0.62 235:0.22 239:0.82 242:0.24 243:0.21 244:0.97 245:0.43 246:0.61 247:0.47 253:0.27 257:0.99 259:0.21 264:0.96 265:0.24 266:0.38 267:0.44 271:0.17 275:0.91 276:0.23 277:0.69 287:0.61 294:0.93 300:0.33 +0 10:0.81 11:0.54 12:0.69 17:0.96 18:0.58 21:0.78 27:0.72 29:0.62 30:0.11 34:0.94 36:0.38 39:0.58 43:0.05 45:0.66 66:0.30 69:0.97 70:0.54 71:0.67 74:0.26 86:0.59 91:0.14 98:0.09 101:0.45 108:0.95 122:0.76 123:0.95 124:0.78 127:0.19 129:0.05 131:0.61 133:0.73 135:0.37 139:0.57 144:0.76 146:0.20 147:0.05 149:0.05 154:0.08 157:0.76 159:0.67 163:0.90 166:0.61 170:0.90 172:0.16 173:0.97 174:0.79 175:0.97 177:0.05 178:0.55 179:0.75 182:0.72 187:0.21 197:0.79 212:0.30 216:0.06 222:0.21 227:0.61 229:0.61 231:0.63 235:0.60 239:0.80 241:0.07 242:0.33 243:0.23 244:0.93 245:0.40 246:0.61 247:0.39 253:0.24 254:0.88 257:0.99 264:0.96 265:0.10 266:0.27 267:0.17 271:0.17 275:0.91 276:0.26 277:0.95 287:0.61 294:0.92 300:0.33 +0 10:0.84 11:0.64 12:0.69 17:0.64 18:0.57 21:0.78 27:0.67 29:0.62 30:0.18 34:0.30 36:0.20 37:0.48 39:0.58 43:0.24 45:0.77 55:0.59 66:0.07 69:0.98 70:0.93 71:0.67 74:0.27 80:0.98 86:0.59 91:0.40 98:0.16 101:0.64 102:0.94 108:0.27 122:0.55 123:0.26 124:0.98 127:0.95 129:0.05 131:0.61 133:0.98 135:0.69 139:0.57 144:0.76 145:0.45 146:0.32 147:0.55 149:0.25 154:0.07 157:0.75 159:0.95 166:0.61 170:0.90 172:0.32 173:0.96 174:0.92 175:0.91 177:0.38 178:0.55 179:0.39 182:0.72 187:0.68 193:0.94 195:0.99 197:0.94 212:0.13 216:0.24 222:0.21 227:0.61 229:0.61 231:0.81 235:0.42 239:0.84 242:0.13 243:0.22 244:0.93 245:0.68 246:0.61 247:0.95 253:0.24 257:0.97 259:0.21 264:0.96 265:0.17 266:0.98 267:0.28 271:0.17 275:0.98 276:0.79 277:0.98 287:0.61 294:0.94 300:0.84 +0 10:0.79 12:0.69 17:0.99 18:0.60 21:0.78 27:0.71 29:0.62 30:0.68 34:0.73 36:0.28 39:0.58 43:0.06 55:0.96 66:0.12 69:0.94 70:0.43 71:0.67 74:0.27 86:0.60 91:0.01 98:0.17 108:0.89 122:0.85 123:0.93 124:0.86 127:0.19 129:0.59 131:0.61 133:0.82 135:0.71 139:0.58 146:0.17 147:0.05 149:0.06 154:0.09 157:0.61 159:0.66 163:0.98 166:0.61 170:0.90 172:0.10 173:0.86 174:0.83 175:0.99 177:0.05 178:0.55 179:0.65 182:0.61 187:0.39 197:0.82 212:0.42 216:0.14 222:0.21 227:0.61 229:0.61 231:0.64 239:0.79 241:0.05 242:0.19 243:0.19 244:0.97 245:0.44 246:0.61 247:0.55 253:0.24 254:0.74 257:0.99 259:0.21 264:0.96 265:0.07 266:0.38 267:0.36 271:0.17 275:0.91 276:0.33 277:0.98 287:0.61 294:0.91 300:0.33 +0 10:0.84 11:0.64 12:0.69 17:0.62 18:0.59 21:0.78 27:0.67 29:0.62 30:0.33 34:0.26 36:0.21 37:0.48 39:0.58 43:0.24 45:0.77 55:0.96 66:0.09 69:0.82 70:0.88 71:0.67 74:0.29 80:0.98 86:0.61 91:0.38 98:0.19 101:0.64 102:0.94 108:0.27 122:0.98 123:0.26 124:0.95 127:0.90 129:0.59 131:0.61 133:0.95 135:0.54 139:0.60 144:0.76 145:0.44 146:0.40 147:0.55 149:0.30 154:0.13 157:0.76 159:0.91 166:0.61 170:0.90 172:0.32 173:0.55 174:0.94 175:0.97 177:0.38 178:0.55 179:0.48 182:0.72 187:0.68 193:0.94 195:0.98 197:0.94 212:0.29 216:0.24 222:0.21 227:0.61 229:0.61 231:0.83 235:0.31 239:0.84 241:0.01 242:0.15 243:0.27 244:0.93 245:0.67 246:0.61 247:0.96 253:0.27 257:0.97 259:0.21 264:0.96 265:0.21 266:0.96 267:0.36 271:0.17 275:0.97 276:0.69 277:0.98 287:0.61 294:0.94 300:0.84 +1 1:0.57 11:0.48 12:0.69 17:0.67 18:0.64 21:0.30 27:0.30 29:0.62 30:0.90 34:0.42 36:0.35 37:0.71 39:0.58 43:0.32 45:0.73 55:0.59 56:0.97 66:0.69 69:0.66 70:0.19 71:0.67 74:0.33 81:0.48 86:0.65 91:0.48 98:0.33 101:0.52 108:0.50 123:0.48 124:0.41 126:0.32 127:0.19 129:0.05 131:0.61 133:0.37 135:0.69 139:0.63 144:0.76 146:0.40 147:0.46 149:0.29 150:0.47 154:0.23 155:0.46 157:0.61 159:0.27 166:0.93 170:0.90 172:0.41 173:0.68 175:0.97 177:0.70 178:0.55 179:0.59 182:0.61 187:0.39 192:0.47 201:0.59 208:0.49 212:0.82 215:0.72 216:0.35 222:0.21 227:0.61 229:0.61 231:0.88 235:0.52 236:0.92 241:0.07 242:0.72 243:0.73 244:0.93 245:0.46 246:0.61 247:0.39 253:0.29 257:0.93 259:0.21 264:0.96 265:0.35 266:0.23 267:0.52 271:0.17 276:0.29 277:0.95 287:0.61 297:0.36 300:0.18 +0 10:0.85 11:0.64 12:0.69 17:0.67 18:0.61 21:0.76 27:1.00 29:0.62 30:0.30 34:0.34 36:0.24 37:0.48 39:0.58 43:0.24 45:0.87 55:0.92 56:0.97 66:0.13 69:0.94 70:0.97 71:0.67 74:0.31 80:0.98 81:0.25 82:0.98 86:0.63 88:0.98 91:0.53 98:0.39 101:0.64 102:0.94 108:0.96 123:0.96 124:0.98 126:0.22 127:0.95 129:0.59 131:0.61 133:0.98 135:0.69 139:0.61 144:0.76 145:0.59 146:0.61 147:0.56 149:0.33 150:0.26 154:0.13 157:0.75 159:0.95 166:0.81 170:0.90 172:0.63 173:0.72 174:0.90 175:0.91 177:0.38 178:0.55 179:0.28 182:0.72 187:0.68 193:0.94 195:0.99 197:0.93 212:0.23 216:0.11 222:0.21 227:0.61 229:0.61 231:0.84 235:0.95 239:0.86 241:0.03 242:0.17 243:0.19 244:0.93 245:0.77 246:0.61 247:0.93 253:0.28 257:0.97 259:0.21 264:0.96 265:0.41 266:0.98 267:0.28 271:0.17 275:0.99 276:0.86 277:0.87 287:0.61 294:0.95 300:0.94 +0 8:0.71 10:0.79 12:0.69 17:0.96 18:0.57 21:0.78 27:0.65 29:0.62 30:0.45 34:0.74 36:0.78 37:0.49 39:0.58 43:0.05 55:1.00 66:0.09 69:1.00 70:0.47 71:0.67 74:0.26 86:0.57 91:0.03 98:0.05 108:0.99 122:0.43 123:0.99 124:0.92 127:0.30 129:0.59 131:0.61 133:0.91 135:0.54 139:0.56 146:0.13 147:0.05 149:0.05 154:0.05 157:0.61 159:0.99 163:1.00 166:0.61 170:0.90 172:0.10 173:0.86 174:0.79 175:0.99 177:0.05 178:0.55 179:0.75 182:0.61 187:0.39 197:0.79 202:0.46 212:0.30 216:0.02 222:0.21 227:0.61 229:0.61 231:0.61 235:0.42 239:0.78 241:0.05 242:0.14 243:0.17 244:0.97 245:0.44 246:0.61 247:0.67 253:0.23 257:0.99 259:0.21 264:0.96 265:0.05 266:0.54 267:0.69 271:0.17 275:0.93 276:0.39 277:0.98 287:0.61 294:0.91 300:0.33 +0 8:0.69 10:0.79 12:0.69 17:0.94 18:0.69 21:0.78 27:0.65 29:0.62 30:0.38 34:0.68 36:0.72 37:0.49 39:0.58 43:0.07 55:1.00 66:0.09 69:1.00 70:0.63 71:0.67 74:0.25 86:0.63 91:0.09 98:0.05 108:1.00 122:0.75 123:1.00 124:0.93 127:0.70 129:0.59 131:0.61 133:0.92 135:0.58 139:0.61 146:0.13 147:0.05 149:0.07 154:0.05 157:0.61 159:1.00 163:1.00 166:0.61 170:0.90 172:0.10 173:0.90 174:0.79 175:0.99 177:0.05 178:0.55 179:0.84 182:0.61 187:0.39 197:0.79 202:0.50 212:0.28 216:0.02 222:0.21 227:0.61 229:0.61 231:0.61 235:0.31 239:0.78 241:0.07 242:0.13 243:0.16 244:0.97 245:0.45 246:0.61 247:0.74 253:0.22 257:0.99 259:0.21 264:0.96 265:0.05 266:0.63 267:0.36 271:0.17 275:0.94 276:0.41 277:0.98 287:0.61 294:0.91 300:0.43 +1 7:0.81 12:0.37 17:0.89 20:0.97 21:0.13 27:0.06 32:0.80 34:0.89 36:0.36 37:0.60 43:0.28 66:0.36 69:0.77 78:0.83 81:0.97 91:0.25 98:0.06 100:0.85 104:0.79 106:0.81 108:0.60 111:0.83 123:0.57 126:0.54 127:0.05 129:0.05 135:0.94 145:0.38 146:0.05 147:0.05 149:0.12 150:0.96 152:0.89 154:0.21 159:0.05 169:0.83 172:0.05 173:0.58 177:0.05 178:0.55 186:0.84 187:0.68 195:0.93 206:0.81 215:0.84 216:0.85 230:0.65 233:0.63 235:0.12 241:0.32 243:0.51 259:0.21 261:0.94 265:0.06 267:0.84 297:0.36 +2 7:0.81 12:0.37 17:0.16 20:0.83 21:0.05 27:0.65 30:0.71 37:0.44 43:0.52 55:0.71 66:0.82 69:0.05 70:0.31 78:0.84 81:0.98 91:0.25 98:0.78 100:0.92 104:0.84 106:0.81 108:0.05 111:0.91 120:0.76 123:0.05 126:0.54 127:0.27 129:0.05 140:0.45 146:0.66 147:0.71 149:0.54 150:0.97 151:0.74 152:0.79 153:0.82 154:0.41 159:0.25 162:0.55 164:0.70 169:0.93 172:0.48 173:0.21 177:0.05 179:0.71 186:0.73 187:0.39 202:0.67 206:0.81 212:0.61 215:0.91 216:0.94 230:0.87 233:0.76 235:0.05 241:0.67 242:0.82 243:0.83 247:0.12 248:0.69 256:0.58 260:0.77 261:0.89 265:0.79 266:0.27 268:0.64 276:0.40 283:0.82 285:0.62 297:0.36 300:0.25 +0 12:0.37 17:0.72 20:0.84 21:0.78 25:0.88 27:0.72 30:0.33 43:0.52 55:0.59 66:0.61 69:0.35 70:0.49 78:0.83 91:0.72 98:0.77 100:0.73 104:0.73 108:0.59 111:0.81 120:0.79 123:0.57 124:0.51 127:0.92 129:0.81 133:0.67 140:0.45 146:0.05 147:0.05 149:0.48 151:0.66 152:0.80 153:0.48 154:0.41 159:0.34 162:0.70 163:0.75 164:0.72 169:0.72 172:0.37 173:0.52 177:0.05 179:0.91 186:0.86 202:0.63 212:0.19 216:0.06 235:0.12 241:0.97 242:0.82 243:0.18 245:0.45 247:0.12 248:0.72 256:0.64 260:0.80 261:0.89 265:0.77 266:0.47 268:0.65 276:0.33 285:0.62 300:0.33 +2 7:0.81 12:0.37 17:0.79 20:0.80 21:0.22 27:0.11 30:0.76 32:0.80 34:0.33 36:0.55 37:0.86 43:0.39 55:0.59 66:0.64 69:0.56 70:0.15 78:0.86 81:0.97 91:0.40 98:0.44 100:0.92 104:0.93 106:0.81 108:0.43 111:0.88 123:0.41 126:0.54 127:0.14 129:0.05 135:0.78 145:0.63 146:0.37 147:0.44 149:0.27 150:0.95 152:0.77 154:0.29 159:0.14 163:0.93 169:0.90 172:0.16 173:0.69 177:0.05 178:0.55 179:0.81 186:0.87 187:0.39 195:0.63 202:0.36 206:0.81 212:0.95 215:0.79 216:0.84 230:0.87 233:0.76 235:0.22 241:0.55 242:0.82 243:0.69 247:0.12 259:0.21 261:0.89 265:0.46 266:0.12 267:0.69 276:0.17 297:0.36 300:0.13 +1 12:0.37 17:0.02 21:0.05 27:0.72 30:0.95 34:0.86 36:0.77 37:0.32 43:0.50 55:0.92 66:0.54 69:0.12 81:0.98 91:0.44 98:0.77 104:0.96 108:0.10 123:0.10 126:0.54 127:0.25 129:0.05 135:0.89 146:0.46 147:0.52 149:0.22 150:0.97 154:0.39 159:0.17 172:0.10 173:0.45 177:0.05 178:0.55 179:0.99 187:0.68 215:0.91 216:0.14 235:0.05 241:0.15 243:0.63 259:0.21 265:0.77 267:0.89 297:0.36 300:0.08 +1 7:0.81 8:0.58 12:0.37 17:0.70 20:0.89 25:0.88 27:0.72 34:0.52 36:0.49 78:0.85 81:0.99 91:0.86 100:0.93 104:0.58 111:0.88 120:0.75 126:0.54 135:0.49 140:0.90 150:0.98 151:0.64 152:0.83 153:0.46 162:0.53 164:0.65 169:0.91 175:0.75 178:0.50 186:0.87 202:0.62 215:0.96 216:0.04 230:0.87 233:0.76 235:0.02 238:0.96 241:0.87 244:0.61 248:0.68 256:0.58 257:0.65 259:0.21 260:0.76 261:0.96 267:0.23 268:0.58 277:0.69 285:0.62 297:0.36 +2 12:0.37 17:0.32 21:0.47 27:0.43 30:0.76 34:0.95 36:0.39 37:0.67 43:0.43 55:0.92 66:0.64 69:0.49 70:0.29 81:0.93 91:0.17 98:0.69 108:0.66 123:0.64 124:0.42 126:0.54 127:0.37 129:0.59 133:0.47 135:0.47 145:0.67 146:0.05 147:0.05 149:0.37 150:0.87 154:0.32 159:0.46 163:0.75 172:0.32 173:0.47 177:0.05 178:0.55 179:0.88 187:0.95 212:0.23 215:0.63 216:0.60 235:0.52 241:0.41 242:0.82 243:0.21 245:0.43 247:0.12 259:0.21 265:0.69 266:0.38 267:0.28 276:0.31 283:0.60 297:0.36 300:0.25 +2 12:0.37 17:0.81 21:0.54 27:0.54 30:0.61 32:0.80 34:0.30 36:0.90 37:0.39 43:0.46 55:0.71 66:0.67 69:0.44 70:0.62 81:0.98 91:0.10 98:0.79 104:0.97 106:0.81 108:0.73 123:0.71 124:0.47 126:0.54 127:0.47 129:0.59 133:0.47 135:0.65 145:0.63 146:0.68 147:0.73 149:0.74 150:0.97 154:0.35 159:0.72 172:0.79 173:0.27 177:0.05 178:0.55 179:0.36 187:0.39 195:0.88 206:0.81 212:0.47 215:0.58 216:0.79 235:0.68 241:0.03 242:0.82 243:0.31 245:0.87 247:0.12 259:0.21 265:0.79 266:0.80 267:0.44 276:0.75 297:0.36 300:0.70 +1 7:0.81 12:0.37 17:0.18 20:0.83 21:0.13 27:0.63 30:0.38 37:0.58 43:0.52 55:0.84 66:0.93 69:0.07 70:0.57 78:0.72 81:0.97 91:0.33 98:0.91 100:0.73 108:0.06 111:0.72 120:0.85 123:0.06 126:0.54 127:0.51 129:0.05 140:0.45 146:0.94 147:0.95 149:0.73 150:0.96 151:0.71 152:0.79 153:0.72 154:0.41 159:0.60 162:0.86 163:0.75 164:0.80 169:0.72 172:0.81 173:0.12 177:0.05 179:0.44 186:0.73 187:0.39 202:0.66 212:0.30 215:0.84 216:0.91 220:0.62 230:0.65 233:0.63 235:0.12 241:0.37 242:0.82 243:0.93 247:0.12 248:0.77 256:0.73 260:0.85 261:0.73 265:0.91 266:0.54 268:0.75 276:0.71 283:0.82 285:0.62 297:0.36 300:0.43 +1 12:0.37 17:0.64 21:0.64 30:0.21 32:0.80 34:0.31 36:0.36 37:0.97 43:0.39 55:0.96 66:0.33 69:0.58 70:0.87 81:0.88 91:0.09 98:0.33 104:0.83 108:0.58 120:0.69 123:0.55 124:0.92 126:0.54 127:0.81 129:0.98 133:0.93 135:0.47 140:0.45 145:0.72 146:0.05 147:0.05 149:0.58 150:0.75 151:0.66 153:0.48 154:0.29 159:0.86 162:0.86 163:0.75 164:0.57 172:0.48 173:0.31 177:0.05 179:0.64 187:0.21 195:0.95 202:0.36 212:0.18 215:0.53 216:0.98 235:0.74 242:0.82 243:0.10 245:0.57 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.36 266:0.87 267:0.65 268:0.46 276:0.59 283:0.60 285:0.62 297:0.36 300:0.70 +1 12:0.37 17:0.77 25:0.88 27:0.72 78:0.87 81:0.99 91:0.85 104:0.86 111:0.90 120:0.80 126:0.54 140:0.45 150:0.98 151:0.66 153:0.48 162:0.81 164:0.79 169:0.93 175:0.75 202:0.69 215:0.96 216:0.16 220:0.82 235:0.02 241:0.89 244:0.61 248:0.72 256:0.73 257:0.65 260:0.81 268:0.74 277:0.69 285:0.62 297:0.36 +2 7:0.81 12:0.37 17:0.75 21:0.47 27:0.39 30:0.76 32:0.80 34:0.90 36:0.44 37:0.63 43:0.52 55:0.45 66:0.64 69:0.15 70:0.15 81:0.93 91:0.44 98:0.39 104:0.86 106:0.81 108:0.12 123:0.12 126:0.54 127:0.13 129:0.05 135:0.32 145:0.39 146:0.27 147:0.33 149:0.30 150:0.87 154:0.41 159:0.12 172:0.16 173:0.48 177:0.05 178:0.55 179:0.74 187:0.21 195:0.55 206:0.81 212:0.95 215:0.63 216:0.75 230:0.87 233:0.76 235:0.52 241:0.39 242:0.82 243:0.69 247:0.12 259:0.21 265:0.41 266:0.12 267:0.44 276:0.16 283:0.82 297:0.36 300:0.13 +2 12:0.37 17:0.01 20:0.80 27:0.43 34:0.42 36:0.29 37:0.41 43:0.52 66:0.36 69:0.05 78:0.72 81:0.99 91:0.20 98:0.12 100:0.73 104:0.96 106:0.81 108:0.05 111:0.72 123:0.05 126:0.54 127:0.08 129:0.05 135:0.74 146:0.05 147:0.05 149:0.16 150:0.98 152:0.77 154:0.41 159:0.05 169:0.72 172:0.05 173:0.39 177:0.05 178:0.55 186:0.73 187:0.68 206:0.81 215:0.96 216:0.55 235:0.02 241:0.35 243:0.51 259:0.21 261:0.73 265:0.13 267:0.52 283:0.82 297:0.36 +1 12:0.37 17:0.26 21:0.76 27:0.48 30:0.36 32:0.80 34:0.68 36:0.19 37:0.55 43:0.30 55:0.52 66:0.48 69:0.74 70:0.88 81:0.79 91:0.31 98:0.35 108:0.57 123:0.54 124:0.83 126:0.54 127:0.58 129:0.95 133:0.87 135:0.51 145:0.63 146:0.76 147:0.79 149:0.65 150:0.59 154:0.22 159:0.86 172:0.61 173:0.48 177:0.05 178:0.55 179:0.54 187:0.39 195:0.96 212:0.60 215:0.46 216:0.92 235:0.95 241:0.30 242:0.82 243:0.59 245:0.65 247:0.12 259:0.21 265:0.38 266:0.75 267:0.28 276:0.62 283:0.60 297:0.36 300:0.84 +0 12:0.37 17:0.18 21:0.30 27:0.32 30:0.87 34:0.96 36:0.39 37:0.54 43:0.44 55:0.84 66:0.45 69:0.47 70:0.15 81:0.95 91:0.06 98:0.64 108:0.36 123:0.34 124:0.67 126:0.54 127:0.36 129:0.81 133:0.67 135:0.21 146:0.77 147:0.81 149:0.45 150:0.93 154:0.33 159:0.52 163:0.94 172:0.32 173:0.39 177:0.05 178:0.55 179:0.78 187:0.68 212:0.95 215:0.72 216:0.85 235:0.31 241:0.24 242:0.82 243:0.58 245:0.52 247:0.12 259:0.21 265:0.65 266:0.12 267:0.28 276:0.41 283:0.60 297:0.36 300:0.13 +0 8:0.80 12:0.37 17:0.26 20:0.81 21:0.78 27:0.05 34:0.44 36:0.44 37:0.97 78:0.72 91:0.68 100:0.73 111:0.72 120:0.60 135:0.34 140:0.95 151:0.54 152:0.78 153:0.48 162:0.47 164:0.57 169:0.72 175:0.75 178:0.53 186:0.73 187:0.39 191:0.76 202:0.75 216:0.58 220:0.62 235:0.31 241:0.68 244:0.61 248:0.54 256:0.45 257:0.65 259:0.21 260:0.61 261:0.73 267:0.52 268:0.46 277:0.69 285:0.62 +2 7:0.81 12:0.37 17:0.45 20:0.80 21:0.30 27:0.50 30:0.95 32:0.80 34:0.37 36:0.91 37:0.60 43:0.50 55:0.92 66:0.36 69:0.21 78:0.72 81:0.95 91:0.44 98:0.43 100:0.73 104:0.84 106:0.81 108:0.17 111:0.72 120:0.69 123:0.16 124:0.52 126:0.54 127:0.22 129:0.59 133:0.47 135:0.78 140:0.45 145:0.69 146:0.39 147:0.46 149:0.27 150:0.93 151:0.66 152:0.77 153:0.48 154:0.39 159:0.24 162:0.86 163:0.86 164:0.57 169:0.72 172:0.10 173:0.34 177:0.05 179:0.96 186:0.73 187:0.39 195:0.60 202:0.36 206:0.81 212:0.95 215:0.72 216:0.86 230:0.87 233:0.76 235:0.31 241:0.27 242:0.82 243:0.51 245:0.37 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.73 265:0.45 266:0.12 267:0.52 268:0.46 276:0.18 283:0.82 285:0.62 297:0.36 300:0.08 +2 7:0.81 12:0.37 17:0.76 21:0.54 27:0.62 30:0.68 32:0.80 34:0.91 36:0.81 37:0.55 43:0.47 55:0.71 66:0.89 69:0.41 70:0.45 81:0.98 91:0.14 98:0.90 106:0.81 108:0.31 123:0.30 124:0.36 126:0.54 127:0.64 129:0.59 133:0.47 135:0.61 145:0.76 146:0.94 147:0.95 149:0.80 150:0.97 154:0.36 159:0.63 172:0.86 173:0.32 177:0.05 178:0.55 179:0.37 187:0.98 195:0.80 206:0.81 212:0.81 215:0.58 216:0.70 230:0.65 233:0.63 235:0.68 242:0.82 243:0.90 245:0.66 247:0.12 259:0.21 265:0.90 266:0.27 267:0.94 276:0.78 283:0.60 297:0.36 300:0.43 +2 7:0.81 8:0.98 12:0.37 17:0.95 21:0.77 27:0.72 28:0.66 32:0.80 34:0.36 36:0.55 37:0.99 81:0.80 91:0.92 104:0.54 126:0.54 135:0.30 145:1.00 150:0.60 161:0.61 167:0.82 175:0.75 178:0.55 181:0.91 191:0.86 195:0.61 202:0.36 215:0.43 216:0.06 230:0.65 233:0.63 235:0.98 241:0.87 244:0.61 257:0.65 259:0.21 267:0.65 271:0.55 277:0.69 297:0.99 +1 8:0.62 12:0.37 17:0.40 20:0.88 21:0.71 28:0.66 30:0.37 32:0.80 34:0.40 36:0.28 37:0.97 43:0.30 55:0.71 66:0.62 69:0.73 70:0.89 78:0.74 81:0.76 91:0.54 98:0.47 100:0.77 104:0.80 108:0.70 111:0.73 120:0.93 123:0.68 124:0.75 126:0.54 127:0.66 129:0.95 133:0.87 135:0.69 140:0.45 145:0.63 146:0.40 147:0.46 149:0.71 150:0.57 151:0.80 152:0.83 153:0.93 154:0.22 159:0.89 161:0.61 162:0.72 164:0.92 167:0.55 169:0.75 172:0.77 173:0.44 177:0.05 179:0.43 181:0.91 186:0.75 187:0.21 195:0.97 202:0.87 212:0.42 215:0.48 216:0.98 235:0.84 241:0.49 242:0.82 243:0.23 245:0.70 247:0.12 248:0.90 256:0.89 259:0.21 260:0.93 261:0.75 265:0.49 266:0.75 267:0.44 268:0.90 271:0.55 276:0.70 283:0.60 285:0.62 297:0.36 300:0.70 +3 8:0.88 12:0.37 17:0.57 20:0.76 21:0.22 27:0.72 28:0.66 30:0.21 34:0.19 36:0.25 37:0.86 43:0.44 55:0.45 66:0.54 69:0.45 70:0.37 78:0.76 81:0.92 91:0.87 98:0.33 100:0.80 104:0.54 108:0.35 111:0.76 120:0.77 123:0.33 124:0.45 126:0.54 127:0.83 129:0.59 133:0.47 135:0.54 140:0.45 145:0.70 146:0.41 147:0.48 149:0.35 150:0.86 151:0.72 152:0.75 153:0.78 154:0.34 159:0.57 161:0.61 162:0.72 163:0.75 164:0.79 167:0.85 169:0.78 172:0.21 173:0.42 177:0.05 179:0.97 181:0.91 186:0.77 202:0.71 212:0.42 215:0.79 216:0.21 220:0.62 235:0.22 241:0.97 242:0.82 243:0.63 245:0.40 247:0.12 248:0.69 256:0.71 259:0.21 260:0.78 261:0.75 265:0.36 266:0.23 267:0.44 268:0.74 271:0.55 276:0.15 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.81 7:0.81 8:0.68 12:0.37 17:0.36 20:0.98 21:0.78 27:0.66 28:0.66 30:0.38 32:0.80 34:0.87 36:0.61 37:0.29 43:0.50 55:0.59 66:0.72 69:0.25 70:0.45 78:0.76 91:0.82 98:0.84 100:0.83 104:0.58 108:0.57 111:0.77 120:0.85 123:0.55 124:0.40 127:0.89 129:0.59 133:0.47 135:0.30 140:0.45 145:0.98 146:0.05 147:0.05 149:0.50 151:0.77 152:0.90 153:0.88 154:0.39 159:0.40 161:0.61 162:0.78 164:0.89 167:0.83 169:0.81 172:0.48 173:0.38 177:0.05 179:0.85 181:0.91 186:0.77 189:0.84 191:0.77 195:0.64 202:0.82 212:0.28 216:0.20 220:0.62 230:0.65 233:0.63 235:0.60 238:0.97 241:0.90 242:0.82 243:0.16 245:0.48 247:0.12 248:0.79 256:0.85 259:0.21 260:0.86 261:0.82 265:0.84 266:0.54 267:0.36 268:0.86 271:0.55 276:0.41 283:0.60 285:0.62 300:0.33 +1 12:0.37 17:0.36 21:0.30 27:0.45 28:0.66 30:0.68 34:0.71 36:0.30 37:0.50 43:0.33 55:0.71 66:0.68 69:0.68 70:0.43 81:0.91 91:0.22 98:0.53 108:0.52 123:0.49 124:0.41 126:0.54 127:0.37 129:0.59 133:0.47 135:0.69 145:0.52 146:0.64 147:0.69 149:0.42 150:0.83 154:0.24 159:0.48 161:0.61 163:0.75 167:0.49 172:0.37 173:0.67 177:0.05 178:0.55 179:0.85 181:0.91 187:0.68 212:0.42 215:0.72 216:0.77 235:0.31 241:0.21 242:0.82 243:0.72 245:0.45 247:0.12 259:0.21 265:0.55 266:0.27 267:0.28 271:0.55 276:0.28 297:0.36 300:0.33 +3 6:0.85 7:0.81 8:0.78 12:0.37 17:0.73 20:0.76 21:0.74 27:0.43 28:0.66 30:0.21 32:0.80 34:0.59 36:0.62 37:0.69 43:0.49 55:0.45 66:0.54 69:0.38 70:0.37 78:0.89 81:0.73 91:0.76 98:0.49 100:0.95 104:0.58 108:0.59 111:0.91 120:0.85 123:0.56 124:0.45 126:0.54 127:0.75 129:0.59 133:0.47 135:0.42 140:0.45 145:0.98 146:0.05 147:0.05 149:0.35 150:0.54 151:0.75 152:0.74 153:0.86 154:0.38 159:0.24 161:0.61 162:0.72 164:0.91 167:0.83 169:0.92 172:0.21 173:0.64 177:0.05 179:0.97 181:0.91 186:0.89 189:0.87 191:0.83 195:0.55 202:0.85 212:0.42 215:0.46 216:0.26 220:0.62 230:0.65 233:0.63 235:0.88 238:0.80 241:0.90 242:0.82 243:0.26 245:0.40 247:0.12 248:0.79 256:0.86 259:0.21 260:0.86 261:0.77 265:0.51 266:0.23 267:0.19 268:0.88 271:0.55 276:0.18 283:0.60 285:0.62 297:0.36 300:0.25 +3 6:0.98 8:0.96 12:0.37 17:0.15 20:0.96 21:0.13 27:0.05 28:0.66 30:0.76 32:0.80 34:0.37 36:0.86 37:0.96 43:0.32 55:0.59 66:0.36 69:0.68 70:0.15 78:0.90 81:0.94 91:0.97 98:0.21 100:0.99 108:0.64 111:0.98 120:0.95 123:0.62 124:0.52 126:0.54 127:0.28 129:0.59 133:0.47 135:0.78 140:0.45 145:0.70 146:0.05 147:0.05 149:0.24 150:0.89 151:0.83 152:0.89 153:0.95 154:0.24 159:0.20 161:0.61 162:0.58 163:0.93 164:0.99 167:0.84 169:1.00 172:0.10 173:0.88 177:0.05 179:0.98 181:0.91 186:0.90 189:0.99 191:0.96 195:0.56 202:0.98 212:0.95 215:0.84 216:0.55 220:0.62 235:0.12 238:1.00 241:0.97 242:0.82 243:0.34 245:0.37 247:0.12 248:0.92 256:0.98 259:0.21 260:0.95 261:0.99 265:0.23 266:0.12 267:0.69 268:0.98 271:0.55 276:0.14 283:0.60 285:0.62 297:0.36 300:0.13 +3 8:1.00 12:0.37 17:0.77 20:0.80 21:0.13 27:0.54 28:0.66 30:0.76 34:0.45 36:0.97 37:0.85 43:0.32 55:0.92 66:0.72 69:0.70 70:0.22 78:0.72 81:0.94 91:0.85 98:0.78 100:0.78 104:0.72 108:0.53 111:0.73 120:0.85 123:0.51 126:0.54 127:0.26 129:0.05 135:0.74 140:0.45 146:0.66 147:0.71 149:0.31 150:0.89 151:0.78 152:0.78 153:0.89 154:0.23 159:0.25 161:0.61 162:0.79 163:0.90 164:0.88 167:0.83 169:0.75 172:0.27 173:0.82 177:0.05 179:0.91 181:0.91 186:0.73 191:0.90 202:0.81 212:0.42 215:0.84 216:0.24 220:0.74 235:0.12 241:0.89 242:0.82 243:0.75 247:0.12 248:0.79 256:0.83 259:0.21 260:0.86 261:0.75 265:0.78 266:0.23 267:0.28 268:0.85 271:0.55 276:0.24 283:0.60 285:0.62 297:0.36 300:0.18 +1 7:0.81 8:0.85 12:0.37 17:0.57 20:0.76 27:0.20 28:0.66 30:0.94 32:0.80 34:0.24 36:0.41 37:0.73 43:0.43 55:0.71 66:0.64 69:0.45 70:0.20 78:0.72 81:0.96 91:0.29 98:0.81 100:0.73 104:0.88 106:0.81 108:0.68 111:0.72 123:0.66 124:0.47 126:0.54 127:0.52 129:0.59 133:0.47 135:0.71 145:0.39 146:0.46 147:0.53 149:0.76 150:0.94 152:0.75 154:0.33 159:0.65 161:0.61 167:0.54 169:0.72 172:0.80 173:0.34 177:0.05 178:0.55 179:0.36 181:0.91 186:0.73 187:0.68 195:0.82 206:0.81 212:0.86 215:0.96 216:0.72 230:0.87 233:0.76 235:0.02 241:0.44 242:0.82 243:0.32 245:0.88 247:0.12 259:0.21 261:0.73 265:0.81 266:0.63 267:0.23 271:0.55 276:0.77 283:0.82 297:0.36 300:0.43 +2 7:0.81 8:0.93 12:0.37 17:0.13 21:0.54 27:0.54 28:0.66 30:0.33 32:0.80 34:0.55 36:0.56 37:0.95 43:0.50 55:0.52 66:0.86 69:0.27 70:0.69 81:0.94 91:0.67 98:0.92 104:0.58 108:0.21 120:0.75 123:0.20 126:0.54 127:0.54 129:0.05 135:0.60 140:0.45 145:0.47 146:0.57 147:0.63 149:0.65 150:0.90 151:0.73 153:0.78 154:0.39 159:0.21 161:0.61 162:0.60 164:0.73 167:0.73 172:0.61 173:0.58 177:0.05 179:0.71 181:0.91 187:0.39 191:0.82 195:0.56 202:0.67 212:0.85 215:0.58 216:0.24 230:0.87 233:0.76 235:0.68 241:0.74 242:0.82 243:0.87 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 265:0.92 266:0.27 267:0.28 268:0.67 271:0.55 276:0.51 283:0.60 285:0.62 297:0.36 300:0.55 +2 7:0.81 12:0.37 17:0.49 20:0.81 21:0.22 27:0.64 28:0.66 30:0.54 32:0.80 34:0.92 36:0.90 37:0.55 43:0.49 55:0.71 66:0.88 69:0.27 70:0.42 78:0.74 81:0.92 91:0.74 98:0.98 100:0.79 104:0.58 108:0.21 111:0.74 120:0.73 123:0.20 126:0.54 127:0.80 129:0.05 135:0.77 140:0.45 145:0.84 146:0.85 147:0.88 149:0.62 150:0.86 151:0.66 152:0.82 153:0.48 154:0.38 159:0.42 161:0.61 162:0.74 164:0.67 167:0.83 169:0.75 172:0.67 173:0.37 177:0.05 179:0.69 181:0.91 186:0.75 187:0.21 195:0.67 202:0.56 212:0.61 215:0.79 216:0.21 230:0.87 233:0.76 235:0.22 241:0.91 242:0.82 243:0.89 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 261:0.76 265:0.98 266:0.54 267:0.59 268:0.59 271:0.55 276:0.56 285:0.62 297:0.36 300:0.33 +2 7:0.81 8:0.80 12:0.37 17:0.36 20:0.83 21:0.05 25:0.88 27:0.37 28:0.66 30:0.72 32:0.80 34:0.42 36:0.78 37:0.67 43:0.49 55:0.71 66:0.84 69:0.25 70:0.29 78:0.75 81:0.95 91:0.83 98:0.98 100:0.79 104:0.58 108:0.20 111:0.75 120:0.64 123:0.19 126:0.54 127:0.85 129:0.05 135:0.39 140:0.45 145:0.76 146:0.82 147:0.85 149:0.54 150:0.92 151:0.59 152:0.79 153:0.91 154:0.37 159:0.38 161:0.61 162:0.69 163:0.75 164:0.82 167:0.83 169:0.77 172:0.54 173:0.39 177:0.05 179:0.83 181:0.91 186:0.76 191:0.73 195:0.63 202:0.74 212:0.39 215:0.91 216:0.24 220:0.62 230:0.65 233:0.63 235:0.05 241:0.90 242:0.82 243:0.85 247:0.12 248:0.58 256:0.75 259:0.21 260:0.65 261:0.76 265:0.98 266:0.47 267:0.28 268:0.77 271:0.55 276:0.45 283:0.60 285:0.62 297:0.36 300:0.25 +3 6:0.94 7:0.81 8:0.94 12:0.37 17:0.20 20:0.99 21:0.05 27:0.16 28:0.66 30:0.91 32:0.80 34:0.64 36:0.73 37:0.90 43:0.42 55:0.59 66:0.69 69:0.47 70:0.13 78:0.78 81:0.95 91:0.98 98:0.44 100:0.90 104:0.58 108:0.36 111:0.83 120:0.97 123:0.35 126:0.54 127:0.14 129:0.05 135:0.34 140:0.45 145:0.47 146:0.32 147:0.39 149:0.33 150:0.92 151:0.83 152:0.91 153:0.96 154:0.32 159:0.13 161:0.61 162:0.80 164:0.96 167:0.84 169:0.87 172:0.21 173:0.68 177:0.05 179:0.69 181:0.91 186:0.78 189:0.95 191:0.88 195:0.65 202:0.93 212:0.61 215:0.91 216:0.48 220:0.62 230:0.65 233:0.63 235:0.05 238:0.98 241:0.93 242:0.82 243:0.73 247:0.12 248:0.95 256:0.95 259:0.21 260:0.97 261:0.88 265:0.46 266:0.17 267:0.79 268:0.95 271:0.55 276:0.23 283:0.82 285:0.62 297:0.36 300:0.13 +2 7:0.81 8:0.92 12:0.37 17:0.32 20:0.84 21:0.22 27:0.64 28:0.66 30:0.54 32:0.80 34:0.91 36:0.90 37:0.55 43:0.49 55:0.71 66:0.88 69:0.27 70:0.42 78:0.74 81:0.92 91:0.76 98:0.98 100:0.73 104:0.58 108:0.21 111:0.73 120:0.78 123:0.20 126:0.54 127:0.80 129:0.05 135:0.78 140:0.45 145:0.84 146:0.85 147:0.88 149:0.62 150:0.86 151:0.73 152:0.82 153:0.78 154:0.38 159:0.42 161:0.61 162:0.73 164:0.85 167:0.84 169:0.75 172:0.67 173:0.37 177:0.05 179:0.69 181:0.91 186:0.75 195:0.67 202:0.77 212:0.61 215:0.79 216:0.21 220:0.82 230:0.87 233:0.76 235:0.22 241:0.93 242:0.82 243:0.89 247:0.12 248:0.70 256:0.80 259:0.21 260:0.79 261:0.76 265:0.98 266:0.54 267:0.59 268:0.81 271:0.55 276:0.56 285:0.62 297:0.36 300:0.33 +2 8:0.85 12:0.37 17:0.84 21:0.05 27:1.00 28:0.66 30:0.76 32:0.80 34:0.59 36:0.57 37:0.31 43:0.46 55:0.59 66:0.64 69:0.41 70:0.15 81:0.95 91:0.93 98:0.74 104:0.58 108:0.31 120:0.79 123:0.30 126:0.54 127:0.24 129:0.05 135:0.49 140:0.45 145:0.63 146:0.35 147:0.42 149:0.28 150:0.92 151:0.74 153:0.82 154:0.35 159:0.14 161:0.61 162:0.80 163:0.75 164:0.82 167:0.84 172:0.16 173:0.78 177:0.05 179:0.97 181:0.91 195:0.53 202:0.73 212:0.95 215:0.91 216:0.18 220:0.62 235:0.05 241:0.95 242:0.82 243:0.69 247:0.12 248:0.72 256:0.75 259:0.21 260:0.80 265:0.75 266:0.12 267:0.44 268:0.78 271:0.55 276:0.17 285:0.62 297:0.36 300:0.13 +3 8:0.89 12:0.37 17:0.47 20:0.82 21:0.64 27:0.51 28:0.66 30:0.72 32:0.80 34:0.55 36:0.44 37:0.96 43:0.39 55:0.45 66:0.90 69:0.57 70:0.37 78:0.88 81:0.81 91:0.91 98:0.91 100:0.80 104:0.58 108:0.44 111:0.95 120:0.91 123:0.42 126:0.54 127:0.51 129:0.05 135:0.32 140:0.45 145:0.74 146:0.70 147:0.74 149:0.70 150:0.61 151:0.77 152:0.78 153:0.88 154:0.30 159:0.27 161:0.61 162:0.69 164:0.95 167:0.84 169:0.96 172:0.71 173:0.75 177:0.05 179:0.58 181:0.91 186:0.75 191:0.80 195:0.56 202:0.93 212:0.83 215:0.53 216:0.49 220:0.74 235:0.74 238:0.99 241:0.97 242:0.82 243:0.90 247:0.12 248:0.87 256:0.94 260:0.92 261:0.76 265:0.91 266:0.38 267:0.18 268:0.94 271:0.55 276:0.60 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.37 17:0.16 21:0.40 27:0.45 28:0.66 30:0.45 32:0.80 34:0.75 36:0.18 37:0.50 43:0.32 55:0.92 66:0.13 69:0.69 70:0.33 81:0.89 91:0.14 98:0.21 108:0.52 123:0.82 124:0.82 126:0.54 127:0.21 129:0.89 133:0.78 135:0.78 145:0.40 146:0.34 147:0.40 149:0.35 150:0.79 154:0.24 159:0.53 161:0.61 163:0.89 167:0.53 172:0.10 173:0.52 177:0.05 178:0.55 179:0.75 181:0.91 187:0.68 195:0.90 212:0.23 215:0.67 216:0.77 235:0.42 241:0.41 242:0.82 243:0.34 245:0.43 247:0.12 259:0.21 265:0.20 266:0.38 267:0.36 271:0.55 276:0.32 283:0.60 297:0.36 300:0.25 +3 6:0.83 7:0.81 8:0.89 12:0.37 17:0.66 20:0.84 21:0.05 27:0.64 28:0.66 30:0.95 32:0.80 34:0.75 36:0.45 37:0.90 43:0.46 55:0.45 66:0.54 69:0.39 78:0.76 81:0.95 91:0.91 98:0.19 100:0.80 104:0.58 108:0.30 111:0.76 120:0.74 123:0.29 126:0.54 127:0.10 129:0.05 135:0.39 140:0.45 145:0.70 146:0.19 147:0.24 149:0.23 150:0.92 151:0.64 152:0.80 153:0.46 154:0.35 159:0.10 161:0.61 162:0.86 164:0.78 167:0.84 169:0.78 172:0.10 173:0.71 177:0.05 179:0.43 181:0.91 186:0.77 189:0.85 191:0.77 195:0.80 202:0.65 215:0.91 216:0.08 220:0.62 230:0.87 233:0.76 235:0.05 238:0.93 241:0.95 243:0.63 248:0.67 256:0.71 259:0.21 260:0.75 261:0.78 265:0.21 267:0.36 268:0.73 271:0.55 285:0.62 297:0.36 300:0.08 +2 6:0.96 7:0.81 8:0.93 12:0.37 17:0.43 20:0.99 21:0.64 27:0.54 28:0.66 30:0.61 32:0.80 34:0.49 36:0.48 37:0.95 43:0.50 55:0.59 66:0.89 69:0.32 70:0.48 78:0.77 81:0.81 91:0.87 98:0.96 100:0.83 104:0.58 108:0.25 111:0.82 120:0.82 123:0.24 126:0.54 127:0.71 129:0.05 135:0.66 140:0.45 145:0.52 146:0.61 147:0.66 149:0.71 150:0.61 151:0.75 152:0.90 153:0.85 154:0.39 159:0.22 161:0.61 162:0.76 164:0.91 167:0.83 169:0.87 172:0.68 173:0.61 177:0.05 179:0.66 181:0.91 186:0.76 189:0.97 191:0.85 195:0.56 202:0.86 212:0.95 215:0.53 216:0.24 220:0.82 230:0.87 233:0.76 235:0.74 238:0.99 241:0.91 242:0.82 243:0.89 247:0.12 248:0.74 256:0.89 259:0.21 260:0.82 261:0.82 265:0.96 266:0.12 267:0.17 268:0.89 271:0.55 276:0.58 283:0.60 285:0.62 297:0.36 300:0.43 +1 7:0.81 8:0.93 12:0.37 17:0.11 21:0.54 27:0.54 28:0.66 30:0.41 32:0.80 34:0.66 36:0.45 37:0.95 43:0.50 55:0.52 66:0.86 69:0.27 70:0.72 81:0.94 91:0.73 98:0.89 104:0.58 108:0.21 120:0.75 123:0.20 126:0.54 127:0.46 129:0.05 135:0.60 140:0.45 145:0.47 146:0.55 147:0.61 149:0.64 150:0.90 151:0.73 153:0.78 154:0.39 159:0.20 161:0.61 162:0.60 164:0.73 167:0.74 172:0.59 173:0.58 177:0.05 179:0.71 181:0.91 187:0.39 191:0.82 195:0.56 202:0.67 212:0.84 215:0.58 216:0.24 230:0.87 233:0.76 235:0.68 241:0.74 242:0.82 243:0.86 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 265:0.89 266:0.27 267:0.28 268:0.67 271:0.55 276:0.49 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.85 8:0.75 12:0.37 17:0.51 20:0.85 21:0.68 25:0.88 27:0.28 28:0.66 30:0.62 34:0.63 36:0.46 37:0.92 43:0.47 55:0.71 66:0.75 69:0.43 70:0.34 78:0.74 81:0.79 91:0.76 98:0.94 100:0.77 104:0.58 108:0.33 111:0.74 123:0.32 126:0.54 127:0.60 129:0.05 135:0.32 146:0.64 147:0.69 149:0.40 150:0.59 152:0.89 154:0.36 159:0.24 161:0.61 163:0.81 167:0.84 169:0.75 172:0.32 173:0.67 177:0.05 178:0.55 179:0.94 181:0.91 186:0.75 187:0.21 212:0.61 215:0.49 216:0.38 235:0.80 241:0.93 242:0.82 243:0.77 247:0.12 259:0.21 261:0.77 265:0.94 266:0.17 267:0.36 271:0.55 276:0.29 297:0.36 300:0.25 +2 7:0.81 12:0.37 17:0.49 21:0.22 27:0.19 28:0.66 30:0.72 32:0.80 34:0.37 36:0.25 37:0.70 43:0.25 55:0.84 66:0.33 69:0.82 70:0.26 81:0.92 91:0.25 98:0.66 108:0.74 120:0.72 123:0.78 124:0.67 126:0.54 127:0.29 129:0.81 133:0.67 135:0.73 140:0.45 145:0.52 146:0.05 147:0.05 149:0.57 150:0.86 151:0.70 153:0.69 154:0.18 159:0.55 161:0.61 162:0.86 163:0.94 164:0.62 167:0.53 172:0.48 173:0.77 177:0.05 179:0.46 181:0.91 187:0.87 195:0.84 202:0.46 212:0.55 215:0.79 216:0.63 230:0.87 233:0.76 235:0.22 241:0.39 242:0.82 243:0.10 245:0.71 247:0.12 248:0.66 256:0.45 259:0.21 260:0.73 265:0.23 266:0.47 267:0.93 268:0.55 271:0.55 276:0.60 285:0.62 297:0.36 300:0.25 +1 8:0.94 12:0.37 17:0.55 21:0.78 27:0.21 28:0.66 30:0.33 34:0.39 36:0.90 37:0.74 43:0.43 55:0.71 66:0.13 69:0.48 70:0.74 78:0.72 91:0.53 98:0.39 108:0.94 111:0.72 120:0.78 123:0.94 124:0.96 127:0.79 129:0.99 133:0.96 135:0.28 140:0.45 146:0.50 147:0.57 149:0.84 151:0.59 153:0.45 154:0.33 159:0.95 161:0.61 162:0.68 164:0.87 167:0.51 169:0.82 172:0.84 173:0.11 177:0.05 179:0.14 181:0.91 187:0.39 191:0.80 202:0.81 212:0.35 216:0.95 220:0.62 235:0.42 241:0.30 242:0.82 243:0.11 245:0.94 247:0.12 248:0.71 256:0.84 259:0.21 260:0.79 265:0.41 266:0.97 267:0.82 268:0.84 271:0.55 276:0.96 283:0.82 285:0.62 300:0.84 +0 8:0.85 11:0.81 12:0.20 17:0.62 18:0.56 21:0.78 27:0.72 28:0.68 29:0.62 30:0.45 34:0.24 36:0.61 39:0.66 43:0.06 45:0.66 66:0.27 69:1.00 70:0.25 71:0.92 74:0.26 86:0.58 91:0.27 98:0.05 101:0.52 108:0.99 122:0.80 123:0.99 124:0.72 127:0.12 129:0.05 131:0.61 133:0.62 135:0.92 139:0.57 144:0.76 145:0.91 146:0.13 147:0.18 149:0.06 154:0.06 157:0.75 159:0.85 161:0.96 163:0.90 166:0.61 167:0.82 172:0.10 173:0.93 175:0.75 177:0.38 178:0.55 179:0.51 181:0.73 182:0.71 202:0.64 212:0.61 216:0.02 222:0.48 227:0.61 229:0.61 231:0.61 235:0.42 241:0.78 242:0.63 243:0.46 244:0.61 245:0.38 246:0.61 247:0.30 253:0.23 257:0.65 259:0.21 265:0.05 266:0.17 267:0.28 271:0.37 276:0.17 277:0.69 287:0.61 300:0.18 +0 11:0.81 12:0.20 17:0.34 18:0.59 21:0.78 27:0.34 28:0.68 29:0.62 30:0.21 34:0.66 36:0.77 37:0.46 39:0.66 43:0.26 45:0.66 66:0.20 69:0.86 70:0.37 71:0.92 74:0.32 86:0.63 91:0.07 98:0.19 101:0.52 108:0.72 122:0.41 123:0.70 124:0.73 127:0.21 129:0.59 131:0.61 133:0.64 135:0.71 139:0.62 144:0.76 145:0.63 146:0.33 147:0.40 149:0.20 154:0.18 157:0.76 159:0.50 161:0.96 163:0.97 166:0.61 167:0.54 172:0.10 173:0.77 175:0.75 177:0.38 178:0.55 179:0.85 181:0.73 182:0.72 187:0.87 212:0.42 216:0.53 222:0.48 227:0.61 229:0.61 231:0.62 235:0.22 241:0.30 242:0.45 243:0.40 244:0.61 245:0.40 246:0.61 247:0.35 253:0.27 257:0.65 259:0.21 265:0.20 266:0.23 267:0.28 271:0.37 276:0.23 277:0.69 287:0.61 300:0.25 +0 9:0.80 12:0.20 17:0.59 18:0.74 20:0.92 21:0.78 27:0.49 28:0.68 29:0.62 30:0.76 31:0.87 34:0.75 36:0.43 37:0.42 39:0.66 41:0.82 43:0.15 55:0.45 66:0.64 69:0.35 70:0.15 71:0.92 78:0.72 79:0.87 83:0.91 86:0.60 91:0.56 96:0.69 98:0.66 100:0.73 108:0.27 111:0.72 120:0.57 122:0.83 123:0.26 127:0.20 129:0.05 131:0.86 135:0.77 137:0.87 139:0.59 140:0.80 144:0.76 146:0.32 147:0.38 149:0.07 151:0.52 152:0.86 153:0.48 154:0.59 155:0.67 157:0.61 159:0.13 161:0.96 162:0.62 163:0.75 164:0.65 166:0.61 167:0.59 169:0.72 172:0.16 173:0.74 175:0.75 177:0.38 179:0.94 181:0.73 182:0.80 186:0.73 187:0.68 192:0.87 196:0.87 202:0.60 208:0.64 212:0.95 216:0.94 220:0.87 222:0.48 227:0.91 229:0.89 231:0.71 235:0.88 241:0.49 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 248:0.51 253:0.39 254:0.80 255:0.95 256:0.58 257:0.65 259:0.21 260:0.57 261:0.73 265:0.67 266:0.12 267:0.44 268:0.59 271:0.37 276:0.14 277:0.69 284:0.89 285:0.62 287:0.93 300:0.13 +0 11:0.81 12:0.20 17:0.40 18:0.85 20:0.90 21:0.78 27:0.04 28:0.68 29:0.62 30:0.71 34:0.66 36:0.44 37:0.96 39:0.66 43:0.65 45:0.77 55:0.59 66:0.33 69:0.67 70:0.29 71:0.92 74:0.49 78:0.82 86:0.79 91:0.37 98:0.35 100:0.88 101:0.52 108:0.51 111:0.83 120:0.69 122:0.86 123:0.49 124:0.61 127:0.21 129:0.59 131:0.61 133:0.47 135:0.79 139:0.76 140:0.80 144:0.76 146:0.37 147:0.44 149:0.57 151:0.60 152:0.96 153:0.48 154:0.54 157:0.82 159:0.27 161:0.96 162:0.62 164:0.53 166:0.61 167:0.62 169:0.86 172:0.27 173:0.73 175:0.75 177:0.38 178:0.42 179:0.62 181:0.73 182:0.75 186:0.82 187:0.95 190:0.89 202:0.50 212:0.50 216:0.65 222:0.48 227:0.61 229:0.61 231:0.66 235:0.22 241:0.54 242:0.53 243:0.50 244:0.61 245:0.60 246:0.61 247:0.47 248:0.59 253:0.34 256:0.45 257:0.65 259:0.21 260:0.66 261:0.90 265:0.37 266:0.47 267:0.59 268:0.46 271:0.37 276:0.34 277:0.69 285:0.47 287:0.61 300:0.25 +1 1:0.74 11:0.81 12:0.20 17:0.55 18:0.86 21:0.64 27:0.45 28:0.68 29:0.62 30:0.15 34:0.86 36:0.18 37:0.50 39:0.66 43:0.11 44:0.85 45:0.66 55:0.52 64:0.77 66:0.33 69:0.70 70:0.84 71:0.92 74:0.43 81:0.42 83:0.60 86:0.75 91:0.25 98:0.39 99:0.83 101:0.52 108:0.54 114:0.57 122:0.86 123:0.51 124:0.61 126:0.54 127:0.21 129:0.05 131:0.61 133:0.37 135:0.66 139:0.73 144:0.76 145:0.49 146:0.48 147:0.54 149:0.14 150:0.66 154:0.76 155:0.67 157:0.76 159:0.32 161:0.96 165:0.70 166:0.80 167:0.51 172:0.27 173:0.71 176:0.73 177:0.70 178:0.55 179:0.62 181:0.73 182:0.80 187:0.68 192:0.87 195:0.72 201:0.93 208:0.64 212:0.15 215:0.38 216:0.77 222:0.48 227:0.61 229:0.61 231:0.71 235:0.88 241:0.12 242:0.77 243:0.50 245:0.60 246:0.91 247:0.35 253:0.39 254:0.74 259:0.21 265:0.41 266:0.63 267:0.28 271:0.37 276:0.37 287:0.61 290:0.57 297:0.36 300:0.55 +2 6:0.90 8:0.80 9:0.76 11:0.81 12:0.20 17:0.55 18:0.89 20:0.96 21:0.13 27:0.53 28:0.68 29:0.62 30:0.37 31:0.92 34:0.26 36:0.98 37:0.39 39:0.66 43:0.59 45:0.66 55:0.92 66:0.43 69:0.45 70:0.90 71:0.92 74:0.44 78:0.96 79:0.92 81:0.49 83:0.60 86:0.76 91:0.15 96:0.79 98:0.51 100:0.98 101:0.52 108:0.64 111:0.98 120:0.57 122:0.86 123:0.62 124:0.82 126:0.26 127:0.67 129:0.59 131:0.86 133:0.82 135:0.96 137:0.92 139:0.73 140:0.45 144:0.76 146:0.43 147:0.49 149:0.37 150:0.38 151:0.70 152:0.89 153:0.72 154:0.49 155:0.58 157:0.76 159:0.73 161:0.96 162:0.64 163:0.81 164:0.53 166:0.61 167:0.51 169:0.98 172:0.48 173:0.30 175:0.75 177:0.38 179:0.67 181:0.73 182:0.81 186:0.96 187:0.87 189:0.91 190:0.77 192:0.51 196:0.91 202:0.65 208:0.54 212:0.22 215:0.61 216:0.68 220:0.62 222:0.48 227:0.92 229:0.89 231:0.72 235:0.22 238:0.96 241:0.12 242:0.53 243:0.25 244:0.61 245:0.60 246:0.61 247:0.60 248:0.72 253:0.65 255:0.74 256:0.64 257:0.65 259:0.21 260:0.56 261:0.96 265:0.53 266:0.80 267:0.52 268:0.66 271:0.37 276:0.55 277:0.69 284:0.92 285:0.62 287:0.93 297:0.36 300:0.70 +2 8:0.89 11:0.81 12:0.20 17:0.57 18:0.69 20:0.88 27:0.41 28:0.68 29:0.62 30:0.41 34:0.26 36:0.99 37:0.39 39:0.66 43:0.21 45:0.66 55:0.59 66:0.49 69:0.97 70:0.54 71:0.92 74:0.30 78:0.89 81:0.69 86:0.62 91:0.38 98:0.26 100:0.92 101:0.52 108:0.94 111:0.89 120:0.81 122:0.86 123:0.94 124:0.66 126:0.26 127:0.92 129:0.05 131:0.61 133:0.62 135:0.89 139:0.60 140:0.45 144:0.76 146:0.61 147:0.44 149:0.19 150:0.50 151:0.66 152:0.83 153:0.89 154:0.13 157:0.76 159:0.88 161:0.96 162:0.59 164:0.71 166:0.61 167:0.52 169:0.90 172:0.45 173:0.97 175:0.75 177:0.05 179:0.79 181:0.73 182:0.72 186:0.89 187:0.68 190:0.89 202:0.73 212:0.30 215:0.96 216:0.63 222:0.48 227:0.61 229:0.61 231:0.63 235:0.05 241:0.21 242:0.63 243:0.22 244:0.61 245:0.60 246:0.61 247:0.60 248:0.66 253:0.26 256:0.68 257:0.84 259:0.21 260:0.74 261:0.88 265:0.29 266:0.75 267:0.52 268:0.73 271:0.37 276:0.30 277:0.69 283:0.82 285:0.47 287:0.61 297:0.36 300:0.43 +1 1:0.74 8:0.66 11:0.78 12:0.20 17:0.51 18:0.75 21:0.59 27:0.45 28:0.68 29:0.62 30:0.21 34:0.75 36:0.19 37:0.50 39:0.66 43:0.81 44:0.85 64:0.77 66:0.54 69:0.70 70:0.37 71:0.92 74:0.43 81:0.47 83:0.91 85:0.72 86:0.75 91:0.27 98:0.14 101:0.87 108:0.54 114:0.58 122:0.57 123:0.51 124:0.45 126:0.54 127:0.18 129:0.05 131:0.61 133:0.37 135:0.85 139:0.72 144:0.76 145:0.47 146:0.21 147:0.27 149:0.57 150:0.71 154:0.76 155:0.67 157:0.76 159:0.29 161:0.96 165:0.93 166:0.80 167:0.52 172:0.21 173:0.69 176:0.73 177:0.70 178:0.55 179:0.80 181:0.73 182:0.80 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.42 215:0.39 216:0.77 222:0.48 227:0.61 229:0.61 231:0.71 235:0.84 241:0.21 242:0.45 243:0.63 245:0.40 246:0.91 247:0.35 253:0.40 259:0.21 265:0.15 266:0.23 267:0.36 271:0.37 276:0.14 287:0.61 290:0.58 297:0.36 300:0.25 +2 6:0.97 7:0.66 8:0.83 11:0.81 12:0.20 17:0.32 18:0.83 20:0.96 21:0.47 27:0.21 28:0.68 29:0.62 30:0.10 34:0.49 36:0.82 37:0.74 39:0.66 43:0.62 45:0.87 55:0.84 66:0.29 69:0.19 70:0.99 71:0.92 74:0.46 78:0.90 81:0.28 86:0.78 91:0.51 98:0.38 100:0.97 101:0.52 106:0.81 108:0.96 111:0.95 120:0.79 123:0.96 124:0.96 126:0.26 127:0.91 129:0.81 131:0.61 133:0.96 135:0.78 139:0.74 140:0.80 144:0.76 146:0.57 147:0.63 149:0.58 150:0.27 151:0.63 152:0.91 153:0.72 154:0.15 157:0.81 159:0.95 161:0.96 162:0.49 163:0.81 164:0.77 166:0.61 167:0.57 169:0.96 172:0.79 173:0.06 177:0.70 178:0.42 179:0.26 181:0.73 182:0.74 186:0.90 187:0.39 189:0.97 190:0.89 202:0.86 206:0.81 212:0.18 215:0.41 216:0.95 220:0.74 222:0.48 227:0.61 229:0.61 230:0.69 231:0.68 233:0.73 235:0.60 238:0.97 241:0.41 242:0.30 243:0.22 245:0.81 246:0.61 247:0.91 248:0.65 253:0.34 254:0.98 256:0.78 259:0.21 260:0.72 261:0.95 265:0.40 266:0.99 267:0.52 268:0.79 271:0.37 276:0.87 277:0.69 283:0.82 285:0.47 287:0.61 297:0.36 300:0.98 +0 7:0.66 8:0.86 11:0.81 12:0.20 17:0.89 18:0.73 20:0.90 21:0.47 27:0.64 28:0.68 29:0.62 30:0.45 32:0.79 34:0.91 36:0.57 37:0.35 39:0.66 43:0.66 44:0.93 45:0.66 48:0.91 55:0.45 66:0.77 69:0.52 70:0.33 71:0.92 74:0.57 77:0.70 78:0.82 81:0.28 86:0.76 91:0.23 98:0.58 100:0.73 101:0.52 106:0.81 108:0.40 111:0.80 122:0.43 123:0.38 126:0.26 127:0.17 129:0.05 131:0.61 135:0.98 139:0.82 144:0.76 145:0.89 146:0.34 147:0.41 149:0.32 150:0.27 152:0.84 154:0.84 157:0.77 159:0.14 161:0.96 166:0.61 167:0.51 169:0.72 172:0.37 173:0.79 177:0.70 178:0.55 179:0.62 181:0.73 182:0.74 186:0.82 187:0.39 195:0.54 206:0.81 212:0.87 215:0.41 216:0.08 222:0.48 227:0.61 229:0.61 230:0.69 231:0.64 233:0.76 235:0.60 241:0.12 242:0.24 243:0.79 246:0.61 247:0.47 253:0.38 254:0.80 259:0.21 261:0.82 265:0.59 266:0.17 267:0.59 271:0.37 276:0.31 281:0.91 282:0.87 283:0.78 287:0.61 297:0.36 300:0.25 +1 6:0.93 8:0.86 12:0.06 17:0.16 20:0.93 21:0.78 27:0.03 28:0.68 30:0.21 34:0.17 36:0.78 37:0.45 43:0.44 55:0.96 66:0.27 69:0.45 70:0.67 78:0.90 91:0.97 98:0.23 100:0.91 108:0.80 111:0.89 120:0.75 123:0.79 124:0.87 127:0.83 129:0.95 133:0.87 135:0.75 140:0.98 146:0.05 147:0.05 149:0.34 151:0.69 152:0.87 153:0.69 154:0.33 159:0.84 161:0.61 162:0.46 163:0.81 164:0.83 167:0.81 169:0.89 172:0.21 173:0.22 177:0.05 178:0.54 179:0.89 181:0.76 186:0.89 189:0.94 191:0.94 202:0.95 212:0.16 216:0.52 220:0.91 235:0.12 238:0.91 241:0.82 242:0.82 243:0.18 245:0.43 247:0.12 248:0.68 256:0.82 259:0.21 260:0.76 261:0.91 265:0.25 266:0.54 267:0.95 268:0.79 271:0.97 276:0.36 285:0.62 300:0.43 +1 8:0.74 12:0.06 17:0.06 27:0.69 28:0.68 30:0.60 34:0.30 36:0.99 37:0.26 43:0.52 55:0.71 66:0.72 69:0.05 70:0.62 78:1.00 81:0.87 91:0.85 98:0.87 104:0.94 108:0.05 111:0.99 120:0.75 123:0.05 124:0.43 126:0.54 127:0.92 129:0.59 133:0.47 135:0.85 140:0.45 146:0.95 147:0.96 149:0.75 150:0.74 151:0.71 153:0.72 154:0.41 159:0.72 161:0.61 162:0.55 164:0.78 167:0.48 169:0.91 172:0.82 173:0.08 177:0.05 179:0.43 181:0.76 187:0.68 191:0.70 202:0.75 212:0.37 215:0.96 216:0.42 235:0.02 238:0.80 241:0.12 242:0.82 243:0.75 245:0.85 247:0.12 248:0.68 256:0.75 259:0.21 260:0.76 265:0.87 266:0.63 267:0.91 268:0.73 271:0.97 276:0.76 283:0.60 285:0.62 297:0.36 300:0.55 +1 6:0.79 7:0.81 8:0.60 12:0.06 17:0.24 20:0.80 21:0.13 27:0.08 28:0.68 30:0.38 32:0.80 34:0.76 36:0.76 37:0.24 43:0.52 55:0.71 66:0.36 69:0.07 70:0.50 78:0.92 81:0.84 91:0.92 98:0.49 100:0.87 104:0.58 108:0.06 111:0.89 120:0.85 123:0.06 124:0.70 126:0.54 127:0.95 129:0.81 133:0.67 135:0.32 140:0.45 145:0.88 146:0.49 147:0.55 149:0.53 150:0.64 151:0.74 152:0.78 153:0.82 154:0.41 159:0.47 161:0.61 162:0.69 164:0.95 167:0.84 169:0.85 172:0.32 173:0.18 177:0.05 179:0.86 181:0.76 186:0.91 189:0.81 191:0.90 195:0.68 202:0.93 212:0.14 215:0.84 216:0.53 220:0.74 230:0.87 233:0.76 235:0.12 238:0.84 241:0.92 242:0.82 243:0.51 245:0.55 247:0.12 248:0.77 256:0.93 259:0.21 260:0.85 261:0.84 265:0.51 266:0.71 267:0.59 268:0.94 271:0.97 276:0.40 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.82 7:0.81 8:0.61 12:0.06 17:0.04 20:0.95 21:0.30 27:0.21 28:0.68 30:0.11 34:0.74 36:0.85 37:0.74 43:0.52 55:0.71 66:0.32 69:0.10 70:0.88 78:0.89 81:0.80 91:0.94 98:0.47 100:0.90 104:0.84 106:0.81 108:0.94 111:0.88 120:0.96 123:0.93 124:0.93 126:0.54 127:0.77 129:0.98 133:0.94 135:0.18 140:0.45 146:0.72 147:0.76 149:0.73 150:0.59 151:0.85 152:0.92 153:0.98 154:0.41 159:0.91 161:0.61 162:0.58 164:0.96 167:0.63 169:0.87 172:0.82 173:0.07 177:0.05 179:0.24 181:0.76 186:0.89 187:0.39 189:0.84 191:0.94 202:0.94 206:0.81 212:0.40 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.87 241:0.62 242:0.82 243:0.19 245:0.86 247:0.12 248:0.95 256:0.94 259:0.21 260:0.97 261:0.91 265:0.48 266:0.95 267:0.94 268:0.95 271:0.97 276:0.88 283:0.82 285:0.62 297:0.36 300:0.84 +1 6:0.82 8:0.62 12:0.06 17:0.03 20:0.88 21:0.47 27:0.05 28:0.68 30:0.89 32:0.80 34:0.58 36:0.45 37:0.28 43:0.52 55:0.84 66:0.83 69:0.17 70:0.20 78:0.93 81:0.86 91:0.97 98:0.93 100:0.93 104:0.58 108:0.14 111:0.92 120:0.90 123:0.13 126:0.54 127:0.59 129:0.05 135:0.49 140:0.45 145:0.74 146:0.81 147:0.84 149:0.52 150:0.71 151:0.66 152:0.83 153:0.48 154:0.41 159:0.37 161:0.61 162:0.72 163:0.86 164:0.96 167:0.85 169:0.91 172:0.51 173:0.32 177:0.05 179:0.82 181:0.76 186:0.93 189:0.84 191:0.94 195:0.59 202:0.93 212:0.28 215:0.63 216:0.51 220:0.62 235:0.60 238:0.88 241:0.96 242:0.82 243:0.84 247:0.12 248:0.86 256:0.95 259:0.21 260:0.90 261:0.90 265:0.93 266:0.47 267:0.87 268:0.95 271:0.97 276:0.43 283:0.60 285:0.62 297:0.36 300:0.18 +1 8:0.58 12:0.06 17:0.16 21:0.13 27:0.45 28:0.68 30:0.28 32:0.80 34:0.80 36:0.17 37:0.50 43:0.32 55:0.59 66:0.72 69:0.68 70:0.75 81:0.84 91:0.54 98:0.61 108:0.51 123:0.49 124:0.41 126:0.54 127:0.31 129:0.59 133:0.47 135:0.84 145:0.47 146:0.85 147:0.87 149:0.56 150:0.64 154:0.24 159:0.63 161:0.61 163:0.75 167:0.50 172:0.59 173:0.54 177:0.05 178:0.55 179:0.58 181:0.76 187:0.68 195:0.88 212:0.27 215:0.84 216:0.77 235:0.12 241:0.27 242:0.82 243:0.75 245:0.61 247:0.12 259:0.21 265:0.62 266:0.63 267:0.73 271:0.97 276:0.50 297:0.36 300:0.55 +1 6:0.84 8:0.68 12:0.06 17:0.24 20:0.78 21:0.30 27:0.04 28:0.68 30:0.55 34:0.56 36:0.47 37:0.40 43:0.33 55:0.92 66:0.19 69:0.67 70:0.36 78:0.94 81:0.93 91:0.82 98:0.44 100:0.96 104:0.83 106:0.81 108:0.73 111:0.93 120:0.90 123:0.71 124:0.82 126:0.54 127:0.42 129:0.89 133:0.78 135:0.85 140:0.45 146:0.57 147:0.62 149:0.51 150:0.87 151:0.80 152:0.88 153:0.93 154:0.25 159:0.61 161:0.61 162:0.77 163:0.94 164:0.92 167:0.66 169:0.94 172:0.27 173:0.59 177:0.05 179:0.67 181:0.76 186:0.95 187:0.39 189:0.86 191:0.93 202:0.86 206:0.81 212:0.40 215:0.72 216:0.67 235:0.52 238:0.89 241:0.66 242:0.82 243:0.34 245:0.61 247:0.12 248:0.86 256:0.88 259:0.21 260:0.91 261:0.94 265:0.46 266:0.47 267:0.23 268:0.89 271:0.97 276:0.51 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.81 7:0.81 8:0.60 12:0.06 17:0.03 20:0.95 21:0.78 27:0.06 28:0.68 30:0.11 34:0.52 36:0.95 37:0.29 43:0.46 66:0.75 69:0.42 70:0.54 78:0.89 91:0.95 98:0.78 100:0.88 104:0.58 108:0.32 111:0.87 120:0.84 123:0.31 127:0.27 129:0.05 135:0.44 140:0.95 145:0.50 146:0.46 147:0.52 149:0.42 151:0.71 152:0.88 153:0.77 154:0.35 159:0.17 161:0.61 162:0.47 164:0.86 167:0.82 169:0.85 172:0.32 173:0.71 177:0.05 178:0.53 179:0.87 181:0.76 186:0.89 189:0.83 191:0.92 202:0.95 212:0.61 216:0.56 220:0.62 230:0.87 233:0.76 235:0.22 238:0.85 241:0.84 242:0.82 243:0.77 247:0.12 248:0.76 256:0.86 259:0.21 260:0.85 261:0.90 265:0.78 266:0.23 267:0.95 268:0.83 271:0.97 276:0.26 285:0.62 300:0.33 +1 6:0.84 8:0.66 12:0.06 17:0.32 20:0.94 21:0.30 27:0.04 28:0.68 30:0.41 34:0.58 36:0.42 37:0.40 43:0.33 55:0.84 66:0.20 69:0.67 70:0.54 78:0.94 81:0.93 91:0.80 98:0.44 100:0.96 104:0.83 106:0.81 108:0.51 111:0.94 120:0.90 123:0.72 124:0.80 126:0.54 127:0.41 129:0.89 133:0.78 135:0.84 140:0.45 146:0.61 147:0.66 149:0.51 150:0.87 151:0.80 152:0.88 153:0.93 154:0.25 159:0.60 161:0.61 162:0.79 163:0.94 164:0.93 167:0.63 169:0.94 172:0.27 173:0.59 177:0.05 179:0.69 181:0.76 186:0.94 187:0.39 189:0.86 191:0.92 202:0.87 206:0.81 212:0.30 215:0.72 216:0.67 235:0.52 238:0.92 241:0.63 242:0.82 243:0.40 245:0.58 247:0.12 248:0.86 256:0.90 259:0.21 260:0.91 261:0.94 265:0.43 266:0.63 267:0.23 268:0.91 271:0.97 276:0.49 283:0.82 285:0.62 297:0.36 300:0.43 +1 7:0.81 8:0.66 12:0.06 17:0.26 21:0.78 27:0.06 28:0.68 30:0.21 34:0.88 36:0.92 37:0.85 43:0.27 55:0.71 66:0.35 69:0.80 70:0.87 78:0.98 91:0.79 98:0.51 104:0.79 106:0.81 108:0.90 111:0.96 120:0.61 123:0.90 124:0.87 127:0.55 129:0.96 133:0.90 135:0.92 140:0.90 145:0.47 146:0.05 147:0.05 149:0.67 151:0.56 153:0.71 154:0.20 159:0.81 161:0.61 162:0.47 164:0.63 167:0.71 169:0.84 172:0.71 173:0.62 177:0.05 178:0.50 179:0.35 181:0.76 187:0.68 191:0.86 202:0.73 206:0.81 212:0.50 216:0.87 220:0.62 230:0.87 233:0.76 235:0.88 241:0.71 242:0.82 243:0.07 245:0.78 247:0.12 248:0.55 256:0.45 259:0.21 260:0.62 265:0.53 266:0.87 267:0.89 268:0.57 271:0.97 276:0.78 285:0.62 300:0.70 +2 6:0.82 8:0.72 12:0.06 17:0.20 20:0.80 21:0.78 27:0.21 28:0.68 30:0.10 34:0.71 36:0.88 37:0.74 43:0.39 55:0.59 66:0.12 69:0.57 70:0.98 78:0.90 91:0.90 98:0.40 100:0.88 104:0.84 108:0.96 111:0.88 120:0.92 123:0.96 124:0.95 127:0.74 129:0.99 133:0.95 135:0.28 140:0.80 146:0.05 147:0.05 149:0.69 151:0.82 152:0.92 153:0.95 154:0.29 159:0.93 161:0.61 162:0.48 164:0.89 167:0.53 169:0.88 172:0.65 173:0.19 177:0.05 178:0.42 179:0.22 181:0.76 186:0.89 187:0.39 189:0.88 191:0.92 202:0.94 212:0.29 216:0.95 235:0.42 238:0.86 241:0.41 242:0.82 243:0.06 245:0.87 247:0.12 248:0.89 256:0.87 259:0.21 260:0.93 261:0.91 265:0.42 266:0.97 267:0.59 268:0.86 271:0.97 276:0.89 283:0.82 285:0.62 300:0.94 +1 8:0.59 12:0.06 17:0.16 21:0.30 27:0.45 28:0.68 30:0.76 34:0.80 36:0.19 37:0.50 43:0.32 55:0.71 66:0.75 69:0.68 70:0.57 81:0.80 91:0.42 98:0.57 108:0.52 123:0.50 124:0.41 126:0.54 127:0.36 129:0.81 133:0.67 135:0.87 145:0.50 146:0.81 147:0.84 149:0.60 150:0.59 154:0.24 159:0.64 161:0.61 167:0.51 172:0.65 173:0.56 177:0.05 178:0.55 179:0.55 181:0.76 187:0.68 212:0.77 215:0.72 216:0.77 235:0.31 241:0.32 242:0.82 243:0.77 245:0.55 247:0.12 259:0.21 265:0.58 266:0.54 267:0.44 271:0.97 276:0.54 283:0.60 297:0.36 300:0.70 +0 12:0.01 17:0.73 21:0.78 27:0.72 37:0.77 39:0.27 91:0.27 104:1.00 106:0.81 117:0.86 145:0.56 175:0.91 178:0.55 187:0.95 206:0.81 216:0.52 222:0.35 232:1.00 235:0.84 241:0.41 244:0.93 253:0.20 257:0.84 271:0.14 277:0.87 +0 8:0.82 12:0.01 17:0.85 21:0.78 27:0.31 30:0.37 37:0.77 39:0.27 43:0.15 55:0.92 66:0.24 69:0.74 70:0.46 74:0.71 91:0.38 98:0.48 108:0.57 122:0.67 123:0.55 124:0.93 127:0.49 129:0.59 133:0.92 145:0.74 146:0.85 147:0.05 149:0.32 154:0.15 158:0.74 159:0.83 163:0.94 172:0.57 173:0.51 177:0.05 178:0.55 179:0.39 187:0.87 191:0.76 202:0.36 212:0.27 216:0.43 222:0.35 232:0.71 235:0.80 241:0.18 242:0.79 243:0.08 244:0.61 245:0.72 247:0.39 253:0.42 254:0.96 257:0.65 259:0.21 265:0.49 266:0.80 271:0.14 276:0.74 300:0.33 +0 7:0.66 12:0.01 17:0.90 21:0.22 27:0.64 30:0.93 32:0.64 34:0.29 36:0.29 39:0.27 43:0.14 55:0.59 66:0.88 69:0.56 70:0.19 74:0.35 81:0.56 91:0.57 98:0.70 104:0.80 106:0.81 108:0.43 123:0.42 126:0.32 127:0.21 129:0.05 135:0.56 145:0.63 146:0.78 147:0.82 149:0.26 150:0.42 154:0.40 159:0.34 172:0.65 173:0.53 177:0.38 178:0.55 179:0.41 187:0.39 195:0.77 206:0.81 212:0.73 215:0.51 216:0.30 222:0.35 230:0.69 233:0.76 235:0.22 241:0.49 242:0.78 243:0.88 244:0.61 247:0.39 253:0.29 254:0.94 259:0.21 265:0.70 266:0.47 267:0.52 271:0.14 276:0.55 283:0.82 297:0.36 300:0.18 +0 7:0.66 8:0.93 12:0.01 17:0.78 21:0.78 27:0.18 34:0.53 36:0.43 37:0.84 39:0.27 74:0.34 77:0.89 91:0.64 135:0.54 145:0.90 175:0.91 178:0.55 187:0.21 191:0.73 195:0.57 202:0.64 216:0.35 222:0.35 230:0.69 233:0.73 235:0.12 241:0.78 244:0.93 253:0.28 257:0.84 259:0.21 267:0.28 271:0.14 277:0.87 282:0.73 +1 12:0.01 17:0.32 21:0.78 27:0.72 37:0.25 39:0.27 74:0.34 91:0.89 96:0.93 120:0.68 140:0.45 151:0.60 153:0.95 158:0.74 162:0.57 164:0.62 175:0.91 190:0.89 202:0.88 216:0.36 220:0.62 222:0.35 232:0.74 235:0.74 241:0.88 244:0.93 248:0.74 253:0.84 256:0.87 257:0.84 260:0.65 268:0.88 271:0.14 277:0.87 285:0.62 +0 12:0.01 17:0.34 21:0.78 27:0.72 30:0.95 34:0.36 36:0.90 39:0.27 43:0.13 55:0.92 66:0.54 69:0.67 74:0.33 91:0.06 98:0.13 108:0.51 123:0.49 127:0.08 129:0.05 135:0.54 146:0.43 147:0.50 149:0.09 154:0.10 159:0.16 172:0.10 173:0.16 177:0.38 178:0.55 179:0.17 187:0.39 216:0.11 222:0.35 232:0.79 235:0.88 241:0.07 243:0.63 244:0.83 253:0.28 259:0.21 265:0.14 267:0.23 271:0.14 300:0.08 +0 7:0.66 8:0.68 12:0.01 17:0.97 21:0.78 27:0.70 30:0.45 34:0.39 36:0.75 37:0.39 39:0.27 43:0.13 55:0.59 66:0.64 69:0.68 70:0.50 74:0.51 91:0.29 98:0.43 108:0.52 122:0.67 123:0.49 124:0.42 127:0.20 129:0.59 133:0.47 135:0.58 145:0.77 146:0.52 147:0.58 149:0.18 154:0.10 159:0.31 172:0.32 173:0.68 175:0.75 177:0.05 178:0.55 179:0.73 187:0.21 212:0.52 216:0.15 222:0.35 230:0.69 232:0.92 233:0.76 235:0.80 241:0.01 242:0.82 243:0.69 244:0.83 245:0.43 247:0.12 253:0.35 257:0.65 259:0.21 265:0.45 266:0.27 267:0.52 271:0.14 276:0.28 277:0.69 300:0.33 +1 12:0.01 17:0.92 21:0.78 27:0.68 34:0.45 36:0.34 39:0.27 91:0.04 104:0.99 106:0.81 117:0.86 135:0.39 145:0.70 175:0.91 178:0.55 187:0.39 206:0.81 216:0.31 222:0.35 232:0.96 235:0.68 241:0.39 244:0.93 253:0.20 257:0.84 259:0.21 267:0.52 271:0.14 277:0.87 +0 7:0.66 12:0.01 17:0.36 21:0.22 27:0.44 34:0.62 36:0.50 37:0.35 39:0.27 74:0.34 77:0.70 81:0.53 91:0.57 96:0.71 114:0.66 126:0.32 135:0.28 140:0.45 145:0.67 150:0.40 151:0.49 153:0.48 162:0.62 175:0.91 176:0.61 187:0.39 190:0.89 195:0.57 202:0.50 216:0.85 220:0.82 222:0.35 230:0.69 233:0.73 235:0.22 241:0.63 244:0.93 248:0.49 253:0.50 256:0.45 257:0.84 259:0.21 267:0.97 268:0.46 271:0.14 277:0.87 282:0.73 285:0.50 290:0.66 +0 12:0.01 17:0.18 21:0.68 27:0.48 30:0.42 34:0.31 36:0.21 37:0.55 39:0.27 43:0.14 55:0.92 66:0.45 69:0.59 70:0.75 74:0.63 77:0.96 81:0.24 91:0.23 98:0.47 108:0.58 114:0.55 120:0.63 122:0.67 123:0.55 124:0.75 126:0.32 127:0.40 129:0.89 133:0.78 135:0.68 140:0.45 145:0.77 146:0.05 147:0.05 149:0.25 150:0.24 151:0.55 153:0.48 154:0.10 159:0.72 162:0.86 163:0.81 164:0.53 172:0.48 173:0.40 175:0.75 176:0.61 177:0.05 179:0.62 187:0.39 190:0.89 195:0.90 202:0.36 212:0.23 216:0.92 220:0.62 222:0.35 235:0.80 241:0.55 242:0.82 243:0.12 244:0.83 245:0.60 247:0.12 248:0.54 253:0.41 256:0.45 257:0.65 259:0.21 260:0.61 265:0.48 266:0.75 267:0.69 268:0.46 271:0.14 276:0.53 277:0.69 282:0.73 285:0.50 290:0.55 300:0.55 +0 12:0.01 17:0.77 21:0.78 27:0.68 39:0.27 91:0.05 104:0.99 106:0.81 117:0.86 145:0.67 175:0.91 178:0.55 187:0.39 202:0.36 206:0.81 216:0.31 222:0.35 232:0.96 235:0.52 241:0.46 244:0.93 253:0.20 257:0.84 259:0.21 271:0.14 277:0.87 +1 12:0.01 17:0.85 21:0.78 27:0.68 39:0.27 91:0.05 104:0.99 106:0.81 117:0.86 145:0.67 175:0.91 178:0.55 187:0.39 206:0.81 216:0.31 222:0.35 232:0.96 235:0.52 241:0.35 244:0.93 253:0.20 257:0.84 259:0.21 271:0.14 277:0.87 +0 8:0.60 12:0.20 17:0.81 18:0.56 21:0.78 26:0.93 27:0.50 29:0.77 30:0.56 34:0.29 36:0.56 37:0.42 39:0.21 43:0.06 55:0.99 58:0.93 66:0.05 69:0.98 70:0.58 71:0.89 74:0.25 86:0.57 89:0.95 98:0.05 108:0.96 123:0.96 124:0.96 127:0.47 129:0.99 133:0.95 135:0.83 139:0.56 141:0.91 146:0.05 147:0.05 149:0.06 154:0.06 159:1.00 163:0.97 170:0.77 172:0.05 173:0.55 175:0.97 177:0.05 178:0.55 179:0.77 187:0.39 199:0.94 202:0.36 212:0.13 216:0.92 222:0.48 223:0.91 224:0.93 225:0.95 234:0.91 240:0.95 241:0.03 242:0.73 243:0.15 244:0.93 245:0.43 247:0.47 253:0.23 257:0.93 259:0.21 265:0.05 266:0.75 267:0.19 271:0.50 274:0.91 276:0.43 277:0.95 300:0.43 +0 8:0.80 10:0.83 12:0.20 17:0.83 18:0.57 21:0.78 26:0.94 27:0.69 29:0.77 30:0.45 34:0.45 36:0.30 37:0.63 39:0.21 43:0.07 55:0.99 58:0.93 66:0.07 69:0.95 70:0.61 71:0.89 74:0.25 86:0.58 89:0.96 91:0.01 98:0.12 108:0.90 123:0.90 124:0.94 127:0.60 129:0.05 133:0.94 135:0.78 139:0.56 141:0.91 145:0.52 146:0.43 147:0.50 149:0.08 154:0.06 159:0.98 163:0.99 170:0.77 172:0.10 173:0.56 174:0.90 175:0.91 177:0.38 178:0.55 179:0.77 187:0.21 197:0.81 199:0.94 212:0.22 216:0.32 222:0.48 223:0.91 224:0.93 225:0.96 234:0.91 235:0.42 239:0.82 240:0.95 241:0.01 242:0.22 243:0.23 244:0.93 245:0.47 247:0.76 253:0.23 257:0.84 259:0.21 265:0.12 266:0.75 267:0.28 271:0.50 274:0.91 276:0.46 277:0.87 300:0.43 +0 10:0.91 11:0.46 12:0.20 17:1.00 18:0.62 21:0.78 26:0.94 27:0.72 29:0.77 30:0.36 34:0.88 36:0.54 39:0.21 43:0.19 45:0.83 55:0.98 58:0.93 66:0.05 69:0.98 70:0.88 71:0.89 74:0.25 86:0.65 89:0.98 98:0.11 101:0.47 108:0.98 122:0.95 123:0.98 124:1.00 127:0.94 129:0.95 133:1.00 135:0.28 139:0.63 141:0.91 146:0.13 147:0.05 149:0.23 154:0.09 159:1.00 163:0.99 170:0.77 172:0.16 173:0.60 174:0.99 175:0.91 177:0.05 178:0.55 179:0.29 187:0.21 197:0.93 199:0.97 212:0.18 222:0.48 223:0.92 224:0.93 225:0.97 234:0.91 235:0.02 239:0.90 240:0.95 242:0.44 243:0.06 244:0.83 245:0.64 247:0.86 253:0.22 257:0.93 265:0.11 266:0.99 267:0.98 271:0.50 274:0.91 276:0.85 277:0.95 300:0.84 +0 10:0.83 12:0.20 17:0.84 18:0.61 21:0.78 26:0.94 27:0.58 29:0.77 30:0.76 34:0.61 36:0.41 37:0.32 39:0.21 43:0.31 55:0.92 58:0.93 66:0.18 69:0.88 70:0.37 71:0.89 74:0.26 86:0.63 89:0.96 91:0.10 98:0.29 108:0.81 123:0.89 124:0.73 127:0.22 129:0.59 133:0.64 135:0.58 139:0.61 141:0.91 145:0.69 146:0.13 147:0.18 149:0.25 154:0.23 159:0.63 163:0.81 170:0.77 172:0.21 173:0.77 174:0.79 175:0.91 177:0.38 178:0.55 179:0.68 187:0.21 197:0.81 199:0.94 202:0.36 212:0.37 216:0.69 222:0.48 223:0.91 224:0.93 225:0.95 234:0.91 235:0.31 239:0.82 240:0.95 242:0.40 243:0.29 244:0.83 245:0.51 247:0.55 253:0.24 257:0.84 259:0.21 265:0.23 266:0.47 267:0.98 271:0.50 274:0.91 276:0.32 277:0.87 300:0.33 +1 1:0.63 7:0.70 9:0.79 12:0.51 17:0.66 18:0.91 21:0.22 22:0.93 26:0.98 29:0.99 30:0.58 31:0.94 34:0.80 36:0.92 37:0.98 39:0.79 43:0.14 44:0.88 47:0.93 48:0.97 55:0.45 58:0.98 64:0.77 66:0.33 69:0.80 70:0.48 71:0.59 74:0.42 76:0.85 77:0.70 79:0.93 81:0.60 83:0.60 86:0.90 91:0.10 96:0.80 98:0.31 99:0.83 108:0.79 114:0.77 117:0.86 120:0.69 122:0.86 123:0.78 124:0.76 126:0.44 127:0.45 129:0.05 131:0.85 133:0.73 135:0.32 137:0.94 139:0.88 140:0.45 141:0.99 144:0.57 145:0.59 146:0.40 147:0.30 149:0.26 150:0.49 151:0.86 153:0.48 154:0.34 155:0.66 157:0.61 159:0.52 162:0.86 164:0.56 165:0.70 166:0.73 172:0.54 173:0.81 176:0.59 177:0.05 179:0.49 182:0.82 187:0.68 190:0.77 192:0.87 195:0.74 196:0.95 199:0.98 201:0.59 202:0.36 204:0.81 208:0.64 212:0.81 216:0.95 222:0.25 223:0.97 224:1.00 227:0.85 229:0.90 230:0.73 231:0.69 232:0.72 233:0.76 234:1.00 235:0.42 241:0.66 242:0.59 243:0.15 244:0.61 245:0.72 246:0.61 247:0.74 248:0.77 253:0.78 254:0.99 255:0.78 256:0.45 257:0.84 259:0.21 260:0.70 262:0.93 265:0.33 266:0.63 267:0.44 268:0.46 271:0.79 274:0.97 276:0.61 277:0.69 281:0.91 282:0.72 284:0.88 285:0.62 287:0.88 290:0.77 300:0.43 +2 1:0.69 11:0.82 12:0.51 17:0.85 18:0.89 21:0.30 22:0.93 26:0.97 27:0.54 29:0.99 30:0.45 34:0.85 36:0.30 37:0.73 39:0.79 43:0.89 44:0.92 45:0.66 47:0.93 48:0.91 55:0.52 58:0.93 64:0.77 66:0.61 69:0.50 70:0.80 71:0.59 74:0.59 76:0.97 77:0.89 81:0.79 83:0.60 85:0.80 86:0.95 91:0.20 97:0.89 98:0.60 99:0.95 101:0.97 108:0.67 114:0.86 117:0.86 122:0.82 123:0.65 124:0.50 126:0.54 127:0.57 129:0.59 131:0.61 133:0.46 135:0.37 139:0.95 141:0.91 144:0.57 145:0.79 146:0.24 147:0.30 149:0.77 150:0.66 154:0.88 155:0.57 157:0.81 158:0.73 159:0.37 165:0.93 166:0.78 172:0.59 173:0.60 176:0.73 177:0.70 178:0.55 179:0.63 182:0.81 187:0.87 192:0.54 195:0.61 199:0.98 201:0.67 204:0.83 208:0.64 212:0.81 215:0.72 216:0.67 219:0.94 222:0.25 223:0.97 224:0.93 227:0.61 229:0.61 231:0.68 232:0.99 234:0.91 235:0.74 241:0.39 242:0.21 243:0.34 245:0.74 246:0.86 247:0.74 253:0.63 259:0.21 262:0.93 265:0.61 266:0.47 267:0.28 271:0.79 274:0.91 276:0.47 279:0.77 281:0.86 282:0.73 287:0.61 290:0.86 292:0.88 297:0.36 300:0.70 +1 1:0.63 7:0.70 9:0.73 12:0.51 17:0.64 18:0.94 21:0.22 22:0.93 26:0.98 29:0.99 30:0.53 31:0.92 34:0.80 36:0.80 37:0.98 39:0.79 43:0.14 44:0.88 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.64 69:0.78 70:0.57 71:0.59 74:0.41 76:0.85 77:0.70 79:0.91 81:0.60 83:0.60 86:0.91 91:0.09 96:0.77 98:0.74 99:0.83 108:0.72 114:0.77 117:0.86 122:0.86 123:0.70 124:0.52 126:0.44 127:0.45 129:0.59 131:0.81 133:0.64 135:0.54 137:0.92 139:0.90 140:0.45 141:0.99 144:0.57 145:0.59 146:0.33 147:0.30 149:0.30 150:0.49 151:0.76 153:0.48 154:0.21 155:0.64 157:0.61 159:0.54 162:0.86 165:0.70 166:0.73 172:0.74 173:0.77 175:0.75 176:0.59 177:0.05 179:0.43 182:0.61 187:0.68 190:0.77 192:0.56 195:0.76 196:0.94 199:0.99 201:0.59 202:0.36 204:0.81 208:0.64 212:0.71 216:0.95 220:0.62 222:0.25 223:0.97 224:1.00 227:0.83 229:0.61 230:0.73 231:0.64 232:0.72 233:0.76 234:0.99 235:0.42 241:0.64 242:0.72 243:0.13 244:0.83 245:0.76 246:0.61 247:0.74 248:0.70 253:0.70 254:0.90 255:0.72 256:0.45 257:0.84 259:0.21 262:0.93 265:0.74 266:0.54 267:0.79 268:0.46 271:0.79 274:0.97 276:0.69 277:0.69 281:0.91 282:0.72 284:0.88 285:0.56 287:0.61 290:0.77 300:0.43 +0 12:0.51 17:0.36 18:0.74 21:0.78 26:0.97 27:0.45 29:0.99 30:0.33 34:0.70 36:0.20 37:0.50 39:0.79 43:0.13 55:0.84 58:0.93 64:0.77 66:0.12 69:0.88 70:0.69 71:0.59 74:0.26 81:0.25 86:0.71 91:0.04 98:0.16 108:0.76 122:0.41 123:0.52 124:0.81 126:0.26 127:0.41 129:0.05 131:0.61 133:0.74 135:0.85 139:0.68 141:0.91 145:0.50 146:0.26 147:0.40 149:0.12 150:0.27 154:0.18 157:0.61 159:0.80 163:0.80 166:0.61 172:0.16 173:0.74 175:0.75 177:0.05 178:0.55 179:0.86 182:0.61 187:0.68 199:0.95 212:0.19 216:0.77 222:0.25 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.99 241:0.49 242:0.19 243:0.32 244:0.83 245:0.46 246:0.61 247:0.55 253:0.24 254:0.92 257:0.84 259:0.21 265:0.14 266:0.47 267:0.73 271:0.79 274:0.91 276:0.29 277:0.87 287:0.61 300:0.43 +1 1:0.63 7:0.70 9:0.71 12:0.51 17:0.69 18:0.90 21:0.22 22:0.93 26:0.98 29:0.99 30:0.53 31:0.88 34:0.80 36:0.82 37:0.98 39:0.79 41:0.82 43:0.14 44:0.88 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.43 69:0.78 70:0.65 71:0.59 74:0.41 76:0.85 77:0.70 79:0.88 81:0.60 83:0.60 86:0.88 91:0.06 96:0.71 98:0.36 99:0.83 108:0.82 114:0.77 117:0.86 120:0.58 122:0.86 123:0.81 124:0.77 126:0.44 127:0.46 129:0.59 131:0.85 133:0.76 135:0.49 137:0.88 139:0.86 140:0.45 141:0.99 144:0.57 145:0.59 146:0.33 147:0.30 149:0.24 150:0.49 151:0.58 153:0.48 154:0.21 155:0.64 157:0.61 159:0.54 162:0.86 164:0.57 165:0.70 166:0.73 172:0.57 173:0.77 175:0.75 176:0.59 177:0.05 179:0.51 182:0.82 187:0.68 192:0.59 195:0.77 196:0.91 199:0.98 201:0.59 202:0.36 204:0.81 208:0.64 212:0.73 216:0.95 220:0.87 222:0.25 223:0.97 224:0.99 227:0.85 229:0.90 230:0.73 231:0.69 232:0.72 233:0.76 234:0.98 235:0.42 241:0.67 242:0.61 243:0.15 244:0.83 245:0.72 246:0.61 247:0.74 248:0.56 253:0.59 254:0.90 255:0.71 256:0.45 257:0.84 259:0.21 260:0.58 262:0.93 265:0.38 266:0.54 267:0.76 268:0.46 271:0.79 274:0.97 276:0.60 277:0.69 281:0.91 282:0.72 284:0.89 285:0.62 287:0.88 290:0.77 300:0.55 +0 12:0.51 17:0.83 18:0.93 21:0.68 26:0.97 27:0.52 29:0.99 30:0.89 34:0.55 36:0.26 37:0.43 39:0.79 43:0.14 44:0.88 48:1.00 55:0.59 58:0.93 64:0.77 66:0.60 69:0.67 70:0.33 71:0.59 74:0.30 77:0.70 81:0.35 86:0.90 91:0.14 98:0.54 108:0.51 122:0.86 123:0.49 124:0.50 126:0.26 127:0.21 129:0.59 131:0.61 133:0.47 135:0.70 139:0.90 141:0.91 145:0.82 146:0.71 147:0.76 149:0.26 150:0.39 154:0.17 157:0.61 159:0.40 166:0.61 172:0.57 173:0.60 175:0.75 177:0.38 178:0.55 179:0.39 182:0.61 187:0.39 195:0.81 199:0.99 212:0.80 216:0.79 219:0.90 222:0.25 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.88 241:0.07 242:0.76 243:0.67 244:0.83 245:0.73 246:0.61 247:0.55 253:0.27 254:0.97 257:0.65 259:0.21 265:0.55 266:0.47 267:0.17 271:0.79 274:0.91 276:0.54 277:0.69 281:0.86 282:0.72 287:0.61 292:0.88 300:0.43 +2 1:0.69 6:0.94 8:0.81 9:0.73 11:0.64 12:0.51 17:0.73 18:0.98 20:0.99 21:0.54 22:0.93 26:0.98 27:0.63 29:0.99 30:0.33 31:0.93 32:0.66 34:0.83 36:0.80 37:0.70 39:0.79 43:0.65 44:0.92 45:0.83 47:0.93 48:0.91 58:0.99 64:0.77 66:0.97 69:0.27 70:0.72 71:0.59 74:0.65 77:0.96 78:0.97 79:0.92 81:0.67 83:0.91 86:0.98 91:0.61 96:0.87 98:0.93 99:0.83 100:0.91 101:0.87 108:0.21 111:0.95 114:0.67 120:0.57 122:0.80 123:0.20 126:0.54 127:0.57 129:0.05 131:0.85 135:0.54 137:0.93 138:0.96 139:0.98 140:0.80 141:0.99 144:0.57 145:0.59 146:0.86 147:0.89 149:0.83 150:0.58 151:0.61 152:0.95 153:0.48 154:0.66 155:0.65 157:0.81 158:0.72 159:0.43 162:0.82 164:0.55 165:0.70 166:0.78 169:0.87 172:0.93 173:0.33 176:0.62 177:0.70 179:0.24 182:0.81 186:0.97 187:0.39 190:0.95 191:0.83 192:0.86 195:0.66 196:0.97 199:0.99 201:0.66 202:0.71 208:0.64 212:0.87 215:0.53 216:0.64 219:0.90 220:0.74 222:0.25 223:0.98 224:0.99 227:0.85 229:0.61 231:0.69 232:0.83 234:0.99 235:0.88 241:0.12 242:0.22 243:0.97 244:0.61 246:0.86 247:0.79 248:0.60 253:0.85 255:0.72 256:0.78 260:0.57 261:0.93 262:0.93 265:0.93 266:0.47 267:0.36 268:0.77 271:0.79 274:0.99 276:0.86 279:0.77 281:0.91 282:0.75 284:0.95 285:0.62 287:0.61 290:0.67 292:0.95 297:0.36 300:0.55 +2 7:0.70 12:0.51 17:0.70 18:0.84 21:0.78 26:0.97 27:0.59 29:0.99 30:0.66 34:0.92 36:0.41 37:1.00 39:0.79 43:0.31 55:0.59 58:0.93 66:0.97 69:0.58 70:0.57 71:0.59 74:0.38 76:0.94 83:0.60 86:0.82 91:0.18 98:0.98 108:0.44 117:0.86 123:0.43 127:0.80 129:0.05 131:0.61 135:0.39 139:0.78 141:0.91 144:0.57 145:0.83 146:0.96 147:0.97 149:0.50 154:0.47 155:0.51 157:0.61 159:0.67 166:0.61 172:0.93 173:0.49 177:0.70 178:0.55 179:0.26 182:0.61 187:0.68 192:0.48 199:0.96 204:0.81 208:0.54 212:0.77 216:0.48 222:0.25 223:0.96 224:0.93 227:0.61 229:0.61 230:0.73 231:0.65 232:0.78 233:0.76 234:0.91 235:0.80 241:0.10 242:0.75 243:0.97 244:0.83 246:0.61 247:0.76 253:0.33 265:0.98 266:0.63 267:0.23 271:0.79 274:0.91 276:0.87 287:0.61 300:0.70 +1 1:0.69 7:0.81 8:0.73 9:0.74 11:0.82 12:0.51 17:0.95 18:0.98 21:0.13 26:0.98 27:0.53 29:0.99 30:0.15 31:0.92 32:0.80 34:0.90 36:0.98 37:0.39 39:0.79 43:0.85 44:0.93 45:0.87 48:1.00 55:0.59 58:0.98 64:0.77 66:0.96 69:0.17 70:0.82 71:0.59 74:0.77 76:0.85 77:0.89 79:0.91 81:0.82 83:0.91 85:0.80 86:0.99 91:0.35 96:0.77 97:0.89 98:0.96 99:0.95 101:0.97 108:0.14 114:0.92 121:0.90 122:0.86 123:0.13 126:0.54 127:0.72 129:0.05 131:0.81 135:0.26 137:0.92 139:0.99 140:0.45 141:0.99 144:0.57 145:0.72 146:0.86 147:0.88 149:0.92 150:0.78 151:0.76 153:0.48 154:0.82 155:0.65 157:0.81 158:0.73 159:0.43 162:0.86 165:0.93 166:0.79 172:0.90 173:0.29 176:0.73 177:0.70 179:0.32 182:0.82 187:0.39 190:0.77 192:0.58 195:0.65 196:0.96 199:0.99 201:0.68 202:0.36 204:0.84 208:0.64 212:0.92 215:0.84 216:0.29 219:0.94 220:0.62 222:0.25 223:0.97 224:1.00 227:0.83 229:0.61 230:0.87 231:0.69 232:0.75 233:0.76 234:1.00 235:0.60 241:0.07 242:0.33 243:0.96 246:0.86 247:0.82 248:0.70 253:0.81 255:0.73 256:0.45 259:0.21 265:0.96 266:0.27 267:0.98 268:0.46 271:0.79 274:0.98 276:0.82 279:0.77 281:0.91 282:0.88 284:0.88 285:0.56 287:0.61 290:0.92 292:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.66 6:0.82 7:0.64 8:0.65 9:0.71 11:0.50 12:0.51 17:0.47 18:0.77 20:0.93 21:0.40 26:0.98 27:0.49 29:0.99 30:0.44 31:0.88 32:0.71 34:0.89 36:0.62 37:0.27 39:0.79 43:0.63 44:0.92 45:0.92 48:0.91 55:0.71 58:0.98 64:0.77 66:0.72 69:0.23 70:0.63 71:0.59 74:0.82 76:0.85 77:0.70 78:1.00 79:0.87 81:0.65 83:0.60 86:0.83 91:0.66 96:0.70 97:0.89 98:0.92 99:0.83 100:0.94 101:0.52 104:0.97 106:0.81 108:0.73 111:0.98 114:0.82 117:0.86 120:0.57 122:0.77 123:0.71 124:0.44 126:0.54 127:0.95 129:0.59 131:0.85 133:0.46 135:0.70 137:0.88 139:0.87 140:0.45 141:0.99 144:0.57 145:0.76 146:0.73 147:0.77 149:0.74 150:0.56 151:0.55 152:0.86 153:0.69 154:0.66 155:0.58 157:0.89 158:0.81 159:0.85 162:0.86 164:0.62 165:0.70 166:0.79 169:0.92 172:0.97 173:0.12 176:0.70 177:0.70 179:0.16 182:0.82 186:0.99 187:0.39 189:0.84 190:0.77 192:0.57 195:0.94 196:0.90 199:0.97 201:0.63 202:0.46 206:0.81 208:0.64 212:0.81 215:0.67 216:0.33 219:0.94 220:0.91 222:0.25 223:0.97 224:0.99 227:0.85 229:0.90 230:0.65 231:0.69 232:0.72 233:0.76 234:0.98 235:0.60 238:0.84 241:0.37 242:0.70 243:0.27 244:0.61 245:0.98 246:0.86 247:0.92 248:0.54 253:0.69 254:0.80 255:0.70 256:0.45 259:0.21 260:0.57 261:0.95 265:0.92 266:0.54 267:0.28 268:0.55 271:0.79 274:0.97 276:0.95 279:0.79 281:0.91 282:0.79 283:0.66 284:0.90 285:0.62 287:0.88 290:0.82 292:0.87 297:0.36 300:0.70 +1 1:0.63 7:0.70 9:0.79 12:0.51 17:0.69 18:0.91 21:0.22 22:0.93 26:0.98 29:0.99 30:0.53 31:0.94 34:0.77 36:0.78 37:0.98 39:0.79 43:0.14 44:0.88 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.43 69:0.78 70:0.65 71:0.59 74:0.41 76:0.85 77:0.70 79:0.93 81:0.60 83:0.60 86:0.88 91:0.06 96:0.80 98:0.38 99:0.83 108:0.82 114:0.77 117:0.86 120:0.69 122:0.86 123:0.81 124:0.77 126:0.44 127:0.53 129:0.59 131:0.85 133:0.76 135:0.51 137:0.94 139:0.86 140:0.45 141:0.99 144:0.57 145:0.59 146:0.33 147:0.30 149:0.25 150:0.49 151:0.86 153:0.48 154:0.21 155:0.66 157:0.61 159:0.54 162:0.86 164:0.56 165:0.70 166:0.73 172:0.57 173:0.78 175:0.75 176:0.59 177:0.05 179:0.54 182:0.82 187:0.68 190:0.77 192:0.87 195:0.74 196:0.95 199:0.98 201:0.59 202:0.36 204:0.81 208:0.64 212:0.73 216:0.95 222:0.25 223:0.97 224:1.00 227:0.85 229:0.90 230:0.73 231:0.69 232:0.72 233:0.76 234:1.00 235:0.42 241:0.63 242:0.61 243:0.15 244:0.83 245:0.72 246:0.61 247:0.74 248:0.77 253:0.78 254:0.90 255:0.78 256:0.45 257:0.84 259:0.21 260:0.70 262:0.93 265:0.40 266:0.54 267:0.73 268:0.46 271:0.79 274:0.97 276:0.60 277:0.69 281:0.91 282:0.72 284:0.88 285:0.62 287:0.88 290:0.77 300:0.55 +0 11:0.42 12:0.51 17:0.49 18:0.60 21:0.78 26:0.94 27:0.45 29:0.99 30:0.11 34:0.86 36:0.19 37:0.50 39:0.79 43:0.08 55:0.45 58:0.93 66:0.16 69:0.86 70:0.54 71:0.59 74:0.33 86:0.62 91:0.17 98:0.18 108:0.73 122:0.51 123:0.77 124:0.81 127:0.22 129:0.59 131:0.61 133:0.76 135:0.95 139:0.61 141:0.91 144:0.57 145:0.49 146:0.19 147:0.24 149:0.08 154:0.23 157:0.61 159:0.64 163:0.89 166:0.61 172:0.10 173:0.71 175:0.75 177:0.38 178:0.55 179:0.81 182:0.61 187:0.68 199:0.94 212:0.30 216:0.77 222:0.25 223:0.92 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.84 241:0.21 242:0.33 243:0.37 244:0.83 245:0.42 246:0.61 247:0.39 253:0.29 254:0.84 257:0.65 259:0.21 265:0.09 266:0.27 267:0.65 271:0.79 274:0.91 276:0.25 277:0.69 287:0.61 300:0.33 +2 1:0.69 8:0.84 11:0.52 12:0.51 17:0.91 18:0.96 22:0.93 26:0.98 27:0.63 29:0.99 30:0.53 31:0.92 34:0.22 36:0.85 37:0.78 39:0.79 43:0.77 44:0.92 45:0.91 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.77 69:0.10 70:0.68 71:0.59 74:0.68 76:0.97 77:0.96 79:0.91 81:0.74 86:0.95 91:0.07 96:0.80 97:0.89 98:0.70 99:0.83 101:0.52 108:0.65 114:0.81 122:0.77 123:0.62 124:0.58 126:0.44 127:0.88 129:0.89 131:0.81 133:0.89 135:0.63 137:0.92 139:0.95 140:0.45 141:0.99 145:0.47 146:0.22 147:0.28 149:0.89 150:0.69 151:0.65 153:0.48 154:0.69 155:0.56 157:0.61 158:0.73 159:0.80 162:0.86 165:0.70 166:0.73 172:0.98 173:0.10 175:0.75 176:0.59 177:0.38 179:0.14 182:0.61 187:0.39 190:0.95 191:0.75 192:0.50 195:0.90 196:0.96 199:0.99 201:0.66 202:0.50 204:0.84 208:0.54 212:0.91 216:0.46 219:0.94 222:0.25 223:0.97 224:1.00 227:0.83 229:0.61 231:0.64 232:0.83 234:1.00 235:0.31 241:0.10 242:0.73 243:0.09 244:0.61 245:0.91 246:0.61 247:0.79 248:0.68 253:0.82 256:0.58 257:0.65 262:0.93 265:0.70 266:0.75 267:0.17 268:0.59 271:0.79 274:0.97 276:0.95 277:0.69 279:0.77 281:0.91 282:0.73 284:0.88 285:0.56 287:0.61 290:0.81 292:0.95 300:0.84 +2 1:0.60 8:0.69 9:0.70 11:0.64 12:0.51 17:0.76 18:0.84 20:0.93 21:0.71 26:0.98 27:0.08 29:0.99 30:0.38 31:0.87 34:1.00 36:0.57 37:0.32 39:0.79 43:0.73 44:0.92 45:0.85 55:0.45 58:0.98 64:0.77 66:0.93 69:0.42 70:0.74 71:0.59 74:0.74 76:0.94 77:0.99 78:0.96 79:0.86 81:0.51 86:0.91 91:0.20 96:0.69 97:0.89 98:0.93 99:0.83 100:0.73 101:0.87 108:0.32 111:0.92 114:0.63 117:0.86 122:0.75 123:0.31 126:0.44 127:0.59 129:0.05 131:0.81 135:0.72 137:0.87 139:0.93 140:0.45 141:0.99 144:0.57 145:0.95 146:0.84 147:0.87 149:0.43 150:0.40 151:0.50 152:0.96 153:0.48 154:0.64 155:0.56 157:0.85 158:0.73 159:0.40 162:0.86 165:0.70 166:0.73 169:0.72 172:0.81 173:0.49 176:0.59 177:0.70 179:0.46 182:0.76 186:0.95 187:0.39 190:0.77 192:0.49 195:0.63 196:0.90 199:0.97 201:0.57 202:0.36 204:0.84 208:0.54 212:0.70 216:0.66 219:0.94 220:0.96 222:0.25 223:0.97 224:0.98 227:0.83 229:0.61 231:0.67 232:0.72 234:0.98 235:0.97 241:0.65 242:0.16 243:0.93 246:0.61 247:0.84 248:0.50 253:0.57 254:0.88 255:0.69 256:0.45 259:0.21 261:0.92 265:0.93 266:0.54 267:0.59 268:0.46 271:0.79 274:0.97 276:0.70 279:0.77 281:0.86 282:0.73 284:0.88 285:0.56 287:0.61 290:0.63 292:0.88 300:0.55 +2 6:0.96 8:0.91 12:0.51 17:0.16 20:0.86 21:0.40 27:0.04 28:0.73 30:0.76 32:0.80 34:0.97 36:0.21 37:0.88 43:0.33 55:0.71 66:0.52 69:0.67 70:0.29 78:0.95 81:0.92 91:0.54 98:0.45 100:0.99 104:0.95 106:0.81 108:0.63 111:0.99 120:0.55 123:0.61 124:0.63 126:0.54 127:0.31 129:0.81 133:0.67 135:0.99 140:0.45 145:0.76 146:0.05 147:0.05 149:0.37 150:0.86 151:0.47 152:1.00 153:0.69 154:0.25 159:0.42 161:0.65 162:0.57 164:0.68 167:0.57 169:0.94 172:0.27 173:0.68 177:0.05 179:0.86 181:0.77 186:0.95 187:0.39 189:0.98 191:0.90 195:0.70 202:0.63 206:0.81 212:0.23 215:0.67 216:0.55 220:0.82 235:0.42 238:0.99 241:0.60 242:0.82 243:0.21 245:0.43 247:0.12 248:0.48 256:0.58 259:0.21 260:0.55 261:0.96 265:0.47 266:0.38 267:0.69 268:0.61 276:0.30 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.79 8:0.58 12:0.51 17:0.13 20:0.86 21:0.47 27:0.62 28:0.73 30:0.21 34:0.56 36:0.87 37:0.26 43:0.52 55:0.71 66:0.20 69:0.15 70:0.70 78:0.80 81:0.91 91:0.40 98:0.32 100:0.78 104:0.88 106:0.81 108:0.80 111:0.78 120:0.89 123:0.79 124:0.90 126:0.54 127:0.83 129:0.96 133:0.90 135:0.24 140:0.45 146:0.05 147:0.05 149:0.54 150:0.83 151:0.76 152:0.81 153:0.87 154:0.41 159:0.78 161:0.65 162:0.85 163:0.90 164:0.85 167:0.52 169:0.76 172:0.32 173:0.12 177:0.05 179:0.72 181:0.77 186:0.80 187:0.39 189:0.81 202:0.75 206:0.81 212:0.16 215:0.63 216:0.75 235:0.52 238:0.82 241:0.50 242:0.82 243:0.12 245:0.56 247:0.12 248:0.85 256:0.81 259:0.21 260:0.90 261:0.80 265:0.34 266:0.89 267:0.88 268:0.81 276:0.54 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.93 7:0.81 8:0.81 12:0.51 17:0.13 20:0.80 21:0.30 27:0.16 28:0.73 30:0.76 32:0.80 34:0.93 36:0.22 37:0.68 43:0.46 55:0.71 66:0.36 69:0.44 70:0.29 78:0.92 81:0.94 91:0.72 98:0.29 100:0.91 104:0.86 106:0.81 108:0.87 111:0.91 120:0.65 123:0.87 124:0.58 126:0.54 127:0.61 129:0.59 133:0.47 135:1.00 140:0.45 145:0.98 146:0.45 147:0.52 149:0.34 150:0.89 151:0.54 152:0.77 153:0.48 154:0.35 159:0.68 161:0.65 162:0.74 163:0.86 164:0.75 167:0.56 169:0.90 172:0.21 173:0.32 177:0.05 179:0.92 181:0.77 186:0.91 187:0.39 189:0.94 191:0.89 195:0.84 202:0.65 206:0.81 212:0.23 215:0.72 216:0.52 220:0.62 230:0.65 233:0.63 235:0.31 238:0.89 241:0.59 242:0.82 243:0.45 245:0.50 247:0.12 248:0.58 256:0.68 259:0.21 260:0.65 261:0.84 265:0.31 266:0.38 267:0.65 268:0.69 276:0.20 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.94 7:0.81 8:0.85 12:0.51 17:0.84 20:0.80 21:0.22 27:0.11 28:0.73 30:0.33 32:0.80 34:0.83 36:0.66 37:0.86 43:0.40 55:0.84 66:0.51 69:0.53 70:0.73 78:0.82 81:0.95 91:0.58 98:0.81 100:0.90 104:0.93 106:0.81 108:0.76 111:0.84 120:0.76 123:0.75 124:0.64 126:0.54 127:0.79 129:0.81 133:0.67 135:0.56 140:0.45 145:0.63 146:0.67 147:0.71 149:0.72 150:0.92 151:0.73 152:0.77 153:0.80 154:0.30 159:0.69 161:0.65 162:0.78 164:0.71 167:0.52 169:0.87 172:0.71 173:0.42 177:0.05 179:0.47 181:0.77 186:0.82 187:0.39 189:0.95 191:0.86 195:0.84 202:0.61 206:0.81 212:0.35 215:0.79 216:0.84 230:0.87 233:0.76 235:0.22 238:0.94 241:0.49 242:0.82 243:0.40 245:0.81 247:0.12 248:0.68 256:0.58 259:0.21 260:0.77 261:0.80 265:0.81 266:0.63 267:0.79 268:0.65 276:0.73 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.51 17:0.55 21:0.68 27:0.45 28:0.73 30:0.76 34:0.97 36:0.17 37:0.50 43:0.32 55:0.71 66:0.64 69:0.70 70:0.15 81:0.83 91:0.42 98:0.36 108:0.54 123:0.51 126:0.54 127:0.13 129:0.05 135:0.89 145:0.45 146:0.51 147:0.57 149:0.25 150:0.64 154:0.24 159:0.18 161:0.65 163:0.98 167:0.50 172:0.16 173:0.65 177:0.05 178:0.55 179:0.70 181:0.77 187:0.68 212:0.95 215:0.49 216:0.77 235:0.80 241:0.39 242:0.82 243:0.69 247:0.12 259:0.21 265:0.38 266:0.12 267:0.44 276:0.16 297:0.36 300:0.13 +2 6:0.94 8:0.83 12:0.51 17:0.78 20:0.85 21:0.68 27:0.18 28:0.73 30:0.76 34:0.86 36:0.55 37:0.42 43:0.51 55:0.84 66:0.85 69:0.29 70:0.27 78:0.96 81:0.83 91:0.80 98:0.99 100:0.97 104:0.58 108:0.22 111:0.96 120:0.79 123:0.22 126:0.54 127:0.90 129:0.05 135:0.75 140:0.45 145:0.45 146:0.86 147:0.88 149:0.56 150:0.64 151:0.66 152:0.81 153:0.48 154:0.40 159:0.42 161:0.65 162:0.73 163:0.81 164:0.80 167:0.80 169:0.96 172:0.57 173:0.39 177:0.05 179:0.81 181:0.77 186:0.96 189:0.95 191:0.91 202:0.72 212:0.22 215:0.49 216:0.09 220:0.62 235:0.80 238:0.95 241:0.95 242:0.82 243:0.86 247:0.12 248:0.72 256:0.75 259:0.21 260:0.80 261:0.92 265:0.99 266:0.54 267:0.69 268:0.76 276:0.47 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.79 7:0.81 8:0.58 12:0.51 17:0.61 20:0.89 25:0.88 27:0.12 28:0.73 32:0.80 34:0.61 36:0.38 37:0.23 78:0.97 81:0.98 91:0.91 100:0.97 104:0.58 111:0.96 120:0.75 126:0.54 135:0.37 140:0.45 145:0.98 150:0.97 151:0.71 152:0.84 153:0.72 161:0.65 162:0.81 164:0.73 167:0.80 169:0.95 175:0.75 181:0.77 186:0.97 189:0.82 191:0.90 195:0.52 202:0.61 215:0.96 216:0.13 230:0.87 233:0.76 235:0.02 238:0.91 241:0.90 244:0.61 248:0.68 256:0.64 257:0.65 259:0.21 260:0.76 261:0.94 267:0.28 268:0.66 277:0.69 285:0.62 297:0.36 +1 6:0.89 7:0.81 8:0.74 12:0.51 17:0.77 20:0.75 21:0.30 27:0.43 28:0.73 30:0.27 32:0.80 34:0.97 36:0.33 37:0.47 43:0.44 55:0.71 66:0.35 69:0.45 70:0.78 78:0.99 81:0.94 91:0.27 98:0.44 100:0.97 104:0.91 106:0.81 108:0.64 111:0.97 123:0.62 124:0.78 126:0.54 127:0.44 129:0.89 133:0.78 135:0.97 145:0.67 146:0.40 147:0.47 149:0.66 150:0.89 152:0.74 154:0.34 159:0.79 161:0.65 163:0.81 167:0.46 169:0.94 172:0.54 173:0.23 177:0.05 178:0.55 179:0.51 181:0.77 186:1.00 187:0.39 189:0.91 191:0.73 195:0.93 206:0.81 212:0.16 215:0.72 216:0.24 230:0.87 233:0.76 235:0.31 238:0.94 241:0.12 242:0.82 243:0.33 245:0.72 247:0.12 259:0.21 261:0.79 265:0.46 266:0.89 267:0.65 276:0.61 283:0.82 297:0.36 300:0.55 +1 12:0.51 17:0.22 21:0.30 27:0.45 28:0.73 30:0.62 32:0.80 34:0.74 36:0.18 37:0.50 43:0.32 55:0.59 66:0.47 69:0.69 70:0.23 81:0.94 91:0.12 98:0.38 108:0.52 123:0.50 124:0.52 126:0.54 127:0.20 129:0.59 133:0.47 135:0.92 145:0.39 146:0.49 147:0.55 149:0.35 150:0.89 154:0.24 159:0.31 161:0.65 163:0.87 167:0.47 172:0.21 173:0.67 177:0.05 178:0.55 179:0.77 181:0.77 187:0.68 195:0.73 212:0.30 215:0.72 216:0.77 235:0.31 241:0.18 242:0.82 243:0.59 245:0.46 247:0.12 259:0.21 265:0.41 266:0.27 267:0.36 276:0.26 283:0.60 297:0.36 300:0.18 +2 6:0.85 7:0.81 8:0.71 12:0.51 17:0.26 20:0.88 21:0.54 27:0.04 28:0.73 30:0.33 32:0.80 34:0.81 36:0.57 37:0.68 43:0.26 55:0.59 66:0.20 69:0.81 70:0.36 78:0.82 81:0.89 91:0.29 98:0.39 100:0.88 104:0.94 106:0.81 108:0.76 111:0.83 120:0.81 123:0.64 124:0.56 126:0.54 127:0.23 129:0.59 133:0.47 135:0.66 140:0.45 145:0.67 146:0.43 147:0.50 149:0.36 150:0.79 151:0.74 152:0.84 153:0.82 154:0.19 159:0.31 161:0.65 162:0.73 163:0.75 164:0.79 167:0.62 169:0.85 172:0.27 173:0.87 177:0.05 179:0.74 181:0.77 186:0.83 187:0.39 189:0.87 191:0.81 195:0.71 202:0.70 206:0.81 212:0.19 215:0.58 216:0.80 230:0.87 233:0.76 235:0.60 238:0.91 241:0.66 242:0.82 243:0.40 245:0.53 247:0.12 248:0.73 256:0.71 259:0.21 260:0.82 261:0.85 265:0.40 266:0.47 267:0.69 268:0.74 276:0.34 283:0.82 285:0.62 297:0.36 300:0.25 +1 7:0.81 8:0.81 12:0.51 17:0.87 20:0.75 21:0.30 25:0.88 27:0.56 28:0.73 30:0.21 32:0.80 34:0.23 36:0.47 43:0.29 55:0.59 66:0.72 69:0.75 70:0.78 78:0.94 81:0.94 91:0.62 98:0.58 100:0.86 104:0.54 108:0.59 111:0.91 120:0.77 123:0.56 124:0.44 126:0.54 127:0.43 129:0.81 133:0.67 135:0.86 140:0.45 145:0.88 146:0.79 147:0.83 149:0.62 150:0.89 151:0.73 152:0.74 153:0.78 154:0.21 159:0.64 161:0.65 162:0.86 164:0.74 167:0.77 169:0.84 172:0.67 173:0.67 177:0.05 179:0.55 181:0.77 186:0.94 191:0.73 195:0.84 202:0.60 212:0.49 215:0.72 216:0.19 230:0.87 233:0.76 235:0.31 241:0.79 242:0.82 243:0.75 245:0.61 247:0.12 248:0.69 256:0.64 259:0.21 260:0.77 261:0.76 265:0.59 266:0.75 267:0.96 268:0.68 276:0.56 283:0.82 285:0.62 297:0.36 300:0.55 +1 6:0.98 7:0.81 8:0.96 12:0.51 17:0.76 20:0.87 21:0.64 27:0.17 28:0.73 30:0.60 32:0.80 34:0.68 36:0.31 37:0.97 43:0.36 55:0.71 66:0.77 69:0.63 70:0.66 78:0.94 81:0.85 91:0.82 98:0.78 100:0.96 104:0.58 108:0.48 111:0.94 123:0.46 124:0.40 126:0.54 127:0.64 129:0.81 133:0.67 135:0.54 140:0.45 145:0.96 146:0.85 147:0.88 149:0.66 150:0.68 152:0.82 153:0.48 154:0.27 159:0.57 161:0.65 162:0.86 164:0.67 167:0.79 169:0.94 172:0.70 173:0.59 177:0.05 179:0.60 181:0.77 186:0.93 189:0.99 191:0.87 195:0.79 202:0.50 212:0.48 215:0.53 216:0.07 220:0.99 230:0.87 233:0.76 235:0.74 238:0.94 241:0.88 242:0.82 243:0.79 245:0.57 247:0.12 256:0.58 259:0.21 261:0.92 265:0.78 266:0.47 267:0.59 268:0.59 276:0.61 283:0.60 285:0.62 297:0.36 300:0.70 +0 7:0.66 12:0.01 17:0.98 18:0.66 21:0.68 27:0.72 29:0.62 30:0.90 32:0.64 34:0.56 36:0.30 39:0.82 43:0.17 48:0.91 55:0.71 64:0.77 66:0.86 69:0.39 70:0.24 71:0.56 74:0.33 81:0.30 86:0.70 89:1.00 91:0.67 98:0.91 104:0.58 108:0.30 123:0.29 126:0.44 127:0.49 129:0.05 135:0.32 139:0.71 141:0.91 145:0.52 146:0.73 147:0.77 149:0.24 150:0.25 154:0.54 159:0.30 172:0.59 173:0.54 177:0.70 178:0.55 179:0.72 192:0.51 195:0.60 201:0.68 212:0.84 215:0.37 216:0.01 222:0.76 223:0.95 225:1.00 230:0.69 233:0.76 234:0.91 235:0.88 240:0.95 241:0.77 242:0.72 243:0.86 244:0.61 247:0.55 253:0.28 254:0.90 259:0.21 265:0.91 266:0.27 267:0.23 271:0.57 274:0.91 276:0.48 281:0.89 297:0.36 300:0.25 +0 12:0.01 17:0.66 21:0.78 25:0.88 27:0.34 29:0.62 30:0.15 34:0.58 36:0.66 37:0.74 39:0.82 43:0.18 55:0.59 66:0.27 69:0.19 70:0.97 71:0.56 74:0.44 89:0.99 91:0.01 98:0.15 104:0.58 108:0.83 123:0.82 124:0.91 127:0.97 129:0.81 133:0.91 135:0.58 141:0.91 146:0.47 147:0.53 149:0.19 154:0.46 159:0.95 172:0.41 173:0.06 175:0.75 177:0.38 178:0.55 179:0.70 187:0.95 212:0.48 216:0.72 222:0.76 223:0.93 225:0.97 234:0.91 235:0.52 240:0.95 241:0.05 242:0.51 243:0.29 244:0.61 245:0.56 247:0.60 253:0.33 254:0.80 257:0.65 259:0.21 265:0.17 266:0.75 267:0.23 271:0.57 274:0.91 276:0.55 277:0.69 300:0.84 +2 7:0.72 11:0.64 12:0.01 17:0.90 21:0.71 27:0.38 29:0.62 30:0.38 32:0.69 34:0.80 36:0.86 37:0.72 39:0.82 43:0.67 45:0.77 55:0.59 66:0.36 69:0.51 70:0.83 71:0.56 74:0.76 76:0.99 77:0.70 81:0.23 89:0.99 91:0.04 98:0.62 101:0.52 108:0.39 114:0.54 122:0.55 123:0.37 124:0.60 126:0.26 127:0.50 129:0.59 133:0.47 135:0.42 141:0.91 145:0.79 146:0.61 147:0.67 149:0.56 150:0.23 154:0.56 155:0.58 159:0.42 172:0.51 173:0.55 175:0.75 176:0.61 177:0.38 178:0.55 179:0.58 187:0.39 192:0.59 195:0.70 204:0.85 208:0.54 212:0.68 216:0.46 222:0.76 223:0.94 225:0.98 230:0.74 232:0.74 233:0.73 234:0.91 235:0.88 240:0.95 241:0.32 242:0.27 243:0.51 244:0.61 245:0.79 247:0.60 253:0.43 257:0.65 259:0.21 265:0.63 266:0.54 267:0.28 271:0.57 274:0.91 276:0.59 277:0.69 282:0.77 290:0.54 300:0.70 +0 8:0.85 12:0.01 17:0.78 21:0.30 27:0.51 29:0.62 30:0.33 34:0.47 36:0.53 37:0.55 39:0.82 43:0.18 55:0.92 66:0.54 69:0.15 70:0.69 71:0.56 74:0.56 76:0.98 77:0.96 81:0.33 89:0.97 91:0.89 96:0.76 98:0.61 108:0.12 114:0.63 122:0.77 123:0.12 124:0.48 126:0.26 127:0.89 129:0.05 133:0.39 135:0.68 140:0.45 141:0.91 145:0.72 146:0.44 147:0.51 149:0.22 150:0.30 151:0.57 153:0.72 154:0.12 159:0.31 162:0.67 172:0.32 173:0.38 175:0.75 176:0.61 177:0.38 179:0.92 190:0.89 195:0.59 202:0.53 212:0.42 216:0.08 220:0.62 222:0.76 223:0.92 225:0.96 232:0.79 234:0.91 235:0.42 240:0.95 241:0.95 242:0.45 243:0.63 244:0.83 245:0.50 247:0.47 248:0.55 253:0.59 256:0.45 257:0.65 259:0.21 265:0.62 266:0.38 267:0.44 268:0.57 271:0.57 274:0.91 276:0.33 277:0.69 282:0.73 285:0.47 290:0.63 300:0.43 +0 8:0.75 11:0.64 12:0.01 17:0.64 21:0.71 27:0.54 29:0.62 30:0.75 32:0.64 34:0.92 36:0.48 37:0.62 39:0.82 43:0.21 45:0.66 55:0.59 66:0.35 69:0.97 70:0.64 71:0.56 74:0.37 81:0.23 89:1.00 91:0.10 98:0.46 101:0.52 104:0.91 106:0.81 108:0.97 120:0.68 123:0.97 124:0.95 126:0.26 127:0.93 129:0.05 133:0.96 135:0.42 140:0.80 141:0.98 145:0.40 146:0.71 147:0.72 149:0.51 150:0.23 151:0.57 153:0.72 154:0.14 159:0.94 162:0.75 164:0.70 172:0.93 173:0.85 175:0.75 177:0.05 178:0.42 179:0.15 187:0.39 190:0.89 195:0.99 202:0.68 206:0.81 212:0.78 215:0.37 216:0.80 220:0.62 222:0.76 223:0.96 225:1.00 234:0.99 235:0.88 240:1.00 241:0.41 242:0.81 243:0.15 244:0.61 245:0.93 247:0.60 248:0.58 253:0.30 256:0.71 257:0.84 259:0.21 260:0.65 265:0.48 266:0.97 267:0.28 268:0.72 271:0.57 274:0.99 276:0.95 277:0.87 283:0.78 285:0.47 297:0.36 300:0.94 +2 11:0.64 12:0.01 17:0.45 18:0.65 21:0.05 27:0.39 29:0.62 30:0.91 34:0.71 36:0.99 37:0.86 39:0.82 43:0.64 45:0.66 55:0.84 66:0.69 69:0.36 70:0.13 71:0.56 74:0.40 81:0.39 86:0.68 89:0.96 91:0.25 98:0.80 101:0.52 108:0.28 114:0.81 122:0.55 123:0.27 126:0.26 127:0.29 129:0.05 135:0.23 139:0.66 141:0.91 146:0.49 147:0.55 149:0.34 150:0.34 154:0.53 159:0.18 163:0.86 172:0.21 173:0.65 176:0.61 177:0.70 178:0.55 179:0.95 187:0.39 212:0.61 216:0.19 222:0.76 223:0.92 225:0.96 232:0.76 234:0.91 235:0.05 240:0.95 241:0.59 242:0.63 243:0.73 244:0.61 247:0.30 253:0.56 259:0.21 265:0.80 266:0.17 267:0.79 271:0.57 274:0.91 276:0.20 290:0.81 300:0.13 +0 8:0.95 12:0.01 17:0.02 21:0.40 25:0.88 27:0.44 29:0.62 30:0.95 34:0.53 36:0.55 37:0.65 39:0.82 43:0.17 55:0.84 66:0.54 69:0.39 71:0.56 74:0.34 81:0.28 89:0.96 91:0.38 98:0.79 108:0.30 123:0.29 126:0.26 127:0.27 129:0.05 135:0.37 141:0.91 145:0.38 146:0.38 147:0.45 149:0.10 150:0.27 154:0.11 159:0.15 172:0.10 173:0.76 175:0.75 177:0.38 178:0.55 179:0.99 187:0.39 215:0.42 216:0.45 222:0.76 223:0.95 225:1.00 234:0.91 235:0.42 240:0.95 241:0.68 243:0.63 244:0.83 253:0.28 257:0.65 259:0.21 265:0.79 267:0.28 271:0.57 274:0.91 277:0.69 297:0.36 300:0.08 +0 11:0.64 12:0.01 17:0.09 18:0.80 21:0.78 27:0.27 29:0.62 30:0.27 34:0.26 36:0.96 37:0.74 39:0.82 43:0.69 45:0.81 55:0.71 66:0.13 69:0.46 70:0.79 71:0.56 74:0.74 86:0.88 89:0.99 91:0.07 98:0.53 101:0.52 108:0.35 122:0.80 123:0.70 124:0.64 127:0.53 129:0.59 133:0.61 135:0.66 139:0.84 141:0.91 146:0.63 147:0.68 149:0.49 154:0.78 159:0.57 172:0.63 173:0.40 177:0.70 178:0.55 179:0.54 187:0.95 202:0.36 212:0.73 216:0.63 222:0.76 223:0.93 225:0.97 234:0.91 235:0.12 240:0.95 241:0.55 242:0.16 243:0.62 245:0.74 247:0.88 253:0.42 254:0.84 259:0.21 265:0.52 266:0.71 267:0.59 271:0.57 274:0.91 276:0.62 300:0.55 +1 7:0.72 11:0.64 12:0.01 17:0.73 21:0.22 27:0.38 29:0.62 30:0.37 32:0.69 34:0.34 36:0.54 37:0.72 39:0.82 43:0.67 45:0.83 55:0.71 66:0.10 69:0.53 70:0.82 71:0.56 74:0.80 76:0.99 77:0.70 81:0.31 89:0.99 91:0.04 98:0.37 101:0.52 108:0.94 114:0.66 122:0.77 123:0.93 124:0.94 126:0.26 127:0.91 129:0.89 133:0.93 135:0.42 141:0.91 145:0.79 146:0.44 147:0.50 149:0.60 150:0.29 154:0.57 155:0.58 159:0.89 172:0.51 173:0.24 175:0.75 176:0.61 177:0.38 178:0.55 179:0.30 187:0.39 192:0.59 195:0.97 204:0.85 208:0.54 212:0.29 216:0.46 222:0.76 223:0.93 225:0.97 230:0.74 232:0.74 233:0.73 234:0.91 235:0.22 240:0.95 241:0.47 242:0.76 243:0.16 244:0.61 245:0.84 247:0.60 253:0.52 257:0.65 259:0.21 265:0.39 266:0.94 267:0.36 271:0.57 274:0.91 276:0.85 277:0.87 282:0.77 290:0.66 300:0.84 +2 11:0.64 12:0.01 17:0.09 18:0.79 21:0.78 27:0.27 29:0.62 30:0.27 34:0.24 36:0.95 37:0.74 39:0.82 43:0.69 45:0.81 55:0.71 66:0.13 69:0.52 70:0.79 71:0.56 74:0.74 86:0.87 89:0.99 91:0.07 98:0.53 101:0.52 108:0.40 122:0.80 123:0.70 124:0.64 127:0.54 129:0.59 133:0.61 135:0.51 139:0.83 141:0.91 146:0.63 147:0.68 149:0.48 154:0.76 159:0.57 172:0.63 173:0.46 177:0.70 178:0.55 179:0.54 187:0.39 212:0.73 216:0.63 222:0.76 223:0.93 225:0.97 234:0.91 235:0.12 240:0.95 241:0.65 242:0.16 243:0.62 245:0.74 247:0.88 253:0.42 254:0.84 259:0.21 265:0.52 266:0.71 267:0.59 271:0.57 274:0.91 276:0.62 300:0.55 +0 11:0.64 12:0.01 17:0.34 18:0.60 21:0.13 27:0.61 29:0.62 30:0.56 34:0.85 36:0.60 39:0.82 43:0.14 45:0.66 55:0.84 66:0.33 69:0.84 70:0.32 71:0.56 74:0.58 81:0.35 86:0.70 89:0.96 91:0.51 98:0.58 101:0.52 108:0.44 114:0.72 117:0.86 122:0.77 123:0.68 124:0.61 126:0.26 127:0.29 129:0.05 133:0.37 135:0.24 139:0.67 141:0.91 146:0.70 147:0.67 149:0.20 150:0.31 154:0.54 159:0.46 163:0.94 172:0.32 173:0.85 176:0.61 177:0.05 178:0.55 179:0.67 187:0.87 212:0.39 216:0.54 222:0.76 223:0.91 225:0.96 232:0.88 234:0.91 235:0.12 240:0.95 241:0.56 242:0.73 243:0.50 245:0.64 247:0.47 253:0.53 254:0.95 257:0.84 259:0.21 265:0.50 266:0.38 267:0.76 271:0.57 274:0.91 276:0.45 290:0.72 300:0.25 +1 8:0.96 9:0.76 12:0.01 17:0.32 21:0.13 27:0.26 29:0.62 30:0.45 34:0.55 36:0.74 37:0.85 39:0.82 43:0.16 55:0.59 66:0.69 69:0.45 70:0.25 71:0.56 74:0.49 81:0.35 89:0.95 91:0.99 96:0.94 98:0.72 108:0.35 114:0.72 120:0.68 122:0.77 123:0.34 126:0.26 127:0.22 129:0.05 135:0.24 140:0.97 141:0.98 146:0.41 147:0.48 149:0.14 150:0.31 151:0.61 153:0.80 154:0.11 155:0.58 158:0.74 159:0.15 162:0.74 163:0.81 164:0.78 172:0.21 173:0.74 175:0.91 176:0.61 177:0.05 179:0.92 190:0.89 191:0.94 192:0.59 202:0.92 208:0.54 212:0.61 216:0.53 220:0.74 222:0.76 223:0.96 225:1.00 234:0.97 235:0.12 240:1.00 241:0.98 242:0.82 243:0.73 244:0.83 247:0.12 248:0.72 253:0.87 255:0.74 256:0.94 257:0.84 259:0.21 260:0.67 265:0.72 266:0.17 267:0.52 268:0.95 271:0.57 274:0.99 276:0.21 277:0.87 285:0.56 290:0.72 300:0.18 +0 7:0.66 12:0.01 17:0.98 18:0.66 21:0.68 27:0.72 29:0.62 30:0.90 32:0.64 34:0.49 36:0.28 39:0.82 43:0.17 48:0.91 55:0.71 64:0.77 66:0.86 69:0.39 70:0.24 71:0.56 74:0.33 81:0.30 86:0.70 89:1.00 91:0.67 98:0.91 104:0.58 108:0.30 123:0.29 126:0.44 127:0.49 129:0.05 135:0.37 139:0.71 141:0.91 145:0.52 146:0.73 147:0.77 149:0.24 150:0.25 154:0.54 159:0.30 172:0.59 173:0.54 177:0.70 178:0.55 179:0.72 192:0.51 195:0.60 201:0.68 212:0.84 215:0.37 216:0.01 222:0.76 223:0.95 225:1.00 230:0.69 233:0.76 234:0.91 235:0.88 240:0.95 241:0.77 242:0.72 243:0.86 244:0.61 247:0.55 253:0.28 254:0.90 259:0.21 265:0.91 266:0.27 267:0.23 271:0.57 274:0.91 276:0.48 281:0.89 297:0.36 300:0.25 +0 8:0.78 12:0.01 17:0.06 21:0.40 25:0.88 27:0.44 29:0.62 30:0.76 34:0.34 36:0.68 37:0.65 39:0.82 43:0.17 55:0.92 66:0.36 69:0.39 70:0.22 71:0.56 74:0.44 81:0.28 89:0.96 91:0.60 98:0.27 108:0.53 122:0.77 123:0.51 124:0.67 126:0.26 127:0.66 129:0.81 133:0.67 135:0.34 141:0.91 145:0.38 146:0.05 147:0.05 149:0.16 150:0.27 154:0.11 159:0.26 163:0.75 172:0.16 173:0.61 175:0.91 177:0.05 178:0.55 179:0.96 187:0.39 202:0.46 212:0.42 215:0.42 216:0.45 222:0.76 223:0.95 225:1.00 234:0.91 235:0.42 240:0.95 241:0.73 242:0.75 243:0.26 244:0.83 245:0.40 247:0.30 253:0.33 257:0.84 259:0.21 265:0.29 266:0.23 267:0.23 271:0.57 274:0.91 276:0.23 277:0.87 297:0.36 300:0.18 +1 7:0.72 11:0.64 12:0.01 17:0.78 21:0.54 27:0.38 29:0.62 30:0.54 32:0.69 34:0.39 36:0.68 37:0.72 39:0.82 43:0.22 45:0.73 55:0.71 66:0.51 69:0.92 70:0.78 71:0.56 74:0.70 76:0.99 77:0.70 81:0.24 89:0.98 91:0.18 98:0.63 101:0.52 108:0.84 114:0.58 122:0.77 123:0.83 124:0.70 126:0.26 127:0.82 129:0.59 133:0.76 135:0.44 141:0.91 145:0.79 146:0.88 147:0.05 149:0.34 150:0.24 154:0.16 155:0.58 159:0.79 172:0.70 173:0.88 175:0.75 176:0.61 177:0.05 178:0.55 179:0.49 187:0.39 192:0.59 195:0.91 204:0.85 208:0.54 212:0.35 216:0.46 222:0.76 223:0.93 225:0.97 230:0.74 232:0.74 233:0.73 234:0.91 235:0.60 240:0.95 241:0.32 242:0.76 243:0.08 244:0.61 245:0.76 247:0.60 253:0.44 257:0.84 259:0.21 265:0.64 266:0.80 267:0.59 271:0.57 274:0.91 276:0.69 277:0.69 282:0.77 290:0.58 300:0.84 +0 1:0.58 9:0.71 10:0.88 12:0.79 17:0.62 18:0.66 21:0.47 23:0.95 27:0.57 29:0.77 30:0.33 31:0.89 34:0.82 36:0.31 37:0.54 39:0.46 43:0.08 46:0.88 55:0.84 62:0.96 64:0.77 66:0.54 69:0.32 70:0.49 71:0.84 74:0.29 79:0.89 81:0.47 83:0.56 86:0.66 91:0.22 98:0.53 107:0.89 108:0.25 110:0.90 122:0.95 123:0.24 124:0.55 125:0.88 126:0.51 127:0.53 128:0.96 129:0.05 133:0.59 135:0.65 137:0.89 139:0.64 140:0.45 146:0.59 147:0.64 149:0.09 150:0.45 151:0.61 153:0.48 154:0.35 155:0.54 159:0.49 160:0.88 162:0.86 168:0.96 170:0.77 172:0.32 173:0.33 174:0.89 175:0.75 177:0.70 179:0.89 187:0.39 190:0.98 192:0.49 196:0.88 197:0.93 201:0.60 202:0.36 205:0.95 208:0.50 212:0.19 215:0.63 216:0.44 220:0.82 222:0.35 235:0.74 236:0.92 239:0.87 241:0.61 242:0.45 243:0.63 244:0.93 245:0.45 247:0.47 248:0.59 253:0.26 254:0.84 255:0.70 256:0.45 257:0.65 259:0.21 265:0.54 266:0.47 267:0.69 268:0.46 271:0.50 276:0.33 277:0.69 284:0.88 285:0.50 289:0.92 297:0.36 300:0.33 +0 8:0.69 10:0.81 12:0.79 17:0.66 18:0.60 21:0.64 25:0.88 27:0.70 29:0.77 30:0.45 34:0.33 36:0.70 37:0.37 39:0.46 43:0.06 46:0.88 55:0.52 66:0.27 69:0.86 70:0.25 71:0.84 74:0.27 81:0.26 86:0.60 91:0.20 98:0.11 104:0.58 107:0.89 108:0.93 110:0.89 122:0.86 123:0.92 124:0.72 125:0.88 126:0.24 127:0.42 129:0.81 133:0.67 135:0.89 139:0.58 145:0.93 146:0.05 147:0.05 149:0.06 150:0.28 154:0.06 159:0.75 160:0.88 163:0.99 170:0.77 172:0.10 173:0.75 174:0.79 175:0.97 177:0.05 178:0.55 179:0.97 187:0.68 197:0.82 202:0.36 212:0.61 215:0.53 216:0.38 222:0.35 235:0.74 239:0.81 241:0.07 242:0.63 243:0.29 244:0.98 245:0.38 247:0.30 253:0.25 257:0.93 259:0.21 265:0.12 266:0.17 267:0.44 271:0.50 276:0.17 277:0.95 289:0.90 297:0.36 300:0.18 +2 1:0.68 7:0.65 10:0.85 11:0.51 12:0.79 17:0.61 18:0.85 21:0.30 27:0.60 29:0.77 30:0.38 32:0.64 34:0.85 36:0.50 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 64:0.77 66:0.77 69:0.40 70:0.78 71:0.84 74:0.46 81:0.71 86:0.77 91:0.44 98:0.87 99:0.83 101:0.47 102:0.94 107:0.95 108:0.31 110:0.96 114:0.82 120:0.69 122:0.75 123:0.30 124:0.40 125:0.88 126:0.54 127:0.71 129:0.59 132:0.97 133:0.46 135:0.28 139:0.73 140:0.45 145:0.41 146:0.89 147:0.91 149:0.09 150:0.61 151:0.65 153:0.48 154:0.30 155:0.52 159:0.55 160:0.88 162:0.86 163:0.75 164:0.55 165:0.65 170:0.77 172:0.76 173:0.38 174:0.79 175:0.75 176:0.70 177:0.70 179:0.52 187:0.39 190:0.99 192:0.51 193:0.95 195:0.75 197:0.81 201:0.67 202:0.36 208:0.64 212:0.29 215:0.67 216:0.20 222:0.35 230:0.68 233:0.76 235:0.88 236:0.95 239:0.87 241:0.65 242:0.38 243:0.79 244:0.61 245:0.74 247:0.67 248:0.62 253:0.58 254:0.74 256:0.45 257:0.65 260:0.69 265:0.87 266:0.54 267:0.28 268:0.46 271:0.50 276:0.67 277:0.69 283:0.82 285:0.46 289:0.95 290:0.82 297:0.36 300:0.84 +0 7:0.65 10:0.87 11:0.54 12:0.79 17:0.82 18:0.94 21:0.74 27:0.61 29:0.77 30:0.09 32:0.64 34:0.85 36:0.42 37:0.45 39:0.46 43:0.09 45:0.81 46:0.88 55:0.92 66:0.05 69:0.97 70:1.00 71:0.84 74:0.34 81:0.25 86:0.80 91:0.01 98:0.16 101:0.65 107:0.95 108:1.00 110:0.96 122:0.92 123:1.00 124:1.00 125:0.88 126:0.24 127:0.99 129:0.93 133:1.00 135:1.00 139:0.77 145:0.56 146:0.79 147:0.66 149:0.24 150:0.27 154:0.06 159:0.99 160:0.88 170:0.77 172:0.57 173:0.58 174:0.96 177:0.38 178:0.55 179:0.11 187:0.21 195:1.00 197:0.90 212:0.15 215:0.46 216:0.17 222:0.35 230:0.67 233:0.66 235:0.88 239:0.85 241:0.10 242:0.52 243:0.10 244:0.61 245:0.92 247:0.99 253:0.31 254:0.88 257:0.84 259:0.21 265:0.14 266:1.00 267:0.73 271:0.50 276:0.98 277:0.87 289:0.93 297:0.36 300:0.98 +2 1:0.63 7:0.65 10:0.85 11:0.51 12:0.79 17:0.75 18:0.73 21:0.30 23:0.95 27:0.60 29:0.77 30:0.09 32:0.64 34:0.85 36:0.95 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 62:0.95 64:0.77 66:0.19 69:0.91 70:0.95 71:0.84 74:0.35 81:0.59 86:0.70 91:0.46 98:0.39 99:0.83 101:0.47 102:0.94 107:0.93 108:0.74 110:0.94 114:0.84 123:0.72 124:0.82 125:0.88 126:0.51 127:0.70 128:0.95 129:0.05 132:0.97 133:0.73 135:0.61 139:0.67 140:0.45 145:0.41 146:0.19 147:0.38 149:0.11 150:0.51 151:0.50 153:0.48 154:0.35 155:0.49 159:0.52 160:0.88 162:0.86 163:0.81 165:0.65 168:0.95 170:0.77 172:0.21 173:0.99 174:0.79 175:0.75 176:0.70 177:0.05 179:0.81 187:0.39 190:0.99 192:0.46 193:0.95 195:0.73 197:0.81 201:0.62 202:0.36 205:0.95 208:0.61 212:0.23 216:0.20 220:0.91 222:0.35 230:0.68 233:0.76 235:0.74 236:0.95 239:0.87 241:0.67 242:0.42 243:0.33 244:0.61 245:0.55 247:0.67 248:0.50 253:0.59 254:0.74 256:0.45 257:0.93 265:0.41 266:0.54 267:0.52 268:0.46 271:0.50 276:0.42 277:0.87 285:0.46 289:0.94 290:0.84 300:0.70 +1 10:0.85 12:0.79 17:0.67 18:0.89 21:0.59 27:0.43 29:0.77 30:0.09 34:0.98 36:0.58 37:0.78 39:0.46 43:0.08 46:0.88 55:0.71 66:0.26 69:0.74 70:0.94 71:0.84 74:0.41 76:0.94 77:0.70 81:0.34 86:0.77 91:0.20 98:0.47 102:0.94 107:0.94 108:0.58 110:0.95 114:0.67 122:0.65 123:0.55 124:0.83 125:0.88 126:0.32 127:0.56 129:0.59 133:0.81 135:0.68 139:0.73 145:0.61 146:0.71 147:0.05 149:0.11 150:0.34 154:0.11 159:0.70 160:0.88 170:0.77 172:0.51 173:0.64 174:0.89 176:0.60 177:0.05 178:0.55 179:0.49 187:0.98 192:0.44 193:0.95 195:0.86 197:0.88 201:0.55 212:0.48 216:0.60 222:0.35 235:0.80 236:0.92 239:0.87 241:0.15 242:0.14 243:0.09 244:0.93 245:0.73 247:0.94 253:0.52 254:0.95 257:0.93 259:0.21 265:0.49 266:0.84 267:0.76 271:0.50 276:0.68 282:0.73 289:0.93 290:0.67 300:0.70 +0 1:0.59 10:0.88 12:0.79 17:0.57 18:0.67 21:0.30 23:0.95 27:0.57 29:0.77 30:0.33 34:0.83 36:0.33 37:0.54 39:0.46 43:0.56 46:0.88 62:0.96 64:0.77 66:0.12 69:0.30 70:0.36 71:0.84 74:0.29 81:0.51 83:0.56 86:0.66 91:0.20 98:0.28 107:0.89 108:0.24 110:0.90 120:0.62 122:0.95 123:0.54 124:0.78 125:0.88 126:0.51 127:0.61 128:0.96 129:0.05 133:0.73 135:0.66 139:0.64 140:0.45 146:0.31 147:0.37 149:0.33 150:0.49 151:0.65 153:0.48 154:0.45 155:0.47 159:0.50 160:0.88 162:0.86 164:0.55 168:0.96 170:0.77 172:0.21 173:0.31 174:0.89 175:0.75 177:0.70 179:0.90 187:0.39 190:0.98 192:0.50 197:0.93 201:0.62 202:0.36 205:0.95 208:0.50 212:0.19 215:0.72 216:0.44 220:0.74 222:0.35 235:0.60 236:0.92 239:0.87 241:0.60 242:0.45 243:0.49 244:0.93 245:0.45 247:0.47 248:0.63 253:0.26 255:0.70 256:0.45 257:0.65 259:0.21 260:0.62 265:0.26 266:0.47 267:0.36 268:0.46 271:0.50 276:0.33 277:0.95 285:0.50 289:0.92 297:0.36 300:0.25 +2 1:0.65 7:0.65 8:0.85 9:0.75 10:0.87 11:0.51 12:0.79 17:0.62 18:0.74 21:0.13 23:0.98 27:0.58 29:0.77 30:0.30 31:0.98 32:0.64 34:0.31 36:0.71 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 62:0.97 64:0.77 66:0.51 69:0.35 70:0.92 71:0.84 74:0.41 79:0.98 81:0.64 86:0.72 91:0.82 96:0.91 98:0.61 99:0.83 101:0.47 102:0.94 107:0.93 108:0.27 110:0.94 114:0.92 120:0.64 123:0.26 124:0.52 125:0.88 126:0.51 127:0.79 128:0.98 129:0.59 133:0.46 135:0.47 137:0.98 139:0.71 140:0.97 145:0.41 146:0.49 147:0.55 149:0.09 150:0.56 151:0.76 153:0.86 154:0.49 155:0.64 158:0.73 159:0.35 160:0.88 162:0.57 164:0.65 165:0.65 168:0.98 170:0.77 172:0.41 173:0.50 174:0.83 175:0.75 176:0.70 177:0.70 178:0.42 179:0.82 190:0.77 191:0.70 192:0.97 193:0.95 195:0.61 196:0.93 197:0.86 201:0.63 202:0.82 205:0.98 208:0.61 212:0.39 216:0.27 219:0.88 220:0.62 222:0.35 230:0.68 233:0.76 235:0.60 236:0.95 239:0.88 241:0.85 242:0.13 243:0.61 244:0.61 245:0.63 247:0.76 248:0.75 253:0.87 254:0.74 255:0.78 256:0.81 257:0.65 260:0.65 265:0.62 266:0.54 267:0.19 268:0.82 271:0.50 276:0.41 277:0.69 279:0.75 284:0.98 285:0.62 289:0.95 290:0.92 292:0.88 300:0.70 +0 8:0.72 10:0.81 12:0.79 17:0.73 18:0.78 21:0.78 22:0.93 27:0.43 29:0.77 30:0.68 34:0.82 36:0.29 37:0.84 39:0.46 43:0.08 46:0.88 47:0.93 55:0.71 66:0.79 69:0.56 70:0.43 71:0.84 74:0.30 86:0.70 91:0.44 98:0.77 107:0.91 108:0.43 110:0.92 122:0.75 123:0.41 125:0.88 127:0.26 129:0.05 135:0.47 139:0.68 146:0.68 147:0.73 149:0.09 154:0.15 159:0.26 160:0.88 163:0.81 170:0.77 172:0.41 173:0.66 174:0.79 177:0.88 178:0.55 179:0.77 187:0.39 197:0.82 212:0.42 216:0.29 219:0.88 222:0.35 235:0.22 239:0.81 241:0.18 242:0.19 243:0.80 244:0.93 247:0.55 253:0.27 254:0.99 259:0.21 262:0.93 265:0.77 266:0.38 267:0.19 271:0.50 276:0.34 289:0.90 292:0.88 300:0.33 +0 1:0.57 8:0.72 9:0.71 10:0.82 11:0.42 12:0.79 17:0.47 18:0.96 21:0.64 27:0.43 29:0.77 30:0.55 31:0.90 34:0.25 36:0.49 37:0.52 39:0.46 43:0.21 44:0.86 45:0.66 46:0.88 48:0.91 55:0.52 64:0.77 66:0.20 69:0.25 70:0.52 71:0.84 74:0.35 76:0.85 79:0.89 81:0.33 83:0.56 86:0.83 91:0.89 96:0.79 98:0.69 101:0.47 107:0.92 108:0.73 110:0.93 114:0.66 122:0.92 123:0.19 124:0.55 125:0.88 126:0.32 127:0.95 129:0.59 133:0.46 135:0.49 137:0.90 139:0.81 140:0.94 145:0.80 146:0.65 147:0.70 149:0.24 150:0.37 151:0.56 153:0.48 154:0.14 155:0.54 159:0.46 160:0.88 162:0.48 170:0.77 172:0.65 173:0.34 174:0.79 175:0.75 176:0.60 177:0.70 178:0.53 179:0.58 190:0.99 191:0.86 192:0.51 195:0.68 196:0.91 197:0.81 201:0.54 202:0.90 208:0.50 212:0.87 216:0.28 220:0.91 222:0.35 235:0.80 239:0.81 241:0.94 242:0.75 243:0.40 244:0.97 245:0.83 247:0.74 248:0.61 253:0.70 255:0.70 256:0.78 257:0.65 259:0.21 265:0.58 266:0.47 267:0.23 268:0.79 271:0.50 276:0.64 277:0.69 281:0.86 284:0.97 285:0.50 289:0.93 290:0.66 300:0.55 +1 1:0.59 7:0.66 10:0.84 12:0.79 17:0.69 18:0.96 21:0.64 22:0.93 27:0.39 29:0.77 30:0.21 31:0.87 32:0.64 33:0.97 34:0.52 36:0.35 37:0.67 39:0.46 43:0.06 44:0.88 46:0.88 47:0.93 48:0.97 64:0.77 66:0.12 69:0.90 70:0.97 71:0.84 74:0.57 76:0.85 77:0.89 79:0.87 81:0.42 86:0.87 91:0.07 98:0.27 102:0.95 104:1.00 106:0.81 107:0.96 108:0.98 110:0.97 114:0.64 117:0.86 122:0.82 123:0.84 124:0.97 125:0.88 126:0.51 127:0.86 129:0.81 133:0.97 135:0.99 137:0.87 139:0.85 140:0.45 145:0.84 146:0.49 147:0.23 149:0.10 150:0.44 151:0.50 153:0.48 154:0.16 155:0.53 159:0.95 160:0.88 162:0.86 170:0.77 172:0.90 173:0.57 174:0.92 176:0.60 177:0.70 179:0.14 187:0.21 190:0.99 192:0.56 193:0.95 195:0.99 196:0.90 197:0.85 201:0.57 202:0.36 204:0.82 206:0.81 208:0.61 212:0.68 215:0.49 216:0.89 219:0.88 220:0.91 222:0.35 230:0.68 233:0.65 235:0.88 239:0.87 241:0.02 242:0.33 243:0.07 244:0.83 245:0.93 247:0.96 248:0.50 253:0.52 254:0.86 256:0.45 257:0.65 259:0.21 262:0.93 265:0.26 266:0.98 267:0.36 268:0.46 271:0.50 276:0.95 281:0.86 282:0.73 284:0.87 285:0.46 289:0.95 290:0.64 291:0.97 292:0.88 297:0.36 300:0.98 +0 10:0.81 12:0.79 17:0.89 18:0.57 21:0.78 27:0.72 29:0.77 30:0.45 34:0.52 36:0.34 39:0.46 43:0.06 46:0.88 55:0.96 66:0.10 69:0.90 70:0.25 71:0.84 74:0.26 86:0.58 91:0.27 98:0.05 107:0.88 108:0.80 110:0.89 122:0.82 123:0.78 124:0.82 125:0.88 127:0.31 129:0.89 132:0.97 133:0.78 135:0.30 139:0.56 145:0.58 146:0.05 147:0.05 149:0.05 154:0.06 159:0.91 160:0.88 163:1.00 170:0.77 172:0.05 173:0.59 174:0.79 175:0.97 177:0.05 178:0.55 179:0.96 187:0.39 197:0.81 212:0.61 216:0.17 222:0.35 235:0.05 239:0.80 241:0.15 242:0.63 243:0.29 244:0.98 245:0.37 247:0.30 253:0.24 257:0.93 265:0.05 266:0.17 267:0.73 271:0.50 276:0.17 277:0.95 289:0.88 300:0.18 +0 8:0.93 9:0.70 12:0.79 17:0.49 21:0.59 27:0.53 29:0.77 31:0.87 34:0.90 36:0.37 37:0.88 39:0.46 46:0.88 71:0.84 79:0.87 81:0.27 91:0.76 96:0.73 107:0.88 110:0.88 120:0.57 125:0.88 126:0.24 135:0.34 137:0.87 138:0.97 140:0.92 145:0.40 150:0.30 151:0.57 153:0.48 155:0.54 160:0.88 162:0.49 164:0.55 170:0.77 175:0.99 178:0.51 187:0.21 190:0.99 191:0.77 192:0.57 193:0.95 202:0.72 208:0.61 216:0.18 220:0.87 222:0.35 235:0.74 236:0.91 239:0.82 241:0.78 244:0.99 248:0.56 253:0.50 255:0.69 256:0.58 257:0.97 259:0.21 260:0.57 267:0.23 268:0.59 271:0.50 277:0.98 284:0.88 285:0.60 286:0.99 289:0.95 298:0.99 +0 10:0.89 11:0.51 12:0.79 17:0.84 18:0.90 21:0.22 27:0.25 29:0.77 30:0.21 34:0.52 36:0.95 37:0.36 39:0.46 43:0.63 45:0.66 46:0.88 55:0.52 64:0.77 66:0.51 69:0.72 70:0.73 71:0.84 74:0.30 81:0.31 86:0.82 91:0.12 98:0.75 101:0.47 107:0.91 108:0.55 110:0.92 122:0.92 123:0.53 124:0.66 125:0.88 126:0.24 127:0.61 129:0.05 133:0.66 135:0.54 139:0.77 146:0.78 147:0.28 149:0.33 150:0.35 154:0.52 159:0.51 160:0.88 170:0.77 172:0.51 173:0.74 174:0.88 175:0.75 177:0.38 178:0.55 179:0.68 187:0.39 197:0.90 212:0.35 216:0.68 222:0.35 235:0.22 239:0.88 241:0.07 242:0.24 243:0.26 244:0.83 245:0.65 247:0.76 253:0.27 257:0.84 259:0.21 265:0.75 266:0.63 267:0.28 271:0.50 276:0.54 277:0.69 289:0.90 300:0.55 +2 1:0.65 7:0.66 9:0.74 10:0.84 12:0.79 17:0.67 18:0.96 21:0.47 22:0.93 27:0.39 29:0.77 30:0.28 31:0.91 32:0.64 33:0.97 34:0.59 36:0.30 37:0.67 39:0.46 43:0.07 44:0.86 46:0.88 47:0.93 48:0.97 55:0.42 64:0.77 66:0.27 69:0.92 70:0.90 71:0.84 74:0.59 75:0.96 76:0.85 77:0.89 79:0.91 81:0.59 86:0.87 91:0.22 96:0.77 98:0.37 104:1.00 106:0.81 107:0.97 108:0.95 110:0.97 114:0.72 117:0.86 120:0.65 122:0.82 123:0.95 124:0.94 125:0.88 126:0.54 127:0.74 129:0.81 133:0.94 135:0.98 137:0.91 138:0.96 139:0.85 140:0.80 145:0.90 146:0.65 147:0.25 149:0.10 150:0.55 151:0.72 153:0.46 154:0.15 155:0.64 158:0.74 159:0.89 160:0.88 162:0.80 164:0.67 170:0.77 172:0.89 173:0.77 174:0.90 176:0.62 177:0.70 178:0.42 179:0.16 187:0.21 190:0.95 191:0.73 192:0.97 193:0.95 195:0.97 196:0.93 197:0.85 201:0.64 202:0.56 204:0.82 206:0.81 208:0.64 212:0.77 215:0.58 216:0.89 219:0.90 220:0.62 222:0.35 226:0.95 230:0.68 233:0.65 235:0.84 236:0.92 239:0.87 241:0.12 242:0.36 243:0.09 244:0.83 245:0.93 247:0.94 248:0.76 253:0.72 254:0.96 255:0.73 256:0.64 257:0.65 259:0.21 260:0.65 262:0.93 265:0.39 266:0.91 267:0.69 268:0.62 271:0.50 276:0.94 279:0.82 281:0.91 282:0.73 283:0.66 284:0.92 285:0.60 286:0.99 289:0.95 290:0.72 291:0.97 292:0.87 297:0.36 298:0.99 300:0.94 +1 1:0.64 10:0.88 12:0.79 17:0.66 18:0.66 21:0.40 27:0.57 29:0.77 30:0.21 34:0.87 36:0.39 37:0.54 39:0.46 43:0.08 46:0.88 55:0.42 64:0.77 66:0.10 69:0.32 70:0.50 71:0.84 74:0.35 81:0.59 86:0.66 91:0.15 98:0.27 107:0.90 108:0.25 110:0.91 114:0.74 122:0.95 123:0.58 124:0.77 125:0.88 126:0.54 127:0.57 129:0.05 133:0.72 135:0.63 139:0.64 146:0.34 147:0.41 149:0.08 150:0.54 154:0.53 155:0.50 159:0.52 160:0.88 170:0.77 172:0.27 173:0.31 174:0.89 176:0.62 177:0.88 178:0.55 179:0.87 187:0.39 192:0.49 197:0.92 201:0.64 208:0.64 212:0.37 215:0.63 216:0.44 222:0.35 235:0.74 236:0.92 239:0.87 241:0.50 242:0.40 243:0.51 244:0.83 245:0.47 247:0.55 253:0.54 254:0.80 259:0.21 265:0.29 266:0.47 267:0.69 271:0.50 276:0.35 277:0.95 289:0.95 290:0.74 297:0.36 300:0.33 +2 1:0.68 7:0.65 9:0.71 10:0.85 11:0.51 12:0.79 17:0.61 18:0.83 21:0.30 27:0.60 29:0.77 30:0.11 31:0.88 32:0.64 34:0.76 36:0.86 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 64:0.77 66:0.49 69:0.84 70:0.93 71:0.84 74:0.37 79:0.88 81:0.71 86:0.75 91:0.44 96:0.71 98:0.66 99:0.83 101:0.47 102:0.94 107:0.94 108:0.31 110:0.95 114:0.82 120:0.58 122:0.92 123:0.30 124:0.56 125:0.88 126:0.54 127:0.70 129:0.05 132:0.97 133:0.45 135:0.81 137:0.88 138:0.96 139:0.72 140:0.80 145:0.41 146:0.59 147:0.67 149:0.11 150:0.61 151:0.61 153:0.48 154:0.46 155:0.58 159:0.42 160:0.88 162:0.54 164:0.55 165:0.65 170:0.77 172:0.48 173:0.93 174:0.79 176:0.70 177:0.70 178:0.42 179:0.72 187:0.39 190:0.95 192:0.86 193:0.95 195:0.67 196:0.89 197:0.81 201:0.67 202:0.56 208:0.64 212:0.37 215:0.67 216:0.20 220:0.82 222:0.35 230:0.68 233:0.76 235:0.88 236:0.95 239:0.87 241:0.66 242:0.16 243:0.60 244:0.61 245:0.71 247:0.84 248:0.59 253:0.60 254:0.74 255:0.70 256:0.45 257:0.65 260:0.58 265:0.66 266:0.54 267:0.28 268:0.46 271:0.50 276:0.51 277:0.69 284:0.88 285:0.60 286:0.99 289:0.95 290:0.82 297:0.36 298:0.99 300:0.70 +1 1:0.65 8:0.78 10:0.85 11:0.42 12:0.79 17:0.86 18:0.90 21:0.13 27:0.15 29:0.77 30:0.21 32:0.64 34:0.92 36:0.95 37:0.94 39:0.46 43:0.07 44:0.86 45:0.66 46:0.88 64:0.77 66:0.49 69:0.49 70:0.91 71:0.84 74:0.53 77:0.70 81:0.55 86:0.84 91:0.23 98:0.55 101:0.47 107:0.96 108:0.38 110:0.96 114:0.80 122:0.82 123:0.36 124:0.75 125:0.88 126:0.51 127:0.55 129:0.05 133:0.76 135:0.93 139:0.80 145:0.41 146:0.67 147:0.72 149:0.08 150:0.55 154:0.48 155:0.50 158:0.73 159:0.57 160:0.88 170:0.77 172:0.70 173:0.43 174:0.83 176:0.60 177:0.88 178:0.55 179:0.43 187:0.39 191:0.82 192:0.50 195:0.78 197:0.86 201:0.63 208:0.61 212:0.58 215:0.79 216:0.26 219:0.88 222:0.35 235:0.31 239:0.84 241:0.30 242:0.30 243:0.60 244:0.83 245:0.78 247:0.92 253:0.59 254:0.90 259:0.21 265:0.56 266:0.63 267:0.36 271:0.50 276:0.70 279:0.78 282:0.73 283:0.66 289:0.95 290:0.80 292:0.87 297:0.36 300:0.84 +1 1:0.59 7:0.66 10:0.84 12:0.79 17:0.62 18:0.95 21:0.64 22:0.93 27:0.39 29:0.77 30:0.21 32:0.64 33:0.97 34:0.58 36:0.25 37:0.67 39:0.46 43:0.06 44:0.88 46:0.88 47:0.93 48:0.91 64:0.77 66:0.11 69:0.91 70:0.98 71:0.84 74:0.56 76:0.85 77:0.89 81:0.40 86:0.86 91:0.10 98:0.26 102:0.95 104:1.00 106:0.81 107:0.96 108:0.98 110:0.97 114:0.64 117:0.86 122:0.82 123:0.84 124:0.97 125:0.88 126:0.51 127:0.92 129:0.81 133:0.97 135:0.99 139:0.84 145:0.80 146:0.26 147:0.23 149:0.09 150:0.42 154:0.16 155:0.54 159:0.96 160:0.88 170:0.77 172:0.90 173:0.58 174:0.92 176:0.60 177:0.70 178:0.55 179:0.15 187:0.21 192:0.56 193:0.95 195:0.99 197:0.85 201:0.58 202:0.36 204:0.82 206:0.81 208:0.61 212:0.68 215:0.49 216:0.89 222:0.35 230:0.68 233:0.65 235:0.84 239:0.87 241:0.10 242:0.33 243:0.07 244:0.83 245:0.92 247:0.97 253:0.52 254:0.86 257:0.65 259:0.21 262:0.93 265:0.27 266:0.98 267:0.28 271:0.50 276:0.95 281:0.86 282:0.73 289:0.95 290:0.64 291:0.97 297:0.36 300:0.98 +1 1:0.59 9:0.70 10:0.86 11:0.51 12:0.79 17:0.18 18:0.81 21:0.64 27:0.45 29:0.77 30:0.11 31:0.86 34:0.75 36:0.17 37:0.50 39:0.46 43:0.57 45:0.66 46:0.88 55:0.71 64:0.77 66:0.29 69:0.70 70:0.99 71:0.84 74:0.35 79:0.86 81:0.40 86:0.72 91:0.29 96:0.68 98:0.31 101:0.47 107:0.90 108:0.53 110:0.92 114:0.64 120:0.54 122:0.92 123:0.51 124:0.69 125:0.88 126:0.51 127:0.43 129:0.05 133:0.60 135:0.89 137:0.86 138:0.95 139:0.72 140:0.45 145:0.54 146:0.45 147:0.52 149:0.26 150:0.42 151:0.48 153:0.48 154:0.45 155:0.48 158:0.73 159:0.57 160:0.88 162:0.86 164:0.55 170:0.77 172:0.27 173:0.65 174:0.83 176:0.60 177:0.88 179:0.78 187:0.68 190:0.95 192:0.47 196:0.87 197:0.85 201:0.58 202:0.36 208:0.61 212:0.14 215:0.49 216:0.77 219:0.88 220:0.99 222:0.35 235:0.84 239:0.85 241:0.37 242:0.29 243:0.48 244:0.83 245:0.55 247:0.60 248:0.48 253:0.50 255:0.68 256:0.45 259:0.21 260:0.54 265:0.34 266:0.71 267:0.28 268:0.46 271:0.50 276:0.37 279:0.78 283:0.66 284:0.88 285:0.60 286:0.99 289:0.95 290:0.64 292:0.87 297:0.36 298:0.99 300:0.84 +2 1:0.67 7:0.66 10:0.87 12:0.79 17:0.81 18:0.96 21:0.47 27:0.33 29:0.77 30:0.27 32:0.64 34:0.77 36:0.25 37:0.79 39:0.46 43:0.07 44:0.86 46:0.88 48:0.98 55:0.42 64:1.00 66:0.26 69:0.93 70:0.92 71:0.84 74:0.56 76:0.85 77:0.89 81:0.66 86:0.88 91:0.14 98:0.36 107:0.97 108:0.95 110:0.97 114:0.72 122:0.82 123:0.95 124:0.94 125:0.88 126:0.54 127:0.73 129:0.81 133:0.95 135:0.95 139:0.85 145:0.97 146:0.65 147:0.30 149:0.10 150:0.61 154:0.15 155:0.54 159:0.89 160:0.88 170:0.77 172:0.89 173:0.79 174:0.92 176:0.62 177:0.38 178:0.55 179:0.15 187:0.39 192:0.56 193:0.95 195:0.97 197:0.88 201:0.66 204:0.80 208:0.64 212:0.75 215:0.58 216:0.27 222:0.35 230:0.68 233:0.65 235:0.84 236:0.92 239:0.86 241:0.01 242:0.36 243:0.06 244:0.83 245:0.93 247:0.95 253:0.55 254:0.96 257:0.84 259:0.21 265:0.38 266:0.91 267:0.79 271:0.50 276:0.94 281:0.91 282:0.73 289:0.95 290:0.72 297:0.99 300:0.94 +2 1:0.67 7:0.66 10:0.84 12:0.79 17:0.66 18:0.95 21:0.22 22:0.93 27:0.39 29:0.77 30:0.30 32:0.64 33:0.97 34:0.63 36:0.47 37:0.67 39:0.46 43:0.10 44:0.86 46:0.88 47:0.93 48:0.97 64:0.77 66:0.29 69:0.92 70:0.89 71:0.84 74:0.59 76:0.85 77:0.89 81:0.71 86:0.86 91:0.07 98:0.39 99:0.83 104:1.00 106:0.81 107:0.97 108:0.95 110:0.97 114:0.84 117:0.86 122:0.82 123:0.95 124:0.93 125:0.88 126:0.54 127:0.73 129:0.81 133:0.93 135:0.97 139:0.85 145:0.89 146:0.65 147:0.23 149:0.14 150:0.61 154:0.15 155:0.55 158:0.73 159:0.89 160:0.88 165:0.65 170:0.77 172:0.90 173:0.77 174:0.90 176:0.70 177:0.70 178:0.55 179:0.15 187:0.21 192:0.57 193:0.95 195:0.97 197:0.85 201:0.66 202:0.36 204:0.82 206:0.81 208:0.64 212:0.73 215:0.72 216:0.89 219:0.88 222:0.35 230:0.68 233:0.65 235:0.80 236:0.95 239:0.85 241:0.10 242:0.37 243:0.07 244:0.83 245:0.94 247:0.96 253:0.62 254:0.96 257:0.65 259:0.21 262:0.93 265:0.41 266:0.89 267:0.23 271:0.50 276:0.94 279:0.82 281:0.91 282:0.73 283:0.66 289:0.95 290:0.84 291:0.97 292:0.87 297:0.36 300:0.94 +1 1:0.65 9:0.72 10:0.85 11:0.42 12:0.79 17:0.85 18:0.90 21:0.13 27:0.15 29:0.77 30:0.21 31:0.89 32:0.64 34:0.83 36:0.96 37:0.94 39:0.46 43:0.07 44:0.86 45:0.66 46:0.88 64:0.77 66:0.49 69:0.49 70:0.91 71:0.84 74:0.53 77:0.70 79:0.89 81:0.55 86:0.84 91:0.20 96:0.72 98:0.55 101:0.47 107:0.96 108:0.38 110:0.96 114:0.80 120:0.59 122:0.82 123:0.36 124:0.75 125:0.88 126:0.51 127:0.55 129:0.05 133:0.76 135:0.93 137:0.89 138:0.95 139:0.80 140:0.45 145:0.41 146:0.67 147:0.72 149:0.08 150:0.55 151:0.65 153:0.48 154:0.48 155:0.58 158:0.73 159:0.57 160:0.88 162:0.86 164:0.55 170:0.77 172:0.70 173:0.43 174:0.83 176:0.60 177:0.88 179:0.43 187:0.39 190:0.95 192:0.87 195:0.78 196:0.91 197:0.86 201:0.63 202:0.36 208:0.61 212:0.58 215:0.79 216:0.26 219:0.88 220:0.74 222:0.35 235:0.31 239:0.84 241:0.27 242:0.30 243:0.60 244:0.83 245:0.78 247:0.92 248:0.63 253:0.63 254:0.90 255:0.71 256:0.45 259:0.21 260:0.60 265:0.56 266:0.63 267:0.52 268:0.46 271:0.50 276:0.70 279:0.78 282:0.73 283:0.66 284:0.88 285:0.60 286:0.99 289:0.95 290:0.80 292:0.87 297:0.36 298:0.99 300:0.84 +1 1:0.62 10:0.87 12:0.79 17:0.38 18:0.80 21:0.40 27:0.45 29:0.77 30:0.45 34:0.80 36:0.18 37:0.50 39:0.46 43:0.36 46:0.88 55:0.71 64:0.77 66:0.49 69:0.61 70:0.85 71:0.84 74:0.35 81:0.48 86:0.69 91:0.22 98:0.57 107:0.90 108:0.78 110:0.91 114:0.72 122:0.94 123:0.77 124:0.65 125:0.88 126:0.51 127:0.42 129:0.05 133:0.60 135:0.87 139:0.69 145:0.47 146:0.39 147:0.46 149:0.31 150:0.48 154:0.27 155:0.49 158:0.73 159:0.55 160:0.88 170:0.77 172:0.48 173:0.55 174:0.92 176:0.60 177:0.88 178:0.55 179:0.65 187:0.68 192:0.48 193:0.95 197:0.93 201:0.61 208:0.61 212:0.48 215:0.63 216:0.77 219:0.88 222:0.35 235:0.60 239:0.87 241:0.15 242:0.28 243:0.38 244:0.93 245:0.63 247:0.74 253:0.53 259:0.21 265:0.58 266:0.63 267:0.23 271:0.50 276:0.50 277:0.87 279:0.78 283:0.66 289:0.95 290:0.72 292:0.87 297:0.36 300:0.70 +0 10:0.96 12:0.79 17:0.85 18:0.92 21:0.47 27:0.43 29:0.77 30:0.40 34:0.82 36:0.25 37:0.78 39:0.46 43:0.07 46:0.88 55:0.71 66:0.60 69:0.73 70:0.71 71:0.84 74:0.42 75:0.95 81:0.33 86:0.82 91:0.15 98:0.81 102:0.94 107:0.96 108:0.56 110:0.96 122:0.94 123:0.54 124:0.51 125:0.88 126:0.24 127:0.72 129:0.05 133:0.39 135:0.71 139:0.77 145:0.74 146:0.98 147:0.99 149:0.20 150:0.36 154:0.16 159:0.86 160:0.88 170:0.77 172:0.94 173:0.49 174:0.99 177:0.88 178:0.55 179:0.17 187:1.00 193:0.95 195:0.95 197:0.98 212:0.57 216:0.60 222:0.35 226:0.96 235:0.74 236:0.91 239:0.95 241:0.05 242:0.13 243:0.67 244:0.83 245:0.98 247:0.96 253:0.36 254:0.84 259:0.21 265:0.81 266:0.80 267:0.28 271:0.50 276:0.93 289:0.94 300:0.84 +0 10:0.87 12:0.79 17:0.72 18:0.84 21:0.78 27:0.43 29:0.77 30:0.38 34:0.89 36:0.63 37:0.84 39:0.46 43:0.08 46:0.88 55:0.71 66:0.77 69:0.56 70:0.83 71:0.84 74:0.30 86:0.74 91:0.22 98:0.62 104:0.94 106:0.81 107:0.92 108:0.43 110:0.93 122:0.51 123:0.41 124:0.41 125:0.88 127:0.63 129:0.05 133:0.59 135:0.69 139:0.70 146:0.86 147:0.88 149:0.11 154:0.16 159:0.74 160:0.88 170:0.77 172:0.68 173:0.39 174:0.88 177:0.88 178:0.55 179:0.61 187:0.39 197:0.87 206:0.81 212:0.55 216:0.29 222:0.35 235:0.22 239:0.85 241:0.10 242:0.18 243:0.79 244:0.93 245:0.56 247:0.86 253:0.27 254:0.90 259:0.21 265:0.63 266:0.71 267:0.73 271:0.50 276:0.55 283:0.67 289:0.91 300:0.70 +0 10:0.93 12:0.79 17:0.73 18:0.93 21:0.54 27:0.57 29:0.77 30:0.30 33:0.97 34:0.71 36:0.92 37:0.54 39:0.46 43:0.08 46:0.88 55:0.84 66:0.36 69:0.73 70:0.80 71:0.84 74:0.27 75:0.95 81:0.28 86:0.82 91:0.05 98:0.61 107:0.91 108:0.56 110:0.93 122:0.92 123:0.54 124:0.85 125:0.88 126:0.24 127:0.88 129:0.81 133:0.86 135:0.87 139:0.77 146:0.91 147:0.37 149:0.18 150:0.30 154:0.17 159:0.84 160:0.88 170:0.77 172:0.76 173:0.52 174:0.97 175:0.75 177:0.38 178:0.55 179:0.35 187:0.68 197:0.97 212:0.30 216:0.44 222:0.35 226:0.98 235:0.68 236:0.91 239:0.92 241:0.21 242:0.15 243:0.10 244:0.93 245:0.83 247:0.84 253:0.24 254:0.92 257:0.84 259:0.21 265:0.62 266:0.80 267:0.28 271:0.50 276:0.81 277:0.69 289:0.90 291:0.97 300:0.70 +0 7:0.76 11:0.42 12:0.37 17:0.34 21:0.40 27:0.21 28:0.62 30:0.30 34:0.47 36:0.99 37:0.74 39:0.68 43:0.45 55:0.84 66:0.54 69:0.90 70:0.75 74:0.37 81:0.54 91:0.48 98:0.72 104:0.84 106:0.81 108:0.80 120:0.84 123:0.78 124:0.67 126:0.32 127:0.65 129:0.05 131:0.89 133:0.74 135:0.28 140:0.45 144:0.57 146:0.93 147:0.31 149:0.68 150:0.41 151:0.70 153:0.90 154:0.53 157:0.61 159:0.78 161:0.67 162:0.63 163:0.81 164:0.81 166:0.84 167:0.59 172:0.77 173:0.83 177:0.05 179:0.40 181:0.97 182:0.61 187:0.39 190:0.77 202:0.77 206:0.81 212:0.19 215:0.67 216:0.95 220:0.62 222:0.35 227:0.91 229:0.61 230:0.78 231:0.82 233:0.69 235:0.42 241:0.32 242:0.81 243:0.11 245:0.80 246:0.61 247:0.39 248:0.76 253:0.33 254:0.80 256:0.78 257:0.65 259:0.21 260:0.83 265:0.73 266:0.89 267:0.87 268:0.78 271:0.33 276:0.76 283:0.71 285:0.50 287:0.61 297:0.36 300:0.55 +0 1:0.74 9:0.80 12:0.37 17:0.40 21:0.47 27:0.68 28:0.62 30:0.45 32:0.72 34:0.26 36:0.71 37:0.35 39:0.68 43:0.31 55:0.59 66:0.27 69:0.67 70:0.25 81:0.65 91:0.12 96:0.69 98:0.18 108:0.51 114:0.80 120:0.55 123:0.66 124:0.61 126:0.54 127:0.14 129:0.59 131:0.96 133:0.47 135:0.54 140:0.45 144:0.57 145:0.56 146:0.24 147:0.30 149:0.27 150:0.71 151:0.51 153:0.48 154:0.23 155:0.67 157:0.61 159:0.27 161:0.67 162:0.86 163:0.89 164:0.55 166:0.94 167:0.63 172:0.10 173:0.52 175:0.75 176:0.73 177:0.05 179:0.67 181:0.97 182:0.61 187:0.39 192:0.59 195:0.91 201:0.74 202:0.36 208:0.64 212:0.61 215:0.63 216:0.63 220:0.96 222:0.35 227:0.96 229:0.61 231:0.90 232:0.98 235:0.60 241:0.47 242:0.82 243:0.46 244:0.61 245:0.40 246:0.61 247:0.12 248:0.50 253:0.61 255:0.79 256:0.45 257:0.65 259:0.21 260:0.55 265:0.15 266:0.17 267:0.28 268:0.46 271:0.33 276:0.18 277:0.69 285:0.62 287:0.61 290:0.80 297:0.36 300:0.18 +0 1:0.74 7:0.76 12:0.37 17:0.05 21:0.59 27:0.12 28:0.62 30:0.42 32:0.72 34:0.75 36:0.96 37:0.88 39:0.68 43:0.21 55:0.45 66:0.86 69:0.93 70:0.53 74:0.51 76:0.85 81:0.57 91:0.10 98:0.64 108:0.86 114:0.54 123:0.85 124:0.37 126:0.54 127:0.23 129:0.05 131:0.61 133:0.39 135:0.54 145:0.89 146:0.91 147:0.05 149:0.51 150:0.65 154:0.14 155:0.67 157:0.61 159:0.61 161:0.67 163:0.81 166:0.93 167:0.56 172:0.79 173:0.89 176:0.53 177:0.05 178:0.55 179:0.27 181:0.97 182:0.61 187:0.68 192:0.59 195:0.92 201:0.74 212:0.30 215:0.55 216:0.82 222:0.35 227:0.61 229:0.61 230:0.78 231:0.90 233:0.69 235:0.74 241:0.18 242:0.79 243:0.08 244:0.61 245:0.62 246:0.61 247:0.47 253:0.43 257:0.65 259:0.21 265:0.65 266:0.71 267:0.85 271:0.33 276:0.69 287:0.61 290:0.54 297:0.36 300:0.55 +0 7:0.76 12:0.37 21:0.13 27:0.38 28:0.62 30:0.67 32:0.72 34:0.56 36:0.99 37:0.59 39:0.68 43:0.35 55:0.84 66:0.51 69:0.55 70:0.39 74:0.42 81:0.76 91:0.65 98:0.76 108:0.81 120:0.92 123:0.80 124:0.56 126:0.32 127:0.64 129:0.05 131:0.89 133:0.39 135:0.96 140:0.45 145:0.79 146:0.82 147:0.85 149:0.62 150:0.56 151:0.70 153:0.69 154:0.26 157:0.61 159:0.74 161:0.67 162:0.79 163:0.92 164:0.88 166:0.84 167:0.54 172:0.65 173:0.39 177:0.38 179:0.52 181:0.97 182:0.61 187:0.87 190:0.77 195:0.89 202:0.82 212:0.23 215:0.84 216:0.93 220:0.62 222:0.35 227:0.91 229:0.61 230:0.79 231:0.84 233:0.76 235:0.12 241:0.12 242:0.79 243:0.44 244:0.61 245:0.84 246:0.61 247:0.47 248:0.88 253:0.37 256:0.86 259:0.21 260:0.92 265:0.76 266:0.75 267:0.96 268:0.86 271:0.33 276:0.68 277:0.69 283:0.82 285:0.50 287:0.61 297:0.36 300:0.33 +4 6:0.99 8:0.96 9:0.80 12:0.37 17:0.34 20:0.82 21:0.78 27:0.10 28:0.62 34:0.45 36:0.22 37:0.97 39:0.68 41:0.82 78:0.99 83:0.74 91:0.88 96:0.77 100:0.99 104:0.54 111:1.00 120:0.65 131:0.96 135:0.78 140:0.87 144:0.57 151:0.77 152:0.86 153:0.48 155:0.67 157:0.61 161:0.67 162:0.49 164:0.57 166:0.61 167:0.91 169:1.00 175:0.91 178:0.48 181:0.97 182:0.93 186:0.98 189:0.99 191:0.87 192:0.59 202:0.64 208:0.64 216:0.29 220:0.62 222:0.35 227:0.96 229:0.97 231:0.90 232:0.74 238:1.00 241:0.90 244:0.83 246:0.61 248:0.71 253:0.63 255:0.79 256:0.45 257:0.84 259:0.21 260:0.66 261:0.98 267:0.36 268:0.46 271:0.33 277:0.87 285:0.62 287:0.95 +1 1:0.74 7:0.81 11:0.64 12:0.37 17:0.28 21:0.71 27:0.35 28:0.62 30:0.76 32:0.80 34:0.97 36:0.30 37:0.62 39:0.68 43:0.83 60:0.87 66:0.36 69:0.66 70:0.29 74:0.80 77:0.70 81:0.50 83:0.85 85:0.85 91:0.40 98:0.12 101:0.74 108:0.50 114:0.68 121:0.90 122:0.43 123:0.48 124:0.69 126:0.54 127:0.15 129:0.59 131:0.61 133:0.64 135:0.47 144:0.57 145:0.49 146:0.22 147:0.28 149:0.75 150:0.66 154:0.80 155:0.67 157:0.88 158:0.87 159:0.34 161:0.67 165:0.69 166:0.93 167:0.62 172:0.21 173:0.48 176:0.73 177:0.38 178:0.55 179:0.49 181:0.97 182:0.93 187:0.87 192:0.59 195:0.91 201:0.74 204:0.89 208:0.64 212:0.23 215:0.48 216:0.62 222:0.35 227:0.61 229:0.61 230:0.87 231:0.89 233:0.76 235:0.88 241:0.43 242:0.73 243:0.51 245:0.45 246:0.93 247:0.30 253:0.65 259:0.21 265:0.13 266:0.38 267:0.95 271:0.33 276:0.25 279:0.86 282:0.88 283:0.71 287:0.61 290:0.68 297:0.36 299:0.88 300:0.25 +1 1:0.74 11:0.64 12:0.37 17:0.38 21:0.78 27:0.45 28:0.62 30:0.95 32:0.80 34:0.80 36:0.18 37:0.50 39:0.68 43:0.82 66:0.54 69:0.72 74:0.56 77:0.70 81:0.40 83:0.71 85:0.72 91:0.17 98:0.08 101:0.71 108:0.55 114:0.63 123:0.53 126:0.54 127:0.06 129:0.05 131:0.61 135:0.91 144:0.57 145:0.70 146:0.13 147:0.18 149:0.46 150:0.61 154:0.77 155:0.67 157:0.79 159:0.08 161:0.67 165:0.63 166:0.93 167:0.62 172:0.10 173:0.65 176:0.73 177:0.38 178:0.55 179:0.08 181:0.97 182:0.92 187:0.68 192:0.59 195:0.80 201:0.74 215:0.43 216:0.77 222:0.35 227:0.61 229:0.61 231:0.89 235:1.00 241:0.44 243:0.63 246:0.93 253:0.53 259:0.21 265:0.09 267:0.28 271:0.33 282:0.88 287:0.61 290:0.63 297:0.36 299:0.88 300:0.08 +0 12:0.37 17:0.49 21:0.78 27:0.45 28:0.62 30:0.91 34:0.79 36:0.17 37:0.50 39:0.68 43:0.26 55:0.59 66:0.69 69:0.79 70:0.13 74:0.52 91:0.20 98:0.21 108:0.63 122:0.51 123:0.61 127:0.10 129:0.05 131:0.61 135:0.89 145:0.47 146:0.32 147:0.39 149:0.26 154:0.19 157:0.61 159:0.13 161:0.67 166:0.61 167:0.56 172:0.21 173:0.75 177:0.38 178:0.55 179:0.24 181:0.97 182:0.61 187:0.68 212:0.61 216:0.77 222:0.35 227:0.61 229:0.61 231:0.69 235:0.74 241:0.21 242:0.63 243:0.73 244:0.61 246:0.61 247:0.30 253:0.43 259:0.21 265:0.23 266:0.17 267:0.52 271:0.33 276:0.23 287:0.61 300:0.13 +0 7:0.76 8:0.61 12:0.37 17:0.75 21:0.13 27:0.63 28:0.62 30:0.54 32:0.72 34:1.00 36:0.38 37:0.69 39:0.68 43:0.21 55:0.59 66:0.88 69:0.91 70:0.42 74:0.49 81:0.76 91:0.35 98:0.62 104:0.88 106:0.81 108:0.81 123:0.80 126:0.32 127:0.18 129:0.05 131:0.61 135:0.94 145:0.44 146:0.86 147:0.88 149:0.39 150:0.56 154:0.30 157:0.61 159:0.43 161:0.67 163:0.81 166:0.84 167:0.59 172:0.67 173:0.87 177:0.38 178:0.55 179:0.32 181:0.97 182:0.61 187:0.39 195:0.87 206:0.81 212:0.30 215:0.84 216:0.24 222:0.35 227:0.61 229:0.61 230:0.78 231:0.87 233:0.69 235:0.12 241:0.32 242:0.59 243:0.89 246:0.61 247:0.55 253:0.41 254:0.93 259:0.21 265:0.63 266:0.63 267:0.65 271:0.33 276:0.56 287:0.61 297:0.36 300:0.33 +0 12:0.37 17:0.84 21:0.59 27:0.72 28:0.62 30:0.68 39:0.68 43:0.24 55:0.96 66:0.32 69:0.93 70:0.31 74:0.46 81:0.33 91:0.65 96:0.77 98:0.33 108:0.85 114:0.54 120:0.69 123:0.84 124:0.83 126:0.32 127:0.41 129:0.05 131:0.96 133:0.82 140:0.45 146:0.63 147:0.05 149:0.26 150:0.30 151:0.66 153:0.48 154:0.17 157:0.61 158:0.82 159:0.74 161:0.67 162:0.82 163:0.94 164:0.55 166:0.77 167:0.86 172:0.21 173:0.88 176:0.53 177:0.05 179:0.86 181:0.97 182:0.61 190:0.77 202:0.58 212:0.19 216:0.04 220:0.74 222:0.35 227:0.96 229:0.61 231:0.90 232:0.74 235:0.68 241:0.78 242:0.63 243:0.19 244:0.61 245:0.43 246:0.61 247:0.39 248:0.68 253:0.61 256:0.64 257:0.65 259:0.21 260:0.69 265:0.36 266:0.47 268:0.64 271:0.33 276:0.34 285:0.62 287:0.61 290:0.54 300:0.25 +0 7:0.76 12:0.37 17:0.59 21:0.22 27:0.34 28:0.62 30:0.45 32:0.72 34:0.86 36:0.65 37:0.46 39:0.68 43:0.32 55:0.59 66:0.75 69:0.64 70:0.43 74:0.37 81:0.68 91:0.07 98:0.59 104:0.86 106:0.81 108:0.49 123:0.47 124:0.39 126:0.32 127:0.22 129:0.05 131:0.61 133:0.39 135:0.91 145:0.59 146:0.71 147:0.75 149:0.48 150:0.49 154:0.62 157:0.61 159:0.38 161:0.67 163:0.90 166:0.84 167:0.58 172:0.54 173:0.60 177:0.38 178:0.55 179:0.54 181:0.97 182:0.61 187:0.87 195:0.75 206:0.81 212:0.36 215:0.79 216:0.53 222:0.35 227:0.61 229:0.61 230:0.78 231:0.82 233:0.69 235:0.22 241:0.30 242:0.77 243:0.77 245:0.50 246:0.61 247:0.39 253:0.34 254:0.96 259:0.21 265:0.60 266:0.54 267:0.23 271:0.33 276:0.46 287:0.61 297:0.36 300:0.33 +1 1:0.74 6:0.79 9:0.80 11:0.64 12:0.37 17:0.51 20:0.81 21:0.05 27:0.38 28:0.62 30:0.30 32:0.72 34:0.29 36:0.73 37:0.63 39:0.68 41:0.82 43:0.83 55:0.92 66:0.62 69:0.65 70:0.82 74:0.48 78:0.80 81:0.90 83:0.79 85:0.72 91:0.11 96:0.77 98:0.81 100:0.80 101:0.69 104:0.88 106:0.81 108:0.75 111:0.79 114:0.95 117:0.86 120:0.65 123:0.73 124:0.55 126:0.54 127:0.61 129:0.59 131:0.96 133:0.64 135:0.61 140:0.45 144:0.57 145:0.41 146:0.70 147:0.74 149:0.76 150:0.95 151:0.77 152:0.82 153:0.48 154:0.78 155:0.67 157:0.78 159:0.82 161:0.67 162:0.86 164:0.57 165:0.90 166:0.94 167:0.49 169:0.78 172:0.84 173:0.42 176:0.73 177:0.38 179:0.31 181:0.97 182:0.94 186:0.81 187:0.68 192:0.59 195:0.93 201:0.74 202:0.36 206:0.81 208:0.64 212:0.47 215:0.91 216:0.72 220:0.62 222:0.35 227:0.96 229:0.97 231:0.91 232:0.82 235:0.12 242:0.80 243:0.32 245:0.87 246:0.94 247:0.55 248:0.71 253:0.79 255:0.79 256:0.45 259:0.21 260:0.66 261:0.81 265:0.81 266:0.75 267:0.95 268:0.46 271:0.33 276:0.82 285:0.62 287:0.95 290:0.95 297:0.36 300:0.84 +0 7:0.76 12:0.37 17:0.95 20:0.89 21:0.76 27:0.63 28:0.62 30:0.62 34:0.99 36:0.43 37:0.28 39:0.68 43:0.18 55:0.96 66:0.30 69:0.94 70:0.34 74:0.50 77:0.96 78:0.72 81:0.29 91:0.25 98:0.22 100:0.73 104:0.93 106:0.81 108:0.91 111:0.72 120:0.55 122:0.41 123:0.90 124:0.71 126:0.32 127:0.18 129:0.81 131:0.89 133:0.67 135:0.82 140:0.45 145:1.00 146:0.23 147:0.29 149:0.19 150:0.28 151:0.48 152:0.83 153:0.48 154:0.12 157:0.61 159:0.47 161:0.67 162:0.86 163:0.87 164:0.55 166:0.84 167:0.52 169:0.72 172:0.16 173:0.93 175:0.75 177:0.05 179:0.73 181:0.97 182:0.61 186:0.73 187:0.21 190:0.77 195:0.91 202:0.36 206:0.81 212:0.30 215:0.46 216:0.28 220:0.96 222:0.35 227:0.91 229:0.61 230:0.79 231:0.88 233:0.76 235:0.95 241:0.05 242:0.33 243:0.37 244:0.61 245:0.43 246:0.61 247:0.39 248:0.48 253:0.42 256:0.45 257:0.65 259:0.21 260:0.55 261:0.73 265:0.24 266:0.27 267:0.95 268:0.46 271:0.33 276:0.27 277:0.69 282:0.81 283:0.71 285:0.50 287:0.61 297:0.36 300:0.25 +1 12:0.37 17:0.53 21:0.05 27:0.58 28:0.62 30:0.54 34:0.18 36:0.46 37:0.35 39:0.68 43:0.34 55:0.59 66:0.95 69:0.59 70:0.57 74:0.66 81:0.84 91:0.12 98:0.95 104:0.80 106:0.81 108:0.45 123:0.43 126:0.32 127:0.64 129:0.05 131:0.61 135:0.66 146:0.95 147:0.96 149:0.75 150:0.64 154:0.61 157:0.61 158:0.82 159:0.63 161:0.67 166:0.84 167:0.57 172:0.88 173:0.51 177:0.38 178:0.55 179:0.35 181:0.97 182:0.61 187:0.21 206:0.81 212:0.70 215:0.91 216:0.86 222:0.35 227:0.61 229:0.61 231:0.87 235:0.05 241:0.24 242:0.80 243:0.95 246:0.61 247:0.47 253:0.50 254:0.96 259:0.21 265:0.95 266:0.38 267:0.36 271:0.33 276:0.80 283:0.82 287:0.61 297:0.36 300:0.55 +0 11:0.42 12:0.37 17:0.75 21:0.30 27:0.58 28:0.62 30:0.67 32:0.77 34:0.19 36:0.98 37:0.35 39:0.68 43:0.32 55:0.71 66:0.51 69:0.39 70:0.47 74:0.60 77:0.70 81:0.60 91:0.06 98:0.70 108:0.30 120:0.58 123:0.29 124:0.55 126:0.32 127:0.67 129:0.59 131:0.89 133:0.47 135:0.32 140:0.45 144:0.57 145:0.77 146:0.72 147:0.76 149:0.58 150:0.45 151:0.52 153:0.48 154:0.81 157:0.61 159:0.49 161:0.67 162:0.86 164:0.55 166:0.84 167:0.54 172:0.57 173:0.41 177:0.38 179:0.64 181:0.97 182:0.61 187:0.95 190:0.77 195:0.66 202:0.36 212:0.61 215:0.72 216:0.85 220:0.87 222:0.35 227:0.91 229:0.61 231:0.87 232:0.80 235:0.31 241:0.12 242:0.79 243:0.61 245:0.77 246:0.61 247:0.39 248:0.52 253:0.47 254:0.84 256:0.45 259:0.21 260:0.58 265:0.71 266:0.38 267:0.91 268:0.46 271:0.33 276:0.59 282:0.85 285:0.50 287:0.61 297:0.36 300:0.43 +0 12:0.37 17:0.20 20:0.77 21:0.30 27:0.15 28:0.62 30:0.76 34:0.39 36:0.70 37:0.56 39:0.68 43:0.39 55:0.92 66:0.64 69:0.43 70:0.21 74:0.42 78:0.72 81:0.60 91:0.64 98:0.81 100:0.73 104:0.91 106:0.81 108:0.54 111:0.72 120:0.63 123:0.52 124:0.42 126:0.32 127:0.53 129:0.59 131:0.89 133:0.47 135:0.90 140:0.45 146:0.05 147:0.05 149:0.38 150:0.45 151:0.59 152:0.75 153:0.72 154:0.29 157:0.61 159:0.36 161:0.67 162:0.67 163:0.94 164:0.63 166:0.84 167:0.68 169:0.72 172:0.32 173:0.52 175:0.75 177:0.05 179:0.91 181:0.97 182:0.61 186:0.73 187:0.68 190:0.77 202:0.53 206:0.81 212:0.52 215:0.72 216:0.86 220:0.74 222:0.35 227:0.91 229:0.61 231:0.83 235:0.31 241:0.57 242:0.78 243:0.21 244:0.61 245:0.43 246:0.61 247:0.30 248:0.57 253:0.37 256:0.45 257:0.65 259:0.21 260:0.64 261:0.73 265:0.81 266:0.27 267:0.52 268:0.57 271:0.33 276:0.32 277:0.69 285:0.50 287:0.61 297:0.36 300:0.18 +2 1:0.74 7:0.72 8:0.73 9:0.80 11:0.57 12:0.69 17:0.70 21:0.68 27:0.40 28:0.78 30:0.45 32:0.69 34:0.88 36:0.53 37:0.54 39:0.50 43:0.63 60:0.99 66:0.07 69:0.73 70:0.81 74:0.77 76:0.85 77:0.89 81:0.42 83:0.91 85:0.96 91:0.37 96:0.79 98:0.25 101:0.87 104:0.94 106:0.81 108:0.89 114:0.55 117:0.86 120:0.78 121:0.90 122:0.57 123:0.86 124:0.93 126:0.54 127:0.60 129:0.96 133:0.93 135:0.88 140:0.80 145:0.76 146:0.13 147:0.18 149:0.76 150:0.71 151:0.70 153:0.86 154:0.53 155:0.67 158:0.78 159:0.78 161:0.90 162:0.86 164:0.82 165:0.93 167:0.67 172:0.54 173:0.55 176:0.66 177:0.38 179:0.36 181:0.48 187:0.21 190:0.77 192:0.59 195:0.92 201:0.74 202:0.75 204:0.89 206:0.81 208:0.64 212:0.56 215:0.37 216:0.80 220:0.62 222:0.60 230:0.74 232:0.96 233:0.73 235:0.95 241:0.47 242:0.43 243:0.11 244:0.61 245:0.76 247:0.67 248:0.70 253:0.76 255:0.79 256:0.81 259:0.21 260:0.72 265:0.24 266:0.84 267:0.96 268:0.83 271:0.18 276:0.77 279:0.86 282:0.77 283:0.78 285:0.62 290:0.55 297:0.36 299:0.97 300:0.70 +1 9:0.80 11:0.57 12:0.69 17:0.47 21:0.78 27:0.27 28:0.78 30:0.41 34:0.89 36:0.91 37:0.83 39:0.50 41:0.82 43:0.63 55:0.92 60:0.87 66:0.86 69:0.71 70:0.57 74:0.60 83:0.91 85:0.85 91:0.18 96:0.70 98:0.84 101:0.87 108:0.54 120:0.57 123:0.52 127:0.34 129:0.05 135:0.68 140:0.45 146:0.85 147:0.88 149:0.58 151:0.53 153:0.46 154:0.53 155:0.67 158:0.78 159:0.42 161:0.90 162:0.62 164:0.53 167:0.67 172:0.59 173:0.73 177:0.38 179:0.65 181:0.48 187:0.95 190:0.77 191:0.73 192:0.59 202:0.49 208:0.64 212:0.30 216:0.66 220:0.82 222:0.60 235:0.31 241:0.46 242:0.45 243:0.86 244:0.61 247:0.55 248:0.52 253:0.43 255:0.79 256:0.45 259:0.21 260:0.57 265:0.84 266:0.63 267:0.36 268:0.45 271:0.18 276:0.48 279:0.86 283:0.78 285:0.62 300:0.43 +3 1:0.74 6:0.97 8:0.89 9:0.80 12:0.69 17:0.64 20:0.87 21:0.68 27:0.09 28:0.78 30:0.89 32:0.64 34:0.88 36:0.67 37:0.89 39:0.50 41:0.88 43:0.18 55:0.59 66:0.47 69:0.27 70:0.20 78:0.80 81:0.35 83:0.91 91:0.58 96:0.68 98:0.31 100:0.86 104:0.58 108:0.74 111:0.81 114:0.55 120:0.78 123:0.72 124:0.65 126:0.54 127:0.64 129:0.81 133:0.67 135:0.58 140:0.96 145:0.61 146:0.05 147:0.05 149:0.15 150:0.62 151:0.48 152:0.98 153:0.72 154:0.12 155:0.67 159:0.41 161:0.90 162:0.54 164:0.70 167:0.82 169:0.97 172:0.21 173:0.36 175:0.75 176:0.66 177:0.05 178:0.42 179:0.94 181:0.48 186:0.81 187:0.21 190:0.89 191:0.83 192:0.59 195:0.63 201:0.74 202:0.74 208:0.64 212:0.30 215:0.37 216:0.52 222:0.60 235:0.84 241:0.74 242:0.82 243:0.23 244:0.83 245:0.42 247:0.12 248:0.48 253:0.38 255:0.79 256:0.71 257:0.65 259:0.21 260:0.70 261:0.98 265:0.33 266:0.27 267:0.23 268:0.71 271:0.18 276:0.24 277:0.69 285:0.62 290:0.55 297:0.36 300:0.18 +2 1:0.74 8:0.86 9:0.80 11:0.57 12:0.69 17:0.83 20:0.88 21:0.22 27:0.07 28:0.78 30:0.17 34:0.70 36:0.57 37:0.88 39:0.50 41:0.88 43:0.62 55:0.96 60:0.95 66:0.13 69:0.74 70:0.95 74:0.45 78:0.80 81:0.70 83:0.91 85:0.80 91:0.14 96:0.72 98:0.18 100:0.73 101:0.87 108:0.72 111:0.78 114:0.67 120:0.81 123:0.70 124:0.97 126:0.54 127:0.42 129:0.59 133:0.96 135:0.98 140:0.80 146:0.13 147:0.18 149:0.39 150:0.83 151:0.59 152:0.83 153:0.69 154:0.52 155:0.67 158:0.78 159:0.95 161:0.90 162:0.86 163:0.81 164:0.71 165:0.93 167:0.66 169:0.72 172:0.37 173:0.25 176:0.66 177:0.38 179:0.46 181:0.48 186:0.80 187:0.68 190:0.89 191:0.70 192:0.59 201:0.74 202:0.62 208:0.64 212:0.12 215:0.51 216:0.81 222:0.60 235:0.31 241:0.44 242:0.78 243:0.14 244:0.61 245:0.58 247:0.39 248:0.58 253:0.54 255:0.79 256:0.81 259:0.21 260:0.75 261:0.81 265:0.19 266:0.96 267:0.36 268:0.70 271:0.18 276:0.66 277:0.69 279:0.86 283:0.82 285:0.62 290:0.67 297:0.36 300:0.84 +0 7:0.66 9:0.80 12:0.69 17:0.64 21:0.68 27:0.26 28:0.78 30:0.54 32:0.68 34:0.96 36:0.97 37:0.87 39:0.50 43:0.14 55:0.71 66:0.63 69:0.25 70:0.71 74:0.70 77:0.89 81:0.24 91:0.38 96:0.71 98:0.81 104:0.96 106:0.81 108:0.61 117:0.86 120:0.65 122:0.67 123:0.59 124:0.47 126:0.32 127:0.64 129:0.59 133:0.47 135:0.81 140:0.92 145:0.95 146:0.36 147:0.43 149:0.21 150:0.24 151:0.48 153:0.71 154:0.60 155:0.67 158:0.74 159:0.48 161:0.90 162:0.60 164:0.66 167:0.82 172:0.59 173:0.30 177:0.38 179:0.67 181:0.48 187:0.39 190:0.89 192:0.59 195:0.68 202:0.70 206:0.81 208:0.64 212:0.41 215:0.37 216:0.53 220:0.82 222:0.60 230:0.69 232:0.76 233:0.73 235:0.80 241:0.74 242:0.44 243:0.31 244:0.61 245:0.71 247:0.60 248:0.48 253:0.46 254:0.80 255:0.79 256:0.68 259:0.21 260:0.62 265:0.81 266:0.54 267:0.36 268:0.70 271:0.18 276:0.55 282:0.77 283:0.78 285:0.62 297:0.36 300:0.55 +1 1:0.74 6:0.98 7:0.72 8:0.78 11:0.57 12:0.69 17:0.49 20:0.87 21:0.68 27:0.27 28:0.78 30:0.13 32:0.64 34:0.74 36:0.96 37:0.56 39:0.50 43:0.65 55:0.84 60:0.97 66:0.69 69:0.59 70:0.89 74:0.74 78:0.92 81:0.37 83:0.91 85:0.91 91:0.22 98:0.78 100:0.91 101:0.87 104:0.98 108:0.45 111:0.90 114:0.55 121:0.90 122:0.43 123:0.43 124:0.46 126:0.54 127:0.63 129:0.59 133:0.64 135:0.85 145:0.86 146:0.89 147:0.91 149:0.69 150:0.63 152:0.82 154:0.56 155:0.67 158:0.78 159:0.65 161:0.90 163:0.81 167:0.52 169:0.89 172:0.79 173:0.50 176:0.66 177:0.38 178:0.55 179:0.43 181:0.48 186:0.91 187:0.87 189:0.99 192:0.59 195:0.80 201:0.74 202:0.36 204:0.89 208:0.64 212:0.37 215:0.37 216:0.44 222:0.60 230:0.74 233:0.73 235:0.95 238:0.91 242:0.33 243:0.73 244:0.61 245:0.76 247:0.60 253:0.43 259:0.21 261:0.90 265:0.78 266:0.63 267:0.36 271:0.18 276:0.72 279:0.86 283:0.78 290:0.55 297:0.36 300:0.70 +1 12:0.69 17:0.47 21:0.47 27:0.45 28:0.78 30:0.60 34:0.78 36:0.21 37:0.50 39:0.50 43:0.13 55:0.92 66:0.44 69:0.80 70:0.44 74:0.35 81:0.30 91:0.17 98:0.42 108:0.64 123:0.62 124:0.67 126:0.32 127:0.24 129:0.05 133:0.62 135:0.88 145:0.52 146:0.66 147:0.05 149:0.20 150:0.28 154:0.46 159:0.52 161:0.90 163:0.75 167:0.65 172:0.41 173:0.72 177:0.05 178:0.55 179:0.55 181:0.48 187:0.68 212:0.19 215:0.41 216:0.77 222:0.60 235:0.52 241:0.39 242:0.77 243:0.13 244:0.61 245:0.60 247:0.39 253:0.29 254:0.86 257:0.65 259:0.21 265:0.44 266:0.63 267:0.28 271:0.18 276:0.49 297:0.36 300:0.33 +2 1:0.74 8:0.92 9:0.80 12:0.69 17:0.67 21:0.68 27:0.09 28:0.78 30:0.62 32:0.64 34:0.82 36:0.94 37:0.89 39:0.50 43:0.18 55:0.59 66:0.61 69:0.27 70:0.23 81:0.35 91:0.62 96:0.67 98:0.36 104:0.58 108:0.77 114:0.55 120:0.53 123:0.75 124:0.43 126:0.54 127:0.61 129:0.59 133:0.47 135:0.54 140:0.93 145:0.61 146:0.05 147:0.05 149:0.14 150:0.62 151:0.46 153:0.48 154:0.12 155:0.67 159:0.47 161:0.90 162:0.47 164:0.54 167:0.83 172:0.27 173:0.31 175:0.75 176:0.66 177:0.05 178:0.52 179:0.94 181:0.48 187:0.21 190:0.77 191:0.75 192:0.59 195:0.67 201:0.74 202:0.76 208:0.64 212:0.61 215:0.37 216:0.52 220:0.99 222:0.60 235:0.84 241:0.74 242:0.82 243:0.23 244:0.83 245:0.42 247:0.12 248:0.46 253:0.37 255:0.79 256:0.45 257:0.65 259:0.21 260:0.53 265:0.38 266:0.23 267:0.87 268:0.46 271:0.18 276:0.17 277:0.69 285:0.62 290:0.55 297:0.36 300:0.18 +2 7:0.72 8:0.73 11:0.57 12:0.69 17:0.90 20:0.90 21:0.64 27:0.30 28:0.78 30:0.36 32:0.69 34:0.97 36:0.90 37:0.84 39:0.50 43:0.23 55:0.71 66:0.30 69:0.84 70:0.73 74:0.58 77:0.70 78:0.83 81:0.25 85:0.72 91:0.17 98:0.52 100:0.86 101:0.87 104:0.86 106:0.81 108:0.70 111:0.82 117:0.86 121:0.97 123:0.68 124:0.89 126:0.32 127:0.72 129:0.81 133:0.89 135:0.65 145:0.98 146:0.82 147:0.65 149:0.37 150:0.25 152:0.85 154:0.45 155:0.67 159:0.79 161:0.90 167:0.60 169:0.83 172:0.59 173:0.73 177:0.05 178:0.55 179:0.47 181:0.48 186:0.83 187:0.39 192:0.59 195:0.91 204:0.89 206:0.81 208:0.64 212:0.19 215:0.38 216:0.32 222:0.60 230:0.75 232:0.91 233:0.76 235:0.74 241:0.21 242:0.75 243:0.28 244:0.61 245:0.72 247:0.60 253:0.38 257:0.65 259:0.21 261:0.86 265:0.53 266:0.84 267:0.28 271:0.18 276:0.71 282:0.77 297:0.36 300:0.70 +2 9:0.80 11:0.57 12:0.69 17:0.69 21:0.30 27:0.08 28:0.78 30:0.45 34:0.80 36:0.97 37:0.85 39:0.50 43:0.58 55:0.96 66:0.30 69:0.83 70:0.61 74:0.55 81:0.36 85:0.80 91:0.63 96:0.78 98:0.36 101:0.87 108:0.68 114:0.63 120:0.67 123:0.66 124:0.87 126:0.32 127:0.74 129:0.05 133:0.87 135:0.77 140:0.87 145:0.77 146:0.53 147:0.44 149:0.43 150:0.32 151:0.60 153:0.46 154:0.47 155:0.67 159:0.67 161:0.90 162:0.63 163:0.93 164:0.78 167:0.80 172:0.32 173:0.80 176:0.61 177:0.05 178:0.42 179:0.79 181:0.48 187:0.39 190:0.77 192:0.59 202:0.78 208:0.64 212:0.22 216:0.67 220:0.82 222:0.60 235:0.31 241:0.72 242:0.22 243:0.31 244:0.61 245:0.50 247:0.60 248:0.68 253:0.64 255:0.79 256:0.81 257:0.65 259:0.21 260:0.65 265:0.38 266:0.75 267:0.73 268:0.79 271:0.18 276:0.46 285:0.62 290:0.63 300:0.43 +2 7:0.66 8:0.83 9:0.80 12:0.69 17:0.49 20:0.96 21:0.40 27:0.21 28:0.78 30:0.37 34:0.52 36:0.97 37:0.74 39:0.50 43:0.18 55:0.84 60:0.95 66:0.51 69:0.15 70:0.70 74:0.60 78:0.83 81:0.34 83:0.91 91:0.62 96:0.78 98:0.73 100:0.85 104:0.89 106:0.81 108:0.12 111:0.83 120:0.87 123:0.12 124:0.56 126:0.32 127:0.47 129:0.05 133:0.39 135:0.24 140:0.96 146:0.92 147:0.94 149:0.32 150:0.31 151:0.64 152:0.92 153:0.82 154:0.46 155:0.67 158:0.78 159:0.73 161:0.90 162:0.78 164:0.85 167:0.61 169:0.83 172:0.73 173:0.12 177:0.38 179:0.38 181:0.48 186:0.83 187:0.39 190:0.89 191:0.73 192:0.59 202:0.83 206:0.81 208:0.64 212:0.22 215:0.42 216:0.95 220:0.62 222:0.60 230:0.69 233:0.73 235:0.42 241:0.24 242:0.70 243:0.61 244:0.61 245:0.89 247:0.60 248:0.68 253:0.64 254:0.74 255:0.79 256:0.85 259:0.21 260:0.81 261:0.87 265:0.73 266:0.80 267:0.84 268:0.87 271:0.18 276:0.74 279:0.86 283:0.82 285:0.62 297:0.36 300:0.70 +3 1:0.74 8:0.84 9:0.80 12:0.69 17:0.93 21:0.68 27:0.09 28:0.78 30:0.58 32:0.64 34:0.87 36:0.68 37:0.89 39:0.50 43:0.18 55:0.71 66:0.80 69:0.27 70:0.33 81:0.35 91:0.66 96:0.72 98:0.91 104:0.58 108:0.21 114:0.55 120:0.75 123:0.20 126:0.54 127:0.51 129:0.05 135:0.58 140:0.45 145:0.80 146:0.76 147:0.80 149:0.21 150:0.62 151:0.56 153:0.48 154:0.12 155:0.67 159:0.32 161:0.90 162:0.84 163:0.81 164:0.81 167:0.81 172:0.45 173:0.42 175:0.75 176:0.66 177:0.05 179:0.86 181:0.48 187:0.21 190:0.89 191:0.70 192:0.59 195:0.59 201:0.74 202:0.75 208:0.64 212:0.55 215:0.37 216:0.52 220:0.99 222:0.60 235:0.84 241:0.73 242:0.82 243:0.82 244:0.83 247:0.12 248:0.58 253:0.47 255:0.79 256:0.79 257:0.65 259:0.21 260:0.68 265:0.91 266:0.27 267:0.96 268:0.82 271:0.18 276:0.37 277:0.69 285:0.62 290:0.55 297:0.36 300:0.25 +2 7:0.72 8:0.83 11:0.57 12:0.69 17:0.62 20:0.88 21:0.30 27:0.12 28:0.78 30:0.32 32:0.64 34:0.69 36:0.97 37:0.85 39:0.50 43:0.65 55:0.84 66:0.45 69:0.59 70:0.88 74:0.45 78:0.81 81:0.39 85:0.72 91:0.48 98:0.63 100:0.88 101:0.87 104:0.84 106:0.81 108:0.67 111:0.83 117:0.86 120:0.77 121:0.90 123:0.64 124:0.66 126:0.32 127:0.52 129:0.05 133:0.62 135:0.95 140:0.45 145:0.59 146:0.44 147:0.51 149:0.43 150:0.34 151:0.64 152:0.83 153:0.82 154:0.54 155:0.67 159:0.60 161:0.90 162:0.82 164:0.66 167:0.68 169:0.86 172:0.51 173:0.51 177:0.38 179:0.62 181:0.48 186:0.82 187:0.39 190:0.89 191:0.70 192:0.59 195:0.79 202:0.63 204:0.89 206:0.81 208:0.64 212:0.30 215:0.44 216:0.66 222:0.60 230:0.75 232:0.82 233:0.76 235:0.31 241:0.50 242:0.79 243:0.43 244:0.61 245:0.68 247:0.39 248:0.62 253:0.33 256:0.64 259:0.21 260:0.69 261:0.85 265:0.64 266:0.71 267:0.59 268:0.69 271:0.18 276:0.55 277:0.69 283:0.82 285:0.50 297:0.36 300:0.70 +0 7:0.66 12:0.69 17:0.67 21:0.68 27:0.26 28:0.78 30:0.30 32:0.68 34:0.93 36:0.80 37:0.87 39:0.50 43:0.11 55:0.59 66:0.77 69:0.85 70:0.65 74:0.62 77:0.96 81:0.24 91:0.37 96:0.67 98:0.79 104:0.86 106:0.81 108:0.71 122:0.43 123:0.69 124:0.39 126:0.32 127:0.37 129:0.05 133:0.39 135:0.79 140:0.45 145:0.97 146:0.86 147:0.30 149:0.18 150:0.24 151:0.46 153:0.48 154:0.29 158:0.74 159:0.50 161:0.90 162:0.86 167:0.74 172:0.71 173:0.86 177:0.05 179:0.49 181:0.48 187:0.39 190:0.89 195:0.75 202:0.36 206:0.81 212:0.42 215:0.37 216:0.53 220:0.99 222:0.60 230:0.69 232:0.76 233:0.73 235:0.80 241:0.64 242:0.32 243:0.16 244:0.61 245:0.67 247:0.67 248:0.46 253:0.39 254:0.80 256:0.45 257:0.65 259:0.21 265:0.79 266:0.63 267:0.52 268:0.46 271:0.18 276:0.63 282:0.77 285:0.50 297:0.36 300:0.43 +1 11:0.57 12:0.69 17:0.64 21:0.78 27:0.45 28:0.78 30:0.45 34:0.85 36:0.27 37:0.50 39:0.50 43:0.61 66:0.27 69:0.77 70:0.25 74:0.37 85:0.72 91:0.04 98:0.07 101:0.87 108:0.61 122:0.51 123:0.58 124:0.72 127:0.35 129:0.05 133:0.62 135:0.78 145:0.50 146:0.13 147:0.18 149:0.30 154:0.50 159:0.74 161:0.90 163:0.90 167:0.72 172:0.10 173:0.60 177:0.38 178:0.55 179:0.96 181:0.48 187:0.68 212:0.61 216:0.77 222:0.60 235:0.68 241:0.61 242:0.63 243:0.46 244:0.61 245:0.38 247:0.30 253:0.30 259:0.21 265:0.07 266:0.17 267:0.28 271:0.18 276:0.19 300:0.18 +3 1:0.74 8:0.75 9:0.80 12:0.69 17:0.70 21:0.68 27:0.09 28:0.78 30:0.76 32:0.64 34:0.92 36:0.79 37:0.89 39:0.50 43:0.18 55:0.59 66:0.54 69:0.27 70:0.22 81:0.35 91:0.67 96:0.67 98:0.56 104:0.58 108:0.65 114:0.55 120:0.53 123:0.63 124:0.45 126:0.54 127:0.51 129:0.59 133:0.47 135:0.44 140:0.87 145:0.61 146:0.05 147:0.05 149:0.14 150:0.62 151:0.46 153:0.48 154:0.12 155:0.67 159:0.36 161:0.90 162:0.50 163:0.89 164:0.54 167:0.78 172:0.21 173:0.39 175:0.75 176:0.66 177:0.05 178:0.48 179:0.96 181:0.48 187:0.21 190:0.77 191:0.70 192:0.59 195:0.61 201:0.74 202:0.63 208:0.64 212:0.42 215:0.37 216:0.52 220:0.97 222:0.60 235:0.84 241:0.71 242:0.82 243:0.26 244:0.83 245:0.40 247:0.12 248:0.46 253:0.37 255:0.79 256:0.45 257:0.65 259:0.21 260:0.53 265:0.57 266:0.23 267:0.23 268:0.46 271:0.18 276:0.21 277:0.69 285:0.62 290:0.55 297:0.36 300:0.18 +1 6:0.84 8:0.61 9:0.80 12:0.69 17:0.69 20:0.99 21:0.47 27:0.20 28:0.78 30:0.21 34:0.64 36:1.00 37:0.48 39:0.50 41:0.88 43:0.18 55:0.92 66:0.85 69:0.27 70:0.69 74:0.45 78:0.87 81:0.30 83:0.91 91:0.79 96:0.79 98:0.87 100:0.85 104:0.84 106:0.81 108:0.21 111:0.85 120:0.92 123:0.20 126:0.32 127:0.39 129:0.05 135:0.66 140:0.93 146:0.83 147:0.86 149:0.20 150:0.28 151:0.64 152:0.90 153:0.90 154:0.53 155:0.67 159:0.39 161:0.90 162:0.85 163:0.81 164:0.89 167:0.81 169:0.83 172:0.57 173:0.33 177:0.38 179:0.71 181:0.48 186:0.87 187:0.21 189:0.86 190:0.89 191:0.84 192:0.59 202:0.86 206:0.81 208:0.64 212:0.36 215:0.41 216:0.44 222:0.60 235:0.52 238:0.87 241:0.73 242:0.52 243:0.86 244:0.61 247:0.47 248:0.72 253:0.65 254:0.74 255:0.79 256:0.90 259:0.21 260:0.88 261:0.89 265:0.87 266:0.47 267:0.88 268:0.91 271:0.18 276:0.46 283:0.78 285:0.62 297:0.36 300:0.43 +4 1:0.74 6:0.97 7:0.66 8:0.97 9:0.80 12:0.69 17:0.73 20:0.99 21:0.40 27:0.09 28:0.78 30:0.91 32:0.68 34:0.67 36:0.53 37:0.89 39:0.50 41:0.97 43:0.18 55:0.59 60:0.87 66:0.69 69:0.17 70:0.13 74:0.46 77:0.70 78:0.95 81:0.51 83:0.91 91:0.95 96:0.94 98:0.86 100:0.99 104:0.58 108:0.14 111:0.99 114:0.61 120:0.96 123:0.13 126:0.54 127:0.37 129:0.05 135:0.44 140:0.90 145:0.77 146:0.38 147:0.45 149:0.15 150:0.68 151:0.91 152:0.98 153:0.96 154:0.12 155:0.67 158:0.78 159:0.15 161:0.90 162:0.75 163:0.81 164:0.94 167:0.95 169:0.97 172:0.21 173:0.64 175:0.75 176:0.66 177:0.05 179:0.96 181:0.48 186:0.94 189:0.98 190:0.77 191:0.93 192:0.59 195:0.53 201:0.74 202:0.93 208:0.64 212:0.61 215:0.42 216:0.52 222:0.60 230:0.69 233:0.73 235:0.52 238:0.99 241:0.96 242:0.82 243:0.73 244:0.83 247:0.12 248:0.91 253:0.88 255:0.79 256:0.95 257:0.65 259:0.21 260:0.94 261:0.98 265:0.86 266:0.17 267:0.84 268:0.95 271:0.18 276:0.22 277:0.69 279:0.86 282:0.77 283:0.82 285:0.62 290:0.61 297:0.36 300:0.13 +0 8:0.79 9:0.80 12:0.69 17:0.03 20:0.80 21:0.22 27:0.26 28:0.78 30:0.76 34:0.75 36:0.61 37:0.87 39:0.50 43:0.10 55:0.59 66:0.77 69:0.81 70:0.21 74:0.42 78:0.72 81:0.42 91:0.61 96:0.68 98:0.70 100:0.89 104:0.86 106:0.81 108:0.65 111:0.81 114:0.66 120:0.55 122:0.51 123:0.63 126:0.32 127:0.21 129:0.05 135:0.65 140:0.45 146:0.65 147:0.70 149:0.11 150:0.35 151:0.48 152:0.85 153:0.48 154:0.25 155:0.67 159:0.25 161:0.90 162:0.86 164:0.54 167:0.74 169:0.80 172:0.37 173:0.90 176:0.61 177:0.38 179:0.75 181:0.48 186:0.73 187:0.39 190:0.77 192:0.59 202:0.36 206:0.81 208:0.64 212:0.23 216:0.53 220:0.91 222:0.60 232:0.76 235:0.22 241:0.64 242:0.55 243:0.79 244:0.61 247:0.39 248:0.48 253:0.49 254:0.95 255:0.79 256:0.45 259:0.21 260:0.54 261:0.80 265:0.70 266:0.38 267:0.44 268:0.46 271:0.18 276:0.31 285:0.62 290:0.66 300:0.18 +2 7:0.66 8:0.83 9:0.80 12:0.69 17:0.53 20:0.96 21:0.40 27:0.21 28:0.78 30:0.17 34:0.63 36:0.96 37:0.74 39:0.50 43:0.18 55:0.59 60:0.95 66:0.45 69:0.71 70:0.80 74:0.60 78:0.83 81:0.34 83:0.91 91:0.64 96:0.78 98:0.68 100:0.85 104:0.89 106:0.81 108:0.54 111:0.83 120:0.87 123:0.52 124:0.67 126:0.32 127:0.47 129:0.05 133:0.62 135:0.19 140:0.96 146:0.89 147:0.34 149:0.30 150:0.31 151:0.64 152:0.92 153:0.82 154:0.46 155:0.67 158:0.78 159:0.71 161:0.90 162:0.78 164:0.85 167:0.69 169:0.83 172:0.67 173:0.58 177:0.05 179:0.41 181:0.48 186:0.83 187:0.39 190:0.89 191:0.73 192:0.59 202:0.83 206:0.81 208:0.64 212:0.23 215:0.42 216:0.95 220:0.62 222:0.60 230:0.69 233:0.73 235:0.42 241:0.53 242:0.62 243:0.17 244:0.61 245:0.82 247:0.60 248:0.68 253:0.65 254:0.74 255:0.79 256:0.85 257:0.65 259:0.21 260:0.81 261:0.87 265:0.69 266:0.75 267:0.79 268:0.87 271:0.18 276:0.72 279:0.86 283:0.82 285:0.62 297:0.36 300:0.70 +1 1:0.57 7:0.70 9:0.70 11:0.50 12:0.51 17:0.47 18:0.72 21:0.68 27:0.72 29:0.62 30:0.73 34:0.49 36:0.37 39:0.63 43:0.64 44:0.88 45:0.85 66:0.86 69:0.51 70:0.47 71:0.70 74:0.67 81:0.53 83:0.60 86:0.74 91:0.12 96:0.69 97:0.89 98:0.46 99:0.83 101:0.52 104:0.99 106:0.81 108:0.39 114:0.67 117:0.86 120:0.55 122:0.51 123:0.37 126:0.44 127:0.14 129:0.05 131:1.00 135:0.47 139:0.74 140:0.87 144:0.57 145:0.79 146:0.52 147:0.58 149:0.67 150:0.44 151:0.50 153:0.46 154:0.59 155:0.55 157:0.99 158:0.76 159:0.19 162:0.48 164:0.53 165:0.70 166:0.99 172:0.59 173:0.51 176:0.63 177:0.70 178:0.48 179:0.24 182:0.98 187:0.95 190:0.89 192:0.55 195:0.71 201:0.55 202:0.60 204:0.84 206:0.81 208:0.54 212:0.78 215:0.49 216:0.10 220:0.93 222:0.25 227:0.61 229:0.98 230:0.73 231:0.99 232:1.00 233:0.66 235:0.84 241:0.18 242:0.45 243:0.86 244:0.61 246:0.61 247:0.55 248:0.50 253:0.57 254:0.84 255:0.69 256:0.45 259:0.21 260:0.55 265:0.48 266:0.27 267:0.28 268:0.45 276:0.47 279:0.77 283:0.67 285:0.56 287:0.61 290:0.67 297:0.36 300:0.43 +1 1:0.57 9:0.72 11:0.50 12:0.51 17:0.22 18:0.68 21:0.68 27:0.49 29:0.62 30:0.75 32:0.67 34:0.58 36:0.23 37:0.38 39:0.63 43:0.62 45:0.93 66:0.35 69:0.62 70:0.54 71:0.70 74:0.73 77:0.96 81:0.53 83:0.60 86:0.67 91:0.64 96:0.76 97:0.99 98:0.51 99:0.83 101:0.52 104:0.84 106:0.81 108:0.47 114:0.67 117:0.86 120:0.65 122:0.51 123:0.45 124:0.77 126:0.44 127:0.43 129:0.59 131:1.00 133:0.77 135:0.81 139:0.65 140:0.45 144:0.57 145:1.00 146:0.75 147:0.79 149:0.81 150:0.44 151:0.66 153:0.48 154:0.51 155:0.57 157:0.99 158:0.76 159:0.66 162:0.75 164:0.80 165:0.70 166:0.99 172:0.61 173:0.48 176:0.63 177:0.70 179:0.42 182:0.99 187:0.87 190:0.89 192:0.57 195:0.85 201:0.55 202:0.72 206:0.81 208:0.54 212:0.52 215:0.49 216:0.88 220:0.91 222:0.25 227:0.61 229:1.00 231:0.99 232:0.91 235:0.84 241:0.52 242:0.55 243:0.51 244:0.83 245:0.77 246:0.61 247:0.47 248:0.69 253:0.77 255:0.71 256:0.77 260:0.65 265:0.53 266:0.84 267:0.76 268:0.76 276:0.68 279:0.82 282:0.75 283:0.67 285:0.56 287:0.61 290:0.67 297:0.36 300:0.55 +0 11:0.42 12:0.51 17:0.43 18:0.62 21:0.78 27:0.45 29:0.62 30:0.76 34:0.89 36:0.17 37:0.50 39:0.63 43:0.63 55:0.42 66:0.64 69:0.79 70:0.15 71:0.70 74:0.37 86:0.69 91:0.06 98:0.13 108:0.63 122:0.57 123:0.61 127:0.08 129:0.05 131:0.61 135:0.51 139:0.66 144:0.57 145:0.45 146:0.19 147:0.25 149:0.39 154:0.53 157:0.61 159:0.10 163:0.97 166:0.61 172:0.16 173:0.81 177:0.70 178:0.55 179:0.10 182:0.61 187:0.68 212:0.95 216:0.77 222:0.25 227:0.61 229:0.61 231:0.78 235:1.00 241:0.01 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.33 254:0.84 259:0.21 265:0.13 266:0.12 267:0.23 276:0.19 287:0.61 300:0.13 +1 1:0.58 11:0.64 12:0.51 17:0.79 18:0.71 21:0.77 22:0.93 27:0.66 29:0.62 30:0.76 34:0.79 36:0.63 37:0.49 39:0.63 43:0.67 47:0.93 64:0.77 66:0.54 69:0.73 70:0.15 71:0.70 74:0.38 81:0.67 83:0.91 85:0.72 86:0.72 91:0.17 98:0.37 101:0.87 104:1.00 106:0.81 108:0.56 114:0.63 117:0.86 122:0.57 123:0.53 124:0.45 126:0.54 127:0.18 129:0.05 131:0.61 133:0.37 135:0.63 139:0.70 144:0.57 146:0.41 147:0.48 149:0.43 150:0.46 154:0.56 155:0.47 157:0.85 159:0.24 163:0.75 165:0.93 166:0.99 172:0.21 173:0.77 176:0.70 177:0.70 178:0.55 179:0.79 182:0.97 187:0.87 192:0.45 201:0.57 206:0.81 208:0.64 212:0.42 215:0.44 216:0.35 222:0.25 227:0.61 229:0.61 231:0.99 232:1.00 235:0.99 241:0.30 242:0.45 243:0.63 244:0.61 245:0.40 246:0.61 247:0.35 253:0.48 259:0.21 262:0.93 265:0.39 266:0.23 267:0.65 276:0.22 287:0.61 290:0.63 297:0.36 300:0.13 +1 1:0.60 7:0.70 11:0.50 12:0.51 17:0.47 18:0.65 21:0.54 27:0.65 29:0.62 30:0.76 32:0.67 34:0.85 36:0.92 37:0.39 39:0.63 43:0.63 45:0.89 66:0.13 69:0.53 70:0.47 71:0.70 74:0.66 77:0.70 81:0.57 83:0.60 86:0.65 91:0.25 97:0.94 98:0.32 99:0.83 101:0.52 104:0.87 106:0.81 108:0.89 114:0.74 117:0.86 122:0.51 123:0.39 124:0.77 126:0.44 127:0.70 129:0.05 131:0.61 132:0.97 133:0.77 135:0.99 139:0.63 144:0.57 145:0.56 146:0.50 147:0.56 149:0.67 150:0.48 154:0.53 155:0.53 157:0.99 158:0.76 159:0.70 165:0.70 166:0.99 172:0.48 173:0.41 176:0.63 177:0.70 178:0.55 179:0.66 182:0.97 187:0.39 192:0.50 195:0.83 201:0.57 202:0.36 204:0.82 206:0.81 208:0.54 212:0.54 215:0.58 216:0.54 222:0.25 227:0.61 229:0.61 230:0.73 231:0.99 232:0.92 233:0.66 235:0.68 241:0.12 242:0.57 243:0.34 244:0.83 245:0.65 246:0.61 247:0.47 253:0.59 259:0.21 265:0.21 266:0.80 267:0.44 276:0.48 279:0.79 282:0.75 283:0.67 287:0.61 290:0.74 297:0.36 300:0.55 +1 1:0.67 9:0.72 11:0.50 12:0.51 17:0.47 18:0.69 21:0.40 22:0.93 27:0.20 29:0.62 30:0.62 34:0.52 36:0.51 37:0.47 39:0.63 43:0.62 45:0.81 47:0.93 64:0.77 66:0.36 69:0.27 70:0.69 71:0.70 74:0.59 81:0.74 83:0.60 86:0.74 91:0.04 96:0.74 97:0.89 98:0.24 99:0.83 101:0.52 104:0.97 106:0.81 108:0.21 114:0.82 117:0.86 120:0.62 122:0.51 123:0.20 124:0.68 126:0.54 127:0.37 129:0.05 131:1.00 133:0.60 135:0.78 139:0.77 140:0.45 144:0.57 146:0.34 147:0.41 149:0.49 150:0.67 151:0.66 153:0.48 154:0.64 155:0.58 157:0.98 158:0.81 159:0.53 162:0.86 164:0.56 165:0.70 166:0.99 172:0.32 173:0.23 176:0.70 177:0.70 179:0.76 182:0.98 187:0.68 190:0.89 192:0.57 201:0.66 202:0.36 206:0.81 208:0.64 212:0.42 215:0.67 216:0.73 219:0.94 220:0.74 222:0.25 227:0.61 229:0.99 231:0.99 232:0.98 235:0.68 241:0.07 242:0.45 243:0.51 244:0.61 245:0.54 246:0.61 247:0.55 248:0.63 253:0.69 254:0.86 255:0.71 256:0.45 259:0.21 260:0.63 262:0.93 265:0.26 266:0.54 267:0.28 268:0.46 276:0.34 279:0.81 283:0.65 285:0.56 287:0.61 290:0.82 292:0.86 297:0.36 300:0.55 +1 1:0.66 7:0.64 11:0.50 12:0.51 17:0.36 18:0.70 21:0.40 27:0.19 29:0.62 30:0.87 32:0.71 34:0.62 36:0.26 37:0.38 39:0.63 43:0.10 44:0.92 45:0.66 48:0.91 55:0.71 64:0.77 66:0.64 69:0.71 70:0.29 71:0.70 74:0.54 76:0.94 77:0.70 81:0.68 83:0.60 86:0.74 91:0.15 98:0.59 99:0.83 101:0.52 108:0.66 114:0.82 122:0.77 123:0.64 124:0.45 126:0.54 127:0.23 129:0.59 131:0.61 133:0.46 135:0.91 139:0.79 144:0.57 145:0.50 146:0.18 147:0.23 149:0.17 150:0.59 154:0.59 155:0.50 157:0.61 159:0.36 165:0.70 166:0.99 172:0.54 173:0.70 176:0.70 177:0.70 178:0.55 179:0.49 182:0.61 187:0.39 192:0.48 195:0.76 201:0.63 208:0.64 212:0.59 215:0.67 216:0.87 222:0.25 227:0.61 229:0.61 230:0.65 231:0.99 233:0.76 235:0.60 241:0.54 242:0.54 243:0.28 244:0.61 245:0.64 246:0.61 247:0.74 253:0.59 254:0.74 259:0.21 265:0.60 266:0.38 267:0.36 276:0.49 281:0.91 282:0.80 287:0.61 290:0.82 297:0.36 300:0.33 +1 11:0.50 12:0.51 17:0.57 18:0.74 21:0.13 27:0.32 29:0.62 30:0.33 34:0.58 36:0.86 37:0.48 39:0.63 43:0.61 45:0.73 64:0.77 66:0.45 69:0.79 70:0.49 71:0.70 74:0.48 81:0.30 83:0.60 86:0.71 91:0.18 97:0.89 98:0.27 101:0.52 104:0.90 106:0.81 108:0.63 117:0.86 122:0.86 123:0.60 124:0.66 126:0.26 127:0.71 129:0.05 131:0.61 133:0.60 135:0.80 139:0.68 144:0.57 146:0.52 147:0.18 149:0.43 150:0.33 154:0.50 157:0.91 158:0.76 159:0.79 163:0.92 166:0.61 172:0.27 173:0.65 177:0.38 178:0.55 179:0.91 182:0.81 187:0.39 206:0.81 208:0.54 212:0.42 216:0.58 222:0.25 227:0.61 229:0.61 231:0.87 232:0.94 235:0.12 241:0.01 242:0.45 243:0.32 244:0.83 245:0.47 246:0.61 247:0.47 253:0.39 257:0.65 259:0.21 265:0.30 266:0.38 267:0.52 276:0.21 279:0.77 283:0.67 287:0.61 300:0.33 +1 1:0.64 8:0.78 9:0.76 11:0.50 12:0.51 17:0.08 21:0.13 27:0.41 29:0.62 30:0.91 31:0.92 32:0.67 34:0.50 36:0.80 37:0.34 39:0.63 43:0.66 45:0.73 66:0.27 69:0.19 70:0.13 71:0.70 74:0.53 77:0.70 79:0.91 81:0.67 83:0.60 91:0.94 96:0.98 97:0.97 98:0.12 99:0.83 101:0.52 104:0.58 108:0.15 114:0.88 120:0.96 122:0.51 123:0.15 124:0.61 126:0.44 127:0.80 129:0.59 131:1.00 133:0.47 135:0.63 137:0.92 140:0.45 144:0.57 145:0.74 146:0.13 147:0.18 149:0.43 150:0.60 151:0.92 153:0.48 154:0.55 155:0.58 157:0.95 158:0.76 159:0.28 162:0.65 163:0.79 164:0.96 165:0.70 166:0.99 172:0.10 173:0.45 175:0.75 176:0.63 177:0.38 179:0.98 182:1.00 190:0.89 192:0.58 195:0.56 201:0.60 202:0.94 208:0.54 212:0.61 215:0.84 216:0.30 220:0.74 222:0.25 227:0.61 229:1.00 231:0.99 232:0.79 235:0.22 241:0.86 242:0.63 243:0.46 244:0.83 245:0.40 246:0.61 247:0.30 248:0.98 253:0.97 255:0.74 256:0.94 257:0.65 260:0.96 265:0.13 266:0.17 267:0.73 268:0.95 276:0.12 277:0.69 279:0.81 282:0.75 283:0.67 284:0.88 285:0.62 287:0.61 290:0.88 297:0.36 300:0.13 +2 1:0.69 11:0.64 12:0.51 17:0.81 18:0.74 21:0.05 27:0.09 29:0.62 30:0.95 34:0.78 36:0.53 37:0.73 39:0.63 43:0.76 64:0.77 66:0.75 69:0.45 70:0.13 71:0.70 74:0.50 81:0.81 83:0.91 85:0.80 86:0.79 91:0.06 98:0.34 101:0.87 108:0.35 114:0.95 122:0.57 123:0.33 126:0.54 127:0.12 129:0.05 131:0.61 135:0.77 139:0.76 144:0.57 146:0.25 147:0.31 149:0.68 150:0.76 154:0.67 155:0.53 157:0.95 159:0.11 165:0.93 166:0.99 172:0.32 173:0.73 176:0.70 177:0.70 178:0.55 179:0.35 182:0.98 187:0.39 192:0.50 201:0.66 208:0.64 212:0.84 215:0.91 216:0.92 222:0.25 227:0.61 229:0.61 231:0.99 235:0.22 241:0.07 242:0.63 243:0.77 244:0.61 246:0.61 247:0.35 253:0.65 259:0.21 265:0.37 266:0.17 267:0.97 276:0.20 287:0.61 290:0.95 297:0.36 300:0.13 +1 1:0.56 8:0.89 9:0.75 11:0.50 12:0.51 17:0.22 18:0.58 21:0.76 29:0.62 30:0.75 32:0.67 34:0.53 36:0.23 37:0.97 39:0.63 43:0.58 45:0.93 66:0.33 69:0.75 70:0.61 71:0.70 74:0.69 77:0.96 81:0.50 83:0.60 86:0.60 91:0.93 96:0.97 97:0.99 98:0.25 99:0.83 101:0.52 104:0.82 108:0.80 114:0.63 120:0.94 122:0.75 123:0.79 124:0.87 126:0.44 127:0.48 129:0.93 131:1.00 133:0.87 135:0.23 139:0.59 140:0.45 144:0.57 145:0.91 146:0.37 147:0.44 149:0.76 150:0.40 151:0.90 153:0.89 154:0.47 155:0.58 157:0.99 158:0.76 159:0.82 162:0.59 164:0.95 165:0.70 166:0.99 172:0.59 173:0.53 175:0.75 176:0.63 177:0.38 179:0.44 182:1.00 187:0.21 190:0.89 191:0.76 192:0.58 195:0.94 201:0.54 202:0.94 208:0.54 212:0.37 215:0.46 216:0.98 220:0.74 222:0.25 227:0.61 229:1.00 231:0.99 232:0.88 235:0.97 241:0.21 242:0.58 243:0.29 244:0.83 245:0.72 246:0.61 247:0.60 248:0.97 253:0.97 255:0.74 256:0.94 257:0.65 259:0.21 260:0.94 265:0.28 266:0.87 267:0.59 268:0.94 276:0.66 277:0.69 279:0.82 282:0.75 283:0.67 285:0.56 287:0.61 290:0.63 297:0.36 300:0.70 +0 12:0.51 17:0.38 18:0.69 21:0.54 27:0.45 29:0.62 30:0.76 34:0.63 36:0.17 37:0.50 39:0.63 43:0.13 55:0.99 64:0.77 66:0.13 69:0.70 70:0.15 71:0.70 81:0.26 86:0.67 91:0.06 98:0.06 108:0.53 122:0.86 123:0.51 124:0.75 126:0.26 127:0.60 129:0.81 131:0.61 133:0.67 135:0.89 139:0.65 145:0.52 146:0.05 147:0.05 149:0.08 150:0.29 154:0.10 157:0.61 159:0.85 163:0.97 166:0.61 172:0.05 173:0.45 175:0.91 177:0.05 178:0.55 179:0.99 182:0.61 187:0.68 212:0.95 216:0.77 222:0.25 227:0.61 229:0.61 231:0.61 235:0.60 241:0.21 242:0.82 243:0.34 244:0.93 245:0.37 246:0.61 247:0.12 253:0.20 257:0.84 259:0.21 265:0.06 266:0.12 267:0.59 276:0.17 277:0.87 287:0.61 300:0.13 +1 1:0.56 9:0.74 11:0.50 12:0.51 17:0.28 18:0.58 21:0.76 27:0.48 29:0.62 30:0.75 32:0.67 34:0.29 36:0.28 37:0.55 39:0.63 43:0.58 45:0.93 66:0.33 69:0.75 70:0.61 71:0.70 74:0.69 77:0.96 81:0.50 83:0.60 86:0.60 91:0.42 96:0.83 97:1.00 98:0.25 99:0.83 101:0.52 108:0.80 114:0.63 120:0.73 122:0.75 123:0.79 124:0.87 126:0.44 127:0.48 129:0.93 131:1.00 133:0.87 135:0.30 139:0.59 140:0.45 144:0.57 145:0.91 146:0.37 147:0.44 149:0.76 150:0.40 151:0.74 153:0.45 154:0.47 155:0.58 157:0.99 158:0.76 159:0.82 162:0.57 164:0.75 165:0.70 166:0.99 172:0.59 173:0.53 175:0.75 176:0.63 177:0.38 179:0.44 182:0.99 187:0.39 190:0.89 192:0.58 195:0.94 201:0.54 202:0.70 208:0.54 212:0.37 215:0.46 216:0.92 220:0.74 222:0.25 227:0.61 229:0.99 231:0.99 235:0.97 241:0.46 242:0.58 243:0.29 244:0.83 245:0.72 246:0.61 247:0.60 248:0.80 253:0.83 255:0.73 256:0.71 257:0.65 259:0.21 260:0.73 265:0.28 266:0.87 267:0.28 268:0.69 276:0.66 277:0.69 279:0.82 282:0.75 283:0.67 285:0.56 287:0.61 290:0.63 297:0.36 300:0.70 +1 1:0.60 8:0.73 9:0.73 10:0.82 11:0.45 12:0.51 17:0.61 18:0.86 21:0.13 27:0.13 29:0.56 30:0.21 31:0.98 34:0.24 36:0.89 37:0.69 39:0.42 43:0.48 44:0.88 45:0.92 55:0.71 64:0.77 66:0.35 69:0.95 70:0.86 71:0.94 74:0.82 76:0.85 77:0.70 79:0.98 81:0.46 83:0.54 86:0.81 89:0.96 91:0.62 96:0.93 97:0.98 98:0.67 99:0.83 101:0.46 108:0.90 114:0.85 122:0.90 123:0.90 124:0.88 126:0.31 127:0.91 129:0.59 133:0.89 135:0.63 137:0.98 139:0.81 140:0.45 141:0.91 145:0.56 146:0.98 147:0.35 149:0.63 150:0.43 151:0.91 153:0.72 154:0.16 155:0.54 158:0.75 159:0.91 162:0.78 165:0.63 170:0.77 172:0.92 173:0.85 174:0.88 175:0.75 176:0.62 177:0.38 179:0.17 187:0.21 190:0.95 191:0.82 192:0.56 195:0.98 196:0.97 197:0.81 201:0.57 202:0.79 208:0.49 212:0.52 216:0.37 219:0.90 220:0.74 222:0.84 223:0.92 225:0.96 232:0.72 234:0.91 235:0.22 239:0.81 240:0.95 241:0.53 242:0.58 243:0.15 244:0.61 245:0.95 247:0.98 248:0.92 253:0.94 254:0.92 255:0.72 256:0.82 257:0.93 259:0.21 264:0.95 265:0.68 266:0.80 267:0.59 268:0.84 271:0.80 274:0.91 275:0.96 276:0.94 277:0.69 279:0.79 282:0.74 284:0.98 285:0.49 290:0.85 292:0.88 294:0.95 300:0.70 +1 1:0.56 8:0.88 9:0.70 10:0.89 11:0.45 12:0.51 17:0.32 18:0.93 21:0.74 27:0.38 29:0.56 30:0.15 31:0.90 34:0.19 36:0.60 37:0.57 39:0.42 43:0.46 45:0.88 55:0.71 64:0.77 66:0.27 69:0.61 70:0.97 71:0.94 74:0.67 77:0.70 79:0.89 81:0.36 82:0.98 83:0.54 86:0.87 88:0.99 89:0.97 91:0.31 96:0.72 97:0.89 98:0.53 99:0.83 101:0.46 102:0.95 108:0.47 114:0.63 122:0.90 123:0.45 124:0.94 126:0.31 127:0.75 129:0.05 133:0.94 135:0.83 137:0.90 139:0.84 140:0.45 141:0.91 145:0.91 146:0.95 147:0.96 149:0.49 150:0.33 151:0.57 153:0.48 154:0.21 155:0.51 158:0.75 159:0.91 162:0.61 165:0.63 170:0.77 172:0.84 173:0.26 174:0.91 176:0.62 177:0.96 179:0.20 187:0.87 190:0.98 192:0.50 195:0.98 196:0.92 197:0.90 201:0.53 202:0.74 208:0.49 212:0.23 216:0.80 219:0.90 220:0.99 222:0.84 223:0.92 225:0.96 234:0.91 235:0.95 239:0.89 240:0.95 241:0.35 242:0.18 243:0.46 244:0.83 245:0.89 247:0.99 248:0.57 253:0.58 254:0.84 255:0.69 256:0.73 259:0.21 264:0.95 265:0.55 266:0.96 267:0.69 268:0.75 271:0.80 274:0.91 275:0.97 276:0.91 279:0.75 282:0.74 284:0.96 285:0.49 290:0.63 292:0.88 294:0.96 300:0.84 +1 1:0.56 10:0.85 11:0.45 12:0.51 17:0.47 18:0.75 21:0.77 27:0.45 29:0.56 30:0.17 34:0.95 36:0.19 37:0.50 39:0.42 43:0.09 45:0.66 55:0.92 64:0.77 66:0.24 69:0.71 70:0.91 71:0.94 74:0.55 81:0.40 83:0.54 86:0.73 89:0.96 91:0.15 98:0.40 99:0.83 101:0.46 108:0.54 114:0.62 122:0.90 123:0.51 124:0.88 126:0.43 127:0.50 129:0.05 133:0.85 135:0.87 139:0.70 141:0.91 145:0.49 146:0.68 147:0.73 149:0.16 150:0.36 154:0.46 155:0.46 159:0.74 163:0.81 165:0.63 170:0.77 172:0.48 173:0.55 174:0.79 176:0.63 177:0.96 178:0.55 179:0.49 187:0.68 192:0.45 197:0.82 201:0.54 208:0.54 212:0.19 215:0.43 216:0.77 222:0.84 223:0.91 225:0.95 234:0.91 235:1.00 239:0.83 240:0.95 241:0.55 242:0.39 243:0.44 244:0.83 245:0.70 247:0.79 253:0.50 254:0.86 259:0.21 264:0.95 265:0.42 266:0.89 267:0.76 271:0.80 274:0.91 275:0.91 276:0.67 290:0.62 294:0.94 297:0.36 300:0.70 +1 1:0.56 10:0.83 12:0.51 17:0.99 18:0.97 21:0.74 27:0.72 29:0.56 30:0.42 34:0.24 36:0.67 39:0.42 43:0.11 44:0.86 48:1.00 55:0.71 64:0.77 66:0.67 69:0.34 70:0.74 71:0.94 80:0.99 81:0.42 83:0.54 86:0.91 89:0.96 91:0.72 98:0.81 99:0.83 104:0.58 108:0.27 114:0.64 122:0.95 123:0.26 124:0.45 126:0.43 127:0.96 129:0.59 133:0.47 135:0.63 139:0.89 141:0.91 145:0.67 146:0.87 147:0.89 149:0.30 150:0.38 154:0.19 155:0.46 159:0.61 165:0.63 170:0.77 172:0.75 173:0.31 174:0.83 175:0.97 176:0.63 177:0.38 178:0.55 179:0.53 192:0.44 195:0.78 197:0.83 201:0.53 208:0.54 212:0.71 215:0.46 222:0.84 223:0.91 225:0.96 232:0.78 234:0.91 235:0.98 239:0.82 240:0.95 241:0.77 242:0.75 243:0.72 244:0.93 245:0.82 247:0.67 253:0.49 254:0.88 257:0.93 259:0.21 264:0.95 265:0.81 266:0.75 267:0.65 271:0.80 274:0.91 276:0.70 277:0.95 281:0.86 290:0.64 294:0.94 297:0.36 300:0.55 +1 1:0.63 2:0.99 9:0.70 10:0.91 11:0.45 12:0.51 17:0.89 18:0.94 21:0.47 23:0.98 27:0.60 29:0.56 30:0.32 31:0.87 32:0.64 33:0.97 34:0.86 36:0.68 37:0.55 39:0.42 43:0.26 45:0.83 55:0.45 56:0.97 62:0.96 64:0.77 66:0.52 69:0.29 70:0.95 71:0.94 74:0.70 75:0.98 79:0.87 81:0.57 83:0.54 86:0.91 89:0.96 91:0.44 96:0.72 97:0.89 98:0.72 99:0.83 101:0.46 108:0.22 114:0.78 117:0.86 122:0.95 123:0.22 124:0.72 126:0.53 127:0.79 128:0.95 129:0.05 133:0.73 135:0.90 137:0.87 139:0.90 140:0.90 141:0.97 143:0.99 145:0.76 146:0.87 147:0.89 149:0.35 150:0.48 151:0.52 153:0.48 154:0.67 155:0.54 158:0.81 159:0.69 162:0.86 165:0.63 168:0.95 170:0.77 172:0.79 173:0.22 174:0.86 176:0.70 177:0.96 179:0.37 187:0.21 190:0.95 192:0.56 195:0.82 196:0.90 197:0.89 201:0.62 202:0.60 204:0.79 205:0.98 208:0.64 212:0.61 216:0.32 219:0.94 220:0.99 222:0.84 223:0.93 225:0.97 226:0.95 232:0.87 234:0.97 235:0.74 236:0.95 239:0.92 240:0.99 241:0.03 242:0.44 243:0.61 244:0.61 245:0.84 247:0.97 248:0.51 253:0.66 254:0.80 255:0.70 256:0.68 259:0.21 264:0.95 265:0.73 266:0.84 267:0.28 268:0.69 271:0.80 274:0.98 275:0.93 276:0.78 279:0.79 281:0.91 284:0.88 285:0.61 290:0.78 291:0.97 292:0.87 294:0.98 295:0.98 300:0.84 +0 7:0.66 8:0.88 10:0.88 12:0.51 17:0.20 18:0.90 21:0.74 27:0.11 29:0.56 30:0.10 31:0.87 34:0.82 36:1.00 37:0.67 39:0.42 43:0.10 44:0.88 55:0.45 64:0.77 66:0.46 69:0.62 70:0.73 71:0.94 74:0.55 76:0.94 77:0.70 79:0.87 81:0.24 86:0.82 89:0.97 91:0.22 98:0.77 102:0.95 108:0.71 122:0.91 123:0.69 124:0.67 126:0.23 127:0.65 129:0.59 133:0.67 135:0.97 137:0.87 139:0.81 140:0.87 141:0.91 145:0.86 146:0.57 147:0.63 149:0.22 150:0.25 151:0.48 153:0.46 154:0.19 158:0.73 159:0.72 162:0.59 163:0.75 170:0.77 172:0.68 173:0.48 174:0.88 175:0.75 177:0.88 178:0.48 179:0.45 187:0.68 190:0.98 195:0.87 196:0.89 197:0.87 202:0.68 212:0.23 216:0.92 219:0.88 220:0.99 222:0.84 223:0.92 225:0.96 230:0.68 233:0.76 234:0.91 235:0.88 239:0.87 240:0.95 241:0.37 242:0.13 243:0.40 244:0.83 245:0.83 247:0.96 248:0.48 253:0.44 254:0.97 256:0.68 257:0.65 259:0.21 264:0.95 265:0.78 266:0.80 267:0.95 268:0.68 271:0.80 274:0.91 275:0.93 276:0.72 277:0.69 282:0.73 284:0.94 285:0.45 292:0.88 294:0.95 300:0.55 +0 8:0.83 9:0.72 12:0.51 17:0.24 18:0.80 21:0.78 23:0.97 27:0.45 29:0.56 30:0.17 31:0.96 34:0.17 36:0.99 37:0.39 39:0.42 43:0.07 55:0.71 66:0.13 69:0.94 70:0.97 71:0.94 74:0.26 79:0.96 83:0.54 86:0.72 89:0.95 91:0.80 96:0.83 98:0.21 108:0.96 120:0.72 122:0.95 123:0.95 124:0.93 127:0.53 128:0.96 129:0.81 133:0.92 135:0.58 137:0.96 139:0.69 140:0.97 141:0.97 145:0.87 146:0.48 147:0.25 149:0.10 151:0.85 153:0.48 154:0.10 155:0.53 159:0.93 162:0.47 163:0.88 164:0.77 168:0.96 170:0.77 172:0.32 173:0.74 175:0.97 177:0.05 178:0.54 179:0.55 187:0.21 190:0.95 191:0.83 192:0.86 196:0.91 202:0.94 205:0.97 208:0.49 212:0.13 216:0.84 220:0.99 222:0.84 223:0.92 225:0.96 234:0.97 235:0.12 240:0.98 241:0.57 242:0.40 243:0.16 244:0.93 245:0.61 247:0.79 248:0.80 253:0.76 254:0.74 255:0.71 256:0.89 257:0.97 259:0.21 260:0.70 264:0.95 265:0.23 266:0.95 267:0.96 268:0.85 271:0.80 274:0.98 275:0.91 276:0.61 277:0.95 284:0.98 285:0.61 294:0.92 300:0.84 +0 10:0.88 11:0.45 12:0.51 17:0.30 18:0.89 21:0.78 27:0.12 29:0.56 30:0.32 32:0.69 34:0.87 36:0.83 37:0.81 39:0.42 43:0.26 44:0.92 45:0.73 55:0.71 66:0.89 69:0.72 70:0.77 71:0.94 74:0.48 77:0.70 86:0.87 89:0.96 91:0.23 98:0.69 101:0.46 102:0.97 108:0.55 122:0.91 123:0.53 127:0.21 129:0.05 135:0.75 139:0.85 141:0.91 145:0.74 146:0.75 147:0.79 149:0.24 154:0.52 159:0.31 170:0.77 172:0.68 173:0.73 174:0.83 175:0.75 177:0.88 178:0.55 179:0.36 187:0.98 195:0.71 197:0.86 202:0.46 212:0.59 216:0.90 222:0.84 223:0.92 225:0.96 234:0.91 235:0.84 239:0.90 240:0.95 241:0.30 242:0.14 243:0.89 244:0.61 247:0.84 253:0.39 254:0.93 257:0.65 259:0.21 264:0.95 265:0.69 266:0.38 267:0.79 271:0.80 274:0.91 276:0.57 277:0.69 282:0.77 300:0.55 +0 8:0.81 10:0.97 11:0.45 12:0.51 17:0.05 18:0.78 21:0.78 23:0.97 27:0.53 29:0.56 30:0.42 34:0.40 36:1.00 37:0.47 39:0.42 43:0.49 44:0.86 45:0.66 48:0.91 55:0.71 62:0.96 66:0.98 69:0.36 70:0.73 71:0.94 74:0.82 75:0.96 86:0.76 89:0.98 91:0.42 96:0.82 98:0.96 101:0.46 108:0.28 117:0.86 122:0.90 123:0.27 127:0.72 128:0.95 129:0.05 135:0.75 139:0.76 140:0.45 141:0.97 145:0.54 146:0.99 147:0.99 149:0.60 151:0.51 153:0.48 154:0.47 158:0.75 159:0.82 162:0.60 168:0.95 170:0.77 172:0.96 173:0.18 174:0.97 175:0.75 177:0.88 179:0.18 187:0.68 190:0.98 192:0.46 195:0.93 197:0.98 202:0.68 205:0.95 212:0.72 216:0.42 220:0.96 222:0.84 223:0.93 225:0.97 226:0.96 232:0.87 234:0.97 235:0.12 239:0.96 240:0.98 241:0.50 242:0.38 243:0.99 244:0.83 247:0.94 248:0.51 253:0.84 254:0.93 255:0.69 256:0.64 257:0.65 259:0.21 264:0.95 265:0.96 266:0.54 267:0.52 268:0.68 271:0.80 274:0.97 276:0.92 277:0.69 281:0.86 285:0.49 300:0.70 +1 7:0.66 8:0.74 10:0.87 11:0.45 12:0.51 17:0.83 18:0.95 21:0.78 22:0.93 27:0.24 29:0.56 30:0.41 31:0.87 34:0.95 36:1.00 37:0.72 39:0.42 43:0.11 44:0.88 45:0.66 47:0.93 48:0.97 55:0.71 66:0.27 69:0.93 70:0.67 71:0.94 74:0.57 76:0.94 77:0.70 79:0.86 86:0.87 89:0.97 91:0.38 96:0.74 98:0.62 101:0.46 108:0.35 122:0.95 123:0.33 124:0.78 127:0.93 129:0.59 133:0.74 135:0.95 137:0.87 139:0.85 140:0.45 141:0.91 145:0.91 146:0.79 147:0.93 149:0.31 151:0.57 153:0.48 154:0.17 159:0.85 162:0.74 170:0.77 172:0.76 173:0.86 174:0.93 175:0.75 177:0.70 179:0.28 187:0.39 190:0.98 191:0.85 195:0.94 196:0.89 197:0.89 202:0.56 212:0.49 216:0.45 220:0.74 222:0.84 223:0.92 225:0.96 230:0.68 233:0.66 234:0.91 235:0.80 239:0.86 240:0.95 241:0.24 242:0.22 243:0.46 244:0.83 245:0.92 247:0.99 248:0.56 253:0.58 254:0.99 256:0.58 257:0.84 259:0.21 262:0.93 264:0.95 265:0.63 266:0.84 267:0.73 268:0.59 271:0.80 274:0.91 275:0.93 276:0.86 277:0.87 281:0.86 282:0.73 284:0.88 285:0.49 294:0.93 300:0.70 +1 1:0.61 7:0.66 9:0.70 10:0.87 11:0.45 12:0.51 17:0.79 18:0.78 21:0.64 27:0.13 29:0.56 30:0.21 32:0.64 34:0.49 36:0.97 37:0.69 39:0.42 43:0.48 45:0.83 55:0.45 64:0.77 66:0.25 69:0.78 70:0.87 71:0.94 74:0.74 76:0.85 77:0.70 81:0.48 83:0.54 86:0.79 89:0.96 91:0.27 96:0.69 98:0.67 99:0.83 101:0.46 104:1.00 106:0.81 108:0.76 114:0.69 117:0.86 120:0.55 122:0.90 123:0.75 124:0.74 126:0.43 127:0.55 129:0.05 133:0.65 135:0.21 139:0.73 140:0.45 141:0.91 145:0.63 146:0.50 147:0.35 149:0.39 150:0.43 151:0.50 153:0.48 154:0.30 155:0.49 158:0.74 159:0.57 162:0.86 164:0.55 165:0.63 170:0.77 172:0.57 173:0.77 174:0.83 175:0.75 176:0.63 177:0.38 179:0.41 187:0.87 190:0.95 192:0.49 193:0.96 195:0.79 197:0.84 201:0.58 202:0.36 206:0.81 208:0.54 212:0.71 215:0.53 216:0.37 220:0.96 222:0.84 223:0.91 225:0.96 230:0.68 232:0.72 233:0.76 234:0.91 235:0.88 239:0.87 240:0.95 241:0.03 242:0.39 243:0.36 244:0.61 245:0.85 247:0.86 248:0.50 253:0.59 254:1.00 255:0.68 256:0.45 257:0.93 259:0.21 260:0.55 264:0.95 265:0.68 266:0.63 267:0.36 268:0.46 271:0.80 274:0.91 276:0.72 277:0.87 279:0.75 282:0.73 283:0.65 285:0.49 290:0.69 297:0.36 300:0.70 +1 1:0.56 10:0.95 11:0.45 12:0.51 17:0.66 18:0.75 21:0.68 27:0.53 29:0.56 30:0.18 34:0.39 36:1.00 37:0.45 39:0.42 43:0.35 45:0.73 55:0.52 64:0.77 66:0.68 69:0.52 70:0.99 71:0.94 74:0.73 75:0.96 76:0.85 81:0.35 86:0.69 89:0.97 91:0.20 98:0.62 101:0.46 108:0.40 114:0.67 122:0.90 123:0.38 124:0.64 126:0.43 127:0.78 129:0.05 133:0.80 135:0.81 139:0.67 141:0.91 145:0.41 146:0.89 147:0.91 149:0.46 150:0.37 154:0.53 155:0.46 158:0.74 159:0.80 170:0.77 172:0.84 173:0.32 174:0.93 176:0.63 177:0.96 178:0.55 179:0.37 187:0.68 192:0.45 197:0.95 201:0.55 202:0.36 208:0.54 212:0.68 216:0.59 222:0.84 223:0.92 225:0.97 226:0.95 234:0.91 235:0.88 236:0.94 239:0.94 240:0.95 241:0.10 242:0.19 243:0.72 244:0.61 245:0.76 247:0.96 253:0.58 254:0.94 259:0.21 264:0.95 265:0.63 266:0.80 267:0.59 271:0.80 274:0.91 275:0.91 276:0.78 290:0.67 294:0.95 300:0.94 +0 1:0.58 2:0.99 9:0.70 10:0.85 11:0.45 12:0.51 17:0.62 18:0.93 21:0.30 22:0.93 27:0.72 29:0.56 30:0.37 31:0.88 34:0.36 36:0.84 39:0.42 43:0.23 45:0.77 47:0.93 55:0.71 56:0.97 64:0.77 66:0.48 69:0.88 70:0.87 71:0.94 74:0.60 79:0.88 81:0.53 83:0.54 86:0.87 89:0.96 91:0.27 96:0.71 97:0.89 98:0.79 99:0.83 101:0.46 108:0.76 114:0.82 117:0.86 122:0.95 123:0.75 124:0.58 126:0.43 127:0.77 129:0.05 133:0.45 135:0.85 137:0.88 139:0.85 140:0.45 141:0.91 143:0.98 146:0.91 147:0.83 149:0.34 150:0.45 151:0.57 153:0.48 154:0.51 155:0.51 158:0.76 159:0.69 162:0.54 165:0.63 170:0.77 172:0.76 173:0.86 174:0.83 176:0.63 177:0.88 179:0.38 187:0.87 190:0.89 192:0.55 196:0.91 197:0.85 201:0.61 202:0.56 208:0.49 212:0.50 216:0.10 219:0.91 220:0.87 222:0.84 223:0.91 225:0.96 232:0.97 234:0.91 235:0.52 239:0.84 240:0.95 241:0.10 242:0.21 243:0.46 244:0.83 245:0.92 247:0.96 248:0.56 253:0.63 254:0.84 255:0.70 256:0.45 257:0.65 259:0.21 262:0.93 264:0.95 265:0.79 266:0.63 267:0.52 268:0.46 271:0.80 274:0.91 275:0.95 276:0.78 279:0.75 284:0.88 285:0.55 290:0.82 292:0.86 294:0.97 295:0.98 300:0.70 +1 1:0.62 8:0.69 9:0.72 10:0.84 11:0.45 12:0.51 17:0.88 18:0.95 22:0.93 27:0.60 29:0.56 30:0.32 31:0.93 34:0.85 36:0.98 37:0.31 39:0.42 43:0.32 45:0.92 47:0.93 64:0.77 66:0.96 69:0.52 70:0.68 71:0.94 74:0.71 79:0.93 81:0.50 83:0.54 86:0.93 89:0.96 91:0.57 96:0.80 97:0.89 98:0.93 99:0.83 101:0.46 108:0.40 114:0.92 117:0.86 122:0.91 123:0.39 126:0.31 127:0.56 129:0.05 135:0.83 137:0.93 139:0.90 140:0.45 141:0.91 146:0.90 147:0.92 149:0.62 150:0.47 151:0.85 153:0.48 154:0.20 155:0.54 158:0.75 159:0.50 162:0.86 165:0.63 170:0.77 172:0.89 173:0.52 174:0.79 176:0.62 177:0.96 179:0.31 187:0.21 190:0.95 192:0.56 196:0.96 197:0.80 201:0.59 202:0.36 208:0.49 212:0.85 216:0.14 219:0.90 222:0.84 223:0.91 225:0.95 232:0.83 234:0.91 235:0.05 239:0.82 240:0.95 241:0.07 242:0.24 243:0.96 244:0.61 247:0.92 248:0.76 253:0.83 254:0.99 255:0.71 256:0.45 259:0.21 262:0.93 264:0.95 265:0.93 266:0.47 267:0.28 268:0.46 271:0.80 274:0.91 275:0.93 276:0.81 279:0.75 284:0.88 285:0.49 290:0.92 292:0.88 294:0.95 300:0.55 +2 1:0.59 10:0.90 11:0.49 12:0.51 17:0.67 18:0.93 21:0.54 27:0.69 29:0.56 30:0.18 32:0.66 34:0.73 36:0.90 37:0.44 39:0.42 43:0.10 45:0.77 55:0.45 64:0.77 66:0.68 69:0.50 70:0.87 71:0.94 74:0.60 77:0.89 81:0.47 83:0.54 86:0.87 89:0.96 91:0.20 98:0.77 99:0.83 101:0.52 102:0.96 108:0.39 114:0.74 122:0.95 123:0.37 124:0.45 126:0.43 127:0.51 129:0.05 133:0.36 135:0.83 139:0.83 141:0.91 145:0.96 146:0.77 147:0.81 149:0.20 150:0.42 154:0.59 155:0.47 159:0.45 165:0.63 170:0.77 172:0.76 173:0.52 174:0.83 175:0.75 176:0.63 177:0.88 178:0.55 179:0.44 187:0.39 192:0.47 195:0.68 197:0.87 201:0.59 208:0.54 212:0.60 215:0.58 216:0.32 222:0.84 223:0.92 225:0.96 234:0.91 235:0.74 239:0.90 240:0.95 241:0.05 242:0.27 243:0.72 244:0.61 245:0.83 247:0.90 253:0.57 254:0.84 257:0.65 259:0.21 264:0.95 265:0.77 266:0.63 267:0.44 271:0.80 274:0.91 276:0.69 277:0.69 282:0.75 290:0.74 297:0.36 300:0.70 +1 7:0.66 10:0.91 12:0.51 17:0.49 18:0.69 21:0.68 27:0.08 29:0.56 30:0.32 32:0.65 34:0.80 36:0.92 37:0.70 39:0.42 43:0.26 55:0.59 66:0.81 69:0.80 70:0.79 71:0.94 74:0.43 81:0.35 82:0.98 86:0.66 88:0.98 89:0.97 91:0.06 98:0.78 108:0.64 114:0.67 122:0.97 123:0.62 124:0.38 126:0.43 127:0.42 129:0.05 133:0.38 135:0.73 139:0.64 141:0.91 145:0.49 146:0.88 147:0.44 149:0.21 150:0.35 154:0.26 159:0.57 170:0.77 172:0.79 173:0.77 174:0.90 176:0.63 177:0.38 178:0.55 179:0.42 187:0.39 192:0.44 195:0.79 197:0.90 201:0.54 208:0.49 212:0.52 215:0.49 216:0.58 222:0.84 223:0.92 225:0.97 230:0.68 233:0.76 234:0.91 235:0.88 236:0.94 239:0.89 240:0.95 241:0.07 242:0.13 243:0.13 244:0.83 245:0.71 247:0.95 253:0.51 254:0.92 257:0.93 259:0.21 264:0.95 265:0.78 266:0.75 267:0.95 271:0.80 274:0.91 275:0.94 276:0.69 290:0.67 294:0.96 297:0.36 300:0.70 +1 8:0.85 9:0.72 10:0.86 11:0.45 12:0.51 17:0.81 18:0.95 21:0.13 27:0.13 29:0.56 30:0.21 31:0.93 34:0.58 36:0.98 37:0.69 39:0.42 43:0.56 45:0.87 55:0.45 66:0.98 69:0.36 70:0.80 71:0.94 74:0.88 76:0.85 77:0.70 79:0.92 80:0.99 81:0.32 82:0.98 83:0.54 86:0.92 88:0.99 89:0.96 91:0.65 96:0.85 98:0.97 101:0.46 108:0.28 114:0.82 122:0.90 123:0.27 126:0.23 127:0.74 129:0.05 135:0.63 137:0.93 139:0.88 140:0.87 141:0.91 145:0.70 146:0.96 147:0.97 149:0.61 150:0.35 151:0.75 153:0.48 154:0.17 155:0.54 158:0.73 159:0.65 162:0.84 170:0.77 172:0.96 173:0.28 174:0.86 176:0.60 177:0.96 179:0.19 187:0.21 190:0.95 191:0.75 192:0.56 195:0.81 196:0.95 197:0.86 202:0.67 208:0.49 212:0.91 216:0.37 220:0.74 222:0.84 223:0.92 225:0.96 232:0.72 234:0.91 235:0.12 239:0.85 240:0.95 241:0.39 242:0.45 243:0.98 244:0.83 247:0.94 248:0.72 253:0.88 254:0.97 255:0.71 256:0.71 259:0.21 264:0.95 265:0.97 266:0.47 267:0.52 268:0.73 271:0.80 274:0.91 275:0.93 276:0.91 282:0.73 284:0.94 285:0.49 290:0.82 294:0.96 300:0.70 +1 1:0.62 8:0.76 10:0.85 11:0.45 12:0.51 17:0.91 18:0.95 22:0.93 27:0.47 29:0.56 30:0.12 34:0.52 36:0.92 37:0.66 39:0.42 43:0.57 44:0.88 45:0.83 47:0.93 48:0.99 55:0.52 64:0.77 66:0.75 69:0.07 70:0.90 71:0.94 74:0.67 77:0.70 80:0.99 81:0.50 83:0.54 86:0.91 89:0.96 91:0.35 97:0.89 98:0.83 99:0.83 101:0.46 108:0.06 114:0.92 117:0.86 122:0.94 123:0.06 124:0.41 126:0.31 127:0.63 129:0.05 133:0.43 135:0.89 139:0.90 141:0.91 145:0.49 146:0.90 147:0.92 149:0.65 150:0.47 154:0.46 155:0.49 158:0.74 159:0.61 165:0.63 170:0.77 172:0.84 173:0.12 174:0.83 176:0.62 177:0.96 178:0.55 179:0.38 187:0.21 192:0.48 195:0.79 197:0.84 201:0.59 204:0.78 208:0.49 212:0.68 216:0.26 219:0.90 222:0.84 223:0.91 225:0.96 232:0.85 234:0.91 235:0.05 239:0.84 240:0.95 241:0.10 242:0.13 243:0.77 244:0.83 245:0.84 247:0.96 253:0.67 259:0.21 262:0.93 264:0.95 265:0.83 266:0.75 267:0.99 271:0.80 274:0.91 275:0.94 276:0.77 279:0.75 281:0.91 282:0.74 290:0.92 292:0.86 294:0.96 300:0.84 +0 8:0.93 10:0.88 11:0.45 12:0.51 17:0.94 18:0.95 21:0.78 27:0.13 29:0.56 30:0.38 34:0.44 36:0.94 37:0.69 39:0.42 43:0.56 44:0.88 45:0.85 48:0.91 55:0.45 66:0.63 69:0.36 70:0.92 71:0.94 74:0.83 76:0.85 77:0.70 86:0.92 89:0.97 91:0.65 98:0.78 101:0.46 108:0.28 122:0.90 123:0.27 124:0.52 127:0.80 129:0.05 133:0.60 135:0.76 139:0.90 141:0.91 145:0.67 146:0.93 147:0.95 149:0.57 154:0.17 155:0.49 159:0.75 170:0.77 172:0.92 173:0.23 174:0.91 177:0.96 178:0.55 179:0.22 187:0.21 191:0.70 192:0.48 193:0.95 195:0.88 197:0.89 202:0.64 204:0.79 208:0.49 212:0.82 216:0.37 222:0.84 223:0.92 225:0.96 232:0.72 234:0.91 235:0.12 239:0.88 240:0.95 241:0.21 242:0.21 243:0.69 244:0.83 245:0.94 247:0.98 253:0.57 254:0.90 259:0.21 264:0.95 265:0.78 266:0.71 267:0.36 271:0.80 274:0.91 275:0.94 276:0.89 281:0.86 282:0.74 294:0.94 300:0.84 +1 1:0.74 8:0.81 11:0.57 12:0.37 17:0.51 20:0.78 21:0.05 27:0.36 28:0.78 30:0.45 34:0.86 36:0.81 37:0.93 39:0.31 43:0.95 60:0.87 66:0.68 69:0.15 70:0.57 74:0.85 78:0.96 81:0.92 83:0.88 85:0.88 91:0.31 98:0.73 100:0.91 101:0.82 108:0.12 111:0.93 114:0.95 117:0.86 122:0.43 123:0.12 124:0.43 126:0.54 127:0.58 129:0.59 133:0.47 135:0.63 146:0.60 147:0.66 149:0.86 150:0.96 152:0.76 154:0.95 155:0.67 158:0.92 159:0.33 161:0.82 165:0.90 167:0.55 169:0.89 172:0.51 173:0.33 176:0.73 177:0.38 178:0.55 179:0.77 181:0.53 186:0.96 187:0.21 192:0.59 201:0.74 208:0.64 212:0.83 215:0.91 216:0.18 222:0.48 232:0.91 235:0.12 241:0.44 242:0.38 243:0.72 245:0.58 247:0.60 253:0.77 259:0.21 261:0.81 265:0.73 266:0.27 267:0.23 271:0.42 276:0.42 279:0.86 283:0.82 290:0.95 297:0.36 300:0.43 +1 7:0.81 11:0.57 12:0.37 17:0.64 21:0.78 27:0.33 28:0.78 30:0.38 34:0.63 36:0.93 37:0.81 39:0.31 43:0.78 55:0.71 66:0.45 69:0.79 70:0.59 74:0.92 85:0.80 91:0.33 98:0.61 101:0.72 108:0.84 121:0.90 122:0.67 123:0.83 124:0.76 127:0.36 129:0.05 133:0.74 135:0.34 145:0.54 146:0.54 147:0.60 149:0.74 154:0.70 155:0.67 159:0.76 161:0.82 163:0.81 167:0.47 172:0.63 173:0.60 177:0.38 178:0.55 179:0.41 181:0.53 187:0.68 192:0.59 204:0.89 208:0.64 212:0.14 216:0.84 222:0.48 230:0.87 233:0.76 235:0.74 241:0.07 242:0.80 243:0.31 245:0.75 247:0.39 253:0.65 259:0.21 265:0.62 266:0.87 267:0.52 271:0.42 276:0.68 277:0.69 300:0.55 +2 1:0.74 6:0.87 7:0.81 8:0.68 11:0.57 12:0.37 17:0.53 20:0.87 21:0.22 27:0.42 28:0.78 30:0.75 32:0.80 34:0.42 36:0.18 37:0.39 39:0.31 43:0.86 60:0.95 66:0.48 69:0.59 70:0.52 74:0.96 77:0.89 78:0.94 81:0.90 83:0.89 85:0.98 91:0.10 98:0.68 100:0.89 101:0.87 104:0.98 106:0.81 108:0.45 111:0.91 114:0.90 117:0.86 121:1.00 122:0.43 123:0.43 124:0.57 126:0.54 127:0.47 129:0.59 133:0.47 135:0.96 145:0.47 146:0.73 147:0.77 149:0.98 150:0.94 152:0.82 154:0.83 155:0.67 158:0.92 159:0.47 161:0.82 165:0.90 167:0.47 169:0.87 172:0.75 173:0.60 176:0.73 177:0.38 178:0.55 179:0.34 181:0.53 186:0.93 187:0.87 189:0.89 192:0.59 195:0.73 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.79 216:0.43 222:0.48 230:0.87 232:0.99 233:0.76 235:0.52 238:0.83 241:0.03 242:0.62 243:0.60 245:0.91 247:0.47 253:0.78 259:0.21 261:0.88 265:0.69 266:0.54 267:0.19 271:0.42 276:0.75 279:0.86 282:0.88 283:0.82 290:0.90 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.89 7:0.81 8:0.74 9:0.80 11:0.57 12:0.37 17:0.16 20:0.88 21:0.71 27:0.03 28:0.78 30:0.62 34:0.97 36:0.70 37:0.92 39:0.31 41:0.88 43:0.77 60:0.87 66:0.75 69:0.80 70:0.34 74:0.76 78:0.93 81:0.61 83:0.83 85:0.80 91:0.38 96:0.72 98:0.39 100:0.91 101:0.76 108:0.64 111:0.91 114:0.68 120:0.59 121:0.90 122:0.43 123:0.61 126:0.54 127:0.13 129:0.05 135:0.65 140:0.45 145:0.54 146:0.43 147:0.49 149:0.62 150:0.74 151:0.58 152:0.93 153:0.48 154:0.70 155:0.67 158:0.87 159:0.16 161:0.82 162:0.64 164:0.71 165:0.78 167:0.51 169:0.88 172:0.32 173:0.85 176:0.73 177:0.38 179:0.43 181:0.53 186:0.93 187:0.68 189:0.90 191:0.75 192:0.59 201:0.74 202:0.63 204:0.89 208:0.64 212:0.30 215:0.48 216:0.79 220:0.93 222:0.48 230:0.87 233:0.76 235:0.97 238:0.84 241:0.27 242:0.33 243:0.77 247:0.39 248:0.58 253:0.68 255:0.79 256:0.58 259:0.21 260:0.60 261:0.91 265:0.41 266:0.27 267:0.76 268:0.64 271:0.42 276:0.27 279:0.86 283:0.71 285:0.62 290:0.68 297:0.36 300:0.25 +2 1:0.74 6:0.83 8:0.71 11:0.57 12:0.37 20:0.91 27:0.14 28:0.78 30:0.86 34:0.90 36:0.18 37:0.67 39:0.31 43:0.86 60:0.87 66:0.15 69:0.57 70:0.32 74:0.81 78:0.97 81:0.96 83:0.86 85:0.93 91:0.33 98:0.38 100:0.98 101:0.83 108:0.74 111:0.98 114:0.98 122:0.43 123:0.57 124:0.74 126:0.54 127:0.30 129:0.81 133:0.67 135:0.88 145:0.58 146:0.22 147:0.28 149:0.88 150:0.98 152:0.98 154:0.84 155:0.67 158:0.92 159:0.37 161:0.82 165:0.92 167:0.49 169:0.96 172:0.32 173:0.59 176:0.73 177:0.38 178:0.55 179:0.62 181:0.53 186:0.97 187:0.39 189:0.88 192:0.59 201:0.74 202:0.36 208:0.64 212:0.42 215:0.96 216:0.93 222:0.48 235:0.05 238:0.94 241:0.15 242:0.63 243:0.36 245:0.63 247:0.30 253:0.78 259:0.21 261:0.97 265:0.20 266:0.63 267:0.44 271:0.42 276:0.43 279:0.86 283:0.82 290:0.98 297:0.36 300:0.33 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.37 17:0.79 21:0.74 27:0.39 28:0.78 30:0.76 32:0.80 34:0.66 36:0.32 37:0.49 39:0.31 41:0.88 43:0.90 60:0.87 66:0.77 69:0.49 70:0.40 74:0.95 77:0.70 81:0.50 83:0.83 85:0.98 91:0.23 96:0.70 98:0.76 101:0.85 104:0.90 106:0.81 108:0.38 114:0.67 120:0.56 121:0.97 122:0.43 123:0.36 124:0.41 126:0.54 127:0.41 129:0.59 133:0.47 135:0.37 140:0.45 145:0.88 146:0.92 147:0.94 149:0.96 150:0.66 151:0.51 153:0.72 154:0.89 155:0.67 158:0.87 159:0.67 161:0.82 162:0.86 163:0.81 164:0.70 165:0.65 167:0.48 172:0.82 173:0.33 176:0.73 177:0.38 179:0.34 181:0.53 187:0.39 192:0.59 195:0.87 201:0.74 202:0.55 204:0.89 206:0.81 208:0.64 212:0.47 215:0.46 216:0.29 220:0.96 222:0.48 230:0.87 233:0.76 235:0.95 241:0.10 242:0.63 243:0.79 245:0.81 247:0.39 248:0.52 253:0.73 255:0.79 256:0.58 259:0.21 260:0.57 265:0.77 266:0.63 267:0.94 268:0.63 271:0.42 276:0.74 279:0.86 282:0.88 283:0.71 285:0.62 290:0.67 297:0.36 299:0.88 300:0.70 +1 11:0.57 12:0.37 17:0.36 21:0.78 27:0.45 28:0.78 30:0.21 34:0.74 36:0.18 37:0.50 39:0.31 43:0.78 66:0.54 69:0.80 70:0.37 74:0.55 85:0.72 91:0.23 98:0.28 101:0.71 108:0.63 122:0.51 123:0.61 124:0.45 127:0.16 129:0.05 133:0.39 135:0.58 145:0.52 146:0.39 147:0.45 149:0.46 154:0.70 159:0.27 161:0.82 163:0.75 167:0.50 172:0.21 173:0.77 177:0.38 178:0.55 179:0.73 181:0.53 187:0.68 212:0.42 216:0.77 222:0.48 235:0.95 241:0.21 242:0.45 243:0.63 245:0.40 247:0.35 253:0.45 259:0.21 265:0.30 266:0.23 267:0.28 271:0.42 276:0.21 300:0.25 +1 1:0.74 7:0.81 11:0.57 12:0.37 17:0.02 20:0.80 21:0.30 27:0.11 28:0.78 30:0.89 34:0.82 36:0.62 37:0.79 39:0.31 43:0.87 66:0.75 69:0.56 70:0.20 74:0.70 78:0.88 81:0.80 83:0.75 85:0.85 91:0.31 98:0.51 100:0.85 101:0.79 104:0.89 106:0.81 108:0.43 111:0.86 114:0.86 117:0.86 121:0.90 122:0.43 123:0.41 126:0.54 127:0.15 129:0.05 135:0.21 145:0.52 146:0.46 147:0.52 149:0.76 150:0.87 152:0.77 154:0.85 155:0.67 159:0.17 161:0.82 165:0.84 167:0.49 169:0.83 172:0.32 173:0.66 176:0.73 177:0.38 178:0.55 179:0.60 181:0.53 186:0.88 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.30 215:0.72 216:0.85 222:0.48 230:0.87 232:0.92 233:0.76 235:0.42 241:0.18 242:0.63 243:0.77 247:0.30 253:0.70 259:0.21 261:0.80 265:0.53 266:0.27 267:0.65 271:0.42 276:0.21 290:0.86 297:0.36 300:0.18 +0 1:0.74 7:0.78 11:0.57 12:0.37 17:0.67 27:0.55 28:0.78 30:0.76 34:0.21 36:0.94 37:0.23 39:0.31 43:0.92 66:0.64 69:0.45 70:0.15 74:0.46 81:0.96 83:0.80 85:0.72 91:0.44 98:0.63 101:0.76 104:0.76 108:0.35 114:0.98 120:0.65 122:0.43 123:0.33 126:0.54 127:0.18 129:0.05 135:0.83 140:0.45 145:0.88 146:0.30 147:0.37 149:0.55 150:0.98 151:0.61 153:0.48 154:0.91 155:0.67 159:0.13 161:0.82 162:0.86 163:0.92 164:0.56 165:0.92 167:0.54 172:0.16 173:0.82 176:0.73 177:0.38 179:0.93 181:0.53 187:0.21 190:0.77 192:0.59 201:0.74 202:0.36 212:0.95 215:0.96 216:0.49 220:0.62 222:0.48 230:0.81 232:0.82 233:0.76 235:0.60 241:0.39 242:0.82 243:0.69 247:0.12 248:0.59 253:0.73 256:0.45 259:0.21 260:0.66 265:0.64 266:0.12 267:0.17 268:0.46 271:0.42 276:0.13 285:0.50 290:0.98 297:0.36 300:0.13 +1 1:0.74 11:0.57 12:0.37 17:0.62 21:0.71 27:0.45 28:0.78 30:0.45 32:0.80 34:0.87 36:0.20 37:0.50 39:0.31 43:0.81 66:0.69 69:0.71 70:0.25 74:0.56 77:0.70 81:0.52 83:0.73 85:0.72 91:0.12 98:0.46 101:0.72 108:0.54 114:0.68 122:0.43 123:0.52 126:0.54 127:0.14 129:0.05 135:0.75 145:0.58 146:0.55 147:0.61 149:0.51 150:0.67 154:0.77 155:0.67 159:0.20 161:0.82 163:0.86 165:0.69 167:0.48 172:0.21 173:0.71 176:0.73 177:0.38 178:0.55 179:0.72 181:0.53 187:0.68 192:0.59 195:0.72 201:0.74 212:0.61 215:0.48 216:0.77 222:0.48 235:0.88 241:0.12 242:0.63 243:0.73 247:0.30 253:0.56 259:0.21 265:0.48 266:0.17 267:0.28 271:0.42 276:0.20 282:0.88 290:0.68 297:0.36 299:0.88 300:0.18 +0 1:0.74 6:0.79 7:0.78 8:0.57 9:0.80 11:0.57 12:0.37 17:0.40 20:0.96 27:0.55 28:0.78 30:0.76 34:0.22 36:0.95 37:0.23 39:0.31 41:0.92 43:0.92 60:0.87 66:0.64 69:0.45 70:0.15 74:0.58 78:0.94 81:0.96 83:0.88 85:0.72 91:0.56 96:0.78 98:0.63 100:0.85 101:0.76 104:0.76 108:0.35 111:0.91 114:0.98 120:0.67 122:0.43 123:0.33 126:0.54 127:0.18 129:0.05 135:0.84 140:0.45 145:0.88 146:0.30 147:0.37 149:0.55 150:0.98 151:0.69 152:0.89 153:0.45 154:0.91 155:0.67 158:0.92 159:0.13 161:0.82 162:0.66 163:0.92 164:0.69 165:0.92 167:0.64 169:0.82 172:0.16 173:0.82 176:0.73 177:0.38 179:0.93 181:0.53 186:0.94 187:0.21 189:0.81 192:0.59 201:0.74 202:0.61 208:0.64 212:0.95 215:0.96 216:0.49 220:0.82 222:0.48 230:0.81 232:0.82 233:0.76 235:0.60 238:0.81 241:0.64 242:0.82 243:0.69 247:0.12 248:0.72 253:0.81 255:0.79 256:0.73 259:0.21 260:0.68 261:0.91 265:0.64 266:0.12 267:0.17 268:0.63 271:0.42 276:0.13 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.13 +1 1:0.74 6:0.79 8:0.58 9:0.80 11:0.57 12:0.37 17:0.30 20:0.93 21:0.22 27:0.32 28:0.78 30:0.87 34:0.55 36:0.41 37:0.26 39:0.31 41:0.82 43:0.85 60:0.97 66:0.63 69:0.60 70:0.27 74:0.77 78:0.97 81:0.84 83:0.87 85:0.90 91:0.62 96:0.80 98:0.67 100:0.86 101:0.82 104:0.83 108:0.46 111:0.94 114:0.90 120:0.69 122:0.43 123:0.44 124:0.45 126:0.54 127:0.33 129:0.59 133:0.47 135:0.91 140:0.45 145:0.54 146:0.63 147:0.68 149:0.84 150:0.90 151:0.87 152:0.86 153:0.48 154:0.83 155:0.67 158:0.92 159:0.33 161:0.82 162:0.86 163:0.81 164:0.57 165:0.86 167:0.49 169:0.83 172:0.41 173:0.67 176:0.73 177:0.38 179:0.76 181:0.53 186:0.97 187:0.39 189:0.81 192:0.59 201:0.74 202:0.36 208:0.64 212:0.30 215:0.79 216:0.25 222:0.48 232:0.88 235:0.31 238:0.81 241:0.18 242:0.63 243:0.68 245:0.54 247:0.30 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 261:0.92 265:0.68 266:0.54 267:0.69 268:0.46 271:0.42 276:0.36 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.25 +2 1:0.74 6:0.83 8:0.61 9:0.80 11:0.57 12:0.37 17:0.04 20:0.98 21:0.05 27:0.14 28:0.78 30:0.76 32:0.80 34:0.80 36:0.30 37:0.67 39:0.31 41:0.82 43:0.86 66:0.36 69:0.57 70:0.28 74:0.74 77:0.70 78:0.99 81:0.92 83:0.80 85:0.88 91:0.42 96:0.80 98:0.51 100:0.97 101:0.80 108:0.60 111:0.97 114:0.95 120:0.69 122:0.43 123:0.58 124:0.59 126:0.54 127:0.23 129:0.59 133:0.47 135:0.87 140:0.45 145:0.47 146:0.22 147:0.28 149:0.81 150:0.96 151:0.87 152:0.98 153:0.48 154:0.84 155:0.67 159:0.29 161:0.82 162:0.86 163:0.81 164:0.57 165:0.90 167:0.49 169:0.96 172:0.27 173:0.62 176:0.73 177:0.38 179:0.69 181:0.53 186:0.98 187:0.68 189:0.84 192:0.59 195:0.65 201:0.74 202:0.36 208:0.64 212:0.37 215:0.91 216:0.93 222:0.48 235:0.12 238:0.87 241:0.15 242:0.72 243:0.46 245:0.56 247:0.30 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 261:0.97 265:0.52 266:0.38 267:0.52 268:0.46 271:0.42 276:0.33 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.25 +2 6:1.00 8:0.98 12:0.06 17:0.01 20:0.99 21:0.78 27:0.01 28:0.49 34:0.28 36:0.57 37:0.96 43:0.21 66:0.36 69:0.93 78:0.91 91:0.99 98:0.07 100:0.99 108:0.85 111:0.98 120:0.92 123:0.84 127:0.06 129:0.05 135:0.28 140:0.98 145:0.74 146:0.05 147:0.05 149:0.08 151:0.83 152:0.93 153:0.95 154:0.14 159:0.05 161:0.71 162:0.45 164:0.92 167:0.92 169:0.98 172:0.05 173:0.99 177:0.05 178:0.54 181:0.63 186:0.91 189:1.00 191:0.99 202:1.00 216:0.94 235:0.68 238:1.00 241:0.96 243:0.51 248:0.89 256:0.95 259:0.21 260:0.93 261:0.97 265:0.07 267:0.84 268:0.90 271:0.28 285:0.62 +2 8:0.67 12:0.06 17:0.30 20:0.82 21:0.78 25:0.88 27:0.53 28:0.49 34:0.59 36:0.50 37:0.33 78:0.74 91:0.88 100:0.78 104:0.58 111:0.74 120:0.74 135:0.28 140:0.94 151:0.66 152:0.79 153:0.48 161:0.71 162:0.47 164:0.75 167:0.92 169:0.76 175:0.75 178:0.53 181:0.63 186:0.75 191:0.95 202:0.85 216:0.23 220:0.91 235:0.02 241:0.95 244:0.61 248:0.67 256:0.68 257:0.65 259:0.21 260:0.75 261:0.75 267:0.23 268:0.69 271:0.28 277:0.69 285:0.62 +3 6:0.88 8:0.93 12:0.06 17:0.07 20:0.99 21:0.54 25:0.88 27:0.40 28:0.49 30:0.15 34:0.47 36:0.77 37:0.74 43:0.47 66:0.16 69:0.39 70:0.68 78:0.74 81:0.91 91:0.90 98:0.63 100:0.79 104:0.58 108:0.63 111:0.74 120:0.80 123:0.29 124:0.51 126:0.54 127:0.78 129:0.59 133:0.47 135:0.26 140:0.93 146:0.47 147:0.53 149:0.49 150:0.83 151:0.74 152:0.94 153:0.83 154:0.36 159:0.31 161:0.71 162:0.48 164:0.89 167:0.92 169:0.77 172:0.37 173:0.57 177:0.05 178:0.52 179:0.87 181:0.63 186:0.75 189:0.94 191:0.84 202:0.94 212:0.30 215:0.58 216:0.59 220:0.96 235:0.60 238:0.97 241:0.96 242:0.82 243:0.37 245:0.57 247:0.12 248:0.72 256:0.86 259:0.21 260:0.81 261:0.78 265:0.60 266:0.54 267:0.28 268:0.86 271:0.28 276:0.37 283:0.60 285:0.62 297:0.36 300:0.43 +1 6:0.99 8:0.98 12:0.06 17:0.28 20:0.78 21:0.78 27:0.32 28:0.49 34:0.68 36:0.29 37:0.95 78:0.76 91:0.92 100:0.86 104:0.58 111:0.79 120:0.69 135:0.80 140:0.97 151:0.66 152:0.76 153:0.48 161:0.71 162:0.46 164:0.57 167:0.92 169:0.83 175:0.75 178:0.54 181:0.63 186:0.77 189:0.99 191:0.94 202:0.79 216:0.23 238:0.99 241:0.94 244:0.61 248:0.63 256:0.45 257:0.65 259:0.21 260:0.70 261:0.77 267:0.44 268:0.46 271:0.28 277:0.69 283:0.60 285:0.62 +2 6:1.00 8:0.91 12:0.06 17:0.30 20:0.99 21:0.78 25:0.88 27:0.10 28:0.49 34:0.26 36:0.72 37:0.97 43:0.33 66:0.36 69:0.67 78:0.78 91:0.73 98:0.09 100:0.86 104:0.58 108:0.51 111:0.80 120:0.75 123:0.49 127:0.07 129:0.05 135:0.18 140:0.94 145:0.45 146:0.05 147:0.05 149:0.13 151:0.71 152:1.00 153:0.72 154:0.25 159:0.05 161:0.71 162:0.47 164:0.76 167:0.73 169:0.95 172:0.05 173:0.80 177:0.05 178:0.53 181:0.63 186:0.79 187:0.68 189:0.98 191:0.93 202:0.85 216:0.67 235:0.42 238:0.97 241:0.65 243:0.51 248:0.68 256:0.68 259:0.21 260:0.76 261:0.96 265:0.10 267:0.44 268:0.70 271:0.28 285:0.62 +1 12:0.06 17:0.59 21:0.54 27:0.45 28:0.49 30:0.62 34:0.94 36:0.19 37:0.50 43:0.32 55:0.71 66:0.54 69:0.69 70:0.54 81:0.91 91:0.09 98:0.52 108:0.53 123:0.51 124:0.50 126:0.54 127:0.22 129:0.59 133:0.47 135:0.80 145:0.50 146:0.72 147:0.76 149:0.47 150:0.83 154:0.24 159:0.43 161:0.71 167:0.56 172:0.41 173:0.61 177:0.05 178:0.55 179:0.59 181:0.63 187:0.68 212:0.14 215:0.58 216:0.77 235:0.60 241:0.15 242:0.82 243:0.63 245:0.59 247:0.12 259:0.21 265:0.54 266:0.71 267:0.28 271:0.28 276:0.41 297:0.36 300:0.43 +1 6:1.00 8:0.99 12:0.06 17:0.02 20:0.99 21:0.78 25:0.88 27:0.10 28:0.49 34:0.24 36:0.37 37:0.97 43:0.52 66:0.36 69:0.12 78:0.90 91:0.97 98:0.10 100:0.98 104:0.58 108:0.10 111:0.97 120:0.82 123:0.10 127:0.07 129:0.05 135:0.37 140:0.93 146:0.05 147:0.05 149:0.16 151:0.79 152:1.00 153:0.91 154:0.41 159:0.05 161:0.71 162:0.48 164:0.91 167:0.92 169:0.95 172:0.05 173:0.35 177:0.05 178:0.52 181:0.63 186:0.90 189:1.00 191:0.96 202:0.95 216:0.67 220:0.82 235:0.42 238:1.00 241:0.93 243:0.51 248:0.74 256:0.89 259:0.21 260:0.83 261:0.96 265:0.10 267:0.73 268:0.89 271:0.28 283:0.82 285:0.62 +1 6:0.81 8:0.61 12:0.06 17:0.11 20:0.92 21:0.54 27:0.11 28:0.49 30:0.30 32:0.80 34:0.90 36:0.39 37:0.89 43:0.26 55:0.96 66:0.33 69:0.82 70:0.66 78:0.74 81:0.91 91:0.40 98:0.33 100:0.79 104:0.84 106:0.81 108:0.83 111:0.74 120:0.87 123:0.83 124:0.83 126:0.54 127:0.32 129:0.93 133:0.84 135:0.86 140:0.45 145:0.77 146:0.20 147:0.25 149:0.41 150:0.83 151:0.79 152:0.89 153:0.90 154:0.19 159:0.61 161:0.71 162:0.61 163:0.81 164:0.81 167:0.65 169:0.77 172:0.32 173:0.74 177:0.05 179:0.70 181:0.63 186:0.75 187:0.39 189:0.83 191:0.80 195:0.87 202:0.75 206:0.81 212:0.23 215:0.58 216:0.90 235:0.60 238:0.94 241:0.50 242:0.82 243:0.25 245:0.51 247:0.12 248:0.81 256:0.75 259:0.21 260:0.87 261:0.78 265:0.35 266:0.63 267:0.69 268:0.76 271:0.28 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.99 8:0.95 12:0.06 17:0.28 20:0.83 21:0.78 27:0.40 28:0.49 30:0.15 34:0.22 36:0.98 37:0.79 43:0.47 55:0.52 66:0.33 69:0.35 70:0.68 78:0.75 91:0.87 98:0.30 100:0.84 104:0.58 108:0.58 111:0.78 120:0.77 123:0.56 124:0.77 127:0.27 129:0.89 133:0.78 135:0.84 140:0.95 146:0.05 147:0.05 149:0.45 151:0.72 152:0.80 153:0.78 154:0.36 159:0.49 161:0.71 162:0.46 163:0.97 164:0.75 167:0.91 169:0.81 172:0.27 173:0.27 177:0.05 178:0.53 179:0.71 181:0.63 186:0.76 189:1.00 191:0.91 202:0.91 212:0.30 216:0.45 235:0.22 238:0.99 241:0.89 242:0.82 243:0.17 245:0.49 247:0.12 248:0.69 256:0.73 259:0.21 260:0.78 261:0.79 265:0.32 266:0.54 267:0.65 268:0.69 271:0.28 276:0.38 285:0.62 300:0.43 +1 7:0.81 8:0.79 12:0.06 17:0.30 20:0.85 21:0.64 27:0.16 28:0.49 30:0.60 32:0.80 34:0.80 36:0.35 37:0.85 43:0.28 55:0.84 66:0.77 69:0.78 70:0.53 78:0.74 81:0.88 91:0.14 98:0.76 100:0.77 104:0.98 106:0.81 108:0.71 111:0.73 120:0.69 123:0.69 124:0.39 126:0.54 127:0.31 129:0.59 133:0.47 135:0.72 140:0.80 145:0.91 146:0.05 147:0.05 149:0.46 150:0.75 151:0.66 152:0.81 153:0.48 154:0.20 159:0.55 161:0.71 162:0.62 163:0.86 164:0.57 167:0.62 169:0.75 172:0.57 173:0.72 177:0.05 178:0.42 179:0.63 181:0.63 186:0.75 187:0.68 195:0.84 202:0.50 206:0.81 212:0.30 215:0.53 216:0.81 230:0.87 233:0.76 235:0.74 241:0.39 242:0.82 243:0.13 245:0.51 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.75 265:0.76 266:0.63 267:0.28 268:0.46 271:0.28 276:0.49 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.88 12:0.06 17:0.18 20:0.75 21:0.59 25:0.88 27:0.40 28:0.49 30:0.45 34:0.80 36:0.87 37:0.74 43:0.47 55:0.59 66:0.24 69:0.40 70:0.61 78:0.72 81:0.89 91:0.22 98:0.45 100:0.73 104:0.58 108:0.65 111:0.72 120:0.69 123:0.30 124:0.57 126:0.54 127:0.58 129:0.59 133:0.47 135:0.80 140:0.80 146:0.35 147:0.41 149:0.51 150:0.79 151:0.66 152:0.94 153:0.48 154:0.36 159:0.29 161:0.71 162:0.62 164:0.57 167:0.65 169:0.77 172:0.32 173:0.57 177:0.05 178:0.42 179:0.84 181:0.63 186:0.73 187:0.39 202:0.50 212:0.61 215:0.55 216:0.59 235:0.68 241:0.47 242:0.82 243:0.44 245:0.59 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.78 265:0.42 266:0.38 267:0.28 268:0.46 271:0.28 276:0.32 285:0.62 297:0.36 300:0.43 +1 12:0.06 17:0.67 21:0.78 27:0.45 28:0.49 30:0.45 32:0.80 34:0.95 36:0.20 37:0.50 43:0.32 55:0.59 66:0.36 69:0.72 70:0.33 81:0.72 91:0.29 98:0.33 108:0.69 123:0.67 124:0.58 126:0.54 127:0.17 129:0.59 133:0.47 135:0.60 145:0.54 146:0.29 147:0.35 149:0.38 150:0.53 154:0.24 159:0.30 161:0.71 163:0.79 167:0.56 172:0.21 173:0.68 177:0.05 178:0.55 179:0.64 181:0.63 187:0.68 195:0.77 212:0.23 215:0.43 216:0.77 235:1.00 241:0.15 242:0.82 243:0.45 245:0.50 247:0.12 259:0.21 265:0.36 266:0.38 267:0.52 271:0.28 276:0.29 297:0.36 300:0.25 +1 6:0.81 8:0.65 12:0.06 17:0.13 20:0.83 21:0.54 27:0.11 28:0.49 30:0.30 32:0.80 34:0.86 36:0.36 37:0.89 43:0.26 55:0.96 66:0.33 69:0.82 70:0.66 78:0.74 81:0.91 91:0.37 98:0.33 100:0.80 104:0.84 106:0.81 108:0.83 111:0.75 120:0.81 123:0.83 124:0.83 126:0.54 127:0.32 129:0.93 133:0.84 135:0.85 140:0.45 145:0.77 146:0.20 147:0.25 149:0.41 150:0.83 151:0.72 152:0.89 153:0.77 154:0.19 159:0.61 161:0.71 162:0.86 163:0.81 164:0.72 167:0.61 169:0.77 172:0.32 173:0.74 177:0.05 179:0.70 181:0.63 186:0.75 187:0.39 189:0.83 191:0.87 195:0.87 202:0.57 206:0.81 212:0.23 215:0.58 216:0.90 235:0.60 238:0.97 241:0.37 242:0.82 243:0.25 245:0.51 247:0.12 248:0.73 256:0.64 259:0.21 260:0.81 261:0.78 265:0.35 266:0.63 267:0.65 268:0.66 271:0.28 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 8:0.99 12:0.06 17:0.69 20:0.82 21:0.59 27:0.49 28:0.49 30:0.61 32:0.80 34:0.52 36:0.56 37:0.94 43:0.44 55:0.59 66:0.89 69:0.48 70:0.42 78:0.74 81:0.89 91:0.74 98:0.95 100:0.78 104:0.58 108:0.37 111:0.74 120:0.74 123:0.35 126:0.54 127:0.64 129:0.05 135:0.30 140:0.45 145:0.98 146:0.71 147:0.75 149:0.68 150:0.79 151:0.66 152:0.79 153:0.48 154:0.34 159:0.28 161:0.71 162:0.75 164:0.84 167:0.92 169:0.75 172:0.68 173:0.67 177:0.05 179:0.65 181:0.63 186:0.75 195:0.57 202:0.76 212:0.85 215:0.55 216:0.24 220:0.62 235:0.68 241:0.92 242:0.82 243:0.89 247:0.12 248:0.67 256:0.79 259:0.21 260:0.75 261:0.75 265:0.95 266:0.27 267:0.52 268:0.80 271:0.28 276:0.58 285:0.62 297:0.36 300:0.33 +1 12:0.06 17:0.36 21:0.05 25:0.88 27:0.40 28:0.49 30:0.21 34:0.58 36:0.45 37:0.74 43:0.47 66:0.36 69:0.33 70:0.67 81:0.98 91:0.31 98:0.42 104:0.58 108:0.26 123:0.25 124:0.59 126:0.54 127:0.54 129:0.59 133:0.47 135:0.26 146:0.31 147:0.37 149:0.51 150:0.97 154:0.36 159:0.25 161:0.71 167:0.73 172:0.27 173:0.56 177:0.05 178:0.55 179:0.86 181:0.63 187:0.21 212:0.71 215:0.91 216:0.59 235:0.05 241:0.66 242:0.82 243:0.51 245:0.56 247:0.12 259:0.21 265:0.44 266:0.27 267:0.28 271:0.28 276:0.28 297:0.36 300:0.43 +2 12:0.06 17:0.28 21:0.22 25:0.88 27:0.40 28:0.49 30:0.21 34:0.50 36:0.81 37:0.74 43:0.47 55:0.59 66:0.16 69:0.35 70:0.92 81:0.96 91:0.15 98:0.25 104:0.58 108:0.85 123:0.26 124:0.78 126:0.54 127:0.89 129:0.89 133:0.78 135:0.28 146:0.35 147:0.41 149:0.49 150:0.94 154:0.36 159:0.62 161:0.71 167:0.72 172:0.32 173:0.30 177:0.05 178:0.55 179:0.81 181:0.63 187:0.21 202:0.62 212:0.49 215:0.79 216:0.59 235:0.22 241:0.64 242:0.82 243:0.37 245:0.56 247:0.12 259:0.21 265:0.25 266:0.47 267:0.23 271:0.28 276:0.40 297:0.36 300:0.70 +1 7:0.81 8:0.74 12:0.06 17:0.34 21:0.78 25:0.88 27:0.10 28:0.49 34:0.40 36:0.96 37:0.97 43:0.22 66:0.36 69:0.90 78:0.79 91:0.33 98:0.08 104:0.58 108:0.79 111:0.77 120:0.69 123:0.78 127:0.06 129:0.05 135:0.20 140:0.96 146:0.05 147:0.05 149:0.09 151:0.66 153:0.48 154:0.15 159:0.05 161:0.71 162:0.46 164:0.57 167:0.59 169:0.84 172:0.05 173:0.96 177:0.05 178:0.53 181:0.63 187:0.39 191:0.88 202:0.77 216:0.67 230:0.87 233:0.76 235:0.31 241:0.30 243:0.51 248:0.63 256:0.45 259:0.21 260:0.70 265:0.08 267:0.82 268:0.46 271:0.28 285:0.62 +2 7:0.78 8:0.70 11:0.57 12:0.69 17:0.34 21:0.13 27:0.48 28:0.70 30:0.11 34:0.23 36:0.93 37:0.47 39:0.76 43:0.28 55:0.71 66:0.11 69:0.33 70:0.94 74:0.98 76:1.00 77:0.89 81:0.70 85:0.72 91:0.56 98:0.36 101:0.64 104:0.95 106:0.81 108:0.95 117:0.86 122:0.67 123:0.95 124:0.96 126:0.32 127:0.95 129:0.97 133:0.96 135:0.74 145:0.61 146:0.63 147:0.68 149:0.72 150:0.50 154:0.88 159:0.93 161:0.88 167:0.45 172:0.73 173:0.10 177:0.38 178:0.55 179:0.18 181:0.45 187:0.87 195:0.98 206:0.81 212:0.42 215:0.84 216:0.74 222:0.84 230:0.81 232:0.86 233:0.76 235:0.12 241:0.05 242:0.58 243:0.19 245:0.91 247:0.67 253:0.70 259:0.21 265:0.38 266:0.96 267:0.59 271:0.34 276:0.93 282:0.84 297:0.36 300:0.94 +2 1:0.74 7:0.81 12:0.69 17:0.28 21:0.40 27:0.21 28:0.70 30:0.15 34:0.55 36:0.58 37:0.74 39:0.76 43:0.37 55:0.59 66:0.64 69:0.15 70:0.84 74:0.97 76:0.94 81:0.65 91:0.17 98:0.86 108:0.12 114:0.82 117:0.86 123:0.12 124:0.64 126:0.54 127:0.81 129:0.89 133:0.78 135:0.18 146:0.99 147:0.99 149:0.70 150:0.71 154:0.92 155:0.67 159:0.91 161:0.88 167:0.47 172:0.96 173:0.07 176:0.73 177:0.38 178:0.55 179:0.15 181:0.45 187:0.39 192:0.59 201:0.74 204:0.89 208:0.64 212:0.54 215:0.67 216:0.95 222:0.84 230:0.84 232:0.85 233:0.69 235:0.52 241:0.21 242:0.22 243:0.69 245:0.96 247:0.67 253:0.77 254:0.74 259:0.21 265:0.86 266:0.80 267:0.52 271:0.34 276:0.95 290:0.82 297:0.36 300:0.70 +1 1:0.74 7:0.81 11:0.57 12:0.69 17:0.72 21:0.71 27:0.72 28:0.70 30:0.38 32:0.80 34:0.49 36:0.98 39:0.76 43:0.78 55:0.84 60:0.87 66:0.80 69:0.80 70:0.64 74:0.89 77:0.70 81:0.48 83:0.82 85:0.72 91:0.18 98:0.85 101:0.70 108:0.63 114:0.68 121:0.90 123:0.61 124:0.42 126:0.54 127:0.70 129:0.05 133:0.74 135:0.93 145:0.41 146:0.97 147:0.05 149:0.72 150:0.65 154:0.70 155:0.67 158:0.87 159:0.79 161:0.88 165:0.69 167:0.45 172:0.87 173:0.66 176:0.73 177:0.05 178:0.55 179:0.35 181:0.45 187:0.95 192:0.59 195:0.92 201:0.74 202:0.36 204:0.89 208:0.64 212:0.26 215:0.48 216:0.16 222:0.84 230:0.87 233:0.76 235:0.88 241:0.10 242:0.43 243:0.07 245:0.71 247:0.67 253:0.69 257:0.65 259:0.21 265:0.85 266:0.75 267:0.89 271:0.34 276:0.80 279:0.86 282:0.88 283:0.71 290:0.68 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.99 7:0.81 8:0.96 9:0.80 12:0.69 17:0.20 20:0.98 21:0.40 27:0.21 28:0.70 30:0.15 34:0.34 36:0.57 37:0.74 39:0.76 43:0.38 55:0.59 66:0.33 69:0.15 70:0.86 74:0.97 76:0.94 78:0.98 81:0.65 91:0.44 96:0.87 98:0.68 100:0.99 104:0.94 106:0.81 108:0.12 111:0.99 114:0.82 117:0.86 120:0.87 123:0.12 124:0.82 126:0.54 127:0.83 129:0.93 133:0.84 135:0.17 140:0.45 146:0.98 147:0.98 149:0.70 150:0.71 151:0.83 152:0.98 153:0.72 154:0.92 155:0.67 158:0.87 159:0.91 161:0.88 162:0.61 164:0.80 167:0.47 169:0.98 172:0.91 173:0.07 176:0.73 177:0.38 179:0.16 181:0.45 186:0.98 187:0.39 189:0.99 190:0.77 192:0.59 201:0.74 202:0.76 204:0.89 206:0.81 208:0.64 212:0.54 215:0.67 216:0.95 220:0.62 222:0.84 230:0.84 232:0.85 233:0.69 235:0.52 238:0.96 241:0.21 242:0.27 243:0.50 245:0.97 247:0.67 248:0.79 253:0.91 254:0.74 255:0.79 256:0.78 259:0.21 260:0.87 261:0.97 265:0.68 266:0.87 267:0.65 268:0.77 271:0.34 276:0.95 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.70 +0 12:0.69 17:0.30 21:0.30 27:0.45 28:0.70 30:0.42 34:0.63 36:0.36 37:0.50 39:0.76 43:0.30 55:0.59 66:0.78 69:0.68 70:0.46 74:0.98 81:0.46 91:0.11 98:0.81 108:0.52 114:0.69 122:0.67 123:0.50 124:0.40 126:0.32 127:0.59 129:0.05 133:0.39 135:0.83 145:0.50 146:0.96 147:0.97 149:0.73 150:0.37 154:0.45 158:0.84 159:0.78 161:0.88 167:0.47 172:0.88 173:0.51 176:0.56 177:0.38 178:0.55 179:0.31 181:0.45 187:0.68 212:0.55 216:0.77 222:0.84 235:0.31 241:0.27 242:0.80 243:0.80 245:0.86 247:0.39 253:0.74 254:0.94 259:0.21 265:0.81 266:0.80 267:0.28 271:0.34 276:0.80 290:0.69 300:0.43 +2 1:0.74 7:0.78 11:0.57 12:0.69 17:0.62 21:0.30 27:0.49 28:0.70 30:0.61 32:0.80 34:0.92 36:0.91 37:0.53 39:0.76 43:0.92 55:0.71 66:0.89 69:0.37 70:0.51 74:0.92 76:0.85 77:0.70 81:0.73 83:0.75 85:0.85 91:0.12 98:0.95 101:0.80 104:0.90 106:0.81 108:0.29 114:0.86 117:0.86 122:0.67 123:0.28 126:0.54 127:0.64 129:0.05 135:0.42 145:0.42 146:0.78 147:0.82 149:0.81 150:0.83 154:0.92 155:0.67 158:0.85 159:0.34 161:0.88 165:0.84 167:0.46 172:0.68 173:0.50 176:0.73 177:0.38 178:0.55 179:0.65 181:0.45 187:0.68 192:0.59 195:0.59 201:0.74 206:0.81 212:0.77 215:0.72 216:0.71 222:0.84 230:0.81 232:0.97 233:0.76 235:0.42 241:0.15 242:0.51 243:0.89 247:0.67 253:0.76 259:0.21 265:0.95 266:0.38 267:0.59 271:0.34 276:0.58 282:0.88 283:0.82 290:0.86 297:0.36 299:0.88 300:0.43 +0 7:0.78 11:0.57 12:0.69 17:0.24 21:0.54 27:0.21 28:0.70 30:0.21 34:0.45 36:0.50 37:0.74 39:0.76 43:0.48 55:0.59 66:0.43 69:0.23 70:0.86 74:0.95 81:0.38 85:0.72 91:0.37 98:0.66 101:0.68 104:0.97 106:0.81 108:0.18 120:0.59 123:0.18 124:0.85 126:0.32 127:0.78 129:0.93 133:0.87 135:0.17 140:0.45 146:0.96 147:0.96 149:0.71 150:0.33 151:0.52 153:0.48 154:0.86 159:0.87 161:0.88 162:0.86 164:0.66 167:0.46 172:0.89 173:0.10 177:0.38 179:0.19 181:0.45 187:0.39 190:0.77 202:0.50 206:0.81 212:0.49 215:0.58 216:0.95 220:0.96 222:0.84 230:0.81 233:0.69 235:0.60 241:0.15 242:0.23 243:0.57 245:0.93 247:0.67 248:0.52 253:0.67 256:0.58 259:0.21 260:0.59 265:0.66 266:0.75 267:0.69 268:0.59 271:0.34 276:0.92 285:0.50 297:0.36 300:0.84 +0 6:0.92 7:0.78 8:0.70 12:0.69 17:0.45 20:0.94 21:0.05 27:0.48 28:0.70 30:0.15 34:0.24 36:0.92 37:0.47 39:0.76 43:0.33 55:0.84 66:0.13 69:0.32 70:0.89 74:0.84 76:1.00 77:0.89 78:0.89 81:0.79 91:0.23 98:0.40 100:0.84 104:0.95 106:0.81 108:0.25 111:0.86 117:0.86 123:0.89 124:0.90 126:0.32 127:0.79 129:0.81 133:0.89 135:0.76 145:0.61 146:0.43 147:0.50 149:0.45 150:0.58 152:0.97 154:0.88 159:0.81 161:0.88 163:0.81 167:0.45 169:0.86 172:0.37 173:0.17 177:0.38 178:0.55 179:0.53 181:0.45 186:0.89 187:0.87 195:0.92 206:0.81 212:0.18 215:0.91 216:0.74 222:0.84 230:0.81 232:0.86 233:0.76 235:0.05 241:0.07 242:0.71 243:0.33 245:0.70 247:0.60 253:0.60 254:0.74 259:0.21 261:0.92 265:0.24 266:0.94 267:0.59 271:0.34 276:0.68 282:0.84 297:0.36 300:0.70 +0 12:0.69 17:0.32 21:0.47 27:0.45 28:0.70 30:0.26 32:0.75 34:0.89 36:0.20 37:0.50 39:0.76 43:0.30 55:0.52 66:0.33 69:0.84 70:0.91 74:0.37 81:0.42 91:0.12 98:0.41 108:0.69 123:0.67 124:0.84 126:0.32 127:0.52 129:0.05 133:0.82 135:0.72 145:0.41 146:0.68 147:0.43 149:0.64 150:0.35 154:0.52 159:0.73 161:0.88 167:0.50 172:0.61 173:0.74 177:0.05 178:0.55 179:0.43 181:0.45 187:0.68 195:0.88 212:0.52 215:0.63 216:0.77 222:0.84 235:0.52 241:0.44 242:0.81 243:0.17 245:0.77 247:0.39 253:0.33 254:0.92 257:0.65 259:0.21 265:0.43 266:0.89 267:0.28 271:0.34 276:0.71 283:0.71 297:0.36 300:0.84 +0 1:0.74 6:0.80 7:0.78 8:0.60 11:0.57 12:0.69 17:0.45 20:0.93 21:0.22 27:0.71 28:0.70 30:0.17 32:0.80 34:0.52 36:0.64 37:0.54 39:0.76 43:0.72 55:0.59 60:0.87 66:0.60 69:0.87 70:0.88 74:0.94 76:0.85 77:0.89 78:0.92 81:0.81 83:0.81 85:0.80 87:0.98 91:0.08 98:0.66 100:0.87 101:0.71 104:0.83 106:0.81 108:0.74 111:0.90 114:0.90 122:0.57 123:0.72 124:0.77 126:0.54 127:0.72 129:0.59 133:0.86 135:0.78 145:0.65 146:0.95 147:0.41 149:0.72 150:0.87 152:0.86 154:0.62 155:0.67 158:0.86 159:0.86 161:0.88 165:0.86 167:0.46 169:0.84 172:0.85 173:0.70 176:0.73 177:0.05 178:0.55 179:0.31 181:0.45 186:0.92 187:0.21 189:0.82 192:0.59 195:0.95 201:0.74 206:0.81 208:0.64 212:0.35 215:0.79 216:0.15 222:0.84 230:0.81 232:0.70 233:0.76 235:0.42 238:0.82 241:0.15 242:0.21 243:0.09 245:0.81 247:0.67 253:0.78 257:0.65 259:0.21 261:0.89 265:0.67 266:0.91 267:0.73 271:0.34 276:0.83 279:0.86 282:0.88 283:0.82 290:0.90 297:0.36 299:0.88 300:0.84 +0 1:0.74 6:0.91 7:0.78 8:0.80 11:0.57 12:0.69 17:0.83 20:0.93 21:0.13 27:0.59 28:0.70 30:0.21 32:0.80 34:0.78 36:0.29 37:0.77 39:0.76 43:0.94 55:0.59 66:0.27 69:0.25 70:0.67 74:0.86 76:0.85 77:0.70 78:0.97 81:0.83 83:0.77 85:0.72 91:0.63 98:0.42 100:0.93 101:0.73 108:0.20 111:0.94 114:0.92 120:0.85 122:0.67 123:0.70 124:0.59 126:0.54 127:0.59 129:0.59 133:0.47 135:0.37 140:0.45 145:0.39 146:0.35 147:0.41 149:0.65 150:0.89 151:0.71 152:0.86 153:0.77 154:0.94 155:0.67 159:0.41 161:0.88 162:0.86 164:0.74 165:0.88 167:0.47 169:0.91 172:0.27 173:0.34 176:0.73 177:0.38 179:0.87 181:0.45 186:0.96 187:0.39 189:0.92 190:0.77 192:0.59 195:0.63 201:0.74 202:0.60 212:0.55 215:0.84 216:0.25 222:0.84 230:0.81 233:0.76 235:0.22 238:0.86 241:0.24 242:0.58 243:0.51 245:0.56 247:0.39 248:0.76 253:0.76 256:0.68 259:0.21 260:0.85 261:0.92 265:0.36 266:0.38 267:0.36 268:0.68 271:0.34 276:0.28 282:0.88 283:0.82 285:0.50 290:0.92 297:0.36 299:0.88 300:0.43 +0 7:0.78 11:0.57 12:0.69 17:0.36 21:0.78 27:0.48 28:0.70 30:0.45 34:0.36 36:0.96 37:0.47 39:0.76 43:0.60 55:0.71 66:0.25 69:0.37 70:0.77 74:0.99 85:0.96 91:0.01 98:0.59 101:0.73 104:0.96 106:0.81 108:0.92 122:0.67 123:0.92 124:0.96 127:0.97 129:0.98 133:0.96 135:0.89 145:0.61 146:0.39 147:0.45 149:0.87 154:0.85 159:0.98 161:0.88 167:0.45 172:0.99 173:0.07 177:0.38 178:0.55 179:0.09 181:0.45 187:0.95 206:0.81 212:0.60 216:0.74 222:0.84 230:0.81 233:0.76 235:0.22 241:0.02 242:0.19 243:0.05 245:0.99 247:0.67 253:0.71 259:0.21 265:0.60 266:0.95 267:0.36 271:0.34 276:0.99 300:0.94 +1 1:0.74 6:0.92 7:0.78 8:0.73 12:0.69 17:0.11 20:0.82 21:0.05 27:0.48 28:0.70 30:0.55 34:0.19 36:0.98 37:0.47 39:0.76 43:0.33 55:0.71 66:0.97 69:0.37 70:0.56 74:0.80 76:1.00 77:0.89 78:0.98 81:0.88 83:0.79 91:0.27 96:0.70 98:0.98 100:0.94 104:0.95 106:0.81 108:0.29 111:0.95 114:0.95 117:0.86 123:0.28 126:0.54 127:0.79 129:0.05 135:0.47 140:0.45 145:0.61 146:0.98 147:0.98 149:0.76 150:0.93 151:0.50 152:0.97 153:0.48 154:0.88 155:0.67 159:0.76 161:0.88 162:0.86 165:0.90 167:0.47 169:0.86 172:0.92 173:0.23 176:0.73 177:0.38 179:0.29 181:0.45 186:0.97 187:0.87 190:0.77 192:0.59 195:0.89 201:0.74 202:0.36 206:0.81 212:0.39 215:0.91 216:0.74 220:0.91 222:0.84 230:0.81 232:0.86 233:0.76 235:0.12 241:0.21 242:0.79 243:0.97 247:0.60 248:0.50 253:0.77 254:0.74 256:0.45 259:0.21 261:0.92 265:0.98 266:0.54 267:0.92 268:0.46 271:0.34 276:0.85 282:0.84 285:0.50 290:0.95 297:0.36 300:0.70 +2 8:0.88 11:0.57 12:0.69 17:0.16 20:0.92 21:0.22 27:0.57 28:0.70 30:0.38 32:0.77 34:0.33 36:0.84 37:0.54 39:0.76 43:0.80 55:0.59 60:0.87 66:0.45 69:0.70 70:0.65 74:0.89 77:0.89 78:0.90 81:0.52 83:0.80 85:0.72 91:0.27 98:0.72 100:0.84 101:0.71 104:0.93 106:0.81 108:0.70 111:0.87 114:0.72 117:0.86 120:0.69 123:0.68 124:0.76 126:0.32 127:0.72 129:0.81 133:0.76 135:0.66 140:0.45 145:0.56 146:0.26 147:0.05 149:0.67 150:0.40 151:0.66 152:0.86 153:0.48 154:0.75 155:0.67 158:0.87 159:0.71 161:0.88 162:0.86 164:0.56 167:0.45 169:0.82 172:0.63 173:0.60 176:0.56 177:0.05 179:0.52 181:0.45 186:0.90 187:0.68 190:0.77 192:0.59 195:0.84 202:0.36 206:0.81 208:0.64 212:0.51 216:0.70 222:0.84 232:0.97 235:0.22 241:0.01 242:0.38 243:0.09 245:0.75 247:0.67 248:0.63 253:0.70 256:0.45 257:0.65 259:0.21 260:0.70 261:0.88 265:0.73 266:0.80 267:0.28 268:0.46 271:0.34 276:0.68 279:0.86 282:0.85 283:0.82 285:0.50 290:0.72 300:0.55 +2 1:0.74 6:0.92 8:0.89 9:0.80 11:0.57 12:0.69 17:0.38 20:0.93 21:0.05 27:0.48 28:0.70 30:0.76 34:0.26 36:0.62 37:0.47 39:0.76 41:0.88 43:0.85 55:0.45 60:0.95 66:0.72 69:0.33 70:0.22 74:0.84 76:1.00 77:0.89 78:0.93 81:0.88 83:0.82 85:0.72 91:0.69 96:0.80 98:0.53 100:0.90 101:0.75 104:0.95 106:0.81 108:0.26 111:0.91 114:0.95 117:0.86 120:0.82 122:0.57 123:0.25 126:0.54 127:0.16 129:0.05 135:0.60 140:0.45 145:0.61 146:0.36 147:0.43 149:0.55 150:0.93 151:0.83 152:0.97 153:0.48 154:0.82 155:0.67 158:0.92 159:0.14 161:0.88 162:0.83 163:0.75 164:0.76 165:0.90 167:0.51 169:0.86 172:0.27 173:0.55 176:0.73 177:0.38 179:0.70 181:0.45 186:0.92 187:0.87 189:0.97 190:0.77 192:0.59 195:0.58 201:0.74 202:0.64 206:0.81 208:0.64 212:0.42 215:0.91 216:0.74 220:0.62 222:0.84 232:0.86 235:0.12 238:0.85 241:0.47 242:0.45 243:0.75 247:0.35 248:0.76 253:0.85 255:0.79 256:0.68 259:0.21 260:0.83 261:0.92 265:0.54 266:0.23 267:0.76 268:0.70 271:0.34 276:0.17 279:0.86 282:0.84 283:0.82 285:0.62 290:0.95 297:0.36 300:0.18 +0 1:0.74 11:0.57 12:0.69 17:0.22 20:0.95 21:0.68 27:0.18 28:0.70 30:0.27 34:0.36 36:0.73 37:0.45 39:0.76 43:0.80 55:0.71 66:0.08 69:0.34 70:0.87 74:0.99 78:0.78 81:0.48 85:0.97 91:0.10 98:0.36 100:0.73 101:0.72 108:0.99 111:0.77 114:0.70 120:0.69 122:0.57 123:0.99 124:0.98 126:0.54 127:1.00 129:1.00 133:0.98 135:0.34 140:0.45 146:0.96 147:0.96 149:0.93 150:0.62 151:0.66 152:0.91 153:0.48 154:0.87 155:0.67 159:0.99 161:0.88 162:0.86 164:0.56 167:0.45 169:0.72 172:0.95 173:0.06 176:0.73 177:0.38 179:0.09 181:0.45 186:0.79 187:0.87 190:0.77 192:0.59 201:0.74 202:0.36 212:0.47 215:0.49 216:0.83 222:0.84 235:0.84 241:0.05 242:0.22 243:0.19 245:0.99 247:0.67 248:0.63 253:0.76 256:0.45 259:0.21 260:0.70 261:0.80 265:0.39 266:0.98 267:0.65 268:0.46 271:0.34 276:1.00 285:0.50 290:0.70 297:0.36 300:0.94 +0 6:0.99 7:0.78 8:0.91 9:0.80 11:0.57 12:0.69 17:0.03 20:0.98 21:0.30 27:0.21 28:0.70 30:0.16 34:0.45 36:0.67 37:0.74 39:0.76 41:0.82 43:0.45 55:0.71 66:0.10 69:0.85 70:0.87 74:0.56 78:0.93 81:0.54 83:0.76 85:0.72 91:0.93 96:0.80 98:0.33 100:0.89 101:0.52 104:0.84 106:0.81 108:0.97 111:0.91 120:0.99 123:0.97 124:0.99 126:0.32 127:1.00 129:0.81 133:0.99 135:0.44 140:1.00 146:0.89 147:0.48 149:0.80 150:0.41 151:0.87 152:0.98 153:0.99 154:0.47 155:0.67 159:0.97 161:0.88 162:0.60 164:0.97 167:0.55 169:0.98 172:0.84 173:0.35 177:0.05 179:0.13 181:0.45 186:0.93 187:0.39 189:0.94 191:0.85 192:0.59 202:0.96 206:0.81 208:0.64 212:0.16 215:0.72 216:0.95 222:0.84 230:0.81 233:0.76 235:0.31 238:0.85 241:0.57 242:0.79 243:0.08 245:0.93 247:0.60 248:0.78 253:0.77 255:0.79 256:0.96 257:0.65 259:0.21 260:0.99 261:0.97 265:0.35 266:0.98 267:0.85 268:0.97 271:0.34 276:0.97 283:0.82 285:0.62 297:0.36 300:0.94 +2 8:0.75 12:0.51 17:0.81 21:0.40 27:0.72 28:0.47 30:0.89 32:0.80 34:0.56 36:0.54 37:0.32 43:0.31 66:0.47 69:0.72 70:0.20 81:0.92 91:0.88 98:0.22 104:0.58 108:0.71 120:0.89 123:0.69 124:0.52 126:0.54 127:0.28 129:0.59 133:0.47 135:0.58 140:0.45 145:0.77 146:0.13 147:0.18 149:0.34 150:0.85 151:0.80 153:0.93 154:0.22 159:0.26 161:0.89 162:0.86 164:0.84 167:0.83 172:0.21 173:0.85 177:0.05 179:0.87 181:0.98 195:0.59 202:0.72 212:0.30 215:0.67 216:0.26 235:0.52 241:0.91 242:0.82 243:0.37 245:0.46 247:0.12 248:0.85 256:0.78 259:0.21 260:0.90 265:0.24 266:0.27 267:0.52 268:0.80 271:0.64 276:0.15 285:0.62 297:0.36 300:0.18 +3 8:0.84 12:0.51 17:0.26 21:0.22 25:0.88 27:0.26 28:0.47 34:0.21 36:0.45 37:0.79 43:0.52 66:0.36 69:0.10 78:0.88 81:0.87 91:0.98 98:0.22 104:0.58 108:0.09 111:0.93 120:0.98 123:0.09 126:0.54 127:0.11 129:0.05 135:0.30 140:0.45 146:0.05 147:0.05 149:0.16 150:0.73 151:0.85 153:0.98 154:0.41 159:0.05 161:0.89 162:0.78 164:0.98 167:0.85 169:0.94 172:0.05 173:0.87 177:0.05 181:0.98 191:0.93 202:0.96 215:0.79 216:0.69 235:0.22 238:0.97 241:1.00 243:0.51 248:0.98 256:0.97 259:0.21 260:0.98 265:0.24 267:0.73 268:0.98 271:0.64 283:0.82 285:0.62 297:0.36 +1 7:0.81 8:0.76 12:0.51 17:0.51 21:0.40 27:0.50 28:0.47 30:0.26 32:0.80 34:0.31 36:0.79 37:0.60 43:0.40 55:0.84 66:0.05 69:0.54 70:0.64 81:0.83 91:0.06 98:0.26 104:0.91 106:0.81 108:0.98 120:0.80 123:0.96 124:0.99 126:0.54 127:0.99 129:1.00 133:0.99 135:0.60 140:0.45 145:0.70 146:0.85 147:0.88 149:0.85 150:0.63 151:0.73 153:0.80 154:0.30 159:0.99 161:0.89 162:0.77 164:0.84 167:0.46 172:0.86 173:0.08 177:0.05 179:0.10 181:0.98 187:0.39 191:0.73 195:1.00 202:0.76 206:0.81 212:0.23 215:0.67 216:0.86 220:0.62 230:0.65 233:0.63 235:0.42 241:0.03 242:0.82 243:0.09 245:0.97 247:0.12 248:0.73 256:0.80 259:0.21 260:0.81 265:0.25 266:0.98 267:0.23 268:0.81 271:0.64 276:0.99 283:0.82 285:0.62 297:0.36 300:0.84 +2 8:0.83 12:0.51 17:0.18 20:0.86 21:0.47 25:0.88 27:0.38 28:0.47 30:0.68 32:0.80 34:0.55 36:0.85 37:0.64 43:0.41 55:0.71 66:0.79 69:0.53 70:0.31 78:0.76 81:0.81 91:0.85 98:0.85 100:0.79 104:0.58 108:0.41 111:0.76 120:0.88 123:0.39 126:0.54 127:0.36 129:0.05 135:0.21 140:0.45 145:0.41 146:0.50 147:0.56 149:0.48 150:0.61 151:0.83 152:0.81 153:0.96 154:0.31 159:0.18 161:0.89 162:0.83 164:0.93 167:0.83 169:0.77 172:0.41 173:0.82 177:0.05 179:0.85 181:0.98 186:0.77 191:0.90 195:0.58 202:0.87 212:0.89 215:0.63 216:0.35 235:0.52 241:0.91 242:0.82 243:0.80 247:0.12 248:0.83 256:0.90 259:0.21 260:0.89 261:0.78 265:0.85 266:0.17 267:0.23 268:0.91 271:0.64 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +2 8:0.86 12:0.51 17:0.07 21:0.30 27:0.50 28:0.47 30:0.76 34:0.17 36:0.92 37:0.31 43:0.36 55:0.52 66:0.72 69:0.61 70:0.15 78:0.79 81:0.85 91:0.97 98:0.79 104:0.73 108:0.46 111:0.79 120:0.92 123:0.45 126:0.54 127:0.27 129:0.05 135:0.37 140:0.80 146:0.45 147:0.52 149:0.36 150:0.68 151:0.78 153:0.90 154:0.27 159:0.16 161:0.89 162:0.77 163:0.86 164:0.98 167:0.85 169:0.82 172:0.27 173:0.88 177:0.05 178:0.42 179:0.91 181:0.98 191:0.87 202:0.95 212:0.42 215:0.72 216:0.61 220:0.74 235:0.31 241:0.98 242:0.82 243:0.75 247:0.12 248:0.88 256:0.96 259:0.21 260:0.92 265:0.79 266:0.23 267:0.44 268:0.97 271:0.64 276:0.24 283:0.60 285:0.62 297:0.36 300:0.13 +1 12:0.51 17:0.45 21:0.54 27:0.45 28:0.47 30:0.66 34:0.50 36:0.59 37:0.50 43:0.32 55:0.71 66:0.54 69:0.69 70:0.31 81:0.79 91:0.05 98:0.65 108:0.86 123:0.85 124:0.64 126:0.54 127:0.39 129:0.81 133:0.67 135:0.73 145:0.44 146:0.56 147:0.62 149:0.64 150:0.58 154:0.24 159:0.78 161:0.89 163:0.93 167:0.50 172:0.75 173:0.48 177:0.05 178:0.55 179:0.35 181:0.98 187:0.68 212:0.22 215:0.58 216:0.77 235:0.60 241:0.27 242:0.82 243:0.19 245:0.82 247:0.12 259:0.21 265:0.66 266:0.80 267:0.28 271:0.64 276:0.74 297:0.36 300:0.33 +0 6:0.90 8:0.72 12:0.51 17:0.59 20:0.81 21:0.22 25:0.88 27:0.26 28:0.47 34:0.55 36:0.66 37:0.79 78:0.82 81:0.87 91:0.82 100:0.88 104:0.58 111:0.83 120:0.88 126:0.54 135:0.47 140:0.45 150:0.73 151:0.74 152:0.84 153:0.84 161:0.89 162:0.86 164:0.85 167:0.80 169:0.89 175:0.75 181:0.98 186:0.82 187:0.21 189:0.88 202:0.74 215:0.79 216:0.69 235:0.22 238:0.94 241:0.82 244:0.61 248:0.84 256:0.80 257:0.65 259:0.21 260:0.89 261:0.89 267:0.18 268:0.81 271:0.64 277:0.69 285:0.62 297:0.36 +0 6:0.90 8:0.92 12:0.51 17:0.32 20:0.87 21:0.22 25:0.88 27:0.26 28:0.47 34:0.68 36:0.68 37:0.79 78:0.84 81:0.87 91:0.93 100:0.91 104:0.58 111:0.86 120:0.96 126:0.54 135:0.56 140:0.45 150:0.73 151:0.82 152:0.84 153:0.95 161:0.89 162:0.77 164:0.95 167:0.78 169:0.94 175:0.75 181:0.98 186:0.85 187:0.87 189:0.92 191:0.83 202:0.92 215:0.79 216:0.69 235:0.22 238:0.95 241:0.77 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.96 261:0.89 267:0.52 268:0.94 271:0.64 277:0.69 283:0.60 285:0.62 297:0.36 +2 6:0.95 8:0.88 12:0.51 17:0.20 20:0.95 21:0.78 27:0.16 28:0.47 34:0.58 36:0.26 37:0.85 78:0.91 91:0.97 100:0.99 104:0.58 111:0.98 120:0.97 135:0.77 140:0.45 151:0.84 152:0.93 153:0.97 161:0.89 162:0.80 164:0.97 167:0.82 169:0.98 175:0.75 181:0.98 186:0.91 189:0.96 191:0.84 202:0.94 216:0.55 238:0.99 241:0.86 244:0.61 248:0.96 256:0.95 257:0.65 259:0.21 260:0.98 261:0.98 267:0.87 268:0.96 271:0.64 277:0.69 285:0.62 +2 6:0.96 8:0.92 12:0.51 17:0.34 20:0.92 21:0.78 27:0.18 28:0.47 34:0.56 36:0.43 37:0.87 78:0.85 91:0.99 100:0.99 104:0.58 111:0.97 120:0.88 135:0.28 140:0.80 145:0.42 151:0.66 152:0.86 153:0.48 161:0.89 162:0.50 164:0.90 167:0.84 169:0.99 175:0.75 178:0.42 181:0.98 186:0.85 189:0.97 191:0.91 202:0.91 216:0.43 220:0.62 235:0.05 238:1.00 241:0.97 244:0.61 248:0.82 256:0.88 257:0.65 259:0.21 260:0.88 261:0.98 267:0.36 268:0.88 271:0.64 277:0.69 285:0.62 +1 6:0.90 8:0.91 12:0.51 17:0.38 20:0.76 21:0.22 25:0.88 27:0.26 28:0.47 34:0.44 36:0.49 37:0.79 43:0.52 66:0.36 69:0.10 78:0.89 81:0.87 91:0.90 98:0.15 100:0.98 104:0.58 106:0.81 108:0.09 111:0.96 120:0.98 123:0.09 126:0.54 127:0.09 129:0.05 135:0.42 140:0.45 146:0.05 147:0.05 149:0.16 150:0.73 151:0.84 152:0.84 153:0.98 154:0.41 159:0.05 161:0.89 162:0.79 164:0.97 167:0.82 169:0.89 172:0.05 173:0.68 177:0.05 181:0.98 186:0.89 187:0.68 189:0.92 191:0.91 202:0.95 206:0.81 215:0.79 216:0.69 235:0.22 238:0.99 241:0.87 243:0.51 248:0.97 256:0.96 259:0.21 260:0.98 261:0.89 265:0.16 267:0.85 268:0.97 271:0.64 283:0.82 285:0.62 297:0.36 +1 12:0.51 17:0.53 21:0.22 27:0.45 28:0.47 30:0.45 34:0.39 36:0.67 37:0.50 43:0.32 55:0.71 66:0.34 69:0.68 70:0.31 81:0.87 91:0.11 98:0.57 108:0.52 123:0.49 124:0.71 126:0.54 127:0.31 129:0.81 133:0.67 135:0.88 145:0.45 146:0.81 147:0.84 149:0.52 150:0.73 154:0.24 159:0.62 161:0.89 163:0.94 167:0.49 172:0.41 173:0.55 177:0.05 178:0.55 179:0.58 181:0.98 187:0.68 212:0.37 215:0.79 216:0.77 235:0.22 241:0.21 242:0.82 243:0.51 245:0.66 247:0.12 259:0.21 265:0.59 266:0.71 267:0.28 271:0.64 276:0.53 297:0.36 300:0.25 +2 7:0.81 8:0.78 12:0.51 17:0.38 21:0.47 27:0.21 28:0.47 30:0.10 34:0.67 36:0.86 37:0.74 43:0.52 55:0.52 66:0.30 69:0.15 70:0.87 78:0.76 81:0.81 91:0.23 98:0.60 104:0.84 106:0.81 108:0.87 111:0.75 120:0.84 123:0.86 124:0.89 126:0.54 127:0.78 129:0.96 133:0.90 135:0.17 140:0.45 146:0.45 147:0.52 149:0.76 150:0.61 151:0.80 153:0.92 154:0.41 159:0.88 161:0.89 162:0.70 164:0.83 167:0.52 169:0.84 172:0.78 173:0.08 177:0.05 179:0.27 181:0.98 187:0.39 202:0.76 206:0.81 212:0.30 215:0.63 216:0.95 230:0.87 233:0.76 235:0.52 241:0.39 242:0.82 243:0.15 245:0.87 247:0.12 248:0.77 256:0.77 259:0.21 260:0.85 265:0.61 266:0.91 267:0.88 268:0.79 271:0.64 276:0.86 283:0.82 285:0.62 297:0.36 300:0.84 +0 8:0.74 12:0.51 17:0.18 21:0.40 25:0.88 27:0.13 28:0.47 34:0.40 36:0.55 37:0.65 81:0.83 91:0.85 104:0.73 120:0.80 126:0.54 135:0.54 140:0.80 150:0.63 151:0.66 153:0.48 161:0.89 162:0.57 164:0.88 167:0.82 175:0.75 178:0.42 181:0.98 191:0.88 202:0.85 215:0.67 216:0.14 220:0.62 235:0.42 241:0.85 244:0.61 248:0.72 256:0.85 257:0.65 259:0.21 260:0.80 267:0.91 268:0.85 271:0.64 277:0.69 285:0.62 297:0.36 +2 6:0.91 7:0.81 8:0.88 12:0.51 17:0.30 20:0.96 21:0.40 27:0.21 28:0.47 30:0.45 34:0.34 36:0.63 37:0.74 43:0.52 55:0.71 66:0.07 69:0.15 70:0.66 78:0.77 81:0.83 91:0.89 98:0.33 100:0.88 104:0.91 106:0.81 108:0.99 111:0.80 120:0.93 123:0.95 124:0.98 126:0.54 127:0.98 129:1.00 133:0.98 135:0.47 140:0.45 146:0.25 147:0.31 149:0.85 150:0.63 151:0.79 152:0.96 153:0.90 154:0.41 159:0.98 161:0.89 162:0.64 164:0.96 167:0.51 169:0.83 172:0.94 173:0.05 177:0.05 179:0.10 181:0.98 186:0.78 187:0.39 189:0.93 191:0.84 202:0.94 206:0.81 212:0.41 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.98 241:0.35 242:0.82 243:0.05 245:0.98 247:0.12 248:0.89 256:0.94 259:0.21 260:0.93 261:0.88 265:0.32 266:0.97 267:0.69 268:0.95 271:0.64 276:0.99 283:0.82 285:0.62 297:0.36 300:0.94 +2 6:0.88 7:0.81 8:0.84 12:0.51 17:0.07 20:0.95 21:0.30 27:0.28 28:0.47 30:0.41 32:0.80 34:0.21 36:0.33 37:0.62 43:0.47 55:0.84 66:0.10 69:0.39 70:0.36 78:0.75 81:0.85 91:0.84 98:0.38 100:0.82 104:0.58 108:0.71 111:0.76 120:0.91 123:0.81 124:0.86 126:0.54 127:0.88 129:0.93 133:0.84 135:0.18 140:0.45 145:0.77 146:0.30 147:0.36 149:0.53 150:0.68 151:0.83 152:0.88 153:0.96 154:0.36 159:0.57 161:0.89 162:0.79 163:0.94 164:0.94 167:0.83 169:0.79 172:0.27 173:0.37 177:0.05 179:0.79 181:0.98 186:0.76 189:0.89 191:0.91 195:0.74 202:0.89 212:0.54 215:0.72 216:0.40 230:0.87 233:0.76 235:0.31 238:0.97 241:0.89 242:0.82 243:0.22 245:0.56 247:0.12 248:0.87 256:0.91 259:0.21 260:0.91 261:0.82 265:0.31 266:0.47 267:0.73 268:0.92 271:0.64 276:0.49 283:0.82 285:0.62 297:0.36 300:0.25 +1 1:0.66 10:0.96 11:0.64 12:0.69 17:0.32 18:0.89 21:0.22 22:0.93 26:0.98 27:0.56 29:0.77 30:0.76 33:0.97 34:0.87 36:0.24 37:0.60 39:0.78 43:0.62 44:0.92 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.53 69:0.83 70:0.53 71:0.81 74:0.61 77:0.89 81:0.73 82:0.99 83:0.69 86:0.83 88:0.99 89:0.99 91:0.10 97:0.89 98:0.79 99:0.99 101:0.97 102:0.97 108:0.68 114:0.88 117:0.86 122:0.98 123:0.66 124:0.66 126:0.54 127:0.58 129:0.05 131:0.61 133:0.62 135:0.66 139:0.83 141:0.91 144:0.57 145:0.70 146:0.92 147:0.60 149:0.66 150:0.54 154:0.24 155:0.50 157:0.94 158:0.73 159:0.69 165:0.88 166:0.88 170:0.79 172:0.92 173:0.77 174:0.97 176:0.70 177:0.96 178:0.55 179:0.18 182:0.89 187:0.95 192:0.51 195:0.84 197:0.97 199:0.98 201:0.66 208:0.64 212:0.69 216:0.76 219:0.88 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.82 234:0.91 235:0.68 236:0.95 239:0.95 240:0.95 241:0.15 242:0.14 243:0.23 244:0.61 245:0.96 246:0.92 247:0.98 253:0.63 254:0.90 257:0.65 259:0.21 262:0.93 264:0.98 265:0.79 266:0.38 267:0.36 274:0.91 275:0.99 276:0.92 279:0.74 282:0.80 287:0.61 290:0.88 291:0.97 292:0.95 294:0.99 300:0.70 +1 1:0.68 10:0.98 11:0.55 12:0.69 17:0.75 18:0.90 21:0.40 22:0.93 26:0.98 27:0.40 29:0.77 30:0.89 32:0.80 34:0.74 36:0.43 37:0.54 39:0.78 43:0.59 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.99 69:0.68 70:0.33 71:0.81 74:0.66 77:0.89 81:0.76 82:0.99 83:0.72 86:0.87 88:0.99 89:0.99 91:0.07 98:0.91 99:1.00 101:0.70 102:0.98 104:0.94 106:0.81 108:0.52 114:0.78 117:0.86 122:0.99 123:0.50 126:0.54 127:0.50 129:0.05 131:0.61 135:0.73 139:0.87 141:0.91 144:0.57 145:0.76 146:0.97 147:0.97 149:0.81 150:0.63 154:0.50 155:0.52 157:0.94 159:0.68 165:0.79 166:0.88 170:0.79 172:0.99 173:0.57 174:0.99 176:0.63 177:0.99 178:0.55 179:0.12 182:0.90 187:0.21 192:0.58 195:0.85 197:0.99 199:0.99 201:0.67 206:0.81 208:0.64 212:0.91 215:0.67 216:0.80 222:0.90 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.81 232:0.96 234:0.91 235:0.88 236:0.94 239:0.98 240:0.95 241:0.07 242:0.27 243:0.99 244:0.61 246:0.93 247:0.96 253:0.61 259:0.21 262:0.93 264:0.98 265:0.91 266:0.27 267:0.52 274:0.91 275:1.00 276:0.96 282:0.88 287:0.61 290:0.78 294:1.00 297:0.36 299:0.88 300:0.70 +1 1:0.58 10:0.95 11:0.55 12:0.69 17:0.36 18:0.69 21:0.22 26:0.98 27:0.42 29:0.77 30:0.89 33:0.97 34:0.30 36:0.32 37:0.66 39:0.78 43:0.27 45:0.96 56:0.97 58:0.93 66:0.77 69:0.10 70:0.36 71:0.81 74:0.37 80:0.98 81:0.38 82:0.98 83:0.54 86:0.70 88:0.99 89:0.98 91:0.31 98:0.75 99:0.83 101:0.70 102:0.94 108:0.09 122:0.98 123:0.09 124:0.40 126:0.26 127:0.41 129:0.05 131:0.61 133:0.37 135:0.80 139:0.67 141:0.91 144:0.57 145:0.63 146:0.76 147:0.80 149:0.57 150:0.37 154:0.53 155:0.50 157:0.79 159:0.43 165:0.63 166:0.61 170:0.79 172:0.83 173:0.20 174:0.95 177:0.99 178:0.55 179:0.34 182:0.73 187:0.95 192:0.47 193:0.95 195:0.69 197:0.97 199:0.98 201:0.55 204:0.80 208:0.49 212:0.82 216:0.52 222:0.90 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.66 234:0.91 235:0.31 236:0.91 239:0.94 240:0.95 241:0.18 242:0.24 243:0.79 244:0.61 245:0.82 246:0.61 247:0.84 253:0.33 254:0.80 259:0.21 264:0.98 265:0.75 266:0.63 267:0.44 274:0.91 275:0.98 276:0.73 287:0.61 291:0.97 294:0.98 300:0.55 +0 1:0.61 10:0.96 11:0.64 12:0.69 17:0.91 18:0.99 21:0.22 22:0.93 26:0.98 27:0.69 29:0.77 30:0.26 34:0.99 36:0.86 37:0.85 39:0.78 43:0.25 44:0.85 45:0.97 47:0.93 55:0.52 56:0.97 58:0.93 64:1.00 66:0.27 69:0.79 70:0.78 71:0.81 74:0.34 80:0.99 81:0.64 82:0.98 83:0.60 86:0.72 88:0.98 89:0.98 91:0.06 98:0.64 99:0.99 101:0.87 102:0.95 104:0.97 106:0.81 108:0.63 122:0.95 123:0.86 124:0.72 126:0.54 127:0.58 129:0.81 131:0.61 133:0.67 135:0.93 139:0.70 141:0.91 144:0.57 145:0.89 146:0.91 147:0.93 149:0.55 150:0.53 154:0.17 155:0.53 157:0.77 159:0.82 165:0.69 166:0.80 170:0.79 172:0.93 173:0.60 174:0.98 175:0.75 177:0.96 178:0.55 179:0.12 182:0.72 187:0.39 192:0.51 193:0.95 195:0.94 197:0.98 199:0.99 201:0.61 204:0.82 206:0.81 208:0.61 212:0.87 215:0.63 216:0.47 219:0.87 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.80 234:0.91 235:0.88 236:0.92 239:0.95 240:0.95 241:0.62 242:0.39 243:0.48 244:0.93 245:0.99 246:0.61 247:0.99 253:0.30 257:0.65 259:0.21 262:0.93 264:0.98 265:0.61 266:0.71 267:0.36 274:0.91 275:0.99 276:0.96 277:0.69 283:0.66 287:0.61 292:0.87 294:0.98 297:0.36 300:0.70 +0 8:0.91 9:0.71 10:0.95 11:0.46 12:0.69 17:0.61 18:0.84 21:0.30 23:0.98 26:0.98 27:0.66 29:0.77 30:0.75 31:0.94 34:0.73 36:0.97 37:0.96 39:0.78 43:0.17 45:0.93 58:0.99 62:0.97 64:0.77 66:0.95 69:0.15 70:0.48 71:0.81 74:0.26 79:0.94 81:0.28 83:0.60 86:0.65 89:0.99 91:0.82 98:0.97 101:0.52 108:0.12 122:0.98 123:0.12 126:0.22 127:0.74 128:0.96 129:0.05 131:0.86 135:0.76 137:0.94 139:0.63 140:0.45 141:0.99 143:0.99 146:0.81 147:0.84 149:0.14 150:0.31 151:0.57 153:0.48 154:0.28 155:0.55 157:0.61 159:0.37 162:0.69 166:0.80 168:0.96 170:0.79 172:0.86 173:0.31 174:0.94 177:0.99 179:0.39 182:0.61 190:0.99 191:0.87 192:0.56 196:0.88 197:0.96 199:0.97 202:0.72 205:0.98 208:0.54 212:0.80 216:0.24 220:0.91 222:0.90 223:0.98 224:0.98 225:0.97 227:0.89 229:0.61 231:0.75 234:0.97 235:0.31 239:0.92 240:0.95 241:0.80 242:0.18 243:0.95 244:0.93 246:0.61 247:0.91 248:0.60 253:0.23 254:0.80 255:0.70 256:0.73 259:0.21 264:0.98 265:0.97 266:0.27 267:0.19 268:0.75 274:0.98 275:0.97 276:0.77 284:0.96 285:0.50 287:0.61 294:0.98 300:0.55 +0 1:0.60 8:0.98 9:0.70 10:0.98 11:0.64 12:0.69 17:0.93 18:0.98 21:0.40 23:0.95 26:0.98 27:0.69 29:0.77 30:0.42 33:0.97 34:0.31 36:0.67 37:0.85 39:0.78 43:0.27 45:0.99 55:0.52 56:0.97 58:0.98 62:0.97 66:0.19 69:0.41 70:0.65 71:0.81 74:0.33 75:0.95 81:0.50 82:0.98 83:0.60 86:0.72 88:0.99 89:0.99 91:0.03 97:0.89 98:0.69 99:0.95 101:0.87 102:0.94 108:0.76 122:0.95 123:0.89 124:0.74 126:0.32 127:0.91 128:0.95 129:0.81 131:0.61 133:0.67 135:0.19 139:0.69 140:0.45 141:0.99 143:0.98 144:0.57 145:0.82 146:0.69 147:0.74 149:0.72 150:0.41 151:0.53 153:0.48 154:0.27 155:0.52 157:0.77 159:0.83 162:0.86 165:0.69 166:0.61 168:0.95 170:0.79 172:0.93 173:0.21 174:0.99 175:0.75 177:0.96 179:0.13 182:0.72 187:0.21 190:0.98 191:0.86 192:0.49 195:0.92 197:0.99 199:0.99 201:0.59 202:0.36 205:0.95 208:0.54 212:0.90 216:0.47 220:0.91 222:0.90 223:0.98 224:0.99 225:0.99 226:0.96 227:0.61 229:0.61 231:0.80 234:0.98 235:0.60 236:0.92 239:0.97 240:0.99 241:0.47 242:0.29 243:0.40 244:0.83 245:0.99 246:0.61 247:0.99 248:0.53 253:0.29 254:0.92 255:0.69 256:0.45 257:0.65 259:0.21 264:0.98 265:0.64 266:0.75 267:0.59 268:0.46 274:0.97 275:0.99 276:0.97 277:0.69 279:0.74 285:0.50 287:0.61 291:0.97 294:0.99 295:0.98 300:0.84 +0 10:0.92 11:0.55 12:0.69 17:0.90 18:0.99 21:0.22 22:0.93 26:0.98 27:0.69 29:0.77 30:0.33 34:0.97 36:0.87 37:0.85 39:0.78 43:0.22 45:0.94 47:0.93 55:0.52 58:0.93 64:0.77 66:0.32 69:0.79 70:0.79 71:0.81 74:0.34 81:0.51 86:0.72 89:0.98 91:0.07 98:0.66 101:0.70 104:0.97 106:0.81 108:0.63 122:0.95 123:0.86 124:0.67 126:0.32 127:0.56 129:0.59 131:0.61 133:0.66 135:0.69 139:0.69 141:0.91 144:0.57 145:0.69 146:0.91 147:0.93 149:0.53 150:0.48 154:0.14 157:0.77 159:0.81 166:0.79 170:0.79 172:0.93 173:0.61 174:0.97 175:0.75 177:0.96 178:0.55 179:0.13 182:0.72 187:0.39 192:0.47 195:0.93 197:0.97 199:0.97 201:0.60 202:0.36 206:0.81 212:0.87 215:0.79 216:0.47 219:0.87 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.80 234:0.91 235:0.52 239:0.91 240:0.95 241:0.55 242:0.44 243:0.51 244:0.93 245:0.98 246:0.61 247:0.99 253:0.30 257:0.65 259:0.21 262:0.93 264:0.98 265:0.63 266:0.71 267:0.28 274:0.91 275:0.98 276:0.95 277:0.69 283:0.67 287:0.61 292:0.88 294:0.96 297:0.36 300:0.70 +0 1:0.60 8:0.79 9:0.71 10:0.93 11:0.64 12:0.69 17:0.86 18:0.99 21:0.22 23:0.95 26:0.98 27:0.69 29:0.77 30:0.43 33:0.97 34:0.97 36:0.77 37:0.85 39:0.78 43:0.25 45:0.95 55:0.52 56:0.97 58:0.98 62:0.96 66:0.30 69:0.79 70:0.77 71:0.81 74:0.34 75:0.95 81:0.53 82:0.98 83:0.60 86:0.71 88:0.99 89:0.98 91:0.17 97:0.89 98:0.63 99:0.95 101:0.87 102:0.94 108:0.63 122:0.95 123:0.86 124:0.74 126:0.32 127:0.54 128:0.95 129:0.59 131:0.61 133:0.74 135:0.95 139:0.68 140:0.80 141:0.99 143:0.98 144:0.57 145:0.70 146:0.91 147:0.92 149:0.53 150:0.44 151:0.57 153:0.48 154:0.14 155:0.54 157:0.77 159:0.82 162:0.51 165:0.69 166:0.61 168:0.95 170:0.79 172:0.93 173:0.60 174:0.97 175:0.75 177:0.96 178:0.42 179:0.13 182:0.72 187:0.39 190:0.98 192:0.50 195:0.94 197:0.98 199:0.98 201:0.59 202:0.60 205:0.95 208:0.54 212:0.86 216:0.47 220:0.87 222:0.90 223:0.98 224:0.98 225:0.99 226:0.96 227:0.61 229:0.61 231:0.80 234:0.97 235:0.52 236:0.92 239:0.92 240:0.99 241:0.57 242:0.44 243:0.57 244:0.93 245:0.97 246:0.61 247:0.99 248:0.56 253:0.30 255:0.70 256:0.45 257:0.65 259:0.21 264:0.98 265:0.60 266:0.80 267:0.59 268:0.46 274:0.97 275:0.98 276:0.95 277:0.69 279:0.74 285:0.50 287:0.61 291:0.97 294:0.97 295:0.98 300:0.84 +0 10:0.92 11:0.55 12:0.69 17:0.89 18:0.99 21:0.22 26:0.98 27:0.69 29:0.77 30:0.43 32:0.63 34:0.97 36:0.79 37:0.85 39:0.78 43:0.20 45:0.93 55:0.52 58:0.93 66:0.29 69:0.79 70:0.78 71:0.81 74:0.34 81:0.41 86:0.71 89:0.98 91:0.03 98:0.63 101:0.70 104:0.99 106:0.81 108:0.63 122:0.95 123:0.86 124:0.69 126:0.26 127:0.62 129:0.59 131:0.61 133:0.66 135:0.89 139:0.68 141:0.91 144:0.57 145:0.97 146:0.91 147:0.92 149:0.42 150:0.43 154:0.14 157:0.77 159:0.83 166:0.61 170:0.79 172:0.93 173:0.60 174:0.97 175:0.75 177:0.96 178:0.55 179:0.14 182:0.72 187:0.39 192:0.44 195:0.94 197:0.97 199:0.97 201:0.54 206:0.81 212:0.86 215:0.67 216:0.47 222:0.90 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.80 234:0.91 235:0.68 239:0.90 240:0.95 241:0.37 242:0.43 243:0.51 244:0.93 245:0.98 246:0.61 247:0.99 253:0.30 257:0.65 259:0.21 264:0.98 265:0.60 266:0.71 267:0.59 274:0.91 275:0.98 276:0.95 277:0.69 283:0.67 287:0.61 294:0.96 297:0.99 300:0.70 +1 1:0.67 10:0.98 11:0.64 12:0.69 17:0.61 18:0.91 21:0.22 22:0.93 26:0.98 27:0.56 29:0.77 30:0.87 32:0.80 33:0.97 34:0.64 36:0.35 37:0.60 39:0.78 43:0.74 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.75 69:0.81 70:0.41 71:0.81 74:0.70 77:0.89 81:0.82 82:0.99 83:0.72 86:0.89 88:0.99 89:0.99 91:0.06 97:0.94 98:0.82 101:0.97 102:0.98 104:0.90 106:0.81 108:0.66 114:0.90 117:0.86 122:0.99 123:0.64 124:0.42 126:0.54 127:0.51 129:0.05 131:0.61 133:0.45 135:0.18 139:0.89 141:0.91 144:0.57 145:0.74 146:0.89 147:0.42 149:0.77 150:0.62 154:0.65 155:0.51 157:0.95 158:0.73 159:0.58 165:0.93 166:0.88 170:0.79 172:0.96 173:0.80 174:0.98 176:0.73 177:0.96 178:0.55 179:0.15 182:0.89 187:0.39 192:0.57 195:0.76 197:0.98 199:0.99 201:0.67 206:0.81 208:0.64 212:0.92 215:0.79 216:0.76 219:0.88 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.81 232:0.94 234:0.91 235:0.74 236:0.97 239:0.98 240:0.95 241:0.30 242:0.18 243:0.23 244:0.61 245:0.97 246:0.92 247:0.97 253:0.67 257:0.65 259:0.21 262:0.93 264:0.98 265:0.82 266:0.54 267:0.73 274:0.91 275:0.99 276:0.93 279:0.77 282:0.88 283:0.67 287:0.61 290:0.90 291:0.97 292:0.88 294:0.99 297:0.36 299:0.88 300:0.84 +1 1:0.65 10:0.97 11:0.82 12:0.69 17:0.53 18:0.91 21:0.40 22:0.93 26:0.98 27:0.56 29:0.77 30:0.87 32:0.72 33:0.97 34:0.95 36:0.20 37:0.60 39:0.78 43:0.70 44:0.92 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.98 69:0.58 70:0.37 71:0.81 74:0.69 77:0.70 81:0.78 82:0.99 83:0.91 85:0.72 86:0.91 88:0.99 89:0.98 91:0.15 98:0.87 99:0.99 101:1.00 102:0.97 104:0.91 106:0.81 108:0.44 114:0.82 117:0.86 122:0.99 123:0.42 126:0.54 127:0.40 129:0.05 131:0.61 135:0.68 139:0.89 141:0.91 144:0.57 145:0.72 146:0.87 147:0.89 149:0.77 150:0.59 154:0.60 155:0.50 157:0.95 159:0.45 165:0.88 166:0.88 170:0.79 172:0.94 173:0.58 174:0.96 176:0.70 177:0.99 178:0.55 179:0.19 182:0.89 187:0.21 192:0.55 195:0.68 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.88 215:0.67 216:0.76 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.81 232:0.95 234:0.91 235:0.80 236:0.95 239:0.96 240:0.95 241:0.18 242:0.16 243:0.98 246:0.92 247:0.94 253:0.63 259:0.21 262:0.93 264:0.98 265:0.87 266:0.27 267:0.36 274:0.91 275:0.98 276:0.88 282:0.80 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.70 +0 1:0.60 8:0.92 9:0.71 10:0.92 11:0.55 12:0.69 17:0.92 18:0.98 21:0.54 23:0.98 26:0.98 27:0.69 29:0.77 30:0.37 31:0.90 34:0.24 36:0.99 37:0.85 39:0.78 43:0.22 45:0.95 55:0.52 56:0.97 58:0.99 62:0.96 66:0.26 69:0.45 70:0.87 71:0.81 74:0.34 79:0.89 81:0.50 83:0.60 86:0.71 89:0.99 91:0.70 96:0.73 98:0.60 99:0.95 101:0.70 108:0.72 122:0.95 123:0.88 124:0.82 126:0.32 127:0.95 128:0.96 129:0.81 131:0.92 133:0.83 135:0.61 137:0.90 139:0.68 140:0.80 141:0.99 143:0.99 144:0.57 145:0.79 146:0.54 147:0.60 149:0.49 150:0.41 151:0.64 153:0.72 154:0.13 155:0.55 157:0.76 159:0.87 162:0.73 165:0.69 166:0.61 168:0.96 170:0.79 172:0.93 173:0.20 174:0.98 175:0.75 177:0.96 179:0.14 182:0.72 190:0.99 191:0.85 192:0.51 195:0.95 196:0.89 197:0.97 199:0.98 201:0.59 202:0.83 205:0.98 208:0.54 212:0.83 216:0.47 220:0.82 222:0.90 223:0.98 224:0.98 225:0.99 227:0.93 229:0.61 231:0.82 234:0.97 235:0.80 236:0.92 239:0.90 240:0.99 241:0.82 242:0.39 243:0.12 244:0.93 245:0.98 246:0.61 247:1.00 248:0.62 253:0.48 255:0.70 256:0.87 257:0.65 259:0.21 264:0.98 265:0.57 266:0.92 267:0.73 268:0.86 274:0.98 275:0.98 276:0.96 277:0.69 284:0.93 285:0.61 287:0.61 294:0.96 300:0.84 +2 6:0.96 7:0.81 8:0.85 11:0.57 12:0.51 17:0.79 20:0.78 21:0.47 27:0.06 28:0.99 30:0.33 34:0.39 36:0.49 37:0.86 39:0.39 43:0.80 55:0.45 66:0.06 69:0.62 70:0.94 74:0.92 76:0.99 77:0.70 78:0.96 81:0.47 85:0.80 91:0.46 96:0.78 98:0.26 100:0.93 101:0.72 108:0.47 111:0.94 114:0.66 117:0.86 121:0.90 122:0.67 123:0.66 124:0.85 126:0.32 127:0.50 129:0.81 133:0.83 135:0.94 140:0.45 145:0.59 146:0.45 147:0.52 149:0.67 150:0.38 151:0.61 152:0.76 153:0.48 154:0.74 155:0.67 158:0.85 159:0.78 161:0.88 162:0.86 167:0.68 169:0.91 172:0.45 173:0.41 176:0.56 177:0.38 179:0.50 181:0.47 186:0.95 187:0.87 189:0.97 190:0.77 191:0.82 192:0.59 195:0.93 202:0.54 204:0.89 208:0.64 212:0.50 216:0.76 220:0.93 222:0.48 230:0.87 232:0.91 233:0.76 235:0.52 238:0.86 241:0.68 242:0.41 243:0.40 245:0.72 247:0.60 248:0.60 253:0.83 256:0.58 259:0.21 261:0.82 265:0.25 266:0.92 267:0.82 268:0.63 276:0.62 277:0.69 282:0.84 285:0.50 290:0.66 300:0.84 +1 1:0.74 7:0.78 8:0.58 11:0.57 12:0.51 17:0.49 21:0.40 27:0.37 28:0.99 30:0.28 32:0.80 34:0.99 36:0.26 37:0.28 39:0.39 43:0.98 60:0.87 66:0.67 69:0.15 70:0.74 74:0.92 76:0.94 77:0.70 81:0.82 83:0.83 85:0.88 91:0.46 98:0.76 101:0.81 104:0.58 108:0.12 114:0.82 123:0.12 124:0.45 126:0.54 127:0.59 129:0.05 133:0.39 135:0.58 145:0.76 146:0.67 147:0.72 149:0.89 150:0.88 154:0.98 155:0.67 158:0.87 159:0.37 161:0.88 165:0.83 167:0.56 172:0.63 173:0.30 176:0.73 177:0.38 178:0.55 179:0.62 181:0.47 187:0.21 192:0.59 195:0.62 201:0.74 208:0.64 212:0.75 215:0.67 216:0.22 222:0.48 230:0.81 232:0.79 233:0.76 235:0.52 241:0.47 242:0.45 243:0.72 245:0.72 247:0.60 253:0.75 259:0.21 265:0.76 266:0.47 267:0.52 276:0.56 279:0.86 282:0.88 283:0.71 290:0.82 297:0.36 299:0.88 300:0.55 +1 7:0.81 8:0.59 9:0.80 11:0.57 12:0.51 17:0.79 21:0.78 27:0.37 28:0.99 30:0.33 34:0.75 36:0.27 37:0.36 39:0.39 41:0.82 43:0.89 66:0.46 69:0.49 70:0.55 74:0.85 83:0.76 85:0.88 91:0.46 96:0.80 98:0.62 101:0.78 104:0.58 108:0.72 120:0.69 121:0.90 122:0.67 123:0.70 124:0.57 127:0.37 129:0.59 133:0.47 135:0.42 140:0.45 145:0.72 146:0.24 147:0.30 149:0.79 151:0.87 153:0.48 154:0.87 155:0.67 159:0.51 161:0.88 162:0.86 164:0.57 167:0.54 172:0.45 173:0.44 177:0.38 179:0.65 181:0.47 187:0.21 192:0.59 202:0.36 204:0.89 208:0.64 212:0.40 216:0.15 222:0.48 230:0.83 232:0.79 233:0.66 235:0.31 241:0.39 242:0.31 243:0.45 245:0.70 247:0.60 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 265:0.63 266:0.63 267:0.36 268:0.46 276:0.44 285:0.62 300:0.43 +1 11:0.57 12:0.51 17:0.20 21:0.64 27:0.45 28:0.99 30:0.58 32:0.75 34:0.79 36:0.17 37:0.50 39:0.39 43:0.77 55:0.84 66:0.36 69:0.80 70:0.60 74:0.49 81:0.45 85:0.72 91:0.09 98:0.27 101:0.70 108:0.64 123:0.61 124:0.77 126:0.32 127:0.55 129:0.05 133:0.74 135:0.88 145:0.45 146:0.48 147:0.05 149:0.54 150:0.37 154:0.69 159:0.73 161:0.88 163:0.92 167:0.63 172:0.27 173:0.69 177:0.05 178:0.55 179:0.87 181:0.47 187:0.68 195:0.87 212:0.37 215:0.53 216:0.77 222:0.48 235:0.74 241:0.61 242:0.40 243:0.18 245:0.47 247:0.47 253:0.41 257:0.65 259:0.21 265:0.30 266:0.47 267:0.44 276:0.32 283:0.71 297:0.36 300:0.43 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.55 21:0.30 27:0.37 28:0.99 30:0.30 32:0.80 34:0.95 36:0.29 37:0.28 39:0.39 43:0.98 60:0.95 66:0.17 69:0.12 70:0.87 74:0.94 77:0.70 81:0.86 83:0.84 85:0.90 91:0.38 98:0.49 101:0.81 104:0.58 106:0.81 108:0.10 114:0.86 117:0.86 121:0.90 123:0.64 124:0.70 126:0.54 127:0.74 129:0.59 133:0.64 135:0.61 145:0.72 146:0.44 147:0.51 149:0.90 150:0.91 154:0.98 155:0.67 158:0.92 159:0.50 161:0.88 165:0.84 167:0.54 172:0.54 173:0.22 176:0.73 177:0.38 178:0.55 179:0.60 181:0.47 187:0.21 192:0.59 195:0.69 201:0.74 204:0.89 206:0.81 208:0.64 212:0.51 215:0.72 216:0.22 222:0.48 230:0.87 232:0.79 233:0.76 235:0.42 241:0.39 242:0.24 243:0.51 245:0.75 247:0.67 253:0.77 259:0.21 265:0.42 266:0.71 267:0.36 276:0.60 279:0.86 282:0.88 283:0.82 290:0.86 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.88 7:0.81 8:0.74 9:0.80 11:0.57 12:0.51 17:0.85 20:0.83 21:0.30 27:0.33 28:0.99 30:0.55 32:0.75 34:0.83 36:0.19 37:0.57 39:0.39 41:0.88 43:0.77 55:0.59 60:0.87 66:0.35 69:0.54 70:0.78 74:0.95 78:0.96 81:0.86 83:0.83 85:0.90 91:0.57 96:0.82 98:0.33 100:0.90 101:0.77 104:0.96 106:0.81 108:0.42 111:0.93 114:0.86 120:0.72 121:0.90 122:0.67 123:0.40 124:0.81 126:0.54 127:0.42 129:0.81 133:0.83 135:0.89 140:0.45 145:0.58 146:0.61 147:0.66 149:0.80 150:0.91 151:0.87 152:0.79 153:0.48 154:0.70 155:0.67 158:0.92 159:0.74 161:0.88 162:0.86 164:0.67 165:0.84 167:0.58 169:0.87 172:0.57 173:0.35 176:0.73 177:0.38 179:0.47 181:0.47 186:0.96 187:0.39 189:0.90 192:0.59 195:0.90 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.69 215:0.72 216:0.31 220:0.87 222:0.48 230:0.87 233:0.76 235:0.42 238:0.83 241:0.52 242:0.53 243:0.51 245:0.70 247:0.60 248:0.79 253:0.88 255:0.79 256:0.58 259:0.21 260:0.73 261:0.85 265:0.35 266:0.75 267:0.36 268:0.59 276:0.62 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +1 1:0.74 6:0.91 7:0.81 8:0.84 9:0.80 11:0.57 12:0.51 17:0.93 20:0.82 21:0.22 27:0.63 28:0.99 30:0.27 32:0.80 34:0.64 36:0.24 37:0.88 39:0.39 41:0.92 43:0.85 55:0.52 60:0.97 66:0.69 69:0.63 70:0.89 74:0.96 77:0.89 78:0.97 81:0.94 83:0.89 85:0.94 91:0.75 96:0.73 98:0.64 100:0.84 101:0.83 104:0.58 108:0.79 111:0.94 114:0.69 120:0.61 121:0.90 122:0.67 123:0.77 124:0.47 126:0.54 127:0.76 129:0.81 133:0.67 135:0.54 138:0.96 140:0.80 145:0.74 146:0.13 147:0.18 149:0.88 150:0.97 151:0.64 152:0.79 153:0.72 154:0.81 155:0.67 158:0.87 159:0.66 161:0.88 162:0.63 164:0.68 165:0.88 167:0.81 169:0.82 172:0.84 173:0.55 176:0.56 177:0.38 178:0.42 179:0.37 181:0.47 186:0.97 189:0.92 192:0.59 195:0.81 201:0.74 202:0.62 204:0.89 208:0.64 212:0.85 215:0.72 216:0.13 220:0.82 222:0.48 230:0.84 232:0.79 233:0.69 235:0.68 238:0.81 241:0.81 242:0.60 243:0.10 245:0.82 247:0.60 248:0.66 253:0.79 255:0.79 256:0.58 259:0.21 260:0.61 261:0.86 265:0.65 266:0.71 267:0.89 268:0.62 276:0.74 279:0.86 282:0.88 283:0.71 285:0.62 286:0.99 290:0.69 297:0.36 298:0.99 299:0.88 300:0.84 +2 1:0.74 7:0.81 8:0.88 11:0.57 12:0.51 17:0.85 21:0.22 27:0.63 28:0.99 30:0.21 32:0.80 34:0.96 36:0.34 37:0.88 39:0.39 43:0.33 66:0.45 69:0.91 70:0.88 74:0.93 77:1.00 81:0.96 83:0.79 85:0.96 91:0.10 98:0.40 101:0.80 104:0.58 106:0.81 108:0.92 114:0.90 117:0.86 121:0.99 122:0.57 123:0.92 124:0.80 126:0.54 127:0.70 129:0.59 133:0.82 135:0.89 145:0.67 146:0.56 147:0.05 149:0.84 150:0.98 154:0.24 155:0.67 159:0.87 161:0.88 165:0.91 167:0.46 172:0.82 173:0.76 176:0.73 177:0.05 178:0.55 179:0.28 181:0.47 187:0.39 192:0.59 195:0.96 201:0.74 204:0.89 206:0.81 208:0.64 212:0.71 215:0.79 216:0.13 222:0.48 230:0.87 232:0.79 233:0.76 235:0.80 241:0.02 242:0.29 243:0.06 245:0.87 247:0.67 253:0.77 257:0.65 259:0.21 265:0.42 266:0.87 267:0.23 276:0.82 282:0.88 290:0.90 297:0.36 299:1.00 300:0.84 +2 1:0.74 6:0.86 7:0.81 8:0.63 9:0.80 11:0.57 12:0.51 17:0.90 20:0.84 21:0.54 27:0.55 28:0.99 30:0.31 32:0.80 34:0.80 36:0.28 39:0.39 41:0.90 43:0.92 66:0.47 69:0.19 70:0.84 74:0.92 76:0.85 77:0.96 78:0.96 81:0.73 83:0.78 85:0.95 91:0.78 96:0.85 98:0.72 100:0.92 101:0.84 104:0.58 108:0.79 111:0.93 114:0.77 120:0.82 121:0.90 123:0.77 124:0.67 126:0.54 127:0.97 129:0.81 133:0.67 135:0.34 140:0.80 145:0.83 146:0.27 147:0.33 149:0.92 150:0.81 151:0.87 152:0.80 153:0.48 154:0.91 155:0.67 159:0.64 161:0.88 162:0.80 164:0.80 165:0.79 167:0.84 169:0.89 172:0.75 173:0.20 176:0.73 177:0.38 179:0.41 181:0.47 186:0.95 189:0.88 192:0.59 195:0.79 201:0.74 202:0.71 204:0.89 208:0.64 212:0.49 215:0.58 216:0.09 220:0.74 222:0.48 230:0.87 232:0.79 233:0.76 235:0.68 238:0.84 241:0.88 242:0.27 243:0.21 245:0.87 247:0.67 248:0.83 253:0.89 255:0.79 256:0.73 259:0.21 260:0.82 261:0.87 265:0.72 266:0.54 267:0.52 268:0.76 276:0.77 282:0.88 283:0.71 285:0.62 290:0.77 297:0.36 299:0.97 300:0.70 +2 1:0.74 11:0.57 12:0.51 17:0.93 21:0.54 27:0.72 28:0.99 30:0.36 32:0.80 34:0.29 36:0.22 39:0.39 43:0.90 55:0.59 60:0.87 66:0.67 69:0.47 70:0.78 74:0.88 77:0.70 81:0.73 83:0.82 85:0.80 87:0.98 91:0.06 98:0.71 101:0.75 104:0.94 106:0.81 108:0.65 114:0.77 117:0.86 123:0.63 124:0.45 126:0.54 127:0.38 129:0.59 133:0.47 135:0.30 145:0.52 146:0.26 147:0.32 149:0.79 150:0.81 154:0.89 155:0.67 158:0.92 159:0.46 161:0.88 165:0.79 167:0.55 172:0.70 173:0.44 176:0.73 177:0.38 178:0.55 179:0.46 181:0.47 187:0.21 192:0.59 195:0.70 201:0.74 206:0.81 208:0.64 212:0.71 215:0.58 216:0.17 222:0.48 232:0.77 235:0.68 241:0.43 242:0.71 243:0.29 245:0.78 247:0.60 253:0.72 259:0.21 265:0.71 266:0.54 267:0.28 276:0.63 279:0.86 282:0.88 283:0.82 290:0.77 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.82 27:0.09 28:0.99 30:0.42 32:0.80 34:0.90 36:0.75 37:0.84 39:0.39 43:0.95 55:0.84 60:0.87 66:0.62 69:0.12 70:0.56 74:0.84 77:0.89 81:0.99 83:0.88 85:0.88 91:0.58 98:0.62 101:0.78 104:0.88 106:0.81 108:0.10 114:0.98 117:0.86 121:0.90 123:0.10 124:0.52 126:0.54 127:0.57 129:0.59 133:0.64 135:0.71 145:0.72 146:0.75 147:0.79 149:0.83 150:1.00 154:0.95 155:0.67 158:0.92 159:0.60 161:0.88 163:0.75 165:0.92 167:0.57 172:0.57 173:0.16 176:0.73 177:0.38 178:0.55 179:0.68 181:0.47 187:0.21 192:0.59 195:0.76 201:0.74 204:0.89 206:0.81 208:0.64 212:0.23 215:0.96 216:0.45 222:0.48 230:0.87 232:0.92 233:0.76 235:0.05 241:0.49 242:0.45 243:0.68 245:0.61 247:0.67 253:0.78 259:0.21 265:0.63 266:0.71 267:0.82 276:0.50 279:0.86 282:0.88 283:0.82 290:0.98 297:0.36 299:0.97 300:0.43 +3 1:0.74 6:0.87 7:0.81 8:0.77 9:0.80 11:0.57 12:0.51 17:0.79 20:0.98 21:0.64 27:0.37 28:0.99 30:0.43 32:0.80 34:0.83 36:0.29 37:0.36 39:0.39 41:0.90 43:0.89 60:0.87 66:0.23 69:0.52 70:0.73 74:0.98 77:0.70 78:0.98 81:0.70 83:0.81 85:0.96 91:0.85 96:0.88 98:0.71 100:0.97 101:0.85 104:0.58 108:0.40 111:0.97 114:0.72 120:0.79 121:0.90 122:0.57 123:0.69 124:0.55 126:0.54 127:0.47 129:0.59 133:0.47 135:0.61 140:0.45 145:0.72 146:0.61 147:0.66 149:0.96 150:0.75 151:0.97 152:0.96 153:0.88 154:0.87 155:0.67 158:0.92 159:0.52 161:0.88 162:0.73 164:0.81 167:0.83 169:0.94 172:0.76 173:0.49 176:0.73 177:0.38 179:0.35 181:0.47 186:0.98 189:0.90 191:0.76 192:0.59 195:0.72 201:0.74 202:0.74 204:0.89 208:0.64 212:0.77 215:0.53 216:0.15 222:0.48 230:0.83 232:0.79 233:0.66 235:0.84 238:0.89 241:0.85 242:0.28 243:0.61 245:0.90 247:0.60 248:0.86 253:0.92 255:0.79 256:0.75 259:0.21 260:0.80 261:0.95 265:0.52 266:0.54 267:0.65 268:0.78 276:0.75 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.88 300:0.55 +0 12:0.51 17:0.18 21:0.78 27:0.45 28:0.99 30:0.09 34:0.87 36:0.17 37:0.50 39:0.39 43:0.30 55:0.45 66:0.44 69:0.83 70:0.99 74:0.80 77:0.70 91:0.37 98:0.21 108:0.68 122:0.57 123:0.66 124:0.76 127:0.51 129:0.05 133:0.74 135:0.90 145:0.52 146:0.38 147:0.40 149:0.25 154:0.64 159:0.74 161:0.88 167:0.52 172:0.37 173:0.72 177:0.05 178:0.55 179:0.78 181:0.47 187:0.68 195:0.89 212:0.52 216:0.77 222:0.48 235:0.52 241:0.30 242:0.42 243:0.25 245:0.52 247:0.47 253:0.57 254:0.74 257:0.65 259:0.21 265:0.23 266:0.54 267:0.52 276:0.39 282:0.84 300:0.84 +3 1:0.74 6:0.86 7:0.81 8:0.71 9:0.80 11:0.57 12:0.51 17:0.95 20:0.87 21:0.30 27:0.34 28:0.99 30:0.28 32:0.80 34:0.75 36:0.33 37:0.38 39:0.39 41:0.90 43:0.78 60:0.87 66:0.36 69:0.77 70:0.88 74:0.85 77:0.98 78:0.98 81:0.89 83:0.86 85:0.94 91:0.33 96:0.85 98:0.32 100:0.95 101:0.77 104:0.87 108:0.89 111:0.96 114:0.68 120:0.76 121:0.90 123:0.88 124:0.92 126:0.54 127:0.69 129:0.89 133:0.93 135:0.58 138:0.96 140:0.45 145:0.98 146:0.13 147:0.18 149:0.82 150:0.93 151:0.94 152:0.82 153:0.80 154:0.71 155:0.67 158:0.92 159:0.88 161:0.88 162:0.86 164:0.74 165:0.83 167:0.55 169:0.93 172:0.70 173:0.51 176:0.56 177:0.38 179:0.40 181:0.47 186:0.98 187:0.21 189:0.88 191:0.73 192:0.59 195:0.97 201:0.74 202:0.60 204:0.89 208:0.64 212:0.51 215:0.67 216:0.40 222:0.48 230:0.87 232:0.91 233:0.76 235:0.52 238:0.84 241:0.43 242:0.57 243:0.11 245:0.71 247:0.60 248:0.84 253:0.87 255:0.79 256:0.64 259:0.21 260:0.77 261:0.91 265:0.35 266:0.95 267:0.76 268:0.68 276:0.75 279:0.86 282:0.88 283:0.82 285:0.62 290:0.68 297:0.36 299:1.00 300:0.84 +1 1:0.74 6:0.86 7:0.81 8:0.69 9:0.80 11:0.57 12:0.51 17:0.93 20:0.98 21:0.40 27:0.37 28:0.99 30:0.19 32:0.80 34:0.86 36:0.40 37:0.28 39:0.39 41:0.93 43:0.98 60:0.87 66:0.35 69:0.17 70:0.85 74:0.99 76:0.85 77:0.70 78:0.97 81:0.84 83:0.87 85:0.95 91:0.80 96:0.91 98:0.81 100:0.93 101:0.84 104:0.58 108:0.14 111:0.95 114:0.82 120:0.85 121:0.99 122:0.67 123:0.70 124:0.61 126:0.54 127:0.79 129:0.59 133:0.47 135:0.68 140:0.45 145:0.54 146:0.69 147:0.74 149:0.95 150:0.89 151:0.97 152:0.94 153:0.90 154:0.98 155:0.67 158:0.92 159:0.65 161:0.88 162:0.82 164:0.81 165:0.81 167:0.83 169:0.90 172:0.75 173:0.18 176:0.73 177:0.38 179:0.34 181:0.47 186:0.97 189:0.89 192:0.59 195:0.78 201:0.74 202:0.71 204:0.89 208:0.64 212:0.61 215:0.67 216:0.22 222:0.48 230:0.87 232:0.79 233:0.76 235:0.60 238:0.83 241:0.84 242:0.55 243:0.51 245:0.94 247:0.47 248:0.91 253:0.95 255:0.79 256:0.73 259:0.21 260:0.86 261:0.93 265:0.55 266:0.54 267:0.87 268:0.76 276:0.81 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.55 +2 6:0.85 8:0.75 12:0.06 17:0.15 20:0.78 21:0.13 28:0.87 30:0.68 34:0.37 36:0.34 37:0.99 43:0.31 55:0.59 66:0.32 69:0.71 70:0.31 78:0.77 81:0.96 91:0.87 98:0.49 100:0.78 104:0.86 106:0.81 108:0.54 111:0.76 120:0.94 123:0.68 124:0.61 126:0.54 127:0.35 129:0.59 132:0.97 133:0.47 135:0.24 140:0.45 145:0.44 146:0.40 147:0.47 149:0.41 150:0.95 151:0.75 152:0.84 153:0.86 154:0.23 159:0.32 161:0.50 162:0.72 163:0.75 164:0.96 167:0.92 169:0.76 172:0.21 173:0.81 177:0.05 179:0.85 181:0.66 186:0.78 187:0.39 189:0.90 191:0.91 202:0.92 206:0.81 212:0.42 215:0.84 216:1.00 220:0.62 235:0.12 238:0.86 241:0.77 242:0.82 243:0.49 245:0.54 247:0.12 248:0.91 256:0.93 259:0.21 260:0.94 261:0.77 265:0.46 266:0.27 267:0.36 268:0.95 271:0.59 276:0.33 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.85 8:0.72 12:0.06 17:0.16 20:0.82 21:0.13 28:0.87 30:0.68 34:0.37 36:0.35 37:0.99 43:0.31 55:0.59 66:0.32 69:0.71 70:0.31 78:0.75 81:0.96 91:0.93 98:0.49 100:0.78 104:0.86 106:0.81 108:0.54 111:0.75 120:0.94 123:0.68 124:0.61 126:0.54 127:0.35 129:0.59 132:0.97 133:0.47 135:0.26 140:0.45 145:0.44 146:0.34 147:0.41 149:0.41 150:0.95 151:0.75 152:0.84 153:0.86 154:0.23 159:0.32 161:0.50 162:0.72 163:0.97 164:0.96 167:0.92 169:0.76 172:0.21 173:0.81 177:0.05 179:0.85 181:0.66 186:0.76 187:0.39 189:0.89 191:0.87 202:0.92 206:0.81 212:0.42 215:0.84 216:1.00 220:0.62 235:0.12 238:0.87 241:0.77 242:0.82 243:0.49 245:0.54 247:0.12 248:0.91 256:0.93 259:0.21 260:0.94 261:0.77 265:0.38 266:0.38 267:0.69 268:0.95 271:0.59 276:0.32 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.88 7:0.81 8:0.72 12:0.06 17:0.79 20:0.85 21:0.47 27:0.04 28:0.87 30:0.58 32:0.80 34:0.52 36:0.98 37:0.42 43:0.51 55:0.71 66:0.93 69:0.21 70:0.46 78:0.79 81:0.91 91:0.89 98:1.00 100:0.81 104:0.58 108:0.17 111:0.78 120:0.95 123:0.16 126:0.54 127:0.95 129:0.05 135:0.39 140:0.45 145:0.74 146:0.93 147:0.95 149:0.76 150:0.84 151:0.83 152:0.80 153:0.96 154:0.40 159:0.57 161:0.50 162:0.77 164:0.97 167:0.97 169:0.79 172:0.82 173:0.24 177:0.05 179:0.49 181:0.66 186:0.79 189:0.89 191:0.93 195:0.73 202:0.95 212:0.58 215:0.63 216:0.29 230:0.87 233:0.76 235:0.52 238:0.89 241:0.83 242:0.82 243:0.94 247:0.12 248:0.93 256:0.95 259:0.21 260:0.95 261:0.79 265:1.00 266:0.54 267:0.82 268:0.97 271:0.59 276:0.73 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.97 8:0.96 12:0.06 17:0.03 20:0.78 21:0.13 25:0.88 27:0.30 28:0.87 30:0.21 34:0.28 36:0.72 37:0.85 43:0.37 55:0.52 66:0.69 69:0.60 70:0.50 78:0.90 81:0.96 91:0.95 98:0.39 100:0.88 104:0.57 108:0.91 111:0.94 120:0.98 123:0.90 124:0.41 126:0.54 127:0.87 129:0.59 132:0.97 133:0.47 135:0.42 140:0.45 145:0.47 146:0.05 147:0.05 149:0.28 150:0.95 151:0.85 152:0.76 153:0.98 154:0.28 159:0.78 161:0.50 162:0.68 163:0.81 164:0.98 167:0.99 169:0.95 172:0.41 173:0.43 177:0.05 179:0.89 181:0.66 186:0.81 189:0.98 191:0.98 202:0.96 212:0.16 215:0.84 216:0.88 235:0.12 238:0.99 241:0.92 242:0.82 243:0.18 245:0.46 247:0.12 248:0.97 256:0.97 259:0.21 260:0.98 261:0.78 265:0.41 266:0.54 267:0.89 268:0.97 271:0.59 276:0.21 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.81 7:0.81 8:0.67 12:0.06 17:0.38 20:0.77 21:0.47 27:0.21 28:0.87 30:0.52 34:0.37 36:0.81 37:0.74 43:0.51 55:0.71 66:0.53 69:0.19 70:0.59 78:0.74 81:0.91 91:0.38 98:0.78 100:0.77 104:0.93 106:0.81 108:0.15 111:0.74 120:0.80 123:0.15 124:0.65 126:0.54 127:0.76 129:0.81 133:0.67 135:0.34 140:0.45 146:0.98 147:0.98 149:0.83 150:0.84 151:0.72 152:0.92 153:0.77 154:0.40 159:0.87 161:0.50 162:0.78 164:0.86 167:0.64 169:0.77 172:0.90 173:0.10 177:0.05 179:0.22 181:0.66 186:0.75 187:0.39 189:0.84 191:0.70 202:0.78 206:0.81 212:0.42 215:0.63 216:0.95 220:0.93 230:0.65 233:0.63 235:0.52 238:0.85 241:0.15 242:0.82 243:0.62 245:0.95 247:0.12 248:0.73 256:0.81 259:0.21 260:0.81 261:0.81 265:0.78 266:0.87 267:0.94 268:0.83 271:0.59 276:0.90 283:0.60 285:0.62 297:0.36 300:0.84 +2 7:0.81 12:0.06 17:0.55 21:0.05 27:0.18 28:0.87 30:0.38 32:0.80 34:0.40 36:0.68 37:0.49 43:0.47 55:0.59 66:0.98 69:0.34 70:0.62 81:0.97 91:0.37 98:0.97 104:0.79 106:0.81 108:0.27 120:0.79 123:0.26 126:0.54 127:0.74 129:0.05 135:0.19 140:0.45 145:0.77 146:0.97 147:0.98 149:0.89 150:0.96 151:0.74 153:0.83 154:0.36 159:0.72 161:0.50 162:0.83 164:0.76 167:0.70 172:0.96 173:0.23 177:0.05 179:0.19 181:0.66 187:0.21 195:0.84 202:0.64 206:0.81 212:0.84 215:0.91 216:0.63 230:0.87 233:0.76 235:0.05 241:0.41 242:0.82 243:0.98 247:0.12 248:0.71 256:0.68 259:0.21 260:0.80 265:0.97 266:0.54 267:0.73 268:0.70 271:0.59 276:0.92 283:0.82 285:0.62 297:0.36 300:0.55 +1 7:0.81 12:0.06 17:0.40 21:0.13 27:0.45 28:0.87 30:0.33 32:0.80 34:0.83 36:0.22 37:0.50 43:0.36 55:0.59 66:0.20 69:0.60 70:0.88 81:0.96 91:0.12 98:0.51 108:0.46 123:0.44 124:0.84 126:0.54 127:0.52 129:0.93 133:0.84 135:0.82 145:0.50 146:0.80 147:0.83 149:0.64 150:0.95 154:0.27 159:0.75 161:0.50 167:0.62 172:0.48 173:0.41 177:0.05 178:0.55 179:0.46 181:0.66 187:0.68 195:0.90 212:0.35 215:0.84 216:0.77 230:0.87 233:0.76 235:0.12 241:0.07 242:0.82 243:0.40 245:0.75 247:0.12 259:0.21 265:0.52 266:0.89 267:0.59 271:0.59 276:0.68 297:0.36 300:0.70 +1 7:0.81 12:0.06 17:0.45 21:0.22 27:0.45 28:0.87 30:0.38 32:0.80 34:0.79 36:0.21 37:0.50 43:0.36 55:0.59 66:0.46 69:0.60 70:0.62 81:0.95 91:0.11 98:0.76 108:0.46 123:0.44 124:0.58 126:0.54 127:0.45 129:0.59 133:0.47 135:0.84 145:0.47 146:0.91 147:0.93 149:0.68 150:0.93 154:0.27 159:0.67 161:0.50 167:0.65 172:0.67 173:0.46 177:0.05 178:0.55 179:0.42 181:0.66 187:0.68 195:0.86 212:0.26 215:0.79 216:0.77 230:0.87 233:0.76 235:0.22 241:0.18 242:0.82 243:0.59 245:0.87 247:0.12 259:0.21 265:0.76 266:0.80 267:0.59 271:0.59 276:0.71 297:0.36 300:0.55 +2 6:0.80 8:0.58 12:0.06 17:0.13 20:0.92 21:0.13 25:0.88 27:0.01 28:0.87 34:0.37 36:0.36 37:0.23 43:0.51 66:0.36 69:0.12 78:0.83 81:0.96 91:0.95 98:0.16 100:0.86 104:0.57 108:0.10 111:0.82 120:0.99 123:0.10 126:0.54 127:0.09 129:0.05 135:0.39 140:0.45 146:0.05 147:0.05 149:0.16 150:0.95 151:0.84 152:0.93 153:0.97 154:0.40 159:0.05 161:0.50 162:0.75 164:0.99 167:0.99 169:0.81 172:0.05 173:0.71 177:0.05 181:0.66 186:0.85 189:0.82 191:0.95 202:0.97 215:0.84 216:0.68 220:0.74 235:0.12 238:0.89 241:0.92 243:0.51 248:0.98 256:0.98 259:0.21 260:0.99 261:0.87 265:0.17 267:0.79 268:0.98 271:0.59 283:0.82 285:0.62 297:0.36 +4 6:0.99 8:0.99 12:0.06 17:0.05 20:0.97 21:0.13 25:0.88 27:0.02 28:0.87 30:0.33 34:0.40 36:0.34 37:0.92 43:0.37 55:0.52 66:0.68 69:0.60 70:0.69 78:0.97 81:0.96 91:1.00 98:0.44 100:0.99 104:0.58 108:0.87 111:0.98 120:1.00 123:0.86 124:0.41 126:0.54 127:0.81 129:0.59 133:0.47 135:0.47 140:0.45 145:0.47 146:0.05 147:0.05 149:0.29 150:0.95 151:0.93 152:0.96 153:1.00 154:0.28 159:0.68 161:0.50 162:0.57 163:0.75 164:1.00 167:0.99 169:0.97 172:0.37 173:0.50 177:0.05 179:0.91 181:0.66 186:0.98 189:1.00 191:1.00 202:1.00 212:0.19 215:0.84 216:0.99 235:0.12 238:0.99 241:0.91 242:0.82 243:0.19 245:0.45 247:0.12 248:1.00 256:1.00 259:0.21 260:1.00 261:0.99 265:0.46 266:0.47 267:0.91 268:1.00 271:0.59 276:0.21 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.80 8:0.61 12:0.06 17:0.51 20:0.81 21:0.13 25:0.88 27:0.01 28:0.87 34:0.33 36:0.22 37:0.23 78:0.78 81:0.96 91:0.78 100:0.81 104:0.57 111:0.78 120:0.59 126:0.54 135:0.51 140:0.45 150:0.95 151:0.51 152:0.93 153:0.83 161:0.50 162:0.85 164:0.79 167:0.85 169:0.81 175:0.75 181:0.66 186:0.79 187:0.39 189:0.84 191:0.82 202:0.67 215:0.84 216:0.68 220:0.87 235:0.12 238:0.92 241:0.73 244:0.61 248:0.52 256:0.71 257:0.65 259:0.21 260:0.59 261:0.87 267:0.88 268:0.74 271:0.59 277:0.69 285:0.62 297:0.36 +3 6:0.83 7:0.81 8:0.65 12:0.06 17:0.45 20:0.81 21:0.54 27:0.50 28:0.87 30:0.21 32:0.80 34:0.42 36:0.77 37:0.60 43:0.51 55:0.71 66:0.18 69:0.21 70:0.70 78:0.74 81:0.90 91:0.14 98:0.56 100:0.77 104:0.84 106:0.81 108:0.84 111:0.74 120:0.88 123:0.84 124:0.95 126:0.54 127:0.98 129:0.99 133:0.95 135:0.51 140:0.45 145:0.80 146:0.61 147:0.67 149:0.90 150:0.80 151:0.79 152:0.78 153:0.90 154:0.40 159:0.97 161:0.50 162:0.86 164:0.88 167:0.63 169:0.75 172:0.95 173:0.06 177:0.05 179:0.10 181:0.66 186:0.75 187:0.39 189:0.85 195:1.00 202:0.79 206:0.81 212:0.42 215:0.58 216:0.86 220:0.87 230:0.87 233:0.76 235:0.60 238:0.86 241:0.12 242:0.82 243:0.07 245:0.99 247:0.12 248:0.83 256:0.85 259:0.21 260:0.89 261:0.75 265:0.57 266:0.92 267:0.28 268:0.85 271:0.59 276:0.99 283:0.82 285:0.62 297:0.36 300:0.70 +3 6:0.99 8:0.95 12:0.06 17:0.43 20:0.81 21:0.13 25:0.88 27:0.02 28:0.87 30:0.15 34:0.25 36:0.29 37:0.92 43:0.36 55:0.59 66:0.45 69:0.60 70:0.84 78:0.85 81:0.96 91:0.96 98:0.27 100:0.93 104:0.58 108:0.91 111:0.88 120:0.98 123:0.91 124:0.75 126:0.54 127:0.80 129:0.89 133:0.78 135:0.63 140:0.80 145:0.47 146:0.05 147:0.05 149:0.33 150:0.95 151:0.82 152:0.96 153:0.95 154:0.27 159:0.77 161:0.50 162:0.60 163:0.75 164:0.98 167:0.83 169:0.99 172:0.32 173:0.43 177:0.05 178:0.42 179:0.87 181:0.66 186:0.86 187:0.39 189:0.98 191:0.99 202:0.97 212:0.15 215:0.84 216:0.99 235:0.12 238:0.96 241:0.71 242:0.82 243:0.17 245:0.48 247:0.12 248:0.98 256:0.97 259:0.21 260:0.98 261:0.99 265:0.29 266:0.63 267:0.97 268:0.97 271:0.59 276:0.33 285:0.62 297:0.36 300:0.55 +3 6:0.81 7:0.81 8:0.60 12:0.06 17:0.11 20:0.93 21:0.30 27:0.21 28:0.87 30:0.55 34:0.45 36:0.82 37:0.74 43:0.52 55:0.71 66:0.52 69:0.10 70:0.62 78:0.78 81:0.94 91:0.88 98:0.75 100:0.80 104:0.84 106:0.81 108:0.96 111:0.77 120:0.98 123:0.96 124:0.84 126:0.54 127:0.87 129:0.96 133:0.90 135:0.17 140:0.45 146:0.65 147:0.70 149:0.88 150:0.90 151:0.86 152:0.92 153:0.99 154:0.41 159:0.96 161:0.50 162:0.66 164:0.97 167:0.82 169:0.77 172:0.98 173:0.05 177:0.05 179:0.11 181:0.66 186:0.79 187:0.39 189:0.83 191:0.91 202:0.96 206:0.81 212:0.51 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.87 241:0.70 242:0.82 243:0.08 245:0.99 247:0.12 248:0.97 256:0.96 259:0.21 260:0.98 261:0.81 265:0.75 266:0.94 267:0.44 268:0.97 271:0.59 276:0.98 283:0.82 285:0.62 297:0.36 300:0.84 +1 7:0.81 8:0.90 12:0.95 17:0.90 21:0.78 27:0.72 34:0.23 36:0.66 91:0.85 135:0.20 145:1.00 175:0.75 178:0.55 202:0.36 216:0.02 230:0.87 233:0.76 235:0.12 241:0.86 244:0.61 257:0.65 259:0.21 267:0.23 277:0.69 +1 12:0.95 17:0.87 21:0.76 27:0.72 30:0.95 34:0.87 36:0.46 37:0.47 43:0.43 55:0.92 66:0.20 69:0.52 81:0.44 91:0.20 98:0.06 106:0.81 108:0.40 123:0.39 124:0.61 126:0.54 127:0.11 129:0.59 133:0.47 135:0.78 146:0.05 147:0.05 149:0.21 150:0.36 154:0.32 159:0.17 172:0.05 173:0.34 177:0.05 178:0.55 179:0.75 187:0.39 206:0.81 215:0.46 216:0.10 235:0.95 241:0.21 243:0.40 245:0.36 259:0.21 265:0.06 267:0.65 297:0.36 300:0.08 +0 7:0.81 12:0.95 17:0.85 21:0.78 27:0.72 34:0.36 36:0.76 91:0.85 135:0.30 145:1.00 175:0.75 178:0.55 187:0.21 216:0.02 230:0.87 233:0.76 235:0.12 241:0.79 244:0.61 257:0.65 259:0.21 267:0.23 277:0.69 +2 12:0.95 17:0.91 21:0.78 27:0.60 34:0.20 36:0.30 37:0.81 43:0.34 66:0.36 69:0.64 91:0.17 98:0.08 106:0.81 108:0.49 123:0.47 127:0.06 129:0.05 135:0.81 146:0.05 147:0.05 149:0.13 154:0.26 159:0.05 172:0.05 173:0.72 177:0.05 178:0.55 187:0.39 206:0.81 216:0.20 235:0.12 241:0.01 243:0.51 259:0.21 265:0.09 267:0.19 283:0.82 +1 12:0.95 17:0.49 21:0.78 27:0.50 30:0.94 34:0.91 36:0.55 37:0.60 43:0.39 55:0.59 66:0.72 69:0.56 70:0.13 91:0.29 98:0.39 108:0.43 123:0.41 127:0.13 129:0.05 135:0.75 145:0.67 146:0.34 147:0.41 149:0.37 154:0.29 159:0.14 172:0.27 173:0.69 177:0.05 178:0.55 179:0.52 187:0.39 212:0.78 216:0.86 235:0.31 241:0.01 242:0.82 243:0.75 247:0.12 259:0.21 265:0.41 266:0.17 267:0.59 276:0.26 300:0.13 +1 8:0.73 12:0.95 17:0.55 21:0.78 27:0.72 34:0.26 36:0.62 37:0.47 43:0.27 66:0.36 69:0.80 91:0.66 98:0.06 108:0.64 123:0.61 127:0.05 129:0.05 135:0.44 146:0.05 147:0.05 149:0.11 154:0.19 159:0.05 172:0.05 173:0.69 177:0.05 178:0.55 187:0.21 191:0.84 216:0.10 235:0.22 241:0.77 243:0.51 259:0.21 265:0.06 267:0.79 +2 12:0.95 17:0.36 21:0.77 27:0.45 32:0.80 34:0.83 36:0.19 37:0.50 43:0.32 66:0.36 69:0.71 81:0.41 91:0.07 98:0.06 108:0.54 123:0.52 126:0.54 127:0.05 129:0.05 135:0.82 145:0.54 146:0.05 147:0.05 149:0.13 150:0.35 154:0.24 159:0.05 172:0.05 173:0.50 177:0.05 178:0.55 187:0.68 195:0.93 215:0.44 216:0.77 235:0.97 241:0.10 243:0.51 259:0.21 265:0.06 267:0.98 297:0.36 +1 12:0.95 17:0.32 21:0.78 27:0.70 30:0.71 34:0.59 36:0.21 37:0.38 43:0.24 55:0.52 66:0.82 69:0.85 70:0.29 91:0.14 98:0.46 108:0.71 123:0.69 127:0.14 129:0.05 135:0.32 146:0.53 147:0.59 149:0.41 154:0.17 159:0.19 172:0.48 173:0.90 177:0.05 178:0.55 179:0.34 187:0.39 212:0.84 216:0.45 235:0.42 241:0.32 242:0.82 243:0.83 247:0.12 259:0.21 265:0.48 266:0.23 267:0.23 276:0.40 300:0.25 +1 12:0.95 17:0.62 21:0.78 27:0.72 30:0.76 34:0.53 36:0.29 37:0.47 43:0.29 55:0.59 66:0.54 69:0.75 70:0.22 91:0.31 98:0.30 108:0.58 123:0.56 124:0.45 127:0.35 129:0.59 133:0.47 135:0.26 146:0.44 147:0.50 149:0.30 154:0.21 159:0.53 163:0.81 172:0.21 173:0.71 177:0.05 178:0.55 179:0.94 187:0.39 212:0.42 216:0.10 235:0.42 241:0.27 242:0.82 243:0.63 245:0.40 247:0.12 259:0.21 265:0.32 266:0.23 267:0.52 276:0.16 300:0.18 +1 12:0.95 17:0.90 21:0.13 27:0.72 30:0.62 34:0.40 36:0.17 37:0.90 43:0.35 55:0.96 66:0.16 69:0.64 70:0.34 81:0.74 91:0.12 98:0.17 106:0.81 108:0.81 123:0.80 124:0.81 126:0.54 127:0.22 129:0.89 133:0.78 135:0.65 146:0.05 147:0.05 149:0.29 150:0.55 154:0.26 159:0.61 163:0.96 172:0.10 173:0.41 177:0.05 178:0.55 179:0.81 187:0.68 206:0.81 212:0.30 215:0.84 216:0.06 235:0.12 241:0.01 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.18 266:0.27 267:0.28 276:0.28 283:0.60 297:0.36 300:0.25 +1 7:0.81 12:0.95 17:0.81 21:0.40 27:1.00 30:0.89 32:0.80 37:0.26 43:0.25 55:0.71 66:0.75 69:0.83 70:0.20 81:0.66 91:0.17 98:0.41 106:0.81 108:0.67 123:0.65 126:0.54 127:0.13 129:0.05 145:0.84 146:0.56 147:0.62 149:0.31 150:0.48 154:0.18 159:0.20 172:0.32 173:0.81 177:0.05 178:0.55 179:0.47 187:0.39 195:0.79 206:0.81 212:0.61 215:0.67 216:0.41 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.77 247:0.12 265:0.43 266:0.23 276:0.28 283:0.82 297:0.36 300:0.18 +1 12:0.95 17:0.57 21:0.40 27:0.72 30:0.95 34:0.95 36:0.35 37:0.47 43:0.46 55:0.84 66:0.54 69:0.43 81:0.66 91:0.20 98:0.42 108:0.33 123:0.32 126:0.54 127:0.14 129:0.05 135:0.26 146:0.36 147:0.43 149:0.22 150:0.48 154:0.35 159:0.14 172:0.10 173:0.56 177:0.05 178:0.55 179:0.93 187:0.39 215:0.67 216:0.10 235:0.42 241:0.35 243:0.63 259:0.21 265:0.44 267:0.44 297:0.36 300:0.08 +0 12:0.95 17:0.64 21:0.78 27:0.72 30:0.89 34:0.18 36:0.31 43:0.34 55:0.84 66:0.47 69:0.66 70:0.20 91:0.60 98:0.20 108:0.96 123:0.96 124:0.65 127:0.87 129:0.81 133:0.67 135:0.77 146:0.05 147:0.05 149:0.18 154:0.25 159:0.92 163:0.94 172:0.21 173:0.30 177:0.05 178:0.55 179:0.95 202:0.50 212:0.30 216:0.01 235:0.22 241:0.78 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.22 266:0.27 267:0.36 276:0.16 300:0.18 +1 8:0.75 12:0.95 17:0.22 21:0.78 27:0.49 30:0.62 34:0.49 36:0.37 37:0.38 43:0.24 55:0.84 66:0.75 69:0.85 70:0.34 91:0.35 98:0.40 108:0.72 123:0.70 127:0.13 129:0.05 135:0.61 145:0.98 146:0.66 147:0.71 149:0.29 154:0.17 159:0.25 163:0.75 172:0.32 173:0.78 177:0.05 178:0.55 179:0.46 187:0.39 191:0.75 202:0.58 212:0.30 216:0.88 235:0.52 241:0.24 242:0.82 243:0.77 247:0.12 265:0.42 266:0.27 267:0.85 276:0.29 300:0.25 +3 6:0.97 8:0.97 12:0.06 17:0.70 20:0.81 21:0.47 27:0.43 28:0.48 30:0.60 34:0.34 36:0.32 37:0.96 43:0.52 55:0.59 66:0.49 69:0.17 70:0.56 78:0.74 81:0.94 91:0.88 98:0.54 100:0.78 104:0.73 108:0.84 111:0.74 120:0.80 123:0.84 124:0.55 126:0.54 127:0.98 129:0.59 133:0.47 135:0.42 140:0.45 146:0.54 147:0.60 149:0.51 150:0.89 151:0.77 152:0.78 153:0.88 154:0.41 159:0.67 161:0.74 162:0.83 164:0.89 167:0.98 169:0.76 172:0.45 173:0.17 177:0.05 179:0.80 181:0.64 186:0.75 189:0.97 191:0.78 202:0.81 212:0.42 215:0.63 216:0.23 220:0.93 235:0.60 238:0.92 241:0.97 242:0.82 243:0.40 245:0.68 247:0.12 248:0.72 256:0.85 259:0.21 260:0.81 261:0.76 265:0.55 266:0.63 267:0.18 268:0.86 271:0.61 276:0.39 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.98 8:0.94 12:0.06 17:0.15 20:0.83 21:0.13 25:0.88 27:0.04 28:0.48 30:0.91 34:0.55 36:0.70 37:0.98 43:0.48 55:0.52 66:0.69 69:0.33 70:0.13 78:0.81 81:0.92 91:0.95 98:0.59 100:0.91 104:0.58 108:0.26 111:0.85 120:0.95 123:0.25 126:0.54 127:0.17 129:0.05 135:0.49 140:0.45 146:0.30 147:0.36 149:0.35 150:0.85 151:0.84 152:0.93 153:0.98 154:0.36 159:0.13 161:0.74 162:0.76 164:0.96 167:0.86 169:0.98 172:0.21 173:0.70 177:0.05 179:0.84 181:0.64 186:0.81 187:0.68 189:0.97 191:0.91 202:0.92 212:0.95 215:0.84 216:0.81 235:0.12 238:0.97 241:0.75 242:0.82 243:0.73 247:0.12 248:0.93 256:0.94 259:0.21 260:0.95 261:0.98 265:0.60 266:0.12 267:0.52 268:0.94 271:0.61 276:0.21 283:0.60 285:0.62 297:0.36 300:0.13 +3 6:0.97 8:0.97 12:0.06 17:0.47 20:0.92 21:0.13 27:0.33 28:0.48 30:0.38 32:0.80 34:0.61 36:0.40 37:0.84 43:0.39 55:0.52 66:0.83 69:0.55 70:0.45 78:0.76 81:0.92 91:0.95 98:0.90 100:0.81 104:0.74 108:0.43 111:0.76 120:0.90 123:0.41 126:0.54 127:0.48 129:0.05 135:0.58 140:0.45 145:0.61 146:0.74 147:0.78 149:0.52 150:0.85 151:0.80 152:0.86 153:0.92 154:0.29 159:0.30 161:0.74 162:0.82 163:0.90 164:0.94 167:0.97 169:0.79 172:0.51 173:0.70 177:0.05 179:0.80 181:0.64 186:0.77 189:0.98 191:0.90 195:0.56 202:0.89 212:0.28 215:0.84 216:0.42 220:0.74 235:0.12 238:0.95 241:0.93 242:0.82 243:0.84 247:0.12 248:0.86 256:0.92 259:0.21 260:0.90 261:0.82 265:0.90 266:0.54 267:0.17 268:0.92 271:0.61 276:0.42 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.92 8:0.91 12:0.06 20:0.90 21:0.22 27:0.42 28:0.48 30:0.45 32:0.80 34:0.34 36:0.72 37:0.53 43:0.47 55:0.84 66:0.85 69:0.35 70:0.46 78:0.74 81:0.91 91:0.98 98:0.94 100:0.78 104:0.58 108:0.27 111:0.74 120:0.94 123:0.26 126:0.54 127:0.63 129:0.05 135:0.20 140:0.80 145:0.40 146:0.85 147:0.88 149:0.56 150:0.82 151:0.82 152:0.84 153:0.95 154:0.36 159:0.42 161:0.74 162:0.52 163:0.94 164:0.96 167:0.97 169:0.77 172:0.57 173:0.42 177:0.05 178:0.42 179:0.78 181:0.64 186:0.75 189:0.93 191:0.88 195:0.62 202:0.96 212:0.58 215:0.79 216:0.66 220:0.82 235:0.22 238:0.95 241:0.93 242:0.82 243:0.86 247:0.12 248:0.92 256:0.95 259:0.21 260:0.95 261:0.78 265:0.94 266:0.47 267:0.44 268:0.95 271:0.61 276:0.47 283:0.82 285:0.62 297:0.36 300:0.33 +0 12:0.06 17:0.12 21:0.22 25:0.88 27:0.12 28:0.48 30:0.45 34:0.79 36:0.68 37:0.98 43:0.44 55:0.42 66:0.69 69:0.45 70:0.25 81:0.91 91:0.73 98:0.84 104:0.58 108:0.35 120:0.83 123:0.34 126:0.54 127:0.33 129:0.05 135:0.75 140:0.87 146:0.42 147:0.48 149:0.33 150:0.82 151:0.71 153:0.72 154:0.33 159:0.16 161:0.74 162:0.53 163:0.75 164:0.81 167:0.85 172:0.21 173:0.81 177:0.05 178:0.48 179:0.96 181:0.64 187:0.39 191:0.93 202:0.79 212:0.61 215:0.79 216:0.58 235:0.22 241:0.74 242:0.82 243:0.73 247:0.12 248:0.75 256:0.77 259:0.21 260:0.84 265:0.84 266:0.17 267:0.87 268:0.76 271:0.61 276:0.20 285:0.62 297:0.36 300:0.18 +4 6:0.97 8:0.94 12:0.06 17:0.49 20:0.78 21:0.13 27:0.33 28:0.48 30:0.56 32:0.80 34:0.49 36:0.42 37:0.84 43:0.34 55:0.52 66:0.44 69:0.65 70:0.42 78:0.76 81:0.92 91:0.86 98:0.46 100:0.80 104:0.74 108:0.49 111:0.76 120:0.84 123:0.47 124:0.58 126:0.54 127:0.53 129:0.59 133:0.47 135:0.54 140:0.45 145:0.61 146:0.48 147:0.55 149:0.51 150:0.85 151:0.73 152:0.86 153:0.81 154:0.26 159:0.44 161:0.74 162:0.86 164:0.84 167:0.97 169:0.79 172:0.37 173:0.69 177:0.05 179:0.78 181:0.64 186:0.77 187:0.21 189:0.96 191:0.92 195:0.64 202:0.72 212:0.23 215:0.84 216:0.42 220:0.74 235:0.12 238:0.93 241:0.95 242:0.82 243:0.57 245:0.64 247:0.12 248:0.77 256:0.80 259:0.21 260:0.85 261:0.82 265:0.47 266:0.63 267:0.19 268:0.80 271:0.61 276:0.38 283:0.60 285:0.62 297:0.36 300:0.33 +3 6:0.96 8:0.97 12:0.06 17:0.07 20:0.99 21:0.22 25:0.88 27:0.12 28:0.48 30:0.45 34:0.66 36:0.66 37:0.98 43:0.44 55:0.42 66:0.69 69:0.45 70:0.25 78:0.76 81:0.91 91:0.97 98:0.85 100:0.82 104:0.58 108:0.35 111:0.77 120:0.94 123:0.34 126:0.54 127:0.36 129:0.05 135:0.63 140:0.87 146:0.42 147:0.48 149:0.33 150:0.82 151:0.82 152:0.90 153:0.95 154:0.33 159:0.16 161:0.74 162:0.56 163:0.75 164:0.96 167:0.96 169:0.79 172:0.21 173:0.82 177:0.05 178:0.48 179:0.96 181:0.64 186:0.77 189:0.97 191:0.89 202:0.95 212:0.61 215:0.79 216:0.58 235:0.22 238:0.95 241:0.90 242:0.82 243:0.73 247:0.12 248:0.91 256:0.95 259:0.21 260:0.94 261:0.83 265:0.85 266:0.17 267:0.94 268:0.95 271:0.61 276:0.20 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.94 8:0.95 12:0.06 17:0.02 20:0.85 21:0.68 27:0.12 28:0.48 30:0.91 34:0.67 36:0.76 37:0.88 43:0.41 55:0.84 66:0.69 69:0.54 70:0.13 78:0.74 81:0.76 91:0.98 98:0.80 100:0.79 108:0.41 111:0.74 120:0.94 123:0.40 126:0.54 127:0.29 129:0.05 135:0.73 140:0.80 146:0.44 147:0.50 149:0.32 150:0.56 151:0.81 152:0.81 153:0.94 154:0.31 159:0.16 161:0.74 162:0.51 163:0.93 164:0.96 167:0.97 169:0.77 172:0.21 173:0.84 177:0.05 178:0.42 179:0.95 181:0.64 186:0.75 189:0.95 191:0.91 202:0.96 212:0.61 215:0.49 216:0.74 220:0.82 235:0.80 238:0.96 241:0.95 242:0.82 243:0.73 247:0.12 248:0.91 256:0.95 259:0.21 260:0.94 261:0.77 265:0.80 266:0.17 267:0.84 268:0.95 271:0.61 276:0.21 283:0.82 285:0.62 297:0.36 300:0.13 +1 12:0.06 17:0.32 21:0.59 27:0.45 28:0.48 30:0.21 34:0.93 36:0.21 37:0.50 43:0.32 55:0.59 66:0.68 69:0.69 70:0.73 81:0.81 91:0.29 98:0.59 108:0.53 123:0.50 124:0.51 126:0.54 127:0.49 129:0.89 133:0.78 135:0.65 145:0.50 146:0.81 147:0.84 149:0.56 150:0.61 154:0.24 159:0.68 161:0.74 167:0.59 172:0.59 173:0.58 177:0.05 178:0.55 179:0.65 181:0.64 187:0.68 212:0.23 215:0.55 216:0.77 235:0.68 241:0.07 242:0.82 243:0.72 245:0.53 247:0.12 259:0.21 265:0.60 266:0.71 267:0.89 271:0.61 276:0.53 297:0.36 300:0.55 +4 6:0.98 7:0.81 8:0.96 12:0.06 17:0.26 20:0.98 21:0.40 25:0.88 27:0.04 28:0.48 30:0.58 32:0.80 34:0.40 36:0.83 37:0.98 43:0.52 55:0.59 66:0.88 69:0.12 70:0.42 78:0.87 81:0.87 91:0.99 98:0.98 100:0.99 104:0.58 108:0.10 111:0.96 120:0.99 123:0.10 126:0.54 127:0.85 129:0.05 135:0.30 140:0.45 145:0.80 146:0.82 147:0.85 149:0.64 150:0.74 151:0.90 152:0.93 153:0.99 154:0.41 159:0.38 161:0.74 162:0.69 164:0.99 167:0.95 169:0.97 172:0.65 173:0.30 177:0.05 179:0.72 181:0.64 186:0.89 189:0.99 191:0.97 195:0.60 202:0.99 212:0.35 215:0.67 216:0.81 230:0.87 233:0.76 235:0.42 238:0.99 241:0.86 242:0.82 243:0.88 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.98 266:0.38 267:0.97 268:0.99 271:0.61 276:0.54 283:0.82 285:0.62 297:0.36 300:0.33 +3 6:0.95 7:0.81 8:0.96 12:0.06 17:0.32 20:0.96 21:0.78 27:0.11 28:0.48 30:0.91 32:0.80 34:0.75 36:0.61 37:0.98 43:0.48 55:0.84 66:0.69 69:0.41 70:0.13 78:0.80 91:0.96 98:0.87 100:0.88 108:0.31 111:0.82 120:0.93 123:0.30 127:0.40 129:0.05 135:0.39 140:0.80 145:0.76 146:0.43 147:0.49 149:0.34 151:0.82 152:0.90 153:0.95 154:0.37 159:0.16 161:0.74 162:0.66 163:0.75 164:0.96 167:0.98 169:0.86 172:0.21 173:0.78 177:0.05 178:0.42 179:0.97 181:0.64 186:0.80 189:0.96 191:0.93 195:0.53 202:0.94 212:0.61 216:0.57 230:0.87 233:0.76 235:0.84 238:0.96 241:0.98 242:0.82 243:0.73 247:0.12 248:0.90 256:0.95 259:0.21 260:0.93 261:0.88 265:0.87 266:0.17 267:0.79 268:0.96 271:0.61 276:0.22 283:0.82 285:0.62 300:0.13 +0 8:0.94 12:0.06 17:0.38 21:0.78 27:0.37 28:0.48 34:0.50 36:0.21 37:0.57 78:0.91 91:0.91 111:0.98 120:0.93 135:0.83 140:0.45 151:0.78 153:0.88 161:0.74 162:0.67 164:0.92 167:0.97 169:0.97 175:0.75 181:0.64 191:0.88 202:0.88 216:0.17 238:0.99 241:0.96 244:0.61 248:0.90 256:0.91 257:0.65 259:0.21 260:0.93 267:0.65 268:0.90 271:0.61 277:0.69 285:0.62 +1 12:0.06 17:0.24 21:0.78 27:0.45 28:0.48 30:0.40 34:0.63 36:0.21 37:0.50 43:0.29 55:0.52 66:0.35 69:0.75 70:0.70 91:0.14 98:0.31 108:0.70 123:0.68 124:0.77 127:0.19 129:0.89 133:0.78 135:0.78 145:0.42 146:0.27 147:0.33 149:0.64 154:0.21 159:0.55 161:0.74 167:0.67 172:0.54 173:0.56 177:0.05 178:0.55 179:0.26 181:0.64 187:0.68 212:0.42 216:0.77 235:0.60 241:0.39 242:0.82 243:0.29 245:0.72 247:0.12 259:0.21 265:0.33 266:0.80 267:0.69 271:0.61 276:0.63 300:0.55 +2 6:0.96 8:0.95 12:0.06 17:0.22 20:0.88 21:0.78 27:0.20 28:0.48 34:0.47 36:0.27 37:0.85 78:0.75 91:0.92 100:0.80 104:0.58 111:0.76 120:0.85 135:0.88 140:0.45 151:0.80 152:0.86 153:0.92 161:0.74 162:0.79 164:0.95 167:0.97 169:0.78 175:0.75 181:0.64 186:0.76 189:0.96 191:0.89 202:0.90 216:0.53 220:0.99 238:0.95 241:0.93 244:0.61 248:0.77 256:0.93 257:0.65 259:0.21 260:0.85 261:0.81 267:0.65 268:0.94 271:0.61 277:0.69 283:0.60 285:0.62 +2 6:0.90 8:0.92 12:0.06 17:0.13 20:0.78 21:0.78 27:0.36 28:0.48 30:0.56 34:0.30 36:0.99 37:0.41 43:0.52 55:0.84 66:0.44 69:0.08 70:0.53 78:0.75 91:0.82 98:0.72 100:0.79 104:0.54 108:0.77 111:0.75 120:0.94 123:0.75 124:0.58 127:0.98 129:0.59 133:0.47 135:0.32 140:0.80 145:0.47 146:0.49 147:0.55 149:0.50 151:0.80 152:0.76 153:0.93 154:0.41 159:0.61 161:0.74 162:0.56 163:0.92 164:0.94 167:0.96 169:0.77 172:0.37 173:0.15 177:0.05 178:0.42 179:0.84 181:0.64 186:0.76 189:0.91 191:0.87 202:0.92 212:0.23 216:0.56 220:0.82 235:0.22 238:0.88 241:0.88 242:0.82 243:0.45 245:0.64 247:0.12 248:0.91 256:0.92 259:0.21 260:0.94 261:0.75 265:0.72 266:0.71 267:0.44 268:0.92 271:0.61 276:0.42 283:0.82 285:0.62 300:0.43 +4 6:0.98 7:0.81 8:0.95 12:0.06 17:0.36 20:0.76 21:0.40 25:0.88 27:0.04 28:0.48 30:0.58 32:0.80 34:0.67 36:0.86 37:0.98 43:0.52 55:0.59 66:0.88 69:0.12 70:0.42 78:0.81 81:0.87 91:0.90 98:0.94 100:0.91 104:0.58 108:0.10 111:0.84 120:0.95 123:0.10 126:0.54 127:0.63 129:0.05 135:0.42 140:0.45 145:0.80 146:0.82 147:0.85 149:0.64 150:0.74 151:0.84 152:0.93 153:0.97 154:0.41 159:0.38 161:0.74 162:0.73 164:0.96 167:0.83 169:0.98 172:0.65 173:0.28 177:0.05 179:0.69 181:0.64 186:0.82 187:0.95 189:0.99 191:0.92 195:0.62 202:0.93 212:0.35 215:0.67 216:0.81 230:0.87 233:0.76 235:0.42 238:0.96 241:0.73 242:0.82 243:0.88 247:0.12 248:0.93 256:0.94 259:0.21 260:0.95 261:0.98 265:0.94 266:0.38 267:0.95 268:0.95 271:0.61 276:0.54 285:0.62 297:0.36 300:0.33 +4 6:0.89 8:0.92 12:0.06 17:0.83 20:0.81 21:0.22 25:0.88 27:0.51 28:0.48 30:0.66 34:0.36 36:0.44 37:1.00 43:0.49 55:0.59 66:0.85 69:0.23 70:0.29 78:0.75 81:0.91 91:0.82 98:0.98 100:0.79 104:0.58 108:0.18 111:0.75 120:0.86 123:0.18 126:0.54 127:0.83 129:0.05 135:0.69 140:0.45 146:0.72 147:0.76 149:0.58 150:0.82 151:0.71 152:0.78 153:0.72 154:0.38 159:0.29 161:0.74 162:0.83 164:0.88 167:0.97 169:0.76 172:0.57 173:0.47 177:0.05 179:0.80 181:0.64 186:0.76 189:0.90 202:0.79 212:0.58 215:0.79 216:0.23 220:0.82 235:0.22 238:0.93 241:0.93 242:0.82 243:0.86 247:0.12 248:0.80 256:0.83 259:0.21 260:0.86 261:0.76 265:0.98 266:0.27 267:0.28 268:0.84 271:0.61 276:0.47 285:0.62 297:0.36 300:0.25 +1 8:0.62 12:0.06 17:0.07 20:0.74 21:0.78 27:0.44 28:0.48 34:0.55 36:0.62 37:0.32 78:0.75 91:0.92 100:0.73 104:0.82 111:0.75 120:0.97 135:0.47 140:0.45 151:0.85 152:0.74 153:0.98 161:0.74 162:0.59 164:0.99 167:0.71 169:0.76 175:0.75 181:0.64 186:0.73 187:0.39 191:0.78 202:0.99 216:0.94 220:0.62 235:0.74 241:0.53 244:0.61 248:0.96 256:0.99 257:0.65 259:0.21 260:0.98 261:0.73 267:0.73 268:0.99 271:0.61 277:0.69 283:0.82 285:0.62 +2 6:0.96 7:0.81 8:0.96 12:0.06 17:0.55 20:0.85 21:0.64 27:0.20 28:0.48 30:0.57 34:0.80 36:0.59 37:0.85 43:0.52 55:0.52 66:0.26 69:0.21 70:0.53 78:0.75 81:0.79 91:0.77 98:0.67 100:0.80 104:0.58 108:0.83 111:0.76 120:0.90 123:0.16 124:0.71 126:0.54 127:0.77 129:0.81 133:0.67 135:0.51 140:0.45 145:0.72 146:0.81 147:0.84 149:0.68 150:0.58 151:0.80 152:0.86 153:0.93 154:0.41 159:0.65 161:0.74 162:0.79 164:0.93 167:0.82 169:0.78 172:0.57 173:0.19 177:0.05 179:0.53 181:0.64 186:0.76 187:0.21 189:0.97 191:0.93 202:0.87 212:0.29 215:0.53 216:0.53 220:0.82 230:0.87 233:0.76 235:0.74 238:0.95 241:0.72 242:0.82 243:0.45 245:0.80 247:0.12 248:0.85 256:0.90 259:0.21 260:0.90 261:0.81 265:0.56 266:0.75 267:0.94 268:0.91 271:0.61 276:0.68 285:0.62 297:0.36 300:0.43 +3 6:0.98 8:0.95 12:0.20 17:0.34 20:0.81 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.52 36:0.93 37:0.82 43:0.52 55:0.52 66:0.27 69:0.27 70:0.73 78:0.81 81:0.77 91:0.78 98:0.75 100:0.90 104:0.58 108:0.81 111:0.83 120:0.79 123:0.20 124:0.58 126:0.54 127:0.78 129:0.59 133:0.47 135:0.81 140:0.45 145:0.97 146:0.85 147:0.88 149:0.76 150:0.57 151:0.75 152:1.00 153:0.86 154:0.41 159:0.63 161:0.69 162:0.84 164:0.81 167:0.82 169:0.96 172:0.73 173:0.24 177:0.05 179:0.43 181:0.72 186:0.81 187:0.21 189:0.98 195:0.80 202:0.69 212:0.58 215:0.46 216:0.87 235:0.88 238:0.97 241:0.75 242:0.82 243:0.46 245:0.90 247:0.12 248:0.71 256:0.73 259:0.21 260:0.79 261:0.98 265:0.62 266:0.80 267:0.65 268:0.76 271:0.50 276:0.75 285:0.62 297:0.36 300:0.55 +1 8:0.82 12:0.20 17:0.66 21:0.40 27:0.26 28:0.78 30:0.33 32:0.80 34:0.71 36:0.62 37:0.91 43:0.52 55:0.52 66:0.36 69:0.12 70:0.61 81:0.92 91:0.65 98:0.66 104:0.73 108:0.63 120:0.73 123:0.61 124:0.60 126:0.54 127:0.58 129:0.59 133:0.47 135:0.54 140:0.87 145:0.52 146:0.47 147:0.53 149:0.62 150:0.85 151:0.66 153:0.48 154:0.41 159:0.37 161:0.69 162:0.61 164:0.75 167:0.78 172:0.41 173:0.28 177:0.05 178:0.48 179:0.72 181:0.72 187:0.21 191:0.75 195:0.64 202:0.68 212:0.59 215:0.67 216:0.45 220:0.82 235:0.42 241:0.72 242:0.82 243:0.49 245:0.71 247:0.12 248:0.66 256:0.68 259:0.21 260:0.74 265:0.66 266:0.63 267:0.65 268:0.69 271:0.50 276:0.48 285:0.62 297:0.36 300:0.43 +3 6:0.96 7:0.81 8:0.92 12:0.20 17:0.47 20:0.99 21:0.30 27:0.21 28:0.78 30:0.27 34:0.66 36:0.87 37:0.74 43:0.52 55:0.71 66:0.44 69:0.10 70:0.80 78:0.79 81:0.93 91:0.80 98:0.75 100:0.85 104:0.84 106:0.81 108:0.87 111:0.80 120:0.92 123:0.86 124:0.67 126:0.54 127:0.66 129:0.81 133:0.67 135:0.20 140:0.45 146:0.92 147:0.93 149:0.85 150:0.88 151:0.82 152:0.90 153:0.95 154:0.41 159:0.84 161:0.69 162:0.61 164:0.90 167:0.72 169:0.82 172:0.89 173:0.08 177:0.05 179:0.19 181:0.72 186:0.79 187:0.39 189:0.97 191:0.70 202:0.86 206:0.81 212:0.60 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.94 241:0.62 242:0.82 243:0.45 245:0.96 247:0.12 248:0.88 256:0.89 259:0.21 260:0.92 261:0.85 265:0.75 266:0.63 267:0.44 268:0.88 271:0.50 276:0.91 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.98 8:0.97 12:0.20 17:0.06 20:0.99 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.50 36:0.74 37:0.82 43:0.47 55:0.59 66:0.33 69:0.46 70:0.81 78:0.92 81:0.77 91:1.00 98:0.56 100:0.99 104:0.58 108:0.81 111:0.98 120:0.99 123:0.80 124:0.78 126:0.54 127:0.90 129:0.89 133:0.78 135:0.32 140:0.45 145:0.97 146:0.25 147:0.31 149:0.67 150:0.57 151:0.91 152:1.00 153:1.00 154:0.35 159:0.59 161:0.69 162:0.69 164:0.99 167:0.94 169:0.96 172:0.54 173:0.42 177:0.05 179:0.59 181:0.72 186:0.91 189:0.99 191:0.97 195:0.76 202:0.99 212:0.40 215:0.46 216:0.87 235:0.88 238:0.99 241:0.98 242:0.82 243:0.29 245:0.73 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.57 266:0.63 267:0.79 268:0.99 271:0.50 276:0.64 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.98 8:0.93 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.94 98:0.71 100:0.85 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.86 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.79 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +0 12:0.20 17:0.53 21:0.59 27:0.45 28:0.78 30:0.11 32:0.80 34:0.91 36:0.18 37:0.50 43:0.32 55:0.59 66:0.30 69:0.70 70:0.54 81:0.87 91:0.27 98:0.30 108:0.53 123:0.51 124:0.61 126:0.54 127:0.21 129:0.59 133:0.47 135:0.75 145:0.44 146:0.48 147:0.54 149:0.33 150:0.72 154:0.24 159:0.44 161:0.69 163:0.88 167:0.68 172:0.16 173:0.60 177:0.05 178:0.55 179:0.80 181:0.72 187:0.68 195:0.83 212:0.30 215:0.55 216:0.77 235:0.68 241:0.53 242:0.82 243:0.48 245:0.47 247:0.12 259:0.21 265:0.32 266:0.27 267:0.28 271:0.50 276:0.25 283:0.60 297:0.36 300:0.33 +2 6:1.00 8:1.00 12:0.20 17:0.59 20:0.82 21:0.74 25:0.88 27:0.05 28:0.78 30:0.45 32:0.80 34:0.21 36:0.55 43:0.52 55:0.45 66:0.36 69:0.27 70:0.53 78:0.79 81:0.77 91:0.89 98:0.75 100:0.89 104:0.73 108:0.67 111:0.82 120:0.84 123:0.64 124:0.60 126:0.54 127:0.95 129:0.59 133:0.47 135:0.54 140:0.90 145:0.92 146:0.51 147:0.57 149:0.68 150:0.57 151:0.76 152:0.78 153:0.87 154:0.41 159:0.44 161:0.69 162:0.59 164:0.92 167:0.94 169:0.87 172:0.48 173:0.36 177:0.05 178:0.50 179:0.69 181:0.72 186:0.80 189:1.00 191:0.95 195:0.65 202:0.89 212:0.68 215:0.46 216:0.16 220:0.62 235:0.88 238:0.98 241:0.99 242:0.82 243:0.50 245:0.77 247:0.12 248:0.76 256:0.88 259:0.21 260:0.84 261:0.82 265:0.75 266:0.63 267:0.92 268:0.90 271:0.50 276:0.55 283:0.60 285:0.62 297:0.36 300:0.43 +3 6:0.96 7:0.81 8:0.97 12:0.20 17:0.12 20:0.85 21:0.78 25:0.88 27:0.44 28:0.78 30:0.45 32:0.80 34:0.55 36:0.63 43:0.47 55:0.45 66:0.43 69:0.46 70:0.57 78:0.77 91:0.88 98:0.39 100:0.82 104:0.76 108:0.86 111:0.77 120:0.94 123:0.85 124:0.66 127:0.93 129:0.81 133:0.67 135:0.65 140:0.45 145:0.85 146:0.27 147:0.33 149:0.56 151:0.83 152:0.81 153:0.96 154:0.35 159:0.67 161:0.69 162:0.55 164:0.99 167:0.92 169:0.80 172:0.45 173:0.37 177:0.05 179:0.75 181:0.72 186:0.77 189:0.97 191:0.92 195:0.81 202:0.98 212:0.55 216:0.49 220:0.62 230:0.65 233:0.63 235:0.88 238:0.96 241:0.92 242:0.82 243:0.43 245:0.64 247:0.12 248:0.91 256:0.98 259:0.21 260:0.94 261:0.80 265:0.41 266:0.63 267:0.44 268:0.98 271:0.50 276:0.39 283:0.60 285:0.62 300:0.55 +2 6:0.98 8:0.97 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.87 98:0.71 100:0.82 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.75 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.28 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +1 12:0.20 17:0.64 21:0.54 27:0.45 28:0.78 30:0.76 34:0.80 36:0.17 37:0.50 43:0.32 55:0.92 66:0.36 69:0.69 70:0.15 81:0.89 91:0.35 98:0.34 108:0.53 123:0.50 124:0.52 126:0.54 127:0.19 129:0.59 133:0.47 135:0.56 145:0.52 146:0.46 147:0.52 149:0.23 150:0.77 154:0.24 159:0.32 161:0.69 163:0.96 167:0.56 172:0.10 173:0.66 177:0.05 178:0.55 179:0.94 181:0.72 187:0.68 212:0.95 215:0.58 216:0.77 235:0.60 241:0.12 242:0.82 243:0.51 245:0.37 247:0.12 259:0.21 265:0.36 266:0.12 267:0.28 271:0.50 276:0.18 297:0.36 300:0.13 +3 6:1.00 7:0.81 8:1.00 12:0.20 17:0.11 20:0.99 21:0.13 27:0.07 28:0.78 30:0.45 32:0.80 34:0.67 36:0.82 37:0.84 43:0.47 55:0.45 66:0.33 69:0.37 70:0.42 78:0.83 81:0.96 91:0.96 98:0.55 100:0.93 104:0.58 108:0.68 111:0.88 120:0.94 123:0.66 124:0.71 126:0.54 127:0.88 129:0.81 133:0.67 135:0.30 140:0.45 145:0.86 146:0.19 147:0.25 149:0.47 150:0.93 151:0.81 152:0.95 153:0.94 154:0.36 159:0.51 161:0.69 162:0.58 164:0.97 167:0.92 169:0.89 172:0.27 173:0.39 177:0.05 179:0.87 181:0.72 186:0.84 189:1.00 191:0.94 195:0.70 202:0.96 212:0.15 215:0.84 216:0.63 220:0.74 230:0.87 233:0.76 235:0.12 238:0.98 241:0.91 242:0.82 243:0.37 245:0.53 247:0.12 248:0.92 256:0.95 259:0.21 260:0.95 261:0.92 265:0.56 266:0.63 267:0.99 268:0.96 271:0.50 276:0.36 283:0.60 285:0.62 297:0.36 300:0.33 +1 12:0.20 17:0.18 21:0.78 25:0.88 27:0.13 28:0.78 30:0.21 34:0.37 36:0.92 37:0.82 43:0.52 55:0.52 66:0.63 69:0.05 70:0.80 91:0.58 98:0.80 104:0.58 108:0.05 120:0.80 123:0.05 124:0.52 127:0.86 129:0.81 133:0.67 135:0.34 140:0.45 146:0.87 147:0.90 149:0.75 151:0.74 153:0.83 154:0.41 159:0.62 161:0.69 162:0.85 164:0.82 167:0.86 172:0.73 173:0.10 177:0.05 179:0.53 181:0.72 187:0.21 202:0.70 212:0.54 216:0.87 235:0.02 241:0.77 242:0.82 243:0.69 245:0.75 247:0.12 248:0.72 256:0.77 259:0.21 260:0.81 265:0.80 266:0.80 267:0.44 268:0.78 271:0.50 276:0.68 283:0.60 285:0.62 300:0.55 +1 12:0.06 17:0.38 21:0.54 25:0.88 27:0.28 30:0.41 32:0.75 34:0.93 36:0.88 37:0.85 39:0.71 43:0.41 55:0.59 66:0.63 69:0.37 70:0.47 74:0.59 81:0.56 89:0.97 91:0.17 98:0.82 104:0.58 108:0.29 120:0.62 123:0.28 124:0.46 126:0.32 127:0.84 129:0.05 133:0.39 135:0.68 140:0.45 141:0.91 145:0.59 146:0.76 147:0.80 149:0.45 150:0.42 151:0.58 153:0.48 154:0.69 159:0.44 162:0.86 164:0.56 172:0.51 173:0.44 177:0.38 179:0.78 187:0.68 190:0.77 195:0.61 202:0.36 212:0.42 215:0.58 216:0.39 220:0.74 222:0.21 223:0.92 225:0.96 234:0.91 235:0.68 240:0.95 241:0.37 242:0.58 243:0.69 245:0.63 247:0.47 248:0.56 253:0.47 254:0.95 256:0.45 259:0.21 260:0.63 265:0.82 266:0.54 267:0.36 268:0.46 271:0.26 274:0.91 276:0.48 283:0.71 285:0.50 297:0.36 300:0.33 +2 1:0.74 12:0.06 17:0.91 21:0.30 27:0.72 30:0.45 34:0.36 36:0.79 39:0.71 43:0.36 55:0.45 66:0.53 69:0.29 70:0.61 74:0.74 81:0.75 89:0.98 91:0.72 98:0.58 104:0.58 108:0.64 114:0.86 122:0.51 123:0.61 124:0.51 126:0.54 127:0.84 129:0.59 133:0.47 135:0.47 141:0.91 146:0.30 147:0.37 149:0.19 150:0.78 154:0.87 155:0.67 159:0.32 172:0.37 173:0.47 176:0.73 177:0.38 178:0.55 179:0.87 192:0.59 201:0.74 202:0.36 212:0.50 215:0.72 216:0.08 222:0.21 223:0.94 225:0.98 234:0.91 235:0.42 240:0.95 241:0.86 242:0.33 243:0.37 245:0.57 247:0.55 253:0.71 254:0.80 259:0.21 265:0.60 266:0.47 267:0.28 271:0.26 274:0.91 276:0.34 290:0.86 297:0.36 300:0.43 +1 1:0.74 8:0.70 12:0.06 17:0.57 21:0.54 27:0.59 30:0.76 32:0.75 34:0.99 36:0.33 37:0.42 39:0.71 43:0.79 55:0.45 66:0.64 69:0.27 70:0.15 74:0.55 77:0.70 81:0.60 89:0.96 91:0.31 98:0.23 104:0.99 106:0.81 108:0.21 114:0.77 122:0.41 123:0.20 126:0.54 127:0.11 129:0.05 135:0.51 141:0.91 145:0.82 146:0.24 147:0.30 149:0.43 150:0.67 154:0.72 155:0.67 159:0.11 172:0.16 173:0.46 176:0.73 177:0.38 178:0.55 179:0.41 187:0.39 192:0.59 195:0.60 201:0.74 206:0.81 212:0.95 215:0.58 216:0.18 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.41 242:0.82 243:0.69 247:0.12 253:0.61 254:0.95 259:0.21 265:0.26 266:0.12 267:0.59 271:0.26 274:0.91 276:0.17 282:0.83 283:0.71 290:0.77 297:0.36 300:0.13 +1 8:0.97 12:0.06 17:0.55 21:0.40 25:0.88 27:0.72 30:0.66 32:0.75 34:0.49 36:0.81 37:1.00 39:0.71 43:0.40 55:0.59 66:0.67 69:0.38 70:0.57 74:0.39 81:0.67 89:0.96 91:0.66 98:0.78 104:0.76 108:0.30 120:0.76 123:0.28 124:0.48 126:0.32 127:0.92 129:0.05 133:0.62 135:0.76 140:0.45 141:0.91 145:0.42 146:0.82 147:0.85 149:0.68 150:0.49 151:0.64 153:0.46 154:0.62 159:0.57 162:0.78 164:0.77 172:0.70 173:0.37 177:0.38 179:0.59 190:0.77 191:0.87 195:0.69 202:0.67 212:0.48 215:0.67 216:0.17 220:0.91 222:0.21 223:0.91 225:0.96 234:0.91 235:0.52 240:0.95 241:0.96 242:0.80 243:0.72 245:0.70 247:0.39 248:0.68 253:0.35 254:0.80 256:0.71 260:0.77 265:0.78 266:0.71 267:0.65 268:0.72 271:0.26 274:0.91 276:0.64 283:0.82 285:0.50 297:0.36 300:0.55 +1 12:0.06 17:0.32 21:0.78 27:0.45 30:0.95 34:0.85 36:0.17 37:0.50 39:0.71 43:0.28 55:0.84 66:0.54 69:0.74 74:0.42 89:0.96 91:0.37 98:0.19 108:0.57 123:0.55 127:0.10 129:0.05 135:0.82 141:0.91 145:0.50 146:0.32 147:0.38 149:0.18 154:0.21 159:0.13 172:0.10 173:0.65 177:0.38 178:0.55 179:0.50 187:0.68 216:0.77 222:0.21 223:0.91 225:0.96 234:0.91 235:0.74 240:0.95 241:0.21 243:0.63 244:0.61 253:0.37 259:0.21 265:0.21 267:0.65 271:0.26 274:0.91 300:0.08 +0 7:0.78 9:0.80 12:0.06 17:0.67 21:0.74 27:0.25 30:0.33 34:0.99 36:0.39 37:0.74 39:0.71 43:0.35 55:0.59 66:0.54 69:0.37 70:0.36 74:0.71 81:0.30 89:0.98 91:0.40 96:0.69 98:0.39 104:0.89 106:0.81 108:0.29 120:0.55 122:0.41 123:0.28 124:0.55 126:0.32 127:0.56 129:0.05 133:0.62 135:0.56 140:0.45 141:0.97 145:0.45 146:0.43 147:0.49 149:0.37 150:0.28 151:0.51 153:0.48 154:0.70 155:0.67 158:0.84 159:0.46 162:0.86 164:0.57 172:0.32 173:0.39 177:0.38 179:0.89 187:0.68 192:0.59 202:0.36 206:0.81 208:0.64 212:0.78 215:0.46 216:0.66 220:0.96 222:0.21 223:0.94 225:0.98 230:0.81 233:0.76 234:0.98 235:0.88 240:0.99 241:0.24 242:0.19 243:0.63 245:0.45 247:0.55 248:0.50 253:0.54 254:0.96 255:0.79 256:0.45 259:0.21 260:0.55 265:0.41 266:0.23 267:0.59 268:0.46 271:0.26 274:0.97 276:0.31 279:0.86 283:0.71 285:0.62 297:0.36 300:0.25 +2 8:0.77 12:0.06 17:0.95 21:0.54 27:0.60 30:0.40 34:0.92 36:0.71 37:0.55 39:0.71 43:0.34 55:0.45 66:0.27 69:0.17 70:0.59 74:0.95 81:0.35 89:0.99 91:0.51 96:0.76 98:0.66 104:0.58 108:0.14 114:0.65 117:0.86 122:0.67 123:0.66 124:0.58 126:0.32 127:0.72 129:0.59 133:0.47 135:0.39 140:0.45 141:0.97 146:0.56 147:0.62 149:0.50 150:0.31 151:0.59 153:0.72 154:0.86 158:0.84 159:0.43 162:0.79 172:0.59 173:0.29 176:0.56 177:0.38 179:0.58 187:0.21 190:0.77 202:0.64 212:0.86 216:0.26 220:0.96 222:0.21 223:0.94 225:0.99 232:0.83 234:0.98 235:0.60 240:0.99 241:0.18 242:0.52 243:0.58 245:0.82 247:0.39 248:0.58 253:0.81 254:0.80 256:0.64 259:0.21 265:0.61 266:0.47 267:0.76 268:0.69 271:0.26 274:0.98 276:0.62 285:0.50 290:0.65 300:0.55 +0 12:0.06 17:0.64 21:0.68 27:0.64 30:0.38 34:0.96 36:0.17 37:0.97 39:0.71 43:0.31 55:0.59 66:0.83 69:0.68 70:0.50 74:0.79 77:0.70 81:0.30 89:0.98 91:0.48 98:0.71 104:0.99 106:0.81 108:0.51 114:0.62 117:0.86 122:0.57 123:0.49 126:0.32 127:0.22 129:0.05 135:0.98 141:0.91 145:0.47 146:0.81 147:0.84 149:0.31 150:0.28 154:0.59 159:0.36 163:0.81 172:0.51 173:0.65 176:0.56 177:0.38 178:0.55 179:0.59 187:0.95 195:0.75 206:0.81 212:0.42 216:0.57 222:0.21 223:0.94 225:0.98 234:0.91 235:0.80 240:0.95 241:0.44 242:0.60 243:0.84 247:0.39 253:0.61 254:0.92 259:0.21 265:0.71 266:0.38 267:0.76 271:0.26 274:0.91 276:0.41 282:0.84 290:0.62 300:0.33 +0 12:0.06 17:0.75 21:0.54 27:0.72 30:0.45 34:0.42 36:0.57 39:0.71 43:0.43 55:0.71 66:0.82 69:0.29 70:0.61 74:0.57 76:0.85 81:0.55 89:0.96 91:0.75 98:0.96 104:0.58 108:0.22 114:0.77 123:0.22 126:0.54 127:0.69 129:0.05 135:0.51 141:0.91 145:0.88 146:0.65 147:0.70 149:0.52 150:0.58 154:0.33 159:0.25 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.86 202:0.36 212:0.61 215:0.58 216:0.03 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.87 242:0.80 243:0.83 244:0.61 247:0.30 253:0.61 259:0.21 265:0.96 266:0.38 267:0.36 271:0.26 274:0.91 276:0.39 290:0.77 297:0.36 300:0.43 +1 1:0.74 7:0.78 12:0.06 17:0.49 21:0.22 27:0.43 30:0.68 32:0.74 34:0.45 36:0.24 37:0.71 39:0.71 43:0.27 55:0.45 66:0.79 69:0.77 70:0.31 74:0.75 77:0.70 81:0.80 89:0.98 91:0.42 98:0.55 104:0.83 106:0.81 108:0.60 114:0.90 117:0.86 122:0.41 123:0.58 126:0.54 127:0.16 129:0.05 135:0.20 141:0.91 145:0.50 146:0.49 147:0.56 149:0.11 150:0.83 154:0.66 155:0.67 158:0.86 159:0.18 172:0.41 173:0.87 176:0.73 177:0.38 178:0.55 179:0.53 187:0.39 192:0.59 195:0.60 201:0.74 206:0.81 208:0.64 212:0.78 215:0.79 216:0.51 222:0.21 223:0.94 225:0.98 230:0.81 233:0.76 234:0.91 235:0.31 240:0.95 241:0.32 242:0.19 243:0.80 247:0.55 253:0.72 254:0.74 259:0.21 265:0.57 266:0.23 267:0.84 271:0.26 274:0.91 276:0.32 279:0.86 282:0.83 283:0.71 290:0.90 297:0.36 300:0.25 +2 8:0.84 12:0.06 17:0.11 21:0.13 27:0.37 30:0.68 34:0.92 36:0.83 37:0.58 39:0.71 43:0.42 55:0.59 66:0.79 69:0.21 70:0.31 74:0.39 81:0.76 89:0.96 91:0.38 98:0.90 104:0.78 106:0.81 108:0.17 120:0.59 123:0.16 126:0.32 127:0.48 129:0.05 135:0.26 140:0.80 141:0.91 146:0.65 147:0.70 149:0.39 150:0.56 151:0.51 153:0.46 154:0.56 159:0.24 162:0.66 164:0.65 172:0.41 173:0.47 177:0.38 178:0.42 179:0.88 187:0.21 190:0.77 202:0.56 206:0.81 212:0.42 215:0.84 216:0.45 220:0.87 222:0.21 223:0.91 225:0.96 234:0.91 235:0.12 240:0.95 241:0.65 242:0.75 243:0.80 247:0.35 248:0.52 253:0.35 254:0.97 256:0.58 259:0.21 260:0.59 265:0.90 266:0.27 267:0.69 268:0.58 271:0.26 274:0.91 276:0.34 285:0.50 297:0.36 300:0.25 +2 12:0.06 17:0.90 21:0.54 27:0.72 30:0.45 34:0.49 36:0.55 39:0.71 43:0.43 55:0.71 66:0.82 69:0.29 70:0.61 74:0.57 76:0.85 81:0.55 89:0.96 91:0.71 98:0.96 104:0.54 108:0.22 114:0.77 123:0.22 126:0.54 127:0.69 129:0.05 135:0.51 141:0.91 145:0.88 146:0.65 147:0.70 149:0.52 150:0.58 154:0.33 159:0.25 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.86 187:0.21 212:0.61 215:0.58 216:0.04 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.78 242:0.80 243:0.83 244:0.61 247:0.30 253:0.61 259:0.21 265:0.96 266:0.38 267:0.23 271:0.26 274:0.91 276:0.39 290:0.77 297:0.36 300:0.43 +1 12:0.06 17:0.77 21:0.13 25:0.88 27:0.72 30:0.74 34:0.83 36:0.59 37:0.23 39:0.71 43:0.45 55:0.52 66:0.67 69:0.07 70:0.59 74:0.54 81:0.76 89:0.97 91:0.62 98:0.53 104:0.58 108:0.82 123:0.81 124:0.49 126:0.32 127:0.57 129:0.05 133:0.62 135:0.19 141:0.91 146:0.40 147:0.47 149:0.54 150:0.56 154:0.34 159:0.65 172:0.63 173:0.11 177:0.38 178:0.55 179:0.62 202:0.55 212:0.28 215:0.84 216:0.20 222:0.21 223:0.92 225:0.96 234:0.91 235:0.12 240:0.95 241:0.80 242:0.79 243:0.23 244:0.61 245:0.64 247:0.39 253:0.44 259:0.21 265:0.54 266:0.63 267:0.76 271:0.26 274:0.91 276:0.54 277:0.69 297:0.36 300:0.70 +0 8:0.70 12:0.06 17:0.43 21:0.78 27:0.25 30:0.62 34:0.90 36:0.37 37:0.74 39:0.71 43:0.27 55:0.71 66:0.16 69:0.90 70:0.23 74:0.57 89:0.98 91:0.46 98:0.20 104:0.89 106:0.81 108:0.68 122:0.67 123:0.66 124:0.81 127:0.29 129:0.05 133:0.74 135:0.42 141:0.91 145:0.54 146:0.27 147:0.05 149:0.30 154:0.20 159:0.41 163:0.98 172:0.10 173:0.96 177:0.05 178:0.55 179:0.88 187:0.68 191:0.86 202:0.36 206:0.81 212:0.61 216:0.66 222:0.21 223:0.92 225:0.96 234:0.91 235:0.88 240:0.95 241:0.18 242:0.63 243:0.23 244:0.61 245:0.42 247:0.35 253:0.46 257:0.65 259:0.21 265:0.22 266:0.23 267:0.65 271:0.26 274:0.91 276:0.29 277:0.69 283:0.82 300:0.18 +2 1:0.74 12:0.06 17:0.95 21:0.30 27:0.72 30:0.45 34:0.40 36:0.80 39:0.71 43:0.36 55:0.45 66:0.53 69:0.29 70:0.61 74:0.74 81:0.75 89:0.98 91:0.71 98:0.58 104:0.58 108:0.64 114:0.86 122:0.51 123:0.61 124:0.51 126:0.54 127:0.84 129:0.59 133:0.47 135:0.47 141:0.91 146:0.30 147:0.37 149:0.19 150:0.78 154:0.87 155:0.67 159:0.32 172:0.37 173:0.47 176:0.73 177:0.38 178:0.55 179:0.87 187:0.21 192:0.59 201:0.74 212:0.50 215:0.72 216:0.08 222:0.21 223:0.94 225:0.98 234:0.91 235:0.42 240:0.95 241:0.77 242:0.33 243:0.37 245:0.57 247:0.55 253:0.71 254:0.80 259:0.21 265:0.60 266:0.47 267:0.36 271:0.26 274:0.91 276:0.34 290:0.86 297:0.36 300:0.43 +1 12:0.06 17:0.53 21:0.59 25:0.88 27:0.28 30:0.21 32:0.77 34:0.78 36:0.81 37:0.85 39:0.71 43:0.41 55:0.71 66:0.11 69:0.39 70:0.64 74:0.70 77:0.70 81:0.58 89:0.97 91:0.11 98:0.57 104:0.58 108:0.30 114:0.74 123:0.61 124:0.73 126:0.54 127:0.68 129:0.05 133:0.62 135:0.82 141:0.91 145:0.87 146:0.40 147:0.47 149:0.42 150:0.59 154:0.75 158:0.84 159:0.45 172:0.32 173:0.43 176:0.73 177:0.38 178:0.55 179:0.78 187:0.68 195:0.64 202:0.36 208:0.64 212:0.58 215:0.55 216:0.39 222:0.21 223:0.94 225:0.98 234:0.91 235:0.80 240:0.95 241:0.21 242:0.74 243:0.48 245:0.60 247:0.47 253:0.64 254:0.84 259:0.21 265:0.41 266:0.54 267:0.65 271:0.26 274:0.91 276:0.45 277:0.69 282:0.85 283:0.65 290:0.74 297:0.36 300:0.43 +2 1:0.74 12:0.06 17:0.30 21:0.40 25:0.88 27:0.27 30:0.30 32:0.74 34:0.94 36:0.62 37:0.90 39:0.71 43:0.88 55:0.71 66:0.60 69:0.41 70:0.80 74:0.80 77:0.89 81:0.70 89:0.98 91:0.23 98:0.65 104:0.58 108:0.31 114:0.82 122:0.43 123:0.30 124:0.55 126:0.54 127:0.70 129:0.05 132:0.97 133:0.62 135:0.91 141:0.91 145:0.59 146:0.57 147:0.63 149:0.66 150:0.75 154:0.86 155:0.67 158:0.84 159:0.39 172:0.45 173:0.50 176:0.73 177:0.38 178:0.55 179:0.81 187:0.68 192:0.59 195:0.59 201:0.74 212:0.52 215:0.67 216:0.75 222:0.21 223:0.94 225:0.98 234:0.91 235:0.52 240:0.95 241:0.35 242:0.24 243:0.67 245:0.53 247:0.67 253:0.71 265:0.66 266:0.47 267:0.59 271:0.26 274:0.91 276:0.42 282:0.82 290:0.82 297:0.36 300:0.55 +2 12:0.06 17:0.75 21:0.54 27:0.72 30:0.45 34:0.42 36:0.57 39:0.71 43:0.43 55:0.71 66:0.82 69:0.29 70:0.61 74:0.57 76:0.85 81:0.55 89:0.96 91:0.75 98:0.96 104:0.58 108:0.22 114:0.77 123:0.22 126:0.54 127:0.69 129:0.05 135:0.51 141:0.91 145:0.88 146:0.65 147:0.70 149:0.52 150:0.58 154:0.33 159:0.25 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.86 202:0.36 212:0.61 215:0.58 216:0.03 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.87 242:0.80 243:0.83 244:0.61 247:0.30 253:0.61 259:0.21 265:0.96 266:0.38 267:0.36 271:0.26 274:0.91 276:0.39 290:0.77 297:0.36 300:0.43 +1 8:0.96 12:0.06 17:0.70 21:0.05 25:0.88 27:0.71 30:0.41 32:0.75 34:0.93 36:0.77 37:0.89 39:0.71 43:0.41 55:0.59 66:0.68 69:0.29 70:0.65 74:0.38 81:0.84 89:0.96 91:0.67 98:0.85 104:0.58 108:0.22 120:0.85 123:0.22 124:0.45 126:0.32 127:0.91 129:0.05 133:0.39 135:0.60 140:0.45 141:0.91 145:0.92 146:0.87 147:0.90 149:0.69 150:0.64 151:0.70 153:0.71 154:0.57 159:0.57 162:0.82 164:0.84 172:0.73 173:0.29 177:0.38 179:0.56 190:0.77 191:0.84 195:0.70 202:0.75 212:0.71 215:0.91 216:0.25 220:0.62 222:0.21 223:0.91 225:0.96 234:0.91 235:0.05 240:0.95 241:0.89 242:0.80 243:0.72 245:0.79 247:0.39 248:0.79 253:0.34 254:0.80 256:0.80 260:0.86 265:0.85 266:0.63 267:0.23 268:0.80 271:0.26 274:0.91 276:0.66 283:0.82 285:0.50 297:0.36 300:0.55 +2 8:0.91 12:0.06 17:0.57 21:0.30 25:0.88 27:0.28 30:0.55 32:0.75 34:0.45 36:0.45 37:0.85 39:0.71 43:0.41 55:0.71 66:0.52 69:0.33 70:0.45 74:0.41 81:0.61 89:0.96 91:0.77 98:0.66 104:0.58 108:0.69 120:0.91 123:0.67 124:0.52 126:0.32 127:0.61 129:0.05 133:0.39 135:0.78 140:0.45 141:0.91 145:0.47 146:0.63 147:0.68 149:0.56 150:0.45 151:0.75 153:0.86 154:0.31 159:0.45 162:0.79 164:0.90 172:0.48 173:0.38 177:0.38 179:0.73 187:0.21 190:0.77 191:0.86 195:0.64 202:0.83 212:0.59 215:0.72 216:0.39 220:0.87 222:0.21 223:0.91 225:0.96 234:0.91 235:0.31 240:0.95 241:0.75 242:0.78 243:0.40 244:0.61 245:0.69 247:0.39 248:0.86 253:0.36 256:0.87 259:0.21 260:0.91 265:0.67 266:0.47 267:0.59 268:0.87 271:0.26 274:0.91 276:0.51 277:0.69 283:0.71 285:0.50 297:0.36 300:0.33 +1 12:0.06 17:0.36 21:0.78 27:0.45 30:0.95 34:0.75 36:0.17 37:0.50 39:0.71 43:0.24 55:0.71 66:0.54 69:0.84 74:0.39 89:0.96 91:0.27 98:0.17 108:0.69 123:0.67 127:0.10 129:0.05 135:0.83 141:0.91 145:0.47 146:0.27 147:0.33 149:0.16 154:0.17 159:0.12 172:0.10 173:0.79 177:0.38 178:0.55 179:0.36 187:0.68 216:0.77 222:0.21 223:0.91 225:0.96 234:0.91 235:0.31 240:0.95 241:0.44 243:0.63 244:0.61 253:0.35 259:0.21 265:0.18 267:0.44 271:0.26 274:0.91 300:0.08 +1 7:0.78 12:0.06 17:0.97 21:0.54 27:0.72 30:0.87 32:0.75 34:0.75 36:0.42 39:0.71 43:0.36 55:0.71 66:0.86 69:0.96 70:0.33 74:0.74 81:0.56 89:0.99 91:0.56 98:0.69 104:0.80 106:0.81 108:0.93 120:0.69 122:0.57 123:0.93 124:0.38 126:0.32 127:0.74 129:0.05 133:0.62 135:0.84 140:0.45 141:0.91 145:0.52 146:0.98 147:0.05 149:0.43 150:0.42 151:0.66 153:0.48 154:0.17 159:0.90 162:0.86 164:0.56 172:0.91 173:0.91 177:0.05 179:0.28 187:0.21 190:0.77 195:0.97 202:0.36 206:0.81 212:0.76 215:0.58 216:0.40 222:0.21 223:0.94 225:0.98 230:0.81 233:0.76 234:0.91 235:0.68 240:0.95 241:0.12 242:0.30 243:0.06 245:0.76 247:0.67 248:0.63 253:0.55 254:0.92 256:0.45 257:0.65 259:0.21 260:0.70 265:0.69 266:0.80 267:0.28 268:0.46 271:0.26 274:0.91 276:0.80 283:0.82 285:0.50 297:0.36 300:0.70 +2 8:0.77 12:0.06 17:0.85 21:0.40 27:0.60 30:0.44 34:0.66 36:0.91 37:0.55 39:0.71 43:0.45 55:0.71 66:0.92 69:0.15 70:0.57 74:0.95 81:0.54 89:0.99 91:0.78 96:0.85 98:1.00 104:0.58 108:0.12 114:0.68 120:0.69 122:0.67 123:0.12 126:0.32 127:0.96 129:0.05 135:0.51 140:0.80 141:0.97 146:0.89 147:0.91 149:0.65 150:0.41 151:0.66 153:0.48 154:0.61 159:0.48 162:0.56 164:0.56 172:0.77 173:0.25 176:0.56 177:0.38 178:0.42 179:0.58 190:0.77 191:0.82 202:0.83 212:0.69 216:0.26 220:0.91 222:0.21 223:0.94 225:0.99 234:0.98 235:0.52 240:0.99 241:0.86 242:0.72 243:0.92 247:0.47 248:0.73 253:0.89 254:0.80 256:0.81 259:0.21 260:0.70 265:1.00 266:0.54 267:0.23 268:0.83 271:0.26 274:0.99 276:0.66 285:0.62 290:0.68 300:0.43 +3 1:0.74 9:0.80 11:0.64 12:0.69 17:0.79 21:0.30 27:0.17 28:0.78 30:0.56 32:0.80 34:0.67 36:0.67 37:0.80 39:0.86 41:0.82 43:0.78 46:0.98 66:0.33 69:0.23 70:0.66 74:0.78 77:0.70 81:0.70 83:0.76 85:0.94 91:0.58 96:0.70 98:0.66 101:0.83 104:0.58 107:0.96 108:0.78 110:0.96 114:0.86 120:0.57 122:0.43 123:0.18 124:0.60 125:0.98 126:0.54 127:0.67 129:0.59 131:0.87 133:0.47 135:0.24 140:0.45 144:0.57 145:0.52 146:0.75 147:0.79 149:0.88 150:0.81 151:0.54 153:0.48 154:0.71 155:0.67 157:0.89 159:0.57 160:0.96 161:0.45 162:0.86 163:0.81 164:0.57 165:0.84 166:0.81 167:0.76 172:0.54 173:0.24 176:0.73 177:0.38 179:0.59 181:0.94 182:0.83 187:0.39 192:0.59 195:0.74 201:0.74 202:0.36 208:0.64 212:0.30 215:0.72 216:0.22 220:0.91 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.42 241:0.59 242:0.54 243:0.50 245:0.81 246:0.88 247:0.47 248:0.54 253:0.73 255:0.79 256:0.45 259:0.21 260:0.57 265:0.60 266:0.75 267:0.96 268:0.46 271:0.72 276:0.61 282:0.88 285:0.62 287:0.90 289:0.94 290:0.86 297:0.36 299:0.88 300:0.55 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.26 21:0.30 25:0.88 27:0.06 28:0.78 30:0.73 32:0.80 34:0.83 36:0.70 37:0.95 39:0.86 41:0.82 43:0.85 46:0.99 66:0.86 69:0.60 70:0.29 74:0.81 77:0.89 81:0.70 83:0.76 85:0.91 91:0.53 96:0.72 98:0.83 101:0.84 104:0.58 107:0.95 108:0.46 110:0.95 114:0.86 120:0.72 122:0.43 123:0.44 125:0.98 126:0.54 127:0.32 129:0.05 131:0.87 135:0.60 140:0.45 144:0.57 145:0.52 146:0.69 147:0.74 149:0.88 150:0.81 151:0.60 153:0.48 154:0.83 155:0.67 157:0.88 159:0.27 160:0.96 161:0.45 162:0.86 163:0.81 164:0.68 165:0.84 166:0.81 167:0.87 172:0.59 173:0.73 176:0.73 177:0.38 179:0.64 181:0.94 182:0.83 187:0.39 190:0.77 192:0.59 195:0.57 201:0.74 202:0.53 208:0.64 212:0.54 215:0.72 216:0.58 220:0.87 222:0.60 227:0.88 229:0.91 231:0.72 235:0.42 241:0.73 242:0.63 243:0.86 246:0.88 247:0.35 248:0.58 253:0.76 255:0.79 256:0.58 259:0.21 260:0.70 265:0.83 266:0.27 267:0.44 268:0.62 271:0.72 276:0.48 282:0.88 285:0.62 287:0.90 289:0.95 290:0.86 297:0.36 299:0.88 300:0.25 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.67 21:0.22 27:0.17 28:0.78 30:0.43 32:0.80 34:0.45 36:0.72 37:0.80 39:0.86 41:0.82 43:0.79 46:0.98 66:0.89 69:0.46 70:0.71 74:0.78 77:0.70 81:0.75 83:0.77 85:0.91 91:0.57 96:0.73 98:0.90 101:0.83 104:0.58 107:0.96 108:0.35 110:0.96 114:0.90 120:0.61 122:0.43 123:0.34 125:0.99 126:0.54 127:0.46 129:0.05 131:0.87 135:0.54 140:0.45 144:0.57 145:0.84 146:0.82 147:0.85 149:0.88 150:0.84 151:0.63 153:0.69 154:0.73 155:0.67 157:0.88 159:0.38 160:0.96 161:0.45 162:0.86 163:0.81 164:0.62 165:0.86 166:0.81 167:0.76 172:0.70 173:0.53 176:0.73 177:0.38 179:0.58 181:0.94 182:0.84 187:0.39 192:0.59 195:0.65 201:0.74 202:0.46 208:0.64 212:0.42 215:0.79 216:0.22 220:0.82 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.31 241:0.61 242:0.45 243:0.90 246:0.88 247:0.47 248:0.61 253:0.77 255:0.79 256:0.45 259:0.21 260:0.61 265:0.90 266:0.38 267:0.73 268:0.55 271:0.72 276:0.58 282:0.88 285:0.62 287:0.90 289:0.94 290:0.90 297:0.36 299:0.88 300:0.55 +3 9:0.80 11:0.64 12:0.69 17:0.72 21:0.78 25:0.88 27:0.06 28:0.78 30:0.76 32:0.80 34:0.58 36:0.49 37:0.95 39:0.86 41:0.93 43:0.86 46:0.98 55:0.71 66:0.77 69:0.47 70:0.21 74:0.65 77:0.70 83:0.80 85:0.80 91:0.72 96:0.90 98:0.73 101:0.79 104:0.58 107:0.93 108:0.36 110:0.94 120:0.86 122:0.43 123:0.35 125:0.97 127:0.23 129:0.05 131:0.87 135:0.26 140:0.80 144:0.57 145:0.63 146:0.43 147:0.49 149:0.72 151:0.94 153:0.82 154:0.83 155:0.67 157:0.82 159:0.16 160:0.98 161:0.45 162:0.67 164:0.81 166:0.61 167:0.90 172:0.37 173:0.75 177:0.38 178:0.42 179:0.78 181:0.94 182:0.84 187:0.39 192:0.59 195:0.54 202:0.74 208:0.64 212:0.87 216:0.58 222:0.60 227:0.88 229:0.91 231:0.72 235:0.52 241:0.75 242:0.55 243:0.79 246:0.61 247:0.39 248:0.89 253:0.88 255:0.79 256:0.77 259:0.21 260:0.86 265:0.73 266:0.17 267:0.23 268:0.76 271:0.72 276:0.31 282:0.88 285:0.62 287:0.90 289:0.95 299:0.88 300:0.18 +0 1:0.74 7:0.81 8:0.61 9:0.80 11:0.64 12:0.69 17:0.08 20:0.75 21:0.13 25:0.88 27:0.72 28:0.78 30:0.91 34:0.40 36:0.71 37:0.23 39:0.86 41:0.94 43:0.98 46:1.00 60:0.87 66:0.69 69:0.08 70:0.13 74:0.73 78:0.89 81:0.80 83:0.88 85:0.80 87:0.98 91:0.78 96:0.91 98:0.88 100:0.85 101:0.81 104:0.58 107:0.92 108:0.07 110:0.92 111:0.89 114:0.92 120:0.85 121:0.90 122:0.43 123:0.07 125:0.99 126:0.54 127:0.41 129:0.05 131:0.87 132:0.97 135:0.58 140:0.45 144:0.57 145:0.49 146:0.32 147:0.38 149:0.70 150:0.87 151:0.96 152:0.74 153:0.86 154:0.98 155:0.67 157:0.82 158:0.92 159:0.13 160:0.98 161:0.45 162:0.83 163:0.75 164:0.82 165:0.88 166:0.81 167:1.00 169:0.89 172:0.21 173:0.66 176:0.73 177:0.38 179:0.97 181:0.94 182:0.84 186:0.80 192:0.59 201:0.74 202:0.71 204:0.89 208:0.64 212:0.61 215:0.84 216:0.27 220:0.82 222:0.60 227:0.88 229:0.91 230:0.84 231:0.72 232:0.80 233:0.69 235:0.22 238:0.88 241:0.92 242:0.63 243:0.73 246:0.88 247:0.30 248:0.91 253:0.92 255:0.79 256:0.73 260:0.86 261:0.74 265:0.88 266:0.17 267:0.91 268:0.77 271:0.72 276:0.14 279:0.86 283:0.82 285:0.62 287:0.90 289:0.95 290:0.92 297:0.36 300:0.13 +3 1:0.74 12:0.69 17:0.89 21:0.22 27:0.17 28:0.78 30:0.45 34:0.25 36:0.62 37:0.80 39:0.86 43:0.43 46:0.88 55:0.45 66:0.69 69:0.23 70:0.25 74:0.75 76:0.85 77:0.70 81:0.73 91:0.33 98:0.36 104:0.58 107:0.89 108:0.18 110:0.89 114:0.90 117:0.86 122:0.67 123:0.18 125:0.88 126:0.54 127:0.13 129:0.05 131:0.61 135:0.34 144:0.57 145:0.58 146:0.38 147:0.45 149:0.33 150:0.77 154:0.32 155:0.67 157:0.61 159:0.15 160:0.88 161:0.45 163:0.75 166:0.81 167:0.70 172:0.21 173:0.31 175:0.75 176:0.73 177:0.05 178:0.55 179:0.56 181:0.94 182:0.61 187:0.39 192:0.59 195:0.67 201:0.74 212:0.61 215:0.79 216:0.22 222:0.60 227:0.61 229:0.61 231:0.72 232:0.79 235:0.31 241:0.35 242:0.82 243:0.73 244:0.61 246:0.61 247:0.12 253:0.72 257:0.65 259:0.21 265:0.38 266:0.17 267:0.59 271:0.72 276:0.21 277:0.69 282:0.81 287:0.61 289:0.94 290:0.90 297:0.36 300:0.18 +3 1:0.74 9:0.80 11:0.64 12:0.69 17:0.82 21:0.13 27:0.17 28:0.78 30:0.56 32:0.80 34:0.70 36:0.93 37:0.80 39:0.86 41:0.90 43:0.80 46:0.98 55:0.71 66:0.91 69:0.45 70:0.70 74:0.79 77:0.70 81:0.80 83:0.79 85:0.90 91:0.61 96:0.79 98:0.92 101:0.82 104:0.58 107:0.96 108:0.35 110:0.96 114:0.92 120:0.68 123:0.34 125:0.99 126:0.54 127:0.53 129:0.05 131:0.87 135:0.76 140:0.45 144:0.57 145:0.44 146:0.86 147:0.88 149:0.85 150:0.87 151:0.73 153:0.78 154:0.73 155:0.67 157:0.87 159:0.43 160:0.98 161:0.45 162:0.86 164:0.73 165:0.88 166:0.81 167:0.78 172:0.74 173:0.49 176:0.73 177:0.38 179:0.55 181:0.94 182:0.84 187:0.39 192:0.59 195:0.65 201:0.74 202:0.59 208:0.64 212:0.71 215:0.84 216:0.22 220:0.74 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.22 241:0.65 242:0.40 243:0.91 246:0.88 247:0.60 248:0.75 253:0.84 255:0.79 256:0.64 259:0.21 260:0.69 265:0.92 266:0.38 267:0.69 268:0.67 271:0.72 276:0.62 282:0.88 285:0.62 287:0.90 289:0.95 290:0.92 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.82 7:0.81 8:0.68 9:0.80 11:0.64 12:0.69 17:0.59 20:0.88 21:0.47 27:0.21 28:0.78 30:0.30 34:0.62 36:0.80 37:0.74 39:0.86 41:0.82 43:0.98 46:0.97 55:0.71 60:0.87 66:0.94 69:0.17 70:0.74 74:0.89 78:0.75 81:0.61 83:0.83 85:0.93 91:0.48 96:0.70 98:0.88 100:0.78 101:0.81 104:0.84 106:0.81 107:0.96 108:0.14 110:0.96 111:0.74 114:0.80 117:0.86 120:0.56 121:0.90 122:0.43 123:0.13 125:0.98 126:0.54 127:0.41 129:0.05 131:0.87 135:0.20 140:0.45 144:0.57 146:0.93 147:0.94 149:0.91 150:0.76 151:0.53 152:0.83 153:0.77 154:0.98 155:0.67 157:0.88 158:0.87 159:0.57 160:0.96 161:0.45 162:0.68 164:0.64 165:0.80 166:0.81 167:0.64 169:0.75 172:0.83 173:0.17 176:0.73 177:0.38 179:0.37 181:0.94 182:0.83 186:0.76 187:0.39 189:0.84 192:0.59 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.54 215:0.63 216:0.95 220:0.93 222:0.60 227:0.88 229:0.91 230:0.84 231:0.72 232:0.91 233:0.69 235:0.60 238:0.87 241:0.10 242:0.39 243:0.94 246:0.88 247:0.60 248:0.52 253:0.74 255:0.79 256:0.45 259:0.21 260:0.57 261:0.76 265:0.88 266:0.63 267:0.88 268:0.57 271:0.72 276:0.73 279:0.86 283:0.71 285:0.62 287:0.90 289:0.94 290:0.80 297:0.36 300:0.70 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.09 21:0.30 25:0.88 27:0.06 28:0.78 30:0.45 32:0.80 34:0.96 36:0.48 37:0.95 39:0.86 41:0.88 43:0.90 46:0.97 66:0.36 69:0.23 70:0.33 74:0.64 77:0.89 81:0.70 83:0.78 85:0.80 91:0.54 96:0.82 98:0.37 101:0.79 104:0.58 107:0.93 108:0.57 110:0.94 114:0.86 120:0.72 122:0.43 123:0.55 124:0.58 125:0.99 126:0.54 127:0.40 129:0.59 131:0.87 133:0.47 135:0.30 140:0.45 144:0.57 145:0.52 146:0.26 147:0.32 149:0.71 150:0.81 151:0.87 153:0.48 154:0.89 155:0.67 157:0.82 159:0.23 160:0.97 161:0.45 162:0.86 164:0.67 165:0.84 166:0.81 167:0.83 172:0.21 173:0.48 176:0.73 177:0.38 179:0.89 181:0.94 182:0.84 187:0.39 192:0.59 195:0.54 201:0.74 202:0.50 208:0.64 212:0.52 215:0.72 216:0.58 222:0.60 227:0.88 229:0.91 231:0.72 235:0.42 241:0.70 242:0.55 243:0.45 245:0.50 246:0.88 247:0.39 248:0.79 253:0.82 255:0.79 256:0.58 259:0.21 260:0.73 265:0.39 266:0.27 267:0.44 268:0.59 271:0.72 276:0.25 282:0.88 285:0.62 287:0.90 289:0.95 290:0.86 297:0.36 299:0.88 300:0.25 +1 11:0.64 12:0.69 17:0.36 21:0.78 27:0.45 28:0.78 30:0.76 34:0.77 36:0.19 37:0.50 39:0.86 43:0.79 46:0.97 55:0.59 66:0.72 69:0.76 70:0.28 74:0.76 85:0.93 91:0.27 98:0.41 101:0.81 107:0.95 108:0.59 110:0.96 122:0.43 123:0.56 124:0.42 125:0.88 127:0.39 129:0.05 131:0.61 133:0.39 135:0.51 144:0.57 145:0.38 146:0.60 147:0.66 149:0.88 154:0.72 157:0.88 159:0.60 160:0.88 161:0.45 166:0.61 167:0.70 172:0.67 173:0.69 177:0.38 178:0.55 179:0.53 181:0.94 182:0.78 187:0.68 212:0.87 216:0.77 222:0.60 227:0.61 229:0.61 231:0.69 235:0.52 241:0.35 242:0.52 243:0.75 245:0.70 246:0.61 247:0.47 253:0.56 259:0.21 265:0.43 266:0.38 267:0.19 271:0.72 276:0.39 287:0.61 289:0.93 300:0.33 +0 1:0.74 11:0.42 12:0.69 17:0.67 21:0.13 27:0.72 28:0.78 30:0.40 34:0.59 36:0.68 39:0.86 43:0.43 46:0.88 55:0.45 66:0.25 69:0.21 70:0.83 74:0.93 81:0.82 83:0.77 87:0.98 91:0.25 98:0.30 107:0.96 108:0.17 110:0.96 114:0.92 122:0.67 123:0.16 124:0.84 125:0.88 126:0.54 127:0.78 129:0.89 131:0.61 133:0.83 135:0.79 144:0.57 146:0.55 147:0.61 149:0.58 150:0.88 154:0.84 155:0.67 157:0.61 159:0.78 160:0.88 161:0.45 165:0.88 166:0.81 167:0.77 172:0.59 173:0.14 176:0.73 177:0.38 178:0.55 179:0.43 181:0.94 182:0.83 187:0.39 192:0.59 201:0.74 212:0.76 215:0.84 222:0.60 227:0.61 229:0.61 231:0.72 232:0.70 235:0.31 241:0.63 242:0.36 243:0.44 245:0.81 246:0.88 247:0.67 253:0.78 254:0.74 265:0.33 266:0.71 267:0.79 271:0.72 276:0.71 287:0.61 289:0.94 290:0.92 297:0.36 300:0.84 +2 1:0.74 11:0.64 12:0.69 17:0.90 21:0.40 27:0.17 28:0.78 30:0.68 32:0.80 34:0.75 36:0.58 37:0.80 39:0.86 43:0.84 46:0.99 55:0.59 66:0.86 69:0.35 70:0.31 74:0.73 77:0.70 81:0.65 83:0.75 85:0.88 91:0.33 98:0.91 101:0.83 104:0.58 107:0.95 108:0.27 110:0.95 114:0.82 123:0.26 125:0.88 126:0.54 127:0.50 129:0.05 131:0.61 135:0.30 144:0.57 145:0.44 146:0.65 147:0.70 149:0.84 150:0.78 154:0.81 155:0.67 157:0.86 159:0.25 160:0.88 161:0.45 165:0.83 166:0.81 167:0.71 172:0.61 173:0.58 176:0.73 177:0.38 178:0.55 179:0.70 181:0.94 182:0.83 187:0.39 192:0.59 195:0.54 201:0.74 212:0.69 215:0.67 216:0.22 222:0.60 227:0.61 229:0.61 231:0.72 232:0.79 235:0.52 241:0.39 242:0.54 243:0.87 246:0.88 247:0.47 253:0.69 259:0.21 265:0.91 266:0.27 267:0.82 271:0.72 276:0.51 282:0.88 283:0.71 287:0.61 289:0.94 290:0.82 297:0.36 299:0.88 300:0.25 +1 1:0.74 6:0.84 8:0.70 9:0.80 11:0.64 12:0.69 17:0.73 20:0.87 21:0.05 27:0.72 28:0.78 30:0.70 34:0.28 36:0.81 37:0.23 39:0.86 41:0.88 43:0.94 46:0.99 66:0.62 69:0.21 70:0.48 74:0.84 76:0.85 78:0.74 81:0.85 83:0.80 85:0.93 91:0.80 96:0.80 98:0.66 100:0.77 101:0.84 104:0.58 107:0.96 108:0.68 110:0.95 111:0.73 114:0.95 120:0.69 122:0.57 123:0.66 124:0.52 125:1.00 126:0.54 127:0.79 129:0.81 131:0.87 133:0.67 135:0.49 140:0.45 144:0.57 146:0.13 147:0.18 149:0.87 150:0.91 151:0.82 152:0.82 153:0.77 154:0.94 155:0.67 157:0.89 159:0.43 160:0.97 161:0.45 162:0.86 164:0.69 165:0.90 166:0.81 167:0.98 169:0.75 172:0.57 173:0.32 176:0.73 177:0.38 179:0.71 181:0.94 182:0.84 186:0.75 189:0.86 192:0.59 201:0.74 202:0.54 208:0.64 212:0.35 215:0.91 216:0.10 220:0.62 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.12 238:0.83 241:0.83 242:0.55 243:0.19 245:0.61 246:0.88 247:0.39 248:0.77 253:0.86 255:0.79 256:0.58 259:0.21 260:0.70 261:0.75 265:0.67 266:0.63 267:0.69 268:0.63 271:0.72 276:0.51 285:0.62 287:0.90 289:0.95 290:0.95 297:0.36 300:0.43 +4 1:0.74 6:0.99 7:0.81 8:0.98 9:0.80 12:0.69 17:0.13 20:0.99 21:0.30 25:0.88 27:0.06 28:0.78 30:0.11 32:0.80 34:0.33 36:0.47 37:0.95 39:0.86 41:0.99 43:0.43 46:0.88 55:0.45 60:0.87 66:0.47 69:0.23 70:0.54 74:0.67 76:0.85 77:0.89 78:0.92 81:0.70 83:0.90 91:0.98 96:0.98 98:0.52 100:1.00 104:0.58 107:0.90 108:0.18 110:0.91 111:1.00 114:0.86 120:0.98 121:0.90 123:0.18 124:0.52 125:0.98 126:0.54 127:0.65 129:0.59 131:0.87 133:0.47 135:0.54 138:0.98 140:0.45 144:0.57 145:0.88 146:0.42 147:0.49 149:0.38 150:0.81 151:0.99 152:0.99 153:0.97 154:0.33 155:0.67 157:0.61 158:0.87 159:0.33 160:1.00 161:0.45 162:0.78 163:0.75 164:0.96 165:0.84 166:0.81 167:1.00 169:0.99 172:0.21 173:0.40 175:0.75 176:0.73 177:0.05 179:0.94 181:0.94 182:0.84 186:0.92 189:0.99 191:0.97 192:0.59 195:0.63 201:0.74 202:0.94 204:0.89 208:0.64 212:0.30 215:0.72 216:0.58 220:0.74 222:0.60 227:0.88 229:0.91 230:0.87 231:0.72 233:0.76 235:0.42 238:1.00 241:0.93 242:0.82 243:0.59 244:0.61 245:0.46 246:0.88 247:0.12 248:0.99 253:0.98 255:0.79 256:0.95 257:0.65 259:0.21 260:0.98 261:0.99 265:0.53 266:0.27 267:0.82 268:0.96 271:0.72 276:0.22 277:0.69 279:0.86 282:0.88 283:0.71 285:0.62 287:0.90 289:0.95 290:0.86 297:0.36 299:0.88 300:0.33 +3 1:0.74 12:0.69 17:0.79 21:0.30 27:0.17 28:0.78 30:0.95 32:0.80 34:0.52 36:0.59 37:0.80 39:0.86 43:0.42 46:0.88 55:0.84 66:0.54 69:0.27 74:0.47 77:0.70 81:0.70 83:0.75 91:0.42 98:0.55 104:0.58 107:0.89 108:0.21 110:0.90 114:0.86 123:0.20 125:0.88 126:0.54 127:0.16 129:0.05 131:0.61 135:0.19 144:0.57 145:0.52 146:0.33 147:0.40 149:0.22 150:0.81 154:0.32 155:0.67 157:0.61 159:0.13 160:0.88 161:0.45 165:0.84 166:0.81 167:0.71 172:0.10 173:0.57 175:0.75 176:0.73 177:0.05 178:0.55 179:0.97 181:0.94 182:0.83 187:0.39 192:0.59 195:0.54 201:0.74 215:0.72 216:0.22 222:0.60 227:0.61 229:0.61 231:0.71 232:0.79 235:0.42 241:0.43 243:0.63 244:0.61 246:0.88 253:0.65 257:0.65 259:0.21 265:0.57 267:0.65 271:0.72 277:0.69 282:0.88 287:0.61 289:0.94 290:0.86 297:0.36 299:0.88 300:0.08 +1 11:0.64 12:0.69 17:0.47 21:0.78 27:0.45 28:0.78 30:0.19 34:0.90 36:0.19 37:0.50 39:0.86 43:0.78 46:0.95 66:0.53 69:0.78 70:0.86 74:0.83 85:0.88 91:0.11 98:0.41 101:0.78 107:0.95 108:0.61 110:0.95 122:0.67 123:0.59 124:0.64 125:0.88 127:0.38 129:0.05 131:0.61 133:0.62 135:0.68 144:0.57 145:0.56 146:0.59 147:0.05 149:0.79 154:0.70 157:0.85 159:0.57 160:0.88 161:0.45 166:0.61 167:0.65 172:0.54 173:0.73 177:0.05 178:0.55 179:0.59 181:0.94 182:0.76 187:0.68 212:0.50 216:0.77 222:0.60 227:0.61 229:0.61 231:0.67 235:0.98 241:0.12 242:0.44 243:0.11 245:0.65 246:0.61 247:0.47 253:0.59 257:0.65 259:0.21 265:0.44 266:0.71 267:0.44 271:0.72 276:0.49 287:0.61 289:0.92 300:0.70 +2 1:0.74 11:0.64 12:0.69 17:0.40 21:0.30 27:0.17 28:0.78 30:0.45 32:0.80 34:0.96 36:0.47 37:0.80 39:0.86 43:0.90 46:0.97 66:0.36 69:0.23 70:0.33 74:0.64 77:0.89 81:0.70 83:0.75 85:0.80 91:0.38 98:0.37 101:0.79 104:0.58 107:0.93 108:0.57 110:0.94 114:0.86 122:0.43 123:0.54 124:0.58 125:0.88 126:0.54 127:0.38 129:0.59 131:0.61 133:0.47 135:0.23 144:0.57 145:0.52 146:0.26 147:0.32 149:0.71 150:0.81 154:0.89 155:0.67 157:0.82 159:0.23 160:0.88 161:0.45 165:0.84 166:0.81 167:0.74 172:0.21 173:0.48 176:0.73 177:0.38 178:0.55 179:0.89 181:0.94 182:0.83 187:0.39 192:0.59 195:0.54 201:0.74 212:0.52 215:0.72 216:0.22 222:0.60 227:0.61 229:0.61 231:0.72 232:0.79 235:0.42 241:0.55 242:0.55 243:0.45 245:0.50 246:0.88 247:0.39 253:0.68 259:0.21 265:0.39 266:0.27 267:0.28 271:0.72 276:0.25 282:0.88 287:0.61 289:0.94 290:0.86 297:0.36 299:0.88 300:0.25 +2 12:0.69 17:0.51 21:0.13 27:0.17 28:0.78 30:0.33 32:0.80 34:0.34 36:0.34 37:0.80 39:0.86 43:0.43 46:0.88 55:0.59 66:0.54 69:0.17 70:0.69 74:0.47 77:0.70 81:0.67 91:0.35 98:0.67 104:0.58 107:0.90 108:0.14 110:0.91 120:0.62 123:0.13 124:0.48 125:0.88 126:0.32 127:0.49 129:0.59 131:0.82 133:0.47 135:0.24 140:0.45 144:0.57 145:0.54 146:0.60 147:0.65 149:0.44 150:0.48 151:0.58 153:0.48 154:0.33 157:0.61 159:0.36 160:0.88 161:0.45 162:0.86 163:0.81 164:0.55 166:0.75 167:0.78 172:0.32 173:0.31 175:0.75 177:0.05 179:0.88 181:0.94 182:0.73 187:0.39 190:0.77 195:0.60 202:0.36 212:0.42 215:0.84 216:0.22 220:0.74 222:0.60 227:0.85 229:0.61 231:0.67 232:0.79 235:0.12 241:0.65 242:0.82 243:0.63 244:0.61 245:0.50 246:0.61 247:0.12 248:0.56 253:0.40 256:0.45 257:0.65 259:0.21 260:0.62 265:0.68 266:0.38 267:0.36 268:0.46 271:0.72 276:0.31 277:0.69 282:0.88 285:0.50 287:0.61 289:0.92 297:0.36 299:0.88 300:0.43 +1 12:0.51 17:0.26 18:0.78 21:0.78 27:0.72 29:0.62 30:0.19 34:0.66 36:0.70 37:0.36 39:0.71 43:0.09 55:0.92 66:0.15 69:0.37 70:0.90 71:0.56 74:0.28 86:0.73 91:0.18 98:0.21 108:0.29 123:0.28 124:0.93 127:0.81 129:0.59 133:0.92 135:0.81 139:0.70 146:0.55 147:0.61 149:0.11 154:0.29 159:0.91 163:0.75 170:0.99 172:0.27 173:0.12 175:0.91 177:0.38 178:0.55 179:0.70 187:0.68 202:0.36 212:0.15 216:0.30 222:0.35 235:0.12 241:0.10 242:0.33 243:0.36 244:0.93 245:0.55 247:0.76 253:0.25 254:0.80 257:0.84 259:0.21 265:0.22 266:0.91 267:0.23 271:0.61 276:0.55 277:0.87 300:0.70 +0 10:0.92 12:0.51 17:0.20 18:0.68 21:0.78 27:0.68 29:0.62 30:0.30 34:0.61 36:0.69 37:0.63 39:0.71 43:0.09 55:0.92 66:0.07 69:0.56 70:0.63 71:0.56 74:0.30 86:0.64 91:0.48 98:0.21 108:0.43 122:0.95 123:0.94 124:0.94 127:0.71 129:0.93 133:0.93 135:0.58 139:0.63 146:0.49 147:0.55 149:0.17 154:0.17 159:0.89 163:0.94 170:0.99 172:0.27 173:0.24 174:0.95 175:0.91 177:0.38 178:0.55 179:0.59 187:0.98 197:0.95 212:0.23 216:0.68 222:0.35 235:0.12 239:0.90 241:0.07 242:0.40 243:0.31 244:0.93 245:0.60 247:0.88 253:0.27 254:0.96 257:0.84 259:0.21 265:0.21 266:0.94 267:0.65 271:0.61 276:0.59 277:0.87 300:0.43 +0 10:0.89 12:0.51 17:0.22 18:0.69 21:0.78 27:0.36 29:0.62 30:0.16 34:0.62 36:0.93 37:0.31 39:0.71 43:0.07 55:0.92 66:0.07 69:0.99 70:0.99 71:0.56 86:0.66 91:0.06 98:0.21 108:0.99 122:0.95 123:0.99 124:0.99 127:0.72 129:0.93 133:0.99 135:0.70 139:0.64 146:0.62 147:0.05 149:0.13 154:0.06 159:0.98 170:0.99 172:0.54 173:0.91 174:0.99 175:0.91 177:0.05 178:0.55 179:0.20 187:0.68 197:0.92 212:0.12 216:0.70 222:0.35 239:0.88 241:0.01 242:0.55 243:0.06 244:0.93 245:0.81 247:0.74 253:0.20 254:0.74 257:0.93 259:0.21 265:0.23 266:1.00 267:0.97 271:0.61 276:0.91 277:0.87 300:0.94 +0 8:0.61 10:0.80 12:0.51 17:0.38 18:0.57 21:0.78 27:0.27 29:0.62 30:0.20 34:0.80 36:0.61 37:0.64 39:0.71 43:0.09 55:0.99 66:0.07 69:0.73 70:0.90 71:0.56 74:0.28 86:0.58 91:0.15 98:0.13 108:1.00 123:0.54 124:0.95 127:0.63 129:0.99 133:0.95 135:0.76 139:0.57 146:0.55 147:0.61 149:0.11 154:0.08 159:0.98 170:0.99 172:0.32 173:0.15 174:0.90 175:0.97 177:0.05 178:0.55 179:0.59 187:0.68 197:0.80 202:0.50 212:0.18 216:0.75 222:0.35 235:0.12 239:0.80 241:0.18 242:0.51 243:0.22 244:0.97 245:0.56 247:0.76 253:0.25 257:0.93 259:0.21 265:0.11 266:0.95 267:0.44 271:0.61 276:0.60 277:0.95 300:0.70 +0 10:0.84 12:0.51 17:0.59 18:0.57 21:0.78 27:0.55 29:0.62 30:0.28 34:0.28 36:0.44 37:0.31 39:0.71 43:0.17 55:0.71 66:0.32 69:0.95 70:0.99 71:0.56 74:0.32 86:0.57 91:0.02 98:0.25 108:0.91 123:0.90 124:0.98 127:0.66 129:0.59 133:0.98 135:0.26 139:0.56 146:0.91 147:0.05 149:0.31 154:0.11 159:0.97 170:0.99 172:0.83 173:0.64 174:0.89 175:0.91 177:0.05 178:0.55 179:0.22 187:0.68 197:0.84 212:0.37 216:0.60 222:0.35 235:0.02 239:0.84 241:0.46 242:0.77 243:0.06 244:0.93 245:0.77 247:0.88 253:0.29 257:0.93 259:0.21 265:0.27 266:0.98 267:0.23 271:0.61 276:0.89 277:0.87 300:0.98 +0 10:0.82 12:0.51 17:0.32 18:0.82 21:0.47 27:0.69 29:0.62 30:0.68 34:0.87 36:0.20 37:0.34 39:0.71 43:0.11 55:0.84 64:0.77 66:0.45 69:0.86 70:0.21 71:0.56 74:0.29 81:0.31 86:0.72 91:0.33 98:0.62 108:0.18 122:0.92 123:0.18 124:0.56 126:0.24 127:0.51 129:0.05 133:0.43 135:0.60 139:0.70 146:0.55 147:0.70 149:0.16 150:0.35 154:0.09 159:0.46 163:0.94 170:0.99 172:0.27 173:0.92 174:0.79 175:0.75 177:0.38 178:0.55 179:0.89 187:0.39 197:0.82 212:0.42 216:0.49 222:0.35 235:0.52 239:0.81 241:0.01 242:0.63 243:0.58 244:0.97 245:0.53 247:0.39 253:0.27 257:0.84 259:0.21 265:0.63 266:0.38 267:0.28 271:0.61 276:0.35 277:0.87 300:0.18 +1 12:0.51 17:0.26 18:0.83 21:0.78 27:0.72 29:0.62 30:0.40 34:0.55 36:0.81 37:0.36 39:0.71 43:0.11 55:0.96 66:0.13 69:0.74 70:0.76 71:0.56 74:0.28 86:0.75 91:0.33 98:0.26 108:0.91 122:0.92 123:0.91 124:0.95 127:0.61 129:0.81 133:0.95 135:0.83 139:0.72 146:0.28 147:0.05 149:0.16 154:0.20 159:0.88 163:0.88 170:0.99 172:0.32 173:0.46 175:0.91 177:0.05 178:0.55 179:0.56 187:0.68 202:0.46 212:0.16 216:0.30 222:0.35 235:0.22 241:0.12 242:0.45 243:0.10 244:0.93 245:0.58 247:0.79 253:0.25 254:0.80 257:0.93 259:0.21 265:0.28 266:0.94 267:0.36 271:0.61 276:0.64 277:0.87 300:0.70 +0 10:0.90 12:0.51 17:0.24 18:0.70 21:0.78 27:0.36 29:0.62 30:0.09 34:0.64 36:0.95 37:0.31 39:0.71 43:0.07 55:0.84 66:0.06 69:0.99 70:0.97 71:0.56 86:0.67 91:0.03 98:0.18 108:1.00 122:0.95 123:0.98 124:0.99 127:0.79 129:0.99 133:0.99 135:0.63 139:0.65 146:0.39 147:0.05 149:0.15 154:0.06 159:0.98 163:0.75 170:0.99 172:0.51 173:0.87 174:0.99 175:0.91 177:0.05 178:0.55 179:0.16 187:0.68 197:0.94 212:0.12 216:0.70 222:0.35 239:0.89 241:0.01 242:0.57 243:0.05 244:0.93 245:0.87 247:0.82 253:0.20 254:0.84 257:0.93 259:0.21 265:0.17 266:1.00 267:0.97 271:0.61 276:0.94 277:0.87 300:0.94 +1 12:0.51 17:0.22 18:0.56 21:0.78 25:0.88 27:0.38 29:0.62 30:0.17 34:0.33 36:0.79 37:0.66 39:0.71 43:0.08 55:0.92 66:0.05 69:0.81 70:0.98 71:0.56 74:0.26 86:0.57 98:0.16 104:0.58 108:0.98 123:0.98 124:1.00 127:0.98 129:1.00 133:1.00 135:0.71 139:0.55 146:0.05 147:0.05 149:0.21 154:0.07 159:1.00 163:0.92 170:0.99 172:0.54 173:0.13 175:0.97 177:0.05 178:0.55 179:0.14 187:0.95 202:0.56 212:0.12 216:0.88 222:0.35 235:0.88 241:0.07 242:0.82 243:0.05 244:0.97 245:0.85 247:0.67 253:0.23 257:0.93 259:0.21 265:0.17 266:1.00 267:0.59 271:0.61 276:0.96 277:0.95 300:0.99 +1 12:0.51 17:0.09 18:0.78 21:0.78 27:0.56 29:0.62 30:0.09 34:0.53 36:0.76 37:0.39 39:0.71 43:0.09 55:0.71 66:0.15 69:0.37 70:0.94 71:0.56 74:0.28 86:0.73 91:0.17 98:0.21 108:0.29 123:0.28 124:0.93 127:0.83 129:0.59 133:0.92 135:0.86 139:0.70 146:0.55 147:0.61 149:0.11 154:0.29 159:0.91 163:0.75 170:0.99 172:0.27 173:0.12 175:0.91 177:0.38 178:0.55 179:0.70 187:0.87 212:0.15 216:0.61 222:0.35 235:0.12 241:0.05 242:0.33 243:0.36 244:0.93 245:0.55 247:0.76 253:0.25 254:0.80 257:0.84 259:0.21 265:0.22 266:0.87 267:0.52 271:0.61 276:0.55 277:0.87 300:0.70 +1 8:0.74 10:0.88 12:0.51 17:0.15 18:0.57 21:0.54 27:0.60 29:0.62 30:0.21 34:0.75 36:0.75 37:0.42 39:0.71 43:0.22 55:0.71 66:0.11 69:0.91 70:0.83 71:0.56 74:0.31 81:0.26 86:0.58 91:0.04 98:0.33 104:0.99 106:0.81 108:0.93 123:0.93 124:0.99 126:0.24 127:0.97 129:0.96 133:0.99 135:0.65 139:0.57 146:0.59 147:0.40 149:0.40 150:0.28 154:0.15 159:0.98 163:0.94 170:0.99 172:0.85 173:0.41 174:0.93 175:0.91 177:0.05 178:0.55 179:0.13 187:0.95 197:0.89 206:0.81 212:0.15 215:0.58 216:0.59 222:0.35 235:0.60 239:0.86 241:0.07 242:0.79 243:0.06 244:0.93 245:0.91 247:0.91 253:0.28 257:0.93 259:0.21 265:0.35 266:0.99 267:0.36 271:0.61 276:0.97 277:0.87 297:0.36 300:0.98 +0 12:0.51 17:0.18 18:0.84 21:0.78 27:0.50 29:0.62 30:0.14 34:0.62 36:0.75 37:0.66 39:0.71 43:0.11 55:0.71 66:0.13 69:0.34 70:0.84 71:0.56 74:0.28 86:0.75 91:0.27 98:0.26 108:0.27 122:0.92 123:0.91 124:0.95 127:0.59 129:0.81 133:0.94 135:0.87 139:0.72 146:0.61 147:0.66 149:0.17 154:0.20 159:0.88 170:0.99 172:0.32 173:0.12 175:0.91 177:0.38 178:0.55 179:0.55 187:0.87 212:0.13 216:0.51 222:0.35 235:0.22 241:0.07 242:0.45 243:0.33 244:0.93 245:0.59 247:0.79 253:0.25 254:0.80 257:0.84 259:0.21 265:0.27 266:0.92 267:0.52 271:0.61 276:0.64 277:0.87 300:0.70 +1 10:0.80 12:0.51 17:0.43 18:0.56 21:0.13 27:0.66 29:0.62 30:0.16 34:0.37 36:0.72 37:0.55 39:0.71 43:0.11 55:0.84 66:0.08 69:1.00 70:0.91 71:0.56 74:0.26 81:0.28 86:0.57 91:0.04 98:0.25 108:0.96 120:0.65 123:0.96 124:1.00 126:0.24 127:1.00 129:0.93 133:1.00 135:0.44 139:0.56 140:0.45 146:0.94 147:0.51 149:0.47 150:0.31 151:0.58 153:0.69 154:0.05 159:0.99 162:0.86 164:0.66 170:0.99 172:0.93 173:0.91 174:0.88 175:0.91 177:0.05 179:0.09 187:0.99 190:0.98 197:0.79 202:0.50 212:0.27 215:0.84 216:0.68 220:0.74 222:0.35 235:0.12 239:0.79 241:0.21 242:0.81 243:0.05 244:0.93 245:0.97 247:0.82 248:0.58 253:0.24 254:0.80 256:0.58 257:0.93 259:0.21 260:0.65 265:0.27 266:0.99 267:0.84 268:0.59 271:0.61 276:0.99 277:0.95 285:0.46 297:0.36 300:0.98 +1 12:0.51 17:0.12 18:0.83 21:0.78 27:0.25 29:0.62 30:0.40 34:0.49 36:0.87 37:0.65 39:0.71 43:0.11 55:0.96 66:0.13 69:0.74 70:0.76 71:0.56 74:0.28 86:0.75 91:0.25 98:0.26 108:0.91 122:0.92 123:0.91 124:0.95 127:0.61 129:0.81 133:0.95 135:0.88 139:0.72 146:0.28 147:0.05 149:0.16 154:0.20 159:0.88 163:0.88 170:0.99 172:0.32 173:0.46 175:0.91 177:0.05 178:0.55 179:0.56 187:0.68 212:0.16 216:0.66 222:0.35 235:0.22 241:0.05 242:0.45 243:0.10 244:0.93 245:0.58 247:0.79 253:0.25 254:0.80 257:0.93 259:0.21 265:0.28 266:0.94 267:0.52 271:0.61 276:0.64 277:0.87 300:0.70 +1 10:0.79 12:0.51 17:0.49 18:0.57 21:0.78 25:0.88 27:0.67 29:0.62 30:0.11 34:0.19 36:0.64 37:0.27 39:0.71 43:0.11 55:0.42 66:0.16 69:0.41 70:0.54 71:0.56 74:0.25 86:0.58 91:0.01 98:0.05 104:0.75 108:0.31 123:0.30 124:0.86 127:0.99 129:0.93 133:0.84 135:0.51 139:0.57 146:0.17 147:0.22 149:0.09 154:0.09 159:1.00 163:0.80 170:0.99 172:0.10 173:0.05 174:0.79 175:0.97 177:0.05 178:0.55 179:0.96 187:0.39 197:0.79 202:0.56 212:0.30 216:0.75 222:0.35 235:0.22 239:0.79 241:0.07 242:0.33 243:0.37 244:0.97 245:0.40 247:0.39 253:0.22 257:0.93 259:0.21 265:0.05 266:0.27 267:0.19 271:0.61 276:0.24 277:0.95 300:0.33 +1 10:0.95 12:0.51 17:0.11 18:0.61 21:0.78 27:0.62 29:0.62 30:0.17 34:0.87 36:0.66 37:0.35 39:0.71 43:0.41 55:0.59 66:0.08 69:0.36 70:0.93 71:0.56 74:0.25 86:0.62 91:0.08 98:0.29 108:0.94 123:0.94 124:0.98 127:0.99 129:0.05 133:0.99 135:0.68 139:0.60 146:0.95 147:0.96 149:0.67 154:0.31 159:0.98 170:0.99 172:0.90 173:0.06 174:0.99 175:0.75 177:0.70 178:0.55 179:0.10 187:0.95 197:0.97 212:0.55 216:0.66 222:0.35 235:0.31 239:0.94 241:0.07 242:0.70 243:0.23 244:0.93 245:0.98 247:0.86 253:0.23 257:0.65 259:0.21 265:0.31 266:0.98 267:0.44 271:0.61 276:0.99 277:0.87 300:0.98 +1 12:1.00 17:0.59 21:0.78 27:0.45 28:0.49 34:0.73 36:0.18 37:0.50 43:0.29 66:0.36 69:0.77 91:0.23 98:0.07 108:0.60 123:0.57 127:0.06 129:0.05 135:0.82 145:0.52 146:0.05 147:0.05 149:0.12 154:0.21 159:0.05 161:0.59 167:0.61 172:0.05 173:0.72 177:0.05 178:0.55 181:0.52 187:0.68 216:0.77 235:0.95 241:0.32 243:0.51 259:0.21 265:0.07 267:0.23 +2 6:0.81 8:0.74 12:1.00 17:0.62 20:0.75 21:0.78 27:0.28 28:0.49 30:0.21 32:0.80 34:0.52 36:0.69 37:0.41 43:0.39 55:0.71 66:0.20 69:0.56 70:0.37 78:0.83 91:0.22 98:0.18 100:0.89 108:0.43 111:0.84 123:0.41 124:0.81 127:0.50 129:0.89 133:0.78 135:0.94 145:0.82 146:0.27 147:0.33 149:0.32 152:0.93 154:0.29 159:0.56 161:0.59 163:0.89 167:0.50 169:0.80 172:0.10 173:0.51 177:0.05 178:0.55 179:0.96 181:0.52 186:0.83 187:0.39 195:0.74 212:0.42 216:0.67 235:0.12 242:0.82 243:0.40 245:0.39 247:0.12 259:0.21 261:0.85 265:0.20 266:0.23 267:0.79 276:0.24 300:0.25 +1 6:0.83 8:0.63 12:1.00 17:0.70 20:0.87 21:0.22 27:0.66 28:0.49 30:0.18 34:0.58 36:0.41 37:0.56 43:0.48 55:0.96 66:0.08 69:0.33 70:0.81 78:0.82 81:0.84 91:0.18 98:0.21 100:0.80 106:0.81 108:0.89 111:0.81 120:0.54 123:0.88 124:0.97 126:0.54 127:0.95 129:0.99 133:0.97 135:0.69 140:0.45 146:0.29 147:0.35 149:0.50 150:0.64 151:0.47 152:0.85 153:0.48 154:0.37 159:0.93 161:0.59 162:0.86 163:0.99 164:0.57 167:0.56 169:0.78 172:0.27 173:0.09 177:0.05 179:0.51 181:0.52 186:0.83 187:0.68 189:0.84 202:0.36 206:0.81 212:0.16 215:0.79 216:0.07 220:0.82 235:0.31 238:0.84 241:0.12 242:0.82 243:0.13 245:0.61 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 261:0.82 265:0.23 266:0.95 267:0.17 268:0.46 276:0.71 283:0.60 285:0.62 297:0.36 300:0.55 +0 6:0.80 8:0.59 12:1.00 17:0.99 20:0.86 21:0.13 25:0.88 27:0.20 28:0.49 34:0.44 36:0.59 37:0.23 78:0.92 81:0.74 91:0.87 100:0.92 111:0.91 126:0.54 135:0.23 150:0.55 152:0.81 161:0.59 167:0.91 169:0.90 175:0.75 178:0.55 181:0.52 186:0.92 189:0.82 215:0.84 235:0.12 238:0.88 241:0.83 244:0.61 257:0.65 259:0.21 261:0.88 267:0.28 277:0.69 297:0.36 +1 6:0.81 8:0.62 12:1.00 17:0.73 20:0.93 21:0.13 27:0.28 28:0.49 30:0.45 32:0.80 34:0.22 36:0.18 37:0.41 43:0.44 55:0.99 66:0.25 69:0.45 70:0.50 78:0.84 81:0.74 91:0.03 98:0.13 100:0.83 108:0.35 111:0.83 123:0.34 124:0.84 126:0.54 127:0.89 129:0.93 133:0.84 135:0.91 145:0.80 146:0.31 147:0.37 149:0.34 150:0.55 152:0.93 154:0.33 159:0.93 161:0.59 163:0.79 167:0.53 169:0.80 172:0.16 173:0.13 177:0.05 178:0.55 179:0.94 181:0.52 186:0.84 187:0.39 189:0.83 191:0.84 195:0.98 202:0.36 212:0.23 215:0.84 216:0.67 235:0.12 238:0.85 241:0.02 242:0.82 243:0.45 245:0.42 247:0.12 259:0.21 261:0.85 265:0.14 266:0.38 267:0.73 276:0.29 297:0.36 300:0.33 +3 6:0.96 8:0.93 12:1.00 17:0.78 20:0.99 21:0.78 25:0.88 27:0.07 28:0.49 34:0.36 36:0.39 37:0.95 78:0.91 91:0.92 100:0.98 111:0.97 120:0.62 135:0.44 140:0.90 151:0.56 152:0.99 153:0.78 161:0.59 162:0.46 164:0.65 167:0.93 169:0.99 175:0.75 178:0.50 181:0.52 186:0.91 189:0.96 191:0.91 202:0.84 216:0.26 220:0.62 235:0.12 238:0.98 241:0.95 244:0.61 248:0.55 256:0.45 257:0.65 259:0.21 260:0.62 261:0.98 267:0.44 268:0.58 277:0.69 285:0.62 +1 6:0.83 12:1.00 17:0.66 20:0.80 21:0.30 27:0.66 28:0.49 30:0.76 34:0.95 36:0.75 37:0.56 43:0.51 55:0.45 66:0.36 69:0.17 70:0.15 78:0.75 81:0.82 91:0.29 98:0.15 100:0.73 106:0.81 108:0.14 111:0.74 123:0.13 124:0.52 126:0.54 127:0.27 129:0.59 133:0.47 135:0.24 146:0.18 147:0.23 149:0.29 150:0.61 152:0.85 154:0.40 159:0.19 161:0.59 163:0.75 167:0.53 169:0.78 172:0.10 173:0.43 177:0.05 178:0.55 179:0.97 181:0.52 186:0.76 187:0.68 206:0.81 212:0.95 215:0.72 216:0.07 235:0.42 241:0.03 242:0.82 243:0.51 245:0.37 247:0.12 259:0.21 261:0.82 265:0.16 266:0.12 267:0.82 276:0.13 297:0.36 300:0.13 +1 8:0.64 12:1.00 17:0.75 20:0.83 21:0.30 27:0.34 28:0.49 30:0.68 32:0.80 34:0.69 36:0.68 37:0.28 43:0.40 55:0.92 66:0.32 69:0.54 70:0.43 78:0.76 81:0.69 91:0.42 98:0.38 100:0.73 106:0.81 108:0.41 111:0.75 123:0.39 124:0.78 126:0.54 127:0.24 129:0.89 133:0.78 135:0.70 145:0.90 146:0.59 147:0.64 149:0.41 150:0.50 152:0.79 154:0.30 159:0.48 161:0.59 163:0.86 167:0.55 169:0.72 172:0.21 173:0.41 177:0.05 178:0.55 179:0.75 181:0.52 186:0.77 187:0.21 191:0.73 195:0.82 206:0.81 212:0.19 215:0.72 216:0.94 235:0.31 241:0.07 242:0.82 243:0.49 245:0.45 247:0.12 259:0.21 261:0.76 265:0.41 266:0.47 267:0.69 276:0.35 297:0.36 300:0.33 +1 6:0.81 12:1.00 17:0.84 20:0.86 21:0.13 27:0.28 28:0.49 30:0.91 34:0.34 36:0.51 37:0.41 43:0.43 55:0.59 66:0.77 69:0.46 70:0.19 78:0.74 81:0.74 91:0.15 98:0.77 100:0.73 106:0.81 108:0.35 111:0.74 123:0.34 126:0.54 127:0.25 129:0.05 135:0.86 146:0.53 147:0.59 149:0.44 150:0.55 152:0.93 154:0.33 159:0.19 161:0.59 163:0.90 167:0.51 169:0.80 172:0.37 173:0.68 177:0.05 178:0.55 179:0.81 181:0.52 186:0.75 187:0.68 206:0.81 212:0.52 215:0.84 216:0.67 235:0.12 241:0.01 242:0.82 243:0.79 247:0.12 259:0.21 261:0.85 265:0.77 266:0.27 267:0.76 276:0.32 283:0.82 297:0.36 300:0.18 +2 7:0.81 12:0.20 17:0.36 21:0.54 27:0.20 28:0.92 30:0.45 34:0.75 36:0.37 37:0.81 43:0.37 55:0.52 66:0.25 69:0.61 70:0.50 81:0.89 91:0.48 98:0.18 108:0.62 120:0.79 123:0.87 124:0.71 126:0.54 127:0.47 129:0.81 133:0.67 135:0.54 140:0.80 145:0.61 146:0.05 147:0.05 149:0.34 150:0.78 151:0.76 153:0.87 154:0.28 159:0.66 161:0.57 162:0.48 163:0.80 164:0.83 167:0.82 172:0.16 173:0.49 177:0.05 178:0.42 179:0.90 181:0.57 187:0.68 202:0.88 212:0.23 215:0.58 216:0.84 230:0.87 233:0.76 235:0.68 241:0.72 242:0.82 243:0.21 245:0.45 247:0.12 248:0.72 256:0.77 259:0.21 260:0.80 265:0.15 266:0.38 267:0.23 268:0.78 271:0.32 276:0.21 283:0.60 285:0.62 297:0.36 300:0.33 +1 12:0.20 17:0.26 21:0.71 27:0.45 28:0.92 30:0.62 34:0.85 36:0.30 37:0.50 43:0.32 55:0.92 66:0.16 69:0.70 70:0.34 81:0.80 91:0.14 98:0.14 108:0.87 123:0.86 124:0.86 126:0.54 127:0.45 129:0.93 133:0.84 135:0.77 145:0.52 146:0.05 147:0.05 149:0.30 150:0.60 154:0.24 159:0.60 161:0.57 163:0.94 167:0.62 172:0.10 173:0.63 177:0.05 178:0.55 179:0.93 181:0.57 187:0.68 212:0.30 215:0.48 216:0.77 235:0.84 241:0.18 242:0.82 243:0.23 245:0.40 247:0.12 259:0.21 265:0.15 266:0.27 267:0.59 271:0.32 276:0.28 283:0.60 297:0.36 300:0.25 +2 6:0.83 8:0.76 12:0.20 17:0.81 20:0.82 21:0.13 25:0.88 27:0.68 28:0.92 30:0.45 34:0.31 36:0.52 37:0.41 43:0.46 55:0.45 66:0.35 69:0.42 70:0.49 78:0.74 81:0.96 91:0.86 98:0.69 100:0.77 104:0.76 108:0.32 111:0.74 120:0.85 123:0.62 124:0.61 126:0.54 127:0.73 129:0.59 133:0.47 135:0.23 140:0.80 146:0.45 147:0.51 149:0.71 150:0.94 151:0.79 152:0.79 153:0.90 154:0.35 159:0.36 161:0.57 162:0.64 164:0.88 167:0.94 169:0.75 172:0.51 173:0.54 177:0.05 178:0.42 179:0.61 181:0.57 186:0.75 189:0.85 191:0.89 202:0.84 212:0.81 215:0.84 216:0.56 220:0.74 235:0.12 238:0.87 241:0.83 242:0.82 243:0.51 245:0.80 247:0.12 248:0.79 256:0.83 259:0.21 260:0.86 261:0.75 265:0.55 266:0.27 267:0.23 268:0.85 271:0.32 276:0.59 283:0.60 285:0.62 297:0.36 300:0.43 +1 7:0.81 12:0.20 17:0.40 21:0.54 27:0.20 28:0.92 30:0.45 34:0.89 36:0.36 37:0.81 43:0.38 55:0.59 66:0.25 69:0.60 70:0.50 81:0.89 91:0.12 98:0.18 108:0.66 123:0.88 124:0.71 126:0.54 127:0.51 129:0.81 133:0.67 135:0.49 145:0.61 146:0.05 147:0.05 149:0.33 150:0.78 154:0.28 159:0.68 161:0.57 163:0.80 167:0.78 172:0.16 173:0.47 177:0.05 178:0.55 179:0.91 181:0.57 187:0.68 212:0.23 215:0.58 216:0.84 230:0.87 233:0.76 235:0.68 241:0.69 242:0.82 243:0.21 245:0.45 247:0.12 259:0.21 265:0.15 266:0.38 267:0.28 271:0.32 276:0.22 283:0.60 297:0.36 300:0.33 +2 6:0.99 8:0.84 12:0.20 17:0.08 20:0.75 21:0.54 25:0.88 27:0.03 28:0.92 30:0.66 32:0.80 34:0.92 36:0.49 37:0.96 43:0.40 55:0.71 66:0.75 69:0.54 70:0.64 78:0.85 81:0.89 91:0.61 98:0.75 100:0.92 104:0.58 108:0.68 111:0.87 120:0.96 123:0.66 124:0.39 126:0.54 127:0.62 129:0.59 133:0.47 135:0.60 140:0.45 145:0.54 146:0.05 147:0.05 149:0.48 150:0.78 151:0.79 152:0.99 153:0.91 154:0.30 159:0.48 161:0.57 162:0.53 164:0.95 167:0.74 169:0.99 172:0.54 173:0.57 177:0.05 179:0.78 181:0.57 186:0.88 187:0.21 189:0.93 191:0.87 195:0.68 202:0.95 212:0.22 215:0.58 216:0.89 235:0.60 238:0.95 241:0.62 242:0.82 243:0.14 245:0.50 247:0.12 248:0.94 256:0.95 259:0.21 260:0.96 261:1.00 265:0.76 266:0.63 267:0.19 268:0.94 271:0.32 276:0.43 283:0.82 285:0.62 297:0.36 300:0.55 +2 8:0.85 12:0.20 17:0.61 21:0.54 25:0.88 27:0.03 28:0.92 30:0.15 32:0.80 34:0.92 36:0.41 37:0.96 43:0.22 55:0.42 66:0.33 69:0.90 70:0.68 81:0.89 91:0.78 98:0.24 104:0.58 108:0.88 120:0.87 123:0.88 124:0.71 126:0.54 127:0.23 129:0.81 133:0.67 135:0.42 140:0.45 145:0.54 146:0.19 147:0.24 149:0.31 150:0.78 151:0.79 153:0.90 154:0.15 159:0.47 161:0.57 162:0.77 164:0.89 167:0.76 172:0.27 173:0.90 177:0.05 179:0.65 181:0.57 187:0.21 191:0.83 195:0.83 202:0.82 212:0.30 215:0.58 216:0.89 220:0.87 235:0.60 241:0.65 242:0.82 243:0.28 245:0.53 247:0.12 248:0.82 256:0.86 259:0.21 260:0.88 265:0.26 266:0.54 267:0.84 268:0.86 271:0.32 276:0.36 283:0.60 285:0.62 297:0.36 300:0.43 +2 12:0.20 17:0.08 21:0.40 25:0.88 27:0.03 28:0.92 30:0.15 32:0.80 34:0.19 36:0.67 37:0.96 43:0.50 55:0.42 66:0.33 69:0.23 70:0.84 81:0.92 91:0.63 98:0.21 104:0.58 106:0.81 108:0.94 120:0.87 123:0.94 124:0.87 126:0.54 127:0.93 129:0.95 133:0.87 135:0.49 140:0.45 145:0.54 146:0.05 147:0.05 149:0.29 150:0.86 151:0.77 153:0.88 154:0.39 159:0.85 161:0.57 162:0.48 164:0.89 167:0.78 172:0.27 173:0.12 177:0.05 179:0.88 181:0.57 187:0.39 195:0.94 202:0.92 206:0.81 212:0.15 215:0.67 216:0.89 220:0.91 235:0.42 241:0.69 242:0.82 243:0.17 245:0.45 247:0.12 248:0.82 256:0.90 259:0.21 260:0.88 265:0.23 266:0.63 267:0.36 268:0.86 271:0.32 276:0.37 283:0.60 285:0.62 297:0.36 300:0.55 +3 6:0.80 8:0.59 12:0.20 17:0.47 20:0.83 21:0.13 25:0.88 27:0.39 28:0.92 34:0.50 36:0.61 78:0.76 81:0.96 91:0.88 100:0.78 104:0.73 111:0.75 120:0.89 126:0.54 135:0.30 140:0.45 150:0.94 151:0.80 152:0.79 153:0.93 161:0.57 162:0.69 164:0.89 167:0.82 169:0.76 175:0.75 181:0.57 186:0.76 187:0.68 189:0.82 191:0.76 202:0.84 215:0.84 216:0.16 235:0.12 238:0.87 241:0.72 244:0.61 248:0.85 256:0.86 257:0.65 259:0.21 260:0.90 261:0.76 267:0.28 268:0.87 271:0.32 277:0.69 283:0.82 285:0.62 297:0.36 +2 6:0.80 8:0.76 12:0.20 17:0.30 20:0.75 21:0.78 25:0.88 27:0.72 28:0.92 30:0.62 34:0.52 36:0.56 37:0.65 43:0.34 55:0.45 66:0.75 69:0.65 70:0.23 78:0.75 91:0.84 98:0.82 100:0.73 104:0.77 108:0.49 111:0.74 120:0.84 123:0.47 127:0.31 129:0.05 135:0.21 140:0.87 146:0.60 147:0.66 149:0.37 151:0.73 152:0.81 153:0.78 154:0.25 159:0.22 161:0.57 162:0.52 163:0.86 164:0.79 167:0.95 169:0.75 172:0.32 173:0.84 177:0.05 178:0.48 179:0.89 181:0.57 186:0.73 191:0.89 202:0.79 212:0.30 216:0.53 235:0.05 241:0.84 242:0.82 243:0.77 247:0.12 248:0.76 256:0.73 259:0.21 260:0.85 261:0.75 265:0.82 266:0.27 267:0.36 268:0.74 271:0.32 276:0.26 283:0.60 285:0.62 300:0.18 +3 6:0.99 8:0.97 12:0.20 17:0.02 20:0.98 21:0.54 25:0.88 27:0.03 28:0.92 30:0.44 32:0.80 34:0.39 36:0.53 37:0.96 43:0.22 55:0.71 66:0.54 69:0.90 70:0.90 78:0.97 81:0.89 91:0.98 98:0.59 100:1.00 104:0.58 108:0.91 111:1.00 120:0.99 123:0.91 124:0.68 126:0.54 127:0.38 129:0.89 133:0.78 135:0.32 140:0.45 145:0.65 146:0.19 147:0.24 149:0.45 150:0.78 151:0.91 152:0.99 153:1.00 154:0.15 159:0.75 161:0.57 162:0.66 164:0.99 167:0.97 169:0.99 172:0.67 173:0.82 177:0.05 179:0.45 181:0.57 186:0.97 189:0.99 191:0.98 195:0.93 202:0.99 212:0.30 215:0.58 216:0.89 235:0.60 238:1.00 241:0.96 242:0.82 243:0.15 245:0.70 247:0.12 248:0.99 256:0.99 259:0.21 260:0.99 261:1.00 265:0.60 266:0.87 267:0.91 268:0.99 271:0.32 276:0.64 283:0.82 285:0.62 297:0.36 300:0.84 +1 6:0.99 8:0.75 12:0.20 17:0.02 20:0.86 21:0.30 25:0.88 27:0.03 28:0.92 30:0.75 32:0.80 34:0.79 36:0.88 37:0.96 43:0.25 55:0.71 66:0.68 69:0.84 70:0.47 78:0.82 81:0.93 91:0.71 98:0.66 100:0.82 104:0.58 108:0.86 111:0.85 120:0.92 123:0.85 124:0.44 126:0.54 127:0.43 129:0.59 133:0.47 135:0.47 140:0.90 145:0.69 146:0.52 147:0.58 149:0.49 150:0.89 151:0.81 152:0.99 153:0.94 154:0.18 159:0.69 161:0.57 162:0.47 164:0.94 167:0.90 169:0.99 172:0.67 173:0.75 177:0.05 178:0.50 179:0.54 181:0.57 186:0.79 187:0.39 189:0.86 191:0.88 195:0.89 202:0.98 212:0.39 215:0.72 216:0.89 220:0.87 235:0.31 238:0.97 241:0.77 242:0.82 243:0.27 245:0.74 247:0.12 248:0.89 256:0.91 259:0.21 260:0.93 261:1.00 265:0.67 266:0.75 267:0.44 268:0.92 271:0.32 276:0.58 283:0.82 285:0.62 297:0.36 300:0.55 +3 6:0.83 7:0.81 8:0.65 12:0.20 17:0.18 20:0.97 21:0.30 27:0.21 28:0.92 30:0.45 34:0.73 36:0.77 37:0.74 43:0.52 55:0.71 66:0.42 69:0.10 70:0.62 78:0.75 81:0.93 91:0.37 98:0.60 100:0.77 104:0.84 106:0.81 108:0.09 111:0.74 120:0.81 123:0.09 124:0.86 126:0.54 127:0.68 129:0.95 133:0.87 135:0.44 140:0.45 146:0.91 147:0.93 149:0.79 150:0.89 151:0.76 152:0.90 153:0.87 154:0.41 159:0.84 161:0.57 162:0.64 164:0.78 167:0.63 169:0.75 172:0.78 173:0.09 177:0.05 179:0.31 181:0.57 186:0.75 187:0.39 189:0.85 202:0.71 206:0.81 212:0.35 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.86 241:0.21 242:0.82 243:0.57 245:0.85 247:0.12 248:0.73 256:0.68 259:0.21 260:0.82 261:0.77 265:0.61 266:0.89 267:0.85 268:0.72 271:0.32 276:0.83 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.20 17:0.61 21:0.05 25:0.88 27:0.68 28:0.92 30:0.66 32:0.80 34:0.30 36:0.75 37:0.41 43:0.52 55:0.71 66:0.93 69:0.07 70:0.32 81:0.97 91:0.17 98:0.98 104:0.76 106:0.81 108:0.06 123:0.06 126:0.54 127:0.81 129:0.05 135:0.30 145:0.65 146:0.87 147:0.89 149:0.76 150:0.95 154:0.41 159:0.44 161:0.57 167:0.68 172:0.80 173:0.19 177:0.05 178:0.55 179:0.51 181:0.57 187:0.39 191:0.89 195:0.65 202:0.36 206:0.81 212:0.94 215:0.91 216:0.56 235:0.05 241:0.43 242:0.82 243:0.93 247:0.12 259:0.21 265:0.98 266:0.17 267:0.44 271:0.32 276:0.70 297:0.36 300:0.33 +2 6:0.90 8:0.73 12:0.20 17:0.13 20:0.93 21:0.54 25:0.88 27:0.42 28:0.92 30:0.33 32:0.80 34:0.30 36:0.90 37:0.53 43:0.36 55:0.71 66:0.45 69:0.62 70:0.69 78:0.74 81:0.89 91:0.67 98:0.14 100:0.77 104:0.79 108:0.97 111:0.74 120:0.87 123:0.97 124:0.66 126:0.54 127:0.94 129:0.81 133:0.67 135:0.60 140:0.45 145:0.84 146:0.05 147:0.05 149:0.24 150:0.78 151:0.75 152:0.86 153:0.86 154:0.27 159:0.93 161:0.57 162:0.51 164:0.92 167:0.94 169:0.75 172:0.27 173:0.25 177:0.05 179:0.92 181:0.57 186:0.75 189:0.91 191:0.79 195:0.98 202:0.93 212:0.42 215:0.58 216:0.53 220:0.96 235:0.60 238:0.87 241:0.82 242:0.82 243:0.19 245:0.47 247:0.12 248:0.81 256:0.92 259:0.21 260:0.87 261:0.76 265:0.15 266:0.38 267:0.96 268:0.90 271:0.32 276:0.21 285:0.62 297:0.36 300:0.43 +1 12:0.20 17:0.43 21:0.78 27:0.45 28:0.92 30:0.45 34:0.83 36:0.47 37:0.50 43:0.29 55:0.92 66:0.53 69:0.77 70:0.47 91:0.10 98:0.55 108:0.68 123:0.66 124:0.63 127:0.36 129:0.81 133:0.67 135:0.30 145:0.49 146:0.05 147:0.05 149:0.40 154:0.21 159:0.56 161:0.57 163:0.75 167:0.64 172:0.37 173:0.71 177:0.05 178:0.55 179:0.79 181:0.57 187:0.68 212:0.15 216:0.77 235:0.84 241:0.27 242:0.82 243:0.17 245:0.50 247:0.12 259:0.21 265:0.57 266:0.63 267:0.91 271:0.32 276:0.37 300:0.33 +1 7:0.81 12:0.20 17:0.36 21:0.54 27:0.20 28:0.92 30:0.45 34:0.89 36:0.39 37:0.81 43:0.36 55:0.59 66:0.25 69:0.63 70:0.50 81:0.89 91:0.10 98:0.18 108:0.66 123:0.88 124:0.71 126:0.54 127:0.50 129:0.81 133:0.67 135:0.49 145:0.61 146:0.05 147:0.05 149:0.33 150:0.78 154:0.27 159:0.67 161:0.57 163:0.80 167:0.78 172:0.16 173:0.51 177:0.05 178:0.55 179:0.91 181:0.57 187:0.68 212:0.23 215:0.58 216:0.84 230:0.87 233:0.76 235:0.68 241:0.69 242:0.82 243:0.21 245:0.45 247:0.12 259:0.21 265:0.15 266:0.38 267:0.28 271:0.32 276:0.22 297:0.36 300:0.33 +1 8:0.74 12:0.20 17:0.34 21:0.30 25:0.88 27:0.03 28:0.92 30:0.76 32:0.80 34:0.67 36:0.86 37:0.96 43:0.25 55:0.71 66:0.69 69:0.83 70:0.44 78:0.96 81:0.93 91:0.40 98:0.69 104:0.58 108:0.86 111:1.00 120:0.82 123:0.85 124:0.44 126:0.54 127:0.47 129:0.59 133:0.47 135:0.60 140:0.45 145:0.69 146:0.52 147:0.58 149:0.50 150:0.89 151:0.74 153:0.84 154:0.18 159:0.71 161:0.57 162:0.66 164:0.80 167:0.86 169:0.99 172:0.68 173:0.74 177:0.05 179:0.53 181:0.57 187:0.21 195:0.89 202:0.74 212:0.30 215:0.72 216:0.89 220:0.87 235:0.31 238:0.99 241:0.75 242:0.82 243:0.26 245:0.75 247:0.12 248:0.74 256:0.73 259:0.21 260:0.83 265:0.69 266:0.80 267:0.76 268:0.76 271:0.32 276:0.60 285:0.62 297:0.36 300:0.55 +1 12:0.20 17:0.55 21:0.13 25:0.88 27:0.72 28:0.92 30:0.40 32:0.80 34:0.68 36:0.84 43:0.21 55:0.71 66:0.25 69:0.92 70:0.88 81:0.96 91:0.22 98:0.36 104:0.72 108:0.84 123:0.84 124:0.84 126:0.54 127:0.39 129:0.93 133:0.84 135:0.61 145:0.61 146:0.78 147:0.81 149:0.51 150:0.94 154:0.14 159:0.83 161:0.57 167:0.67 172:0.57 173:0.81 177:0.05 178:0.55 179:0.36 181:0.57 187:0.21 191:0.70 195:0.96 212:0.22 215:0.84 216:0.48 235:0.12 241:0.37 242:0.82 243:0.44 245:0.79 247:0.12 259:0.21 265:0.39 266:0.94 267:0.65 271:0.32 276:0.72 297:0.36 300:0.84 +2 12:0.20 17:0.36 21:0.64 25:0.88 27:0.03 28:0.92 30:0.72 32:0.80 34:0.56 36:0.63 37:0.96 43:0.40 55:0.92 66:0.74 69:0.55 70:0.37 81:0.85 91:0.84 98:0.81 104:0.58 106:0.81 108:0.68 120:0.96 123:0.66 124:0.39 126:0.54 127:0.64 129:0.59 133:0.47 135:0.56 140:0.80 145:0.65 146:0.05 147:0.05 149:0.44 150:0.67 151:0.73 153:0.78 154:0.30 159:0.56 161:0.57 162:0.72 163:0.94 164:0.94 167:0.77 172:0.51 173:0.52 177:0.05 178:0.42 179:0.80 181:0.57 187:0.39 195:0.75 202:0.90 206:0.81 212:0.39 215:0.53 216:0.89 220:0.96 235:0.74 241:0.67 242:0.82 243:0.15 245:0.49 247:0.12 248:0.94 256:0.92 259:0.21 260:0.96 265:0.81 266:0.63 267:0.28 268:0.93 271:0.32 276:0.43 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.20 17:0.47 21:0.78 25:0.88 27:0.72 28:0.92 30:0.62 34:0.70 36:0.57 37:0.65 43:0.34 55:0.45 66:0.75 69:0.65 70:0.23 91:0.68 98:0.78 104:0.77 108:0.49 120:0.84 123:0.47 127:0.27 129:0.05 135:0.24 140:0.45 146:0.60 147:0.66 149:0.37 151:0.73 153:0.78 154:0.25 159:0.22 161:0.57 162:0.53 163:0.86 164:0.79 167:0.78 172:0.32 173:0.82 177:0.05 179:0.87 181:0.57 187:0.39 202:0.78 212:0.30 216:0.53 220:0.97 235:0.05 241:0.68 242:0.82 243:0.77 247:0.12 248:0.76 256:0.73 259:0.21 260:0.84 265:0.78 266:0.27 267:0.65 268:0.74 271:0.32 276:0.26 285:0.62 300:0.18 +2 7:0.81 12:0.20 17:0.36 21:0.40 27:0.20 28:0.92 30:0.21 34:0.92 36:0.39 37:0.81 43:0.35 55:0.59 66:0.20 69:0.63 70:0.37 81:0.92 91:0.29 98:0.13 108:0.48 123:0.46 124:0.81 126:0.54 127:0.31 129:0.89 133:0.78 135:0.65 146:0.22 147:0.27 149:0.31 150:0.86 154:0.26 159:0.53 161:0.57 163:0.87 167:0.77 172:0.10 173:0.55 177:0.05 178:0.55 179:0.93 181:0.57 187:0.68 191:0.81 212:0.42 215:0.67 216:0.84 230:0.87 233:0.76 235:0.42 241:0.67 242:0.82 243:0.40 245:0.39 247:0.12 259:0.21 265:0.14 266:0.23 267:0.23 271:0.32 276:0.24 297:0.36 300:0.25 +1 6:0.93 7:0.81 8:0.88 12:0.20 17:0.26 20:0.83 21:0.59 25:0.88 27:0.71 28:0.92 30:0.11 32:0.80 34:0.31 36:0.34 37:0.42 43:0.36 55:0.42 66:0.61 69:0.62 70:0.54 78:0.74 81:0.87 91:0.80 98:0.36 100:0.77 104:0.73 108:0.87 111:0.74 120:0.72 123:0.86 124:0.43 126:0.54 127:0.74 129:0.59 133:0.47 135:0.51 140:0.80 145:0.61 146:0.05 147:0.05 149:0.26 150:0.73 151:0.66 152:0.79 153:0.48 154:0.27 159:0.66 161:0.57 162:0.73 164:0.86 167:0.96 169:0.75 172:0.27 173:0.54 177:0.05 178:0.42 179:0.95 181:0.57 186:0.75 189:0.94 191:0.78 195:0.80 202:0.80 212:0.30 215:0.55 216:0.54 220:0.87 230:0.87 233:0.76 235:0.68 238:0.89 241:0.89 242:0.82 243:0.23 245:0.42 247:0.12 248:0.65 256:0.86 259:0.21 260:0.73 261:0.75 265:0.38 266:0.27 267:0.28 268:0.83 271:0.32 276:0.17 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.89 8:0.75 12:0.20 17:0.45 20:0.81 21:0.13 27:0.29 28:0.92 30:0.38 34:0.59 36:0.60 37:0.92 43:0.45 55:0.59 66:0.48 69:0.44 70:0.50 78:0.74 81:0.96 91:0.61 98:0.59 100:0.77 104:0.78 106:0.81 108:0.34 111:0.74 120:0.83 123:0.32 124:0.56 126:0.54 127:0.29 129:0.59 133:0.47 135:0.47 140:0.45 146:0.62 147:0.67 149:0.52 150:0.94 151:0.72 152:0.78 153:0.77 154:0.34 159:0.37 161:0.57 162:0.86 163:0.75 164:0.72 167:0.77 169:0.75 172:0.37 173:0.45 177:0.05 179:0.70 181:0.57 186:0.75 187:0.21 189:0.90 202:0.58 206:0.81 212:0.28 215:0.84 216:0.93 235:0.12 238:0.91 241:0.68 242:0.82 243:0.60 245:0.61 247:0.12 248:0.75 256:0.64 259:0.21 260:0.84 261:0.75 265:0.60 266:0.54 267:0.82 268:0.66 271:0.32 276:0.42 285:0.62 297:0.36 300:0.33 +1 1:0.68 8:0.77 9:0.76 10:0.99 11:0.83 12:0.86 17:0.69 18:0.94 23:0.98 27:0.60 29:0.91 30:0.75 31:0.96 34:0.30 36:0.95 37:0.49 39:0.82 43:0.62 45:0.99 56:0.97 62:0.99 64:0.77 66:0.63 69:0.54 70:0.58 71:0.59 74:0.76 75:0.95 79:0.95 81:0.73 83:0.72 85:0.72 86:0.94 91:0.57 96:0.86 97:0.89 98:0.79 99:0.99 101:1.00 108:0.42 114:0.95 117:0.86 122:0.91 123:0.40 124:0.55 126:0.53 127:0.84 128:0.98 129:0.05 131:0.89 133:0.67 135:0.61 137:0.96 139:0.91 140:0.45 143:0.99 144:0.57 146:0.97 147:0.98 149:0.95 150:0.58 151:0.93 153:0.78 154:0.51 155:0.65 157:0.98 159:0.84 162:0.84 165:0.86 166:0.84 168:0.98 170:0.82 172:0.97 173:0.31 174:0.99 176:0.63 177:0.96 179:0.14 182:0.94 187:0.68 190:0.89 191:0.73 192:0.87 196:0.97 197:0.99 201:0.67 202:0.62 205:0.97 208:0.64 212:0.77 216:0.60 220:0.82 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 232:0.92 235:0.22 236:0.94 239:0.98 241:0.30 242:0.18 243:0.69 245:0.98 246:0.89 247:0.99 248:0.83 253:0.88 255:0.79 256:0.64 259:0.21 264:0.95 265:0.79 266:0.63 267:0.82 268:0.68 271:0.29 275:0.99 276:0.96 279:0.75 284:0.95 285:0.61 287:0.91 290:0.95 294:0.99 295:0.99 300:0.70 +1 1:0.68 8:0.73 9:0.75 10:0.95 11:0.54 12:0.86 17:0.91 18:0.81 23:0.96 27:0.33 29:0.91 30:0.84 31:0.94 33:0.97 34:0.94 36:1.00 37:0.45 39:0.82 43:0.63 45:0.96 56:0.97 62:0.98 64:0.77 66:0.96 69:0.51 70:0.36 71:0.59 74:0.57 75:0.97 76:0.85 79:0.94 80:0.99 81:0.73 82:0.98 83:0.72 86:0.80 88:0.99 91:0.58 96:0.83 97:0.97 98:0.91 99:0.99 101:0.87 108:0.39 114:0.95 122:0.99 123:0.37 126:0.53 127:0.52 128:0.98 129:0.05 131:0.88 135:0.73 137:0.94 139:0.81 140:0.45 143:0.99 144:0.57 145:0.47 146:0.91 147:0.92 149:0.82 150:0.58 151:0.90 153:0.69 154:0.53 155:0.65 157:0.97 158:0.77 159:0.51 162:0.86 165:0.86 166:0.84 168:0.98 170:0.82 172:0.90 173:0.48 174:0.96 175:0.75 176:0.63 177:0.88 179:0.29 182:0.94 187:0.21 190:0.89 192:0.87 193:0.96 195:0.72 196:0.94 197:0.97 201:0.67 202:0.46 204:0.81 205:0.95 208:0.64 212:0.80 216:0.35 219:0.91 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 235:0.22 236:0.94 239:0.95 241:0.12 242:0.14 243:0.96 244:0.83 246:0.89 247:0.88 248:0.79 253:0.82 255:0.78 256:0.45 257:0.65 259:0.21 264:0.95 265:0.91 266:0.54 267:0.69 268:0.55 271:0.29 275:0.99 276:0.82 277:0.69 279:0.79 281:0.91 284:0.90 285:0.61 287:0.91 290:0.95 291:0.97 292:0.95 294:0.99 295:0.99 300:0.43 +1 1:0.66 9:0.72 10:0.94 11:0.54 12:0.86 17:0.78 18:0.82 21:0.22 22:0.93 23:0.95 27:0.55 29:0.91 30:0.84 31:0.89 33:0.97 34:0.97 36:0.71 37:0.58 39:0.82 43:0.58 44:0.88 45:0.97 47:0.93 56:0.97 62:0.97 64:0.77 66:0.16 69:0.75 70:0.48 71:0.59 74:0.65 75:0.97 77:0.70 79:0.89 80:0.98 81:0.69 82:0.99 83:0.72 86:0.84 88:0.99 91:0.27 96:0.72 97:0.97 98:0.67 99:0.99 101:0.69 102:0.96 108:0.82 114:0.85 117:0.86 122:0.99 123:0.56 124:0.51 126:0.53 127:0.52 128:0.96 129:0.05 131:0.88 133:0.45 135:0.90 137:0.89 139:0.86 140:0.45 143:0.99 144:0.57 145:0.44 146:0.82 147:0.85 149:0.78 150:0.52 151:0.61 153:0.48 154:0.47 155:0.64 157:0.97 158:0.76 159:0.63 162:0.86 165:0.86 166:0.83 168:0.96 170:0.82 172:0.87 173:0.70 174:0.94 176:0.63 177:0.96 179:0.25 182:0.94 187:0.39 190:0.89 192:0.86 195:0.80 196:0.92 197:0.93 201:0.65 202:0.36 204:0.79 205:0.95 208:0.64 212:0.73 216:0.70 219:0.91 220:0.82 222:0.35 226:0.96 227:0.88 229:0.97 231:0.76 232:0.88 235:0.52 236:0.94 239:0.95 241:0.37 242:0.27 243:0.37 244:0.61 245:0.95 246:0.89 247:0.84 248:0.59 253:0.68 254:0.94 255:0.72 256:0.45 259:0.21 262:0.93 264:0.95 265:0.65 266:0.63 267:0.73 268:0.46 271:0.29 275:0.99 276:0.85 279:0.79 281:0.91 282:0.75 284:0.88 285:0.61 287:0.91 290:0.85 291:0.97 292:0.88 294:0.99 295:0.98 300:0.55 +1 10:0.94 11:0.54 12:0.86 17:0.89 18:0.81 21:0.78 27:0.72 29:0.91 30:0.61 34:0.97 36:0.89 37:0.23 39:0.82 43:0.66 44:0.88 45:0.92 48:0.91 66:0.53 69:0.10 70:0.79 71:0.59 74:0.62 76:0.85 77:0.70 80:1.00 82:0.99 83:0.72 86:0.82 88:0.99 91:0.17 98:0.46 101:0.87 102:0.96 108:0.09 122:0.91 123:0.09 124:0.65 127:0.78 129:0.05 131:0.61 133:0.65 135:0.74 139:0.84 144:0.57 145:0.59 146:0.60 147:0.66 149:0.75 154:0.55 155:0.54 157:0.92 159:0.63 166:0.61 170:0.82 172:0.71 173:0.15 174:0.94 175:0.75 177:0.88 178:0.55 179:0.48 182:0.85 187:0.21 192:0.56 193:0.98 195:0.78 197:0.96 204:0.84 208:0.64 212:0.76 216:0.26 222:0.35 227:0.61 229:0.61 231:0.72 232:0.72 235:0.31 239:0.95 241:0.35 242:0.15 243:0.62 244:0.83 245:0.81 246:0.61 247:0.91 253:0.47 257:0.65 259:0.21 264:0.95 265:0.48 266:0.71 267:0.65 271:0.29 275:0.97 276:0.68 277:0.69 281:0.91 282:0.75 287:0.61 294:0.99 300:0.84 +1 10:0.94 11:0.54 12:0.86 17:0.53 18:0.84 21:0.78 27:0.72 29:0.91 30:0.91 34:0.89 36:0.56 37:0.23 39:0.82 43:0.66 44:0.88 45:0.93 48:0.91 66:0.19 69:0.12 70:0.28 71:0.59 74:0.66 75:0.97 76:0.85 77:0.70 80:1.00 82:0.99 83:0.72 86:0.83 88:0.99 91:0.22 97:0.97 98:0.56 101:0.87 102:0.96 108:0.10 122:0.91 123:0.56 124:0.52 127:0.48 129:0.59 131:0.61 133:0.47 135:0.44 139:0.87 144:0.57 145:0.59 146:0.38 147:0.45 149:0.82 154:0.55 155:0.54 157:0.95 158:0.76 159:0.28 166:0.61 170:0.82 172:0.63 173:0.35 174:0.89 175:0.75 177:0.88 178:0.55 179:0.52 182:0.87 187:0.21 192:0.56 193:0.98 195:0.58 197:0.94 204:0.84 208:0.64 212:0.88 216:0.26 219:0.91 222:0.35 226:0.96 227:0.61 229:0.61 231:0.72 232:0.72 235:0.42 239:0.96 241:0.39 242:0.22 243:0.62 244:0.83 245:0.81 246:0.61 247:0.79 253:0.49 257:0.65 259:0.21 264:0.95 265:0.52 266:0.38 267:0.59 271:0.29 275:0.97 276:0.59 277:0.69 279:0.79 281:0.91 282:0.75 287:0.61 292:0.88 294:0.99 295:0.98 300:0.43 +1 1:0.65 10:0.92 11:0.54 12:0.86 17:0.84 18:0.73 21:0.05 27:0.72 29:0.91 30:0.76 34:0.90 36:0.63 37:0.23 39:0.82 43:0.66 44:0.88 45:0.85 48:0.91 56:0.97 64:0.77 66:0.85 69:0.12 70:0.28 71:0.59 74:0.54 76:0.85 77:0.70 80:1.00 81:0.69 82:0.99 83:0.72 86:0.75 88:0.99 91:0.40 98:0.82 99:0.95 101:0.87 102:0.96 108:0.10 114:0.95 122:0.91 123:0.10 126:0.53 127:0.32 129:0.05 131:0.61 135:0.44 139:0.79 144:0.57 145:0.59 146:0.50 147:0.56 149:0.49 150:0.55 154:0.55 155:0.56 157:0.92 159:0.18 165:0.70 166:0.76 170:0.82 172:0.57 173:0.46 174:0.86 175:0.75 176:0.70 177:0.88 178:0.55 179:0.66 182:0.84 187:0.21 192:0.86 193:0.98 195:0.55 197:0.91 201:0.66 204:0.84 208:0.64 212:0.92 215:0.91 216:0.26 222:0.35 227:0.61 229:0.61 231:0.73 232:0.72 235:0.42 239:0.93 241:0.37 242:0.22 243:0.86 244:0.83 246:0.61 247:0.67 253:0.66 257:0.65 259:0.21 264:0.95 265:0.82 266:0.17 267:0.28 271:0.29 275:0.96 276:0.42 277:0.69 281:0.91 282:0.75 287:0.61 290:0.95 294:0.99 297:0.36 300:0.25 +1 1:0.57 7:0.69 8:0.76 9:0.70 10:0.95 11:0.64 12:0.86 17:0.75 18:0.64 21:0.68 23:0.96 27:0.48 29:0.91 30:0.28 34:0.89 36:0.94 37:0.45 39:0.82 43:0.21 45:0.99 56:0.97 62:0.96 66:0.47 69:0.83 70:0.94 71:0.59 74:0.69 75:0.98 77:0.70 80:0.99 81:0.57 82:0.98 83:0.54 86:0.67 88:0.98 91:0.07 96:0.69 97:1.00 98:0.73 99:0.95 101:0.96 102:0.95 108:0.91 114:0.69 120:0.56 122:0.93 123:0.90 124:0.81 126:0.53 127:0.58 128:0.95 129:0.81 131:0.88 133:0.84 135:0.91 139:0.65 140:0.45 143:0.98 144:0.57 145:0.96 146:0.78 147:0.82 149:0.44 150:0.39 151:0.52 153:0.48 154:0.52 155:0.54 157:0.98 158:0.81 159:0.92 162:0.86 164:0.56 165:0.70 166:0.83 168:0.95 170:0.82 172:0.97 173:0.50 174:0.99 175:0.75 176:0.70 177:0.88 179:0.12 182:0.94 187:0.68 190:0.77 192:0.57 193:0.95 195:0.99 197:0.99 201:0.59 202:0.36 204:0.83 205:0.95 208:0.54 212:0.69 215:0.49 216:0.74 220:0.93 222:0.35 226:0.95 227:0.88 229:0.97 230:0.71 231:0.76 233:0.66 235:0.97 236:0.95 239:0.96 241:0.12 242:0.14 243:0.19 244:0.61 245:0.98 246:0.89 247:0.99 248:0.51 253:0.58 254:0.74 255:0.69 256:0.45 257:0.65 259:0.21 260:0.56 264:0.95 265:0.73 266:0.89 267:0.73 268:0.46 271:0.29 275:0.99 276:0.97 277:0.69 279:0.82 282:0.74 283:0.66 285:0.61 287:0.91 290:0.69 294:0.99 295:0.98 297:0.36 300:0.94 +1 1:0.66 9:0.75 10:0.98 11:0.54 12:0.86 17:0.92 18:0.97 21:0.22 22:0.93 23:0.95 27:0.42 29:0.91 30:0.89 31:0.93 33:0.97 34:0.96 36:0.67 37:0.47 39:0.82 43:0.65 44:0.88 45:1.00 47:0.93 56:0.97 62:0.99 64:0.77 66:0.45 69:0.82 70:0.40 71:0.59 74:0.88 75:0.97 77:0.70 79:0.93 80:1.00 81:0.69 82:0.99 83:0.72 86:0.97 88:0.99 91:0.22 96:0.80 97:0.97 98:0.71 99:0.99 101:0.87 102:0.96 108:0.66 114:0.85 117:0.86 122:0.91 123:0.64 124:0.77 126:0.53 127:0.82 128:0.98 129:0.59 131:0.88 133:0.78 135:0.91 137:0.93 139:0.97 140:0.45 143:0.99 144:0.57 145:0.42 146:0.92 147:0.64 149:0.95 150:0.52 151:0.85 153:0.48 154:0.37 155:0.65 157:0.98 158:0.77 159:0.79 162:0.86 165:0.86 166:0.83 168:0.98 170:0.82 172:0.96 173:0.69 174:0.99 176:0.63 177:0.88 179:0.13 182:0.94 187:0.95 190:0.89 192:0.87 193:0.98 195:0.90 196:0.97 197:0.99 201:0.65 202:0.36 204:0.85 205:0.95 208:0.64 212:0.87 216:0.72 219:0.91 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 235:0.52 236:0.94 239:0.99 241:0.03 242:0.21 243:0.21 244:0.61 245:0.98 246:0.89 247:0.98 248:0.76 253:0.87 254:0.80 255:0.78 256:0.45 257:0.65 259:0.21 262:0.93 264:0.95 265:0.71 266:0.47 267:0.82 268:0.46 271:0.29 275:0.99 276:0.96 279:0.79 281:0.91 282:0.75 284:0.88 285:0.61 287:0.91 290:0.85 291:0.97 292:0.95 294:1.00 295:0.99 300:0.70 +1 8:0.77 10:0.94 11:0.54 12:0.86 17:0.78 18:0.83 21:0.78 27:0.50 29:0.91 30:0.85 34:0.79 36:0.90 37:0.30 39:0.82 43:0.64 44:0.88 45:0.96 66:0.24 69:0.76 70:0.49 71:0.59 74:0.64 75:0.97 77:0.70 82:0.99 83:0.72 86:0.85 88:0.99 91:0.15 97:0.97 98:0.27 101:0.69 102:0.96 108:0.79 122:0.91 123:0.77 124:0.91 127:0.66 129:0.05 131:0.61 133:0.91 135:0.42 139:0.85 144:0.57 145:0.50 146:0.27 147:0.26 149:0.73 154:0.51 157:0.94 158:0.77 159:0.80 166:0.61 170:0.82 172:0.63 173:0.59 174:0.95 177:0.88 178:0.55 179:0.36 182:0.86 187:0.39 195:0.91 197:0.95 208:0.64 212:0.60 216:0.36 219:0.91 222:0.35 226:0.98 227:0.61 229:0.61 231:0.73 235:0.42 239:0.95 241:0.07 242:0.24 243:0.20 244:0.61 245:0.78 246:0.61 247:0.90 253:0.48 254:0.92 257:0.65 259:0.21 264:0.95 265:0.29 266:0.80 267:0.65 271:0.29 275:0.98 276:0.77 277:0.69 279:0.79 282:0.75 287:0.61 292:0.95 294:0.99 295:0.99 300:0.70 +1 1:0.65 8:0.58 10:0.98 11:0.54 12:0.86 17:0.40 18:0.93 21:0.05 27:0.72 29:0.91 30:0.90 34:0.67 36:0.89 37:0.23 39:0.82 43:0.66 44:0.88 45:0.98 48:0.97 56:0.97 64:0.77 66:0.64 69:0.51 70:0.28 71:0.59 74:0.75 76:0.94 77:0.96 80:1.00 81:0.69 82:0.99 83:0.72 86:0.92 88:0.99 91:0.72 98:0.77 99:0.95 101:0.87 102:0.96 108:0.39 114:0.95 122:0.91 123:0.38 124:0.48 126:0.53 127:0.56 129:0.59 131:0.61 133:0.47 135:0.24 139:0.92 144:0.57 145:0.98 146:0.69 147:0.74 149:0.92 150:0.55 154:0.55 155:0.55 157:0.97 159:0.38 165:0.70 166:0.76 170:0.82 172:0.87 173:0.60 174:0.95 175:0.75 176:0.70 177:0.88 178:0.55 179:0.25 182:0.92 192:0.85 193:0.98 195:0.70 197:0.98 201:0.66 202:0.74 204:0.83 208:0.64 212:0.93 215:0.91 216:0.26 222:0.35 227:0.61 229:0.61 231:0.74 232:0.72 235:0.95 239:0.98 241:0.86 242:0.24 243:0.69 244:0.83 245:0.93 246:0.61 247:0.82 253:0.70 257:0.65 259:0.21 264:0.95 265:0.77 266:0.27 267:0.19 271:0.29 275:0.99 276:0.83 277:0.69 281:0.91 282:0.75 287:0.61 290:0.95 294:1.00 297:0.36 300:0.43 +1 1:0.60 9:0.73 10:0.97 11:0.54 12:0.86 17:0.81 18:0.96 21:0.64 22:0.93 23:0.95 27:0.52 29:0.91 30:0.88 31:0.90 33:0.97 34:0.99 36:0.76 37:0.60 39:0.82 43:0.64 44:0.88 45:0.99 47:0.93 56:0.97 62:0.98 64:0.77 66:0.54 69:0.48 70:0.45 71:0.59 74:0.85 75:0.97 77:0.96 79:0.90 81:0.63 82:0.99 83:0.72 86:0.96 88:0.98 91:0.25 96:0.74 97:0.97 98:0.69 99:0.99 101:0.87 102:0.96 108:0.37 114:0.69 117:0.86 122:0.91 123:0.35 124:0.70 126:0.53 127:0.77 128:0.96 129:0.05 131:0.88 133:0.74 135:0.95 137:0.90 139:0.95 140:0.45 143:0.99 144:0.57 145:0.87 146:0.86 147:0.88 149:0.91 150:0.45 151:0.66 153:0.48 154:0.57 155:0.64 157:0.97 158:0.77 159:0.70 162:0.62 165:0.86 166:0.83 168:0.96 170:0.82 172:0.94 173:0.35 174:0.98 176:0.63 177:0.96 179:0.17 182:0.94 187:0.21 190:0.89 192:0.87 195:0.82 196:0.94 197:0.99 201:0.60 202:0.50 205:0.95 208:0.64 212:0.84 216:0.48 219:0.91 220:0.74 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 232:0.81 235:0.95 236:0.94 239:0.98 241:0.02 242:0.21 243:0.63 244:0.61 245:0.96 246:0.89 247:0.98 248:0.63 251:1.00 253:0.77 254:0.80 255:0.73 256:0.45 259:0.21 262:0.93 264:0.95 265:0.69 266:0.71 267:0.92 268:0.46 271:0.29 275:0.99 276:0.93 279:0.79 282:0.75 284:0.88 285:0.61 287:0.91 290:0.69 291:0.97 292:0.95 294:0.99 295:0.99 300:0.84 +1 6:0.89 7:0.81 8:0.84 12:0.37 17:0.67 20:0.90 21:0.13 27:0.43 28:0.62 30:0.66 32:0.80 34:0.58 36:0.27 37:0.98 43:0.35 55:0.99 66:0.07 69:0.63 70:0.36 78:0.81 81:0.99 91:0.29 98:0.06 100:0.81 106:0.81 108:0.48 111:0.79 120:0.76 123:0.46 124:0.95 126:0.54 127:0.99 129:0.99 133:0.95 135:0.77 140:0.45 145:0.59 146:0.22 147:0.28 149:0.27 150:0.99 151:0.73 152:0.91 153:0.80 154:0.26 159:0.99 161:0.60 162:0.79 163:0.90 164:0.71 167:0.64 169:0.78 172:0.10 173:0.09 177:0.05 179:0.82 181:0.84 186:0.81 187:0.87 189:0.91 191:0.84 195:1.00 202:0.60 206:0.81 212:0.13 215:0.84 216:0.64 230:0.87 233:0.76 235:0.22 238:0.89 241:0.43 242:0.82 243:0.23 245:0.46 247:0.12 248:0.68 256:0.58 259:0.21 260:0.77 261:0.82 265:0.06 266:0.80 267:0.69 268:0.65 271:0.23 276:0.47 283:0.82 285:0.62 297:0.36 300:0.33 +1 6:0.79 8:0.58 12:0.37 17:0.73 20:0.92 25:0.88 27:0.12 28:0.62 30:0.94 34:0.39 36:0.36 37:0.23 43:0.48 55:0.59 66:0.69 69:0.30 70:0.18 78:0.85 81:1.00 91:0.84 98:0.70 100:0.84 104:0.58 108:0.62 111:0.84 120:0.83 123:0.59 124:0.41 126:0.54 127:0.80 129:0.59 133:0.47 135:0.49 140:0.45 146:0.05 147:0.05 149:0.44 150:1.00 151:0.79 152:0.86 153:0.90 154:0.37 159:0.35 161:0.60 162:0.79 164:0.85 167:0.93 169:0.83 172:0.41 173:0.46 177:0.05 179:0.89 181:0.84 186:0.84 189:0.81 191:0.80 202:0.77 212:0.71 215:0.96 216:0.34 235:0.05 238:0.88 241:0.97 242:0.82 243:0.18 245:0.46 247:0.12 248:0.75 256:0.79 259:0.21 260:0.84 261:0.86 265:0.71 266:0.27 267:0.23 268:0.81 271:0.23 276:0.32 283:0.60 285:0.62 297:0.36 300:0.18 +2 8:0.93 12:0.37 17:0.92 20:0.75 21:0.30 27:0.56 28:0.62 30:0.76 34:0.45 36:0.86 37:0.97 43:0.41 55:0.92 66:0.36 69:0.53 70:0.15 78:0.79 81:0.98 91:0.85 98:0.58 100:0.73 108:0.41 111:0.77 120:0.62 123:0.39 124:0.52 126:0.54 127:0.52 129:0.59 133:0.47 135:0.37 140:0.45 146:0.40 147:0.46 149:0.26 150:0.97 151:0.56 152:0.74 153:0.78 154:0.31 159:0.26 161:0.60 162:0.81 163:0.96 164:0.73 167:0.90 169:0.72 172:0.10 173:0.73 177:0.05 179:0.99 181:0.84 186:0.79 191:0.79 202:0.62 212:0.95 215:0.72 216:0.07 220:0.62 235:0.42 241:0.83 242:0.82 243:0.51 245:0.37 247:0.12 248:0.56 256:0.64 259:0.21 260:0.62 261:0.74 265:0.59 266:0.12 267:0.65 268:0.67 271:0.23 276:0.18 285:0.62 297:0.36 300:0.13 +3 6:0.84 7:0.81 8:0.68 12:0.37 17:0.28 20:0.80 21:0.30 27:0.21 28:0.62 30:0.14 34:0.69 36:0.91 37:0.74 43:0.52 55:0.96 66:0.11 69:0.10 70:0.97 78:0.80 81:0.98 91:0.51 98:0.22 100:0.82 104:0.84 106:0.81 108:0.99 111:0.79 120:0.81 123:0.98 124:0.97 126:0.54 127:0.92 129:1.00 133:0.97 135:0.39 140:0.45 146:0.05 147:0.05 149:0.48 150:0.97 151:0.78 152:0.93 153:0.89 154:0.41 159:0.96 161:0.60 162:0.56 164:0.81 167:0.56 169:0.78 172:0.51 173:0.05 177:0.05 179:0.39 181:0.84 186:0.80 187:0.39 189:0.84 191:0.76 202:0.78 206:0.81 212:0.23 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.89 241:0.12 242:0.82 243:0.07 245:0.68 247:0.12 248:0.73 256:0.73 259:0.21 260:0.82 261:0.82 265:0.16 266:0.98 267:0.84 268:0.76 271:0.23 276:0.76 283:0.82 285:0.62 297:0.36 300:0.94 +2 6:0.89 7:0.81 8:0.63 12:0.37 17:0.30 20:0.86 21:0.22 27:0.43 28:0.62 30:0.21 32:0.80 34:0.20 36:0.62 37:0.98 43:0.35 55:0.45 66:0.36 69:0.63 70:0.37 78:0.75 81:0.98 91:0.62 98:0.30 100:0.78 106:0.81 108:0.82 111:0.74 120:0.71 123:0.81 124:0.57 126:0.54 127:0.43 129:0.59 133:0.47 135:0.99 140:0.45 145:0.65 146:0.22 147:0.28 149:0.29 150:0.98 151:0.66 152:0.91 153:0.48 154:0.26 159:0.53 161:0.60 162:0.86 163:0.90 164:0.73 167:0.58 169:0.78 172:0.16 173:0.59 177:0.05 179:0.95 181:0.84 186:0.76 187:0.87 189:0.85 195:0.78 202:0.58 206:0.81 212:0.42 215:0.79 216:0.64 220:0.82 230:0.87 233:0.76 235:0.31 238:0.84 241:0.21 242:0.82 243:0.40 245:0.43 247:0.12 248:0.64 256:0.64 259:0.21 260:0.72 261:0.83 265:0.32 266:0.23 267:0.76 268:0.66 271:0.23 276:0.16 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.94 8:0.89 12:0.37 17:0.84 20:0.96 27:0.08 28:0.62 34:0.63 36:0.51 37:0.85 78:0.91 81:1.00 91:0.84 100:0.95 111:0.92 120:0.95 126:0.54 135:0.47 140:0.45 150:1.00 151:0.78 152:0.96 153:0.89 161:0.60 162:0.59 164:0.91 167:0.76 169:0.90 175:0.75 181:0.84 186:0.91 187:0.39 189:0.96 191:0.95 202:0.88 215:0.96 216:0.27 235:0.02 238:0.97 241:0.69 244:0.61 248:0.92 256:0.89 257:0.65 259:0.21 260:0.95 261:0.93 267:0.18 268:0.89 271:0.23 277:0.69 283:0.82 285:0.62 297:0.36 +2 6:0.97 8:0.82 12:0.37 17:0.30 20:0.93 21:0.78 27:0.05 28:0.62 30:0.76 34:0.77 36:0.85 37:0.94 43:0.30 55:0.59 66:0.64 69:0.72 70:0.15 78:0.77 91:0.68 98:0.53 100:0.80 108:0.56 111:0.77 120:0.83 123:0.53 127:0.16 129:0.05 135:0.49 140:0.87 146:0.44 147:0.51 149:0.24 151:0.71 152:0.96 153:0.72 154:0.22 159:0.16 161:0.60 162:0.56 163:0.98 164:0.73 167:0.64 169:0.98 172:0.16 173:0.85 177:0.05 178:0.48 179:0.88 181:0.84 186:0.78 187:0.21 189:0.93 191:0.92 202:0.69 212:0.95 216:0.64 235:0.02 238:0.93 241:0.44 242:0.82 243:0.69 247:0.12 248:0.75 256:0.64 259:0.21 260:0.84 261:0.98 265:0.55 266:0.12 267:0.91 268:0.66 271:0.23 276:0.16 285:0.62 300:0.13 +3 6:0.89 8:0.78 12:0.37 17:0.30 20:0.83 27:0.09 28:0.62 30:0.28 34:0.78 36:0.40 37:0.82 43:0.35 55:0.84 66:0.23 69:0.62 70:0.47 78:0.80 81:1.00 91:0.38 98:0.53 100:0.83 104:0.91 106:0.81 108:0.47 111:0.80 120:0.80 123:0.45 124:0.85 126:0.54 127:0.54 129:0.93 133:0.84 135:0.98 140:0.45 145:0.61 146:0.76 147:0.79 149:0.51 150:1.00 151:0.71 152:0.79 153:0.72 154:0.26 159:0.68 161:0.60 162:0.86 163:0.96 164:0.73 167:0.67 169:0.81 172:0.32 173:0.50 177:0.05 179:0.69 181:0.84 186:0.80 187:0.39 189:0.91 191:0.76 202:0.58 206:0.81 212:0.16 215:0.96 216:0.94 235:0.02 238:0.93 241:0.51 242:0.82 243:0.43 245:0.59 247:0.12 248:0.72 256:0.64 259:0.21 260:0.81 261:0.79 265:0.55 266:0.80 267:0.52 268:0.66 271:0.23 276:0.53 283:0.82 285:0.62 297:0.36 300:0.33 +2 6:0.97 8:0.92 12:0.37 17:0.30 20:0.79 27:0.05 28:0.62 30:0.95 34:0.73 36:0.87 37:0.94 43:0.30 55:0.92 66:0.20 69:0.72 78:0.85 81:1.00 91:0.56 98:0.09 100:0.92 108:0.56 111:0.87 123:0.53 124:0.61 126:0.54 127:0.17 129:0.59 133:0.47 135:0.60 146:0.05 147:0.05 149:0.19 150:1.00 152:0.98 154:0.22 159:0.17 161:0.60 167:0.58 169:0.98 172:0.05 173:0.85 177:0.05 178:0.55 179:0.97 181:0.84 186:0.85 187:0.21 189:0.96 191:0.98 202:0.36 215:0.96 216:0.64 235:0.02 238:0.97 241:0.21 243:0.40 245:0.36 259:0.21 261:0.98 265:0.10 267:0.84 271:0.23 283:0.82 297:0.36 300:0.08 +3 6:0.95 8:0.89 12:0.37 17:0.82 20:0.82 27:0.08 28:0.62 34:0.63 36:0.51 37:0.85 78:0.84 81:1.00 91:0.76 100:0.92 111:0.86 120:0.88 126:0.54 135:0.44 140:0.45 150:1.00 151:0.74 152:0.97 153:0.84 161:0.60 162:0.53 164:0.84 167:0.74 169:0.88 175:0.75 181:0.84 186:0.85 187:0.68 189:0.96 191:0.98 202:0.83 215:0.96 216:0.27 235:0.02 238:0.97 241:0.67 244:0.61 248:0.83 256:0.79 257:0.65 259:0.21 260:0.89 261:0.92 267:0.19 268:0.80 271:0.23 277:0.69 285:0.62 297:0.36 +1 12:0.37 17:0.55 21:0.64 27:0.45 28:0.62 30:0.45 34:0.90 36:0.23 37:0.50 43:0.32 55:0.52 66:0.49 69:0.70 70:0.25 81:0.98 91:0.11 98:0.34 108:0.53 123:0.51 124:0.48 126:0.54 127:0.32 129:0.59 133:0.47 135:0.60 145:0.50 146:0.44 147:0.50 149:0.28 150:0.97 154:0.24 159:0.45 161:0.60 163:0.87 167:0.55 172:0.16 173:0.69 177:0.05 178:0.55 179:0.96 181:0.84 187:0.68 212:0.61 215:0.53 216:0.77 235:0.88 241:0.10 242:0.82 243:0.60 245:0.39 247:0.12 259:0.21 265:0.36 266:0.17 267:0.36 271:0.23 276:0.16 297:0.36 300:0.18 +1 6:0.84 7:0.81 8:0.65 12:0.37 17:0.18 20:0.95 21:0.30 27:0.21 28:0.62 30:0.91 34:0.66 36:0.61 37:0.74 43:0.52 55:0.59 66:0.69 69:0.12 70:0.13 78:0.78 81:0.98 91:0.51 98:0.39 100:0.81 106:0.81 108:0.10 111:0.78 120:0.75 123:0.10 126:0.54 127:0.13 129:0.05 135:0.28 140:0.45 146:0.32 147:0.38 149:0.35 150:0.97 151:0.73 152:0.93 153:0.78 154:0.41 159:0.13 161:0.60 162:0.60 164:0.74 167:0.67 169:0.78 172:0.21 173:0.35 177:0.05 179:0.62 181:0.84 186:0.79 187:0.39 189:0.86 191:0.76 202:0.68 206:0.81 212:0.95 215:0.72 216:0.95 230:0.87 233:0.76 235:0.42 238:0.89 241:0.52 242:0.82 243:0.73 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 261:0.82 265:0.41 266:0.12 267:0.97 268:0.68 271:0.23 276:0.23 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.82 7:0.81 8:0.64 12:0.37 17:0.30 20:0.89 27:0.34 28:0.62 30:0.40 32:0.80 34:0.70 36:0.90 37:0.36 43:0.39 55:0.71 66:0.59 69:0.54 70:0.76 78:0.78 81:1.00 91:0.42 98:0.35 100:0.79 104:0.80 106:0.81 108:0.94 111:0.77 120:0.78 123:0.94 124:0.67 126:0.54 127:0.51 129:0.89 133:0.78 135:0.98 140:0.45 145:1.00 146:0.38 147:0.45 149:0.43 150:1.00 151:0.74 152:0.84 153:0.83 154:0.30 159:0.87 161:0.60 162:0.82 164:0.74 167:0.55 169:0.77 172:0.65 173:0.24 177:0.05 179:0.53 181:0.84 186:0.79 187:0.21 189:0.84 191:0.70 195:0.97 202:0.63 206:0.81 212:0.49 215:0.96 216:0.84 230:0.87 233:0.76 235:0.05 238:0.86 241:0.07 242:0.82 243:0.15 245:0.67 247:0.12 248:0.70 256:0.64 259:0.21 260:0.79 261:0.80 265:0.37 266:0.80 267:0.95 268:0.68 271:0.23 276:0.53 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.97 8:0.95 12:0.37 17:0.03 20:0.97 27:0.05 28:0.62 30:0.76 34:0.23 36:0.94 37:0.94 43:0.30 55:0.71 66:0.20 69:0.73 70:0.22 78:0.99 81:1.00 91:0.97 98:0.20 100:0.99 108:0.80 111:0.99 120:0.99 123:0.79 124:0.73 126:0.54 127:0.54 129:0.81 133:0.67 135:0.37 140:0.45 146:0.05 147:0.05 149:0.25 150:1.00 151:0.86 152:0.96 153:0.99 154:0.22 159:0.57 161:0.60 162:0.53 163:0.88 164:0.98 167:0.91 169:0.98 172:0.10 173:0.70 177:0.05 179:0.96 181:0.84 186:0.98 189:0.98 191:0.99 202:0.98 212:0.42 215:0.96 216:0.64 235:0.02 238:0.99 241:0.84 242:0.82 243:0.26 245:0.40 247:0.12 248:0.98 256:0.98 259:0.21 260:0.99 261:0.98 265:0.22 266:0.23 267:0.97 268:0.97 271:0.23 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +1 8:0.73 12:0.37 17:0.47 27:0.05 28:0.62 34:0.52 36:0.86 37:0.94 43:0.31 66:0.36 69:0.71 81:1.00 91:0.58 98:0.09 108:0.54 120:0.78 123:0.52 126:0.54 127:0.06 129:0.05 135:0.42 140:0.45 146:0.05 147:0.05 149:0.13 150:1.00 151:0.66 153:0.48 154:0.23 159:0.05 161:0.60 162:0.74 164:0.67 167:0.61 172:0.05 173:0.81 177:0.05 181:0.84 187:0.21 191:0.99 202:0.56 215:0.96 216:0.64 235:0.02 241:0.35 243:0.51 248:0.70 256:0.58 259:0.21 260:0.79 265:0.09 267:0.89 268:0.59 271:0.23 285:0.62 297:0.36 +1 1:0.74 11:0.85 12:0.06 17:0.57 18:0.96 21:0.54 27:0.45 28:0.70 29:0.94 30:0.15 34:0.89 36:0.21 37:0.50 39:0.86 43:0.82 45:0.85 46:0.93 48:0.91 64:0.77 66:0.48 69:0.61 70:0.94 71:0.93 74:0.44 81:0.49 83:0.91 85:0.72 86:0.75 91:0.18 98:0.47 101:0.97 107:0.91 108:0.85 110:0.91 114:0.59 122:0.86 123:0.84 124:0.76 125:0.88 126:0.54 127:0.49 129:0.05 131:0.61 133:0.73 135:0.90 139:0.73 144:0.57 145:0.50 146:0.58 147:0.63 149:0.75 150:0.72 154:0.77 155:0.67 157:0.83 159:0.65 160:0.88 161:0.74 165:0.93 166:0.91 167:0.66 172:0.73 173:0.50 176:0.73 177:0.70 178:0.55 179:0.37 181:0.84 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.67 215:0.39 216:0.77 222:0.35 227:0.61 229:0.61 231:0.86 235:0.74 241:0.37 242:0.70 243:0.28 245:0.81 246:0.96 247:0.60 253:0.42 259:0.21 265:0.49 266:0.87 267:0.28 271:0.39 276:0.75 277:0.69 281:0.91 287:0.61 289:0.93 290:0.59 297:0.36 300:0.70 +1 6:0.91 8:0.93 9:0.80 11:0.75 12:0.06 17:0.75 18:0.86 20:0.98 21:0.78 27:0.19 28:0.70 29:0.94 30:0.41 31:0.97 34:0.74 36:0.65 37:0.95 39:0.86 41:0.93 43:0.12 45:0.66 46:0.88 55:0.42 66:0.86 69:0.58 70:0.60 71:0.93 74:0.52 78:0.82 79:0.98 83:0.91 86:0.82 91:0.87 96:0.94 98:0.86 100:0.86 101:0.52 104:0.58 107:0.92 108:0.45 110:0.93 111:0.82 120:0.93 122:0.57 123:0.43 125:0.97 127:0.37 129:0.05 131:0.95 135:0.23 137:0.97 138:0.97 139:0.78 140:0.95 144:0.57 146:0.62 147:0.67 149:0.06 151:0.87 152:0.93 153:0.92 154:0.78 155:0.67 157:0.61 159:0.23 160:0.98 161:0.74 162:0.77 164:0.82 166:0.61 167:0.96 169:0.83 172:0.59 173:0.79 177:0.70 179:0.67 181:0.84 182:0.99 186:0.82 189:0.92 191:0.87 192:0.87 196:0.95 202:0.84 208:0.64 212:0.54 216:0.38 220:0.62 222:0.35 227:0.94 229:0.99 231:0.87 232:0.76 235:0.05 238:0.91 241:0.94 242:0.19 243:0.86 246:0.61 247:0.74 248:0.89 253:0.87 254:0.80 255:0.95 256:0.86 259:0.21 260:0.85 261:0.88 265:0.86 266:0.27 267:0.65 268:0.87 271:0.39 276:0.47 284:0.97 285:0.62 287:0.97 289:0.93 300:0.43 +1 6:0.96 8:0.91 9:0.80 12:0.06 17:0.32 20:0.92 21:0.78 27:0.15 28:0.70 29:0.94 31:0.98 34:0.64 36:0.27 37:0.94 39:0.86 41:0.95 46:0.88 60:0.87 71:0.93 74:0.47 78:0.86 79:0.99 83:0.91 91:0.93 96:0.93 100:0.93 104:0.58 107:0.88 110:0.88 111:0.88 120:0.93 125:0.96 131:0.95 135:0.80 137:0.98 138:0.98 139:0.74 140:0.94 144:0.57 151:0.94 152:0.94 153:0.89 155:0.67 157:0.61 158:0.92 160:0.99 161:0.74 162:0.78 164:0.86 166:0.61 167:0.96 169:0.92 175:0.97 181:0.84 182:0.99 186:0.86 189:0.96 191:0.95 192:0.87 196:0.94 202:0.87 208:0.64 216:0.47 219:0.95 220:0.87 222:0.35 227:0.94 229:0.99 231:0.87 238:0.94 241:0.94 244:0.93 246:0.61 248:0.93 253:0.89 255:0.95 256:0.90 257:0.93 259:0.21 260:0.88 261:0.95 267:0.36 268:0.91 271:0.39 277:0.95 279:0.86 283:0.82 284:0.98 285:0.62 287:0.97 289:0.93 292:0.95 +1 9:0.80 12:0.06 17:0.64 21:0.78 25:0.88 27:0.72 28:0.70 29:0.94 31:0.91 34:0.53 36:0.25 37:0.73 39:0.86 41:0.82 46:0.88 71:0.93 74:0.28 79:0.94 83:0.91 87:0.98 91:0.80 96:0.82 97:0.89 104:0.79 107:0.88 110:0.88 120:0.59 125:0.98 131:0.95 135:0.82 137:0.91 139:0.58 140:0.80 144:0.57 151:0.61 153:0.48 155:0.67 157:0.61 158:0.72 160:0.96 161:0.74 162:0.86 164:0.57 166:0.61 167:0.94 175:0.97 181:0.84 182:0.98 190:0.77 191:0.76 192:0.87 196:0.87 202:0.50 208:0.64 216:0.20 219:0.87 222:0.35 227:0.94 228:0.99 229:0.99 231:0.87 232:0.70 241:0.85 244:0.93 246:0.61 248:0.59 253:0.56 255:0.95 256:0.58 257:0.93 259:0.21 260:0.60 267:0.59 268:0.59 271:0.39 277:0.95 279:0.82 284:0.91 285:0.62 287:0.97 289:0.93 292:0.95 +1 1:0.74 6:1.00 7:0.81 8:0.89 9:0.80 11:0.85 12:0.06 17:0.77 18:0.98 20:0.80 21:0.22 22:0.93 27:0.49 28:0.70 29:0.94 30:0.14 31:0.98 32:0.80 34:0.28 36:0.33 37:0.68 39:0.86 41:0.94 43:0.86 44:0.93 45:0.85 46:0.93 47:0.93 55:0.71 60:0.87 64:0.77 66:0.09 69:0.56 70:0.97 71:0.93 74:0.81 77:0.70 78:0.78 79:0.98 81:0.66 83:0.91 85:0.90 86:0.92 91:0.18 96:0.91 98:0.36 100:0.84 101:0.97 104:0.94 106:0.81 107:0.94 108:0.85 110:0.95 111:0.79 114:0.71 117:0.86 120:0.84 121:0.90 123:0.84 124:0.98 125:0.98 126:0.54 127:0.85 129:0.99 131:0.95 133:0.98 135:0.93 137:0.98 138:0.97 139:0.96 140:0.45 144:0.57 145:0.42 146:0.60 147:0.65 149:0.88 150:0.80 151:0.94 152:0.77 153:0.82 154:0.83 155:0.67 157:0.90 158:0.92 159:0.97 160:0.98 161:0.74 162:0.85 164:0.83 165:0.93 166:0.91 167:0.56 169:0.82 172:0.87 173:0.10 176:0.73 177:0.70 179:0.11 181:0.84 182:0.99 186:0.79 187:0.39 189:1.00 192:0.87 195:1.00 196:0.99 201:0.93 202:0.74 204:0.89 206:0.81 208:0.64 212:0.30 215:0.51 216:0.67 219:0.95 220:0.82 222:0.35 227:0.94 229:1.00 230:0.87 231:0.87 232:0.95 233:0.76 235:0.42 238:0.96 241:0.02 242:0.33 243:0.19 245:0.97 246:0.96 247:0.94 248:0.91 253:0.92 255:0.95 256:0.80 259:0.21 260:0.85 261:0.79 262:0.93 265:0.38 266:0.96 267:0.88 268:0.81 271:0.39 276:0.98 279:0.86 281:0.91 282:0.88 283:0.82 284:0.97 285:0.62 287:0.97 289:0.93 290:0.71 292:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 9:0.80 11:0.64 12:0.06 17:0.67 18:0.79 21:0.05 22:0.93 25:0.88 27:0.72 28:0.70 29:0.94 30:0.45 31:0.91 34:0.90 36:0.81 39:0.86 41:0.82 43:0.72 45:0.66 46:0.88 47:0.93 64:0.77 66:0.69 69:0.10 70:0.25 71:0.93 74:0.28 79:0.90 81:0.81 83:0.91 86:0.59 91:0.25 96:0.75 98:0.79 101:0.52 104:0.58 107:0.89 108:0.09 110:0.89 114:0.89 120:0.74 122:0.82 123:0.09 125:0.99 126:0.54 127:0.28 129:0.05 131:0.95 135:0.79 137:0.91 139:0.58 140:0.80 144:0.57 146:0.48 147:0.54 149:0.38 150:0.88 151:0.72 153:0.48 154:0.62 155:0.67 157:0.61 159:0.17 160:0.96 161:0.74 162:0.86 164:0.62 165:0.93 166:0.91 167:0.78 172:0.21 173:0.43 175:0.75 176:0.73 177:0.38 179:0.95 181:0.84 182:0.98 187:0.87 190:0.89 192:0.87 196:0.87 201:0.93 202:0.50 208:0.64 212:0.61 215:0.78 216:0.39 222:0.35 227:0.94 229:0.99 231:0.87 235:0.22 241:0.69 242:0.63 243:0.73 244:0.61 246:0.96 247:0.30 248:0.67 253:0.69 255:0.95 256:0.58 257:0.65 259:0.21 260:0.65 262:0.93 265:0.79 266:0.17 267:0.28 268:0.59 271:0.39 276:0.16 277:0.69 284:0.89 285:0.62 287:0.97 289:0.93 290:0.89 297:0.36 300:0.18 +1 1:0.69 6:0.93 8:0.84 9:0.80 11:0.75 12:0.06 17:0.84 18:0.99 20:0.75 21:0.59 22:0.93 25:0.88 27:0.11 28:0.70 29:0.94 30:0.45 31:0.98 32:0.80 34:0.87 36:0.60 37:0.86 39:0.86 41:0.94 43:0.65 44:0.93 45:0.90 46:0.88 47:0.93 48:0.98 55:0.45 64:0.77 66:0.97 69:0.27 70:0.78 71:0.93 74:0.55 77:0.96 78:0.88 79:0.98 81:0.42 83:0.91 86:0.82 91:0.62 96:0.94 97:0.89 98:0.98 99:0.83 100:0.73 101:0.52 104:0.58 107:0.94 108:0.21 110:0.95 111:0.88 114:0.56 117:0.86 120:0.90 122:0.86 123:0.20 125:0.97 126:0.54 127:0.79 129:0.05 131:0.95 135:0.24 137:0.98 138:0.97 139:0.85 140:0.90 144:0.57 145:0.77 146:0.92 147:0.93 149:0.79 150:0.59 151:0.96 152:0.91 153:0.88 154:0.77 155:0.67 157:0.61 158:0.72 159:0.53 160:0.99 161:0.74 162:0.86 164:0.83 165:0.70 166:0.91 167:0.76 169:0.96 172:0.92 173:0.29 176:0.55 177:0.70 179:0.28 181:0.84 182:0.99 186:0.83 187:0.21 191:0.95 192:0.87 195:0.69 196:0.97 201:0.74 202:0.83 204:0.85 208:0.64 212:0.83 215:0.39 216:0.75 219:0.87 220:0.62 222:0.35 227:0.94 229:0.99 231:0.87 232:0.80 235:0.88 241:0.67 242:0.30 243:0.97 246:0.61 247:0.90 248:0.92 253:0.90 254:0.86 255:0.95 256:0.87 259:0.21 260:0.86 261:0.97 262:0.93 265:0.98 266:0.27 267:0.73 268:0.89 271:0.39 276:0.85 279:0.82 281:0.88 282:0.88 284:0.98 285:0.62 287:0.97 289:0.93 290:0.56 292:0.95 297:0.36 300:0.70 +1 1:0.69 6:0.93 8:0.90 9:0.80 11:0.75 12:0.06 17:0.15 18:0.94 20:0.98 21:0.59 25:0.88 27:0.11 28:0.70 29:0.94 30:0.17 31:0.99 32:0.63 34:0.73 36:0.49 37:0.86 39:0.86 41:0.94 43:0.64 44:0.85 45:0.81 46:0.88 48:0.98 55:0.71 64:0.77 66:0.45 69:0.27 70:0.93 71:0.93 74:0.39 77:0.70 78:0.91 79:1.00 81:0.42 83:0.91 86:0.72 91:0.98 96:0.95 98:0.73 99:0.83 100:0.98 101:0.52 104:0.58 107:0.94 108:0.21 110:0.95 111:0.96 114:0.56 120:0.99 122:0.86 123:0.20 124:0.59 125:0.96 126:0.54 127:0.97 129:0.59 131:0.95 133:0.46 135:0.37 137:0.99 138:1.00 139:0.70 140:0.94 144:0.57 145:0.83 146:0.73 147:0.77 149:0.57 150:0.59 151:0.93 152:0.91 153:0.99 154:0.78 155:0.67 157:0.61 159:0.50 160:0.98 161:0.74 162:0.73 164:0.93 165:0.70 166:0.91 167:0.96 169:0.96 172:0.67 173:0.32 176:0.55 177:0.70 179:0.50 181:0.84 182:0.96 186:0.91 189:0.94 191:0.95 192:0.87 195:0.66 196:0.93 201:0.74 202:0.98 204:0.85 208:0.64 212:0.71 215:0.39 216:0.75 219:0.86 222:0.35 227:0.94 229:0.98 231:0.87 235:0.88 238:0.97 241:0.93 242:0.24 243:0.58 245:0.88 246:0.61 247:0.88 248:0.92 253:0.89 254:0.86 255:0.95 256:0.99 259:0.21 260:0.92 261:0.97 265:0.73 266:0.63 267:0.82 268:0.99 271:0.39 276:0.71 281:0.89 282:0.70 283:0.82 284:0.99 285:0.62 287:0.96 289:0.93 290:0.56 292:0.95 297:0.36 300:0.70 +1 6:0.99 8:0.96 9:0.80 12:0.06 17:0.73 20:0.99 21:0.78 27:0.47 28:0.83 30:0.11 34:0.21 36:0.49 37:0.91 39:0.69 41:0.90 43:0.20 66:0.29 69:0.39 70:0.84 74:0.49 78:0.82 83:0.80 91:0.84 96:0.91 98:0.24 100:0.93 104:0.58 108:0.86 111:0.86 120:0.94 123:0.86 124:0.72 127:0.63 129:0.59 133:0.64 135:0.18 140:0.96 145:0.79 146:0.23 147:0.29 149:0.19 151:0.93 152:0.91 153:0.78 154:0.82 155:0.67 159:0.69 161:0.71 162:0.56 164:0.91 167:0.97 169:0.91 172:0.27 173:0.27 177:0.38 178:0.52 179:0.83 181:0.71 186:0.82 189:0.99 191:0.91 192:0.59 202:0.89 208:0.64 212:0.57 216:0.36 220:0.62 222:0.95 235:0.97 238:0.98 241:0.91 242:0.71 243:0.40 245:0.56 247:0.39 248:0.90 253:0.87 254:0.86 255:0.79 256:0.89 259:0.21 260:0.94 261:0.90 265:0.26 266:0.47 267:0.73 268:0.89 271:0.40 276:0.25 283:0.82 285:0.62 300:0.55 +2 6:0.99 8:0.98 9:0.80 12:0.06 17:0.01 20:0.97 21:0.59 25:0.88 27:0.08 28:0.83 30:0.09 32:0.75 34:0.33 36:0.86 37:0.87 39:0.69 41:0.82 43:0.38 55:0.52 66:0.24 69:0.33 70:0.98 74:0.48 78:0.84 81:0.44 83:0.73 91:0.99 96:0.75 98:0.34 100:0.93 104:0.58 108:0.94 111:0.87 120:1.00 123:0.94 124:0.90 126:0.32 127:0.99 129:0.59 133:0.89 135:0.54 140:1.00 145:0.69 146:0.49 147:0.55 149:0.44 150:0.36 151:0.63 152:0.91 153:1.00 154:0.82 155:0.67 159:0.86 161:0.71 162:0.60 164:0.99 167:0.96 169:0.91 172:0.45 173:0.15 177:0.38 179:0.61 181:0.71 186:0.84 189:0.99 191:0.98 192:0.59 195:0.94 202:0.99 208:0.64 212:0.29 215:0.55 216:0.83 222:0.95 235:0.68 238:0.97 241:0.89 242:0.79 243:0.33 245:0.65 247:0.39 248:0.65 253:0.58 254:0.86 255:0.79 256:0.99 259:0.21 260:1.00 261:0.91 265:0.36 266:0.91 267:1.00 268:0.99 271:0.40 276:0.62 283:0.82 285:0.62 297:0.36 300:0.84 +3 6:0.98 8:0.96 9:0.80 12:0.06 17:0.82 20:0.97 21:0.59 27:0.11 28:0.83 30:0.62 34:0.42 36:0.35 37:0.94 39:0.69 41:0.95 43:0.28 55:0.52 66:0.30 69:0.33 70:0.34 74:0.66 78:0.93 81:0.33 83:0.85 91:0.92 96:0.97 98:0.14 100:0.99 104:0.58 108:0.85 111:0.98 114:0.64 120:0.94 122:0.51 123:0.85 124:0.71 126:0.32 127:0.89 129:0.59 133:0.64 135:0.42 140:0.80 146:0.20 147:0.25 149:0.12 150:0.30 151:0.99 152:0.98 153:0.98 154:0.80 155:0.67 158:0.85 159:0.61 161:0.71 162:0.64 163:0.79 164:0.87 167:0.94 169:0.97 172:0.16 173:0.29 176:0.56 177:0.38 179:0.95 181:0.71 186:0.93 189:0.99 191:0.92 192:0.59 202:0.88 208:0.64 212:0.30 216:0.37 222:0.95 232:0.75 235:0.68 238:0.99 241:0.82 242:0.33 243:0.37 245:0.43 247:0.39 248:0.95 253:0.97 254:0.80 255:0.79 256:0.88 259:0.21 260:0.94 261:0.98 265:0.15 266:0.27 267:0.92 268:0.90 271:0.40 276:0.20 285:0.62 290:0.64 300:0.25 +1 12:0.06 17:0.45 21:0.78 27:0.45 28:0.83 30:0.76 34:0.83 36:0.18 37:0.50 39:0.69 43:0.76 66:0.64 69:0.82 70:0.15 74:0.50 91:0.14 98:0.10 108:0.66 122:0.57 123:0.64 127:0.07 129:0.05 135:0.90 145:0.47 146:0.18 147:0.23 149:0.41 154:0.68 159:0.09 161:0.71 163:0.97 167:0.68 172:0.16 173:0.75 177:0.38 178:0.55 179:0.08 181:0.71 187:0.68 212:0.95 216:0.77 222:0.95 235:0.98 241:0.43 242:0.82 243:0.69 247:0.12 253:0.41 259:0.21 265:0.10 266:0.12 267:0.28 271:0.40 276:0.18 300:0.13 +2 1:0.74 6:0.98 8:0.95 9:0.80 12:0.06 17:0.85 20:0.82 21:0.05 27:0.11 28:0.83 30:0.68 34:0.74 36:0.29 37:0.94 39:0.69 41:0.88 43:0.35 55:0.45 66:0.32 69:0.15 70:0.31 74:0.81 76:0.85 77:0.70 78:0.86 81:0.93 83:0.81 91:0.49 96:0.87 98:0.34 100:0.95 104:0.58 108:0.12 111:0.91 114:0.95 120:0.78 122:0.67 123:0.12 124:0.70 126:0.54 127:0.59 129:0.59 133:0.64 135:0.39 140:0.45 145:0.41 146:0.35 147:0.42 149:0.25 150:0.96 151:0.90 152:0.99 153:0.69 154:0.88 155:0.67 159:0.41 161:0.71 162:0.74 163:0.75 164:0.71 165:0.90 167:0.73 169:0.97 172:0.21 173:0.27 176:0.73 177:0.38 179:0.90 181:0.71 186:0.86 187:0.21 189:0.98 192:0.59 195:0.63 201:0.74 202:0.61 208:0.64 212:0.19 215:0.91 216:0.37 220:0.62 222:0.95 232:0.75 235:0.12 238:0.98 241:0.58 242:0.45 243:0.49 245:0.48 247:0.47 248:0.85 253:0.89 254:0.80 255:0.79 256:0.58 259:0.21 260:0.79 261:0.98 265:0.37 266:0.47 267:0.98 268:0.64 271:0.40 276:0.30 282:0.84 285:0.62 290:0.95 297:0.36 300:0.25 +2 1:0.74 12:0.06 17:0.87 25:0.88 27:0.61 28:0.83 30:0.76 34:0.17 36:0.91 37:0.31 39:0.69 43:0.30 55:0.52 66:0.36 69:0.21 70:0.22 74:0.48 81:0.96 83:0.80 87:0.98 91:0.20 98:0.12 104:0.78 106:0.81 108:0.92 114:0.98 117:0.86 122:0.43 123:0.92 124:0.57 126:0.54 127:0.90 129:0.59 133:0.47 135:0.34 146:0.20 147:0.25 149:0.19 150:0.98 154:0.80 155:0.67 159:0.80 161:0.71 163:0.80 165:0.92 167:0.57 172:0.16 173:0.13 176:0.73 177:0.38 178:0.55 179:0.97 181:0.71 187:0.68 192:0.59 201:0.74 206:0.81 212:0.42 215:0.96 216:0.17 222:0.95 232:0.70 235:0.05 241:0.02 242:0.45 243:0.40 245:0.43 247:0.35 253:0.73 254:0.80 259:0.21 265:0.13 266:0.23 267:0.28 271:0.40 276:0.12 290:0.98 297:0.36 300:0.18 +1 12:0.06 17:0.43 21:0.40 25:0.88 27:0.08 28:0.83 30:0.45 34:0.66 36:0.34 37:0.87 39:0.69 43:0.23 55:0.71 66:0.53 69:0.21 70:0.74 74:0.56 81:0.42 91:0.17 98:0.60 104:0.58 108:0.74 114:0.68 117:0.86 123:0.72 124:0.52 126:0.32 127:0.75 129:0.59 133:0.47 135:0.60 146:0.22 147:0.27 149:0.30 150:0.36 154:0.88 159:0.50 161:0.71 167:0.66 172:0.45 173:0.27 176:0.56 177:0.38 178:0.55 179:0.79 181:0.71 187:0.95 191:0.76 212:0.58 216:0.83 222:0.95 232:0.84 235:0.42 241:0.32 242:0.52 243:0.37 245:0.64 247:0.39 253:0.56 254:0.80 259:0.21 265:0.61 266:0.54 267:0.98 271:0.40 276:0.36 290:0.68 300:0.55 +1 12:0.06 17:0.59 21:0.78 27:0.45 28:0.83 30:0.76 34:0.89 36:0.20 37:0.50 39:0.69 43:0.77 66:0.64 69:0.80 70:0.15 74:0.41 91:0.15 98:0.11 108:0.63 122:0.55 123:0.61 127:0.08 129:0.05 135:0.61 145:0.50 146:0.18 147:0.23 149:0.27 154:0.69 159:0.09 161:0.71 163:0.97 167:0.60 172:0.16 173:0.82 177:0.38 178:0.55 179:0.09 181:0.71 187:0.68 212:0.95 216:0.77 222:0.95 235:0.99 241:0.10 242:0.82 243:0.69 247:0.12 253:0.36 259:0.21 265:0.12 266:0.12 267:0.23 271:0.40 276:0.18 300:0.13 +1 6:0.98 8:0.97 9:0.80 12:0.06 17:0.24 20:0.97 21:0.78 27:0.10 28:0.83 34:0.56 36:0.26 37:0.92 39:0.69 41:0.90 78:0.85 83:0.80 91:0.95 96:0.86 100:0.96 104:0.58 111:0.92 120:0.94 135:0.80 140:0.97 151:0.86 152:0.93 153:0.84 155:0.67 161:0.71 162:0.55 164:0.90 167:0.97 169:0.94 175:0.91 178:0.48 181:0.71 186:0.86 189:0.99 191:0.95 192:0.59 202:0.88 208:0.64 216:0.41 220:0.62 222:0.95 238:0.98 241:0.93 244:0.83 248:0.84 253:0.80 255:0.79 256:0.86 257:0.84 259:0.21 260:0.94 261:0.94 267:0.85 268:0.87 271:0.40 277:0.87 285:0.62 +1 1:0.74 6:0.98 8:0.86 9:0.80 12:0.06 17:0.61 20:0.92 21:0.13 27:0.11 28:0.83 30:0.21 34:0.36 36:0.56 37:0.94 39:0.69 41:0.82 43:0.30 66:0.61 69:0.17 70:0.50 74:0.70 76:0.85 77:0.70 78:0.85 81:0.89 83:0.78 91:0.44 96:0.75 98:0.67 100:0.93 104:0.58 108:0.53 111:0.88 114:0.92 120:0.63 123:0.51 124:0.47 126:0.54 127:0.42 129:0.59 133:0.47 135:0.39 140:0.45 145:0.42 146:0.18 147:0.23 149:0.29 150:0.94 151:0.72 152:0.99 153:0.72 154:0.88 155:0.67 159:0.27 161:0.71 162:0.65 163:0.81 164:0.64 165:0.88 167:0.72 169:0.97 172:0.37 173:0.38 176:0.73 177:0.38 179:0.84 181:0.71 186:0.85 187:0.21 189:0.94 192:0.59 195:0.57 201:0.74 202:0.55 208:0.64 212:0.55 215:0.84 216:0.37 220:0.74 222:0.95 232:0.75 235:0.22 238:0.96 241:0.54 242:0.58 243:0.29 245:0.52 247:0.39 248:0.67 253:0.79 254:0.80 255:0.79 256:0.45 259:0.21 260:0.64 261:0.98 265:0.67 266:0.27 267:0.82 268:0.57 271:0.40 276:0.33 282:0.84 285:0.62 290:0.92 297:0.36 300:0.33 +0 11:0.57 12:0.51 17:0.34 21:0.78 27:0.45 28:0.99 30:0.45 34:0.84 36:0.17 37:0.50 39:0.79 43:0.78 66:0.49 69:0.78 70:0.25 74:0.46 85:0.72 91:0.27 98:0.19 101:0.71 108:0.62 122:0.43 123:0.59 124:0.48 127:0.40 129:0.05 133:0.39 135:0.73 145:0.52 146:0.29 147:0.35 149:0.46 154:0.70 159:0.59 161:0.84 163:0.80 167:0.55 172:0.16 173:0.73 177:0.38 178:0.55 179:0.97 181:0.56 187:0.68 212:0.61 216:0.77 222:0.60 235:0.31 241:0.37 242:0.63 243:0.60 245:0.39 247:0.30 253:0.39 259:0.21 265:0.20 266:0.17 267:0.44 271:0.20 276:0.13 300:0.18 +1 1:0.74 6:0.90 7:0.78 8:0.68 9:0.80 11:0.57 12:0.51 17:0.84 20:0.86 25:0.88 27:0.51 28:0.99 30:0.30 32:0.75 34:0.49 36:0.99 37:0.69 39:0.79 41:0.82 43:0.98 66:0.91 69:0.05 70:0.76 74:0.76 78:0.88 81:0.96 83:0.81 85:0.91 91:0.73 96:0.83 98:0.96 100:0.88 101:0.83 104:0.58 106:0.81 108:0.05 111:0.86 114:0.98 120:0.81 122:0.43 123:0.05 126:0.54 127:0.72 129:0.05 135:0.89 140:0.80 145:0.47 146:0.83 147:0.86 149:0.89 150:0.98 151:0.90 152:0.93 153:0.77 154:0.98 155:0.67 159:0.39 161:0.84 162:0.86 164:0.70 165:0.92 167:0.51 169:0.87 172:0.74 173:0.19 176:0.73 177:0.38 179:0.59 181:0.56 186:0.88 187:0.68 189:0.90 192:0.59 195:0.63 201:0.74 202:0.55 206:0.81 208:0.64 212:0.60 215:0.96 216:0.28 222:0.60 230:0.81 233:0.76 235:0.05 238:0.86 241:0.18 242:0.40 243:0.91 247:0.39 248:0.80 253:0.86 255:0.79 256:0.58 259:0.21 260:0.81 261:0.90 265:0.96 266:0.38 267:0.59 268:0.64 271:0.20 276:0.62 285:0.62 290:0.98 297:0.36 300:0.55 +1 1:0.74 6:0.97 7:0.78 8:0.86 9:0.80 12:0.51 17:0.16 20:0.92 21:0.30 27:0.28 28:0.99 30:0.29 32:0.75 34:0.56 36:0.92 37:0.62 39:0.79 43:0.39 55:0.59 66:0.95 69:0.15 70:0.66 74:0.52 78:0.91 81:0.86 83:0.75 91:0.62 96:0.74 98:0.94 100:0.93 104:0.95 106:0.81 108:0.12 111:0.91 114:0.86 120:0.85 123:0.12 126:0.54 127:0.60 129:0.05 135:0.51 140:0.94 145:0.61 146:0.92 147:0.93 149:0.77 150:0.91 151:0.69 152:0.97 153:0.72 154:0.88 155:0.67 159:0.53 161:0.84 162:0.81 164:0.80 165:0.84 167:0.57 169:0.96 172:0.87 173:0.20 176:0.73 177:0.38 179:0.36 181:0.56 186:0.91 187:0.68 189:0.96 190:0.77 192:0.59 195:0.73 201:0.74 202:0.70 206:0.81 208:0.64 212:0.84 215:0.72 216:0.50 220:0.62 222:0.60 230:0.81 233:0.76 235:0.52 238:0.91 241:0.46 242:0.80 243:0.95 247:0.39 248:0.65 253:0.72 254:0.74 255:0.79 256:0.75 259:0.21 260:0.85 261:0.97 265:0.94 266:0.54 267:0.52 268:0.76 271:0.20 276:0.78 283:0.71 285:0.62 290:0.86 297:0.36 300:0.55 +3 1:0.74 6:0.94 7:0.81 8:0.87 9:0.80 11:0.57 12:0.51 17:0.55 20:0.91 21:0.30 27:0.36 28:0.99 30:0.09 32:0.80 34:0.82 36:0.87 37:0.71 39:0.79 41:0.82 43:0.98 55:0.59 66:0.52 69:0.12 70:0.92 74:0.86 76:0.85 77:0.70 78:0.89 81:0.80 83:0.76 85:0.91 91:0.51 96:0.82 98:0.77 100:0.88 101:0.78 104:0.84 106:0.81 108:0.60 111:0.88 114:0.86 117:0.86 120:0.79 121:0.90 123:0.58 124:0.70 126:0.54 127:0.60 129:0.59 133:0.76 135:0.60 140:0.87 145:0.40 146:0.44 147:0.50 149:0.90 150:0.87 151:0.58 152:0.85 153:0.48 154:0.98 155:0.67 158:0.86 159:0.76 161:0.84 162:0.86 164:0.71 165:0.84 167:0.49 169:0.87 172:0.76 173:0.11 176:0.73 177:0.38 179:0.38 181:0.56 186:0.89 187:0.21 189:0.95 190:0.77 191:0.70 192:0.59 195:0.89 201:0.74 202:0.60 204:0.89 206:0.81 208:0.64 212:0.54 215:0.72 216:0.44 222:0.60 230:0.87 232:0.90 233:0.76 235:0.42 238:0.88 241:0.10 242:0.74 243:0.37 245:0.81 247:0.47 248:0.57 253:0.86 255:0.79 256:0.68 259:0.21 260:0.79 261:0.88 265:0.77 266:0.75 267:0.89 268:0.69 271:0.20 276:0.76 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.95 7:0.81 8:0.81 9:0.80 11:0.57 12:0.51 17:0.95 20:0.88 21:0.68 27:0.55 28:0.99 30:0.21 32:0.80 34:0.61 36:0.98 37:0.66 39:0.79 41:0.90 43:0.98 60:0.87 66:0.99 69:0.25 70:0.87 74:0.98 77:0.70 78:0.90 81:0.55 83:0.86 85:0.99 91:0.46 96:0.86 98:0.99 100:0.91 101:0.86 104:0.58 106:0.81 108:0.20 111:0.90 114:0.70 117:0.86 120:0.82 121:0.90 123:0.19 126:0.54 127:0.90 129:0.05 135:0.96 140:0.45 145:0.96 146:0.99 147:0.99 149:0.99 150:0.69 151:0.95 152:0.84 153:0.84 154:0.98 155:0.67 158:0.92 159:0.83 161:0.84 162:0.86 164:0.78 165:0.70 167:0.51 169:0.89 172:0.98 173:0.13 176:0.73 177:0.38 179:0.15 181:0.56 186:0.90 187:0.21 189:0.96 192:0.59 195:0.93 201:0.74 202:0.65 204:0.89 206:0.81 208:0.64 212:0.74 215:0.49 216:0.36 222:0.60 230:0.87 232:0.88 233:0.76 235:0.84 238:0.88 241:0.18 242:0.50 243:0.99 247:0.60 248:0.84 253:0.90 255:0.79 256:0.71 259:0.21 260:0.82 261:0.89 265:0.99 266:0.63 267:0.73 268:0.73 271:0.20 276:0.95 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.90 7:0.81 8:0.77 9:0.80 11:0.57 12:0.51 17:0.73 20:0.92 21:0.22 25:0.88 27:0.51 28:0.99 30:0.26 32:0.75 34:0.39 36:0.97 37:0.69 39:0.79 41:0.94 43:0.84 55:0.45 66:0.45 69:0.15 70:0.89 74:0.72 78:0.88 81:0.91 83:0.82 85:0.85 91:0.82 96:0.92 98:0.70 100:0.90 101:0.79 104:0.58 108:0.12 111:0.88 114:0.90 120:0.93 121:0.90 123:0.12 124:0.59 126:0.54 127:0.91 129:0.59 133:0.47 135:0.79 138:0.98 140:0.87 145:0.49 146:0.65 147:0.70 149:0.83 150:0.95 151:0.97 152:0.93 153:0.96 154:0.81 155:0.67 159:0.45 161:0.84 162:0.86 164:0.92 165:0.84 167:0.82 169:0.87 172:0.67 173:0.27 176:0.73 177:0.38 179:0.50 181:0.56 186:0.89 189:0.92 192:0.59 195:0.63 201:0.74 202:0.84 204:0.89 208:0.64 212:0.71 215:0.79 216:0.28 222:0.60 230:0.87 233:0.76 235:0.52 238:0.88 241:0.79 242:0.74 243:0.58 245:0.88 247:0.39 248:0.93 253:0.92 255:0.79 256:0.89 259:0.21 260:0.94 261:0.90 265:0.70 266:0.47 267:0.69 268:0.90 271:0.20 276:0.71 283:0.71 285:0.62 290:0.90 297:0.36 300:0.70 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.98 21:0.30 25:0.88 27:0.62 28:0.99 30:0.38 32:0.80 34:0.36 36:0.94 37:0.78 39:0.79 43:0.79 55:0.59 66:0.92 69:0.53 70:0.86 74:0.83 76:0.85 77:0.70 81:0.80 83:0.75 85:0.85 91:0.72 98:0.95 101:0.78 104:0.73 108:0.41 114:0.86 121:0.90 123:0.39 126:0.54 127:0.66 129:0.05 135:0.51 145:0.88 146:0.89 147:0.91 149:0.80 150:0.87 154:0.73 155:0.67 159:0.48 161:0.84 165:0.84 167:0.79 172:0.79 173:0.55 176:0.73 177:0.38 178:0.55 179:0.51 181:0.56 187:0.21 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 208:0.64 212:0.61 215:0.72 216:0.11 222:0.60 230:0.87 233:0.76 235:0.42 241:0.77 242:0.71 243:0.92 247:0.60 253:0.73 259:0.21 265:0.95 266:0.54 267:0.52 271:0.20 276:0.68 282:0.88 290:0.86 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.97 7:0.81 8:0.95 9:0.80 11:0.57 12:0.51 17:0.16 20:0.85 27:0.28 28:0.99 30:0.57 32:0.80 34:0.37 36:0.96 37:0.62 39:0.79 41:0.88 43:0.98 55:0.52 66:0.97 69:0.05 70:0.66 74:0.85 76:0.94 77:0.89 78:0.96 81:0.96 83:0.82 85:0.93 91:0.76 96:0.91 98:0.94 100:0.98 101:0.83 104:0.82 106:0.81 108:0.05 111:0.97 114:0.98 120:0.93 121:0.90 123:0.05 126:0.54 127:0.60 129:0.05 135:0.60 140:0.80 145:0.89 146:0.90 147:0.92 149:0.94 150:0.98 151:0.95 152:0.97 153:0.93 154:0.98 155:0.67 159:0.50 161:0.84 162:0.80 164:0.89 165:0.92 167:0.71 169:0.96 172:0.93 173:0.14 176:0.73 177:0.38 179:0.24 181:0.56 186:0.96 187:0.21 189:0.99 192:0.59 195:0.72 201:0.74 202:0.82 204:0.89 206:0.81 208:0.64 212:0.90 215:0.96 216:0.50 222:0.60 230:0.87 233:0.76 235:0.05 238:0.96 241:0.69 242:0.75 243:0.97 247:0.39 248:0.90 253:0.93 255:0.79 256:0.85 259:0.21 260:0.93 261:0.97 265:0.94 266:0.27 267:0.36 268:0.86 271:0.20 276:0.87 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.55 +2 6:0.94 7:0.78 8:0.81 9:0.80 11:0.57 12:0.51 17:0.26 20:0.99 21:0.30 27:0.21 28:0.99 30:0.33 34:0.34 36:0.84 37:0.74 39:0.79 41:0.82 43:0.75 55:0.52 66:0.92 69:0.10 70:0.76 74:0.69 76:0.97 78:0.88 81:0.70 83:0.76 85:0.80 91:0.63 96:0.80 98:0.86 100:0.92 101:0.76 104:0.84 106:0.81 108:0.09 111:0.88 120:0.89 123:0.09 126:0.32 127:0.37 129:0.05 135:0.19 140:0.93 146:0.82 147:0.85 149:0.75 150:0.50 151:0.87 152:0.90 153:0.90 154:0.65 155:0.67 159:0.38 161:0.84 162:0.82 164:0.82 167:0.62 169:0.89 172:0.77 173:0.21 177:0.38 179:0.44 181:0.56 186:0.88 187:0.39 189:0.95 192:0.59 202:0.73 206:0.81 208:0.64 212:0.61 215:0.72 216:0.95 222:0.60 230:0.81 233:0.76 235:0.31 238:0.91 241:0.57 242:0.72 243:0.92 247:0.39 248:0.78 253:0.80 255:0.79 256:0.77 259:0.21 260:0.89 261:0.90 265:0.86 266:0.38 267:0.79 268:0.78 271:0.20 276:0.66 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.91 8:0.80 9:0.80 12:0.51 17:0.47 20:0.97 21:0.78 27:0.54 28:0.99 30:0.76 34:0.75 36:0.84 37:0.58 39:0.79 41:0.92 43:0.27 55:0.42 60:0.87 66:0.64 69:0.75 70:0.15 74:0.55 78:0.93 83:0.79 91:0.74 96:0.85 98:0.50 100:0.95 104:0.58 108:0.59 111:0.93 120:0.79 122:0.55 123:0.56 127:0.15 129:0.05 135:0.63 140:0.87 146:0.33 147:0.40 149:0.23 151:0.87 152:0.93 153:0.48 154:0.20 155:0.67 158:0.92 159:0.13 161:0.84 162:0.69 163:0.98 164:0.78 167:0.86 169:0.92 172:0.16 173:0.96 177:0.38 178:0.42 179:0.86 181:0.56 186:0.93 189:0.92 192:0.59 202:0.71 208:0.64 212:0.95 216:0.17 220:0.82 222:0.60 232:0.78 235:0.31 238:0.92 241:0.89 242:0.82 243:0.69 244:0.61 247:0.12 248:0.83 253:0.81 255:0.79 256:0.73 259:0.21 260:0.80 261:0.93 265:0.51 266:0.12 267:0.88 268:0.73 271:0.20 276:0.13 279:0.86 283:0.82 285:0.62 300:0.13 +1 1:0.74 7:0.81 12:0.69 17:0.47 21:0.59 27:0.70 28:0.66 32:0.75 34:0.50 36:0.99 37:0.43 39:0.50 43:0.22 66:0.36 69:0.89 74:0.47 81:0.65 83:0.73 91:0.11 98:0.08 104:0.58 108:0.78 114:0.74 121:0.90 123:0.76 126:0.54 127:0.06 129:0.05 135:0.73 145:0.59 146:0.05 147:0.05 149:0.09 150:0.76 154:0.15 155:0.67 159:0.05 161:0.86 165:0.70 167:0.65 172:0.05 173:0.98 175:0.75 176:0.73 177:0.05 178:0.55 181:0.59 187:0.39 192:0.59 195:0.67 201:0.74 204:0.89 208:0.64 215:0.55 216:0.27 222:0.76 230:0.87 233:0.76 235:0.84 241:0.44 243:0.51 244:0.61 253:0.58 257:0.65 259:0.21 265:0.09 267:0.44 271:0.66 277:0.69 290:0.74 297:0.36 +3 1:0.74 7:0.81 8:0.89 12:0.69 17:0.55 20:0.79 21:0.54 27:0.21 28:0.66 30:0.10 34:0.85 36:0.51 37:0.74 39:0.50 43:0.35 55:0.42 66:0.23 69:0.23 70:0.93 74:0.97 76:0.94 78:0.83 81:0.59 91:0.05 98:0.38 100:0.89 104:0.97 106:0.81 108:0.93 111:0.84 114:0.77 117:0.86 122:0.67 123:0.93 124:0.91 126:0.54 127:0.72 129:0.89 133:0.91 135:0.24 146:0.52 147:0.58 149:0.59 150:0.66 152:0.93 154:0.91 155:0.67 158:0.86 159:0.86 161:0.86 167:0.52 169:0.82 172:0.61 173:0.11 176:0.73 177:0.38 178:0.55 179:0.37 181:0.59 186:0.84 187:0.39 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.49 215:0.58 216:0.95 222:0.76 230:0.83 232:0.91 233:0.76 235:0.68 241:0.01 242:0.55 243:0.34 245:0.78 247:0.67 253:0.76 254:0.74 259:0.21 261:0.86 265:0.40 266:0.87 267:0.23 271:0.66 276:0.78 279:0.86 283:0.82 290:0.77 297:0.36 300:0.84 +1 1:0.74 6:0.92 8:0.86 9:0.80 11:0.57 12:0.69 17:0.82 20:0.94 21:0.30 27:0.52 28:0.66 30:0.54 32:0.80 34:0.56 36:0.75 37:0.69 39:0.50 41:0.88 43:0.98 55:0.52 60:0.87 66:0.90 69:0.12 70:0.72 74:0.93 77:0.70 78:0.93 81:0.75 83:0.81 85:0.90 91:0.38 96:0.91 98:0.94 100:0.99 101:0.84 104:0.95 106:0.81 108:0.10 111:0.99 114:0.86 117:0.86 120:0.84 122:0.43 123:0.10 126:0.54 127:0.60 129:0.05 135:0.18 140:0.45 145:0.47 146:0.73 147:0.77 149:0.87 150:0.84 151:0.96 152:0.87 153:0.87 154:0.98 155:0.67 158:0.92 159:0.30 161:0.86 162:0.85 164:0.77 165:0.84 167:0.58 169:1.00 172:0.71 173:0.35 176:0.73 177:0.38 179:0.60 181:0.59 186:0.93 187:0.68 189:0.94 192:0.59 195:0.56 201:0.74 202:0.69 206:0.81 208:0.64 212:0.80 215:0.72 216:0.38 222:0.76 232:0.97 235:0.42 238:0.98 241:0.18 242:0.37 243:0.90 247:0.60 248:0.86 253:0.93 255:0.79 256:0.73 259:0.21 260:0.85 261:0.97 265:0.94 266:0.54 267:0.59 268:0.76 271:0.66 276:0.58 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.95 7:0.81 8:0.79 12:0.69 17:0.55 20:0.83 21:0.13 27:0.51 28:0.66 30:0.45 32:0.78 34:0.87 36:0.26 37:0.68 39:0.50 43:0.21 55:0.42 66:0.27 69:0.48 70:0.25 74:0.75 76:0.85 77:0.70 78:0.85 81:0.84 91:0.37 98:0.10 100:0.90 104:0.87 106:0.81 108:0.37 111:0.86 114:0.92 117:0.86 121:0.90 122:0.41 123:0.35 124:0.72 126:0.54 127:0.49 129:0.05 133:0.62 135:0.42 145:0.86 146:0.17 147:0.22 149:0.12 150:0.85 152:0.82 154:0.85 155:0.67 158:0.87 159:0.60 161:0.86 163:0.90 167:0.67 169:0.90 172:0.10 173:0.39 176:0.73 177:0.38 178:0.55 179:0.97 181:0.59 186:0.85 187:0.68 192:0.59 195:0.78 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.84 216:0.40 222:0.76 230:0.87 232:0.82 233:0.76 235:0.22 241:0.50 242:0.63 243:0.46 245:0.38 247:0.30 253:0.74 254:0.74 259:0.21 261:0.86 265:0.10 266:0.17 267:0.19 271:0.66 276:0.18 279:0.86 282:0.86 283:0.71 290:0.92 297:0.36 300:0.18 +0 12:0.69 17:0.32 21:0.71 27:0.45 28:0.66 30:0.38 34:0.77 36:0.22 37:0.50 39:0.50 43:0.27 55:0.59 66:0.20 69:0.71 70:0.78 74:0.60 77:0.70 81:0.29 91:0.10 98:0.35 108:0.86 123:0.52 124:0.72 126:0.32 127:0.42 129:0.05 133:0.62 135:0.81 145:0.44 146:0.52 147:0.58 149:0.34 150:0.27 154:0.65 159:0.59 161:0.86 167:0.59 172:0.27 173:0.64 177:0.38 178:0.55 179:0.78 181:0.59 187:0.68 195:0.80 212:0.28 215:0.48 216:0.77 222:0.76 235:0.84 241:0.21 242:0.75 243:0.40 245:0.56 247:0.39 253:0.48 254:0.94 259:0.21 265:0.24 266:0.63 267:0.89 271:0.66 276:0.38 282:0.84 283:0.71 297:0.36 300:0.55 +2 1:0.74 7:0.81 8:0.73 11:0.57 12:0.69 17:0.88 20:0.81 21:0.30 27:0.32 28:0.66 30:0.14 32:0.80 34:0.89 36:0.63 37:0.86 39:0.50 43:0.98 55:0.84 60:0.95 66:0.60 69:0.19 70:0.94 74:0.84 76:0.97 77:0.89 78:0.81 81:0.83 83:0.84 85:0.80 91:0.20 98:0.61 100:0.73 101:0.75 104:0.90 106:0.81 108:0.71 111:0.79 114:0.86 117:0.86 121:0.90 123:0.69 124:0.65 126:0.54 127:0.82 129:0.59 133:0.76 135:0.49 145:0.47 146:0.13 147:0.18 149:0.70 150:0.89 152:0.78 154:0.98 155:0.67 158:0.92 159:0.54 161:0.86 165:0.83 167:0.52 169:0.72 172:0.51 173:0.24 176:0.73 177:0.38 178:0.55 179:0.76 181:0.59 186:0.81 187:0.39 192:0.59 195:0.73 201:0.74 204:0.89 206:0.81 208:0.64 212:0.51 215:0.72 216:0.29 222:0.76 230:0.87 232:0.84 233:0.76 235:0.68 241:0.01 242:0.31 243:0.21 245:0.54 247:0.67 253:0.74 259:0.21 261:0.79 265:0.62 266:0.54 267:0.23 271:0.66 276:0.48 279:0.86 282:0.86 283:0.82 290:0.86 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.81 12:0.69 17:0.73 20:0.80 21:0.59 27:0.72 28:0.66 30:0.33 32:0.75 34:0.26 36:0.95 39:0.50 43:0.39 55:0.71 66:0.54 69:0.90 70:0.69 74:0.64 78:0.72 81:0.65 83:0.73 91:0.65 98:0.57 100:0.73 104:0.74 108:0.79 111:0.72 114:0.74 121:0.90 122:0.41 123:0.78 124:0.55 126:0.54 127:0.80 129:0.05 133:0.62 135:0.23 145:0.59 146:0.65 147:0.05 149:0.34 150:0.76 152:0.77 154:0.29 155:0.67 159:0.56 161:0.86 165:0.70 167:0.92 169:0.72 172:0.32 173:0.95 176:0.73 177:0.05 178:0.55 179:0.91 181:0.59 186:0.73 192:0.59 195:0.73 201:0.74 204:0.89 208:0.64 212:0.19 215:0.55 216:0.02 222:0.76 230:0.87 233:0.76 235:0.84 241:0.87 242:0.19 243:0.19 244:0.61 245:0.45 247:0.55 253:0.62 257:0.65 259:0.21 261:0.73 265:0.58 266:0.47 267:0.69 271:0.66 276:0.31 290:0.74 297:0.36 300:0.43 +1 1:0.74 7:0.81 12:0.69 17:0.61 21:0.59 27:0.70 28:0.66 32:0.75 34:0.26 36:0.98 37:0.43 39:0.50 43:0.21 66:0.36 69:0.91 74:0.47 81:0.65 83:0.73 91:0.37 98:0.08 104:0.58 108:0.81 114:0.74 121:0.90 123:0.80 126:0.54 127:0.06 129:0.05 135:0.75 145:0.59 146:0.05 147:0.05 149:0.08 150:0.76 154:0.14 155:0.67 159:0.05 161:0.86 165:0.70 167:0.70 172:0.05 173:0.99 175:0.75 176:0.73 177:0.05 178:0.55 181:0.59 187:0.39 192:0.59 195:0.71 201:0.74 204:0.89 208:0.64 215:0.55 216:0.27 222:0.76 230:0.87 233:0.76 235:0.84 241:0.58 243:0.51 244:0.61 253:0.58 257:0.65 259:0.21 265:0.08 267:0.36 271:0.66 277:0.69 290:0.74 297:0.36 +2 1:0.74 6:0.93 8:0.85 12:0.69 17:0.51 20:0.84 21:0.05 27:0.62 28:0.66 30:0.33 34:0.70 36:0.49 37:0.50 39:0.50 43:0.79 55:0.42 66:0.79 69:0.74 70:0.49 74:0.74 77:0.89 78:0.87 81:0.88 91:0.44 98:0.30 100:0.95 108:0.57 111:0.91 114:0.95 122:0.57 123:0.55 126:0.54 127:0.12 129:0.05 135:0.44 145:0.85 146:0.35 147:0.42 149:0.61 150:0.90 152:0.80 154:0.72 155:0.67 159:0.14 161:0.86 167:0.62 169:0.93 172:0.41 173:0.79 176:0.73 177:0.38 178:0.55 179:0.22 181:0.59 186:0.87 187:0.39 189:0.94 192:0.59 195:0.69 201:0.74 212:0.89 215:0.91 216:0.19 222:0.76 235:0.12 238:0.96 241:0.35 242:0.63 243:0.80 247:0.39 253:0.75 254:0.74 259:0.21 261:0.87 265:0.32 266:0.17 267:0.23 271:0.66 276:0.34 282:0.84 283:0.82 290:0.95 297:0.36 300:0.33 +1 1:0.74 6:0.87 7:0.81 8:0.73 9:0.80 12:0.69 17:0.51 20:0.91 21:0.47 27:0.21 28:0.66 30:0.72 34:0.77 36:0.77 37:0.74 39:0.50 43:0.33 55:0.42 66:0.33 69:0.19 70:0.56 74:0.87 76:0.85 78:0.79 81:0.63 91:0.40 96:0.78 98:0.68 100:0.82 104:0.93 106:0.81 108:0.15 111:0.79 114:0.80 117:0.86 120:0.66 122:0.43 123:0.15 124:0.73 126:0.54 127:0.49 129:0.81 133:0.67 135:0.26 140:0.45 146:0.78 147:0.82 149:0.24 150:0.69 151:0.73 152:0.94 153:0.78 154:0.92 155:0.67 158:0.86 159:0.55 161:0.86 162:0.57 164:0.74 167:0.62 169:0.82 172:0.51 173:0.20 176:0.73 177:0.38 179:0.54 181:0.59 186:0.80 187:0.39 192:0.59 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.37 215:0.63 216:0.95 220:0.74 222:0.76 230:0.83 232:0.98 233:0.76 235:0.60 241:0.32 242:0.40 243:0.50 245:0.76 247:0.60 248:0.72 253:0.83 254:0.74 255:0.79 256:0.64 259:0.21 260:0.67 261:0.86 265:0.68 266:0.75 267:0.44 268:0.68 271:0.66 276:0.61 279:0.86 283:0.67 285:0.62 290:0.80 297:0.36 300:0.55 +2 1:0.74 6:0.87 7:0.81 8:0.82 11:0.57 12:0.69 17:0.66 20:0.92 21:0.22 25:0.88 27:0.62 28:0.66 30:0.30 32:0.80 34:0.64 36:0.45 37:0.55 39:0.50 43:0.98 66:0.33 69:0.15 70:0.60 74:0.86 76:0.85 77:0.89 78:0.86 81:0.86 83:0.75 85:0.85 91:0.20 98:0.62 100:0.91 101:0.80 104:0.58 108:0.12 111:0.86 114:0.90 117:0.86 121:0.90 122:0.41 123:0.62 124:0.61 126:0.54 127:0.75 129:0.59 133:0.47 135:0.60 145:0.50 146:0.26 147:0.32 149:0.74 150:0.91 152:0.86 154:0.98 155:0.67 158:0.85 159:0.36 161:0.86 165:0.84 167:0.65 169:0.88 172:0.32 173:0.33 176:0.73 177:0.38 178:0.55 179:0.82 181:0.59 186:0.86 187:0.39 189:0.88 192:0.59 195:0.58 201:0.74 204:0.89 208:0.64 212:0.61 215:0.79 216:0.18 222:0.76 230:0.87 232:0.95 233:0.76 235:0.52 238:0.92 241:0.44 242:0.24 243:0.50 245:0.64 247:0.60 253:0.75 259:0.21 261:0.88 265:0.28 266:0.47 267:0.59 271:0.66 276:0.35 282:0.88 283:0.82 290:0.90 297:0.36 299:0.88 300:0.43 +3 6:0.86 8:0.80 9:0.80 12:0.69 17:0.53 20:0.96 21:0.13 27:0.34 28:0.66 30:0.58 34:0.53 36:0.64 37:0.62 39:0.50 41:0.82 43:0.19 55:0.42 66:0.27 69:0.21 70:0.44 74:0.62 78:0.83 81:0.71 83:0.74 91:0.11 96:0.89 98:0.30 100:0.89 108:0.17 111:0.84 114:0.74 117:0.86 120:0.80 122:0.43 123:0.16 124:0.80 126:0.32 127:0.75 129:0.81 133:0.76 135:0.69 138:0.97 140:0.87 146:0.45 147:0.51 149:0.07 150:0.52 151:0.87 152:0.89 153:0.48 154:0.77 155:0.67 159:0.67 161:0.86 162:0.68 163:0.88 164:0.78 167:0.62 169:0.86 172:0.21 173:0.18 176:0.56 177:0.38 178:0.48 179:0.89 181:0.59 186:0.83 187:0.68 189:0.88 192:0.59 202:0.73 208:0.64 212:0.37 216:0.79 220:0.62 222:0.76 232:0.82 235:0.12 238:0.91 241:0.35 242:0.58 243:0.46 245:0.48 247:0.39 248:0.87 253:0.87 254:0.74 255:0.79 256:0.75 259:0.21 260:0.81 261:0.87 265:0.32 266:0.38 267:0.17 268:0.75 271:0.66 276:0.35 283:0.82 285:0.62 290:0.74 300:0.33 +0 11:0.57 12:0.69 17:0.32 21:0.13 27:0.45 28:0.66 30:0.30 32:0.75 34:0.52 36:0.17 37:0.50 39:0.50 43:0.76 55:0.84 66:0.25 69:0.68 70:0.88 74:0.50 81:0.65 85:0.72 91:0.09 98:0.43 101:0.69 108:0.83 123:0.82 124:0.84 126:0.32 127:0.61 129:0.05 133:0.82 135:0.76 145:0.49 146:0.54 147:0.60 149:0.64 150:0.47 154:0.68 159:0.80 161:0.86 163:0.93 167:0.60 172:0.45 173:0.49 177:0.38 178:0.55 179:0.57 181:0.59 187:0.68 195:0.92 212:0.16 215:0.84 216:0.77 222:0.76 235:0.12 241:0.24 242:0.79 243:0.34 245:0.69 247:0.47 253:0.41 259:0.21 265:0.45 266:0.91 267:0.36 271:0.66 276:0.61 277:0.69 283:0.82 297:0.36 300:0.70 +1 7:0.81 8:0.92 9:0.80 12:0.69 17:0.43 20:0.91 21:0.78 27:0.29 28:0.66 34:0.52 36:0.51 37:0.89 39:0.50 41:0.82 74:0.47 78:0.82 83:0.73 91:0.94 96:0.89 100:0.87 111:0.82 120:0.76 121:0.90 135:0.23 138:0.99 140:0.98 145:0.99 151:0.69 152:0.85 153:0.72 155:0.67 161:0.86 162:0.47 164:0.81 167:0.92 169:0.85 175:0.91 178:0.54 181:0.59 186:0.82 190:0.77 191:0.82 192:0.59 202:0.93 204:0.89 208:0.64 216:0.39 222:0.76 230:0.87 233:0.76 235:0.12 241:0.86 244:0.83 248:0.79 253:0.85 255:0.79 256:0.81 257:0.84 259:0.21 260:0.77 261:0.85 267:0.44 268:0.80 271:0.66 277:0.87 285:0.62 +2 1:0.74 6:0.87 7:0.81 8:0.77 11:0.57 12:0.69 17:0.55 20:0.88 21:0.47 27:0.21 28:0.66 30:0.16 34:0.85 36:0.53 37:0.74 39:0.50 43:0.86 55:0.42 66:0.95 69:0.19 70:0.81 74:0.96 76:0.85 78:0.82 81:0.63 85:0.80 91:0.14 98:0.90 100:0.83 101:0.75 104:0.97 106:0.81 108:0.15 111:0.81 114:0.80 117:0.86 122:0.57 123:0.15 126:0.54 127:0.47 129:0.05 135:0.17 146:0.90 147:0.92 149:0.89 150:0.69 152:0.94 154:0.92 155:0.67 158:0.87 159:0.50 161:0.86 167:0.56 169:0.82 172:0.87 173:0.23 176:0.73 177:0.38 178:0.55 179:0.33 181:0.59 186:0.82 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.71 215:0.63 216:0.95 222:0.76 230:0.83 232:0.91 233:0.66 235:0.60 241:0.10 242:0.36 243:0.95 247:0.60 253:0.76 259:0.21 261:0.86 265:0.90 266:0.38 267:0.76 271:0.66 276:0.77 279:0.86 283:0.71 290:0.80 297:0.36 300:0.70 +3 1:0.74 6:0.95 7:0.81 8:0.92 12:0.69 17:0.69 20:0.84 21:0.13 27:0.51 28:0.66 30:0.45 32:0.78 34:0.85 36:0.25 37:0.68 39:0.50 43:0.21 55:0.42 66:0.27 69:0.48 70:0.25 74:0.75 76:0.85 77:0.70 78:0.81 81:0.84 91:0.37 98:0.10 100:0.94 104:0.87 106:0.81 108:0.37 111:0.87 114:0.92 117:0.86 121:0.90 122:0.41 123:0.35 124:0.72 126:0.54 127:0.49 129:0.05 133:0.62 135:0.47 145:0.86 146:0.17 147:0.22 149:0.12 150:0.85 152:0.82 154:0.85 155:0.67 158:0.87 159:0.60 161:0.86 163:0.90 167:0.67 169:0.90 172:0.10 173:0.39 176:0.73 177:0.38 178:0.55 179:0.97 181:0.59 186:0.82 187:0.39 192:0.59 195:0.78 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.84 216:0.40 222:0.76 230:0.87 232:0.82 233:0.76 235:0.22 241:0.51 242:0.63 243:0.46 245:0.38 247:0.30 253:0.74 254:0.74 259:0.21 261:0.87 265:0.10 266:0.17 267:0.19 271:0.66 276:0.18 279:0.86 282:0.86 283:0.71 290:0.92 297:0.36 300:0.18 +2 1:0.74 6:0.97 7:0.81 8:0.84 9:0.80 11:0.87 12:0.51 17:0.15 18:0.98 20:0.99 21:0.30 22:0.93 27:0.21 28:0.73 29:0.62 30:0.42 31:0.99 34:0.44 36:0.84 37:0.74 39:0.67 41:0.95 43:0.97 45:0.93 47:0.93 60:0.98 64:0.77 66:0.43 69:0.15 70:0.82 71:0.73 74:0.85 78:0.85 79:0.99 81:0.59 83:0.91 85:0.90 86:0.96 91:0.73 96:0.96 97:0.89 98:0.58 100:0.91 101:0.97 104:0.84 106:0.81 108:0.12 111:0.86 114:0.65 117:0.86 120:0.92 121:0.90 122:0.85 123:0.12 124:0.79 126:0.54 127:0.81 129:0.59 131:0.93 133:0.81 135:0.44 137:0.99 139:0.97 140:0.45 144:0.76 146:0.88 147:0.90 149:0.95 150:0.76 151:0.99 152:0.99 153:0.95 154:0.97 155:0.67 157:0.98 158:0.92 159:0.82 161:0.71 162:0.61 164:0.86 165:0.93 166:0.89 167:0.73 169:0.86 172:0.87 173:0.10 176:0.73 177:0.70 179:0.22 181:0.73 182:0.99 186:0.85 187:0.39 189:0.98 192:0.87 196:0.99 201:0.93 202:0.88 204:0.89 206:0.81 208:0.64 212:0.61 215:0.44 216:0.95 219:0.95 222:0.90 227:0.96 229:0.99 230:0.87 231:0.85 232:0.90 233:0.76 235:0.52 238:0.94 241:0.51 242:0.19 243:0.57 245:0.93 246:0.99 247:0.90 248:0.94 253:0.96 255:0.95 256:0.88 259:0.21 260:0.90 261:0.89 262:0.93 265:0.59 266:0.87 267:0.52 268:0.89 271:0.69 276:0.90 279:0.86 281:0.91 283:0.82 284:0.98 285:0.62 287:0.99 290:0.65 292:0.95 297:0.36 300:0.84 +3 1:0.74 6:0.91 9:0.80 11:0.87 12:0.51 17:0.02 18:0.96 20:0.84 21:0.05 27:0.52 28:0.73 29:0.62 30:0.56 31:0.96 34:0.66 36:0.76 37:0.45 39:0.67 41:0.90 43:0.96 45:0.77 64:0.77 66:0.94 69:0.10 70:0.62 71:0.73 74:0.77 78:0.80 79:0.95 81:0.79 83:0.91 85:0.88 86:0.97 91:0.54 96:0.86 98:0.98 100:0.84 101:0.97 108:0.09 111:0.80 114:0.89 120:0.78 122:0.57 123:0.09 126:0.54 127:0.85 129:0.05 131:0.93 135:0.42 137:0.96 139:0.96 140:0.45 144:0.76 146:0.81 147:0.84 149:0.90 150:0.87 151:0.93 152:0.96 153:0.78 154:0.96 155:0.67 157:0.98 159:0.37 161:0.71 162:0.72 164:0.73 165:0.93 166:0.90 167:0.74 169:0.92 172:0.83 173:0.28 176:0.73 177:0.70 179:0.47 181:0.73 182:0.99 186:0.81 187:0.87 192:0.87 196:0.98 201:0.93 202:0.64 208:0.64 212:0.73 215:0.78 216:0.46 222:0.90 227:0.96 229:0.99 231:0.84 235:0.22 241:0.56 242:0.18 243:0.94 246:0.99 247:0.86 248:0.85 253:0.88 255:0.95 256:0.64 259:0.21 260:0.78 261:0.94 265:0.98 266:0.47 267:0.44 268:0.67 271:0.69 276:0.72 284:0.94 285:0.62 287:0.99 290:0.89 297:0.36 300:0.55 +4 1:0.74 6:0.91 8:0.77 9:0.80 11:0.87 12:0.51 17:0.04 18:0.95 20:0.99 21:0.05 27:0.52 28:0.73 29:0.62 30:0.66 31:0.99 34:0.36 36:0.55 37:0.45 39:0.67 41:0.98 43:0.81 45:0.73 60:0.95 64:0.77 66:0.93 69:0.23 70:0.49 71:0.73 74:0.75 78:0.92 79:0.99 81:0.79 83:0.91 85:0.90 86:0.91 91:0.90 96:0.97 98:0.92 100:0.96 101:0.97 108:0.18 111:0.93 114:0.89 120:0.95 122:0.57 123:0.18 126:0.54 127:0.53 129:0.05 131:0.93 135:0.28 137:0.99 139:0.92 140:0.45 144:0.76 146:0.85 147:0.88 149:0.91 150:0.87 151:0.99 152:0.96 153:0.95 154:0.76 155:0.67 157:0.99 158:0.92 159:0.42 161:0.71 162:0.80 164:0.92 165:0.93 166:0.90 167:1.00 169:0.92 172:0.80 173:0.32 176:0.73 177:0.70 179:0.45 181:0.73 182:0.99 186:0.92 189:0.93 191:0.89 192:0.87 196:0.99 201:0.93 202:0.90 208:0.64 212:0.77 215:0.78 216:0.46 219:0.95 220:0.62 222:0.90 227:0.96 229:1.00 231:0.85 235:0.22 238:0.94 241:0.95 242:0.22 243:0.93 246:0.99 247:0.76 248:0.97 253:0.97 255:0.95 256:0.92 259:0.21 260:0.94 261:0.94 265:0.92 266:0.27 267:0.52 268:0.93 271:0.69 276:0.69 279:0.86 283:0.82 284:0.99 285:0.62 287:0.99 290:0.89 292:0.95 297:0.36 300:0.43 +2 1:0.74 6:0.99 8:0.88 9:0.80 11:0.78 12:0.51 17:0.16 18:0.64 20:0.80 21:0.30 27:0.17 28:0.73 29:0.62 30:0.62 31:0.87 34:0.55 36:0.77 37:1.00 39:0.67 41:0.82 43:0.80 64:0.77 66:0.75 69:0.41 70:0.34 71:0.73 74:0.42 78:0.85 79:0.87 81:0.59 83:0.91 85:0.72 86:0.74 91:0.70 96:0.70 98:0.81 100:0.91 101:0.87 108:0.31 111:0.86 114:0.65 120:0.56 123:0.30 126:0.54 127:0.30 129:0.05 131:0.93 135:0.51 137:0.87 139:0.71 140:0.45 144:0.76 146:0.56 147:0.62 149:0.57 150:0.76 151:0.53 152:0.92 153:0.78 154:0.74 155:0.67 157:0.87 159:0.20 161:0.71 162:0.53 164:0.65 165:0.93 166:0.89 167:0.79 169:1.00 172:0.32 173:0.63 176:0.73 177:0.70 179:0.89 181:0.73 182:0.98 186:0.85 187:0.21 191:0.80 192:0.87 196:0.88 201:0.93 202:0.62 208:0.64 212:0.30 215:0.44 216:0.48 220:0.87 222:0.90 227:0.96 229:0.98 231:0.84 235:0.52 241:0.67 242:0.33 243:0.77 246:0.99 247:0.39 248:0.52 253:0.50 255:0.95 256:0.45 259:0.21 260:0.57 261:0.98 265:0.81 266:0.27 267:0.36 268:0.58 271:0.69 276:0.25 284:0.91 285:0.62 287:0.99 290:0.65 297:0.36 300:0.25 +2 1:0.74 6:0.97 7:0.81 8:0.86 9:0.80 11:0.87 12:0.51 17:0.12 18:0.88 20:0.98 21:0.40 22:0.93 27:0.21 28:0.73 29:0.62 30:0.72 31:0.96 34:0.50 36:0.61 37:0.74 39:0.67 41:0.92 43:0.97 45:0.66 47:0.93 48:0.91 60:0.87 64:0.77 66:0.77 69:0.17 70:0.50 71:0.73 74:0.82 78:0.79 79:0.96 81:0.54 83:0.91 85:0.91 86:0.93 91:0.44 96:0.87 98:0.80 100:0.81 101:0.97 104:0.84 106:0.81 108:0.61 111:0.78 114:0.62 117:0.86 120:0.77 121:0.99 122:0.57 123:0.59 124:0.40 126:0.54 127:0.56 129:0.59 131:0.93 133:0.46 135:0.17 137:0.96 139:0.95 140:0.45 144:0.76 146:0.17 147:0.22 149:0.92 150:0.74 151:0.82 152:0.99 153:0.46 154:0.97 155:0.67 157:0.99 158:0.91 159:0.49 161:0.71 162:0.68 164:0.76 165:0.93 166:0.89 167:0.72 169:0.86 172:0.78 173:0.23 176:0.73 177:0.70 179:0.46 181:0.73 182:0.99 186:0.80 187:0.39 192:0.87 196:0.98 201:0.93 202:0.76 204:0.89 206:0.81 208:0.64 212:0.82 215:0.42 216:0.95 219:0.95 220:0.62 222:0.90 227:0.96 229:0.99 230:0.87 231:0.84 232:0.90 233:0.76 235:0.60 241:0.46 242:0.33 243:0.18 245:0.76 246:0.99 247:0.74 248:0.84 253:0.88 255:0.95 256:0.79 259:0.21 260:0.78 261:0.89 262:0.93 265:0.80 266:0.63 267:0.85 268:0.79 271:0.69 276:0.67 279:0.86 281:0.91 283:0.78 284:0.96 285:0.62 287:0.99 290:0.62 292:0.91 297:0.36 300:0.55 +2 1:0.74 8:0.84 9:0.80 11:0.78 12:0.51 17:0.15 18:0.64 20:0.86 21:0.30 25:0.88 27:0.16 28:0.73 29:0.62 30:0.76 31:0.87 34:0.49 36:0.39 37:1.00 39:0.67 41:0.82 43:0.79 64:0.77 66:0.54 69:0.75 70:0.22 71:0.73 74:0.42 78:0.81 79:0.87 81:0.59 83:0.91 85:0.72 86:0.73 91:0.69 96:0.69 98:0.23 100:0.87 101:0.87 104:0.75 108:0.58 111:0.82 114:0.65 120:0.55 122:0.41 123:0.56 124:0.45 126:0.54 127:0.32 129:0.05 131:0.93 133:0.37 135:0.34 137:0.87 139:0.71 140:0.45 144:0.76 146:0.22 147:0.05 149:0.56 150:0.76 151:0.52 152:0.81 153:0.48 154:0.73 155:0.67 157:0.87 159:0.21 161:0.71 162:0.86 164:0.57 165:0.93 166:0.89 167:0.72 169:0.85 172:0.21 173:0.94 176:0.73 177:0.05 179:0.93 181:0.73 182:0.98 186:0.81 187:0.39 191:0.86 192:0.87 196:0.88 201:0.93 202:0.36 208:0.64 212:0.42 215:0.44 216:0.22 220:0.87 222:0.90 227:0.96 229:0.99 231:0.84 235:0.52 241:0.46 242:0.45 243:0.26 245:0.40 246:0.99 247:0.35 248:0.51 253:0.50 255:0.95 256:0.45 257:0.84 259:0.21 260:0.56 261:0.82 265:0.25 266:0.23 267:0.59 268:0.46 271:0.69 276:0.15 284:0.89 285:0.62 287:0.99 290:0.65 297:0.36 300:0.18 +2 1:0.74 8:0.79 9:0.80 11:0.92 12:0.51 17:0.47 18:0.98 20:0.96 21:0.71 27:0.68 28:0.73 29:0.62 30:0.42 31:0.95 34:0.70 36:0.95 37:0.47 39:0.67 41:0.94 43:0.91 45:0.88 60:0.87 64:0.77 66:0.20 69:0.45 70:0.86 71:0.73 74:0.84 78:0.76 79:0.95 81:0.44 83:0.91 85:0.98 86:0.96 91:0.40 96:0.84 98:0.43 100:0.73 101:0.97 104:0.88 106:0.81 108:0.80 111:0.75 114:0.55 120:0.81 122:0.57 123:0.78 124:0.95 126:0.54 127:0.98 129:0.98 131:0.93 133:0.95 135:0.66 137:0.95 139:0.96 140:0.80 144:0.76 146:0.17 147:0.22 149:0.96 150:0.69 151:0.92 152:0.89 153:0.72 154:0.91 155:0.67 157:0.99 158:0.91 159:0.95 161:0.71 162:0.60 164:0.76 165:0.93 166:0.89 167:0.67 169:0.72 172:0.90 173:0.11 176:0.73 177:0.70 178:0.42 179:0.14 181:0.73 182:0.99 186:0.77 187:0.39 192:0.87 196:0.97 201:0.93 202:0.74 206:0.81 208:0.64 212:0.48 215:0.37 216:0.29 219:0.95 222:0.90 227:0.96 229:0.99 231:0.84 235:0.95 241:0.24 242:0.16 243:0.07 245:0.96 246:0.99 247:0.93 248:0.82 253:0.87 255:0.95 256:0.79 259:0.21 260:0.76 261:0.78 265:0.45 266:0.96 267:0.52 268:0.75 271:0.69 276:0.96 279:0.86 283:0.78 284:0.95 285:0.62 287:0.99 290:0.55 292:0.91 297:0.36 300:0.94 +1 1:0.74 11:0.78 12:0.51 17:0.30 18:0.65 21:0.54 27:0.45 28:0.73 29:0.62 30:0.91 34:0.89 36:0.18 37:0.50 39:0.67 43:0.82 64:0.77 66:0.69 69:0.70 70:0.13 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.74 91:0.20 98:0.34 101:0.87 108:0.53 114:0.59 122:0.57 123:0.51 126:0.54 127:0.12 129:0.05 131:0.61 135:0.26 139:0.72 144:0.76 145:0.52 146:0.42 147:0.49 149:0.58 150:0.72 154:0.77 155:0.67 157:0.85 159:0.16 161:0.71 163:0.75 165:0.93 166:0.89 167:0.65 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.54 181:0.73 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.61 215:0.39 216:0.77 222:0.90 227:0.61 229:0.61 231:0.83 235:0.74 241:0.12 242:0.63 243:0.73 246:0.99 247:0.30 253:0.42 259:0.21 265:0.36 266:0.17 267:0.23 271:0.69 276:0.13 287:0.61 290:0.59 297:0.36 300:0.13 +1 1:0.74 11:0.78 12:0.51 17:0.24 18:0.83 21:0.54 27:0.45 28:0.73 29:0.62 30:0.44 34:0.86 36:0.18 37:0.50 39:0.67 43:0.82 55:0.71 64:0.77 66:0.10 69:0.69 70:0.82 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.75 91:0.18 98:0.25 101:0.87 108:0.53 114:0.59 123:0.88 124:0.90 126:0.54 127:0.38 129:0.05 131:0.61 133:0.89 135:0.85 139:0.72 144:0.76 145:0.49 146:0.39 147:0.46 149:0.73 150:0.72 154:0.77 155:0.67 157:0.82 159:0.73 161:0.71 165:0.93 166:0.89 167:0.71 172:0.48 173:0.51 176:0.73 177:0.70 178:0.55 179:0.45 181:0.73 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.50 215:0.39 216:0.77 222:0.90 227:0.61 229:0.61 231:0.83 235:0.74 241:0.44 242:0.45 243:0.44 245:0.67 246:0.99 247:0.74 253:0.42 259:0.21 265:0.23 266:0.87 267:0.52 271:0.69 276:0.65 277:0.69 287:0.61 290:0.59 297:0.36 300:0.70 +2 9:0.80 11:0.87 12:0.51 17:0.30 18:0.94 20:0.87 21:0.30 27:0.55 28:0.73 29:0.62 30:0.17 31:0.93 32:0.80 34:0.36 36:0.93 37:0.34 39:0.67 41:0.82 43:0.74 44:0.93 45:0.66 55:0.71 60:0.87 64:0.77 66:0.30 69:0.83 70:0.92 71:0.73 74:0.73 77:0.70 78:0.76 79:0.94 81:0.30 83:0.91 85:0.90 86:0.89 91:0.15 96:0.79 98:0.45 100:0.73 101:0.97 108:0.89 111:0.75 120:0.67 122:0.85 123:0.74 124:0.76 126:0.26 127:0.29 129:0.81 131:0.93 133:0.74 135:0.92 137:0.93 139:0.92 140:0.80 144:0.76 145:0.91 146:0.31 147:0.38 149:0.81 150:0.28 151:0.82 152:0.82 153:0.46 154:0.65 155:0.67 157:0.97 158:0.92 159:0.81 161:0.71 162:0.77 164:0.54 166:0.74 167:0.67 169:0.72 172:0.75 173:0.59 177:0.70 179:0.20 181:0.73 182:0.98 186:0.77 187:0.68 192:0.87 195:0.97 196:0.96 202:0.55 208:0.64 212:0.58 216:0.72 219:0.95 220:0.62 222:0.90 227:0.96 229:0.99 231:0.84 235:0.31 241:0.24 242:0.21 243:0.17 245:0.89 246:0.61 247:0.90 248:0.74 253:0.79 255:0.95 256:0.58 259:0.21 260:0.68 261:0.77 265:0.38 266:0.63 267:0.36 268:0.58 271:0.69 276:0.82 279:0.86 282:0.88 283:0.82 284:0.87 285:0.62 287:0.99 292:0.95 299:0.88 300:0.94 +3 1:0.74 6:0.99 7:0.63 8:0.99 9:0.80 12:0.51 17:0.13 20:0.95 21:0.30 27:0.17 28:0.73 29:0.62 30:0.11 31:0.99 32:0.80 34:0.62 36:0.73 37:1.00 39:0.67 41:0.98 43:0.14 44:0.93 48:0.91 55:0.45 60:0.87 64:0.77 66:0.61 69:0.63 70:0.54 71:0.73 74:0.59 76:0.85 77:0.70 78:0.96 79:0.99 81:0.59 83:0.91 91:0.92 96:0.97 97:0.99 98:0.54 100:1.00 108:0.48 111:0.99 114:0.65 120:0.92 123:0.46 124:0.43 126:0.54 127:0.48 129:0.59 131:0.93 133:0.47 135:0.42 137:0.99 139:0.82 140:0.87 144:0.76 145:0.72 146:0.48 147:0.54 149:0.17 150:0.76 151:0.96 152:0.92 153:0.92 154:0.10 155:0.67 157:0.61 158:0.91 159:0.35 161:0.71 162:0.72 164:0.91 165:0.93 166:0.89 167:1.00 169:1.00 172:0.27 173:0.73 175:0.91 176:0.73 177:0.05 179:0.93 181:0.73 182:0.99 186:0.95 189:0.99 191:0.91 192:0.87 195:0.65 196:0.98 201:0.93 202:0.91 204:0.85 208:0.64 212:0.30 215:0.44 216:0.48 219:0.95 220:0.62 222:0.90 227:0.96 229:1.00 230:0.63 231:0.85 233:0.73 235:0.52 238:0.99 241:0.95 242:0.82 243:0.68 244:0.83 245:0.42 246:0.99 247:0.12 248:0.95 253:0.94 255:0.95 256:0.92 257:0.84 259:0.21 260:0.91 261:0.98 265:0.55 266:0.27 267:0.44 268:0.93 271:0.69 276:0.23 277:0.87 279:0.86 281:0.91 282:0.88 283:0.78 284:0.99 285:0.62 287:0.99 290:0.65 292:0.95 297:0.36 299:0.88 300:0.33 +1 12:0.92 17:0.43 21:0.40 27:0.45 28:0.56 30:0.45 32:0.80 34:0.87 36:0.19 37:0.50 43:0.32 55:0.59 66:0.69 69:0.69 70:0.25 81:0.95 91:0.11 98:0.39 108:0.53 123:0.50 126:0.54 127:0.13 129:0.05 135:0.75 145:0.47 146:0.48 147:0.54 149:0.30 150:0.91 154:0.24 159:0.17 161:0.65 163:0.81 167:0.64 172:0.21 173:0.70 177:0.05 178:0.55 179:0.62 181:0.53 187:0.68 195:0.72 212:0.61 215:0.67 216:0.77 235:0.42 241:0.18 242:0.82 243:0.73 247:0.12 259:0.21 265:0.41 266:0.17 267:0.23 271:0.43 276:0.20 297:0.36 300:0.18 +1 12:0.92 17:0.38 21:0.30 27:0.45 28:0.56 30:0.33 32:0.80 34:0.86 36:0.17 37:0.50 43:0.32 55:0.71 66:0.68 69:0.69 70:0.69 81:0.96 91:0.18 98:0.48 108:0.52 123:0.50 124:0.41 126:0.54 127:0.19 129:0.59 133:0.47 135:0.72 145:0.49 146:0.68 147:0.73 149:0.41 150:0.93 154:0.24 159:0.37 161:0.65 167:0.62 172:0.37 173:0.60 177:0.05 178:0.55 179:0.64 181:0.53 187:0.68 195:0.82 212:0.19 215:0.72 216:0.77 235:0.31 241:0.12 242:0.82 243:0.72 245:0.45 247:0.12 259:0.21 265:0.50 266:0.47 267:0.36 271:0.43 276:0.33 283:0.60 297:0.36 300:0.43 +1 6:0.93 7:0.81 8:0.89 12:0.92 17:0.06 20:0.93 25:0.88 27:0.12 28:0.56 30:0.76 32:0.80 34:0.61 36:0.29 37:0.82 43:0.40 55:0.71 66:0.72 69:0.53 70:0.22 78:0.87 81:0.99 91:0.72 98:0.83 100:0.94 104:0.72 108:0.41 111:0.90 120:0.81 123:0.39 126:0.54 127:0.32 129:0.05 135:0.23 140:0.45 145:0.65 146:0.51 147:0.57 149:0.36 150:0.98 151:0.73 152:0.92 153:0.80 154:0.30 159:0.18 161:0.65 162:0.78 163:0.81 164:0.70 167:0.91 169:0.92 172:0.27 173:0.79 177:0.05 179:0.93 181:0.53 186:0.87 187:0.39 189:0.95 191:0.77 195:0.56 202:0.59 212:0.42 215:0.96 216:0.45 230:0.87 233:0.76 235:0.02 238:0.96 241:0.77 242:0.82 243:0.75 247:0.12 248:0.73 256:0.58 259:0.21 260:0.81 261:0.92 265:0.83 266:0.23 267:0.18 268:0.64 271:0.43 276:0.26 285:0.62 297:0.36 300:0.18 +0 12:0.92 17:0.81 25:0.88 27:0.07 28:0.56 34:0.83 36:0.39 37:0.91 81:0.99 91:0.37 104:0.58 120:0.75 126:0.54 135:0.72 140:0.45 150:0.98 151:0.71 153:0.72 161:0.65 162:0.86 164:0.69 167:0.77 175:0.75 181:0.53 187:0.39 202:0.53 215:0.96 216:0.69 235:0.02 241:0.66 244:0.61 248:0.68 256:0.58 257:0.65 259:0.21 260:0.76 267:0.44 268:0.62 271:0.43 277:0.69 285:0.62 297:0.36 +0 6:0.88 7:0.81 8:0.73 12:0.92 17:0.24 20:0.93 21:0.30 27:0.21 28:0.56 30:0.33 34:0.61 36:0.69 37:0.74 43:0.52 55:0.52 66:0.79 69:0.10 70:0.36 78:0.77 81:0.96 91:0.63 98:0.74 100:0.81 104:0.84 106:0.81 108:0.09 111:0.77 120:0.85 123:0.09 126:0.54 127:0.23 129:0.05 135:0.26 140:0.45 146:0.56 147:0.62 149:0.50 150:0.93 151:0.80 152:0.99 153:0.92 154:0.41 159:0.20 161:0.65 162:0.56 164:0.80 167:0.80 169:0.78 172:0.41 173:0.32 177:0.05 179:0.74 181:0.53 186:0.78 187:0.39 189:0.89 202:0.77 206:0.81 212:0.89 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.90 241:0.70 242:0.82 243:0.80 247:0.12 248:0.79 256:0.73 259:0.21 260:0.86 261:0.82 265:0.74 266:0.17 267:0.28 268:0.76 271:0.43 276:0.33 283:0.82 285:0.62 297:0.36 300:0.25 +2 7:0.81 12:0.92 17:0.59 21:0.40 27:0.21 28:0.56 30:0.52 34:0.67 36:0.77 37:0.74 43:0.52 55:0.71 66:0.45 69:0.15 70:0.74 81:0.95 91:0.11 98:0.67 104:0.89 106:0.81 108:0.74 120:0.71 123:0.73 124:0.79 126:0.54 127:0.67 129:0.93 133:0.84 135:0.18 140:0.45 146:0.52 147:0.58 149:0.79 150:0.91 151:0.66 153:0.48 154:0.41 159:0.88 161:0.65 162:0.78 163:0.81 164:0.70 167:0.63 172:0.85 173:0.08 177:0.05 179:0.24 181:0.53 187:0.39 202:0.59 206:0.81 212:0.30 215:0.67 216:0.95 220:0.74 230:0.65 233:0.63 235:0.42 241:0.15 242:0.82 243:0.14 245:0.90 247:0.12 248:0.65 256:0.58 259:0.21 260:0.72 265:0.67 266:0.75 267:0.52 268:0.64 271:0.43 276:0.88 283:0.82 285:0.62 297:0.36 300:0.84 +1 6:0.90 7:0.81 8:0.80 12:0.92 17:0.45 20:0.99 21:0.05 27:0.19 28:0.56 30:0.21 32:0.80 34:0.80 36:0.74 37:0.82 43:0.37 55:0.84 66:0.36 69:0.59 70:0.67 78:0.76 81:0.98 91:0.68 98:0.47 100:0.80 104:0.97 106:0.81 108:0.75 111:0.76 120:0.79 123:0.73 124:0.70 126:0.54 127:0.39 129:0.81 133:0.67 135:0.69 140:0.45 145:0.88 146:0.46 147:0.52 149:0.41 150:0.98 151:0.74 152:0.90 153:0.82 154:0.28 159:0.45 161:0.65 162:0.80 163:0.75 164:0.78 167:0.74 169:0.78 172:0.27 173:0.59 177:0.05 179:0.83 181:0.53 186:0.77 187:0.87 189:0.92 191:0.70 195:0.69 202:0.68 206:0.81 212:0.16 215:0.91 216:0.59 230:0.87 233:0.76 235:0.05 238:0.91 241:0.59 242:0.82 243:0.29 245:0.50 247:0.12 248:0.71 256:0.71 259:0.21 260:0.80 261:0.80 265:0.48 266:0.54 267:0.52 268:0.73 271:0.43 276:0.37 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 8:0.85 12:0.92 17:0.53 21:0.05 25:0.88 27:0.07 28:0.56 30:0.62 32:0.80 34:0.68 36:0.81 37:0.91 43:0.49 55:0.71 66:0.79 69:0.25 70:0.41 81:0.98 91:0.72 98:0.83 104:0.58 108:0.20 120:0.73 123:0.19 124:0.38 126:0.54 127:0.51 129:0.59 133:0.47 135:0.72 140:0.45 145:0.65 146:0.80 147:0.83 149:0.64 150:0.98 151:0.66 153:0.48 154:0.37 159:0.43 161:0.65 162:0.64 164:0.76 167:0.73 172:0.61 173:0.32 177:0.05 179:0.68 181:0.53 187:0.39 191:0.85 195:0.69 202:0.69 212:0.55 215:0.91 216:0.69 220:0.87 230:0.87 233:0.76 235:0.05 241:0.56 242:0.82 243:0.80 245:0.53 247:0.12 248:0.66 256:0.68 259:0.21 260:0.74 265:0.83 266:0.47 267:0.59 268:0.70 271:0.43 276:0.53 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.93 8:0.90 12:0.92 17:0.32 20:0.99 21:0.05 27:0.31 28:0.56 34:0.24 36:0.65 37:0.85 78:0.79 81:0.98 91:0.92 100:0.84 104:0.58 111:0.79 120:0.85 126:0.54 135:0.44 140:0.45 150:0.98 151:0.79 152:0.92 153:0.90 161:0.65 162:0.60 164:0.82 167:0.99 169:0.81 175:0.75 181:0.53 186:0.80 189:0.95 191:0.95 202:0.77 215:0.91 216:0.25 235:0.05 238:0.92 241:0.97 244:0.61 248:0.77 256:0.88 257:0.65 259:0.21 260:0.85 261:0.84 267:0.84 268:0.78 271:0.43 277:0.69 285:0.62 297:0.36 +1 6:0.96 7:0.81 8:0.91 12:0.92 17:0.45 20:0.93 21:0.05 25:0.88 27:0.07 28:0.56 30:0.68 32:0.80 34:0.69 36:0.83 37:0.91 43:0.48 55:0.71 66:0.77 69:0.29 70:0.37 78:0.84 81:0.98 91:0.80 98:0.83 100:0.93 104:0.58 108:0.22 111:0.87 120:0.82 123:0.22 124:0.38 126:0.54 127:0.49 129:0.59 133:0.47 135:0.65 140:0.45 145:0.65 146:0.80 147:0.83 149:0.62 150:0.98 151:0.78 152:0.97 153:0.89 154:0.37 159:0.42 161:0.65 162:0.54 164:0.83 167:0.77 169:0.98 172:0.59 173:0.34 177:0.05 179:0.70 181:0.53 186:0.85 187:0.21 189:0.96 191:0.92 195:0.69 202:0.81 212:0.51 215:0.91 216:0.69 230:0.87 233:0.76 235:0.05 238:0.96 241:0.65 242:0.82 243:0.79 245:0.52 247:0.12 248:0.74 256:0.80 259:0.21 260:0.82 261:0.98 265:0.83 266:0.47 267:0.52 268:0.78 271:0.43 276:0.51 285:0.62 297:0.36 300:0.33 +2 6:0.88 7:0.81 8:0.78 12:0.92 17:0.62 20:0.81 21:0.47 27:0.21 28:0.56 30:0.52 34:0.66 36:0.77 37:0.74 43:0.52 55:0.71 66:0.43 69:0.17 70:0.70 78:0.76 81:0.93 91:0.06 98:0.62 100:0.78 104:0.89 106:0.81 108:0.74 111:0.75 120:0.77 123:0.73 124:0.84 126:0.54 127:0.65 129:0.95 133:0.87 135:0.17 140:0.45 146:0.52 147:0.58 149:0.78 150:0.88 151:0.74 152:0.99 153:0.83 154:0.41 159:0.88 161:0.65 162:0.61 163:0.81 164:0.71 167:0.59 169:0.78 172:0.84 173:0.09 177:0.05 179:0.24 181:0.53 186:0.77 187:0.39 189:0.93 202:0.64 206:0.81 212:0.30 215:0.63 216:0.95 230:0.65 233:0.63 235:0.52 238:0.89 241:0.03 242:0.82 243:0.15 245:0.88 247:0.12 248:0.69 256:0.58 259:0.21 260:0.77 261:0.82 265:0.63 266:0.75 267:0.28 268:0.64 271:0.43 276:0.87 283:0.82 285:0.62 297:0.36 300:0.84 +1 8:0.87 12:0.92 17:0.04 21:0.05 25:0.88 27:0.07 28:0.56 30:0.95 34:0.90 36:0.71 37:0.91 43:0.47 55:0.96 66:0.20 69:0.38 81:0.98 91:0.73 98:0.10 104:0.58 108:0.30 120:0.73 123:0.28 124:0.61 126:0.54 127:0.33 129:0.59 133:0.47 135:0.58 140:0.45 146:0.05 147:0.05 149:0.21 150:0.98 151:0.66 153:0.48 154:0.35 159:0.25 161:0.65 162:0.82 164:0.74 167:0.82 172:0.05 173:0.55 177:0.05 179:1.00 181:0.53 187:0.68 191:0.89 202:0.62 215:0.91 216:0.69 220:0.93 235:0.05 241:0.71 243:0.40 245:0.36 248:0.66 256:0.64 259:0.21 260:0.74 265:0.10 267:0.69 268:0.68 271:0.43 285:0.62 297:0.36 300:0.08 +2 7:0.81 8:0.79 12:0.92 17:0.18 25:0.88 27:0.12 28:0.56 30:0.76 32:0.80 34:0.59 36:0.28 37:0.82 43:0.33 55:0.71 66:0.36 69:0.67 70:0.22 81:0.99 91:0.58 98:0.36 104:0.72 108:0.68 120:0.69 123:0.66 124:0.57 126:0.54 127:0.31 129:0.59 133:0.47 135:0.54 140:0.45 145:0.65 146:0.26 147:0.32 149:0.32 150:0.98 151:0.66 153:0.48 154:0.24 159:0.27 161:0.65 162:0.62 163:0.88 164:0.57 167:0.77 172:0.16 173:0.80 177:0.05 179:0.93 181:0.53 187:0.68 191:0.73 195:0.62 202:0.50 212:0.42 215:0.96 216:0.45 230:0.87 233:0.76 235:0.02 241:0.66 242:0.82 243:0.40 245:0.43 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.38 266:0.23 267:0.59 268:0.46 271:0.43 276:0.21 285:0.62 297:0.36 300:0.18 +0 8:0.84 12:0.92 17:0.61 21:0.05 27:0.40 28:0.56 34:0.45 36:0.30 37:0.85 81:0.98 91:0.67 104:0.54 120:0.73 126:0.54 135:0.61 140:0.45 150:0.98 151:0.69 153:0.69 161:0.65 162:0.64 164:0.68 167:0.85 175:0.75 181:0.53 187:0.39 191:0.78 202:0.61 215:0.91 216:0.19 235:0.05 241:0.73 244:0.61 248:0.66 256:0.68 257:0.65 259:0.21 260:0.74 267:0.59 268:0.62 271:0.43 277:0.69 285:0.62 297:0.36 +4 6:0.96 8:0.92 12:0.92 17:0.11 20:0.98 21:0.78 25:0.88 27:0.07 28:0.56 34:0.64 36:0.27 37:0.91 78:0.93 91:0.98 100:0.99 104:0.58 111:0.99 120:0.97 135:0.75 140:0.45 151:0.86 152:0.97 153:0.99 161:0.65 162:0.56 164:0.97 167:0.98 169:0.98 175:0.75 181:0.53 186:0.93 189:0.97 191:0.93 202:0.96 216:0.69 238:0.99 241:0.94 244:0.61 248:0.96 256:0.96 257:0.65 259:0.21 260:0.97 261:0.98 267:0.84 268:0.96 271:0.43 277:0.69 283:0.82 285:0.62 +1 1:0.74 6:0.82 8:0.64 9:0.80 11:0.91 12:0.51 17:0.26 18:0.90 20:0.79 21:0.40 22:0.93 27:0.25 28:0.76 29:0.56 30:0.17 31:0.88 34:0.42 36:0.38 37:0.34 39:0.25 43:0.67 45:0.81 47:0.93 55:0.84 64:0.77 66:0.11 69:0.55 70:0.91 71:0.97 74:0.60 78:0.88 79:0.88 81:0.57 83:0.60 86:0.89 91:0.25 96:0.71 98:0.30 99:0.83 100:0.89 101:0.52 104:0.99 106:0.81 108:0.42 111:0.87 114:0.62 117:0.86 120:0.57 122:0.86 123:0.89 124:0.77 126:0.54 127:0.46 129:0.59 133:0.74 135:0.90 137:0.88 139:0.86 140:0.80 144:0.85 146:0.60 147:0.65 149:0.53 150:0.73 151:0.56 152:0.78 153:0.48 154:0.83 155:0.67 159:0.78 161:0.78 162:0.54 164:0.57 165:0.70 167:0.45 169:0.88 172:0.54 173:0.33 176:0.73 177:0.70 178:0.42 179:0.53 181:0.96 186:0.88 187:0.95 189:0.86 192:0.87 196:0.91 201:0.93 202:0.56 206:0.81 208:0.64 212:0.42 215:0.42 216:0.40 220:0.82 222:0.60 232:1.00 235:0.60 238:0.88 241:0.05 242:0.54 243:0.51 245:0.71 247:0.67 248:0.55 253:0.51 254:0.74 255:0.95 256:0.45 259:0.21 260:0.58 261:0.86 262:0.93 265:0.32 266:0.87 267:0.73 268:0.46 271:0.52 276:0.59 277:0.69 284:0.89 285:0.62 290:0.62 297:0.36 300:0.84 +2 1:0.69 7:0.72 8:0.63 9:0.76 11:0.91 12:0.51 17:0.84 21:0.64 27:0.72 28:0.76 29:0.56 30:0.21 32:0.69 34:0.96 36:0.89 37:0.80 39:0.25 43:0.70 45:0.81 48:0.91 66:0.12 69:0.43 70:0.91 71:0.97 74:0.66 76:0.85 77:0.70 81:0.34 83:0.60 91:0.51 96:0.72 98:0.41 99:0.83 101:0.52 104:0.83 106:0.81 108:0.33 114:0.56 117:0.86 120:0.59 122:0.51 123:0.81 124:0.71 126:0.44 127:0.61 129:0.05 133:0.74 135:0.70 139:0.64 140:0.45 144:0.85 145:0.50 146:0.50 147:0.56 149:0.70 150:0.52 151:0.53 153:0.48 154:0.60 155:0.58 159:0.58 161:0.78 162:0.86 164:0.68 165:0.70 167:0.47 172:0.51 173:0.37 175:0.75 176:0.66 177:0.38 179:0.68 181:0.96 187:0.39 190:0.77 192:0.59 195:0.80 201:0.68 202:0.56 204:0.85 206:0.81 208:0.54 212:0.42 215:0.38 216:0.09 220:0.99 222:0.60 230:0.75 232:0.89 233:0.76 235:0.80 241:0.18 242:0.37 243:0.61 244:0.61 245:0.60 247:0.60 248:0.56 253:0.50 255:0.74 256:0.64 257:0.65 259:0.21 260:0.58 265:0.40 266:0.75 267:0.52 268:0.65 271:0.52 276:0.52 277:0.87 281:0.91 282:0.77 285:0.56 290:0.56 297:0.36 300:0.70 +2 1:0.74 9:0.80 11:0.91 12:0.51 17:0.64 18:0.69 21:0.05 22:0.93 27:0.21 28:0.76 29:0.56 30:0.60 31:0.91 32:0.69 34:0.50 36:0.71 37:0.60 39:0.25 43:0.10 45:0.77 47:0.93 55:0.71 64:0.77 66:0.86 69:0.78 70:0.44 71:0.97 74:0.69 77:0.70 79:0.90 81:0.86 83:0.60 86:0.79 91:0.37 96:0.75 97:0.94 98:0.64 99:0.83 101:0.52 104:0.98 106:0.81 108:0.62 114:0.89 117:0.86 120:0.63 122:0.82 123:0.59 126:0.54 127:0.19 129:0.05 135:0.94 137:0.91 139:0.83 140:0.45 144:0.85 145:0.83 146:0.69 147:0.73 149:0.14 150:0.90 151:0.72 153:0.48 154:0.70 155:0.67 158:0.91 159:0.27 161:0.78 162:0.86 164:0.57 165:0.70 167:0.46 172:0.59 173:0.81 176:0.73 177:0.70 179:0.41 181:0.96 187:0.68 192:0.87 195:0.70 196:0.93 201:0.93 202:0.36 206:0.81 208:0.64 212:0.54 215:0.78 216:0.62 219:0.95 220:0.62 222:0.60 232:0.99 235:0.22 241:0.12 242:0.19 243:0.86 247:0.79 248:0.67 253:0.75 254:0.74 255:0.95 256:0.45 259:0.21 260:0.64 262:0.93 265:0.64 266:0.38 267:0.93 268:0.46 271:0.52 276:0.49 279:0.86 282:0.77 283:0.78 284:0.89 285:0.62 290:0.89 292:0.91 297:0.36 300:0.33 +0 11:0.91 12:0.51 17:0.43 18:0.74 21:0.40 27:0.45 28:0.76 29:0.56 30:0.21 32:0.69 34:0.58 36:0.17 37:0.50 39:0.25 43:0.11 45:0.66 64:0.77 66:0.54 69:0.69 70:0.37 71:0.97 74:0.50 77:0.70 81:0.26 86:0.79 91:0.18 98:0.32 101:0.52 108:0.52 122:0.57 123:0.50 124:0.45 126:0.26 127:0.19 129:0.05 133:0.37 135:0.83 139:0.75 144:0.85 145:0.49 146:0.43 147:0.50 149:0.08 150:0.25 154:0.71 159:0.32 161:0.78 163:0.75 167:0.51 172:0.21 173:0.66 177:0.70 178:0.55 179:0.82 181:0.96 187:0.68 195:0.75 212:0.42 216:0.77 222:0.60 235:0.42 241:0.41 242:0.45 243:0.63 245:0.40 247:0.35 253:0.35 254:0.94 259:0.21 265:0.34 266:0.23 267:0.36 271:0.52 276:0.20 282:0.77 300:0.25 +2 7:0.72 8:0.70 9:0.76 11:0.91 12:0.51 17:0.59 20:0.75 21:0.78 27:0.12 28:0.76 29:0.56 30:0.21 32:0.69 34:0.25 36:0.26 37:0.29 39:0.25 43:0.40 45:0.73 55:0.59 66:0.85 69:0.81 70:0.64 71:0.97 74:0.53 76:0.85 77:0.70 78:0.82 83:0.60 91:0.77 96:0.98 98:0.78 100:0.91 101:0.52 104:0.58 108:0.65 111:0.85 120:0.95 123:0.63 127:0.27 129:0.05 135:0.20 138:0.99 140:0.45 144:0.85 145:0.74 146:0.76 147:0.80 149:0.39 151:0.90 152:0.74 153:0.83 154:0.30 155:0.58 159:0.32 161:0.78 162:0.71 164:0.90 167:0.79 169:0.89 172:0.57 173:0.87 175:0.75 177:0.38 179:0.61 181:0.96 186:0.83 190:0.77 191:0.77 192:0.59 195:0.67 202:0.88 204:0.85 208:0.54 212:0.36 216:0.15 220:0.62 222:0.60 230:0.75 232:0.78 233:0.76 235:0.05 241:0.85 242:0.71 243:0.86 244:0.61 247:0.47 248:0.97 253:0.95 255:0.74 256:0.90 257:0.65 259:0.21 260:0.94 261:0.76 265:0.78 266:0.38 267:0.44 268:0.90 271:0.52 276:0.46 277:0.69 282:0.77 285:0.56 300:0.43 +2 6:0.87 11:0.91 12:0.51 17:0.18 18:0.87 20:0.76 21:0.78 27:0.19 28:0.76 29:0.56 30:0.40 31:0.90 34:0.61 36:0.29 37:0.73 39:0.25 43:0.56 45:0.77 55:0.71 66:0.91 69:0.73 70:0.57 71:0.97 74:0.55 78:0.95 79:0.90 83:0.60 86:0.85 91:0.08 97:0.89 98:0.85 100:0.93 101:0.52 108:0.56 111:0.93 120:0.62 122:0.86 123:0.53 127:0.35 129:0.05 135:0.84 137:0.90 139:0.84 140:0.45 144:0.85 146:0.93 147:0.95 149:0.45 151:0.59 152:0.75 153:0.48 154:0.66 155:0.58 158:0.79 159:0.58 161:0.78 162:0.86 163:0.90 164:0.53 167:0.49 169:0.91 172:0.75 173:0.65 177:0.70 179:0.46 181:0.96 186:0.94 187:0.68 189:0.88 190:0.77 192:0.59 196:0.92 202:0.50 208:0.54 212:0.29 216:0.63 219:0.92 220:0.74 222:0.60 235:0.05 238:0.90 241:0.35 242:0.38 243:0.91 247:0.84 248:0.61 253:0.37 254:0.80 255:0.74 256:0.58 259:0.21 260:0.58 261:0.80 265:0.85 266:0.71 267:0.84 268:0.59 271:0.52 276:0.64 279:0.82 283:0.82 284:0.91 285:0.56 292:0.91 300:0.43 +2 1:0.69 6:0.86 7:0.72 8:0.83 9:0.76 11:0.91 12:0.51 17:0.49 18:0.61 20:0.98 21:0.30 27:0.21 28:0.76 29:0.56 30:0.43 34:0.69 36:0.72 37:0.74 39:0.25 43:0.72 45:0.94 66:0.15 69:0.85 70:0.81 71:0.97 74:0.80 78:0.78 81:0.54 83:0.60 86:0.67 91:0.73 96:0.96 97:0.97 98:0.65 99:0.83 100:0.81 101:0.52 104:0.84 106:0.81 108:0.91 111:0.77 114:0.63 117:0.86 120:0.94 122:0.77 123:0.69 124:0.72 126:0.44 127:0.56 129:0.59 133:0.66 135:0.18 139:0.65 140:0.45 144:0.85 146:0.92 147:0.74 149:0.85 150:0.58 151:0.96 152:0.90 153:0.94 154:0.22 155:0.58 158:0.79 159:0.80 161:0.78 162:0.60 164:0.87 165:0.70 167:0.49 169:0.79 172:0.79 173:0.71 176:0.66 177:0.38 179:0.26 181:0.96 186:0.79 187:0.39 189:0.87 190:0.77 191:0.81 192:0.59 201:0.68 202:0.86 204:0.85 206:0.81 208:0.54 212:0.60 215:0.44 216:0.95 222:0.60 230:0.75 232:0.90 233:0.76 235:0.42 238:0.87 241:0.35 242:0.40 243:0.33 245:0.93 247:0.74 248:0.95 253:0.96 254:0.96 255:0.74 256:0.86 257:0.65 259:0.21 260:0.91 261:0.83 265:0.41 266:0.80 267:0.23 268:0.87 271:0.52 276:0.84 279:0.82 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.74 6:0.84 8:0.66 9:0.80 11:0.91 12:0.51 17:0.67 18:0.71 20:0.99 21:0.22 22:0.93 27:0.21 28:0.76 29:0.56 30:0.66 31:0.90 32:0.69 34:0.61 36:0.36 37:0.60 39:0.25 43:0.56 45:0.83 47:0.93 60:0.87 64:0.77 66:0.47 69:0.78 70:0.64 71:0.97 74:0.72 77:0.70 78:0.92 79:0.89 81:0.71 83:0.91 86:0.83 91:0.57 96:0.73 97:0.94 98:0.45 99:0.83 100:0.97 101:0.52 104:1.00 106:0.81 108:0.73 111:0.95 114:0.71 120:0.61 122:0.51 123:0.71 124:0.56 126:0.54 127:0.21 129:0.59 133:0.46 135:0.96 137:0.90 139:0.85 140:0.45 144:0.85 145:0.93 146:0.24 147:0.30 149:0.36 150:0.80 151:0.58 152:0.99 153:0.78 154:0.69 155:0.67 158:0.91 159:0.33 161:0.78 162:0.86 164:0.73 165:0.70 167:0.46 169:0.94 172:0.41 173:0.78 176:0.73 177:0.70 179:0.50 181:0.96 186:0.92 187:0.68 189:0.86 192:0.87 195:0.74 196:0.92 201:0.93 202:0.59 206:0.81 208:0.64 212:0.36 215:0.51 216:0.62 219:0.95 220:0.82 222:0.60 235:0.42 238:0.96 241:0.07 242:0.38 243:0.44 245:0.66 247:0.55 248:0.61 253:0.63 254:0.84 255:0.95 256:0.64 259:0.21 260:0.61 261:0.97 262:0.93 265:0.46 266:0.63 267:0.84 268:0.67 271:0.52 276:0.40 279:0.86 282:0.77 283:0.78 284:0.94 285:0.62 290:0.71 292:0.91 297:0.36 300:0.55 +1 7:0.72 8:0.70 9:0.76 11:0.91 12:0.51 17:0.66 20:0.75 21:0.78 27:0.12 28:0.76 29:0.56 30:0.38 32:0.69 34:0.21 36:0.41 37:0.29 39:0.25 43:0.62 45:0.83 55:0.92 66:0.24 69:0.70 70:0.70 71:0.97 74:0.60 76:0.85 77:0.70 78:0.82 83:0.60 91:0.73 96:0.98 98:0.67 100:0.91 101:0.52 104:0.58 108:0.84 111:0.85 120:0.95 123:0.64 124:0.78 127:0.81 129:0.81 133:0.76 135:0.28 138:0.99 140:0.45 144:0.85 145:0.74 146:0.29 147:0.05 149:0.61 151:0.90 152:0.74 153:0.83 154:0.52 155:0.58 159:0.62 161:0.78 162:0.72 164:0.90 167:0.79 169:0.89 172:0.45 173:0.67 175:0.75 177:0.05 179:0.64 181:0.96 186:0.83 190:0.77 191:0.77 192:0.59 195:0.78 202:0.87 204:0.85 208:0.54 212:0.18 216:0.15 220:0.62 222:0.60 230:0.75 232:0.78 233:0.76 235:0.05 241:0.84 242:0.18 243:0.10 244:0.61 245:0.68 247:0.67 248:0.97 253:0.96 255:0.74 256:0.90 257:0.84 259:0.21 260:0.94 261:0.76 265:0.44 266:0.80 267:0.65 268:0.90 271:0.52 276:0.60 277:0.69 282:0.77 285:0.56 300:0.55 +2 1:0.69 6:0.87 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.49 20:0.78 21:0.05 27:0.18 28:0.76 29:0.56 30:0.75 32:0.69 34:0.61 36:0.81 37:0.96 39:0.25 43:0.70 44:0.87 45:0.97 48:0.91 66:0.05 69:0.44 70:0.43 71:0.97 74:0.89 77:0.70 78:0.86 81:0.82 83:0.60 91:0.51 96:0.90 97:0.89 98:0.46 99:0.83 100:0.86 101:0.52 104:0.82 106:0.81 108:0.80 111:0.85 114:0.85 117:0.86 120:0.83 122:0.51 123:0.64 124:0.49 126:0.44 127:0.70 129:0.81 133:0.67 135:0.42 139:0.68 140:0.45 144:0.85 145:0.93 146:0.43 147:0.50 149:0.93 150:0.79 151:0.90 152:0.76 153:0.83 154:0.59 155:0.58 158:0.79 159:0.55 161:0.78 162:0.84 164:0.76 165:0.70 167:0.52 169:0.84 172:0.89 173:0.42 175:0.75 176:0.66 177:0.38 179:0.27 181:0.96 186:0.86 187:0.21 189:0.91 190:0.77 191:0.70 192:0.59 195:0.76 201:0.68 202:0.68 204:0.85 206:0.81 208:0.54 212:0.87 215:0.78 216:0.50 222:0.60 230:0.75 232:0.88 233:0.76 235:0.12 238:0.84 241:0.47 242:0.62 243:0.22 244:0.61 245:0.89 247:0.47 248:0.87 253:0.91 255:0.74 256:0.71 257:0.65 259:0.21 260:0.80 261:0.81 265:0.46 266:0.84 267:0.44 268:0.74 271:0.52 276:0.83 277:0.69 279:0.82 281:0.89 282:0.77 283:0.82 285:0.56 290:0.85 297:0.36 300:0.70 +0 1:0.74 11:0.91 12:0.51 17:0.36 18:0.76 21:0.59 27:0.45 28:0.76 29:0.56 30:0.33 32:0.80 34:0.81 36:0.17 37:0.50 39:0.25 43:0.32 44:0.93 45:0.73 64:0.77 66:0.45 69:0.69 70:0.69 71:0.97 74:0.67 77:0.70 81:0.45 83:0.60 86:0.79 91:0.17 97:0.89 98:0.30 99:0.83 101:0.52 108:0.53 114:0.58 122:0.41 123:0.51 124:0.66 126:0.54 127:0.34 129:0.05 133:0.60 135:0.86 139:0.88 144:0.85 145:0.47 146:0.39 147:0.46 149:0.24 150:0.67 154:0.76 155:0.67 158:0.91 159:0.47 161:0.78 163:0.75 165:0.70 167:0.46 172:0.27 173:0.68 176:0.73 177:0.70 178:0.55 179:0.84 181:0.96 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.19 215:0.39 216:0.77 219:0.95 222:0.60 235:0.80 241:0.12 242:0.19 243:0.58 245:0.47 247:0.55 253:0.43 254:0.74 259:0.21 265:0.32 266:0.47 267:0.28 271:0.52 276:0.30 279:0.86 282:0.88 283:0.77 290:0.58 292:0.91 297:0.36 300:0.43 +2 1:0.69 6:0.84 8:0.77 9:0.76 11:0.91 12:0.51 17:0.03 18:0.60 20:0.94 21:0.05 27:0.14 28:0.76 29:0.56 30:0.41 32:0.69 34:0.37 36:0.25 37:0.67 39:0.25 43:0.67 45:0.81 66:0.49 69:0.57 70:0.45 71:0.97 74:0.58 77:0.70 78:0.81 81:0.82 83:0.60 86:0.62 91:0.44 96:0.79 98:0.36 99:0.83 100:0.84 101:0.52 108:0.61 111:0.81 114:0.85 120:0.68 122:0.51 123:0.59 124:0.66 126:0.44 127:0.18 129:0.81 133:0.67 135:0.95 139:0.60 140:0.45 144:0.85 145:0.47 146:0.22 147:0.28 149:0.64 150:0.79 151:0.70 152:0.96 153:0.72 154:0.57 155:0.58 159:0.48 161:0.78 162:0.55 164:0.65 165:0.70 167:0.47 169:0.82 172:0.45 173:0.37 175:0.75 176:0.66 177:0.38 179:0.39 181:0.96 186:0.82 187:0.68 189:0.86 190:0.77 192:0.59 195:0.91 201:0.68 202:0.65 208:0.54 212:0.19 215:0.78 216:0.93 220:0.62 222:0.60 235:0.12 238:0.88 241:0.18 242:0.33 243:0.29 244:0.61 245:0.60 247:0.55 248:0.72 253:0.75 255:0.74 256:0.58 257:0.65 259:0.21 260:0.67 261:0.90 265:0.38 266:0.54 267:0.36 268:0.62 271:0.52 276:0.48 277:0.69 282:0.77 285:0.56 290:0.85 297:0.36 300:0.33 +1 1:0.69 6:0.89 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.91 18:0.70 20:0.94 21:0.30 27:0.35 28:0.76 29:0.56 30:0.75 32:0.69 34:0.39 36:0.47 37:0.55 39:0.25 43:0.72 45:0.98 66:0.77 69:0.80 70:0.41 71:0.97 74:0.92 77:0.70 78:0.85 81:0.54 83:0.60 86:0.79 91:0.67 96:0.76 97:0.89 98:0.76 99:0.83 100:0.87 101:0.52 104:0.91 108:0.77 111:0.85 114:0.63 120:0.64 122:0.51 123:0.75 124:0.42 126:0.44 127:0.81 129:0.59 133:0.66 135:0.37 138:0.96 139:0.75 140:0.45 144:0.85 145:0.41 146:0.37 147:0.18 149:0.94 150:0.58 151:0.81 152:0.88 153:0.48 154:0.65 155:0.58 158:0.79 159:0.66 161:0.78 162:0.81 164:0.68 165:0.70 167:0.47 169:0.85 172:0.93 173:0.77 176:0.66 177:0.38 179:0.23 181:0.96 186:0.85 187:0.39 189:0.90 190:0.77 192:0.59 195:0.79 201:0.68 202:0.61 204:0.85 208:0.54 212:0.92 215:0.44 216:0.52 220:0.87 222:0.60 230:0.74 232:0.93 233:0.73 235:0.42 238:0.88 241:0.15 242:0.60 243:0.08 245:0.89 247:0.60 248:0.74 253:0.71 254:0.80 255:0.74 256:0.64 257:0.65 259:0.21 260:0.62 261:0.90 265:0.76 266:0.54 267:0.36 268:0.66 271:0.52 276:0.88 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.70 +1 1:0.74 7:0.72 8:0.77 9:0.76 11:0.91 12:0.51 17:0.85 18:0.76 21:0.71 27:0.27 28:0.76 29:0.56 30:0.74 32:0.79 34:0.75 36:0.47 37:0.40 39:0.25 43:0.59 44:0.93 45:0.93 48:0.97 64:0.77 66:0.93 69:0.73 70:0.63 71:0.97 74:0.84 77:0.70 81:0.41 83:0.60 86:0.83 91:0.64 96:0.72 97:0.89 98:0.65 99:0.83 101:0.52 104:0.86 106:0.81 108:0.56 114:0.55 117:0.86 120:0.59 122:0.51 123:0.54 126:0.54 127:0.19 129:0.05 135:0.86 139:0.87 140:0.45 144:0.85 145:0.93 146:0.82 147:0.85 149:0.85 150:0.65 151:0.59 153:0.48 154:0.73 155:0.67 158:0.79 159:0.38 161:0.78 162:0.86 164:0.54 165:0.70 167:0.45 172:0.82 173:0.66 176:0.73 177:0.70 179:0.20 181:0.96 187:0.68 190:0.77 192:0.87 195:0.84 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.72 215:0.37 216:0.40 220:0.74 222:0.60 230:0.74 232:0.91 233:0.73 235:0.97 241:0.01 242:0.58 243:0.94 247:0.60 248:0.58 253:0.56 254:0.80 255:0.74 256:0.45 259:0.21 260:0.59 265:0.66 266:0.38 267:0.28 268:0.46 271:0.52 276:0.71 279:0.82 281:0.89 282:0.87 283:0.82 285:0.56 290:0.55 297:0.36 300:0.70 +1 1:0.69 6:0.87 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.57 20:0.78 21:0.05 27:0.18 28:0.76 29:0.56 30:0.74 32:0.69 34:0.49 36:0.87 37:0.96 39:0.25 43:0.70 44:0.87 45:0.98 48:0.91 66:0.72 69:0.44 70:0.41 71:0.97 74:0.91 77:0.70 78:0.86 81:0.82 83:0.60 91:0.54 96:0.90 97:0.89 98:0.63 99:0.83 100:0.86 101:0.52 104:0.82 106:0.81 108:0.78 111:0.85 114:0.85 117:0.86 120:0.83 122:0.51 123:0.77 124:0.45 126:0.44 127:0.73 129:0.81 133:0.67 135:0.61 139:0.68 140:0.45 144:0.85 145:0.93 146:0.43 147:0.50 149:0.95 150:0.79 151:0.90 152:0.76 153:0.83 154:0.59 155:0.58 158:0.79 159:0.58 161:0.78 162:0.84 164:0.76 165:0.70 167:0.50 169:0.84 172:0.93 173:0.40 175:0.75 176:0.66 177:0.38 179:0.22 181:0.96 186:0.86 187:0.21 189:0.91 190:0.77 191:0.70 192:0.59 195:0.77 201:0.68 202:0.68 204:0.85 206:0.81 208:0.54 212:0.88 215:0.78 216:0.50 222:0.60 230:0.75 232:0.88 233:0.76 235:0.12 238:0.84 241:0.39 242:0.62 243:0.19 244:0.61 245:0.91 247:0.47 248:0.87 253:0.91 255:0.74 256:0.71 257:0.65 259:0.21 260:0.80 261:0.81 265:0.64 266:0.80 267:0.52 268:0.74 271:0.52 276:0.88 277:0.69 279:0.82 281:0.89 282:0.77 283:0.82 285:0.56 290:0.85 297:0.36 300:0.70 +2 1:0.69 6:0.84 8:0.84 9:0.76 11:0.91 12:0.51 17:0.02 20:0.75 27:0.14 28:0.76 29:0.56 30:0.76 32:0.69 34:0.85 36:0.23 37:0.67 39:0.25 43:0.67 45:0.88 66:0.32 69:0.57 70:0.58 71:0.97 74:0.66 77:0.70 78:0.72 81:0.88 83:0.60 91:0.65 96:0.99 97:0.98 98:0.22 99:0.83 100:0.73 101:0.52 108:0.89 111:0.72 114:0.95 120:0.97 122:0.51 123:0.89 124:0.79 126:0.44 127:0.53 129:0.89 133:0.78 135:0.85 138:0.99 140:0.45 144:0.85 145:0.80 146:0.22 147:0.28 149:0.70 150:0.87 151:0.97 152:0.96 153:0.97 154:0.57 155:0.58 158:0.79 159:0.71 161:0.78 162:0.66 164:0.94 165:0.70 167:0.47 169:0.82 172:0.41 173:0.41 175:0.75 176:0.66 177:0.38 179:0.67 181:0.96 186:0.73 187:0.39 190:0.77 192:0.59 195:0.87 201:0.68 202:0.93 208:0.54 212:0.42 215:0.96 216:0.93 222:0.60 235:0.05 241:0.15 242:0.62 243:0.26 244:0.61 245:0.63 247:0.35 248:0.99 253:0.98 255:0.74 256:0.93 257:0.65 259:0.21 260:0.97 261:0.90 265:0.24 266:0.75 267:0.19 268:0.94 271:0.52 276:0.42 277:0.69 279:0.82 282:0.77 283:0.82 285:0.56 290:0.95 297:0.36 300:0.70 +1 7:0.72 11:0.91 12:0.51 17:0.81 18:0.60 21:0.78 27:0.72 28:0.76 29:0.56 30:0.33 32:0.69 34:0.33 36:0.79 37:0.29 39:0.25 43:0.68 45:0.85 66:0.43 69:0.57 70:0.77 71:0.97 74:0.63 76:0.85 77:0.70 86:0.62 91:0.22 98:0.51 101:0.52 104:0.54 108:0.87 122:0.51 123:0.86 124:0.76 127:0.67 129:0.89 133:0.78 135:0.44 139:0.60 144:0.85 145:0.74 146:0.13 147:0.18 149:0.66 154:0.57 155:0.58 159:0.71 161:0.78 167:0.45 172:0.54 173:0.44 175:0.75 177:0.38 178:0.55 179:0.60 181:0.96 187:0.39 192:0.59 195:0.86 202:0.36 204:0.85 208:0.54 212:0.18 216:0.13 222:0.60 230:0.75 232:0.74 233:0.76 235:0.22 241:0.05 242:0.24 243:0.16 244:0.61 245:0.69 247:0.76 253:0.40 257:0.65 259:0.21 265:0.53 266:0.84 267:0.91 271:0.52 276:0.59 277:0.69 282:0.77 300:0.55 +1 1:0.74 7:0.72 11:0.91 12:0.51 17:0.88 18:0.67 21:0.71 27:0.16 28:0.76 29:0.56 30:0.76 32:0.77 34:0.80 36:0.63 37:0.27 39:0.25 43:0.68 44:0.93 45:0.81 48:0.91 60:0.87 64:0.77 66:0.80 69:0.29 70:0.28 71:0.97 74:0.80 77:0.89 81:0.41 83:0.91 86:0.77 91:0.66 97:0.89 98:0.55 99:0.83 101:0.52 104:0.97 106:0.81 108:0.22 114:0.55 117:0.86 122:0.51 123:0.22 126:0.54 127:0.16 129:0.05 135:0.60 139:0.88 144:0.85 145:0.74 146:0.45 147:0.51 149:0.49 150:0.65 154:0.94 155:0.67 158:0.91 159:0.16 161:0.78 165:0.70 167:0.46 172:0.45 173:0.44 176:0.73 177:0.70 178:0.55 179:0.48 181:0.96 187:0.87 192:0.87 195:0.63 201:0.93 204:0.85 206:0.81 208:0.64 212:0.55 215:0.37 216:0.30 219:0.95 222:0.60 230:0.75 232:0.98 233:0.76 235:0.95 241:0.10 242:0.40 243:0.82 247:0.47 253:0.44 254:0.74 259:0.21 265:0.56 266:0.27 267:0.59 271:0.52 276:0.33 279:0.86 281:0.91 282:0.85 283:0.78 290:0.55 292:0.91 297:0.36 300:0.25 +2 1:0.69 6:0.86 7:0.81 8:0.62 9:0.76 11:0.92 12:0.51 17:0.82 18:0.61 20:0.77 27:0.34 28:0.76 29:0.56 30:0.70 32:0.69 34:0.29 36:0.73 37:0.36 39:0.25 43:0.69 45:0.99 66:0.99 69:0.45 70:0.67 71:0.97 74:0.96 76:0.85 77:0.89 78:0.88 81:0.88 83:0.60 85:0.80 86:0.70 91:0.38 96:0.80 97:0.89 98:0.90 99:0.83 100:0.91 101:0.87 104:0.80 106:0.81 108:0.35 111:0.88 114:0.95 117:0.86 120:0.69 121:0.90 122:0.51 123:0.34 126:0.44 127:0.47 129:0.05 135:0.97 139:0.79 140:0.45 144:0.85 145:0.91 146:0.98 147:0.98 149:0.97 150:0.87 151:0.81 152:0.75 153:0.48 154:0.45 155:0.67 158:0.79 159:0.76 161:0.78 162:0.86 164:0.54 165:0.70 167:0.45 169:0.88 172:0.98 173:0.26 176:0.66 177:0.70 179:0.13 181:0.96 186:0.88 187:0.21 189:0.88 190:0.77 192:0.87 195:0.92 201:0.68 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.96 216:0.84 222:0.60 230:0.87 232:0.86 233:0.76 235:0.05 238:0.87 241:0.01 242:0.53 243:0.99 247:0.76 248:0.73 253:0.86 255:0.74 256:0.45 259:0.21 260:0.68 261:0.80 265:0.90 266:0.63 267:0.23 268:0.46 271:0.52 276:0.95 279:0.82 281:0.91 282:0.77 283:0.82 285:0.56 290:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.82 8:0.61 9:0.76 11:0.91 12:0.51 17:0.32 18:0.96 20:0.79 21:0.40 22:0.93 27:0.25 28:0.76 29:0.56 30:0.12 34:0.52 36:0.40 37:0.34 39:0.25 43:0.67 45:0.81 47:0.93 55:0.84 64:0.77 66:0.13 69:0.55 70:0.94 71:0.97 74:0.64 78:0.92 81:0.57 83:0.60 86:0.93 91:0.25 96:0.80 97:0.89 98:0.39 99:0.83 100:0.91 101:0.52 104:0.99 106:0.81 108:0.42 111:0.90 114:0.62 117:0.86 120:0.69 122:0.86 123:0.88 124:0.85 126:0.54 127:0.45 129:0.81 133:0.81 135:0.79 139:0.93 140:0.45 144:0.85 146:0.72 147:0.77 149:0.52 150:0.73 151:0.81 152:0.78 153:0.48 154:0.83 155:0.67 158:0.87 159:0.79 161:0.78 162:0.86 164:0.54 165:0.70 167:0.45 169:0.88 172:0.59 173:0.32 176:0.73 177:0.70 179:0.33 181:0.96 186:0.92 187:0.95 189:0.83 190:0.77 192:0.87 201:0.93 202:0.36 206:0.81 208:0.64 212:0.18 215:0.42 216:0.40 219:0.95 222:0.60 232:1.00 235:0.60 238:0.86 241:0.05 242:0.74 243:0.43 245:0.83 247:0.76 248:0.73 253:0.76 254:0.80 255:0.74 256:0.45 259:0.21 260:0.68 261:0.86 262:0.93 265:0.40 266:0.89 267:0.52 268:0.46 271:0.52 276:0.77 279:0.86 283:0.71 285:0.56 290:0.62 292:0.91 297:0.36 300:0.84 +2 1:0.69 7:0.72 8:0.67 9:0.76 11:0.91 12:0.51 17:0.67 18:0.61 21:0.30 27:0.50 28:0.76 29:0.56 30:0.60 32:0.69 34:0.69 36:0.88 37:0.60 39:0.25 43:0.72 45:0.97 66:0.17 69:0.94 70:0.75 71:0.97 74:0.87 77:0.70 81:0.54 83:0.60 86:0.66 91:0.14 96:0.69 97:0.94 98:0.57 99:0.83 101:0.52 104:0.84 106:0.81 108:0.92 114:0.63 117:0.86 120:0.55 122:0.75 123:0.13 124:0.76 126:0.44 127:0.64 129:0.59 133:0.74 135:0.85 139:0.64 140:0.45 144:0.85 145:0.70 146:0.68 147:0.92 149:0.90 150:0.58 151:0.51 153:0.48 154:0.20 155:0.58 158:0.79 159:0.84 161:0.78 162:0.62 164:0.54 165:0.70 167:0.45 172:0.90 173:0.88 176:0.66 177:0.38 179:0.19 181:0.96 187:0.39 190:0.77 192:0.59 195:0.94 201:0.68 202:0.50 204:0.85 206:0.81 208:0.54 212:0.71 215:0.44 216:0.86 220:0.87 222:0.60 230:0.75 232:0.90 233:0.76 235:0.42 241:0.01 242:0.45 243:0.38 245:0.95 247:0.79 248:0.50 253:0.53 254:0.97 255:0.74 256:0.45 257:0.65 259:0.21 260:0.55 265:0.50 266:0.80 267:0.69 268:0.46 271:0.52 276:0.91 277:0.69 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +1 1:0.69 8:0.71 9:0.76 11:0.91 12:0.51 17:0.85 18:0.68 20:0.76 21:0.05 27:0.11 28:0.76 29:0.56 30:0.76 34:0.18 36:0.33 37:0.68 39:0.25 43:0.59 45:0.91 66:0.20 69:0.80 70:0.39 71:0.97 74:0.67 78:0.89 81:0.82 83:0.60 86:0.66 91:0.60 96:0.93 97:0.89 98:0.27 99:0.83 100:0.92 101:0.52 108:0.84 111:0.89 114:0.85 120:0.88 122:0.51 123:0.62 124:0.65 126:0.44 127:0.53 129:0.59 133:0.64 135:0.63 139:0.64 140:0.45 144:0.85 146:0.39 147:0.05 149:0.77 150:0.79 151:0.94 152:0.74 153:0.89 154:0.49 155:0.58 158:0.79 159:0.59 161:0.78 162:0.84 164:0.80 165:0.70 167:0.59 169:0.90 172:0.59 173:0.78 175:0.75 176:0.66 177:0.05 179:0.55 181:0.96 186:0.89 187:0.39 190:0.77 191:0.77 192:0.59 201:0.68 202:0.73 208:0.54 212:0.78 215:0.78 216:0.47 222:0.60 235:0.12 241:0.62 242:0.62 243:0.10 244:0.61 245:0.73 247:0.35 248:0.91 253:0.91 255:0.74 256:0.77 257:0.84 259:0.21 260:0.86 261:0.77 265:0.21 266:0.47 267:0.44 268:0.79 271:0.52 276:0.51 277:0.69 279:0.82 283:0.82 285:0.56 290:0.85 297:0.36 300:0.55 +1 1:0.74 7:0.81 11:0.78 12:0.86 17:0.87 27:0.13 28:0.89 30:0.70 32:0.80 34:0.36 36:0.79 37:0.85 39:0.86 43:0.96 66:0.99 69:0.08 70:0.36 74:0.98 77:0.96 81:0.96 83:0.80 85:0.99 91:0.09 98:1.00 101:0.87 104:0.57 108:0.07 114:0.98 121:1.00 122:0.43 123:0.07 126:0.54 127:0.96 129:0.05 131:0.61 135:0.44 144:0.76 145:0.98 146:0.99 147:0.99 149:0.99 150:0.98 154:0.96 155:0.67 157:0.95 159:0.84 161:0.63 163:0.81 165:0.92 166:0.93 167:0.57 172:0.97 173:0.08 176:0.73 177:0.38 178:0.55 179:0.19 181:0.50 182:0.86 187:0.39 192:0.59 195:0.93 201:0.74 204:0.89 208:0.64 212:0.48 215:0.96 216:0.31 222:0.25 227:0.61 229:0.61 230:0.87 231:0.89 232:0.80 233:0.76 235:0.05 241:0.02 242:0.54 243:0.99 246:0.94 247:0.60 253:0.81 259:0.21 265:1.00 266:0.38 267:0.23 271:0.44 276:0.93 282:0.88 287:0.61 290:0.98 297:0.36 299:0.97 300:0.43 +1 1:0.74 6:0.92 7:0.81 8:0.84 9:0.80 11:0.78 12:0.86 17:0.43 20:0.99 27:0.13 28:0.89 30:0.71 32:0.80 34:0.36 36:0.40 37:0.85 39:0.86 41:0.82 43:0.96 55:0.59 66:0.69 69:0.08 70:0.64 74:0.76 77:0.96 78:0.83 81:0.96 83:0.82 85:0.85 91:0.83 96:0.89 98:0.81 100:0.91 101:0.81 104:0.57 108:0.07 111:0.85 114:0.98 120:0.80 121:1.00 123:0.07 124:0.43 126:0.54 127:0.89 129:0.59 131:0.96 133:0.47 135:0.20 140:0.80 144:0.76 145:0.98 146:0.63 147:0.68 149:0.82 150:0.98 151:0.97 152:0.95 153:0.90 154:0.96 155:0.67 157:0.86 159:0.33 161:0.63 162:0.52 164:0.72 165:0.92 166:0.93 167:0.94 169:0.87 172:0.63 173:0.30 176:0.73 177:0.38 178:0.42 179:0.69 181:0.50 182:0.87 186:0.83 189:0.94 191:0.84 192:0.59 195:0.59 201:0.74 202:0.75 204:0.89 208:0.64 212:0.77 215:0.96 216:0.31 222:0.25 227:0.97 229:0.92 230:0.87 231:0.89 232:0.80 233:0.76 235:0.05 238:0.96 241:0.83 242:0.74 243:0.73 245:0.68 246:0.94 247:0.39 248:0.86 253:0.90 255:0.79 256:0.64 259:0.21 260:0.81 261:0.90 265:0.81 266:0.27 267:0.76 268:0.70 271:0.44 276:0.56 282:0.88 283:0.71 285:0.62 287:0.95 290:0.98 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.80 7:0.81 8:0.59 9:0.80 11:0.78 12:0.86 17:0.75 20:0.93 21:0.05 27:0.40 28:0.89 30:0.41 32:0.80 34:0.37 36:0.72 37:0.23 39:0.86 41:0.82 43:0.84 60:0.95 66:0.69 69:0.33 70:0.57 74:0.84 77:0.89 78:0.76 81:0.93 83:0.87 85:0.88 91:0.71 96:0.79 98:0.72 100:0.78 101:0.80 104:0.74 108:0.26 111:0.75 114:0.95 120:0.68 121:0.99 122:0.43 123:0.25 124:0.42 126:0.54 127:0.54 129:0.59 131:0.95 133:0.47 135:0.54 140:0.45 144:0.76 145:0.97 146:0.72 147:0.76 149:0.83 150:0.96 151:0.83 152:0.90 153:0.78 154:0.81 155:0.67 157:0.87 158:0.92 159:0.45 161:0.63 162:0.57 164:0.65 165:0.90 166:0.93 167:0.96 169:0.76 172:0.54 173:0.36 176:0.73 177:0.38 179:0.73 181:0.50 182:0.87 186:0.77 189:0.83 192:0.59 195:0.67 201:0.74 202:0.59 204:0.89 208:0.64 212:0.30 215:0.91 216:0.11 220:0.62 222:0.25 227:0.97 229:0.92 230:0.84 231:0.89 233:0.69 235:0.12 238:0.85 241:0.86 242:0.45 243:0.73 245:0.59 246:0.94 247:0.47 248:0.75 253:0.85 255:0.79 256:0.45 259:0.21 260:0.69 261:0.80 265:0.72 266:0.54 267:0.18 268:0.58 271:0.44 276:0.46 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.95 297:0.36 299:0.97 300:0.43 +2 1:0.74 11:0.78 12:0.86 17:0.79 21:0.05 27:0.28 28:0.89 30:0.62 34:0.86 36:0.42 37:0.67 39:0.86 43:0.85 66:0.83 69:0.61 70:0.40 74:0.66 81:0.93 83:0.79 85:0.88 91:0.58 98:0.77 101:0.83 104:0.77 108:0.47 114:0.95 122:0.43 123:0.45 126:0.54 127:0.26 129:0.05 131:0.61 135:0.21 144:0.76 146:0.53 147:0.59 149:0.81 150:0.96 154:0.82 155:0.67 157:0.88 159:0.19 161:0.63 165:0.90 166:0.93 167:0.72 172:0.51 173:0.82 176:0.73 177:0.38 178:0.55 179:0.66 181:0.50 182:0.86 187:0.21 192:0.59 201:0.74 202:0.36 212:0.78 215:0.91 216:0.06 222:0.25 227:0.61 229:0.61 231:0.89 232:0.84 235:0.12 241:0.54 242:0.45 243:0.84 246:0.94 247:0.47 253:0.74 259:0.21 265:0.77 266:0.27 267:0.19 271:0.44 276:0.34 283:0.71 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.81 11:0.78 12:0.86 17:0.83 21:0.30 27:0.50 28:0.89 30:0.16 32:0.80 34:0.75 36:0.79 37:0.60 39:0.86 43:0.86 60:0.95 66:0.35 69:0.57 70:0.95 74:0.97 77:0.89 81:0.80 83:0.81 85:0.99 91:0.06 98:0.55 101:0.84 104:0.84 106:0.81 108:0.94 114:0.86 117:0.86 121:0.90 122:0.67 123:0.94 124:0.87 126:0.54 127:0.86 129:0.95 131:0.61 133:0.87 135:0.21 144:0.76 145:0.70 146:0.85 147:0.88 149:0.97 150:0.87 154:0.84 155:0.67 157:0.94 158:0.92 159:0.91 161:0.63 165:0.84 166:0.93 167:0.54 172:0.95 173:0.24 176:0.73 177:0.38 178:0.55 179:0.13 181:0.50 182:0.86 187:0.39 192:0.59 195:0.97 201:0.74 204:0.89 206:0.81 208:0.64 212:0.68 215:0.72 216:0.86 222:0.25 227:0.61 229:0.61 230:0.87 231:0.89 232:0.92 233:0.76 235:0.42 242:0.43 243:0.22 245:0.98 246:0.94 247:0.67 253:0.78 259:0.21 265:0.57 266:0.80 267:0.28 271:0.44 276:0.96 279:0.86 282:0.88 283:0.82 287:0.61 290:0.86 297:0.36 299:0.97 300:0.84 +1 1:0.74 11:0.78 12:0.86 17:0.61 21:0.13 27:0.45 28:0.89 30:0.21 32:0.80 34:0.75 36:0.20 37:0.50 39:0.86 43:0.82 55:0.92 60:0.87 66:0.11 69:0.60 70:0.69 74:0.86 76:0.85 77:0.70 81:0.89 83:0.87 85:0.80 91:0.08 98:0.40 101:0.74 108:0.45 114:0.92 122:0.67 123:0.78 124:0.84 126:0.54 127:0.43 129:0.05 131:0.61 133:0.82 135:0.89 144:0.76 145:0.50 146:0.35 147:0.42 149:0.72 150:0.94 154:0.78 155:0.67 157:0.81 158:0.92 159:0.58 161:0.63 163:0.90 165:0.88 166:0.93 167:0.63 172:0.27 173:0.52 176:0.73 177:0.38 178:0.55 179:0.73 181:0.50 182:0.86 187:0.68 192:0.59 195:0.79 201:0.74 208:0.64 212:0.22 215:0.84 216:0.77 222:0.25 227:0.61 229:0.61 231:0.89 235:0.22 241:0.21 242:0.61 243:0.44 245:0.53 246:0.94 247:0.39 253:0.76 259:0.21 265:0.26 266:0.71 267:0.28 271:0.44 276:0.46 277:0.69 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.43 +1 1:0.74 6:0.96 8:0.91 9:0.80 11:0.78 12:0.86 17:0.88 20:0.76 21:0.54 27:0.67 28:0.89 30:0.20 34:0.20 36:0.22 37:0.72 39:0.86 41:0.90 43:0.92 55:0.84 60:0.95 66:0.06 69:0.41 70:0.86 74:0.84 78:0.82 81:0.66 83:0.83 85:0.99 91:0.05 96:0.74 98:0.23 100:0.82 101:0.75 104:0.97 106:0.81 108:0.98 111:0.81 114:0.77 117:0.86 120:0.68 123:0.92 124:1.00 126:0.54 127:0.97 129:1.00 131:0.96 133:1.00 135:0.69 140:0.45 144:0.76 146:0.37 147:0.44 149:0.92 150:0.78 151:0.60 152:0.75 153:0.46 154:0.91 155:0.67 157:0.89 158:0.87 159:0.99 161:0.63 162:0.86 163:0.94 164:0.74 165:0.79 166:0.93 167:0.59 169:0.80 172:0.71 173:0.06 176:0.73 177:0.38 179:0.12 181:0.50 182:0.87 186:0.83 187:0.68 189:0.97 190:0.77 192:0.59 201:0.74 202:0.59 206:0.81 208:0.64 212:0.13 215:0.58 216:0.14 220:0.96 222:0.25 227:0.97 229:0.93 231:0.89 232:0.98 235:0.68 238:1.00 241:0.07 242:0.63 243:0.08 245:0.91 246:0.94 247:0.67 248:0.62 253:0.77 255:0.79 256:0.68 259:0.21 260:0.69 261:0.75 265:0.20 266:1.00 267:0.82 268:0.68 271:0.44 276:0.97 279:0.86 283:0.82 285:0.62 287:0.96 290:0.77 297:0.36 300:0.84 +1 1:0.74 11:0.78 12:0.86 17:0.55 20:0.74 21:0.22 27:0.45 28:0.89 30:0.66 32:0.72 34:0.80 36:0.20 37:0.50 39:0.86 43:0.82 55:0.92 60:0.87 66:0.53 69:0.60 70:0.42 74:0.81 76:0.85 78:0.84 81:0.85 83:0.87 85:0.80 91:0.10 98:0.58 100:0.73 101:0.74 108:0.46 111:0.82 114:0.90 122:0.43 123:0.44 124:0.52 126:0.54 127:0.35 129:0.05 131:0.61 133:0.39 135:0.89 144:0.76 145:0.47 146:0.70 147:0.75 149:0.68 150:0.90 152:0.74 154:0.78 155:0.67 157:0.82 158:0.87 159:0.50 161:0.63 163:0.75 165:0.86 166:0.93 167:0.61 169:0.72 172:0.45 173:0.55 176:0.73 177:0.38 178:0.55 179:0.69 181:0.50 182:0.86 186:0.85 187:0.68 192:0.59 195:0.76 201:0.74 208:0.64 212:0.22 215:0.79 216:0.77 222:0.25 227:0.61 229:0.61 231:0.89 235:0.31 241:0.12 242:0.38 243:0.62 245:0.64 246:0.94 247:0.55 253:0.74 259:0.21 261:0.75 265:0.59 266:0.54 267:0.28 271:0.44 276:0.47 279:0.86 283:0.71 287:0.61 290:0.90 297:0.36 300:0.33 +1 1:0.74 7:0.81 11:0.78 12:0.86 17:0.88 27:0.13 28:0.89 30:0.66 32:0.80 34:0.31 36:0.89 37:0.85 39:0.86 43:0.96 66:0.31 69:0.08 70:0.52 74:0.97 77:0.96 81:0.96 83:0.80 85:0.99 91:0.14 98:0.69 101:0.86 104:0.57 108:0.58 114:0.98 121:1.00 122:0.57 123:0.88 124:0.67 126:0.54 127:0.90 129:0.81 131:0.61 133:0.67 135:0.56 144:0.76 145:0.98 146:0.25 147:0.31 149:0.98 150:0.98 154:0.96 155:0.67 157:0.94 159:0.83 161:0.63 163:0.81 165:0.92 166:0.93 167:0.58 172:0.86 173:0.09 176:0.73 177:0.38 178:0.55 179:0.23 181:0.50 182:0.86 187:0.39 192:0.59 195:0.93 201:0.74 204:0.89 208:0.64 212:0.39 215:0.96 216:0.31 222:0.25 227:0.61 229:0.61 230:0.87 231:0.89 232:0.80 233:0.76 235:0.05 241:0.03 242:0.50 243:0.11 245:0.95 246:0.94 247:0.60 253:0.80 259:0.21 265:0.69 266:0.71 267:0.59 271:0.44 276:0.90 282:0.88 287:0.61 290:0.98 297:0.36 299:0.97 300:0.55 +1 1:0.74 6:0.80 7:0.81 8:0.59 9:0.80 11:0.78 12:0.86 17:0.99 20:0.81 27:0.72 28:0.89 30:0.71 32:0.80 34:0.23 36:0.74 39:0.86 41:0.88 43:0.78 60:0.87 66:0.82 69:0.78 70:0.31 74:0.83 77:0.70 78:0.78 81:0.94 83:0.88 85:0.88 91:0.79 96:0.87 98:0.67 100:0.79 101:0.80 104:0.58 108:0.61 111:0.77 114:0.98 120:0.78 121:0.90 122:0.43 123:0.59 126:0.54 127:0.20 129:0.05 131:0.96 135:0.30 140:0.45 144:0.76 145:0.38 146:0.67 147:0.72 149:0.78 150:0.97 151:0.93 152:0.78 153:0.77 154:0.71 155:0.67 157:0.87 158:0.87 159:0.26 161:0.63 162:0.86 164:0.69 165:0.91 166:0.93 167:0.91 169:0.77 172:0.48 173:0.84 176:0.73 177:0.38 179:0.58 181:0.50 182:0.87 186:0.79 189:0.82 192:0.59 195:0.64 201:0.74 202:0.54 204:0.89 208:0.64 212:0.61 215:0.96 216:0.06 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.81 233:0.76 235:0.22 238:0.84 241:0.78 242:0.33 243:0.83 246:0.94 247:0.55 248:0.85 253:0.89 255:0.79 256:0.58 259:0.21 260:0.79 261:0.78 265:0.68 266:0.38 267:0.18 268:0.63 271:0.44 276:0.35 279:0.86 282:0.88 283:0.71 285:0.62 287:0.96 290:0.98 297:0.99 299:0.88 300:0.25 +1 1:0.74 6:0.92 8:0.65 11:0.78 12:0.86 17:0.93 20:0.81 21:0.64 27:0.13 28:0.89 30:0.38 34:0.71 36:0.68 37:0.85 39:0.86 43:0.92 66:0.45 69:0.44 70:0.65 74:0.86 78:0.77 81:0.67 83:0.73 85:0.94 91:0.02 98:0.72 100:0.79 101:0.83 104:0.57 108:0.76 111:0.76 114:0.72 122:0.43 123:0.75 124:0.67 126:0.54 127:0.75 129:0.81 131:0.61 133:0.67 135:0.60 144:0.76 146:0.62 147:0.67 149:0.91 150:0.77 152:0.95 154:0.91 155:0.67 157:0.91 159:0.60 161:0.63 163:0.81 165:0.70 166:0.92 167:0.60 169:0.86 172:0.63 173:0.38 176:0.73 177:0.38 178:0.55 179:0.53 181:0.50 182:0.85 186:0.78 187:0.39 189:0.85 192:0.59 201:0.74 212:0.40 215:0.53 216:0.31 222:0.25 227:0.61 229:0.61 231:0.88 232:0.80 235:0.88 238:0.88 241:0.10 242:0.24 243:0.40 245:0.79 246:0.94 247:0.67 253:0.69 259:0.21 261:0.90 265:0.72 266:0.54 267:0.36 271:0.44 276:0.68 287:0.61 290:0.72 297:0.36 300:0.55 +1 1:0.74 6:0.98 7:0.81 8:0.92 9:0.80 11:0.78 12:0.86 17:0.61 20:0.93 21:0.54 27:0.05 28:0.89 30:0.68 32:0.80 34:0.74 36:0.36 37:0.96 39:0.86 41:0.93 43:0.96 66:0.17 69:0.23 70:0.65 74:0.98 77:0.89 78:0.88 81:0.66 83:0.81 85:1.00 91:0.56 96:0.93 98:0.39 100:0.96 101:0.85 104:0.58 108:0.64 111:0.92 114:0.77 120:0.87 121:1.00 122:0.43 123:0.62 124:0.97 126:0.54 127:1.00 129:0.99 131:0.96 133:0.97 135:0.86 140:0.45 144:0.76 145:0.98 146:0.49 147:0.55 149:0.99 150:0.78 151:0.98 152:0.99 153:0.92 154:0.96 155:0.67 157:0.94 159:0.96 161:0.63 162:0.65 164:0.83 165:0.79 166:0.93 167:0.72 169:0.97 172:0.90 173:0.06 176:0.73 177:0.38 179:0.13 181:0.50 182:0.87 186:0.88 187:0.21 189:0.97 191:0.87 192:0.59 195:0.99 201:0.74 202:0.80 204:0.89 208:0.64 212:0.39 215:0.58 216:0.58 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.81 233:0.76 235:0.68 238:0.97 241:0.54 242:0.57 243:0.10 245:0.96 246:0.94 247:0.60 248:0.91 253:0.96 255:0.79 256:0.80 259:0.21 260:0.88 261:0.99 265:0.42 266:0.95 267:0.36 268:0.82 271:0.44 276:0.97 282:0.88 285:0.62 287:0.96 290:0.77 297:0.36 299:0.97 300:0.84 +2 1:0.74 6:0.90 7:0.81 8:0.78 9:0.80 11:0.78 12:0.86 17:0.67 20:0.99 21:0.30 27:0.21 28:0.89 30:0.14 34:0.80 36:0.76 37:0.74 39:0.86 41:0.95 43:0.98 60:0.97 66:0.13 69:0.12 70:0.96 74:0.96 76:0.85 78:0.78 81:0.80 83:0.88 85:0.99 91:0.37 96:0.94 98:0.63 100:0.81 101:0.83 104:0.84 106:0.81 108:0.94 111:0.77 114:0.86 117:0.86 120:0.88 121:0.90 122:0.67 123:0.77 124:0.77 126:0.54 127:0.76 129:0.89 131:0.96 133:0.78 135:0.17 140:0.45 144:0.76 146:0.82 147:0.85 149:0.97 150:0.87 151:0.98 152:0.92 153:0.92 154:0.98 155:0.67 157:0.93 158:0.92 159:0.89 161:0.63 162:0.70 164:0.84 165:0.84 166:0.93 167:0.65 169:0.79 172:0.93 173:0.08 176:0.73 177:0.38 179:0.15 181:0.50 182:0.87 186:0.78 187:0.39 189:0.91 191:0.78 192:0.59 201:0.74 202:0.78 204:0.89 206:0.81 208:0.64 212:0.60 215:0.72 216:0.95 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.92 233:0.76 235:0.42 238:0.90 241:0.30 242:0.44 243:0.23 245:0.98 246:0.94 247:0.67 248:0.93 253:0.96 255:0.79 256:0.78 259:0.21 260:0.89 261:0.82 265:0.56 266:0.80 267:0.36 268:0.81 271:0.44 276:0.95 279:0.86 283:0.82 285:0.62 287:0.96 290:0.86 297:0.36 300:0.84 +4 1:0.74 6:0.98 8:0.98 9:0.80 11:0.78 12:0.86 17:0.69 20:0.99 21:0.64 27:0.05 28:0.89 30:0.45 32:0.80 34:0.71 36:0.59 37:0.96 39:0.86 41:0.99 43:0.96 60:0.98 66:0.07 69:0.27 70:0.69 74:0.80 77:0.96 78:0.94 81:0.59 83:0.90 85:0.88 91:0.98 96:0.99 98:0.38 100:0.99 101:0.79 104:0.58 108:0.72 111:0.98 114:0.72 120:0.98 122:0.43 123:0.76 124:0.81 126:0.54 127:0.96 129:0.89 131:0.96 133:0.78 135:0.49 138:0.99 140:0.45 144:0.76 145:0.98 146:0.19 147:0.24 149:0.77 150:0.72 151:1.00 152:0.99 153:0.99 154:0.96 155:0.67 157:0.87 158:0.92 159:0.57 161:0.63 162:0.65 164:0.96 165:0.70 166:0.92 167:0.97 169:0.97 172:0.27 173:0.28 176:0.73 177:0.38 179:0.82 181:0.50 182:0.87 186:0.94 189:0.99 191:0.97 192:0.59 195:0.72 201:0.74 202:0.95 208:0.64 212:0.36 215:0.53 216:0.58 222:0.25 227:0.97 229:0.93 231:0.89 232:0.81 235:0.80 238:0.99 241:0.93 242:0.38 243:0.31 245:0.57 246:0.94 247:0.60 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.98 261:0.99 265:0.19 266:0.71 267:0.98 268:0.96 271:0.44 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 287:0.96 290:0.72 297:0.36 299:0.97 300:0.55 +3 1:0.74 6:0.98 8:0.97 9:0.80 11:0.78 12:0.86 17:0.59 20:0.81 27:0.05 28:0.89 30:0.66 34:0.90 36:0.27 37:0.96 39:0.86 41:0.90 43:0.96 66:0.30 69:0.08 70:0.76 74:0.63 78:0.89 81:0.96 83:0.82 85:0.90 91:0.44 96:0.89 98:0.32 100:0.97 101:0.80 104:0.58 108:0.83 111:0.94 114:0.98 120:0.82 122:0.43 123:0.82 124:0.73 126:0.54 127:0.85 129:0.81 131:0.96 133:0.67 135:0.37 140:0.80 144:0.76 146:0.19 147:0.24 149:0.78 150:0.98 151:0.92 152:0.99 153:0.72 154:0.96 155:0.67 157:0.88 159:0.60 161:0.63 162:0.60 164:0.75 165:0.92 166:0.93 167:0.79 169:0.97 172:0.32 173:0.15 176:0.73 177:0.38 178:0.42 179:0.81 181:0.50 182:0.87 186:0.89 187:0.68 189:0.99 191:0.91 192:0.59 201:0.74 202:0.70 208:0.64 212:0.36 215:0.96 216:0.58 222:0.25 227:0.97 229:0.93 231:0.89 232:0.81 235:0.05 238:0.98 241:0.69 242:0.52 243:0.31 245:0.60 246:0.94 247:0.47 248:0.88 253:0.89 255:0.79 256:0.68 259:0.21 260:0.83 261:0.99 265:0.34 266:0.71 267:0.18 268:0.70 271:0.44 276:0.34 285:0.62 287:0.96 290:0.98 297:0.36 300:0.70 +0 7:0.81 8:0.97 9:0.80 11:0.78 12:0.86 17:0.70 21:0.78 27:0.05 28:0.89 30:0.33 32:0.72 34:0.94 36:0.51 37:0.96 39:0.86 41:0.88 43:0.96 66:0.45 69:0.12 70:0.49 74:0.66 78:0.94 83:0.77 85:0.80 91:0.57 96:0.84 98:0.55 101:0.77 104:0.58 108:0.71 111:0.96 120:0.74 121:0.90 122:0.41 123:0.69 124:0.56 127:0.76 129:0.59 131:0.96 133:0.47 135:0.49 140:0.45 144:0.76 145:1.00 146:0.19 147:0.24 149:0.66 151:0.87 153:0.48 154:0.96 155:0.67 157:0.82 159:0.43 161:0.63 162:0.74 164:0.67 166:0.61 167:0.77 169:0.99 172:0.27 173:0.25 177:0.38 179:0.91 181:0.50 182:0.87 187:0.21 192:0.59 195:0.66 202:0.56 204:0.89 208:0.64 212:0.42 216:0.58 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.81 233:0.76 235:0.22 241:0.67 242:0.45 243:0.40 245:0.53 246:0.61 247:0.35 248:0.81 253:0.81 255:0.79 256:0.58 259:0.21 260:0.74 265:0.56 266:0.38 267:0.28 268:0.59 271:0.44 276:0.25 285:0.62 287:0.96 300:0.33 +0 17:0.53 21:0.78 27:0.72 28:0.83 91:0.93 120:0.75 140:0.99 151:0.73 153:0.80 161:0.99 162:0.45 164:0.65 167:0.67 175:0.75 178:0.55 181:0.98 187:0.39 202:0.98 216:0.09 235:0.05 241:0.64 244:0.61 248:0.68 256:0.58 257:0.65 260:0.76 268:0.58 277:0.69 285:0.62 +0 17:0.92 20:0.80 21:0.78 27:0.62 28:0.83 34:0.99 36:0.37 37:0.27 43:0.32 66:0.36 69:0.70 78:0.72 91:0.10 98:0.08 100:0.73 108:0.53 111:0.72 123:0.51 127:0.06 129:0.05 135:0.21 145:0.44 146:0.05 147:0.05 149:0.13 152:0.77 154:0.23 159:0.05 161:0.99 167:0.53 169:0.72 172:0.05 173:0.75 177:0.05 178:0.55 181:0.98 186:0.73 187:0.21 216:0.60 235:0.05 241:0.24 243:0.51 259:0.21 261:0.73 265:0.08 267:0.59 +1 17:0.24 20:0.76 21:0.22 27:0.17 28:0.83 30:0.45 34:0.86 36:0.96 37:0.58 43:0.51 55:0.59 66:0.27 69:0.15 70:0.25 78:0.84 81:0.72 91:0.04 98:0.12 100:0.73 108:0.12 111:0.81 123:0.12 124:0.72 126:0.54 127:0.85 129:0.81 133:0.67 135:1.00 146:0.24 147:0.30 149:0.28 150:0.53 152:0.76 154:0.40 159:0.84 161:0.99 163:0.79 167:0.52 169:0.85 172:0.10 173:0.10 177:0.05 178:0.55 179:0.98 181:0.98 186:0.84 187:0.68 202:0.36 212:0.61 215:0.79 216:0.90 235:0.31 241:0.18 242:0.82 243:0.46 245:0.38 247:0.12 259:0.21 261:0.80 265:0.13 266:0.17 267:0.52 276:0.16 297:0.36 300:0.18 +0 8:0.79 17:0.26 20:0.78 21:0.78 27:0.40 28:0.83 32:0.80 34:0.40 36:0.18 37:0.90 43:0.33 66:0.36 69:0.67 78:0.72 91:0.14 98:0.07 100:0.73 108:0.51 111:0.72 123:0.49 127:0.06 129:0.05 135:0.56 145:0.88 146:0.05 147:0.05 149:0.13 152:0.76 154:0.24 159:0.05 161:0.99 167:0.52 169:0.72 172:0.05 173:0.64 177:0.05 178:0.55 181:0.98 186:0.73 187:0.68 195:0.80 216:0.65 235:0.05 241:0.18 243:0.51 259:0.21 261:0.73 265:0.07 267:0.28 +0 17:0.57 21:0.78 27:0.25 28:0.83 34:0.70 36:0.29 91:0.78 120:0.73 135:0.63 140:1.00 151:0.70 153:0.71 161:0.99 162:0.45 164:0.63 167:0.67 175:0.75 178:0.55 181:0.98 187:0.68 202:0.99 216:0.14 241:0.63 244:0.61 248:0.66 256:0.58 257:0.65 259:0.21 260:0.74 267:0.94 268:0.57 277:0.69 285:0.62 +0 6:0.78 7:0.81 8:0.59 17:0.24 20:0.81 21:0.30 27:0.24 28:0.83 30:0.95 32:0.80 34:0.95 36:0.70 37:0.90 43:0.42 55:0.92 66:0.20 69:0.50 78:0.85 81:0.69 91:0.48 98:0.10 100:0.85 108:0.39 111:0.83 120:0.63 123:0.37 124:0.61 126:0.54 127:0.23 129:0.59 133:0.47 135:0.79 140:0.45 145:0.42 146:0.05 147:0.05 149:0.21 150:0.50 151:0.54 152:0.78 153:0.48 154:0.32 159:0.18 161:0.99 162:0.61 164:0.76 167:0.62 169:0.83 172:0.05 173:0.72 177:0.05 179:0.99 181:0.98 186:0.85 187:0.21 189:0.81 191:0.77 195:0.57 202:0.70 215:0.72 216:0.74 220:0.87 230:0.65 233:0.63 235:0.42 238:0.89 241:0.56 243:0.40 245:0.36 248:0.57 256:0.68 259:0.21 260:0.64 261:0.83 265:0.11 267:0.44 268:0.70 283:0.60 285:0.62 297:0.36 300:0.08 +0 17:0.85 21:0.78 27:0.64 28:0.83 34:0.68 36:0.42 37:0.35 91:0.14 120:0.86 135:0.49 140:0.45 151:0.74 153:0.83 161:0.99 162:0.86 164:0.76 167:0.54 175:0.75 181:0.98 187:0.68 202:0.62 216:0.12 241:0.30 244:0.61 248:0.80 256:0.68 257:0.65 259:0.21 260:0.87 267:0.69 268:0.71 277:0.69 285:0.62 +0 17:0.24 21:0.78 27:0.72 28:0.83 34:0.82 36:0.37 37:0.33 91:0.92 120:0.72 135:0.34 140:1.00 151:0.69 153:0.69 161:0.99 162:0.45 164:0.62 167:0.54 175:0.75 178:0.55 181:0.98 187:0.68 202:0.98 216:0.19 235:0.68 241:0.30 244:0.61 248:0.65 256:0.58 257:0.65 259:0.21 260:0.73 267:0.87 268:0.55 277:0.69 285:0.62 +2 17:0.62 21:0.30 27:0.72 28:0.83 30:0.73 34:0.56 36:0.62 37:0.57 43:0.46 55:0.92 66:0.69 69:0.41 70:0.22 81:0.69 91:0.18 98:0.77 106:0.81 108:0.32 123:0.31 124:0.42 126:0.54 127:0.38 129:0.59 133:0.47 135:0.74 146:0.89 147:0.91 149:0.55 150:0.50 154:0.35 159:0.57 161:0.99 163:0.94 167:0.49 172:0.54 173:0.32 177:0.05 178:0.55 179:0.68 181:0.98 187:0.39 206:0.81 212:0.72 215:0.72 216:0.32 235:0.31 241:0.05 242:0.82 243:0.73 245:0.59 247:0.12 259:0.21 265:0.77 266:0.38 267:0.36 276:0.50 297:0.36 300:0.18 +0 6:0.79 8:0.61 17:0.88 20:0.76 21:0.22 27:0.63 28:0.83 30:0.95 34:0.78 36:0.37 37:0.54 43:0.48 55:0.99 66:0.20 69:0.35 78:0.93 81:0.72 91:0.37 98:0.07 100:0.94 108:0.27 111:0.92 123:0.26 124:0.61 126:0.54 127:0.24 129:0.59 133:0.47 135:0.74 146:0.05 147:0.05 149:0.19 150:0.53 152:0.76 154:0.36 159:0.49 161:0.99 167:0.56 169:0.91 172:0.05 173:0.25 177:0.05 178:0.55 179:0.99 181:0.98 186:0.92 187:0.21 202:0.36 215:0.79 216:0.13 235:0.31 241:0.39 243:0.40 245:0.36 259:0.21 261:0.83 265:0.07 267:0.97 283:0.60 297:0.36 300:0.08 +2 17:0.51 21:0.64 27:0.45 28:0.83 30:0.76 34:0.89 36:0.32 37:0.50 43:0.32 55:0.96 66:0.20 69:0.70 70:0.22 81:0.55 91:0.03 98:0.25 108:0.65 123:0.63 124:0.73 126:0.54 127:0.22 129:0.81 133:0.67 135:0.69 145:0.52 146:0.05 147:0.05 149:0.30 150:0.42 154:0.24 159:0.39 161:0.99 163:0.96 167:0.52 172:0.10 173:0.66 177:0.05 178:0.55 179:0.87 181:0.98 187:0.68 212:0.42 215:0.53 216:0.77 235:0.80 241:0.18 242:0.82 243:0.26 245:0.40 247:0.12 259:0.21 265:0.27 266:0.23 267:0.76 276:0.24 297:0.36 300:0.18 +1 6:0.79 7:0.81 8:0.58 17:0.97 20:0.83 21:0.22 27:0.58 28:0.83 32:0.80 34:0.36 36:0.33 37:0.23 78:0.99 81:0.72 91:0.85 100:0.99 111:0.99 126:0.54 135:0.39 145:0.91 150:0.53 152:0.79 161:0.99 167:0.85 169:0.99 175:0.75 178:0.55 181:0.98 186:0.99 189:0.81 195:0.73 202:0.36 215:0.79 216:0.01 230:0.87 233:0.76 235:0.31 238:0.97 241:0.83 244:0.61 257:0.65 259:0.21 261:0.94 267:0.23 277:0.69 297:0.36 +0 17:0.15 20:0.78 21:0.22 27:0.17 28:0.83 30:0.45 34:0.52 36:0.99 37:0.58 43:0.51 55:0.59 66:0.27 69:0.15 70:0.25 78:0.86 81:0.72 91:0.04 98:0.13 100:0.87 108:0.12 111:0.85 120:0.54 123:0.12 124:0.72 126:0.54 127:0.88 129:0.81 133:0.67 135:0.99 140:0.45 146:0.24 147:0.30 149:0.28 150:0.53 151:0.47 152:0.76 153:0.48 154:0.40 159:0.83 161:0.99 162:0.52 163:0.79 164:0.57 167:0.57 169:0.83 172:0.10 173:0.10 177:0.05 179:0.98 181:0.98 186:0.87 187:0.68 202:0.58 212:0.61 215:0.79 216:0.90 220:0.82 235:0.31 241:0.41 242:0.82 243:0.46 245:0.38 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 261:0.81 265:0.13 266:0.17 267:0.84 268:0.46 276:0.16 285:0.62 297:0.36 300:0.18 +0 6:0.79 8:0.65 17:0.76 20:0.78 21:0.40 27:0.63 28:0.83 30:0.62 34:0.75 36:0.48 37:0.54 43:0.47 55:0.98 66:0.16 69:0.39 70:0.34 78:0.81 81:0.66 91:0.48 98:0.11 100:0.93 108:0.88 111:0.86 120:0.53 123:0.87 124:0.86 126:0.54 127:0.24 129:0.93 133:0.84 135:0.66 140:0.45 146:0.05 147:0.05 149:0.27 150:0.48 151:0.46 152:0.76 153:0.48 154:0.36 159:0.77 161:0.99 162:0.65 163:0.89 164:0.69 167:0.59 169:0.91 172:0.10 173:0.14 177:0.05 179:0.85 181:0.98 186:0.81 187:0.21 202:0.61 212:0.30 215:0.67 216:0.13 220:0.91 235:0.52 241:0.49 242:0.82 243:0.23 245:0.40 247:0.12 248:0.46 256:0.58 259:0.21 260:0.53 261:0.83 265:0.12 266:0.27 267:0.76 268:0.62 276:0.28 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.78 17:0.88 20:0.87 21:0.13 27:0.44 28:0.83 30:0.45 32:0.80 34:0.24 36:0.21 37:0.28 43:0.52 55:0.59 66:0.82 69:0.07 70:0.42 78:0.80 81:0.74 91:0.61 98:0.83 100:0.79 106:0.81 108:0.06 111:0.79 120:0.57 123:0.06 126:0.54 127:0.32 129:0.05 135:0.66 140:0.45 145:0.84 146:0.73 147:0.77 149:0.50 150:0.55 151:0.50 152:0.86 153:0.77 154:0.41 159:0.30 161:0.99 162:0.86 163:0.81 164:0.72 167:0.53 169:0.79 172:0.48 173:0.21 177:0.05 179:0.76 181:0.98 186:0.81 187:0.39 189:0.81 195:0.59 202:0.57 206:0.81 212:0.30 215:0.84 216:0.91 220:0.74 235:0.12 238:0.82 241:0.24 242:0.82 243:0.83 247:0.12 248:0.51 256:0.64 259:0.21 260:0.58 261:0.85 265:0.83 266:0.47 267:0.44 268:0.66 276:0.40 283:0.60 285:0.62 297:0.36 300:0.33 +0 17:0.38 20:0.76 21:0.47 27:0.48 28:0.83 30:0.66 34:0.23 36:0.25 37:0.39 43:0.41 55:0.92 66:0.13 69:0.53 70:0.33 78:0.72 81:0.63 91:0.25 98:0.28 100:0.94 108:0.87 111:0.87 120:0.53 123:0.86 124:0.95 126:0.54 127:0.79 129:0.99 133:0.95 135:0.88 140:0.45 146:0.50 147:0.56 149:0.54 150:0.46 151:0.45 152:0.75 153:0.48 154:0.31 159:0.88 161:0.99 162:0.86 163:0.92 164:0.57 167:0.52 169:0.92 172:0.32 173:0.25 177:0.05 179:0.61 181:0.98 186:0.73 187:0.95 202:0.36 212:0.23 215:0.63 216:0.51 220:0.93 235:0.60 241:0.18 242:0.82 243:0.21 245:0.57 247:0.12 248:0.45 256:0.45 259:0.21 260:0.53 261:0.78 265:0.30 266:0.75 267:0.65 268:0.46 276:0.63 285:0.62 297:0.36 300:0.33 +1 17:0.08 20:0.76 21:0.30 27:0.52 28:0.83 30:0.91 34:0.47 36:0.58 37:0.34 43:0.32 55:0.92 66:0.49 69:0.70 70:0.13 78:0.87 81:0.69 91:0.33 98:0.58 100:0.92 108:0.53 111:0.86 120:0.73 123:0.51 124:0.48 126:0.54 127:0.28 129:0.59 133:0.47 135:0.20 140:0.45 146:0.48 147:0.54 149:0.29 150:0.50 151:0.66 152:0.75 153:0.48 154:0.23 159:0.25 161:0.99 162:0.86 163:0.86 164:0.73 167:0.64 169:0.87 172:0.16 173:0.83 177:0.05 179:0.95 181:0.98 186:0.73 187:0.39 202:0.59 212:0.61 215:0.72 216:0.81 220:0.62 235:0.31 241:0.59 242:0.82 243:0.60 245:0.39 247:0.12 248:0.66 256:0.64 259:0.21 260:0.74 261:0.76 265:0.59 266:0.17 267:0.79 268:0.68 276:0.21 283:0.60 285:0.62 297:0.36 300:0.13 +1 7:0.81 12:0.51 17:0.38 20:0.77 21:0.40 27:0.72 28:0.96 30:0.58 32:0.80 34:0.85 36:0.81 43:0.52 55:0.52 66:0.69 69:0.12 70:0.44 78:0.74 81:0.78 91:0.79 98:0.74 100:0.73 104:0.58 108:0.10 111:0.73 120:0.62 123:0.10 124:0.41 126:0.54 127:0.59 129:0.59 133:0.47 135:0.32 140:0.45 145:0.93 146:0.56 147:0.62 149:0.52 150:0.58 151:0.56 152:0.75 153:0.78 154:0.41 159:0.30 161:0.55 162:0.86 164:0.84 167:0.93 169:0.72 172:0.41 173:0.34 177:0.05 179:0.87 181:0.57 186:0.75 195:0.61 202:0.73 212:0.55 215:0.67 216:0.17 220:0.74 230:0.65 233:0.63 235:0.42 241:0.87 242:0.82 243:0.73 245:0.46 247:0.12 248:0.56 256:0.81 259:0.21 260:0.63 261:0.74 265:0.74 266:0.27 267:0.19 268:0.80 271:0.82 276:0.35 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.98 7:0.81 8:0.97 12:0.51 17:0.03 20:0.92 21:0.68 25:0.88 27:0.13 28:0.96 30:0.21 34:0.44 36:0.33 37:0.46 43:0.48 55:0.59 66:0.30 69:0.41 70:0.64 78:0.83 81:0.63 91:1.00 98:0.19 100:0.84 104:0.58 108:0.74 111:0.90 120:0.99 123:0.72 124:0.78 126:0.54 127:0.97 129:0.89 132:0.97 133:0.78 135:0.42 140:0.45 145:0.77 146:0.27 147:0.33 149:0.45 150:0.46 151:0.91 152:0.86 153:1.00 154:0.36 159:0.82 161:0.55 162:0.71 164:1.00 167:0.94 169:0.93 172:0.32 173:0.22 177:0.05 179:0.82 181:0.57 186:0.77 189:0.99 191:0.98 202:0.99 212:0.49 215:0.49 216:0.93 230:0.87 233:0.76 235:0.80 238:0.99 241:0.95 242:0.82 243:0.23 245:0.56 247:0.12 248:0.99 256:1.00 260:0.99 261:0.82 265:0.21 266:0.54 267:0.97 268:1.00 271:0.82 276:0.40 283:0.60 285:0.62 297:0.36 300:0.43 +1 6:0.87 7:0.81 8:0.74 12:0.51 17:0.12 20:0.97 21:0.40 27:0.21 28:0.96 30:0.30 34:0.34 36:0.76 37:0.74 43:0.52 55:0.84 66:0.10 69:0.12 70:0.85 78:0.75 81:0.78 91:0.74 98:0.42 100:0.78 104:0.84 106:0.81 108:0.81 111:0.74 120:0.90 123:0.80 124:0.95 126:0.54 127:0.83 129:0.99 133:0.95 135:0.17 140:0.45 146:0.75 147:0.79 149:0.82 150:0.58 151:0.80 152:0.98 153:0.93 154:0.41 159:0.94 161:0.55 162:0.59 164:0.93 167:0.71 169:0.76 172:0.71 173:0.06 177:0.05 179:0.17 181:0.57 186:0.75 187:0.39 189:0.88 191:0.80 202:0.91 206:0.81 212:0.29 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.92 241:0.58 242:0.82 243:0.22 245:0.92 247:0.12 248:0.86 256:0.91 259:0.21 260:0.91 261:0.79 265:0.44 266:0.96 267:0.28 268:0.92 271:0.82 276:0.93 283:0.82 285:0.62 297:0.36 300:0.84 +1 7:0.81 12:0.51 17:0.36 21:0.77 25:0.88 27:0.37 28:0.96 30:0.21 32:0.80 34:0.66 36:0.46 37:0.31 43:0.41 55:0.59 66:0.49 69:0.55 70:0.67 81:0.52 91:0.64 98:0.21 104:0.58 108:0.92 120:0.72 123:0.92 124:0.64 126:0.54 127:0.81 129:0.81 133:0.67 135:0.54 140:0.80 145:0.67 146:0.05 147:0.05 149:0.33 150:0.40 151:0.58 153:0.86 154:0.31 159:0.80 161:0.55 162:0.72 164:0.89 167:0.76 172:0.32 173:0.36 177:0.05 178:0.42 179:0.89 181:0.57 187:0.68 195:0.90 202:0.83 212:0.55 215:0.44 216:0.86 220:1.00 230:0.65 233:0.63 235:0.97 241:0.69 242:0.82 243:0.18 245:0.48 247:0.12 248:0.65 256:0.86 259:0.21 260:0.73 265:0.23 266:0.38 267:0.65 268:0.87 271:0.82 276:0.21 285:0.62 297:0.36 300:0.43 +1 12:0.51 17:0.57 21:0.22 27:0.45 28:0.96 30:0.62 32:0.80 34:0.70 36:0.31 37:0.50 43:0.32 55:0.52 66:0.75 69:0.68 70:0.34 81:0.83 91:0.14 98:0.51 108:0.52 123:0.50 126:0.54 127:0.15 129:0.05 135:0.69 145:0.41 146:0.62 147:0.67 149:0.37 150:0.62 154:0.24 159:0.23 161:0.55 163:0.75 167:0.58 172:0.32 173:0.67 177:0.05 178:0.55 179:0.60 181:0.57 187:0.68 195:0.73 212:0.30 215:0.79 216:0.77 235:0.22 241:0.15 242:0.82 243:0.77 247:0.12 259:0.21 265:0.52 266:0.27 267:0.23 271:0.82 276:0.27 283:0.60 297:0.36 300:0.25 +1 8:0.94 12:0.51 17:0.88 21:0.71 27:0.59 28:0.96 30:0.62 32:0.80 34:0.81 36:0.66 37:0.48 43:0.34 55:0.84 66:0.75 69:0.66 70:0.34 81:0.60 91:0.78 98:0.74 104:0.58 108:0.50 120:0.64 123:0.48 126:0.54 127:0.24 129:0.05 135:0.47 140:0.45 145:0.97 146:0.55 147:0.61 149:0.39 150:0.45 151:0.56 153:0.78 154:0.25 159:0.20 161:0.55 162:0.79 163:0.75 164:0.89 167:0.94 172:0.32 173:0.84 177:0.05 179:0.84 181:0.57 191:0.75 195:0.56 202:0.81 212:0.30 215:0.48 216:0.23 220:0.82 235:0.84 241:0.93 242:0.82 243:0.77 247:0.12 248:0.58 256:0.85 259:0.21 260:0.65 265:0.74 266:0.27 267:0.23 268:0.86 271:0.82 276:0.29 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.87 7:0.81 8:0.79 12:0.51 17:0.40 20:0.98 21:0.40 27:0.21 28:0.96 30:0.36 34:0.89 36:0.85 37:0.74 43:0.52 55:0.71 66:0.11 69:0.12 70:0.78 78:0.74 81:0.78 91:0.76 98:0.42 100:0.78 104:0.84 106:0.81 108:0.96 111:0.74 120:0.93 123:0.96 124:0.96 126:0.54 127:0.76 129:0.99 133:0.96 135:0.21 140:0.45 146:0.30 147:0.37 149:0.81 150:0.58 151:0.81 152:0.98 153:0.94 154:0.41 159:0.94 161:0.55 162:0.69 164:0.95 167:0.71 169:0.76 172:0.73 173:0.06 177:0.05 179:0.17 181:0.57 186:0.75 187:0.39 189:0.89 191:0.85 202:0.91 206:0.81 212:0.27 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.92 241:0.57 242:0.82 243:0.08 245:0.92 247:0.12 248:0.90 256:0.93 259:0.21 260:0.93 261:0.79 265:0.44 266:0.95 267:0.69 268:0.93 271:0.82 276:0.93 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.87 8:0.81 12:0.51 17:0.34 20:0.73 21:0.78 27:0.21 28:0.96 30:0.33 34:0.56 36:0.86 37:0.74 43:0.46 55:0.84 66:0.36 69:0.41 70:0.91 78:0.74 91:0.72 98:0.36 100:0.86 104:0.84 108:0.96 111:0.73 120:0.92 123:0.96 124:0.94 127:0.55 129:0.99 133:0.95 135:0.21 140:0.80 146:0.19 147:0.24 149:0.51 151:0.82 152:0.98 153:0.95 154:0.35 159:0.92 161:0.55 162:0.49 163:0.86 164:0.88 167:0.58 169:0.76 172:0.61 173:0.11 177:0.05 178:0.42 179:0.47 181:0.57 186:0.85 187:0.39 191:0.70 202:0.91 212:0.14 216:0.95 235:0.31 238:0.88 241:0.15 242:0.82 243:0.13 245:0.61 247:0.12 248:0.89 256:0.85 259:0.21 260:0.92 261:0.79 265:0.39 266:0.92 267:0.73 268:0.85 271:0.82 276:0.69 283:0.82 285:0.62 300:0.84 +1 6:0.89 7:0.81 8:0.91 12:0.51 17:0.24 20:0.91 21:0.74 25:0.88 27:0.37 28:0.96 30:0.45 32:0.80 34:0.50 36:0.63 37:0.31 43:0.46 55:0.84 66:0.36 69:0.48 70:0.50 78:0.75 81:0.57 91:1.00 98:0.19 100:0.79 104:0.58 108:0.93 111:0.76 120:0.97 123:0.92 124:0.69 126:0.54 127:0.96 129:0.81 133:0.67 135:0.44 140:0.45 145:0.61 146:0.31 147:0.38 149:0.29 150:0.43 151:0.85 152:0.87 153:0.98 154:0.35 159:0.81 161:0.55 162:0.74 163:0.90 164:0.99 167:0.93 169:0.78 172:0.21 173:0.28 177:0.05 179:0.94 181:0.57 186:0.76 189:0.90 191:0.95 195:0.90 202:0.98 212:0.23 215:0.46 216:0.86 220:0.62 230:0.65 233:0.63 235:0.88 238:0.96 241:0.90 242:0.82 243:0.34 245:0.45 247:0.12 248:0.96 256:0.99 259:0.21 260:0.97 261:0.78 265:0.20 266:0.38 267:0.89 268:0.99 271:0.82 276:0.22 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.92 8:0.89 12:0.51 17:0.12 20:0.87 21:0.78 25:0.88 27:0.47 28:0.96 34:0.62 36:0.24 37:0.90 78:0.91 91:0.97 100:0.99 104:0.58 111:0.98 120:0.97 135:0.87 140:0.45 151:0.84 152:0.82 153:0.97 161:0.55 162:0.69 164:0.99 167:0.93 169:1.00 175:0.75 181:0.57 186:0.91 189:0.93 191:0.89 202:0.98 216:0.68 220:0.74 238:0.98 241:0.90 244:0.61 248:0.96 256:0.99 257:0.65 259:0.21 260:0.97 261:0.95 267:0.91 268:0.99 271:0.82 277:0.69 283:0.60 285:0.62 +1 6:0.83 7:0.81 8:0.91 12:0.51 17:0.16 20:0.78 21:0.68 25:0.88 27:0.41 28:0.96 30:0.66 34:0.89 36:0.84 37:0.84 43:0.32 55:0.71 66:0.47 69:0.71 70:0.40 78:0.75 81:0.63 91:0.92 98:0.61 100:0.79 104:0.81 108:0.73 111:0.74 120:0.96 123:0.71 124:0.56 126:0.54 127:0.43 129:0.59 133:0.47 135:0.65 140:0.45 145:0.77 146:0.55 147:0.61 149:0.51 150:0.46 151:0.83 152:0.80 153:0.97 154:0.23 159:0.40 161:0.55 162:0.79 164:0.98 167:0.68 169:0.76 172:0.41 173:0.77 177:0.05 179:0.73 181:0.57 186:0.75 187:0.68 189:0.89 191:0.91 202:0.96 212:0.49 215:0.49 216:0.68 220:0.62 230:0.65 233:0.63 235:0.80 238:0.92 241:0.50 242:0.82 243:0.44 245:0.66 247:0.12 248:0.94 256:0.97 259:0.21 260:0.96 261:0.76 265:0.62 266:0.63 267:0.91 268:0.98 271:0.82 276:0.46 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.51 17:0.45 21:0.77 25:0.88 27:0.07 28:0.96 30:0.66 34:0.87 36:0.67 37:0.63 43:0.46 55:0.84 66:0.30 69:0.50 70:0.53 81:0.52 91:0.97 98:0.31 104:0.58 108:0.76 120:0.97 123:0.75 124:0.73 126:0.54 127:0.42 129:0.81 133:0.67 135:0.71 140:0.45 145:0.72 146:0.50 147:0.56 149:0.48 150:0.40 151:0.84 153:0.97 154:0.35 159:0.79 161:0.55 162:0.65 163:0.75 164:0.99 167:0.75 172:0.32 173:0.26 177:0.05 179:0.73 181:0.57 187:0.68 202:0.98 212:0.13 215:0.44 216:0.95 220:0.62 235:0.97 241:0.67 242:0.82 243:0.37 245:0.60 247:0.12 248:0.96 256:0.99 259:0.21 260:0.98 265:0.34 266:0.80 267:0.87 268:0.99 271:0.82 276:0.39 283:0.82 285:0.62 297:0.36 300:0.43 +0 6:0.89 7:0.81 8:0.96 12:0.51 17:0.16 20:0.75 21:0.74 25:0.88 27:0.25 28:0.96 30:0.37 37:0.30 43:0.43 55:0.71 66:0.19 69:0.52 70:0.70 78:0.97 81:0.57 91:0.98 98:0.26 100:0.82 104:0.58 108:0.73 111:1.00 120:0.99 123:0.92 124:0.68 126:0.54 127:0.94 129:0.81 133:0.67 140:0.45 145:0.89 146:0.26 147:0.32 149:0.53 150:0.43 151:0.91 152:0.74 153:1.00 154:0.33 159:0.82 161:0.55 162:0.66 164:1.00 167:0.93 169:0.99 172:0.48 173:0.31 177:0.05 179:0.71 181:0.57 186:0.78 189:0.91 191:0.96 202:0.99 212:0.71 215:0.46 216:0.92 220:0.74 230:0.87 233:0.76 235:0.88 238:1.00 241:0.87 242:0.82 243:0.19 245:0.68 247:0.12 248:0.99 256:1.00 260:0.99 261:0.74 265:0.25 266:0.63 268:0.99 271:0.82 276:0.41 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.51 17:0.59 21:0.74 25:0.88 27:0.07 28:0.96 30:0.71 34:0.96 36:0.40 37:0.63 43:0.46 55:0.71 66:0.24 69:0.47 70:0.40 81:0.57 91:0.81 98:0.25 104:0.58 108:0.90 120:0.81 123:0.89 124:0.85 126:0.54 127:0.91 129:0.93 133:0.84 135:0.87 140:0.80 145:0.77 146:0.26 147:0.32 149:0.37 150:0.43 151:0.75 153:0.84 154:0.35 159:0.73 161:0.55 162:0.60 163:0.81 164:0.91 167:0.75 172:0.21 173:0.34 177:0.05 178:0.42 179:0.88 181:0.57 187:0.68 202:0.88 212:0.15 215:0.46 216:0.95 220:0.74 235:0.88 241:0.66 242:0.82 243:0.28 245:0.48 247:0.12 248:0.73 256:0.89 259:0.21 260:0.82 265:0.27 266:0.63 267:0.44 268:0.89 271:0.82 276:0.37 283:0.82 285:0.62 297:0.36 300:0.33 +1 7:0.81 12:0.51 17:0.43 21:0.78 25:0.88 27:0.47 28:0.96 30:0.17 32:0.80 34:0.68 36:0.88 37:0.90 43:0.43 55:0.45 66:0.23 69:0.46 70:0.70 91:0.72 98:0.72 104:0.58 108:0.91 120:0.76 123:0.77 124:0.74 127:0.89 129:0.81 133:0.67 135:0.92 140:0.80 145:0.93 146:0.80 147:0.83 149:0.84 151:0.57 153:0.83 154:0.33 159:0.82 161:0.55 162:0.57 164:0.92 167:0.75 172:0.81 173:0.25 177:0.05 178:0.42 179:0.24 181:0.57 187:0.21 195:0.92 202:0.90 212:0.58 216:0.68 220:0.82 230:0.65 233:0.63 235:0.12 241:0.66 242:0.82 243:0.34 245:0.96 247:0.12 248:0.68 256:0.89 259:0.21 260:0.76 265:0.51 266:0.84 267:0.73 268:0.90 271:0.82 276:0.89 283:0.60 285:0.62 300:0.55 +1 12:0.51 17:0.57 21:0.22 25:0.88 27:0.37 28:0.96 30:0.62 34:0.45 36:0.54 37:0.31 43:0.41 55:0.71 66:0.61 69:0.52 70:0.34 81:0.83 91:0.22 98:0.22 104:0.58 108:0.89 120:0.54 123:0.89 124:0.43 126:0.54 127:0.75 129:0.59 133:0.47 135:0.88 140:0.80 146:0.05 147:0.05 149:0.25 150:0.62 151:0.47 153:0.48 154:0.31 159:0.72 161:0.55 162:0.53 164:0.57 167:0.67 172:0.27 173:0.38 177:0.05 178:0.42 179:0.95 181:0.57 187:0.68 202:0.57 212:0.61 215:0.79 216:0.86 220:0.82 235:0.22 241:0.49 242:0.82 243:0.23 245:0.42 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 265:0.24 266:0.23 267:0.79 268:0.46 271:0.82 276:0.14 285:0.62 297:0.36 300:0.25 +1 6:0.97 7:0.81 8:0.97 12:0.51 17:0.24 20:0.75 21:0.76 25:0.88 27:0.18 28:0.96 30:0.21 32:0.80 37:0.37 43:0.47 55:0.71 66:0.20 69:0.45 70:0.91 78:0.78 81:0.54 91:1.00 98:0.30 100:0.86 104:0.58 108:0.75 111:0.81 120:0.99 123:0.73 124:0.85 126:0.54 127:0.95 129:0.93 133:0.84 140:0.45 145:0.76 146:0.27 147:0.33 149:0.49 150:0.41 151:0.91 152:0.74 153:1.00 154:0.36 159:0.76 161:0.55 162:0.69 164:1.00 167:0.94 169:0.86 172:0.32 173:0.30 177:0.05 179:0.73 181:0.57 186:0.77 189:0.98 191:0.96 195:0.86 202:0.99 212:0.23 215:0.46 216:0.94 230:0.65 233:0.63 235:0.95 238:1.00 241:0.91 242:0.82 243:0.19 245:0.60 247:0.12 248:0.99 256:1.00 260:0.99 261:0.74 265:0.32 266:0.71 268:1.00 271:0.82 276:0.50 283:0.82 285:0.62 297:0.36 300:0.70 +2 12:0.51 17:0.24 21:0.74 25:0.88 27:0.07 28:0.96 30:0.53 34:0.90 36:0.67 37:0.63 43:0.46 55:0.71 66:0.34 69:0.49 70:0.63 81:0.57 91:0.95 98:0.37 104:0.58 108:0.71 120:0.96 123:0.69 124:0.89 126:0.54 127:0.89 129:0.96 133:0.90 135:0.82 140:0.80 145:0.59 146:0.54 147:0.60 149:0.65 150:0.43 151:0.84 153:0.97 154:0.35 159:0.86 161:0.55 162:0.63 164:0.98 167:0.79 172:0.57 173:0.24 177:0.05 178:0.42 179:0.56 181:0.57 187:0.68 202:0.97 212:0.19 215:0.46 216:0.95 220:0.62 235:0.88 241:0.72 242:0.82 243:0.28 245:0.67 247:0.12 248:0.95 256:1.00 259:0.21 260:0.96 265:0.39 266:0.92 267:0.94 268:0.98 271:0.82 276:0.65 283:0.60 285:0.62 297:0.36 300:0.55 +4 6:0.99 7:0.81 8:0.98 12:0.51 17:0.05 20:0.98 21:0.68 25:0.88 27:0.07 28:0.96 30:0.38 34:0.53 36:0.35 37:0.63 43:0.46 55:0.71 66:0.13 69:0.48 70:0.83 78:0.90 81:0.63 91:1.00 98:0.44 100:1.00 104:0.58 108:0.78 111:0.97 120:1.00 123:0.89 124:0.82 126:0.54 127:0.94 129:0.93 133:0.84 135:0.56 140:0.45 145:0.89 146:0.35 147:0.42 149:0.64 150:0.46 151:0.91 152:0.99 153:1.00 154:0.35 159:0.77 161:0.55 162:0.64 164:1.00 167:0.94 169:0.96 172:0.61 173:0.32 177:0.05 179:0.55 181:0.57 186:0.96 189:0.99 191:0.99 202:1.00 212:0.48 215:0.49 216:0.95 220:0.62 230:0.87 233:0.76 235:0.80 238:1.00 241:0.94 242:0.82 243:0.19 245:0.72 247:0.12 248:1.00 256:1.00 259:0.21 260:1.00 261:1.00 265:0.40 266:0.89 267:0.82 268:1.00 271:0.82 276:0.66 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.83 8:0.93 12:0.51 17:0.28 20:0.76 21:0.22 25:0.88 27:0.41 28:0.96 30:0.36 34:0.79 36:0.62 37:0.84 43:0.34 55:0.71 66:0.91 69:0.65 70:0.44 78:0.75 81:0.83 91:0.84 98:0.95 100:0.77 104:0.81 108:0.50 111:0.74 120:0.96 123:0.48 126:0.54 127:0.64 129:0.05 135:0.65 140:0.45 146:0.92 147:0.93 149:0.67 150:0.62 151:0.81 152:0.80 153:0.94 154:0.25 159:0.53 161:0.55 162:0.77 164:0.97 167:0.91 169:0.76 172:0.76 173:0.66 177:0.05 179:0.55 181:0.57 186:0.75 191:0.91 202:0.94 212:0.48 215:0.79 216:0.68 220:0.62 235:0.22 238:0.84 241:0.82 242:0.82 243:0.92 247:0.12 248:0.94 256:0.96 259:0.21 260:0.96 261:0.76 265:0.95 266:0.47 267:0.65 268:0.96 271:0.82 276:0.65 283:0.60 285:0.62 297:0.36 300:0.33 +1 12:0.51 17:0.76 21:0.40 25:0.88 27:0.37 28:0.96 30:0.62 34:0.52 36:0.48 37:0.31 43:0.41 55:0.71 66:0.61 69:0.52 70:0.34 81:0.78 91:0.09 98:0.24 104:0.58 108:0.87 123:0.86 124:0.43 126:0.54 127:0.77 129:0.59 133:0.47 135:0.81 146:0.05 147:0.05 149:0.26 150:0.58 154:0.31 159:0.65 161:0.55 167:0.56 172:0.27 173:0.43 177:0.05 178:0.55 179:0.95 181:0.57 187:0.68 212:0.61 215:0.67 216:0.86 235:0.42 241:0.07 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.26 266:0.23 267:0.69 271:0.82 276:0.15 297:0.36 300:0.25 +1 12:0.51 17:0.72 21:0.54 27:0.45 28:0.96 30:0.66 32:0.80 34:0.90 36:0.18 37:0.50 43:0.32 55:0.59 66:0.91 69:0.69 70:0.41 81:0.72 91:0.15 98:0.78 108:0.53 123:0.51 126:0.54 127:0.27 129:0.05 135:0.87 145:0.50 146:0.90 147:0.92 149:0.66 150:0.54 154:0.24 159:0.51 161:0.55 167:0.59 172:0.76 173:0.61 177:0.05 178:0.55 179:0.37 181:0.57 187:0.68 195:0.82 212:0.52 215:0.58 216:0.77 235:0.60 241:0.18 242:0.82 243:0.92 247:0.12 259:0.21 265:0.78 266:0.54 267:0.79 271:0.82 276:0.65 297:0.36 300:0.43 +1 6:0.84 7:0.81 8:0.65 12:0.51 17:0.69 20:0.79 21:0.74 27:0.55 28:0.96 34:0.50 36:0.79 37:0.86 43:0.26 66:0.36 69:0.82 78:0.74 81:0.57 91:0.81 98:0.09 100:0.77 104:0.76 106:0.81 108:0.67 111:0.73 120:0.71 123:0.65 126:0.54 127:0.07 129:0.05 135:0.68 140:0.45 145:0.91 146:0.05 147:0.05 149:0.11 150:0.43 151:0.66 152:0.77 153:0.48 154:0.18 159:0.05 161:0.55 162:0.86 164:0.76 167:0.77 169:0.75 172:0.05 173:0.96 177:0.05 181:0.57 186:0.75 187:0.21 189:0.86 202:0.62 206:0.81 215:0.46 216:0.41 220:0.99 230:0.87 233:0.76 235:0.88 238:0.90 241:0.70 243:0.51 248:0.64 256:0.68 259:0.21 260:0.72 261:0.74 265:0.10 267:0.18 268:0.70 271:0.82 283:0.82 285:0.62 297:0.36 +1 1:0.74 11:0.57 12:0.79 17:0.55 21:0.13 27:0.45 28:0.80 30:0.86 34:0.75 36:0.27 37:0.50 39:0.79 43:0.82 46:0.96 66:0.33 69:0.68 70:0.32 74:0.71 81:0.75 83:0.77 85:0.91 91:0.11 98:0.34 101:0.80 107:0.94 108:0.51 110:0.92 114:0.92 122:0.43 123:0.49 124:0.61 125:0.88 126:0.54 127:0.18 129:0.59 133:0.47 135:0.90 145:0.50 146:0.52 147:0.58 149:0.84 150:0.85 154:0.78 155:0.67 159:0.34 160:0.88 161:0.66 163:0.81 165:0.88 167:0.65 172:0.32 173:0.59 176:0.73 177:0.38 178:0.55 179:0.43 181:0.52 187:0.68 192:0.59 201:0.74 212:0.39 215:0.84 216:0.77 222:0.76 235:0.22 241:0.18 242:0.63 243:0.50 245:0.64 247:0.30 253:0.73 259:0.21 265:0.36 266:0.54 267:0.44 271:0.95 276:0.41 289:0.92 290:0.92 297:0.36 300:0.33 +2 8:0.90 9:0.80 11:0.57 12:0.79 17:0.02 21:0.76 27:0.05 28:0.80 30:0.85 32:0.80 34:0.93 36:0.80 37:0.98 39:0.79 41:0.96 43:0.91 46:0.99 66:0.60 69:0.48 70:0.57 74:0.87 77:0.70 78:0.72 81:0.25 83:0.80 85:0.95 91:0.78 96:0.91 98:0.61 101:0.86 104:0.72 107:0.95 108:0.70 110:0.94 111:0.77 120:0.84 122:0.43 123:0.68 124:0.50 125:0.97 126:0.32 127:0.67 129:0.59 133:0.47 135:0.34 140:0.87 145:0.76 146:0.52 147:0.58 149:0.93 150:0.25 151:0.95 153:0.83 154:0.90 155:0.67 159:0.42 160:0.99 161:0.66 162:0.60 164:0.85 167:0.89 169:0.90 172:0.63 173:0.55 177:0.38 178:0.48 179:0.60 181:0.52 187:0.39 192:0.59 195:0.63 202:0.81 208:0.64 212:0.58 215:0.46 216:0.70 222:0.76 232:0.81 235:0.95 241:0.75 242:0.63 243:0.36 245:0.78 247:0.30 248:0.91 253:0.93 255:0.79 256:0.81 259:0.21 260:0.85 265:0.62 266:0.54 267:0.87 268:0.82 271:0.95 276:0.58 282:0.88 285:0.62 289:0.92 297:0.36 299:0.88 300:0.84 +3 6:0.98 8:0.95 9:0.80 11:0.57 12:0.79 17:0.01 20:0.81 21:0.76 27:0.05 28:0.80 30:0.86 32:0.80 34:0.77 36:0.77 37:0.98 39:0.79 41:0.94 43:0.87 46:1.00 66:0.86 69:0.57 70:0.46 74:0.84 77:0.70 78:0.77 81:0.25 83:0.80 85:0.93 91:0.68 96:0.90 98:0.76 100:0.83 101:0.86 104:0.72 107:0.95 108:0.44 110:0.93 111:0.78 120:0.84 122:0.43 123:0.42 125:0.99 126:0.32 127:0.25 129:0.05 135:0.68 140:0.45 145:0.76 146:0.52 147:0.58 149:0.91 150:0.25 151:0.95 152:0.93 153:0.84 154:0.86 155:0.67 159:0.18 160:0.99 161:0.66 162:0.82 164:0.81 167:0.88 169:0.94 172:0.59 173:0.79 177:0.38 179:0.56 181:0.52 186:0.78 187:0.68 189:0.98 192:0.59 195:0.55 202:0.70 208:0.64 212:0.84 215:0.46 216:0.70 222:0.76 232:0.81 235:0.95 238:0.96 241:0.74 242:0.63 243:0.86 247:0.30 248:0.90 253:0.92 255:0.79 256:0.75 259:0.21 260:0.84 261:0.94 265:0.76 266:0.27 267:0.85 268:0.76 271:0.95 276:0.41 282:0.88 285:0.62 289:0.92 297:0.36 299:0.88 300:0.55 +2 1:0.74 9:0.80 11:0.57 12:0.79 17:0.16 21:0.13 27:0.50 28:0.80 30:0.76 34:0.94 36:0.94 37:0.69 39:0.79 41:0.97 43:0.90 46:1.00 60:0.97 66:0.71 69:0.44 70:0.46 74:0.93 81:0.75 83:0.88 85:0.97 91:0.60 96:0.94 98:0.81 101:0.87 104:0.72 107:0.96 108:0.61 110:0.94 114:0.92 120:0.90 122:0.43 123:0.59 124:0.44 125:0.99 126:0.54 127:0.58 129:0.59 133:0.47 135:0.34 140:0.45 146:0.32 147:0.38 149:0.97 150:0.85 151:0.97 153:0.89 154:0.89 155:0.67 158:0.87 159:0.47 160:0.99 161:0.66 162:0.73 164:0.87 165:0.88 167:0.86 172:0.79 173:0.46 176:0.73 177:0.38 179:0.41 181:0.52 187:0.39 192:0.59 201:0.74 202:0.81 208:0.64 212:0.69 215:0.84 216:0.52 222:0.76 232:0.81 235:0.22 241:0.73 242:0.63 243:0.26 245:0.84 247:0.39 248:0.94 253:0.96 255:0.79 256:0.83 259:0.21 260:0.90 265:0.81 266:0.63 267:0.84 268:0.84 271:0.95 276:0.72 279:0.86 283:0.71 285:0.62 289:0.92 290:0.92 297:0.36 300:0.70 +1 1:0.74 6:0.98 8:0.88 9:0.80 11:0.57 12:0.79 17:0.08 20:0.82 21:0.22 27:0.02 28:0.80 30:0.85 34:0.63 36:0.74 37:0.96 39:0.79 41:1.00 43:0.89 46:0.99 66:0.72 69:0.49 70:0.54 74:0.78 78:0.76 81:0.70 83:0.90 85:0.94 91:0.97 96:0.98 98:0.55 100:0.80 101:0.85 104:0.58 107:0.95 108:0.67 110:0.93 111:0.76 114:0.90 120:0.96 122:0.43 123:0.65 124:0.41 125:0.96 126:0.54 127:0.48 129:0.59 133:0.47 135:0.30 140:0.80 146:0.13 147:0.18 149:0.89 150:0.82 151:0.99 152:1.00 153:0.97 154:0.88 155:0.67 159:0.35 160:1.00 161:0.66 162:0.58 164:0.97 165:0.86 167:0.87 169:0.96 172:0.59 173:0.58 176:0.73 177:0.38 178:0.42 179:0.67 181:0.52 186:0.76 187:0.39 189:0.93 191:0.83 192:0.59 201:0.74 202:0.96 208:0.64 212:0.77 215:0.79 216:0.87 222:0.76 232:0.80 235:0.31 238:0.94 241:0.73 242:0.63 243:0.20 245:0.61 247:0.30 248:0.99 253:0.99 255:0.79 256:0.96 259:0.21 260:0.97 261:0.98 265:0.56 266:0.47 267:0.88 268:0.97 271:0.95 276:0.39 285:0.62 289:0.92 290:0.90 297:0.36 300:0.70 +1 1:0.74 6:0.94 8:0.94 9:0.80 11:0.57 12:0.79 17:0.05 20:0.81 21:0.13 27:0.50 28:0.80 30:0.76 34:0.88 36:0.93 37:0.69 39:0.79 41:0.97 43:0.90 46:1.00 60:0.87 66:0.71 69:0.44 70:0.46 74:0.93 78:0.74 81:0.75 83:0.87 85:0.97 91:0.77 96:0.92 98:0.84 100:0.78 101:0.87 104:0.72 107:0.96 108:0.61 110:0.94 111:0.74 114:0.92 120:0.86 122:0.43 123:0.59 124:0.44 125:0.98 126:0.54 127:0.81 129:0.59 133:0.47 135:0.26 140:0.45 146:0.32 147:0.38 149:0.97 150:0.85 151:0.87 152:0.83 153:0.48 154:0.89 155:0.67 158:0.87 159:0.47 160:0.99 161:0.66 162:0.80 164:0.88 165:0.88 167:1.00 169:0.76 172:0.79 173:0.48 176:0.73 177:0.38 179:0.45 181:0.52 186:0.75 189:0.96 192:0.59 201:0.74 202:0.81 208:0.64 212:0.69 215:0.84 216:0.52 220:0.74 222:0.76 232:0.81 235:0.22 238:0.94 241:0.95 242:0.63 243:0.26 245:0.84 247:0.39 248:0.92 253:0.95 255:0.79 256:0.84 259:0.21 260:0.87 261:0.77 265:0.84 266:0.63 267:0.85 268:0.86 271:0.95 276:0.72 279:0.86 283:0.71 285:0.62 289:0.92 290:0.92 297:0.36 300:0.70 +2 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.57 12:0.79 17:0.13 20:0.76 21:0.47 27:0.02 28:0.80 30:0.86 34:0.42 36:0.45 37:0.96 39:0.79 41:0.97 43:0.98 46:1.00 66:0.86 69:0.17 70:0.46 74:0.86 76:0.85 78:0.88 81:0.57 83:0.87 85:0.93 91:0.79 96:0.96 98:0.83 100:0.98 101:0.86 104:0.58 107:0.95 108:0.14 110:0.93 111:0.96 114:0.80 120:0.92 121:0.90 122:0.43 123:0.13 125:0.99 126:0.54 127:0.32 129:0.05 135:0.37 140:0.45 146:0.50 147:0.56 149:0.92 150:0.74 151:0.98 152:1.00 153:0.91 154:0.98 155:0.67 159:0.18 160:0.99 161:0.66 162:0.81 164:0.88 165:0.80 167:0.97 169:0.96 172:0.59 173:0.50 176:0.73 177:0.38 179:0.64 181:0.52 186:0.88 187:0.21 189:0.99 191:0.82 192:0.59 201:0.74 202:0.80 204:0.89 208:0.64 212:0.84 215:0.63 216:0.87 222:0.76 230:0.84 232:0.80 233:0.69 235:0.60 238:0.99 241:0.82 242:0.63 243:0.86 247:0.30 248:0.96 253:0.97 255:0.79 256:0.85 259:0.21 260:0.93 261:0.98 265:0.83 266:0.27 267:0.95 268:0.85 271:0.95 276:0.42 285:0.62 289:0.92 290:0.80 297:0.36 300:0.55 +2 1:0.74 8:0.97 9:0.80 11:0.57 12:0.79 17:0.03 21:0.68 27:0.02 28:0.80 30:0.85 34:0.73 36:0.86 37:0.96 39:0.79 41:0.90 43:0.87 46:0.99 66:0.72 69:0.58 70:0.54 74:0.77 81:0.47 83:0.78 85:0.94 91:0.74 96:0.84 98:0.55 101:0.85 104:0.58 107:0.95 108:0.68 110:0.93 114:0.70 120:0.75 122:0.43 123:0.66 124:0.41 125:0.98 126:0.54 127:0.47 129:0.59 133:0.47 135:0.54 140:0.45 146:0.13 147:0.18 149:0.88 150:0.65 151:0.93 153:0.77 154:0.85 155:0.67 159:0.35 160:0.98 161:0.66 162:0.86 164:0.74 165:0.70 167:0.83 172:0.59 173:0.69 176:0.73 177:0.38 179:0.67 181:0.52 187:0.21 191:0.91 192:0.59 201:0.74 202:0.60 208:0.64 212:0.77 215:0.49 216:0.87 220:0.99 222:0.76 232:0.80 235:0.84 241:0.71 242:0.63 243:0.20 245:0.61 247:0.30 248:0.82 253:0.85 255:0.79 256:0.64 259:0.21 260:0.76 265:0.56 266:0.47 267:0.84 268:0.69 271:0.95 276:0.39 285:0.62 289:0.92 290:0.70 297:0.36 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.57 12:0.79 17:0.55 20:0.77 21:0.30 27:0.21 28:0.80 30:0.74 34:0.53 36:0.82 37:0.74 39:0.79 41:0.90 43:0.98 46:0.99 60:0.95 66:0.16 69:0.12 70:0.55 74:0.99 78:0.74 81:0.65 83:0.88 85:1.00 91:0.04 96:0.85 98:0.54 100:0.77 101:0.87 104:0.84 106:0.81 107:0.97 108:0.96 110:0.95 111:0.73 114:0.86 117:0.86 120:0.75 121:0.90 122:0.57 123:0.87 124:0.95 125:0.99 126:0.54 127:0.88 129:0.99 133:0.96 135:0.44 140:0.45 146:0.53 147:0.59 149:1.00 150:0.79 151:0.82 152:0.91 153:0.46 154:0.98 155:0.67 158:0.92 159:0.95 160:0.98 161:0.66 162:0.81 164:0.73 165:0.84 167:0.64 169:0.77 172:0.96 173:0.06 176:0.73 177:0.38 179:0.10 181:0.52 186:0.75 187:0.39 192:0.59 201:0.74 202:0.61 204:0.89 206:0.81 208:0.64 212:0.56 215:0.72 216:0.95 220:0.87 222:0.76 230:0.87 232:0.91 233:0.76 235:0.42 241:0.15 242:0.60 243:0.09 245:0.99 247:0.67 248:0.83 253:0.90 255:0.79 256:0.64 259:0.21 260:0.76 261:0.79 265:0.51 266:0.87 267:0.52 268:0.67 271:0.95 276:0.99 279:0.86 283:0.82 285:0.62 289:0.92 290:0.86 297:0.36 300:0.84 +1 8:0.97 9:0.80 12:0.79 17:0.08 21:0.78 27:0.28 28:0.80 34:0.56 36:0.22 37:0.94 39:0.79 41:0.92 46:0.88 78:0.79 83:0.78 91:0.81 96:0.87 104:0.54 107:0.88 110:0.88 111:0.86 120:0.80 125:0.98 132:0.97 135:0.75 140:0.45 151:0.93 153:0.78 155:0.67 160:0.98 161:0.66 162:0.81 164:0.78 167:0.98 169:0.91 175:0.91 181:0.52 191:0.86 192:0.59 202:0.67 208:0.64 216:0.18 222:0.76 232:0.76 238:0.98 241:0.87 244:0.83 248:0.85 253:0.81 255:0.79 256:0.71 257:0.84 259:0.21 260:0.81 267:0.65 268:0.73 271:0.95 277:0.87 285:0.62 289:0.92 +1 1:0.74 8:0.88 9:0.80 11:0.57 12:0.79 17:0.04 27:0.02 28:0.80 30:0.86 34:0.63 36:0.66 37:0.96 39:0.79 41:0.94 43:0.76 46:0.99 66:0.84 69:0.82 70:0.40 74:0.71 78:0.80 81:0.86 83:0.83 85:0.91 91:0.77 96:0.92 98:0.39 101:0.84 104:0.58 107:0.94 108:0.66 110:0.93 111:0.79 114:0.98 120:0.86 122:0.43 123:0.64 125:1.00 126:0.54 127:0.13 129:0.05 135:0.60 140:0.45 146:0.34 147:0.41 149:0.84 150:0.92 151:0.93 153:0.78 154:0.67 155:0.67 159:0.14 160:0.99 161:0.66 162:0.85 164:0.81 165:0.92 167:0.85 169:0.92 172:0.54 173:0.95 176:0.73 177:0.38 179:0.22 181:0.52 187:0.21 192:0.59 201:0.74 202:0.69 208:0.64 212:0.92 215:0.96 216:0.87 222:0.76 232:0.80 235:0.05 241:0.73 242:0.63 243:0.85 247:0.30 248:0.91 253:0.92 255:0.79 256:0.75 259:0.21 260:0.86 265:0.41 266:0.17 267:0.59 268:0.76 271:0.95 276:0.42 285:0.62 289:0.92 290:0.98 297:0.36 300:0.43 +3 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.57 12:0.79 17:0.36 20:0.98 21:0.30 27:0.21 28:0.80 30:0.74 34:0.58 36:0.84 37:0.74 39:0.79 41:0.99 43:0.98 46:0.99 60:1.00 66:0.16 69:0.12 70:0.55 74:0.99 78:0.75 81:0.65 83:0.90 85:1.00 91:0.73 96:0.98 98:0.54 100:0.79 101:0.87 104:0.84 106:0.81 107:0.97 108:0.96 110:0.95 111:0.75 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.87 124:0.95 125:0.99 126:0.54 127:0.87 129:0.99 133:0.96 135:0.39 140:0.45 146:0.51 147:0.57 149:1.00 150:0.79 151:0.99 152:0.91 153:0.97 154:0.98 155:0.67 158:0.92 159:0.95 160:1.00 161:0.66 162:0.68 164:0.93 165:0.84 167:0.71 169:0.77 172:0.96 173:0.06 176:0.73 177:0.38 179:0.10 181:0.52 186:0.76 187:0.39 189:0.93 191:0.75 192:0.59 201:0.74 202:0.90 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.95 222:0.76 230:0.87 232:0.91 233:0.76 235:0.42 238:0.94 241:0.46 242:0.60 243:0.08 245:0.99 247:0.67 248:0.98 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.79 265:0.51 266:0.87 267:0.36 268:0.92 271:0.95 276:0.99 279:0.86 283:0.82 285:0.62 289:0.92 290:0.86 297:0.36 300:0.84 +2 6:0.97 8:0.98 9:0.80 12:0.79 17:0.01 20:0.79 21:0.78 27:0.14 28:0.80 34:0.68 36:0.28 37:1.00 39:0.79 41:0.95 46:0.88 78:0.75 83:0.80 91:0.85 96:0.90 100:0.80 104:0.72 107:0.88 110:0.88 111:0.75 120:0.83 125:0.97 135:0.81 140:0.80 151:0.96 152:0.76 153:0.85 155:0.67 160:0.99 161:0.66 162:0.55 164:0.81 167:0.98 169:0.78 175:0.91 178:0.42 181:0.52 186:0.76 189:0.98 191:0.92 192:0.59 202:0.78 208:0.64 216:0.41 222:0.76 232:0.81 238:0.95 241:0.85 244:0.83 248:0.89 253:0.85 255:0.79 256:0.77 257:0.84 259:0.21 260:0.83 261:0.76 267:0.52 268:0.77 271:0.95 277:0.87 285:0.62 289:0.92 +2 1:0.74 8:0.95 9:0.80 11:0.57 12:0.79 17:0.78 27:0.72 28:0.80 30:0.91 34:0.36 36:0.40 37:0.87 39:0.79 41:0.82 43:0.89 46:0.99 66:0.69 69:0.58 70:0.13 74:0.56 81:0.83 83:0.81 85:0.80 91:0.88 96:0.84 98:0.52 101:0.81 107:0.92 108:0.44 110:0.91 114:0.98 120:0.75 122:0.43 123:0.43 125:0.99 126:0.54 127:0.16 129:0.05 135:0.51 140:0.45 145:0.96 146:0.26 147:0.32 149:0.69 150:0.90 151:0.93 153:0.77 154:0.88 155:0.67 159:0.11 160:0.96 161:0.66 162:0.68 164:0.64 165:0.92 167:1.00 172:0.21 173:0.95 176:0.73 177:0.38 179:0.77 181:0.52 192:0.59 201:0.74 202:0.54 208:0.64 212:0.61 215:0.96 216:0.07 222:0.76 235:0.22 241:0.99 242:0.63 243:0.73 247:0.30 248:0.82 253:0.84 255:0.79 256:0.45 259:0.21 260:0.75 265:0.54 266:0.17 267:0.59 268:0.57 271:0.95 276:0.15 285:0.62 289:0.92 290:0.98 297:1.00 300:0.13 +3 1:0.74 6:0.98 7:0.81 8:0.97 9:0.80 11:0.57 12:0.79 17:0.15 20:0.97 21:0.13 27:0.02 28:0.80 30:0.86 34:0.25 36:0.49 37:0.96 39:0.79 41:0.98 43:0.98 46:1.00 66:0.86 69:0.08 70:0.46 74:0.86 78:0.87 81:0.75 83:0.89 85:0.93 91:0.87 96:0.97 98:0.82 100:0.97 101:0.86 104:0.58 107:0.95 108:0.07 110:0.93 111:0.94 114:0.92 120:0.93 121:0.90 122:0.43 123:0.07 125:0.99 126:0.54 127:0.31 129:0.05 135:0.30 140:0.45 146:0.50 147:0.56 149:0.92 150:0.85 151:0.98 152:1.00 153:0.92 154:0.98 155:0.67 159:0.18 160:0.99 161:0.66 162:0.75 164:0.91 165:0.88 167:0.96 169:0.96 172:0.59 173:0.41 176:0.73 177:0.38 179:0.63 181:0.52 186:0.87 187:0.21 189:0.99 191:0.81 192:0.59 201:0.74 202:0.85 204:0.89 208:0.64 212:0.84 215:0.84 216:0.87 222:0.76 230:0.84 232:0.80 233:0.69 235:0.22 238:0.99 241:0.81 242:0.63 243:0.86 247:0.30 248:0.97 253:0.97 255:0.79 256:0.87 259:0.21 260:0.94 261:0.98 265:0.82 266:0.27 267:0.44 268:0.88 271:0.95 276:0.42 285:0.62 289:0.92 290:0.92 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.79 17:0.47 21:0.13 27:0.45 28:0.80 30:0.58 32:0.80 34:0.77 36:0.19 37:0.50 39:0.79 43:0.82 46:0.97 66:0.67 69:0.68 70:0.67 74:0.89 77:0.70 81:0.75 83:0.77 85:0.94 91:0.18 98:0.48 101:0.82 107:0.95 108:0.51 110:0.94 114:0.92 122:0.43 123:0.49 124:0.48 125:0.88 126:0.54 127:0.46 129:0.59 133:0.64 135:0.86 145:0.50 146:0.66 147:0.71 149:0.89 150:0.85 154:0.78 155:0.67 159:0.61 160:0.88 161:0.66 165:0.88 167:0.65 172:0.70 173:0.61 176:0.73 177:0.38 178:0.55 179:0.50 181:0.52 187:0.68 192:0.59 195:0.80 201:0.74 212:0.60 215:0.84 216:0.77 222:0.76 235:0.22 241:0.18 242:0.70 243:0.72 245:0.70 247:0.30 253:0.77 259:0.21 265:0.50 266:0.63 267:0.19 271:0.95 276:0.59 282:0.88 289:0.92 290:0.92 297:0.36 299:0.88 300:0.55 +0 1:0.66 8:0.77 9:0.75 11:0.45 12:0.79 17:0.62 18:0.96 27:0.71 29:0.77 30:0.57 31:0.99 34:0.28 36:0.97 37:0.40 39:0.95 43:0.56 44:0.88 45:0.90 46:0.88 48:0.98 55:0.71 64:0.77 66:0.46 69:0.88 70:0.62 71:0.66 74:0.66 77:0.70 79:0.99 81:0.62 83:0.60 86:0.92 91:0.87 96:0.95 97:0.97 98:0.66 99:0.83 101:0.52 107:0.91 108:0.75 110:0.92 114:0.92 122:0.85 123:0.74 124:0.65 125:0.88 126:0.44 127:0.89 129:0.05 133:0.60 135:0.30 137:0.99 139:0.91 140:0.80 145:0.65 146:0.77 147:0.75 149:0.67 150:0.54 151:0.98 153:0.94 154:0.16 155:0.58 158:0.75 159:0.62 160:0.88 162:0.63 165:0.70 172:0.75 173:0.90 176:0.62 177:0.38 178:0.42 179:0.40 190:0.95 191:0.76 192:0.51 195:0.76 196:0.99 201:0.62 202:0.87 204:0.81 208:0.54 212:0.72 216:0.39 219:0.90 222:0.25 232:0.72 235:0.05 241:0.80 242:0.33 243:0.46 244:0.83 245:0.87 247:0.86 248:0.95 253:0.95 254:0.96 255:0.74 256:0.90 257:0.65 259:0.21 265:0.67 266:0.75 267:0.87 268:0.88 271:0.95 276:0.78 279:0.81 281:0.91 282:0.74 284:0.99 285:0.56 289:0.90 290:0.92 292:0.95 300:0.55 +1 8:0.89 11:0.45 12:0.79 17:0.24 18:1.00 21:0.71 22:0.93 27:0.21 29:0.77 30:0.28 31:0.98 34:0.29 36:0.75 37:0.74 39:0.95 43:0.11 45:0.66 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.10 69:0.27 70:0.82 71:0.66 74:0.49 79:0.98 81:0.27 86:0.98 91:0.60 98:0.39 101:0.52 107:0.92 108:0.99 110:0.92 120:0.77 122:0.86 123:0.99 124:0.98 125:0.88 126:0.26 127:0.99 129:0.99 133:0.98 135:0.26 137:0.98 139:0.97 140:0.96 146:0.87 147:0.89 149:0.52 150:0.29 151:0.80 153:0.48 154:0.08 159:0.98 160:0.88 162:0.60 164:0.72 172:0.95 173:0.06 177:0.70 178:0.48 179:0.09 187:0.39 190:0.98 191:0.70 192:0.50 196:0.99 202:0.90 212:0.52 216:0.95 219:0.90 220:0.62 222:0.25 235:0.84 241:0.57 242:0.71 243:0.11 244:0.83 245:0.99 247:0.97 248:0.73 253:0.40 254:0.97 255:0.71 256:0.91 259:0.21 260:0.77 262:0.93 265:0.41 266:0.96 267:0.18 268:0.91 271:0.95 276:0.99 281:0.86 283:0.67 284:0.99 285:0.56 289:0.90 292:0.88 300:0.94 +0 7:0.69 8:0.65 11:0.45 12:0.79 17:0.45 18:0.99 21:0.22 22:0.93 27:0.66 29:0.77 30:0.13 31:0.91 34:0.40 36:0.93 37:0.54 39:0.95 43:0.22 45:0.95 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.42 69:0.74 70:0.93 71:0.66 74:0.58 79:0.90 81:0.43 86:0.96 91:0.20 98:0.72 101:0.52 104:0.83 106:0.81 107:0.92 108:0.57 110:0.93 120:0.63 122:0.85 123:0.54 124:0.86 125:0.88 126:0.44 127:0.90 129:0.93 133:0.87 135:0.60 137:0.91 139:0.95 140:0.45 146:0.99 147:0.37 149:0.66 150:0.42 151:0.70 153:0.72 154:0.14 159:0.93 160:0.88 162:0.65 164:0.63 172:0.96 173:0.37 177:0.38 179:0.13 187:0.21 190:0.95 192:0.51 196:0.95 201:0.60 202:0.55 206:0.81 212:0.47 215:0.79 216:0.45 219:0.90 220:0.74 222:0.25 230:0.69 233:0.66 235:0.31 242:0.18 243:0.07 244:0.83 245:0.98 247:0.95 248:0.66 253:0.45 255:0.71 256:0.45 257:0.65 259:0.21 260:0.64 262:0.93 265:0.72 266:0.80 267:0.44 268:0.57 271:0.95 276:0.97 281:0.86 283:0.66 284:0.91 285:0.56 289:0.90 292:0.88 297:0.36 300:0.84 +0 1:0.67 12:0.79 17:0.57 18:0.86 21:0.22 22:0.93 27:0.47 29:0.77 30:0.65 31:0.92 32:0.66 34:0.64 36:0.28 37:0.66 39:0.95 43:0.60 44:0.88 46:0.88 47:0.93 64:0.77 66:0.29 69:0.61 70:0.66 71:0.66 74:0.73 76:0.85 77:0.70 79:0.91 81:0.55 83:0.60 86:0.85 91:0.18 98:0.75 104:0.94 107:0.92 108:0.47 110:0.92 114:0.85 120:0.65 122:0.51 123:0.80 124:0.67 125:0.88 126:0.54 127:0.73 129:0.81 133:0.67 135:0.90 137:0.92 139:0.83 140:0.45 145:0.79 146:0.66 147:0.71 149:0.70 150:0.56 151:0.75 153:0.48 154:0.50 155:0.52 158:0.75 159:0.70 160:0.88 162:0.86 164:0.55 172:0.73 173:0.50 176:0.63 177:0.70 179:0.37 187:0.68 190:0.95 192:0.55 195:0.86 196:0.93 201:0.65 202:0.36 208:0.64 212:0.59 215:0.79 216:0.82 220:0.62 222:0.25 232:0.91 235:0.42 241:0.12 242:0.21 243:0.51 244:0.83 245:0.88 247:0.79 248:0.70 253:0.65 255:0.72 256:0.45 259:0.21 260:0.66 262:0.93 265:0.46 266:0.80 267:0.44 268:0.46 271:0.95 276:0.78 279:0.77 282:0.75 283:0.82 284:0.88 285:0.56 289:0.90 290:0.85 297:0.36 300:0.70 +0 1:0.63 8:0.87 9:0.75 12:0.79 17:0.01 18:0.67 21:0.22 27:0.56 29:0.77 30:0.60 34:0.75 36:0.66 37:0.58 39:0.95 43:0.57 46:0.88 66:0.10 69:0.12 70:0.79 71:0.66 74:0.44 81:0.35 86:0.64 91:0.93 96:0.98 98:0.20 107:0.89 108:0.10 110:0.89 114:0.76 120:0.96 122:0.75 123:0.60 124:0.85 125:0.88 126:0.44 127:0.94 129:0.05 133:0.83 135:0.20 138:0.95 139:0.62 140:0.45 146:0.30 147:0.36 149:0.48 150:0.39 151:0.96 153:0.84 154:0.45 155:0.58 158:0.73 159:0.86 160:0.88 162:0.61 164:0.97 172:0.27 173:0.09 176:0.60 177:0.70 179:0.79 187:0.68 190:0.95 191:0.92 192:0.58 201:0.59 202:0.96 208:0.54 212:0.30 215:0.72 216:0.79 220:0.62 222:0.25 235:0.31 241:0.79 242:0.33 243:0.40 244:0.93 245:0.56 247:0.67 248:0.99 253:0.96 255:0.73 256:0.97 259:0.21 260:0.95 265:0.16 266:0.71 267:0.44 268:0.97 271:0.95 276:0.38 277:0.69 279:0.79 283:0.66 285:0.56 286:0.99 289:0.88 290:0.76 297:0.36 300:0.70 +0 7:0.66 8:0.88 9:0.75 11:0.45 12:0.79 17:0.32 18:0.91 21:0.78 27:0.67 29:0.77 30:0.44 31:0.94 32:0.67 34:0.77 36:0.64 37:0.54 39:0.95 43:0.21 44:0.88 45:0.66 46:0.88 48:0.91 55:0.92 66:0.32 69:0.96 70:0.77 71:0.66 74:0.49 76:0.85 77:0.70 79:0.94 86:0.82 91:0.66 96:0.86 98:0.61 101:0.52 107:0.90 108:0.72 110:0.91 120:0.79 122:0.86 123:0.71 124:0.87 125:0.88 127:0.96 129:0.05 133:0.86 135:0.18 137:0.94 139:0.82 140:0.96 145:0.52 146:0.48 147:0.46 149:0.25 151:0.76 153:0.48 154:0.13 155:0.65 159:0.80 160:0.88 162:0.77 163:0.94 164:0.85 172:0.54 173:0.99 177:0.38 179:0.58 190:0.77 191:0.73 192:0.87 195:0.90 196:0.94 202:0.79 208:0.64 212:0.19 216:0.39 220:0.82 222:0.25 230:0.68 233:0.66 235:0.22 241:0.85 242:0.58 243:0.15 244:0.83 245:0.69 247:0.74 248:0.71 253:0.81 254:0.80 255:0.78 256:0.84 257:0.65 259:0.21 260:0.79 265:0.62 266:0.84 267:0.23 268:0.84 271:0.95 276:0.66 277:0.69 281:0.91 282:0.75 284:0.97 285:0.62 289:0.90 300:0.70 +0 1:0.61 7:0.66 11:0.75 12:0.79 17:0.38 18:0.96 21:0.64 27:0.26 29:0.77 30:0.32 32:0.66 34:0.99 36:0.32 37:0.68 39:0.95 43:0.75 44:0.88 45:0.77 46:0.94 48:0.91 55:0.45 64:0.77 66:0.93 69:0.61 70:0.64 71:0.66 74:0.60 77:0.70 81:0.61 83:0.60 85:0.72 86:0.95 91:0.29 97:0.89 98:0.85 99:0.83 101:0.97 107:0.91 108:0.47 110:0.92 114:0.69 122:0.86 123:0.45 125:0.88 126:0.54 127:0.35 129:0.05 135:0.93 139:0.94 145:0.54 146:0.78 147:0.82 149:0.62 150:0.49 154:0.66 155:0.54 158:0.74 159:0.34 160:0.88 165:0.70 172:0.80 173:0.69 176:0.63 177:0.70 178:0.55 179:0.38 187:0.68 192:0.49 195:0.63 201:0.60 204:0.84 208:0.64 212:0.88 215:0.53 216:0.69 219:0.90 222:0.25 230:0.68 233:0.66 235:0.88 241:0.07 242:0.28 243:0.93 247:0.79 253:0.55 259:0.21 265:0.85 266:0.27 267:0.65 271:0.95 276:0.69 279:0.79 281:0.86 282:0.75 289:0.90 290:0.69 292:0.87 297:0.36 300:0.55 +2 1:0.66 8:0.96 9:0.75 12:0.79 17:0.69 18:0.89 21:0.30 27:0.72 29:0.77 30:0.76 31:0.92 34:0.29 36:0.96 39:0.95 43:0.10 46:0.88 55:0.71 64:0.77 66:0.47 69:0.89 70:0.43 71:0.66 74:0.34 79:0.91 81:0.53 86:0.80 91:0.71 96:0.78 98:0.65 107:0.89 108:0.62 110:0.90 114:0.82 120:0.68 122:0.86 123:0.60 124:0.67 125:0.88 126:0.54 127:0.48 129:0.05 133:0.60 135:0.69 137:0.92 139:0.76 140:0.90 146:0.19 147:0.28 149:0.17 150:0.52 151:0.69 153:0.46 154:0.20 155:0.65 159:0.43 160:0.88 162:0.85 164:0.75 172:0.41 173:0.98 176:0.63 177:0.05 179:0.74 190:0.77 192:0.87 196:0.92 201:0.64 202:0.63 208:0.64 212:0.22 215:0.72 216:0.04 220:0.82 222:0.25 235:0.52 241:0.86 242:0.61 243:0.23 244:0.83 245:0.58 247:0.60 248:0.64 253:0.73 254:0.80 255:0.74 256:0.71 257:0.84 259:0.21 260:0.68 265:0.65 266:0.75 267:0.19 268:0.70 271:0.95 276:0.46 277:0.69 284:0.95 285:0.62 289:0.90 290:0.82 297:0.36 300:0.43 +2 1:0.66 9:0.74 12:0.79 17:0.75 18:0.89 21:0.30 27:0.72 29:0.77 30:0.76 31:0.90 34:0.31 36:0.96 39:0.95 43:0.10 46:0.88 55:0.71 64:0.77 66:0.47 69:0.89 70:0.43 71:0.66 74:0.34 79:0.89 81:0.53 86:0.80 91:0.69 96:0.76 98:0.65 107:0.89 108:0.62 110:0.90 114:0.82 120:0.65 122:0.86 123:0.60 124:0.67 125:0.88 126:0.54 127:0.48 129:0.05 133:0.60 135:0.69 137:0.90 139:0.76 140:0.80 146:0.19 147:0.28 149:0.17 150:0.52 151:0.69 153:0.46 154:0.20 155:0.65 159:0.43 160:0.88 162:0.82 164:0.67 172:0.41 173:0.98 176:0.63 177:0.05 179:0.74 187:0.21 190:0.77 192:0.86 196:0.90 201:0.64 202:0.56 208:0.64 212:0.22 215:0.72 216:0.04 220:0.82 222:0.25 235:0.52 241:0.83 242:0.61 243:0.23 244:0.83 245:0.58 247:0.60 248:0.64 253:0.70 254:0.80 255:0.73 256:0.64 257:0.84 259:0.21 260:0.65 265:0.65 266:0.75 267:0.19 268:0.61 271:0.95 276:0.46 277:0.69 284:0.91 285:0.62 289:0.90 290:0.82 297:0.36 300:0.43 +0 1:0.59 7:0.66 8:0.65 9:0.71 11:0.75 12:0.79 17:0.40 18:0.99 21:0.59 27:0.31 29:0.77 30:0.28 31:0.88 34:0.59 36:0.87 37:0.42 39:0.95 43:0.56 44:0.88 45:0.95 46:0.93 55:0.71 64:0.77 66:0.48 69:0.33 70:0.85 71:0.66 74:0.69 77:0.70 79:0.88 81:0.51 83:0.60 85:0.72 86:0.96 91:0.23 96:0.71 98:0.77 99:0.83 101:0.97 107:0.92 108:0.94 110:0.93 114:0.69 122:0.86 123:0.94 124:0.86 125:0.88 126:0.44 127:0.96 129:0.89 133:0.90 135:0.69 137:0.88 139:0.95 140:0.45 145:0.45 146:0.88 147:0.90 149:0.71 150:0.41 151:0.57 153:0.48 154:0.14 155:0.56 159:0.93 160:0.88 162:0.86 165:0.70 172:0.96 173:0.09 176:0.62 177:0.70 179:0.14 187:0.39 190:0.95 192:0.49 195:0.98 196:0.92 201:0.56 202:0.36 204:0.81 208:0.54 212:0.57 216:0.87 220:0.87 222:0.25 230:0.68 233:0.66 235:0.74 241:0.07 242:0.19 243:0.32 245:0.97 247:0.93 248:0.56 253:0.61 255:0.69 256:0.45 259:0.21 265:0.77 266:0.92 267:0.36 268:0.46 271:0.95 276:0.96 281:0.86 282:0.74 284:0.88 285:0.56 289:0.90 290:0.69 300:0.84 +0 11:0.54 12:0.79 17:0.30 18:0.69 21:0.59 27:0.45 29:0.77 30:0.45 34:0.71 36:0.51 37:0.50 39:0.95 43:0.60 45:0.66 46:0.88 55:0.84 66:0.64 69:0.47 70:0.59 71:0.66 74:0.59 77:0.70 81:0.24 86:0.65 91:0.04 98:0.72 101:0.52 107:0.90 108:0.36 110:0.90 114:0.67 122:0.77 123:0.34 124:0.46 125:0.88 126:0.26 127:0.61 129:0.05 133:0.37 135:0.60 139:0.63 145:0.44 146:0.84 147:0.87 149:0.36 150:0.26 154:0.49 158:0.73 159:0.63 160:0.88 163:0.75 172:0.61 173:0.38 176:0.60 177:0.70 178:0.55 179:0.64 187:0.68 195:0.79 212:0.21 216:0.77 222:0.25 235:0.68 241:0.24 242:0.70 243:0.69 244:0.61 245:0.71 247:0.67 253:0.54 259:0.21 265:0.72 266:0.75 267:0.23 271:0.95 276:0.55 282:0.73 289:0.88 290:0.67 300:0.55 +0 1:0.56 11:0.54 12:0.79 17:0.38 18:0.63 21:0.76 27:0.45 29:0.77 30:0.09 32:0.64 34:0.92 36:0.18 37:0.50 39:0.95 43:0.70 45:0.73 46:0.88 55:0.59 66:0.11 69:0.85 70:0.93 71:0.66 74:0.60 81:0.46 86:0.67 91:0.14 98:0.25 99:0.83 101:0.52 107:0.90 108:0.56 110:0.90 114:0.64 122:0.77 123:0.69 124:0.94 125:0.88 126:0.44 127:0.78 129:0.05 133:0.93 135:0.77 139:0.64 145:0.47 146:0.49 147:0.05 149:0.54 150:0.33 154:0.59 155:0.46 159:0.82 160:0.88 165:0.70 172:0.32 173:0.72 176:0.70 177:0.05 178:0.55 179:0.53 187:0.68 192:0.44 195:0.92 201:0.54 208:0.54 212:0.23 215:0.46 216:0.77 222:0.25 235:0.99 241:0.53 242:0.58 243:0.09 244:0.61 245:0.65 247:0.74 253:0.53 257:0.84 259:0.21 265:0.21 266:0.91 267:0.36 271:0.95 276:0.68 289:0.89 290:0.64 297:0.36 300:0.70 +0 1:0.66 7:0.66 11:0.75 12:0.79 17:0.13 18:0.85 21:0.30 27:0.31 29:0.77 30:0.21 32:0.80 34:0.82 36:0.71 37:0.42 39:0.95 43:0.94 44:0.93 45:0.73 46:0.95 48:0.91 55:0.52 64:0.77 66:0.90 69:0.23 70:0.70 71:0.66 74:0.68 76:0.85 77:0.70 81:0.72 83:0.60 85:0.72 86:0.86 91:0.27 97:0.89 98:0.97 101:0.97 104:0.82 107:0.91 108:0.18 110:0.91 114:0.86 120:0.56 123:0.18 125:0.88 126:0.54 127:0.77 129:0.05 135:0.24 139:0.89 140:0.45 145:0.44 146:0.75 147:0.79 149:0.68 150:0.53 151:0.49 153:0.48 154:0.94 155:0.51 158:0.75 159:0.31 160:0.88 162:0.86 164:0.55 165:0.93 172:0.71 173:0.43 176:0.73 177:0.70 179:0.63 187:0.39 190:0.98 192:0.48 195:0.57 201:0.64 202:0.36 208:0.64 212:0.80 215:0.72 216:0.87 219:0.90 220:0.93 222:0.25 230:0.68 233:0.76 235:0.68 241:0.41 242:0.37 243:0.90 247:0.82 248:0.48 253:0.65 256:0.45 259:0.21 260:0.56 265:0.97 266:0.38 267:0.36 268:0.46 271:0.95 276:0.59 279:0.77 281:0.91 282:0.88 285:0.47 289:0.90 290:0.86 292:0.88 297:0.36 299:0.88 300:0.43 +0 8:0.97 11:0.54 12:0.79 17:0.55 18:0.92 21:0.54 27:0.72 29:0.77 30:0.21 31:0.87 34:0.91 36:0.30 37:0.92 39:0.95 43:0.11 44:0.86 45:0.66 46:0.88 48:0.91 55:0.59 64:0.77 66:0.68 69:0.38 70:0.64 71:0.66 74:0.40 79:0.86 81:0.30 86:0.86 91:0.81 98:0.71 101:0.52 107:0.89 108:0.30 110:0.90 122:0.86 123:0.28 124:0.43 125:0.88 126:0.26 127:0.65 129:0.05 133:0.37 135:0.42 137:0.87 139:0.84 140:0.45 145:0.45 146:0.69 147:0.74 149:0.19 150:0.33 151:0.48 153:0.48 154:0.66 159:0.45 160:0.88 162:0.54 172:0.51 173:0.42 177:0.70 179:0.78 190:0.98 191:0.80 195:0.66 196:0.89 202:0.56 212:0.49 216:0.10 220:0.96 222:0.25 235:0.60 241:0.86 242:0.74 243:0.72 245:0.58 247:0.47 248:0.48 253:0.35 254:0.84 256:0.45 259:0.21 265:0.71 266:0.54 267:0.28 268:0.46 271:0.95 276:0.42 281:0.91 284:0.88 285:0.47 289:0.89 300:0.43 +1 8:0.62 12:0.79 17:0.67 18:0.87 21:0.22 27:0.72 29:0.77 30:0.68 31:0.95 34:0.66 36:0.91 37:0.28 39:0.95 43:0.44 44:0.86 46:0.88 48:1.00 64:0.77 66:0.86 69:0.56 70:0.48 71:0.66 74:0.34 79:0.94 81:0.52 86:0.86 91:0.84 98:0.87 104:0.57 107:0.90 108:0.43 110:0.91 120:0.74 122:0.83 123:0.42 125:0.88 126:0.44 127:0.40 129:0.05 135:0.63 137:0.95 139:0.83 140:0.80 145:0.58 146:0.56 147:0.62 149:0.41 150:0.49 151:0.80 153:0.72 154:0.45 159:0.20 160:0.88 162:0.59 164:0.71 172:0.61 173:0.81 177:0.70 178:0.42 179:0.66 190:0.95 192:0.51 195:0.55 196:0.95 201:0.62 202:0.66 212:0.81 215:0.79 216:0.15 220:0.62 222:0.25 235:0.42 241:0.89 242:0.54 243:0.87 244:0.83 247:0.60 248:0.81 253:0.31 254:0.84 255:0.73 256:0.64 259:0.21 260:0.75 265:0.87 266:0.27 267:0.23 268:0.66 271:0.95 276:0.47 281:0.91 284:0.94 285:0.56 289:0.90 297:0.36 300:0.43 +0 1:0.65 8:0.66 9:0.75 11:0.45 12:0.79 17:0.32 18:0.98 21:0.05 27:0.17 29:0.77 30:0.26 31:0.97 34:0.42 36:0.93 37:0.59 39:0.95 43:0.28 45:0.96 46:0.88 64:0.77 66:0.68 69:0.93 70:0.75 71:0.66 74:0.65 79:0.96 81:0.60 83:0.60 86:0.94 91:0.58 96:0.89 97:0.94 98:0.75 99:0.83 101:0.52 107:0.92 108:0.86 110:0.92 114:0.89 122:0.85 123:0.85 124:0.50 125:0.88 126:0.44 127:0.75 129:0.05 133:0.60 135:0.69 137:0.97 139:0.93 140:0.45 146:0.94 147:0.05 149:0.72 150:0.51 151:0.92 153:0.77 154:0.20 155:0.58 158:0.75 159:0.80 160:0.88 162:0.86 165:0.70 172:0.95 173:0.90 176:0.62 177:0.05 179:0.18 187:0.21 190:0.95 192:0.51 196:0.98 201:0.61 202:0.60 208:0.54 212:0.84 216:0.79 219:0.90 220:0.62 222:0.25 232:0.80 235:0.12 241:0.71 242:0.39 243:0.05 244:0.93 245:0.95 247:0.90 248:0.88 253:0.88 255:0.73 256:0.64 257:0.84 259:0.21 265:0.75 266:0.75 267:0.84 268:0.69 271:0.95 276:0.92 279:0.79 284:0.95 285:0.56 289:0.90 290:0.89 292:0.88 300:0.70 +0 11:0.45 12:0.79 17:0.32 18:0.88 21:0.22 22:0.93 27:0.38 29:0.77 30:0.11 34:0.67 36:0.68 37:0.50 39:0.95 43:0.21 45:0.73 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.26 69:0.51 70:0.96 71:0.66 74:0.49 81:0.43 86:0.80 91:0.07 98:0.31 101:0.52 104:0.73 107:0.91 108:0.76 110:0.91 122:0.85 123:0.74 124:0.92 125:0.88 126:0.44 127:0.50 129:0.93 133:0.93 135:0.60 139:0.79 146:0.22 147:0.27 149:0.27 150:0.42 154:0.20 159:0.85 160:0.88 163:0.81 172:0.59 173:0.23 175:0.75 177:0.38 178:0.55 179:0.38 187:0.39 192:0.45 201:0.60 212:0.18 215:0.79 216:0.30 219:0.88 222:0.25 232:0.84 235:0.31 241:0.21 242:0.21 243:0.12 244:0.93 245:0.72 247:0.91 253:0.40 257:0.65 259:0.21 262:0.93 265:0.34 266:0.94 267:0.36 271:0.95 276:0.74 277:0.69 281:0.86 289:0.90 292:0.88 297:0.36 300:0.84 +1 1:0.60 7:0.75 9:0.71 11:0.54 12:0.79 17:0.22 18:0.87 21:0.68 22:0.93 27:0.07 29:0.77 30:0.30 31:0.88 32:0.74 34:0.98 36:0.23 37:0.63 39:0.95 43:0.11 44:0.93 45:0.73 46:0.88 47:0.93 48:0.97 55:0.84 64:0.77 66:0.18 69:0.38 70:0.92 71:0.66 74:0.75 77:0.70 79:0.87 81:0.57 86:0.85 91:0.22 96:0.70 97:0.89 98:0.45 99:0.83 101:0.52 104:0.79 106:0.81 107:0.92 108:0.92 110:0.92 114:0.70 120:0.63 122:0.86 123:0.92 124:0.88 125:0.88 126:0.54 127:0.82 129:0.81 133:0.87 135:1.00 137:0.88 139:0.89 140:0.45 145:0.61 146:0.69 147:0.74 149:0.24 150:0.43 151:0.52 153:0.48 154:0.62 155:0.58 158:0.83 159:0.85 160:0.88 162:0.70 163:0.90 164:0.71 165:0.70 172:0.59 173:0.17 176:0.73 177:0.70 179:0.37 187:0.68 190:0.98 192:0.56 195:0.94 196:0.91 201:0.57 202:0.63 204:0.81 206:0.81 208:0.64 212:0.16 215:0.49 216:0.95 219:0.95 220:0.93 222:0.25 230:0.77 233:0.66 235:0.97 241:0.01 242:0.27 243:0.27 245:0.83 247:0.90 248:0.53 253:0.62 254:0.90 255:0.69 256:0.64 259:0.21 260:0.63 262:0.93 265:0.47 266:0.94 267:0.69 268:0.65 271:0.95 276:0.80 277:0.69 279:0.79 281:0.86 282:0.82 283:0.67 284:0.92 285:0.62 289:0.90 290:0.70 292:0.86 297:0.36 300:0.84 +1 1:0.62 8:0.61 9:0.74 11:0.54 12:0.79 17:0.02 18:0.86 21:0.30 27:0.05 29:0.77 30:0.45 31:0.87 32:0.72 34:0.64 36:0.82 37:0.79 39:0.95 43:0.74 45:0.66 46:0.88 55:0.92 66:0.29 69:0.61 70:0.57 71:0.66 74:0.58 77:0.70 79:0.87 81:0.51 86:0.77 91:0.22 96:0.82 97:0.89 98:0.55 99:0.83 101:0.52 107:0.90 108:0.77 110:0.91 114:0.84 120:0.76 122:0.75 123:0.76 124:0.84 125:0.88 126:0.44 127:0.35 129:0.59 133:0.81 135:0.72 137:0.87 139:0.73 140:0.45 145:0.42 146:0.34 147:0.41 149:0.32 150:0.39 151:0.76 153:0.48 154:0.64 155:0.58 158:0.81 159:0.65 160:0.88 162:0.60 163:0.92 164:0.77 165:0.70 172:0.37 173:0.45 176:0.70 177:0.70 179:0.61 187:0.87 190:0.77 192:0.58 195:0.89 196:0.88 201:0.59 202:0.75 208:0.54 212:0.37 215:0.72 216:0.90 220:0.74 222:0.25 232:0.71 235:0.60 241:0.32 242:0.28 243:0.33 245:0.58 247:0.74 248:0.79 253:0.81 254:0.97 255:0.72 256:0.75 259:0.21 260:0.77 265:0.56 266:0.63 267:0.59 268:0.75 271:0.95 276:0.53 279:0.77 282:0.80 283:0.67 284:0.88 285:0.62 289:0.90 290:0.84 297:0.36 300:0.43 +0 1:0.66 11:0.75 12:0.79 17:0.55 18:0.93 21:0.40 27:0.25 29:0.77 30:0.17 34:0.76 36:0.97 37:0.54 39:0.95 43:0.56 45:0.85 46:0.93 55:0.84 64:0.77 66:0.49 69:0.65 70:0.92 71:0.66 74:0.52 81:0.51 85:0.72 86:0.90 91:0.03 98:0.73 101:0.97 107:0.92 108:0.87 110:0.92 114:0.78 122:0.82 123:0.86 124:0.64 125:0.88 126:0.54 127:0.75 129:0.59 133:0.61 135:0.54 139:0.85 145:0.45 146:0.93 147:0.94 149:0.54 150:0.50 154:0.45 155:0.50 159:0.83 160:0.88 172:0.86 173:0.43 176:0.63 177:0.70 178:0.55 179:0.25 187:0.68 192:0.48 201:0.63 208:0.64 212:0.29 215:0.67 216:0.86 222:0.25 232:0.82 235:0.60 241:0.12 242:0.14 243:0.45 245:0.93 247:0.95 253:0.58 259:0.21 265:0.73 266:0.80 267:0.44 271:0.95 276:0.87 289:0.90 290:0.78 297:0.36 300:0.84 +0 1:0.63 8:0.89 9:0.75 12:0.79 17:0.02 18:0.67 21:0.22 27:0.56 29:0.77 30:0.60 34:0.75 36:0.66 37:0.58 39:0.95 43:0.57 46:0.88 66:0.10 69:0.12 70:0.79 71:0.66 74:0.44 81:0.35 86:0.64 91:0.89 96:0.96 98:0.20 107:0.89 108:0.10 110:0.89 114:0.76 120:0.93 122:0.75 123:0.60 124:0.85 125:0.88 126:0.44 127:0.94 129:0.05 133:0.83 135:0.26 138:0.95 139:0.62 140:0.45 146:0.30 147:0.36 149:0.48 150:0.39 151:0.94 153:0.78 154:0.45 155:0.58 158:0.73 159:0.86 160:0.88 162:0.63 164:0.96 172:0.27 173:0.09 176:0.60 177:0.70 179:0.79 187:0.39 190:0.95 191:0.95 192:0.58 201:0.59 202:0.94 208:0.54 212:0.30 215:0.72 216:0.79 220:0.74 222:0.25 235:0.31 241:0.78 242:0.33 243:0.40 244:0.93 245:0.56 247:0.67 248:0.98 253:0.94 255:0.73 256:0.95 259:0.21 260:0.93 265:0.16 266:0.71 267:0.36 268:0.95 271:0.95 276:0.38 277:0.69 279:0.79 283:0.66 285:0.56 286:0.99 289:0.88 290:0.76 297:0.36 298:0.99 300:0.70 +0 8:0.84 12:0.79 17:0.94 18:0.71 21:0.78 27:0.47 29:0.77 30:0.15 31:0.92 34:0.24 36:0.75 37:0.77 39:0.95 43:0.11 46:0.88 48:0.91 55:0.84 66:0.16 69:0.90 70:0.94 71:0.66 74:0.64 79:0.91 86:0.69 91:0.87 98:0.54 107:0.90 108:0.29 110:0.90 122:0.77 123:0.84 124:0.77 125:0.88 127:0.90 129:0.05 133:0.74 135:0.79 137:0.92 139:0.69 140:0.96 145:0.74 146:0.54 147:0.72 149:0.24 151:0.60 153:0.48 154:0.21 159:0.67 160:0.88 162:0.46 172:0.59 173:0.91 177:0.38 178:0.54 179:0.55 190:0.98 191:0.88 196:0.90 202:0.78 212:0.51 216:0.10 220:0.62 222:0.25 235:0.68 241:0.82 242:0.72 243:0.51 244:0.93 245:0.76 247:0.67 248:0.59 253:0.48 254:0.74 256:0.45 257:0.65 259:0.21 265:0.52 266:0.75 267:0.23 268:0.46 271:0.95 276:0.68 277:0.69 281:0.86 284:0.88 285:0.47 289:0.89 300:0.70 +3 6:0.95 8:0.89 9:0.80 12:0.79 17:0.01 20:0.96 21:0.40 27:0.13 28:0.53 30:0.21 34:0.42 36:0.70 37:0.64 39:0.42 41:0.82 43:0.36 55:0.45 66:0.61 69:0.54 70:0.67 74:0.84 78:0.91 81:0.52 83:0.77 91:0.86 96:0.97 98:0.39 100:0.94 108:0.42 111:0.91 114:0.68 117:0.86 120:0.74 122:0.67 123:0.40 124:0.51 126:0.32 127:0.64 129:0.05 133:0.62 135:0.34 140:0.96 146:0.47 147:0.54 149:0.39 150:0.40 151:0.92 152:0.98 153:0.90 154:0.75 155:0.67 158:0.84 159:0.54 161:0.71 162:0.61 163:0.90 164:0.64 167:0.85 169:0.96 172:0.37 173:0.52 176:0.56 177:0.38 179:0.88 181:0.81 186:0.91 187:0.68 189:0.97 191:0.81 192:0.59 202:0.91 208:0.64 212:0.37 216:0.92 220:0.91 222:0.95 232:0.88 235:0.42 238:0.91 241:0.76 242:0.72 243:0.67 245:0.45 247:0.39 248:0.81 253:0.97 254:0.84 255:0.79 256:0.92 259:0.21 260:0.75 261:0.97 265:0.41 266:0.47 267:0.19 268:0.92 276:0.32 285:0.62 290:0.68 300:0.43 +1 1:0.74 12:0.79 17:0.47 21:0.59 27:0.45 28:0.53 30:0.45 32:0.78 34:0.85 36:0.17 37:0.50 39:0.42 43:0.27 55:0.59 66:0.33 69:0.69 70:0.61 74:0.67 77:0.70 81:0.53 91:0.07 98:0.30 108:0.53 114:0.74 123:0.50 124:0.61 126:0.54 127:0.55 129:0.05 133:0.39 135:0.58 145:0.45 146:0.55 147:0.61 149:0.29 150:0.63 154:0.75 155:0.67 159:0.76 161:0.71 167:0.66 172:0.27 173:0.53 176:0.73 177:0.38 178:0.55 179:0.84 181:0.81 187:0.68 192:0.59 195:0.90 201:0.74 212:0.30 215:0.55 216:0.77 222:0.95 235:0.74 241:0.49 242:0.63 243:0.50 245:0.60 247:0.47 253:0.63 254:0.74 259:0.21 265:0.32 266:0.54 267:0.28 276:0.21 282:0.86 290:0.74 297:0.36 300:0.43 +2 1:0.74 6:0.82 8:0.73 9:0.80 12:0.79 20:0.76 21:0.22 27:0.19 28:0.53 30:0.95 34:0.42 36:0.91 37:0.56 39:0.42 41:0.95 43:0.61 55:0.59 66:0.54 69:0.85 74:0.51 78:0.83 81:0.73 83:0.81 91:0.81 96:0.98 98:0.19 100:0.73 108:0.72 111:0.81 114:0.69 117:0.86 120:0.93 123:0.70 126:0.54 127:0.10 129:0.05 135:0.63 138:0.98 140:0.45 145:0.96 146:0.23 147:0.29 149:0.29 150:0.77 151:0.95 152:0.98 153:0.84 154:0.50 155:0.67 158:0.86 159:0.11 161:0.71 162:0.83 164:0.94 167:0.77 169:0.80 172:0.10 173:0.97 176:0.56 177:0.38 179:0.47 181:0.81 186:0.83 187:0.87 191:0.80 192:0.59 201:0.74 202:0.91 208:0.64 215:0.72 216:0.92 220:0.74 222:0.95 232:0.85 235:0.31 241:0.71 243:0.63 248:0.96 253:0.97 254:0.97 255:0.79 256:0.94 259:0.21 260:0.94 261:0.89 265:0.21 267:0.23 268:0.94 279:0.86 283:0.66 285:0.62 290:0.69 297:0.36 300:0.08 +2 6:0.95 8:0.88 9:0.80 12:0.79 20:0.98 21:0.40 27:0.13 28:0.53 30:0.58 34:0.75 36:0.62 37:0.64 39:0.42 41:0.97 43:0.36 55:0.45 60:0.87 66:0.69 69:0.54 70:0.60 74:0.88 77:0.70 78:0.97 81:0.52 83:0.83 91:0.91 96:0.99 98:0.56 100:0.98 108:0.42 111:0.98 114:0.68 117:0.86 120:0.91 122:0.67 123:0.40 124:0.41 126:0.32 127:0.41 129:0.05 133:0.39 135:0.37 140:0.96 145:0.63 146:0.50 147:0.56 149:0.41 150:0.40 151:0.98 152:0.98 153:0.97 154:0.75 155:0.67 158:0.87 159:0.33 161:0.71 162:0.65 164:0.88 167:0.85 169:0.96 172:0.41 173:0.64 176:0.56 177:0.38 179:0.84 181:0.81 186:0.96 187:0.39 189:0.96 191:0.88 192:0.59 195:0.64 202:0.98 208:0.64 212:0.37 216:0.92 220:0.91 222:0.95 232:0.88 235:0.42 238:0.95 241:0.76 242:0.72 243:0.73 245:0.46 247:0.39 248:0.94 253:1.00 254:0.84 255:0.79 256:0.99 259:0.21 260:0.92 261:0.97 265:0.57 266:0.38 267:0.59 268:0.99 276:0.30 279:0.86 282:0.84 283:0.71 285:0.62 290:0.68 300:0.43 +2 1:0.74 11:0.57 12:0.79 17:0.51 21:0.40 27:0.45 28:0.53 30:0.68 34:0.80 36:0.18 37:0.50 39:0.42 43:0.82 55:0.59 66:0.77 69:0.69 70:0.43 74:0.81 77:0.70 81:0.67 83:0.75 85:0.88 91:0.07 98:0.47 101:0.79 108:0.52 114:0.82 122:0.43 123:0.50 124:0.38 126:0.54 127:0.33 129:0.05 133:0.39 135:0.51 145:0.49 146:0.56 147:0.62 149:0.81 150:0.79 154:0.77 155:0.67 159:0.43 161:0.71 165:0.83 167:0.57 172:0.59 173:0.69 176:0.73 177:0.38 178:0.55 179:0.62 181:0.81 187:0.68 192:0.59 195:0.72 201:0.74 212:0.75 215:0.67 216:0.77 222:0.95 235:0.52 241:0.15 242:0.43 243:0.79 245:0.52 247:0.60 253:0.71 259:0.21 265:0.49 266:0.38 267:0.28 276:0.37 282:0.84 290:0.82 297:0.36 300:0.43 +2 9:0.80 11:0.57 12:0.79 17:0.45 21:0.68 27:0.21 28:0.53 30:0.45 34:0.45 36:0.50 37:0.74 39:0.42 41:0.82 43:0.91 55:0.71 60:0.87 66:0.16 69:0.27 70:0.66 74:0.92 76:0.94 78:0.95 81:0.33 83:0.83 85:0.72 91:0.09 96:0.80 98:0.22 101:0.68 104:0.84 106:0.81 108:0.21 111:0.99 114:0.62 117:0.86 120:0.69 122:0.67 123:0.20 124:0.91 126:0.32 127:0.66 129:0.59 133:0.89 135:0.30 140:0.45 146:0.49 147:0.56 149:0.70 150:0.30 151:0.87 153:0.48 154:0.91 155:0.67 158:0.92 159:0.85 161:0.71 162:0.86 164:0.57 167:0.63 169:0.97 172:0.37 173:0.12 176:0.56 177:0.38 179:0.55 181:0.81 187:0.39 192:0.59 202:0.36 206:0.81 208:0.64 212:0.27 216:0.95 222:0.95 232:0.91 235:0.80 238:0.98 241:0.39 242:0.74 243:0.36 245:0.67 247:0.39 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 265:0.24 266:0.80 267:0.19 268:0.46 276:0.56 279:0.86 283:0.82 285:0.62 290:0.62 300:0.55 +2 1:0.74 7:0.81 8:0.95 9:0.80 12:0.79 17:0.11 21:0.68 27:0.10 28:0.53 30:0.94 34:0.70 36:0.68 37:0.81 39:0.42 41:0.93 43:0.34 55:0.92 60:0.87 66:0.36 69:0.61 70:0.13 74:0.78 76:0.97 77:0.89 81:0.56 83:0.82 91:0.75 96:0.93 98:0.62 108:0.46 114:0.70 117:0.86 120:0.80 121:0.90 122:0.51 123:0.45 124:0.67 126:0.54 127:0.44 129:0.05 133:0.62 135:0.24 140:0.80 145:0.76 146:0.62 147:0.67 149:0.31 150:0.69 151:0.96 153:0.86 154:0.25 155:0.67 158:0.92 159:0.40 161:0.71 162:0.82 163:0.94 164:0.79 165:0.69 167:0.89 172:0.16 173:0.66 176:0.73 177:0.38 179:0.95 181:0.81 187:0.21 191:0.76 192:0.59 195:0.67 201:0.74 202:0.77 204:0.89 208:0.64 212:0.78 215:0.49 216:0.76 220:0.62 222:0.95 230:0.87 232:0.83 233:0.76 235:0.88 241:0.79 242:0.45 243:0.51 244:0.61 245:0.40 247:0.35 248:0.87 253:0.94 255:0.79 256:0.81 259:0.21 260:0.81 265:0.63 266:0.17 267:0.36 268:0.83 276:0.27 279:0.86 282:0.84 283:0.82 285:0.62 290:0.70 297:0.36 300:0.13 +3 1:0.74 7:0.78 8:0.78 9:0.80 12:0.79 17:0.45 21:0.40 27:0.38 28:0.53 30:0.95 32:0.74 34:0.74 36:0.90 39:0.42 41:0.94 43:0.60 55:0.59 66:0.54 69:0.85 74:0.68 76:0.94 77:0.70 78:0.82 81:0.64 83:0.80 91:0.79 96:0.94 98:0.19 104:0.89 108:0.71 111:0.80 114:0.82 120:0.89 123:0.69 126:0.54 127:0.10 129:0.05 135:0.23 140:0.80 145:0.69 146:0.24 147:0.30 149:0.28 150:0.70 151:0.96 153:0.86 154:0.49 155:0.67 158:0.84 159:0.11 161:0.71 162:0.57 164:0.90 167:0.77 169:0.80 172:0.10 173:0.95 176:0.73 177:0.38 179:0.48 181:0.81 187:0.39 191:0.86 192:0.59 195:0.64 201:0.74 202:0.89 208:0.64 215:0.67 216:0.50 220:0.91 222:0.95 230:0.81 233:0.69 235:0.52 241:0.71 243:0.63 248:0.91 253:0.93 254:0.98 255:0.79 256:0.88 259:0.21 260:0.90 265:0.21 267:0.18 268:0.89 282:0.83 283:0.71 285:0.62 290:0.82 297:0.36 300:0.08 +2 1:0.74 6:0.82 8:0.63 9:0.80 11:0.57 12:0.79 17:0.22 20:0.80 21:0.13 27:0.35 28:0.53 30:0.72 34:0.75 36:0.89 37:0.31 39:0.42 41:0.90 43:0.81 66:0.44 69:0.70 70:0.40 74:0.72 78:0.84 81:0.82 83:0.81 85:0.90 91:0.29 96:0.91 98:0.51 100:0.86 101:0.83 104:0.58 108:0.53 111:0.83 114:0.92 120:0.81 122:0.43 123:0.51 124:0.58 126:0.54 127:0.34 129:0.59 133:0.47 135:0.39 140:0.45 146:0.45 147:0.52 149:0.82 150:0.89 151:0.97 152:0.98 153:0.88 154:0.76 155:0.67 159:0.31 161:0.71 162:0.85 163:0.75 164:0.75 165:0.88 167:0.74 169:0.92 172:0.37 173:0.80 176:0.73 177:0.38 179:0.71 181:0.81 186:0.84 187:0.21 189:0.85 192:0.59 201:0.74 202:0.69 208:0.64 212:0.39 215:0.84 216:0.57 222:0.95 232:0.80 235:0.22 238:0.86 241:0.67 242:0.42 243:0.57 245:0.64 247:0.55 248:0.88 253:0.91 255:0.79 256:0.73 259:0.21 260:0.82 261:0.95 265:0.52 266:0.27 267:0.28 268:0.76 276:0.41 285:0.62 290:0.92 297:0.36 300:0.33 +3 6:0.92 8:0.77 9:0.80 12:0.79 17:0.01 20:0.98 21:0.78 27:0.31 28:0.53 34:0.61 36:0.28 37:0.58 39:0.42 41:0.96 78:0.92 83:0.82 91:0.96 96:0.96 100:0.93 104:0.58 111:0.91 120:0.95 135:0.89 140:0.90 151:0.99 152:0.90 153:0.95 155:0.67 161:0.71 162:0.70 164:0.95 167:0.92 169:0.90 175:0.91 181:0.81 186:0.92 189:0.93 191:0.84 192:0.59 202:0.93 208:0.64 216:0.54 220:0.62 222:0.95 238:0.89 241:0.88 244:0.83 248:0.94 253:0.93 255:0.79 256:0.94 257:0.84 259:0.21 260:0.95 261:0.92 267:0.52 268:0.95 277:0.87 285:0.62 +2 6:0.96 8:0.95 9:0.80 11:0.57 12:0.79 17:0.32 20:0.80 21:0.68 27:0.21 28:0.53 30:0.45 34:0.44 36:0.48 37:0.74 39:0.42 41:0.82 43:0.91 55:0.71 60:0.87 66:0.16 69:0.27 70:0.66 74:0.92 76:0.94 78:0.92 81:0.33 83:0.82 85:0.72 91:0.33 96:0.86 98:0.22 100:0.95 101:0.68 104:0.84 106:0.81 108:0.21 111:0.95 114:0.62 117:0.86 120:0.69 122:0.67 123:0.20 124:0.91 126:0.32 127:0.66 129:0.59 133:0.89 135:0.24 140:0.80 146:0.49 147:0.56 149:0.70 150:0.30 151:0.87 152:0.84 153:0.48 154:0.91 155:0.67 158:0.92 159:0.85 161:0.71 162:0.72 164:0.57 167:0.71 169:0.93 172:0.37 173:0.12 176:0.56 177:0.38 178:0.42 179:0.55 181:0.81 186:0.91 187:0.39 189:1.00 191:0.70 192:0.59 202:0.68 206:0.81 208:0.64 212:0.27 216:0.95 222:0.95 232:0.91 235:0.80 238:0.95 241:0.61 242:0.74 243:0.36 245:0.67 247:0.39 248:0.78 253:0.89 255:0.79 256:0.71 259:0.21 260:0.70 261:0.89 265:0.24 266:0.80 267:0.59 268:0.71 276:0.56 279:0.86 283:0.82 285:0.62 290:0.62 300:0.55 +2 1:0.74 6:0.82 8:0.63 9:0.80 11:0.57 12:0.79 20:0.98 21:0.13 27:0.35 28:0.53 30:0.72 34:0.53 36:0.86 37:0.31 39:0.42 41:1.00 43:0.81 60:1.00 66:0.90 69:0.70 70:0.41 74:0.87 78:0.96 81:0.82 83:0.90 85:0.94 91:0.94 96:0.99 98:0.85 100:0.96 101:0.85 104:0.58 108:0.53 111:0.96 114:0.92 120:0.98 122:0.43 123:0.51 126:0.54 127:0.35 129:0.05 135:0.28 138:0.99 140:0.45 146:0.75 147:0.79 149:0.92 150:0.89 151:1.00 152:0.98 153:0.98 154:0.76 155:0.67 158:0.92 159:0.32 161:0.71 162:0.79 164:0.98 165:0.88 167:0.92 169:0.92 172:0.71 173:0.80 176:0.73 177:0.38 179:0.51 181:0.81 186:0.96 189:0.84 191:0.88 192:0.59 201:0.74 202:0.96 208:0.64 212:0.83 215:0.84 216:0.57 220:0.74 222:0.95 232:0.80 235:0.22 238:0.91 241:0.91 242:0.62 243:0.90 247:0.39 248:0.99 253:1.00 255:0.79 256:0.97 259:0.21 260:0.98 261:0.95 265:0.85 266:0.27 267:0.79 268:0.98 276:0.59 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.43 +2 1:0.74 8:0.64 9:0.80 11:0.57 12:0.79 17:0.04 21:0.13 27:0.35 28:0.53 30:0.87 34:0.47 36:0.97 37:0.31 39:0.42 41:0.97 43:0.81 66:0.54 69:0.70 70:0.20 74:0.66 81:0.82 83:0.83 85:0.88 91:0.81 96:0.95 98:0.19 101:0.79 104:0.58 108:0.53 114:0.92 120:0.88 122:0.43 123:0.51 124:0.48 126:0.54 127:0.15 129:0.59 133:0.47 135:0.58 140:0.45 146:0.27 147:0.33 149:0.80 150:0.89 151:0.94 153:0.82 154:0.76 155:0.67 159:0.22 161:0.71 162:0.59 164:0.88 165:0.88 167:0.74 172:0.32 173:0.68 176:0.73 177:0.38 179:0.45 181:0.81 187:0.21 191:0.82 192:0.59 201:0.74 202:0.87 208:0.64 212:0.61 215:0.84 216:0.57 220:0.74 222:0.95 232:0.80 235:0.22 241:0.67 242:0.63 243:0.63 245:0.50 247:0.30 248:0.93 253:0.94 255:0.79 256:0.86 259:0.21 260:0.89 265:0.21 266:0.27 267:0.65 268:0.87 276:0.21 285:0.62 290:0.92 297:0.36 300:0.18 +1 1:0.74 6:0.82 8:0.61 9:0.80 11:0.57 12:0.79 17:0.18 20:0.93 21:0.13 27:0.35 28:0.53 30:0.73 34:0.71 36:0.59 37:0.31 39:0.42 41:0.97 43:0.81 60:0.87 66:0.92 69:0.70 70:0.39 74:0.92 78:0.86 81:0.82 83:0.88 85:0.96 91:0.65 96:0.96 98:0.85 100:0.84 101:0.86 104:0.58 108:0.53 111:0.84 114:0.92 120:0.91 122:0.57 123:0.51 126:0.54 127:0.35 129:0.05 135:0.30 138:0.98 140:0.45 146:0.75 147:0.79 149:0.95 150:0.89 151:0.96 152:0.98 153:0.87 154:0.76 155:0.67 158:0.92 159:0.31 161:0.71 162:0.76 164:0.90 165:0.88 167:0.77 169:0.92 172:0.78 173:0.80 176:0.73 177:0.38 179:0.41 181:0.81 186:0.86 187:0.21 189:0.83 192:0.59 201:0.74 202:0.85 208:0.64 212:0.82 215:0.84 216:0.57 220:0.74 222:0.95 232:0.80 235:0.22 238:0.83 241:0.71 242:0.60 243:0.92 247:0.47 248:0.95 253:0.97 255:0.79 256:0.88 259:0.21 260:0.91 261:0.95 265:0.85 266:0.27 267:0.19 268:0.89 276:0.67 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.43 +3 1:0.74 6:0.87 7:0.81 8:0.85 9:0.80 12:0.79 17:0.08 20:0.79 21:0.71 27:0.10 28:0.53 30:0.95 34:0.70 36:0.75 37:0.81 39:0.42 41:0.96 43:0.30 55:0.92 60:0.98 66:0.27 69:0.69 74:0.72 76:0.97 77:0.89 78:0.87 81:0.53 83:0.82 91:0.75 96:0.94 98:0.45 100:0.83 108:0.53 111:0.85 114:0.68 120:0.85 121:0.90 123:0.50 124:0.72 126:0.54 127:0.35 129:0.05 133:0.62 135:0.47 140:0.45 145:0.76 146:0.44 147:0.50 149:0.26 150:0.67 151:0.96 152:0.77 153:0.86 154:0.22 155:0.67 158:0.92 159:0.34 161:0.71 162:0.81 163:0.94 164:0.86 165:0.69 167:0.88 169:0.80 172:0.10 173:0.78 176:0.73 177:0.38 179:0.96 181:0.81 186:0.88 187:0.39 189:0.89 191:0.85 192:0.59 195:0.65 201:0.74 202:0.82 204:0.89 208:0.64 212:0.95 215:0.48 216:0.76 222:0.95 230:0.87 233:0.76 235:0.95 238:0.82 241:0.78 242:0.63 243:0.46 244:0.61 245:0.38 247:0.30 248:0.90 253:0.94 255:0.79 256:0.86 259:0.21 260:0.86 261:0.81 265:0.47 266:0.12 267:0.52 268:0.87 276:0.23 279:0.86 282:0.84 283:0.82 285:0.62 290:0.68 297:0.36 300:0.08 +2 6:0.90 7:0.78 8:0.77 9:0.80 11:0.57 12:0.79 17:0.08 20:0.80 21:0.30 27:0.27 28:0.53 30:0.76 34:0.47 36:0.64 37:0.32 39:0.42 41:0.96 43:0.75 55:0.59 66:0.64 69:0.83 70:0.37 74:0.92 76:0.85 77:0.70 78:0.82 81:0.58 83:0.83 85:0.80 91:0.80 96:0.99 98:0.51 100:0.83 101:0.77 108:0.68 111:0.81 114:0.69 120:0.91 122:0.67 123:0.66 124:0.45 126:0.32 127:0.60 129:0.05 133:0.39 135:0.30 140:0.95 145:0.45 146:0.45 147:0.30 149:0.67 150:0.43 151:0.97 152:0.86 153:0.97 154:0.66 155:0.67 158:0.85 159:0.38 161:0.71 162:0.70 164:0.89 167:0.75 169:0.77 172:0.54 173:0.94 176:0.56 177:0.05 179:0.73 181:0.81 186:0.83 187:0.39 189:0.93 192:0.59 195:0.61 202:0.95 208:0.64 212:0.85 216:0.70 222:0.95 230:0.81 233:0.69 235:0.31 238:0.86 241:0.68 242:0.61 243:0.28 245:0.64 247:0.39 248:0.95 253:0.99 255:0.79 256:0.96 257:0.65 259:0.21 260:0.92 261:0.82 265:0.52 266:0.27 267:0.23 268:0.96 276:0.37 282:0.84 285:0.62 290:0.69 300:0.33 +3 8:0.93 9:0.80 11:0.57 12:0.79 17:0.12 21:0.68 27:0.21 28:0.53 30:0.44 34:0.30 36:0.55 37:0.74 39:0.42 41:0.90 43:0.44 55:0.52 66:0.92 69:0.80 70:0.41 74:0.99 76:0.98 81:0.33 83:0.77 85:1.00 91:0.74 96:0.96 98:0.80 101:0.85 108:0.94 114:0.62 117:0.86 120:0.76 122:0.57 123:0.94 124:0.37 126:0.32 127:0.98 129:0.59 133:0.76 135:0.17 140:0.98 146:0.25 147:0.34 149:0.93 150:0.30 151:0.87 153:0.85 154:0.19 155:0.67 158:0.85 159:0.93 161:0.71 162:0.56 164:0.72 167:0.84 172:0.99 173:0.45 176:0.56 177:0.05 178:0.42 179:0.11 181:0.81 187:0.39 191:0.88 192:0.59 202:0.92 208:0.64 212:0.94 216:0.95 220:0.93 222:0.95 232:0.91 235:0.80 241:0.76 242:0.45 243:0.06 245:0.93 247:0.60 248:0.81 253:0.98 255:0.79 256:0.92 257:0.65 259:0.21 260:0.77 265:0.81 266:0.71 267:0.98 268:0.92 276:0.98 285:0.62 290:0.62 300:0.70 +1 1:0.67 7:0.75 11:0.55 12:0.37 17:0.79 18:0.65 21:0.54 27:0.61 29:0.94 30:0.58 32:0.74 34:0.97 36:0.63 37:0.30 39:0.47 43:0.76 44:0.93 45:0.83 55:0.84 66:0.26 69:0.44 70:0.54 71:0.61 74:0.72 77:0.89 81:0.59 86:0.65 91:0.07 97:0.89 98:0.22 99:0.95 101:0.52 108:0.34 114:0.76 122:0.51 123:0.33 124:0.84 126:0.44 127:0.45 129:0.59 131:0.61 133:0.82 135:0.56 139:0.74 144:0.57 145:0.98 146:0.63 147:0.68 149:0.65 150:0.49 154:0.67 155:0.56 157:0.96 158:0.82 159:0.90 163:0.86 165:0.70 166:0.95 172:0.37 173:0.13 176:0.70 177:0.70 178:0.55 179:0.64 182:0.98 187:0.21 192:0.56 195:0.98 201:0.62 204:0.85 208:0.54 212:0.16 215:0.58 216:0.37 222:0.35 227:0.61 229:0.61 230:0.77 231:0.93 233:0.76 235:0.88 241:0.21 242:0.24 243:0.46 244:0.61 245:0.60 246:0.85 247:0.60 253:0.61 259:0.21 265:0.24 266:0.84 267:0.19 271:0.24 276:0.46 279:0.77 282:0.82 283:0.82 287:0.61 290:0.76 297:0.36 300:0.55 +0 1:0.61 9:0.71 11:0.46 12:0.37 17:0.72 18:0.75 21:0.40 22:0.93 27:0.72 29:0.94 30:0.33 31:0.88 34:0.37 36:0.63 39:0.47 43:0.66 45:0.73 47:0.93 64:0.77 66:0.12 69:0.17 70:0.69 71:0.61 74:0.35 79:0.88 81:0.59 83:0.60 86:0.75 91:0.27 96:0.71 97:0.89 98:0.11 99:0.83 101:0.52 108:0.14 114:0.70 117:0.86 122:0.80 123:0.98 124:0.78 126:0.44 127:0.79 129:0.59 131:0.92 133:0.76 135:0.32 137:0.88 139:0.80 140:0.45 146:0.25 147:0.31 149:0.48 150:0.51 151:0.57 153:0.48 154:0.56 155:0.56 157:0.61 158:0.73 159:0.96 162:0.86 165:0.70 166:0.87 172:0.21 173:0.06 175:0.75 176:0.59 177:0.38 179:0.91 182:0.61 187:0.39 190:0.89 192:0.49 196:0.90 201:0.58 202:0.36 204:0.82 208:0.54 212:0.42 216:0.06 219:0.91 220:0.87 222:0.35 227:0.83 229:0.61 231:0.86 232:0.85 235:0.52 241:0.01 242:0.45 243:0.49 244:0.83 245:0.45 246:0.61 247:0.47 248:0.56 253:0.55 255:0.69 256:0.45 257:0.65 259:0.21 262:0.93 265:0.09 266:0.38 267:0.59 268:0.46 271:0.24 276:0.28 277:0.69 279:0.77 281:0.86 284:0.88 285:0.56 287:0.61 290:0.70 292:0.88 300:0.43 +1 8:0.60 11:0.46 12:0.37 17:0.57 18:0.80 21:0.22 27:0.45 29:0.94 30:0.45 34:0.55 36:0.38 37:0.50 39:0.47 43:0.57 45:0.66 55:0.92 64:0.77 66:0.36 69:0.68 70:0.33 71:0.61 74:0.28 81:0.37 86:0.77 91:0.09 98:0.28 101:0.52 108:0.52 122:0.75 123:0.50 124:0.76 126:0.26 127:0.21 129:0.05 131:0.61 133:0.74 135:0.61 139:0.73 145:0.50 146:0.46 147:0.52 149:0.30 150:0.41 154:0.46 157:0.61 159:0.46 163:0.81 166:0.61 172:0.21 173:0.58 175:0.75 177:0.38 178:0.55 179:0.76 182:0.61 187:0.68 212:0.23 216:0.77 222:0.35 227:0.61 229:0.61 231:0.66 235:0.22 241:0.24 242:0.24 243:0.51 244:0.83 245:0.42 246:0.61 247:0.47 253:0.26 257:0.65 259:0.21 265:0.30 266:0.38 267:0.65 271:0.24 276:0.31 277:0.69 287:0.61 300:0.25 +0 1:0.57 8:0.58 12:0.37 17:0.36 18:0.74 21:0.71 22:0.93 27:0.13 29:0.94 30:0.76 34:0.91 36:0.90 37:0.98 39:0.47 43:0.14 47:0.93 55:0.42 64:0.77 66:0.72 69:0.32 70:0.15 71:0.61 74:0.33 81:0.35 86:0.75 91:0.10 98:0.52 108:0.25 114:0.63 122:0.80 123:0.24 126:0.44 127:0.16 129:0.05 131:0.61 135:0.76 139:0.76 145:0.83 146:0.43 147:0.49 149:0.07 150:0.40 154:0.54 155:0.46 157:0.61 158:0.73 159:0.16 163:0.75 166:0.87 172:0.27 173:0.46 176:0.59 177:0.70 178:0.55 179:0.69 182:0.61 187:0.39 192:0.44 201:0.55 202:0.36 208:0.54 212:0.42 216:0.38 219:0.91 222:0.35 227:0.61 229:0.61 231:0.86 235:0.88 241:0.12 242:0.45 243:0.75 244:0.83 246:0.61 247:0.35 253:0.47 254:0.74 259:0.21 262:0.93 265:0.54 266:0.23 267:0.36 271:0.24 276:0.23 279:0.77 287:0.61 290:0.63 292:0.87 300:0.13 +1 8:0.74 12:0.37 17:0.59 18:0.85 21:0.78 22:0.93 27:0.31 29:0.94 30:0.17 34:0.53 36:0.96 37:0.64 39:0.47 43:0.12 44:0.88 47:0.93 48:0.91 55:0.45 66:0.34 69:0.78 70:0.68 71:0.61 74:0.32 76:0.85 77:0.70 86:0.81 91:0.37 98:0.37 108:0.61 122:0.82 123:0.59 124:0.77 127:0.40 129:0.81 131:0.61 133:0.76 135:0.96 139:0.81 145:0.99 146:0.63 147:0.68 149:0.18 154:0.26 157:0.61 159:0.69 163:0.75 166:0.61 172:0.37 173:0.65 175:0.75 177:0.38 178:0.55 179:0.69 182:0.61 187:0.68 195:0.90 202:0.65 212:0.19 216:0.43 222:0.35 227:0.61 229:0.61 231:0.80 235:0.84 241:0.15 242:0.19 243:0.50 244:0.83 245:0.57 246:0.61 247:0.67 253:0.29 254:0.84 257:0.65 259:0.21 262:0.93 265:0.39 266:0.47 267:0.84 271:0.24 276:0.46 277:0.69 281:0.86 282:0.72 287:0.61 300:0.43 +1 7:0.64 11:0.55 12:0.37 17:0.69 18:0.65 21:0.68 27:0.61 29:0.94 30:0.41 32:0.63 34:0.95 36:0.61 37:0.30 39:0.47 43:0.75 45:0.81 55:0.42 66:0.15 69:0.78 70:0.88 71:0.61 74:0.51 81:0.28 86:0.65 91:0.03 98:0.14 101:0.52 108:0.61 122:0.41 123:0.59 124:0.86 126:0.26 127:0.84 129:0.59 131:0.61 133:0.82 135:0.58 139:0.63 144:0.57 145:0.92 146:0.30 147:0.30 149:0.61 150:0.30 154:0.66 157:0.96 159:0.89 166:0.78 172:0.21 173:0.52 177:0.38 178:0.55 179:0.78 182:0.88 187:0.21 195:0.96 212:0.19 215:0.49 216:0.37 222:0.35 227:0.61 229:0.61 230:0.64 231:0.85 233:0.66 235:0.84 241:0.07 242:0.33 243:0.29 244:0.61 245:0.56 246:0.61 247:0.67 253:0.41 257:0.65 259:0.21 265:0.15 266:0.80 267:0.28 271:0.24 276:0.36 283:0.67 287:0.61 297:0.36 300:0.70 +0 8:0.66 12:0.37 17:0.28 18:0.87 21:0.40 22:0.93 27:0.21 29:0.94 30:0.15 31:0.98 34:0.59 36:0.83 37:0.74 39:0.47 43:0.20 47:0.93 48:0.91 55:0.71 64:0.77 66:0.10 69:0.98 70:0.81 71:0.61 74:0.25 79:0.98 81:0.34 86:0.82 91:0.65 98:0.19 108:0.62 122:0.86 123:0.59 124:0.95 126:0.26 127:0.80 129:0.05 131:0.61 133:0.94 135:0.17 137:0.98 139:0.82 140:0.45 146:0.30 147:0.05 149:0.22 150:0.38 151:0.70 153:0.46 154:0.13 157:0.61 159:0.89 162:0.61 163:0.79 166:0.61 172:0.21 173:0.99 175:0.75 177:0.05 179:0.68 182:0.61 187:0.39 190:0.95 191:0.76 196:0.97 202:0.84 212:0.12 216:0.95 219:0.90 220:0.62 222:0.35 227:0.61 229:0.61 231:0.68 235:0.42 241:0.15 242:0.70 243:0.11 244:0.93 245:0.54 246:0.61 247:0.74 248:0.84 253:0.23 256:0.85 257:0.84 259:0.21 262:0.93 265:0.20 266:0.94 267:0.87 268:0.85 271:0.24 276:0.57 277:0.87 281:0.86 284:0.98 285:0.47 287:0.61 292:0.95 300:0.55 +0 8:0.71 12:0.37 17:0.43 18:0.88 21:0.13 27:0.61 29:0.94 30:0.38 32:0.63 34:0.95 36:0.52 37:0.35 39:0.47 43:0.15 48:0.98 55:0.71 64:0.77 66:0.36 69:0.61 70:0.63 71:0.61 74:0.29 81:0.38 86:0.83 91:0.57 98:0.36 108:0.46 122:0.86 123:0.45 124:0.77 126:0.26 127:0.29 129:0.05 131:0.61 133:0.73 135:0.34 139:0.81 145:0.69 146:0.50 147:0.56 149:0.20 150:0.43 154:0.47 157:0.61 159:0.47 166:0.61 172:0.32 173:0.55 177:0.70 178:0.55 179:0.70 182:0.61 187:0.39 195:0.80 212:0.28 216:0.10 222:0.35 227:0.61 228:0.99 229:0.61 231:0.71 235:0.12 241:0.15 242:0.71 243:0.51 244:0.83 245:0.51 246:0.61 247:0.47 253:0.26 254:0.84 259:0.21 265:0.38 266:0.63 267:0.65 271:0.24 276:0.41 281:0.91 287:0.61 300:0.43 +0 8:0.63 11:0.55 12:0.37 17:0.28 18:0.80 21:0.59 22:0.93 27:0.41 29:0.94 30:0.21 31:0.86 34:0.80 36:0.53 37:0.44 39:0.47 43:0.59 45:0.66 47:0.93 48:0.91 55:0.84 64:0.77 66:0.10 69:0.86 70:0.67 71:0.61 74:0.35 79:0.86 81:0.30 86:0.75 91:0.49 98:0.25 101:0.52 108:0.72 122:0.86 123:0.85 124:0.86 126:0.26 127:0.76 129:0.05 131:0.61 133:0.81 135:0.80 137:0.86 139:0.78 140:0.45 144:0.57 145:0.59 146:0.35 147:0.05 149:0.32 150:0.33 151:0.47 153:0.48 154:0.48 157:0.82 159:0.79 162:0.86 163:0.96 166:0.61 172:0.16 173:0.76 177:0.05 179:0.89 182:0.75 187:0.68 190:0.95 196:0.88 202:0.36 212:0.37 216:0.75 219:0.90 220:0.97 222:0.35 227:0.61 229:0.61 231:0.66 235:0.68 241:0.01 242:0.58 243:0.18 244:0.61 245:0.46 246:0.61 247:0.47 248:0.47 253:0.32 256:0.45 257:0.84 259:0.21 262:0.93 265:0.21 266:0.47 267:0.69 268:0.46 271:0.24 276:0.36 277:0.87 281:0.91 284:0.88 285:0.47 287:0.61 292:0.88 300:0.43 +1 1:0.57 11:0.46 12:0.37 17:0.86 18:0.73 21:0.68 27:0.61 29:0.94 30:0.15 34:0.55 36:0.99 37:0.53 39:0.47 43:0.21 45:0.66 55:0.71 64:0.77 66:0.08 69:0.90 70:0.76 71:0.61 74:0.32 81:0.36 86:0.73 91:0.49 98:0.19 101:0.52 108:0.80 114:0.63 122:0.77 123:0.86 124:0.93 126:0.44 127:0.30 129:0.05 131:0.61 133:0.92 135:0.93 139:0.74 145:0.61 146:0.49 147:0.55 149:0.19 150:0.41 154:0.17 155:0.47 157:0.61 158:0.73 159:0.85 163:0.87 166:0.87 172:0.21 173:0.69 176:0.59 177:0.70 178:0.55 179:0.51 182:0.61 187:0.21 192:0.44 201:0.55 208:0.54 212:0.14 216:0.26 219:0.91 222:0.35 227:0.61 229:0.61 231:0.84 235:0.84 241:0.10 242:0.21 243:0.29 244:0.83 245:0.56 246:0.61 247:0.79 253:0.48 259:0.21 265:0.20 266:0.89 267:0.44 271:0.24 276:0.57 277:0.87 279:0.77 287:0.61 290:0.63 292:0.86 300:0.55 +1 11:0.46 12:0.37 17:0.91 18:0.85 21:0.54 27:0.72 29:0.94 30:0.09 31:0.89 32:0.63 34:0.31 36:0.96 39:0.47 43:0.58 45:0.66 48:0.91 55:0.59 64:0.77 66:0.67 69:0.32 70:0.95 71:0.61 74:0.34 77:0.70 79:0.88 81:0.31 83:0.60 86:0.83 91:0.18 98:0.53 101:0.52 108:0.25 122:0.83 123:0.24 124:0.46 126:0.26 127:0.46 129:0.05 131:0.61 133:0.59 135:0.56 137:0.89 139:0.84 140:0.45 145:0.72 146:0.61 147:0.67 149:0.32 150:0.35 151:0.52 153:0.69 154:0.47 155:0.51 157:0.61 159:0.49 162:0.86 166:0.61 172:0.48 173:0.31 177:0.70 179:0.76 182:0.61 187:0.87 190:0.95 192:0.46 195:0.74 196:0.91 202:0.46 204:0.81 208:0.54 212:0.52 216:0.09 219:0.90 220:0.87 222:0.35 227:0.61 229:0.61 231:0.85 235:0.60 242:0.24 243:0.72 244:0.83 245:0.49 246:0.61 247:0.60 248:0.52 253:0.31 256:0.45 259:0.21 265:0.54 266:0.47 267:0.69 268:0.55 271:0.24 276:0.39 281:0.91 282:0.71 284:0.90 285:0.47 287:0.61 292:0.88 300:0.70 +0 8:0.66 12:0.37 17:0.55 18:0.96 21:0.30 22:0.93 27:0.50 29:0.94 30:0.26 31:0.97 34:0.42 36:0.77 37:0.60 39:0.47 43:0.20 44:0.88 47:0.93 48:0.91 55:0.84 64:0.77 66:0.07 69:0.95 70:0.89 71:0.61 74:0.26 79:0.96 81:0.35 86:0.92 91:0.29 98:0.28 108:0.97 122:0.86 123:0.95 124:0.97 126:0.26 127:0.86 129:0.59 131:0.61 133:0.97 135:0.69 137:0.97 139:0.92 140:0.80 145:0.70 146:0.45 147:0.77 149:0.34 150:0.39 151:0.71 153:0.78 154:0.08 157:0.61 159:0.93 162:0.69 166:0.61 172:0.67 173:0.80 175:0.75 177:0.05 178:0.42 179:0.27 182:0.61 187:0.39 190:0.95 195:0.99 196:0.98 202:0.66 212:0.21 216:0.86 219:0.90 222:0.35 227:0.61 229:0.61 231:0.65 235:0.31 241:0.07 242:0.79 243:0.17 244:0.83 245:0.79 246:0.61 247:0.60 248:0.72 253:0.23 254:0.88 256:0.64 257:0.84 259:0.21 262:0.93 265:0.28 266:0.96 267:0.44 268:0.68 271:0.24 276:0.87 277:0.87 281:0.91 284:0.95 285:0.47 287:0.61 292:0.95 300:0.70 +0 12:0.37 17:0.55 18:0.57 21:0.78 27:0.71 29:0.94 30:0.21 34:0.61 36:0.73 37:0.29 39:0.47 43:0.06 55:0.96 66:0.07 69:0.96 70:0.64 71:0.61 74:0.26 86:0.59 91:0.02 98:0.09 108:0.98 123:0.98 124:0.96 127:0.25 129:0.99 131:0.61 133:0.95 135:0.60 139:0.57 145:0.76 146:0.05 147:0.05 149:0.07 154:0.06 157:0.61 159:0.97 163:0.98 166:0.61 172:0.10 173:0.61 175:0.91 177:0.05 178:0.55 179:0.59 182:0.61 187:0.21 202:0.50 212:0.22 216:0.31 222:0.35 227:0.61 229:0.61 231:0.65 235:0.80 241:0.27 242:0.38 243:0.14 244:0.93 245:0.45 246:0.61 247:0.67 253:0.23 257:0.84 259:0.21 265:0.10 266:0.71 267:0.44 271:0.24 276:0.45 277:0.87 287:0.61 300:0.43 +0 8:0.66 12:0.37 17:0.40 18:0.83 21:0.13 27:0.61 29:0.94 30:0.76 34:0.95 36:0.54 37:0.35 39:0.47 43:0.15 44:0.88 48:0.99 55:0.92 64:0.77 66:0.36 69:0.61 70:0.21 71:0.61 74:0.29 81:0.38 86:0.79 91:0.54 98:0.43 108:0.46 122:0.43 123:0.45 124:0.68 126:0.26 127:0.28 129:0.05 131:0.61 133:0.60 135:0.75 139:0.80 145:0.80 146:0.56 147:0.62 149:0.17 150:0.43 154:0.47 157:0.61 159:0.44 166:0.61 172:0.27 173:0.56 177:0.70 178:0.55 179:0.76 182:0.61 187:0.39 195:0.77 212:0.16 216:0.10 222:0.35 227:0.61 228:0.99 229:0.61 231:0.70 235:0.12 241:0.27 242:0.16 243:0.51 244:0.83 245:0.50 246:0.61 247:0.60 253:0.26 254:0.84 259:0.21 265:0.45 266:0.54 267:0.65 271:0.24 276:0.37 281:0.91 287:0.61 300:0.18 +1 7:0.76 11:0.42 12:0.86 17:0.51 21:0.54 27:0.19 28:0.96 30:0.42 32:0.72 34:0.55 36:0.64 37:0.67 39:0.70 43:0.34 55:0.84 66:0.51 69:0.60 70:0.70 74:0.74 77:0.70 81:0.47 91:0.44 98:0.43 104:0.96 106:0.81 108:0.46 120:0.84 122:0.41 123:0.44 124:0.66 126:0.32 127:0.60 129:0.05 131:0.97 133:0.62 135:0.95 140:0.45 144:0.57 145:0.69 146:0.66 147:0.71 149:0.41 150:0.37 151:0.66 153:0.48 154:0.68 157:0.61 159:0.71 161:0.53 162:0.81 164:0.72 166:0.94 167:0.50 172:0.51 173:0.46 177:0.38 179:0.68 181:0.86 182:0.61 187:0.68 190:0.77 195:0.86 202:0.62 206:0.81 212:0.23 215:0.58 216:0.63 222:0.48 227:0.61 229:0.61 230:0.78 231:0.97 233:0.69 235:0.60 241:0.21 242:0.15 243:0.61 245:0.65 246:0.61 247:0.67 248:0.76 253:0.54 254:0.74 256:0.68 259:0.21 260:0.83 265:0.45 266:0.75 267:0.73 268:0.67 271:0.13 276:0.47 282:0.81 285:0.50 287:0.61 297:0.36 300:0.55 +1 6:0.92 7:0.76 8:0.94 11:0.42 12:0.86 17:0.53 20:0.91 21:0.54 27:0.19 28:0.96 30:0.42 32:0.72 34:0.59 36:0.62 37:0.67 39:0.70 43:0.34 55:0.84 66:0.51 69:0.60 70:0.70 74:0.74 77:0.70 78:0.82 81:0.47 91:0.72 98:0.43 100:0.73 104:0.96 106:0.81 108:0.46 111:0.80 120:0.95 122:0.41 123:0.44 124:0.66 126:0.32 127:0.60 129:0.05 131:0.97 133:0.62 135:0.95 140:0.45 144:0.57 145:0.69 146:0.66 147:0.71 149:0.41 150:0.37 151:0.78 152:0.93 153:0.89 154:0.68 157:0.61 159:0.71 161:0.53 162:0.67 164:0.91 166:0.94 167:0.54 169:0.84 172:0.51 173:0.46 177:0.38 179:0.68 181:0.86 182:0.61 186:0.82 187:0.68 190:0.77 195:0.86 202:0.87 206:0.81 212:0.23 215:0.58 216:0.63 220:0.62 222:0.48 227:0.61 229:0.61 230:0.78 231:0.97 233:0.69 235:0.60 241:0.43 242:0.15 243:0.61 245:0.65 246:0.61 247:0.67 248:0.92 253:0.54 254:0.74 256:0.88 259:0.21 260:0.94 261:0.90 265:0.45 266:0.75 267:0.65 268:0.89 271:0.13 276:0.47 282:0.81 283:0.82 285:0.50 287:0.61 297:0.36 300:0.55 +2 6:0.94 8:0.89 11:0.64 12:0.86 17:0.34 20:0.92 21:0.68 27:0.33 28:0.96 30:0.33 34:0.20 36:0.29 37:0.64 39:0.70 43:0.38 55:0.98 66:0.08 69:0.98 70:0.97 74:0.51 78:0.95 81:0.36 85:0.72 91:0.53 98:0.14 100:0.90 101:0.47 104:0.93 106:0.81 108:0.38 111:0.92 120:0.86 122:0.67 123:0.37 124:0.98 126:0.32 127:0.81 129:0.05 131:0.97 133:0.98 135:0.97 140:0.45 144:0.57 145:0.88 146:0.24 147:0.67 149:0.56 150:0.32 151:0.74 152:0.86 153:0.83 154:0.14 157:0.75 159:0.98 161:0.53 162:0.84 163:0.99 164:0.78 166:0.94 167:0.48 169:0.88 172:0.32 173:0.82 177:0.05 179:0.40 181:0.86 182:0.72 186:0.94 187:0.87 189:0.95 190:0.77 202:0.67 206:0.81 212:0.12 215:0.49 216:0.55 222:0.48 227:0.61 229:0.61 231:0.97 235:0.80 238:0.87 241:0.12 242:0.62 243:0.24 245:0.63 246:0.61 247:0.55 248:0.80 253:0.42 256:0.73 257:0.65 259:0.21 260:0.86 261:0.91 265:0.15 266:0.99 267:0.99 268:0.74 271:0.13 276:0.77 277:0.69 283:0.82 285:0.50 287:0.61 297:0.36 300:0.98 +1 1:0.74 11:0.42 12:0.86 17:0.43 21:0.59 27:0.45 28:0.96 30:0.76 32:0.72 34:0.91 36:0.25 37:0.50 39:0.70 43:0.80 55:0.42 66:0.64 69:0.70 70:0.15 74:0.43 81:0.59 91:0.31 98:0.15 108:0.53 114:0.74 122:0.55 123:0.51 126:0.54 127:0.09 129:0.05 131:0.61 135:0.19 144:0.57 145:0.45 146:0.22 147:0.27 149:0.45 150:0.66 154:0.75 155:0.67 157:0.61 159:0.10 161:0.53 163:0.97 166:0.98 167:0.49 172:0.16 173:0.71 176:0.73 177:0.38 178:0.55 179:0.14 181:0.86 182:0.61 187:0.68 192:0.59 195:0.72 201:0.74 212:0.95 215:0.55 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.74 241:0.15 242:0.82 243:0.69 246:0.61 247:0.12 253:0.58 254:0.74 259:0.21 265:0.16 266:0.12 267:0.69 271:0.13 276:0.16 287:0.61 290:0.74 297:0.36 300:0.13 +2 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.64 12:0.86 17:0.38 20:0.96 21:0.13 27:0.68 28:0.96 30:0.10 32:0.80 34:0.23 36:0.31 37:0.82 39:0.70 41:0.88 43:0.87 60:0.95 66:0.23 69:0.54 70:1.00 74:0.97 76:0.94 77:0.96 78:0.99 81:0.88 83:0.88 85:0.97 91:0.42 96:0.84 98:0.26 100:0.99 101:0.76 104:0.77 106:0.81 108:0.41 111:1.00 114:0.92 117:0.86 120:0.80 121:0.90 122:0.43 123:0.39 124:0.97 126:0.54 127:0.77 129:0.98 131:0.99 133:0.98 135:0.65 140:0.45 144:0.57 145:0.63 146:0.85 147:0.87 149:0.96 150:0.93 151:0.87 152:0.98 153:0.48 154:0.86 155:0.67 157:0.99 158:0.92 159:0.95 161:0.53 162:0.83 164:0.72 165:0.88 166:0.99 167:0.55 169:0.98 172:0.81 173:0.12 176:0.73 177:0.38 179:0.20 181:0.86 182:0.99 186:0.99 187:0.21 189:0.97 192:0.59 195:0.99 201:0.74 202:0.63 204:0.89 206:0.81 208:0.64 212:0.29 215:0.84 216:0.50 220:0.74 222:0.48 227:0.61 229:0.99 230:0.87 231:0.98 232:0.85 233:0.76 235:0.22 238:0.97 241:0.44 242:0.22 243:0.43 245:0.85 246:0.61 247:0.67 248:0.82 253:0.90 255:0.79 256:0.68 259:0.21 260:0.80 261:0.98 265:0.28 266:0.99 267:0.65 268:0.70 271:0.13 276:0.91 279:0.86 282:0.88 283:0.82 285:0.62 287:0.61 290:0.92 297:0.36 299:0.99 300:0.98 +2 6:0.96 7:0.76 8:0.92 11:0.64 12:0.86 17:0.66 20:0.92 21:0.22 27:0.68 28:0.96 30:0.40 32:0.80 34:0.26 36:0.33 37:0.82 39:0.70 43:0.80 55:0.71 66:0.49 69:0.54 70:0.92 74:0.83 77:0.70 78:0.98 81:0.73 85:0.93 91:0.44 98:0.57 100:0.95 101:0.74 104:0.77 106:0.81 108:0.41 111:0.96 120:0.69 123:0.39 124:0.92 126:0.32 127:0.64 129:0.93 131:0.97 133:0.94 135:0.85 140:0.45 144:0.57 145:0.72 146:0.97 147:0.97 149:0.90 150:0.54 151:0.63 152:0.98 153:0.72 154:0.74 157:0.98 159:0.91 161:0.53 162:0.86 164:0.72 166:0.94 167:0.46 169:0.98 172:0.89 173:0.19 177:0.38 179:0.20 181:0.86 182:0.94 186:0.98 187:0.21 189:0.94 190:0.77 195:0.98 202:0.59 206:0.81 212:0.56 215:0.79 216:0.50 220:0.62 222:0.48 227:0.61 229:0.61 230:0.79 231:0.98 233:0.76 235:0.22 238:0.89 241:0.03 242:0.71 243:0.60 245:0.85 246:0.61 247:0.67 248:0.63 253:0.59 256:0.64 259:0.21 260:0.69 261:0.98 265:0.58 266:0.95 267:0.52 268:0.68 271:0.13 276:0.90 282:0.88 283:0.71 285:0.50 287:0.61 297:0.36 299:0.88 300:0.84 +1 1:0.74 9:0.80 11:0.42 12:0.86 17:0.34 21:0.30 27:0.05 28:0.96 30:0.62 34:0.81 36:0.51 37:0.91 39:0.70 43:0.28 55:0.92 66:0.23 69:0.69 70:0.53 74:0.78 81:0.78 91:0.49 96:0.71 98:0.48 104:0.97 106:0.81 108:0.83 114:0.86 117:0.86 120:0.58 122:0.67 123:0.82 124:0.80 126:0.54 127:0.67 129:0.59 131:0.99 133:0.76 135:0.74 140:0.45 144:0.57 146:0.32 147:0.39 149:0.34 150:0.80 151:0.58 153:0.48 154:0.75 155:0.67 157:0.61 158:0.87 159:0.75 161:0.53 162:0.62 163:0.92 164:0.57 166:0.99 167:0.46 172:0.32 173:0.55 176:0.73 177:0.38 179:0.72 181:0.86 182:0.61 187:0.87 192:0.59 201:0.74 202:0.50 206:0.81 208:0.64 212:0.37 215:0.72 216:0.62 220:0.87 222:0.48 227:0.61 229:0.61 231:0.98 232:0.99 235:0.42 241:0.02 242:0.78 243:0.27 245:0.62 246:0.61 247:0.39 248:0.57 253:0.74 254:0.74 255:0.79 256:0.45 259:0.21 260:0.59 265:0.50 266:0.63 267:0.82 268:0.46 271:0.13 276:0.49 277:0.69 279:0.86 283:0.71 285:0.62 287:0.61 290:0.86 297:0.36 300:0.43 +1 6:0.96 7:0.76 8:0.70 11:0.64 12:0.86 17:0.55 20:0.84 21:0.40 27:0.68 28:0.96 30:0.21 32:0.72 34:0.24 36:0.61 37:0.82 39:0.70 43:0.73 55:0.52 66:0.53 69:0.54 70:0.96 74:0.66 78:0.88 81:0.59 85:0.85 91:0.44 98:0.50 100:0.73 101:0.73 104:0.77 106:0.81 108:0.42 111:0.85 120:0.65 123:0.40 124:0.84 126:0.32 127:0.46 129:0.89 131:0.97 133:0.89 135:0.80 140:0.45 144:0.57 145:0.77 146:0.86 147:0.88 149:0.78 150:0.44 151:0.61 152:0.98 153:0.48 154:0.67 157:0.95 159:0.81 161:0.53 162:0.86 164:0.55 166:0.94 167:0.47 169:0.97 172:0.80 173:0.29 177:0.38 179:0.30 181:0.86 182:0.85 186:0.88 187:0.21 190:0.77 195:0.95 202:0.36 206:0.81 212:0.69 215:0.67 216:0.50 220:0.62 222:0.48 227:0.61 229:0.61 230:0.79 231:0.97 233:0.76 235:0.42 241:0.05 242:0.73 243:0.62 245:0.78 246:0.61 247:0.55 248:0.59 253:0.51 256:0.45 259:0.21 260:0.65 261:0.98 265:0.51 266:0.84 267:0.59 268:0.46 271:0.13 276:0.80 283:0.82 285:0.50 287:0.61 297:0.36 300:0.94 +1 1:0.74 6:0.95 7:0.81 8:0.88 9:0.80 11:0.64 12:0.86 17:0.09 20:0.82 21:0.71 27:0.19 28:0.96 30:0.42 32:0.80 34:0.69 36:0.28 37:0.67 39:0.70 41:0.82 43:0.86 55:0.52 60:0.87 66:0.36 69:0.61 70:0.62 74:0.82 77:0.70 78:0.98 81:0.52 83:0.84 85:0.80 91:0.53 96:0.70 98:0.32 100:0.92 101:0.72 108:0.61 111:0.95 114:0.68 120:0.56 121:0.90 123:0.58 124:0.86 126:0.54 127:0.52 129:0.81 131:0.99 133:0.86 135:0.18 140:0.45 144:0.57 145:0.76 146:0.13 147:0.18 149:0.73 150:0.67 151:0.52 152:0.94 153:0.48 154:0.83 155:0.67 157:0.91 158:0.87 159:0.75 161:0.53 162:0.62 164:0.57 165:0.69 166:0.98 167:0.48 169:0.86 172:0.45 173:0.43 176:0.73 177:0.38 179:0.66 181:0.86 182:0.98 186:0.98 187:0.87 192:0.59 195:0.91 201:0.74 202:0.50 204:0.89 208:0.64 212:0.35 215:0.48 216:0.63 220:0.93 222:0.48 227:0.61 229:0.98 230:0.87 231:0.98 233:0.76 235:0.88 241:0.12 242:0.37 243:0.19 245:0.56 246:0.61 247:0.55 248:0.52 253:0.67 255:0.79 256:0.45 259:0.21 260:0.56 261:0.91 265:0.34 266:0.84 267:0.97 268:0.46 271:0.13 276:0.52 279:0.86 282:0.88 283:0.71 285:0.62 287:0.61 290:0.68 297:0.36 299:0.88 300:0.43 +0 12:0.86 17:0.51 21:0.78 27:0.45 28:0.96 30:0.95 34:0.75 36:0.36 37:0.50 39:0.70 43:0.71 55:0.59 66:0.54 69:0.71 74:0.37 91:0.09 98:0.16 108:0.54 123:0.52 127:0.09 129:0.05 131:0.61 135:0.24 145:0.49 146:0.23 147:0.29 149:0.35 154:0.61 157:0.61 159:0.11 161:0.53 166:0.61 167:0.62 172:0.10 173:0.73 177:0.38 178:0.55 179:0.33 181:0.86 182:0.61 187:0.68 216:0.77 222:0.48 227:0.61 229:0.61 231:0.74 235:0.31 241:0.61 243:0.63 246:0.61 253:0.34 254:0.97 259:0.21 265:0.18 267:0.89 271:0.13 287:0.61 300:0.08 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.86 17:0.08 21:0.30 27:0.19 28:0.96 30:0.72 32:0.80 34:0.61 36:0.30 37:0.67 39:0.70 41:0.90 43:0.86 55:0.84 60:0.95 66:0.26 69:0.59 70:0.48 74:0.84 77:0.70 81:0.79 83:0.87 85:0.72 91:0.49 96:0.72 98:0.38 101:0.71 108:0.45 114:0.86 120:0.59 121:0.90 122:0.67 123:0.43 124:0.80 126:0.54 127:0.46 129:0.81 131:0.99 133:0.76 135:0.42 140:0.45 144:0.57 145:0.59 146:0.58 147:0.64 149:0.63 150:0.86 151:0.58 153:0.48 154:0.83 155:0.67 157:0.83 158:0.87 159:0.64 161:0.53 162:0.86 163:0.94 164:0.72 165:0.84 166:0.99 167:0.47 172:0.27 173:0.48 176:0.73 177:0.38 179:0.76 181:0.86 182:0.99 187:0.87 192:0.59 195:0.84 201:0.74 202:0.56 204:0.89 208:0.64 212:0.39 215:0.72 216:0.63 220:0.87 222:0.48 227:0.61 229:0.99 230:0.84 231:0.98 233:0.69 235:0.42 241:0.05 242:0.42 243:0.45 245:0.54 246:0.61 247:0.60 248:0.57 253:0.76 255:0.79 256:0.64 259:0.21 260:0.59 265:0.40 266:0.47 267:0.59 268:0.65 271:0.13 276:0.43 279:0.86 282:0.88 283:0.71 285:0.62 287:0.61 290:0.86 297:0.36 299:0.88 300:0.43 +1 1:0.74 11:0.42 12:0.86 17:0.45 21:0.30 27:0.05 28:0.96 30:0.62 34:0.88 36:0.49 37:0.91 39:0.70 43:0.28 55:0.92 66:0.23 69:0.69 70:0.53 74:0.73 81:0.78 91:0.35 98:0.48 104:0.97 106:0.81 108:0.83 114:0.86 117:0.86 122:0.67 123:0.82 124:0.80 126:0.54 127:0.67 129:0.59 131:0.61 133:0.76 135:0.74 144:0.57 146:0.32 147:0.39 149:0.34 150:0.80 154:0.75 155:0.67 157:0.61 159:0.75 161:0.53 163:0.92 166:0.99 167:0.46 172:0.32 173:0.55 176:0.73 177:0.38 178:0.55 179:0.72 181:0.86 182:0.61 187:0.98 192:0.59 201:0.74 206:0.81 212:0.37 215:0.72 216:0.62 222:0.48 227:0.61 229:0.61 231:0.98 232:0.99 235:0.42 241:0.03 242:0.78 243:0.27 245:0.62 246:0.61 247:0.39 253:0.70 254:0.74 259:0.21 265:0.50 266:0.63 267:0.73 271:0.13 276:0.49 277:0.69 287:0.61 290:0.86 297:0.36 300:0.43 +0 1:0.69 7:0.72 11:0.91 12:0.06 17:0.96 18:0.67 21:0.68 27:0.72 29:0.53 30:0.40 32:0.69 34:0.84 36:0.39 39:0.44 43:0.72 44:0.90 45:0.66 55:0.45 66:0.36 69:0.30 70:0.55 71:0.83 74:0.53 77:0.70 81:0.34 83:0.60 86:0.70 91:0.08 98:0.35 99:0.83 101:0.52 104:0.94 106:0.81 108:0.24 114:0.55 123:0.23 124:0.78 126:0.44 127:0.85 129:0.05 133:0.73 135:0.24 139:0.74 144:0.85 145:0.88 146:0.45 147:0.51 149:0.67 150:0.52 154:0.62 155:0.58 159:0.59 165:0.70 172:0.75 173:0.28 176:0.66 177:0.70 178:0.55 179:0.36 187:0.87 192:0.59 195:0.74 201:0.68 204:0.85 206:0.81 208:0.54 212:0.87 215:0.37 216:0.05 222:0.76 230:0.74 233:0.73 235:0.97 241:0.10 242:0.80 243:0.51 244:0.61 245:0.87 247:0.55 253:0.39 259:0.21 265:0.37 266:0.27 267:0.19 271:0.62 276:0.76 282:0.77 283:0.78 290:0.55 297:0.36 300:0.55 +2 8:0.87 11:0.93 12:0.06 17:0.47 18:1.00 21:0.78 27:0.62 29:0.53 30:0.95 34:0.29 36:0.37 37:0.59 39:0.44 43:0.62 45:0.85 55:0.52 66:0.98 69:0.66 70:0.13 71:0.83 74:0.44 86:0.99 91:0.08 98:0.85 101:0.87 108:0.50 122:0.86 123:0.48 127:0.36 129:0.05 135:0.30 139:0.99 144:0.85 145:0.40 146:0.84 147:0.87 149:0.72 154:0.73 159:0.40 172:0.95 173:0.70 177:0.70 178:0.55 179:0.17 187:0.39 212:0.94 216:0.67 222:0.76 235:0.22 241:0.39 242:0.78 243:0.98 247:0.76 253:0.33 254:0.80 259:0.21 265:0.85 266:0.17 267:0.59 271:0.62 276:0.90 300:0.25 +1 11:0.91 12:0.06 17:0.77 18:0.80 21:0.78 27:0.42 29:0.53 30:0.30 34:0.94 36:0.51 37:0.78 39:0.44 43:0.41 45:0.73 55:0.71 66:0.84 69:0.89 70:0.48 71:0.83 74:0.39 86:0.78 91:0.03 98:0.71 101:0.52 108:0.77 122:0.86 123:0.76 127:0.22 129:0.05 135:0.58 139:0.74 144:0.85 146:0.81 147:0.84 149:0.27 154:0.47 159:0.37 163:0.75 172:0.54 173:0.91 177:0.70 178:0.55 179:0.56 187:0.99 212:0.23 216:0.30 222:0.76 235:0.84 241:0.12 242:0.55 243:0.85 247:0.47 253:0.31 254:0.93 259:0.21 265:0.71 266:0.63 267:0.69 271:0.62 276:0.45 300:0.33 +0 12:0.06 17:1.00 18:0.59 21:0.78 27:0.72 29:0.53 30:0.21 34:0.94 36:0.47 37:0.67 39:0.44 43:0.07 55:0.84 66:0.20 69:0.94 70:0.37 71:0.83 74:0.25 86:0.61 91:0.23 98:0.12 108:0.88 122:0.75 123:0.87 124:0.81 127:0.26 129:0.05 133:0.74 135:0.42 139:0.59 145:0.77 146:0.37 147:0.44 149:0.07 154:0.07 159:0.94 163:0.97 172:0.10 173:0.60 175:0.75 177:0.38 178:0.55 179:0.91 187:0.68 212:0.42 216:0.16 222:0.76 235:0.52 242:0.45 243:0.40 244:0.83 245:0.39 247:0.35 253:0.22 257:0.65 259:0.21 265:0.12 266:0.23 267:0.28 271:0.62 276:0.24 277:0.69 300:0.25 +2 11:0.88 12:0.06 17:0.34 18:0.76 21:0.78 27:0.71 29:0.53 30:0.71 34:0.50 36:0.93 37:0.59 39:0.44 43:0.24 55:0.45 66:0.89 69:0.97 70:0.41 71:0.83 74:0.31 85:0.72 86:0.81 91:0.42 98:0.25 101:0.87 108:0.95 122:0.86 123:0.95 127:0.11 129:0.05 135:0.60 139:0.77 144:0.85 146:0.57 147:0.63 149:0.23 154:0.17 159:0.21 172:0.68 173:0.99 177:0.70 178:0.55 179:0.10 187:0.39 212:0.85 216:0.42 222:0.76 235:0.22 241:0.24 242:0.78 243:0.89 247:0.47 253:0.27 259:0.21 265:0.27 266:0.38 267:0.28 271:0.62 276:0.54 300:0.43 +1 11:0.91 12:0.06 17:0.84 21:0.71 27:0.58 29:0.53 30:0.40 34:0.47 36:0.42 37:0.69 39:0.44 43:0.52 44:0.87 45:0.66 55:0.71 64:0.77 66:0.35 69:0.79 70:0.58 71:0.83 74:0.35 81:0.23 91:0.06 98:0.60 101:0.52 104:0.91 106:0.81 108:0.73 117:0.86 123:0.71 124:0.76 126:0.26 127:0.59 129:0.59 133:0.76 135:0.44 139:0.64 144:0.85 145:0.63 146:0.32 147:0.38 149:0.44 150:0.23 154:0.45 159:0.88 172:0.84 173:0.52 175:0.75 177:0.38 178:0.55 179:0.21 187:0.68 195:0.97 206:0.81 212:0.23 216:0.18 222:0.76 232:0.95 235:0.84 241:0.10 242:0.76 243:0.10 244:0.61 245:0.93 247:0.47 253:0.29 257:0.65 259:0.21 265:0.61 266:0.91 267:0.17 271:0.62 276:0.89 277:0.69 300:0.84 +1 11:0.91 12:0.06 17:0.28 18:0.62 21:0.13 27:0.58 29:0.53 30:0.91 34:0.86 36:0.49 37:0.43 39:0.44 43:0.72 45:0.73 66:0.36 69:0.84 70:0.13 71:0.83 74:0.49 81:0.41 86:0.72 91:0.08 98:0.19 101:0.52 108:0.69 122:0.82 123:0.67 124:0.57 126:0.26 127:0.58 129:0.05 133:0.43 135:0.63 139:0.69 144:0.85 146:0.22 147:0.18 149:0.36 150:0.35 154:0.65 159:0.33 163:0.97 172:0.16 173:0.97 177:0.38 178:0.55 179:0.19 187:0.87 212:0.95 215:0.61 216:0.50 222:0.76 235:0.22 241:0.47 242:0.82 243:0.40 245:0.43 247:0.12 253:0.35 254:0.93 257:0.65 259:0.21 265:0.21 266:0.12 267:0.44 271:0.62 276:0.17 297:0.36 300:0.13 +2 1:0.74 7:0.72 11:0.92 12:0.06 17:0.20 18:0.73 21:0.68 22:0.93 27:0.62 29:0.53 30:0.72 32:0.69 34:0.97 36:0.52 37:0.59 39:0.44 43:0.89 45:0.66 47:0.93 48:0.91 60:0.87 64:0.77 66:0.67 69:0.52 70:0.60 71:0.83 74:0.76 77:0.70 81:0.44 83:0.91 85:0.80 86:0.84 91:0.42 98:0.58 101:0.87 106:0.81 108:0.40 114:0.56 117:0.86 122:0.57 123:0.38 124:0.43 126:0.54 127:0.35 129:0.05 133:0.37 135:0.49 139:0.87 144:0.85 145:0.80 146:0.48 147:0.54 149:0.77 150:0.70 154:0.88 155:0.67 158:0.91 159:0.28 165:0.93 172:0.48 173:0.65 176:0.73 177:0.70 178:0.55 179:0.71 187:0.95 192:0.87 195:0.62 201:0.93 204:0.85 206:0.81 208:0.64 212:0.52 215:0.37 216:0.67 219:0.95 222:0.76 230:0.74 233:0.73 235:0.88 241:0.63 242:0.24 243:0.72 245:0.56 247:0.74 253:0.44 259:0.21 262:0.93 265:0.59 266:0.47 267:0.28 271:0.62 276:0.37 279:0.86 281:0.91 282:0.77 283:0.78 290:0.56 292:0.91 297:0.36 300:0.55 +1 1:0.69 7:0.72 11:0.91 12:0.06 17:0.32 21:0.71 22:0.93 27:0.62 29:0.53 30:0.56 32:0.69 34:0.95 36:0.73 37:0.59 39:0.44 43:0.69 45:0.81 47:0.93 66:0.12 69:0.57 70:0.53 71:0.83 74:0.68 77:0.70 81:0.32 83:0.60 91:0.15 97:0.89 98:0.27 99:0.83 101:0.52 106:0.81 108:0.44 114:0.54 117:0.86 122:0.75 123:0.79 124:0.74 126:0.44 127:0.55 129:0.59 133:0.64 135:0.32 144:0.85 145:0.67 146:0.27 147:0.33 149:0.66 150:0.51 154:0.58 155:0.58 158:0.78 159:0.49 165:0.70 172:0.27 173:0.57 175:0.75 176:0.66 177:0.38 178:0.55 179:0.79 187:0.95 192:0.59 195:0.73 201:0.68 204:0.85 206:0.81 208:0.54 212:0.23 215:0.37 216:0.67 222:0.76 230:0.74 233:0.73 235:0.88 241:0.27 242:0.42 243:0.45 244:0.61 245:0.59 247:0.47 253:0.42 257:0.65 259:0.21 262:0.93 265:0.22 266:0.63 267:0.91 271:0.62 276:0.36 277:0.87 279:0.82 282:0.77 283:0.78 290:0.54 297:0.36 300:0.43 +1 11:0.91 12:0.06 17:0.49 18:0.62 21:0.13 27:0.58 29:0.53 30:0.91 34:0.53 36:0.56 37:0.43 39:0.44 43:0.72 45:0.73 66:0.36 69:0.86 70:0.13 71:0.83 74:0.48 81:0.54 86:0.72 91:0.04 98:0.18 101:0.52 108:0.72 122:0.82 123:0.70 124:0.57 126:0.26 127:0.59 129:0.05 133:0.43 135:0.58 139:0.69 144:0.85 146:0.22 147:0.18 149:0.36 150:0.41 154:0.63 159:0.37 163:0.97 172:0.16 173:0.98 177:0.38 178:0.55 179:0.17 187:0.68 212:0.95 215:0.61 216:0.50 222:0.76 232:0.85 235:0.12 241:0.46 242:0.82 243:0.40 245:0.43 247:0.12 253:0.34 254:0.93 257:0.65 259:0.21 265:0.19 266:0.12 267:0.23 271:0.62 276:0.17 297:0.99 300:0.13 +1 11:0.91 12:0.06 17:0.01 18:0.75 21:0.78 27:0.67 29:0.53 30:0.45 34:0.34 36:0.57 37:0.41 39:0.44 43:0.64 45:0.73 66:0.52 69:0.44 70:0.50 71:0.83 74:0.43 86:0.71 91:0.38 98:0.59 101:0.52 108:0.34 122:0.51 123:0.32 124:0.50 127:0.41 129:0.05 133:0.37 135:0.54 139:0.69 144:0.85 145:0.84 146:0.49 147:0.55 149:0.48 154:0.54 159:0.30 172:0.27 173:0.57 177:0.70 178:0.55 179:0.89 187:0.68 202:0.46 212:0.23 216:0.44 222:0.76 235:0.31 241:0.41 242:0.24 243:0.61 244:0.61 245:0.48 247:0.47 253:0.33 259:0.21 265:0.60 266:0.38 267:0.44 271:0.62 276:0.28 300:0.33 +0 11:0.91 12:0.06 17:0.76 18:0.63 21:0.13 27:0.60 29:0.53 30:0.17 34:0.37 36:0.64 37:0.49 39:0.44 43:0.18 45:0.73 55:0.59 66:0.07 69:0.98 70:0.95 71:0.83 74:0.48 81:0.44 83:0.60 86:0.65 91:0.11 96:0.68 97:0.89 98:0.26 101:0.52 108:0.85 114:0.63 117:0.86 122:0.77 123:0.84 124:0.97 126:0.26 127:0.91 129:0.05 133:0.97 135:0.24 139:0.63 140:0.45 144:0.85 146:0.61 147:0.38 149:0.41 150:0.36 151:0.47 153:0.48 154:0.16 155:0.58 158:0.78 159:0.94 162:0.86 172:0.54 173:0.95 176:0.55 177:0.05 179:0.20 187:0.68 190:0.89 192:0.59 202:0.36 208:0.54 212:0.39 216:0.21 220:0.91 222:0.76 232:0.91 235:0.12 241:0.01 242:0.71 243:0.08 244:0.61 245:0.88 247:0.92 248:0.47 253:0.47 256:0.45 257:0.84 259:0.21 265:0.28 266:0.96 267:0.52 268:0.46 271:0.62 276:0.92 277:0.69 279:0.82 283:0.78 285:0.47 290:0.63 300:0.94 +0 8:0.78 12:0.06 17:0.96 18:0.61 21:0.78 27:0.42 29:0.53 30:0.45 34:0.42 36:0.30 37:0.35 39:0.44 43:0.05 55:0.52 66:0.09 69:0.98 70:0.61 71:0.83 74:0.25 86:0.63 91:0.35 98:0.07 108:0.96 122:0.86 123:0.97 124:0.89 127:0.21 129:0.95 133:0.87 135:0.26 139:0.61 145:0.70 146:0.17 147:0.22 149:0.07 154:0.05 159:0.92 163:0.81 172:0.10 173:0.85 175:0.91 177:0.05 178:0.55 179:0.62 191:0.78 202:0.69 212:0.15 216:0.10 222:0.76 235:0.05 241:0.85 242:0.63 243:0.28 244:0.83 245:0.47 247:0.47 253:0.22 257:0.84 259:0.21 265:0.06 266:0.63 267:0.23 271:0.62 276:0.36 277:0.87 300:0.43 +1 11:0.91 12:0.06 17:0.02 18:0.65 21:0.13 27:0.58 29:0.53 30:0.91 34:0.52 36:0.57 37:0.43 39:0.44 43:0.72 45:0.73 66:0.36 69:0.66 70:0.13 71:0.83 74:0.51 81:0.54 86:0.76 91:0.05 98:0.33 101:0.52 108:0.50 122:0.82 123:0.48 124:0.57 126:0.26 127:0.77 129:0.05 133:0.43 135:0.73 139:0.72 144:0.85 146:0.22 147:0.18 149:0.36 150:0.41 154:0.77 159:0.17 163:0.90 172:0.16 173:0.99 177:0.38 178:0.55 179:0.52 187:0.39 212:0.95 215:0.61 216:0.50 222:0.76 232:0.85 235:0.12 241:0.57 242:0.82 243:0.40 245:0.43 247:0.12 253:0.35 254:0.93 257:0.65 259:0.21 265:0.36 266:0.12 267:0.28 271:0.62 276:0.17 283:0.78 297:0.99 300:0.13 +1 8:0.69 12:0.06 17:0.75 20:0.91 21:0.78 27:0.41 29:0.86 34:0.42 36:0.45 37:0.66 39:0.70 71:0.59 74:0.41 78:0.97 83:0.69 91:0.44 97:0.99 100:0.73 104:0.86 111:0.94 135:0.77 139:0.70 144:0.85 152:0.86 158:0.82 169:0.72 170:0.87 175:0.99 178:0.55 186:0.97 187:0.21 208:0.61 216:0.34 219:0.94 222:0.48 232:0.88 235:0.22 241:0.32 244:0.97 253:0.36 257:0.97 259:0.21 261:0.89 267:0.36 271:0.52 277:0.98 279:0.81 283:0.82 292:0.95 +3 1:0.67 8:0.59 10:0.89 11:0.87 12:0.06 17:0.05 18:0.79 27:0.14 29:0.86 30:0.91 34:0.78 36:0.18 37:0.67 39:0.70 43:0.74 45:0.91 64:0.77 66:0.35 69:0.76 70:0.24 71:0.59 74:0.65 81:0.76 83:0.69 86:0.86 91:0.40 97:0.94 98:0.18 99:0.95 101:0.65 108:0.77 114:0.97 122:0.65 123:0.76 124:0.72 126:0.51 127:0.34 129:0.05 133:0.67 135:0.94 139:0.86 144:0.85 145:0.58 146:0.20 147:0.28 149:0.73 150:0.68 154:0.67 155:0.52 158:0.82 159:0.42 165:0.81 170:0.87 172:0.45 173:0.79 174:0.79 176:0.70 177:0.70 178:0.55 179:0.56 187:0.39 192:0.51 197:0.83 201:0.65 208:0.61 212:0.71 215:0.96 216:0.93 219:0.94 222:0.48 235:0.12 239:0.87 241:0.10 242:0.44 243:0.36 245:0.69 247:0.67 253:0.70 254:0.86 257:0.65 259:0.21 265:0.20 266:0.54 267:0.36 271:0.52 276:0.41 277:0.69 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.33 +0 11:0.87 12:0.06 17:0.38 18:0.80 21:0.54 27:0.27 29:0.86 30:0.94 34:0.90 36:0.57 37:0.46 39:0.70 43:0.77 45:0.88 66:0.60 69:0.27 70:0.19 71:0.59 74:0.64 81:0.26 83:0.69 86:0.86 91:0.18 97:0.94 98:0.35 101:0.65 108:0.57 122:0.65 123:0.55 124:0.48 126:0.24 127:0.26 129:0.59 133:0.46 135:0.65 139:0.86 144:0.85 146:0.17 147:0.22 149:0.81 150:0.28 154:0.69 158:0.82 159:0.24 170:0.87 172:0.45 173:0.42 175:0.75 177:0.70 178:0.55 179:0.63 187:0.39 208:0.61 212:0.81 216:0.70 219:0.94 222:0.48 235:0.60 236:0.92 241:0.30 242:0.55 243:0.33 244:0.61 245:0.60 247:0.35 253:0.48 257:0.65 259:0.21 265:0.37 266:0.27 267:0.76 271:0.52 276:0.29 277:0.69 279:0.78 283:0.82 292:0.95 300:0.18 +0 1:0.67 8:0.58 10:0.89 11:0.87 12:0.06 17:0.06 18:0.79 27:0.33 29:0.86 30:0.91 34:0.83 36:0.18 37:0.41 39:0.70 43:0.74 45:0.91 64:0.77 66:0.35 69:0.76 70:0.24 71:0.59 74:0.65 81:0.76 83:0.69 86:0.86 91:0.42 97:0.94 98:0.18 99:0.95 101:0.65 108:0.77 114:0.97 122:0.65 123:0.76 124:0.72 126:0.51 127:0.34 129:0.05 133:0.67 135:0.96 139:0.86 144:0.85 145:0.58 146:0.20 147:0.28 149:0.73 150:0.68 154:0.67 155:0.52 158:0.82 159:0.42 165:0.81 170:0.87 172:0.45 173:0.79 174:0.79 176:0.70 177:0.70 178:0.55 179:0.56 187:0.39 192:0.51 197:0.83 201:0.65 208:0.61 212:0.71 215:0.96 216:0.88 219:0.94 222:0.48 235:0.12 239:0.87 241:0.10 242:0.44 243:0.36 245:0.69 247:0.67 253:0.70 254:0.86 257:0.65 259:0.21 265:0.20 266:0.54 267:0.52 271:0.52 276:0.41 277:0.69 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.33 +0 11:0.87 12:0.06 17:0.82 18:0.94 21:0.78 22:0.93 27:0.27 29:0.86 30:0.93 34:0.25 36:0.44 37:0.46 39:0.70 43:0.42 45:0.99 47:0.93 66:0.99 69:0.92 70:0.19 71:0.59 74:0.84 86:0.97 91:0.03 98:0.84 101:0.65 102:0.95 104:1.00 106:0.81 108:0.83 117:0.86 122:0.65 123:0.82 127:0.33 129:0.05 135:0.58 139:0.95 144:0.85 145:0.83 146:0.98 147:0.99 149:0.96 154:0.32 159:0.77 170:0.87 172:0.98 173:0.82 175:0.75 177:0.70 178:0.55 179:0.12 187:0.39 195:0.95 206:0.81 212:0.71 216:0.70 222:0.48 232:1.00 235:0.05 239:0.84 241:0.15 242:0.50 243:0.99 244:0.61 247:0.35 253:0.57 257:0.65 259:0.21 262:0.93 265:0.84 266:0.38 267:0.28 271:0.52 276:0.94 277:0.69 300:0.18 +0 7:0.75 8:0.61 10:0.82 11:0.87 12:0.06 17:0.40 18:0.70 21:0.78 27:0.21 29:0.86 30:0.90 34:0.58 36:0.88 37:0.74 39:0.70 43:0.75 45:0.83 66:0.36 69:0.56 70:0.19 71:0.59 74:0.56 83:0.69 86:0.76 91:0.35 98:0.34 101:0.65 108:0.73 122:0.65 123:0.71 124:0.68 127:0.29 129:0.59 133:0.61 135:0.19 139:0.79 144:0.85 146:0.17 147:0.22 149:0.65 154:0.65 155:0.54 159:0.48 163:0.75 170:0.87 172:0.27 173:0.49 174:0.79 175:0.75 177:0.70 178:0.55 179:0.77 187:0.39 192:0.56 197:0.81 202:0.58 204:0.83 208:0.61 212:0.16 216:0.95 222:0.48 230:0.77 233:0.76 235:0.42 239:0.82 241:0.44 242:0.40 243:0.39 244:0.61 245:0.50 247:0.39 253:0.44 257:0.65 259:0.21 265:0.36 266:0.54 267:0.76 271:0.52 276:0.31 277:0.69 281:0.91 300:0.18 +0 1:0.67 6:0.79 8:0.59 10:0.89 11:0.87 12:0.06 17:0.01 18:0.79 20:0.90 27:0.15 29:0.86 30:0.91 34:0.83 36:0.18 37:0.58 39:0.70 43:0.74 45:0.91 64:0.77 66:0.35 69:0.76 70:0.24 71:0.59 74:0.65 78:1.00 81:0.76 83:0.69 86:0.86 91:0.42 97:0.94 98:0.18 99:0.95 100:0.97 101:0.65 108:0.77 111:0.98 114:0.97 122:0.65 123:0.76 124:0.72 126:0.51 127:0.34 129:0.05 132:0.97 133:0.67 135:0.95 139:0.86 144:0.85 145:0.58 146:0.20 147:0.28 149:0.73 150:0.68 152:0.84 154:0.67 155:0.52 158:0.82 159:0.42 165:0.81 169:0.95 170:0.87 172:0.45 173:0.79 174:0.79 176:0.70 177:0.70 178:0.55 179:0.56 186:0.99 187:0.39 189:0.81 192:0.51 197:0.83 201:0.65 208:0.61 212:0.71 215:0.96 216:0.68 219:0.94 222:0.48 235:0.12 238:0.84 239:0.87 241:0.10 242:0.44 243:0.36 245:0.69 247:0.67 253:0.70 254:0.86 257:0.65 259:0.21 261:0.93 265:0.20 266:0.54 267:0.44 271:0.52 276:0.41 277:0.69 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.33 +1 1:0.61 10:0.92 11:0.87 12:0.06 17:0.38 18:0.84 21:0.54 27:0.02 29:0.86 30:0.66 32:0.72 34:0.75 36:0.43 37:0.92 39:0.70 43:0.75 44:0.92 45:0.92 64:0.77 66:0.36 69:0.44 70:0.72 71:0.59 74:0.71 77:0.70 81:0.63 83:0.69 86:0.89 91:0.06 98:0.53 99:0.95 101:0.65 108:0.34 114:0.76 122:0.95 123:0.32 124:0.70 126:0.51 127:0.42 129:0.05 133:0.60 135:0.61 139:0.89 144:0.85 145:0.49 146:0.75 147:0.79 149:0.88 150:0.50 154:0.66 155:0.48 159:0.64 165:0.81 170:0.87 172:0.65 173:0.30 174:0.94 176:0.70 177:0.88 178:0.55 179:0.38 187:0.39 192:0.47 195:0.84 197:0.94 201:0.60 208:0.61 212:0.48 215:0.58 216:0.99 222:0.48 235:0.74 239:0.91 241:0.32 242:0.41 243:0.51 244:0.61 245:0.84 247:0.79 253:0.61 259:0.21 265:0.55 266:0.71 267:0.44 271:0.52 276:0.70 282:0.80 290:0.76 297:0.36 300:0.84 +1 1:0.65 10:0.89 11:0.87 12:0.06 17:0.02 18:0.76 21:0.22 27:0.33 29:0.86 30:0.91 32:0.72 34:0.52 36:0.30 37:0.41 39:0.70 43:0.74 44:0.92 45:0.85 64:0.77 66:0.45 69:0.79 70:0.21 71:0.59 74:0.61 77:0.70 81:0.70 83:0.69 86:0.84 91:0.31 98:0.22 99:0.95 101:0.65 108:0.44 114:0.88 122:0.65 123:0.43 124:0.57 126:0.51 127:0.15 129:0.05 133:0.45 135:0.94 139:0.84 144:0.85 145:0.49 146:0.22 147:0.37 149:0.66 150:0.58 154:0.59 155:0.50 159:0.23 163:0.90 165:0.81 170:0.87 172:0.32 173:0.78 174:0.79 176:0.70 177:0.70 178:0.55 179:0.36 187:0.68 192:0.49 195:0.76 197:0.82 201:0.63 208:0.61 212:0.50 215:0.79 216:0.88 222:0.48 235:0.42 239:0.86 241:0.05 242:0.53 243:0.58 245:0.59 247:0.55 253:0.63 254:0.94 257:0.65 259:0.21 265:0.24 266:0.27 267:0.36 271:0.52 276:0.29 277:0.69 282:0.80 290:0.88 297:0.36 300:0.25 +0 1:0.65 8:0.58 10:0.89 11:0.87 12:0.06 18:0.70 21:0.22 27:0.14 29:0.86 30:0.90 32:0.72 34:0.75 36:0.19 37:0.67 39:0.70 43:0.74 44:0.92 45:0.83 64:0.77 66:0.36 69:0.77 70:0.19 71:0.59 74:0.56 77:0.70 81:0.70 83:0.69 86:0.80 91:0.27 98:0.17 99:0.95 101:0.65 108:0.44 114:0.88 122:0.65 123:0.43 124:0.59 126:0.51 127:0.16 129:0.05 133:0.45 135:0.90 139:0.80 144:0.85 145:0.49 146:0.22 147:0.28 149:0.48 150:0.58 154:0.61 155:0.50 159:0.20 165:0.81 170:0.87 172:0.27 173:0.83 174:0.79 176:0.70 177:0.70 178:0.55 179:0.47 187:0.68 192:0.49 195:0.65 197:0.82 201:0.63 208:0.61 212:0.55 215:0.79 216:0.93 222:0.48 235:0.42 239:0.87 241:0.02 242:0.40 243:0.51 245:0.56 247:0.55 253:0.63 254:0.94 257:0.65 259:0.21 265:0.18 266:0.27 267:0.36 271:0.52 276:0.23 277:0.69 282:0.80 290:0.88 297:0.36 300:0.18 +1 11:0.87 12:0.06 17:0.32 18:0.77 21:0.54 27:0.27 29:0.86 30:0.91 34:0.96 36:0.43 37:0.46 39:0.70 43:0.77 45:0.85 66:0.82 69:0.25 70:0.13 71:0.59 74:0.55 81:0.26 86:0.83 91:0.22 98:0.72 101:0.65 108:0.20 122:0.65 123:0.19 126:0.24 127:0.22 129:0.05 135:0.47 139:0.79 144:0.85 146:0.59 147:0.65 149:0.76 150:0.28 154:0.69 159:0.22 163:0.75 170:0.87 172:0.48 173:0.41 175:0.75 177:0.70 178:0.55 179:0.64 187:0.39 212:0.30 216:0.70 222:0.48 235:0.60 236:0.92 241:0.32 242:0.53 243:0.83 244:0.61 247:0.55 253:0.43 257:0.65 259:0.21 265:0.72 266:0.47 267:0.94 271:0.52 276:0.36 277:0.69 300:0.13 +0 10:0.83 11:0.87 12:0.06 17:0.02 18:0.61 21:0.78 27:0.14 29:0.86 30:0.76 34:0.78 36:0.20 37:0.67 39:0.70 43:0.60 45:0.73 66:0.54 69:0.86 70:0.22 71:0.59 74:0.36 86:0.67 91:0.37 98:0.35 101:0.65 108:0.73 122:0.65 123:0.71 124:0.45 127:0.18 129:0.05 133:0.37 135:0.97 139:0.66 144:0.85 145:0.74 146:0.41 147:0.05 149:0.37 154:0.49 159:0.25 163:0.75 170:0.87 172:0.21 173:0.91 174:0.79 175:0.75 177:0.05 178:0.55 179:0.79 187:0.39 197:0.83 212:0.42 216:0.93 222:0.48 235:0.60 239:0.83 241:0.05 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.32 257:0.93 259:0.21 265:0.37 266:0.23 267:0.44 271:0.52 276:0.21 277:0.69 300:0.18 +0 11:0.87 12:0.06 17:0.59 18:0.88 21:0.54 27:0.27 29:0.86 30:0.90 34:0.75 36:0.49 37:0.46 39:0.70 43:0.76 45:0.96 66:0.71 69:0.43 70:0.21 71:0.59 74:0.72 81:0.26 86:0.92 91:0.18 98:0.43 101:0.65 108:0.75 122:0.65 123:0.74 124:0.46 126:0.24 127:0.44 129:0.59 133:0.61 135:0.69 139:0.90 144:0.85 146:0.42 147:0.49 149:0.91 150:0.28 154:0.67 159:0.46 170:0.87 172:0.80 173:0.42 175:0.75 177:0.70 178:0.55 179:0.36 187:0.39 212:0.88 216:0.70 222:0.48 235:0.60 236:0.92 241:0.30 242:0.45 243:0.23 244:0.61 245:0.77 247:0.67 253:0.51 257:0.65 259:0.21 265:0.45 266:0.54 267:0.79 271:0.52 276:0.70 277:0.69 300:0.33 +0 11:0.87 12:0.06 17:0.55 18:0.89 21:0.54 27:0.27 29:0.86 30:0.91 34:0.75 36:0.49 37:0.46 39:0.70 43:0.76 45:0.96 66:0.77 69:0.43 70:0.19 71:0.59 74:0.78 81:0.26 83:0.69 86:0.93 91:0.15 97:0.94 98:0.49 101:0.65 108:0.73 122:0.65 123:0.71 124:0.41 126:0.24 127:0.45 129:0.59 133:0.46 135:0.61 139:0.93 144:0.85 146:0.21 147:0.27 149:0.92 150:0.28 154:0.67 158:0.82 159:0.44 170:0.87 172:0.82 173:0.45 175:0.75 177:0.70 178:0.55 179:0.36 187:0.39 208:0.61 212:0.91 216:0.70 219:0.94 222:0.48 235:0.60 236:0.92 241:0.27 242:0.50 243:0.19 244:0.61 245:0.81 247:0.39 253:0.54 257:0.65 259:0.21 265:0.50 266:0.38 267:0.59 271:0.52 276:0.58 277:0.69 279:0.78 283:0.82 292:0.95 300:0.25 +0 1:0.65 7:0.75 8:0.58 9:0.75 11:0.87 12:0.06 17:0.64 18:0.72 21:0.22 22:0.93 27:0.09 29:0.86 30:0.94 31:0.94 32:0.72 34:0.92 36:0.54 37:0.44 39:0.70 43:0.64 44:0.92 45:0.87 47:0.93 64:0.77 66:0.83 69:0.81 70:0.19 71:0.59 74:0.64 77:0.89 79:0.93 81:0.70 83:0.69 86:0.78 91:0.37 96:0.80 98:0.62 99:0.95 101:0.65 104:0.99 106:0.81 108:0.65 114:0.88 117:0.86 120:0.69 122:0.65 123:0.63 126:0.51 127:0.18 129:0.05 135:0.97 137:0.94 139:0.84 140:0.45 144:0.85 145:0.69 146:0.62 147:0.67 149:0.66 150:0.58 151:0.86 153:0.48 154:0.54 155:0.64 159:0.23 162:0.86 164:0.56 165:0.81 170:0.87 172:0.51 173:0.88 175:0.75 176:0.70 177:0.70 179:0.49 187:0.39 190:0.77 192:0.98 195:0.66 196:0.95 201:0.63 202:0.36 204:0.83 206:0.81 208:0.61 212:0.28 215:0.79 216:0.40 222:0.48 230:0.77 232:0.99 233:0.66 235:0.42 241:0.37 242:0.45 243:0.84 244:0.61 247:0.35 248:0.77 253:0.82 255:0.77 256:0.45 257:0.65 259:0.21 260:0.70 262:0.93 265:0.63 266:0.38 267:0.79 268:0.46 271:0.52 276:0.34 277:0.69 281:0.86 282:0.80 284:0.88 285:0.60 290:0.88 297:0.36 300:0.18 +0 11:0.87 12:0.06 17:0.43 18:0.72 21:0.54 27:0.27 29:0.86 30:0.95 34:0.83 36:0.57 37:0.46 39:0.70 43:0.76 45:0.81 66:0.30 69:0.39 70:0.13 71:0.59 74:0.48 81:0.26 86:0.78 91:0.27 98:0.12 101:0.65 108:0.30 122:0.65 123:0.29 124:0.61 126:0.24 127:0.31 129:0.59 133:0.46 135:0.63 139:0.74 144:0.85 146:0.17 147:0.22 149:0.64 150:0.28 154:0.68 159:0.29 170:0.87 172:0.16 173:0.49 175:0.75 177:0.70 178:0.55 179:0.89 187:0.39 212:0.30 216:0.70 222:0.48 235:0.60 236:0.92 241:0.44 242:0.63 243:0.48 244:0.61 245:0.47 247:0.35 253:0.39 257:0.65 259:0.21 265:0.13 266:0.27 267:0.59 271:0.52 276:0.13 277:0.69 300:0.13 +1 1:0.65 7:0.75 8:0.58 9:0.75 11:0.87 12:0.06 17:0.45 18:0.73 21:0.22 22:0.93 27:0.09 29:0.86 30:0.94 31:0.94 32:0.72 34:0.94 36:0.57 37:0.44 39:0.70 43:0.65 44:0.92 45:0.87 47:0.93 64:0.77 66:0.83 69:0.81 70:0.19 71:0.59 74:0.64 77:0.89 79:0.93 81:0.70 83:0.69 86:0.79 91:0.42 96:0.80 98:0.62 99:0.95 101:0.65 104:0.99 106:0.81 108:0.65 114:0.88 117:0.86 120:0.69 122:0.65 123:0.62 126:0.51 127:0.18 129:0.05 135:0.96 137:0.94 139:0.84 140:0.45 144:0.85 145:0.69 146:0.62 147:0.67 149:0.66 150:0.58 151:0.86 153:0.48 154:0.55 155:0.64 159:0.23 162:0.86 164:0.56 165:0.81 170:0.87 172:0.51 173:0.87 175:0.75 176:0.70 177:0.70 179:0.49 187:0.39 190:0.77 192:0.98 195:0.66 196:0.95 201:0.63 202:0.36 204:0.83 206:0.81 208:0.61 212:0.28 215:0.79 216:0.40 222:0.48 230:0.77 232:0.99 233:0.66 235:0.42 241:0.35 242:0.45 243:0.84 244:0.61 247:0.35 248:0.77 253:0.82 255:0.77 256:0.45 257:0.65 259:0.21 260:0.70 262:0.93 265:0.63 266:0.38 267:0.85 268:0.46 271:0.52 276:0.34 277:0.69 281:0.86 282:0.80 284:0.88 285:0.60 290:0.88 297:0.36 300:0.18 +0 1:0.57 12:0.06 17:0.95 21:0.76 22:0.93 27:0.71 29:0.86 34:0.47 36:0.81 37:0.23 39:0.70 47:0.93 64:0.77 71:0.59 81:0.58 83:0.69 91:0.15 99:0.95 104:0.96 106:0.81 114:0.64 117:0.86 126:0.51 135:0.39 144:0.85 145:0.79 150:0.42 155:0.46 165:0.81 170:0.87 175:0.99 176:0.70 178:0.55 187:0.39 192:0.45 193:0.97 201:0.56 206:0.81 208:0.61 215:0.46 216:0.25 222:0.48 232:0.96 235:0.98 239:0.84 241:0.41 244:0.97 253:0.49 257:0.97 259:0.21 262:0.93 267:0.44 271:0.52 277:0.98 290:0.64 297:0.36 +1 1:0.67 10:0.89 11:0.87 12:0.06 17:0.03 18:0.76 21:0.05 27:0.14 29:0.86 30:0.91 32:0.72 34:0.33 36:0.34 37:0.67 39:0.70 43:0.74 44:0.92 45:0.85 64:0.77 66:0.45 69:0.79 70:0.21 71:0.59 74:0.61 77:0.70 81:0.74 83:0.69 86:0.84 91:0.25 98:0.20 99:0.95 101:0.65 108:0.44 114:0.95 122:0.65 123:0.42 124:0.57 126:0.51 127:0.14 129:0.05 133:0.45 135:0.78 139:0.84 144:0.85 145:0.49 146:0.22 147:0.37 149:0.66 150:0.64 154:0.62 155:0.51 159:0.22 165:0.81 170:0.87 172:0.32 173:0.74 174:0.79 176:0.70 177:0.70 178:0.55 179:0.30 187:0.68 192:0.51 195:0.81 197:0.82 201:0.64 208:0.61 212:0.61 215:0.91 216:0.93 222:0.48 235:0.22 239:0.87 241:0.01 242:0.53 243:0.58 245:0.59 247:0.55 253:0.67 254:0.92 257:0.65 259:0.21 265:0.21 266:0.27 267:0.87 271:0.52 276:0.28 277:0.69 282:0.80 290:0.95 297:0.36 300:0.25 +0 11:0.87 12:0.06 17:0.40 18:0.82 21:0.54 27:0.27 29:0.86 30:0.94 34:0.87 36:0.55 37:0.46 39:0.70 43:0.77 45:0.90 66:0.64 69:0.27 70:0.19 71:0.59 74:0.67 81:0.26 83:0.69 86:0.87 91:0.25 97:0.94 98:0.40 101:0.65 108:0.62 122:0.65 123:0.60 124:0.45 126:0.24 127:0.31 129:0.59 133:0.46 135:0.68 139:0.87 144:0.85 146:0.17 147:0.22 149:0.83 150:0.28 154:0.69 158:0.82 159:0.29 170:0.87 172:0.54 173:0.39 175:0.75 177:0.70 178:0.55 179:0.60 187:0.39 208:0.61 212:0.85 216:0.70 219:0.94 222:0.48 235:0.60 236:0.92 241:0.24 242:0.54 243:0.28 244:0.61 245:0.64 247:0.35 253:0.49 257:0.65 259:0.21 265:0.42 266:0.27 267:0.59 271:0.52 276:0.34 277:0.69 279:0.78 283:0.82 292:0.95 300:0.25 +2 1:0.74 7:0.81 11:0.88 12:0.20 17:0.57 21:0.22 27:0.72 30:0.45 34:0.58 36:0.97 39:0.53 43:0.97 46:0.96 66:0.36 69:0.12 70:0.33 74:0.75 76:0.85 81:0.80 83:0.76 85:0.80 91:0.75 98:0.59 101:0.78 104:0.72 107:0.96 108:0.10 110:0.96 114:0.90 121:0.90 122:0.51 123:0.10 124:0.69 125:0.88 126:0.54 127:0.84 129:0.59 133:0.64 135:0.89 144:0.85 145:0.74 146:0.49 147:0.55 149:0.70 150:0.87 154:0.97 155:0.67 159:0.37 160:0.88 163:0.75 165:0.86 172:0.21 173:0.30 176:0.73 177:0.38 178:0.55 179:0.93 187:0.21 192:0.59 201:0.74 204:0.89 208:0.64 212:0.23 215:0.79 216:0.04 222:0.84 230:0.84 232:0.77 233:0.69 235:0.31 241:0.77 242:0.55 243:0.51 245:0.45 247:0.39 253:0.72 259:0.21 265:0.60 266:0.38 267:0.23 271:0.62 276:0.30 289:0.97 290:0.90 297:0.36 300:0.25 +3 9:0.80 12:0.20 17:0.53 21:0.78 27:0.72 34:0.37 36:0.67 39:0.53 41:0.82 46:0.88 60:0.87 74:0.47 83:0.86 91:0.88 96:0.86 104:0.58 107:0.88 110:0.88 120:0.77 125:0.97 135:0.47 140:0.45 144:0.85 151:0.95 153:0.83 155:0.67 158:0.92 160:0.96 162:0.55 164:0.65 175:0.91 192:0.59 202:0.62 208:0.64 216:0.13 222:0.84 232:0.76 235:0.05 241:0.91 244:0.83 248:0.84 253:0.81 255:0.79 256:0.45 257:0.84 259:0.21 260:0.77 267:0.36 268:0.58 271:0.62 277:0.87 279:0.86 283:0.82 285:0.62 289:0.97 +1 11:0.88 12:0.20 17:0.32 21:0.78 27:0.45 30:0.76 34:0.83 36:0.18 37:0.50 39:0.53 43:0.75 46:0.94 66:0.64 69:0.83 70:0.15 74:0.47 85:0.72 91:0.07 98:0.25 101:0.71 107:0.92 108:0.68 110:0.92 122:0.51 123:0.66 125:0.88 127:0.11 129:0.05 135:0.72 144:0.85 145:0.49 146:0.34 147:0.41 149:0.41 154:0.66 159:0.14 160:0.88 163:0.98 172:0.16 173:0.84 177:0.38 178:0.55 179:0.46 187:0.68 212:0.95 216:0.77 222:0.84 235:0.84 241:0.21 242:0.82 243:0.69 247:0.12 253:0.40 259:0.21 265:0.27 266:0.12 267:0.36 271:0.62 276:0.13 289:0.90 300:0.13 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.20 17:0.16 21:0.30 27:0.21 30:0.68 34:0.52 36:0.81 37:0.74 39:0.53 41:0.82 43:0.98 46:0.94 60:0.87 66:0.54 69:0.12 70:0.43 74:0.79 81:0.75 83:0.84 85:0.85 91:0.22 96:0.77 98:0.20 101:0.73 104:0.84 106:0.81 107:0.95 108:0.10 110:0.95 114:0.86 117:0.86 120:0.65 121:0.90 122:0.43 123:0.10 124:0.55 125:0.98 126:0.54 127:0.50 129:0.05 133:0.62 135:0.42 140:0.87 144:0.85 146:0.41 147:0.47 149:0.80 150:0.84 151:0.77 153:0.48 154:0.98 155:0.67 158:0.92 159:0.82 160:0.96 162:0.54 164:0.57 165:0.84 172:0.32 173:0.09 176:0.73 177:0.38 178:0.48 179:0.88 187:0.39 192:0.59 201:0.74 202:0.56 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 220:0.62 222:0.84 230:0.87 232:0.88 233:0.76 235:0.42 241:0.21 242:0.63 243:0.63 245:0.45 247:0.30 248:0.71 253:0.81 255:0.79 256:0.45 259:0.21 260:0.66 265:0.21 266:0.38 267:0.44 268:0.46 271:0.62 276:0.20 279:0.86 283:0.82 285:0.62 289:0.97 290:0.86 297:0.36 300:0.33 +2 1:0.74 11:0.88 12:0.20 17:0.82 21:0.05 27:0.72 30:0.76 34:0.67 36:0.33 39:0.53 43:0.98 46:0.97 66:0.64 69:0.07 70:0.15 74:0.55 81:0.89 83:0.79 85:0.72 91:0.76 98:0.44 101:0.76 107:0.93 108:0.06 110:0.93 114:0.95 120:0.65 122:0.51 123:0.06 125:0.88 126:0.54 127:0.14 129:0.05 135:0.51 140:0.80 144:0.85 146:0.26 147:0.32 149:0.56 150:0.94 151:0.61 153:0.48 154:0.98 155:0.67 159:0.11 160:0.88 162:0.62 164:0.55 165:0.90 172:0.16 173:0.46 176:0.73 177:0.38 178:0.42 179:0.80 190:0.77 192:0.59 201:0.74 202:0.50 212:0.95 215:0.91 216:0.03 220:0.62 222:0.84 235:0.12 241:0.86 242:0.82 243:0.69 247:0.12 248:0.59 253:0.72 256:0.45 259:0.21 260:0.65 265:0.46 266:0.12 267:0.17 268:0.46 271:0.62 276:0.14 285:0.50 289:0.97 290:0.95 297:0.36 300:0.13 +1 1:0.74 7:0.81 9:0.80 11:0.88 12:0.20 17:0.34 20:0.99 21:0.54 27:0.30 30:0.91 32:0.80 34:0.92 36:0.96 37:0.29 39:0.53 41:0.92 43:0.87 46:0.95 66:0.27 69:0.55 70:0.13 74:0.70 77:0.70 78:0.90 81:0.61 83:0.82 85:0.80 91:0.61 96:0.93 98:0.09 100:0.94 101:0.75 107:0.93 108:0.43 110:0.94 111:0.91 114:0.77 117:0.86 120:0.88 121:0.90 122:0.43 123:0.41 124:0.61 125:0.99 126:0.54 127:0.25 129:0.59 133:0.47 135:0.93 140:0.45 144:0.85 145:0.50 146:0.13 147:0.18 149:0.62 150:0.75 151:0.98 152:0.91 153:0.92 154:0.86 155:0.67 159:0.37 160:0.98 162:0.69 163:0.88 164:0.79 165:0.79 169:0.92 172:0.10 173:0.54 176:0.73 177:0.38 179:0.93 186:0.90 187:0.68 192:0.59 195:0.72 201:0.74 202:0.71 204:0.89 208:0.64 212:0.61 215:0.58 216:0.63 222:0.84 230:0.87 232:0.88 233:0.76 235:0.68 241:0.57 242:0.63 243:0.46 245:0.40 247:0.30 248:0.93 253:0.93 255:0.79 256:0.68 259:0.21 260:0.89 261:0.94 265:0.10 266:0.17 267:0.44 268:0.74 271:0.62 276:0.12 282:0.88 285:0.62 289:0.97 290:0.77 297:0.36 299:0.88 300:0.13 +1 1:0.74 11:0.88 12:0.20 17:0.13 21:0.22 27:0.72 30:0.76 34:0.94 36:0.86 39:0.53 43:0.90 46:0.95 66:0.36 69:0.45 70:0.15 74:0.52 81:0.80 83:0.76 85:0.72 91:0.54 98:0.12 101:0.74 104:0.72 107:0.92 108:0.35 110:0.93 114:0.90 122:0.51 123:0.34 124:0.52 125:0.88 126:0.54 127:0.28 129:0.05 133:0.39 135:0.37 144:0.85 146:0.13 147:0.18 149:0.54 150:0.87 154:0.89 155:0.67 159:0.23 160:0.88 163:0.93 165:0.86 172:0.10 173:0.63 176:0.73 177:0.38 178:0.55 179:0.98 187:0.21 192:0.59 201:0.74 212:0.95 215:0.79 216:0.04 222:0.84 232:0.77 235:0.31 241:0.50 242:0.82 243:0.51 245:0.37 247:0.12 253:0.68 259:0.21 265:0.12 266:0.12 267:0.23 271:0.62 276:0.12 289:0.97 290:0.90 297:0.36 300:0.13 +2 1:0.74 7:0.81 8:0.70 9:0.80 11:0.88 12:0.20 17:0.43 20:0.91 21:0.22 27:0.71 30:0.45 34:0.45 36:0.98 37:0.32 39:0.53 41:0.96 43:0.97 46:0.96 66:0.36 69:0.12 70:0.33 74:0.75 76:0.85 78:0.91 81:0.80 83:0.82 85:0.80 91:0.82 96:0.95 98:0.59 100:0.73 101:0.78 104:0.76 107:0.96 108:0.10 110:0.96 111:0.88 114:0.90 120:0.88 121:0.90 122:0.51 123:0.10 124:0.69 125:0.98 126:0.54 127:0.84 129:0.59 133:0.64 135:0.90 140:0.45 144:0.85 145:0.74 146:0.49 147:0.55 149:0.70 150:0.87 151:0.91 152:0.95 153:0.48 154:0.97 155:0.67 159:0.37 160:0.99 162:0.81 163:0.75 164:0.86 165:0.86 169:0.72 172:0.21 173:0.30 176:0.73 177:0.38 179:0.93 186:0.91 190:0.77 192:0.59 201:0.74 202:0.80 204:0.89 208:0.64 212:0.23 215:0.79 216:0.14 220:0.62 222:0.84 230:0.84 232:0.81 233:0.69 235:0.31 241:0.96 242:0.55 243:0.51 245:0.45 247:0.39 248:0.93 253:0.95 255:0.79 256:0.85 259:0.21 260:0.89 261:0.92 265:0.60 266:0.38 267:0.36 268:0.85 271:0.62 276:0.30 285:0.62 289:0.97 290:0.90 297:0.36 300:0.25 +0 9:0.80 12:0.20 17:0.73 21:0.05 27:0.72 34:0.30 36:0.64 39:0.53 41:0.82 46:0.88 81:0.78 83:0.76 91:0.80 96:0.80 107:0.88 110:0.88 114:0.56 120:0.69 125:1.00 126:0.32 135:0.24 140:0.45 144:0.85 150:0.58 151:0.87 153:0.48 155:0.67 160:0.96 162:0.86 164:0.57 175:0.91 176:0.53 192:0.59 202:0.36 208:0.64 216:0.02 222:0.84 232:0.74 235:0.05 241:1.00 244:0.83 248:0.78 253:0.74 255:0.79 256:0.45 257:0.84 259:0.21 260:0.70 267:0.28 268:0.46 271:0.62 277:0.87 285:0.62 289:0.97 290:0.56 +2 1:0.74 9:0.80 11:0.88 12:0.20 17:0.81 20:0.88 21:0.30 27:0.40 30:0.45 34:0.34 36:0.90 37:0.69 39:0.53 41:0.82 43:0.79 46:0.95 55:0.84 60:0.87 66:0.61 69:0.75 70:0.72 74:0.85 78:0.72 81:0.75 83:0.86 85:0.85 91:0.31 96:0.80 98:0.81 100:0.95 101:0.76 104:0.98 107:0.97 108:0.71 110:0.97 111:0.88 114:0.86 117:0.86 120:0.69 122:0.67 123:0.69 124:0.50 125:1.00 126:0.54 127:0.45 129:0.59 133:0.47 135:0.66 140:0.45 144:0.85 145:0.79 146:0.26 147:0.32 149:0.74 150:0.84 151:0.87 152:0.83 153:0.48 154:0.73 155:0.67 158:0.92 159:0.58 160:0.96 162:0.86 163:0.92 164:0.57 165:0.84 169:0.93 172:0.59 173:0.70 176:0.73 177:0.38 179:0.60 186:0.73 187:0.87 192:0.59 201:0.74 202:0.36 208:0.64 212:0.30 215:0.72 216:0.30 222:0.84 232:0.96 235:0.42 241:0.07 242:0.57 243:0.34 245:0.74 247:0.39 248:0.78 253:0.85 255:0.79 256:0.45 259:0.21 260:0.70 261:0.90 265:0.81 266:0.75 267:0.84 268:0.46 271:0.62 276:0.57 279:0.86 283:0.82 285:0.62 289:0.97 290:0.86 297:0.36 300:0.55 +1 1:0.74 11:0.88 12:0.20 17:0.72 21:0.13 27:0.72 30:0.91 34:0.61 36:0.57 39:0.53 43:0.84 46:0.97 55:0.71 66:0.69 69:0.63 70:0.13 74:0.44 81:0.84 83:0.77 85:0.72 91:0.78 98:0.59 101:0.76 107:0.94 108:0.48 110:0.93 114:0.92 122:0.43 123:0.46 125:0.88 126:0.54 127:0.17 129:0.05 135:0.28 144:0.85 146:0.32 147:0.38 149:0.54 150:0.90 154:0.81 155:0.67 159:0.13 160:0.88 165:0.88 172:0.21 173:0.91 176:0.73 177:0.38 178:0.55 179:0.84 187:0.21 192:0.59 201:0.74 212:0.61 215:0.84 222:0.84 232:0.77 235:0.22 241:0.77 242:0.63 243:0.73 247:0.30 253:0.69 259:0.21 265:0.60 266:0.17 267:0.23 271:0.62 276:0.21 289:0.97 290:0.92 297:0.36 300:0.13 +0 1:0.74 11:0.88 12:0.20 17:0.70 21:0.71 30:0.56 32:0.80 34:0.61 36:0.25 37:0.97 39:0.53 43:0.80 46:0.97 60:0.87 66:0.35 69:0.74 70:0.71 74:0.88 77:0.70 81:0.49 83:0.86 85:0.96 91:0.25 98:0.49 101:0.82 104:0.82 107:0.99 108:0.78 110:0.98 114:0.68 122:0.43 123:0.76 124:0.77 125:0.88 126:0.54 127:0.55 129:0.81 133:0.76 135:0.61 144:0.85 145:0.65 146:0.58 147:0.64 149:0.93 150:0.66 154:0.74 155:0.67 158:0.87 159:0.75 160:0.88 165:0.69 172:0.65 173:0.59 176:0.73 177:0.38 178:0.55 179:0.41 187:0.21 192:0.59 195:0.89 201:0.74 208:0.64 212:0.27 215:0.48 216:0.98 222:0.84 232:0.87 235:0.88 241:0.07 242:0.53 243:0.40 245:0.80 247:0.47 253:0.69 259:0.21 265:0.50 266:0.92 267:0.28 271:0.62 276:0.72 279:0.86 282:0.88 283:0.71 289:0.97 290:0.68 297:0.36 299:0.88 300:0.70 +1 1:0.74 11:0.88 12:0.20 17:0.03 21:0.22 27:0.72 30:0.76 34:0.97 36:0.88 39:0.53 43:0.85 46:0.95 66:0.36 69:0.62 70:0.15 74:0.51 81:0.80 83:0.76 85:0.72 91:0.54 98:0.12 101:0.74 104:0.72 107:0.92 108:0.47 110:0.93 114:0.90 122:0.51 123:0.46 124:0.52 125:0.88 126:0.54 127:0.24 129:0.05 133:0.39 135:0.42 144:0.85 146:0.13 147:0.18 149:0.50 150:0.87 154:0.81 155:0.67 159:0.21 160:0.88 163:0.93 165:0.86 172:0.10 173:0.79 176:0.73 177:0.38 178:0.55 179:0.97 187:0.21 192:0.59 201:0.74 212:0.95 215:0.79 216:0.04 222:0.84 232:0.77 235:0.31 241:0.50 242:0.82 243:0.51 245:0.37 247:0.12 253:0.67 259:0.21 265:0.12 266:0.12 267:0.28 271:0.62 276:0.12 289:0.97 290:0.90 297:0.36 300:0.13 +1 11:0.88 12:0.20 17:0.32 21:0.78 27:0.45 30:0.95 34:0.62 36:0.17 37:0.50 39:0.53 43:0.78 46:0.93 66:0.54 69:0.78 74:0.41 85:0.72 91:0.06 98:0.07 101:0.70 107:0.90 108:0.61 110:0.90 123:0.59 125:0.88 127:0.06 129:0.05 135:0.85 144:0.85 145:0.50 146:0.13 147:0.18 149:0.42 154:0.71 159:0.08 160:0.88 172:0.10 173:0.65 177:0.38 178:0.55 179:0.08 187:0.68 216:0.77 222:0.84 235:0.22 241:0.27 243:0.63 253:0.37 259:0.21 265:0.08 267:0.36 271:0.62 289:0.89 300:0.08 +1 1:0.74 11:0.88 12:0.20 17:0.06 21:0.22 27:0.72 30:0.45 34:0.96 36:0.55 39:0.53 43:0.97 46:0.97 66:0.52 69:0.12 70:0.33 74:0.69 81:0.80 83:0.76 85:0.80 91:0.67 98:0.57 101:0.79 104:0.72 107:0.96 108:0.52 110:0.96 114:0.90 122:0.67 123:0.50 124:0.50 125:0.88 126:0.54 127:0.40 129:0.59 133:0.47 135:0.30 144:0.85 146:0.13 147:0.18 149:0.71 150:0.87 154:0.97 155:0.67 159:0.23 160:0.88 163:0.75 165:0.86 172:0.27 173:0.39 176:0.73 177:0.38 178:0.55 179:0.89 187:0.21 192:0.59 201:0.74 212:0.52 215:0.79 216:0.04 222:0.84 232:0.77 235:0.31 241:0.47 242:0.55 243:0.34 245:0.48 247:0.39 253:0.71 259:0.21 265:0.58 266:0.27 267:0.82 271:0.62 276:0.27 289:0.97 290:0.90 297:0.36 300:0.25 +2 1:0.74 7:0.81 11:0.64 12:0.79 17:0.51 21:0.40 26:0.99 27:0.21 28:0.80 30:0.38 34:0.40 36:0.87 37:0.74 39:0.85 43:0.97 58:0.93 66:0.63 69:0.17 70:0.65 74:0.89 81:0.73 83:0.75 85:0.91 91:0.14 98:0.70 101:0.82 108:0.14 114:0.82 117:0.86 121:0.90 122:0.43 123:0.13 124:0.48 126:0.54 127:0.33 129:0.59 131:0.61 133:0.47 135:0.18 141:0.91 144:0.57 146:0.71 147:0.76 149:0.90 150:0.83 154:0.97 155:0.67 157:0.92 159:0.39 161:0.61 165:0.83 166:0.93 167:0.67 172:0.63 173:0.24 176:0.73 177:0.38 178:0.55 179:0.50 181:0.60 182:0.89 187:0.39 192:0.59 199:0.97 201:0.74 202:0.36 204:0.89 208:0.64 212:0.55 215:0.67 216:0.95 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 230:0.84 231:0.89 232:0.91 233:0.76 234:0.91 235:0.52 241:0.21 242:0.37 243:0.68 245:0.75 246:0.94 247:0.67 253:0.74 259:0.21 265:0.70 266:0.47 267:0.28 271:0.27 274:0.91 276:0.59 287:0.61 290:0.82 297:0.36 300:0.55 +3 1:0.74 6:0.98 8:0.85 9:0.80 11:0.64 12:0.79 17:0.67 20:0.82 21:0.13 26:0.99 27:0.16 28:0.80 30:0.09 34:0.23 36:0.94 37:0.84 39:0.85 41:0.93 43:0.31 55:0.52 58:0.99 66:0.12 69:0.99 70:0.85 74:0.60 78:0.83 81:0.87 83:0.81 85:0.72 91:0.58 96:0.90 98:0.09 100:0.90 101:0.64 104:0.58 108:0.50 111:0.85 114:0.92 120:0.83 122:0.57 123:0.48 124:0.93 126:0.54 127:0.88 129:0.05 131:0.96 133:0.93 135:0.63 140:0.80 141:1.00 144:0.57 145:0.74 146:0.13 147:0.29 149:0.33 150:0.92 151:0.96 152:0.97 153:0.85 154:0.11 155:0.67 157:0.75 159:0.95 161:0.61 162:0.64 163:0.80 164:0.79 165:0.88 166:0.93 167:0.78 169:0.99 172:0.16 173:1.00 176:0.73 177:0.05 178:0.42 179:0.83 181:0.60 182:0.90 186:0.84 187:0.21 189:0.94 192:0.59 199:0.96 201:0.74 202:0.72 208:0.64 212:0.13 215:0.84 216:0.53 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.22 238:0.94 241:0.64 242:0.42 243:0.33 245:0.46 246:0.94 247:0.47 248:0.90 253:0.89 255:0.79 256:0.71 257:0.65 259:0.21 260:0.84 261:0.99 265:0.09 266:0.75 267:0.59 268:0.74 271:0.27 274:0.99 276:0.43 277:0.69 285:0.62 287:0.95 290:0.92 297:0.36 300:0.55 +2 1:0.74 9:0.80 11:0.64 12:0.79 17:0.24 21:0.13 26:0.99 27:0.20 28:0.80 30:0.33 32:0.80 34:0.36 36:0.46 37:0.77 39:0.85 41:0.82 43:0.62 58:0.98 60:0.95 66:0.20 69:0.91 70:0.49 74:0.74 77:0.70 81:0.87 83:0.85 85:0.80 91:0.42 96:0.80 98:0.21 101:0.74 104:0.79 108:0.67 114:0.92 120:0.69 122:0.41 123:0.65 124:0.81 126:0.54 127:0.33 129:0.59 131:0.95 133:0.76 135:0.58 140:0.80 141:1.00 144:0.57 145:0.52 146:0.13 147:0.36 149:0.52 150:0.92 151:0.87 153:0.48 154:0.52 155:0.67 157:0.83 158:0.87 159:0.48 161:0.61 162:0.53 163:0.79 164:0.57 165:0.88 166:0.93 167:0.74 172:0.16 173:0.96 176:0.73 177:0.05 178:0.42 179:0.83 181:0.60 182:0.90 187:0.39 192:0.59 195:0.74 199:0.96 201:0.74 202:0.57 208:0.64 212:0.19 215:0.84 216:0.83 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 232:0.86 234:0.99 235:0.22 241:0.53 242:0.19 243:0.40 245:0.46 246:0.94 247:0.55 248:0.78 253:0.84 255:0.79 256:0.45 257:0.65 259:0.21 260:0.70 265:0.23 266:0.47 267:0.76 268:0.46 271:0.27 274:0.97 276:0.32 277:0.69 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 6:0.96 8:0.93 9:0.80 11:0.64 12:0.79 17:0.91 20:0.98 21:0.05 26:0.99 27:0.54 28:0.80 30:0.45 34:0.55 36:0.54 37:0.88 39:0.85 41:0.82 43:0.58 58:0.98 66:0.49 69:0.94 70:0.25 74:0.50 78:0.84 81:0.91 83:0.79 85:0.72 91:0.66 96:0.84 98:0.23 100:0.92 101:0.71 104:0.54 108:0.87 111:0.86 114:0.95 120:0.66 122:0.51 123:0.86 124:0.48 126:0.54 127:0.88 129:0.05 131:0.95 133:0.39 135:0.32 140:0.80 141:1.00 144:0.57 146:0.34 147:0.05 149:0.34 150:0.96 151:0.79 152:0.92 153:0.77 154:0.47 155:0.67 157:0.78 159:0.67 161:0.61 162:0.86 163:0.90 164:0.62 165:0.90 166:0.93 167:0.99 169:0.89 172:0.16 173:0.99 176:0.73 177:0.05 179:0.98 181:0.60 182:0.90 186:0.84 189:0.97 192:0.59 199:0.95 201:0.74 202:0.55 208:0.64 212:0.61 215:0.91 216:0.12 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.76 234:0.99 235:0.12 238:0.97 241:0.89 242:0.63 243:0.29 245:0.39 246:0.94 247:0.30 248:0.72 253:0.83 255:0.79 256:0.58 257:0.65 259:0.21 260:0.67 261:0.89 265:0.25 266:0.17 267:0.73 268:0.64 271:0.27 274:0.97 276:0.14 285:0.62 287:0.95 290:0.95 297:0.36 300:0.18 +3 1:0.74 6:0.81 7:0.81 8:0.59 9:0.80 11:0.64 12:0.79 17:0.24 20:0.85 21:0.40 26:0.99 27:0.21 28:0.80 30:0.45 34:0.62 36:0.92 37:0.74 39:0.85 41:0.88 43:0.97 58:0.98 60:0.95 66:0.90 69:0.17 70:0.53 74:0.92 78:0.77 81:0.73 83:0.85 85:0.93 91:0.27 96:0.82 98:0.85 100:0.79 101:0.84 104:0.84 106:0.81 108:0.14 111:0.76 114:0.82 117:0.86 120:0.72 121:0.90 122:0.43 123:0.13 126:0.54 127:0.36 129:0.05 131:0.96 135:0.18 140:0.45 141:1.00 144:0.57 146:0.82 147:0.85 149:0.93 150:0.83 151:0.87 152:0.90 153:0.48 154:0.97 155:0.67 157:0.93 158:0.92 159:0.38 161:0.61 162:0.86 164:0.69 165:0.83 166:0.93 167:0.68 169:0.76 172:0.73 173:0.26 176:0.73 177:0.38 179:0.50 181:0.60 182:0.90 186:0.78 187:0.39 192:0.59 199:0.98 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.58 215:0.67 216:0.95 220:0.91 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 230:0.84 231:0.89 232:0.91 233:0.76 234:0.99 235:0.52 241:0.30 242:0.51 243:0.91 246:0.94 247:0.67 248:0.79 253:0.87 255:0.79 256:0.58 259:0.21 260:0.72 261:0.79 265:0.85 266:0.47 267:0.69 268:0.62 271:0.27 274:0.98 276:0.61 279:0.86 283:0.82 285:0.62 287:0.95 290:0.82 297:0.36 300:0.43 +2 1:0.74 9:0.80 11:0.64 12:0.79 17:0.30 21:0.13 26:0.99 27:0.20 28:0.80 30:0.33 32:0.80 34:0.37 36:0.44 37:0.77 39:0.85 41:0.82 43:0.62 58:0.98 60:0.87 66:0.20 69:0.91 70:0.49 74:0.74 77:0.70 81:0.87 83:0.86 85:0.80 91:0.53 96:0.75 98:0.21 101:0.74 104:0.79 108:0.67 114:0.92 120:0.63 122:0.41 123:0.65 124:0.81 126:0.54 127:0.33 129:0.59 131:0.95 133:0.76 135:0.54 140:0.45 141:1.00 144:0.57 145:0.52 146:0.13 147:0.36 149:0.52 150:0.92 151:0.70 153:0.69 154:0.52 155:0.67 157:0.83 158:0.87 159:0.48 161:0.61 162:0.86 163:0.79 164:0.62 165:0.88 166:0.93 167:0.74 172:0.16 173:0.96 176:0.73 177:0.05 179:0.83 181:0.60 182:0.90 187:0.21 192:0.59 195:0.74 199:0.96 201:0.74 202:0.46 208:0.64 212:0.19 215:0.84 216:0.83 220:0.74 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 232:0.86 234:0.99 235:0.22 241:0.53 242:0.19 243:0.40 245:0.46 246:0.94 247:0.55 248:0.66 253:0.79 255:0.79 256:0.45 257:0.65 259:0.21 260:0.64 265:0.23 266:0.47 267:0.76 268:0.55 271:0.27 274:0.97 276:0.32 277:0.69 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.64 12:0.79 17:0.30 21:0.13 26:0.99 27:0.20 28:0.80 30:0.38 32:0.80 34:0.42 36:0.46 37:0.77 39:0.85 43:0.62 55:0.52 58:0.93 60:0.95 66:0.20 69:0.92 70:0.45 74:0.74 77:0.70 81:0.87 83:0.84 85:0.80 91:0.48 98:0.19 101:0.74 104:0.79 108:0.78 114:0.92 123:0.77 124:0.81 126:0.54 127:0.34 129:0.59 131:0.61 133:0.76 135:0.44 141:0.91 144:0.57 145:0.52 146:0.13 147:0.33 149:0.56 150:0.92 154:0.52 155:0.67 157:0.83 158:0.87 159:0.49 161:0.61 163:0.88 165:0.88 166:0.93 167:0.70 172:0.21 173:0.96 176:0.73 177:0.05 178:0.55 179:0.74 181:0.60 182:0.90 187:0.39 192:0.59 195:0.74 199:0.97 201:0.74 202:0.50 208:0.64 212:0.42 215:0.84 216:0.83 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.89 232:0.86 234:0.91 235:0.22 241:0.39 242:0.45 243:0.34 245:0.53 246:0.94 247:0.60 253:0.73 257:0.65 259:0.21 265:0.21 266:0.38 267:0.76 271:0.27 274:0.91 276:0.38 277:0.69 279:0.86 282:0.88 283:0.71 287:0.61 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.64 12:0.79 17:0.61 21:0.22 26:0.99 27:0.72 28:0.80 30:0.71 34:0.23 36:0.46 39:0.85 43:0.69 58:0.93 66:0.16 69:0.88 70:0.29 74:0.54 81:0.83 83:0.76 85:0.88 91:0.09 98:0.18 101:0.76 104:0.54 108:0.84 114:0.90 122:0.43 123:0.83 124:0.86 126:0.54 127:0.23 129:0.89 131:0.61 133:0.83 135:0.21 141:0.91 144:0.57 145:0.61 146:0.13 147:0.18 149:0.64 150:0.89 154:0.58 155:0.67 157:0.88 159:0.53 161:0.61 163:0.92 165:0.86 166:0.93 167:0.64 172:0.16 173:0.83 176:0.73 177:0.38 178:0.55 179:0.65 181:0.60 182:0.89 187:0.39 192:0.59 199:0.96 201:0.74 212:0.30 215:0.79 216:0.06 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.89 232:0.76 234:0.91 235:0.31 241:0.10 242:0.53 243:0.28 245:0.48 246:0.94 247:0.47 253:0.68 259:0.21 265:0.20 266:0.54 267:0.69 271:0.27 274:0.91 276:0.37 287:0.61 290:0.90 297:0.36 300:0.25 +1 1:0.74 11:0.64 12:0.79 17:0.59 21:0.22 26:0.99 27:0.72 28:0.80 30:0.62 34:0.23 36:0.51 39:0.85 43:0.69 55:0.96 58:0.93 66:0.16 69:0.88 70:0.34 74:0.38 81:0.83 83:0.76 85:0.72 91:0.08 98:0.17 101:0.70 104:0.54 108:0.77 114:0.90 123:0.84 124:0.81 126:0.54 127:0.28 129:0.05 131:0.61 133:0.74 135:0.17 141:0.91 144:0.57 145:0.61 146:0.13 147:0.18 149:0.40 150:0.89 154:0.58 155:0.67 157:0.77 159:0.60 161:0.61 163:0.98 165:0.86 166:0.93 167:0.64 172:0.10 173:0.83 176:0.73 177:0.38 178:0.55 179:0.88 181:0.60 182:0.89 187:0.39 192:0.59 199:0.96 201:0.74 212:0.61 215:0.79 216:0.06 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.89 232:0.76 234:0.91 235:0.31 241:0.10 242:0.63 243:0.37 245:0.42 246:0.94 247:0.35 253:0.67 259:0.21 265:0.08 266:0.23 267:0.84 271:0.27 274:0.91 276:0.27 277:0.69 287:0.61 290:0.90 297:0.36 300:0.25 +1 1:0.74 9:0.80 11:0.64 12:0.79 17:0.26 21:0.22 26:0.99 27:0.70 28:0.80 30:0.11 34:0.45 36:0.84 37:0.28 39:0.85 41:0.82 43:0.71 58:0.98 66:0.30 69:0.40 70:0.54 74:0.50 81:0.83 83:0.78 85:0.72 91:0.25 96:0.80 98:0.26 101:0.73 108:0.31 114:0.90 120:0.69 123:0.30 124:0.61 126:0.54 127:0.48 129:0.59 131:0.95 133:0.47 135:0.56 140:0.45 141:1.00 144:0.57 146:0.29 147:0.35 149:0.47 150:0.89 151:0.87 153:0.48 154:0.61 155:0.67 157:0.79 159:0.39 161:0.61 162:0.86 164:0.57 165:0.86 166:0.93 167:0.70 172:0.16 173:0.47 176:0.73 177:0.38 179:0.93 181:0.60 182:0.90 187:0.21 192:0.59 199:0.96 201:0.74 202:0.36 208:0.64 212:0.30 215:0.79 216:0.41 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 234:1.00 235:0.31 241:0.37 242:0.33 243:0.48 245:0.47 246:0.94 247:0.39 248:0.78 253:0.81 255:0.79 256:0.45 259:0.21 260:0.70 265:0.28 266:0.27 267:0.91 268:0.46 271:0.27 274:0.97 276:0.16 285:0.62 287:0.95 290:0.90 297:0.36 300:0.33 +2 1:0.74 6:0.98 8:0.89 9:0.80 11:0.64 12:0.79 17:0.20 20:0.91 21:0.05 26:0.99 27:0.16 28:0.80 30:0.14 34:0.25 36:0.93 37:0.84 39:0.85 41:0.92 43:0.90 55:0.92 58:0.99 66:0.59 69:0.45 70:0.94 74:0.81 78:0.89 81:0.91 83:0.81 85:0.88 91:0.35 96:0.88 98:0.54 100:0.95 101:0.75 104:0.58 108:0.35 111:0.92 114:0.95 120:0.80 122:0.51 123:0.34 124:0.67 126:0.54 127:0.76 129:0.81 131:0.96 133:0.76 135:0.90 140:0.80 141:1.00 144:0.57 145:0.74 146:0.86 147:0.89 149:0.79 150:0.96 151:0.87 152:0.97 153:0.48 154:0.89 155:0.67 157:0.87 159:0.83 161:0.61 162:0.83 163:0.81 164:0.77 165:0.90 166:0.93 167:0.74 169:0.99 172:0.65 173:0.24 176:0.73 177:0.38 178:0.42 179:0.58 181:0.60 182:0.90 186:0.89 187:0.68 189:0.96 192:0.59 199:0.98 201:0.74 202:0.66 208:0.64 212:0.22 215:0.91 216:0.53 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.12 238:0.96 241:0.55 242:0.30 243:0.67 245:0.68 246:0.94 247:0.60 248:0.87 253:0.90 255:0.79 256:0.71 259:0.21 260:0.81 261:0.99 265:0.56 266:0.80 267:0.69 268:0.72 271:0.27 274:0.99 276:0.56 285:0.62 287:0.95 290:0.95 297:0.36 300:0.84 +0 12:0.79 17:0.34 21:0.78 26:0.98 27:0.45 28:0.80 30:0.21 32:0.72 34:0.82 36:0.23 37:0.50 39:0.85 43:0.30 55:0.52 58:0.93 66:0.36 69:0.71 70:0.50 74:0.59 91:0.10 98:0.41 108:0.54 122:0.57 123:0.52 124:0.59 127:0.24 129:0.59 131:0.61 133:0.47 135:0.47 141:0.91 145:0.45 146:0.57 147:0.63 149:0.32 154:0.50 157:0.61 159:0.43 161:0.61 163:0.98 166:0.61 167:0.73 172:0.27 173:0.65 177:0.38 178:0.55 179:0.71 181:0.60 182:0.61 187:0.68 195:0.79 199:0.95 202:0.36 212:0.37 216:0.77 222:0.35 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 234:0.91 235:0.68 241:0.50 242:0.58 243:0.51 245:0.56 246:0.61 247:0.39 253:0.47 254:0.92 259:0.21 265:0.43 266:0.47 267:0.28 271:0.27 274:0.91 276:0.35 287:0.61 300:0.33 +1 1:0.74 6:0.86 8:0.74 9:0.80 11:0.64 12:0.79 17:0.22 20:0.88 21:0.47 26:0.99 27:0.06 28:0.80 30:0.21 32:0.80 34:0.45 36:0.71 37:0.82 39:0.85 41:0.90 43:0.75 58:0.99 60:0.97 66:0.54 69:0.83 70:0.37 74:0.66 77:0.70 78:0.83 81:0.77 83:0.82 85:0.72 91:0.42 96:0.78 98:0.24 100:0.83 101:0.72 108:0.67 111:0.82 114:0.80 120:0.67 123:0.65 124:0.45 126:0.54 127:0.17 129:0.05 131:0.96 133:0.39 135:0.68 140:0.45 141:1.00 144:0.57 145:0.44 146:0.31 147:0.05 149:0.45 150:0.84 151:0.79 152:0.89 153:0.69 154:0.65 155:0.67 157:0.79 158:0.92 159:0.24 161:0.61 162:0.86 164:0.75 165:0.80 166:0.93 167:0.73 169:0.80 172:0.21 173:0.87 176:0.73 177:0.05 179:0.76 181:0.60 182:0.90 186:0.83 187:0.39 189:0.89 191:0.83 192:0.59 195:0.68 199:0.98 201:0.74 202:0.61 208:0.64 212:0.42 215:0.63 216:0.92 220:0.93 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 234:0.99 235:0.68 238:0.87 241:0.50 242:0.45 243:0.26 245:0.40 246:0.94 247:0.35 248:0.73 253:0.79 255:0.79 256:0.64 257:0.65 259:0.21 260:0.68 261:0.85 265:0.26 266:0.23 267:0.44 268:0.69 271:0.27 274:0.99 276:0.18 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.80 297:0.36 299:0.88 300:0.25 +4 1:0.74 6:0.98 8:0.97 9:0.80 11:0.64 12:0.79 17:0.13 20:0.99 21:0.05 26:0.99 27:0.16 28:0.80 30:0.21 34:0.20 36:0.76 37:0.84 39:0.85 41:0.99 43:0.43 58:1.00 60:0.99 66:0.16 69:0.15 70:0.64 74:0.71 78:0.97 81:0.91 83:0.90 85:0.85 91:0.94 96:0.99 98:0.21 100:1.00 101:0.73 104:0.58 108:0.12 111:1.00 114:0.95 120:0.97 122:0.51 123:0.12 124:0.85 126:0.54 127:0.93 129:0.81 131:0.96 133:0.83 135:0.28 138:0.99 140:0.45 141:1.00 144:0.57 145:0.74 146:0.41 147:0.47 149:0.58 150:0.96 151:0.99 152:0.97 153:0.97 154:0.27 155:0.67 157:0.85 158:0.92 159:0.83 161:0.61 162:0.80 163:0.79 164:0.96 165:0.90 166:0.93 167:1.00 169:0.99 172:0.21 173:0.10 176:0.73 177:0.38 179:0.81 181:0.60 182:0.90 186:0.97 189:0.99 191:0.93 192:0.59 199:1.00 201:0.74 202:0.92 208:0.64 212:0.22 215:0.91 216:0.53 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.12 238:0.99 241:0.93 242:0.22 243:0.37 245:0.54 246:0.94 247:0.67 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.97 261:0.99 265:0.23 266:0.75 267:0.93 268:0.95 271:0.27 274:1.00 276:0.38 279:0.86 283:0.82 285:0.62 287:0.95 290:0.95 297:0.36 300:0.43 +0 1:0.74 6:0.94 8:0.88 9:0.80 11:0.64 12:0.79 17:0.20 20:0.88 21:0.05 26:0.99 27:0.16 28:0.80 30:0.21 34:0.76 36:0.59 37:0.80 39:0.85 41:0.82 43:0.75 58:0.99 66:0.54 69:0.73 70:0.37 74:0.49 78:0.77 81:0.94 83:0.79 85:0.72 91:0.57 96:0.79 98:0.65 100:0.83 101:0.74 104:0.92 106:0.81 108:0.57 111:0.78 114:0.95 120:0.77 122:0.41 123:0.54 124:0.45 126:0.54 127:0.40 129:0.05 131:0.96 133:0.39 135:0.54 140:0.45 141:1.00 144:0.57 146:0.54 147:0.05 149:0.49 150:0.97 151:0.81 152:0.94 153:0.48 154:0.66 155:0.67 157:0.79 159:0.30 161:0.61 162:0.86 163:0.89 164:0.68 165:0.90 166:0.93 167:0.68 169:0.79 172:0.21 173:0.87 176:0.73 177:0.05 179:0.94 181:0.60 182:0.90 186:0.78 187:0.39 189:0.97 190:0.77 192:0.59 199:0.97 201:0.74 202:0.53 206:0.81 208:0.64 212:0.42 215:0.91 216:0.45 220:0.62 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 234:1.00 235:0.22 238:0.95 241:0.27 242:0.45 243:0.26 245:0.40 246:0.94 247:0.35 248:0.73 253:0.80 255:0.79 256:0.58 257:0.65 259:0.21 260:0.77 261:0.81 265:0.66 266:0.23 267:0.19 268:0.62 271:0.27 274:0.98 276:0.24 283:0.82 285:0.62 287:0.95 290:0.95 297:0.36 300:0.25 +0 1:0.74 11:0.64 12:0.79 17:0.64 21:0.78 26:0.99 27:0.45 28:0.80 30:0.95 34:0.74 36:0.18 37:0.50 39:0.85 43:0.82 58:0.93 66:0.54 69:0.71 74:0.43 81:0.41 83:0.71 85:0.72 91:0.09 98:0.09 101:0.72 108:0.54 114:0.63 123:0.52 126:0.54 127:0.07 129:0.05 131:0.61 135:0.82 141:0.91 144:0.57 145:0.50 146:0.13 147:0.18 149:0.46 150:0.61 154:0.77 155:0.67 157:0.78 159:0.08 161:0.61 165:0.63 166:0.92 167:0.67 172:0.10 173:0.70 176:0.73 177:0.38 178:0.55 179:0.08 181:0.60 182:0.88 187:0.68 192:0.59 199:0.94 201:0.74 215:0.43 216:0.77 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.87 234:0.91 235:1.00 241:0.21 243:0.63 246:0.93 253:0.50 259:0.21 265:0.09 267:0.28 271:0.27 274:0.91 287:0.61 290:0.63 297:0.36 300:0.08 +2 1:0.74 8:0.94 9:0.80 11:0.64 12:0.79 17:0.12 21:0.13 26:0.99 27:0.16 28:0.80 30:0.33 34:0.23 36:0.75 37:0.84 39:0.85 41:0.90 43:0.78 58:0.99 66:0.32 69:0.79 70:0.49 74:0.71 81:0.87 83:0.80 85:0.80 91:0.51 96:0.86 98:0.22 101:0.75 104:0.58 108:0.42 114:0.92 120:0.78 122:0.57 123:0.40 124:0.72 126:0.54 127:0.43 129:0.59 131:0.96 133:0.64 135:0.18 140:0.80 141:1.00 144:0.57 145:0.74 146:0.13 147:0.36 149:0.63 150:0.92 151:0.87 153:0.48 154:0.70 155:0.67 157:0.83 159:0.48 161:0.61 162:0.60 163:0.88 164:0.73 165:0.88 166:0.93 167:0.79 172:0.21 173:0.80 176:0.73 177:0.05 178:0.42 179:0.87 181:0.60 182:0.90 187:0.21 192:0.59 199:0.96 201:0.74 202:0.68 208:0.64 212:0.42 215:0.84 216:0.53 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.22 241:0.67 242:0.45 243:0.49 245:0.48 246:0.94 247:0.39 248:0.85 253:0.87 255:0.79 256:0.64 257:0.65 259:0.21 260:0.78 265:0.24 266:0.38 267:0.79 268:0.68 271:0.27 274:0.98 276:0.26 277:0.69 285:0.62 287:0.95 290:0.92 297:0.36 300:0.33 +0 6:0.95 8:0.88 9:0.80 12:0.06 17:0.11 20:0.98 21:0.78 25:0.88 27:0.25 28:0.68 30:0.76 34:0.22 36:0.57 37:0.74 39:0.33 41:0.88 43:0.24 55:0.52 66:0.72 69:0.85 70:0.22 74:0.39 78:0.95 83:0.77 91:0.95 96:0.88 98:0.34 100:0.98 104:0.80 108:0.71 111:0.97 120:0.98 123:0.69 127:0.12 129:0.05 135:0.20 140:1.00 145:0.38 146:0.31 147:0.38 149:0.27 151:0.90 152:0.92 153:0.91 154:0.16 155:0.67 159:0.13 161:0.60 162:0.46 164:0.98 167:0.88 169:0.97 172:0.27 173:0.98 177:0.38 178:0.51 179:0.42 181:0.93 186:0.95 189:0.96 191:0.92 192:0.59 202:1.00 208:0.64 212:0.78 216:0.50 220:0.74 222:0.35 235:0.12 238:0.97 241:0.86 242:0.75 243:0.75 244:0.61 247:0.30 248:0.86 253:0.82 255:0.79 256:0.97 259:0.21 260:0.98 261:0.97 265:0.36 266:0.17 267:0.65 268:0.97 271:0.90 276:0.24 285:0.62 300:0.18 +1 1:0.74 6:0.90 8:0.81 9:0.80 11:0.57 12:0.06 17:0.57 20:0.88 21:0.40 27:0.34 28:0.68 30:0.17 32:0.80 34:0.44 36:0.94 37:0.57 39:0.33 41:0.88 43:0.86 60:0.87 66:0.72 69:0.58 70:0.78 74:0.91 77:0.96 78:0.83 81:0.71 83:0.85 85:0.91 91:0.51 96:0.74 98:0.82 100:0.88 101:0.81 104:0.91 106:0.81 108:0.60 111:0.83 114:0.82 117:0.86 120:0.62 122:0.41 123:0.57 124:0.43 126:0.54 127:0.44 129:0.59 133:0.47 135:0.94 140:0.45 145:0.92 146:0.27 147:0.33 149:0.86 150:0.81 151:0.64 152:0.83 153:0.77 154:0.83 155:0.67 158:0.87 159:0.51 161:0.60 162:0.86 164:0.70 165:0.81 167:0.59 169:0.86 172:0.74 173:0.54 176:0.73 177:0.38 179:0.46 181:0.93 186:0.83 187:0.21 189:0.91 192:0.59 195:0.72 201:0.74 202:0.55 206:0.81 208:0.64 212:0.57 215:0.67 216:0.76 220:0.82 222:0.35 235:0.60 238:0.92 241:0.39 242:0.19 243:0.23 245:0.77 247:0.60 248:0.63 253:0.80 255:0.79 256:0.58 259:0.21 260:0.62 261:0.85 265:0.82 266:0.54 267:0.98 268:0.64 271:0.90 276:0.67 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.97 300:0.55 +2 1:0.74 7:0.81 11:0.57 12:0.06 17:0.89 21:0.22 27:0.28 28:0.68 30:0.27 34:0.85 36:0.93 39:0.33 43:0.98 55:0.52 60:0.87 66:0.51 69:0.12 70:0.89 74:0.94 76:0.85 81:0.80 83:0.81 85:0.94 91:0.48 98:0.64 101:0.81 104:0.93 106:0.81 108:0.10 114:0.90 117:0.86 121:0.90 123:0.10 124:0.65 126:0.54 127:0.86 129:0.81 133:0.67 135:0.24 146:0.85 147:0.88 149:0.92 150:0.87 154:0.98 155:0.67 158:0.92 159:0.74 161:0.60 165:0.86 167:0.48 172:0.79 173:0.12 176:0.73 177:0.38 178:0.55 179:0.36 181:0.93 187:0.21 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.60 215:0.79 216:0.24 222:0.35 230:0.87 233:0.76 235:0.42 242:0.29 243:0.61 245:0.89 247:0.67 253:0.78 259:0.21 265:0.65 266:0.87 267:0.52 271:0.90 276:0.79 279:0.86 283:0.82 290:0.90 297:0.36 300:0.70 +0 6:0.97 8:0.95 9:0.80 12:0.06 17:0.28 20:0.98 21:0.78 27:0.16 28:0.68 34:0.21 36:0.40 37:0.54 39:0.33 41:0.88 43:0.35 66:0.36 69:0.54 74:0.56 78:0.94 83:0.78 91:0.90 96:0.94 98:0.07 100:0.97 108:0.42 111:0.96 120:0.88 123:0.40 127:0.05 129:0.05 135:0.21 140:0.97 145:0.49 146:0.05 147:0.05 149:0.14 151:0.90 152:0.90 153:0.86 154:0.26 155:0.67 158:0.85 159:0.05 161:0.60 162:0.52 164:0.85 167:0.89 169:0.96 172:0.05 173:0.45 175:0.75 177:0.05 178:0.50 181:0.93 186:0.94 189:0.98 191:0.97 192:0.59 202:0.89 208:0.64 216:0.41 220:0.62 222:0.35 235:0.05 238:0.97 241:0.88 243:0.51 244:0.61 248:0.87 253:0.92 255:0.79 256:0.86 257:0.65 259:0.21 260:0.89 261:0.95 265:0.07 267:0.94 268:0.86 271:0.90 277:0.69 285:0.62 +1 11:0.57 12:0.06 17:0.13 21:0.22 25:0.88 27:0.36 28:0.68 30:0.38 34:0.98 36:0.80 37:0.78 39:0.33 43:0.82 55:0.71 66:0.93 69:0.67 70:0.61 74:0.71 81:0.55 85:0.72 91:0.38 98:0.92 101:0.72 104:0.72 108:0.51 123:0.49 126:0.32 127:0.53 129:0.05 135:0.84 146:0.91 147:0.93 149:0.70 150:0.42 154:0.78 159:0.52 161:0.60 167:0.56 172:0.81 173:0.68 177:0.38 178:0.55 179:0.44 181:0.93 187:0.87 212:0.73 215:0.79 216:0.66 222:0.35 235:0.22 241:0.27 242:0.73 243:0.93 247:0.60 253:0.53 259:0.21 265:0.92 266:0.38 267:0.73 271:0.90 276:0.71 297:0.36 300:0.55 +0 6:0.82 8:0.62 9:0.80 12:0.06 17:0.13 20:0.91 21:0.78 27:0.43 28:0.68 30:0.13 34:0.42 36:0.67 37:0.45 39:0.33 41:0.82 43:0.38 55:0.71 66:0.10 69:0.45 70:0.94 74:0.42 78:0.76 83:0.76 91:0.60 96:0.80 98:0.27 100:0.78 104:0.58 108:0.96 111:0.75 120:0.69 123:0.96 124:0.94 127:0.89 129:0.05 133:0.94 135:0.84 140:0.94 146:0.35 147:0.42 149:0.55 151:0.87 152:0.92 153:0.48 154:0.69 155:0.67 159:0.90 161:0.60 162:0.48 164:0.57 167:0.57 169:0.77 172:0.32 173:0.16 177:0.38 178:0.52 179:0.52 181:0.93 186:0.77 187:0.21 189:0.83 192:0.59 202:0.68 208:0.64 212:0.12 216:0.37 222:0.35 235:0.05 238:0.85 241:0.32 242:0.81 243:0.25 245:0.67 247:0.39 248:0.78 253:0.74 254:0.94 255:0.79 256:0.45 259:0.21 260:0.70 261:0.80 265:0.29 266:0.96 267:0.91 268:0.46 271:0.90 276:0.69 277:0.69 285:0.62 300:0.84 +2 1:0.74 6:0.82 8:0.63 9:0.80 12:0.06 17:0.13 20:0.96 21:0.22 25:0.88 27:0.36 28:0.68 30:0.41 34:0.56 36:0.47 37:0.78 39:0.33 43:0.41 55:0.52 66:0.86 69:0.35 70:0.57 74:0.55 78:0.80 81:0.79 91:0.68 96:0.75 98:0.92 100:0.81 104:0.72 108:0.27 111:0.79 114:0.90 117:0.86 120:0.63 123:0.26 126:0.54 127:0.54 129:0.05 135:0.61 140:0.45 146:0.72 147:0.76 149:0.58 150:0.81 151:0.64 152:0.92 153:0.77 154:0.31 155:0.67 158:0.85 159:0.29 161:0.60 162:0.86 164:0.70 167:0.71 169:0.78 172:0.59 173:0.53 176:0.73 177:0.38 179:0.73 181:0.93 186:0.80 187:0.87 189:0.84 192:0.59 201:0.74 202:0.55 208:0.64 212:0.78 215:0.79 216:0.66 220:0.82 222:0.35 235:0.42 238:0.84 241:0.65 242:0.77 243:0.86 244:0.61 247:0.39 248:0.67 253:0.76 255:0.79 256:0.58 259:0.21 260:0.64 261:0.83 265:0.92 266:0.38 267:0.36 268:0.64 271:0.90 276:0.48 285:0.62 290:0.90 297:0.36 300:0.43 +2 11:0.57 12:0.06 17:0.30 21:0.22 25:0.88 27:0.36 28:0.68 30:0.42 34:0.96 36:0.75 37:0.78 39:0.33 43:0.83 55:0.71 66:0.93 69:0.43 70:0.64 74:0.71 81:0.55 85:0.72 91:0.56 98:0.96 101:0.72 104:0.72 108:0.33 120:0.61 123:0.32 126:0.32 127:0.71 129:0.05 135:0.86 140:0.45 146:0.92 147:0.94 149:0.73 150:0.42 151:0.55 153:0.69 154:0.79 159:0.55 161:0.60 162:0.86 164:0.62 167:0.68 172:0.82 173:0.40 177:0.38 179:0.46 181:0.93 187:0.68 190:0.77 202:0.46 212:0.70 215:0.79 216:0.66 220:0.82 222:0.35 235:0.22 241:0.59 242:0.73 243:0.94 247:0.60 248:0.54 253:0.53 256:0.45 259:0.21 260:0.61 265:0.96 266:0.38 267:0.85 268:0.55 271:0.90 276:0.72 285:0.50 297:0.36 300:0.55 +1 1:0.74 6:0.84 7:0.81 8:0.63 9:0.80 11:0.57 12:0.06 17:0.51 20:0.82 21:0.40 27:0.59 28:0.68 30:0.09 32:0.80 34:0.67 36:0.21 37:0.56 39:0.33 41:0.92 43:0.95 60:0.98 66:0.18 69:0.32 70:1.00 74:0.88 77:0.89 78:0.78 81:0.78 83:0.87 85:0.90 91:0.37 96:0.74 98:0.18 100:0.82 101:0.72 108:0.25 111:0.78 114:0.82 120:0.62 121:0.90 123:0.24 124:0.95 126:0.54 127:0.75 129:0.89 133:0.94 135:0.90 140:0.45 145:0.49 146:0.53 147:0.59 149:0.84 150:0.86 151:0.64 152:0.78 153:0.77 154:0.95 155:0.67 158:0.92 159:0.93 161:0.60 162:0.86 164:0.69 165:0.86 167:0.54 169:0.80 172:0.45 173:0.09 176:0.73 177:0.38 179:0.51 181:0.93 186:0.79 187:0.39 189:0.86 192:0.59 195:0.98 201:0.74 202:0.54 204:0.89 208:0.64 212:0.22 215:0.67 216:0.37 220:0.82 222:0.35 230:0.87 233:0.76 235:0.80 238:0.86 241:0.18 242:0.42 243:0.39 245:0.64 247:0.60 248:0.62 253:0.79 255:0.79 256:0.58 259:0.21 260:0.62 261:0.78 265:0.20 266:0.91 267:0.23 268:0.63 271:0.90 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.97 300:0.99 +0 11:0.57 12:0.06 17:0.43 21:0.59 27:0.45 28:0.68 30:0.58 32:0.75 34:0.86 36:0.18 37:0.50 39:0.33 43:0.75 55:0.59 66:0.80 69:0.70 70:0.44 74:0.40 81:0.33 85:0.72 91:0.15 98:0.49 101:0.72 108:0.53 123:0.51 126:0.32 127:0.15 129:0.05 135:0.76 145:0.52 146:0.60 147:0.66 149:0.56 150:0.30 154:0.67 159:0.22 161:0.60 163:0.81 167:0.54 172:0.45 173:0.68 177:0.38 178:0.55 179:0.42 181:0.93 187:0.68 195:0.73 212:0.37 215:0.55 216:0.77 222:0.35 235:0.68 241:0.15 242:0.76 243:0.82 247:0.35 253:0.36 259:0.21 265:0.51 266:0.38 267:0.36 271:0.90 276:0.36 297:0.36 300:0.33 +1 1:0.74 6:0.83 8:0.65 9:0.80 12:0.06 17:0.06 20:0.90 21:0.13 25:0.88 27:0.31 28:0.68 30:0.41 34:0.77 36:0.65 37:0.81 39:0.33 43:0.27 55:0.59 66:0.86 69:0.53 70:0.60 74:0.62 78:0.83 81:0.83 91:0.71 96:0.82 98:0.90 100:0.84 104:0.54 108:0.41 111:0.82 114:0.92 117:0.86 120:0.66 123:0.39 126:0.54 127:0.46 129:0.05 135:0.42 140:0.45 146:0.75 147:0.79 149:0.44 150:0.85 151:0.74 152:0.85 153:0.48 154:0.76 155:0.67 158:0.84 159:0.31 161:0.60 162:0.83 164:0.72 167:0.71 169:0.82 172:0.59 173:0.66 176:0.73 177:0.38 179:0.71 181:0.93 186:0.83 187:0.68 189:0.85 190:0.77 191:0.70 192:0.59 201:0.74 202:0.63 208:0.64 212:0.30 215:0.84 216:0.68 220:0.87 222:0.35 232:0.93 235:0.31 238:0.86 241:0.65 242:0.77 243:0.86 247:0.39 248:0.71 253:0.83 254:0.86 255:0.79 256:0.64 259:0.21 260:0.67 261:0.84 265:0.90 266:0.38 267:0.65 268:0.69 271:0.90 276:0.48 285:0.62 290:0.92 297:0.36 300:0.43 +0 1:0.74 11:0.57 12:0.06 17:0.61 21:0.78 27:0.45 28:0.68 30:0.33 34:0.76 36:0.18 37:0.50 39:0.33 43:0.81 55:0.71 66:0.45 69:0.72 70:0.69 74:0.64 81:0.41 83:0.71 85:0.72 91:0.14 98:0.35 101:0.71 108:0.55 114:0.63 122:0.51 123:0.53 124:0.66 126:0.54 127:0.41 129:0.05 133:0.62 135:0.73 145:0.49 146:0.52 147:0.58 149:0.56 150:0.61 154:0.76 155:0.67 159:0.59 161:0.60 163:0.79 165:0.63 167:0.54 172:0.27 173:0.66 176:0.73 177:0.38 178:0.55 179:0.87 181:0.93 187:0.68 192:0.59 201:0.74 212:0.19 215:0.43 216:0.77 222:0.35 235:1.00 241:0.18 242:0.45 243:0.58 245:0.47 247:0.47 253:0.55 259:0.21 265:0.37 266:0.47 267:0.87 271:0.90 276:0.31 290:0.63 297:0.36 300:0.43 +3 6:0.95 8:0.71 9:0.80 12:0.06 17:0.45 20:0.85 21:0.78 25:0.88 27:0.25 28:0.68 30:0.76 34:0.28 36:0.55 37:0.74 39:0.33 41:0.82 43:0.24 55:0.52 66:0.72 69:0.85 70:0.22 74:0.39 78:0.95 83:0.76 91:0.92 96:0.80 98:0.31 100:0.98 104:0.80 108:0.71 111:0.96 120:0.91 123:0.69 127:0.12 129:0.05 135:0.30 140:0.98 145:0.38 146:0.31 147:0.38 149:0.27 151:0.87 152:0.82 153:0.72 154:0.16 155:0.67 159:0.13 161:0.60 162:0.66 164:0.91 167:0.82 169:0.97 172:0.27 173:0.96 177:0.38 178:0.52 179:0.38 181:0.93 186:0.94 187:0.21 191:0.93 192:0.59 202:0.87 208:0.64 212:0.78 216:0.50 220:0.74 222:0.35 235:0.12 241:0.77 242:0.75 243:0.75 244:0.61 247:0.30 248:0.78 253:0.74 255:0.79 256:0.89 259:0.21 260:0.91 261:0.95 265:0.34 266:0.17 267:0.69 268:0.89 271:0.90 276:0.24 283:0.71 285:0.62 300:0.18 +1 6:0.86 7:0.78 8:0.82 9:0.80 12:0.06 17:0.45 20:0.96 21:0.78 25:0.88 27:0.08 28:0.68 30:0.62 34:0.40 36:0.51 37:0.68 39:0.33 43:0.43 55:0.52 66:0.87 69:0.17 70:0.53 74:0.54 76:0.94 78:0.84 91:0.57 96:0.77 98:0.92 100:0.86 104:0.72 108:0.14 111:0.83 120:0.76 123:0.13 127:0.54 129:0.05 135:0.51 140:0.90 146:0.72 147:0.76 149:0.64 151:0.77 152:0.95 153:0.48 154:0.33 155:0.67 159:0.29 161:0.60 162:0.53 164:0.67 167:0.68 169:0.83 172:0.63 173:0.38 177:0.38 178:0.48 179:0.69 181:0.93 186:0.84 187:0.39 189:0.88 190:0.77 191:0.70 192:0.59 202:0.66 208:0.64 212:0.82 216:0.48 222:0.35 230:0.81 233:0.69 235:0.22 238:0.87 241:0.60 242:0.78 243:0.88 244:0.61 247:0.39 248:0.71 253:0.67 255:0.79 256:0.58 259:0.21 260:0.76 261:0.88 265:0.92 266:0.38 267:0.52 268:0.59 271:0.90 276:0.52 285:0.62 300:0.43 +2 6:0.95 8:0.84 12:0.06 17:0.61 20:0.79 21:0.30 25:0.88 27:0.25 28:0.68 30:0.62 34:0.64 36:0.49 37:0.74 39:0.33 43:0.25 55:0.59 66:0.75 69:0.82 70:0.34 74:0.48 78:0.87 81:0.60 91:0.82 98:0.56 100:0.92 104:0.80 108:0.66 111:0.88 120:0.65 123:0.64 126:0.32 127:0.17 129:0.05 135:0.49 140:0.45 146:0.50 147:0.56 149:0.24 150:0.44 151:0.54 152:0.93 153:0.88 154:0.53 159:0.18 161:0.60 162:0.85 164:0.80 167:0.69 169:0.97 172:0.32 173:0.92 177:0.38 179:0.66 181:0.93 186:0.87 187:0.21 189:0.92 190:0.77 191:0.83 202:0.68 212:0.61 215:0.72 216:0.50 220:0.87 222:0.35 235:0.42 238:0.92 241:0.63 242:0.63 243:0.77 247:0.35 248:0.59 253:0.40 254:0.94 256:0.71 259:0.21 260:0.66 261:0.97 265:0.57 266:0.23 267:0.73 268:0.75 271:0.90 276:0.24 283:0.66 285:0.50 297:0.36 300:0.25 +1 1:0.74 6:0.91 7:0.81 8:0.86 9:0.80 11:0.57 12:0.86 17:0.75 18:0.94 20:0.96 21:0.54 27:0.52 28:0.56 29:0.62 30:0.58 31:0.97 32:0.80 34:0.62 36:0.93 37:0.56 39:0.99 41:0.96 43:0.92 44:0.93 48:0.91 60:0.98 64:0.77 66:0.96 69:0.42 70:0.71 71:0.89 74:0.90 77:0.70 78:0.89 79:0.97 81:0.50 83:0.91 85:0.94 86:0.98 91:0.76 96:0.91 98:0.99 100:0.89 101:0.87 108:0.32 111:0.87 114:0.59 120:0.87 121:0.90 122:0.57 123:0.31 126:0.54 127:0.86 129:0.05 135:0.24 137:0.97 139:0.99 140:0.87 145:0.79 146:0.91 147:0.92 149:0.96 150:0.72 151:0.98 152:0.96 153:0.92 154:0.91 155:0.67 158:0.92 159:0.51 161:0.69 162:0.52 164:0.89 165:0.93 167:0.93 169:0.84 172:0.90 173:0.44 176:0.73 177:0.70 178:0.48 179:0.33 181:0.48 186:0.88 189:0.93 191:0.94 192:0.87 195:0.69 196:0.99 201:0.93 202:0.90 204:0.89 208:0.64 212:0.60 215:0.39 216:0.50 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 238:0.86 241:0.81 242:0.22 243:0.96 247:0.79 248:0.90 253:0.94 255:0.95 256:0.85 259:0.21 260:0.86 261:0.91 265:0.99 266:0.38 267:0.88 268:0.87 271:0.66 276:0.82 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 290:0.59 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.92 9:0.80 11:0.57 12:0.86 17:0.61 18:0.77 20:0.98 21:0.64 27:0.44 28:0.56 29:0.62 30:0.71 31:1.00 32:0.69 34:0.67 36:0.63 37:0.78 39:0.99 41:0.99 43:0.95 44:0.90 48:0.99 55:0.45 60:0.87 64:0.77 66:0.72 69:0.30 70:0.33 71:0.89 74:0.67 78:0.89 79:1.00 81:0.44 83:0.91 85:0.72 86:0.83 91:0.94 96:0.98 98:0.64 100:0.88 101:0.87 108:0.76 111:0.88 114:0.57 120:0.96 121:0.90 123:0.75 124:0.45 126:0.54 127:0.63 129:0.05 133:0.59 135:0.19 137:1.00 139:0.91 140:0.80 145:0.80 146:0.50 147:0.57 149:0.74 150:0.70 151:1.00 152:0.90 153:0.98 154:0.95 155:0.67 158:0.92 159:0.57 161:0.69 162:0.64 164:0.96 165:0.93 167:0.96 169:0.86 172:0.91 173:0.28 176:0.73 177:0.70 178:0.42 179:0.25 181:0.48 186:0.89 189:0.93 191:0.97 192:0.87 195:0.77 196:1.00 201:0.93 202:0.96 204:0.89 208:0.64 212:0.93 215:0.38 216:0.56 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 238:0.85 241:0.91 242:0.80 243:0.23 245:0.88 247:0.55 248:0.98 253:0.97 255:0.95 256:0.96 259:0.21 260:0.96 261:0.91 265:0.65 266:0.47 267:0.89 268:0.96 271:0.66 276:0.85 277:0.87 279:0.86 281:0.91 283:0.82 284:1.00 285:0.62 290:0.57 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.81 7:0.81 8:0.60 9:0.80 11:0.57 12:0.86 17:0.69 18:0.76 20:0.80 21:0.13 27:0.44 28:0.56 29:0.62 30:0.62 31:0.96 32:0.80 34:0.40 36:0.76 37:0.25 39:0.99 41:0.90 43:0.80 44:0.93 48:0.91 60:0.97 64:0.77 66:0.83 69:0.19 70:0.43 71:0.89 74:0.76 77:0.70 78:0.82 79:0.96 81:0.73 83:0.91 85:0.80 86:0.84 91:0.76 96:0.87 98:0.98 100:0.79 101:0.87 108:0.15 111:0.81 114:0.79 120:0.79 121:0.90 122:0.57 123:0.15 126:0.54 127:0.85 129:0.05 135:0.60 137:0.96 139:0.93 140:0.45 145:0.67 146:0.71 147:0.76 149:0.73 150:0.83 151:0.95 152:0.77 153:0.85 154:0.75 155:0.67 158:0.91 159:0.28 161:0.69 162:0.79 163:0.81 164:0.77 165:0.93 167:0.94 169:0.79 172:0.51 173:0.44 176:0.73 177:0.70 179:0.85 181:0.48 186:0.82 189:0.83 192:0.87 195:0.59 196:0.98 201:0.93 202:0.67 204:0.89 208:0.64 212:0.28 215:0.61 216:0.22 219:0.95 222:0.25 230:0.86 233:0.73 235:0.42 238:0.82 241:0.84 242:0.29 243:0.84 247:0.67 248:0.85 253:0.88 255:0.95 256:0.68 259:0.21 260:0.79 261:0.79 265:0.98 266:0.54 267:0.36 268:0.72 271:0.66 276:0.41 279:0.86 281:0.89 282:0.88 283:0.78 284:0.96 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.91 7:0.66 8:0.90 9:0.80 11:0.57 12:0.86 17:0.86 18:0.69 20:0.94 21:0.13 27:0.10 28:0.56 29:0.62 30:0.95 31:0.99 32:0.80 34:0.53 36:0.50 37:0.88 39:0.99 41:0.97 43:0.83 44:0.93 48:0.91 55:0.45 60:0.87 64:0.77 66:0.90 69:0.40 70:0.13 71:0.89 74:0.65 77:0.70 78:0.87 79:0.99 81:0.67 83:0.91 85:0.72 86:0.77 91:0.97 96:0.96 98:0.79 100:0.86 101:0.87 108:0.73 111:0.86 114:0.79 120:0.94 123:0.71 124:0.37 126:0.54 127:0.77 129:0.05 133:0.43 135:0.20 137:0.99 139:0.88 140:0.87 145:0.59 146:0.41 147:0.47 149:0.80 150:0.80 151:0.99 152:0.87 153:0.99 154:0.79 155:0.67 158:0.91 159:0.61 161:0.69 162:0.51 164:0.91 165:0.93 167:0.97 169:0.84 172:0.98 173:0.34 176:0.73 177:0.70 179:0.15 181:0.48 186:0.87 189:0.92 191:0.96 192:0.87 195:0.80 196:0.99 201:0.93 202:0.92 208:0.64 212:0.93 215:0.61 216:0.47 219:0.95 222:0.25 230:0.69 233:0.76 235:0.31 238:0.84 241:0.95 242:0.82 243:0.09 245:0.94 247:0.60 248:0.95 253:0.95 255:0.95 256:0.88 259:0.21 260:0.94 261:0.89 265:0.79 266:0.54 267:0.52 268:0.90 271:0.66 276:0.94 277:0.69 279:0.86 281:0.89 282:0.88 283:0.82 284:0.99 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.57 12:0.86 17:0.47 18:0.76 21:0.59 27:0.45 28:0.56 29:0.62 30:0.62 32:0.80 34:0.84 36:0.27 37:0.50 39:0.99 43:0.81 44:0.93 60:0.87 64:0.77 66:0.13 69:0.70 70:0.62 71:0.89 74:0.71 77:0.70 81:0.45 83:0.91 85:0.85 86:0.84 91:0.07 98:0.35 101:0.87 108:0.82 114:0.58 123:0.51 124:0.48 126:0.54 127:0.35 129:0.59 133:0.46 135:0.83 139:0.90 145:0.47 146:0.47 147:0.54 149:0.75 150:0.70 154:0.77 155:0.67 158:0.91 159:0.49 161:0.69 165:0.93 167:0.64 172:0.54 173:0.67 176:0.73 177:0.70 178:0.55 179:0.61 181:0.48 187:0.68 192:0.87 195:0.75 201:0.93 208:0.64 212:0.77 215:0.39 216:0.77 219:0.95 222:0.25 235:0.80 241:0.27 242:0.51 243:0.33 245:0.68 247:0.67 253:0.44 259:0.21 265:0.26 266:0.38 267:0.52 271:0.66 276:0.35 279:0.86 282:0.88 283:0.78 290:0.58 292:0.91 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.96 7:0.66 8:0.93 9:0.80 12:0.86 17:0.47 18:0.70 20:0.98 21:0.64 25:0.88 27:0.15 28:0.56 29:0.62 30:0.95 31:0.98 32:0.80 34:0.61 36:0.60 37:0.87 39:0.99 41:0.97 43:0.14 44:0.93 48:0.98 55:0.45 64:0.77 66:0.98 69:0.48 70:0.13 71:0.89 74:0.47 77:0.70 78:0.94 79:0.98 81:0.45 83:0.91 86:0.72 91:0.99 96:0.91 98:0.94 100:0.98 104:0.58 108:0.37 111:0.96 114:0.57 120:0.98 123:0.35 126:0.54 127:0.63 129:0.05 135:0.23 137:0.98 139:0.84 140:0.95 145:0.76 146:0.83 147:0.86 149:0.68 150:0.70 151:0.98 152:0.93 153:0.98 154:0.57 155:0.67 159:0.39 161:0.69 162:0.68 164:0.97 165:0.93 167:0.98 169:0.95 172:0.95 173:0.56 175:0.75 176:0.73 177:0.38 179:0.19 181:0.48 186:0.94 189:0.97 191:0.97 192:0.87 195:0.65 196:0.98 201:0.93 202:0.97 208:0.64 212:0.93 215:0.38 216:0.73 219:0.92 220:0.62 222:0.25 230:0.69 233:0.76 235:0.88 238:0.95 241:0.99 242:0.82 243:0.98 244:0.61 247:0.39 248:0.91 253:0.88 254:0.84 255:0.95 256:0.97 257:0.65 259:0.21 260:0.97 261:0.96 265:0.94 266:0.47 267:0.82 268:0.98 271:0.66 276:0.91 277:0.69 281:0.91 282:0.88 283:0.78 284:0.99 285:0.62 290:0.57 292:0.87 297:0.36 299:0.88 300:0.18 +1 1:0.74 6:0.96 7:0.81 8:0.65 9:0.80 12:0.86 17:0.87 20:0.82 21:0.59 25:0.88 27:0.15 28:0.56 29:0.62 31:0.87 32:0.64 34:0.58 36:0.70 37:0.87 39:0.99 41:0.82 64:0.77 71:0.89 74:0.47 78:0.78 79:0.86 81:0.48 83:0.91 91:0.68 96:0.69 100:0.79 104:0.58 111:0.77 114:0.58 120:0.84 121:0.90 126:0.54 135:0.32 137:0.87 139:0.74 140:0.90 145:0.50 150:0.71 151:0.50 152:0.93 153:0.87 155:0.67 161:0.69 162:0.70 164:0.73 165:0.93 167:0.87 169:0.95 175:0.97 176:0.73 181:0.48 186:0.79 187:0.21 189:0.84 190:0.89 191:0.82 192:0.87 195:0.53 196:0.88 201:0.93 202:0.71 204:0.89 208:0.64 215:0.39 216:0.73 222:0.25 230:0.87 233:0.76 235:0.88 238:0.84 241:0.75 244:0.93 248:0.50 253:0.42 255:0.95 256:0.71 257:0.93 259:0.21 260:0.77 261:0.96 267:0.65 268:0.74 271:0.66 277:0.95 281:0.91 283:0.82 284:0.91 285:0.62 290:0.58 297:0.36 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.86 17:0.90 18:0.77 21:0.64 27:0.44 28:0.56 29:0.62 30:0.71 31:0.97 32:0.69 34:0.68 36:0.64 37:0.78 39:0.99 41:0.88 43:0.95 44:0.90 48:0.99 55:0.45 60:0.87 64:0.77 66:0.72 69:0.30 70:0.33 71:0.89 74:0.67 79:0.97 81:0.44 83:0.91 85:0.72 86:0.83 91:0.38 96:0.86 98:0.63 101:0.87 108:0.76 114:0.57 120:0.87 121:0.90 123:0.75 124:0.45 126:0.54 127:0.56 129:0.05 133:0.59 135:0.34 137:0.97 139:0.91 140:0.90 145:0.80 146:0.50 147:0.57 149:0.74 150:0.70 151:0.94 153:0.90 154:0.95 155:0.67 158:0.92 159:0.57 161:0.69 162:0.64 164:0.77 165:0.93 167:0.79 172:0.91 173:0.27 176:0.73 177:0.70 179:0.24 181:0.48 187:0.39 192:0.87 195:0.78 196:0.98 201:0.93 202:0.75 204:0.89 208:0.64 212:0.93 215:0.38 216:0.56 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 241:0.70 242:0.80 243:0.23 245:0.88 247:0.55 248:0.85 253:0.85 255:0.95 256:0.75 259:0.21 260:0.85 265:0.64 266:0.47 267:0.69 268:0.77 271:0.66 276:0.85 277:0.87 279:0.86 281:0.91 283:0.82 284:0.95 285:0.62 290:0.57 292:0.95 297:0.36 300:0.70 +2 1:0.74 11:0.57 12:0.86 17:0.45 18:0.83 21:0.13 27:0.45 28:0.56 29:0.62 30:0.19 34:0.85 36:0.17 37:0.50 39:0.99 43:0.82 44:0.87 60:0.87 64:0.77 66:0.30 69:0.68 70:0.90 71:0.89 74:0.60 81:0.67 83:0.91 85:0.80 86:0.90 91:0.10 98:0.20 101:0.87 108:0.84 114:0.79 123:0.83 124:0.83 126:0.54 127:0.39 129:0.59 133:0.81 135:0.60 139:0.91 145:0.50 146:0.24 147:0.30 149:0.68 150:0.80 154:0.77 155:0.67 158:0.92 159:0.54 161:0.69 165:0.93 167:0.64 172:0.41 173:0.64 176:0.73 177:0.70 178:0.55 179:0.59 181:0.48 187:0.68 192:0.87 195:0.77 201:0.93 208:0.64 212:0.57 215:0.61 216:0.77 219:0.95 222:0.25 235:0.31 241:0.30 242:0.23 243:0.36 245:0.61 247:0.76 253:0.56 259:0.21 265:0.22 266:0.71 267:0.28 271:0.66 276:0.53 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.70 +1 1:0.74 7:0.81 8:0.82 9:0.80 12:0.86 17:0.88 21:0.64 25:0.88 27:0.15 28:0.56 29:0.62 31:0.86 32:0.64 34:0.52 36:0.54 37:0.87 39:0.99 41:0.88 64:0.77 71:0.89 74:0.47 79:0.86 81:0.45 83:0.91 91:0.78 96:0.68 104:0.58 114:0.57 120:0.84 121:0.90 126:0.54 135:0.34 137:0.86 139:0.74 140:0.87 145:0.54 150:0.70 151:0.47 153:0.90 155:0.67 161:0.69 162:0.64 164:0.75 165:0.93 167:0.86 175:0.97 176:0.73 181:0.48 187:0.21 190:0.89 191:0.80 192:0.87 195:0.53 196:0.87 201:0.93 202:0.72 204:0.89 208:0.64 215:0.38 216:0.73 222:0.25 230:0.87 233:0.76 235:0.88 241:0.75 244:0.93 248:0.47 253:0.40 255:0.95 256:0.71 257:0.93 259:0.21 260:0.77 267:0.23 268:0.74 271:0.66 277:0.95 281:0.91 284:0.93 285:0.62 290:0.57 297:0.36 +1 1:0.74 6:0.95 7:0.81 8:0.90 9:0.80 11:0.57 12:0.86 17:0.64 18:0.91 20:0.92 21:0.64 22:0.93 27:0.30 28:0.56 29:0.62 30:0.45 31:0.98 32:0.80 34:0.77 36:0.42 37:0.80 39:0.99 41:0.96 43:0.83 44:0.93 47:0.93 48:0.98 55:0.71 60:0.95 64:0.77 66:0.54 69:0.67 70:0.64 71:0.89 74:0.75 77:0.70 78:0.90 79:0.97 81:0.44 83:0.91 85:0.80 86:0.93 91:0.87 96:0.91 98:0.57 100:0.94 101:0.87 104:0.83 106:0.81 108:0.78 111:0.91 114:0.57 117:0.86 120:0.84 121:0.97 122:0.65 123:0.76 124:0.52 126:0.54 127:0.38 129:0.59 133:0.46 135:0.49 137:0.98 139:0.96 140:0.80 145:0.85 146:0.60 147:0.66 149:0.72 150:0.70 151:1.00 152:0.86 153:0.98 154:0.79 155:0.67 158:0.92 159:0.53 161:0.69 162:0.52 164:0.87 165:0.93 167:0.76 169:0.92 172:0.74 173:0.63 176:0.73 177:0.70 178:0.42 179:0.36 181:0.48 186:0.90 187:0.21 189:0.96 191:0.88 192:0.87 195:0.79 196:0.99 201:0.93 202:0.87 204:0.89 206:0.81 208:0.64 212:0.78 215:0.38 216:0.74 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 238:0.92 241:0.65 242:0.29 243:0.40 245:0.88 247:0.76 248:0.90 253:0.91 255:0.95 256:0.81 259:0.21 260:0.85 261:0.92 262:0.93 265:0.59 266:0.71 267:0.36 268:0.84 271:0.66 276:0.71 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 290:0.57 292:0.95 297:0.36 299:0.88 300:0.55 +0 1:0.74 6:0.96 7:0.81 8:0.92 9:0.80 12:0.86 17:0.85 20:0.74 21:0.64 25:0.88 27:0.15 28:0.56 29:0.62 31:0.91 32:0.64 34:0.55 36:0.81 37:0.87 39:0.99 41:0.88 64:0.77 71:0.89 74:0.47 78:0.87 79:0.91 81:0.45 83:0.91 91:0.61 96:0.76 100:0.89 104:0.58 111:0.86 114:0.57 120:0.79 121:0.90 126:0.54 135:0.30 137:0.91 139:0.74 140:0.87 145:0.54 150:0.70 151:0.72 152:0.93 153:0.82 155:0.67 161:0.69 162:0.76 164:0.74 165:0.93 167:0.91 169:0.95 175:0.97 176:0.73 181:0.48 186:0.87 187:0.21 190:0.89 192:0.87 195:0.53 196:0.91 201:0.93 202:0.68 204:0.89 208:0.64 215:0.38 216:0.73 222:0.25 230:0.87 233:0.76 235:0.88 241:0.78 244:0.93 248:0.68 253:0.60 255:0.95 256:0.68 257:0.93 259:0.21 260:0.74 261:0.96 267:0.19 268:0.72 271:0.66 277:0.95 281:0.91 284:0.93 285:0.62 290:0.57 297:0.36 +2 1:0.74 6:0.95 7:0.81 8:0.93 9:0.80 11:0.57 12:0.86 17:0.55 18:0.60 20:0.98 27:0.19 28:0.56 29:0.62 30:0.95 31:0.99 34:0.64 36:0.58 37:0.84 39:0.99 41:0.98 43:0.66 48:0.91 55:0.45 60:0.95 64:0.77 66:0.92 69:0.81 70:0.13 71:0.89 74:0.62 78:0.92 79:0.99 81:0.81 83:0.91 85:0.72 86:0.69 91:0.97 96:0.95 98:0.80 100:0.94 101:0.87 108:0.65 111:0.92 114:0.98 120:0.95 121:0.90 123:0.63 124:0.36 126:0.54 127:0.82 129:0.05 133:0.37 135:0.18 137:0.99 139:0.85 140:0.90 145:0.67 146:0.78 147:0.50 149:0.70 150:0.88 151:0.98 152:0.90 153:0.93 154:0.55 155:0.67 158:0.92 159:0.48 161:0.69 162:0.51 164:0.95 165:0.93 167:0.96 169:0.91 172:0.95 173:0.88 176:0.73 177:0.05 178:0.50 179:0.22 181:0.48 186:0.92 189:0.96 191:0.97 192:0.87 196:0.98 201:0.93 202:0.96 204:0.89 208:0.64 212:0.93 215:0.96 216:0.59 219:0.95 220:0.82 222:0.25 230:0.87 233:0.76 235:0.12 238:0.90 241:0.91 242:0.82 243:0.08 245:0.83 247:0.35 248:0.95 253:0.95 255:0.95 256:0.95 257:0.84 259:0.21 260:0.94 261:0.93 265:0.80 266:0.54 267:0.52 268:0.95 271:0.66 276:0.88 279:0.86 281:0.91 283:0.82 284:0.99 285:0.62 290:0.98 292:0.95 297:0.36 300:0.55 +2 1:0.74 6:0.90 7:0.81 8:0.88 9:0.80 11:0.57 12:0.86 17:0.28 18:0.86 20:0.98 21:0.22 27:0.29 28:0.56 29:0.62 30:0.41 31:1.00 32:0.68 34:0.80 36:0.73 37:0.63 39:0.99 41:0.99 43:0.76 44:0.90 48:0.98 55:0.59 60:0.87 64:0.77 66:0.54 69:0.77 70:0.51 71:0.89 74:0.64 78:0.92 79:1.00 81:0.65 83:0.91 85:0.72 86:0.90 91:0.98 96:0.96 98:0.69 100:0.94 101:0.87 108:0.61 111:0.92 114:0.71 120:0.95 121:0.90 122:0.43 123:0.58 124:0.50 126:0.54 127:0.54 129:0.05 133:0.43 135:0.20 137:1.00 139:0.94 140:0.45 145:0.79 146:0.56 147:0.37 149:0.54 150:0.79 151:1.00 152:0.90 153:0.99 154:0.68 155:0.67 158:0.87 159:0.32 161:0.69 162:0.56 164:0.93 165:0.93 167:0.95 169:0.92 172:0.48 173:0.91 176:0.73 177:0.38 179:0.73 181:0.48 186:0.92 189:0.92 191:0.97 192:0.87 195:0.59 196:1.00 201:0.93 202:0.97 204:0.89 208:0.64 212:0.72 215:0.51 216:0.64 219:0.95 222:0.25 228:0.99 230:0.87 233:0.76 235:0.52 238:0.90 241:0.89 242:0.19 243:0.36 245:0.66 247:0.74 248:0.96 253:0.96 255:0.95 256:1.00 257:0.65 259:0.21 260:0.94 261:0.94 265:0.70 266:0.27 267:0.28 268:0.97 271:0.66 276:0.45 279:0.86 281:0.91 283:0.82 284:1.00 285:0.62 290:0.71 292:0.95 297:0.36 300:0.33 +1 1:0.74 6:0.81 7:0.81 8:0.94 9:0.80 12:0.86 17:0.49 20:0.76 21:0.05 27:0.56 28:0.56 29:0.62 30:0.95 31:0.95 32:0.80 34:0.84 36:0.73 37:0.84 39:0.99 41:0.88 43:0.10 44:0.93 48:0.98 55:0.45 64:0.77 66:0.54 69:0.81 71:0.89 74:0.59 77:0.70 78:0.85 79:0.94 81:0.80 83:0.91 91:0.88 96:0.83 98:0.15 100:0.81 104:0.76 108:0.66 111:0.83 114:0.89 120:0.82 121:0.90 123:0.63 126:0.54 127:0.09 129:0.05 135:0.28 137:0.95 139:0.82 140:0.45 145:0.65 146:0.19 147:0.24 149:0.08 150:0.87 151:0.82 152:0.75 153:0.72 154:0.09 155:0.67 159:0.10 161:0.69 162:0.72 164:0.77 165:0.93 167:0.97 169:0.79 172:0.10 173:0.96 175:0.91 176:0.73 177:0.05 179:0.22 181:0.48 186:0.85 189:0.83 190:0.89 191:0.86 192:0.87 195:0.58 196:0.94 201:0.93 202:0.72 204:0.89 208:0.64 215:0.78 216:0.13 220:0.62 222:0.25 230:0.87 233:0.76 235:0.31 238:0.82 241:0.95 243:0.63 244:0.83 248:0.80 253:0.82 255:0.95 256:0.73 257:0.84 259:0.21 260:0.80 261:0.76 265:0.16 267:0.36 268:0.75 271:0.66 277:0.87 281:0.91 282:0.88 284:0.94 285:0.62 290:0.89 297:0.36 299:0.88 300:0.08 +2 1:0.60 6:0.80 7:0.75 8:0.60 9:0.80 10:0.95 11:0.46 12:0.86 17:0.82 18:0.97 20:0.92 21:0.54 23:1.00 25:0.88 26:0.95 27:0.43 28:0.76 29:0.86 30:0.62 31:1.00 32:0.67 34:0.78 36:0.81 37:0.26 39:0.99 41:0.90 43:0.66 44:0.88 45:0.83 48:1.00 58:1.00 62:1.00 64:0.77 66:0.61 69:0.27 70:0.66 71:0.92 74:0.41 75:0.98 78:0.96 79:1.00 81:0.60 83:0.91 86:0.97 89:0.98 91:0.85 96:0.96 97:0.99 98:0.80 99:0.83 100:0.92 101:0.47 108:0.21 111:0.93 120:0.97 122:0.91 123:0.20 124:0.50 126:0.51 127:0.94 128:1.00 129:0.59 133:0.46 135:0.19 137:1.00 139:0.98 140:0.96 141:0.98 145:0.94 146:0.72 147:0.76 149:0.82 150:0.49 151:0.96 152:0.92 153:0.93 154:0.65 155:0.67 159:0.43 161:0.90 162:0.86 164:0.96 165:0.65 167:0.81 168:1.00 169:0.88 170:0.77 172:0.80 173:0.37 174:0.89 175:0.75 177:0.70 179:0.41 181:0.50 186:0.96 189:0.82 191:0.73 192:0.99 193:0.99 195:0.64 196:1.00 197:0.92 199:1.00 201:0.64 202:0.91 204:0.82 205:0.99 208:0.64 212:0.84 215:0.58 216:0.29 219:0.94 220:0.62 222:0.21 223:0.95 224:0.99 225:0.99 226:0.98 228:0.99 230:0.77 233:0.76 234:0.99 235:0.88 236:0.95 238:0.84 239:0.96 240:0.99 241:0.82 242:0.27 243:0.68 244:0.61 245:0.90 247:0.84 248:0.91 253:0.93 255:1.00 256:0.93 257:0.65 259:0.21 260:0.97 261:0.93 265:0.80 266:0.47 267:0.17 268:0.95 271:0.66 274:1.00 276:0.76 277:0.69 279:0.81 281:0.91 283:0.82 284:1.00 285:0.62 292:0.95 297:0.36 300:0.70 +2 1:0.66 6:0.84 8:0.67 10:0.97 11:0.75 12:0.86 17:0.64 18:0.92 20:0.89 21:0.47 22:0.93 26:0.95 27:0.42 28:0.76 29:0.86 30:0.74 32:0.80 33:0.97 34:0.31 36:0.34 37:0.63 39:0.99 43:0.77 44:0.93 45:0.91 47:0.93 58:0.93 64:0.77 66:0.82 69:0.80 70:0.33 71:0.92 74:0.54 77:0.70 78:0.82 81:0.74 83:0.91 85:0.72 86:0.95 89:0.99 91:0.12 98:0.69 100:0.80 101:0.96 102:0.98 104:0.90 106:0.81 108:0.63 111:0.80 114:0.80 122:0.93 123:0.61 124:0.38 126:0.54 127:0.26 129:0.05 133:0.39 135:0.20 139:0.94 141:0.91 145:0.59 146:0.79 147:0.18 149:0.84 150:0.54 152:0.84 154:0.69 155:0.51 159:0.43 161:0.90 165:0.93 167:0.53 169:0.77 170:0.77 172:0.79 173:0.79 174:0.94 176:0.73 177:0.38 178:0.55 179:0.30 181:0.50 186:0.82 187:0.87 189:0.86 192:0.50 195:0.74 197:0.96 199:0.98 201:0.66 206:0.81 208:0.64 212:0.83 215:0.63 216:0.68 219:0.91 222:0.21 223:0.94 224:0.93 225:0.98 234:0.91 235:0.80 236:0.97 238:0.82 239:0.97 240:0.95 241:0.37 242:0.44 243:0.13 245:0.71 247:0.76 253:0.59 257:0.84 259:0.21 261:0.82 262:0.93 265:0.69 266:0.38 267:0.23 271:0.66 274:0.91 276:0.70 282:0.88 283:0.82 290:0.80 291:0.97 292:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.64 7:0.69 9:0.71 10:0.99 11:0.46 12:0.86 17:0.53 18:0.97 21:0.30 26:0.95 27:0.51 28:0.76 29:0.86 30:0.73 31:0.88 32:0.80 34:0.36 36:0.35 37:0.72 39:0.99 43:0.63 44:0.93 45:0.95 48:0.97 55:0.59 58:0.98 64:0.77 66:0.74 69:0.15 70:0.45 71:0.92 74:0.47 77:0.70 79:0.88 81:0.62 83:0.69 86:0.98 89:0.99 91:0.22 96:0.71 98:0.80 99:0.95 101:0.47 102:0.98 108:0.12 114:0.84 120:0.58 122:0.76 123:0.12 124:0.43 126:0.51 127:0.63 129:0.05 133:0.37 135:0.82 137:0.88 139:0.97 140:0.45 141:0.98 145:0.61 146:0.91 147:0.93 149:0.46 150:0.47 151:0.58 153:0.48 154:0.48 155:0.57 159:0.67 161:0.90 162:0.86 164:0.56 165:0.81 167:0.58 170:0.77 172:0.93 173:0.15 174:0.96 175:0.75 176:0.70 177:0.70 179:0.21 181:0.50 187:0.39 190:0.77 192:0.86 195:0.83 196:0.93 197:0.97 199:0.99 201:0.62 202:0.36 208:0.61 212:0.89 215:0.72 216:0.59 220:0.87 222:0.21 223:0.95 224:0.99 225:0.98 228:0.99 230:0.71 233:0.76 234:0.98 235:0.52 239:0.99 240:0.95 241:0.54 242:0.31 243:0.77 244:0.61 245:0.95 247:0.86 248:0.56 253:0.62 254:0.92 255:0.70 256:0.45 257:0.65 259:0.21 260:0.58 265:0.80 266:0.54 267:0.44 268:0.46 271:0.66 274:0.97 276:0.89 277:0.69 281:0.91 282:0.88 284:0.88 285:0.60 290:0.84 297:0.36 299:0.88 300:0.70 +2 6:0.97 7:0.75 8:0.90 9:0.72 10:0.96 11:0.57 12:0.86 17:0.75 18:0.89 20:0.82 22:0.93 26:0.95 27:0.19 28:0.76 29:0.86 30:0.73 31:0.97 32:0.67 33:0.97 34:0.59 36:0.47 37:0.84 39:0.99 43:0.73 44:0.88 45:0.93 47:0.93 48:0.98 58:0.99 64:0.77 66:0.61 69:0.40 70:0.40 71:0.92 74:0.56 75:0.97 78:0.90 79:0.97 81:0.46 83:0.91 86:0.91 89:0.99 91:0.42 96:0.73 97:0.97 98:0.76 100:0.93 101:0.87 104:0.86 106:0.81 108:0.74 111:0.90 120:0.81 122:0.93 123:0.72 124:0.58 126:0.32 127:0.80 129:0.05 133:0.65 135:0.68 137:0.97 139:0.93 140:0.95 141:0.98 145:0.54 146:0.37 147:0.44 149:0.76 150:0.46 151:0.63 152:0.78 153:0.83 154:0.66 155:0.64 158:0.81 159:0.76 161:0.90 162:0.75 163:0.81 164:0.74 167:0.54 169:0.91 170:0.77 172:0.77 173:0.25 174:0.95 177:0.88 179:0.44 181:0.50 186:0.90 187:0.68 189:0.98 190:0.77 191:0.73 192:0.87 193:0.99 195:0.88 196:0.98 197:0.95 199:0.99 201:0.60 202:0.65 204:0.83 206:0.81 208:0.64 212:0.48 215:0.96 216:0.59 219:0.94 222:0.21 223:0.95 224:1.00 225:0.97 226:0.98 230:0.77 233:0.76 234:1.00 235:0.05 238:0.93 239:0.97 240:0.95 241:0.43 242:0.41 243:0.29 244:0.61 245:0.82 247:0.82 248:0.61 253:0.54 255:0.73 256:0.80 259:0.21 260:0.82 261:0.84 262:0.93 265:0.76 266:0.71 267:0.52 268:0.69 271:0.66 274:0.99 276:0.73 277:0.69 279:0.80 281:0.91 283:0.82 284:0.95 285:0.60 291:0.97 292:0.95 297:0.36 300:0.55 +2 1:0.59 7:0.75 8:0.89 9:0.70 10:0.95 11:0.52 12:0.86 17:0.85 18:0.89 21:0.40 22:0.93 23:0.96 26:0.95 27:0.35 28:0.76 29:0.86 30:0.75 31:0.88 32:0.72 33:0.97 34:0.23 36:0.29 37:0.79 39:0.99 43:0.32 44:0.92 45:0.92 47:0.93 48:0.98 55:0.45 58:0.98 62:0.96 64:0.77 66:0.91 69:0.88 70:0.41 71:0.92 74:0.54 77:0.70 79:0.87 81:0.54 83:0.69 86:0.89 89:0.99 91:0.38 98:0.77 101:0.65 104:0.78 106:0.81 108:0.77 120:0.57 123:0.75 124:0.37 126:0.51 127:0.38 128:0.95 129:0.05 133:0.39 135:0.51 137:0.88 139:0.92 140:0.45 141:0.98 145:0.77 146:0.88 147:0.23 149:0.74 150:0.50 151:0.54 153:0.48 154:0.25 155:0.55 159:0.55 161:0.90 162:0.86 164:0.56 167:0.52 168:0.95 170:0.77 172:0.94 173:0.89 174:0.94 177:0.38 179:0.19 181:0.50 187:0.39 190:0.77 192:0.58 193:0.96 195:0.80 196:0.91 197:0.94 199:0.99 201:0.65 202:0.36 204:0.81 205:0.95 206:0.81 208:0.64 212:0.91 215:0.67 216:0.70 219:0.91 220:0.91 222:0.21 223:0.95 224:0.99 225:0.99 230:0.77 233:0.66 234:0.99 235:0.74 236:0.95 239:0.94 240:0.99 241:0.32 242:0.63 243:0.08 244:0.61 245:0.82 247:0.91 248:0.53 253:0.43 254:0.80 255:0.70 256:0.45 257:0.84 259:0.21 260:0.57 262:0.93 265:0.77 266:0.54 267:0.79 268:0.46 271:0.66 274:0.98 276:0.87 281:0.86 282:0.80 283:0.67 284:0.88 285:0.60 291:0.97 292:0.88 297:0.36 300:0.70 +2 1:0.69 6:0.83 7:0.81 8:0.69 9:0.80 11:0.52 12:0.86 17:0.89 18:0.96 20:0.81 22:0.93 23:0.96 26:0.95 27:0.61 28:0.76 29:0.86 30:0.85 31:0.94 32:0.72 34:0.64 36:0.55 37:0.43 39:0.99 41:0.82 43:0.73 44:0.92 45:0.95 47:0.93 48:0.98 58:0.99 62:0.97 64:0.77 66:0.97 69:0.21 70:0.41 71:0.92 74:0.82 77:0.70 78:0.91 79:0.93 81:0.86 83:0.91 86:0.98 89:0.95 91:0.46 96:0.80 97:0.94 98:0.97 99:0.95 100:0.89 101:0.65 104:0.91 106:0.81 108:0.17 111:0.89 114:0.98 120:0.69 121:0.90 122:0.65 123:0.16 126:0.54 127:0.75 128:0.98 129:0.05 135:0.42 137:0.94 139:0.98 140:0.45 141:0.98 145:0.89 146:0.91 147:0.93 149:0.94 150:0.83 151:0.86 152:0.79 153:0.48 154:0.63 155:0.67 158:0.82 159:0.53 161:0.90 162:0.86 164:0.57 165:0.93 167:0.56 168:0.98 169:0.86 170:0.77 172:0.92 173:0.26 175:0.75 176:0.73 177:0.70 179:0.28 181:0.50 186:0.91 187:0.39 189:0.84 192:0.99 193:1.00 195:0.71 196:0.97 199:0.99 201:0.74 202:0.36 204:0.85 205:0.95 206:0.81 208:0.64 212:0.78 215:0.96 216:0.30 219:0.94 222:0.21 223:0.95 224:1.00 225:0.98 230:0.87 233:0.76 234:1.00 235:0.68 236:0.97 238:0.84 239:0.90 240:0.99 241:0.49 242:0.18 243:0.97 244:0.61 247:0.84 248:0.77 253:0.86 255:1.00 256:0.45 257:0.65 259:0.21 260:0.70 261:0.84 262:0.93 265:0.97 266:0.38 267:0.28 268:0.46 271:0.66 274:0.98 276:0.85 277:0.69 279:0.78 281:0.91 282:0.80 283:0.82 284:0.89 285:0.62 290:0.98 292:0.95 297:0.36 300:0.43 +1 1:0.63 7:0.81 8:0.83 9:0.73 10:0.90 11:0.46 12:0.86 17:0.90 18:0.92 23:0.96 26:0.95 27:0.68 28:0.76 29:0.86 30:0.11 31:0.94 32:0.71 34:0.45 36:0.77 37:0.87 39:0.99 43:0.59 44:0.92 45:0.66 48:0.99 55:0.84 58:0.98 62:0.98 64:0.77 66:0.16 69:0.73 70:0.97 71:0.92 74:0.47 79:0.94 81:0.66 83:0.91 86:0.92 89:0.97 91:0.23 98:0.48 99:0.83 101:0.47 102:0.97 104:0.81 106:0.81 108:0.56 120:0.72 121:0.90 122:0.92 123:0.72 124:0.89 126:0.51 127:0.53 128:0.98 129:0.05 133:0.89 135:0.85 137:0.94 139:0.94 140:0.45 141:0.98 145:0.94 146:0.50 147:0.05 149:0.38 150:0.57 151:0.90 153:0.69 154:0.49 155:0.63 159:0.78 161:0.90 162:0.86 164:0.62 165:0.65 167:0.50 168:0.98 170:0.77 172:0.48 173:0.55 174:0.86 175:0.75 177:0.05 179:0.44 181:0.50 187:0.21 190:0.77 192:0.87 193:1.00 195:0.92 196:0.97 197:0.88 199:0.99 201:0.65 202:0.46 204:0.84 205:0.95 206:0.81 208:0.64 212:0.26 215:0.96 216:0.04 219:0.91 222:0.21 223:0.95 224:1.00 225:0.99 228:0.99 230:0.87 233:0.76 234:1.00 235:0.12 236:0.95 239:0.94 240:0.99 241:0.24 242:0.32 243:0.08 244:0.61 245:0.71 247:0.82 248:0.80 251:1.00 253:0.39 254:0.95 255:0.78 256:0.45 257:0.93 259:0.21 260:0.73 265:0.28 266:0.94 267:0.28 268:0.55 271:0.66 274:0.97 276:0.71 277:0.87 281:0.91 283:0.82 284:0.90 285:0.60 292:0.95 297:0.36 300:0.84 +1 7:0.70 8:0.80 9:0.72 10:0.96 11:0.52 12:0.86 17:0.79 18:0.85 21:0.59 23:0.99 26:0.95 27:0.66 28:0.76 29:0.86 30:0.19 31:0.98 32:0.67 34:0.40 36:0.62 37:0.45 39:0.99 43:0.34 44:0.88 45:0.81 58:1.00 62:0.98 66:0.35 69:0.86 70:0.97 71:0.92 74:0.44 75:0.97 79:0.98 81:0.33 83:0.56 86:0.88 89:0.99 91:0.84 96:0.80 98:0.66 101:0.65 108:0.85 120:0.92 122:0.75 123:0.84 124:0.76 126:0.32 127:0.96 128:0.98 129:0.59 133:0.74 135:0.42 137:0.98 139:0.87 140:0.99 141:0.98 145:0.99 146:0.74 147:0.47 149:0.47 150:0.33 151:0.53 153:0.82 154:0.18 155:0.56 159:0.73 161:0.90 162:0.70 164:0.93 167:0.83 168:0.98 170:0.77 172:0.77 173:0.81 174:0.95 177:0.70 179:0.34 181:0.50 190:0.89 191:0.88 192:0.58 193:0.97 195:0.87 196:0.98 197:0.96 199:0.99 201:0.55 202:0.90 204:0.79 205:0.99 208:0.61 212:0.55 215:0.55 216:0.21 219:0.91 220:0.62 222:0.21 223:0.95 224:0.98 225:0.99 226:0.96 230:0.73 233:0.66 234:0.97 235:0.74 236:0.94 239:0.96 240:0.99 241:0.88 242:0.13 243:0.20 245:0.88 247:0.97 248:0.52 253:0.74 254:0.99 255:0.73 256:0.91 257:0.65 259:0.21 260:0.92 265:0.66 266:0.75 267:0.93 268:0.92 271:0.66 274:0.99 276:0.82 283:0.67 284:0.99 285:0.62 292:0.88 297:0.36 300:0.84 +3 1:0.69 6:0.94 7:0.70 8:0.80 9:0.71 10:0.99 11:0.75 12:0.86 17:0.81 18:0.96 20:0.84 21:0.22 22:0.93 23:0.96 26:0.95 27:0.52 28:0.76 29:0.86 30:0.54 31:0.89 32:0.80 33:0.97 34:0.53 36:0.64 37:0.56 39:0.99 43:0.93 44:0.93 45:0.97 47:0.93 48:0.97 58:0.98 62:0.98 64:0.77 66:0.15 69:0.35 70:0.75 71:0.92 74:0.58 77:0.70 78:0.97 79:0.89 81:0.84 83:0.91 85:0.72 86:0.97 89:0.99 91:0.18 98:0.58 99:0.83 100:0.96 101:0.96 102:0.98 106:0.81 108:0.94 111:0.96 114:0.90 120:0.59 122:0.93 123:0.85 124:0.90 126:0.54 127:0.96 128:0.96 129:0.05 133:0.90 135:0.32 137:0.89 139:0.97 140:0.45 141:0.98 145:0.80 146:0.22 147:0.28 149:0.88 150:0.67 151:0.61 152:0.87 153:0.48 154:0.93 155:0.57 159:0.88 161:0.90 162:0.86 163:0.81 164:0.56 165:1.00 167:0.47 168:0.96 169:0.95 170:0.77 172:0.82 173:0.14 174:0.98 176:0.73 177:0.88 179:0.20 181:0.50 186:0.97 187:0.21 189:0.93 190:0.77 192:0.86 195:0.96 196:0.94 197:0.97 199:0.99 201:0.68 202:0.36 205:0.95 206:0.81 208:0.64 212:0.30 215:0.79 216:0.50 219:0.91 220:0.82 222:0.21 223:0.95 224:0.99 225:0.99 230:0.73 233:0.76 234:0.99 235:0.74 236:0.97 238:0.88 239:0.99 240:0.99 241:0.07 242:0.29 243:0.15 245:0.93 247:0.94 248:0.59 253:0.64 255:0.71 256:0.45 259:0.21 260:0.60 261:0.95 262:0.93 265:0.56 266:0.87 267:0.23 268:0.46 271:0.66 274:0.97 276:0.92 277:0.69 281:0.91 282:0.88 283:0.67 284:0.88 285:0.60 290:0.90 291:0.97 292:0.88 297:0.36 299:0.88 300:0.84 +2 7:0.75 8:0.95 10:0.96 11:0.57 12:0.86 17:0.85 18:0.91 21:0.54 23:0.95 26:0.95 27:0.54 28:0.76 29:0.86 30:0.54 31:0.98 32:0.71 34:0.34 36:0.49 37:0.95 39:0.99 43:0.71 44:0.92 45:0.94 48:0.91 58:0.99 62:0.98 64:0.77 66:0.43 69:0.46 70:0.77 71:0.92 74:0.75 79:0.98 81:0.51 83:0.91 86:0.95 89:0.99 91:0.62 98:0.72 101:0.87 102:0.97 108:0.35 120:0.88 123:0.34 124:0.60 126:0.51 127:0.78 128:0.96 129:0.59 133:0.47 135:0.26 137:0.98 139:0.95 140:0.45 141:0.98 145:0.82 146:0.77 147:0.81 149:0.85 150:0.47 151:0.95 153:0.85 154:0.50 155:0.55 159:0.55 161:0.90 162:0.70 164:0.85 167:0.79 168:0.96 170:0.77 172:0.86 173:0.44 174:0.93 177:0.88 179:0.23 181:0.50 190:0.89 191:0.86 192:0.86 193:0.99 195:0.72 196:0.99 197:0.93 199:0.98 201:0.62 202:0.80 204:0.84 205:0.95 208:0.64 212:0.87 215:0.58 216:0.24 220:0.62 222:0.21 223:0.95 224:0.98 225:0.98 230:0.77 233:0.76 234:0.98 235:0.88 236:0.95 239:0.96 240:0.99 241:0.79 242:0.16 243:0.57 245:0.97 247:0.97 248:0.91 253:0.53 254:0.84 255:0.73 256:0.81 259:0.21 260:0.88 265:0.72 266:0.54 267:0.18 268:0.83 271:0.66 274:0.99 276:0.89 281:0.91 284:0.97 285:0.60 297:0.36 300:0.70 +0 1:0.64 6:0.80 7:0.75 8:0.60 9:0.80 10:0.95 11:0.82 12:0.86 17:0.86 18:0.97 20:0.88 21:0.40 23:1.00 26:0.95 27:0.55 28:0.76 29:0.86 30:0.74 31:1.00 32:0.72 34:0.59 36:0.88 37:0.25 39:0.99 41:0.94 43:0.68 44:0.92 45:0.89 48:0.97 58:1.00 60:0.87 62:1.00 64:0.77 66:0.48 69:0.45 70:0.53 71:0.92 74:0.66 75:0.99 78:0.93 79:1.00 81:0.68 83:0.91 85:0.72 86:0.98 89:0.98 91:0.88 96:0.96 97:0.99 98:0.75 99:0.83 100:0.89 101:0.96 102:0.97 108:0.35 111:0.91 120:0.97 122:0.91 123:0.34 124:0.58 126:0.51 127:0.78 128:1.00 129:0.59 133:0.47 135:0.30 137:1.00 139:0.98 140:0.80 141:0.98 145:0.77 146:0.78 147:0.81 149:0.92 150:0.58 151:0.99 152:0.82 153:0.96 154:0.56 155:0.67 158:0.92 159:0.53 161:0.90 162:0.86 164:0.96 165:0.65 167:0.81 168:1.00 169:0.87 170:0.77 172:0.83 173:0.45 174:0.89 177:0.88 179:0.29 181:0.50 186:0.93 189:0.83 191:0.70 192:0.99 193:0.99 195:0.70 196:1.00 197:0.90 199:1.00 201:0.67 202:0.91 204:0.84 205:1.00 208:0.64 212:0.89 215:0.67 216:0.21 219:0.95 222:0.21 223:0.95 224:0.99 225:0.99 226:0.98 230:0.77 233:0.76 234:0.99 235:0.88 236:0.95 238:0.83 239:0.98 240:0.99 241:0.81 242:0.22 243:0.59 245:0.95 247:0.90 248:0.95 253:0.96 255:1.00 256:0.93 259:0.21 260:0.98 261:0.89 265:0.75 266:0.54 267:0.96 268:0.95 271:0.66 274:1.00 276:0.84 279:0.82 281:0.91 283:0.82 284:1.00 285:0.62 292:0.95 297:0.36 300:0.84 +3 1:0.57 6:0.94 7:0.70 8:0.87 9:0.73 10:0.98 11:0.52 12:0.86 17:0.88 18:0.96 20:0.81 21:0.59 22:0.93 23:0.96 26:0.95 27:0.44 28:0.76 29:0.86 30:0.19 31:0.96 32:0.71 34:0.44 36:0.52 37:0.78 39:0.99 43:0.25 44:0.92 45:0.94 47:0.93 48:0.99 58:0.99 62:0.99 64:0.77 66:0.11 69:0.33 70:0.97 71:0.92 74:0.51 75:0.98 78:0.89 79:0.96 81:0.47 83:0.56 86:0.97 89:0.99 91:0.42 97:0.89 98:0.50 100:0.91 101:0.65 102:0.97 104:0.86 106:0.81 108:0.62 111:0.89 120:0.71 122:0.93 123:0.90 124:0.89 126:0.51 127:0.84 128:0.98 129:0.59 133:0.89 135:0.78 137:0.96 139:0.97 140:0.90 141:0.98 145:0.91 146:0.45 147:0.52 149:0.62 150:0.44 151:0.86 152:0.83 153:0.48 154:0.86 155:0.55 159:0.86 161:0.90 162:0.86 164:0.73 167:0.57 168:0.98 169:0.87 170:0.77 172:0.80 173:0.14 174:0.96 177:0.88 179:0.25 181:0.50 186:0.89 187:0.68 189:0.97 190:0.77 191:0.80 192:0.58 193:0.98 195:0.95 196:0.98 197:0.95 199:0.99 201:0.62 202:0.62 204:0.79 205:0.95 206:0.81 208:0.50 212:0.49 215:0.55 216:0.56 219:0.94 220:0.96 222:0.21 223:0.95 224:0.99 225:0.98 226:0.98 230:0.72 233:0.66 234:0.99 235:0.88 236:0.95 238:0.90 239:0.98 240:0.99 241:0.51 242:0.19 243:0.16 245:0.89 247:0.95 248:0.77 253:0.41 254:0.80 255:0.74 256:0.68 259:0.21 260:0.72 261:0.87 262:0.93 265:0.38 266:0.80 267:0.65 268:0.70 271:0.66 274:0.98 276:0.87 277:0.69 279:0.76 281:0.86 283:0.82 284:0.95 285:0.60 292:0.95 297:0.36 300:0.94 +0 1:0.68 10:0.94 11:0.75 12:0.86 17:0.40 18:0.88 21:0.13 26:0.95 27:0.45 28:0.76 29:0.86 30:0.60 34:0.75 36:0.20 37:0.50 39:0.99 43:0.82 45:0.83 48:0.91 58:0.93 60:0.87 64:0.77 66:0.60 69:0.61 70:0.71 71:0.92 74:0.56 75:0.99 81:0.77 83:0.91 85:0.72 86:0.90 89:0.98 91:0.09 98:0.60 101:0.96 108:0.78 114:0.92 122:0.93 123:0.77 124:0.50 126:0.54 127:0.46 129:0.05 133:0.45 135:0.66 139:0.91 141:0.91 145:0.50 146:0.37 147:0.44 149:0.75 150:0.59 154:0.77 155:0.52 158:0.92 159:0.57 161:0.90 165:0.93 167:0.49 170:0.77 172:0.63 173:0.55 174:0.91 176:0.73 177:0.88 178:0.55 179:0.55 181:0.50 187:0.68 192:0.54 197:0.91 199:0.97 201:0.67 208:0.64 212:0.58 215:0.84 216:0.77 219:0.95 222:0.21 223:0.95 224:0.93 225:0.98 226:0.98 234:0.91 235:0.42 236:0.97 239:0.95 240:0.95 241:0.15 242:0.17 243:0.36 245:0.78 247:0.84 253:0.66 259:0.21 265:0.61 266:0.75 267:0.23 271:0.66 274:0.91 276:0.54 277:0.69 279:0.80 281:0.91 283:0.82 290:0.92 292:0.95 297:0.36 300:0.70 +2 1:0.67 6:0.88 8:0.74 9:0.73 10:0.97 11:0.57 12:0.86 17:0.34 18:0.92 20:0.93 21:0.22 22:0.93 23:0.97 26:0.95 27:0.56 28:0.76 29:0.86 30:0.28 31:0.94 32:0.80 33:0.97 34:0.80 36:0.22 37:0.60 39:0.99 41:0.82 43:0.75 44:0.93 45:0.94 47:0.93 58:0.98 62:0.97 64:0.77 66:0.54 69:0.57 70:0.89 71:0.92 74:0.56 77:0.70 78:0.90 79:0.94 81:0.76 83:0.91 86:0.94 89:0.99 91:0.14 96:0.73 98:0.70 100:0.91 101:0.87 102:0.98 104:0.96 106:0.81 108:0.86 111:0.90 114:0.90 120:0.73 122:0.93 123:0.85 124:0.71 126:0.54 127:0.72 128:0.96 129:0.59 133:0.74 135:0.60 137:0.94 139:0.93 140:0.45 141:0.98 145:0.65 146:0.44 147:0.50 149:0.67 150:0.57 151:0.63 152:0.87 153:0.48 154:0.19 155:0.65 159:0.81 161:0.90 162:0.86 164:0.68 165:0.93 167:0.57 168:0.96 169:0.89 170:0.77 172:0.87 173:0.35 174:0.96 176:0.73 177:0.88 179:0.25 181:0.50 186:0.90 187:0.87 189:0.89 190:0.89 191:0.70 192:0.98 195:0.92 196:0.97 197:0.94 199:0.99 201:0.66 202:0.53 205:0.95 206:0.81 208:0.64 212:0.42 215:0.79 216:0.76 220:0.82 222:0.21 223:0.95 224:0.99 225:0.99 234:0.99 235:0.52 236:0.97 238:0.87 239:0.97 240:0.99 241:0.52 242:0.19 243:0.23 245:0.91 247:0.95 248:0.61 253:0.70 254:0.97 255:0.74 256:0.58 259:0.21 260:0.73 261:0.90 262:0.93 265:0.71 266:0.80 267:0.44 268:0.62 271:0.66 274:0.98 276:0.86 282:0.88 283:0.82 284:0.93 285:0.62 290:0.90 291:0.97 297:0.36 299:0.88 300:0.84 +2 1:0.64 6:0.96 7:0.75 8:0.90 9:0.73 10:0.98 11:0.52 12:0.86 17:0.66 18:0.97 20:0.85 21:0.22 22:0.93 23:0.97 26:0.95 27:0.35 28:0.76 29:0.86 30:0.21 31:0.91 32:0.75 33:0.97 34:0.22 36:0.63 37:0.79 39:0.99 43:0.62 44:0.93 45:0.98 47:0.93 48:0.98 58:0.99 62:0.98 64:0.77 66:0.48 69:0.98 70:0.83 71:0.92 74:0.55 77:0.70 78:0.94 79:0.91 81:0.67 83:0.91 86:0.98 89:0.99 91:0.27 96:0.72 98:0.79 99:0.83 100:0.91 101:0.65 102:0.98 104:0.78 106:0.81 108:0.90 111:0.92 120:0.65 123:0.89 124:0.83 126:0.51 127:0.72 128:0.96 129:0.05 133:0.87 135:0.61 137:0.91 139:0.98 140:0.45 141:0.98 145:0.67 146:0.71 147:0.33 149:0.81 150:0.57 151:0.63 152:0.81 153:0.72 154:0.50 155:0.65 159:0.92 161:0.90 162:0.86 164:0.72 165:0.65 167:0.46 168:0.96 169:0.89 170:0.77 172:0.97 173:0.98 174:0.99 177:0.70 179:0.12 181:0.50 186:0.94 187:0.39 189:0.97 190:0.77 192:0.98 193:0.99 195:0.98 196:0.96 197:0.97 199:1.00 201:0.67 202:0.58 204:0.83 205:0.97 206:0.81 208:0.64 212:0.48 215:0.79 216:0.70 219:0.91 220:0.82 222:0.21 223:0.95 224:0.99 225:0.99 230:0.77 233:0.76 234:0.99 235:0.60 236:0.95 238:0.85 239:0.98 240:0.99 241:0.02 242:0.21 243:0.08 244:0.61 245:0.98 247:0.95 248:0.69 253:0.53 254:0.74 255:0.77 256:0.64 257:0.65 259:0.21 260:0.65 261:0.88 262:0.93 265:0.79 266:0.87 267:0.18 268:0.66 271:0.66 274:0.99 276:0.97 277:0.69 281:0.91 282:0.83 283:0.82 284:0.94 285:0.62 291:0.97 292:0.95 297:0.36 300:0.84 +1 1:0.69 7:0.75 8:0.92 9:0.76 10:0.94 11:0.57 12:0.86 17:0.92 18:0.96 20:0.80 21:0.13 22:0.93 26:0.95 27:0.54 28:0.76 29:0.86 30:0.72 31:0.98 32:0.80 34:0.88 36:0.60 37:0.95 39:0.99 43:0.74 44:0.93 45:0.89 47:0.93 48:1.00 58:0.99 64:0.77 66:0.97 69:0.27 70:0.56 71:0.92 74:0.70 77:0.70 78:0.83 79:0.97 81:0.86 83:0.91 86:0.98 89:0.98 91:0.25 96:0.79 98:0.95 99:0.99 100:0.87 101:0.87 102:0.98 104:0.98 106:0.81 108:0.21 111:0.83 114:0.92 120:0.84 122:0.75 123:0.20 126:0.54 127:0.67 129:0.05 135:0.51 137:0.98 139:0.98 140:0.80 141:0.98 145:0.86 146:0.89 147:0.91 149:0.87 150:0.81 151:0.82 152:0.77 153:0.78 154:0.55 155:0.65 159:0.48 161:0.90 162:0.77 164:0.78 165:0.93 167:0.52 169:0.85 170:0.77 172:0.92 173:0.31 174:0.89 176:0.73 177:0.88 179:0.26 181:0.50 186:0.83 187:0.39 190:0.89 192:0.98 193:0.99 195:0.69 196:0.99 197:0.88 199:0.99 201:0.68 202:0.69 204:0.83 206:0.81 208:0.64 212:0.87 215:0.84 216:0.24 219:0.91 220:0.87 222:0.21 223:0.95 224:1.00 225:0.98 230:0.77 233:0.76 234:1.00 235:0.88 236:0.97 239:0.96 240:0.95 241:0.32 242:0.23 243:0.97 247:0.93 248:0.74 253:0.82 254:0.84 255:0.78 256:0.71 259:0.21 260:0.85 261:0.80 262:0.93 265:0.95 266:0.27 267:0.93 268:0.73 271:0.66 274:0.99 276:0.85 281:0.91 282:0.88 283:0.82 284:0.96 285:0.60 290:0.92 292:0.95 297:0.36 300:0.70 +1 1:0.57 7:0.69 9:0.70 12:0.86 17:0.77 21:0.64 22:0.93 23:0.96 25:0.88 26:0.95 27:0.15 28:0.76 29:0.86 31:0.87 32:0.71 33:0.97 34:0.58 36:0.85 37:0.87 39:0.99 44:0.92 47:0.93 48:0.91 58:0.98 62:0.95 64:0.77 71:0.92 79:0.87 81:0.42 89:0.95 91:0.37 102:0.97 104:0.58 120:0.77 126:0.51 128:0.95 135:0.34 137:0.87 139:0.73 140:0.87 141:0.98 145:0.77 150:0.40 151:0.52 153:0.48 155:0.50 161:0.90 162:0.86 164:0.72 167:0.73 168:0.95 170:0.77 175:0.99 181:0.50 187:0.39 190:0.95 192:0.50 193:0.96 195:0.53 196:0.88 199:0.96 201:0.59 202:0.58 205:0.95 208:0.50 215:0.53 216:0.73 220:0.62 222:0.21 223:0.95 224:0.98 225:0.98 230:0.71 233:0.66 234:0.98 235:0.88 236:0.95 239:0.89 240:0.99 241:0.73 244:0.97 248:0.51 253:0.20 255:0.69 256:0.64 257:0.97 259:0.21 260:0.77 262:0.93 267:0.19 268:0.66 271:0.66 274:0.97 277:0.98 281:0.86 284:0.88 285:0.60 291:0.97 297:0.36 +1 1:0.68 8:0.60 10:0.93 11:0.82 12:0.86 17:0.34 18:0.90 21:0.13 26:0.95 27:0.45 28:0.76 29:0.86 30:0.61 32:0.67 34:0.75 36:0.19 37:0.50 39:0.99 43:0.82 45:0.85 48:0.91 58:0.93 60:0.87 64:0.77 66:0.51 69:0.61 70:0.65 71:0.92 74:0.74 75:0.99 77:0.70 81:0.77 83:0.91 85:0.80 86:0.93 89:0.98 91:0.06 98:0.67 101:0.96 108:0.78 114:0.92 122:0.65 123:0.77 124:0.66 126:0.54 127:0.48 129:0.59 133:0.64 135:0.85 139:0.93 141:0.91 145:0.52 146:0.40 147:0.46 149:0.81 150:0.59 154:0.77 155:0.52 158:0.92 159:0.61 161:0.90 165:0.93 167:0.49 170:0.77 172:0.74 173:0.52 174:0.89 176:0.73 177:0.88 178:0.55 179:0.37 181:0.50 187:0.68 192:0.54 195:0.81 197:0.89 199:0.98 201:0.67 208:0.64 212:0.60 215:0.84 216:0.77 219:0.95 222:0.21 223:0.95 224:0.93 225:0.98 226:0.98 234:0.91 235:0.42 236:0.97 239:0.95 240:0.95 241:0.18 242:0.14 243:0.36 245:0.85 247:0.93 253:0.70 259:0.21 265:0.68 266:0.80 267:0.19 271:0.66 274:0.91 276:0.74 279:0.80 281:0.91 282:0.75 283:0.82 290:0.92 292:0.95 297:0.36 300:0.70 +1 1:0.66 6:0.91 7:0.69 8:0.82 9:0.74 10:0.92 11:0.46 12:0.86 17:0.51 18:0.93 20:0.93 21:0.13 22:0.93 23:0.97 26:0.95 27:0.31 28:0.76 29:0.86 30:0.45 31:0.91 32:0.80 33:0.97 34:0.56 36:0.50 37:0.55 39:0.99 41:0.88 43:0.22 44:0.93 45:0.95 47:0.93 48:0.91 58:0.98 62:0.97 64:0.77 66:0.42 69:0.95 70:0.79 71:0.92 74:0.47 77:0.96 78:0.89 79:0.90 81:0.64 83:0.91 86:0.91 89:0.99 91:0.06 96:0.75 98:0.59 99:0.95 100:0.91 101:0.47 102:0.98 104:0.92 106:0.81 108:0.94 111:0.89 114:0.92 120:0.74 122:0.93 123:0.94 124:0.86 126:0.51 127:0.81 128:0.96 129:0.81 133:0.86 135:0.92 137:0.91 139:0.93 140:0.80 141:0.98 145:0.92 146:0.69 147:0.63 149:0.60 150:0.50 151:0.69 152:0.86 153:0.48 154:0.16 155:0.65 159:0.90 161:0.90 162:0.86 163:0.81 164:0.71 165:0.81 167:0.51 168:0.96 169:0.89 170:0.77 172:0.79 173:0.84 174:0.96 175:0.75 176:0.70 177:0.38 179:0.30 181:0.50 186:0.89 187:0.39 189:0.92 190:0.95 192:0.98 195:0.97 196:0.94 197:0.91 199:0.99 201:0.63 202:0.56 205:0.97 206:0.81 208:0.64 212:0.39 215:0.84 216:0.85 219:0.91 222:0.21 223:0.95 224:0.99 225:0.99 230:0.71 233:0.76 234:0.98 235:0.31 238:0.88 239:0.94 240:0.99 241:0.27 242:0.42 243:0.19 244:0.61 245:0.86 247:0.90 248:0.66 253:0.71 254:0.90 255:0.74 256:0.64 257:0.84 259:0.21 260:0.74 261:0.90 262:0.93 265:0.60 266:0.94 267:0.28 268:0.65 271:0.66 274:0.98 276:0.84 277:0.69 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 290:0.92 291:0.97 292:0.95 297:0.36 299:0.88 300:0.94 +3 1:0.69 7:0.75 9:0.79 10:0.99 11:0.75 12:0.86 17:0.82 18:0.96 22:0.93 23:0.96 25:0.88 26:0.95 27:0.69 28:0.76 29:0.86 30:0.84 31:0.95 32:0.67 34:0.19 36:0.42 37:0.58 39:0.99 41:0.82 43:0.78 44:0.88 45:0.96 47:0.93 48:0.97 58:0.98 60:0.87 62:0.99 64:0.77 66:0.97 69:0.73 70:0.41 71:0.92 74:0.61 75:0.99 79:0.94 81:0.80 83:0.91 85:0.72 86:0.97 89:0.99 91:0.37 96:0.83 98:0.87 101:0.96 104:0.54 108:0.57 114:0.98 120:0.72 122:0.93 123:0.54 126:0.54 127:0.39 128:0.98 129:0.05 135:0.37 137:0.95 139:0.97 140:0.45 141:0.98 145:0.56 146:0.88 147:0.90 149:0.93 150:0.63 151:0.90 153:0.69 154:0.70 155:0.66 158:0.92 159:0.47 161:0.90 162:0.86 164:0.62 165:0.93 167:0.52 168:0.98 170:0.77 172:0.91 173:0.74 174:0.97 176:0.73 177:0.88 179:0.23 181:0.50 187:0.39 192:0.99 195:0.72 196:0.97 197:0.99 199:0.99 201:0.68 202:0.46 204:0.83 205:0.95 208:0.64 212:0.69 215:0.96 216:0.03 219:0.95 222:0.21 223:0.95 224:1.00 225:0.99 226:0.98 230:0.77 233:0.76 234:0.99 235:0.22 236:0.97 239:0.99 240:0.99 241:0.35 242:0.60 243:0.97 247:0.60 248:0.80 253:0.83 255:0.94 256:0.45 259:0.21 260:0.73 262:0.93 265:0.87 266:0.38 267:0.28 268:0.55 271:0.66 274:0.97 276:0.84 279:0.80 281:0.91 283:0.82 284:0.90 285:0.62 290:0.98 292:0.95 297:0.36 300:0.43 +3 1:0.57 6:0.95 7:0.75 8:0.82 9:0.70 10:0.99 11:0.75 12:0.86 17:0.82 18:0.97 20:0.79 21:0.59 22:0.93 23:0.97 26:0.95 27:0.44 28:0.76 29:0.86 30:0.66 31:0.87 32:0.71 34:0.40 36:0.50 37:0.78 39:0.99 43:0.92 44:0.92 45:0.97 47:0.93 48:0.98 58:0.98 62:0.97 64:0.77 66:0.46 69:0.33 70:0.62 71:0.92 74:0.52 78:0.87 79:0.87 81:0.56 83:0.69 85:0.72 86:0.99 89:0.99 91:0.20 98:0.86 99:0.83 100:0.91 101:0.96 102:0.97 104:0.86 106:0.81 108:0.80 111:0.87 120:0.56 122:0.93 123:0.79 124:0.67 126:0.51 127:0.80 128:0.95 129:0.05 133:0.67 135:0.87 137:0.87 139:0.98 140:0.45 141:0.98 145:0.86 146:0.46 147:0.52 149:0.89 150:0.45 151:0.53 152:0.81 153:0.72 154:0.92 155:0.54 159:0.85 161:0.90 162:0.67 164:0.64 165:0.65 167:0.56 168:0.95 169:0.87 170:0.77 172:0.88 173:0.15 174:0.98 177:0.88 179:0.22 181:0.50 186:0.87 187:0.95 190:0.77 191:0.79 192:0.57 193:0.99 195:0.94 196:0.91 197:0.97 199:0.99 201:0.61 202:0.53 204:0.82 205:0.95 206:0.81 208:0.61 212:0.37 215:0.55 216:0.56 219:0.90 220:0.93 222:0.21 223:0.95 224:0.99 225:0.99 230:0.77 233:0.76 234:0.99 235:0.88 236:0.95 239:0.99 240:0.99 241:0.50 242:0.33 243:0.18 245:0.96 247:0.93 248:0.52 253:0.42 255:0.70 256:0.45 259:0.21 260:0.56 261:0.86 262:0.93 265:0.86 266:0.54 267:0.44 268:0.57 271:0.66 274:0.98 276:0.90 277:0.69 281:0.91 284:0.91 285:0.60 292:0.95 297:0.36 300:0.70 +2 1:0.69 7:0.70 8:0.81 9:0.72 10:0.99 11:0.82 12:0.86 17:0.75 18:0.99 21:0.30 22:0.93 23:0.98 26:0.95 27:0.52 28:0.76 29:0.86 30:0.44 31:0.95 32:0.80 33:0.97 34:0.91 36:0.76 37:0.56 39:0.99 43:0.78 44:0.93 45:0.97 47:0.93 48:0.99 58:0.99 62:0.98 64:0.77 66:0.19 69:0.40 70:0.74 71:0.92 74:0.79 75:0.98 77:0.70 79:0.95 81:0.81 83:0.91 85:0.80 86:0.99 89:0.99 91:0.20 97:0.98 98:0.65 99:0.99 101:0.96 102:0.98 104:0.93 106:0.81 108:0.94 114:0.86 120:0.75 123:0.86 124:0.79 126:0.54 127:0.97 128:0.97 129:0.81 133:0.78 135:0.30 137:0.95 139:0.99 140:0.45 141:0.98 145:0.70 146:0.80 147:0.83 149:0.95 150:0.65 151:0.74 153:0.48 154:0.71 155:0.58 158:0.82 159:0.87 161:0.90 162:0.82 164:0.73 165:0.93 167:0.49 168:0.97 170:0.77 172:0.92 173:0.17 174:0.97 176:0.73 177:0.88 179:0.13 181:0.50 187:0.39 190:0.89 192:0.86 195:0.95 196:0.98 197:0.97 199:1.00 201:0.68 202:0.62 205:0.97 206:0.81 208:0.64 212:0.85 215:0.72 216:0.50 219:0.94 220:0.74 222:0.21 223:0.95 224:0.99 225:0.99 226:0.98 230:0.73 233:0.76 234:0.99 235:0.84 236:0.97 239:0.99 240:0.99 241:0.18 242:0.23 243:0.38 245:0.99 247:0.98 248:0.69 253:0.68 255:0.74 256:0.64 259:0.21 260:0.76 262:0.93 265:0.43 266:0.87 267:0.76 268:0.68 271:0.66 274:0.98 276:0.96 279:0.81 281:0.91 282:0.88 283:0.82 284:0.95 285:0.60 290:0.86 291:0.97 292:0.95 297:0.36 299:0.88 300:0.94 +1 1:0.74 11:0.64 12:0.20 17:0.95 21:0.22 27:0.72 30:0.94 34:0.34 36:0.32 39:0.55 43:0.96 55:0.45 60:0.87 66:0.79 69:0.17 70:0.24 74:0.99 81:0.90 83:0.87 85:0.88 91:0.20 98:0.64 101:0.79 104:0.97 106:0.81 108:0.66 114:0.90 117:0.86 122:0.67 123:0.64 124:0.41 126:0.54 127:0.66 129:0.59 131:0.61 133:0.64 135:0.63 144:0.57 146:0.39 147:0.45 149:0.94 150:0.94 154:0.96 155:0.67 157:0.98 158:0.92 159:0.57 165:0.86 166:1.00 172:0.94 173:0.20 176:0.73 177:0.38 178:0.55 179:0.21 182:0.98 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.92 215:0.79 216:0.07 222:0.84 227:0.61 229:0.61 231:1.00 232:0.87 235:0.31 241:0.03 242:0.80 243:0.17 245:0.89 246:0.61 247:0.47 253:0.79 259:0.21 265:0.65 266:0.54 267:0.19 276:0.87 279:0.86 283:0.82 287:0.61 290:0.90 297:0.36 300:0.70 +1 11:0.64 12:0.20 17:0.22 21:0.78 27:0.25 30:0.28 34:0.64 36:0.91 37:0.44 39:0.55 43:0.32 55:0.71 66:0.72 69:0.94 70:0.89 74:0.50 85:0.85 91:0.02 98:0.40 101:0.72 108:0.92 122:0.43 123:0.92 124:0.41 127:0.21 129:0.59 131:0.61 133:0.47 135:0.80 144:0.57 146:0.18 147:0.23 149:0.45 154:0.45 157:0.94 159:0.65 166:0.61 172:0.59 173:0.87 177:0.38 178:0.55 179:0.43 182:0.85 187:0.39 202:0.50 212:0.55 216:0.81 222:0.84 227:0.61 229:0.61 231:0.99 235:0.05 241:0.07 242:0.40 243:0.20 245:0.61 246:0.61 247:0.60 253:0.42 259:0.21 265:0.42 266:0.63 267:0.28 276:0.42 287:0.61 300:0.70 +1 7:0.76 11:0.64 12:0.20 17:0.76 21:0.22 27:0.67 30:0.15 32:0.72 34:0.88 36:0.53 37:0.32 39:0.55 43:0.75 55:0.71 66:0.89 69:0.59 70:0.80 74:0.54 81:0.84 85:0.80 91:0.23 98:0.84 101:0.76 104:0.93 106:0.81 108:0.45 123:0.43 126:0.32 127:0.34 129:0.05 131:0.61 132:0.97 135:0.49 144:0.57 145:0.40 146:0.81 147:0.84 149:0.73 150:0.65 154:0.66 157:0.94 159:0.36 166:0.99 172:0.68 173:0.64 177:0.38 178:0.55 179:0.53 182:0.85 187:0.39 195:0.66 206:0.81 212:0.54 215:0.79 216:0.44 222:0.84 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.22 242:0.72 243:0.89 246:0.61 247:0.47 253:0.44 259:0.21 265:0.84 266:0.38 267:0.59 276:0.57 287:0.61 297:0.36 300:0.55 +1 12:0.20 17:0.84 21:0.78 27:0.72 30:0.95 34:0.21 36:0.22 39:0.55 43:0.24 55:0.99 66:0.20 69:0.84 74:0.39 91:0.20 98:0.06 104:0.58 108:0.70 120:0.69 123:0.68 124:0.61 127:0.53 129:0.59 131:0.99 133:0.47 135:0.60 140:0.45 146:0.05 147:0.05 149:0.12 151:0.66 153:0.48 154:0.17 157:0.61 159:0.84 162:0.86 164:0.55 166:0.61 172:0.05 173:0.65 175:0.75 177:0.05 179:1.00 182:0.61 190:0.77 202:0.36 216:0.03 222:0.84 227:0.61 229:0.61 231:0.99 235:0.12 241:0.79 243:0.40 244:0.61 245:0.36 246:0.61 248:0.63 253:0.35 256:0.45 257:0.65 259:0.21 260:0.69 265:0.06 267:0.52 268:0.46 277:0.69 283:0.82 285:0.50 287:0.61 300:0.08 +1 11:0.64 12:0.20 17:0.40 21:0.78 27:0.72 30:0.17 34:0.53 36:0.87 37:0.41 39:0.55 43:0.76 55:0.71 66:0.13 69:0.72 70:0.78 74:0.85 85:0.72 91:0.25 98:0.34 101:0.70 108:0.55 122:0.67 123:0.52 124:0.89 127:0.61 129:0.05 131:0.61 133:0.87 135:0.51 144:0.57 146:0.59 147:0.65 149:0.66 154:0.67 157:0.83 159:0.75 163:0.93 166:0.61 172:0.32 173:0.57 177:0.38 178:0.55 179:0.57 182:0.75 187:0.39 212:0.16 216:0.33 222:0.84 227:0.61 229:0.61 231:0.93 232:0.93 235:0.68 241:0.30 242:0.80 243:0.34 245:0.67 246:0.61 247:0.39 253:0.61 259:0.21 265:0.36 266:0.89 267:0.44 276:0.62 287:0.61 300:0.55 +1 1:0.74 7:0.81 11:0.64 12:0.20 17:0.59 21:0.13 27:0.68 30:0.62 34:0.58 36:0.59 37:0.81 39:0.55 43:0.78 55:0.59 60:0.87 66:0.75 69:0.77 70:0.34 74:0.71 81:0.93 83:0.87 85:0.72 91:0.54 98:0.44 101:0.74 108:0.61 114:0.92 121:0.90 122:0.43 123:0.58 126:0.54 127:0.14 129:0.05 131:0.61 135:0.80 144:0.57 145:0.83 146:0.34 147:0.41 149:0.48 150:0.97 154:0.71 155:0.67 157:0.87 158:0.92 159:0.14 165:0.88 166:1.00 172:0.32 173:0.93 176:0.73 177:0.38 178:0.55 179:0.50 182:0.98 187:0.68 192:0.59 201:0.74 204:0.89 208:0.64 212:0.84 215:0.84 216:0.21 222:0.84 227:0.61 229:0.61 230:0.87 231:1.00 233:0.76 235:0.22 241:0.51 242:0.33 243:0.77 246:0.61 247:0.39 253:0.73 259:0.21 265:0.46 266:0.17 267:0.76 276:0.28 279:0.86 283:0.82 287:0.61 290:0.92 297:0.36 300:0.25 +1 8:0.79 11:0.64 12:0.20 17:0.32 21:0.78 27:0.70 30:0.53 34:0.49 36:0.97 37:0.79 39:0.55 43:0.31 55:0.71 66:0.33 69:0.98 70:0.73 74:0.98 85:0.72 91:0.51 98:0.73 101:0.65 108:0.91 122:0.67 123:0.90 124:0.83 127:0.83 129:0.05 131:0.61 133:0.82 135:0.21 144:0.57 145:0.88 146:0.66 147:0.93 149:0.83 154:0.16 157:0.79 159:0.90 166:0.61 172:0.89 173:0.98 177:0.05 178:0.55 179:0.18 182:0.74 187:0.21 202:0.53 212:0.56 216:0.27 222:0.84 227:0.61 229:0.61 231:0.98 232:0.85 235:0.22 241:0.59 242:0.81 243:0.36 245:0.96 246:0.61 247:0.47 253:0.70 257:0.65 259:0.21 265:0.73 266:0.80 267:0.73 276:0.93 277:0.69 287:0.61 300:0.84 +1 11:0.64 12:0.20 17:0.08 21:0.78 27:0.70 30:0.41 34:0.30 36:0.50 37:0.79 39:0.55 43:0.98 55:0.71 66:0.86 69:0.05 70:0.60 74:0.85 85:0.72 91:0.15 98:0.88 101:0.74 108:0.05 122:0.67 123:0.05 127:0.42 129:0.05 131:0.61 135:0.78 144:0.57 146:0.69 147:0.74 149:0.70 154:0.98 157:0.87 159:0.27 166:0.61 172:0.59 173:0.24 177:0.38 178:0.55 179:0.70 182:0.77 187:0.87 212:0.72 216:0.27 222:0.84 227:0.61 229:0.61 231:0.98 235:0.05 241:0.24 242:0.63 243:0.86 246:0.61 247:0.47 253:0.60 259:0.21 265:0.88 266:0.27 267:0.36 276:0.49 287:0.61 300:0.43 +2 11:0.64 12:0.20 17:0.73 21:0.40 27:0.62 30:0.11 34:0.55 36:0.66 37:0.73 39:0.55 43:0.88 66:0.83 69:0.12 70:0.69 74:0.56 81:0.72 85:0.72 91:0.38 98:0.51 101:0.72 104:0.80 108:0.10 123:0.10 126:0.32 127:0.15 129:0.05 131:0.61 135:0.77 144:0.57 145:0.98 146:0.55 147:0.61 149:0.63 150:0.52 154:0.86 157:0.85 159:0.20 166:0.99 172:0.51 173:0.22 177:0.38 178:0.55 179:0.36 182:0.76 187:0.21 212:0.86 215:0.67 216:0.10 222:0.84 227:0.61 229:0.61 231:0.99 235:0.42 241:0.12 242:0.45 243:0.84 246:0.61 247:0.39 253:0.46 259:0.21 265:0.52 266:0.23 267:0.44 276:0.41 283:0.71 287:0.61 297:0.36 300:0.43 +3 1:0.69 7:0.72 8:0.67 11:0.64 12:0.86 17:0.97 18:0.71 27:0.72 29:0.71 30:0.17 32:0.69 34:0.80 36:0.61 37:0.81 39:0.84 43:0.72 45:0.73 55:0.71 66:0.91 69:0.05 70:0.79 71:0.89 74:0.82 76:0.85 77:0.89 81:0.89 83:0.91 86:0.78 91:0.62 97:0.89 98:0.97 99:0.83 101:0.52 104:0.58 108:0.05 114:0.95 122:0.77 123:0.05 126:0.44 127:0.79 129:0.05 135:0.51 139:0.77 145:0.65 146:0.80 147:0.83 149:0.48 150:0.88 154:0.65 155:0.67 158:0.74 159:0.36 165:0.70 172:0.74 173:0.21 176:0.66 177:0.70 178:0.55 179:0.60 187:0.21 192:0.87 195:0.58 201:0.68 204:0.85 208:0.64 212:0.55 215:0.96 216:0.05 219:0.89 222:0.48 230:0.75 232:0.81 233:0.76 235:0.05 241:0.07 242:0.73 243:0.91 247:0.60 253:0.65 254:0.88 259:0.21 265:0.97 266:0.27 267:0.87 276:0.62 279:0.82 282:0.77 290:0.95 292:0.91 297:0.36 300:0.55 +1 1:0.69 8:0.69 9:0.76 11:0.64 12:0.86 17:0.26 18:0.64 21:0.22 27:0.20 29:0.71 30:0.11 34:0.67 36:0.24 37:0.65 39:0.84 43:0.80 45:0.66 66:0.43 69:0.67 70:0.99 71:0.89 74:0.70 81:0.54 86:0.74 91:0.53 96:0.69 98:0.29 101:0.52 104:0.95 106:0.81 108:0.51 114:0.63 117:0.86 120:0.55 122:0.55 123:0.49 124:0.89 126:0.44 127:0.66 129:0.05 133:0.91 135:0.99 138:0.96 139:0.71 140:0.80 145:0.59 146:0.63 147:0.68 149:0.67 150:0.56 151:0.53 153:0.48 154:0.75 155:0.58 158:0.74 159:0.84 162:0.62 164:0.53 172:0.59 173:0.43 176:0.61 177:0.70 178:0.42 179:0.54 187:0.68 190:0.77 191:0.73 192:0.59 201:0.68 202:0.50 206:0.81 208:0.54 212:0.39 215:0.44 216:0.70 220:0.82 222:0.48 232:0.92 235:0.31 241:0.32 242:0.19 243:0.57 245:0.63 247:0.79 248:0.53 253:0.49 255:0.74 256:0.45 259:0.21 260:0.55 265:0.31 266:0.91 267:0.79 268:0.46 276:0.65 279:0.82 283:0.77 285:0.56 286:0.99 290:0.63 297:0.36 298:0.99 300:0.94 +1 8:0.83 12:0.86 17:0.43 18:0.66 21:0.05 25:0.88 27:0.36 29:0.71 30:0.38 34:0.22 36:0.50 37:0.57 39:0.84 43:0.15 55:0.71 66:0.54 69:0.78 70:0.78 71:0.89 74:0.41 76:0.85 77:0.70 81:0.77 86:0.65 91:0.80 98:0.61 104:0.54 108:0.62 120:0.75 123:0.59 124:0.58 126:0.26 127:0.50 129:0.05 133:0.62 135:0.68 139:0.64 140:0.45 145:0.74 146:0.60 147:0.05 149:0.21 150:0.56 151:0.59 153:0.85 154:0.11 159:0.42 162:0.71 164:0.71 172:0.41 173:0.85 175:0.75 177:0.05 179:0.80 190:0.89 191:0.83 195:0.65 202:0.70 212:0.28 215:0.78 216:0.18 220:0.62 222:0.48 235:0.05 241:0.82 242:0.60 243:0.16 244:0.83 245:0.51 247:0.55 248:0.61 253:0.32 256:0.68 257:0.84 259:0.21 260:0.68 265:0.62 266:0.47 267:0.44 268:0.73 276:0.40 277:0.69 282:0.73 283:0.78 285:0.47 297:0.36 300:0.55 +1 1:0.69 11:0.64 12:0.86 17:0.36 18:0.83 21:0.13 27:0.45 29:0.71 30:0.28 34:0.79 36:0.18 37:0.50 39:0.84 43:0.65 44:0.90 45:0.66 55:0.59 64:0.77 66:0.30 69:0.68 70:0.96 71:0.89 74:0.70 77:0.70 81:0.52 83:0.60 86:0.83 91:0.09 97:0.89 98:0.31 99:0.83 101:0.52 108:0.51 114:0.75 122:0.77 123:0.49 124:0.79 126:0.44 127:0.45 129:0.81 133:0.76 135:0.75 139:0.85 145:0.42 146:0.50 147:0.56 149:0.38 150:0.58 154:0.54 155:0.58 158:0.79 159:0.64 165:0.70 172:0.45 173:0.58 175:0.75 176:0.66 177:0.38 178:0.55 179:0.58 187:0.68 192:0.51 195:0.83 201:0.68 208:0.54 212:0.42 216:0.77 219:0.92 222:0.48 235:0.22 241:0.18 242:0.45 243:0.48 244:0.61 245:0.68 247:0.76 253:0.55 257:0.65 259:0.21 265:0.34 266:0.80 267:0.92 276:0.55 277:0.69 279:0.82 282:0.77 290:0.75 292:0.95 300:0.84 +1 1:0.74 7:0.66 8:0.81 9:0.80 11:0.64 12:0.86 17:0.72 18:0.93 21:0.22 27:0.52 29:0.71 30:0.76 31:0.87 32:0.69 34:0.86 36:0.41 37:0.55 39:0.84 43:0.76 44:0.90 45:0.66 48:0.91 64:0.77 66:0.62 69:0.81 70:0.58 71:0.89 74:0.87 76:0.85 77:0.89 79:0.87 81:0.63 86:0.95 91:0.25 96:0.69 98:0.68 101:0.52 108:0.65 114:0.63 120:0.55 122:0.76 123:0.63 124:0.56 126:0.54 127:0.34 129:0.81 133:0.67 135:0.90 137:0.87 138:0.95 139:0.95 140:0.45 145:0.77 146:0.92 147:0.94 149:0.96 150:0.73 151:0.56 153:0.48 154:0.68 155:0.67 158:0.78 159:0.72 162:0.86 164:0.54 172:0.90 173:0.66 176:0.66 177:0.70 179:0.18 187:0.21 192:0.87 195:0.91 196:0.90 201:0.93 202:0.36 204:0.85 208:0.64 212:0.52 215:0.44 216:0.46 219:0.92 220:0.82 222:0.48 230:0.68 233:0.73 235:0.42 241:0.01 242:0.32 243:0.68 245:0.93 247:0.90 248:0.55 253:0.53 255:0.95 256:0.45 259:0.21 260:0.55 265:0.69 266:0.38 267:0.44 268:0.46 276:0.87 279:0.86 281:0.89 282:0.86 283:0.77 284:0.87 285:0.62 286:0.99 290:0.63 292:0.91 297:0.36 298:0.99 300:0.70 +0 12:0.86 17:0.64 21:0.78 27:0.72 29:0.71 30:0.76 34:0.17 36:0.32 37:0.26 39:0.84 43:0.13 44:0.87 55:0.99 66:0.13 69:0.65 70:0.15 71:0.89 74:0.34 91:0.65 98:0.05 108:0.50 122:0.77 123:0.48 124:0.75 127:0.53 129:0.81 133:0.67 135:0.76 139:0.64 145:0.84 146:0.05 147:0.05 149:0.08 154:0.10 159:0.93 163:0.97 172:0.05 173:0.25 175:0.91 177:0.05 178:0.55 179:0.99 195:0.99 202:0.66 212:0.95 216:0.07 222:0.48 235:0.12 241:0.85 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.28 257:0.84 259:0.21 265:0.05 266:0.12 267:0.98 276:0.18 277:0.87 300:0.13 +2 11:0.85 12:0.86 17:0.18 18:0.91 21:0.47 22:0.93 27:0.41 29:0.71 30:0.15 32:0.68 34:0.53 36:0.30 37:0.44 39:0.84 43:0.60 44:0.90 45:0.81 47:0.93 48:0.91 55:0.92 64:0.77 66:0.35 69:0.53 70:0.97 71:0.89 74:0.59 76:0.85 81:0.25 86:0.90 91:0.38 98:0.58 101:0.87 104:0.99 108:0.72 122:0.86 123:0.70 124:0.83 126:0.26 127:0.70 129:0.59 133:0.84 135:0.82 139:0.90 145:0.89 146:0.26 147:0.32 149:0.52 150:0.25 154:0.74 159:0.78 172:0.59 173:0.34 177:0.70 178:0.55 179:0.52 187:0.68 195:0.90 212:0.40 216:0.75 219:0.89 222:0.48 235:0.52 241:0.05 242:0.24 243:0.23 245:0.73 247:0.82 253:0.38 254:0.96 259:0.21 262:0.93 265:0.59 266:0.89 267:0.92 276:0.67 277:0.69 281:0.91 292:0.91 300:0.84 +0 11:0.64 12:0.86 17:0.75 18:0.80 21:0.13 27:0.63 29:0.71 30:0.10 32:0.78 34:0.71 36:0.31 37:0.32 39:0.84 43:0.13 44:0.93 45:0.66 55:0.52 66:0.16 69:0.69 70:0.97 71:0.89 74:0.70 77:0.89 81:0.66 86:0.82 91:0.23 98:0.41 101:0.52 104:0.83 108:0.52 122:0.86 123:0.85 124:0.87 126:0.26 127:0.62 129:0.59 133:0.86 135:0.88 139:0.84 145:0.80 146:0.65 147:0.70 149:0.22 150:0.48 154:0.67 159:0.74 172:0.51 173:0.55 177:0.70 178:0.55 179:0.45 187:0.39 195:0.87 212:0.30 215:0.61 216:0.56 222:0.48 235:0.12 241:0.18 242:0.24 243:0.40 245:0.75 247:0.84 253:0.41 254:0.95 259:0.21 265:0.41 266:0.84 267:0.59 276:0.71 282:0.86 283:0.78 297:0.36 300:0.84 +1 1:0.74 8:0.69 9:0.80 12:0.86 17:0.62 18:0.75 21:0.59 22:0.93 27:0.28 29:0.71 30:0.56 31:0.86 34:0.95 36:0.26 37:0.41 39:0.84 43:0.14 47:0.93 55:0.96 64:0.77 66:0.08 69:0.51 70:0.71 71:0.89 74:0.60 79:0.86 81:0.42 86:0.80 91:0.40 96:0.68 98:0.23 104:0.94 106:0.81 108:0.39 114:0.58 117:0.86 120:0.54 122:0.77 123:0.65 124:0.85 126:0.54 127:0.58 129:0.05 133:0.81 135:0.99 137:0.86 139:0.83 140:0.45 145:0.85 146:0.27 147:0.33 149:0.16 150:0.63 151:0.47 153:0.48 154:0.83 155:0.67 158:0.86 159:0.67 162:0.78 164:0.70 172:0.21 173:0.39 176:0.73 177:0.70 179:0.79 187:0.39 192:0.87 196:0.88 201:0.93 202:0.59 206:0.81 208:0.64 212:0.13 215:0.39 216:0.67 219:0.95 220:0.97 222:0.48 232:0.98 235:0.80 241:0.01 242:0.24 243:0.39 245:0.52 247:0.74 248:0.47 253:0.42 254:0.86 255:0.95 256:0.58 259:0.21 260:0.54 262:0.93 265:0.18 266:0.75 267:0.59 268:0.64 276:0.43 277:0.87 279:0.86 283:0.77 284:0.93 285:0.62 290:0.58 292:0.87 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.64 12:0.86 17:0.49 18:0.69 21:0.13 22:0.93 27:0.20 29:0.71 30:0.62 31:0.91 34:0.56 36:0.41 37:0.65 39:0.84 43:0.79 45:0.66 47:0.93 64:0.77 66:0.47 69:0.66 70:0.34 71:0.89 74:0.57 79:0.91 81:0.80 83:0.60 86:0.77 91:0.68 96:0.76 97:0.94 98:0.33 99:0.83 101:0.52 104:0.93 106:0.81 108:0.50 114:0.79 117:0.86 120:0.64 122:0.80 123:0.48 124:0.52 126:0.54 127:0.23 129:0.05 133:0.43 135:0.75 137:0.91 139:0.82 140:0.45 145:0.47 146:0.39 147:0.46 149:0.56 150:0.86 151:0.74 153:0.69 154:0.73 155:0.67 158:0.91 159:0.33 162:0.86 163:0.79 164:0.62 165:0.70 172:0.21 173:0.68 176:0.73 177:0.70 179:0.83 187:0.68 192:0.87 196:0.93 201:0.93 202:0.46 206:0.81 208:0.64 212:0.30 215:0.61 216:0.70 219:0.95 220:0.62 222:0.48 232:0.96 235:0.31 241:0.47 242:0.63 243:0.59 245:0.46 247:0.35 248:0.69 253:0.71 255:0.95 256:0.45 259:0.21 260:0.65 262:0.93 265:0.35 266:0.27 267:0.65 268:0.55 276:0.21 279:0.86 283:0.82 284:0.90 285:0.62 290:0.79 292:0.91 297:0.36 300:0.25 +2 7:0.66 11:0.64 12:0.86 17:0.96 18:0.83 21:0.22 27:0.53 29:0.71 30:0.62 32:0.68 34:0.81 36:0.52 37:0.25 39:0.84 43:0.58 45:0.73 48:0.91 55:0.59 64:0.77 66:0.92 69:0.12 70:0.58 71:0.89 74:0.69 76:0.99 77:0.70 81:0.63 86:0.92 91:0.56 98:0.98 101:0.52 104:0.89 106:0.81 108:0.10 123:0.10 126:0.44 127:0.81 129:0.05 135:0.47 139:0.90 145:0.49 146:0.76 147:0.80 149:0.37 150:0.44 154:0.83 155:0.58 159:0.32 172:0.77 173:0.35 177:0.70 178:0.55 179:0.56 187:0.39 192:0.59 195:0.60 201:0.68 204:0.85 206:0.81 208:0.54 212:0.91 215:0.51 216:0.18 222:0.48 230:0.69 232:0.74 233:0.76 235:0.31 241:0.05 242:0.58 243:0.92 247:0.79 253:0.41 254:0.95 259:0.21 265:0.98 266:0.27 267:0.52 276:0.66 281:0.91 282:0.77 283:0.82 297:0.36 300:0.55 +1 1:0.74 7:0.81 8:0.58 11:0.64 12:0.86 17:0.73 18:0.90 21:0.22 22:0.93 27:0.51 29:0.71 30:0.53 32:0.80 34:0.75 36:0.62 37:0.35 39:0.84 43:0.40 44:0.93 45:0.89 47:0.93 64:0.77 66:0.33 69:0.55 70:0.83 71:0.89 74:0.91 77:0.70 81:0.72 83:0.60 86:0.97 91:0.37 97:0.94 98:0.65 99:0.83 101:0.52 104:0.92 106:0.81 108:0.42 114:0.71 117:0.86 122:0.80 123:0.68 124:0.60 126:0.54 127:0.37 129:0.59 133:0.46 135:0.94 139:0.98 145:0.42 146:0.51 147:0.57 149:0.35 150:0.81 154:0.84 155:0.67 158:0.91 159:0.48 165:0.70 172:0.68 173:0.52 176:0.73 177:0.70 178:0.55 179:0.33 187:0.21 192:0.87 195:0.74 201:0.93 204:0.89 206:0.81 208:0.64 212:0.77 215:0.51 216:0.39 219:0.95 222:0.48 230:0.86 232:0.97 233:0.73 235:0.42 241:0.01 242:0.27 243:0.57 245:0.90 247:0.90 253:0.55 254:0.74 259:0.21 262:0.93 265:0.42 266:0.75 267:0.94 276:0.72 279:0.86 281:0.89 282:0.88 283:0.77 290:0.71 292:0.91 297:0.36 300:0.84 +1 7:0.66 12:0.86 17:0.66 18:0.79 21:0.05 27:0.72 29:0.71 30:0.27 31:0.88 32:0.75 34:0.97 36:0.65 39:0.84 43:0.14 44:0.93 55:0.42 64:0.77 66:0.43 69:0.08 70:0.73 71:0.89 74:0.72 76:0.97 77:0.70 79:0.88 81:0.82 86:0.86 87:0.98 91:0.65 98:0.39 104:0.88 106:0.81 108:0.07 120:0.57 123:0.07 124:0.90 126:0.44 127:0.91 129:0.59 133:0.91 135:0.74 137:0.88 139:0.87 140:0.45 145:0.59 146:0.71 147:0.75 149:0.20 150:0.60 151:0.53 153:0.48 154:0.78 159:0.82 162:0.86 164:0.54 172:0.61 173:0.09 177:0.70 179:0.55 187:0.21 190:0.77 192:0.51 195:0.92 196:0.90 201:0.68 202:0.36 206:0.81 212:0.55 215:0.78 216:0.07 220:0.82 222:0.48 228:0.99 230:0.69 232:0.77 233:0.73 235:0.12 241:0.07 242:0.14 243:0.57 245:0.65 247:0.86 248:0.53 253:0.42 254:0.80 255:0.74 256:0.45 259:0.21 260:0.57 265:0.41 266:0.84 267:0.88 268:0.46 276:0.67 282:0.84 283:0.78 284:0.87 285:0.56 297:0.36 300:0.55 +2 7:0.72 11:0.64 12:0.86 17:0.97 18:0.63 21:0.05 27:0.72 29:0.71 30:0.41 32:0.69 34:0.44 36:0.84 37:0.81 39:0.84 43:0.18 45:0.66 55:0.52 66:0.86 69:0.05 70:0.65 71:0.89 74:0.69 76:0.85 77:0.89 81:0.77 86:0.68 91:0.85 98:0.99 101:0.52 104:0.58 108:0.05 122:0.55 123:0.05 126:0.26 127:0.87 129:0.05 135:0.44 139:0.66 145:0.65 146:0.64 147:0.69 149:0.19 150:0.56 154:0.70 155:0.58 159:0.24 172:0.59 173:0.34 177:0.70 178:0.55 179:0.78 187:0.21 192:0.59 195:0.53 204:0.85 208:0.54 212:0.61 215:0.78 216:0.05 222:0.48 230:0.74 232:0.81 233:0.73 235:0.05 241:0.75 242:0.19 243:0.86 247:0.74 253:0.41 254:0.88 259:0.21 265:0.99 266:0.27 267:0.93 276:0.48 282:0.77 297:0.36 300:0.43 +1 1:0.74 9:0.80 11:0.64 12:0.86 17:0.47 18:0.68 21:0.13 22:0.93 27:0.20 29:0.71 30:0.62 31:0.91 34:0.71 36:0.33 37:0.65 39:0.84 43:0.78 45:0.66 47:0.93 64:0.77 66:0.47 69:0.71 70:0.34 71:0.89 74:0.57 79:0.91 81:0.80 83:0.60 86:0.76 91:0.66 96:0.76 97:0.94 98:0.33 99:0.83 101:0.52 104:0.93 106:0.81 108:0.54 114:0.79 117:0.86 120:0.64 122:0.80 123:0.52 124:0.52 126:0.54 127:0.23 129:0.05 133:0.43 135:0.74 137:0.91 139:0.81 140:0.45 145:0.47 146:0.39 147:0.46 149:0.54 150:0.86 151:0.74 153:0.69 154:0.71 155:0.67 158:0.91 159:0.33 162:0.86 163:0.79 164:0.62 165:0.70 172:0.21 173:0.73 176:0.73 177:0.70 179:0.83 187:0.68 192:0.87 196:0.92 201:0.93 202:0.46 206:0.81 208:0.64 212:0.30 215:0.61 216:0.70 219:0.95 220:0.62 222:0.48 232:0.96 235:0.31 241:0.50 242:0.63 243:0.59 245:0.46 247:0.35 248:0.69 253:0.70 255:0.95 256:0.45 259:0.21 260:0.65 262:0.93 265:0.35 266:0.27 267:0.84 268:0.55 276:0.21 279:0.86 283:0.82 284:0.90 285:0.62 290:0.79 292:0.91 297:0.36 300:0.25 +2 1:0.69 7:0.66 8:0.73 9:0.76 11:0.64 12:0.86 17:0.53 18:0.80 22:0.93 27:0.17 29:0.71 30:0.28 31:0.90 34:0.71 36:0.67 37:0.55 39:0.84 43:0.58 45:0.81 47:0.93 64:0.77 66:0.61 69:0.84 70:0.83 71:0.89 74:0.68 77:0.70 79:0.90 81:0.69 83:0.60 86:0.84 91:0.40 96:0.78 97:0.89 98:0.31 99:0.83 101:0.52 108:0.69 114:0.95 117:0.86 122:0.80 123:0.67 124:0.55 126:0.44 127:0.24 129:0.05 133:0.60 135:0.98 137:0.90 139:0.83 140:0.80 145:0.72 146:0.53 147:0.59 149:0.46 150:0.65 151:0.65 153:0.48 154:0.60 155:0.58 158:0.79 159:0.54 162:0.86 165:0.70 172:0.54 173:0.76 176:0.66 177:0.70 179:0.50 187:0.68 190:0.77 191:0.70 192:0.51 195:0.88 196:0.92 201:0.68 202:0.53 208:0.54 212:0.61 216:0.71 219:0.92 220:0.74 222:0.48 230:0.69 232:0.92 233:0.73 235:0.05 241:0.56 242:0.28 243:0.68 245:0.60 247:0.76 248:0.63 253:0.75 254:0.93 255:0.74 256:0.58 259:0.21 262:0.93 265:0.33 266:0.63 267:0.69 268:0.62 276:0.45 279:0.82 282:0.73 284:0.87 285:0.56 290:0.95 292:0.95 300:0.70 +1 7:0.72 8:0.83 11:0.64 12:0.86 17:0.92 18:0.72 21:0.05 27:0.72 29:0.71 30:0.21 34:0.36 36:0.43 39:0.84 43:0.59 45:0.73 48:0.97 55:0.84 66:0.93 69:0.08 70:0.88 71:0.89 74:0.68 81:0.77 86:0.83 91:0.80 98:0.92 101:0.52 104:0.58 108:0.07 120:0.74 123:0.07 126:0.26 127:0.55 129:0.05 135:0.54 139:0.83 140:0.45 146:0.92 147:0.93 149:0.45 150:0.56 151:0.60 153:0.48 154:0.92 155:0.58 159:0.53 162:0.86 164:0.56 172:0.82 173:0.15 177:0.70 179:0.42 190:0.89 192:0.59 202:0.50 204:0.85 208:0.54 212:0.60 215:0.78 216:0.02 220:0.62 222:0.48 230:0.75 233:0.76 235:0.05 241:0.79 242:0.72 243:0.94 247:0.79 248:0.61 253:0.41 254:0.74 256:0.58 259:0.21 260:0.68 265:0.92 266:0.63 267:0.84 268:0.59 276:0.72 281:0.91 283:0.78 285:0.47 297:0.36 300:0.70 +3 7:0.72 8:0.73 9:0.76 11:0.64 12:0.86 17:0.91 18:0.63 21:0.05 27:0.72 29:0.71 30:0.41 32:0.69 34:0.50 36:0.83 37:0.81 39:0.84 43:0.18 45:0.66 55:0.52 66:0.86 69:0.05 70:0.65 71:0.89 74:0.73 76:0.85 77:0.89 81:0.77 83:0.60 86:0.68 91:0.90 96:0.84 97:0.89 98:0.99 101:0.52 104:0.58 108:0.05 120:0.78 122:0.55 123:0.05 126:0.26 127:0.87 129:0.05 135:0.42 139:0.66 140:0.80 145:0.65 146:0.64 147:0.69 149:0.19 150:0.56 151:0.85 153:0.77 154:0.70 155:0.58 158:0.79 159:0.24 162:0.70 164:0.65 172:0.59 173:0.34 177:0.70 179:0.78 190:0.77 192:0.59 195:0.53 202:0.62 204:0.85 208:0.54 212:0.61 215:0.78 216:0.05 220:0.62 222:0.48 230:0.74 232:0.81 233:0.73 235:0.05 241:0.80 242:0.19 243:0.86 247:0.74 248:0.76 253:0.81 254:0.88 255:0.74 256:0.58 259:0.21 260:0.74 265:0.99 266:0.27 267:0.96 268:0.65 276:0.48 279:0.82 282:0.77 283:0.82 285:0.56 297:0.36 300:0.43 +1 1:0.69 7:0.72 8:0.68 9:0.76 11:0.64 12:0.86 17:0.88 18:0.73 21:0.22 27:0.49 29:0.71 30:0.73 32:0.69 34:0.63 36:0.73 37:0.38 39:0.84 43:0.65 45:0.81 55:0.52 66:0.91 69:0.10 70:0.39 71:0.89 74:0.87 76:0.97 77:0.99 81:0.62 86:0.79 91:0.46 96:0.71 98:0.85 101:0.52 108:0.09 114:0.67 117:0.86 120:0.57 122:0.75 123:0.09 126:0.44 127:0.36 129:0.05 135:0.49 139:0.79 140:0.45 145:0.92 146:0.66 147:0.71 149:0.66 150:0.59 151:0.53 153:0.48 154:0.77 155:0.67 158:0.78 159:0.25 162:0.86 164:0.54 172:0.76 173:0.32 176:0.66 177:0.70 179:0.45 187:0.21 190:0.77 192:0.87 195:0.57 201:0.68 202:0.36 204:0.85 208:0.64 212:0.95 215:0.51 216:0.30 219:0.92 220:0.82 222:0.48 230:0.74 232:0.84 233:0.73 235:0.31 241:0.01 242:0.28 243:0.92 247:0.84 248:0.53 253:0.57 254:0.93 255:0.74 256:0.45 259:0.21 260:0.57 265:0.85 266:0.12 267:0.28 268:0.46 276:0.65 279:0.82 282:0.77 285:0.56 290:0.67 292:0.91 297:0.36 300:0.33 +2 7:0.72 8:0.83 11:0.64 12:0.86 17:0.92 18:0.72 21:0.05 27:0.72 29:0.71 30:0.21 34:0.36 36:0.43 39:0.84 43:0.59 45:0.73 48:0.97 55:0.84 66:0.93 69:0.08 70:0.88 71:0.89 74:0.68 81:0.77 86:0.83 91:0.80 98:0.92 101:0.52 104:0.58 108:0.07 120:0.74 123:0.07 126:0.26 127:0.55 129:0.05 135:0.54 139:0.83 140:0.45 146:0.92 147:0.93 149:0.45 150:0.56 151:0.60 153:0.48 154:0.92 155:0.58 159:0.53 162:0.86 164:0.56 172:0.82 173:0.15 177:0.70 179:0.42 190:0.89 192:0.59 202:0.50 204:0.85 208:0.54 212:0.60 215:0.78 216:0.02 220:0.62 222:0.48 230:0.75 233:0.76 235:0.05 241:0.79 242:0.72 243:0.94 247:0.79 248:0.61 253:0.41 254:0.74 256:0.58 259:0.21 260:0.68 265:0.92 266:0.63 267:0.84 268:0.59 276:0.72 281:0.91 285:0.47 297:0.36 300:0.70 +2 1:0.74 11:0.64 12:0.86 17:0.18 18:0.76 21:0.30 27:0.45 29:0.71 30:0.30 34:0.91 36:0.21 37:0.50 39:0.84 43:0.20 45:0.73 55:0.59 64:0.77 66:0.33 69:0.68 70:0.92 71:0.89 74:0.52 81:0.64 83:0.60 86:0.83 91:0.11 98:0.15 99:0.83 101:0.52 108:0.52 114:0.65 122:0.55 123:0.50 124:0.77 126:0.54 127:0.69 129:0.59 133:0.73 135:0.87 139:0.79 145:0.49 146:0.31 147:0.38 149:0.11 150:0.77 154:0.76 155:0.67 159:0.86 165:0.70 172:0.32 173:0.43 176:0.73 177:0.70 178:0.55 179:0.81 187:0.68 192:0.87 201:0.93 208:0.64 212:0.52 215:0.44 216:0.77 222:0.48 235:0.52 241:0.27 242:0.13 243:0.50 245:0.53 247:0.76 253:0.49 254:0.80 259:0.21 265:0.16 266:0.54 267:0.84 276:0.34 290:0.65 297:0.36 300:0.70 +2 1:0.74 6:0.95 8:0.90 9:0.80 11:0.51 12:0.37 17:0.16 20:0.83 21:0.22 25:0.88 27:0.07 28:0.85 30:0.17 34:0.95 36:0.88 37:0.90 39:0.27 41:0.96 43:0.35 55:0.71 66:0.43 69:0.10 70:0.81 74:0.94 77:0.70 78:0.89 81:0.84 83:0.81 91:0.77 96:0.98 98:0.68 100:0.95 104:0.73 108:0.09 111:0.92 114:0.90 117:0.86 120:0.97 122:0.67 123:0.09 124:0.71 126:0.54 127:0.86 129:0.59 131:0.98 133:0.64 135:0.81 138:1.00 140:0.80 144:0.76 145:0.38 146:0.83 147:0.86 149:0.65 150:0.86 151:0.92 152:0.91 153:0.91 154:0.77 155:0.67 157:0.61 159:0.68 161:0.68 162:0.66 164:0.97 166:0.97 167:0.79 169:0.96 172:0.68 173:0.13 176:0.73 177:0.38 178:0.42 179:0.45 181:0.76 182:0.94 186:0.89 187:0.98 189:0.97 191:0.83 192:0.59 195:0.80 201:0.74 202:0.96 208:0.64 212:0.51 215:0.79 216:0.92 220:0.82 222:0.60 227:0.98 229:0.97 231:0.96 232:0.94 235:0.31 238:0.95 241:0.73 242:0.73 243:0.57 245:0.85 246:0.61 247:0.67 248:0.95 253:0.99 254:0.74 255:0.79 256:0.96 259:0.21 260:0.97 261:0.96 265:0.68 266:0.75 267:0.36 268:0.97 271:0.77 276:0.74 282:0.81 285:0.62 287:0.97 290:0.90 297:0.36 300:0.70 +2 1:0.74 6:0.94 7:0.81 8:0.89 9:0.80 11:0.78 12:0.37 17:0.22 20:0.97 21:0.59 27:0.08 28:0.85 30:0.31 32:0.80 34:0.55 36:0.96 37:0.90 39:0.27 41:0.99 43:0.83 55:0.59 60:0.95 66:0.34 69:0.67 70:0.76 74:1.00 76:0.98 77:0.98 78:0.83 81:0.77 83:0.89 85:0.99 91:0.89 96:0.99 98:0.72 100:0.87 101:0.84 104:0.78 106:0.81 108:0.94 111:0.83 114:0.74 117:0.86 120:0.95 121:0.90 122:0.67 123:0.94 124:0.72 126:0.54 127:0.83 129:0.81 131:0.98 133:0.67 135:1.00 140:0.87 144:0.76 145:0.85 146:0.92 147:0.93 149:0.98 150:0.83 151:0.99 152:0.89 153:0.98 154:0.79 155:0.67 157:0.98 158:0.92 159:0.91 161:0.68 162:0.80 164:0.93 165:0.70 166:0.97 167:0.73 169:0.85 172:0.97 173:0.33 176:0.73 177:0.38 179:0.11 181:0.76 182:0.95 186:0.83 187:0.21 189:0.95 191:0.87 192:0.59 195:0.98 201:0.74 202:0.93 204:0.89 206:0.81 208:0.64 212:0.79 215:0.55 216:0.85 220:0.62 222:0.60 227:0.98 229:0.97 230:0.87 231:0.96 232:0.84 233:0.76 235:0.88 238:0.91 241:0.65 242:0.43 243:0.28 245:1.00 246:0.96 247:0.60 248:0.97 253:1.00 255:0.79 256:0.96 259:0.21 260:0.95 261:0.88 265:0.72 266:0.54 267:0.85 268:0.96 271:0.77 276:0.98 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.74 297:0.36 299:0.88 300:0.70 +4 1:0.74 6:0.97 8:0.93 9:0.80 11:0.51 12:0.37 17:0.01 20:0.82 25:0.88 27:0.07 28:0.85 30:0.68 34:0.18 36:0.68 37:0.88 39:0.27 41:1.00 43:0.39 55:0.59 66:0.67 69:0.05 70:0.54 74:0.96 76:0.85 77:0.89 78:0.93 81:0.96 83:0.91 91:0.98 96:1.00 98:0.80 100:0.99 104:0.58 108:0.63 111:0.97 114:0.98 117:0.86 120:1.00 122:0.67 123:0.61 124:0.46 126:0.54 127:0.84 129:0.59 131:0.98 133:0.47 135:0.47 138:0.99 140:0.80 144:0.76 145:0.96 146:0.53 147:0.59 149:0.67 150:0.99 151:1.00 152:0.94 153:1.00 154:0.75 155:0.67 157:0.61 158:0.86 159:0.48 161:0.68 162:0.65 164:1.00 165:0.92 166:0.97 167:0.84 169:0.96 172:0.78 173:0.16 176:0.73 177:0.38 179:0.47 181:0.76 182:0.95 186:0.92 187:0.21 189:0.99 191:0.97 192:0.59 195:0.72 201:0.74 202:1.00 208:0.64 212:0.84 215:0.96 216:0.99 222:0.60 227:0.98 229:0.97 231:0.96 232:0.82 235:0.05 238:0.98 241:0.76 242:0.72 243:0.29 245:0.85 246:0.97 247:0.55 248:1.00 253:1.00 254:0.84 255:0.79 256:1.00 259:0.21 260:1.00 261:0.97 265:0.80 266:0.38 267:0.76 268:1.00 271:0.77 276:0.73 279:0.86 282:0.81 283:0.67 285:0.62 287:0.98 290:0.98 297:0.36 300:0.55 +1 1:0.74 8:0.64 11:0.78 12:0.37 17:0.53 20:0.74 21:0.13 27:0.45 28:0.85 30:0.17 32:0.80 34:0.71 36:0.27 37:0.50 39:0.27 43:0.82 55:0.84 60:0.87 66:0.20 69:0.68 70:0.90 74:0.90 77:0.70 78:0.85 81:0.89 83:0.85 85:0.80 91:0.11 98:0.40 100:0.88 101:0.72 108:0.52 111:0.85 114:0.92 122:0.67 123:0.50 124:0.85 126:0.54 127:0.59 129:0.93 131:0.61 133:0.84 135:0.90 144:0.76 145:0.49 146:0.68 147:0.73 149:0.77 150:0.94 152:0.74 154:0.77 155:0.67 157:0.85 158:0.92 159:0.76 161:0.68 165:0.88 166:0.97 167:0.66 169:0.85 172:0.51 173:0.53 176:0.73 177:0.38 178:0.55 179:0.45 181:0.76 182:0.94 186:0.86 187:0.68 192:0.59 195:0.89 201:0.74 208:0.64 212:0.30 215:0.84 216:0.77 222:0.60 227:0.61 229:0.61 231:0.95 235:0.22 241:0.47 242:0.55 243:0.40 245:0.78 246:0.97 247:0.67 253:0.78 259:0.21 261:0.75 265:0.42 266:0.87 267:0.36 271:0.77 276:0.70 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.84 +2 6:0.90 8:0.85 9:0.80 11:0.51 12:0.37 17:0.08 20:0.78 21:0.05 27:0.37 28:0.85 30:0.20 34:0.18 36:0.78 37:0.48 39:0.27 41:0.88 43:0.23 55:0.84 60:0.87 66:0.29 69:0.85 70:0.90 74:0.93 78:0.82 81:0.88 83:0.78 91:0.65 96:0.99 98:0.57 100:0.84 108:0.94 111:0.81 114:0.56 117:0.86 120:0.76 122:0.67 123:0.94 124:0.91 126:0.32 127:0.57 129:0.96 131:0.98 133:0.91 135:0.99 140:0.99 144:0.76 146:0.77 147:0.81 149:0.59 150:0.76 151:0.82 152:0.87 153:0.97 154:0.54 155:0.67 157:0.61 158:0.92 159:0.88 161:0.68 162:0.71 164:0.70 166:0.91 167:0.68 169:0.84 172:0.80 173:0.63 176:0.53 177:0.38 179:0.22 181:0.76 182:0.94 186:0.83 187:0.39 189:0.91 191:0.70 192:0.59 202:0.91 208:0.64 212:0.30 216:0.82 222:0.60 227:0.98 229:0.97 231:0.95 232:0.83 235:0.05 238:0.88 241:0.54 242:0.50 243:0.31 245:0.89 246:0.61 247:0.60 248:0.83 253:0.99 255:0.79 256:0.93 259:0.21 260:0.77 261:0.88 265:0.58 266:0.92 267:0.65 268:0.93 271:0.77 276:0.88 279:0.86 283:0.82 285:0.62 287:0.97 290:0.56 300:0.84 +2 6:0.95 7:0.76 8:0.87 9:0.80 11:0.78 12:0.37 17:0.09 20:0.96 21:0.30 27:0.21 28:0.85 30:0.27 34:0.37 36:0.84 37:0.74 39:0.27 41:0.98 43:0.45 55:0.59 60:0.95 66:0.35 69:0.10 70:0.81 74:0.99 76:0.85 78:0.79 81:0.67 83:0.89 85:0.97 91:0.92 96:1.00 98:0.59 100:0.84 101:0.74 108:0.98 111:0.79 114:0.55 117:0.86 120:0.94 122:0.67 123:0.98 124:0.92 126:0.32 127:0.99 129:0.97 131:0.98 133:0.94 135:0.23 138:1.00 140:0.90 144:0.76 146:0.45 147:0.51 149:0.84 150:0.49 151:0.98 152:0.89 153:0.99 154:0.77 155:0.67 157:0.95 158:0.92 159:0.97 161:0.68 162:0.66 164:0.90 166:0.90 167:0.82 169:0.81 172:0.99 173:0.05 176:0.53 177:0.38 179:0.09 181:0.76 182:0.95 186:0.80 187:0.39 189:0.96 191:0.87 192:0.59 202:0.96 208:0.64 212:0.73 216:0.95 222:0.60 227:0.98 229:0.97 230:0.78 231:0.96 232:0.85 233:0.69 235:0.31 238:0.91 241:0.75 242:0.33 243:0.06 245:1.00 246:0.61 247:0.67 248:0.97 253:1.00 255:0.79 256:0.97 259:0.21 260:0.95 261:0.85 265:0.60 266:0.94 267:0.69 268:0.97 271:0.77 276:0.99 279:0.86 283:0.82 285:0.62 287:0.98 290:0.55 300:0.84 +1 11:0.78 12:0.37 17:0.34 21:0.54 27:0.45 28:0.85 30:0.20 34:0.89 36:0.18 37:0.50 39:0.27 43:0.79 55:0.59 66:0.35 69:0.69 70:0.88 74:0.90 77:0.70 81:0.48 85:0.85 91:0.25 96:0.69 98:0.52 101:0.76 108:0.52 114:0.54 122:0.67 123:0.50 124:0.78 126:0.32 127:0.42 129:0.89 131:0.94 133:0.78 135:0.79 140:0.45 144:0.76 145:0.42 146:0.69 147:0.73 149:0.77 150:0.38 151:0.48 153:0.48 154:0.73 157:0.91 158:0.82 159:0.57 161:0.68 162:0.86 166:0.90 167:0.58 172:0.57 173:0.63 176:0.53 177:0.38 179:0.47 181:0.76 182:0.80 187:0.68 190:0.77 195:0.79 202:0.36 212:0.61 216:0.77 220:0.96 222:0.60 227:0.96 229:0.61 231:0.94 235:0.60 241:0.18 242:0.41 243:0.51 245:0.74 246:0.61 247:0.60 248:0.48 253:0.65 256:0.45 259:0.21 265:0.54 266:0.54 267:0.28 268:0.46 271:0.77 276:0.65 282:0.81 285:0.50 287:0.61 290:0.54 300:0.70 +2 1:0.74 6:0.94 7:0.76 8:0.92 9:0.80 12:0.37 17:0.03 20:0.84 21:0.68 27:0.06 28:0.85 30:0.58 32:0.77 34:0.93 36:0.92 37:0.92 39:0.27 41:0.96 43:0.29 55:0.71 66:0.88 69:0.72 70:0.39 74:0.90 76:0.94 77:0.70 78:0.86 81:0.53 83:0.80 91:0.93 96:0.99 98:0.90 100:0.91 108:0.55 111:0.87 114:0.54 117:0.86 120:0.88 122:0.67 123:0.52 126:0.54 127:0.46 129:0.05 131:0.98 135:0.81 140:0.95 144:0.76 145:0.63 146:0.92 147:0.94 149:0.53 150:0.63 151:0.94 152:0.88 153:0.95 154:0.22 155:0.67 157:0.61 158:0.83 159:0.54 161:0.68 162:0.57 163:0.92 164:0.93 166:0.97 167:0.79 169:0.88 172:0.65 173:0.69 175:0.75 176:0.53 177:0.05 178:0.42 179:0.64 181:0.76 182:0.94 186:0.86 187:0.68 189:0.97 191:0.95 192:0.59 195:0.77 201:0.74 202:0.97 208:0.64 212:0.23 215:0.49 216:0.75 222:0.60 227:0.98 229:0.97 230:0.79 231:0.96 232:0.98 233:0.76 235:0.84 238:0.93 241:0.72 242:0.82 243:0.88 244:0.61 246:0.61 247:0.12 248:0.91 253:0.99 255:0.79 256:0.97 257:0.65 259:0.21 260:0.88 261:0.90 265:0.90 266:0.63 267:0.28 268:0.97 271:0.77 276:0.55 277:0.69 282:0.85 283:0.82 285:0.62 287:0.97 290:0.54 297:0.36 300:0.33 +3 1:0.74 6:0.97 8:0.86 9:0.80 12:0.37 20:0.77 21:0.05 25:0.88 27:0.07 28:0.85 30:0.45 34:0.28 36:0.92 37:0.88 39:0.27 41:1.00 43:0.39 55:0.59 60:0.98 66:0.29 69:0.07 70:0.59 74:0.95 76:0.85 77:0.89 78:0.88 81:0.93 83:0.90 91:0.92 96:1.00 98:0.73 100:0.89 104:0.58 108:0.78 111:0.87 114:0.95 117:0.86 120:0.99 122:0.67 123:0.06 124:0.58 126:0.54 127:0.82 129:0.59 131:0.98 133:0.47 135:0.49 138:1.00 140:0.80 144:0.76 145:0.96 146:0.79 147:0.82 149:0.69 150:0.96 151:0.98 152:0.94 153:0.98 154:0.75 155:0.67 157:0.61 158:0.87 159:0.57 161:0.68 162:0.56 164:0.98 165:0.90 166:0.97 167:0.76 169:0.96 172:0.67 173:0.14 176:0.73 177:0.38 179:0.50 181:0.76 182:0.95 186:0.83 187:0.68 189:0.96 191:0.89 192:0.59 195:0.76 201:0.74 202:0.99 208:0.64 212:0.83 215:0.91 216:0.99 222:0.60 227:0.98 229:0.97 231:0.96 232:0.82 235:0.12 238:0.90 241:0.69 242:0.74 243:0.47 245:0.87 246:0.97 247:0.55 248:1.00 253:1.00 254:0.93 255:0.79 256:0.99 259:0.21 260:0.99 261:0.97 265:0.62 266:0.63 267:0.52 268:0.99 271:0.77 276:0.70 279:0.86 282:0.81 283:0.82 285:0.62 287:0.98 290:0.95 297:0.36 300:0.55 +1 1:0.74 8:0.64 11:0.57 12:0.51 17:0.36 20:0.81 21:0.59 27:0.45 28:0.73 30:0.28 34:0.83 36:0.17 37:0.50 39:0.38 43:0.82 66:0.51 69:0.70 70:0.79 74:0.79 78:0.90 81:0.67 83:0.73 85:0.90 91:0.25 98:0.45 100:0.73 101:0.79 108:0.53 111:0.87 114:0.74 122:0.43 123:0.51 124:0.65 126:0.54 127:0.38 129:0.59 133:0.64 135:0.87 145:0.49 146:0.63 147:0.68 149:0.81 150:0.78 152:0.78 154:0.77 155:0.67 159:0.57 161:0.78 165:0.78 167:0.54 169:0.72 172:0.57 173:0.63 176:0.73 177:0.38 178:0.55 179:0.55 181:0.97 186:0.90 187:0.68 192:0.59 201:0.74 212:0.61 215:0.55 216:0.77 222:0.48 235:0.74 241:0.39 242:0.39 243:0.61 245:0.69 247:0.67 253:0.67 259:0.21 261:0.84 265:0.47 266:0.75 267:0.28 271:0.11 276:0.56 290:0.74 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.57 20:0.79 21:0.71 28:0.73 30:0.85 32:0.80 34:0.28 36:0.28 37:0.97 39:0.38 41:0.82 43:0.80 60:0.87 66:0.69 69:0.73 70:0.44 74:0.90 77:0.70 78:0.72 81:0.57 83:0.85 85:0.96 91:0.37 96:0.80 98:0.39 100:0.73 101:0.81 104:0.80 108:0.57 111:0.72 114:0.68 120:0.69 122:0.43 123:0.54 124:0.46 126:0.54 127:0.25 129:0.81 133:0.67 135:0.66 140:0.80 145:0.63 146:0.73 147:0.77 149:0.93 150:0.70 151:0.87 152:0.82 153:0.48 154:0.74 155:0.67 158:0.87 159:0.65 161:0.78 162:0.51 164:0.57 165:0.69 167:0.57 169:0.79 172:0.70 173:0.54 176:0.73 177:0.38 178:0.42 179:0.35 181:0.97 186:0.73 187:0.21 192:0.59 195:0.93 201:0.74 202:0.59 208:0.64 212:0.42 215:0.48 216:0.98 222:0.48 232:0.85 235:0.88 241:0.50 242:0.63 243:0.73 245:0.67 247:0.30 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 261:0.81 265:0.42 266:0.63 267:0.73 268:0.46 271:0.11 276:0.53 279:0.86 282:0.88 283:0.71 285:0.62 290:0.68 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.93 7:0.81 8:0.85 9:0.80 11:0.57 12:0.51 17:0.55 20:0.98 21:0.30 27:0.21 28:0.73 30:0.45 34:0.42 36:0.95 37:0.74 39:0.38 41:0.96 43:0.98 55:0.71 60:0.97 66:0.63 69:0.12 70:0.64 74:0.98 76:0.85 78:0.93 81:0.85 83:0.88 85:0.97 91:0.70 96:0.96 98:0.82 100:0.99 101:0.82 104:0.84 106:0.81 108:0.84 111:0.98 114:0.86 117:0.86 120:0.92 121:0.90 122:0.57 123:0.83 124:0.49 126:0.54 127:0.68 129:0.59 133:0.47 135:0.23 140:0.45 146:0.91 147:0.93 149:0.93 150:0.91 151:0.98 152:0.92 153:0.92 154:0.98 155:0.67 158:0.92 159:0.85 161:0.78 162:0.71 164:0.86 165:0.84 167:0.54 169:0.98 172:0.94 173:0.09 176:0.73 177:0.38 179:0.18 181:0.97 186:0.93 187:0.39 189:0.94 192:0.59 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.58 215:0.72 216:0.95 222:0.48 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.39 242:0.36 243:0.34 245:0.98 247:0.67 248:0.96 253:0.97 255:0.79 256:0.83 259:0.21 260:0.93 261:0.97 265:0.82 266:0.63 267:0.88 268:0.83 271:0.11 276:0.92 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.51 17:0.83 21:0.64 27:0.72 28:0.73 30:0.13 32:0.80 34:0.22 36:0.68 39:0.38 41:0.93 43:0.98 55:0.59 60:0.99 66:0.97 69:0.27 70:0.86 74:0.90 77:0.89 81:0.74 83:0.82 85:0.95 91:0.72 96:0.82 98:1.00 101:0.83 104:0.54 108:0.21 114:0.72 120:0.72 121:0.99 123:0.20 126:0.54 127:0.95 129:0.05 135:0.30 140:0.45 145:0.65 146:0.98 147:0.98 149:0.93 150:0.82 151:0.77 153:0.48 154:0.98 155:0.67 158:0.87 159:0.75 161:0.78 162:0.78 164:0.77 165:0.80 167:0.81 172:0.93 173:0.18 176:0.73 177:0.38 179:0.27 181:0.97 192:0.59 195:0.86 201:0.74 202:0.67 204:0.89 208:0.64 212:0.60 215:0.53 216:0.08 220:0.62 222:0.48 230:0.87 232:0.74 233:0.76 235:0.88 241:0.81 242:0.70 243:0.97 247:0.47 248:0.80 253:0.86 255:0.79 256:0.71 259:0.21 260:0.73 265:1.00 266:0.54 267:0.94 268:0.71 271:0.11 276:0.87 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.83 8:0.64 9:0.80 11:0.57 12:0.51 17:0.62 20:0.96 27:0.44 28:0.73 30:0.85 34:0.39 36:0.84 37:0.41 39:0.38 41:0.88 43:0.98 55:0.71 66:0.87 69:0.05 70:0.28 74:0.75 78:0.92 81:0.99 83:0.81 85:0.80 91:0.68 96:0.88 98:0.92 100:0.94 101:0.78 104:0.92 106:0.81 108:0.05 111:0.91 114:0.98 120:0.78 122:0.43 123:0.05 126:0.54 127:0.55 129:0.05 135:0.69 140:0.45 146:0.77 147:0.81 149:0.75 150:0.99 151:0.91 152:0.89 153:0.71 154:0.98 155:0.67 159:0.33 161:0.78 162:0.86 164:0.66 165:0.92 167:0.55 169:0.92 172:0.63 173:0.21 176:0.73 177:0.38 179:0.69 181:0.97 186:0.92 187:0.68 189:0.85 192:0.59 201:0.74 202:0.56 206:0.81 208:0.64 212:0.71 215:0.96 216:0.93 222:0.48 235:0.05 238:0.90 241:0.43 242:0.40 243:0.88 247:0.60 248:0.85 253:0.90 255:0.79 256:0.64 259:0.21 260:0.79 261:0.93 265:0.92 266:0.27 267:0.44 268:0.65 271:0.11 276:0.53 285:0.62 290:0.98 297:0.36 300:0.25 +1 1:0.74 6:0.87 8:0.60 9:0.80 11:0.57 12:0.51 17:0.01 20:0.89 21:0.05 27:0.14 28:0.73 30:0.76 32:0.80 34:0.82 36:0.35 37:0.67 39:0.38 41:0.88 43:0.86 60:0.95 66:0.64 69:0.59 70:0.21 74:0.76 77:0.70 78:0.87 81:0.96 83:0.88 85:0.85 91:0.54 96:0.88 98:0.55 100:0.83 101:0.79 108:0.45 111:0.85 114:0.95 120:0.80 122:0.43 123:0.43 124:0.42 126:0.54 127:0.25 129:0.05 133:0.39 135:0.98 140:0.45 145:0.49 146:0.53 147:0.59 149:0.75 150:0.98 151:0.92 152:0.95 153:0.72 154:0.83 155:0.67 158:0.92 159:0.28 161:0.78 162:0.86 163:0.92 164:0.69 165:0.90 167:0.51 169:0.88 172:0.32 173:0.66 176:0.73 177:0.38 179:0.81 181:0.97 186:0.88 187:0.39 189:0.82 192:0.59 195:0.62 201:0.74 202:0.53 208:0.64 212:0.73 215:0.91 216:0.93 222:0.48 235:0.12 238:0.83 241:0.24 242:0.73 243:0.69 245:0.43 247:0.30 248:0.87 253:0.89 255:0.79 256:0.58 259:0.21 260:0.80 261:0.93 265:0.56 266:0.17 267:0.44 268:0.62 271:0.11 276:0.28 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.18 +1 11:0.57 12:0.51 17:0.53 21:0.77 27:0.45 28:0.73 30:0.30 32:0.75 34:0.77 36:0.26 37:0.50 39:0.38 43:0.79 66:0.15 69:0.71 70:0.94 74:0.72 81:0.37 85:0.94 91:0.06 98:0.32 101:0.78 108:0.90 123:0.83 124:0.90 126:0.32 127:0.62 129:0.93 133:0.90 135:0.78 145:0.58 146:0.49 147:0.55 149:0.83 150:0.33 154:0.72 159:0.84 161:0.78 163:0.81 167:0.60 172:0.48 173:0.47 177:0.38 178:0.55 179:0.43 181:0.97 187:0.68 195:0.95 212:0.18 215:0.44 216:0.77 222:0.48 235:0.98 241:0.57 242:0.33 243:0.29 245:0.75 247:0.67 253:0.53 259:0.21 265:0.30 266:0.87 267:0.65 271:0.11 276:0.72 297:0.36 300:0.84 +1 1:0.74 6:0.97 7:0.78 8:0.93 9:0.80 11:0.57 12:0.51 17:0.11 20:0.95 21:0.13 27:0.02 28:0.73 30:0.65 32:0.80 34:0.28 36:0.30 37:0.92 39:0.38 41:0.90 43:0.82 60:0.87 66:0.67 69:0.67 70:0.44 74:0.88 76:0.85 77:0.70 78:0.84 81:0.93 83:0.86 85:0.97 91:0.44 96:0.88 98:0.60 100:0.88 101:0.84 108:0.86 111:0.84 114:0.92 120:0.80 122:0.43 123:0.86 124:0.50 126:0.54 127:0.49 129:0.59 133:0.64 135:0.85 140:0.80 145:0.67 146:0.13 147:0.18 149:0.89 150:0.96 151:0.87 152:0.89 153:0.48 154:0.78 155:0.67 158:0.87 159:0.74 161:0.78 162:0.52 164:0.74 165:0.88 167:0.68 169:0.86 172:0.80 173:0.52 176:0.73 177:0.38 178:0.42 179:0.36 181:0.97 186:0.84 187:0.39 189:0.97 192:0.59 195:0.90 201:0.74 202:0.73 208:0.64 212:0.54 215:0.84 216:0.99 220:0.62 222:0.48 230:0.81 233:0.76 235:0.22 238:0.90 241:0.68 242:0.52 243:0.11 245:0.81 247:0.60 248:0.87 253:0.91 255:0.79 256:0.64 259:0.21 260:0.81 261:0.88 265:0.61 266:0.87 267:0.82 268:0.68 271:0.11 276:0.74 279:0.86 282:0.88 283:0.71 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +2 6:0.88 8:0.89 9:0.80 11:0.57 12:0.51 17:0.05 20:0.97 21:0.78 27:0.14 28:0.73 30:0.87 34:0.75 36:0.31 37:0.67 39:0.38 41:0.92 43:0.59 60:0.87 66:0.54 69:0.93 70:0.20 74:0.60 78:0.88 83:0.82 85:0.88 91:0.40 96:0.91 98:0.14 100:0.92 101:0.74 108:0.87 111:0.89 120:0.89 122:0.43 123:0.86 124:0.48 127:0.13 129:0.59 133:0.47 135:0.78 140:0.87 145:0.77 146:0.28 147:0.35 149:0.57 151:0.96 152:0.95 153:0.87 154:0.47 155:0.67 158:0.92 159:0.28 161:0.78 162:0.48 164:0.80 167:0.55 169:0.88 172:0.32 173:0.87 177:0.38 178:0.48 179:0.28 181:0.97 186:0.88 187:0.39 189:0.93 192:0.59 202:0.87 208:0.64 212:0.61 216:0.93 222:0.48 235:0.05 238:0.91 241:0.46 242:0.63 243:0.63 245:0.50 247:0.30 248:0.91 253:0.89 255:0.79 256:0.75 259:0.21 260:0.89 261:0.93 265:0.16 266:0.27 267:0.28 268:0.76 271:0.11 276:0.18 279:0.86 283:0.82 285:0.62 300:0.18 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.75 21:0.59 27:0.57 28:0.73 30:0.84 32:0.80 34:0.55 36:0.42 37:0.61 39:0.38 43:0.86 66:0.97 69:0.59 70:0.26 74:0.99 77:0.70 81:0.67 83:0.73 85:1.00 91:0.08 98:0.88 101:0.87 104:0.80 106:0.81 108:0.73 114:0.74 117:0.86 121:0.90 122:0.43 123:0.72 124:0.36 126:0.54 127:0.62 129:0.59 133:0.47 135:0.74 145:0.74 146:0.13 147:0.18 149:1.00 150:0.78 154:0.84 155:0.67 159:0.85 161:0.78 165:0.78 167:0.51 172:0.99 173:0.32 176:0.73 177:0.38 178:0.55 179:0.12 181:0.97 187:0.21 192:0.59 195:0.95 201:0.74 204:0.89 206:0.81 208:0.64 212:0.94 215:0.55 216:0.86 222:0.48 230:0.87 232:0.86 233:0.76 235:0.74 241:0.24 242:0.63 243:0.06 245:0.91 247:0.30 253:0.76 265:0.88 266:0.54 267:0.44 271:0.11 276:0.97 282:0.88 290:0.74 297:0.36 299:0.88 300:0.33 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.73 20:0.82 21:0.74 28:0.73 30:0.76 32:0.80 34:0.66 36:0.32 37:0.97 39:0.38 41:0.88 43:0.80 60:0.87 66:0.36 69:0.74 70:0.62 74:0.89 77:0.70 78:0.82 81:0.54 83:0.85 85:0.96 91:0.37 96:0.88 98:0.35 100:0.73 101:0.81 104:0.86 108:0.57 111:0.80 114:0.67 120:0.80 122:0.43 123:0.54 124:0.82 126:0.54 127:0.59 129:0.89 133:0.83 135:0.42 140:0.45 145:0.69 146:0.73 147:0.77 149:0.92 150:0.67 151:0.93 152:0.82 153:0.78 154:0.74 155:0.67 158:0.87 159:0.85 161:0.78 162:0.78 163:0.81 164:0.70 165:0.65 167:0.52 169:0.79 172:0.59 173:0.49 176:0.73 177:0.38 179:0.51 181:0.97 186:0.82 187:0.21 192:0.59 195:0.95 201:0.74 202:0.59 208:0.64 212:0.23 215:0.46 216:0.98 222:0.48 232:0.90 235:0.95 241:0.32 242:0.63 243:0.51 245:0.71 247:0.39 248:0.87 253:0.91 255:0.79 256:0.58 259:0.21 260:0.81 261:0.81 265:0.37 266:0.84 267:0.36 268:0.64 271:0.11 276:0.62 279:0.86 282:0.88 283:0.71 285:0.62 290:0.67 297:0.36 299:0.88 300:0.84 +2 1:0.74 11:0.57 12:0.51 17:0.47 21:0.64 28:0.73 30:0.75 32:0.80 34:0.50 36:0.25 37:0.97 39:0.38 43:0.80 60:0.87 66:0.35 69:0.75 70:0.59 74:0.91 77:0.96 81:0.63 83:0.86 85:0.96 91:0.22 98:0.27 101:0.82 104:0.80 108:0.58 114:0.72 122:0.43 123:0.55 124:0.86 126:0.54 127:0.68 129:0.89 133:0.87 135:0.54 145:0.74 146:0.52 147:0.05 149:0.94 150:0.75 154:0.73 155:0.67 158:0.87 159:0.79 161:0.78 165:0.70 167:0.47 172:0.59 173:0.59 176:0.73 177:0.05 178:0.55 179:0.51 181:0.97 187:0.21 192:0.59 195:0.90 201:0.74 208:0.64 212:0.59 215:0.53 216:0.98 222:0.48 232:0.85 235:0.80 241:0.03 242:0.63 243:0.09 245:0.70 247:0.35 253:0.71 257:0.65 259:0.21 265:0.29 266:0.71 267:0.73 271:0.11 276:0.62 279:0.86 282:0.88 283:0.71 290:0.72 297:0.36 299:0.99 300:0.70 +2 1:0.74 9:0.80 11:0.57 12:0.51 17:0.28 21:0.64 27:0.48 28:0.73 30:0.60 32:0.80 34:0.53 36:0.30 37:0.55 39:0.38 41:0.88 43:0.80 60:0.98 66:0.71 69:0.75 70:0.74 74:0.91 77:0.96 81:0.63 83:0.84 85:0.97 91:0.20 96:0.87 98:0.52 101:0.82 108:0.58 114:0.72 120:0.78 122:0.43 123:0.55 124:0.51 126:0.54 127:0.35 129:0.81 133:0.76 135:0.42 140:0.45 145:0.79 146:0.88 147:0.05 149:0.95 150:0.75 151:0.87 153:0.48 154:0.73 155:0.67 158:0.92 159:0.78 161:0.78 162:0.86 164:0.67 165:0.70 167:0.60 172:0.82 173:0.53 176:0.73 177:0.05 179:0.30 181:0.97 187:0.39 192:0.59 195:0.94 201:0.74 202:0.50 208:0.64 212:0.47 215:0.53 216:0.92 222:0.48 235:0.80 241:0.57 242:0.55 243:0.07 245:0.76 247:0.47 248:0.86 253:0.90 255:0.79 256:0.58 257:0.65 259:0.21 260:0.79 265:0.53 266:0.87 267:0.92 268:0.59 271:0.11 276:0.74 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.99 300:0.84 +1 6:0.79 8:0.58 9:0.80 12:0.51 17:0.53 20:0.91 21:0.78 27:0.72 28:0.73 34:0.49 36:0.24 39:0.38 41:0.82 78:0.87 83:0.77 91:0.89 96:0.84 100:0.82 104:0.58 111:0.84 120:0.74 135:0.61 140:0.45 151:0.92 152:0.85 153:0.72 155:0.67 161:0.78 162:0.67 164:0.64 167:0.84 169:0.79 175:0.91 181:0.97 186:0.86 189:0.81 192:0.59 202:0.53 208:0.64 216:0.05 222:0.48 232:0.78 238:0.82 241:0.91 244:0.83 248:0.81 253:0.77 255:0.79 256:0.45 257:0.84 259:0.21 260:0.75 261:0.87 267:0.52 268:0.57 271:0.11 277:0.87 285:0.62 +2 1:0.74 7:0.81 8:0.82 9:0.80 11:0.57 12:0.51 17:0.49 20:0.81 21:0.40 27:0.72 28:0.73 30:0.45 32:0.80 34:0.96 36:0.73 37:0.55 39:0.38 41:0.82 43:0.83 55:0.71 60:0.87 66:0.75 69:0.66 70:0.79 74:0.97 77:0.70 78:0.90 81:0.81 83:0.83 85:0.94 91:0.33 96:0.80 98:0.81 100:0.88 101:0.80 104:0.83 106:0.81 108:0.50 111:0.89 114:0.82 117:0.86 120:0.69 121:0.90 122:0.67 123:0.48 124:0.43 126:0.54 127:0.59 129:0.81 133:0.67 135:0.78 140:0.45 145:0.79 146:0.96 147:0.97 149:0.89 150:0.87 151:0.87 152:0.78 153:0.48 154:0.79 155:0.67 158:0.92 159:0.77 161:0.78 162:0.86 164:0.57 165:0.83 167:0.51 169:0.86 172:0.86 173:0.49 176:0.73 177:0.38 179:0.33 181:0.97 186:0.90 187:0.39 192:0.59 195:0.91 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.39 215:0.67 216:0.09 222:0.48 230:0.87 232:0.89 233:0.76 235:0.52 241:0.27 242:0.54 243:0.77 245:0.81 247:0.60 248:0.78 253:0.88 255:0.79 256:0.45 259:0.21 260:0.70 261:0.84 265:0.81 266:0.80 267:0.85 268:0.46 271:0.11 276:0.80 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.84 +2 9:0.80 11:0.57 12:0.51 17:0.05 21:0.78 27:0.14 28:0.73 30:0.87 34:0.66 36:0.41 37:0.67 39:0.38 41:0.92 43:0.58 60:0.87 66:0.45 69:0.94 70:0.27 74:0.61 78:0.86 83:0.82 85:0.90 91:0.73 96:0.91 98:0.14 101:0.75 108:0.88 111:0.89 120:0.89 122:0.43 123:0.87 124:0.67 127:0.22 129:0.81 133:0.67 135:0.70 140:0.80 145:0.77 146:0.28 147:0.35 149:0.60 151:0.96 153:0.87 154:0.46 155:0.67 158:0.92 159:0.66 161:0.78 162:0.48 164:0.80 167:0.53 169:0.91 172:0.32 173:0.87 177:0.38 178:0.42 179:0.63 181:0.97 187:0.39 191:0.75 192:0.59 202:0.87 208:0.64 212:0.50 216:0.93 222:0.48 235:0.05 241:0.35 242:0.63 243:0.58 245:0.52 247:0.30 248:0.91 253:0.89 255:0.79 256:0.75 259:0.21 260:0.89 265:0.15 266:0.47 267:0.28 268:0.76 271:0.11 276:0.27 279:0.86 283:0.82 285:0.62 300:0.25 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.01 20:0.95 21:0.05 27:0.63 28:0.73 30:0.72 34:0.68 36:0.50 37:0.28 39:0.38 41:0.90 43:0.87 66:0.84 69:0.55 70:0.48 74:0.72 78:0.79 81:0.96 83:0.81 85:0.90 91:0.44 96:0.88 98:0.82 100:0.80 101:0.83 108:0.42 111:0.78 114:0.95 120:0.81 122:0.43 123:0.41 126:0.54 127:0.31 129:0.05 135:0.58 140:0.45 145:0.49 146:0.69 147:0.74 149:0.85 150:0.98 151:0.87 152:0.88 153:0.48 154:0.85 155:0.67 159:0.27 161:0.78 162:0.86 164:0.72 165:0.90 167:0.52 169:0.77 172:0.54 173:0.68 176:0.73 177:0.38 179:0.69 181:0.97 186:0.80 187:0.87 192:0.59 201:0.74 202:0.56 208:0.64 212:0.39 215:0.91 216:0.59 222:0.48 235:0.12 241:0.30 242:0.55 243:0.85 247:0.47 248:0.88 253:0.89 255:0.79 256:0.64 259:0.21 260:0.81 261:0.83 265:0.82 266:0.27 267:0.44 268:0.65 271:0.11 276:0.39 285:0.62 290:0.95 297:0.36 300:0.43 +2 8:0.98 9:0.80 12:0.20 17:0.08 21:0.78 27:0.42 34:0.45 36:0.26 37:0.86 39:0.96 41:0.98 60:0.87 74:0.46 83:0.88 91:0.92 96:0.94 104:0.76 120:0.90 135:0.71 140:0.45 151:0.94 153:0.96 155:0.67 158:0.87 162:0.79 164:0.90 175:0.91 192:0.59 202:0.83 208:0.64 216:0.38 220:0.62 222:0.35 232:0.81 241:0.90 244:0.83 248:0.94 253:0.91 255:0.79 256:0.87 257:0.84 260:0.90 267:0.28 268:0.88 271:0.36 277:0.87 279:0.86 283:0.71 285:0.62 +1 1:0.74 7:0.81 8:0.60 9:0.80 12:0.20 17:0.26 21:0.47 27:0.21 30:0.38 34:0.44 36:0.89 37:0.74 39:0.96 43:0.32 55:0.71 66:0.63 69:0.19 70:0.73 74:0.92 76:0.85 81:0.56 91:0.42 96:0.77 98:0.80 104:0.97 106:0.81 108:0.15 114:0.80 117:0.86 120:0.69 123:0.15 124:0.48 126:0.54 127:0.58 129:0.59 133:0.47 135:0.23 140:0.45 146:0.92 147:0.93 149:0.59 150:0.64 151:0.75 153:0.48 154:0.92 155:0.67 158:0.86 159:0.67 162:0.59 164:0.71 172:0.83 173:0.16 176:0.73 177:0.38 179:0.33 187:0.39 190:0.77 192:0.59 201:0.74 202:0.65 204:0.89 206:0.81 208:0.64 212:0.77 215:0.63 216:0.95 220:0.74 222:0.35 230:0.83 232:0.91 233:0.66 235:0.60 241:0.10 242:0.36 243:0.69 245:0.91 247:0.60 248:0.69 253:0.83 254:0.74 255:0.79 256:0.58 259:0.21 260:0.70 265:0.80 266:0.71 267:0.82 268:0.65 271:0.36 276:0.80 279:0.86 283:0.71 285:0.62 290:0.80 297:0.36 300:0.70 +2 1:0.74 7:0.81 11:0.57 12:0.20 17:0.61 21:0.40 27:0.34 30:0.53 34:0.67 36:0.93 37:0.42 39:0.96 43:0.59 66:0.74 69:0.94 70:0.50 74:0.89 76:0.97 77:0.96 81:0.46 83:0.72 85:0.94 91:0.46 98:0.58 101:0.80 104:0.58 108:0.88 114:0.68 121:0.90 122:0.57 123:0.87 124:0.42 126:0.54 127:0.77 129:0.05 133:0.62 135:0.72 145:0.86 146:0.82 147:0.05 149:0.78 150:0.63 154:0.47 155:0.67 159:0.75 165:0.63 172:0.73 173:0.95 176:0.73 177:0.05 178:0.55 179:0.56 187:0.39 192:0.59 195:0.90 201:0.74 204:0.89 208:0.64 212:0.54 215:0.46 216:0.38 222:0.35 230:0.84 232:0.82 233:0.69 235:0.99 241:0.41 242:0.52 243:0.09 245:0.64 247:0.47 253:0.69 257:0.65 259:0.21 265:0.59 266:0.63 267:0.79 271:0.36 276:0.52 282:0.84 290:0.68 297:0.36 300:0.43 +1 1:0.74 9:0.80 12:0.20 17:0.38 21:0.05 27:0.35 30:0.52 34:0.18 36:0.79 37:0.24 39:0.96 43:0.45 55:0.71 66:0.34 69:0.07 70:0.64 74:0.58 81:0.82 91:0.11 96:0.85 98:0.44 104:0.58 108:0.96 114:0.95 117:0.86 120:0.65 123:0.96 124:0.92 126:0.54 127:0.96 129:0.59 133:0.93 135:0.39 140:0.45 146:0.25 147:0.31 149:0.58 150:0.84 151:0.77 153:0.48 154:0.15 155:0.67 158:0.84 159:0.94 162:0.80 164:0.57 172:0.82 173:0.06 176:0.73 177:0.38 179:0.26 187:0.21 192:0.59 201:0.74 202:0.60 208:0.64 212:0.49 215:0.91 216:0.55 220:0.62 222:0.35 232:0.83 235:0.12 241:0.41 242:0.81 243:0.08 245:0.85 247:0.39 248:0.71 253:0.84 254:0.80 255:0.79 256:0.64 259:0.21 260:0.66 265:0.46 266:0.96 267:0.52 268:0.65 271:0.36 276:0.87 277:0.69 285:0.62 290:0.95 297:0.36 300:0.84 +2 1:0.74 7:0.78 9:0.80 12:0.20 17:0.28 21:0.22 27:0.64 30:0.73 32:0.75 34:0.66 36:0.67 37:0.37 39:0.96 43:0.41 55:0.71 66:0.97 69:0.33 70:0.39 74:0.88 81:0.70 87:0.98 91:0.20 96:0.78 98:0.99 104:0.72 108:0.26 114:0.90 120:0.60 123:0.25 126:0.54 127:0.92 129:0.05 135:0.21 140:0.45 145:0.93 146:0.99 147:0.99 149:0.83 150:0.75 151:0.62 153:0.48 154:0.31 155:0.67 158:0.85 159:0.82 162:0.86 164:0.57 172:0.93 173:0.17 176:0.73 177:0.38 179:0.26 187:0.21 190:0.77 192:0.59 195:0.92 201:0.74 202:0.50 208:0.64 212:0.48 215:0.79 216:0.21 220:0.82 222:0.35 230:0.81 232:0.70 233:0.69 235:0.31 241:0.37 242:0.80 243:0.97 244:0.61 247:0.39 248:0.60 253:0.84 255:0.79 256:0.58 259:0.21 260:0.60 265:0.99 266:0.63 267:0.85 268:0.59 271:0.36 276:0.88 285:0.62 290:0.90 297:0.36 300:0.55 +2 8:0.63 9:0.80 12:0.20 17:0.02 21:0.78 25:0.88 27:0.72 30:0.76 34:0.24 36:0.96 37:0.28 39:0.96 41:0.88 43:0.45 55:0.71 66:0.83 69:0.10 70:0.29 83:0.78 91:0.86 96:0.87 98:0.97 104:0.58 108:0.09 120:0.92 123:0.09 127:0.76 129:0.05 132:0.97 135:0.54 140:0.98 145:0.70 146:0.68 147:0.73 149:0.54 151:0.87 153:0.92 154:0.34 155:0.67 159:0.27 162:0.60 164:0.90 172:0.51 173:0.38 175:0.75 177:0.05 178:0.48 179:0.84 192:0.59 202:0.86 208:0.64 212:0.57 216:0.41 222:0.35 235:0.31 241:0.90 242:0.82 243:0.84 244:0.61 247:0.12 248:0.86 253:0.82 255:0.79 256:0.87 257:0.65 260:0.93 265:0.97 266:0.38 267:0.96 268:0.87 271:0.36 276:0.43 277:0.69 285:0.62 300:0.25 +2 8:0.76 9:0.80 12:0.20 17:0.47 21:0.78 27:1.00 30:0.45 34:0.20 36:0.94 37:0.33 39:0.96 41:0.95 43:0.44 55:0.45 66:0.27 69:0.05 70:0.25 74:0.77 76:0.94 77:0.70 83:0.80 91:0.88 96:0.93 98:0.26 104:0.58 108:0.05 120:0.85 122:0.67 123:0.05 124:0.61 127:0.33 129:0.59 133:0.47 135:0.76 140:0.87 145:0.61 146:0.32 147:0.38 149:0.28 151:0.91 153:0.90 154:0.34 155:0.67 159:0.40 162:0.49 163:0.87 164:0.82 172:0.10 173:0.14 175:0.75 177:0.05 178:0.48 179:0.96 191:0.81 192:0.59 195:0.70 202:0.90 208:0.64 212:0.61 216:0.43 220:0.62 222:0.35 232:0.76 235:0.02 241:0.80 242:0.82 243:0.46 244:0.61 245:0.40 247:0.12 248:0.91 253:0.94 255:0.79 256:0.84 257:0.65 259:0.21 260:0.85 265:0.28 266:0.17 267:0.87 268:0.83 271:0.36 276:0.15 277:0.69 282:0.84 285:0.62 300:0.18 +0 12:0.20 17:0.38 21:0.77 27:0.45 30:0.56 34:0.78 36:0.18 37:0.50 39:0.96 43:0.34 55:0.84 66:0.33 69:0.62 70:0.42 74:0.83 77:0.70 81:0.24 91:0.10 98:0.43 108:0.47 114:0.60 122:0.67 123:0.45 124:0.78 126:0.32 127:0.47 129:0.05 133:0.74 135:0.76 145:0.61 146:0.67 147:0.72 149:0.39 150:0.24 154:0.60 159:0.69 163:0.94 172:0.32 173:0.47 176:0.56 177:0.38 178:0.55 179:0.77 187:0.68 195:0.87 212:0.39 216:0.77 222:0.35 235:0.97 241:0.53 242:0.76 243:0.50 245:0.53 247:0.39 253:0.62 254:0.93 259:0.21 265:0.44 266:0.63 267:0.44 271:0.36 276:0.44 282:0.84 290:0.60 300:0.33 +1 8:0.86 9:0.80 12:0.20 17:0.20 21:0.30 27:0.37 30:0.76 34:0.20 36:1.00 37:0.44 39:0.96 41:0.82 43:0.44 55:0.84 66:0.25 69:0.15 70:0.29 74:0.78 81:0.31 83:0.76 91:0.44 96:0.96 98:0.28 104:0.58 108:0.12 114:0.69 117:0.86 120:0.69 122:0.67 123:0.12 124:0.71 126:0.32 127:0.65 129:0.81 133:0.67 135:0.90 140:0.98 145:0.44 146:0.41 147:0.47 149:0.35 150:0.29 151:0.87 153:0.89 154:0.33 155:0.67 158:0.85 159:0.63 162:0.67 163:0.86 164:0.57 172:0.16 173:0.16 175:0.75 176:0.56 177:0.05 179:0.92 187:0.68 191:0.82 192:0.59 202:0.87 208:0.64 212:0.23 216:0.67 222:0.35 232:0.88 235:0.31 241:0.56 242:0.82 243:0.45 244:0.61 245:0.45 247:0.12 248:0.78 253:0.96 255:0.79 256:0.89 257:0.65 259:0.21 260:0.70 265:0.30 266:0.38 267:0.23 268:0.89 271:0.36 276:0.24 277:0.69 285:0.62 290:0.69 300:0.25 +2 1:0.74 7:0.81 11:0.57 12:0.20 17:0.49 21:0.74 27:0.64 30:0.67 34:0.39 36:0.73 37:0.64 39:0.96 43:0.83 66:0.92 69:0.37 70:0.29 74:0.93 76:0.94 81:0.44 83:0.72 85:0.95 91:0.23 96:0.84 98:0.96 101:0.86 104:0.95 108:0.29 114:0.67 117:0.86 121:0.90 122:0.43 123:0.28 126:0.54 127:0.70 129:0.05 135:0.68 140:0.45 146:0.88 147:0.90 149:0.94 150:0.63 151:0.71 153:0.77 154:0.79 155:0.67 158:0.85 159:0.45 162:0.68 163:0.81 165:0.65 172:0.78 173:0.42 176:0.73 177:0.38 179:0.53 187:0.68 190:0.77 192:0.59 201:0.74 202:0.54 204:0.89 208:0.64 212:0.42 215:0.46 216:0.62 222:0.35 230:0.84 232:0.79 233:0.69 235:0.97 241:0.50 242:0.55 243:0.92 247:0.55 248:0.67 253:0.88 256:0.45 259:0.21 265:0.96 266:0.54 267:0.23 268:0.57 271:0.36 276:0.67 285:0.50 290:0.67 297:0.36 300:0.33 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.69 21:0.76 27:0.64 30:0.66 34:0.80 36:0.90 37:0.64 39:0.96 43:0.85 66:0.91 69:0.34 70:0.36 74:0.94 76:0.94 81:0.43 83:0.72 85:0.95 91:0.33 98:0.91 101:0.85 108:0.27 114:0.66 121:0.90 122:0.57 123:0.26 126:0.54 127:0.51 129:0.05 135:0.73 146:0.85 147:0.88 149:0.93 150:0.63 154:0.82 155:0.67 159:0.42 163:0.81 165:0.65 172:0.76 173:0.39 176:0.73 177:0.38 178:0.55 179:0.52 187:0.87 192:0.59 201:0.74 204:0.89 208:0.64 212:0.56 215:0.46 216:0.62 222:0.35 230:0.84 232:0.79 233:0.69 235:0.98 241:0.37 242:0.59 243:0.92 247:0.39 253:0.71 259:0.21 265:0.91 266:0.27 267:0.96 271:0.36 276:0.65 290:0.66 297:0.36 300:0.43 +2 9:0.80 11:0.57 12:0.20 17:0.01 21:0.78 27:0.37 30:0.86 34:0.93 36:0.86 37:0.42 39:0.96 41:0.88 43:0.98 66:0.84 69:0.05 70:0.28 74:0.79 83:0.77 85:0.91 91:0.20 96:0.85 98:0.86 101:0.84 108:0.05 120:0.76 122:0.43 123:0.05 127:0.38 129:0.05 135:0.34 140:0.45 146:0.68 147:0.72 149:0.89 151:0.93 153:0.77 154:0.98 155:0.67 159:0.26 162:0.86 164:0.69 172:0.54 173:0.21 177:0.38 179:0.73 187:0.21 192:0.59 202:0.54 208:0.64 212:0.39 216:0.35 222:0.35 232:0.82 235:0.02 241:0.39 242:0.63 243:0.85 247:0.30 248:0.83 253:0.85 255:0.79 256:0.58 259:0.21 260:0.76 265:0.86 266:0.38 267:0.65 268:0.63 271:0.36 276:0.42 285:0.62 300:0.25 +2 8:0.86 9:0.80 12:0.20 17:0.64 21:0.05 27:0.34 30:0.45 34:0.18 36:0.63 37:0.42 39:0.96 41:0.82 43:0.41 55:0.98 66:0.13 69:0.30 70:0.50 74:0.84 76:0.85 77:0.89 81:0.42 83:0.76 91:0.74 96:0.97 98:0.18 104:0.58 108:0.24 114:0.77 120:0.69 122:0.67 123:0.23 124:0.86 126:0.32 127:0.90 129:0.93 133:0.84 135:0.21 140:0.98 145:0.70 146:0.32 147:0.38 149:0.33 150:0.35 151:0.87 153:0.48 154:0.31 155:0.67 158:0.84 159:0.78 162:0.76 163:0.87 164:0.57 172:0.10 173:0.18 175:0.75 176:0.56 177:0.05 179:0.94 191:0.89 192:0.59 195:0.88 202:0.85 208:0.64 212:0.23 216:0.38 220:0.62 222:0.35 232:0.82 235:0.05 241:0.77 242:0.82 243:0.34 244:0.61 245:0.42 247:0.12 248:0.78 253:0.97 255:0.79 256:0.89 257:0.65 259:0.21 260:0.70 265:0.20 266:0.38 267:0.65 268:0.89 271:0.36 276:0.30 277:0.69 282:0.84 285:0.62 290:0.77 300:0.33 +2 1:0.74 7:0.81 8:0.97 9:0.80 11:0.57 12:0.20 17:0.16 21:0.74 27:0.64 30:0.58 34:0.62 36:0.89 37:0.64 39:0.96 41:0.98 43:0.59 60:0.95 66:0.68 69:0.93 70:0.52 74:0.82 76:0.94 81:0.44 83:0.88 85:0.91 91:0.57 96:0.94 98:0.54 101:0.79 104:0.95 108:0.87 114:0.67 120:0.90 121:0.90 122:0.43 123:0.86 124:0.47 126:0.54 127:0.66 129:0.05 133:0.62 135:0.95 140:0.45 146:0.77 147:0.41 149:0.72 150:0.63 151:0.94 153:0.96 154:0.47 155:0.67 158:0.87 159:0.72 162:0.75 164:0.90 165:0.65 172:0.59 173:0.95 176:0.73 177:0.05 179:0.69 187:0.39 191:0.81 192:0.59 201:0.74 202:0.84 204:0.89 208:0.64 212:0.23 215:0.46 216:0.62 220:0.62 222:0.35 230:0.84 232:0.79 233:0.69 235:0.97 241:0.74 242:0.45 243:0.19 245:0.58 247:0.60 248:0.94 253:0.95 255:0.79 256:0.87 257:0.65 259:0.21 260:0.90 265:0.55 266:0.71 267:0.52 268:0.88 271:0.36 276:0.45 279:0.86 283:0.71 285:0.62 290:0.67 297:0.36 300:0.43 +0 12:0.20 17:0.11 21:0.78 27:0.45 30:0.41 34:0.70 36:0.18 37:0.50 39:0.96 43:0.34 55:0.71 66:0.34 69:0.80 70:0.77 74:0.80 91:0.15 98:0.32 108:0.64 122:0.67 123:0.62 124:0.83 127:0.63 129:0.05 133:0.82 135:0.68 145:0.45 146:0.69 147:0.05 149:0.45 154:0.25 159:0.85 163:0.90 172:0.37 173:0.59 177:0.05 178:0.55 179:0.76 187:0.68 212:0.19 216:0.77 222:0.35 235:0.68 241:0.68 242:0.79 243:0.13 244:0.61 245:0.54 247:0.35 253:0.58 257:0.65 259:0.21 265:0.35 266:0.80 267:0.28 271:0.36 276:0.44 300:0.55 +1 8:0.74 9:0.80 12:0.20 17:0.04 21:0.74 27:0.38 30:0.21 34:0.30 36:0.92 37:0.54 39:0.96 41:0.82 43:0.40 55:0.71 66:0.36 69:0.78 70:0.67 74:0.89 76:0.85 77:0.70 81:0.24 83:0.76 91:0.92 96:0.89 98:0.31 104:0.58 108:0.62 114:0.61 120:0.83 122:0.67 123:0.59 124:0.68 126:0.32 127:0.22 129:0.05 133:0.62 135:0.18 140:0.99 145:0.84 146:0.45 147:0.47 149:0.45 150:0.24 151:0.87 153:0.48 154:0.30 155:0.67 158:0.84 159:0.40 162:0.46 164:0.77 172:0.27 173:0.74 176:0.56 177:0.05 178:0.54 179:0.67 191:0.87 192:0.59 195:0.80 202:0.96 208:0.64 212:0.16 216:0.50 220:0.91 222:0.35 235:0.88 241:0.83 242:0.76 243:0.39 244:0.61 245:0.50 247:0.35 248:0.78 253:0.91 255:0.79 256:0.82 257:0.65 259:0.21 260:0.84 265:0.33 266:0.54 267:0.79 268:0.82 271:0.36 276:0.36 282:0.84 285:0.62 290:0.61 300:0.43 +1 1:0.74 6:0.79 8:0.58 9:0.80 11:0.87 12:0.98 17:0.70 18:0.82 20:0.83 22:0.93 27:0.53 28:0.91 29:0.77 30:0.70 31:0.97 34:0.88 36:0.35 37:0.27 39:0.53 41:0.92 43:0.83 45:0.94 47:0.93 60:0.95 64:0.77 66:0.93 69:0.41 70:0.54 71:0.73 74:0.84 78:0.96 79:0.97 81:0.83 83:0.91 85:0.90 86:0.92 91:0.65 96:0.91 97:0.94 98:0.91 100:0.84 101:0.87 104:0.94 106:0.81 108:0.31 111:0.93 114:0.98 117:0.86 120:0.84 122:0.75 123:0.30 124:0.36 126:0.54 127:0.76 129:0.05 131:0.89 133:0.37 135:0.32 137:0.97 139:0.92 140:0.45 144:0.76 146:0.93 147:0.94 149:0.96 150:0.89 151:0.95 152:0.79 153:0.83 154:0.79 155:0.67 157:0.89 158:0.92 159:0.61 161:0.73 162:0.86 164:0.77 165:0.93 166:0.84 167:0.47 169:0.82 172:0.92 173:0.35 176:0.73 177:0.70 179:0.27 181:0.49 182:0.88 186:0.96 187:0.95 189:0.82 192:0.87 196:0.98 201:0.93 202:0.66 206:0.81 208:0.64 212:0.86 215:0.96 216:0.29 219:0.95 222:0.48 227:0.95 229:0.93 231:0.77 232:0.77 235:0.12 238:0.81 241:0.24 242:0.36 243:0.93 245:0.71 246:0.96 247:0.82 248:0.89 253:0.93 255:0.95 256:0.73 259:0.21 260:0.84 261:0.85 262:0.93 265:0.91 266:0.63 267:0.79 268:0.74 276:0.85 279:0.86 283:0.82 284:0.95 285:0.62 287:0.97 290:0.98 292:0.95 297:0.36 300:0.55 +1 1:0.69 6:0.79 7:0.72 8:0.59 9:0.80 11:0.64 12:0.98 17:0.85 18:0.69 20:0.77 21:0.22 27:0.35 28:0.91 29:0.77 30:0.13 31:0.98 32:0.69 34:0.97 36:0.61 37:0.27 39:0.53 41:0.90 43:0.72 45:0.83 55:0.52 66:0.35 69:0.10 70:0.97 71:0.73 74:0.37 76:0.85 77:0.70 78:1.00 79:0.97 81:0.48 83:0.91 86:0.59 91:0.81 96:0.96 97:0.94 98:0.64 99:0.83 100:0.94 101:0.52 104:0.83 106:0.81 108:0.09 111:0.98 114:0.62 117:0.86 120:0.93 122:0.77 123:0.09 124:0.67 126:0.44 127:0.62 129:0.05 131:0.89 133:0.60 135:0.63 137:0.98 138:0.97 139:0.57 140:0.80 144:0.76 145:0.56 146:0.72 147:0.76 149:0.75 150:0.57 151:0.97 152:0.75 153:0.94 154:0.62 155:0.67 157:0.61 158:0.72 159:0.53 161:0.73 162:0.80 164:0.85 165:0.70 166:0.77 167:0.52 169:0.91 172:0.61 173:0.17 176:0.55 177:0.70 179:0.47 181:0.49 182:0.88 186:0.99 187:0.21 189:0.82 191:0.73 192:0.87 195:0.72 196:0.87 201:0.68 202:0.79 204:0.85 206:0.81 208:0.64 212:0.74 215:0.51 216:0.46 222:0.48 227:0.95 229:0.93 230:0.75 231:0.77 232:0.93 233:0.76 235:0.31 238:0.81 241:0.47 242:0.62 243:0.51 244:0.61 245:0.81 246:0.61 247:0.79 248:0.91 253:0.89 255:0.95 256:0.83 259:0.21 260:0.92 261:0.80 265:0.65 266:0.75 267:0.69 268:0.84 276:0.66 279:0.82 282:0.71 283:0.82 284:0.95 285:0.62 287:0.97 290:0.62 297:0.36 300:0.84 +3 8:0.61 9:0.80 11:0.87 12:0.98 17:0.26 18:0.83 27:0.18 28:0.91 29:0.77 30:0.68 31:0.98 34:0.42 36:0.79 37:0.64 39:0.53 41:0.93 43:0.97 45:0.66 64:0.77 66:0.79 69:0.05 70:0.31 71:0.73 74:0.58 79:0.98 81:0.63 83:0.91 85:0.72 86:0.86 91:0.64 96:0.91 98:0.61 101:0.87 108:0.05 120:0.88 122:0.57 123:0.05 126:0.44 127:0.18 129:0.05 131:0.89 135:0.39 137:0.98 139:0.82 140:0.45 144:0.76 146:0.41 147:0.48 149:0.65 150:0.44 151:0.98 153:0.92 154:0.97 155:0.67 157:0.79 159:0.15 161:0.73 162:0.76 164:0.82 166:0.73 167:0.52 172:0.41 173:0.28 177:0.70 179:0.60 181:0.49 182:0.88 187:0.39 191:0.76 192:0.87 196:0.96 201:0.68 202:0.79 208:0.64 212:0.78 215:0.96 216:0.53 222:0.48 227:0.95 229:0.93 231:0.77 235:0.05 241:0.50 242:0.45 243:0.80 246:0.61 247:0.39 248:0.89 253:0.88 255:0.95 256:0.81 259:0.21 260:0.87 265:0.62 266:0.23 267:0.44 268:0.83 276:0.24 284:0.97 285:0.62 287:0.97 297:0.36 300:0.25 +2 1:0.74 8:0.67 9:0.80 11:0.92 12:0.98 17:0.94 18:0.96 21:0.40 25:0.88 27:0.72 28:0.91 29:0.77 30:0.73 31:0.96 34:0.28 36:0.34 39:0.53 41:0.93 43:0.97 45:0.98 60:0.97 64:0.77 66:0.16 69:0.17 70:0.63 71:0.73 74:0.94 79:0.96 81:0.53 83:0.91 85:0.99 86:0.99 87:0.98 91:0.62 96:0.94 98:0.54 101:0.97 104:0.54 108:0.80 114:0.62 120:0.89 122:0.57 123:0.79 124:0.89 126:0.54 127:0.98 129:0.95 131:0.89 133:0.87 135:0.30 137:0.96 139:0.99 140:0.80 144:0.76 146:0.60 147:0.65 149:0.99 150:0.74 151:0.87 153:0.86 154:0.97 155:0.67 157:0.94 158:0.91 159:0.90 161:0.73 162:0.86 164:0.82 165:0.93 166:0.84 167:0.75 172:0.90 173:0.08 176:0.73 177:0.70 179:0.12 181:0.49 182:0.88 192:0.87 196:0.99 201:0.93 202:0.72 208:0.64 212:0.75 215:0.42 216:0.06 219:0.95 222:0.48 227:0.95 228:0.99 229:0.93 231:0.77 232:0.82 235:0.60 241:0.77 242:0.18 243:0.18 245:0.99 246:0.96 247:0.95 248:0.86 253:0.93 255:0.95 256:0.79 259:0.21 260:0.88 265:0.55 266:0.80 267:0.96 268:0.79 276:0.97 279:0.86 283:0.78 284:0.96 285:0.62 287:0.97 290:0.62 292:0.91 297:0.36 300:0.84 +3 1:0.74 6:0.86 7:0.81 8:0.74 9:0.80 11:0.92 12:0.98 17:0.92 18:0.98 20:0.83 21:0.22 22:0.93 27:0.33 28:0.91 29:0.77 30:0.73 31:0.95 32:0.80 34:0.63 36:0.88 37:0.57 39:0.53 41:0.92 43:0.85 44:0.93 45:0.95 47:0.93 48:0.91 60:0.87 64:0.77 66:0.90 69:0.23 70:0.47 71:0.73 74:0.96 76:0.97 77:0.89 78:0.99 79:0.95 81:0.70 83:0.91 85:0.90 86:0.99 91:0.78 96:0.85 98:0.69 100:0.96 101:0.97 104:0.87 106:0.81 108:0.18 111:0.98 114:0.71 117:0.86 120:0.75 121:0.90 122:0.80 123:0.18 124:0.37 126:0.54 127:0.84 129:0.05 131:0.89 133:0.43 135:0.69 137:0.95 139:0.99 140:0.45 144:0.76 145:0.67 146:0.86 147:0.88 149:0.97 150:0.82 151:0.87 152:0.79 153:0.48 154:0.82 155:0.67 157:0.94 158:0.92 159:0.71 161:0.73 162:0.85 164:0.76 165:0.93 166:0.84 167:0.50 169:0.94 172:0.93 173:0.18 176:0.73 177:0.70 179:0.26 181:0.49 182:0.88 186:0.99 187:0.21 189:0.88 191:0.78 192:0.87 195:0.84 196:0.98 201:0.93 202:0.67 204:0.89 206:0.81 208:0.64 212:0.89 215:0.51 216:0.17 219:0.95 220:0.82 222:0.48 227:0.95 229:0.93 230:0.87 231:0.77 232:0.76 233:0.76 235:0.60 238:0.85 241:0.39 242:0.37 243:0.91 245:0.81 246:0.96 247:0.86 248:0.83 253:0.90 255:0.95 256:0.73 259:0.21 260:0.76 261:0.88 262:0.93 265:0.69 266:0.54 267:0.99 268:0.74 276:0.79 279:0.86 281:0.91 282:0.88 283:0.82 284:0.95 285:0.62 287:0.97 290:0.71 292:0.95 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.81 12:0.98 17:0.28 18:0.77 21:0.22 27:0.45 28:0.91 29:0.77 30:0.21 34:0.80 36:0.23 37:0.50 39:0.53 43:0.60 45:0.73 64:0.77 66:0.36 69:0.68 70:0.86 71:0.73 74:0.62 81:0.60 83:0.60 86:0.79 91:0.20 97:0.89 98:0.26 99:0.83 101:0.52 108:0.64 114:0.71 122:0.57 123:0.61 124:0.68 126:0.54 127:0.24 129:0.05 131:0.61 133:0.60 135:0.65 139:0.83 144:0.76 145:0.50 146:0.18 147:0.23 149:0.29 150:0.75 154:0.76 155:0.67 157:0.82 158:0.91 159:0.45 161:0.73 163:0.75 165:0.70 166:0.84 167:0.46 172:0.27 173:0.61 176:0.73 177:0.70 178:0.55 179:0.71 181:0.49 182:0.87 187:0.68 192:0.87 201:0.93 208:0.64 212:0.37 215:0.51 216:0.77 219:0.95 222:0.48 227:0.61 229:0.61 231:0.76 235:0.42 241:0.15 242:0.16 243:0.39 245:0.50 246:0.96 247:0.60 253:0.52 254:0.74 259:0.21 265:0.28 266:0.38 267:0.36 276:0.30 277:0.69 279:0.86 283:0.78 287:0.61 290:0.71 292:0.91 297:0.36 300:0.55 +0 6:0.80 8:0.59 9:0.80 11:0.87 12:0.98 17:0.79 18:0.68 20:0.95 21:0.47 27:0.40 28:0.91 29:0.77 30:0.17 31:0.97 34:0.98 36:0.69 37:0.38 39:0.53 41:0.90 43:0.70 45:0.91 66:0.45 69:0.43 70:0.91 71:0.73 74:0.52 78:0.94 79:0.97 81:0.24 83:0.91 85:0.72 86:0.78 91:0.60 96:0.94 97:0.94 98:0.72 100:0.82 101:0.87 108:0.33 111:0.91 120:0.89 122:0.77 123:0.32 124:0.77 126:0.26 127:0.86 129:0.05 131:0.89 133:0.74 135:0.92 137:0.97 139:0.74 140:0.87 144:0.76 146:0.91 147:0.93 149:0.84 150:0.24 151:0.92 152:0.89 153:0.78 154:0.63 155:0.67 157:0.77 158:0.72 159:0.77 161:0.73 162:0.86 164:0.82 166:0.61 167:0.45 169:0.80 172:0.85 173:0.27 177:0.70 179:0.26 181:0.49 182:0.88 186:0.94 187:0.68 189:0.82 192:0.87 196:0.94 202:0.76 208:0.64 212:0.57 215:0.41 216:0.31 220:0.62 222:0.48 227:0.95 229:0.93 231:0.77 235:0.52 238:0.81 241:0.03 242:0.43 243:0.58 245:0.92 246:0.61 247:0.84 248:0.89 253:0.87 255:0.95 256:0.83 259:0.21 260:0.88 261:0.89 265:0.72 266:0.63 267:0.96 268:0.83 276:0.87 279:0.82 283:0.82 284:0.95 285:0.62 287:0.97 297:0.36 300:0.70 +2 8:0.75 9:0.76 11:0.81 12:0.98 17:0.43 18:0.81 22:0.93 27:0.50 28:0.91 29:0.77 30:0.15 31:0.92 34:0.55 36:0.99 37:0.45 39:0.53 43:0.68 45:0.66 47:0.93 55:0.59 64:0.77 66:0.33 69:0.30 70:0.96 71:0.73 74:0.48 76:0.85 79:0.96 81:0.46 83:0.60 86:0.72 91:0.72 96:0.86 97:0.89 98:0.20 101:0.52 108:0.93 122:0.85 123:0.93 124:0.69 126:0.26 127:0.90 129:0.59 131:0.89 133:0.64 135:0.78 137:0.92 139:0.75 140:0.80 144:0.76 145:0.63 146:0.32 147:0.39 149:0.36 150:0.37 151:0.70 153:0.78 154:0.57 155:0.58 157:0.76 158:0.79 159:0.83 161:0.73 162:0.79 166:0.73 167:0.48 172:0.27 173:0.15 175:0.75 177:0.38 179:0.88 181:0.49 182:0.87 187:0.39 190:0.89 191:0.89 192:0.51 196:0.92 202:0.72 208:0.54 212:0.30 216:0.29 219:0.92 222:0.48 227:0.95 229:0.93 231:0.77 235:0.02 241:0.30 242:0.53 243:0.44 244:0.61 245:0.53 246:0.61 247:0.47 248:0.69 253:0.64 255:0.74 256:0.77 257:0.65 259:0.21 262:0.93 265:0.22 266:0.54 267:0.76 268:0.77 276:0.32 277:0.69 279:0.82 284:0.93 285:0.56 287:0.97 292:0.95 300:0.70 +2 1:0.74 6:0.80 8:0.59 9:0.80 11:0.87 12:0.98 17:0.67 18:0.88 20:0.93 21:0.05 22:0.93 27:0.40 28:0.91 29:0.77 30:0.15 31:0.93 34:0.81 36:0.55 37:0.28 39:0.53 43:0.78 45:0.73 47:0.93 64:0.77 66:0.27 69:0.49 70:0.97 71:0.73 74:0.72 78:0.98 79:0.95 81:0.75 83:0.60 85:0.72 86:0.91 87:0.98 91:0.53 96:0.85 97:0.94 98:0.64 99:0.83 100:0.84 101:0.97 104:0.93 106:0.81 108:0.38 111:0.95 114:0.89 117:0.86 120:0.76 122:0.77 123:0.78 124:0.58 126:0.54 127:0.70 129:0.59 131:0.89 133:0.46 135:0.78 137:0.93 139:0.92 140:0.80 144:0.76 146:0.66 147:0.71 149:0.59 150:0.83 151:0.79 152:0.96 153:0.82 154:0.74 155:0.67 157:0.85 158:0.91 159:0.60 161:0.73 162:0.86 164:0.81 165:0.70 166:0.84 167:0.45 169:0.85 172:0.73 173:0.43 176:0.73 177:0.70 179:0.41 181:0.49 182:0.88 186:0.98 187:0.95 189:0.82 192:0.87 196:0.96 201:0.93 202:0.74 206:0.81 208:0.64 212:0.67 215:0.78 216:0.29 219:0.95 220:0.74 222:0.48 227:0.95 229:0.93 231:0.77 232:0.75 235:0.22 238:0.81 241:0.07 242:0.40 243:0.59 245:0.90 246:0.96 247:0.88 248:0.74 253:0.82 255:0.95 256:0.81 259:0.21 260:0.75 261:0.94 262:0.93 265:0.55 266:0.63 267:0.82 268:0.82 276:0.72 279:0.86 283:0.78 284:0.95 285:0.62 287:0.97 290:0.89 292:0.91 297:0.36 300:0.84 +3 6:0.82 8:0.61 9:0.80 11:0.87 12:0.98 17:0.20 18:0.83 20:0.96 27:0.18 28:0.91 29:0.77 30:0.68 31:0.98 34:0.47 36:0.83 37:0.64 39:0.53 41:0.94 43:0.97 45:0.66 60:0.87 64:0.77 66:0.79 69:0.05 70:0.31 71:0.73 74:0.67 78:1.00 79:0.99 81:0.63 83:0.91 85:0.72 86:0.86 91:0.67 96:0.94 98:0.61 100:0.98 101:0.87 108:0.05 111:0.99 120:0.91 122:0.57 123:0.05 126:0.44 127:0.18 129:0.05 131:0.89 135:0.39 137:0.98 139:0.87 140:0.45 144:0.76 146:0.41 147:0.48 149:0.65 150:0.44 151:0.98 152:0.89 153:0.92 154:0.97 155:0.67 157:0.79 158:0.92 159:0.15 161:0.73 162:0.78 164:0.85 166:0.73 167:0.56 169:0.97 172:0.41 173:0.28 177:0.70 179:0.60 181:0.49 182:0.88 186:1.00 187:0.39 189:0.84 191:0.86 192:0.87 196:0.98 201:0.68 202:0.82 208:0.64 212:0.78 215:0.96 216:0.53 219:0.95 222:0.48 227:0.95 229:0.93 231:0.77 235:0.05 238:0.86 241:0.58 242:0.45 243:0.80 246:0.61 247:0.39 248:0.90 253:0.92 255:0.95 256:0.86 259:0.21 260:0.90 261:0.95 265:0.62 266:0.23 267:0.59 268:0.87 276:0.24 279:0.86 283:0.82 284:0.97 285:0.62 287:0.97 292:0.95 297:0.36 300:0.25 +1 1:0.74 7:0.81 11:0.92 12:0.98 17:0.85 18:0.92 21:0.05 22:0.93 27:0.35 28:0.91 29:0.77 30:0.54 32:0.69 34:0.97 36:0.52 37:0.27 39:0.53 43:0.97 45:0.88 47:0.93 64:0.77 66:0.43 69:0.08 70:0.89 71:0.73 74:0.90 76:0.85 77:0.70 81:0.76 83:0.91 85:0.90 86:0.97 91:0.62 98:0.62 101:0.97 104:0.83 106:0.81 108:0.07 114:0.89 117:0.86 121:0.90 122:0.77 123:0.07 124:0.70 126:0.54 127:0.87 129:0.81 131:0.61 133:0.67 135:0.65 139:0.97 144:0.76 145:0.69 146:0.77 147:0.80 149:0.94 150:0.85 154:0.97 155:0.67 157:0.93 159:0.66 161:0.73 165:0.93 166:0.84 167:0.48 172:0.79 173:0.13 176:0.73 177:0.70 178:0.55 179:0.31 181:0.49 182:0.87 187:0.21 192:0.87 195:0.79 201:0.93 204:0.89 206:0.81 208:0.64 212:0.86 215:0.78 216:0.46 222:0.48 227:0.61 229:0.61 230:0.87 231:0.76 232:0.96 233:0.76 235:0.22 241:0.27 242:0.30 243:0.57 245:0.92 246:0.96 247:0.86 253:0.62 259:0.21 262:0.93 265:0.62 266:0.71 267:0.82 276:0.82 281:0.91 282:0.71 287:0.61 290:0.89 297:0.36 300:0.94 +3 1:0.69 6:0.87 7:0.72 8:0.69 9:0.76 11:0.92 12:0.98 17:0.72 18:0.85 20:0.91 21:0.05 27:0.54 28:0.91 29:0.77 30:0.28 32:0.80 34:0.99 36:0.47 37:0.86 39:0.53 43:0.79 44:0.93 45:0.91 60:0.87 66:0.16 69:0.30 70:0.92 71:0.73 74:0.88 76:0.94 77:0.70 78:0.99 81:0.64 83:0.91 85:0.88 86:0.94 91:0.61 96:0.77 97:0.94 98:0.49 99:0.83 100:0.90 101:0.97 104:0.86 106:0.81 108:0.93 111:0.96 114:0.70 117:0.86 120:0.65 122:0.77 123:0.23 124:0.88 126:0.44 127:0.97 129:0.81 131:0.84 133:0.86 135:0.95 139:0.95 140:0.45 144:0.76 145:0.41 146:0.83 147:0.86 149:0.91 150:0.62 151:0.65 152:0.85 153:0.48 154:0.73 155:0.67 157:0.88 158:0.92 159:0.84 161:0.73 162:0.86 164:0.64 165:0.70 166:0.77 167:0.47 169:0.87 172:0.79 173:0.14 176:0.55 177:0.70 179:0.21 181:0.49 182:0.79 186:0.99 187:0.21 189:0.89 190:0.77 192:0.87 195:0.93 201:0.68 202:0.50 204:0.85 206:0.81 208:0.64 212:0.80 215:0.78 216:0.12 219:0.95 220:0.74 222:0.48 227:0.91 229:0.61 230:0.75 231:0.75 232:0.94 233:0.76 235:0.12 238:0.81 241:0.21 242:0.71 243:0.37 245:0.94 246:0.61 247:0.88 248:0.67 253:0.61 255:0.74 256:0.58 259:0.21 260:0.64 261:0.91 265:0.29 266:0.80 267:0.85 268:0.59 276:0.90 279:0.86 282:0.88 283:0.82 285:0.56 287:0.61 290:0.70 292:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.80 8:0.59 9:0.80 11:0.87 12:0.98 17:0.73 18:0.88 20:0.95 22:0.93 27:0.40 28:0.91 29:0.77 30:0.53 31:0.96 34:0.88 36:0.55 37:0.28 39:0.53 41:0.88 43:0.80 45:0.83 47:0.93 60:0.95 64:0.77 66:0.23 69:0.49 70:0.77 71:0.73 74:0.73 78:0.99 79:0.96 81:0.83 83:0.91 85:0.80 86:0.89 87:0.98 91:0.57 96:0.93 97:0.89 98:0.65 100:0.89 101:0.87 104:0.93 106:0.81 108:0.38 111:0.97 114:0.98 117:0.86 120:0.86 122:0.77 123:0.76 124:0.67 126:0.54 127:0.66 129:0.81 131:0.89 133:0.67 135:0.81 137:0.96 139:0.90 140:0.80 144:0.76 146:0.63 147:0.68 149:0.82 150:0.89 151:0.93 152:0.96 153:0.78 154:0.75 155:0.67 157:0.82 158:0.92 159:0.57 161:0.73 162:0.85 164:0.82 165:0.93 166:0.84 167:0.46 169:0.85 172:0.73 173:0.44 176:0.73 177:0.70 179:0.39 181:0.49 182:0.88 186:0.99 187:0.95 189:0.83 192:0.87 196:0.97 201:0.93 202:0.76 206:0.81 208:0.64 212:0.73 215:0.96 216:0.29 219:0.95 220:0.62 222:0.48 227:0.95 229:0.93 231:0.77 232:0.75 235:0.12 238:0.81 241:0.12 242:0.32 243:0.58 245:0.86 246:0.96 247:0.86 248:0.83 253:0.89 255:0.95 256:0.83 259:0.21 260:0.84 261:0.94 262:0.93 265:0.53 266:0.71 267:0.93 268:0.83 276:0.75 279:0.86 283:0.82 284:0.94 285:0.62 287:0.97 290:0.98 292:0.95 297:0.36 300:0.70 +2 1:0.74 11:0.81 12:0.98 17:0.32 18:0.80 21:0.30 27:0.45 28:0.91 29:0.77 30:0.11 32:0.69 34:0.84 36:0.17 37:0.50 39:0.53 43:0.12 45:0.73 64:0.77 66:0.48 69:0.69 70:0.95 71:0.73 74:0.54 77:0.70 81:0.55 83:0.60 86:0.83 91:0.07 98:0.20 99:0.83 101:0.52 108:0.52 114:0.65 122:0.57 123:0.50 124:0.74 126:0.54 127:0.25 129:0.05 131:0.61 133:0.73 135:0.77 139:0.79 144:0.76 145:0.49 146:0.35 147:0.42 149:0.08 150:0.71 154:0.76 155:0.67 157:0.61 159:0.57 161:0.73 165:0.70 166:0.84 167:0.47 172:0.37 173:0.55 176:0.73 177:0.70 178:0.55 179:0.66 181:0.49 182:0.61 187:0.68 192:0.87 195:0.87 201:0.93 208:0.64 212:0.42 215:0.44 216:0.77 222:0.48 227:0.61 229:0.61 231:0.76 235:0.52 241:0.24 242:0.29 243:0.60 245:0.49 246:0.61 247:0.67 253:0.49 254:0.74 259:0.21 265:0.22 266:0.54 267:0.69 276:0.37 282:0.71 287:0.61 290:0.65 297:0.36 300:0.70 +2 1:0.74 6:0.82 8:0.64 9:0.80 11:0.92 12:0.98 17:0.98 18:0.83 20:0.80 21:0.54 27:0.53 28:0.91 29:0.77 30:0.36 31:0.86 32:0.78 34:0.98 36:0.51 37:0.36 39:0.53 43:0.77 44:0.93 45:0.98 60:0.87 64:0.77 66:0.99 69:0.32 70:0.70 71:0.73 74:0.88 77:0.96 78:0.97 79:0.86 81:0.44 83:0.91 85:0.80 86:0.88 91:0.51 96:0.68 97:1.00 98:0.98 99:0.83 100:0.90 101:0.97 104:0.94 106:0.81 108:0.25 111:0.94 114:0.59 117:0.86 120:0.55 122:0.77 123:0.24 126:0.54 127:0.82 129:0.05 131:0.89 135:0.88 137:0.86 139:0.92 140:0.45 144:0.76 145:0.67 146:0.96 147:0.97 149:0.96 150:0.66 151:0.50 152:0.77 153:0.48 154:0.80 155:0.67 157:0.87 158:0.92 159:0.68 161:0.73 162:0.86 164:0.57 165:0.70 166:0.84 167:0.46 169:0.88 172:0.98 173:0.24 176:0.73 177:0.70 179:0.15 181:0.49 182:0.77 186:0.97 187:0.39 189:0.84 191:0.70 192:0.87 195:0.80 196:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.91 215:0.39 216:0.09 219:0.95 220:0.91 222:0.48 227:0.95 229:0.61 231:0.77 232:0.79 235:0.74 238:0.81 241:0.10 242:0.38 243:0.99 246:0.61 247:0.92 248:0.49 253:0.49 255:0.95 256:0.45 259:0.21 260:0.55 261:0.83 265:0.98 266:0.27 267:0.89 268:0.46 276:0.95 279:0.86 282:0.86 283:0.82 284:0.89 285:0.62 287:0.61 290:0.59 292:0.95 297:0.36 300:0.70 +1 12:0.20 17:0.22 21:0.64 27:0.45 28:0.48 30:0.33 34:0.86 36:0.17 37:0.50 39:0.39 43:0.30 55:0.96 66:0.32 69:0.80 70:0.69 74:0.59 81:0.32 91:0.18 98:0.20 108:0.63 122:0.43 123:0.61 124:0.83 126:0.32 127:0.36 129:0.05 133:0.82 135:0.86 145:0.52 146:0.32 147:0.05 149:0.26 150:0.30 154:0.65 159:0.58 161:0.54 167:0.60 172:0.21 173:0.74 177:0.05 178:0.55 179:0.85 181:0.73 187:0.68 212:0.19 215:0.53 216:0.77 222:0.25 235:0.74 241:0.12 242:0.45 243:0.19 245:0.43 247:0.39 253:0.47 254:0.84 257:0.65 259:0.21 265:0.22 266:0.47 267:0.73 271:0.74 276:0.33 283:0.71 297:0.36 300:0.43 +3 6:0.92 8:0.88 9:0.80 12:0.20 17:0.49 20:0.99 25:0.88 27:0.26 28:0.48 30:0.76 34:0.34 36:0.53 37:0.60 39:0.39 41:0.88 43:0.42 55:0.52 66:0.80 69:0.21 70:0.21 74:0.87 78:0.89 81:0.80 83:0.76 91:0.72 96:0.97 98:0.92 100:0.93 104:0.58 108:0.17 111:0.90 114:0.80 117:0.86 120:0.87 122:0.67 123:0.16 126:0.32 127:0.54 129:0.05 135:0.61 138:0.98 140:0.95 146:0.48 147:0.54 149:0.51 150:0.60 151:0.77 152:0.99 153:0.95 154:0.32 155:0.67 159:0.17 161:0.54 162:0.84 164:0.87 167:0.87 169:0.88 172:0.45 173:0.62 175:0.75 176:0.56 177:0.05 179:0.86 181:0.73 186:0.89 187:0.21 189:0.96 190:0.77 191:0.93 192:0.59 202:0.85 208:0.64 212:0.95 216:0.83 222:0.25 232:0.81 235:0.12 238:0.94 241:0.76 242:0.82 243:0.82 244:0.61 247:0.12 248:0.85 253:0.98 255:0.79 256:0.89 257:0.65 259:0.21 260:0.87 261:0.92 265:0.92 266:0.12 267:0.44 268:0.90 271:0.74 276:0.37 277:0.69 283:0.71 285:0.62 290:0.80 300:0.18 +1 12:0.20 17:0.34 21:0.47 27:0.45 28:0.48 30:0.20 34:0.77 36:0.21 37:0.50 39:0.39 43:0.30 55:0.52 66:0.83 69:0.68 70:0.84 74:0.69 81:0.39 91:0.11 98:0.64 108:0.52 123:0.50 124:0.37 126:0.32 127:0.34 129:0.05 133:0.39 135:0.72 145:0.47 146:0.82 147:0.85 149:0.54 150:0.34 154:0.63 159:0.57 161:0.54 167:0.59 172:0.71 173:0.60 177:0.38 178:0.55 179:0.48 181:0.73 187:0.68 212:0.77 215:0.63 216:0.77 222:0.25 235:0.52 241:0.10 242:0.63 243:0.84 245:0.57 247:0.55 253:0.52 254:0.88 259:0.21 265:0.65 266:0.54 267:0.19 271:0.74 276:0.56 283:0.71 297:0.36 300:0.70 +2 7:0.78 12:0.20 17:0.34 21:0.30 25:0.88 27:0.26 28:0.48 30:0.36 32:0.75 34:0.47 36:0.80 37:0.60 39:0.39 43:0.45 55:0.71 66:0.71 69:0.77 70:0.48 74:0.51 76:0.85 81:0.51 91:0.74 98:0.84 104:0.58 108:0.60 123:0.57 124:0.43 126:0.32 127:0.65 129:0.05 133:0.39 135:0.49 145:0.58 146:0.87 147:0.49 149:0.68 150:0.40 154:0.34 159:0.54 161:0.54 167:0.92 172:0.71 173:0.78 177:0.05 178:0.55 179:0.55 181:0.73 187:0.21 195:0.75 212:0.27 215:0.72 216:0.83 222:0.25 230:0.81 233:0.76 235:0.31 241:0.80 242:0.82 243:0.25 244:0.61 245:0.76 247:0.30 253:0.42 257:0.65 259:0.21 265:0.84 266:0.54 267:0.65 271:0.74 276:0.65 297:0.36 300:0.33 +0 8:0.98 12:0.20 17:0.08 20:0.85 21:0.54 25:0.88 27:0.07 28:0.48 30:0.62 34:0.52 36:0.49 37:0.95 39:0.39 43:0.38 55:0.71 66:0.83 69:0.47 70:0.43 74:0.50 78:0.76 81:0.36 91:0.93 98:0.87 100:0.77 104:0.76 108:0.36 111:0.75 120:0.95 123:0.35 126:0.32 127:0.40 129:0.05 135:0.37 140:0.45 146:0.65 147:0.70 149:0.49 150:0.32 151:0.75 152:0.80 153:0.86 154:0.63 159:0.24 161:0.54 162:0.68 164:0.96 167:0.97 169:0.75 172:0.51 173:0.67 177:0.38 179:0.77 181:0.73 186:0.77 190:0.77 191:0.97 202:0.94 212:0.70 215:0.58 216:0.54 220:0.82 222:0.25 235:0.60 241:0.96 242:0.78 243:0.84 247:0.35 248:0.93 253:0.41 254:0.92 256:0.95 259:0.21 260:0.95 261:0.76 265:0.87 266:0.27 267:0.69 268:0.96 271:0.74 276:0.42 283:0.71 285:0.50 297:0.36 300:0.33 +1 12:0.20 17:0.34 25:0.88 27:0.25 28:0.48 30:0.45 34:0.62 36:0.95 37:0.88 39:0.39 43:0.40 55:0.59 66:0.87 69:0.05 70:0.40 74:0.46 81:0.90 91:0.20 96:0.80 98:0.82 104:0.76 108:0.05 114:0.80 123:0.05 126:0.32 127:0.32 129:0.05 135:0.39 140:0.45 146:0.80 147:0.83 149:0.54 150:0.80 151:0.66 153:0.48 154:0.92 159:0.36 161:0.54 162:0.86 167:0.70 172:0.63 173:0.14 176:0.56 177:0.38 179:0.58 181:0.73 187:0.39 190:0.77 202:0.36 212:0.37 216:0.50 222:0.25 235:0.02 241:0.51 242:0.81 243:0.88 247:0.30 248:0.63 253:0.79 254:0.74 256:0.45 259:0.21 265:0.82 266:0.38 267:0.44 268:0.46 271:0.74 276:0.52 285:0.50 290:0.80 300:0.33 +0 8:0.92 12:0.20 17:0.40 21:0.78 25:0.88 27:0.72 28:0.48 30:0.89 34:0.21 36:0.63 37:0.38 39:0.39 43:0.43 55:0.71 66:0.47 69:0.15 70:0.20 74:0.75 91:0.70 96:0.74 98:0.42 104:0.75 108:0.59 120:0.60 122:0.67 123:0.57 124:0.52 127:0.27 129:0.59 133:0.47 135:0.56 140:0.80 146:0.27 147:0.33 149:0.38 151:0.58 153:0.48 154:0.33 159:0.28 161:0.54 162:0.59 163:0.79 164:0.56 167:0.96 172:0.21 173:0.28 175:0.75 177:0.05 178:0.42 179:0.87 181:0.73 190:0.77 191:0.85 202:0.62 212:0.30 216:0.56 220:0.74 222:0.25 235:0.12 241:0.94 242:0.82 243:0.37 244:0.61 245:0.46 247:0.12 248:0.58 253:0.69 256:0.58 257:0.65 259:0.21 260:0.60 265:0.44 266:0.27 267:0.59 268:0.59 271:0.74 276:0.26 277:0.69 285:0.62 300:0.18 +0 11:0.57 12:0.20 17:0.02 21:0.68 25:0.88 27:0.47 28:0.48 30:0.58 34:0.55 36:0.55 37:0.55 39:0.39 43:0.77 66:0.61 69:0.79 70:0.44 74:0.62 81:0.31 85:0.72 91:0.48 98:0.48 101:0.74 104:0.74 108:0.63 120:0.71 123:0.60 124:0.47 126:0.32 127:0.52 129:0.05 133:0.39 135:0.68 140:0.45 146:0.37 147:0.18 149:0.61 150:0.29 151:0.66 153:0.48 154:0.69 159:0.28 161:0.54 162:0.59 164:0.70 167:0.80 172:0.37 173:0.95 177:0.05 179:0.86 181:0.73 187:0.39 190:0.77 202:0.64 212:0.55 215:0.49 216:0.50 220:0.99 222:0.25 235:0.80 241:0.71 242:0.40 243:0.29 245:0.52 247:0.47 248:0.63 253:0.48 256:0.58 257:0.65 259:0.21 260:0.70 265:0.50 266:0.38 267:0.36 268:0.64 271:0.74 276:0.25 283:0.71 285:0.50 297:0.36 300:0.33 +0 6:0.88 8:0.75 9:0.80 12:0.20 17:0.26 20:0.88 21:0.78 25:0.88 27:0.03 28:0.48 34:0.58 36:0.28 37:0.70 39:0.39 41:0.95 78:1.00 83:0.82 91:0.94 96:0.99 100:0.99 104:0.74 111:1.00 120:0.96 135:0.72 138:0.98 140:0.95 151:0.95 152:0.92 153:0.97 155:0.67 161:0.54 162:0.80 164:0.96 167:0.96 169:0.98 175:0.91 181:0.73 186:1.00 189:0.90 191:0.98 192:0.59 202:0.95 208:0.64 216:0.67 222:0.25 238:0.99 241:0.94 244:0.83 248:0.96 253:0.98 255:0.79 256:0.97 257:0.84 259:0.21 260:0.97 261:0.99 267:0.91 268:0.97 271:0.74 277:0.87 285:0.62 +2 11:0.57 12:0.20 17:0.34 21:0.13 25:0.88 27:0.26 28:0.48 30:0.26 34:0.39 36:0.98 37:0.60 39:0.39 43:0.64 55:0.59 66:0.61 69:0.07 70:0.87 74:0.97 81:0.66 85:0.72 91:0.44 98:0.82 101:0.71 104:0.58 108:0.06 117:0.86 120:0.63 122:0.65 123:0.06 124:0.50 126:0.32 127:0.85 129:0.59 133:0.47 135:0.76 140:0.45 146:0.92 147:0.94 149:0.64 150:0.48 151:0.59 153:0.69 154:0.82 159:0.70 161:0.54 162:0.86 164:0.62 167:0.64 172:0.88 173:0.11 177:0.38 179:0.27 181:0.73 187:0.87 190:0.77 191:0.75 202:0.46 212:0.77 215:0.84 216:0.83 220:0.74 222:0.25 232:0.81 235:0.12 241:0.30 242:0.22 243:0.68 245:0.95 247:0.67 248:0.57 253:0.69 256:0.45 259:0.21 260:0.64 265:0.82 266:0.75 267:0.73 268:0.55 271:0.74 276:0.86 285:0.50 297:0.36 300:0.84 +2 6:0.92 7:0.78 8:0.85 9:0.80 12:0.20 20:0.91 21:0.30 25:0.88 27:0.26 28:0.48 30:0.33 32:0.75 34:0.39 36:0.79 37:0.60 39:0.39 43:0.42 55:0.59 66:0.36 69:0.25 70:0.58 74:0.67 76:0.85 78:0.83 81:0.51 91:0.97 96:0.80 98:0.73 100:0.83 104:0.58 108:0.72 111:0.82 120:1.00 123:0.70 124:0.60 126:0.32 127:0.94 129:0.05 133:0.39 135:0.18 140:1.00 145:0.58 146:0.65 147:0.70 149:0.73 150:0.40 151:0.49 152:0.99 153:0.99 154:0.32 155:0.67 159:0.53 161:0.54 162:0.74 164:1.00 167:0.97 169:0.89 172:0.61 173:0.29 177:0.38 179:0.54 181:0.73 186:0.83 189:0.91 190:0.77 191:0.97 192:0.59 195:0.69 202:0.99 208:0.64 212:0.60 215:0.72 216:0.83 222:0.25 230:0.81 233:0.69 235:0.31 238:0.87 241:0.98 242:0.79 243:0.50 244:0.61 245:0.86 247:0.55 248:0.49 253:0.79 255:0.79 256:0.99 259:0.21 260:1.00 261:0.92 265:0.73 266:0.54 267:0.92 268:1.00 271:0.74 276:0.68 277:0.69 283:0.71 285:0.62 297:0.36 300:0.43 +2 7:0.78 8:0.74 11:0.57 12:0.20 17:0.16 21:0.40 25:0.88 27:0.61 28:0.48 30:0.45 32:0.75 34:0.20 36:0.25 37:0.79 39:0.39 43:0.98 55:0.84 66:0.72 69:0.12 70:0.61 74:0.70 81:0.45 85:0.72 91:0.80 98:0.79 101:0.74 104:0.78 108:0.10 120:0.89 122:0.55 123:0.10 124:0.40 126:0.32 127:0.78 129:0.05 133:0.39 135:0.23 140:0.80 145:0.45 146:0.59 147:0.65 149:0.67 150:0.37 151:0.69 153:0.69 154:0.98 159:0.30 161:0.54 162:0.68 164:0.92 167:0.96 172:0.45 173:0.36 177:0.38 178:0.42 179:0.87 181:0.73 190:0.77 191:0.80 195:0.56 202:0.89 212:0.30 215:0.67 216:0.37 220:0.82 222:0.25 230:0.81 233:0.69 235:0.42 241:0.93 242:0.33 243:0.75 245:0.47 247:0.60 248:0.85 253:0.53 256:0.90 259:0.21 260:0.90 265:0.79 266:0.47 267:0.19 268:0.91 271:0.74 276:0.38 283:0.71 285:0.50 297:0.36 300:0.43 +1 8:0.64 12:0.20 17:0.32 25:0.88 27:0.25 28:0.48 30:0.58 34:0.75 36:0.81 37:0.88 39:0.39 43:0.40 55:0.45 66:0.80 69:0.05 70:0.33 74:0.55 81:0.90 91:0.48 98:0.67 104:0.76 108:0.05 114:0.80 123:0.05 126:0.32 127:0.20 129:0.05 135:0.44 146:0.55 147:0.61 149:0.36 150:0.80 154:0.92 159:0.20 161:0.54 167:0.68 172:0.45 173:0.19 176:0.56 177:0.38 178:0.55 179:0.62 181:0.73 187:0.39 212:0.71 216:0.50 222:0.25 235:0.02 241:0.46 242:0.76 243:0.82 247:0.35 253:0.62 254:0.74 259:0.21 265:0.68 266:0.27 267:0.44 271:0.74 276:0.34 290:0.80 300:0.25 +1 1:0.66 10:0.91 11:0.64 12:0.20 17:0.45 18:0.76 21:0.30 27:0.45 28:0.92 29:0.91 30:0.56 34:0.56 36:0.21 37:0.50 39:0.44 43:0.58 45:0.81 64:0.77 66:0.44 69:0.69 70:0.71 71:0.74 74:0.48 81:0.73 83:0.69 86:0.78 91:0.18 98:0.44 99:0.95 101:0.65 108:0.52 114:0.86 122:0.65 123:0.50 124:0.67 126:0.54 127:0.36 129:0.05 131:0.61 133:0.60 135:0.81 139:0.74 144:0.76 145:0.50 146:0.55 147:0.61 149:0.28 150:0.59 154:0.75 155:0.51 157:0.98 159:0.48 161:0.57 165:0.81 166:0.89 167:0.68 170:0.77 172:0.37 173:0.68 174:0.83 176:0.73 177:0.88 178:0.55 179:0.72 181:0.62 182:1.00 187:0.68 192:0.50 197:0.85 201:0.65 208:0.64 212:0.23 215:0.72 216:0.77 222:0.35 227:0.61 229:0.61 231:0.83 235:0.60 236:0.97 239:0.89 241:0.39 242:0.24 243:0.57 245:0.56 246:0.97 247:0.74 253:0.61 254:0.74 259:0.21 265:0.46 266:0.71 267:0.28 271:0.74 276:0.40 287:0.61 290:0.86 297:0.36 300:0.55 +2 7:0.70 10:0.97 11:0.75 12:0.20 17:0.57 18:0.92 21:0.78 27:0.72 28:0.92 29:0.91 30:0.45 32:0.77 34:0.86 36:0.54 37:0.71 39:0.44 43:0.63 44:0.93 45:1.00 66:0.07 69:0.25 70:0.90 71:0.74 74:0.96 75:0.95 77:0.89 83:0.56 86:0.93 91:0.02 97:0.94 98:0.34 101:0.87 102:0.98 108:0.99 122:0.55 123:0.87 124:0.98 127:0.98 129:0.98 131:0.61 133:0.98 135:0.87 139:0.91 144:0.76 145:0.74 146:0.78 147:0.81 149:0.89 154:0.79 155:0.51 157:1.00 158:0.81 159:0.97 161:0.57 166:0.61 167:0.67 170:0.77 172:0.92 173:0.06 174:0.97 177:0.88 178:0.55 179:0.09 181:0.62 182:1.00 187:0.21 192:0.51 195:1.00 197:0.93 204:0.81 208:0.61 212:0.55 216:0.06 219:0.88 222:0.35 226:0.96 227:0.61 229:0.61 230:0.73 231:0.82 233:0.76 235:0.74 239:0.97 241:0.32 242:0.21 243:0.20 245:0.99 246:0.61 247:1.00 253:0.64 254:0.86 259:0.21 265:0.36 266:0.97 267:0.69 271:0.74 276:0.99 277:0.69 279:0.79 282:0.85 283:0.82 287:0.61 292:0.86 300:0.94 +1 1:0.62 6:0.85 8:0.69 9:0.71 10:0.87 11:0.64 12:0.20 17:0.22 18:0.77 20:0.88 21:0.59 23:0.95 27:0.19 28:0.92 29:0.91 30:0.09 32:0.63 33:0.97 34:0.80 36:0.97 37:0.78 39:0.44 43:0.57 45:0.85 62:0.96 64:0.77 66:0.43 69:0.29 70:0.98 71:0.74 74:0.56 78:0.79 81:0.62 83:0.56 86:0.69 91:0.11 96:0.74 98:0.57 99:0.83 100:0.81 101:0.65 102:0.94 104:0.58 108:0.22 111:0.78 114:0.74 120:0.62 123:0.22 124:0.77 126:0.54 127:0.88 128:0.96 129:0.59 131:0.89 133:0.73 135:0.79 139:0.66 140:0.45 144:0.76 145:0.52 146:0.71 147:0.75 149:0.42 150:0.52 151:0.66 152:0.83 153:0.48 154:0.71 155:0.56 157:0.98 159:0.64 161:0.57 162:0.86 164:0.56 165:0.65 166:0.89 167:0.67 168:0.96 169:0.79 170:0.77 172:0.67 173:0.25 174:0.89 176:0.73 177:0.88 179:0.47 181:0.62 182:0.96 186:0.80 187:0.68 189:0.87 190:0.77 192:0.86 193:0.95 195:0.77 197:0.92 201:0.61 202:0.36 205:0.95 208:0.64 212:0.59 215:0.55 216:0.65 220:0.74 222:0.35 227:0.87 229:0.96 231:0.84 232:0.98 235:0.88 236:0.91 238:0.86 239:0.87 241:0.32 242:0.29 243:0.57 245:0.80 246:0.87 247:0.93 248:0.63 253:0.66 254:0.86 255:0.72 256:0.45 259:0.21 260:0.63 261:0.81 265:0.58 266:0.75 267:0.87 268:0.46 271:0.74 276:0.72 285:0.60 287:0.89 290:0.74 291:0.97 297:0.36 300:0.84 +3 1:0.64 6:0.95 7:0.70 8:0.85 9:0.74 10:0.89 11:0.50 12:0.20 17:0.61 18:0.83 20:0.88 21:0.54 23:0.98 27:0.08 28:0.92 29:0.91 30:0.61 32:0.67 34:0.53 36:0.36 37:0.81 39:0.44 43:0.74 45:0.98 62:0.97 66:0.99 69:0.38 70:0.64 71:0.74 74:0.93 75:0.95 77:0.96 78:0.87 81:0.51 83:0.56 86:0.68 91:0.63 96:0.91 97:1.00 98:0.99 99:0.95 100:0.90 101:0.47 104:0.87 106:0.81 108:0.30 111:0.87 114:0.69 117:0.86 120:0.84 122:0.55 123:0.28 126:0.32 127:0.93 128:0.98 129:0.05 131:0.89 135:0.86 138:0.96 139:0.66 140:0.80 144:0.57 145:0.65 146:0.99 147:0.99 149:0.94 150:0.46 151:0.58 152:0.86 153:0.93 154:0.66 155:0.56 157:0.98 158:0.81 159:0.80 161:0.57 162:0.82 164:0.82 165:0.65 166:0.80 167:0.62 168:0.98 169:0.86 170:0.77 172:0.97 173:0.21 174:0.95 176:0.62 177:0.88 179:0.17 181:0.62 182:0.93 186:0.87 187:0.68 189:0.96 190:0.89 191:0.79 192:0.86 195:0.90 197:0.95 201:0.60 202:0.74 204:0.81 205:0.98 206:0.81 208:0.50 212:0.77 215:0.55 216:0.79 222:0.35 226:0.96 227:0.87 229:0.97 230:0.73 231:0.83 232:0.90 233:0.76 235:0.80 238:0.90 239:0.88 241:0.12 242:0.28 243:0.99 244:0.61 246:0.87 247:0.97 248:0.59 253:0.94 255:0.73 256:0.78 259:0.21 260:0.84 261:0.88 265:0.99 266:0.54 267:0.98 268:0.80 271:0.74 276:0.94 279:0.82 282:0.75 283:0.82 285:0.60 287:0.89 290:0.69 297:0.36 300:0.70 +2 1:0.60 6:0.95 7:0.75 8:0.81 9:0.70 10:0.90 11:0.50 12:0.20 17:0.51 18:0.90 20:0.85 21:0.30 23:0.95 27:0.08 28:0.92 29:0.91 30:0.62 32:0.72 34:0.31 36:0.72 37:0.81 39:0.44 43:0.75 45:0.99 62:0.96 66:0.99 69:0.34 70:0.63 71:0.74 74:0.94 77:0.70 78:0.83 81:0.59 83:0.56 86:0.73 91:0.06 96:0.71 98:0.99 99:0.83 100:0.84 101:0.47 102:0.94 104:0.87 106:0.81 108:0.27 111:0.82 114:0.84 117:0.86 120:0.58 122:0.55 123:0.26 126:0.51 127:0.92 128:0.95 129:0.05 131:0.89 135:0.89 139:0.70 140:0.45 144:0.57 145:0.45 146:0.99 147:1.00 149:0.94 150:0.48 151:0.58 152:0.86 153:0.48 154:0.20 155:0.53 157:0.98 159:0.88 161:0.57 162:0.86 164:0.56 165:0.65 166:0.84 167:0.62 168:0.95 169:0.86 170:0.77 172:0.99 173:0.14 174:0.97 176:0.70 177:0.88 179:0.14 181:0.62 182:0.93 186:0.83 187:0.87 189:0.94 190:0.77 192:0.58 193:0.95 195:0.95 197:0.96 201:0.62 202:0.36 204:0.81 205:0.95 206:0.81 208:0.50 212:0.67 215:0.72 216:0.79 220:0.87 222:0.35 227:0.87 229:0.96 230:0.77 231:0.83 232:0.90 233:0.66 235:0.52 236:0.91 238:0.88 239:0.90 241:0.12 242:0.18 243:0.99 246:0.87 247:0.97 248:0.56 253:0.76 254:1.00 255:0.70 256:0.45 259:0.21 260:0.58 261:0.88 265:0.99 266:0.54 267:0.44 268:0.46 271:0.74 276:0.96 282:0.80 285:0.60 287:0.89 290:0.84 297:0.36 300:0.70 +3 1:0.60 7:0.70 8:0.86 9:0.75 10:0.92 11:0.64 12:0.20 17:0.20 18:0.80 20:0.80 21:0.30 27:0.21 28:0.92 29:0.91 30:0.61 34:0.66 36:0.89 37:0.74 39:0.44 43:0.66 45:0.96 66:0.46 69:0.12 70:0.81 71:0.74 74:0.82 78:0.79 81:0.50 83:0.56 86:0.80 91:0.70 96:0.95 97:0.89 98:0.71 99:0.83 100:0.81 101:0.65 104:0.84 106:0.81 108:0.10 111:0.78 114:0.82 117:0.86 120:0.91 122:0.55 123:0.10 124:0.67 126:0.32 127:0.64 129:0.59 131:0.86 133:0.64 135:0.21 139:0.75 140:0.45 144:0.76 146:0.90 147:0.92 149:0.83 150:0.45 151:0.97 152:0.77 153:0.90 154:0.63 155:0.56 157:1.00 158:0.77 159:0.73 161:0.57 162:0.72 164:0.87 165:0.65 166:0.80 167:0.73 169:0.79 170:0.77 172:0.83 173:0.12 174:0.89 176:0.63 177:0.88 179:0.26 181:0.62 182:0.98 186:0.79 187:0.39 190:0.89 191:0.79 192:0.59 197:0.90 201:0.57 202:0.80 204:0.80 206:0.81 208:0.50 212:0.78 215:0.72 216:0.95 222:0.35 227:0.85 229:0.97 230:0.73 231:0.81 232:0.88 233:0.76 235:0.42 239:0.90 241:0.56 242:0.39 243:0.58 245:0.93 246:0.87 247:0.96 248:0.95 253:0.96 254:0.93 255:0.73 256:0.82 259:0.21 260:0.91 261:0.77 265:0.72 266:0.75 267:0.82 268:0.83 271:0.74 276:0.84 279:0.76 283:0.82 285:0.50 287:0.89 290:0.82 297:0.36 300:0.84 +2 1:0.67 6:0.93 7:0.75 8:0.76 9:0.74 10:0.88 11:0.50 12:0.20 17:0.20 20:0.88 21:0.30 23:0.97 27:0.13 28:0.92 29:0.91 30:0.27 31:0.89 32:0.72 34:0.81 36:0.28 37:0.62 39:0.44 43:0.73 45:0.85 62:0.97 66:0.26 69:0.59 70:0.92 71:0.74 74:0.75 75:0.95 77:0.89 78:0.82 79:0.89 81:0.68 83:0.56 91:0.35 96:0.88 97:0.97 98:0.24 99:0.95 100:0.85 101:0.47 102:0.94 104:0.78 106:0.81 108:0.45 111:0.82 114:0.84 117:0.86 120:0.79 122:0.95 123:0.64 124:0.78 126:0.51 127:0.93 128:0.97 129:0.59 131:0.93 133:0.73 135:0.92 137:0.89 139:0.58 140:0.80 144:0.57 145:0.77 146:0.34 147:0.41 149:0.60 150:0.58 151:0.76 152:0.85 153:0.82 154:0.63 155:0.56 157:0.90 158:0.81 159:0.93 161:0.57 162:0.83 164:0.73 165:0.65 166:0.84 167:0.71 168:0.97 169:0.83 170:0.77 172:0.51 173:0.22 174:0.94 175:0.75 176:0.70 177:0.70 179:0.55 181:0.62 182:0.93 186:0.82 187:0.68 189:0.95 190:0.89 191:0.90 192:0.87 193:0.95 195:0.98 196:0.87 197:0.94 201:0.67 202:0.64 204:0.84 205:0.97 206:0.81 208:0.50 212:0.69 215:0.72 216:0.70 219:0.87 222:0.35 226:0.96 227:0.91 229:0.97 230:0.77 231:0.84 232:0.83 233:0.66 235:0.74 236:0.91 238:0.89 239:0.89 241:0.47 242:0.24 243:0.45 244:0.61 245:0.76 246:0.87 247:0.82 248:0.72 251:1.00 253:0.89 255:0.77 256:0.68 257:0.65 259:0.21 260:0.80 261:0.85 265:0.15 266:0.75 267:0.69 268:0.70 271:0.74 276:0.38 277:0.69 279:0.79 282:0.80 283:0.82 284:0.87 285:0.62 287:0.89 290:0.84 292:0.88 297:0.36 300:0.84 +1 1:0.61 6:0.86 8:0.72 9:0.74 10:0.90 11:0.83 12:0.20 17:0.73 18:0.79 20:0.88 21:0.13 23:0.95 27:0.25 28:0.92 29:0.91 30:0.66 34:0.22 36:0.38 37:0.87 39:0.44 43:0.61 45:0.98 62:0.96 66:0.30 69:0.12 70:0.70 71:0.74 74:0.86 78:0.79 81:0.62 83:0.56 85:0.72 86:0.74 91:0.53 96:0.87 97:0.97 98:0.57 99:0.83 100:0.80 101:0.96 104:0.80 106:0.81 108:0.81 111:0.78 114:0.92 117:0.86 120:0.78 122:0.55 123:0.80 124:0.78 126:0.51 127:0.78 128:0.96 129:0.81 131:0.89 133:0.74 135:0.98 139:0.71 140:0.80 144:0.76 146:0.87 147:0.89 149:0.91 150:0.53 151:0.68 152:0.84 153:0.78 154:0.51 155:0.56 157:1.00 158:0.77 159:0.83 161:0.57 162:0.86 164:0.73 165:0.65 166:0.84 167:0.66 168:0.96 169:0.78 170:0.77 172:0.86 173:0.09 174:0.94 176:0.70 177:0.88 179:0.19 181:0.62 182:0.97 186:0.80 187:0.21 189:0.88 190:0.89 191:0.70 192:0.86 197:0.95 201:0.63 202:0.59 205:0.95 206:0.81 208:0.50 212:0.59 215:0.84 216:0.44 222:0.35 227:0.87 229:0.97 231:0.83 232:0.85 235:0.31 236:0.91 238:0.84 239:0.88 241:0.30 242:0.32 243:0.39 245:0.96 246:0.87 247:0.97 248:0.64 253:0.90 255:0.73 256:0.64 259:0.21 260:0.79 261:0.81 265:0.58 266:0.75 267:0.91 268:0.67 271:0.74 276:0.92 279:0.81 283:0.82 285:0.60 287:0.89 290:0.92 297:0.36 300:0.84 +2 1:0.62 6:0.93 7:0.75 8:0.86 9:0.74 10:0.86 11:0.50 12:0.20 17:0.24 18:0.69 20:0.83 21:0.05 23:0.99 27:0.18 28:0.92 29:0.91 30:0.73 32:0.72 34:0.64 36:0.23 37:0.46 39:0.44 43:0.76 45:0.96 62:0.97 66:0.18 69:0.19 70:0.68 71:0.74 74:0.85 77:0.89 78:0.84 81:0.65 83:0.56 86:0.62 91:0.64 96:0.93 97:0.89 98:0.54 99:0.83 100:0.87 101:0.47 102:0.94 104:0.77 106:0.81 108:0.88 111:0.83 114:0.95 117:0.86 120:0.88 122:0.82 123:0.69 124:0.83 126:0.51 127:0.84 128:0.99 129:0.59 131:0.89 133:0.81 135:0.96 139:0.60 140:0.80 144:0.57 145:0.77 146:0.65 147:0.70 149:0.89 150:0.56 151:0.84 152:0.79 153:0.72 154:0.18 155:0.56 157:0.97 158:0.77 159:0.76 161:0.57 162:0.86 164:0.82 165:0.65 166:0.84 167:0.68 168:0.99 169:0.84 170:0.77 172:0.75 173:0.14 174:0.90 176:0.70 177:0.88 179:0.29 181:0.62 182:0.93 186:0.84 187:0.87 189:0.94 190:0.89 191:0.70 192:0.87 193:0.95 195:0.87 197:0.91 201:0.64 202:0.76 204:0.84 205:0.99 206:0.81 208:0.50 212:0.61 215:0.91 216:0.75 220:0.62 222:0.35 227:0.87 229:0.97 230:0.77 231:0.81 232:0.83 233:0.66 235:0.22 236:0.91 238:0.88 239:0.87 241:0.37 242:0.41 243:0.31 245:0.90 246:0.87 247:0.91 248:0.85 251:1.00 253:0.95 254:0.99 255:0.78 256:0.87 259:0.21 260:0.89 261:0.82 265:0.49 266:0.80 267:0.59 268:0.84 271:0.74 276:0.85 277:0.87 279:0.76 282:0.80 283:0.82 285:0.60 287:0.89 290:0.95 297:0.36 300:0.84 +1 1:0.65 10:0.80 11:0.50 12:0.20 17:0.20 18:0.71 21:0.22 27:0.45 28:0.92 29:0.91 30:0.60 32:0.71 34:0.75 36:0.20 37:0.50 39:0.44 43:0.57 44:0.86 45:0.83 64:0.77 66:0.54 69:0.68 70:0.67 71:0.74 74:0.67 77:0.70 81:0.62 83:0.56 86:0.64 91:0.14 97:0.89 98:0.33 99:0.83 101:0.47 108:0.52 114:0.88 122:0.55 123:0.50 124:0.67 126:0.51 127:0.43 129:0.05 131:0.61 133:0.73 135:0.68 139:0.67 144:0.57 145:0.47 146:0.50 147:0.57 149:0.45 150:0.55 154:0.61 155:0.50 157:0.93 158:0.81 159:0.62 161:0.57 165:0.65 166:0.86 167:0.69 170:0.77 172:0.48 173:0.60 174:0.79 175:0.75 176:0.70 177:0.70 178:0.55 179:0.70 181:0.62 182:0.93 187:0.68 192:0.49 195:0.82 197:0.82 201:0.63 208:0.61 212:0.42 215:0.79 216:0.77 219:0.88 222:0.35 227:0.61 229:0.61 231:0.80 235:0.42 239:0.80 241:0.41 242:0.19 243:0.63 244:0.61 245:0.53 246:0.87 247:0.67 253:0.65 254:0.74 257:0.65 259:0.21 265:0.35 266:0.63 267:0.23 271:0.74 276:0.43 277:0.69 279:0.78 282:0.80 283:0.67 287:0.61 290:0.88 292:0.88 297:0.36 300:0.55 +4 1:0.62 6:0.97 7:0.70 8:0.95 9:0.80 10:0.86 11:0.50 12:0.20 17:0.06 18:0.87 20:0.95 21:0.13 23:0.99 27:0.07 28:0.92 29:0.91 30:0.44 31:1.00 33:0.97 34:0.44 36:0.43 37:0.93 39:0.44 41:0.82 43:0.57 45:0.94 62:0.97 66:0.17 69:0.21 70:0.92 71:0.74 74:0.68 78:0.96 79:1.00 81:0.54 83:0.91 86:0.67 91:0.80 96:0.99 97:1.00 98:0.35 99:0.83 100:1.00 101:0.47 104:0.92 106:0.81 108:0.89 111:0.99 114:0.88 117:0.86 120:0.97 122:0.55 123:0.16 124:0.87 126:0.32 127:0.98 128:1.00 129:0.59 131:0.93 133:0.87 135:0.58 137:1.00 138:0.99 139:0.67 140:0.97 144:0.76 145:0.98 146:0.78 147:0.81 149:0.72 150:0.48 151:0.91 152:0.97 153:0.97 154:0.64 155:0.67 157:0.96 158:0.82 159:0.89 161:0.57 162:0.78 164:0.95 165:0.65 166:0.80 167:0.79 168:1.00 169:0.99 170:0.77 172:0.70 173:0.09 174:0.92 176:0.63 177:0.88 179:0.33 181:0.62 182:1.00 186:0.95 187:0.68 189:0.98 191:0.94 192:0.99 196:0.93 197:0.96 201:0.58 202:0.92 204:0.80 205:0.99 206:0.81 208:0.64 212:0.57 215:0.84 216:0.61 219:0.88 222:0.35 227:0.91 229:1.00 230:0.73 231:0.84 232:0.94 233:0.76 235:0.22 238:0.99 239:0.85 241:0.68 242:0.28 243:0.38 245:0.86 246:0.87 247:0.92 248:0.94 253:0.99 254:0.97 255:1.00 256:0.95 259:0.21 260:0.97 261:0.98 265:0.25 266:0.92 267:0.28 268:0.95 271:0.74 276:0.81 279:0.82 283:0.82 284:0.99 285:0.62 287:0.98 290:0.88 291:0.97 292:0.95 297:0.36 300:0.94 +1 1:0.58 7:0.75 10:0.84 11:0.50 12:0.20 17:0.43 18:0.78 21:0.47 27:0.34 28:0.92 29:0.91 30:0.73 32:0.67 33:0.97 34:0.63 36:0.41 37:0.46 39:0.44 43:0.61 45:1.00 66:0.95 69:0.19 70:0.42 71:0.74 74:0.98 76:0.85 77:0.70 81:0.47 83:0.56 86:0.68 91:0.03 98:0.92 99:0.83 101:0.47 104:0.82 106:0.81 108:0.15 114:0.76 117:0.86 122:0.55 123:0.15 124:0.37 126:0.32 127:0.94 129:0.05 131:0.61 133:0.72 135:0.24 139:0.65 144:0.57 145:0.54 146:0.99 147:0.99 149:0.98 150:0.42 154:0.22 155:0.52 157:0.98 159:0.88 161:0.57 165:0.65 166:0.80 167:0.63 170:0.77 172:0.99 173:0.09 174:0.92 176:0.63 177:0.88 178:0.55 179:0.11 181:0.62 182:0.93 187:0.98 192:0.56 193:0.95 195:0.96 197:0.92 201:0.55 204:0.82 206:0.81 208:0.50 212:0.92 215:0.63 216:0.53 222:0.35 227:0.61 229:0.61 230:0.77 231:0.81 232:0.87 233:0.66 235:0.60 239:0.85 241:0.15 242:0.57 243:0.95 244:0.61 245:0.91 246:0.87 247:0.82 253:0.72 254:0.74 259:0.21 265:0.92 266:0.71 267:0.88 271:0.74 276:0.98 282:0.75 287:0.61 290:0.76 291:0.97 297:0.36 300:0.84 +2 6:0.97 7:0.70 8:0.86 9:0.73 10:0.86 11:0.50 12:0.20 17:0.20 18:0.87 20:0.90 21:0.30 23:0.95 27:0.07 28:0.92 29:0.91 30:0.13 31:0.93 33:0.97 34:0.59 36:0.78 37:0.93 39:0.44 43:0.57 45:0.81 55:0.84 62:0.97 66:0.43 69:0.23 70:0.97 71:0.74 74:0.56 78:0.87 79:0.93 81:0.28 83:0.69 86:0.67 91:0.23 97:0.94 98:0.52 100:0.92 101:0.47 104:0.92 106:0.81 108:0.18 111:0.88 117:0.86 122:0.92 123:0.18 124:0.77 126:0.24 127:0.85 128:0.98 129:0.59 131:0.82 133:0.74 135:0.86 137:0.93 139:0.71 140:0.45 144:0.76 146:0.78 147:0.81 149:0.48 150:0.30 151:0.85 152:0.97 153:0.48 154:0.64 155:0.57 157:0.91 158:0.77 159:0.77 161:0.57 162:0.86 166:0.72 167:0.70 168:0.98 169:0.99 170:0.77 172:0.63 173:0.15 174:0.93 177:0.88 179:0.51 181:0.62 182:1.00 186:0.87 187:0.68 189:0.94 190:0.89 191:0.86 192:0.56 193:0.98 196:0.91 197:0.96 202:0.36 204:0.83 205:0.95 206:0.81 208:0.61 212:0.48 216:0.61 222:0.35 227:0.85 229:0.92 230:0.73 231:0.77 232:0.94 233:0.76 235:0.31 236:0.91 238:0.93 239:0.88 241:0.46 242:0.33 243:0.57 245:0.77 246:0.61 247:0.90 248:0.77 253:0.44 254:0.97 255:0.72 256:0.45 259:0.21 261:0.98 265:0.54 266:0.84 267:0.69 268:0.46 271:0.74 276:0.67 279:0.78 281:0.91 283:0.82 284:0.88 285:0.50 287:0.92 291:0.97 300:0.84 +1 8:0.72 11:0.75 12:0.37 17:0.34 18:0.92 21:0.30 27:0.20 29:0.56 30:0.32 34:0.45 36:0.93 37:0.58 39:0.39 43:0.16 45:0.66 55:0.52 66:0.35 69:0.78 70:0.84 71:0.65 74:0.42 77:0.89 81:0.34 86:0.73 91:0.56 96:0.70 98:0.59 101:0.52 108:0.61 114:0.59 122:0.86 123:0.59 124:0.68 126:0.26 127:0.46 129:0.59 131:0.88 133:0.61 135:0.89 139:0.70 140:0.45 144:0.57 145:0.74 146:0.78 147:0.38 149:0.29 150:0.30 151:0.48 153:0.71 154:0.62 157:0.61 158:0.71 159:0.63 162:0.67 166:0.82 172:0.67 173:0.72 176:0.55 177:0.05 179:0.36 182:0.61 187:0.68 190:0.89 195:0.81 202:0.60 212:0.76 216:0.68 220:0.87 222:0.76 227:0.92 229:0.61 231:0.90 235:0.31 241:0.35 242:0.44 243:0.15 245:0.85 246:0.61 247:0.86 248:0.48 253:0.43 254:0.90 256:0.58 257:0.84 259:0.21 265:0.60 266:0.87 267:0.79 268:0.62 271:0.37 276:0.75 282:0.71 285:0.47 287:0.61 290:0.59 300:0.84 +1 11:0.42 12:0.37 17:0.28 18:0.62 21:0.78 27:0.45 29:0.56 30:0.76 34:0.75 36:0.18 37:0.50 39:0.39 43:0.10 55:0.84 66:0.54 69:0.81 70:0.22 71:0.65 74:0.35 86:0.67 91:0.22 98:0.25 108:0.65 122:0.75 123:0.63 124:0.45 127:0.15 129:0.05 131:0.61 133:0.37 135:0.90 139:0.65 144:0.57 145:0.49 146:0.43 147:0.49 149:0.09 154:0.46 157:0.61 159:0.29 163:0.87 166:0.61 172:0.21 173:0.72 177:0.70 178:0.55 179:0.63 182:0.61 187:0.68 212:0.42 216:0.77 222:0.76 227:0.61 229:0.61 231:0.71 235:0.31 241:0.43 242:0.45 243:0.63 244:0.61 245:0.40 246:0.61 247:0.35 253:0.29 254:0.80 259:0.21 265:0.27 266:0.23 267:0.89 271:0.37 276:0.21 287:0.61 300:0.18 +0 8:0.74 11:0.75 12:0.37 17:0.92 18:0.97 21:0.22 22:0.93 27:0.50 29:0.56 30:0.28 31:0.89 34:0.94 36:0.25 37:0.37 39:0.39 43:0.49 44:0.90 45:0.88 47:0.93 48:0.99 55:0.45 64:0.77 66:0.67 69:0.97 70:0.93 71:0.65 74:0.71 77:0.89 79:0.93 81:0.36 86:0.91 91:0.40 98:0.47 101:0.52 108:0.74 122:0.86 123:0.72 124:0.71 126:0.26 127:0.80 129:0.05 131:0.86 133:0.86 135:0.69 137:0.89 139:0.91 140:0.45 144:0.57 145:0.61 146:0.28 147:0.33 149:0.70 150:0.32 151:0.60 153:0.48 154:0.38 155:0.58 157:0.93 159:0.83 162:0.86 166:0.80 172:0.87 173:0.99 177:0.38 179:0.31 182:0.86 187:0.21 190:0.89 192:0.51 195:0.93 196:0.92 202:0.53 204:0.85 208:0.54 212:0.82 216:0.40 219:0.87 220:0.96 222:0.76 227:0.90 229:0.61 231:0.90 235:0.22 241:0.64 242:0.28 243:0.09 244:0.61 245:0.79 246:0.61 247:0.82 248:0.59 253:0.41 256:0.58 257:0.65 259:0.21 262:0.93 265:0.49 266:0.89 267:0.23 268:0.62 271:0.37 276:0.80 277:0.69 281:0.91 282:0.77 284:0.86 285:0.47 287:0.61 292:0.95 300:0.94 +0 8:0.65 11:0.64 12:0.37 17:0.55 18:0.95 21:0.78 27:0.57 29:0.56 30:0.53 34:0.80 36:0.63 37:0.60 39:0.39 43:0.56 45:0.66 55:0.59 66:0.74 69:0.75 70:0.81 71:0.65 74:0.30 86:0.65 91:0.48 96:0.85 98:0.73 101:0.52 108:0.59 122:0.86 123:0.56 124:0.44 127:0.48 129:0.05 131:0.88 133:0.59 135:0.81 139:0.63 140:0.45 146:0.89 147:0.91 149:0.40 151:0.60 153:0.48 154:0.52 157:0.61 159:0.65 162:0.52 166:0.61 172:0.81 173:0.67 177:0.70 179:0.38 182:0.61 187:0.68 190:0.89 191:0.81 202:0.78 212:0.71 216:0.58 220:0.87 222:0.76 227:0.92 229:0.61 231:0.90 232:0.92 235:0.05 241:0.37 242:0.45 243:0.77 245:0.75 246:0.61 247:0.88 248:0.62 253:0.51 254:0.94 256:0.71 259:0.21 265:0.73 266:0.63 267:0.92 268:0.74 271:0.37 276:0.74 285:0.47 287:0.61 300:0.84 +2 11:0.75 12:0.37 17:0.77 18:0.84 21:0.22 27:0.34 29:0.56 30:0.60 34:0.74 36:0.44 37:0.64 39:0.39 43:0.59 45:0.91 64:0.77 66:0.74 69:0.78 70:0.68 71:0.65 74:0.43 81:0.36 86:0.72 91:0.27 98:0.49 101:0.52 108:0.62 122:0.75 123:0.60 124:0.43 126:0.26 127:0.41 129:0.05 131:0.61 133:0.59 135:0.66 139:0.70 144:0.57 146:0.59 147:0.05 149:0.76 150:0.32 154:0.65 157:0.61 159:0.50 166:0.80 172:0.77 173:0.78 177:0.05 178:0.55 179:0.41 182:0.61 187:0.68 212:0.88 216:0.32 219:0.87 222:0.76 227:0.61 229:0.61 231:0.89 235:0.22 241:0.39 242:0.45 243:0.08 245:0.70 246:0.61 247:0.76 253:0.33 254:0.74 257:0.84 259:0.21 265:0.50 266:0.47 267:0.76 271:0.37 276:0.67 287:0.61 292:0.91 300:0.84 +1 12:0.37 17:0.43 18:0.68 21:0.54 27:0.45 29:0.56 30:0.21 34:0.93 36:0.27 37:0.50 39:0.39 43:0.13 55:0.52 66:0.18 69:0.70 70:0.67 71:0.65 74:0.29 81:0.26 86:0.58 91:0.15 98:0.35 108:0.53 114:0.56 122:0.77 123:0.51 124:0.75 126:0.26 127:0.37 129:0.59 131:0.61 133:0.64 135:0.65 139:0.57 145:0.40 146:0.48 147:0.55 149:0.18 150:0.25 154:0.10 157:0.61 158:0.71 159:0.53 163:0.92 166:0.82 172:0.16 173:0.65 175:0.75 176:0.55 177:0.38 178:0.55 179:0.82 182:0.61 187:0.68 212:0.37 216:0.77 222:0.76 227:0.61 229:0.61 231:0.83 235:0.60 241:0.39 242:0.40 243:0.39 244:0.83 245:0.52 246:0.61 247:0.55 253:0.38 257:0.65 259:0.21 265:0.37 266:0.47 267:0.52 271:0.37 276:0.32 277:0.69 287:0.61 290:0.56 300:0.43 +2 1:0.74 11:0.75 12:0.37 17:0.85 18:0.82 21:0.76 27:0.58 29:0.56 30:0.38 34:1.00 36:0.40 37:0.58 39:0.39 43:0.68 45:0.77 64:0.77 66:0.48 69:0.54 70:0.92 71:0.65 74:0.39 81:0.40 83:0.60 86:0.71 91:0.18 98:0.43 99:0.83 101:0.52 108:0.41 114:0.54 122:0.82 123:0.40 124:0.65 126:0.54 127:0.53 129:0.59 131:0.61 133:0.61 135:0.80 139:0.69 144:0.57 146:0.41 147:0.48 149:0.36 150:0.65 154:0.74 155:0.67 157:0.61 159:0.39 163:0.75 165:0.70 166:0.94 172:0.37 173:0.61 176:0.73 177:0.70 178:0.55 179:0.81 182:0.61 187:0.39 192:0.87 201:0.93 208:0.64 212:0.28 215:0.36 216:0.68 222:0.76 227:0.61 229:0.61 231:0.90 235:0.98 241:0.12 242:0.29 243:0.60 245:0.53 246:0.61 247:0.60 253:0.34 254:0.80 259:0.21 265:0.45 266:0.54 267:0.87 271:0.37 276:0.36 287:0.61 290:0.54 297:0.36 300:0.70 +0 1:0.69 8:0.65 9:0.76 11:0.75 12:0.37 17:0.93 18:0.97 21:0.22 22:0.93 27:0.50 29:0.56 30:0.40 31:0.95 34:0.71 36:0.19 37:0.37 39:0.39 43:0.56 44:0.90 45:0.96 47:0.93 64:0.77 66:0.77 69:0.97 70:0.77 71:0.65 74:0.87 77:0.70 79:0.96 81:0.56 83:0.60 86:0.97 91:0.29 96:0.87 97:0.89 98:0.65 99:0.83 101:0.52 108:0.74 114:0.67 117:0.86 122:0.80 123:0.72 124:0.51 126:0.44 127:0.85 129:0.05 131:0.96 133:0.86 135:0.72 137:0.95 139:0.97 140:0.45 144:0.57 145:0.67 146:0.28 147:0.33 149:0.88 150:0.59 151:0.81 153:0.48 154:0.44 155:0.58 157:0.97 158:0.79 159:0.87 162:0.86 165:0.70 166:0.94 172:0.95 173:0.99 176:0.66 177:0.38 179:0.19 182:0.92 187:0.21 190:0.77 192:0.51 195:0.95 196:0.98 201:0.68 202:0.50 204:0.85 208:0.54 212:0.88 216:0.40 219:0.92 222:0.76 227:0.97 229:0.96 231:0.91 232:0.83 235:0.31 241:0.65 242:0.45 243:0.07 244:0.61 245:0.86 246:0.95 247:0.84 248:0.82 253:0.87 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.66 266:0.89 267:0.23 268:0.59 271:0.37 276:0.91 277:0.69 279:0.82 281:0.91 282:0.77 284:0.91 285:0.56 287:0.96 290:0.67 292:0.95 300:0.94 +2 1:0.74 11:0.75 12:0.37 17:0.77 18:0.84 21:0.54 27:0.34 29:0.56 30:0.11 34:0.99 36:0.27 37:0.64 39:0.39 43:0.15 45:0.73 64:0.77 66:0.13 69:0.79 70:0.69 71:0.65 74:0.48 81:0.47 83:0.60 86:0.79 91:0.11 98:0.48 99:0.83 101:0.52 108:0.62 114:0.59 122:0.57 123:0.68 124:0.80 126:0.54 127:0.62 129:0.59 131:0.61 133:0.73 135:0.44 139:0.75 144:0.57 146:0.22 147:0.05 149:0.11 150:0.67 154:0.69 155:0.67 157:0.61 159:0.48 163:0.96 165:0.70 166:0.94 172:0.21 173:0.83 176:0.73 177:0.05 178:0.55 179:0.83 182:0.61 187:0.68 192:0.87 201:0.93 208:0.64 212:0.28 215:0.39 216:0.32 222:0.76 227:0.61 229:0.61 231:0.90 235:0.74 241:0.39 242:0.29 243:0.16 245:0.52 246:0.61 247:0.47 253:0.42 254:0.74 257:0.84 259:0.21 265:0.18 266:0.54 267:0.73 271:0.37 276:0.40 277:0.87 287:0.61 290:0.59 297:0.36 300:0.43 +2 11:0.75 12:0.37 17:0.78 18:0.84 21:0.22 27:0.34 29:0.56 30:0.53 34:0.73 36:0.41 37:0.64 39:0.39 43:0.58 45:0.90 64:0.77 66:0.74 69:0.78 70:0.62 71:0.65 74:0.43 81:0.36 86:0.72 91:0.29 98:0.51 101:0.52 108:0.62 122:0.75 123:0.59 124:0.43 126:0.26 127:0.41 129:0.05 131:0.61 133:0.59 135:0.66 139:0.70 144:0.57 146:0.62 147:0.05 149:0.74 150:0.32 154:0.65 157:0.61 159:0.50 166:0.80 172:0.77 173:0.78 177:0.05 178:0.55 179:0.41 182:0.61 187:0.68 212:0.88 216:0.32 219:0.87 222:0.76 227:0.61 229:0.61 231:0.89 235:0.22 241:0.39 242:0.40 243:0.08 245:0.70 246:0.61 247:0.84 253:0.32 254:0.74 257:0.84 259:0.21 265:0.53 266:0.47 267:0.76 271:0.37 276:0.68 287:0.61 292:0.91 300:0.84 +1 8:0.67 11:0.64 12:0.37 17:0.47 18:0.95 21:0.78 27:0.57 29:0.56 30:0.53 34:0.80 36:0.61 37:0.60 39:0.39 43:0.56 45:0.66 55:0.59 66:0.74 69:0.75 70:0.81 71:0.65 74:0.30 86:0.65 91:0.58 96:0.78 98:0.73 101:0.52 108:0.59 122:0.86 123:0.56 124:0.44 127:0.48 129:0.05 131:0.88 133:0.59 135:0.77 139:0.63 140:0.45 146:0.89 147:0.91 149:0.40 151:0.55 153:0.48 154:0.52 157:0.61 159:0.65 162:0.61 166:0.61 172:0.81 173:0.67 177:0.70 179:0.38 182:0.61 187:0.68 190:0.89 191:0.84 202:0.78 212:0.71 216:0.58 220:0.91 222:0.76 227:0.92 229:0.61 231:0.90 232:0.92 235:0.05 241:0.46 242:0.45 243:0.77 245:0.75 246:0.61 247:0.88 248:0.57 253:0.45 254:0.94 256:0.77 259:0.21 265:0.73 266:0.63 267:0.92 268:0.79 271:0.37 276:0.74 285:0.47 287:0.61 300:0.84 +0 1:0.69 8:0.72 9:0.76 11:0.75 12:0.37 17:0.88 18:0.97 21:0.22 22:0.93 27:0.50 29:0.56 30:0.40 31:0.96 34:0.66 36:0.20 37:0.37 39:0.39 43:0.56 44:0.90 45:0.96 47:0.93 64:0.77 66:0.77 69:0.97 70:0.77 71:0.65 74:0.87 77:0.70 79:0.96 81:0.56 83:0.60 86:0.97 91:0.38 96:0.89 97:0.97 98:0.65 99:0.83 101:0.52 108:0.74 114:0.67 117:0.86 122:0.80 123:0.72 124:0.51 126:0.44 127:0.85 129:0.05 131:0.97 133:0.86 135:0.73 137:0.96 139:0.97 140:0.45 144:0.57 145:0.67 146:0.28 147:0.33 149:0.88 150:0.59 151:0.89 153:0.78 154:0.44 155:0.58 157:0.97 158:0.79 159:0.87 162:0.83 165:0.70 166:0.94 172:0.95 173:0.99 176:0.66 177:0.38 179:0.19 182:0.92 187:0.21 190:0.77 192:0.51 195:0.95 196:0.98 201:0.68 202:0.65 204:0.85 208:0.54 212:0.88 216:0.40 219:0.92 222:0.76 227:0.97 229:0.96 231:0.91 232:0.83 235:0.31 241:0.67 242:0.45 243:0.07 244:0.61 245:0.86 246:0.95 247:0.84 248:0.85 253:0.89 255:0.74 256:0.68 257:0.65 259:0.21 262:0.93 265:0.66 266:0.89 267:0.23 268:0.71 271:0.37 276:0.91 277:0.69 279:0.82 281:0.91 282:0.77 284:0.94 285:0.56 287:0.96 290:0.67 292:0.95 300:0.94 +0 1:0.69 11:0.75 12:0.37 17:0.96 18:0.92 21:0.59 22:0.93 27:0.50 29:0.56 30:0.15 34:0.90 36:0.20 37:0.37 39:0.39 43:0.38 44:0.90 45:0.77 47:0.93 55:0.42 64:0.77 66:0.59 69:0.97 70:0.91 71:0.65 74:0.71 77:0.70 81:0.34 83:0.60 86:0.89 91:0.29 98:0.43 99:0.83 101:0.52 108:0.82 114:0.57 117:0.86 122:0.85 123:0.81 124:0.78 126:0.44 127:0.87 129:0.05 131:0.61 133:0.87 135:0.51 139:0.89 144:0.57 145:0.47 146:0.28 147:0.35 149:0.39 150:0.52 154:0.28 155:0.58 157:0.85 159:0.85 165:0.70 166:0.94 172:0.82 173:1.00 176:0.66 177:0.38 178:0.55 179:0.37 182:0.91 187:0.39 192:0.51 195:0.94 201:0.68 204:0.85 208:0.54 212:0.78 216:0.40 222:0.76 227:0.61 229:0.61 231:0.90 232:0.94 235:0.74 241:0.57 242:0.45 243:0.13 244:0.61 245:0.79 246:0.94 247:0.82 253:0.44 257:0.65 259:0.21 262:0.93 265:0.45 266:0.91 267:0.52 271:0.37 276:0.77 277:0.69 281:0.91 282:0.77 287:0.61 290:0.57 300:0.94 +1 8:0.77 11:0.75 12:0.37 17:0.73 18:0.80 21:0.78 27:0.53 29:0.56 30:0.37 34:0.69 36:0.19 37:0.31 39:0.39 43:0.59 45:0.66 55:0.45 66:0.68 69:0.73 70:0.76 71:0.65 74:0.59 86:0.81 91:0.71 98:0.52 101:0.52 108:0.56 120:0.69 122:0.77 123:0.54 124:0.43 127:0.32 129:0.59 131:0.61 133:0.47 135:0.71 139:0.77 140:0.80 144:0.57 145:0.67 146:0.53 147:0.59 149:0.30 151:0.60 153:0.48 154:0.51 157:0.79 159:0.36 162:0.51 164:0.53 166:0.61 172:0.61 173:0.79 175:0.75 177:0.38 178:0.42 179:0.54 182:0.74 187:0.39 190:0.89 191:0.75 202:0.60 212:0.75 216:0.35 222:0.76 227:0.61 229:0.61 231:0.86 235:0.31 241:0.59 242:0.53 243:0.72 244:0.61 245:0.68 246:0.61 247:0.47 248:0.59 253:0.38 256:0.45 257:0.65 259:0.21 260:0.66 265:0.54 266:0.54 267:0.23 268:0.46 271:0.37 276:0.53 277:0.69 285:0.47 287:0.61 300:0.55 +1 1:0.69 8:0.66 9:0.76 11:0.75 12:0.37 17:0.95 18:0.89 22:0.93 27:0.57 29:0.56 30:0.21 31:0.93 34:0.90 36:0.22 37:0.43 39:0.39 43:0.26 44:0.90 45:0.88 47:0.93 48:0.97 55:0.59 64:0.77 66:0.86 69:0.93 70:0.63 71:0.65 74:0.74 77:0.70 79:0.93 81:0.83 83:0.60 86:0.89 91:0.27 96:0.80 97:0.94 98:0.67 99:0.83 101:0.52 108:0.86 114:0.95 117:0.86 122:0.80 123:0.86 124:0.37 126:0.44 127:0.61 129:0.05 131:0.96 133:0.62 135:0.37 137:0.93 139:0.91 140:0.45 144:0.57 145:0.54 146:0.89 147:0.05 149:0.62 150:0.80 151:0.81 153:0.48 154:0.19 155:0.58 157:0.94 158:0.79 159:0.75 162:0.62 165:0.70 166:0.94 172:0.88 173:0.92 175:0.75 176:0.66 177:0.05 179:0.33 182:0.92 187:0.39 190:0.77 192:0.51 195:0.89 196:0.95 201:0.68 202:0.50 204:0.85 208:0.54 212:0.87 216:0.15 219:0.92 222:0.76 227:0.97 229:0.96 231:0.91 232:0.85 235:0.05 241:0.30 242:0.52 243:0.07 244:0.61 245:0.67 246:0.95 247:0.67 248:0.73 253:0.81 255:0.74 256:0.45 257:0.84 259:0.21 262:0.93 265:0.67 266:0.54 267:0.79 268:0.46 271:0.37 276:0.77 277:0.69 279:0.82 281:0.91 282:0.77 284:0.87 285:0.56 287:0.96 290:0.95 292:0.95 300:0.55 +4 1:0.74 7:0.81 11:0.64 12:0.79 17:0.88 20:0.88 27:0.72 28:0.56 30:0.95 32:0.80 34:0.56 36:0.32 39:0.80 43:0.87 46:0.97 66:0.54 69:0.56 74:0.66 76:0.94 77:0.89 78:0.79 81:0.97 83:0.80 85:0.72 91:0.79 98:0.14 100:0.81 101:0.76 104:0.58 107:0.91 108:0.43 110:0.94 111:0.78 114:0.98 121:0.99 123:0.41 125:0.88 126:0.54 127:0.09 129:0.05 131:0.61 135:0.20 144:0.57 145:0.92 146:0.13 147:0.18 149:0.49 150:0.99 152:0.82 154:0.85 155:0.67 157:0.81 159:0.08 160:0.88 161:0.68 165:0.92 166:0.97 167:0.94 169:0.79 172:0.10 173:0.85 176:0.73 177:0.38 178:0.55 179:0.14 181:0.60 182:0.94 186:0.80 192:0.59 195:0.55 201:0.74 204:0.89 208:0.64 215:0.96 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 232:0.80 233:0.76 235:0.05 241:0.80 243:0.63 246:0.94 253:0.75 261:0.80 265:0.15 267:0.69 282:0.88 287:0.61 289:0.99 290:0.98 297:0.36 299:0.88 300:0.08 +0 12:0.79 17:0.51 21:0.78 27:0.45 28:0.56 30:0.95 34:0.89 36:0.21 37:0.50 39:0.80 43:0.26 46:0.88 55:0.71 66:0.54 69:0.80 74:0.38 91:0.15 98:0.19 107:0.90 108:0.64 110:0.93 123:0.61 125:0.88 127:0.10 129:0.05 131:0.61 135:0.66 145:0.42 146:0.27 147:0.33 149:0.17 154:0.18 157:0.61 159:0.12 160:0.88 161:0.68 166:0.61 167:0.60 172:0.10 173:0.80 177:0.38 178:0.55 179:0.46 181:0.60 182:0.61 187:0.68 216:0.77 222:0.35 227:0.61 229:0.61 231:0.69 235:0.60 241:0.05 243:0.63 244:0.61 246:0.61 253:0.34 259:0.21 265:0.20 267:0.79 287:0.61 289:0.90 300:0.08 +1 1:0.74 11:0.64 12:0.79 17:0.79 27:0.72 28:0.56 30:0.76 34:0.49 36:0.49 37:0.36 39:0.80 43:0.93 46:1.00 60:0.95 66:0.77 69:0.25 70:0.21 74:0.77 81:0.97 83:0.89 85:0.85 91:0.48 98:0.39 101:0.83 107:0.96 108:0.20 110:0.98 114:0.98 122:0.43 123:0.19 125:0.88 126:0.54 127:0.13 129:0.05 131:0.61 135:0.56 144:0.57 146:0.24 147:0.30 149:0.80 150:0.99 154:0.93 155:0.67 157:0.92 158:0.92 159:0.11 160:0.88 161:0.68 165:0.92 166:0.97 167:0.65 172:0.37 173:0.65 176:0.73 177:0.38 178:0.55 179:0.36 181:0.60 182:0.94 187:0.68 192:0.59 201:0.74 208:0.64 212:0.95 215:0.96 216:0.14 222:0.35 227:0.61 229:0.61 231:0.95 235:0.05 241:0.24 242:0.55 243:0.79 246:0.94 247:0.35 253:0.77 259:0.21 265:0.41 266:0.12 267:0.28 276:0.31 279:0.86 283:0.82 287:0.61 289:0.99 290:0.98 297:0.36 300:0.18 +2 7:0.81 11:0.64 12:0.79 17:0.85 20:0.87 21:0.05 27:0.72 28:0.56 30:0.38 32:0.80 34:0.62 36:0.74 39:0.80 43:0.82 46:0.99 66:0.67 69:0.51 70:0.71 74:0.87 76:0.94 77:0.89 78:0.79 81:0.91 85:0.91 91:0.73 98:0.74 100:0.81 101:0.84 104:0.58 107:0.99 108:0.64 110:1.00 111:0.78 121:0.99 122:0.43 123:0.61 124:0.45 125:0.88 126:0.32 127:0.66 129:0.59 131:0.61 133:0.47 135:0.17 144:0.57 145:0.92 146:0.39 147:0.45 149:0.84 150:0.82 152:0.82 154:0.78 155:0.67 157:0.96 159:0.38 160:0.88 161:0.68 166:0.93 167:0.93 169:0.79 172:0.65 173:0.62 177:0.38 178:0.55 179:0.62 181:0.60 182:0.88 186:0.80 192:0.59 195:0.63 204:0.89 208:0.64 212:0.80 215:0.91 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 233:0.76 235:0.05 241:0.79 242:0.27 243:0.28 245:0.73 246:0.61 247:0.67 253:0.62 259:0.21 261:0.80 265:0.74 266:0.38 267:0.91 276:0.58 282:0.88 287:0.61 289:0.99 297:0.36 299:0.88 300:0.55 +1 7:0.81 11:0.64 12:0.79 17:0.85 20:0.87 21:0.05 27:0.72 28:0.56 30:0.38 32:0.80 34:0.62 36:0.74 39:0.80 43:0.82 46:0.99 66:0.67 69:0.51 70:0.71 74:0.87 76:0.94 77:0.89 78:0.79 81:0.91 85:0.91 91:0.73 98:0.74 100:0.81 101:0.84 104:0.58 107:0.99 108:0.64 110:1.00 111:0.78 121:0.99 122:0.43 123:0.61 124:0.45 125:0.88 126:0.32 127:0.66 129:0.59 131:0.61 133:0.47 135:0.17 144:0.57 145:0.92 146:0.39 147:0.45 149:0.84 150:0.82 152:0.82 154:0.78 155:0.67 157:0.96 159:0.38 160:0.88 161:0.68 166:0.93 167:0.93 169:0.79 172:0.65 173:0.62 177:0.38 178:0.55 179:0.62 181:0.60 182:0.88 186:0.80 192:0.59 195:0.63 204:0.89 208:0.64 212:0.80 215:0.91 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 233:0.76 235:0.05 241:0.79 242:0.27 243:0.28 245:0.73 246:0.61 247:0.67 253:0.62 259:0.21 261:0.80 265:0.74 266:0.38 267:0.91 276:0.58 282:0.88 287:0.61 289:0.99 297:0.36 299:0.88 300:0.55 +1 1:0.74 9:0.80 11:0.64 12:0.79 17:0.08 20:0.90 21:0.71 27:0.14 28:0.56 30:0.62 34:0.33 36:0.99 37:0.68 39:0.80 41:0.82 43:0.84 46:0.96 66:0.83 69:0.65 70:0.32 74:0.76 78:0.72 81:0.53 83:0.76 85:0.88 91:0.04 96:0.79 98:0.61 100:0.73 101:0.80 107:0.97 108:0.50 110:0.99 111:0.72 114:0.68 120:0.67 122:0.57 123:0.48 125:0.99 126:0.54 127:0.18 129:0.05 131:0.98 135:0.58 140:0.45 144:0.57 146:0.67 147:0.72 149:0.81 150:0.68 151:0.82 152:0.85 153:0.46 154:0.80 155:0.67 157:0.93 159:0.25 160:0.96 161:0.68 162:0.62 164:0.54 165:0.69 166:0.97 167:0.64 169:0.72 172:0.51 173:0.67 176:0.73 177:0.38 179:0.48 181:0.60 182:0.94 186:0.73 187:0.95 192:0.59 201:0.74 202:0.49 208:0.64 212:0.28 215:0.48 216:0.86 222:0.35 227:0.98 229:0.97 231:0.95 235:0.88 241:0.21 242:0.45 243:0.84 246:0.94 247:0.47 248:0.74 253:0.81 255:0.79 256:0.45 259:0.21 260:0.68 261:0.73 265:0.62 266:0.47 267:0.59 268:0.45 276:0.39 285:0.62 287:0.96 289:0.99 290:0.68 297:0.36 300:0.25 +1 1:0.74 7:0.81 11:0.64 12:0.79 17:0.43 20:0.85 21:0.40 27:0.31 28:0.56 30:0.91 32:0.80 34:0.93 36:0.31 37:0.55 39:0.80 43:0.79 46:0.95 60:0.87 66:0.69 69:0.75 70:0.13 74:0.76 77:0.70 78:0.78 81:0.76 83:0.87 85:0.80 91:0.14 98:0.17 100:0.82 101:0.76 104:0.95 106:0.81 107:0.94 108:0.58 110:0.97 111:0.78 114:0.82 117:0.86 121:0.90 122:0.43 123:0.56 125:0.88 126:0.54 127:0.10 129:0.05 131:0.61 135:0.87 144:0.57 145:0.63 146:0.22 147:0.28 149:0.62 150:0.84 152:0.83 154:0.73 155:0.67 157:0.87 158:0.92 159:0.10 160:0.88 161:0.68 165:0.83 166:0.97 167:0.66 169:0.79 172:0.21 173:0.82 176:0.73 177:0.38 178:0.55 179:0.14 181:0.60 182:0.93 186:0.79 187:0.39 192:0.59 195:0.67 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.67 216:0.85 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 232:0.97 233:0.76 235:0.52 241:0.27 242:0.63 243:0.73 246:0.94 247:0.30 253:0.70 259:0.21 261:0.80 265:0.18 266:0.17 267:0.28 276:0.17 279:0.86 282:0.88 283:0.82 287:0.61 289:0.99 290:0.82 297:0.36 299:0.88 300:0.13 +1 1:0.74 11:0.64 12:0.79 17:0.75 20:0.89 21:0.13 27:0.71 28:0.56 30:0.62 34:0.92 36:0.62 37:0.57 39:0.80 43:0.92 46:0.98 66:0.83 69:0.38 70:0.43 74:0.71 78:0.75 81:0.89 83:0.77 85:0.88 91:0.33 98:0.85 100:0.73 101:0.82 104:0.82 106:0.81 107:0.97 108:0.30 110:0.99 111:0.74 114:0.92 117:0.86 122:0.43 123:0.28 125:0.88 126:0.54 127:0.35 129:0.05 131:0.61 135:0.74 144:0.57 145:0.58 146:0.71 147:0.76 149:0.85 150:0.94 152:0.84 154:0.91 155:0.67 157:0.93 159:0.28 160:0.88 161:0.68 165:0.88 166:0.97 167:0.69 169:0.72 172:0.51 173:0.51 176:0.73 177:0.38 178:0.55 179:0.75 181:0.60 182:0.93 186:0.76 187:0.21 192:0.59 201:0.74 206:0.81 212:0.57 215:0.84 216:0.22 222:0.35 227:0.61 229:0.61 231:0.95 232:0.90 235:0.22 241:0.41 242:0.60 243:0.84 246:0.94 247:0.39 253:0.73 259:0.21 261:0.77 265:0.85 266:0.47 267:0.36 276:0.40 287:0.61 289:0.99 290:0.92 297:0.36 300:0.33 +0 12:0.79 17:0.38 21:0.78 27:0.45 28:0.56 30:0.44 34:0.78 36:0.18 37:0.50 39:0.80 43:0.28 46:0.88 55:0.71 66:0.51 69:0.75 70:0.73 74:0.88 91:0.06 98:0.60 107:0.94 108:0.87 110:0.95 122:0.67 123:0.86 124:0.74 125:0.88 127:0.42 129:0.05 131:0.61 133:0.74 135:0.82 145:0.44 146:0.50 147:0.57 149:0.58 154:0.64 157:0.61 159:0.78 160:0.88 161:0.68 163:0.75 166:0.61 167:0.73 172:0.70 173:0.56 177:0.38 178:0.55 179:0.39 181:0.60 182:0.61 187:0.68 212:0.23 216:0.77 222:0.35 227:0.61 229:0.61 231:0.75 235:0.68 241:0.56 242:0.81 243:0.31 245:0.77 246:0.61 247:0.39 253:0.62 254:0.88 259:0.21 265:0.61 266:0.87 267:0.84 276:0.71 277:0.69 287:0.61 289:0.92 300:0.70 +1 7:0.76 8:0.85 11:0.64 12:0.79 17:0.93 21:0.30 27:0.58 28:0.56 30:0.44 34:0.24 36:0.98 37:0.71 39:0.80 43:0.84 46:0.96 55:0.71 66:0.94 69:0.54 70:0.55 74:0.98 76:0.94 77:0.89 81:0.44 85:0.85 91:0.49 96:0.72 98:0.89 101:0.79 107:0.97 108:0.41 110:0.99 114:0.55 122:0.67 123:0.39 125:0.88 126:0.32 127:0.43 129:0.05 131:0.82 135:0.84 140:0.45 144:0.57 145:0.84 146:0.84 147:0.87 149:0.86 150:0.36 151:0.53 153:0.77 154:0.80 157:0.91 158:0.82 159:0.40 160:0.88 161:0.68 162:0.68 166:0.75 167:0.67 172:0.84 173:0.58 176:0.53 177:0.38 179:0.37 181:0.60 182:0.80 187:0.39 190:0.77 195:0.68 202:0.54 212:0.91 216:0.28 220:0.87 222:0.35 227:0.86 229:0.61 230:0.79 231:0.88 232:0.92 233:0.76 235:0.31 241:0.32 242:0.77 243:0.94 246:0.61 247:0.39 248:0.52 253:0.75 256:0.45 259:0.21 265:0.89 266:0.27 267:0.52 268:0.57 276:0.74 282:0.81 285:0.50 287:0.61 289:0.97 290:0.55 300:0.43 +4 1:0.74 7:0.81 8:0.83 11:0.64 12:0.79 17:0.89 20:0.85 27:0.72 28:0.56 30:0.95 32:0.80 34:0.56 36:0.32 39:0.80 43:0.87 46:0.97 66:0.54 69:0.56 74:0.66 76:0.94 77:0.89 78:0.82 81:0.97 83:0.80 85:0.72 91:0.79 98:0.14 100:0.87 101:0.76 104:0.58 107:0.91 108:0.43 110:0.94 111:0.82 114:0.98 121:0.99 123:0.41 125:0.88 126:0.54 127:0.09 129:0.05 131:0.61 135:0.20 144:0.57 145:0.92 146:0.13 147:0.18 149:0.49 150:0.99 152:0.81 154:0.85 155:0.67 157:0.81 159:0.08 160:0.88 161:0.68 165:0.92 166:0.97 167:0.96 169:0.85 172:0.10 173:0.85 176:0.73 177:0.38 178:0.55 179:0.14 181:0.60 182:0.94 186:0.82 192:0.59 195:0.55 201:0.74 204:0.89 208:0.64 215:0.96 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 232:0.80 233:0.76 235:0.05 241:0.85 243:0.63 246:0.94 253:0.75 261:0.82 265:0.15 267:0.69 282:0.88 287:0.61 289:0.99 290:0.98 297:0.36 299:0.88 300:0.08 +1 1:0.74 11:0.64 12:0.79 17:0.43 21:0.54 27:0.56 28:0.56 30:0.73 32:0.80 34:0.93 36:0.24 37:0.60 39:0.80 43:0.86 46:0.97 60:0.87 66:0.63 69:0.60 70:0.47 74:0.87 77:0.70 81:0.67 83:0.86 85:0.91 91:0.12 98:0.61 101:0.82 104:0.91 106:0.81 107:0.98 108:0.46 110:0.99 114:0.77 117:0.86 122:0.57 123:0.44 124:0.46 125:0.88 126:0.54 127:0.30 129:0.59 131:0.61 133:0.47 135:0.66 144:0.57 145:0.69 146:0.67 147:0.72 149:0.88 150:0.78 154:0.83 155:0.67 157:0.95 158:0.92 159:0.39 160:0.88 161:0.68 165:0.79 166:0.97 167:0.63 172:0.51 173:0.60 176:0.73 177:0.38 178:0.55 179:0.62 181:0.60 182:0.93 187:0.21 192:0.59 195:0.68 201:0.74 206:0.81 208:0.64 212:0.30 215:0.58 216:0.76 222:0.35 227:0.61 229:0.61 231:0.95 232:0.95 235:0.68 241:0.15 242:0.58 243:0.69 245:0.63 246:0.94 247:0.39 253:0.71 259:0.21 265:0.62 266:0.75 267:0.28 276:0.45 279:0.86 282:0.88 283:0.82 287:0.61 289:0.99 290:0.77 297:0.36 299:0.88 300:0.43 +1 1:0.74 11:0.64 12:0.79 17:0.57 21:0.30 27:0.20 28:0.56 30:0.86 34:0.83 36:0.40 37:0.94 39:0.80 43:0.77 46:0.95 66:0.33 69:0.79 70:0.32 74:0.62 81:0.81 83:0.75 85:0.91 91:0.27 98:0.19 101:0.77 104:0.58 107:0.97 108:0.83 110:0.99 114:0.86 122:0.43 123:0.82 124:0.78 125:0.88 126:0.54 127:0.22 129:0.89 131:0.61 133:0.78 135:0.73 144:0.57 146:0.13 147:0.18 149:0.75 150:0.87 154:0.69 155:0.67 157:0.94 159:0.59 160:0.88 161:0.68 163:0.81 165:0.84 166:0.97 167:0.57 172:0.32 173:0.64 176:0.73 177:0.38 178:0.55 179:0.56 181:0.60 182:0.93 187:0.39 192:0.59 201:0.74 212:0.39 215:0.72 216:0.29 222:0.35 227:0.61 229:0.61 231:0.95 232:0.80 235:0.42 241:0.01 242:0.63 243:0.25 245:0.54 246:0.94 247:0.30 253:0.67 259:0.21 265:0.21 266:0.54 267:0.52 276:0.36 287:0.61 289:0.99 290:0.86 297:0.36 300:0.33 +4 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 11:0.64 12:0.79 17:0.45 20:0.99 27:0.20 28:0.56 30:0.95 32:0.80 34:0.59 36:0.33 37:0.94 39:0.80 41:0.96 43:0.87 46:0.97 66:0.54 69:0.54 74:0.66 76:0.94 77:0.89 78:0.97 81:0.97 83:0.89 85:0.72 91:0.91 96:0.97 98:0.14 100:1.00 101:0.76 104:0.58 107:0.91 108:0.41 110:0.94 111:1.00 114:0.98 120:0.93 121:0.99 123:0.40 125:0.99 126:0.54 127:0.09 129:0.05 131:0.98 135:0.20 140:0.45 144:0.57 145:0.92 146:0.13 147:0.18 149:0.49 150:0.99 151:0.99 152:0.91 153:0.95 154:0.85 155:0.67 157:0.81 159:0.08 160:0.99 161:0.68 162:0.78 164:0.87 165:0.92 166:0.97 167:0.98 169:1.00 172:0.10 173:0.84 176:0.73 177:0.38 179:0.14 181:0.60 182:0.94 186:0.96 189:0.98 191:0.93 192:0.59 195:0.55 201:0.74 202:0.79 204:0.89 208:0.64 215:0.96 216:0.29 222:0.35 227:0.98 229:0.97 230:0.87 231:0.95 232:0.80 233:0.76 235:0.05 238:1.00 241:0.94 243:0.63 246:0.94 248:0.97 253:0.96 255:0.79 256:0.81 259:0.21 260:0.94 261:1.00 265:0.15 267:0.59 268:0.84 282:0.88 285:0.62 287:0.96 289:0.99 290:0.98 297:0.36 299:0.88 300:0.08 +2 1:0.74 6:0.89 8:0.76 11:0.75 12:0.79 17:0.22 18:0.65 20:0.84 21:0.54 27:0.45 28:0.60 29:0.77 30:0.72 32:0.80 34:0.91 36:0.17 37:0.50 39:0.60 43:0.14 44:0.93 45:0.66 46:0.93 55:0.84 64:0.77 66:0.26 69:0.62 70:0.48 71:0.99 74:0.66 76:0.85 77:0.70 78:0.84 81:0.46 83:0.60 86:0.74 91:0.17 97:0.89 98:0.53 99:0.83 100:0.86 101:0.52 107:0.97 108:0.47 110:0.99 111:0.83 114:0.59 122:0.55 123:0.79 124:0.58 125:0.88 126:0.54 127:0.44 129:0.05 131:0.61 133:0.43 135:0.94 139:0.86 144:0.57 145:0.45 146:0.65 147:0.70 149:0.18 150:0.67 152:0.81 154:0.76 155:0.67 157:0.81 158:0.91 159:0.58 160:0.88 161:0.68 163:0.94 165:0.70 166:0.98 167:0.66 169:0.83 172:0.37 173:0.54 176:0.73 177:0.70 178:0.55 179:0.76 181:0.80 182:0.95 186:0.84 187:0.68 189:0.93 192:0.87 195:0.79 201:0.93 208:0.64 212:0.23 215:0.39 216:0.77 219:0.95 222:0.95 227:0.61 229:0.61 231:0.96 235:0.74 238:0.89 241:0.41 242:0.24 243:0.57 245:0.64 246:0.85 247:0.67 253:0.44 254:0.74 259:0.21 261:0.84 265:0.51 266:0.71 267:0.52 271:0.24 276:0.42 277:0.69 279:0.86 282:0.88 283:0.77 287:0.61 289:0.99 290:0.59 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.64 9:0.80 11:0.85 12:0.79 17:0.98 18:0.88 21:0.22 27:0.70 28:0.60 29:0.77 30:0.38 31:0.88 32:0.80 34:0.59 36:0.99 37:0.65 39:0.60 41:0.82 43:0.68 44:0.93 45:0.87 46:0.97 48:0.91 60:0.87 64:0.77 66:0.64 69:0.25 70:0.94 71:0.99 74:0.86 76:0.85 77:0.89 79:0.88 81:0.67 83:0.91 85:0.72 86:0.92 91:0.42 96:0.71 98:0.68 101:0.87 104:0.90 106:0.81 107:0.99 108:0.20 110:0.99 114:0.71 120:0.57 122:0.57 123:0.19 124:0.51 125:0.99 126:0.54 127:0.53 129:0.59 131:0.99 133:0.61 135:0.78 137:0.88 139:0.95 140:0.45 144:0.57 145:0.96 146:0.86 147:0.89 149:0.72 150:0.80 151:0.56 153:0.48 154:0.89 155:0.67 157:0.97 158:0.92 159:0.68 160:0.96 161:0.68 162:0.86 164:0.57 165:0.93 166:0.98 167:0.56 172:0.75 173:0.18 176:0.73 177:0.70 179:0.44 181:0.80 182:0.96 187:0.21 192:0.87 195:0.84 196:0.92 201:0.93 202:0.36 206:0.81 208:0.64 212:0.70 215:0.51 216:0.18 219:0.95 220:0.82 222:0.95 227:0.88 229:0.98 230:0.64 231:0.96 233:0.73 235:0.42 241:0.05 242:0.16 243:0.69 245:0.77 246:0.85 247:0.88 248:0.55 253:0.59 255:0.95 256:0.45 259:0.21 260:0.58 265:0.68 266:0.80 267:0.79 268:0.46 271:0.24 276:0.67 279:0.86 281:0.88 282:0.88 283:0.82 284:0.89 285:0.62 287:0.88 289:0.99 290:0.71 292:0.95 297:0.36 300:0.94 +0 9:0.76 11:0.64 12:0.79 17:0.88 18:0.93 21:0.59 22:0.93 27:0.40 28:0.60 29:0.77 30:0.71 34:0.61 36:0.49 37:0.57 39:0.60 43:0.60 44:0.87 45:0.73 46:0.88 47:0.93 48:0.99 55:0.45 64:0.77 66:0.98 69:0.41 70:0.37 71:0.99 74:0.53 77:0.70 81:0.23 83:0.60 86:0.95 91:0.25 96:0.83 97:0.89 98:0.95 101:0.52 104:0.95 107:0.91 108:0.32 110:0.93 120:0.72 122:0.77 123:0.31 125:1.00 126:0.26 127:0.64 129:0.05 131:0.99 135:0.32 139:0.93 140:0.45 144:0.57 145:0.88 146:0.91 147:0.92 149:0.60 150:0.23 151:0.83 153:0.69 154:0.57 155:0.58 157:0.61 158:0.79 159:0.51 160:0.96 161:0.68 162:0.86 164:0.55 166:0.61 167:0.62 172:0.95 173:0.41 175:0.75 177:0.38 179:0.21 181:0.80 182:0.96 187:0.95 190:0.77 191:0.75 192:0.59 195:0.71 202:0.46 208:0.54 212:0.93 216:0.55 222:0.95 227:0.88 229:0.98 231:0.96 232:0.97 235:0.68 241:0.27 242:0.74 243:0.98 244:0.61 246:0.61 247:0.67 248:0.75 253:0.73 255:0.74 256:0.45 257:0.65 259:0.21 260:0.69 262:0.93 265:0.95 266:0.38 267:0.28 268:0.55 271:0.24 276:0.90 277:0.69 279:0.82 281:0.89 282:0.71 283:0.82 285:0.56 287:0.88 289:0.99 300:0.55 +1 11:0.75 12:0.79 17:0.57 18:0.85 21:0.74 28:0.60 29:0.77 30:0.17 32:0.62 34:0.36 36:0.18 37:0.97 39:0.60 43:0.59 45:0.85 46:0.94 48:0.91 55:0.71 66:0.69 69:0.59 70:0.94 71:0.99 74:0.49 81:0.23 86:0.84 91:0.08 98:0.74 101:0.52 104:0.80 106:0.81 107:0.99 108:0.45 110:0.99 123:0.43 124:0.76 125:0.88 126:0.26 127:0.82 129:0.59 131:0.61 133:0.91 135:0.32 139:0.82 144:0.57 145:0.63 146:0.98 147:0.98 149:0.66 150:0.23 154:0.48 157:0.95 159:0.89 160:0.88 161:0.68 166:0.90 167:0.49 172:0.92 173:0.29 177:0.70 178:0.55 179:0.24 181:0.80 182:0.86 187:0.39 195:0.96 206:0.81 212:0.54 215:0.36 216:0.98 222:0.95 227:0.61 229:0.61 231:0.94 232:0.87 235:0.88 242:0.50 243:0.72 244:0.61 245:0.81 246:0.61 247:0.86 253:0.34 259:0.21 265:0.74 266:0.95 267:0.36 271:0.24 276:0.88 281:0.89 287:0.61 289:0.98 297:0.36 300:0.84 +1 1:0.69 7:0.72 8:0.64 9:0.76 11:0.75 12:0.79 17:0.95 18:0.80 20:0.89 27:0.57 28:0.60 29:0.77 30:0.70 32:0.69 34:0.64 36:0.79 37:0.60 39:0.60 43:0.71 45:0.95 46:0.99 48:0.91 66:0.84 69:0.32 70:0.58 71:0.99 74:0.92 77:0.96 78:0.78 81:0.84 83:0.60 86:0.88 91:0.37 96:0.84 97:0.89 98:0.71 99:0.83 100:0.79 101:0.52 104:0.95 106:0.81 107:0.99 108:0.25 110:1.00 111:0.77 114:0.95 117:0.86 120:0.74 122:0.75 123:0.24 124:0.38 125:0.99 126:0.44 127:0.80 129:0.59 131:0.99 133:0.46 135:0.75 139:0.85 140:0.45 144:0.57 145:0.86 146:0.90 147:0.92 149:0.91 150:0.82 151:0.84 152:0.84 153:0.72 154:0.70 155:0.58 157:0.99 158:0.79 159:0.75 160:0.96 161:0.68 162:0.67 164:0.55 165:0.70 166:0.98 167:0.55 169:0.77 172:0.88 173:0.20 176:0.66 177:0.70 179:0.34 181:0.80 182:0.96 186:0.79 187:0.39 190:0.77 191:0.75 192:0.59 195:0.87 201:0.68 202:0.53 204:0.85 206:0.81 208:0.54 212:0.73 215:0.96 216:0.56 222:0.95 227:0.88 229:0.98 230:0.75 231:0.96 232:0.97 233:0.76 235:0.05 241:0.03 242:0.50 243:0.85 245:0.82 246:0.85 247:0.74 248:0.76 253:0.86 254:0.88 255:0.74 256:0.45 259:0.21 260:0.69 261:0.80 265:0.71 266:0.54 267:0.69 268:0.57 271:0.24 276:0.75 279:0.82 281:0.91 282:0.77 283:0.82 285:0.56 287:0.88 289:0.99 290:0.95 297:0.36 300:0.70 +1 1:0.69 7:0.72 9:0.76 11:0.85 12:0.79 17:0.36 18:0.70 21:0.47 27:0.19 28:0.60 29:0.77 30:0.58 32:0.69 34:0.52 36:0.28 37:0.38 39:0.60 43:0.71 45:0.98 46:0.99 48:0.91 66:0.99 69:0.35 70:0.58 71:0.99 74:0.90 76:0.94 77:0.89 81:0.38 83:0.60 85:0.72 86:0.81 91:0.27 96:0.86 97:0.89 98:0.96 99:0.83 101:0.87 107:1.00 108:0.27 110:1.00 114:0.59 120:0.77 122:0.51 123:0.26 125:0.97 126:0.44 127:0.68 129:0.05 131:0.99 135:0.95 139:0.79 140:0.45 144:0.57 145:0.59 146:0.99 147:0.99 149:0.93 150:0.54 151:0.90 153:0.83 154:0.70 155:0.58 157:0.99 158:0.78 159:0.80 160:0.97 161:0.68 162:0.82 164:0.71 165:0.70 166:0.98 167:0.81 172:0.96 173:0.18 176:0.66 177:0.70 179:0.18 181:0.80 182:0.96 187:0.39 190:0.77 192:0.59 195:0.93 201:0.68 202:0.63 204:0.85 208:0.54 212:0.54 215:0.41 216:0.87 222:0.95 227:0.88 229:0.98 230:0.74 231:0.96 233:0.76 235:0.60 241:0.73 242:0.45 243:0.99 246:0.85 247:0.84 248:0.81 253:0.87 255:0.74 256:0.64 259:0.21 260:0.74 265:0.96 266:0.54 267:0.59 268:0.69 271:0.24 276:0.92 279:0.82 281:0.91 282:0.77 283:0.78 285:0.56 287:0.88 289:0.99 290:0.59 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.79 17:0.51 18:0.73 21:0.64 27:0.45 28:0.60 29:0.77 30:0.62 34:0.83 36:0.25 37:0.50 39:0.60 43:0.81 46:0.94 60:0.87 64:0.77 66:0.16 69:0.70 70:0.23 71:0.99 74:0.56 81:0.45 83:0.91 85:0.72 86:0.78 91:0.15 98:0.34 101:0.87 107:0.92 108:0.54 110:0.94 114:0.57 122:0.57 123:0.77 124:0.71 125:0.88 126:0.54 127:0.31 129:0.05 131:0.61 133:0.60 135:0.34 139:0.82 144:0.57 145:0.47 146:0.17 147:0.22 149:0.58 150:0.70 154:0.77 155:0.67 157:0.81 158:0.91 159:0.44 160:0.88 161:0.68 163:0.79 165:0.93 166:0.97 167:0.66 172:0.16 173:0.69 176:0.73 177:0.70 178:0.55 179:0.89 181:0.80 182:0.95 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.38 216:0.77 219:0.95 222:0.95 227:0.61 229:0.61 231:0.96 235:0.84 241:0.41 242:0.33 243:0.48 245:0.43 246:0.85 247:0.39 253:0.41 259:0.21 265:0.11 266:0.27 267:0.28 271:0.24 276:0.25 277:0.69 279:0.86 283:0.78 287:0.61 289:0.99 290:0.57 292:0.91 297:0.36 300:0.18 +2 6:0.87 7:0.64 8:0.77 11:0.75 12:0.79 17:0.89 18:0.80 20:0.87 21:0.30 22:0.93 27:0.56 28:0.60 29:0.77 30:0.33 34:0.91 36:0.55 37:0.69 39:0.60 43:0.58 45:0.81 46:0.96 47:0.93 48:0.91 60:0.95 64:0.77 66:0.60 69:0.83 70:0.89 71:0.99 74:0.61 78:0.80 81:0.42 83:0.91 86:0.79 91:0.42 97:0.89 98:0.52 100:0.81 101:0.52 104:0.89 106:0.81 107:0.97 108:0.68 110:0.99 111:0.79 120:0.64 122:0.51 123:0.66 124:0.65 125:0.88 126:0.44 127:0.76 129:0.05 131:0.94 133:0.73 135:0.54 139:0.86 140:0.45 144:0.57 146:0.65 147:0.05 149:0.50 150:0.33 151:0.56 152:0.82 153:0.69 154:0.57 155:0.67 157:0.95 158:0.92 159:0.61 160:0.88 161:0.68 162:0.86 164:0.52 166:0.91 167:0.65 169:0.79 172:0.51 173:0.83 177:0.05 179:0.75 181:0.80 182:0.86 186:0.81 187:0.39 189:0.89 190:0.89 191:0.70 192:0.87 201:0.68 202:0.46 206:0.81 208:0.64 212:0.18 215:0.44 216:0.48 219:0.95 220:0.62 222:0.95 227:0.85 229:0.61 230:0.64 231:0.94 233:0.73 235:0.42 238:0.85 241:0.39 242:0.18 243:0.13 245:0.54 246:0.61 247:0.74 248:0.55 253:0.39 254:0.95 256:0.45 257:0.84 259:0.21 260:0.58 261:0.81 262:0.93 265:0.54 266:0.71 267:0.52 268:0.55 271:0.24 276:0.48 279:0.86 281:0.88 283:0.82 285:0.47 287:0.61 289:0.98 292:0.95 297:0.36 300:0.70 +1 1:0.74 7:0.81 9:0.76 11:0.85 12:0.79 17:0.98 18:0.79 21:0.47 22:0.93 27:0.64 28:0.60 29:0.77 30:0.75 32:0.69 34:0.39 36:0.56 37:0.65 39:0.60 43:0.86 45:0.92 46:0.99 47:0.93 48:0.91 64:0.77 66:0.20 69:0.60 70:0.56 71:0.99 74:0.83 76:0.85 77:0.70 81:0.52 83:0.91 85:0.72 86:0.87 91:0.35 96:0.80 98:0.49 101:0.87 104:0.98 106:0.81 107:0.99 108:0.80 110:1.00 114:0.60 117:0.86 120:0.69 122:0.51 123:0.44 124:0.69 125:1.00 126:0.54 127:0.62 129:0.59 131:0.99 133:0.66 135:0.39 139:0.88 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.88 150:0.74 151:0.81 153:0.48 154:0.83 155:0.67 157:0.98 159:0.48 160:0.96 161:0.68 162:0.86 164:0.54 165:0.93 166:0.98 167:0.67 172:0.65 173:0.62 176:0.73 177:0.70 179:0.45 181:0.80 182:0.96 187:0.87 190:0.77 192:0.87 195:0.67 201:0.93 202:0.36 204:0.89 206:0.81 208:0.64 212:0.71 215:0.41 216:0.44 222:0.95 227:0.88 229:0.98 230:0.86 231:0.96 232:0.99 233:0.73 235:0.74 241:0.46 242:0.55 243:0.40 245:0.83 246:0.85 247:0.60 248:0.73 253:0.81 255:0.74 256:0.45 259:0.21 260:0.68 262:0.93 265:0.30 266:0.63 267:0.59 268:0.46 271:0.24 276:0.68 281:0.89 282:0.77 285:0.56 287:0.88 289:0.99 290:0.60 297:0.36 300:0.70 +1 1:0.69 7:0.72 8:0.95 9:0.76 11:0.75 12:0.79 17:0.49 20:0.76 21:0.30 27:0.47 28:0.60 29:0.77 30:0.66 31:0.90 32:0.69 34:0.36 36:0.31 37:0.26 39:0.60 43:0.71 45:0.83 46:0.99 66:0.85 69:0.32 70:0.40 71:0.99 74:0.71 77:0.89 78:0.72 79:0.90 81:0.50 83:0.60 91:0.58 96:0.72 97:0.89 98:0.90 99:0.83 100:0.73 101:0.52 104:0.84 107:0.99 108:0.25 110:0.99 111:0.72 114:0.63 120:0.67 122:0.75 123:0.24 125:0.97 126:0.44 127:0.48 129:0.05 131:0.99 135:0.47 137:0.90 140:0.87 144:0.57 145:0.74 146:0.71 147:0.75 149:0.72 150:0.57 151:0.64 152:0.75 153:0.48 154:0.61 155:0.58 157:0.97 158:0.79 159:0.28 160:0.96 161:0.68 162:0.65 164:0.54 165:0.70 166:0.98 167:0.72 169:0.72 172:0.57 173:0.50 175:0.75 176:0.66 177:0.38 178:0.42 179:0.74 181:0.80 182:0.96 186:0.73 187:0.21 190:0.77 191:0.78 192:0.59 195:0.58 201:0.68 202:0.62 204:0.85 208:0.54 212:0.58 215:0.44 216:0.93 220:0.62 222:0.95 227:0.88 229:0.98 230:0.74 231:0.96 233:0.73 235:0.42 241:0.61 242:0.52 243:0.86 244:0.61 246:0.85 247:0.39 248:0.63 253:0.54 255:0.79 256:0.64 257:0.65 259:0.21 260:0.61 261:0.73 265:0.90 266:0.27 267:0.76 268:0.63 271:0.24 276:0.44 277:0.69 279:0.82 282:0.77 283:0.82 284:0.86 285:0.62 287:0.88 289:0.99 290:0.63 297:0.36 300:0.33 +4 1:0.69 6:0.96 7:0.72 8:0.93 9:0.80 11:0.75 12:0.79 17:0.36 20:0.88 21:0.71 27:0.12 28:0.60 29:0.77 30:0.76 31:0.95 32:0.69 34:0.69 36:0.55 37:0.95 39:0.60 41:0.82 43:0.68 45:0.95 46:1.00 66:0.67 69:0.58 70:0.29 71:0.99 74:0.86 77:0.70 78:0.94 79:0.94 81:0.32 83:0.91 91:0.78 96:0.93 97:0.94 98:0.57 99:0.83 100:0.99 101:0.52 104:0.58 107:0.99 108:0.69 110:1.00 111:0.98 114:0.54 120:0.91 122:0.51 123:0.67 124:0.50 125:0.96 126:0.44 127:0.63 129:0.81 131:0.99 133:0.67 135:0.34 137:0.95 140:0.99 144:0.57 145:0.82 146:0.23 147:0.29 149:0.91 150:0.51 151:0.87 152:0.93 153:0.94 154:0.58 155:0.67 157:0.99 158:0.78 159:0.36 160:0.99 161:0.68 162:0.50 164:0.86 165:0.70 166:0.97 167:0.94 169:0.97 172:0.79 173:0.70 175:0.75 176:0.66 177:0.38 178:0.50 179:0.40 181:0.80 182:0.96 186:0.94 189:0.97 191:0.95 192:0.87 195:0.62 201:0.68 202:0.92 204:0.85 208:0.64 212:0.89 215:0.37 216:0.24 222:0.95 227:0.88 229:0.98 230:0.75 231:0.96 232:0.80 233:0.76 235:0.88 238:0.98 241:0.89 242:0.63 243:0.25 244:0.61 245:0.80 246:0.85 247:0.39 248:0.77 253:0.93 255:0.95 256:0.87 257:0.65 259:0.21 260:0.86 261:0.97 265:0.58 266:0.38 267:0.28 268:0.88 271:0.24 276:0.70 277:0.69 279:0.82 282:0.77 283:0.82 284:0.93 285:0.62 287:0.88 289:0.99 290:0.54 297:0.36 300:0.33 +2 7:0.72 12:0.79 17:0.73 18:0.82 21:0.22 27:0.54 28:0.60 29:0.77 30:0.58 32:0.69 34:0.89 36:0.54 37:0.27 39:0.60 43:0.15 46:0.88 55:0.59 66:0.80 69:0.52 70:0.33 71:0.99 74:0.48 77:0.89 81:0.57 86:0.76 91:0.53 98:0.87 104:0.58 107:0.93 108:0.40 110:0.95 122:0.86 123:0.38 125:0.88 126:0.26 127:0.39 129:0.05 131:0.61 135:0.61 139:0.73 144:0.57 145:0.95 146:0.77 147:0.80 149:0.21 150:0.42 154:0.11 155:0.58 157:0.61 159:0.33 160:0.88 161:0.68 163:0.81 166:0.91 167:0.66 172:0.45 173:0.61 175:0.75 177:0.38 178:0.55 179:0.83 181:0.80 182:0.81 187:0.39 192:0.59 195:0.63 204:0.85 208:0.54 212:0.37 215:0.51 216:0.13 222:0.95 227:0.61 229:0.61 230:0.75 231:0.93 233:0.76 235:0.31 241:0.43 242:0.58 243:0.82 244:0.83 246:0.61 247:0.47 253:0.34 257:0.65 259:0.21 265:0.87 266:0.38 267:0.28 271:0.24 276:0.37 277:0.69 282:0.77 287:0.61 289:0.98 297:0.36 300:0.25 +4 6:0.79 7:0.63 8:0.58 12:0.79 17:0.47 18:0.80 20:0.89 21:0.74 27:0.54 28:0.60 29:0.77 30:0.21 32:0.62 34:0.58 36:0.31 37:0.27 39:0.60 43:0.16 46:0.88 55:0.45 66:0.80 69:0.52 70:0.50 71:0.99 78:0.92 81:0.23 86:0.81 91:0.88 98:0.91 100:0.92 104:0.58 107:0.93 108:0.40 110:0.96 111:0.91 120:0.66 122:0.65 123:0.38 125:0.88 126:0.26 127:0.51 129:0.05 131:0.94 135:0.51 139:0.77 140:0.45 145:0.82 146:0.69 147:0.74 149:0.11 150:0.23 151:0.57 152:0.84 153:0.78 154:0.56 157:0.61 159:0.27 160:0.88 161:0.68 162:0.51 163:0.75 164:0.53 166:0.90 167:0.95 169:0.89 172:0.45 173:0.70 175:0.75 177:0.38 179:0.86 181:0.80 182:0.61 186:0.92 189:0.81 190:0.89 191:0.81 195:0.59 202:0.72 212:0.37 215:0.36 216:0.13 220:0.62 222:0.95 227:0.85 229:0.61 230:0.63 231:0.87 233:0.73 235:0.88 238:0.88 241:0.93 242:0.40 243:0.82 244:0.61 246:0.61 247:0.47 248:0.57 253:0.20 254:0.74 256:0.64 257:0.65 259:0.21 260:0.58 261:0.91 265:0.91 266:0.38 267:0.28 268:0.65 271:0.24 276:0.35 277:0.69 283:0.78 285:0.47 287:0.61 289:0.95 297:0.36 300:0.33 +1 1:0.74 6:0.84 8:0.67 11:0.75 12:0.79 17:0.89 18:0.89 20:0.99 21:0.13 22:0.93 27:0.54 28:0.60 29:0.77 30:0.45 32:0.69 34:0.81 36:0.56 37:0.65 39:0.60 43:0.67 44:0.87 45:0.87 46:0.99 47:0.93 64:0.77 66:0.90 69:0.29 70:0.62 71:0.99 74:0.82 77:0.89 78:0.88 81:0.74 83:0.91 86:0.94 91:0.37 97:0.89 98:0.91 100:0.90 101:0.52 104:0.99 106:0.81 107:0.99 108:0.22 110:0.99 111:0.87 114:0.79 117:0.86 122:0.57 123:0.22 125:0.88 126:0.54 127:0.49 129:0.05 131:0.61 135:0.65 139:0.91 144:0.57 145:0.82 146:0.76 147:0.80 149:0.50 150:0.84 152:0.91 154:0.85 155:0.67 157:0.98 158:0.79 159:0.32 160:0.88 161:0.68 165:0.93 166:0.98 167:0.58 169:0.87 172:0.73 173:0.43 176:0.73 177:0.70 178:0.55 179:0.56 181:0.80 182:0.95 186:0.88 187:0.68 189:0.86 192:0.87 195:0.57 201:0.93 206:0.81 208:0.64 212:0.84 215:0.61 216:0.62 222:0.95 227:0.61 229:0.61 231:0.96 235:0.31 238:0.88 241:0.12 242:0.24 243:0.91 246:0.85 247:0.67 253:0.57 254:0.74 259:0.21 261:0.90 262:0.93 265:0.91 266:0.27 267:0.44 271:0.24 276:0.60 279:0.82 282:0.77 283:0.82 287:0.61 289:0.99 290:0.79 297:0.36 300:0.55 +2 1:0.74 9:0.76 11:0.85 12:0.79 17:0.90 18:0.77 21:0.13 27:0.72 28:0.60 29:0.77 30:0.89 34:0.85 36:0.57 39:0.60 43:0.92 45:0.87 46:1.00 64:0.77 66:0.91 69:0.38 70:0.22 71:0.99 74:0.79 81:0.74 83:0.91 85:0.85 86:0.88 91:0.89 96:0.75 98:0.95 101:0.87 104:0.75 107:0.99 108:0.30 110:0.99 114:0.79 120:0.63 122:0.51 123:0.28 125:1.00 126:0.54 127:0.68 129:0.05 131:0.99 135:0.47 139:0.85 140:0.45 144:0.57 146:0.72 147:0.76 149:0.93 150:0.84 151:0.65 153:0.48 154:0.91 155:0.67 157:0.98 159:0.29 160:0.96 161:0.68 162:0.86 164:0.54 165:0.93 166:0.98 167:0.91 172:0.74 173:0.58 176:0.73 177:0.70 179:0.59 181:0.80 182:0.96 190:0.77 192:0.87 201:0.93 202:0.36 208:0.64 212:0.82 215:0.61 220:0.62 222:0.95 227:0.88 229:0.98 231:0.96 235:0.31 241:0.80 242:0.45 243:0.91 246:0.85 247:0.47 248:0.63 253:0.72 255:0.74 256:0.45 259:0.21 260:0.63 265:0.95 266:0.27 267:0.23 268:0.46 271:0.24 276:0.62 285:0.56 287:0.88 289:0.99 290:0.79 297:0.36 300:0.25 +2 9:0.76 11:0.75 12:0.79 17:0.30 18:0.84 21:0.74 27:0.48 28:0.60 29:0.77 30:0.14 32:0.62 34:0.21 36:0.20 37:0.55 39:0.60 43:0.59 45:0.85 46:0.94 48:0.91 55:0.71 66:0.68 69:0.59 70:0.94 71:0.99 74:0.54 81:0.23 83:0.60 86:0.84 91:0.35 96:0.86 97:0.97 98:0.74 101:0.52 107:0.99 108:0.45 110:0.99 120:0.79 123:0.43 124:0.76 125:0.97 126:0.26 127:0.81 129:0.59 131:0.99 133:0.91 135:0.34 139:0.81 140:0.45 144:0.57 145:0.63 146:0.98 147:0.98 149:0.66 150:0.23 151:0.90 153:0.83 154:0.48 155:0.58 157:0.95 158:0.78 159:0.89 160:0.98 161:0.68 162:0.76 164:0.73 166:0.90 167:0.69 172:0.91 173:0.29 177:0.70 179:0.25 181:0.80 182:0.96 187:0.39 190:0.77 192:0.59 195:0.96 202:0.68 208:0.54 212:0.54 215:0.36 216:0.92 222:0.95 227:0.88 229:0.98 231:0.96 235:0.88 241:0.52 242:0.54 243:0.72 244:0.61 245:0.81 246:0.61 247:0.86 248:0.81 253:0.79 255:0.74 256:0.71 259:0.21 260:0.74 265:0.74 266:0.95 267:0.76 268:0.73 271:0.24 276:0.88 279:0.82 281:0.89 283:0.82 285:0.56 287:0.88 289:0.99 297:0.36 300:0.84 +2 7:0.63 12:0.79 17:0.70 18:0.66 21:0.30 27:0.54 28:0.60 29:0.77 30:0.76 32:0.69 34:0.79 36:0.44 37:0.27 39:0.60 43:0.16 46:0.88 55:0.71 66:0.36 69:0.41 70:0.15 71:0.99 74:0.39 77:0.89 81:0.36 86:0.65 91:0.46 98:0.27 104:0.58 107:0.91 108:0.32 110:0.93 122:0.65 123:0.31 124:0.52 125:0.88 126:0.26 127:0.42 129:0.05 131:0.61 133:0.39 135:0.42 139:0.64 144:0.57 145:0.76 146:0.27 147:0.33 149:0.11 150:0.32 154:0.11 157:0.61 159:0.30 160:0.88 161:0.68 163:0.79 166:0.91 167:0.72 172:0.10 173:0.54 175:0.75 177:0.38 178:0.55 179:0.99 181:0.80 182:0.81 187:0.39 195:0.61 212:0.95 215:0.44 216:0.13 222:0.95 227:0.61 229:0.61 230:0.63 231:0.93 233:0.73 235:0.31 241:0.59 242:0.82 243:0.51 244:0.83 245:0.37 246:0.61 247:0.12 253:0.31 257:0.65 259:0.21 265:0.29 266:0.12 267:0.36 271:0.24 276:0.14 277:0.69 282:0.77 287:0.61 289:0.98 297:0.36 300:0.13 +2 7:0.81 8:0.68 12:0.79 17:0.94 18:0.80 20:0.80 21:0.54 22:0.93 27:0.64 28:0.60 29:0.77 30:0.66 32:0.62 34:0.58 36:0.54 37:0.65 39:0.60 43:0.13 46:0.88 47:0.93 48:0.91 55:0.45 60:0.95 64:0.77 66:0.25 69:0.60 70:0.61 71:0.99 74:0.58 76:0.85 78:0.81 81:0.33 83:0.91 86:0.80 91:0.35 96:0.75 97:0.94 98:0.49 100:0.84 104:0.98 106:0.81 107:0.95 108:0.80 110:0.97 111:0.81 117:0.86 123:0.44 124:0.69 125:0.88 126:0.44 127:0.63 129:0.59 131:0.86 133:0.64 135:0.39 139:0.88 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.24 150:0.27 151:0.55 152:0.78 153:0.48 154:0.57 155:0.67 157:0.61 158:0.91 159:0.48 160:0.88 161:0.68 162:0.86 166:0.91 167:0.61 169:0.83 172:0.45 173:0.62 175:0.75 177:0.38 179:0.67 181:0.80 182:0.61 186:0.82 187:0.68 190:0.89 192:0.87 195:0.67 201:0.68 202:0.36 204:0.89 206:0.81 208:0.64 212:0.57 215:0.39 216:0.44 219:0.95 220:0.62 222:0.95 227:0.83 229:0.61 230:0.86 231:0.96 232:0.99 233:0.73 235:0.74 241:0.24 242:0.76 243:0.44 244:0.61 245:0.68 246:0.61 247:0.55 248:0.54 253:0.45 254:0.74 256:0.45 257:0.65 259:0.21 261:0.80 262:0.93 265:0.30 266:0.75 267:0.59 268:0.46 271:0.24 276:0.53 277:0.69 279:0.86 281:0.88 283:0.82 285:0.47 287:0.61 289:0.99 292:0.91 297:0.36 300:0.55 +1 8:0.94 12:0.37 17:0.96 18:0.65 21:0.54 22:0.93 27:0.04 29:0.71 30:0.62 31:0.96 34:0.42 36:0.60 37:0.96 39:0.58 43:0.17 47:0.93 55:0.84 66:0.72 69:0.30 70:0.47 71:0.80 74:0.51 79:0.96 81:0.25 86:0.76 91:0.78 98:0.84 104:0.80 106:0.81 108:0.24 114:0.58 120:0.81 123:0.23 124:0.41 126:0.26 127:0.76 129:0.05 133:0.37 135:0.81 137:0.96 139:0.77 140:0.45 146:0.83 147:0.86 149:0.24 150:0.25 151:0.90 153:0.83 154:0.78 159:0.50 162:0.64 163:0.86 164:0.66 172:0.59 173:0.33 176:0.61 177:0.70 179:0.73 187:0.39 190:0.77 192:0.51 196:0.94 202:0.63 206:0.81 212:0.27 216:0.31 219:0.92 222:0.35 235:0.60 241:0.37 242:0.72 243:0.75 245:0.61 247:0.60 248:0.85 253:0.41 254:0.95 255:0.74 256:0.58 259:0.21 260:0.78 262:0.93 265:0.84 266:0.63 267:0.44 268:0.64 271:0.79 276:0.52 283:0.82 284:0.92 285:0.56 290:0.58 292:0.95 300:0.43 +1 7:0.72 8:0.90 12:0.37 17:0.49 21:0.30 27:0.01 29:0.71 31:0.88 32:0.69 34:0.64 36:0.79 37:0.93 39:0.58 43:0.12 44:0.90 48:0.91 64:0.77 66:0.36 69:0.75 71:0.80 74:0.42 76:0.85 77:0.70 79:0.88 81:0.39 91:0.93 98:0.10 108:0.59 120:0.59 123:0.56 126:0.44 127:0.07 129:0.05 135:0.86 137:0.88 139:0.79 140:0.45 145:0.70 146:0.05 147:0.05 149:0.06 150:0.31 151:0.56 153:0.78 154:0.09 159:0.05 162:0.78 164:0.65 172:0.05 173:0.95 175:0.91 177:0.05 187:0.21 190:0.77 191:0.93 192:0.51 195:0.61 196:0.90 201:0.68 202:0.59 215:0.44 216:0.51 219:0.92 220:0.82 222:0.35 230:0.74 233:0.73 235:0.42 241:0.76 243:0.51 244:0.83 248:0.56 253:0.32 255:0.74 256:0.58 257:0.84 259:0.21 260:0.58 265:0.11 267:0.65 268:0.64 271:0.79 277:0.87 281:0.89 282:0.73 283:0.78 284:0.92 285:0.56 292:0.91 297:0.36 +1 7:0.72 12:0.37 17:0.59 21:0.30 22:0.93 27:0.33 29:0.71 32:0.64 37:0.60 39:0.58 47:0.93 48:0.91 71:0.80 74:0.39 81:0.35 91:0.58 104:0.77 126:0.26 139:0.68 145:0.45 150:0.32 155:0.58 175:0.97 178:0.55 187:0.39 192:0.59 195:0.77 204:0.85 208:0.54 215:0.44 216:0.24 222:0.35 230:0.75 233:0.76 235:0.42 241:0.65 244:0.93 253:0.31 257:0.93 262:0.93 271:0.79 277:0.95 281:0.91 297:0.36 +1 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.97 21:0.59 27:0.72 29:0.71 30:0.59 32:0.69 34:0.17 36:0.37 39:0.58 43:0.38 45:0.77 55:0.45 66:0.98 69:0.32 70:0.42 71:0.80 74:0.91 76:0.94 77:0.98 81:0.36 83:0.60 91:0.92 96:0.77 98:0.99 99:0.83 101:0.52 104:0.58 108:0.25 114:0.56 120:0.65 123:0.24 126:0.44 127:0.93 129:0.05 135:0.23 138:0.96 140:0.45 145:0.84 146:0.95 147:0.96 149:0.48 150:0.53 151:0.71 153:0.77 154:0.58 155:0.58 158:0.78 159:0.61 162:0.86 164:0.65 165:0.70 172:0.96 173:0.29 175:0.75 176:0.61 177:0.38 179:0.21 190:0.77 192:0.59 195:0.76 201:0.68 202:0.54 204:0.85 208:0.54 212:0.89 215:0.38 216:0.02 220:0.62 222:0.35 230:0.75 232:0.79 233:0.76 235:0.97 241:0.79 242:0.54 243:0.98 244:0.61 247:0.55 248:0.67 253:0.76 255:0.74 256:0.58 257:0.65 259:0.21 260:0.64 265:0.99 266:0.54 267:0.18 268:0.63 271:0.79 276:0.91 277:0.69 279:0.82 282:0.77 283:0.78 285:0.56 290:0.56 297:0.36 300:0.55 +1 7:0.72 12:0.37 17:0.51 21:0.30 27:0.33 29:0.71 30:0.76 32:0.64 34:0.21 36:0.43 37:0.60 39:0.58 43:0.16 48:0.91 55:0.84 66:0.36 69:0.47 70:0.15 71:0.80 74:0.39 81:0.35 91:0.81 96:0.73 98:0.24 104:0.77 108:0.36 120:0.68 123:0.34 124:0.52 126:0.26 127:0.23 129:0.59 133:0.47 135:0.30 139:0.68 140:0.80 145:0.45 146:0.32 147:0.38 149:0.11 150:0.32 151:0.53 153:0.46 154:0.11 155:0.58 159:0.34 162:0.77 163:0.96 164:0.71 172:0.10 173:0.44 175:0.91 177:0.05 178:0.42 179:0.96 187:0.39 190:0.89 192:0.59 195:0.76 202:0.70 204:0.85 208:0.54 212:0.95 215:0.44 216:0.24 220:0.74 222:0.35 230:0.75 233:0.76 235:0.42 241:0.70 242:0.82 243:0.51 244:0.83 245:0.37 247:0.12 248:0.59 253:0.45 256:0.81 257:0.84 260:0.65 265:0.26 266:0.12 267:0.69 268:0.74 271:0.79 276:0.15 277:0.87 281:0.91 285:0.56 297:0.36 300:0.13 +2 1:0.69 7:0.72 8:0.58 9:0.76 11:0.64 12:0.37 17:0.89 18:0.79 21:0.30 27:0.39 29:0.71 30:0.56 32:0.69 34:0.40 36:0.64 37:0.23 39:0.58 43:0.80 45:0.66 66:0.84 69:0.25 70:0.71 71:0.80 74:0.64 77:0.89 81:0.63 83:0.60 86:0.86 91:0.96 96:0.75 98:0.86 99:0.95 101:0.52 104:0.58 108:0.20 114:0.63 120:0.63 123:0.19 126:0.44 127:0.37 129:0.05 135:0.23 139:0.82 140:0.45 145:0.65 146:0.65 147:0.70 149:0.70 150:0.61 151:0.65 153:0.48 154:0.75 155:0.58 159:0.25 162:0.86 164:0.54 165:0.70 172:0.54 173:0.46 176:0.66 177:0.70 179:0.73 190:0.77 192:0.59 195:0.59 201:0.68 202:0.36 204:0.85 208:0.54 212:0.61 215:0.44 216:0.02 220:0.62 222:0.35 230:0.75 232:0.77 233:0.76 235:0.80 241:0.98 242:0.24 243:0.85 247:0.74 248:0.63 253:0.62 255:0.74 256:0.45 259:0.21 260:0.63 265:0.86 266:0.27 267:0.93 268:0.46 271:0.79 276:0.40 282:0.77 285:0.56 290:0.63 297:0.36 300:0.55 +2 7:0.66 8:0.90 9:0.76 11:0.64 12:0.37 17:0.95 21:0.30 27:0.52 29:0.71 30:0.36 32:0.64 34:0.19 36:0.62 37:0.88 39:0.58 43:0.24 45:0.77 55:0.52 66:0.78 69:0.86 70:0.85 71:0.80 74:0.55 81:0.30 83:0.60 91:0.92 96:0.76 97:0.89 98:0.48 101:0.52 104:0.58 108:0.89 120:0.83 122:0.51 123:0.89 124:0.41 126:0.26 127:0.43 129:0.59 133:0.64 135:0.30 140:0.87 145:0.56 146:0.40 147:0.46 149:0.35 150:0.28 151:0.65 153:0.78 154:0.17 155:0.58 158:0.78 159:0.69 162:0.73 164:0.84 172:0.79 173:0.78 175:0.75 177:0.38 178:0.42 179:0.40 190:0.77 191:0.93 192:0.59 195:0.88 202:0.83 208:0.54 212:0.87 215:0.44 216:0.09 220:0.93 222:0.35 230:0.69 232:0.80 233:0.73 235:0.31 241:0.85 242:0.37 243:0.13 244:0.61 245:0.67 247:0.60 248:0.65 253:0.58 255:0.74 256:0.84 257:0.65 259:0.21 260:0.77 265:0.49 266:0.54 267:0.59 268:0.86 271:0.79 276:0.64 277:0.69 279:0.82 283:0.78 285:0.56 297:0.36 300:0.84 +1 1:0.69 7:0.66 8:0.97 11:0.64 12:0.37 17:0.83 18:0.79 21:0.74 25:0.88 27:0.71 29:0.71 30:0.21 32:0.64 34:0.42 36:0.39 37:0.71 39:0.58 43:0.23 45:0.81 55:0.59 66:0.49 69:0.82 70:0.95 71:0.80 74:0.57 81:0.32 83:0.60 86:0.73 87:0.98 91:0.90 98:0.49 99:0.83 101:0.52 104:0.76 108:0.90 114:0.54 123:0.89 124:0.78 126:0.44 127:0.93 129:0.05 133:0.83 135:0.49 139:0.71 145:0.94 146:0.71 147:0.31 149:0.46 150:0.51 154:0.16 155:0.58 159:0.84 165:0.70 172:0.85 173:0.66 176:0.66 177:0.38 178:0.55 179:0.29 187:0.21 192:0.59 195:0.93 201:0.68 208:0.54 212:0.69 215:0.36 216:0.07 222:0.35 230:0.69 232:0.82 233:0.73 235:0.95 241:0.75 242:0.38 243:0.08 244:0.61 245:0.88 247:0.90 253:0.39 257:0.65 259:0.21 265:0.51 266:0.89 267:0.79 271:0.79 276:0.85 277:0.69 290:0.54 297:0.36 300:0.94 +2 7:0.72 8:0.72 12:0.37 17:0.64 21:0.54 27:0.34 29:0.71 30:0.76 32:0.64 34:0.26 36:0.51 37:0.36 39:0.58 43:0.17 48:0.91 55:0.92 66:0.36 69:0.47 70:0.22 71:0.80 81:0.25 91:0.95 98:0.51 108:0.67 120:0.64 123:0.65 124:0.57 126:0.26 127:0.37 129:0.59 133:0.47 135:0.47 139:0.68 140:0.80 145:0.69 146:0.40 147:0.46 149:0.15 150:0.25 151:0.55 153:0.48 154:0.11 159:0.36 162:0.53 163:0.96 164:0.56 172:0.16 173:0.53 175:0.91 177:0.05 178:0.42 179:0.94 190:0.89 191:0.84 192:0.51 195:0.67 202:0.66 212:0.42 215:0.39 216:0.21 220:0.99 222:0.35 230:0.74 233:0.73 235:0.74 241:0.97 242:0.82 243:0.40 244:0.83 245:0.43 247:0.12 248:0.54 253:0.20 256:0.58 257:0.84 259:0.21 260:0.61 265:0.53 266:0.23 267:0.59 268:0.59 271:0.79 276:0.25 277:0.87 281:0.89 285:0.47 297:0.99 300:0.18 +1 7:0.66 9:0.76 12:0.37 17:0.94 21:0.40 27:0.72 29:0.71 30:0.94 32:0.68 34:0.90 36:0.58 37:0.23 39:0.58 43:0.18 44:0.90 48:0.97 55:0.71 66:0.79 69:0.21 70:0.19 71:0.80 74:0.46 76:0.94 77:0.70 81:0.28 83:0.60 91:0.77 96:0.80 98:0.85 104:0.58 108:0.17 120:0.69 123:0.16 126:0.26 127:0.36 129:0.05 135:0.18 139:0.71 140:0.45 145:0.80 146:0.52 147:0.58 149:0.22 150:0.27 151:0.81 153:0.48 154:0.12 155:0.58 159:0.19 162:0.86 164:0.54 172:0.41 173:0.53 175:0.75 177:0.38 179:0.84 187:0.21 190:0.77 191:0.70 192:0.59 195:0.55 202:0.36 208:0.54 212:0.95 215:0.42 216:0.08 222:0.35 230:0.69 232:0.77 233:0.76 235:0.42 241:0.47 242:0.75 243:0.80 244:0.83 247:0.35 248:0.73 253:0.69 255:0.74 256:0.45 257:0.65 259:0.21 260:0.68 265:0.85 266:0.12 267:0.23 268:0.46 271:0.79 276:0.36 277:0.69 281:0.89 282:0.77 285:0.56 297:0.36 300:0.18 +1 1:0.69 7:0.66 8:0.80 9:0.76 11:0.64 12:0.37 17:0.59 21:0.40 27:0.02 29:0.71 30:0.62 34:0.50 36:0.37 37:0.99 39:0.58 43:0.65 45:0.66 55:0.45 66:0.77 69:0.17 70:0.56 71:0.80 74:0.93 76:0.98 77:0.70 81:0.34 83:0.60 91:0.85 96:0.87 97:0.89 98:0.77 101:0.52 104:0.86 106:0.81 108:0.14 114:0.59 120:0.81 122:0.77 123:0.13 124:0.42 126:0.44 127:0.94 129:0.05 133:0.62 135:0.65 138:0.96 140:0.45 145:0.54 146:0.86 147:0.89 149:0.63 150:0.49 151:0.91 153:0.84 154:0.55 155:0.58 158:0.79 159:0.65 162:0.84 164:0.73 172:0.91 173:0.18 175:0.75 176:0.61 177:0.38 179:0.29 187:0.39 190:0.77 191:0.91 192:0.59 195:0.79 201:0.68 202:0.67 206:0.81 208:0.54 212:0.87 215:0.41 216:0.47 222:0.35 230:0.69 233:0.76 235:0.52 241:0.12 242:0.77 243:0.79 244:0.61 245:0.85 247:0.60 248:0.82 253:0.89 255:0.74 256:0.71 257:0.65 260:0.77 265:0.77 266:0.63 267:0.65 268:0.74 271:0.79 276:0.85 277:0.69 279:0.82 282:0.73 283:0.82 285:0.56 290:0.59 297:0.36 300:0.84 +1 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.95 18:0.59 21:0.59 27:0.72 29:0.71 30:0.37 32:0.69 34:0.25 36:0.87 39:0.58 43:0.26 45:0.88 55:0.52 66:0.99 69:0.32 70:0.75 71:0.80 74:0.74 76:0.97 77:0.96 81:0.35 83:0.60 86:0.64 91:0.92 96:0.86 97:0.97 98:1.00 99:0.83 101:0.52 104:0.72 108:0.25 114:0.57 120:0.77 123:0.24 126:0.44 127:0.98 129:0.05 135:0.23 139:0.62 140:0.45 145:0.63 146:0.99 147:0.99 149:0.73 150:0.53 151:0.85 153:0.77 154:0.45 155:0.58 158:0.79 159:0.81 162:0.86 164:0.66 165:0.70 172:0.98 173:0.17 176:0.66 177:0.70 179:0.15 190:0.77 192:0.59 195:0.91 201:0.68 202:0.55 204:0.85 208:0.54 212:0.86 215:0.39 216:0.08 222:0.35 230:0.75 232:0.78 233:0.76 235:0.84 241:0.77 242:0.74 243:0.99 244:0.61 247:0.79 248:0.81 253:0.83 255:0.74 256:0.58 259:0.21 260:0.74 265:1.00 266:0.54 267:0.85 268:0.64 271:0.79 276:0.95 279:0.82 282:0.77 283:0.82 285:0.56 290:0.57 297:0.36 300:0.70 +0 11:0.64 12:0.37 17:0.20 18:0.87 21:0.78 27:0.45 29:0.71 30:0.10 34:0.87 36:0.21 37:0.50 39:0.58 43:0.61 45:0.73 55:0.71 66:0.54 69:0.84 70:0.98 71:0.80 74:0.72 86:0.91 91:0.15 98:0.52 101:0.52 108:0.69 123:0.67 124:0.85 127:0.63 129:0.05 133:0.91 135:0.54 139:0.88 145:0.52 146:0.89 147:0.38 149:0.47 154:0.62 159:0.85 172:0.84 173:0.65 177:0.38 178:0.55 179:0.29 187:0.68 212:0.56 216:0.77 222:0.35 235:0.68 241:0.32 242:0.18 243:0.12 245:0.79 247:0.95 253:0.42 254:0.74 257:0.65 259:0.21 265:0.54 266:0.95 267:0.52 271:0.79 276:0.83 300:0.84 +2 1:0.69 7:0.72 8:0.58 9:0.76 11:0.64 12:0.37 17:0.85 18:0.69 21:0.30 27:0.54 29:0.71 30:0.45 32:0.69 34:0.44 36:0.63 37:0.30 39:0.58 43:0.72 45:0.73 66:0.33 69:0.76 70:0.61 71:0.80 74:0.65 77:0.89 81:0.63 83:0.60 86:0.75 91:0.94 96:0.75 98:0.52 99:0.95 101:0.52 104:0.58 108:0.18 114:0.63 120:0.63 122:0.51 123:0.18 124:0.61 126:0.44 127:0.41 129:0.05 133:0.43 135:0.18 139:0.71 140:0.45 145:0.65 146:0.37 147:0.47 149:0.59 150:0.61 151:0.65 153:0.48 154:0.65 155:0.58 159:0.27 162:0.86 163:0.81 164:0.54 165:0.70 172:0.27 173:0.92 176:0.66 177:0.38 179:0.80 190:0.77 191:0.84 192:0.59 195:0.60 201:0.68 202:0.36 204:0.85 208:0.54 212:0.50 215:0.44 216:0.02 220:0.62 222:0.35 230:0.75 232:0.77 233:0.76 235:0.80 241:0.91 242:0.33 243:0.50 245:0.60 247:0.60 248:0.63 253:0.63 254:0.97 255:0.74 256:0.45 257:0.65 259:0.21 260:0.63 265:0.54 266:0.38 267:0.94 268:0.46 271:0.79 276:0.36 277:0.69 282:0.77 285:0.56 290:0.63 297:0.36 300:0.43 +2 1:0.69 7:0.72 8:0.84 9:0.76 11:0.64 12:0.37 17:0.96 21:0.59 27:0.72 29:0.71 30:0.62 32:0.68 34:0.21 36:0.34 39:0.58 43:0.67 45:0.83 48:0.91 55:0.52 66:0.96 69:0.41 70:0.51 71:0.80 74:0.69 77:0.70 81:0.31 91:0.94 96:0.75 98:0.94 101:0.52 104:0.75 108:0.31 114:0.56 120:0.63 123:0.30 126:0.44 127:0.62 129:0.05 135:0.24 138:0.95 139:0.68 140:0.45 145:0.54 146:0.85 147:0.87 149:0.76 150:0.48 151:0.73 153:0.80 154:0.56 155:0.58 158:0.74 159:0.41 162:0.78 164:0.66 172:0.90 173:0.47 175:0.75 176:0.61 177:0.38 179:0.30 190:0.77 191:0.80 192:0.59 195:0.67 201:0.68 202:0.63 204:0.85 208:0.54 212:0.91 215:0.38 216:0.03 220:0.62 222:0.35 230:0.75 232:0.72 233:0.76 235:0.80 241:0.84 242:0.75 243:0.96 244:0.61 247:0.67 248:0.73 253:0.57 255:0.74 256:0.64 257:0.65 260:0.61 265:0.94 266:0.27 267:0.44 268:0.68 271:0.79 276:0.83 277:0.69 279:0.82 281:0.91 282:0.77 283:0.78 285:0.56 286:0.99 290:0.56 297:0.36 298:0.99 300:0.55 +0 8:0.84 9:0.80 12:0.37 17:0.69 18:0.62 21:0.13 27:0.06 29:0.71 30:0.38 31:0.93 34:0.23 36:0.72 37:0.72 39:0.58 43:0.18 55:0.59 66:0.26 69:0.83 70:0.90 71:0.80 74:0.76 76:0.97 77:0.89 79:0.94 81:0.40 83:0.60 86:0.67 91:0.97 96:1.00 98:0.41 108:0.67 114:0.72 120:0.87 122:0.77 123:0.65 124:0.90 126:0.26 127:0.98 129:0.05 133:0.89 135:0.32 137:0.93 138:0.99 139:0.65 140:0.94 145:0.72 146:0.73 147:0.67 149:0.36 150:0.34 151:0.94 153:0.99 154:0.47 155:0.67 158:0.74 159:0.83 162:0.78 164:0.81 172:0.63 173:0.68 176:0.61 177:0.38 179:0.42 190:0.77 191:0.96 192:0.87 195:0.93 196:0.89 202:0.98 208:0.64 212:0.60 216:0.54 222:0.35 235:0.12 241:0.94 242:0.72 243:0.21 244:0.61 245:0.79 247:0.76 248:0.92 253:0.99 254:0.74 255:0.79 256:0.98 257:0.65 259:0.21 260:0.84 265:0.43 266:0.92 267:0.85 268:0.99 271:0.79 276:0.77 282:0.73 284:0.98 285:0.62 290:0.72 300:0.84 +1 7:0.72 8:0.60 11:0.64 12:0.37 17:0.97 18:0.72 21:0.78 22:0.93 27:0.49 29:0.71 30:0.62 32:0.80 34:0.78 36:0.53 37:0.23 39:0.58 43:0.61 44:0.93 45:0.73 47:0.93 66:0.54 69:0.05 70:0.54 71:0.80 74:0.75 77:0.70 86:0.81 91:0.83 98:0.37 101:0.52 104:0.58 106:0.81 108:0.05 120:0.53 122:0.51 123:0.05 124:0.50 127:0.40 129:0.05 133:0.43 135:0.51 139:0.84 140:0.45 145:0.47 146:0.29 147:0.35 149:0.29 151:0.46 153:0.48 154:0.88 155:0.58 159:0.24 162:0.86 164:0.53 172:0.41 173:0.17 177:0.70 179:0.77 187:0.39 190:0.89 192:0.59 195:0.58 202:0.36 204:0.85 206:0.81 208:0.54 212:0.78 216:0.18 220:0.99 222:0.35 230:0.75 232:0.77 233:0.76 241:0.49 242:0.29 243:0.63 245:0.59 247:0.67 248:0.46 253:0.43 254:0.86 256:0.45 259:0.21 260:0.53 262:0.93 265:0.40 266:0.27 267:0.65 268:0.46 271:0.79 276:0.26 282:0.88 285:0.47 300:0.43 +2 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.99 18:0.70 21:0.05 27:0.72 29:0.71 30:0.21 32:0.80 34:0.23 36:0.69 37:0.55 39:0.58 43:0.60 44:0.93 45:0.95 55:0.45 66:0.99 69:0.08 70:0.64 71:0.80 74:0.96 77:0.89 81:0.68 83:0.60 86:0.80 91:0.92 96:0.89 98:1.00 99:0.83 101:0.52 104:0.54 108:0.07 114:0.85 120:0.82 123:0.07 126:0.44 127:0.96 129:0.05 135:0.18 139:0.84 140:0.45 145:0.74 146:0.92 147:0.93 149:0.87 150:0.64 151:0.90 153:0.83 154:0.74 155:0.58 159:0.54 162:0.79 164:0.69 165:0.70 172:0.98 173:0.17 176:0.66 177:0.70 179:0.16 190:0.77 191:0.76 192:0.59 195:0.68 201:0.68 202:0.63 204:0.85 208:0.54 212:0.94 215:0.78 216:0.04 222:0.35 230:0.75 232:0.74 233:0.76 235:0.12 241:0.93 242:0.41 243:0.99 247:0.79 248:0.85 253:0.91 254:0.74 255:0.74 256:0.64 259:0.21 260:0.79 265:1.00 266:0.38 267:0.76 268:0.68 271:0.79 276:0.95 282:0.88 285:0.56 290:0.85 297:0.36 300:0.55 +0 8:0.67 12:0.37 17:0.26 21:0.30 27:0.01 29:0.71 31:0.89 34:0.67 36:0.84 37:1.00 39:0.58 71:0.80 79:0.88 81:0.31 91:0.57 114:0.63 120:0.59 126:0.26 135:0.51 137:0.89 140:0.45 150:0.29 151:0.59 153:0.48 162:0.62 164:0.54 175:0.97 176:0.61 187:0.39 190:0.77 192:0.51 202:0.50 216:0.53 220:0.74 222:0.35 232:0.75 235:0.31 241:0.69 244:0.93 248:0.58 253:0.46 255:0.74 256:0.45 257:0.93 259:0.21 260:0.59 267:0.52 268:0.46 271:0.79 277:0.95 284:0.87 285:0.56 290:0.63 +0 9:0.80 11:0.64 12:0.37 17:0.72 18:0.63 21:0.71 27:0.51 29:0.71 30:0.61 31:0.93 34:0.71 36:0.32 37:0.46 39:0.58 43:0.63 45:0.90 55:0.42 66:0.94 69:0.60 70:0.42 71:0.80 74:0.89 79:0.92 81:0.23 83:0.60 86:0.73 91:0.89 96:0.92 98:0.72 101:0.52 108:0.46 120:0.85 122:0.51 123:0.44 126:0.26 127:0.23 129:0.05 135:0.30 137:0.93 139:0.70 140:0.97 145:0.80 146:0.64 147:0.69 149:0.72 150:0.23 151:0.65 153:0.69 154:0.71 155:0.67 159:0.24 162:0.81 164:0.86 172:0.85 173:0.70 177:0.70 179:0.22 187:0.68 192:0.87 196:0.91 202:0.80 208:0.64 212:0.93 215:0.37 216:0.42 220:0.62 222:0.35 235:0.84 241:0.41 242:0.58 243:0.94 247:0.67 248:0.75 253:0.93 254:0.74 255:0.95 256:0.89 259:0.21 260:0.83 265:0.73 266:0.23 267:0.73 268:0.85 271:0.79 276:0.73 283:0.78 284:0.93 285:0.62 297:0.36 300:0.55 +1 1:0.69 7:0.72 8:0.89 9:0.76 11:0.64 12:0.37 17:0.90 21:0.40 27:0.72 29:0.71 30:0.90 32:0.64 34:0.19 36:0.39 37:0.93 39:0.58 43:0.55 45:0.73 55:0.45 66:0.74 69:0.41 70:0.31 71:0.80 74:0.65 81:0.40 91:0.92 96:0.69 98:0.75 101:0.52 104:0.54 108:0.32 114:0.61 120:0.64 123:0.31 124:0.45 126:0.44 127:0.81 129:0.59 133:0.64 135:0.47 140:0.45 145:0.94 146:0.95 147:0.96 149:0.79 150:0.52 151:0.50 153:0.48 154:0.44 155:0.58 159:0.81 162:0.78 164:0.65 172:0.98 173:0.22 175:0.75 176:0.66 177:0.38 179:0.13 190:0.89 191:0.82 192:0.59 195:0.92 201:0.68 202:0.59 204:0.85 208:0.54 212:0.91 215:0.42 216:0.02 220:0.91 222:0.35 230:0.75 233:0.76 235:0.60 241:0.94 242:0.78 243:0.77 244:0.61 245:0.98 247:0.67 248:0.49 253:0.47 255:0.74 256:0.58 257:0.65 259:0.21 260:0.62 265:0.75 266:0.80 267:0.69 268:0.64 271:0.79 276:0.96 277:0.69 283:0.78 285:0.56 290:0.61 297:0.36 300:0.94 +0 11:0.64 12:0.79 17:0.55 18:0.73 21:0.78 27:0.45 29:0.52 30:0.28 34:0.77 36:0.18 37:0.50 39:0.51 43:0.62 45:0.73 55:0.59 66:0.54 69:0.72 70:0.87 71:0.86 74:0.42 81:0.25 86:0.83 91:0.17 98:0.51 101:0.87 108:0.55 114:0.60 122:0.80 123:0.53 124:0.63 126:0.26 127:0.36 129:0.05 131:0.61 133:0.60 135:0.72 139:0.76 144:0.57 145:0.45 146:0.66 147:0.71 149:0.23 150:0.26 154:0.51 157:0.84 159:0.52 166:0.86 172:0.59 173:0.68 176:0.59 177:0.70 178:0.55 179:0.54 182:0.76 187:0.68 212:0.61 216:0.77 222:0.76 227:0.61 229:0.61 231:0.94 235:1.00 241:0.24 242:0.19 243:0.63 245:0.68 246:0.61 247:0.79 253:0.44 254:0.98 259:0.21 265:0.53 266:0.54 267:0.28 271:0.36 276:0.54 287:0.61 290:0.60 300:0.70 +0 12:0.79 17:0.89 18:0.60 21:0.78 27:0.72 29:0.52 30:0.21 34:0.34 36:0.99 39:0.51 43:0.08 55:0.59 66:0.54 69:0.90 70:0.37 71:0.86 86:0.62 91:0.74 98:0.24 108:0.84 123:0.83 124:0.45 127:0.15 129:0.59 131:0.61 133:0.47 135:0.88 139:0.61 146:0.05 147:0.05 149:0.10 154:0.08 157:0.61 159:0.28 163:0.90 166:0.61 172:0.21 173:0.89 175:0.91 177:0.05 178:0.55 179:0.66 182:0.61 212:0.42 216:0.07 222:0.76 227:0.61 229:0.61 231:0.67 235:0.12 241:0.79 242:0.75 243:0.26 244:0.93 245:0.40 246:0.61 247:0.30 253:0.20 257:0.84 259:0.21 265:0.26 266:0.23 267:0.44 271:0.36 276:0.21 277:0.87 287:0.61 300:0.25 +0 8:0.62 11:0.64 12:0.79 17:0.03 18:1.00 21:0.30 27:0.66 29:0.52 30:0.20 31:0.88 34:0.64 36:0.59 37:0.33 39:0.51 43:0.66 45:0.83 64:0.77 66:0.05 69:0.88 70:0.97 71:0.86 74:0.40 79:0.88 81:0.38 86:1.00 91:0.23 98:0.23 101:0.87 108:1.00 120:0.58 122:0.86 123:0.96 124:0.99 126:0.44 127:1.00 129:0.99 131:0.88 133:0.99 135:0.54 137:0.88 139:1.00 140:0.45 144:0.57 146:0.94 147:0.84 149:0.70 150:0.37 151:0.57 153:0.48 154:0.45 157:0.75 159:0.99 162:0.86 164:0.55 166:0.83 172:0.92 173:0.29 177:0.38 179:0.09 182:0.71 187:0.87 190:0.89 192:0.49 196:0.93 201:0.59 202:0.36 212:0.54 215:0.72 216:0.52 219:0.91 220:0.87 222:0.76 227:0.61 229:0.61 231:0.93 235:0.42 241:0.56 242:0.62 243:0.10 245:0.99 246:0.61 247:0.97 248:0.56 253:0.35 254:0.84 255:0.69 256:0.45 257:0.65 259:0.21 260:0.58 265:0.25 266:0.98 267:0.59 268:0.46 271:0.36 276:1.00 277:0.69 283:0.67 284:0.88 285:0.56 287:0.61 292:0.88 297:0.36 300:0.98 +0 1:0.66 11:0.42 12:0.79 17:0.34 18:0.82 21:0.30 27:0.64 29:0.52 30:0.10 34:0.73 36:0.56 37:0.37 39:0.51 43:0.14 55:0.42 64:0.77 66:0.88 69:0.38 70:0.81 71:0.86 74:0.50 81:0.56 86:0.84 91:0.18 98:0.78 108:0.30 114:0.84 123:0.28 126:0.54 127:0.27 129:0.05 131:0.61 135:0.73 139:0.83 144:0.57 146:0.78 147:0.82 149:0.17 150:0.54 154:0.65 155:0.51 157:0.61 158:0.81 159:0.34 166:0.97 172:0.65 173:0.40 176:0.70 177:0.70 178:0.55 179:0.51 182:0.61 187:0.39 192:0.48 201:0.64 202:0.36 208:0.64 212:0.59 215:0.72 216:0.61 219:0.94 222:0.76 227:0.61 229:0.61 231:0.95 235:0.52 241:0.66 242:0.37 243:0.88 244:0.61 246:0.61 247:0.67 253:0.60 254:0.80 259:0.21 265:0.78 266:0.38 267:0.76 271:0.36 276:0.53 279:0.80 283:0.66 287:0.61 290:0.84 292:0.87 297:0.36 300:0.55 +0 11:0.64 12:0.79 17:0.18 18:0.89 21:0.78 27:0.72 29:0.52 30:0.33 34:0.97 36:0.97 39:0.51 43:0.26 45:0.87 55:0.71 66:0.72 69:0.84 70:0.91 71:0.86 74:0.48 86:0.91 91:0.27 98:0.75 101:0.87 108:0.84 123:0.83 124:0.50 127:0.48 129:0.59 131:0.61 133:0.74 135:0.88 139:0.87 144:0.57 146:0.65 147:0.70 149:0.43 154:0.46 157:0.97 159:0.80 166:0.61 172:0.93 173:0.68 177:0.70 178:0.55 179:0.19 182:0.90 187:0.68 212:0.71 216:0.29 222:0.76 227:0.61 229:0.61 231:0.93 235:0.88 241:0.01 242:0.27 243:0.19 245:0.89 246:0.61 247:0.94 253:0.39 254:0.86 259:0.21 265:0.75 266:0.71 267:0.73 271:0.36 276:0.89 287:0.61 300:0.70 +0 1:0.64 11:0.55 12:0.79 17:0.28 18:0.73 21:0.47 27:0.45 29:0.52 30:0.08 34:0.91 36:0.20 37:0.50 39:0.51 43:0.13 45:0.66 55:0.42 64:0.77 66:0.16 69:0.69 70:0.95 71:0.86 74:0.43 81:0.62 86:0.79 91:0.09 98:0.16 99:0.83 101:0.52 108:0.53 114:0.80 122:0.82 123:0.51 124:0.91 126:0.54 127:0.60 129:0.05 131:0.61 133:0.89 135:0.92 139:0.76 144:0.57 145:0.47 146:0.30 147:0.36 149:0.12 150:0.49 154:0.75 155:0.50 157:0.61 159:0.79 165:0.70 166:0.97 172:0.21 173:0.52 176:0.73 177:0.70 178:0.55 179:0.77 182:0.61 187:0.68 192:0.47 201:0.62 208:0.64 212:0.13 215:0.63 216:0.77 222:0.76 227:0.61 229:0.61 231:0.95 235:0.74 241:0.15 242:0.22 243:0.37 245:0.50 246:0.61 247:0.76 253:0.58 254:0.74 259:0.21 265:0.18 266:0.80 267:0.52 271:0.36 276:0.44 287:0.61 290:0.80 297:0.36 300:0.70 +0 12:0.79 17:0.51 18:0.96 21:0.77 27:0.52 29:0.52 30:0.61 34:0.40 36:0.42 37:0.56 39:0.51 43:0.13 48:0.91 55:0.71 64:0.77 66:0.95 69:0.72 70:0.47 71:0.86 74:0.29 81:0.23 86:0.94 91:0.15 98:0.85 108:0.56 122:0.86 123:0.53 126:0.26 127:0.35 129:0.05 131:0.61 135:0.66 139:0.93 146:0.98 147:0.99 149:0.31 150:0.24 154:0.25 157:0.61 159:0.77 166:0.61 172:0.87 173:0.50 177:0.70 178:0.55 179:0.28 182:0.61 187:0.21 212:0.21 216:0.48 219:0.90 222:0.76 227:0.61 229:0.61 231:0.80 235:0.97 241:0.24 242:0.72 243:0.95 244:0.83 246:0.61 247:0.74 253:0.27 254:0.80 259:0.21 265:0.85 266:0.71 267:0.52 271:0.36 276:0.79 281:0.86 287:0.61 292:0.88 300:0.43 +0 1:0.62 7:0.64 8:0.62 9:0.71 11:0.42 12:0.79 17:0.57 18:0.61 21:0.30 27:0.70 29:0.52 30:0.84 32:0.67 34:0.31 36:0.86 37:0.45 39:0.51 43:0.16 55:0.52 66:0.54 69:0.32 70:0.52 71:0.86 74:0.53 77:0.70 81:0.44 86:0.63 91:0.62 96:0.71 98:0.50 108:0.66 114:0.82 117:0.86 120:0.58 122:0.77 123:0.64 124:0.69 126:0.44 127:0.52 129:0.59 131:0.98 133:0.76 135:0.78 139:0.61 140:0.80 144:0.57 145:0.44 146:0.53 147:0.59 149:0.42 150:0.48 151:0.57 153:0.48 154:0.51 155:0.55 157:0.61 159:0.72 162:0.58 164:0.56 166:0.97 172:0.78 173:0.19 175:0.75 176:0.63 177:0.38 178:0.42 179:0.35 182:0.61 187:0.87 190:0.89 192:0.55 195:0.88 201:0.59 202:0.53 208:0.54 212:0.78 215:0.72 216:0.36 220:0.87 222:0.76 227:0.61 229:0.61 230:0.64 231:0.95 232:0.93 233:0.66 235:0.42 241:0.03 242:0.76 243:0.29 244:0.83 245:0.81 246:0.61 247:0.74 248:0.56 253:0.62 254:0.80 255:0.69 256:0.45 257:0.65 259:0.21 260:0.58 265:0.52 266:0.80 267:0.73 268:0.46 271:0.36 276:0.74 277:0.87 282:0.75 285:0.56 287:0.61 290:0.82 297:0.36 300:0.84 +0 7:0.64 8:0.80 12:0.79 17:0.92 18:0.91 21:0.22 27:0.72 29:0.52 30:0.55 31:0.89 32:0.63 34:0.34 36:0.46 37:0.93 39:0.51 43:0.20 44:0.88 48:0.97 55:0.59 64:0.77 66:0.52 69:0.08 70:0.60 71:0.86 74:0.31 79:0.89 81:0.26 86:0.87 91:0.60 98:0.53 108:0.07 122:0.86 123:0.07 124:0.65 126:0.26 127:0.74 129:0.05 131:0.61 133:0.62 135:0.44 137:0.89 139:0.88 140:0.45 145:0.54 146:0.66 147:0.71 149:0.26 150:0.29 151:0.53 153:0.48 154:0.29 157:0.61 159:0.61 162:0.86 166:0.61 172:0.48 173:0.14 175:0.75 177:0.38 179:0.75 182:0.61 187:0.39 190:0.95 191:0.82 195:0.79 196:0.92 202:0.36 212:0.29 216:0.15 219:0.90 220:0.82 222:0.76 227:0.61 228:0.99 229:0.61 230:0.65 231:0.81 233:0.76 235:0.22 241:0.37 242:0.54 243:0.61 244:0.83 245:0.61 246:0.61 247:0.60 248:0.53 253:0.28 254:0.95 256:0.45 257:0.65 259:0.21 265:0.54 266:0.71 267:0.28 268:0.46 271:0.36 276:0.45 277:0.69 281:0.91 284:0.88 285:0.47 287:0.61 292:0.88 300:0.55 +0 8:0.63 11:0.55 12:0.79 17:0.05 18:0.88 21:0.54 27:0.66 29:0.52 30:0.19 34:0.89 36:0.79 37:0.33 39:0.51 43:0.33 45:0.66 55:0.59 66:0.27 69:0.95 70:0.91 71:0.86 74:0.35 81:0.28 86:0.86 91:0.31 98:0.45 101:0.52 108:0.84 120:0.55 122:0.85 123:0.83 124:0.89 126:0.26 127:0.66 129:0.05 131:0.88 133:0.89 135:0.51 139:0.81 140:0.45 144:0.57 146:0.58 147:0.50 149:0.38 150:0.31 151:0.48 153:0.48 154:0.25 157:0.61 159:0.80 162:0.86 164:0.55 166:0.83 172:0.63 173:0.94 177:0.05 179:0.39 182:0.61 187:0.87 190:0.95 202:0.36 212:0.42 215:0.58 216:0.52 220:0.96 222:0.76 227:0.61 229:0.61 231:0.94 235:0.60 241:0.15 242:0.13 243:0.18 244:0.61 245:0.77 246:0.61 247:0.95 248:0.48 253:0.31 256:0.45 257:0.84 259:0.21 260:0.55 265:0.47 266:0.87 267:0.52 268:0.46 271:0.36 276:0.76 277:0.69 283:0.67 285:0.47 287:0.61 297:0.36 300:0.84 +0 7:0.64 8:0.62 12:0.79 17:0.09 18:0.97 21:0.71 22:0.93 27:0.56 29:0.52 30:0.26 34:0.90 36:0.70 37:0.53 39:0.51 43:0.11 47:0.93 48:0.91 55:0.52 64:0.77 66:0.67 69:0.32 70:0.87 71:0.86 81:0.32 86:0.97 91:0.44 98:0.70 104:0.99 106:0.81 108:0.60 122:0.43 123:0.58 124:0.63 126:0.44 127:0.78 129:0.81 131:0.61 133:0.76 135:0.47 139:0.96 146:0.40 147:0.46 149:0.31 150:0.29 154:0.54 157:0.61 159:0.89 166:0.82 172:0.94 173:0.11 175:0.75 177:0.38 178:0.55 179:0.19 182:0.61 187:0.68 192:0.44 201:0.55 206:0.81 212:0.74 215:0.48 216:0.87 219:0.91 222:0.76 227:0.61 229:0.61 230:0.64 231:0.74 233:0.66 235:0.88 241:0.07 242:0.17 243:0.19 244:0.83 245:0.93 246:0.61 247:0.67 253:0.20 254:0.74 257:0.65 259:0.21 262:0.93 265:0.71 266:0.89 267:0.79 271:0.36 276:0.91 277:0.69 281:0.86 283:0.66 287:0.61 292:0.87 297:0.36 300:0.84 +0 1:0.58 11:0.50 12:0.79 17:0.36 18:0.85 21:0.64 27:0.69 29:0.52 30:0.33 34:0.79 36:0.23 37:0.38 39:0.51 43:0.33 45:0.66 55:0.42 66:0.36 69:0.63 70:0.69 71:0.86 74:0.36 77:0.89 81:0.36 86:0.83 91:0.07 98:0.50 101:0.52 108:0.76 114:0.63 117:0.86 122:0.86 123:0.74 124:0.60 126:0.44 127:0.44 129:0.59 131:0.61 133:0.47 135:0.63 139:0.79 144:0.57 145:0.65 146:0.48 147:0.55 149:0.29 150:0.40 154:0.48 155:0.47 157:0.85 158:0.72 159:0.46 166:0.97 172:0.41 173:0.64 175:0.75 176:0.59 177:0.38 178:0.55 179:0.68 182:0.76 187:0.98 192:0.45 195:0.68 201:0.55 208:0.54 212:0.29 215:0.53 216:0.68 222:0.76 227:0.61 229:0.61 231:0.95 232:0.98 235:0.80 241:0.02 242:0.43 243:0.49 244:0.83 245:0.71 246:0.61 247:0.74 253:0.48 257:0.65 259:0.21 265:0.51 266:0.54 267:0.76 271:0.36 276:0.44 277:0.69 282:0.72 287:0.61 290:0.63 297:0.36 300:0.55 +2 1:0.64 9:0.71 11:0.42 12:0.79 17:0.38 18:0.84 21:0.13 27:0.72 29:0.52 30:0.13 34:0.45 36:0.97 37:0.25 39:0.51 43:0.59 55:0.71 66:0.45 69:0.66 70:0.97 71:0.86 74:0.48 81:0.47 86:0.84 91:0.73 96:0.72 98:0.47 104:0.95 106:0.81 108:0.50 114:0.82 117:0.86 120:0.59 123:0.48 124:0.89 126:0.44 127:0.65 129:0.81 131:0.98 133:0.91 135:0.94 138:0.95 139:0.79 140:0.45 144:0.57 146:0.83 147:0.86 149:0.51 150:0.50 151:0.66 153:0.48 154:0.60 155:0.57 157:0.61 159:0.83 162:0.86 164:0.55 166:0.97 172:0.70 173:0.44 176:0.62 177:0.70 179:0.43 182:0.61 187:0.21 190:0.89 192:0.56 201:0.60 202:0.36 206:0.81 208:0.54 212:0.40 215:0.79 216:0.11 220:0.74 222:0.76 227:0.61 229:0.61 231:0.95 232:0.91 235:0.22 241:0.21 242:0.14 243:0.58 244:0.61 245:0.70 246:0.61 247:0.92 248:0.63 253:0.63 254:0.80 255:0.70 256:0.45 259:0.21 260:0.60 265:0.49 266:0.94 267:0.76 268:0.46 271:0.36 276:0.73 285:0.56 286:0.99 287:0.61 290:0.82 297:0.36 298:0.99 300:0.84 +0 1:0.65 11:0.42 12:0.79 17:0.82 18:0.78 21:0.47 27:0.54 29:0.52 30:0.87 34:0.93 36:0.49 37:0.60 39:0.51 43:0.75 55:0.42 64:0.77 66:0.32 69:0.32 70:0.37 71:0.86 74:0.55 81:0.51 86:0.85 91:0.01 98:0.13 108:0.25 114:0.78 122:0.76 123:0.99 124:0.60 126:0.54 127:0.94 129:0.59 131:0.61 133:0.46 135:0.85 139:0.80 144:0.57 146:0.39 147:0.45 149:0.69 150:0.50 154:0.66 155:0.50 157:0.61 159:0.97 166:0.97 172:0.45 173:0.06 176:0.70 177:0.70 178:0.55 179:0.73 182:0.61 187:0.68 192:0.47 201:0.62 208:0.64 212:0.52 215:0.63 216:0.64 222:0.76 227:0.61 229:0.61 231:0.95 235:0.68 241:0.01 242:0.45 243:0.51 244:0.61 245:0.74 246:0.61 247:0.47 253:0.58 254:0.80 259:0.21 265:0.13 266:0.54 267:0.44 271:0.36 276:0.14 287:0.61 290:0.78 297:0.36 300:0.43 +0 1:0.69 7:0.72 8:0.71 9:0.80 11:0.64 12:0.37 17:0.30 18:0.84 20:0.90 21:0.40 27:0.21 29:0.98 30:0.68 31:0.90 34:0.71 36:0.96 37:0.74 39:0.42 43:0.72 45:0.95 66:0.25 69:0.15 70:0.75 71:0.65 74:0.85 78:0.91 79:0.89 81:0.42 83:0.60 86:0.78 91:0.65 96:0.95 97:0.98 98:0.39 99:0.83 100:0.73 101:0.52 104:0.84 106:0.81 108:0.85 111:0.88 114:0.61 117:0.86 120:0.91 122:0.51 123:0.85 124:0.87 126:0.44 127:0.63 129:0.59 133:0.87 135:0.24 137:0.90 139:0.75 140:0.98 146:0.55 147:0.61 149:0.91 150:0.55 151:0.63 152:0.84 153:0.69 154:0.62 155:0.67 158:0.79 159:0.75 162:0.71 164:0.89 165:0.70 169:0.72 172:0.68 173:0.12 176:0.66 177:0.70 179:0.31 186:0.91 187:0.39 190:0.77 192:0.87 196:0.90 201:0.68 202:0.86 204:0.85 206:0.81 208:0.64 212:0.60 215:0.42 216:0.95 220:0.62 222:0.48 230:0.75 232:0.89 233:0.76 235:0.52 241:0.52 242:0.53 243:0.38 244:0.61 245:0.85 247:0.76 248:0.61 253:0.95 255:0.95 256:0.89 259:0.21 260:0.89 261:0.87 265:0.42 266:0.75 267:0.36 268:0.89 271:0.38 276:0.81 277:0.69 279:0.82 283:0.82 284:0.86 285:0.62 290:0.61 297:0.36 300:0.84 +1 1:0.69 7:0.72 8:0.87 9:0.76 11:0.64 12:0.37 17:0.67 18:0.69 21:0.64 27:0.32 29:0.98 30:0.68 32:0.69 34:0.62 36:0.88 37:0.62 39:0.42 43:0.67 45:0.92 66:0.36 69:0.63 70:0.56 71:0.65 74:0.80 77:0.89 81:0.35 83:0.60 86:0.67 91:0.57 96:0.92 97:0.94 98:0.61 99:0.95 101:0.52 104:0.92 106:0.81 108:0.69 114:0.56 117:0.86 120:0.86 122:0.51 123:0.67 124:0.70 126:0.44 127:0.49 129:0.59 133:0.66 135:0.89 139:0.65 140:0.45 145:0.86 146:0.49 147:0.56 149:0.84 150:0.53 151:0.94 153:0.90 154:0.57 155:0.58 158:0.79 159:0.53 162:0.86 164:0.74 165:0.70 172:0.61 173:0.60 176:0.66 177:0.70 179:0.45 187:0.87 190:0.77 191:0.70 192:0.59 195:0.75 201:0.68 202:0.64 204:0.85 206:0.81 208:0.54 212:0.42 215:0.38 216:0.69 222:0.48 230:0.75 232:0.94 233:0.76 235:0.88 241:0.59 242:0.57 243:0.40 244:0.61 245:0.81 247:0.60 248:0.89 253:0.91 255:0.74 256:0.68 259:0.21 260:0.83 265:0.62 266:0.71 267:0.73 268:0.72 271:0.38 276:0.68 279:0.82 282:0.77 283:0.82 285:0.56 290:0.56 297:0.36 300:0.55 +1 1:0.69 11:0.64 12:0.37 17:0.16 18:0.78 22:0.93 27:0.72 29:0.98 30:0.71 34:0.84 36:0.67 39:0.42 43:0.70 45:0.73 47:0.93 55:0.71 66:0.82 69:0.38 70:0.24 71:0.65 74:0.50 81:0.84 83:0.60 86:0.81 91:0.54 98:0.89 99:0.83 101:0.52 104:0.80 106:0.81 108:0.30 114:0.95 117:0.86 123:0.28 126:0.44 127:0.44 129:0.05 135:0.37 139:0.78 146:0.56 147:0.62 149:0.40 150:0.81 154:0.72 155:0.58 159:0.20 165:0.70 172:0.48 173:0.66 176:0.66 177:0.70 178:0.55 179:0.81 187:0.39 192:0.59 201:0.68 206:0.81 208:0.54 212:0.95 215:0.96 216:0.17 219:0.89 222:0.48 232:0.85 235:0.05 241:0.24 242:0.14 243:0.83 247:0.67 253:0.63 254:0.97 259:0.21 262:0.93 265:0.89 266:0.12 267:0.28 271:0.38 276:0.40 290:0.95 292:0.95 297:0.36 300:0.18 +1 1:0.69 6:0.87 8:0.75 9:0.80 11:0.64 12:0.37 17:0.18 20:0.89 21:0.22 27:0.09 29:0.98 30:0.89 31:0.88 34:0.74 36:0.84 37:0.91 39:0.42 43:0.67 45:0.77 66:0.75 69:0.61 70:0.20 71:0.65 74:0.55 78:0.98 79:0.88 81:0.58 83:0.60 91:0.78 96:0.91 97:0.89 98:0.75 99:0.83 100:0.89 101:0.52 104:0.58 108:0.46 111:0.95 114:0.67 120:0.86 122:0.51 123:0.44 126:0.44 127:0.24 129:0.05 132:0.97 135:0.58 137:0.88 140:0.97 145:0.38 146:0.44 147:0.51 149:0.59 150:0.60 151:0.56 152:0.94 153:0.69 154:0.57 155:0.67 158:0.78 159:0.16 162:0.59 164:0.82 165:0.70 169:0.93 172:0.32 173:0.86 175:0.75 176:0.66 177:0.38 179:0.84 186:0.98 187:0.68 189:0.83 190:0.77 191:0.73 192:0.87 201:0.68 202:0.81 208:0.64 212:0.30 215:0.51 216:0.66 220:0.62 222:0.48 232:0.77 235:0.31 238:0.82 241:0.76 242:0.63 243:0.77 244:0.61 247:0.30 248:0.55 253:0.86 255:0.95 256:0.82 257:0.65 260:0.83 261:0.94 265:0.75 266:0.27 267:0.19 268:0.81 271:0.38 276:0.20 277:0.69 279:0.82 283:0.78 284:0.86 285:0.62 290:0.67 297:0.36 300:0.18 +2 1:0.69 11:0.64 12:0.37 17:0.82 18:0.87 21:0.22 27:0.53 29:0.98 30:0.74 32:0.69 34:0.21 36:0.49 37:0.58 39:0.42 43:0.72 45:0.99 66:0.99 69:0.10 70:0.39 71:0.65 74:0.99 77:0.89 81:0.58 83:0.60 86:0.95 91:0.12 97:0.89 98:0.96 99:0.83 101:0.52 108:0.09 114:0.67 122:0.51 123:0.09 126:0.44 127:0.72 129:0.05 135:0.56 139:0.93 145:0.69 146:0.97 147:0.98 149:0.99 150:0.60 154:0.65 155:0.58 158:0.79 159:0.71 165:0.70 172:0.99 173:0.12 176:0.66 177:0.70 178:0.55 179:0.12 187:0.39 192:0.59 195:0.84 201:0.68 208:0.54 212:0.94 215:0.51 216:0.17 222:0.48 235:0.31 241:0.56 242:0.54 243:0.99 247:0.47 253:0.55 254:0.80 259:0.21 265:0.96 266:0.27 267:0.17 271:0.38 276:0.97 279:0.82 282:0.77 283:0.82 290:0.67 297:0.36 300:0.43 +0 11:0.64 12:0.37 17:0.51 18:0.63 21:0.78 27:0.45 29:0.98 30:0.73 34:0.82 36:0.25 37:0.50 39:0.42 43:0.60 45:0.85 66:0.20 69:0.74 70:0.44 71:0.65 74:0.56 86:0.74 91:0.09 98:0.32 101:0.52 108:0.82 122:0.51 123:0.55 124:0.55 127:0.35 129:0.05 133:0.43 135:0.81 139:0.70 145:0.47 146:0.41 147:0.48 149:0.56 154:0.70 159:0.47 172:0.45 173:0.73 177:0.70 178:0.55 179:0.66 187:0.68 212:0.61 216:0.77 222:0.48 235:0.95 241:0.44 242:0.58 243:0.40 245:0.68 247:0.47 253:0.37 254:0.94 259:0.21 265:0.26 266:0.47 267:0.95 271:0.38 276:0.31 300:0.43 +0 1:0.69 11:0.64 12:0.37 17:0.43 18:0.70 21:0.71 27:0.45 29:0.98 30:0.74 32:0.69 34:0.83 36:0.20 37:0.50 39:0.42 43:0.64 45:0.87 66:0.49 69:0.70 70:0.52 71:0.65 74:0.65 77:0.70 81:0.32 83:0.60 86:0.75 91:0.18 98:0.39 99:0.83 101:0.52 108:0.53 114:0.54 122:0.51 123:0.51 124:0.66 126:0.44 127:0.40 129:0.05 133:0.66 135:0.78 139:0.72 145:0.47 146:0.57 147:0.63 149:0.67 150:0.51 154:0.66 155:0.58 159:0.58 163:0.81 165:0.70 172:0.48 173:0.63 176:0.66 177:0.70 178:0.55 179:0.64 187:0.68 192:0.59 195:0.80 201:0.68 208:0.54 212:0.27 215:0.37 216:0.77 222:0.48 235:0.88 241:0.21 242:0.40 243:0.60 245:0.64 247:0.47 253:0.41 254:0.84 259:0.21 265:0.42 266:0.75 267:0.19 271:0.38 276:0.46 282:0.77 290:0.54 297:0.36 300:0.55 +1 1:0.74 6:0.79 7:0.66 8:0.58 9:0.80 11:0.64 12:0.37 17:0.34 18:0.88 20:0.88 21:0.22 22:0.93 27:0.40 29:0.98 30:0.21 31:0.88 32:0.80 34:0.85 36:0.68 37:0.36 39:0.42 43:0.13 44:0.93 45:0.66 47:0.93 48:0.91 55:0.71 64:0.77 66:0.80 69:0.17 70:0.86 71:0.65 74:0.61 76:0.85 77:0.70 78:0.97 79:0.88 81:0.79 83:0.60 86:0.90 91:0.33 96:0.71 98:0.89 99:0.95 100:0.94 101:0.52 104:0.58 108:0.14 111:0.95 114:0.71 120:0.58 122:0.86 123:0.13 126:0.54 127:0.44 129:0.05 132:0.97 135:0.75 137:0.88 139:0.92 140:0.45 145:0.65 146:0.63 147:0.68 149:0.15 150:0.85 151:0.54 152:0.93 153:0.46 154:0.94 155:0.67 159:0.24 162:0.86 164:0.65 165:0.70 169:0.93 172:0.45 173:0.44 176:0.73 177:0.70 179:0.84 186:0.97 187:0.39 192:0.87 195:0.57 196:0.92 201:0.93 202:0.49 208:0.64 212:0.37 215:0.51 216:0.37 219:0.89 220:0.82 222:0.48 230:0.69 232:0.77 233:0.76 235:0.60 241:0.44 242:0.76 243:0.82 247:0.35 248:0.56 253:0.56 254:0.74 255:0.95 256:0.58 260:0.58 261:0.95 262:0.93 265:0.89 266:0.27 267:0.44 268:0.58 271:0.38 276:0.35 281:0.91 282:0.88 284:0.91 285:0.62 290:0.71 292:0.91 297:0.36 300:0.55 +1 1:0.69 7:0.72 8:0.83 9:0.76 11:0.64 12:0.37 17:0.61 18:0.69 21:0.30 27:0.19 29:0.98 30:0.75 32:0.78 34:0.24 36:1.00 37:0.73 39:0.42 43:0.69 44:0.93 45:0.98 48:0.91 66:0.23 69:0.50 70:0.43 71:0.65 74:0.93 77:0.70 81:0.50 83:0.60 86:0.81 91:0.33 96:0.76 97:0.94 98:0.65 99:0.83 101:0.52 104:0.80 108:0.82 114:0.63 120:0.64 122:0.51 123:0.37 124:0.55 126:0.44 127:0.69 129:0.05 133:0.43 135:0.99 139:0.85 140:0.45 145:0.63 146:0.80 147:0.83 149:0.94 150:0.57 151:0.64 153:0.46 154:0.73 155:0.58 158:0.78 159:0.64 162:0.73 164:0.68 165:0.70 172:0.89 173:0.42 176:0.66 177:0.70 179:0.23 187:0.68 190:0.77 192:0.59 195:0.79 201:0.68 202:0.63 204:0.85 208:0.54 212:0.89 215:0.44 216:0.81 220:0.87 222:0.48 230:0.74 232:0.85 233:0.73 235:0.42 241:0.55 242:0.61 243:0.43 245:0.97 247:0.60 248:0.66 253:0.76 254:0.86 255:0.74 256:0.64 259:0.21 260:0.64 265:0.62 266:0.54 267:0.87 268:0.66 271:0.38 276:0.87 279:0.82 281:0.91 282:0.86 283:0.78 285:0.56 290:0.63 297:0.36 300:0.70 +1 1:0.74 6:0.79 7:0.66 8:0.57 11:0.64 12:0.37 17:0.04 18:0.86 20:0.87 21:0.22 22:0.93 27:0.40 29:0.98 30:0.45 32:0.79 34:0.50 36:0.66 37:0.36 39:0.42 43:0.12 44:0.93 45:0.73 47:0.93 48:0.91 55:0.52 64:0.77 66:0.63 69:0.17 70:0.61 71:0.65 74:0.68 76:0.85 77:0.70 78:0.98 81:0.79 83:0.60 86:0.94 91:0.35 98:0.37 99:0.95 100:0.94 101:0.52 104:0.58 108:0.14 111:0.96 114:0.71 122:0.57 123:0.13 124:0.49 126:0.54 127:0.51 129:0.05 132:0.97 133:0.59 135:0.76 139:0.94 145:0.65 146:0.35 147:0.42 149:0.08 150:0.85 152:0.93 154:0.94 155:0.67 159:0.36 165:0.70 169:0.93 172:0.41 173:0.31 176:0.73 177:0.70 178:0.55 179:0.83 186:0.98 187:0.39 192:0.87 195:0.63 201:0.93 208:0.64 212:0.84 215:0.51 216:0.37 219:0.89 222:0.48 230:0.69 232:0.77 233:0.76 235:0.60 241:0.21 242:0.33 243:0.68 245:0.47 247:0.47 253:0.53 254:0.74 261:0.95 262:0.93 265:0.39 266:0.23 267:0.65 271:0.38 276:0.33 281:0.91 282:0.87 290:0.71 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.81 11:0.83 12:0.37 17:0.88 18:0.89 20:0.85 21:0.30 22:0.93 27:0.62 29:0.98 30:0.75 32:0.80 34:0.97 36:0.90 37:0.59 39:0.42 43:0.95 44:0.93 45:0.94 47:0.93 64:0.77 66:0.96 69:0.27 70:0.52 71:0.65 74:0.93 77:0.89 78:0.96 81:0.72 83:0.91 85:0.88 86:0.95 91:0.20 97:0.89 98:0.92 99:0.83 100:0.92 101:0.87 104:0.83 106:0.81 108:0.21 111:0.94 114:0.65 117:0.86 121:0.97 122:0.51 123:0.20 126:0.54 127:0.54 129:0.05 135:0.93 139:0.96 145:0.88 146:0.93 147:0.94 149:0.96 150:0.83 152:0.81 154:0.95 155:0.67 158:0.79 159:0.57 165:0.93 169:0.89 172:0.91 173:0.25 176:0.73 177:0.70 178:0.55 179:0.28 186:0.96 187:0.95 192:0.87 195:0.76 201:0.93 204:0.89 206:0.81 208:0.64 212:0.74 215:0.44 216:0.31 222:0.48 230:0.87 232:0.88 233:0.76 235:0.68 241:0.30 242:0.41 243:0.96 247:0.82 253:0.53 259:0.21 261:0.88 262:0.93 265:0.92 266:0.38 267:0.44 271:0.38 276:0.83 279:0.82 281:0.91 282:0.88 283:0.82 290:0.65 297:0.36 299:0.88 300:0.55 +1 1:0.74 7:0.81 11:0.83 12:0.37 17:0.30 18:0.89 21:0.13 27:0.32 29:0.98 30:0.54 32:0.80 34:0.44 36:0.53 37:0.80 39:0.42 43:0.94 44:0.93 45:0.81 48:0.97 60:0.87 64:0.77 66:0.88 69:0.27 70:0.48 71:0.65 74:0.85 77:0.70 81:0.80 83:0.91 85:0.72 86:0.97 91:0.05 97:0.89 98:0.55 101:0.87 108:0.21 114:0.79 121:0.90 122:0.57 123:0.20 126:0.54 127:0.16 129:0.05 135:0.98 139:0.98 145:0.76 146:0.59 147:0.64 149:0.70 150:0.87 154:0.94 155:0.67 158:0.92 159:0.21 165:0.93 172:0.67 173:0.31 176:0.73 177:0.70 178:0.55 179:0.26 187:0.39 192:0.87 195:0.67 201:0.93 204:0.89 208:0.64 212:0.80 215:0.61 216:0.86 219:0.95 222:0.48 230:0.87 233:0.76 235:0.31 241:0.05 242:0.33 243:0.89 247:0.55 253:0.57 259:0.21 265:0.57 266:0.38 267:0.94 271:0.38 276:0.54 279:0.86 281:0.91 282:0.88 283:0.82 290:0.79 292:0.95 297:0.36 299:0.88 300:0.33 +0 6:0.78 11:0.83 12:0.37 17:0.89 18:0.85 20:0.94 21:0.78 22:0.93 27:0.30 29:0.98 30:0.68 34:0.99 36:0.32 37:0.65 39:0.42 43:0.86 45:0.73 47:0.93 60:0.87 66:0.52 69:0.54 70:0.52 71:0.65 74:0.71 78:0.97 83:0.91 85:0.80 86:0.90 91:0.12 98:0.44 100:0.90 101:0.87 104:0.81 106:0.81 108:0.41 111:0.94 117:0.86 122:0.86 123:0.40 124:0.52 127:0.42 129:0.59 133:0.46 135:0.99 139:0.91 146:0.51 147:0.57 149:0.78 152:0.91 154:0.83 155:0.67 158:0.91 159:0.46 169:0.87 172:0.48 173:0.54 177:0.70 178:0.55 179:0.67 186:0.97 187:0.39 189:0.81 192:0.87 206:0.81 208:0.64 212:0.40 216:0.61 219:0.95 222:0.48 232:0.87 238:0.82 241:0.59 242:0.31 243:0.61 245:0.69 247:0.60 253:0.42 259:0.21 261:0.93 262:0.93 265:0.46 266:0.63 267:0.59 271:0.38 276:0.42 279:0.86 283:0.78 292:0.91 300:0.43 +2 1:0.74 6:0.91 7:0.81 8:0.79 11:0.83 12:0.37 17:0.75 18:0.87 20:0.86 21:0.59 27:0.32 29:0.98 30:0.76 32:0.80 34:0.91 36:0.90 37:0.62 39:0.42 43:0.82 44:0.93 45:0.93 64:0.77 66:0.96 69:0.63 70:0.37 71:0.65 74:0.94 77:0.89 78:0.97 81:0.51 83:0.91 85:0.90 86:0.94 91:0.25 97:0.89 98:0.90 99:0.83 100:0.96 101:0.87 104:0.92 106:0.81 108:0.48 111:0.96 114:0.58 117:0.86 121:0.97 122:0.51 123:0.46 126:0.54 127:0.46 129:0.05 135:0.94 139:0.96 145:0.85 146:0.87 147:0.89 149:0.95 150:0.73 152:0.87 154:0.78 155:0.67 158:0.79 159:0.44 165:0.93 169:0.92 172:0.89 173:0.66 176:0.73 177:0.70 178:0.55 179:0.28 186:0.96 187:0.95 192:0.87 195:0.69 201:0.93 204:0.89 206:0.81 208:0.64 212:0.78 215:0.39 216:0.69 219:0.89 222:0.48 230:0.87 232:0.94 233:0.76 235:0.88 241:0.46 242:0.36 243:0.96 247:0.60 253:0.49 259:0.21 261:0.92 265:0.90 266:0.27 267:0.65 271:0.38 276:0.81 279:0.82 281:0.91 282:0.88 283:0.82 290:0.58 292:0.95 297:0.36 299:0.97 300:0.33 +2 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.28 18:0.80 21:0.47 27:0.52 29:0.98 30:0.74 34:0.31 36:0.57 37:0.56 39:0.42 43:0.64 45:0.99 66:0.99 69:0.70 70:0.46 71:0.65 74:0.95 81:0.38 83:0.60 86:0.91 91:0.14 96:0.88 98:0.83 99:0.83 101:0.52 104:0.98 108:0.54 114:0.59 120:0.80 122:0.51 123:0.51 126:0.44 127:0.32 129:0.05 135:0.34 139:0.88 140:0.45 146:0.97 147:0.98 149:0.93 150:0.54 151:0.85 153:0.77 154:0.65 155:0.58 159:0.72 162:0.86 164:0.65 165:0.70 172:0.97 173:0.51 176:0.66 177:0.70 179:0.13 187:0.21 190:0.77 192:0.59 201:0.68 202:0.54 204:0.85 208:0.54 212:0.58 215:0.41 216:0.48 222:0.48 230:0.75 232:0.99 233:0.76 235:0.60 241:0.32 242:0.54 243:0.99 247:0.60 248:0.84 253:0.90 254:0.74 255:0.74 256:0.58 259:0.21 260:0.77 265:0.83 266:0.47 267:0.79 268:0.63 271:0.38 276:0.93 285:0.56 290:0.59 297:0.36 300:0.55 +2 1:0.74 7:0.81 11:0.64 12:0.37 17:0.47 21:0.54 22:0.93 27:0.02 29:0.98 30:0.89 32:0.69 34:0.84 36:0.49 37:0.99 39:0.42 43:0.64 45:0.77 47:0.93 48:0.97 64:0.77 66:0.75 69:0.70 70:0.20 71:0.65 74:0.69 77:0.70 81:0.51 83:0.91 91:0.64 97:0.89 98:0.30 101:0.52 104:0.95 106:0.81 108:0.53 114:0.59 117:0.86 121:0.97 122:0.51 123:0.51 126:0.54 127:0.12 129:0.05 135:0.86 139:0.74 145:0.54 146:0.26 147:0.32 149:0.57 150:0.73 154:0.54 155:0.67 158:0.79 159:0.11 165:0.93 172:0.32 173:0.90 175:0.75 176:0.73 177:0.38 178:0.55 179:0.29 187:0.68 192:0.87 195:0.55 201:0.93 204:0.89 206:0.81 208:0.64 212:0.84 215:0.39 216:0.85 222:0.48 230:0.86 232:0.96 233:0.73 235:0.74 241:0.47 242:0.63 243:0.77 244:0.61 247:0.30 253:0.45 257:0.65 259:0.21 262:0.93 265:0.33 266:0.17 267:0.52 271:0.38 276:0.25 277:0.69 279:0.82 281:0.89 282:0.77 283:0.82 290:0.59 297:0.36 300:0.18 +1 1:0.69 7:0.72 9:0.80 11:0.64 12:0.37 17:0.59 20:0.89 21:0.47 27:0.31 29:0.98 30:0.85 31:0.94 32:0.69 34:0.75 36:0.88 37:0.55 39:0.42 41:0.82 43:0.70 45:0.91 66:0.51 69:0.47 70:0.41 71:0.65 74:0.73 77:0.89 78:0.92 79:0.93 81:0.38 83:0.91 91:0.22 96:0.80 98:0.35 99:0.83 100:0.89 101:0.52 104:0.97 106:0.81 108:0.83 111:0.90 114:0.59 117:0.86 120:0.69 122:0.51 123:0.82 124:0.65 126:0.44 127:0.53 129:0.81 133:0.67 135:0.99 137:0.94 140:0.45 145:0.95 146:0.33 147:0.39 149:0.80 150:0.54 151:0.87 152:0.93 153:0.48 154:0.59 155:0.67 159:0.59 162:0.86 164:0.57 165:0.70 169:0.83 172:0.59 173:0.39 175:0.75 176:0.66 177:0.38 179:0.57 186:0.92 187:0.87 192:0.87 195:0.77 201:0.68 202:0.36 204:0.85 206:0.81 208:0.64 212:0.61 215:0.41 216:0.64 222:0.48 230:0.74 232:0.98 233:0.73 235:0.60 241:0.27 242:0.63 243:0.36 244:0.61 245:0.72 247:0.30 248:0.77 253:0.82 255:0.95 256:0.45 257:0.65 259:0.21 260:0.70 261:0.90 265:0.37 266:0.63 267:0.52 268:0.46 271:0.38 276:0.53 277:0.69 282:0.77 284:0.89 285:0.62 290:0.59 297:0.36 300:0.43 +1 1:0.74 6:0.81 7:0.81 8:0.64 12:0.06 17:0.93 20:0.81 27:0.72 28:0.64 29:0.71 32:0.80 34:0.40 36:0.78 37:0.56 39:0.94 44:0.93 64:1.00 71:0.91 74:0.59 77:0.89 78:0.84 81:0.91 83:0.91 91:0.86 100:0.85 104:0.54 111:0.83 114:0.98 121:0.97 126:0.54 135:0.32 139:0.82 145:0.45 150:0.95 152:0.78 155:0.67 161:0.73 165:0.93 167:0.86 169:0.83 175:0.97 176:0.73 178:0.55 181:0.78 186:0.84 189:0.83 191:0.88 192:0.87 195:0.52 201:0.93 204:0.89 208:0.64 215:0.96 222:0.21 230:0.87 233:0.76 235:0.12 238:0.87 241:0.80 244:0.93 253:0.66 257:0.93 259:0.21 261:0.81 267:0.19 277:0.95 281:0.91 282:0.88 290:0.98 297:0.99 299:0.97 +4 6:0.98 7:0.66 8:0.95 9:0.76 12:0.06 17:0.20 18:0.85 20:0.79 21:0.54 22:0.93 27:0.06 28:0.64 29:0.71 30:0.41 31:0.94 32:0.68 34:0.76 36:0.36 37:0.92 39:0.94 43:0.17 44:0.90 47:0.93 48:0.91 55:0.71 64:0.77 66:0.86 69:0.37 70:0.60 71:0.91 74:0.32 78:0.90 79:0.95 81:0.32 83:0.60 86:0.79 91:0.81 96:0.84 98:0.93 100:0.98 108:0.29 111:0.96 117:0.86 120:0.76 123:0.28 126:0.44 127:0.58 129:0.05 135:0.37 137:0.94 139:0.81 140:0.80 145:0.72 146:0.81 147:0.84 149:0.26 150:0.26 151:0.86 152:0.97 153:0.78 154:0.11 155:0.58 159:0.37 161:0.73 162:0.69 164:0.77 167:0.84 169:0.97 172:0.59 173:0.47 177:0.70 178:0.42 179:0.74 181:0.78 186:0.90 187:0.68 189:1.00 190:0.77 191:0.94 192:0.59 195:0.62 196:0.94 201:0.68 202:0.83 208:0.54 212:0.54 215:0.39 216:0.75 220:0.96 222:0.21 230:0.69 233:0.73 235:0.68 238:0.98 241:0.78 242:0.33 243:0.86 244:0.83 247:0.67 248:0.77 253:0.73 255:0.79 256:0.85 259:0.21 260:0.68 261:0.98 262:0.93 265:0.93 266:0.47 267:0.23 268:0.85 276:0.49 281:0.89 284:0.97 285:0.62 297:0.36 300:0.43 +1 1:0.74 7:0.66 9:0.76 12:0.06 17:0.32 18:0.89 21:0.71 22:0.93 27:0.06 28:0.64 29:0.71 30:0.21 31:0.93 32:0.79 34:1.00 36:0.26 37:0.92 39:0.94 43:0.17 44:0.93 47:0.93 48:0.91 55:0.71 64:0.77 66:0.45 69:0.41 70:0.87 71:0.91 74:0.60 76:0.85 77:0.70 79:0.93 81:0.38 83:0.60 86:0.88 91:0.20 96:0.80 98:0.63 108:0.76 114:0.55 117:0.86 123:0.75 124:0.58 126:0.54 127:0.83 129:0.05 133:0.43 135:0.70 137:0.93 139:0.90 140:0.45 145:0.59 146:0.65 147:0.70 149:0.22 150:0.62 151:0.81 153:0.48 154:0.48 155:0.67 159:0.53 161:0.73 162:0.86 167:0.70 172:0.54 173:0.41 176:0.73 177:0.70 179:0.64 181:0.78 187:0.68 190:0.77 192:0.87 195:0.72 196:0.95 201:0.93 202:0.36 204:0.85 208:0.64 212:0.49 215:0.37 216:0.75 222:0.21 230:0.69 233:0.76 235:0.95 241:0.65 242:0.18 243:0.47 244:0.61 245:0.79 247:0.82 248:0.73 253:0.73 254:0.80 255:0.74 256:0.45 259:0.21 262:0.93 265:0.64 266:0.71 267:0.52 268:0.46 276:0.58 277:0.69 281:0.88 282:0.87 284:0.87 285:0.56 290:0.55 297:0.36 300:0.70 +1 6:0.95 7:0.66 8:0.97 12:0.06 17:0.16 18:0.79 20:0.84 21:0.54 27:0.05 28:0.64 29:0.71 30:0.21 31:0.94 34:0.45 36:0.65 37:0.94 39:0.94 43:0.16 44:0.87 48:0.91 55:0.52 64:0.77 66:0.16 69:0.46 70:0.82 71:0.91 78:0.83 79:0.95 81:0.32 86:0.74 91:0.93 96:0.68 98:0.42 100:0.88 108:0.75 111:0.84 120:0.96 123:0.34 124:0.70 126:0.44 127:0.79 129:0.05 133:0.62 135:0.18 137:0.94 139:0.77 140:0.97 145:0.59 146:0.40 147:0.46 149:0.25 150:0.26 151:0.81 152:0.80 153:0.48 154:0.11 159:0.41 161:0.73 162:0.56 164:0.92 167:0.89 169:0.86 172:0.37 173:0.55 175:0.75 177:0.38 179:0.80 181:0.78 186:0.83 189:0.96 190:0.77 191:0.98 192:0.51 195:0.65 196:0.93 201:0.68 202:0.94 212:0.49 215:0.39 216:0.55 222:0.21 230:0.69 233:0.76 235:0.68 238:0.93 241:0.96 242:0.61 243:0.37 244:0.83 245:0.60 247:0.55 248:0.79 253:0.32 255:0.74 256:0.93 257:0.65 259:0.21 260:0.94 261:0.83 265:0.30 266:0.63 267:0.73 268:0.94 276:0.43 277:0.69 281:0.89 284:0.94 285:0.62 297:0.36 300:0.55 +2 7:0.66 8:0.97 12:0.06 17:0.24 18:0.92 21:0.64 27:0.04 28:0.64 29:0.71 30:0.45 31:0.90 34:0.83 36:0.42 37:0.94 39:0.94 43:0.13 48:0.91 55:0.71 64:0.77 66:0.93 69:0.55 70:0.63 71:0.91 74:0.45 79:0.91 81:0.31 86:0.92 91:0.27 98:0.94 108:0.42 120:0.64 123:0.40 126:0.44 127:0.63 129:0.05 135:0.39 137:0.90 139:0.89 140:0.45 146:0.87 147:0.89 149:0.25 150:0.25 151:0.48 153:0.48 154:0.51 158:0.74 159:0.45 161:0.73 162:0.78 164:0.71 167:0.79 172:0.81 173:0.59 177:0.70 179:0.47 181:0.78 187:0.68 190:0.89 191:0.94 192:0.51 196:0.93 201:0.68 202:0.68 212:0.80 215:0.38 216:0.87 220:0.93 222:0.21 230:0.69 233:0.73 235:0.84 241:0.75 242:0.21 243:0.93 247:0.84 248:0.48 253:0.34 254:0.99 255:0.74 256:0.71 259:0.21 260:0.62 265:0.94 266:0.38 267:0.36 268:0.73 276:0.71 281:0.89 283:0.78 284:0.92 285:0.56 297:0.36 300:0.55 +1 1:0.74 11:0.64 12:0.06 17:0.36 18:0.89 21:0.40 27:0.45 28:0.64 29:0.71 30:0.09 34:0.89 36:0.17 37:0.50 39:0.94 43:0.63 45:0.81 64:0.77 66:0.53 69:0.69 70:0.99 71:0.91 74:0.68 81:0.51 83:0.60 86:0.89 91:0.29 97:0.89 98:0.52 99:0.83 101:0.52 108:0.52 114:0.62 122:0.86 123:0.50 124:0.64 126:0.54 127:0.51 129:0.05 133:0.60 135:0.89 139:0.90 145:0.47 146:0.73 147:0.77 149:0.56 150:0.69 154:0.76 155:0.67 158:0.91 159:0.66 161:0.73 165:0.70 167:0.55 172:0.63 173:0.59 176:0.73 177:0.70 178:0.55 179:0.53 181:0.78 187:0.68 192:0.87 201:0.93 208:0.64 212:0.29 215:0.42 216:0.77 219:0.95 222:0.21 235:0.60 241:0.24 242:0.30 243:0.62 245:0.74 247:0.88 253:0.48 254:0.74 259:0.21 265:0.53 266:0.80 267:0.44 276:0.61 279:0.86 283:0.77 290:0.62 292:0.91 297:0.36 300:0.94 +3 6:0.96 8:0.93 12:0.06 17:0.38 18:0.70 20:0.82 21:0.68 22:0.93 27:0.06 28:0.64 29:0.71 30:0.62 31:0.91 32:0.64 34:0.71 36:0.63 37:0.92 39:0.94 43:0.15 47:0.93 48:0.97 55:0.52 64:0.77 66:0.64 69:0.57 70:0.40 71:0.91 78:0.86 79:0.92 81:0.29 86:0.68 91:0.82 96:0.72 98:0.51 100:0.91 108:0.68 111:0.86 120:0.83 123:0.66 124:0.44 126:0.44 127:0.46 129:0.59 133:0.47 135:0.65 137:0.91 139:0.73 140:0.80 145:0.69 146:0.28 147:0.34 149:0.21 150:0.24 151:0.53 152:0.97 153:0.48 154:0.11 159:0.34 161:0.73 162:0.59 164:0.79 167:0.83 169:0.96 172:0.45 173:0.68 175:0.75 177:0.38 178:0.42 179:0.79 181:0.78 186:0.86 187:0.68 189:0.98 190:0.89 191:0.95 192:0.51 195:0.62 196:0.90 201:0.68 202:0.85 212:0.78 215:0.37 216:0.75 219:0.89 220:0.87 222:0.21 235:0.84 238:0.94 241:0.77 242:0.60 243:0.26 244:0.83 245:0.55 247:0.39 248:0.55 253:0.45 255:0.74 256:0.84 257:0.65 259:0.21 260:0.77 261:0.98 262:0.93 265:0.53 266:0.27 267:0.19 268:0.85 276:0.34 277:0.69 281:0.89 284:0.97 285:0.62 292:0.91 297:0.36 300:0.33 +4 6:0.98 7:0.66 8:0.93 9:0.80 12:0.06 17:0.24 18:0.85 20:0.94 21:0.54 22:0.93 27:0.06 28:0.64 29:0.71 30:0.41 31:0.98 32:0.68 34:0.78 36:0.35 37:0.92 39:0.94 41:0.88 43:0.17 44:0.90 47:0.93 48:0.91 55:0.71 64:0.77 66:0.86 69:0.37 70:0.60 71:0.91 74:0.38 78:0.94 79:0.98 81:0.32 83:0.91 86:0.79 91:0.84 96:0.94 98:0.93 100:0.99 108:0.29 111:0.98 117:0.86 120:0.87 123:0.28 126:0.44 127:0.58 129:0.05 135:0.37 137:0.98 139:0.81 140:0.96 145:0.72 146:0.81 147:0.84 149:0.26 150:0.26 151:0.87 152:0.95 153:0.83 154:0.11 155:0.67 158:0.75 159:0.37 161:0.73 162:0.59 164:0.86 167:0.80 169:0.97 172:0.59 173:0.47 177:0.70 178:0.42 179:0.75 181:0.78 186:0.94 187:0.39 189:0.98 191:0.93 192:0.87 195:0.62 196:0.97 201:0.68 202:0.89 208:0.64 212:0.54 215:0.39 216:0.75 220:0.74 222:0.21 230:0.69 233:0.73 235:0.68 238:0.98 241:0.76 242:0.33 243:0.86 244:0.83 247:0.67 248:0.88 253:0.89 255:0.95 256:0.89 259:0.21 260:0.86 261:0.98 262:0.93 265:0.93 266:0.47 267:0.18 268:0.90 276:0.49 281:0.89 284:0.98 285:0.62 297:0.36 300:0.43 +2 1:0.74 6:0.84 7:0.81 8:0.68 9:0.80 11:0.83 12:0.06 17:0.82 18:0.96 20:0.92 21:0.22 27:0.29 28:0.64 29:0.71 30:0.15 31:0.90 32:0.80 34:0.67 36:0.57 37:0.69 39:0.94 41:0.90 43:0.89 44:0.93 45:0.66 48:0.91 64:0.77 66:0.29 69:0.51 70:0.92 71:0.91 74:0.85 76:0.85 77:0.70 78:0.88 79:0.89 81:0.67 83:0.91 85:0.90 86:0.99 91:0.70 96:0.73 98:0.62 100:0.91 101:0.97 108:0.78 111:0.88 114:0.71 120:0.61 121:0.90 122:0.57 123:0.56 124:0.70 126:0.54 127:0.66 129:0.81 133:0.67 135:0.89 137:0.90 139:0.99 140:0.45 145:0.80 146:0.29 147:0.35 149:0.90 150:0.80 151:0.59 152:0.87 153:0.83 154:0.87 155:0.67 159:0.54 161:0.73 162:0.86 164:0.74 165:0.93 167:0.56 169:0.88 172:0.75 173:0.48 176:0.73 177:0.70 179:0.31 181:0.78 186:0.88 187:0.87 189:0.86 191:0.73 192:0.87 195:0.75 196:0.95 201:0.93 202:0.62 204:0.89 208:0.64 212:0.85 215:0.51 216:0.61 220:0.82 222:0.21 230:0.87 233:0.76 235:0.52 238:0.91 241:0.30 242:0.16 243:0.20 245:0.91 247:0.88 248:0.61 253:0.68 255:0.95 256:0.68 259:0.21 260:0.61 261:0.91 265:0.60 266:0.54 267:0.73 268:0.71 276:0.82 281:0.91 282:0.88 283:0.78 284:0.95 285:0.62 290:0.71 297:0.36 299:0.88 300:0.70 +1 6:0.82 7:0.66 8:0.73 12:0.06 17:0.43 18:0.83 20:0.85 21:0.47 27:0.15 28:0.64 29:0.71 30:0.62 31:0.93 32:0.69 34:0.44 36:0.64 37:0.79 39:0.94 43:0.17 44:0.90 48:0.97 55:0.59 64:0.77 66:0.61 69:0.37 70:0.47 71:0.91 78:0.85 79:0.94 81:0.37 86:0.83 91:0.90 98:0.79 100:0.87 108:0.29 111:0.84 120:0.85 123:0.28 124:0.48 126:0.44 127:0.76 129:0.05 133:0.39 135:0.49 137:0.93 139:0.84 140:0.80 145:0.74 146:0.61 147:0.66 149:0.24 150:0.30 151:0.65 152:0.81 153:0.48 154:0.51 159:0.31 161:0.73 162:0.59 164:0.86 167:0.89 169:0.85 172:0.54 173:0.55 175:0.75 177:0.38 178:0.42 179:0.73 181:0.78 186:0.85 189:0.84 190:0.89 191:0.91 192:0.51 195:0.58 196:0.94 201:0.68 202:0.86 212:0.71 215:0.41 216:0.33 220:0.93 222:0.21 230:0.69 233:0.76 235:0.68 238:0.89 241:0.94 242:0.40 243:0.68 244:0.61 245:0.68 247:0.60 248:0.75 253:0.20 254:0.80 255:0.74 256:0.86 257:0.65 259:0.21 260:0.80 261:0.85 265:0.79 266:0.27 267:0.88 268:0.86 276:0.51 277:0.69 281:0.91 284:0.97 285:0.56 297:0.36 300:0.43 +1 6:0.98 7:0.66 8:0.88 12:0.06 17:0.82 18:0.85 20:0.79 21:0.54 22:0.93 27:0.06 28:0.64 29:0.71 30:0.45 31:0.87 32:0.68 34:0.83 36:0.61 37:0.92 39:0.94 43:0.15 44:0.90 47:0.93 48:0.97 55:0.92 64:0.77 66:0.97 69:0.55 70:0.43 71:0.91 74:0.67 78:0.91 79:0.87 81:0.32 86:0.85 91:0.78 96:0.71 98:0.99 100:0.98 108:0.42 111:0.96 120:0.55 122:0.77 123:0.41 126:0.44 127:0.91 129:0.05 135:0.66 137:0.87 139:0.85 140:0.45 145:0.54 146:0.99 147:1.00 149:0.30 150:0.26 151:0.50 152:0.95 153:0.46 154:0.29 158:0.74 159:0.87 161:0.73 162:0.55 163:0.94 164:0.70 167:0.74 169:0.97 172:0.91 173:0.28 177:0.70 179:0.32 181:0.78 186:0.91 187:0.68 189:0.97 190:0.89 191:0.92 192:0.51 195:0.96 196:0.89 201:0.68 202:0.77 212:0.35 215:0.39 216:0.75 220:0.91 222:0.21 230:0.69 233:0.76 235:0.68 238:0.98 241:0.70 242:0.30 243:0.97 244:0.61 247:0.92 248:0.50 253:0.45 254:0.93 255:0.74 256:0.73 259:0.21 260:0.55 261:0.98 262:0.93 265:0.99 266:0.54 267:0.69 268:0.74 276:0.84 281:0.89 284:0.93 285:0.62 297:0.36 300:0.33 +1 1:0.74 11:0.57 12:0.06 17:0.45 18:0.83 21:0.13 27:0.45 28:0.64 29:0.71 30:0.58 34:0.95 36:0.20 37:0.50 39:0.94 43:0.82 60:0.87 64:0.77 66:0.20 69:0.68 70:0.79 71:0.91 74:0.72 81:0.70 83:0.91 85:0.85 86:0.88 91:0.29 98:0.40 101:0.87 108:0.52 114:0.79 122:0.57 123:0.85 124:0.79 126:0.54 127:0.44 129:0.59 133:0.74 135:0.87 139:0.89 145:0.41 146:0.40 147:0.46 149:0.81 150:0.82 154:0.77 155:0.67 158:0.92 159:0.65 161:0.73 163:0.90 165:0.93 167:0.60 172:0.37 173:0.58 176:0.73 177:0.70 178:0.55 179:0.64 181:0.78 187:0.68 192:0.87 201:0.93 208:0.64 212:0.23 215:0.61 216:0.77 219:0.95 222:0.21 235:0.31 241:0.46 242:0.24 243:0.46 245:0.63 247:0.79 253:0.56 259:0.21 265:0.26 266:0.80 267:0.59 276:0.50 277:0.69 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.89 8:0.78 9:0.80 11:0.83 12:0.06 17:0.81 18:0.98 20:0.91 21:0.40 27:0.49 28:0.64 29:0.71 30:0.15 31:0.96 32:0.68 34:0.18 36:0.26 37:0.72 39:0.94 41:0.94 43:0.78 44:0.90 45:0.95 55:0.71 60:0.87 64:0.77 66:0.13 69:0.77 70:0.92 71:0.91 74:0.87 78:0.83 79:0.96 81:0.53 83:0.91 85:0.95 86:0.99 91:0.61 96:0.87 97:0.94 98:0.45 100:0.84 101:0.97 104:0.86 108:0.60 111:0.82 114:0.62 120:0.81 122:0.86 123:0.58 124:0.97 126:0.54 127:0.97 129:0.99 133:0.97 135:1.00 137:0.96 139:0.99 140:0.45 145:0.69 146:0.99 147:0.05 149:0.90 150:0.74 151:0.94 152:0.85 153:0.82 154:0.71 155:0.67 158:0.92 159:0.97 161:0.73 162:0.73 164:0.81 165:0.93 167:0.57 169:0.82 172:0.95 173:0.25 176:0.73 177:0.05 179:0.10 181:0.78 186:0.83 187:0.21 189:0.90 191:0.70 192:0.87 195:1.00 196:0.99 201:0.93 202:0.77 208:0.64 212:0.48 215:0.42 216:0.21 219:0.95 220:0.91 222:0.21 235:0.60 238:0.88 241:0.32 242:0.18 243:0.05 245:0.99 247:0.97 248:0.85 253:0.90 255:0.95 256:0.80 257:0.84 259:0.21 260:0.80 261:0.86 265:0.47 266:0.95 267:0.85 268:0.81 276:0.99 279:0.86 283:0.82 284:0.97 285:0.62 290:0.62 292:0.95 297:0.36 300:0.94 +1 6:0.89 8:0.96 9:0.76 12:0.06 17:0.22 20:0.90 21:0.78 27:0.06 28:0.64 29:0.71 31:0.93 34:0.59 36:0.26 37:0.93 39:0.94 71:0.91 74:0.38 78:0.78 79:0.94 91:0.91 96:0.76 100:0.82 111:0.78 120:0.92 135:0.78 137:0.93 140:0.80 151:0.76 152:0.88 153:0.48 155:0.58 158:0.78 161:0.73 162:0.59 164:0.90 167:0.88 169:0.79 175:0.97 181:0.78 186:0.78 189:0.92 190:0.77 191:0.94 192:0.59 202:0.91 208:0.54 216:0.59 222:0.21 238:0.92 241:0.87 244:0.93 248:0.75 253:0.51 255:0.79 256:0.92 257:0.93 259:0.21 260:0.88 261:0.83 267:0.88 268:0.91 277:0.95 279:0.82 283:0.78 284:0.97 285:0.62 +1 6:0.98 8:0.98 12:0.06 17:0.16 18:0.68 20:0.76 21:0.64 22:0.93 27:0.06 28:0.64 29:0.71 30:0.21 31:0.91 32:0.68 34:0.89 36:0.38 37:0.92 39:0.94 43:0.15 44:0.90 47:0.93 48:0.97 55:0.52 64:0.77 66:0.69 69:0.82 70:0.67 71:0.91 78:0.90 79:0.92 81:0.30 86:0.70 91:0.90 98:0.54 100:0.95 108:0.67 111:0.92 120:0.95 123:0.65 124:0.41 126:0.44 127:0.43 129:0.05 133:0.39 135:0.32 137:0.91 139:0.76 140:0.94 145:0.76 146:0.48 147:0.05 149:0.16 150:0.24 151:0.59 152:0.97 153:0.48 154:0.30 159:0.34 161:0.73 162:0.55 164:0.91 167:0.84 169:0.97 172:0.41 173:0.93 175:0.75 177:0.05 178:0.48 179:0.84 181:0.78 186:0.90 187:0.68 190:0.89 191:0.97 192:0.51 195:0.61 196:0.91 201:0.68 202:0.95 212:0.37 215:0.38 216:0.75 222:0.21 235:0.80 241:0.78 242:0.58 243:0.18 244:0.61 245:0.46 247:0.39 248:0.58 253:0.20 254:0.97 255:0.74 256:0.94 257:0.84 259:0.21 260:0.91 261:0.98 262:0.93 265:0.55 266:0.47 267:0.23 268:0.94 276:0.29 277:0.69 281:0.89 283:0.78 284:0.98 285:0.56 297:0.36 300:0.43 +1 7:0.70 9:0.74 12:0.06 17:0.36 18:0.77 21:0.05 23:0.96 27:0.28 29:0.71 30:0.61 31:0.90 32:0.65 34:0.80 36:0.48 37:0.91 39:0.95 43:0.65 48:0.91 55:0.52 66:0.78 69:0.25 70:0.66 79:0.90 81:0.34 86:0.78 91:0.49 96:0.74 98:0.83 108:0.52 120:0.68 123:0.50 124:0.39 126:0.24 127:0.61 128:0.96 129:0.59 133:0.47 135:0.30 137:0.90 138:0.96 139:0.78 140:0.80 145:0.83 146:0.13 147:0.18 149:0.61 150:0.38 151:0.77 153:0.48 154:0.55 155:0.65 159:0.38 162:0.86 164:0.66 168:0.96 170:0.99 172:0.73 173:0.37 175:0.91 177:0.38 179:0.56 187:0.39 192:0.98 195:0.63 196:0.91 202:0.50 205:0.95 208:0.64 212:0.69 215:0.91 216:0.41 220:0.62 222:0.60 230:0.73 233:0.66 235:0.05 241:0.70 242:0.76 243:0.15 244:0.83 245:0.67 247:0.47 248:0.70 253:0.54 255:0.74 256:0.58 257:0.84 259:0.21 260:0.68 265:0.83 266:0.47 267:0.96 268:0.59 276:0.63 277:0.87 281:0.86 284:0.88 285:0.62 297:0.36 298:0.99 300:0.55 +1 1:0.69 8:0.60 9:0.75 10:0.95 12:0.06 17:0.09 18:0.74 23:0.96 27:0.42 29:0.71 30:0.27 31:0.92 34:0.69 36:0.27 37:0.60 39:0.95 43:0.78 62:0.98 64:0.77 66:0.32 69:0.77 70:0.76 74:0.48 79:0.91 81:0.69 86:0.80 91:0.23 96:0.77 98:0.27 108:0.60 114:0.95 120:0.65 122:0.95 123:0.58 124:0.91 126:0.54 127:0.45 128:0.97 129:0.05 133:0.90 135:0.88 137:0.92 138:0.95 139:0.77 140:0.45 146:0.52 147:0.58 149:0.66 150:0.64 151:0.86 153:0.48 154:0.71 155:0.65 159:0.75 162:0.86 164:0.56 168:0.97 170:0.99 172:0.51 173:0.62 174:0.94 176:0.70 177:0.88 179:0.51 187:0.39 192:0.98 196:0.92 197:0.94 201:0.67 202:0.36 205:0.95 208:0.64 212:0.16 215:0.91 216:0.56 222:0.60 235:0.22 236:0.95 239:0.94 241:0.01 242:0.22 243:0.49 245:0.62 247:0.86 248:0.77 253:0.76 255:0.78 256:0.45 259:0.21 260:0.66 265:0.30 266:0.87 267:0.89 268:0.46 276:0.63 284:0.88 285:0.62 286:0.99 290:0.95 297:0.36 298:0.99 300:0.55 +1 1:0.67 8:0.77 10:0.95 11:0.46 12:0.06 17:0.53 18:0.96 21:0.47 27:0.45 29:0.71 30:0.43 32:0.80 34:0.87 36:0.24 37:0.50 39:0.95 43:0.82 44:0.93 45:0.66 48:0.91 64:0.77 66:0.77 69:0.23 70:0.64 74:0.70 75:0.99 77:0.70 81:0.65 83:0.56 86:0.96 91:0.10 97:0.89 98:0.90 99:0.83 101:0.47 102:0.98 108:0.18 114:0.80 122:0.92 123:0.18 124:0.41 126:0.54 127:0.80 129:0.05 133:0.36 135:0.54 139:0.97 145:0.47 146:0.92 147:0.94 149:0.82 150:0.55 154:0.77 155:0.52 158:0.86 159:0.61 165:0.65 170:0.99 172:0.82 173:0.23 174:0.89 176:0.73 177:0.88 178:0.55 179:0.44 187:0.68 192:0.50 195:0.75 197:0.91 201:0.66 208:0.64 212:0.71 215:0.63 216:0.77 219:0.95 222:0.60 226:0.96 228:0.99 235:0.80 236:0.97 239:0.97 241:0.27 242:0.31 243:0.79 245:0.81 247:0.94 253:0.63 259:0.21 265:0.90 266:0.63 267:0.44 276:0.74 279:0.80 281:0.91 282:0.88 283:0.67 290:0.80 292:0.88 297:0.36 300:0.55 +0 10:0.82 12:0.06 17:0.94 18:0.64 21:0.78 27:0.72 29:0.71 30:0.58 34:0.55 36:0.67 39:0.95 43:0.09 55:0.92 66:0.49 69:0.55 70:0.44 74:0.48 86:0.72 91:0.60 98:0.58 108:0.75 122:0.83 123:0.73 124:0.52 127:0.32 129:0.05 133:0.43 135:0.85 139:0.68 145:0.50 146:0.21 147:0.27 149:0.12 154:0.58 159:0.54 163:0.79 170:0.99 172:0.32 173:0.46 174:0.79 175:0.75 177:0.70 178:0.55 179:0.79 197:0.81 202:0.36 212:0.16 216:0.07 222:0.60 235:0.80 239:0.82 241:0.79 242:0.58 243:0.39 244:0.61 245:0.55 247:0.47 253:0.40 254:0.92 257:0.65 259:0.21 265:0.59 266:0.54 267:0.82 276:0.33 277:0.87 300:0.33 +1 1:0.65 7:0.75 8:0.63 9:0.73 10:0.92 12:0.06 17:0.40 18:0.83 21:0.40 22:0.93 23:0.99 27:0.48 29:0.71 30:0.62 31:0.91 32:0.71 33:0.97 34:0.63 36:0.67 37:0.57 39:0.95 43:0.86 44:0.92 47:0.93 48:0.97 62:0.98 64:0.77 66:0.52 69:0.58 70:0.62 74:0.69 75:0.98 76:0.94 77:0.70 79:0.91 81:0.57 86:0.86 91:0.58 96:0.79 98:0.48 102:0.97 106:0.81 108:0.44 114:0.78 117:0.86 120:0.56 123:0.43 124:0.65 126:0.54 127:0.52 128:0.97 129:0.05 133:0.65 135:0.93 137:0.91 138:0.95 139:0.90 140:0.45 145:0.47 146:0.69 147:0.74 149:0.85 150:0.52 151:0.56 153:0.48 154:0.83 155:0.65 158:0.81 159:0.66 162:0.86 164:0.65 168:0.97 170:0.99 172:0.65 173:0.46 174:0.86 176:0.70 177:0.88 179:0.50 187:0.39 190:0.77 192:0.98 193:0.99 195:0.83 196:0.94 197:0.89 201:0.64 202:0.69 204:0.85 205:0.99 206:0.81 208:0.64 212:0.26 215:0.63 216:0.76 219:0.94 220:0.91 222:0.60 226:0.95 230:0.77 232:0.99 233:0.65 235:0.68 236:0.95 239:0.96 241:0.10 242:0.19 243:0.62 245:0.76 247:0.90 248:0.55 253:0.81 255:0.73 256:0.78 259:0.21 260:0.57 262:0.93 265:0.50 266:0.87 267:0.73 268:0.77 276:0.63 279:0.82 281:0.86 282:0.80 283:0.66 284:0.96 285:0.62 286:0.99 290:0.78 291:0.97 292:0.87 297:0.36 298:0.99 300:0.70 +1 1:0.68 8:0.98 9:0.75 10:0.97 12:0.06 17:0.36 18:0.92 21:0.22 23:0.99 27:0.27 29:0.71 30:0.53 31:0.95 34:0.86 36:0.48 37:0.98 39:0.95 43:0.75 62:0.99 64:0.77 66:0.93 69:0.15 70:0.55 74:0.69 75:0.97 79:0.95 81:0.52 86:0.94 91:0.64 96:0.93 98:0.97 108:0.12 114:0.82 122:0.92 123:0.12 126:0.51 127:0.78 128:0.99 129:0.05 135:0.56 137:0.95 138:0.95 139:0.90 140:0.45 146:0.86 147:0.89 149:0.79 150:0.51 151:0.91 153:0.85 154:0.70 155:0.64 158:0.76 159:0.43 162:0.84 168:0.99 170:0.99 172:0.82 173:0.27 174:0.92 176:0.63 177:0.88 179:0.47 187:0.21 190:0.77 191:0.91 192:0.58 196:0.96 197:0.94 201:0.64 202:0.74 205:0.99 208:0.61 212:0.72 216:0.29 220:0.82 222:0.60 226:0.96 228:0.99 232:0.70 235:0.52 236:0.94 239:0.95 241:0.74 242:0.24 243:0.94 247:0.88 248:0.84 253:0.93 254:0.97 255:0.78 256:0.79 259:0.21 265:0.97 266:0.47 267:0.59 268:0.80 276:0.72 284:0.96 285:0.60 286:0.99 290:0.82 298:0.99 300:0.43 +3 8:0.86 10:0.99 12:0.06 17:0.18 18:0.94 21:0.30 23:0.95 27:0.15 29:0.71 30:0.87 34:0.75 36:0.60 37:0.78 39:0.95 43:0.88 62:0.97 66:0.25 69:0.12 70:0.44 74:0.83 75:0.97 81:0.38 86:0.96 91:0.48 96:0.71 98:0.58 108:0.71 114:0.82 122:0.94 123:0.69 124:0.74 126:0.32 127:0.76 128:0.95 129:0.81 133:0.67 135:0.42 139:0.94 140:0.45 146:0.54 147:0.60 149:0.96 150:0.40 151:0.57 153:0.48 154:0.87 158:0.76 159:0.63 162:0.86 168:0.95 170:0.99 172:0.77 173:0.16 174:0.96 176:0.63 177:0.88 179:0.24 187:0.87 190:0.89 191:0.80 192:0.47 197:0.97 201:0.57 202:0.36 205:0.95 212:0.80 216:0.91 220:0.87 222:0.60 226:0.96 228:0.99 235:0.42 236:0.94 239:0.99 241:0.52 242:0.19 243:0.43 245:0.95 247:0.86 248:0.56 253:0.71 255:0.69 256:0.45 259:0.21 265:0.59 266:0.63 267:0.59 268:0.46 276:0.87 285:0.50 290:0.82 300:0.84 +1 1:0.65 10:0.98 11:0.57 12:0.06 17:0.67 18:0.94 21:0.54 27:0.45 29:0.71 30:0.58 32:0.80 34:0.88 36:0.20 37:0.50 39:0.95 43:0.82 44:0.93 48:0.91 60:0.87 64:0.77 66:0.75 69:0.25 70:0.63 74:0.79 75:0.99 77:0.70 81:0.74 83:0.91 85:0.88 86:0.96 91:0.15 98:0.90 101:0.87 102:0.98 108:0.20 114:0.77 122:0.92 123:0.19 124:0.41 126:0.54 127:0.81 129:0.05 133:0.36 135:0.83 139:0.97 145:0.44 146:0.90 147:0.92 149:0.92 150:0.55 154:0.77 155:0.50 158:0.86 159:0.56 165:0.93 170:0.99 172:0.79 173:0.27 174:0.92 176:0.73 177:0.88 178:0.55 179:0.47 187:0.68 192:0.49 195:0.71 197:0.94 201:0.64 208:0.64 212:0.58 215:0.58 216:0.77 219:0.95 222:0.60 226:0.96 228:0.99 235:0.84 236:0.97 239:0.99 241:0.10 242:0.19 243:0.77 245:0.80 247:0.93 253:0.64 259:0.21 265:0.90 266:0.63 267:0.28 276:0.72 279:0.80 281:0.91 282:0.88 283:0.67 290:0.77 292:0.88 297:0.36 299:0.88 300:0.55 +1 7:0.70 9:0.74 12:0.06 17:0.40 18:0.77 21:0.05 23:0.96 27:0.28 29:0.71 30:0.71 31:0.90 32:0.67 34:0.78 36:0.37 37:0.91 39:0.95 43:0.65 44:0.88 48:0.91 55:0.52 66:0.75 69:0.25 70:0.47 79:0.90 81:0.34 86:0.78 91:0.49 96:0.74 98:0.83 108:0.53 120:0.68 123:0.50 124:0.40 126:0.24 127:0.67 128:0.96 129:0.59 133:0.47 135:0.21 137:0.90 138:0.96 139:0.81 140:0.80 145:0.84 146:0.13 147:0.18 149:0.59 150:0.38 151:0.77 153:0.72 154:0.55 155:0.65 159:0.35 162:0.76 164:0.68 168:0.96 170:0.99 172:0.65 173:0.40 175:0.91 177:0.38 179:0.65 187:0.39 192:0.98 195:0.61 196:0.92 202:0.58 205:0.95 208:0.64 212:0.54 215:0.91 216:0.41 220:0.62 222:0.60 230:0.73 233:0.66 235:0.05 241:0.62 242:0.72 243:0.18 244:0.83 245:0.64 247:0.47 248:0.70 253:0.54 255:0.74 256:0.58 257:0.84 259:0.21 260:0.69 265:0.83 266:0.47 267:0.52 268:0.62 276:0.55 277:0.87 281:0.86 284:0.88 285:0.62 297:0.36 298:0.99 300:0.43 +0 7:0.69 8:0.97 9:0.70 12:0.06 17:0.08 21:0.59 27:0.04 29:0.71 30:0.21 31:1.00 34:0.39 36:0.68 37:0.94 39:0.95 43:0.18 55:0.52 66:0.16 69:0.46 70:0.77 74:0.42 79:1.00 81:0.27 83:0.56 91:0.98 96:0.93 98:0.67 108:0.71 120:1.00 123:0.34 124:0.52 126:0.24 127:0.80 129:0.59 133:0.47 135:0.18 137:1.00 138:1.00 139:0.64 140:1.00 145:0.59 146:0.60 147:0.65 149:0.25 150:0.30 151:0.56 153:0.48 154:0.12 155:0.53 159:0.41 162:0.55 164:0.99 170:0.99 172:0.45 173:0.55 175:0.91 177:0.38 179:0.80 190:0.89 191:0.99 192:0.57 196:0.92 202:1.00 208:0.61 212:0.49 215:0.55 216:0.87 219:0.90 222:0.60 230:0.71 233:0.76 235:0.68 241:0.93 242:0.61 243:0.37 244:0.93 245:0.64 247:0.55 248:0.55 253:0.89 255:0.70 256:0.99 257:0.84 259:0.21 260:1.00 265:0.58 266:0.63 267:0.79 268:0.99 276:0.44 277:0.87 283:0.82 284:1.00 285:0.60 292:0.88 297:0.36 298:0.99 300:0.55 +2 1:0.68 8:0.70 9:0.76 10:0.97 12:0.06 17:0.57 18:0.92 21:0.05 23:0.97 27:0.15 29:0.71 30:0.76 31:0.93 34:0.53 36:0.28 37:0.78 39:0.95 43:0.88 62:0.98 64:0.77 66:0.92 69:0.10 70:0.41 74:0.65 79:0.92 81:0.68 86:0.94 91:0.51 96:0.79 98:0.92 108:0.09 114:0.95 120:0.67 122:0.75 123:0.09 126:0.54 127:0.55 128:0.97 129:0.05 135:0.42 137:0.93 139:0.92 140:0.45 146:0.67 147:0.71 149:0.90 150:0.63 151:0.82 153:0.77 154:0.87 155:0.66 159:0.25 162:0.68 164:0.64 168:0.97 170:0.99 172:0.79 173:0.37 174:0.89 176:0.73 177:0.88 179:0.47 187:0.95 191:0.88 192:0.99 196:0.95 197:0.93 201:0.67 202:0.54 205:0.95 208:0.64 212:0.86 215:0.91 216:0.91 220:0.62 222:0.60 228:0.99 235:0.31 236:0.97 239:0.95 241:0.59 242:0.13 243:0.93 247:0.90 248:0.74 253:0.81 255:0.79 256:0.45 259:0.21 260:0.68 265:0.92 266:0.27 267:0.97 268:0.57 276:0.67 284:0.91 285:0.62 290:0.95 297:0.36 300:0.43 +0 8:0.95 9:0.71 12:0.06 17:0.20 21:0.78 23:0.97 27:0.18 29:0.71 31:0.96 34:0.61 36:0.18 37:0.86 39:0.95 79:0.96 91:0.84 96:0.72 120:0.76 128:0.96 135:0.86 137:0.96 138:0.97 139:0.67 140:0.94 151:0.64 153:0.78 155:0.57 162:0.85 164:0.73 168:0.96 170:0.99 175:0.99 190:0.89 191:0.96 192:0.87 196:0.90 202:0.68 205:0.97 208:0.61 216:0.20 219:0.91 220:0.62 222:0.60 228:0.99 241:0.86 244:0.97 248:0.62 253:0.48 255:0.73 256:0.75 257:0.97 259:0.21 260:0.76 267:0.59 268:0.75 277:0.98 283:0.67 284:0.96 285:0.62 292:0.88 298:0.99 +4 1:0.66 8:0.68 9:0.70 10:0.88 12:0.06 17:0.55 18:0.79 21:0.22 22:0.93 23:0.98 27:0.28 29:0.71 30:0.62 31:0.87 33:0.97 34:0.28 36:0.70 37:0.91 39:0.95 43:0.91 47:0.93 62:0.95 64:0.77 66:0.83 69:0.15 70:0.43 74:0.40 79:0.87 81:0.61 86:0.82 91:0.62 96:0.69 98:0.74 108:0.12 114:0.84 117:0.86 120:0.55 122:0.75 123:0.12 126:0.54 127:0.24 128:0.95 129:0.05 135:0.54 137:0.87 138:0.95 139:0.78 140:0.45 146:0.36 147:0.42 149:0.75 150:0.57 151:0.50 153:0.48 154:0.91 155:0.54 159:0.14 162:0.86 164:0.70 168:0.95 170:0.99 172:0.51 173:0.57 174:0.79 176:0.70 177:0.88 179:0.62 187:0.87 192:0.56 196:0.88 197:0.85 201:0.66 202:0.55 205:0.98 208:0.64 212:0.95 215:0.72 216:0.41 220:0.96 222:0.60 232:0.93 235:0.52 236:0.95 239:0.87 241:0.55 242:0.29 243:0.84 247:0.67 248:0.52 253:0.60 255:0.69 256:0.64 259:0.21 260:0.55 262:0.93 265:0.74 266:0.12 267:0.59 268:0.63 276:0.40 284:0.93 285:0.62 286:0.99 290:0.84 291:0.97 297:0.36 298:0.99 300:0.33 +4 1:0.68 10:0.97 12:0.06 17:0.51 18:0.92 21:0.05 27:0.15 29:0.71 30:0.76 34:0.73 36:0.28 37:0.78 39:0.95 43:0.88 64:0.77 66:0.92 69:0.10 70:0.41 74:0.70 75:0.98 81:0.68 86:0.94 91:0.29 98:0.92 108:0.09 114:0.95 122:0.75 123:0.09 126:0.54 127:0.55 129:0.05 135:0.42 139:0.93 146:0.67 147:0.71 149:0.90 150:0.63 154:0.87 155:0.53 158:0.81 159:0.25 170:0.99 172:0.79 173:0.37 174:0.89 176:0.73 177:0.88 178:0.55 179:0.47 187:0.87 191:0.77 192:0.55 197:0.93 201:0.67 208:0.64 212:0.86 215:0.91 216:0.91 219:0.94 222:0.60 226:0.96 228:0.99 235:0.31 236:0.97 239:0.96 241:0.30 242:0.13 243:0.93 247:0.90 253:0.70 259:0.21 265:0.92 266:0.27 267:0.44 276:0.67 279:0.81 283:0.67 290:0.95 292:0.88 297:0.36 300:0.43 +0 1:0.74 11:0.64 12:0.79 17:0.81 18:0.83 21:0.05 26:0.98 27:0.72 28:0.47 29:0.91 30:0.87 32:0.80 34:0.62 36:0.42 39:0.88 43:0.92 44:0.93 58:0.93 64:0.77 66:0.79 69:0.36 70:0.27 71:0.56 74:0.66 77:0.89 81:0.82 83:0.91 85:0.80 86:0.91 91:0.82 98:0.62 101:0.87 104:0.54 108:0.28 114:0.89 122:0.57 123:0.27 126:0.54 127:0.18 129:0.05 131:0.61 135:0.34 139:0.91 141:0.91 144:0.57 145:0.56 146:0.32 147:0.39 149:0.79 150:0.88 154:0.91 155:0.67 157:0.80 159:0.13 161:0.68 165:0.93 166:0.78 167:0.89 172:0.41 173:0.71 176:0.73 177:0.70 178:0.55 179:0.61 181:0.60 182:0.79 187:0.21 192:0.87 195:0.53 199:0.95 201:0.93 208:0.64 212:0.95 215:0.78 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.76 234:0.91 235:0.22 241:0.75 242:0.45 243:0.80 246:0.89 247:0.39 253:0.60 259:0.21 265:0.63 266:0.12 267:0.18 271:0.63 274:0.91 276:0.30 282:0.88 287:0.61 290:0.89 297:0.36 299:0.88 300:0.25 +4 1:0.74 6:0.99 8:0.99 9:0.80 11:0.64 12:0.79 17:0.38 18:0.92 20:0.93 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.91 31:0.98 34:0.31 36:0.40 37:0.96 39:0.88 41:0.95 43:0.94 58:0.99 60:0.87 64:0.77 66:0.71 69:0.25 70:0.27 71:0.56 74:0.88 78:0.92 79:0.98 81:0.75 83:0.91 85:0.95 86:0.97 91:0.88 96:0.93 98:0.84 100:1.00 101:0.87 104:0.58 108:0.54 111:1.00 114:0.79 120:0.87 122:0.57 123:0.52 124:0.46 126:0.54 127:0.93 129:0.81 131:0.85 133:0.67 135:0.32 137:0.98 139:0.97 140:0.45 141:0.99 144:0.57 146:0.17 147:0.22 149:0.97 150:0.84 151:0.98 152:0.99 153:0.93 154:0.94 155:0.67 157:0.87 158:0.92 159:0.52 161:0.68 162:0.81 164:0.84 165:0.93 166:0.78 167:1.00 169:1.00 172:0.79 173:0.30 176:0.73 177:0.70 179:0.47 181:0.60 182:0.80 186:0.92 189:1.00 191:0.90 192:0.87 196:0.99 199:0.98 201:0.93 202:0.78 208:0.64 212:0.72 215:0.61 216:0.34 219:0.95 222:0.35 223:0.98 224:0.99 227:0.88 229:0.89 231:0.68 232:0.80 234:0.99 235:0.31 238:1.00 241:0.94 242:0.45 243:0.16 245:0.77 246:0.89 247:0.55 248:0.92 253:0.95 255:0.95 256:0.81 259:0.21 260:0.88 261:1.00 265:0.84 266:0.54 267:0.19 268:0.83 271:0.63 274:0.99 276:0.72 279:0.86 283:0.82 284:0.98 285:0.62 287:0.91 290:0.79 292:0.95 297:0.36 300:0.33 +0 1:0.74 11:0.64 12:0.79 17:0.84 18:0.83 20:0.90 21:0.05 26:0.98 27:0.72 28:0.47 29:0.91 30:0.87 32:0.80 34:0.62 36:0.42 39:0.88 43:0.92 44:0.93 58:0.93 64:0.77 66:0.79 69:0.36 70:0.27 71:0.56 74:0.66 77:0.89 78:0.76 81:0.82 83:0.91 85:0.80 86:0.91 91:0.82 98:0.62 100:0.73 101:0.87 104:0.54 108:0.28 111:0.75 114:0.89 122:0.57 123:0.27 126:0.54 127:0.18 129:0.05 131:0.61 135:0.34 139:0.91 141:0.91 144:0.57 145:0.56 146:0.32 147:0.39 149:0.79 150:0.88 152:0.84 154:0.91 155:0.67 157:0.80 159:0.13 161:0.68 165:0.93 166:0.78 167:0.94 169:0.72 172:0.41 173:0.71 176:0.73 177:0.70 178:0.55 179:0.61 181:0.60 182:0.79 186:0.77 192:0.87 195:0.53 199:0.95 201:0.93 208:0.64 212:0.95 215:0.78 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.76 234:0.91 235:0.22 241:0.78 242:0.45 243:0.80 246:0.89 247:0.39 253:0.60 259:0.21 261:0.78 265:0.63 266:0.12 267:0.18 271:0.63 274:0.91 276:0.30 282:0.88 287:0.61 290:0.89 297:0.36 299:0.88 300:0.25 +2 1:0.74 7:0.81 8:0.65 11:0.85 12:0.79 17:0.61 18:0.86 20:0.92 21:0.30 22:0.93 26:0.98 27:0.31 28:0.47 29:0.91 30:0.45 32:0.80 34:0.97 36:0.27 37:0.55 39:0.88 43:0.79 44:0.93 45:0.66 47:0.93 48:0.99 58:0.93 60:0.87 64:0.77 66:0.87 69:0.60 70:0.61 71:0.56 74:0.80 77:0.96 78:0.77 81:0.66 83:0.91 85:0.80 86:0.87 91:0.08 98:0.75 99:0.83 100:0.78 101:0.87 104:0.95 106:0.81 108:0.46 111:0.76 114:0.65 117:0.86 121:0.90 122:0.86 123:0.44 126:0.54 127:0.24 129:0.05 131:0.61 135:0.85 139:0.94 141:0.91 144:0.57 145:0.70 146:0.76 147:0.79 149:0.74 150:0.80 152:0.87 154:0.73 155:0.67 157:0.81 158:0.92 159:0.32 161:0.68 165:0.93 166:0.78 167:0.67 169:0.75 172:0.63 173:0.62 176:0.73 177:0.70 178:0.55 179:0.49 181:0.60 182:0.80 186:0.78 187:0.39 192:0.87 195:0.70 199:0.97 201:0.93 204:0.89 206:0.81 208:0.64 212:0.55 215:0.44 216:0.85 219:0.95 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 230:0.87 231:0.68 232:0.97 233:0.76 234:0.91 235:0.68 241:0.24 242:0.40 243:0.88 246:0.89 247:0.60 253:0.51 259:0.21 261:0.80 262:0.93 265:0.75 266:0.47 267:0.28 271:0.63 274:0.91 276:0.50 279:0.86 281:0.91 282:0.88 283:0.82 287:0.61 290:0.65 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 8:0.96 9:0.80 11:0.64 12:0.79 17:0.55 18:0.71 20:0.85 21:0.22 26:0.98 27:0.72 28:0.47 29:0.91 30:0.95 31:0.91 34:0.56 36:0.30 37:0.86 39:0.88 41:0.95 43:0.83 58:0.99 64:0.77 66:0.75 69:0.66 70:0.13 71:0.56 74:0.53 78:0.76 79:0.91 81:0.67 83:0.91 85:0.80 86:0.82 91:0.91 96:0.76 98:0.71 100:0.79 101:0.87 108:0.50 111:0.75 114:0.71 120:0.65 122:0.57 123:0.48 126:0.54 127:0.22 129:0.05 131:0.85 135:0.37 137:0.91 139:0.79 140:0.45 141:0.99 144:0.57 146:0.41 147:0.48 149:0.74 150:0.80 151:0.61 152:0.80 153:0.48 154:0.79 155:0.67 157:0.80 159:0.15 161:0.68 162:0.82 163:0.90 164:0.80 165:0.93 166:0.78 167:1.00 169:0.77 172:0.32 173:0.91 176:0.73 177:0.70 179:0.81 181:0.60 182:0.80 186:0.77 192:0.87 196:0.92 199:0.97 201:0.93 202:0.70 208:0.64 212:0.61 215:0.51 216:0.14 220:0.91 222:0.35 223:0.98 224:0.99 227:0.88 229:0.89 231:0.68 234:0.98 235:0.42 241:0.94 242:0.63 243:0.77 246:0.89 247:0.35 248:0.69 253:0.68 255:0.95 256:0.77 259:0.21 260:0.65 261:0.77 265:0.71 266:0.23 267:0.44 268:0.76 271:0.63 274:0.99 276:0.14 284:0.97 285:0.62 287:0.91 290:0.71 297:0.36 300:0.13 +1 6:0.78 7:0.64 8:0.58 9:0.80 12:0.79 17:0.88 18:0.84 20:0.96 21:0.78 26:0.98 27:0.52 28:0.47 29:0.91 30:0.87 31:0.97 32:0.63 34:0.68 36:0.54 37:0.23 39:0.88 41:0.82 43:0.72 44:0.90 48:0.91 58:0.98 66:0.82 69:0.25 70:0.27 71:0.56 78:0.81 79:0.97 83:0.91 86:0.86 91:0.70 96:0.75 98:0.92 100:0.81 104:0.58 108:0.20 111:0.79 120:0.85 122:0.65 123:0.19 127:0.55 129:0.05 131:0.85 135:0.42 137:0.97 139:0.88 140:0.93 141:0.99 144:0.57 145:0.74 146:0.65 147:0.70 149:0.73 151:0.72 152:0.89 153:0.81 154:0.62 155:0.67 157:0.61 159:0.24 161:0.68 162:0.79 163:0.81 164:0.70 166:0.61 167:0.98 169:0.79 172:0.48 173:0.51 175:0.75 177:0.38 179:0.84 181:0.60 182:0.79 186:0.81 189:0.81 190:0.77 191:0.78 192:0.87 195:0.56 196:0.97 199:0.94 202:0.74 208:0.64 212:0.50 216:0.23 220:0.74 222:0.35 223:0.98 224:0.98 227:0.88 229:0.88 230:0.64 231:0.68 233:0.73 234:0.97 235:0.84 238:0.86 241:0.86 242:0.63 243:0.83 244:0.61 246:0.61 247:0.30 248:0.67 253:0.58 255:0.95 256:0.77 257:0.65 259:0.21 260:0.70 261:0.83 265:0.92 266:0.38 267:0.89 268:0.79 271:0.63 274:0.97 276:0.37 277:0.69 281:0.89 284:0.97 285:0.62 287:0.91 300:0.25 +2 1:0.74 7:0.72 11:0.92 12:0.79 17:0.75 18:0.83 20:0.78 21:0.30 22:0.93 26:0.98 27:0.31 28:0.47 29:0.91 30:0.43 32:0.80 34:0.56 36:0.47 37:0.55 39:0.88 43:0.60 44:0.93 45:0.95 47:0.93 48:0.91 58:0.93 64:0.77 66:0.34 69:0.61 70:0.88 71:0.56 74:0.82 77:0.96 78:0.72 81:0.59 83:0.60 85:0.72 86:0.89 91:0.03 98:0.55 99:0.83 100:0.73 101:0.97 104:0.82 106:0.81 108:0.94 111:0.72 114:0.65 117:0.86 122:0.57 123:0.94 124:0.83 126:0.54 127:0.87 129:0.89 131:0.61 133:0.83 135:0.96 139:0.91 141:0.91 144:0.57 145:0.96 146:0.56 147:0.62 149:0.62 150:0.74 152:0.87 154:0.72 155:0.67 157:0.87 159:0.88 161:0.68 165:0.70 166:0.78 167:0.65 169:0.75 172:0.82 173:0.31 176:0.73 177:0.70 178:0.55 179:0.26 181:0.60 182:0.79 186:0.73 187:0.39 192:0.87 195:0.96 199:0.98 201:0.93 204:0.85 206:0.81 208:0.64 212:0.23 215:0.44 216:0.85 219:0.89 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 230:0.75 231:0.68 232:0.90 233:0.76 234:0.91 235:0.52 241:0.15 242:0.21 243:0.27 245:0.91 246:0.89 247:0.91 253:0.51 259:0.21 261:0.80 262:0.93 265:0.56 266:0.92 267:0.19 271:0.63 274:0.91 276:0.86 281:0.89 282:0.88 287:0.61 290:0.65 292:0.91 297:0.36 299:0.88 300:0.94 +1 1:0.74 8:0.80 9:0.80 11:0.64 12:0.79 17:0.20 18:0.88 21:0.40 26:0.98 27:0.19 28:0.47 29:0.91 30:0.94 31:0.94 34:0.87 36:0.75 37:0.96 39:0.88 41:0.88 43:0.94 58:0.98 64:0.77 66:0.69 69:0.32 70:0.19 71:0.56 74:0.78 79:0.94 81:0.56 83:0.91 85:0.91 86:0.96 91:0.57 96:0.82 98:0.68 101:0.87 104:0.58 108:0.25 114:0.62 120:0.73 122:0.57 123:0.24 124:0.43 126:0.54 127:0.51 129:0.59 131:0.85 133:0.46 135:0.49 137:0.94 139:0.95 140:0.45 141:0.99 144:0.57 146:0.55 147:0.61 149:0.95 150:0.75 151:0.87 153:0.48 154:0.94 155:0.67 157:0.85 159:0.32 161:0.68 162:0.86 164:0.68 165:0.93 166:0.78 167:0.75 172:0.63 173:0.46 176:0.73 177:0.70 179:0.62 181:0.60 182:0.80 187:0.68 192:0.87 196:0.97 199:0.96 201:0.93 202:0.58 208:0.64 212:0.59 215:0.42 216:0.34 220:0.82 222:0.35 223:0.98 224:0.98 227:0.88 229:0.89 231:0.68 232:0.80 234:0.98 235:0.60 241:0.59 242:0.51 243:0.73 245:0.68 246:0.89 247:0.47 248:0.79 253:0.83 255:0.95 256:0.68 259:0.21 260:0.73 265:0.68 266:0.54 267:0.19 268:0.67 271:0.63 274:0.98 276:0.50 284:0.94 285:0.62 287:0.91 290:0.62 297:0.36 300:0.25 +0 1:0.74 11:0.75 12:0.79 17:0.34 18:0.62 20:0.92 21:0.13 26:0.98 27:0.13 28:0.47 29:0.91 30:0.21 34:0.74 36:0.69 37:0.26 39:0.88 43:0.47 45:0.66 55:0.59 58:0.93 64:0.77 66:0.88 69:0.45 70:0.73 71:0.56 74:0.41 78:0.74 81:0.77 83:0.91 86:0.67 91:0.35 98:0.93 100:0.78 101:0.52 104:0.76 108:0.35 111:0.74 114:0.79 122:0.77 123:0.34 126:0.54 127:0.57 129:0.05 131:0.61 135:0.30 139:0.65 141:0.91 144:0.57 146:0.84 147:0.87 149:0.35 150:0.85 152:0.86 154:0.46 155:0.67 157:0.76 159:0.40 161:0.68 165:0.93 166:0.78 167:0.69 169:0.76 172:0.65 173:0.52 176:0.73 177:0.70 178:0.55 179:0.67 181:0.60 182:0.79 186:0.75 187:0.21 192:0.87 199:0.94 201:0.93 208:0.64 212:0.35 215:0.61 216:0.28 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.72 234:0.91 235:0.42 241:0.37 242:0.75 243:0.88 244:0.61 246:0.89 247:0.55 253:0.55 259:0.21 261:0.76 265:0.93 266:0.54 267:0.28 271:0.63 274:0.91 276:0.54 287:0.61 290:0.79 297:0.36 300:0.55 +1 1:0.74 8:0.92 11:0.64 12:0.79 17:0.34 18:0.89 21:0.22 26:0.98 27:0.72 28:0.47 29:0.91 30:0.93 34:0.82 36:0.73 37:0.86 39:0.88 43:0.88 58:0.93 64:0.77 66:0.20 69:0.51 70:0.28 71:0.56 74:0.83 81:0.67 83:0.91 85:0.96 86:0.96 91:0.40 98:0.36 101:0.87 108:0.39 114:0.71 122:0.57 123:0.38 124:0.85 126:0.54 127:0.79 129:0.93 131:0.61 133:0.84 135:0.34 139:0.95 141:0.91 144:0.57 146:0.56 147:0.62 149:0.96 150:0.80 154:0.87 155:0.67 157:0.87 159:0.71 161:0.68 165:0.93 166:0.78 167:0.70 172:0.54 173:0.39 176:0.73 177:0.70 178:0.55 179:0.44 181:0.60 182:0.79 187:0.21 192:0.87 199:0.97 201:0.93 208:0.64 212:0.35 215:0.51 216:0.14 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 234:0.91 235:0.42 241:0.41 242:0.51 243:0.40 245:0.80 246:0.89 247:0.35 253:0.54 259:0.21 265:0.38 266:0.54 267:0.23 271:0.63 274:0.91 276:0.72 287:0.61 290:0.71 297:0.36 300:0.55 +4 1:0.74 6:0.99 8:1.00 9:0.80 11:0.64 12:0.79 17:0.20 18:0.92 20:0.80 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.91 31:0.98 34:0.37 36:0.42 37:0.96 39:0.88 41:0.95 43:0.94 58:0.99 60:0.87 64:0.77 66:0.71 69:0.25 70:0.27 71:0.56 74:0.88 78:0.93 79:0.98 81:0.75 83:0.91 85:0.95 86:0.97 91:0.83 96:0.93 98:0.81 100:1.00 101:0.87 104:0.58 108:0.54 111:1.00 114:0.79 120:0.89 122:0.57 123:0.52 124:0.46 126:0.54 127:0.67 129:0.81 131:0.85 133:0.67 135:0.44 137:0.98 139:0.97 140:0.45 141:0.99 144:0.57 146:0.17 147:0.22 149:0.97 150:0.84 151:0.98 152:0.99 153:0.92 154:0.94 155:0.67 157:0.87 158:0.92 159:0.52 161:0.68 162:0.83 164:0.83 165:0.93 166:0.78 167:0.88 169:1.00 172:0.79 173:0.28 176:0.73 177:0.70 179:0.43 181:0.60 182:0.80 186:0.93 187:0.21 189:1.00 191:0.89 192:0.87 196:0.99 199:0.98 201:0.93 202:0.74 208:0.64 212:0.72 215:0.61 216:0.34 219:0.95 222:0.35 223:0.98 224:0.99 227:0.88 229:0.89 231:0.68 232:0.80 234:0.99 235:0.31 238:1.00 241:0.74 242:0.45 243:0.16 245:0.77 246:0.89 247:0.55 248:0.93 253:0.95 255:0.95 256:0.80 259:0.21 260:0.89 261:1.00 265:0.81 266:0.54 267:0.19 268:0.80 271:0.63 274:0.99 276:0.72 279:0.86 283:0.82 284:0.97 285:0.62 287:0.91 290:0.79 292:0.95 297:0.36 300:0.33 +0 11:0.75 12:0.79 17:0.34 18:0.63 21:0.78 26:0.94 27:0.45 28:0.47 29:0.91 30:0.76 34:0.86 36:0.21 37:0.50 39:0.88 43:0.62 45:0.66 58:0.93 66:0.64 69:0.76 70:0.15 71:0.56 74:0.36 86:0.64 91:0.20 98:0.22 101:0.52 108:0.59 122:0.57 123:0.57 127:0.11 129:0.05 131:0.61 135:0.70 139:0.62 141:0.91 144:0.57 145:0.44 146:0.42 147:0.48 149:0.30 154:0.51 157:0.76 159:0.16 161:0.68 163:0.98 166:0.61 167:0.65 172:0.16 173:0.61 177:0.70 178:0.55 179:0.38 181:0.60 182:0.72 187:0.68 199:0.94 212:0.95 216:0.77 222:0.35 223:0.92 224:0.93 227:0.61 229:0.61 231:0.61 234:0.91 235:0.68 241:0.18 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.29 259:0.21 265:0.24 266:0.12 267:0.59 271:0.63 274:0.91 276:0.12 287:0.61 300:0.13 +0 6:0.93 8:0.60 9:0.80 12:0.79 17:0.53 20:0.78 21:0.78 26:0.98 27:0.51 28:0.47 29:0.91 31:0.96 34:0.40 36:0.39 37:0.94 39:0.88 41:0.90 58:0.99 71:0.56 78:0.78 79:0.96 83:0.91 91:0.67 96:0.85 100:0.73 111:0.77 120:0.75 131:0.85 135:0.47 137:0.96 140:0.97 141:0.99 144:0.57 151:0.87 152:0.89 153:0.48 155:0.67 157:0.61 161:0.68 162:0.50 164:0.72 166:0.61 167:0.85 169:0.81 175:0.97 178:0.53 181:0.60 182:0.80 186:0.79 187:0.39 191:0.92 192:0.87 199:0.95 202:0.81 208:0.64 216:0.43 220:0.62 222:0.35 223:0.98 224:0.98 227:0.88 229:0.89 231:0.68 234:0.97 241:0.72 244:0.93 246:0.61 248:0.83 253:0.79 255:0.95 256:0.73 257:0.93 259:0.21 260:0.76 261:0.82 267:0.44 268:0.73 271:0.63 274:0.98 277:0.95 284:0.95 285:0.62 287:0.91 +2 1:0.74 6:0.99 8:0.94 9:0.80 11:0.85 12:0.79 17:0.06 18:0.82 20:0.81 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.91 31:0.95 34:0.74 36:0.56 37:0.96 39:0.88 41:0.82 43:0.96 45:0.66 58:0.98 64:0.77 66:0.19 69:0.12 70:0.24 71:0.56 74:0.75 78:0.83 79:0.94 81:0.75 83:0.91 85:0.90 86:0.92 91:0.72 96:0.80 98:0.25 100:0.95 101:0.87 104:0.58 108:0.54 111:0.90 114:0.79 120:0.69 122:0.57 123:0.63 124:0.81 126:0.54 127:0.69 129:0.89 131:0.84 133:0.78 135:0.47 137:0.95 139:0.89 140:0.45 141:0.99 144:0.57 146:0.17 147:0.22 149:0.90 150:0.84 151:0.87 152:0.99 153:0.48 154:0.96 155:0.67 157:0.85 159:0.39 161:0.68 162:0.62 164:0.57 165:0.93 166:0.78 167:0.76 169:1.00 172:0.32 173:0.28 176:0.73 177:0.70 179:0.68 181:0.60 182:0.80 186:0.83 187:0.21 189:0.99 191:0.75 192:0.87 196:0.96 199:0.96 201:0.93 202:0.60 208:0.64 212:0.57 215:0.61 216:0.34 222:0.35 223:0.98 224:0.99 227:0.88 229:0.88 231:0.68 232:0.80 234:0.98 235:0.31 238:0.98 241:0.63 242:0.44 243:0.25 245:0.66 246:0.89 247:0.55 248:0.77 253:0.83 255:0.95 256:0.58 259:0.21 260:0.70 261:1.00 265:0.24 266:0.27 267:0.28 268:0.59 271:0.63 274:0.97 276:0.51 284:0.91 285:0.62 287:0.91 290:0.79 297:0.36 300:0.33 +1 1:0.74 8:0.59 11:0.64 12:0.79 17:0.11 18:0.79 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.86 34:0.42 36:0.56 37:0.96 39:0.88 43:0.96 58:0.93 64:0.77 66:0.26 69:0.12 70:0.19 71:0.56 74:0.55 81:0.75 83:0.91 85:0.85 86:0.86 91:0.58 98:0.50 101:0.87 104:0.58 108:0.10 114:0.79 122:0.57 123:0.10 124:0.74 126:0.54 127:0.91 129:0.81 131:0.61 133:0.67 135:0.70 139:0.82 141:0.91 144:0.57 146:0.63 147:0.68 149:0.77 150:0.84 154:0.96 155:0.67 157:0.81 159:0.63 161:0.68 163:0.75 165:0.93 166:0.78 167:0.72 172:0.27 173:0.17 176:0.73 177:0.70 178:0.55 179:0.83 181:0.60 182:0.79 187:0.21 192:0.87 199:0.95 201:0.93 202:0.36 208:0.64 212:0.23 215:0.61 216:0.34 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.80 234:0.91 235:0.31 241:0.47 242:0.42 243:0.45 245:0.59 246:0.89 247:0.60 253:0.56 259:0.21 265:0.52 266:0.63 267:0.91 271:0.63 274:0.91 276:0.38 287:0.61 290:0.79 297:0.36 300:0.18 +0 1:0.74 7:0.81 9:0.80 11:0.64 12:0.79 17:0.59 18:0.91 21:0.40 22:0.93 26:0.98 27:0.68 28:0.47 29:0.91 30:0.68 31:0.86 32:0.80 34:0.76 36:0.35 37:0.37 39:0.88 41:0.82 43:0.91 44:0.93 47:0.93 58:0.98 60:0.87 64:0.77 66:0.86 69:0.44 70:0.41 71:0.56 74:0.81 77:0.89 79:0.86 81:0.56 83:0.91 85:0.85 86:0.92 91:0.38 96:0.68 98:0.80 101:0.87 104:0.95 106:0.81 108:0.34 114:0.62 117:0.86 120:0.55 121:0.97 122:0.86 123:0.33 126:0.54 127:0.29 129:0.05 131:0.84 135:0.74 137:0.86 139:0.96 140:0.45 141:0.99 144:0.57 145:0.70 146:0.61 147:0.67 149:0.85 150:0.75 151:0.50 153:0.48 154:0.90 155:0.67 157:0.82 158:0.91 159:0.23 161:0.68 162:0.86 164:0.57 165:0.93 166:0.78 167:0.65 172:0.61 173:0.61 176:0.73 177:0.70 179:0.58 181:0.60 182:0.80 187:0.39 192:0.87 195:0.58 196:0.90 199:0.97 201:0.93 202:0.36 204:0.89 206:0.81 208:0.64 212:0.59 215:0.42 216:0.40 219:0.95 220:0.91 222:0.35 223:0.98 224:0.99 227:0.88 229:0.88 230:0.86 231:0.68 232:0.97 233:0.73 234:0.98 235:0.60 241:0.15 242:0.54 243:0.87 246:0.89 247:0.55 248:0.49 253:0.50 255:0.95 256:0.45 259:0.21 260:0.55 262:0.93 265:0.80 266:0.27 267:0.23 268:0.46 271:0.63 274:0.97 276:0.48 279:0.86 281:0.89 282:0.88 283:0.78 284:0.89 285:0.62 287:0.91 290:0.62 292:0.91 297:0.36 299:0.97 300:0.33 +2 1:0.74 6:0.82 8:0.67 9:0.80 11:0.78 12:0.37 17:0.64 20:0.86 21:0.05 27:0.71 28:0.56 30:0.89 32:0.80 34:0.37 36:0.60 37:0.65 39:0.79 41:0.90 43:0.79 46:0.97 60:0.87 66:0.47 69:0.75 70:0.20 74:0.72 77:0.70 78:0.75 81:0.91 83:0.87 85:0.85 91:0.87 96:0.83 98:0.49 100:0.78 101:0.80 104:0.58 107:0.97 108:0.70 110:0.97 111:0.74 114:0.95 120:0.73 122:0.43 123:0.68 124:0.52 125:0.98 126:0.54 127:0.30 129:0.59 131:0.95 133:0.47 135:0.89 140:0.45 144:0.76 145:0.69 146:0.13 147:0.18 149:0.68 150:0.96 151:0.85 152:0.87 153:0.84 154:0.72 155:0.67 157:0.86 158:0.87 159:0.28 160:0.98 161:0.45 162:0.60 163:0.81 164:0.73 165:0.90 166:0.91 167:0.95 169:0.75 172:0.21 173:0.87 176:0.73 177:0.38 179:0.89 181:0.96 182:0.87 186:0.76 189:0.84 191:0.80 192:0.59 195:0.58 201:0.74 202:0.67 208:0.64 212:0.30 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 238:0.86 241:0.83 242:0.63 243:0.37 245:0.46 246:0.97 247:0.30 248:0.80 253:0.85 255:0.79 256:0.64 259:0.21 260:0.74 261:0.77 265:0.50 266:0.27 267:0.59 268:0.67 271:0.35 276:0.23 279:0.86 282:0.88 283:0.71 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 299:0.88 300:0.18 +4 1:0.74 6:0.98 8:0.97 9:0.80 11:0.78 12:0.37 17:0.12 20:0.99 27:0.07 28:0.56 30:0.70 34:0.52 36:0.46 37:0.99 39:0.79 41:0.99 43:0.95 46:0.99 60:0.95 66:0.88 69:0.15 70:0.39 74:0.87 78:0.89 81:0.95 83:0.90 85:0.93 91:0.98 96:0.99 98:0.98 100:0.99 101:0.85 104:0.58 107:0.99 108:0.12 110:0.99 111:0.98 114:0.98 120:0.97 122:0.57 123:0.12 125:0.98 126:0.54 127:0.86 129:0.05 131:0.95 135:0.47 138:0.99 140:0.45 144:0.76 146:0.76 147:0.80 149:0.91 150:0.98 151:1.00 152:0.99 153:0.98 154:0.95 155:0.67 157:0.91 158:0.92 159:0.32 160:1.00 161:0.45 162:0.73 164:0.96 165:0.92 166:0.91 167:0.97 169:0.99 172:0.65 173:0.37 176:0.73 177:0.38 179:0.72 181:0.96 182:0.88 186:0.89 189:0.99 191:0.96 192:0.59 201:0.74 202:0.93 208:0.64 212:0.35 215:0.96 216:0.59 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.05 238:0.99 241:0.90 242:0.62 243:0.88 246:0.97 247:0.39 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.97 261:1.00 265:0.98 266:0.38 267:0.79 268:0.95 271:0.35 276:0.52 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.98 297:0.36 300:0.33 +2 1:0.74 6:0.84 8:0.88 9:0.80 11:0.78 12:0.37 17:0.45 20:0.85 21:0.05 27:0.50 28:0.56 30:0.61 34:0.39 36:0.62 37:0.37 39:0.79 41:0.90 43:0.84 46:1.00 60:0.87 66:0.89 69:0.55 70:0.63 74:0.88 78:0.75 81:0.91 83:0.86 85:0.93 91:0.83 96:0.85 98:0.87 100:0.78 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.74 114:0.95 120:0.76 122:0.43 123:0.40 125:0.99 126:0.54 127:0.40 129:0.05 131:0.95 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.89 152:0.95 153:0.87 154:0.81 155:0.67 157:0.91 158:0.87 159:0.23 160:0.98 161:0.45 162:0.68 164:0.75 165:0.90 166:0.91 167:0.95 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.76 192:0.59 201:0.74 202:0.67 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 241:0.84 242:0.57 243:0.89 246:0.97 247:0.47 248:0.83 253:0.89 255:0.79 256:0.64 259:0.21 260:0.77 261:0.81 265:0.87 266:0.23 267:0.28 268:0.69 271:0.35 276:0.56 279:0.86 283:0.71 285:0.62 287:0.98 289:0.99 290:0.95 297:0.36 300:0.55 +2 1:0.74 6:0.84 8:0.70 9:0.80 11:0.78 12:0.37 17:0.66 20:0.90 21:0.05 27:0.50 28:0.56 30:0.61 34:0.44 36:0.62 37:0.37 39:0.79 41:0.82 43:0.84 46:1.00 66:0.89 69:0.55 70:0.63 74:0.86 78:0.76 81:0.91 83:0.79 85:0.93 91:0.80 96:0.78 98:0.87 100:0.79 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.76 114:0.95 120:0.66 122:0.43 123:0.40 125:1.00 126:0.54 127:0.40 129:0.05 131:0.94 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.79 152:0.95 153:0.69 154:0.81 155:0.67 157:0.91 159:0.23 160:0.96 161:0.45 162:0.86 164:0.62 165:0.90 166:0.91 167:0.93 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.77 187:0.21 189:0.88 192:0.59 201:0.74 202:0.46 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 238:0.89 241:0.80 242:0.57 243:0.89 246:0.97 247:0.47 248:0.72 253:0.84 255:0.79 256:0.45 259:0.21 260:0.67 261:0.81 265:0.87 266:0.23 267:0.44 268:0.55 271:0.35 276:0.56 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 300:0.55 +0 1:0.74 9:0.80 11:0.78 12:0.37 17:0.40 21:0.64 27:0.45 28:0.56 30:0.58 32:0.80 34:0.68 36:0.19 37:0.50 39:0.79 41:0.82 43:0.82 46:0.95 60:0.87 66:0.18 69:0.70 70:0.60 74:0.78 77:0.70 81:0.57 83:0.83 85:0.85 91:0.23 96:0.68 98:0.19 101:0.77 107:0.98 108:0.53 110:0.98 114:0.72 120:0.54 122:0.43 123:0.81 124:0.73 125:0.97 126:0.54 127:0.36 129:0.81 131:0.94 133:0.67 135:0.63 140:0.45 144:0.76 145:0.50 146:0.23 147:0.29 149:0.75 150:0.72 151:0.49 153:0.48 154:0.77 155:0.67 157:0.85 158:0.87 159:0.48 160:0.96 161:0.45 162:0.86 163:0.88 164:0.57 165:0.70 166:0.91 167:0.67 172:0.21 173:0.68 176:0.73 177:0.38 179:0.82 181:0.96 182:0.87 187:0.68 192:0.59 195:0.73 201:0.74 202:0.36 208:0.64 212:0.37 215:0.53 216:0.77 220:0.97 222:0.60 227:0.98 229:0.93 231:0.87 235:0.80 241:0.35 242:0.58 243:0.46 245:0.51 246:0.97 247:0.35 248:0.49 253:0.66 255:0.79 256:0.45 259:0.21 260:0.55 265:0.17 266:0.47 267:0.36 268:0.46 271:0.35 276:0.28 279:0.86 282:0.88 283:0.71 285:0.62 287:0.98 289:0.98 290:0.72 297:0.36 299:0.88 300:0.43 +2 1:0.74 7:0.81 8:0.84 9:0.80 11:0.78 12:0.37 17:0.62 20:0.75 21:0.30 27:0.50 28:0.56 30:0.45 32:0.80 34:0.64 36:0.68 37:0.60 39:0.79 41:0.88 43:0.88 46:0.98 60:0.95 66:0.36 69:0.53 70:0.72 74:0.97 77:0.89 78:0.83 81:0.78 83:0.84 85:0.99 91:0.20 96:0.76 98:0.66 100:0.86 101:0.84 104:0.84 106:0.81 107:1.00 108:0.95 110:0.99 111:0.83 114:0.86 117:0.86 120:0.64 121:0.90 122:0.43 123:0.94 124:0.90 125:0.99 126:0.54 127:0.87 129:0.95 131:0.94 133:0.91 135:0.42 140:0.45 144:0.76 145:0.65 146:0.85 147:0.88 149:0.98 150:0.86 151:0.72 152:0.74 153:0.72 154:0.86 155:0.67 157:0.94 158:0.92 159:0.92 160:0.97 161:0.45 162:0.86 164:0.69 165:0.84 166:0.91 167:0.55 169:0.84 172:0.93 173:0.18 176:0.73 177:0.38 179:0.16 181:0.96 182:0.87 186:0.83 187:0.39 192:0.59 195:0.98 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.86 220:0.74 222:0.60 227:0.98 229:0.93 230:0.87 231:0.87 232:0.89 233:0.76 235:0.42 241:0.01 242:0.51 243:0.32 245:0.95 246:0.97 247:0.67 248:0.69 253:0.84 255:0.79 256:0.58 259:0.21 260:0.65 261:0.75 265:0.66 266:0.91 267:0.44 268:0.62 271:0.35 276:0.95 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 289:0.98 290:0.86 297:0.36 299:0.97 300:0.70 +0 1:0.74 9:0.80 11:0.78 12:0.37 17:0.22 27:0.32 28:0.56 30:0.95 32:0.80 34:0.66 36:0.52 37:0.67 39:0.79 41:0.88 43:0.90 46:0.99 66:0.54 69:0.44 74:0.58 77:0.70 81:0.95 83:0.81 85:0.72 91:0.63 96:0.82 98:0.19 101:0.77 104:0.80 106:0.81 107:0.93 108:0.34 110:0.93 114:0.98 120:0.71 123:0.33 125:0.99 126:0.54 127:0.10 129:0.05 131:0.94 135:0.37 140:0.45 144:0.76 145:0.39 146:0.13 147:0.18 149:0.51 150:0.98 151:0.87 153:0.48 154:0.89 155:0.67 157:0.79 159:0.08 160:0.97 161:0.45 162:0.86 164:0.67 165:0.92 166:0.91 167:0.75 172:0.10 173:0.91 176:0.73 177:0.38 179:0.28 181:0.96 182:0.87 187:0.39 192:0.59 195:0.53 201:0.74 202:0.50 206:0.81 208:0.64 215:0.96 216:0.47 222:0.60 227:0.98 229:0.93 231:0.87 235:0.05 241:0.63 243:0.63 246:0.97 248:0.79 253:0.83 255:0.79 256:0.58 259:0.21 260:0.72 265:0.21 267:0.91 268:0.59 271:0.35 282:0.88 285:0.62 287:0.98 289:0.98 290:0.98 297:0.36 299:0.88 300:0.08 +3 1:0.74 6:0.98 8:0.98 9:0.80 11:0.78 12:0.37 17:0.67 20:0.81 27:0.07 28:0.56 30:0.66 34:0.74 36:0.40 37:0.99 39:0.79 41:0.98 43:0.87 46:0.98 60:0.87 66:0.36 69:0.44 70:0.53 74:0.83 78:0.88 81:0.95 83:0.89 85:0.90 91:0.90 96:0.96 98:0.62 100:0.98 101:0.83 104:0.58 107:0.99 108:0.65 110:0.98 111:0.96 114:0.98 120:0.94 122:0.57 123:0.63 124:0.60 125:0.99 126:0.54 127:0.66 129:0.59 131:0.95 133:0.47 135:0.47 140:0.45 144:0.76 146:0.45 147:0.51 149:0.84 150:0.98 151:0.99 152:0.99 153:0.97 154:0.85 155:0.67 157:0.90 158:0.92 159:0.35 160:0.99 161:0.45 162:0.80 164:0.91 165:0.92 166:0.91 167:0.90 169:0.99 172:0.37 173:0.56 176:0.73 177:0.38 179:0.78 181:0.96 182:0.88 186:0.88 187:0.39 189:0.99 191:0.94 192:0.59 201:0.74 202:0.85 208:0.64 212:0.22 215:0.96 216:0.59 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.05 238:0.99 241:0.77 242:0.61 243:0.48 245:0.67 246:0.97 247:0.39 248:0.96 253:0.97 255:0.79 256:0.88 259:0.21 260:0.94 261:1.00 265:0.63 266:0.54 267:0.19 268:0.89 271:0.35 276:0.43 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.98 297:0.36 300:0.43 +2 6:0.78 8:0.58 9:0.80 12:0.37 17:0.09 20:0.86 21:0.05 25:0.88 27:0.51 28:0.56 30:0.95 34:0.80 36:0.92 37:0.23 39:0.79 41:0.82 43:0.45 46:0.88 55:0.71 66:0.77 69:0.05 70:0.13 78:0.75 81:0.88 83:0.77 91:0.83 96:0.84 98:0.96 100:0.77 104:0.58 107:0.95 108:0.05 110:0.96 111:0.74 120:0.89 123:0.05 125:0.96 126:0.32 127:0.69 129:0.05 131:0.94 135:0.34 140:0.93 144:0.76 146:0.61 147:0.66 149:0.43 150:0.74 151:0.92 152:0.81 153:0.85 154:0.34 155:0.67 157:0.61 159:0.22 160:0.96 161:0.45 162:0.73 164:0.85 166:0.84 167:0.97 169:0.75 172:0.37 173:0.35 175:0.75 177:0.05 179:0.93 181:0.96 182:0.87 186:0.76 189:0.81 191:0.92 192:0.59 202:0.78 208:0.64 212:0.52 215:0.91 216:0.23 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 235:0.05 238:0.83 241:0.92 242:0.82 243:0.79 244:0.61 246:0.61 247:0.12 248:0.81 253:0.77 255:0.79 256:0.79 257:0.65 259:0.21 260:0.89 261:0.77 265:0.96 266:0.27 267:0.82 268:0.82 271:0.35 276:0.33 277:0.69 283:0.71 285:0.62 287:0.98 289:0.98 297:0.36 300:0.13 +2 1:0.74 6:0.82 8:0.66 9:0.80 11:0.78 12:0.37 17:0.88 20:0.88 21:0.05 27:0.71 28:0.56 30:0.89 32:0.80 34:0.47 36:0.51 37:0.65 39:0.79 41:0.82 43:0.79 46:0.97 66:0.47 69:0.76 70:0.20 74:0.65 77:0.70 78:0.74 81:0.91 83:0.79 85:0.85 91:0.83 96:0.77 98:0.48 100:0.78 101:0.80 104:0.58 107:0.97 108:0.70 110:0.97 111:0.74 114:0.95 120:0.65 122:0.43 123:0.69 124:0.52 125:1.00 126:0.54 127:0.29 129:0.59 131:0.94 133:0.47 135:0.90 140:0.45 144:0.76 145:0.69 146:0.13 147:0.18 149:0.68 150:0.96 151:0.77 152:0.87 153:0.48 154:0.72 155:0.67 157:0.86 159:0.28 160:0.96 161:0.45 162:0.86 163:0.81 164:0.57 165:0.90 166:0.91 167:0.92 169:0.75 172:0.21 173:0.87 176:0.73 177:0.38 179:0.88 181:0.96 182:0.87 186:0.75 187:0.21 192:0.59 195:0.58 201:0.74 202:0.36 208:0.64 212:0.30 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 241:0.79 242:0.63 243:0.37 245:0.46 246:0.97 247:0.30 248:0.71 253:0.80 255:0.79 256:0.45 259:0.21 260:0.66 261:0.77 265:0.50 266:0.27 267:0.65 268:0.46 271:0.35 276:0.23 282:0.88 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.74 6:0.87 7:0.81 8:0.78 9:0.80 11:0.78 12:0.37 17:0.45 20:0.83 21:0.30 27:0.12 28:0.56 30:0.33 34:0.82 36:0.39 37:0.81 39:0.79 41:0.93 43:0.59 46:0.95 55:0.59 60:0.87 66:0.44 69:0.93 70:0.64 74:0.86 78:0.75 81:0.78 83:0.88 85:0.91 91:0.33 96:0.92 98:0.43 100:0.78 101:0.77 107:0.99 108:0.89 110:0.98 111:0.75 114:0.86 120:0.86 121:0.90 122:0.67 123:0.89 124:0.81 125:1.00 126:0.54 127:0.51 129:0.81 131:0.95 133:0.83 135:0.70 140:0.45 144:0.76 146:0.27 147:0.05 149:0.71 150:0.86 151:0.92 152:0.96 153:0.72 154:0.48 155:0.67 157:0.88 158:0.92 159:0.79 160:0.98 161:0.45 162:0.86 164:0.78 165:0.84 166:0.91 167:0.66 169:0.75 172:0.63 173:0.88 176:0.73 177:0.05 179:0.46 181:0.96 182:0.87 186:0.76 187:0.98 192:0.59 201:0.74 202:0.64 204:0.89 208:0.64 212:0.60 215:0.72 216:0.90 220:0.87 222:0.60 227:0.98 229:0.93 230:0.87 231:0.87 233:0.76 235:0.42 241:0.32 242:0.63 243:0.09 245:0.73 246:0.97 247:0.47 248:0.92 253:0.93 255:0.79 256:0.71 257:0.65 259:0.21 260:0.86 261:0.78 265:0.45 266:0.75 267:0.91 268:0.73 271:0.35 276:0.65 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.86 297:0.36 300:0.55 +2 1:0.74 6:0.84 7:0.81 8:0.68 9:0.80 11:0.78 12:0.37 17:0.36 20:0.99 21:0.30 27:0.21 28:0.56 30:0.73 34:0.66 36:0.89 37:0.74 39:0.79 41:0.92 43:0.98 46:0.99 60:0.95 66:0.94 69:0.12 70:0.42 74:0.94 78:0.77 81:0.78 83:0.88 85:0.97 91:0.58 96:0.91 98:0.88 100:0.80 101:0.85 104:0.84 106:0.81 107:1.00 108:0.10 110:0.99 111:0.76 114:0.86 117:0.86 120:0.84 121:0.90 122:0.43 123:0.10 125:0.99 126:0.54 127:0.41 129:0.05 131:0.95 135:0.17 140:0.45 144:0.76 146:0.93 147:0.95 149:0.97 150:0.86 151:0.96 152:0.90 153:0.86 154:0.98 155:0.67 157:0.94 158:0.92 159:0.57 160:0.98 161:0.45 162:0.71 164:0.78 165:0.84 166:0.91 167:0.66 169:0.78 172:0.84 173:0.15 176:0.73 177:0.38 179:0.35 181:0.96 182:0.87 186:0.77 187:0.39 189:0.86 192:0.59 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.48 215:0.72 216:0.95 222:0.60 227:0.98 229:0.93 230:0.87 231:0.87 232:0.89 233:0.76 235:0.42 238:0.89 241:0.32 242:0.60 243:0.94 246:0.97 247:0.47 248:0.90 253:0.93 255:0.79 256:0.68 259:0.21 260:0.84 261:0.81 265:0.88 266:0.54 267:0.88 268:0.73 271:0.35 276:0.74 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.86 297:0.36 300:0.43 +1 11:0.57 12:0.20 17:0.59 21:0.78 27:0.45 28:0.98 30:0.08 34:0.80 36:0.17 37:0.50 39:0.61 43:0.75 55:0.42 66:0.36 69:0.83 70:0.74 74:0.46 85:0.72 91:0.33 98:0.22 101:0.71 108:0.86 123:0.85 124:0.69 127:0.29 129:0.05 133:0.62 135:0.94 145:0.52 146:0.17 147:0.22 149:0.46 154:0.66 159:0.51 161:0.90 163:0.89 167:0.62 172:0.21 173:0.80 177:0.38 178:0.55 179:0.85 181:0.67 187:0.68 212:0.23 216:0.77 222:0.60 235:1.00 241:0.41 242:0.55 243:0.34 245:0.45 247:0.39 253:0.40 259:0.21 265:0.24 266:0.38 267:0.28 271:0.16 276:0.27 277:0.69 300:0.43 +1 1:0.74 6:0.80 7:0.81 8:0.85 9:0.80 11:0.57 12:0.20 17:0.94 20:0.89 21:0.54 27:0.72 28:0.98 30:0.76 32:0.80 34:0.58 36:0.68 37:0.85 39:0.61 41:0.92 43:0.94 55:0.42 66:0.53 69:0.32 70:0.43 74:0.87 77:0.89 78:0.89 81:0.75 83:0.77 85:0.91 91:0.87 96:0.79 98:0.71 100:0.83 101:0.84 104:0.75 108:0.54 111:0.86 114:0.77 120:0.74 121:0.97 122:0.57 123:0.52 124:0.52 126:0.54 127:0.40 129:0.59 133:0.47 135:0.28 140:0.45 145:0.54 146:0.22 147:0.28 149:0.88 150:0.83 151:0.77 152:0.83 153:0.48 154:0.94 155:0.67 159:0.30 161:0.90 162:0.86 164:0.75 165:0.81 167:0.90 169:0.80 172:0.45 173:0.45 176:0.73 177:0.38 179:0.71 181:0.67 186:0.89 189:0.83 192:0.59 195:0.62 201:0.74 202:0.60 204:0.89 208:0.64 212:0.49 215:0.58 216:0.02 220:0.97 222:0.60 230:0.87 232:0.83 233:0.76 235:0.88 238:0.81 241:0.85 242:0.61 243:0.37 245:0.64 247:0.39 248:0.75 253:0.84 255:0.79 256:0.68 259:0.21 260:0.75 261:0.85 265:0.71 266:0.38 267:0.44 268:0.69 271:0.16 276:0.43 282:0.88 285:0.62 290:0.77 297:0.36 299:0.97 300:0.43 +1 6:0.86 7:0.81 8:0.91 9:0.80 12:0.20 17:0.16 20:0.88 21:0.78 27:0.31 28:0.98 32:0.75 34:0.42 36:0.43 37:0.68 39:0.61 74:0.47 78:0.96 91:0.90 96:0.83 100:0.89 104:0.58 111:0.94 120:0.89 121:0.90 135:0.30 140:0.98 145:0.86 151:0.77 152:0.82 153:0.95 155:0.67 161:0.90 162:0.66 164:0.91 167:0.91 169:0.87 175:0.91 181:0.67 186:0.96 189:0.88 191:0.83 192:0.59 195:0.53 202:0.87 204:0.89 208:0.64 216:0.30 220:0.62 222:0.60 230:0.87 233:0.76 235:0.22 238:0.82 241:0.88 244:0.83 248:0.71 253:0.77 255:0.79 256:0.88 257:0.84 259:0.21 260:0.89 261:0.89 267:0.23 268:0.89 271:0.16 277:0.87 283:0.71 285:0.62 +1 1:0.74 7:0.78 8:0.80 12:0.20 17:0.38 20:0.84 21:0.74 27:0.38 28:0.98 30:0.76 32:0.75 34:0.40 36:0.36 37:0.47 39:0.61 43:0.30 55:0.59 60:0.87 66:0.47 69:0.87 70:0.28 74:0.79 76:0.85 77:0.70 78:0.88 81:0.47 83:0.83 91:0.17 96:0.68 98:0.52 100:0.82 108:0.75 111:0.85 114:0.61 117:0.86 123:0.73 124:0.74 126:0.54 127:0.34 129:0.05 133:0.74 135:0.68 140:0.45 145:0.88 146:0.73 147:0.05 149:0.43 150:0.61 151:0.47 152:0.80 153:0.48 154:0.22 155:0.67 158:0.92 159:0.58 161:0.90 162:0.86 167:0.52 169:0.80 172:0.41 173:0.84 176:0.56 177:0.05 179:0.68 181:0.67 186:0.88 187:0.21 190:0.77 192:0.59 195:0.84 201:0.74 202:0.36 208:0.64 212:0.22 215:0.46 216:0.90 220:0.99 222:0.60 230:0.81 232:0.96 233:0.69 235:0.97 241:0.02 242:0.74 243:0.14 244:0.61 245:0.53 247:0.39 248:0.47 253:0.60 256:0.45 257:0.65 259:0.21 261:0.82 265:0.54 266:0.75 267:0.96 268:0.46 271:0.16 276:0.46 279:0.86 282:0.83 283:0.82 285:0.50 290:0.61 297:0.36 300:0.25 +3 1:0.74 6:0.91 7:0.78 8:0.93 9:0.80 12:0.20 17:0.85 20:0.98 21:0.30 27:0.55 28:0.98 30:0.45 32:0.77 34:0.62 36:0.76 37:0.52 39:0.61 41:0.90 43:0.31 55:0.71 66:0.49 69:0.66 70:0.25 74:0.63 77:0.70 78:0.97 81:0.84 83:0.75 91:0.94 96:0.85 98:0.37 100:0.95 104:0.58 108:0.50 111:0.95 114:0.86 120:0.91 122:0.55 123:0.48 124:0.48 126:0.54 127:0.51 129:0.05 133:0.39 135:0.37 140:0.93 145:0.54 146:0.37 147:0.44 149:0.28 150:0.89 151:0.77 152:0.95 153:0.48 154:0.23 155:0.67 159:0.39 161:0.90 162:0.74 163:0.87 164:0.90 165:0.83 167:0.91 169:0.92 172:0.16 173:0.75 176:0.73 177:0.38 179:0.97 181:0.67 186:0.96 189:0.94 190:0.77 191:0.88 192:0.59 195:0.65 201:0.74 202:0.85 208:0.64 212:0.61 215:0.72 216:0.27 220:0.62 222:0.60 230:0.81 233:0.69 235:0.68 238:0.87 241:0.89 242:0.63 243:0.60 244:0.61 245:0.39 247:0.30 248:0.81 253:0.84 255:0.79 256:0.87 259:0.21 260:0.92 261:0.94 265:0.39 266:0.17 267:0.82 268:0.88 271:0.16 276:0.16 282:0.85 283:0.71 285:0.62 290:0.86 297:0.36 300:0.18 +3 1:0.74 7:0.81 11:0.57 12:0.20 17:0.88 21:0.40 27:0.70 28:0.98 30:0.45 32:0.75 34:0.25 36:0.41 37:0.58 39:0.61 43:0.62 55:0.42 66:0.27 69:0.91 70:0.25 74:0.52 76:0.85 81:0.77 83:0.75 85:0.72 91:0.75 98:0.22 101:0.72 104:0.73 108:0.55 114:0.82 121:0.90 123:0.53 124:0.61 126:0.54 127:0.51 129:0.05 133:0.39 135:0.54 145:0.54 146:0.17 147:0.35 149:0.38 150:0.85 154:0.51 155:0.67 159:0.48 161:0.90 163:0.88 165:0.83 167:0.84 172:0.10 173:1.00 176:0.73 177:0.05 178:0.55 179:0.97 181:0.67 187:0.21 192:0.59 195:0.72 201:0.74 204:0.89 208:0.64 212:0.61 215:0.67 216:0.10 222:0.60 230:0.87 232:0.82 233:0.76 235:0.52 241:0.76 242:0.63 243:0.46 245:0.40 247:0.30 253:0.64 257:0.65 259:0.21 265:0.25 266:0.17 267:0.23 271:0.16 276:0.14 277:0.69 290:0.82 297:0.36 300:0.18 +2 1:0.74 6:0.87 8:0.75 11:0.57 12:0.20 17:0.77 20:0.85 21:0.22 27:0.36 28:0.98 30:0.15 34:0.63 36:0.39 37:0.56 39:0.61 43:0.73 55:0.52 66:0.24 69:0.85 70:0.96 74:0.62 76:0.85 78:0.96 81:0.84 83:0.76 85:0.72 91:0.29 98:0.14 100:0.89 101:0.69 108:0.72 111:0.94 114:0.90 122:0.43 123:0.94 124:0.78 126:0.54 127:0.70 129:0.59 133:0.76 135:0.78 145:0.42 146:0.22 147:0.05 149:0.47 150:0.89 152:0.97 154:0.63 155:0.67 159:0.83 161:0.90 165:0.86 167:0.54 169:0.95 172:0.21 173:0.71 176:0.73 177:0.05 178:0.55 179:0.86 181:0.67 186:0.96 187:0.39 189:0.87 191:0.70 192:0.59 201:0.74 212:0.30 215:0.79 216:0.37 222:0.60 232:0.80 235:0.42 238:0.82 241:0.07 242:0.33 243:0.17 245:0.49 247:0.55 253:0.69 257:0.65 259:0.21 261:0.97 265:0.12 266:0.54 267:0.18 271:0.16 276:0.24 290:0.90 297:0.36 300:0.70 +2 1:0.74 6:0.87 8:0.75 11:0.57 12:0.20 17:0.81 20:0.85 21:0.22 27:0.36 28:0.98 30:0.15 34:0.63 36:0.44 37:0.56 39:0.61 43:0.73 55:0.52 66:0.24 69:0.85 70:0.96 74:0.62 76:0.85 78:0.96 81:0.84 83:0.76 85:0.72 91:0.31 98:0.14 100:0.89 101:0.69 108:0.72 111:0.94 114:0.90 122:0.43 123:0.94 124:0.78 126:0.54 127:0.69 129:0.59 133:0.76 135:0.82 145:0.42 146:0.22 147:0.05 149:0.46 150:0.89 152:0.97 154:0.63 155:0.67 159:0.84 161:0.90 165:0.86 167:0.54 169:0.95 172:0.21 173:0.70 176:0.73 177:0.05 178:0.55 179:0.86 181:0.67 186:0.96 187:0.39 189:0.87 191:0.70 192:0.59 201:0.74 212:0.30 215:0.79 216:0.37 222:0.60 232:0.80 235:0.42 238:0.82 241:0.07 242:0.33 243:0.17 245:0.49 247:0.55 253:0.69 257:0.65 259:0.21 261:0.97 265:0.12 266:0.54 267:0.18 271:0.16 276:0.24 290:0.90 297:0.36 300:0.70 +3 1:0.74 6:0.89 7:0.81 8:0.81 9:0.80 11:0.57 12:0.20 17:0.83 20:0.90 21:0.40 27:0.70 28:0.98 30:0.45 32:0.75 34:0.20 36:0.44 37:0.58 39:0.61 41:0.82 43:0.62 55:0.42 66:0.27 69:0.91 70:0.25 74:0.52 76:0.85 78:0.97 81:0.77 83:0.77 85:0.72 91:0.76 96:0.77 98:0.23 100:0.90 101:0.72 104:0.73 108:0.55 111:0.94 114:0.82 120:0.65 121:0.90 123:0.53 124:0.61 126:0.54 127:0.51 129:0.05 133:0.39 135:0.51 140:0.45 145:0.54 146:0.17 147:0.35 149:0.38 150:0.85 151:0.77 152:0.84 153:0.48 154:0.52 155:0.67 159:0.47 161:0.90 162:0.86 163:0.88 164:0.57 165:0.83 167:0.90 169:0.88 172:0.10 173:1.00 176:0.73 177:0.05 179:0.97 181:0.67 186:0.96 189:0.91 192:0.59 195:0.72 201:0.74 202:0.36 204:0.89 208:0.64 212:0.61 215:0.67 216:0.10 220:0.62 222:0.60 230:0.87 232:0.82 233:0.76 235:0.52 238:0.82 241:0.84 242:0.63 243:0.46 245:0.40 247:0.30 248:0.71 253:0.76 255:0.79 256:0.45 257:0.65 259:0.21 260:0.66 261:0.91 265:0.25 266:0.17 267:0.23 268:0.46 271:0.16 276:0.14 277:0.69 283:0.71 285:0.62 290:0.82 297:0.36 300:0.18 +3 1:0.74 6:0.83 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.97 21:0.05 27:0.63 28:0.98 30:0.88 32:0.80 34:0.36 36:0.49 37:0.45 39:0.61 41:0.97 43:0.61 55:0.92 60:0.87 66:0.24 69:0.75 70:0.29 74:0.87 76:0.85 77:0.89 78:0.98 81:0.91 83:0.88 85:0.72 91:0.91 96:0.96 98:0.67 100:0.95 101:0.72 108:0.80 111:0.96 114:0.95 120:0.93 121:0.90 122:0.67 123:0.56 124:0.67 126:0.54 127:0.80 129:0.59 133:0.64 135:0.66 140:0.80 145:0.72 146:0.73 147:0.05 149:0.51 150:0.95 151:0.98 152:0.95 153:0.91 154:0.54 155:0.67 158:0.87 159:0.55 161:0.90 162:0.64 163:0.81 164:0.89 165:0.90 167:0.91 169:0.91 172:0.37 173:0.77 176:0.73 177:0.05 178:0.42 179:0.80 181:0.67 186:0.98 189:0.85 191:0.83 192:0.59 195:0.71 201:0.74 202:0.84 204:0.89 208:0.64 212:0.22 215:0.91 216:0.28 220:0.62 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.85 241:0.89 242:0.38 243:0.14 245:0.59 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.95 265:0.56 266:0.54 267:0.65 268:0.86 271:0.16 276:0.47 279:0.86 282:0.88 283:0.71 285:0.62 290:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.87 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.95 21:0.05 27:0.36 28:0.98 30:0.33 32:0.80 34:0.87 36:0.64 37:0.56 39:0.61 41:0.96 43:0.77 55:0.42 60:0.95 66:0.60 69:0.75 70:0.58 74:0.85 77:0.89 78:0.99 81:0.91 83:0.89 85:0.85 91:0.86 96:0.95 98:0.55 100:0.97 101:0.77 104:0.58 108:0.58 111:0.97 114:0.95 120:0.91 121:0.97 122:0.41 123:0.56 124:0.50 126:0.54 127:0.50 129:0.59 133:0.47 135:0.49 140:0.45 145:0.52 146:0.66 147:0.71 149:0.69 150:0.95 151:0.98 152:0.95 153:0.92 154:0.69 155:0.67 158:0.92 159:0.54 161:0.90 162:0.64 164:0.87 165:0.90 167:0.90 169:0.93 172:0.51 173:0.74 176:0.73 177:0.38 179:0.70 181:0.67 186:0.99 189:0.89 191:0.78 192:0.59 195:0.76 201:0.74 202:0.82 204:0.89 208:0.64 212:0.69 215:0.91 216:0.37 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.87 241:0.86 242:0.31 243:0.67 245:0.67 247:0.60 248:0.95 253:0.96 255:0.79 256:0.83 259:0.21 260:0.91 261:0.95 265:0.56 266:0.54 267:0.69 268:0.84 271:0.16 276:0.40 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.79 8:0.62 9:0.80 11:0.57 12:0.20 17:0.38 20:0.86 21:0.13 27:0.72 28:0.98 30:0.89 34:0.61 36:0.81 37:0.56 39:0.61 41:0.90 43:0.94 55:0.42 66:0.75 69:0.12 70:0.15 74:0.64 78:0.95 81:0.88 83:0.79 85:0.85 91:0.88 96:0.82 98:0.85 100:0.83 101:0.82 108:0.10 111:0.91 114:0.92 120:0.72 122:0.43 123:0.10 126:0.54 127:0.36 129:0.05 135:0.60 140:0.45 146:0.49 147:0.55 149:0.78 150:0.93 151:0.77 152:0.81 153:0.48 154:0.94 155:0.67 159:0.18 161:0.90 162:0.86 163:0.81 164:0.72 165:0.88 167:0.92 169:0.81 172:0.32 173:0.49 176:0.73 177:0.38 179:0.91 181:0.67 186:0.94 189:0.81 192:0.59 201:0.74 202:0.56 208:0.64 212:0.61 215:0.84 216:0.07 220:0.62 222:0.60 235:0.31 238:0.81 241:0.93 242:0.63 243:0.77 247:0.30 248:0.79 253:0.83 255:0.79 256:0.64 259:0.21 260:0.73 261:0.87 265:0.85 266:0.23 267:0.19 268:0.65 271:0.16 276:0.25 285:0.62 290:0.92 297:0.36 300:0.13 +2 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.90 21:0.78 27:0.54 28:0.98 30:0.76 34:0.91 36:0.54 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.16 69:0.21 70:0.36 74:0.80 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.17 147:0.22 149:0.64 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.64 172:0.21 173:0.13 177:0.38 179:0.62 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.36 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.47 242:0.61 243:0.23 245:0.57 247:0.47 248:0.64 253:0.74 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.47 267:0.89 268:0.71 271:0.16 276:0.44 279:0.86 283:0.82 285:0.62 300:0.33 +2 6:0.87 7:0.78 8:0.66 11:0.57 12:0.20 17:0.97 20:0.81 21:0.59 27:0.36 28:0.98 30:0.15 32:0.80 34:0.90 36:0.55 37:0.56 39:0.61 43:0.28 55:0.92 66:0.16 69:0.99 70:0.96 74:0.55 77:0.70 78:0.85 81:0.43 85:0.72 91:0.23 98:0.19 100:0.85 101:0.64 104:0.58 108:0.98 111:0.84 122:0.41 123:0.56 124:0.84 126:0.32 127:0.89 129:0.05 133:0.82 135:0.49 145:0.59 146:0.28 147:0.63 149:0.30 150:0.36 152:0.97 154:0.13 159:0.94 161:0.90 167:0.52 169:0.94 172:0.21 173:1.00 177:0.05 178:0.55 179:0.87 181:0.67 186:0.85 187:0.39 195:0.99 212:0.15 215:0.55 216:0.37 222:0.60 230:0.81 232:0.80 233:0.69 235:0.74 241:0.02 242:0.33 243:0.37 245:0.47 247:0.55 253:0.44 257:0.65 259:0.21 261:0.96 265:0.12 266:0.63 267:0.91 271:0.16 276:0.31 277:0.69 282:0.88 297:0.36 299:0.88 300:0.70 +2 11:0.57 12:0.20 17:0.89 21:0.22 27:0.63 28:0.98 30:0.38 32:0.80 34:0.93 36:0.36 37:0.45 39:0.61 43:0.63 55:0.42 66:0.09 69:0.91 70:0.63 74:0.76 77:0.89 81:0.73 85:0.85 91:0.67 98:0.25 101:0.78 104:0.58 106:0.81 108:0.81 122:0.43 123:0.54 124:0.74 126:0.32 127:0.62 129:0.05 133:0.74 135:0.58 145:0.69 146:0.31 147:0.05 149:0.65 150:0.54 154:0.53 159:0.51 161:0.90 167:0.57 172:0.37 173:0.98 177:0.05 178:0.55 179:0.82 181:0.67 187:0.21 195:0.69 202:0.36 206:0.81 212:0.57 215:0.79 216:0.28 222:0.60 232:0.80 235:0.31 241:0.21 242:0.29 243:0.16 245:0.49 247:0.60 253:0.55 257:0.65 259:0.21 265:0.27 266:0.47 267:0.52 271:0.16 276:0.39 277:0.69 282:0.88 283:0.71 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.83 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.97 21:0.05 27:0.63 28:0.98 30:0.88 32:0.80 34:0.36 36:0.49 37:0.45 39:0.61 41:0.97 43:0.61 55:0.92 60:0.87 66:0.24 69:0.75 70:0.29 74:0.87 76:0.85 77:0.89 78:0.98 81:0.91 83:0.88 85:0.72 91:0.91 96:0.96 98:0.67 100:0.95 101:0.72 108:0.80 111:0.96 114:0.95 120:0.93 121:0.90 122:0.67 123:0.56 124:0.67 126:0.54 127:0.80 129:0.59 133:0.64 135:0.66 140:0.80 145:0.72 146:0.73 147:0.05 149:0.51 150:0.95 151:0.98 152:0.95 153:0.91 154:0.54 155:0.67 158:0.87 159:0.55 161:0.90 162:0.64 163:0.81 164:0.89 165:0.90 167:0.91 169:0.91 172:0.37 173:0.77 176:0.73 177:0.05 178:0.42 179:0.80 181:0.67 186:0.98 189:0.85 191:0.83 192:0.59 195:0.71 201:0.74 202:0.84 204:0.89 208:0.64 212:0.22 215:0.91 216:0.28 220:0.62 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.85 241:0.89 242:0.38 243:0.14 245:0.59 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.95 265:0.56 266:0.54 267:0.65 268:0.86 271:0.16 276:0.47 279:0.86 282:0.88 283:0.71 285:0.62 290:0.95 297:0.36 299:0.88 300:0.33 +3 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.88 21:0.78 27:0.54 28:0.98 30:0.73 34:0.91 36:0.59 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.15 69:0.21 70:0.37 74:0.84 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.31 147:0.38 149:0.66 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.67 172:0.21 173:0.13 177:0.38 179:0.58 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.30 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.54 242:0.63 243:0.29 245:0.59 247:0.47 248:0.64 253:0.76 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.63 267:0.91 268:0.71 271:0.16 276:0.47 279:0.86 283:0.82 285:0.62 300:0.33 +2 6:0.82 7:0.81 8:0.61 12:0.20 17:0.43 20:0.87 21:0.30 27:0.21 28:0.78 30:0.30 34:0.58 36:0.98 37:0.74 43:0.52 55:0.52 66:0.44 69:0.12 70:0.60 78:0.74 81:0.95 91:0.57 98:0.44 100:0.77 106:0.81 108:0.60 111:0.74 120:0.77 123:0.57 124:0.58 126:0.54 127:0.27 129:0.59 133:0.47 135:0.20 140:0.45 146:0.42 147:0.48 149:0.57 150:0.92 151:0.74 152:0.82 153:0.84 154:0.41 159:0.29 161:0.54 162:0.69 164:0.79 167:0.72 169:0.75 172:0.37 173:0.25 177:0.05 179:0.64 181:0.52 186:0.75 187:0.39 189:0.84 202:0.71 206:0.81 212:0.73 215:0.72 216:0.95 220:0.87 230:0.87 233:0.76 235:0.42 238:0.85 241:0.52 242:0.82 243:0.45 245:0.64 247:0.12 248:0.69 256:0.68 259:0.21 260:0.78 261:0.76 265:0.46 266:0.38 267:0.85 268:0.74 271:0.61 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.90 7:0.81 8:0.89 12:0.20 17:0.75 20:0.94 21:0.71 25:0.88 27:0.06 28:0.78 30:0.95 32:0.80 34:0.53 36:0.80 37:0.95 43:0.37 55:0.84 66:0.54 69:0.61 78:0.82 81:0.83 91:0.93 98:0.54 100:0.86 104:0.58 108:0.46 111:0.85 120:0.96 123:0.45 126:0.54 127:0.16 129:0.05 135:0.26 140:0.45 145:0.89 146:0.33 147:0.39 149:0.21 150:0.63 151:0.83 152:0.88 153:0.95 154:0.28 159:0.13 161:0.54 162:0.60 164:0.95 167:0.97 169:0.90 172:0.10 173:0.86 177:0.05 179:0.97 181:0.52 186:0.81 189:0.91 191:0.95 195:0.58 202:0.93 215:0.48 216:0.52 220:0.62 230:0.87 233:0.76 235:0.84 238:0.97 241:0.86 243:0.63 248:0.94 256:0.93 260:0.96 261:0.84 265:0.55 267:0.76 268:0.94 271:0.61 283:0.60 285:0.62 297:0.36 300:0.08 +1 12:0.20 17:0.64 21:0.30 25:0.88 27:0.05 28:0.78 34:0.67 36:0.41 37:0.96 81:0.95 91:0.62 104:0.58 126:0.54 132:0.97 135:0.49 150:0.92 161:0.54 167:0.74 175:0.75 178:0.55 181:0.52 187:0.39 215:0.72 216:0.64 235:0.42 241:0.61 244:0.61 257:0.65 267:0.93 271:0.61 277:0.69 297:0.36 +2 6:0.81 8:0.82 12:0.20 17:0.55 20:0.86 21:0.22 25:0.88 27:0.16 28:0.78 30:0.76 34:0.56 36:0.74 37:0.95 43:0.48 55:0.52 66:0.64 69:0.34 70:0.15 78:0.77 81:0.96 91:0.88 98:0.83 100:0.80 104:0.75 108:0.27 111:0.77 120:0.62 123:0.26 126:0.54 127:0.33 129:0.05 135:0.51 140:0.45 146:0.30 147:0.36 149:0.29 150:0.94 151:0.56 152:0.81 153:0.72 154:0.36 159:0.13 161:0.54 162:0.85 164:0.81 167:0.98 169:0.78 172:0.16 173:0.86 177:0.05 179:0.98 181:0.52 186:0.78 189:0.83 191:0.89 202:0.70 212:0.95 215:0.79 216:0.32 220:0.87 235:0.22 238:0.90 241:0.92 242:0.82 243:0.69 247:0.12 248:0.56 256:0.73 259:0.21 260:0.63 261:0.79 265:0.83 266:0.12 267:0.36 268:0.77 271:0.61 276:0.16 283:0.60 285:0.62 297:0.36 300:0.13 +1 6:0.84 7:0.81 8:0.89 12:0.20 17:0.96 20:0.80 21:0.76 27:0.24 28:0.78 30:0.42 32:0.80 34:0.50 36:0.83 37:0.68 43:0.47 55:0.84 66:0.88 69:0.47 70:0.48 78:0.76 81:0.78 91:0.66 98:0.95 100:0.78 104:0.58 108:0.36 111:0.75 120:0.73 123:0.34 126:0.54 127:0.67 129:0.05 135:0.47 140:0.80 145:0.63 146:0.83 147:0.86 149:0.62 150:0.58 151:0.66 152:0.80 153:0.48 154:0.35 159:0.39 161:0.54 162:0.49 163:0.93 164:0.67 167:0.97 169:0.77 172:0.65 173:0.56 177:0.05 178:0.42 179:0.69 181:0.52 186:0.77 189:0.88 191:0.90 195:0.64 202:0.73 212:0.35 215:0.46 216:0.10 230:0.87 233:0.76 235:0.97 238:0.87 241:0.89 242:0.82 243:0.88 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 261:0.77 265:0.95 266:0.47 267:0.84 268:0.59 271:0.61 276:0.55 285:0.62 297:0.36 300:0.33 +4 8:0.85 12:0.20 17:0.83 21:0.22 25:0.88 27:0.63 28:0.78 30:0.68 32:0.80 34:0.40 36:0.53 37:0.83 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 81:0.96 91:0.66 98:0.94 104:0.76 108:0.39 120:0.73 123:0.38 126:0.54 127:0.60 129:0.05 135:0.32 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.66 153:0.48 154:0.31 159:0.34 161:0.54 162:0.80 163:0.94 164:0.79 167:0.97 172:0.41 173:0.64 177:0.05 179:0.90 181:0.52 191:0.80 195:0.60 202:0.69 212:0.61 215:0.79 216:0.21 220:0.99 235:0.22 241:0.88 242:0.82 243:0.80 247:0.12 248:0.66 256:0.73 259:0.21 260:0.74 265:0.94 266:0.27 267:0.73 268:0.74 271:0.61 276:0.35 285:0.62 297:0.36 300:0.25 +3 6:0.99 8:0.97 12:0.20 17:0.53 20:0.89 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.49 36:0.64 37:0.98 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 78:0.89 81:0.96 91:0.68 98:0.88 100:0.96 104:0.58 108:0.39 111:0.93 120:0.89 123:0.38 126:0.54 127:0.43 129:0.05 135:0.58 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.71 152:1.00 153:0.72 154:0.31 159:0.34 161:0.54 162:0.81 163:0.94 164:0.88 167:0.84 169:0.98 172:0.41 173:0.61 177:0.05 179:0.87 181:0.52 186:0.89 187:0.68 189:0.99 191:0.96 195:0.63 202:0.81 212:0.61 215:0.79 216:0.73 220:0.99 235:0.22 238:0.98 241:0.72 242:0.82 243:0.80 247:0.12 248:0.84 256:0.85 259:0.21 260:0.89 261:0.99 265:0.88 266:0.27 267:0.69 268:0.85 271:0.61 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.96 8:0.95 12:0.20 17:0.24 20:0.98 21:0.68 25:0.88 27:0.05 28:0.78 30:0.11 34:0.52 36:0.92 37:0.96 43:0.52 55:0.45 66:0.49 69:0.25 70:0.82 78:0.92 81:0.85 91:0.97 98:0.60 100:0.91 104:0.58 108:0.80 111:0.96 120:0.98 123:0.79 124:0.74 126:0.54 127:0.86 129:0.89 132:0.97 133:0.78 135:0.82 140:0.45 145:0.86 146:0.45 147:0.51 149:0.54 150:0.68 151:0.86 152:0.93 153:0.99 154:0.41 159:0.60 161:0.54 162:0.73 164:0.98 167:0.99 169:0.96 172:0.48 173:0.25 177:0.05 179:0.74 181:0.52 186:0.85 189:0.97 191:0.98 202:0.97 212:0.48 215:0.49 216:0.64 235:0.84 238:0.99 241:0.98 242:0.82 243:0.27 245:0.59 247:0.12 248:0.98 256:0.98 260:0.98 261:0.89 265:0.61 266:0.71 267:0.97 268:0.98 271:0.61 276:0.52 283:0.82 285:0.62 297:0.36 300:0.55 +1 6:0.87 7:0.81 8:0.86 12:0.20 17:0.40 20:0.94 21:0.77 25:0.88 27:0.12 28:0.78 30:0.76 32:0.80 34:0.31 36:0.94 37:0.87 43:0.26 55:0.52 66:0.72 69:0.82 70:0.22 78:0.79 81:0.75 91:0.87 98:0.66 100:0.82 104:0.58 108:0.66 111:0.78 120:0.93 123:0.64 126:0.54 127:0.20 129:0.05 135:0.51 140:0.45 145:0.86 146:0.56 147:0.62 149:0.29 150:0.56 151:0.80 152:0.92 153:0.92 154:0.19 159:0.20 161:0.54 162:0.81 163:0.75 164:0.94 167:0.98 169:0.79 172:0.27 173:0.94 177:0.05 179:0.83 181:0.52 186:0.79 189:0.89 191:0.96 195:0.65 202:0.88 212:0.42 215:0.44 216:0.24 230:0.87 233:0.76 235:0.98 238:0.90 241:0.91 242:0.82 243:0.75 247:0.12 248:0.90 256:0.91 260:0.93 261:0.82 265:0.67 266:0.23 267:0.98 268:0.92 271:0.61 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +2 6:0.97 8:0.95 12:0.20 17:0.59 20:0.83 21:0.68 27:0.34 28:0.78 30:0.45 34:0.29 36:0.29 43:0.21 55:0.45 66:0.49 69:0.93 70:0.25 78:0.78 81:0.85 91:0.82 98:0.16 100:0.81 104:0.58 108:0.90 111:0.78 120:0.83 123:0.89 124:0.48 126:0.54 127:0.17 129:0.59 133:0.47 135:0.42 140:0.94 145:0.65 146:0.05 147:0.05 149:0.16 150:0.68 151:0.66 152:0.80 153:0.48 154:0.13 159:0.35 161:0.54 162:0.47 163:0.97 164:0.90 167:0.97 169:0.79 172:0.16 173:0.95 177:0.05 178:0.53 179:0.84 181:0.52 186:0.79 189:0.98 191:0.89 202:0.95 212:0.61 215:0.49 216:0.35 220:0.99 235:0.80 238:0.92 241:0.86 242:0.82 243:0.29 245:0.39 247:0.12 248:0.75 256:0.86 259:0.21 260:0.83 261:0.78 265:0.17 266:0.17 267:0.59 268:0.87 271:0.61 276:0.14 283:0.60 285:0.62 297:0.36 300:0.18 +1 6:0.93 7:0.81 8:0.97 12:0.20 17:0.59 20:0.82 21:0.68 25:0.88 27:0.28 28:0.78 30:0.68 32:0.80 37:0.80 43:0.22 55:0.71 66:0.68 69:0.90 70:0.31 78:0.76 81:0.85 91:0.95 98:0.44 100:0.78 104:0.58 108:0.86 111:0.76 120:0.97 123:0.86 124:0.41 126:0.54 127:0.25 129:0.59 133:0.47 140:0.45 145:0.79 146:0.05 147:0.05 149:0.27 150:0.68 151:0.85 152:0.78 153:0.98 154:0.15 159:0.47 161:0.54 162:0.74 164:0.97 167:0.98 169:0.79 172:0.37 173:0.90 177:0.05 179:0.77 181:0.52 186:0.75 189:0.94 191:0.93 195:0.84 202:0.94 212:0.19 215:0.49 216:0.52 230:0.87 233:0.76 235:0.84 238:0.93 241:0.93 242:0.82 243:0.19 245:0.45 247:0.12 248:0.96 256:0.96 260:0.97 261:0.75 265:0.46 266:0.47 268:0.96 271:0.61 276:0.28 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.99 8:0.97 12:0.20 17:0.34 20:0.75 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.52 36:0.63 37:0.98 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 78:0.80 81:0.96 91:0.70 98:0.88 100:0.92 104:0.58 108:0.39 111:0.79 120:0.81 123:0.38 126:0.54 127:0.43 129:0.05 135:0.44 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.71 152:1.00 153:0.72 154:0.31 159:0.34 161:0.54 162:0.64 163:0.94 164:0.86 167:0.83 169:0.97 172:0.41 173:0.61 177:0.05 179:0.87 181:0.52 186:0.84 187:0.39 189:0.99 191:0.94 195:0.63 202:0.81 212:0.61 215:0.79 216:0.73 220:0.99 235:0.22 238:0.90 241:0.72 242:0.82 243:0.80 247:0.12 248:0.73 256:0.81 259:0.21 260:0.82 261:0.99 265:0.88 266:0.27 267:0.44 268:0.83 271:0.61 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +4 6:0.99 8:0.98 12:0.20 17:0.16 20:0.96 21:0.22 25:0.88 27:0.02 28:0.78 30:0.45 32:0.80 34:0.50 36:0.71 37:0.98 43:0.41 55:0.84 66:0.77 69:0.50 70:0.33 78:0.95 81:0.96 91:0.98 98:0.92 100:0.99 104:0.58 108:0.39 111:0.98 120:0.99 123:0.37 126:0.54 127:0.55 129:0.05 135:0.30 140:0.45 145:0.93 146:0.72 147:0.77 149:0.41 150:0.94 151:0.89 152:1.00 153:0.99 154:0.31 159:0.29 161:0.54 162:0.53 163:0.94 164:0.99 167:0.98 169:0.97 172:0.37 173:0.67 177:0.05 179:0.92 181:0.52 186:0.96 189:1.00 191:0.99 195:0.58 202:0.99 212:0.52 215:0.79 216:0.73 220:0.62 235:0.22 238:0.99 241:0.92 242:0.82 243:0.79 247:0.12 248:0.98 256:0.98 259:0.21 260:0.99 261:0.99 265:0.92 266:0.27 267:0.91 268:0.98 271:0.61 276:0.31 283:0.82 285:0.62 297:0.36 300:0.25 +1 12:0.20 17:0.24 21:0.54 27:0.45 28:0.78 30:0.91 32:0.80 34:0.73 36:0.17 37:0.50 43:0.32 55:0.84 66:0.69 69:0.70 70:0.13 81:0.91 91:0.17 98:0.39 108:0.53 123:0.51 126:0.54 127:0.13 129:0.05 135:0.97 145:0.42 146:0.51 147:0.57 149:0.29 150:0.83 154:0.24 159:0.18 161:0.54 163:0.92 167:0.68 172:0.21 173:0.67 177:0.05 178:0.55 179:0.61 181:0.52 187:0.68 195:0.75 212:0.61 215:0.58 216:0.77 235:0.60 241:0.39 242:0.82 243:0.73 247:0.12 259:0.21 265:0.41 266:0.17 267:0.95 271:0.61 276:0.23 283:0.60 297:0.36 300:0.13 +1 6:0.89 7:0.81 8:0.94 12:0.20 17:0.55 20:0.93 21:0.71 25:0.88 27:0.18 28:0.78 30:0.76 32:0.80 34:0.80 36:0.88 37:0.74 43:0.32 55:0.84 66:0.36 69:0.71 70:0.22 78:0.76 81:0.83 91:0.94 98:0.37 100:0.79 104:0.58 108:0.69 111:0.76 120:0.93 123:0.67 124:0.57 126:0.54 127:0.30 129:0.59 133:0.47 135:0.26 140:0.45 145:0.76 146:0.32 147:0.38 149:0.31 150:0.63 151:0.79 152:0.91 153:0.91 154:0.23 159:0.26 161:0.54 162:0.81 163:0.96 164:0.93 167:0.98 169:0.78 172:0.16 173:0.84 177:0.05 179:0.92 181:0.52 186:0.76 189:0.94 191:0.94 195:0.59 202:0.87 212:0.42 215:0.48 216:0.36 220:0.62 230:0.65 233:0.63 235:0.84 238:0.94 241:0.92 242:0.82 243:0.40 245:0.43 247:0.12 248:0.90 256:0.90 260:0.93 261:0.78 265:0.39 266:0.23 267:0.97 268:0.91 271:0.61 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +1 7:0.81 12:0.20 17:0.85 21:0.68 27:0.31 28:0.78 30:0.76 32:0.80 34:0.50 36:0.82 37:0.83 43:0.25 55:0.59 66:0.36 69:0.83 70:0.15 81:0.85 91:0.78 98:0.19 104:0.89 106:0.81 108:0.76 120:0.83 123:0.74 124:0.52 126:0.54 127:0.18 129:0.59 133:0.47 135:0.93 140:0.45 145:0.93 146:0.05 147:0.05 149:0.20 150:0.68 151:0.70 153:0.71 154:0.18 159:0.23 161:0.54 162:0.73 163:0.98 164:0.84 167:0.91 172:0.10 173:0.90 177:0.05 179:0.93 181:0.52 187:0.39 195:0.65 202:0.76 206:0.81 212:0.95 215:0.49 216:0.59 220:0.97 230:0.65 233:0.63 235:0.84 241:0.77 242:0.82 243:0.34 245:0.37 247:0.12 248:0.75 256:0.80 260:0.84 265:0.21 266:0.12 267:0.87 268:0.80 271:0.61 276:0.14 285:0.62 297:0.36 300:0.13 +1 6:0.84 7:0.81 8:0.79 12:0.20 17:0.79 20:0.80 21:0.77 27:0.24 28:0.78 32:0.80 34:0.31 36:0.65 37:0.68 43:0.17 66:0.36 69:0.95 78:0.74 81:0.75 91:0.38 98:0.07 100:0.77 104:0.58 108:0.90 111:0.74 120:0.60 123:0.89 126:0.54 127:0.05 129:0.05 135:0.34 140:0.92 145:0.65 146:0.05 147:0.05 149:0.07 150:0.56 151:0.54 152:0.80 153:0.48 154:0.11 159:0.05 161:0.54 162:0.48 164:0.57 167:0.83 169:0.77 172:0.05 173:1.00 177:0.05 178:0.51 181:0.52 186:0.75 187:0.68 189:0.89 191:0.86 195:0.86 202:0.69 215:0.44 216:0.10 220:0.62 230:0.65 233:0.63 235:0.98 238:0.87 241:0.72 243:0.51 248:0.54 256:0.45 259:0.21 260:0.61 261:0.77 265:0.07 267:0.52 268:0.46 271:0.61 285:0.62 297:0.36 +2 6:0.99 8:0.86 12:0.20 17:0.57 20:0.73 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.42 36:0.64 37:0.98 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 78:0.84 81:0.96 91:0.31 98:0.88 100:0.73 104:0.58 108:0.39 111:0.82 120:0.69 123:0.38 126:0.54 127:0.43 129:0.05 135:0.61 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.66 152:1.00 153:0.48 154:0.31 159:0.34 161:0.54 162:0.54 163:0.94 164:0.57 167:0.73 169:0.98 172:0.41 173:0.61 177:0.05 179:0.87 181:0.52 186:0.84 187:0.87 195:0.63 202:0.56 212:0.61 215:0.79 216:0.73 235:0.22 241:0.57 242:0.82 243:0.80 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.99 265:0.88 266:0.27 267:0.82 268:0.46 271:0.61 276:0.35 285:0.62 297:0.36 300:0.25 +3 6:0.99 8:0.99 12:0.20 17:0.38 20:0.75 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.55 36:0.76 37:0.98 43:0.37 55:0.84 66:0.79 69:0.60 70:0.31 78:0.90 81:0.96 91:0.29 98:0.88 100:0.97 104:0.58 108:0.46 111:0.94 120:0.72 123:0.44 126:0.54 127:0.43 129:0.05 135:0.78 140:0.45 145:0.93 146:0.78 147:0.82 149:0.41 150:0.94 151:0.70 152:1.00 153:0.69 154:0.28 159:0.34 161:0.54 162:0.86 163:0.94 164:0.62 167:0.78 169:0.98 172:0.41 173:0.70 177:0.05 179:0.87 181:0.52 186:0.90 187:0.68 189:0.99 191:0.86 195:0.63 202:0.46 212:0.61 215:0.79 216:0.73 235:0.22 238:0.98 241:0.67 242:0.82 243:0.80 247:0.12 248:0.66 256:0.45 259:0.21 260:0.73 261:0.99 265:0.88 266:0.27 267:0.97 268:0.55 271:0.61 276:0.35 285:0.62 297:0.36 300:0.25 +1 1:0.69 11:0.92 12:0.37 17:0.70 18:0.87 21:0.47 27:0.54 29:0.62 30:0.57 32:0.80 34:0.66 36:0.75 37:0.61 39:0.37 43:0.81 44:0.93 45:0.96 66:0.98 69:0.53 70:0.77 71:0.70 74:0.85 77:0.89 81:0.38 83:0.60 85:0.80 86:0.95 91:0.38 97:0.89 98:0.91 99:0.83 101:0.97 104:0.96 106:0.81 108:0.41 114:0.57 117:0.86 122:0.75 123:0.39 126:0.44 127:0.50 129:0.05 131:0.61 135:0.69 139:0.95 144:0.76 145:0.89 146:0.97 147:0.98 149:0.91 150:0.54 154:0.76 155:0.58 157:0.95 158:0.72 159:0.70 165:0.70 166:0.82 172:0.95 173:0.38 176:0.55 177:0.70 178:0.55 179:0.20 182:0.86 187:0.39 192:0.59 195:0.86 201:0.68 206:0.81 208:0.54 212:0.60 215:0.41 216:0.65 222:0.48 227:0.61 229:0.61 231:0.88 232:0.97 235:0.60 241:0.12 242:0.28 243:0.98 246:0.61 247:0.88 253:0.47 259:0.21 265:0.91 266:0.54 267:0.99 271:0.14 276:0.89 279:0.82 282:0.88 283:0.78 287:0.61 290:0.57 297:0.36 299:0.88 300:0.70 +2 1:0.69 8:0.77 11:0.87 12:0.37 17:0.28 18:0.85 21:0.13 27:0.45 29:0.62 30:0.73 32:0.80 34:0.75 36:0.17 37:0.50 39:0.37 43:0.81 44:0.93 45:0.83 64:0.77 66:0.08 69:0.69 70:0.62 71:0.70 74:0.78 77:0.70 81:0.65 83:0.60 85:0.85 86:0.88 91:0.29 97:0.89 98:0.35 99:0.83 101:0.97 108:0.52 114:0.75 122:0.80 123:0.80 124:0.85 126:0.44 127:0.49 129:0.59 131:0.61 133:0.84 135:0.90 139:0.91 144:0.76 145:0.47 146:0.54 147:0.60 149:0.82 150:0.62 154:0.76 155:0.58 157:0.93 158:0.79 159:0.73 165:0.70 166:0.94 172:0.45 173:0.54 176:0.66 177:0.70 178:0.55 179:0.51 182:0.90 187:0.68 192:0.51 195:0.88 201:0.68 208:0.54 212:0.27 216:0.77 219:0.92 222:0.48 227:0.61 229:0.61 231:0.90 235:0.22 241:0.21 242:0.28 243:0.43 245:0.71 246:0.95 247:0.76 253:0.55 259:0.21 265:0.33 266:0.91 267:0.52 271:0.14 276:0.63 277:0.69 279:0.82 282:0.88 287:0.61 290:0.75 292:0.95 299:0.88 300:0.70 +2 1:0.74 11:0.87 12:0.37 17:0.26 18:0.81 21:0.40 27:0.45 29:0.62 30:0.86 32:0.80 34:0.75 36:0.18 37:0.50 39:0.37 43:0.82 44:0.93 45:0.77 60:0.87 64:0.77 66:0.54 69:0.69 70:0.46 71:0.70 74:0.76 77:0.70 81:0.58 83:0.91 85:0.80 86:0.88 91:0.22 98:0.36 101:0.97 108:0.52 114:0.62 122:0.80 123:0.50 124:0.50 126:0.54 127:0.17 129:0.05 131:0.61 133:0.43 135:0.93 139:0.92 144:0.76 145:0.50 146:0.55 147:0.61 149:0.82 150:0.76 154:0.77 155:0.67 157:0.91 158:0.91 159:0.33 165:0.93 166:0.93 172:0.48 173:0.61 176:0.73 177:0.70 178:0.55 179:0.36 182:0.90 187:0.68 192:0.87 195:0.82 201:0.93 208:0.64 212:0.42 215:0.42 216:0.77 219:0.95 222:0.48 227:0.61 229:0.61 231:0.90 235:0.60 241:0.41 242:0.33 243:0.63 245:0.66 246:0.95 247:0.67 253:0.49 259:0.21 265:0.38 266:0.63 267:0.82 271:0.14 276:0.43 279:0.86 282:0.88 283:0.78 287:0.61 290:0.62 292:0.91 297:0.36 299:0.88 300:0.55 +1 8:0.83 9:0.76 11:0.81 12:0.37 17:0.72 18:0.80 21:0.78 22:0.93 27:0.21 29:0.62 30:0.15 31:0.95 34:0.56 36:0.81 37:0.74 39:0.37 43:0.71 45:0.90 47:0.93 66:0.13 69:0.89 70:0.98 71:0.70 74:0.64 79:0.96 83:0.60 86:0.84 91:0.60 96:0.87 97:0.89 98:0.27 101:0.52 108:0.77 117:0.86 122:0.77 123:0.76 124:0.95 127:0.72 129:0.05 131:0.96 133:0.94 135:0.68 137:0.95 139:0.84 140:0.45 144:0.76 146:0.69 147:0.54 149:0.66 151:0.90 153:0.83 154:0.55 155:0.58 157:0.93 158:0.79 159:0.90 162:0.82 166:0.61 172:0.51 173:0.68 177:0.38 179:0.35 182:0.91 187:0.39 190:0.77 192:0.51 196:0.95 202:0.62 208:0.54 212:0.26 216:0.95 219:0.92 222:0.48 227:0.97 229:0.95 231:0.90 232:0.85 235:0.31 241:0.50 242:0.59 243:0.24 245:0.77 246:0.61 247:0.67 248:0.82 253:0.82 254:0.80 255:0.74 256:0.64 257:0.65 259:0.21 262:0.93 265:0.29 266:0.95 267:0.99 268:0.68 271:0.14 276:0.80 279:0.82 284:0.93 285:0.56 287:0.96 292:0.95 300:0.84 +2 1:0.74 7:0.81 9:0.76 11:0.81 12:0.37 17:0.93 18:0.88 21:0.59 22:0.93 27:0.72 29:0.62 30:0.60 31:0.93 32:0.75 34:0.90 36:0.32 37:0.37 39:0.37 43:0.65 44:0.93 45:0.88 47:0.93 64:0.77 66:0.90 69:0.23 70:0.74 71:0.70 74:0.86 77:0.98 79:0.93 81:0.45 83:0.60 86:0.94 91:0.61 96:0.80 97:0.89 98:0.98 99:0.83 101:0.52 108:0.18 114:0.58 117:0.86 121:0.90 122:0.80 123:0.18 126:0.54 127:0.81 129:0.05 131:0.96 135:0.70 137:0.93 139:0.96 140:0.45 144:0.76 145:0.76 146:0.86 147:0.89 149:0.64 150:0.67 151:0.81 153:0.48 154:0.94 155:0.67 157:0.94 158:0.79 159:0.44 162:0.86 165:0.70 166:0.93 172:0.73 173:0.33 176:0.73 177:0.70 179:0.62 182:0.91 187:0.21 190:0.77 192:0.87 195:0.63 196:0.96 201:0.93 202:0.36 204:0.89 208:0.64 212:0.52 215:0.39 216:0.10 219:0.92 222:0.48 227:0.97 229:0.95 230:0.87 231:0.90 232:0.92 233:0.76 235:0.80 241:0.21 242:0.42 243:0.91 246:0.95 247:0.76 248:0.73 253:0.82 254:0.74 255:0.74 256:0.45 259:0.21 262:0.93 265:0.98 266:0.63 267:0.76 268:0.46 271:0.14 276:0.60 279:0.82 281:0.91 282:0.84 284:0.87 285:0.56 287:0.96 290:0.58 292:0.95 297:0.36 300:0.70 +1 1:0.74 7:0.66 8:0.63 11:0.87 12:0.37 17:0.95 18:0.87 21:0.59 22:0.93 27:0.68 29:0.62 30:0.29 32:0.80 34:0.58 36:0.87 37:0.28 39:0.37 43:0.75 44:0.93 45:0.89 47:0.93 60:0.95 64:0.77 66:0.90 69:0.29 70:0.81 71:0.70 74:0.83 76:0.94 77:0.96 81:0.49 83:0.91 85:0.72 86:0.91 87:0.98 91:0.44 98:0.91 101:0.97 104:0.97 106:0.81 108:0.22 114:0.56 117:0.86 122:0.77 123:0.22 124:0.36 126:0.54 127:0.79 129:0.05 131:0.61 133:0.37 135:0.79 139:0.95 144:0.76 145:0.49 146:0.92 147:0.93 149:0.82 150:0.72 154:0.66 155:0.67 157:0.95 158:0.91 159:0.58 165:0.93 166:0.93 172:0.87 173:0.27 176:0.66 177:0.70 178:0.55 179:0.38 182:0.90 187:0.39 192:0.87 195:0.74 201:0.93 204:0.85 206:0.81 208:0.64 212:0.61 215:0.38 216:0.10 219:0.95 222:0.48 227:0.61 229:0.61 230:0.69 231:0.90 232:0.72 233:0.76 235:0.95 241:0.02 242:0.51 243:0.90 245:0.66 246:0.95 247:0.67 253:0.46 259:0.21 262:0.93 265:0.91 266:0.38 267:0.92 271:0.14 276:0.78 279:0.86 281:0.91 282:0.88 283:0.78 287:0.61 290:0.56 292:0.91 297:0.36 299:0.88 300:0.55 +1 1:0.74 8:0.77 11:0.87 12:0.37 17:0.77 18:0.93 21:0.22 22:0.93 27:0.37 29:0.62 30:0.84 32:0.80 34:0.84 36:0.95 37:0.70 39:0.37 43:0.95 44:0.93 45:0.83 47:0.93 60:0.87 64:0.77 66:0.86 69:0.19 70:0.40 71:0.70 74:0.92 77:0.70 81:0.71 83:0.91 85:0.94 86:0.97 91:0.54 98:0.86 101:0.97 104:0.89 106:0.81 108:0.15 114:0.71 117:0.86 122:0.57 123:0.15 124:0.37 126:0.54 127:0.57 129:0.05 131:0.61 133:0.43 135:0.89 139:0.98 144:0.76 145:0.49 146:0.88 147:0.90 149:0.96 150:0.82 154:0.95 155:0.67 157:0.96 158:0.91 159:0.52 165:0.93 166:0.94 172:0.86 173:0.23 176:0.73 177:0.70 178:0.55 179:0.35 182:0.90 187:0.21 192:0.87 195:0.72 201:0.93 204:0.85 206:0.81 208:0.64 212:0.81 215:0.51 216:0.65 219:0.95 222:0.48 227:0.61 229:0.61 231:0.90 232:0.92 235:0.42 241:0.10 242:0.24 243:0.86 245:0.75 246:0.95 247:0.79 253:0.55 259:0.21 262:0.93 265:0.86 266:0.47 267:0.95 271:0.14 276:0.77 279:0.86 281:0.91 282:0.88 283:0.78 287:0.61 290:0.71 292:0.91 297:0.36 299:0.88 300:0.43 +1 8:0.61 9:0.80 12:0.37 17:0.90 18:0.57 21:0.78 27:0.55 29:0.62 30:0.21 31:0.96 32:0.64 34:0.96 36:0.85 37:0.46 39:0.37 41:0.82 43:0.17 55:0.92 66:0.18 69:0.97 70:0.86 71:0.70 74:0.27 79:0.96 83:0.91 86:0.57 91:0.71 96:0.89 98:0.18 108:0.95 120:0.73 123:0.94 124:0.88 127:0.98 129:0.59 131:0.96 133:0.86 135:0.90 137:0.96 139:0.55 140:0.95 144:0.76 145:0.80 146:0.47 147:0.05 149:0.12 151:0.91 153:0.72 154:0.10 155:0.67 157:0.61 159:0.92 162:0.80 163:0.87 164:0.64 166:0.61 172:0.16 173:0.93 175:0.75 177:0.05 178:0.48 179:0.90 182:0.91 187:0.21 192:0.87 195:0.98 196:0.87 202:0.75 208:0.64 212:0.37 216:0.14 220:0.74 222:0.48 227:0.97 229:0.95 231:0.90 235:0.52 241:0.32 242:0.16 243:0.18 244:0.61 245:0.45 246:0.61 247:0.60 248:0.81 253:0.82 254:0.90 255:0.95 256:0.77 257:0.84 259:0.21 260:0.74 265:0.19 266:0.47 267:0.69 268:0.80 271:0.14 276:0.33 277:0.69 284:0.95 285:0.62 287:0.96 300:0.55 +2 1:0.74 8:0.75 11:0.87 12:0.37 17:0.49 18:0.87 21:0.47 27:0.34 29:0.62 30:0.62 32:0.80 34:0.40 36:0.36 37:0.57 39:0.37 43:0.86 44:0.93 45:0.88 64:0.77 66:0.09 69:0.60 70:0.57 71:0.70 74:0.79 77:0.70 81:0.53 83:0.91 85:0.88 86:0.93 91:0.44 98:0.37 101:0.97 108:0.62 114:0.60 122:0.80 123:0.91 124:0.93 126:0.54 127:0.69 129:0.81 131:0.61 133:0.93 135:0.70 139:0.93 144:0.76 145:0.87 146:0.23 147:0.29 149:0.91 150:0.74 154:0.83 155:0.67 157:0.95 159:0.81 165:0.93 166:0.93 172:0.41 173:0.38 176:0.73 177:0.70 178:0.55 179:0.38 182:0.90 187:0.21 192:0.87 195:0.92 201:0.93 202:0.36 208:0.64 212:0.21 215:0.41 216:0.76 222:0.48 227:0.61 229:0.61 231:0.90 235:0.68 241:0.37 242:0.31 243:0.23 245:0.77 246:0.95 247:0.76 253:0.48 259:0.21 265:0.32 266:0.91 267:0.59 271:0.14 276:0.77 277:0.69 282:0.88 287:0.61 290:0.60 297:0.36 299:0.88 300:0.55 +2 1:0.69 8:0.80 9:0.76 11:0.81 12:0.37 17:0.95 18:0.95 21:0.05 27:0.72 29:0.62 30:0.70 31:0.95 34:0.20 36:0.45 37:0.73 39:0.37 43:0.71 45:0.95 64:0.77 66:0.95 69:0.33 70:0.57 71:0.70 74:0.87 79:0.95 81:0.75 83:0.60 86:0.97 87:0.98 91:0.86 96:0.86 97:0.94 98:0.94 99:0.83 101:0.52 108:0.26 114:0.85 122:0.80 123:0.25 126:0.44 127:0.62 129:0.05 131:0.96 135:0.76 137:0.95 139:0.96 140:0.45 144:0.76 146:0.92 147:0.94 149:0.90 150:0.70 151:0.90 153:0.80 154:0.54 155:0.58 157:0.96 158:0.79 159:0.54 162:0.78 165:0.70 166:0.94 172:0.88 173:0.31 176:0.66 177:0.70 179:0.34 182:0.91 190:0.77 192:0.51 196:0.97 201:0.68 202:0.59 208:0.54 212:0.55 216:0.04 219:0.92 222:0.48 227:0.97 228:0.99 229:0.95 231:0.90 232:0.72 235:0.12 241:0.84 242:0.55 243:0.96 246:0.95 247:0.67 248:0.80 253:0.87 254:0.84 255:0.74 256:0.58 259:0.21 265:0.94 266:0.38 267:0.76 268:0.64 271:0.14 276:0.80 279:0.82 284:0.92 285:0.56 287:0.96 290:0.85 292:0.95 300:0.55 +2 1:0.69 7:0.81 8:0.71 9:0.76 11:0.87 12:0.37 17:0.87 18:0.97 21:0.40 22:0.93 27:0.31 29:0.62 30:0.67 31:0.86 34:0.59 36:0.79 37:0.77 39:0.37 43:0.70 44:0.90 45:0.96 47:0.93 48:0.91 64:0.77 66:0.67 69:0.80 70:0.64 71:0.70 74:0.91 76:0.85 77:0.70 79:0.86 81:0.42 83:0.60 85:0.72 86:0.97 91:0.75 96:0.68 97:0.97 98:0.88 99:0.83 101:0.97 108:0.64 114:0.61 117:0.86 121:0.90 122:0.80 123:0.62 124:0.47 126:0.44 127:0.71 129:0.05 131:0.96 133:0.43 135:0.89 137:0.86 139:0.98 140:0.80 144:0.76 145:0.49 146:0.94 147:0.55 149:0.94 150:0.55 151:0.49 153:0.48 154:0.53 155:0.67 157:0.96 158:0.79 159:0.68 162:0.62 165:0.70 166:0.93 172:0.89 173:0.75 176:0.66 177:0.38 178:0.42 179:0.26 182:0.91 187:0.21 190:0.77 191:0.70 192:0.87 195:0.84 196:0.90 201:0.68 202:0.50 204:0.89 208:0.64 212:0.60 216:0.31 219:0.92 220:0.91 222:0.48 227:0.97 229:0.95 230:0.87 231:0.90 232:0.93 233:0.76 235:0.52 241:0.03 242:0.50 243:0.31 245:0.94 246:0.95 247:0.84 248:0.48 253:0.51 255:0.74 256:0.45 257:0.65 259:0.21 262:0.93 265:0.88 266:0.54 267:0.79 268:0.46 271:0.14 276:0.86 279:0.82 281:0.91 282:0.77 284:0.87 285:0.56 287:0.96 290:0.61 292:0.95 300:0.55 +2 1:0.74 8:0.61 9:0.80 11:0.87 12:0.37 17:0.40 18:0.81 21:0.30 22:0.93 27:0.34 29:0.62 30:0.74 31:0.95 32:0.80 34:0.64 36:0.71 37:0.57 39:0.37 41:0.90 43:0.86 44:0.93 45:0.77 47:0.93 60:0.95 64:0.77 66:0.26 69:0.59 70:0.41 71:0.70 74:0.77 77:0.70 79:0.94 81:0.64 83:0.91 85:0.85 86:0.89 91:0.53 96:0.83 98:0.42 101:0.97 104:0.84 106:0.81 108:0.74 114:0.65 117:0.86 120:0.73 122:0.80 123:0.73 124:0.81 126:0.54 127:0.40 129:0.81 131:0.96 133:0.78 135:0.99 137:0.95 139:0.93 140:0.45 144:0.76 145:0.89 146:0.17 147:0.22 149:0.84 150:0.79 151:0.87 153:0.48 154:0.83 155:0.67 157:0.92 158:0.92 159:0.60 162:0.81 163:0.75 164:0.73 165:0.93 166:0.93 172:0.41 173:0.48 176:0.73 177:0.70 179:0.56 182:0.91 187:0.21 192:0.87 195:0.81 196:0.97 201:0.93 202:0.61 206:0.81 208:0.64 212:0.28 215:0.44 216:0.76 219:0.95 220:0.74 222:0.48 227:0.97 229:0.95 231:0.90 232:0.89 235:0.52 241:0.32 242:0.29 243:0.23 245:0.68 246:0.95 247:0.67 248:0.80 253:0.84 255:0.95 256:0.64 259:0.21 260:0.74 262:0.93 265:0.44 266:0.80 267:0.69 268:0.66 271:0.14 276:0.57 277:0.69 279:0.86 282:0.88 283:0.82 284:0.94 285:0.62 287:0.96 290:0.65 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 11:0.87 12:0.37 17:0.59 18:0.84 21:0.47 27:0.34 29:0.62 30:0.75 32:0.80 34:0.63 36:0.62 37:0.57 39:0.37 43:0.86 44:0.93 45:0.73 64:0.77 66:0.31 69:0.60 70:0.47 71:0.70 74:0.76 77:0.70 81:0.53 83:0.91 85:0.88 86:0.92 91:0.38 98:0.44 101:0.97 108:0.62 114:0.60 122:0.57 123:0.60 124:0.73 126:0.54 127:0.53 129:0.81 131:0.61 133:0.67 135:0.87 139:0.92 144:0.76 145:0.87 146:0.23 147:0.29 149:0.89 150:0.74 154:0.83 155:0.67 157:0.92 159:0.63 165:0.93 166:0.93 172:0.48 173:0.51 176:0.73 177:0.70 178:0.55 179:0.57 182:0.90 187:0.21 192:0.87 195:0.79 201:0.93 208:0.64 212:0.23 215:0.41 216:0.76 222:0.48 227:0.61 229:0.61 231:0.90 235:0.68 241:0.27 242:0.33 243:0.36 245:0.75 246:0.95 247:0.76 253:0.47 259:0.21 265:0.46 266:0.75 267:0.85 271:0.14 276:0.55 282:0.88 287:0.61 290:0.60 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 8:0.59 9:0.80 11:0.87 12:0.37 17:0.89 18:0.96 21:0.47 22:0.93 27:0.58 29:0.62 30:0.76 31:0.86 34:0.99 36:0.41 37:0.42 39:0.37 41:0.82 43:0.64 44:0.90 45:0.96 47:0.93 64:0.77 66:0.97 69:0.78 70:0.53 71:0.70 74:0.93 77:0.99 79:0.86 81:0.60 83:0.91 85:0.85 86:0.98 91:0.37 96:0.68 97:0.89 98:0.75 99:0.83 101:0.97 108:0.61 114:0.60 117:0.86 120:0.54 121:0.90 122:0.80 123:0.59 126:0.54 127:0.24 129:0.05 131:0.96 135:0.86 137:0.86 139:0.98 140:0.45 144:0.76 145:0.80 146:0.87 147:0.89 149:0.89 150:0.76 151:0.48 153:0.48 154:0.54 155:0.67 157:0.97 158:0.79 159:0.44 162:0.86 164:0.57 165:1.00 166:0.93 172:0.92 173:0.74 176:0.73 177:0.70 179:0.16 182:0.91 187:0.21 192:0.87 195:0.79 196:0.89 201:0.93 202:0.36 204:0.89 208:0.64 212:0.87 215:0.41 216:0.23 219:0.92 220:0.93 222:0.48 227:0.97 229:0.95 230:0.87 231:0.90 232:0.91 233:0.76 235:0.80 241:0.03 242:0.50 243:0.97 246:0.95 247:0.76 248:0.48 253:0.50 255:0.95 256:0.45 259:0.21 260:0.54 262:0.93 265:0.75 266:0.47 267:0.69 268:0.46 271:0.14 276:0.85 279:0.82 281:0.91 282:0.77 284:0.89 285:0.62 287:0.96 290:0.60 292:0.95 297:0.36 300:0.70 +1 1:0.74 11:0.87 12:0.37 17:0.36 18:0.89 21:0.47 27:0.34 29:0.62 30:0.60 32:0.80 34:0.80 36:0.31 37:0.57 39:0.37 43:0.86 44:0.93 45:0.87 64:0.77 66:0.10 69:0.60 70:0.70 71:0.70 74:0.80 77:0.70 81:0.53 83:0.91 85:0.90 86:0.94 91:0.35 98:0.43 101:0.97 108:0.92 114:0.60 122:0.80 123:0.88 124:0.91 126:0.54 127:0.71 129:0.89 131:0.61 133:0.90 135:0.91 139:0.94 144:0.76 145:0.87 146:0.23 147:0.29 149:0.91 150:0.74 154:0.83 155:0.67 157:0.95 159:0.81 165:0.93 166:0.93 172:0.51 173:0.38 176:0.73 177:0.70 178:0.55 179:0.35 182:0.90 187:0.21 192:0.87 195:0.92 201:0.93 208:0.64 212:0.47 215:0.41 216:0.76 222:0.48 227:0.61 229:0.61 231:0.90 235:0.68 241:0.27 242:0.22 243:0.22 245:0.82 246:0.95 247:0.84 253:0.48 259:0.21 265:0.30 266:0.92 267:0.82 271:0.14 276:0.80 282:0.88 287:0.61 290:0.60 297:0.36 299:0.88 300:0.70 +2 1:0.74 8:0.60 11:0.87 12:0.37 17:0.66 18:0.85 21:0.40 22:0.93 27:0.34 29:0.62 30:0.55 32:0.80 34:0.49 36:0.87 37:0.57 39:0.37 43:0.83 44:0.93 45:0.83 47:0.93 64:0.77 66:0.61 69:0.59 70:0.80 71:0.70 74:0.84 77:0.89 81:0.56 83:0.60 85:0.80 86:0.94 91:0.23 97:0.89 98:0.74 99:0.83 101:0.97 104:0.88 106:0.81 108:0.45 114:0.62 117:0.86 122:0.57 123:0.43 124:0.50 126:0.54 127:0.43 129:0.59 131:0.61 133:0.46 135:0.99 139:0.95 144:0.76 145:0.86 146:0.77 147:0.81 149:0.74 150:0.72 154:0.82 155:0.67 157:0.94 158:0.91 159:0.46 165:0.70 166:0.93 172:0.68 173:0.60 176:0.73 177:0.70 178:0.55 179:0.47 182:0.90 187:0.21 192:0.87 195:0.68 201:0.93 206:0.81 208:0.64 212:0.50 215:0.42 216:0.76 219:0.95 222:0.48 227:0.61 229:0.61 231:0.90 232:0.95 235:0.60 241:0.27 242:0.27 243:0.68 245:0.81 246:0.95 247:0.82 253:0.50 259:0.21 262:0.93 265:0.74 266:0.47 267:0.98 271:0.14 276:0.65 279:0.86 282:0.88 283:0.77 287:0.61 290:0.62 292:0.91 297:0.36 300:0.70 +0 7:0.81 12:0.20 17:0.34 18:0.62 21:0.22 26:0.96 27:0.67 29:0.62 30:0.91 32:0.75 34:0.40 36:1.00 37:0.84 39:0.37 43:0.15 44:0.93 48:0.97 55:0.84 58:0.93 66:0.60 69:0.79 70:0.28 71:0.97 74:0.57 77:0.70 81:0.48 86:0.63 91:0.68 98:0.64 104:0.58 108:0.63 120:0.59 121:0.90 123:0.60 124:0.55 126:0.26 127:0.54 129:0.05 133:0.62 135:0.34 139:0.82 140:0.45 141:0.91 145:0.70 146:0.69 147:0.44 149:0.23 150:0.38 151:0.51 153:0.48 154:0.11 155:0.67 159:0.50 162:0.86 163:0.94 164:0.53 172:0.45 173:0.82 175:0.75 177:0.05 179:0.79 187:0.21 190:0.89 192:0.87 195:0.73 199:0.94 202:0.36 204:0.89 208:0.64 212:0.39 215:0.51 216:0.15 220:0.74 222:0.76 223:0.95 224:0.93 230:0.87 233:0.76 234:0.91 235:0.22 241:0.83 242:0.76 243:0.25 244:0.83 245:0.53 247:0.39 248:0.51 253:0.38 256:0.45 257:0.84 259:0.21 260:0.58 265:0.65 266:0.38 267:0.82 268:0.46 271:0.21 274:0.91 276:0.44 277:0.69 281:0.91 282:0.83 285:0.47 297:0.36 300:0.33 +1 1:0.74 7:0.66 11:0.64 12:0.20 17:0.67 18:0.79 21:0.22 26:0.99 27:0.49 29:0.62 30:0.38 32:0.75 34:0.95 36:0.21 37:0.72 39:0.37 43:0.59 44:0.93 45:0.77 58:0.93 64:0.77 66:0.83 69:0.77 70:0.50 71:0.97 74:0.66 77:0.70 81:0.73 83:0.91 86:0.86 91:0.42 98:0.43 101:0.52 108:0.60 114:0.71 122:0.57 123:0.58 126:0.54 127:0.14 129:0.05 135:0.87 139:0.89 141:0.91 145:0.67 146:0.54 147:0.60 149:0.29 150:0.83 154:0.66 155:0.67 159:0.19 165:0.93 172:0.51 173:0.77 176:0.73 177:0.70 178:0.55 179:0.28 187:0.39 192:0.87 195:0.74 199:0.96 201:0.93 204:0.85 208:0.64 212:0.70 215:0.51 216:0.62 222:0.76 223:0.99 224:0.93 230:0.69 233:0.76 234:0.91 235:0.42 241:0.35 242:0.29 243:0.84 247:0.67 253:0.53 254:0.92 259:0.21 265:0.45 266:0.38 267:0.52 271:0.21 274:0.91 276:0.41 281:0.91 282:0.84 290:0.71 297:0.36 300:0.33 +2 1:0.69 7:0.72 11:0.64 12:0.20 17:0.15 18:0.75 21:0.22 26:0.99 27:0.39 29:0.62 30:0.28 32:0.69 34:0.29 36:0.26 37:0.89 39:0.37 43:0.79 45:0.66 55:0.45 58:0.93 66:0.87 69:0.60 70:0.70 71:0.97 74:0.60 76:0.85 77:0.70 81:0.62 86:0.84 91:0.49 98:0.69 101:0.52 104:0.79 108:0.45 114:0.67 123:0.44 126:0.44 127:0.21 129:0.05 135:0.44 139:0.80 141:0.91 145:0.56 146:0.52 147:0.58 149:0.57 150:0.59 154:0.73 155:0.58 159:0.19 172:0.63 173:0.76 176:0.66 177:0.70 178:0.55 179:0.42 187:0.39 192:0.59 195:0.58 199:0.94 201:0.68 204:0.85 208:0.54 212:0.93 215:0.51 216:0.82 222:0.76 223:0.99 224:0.93 230:0.74 233:0.73 234:0.91 235:0.31 241:0.44 242:0.28 243:0.88 247:0.74 253:0.51 259:0.21 265:0.69 266:0.17 267:0.44 271:0.21 274:0.91 276:0.51 282:0.77 290:0.67 297:0.36 300:0.43 +3 7:0.81 8:0.89 12:0.20 17:0.11 18:0.62 21:0.22 26:0.96 27:0.67 29:0.62 30:0.91 32:0.75 34:0.33 36:1.00 37:0.84 39:0.37 43:0.16 44:0.93 48:0.97 55:0.84 58:0.93 66:0.60 69:0.77 70:0.28 71:0.97 74:0.57 77:0.70 81:0.48 86:0.63 91:0.74 98:0.65 104:0.58 108:0.61 120:0.64 121:0.90 123:0.58 124:0.55 126:0.26 127:0.57 129:0.05 133:0.62 135:0.37 139:0.82 140:0.45 141:0.91 145:0.70 146:0.69 147:0.44 149:0.23 150:0.38 151:0.53 153:0.82 154:0.11 155:0.67 159:0.49 162:0.54 163:0.86 164:0.71 172:0.45 173:0.80 175:0.75 177:0.05 179:0.79 190:0.89 192:0.87 195:0.72 199:0.94 202:0.76 204:0.89 208:0.64 212:0.39 215:0.51 216:0.15 220:0.82 222:0.76 223:0.95 224:0.93 230:0.87 233:0.76 234:0.91 235:0.22 241:0.89 242:0.76 243:0.25 244:0.83 245:0.53 247:0.39 248:0.55 253:0.38 256:0.71 257:0.84 259:0.21 260:0.62 265:0.65 266:0.38 267:0.79 268:0.74 271:0.21 274:0.91 276:0.44 277:0.69 281:0.91 282:0.83 285:0.47 297:0.36 300:0.33 +2 1:0.74 11:0.64 12:0.20 17:0.34 18:0.79 21:0.30 26:0.99 27:0.47 29:0.62 30:0.76 32:0.79 34:0.74 36:0.41 37:0.29 39:0.37 43:0.76 44:0.93 45:0.83 58:0.93 64:0.77 66:0.30 69:0.52 70:0.53 71:0.97 74:0.71 77:0.70 81:0.64 83:0.60 86:0.89 91:0.25 98:0.23 99:0.83 101:0.52 104:0.91 108:0.62 114:0.65 122:0.57 123:0.60 124:0.73 126:0.54 127:0.62 129:0.59 133:0.66 135:0.37 139:0.89 141:0.91 145:0.44 146:0.19 147:0.24 149:0.61 150:0.76 154:0.84 155:0.67 159:0.53 165:0.70 172:0.32 173:0.50 176:0.73 177:0.70 178:0.55 179:0.78 187:0.68 192:0.87 195:0.71 199:0.96 201:0.93 208:0.64 212:0.49 215:0.44 216:0.73 222:0.76 223:0.99 224:0.93 234:0.91 235:0.52 241:0.01 242:0.38 243:0.31 245:0.60 247:0.47 253:0.50 259:0.21 265:0.25 266:0.38 267:0.36 271:0.21 274:0.91 276:0.34 282:0.87 290:0.65 297:0.36 300:0.55 +4 1:0.74 6:1.00 8:0.96 9:0.80 11:0.64 12:0.20 17:0.30 18:0.73 20:0.99 21:0.59 26:1.00 27:1.00 29:0.62 30:0.33 31:0.97 34:0.49 36:0.88 37:0.73 39:0.37 43:0.74 45:0.66 58:1.00 64:0.77 66:0.32 69:0.85 70:0.49 71:0.97 74:0.39 78:0.96 79:0.97 81:0.45 83:0.60 86:0.80 91:0.82 96:0.91 98:0.55 99:0.83 100:1.00 101:0.52 104:0.58 108:0.41 111:1.00 114:0.58 120:0.78 123:0.39 124:0.61 126:0.54 127:0.36 129:0.05 133:0.43 135:0.58 137:0.97 139:0.77 140:0.45 141:1.00 146:0.22 147:0.57 149:0.51 150:0.67 151:0.94 152:0.99 153:0.82 154:0.64 155:0.67 159:0.33 162:0.81 164:0.86 165:0.70 169:1.00 172:0.21 173:0.96 176:0.73 177:0.38 179:0.85 186:0.96 189:1.00 192:0.87 196:0.94 199:0.98 201:0.93 202:0.80 208:0.64 212:0.19 215:0.39 216:0.16 222:0.76 223:1.00 224:0.99 234:0.98 235:0.80 238:1.00 241:0.89 242:0.19 243:0.49 245:0.54 247:0.55 248:0.85 253:0.86 255:0.95 256:0.85 257:0.65 259:0.21 260:0.79 261:1.00 265:0.57 266:0.47 267:0.91 268:0.85 271:0.21 274:0.99 276:0.29 277:0.69 284:0.98 285:0.62 290:0.58 297:0.36 300:0.33 +0 8:0.99 11:0.64 12:0.20 17:0.04 18:0.70 21:0.13 26:0.95 27:0.47 29:0.62 30:0.76 34:0.36 36:0.83 37:0.64 39:0.37 43:0.60 45:0.66 55:0.71 58:0.93 66:0.77 69:0.63 70:0.29 71:0.97 74:0.36 81:0.58 86:0.73 91:0.88 98:0.76 101:0.52 104:0.76 108:0.48 120:0.64 123:0.46 126:0.26 127:0.25 129:0.05 135:0.47 139:0.70 140:0.45 141:0.91 146:0.57 147:0.63 149:0.33 150:0.43 151:0.53 153:0.83 154:0.49 159:0.21 162:0.80 164:0.82 172:0.37 173:0.80 177:0.70 179:0.81 190:0.89 199:0.94 202:0.79 212:0.73 215:0.61 216:0.31 220:0.91 222:0.76 223:0.94 224:0.93 234:0.91 235:0.12 241:0.90 242:0.24 243:0.79 244:0.61 247:0.47 248:0.55 253:0.29 256:0.84 259:0.21 260:0.62 265:0.76 266:0.23 267:0.28 268:0.84 271:0.21 274:0.91 276:0.29 283:0.78 285:0.47 297:0.36 300:0.25 +0 11:0.64 12:0.20 17:0.64 18:0.96 21:0.78 26:0.99 27:0.67 29:0.62 30:0.13 34:0.47 36:0.47 37:0.84 39:0.37 43:0.06 45:0.93 58:0.93 66:0.12 69:0.08 70:0.88 71:0.97 74:0.81 86:0.99 91:0.18 98:0.39 101:0.52 104:0.58 108:0.90 122:0.57 123:0.90 124:0.94 127:0.85 129:0.98 133:0.94 135:0.19 139:0.98 141:0.91 146:0.22 147:0.27 149:0.11 154:0.91 159:0.85 172:0.61 173:0.08 177:0.70 178:0.55 179:0.26 187:0.21 199:0.97 212:0.47 216:0.15 222:0.76 223:0.99 224:0.93 234:0.91 235:0.05 241:0.21 242:0.23 243:0.12 245:0.86 247:0.76 253:0.44 254:0.80 259:0.21 265:0.41 266:0.71 267:0.23 271:0.21 274:0.91 276:0.87 300:0.70 +1 6:1.00 9:0.80 11:0.64 12:0.20 17:0.32 18:0.70 20:0.91 21:0.40 26:1.00 27:1.00 29:0.62 30:0.71 31:0.89 34:0.83 36:0.81 37:0.73 39:0.37 43:0.80 45:0.73 58:0.98 66:0.82 69:0.74 70:0.24 71:0.97 74:0.52 78:0.72 79:0.88 81:0.34 83:0.60 86:0.81 91:0.44 96:0.72 98:0.65 100:0.96 101:0.52 104:0.58 108:0.57 111:0.90 120:0.59 123:0.54 126:0.26 127:0.19 129:0.05 135:0.47 137:0.89 139:0.78 140:0.45 141:1.00 146:0.65 147:0.70 149:0.73 150:0.31 151:0.56 152:0.99 153:0.48 154:0.74 155:0.67 159:0.24 162:0.86 164:0.67 169:1.00 172:0.48 173:0.80 177:0.70 179:0.56 186:0.73 187:0.21 192:0.87 196:0.90 199:0.95 202:0.50 208:0.64 212:0.50 215:0.42 216:0.16 220:0.82 222:0.76 223:1.00 224:0.99 234:0.99 235:0.42 241:0.39 242:0.53 243:0.83 247:0.47 248:0.58 253:0.48 255:0.95 256:0.58 259:0.21 260:0.59 261:1.00 265:0.66 266:0.38 267:0.59 268:0.59 271:0.21 274:0.98 276:0.38 284:0.92 285:0.62 297:0.36 300:0.18 +1 1:0.74 7:0.66 11:0.64 12:0.20 17:0.66 18:0.69 20:0.91 21:0.40 26:0.99 27:0.59 29:0.62 30:0.21 32:0.80 34:0.77 36:0.34 37:0.62 39:0.37 43:0.11 44:0.93 45:0.73 58:0.93 64:0.77 66:0.80 69:0.78 70:0.50 71:0.97 74:0.65 77:0.70 78:0.72 81:0.57 83:0.60 86:0.80 91:0.37 98:0.38 99:0.83 100:0.73 101:0.52 108:0.61 111:0.72 114:0.62 123:0.59 126:0.54 127:0.13 129:0.05 135:0.89 139:0.86 141:0.91 145:0.52 146:0.38 147:0.45 149:0.08 150:0.73 152:0.85 154:0.67 155:0.67 159:0.15 165:0.70 169:0.72 172:0.45 173:0.86 176:0.73 177:0.70 178:0.55 179:0.28 186:0.73 187:0.39 192:0.87 195:0.64 199:0.95 201:0.93 204:0.85 208:0.64 212:0.90 215:0.42 216:0.50 222:0.76 223:0.99 224:0.93 230:0.69 233:0.73 234:0.91 235:0.60 241:0.60 242:0.40 243:0.82 247:0.47 253:0.47 254:0.90 259:0.21 261:0.73 265:0.41 266:0.17 267:0.44 271:0.21 274:0.91 276:0.37 281:0.91 282:0.88 290:0.62 297:0.36 300:0.33 +3 11:0.64 12:0.20 17:0.47 18:0.82 21:0.22 26:0.98 27:1.00 29:0.62 30:0.72 34:0.73 36:0.81 37:0.73 39:0.37 43:0.93 45:0.83 58:0.93 66:0.49 69:0.27 70:0.53 71:0.97 74:0.71 81:0.48 86:0.92 91:0.35 98:0.58 101:0.52 104:0.58 108:0.21 122:0.80 123:0.20 124:0.56 126:0.26 127:0.33 129:0.59 133:0.46 135:0.65 139:0.89 141:0.91 146:0.68 147:0.73 149:0.89 150:0.38 154:0.93 159:0.46 172:0.57 173:0.26 177:0.70 178:0.55 179:0.49 187:0.21 199:0.95 212:0.60 215:0.51 216:0.16 222:0.76 223:0.98 224:0.93 234:0.91 235:0.22 241:0.27 242:0.37 243:0.60 245:0.79 247:0.55 253:0.41 259:0.21 265:0.59 266:0.54 267:0.69 271:0.21 274:0.91 276:0.56 297:0.36 300:0.55 +1 1:0.69 7:0.63 11:0.75 12:0.20 17:0.36 18:0.64 21:0.13 27:0.45 28:0.96 29:0.71 30:0.10 34:0.74 36:0.17 37:0.50 39:0.90 43:0.64 45:0.73 55:0.71 66:0.43 69:0.60 70:0.92 71:0.77 74:0.52 81:0.51 83:0.60 86:0.74 91:0.18 98:0.45 99:0.83 101:0.52 108:0.86 114:0.75 123:0.85 124:0.80 126:0.44 127:0.50 129:0.05 131:0.61 133:0.81 135:0.86 139:0.71 144:0.57 145:0.49 146:0.53 147:0.59 149:0.32 150:0.57 154:0.74 155:0.58 157:0.82 159:0.67 161:0.86 165:0.70 166:0.79 167:0.53 172:0.54 173:0.47 176:0.66 177:0.70 178:0.55 179:0.56 181:0.80 182:0.88 187:0.68 192:0.59 201:0.68 208:0.54 212:0.39 215:0.61 216:0.77 222:0.97 227:0.61 229:0.61 230:0.63 231:0.69 233:0.73 235:0.22 241:0.18 242:0.76 243:0.39 245:0.64 246:0.86 247:0.60 253:0.54 254:0.84 259:0.21 265:0.47 266:0.80 267:0.52 271:0.94 276:0.60 277:0.87 287:0.61 290:0.75 297:0.36 300:0.70 +2 1:0.74 11:0.75 12:0.20 17:0.66 18:0.65 21:0.47 27:0.07 28:0.96 29:0.71 30:0.15 32:0.68 34:0.56 36:0.32 37:0.88 39:0.90 43:0.28 44:0.90 45:0.66 55:0.59 64:0.77 66:0.20 69:0.69 70:0.95 71:0.77 74:0.45 77:0.70 81:0.47 83:0.60 86:0.66 91:0.46 96:0.80 98:0.53 99:0.83 101:0.52 104:0.92 106:0.81 108:0.84 114:0.60 117:0.86 123:0.50 124:0.84 126:0.54 127:0.63 129:0.59 131:0.81 133:0.81 135:0.73 139:0.72 140:0.45 144:0.57 145:0.58 146:0.81 147:0.84 149:0.36 150:0.67 151:0.60 153:0.48 154:0.47 155:0.67 157:0.77 158:0.72 159:0.76 161:0.86 162:0.86 165:0.70 166:0.79 167:0.56 172:0.61 173:0.54 176:0.73 177:0.70 179:0.36 181:0.80 182:0.88 187:0.68 190:0.89 192:0.87 195:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.29 215:0.41 216:0.58 222:0.97 227:0.82 229:0.61 231:0.69 232:0.94 235:0.68 241:0.32 242:0.61 243:0.40 244:0.61 245:0.83 246:0.86 247:0.76 248:0.59 253:0.51 256:0.45 259:0.21 265:0.45 266:0.91 267:0.36 268:0.46 271:0.94 276:0.78 282:0.77 285:0.47 287:0.61 290:0.60 297:0.36 300:0.84 +1 1:0.69 7:0.63 11:0.64 12:0.20 17:0.43 18:0.74 21:0.64 27:0.45 28:0.96 29:0.71 30:0.53 34:0.59 36:0.18 37:0.50 39:0.90 43:0.64 45:0.66 55:0.59 64:0.77 66:0.46 69:0.62 70:0.72 71:0.77 74:0.30 77:0.70 81:0.32 83:0.60 86:0.81 91:0.17 98:0.56 99:0.83 101:0.52 108:0.77 114:0.55 123:0.75 124:0.58 126:0.44 127:0.39 129:0.05 131:0.61 133:0.37 135:0.88 139:0.76 145:0.45 146:0.68 147:0.73 149:0.41 150:0.51 154:0.59 155:0.58 157:0.61 159:0.51 161:0.86 165:0.70 166:0.73 167:0.54 172:0.59 173:0.57 176:0.55 177:0.70 178:0.55 179:0.48 181:0.80 182:0.61 187:0.68 192:0.51 195:0.75 201:0.68 208:0.54 212:0.58 216:0.77 222:0.97 227:0.61 229:0.61 230:0.63 231:0.68 233:0.73 235:0.80 241:0.21 242:0.72 243:0.46 245:0.82 246:0.61 247:0.76 253:0.36 254:0.99 259:0.21 265:0.57 266:0.71 267:0.28 271:0.94 276:0.63 277:0.87 282:0.71 287:0.61 290:0.55 300:0.70 +1 8:0.92 9:0.76 12:0.20 17:0.22 21:0.78 27:0.32 28:0.96 29:0.71 31:0.86 34:0.63 36:0.27 37:0.71 39:0.90 71:0.77 79:0.86 91:0.95 96:0.76 104:0.78 120:0.84 131:0.85 135:0.76 137:0.86 140:0.98 144:0.57 151:0.51 153:0.72 155:0.58 157:0.61 161:0.86 162:0.59 164:0.82 166:0.61 167:0.89 175:0.97 181:0.80 182:0.61 190:0.89 191:0.90 192:0.59 202:0.92 208:0.54 216:0.46 220:0.74 222:0.97 227:0.85 229:0.61 231:0.69 241:0.93 244:0.93 246:0.61 248:0.52 253:0.44 255:0.79 256:0.93 257:0.93 259:0.21 260:0.65 267:0.89 268:0.93 271:0.94 277:0.95 283:0.78 284:0.87 285:0.62 287:0.61 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.20 17:0.79 18:0.87 21:0.40 22:0.93 27:0.72 28:0.96 29:0.71 30:0.28 31:0.95 32:0.80 34:0.68 36:0.67 39:0.90 41:0.82 43:0.86 44:0.93 47:0.93 60:0.87 64:0.77 66:0.96 69:0.59 70:0.62 71:0.77 74:0.88 77:0.89 79:0.95 81:0.58 83:0.91 85:0.91 86:0.95 91:0.82 96:0.85 98:0.92 101:0.87 104:0.75 106:0.81 108:0.45 114:0.62 117:0.86 120:0.76 121:0.90 123:0.43 126:0.54 127:0.54 129:0.05 131:0.85 135:0.86 137:0.95 139:0.97 140:0.45 144:0.57 145:0.98 146:0.89 147:0.91 149:0.93 150:0.76 151:0.94 153:0.80 154:0.83 155:0.67 157:0.91 158:0.92 159:0.49 161:0.86 162:0.70 164:0.65 165:0.93 166:0.79 167:0.54 172:0.88 173:0.60 176:0.73 177:0.70 179:0.32 181:0.80 182:0.89 187:0.39 192:0.87 195:0.72 196:0.98 201:0.93 202:0.55 204:0.89 206:0.81 208:0.64 212:0.91 215:0.42 216:0.09 219:0.95 222:0.97 227:0.84 229:0.94 230:0.87 231:0.69 232:0.85 233:0.76 235:0.80 241:0.24 242:0.38 243:0.96 246:0.86 247:0.90 248:0.83 253:0.88 255:0.95 256:0.45 259:0.21 260:0.77 262:0.93 265:0.92 266:0.38 267:0.92 268:0.58 271:0.94 276:0.80 279:0.86 281:0.91 282:0.88 283:0.82 284:0.91 285:0.62 287:0.88 290:0.62 292:0.95 297:0.36 299:0.88 300:0.55 +2 6:0.86 8:0.73 12:0.20 17:0.70 18:0.77 20:0.86 21:0.05 27:0.33 28:0.96 29:0.71 30:0.21 31:0.93 32:0.62 34:0.98 36:0.41 37:0.48 39:0.90 43:0.13 55:0.45 64:0.77 66:0.85 69:0.55 70:0.59 71:0.77 78:0.91 79:0.94 81:0.64 86:0.78 91:0.84 98:0.82 100:0.91 104:0.91 108:0.43 111:0.90 120:0.71 123:0.41 126:0.44 127:0.32 129:0.05 131:0.81 135:0.37 137:0.93 139:0.79 140:0.80 145:0.44 146:0.64 147:0.69 149:0.17 150:0.44 151:0.70 152:0.81 153:0.72 154:0.56 157:0.61 159:0.24 161:0.86 162:0.77 164:0.54 166:0.74 167:0.72 169:0.89 172:0.57 173:0.72 175:0.75 177:0.38 179:0.66 181:0.80 182:0.61 186:0.91 187:0.39 189:0.88 190:0.77 191:0.84 192:0.51 195:0.56 196:0.93 201:0.68 202:0.72 212:0.49 215:0.78 216:0.29 219:0.92 220:0.74 222:0.97 227:0.83 229:0.61 231:0.63 235:0.12 238:0.86 241:0.68 242:0.71 243:0.86 244:0.61 246:0.61 247:0.47 248:0.72 253:0.20 254:0.80 255:0.74 256:0.77 257:0.65 259:0.21 260:0.61 261:0.87 265:0.82 266:0.27 267:0.82 268:0.77 271:0.94 276:0.43 277:0.69 283:0.78 284:0.94 285:0.56 287:0.61 292:0.91 297:0.36 300:0.43 +2 8:0.89 9:0.80 11:0.75 12:0.20 17:0.30 18:0.63 21:0.13 27:0.16 28:0.96 29:0.71 30:0.14 31:0.91 32:0.62 34:0.75 36:0.52 37:0.80 39:0.90 43:0.26 45:0.66 55:0.45 66:0.61 69:0.67 70:0.82 71:0.77 74:0.49 76:1.00 78:0.92 79:0.91 81:0.33 83:0.91 86:0.73 91:0.88 96:0.92 98:0.75 101:0.52 104:0.58 108:0.72 111:0.89 114:0.63 117:0.86 120:0.79 123:0.70 124:0.50 126:0.26 127:0.38 129:0.05 131:0.85 133:0.43 135:0.87 137:0.91 139:0.70 140:0.98 144:0.57 145:0.56 146:0.54 147:0.60 149:0.39 150:0.30 151:0.72 153:0.69 154:0.64 155:0.67 157:0.77 158:0.78 159:0.61 161:0.86 162:0.68 164:0.83 166:0.73 167:0.74 169:0.87 172:0.81 173:0.58 176:0.55 177:0.70 179:0.29 181:0.80 182:0.88 187:0.21 190:0.89 191:0.90 192:0.87 195:0.84 196:0.90 202:0.90 208:0.64 212:0.75 216:0.72 220:0.62 222:0.97 227:0.85 229:0.93 231:0.70 232:0.93 235:0.12 241:0.71 242:0.76 243:0.37 245:0.91 246:0.61 247:0.60 248:0.67 253:0.78 254:0.98 255:0.95 256:0.91 259:0.21 260:0.69 265:0.76 266:0.54 267:0.44 268:0.92 271:0.94 276:0.79 277:0.69 279:0.82 283:0.78 284:0.91 285:0.62 287:0.88 290:0.63 300:0.70 +2 1:0.69 8:0.66 9:0.76 11:0.86 12:0.20 17:0.62 18:0.93 20:0.83 21:0.64 22:0.93 27:0.68 28:0.96 29:0.71 30:0.41 31:0.86 34:0.87 36:0.47 37:0.56 39:0.90 43:0.52 45:0.83 47:0.93 55:0.59 64:0.77 66:0.95 69:0.30 70:0.73 71:0.77 74:0.37 78:0.90 79:0.86 81:0.30 83:0.60 86:0.97 91:0.65 96:0.67 97:0.89 98:0.88 100:0.89 101:0.87 104:0.58 108:0.24 111:0.88 114:0.55 117:0.86 122:0.82 123:0.23 126:0.44 127:0.42 129:0.05 131:0.81 135:0.92 137:0.86 139:0.96 140:0.45 144:0.57 146:0.88 147:0.90 149:0.35 150:0.47 151:0.46 152:0.79 153:0.48 154:0.74 155:0.58 157:0.78 158:0.72 159:0.46 161:0.86 162:0.86 166:0.73 167:0.54 169:0.86 172:0.86 173:0.31 176:0.55 177:0.70 179:0.32 181:0.80 182:0.73 186:0.90 187:0.87 190:0.77 192:0.51 196:0.88 201:0.68 202:0.36 208:0.54 212:0.85 216:0.13 219:0.92 220:0.97 222:0.97 227:0.82 229:0.61 231:0.69 232:0.98 235:0.80 241:0.24 242:0.15 243:0.95 246:0.61 247:0.92 248:0.46 253:0.36 254:0.92 255:0.74 256:0.45 259:0.21 261:0.84 262:0.93 265:0.88 266:0.27 267:0.79 268:0.46 271:0.94 276:0.77 279:0.82 284:0.87 285:0.56 287:0.61 290:0.55 292:0.91 300:0.55 +2 8:0.77 9:0.76 11:0.75 12:0.20 17:0.09 18:0.68 21:0.47 22:0.93 27:0.16 28:0.96 29:0.71 30:0.10 31:0.95 34:0.79 36:0.28 37:0.80 39:0.90 43:0.21 44:0.87 45:0.77 47:0.93 55:0.45 64:0.77 66:0.15 69:0.54 70:0.96 71:0.77 74:0.37 76:0.85 79:0.97 81:0.26 83:0.60 86:0.77 91:0.74 96:0.77 98:0.29 101:0.52 104:0.58 108:0.94 120:0.57 123:0.40 124:0.88 126:0.26 127:0.65 129:0.05 131:0.85 133:0.86 135:0.95 137:0.95 139:0.78 140:0.98 144:0.57 145:0.42 146:0.64 147:0.69 149:0.43 150:0.26 151:0.58 153:0.77 154:0.64 155:0.58 157:0.83 159:0.85 161:0.86 162:0.54 164:0.53 166:0.61 167:0.62 172:0.63 173:0.27 177:0.70 178:0.42 179:0.32 181:0.80 182:0.75 187:0.21 190:0.89 191:0.86 192:0.51 195:0.95 196:0.94 202:0.88 208:0.54 212:0.72 216:0.72 219:0.89 220:0.91 222:0.97 227:0.84 229:0.61 231:0.69 235:0.52 241:0.51 242:0.73 243:0.36 245:0.85 246:0.61 247:0.76 248:0.57 253:0.44 254:0.98 255:0.74 256:0.86 259:0.21 260:0.54 262:0.93 265:0.23 266:0.87 267:0.99 268:0.86 271:0.94 276:0.78 283:0.78 284:0.97 285:0.62 287:0.61 292:0.91 300:0.94 +2 1:0.74 7:0.63 8:0.60 11:0.75 12:0.20 17:0.94 18:0.68 20:0.86 21:0.22 27:0.57 28:0.96 29:0.71 30:0.09 32:0.78 34:0.91 36:0.73 37:0.64 39:0.90 43:0.63 44:0.93 45:0.66 48:0.91 55:0.45 64:0.77 66:0.26 69:0.54 70:0.91 71:0.77 74:0.57 76:0.85 77:0.70 78:0.91 81:0.58 86:0.71 91:0.69 98:0.69 100:0.84 101:0.52 104:0.82 108:0.41 111:0.88 114:0.71 123:0.64 124:0.71 126:0.54 127:0.63 129:0.59 131:0.61 133:0.64 135:0.65 139:0.81 144:0.57 145:0.52 146:0.45 147:0.52 149:0.48 150:0.70 152:0.81 154:0.53 155:0.67 157:0.78 158:0.78 159:0.51 161:0.86 166:0.79 167:0.49 169:0.82 172:0.57 173:0.54 175:0.75 176:0.73 177:0.38 178:0.55 179:0.50 181:0.80 182:0.73 186:0.90 187:0.39 192:0.87 195:0.73 201:0.93 208:0.64 212:0.61 215:0.51 216:0.33 222:0.97 227:0.61 229:0.61 230:0.64 231:0.69 233:0.76 235:0.42 241:0.03 242:0.77 243:0.50 244:0.61 245:0.80 246:0.61 247:0.60 253:0.52 257:0.65 259:0.21 261:0.85 265:0.41 266:0.54 267:0.79 271:0.94 276:0.67 277:0.69 279:0.82 281:0.91 282:0.86 283:0.71 287:0.61 290:0.71 297:0.36 300:0.70 +3 1:0.74 6:0.93 8:0.82 9:0.80 11:0.75 12:0.20 17:0.16 20:0.94 21:0.40 22:0.93 27:0.42 28:0.96 29:0.71 30:0.70 31:0.87 34:0.78 36:0.65 37:0.60 39:0.90 41:0.82 43:0.66 45:0.77 47:0.93 55:0.52 64:0.77 66:0.93 69:0.33 70:0.46 71:0.77 74:0.53 78:0.96 79:0.86 81:0.52 83:0.91 91:0.63 96:0.82 97:0.99 98:0.93 100:0.95 101:0.52 104:0.58 106:0.81 108:0.26 111:0.95 114:0.62 120:0.74 123:0.25 126:0.54 127:0.58 129:0.05 131:0.85 132:0.97 135:0.96 137:0.87 139:0.68 140:0.45 144:0.57 146:0.89 147:0.91 149:0.64 150:0.74 151:0.50 152:0.88 153:0.48 154:0.55 155:0.67 157:0.86 158:0.79 159:0.47 161:0.86 162:0.81 164:0.69 165:0.93 166:0.79 167:0.64 169:0.93 172:0.82 173:0.35 175:0.75 176:0.73 177:0.38 179:0.43 181:0.80 182:0.89 186:0.95 187:0.87 189:0.94 190:0.77 192:0.87 196:0.87 201:0.93 202:0.62 206:0.81 208:0.64 212:0.60 215:0.42 216:0.60 219:0.92 220:0.91 222:0.97 227:0.85 229:0.94 231:0.69 235:0.60 238:0.88 241:0.57 242:0.73 243:0.94 244:0.61 246:0.86 247:0.39 248:0.50 253:0.72 255:0.95 256:0.64 257:0.65 260:0.68 261:0.93 262:0.93 265:0.93 266:0.54 267:0.96 268:0.67 271:0.94 276:0.72 277:0.69 279:0.86 283:0.82 284:0.91 285:0.62 287:0.88 290:0.62 292:0.95 297:0.36 300:0.43 +2 1:0.74 6:0.86 7:0.63 8:0.67 11:0.92 12:0.20 17:0.78 18:0.97 20:0.91 22:0.93 27:0.33 28:0.96 29:0.71 30:0.84 32:0.80 34:0.66 36:0.91 37:0.61 39:0.90 43:0.88 44:0.93 45:0.87 47:0.93 48:0.91 60:0.87 64:0.77 66:0.62 69:0.19 70:0.45 71:0.77 74:0.96 76:0.85 77:0.96 78:0.96 81:0.83 83:0.91 85:0.98 86:0.99 91:0.46 97:0.89 98:0.71 100:0.90 101:0.97 104:0.58 106:0.81 108:0.78 111:0.94 114:0.98 117:0.86 122:0.57 123:0.77 124:0.50 126:0.54 127:0.74 129:0.59 131:0.61 132:0.97 133:0.46 135:0.30 139:0.99 144:0.57 145:0.90 146:0.64 147:0.69 149:0.99 150:0.89 152:0.85 154:0.87 155:0.67 157:0.95 158:0.92 159:0.65 161:0.86 165:0.93 166:0.79 167:0.49 169:0.92 172:0.95 173:0.18 176:0.73 177:0.70 178:0.55 179:0.17 181:0.80 182:0.88 186:0.91 187:0.21 189:0.88 191:0.70 192:0.87 195:0.81 201:0.93 206:0.81 208:0.64 212:0.91 215:0.96 216:0.46 219:0.95 222:0.97 227:0.61 229:0.61 230:0.64 231:0.69 232:0.88 233:0.76 235:0.12 238:0.87 241:0.02 242:0.29 243:0.37 245:0.98 246:0.86 247:0.92 253:0.69 261:0.89 262:0.93 265:0.72 266:0.71 267:0.44 271:0.94 276:0.92 279:0.86 281:0.91 282:0.88 283:0.82 287:0.61 290:0.98 292:0.95 297:0.36 299:0.99 300:0.70 +1 7:0.63 8:0.82 11:0.42 12:0.20 17:0.75 18:0.65 21:0.59 27:0.37 28:0.96 29:0.71 30:0.71 32:0.62 34:0.37 36:0.53 37:0.60 39:0.90 43:0.18 55:0.52 66:0.98 69:0.29 70:0.48 71:0.77 74:0.37 81:0.26 86:0.70 91:0.80 98:0.98 104:0.78 108:0.22 120:0.76 123:0.22 126:0.26 127:0.82 129:0.05 131:0.81 135:0.98 139:0.67 140:0.45 144:0.57 145:0.72 146:0.96 147:0.97 149:0.52 150:0.25 151:0.57 153:0.72 154:0.54 157:0.61 159:0.66 161:0.86 162:0.86 164:0.62 166:0.74 167:0.61 172:0.94 173:0.23 177:0.70 179:0.24 181:0.80 182:0.61 187:0.21 190:0.89 191:0.73 195:0.79 202:0.70 212:0.83 215:0.39 216:0.42 220:0.97 222:0.97 227:0.83 229:0.61 230:0.63 231:0.65 233:0.73 235:0.80 241:0.49 242:0.81 243:0.98 244:0.61 246:0.61 247:0.55 248:0.62 253:0.30 254:0.74 256:0.77 259:0.21 260:0.61 265:0.98 266:0.47 267:0.79 268:0.78 271:0.94 276:0.89 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +2 1:0.69 8:0.68 11:0.42 12:0.20 17:0.82 18:0.65 21:0.54 27:0.37 28:0.96 29:0.71 30:0.38 32:0.68 34:0.66 36:0.85 37:0.60 39:0.90 43:0.18 55:0.59 66:0.53 69:0.25 70:0.74 71:0.77 74:0.51 76:0.97 77:0.89 81:0.31 86:0.69 91:0.71 96:0.76 98:0.71 104:0.78 108:0.20 114:0.56 117:0.86 123:0.19 124:0.73 126:0.44 127:0.82 129:0.05 131:0.81 133:0.73 135:0.94 139:0.67 140:0.45 144:0.57 145:0.74 146:0.90 147:0.92 149:0.40 150:0.48 151:0.55 153:0.48 154:0.58 155:0.58 157:0.61 158:0.71 159:0.75 161:0.86 162:0.74 166:0.79 167:0.52 172:0.82 173:0.17 176:0.55 177:0.70 179:0.33 181:0.80 182:0.61 187:0.68 190:0.89 191:0.73 192:0.59 195:0.87 201:0.68 202:0.56 208:0.54 212:0.48 215:0.39 216:0.42 220:0.62 222:0.97 227:0.82 229:0.61 231:0.69 232:0.98 235:0.68 241:0.12 242:0.78 243:0.62 244:0.61 245:0.87 246:0.61 247:0.74 248:0.54 253:0.45 254:0.74 256:0.58 259:0.21 265:0.72 266:0.75 267:0.89 268:0.59 271:0.94 276:0.82 282:0.77 283:0.78 285:0.47 287:0.61 290:0.56 297:0.36 300:0.70 +2 6:0.97 8:0.93 9:0.76 11:0.75 12:0.20 17:0.15 18:0.81 20:0.93 21:0.78 27:0.16 28:0.96 29:0.71 30:0.21 31:0.93 32:0.79 34:0.82 36:0.45 37:0.80 39:0.90 43:0.56 44:0.93 45:0.73 55:0.52 66:0.31 69:0.70 70:0.86 71:0.77 74:0.56 76:0.94 77:0.70 78:0.97 79:0.95 83:0.60 86:0.81 91:0.68 96:0.82 98:0.56 100:0.99 101:0.52 104:0.58 108:0.87 111:0.98 120:0.71 123:0.87 124:0.78 127:0.48 129:0.59 131:0.85 133:0.74 135:0.98 137:0.93 139:0.84 140:0.95 144:0.57 145:0.49 146:0.54 147:0.60 149:0.47 151:0.81 152:0.98 153:0.48 154:0.64 155:0.58 157:0.81 158:0.71 159:0.74 161:0.86 162:0.53 164:0.54 166:0.61 167:0.60 169:0.96 172:0.75 173:0.53 177:0.70 178:0.50 179:0.26 181:0.80 182:0.76 186:0.96 187:0.21 189:0.98 190:0.77 191:0.83 192:0.51 195:0.90 196:0.94 202:0.81 208:0.54 212:0.68 216:0.72 220:0.62 222:0.97 227:0.85 229:0.61 231:0.69 235:0.84 238:0.96 241:0.47 242:0.52 243:0.31 245:0.90 246:0.61 247:0.88 248:0.74 253:0.53 254:0.98 255:0.74 256:0.79 259:0.21 260:0.60 261:0.97 265:0.57 266:0.80 267:0.99 268:0.78 271:0.94 276:0.83 277:0.69 282:0.87 283:0.78 284:0.92 285:0.62 287:0.61 300:0.84 +1 7:0.72 8:0.91 9:0.76 11:0.75 12:0.20 17:0.34 18:0.81 21:0.78 27:0.16 28:0.96 29:0.71 30:0.45 34:0.56 36:0.29 37:0.80 39:0.90 43:0.25 45:0.77 55:0.52 66:0.74 69:0.66 70:0.56 71:0.77 74:0.62 76:0.94 83:0.60 86:0.85 91:0.74 96:0.94 97:0.98 98:0.85 101:0.52 104:0.58 106:0.81 108:0.79 120:0.86 123:0.77 124:0.45 127:0.60 129:0.81 131:0.85 133:0.67 135:0.86 139:0.81 140:0.80 144:0.57 145:0.38 146:0.54 147:0.60 149:0.58 151:0.84 153:0.87 154:0.65 155:0.58 157:0.83 158:0.79 159:0.84 161:0.86 162:0.60 164:0.79 166:0.61 167:0.67 172:0.97 173:0.42 177:0.70 179:0.14 181:0.80 182:0.89 187:0.21 190:0.77 191:0.76 192:0.59 202:0.84 204:0.85 206:0.81 208:0.54 212:0.87 216:0.72 222:0.97 227:0.85 229:0.94 230:0.74 231:0.70 233:0.73 235:0.05 241:0.62 242:0.54 243:0.17 245:0.97 246:0.61 247:0.95 248:0.88 253:0.87 254:0.93 255:0.74 256:0.84 259:0.21 260:0.82 265:0.85 266:0.63 267:0.98 268:0.84 271:0.94 276:0.95 279:0.82 283:0.82 285:0.56 287:0.88 300:0.70 +2 6:0.98 8:0.88 11:0.75 12:0.20 17:0.09 18:0.82 20:0.85 21:0.47 22:0.93 27:0.16 28:0.96 29:0.71 30:0.20 31:0.92 32:0.62 34:0.91 36:0.49 37:0.80 39:0.90 43:0.22 45:0.77 47:0.93 55:0.52 64:0.77 66:0.36 69:0.69 70:0.93 71:0.77 74:0.48 76:0.85 78:0.94 79:0.94 81:0.26 86:0.86 91:0.65 96:0.76 98:0.60 100:0.98 101:0.52 104:0.58 108:0.89 111:0.97 120:0.66 123:0.88 124:0.78 126:0.26 127:0.52 129:0.89 131:0.85 133:0.78 135:0.98 137:0.92 139:0.83 140:0.96 144:0.57 145:0.56 146:0.54 147:0.60 149:0.35 150:0.26 151:0.65 152:0.98 153:0.46 154:0.64 157:0.84 159:0.79 161:0.86 162:0.60 164:0.53 166:0.61 167:0.55 169:0.97 172:0.77 173:0.50 177:0.70 178:0.48 179:0.28 181:0.80 182:0.76 186:0.94 187:0.21 189:0.97 190:0.89 191:0.87 192:0.51 195:0.93 196:0.93 202:0.78 212:0.73 216:0.72 219:0.89 220:0.91 222:0.97 227:0.84 229:0.61 231:0.69 235:0.52 238:0.95 241:0.30 242:0.57 243:0.33 245:0.89 246:0.61 247:0.91 248:0.67 253:0.44 254:0.88 255:0.74 256:0.80 259:0.21 260:0.59 261:0.97 262:0.93 265:0.61 266:0.87 267:1.00 268:0.78 271:0.94 276:0.81 284:0.95 285:0.62 287:0.61 292:0.91 300:0.94 +2 1:0.69 7:0.63 8:0.79 9:0.76 11:0.75 12:0.20 17:0.78 18:0.62 20:0.83 21:0.47 27:0.39 28:0.96 29:0.71 30:0.21 32:0.69 34:0.61 36:0.61 37:0.48 39:0.90 43:0.66 45:0.66 48:0.91 55:0.45 66:0.63 69:0.51 70:0.79 71:0.77 74:0.55 76:0.85 77:0.70 78:0.93 81:0.34 83:0.60 86:0.72 91:0.72 96:0.76 98:0.71 99:0.83 100:0.86 101:0.52 104:0.75 108:0.39 111:0.91 114:0.58 117:0.86 120:0.54 123:0.37 124:0.48 126:0.44 127:0.55 129:0.05 131:0.85 133:0.43 135:0.73 138:0.95 139:0.72 140:0.45 144:0.57 145:0.50 146:0.72 147:0.77 149:0.41 150:0.52 151:0.48 152:0.79 153:0.48 154:0.65 155:0.58 157:0.78 158:0.71 159:0.46 161:0.86 162:0.86 164:0.53 165:0.70 166:0.79 167:0.58 169:0.86 172:0.73 173:0.53 176:0.61 177:0.70 179:0.47 181:0.80 182:0.89 186:0.93 187:0.21 190:0.89 191:0.70 192:0.59 195:0.69 201:0.68 202:0.58 208:0.54 212:0.73 215:0.39 216:0.35 220:0.93 222:0.97 227:0.84 229:0.94 230:0.64 231:0.69 232:0.83 233:0.76 235:0.68 241:0.41 242:0.45 243:0.69 245:0.83 246:0.86 247:0.76 248:0.48 253:0.47 254:0.74 255:0.74 256:0.64 259:0.21 260:0.53 261:0.85 265:0.71 266:0.63 267:0.79 268:0.66 271:0.94 276:0.66 281:0.91 282:0.77 285:0.56 286:0.99 287:0.88 290:0.58 297:0.36 298:0.99 300:0.70 +2 6:0.93 7:0.81 12:0.20 17:0.34 20:0.98 21:0.40 27:0.21 28:0.76 30:0.20 34:0.42 36:0.88 37:0.74 43:0.52 55:0.59 66:0.25 69:0.15 70:0.82 78:0.76 81:0.95 91:0.54 98:0.62 100:0.78 104:0.89 106:0.81 108:0.72 111:0.75 120:0.74 123:0.70 124:0.89 126:0.54 127:0.74 129:0.96 133:0.90 135:0.34 140:0.45 146:0.71 147:0.76 149:0.78 150:0.92 151:0.59 152:0.99 153:0.91 154:0.41 159:0.88 161:0.64 162:0.65 164:0.85 167:0.68 169:0.77 172:0.78 173:0.08 177:0.05 179:0.23 181:0.84 186:0.76 187:0.39 202:0.80 206:0.81 212:0.27 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 241:0.43 242:0.82 243:0.18 245:0.90 247:0.12 248:0.67 256:0.81 259:0.21 260:0.75 261:0.82 265:0.63 266:0.89 267:0.44 268:0.81 271:0.18 276:0.88 283:0.60 285:0.62 297:0.36 300:0.70 +2 6:0.93 7:0.81 12:0.20 17:0.43 20:0.84 21:0.40 27:0.21 28:0.76 30:0.43 34:0.68 36:0.94 37:0.74 43:0.52 55:0.71 66:0.24 69:0.12 70:0.68 78:0.79 81:0.95 91:0.20 98:0.58 100:0.81 104:0.84 106:0.81 108:0.88 111:0.78 120:0.78 123:0.87 124:0.90 126:0.54 127:0.83 129:0.96 133:0.90 135:0.32 140:0.45 146:0.81 147:0.84 149:0.87 150:0.92 151:0.74 152:0.99 153:0.83 154:0.41 159:0.92 161:0.64 162:0.77 164:0.76 167:0.68 169:0.78 172:0.87 173:0.07 177:0.05 179:0.16 181:0.84 186:0.80 187:0.39 202:0.66 206:0.81 212:0.56 215:0.67 216:0.95 230:0.65 233:0.63 235:0.42 241:0.43 242:0.82 243:0.26 245:0.96 247:0.12 248:0.70 256:0.68 259:0.21 260:0.79 261:0.82 265:0.59 266:0.92 267:0.82 268:0.70 271:0.18 276:0.94 283:0.82 285:0.62 297:0.36 300:0.84 +3 6:1.00 8:1.00 12:0.20 17:0.24 20:0.99 21:0.78 27:0.16 28:0.76 30:0.33 34:0.39 36:0.50 37:0.98 43:0.42 55:0.84 66:0.45 69:0.52 70:0.36 78:0.94 91:0.95 98:0.62 100:0.98 108:0.62 111:0.98 120:0.80 123:0.60 124:0.66 127:0.43 129:0.81 133:0.67 135:0.26 140:0.92 145:0.87 146:0.05 147:0.05 149:0.39 151:0.76 152:0.91 153:0.87 154:0.32 159:0.50 161:0.64 162:0.48 163:0.94 164:0.86 167:0.95 169:0.98 172:0.27 173:0.49 177:0.05 178:0.51 179:0.87 181:0.84 186:0.94 189:1.00 191:0.92 202:0.92 212:0.42 216:0.60 235:0.88 238:1.00 241:0.85 242:0.82 243:0.19 245:0.47 247:0.12 248:0.72 256:0.83 259:0.21 260:0.81 261:0.97 265:0.63 266:0.38 267:0.73 268:0.83 271:0.18 276:0.35 283:0.82 285:0.62 300:0.25 +3 6:0.98 7:0.81 8:0.97 12:0.20 17:0.12 20:0.99 21:0.47 25:0.88 27:0.28 28:0.76 30:0.91 32:0.80 34:0.21 36:0.64 37:0.79 43:0.36 55:0.71 66:0.69 69:0.61 70:0.13 78:0.92 81:0.94 91:0.98 98:0.60 100:0.98 104:0.58 108:0.47 111:0.98 120:0.97 123:0.45 126:0.54 127:0.18 129:0.05 135:0.32 140:0.45 145:0.49 146:0.38 147:0.45 149:0.32 150:0.90 151:0.84 152:0.99 153:0.98 154:0.27 159:0.15 161:0.64 162:0.79 164:0.98 167:0.96 169:0.97 172:0.21 173:0.84 177:0.05 179:0.85 181:0.84 186:0.91 189:0.99 191:0.92 195:0.57 202:0.96 212:0.61 215:0.63 216:0.60 220:0.62 230:0.87 233:0.76 235:0.52 238:0.99 241:0.92 242:0.82 243:0.73 247:0.12 248:0.96 256:0.97 259:0.21 260:0.97 261:0.97 265:0.61 266:0.17 267:0.82 268:0.97 271:0.18 276:0.22 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.93 7:0.81 8:0.77 12:0.20 17:0.32 20:0.91 21:0.40 27:0.21 28:0.76 30:0.45 34:0.68 36:0.97 37:0.74 43:0.52 55:0.71 66:0.24 69:0.12 70:0.68 78:0.78 81:0.95 91:0.37 98:0.58 100:0.80 104:0.84 106:0.81 108:0.88 111:0.77 120:0.83 123:0.87 124:0.90 126:0.54 127:0.83 129:0.96 133:0.90 135:0.32 140:0.45 146:0.80 147:0.84 149:0.87 150:0.92 151:0.73 152:0.99 153:0.78 154:0.41 159:0.92 161:0.64 162:0.69 164:0.83 167:0.64 169:0.77 172:0.87 173:0.07 177:0.05 179:0.16 181:0.84 186:0.78 187:0.39 202:0.76 206:0.81 212:0.56 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.90 241:0.30 242:0.82 243:0.25 245:0.96 247:0.12 248:0.75 256:0.77 259:0.21 260:0.83 261:0.82 265:0.59 266:0.92 267:0.73 268:0.79 271:0.18 276:0.94 283:0.82 285:0.62 297:0.36 300:0.84 +1 12:0.20 17:0.45 21:0.78 27:0.45 28:0.76 30:0.45 34:0.81 36:0.20 37:0.50 43:0.27 55:0.59 66:0.49 69:0.79 70:0.25 91:0.35 98:0.30 108:0.63 123:0.61 124:0.48 127:0.23 129:0.59 133:0.47 135:0.78 145:0.47 146:0.46 147:0.53 149:0.25 154:0.20 159:0.45 161:0.64 163:0.87 167:0.61 172:0.16 173:0.73 177:0.05 178:0.55 179:0.92 181:0.84 187:0.68 212:0.61 216:0.77 235:0.80 241:0.15 242:0.82 243:0.60 245:0.39 247:0.12 259:0.21 265:0.32 266:0.17 267:0.44 271:0.18 276:0.17 300:0.18 +0 8:0.89 12:0.20 17:0.53 20:0.99 21:0.13 25:0.88 27:0.47 28:0.76 34:0.61 36:0.19 37:0.65 43:0.51 66:0.36 69:0.10 78:0.77 81:0.98 91:0.89 98:0.13 100:0.79 104:0.58 108:0.09 111:0.77 120:0.85 123:0.09 126:0.54 127:0.08 129:0.05 135:0.18 140:0.45 146:0.05 147:0.05 149:0.16 150:0.97 151:0.71 152:0.91 153:0.76 154:0.40 159:0.05 161:0.64 162:0.72 164:0.91 167:0.95 169:0.79 172:0.05 173:0.56 177:0.05 181:0.84 186:0.77 191:0.73 202:0.85 215:0.84 216:0.27 220:0.62 235:0.12 238:0.95 241:0.88 243:0.51 248:0.77 256:0.88 259:0.21 260:0.85 261:0.80 265:0.14 267:0.76 268:0.88 271:0.18 283:0.82 285:0.62 297:0.36 +2 6:0.98 7:0.81 8:0.77 12:0.20 17:0.07 20:0.81 21:0.13 25:0.88 27:0.28 28:0.76 30:0.28 34:0.33 36:0.80 37:0.79 43:0.52 55:0.98 66:0.09 69:0.15 70:0.75 78:0.89 81:0.98 91:0.93 98:0.18 100:0.97 104:0.58 108:0.50 111:0.94 120:0.95 123:0.78 124:0.93 126:0.54 127:0.94 129:0.98 133:0.93 135:0.90 140:0.45 145:0.72 146:0.05 147:0.05 149:0.50 150:0.97 151:0.80 152:0.99 153:0.93 154:0.41 159:0.90 161:0.64 162:0.68 163:0.79 164:0.98 167:0.78 169:0.97 172:0.16 173:0.08 177:0.05 179:0.74 181:0.84 186:0.89 187:0.21 189:0.99 191:0.91 202:0.97 212:0.12 215:0.84 216:0.60 220:0.74 230:0.65 233:0.63 235:0.22 238:0.98 241:0.69 242:0.82 243:0.12 245:0.53 247:0.12 248:0.93 256:0.97 259:0.21 260:0.95 261:0.97 265:0.14 266:0.89 267:0.92 268:0.98 271:0.18 276:0.51 283:0.82 285:0.62 297:0.36 300:0.55 +2 12:0.20 17:0.81 21:0.78 27:0.70 28:0.76 30:0.45 34:0.45 36:0.45 37:0.54 43:0.28 55:0.71 66:0.25 69:0.77 70:0.50 91:0.69 98:0.27 108:0.60 123:0.58 124:0.74 127:0.27 129:0.81 133:0.67 135:0.47 145:0.87 146:0.36 147:0.43 149:0.35 154:0.20 159:0.42 161:0.64 163:0.86 167:0.95 172:0.16 173:0.76 177:0.05 178:0.55 179:0.83 181:0.84 202:0.56 212:0.23 216:0.21 241:0.87 242:0.82 243:0.45 245:0.46 247:0.12 259:0.21 265:0.29 266:0.38 267:0.28 271:0.18 276:0.29 283:0.60 300:0.33 +1 6:0.99 8:0.97 12:0.20 17:0.49 20:0.99 21:0.78 27:0.67 28:0.76 30:0.71 34:0.52 36:0.92 37:0.38 43:0.52 55:0.84 66:0.72 69:0.05 70:0.40 78:0.77 91:0.82 98:0.33 100:0.84 108:0.05 111:0.78 120:0.88 123:0.05 124:0.40 127:0.97 129:0.59 133:0.47 135:0.60 140:0.80 145:0.41 146:0.65 147:0.70 149:0.53 151:0.73 152:0.91 153:0.80 154:0.41 159:0.84 161:0.64 162:0.56 164:0.93 167:0.94 169:0.81 172:0.45 173:0.07 177:0.05 178:0.42 179:0.88 181:0.84 186:0.77 189:1.00 191:0.87 202:0.91 212:0.30 216:0.47 220:0.62 235:0.05 238:0.95 241:0.83 242:0.82 243:0.75 245:0.47 247:0.12 248:0.82 256:0.91 259:0.21 260:0.88 261:0.82 265:0.35 266:0.47 267:0.28 268:0.91 271:0.18 276:0.18 283:0.60 285:0.62 300:0.33 +0 12:0.20 17:0.32 21:0.78 27:0.45 28:0.76 30:0.95 34:0.71 36:0.19 37:0.50 43:0.25 55:0.84 66:0.54 69:0.83 91:0.12 98:0.20 108:0.68 123:0.66 127:0.10 129:0.05 135:0.76 145:0.49 146:0.31 147:0.37 149:0.17 154:0.18 159:0.13 161:0.64 167:0.60 172:0.10 173:0.78 177:0.05 178:0.55 179:0.51 181:0.84 187:0.68 216:0.77 235:0.74 241:0.12 243:0.63 259:0.21 265:0.21 267:0.44 271:0.18 300:0.08 +1 12:0.51 17:0.69 18:0.88 21:0.30 27:0.58 29:0.62 30:0.14 32:0.63 34:0.83 36:0.24 37:0.34 39:0.55 43:0.12 44:0.90 55:0.92 64:0.77 66:0.43 69:0.12 70:0.90 71:0.92 81:0.42 86:0.87 91:0.20 98:0.53 108:0.10 123:0.10 124:0.75 126:0.44 127:0.69 129:0.05 131:0.61 133:0.74 135:0.96 139:0.86 145:0.58 146:0.78 147:0.82 149:0.25 150:0.33 154:0.59 157:0.61 159:0.75 166:0.86 172:0.57 173:0.11 175:0.75 177:0.38 178:0.55 179:0.57 182:0.61 187:0.68 192:0.51 195:0.88 201:0.68 212:0.29 215:0.44 216:0.46 222:0.35 227:0.61 229:0.61 231:0.78 235:0.42 242:0.52 243:0.57 244:0.61 245:0.70 246:0.61 247:0.60 253:0.20 254:0.84 257:0.65 259:0.21 265:0.54 266:0.80 267:0.92 271:0.63 276:0.63 277:0.69 287:0.61 297:0.36 300:0.70 +0 7:0.63 8:0.59 12:0.51 17:0.47 18:0.66 21:0.22 27:0.31 29:0.62 30:0.45 31:0.90 32:0.63 34:0.49 36:0.74 37:0.54 39:0.55 43:0.16 44:0.90 48:0.91 55:0.59 66:0.64 69:0.10 70:0.33 71:0.92 74:0.27 79:0.90 81:0.37 86:0.70 91:0.70 98:0.74 104:0.95 108:0.09 120:0.65 122:0.41 123:0.09 124:0.42 126:0.26 127:0.73 129:0.05 131:0.90 133:0.37 135:0.63 137:0.90 139:0.76 140:0.80 145:0.54 146:0.57 147:0.62 149:0.16 150:0.32 151:0.65 153:0.48 154:0.54 157:0.61 159:0.31 162:0.64 164:0.53 166:0.86 172:0.32 173:0.32 177:0.70 179:0.93 182:0.61 187:0.39 190:0.77 191:0.88 192:0.51 195:0.56 196:0.91 202:0.69 212:0.52 215:0.51 216:0.60 220:0.82 222:0.35 227:0.82 229:0.61 230:0.63 231:0.90 233:0.73 235:0.22 241:0.62 242:0.24 243:0.69 244:0.61 245:0.43 246:0.61 247:0.47 248:0.63 253:0.24 254:0.95 255:0.74 256:0.68 259:0.21 260:0.59 265:0.74 266:0.27 267:0.36 268:0.70 271:0.63 276:0.29 281:0.89 283:0.82 284:0.87 285:0.56 287:0.61 297:0.36 300:0.25 +0 7:0.72 8:0.65 11:0.75 12:0.51 17:0.22 21:0.40 27:0.48 29:0.62 30:0.67 32:0.69 34:0.34 36:0.84 37:0.35 39:0.55 43:0.68 45:0.73 55:0.52 66:0.95 69:0.32 70:0.44 71:0.92 74:0.57 77:0.70 81:0.29 91:0.53 98:0.97 101:0.52 108:0.25 120:0.61 123:0.24 126:0.26 127:0.74 129:0.05 131:0.90 135:0.47 140:0.45 144:0.57 145:0.97 146:0.88 147:0.90 149:0.65 150:0.28 151:0.51 153:0.48 154:0.57 155:0.58 157:0.92 159:0.46 162:0.81 164:0.53 166:0.86 172:0.86 173:0.37 175:0.75 177:0.38 179:0.40 182:0.87 187:0.68 190:0.89 192:0.59 195:0.68 202:0.61 204:0.85 208:0.54 212:0.81 215:0.42 216:0.76 220:0.91 222:0.35 227:0.82 229:0.61 230:0.75 231:0.92 233:0.76 235:0.42 241:0.58 242:0.79 243:0.95 244:0.61 246:0.61 247:0.39 248:0.52 253:0.38 256:0.64 257:0.65 259:0.21 260:0.56 265:0.97 266:0.38 267:0.44 268:0.66 271:0.63 276:0.76 277:0.69 282:0.77 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +0 8:0.88 9:0.76 12:0.51 17:0.43 18:0.61 21:0.78 27:0.60 29:0.62 30:0.76 34:0.17 36:0.42 37:0.77 39:0.55 43:0.10 55:0.96 66:0.13 69:0.49 70:0.21 71:0.92 74:0.35 86:0.63 91:0.80 96:0.82 98:0.26 104:0.75 108:0.38 120:0.68 122:0.55 123:0.80 124:0.82 127:0.35 129:0.81 131:0.98 133:0.76 135:0.18 138:0.95 139:0.61 140:0.45 144:0.57 146:0.20 147:0.26 149:0.11 151:0.94 153:0.91 154:0.54 155:0.58 157:0.61 158:0.74 159:0.53 162:0.55 163:0.89 164:0.65 166:0.61 172:0.10 173:0.41 175:0.75 177:0.38 179:0.88 182:0.61 190:0.77 191:0.89 192:0.59 202:0.72 208:0.54 212:0.23 216:0.22 222:0.35 227:0.83 229:0.61 231:0.95 232:0.72 235:0.52 241:0.91 242:0.24 243:0.34 244:0.61 245:0.43 246:0.61 247:0.47 248:0.84 253:0.60 254:0.90 255:0.74 256:0.64 257:0.65 259:0.21 260:0.65 265:0.13 266:0.38 267:0.65 268:0.69 271:0.63 276:0.31 277:0.69 279:0.82 283:0.78 285:0.56 286:0.99 287:0.61 298:0.99 300:0.18 +0 12:0.51 17:0.45 18:0.58 21:0.78 27:0.34 29:0.62 30:0.45 34:0.62 36:0.82 37:0.46 39:0.55 43:0.11 55:0.84 66:0.27 69:0.96 70:0.25 71:0.92 86:0.60 91:0.03 98:0.15 108:0.92 122:0.65 123:0.92 124:0.72 127:0.25 129:0.05 131:0.61 133:0.62 135:0.44 139:0.59 145:0.63 146:0.37 147:0.05 149:0.08 154:0.09 157:0.61 159:0.82 163:0.99 166:0.61 172:0.10 173:0.87 175:0.75 177:0.05 178:0.55 179:0.93 182:0.61 187:0.87 212:0.61 216:0.53 222:0.35 227:0.61 229:0.61 231:0.65 235:0.22 241:0.24 242:0.63 243:0.29 244:0.83 245:0.38 246:0.61 247:0.30 253:0.20 257:0.84 259:0.21 265:0.17 266:0.17 267:0.52 271:0.63 276:0.17 277:0.69 287:0.61 300:0.18 +1 12:0.51 17:0.09 18:0.64 21:0.13 27:0.14 29:0.62 30:0.76 34:0.82 36:0.33 37:0.85 39:0.55 43:0.62 64:0.77 66:0.64 69:0.74 70:0.15 71:0.92 81:0.59 86:0.68 91:0.27 98:0.32 108:0.57 122:0.65 123:0.54 126:0.44 127:0.12 129:0.05 131:0.61 135:0.73 139:0.73 145:0.38 146:0.32 147:0.38 149:0.31 150:0.42 154:0.52 157:0.61 159:0.13 163:0.98 166:0.86 172:0.16 173:0.84 175:0.75 177:0.38 178:0.55 179:0.64 182:0.61 187:0.68 192:0.51 201:0.68 212:0.95 215:0.61 216:0.71 219:0.92 222:0.35 227:0.61 229:0.61 231:0.78 235:0.22 241:0.24 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.20 257:0.65 259:0.21 265:0.34 266:0.12 267:0.36 271:0.63 276:0.13 277:0.69 283:0.82 287:0.61 292:0.95 297:0.36 300:0.13 +1 1:0.74 7:0.64 12:0.51 17:0.87 18:0.69 21:0.40 27:0.36 29:0.62 30:0.62 34:0.81 36:0.84 37:0.36 39:0.55 43:0.09 48:0.91 55:0.42 64:0.77 66:0.47 69:0.83 70:0.34 71:0.92 74:0.28 81:0.51 86:0.72 91:0.62 98:0.19 108:0.68 114:0.62 122:0.65 123:0.66 124:0.52 126:0.54 127:0.16 129:0.05 131:0.61 133:0.39 135:0.70 139:0.79 144:0.57 145:0.69 146:0.25 147:0.31 149:0.07 150:0.66 154:0.46 155:0.67 157:0.61 158:0.71 159:0.20 166:0.97 172:0.21 173:0.89 175:0.75 176:0.73 177:0.38 178:0.55 179:0.62 182:0.61 187:0.21 192:0.87 201:0.93 208:0.64 212:0.30 215:0.42 216:0.39 219:0.92 222:0.35 227:0.61 229:0.61 230:0.64 231:0.95 232:0.79 233:0.73 235:0.60 241:0.02 242:0.33 243:0.59 244:0.61 245:0.46 246:0.61 247:0.39 253:0.46 254:0.74 257:0.65 259:0.21 265:0.20 266:0.27 267:0.36 271:0.63 276:0.18 277:0.69 281:0.88 283:0.77 287:0.61 290:0.62 292:0.91 297:0.36 300:0.25 +1 11:0.64 12:0.51 17:0.30 18:0.69 21:0.78 27:0.45 29:0.62 30:0.21 32:0.62 34:0.75 36:0.17 37:0.50 39:0.55 43:0.61 45:0.66 66:0.36 69:0.70 70:0.37 71:0.92 74:0.27 86:0.70 91:0.22 98:0.25 101:0.52 108:0.53 122:0.57 123:0.51 124:0.57 127:0.16 129:0.05 131:0.61 133:0.43 135:0.92 139:0.68 145:0.41 146:0.39 147:0.46 149:0.31 154:0.50 157:0.61 159:0.30 163:0.88 166:0.61 172:0.16 173:0.62 177:0.70 178:0.55 179:0.71 182:0.61 187:0.68 195:0.82 212:0.42 216:0.77 222:0.35 227:0.61 229:0.61 231:0.77 235:0.22 241:0.43 242:0.45 243:0.51 244:0.61 245:0.43 246:0.61 247:0.35 253:0.24 259:0.21 265:0.28 266:0.23 267:0.36 271:0.63 276:0.22 287:0.61 300:0.25 +1 7:0.64 8:0.91 11:0.42 12:0.51 17:0.07 18:0.82 21:0.59 27:0.29 29:0.62 30:0.76 31:0.86 32:0.63 34:0.80 36:0.38 37:0.41 39:0.55 43:0.68 44:0.90 48:0.91 55:0.42 64:0.77 66:0.30 69:0.80 70:0.53 71:0.92 74:0.41 79:0.86 81:0.31 86:0.86 91:0.57 98:0.37 108:0.44 120:0.53 122:0.65 123:0.43 124:0.71 126:0.44 127:0.43 129:0.05 131:0.90 133:0.66 135:0.93 137:0.86 139:0.90 140:0.45 144:0.57 145:0.80 146:0.24 147:0.48 149:0.63 150:0.25 151:0.46 153:0.48 154:0.67 157:0.61 159:0.43 162:0.86 164:0.53 166:0.85 172:0.32 173:0.85 177:0.38 179:0.73 182:0.61 187:0.87 190:0.77 192:0.51 195:0.67 196:0.87 201:0.68 202:0.36 212:0.36 215:0.39 216:0.63 219:0.92 220:0.97 222:0.35 227:0.82 229:0.61 230:0.64 231:0.87 233:0.73 235:0.74 241:0.03 242:0.38 243:0.48 245:0.60 246:0.61 247:0.67 248:0.46 253:0.32 254:0.84 255:0.74 256:0.45 257:0.65 259:0.21 260:0.53 265:0.39 266:0.54 267:0.59 268:0.46 271:0.63 276:0.38 277:0.69 281:0.88 283:0.77 284:0.87 285:0.56 287:0.61 292:0.91 297:0.36 300:0.55 +1 12:0.51 17:0.34 18:0.79 21:0.40 27:0.45 29:0.62 30:0.45 34:0.71 36:0.17 37:0.50 39:0.55 43:0.08 55:0.59 64:0.77 66:0.52 69:0.69 70:0.50 71:0.92 74:0.28 77:0.70 81:0.27 86:0.81 91:0.27 98:0.23 108:0.52 122:0.75 123:0.50 124:0.63 126:0.26 127:0.26 129:0.05 131:0.61 133:0.59 135:0.82 139:0.77 145:0.52 146:0.35 147:0.42 149:0.07 150:0.26 154:0.51 157:0.61 159:0.49 166:0.61 172:0.27 173:0.61 177:0.70 178:0.55 179:0.82 182:0.61 187:0.68 195:0.82 212:0.52 216:0.77 222:0.35 227:0.61 229:0.61 231:0.73 235:0.42 241:0.47 242:0.24 243:0.61 244:0.61 245:0.43 246:0.61 247:0.47 253:0.24 254:0.74 259:0.21 265:0.25 266:0.27 267:0.52 271:0.63 276:0.26 282:0.71 287:0.61 300:0.33 +0 9:0.76 12:0.51 17:0.81 18:0.63 21:0.78 27:0.60 29:0.62 30:0.91 34:0.39 36:0.55 37:0.77 39:0.55 43:0.10 55:0.96 66:0.27 69:0.47 70:0.13 71:0.92 74:0.26 86:0.64 91:0.40 96:0.75 98:0.14 104:0.75 108:0.36 120:0.63 122:0.57 123:0.35 124:0.72 127:0.25 129:0.05 131:0.98 133:0.62 135:0.19 138:0.95 139:0.62 140:0.45 144:0.57 146:0.20 147:0.26 149:0.08 151:0.81 153:0.48 154:0.54 155:0.58 157:0.61 159:0.35 162:0.86 163:0.89 164:0.53 166:0.61 172:0.10 173:0.47 175:0.75 177:0.38 179:0.94 182:0.61 187:0.21 190:0.77 191:0.73 192:0.59 202:0.36 208:0.54 212:0.61 216:0.22 222:0.35 227:0.83 229:0.61 231:0.95 232:0.72 235:0.12 241:0.52 242:0.63 243:0.46 244:0.61 245:0.38 246:0.61 247:0.30 248:0.73 253:0.51 254:0.90 255:0.74 256:0.45 257:0.65 259:0.21 260:0.61 265:0.15 266:0.17 267:0.36 268:0.46 271:0.63 276:0.21 277:0.69 285:0.56 286:0.99 287:0.61 298:0.99 300:0.13 +1 7:0.64 12:0.51 17:0.73 18:0.69 21:0.22 22:0.93 27:0.39 29:0.62 30:0.76 34:0.61 36:0.25 37:0.27 39:0.55 43:0.60 44:0.87 47:0.93 48:0.91 64:0.77 66:0.64 69:0.80 70:0.15 71:0.92 81:0.50 86:0.73 91:0.79 98:0.17 104:0.89 106:0.81 108:0.64 122:0.75 123:0.61 126:0.44 127:0.10 129:0.05 131:0.61 135:0.94 139:0.78 145:0.67 146:0.21 147:0.26 149:0.27 150:0.37 154:0.49 157:0.61 159:0.10 163:0.97 166:0.86 172:0.16 173:0.93 175:0.75 177:0.38 178:0.55 179:0.20 182:0.61 187:0.39 192:0.51 195:0.60 201:0.68 202:0.50 206:0.81 212:0.95 215:0.51 216:0.30 222:0.35 227:0.61 229:0.61 230:0.64 231:0.78 233:0.73 235:0.31 241:0.41 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.20 257:0.65 259:0.21 262:0.93 265:0.19 266:0.12 267:0.44 271:0.63 276:0.15 277:0.69 281:0.89 287:0.61 297:0.36 300:0.13 +1 1:0.74 7:0.64 8:0.61 9:0.80 12:0.51 17:0.04 18:0.81 21:0.47 27:0.29 29:0.62 30:0.38 31:0.89 34:0.82 36:0.53 37:0.41 39:0.55 43:0.10 48:0.97 55:0.42 64:0.77 66:0.64 69:0.78 70:0.63 71:0.92 74:0.48 76:0.85 79:0.89 81:0.47 86:0.84 91:0.64 96:0.68 98:0.36 108:0.61 114:0.60 120:0.59 122:0.65 123:0.59 124:0.44 126:0.54 127:0.18 129:0.05 131:0.98 133:0.43 135:0.39 137:0.89 139:0.88 140:0.45 144:0.57 145:0.54 146:0.42 147:0.22 149:0.10 150:0.64 151:0.48 153:0.48 154:0.67 155:0.67 157:0.61 158:0.86 159:0.25 162:0.86 164:0.63 166:0.97 172:0.45 173:0.81 176:0.73 177:0.38 179:0.48 182:0.61 187:0.39 190:0.77 192:0.87 196:0.92 201:0.93 202:0.50 208:0.64 212:0.57 215:0.41 216:0.63 219:0.95 220:0.93 222:0.35 227:0.83 229:0.61 230:0.64 231:0.95 232:0.95 233:0.66 235:0.68 241:0.24 242:0.29 243:0.26 245:0.55 246:0.61 247:0.60 248:0.48 253:0.44 254:0.88 255:0.95 256:0.58 257:0.65 259:0.21 260:0.56 265:0.38 266:0.38 267:0.59 268:0.59 271:0.63 276:0.35 279:0.86 281:0.89 283:0.67 284:0.92 285:0.62 287:0.61 290:0.60 292:0.91 297:0.36 300:0.43 +3 1:0.58 6:0.96 8:0.91 9:0.76 11:0.42 12:0.51 17:0.12 20:0.94 21:0.64 27:0.41 28:0.78 29:0.94 30:0.89 32:0.67 34:0.44 36:0.72 37:0.39 39:0.33 43:0.28 66:0.16 69:0.90 70:0.20 71:0.65 74:0.50 77:0.70 78:0.76 81:0.37 83:0.60 91:0.91 96:0.99 98:0.08 100:0.83 104:0.73 108:0.81 111:0.78 114:0.69 120:0.97 122:0.51 123:0.79 124:0.75 126:0.44 127:0.22 129:0.81 131:0.99 133:0.67 135:0.78 140:0.45 144:0.57 145:0.79 146:0.13 147:0.18 149:0.32 150:0.44 151:0.97 152:0.88 153:0.94 154:0.20 155:0.58 157:0.61 158:0.76 159:0.43 161:0.45 162:0.71 163:0.88 164:0.95 166:0.99 167:1.00 169:0.81 172:0.10 173:0.91 175:0.75 176:0.63 177:0.38 179:0.81 181:0.95 182:0.99 186:0.77 189:0.97 190:0.77 191:0.81 192:0.59 195:0.84 201:0.55 202:0.93 208:0.54 212:0.61 215:0.53 216:0.69 220:0.62 222:0.48 227:0.99 229:0.99 231:0.98 232:0.82 235:0.80 238:0.96 241:0.93 242:0.63 243:0.37 244:0.83 245:0.43 246:0.61 247:0.30 248:0.95 253:0.98 255:0.74 256:0.94 257:0.65 259:0.21 260:0.97 261:0.83 265:0.08 266:0.23 267:0.52 268:0.95 271:0.75 276:0.23 277:0.69 279:0.77 282:0.75 283:0.67 285:0.56 287:0.98 290:0.69 297:0.36 300:0.18 +1 1:0.56 11:0.55 12:0.51 17:0.67 18:0.76 21:0.76 27:0.45 28:0.78 29:0.94 30:0.60 32:0.72 34:0.86 36:0.28 37:0.50 39:0.33 43:0.71 45:0.73 66:0.53 69:0.87 70:0.64 71:0.65 74:0.78 77:0.70 81:0.49 86:0.82 91:0.08 98:0.26 99:0.83 101:0.52 108:0.75 114:0.64 122:0.51 123:0.73 124:0.77 126:0.44 127:0.29 129:0.05 131:0.61 133:0.84 135:0.73 139:0.78 144:0.57 145:0.56 146:0.43 147:0.22 149:0.74 150:0.38 154:0.61 155:0.46 157:0.92 159:0.57 161:0.45 165:0.70 166:0.99 167:0.65 172:0.74 173:0.82 176:0.70 177:0.38 178:0.55 179:0.29 181:0.95 182:0.97 187:0.68 192:0.44 195:0.86 201:0.54 208:0.54 212:0.81 215:0.46 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.98 241:0.15 242:0.43 243:0.12 244:0.61 245:0.76 246:0.98 247:0.60 253:0.58 257:0.65 259:0.21 265:0.28 266:0.63 267:0.23 271:0.75 276:0.70 282:0.80 287:0.61 290:0.64 297:0.36 300:0.55 +4 1:0.58 6:0.97 8:0.95 9:0.79 11:0.42 12:0.51 17:0.02 18:0.61 20:0.96 21:0.64 27:0.03 28:0.78 29:0.94 30:0.76 31:1.00 34:0.82 36:0.91 37:0.94 39:0.33 41:0.97 43:0.25 44:0.88 66:0.20 69:0.92 70:0.22 71:0.65 74:0.45 78:0.90 79:1.00 81:0.37 83:0.91 86:0.63 91:1.00 96:1.00 97:0.89 98:0.09 100:0.98 104:0.58 108:0.84 111:0.97 114:0.69 120:1.00 122:0.51 123:0.83 124:0.73 126:0.44 127:0.25 129:0.59 131:0.99 133:0.64 135:0.65 137:1.00 138:1.00 139:0.67 140:0.98 144:0.57 145:0.82 146:0.13 147:0.05 149:0.26 150:0.44 151:0.99 152:1.00 153:1.00 154:0.18 155:0.66 157:0.61 158:0.82 159:0.44 161:0.45 162:0.57 163:0.88 164:1.00 166:0.99 167:1.00 169:0.96 172:0.10 173:0.96 175:0.75 176:0.63 177:0.05 179:0.90 181:0.95 182:0.99 186:0.90 189:0.98 191:0.98 192:0.87 195:0.81 196:0.95 201:0.55 202:1.00 208:0.64 212:0.42 215:0.53 216:0.84 222:0.48 227:0.99 229:0.99 231:0.98 232:0.81 235:0.80 238:0.99 241:0.98 242:0.45 243:0.26 244:0.83 245:0.40 246:0.61 247:0.35 248:0.97 253:1.00 255:0.78 256:1.00 257:0.84 259:0.21 260:1.00 261:0.98 265:0.09 266:0.23 267:0.59 268:1.00 271:0.75 276:0.21 277:0.69 279:0.82 283:0.82 284:1.00 285:0.62 287:0.98 290:0.69 297:0.36 300:0.18 +4 1:0.61 6:0.96 8:0.88 9:0.80 11:0.42 12:0.51 17:0.51 20:0.81 21:0.68 27:0.63 28:0.78 29:0.94 30:0.91 31:0.96 34:0.66 36:0.84 37:0.48 39:0.33 43:0.60 64:0.77 66:0.69 69:0.68 70:0.13 71:0.65 74:0.41 78:0.75 79:0.95 81:0.49 83:0.91 91:0.79 96:0.96 98:0.21 100:0.80 104:0.72 108:0.52 111:0.75 114:0.69 120:0.93 122:0.51 123:0.49 126:0.54 127:0.11 129:0.05 131:0.99 135:0.51 137:0.96 140:0.97 144:0.57 146:0.29 147:0.35 149:0.42 150:0.50 151:0.66 152:0.79 153:0.85 154:0.50 155:0.66 157:0.61 159:0.12 161:0.45 162:0.83 164:0.90 166:0.99 167:1.00 169:0.78 172:0.21 173:0.68 175:0.75 176:0.70 177:0.38 179:0.24 181:0.95 182:0.99 186:0.76 189:0.97 190:0.77 191:0.75 192:0.87 201:0.60 202:0.82 208:0.64 212:0.61 215:0.49 216:0.54 222:0.48 227:0.99 229:0.99 231:0.98 232:0.82 235:0.88 238:0.95 241:0.94 242:0.63 243:0.73 244:0.83 246:0.61 247:0.30 248:0.66 253:0.94 255:0.79 256:0.86 257:0.65 259:0.21 260:0.93 261:0.77 265:0.23 266:0.17 267:0.73 268:0.87 271:0.75 276:0.14 277:0.69 284:0.97 285:0.62 287:0.99 290:0.69 297:0.36 300:0.13 +3 1:0.63 6:0.98 8:0.95 9:0.79 11:0.42 12:0.51 17:0.47 20:0.95 21:0.22 27:0.07 28:0.78 29:0.94 30:0.95 31:0.95 34:0.52 36:0.54 37:0.95 39:0.33 43:0.63 66:0.54 69:0.54 71:0.65 74:0.43 78:0.85 79:0.94 81:0.52 83:0.91 91:0.88 96:0.97 98:0.17 100:0.95 104:0.78 108:0.42 111:0.90 114:0.85 120:0.94 123:0.40 126:0.44 127:0.10 129:0.05 131:0.99 135:0.39 137:0.95 140:0.94 144:0.57 146:0.13 147:0.18 149:0.30 150:0.56 151:0.95 152:0.92 153:0.92 154:0.52 155:0.66 157:0.61 158:0.76 159:0.08 161:0.45 162:0.64 164:0.92 166:0.99 167:0.99 169:0.93 172:0.10 173:0.93 175:0.75 176:0.63 177:0.38 178:0.42 179:0.21 181:0.95 182:0.99 186:0.85 189:0.98 190:0.77 191:0.87 192:0.87 201:0.60 202:0.89 208:0.64 215:0.79 216:0.63 222:0.48 227:0.99 229:0.99 231:0.98 232:0.85 235:0.31 238:0.97 241:0.85 243:0.63 244:0.83 246:0.61 248:0.92 253:0.95 255:0.78 256:0.89 257:0.65 259:0.21 260:0.94 261:0.93 265:0.18 267:0.69 268:0.90 271:0.75 277:0.69 279:0.77 283:0.67 284:0.93 285:0.62 287:0.99 290:0.85 297:0.36 300:0.08 +4 1:0.58 9:0.79 11:0.42 12:0.51 17:0.45 18:0.61 21:0.64 27:0.03 28:0.78 29:0.94 30:0.76 31:0.95 34:0.89 36:0.58 37:0.94 39:0.33 41:0.82 43:0.25 44:0.88 66:0.20 69:0.93 70:0.22 71:0.65 74:0.34 79:0.94 81:0.37 83:0.91 86:0.63 91:0.88 96:0.98 98:0.08 104:0.58 108:0.85 114:0.69 120:0.96 122:0.51 123:0.84 124:0.73 126:0.44 127:0.27 129:0.59 131:0.99 133:0.64 135:0.65 137:0.95 139:0.67 140:0.98 144:0.57 145:0.82 146:0.13 147:0.05 149:0.25 150:0.44 151:0.63 153:0.95 154:0.17 155:0.66 157:0.61 159:0.49 161:0.45 162:0.78 163:0.88 164:0.92 166:0.99 167:0.96 172:0.10 173:0.96 175:0.75 176:0.63 177:0.05 179:0.91 181:0.95 182:0.99 187:0.39 192:0.87 195:0.83 196:0.90 201:0.55 202:0.87 208:0.64 212:0.42 215:0.53 216:0.84 222:0.48 227:0.99 229:0.99 231:0.98 232:0.81 235:0.80 241:0.79 242:0.45 243:0.26 244:0.83 245:0.40 246:0.61 247:0.35 248:0.61 253:0.96 255:0.78 256:0.90 257:0.84 259:0.21 260:0.96 265:0.09 266:0.23 267:0.79 268:0.90 271:0.75 276:0.20 277:0.69 284:0.91 285:0.62 287:0.99 290:0.69 297:0.36 300:0.18 +1 1:0.56 8:0.69 11:0.55 12:0.51 17:0.40 21:0.76 27:0.45 28:0.78 29:0.94 30:0.26 32:0.67 34:0.75 36:0.17 37:0.50 39:0.33 43:0.68 45:0.90 55:0.42 66:0.27 69:0.70 70:0.91 71:0.65 74:0.75 77:0.70 81:0.34 83:0.60 91:0.07 98:0.51 101:0.52 108:0.54 114:0.63 122:0.51 123:0.51 124:0.87 126:0.44 127:0.66 129:0.95 131:0.61 133:0.87 135:0.86 144:0.57 145:0.56 146:0.86 147:0.88 149:0.85 150:0.40 154:0.58 155:0.46 157:0.99 159:0.84 161:0.45 163:0.81 166:0.99 167:0.73 172:0.63 173:0.48 175:0.75 176:0.63 177:0.38 178:0.55 179:0.38 181:0.95 182:0.96 187:0.68 192:0.44 195:0.94 201:0.54 208:0.54 212:0.30 215:0.46 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.97 241:0.51 242:0.60 243:0.46 244:0.61 245:0.80 246:0.61 247:0.47 253:0.57 257:0.65 259:0.21 265:0.52 266:0.84 267:0.28 271:0.75 276:0.76 277:0.69 282:0.75 287:0.61 290:0.63 297:0.36 300:0.84 +3 1:0.62 7:0.70 9:0.75 11:0.50 12:0.51 17:0.43 18:0.83 21:0.30 27:0.21 28:0.78 29:0.94 30:0.73 34:0.58 36:0.83 37:0.74 39:0.33 43:0.66 45:0.77 66:0.35 69:0.12 70:0.64 71:0.65 74:0.89 76:0.85 81:0.49 83:0.60 86:0.88 91:0.27 96:0.88 98:0.60 101:0.52 104:0.84 106:0.81 108:0.91 114:0.82 120:0.80 122:0.51 123:0.91 124:0.78 126:0.44 127:0.69 129:0.81 131:0.99 133:0.78 135:0.47 139:0.82 140:0.80 144:0.57 146:0.69 147:0.73 149:0.92 150:0.54 151:0.90 153:0.69 154:0.49 155:0.58 157:0.61 159:0.85 161:0.45 162:0.86 164:0.74 166:0.99 167:0.69 172:0.92 173:0.09 176:0.63 177:0.70 179:0.15 181:0.95 182:0.98 187:0.39 190:0.77 192:0.58 201:0.59 202:0.60 204:0.82 206:0.81 208:0.54 212:0.69 215:0.72 216:0.95 222:0.48 227:0.99 229:0.98 230:0.73 231:0.98 233:0.76 235:0.42 241:0.32 242:0.53 243:0.21 244:0.61 245:0.98 246:0.61 247:0.84 248:0.79 253:0.91 255:0.73 256:0.68 259:0.21 260:0.81 265:0.61 266:0.71 267:0.59 268:0.68 271:0.75 276:0.95 285:0.56 287:0.98 290:0.82 297:0.36 300:0.84 +3 1:0.63 9:0.80 11:0.42 12:0.51 17:0.40 21:0.22 27:0.07 28:0.78 29:0.94 30:0.95 31:0.98 34:0.40 36:0.55 37:0.95 39:0.33 41:0.82 43:0.63 66:0.54 69:0.54 71:0.65 74:0.36 79:0.98 81:0.52 83:0.91 91:0.58 96:0.98 98:0.15 104:0.78 108:0.42 114:0.85 120:0.96 123:0.40 126:0.44 127:0.09 129:0.05 131:0.99 135:0.49 137:0.98 140:0.96 144:0.57 146:0.13 147:0.18 149:0.30 150:0.56 151:0.91 153:0.94 154:0.52 155:0.67 157:0.61 159:0.08 161:0.45 162:0.69 164:0.94 166:0.99 167:0.87 172:0.10 173:0.88 175:0.75 176:0.63 177:0.38 179:0.16 181:0.95 182:0.99 187:0.39 192:0.87 201:0.60 202:0.91 208:0.64 215:0.79 216:0.63 222:0.48 227:0.99 229:0.99 231:0.98 232:0.85 235:0.31 241:0.73 243:0.63 244:0.83 246:0.61 248:0.81 253:0.97 255:0.94 256:0.92 257:0.65 259:0.21 260:0.96 265:0.16 267:0.19 268:0.93 271:0.75 277:0.69 284:0.96 285:0.62 287:0.99 290:0.85 297:0.36 300:0.08 +1 1:0.63 6:0.98 8:0.88 9:0.79 11:0.42 12:0.51 17:0.06 20:0.80 21:0.22 27:0.07 28:0.78 29:0.94 30:0.87 31:0.95 34:0.95 36:0.21 37:0.95 39:0.33 41:0.82 43:0.65 66:0.32 69:0.41 70:0.27 71:0.65 74:0.51 78:0.78 79:0.94 81:0.52 83:0.91 91:0.53 96:0.90 98:0.54 100:0.84 104:0.78 108:0.31 111:0.79 114:0.85 120:0.83 122:0.51 123:0.30 124:0.61 126:0.44 127:0.63 129:0.59 131:0.99 133:0.47 135:0.63 137:0.95 140:0.87 144:0.57 146:0.37 147:0.43 149:0.60 150:0.56 151:0.91 152:0.92 153:0.72 154:0.54 155:0.66 157:0.61 159:0.26 161:0.45 162:0.72 163:0.81 164:0.79 166:0.99 167:0.81 169:0.92 172:0.21 173:0.63 175:0.75 176:0.63 177:0.38 178:0.42 179:0.90 181:0.95 182:0.99 186:0.79 187:0.39 189:0.94 192:0.87 201:0.60 202:0.71 208:0.64 212:0.42 215:0.79 216:0.63 222:0.48 227:0.99 229:0.99 231:0.98 232:0.85 235:0.31 238:0.94 241:0.68 242:0.63 243:0.49 244:0.83 245:0.54 246:0.61 247:0.30 248:0.81 253:0.87 255:0.79 256:0.73 257:0.65 259:0.21 260:0.84 261:0.93 265:0.56 266:0.38 267:0.36 268:0.74 271:0.75 276:0.29 277:0.69 284:0.91 285:0.62 287:0.99 290:0.85 297:0.36 300:0.25 +3 1:0.62 6:0.87 7:0.70 8:0.74 9:0.79 11:0.64 12:0.51 17:0.34 18:0.83 20:0.83 21:0.30 27:0.21 28:0.78 29:0.94 30:0.72 31:0.94 34:0.69 36:0.87 37:0.74 39:0.33 43:0.76 45:0.81 66:0.35 69:0.12 70:0.66 71:0.65 74:0.91 76:0.85 78:0.74 79:0.93 81:0.49 83:0.91 86:0.88 91:0.64 96:0.95 97:0.94 98:0.60 100:0.78 101:0.87 104:0.84 106:0.81 108:0.91 111:0.74 114:0.82 117:0.86 120:0.91 122:0.51 123:0.91 124:0.79 126:0.44 127:0.69 129:0.81 131:0.99 133:0.78 135:0.32 137:0.94 139:0.82 140:0.45 144:0.57 146:0.69 147:0.73 149:0.93 150:0.54 151:0.98 152:0.79 153:0.93 154:0.68 155:0.66 157:0.81 158:0.82 159:0.85 161:0.45 162:0.85 164:0.86 166:0.99 167:0.71 169:0.76 172:0.92 173:0.09 176:0.63 177:0.70 179:0.15 181:0.95 182:0.99 186:0.75 187:0.39 189:0.88 190:0.77 191:0.70 192:0.87 196:0.94 201:0.59 202:0.76 204:0.82 206:0.81 208:0.64 212:0.69 215:0.72 216:0.95 222:0.48 227:0.99 229:1.00 230:0.73 231:0.98 232:0.92 233:0.76 235:0.42 238:0.91 241:0.41 242:0.54 243:0.21 244:0.61 245:0.98 246:0.61 247:0.82 248:0.94 253:0.97 255:0.79 256:0.82 259:0.21 260:0.92 261:0.76 265:0.61 266:0.71 267:0.52 268:0.83 271:0.75 276:0.95 279:0.81 283:0.82 284:0.88 285:0.62 287:0.99 290:0.82 297:0.36 300:0.84 +3 1:0.59 6:0.97 8:0.92 9:0.79 11:0.42 12:0.51 17:0.32 20:0.78 21:0.59 22:0.93 27:0.03 28:0.78 29:0.94 30:0.95 31:0.95 32:0.67 34:0.81 36:0.83 37:0.94 39:0.33 41:0.82 43:0.21 47:0.93 48:0.91 60:0.87 66:0.54 69:0.95 71:0.65 74:0.53 77:0.70 78:0.86 79:0.95 81:0.39 83:0.91 91:0.84 96:0.97 98:0.08 100:0.95 104:0.58 108:0.91 111:0.91 114:0.71 120:0.94 123:0.90 126:0.44 127:0.06 129:0.05 131:0.99 135:0.32 137:0.95 139:0.77 140:0.98 144:0.57 145:0.80 146:0.13 147:0.18 149:0.13 150:0.45 151:0.86 152:1.00 153:0.91 154:0.14 155:0.66 157:0.61 158:0.92 159:0.08 161:0.45 162:0.76 164:0.92 166:0.99 167:0.88 169:0.96 172:0.10 173:0.99 175:0.75 176:0.63 177:0.38 178:0.42 179:0.08 181:0.95 182:0.99 186:0.86 187:0.21 189:0.97 191:0.95 192:0.87 195:0.89 196:0.93 201:0.56 202:0.86 208:0.64 215:0.55 216:0.84 219:0.95 220:0.62 222:0.48 227:0.99 229:0.99 231:0.98 232:0.81 235:0.74 238:0.97 241:0.74 243:0.63 244:0.83 246:0.61 248:0.77 253:0.95 255:0.79 256:0.88 257:0.65 259:0.21 260:0.94 261:0.98 262:0.93 265:0.08 267:0.69 268:0.90 271:0.75 277:0.69 279:0.80 281:0.91 282:0.75 283:0.82 284:0.92 285:0.62 287:0.99 290:0.71 292:0.95 297:0.36 300:0.08 +3 1:0.69 6:0.97 8:0.93 9:0.76 12:0.51 17:0.04 20:0.95 21:0.13 27:0.03 28:0.78 29:0.94 32:0.67 34:0.59 36:1.00 37:0.94 39:0.33 71:0.65 74:0.44 77:0.70 78:0.78 81:0.69 83:0.60 91:0.80 96:0.98 100:0.85 104:0.58 111:0.79 114:0.88 120:0.96 126:0.44 131:0.99 135:0.98 140:0.94 144:0.57 145:0.40 150:0.77 151:0.96 152:1.00 153:0.89 155:0.58 157:0.61 158:0.76 161:0.45 162:0.65 164:0.94 166:0.99 167:0.85 169:0.95 175:0.97 176:0.63 178:0.50 181:0.95 182:0.99 186:0.78 187:0.39 189:0.97 190:0.77 191:0.80 192:0.59 195:0.72 201:0.66 202:0.92 208:0.54 215:0.84 216:0.84 220:0.62 222:0.48 227:0.99 229:1.00 231:0.98 232:0.81 235:0.42 238:0.95 241:0.72 244:0.97 246:0.61 248:0.95 253:0.97 255:0.74 256:0.92 257:0.93 259:0.21 260:0.96 261:0.98 267:0.84 268:0.93 271:0.75 277:0.95 279:0.79 282:0.75 283:0.67 285:0.56 287:0.99 290:0.88 297:0.36 +3 1:0.62 6:0.97 8:0.93 9:0.76 11:0.42 12:0.51 17:0.07 20:0.94 21:0.30 27:0.03 28:0.78 29:0.94 30:0.95 32:0.67 34:0.61 36:0.61 37:0.94 39:0.33 43:0.23 66:0.54 69:0.94 71:0.65 74:0.39 77:0.89 78:0.84 81:0.49 83:0.60 91:0.86 96:0.97 98:0.08 100:0.95 104:0.58 108:0.88 111:0.89 114:0.82 120:0.95 123:0.88 126:0.44 127:0.06 129:0.05 131:0.99 135:0.61 140:0.87 144:0.57 145:0.70 146:0.13 147:0.18 149:0.15 150:0.54 151:0.97 152:1.00 153:0.92 154:0.16 155:0.58 157:0.61 159:0.08 161:0.45 162:0.77 164:0.94 166:0.99 167:0.87 169:0.95 172:0.10 173:0.99 175:0.75 176:0.63 177:0.38 178:0.42 179:0.08 181:0.95 182:0.99 186:0.85 187:0.39 189:0.98 190:0.77 191:0.89 192:0.58 195:0.84 201:0.59 202:0.89 208:0.54 215:0.72 216:0.84 222:0.48 227:0.99 229:1.00 231:0.98 232:0.81 235:0.42 238:0.97 241:0.73 243:0.63 244:0.83 246:0.61 248:0.94 253:0.96 255:0.74 256:0.92 257:0.65 259:0.21 260:0.95 261:0.98 265:0.08 267:0.89 268:0.92 271:0.75 277:0.69 282:0.75 285:0.56 287:0.99 290:0.82 297:0.36 300:0.08 +2 1:0.74 11:0.91 12:0.37 17:0.49 18:0.89 21:0.47 22:0.93 26:0.99 27:0.56 29:0.86 30:0.33 32:0.80 34:0.55 36:0.19 37:0.60 39:0.75 43:0.65 44:0.93 45:0.95 46:0.99 47:0.93 58:0.93 64:0.77 66:0.67 69:0.57 70:0.86 71:0.62 74:0.90 77:0.70 81:0.54 83:0.91 86:0.93 91:0.11 97:0.89 98:0.84 101:0.52 104:0.91 106:0.81 107:0.99 108:0.44 110:0.99 114:0.60 117:0.86 122:0.75 123:0.42 124:0.47 125:0.88 126:0.54 127:0.58 129:0.59 133:0.46 135:0.89 139:0.93 141:0.91 144:0.85 145:0.69 146:0.92 147:0.94 149:0.85 150:0.74 154:0.77 155:0.67 158:0.79 159:0.64 160:0.88 165:0.93 172:0.90 173:0.48 176:0.73 177:0.70 178:0.55 179:0.24 187:0.21 192:0.87 195:0.79 199:0.98 201:0.93 206:0.81 208:0.64 212:0.82 215:0.41 216:0.76 222:0.76 223:0.99 224:0.93 232:0.94 234:0.91 235:0.68 241:0.10 242:0.36 243:0.71 245:0.95 247:0.88 253:0.49 254:0.74 259:0.21 262:0.93 265:0.84 266:0.38 267:0.28 271:0.16 274:0.91 276:0.87 279:0.82 282:0.88 283:0.82 289:0.98 290:0.60 297:0.36 299:0.88 300:0.70 +1 1:0.69 11:0.91 12:0.37 17:0.15 18:0.94 21:0.40 22:0.93 26:0.99 27:0.56 29:0.86 30:0.19 34:0.88 36:0.25 37:0.60 39:0.75 43:0.56 44:0.90 45:0.85 46:0.96 47:0.93 55:0.52 58:0.93 64:0.77 66:0.54 69:0.83 70:0.81 71:0.62 74:0.64 77:0.89 81:0.39 86:0.96 91:0.10 98:0.78 101:0.52 107:0.98 108:0.68 110:0.98 114:0.58 117:0.86 122:0.77 123:0.66 124:0.65 125:0.88 126:0.44 127:0.54 129:0.05 133:0.60 135:0.42 139:0.95 141:0.91 144:0.85 145:0.65 146:0.92 147:0.55 149:0.39 150:0.51 154:0.61 155:0.58 158:0.72 159:0.68 160:0.88 172:0.87 173:0.77 176:0.55 177:0.38 178:0.55 179:0.23 187:0.95 192:0.51 195:0.84 199:0.99 201:0.68 208:0.54 212:0.71 216:0.76 222:0.76 223:0.99 224:0.93 232:0.98 234:0.91 235:0.52 241:0.46 242:0.21 243:0.28 245:0.93 247:0.88 253:0.44 254:0.84 257:0.65 259:0.21 262:0.93 265:0.78 266:0.80 267:0.36 271:0.16 274:0.91 276:0.87 282:0.71 289:0.96 290:0.58 300:0.84 +2 1:0.74 7:0.72 11:0.91 12:0.37 17:0.53 18:0.68 21:0.40 26:0.99 27:0.49 29:0.86 30:0.41 32:0.80 34:0.67 36:0.25 37:0.65 39:0.75 43:0.60 44:0.93 45:0.81 46:0.96 48:0.91 58:0.93 64:0.77 66:0.86 69:0.72 70:0.36 71:0.62 74:0.68 77:0.70 81:0.57 83:0.60 86:0.74 91:0.40 98:0.66 99:0.83 101:0.52 107:0.97 108:0.55 110:0.98 114:0.62 122:0.75 123:0.53 125:0.88 126:0.54 127:0.19 129:0.05 135:0.95 139:0.82 141:0.91 144:0.85 145:0.52 146:0.75 147:0.79 149:0.53 150:0.72 154:0.67 155:0.67 159:0.31 160:0.88 165:0.70 172:0.59 173:0.71 176:0.73 177:0.70 178:0.55 179:0.44 187:0.39 192:0.87 195:0.74 199:0.95 201:0.93 204:0.85 208:0.64 212:0.42 215:0.42 216:0.61 222:0.76 223:0.99 224:0.93 230:0.74 233:0.73 234:0.91 235:0.60 241:0.62 242:0.33 243:0.86 247:0.67 253:0.48 254:0.92 259:0.21 265:0.67 266:0.38 267:0.44 271:0.16 274:0.91 276:0.48 281:0.91 282:0.88 289:0.98 290:0.62 297:0.36 300:0.25 +4 1:0.74 8:0.74 9:0.80 11:0.91 12:0.37 17:0.90 18:0.86 21:0.30 26:0.99 27:0.72 29:0.86 30:0.21 31:0.87 32:0.78 34:0.67 36:0.63 37:0.92 39:0.75 43:0.67 44:0.93 45:0.83 46:1.00 48:0.91 58:0.98 64:0.77 66:0.90 69:0.15 70:0.62 71:0.62 74:0.78 76:1.00 77:0.96 79:0.87 81:0.63 83:0.60 86:0.94 91:0.86 96:0.82 98:0.95 99:0.83 101:0.52 104:0.58 107:0.98 108:0.12 110:0.98 114:0.65 120:0.71 122:0.57 123:0.12 125:0.99 126:0.54 127:0.64 129:0.05 135:0.39 137:0.87 139:0.94 140:0.45 141:0.99 144:0.85 145:0.86 146:0.61 147:0.67 149:0.33 150:0.76 151:0.53 153:0.48 154:0.87 155:0.67 159:0.23 160:0.97 162:0.78 164:0.68 165:0.70 172:0.71 173:0.47 176:0.73 177:0.70 179:0.61 190:0.77 192:0.87 195:0.56 196:0.91 199:0.96 201:0.93 202:0.59 204:0.85 208:0.64 212:0.89 215:0.44 216:0.03 220:0.87 222:0.76 223:0.99 224:0.98 232:0.77 234:0.98 235:0.52 241:0.88 242:0.27 243:0.90 247:0.76 248:0.52 253:0.81 254:0.88 255:0.95 256:0.58 259:0.21 260:0.68 265:0.95 266:0.27 267:0.36 268:0.64 271:0.16 274:0.97 276:0.60 281:0.89 282:0.86 284:0.91 285:0.62 289:0.98 290:0.65 297:0.36 300:0.43 +1 1:0.74 7:0.72 11:0.91 12:0.37 17:0.55 18:0.63 21:0.30 26:0.99 27:0.31 29:0.86 30:0.33 32:0.79 34:0.97 36:0.32 37:0.55 39:0.75 43:0.62 44:0.93 45:0.73 46:0.95 58:0.93 64:0.77 66:0.20 69:0.75 70:0.49 71:0.62 74:0.68 76:0.94 77:0.70 81:0.65 83:0.91 86:0.73 91:0.10 97:0.89 98:0.35 101:0.52 104:0.95 106:0.81 107:0.95 108:0.58 110:0.96 114:0.65 117:0.86 122:0.51 123:0.66 124:0.56 125:0.88 126:0.54 127:0.18 129:0.05 133:0.37 135:0.86 139:0.80 141:0.91 144:0.85 145:0.67 146:0.25 147:0.31 149:0.32 150:0.79 154:0.68 155:0.67 158:0.79 159:0.24 160:0.88 165:0.93 172:0.27 173:0.80 176:0.73 177:0.70 178:0.55 179:0.60 187:0.39 192:0.87 195:0.69 199:0.95 201:0.93 204:0.85 206:0.81 208:0.64 212:0.42 215:0.44 216:0.85 222:0.76 223:0.99 224:0.93 230:0.75 232:0.96 233:0.76 234:0.91 235:0.52 241:0.30 242:0.19 243:0.58 245:0.53 247:0.55 253:0.50 254:0.92 259:0.21 265:0.21 266:0.38 267:0.28 271:0.16 274:0.91 276:0.30 277:0.87 279:0.82 282:0.87 283:0.82 289:0.98 290:0.65 297:0.36 300:0.33 +2 1:0.74 7:0.72 9:0.80 11:0.93 12:0.37 17:0.72 18:0.98 21:0.54 26:0.99 27:0.53 29:0.86 30:0.87 31:0.86 34:0.88 36:0.31 37:0.85 39:0.75 43:0.66 44:0.90 45:0.94 46:0.94 48:0.99 55:0.59 58:0.98 64:0.77 66:0.69 69:0.57 70:0.37 71:0.62 74:0.64 76:0.99 77:1.00 79:0.86 81:0.52 83:0.60 85:0.80 86:0.99 91:0.15 96:0.68 98:0.76 99:0.83 101:0.97 107:0.97 108:0.79 110:0.97 114:0.59 120:0.55 122:0.85 123:0.78 124:0.49 125:0.97 126:0.54 127:0.59 129:0.59 133:0.61 135:0.88 137:0.86 139:0.98 140:0.45 141:0.99 144:0.85 145:0.97 146:0.51 147:0.57 149:0.85 150:0.70 151:0.50 153:0.48 154:0.70 155:0.67 159:0.71 160:0.96 162:0.86 164:0.57 165:0.70 172:0.95 173:0.43 176:0.73 177:0.70 179:0.16 187:0.68 192:0.87 195:0.86 196:0.90 199:1.00 201:0.93 202:0.36 204:0.89 208:0.64 212:0.93 215:0.39 216:0.41 220:0.91 222:0.76 223:0.99 224:0.98 230:0.75 232:0.79 233:0.76 234:0.98 235:0.88 241:0.18 242:0.30 243:0.16 245:0.96 247:0.84 248:0.49 253:0.45 255:0.95 256:0.45 259:0.21 260:0.55 265:0.76 266:0.38 267:0.87 268:0.46 271:0.16 274:0.97 276:0.93 281:0.88 282:0.71 284:0.89 285:0.62 289:0.98 290:0.59 297:0.36 300:0.70 +1 1:0.74 11:0.91 12:0.37 17:0.49 18:0.90 21:0.47 22:0.93 26:0.99 27:0.56 29:0.86 30:0.17 32:0.80 34:0.61 36:0.18 37:0.60 39:0.75 43:0.65 44:0.93 45:0.90 46:0.98 47:0.93 55:0.45 58:0.93 64:0.77 66:0.48 69:0.67 70:0.90 71:0.62 74:0.83 77:0.70 81:0.54 83:0.91 86:0.93 91:0.07 97:0.89 98:0.75 101:0.52 104:0.91 106:0.81 107:0.99 108:0.51 110:0.99 114:0.60 117:0.86 122:0.86 123:0.49 124:0.58 125:0.88 126:0.54 127:0.42 129:0.59 133:0.46 135:0.86 139:0.93 141:0.91 144:0.85 145:0.69 146:0.84 147:0.87 149:0.66 150:0.74 154:0.70 155:0.67 158:0.79 159:0.53 160:0.88 165:0.93 172:0.79 173:0.64 176:0.73 177:0.70 178:0.55 179:0.27 187:0.39 192:0.87 195:0.74 199:0.98 201:0.93 206:0.81 208:0.64 212:0.80 215:0.41 216:0.76 222:0.76 223:0.99 224:0.93 232:0.94 234:0.91 235:0.68 241:0.01 242:0.21 243:0.59 245:0.94 247:0.88 253:0.48 254:0.74 259:0.21 262:0.93 265:0.76 266:0.38 267:0.28 271:0.16 274:0.91 276:0.81 279:0.82 282:0.88 283:0.82 289:0.98 290:0.60 297:0.36 299:0.88 300:0.70 +1 1:0.74 11:0.91 12:0.37 17:0.55 18:0.92 21:0.47 22:0.93 26:0.99 27:0.56 29:0.86 30:0.43 32:0.80 34:0.93 36:0.17 37:0.60 39:0.75 43:0.67 44:0.93 45:0.83 46:0.97 47:0.93 55:0.45 58:0.93 64:0.77 66:0.95 69:0.64 70:0.81 71:0.62 74:0.76 77:0.70 81:0.54 83:0.91 86:0.94 91:0.08 97:0.89 98:0.86 101:0.52 104:0.91 106:0.81 107:0.98 108:0.49 110:0.98 114:0.60 117:0.86 122:0.77 123:0.47 125:0.88 126:0.54 127:0.38 129:0.05 135:0.60 139:0.94 141:0.91 144:0.85 145:0.69 146:0.88 147:0.90 149:0.55 150:0.74 154:0.71 155:0.67 158:0.79 159:0.46 160:0.88 165:0.93 172:0.88 173:0.64 176:0.73 177:0.70 178:0.55 179:0.28 187:0.39 192:0.87 195:0.70 199:0.98 201:0.93 206:0.81 208:0.64 212:0.77 215:0.41 216:0.76 222:0.76 223:0.99 224:0.93 232:0.94 234:0.91 235:0.68 242:0.27 243:0.96 247:0.90 253:0.47 254:0.74 259:0.21 262:0.93 265:0.86 266:0.38 267:0.28 271:0.16 274:0.91 276:0.79 279:0.82 282:0.88 283:0.82 289:0.98 290:0.60 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.72 11:0.91 12:0.37 17:0.61 18:0.67 21:0.22 22:0.93 26:0.99 27:0.67 29:0.86 30:0.53 32:0.80 34:0.75 36:0.25 37:0.44 39:0.75 43:0.61 44:0.93 45:0.90 46:0.98 47:0.93 48:0.91 58:0.93 64:0.77 66:0.93 69:0.73 70:0.59 71:0.62 74:0.77 77:0.96 81:0.72 83:0.91 86:0.77 91:0.27 98:0.81 101:0.52 104:0.99 106:0.81 107:0.99 108:0.56 110:0.99 114:0.71 117:0.86 122:0.75 123:0.54 125:0.88 126:0.54 127:0.30 129:0.05 135:0.96 139:0.83 141:0.91 144:0.85 145:0.76 146:0.87 147:0.89 149:0.73 150:0.83 154:0.64 155:0.67 159:0.44 160:0.88 165:0.93 172:0.80 173:0.72 176:0.73 177:0.70 178:0.55 179:0.34 187:0.95 192:0.87 195:0.74 199:0.96 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.69 215:0.51 216:0.56 222:0.76 223:0.99 224:0.93 230:0.75 232:0.99 233:0.76 234:0.91 235:0.42 241:0.18 242:0.45 243:0.93 247:0.67 253:0.53 254:0.90 259:0.21 262:0.93 265:0.81 266:0.47 267:0.36 271:0.16 274:0.91 276:0.69 281:0.91 282:0.88 289:0.98 290:0.71 297:0.36 300:0.43 +2 6:0.93 7:0.66 8:0.75 12:0.20 17:0.72 18:0.99 20:0.81 27:0.34 28:0.56 29:0.71 30:0.17 31:0.96 32:0.69 34:0.49 36:0.51 37:0.71 39:0.30 43:0.72 44:0.90 48:0.91 55:0.52 64:0.77 66:0.97 69:0.07 70:0.81 71:0.75 74:0.50 78:0.96 79:0.97 81:0.74 86:0.99 91:0.44 98:0.96 100:0.91 106:0.81 108:0.06 111:0.94 120:0.85 123:0.06 126:0.44 127:0.72 129:0.05 135:0.32 137:0.96 139:0.99 140:0.45 145:0.40 146:0.90 147:0.92 149:0.82 150:0.52 151:0.85 152:0.98 153:0.77 154:0.64 159:0.49 161:0.76 162:0.86 164:0.70 167:0.56 169:0.97 172:0.93 173:0.16 177:0.70 179:0.26 181:0.76 186:0.95 187:0.39 189:0.92 190:0.77 191:0.70 192:0.51 195:0.66 196:0.98 201:0.68 202:0.60 206:0.81 212:0.90 215:0.96 216:0.55 219:0.92 222:0.35 230:0.69 233:0.76 235:0.05 238:0.88 241:0.07 242:0.30 243:0.97 247:0.84 248:0.85 253:0.35 254:0.74 255:0.74 256:0.68 259:0.21 260:0.81 261:0.97 265:0.96 266:0.27 267:0.36 268:0.69 276:0.86 281:0.91 283:0.82 284:0.92 285:0.56 292:0.95 297:0.36 300:0.70 +2 1:0.74 7:0.81 9:0.76 11:0.83 12:0.20 17:0.91 18:0.99 22:0.93 27:0.19 28:0.56 29:0.71 30:0.33 31:0.95 32:0.80 34:0.87 36:0.34 37:0.93 39:0.30 43:0.79 44:0.93 45:0.81 47:0.93 48:0.98 55:0.45 64:0.77 66:0.83 69:0.73 70:0.64 71:0.75 74:0.91 76:0.85 77:0.96 79:0.96 81:0.89 83:0.91 85:0.80 86:0.99 91:0.56 96:0.87 97:0.94 98:0.90 101:0.97 106:0.81 108:0.56 114:0.98 117:0.86 121:0.90 122:0.86 123:0.54 124:0.39 126:0.54 127:0.74 129:0.05 133:0.43 135:0.88 137:0.95 139:0.99 140:0.45 145:0.76 146:0.93 147:0.39 149:0.84 150:0.94 151:0.81 153:0.48 154:0.72 155:0.67 158:0.79 159:0.63 161:0.76 162:0.86 165:0.93 167:0.53 172:0.95 173:0.69 176:0.73 177:0.38 179:0.19 181:0.76 187:0.21 190:0.77 192:0.87 195:0.79 196:0.98 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.93 215:0.96 216:0.61 219:0.92 222:0.35 230:0.87 232:0.84 233:0.76 235:0.12 241:0.01 242:0.70 243:0.15 245:0.94 247:0.91 248:0.82 253:0.89 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.90 266:0.27 267:0.85 268:0.59 276:0.91 279:0.82 281:0.91 282:0.88 284:0.91 285:0.56 290:0.98 292:0.95 297:0.36 299:0.88 300:0.55 +2 6:0.82 7:0.72 8:0.70 12:0.20 17:0.90 18:0.99 20:0.92 22:0.93 27:0.19 28:0.56 29:0.71 30:0.68 31:0.98 32:0.69 34:0.78 36:0.29 37:0.93 39:0.30 43:0.72 44:0.90 47:0.93 48:0.91 55:0.45 64:0.77 66:0.77 69:0.75 70:0.52 71:0.75 74:0.39 78:0.96 79:0.99 81:0.74 86:0.99 91:0.68 98:0.82 100:0.93 106:0.81 108:0.58 111:0.94 120:0.91 122:0.86 123:0.56 124:0.41 126:0.44 127:0.74 129:0.05 133:0.43 135:0.95 137:0.98 139:0.99 140:0.45 145:0.40 146:0.76 147:0.39 149:0.72 150:0.52 151:0.91 152:0.88 153:0.84 154:0.61 159:0.43 161:0.76 162:0.86 164:0.79 167:0.61 169:0.90 172:0.91 173:0.84 177:0.38 179:0.28 181:0.76 186:0.96 187:0.21 189:0.84 190:0.77 192:0.51 195:0.62 196:0.99 201:0.68 202:0.70 206:0.81 212:0.94 215:0.96 216:0.61 219:0.92 222:0.35 230:0.75 233:0.76 235:0.05 238:0.86 241:0.30 242:0.75 243:0.20 245:0.91 247:0.76 248:0.93 253:0.31 254:0.88 255:0.74 256:0.80 257:0.65 259:0.21 260:0.89 261:0.92 262:0.93 265:0.82 266:0.27 267:0.59 268:0.79 276:0.85 281:0.91 283:0.82 284:0.96 285:0.56 292:0.95 297:0.36 300:0.55 +1 6:0.80 7:0.72 8:0.74 12:0.20 17:0.75 18:0.99 20:0.85 22:0.93 27:0.29 28:0.56 29:0.71 30:0.66 31:0.94 32:0.69 34:0.49 36:0.22 37:0.70 39:0.30 43:0.72 44:0.90 47:0.93 48:0.98 55:0.52 64:0.77 66:0.72 69:0.05 70:0.62 71:0.75 78:0.92 79:0.95 81:0.74 86:0.98 91:0.33 98:0.80 100:0.83 106:0.81 108:0.05 111:0.90 120:0.77 122:0.86 123:0.05 124:0.43 126:0.44 127:0.91 129:0.59 133:0.47 135:0.89 137:0.94 139:0.98 140:0.80 145:0.70 146:0.77 147:0.81 149:0.81 150:0.52 151:0.80 152:0.81 153:0.48 154:0.62 159:0.49 161:0.76 162:0.78 164:0.65 167:0.67 169:0.80 172:0.87 173:0.16 175:0.75 177:0.38 179:0.35 181:0.76 186:0.92 187:0.68 189:0.82 190:0.77 191:0.70 192:0.51 195:0.65 196:0.98 201:0.68 202:0.59 206:0.81 212:0.88 215:0.96 216:0.42 219:0.92 220:0.74 222:0.35 230:0.75 233:0.76 235:0.05 238:0.82 241:0.49 242:0.73 243:0.75 244:0.61 245:0.90 247:0.67 248:0.80 253:0.20 255:0.74 256:0.64 257:0.65 259:0.21 260:0.74 261:0.86 262:0.93 265:0.80 266:0.47 267:0.36 268:0.64 276:0.81 277:0.69 281:0.91 283:0.82 284:0.90 285:0.56 292:0.95 297:0.36 300:0.70 +0 1:0.69 7:0.72 9:0.76 11:0.64 12:0.20 17:0.45 21:0.47 27:0.18 28:0.56 29:0.71 30:0.11 32:0.79 34:0.76 36:0.34 37:0.85 39:0.30 43:0.62 44:0.93 45:0.66 48:0.91 55:0.59 66:0.54 69:0.69 70:0.95 71:0.75 74:0.66 76:0.85 77:0.89 81:0.37 83:0.60 91:0.35 96:0.88 97:0.98 98:0.45 99:0.83 101:0.52 106:0.81 108:0.84 114:0.59 117:0.86 120:0.81 122:0.51 123:0.83 124:0.50 126:0.44 127:0.46 129:0.59 133:0.47 135:0.87 138:0.95 139:0.77 140:0.45 145:0.74 146:0.35 147:0.42 149:0.32 150:0.53 151:0.91 153:0.84 154:0.51 155:0.58 158:0.79 159:0.61 161:0.76 162:0.86 163:0.81 164:0.73 165:0.70 167:0.68 172:0.41 173:0.62 175:0.75 176:0.66 177:0.38 179:0.79 181:0.76 187:0.87 190:0.77 192:0.59 195:0.82 201:0.68 202:0.67 204:0.85 206:0.81 208:0.54 212:0.28 215:0.41 216:0.82 222:0.35 230:0.75 232:0.98 233:0.76 235:0.60 241:0.52 242:0.29 243:0.34 244:0.61 245:0.59 247:0.60 248:0.92 253:0.82 255:0.74 256:0.73 257:0.65 259:0.21 260:0.74 265:0.46 266:0.54 267:0.99 268:0.75 276:0.31 277:0.69 279:0.82 281:0.91 282:0.87 283:0.82 285:0.56 286:0.99 290:0.59 297:0.36 298:0.99 300:0.70 +2 7:0.72 8:0.83 11:0.64 12:0.20 17:0.98 18:1.00 20:0.83 22:0.93 27:0.29 28:0.56 29:0.71 30:0.73 31:0.97 32:0.69 34:0.23 36:0.41 37:0.88 39:0.30 43:0.72 44:0.90 45:0.77 47:0.93 48:0.91 55:0.52 64:0.77 66:0.99 69:0.05 70:0.33 71:0.75 74:0.52 78:0.91 79:0.97 81:0.74 86:1.00 91:0.49 98:0.96 100:0.73 101:0.52 106:0.81 108:0.05 111:0.88 120:0.86 122:0.86 123:0.05 126:0.44 127:0.69 129:0.05 135:0.42 137:0.97 139:1.00 140:0.45 145:0.38 146:0.90 147:0.92 149:0.84 150:0.52 151:0.84 152:0.79 153:0.72 154:0.59 159:0.49 161:0.76 162:0.86 164:0.71 167:0.57 169:0.72 172:0.97 173:0.14 177:0.70 179:0.17 181:0.76 186:0.91 187:0.39 190:0.77 191:0.82 192:0.51 195:0.67 196:0.99 201:0.68 202:0.61 206:0.81 212:0.94 215:0.96 216:0.61 219:0.92 222:0.35 230:0.75 233:0.76 235:0.05 241:0.12 242:0.75 243:0.99 247:0.79 248:0.89 253:0.36 254:0.99 255:0.74 256:0.68 259:0.21 260:0.83 261:0.84 262:0.93 265:0.96 266:0.27 267:0.28 268:0.70 276:0.93 281:0.91 283:0.82 284:0.94 285:0.56 292:0.95 297:0.36 300:0.55 +2 6:0.92 8:0.91 9:0.76 11:0.64 12:0.20 17:0.20 20:0.79 21:0.13 27:0.02 28:0.56 29:0.71 30:0.85 34:0.30 36:0.32 37:0.92 39:0.30 43:0.25 45:0.77 55:0.71 66:0.63 69:0.93 70:0.39 71:0.75 74:0.65 76:0.85 77:0.70 78:0.92 81:0.51 83:0.60 91:0.76 96:0.96 98:0.55 100:0.91 101:0.52 108:0.86 111:0.90 114:0.72 120:0.79 122:0.77 123:0.85 124:0.51 126:0.26 127:0.51 129:0.05 133:0.62 135:0.76 140:0.97 145:0.38 146:0.80 147:0.05 149:0.41 150:0.39 151:0.81 152:0.93 153:0.87 154:0.18 155:0.58 158:0.74 159:0.70 161:0.76 162:0.60 164:0.72 167:0.82 169:0.91 172:0.70 173:0.93 175:0.75 176:0.61 177:0.05 179:0.50 181:0.76 186:0.92 187:0.39 190:0.77 191:0.82 192:0.59 195:0.86 202:0.86 208:0.54 212:0.72 216:0.99 222:0.35 235:0.12 241:0.74 242:0.61 243:0.09 244:0.61 245:0.72 247:0.60 248:0.82 253:0.92 255:0.74 256:0.87 257:0.84 259:0.21 260:0.76 261:0.93 265:0.57 266:0.80 267:0.52 268:0.87 276:0.62 277:0.69 282:0.73 285:0.56 290:0.72 300:0.43 +2 1:0.74 6:0.92 7:0.66 8:0.74 9:0.76 11:0.92 12:0.20 17:0.61 18:0.99 20:0.83 21:0.13 22:0.93 27:0.26 28:0.56 29:0.71 30:0.60 31:0.93 32:0.80 37:0.86 39:0.30 43:0.97 44:0.93 45:0.81 47:0.93 48:0.97 55:0.45 64:0.77 66:0.36 69:0.10 70:0.64 71:0.75 74:0.90 76:0.85 77:0.70 78:0.99 79:0.93 81:0.77 83:0.91 85:0.80 86:0.99 91:0.38 96:0.80 97:0.89 98:0.79 100:0.98 101:0.97 106:0.81 108:0.78 111:0.98 114:0.79 117:0.86 122:0.86 123:0.76 124:0.61 126:0.54 127:0.86 129:0.59 133:0.46 137:0.93 139:0.99 140:0.45 145:0.69 146:0.77 147:0.81 149:0.84 150:0.85 151:0.81 152:0.87 153:0.48 154:0.97 155:0.67 158:0.79 159:0.70 161:0.76 162:0.86 165:0.93 167:0.59 169:0.94 172:0.88 173:0.13 176:0.73 177:0.70 179:0.20 181:0.76 186:0.98 187:0.87 189:0.92 190:0.77 192:0.87 195:0.83 196:0.97 201:0.93 202:0.36 206:0.81 208:0.64 212:0.91 215:0.61 216:0.57 219:0.92 222:0.35 230:0.69 232:0.95 233:0.76 235:0.31 238:0.94 241:0.18 242:0.61 243:0.51 245:0.98 247:0.91 248:0.73 253:0.84 255:0.74 256:0.45 261:0.93 262:0.93 265:0.79 266:0.54 268:0.46 276:0.91 279:0.82 281:0.91 282:0.88 284:0.87 285:0.56 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +2 7:0.72 11:0.64 12:0.20 17:0.75 18:1.00 22:0.93 27:0.31 28:0.56 29:0.71 30:0.76 31:0.93 32:0.77 34:0.66 36:0.23 37:0.81 39:0.30 43:0.72 44:0.93 45:0.66 47:0.93 48:0.97 55:0.52 64:0.77 66:0.92 69:0.67 70:0.41 71:0.75 74:0.55 77:0.70 79:0.94 81:0.74 86:1.00 91:0.69 98:0.90 101:0.52 106:0.81 108:0.51 120:0.72 122:0.86 123:0.49 124:0.36 126:0.44 127:0.69 129:0.05 133:0.43 135:0.89 137:0.93 139:1.00 140:0.45 145:0.61 146:0.91 147:0.25 149:0.83 150:0.52 151:0.83 153:0.69 154:0.54 159:0.57 161:0.76 162:0.86 164:0.55 167:0.56 172:0.97 173:0.66 177:0.38 179:0.16 181:0.76 187:0.68 190:0.77 192:0.51 195:0.74 196:0.97 201:0.68 202:0.46 206:0.81 212:0.94 215:0.96 216:0.59 219:0.92 222:0.35 230:0.75 233:0.76 235:0.05 241:0.07 242:0.73 243:0.08 245:0.90 247:0.86 248:0.75 253:0.37 254:0.80 255:0.74 256:0.45 257:0.65 259:0.21 260:0.69 262:0.93 265:0.90 266:0.27 267:0.36 268:0.55 276:0.94 281:0.91 282:0.85 283:0.82 284:0.87 285:0.56 292:0.95 297:0.36 300:0.55 +2 1:0.74 11:0.64 12:0.20 17:0.96 18:0.96 21:0.64 22:0.93 27:0.70 28:0.56 29:0.71 30:0.55 34:0.80 36:0.27 37:0.49 39:0.30 43:0.41 45:0.77 47:0.93 55:0.52 60:0.97 64:0.77 66:0.97 69:0.36 70:0.62 71:0.75 74:0.86 81:0.39 83:0.91 86:0.97 91:0.53 97:0.94 98:0.87 101:0.52 106:0.81 108:0.28 114:0.55 117:0.86 122:0.77 123:0.27 126:0.54 127:0.39 129:0.05 135:0.88 139:0.97 146:0.93 147:0.95 149:0.43 150:0.62 154:0.62 155:0.67 158:0.92 159:0.58 161:0.76 167:0.52 172:0.92 173:0.27 176:0.66 177:0.70 178:0.55 179:0.23 181:0.76 187:0.39 192:0.87 201:0.93 206:0.81 208:0.64 212:0.89 215:0.37 216:0.13 219:0.95 222:0.35 232:0.93 235:0.84 241:0.01 242:0.41 243:0.97 247:0.93 253:0.46 254:0.84 259:0.21 262:0.93 265:0.87 266:0.27 267:0.28 276:0.85 279:0.86 283:0.82 290:0.55 292:0.95 297:0.36 300:0.70 +2 6:0.93 7:0.66 8:0.86 11:0.64 12:0.20 17:0.53 18:0.99 20:0.98 21:0.59 27:0.34 28:0.56 29:0.71 30:0.38 31:0.90 32:0.80 34:0.85 36:0.36 37:0.71 39:0.30 43:0.69 44:0.93 45:0.73 48:0.91 55:0.59 64:0.77 66:0.67 69:0.25 70:0.74 71:0.75 74:0.96 76:0.85 77:0.70 78:0.98 79:0.91 81:0.36 86:1.00 91:0.40 96:0.67 98:0.86 100:0.99 101:0.52 106:0.81 108:0.58 111:0.98 114:0.58 120:0.53 122:0.67 123:0.55 124:0.52 126:0.54 127:0.89 129:0.81 133:0.67 135:0.47 137:0.90 139:1.00 140:0.45 145:0.61 146:0.32 147:0.38 149:0.68 150:0.46 151:0.47 152:0.97 153:0.48 154:0.84 158:0.85 159:0.80 161:0.76 162:0.86 164:0.57 167:0.59 169:0.97 172:0.97 173:0.15 176:0.73 177:0.70 179:0.14 181:0.76 186:0.97 187:0.68 189:0.95 190:0.89 191:0.73 192:0.51 195:0.91 196:0.95 201:0.68 202:0.50 206:0.81 208:0.64 212:0.92 215:0.39 216:0.55 219:0.95 220:0.97 222:0.35 230:0.69 233:0.76 235:0.80 238:0.96 241:0.18 242:0.18 243:0.17 245:0.98 247:0.93 248:0.46 253:0.49 254:0.84 255:0.74 256:0.58 259:0.21 260:0.53 261:0.97 265:0.86 266:0.54 267:0.19 268:0.59 276:0.96 281:0.91 282:0.88 283:0.66 284:0.91 285:0.62 290:0.58 292:0.87 297:0.36 300:0.84 +2 6:0.92 7:0.66 8:0.81 11:0.64 12:0.20 17:0.86 18:0.99 20:0.89 21:0.13 22:0.93 27:0.26 28:0.56 29:0.71 30:0.62 31:0.96 32:0.69 34:0.29 36:0.35 37:0.86 39:0.30 43:0.72 44:0.90 45:0.66 47:0.93 48:0.97 55:0.45 64:0.77 66:0.97 69:0.08 70:0.45 71:0.75 74:0.47 78:0.94 79:0.97 81:0.56 86:0.99 91:0.62 98:0.94 100:0.94 101:0.52 106:0.81 108:0.07 111:0.93 120:0.83 122:0.86 123:0.07 126:0.44 127:0.62 129:0.05 135:0.65 137:0.96 139:0.99 140:0.45 145:0.45 146:0.78 147:0.82 149:0.88 150:0.40 151:0.85 152:0.87 153:0.77 154:0.68 159:0.34 161:0.76 162:0.86 164:0.71 167:0.59 169:0.94 172:0.92 173:0.26 177:0.70 179:0.27 181:0.76 186:0.93 187:0.68 189:0.94 190:0.77 192:0.51 195:0.61 196:0.99 201:0.68 202:0.60 206:0.81 212:0.92 215:0.61 216:0.57 219:0.92 222:0.35 230:0.69 233:0.76 235:0.22 238:0.89 241:0.18 242:0.50 243:0.97 247:0.79 248:0.86 253:0.34 254:0.84 255:0.74 256:0.68 260:0.80 261:0.93 262:0.93 265:0.94 266:0.27 267:0.59 268:0.68 276:0.84 281:0.91 283:0.82 284:0.94 285:0.56 292:0.95 297:0.36 300:0.55 +3 6:0.81 8:0.64 9:0.76 12:0.20 17:0.45 18:0.85 20:0.95 21:0.30 25:0.88 27:0.59 28:0.56 29:0.71 30:0.62 34:0.17 36:0.30 39:0.30 43:0.18 55:0.71 66:0.87 69:0.23 70:0.47 71:0.75 74:0.44 78:0.93 81:0.27 83:0.60 86:0.81 91:0.65 96:0.95 98:0.98 100:0.93 108:0.18 111:0.92 120:0.94 123:0.18 126:0.26 127:0.85 129:0.05 135:0.65 139:0.78 140:0.99 146:0.89 147:0.91 149:0.25 150:0.26 151:0.81 152:0.93 153:0.86 154:0.56 155:0.58 159:0.48 161:0.76 162:0.50 164:0.92 167:0.91 169:0.90 172:0.63 173:0.31 177:0.70 178:0.50 179:0.74 181:0.76 186:0.93 189:0.83 190:0.77 191:0.77 192:0.59 202:0.97 208:0.54 212:0.27 215:0.44 216:0.12 220:0.74 222:0.35 235:0.31 238:0.88 241:0.82 242:0.16 243:0.88 244:0.61 247:0.79 248:0.74 253:0.89 254:0.80 255:0.74 256:0.95 259:0.21 260:0.91 261:0.92 265:0.98 266:0.63 267:0.97 268:0.95 276:0.53 285:0.56 297:0.36 300:0.43 +2 1:0.74 12:0.20 17:0.26 18:0.88 21:0.54 27:0.45 28:0.56 29:0.71 30:0.45 34:0.89 36:0.21 37:0.50 39:0.30 43:0.14 48:0.91 55:0.71 64:0.77 66:0.46 69:0.62 70:0.51 71:0.75 74:0.69 81:0.44 86:0.87 91:0.25 98:0.62 108:0.47 114:0.59 122:0.77 123:0.45 124:0.58 126:0.54 127:0.39 129:0.05 133:0.43 135:0.74 139:0.89 145:0.38 146:0.73 147:0.77 149:0.26 150:0.63 154:0.74 155:0.67 158:0.87 159:0.51 161:0.76 167:0.55 172:0.57 173:0.57 176:0.73 177:0.70 178:0.55 179:0.51 181:0.76 187:0.68 192:0.87 201:0.93 208:0.64 212:0.48 215:0.39 216:0.77 219:0.95 222:0.35 235:0.74 241:0.05 242:0.42 243:0.59 245:0.80 247:0.74 253:0.45 254:0.80 259:0.21 265:0.62 266:0.71 267:0.79 276:0.61 279:0.86 281:0.91 283:0.71 290:0.59 292:0.91 297:0.36 300:0.43 +2 1:0.74 7:0.72 8:0.84 11:0.92 12:0.20 17:0.91 18:1.00 22:0.93 27:0.31 28:0.56 29:0.71 30:0.57 32:0.80 34:0.66 36:0.37 37:0.81 39:0.30 43:0.97 44:0.93 45:0.89 47:0.93 48:0.91 55:0.52 64:0.77 66:0.92 69:0.07 70:0.64 71:0.75 74:0.92 77:0.89 81:0.89 83:0.60 85:0.85 86:1.00 91:0.49 97:0.89 98:0.94 99:0.83 101:0.97 106:0.81 108:0.51 114:0.98 117:0.86 122:0.86 123:0.49 124:0.37 126:0.54 127:0.85 129:0.59 133:0.46 135:0.89 139:1.00 145:0.79 146:0.20 147:0.26 149:0.93 150:0.93 154:0.97 155:0.67 158:0.79 159:0.70 161:0.76 165:0.70 167:0.56 172:0.98 173:0.11 176:0.73 177:0.70 178:0.55 179:0.14 181:0.76 187:0.87 192:0.87 195:0.81 201:0.93 204:0.85 206:0.81 208:0.64 212:0.92 215:0.96 216:0.59 219:0.92 222:0.35 230:0.75 232:0.96 233:0.76 235:0.12 241:0.07 242:0.70 243:0.08 245:0.93 247:0.98 253:0.68 259:0.21 262:0.93 265:0.94 266:0.27 267:0.36 276:0.96 279:0.82 281:0.91 282:0.88 290:0.98 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 9:0.80 11:0.57 12:0.20 17:0.34 18:0.89 21:0.22 27:0.45 28:0.56 29:0.71 30:0.72 31:0.88 32:0.79 34:0.89 36:0.20 37:0.50 39:0.30 41:0.82 43:0.82 44:0.93 48:0.91 55:0.71 60:0.87 64:0.77 66:0.35 69:0.61 70:0.54 71:0.75 74:0.85 77:0.70 79:0.88 81:0.69 83:0.91 85:0.72 86:0.90 91:0.17 96:0.71 98:0.62 101:0.87 108:0.79 114:0.71 120:0.57 122:0.77 123:0.77 124:0.72 126:0.54 127:0.59 129:0.05 133:0.66 135:0.81 137:0.88 139:0.93 140:0.45 145:0.50 146:0.74 147:0.78 149:0.67 150:0.81 151:0.56 153:0.48 154:0.77 155:0.67 158:0.91 159:0.72 161:0.76 162:0.86 164:0.57 165:0.93 167:0.59 172:0.73 173:0.46 176:0.73 177:0.70 179:0.33 181:0.76 187:0.68 192:0.87 195:0.87 196:0.91 201:0.93 202:0.36 208:0.64 212:0.76 215:0.51 216:0.77 219:0.95 220:0.82 222:0.35 235:0.42 241:0.21 242:0.70 243:0.39 245:0.90 247:0.84 248:0.55 253:0.58 255:0.95 256:0.45 259:0.21 260:0.58 265:0.63 266:0.71 267:0.73 268:0.46 276:0.80 277:0.69 279:0.86 281:0.91 282:0.87 283:0.78 284:0.89 285:0.62 290:0.71 292:0.91 297:0.36 300:0.84 +1 1:0.66 9:0.79 11:0.82 12:0.06 17:0.20 18:0.78 21:0.40 27:0.11 28:1.00 29:0.77 30:0.88 31:0.95 32:0.72 34:0.93 36:0.44 37:0.89 39:0.63 43:0.76 44:0.92 45:0.83 64:0.77 66:0.16 69:0.82 70:0.33 71:0.85 74:0.64 77:0.70 79:0.95 81:0.65 83:0.91 85:0.72 86:0.85 91:0.58 96:0.85 98:0.18 99:0.83 101:0.87 108:0.80 114:0.82 120:0.76 122:0.57 123:0.79 124:0.86 126:0.54 127:0.31 129:0.93 131:0.85 133:0.84 135:0.85 137:0.95 139:0.85 140:0.87 144:0.76 145:0.77 146:0.18 147:0.23 149:0.79 150:0.56 151:0.86 153:0.48 154:0.67 155:0.66 157:0.82 159:0.59 161:0.57 162:0.50 164:0.66 165:0.70 166:0.79 167:0.67 172:0.32 173:0.74 176:0.70 177:0.70 178:0.48 179:0.49 181:0.55 182:0.78 187:0.39 190:0.77 192:0.87 195:0.87 196:0.95 201:0.63 202:0.70 208:0.64 212:0.37 215:0.67 216:0.90 220:0.62 222:0.35 227:0.92 229:0.87 231:0.69 235:0.60 241:0.44 242:0.45 243:0.23 245:0.66 246:0.91 247:0.35 248:0.83 253:0.84 255:0.79 256:0.58 259:0.21 260:0.76 265:0.19 266:0.54 267:0.73 268:0.59 271:0.78 276:0.55 282:0.80 284:0.92 285:0.62 287:0.93 290:0.82 297:0.36 300:0.43 +2 1:0.66 7:0.64 8:0.58 9:0.75 11:0.78 12:0.06 17:0.16 18:0.83 20:0.74 21:0.30 22:0.93 27:0.18 28:1.00 29:0.77 30:0.45 31:0.92 32:0.80 34:0.63 36:0.29 37:0.46 39:0.63 41:0.88 43:0.92 44:0.93 47:0.93 60:0.87 64:0.77 66:0.10 69:0.37 70:0.48 71:0.85 74:0.81 77:0.70 78:0.75 79:0.92 81:0.74 83:0.60 85:0.91 86:0.92 91:0.35 96:0.78 98:0.18 100:0.73 101:0.87 104:0.89 106:0.81 108:0.83 111:0.74 114:0.86 117:0.86 120:0.66 122:0.57 123:0.68 124:0.86 126:0.54 127:0.56 129:0.93 131:0.85 133:0.84 135:0.98 137:0.92 139:0.95 140:0.45 144:0.76 145:0.76 146:0.18 147:0.23 149:0.89 150:0.56 151:0.77 152:0.74 153:0.48 154:0.92 155:0.65 157:0.82 158:0.92 159:0.65 161:0.57 162:0.86 164:0.67 165:0.93 166:0.79 167:0.55 169:0.77 172:0.32 173:0.27 176:0.73 177:0.70 179:0.63 181:0.55 182:0.78 186:0.76 187:0.95 192:0.87 195:0.82 196:0.96 201:0.64 202:0.50 204:0.82 206:0.81 208:0.64 212:0.39 215:0.72 216:0.75 219:0.95 220:0.62 222:0.35 227:0.92 229:0.88 230:0.64 231:0.69 232:0.91 233:0.66 235:0.60 241:0.03 242:0.51 243:0.24 245:0.64 246:0.91 247:0.47 248:0.71 253:0.82 255:0.77 256:0.58 259:0.21 260:0.67 261:0.74 262:0.93 265:0.20 266:0.54 267:0.59 268:0.59 271:0.78 276:0.53 279:0.80 281:0.86 282:0.88 283:0.82 284:0.92 285:0.62 287:0.93 290:0.86 292:0.95 297:0.36 299:0.88 300:0.43 +0 11:0.82 12:0.06 17:0.30 18:0.75 21:0.30 27:0.45 28:1.00 29:0.77 30:0.27 34:0.81 36:0.21 37:0.50 39:0.63 43:0.75 45:0.73 66:0.62 69:0.68 70:0.91 71:0.85 74:0.64 81:0.30 85:0.88 86:0.86 91:0.20 98:0.58 101:0.87 108:0.52 123:0.50 124:0.64 126:0.26 127:0.46 129:0.05 131:0.61 133:0.73 135:0.93 139:0.83 144:0.76 145:0.47 146:0.78 147:0.82 149:0.85 150:0.33 154:0.66 157:0.82 159:0.64 161:0.57 166:0.73 167:0.70 172:0.67 173:0.59 177:0.70 178:0.55 179:0.51 181:0.55 182:0.75 187:0.68 212:0.42 215:0.72 216:0.77 222:0.35 227:0.61 229:0.61 231:0.67 235:0.31 241:0.55 242:0.30 243:0.68 245:0.66 246:0.61 247:0.67 253:0.48 259:0.21 265:0.59 266:0.80 267:0.36 271:0.78 276:0.62 283:0.67 287:0.61 297:0.36 300:0.70 +1 1:0.69 7:0.64 9:0.79 11:0.56 12:0.06 17:0.97 18:0.67 21:0.05 27:0.08 28:1.00 29:0.77 30:0.55 31:0.94 32:0.80 34:0.66 36:0.40 37:0.69 39:0.63 43:0.23 44:0.93 45:0.83 64:0.77 66:0.18 69:0.96 70:0.72 71:0.85 74:0.57 76:0.85 77:0.70 79:0.93 81:0.72 83:0.91 86:0.72 91:0.31 96:0.80 98:0.34 99:0.83 101:0.52 104:0.58 108:0.65 114:0.95 120:0.69 122:0.77 123:0.62 124:0.88 126:0.54 127:0.67 129:0.81 131:0.85 133:0.86 135:0.73 137:0.94 139:0.84 140:0.45 144:0.76 145:0.40 146:0.28 147:0.74 149:0.53 150:0.64 151:0.86 153:0.48 154:0.15 155:0.66 157:0.81 159:0.84 161:0.57 162:0.86 164:0.56 165:0.70 166:0.79 167:0.76 172:0.45 173:0.97 176:0.70 177:0.38 179:0.50 181:0.55 182:0.78 187:0.39 190:0.77 192:0.87 195:0.94 196:0.94 201:0.66 202:0.36 204:0.81 208:0.64 212:0.39 215:0.91 216:0.08 222:0.35 227:0.92 229:0.87 230:0.65 231:0.69 232:0.75 233:0.76 235:0.22 241:0.67 242:0.53 243:0.39 244:0.61 245:0.73 246:0.92 247:0.74 248:0.77 253:0.81 255:0.78 256:0.45 257:0.65 259:0.21 260:0.70 265:0.36 266:0.71 267:0.44 268:0.46 271:0.78 276:0.63 277:0.69 281:0.91 282:0.88 284:0.88 285:0.62 287:0.93 290:0.95 297:0.36 299:0.88 300:0.70 +3 1:0.66 7:0.75 9:0.80 11:0.82 12:0.06 17:0.36 18:0.92 21:0.30 22:0.93 27:0.21 28:1.00 29:0.77 30:0.87 31:0.98 34:0.49 36:0.86 37:0.74 39:0.63 41:0.90 43:0.83 45:0.87 47:0.93 48:0.91 64:0.77 66:0.45 69:0.15 70:0.46 71:0.85 74:0.81 79:0.98 81:0.66 83:0.91 85:0.72 86:0.95 91:0.56 96:0.94 97:0.98 98:0.47 99:0.83 101:0.87 104:0.84 106:0.81 108:0.64 114:0.84 117:0.86 120:0.89 122:0.57 123:0.62 124:0.58 126:0.54 127:0.24 129:0.59 131:0.85 133:0.46 135:0.18 137:0.98 139:0.95 140:0.96 144:0.76 146:0.49 147:0.55 149:0.92 150:0.58 151:0.86 153:0.92 154:0.79 155:0.67 157:0.84 158:0.82 159:0.38 161:0.57 162:0.64 164:0.84 165:0.70 166:0.79 167:0.71 172:0.63 173:0.19 176:0.70 177:0.70 179:0.30 181:0.55 182:0.78 187:0.39 192:0.87 196:0.99 201:0.64 202:0.80 204:0.84 206:0.81 208:0.64 212:0.69 215:0.72 216:0.95 219:0.94 222:0.35 227:0.92 229:0.88 230:0.77 231:0.69 232:0.88 233:0.76 235:0.52 241:0.57 242:0.43 243:0.48 245:0.85 246:0.92 247:0.55 248:0.86 253:0.95 255:0.95 256:0.81 259:0.21 260:0.89 262:0.93 265:0.49 266:0.27 267:0.65 268:0.81 271:0.78 276:0.65 279:0.82 281:0.91 283:0.82 284:0.97 285:0.62 287:0.94 290:0.84 292:0.95 297:0.36 300:0.70 +2 1:0.69 6:0.84 7:0.64 8:0.72 9:0.80 11:0.56 12:0.06 17:0.97 18:0.59 20:0.94 21:0.05 27:0.08 28:1.00 29:0.77 30:0.21 31:0.98 32:0.80 34:0.22 36:0.32 37:0.69 39:0.63 41:0.82 43:0.22 44:0.93 45:0.66 55:0.59 64:0.77 66:0.36 69:0.96 70:0.88 71:0.85 74:0.53 76:0.85 77:0.70 78:0.76 79:0.98 81:0.72 83:0.91 86:0.65 91:0.85 96:0.92 98:0.37 99:0.83 100:0.79 101:0.52 104:0.58 108:0.65 111:0.75 114:0.95 120:0.86 122:0.77 123:0.62 124:0.77 126:0.54 127:0.66 129:0.05 131:0.85 133:0.73 135:0.56 137:0.98 139:0.81 140:0.45 144:0.76 145:0.40 146:0.43 147:0.74 149:0.29 150:0.64 151:0.93 152:0.94 153:0.78 154:0.15 155:0.67 157:0.75 159:0.81 161:0.57 162:0.78 164:0.81 165:0.70 166:0.79 167:0.96 169:0.83 172:0.45 173:0.99 176:0.70 177:0.38 179:0.69 181:0.55 182:0.78 186:0.76 189:0.87 191:0.81 192:0.87 195:0.93 196:0.96 201:0.66 202:0.71 204:0.81 208:0.64 212:0.35 215:0.91 216:0.08 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.22 238:0.91 241:0.98 242:0.75 243:0.51 244:0.61 245:0.62 246:0.92 247:0.55 248:0.83 253:0.90 255:0.94 256:0.75 257:0.65 259:0.21 260:0.86 261:0.86 265:0.39 266:0.80 267:0.44 268:0.76 271:0.78 276:0.47 277:0.69 281:0.91 282:0.88 284:0.97 285:0.62 287:0.94 290:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.66 11:0.78 12:0.06 17:0.40 18:0.71 21:0.30 27:0.45 28:1.00 29:0.77 30:0.62 34:0.85 36:0.20 37:0.50 39:0.63 43:0.82 60:0.87 64:0.77 66:0.75 69:0.69 70:0.23 71:0.85 74:0.63 81:0.74 83:0.60 85:0.80 86:0.82 91:0.14 98:0.19 101:0.87 108:0.52 114:0.86 122:0.57 123:0.50 126:0.54 127:0.10 129:0.05 131:0.61 135:0.80 139:0.85 144:0.76 145:0.49 146:0.27 147:0.33 149:0.75 150:0.56 154:0.77 155:0.51 157:0.78 158:0.86 159:0.12 161:0.57 165:0.93 166:0.79 167:0.58 172:0.32 173:0.69 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 182:0.77 187:0.68 192:0.48 201:0.64 208:0.64 212:0.84 215:0.72 216:0.77 219:0.95 222:0.35 227:0.61 229:0.61 231:0.68 235:0.60 241:0.12 242:0.63 243:0.77 246:0.91 247:0.35 253:0.64 259:0.21 265:0.21 266:0.17 267:0.28 271:0.78 276:0.22 279:0.80 283:0.67 287:0.61 290:0.86 292:0.88 297:0.36 300:0.18 +2 1:0.66 6:0.96 8:0.97 9:0.79 12:0.06 17:0.64 20:0.77 21:0.30 27:0.16 28:1.00 29:0.77 31:0.94 34:0.85 36:0.51 37:0.91 39:0.63 64:0.77 71:0.85 74:0.41 78:0.74 79:0.93 81:0.74 83:0.91 91:0.37 96:0.80 97:0.89 100:0.78 104:0.98 111:0.74 114:0.86 120:0.69 126:0.54 131:0.85 135:0.60 137:0.94 139:0.70 140:0.45 144:0.76 150:0.56 151:0.86 152:0.75 153:0.48 155:0.66 157:0.61 158:0.82 161:0.57 162:0.86 164:0.56 165:0.93 166:0.79 167:0.74 169:0.75 175:0.97 176:0.73 181:0.55 182:0.78 186:0.75 187:0.95 189:0.97 190:0.77 192:0.87 196:0.91 201:0.64 202:0.36 208:0.64 215:0.72 216:0.44 219:0.94 222:0.35 227:0.92 229:0.87 231:0.69 232:0.98 235:0.60 238:0.91 241:0.64 244:0.97 246:0.91 248:0.77 253:0.79 255:0.78 256:0.45 257:0.93 259:0.21 260:0.70 261:0.74 267:0.36 268:0.46 271:0.78 277:0.95 279:0.80 283:0.82 284:0.88 285:0.62 287:0.93 290:0.86 292:0.95 297:0.36 +1 1:0.64 6:0.89 7:0.75 8:0.76 9:0.70 11:0.82 12:0.06 17:0.38 18:0.73 20:0.75 21:0.54 27:0.16 28:1.00 29:0.77 30:0.91 31:0.87 32:0.72 34:0.75 36:0.33 37:0.85 39:0.63 43:0.62 44:0.92 45:0.77 48:0.99 64:0.77 66:0.33 69:0.85 70:0.22 71:0.85 74:0.63 77:0.89 78:0.76 79:0.86 81:0.62 83:0.91 85:0.72 86:0.80 91:0.27 96:0.69 98:0.17 99:0.83 100:0.73 101:0.87 108:0.79 111:0.75 114:0.76 120:0.55 122:0.57 123:0.78 124:0.72 126:0.54 127:0.20 129:0.81 131:0.85 133:0.67 135:0.88 137:0.87 139:0.85 140:0.90 144:0.76 145:0.91 146:0.17 147:0.22 149:0.70 150:0.52 151:0.50 152:0.81 153:0.48 154:0.50 155:0.64 157:0.81 159:0.40 161:0.57 162:0.49 164:0.56 165:0.70 166:0.79 167:0.70 169:0.75 172:0.32 173:0.81 176:0.70 177:0.70 178:0.50 179:0.52 181:0.55 182:0.78 186:0.77 187:0.68 190:0.77 191:0.70 192:0.58 195:0.84 196:0.89 201:0.62 202:0.65 204:0.85 208:0.64 212:0.39 215:0.58 216:0.81 220:0.96 222:0.35 227:0.92 229:0.87 230:0.77 231:0.69 233:0.66 235:0.74 241:0.53 242:0.55 243:0.33 245:0.58 246:0.91 247:0.35 248:0.50 253:0.59 255:0.69 256:0.45 259:0.21 260:0.55 261:0.75 265:0.18 266:0.27 267:0.36 268:0.46 271:0.78 276:0.35 281:0.86 282:0.80 284:0.88 285:0.62 287:0.93 290:0.76 297:0.36 300:0.25 +1 1:0.68 7:0.81 11:0.56 12:0.06 17:0.47 18:0.97 21:0.13 27:0.03 28:1.00 29:0.77 30:0.93 32:0.72 34:0.96 36:0.93 37:0.98 39:0.63 43:0.74 44:0.92 45:0.97 64:0.77 66:0.51 69:0.61 70:0.29 71:0.85 74:0.94 77:0.70 81:0.70 83:0.91 86:0.98 91:0.38 97:0.94 98:0.74 99:0.83 101:0.52 104:0.93 108:0.73 114:0.92 121:1.00 122:0.57 123:0.72 124:0.56 126:0.54 127:0.54 129:0.59 131:0.61 133:0.46 135:0.73 139:0.99 144:0.76 145:0.45 146:0.62 147:0.67 149:0.98 150:0.61 154:0.64 155:0.65 157:0.87 158:0.82 159:0.57 161:0.57 165:0.70 166:0.79 167:0.65 172:0.89 173:0.57 176:0.70 177:0.70 178:0.55 179:0.20 181:0.55 182:0.78 187:0.95 192:0.86 195:0.77 201:0.66 204:0.85 208:0.64 212:0.82 215:0.84 216:0.80 219:0.94 222:0.35 227:0.61 229:0.61 230:0.87 231:0.69 232:0.93 233:0.76 235:0.31 241:0.39 242:0.50 243:0.45 244:0.61 245:0.97 246:0.92 247:0.35 253:0.74 259:0.21 265:0.75 266:0.54 267:0.52 271:0.78 276:0.89 279:0.81 281:0.91 282:0.80 283:0.82 287:0.61 290:0.92 292:0.95 297:0.36 300:0.70 +0 1:0.60 11:0.82 12:0.06 17:0.84 18:0.97 21:0.71 22:0.93 27:0.57 28:1.00 29:0.77 30:0.08 32:0.80 34:0.68 36:0.57 37:0.38 39:0.63 43:0.90 44:0.93 45:0.83 47:0.93 60:0.95 64:0.77 66:0.62 69:0.44 70:0.97 71:0.85 74:0.95 77:0.96 81:0.67 83:0.60 85:0.93 86:0.98 91:0.11 98:0.90 101:0.87 104:0.79 106:0.81 108:0.73 114:0.68 117:0.86 122:0.77 123:0.71 124:0.50 126:0.54 127:0.73 129:0.59 131:0.61 133:0.46 135:0.89 139:0.99 144:0.76 145:0.97 146:0.68 147:0.73 149:0.96 150:0.45 154:0.89 155:0.48 157:0.84 158:0.92 159:0.86 161:0.57 165:0.93 166:0.78 167:0.54 172:0.96 173:0.19 176:0.73 177:0.70 178:0.55 179:0.15 181:0.55 182:0.77 187:0.39 191:0.85 192:0.45 195:0.95 201:0.59 206:0.81 208:0.64 212:0.73 215:0.48 216:0.72 219:0.95 222:0.35 227:0.61 229:0.61 231:0.69 232:0.84 235:0.97 241:0.01 242:0.17 243:0.37 245:0.99 246:0.91 247:0.97 253:0.68 259:0.21 262:0.93 265:0.90 266:0.75 267:0.82 271:0.78 276:0.95 279:0.81 282:0.88 283:0.82 287:0.61 290:0.68 292:0.95 297:0.36 299:0.88 300:0.84 +4 1:0.68 6:0.97 7:0.64 8:0.97 9:0.80 11:0.56 12:0.06 17:0.75 18:0.61 20:0.89 21:0.13 27:0.01 28:1.00 29:0.77 30:0.10 31:1.00 32:0.80 34:0.28 36:0.39 37:0.96 39:0.63 41:0.99 43:0.21 44:0.93 45:0.66 55:0.59 60:0.97 64:0.77 66:0.27 69:0.98 70:0.99 71:0.85 74:0.63 76:0.85 77:0.70 78:0.92 79:1.00 81:0.70 83:0.91 86:0.65 91:1.00 96:1.00 97:0.99 98:0.29 99:0.83 100:0.99 101:0.52 104:0.58 108:0.76 111:0.99 114:0.92 120:0.99 122:0.77 123:0.75 124:0.92 126:0.54 127:0.99 129:0.05 131:0.85 133:0.92 135:0.49 137:1.00 138:0.99 139:0.87 140:0.87 144:0.76 145:0.40 146:0.25 147:0.05 149:0.31 150:0.61 151:1.00 152:0.96 153:0.99 154:0.13 155:0.67 157:0.75 158:0.92 159:0.89 161:0.57 162:0.71 164:0.98 165:0.70 166:0.79 167:0.96 169:0.98 172:0.51 173:1.00 176:0.70 177:0.05 178:0.42 179:0.57 181:0.55 182:0.78 186:0.92 189:0.99 191:0.98 192:0.87 195:0.96 196:1.00 201:0.66 202:0.97 204:0.81 208:0.64 212:0.42 215:0.84 216:0.68 219:0.95 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.31 238:0.99 241:0.99 242:0.75 243:0.09 244:0.61 245:0.64 246:0.92 247:0.74 248:1.00 253:1.00 255:0.95 256:0.98 257:0.84 259:0.21 260:0.99 261:0.98 265:0.31 266:0.92 267:0.69 268:0.98 271:0.78 276:0.64 277:0.87 279:0.86 281:0.91 282:0.88 283:0.82 284:1.00 285:0.62 287:0.94 290:0.92 292:0.95 297:0.36 299:0.88 300:0.94 +3 1:0.66 6:0.97 7:0.64 8:0.93 9:0.80 11:0.56 12:0.06 17:0.28 18:0.80 20:0.94 21:0.30 27:0.01 28:1.00 29:0.77 30:0.62 31:1.00 32:0.80 34:0.78 36:0.39 37:0.96 39:0.63 41:0.98 43:0.70 44:0.93 45:0.66 64:0.77 66:0.61 69:0.70 70:0.34 71:0.85 74:0.54 76:0.85 77:0.70 78:0.92 79:1.00 81:0.66 83:0.91 86:0.82 91:0.98 96:0.99 98:0.35 99:0.83 100:0.98 101:0.52 104:0.58 108:0.54 111:0.97 114:0.84 120:0.97 122:0.57 123:0.51 124:0.43 126:0.54 127:0.41 129:0.05 131:0.85 133:0.37 135:0.54 137:1.00 138:0.99 139:0.87 140:0.87 144:0.76 145:0.40 146:0.25 147:0.05 149:0.46 150:0.58 151:0.99 152:0.96 153:0.97 154:0.60 155:0.67 157:0.76 159:0.19 161:0.57 162:0.67 164:0.95 165:0.70 166:0.79 167:0.89 169:0.98 172:0.27 173:0.95 176:0.70 177:0.05 178:0.42 179:0.92 181:0.55 182:0.78 186:0.92 187:0.39 189:0.97 191:0.92 192:0.87 195:0.55 196:1.00 201:0.64 202:0.92 204:0.81 208:0.64 212:0.61 215:0.72 216:0.68 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.52 238:0.98 241:0.77 242:0.33 243:0.23 244:0.61 245:0.42 246:0.92 247:0.39 248:0.97 253:0.98 255:0.95 256:0.94 257:0.84 259:0.21 260:0.97 261:0.98 265:0.37 266:0.23 267:0.52 268:0.94 271:0.78 276:0.17 281:0.91 282:0.88 284:1.00 285:0.62 287:0.94 290:0.84 297:0.36 299:0.88 300:0.25 +1 1:0.69 6:0.84 7:0.64 8:0.65 9:0.80 11:0.56 12:0.06 17:0.43 18:0.71 20:0.84 27:0.08 28:1.00 29:0.77 30:0.91 31:0.97 32:0.80 34:0.59 36:0.49 37:0.69 39:0.63 41:0.82 43:0.74 44:0.93 45:0.66 64:0.77 66:0.69 69:0.56 70:0.13 71:0.85 74:0.55 76:0.85 77:0.70 78:0.88 79:0.96 81:0.74 83:0.91 86:0.79 91:0.80 96:0.89 98:0.35 99:0.83 100:0.92 101:0.52 104:0.58 108:0.43 111:0.89 114:0.97 120:0.81 122:0.57 123:0.41 126:0.54 127:0.13 129:0.05 131:0.85 135:0.73 137:0.97 139:0.86 140:0.80 144:0.76 145:0.40 146:0.23 147:0.29 149:0.48 150:0.68 151:0.90 152:0.94 153:0.77 154:0.65 155:0.66 157:0.76 159:0.11 161:0.57 162:0.86 164:0.70 165:0.70 166:0.79 167:0.81 169:0.83 172:0.21 173:0.89 176:0.70 177:0.70 179:0.53 181:0.55 182:0.78 186:0.88 187:0.21 189:0.86 191:0.76 192:0.87 195:0.54 196:0.97 201:0.67 202:0.55 204:0.81 208:0.64 212:0.61 215:0.96 216:0.08 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.12 238:0.93 241:0.73 242:0.63 243:0.73 244:0.61 246:0.92 247:0.30 248:0.80 253:0.87 255:0.94 256:0.58 259:0.21 260:0.81 261:0.86 265:0.38 266:0.17 267:0.28 268:0.64 271:0.78 276:0.16 281:0.91 282:0.88 284:0.93 285:0.62 287:0.93 290:0.97 297:0.36 299:0.88 300:0.13 +2 1:0.66 7:0.64 9:0.80 11:0.56 12:0.06 17:0.34 18:0.83 21:0.30 27:0.01 28:1.00 29:0.77 30:0.76 31:0.99 32:0.80 34:0.83 36:0.40 37:0.96 39:0.63 41:0.95 43:0.73 44:0.93 45:0.73 60:0.97 64:0.77 66:0.80 69:0.60 70:0.28 71:0.85 74:0.68 76:0.85 77:0.70 79:0.99 81:0.66 83:0.91 86:0.85 91:0.86 96:0.96 97:0.94 98:0.74 99:0.83 101:0.52 104:0.58 108:0.46 114:0.84 120:0.93 122:0.57 123:0.44 126:0.54 127:0.23 129:0.05 131:0.85 135:0.51 137:0.99 139:0.92 140:0.87 144:0.76 145:0.40 146:0.50 147:0.56 149:0.64 150:0.58 151:0.97 153:0.90 154:0.62 155:0.67 157:0.79 158:0.86 159:0.18 161:0.57 162:0.69 163:0.81 164:0.88 165:0.70 166:0.79 167:0.89 172:0.45 173:0.81 176:0.70 177:0.70 178:0.42 179:0.70 181:0.55 182:0.78 187:0.39 192:0.87 195:0.57 196:0.99 201:0.64 202:0.82 204:0.81 208:0.64 212:0.37 215:0.72 216:0.68 219:0.95 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.52 241:0.77 242:0.40 243:0.82 244:0.61 246:0.92 247:0.39 248:0.93 253:0.96 255:0.95 256:0.85 259:0.21 260:0.93 265:0.74 266:0.38 267:0.23 268:0.85 271:0.78 276:0.33 279:0.82 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 287:0.94 290:0.84 292:0.95 297:0.36 299:0.88 300:0.25 +1 12:0.79 17:0.99 21:0.05 25:0.88 27:0.72 28:0.85 30:0.58 34:0.37 36:0.48 43:0.52 55:0.45 66:0.91 69:0.29 70:0.46 81:1.00 91:0.60 98:0.90 104:0.58 108:0.22 123:0.22 126:0.54 127:0.48 129:0.05 135:0.23 146:0.61 147:0.67 149:0.78 150:1.00 154:0.41 159:0.23 161:0.49 167:0.87 172:0.76 173:0.55 177:0.05 178:0.55 179:0.50 181:0.84 212:0.91 215:0.91 216:0.02 235:0.68 241:0.83 242:0.82 243:0.92 247:0.12 259:0.21 265:0.90 266:0.27 267:0.36 271:0.18 276:0.65 297:0.99 300:0.43 +1 6:0.87 7:0.81 8:0.79 12:0.79 17:0.53 20:0.76 21:0.78 27:0.27 28:0.85 32:0.80 34:0.50 36:0.91 37:0.90 78:0.85 91:0.90 100:0.86 111:0.84 120:0.97 135:0.30 140:0.45 145:0.80 151:0.83 152:0.75 153:0.96 161:0.49 162:0.65 164:0.95 167:0.84 169:0.83 175:0.75 181:0.84 186:0.86 187:0.21 189:0.88 191:0.90 195:0.54 202:0.92 216:0.29 230:0.87 233:0.76 235:0.74 238:0.87 241:0.78 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.97 261:0.76 267:0.52 268:0.94 271:0.18 277:0.69 285:0.62 +1 12:0.79 17:0.47 21:0.78 27:0.45 28:0.85 30:0.62 32:0.80 34:0.90 36:0.39 37:0.50 43:0.32 55:0.92 66:0.47 69:0.71 70:0.34 81:0.82 91:0.10 98:0.59 108:0.54 123:0.52 124:0.52 126:0.54 127:0.35 129:0.59 133:0.47 135:0.42 145:0.63 146:0.69 147:0.74 149:0.32 150:0.61 154:0.24 159:0.47 161:0.49 163:0.94 167:0.55 172:0.21 173:0.70 177:0.05 178:0.55 179:0.91 181:0.84 187:0.68 195:0.73 212:0.30 215:0.43 216:0.77 235:1.00 241:0.21 242:0.82 243:0.59 245:0.46 247:0.12 259:0.21 265:0.60 266:0.27 267:0.23 271:0.18 276:0.27 297:0.36 300:0.25 +2 7:0.81 8:0.62 12:0.79 17:0.26 20:0.79 21:0.30 27:0.21 28:0.85 30:0.53 34:0.52 36:0.99 37:0.74 43:0.52 55:0.59 66:0.32 69:0.10 70:0.60 78:0.84 81:0.99 91:0.44 98:0.55 100:0.73 104:0.84 106:0.81 108:0.58 111:0.82 120:0.83 123:0.55 124:0.79 126:0.54 127:0.51 129:0.89 133:0.78 135:0.56 140:0.45 146:0.36 147:0.42 149:0.66 150:0.98 151:0.71 152:0.79 153:0.72 154:0.41 159:0.67 161:0.49 162:0.86 164:0.75 167:0.56 169:0.72 172:0.51 173:0.12 177:0.05 179:0.53 181:0.84 186:0.84 187:0.39 202:0.61 206:0.81 212:0.16 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 241:0.27 242:0.82 243:0.25 245:0.73 247:0.12 248:0.75 256:0.68 259:0.21 260:0.84 261:0.81 265:0.57 266:0.89 267:0.88 268:0.70 271:0.18 276:0.63 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.84 7:0.81 8:0.65 12:0.79 17:0.12 20:0.87 21:0.30 27:0.21 28:0.85 30:0.30 34:0.55 36:0.99 37:0.74 43:0.52 55:0.59 66:0.33 69:0.10 70:0.65 78:0.80 81:0.99 91:0.71 98:0.55 100:0.81 104:0.84 106:0.81 108:0.57 111:0.79 120:0.95 123:0.55 124:0.79 126:0.54 127:0.50 129:0.89 133:0.78 135:0.42 140:0.45 146:0.34 147:0.41 149:0.65 150:0.98 151:0.83 152:0.82 153:0.96 154:0.41 159:0.67 161:0.49 162:0.71 163:0.75 164:0.92 167:0.58 169:0.79 172:0.51 173:0.12 177:0.05 179:0.54 181:0.84 186:0.80 187:0.39 189:0.86 191:0.70 202:0.87 206:0.81 212:0.16 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.85 241:0.37 242:0.82 243:0.21 245:0.71 247:0.12 248:0.93 256:0.88 259:0.21 260:0.95 261:0.80 265:0.57 266:0.89 267:0.84 268:0.90 271:0.18 276:0.62 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 12:0.79 17:0.08 21:0.78 27:0.08 28:0.85 32:0.80 34:0.89 36:0.64 37:0.87 91:0.63 120:0.97 135:0.51 140:0.45 145:0.80 151:0.83 153:0.96 161:0.49 162:0.66 164:0.95 167:0.83 175:0.75 181:0.84 187:0.21 195:0.57 202:0.93 216:0.67 230:0.87 233:0.76 235:0.74 241:0.77 244:0.61 248:0.95 256:0.94 257:0.65 259:0.21 260:0.97 267:0.23 268:0.94 271:0.18 277:0.69 283:0.60 285:0.62 +1 12:0.79 17:0.34 21:0.47 27:0.45 28:0.85 30:0.11 32:0.80 34:0.86 36:0.32 37:0.50 43:0.32 55:0.45 66:0.13 69:0.69 70:0.84 81:0.97 91:0.09 98:0.26 108:0.83 123:0.63 124:0.79 126:0.54 127:0.40 129:0.89 133:0.78 135:0.70 145:0.47 146:0.22 147:0.27 149:0.44 150:0.96 154:0.24 159:0.52 161:0.49 167:0.54 172:0.27 173:0.66 177:0.05 178:0.55 179:0.77 181:0.84 187:0.68 195:0.75 212:0.28 215:0.63 216:0.77 235:0.52 241:0.18 242:0.82 243:0.26 245:0.52 247:0.12 259:0.21 265:0.20 266:0.63 267:0.36 271:0.18 276:0.38 283:0.60 297:0.36 300:0.55 +0 6:0.97 8:0.94 12:0.79 17:0.28 20:0.94 21:0.30 27:0.04 28:0.85 30:0.95 32:0.80 34:0.64 36:0.63 37:0.91 43:0.50 55:0.52 66:0.54 69:0.21 78:0.89 81:0.99 91:0.56 98:0.33 100:0.95 108:0.17 111:0.92 120:0.63 123:0.16 126:0.54 127:0.12 129:0.05 135:0.39 140:0.45 145:0.74 146:0.19 147:0.25 149:0.24 150:0.98 151:0.54 152:0.95 153:0.48 154:0.39 159:0.10 161:0.49 162:0.69 164:0.75 167:0.88 169:0.97 172:0.10 173:0.77 177:0.05 179:0.82 181:0.84 186:0.89 187:0.39 189:0.97 191:0.99 195:0.52 202:0.67 215:0.72 216:0.73 220:0.87 235:0.31 238:0.97 241:0.86 243:0.63 248:0.57 256:0.68 259:0.21 260:0.64 261:0.97 265:0.35 267:0.69 268:0.70 271:0.18 285:0.62 297:0.36 300:0.08 +3 6:0.89 7:0.81 8:0.84 12:0.79 17:0.15 20:0.87 21:0.47 27:0.09 28:0.85 30:0.89 32:0.80 34:0.55 36:0.78 37:0.85 43:0.47 55:0.59 66:0.75 69:0.40 70:0.20 78:0.85 81:0.97 91:0.82 98:0.42 100:0.87 104:0.80 108:0.31 111:0.84 120:0.88 123:0.30 126:0.54 127:0.14 129:0.05 135:0.54 140:0.45 145:0.88 146:0.39 147:0.45 149:0.43 150:0.96 151:0.75 152:0.86 153:0.85 154:0.36 159:0.15 161:0.49 162:0.68 164:0.90 167:0.81 169:0.90 172:0.32 173:0.50 177:0.05 179:0.48 181:0.84 186:0.85 187:0.68 189:0.90 191:0.96 195:0.63 202:0.85 212:0.95 215:0.63 216:0.67 220:0.93 230:0.87 233:0.76 235:0.52 238:0.90 241:0.76 242:0.82 243:0.77 247:0.12 248:0.83 256:0.88 259:0.21 260:0.88 261:0.90 265:0.44 266:0.12 267:0.98 268:0.87 271:0.18 276:0.29 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.89 7:0.81 8:0.77 12:0.79 17:0.40 20:0.75 21:0.40 27:0.09 28:0.85 30:0.45 32:0.80 34:0.61 36:0.87 37:0.85 43:0.48 55:0.52 66:0.69 69:0.35 70:0.25 78:0.89 81:0.98 91:0.65 98:0.36 100:0.92 104:0.80 106:0.81 108:0.27 111:0.89 120:0.95 123:0.26 126:0.54 127:0.13 129:0.05 135:0.51 140:0.45 145:0.79 146:0.32 147:0.39 149:0.34 150:0.97 151:0.80 152:0.86 153:0.93 154:0.37 159:0.13 161:0.49 162:0.69 164:0.91 167:0.83 169:0.90 172:0.21 173:0.49 177:0.05 179:0.56 181:0.84 186:0.89 187:0.39 189:0.93 191:0.70 195:0.61 202:0.87 206:0.81 212:0.95 215:0.67 216:0.67 230:0.87 233:0.76 235:0.42 238:0.92 241:0.77 242:0.82 243:0.73 247:0.12 248:0.92 256:0.89 259:0.21 260:0.95 261:0.90 265:0.38 266:0.12 267:0.94 268:0.89 271:0.18 276:0.21 283:0.82 285:0.62 297:0.36 300:0.18 +1 8:0.76 12:0.79 17:0.40 21:0.78 27:0.04 28:0.85 30:0.76 34:0.64 36:0.39 37:0.91 43:0.31 55:0.71 66:0.36 69:0.71 70:0.15 91:0.78 98:0.16 108:0.80 123:0.78 124:0.52 127:0.19 129:0.59 133:0.47 135:0.94 145:0.69 146:0.05 147:0.05 149:0.20 154:0.23 159:0.43 161:0.49 163:0.98 167:0.77 172:0.10 173:0.60 177:0.05 178:0.55 179:0.94 181:0.84 187:0.21 191:0.97 202:0.80 212:0.95 216:0.73 235:0.31 241:0.73 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.17 266:0.12 267:0.94 271:0.18 276:0.14 300:0.13 +2 6:0.89 7:0.81 8:0.76 12:0.79 17:0.32 20:0.87 21:0.40 27:0.09 28:0.85 30:0.45 32:0.80 34:0.63 36:0.86 37:0.85 43:0.48 55:0.52 66:0.69 69:0.34 70:0.25 78:0.91 81:0.98 91:0.88 98:0.36 100:0.94 104:0.78 106:0.81 108:0.27 111:0.92 120:0.96 123:0.26 126:0.54 127:0.13 129:0.05 135:0.32 140:0.45 145:0.79 146:0.32 147:0.39 149:0.34 150:0.97 151:0.83 152:0.86 153:0.97 154:0.37 159:0.13 161:0.49 162:0.61 164:0.94 167:0.83 169:0.90 172:0.21 173:0.48 177:0.05 179:0.56 181:0.84 186:0.91 187:0.21 189:0.90 195:0.60 202:0.92 206:0.81 212:0.95 215:0.67 216:0.67 230:0.87 233:0.76 235:0.42 238:0.92 241:0.77 242:0.82 243:0.73 247:0.12 248:0.94 256:0.93 259:0.21 260:0.96 261:0.90 265:0.38 266:0.12 267:0.94 268:0.93 271:0.18 276:0.21 283:0.82 285:0.62 297:0.36 300:0.18 +2 6:0.97 8:0.94 12:0.79 17:0.03 20:0.85 21:0.30 27:0.04 28:0.85 30:0.33 32:0.80 34:0.94 36:0.24 37:0.88 43:0.33 55:0.71 66:0.54 69:0.67 70:0.36 78:0.94 81:0.99 91:0.79 98:0.63 100:0.99 104:0.95 108:0.66 111:0.98 120:0.89 123:0.63 124:0.55 126:0.54 127:0.45 129:0.81 133:0.67 135:0.99 140:0.45 145:0.72 146:0.05 147:0.05 149:0.39 150:0.98 151:0.75 152:1.00 153:0.86 154:0.25 159:0.44 161:0.49 162:0.66 163:0.86 164:0.89 167:0.77 169:0.91 172:0.32 173:0.71 177:0.05 179:0.87 181:0.84 186:0.94 187:0.39 189:0.99 191:0.97 195:0.66 202:0.84 212:0.42 215:0.72 216:0.55 220:0.87 235:0.31 238:0.98 241:0.74 242:0.82 243:0.19 245:0.45 247:0.12 248:0.84 256:0.85 259:0.21 260:0.89 261:0.94 265:0.64 266:0.38 267:0.97 268:0.86 271:0.18 276:0.34 283:0.82 285:0.62 297:0.36 300:0.25 +2 6:0.79 7:0.81 8:0.71 12:0.79 17:0.75 20:0.74 21:0.30 27:0.38 28:0.85 30:0.15 32:0.80 34:0.98 36:0.54 37:0.48 43:0.41 55:0.59 66:0.24 69:0.52 70:0.68 78:0.81 81:0.99 91:0.75 98:0.43 100:0.84 104:0.86 106:0.81 108:0.65 111:0.80 120:0.95 123:0.63 124:0.73 126:0.54 127:0.35 129:0.81 132:0.97 133:0.67 135:0.85 140:0.45 145:0.58 146:0.33 147:0.40 149:0.45 150:0.98 151:0.80 152:0.74 153:0.93 154:0.31 159:0.43 161:0.49 162:0.73 163:0.90 164:0.90 167:0.62 169:0.80 172:0.21 173:0.51 177:0.05 179:0.78 181:0.84 186:0.88 187:0.39 191:0.77 195:0.73 202:0.84 206:0.81 212:0.30 215:0.72 216:0.53 230:0.87 233:0.76 235:0.31 238:0.84 241:0.49 242:0.82 243:0.28 245:0.54 247:0.12 248:0.92 256:0.86 260:0.95 261:0.75 265:0.45 266:0.54 267:0.97 268:0.88 271:0.18 276:0.39 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.97 8:0.94 12:0.79 17:0.26 20:0.94 21:0.30 27:0.04 28:0.85 30:0.95 32:0.80 34:0.44 36:0.62 37:0.91 43:0.50 55:0.52 66:0.54 69:0.21 78:0.89 81:0.99 91:0.57 98:0.33 100:0.95 108:0.17 111:0.92 120:0.63 123:0.16 126:0.54 127:0.12 129:0.05 135:0.39 140:0.45 145:0.74 146:0.19 147:0.25 149:0.24 150:0.98 151:0.54 152:0.95 153:0.48 154:0.39 159:0.10 161:0.49 162:0.69 164:0.75 167:0.88 169:0.97 172:0.10 173:0.77 177:0.05 179:0.82 181:0.84 186:0.89 187:0.39 189:0.97 191:0.99 195:0.52 202:0.67 215:0.72 216:0.73 220:0.87 235:0.31 238:0.97 241:0.86 243:0.63 248:0.57 256:0.68 259:0.21 260:0.64 261:0.97 265:0.35 267:0.73 268:0.70 271:0.18 285:0.62 297:0.36 300:0.08 +2 6:0.96 8:0.92 12:0.79 17:0.05 20:0.85 21:0.78 27:0.08 28:0.85 34:0.55 36:0.24 37:0.87 78:0.88 91:0.99 100:0.91 111:0.88 120:0.95 135:0.91 140:0.93 151:0.83 152:0.86 153:0.96 161:0.49 162:0.46 164:0.92 167:0.90 169:0.94 175:0.75 178:0.52 181:0.84 186:0.88 189:0.91 191:0.98 202:0.98 216:0.67 238:0.91 241:0.97 244:0.61 248:0.93 256:0.90 257:0.65 259:0.21 260:0.95 261:0.94 267:0.89 268:0.90 271:0.18 277:0.69 285:0.62 +1 10:0.92 11:0.52 12:0.37 17:0.75 18:0.94 21:0.78 27:0.37 29:0.53 30:0.33 37:0.83 39:0.94 43:0.56 45:0.90 66:0.68 69:0.72 70:0.92 71:0.92 74:0.61 86:0.95 91:0.54 98:0.78 101:0.65 104:0.80 108:0.56 122:0.91 123:0.53 124:0.45 127:0.52 129:0.05 133:0.45 139:0.92 146:0.84 147:0.87 149:0.48 154:0.51 159:0.54 170:0.90 172:0.79 173:0.71 174:0.86 177:0.88 178:0.55 179:0.39 187:0.21 197:0.86 202:0.36 212:0.73 216:0.46 222:0.21 235:0.05 239:0.89 241:0.41 242:0.21 243:0.72 245:0.86 247:0.90 253:0.46 254:0.99 259:0.21 265:0.78 266:0.80 271:0.41 276:0.73 300:0.84 +1 8:0.96 10:0.81 11:0.57 12:0.37 17:0.93 18:1.00 21:0.78 27:0.37 29:0.53 30:0.09 37:0.33 39:0.94 43:0.60 45:0.99 55:0.59 66:0.06 69:0.99 70:0.99 71:0.92 74:0.60 86:0.99 91:0.31 98:0.22 101:0.96 108:1.00 122:0.92 123:0.93 124:1.00 127:0.97 129:0.98 133:1.00 139:0.99 145:0.45 146:0.32 147:0.46 149:0.81 154:0.06 159:1.00 170:0.90 172:0.92 173:0.76 174:0.88 175:0.75 177:0.38 178:0.55 179:0.09 187:0.21 191:0.90 197:0.79 202:0.77 212:0.39 216:0.69 222:0.21 235:0.22 239:0.80 241:0.62 242:0.63 243:0.05 244:0.61 245:0.99 247:0.96 253:0.46 257:0.84 259:0.21 265:0.24 266:0.99 271:0.41 276:1.00 277:0.87 300:0.98 +0 8:0.62 10:0.84 11:0.46 12:0.37 17:0.32 18:0.92 21:0.78 27:0.21 29:0.53 30:0.09 34:0.63 36:0.67 37:0.74 39:0.94 43:0.43 45:0.81 55:0.45 66:0.09 69:0.81 70:0.98 71:0.92 74:0.41 86:0.90 91:0.31 98:0.31 101:0.47 108:0.85 122:0.92 123:0.89 124:0.87 127:0.31 129:0.59 133:0.86 135:0.24 139:0.86 146:0.46 147:0.05 149:0.46 154:0.32 159:0.72 170:0.90 172:0.54 173:0.65 174:0.79 175:0.75 177:0.05 178:0.55 179:0.36 187:0.39 197:0.83 202:0.66 212:0.50 216:0.95 222:0.21 235:0.74 239:0.83 241:0.12 242:0.60 243:0.09 244:0.83 245:0.72 247:0.74 253:0.35 257:0.93 259:0.21 265:0.26 266:0.75 267:0.87 271:0.41 276:0.68 277:0.87 300:0.84 +0 8:0.86 9:0.72 10:0.89 11:0.46 12:0.37 17:0.76 18:0.84 21:0.78 27:0.72 29:0.53 30:0.41 31:0.91 34:0.94 36:0.99 39:0.94 43:0.66 45:0.81 66:0.07 69:0.81 70:0.95 71:0.92 74:0.57 79:0.90 83:0.56 86:0.86 91:0.38 96:0.75 97:0.97 98:0.22 101:0.47 108:0.15 122:0.91 123:0.71 124:0.83 127:0.77 129:0.05 133:0.81 135:0.63 137:0.91 139:0.84 140:0.80 145:0.40 146:0.22 147:0.34 149:0.59 151:0.62 153:0.46 154:0.46 155:0.55 158:0.76 159:0.61 162:0.57 170:0.90 172:0.37 173:0.80 174:0.88 175:0.75 177:0.38 178:0.42 179:0.77 187:0.39 190:0.89 192:0.55 196:0.92 197:0.88 202:0.56 208:0.50 212:0.54 216:0.23 219:0.91 220:0.74 222:0.21 235:0.12 239:0.88 241:0.32 242:0.19 243:0.50 244:0.61 245:0.54 247:0.79 248:0.65 253:0.60 254:0.95 255:0.71 256:0.58 257:0.84 259:0.21 265:0.21 266:0.63 267:0.19 268:0.55 271:0.41 276:0.46 277:0.87 279:0.79 284:0.90 285:0.50 292:0.88 300:0.84 +1 7:0.69 8:0.90 12:0.37 17:0.13 21:0.78 23:0.98 27:0.26 29:0.53 30:0.76 31:0.90 32:0.65 34:0.30 36:0.61 37:0.92 39:0.94 43:0.16 55:0.52 66:0.36 69:0.53 70:0.15 71:0.92 79:0.90 91:0.92 98:0.20 104:0.58 108:0.62 120:0.85 123:0.59 124:0.52 127:0.44 128:0.98 129:0.59 133:0.47 135:0.42 137:0.90 140:0.93 145:0.87 146:0.05 147:0.05 149:0.11 151:0.76 153:0.48 154:0.11 159:0.22 162:0.55 163:0.96 164:0.80 168:0.98 170:0.90 172:0.10 173:0.77 175:0.97 177:0.05 178:0.42 179:0.99 190:0.95 191:0.95 192:0.49 195:0.60 202:0.79 205:0.98 212:0.95 216:0.25 220:0.62 222:0.21 230:0.71 233:0.76 235:0.02 241:0.94 242:0.82 243:0.34 244:0.93 245:0.37 247:0.12 248:0.83 253:0.20 255:0.72 256:0.80 257:0.93 259:0.21 260:0.86 265:0.22 266:0.12 267:0.52 268:0.77 271:0.41 276:0.13 277:0.95 284:0.88 285:0.60 300:0.13 +1 7:0.69 10:0.90 12:0.37 17:0.32 18:0.66 21:0.30 27:0.20 29:0.53 30:0.45 32:0.65 34:0.79 36:0.19 37:0.60 39:0.94 43:0.08 55:0.71 66:0.33 69:0.48 70:0.72 71:0.92 75:0.97 81:0.39 86:0.69 91:0.20 98:0.42 108:0.81 123:0.80 124:0.78 126:0.32 127:0.60 129:0.81 133:0.76 135:0.85 139:0.66 145:0.65 146:0.45 147:0.52 149:0.12 150:0.41 154:0.52 159:0.74 170:0.90 172:0.45 173:0.32 174:0.86 175:0.91 177:0.38 178:0.55 179:0.64 187:0.39 192:0.44 195:0.87 197:0.89 201:0.57 212:0.21 215:0.72 216:0.84 222:0.21 226:0.95 230:0.71 233:0.76 235:0.42 236:0.94 239:0.91 241:0.44 242:0.41 243:0.29 244:0.83 245:0.66 247:0.76 253:0.20 254:0.74 257:0.84 259:0.21 265:0.44 266:0.75 267:0.36 271:0.41 276:0.54 277:0.87 283:0.66 297:0.36 300:0.55 +0 8:0.77 10:0.99 11:0.52 12:0.37 17:0.75 18:0.90 21:0.13 23:0.97 27:0.20 29:0.53 30:0.67 33:0.97 34:0.58 36:0.30 37:0.60 39:0.94 43:0.73 45:0.92 55:0.71 62:0.99 66:0.96 69:0.45 70:0.41 71:0.92 74:0.67 75:0.96 81:0.33 86:0.94 91:0.38 98:0.90 101:0.87 108:0.35 122:0.95 123:0.34 126:0.24 127:0.47 128:0.98 129:0.05 135:0.78 139:0.91 140:0.45 145:0.58 146:0.94 147:0.95 149:0.88 150:0.36 151:0.69 153:0.69 154:0.63 159:0.59 162:0.86 168:0.98 170:0.90 172:0.90 173:0.37 174:0.98 177:0.88 179:0.27 187:0.39 190:0.95 197:0.99 202:0.50 205:0.97 212:0.86 216:0.84 222:0.21 226:0.98 235:0.12 236:0.92 239:0.99 241:0.21 242:0.19 243:0.96 244:0.61 247:0.91 248:0.69 253:0.49 256:0.58 259:0.21 265:0.90 266:0.38 267:0.69 268:0.59 271:0.41 276:0.83 285:0.46 291:0.97 300:0.43 +0 8:0.77 10:0.99 11:0.52 12:0.37 17:0.72 18:0.91 21:0.13 23:0.97 27:0.20 29:0.53 30:0.73 33:0.97 34:0.68 36:0.32 37:0.60 39:0.94 43:0.73 45:0.92 55:0.71 62:0.99 66:0.96 69:0.45 70:0.33 71:0.92 74:0.69 75:0.96 81:0.33 86:0.94 91:0.46 98:0.88 101:0.87 108:0.35 122:0.95 123:0.34 126:0.24 127:0.43 128:0.98 129:0.05 135:0.70 139:0.92 140:0.45 145:0.59 146:0.94 147:0.95 149:0.89 150:0.36 151:0.69 153:0.69 154:0.63 159:0.60 162:0.86 168:0.98 170:0.90 172:0.91 173:0.35 174:0.98 175:0.75 177:0.70 179:0.25 187:0.39 190:0.95 197:0.99 202:0.50 205:0.97 212:0.83 216:0.84 222:0.21 226:0.98 235:0.12 236:0.92 239:0.99 241:0.21 242:0.31 243:0.96 244:0.61 247:0.86 248:0.69 253:0.50 256:0.58 257:0.65 259:0.21 265:0.88 266:0.38 267:0.69 268:0.59 271:0.41 276:0.83 277:0.69 285:0.46 291:0.97 300:0.43 +0 10:0.90 11:0.46 12:0.37 17:0.66 18:0.93 21:0.78 27:0.72 29:0.53 30:0.32 34:0.47 36:0.90 39:0.94 43:0.64 45:0.83 66:0.89 69:0.43 70:0.63 71:0.92 74:0.56 86:0.93 91:0.80 98:0.88 101:0.47 108:0.33 122:0.92 123:0.32 127:0.42 129:0.05 135:0.39 139:0.90 146:0.65 147:0.70 149:0.63 154:0.59 159:0.25 170:0.90 172:0.68 173:0.63 174:0.79 177:0.88 178:0.55 179:0.58 191:0.80 197:0.82 202:0.50 212:0.77 216:0.05 222:0.21 235:0.12 239:0.87 241:0.85 242:0.31 243:0.89 247:0.76 253:0.44 254:0.99 259:0.21 265:0.88 266:0.27 267:0.28 271:0.41 276:0.57 300:0.43 +1 10:0.89 11:0.52 12:0.37 17:0.66 18:0.92 21:0.47 27:0.72 29:0.53 30:0.32 39:0.94 43:0.63 45:0.83 66:0.89 69:0.21 70:0.51 71:0.92 74:0.47 81:0.29 86:0.92 91:0.67 98:0.98 101:0.87 104:0.58 108:0.17 120:0.64 122:0.91 123:0.16 126:0.24 127:0.82 129:0.05 139:0.88 140:0.45 146:0.79 147:0.82 149:0.66 150:0.32 151:0.59 153:0.72 154:0.52 159:0.35 162:0.58 164:0.68 170:0.90 172:0.68 173:0.39 174:0.83 177:0.88 179:0.68 190:0.95 197:0.86 202:0.64 212:0.59 215:0.63 216:0.07 220:0.74 222:0.21 235:0.52 239:0.88 241:0.93 242:0.21 243:0.89 244:0.61 247:0.82 248:0.58 253:0.39 256:0.58 260:0.65 265:0.98 266:0.54 268:0.62 271:0.41 276:0.57 285:0.46 297:0.36 300:0.33 +1 1:0.61 10:0.88 11:0.46 12:0.37 17:0.24 18:0.91 21:0.54 27:0.02 29:0.53 30:0.21 34:0.49 36:0.36 37:0.92 39:0.94 43:0.25 44:0.88 45:0.88 64:0.77 66:0.34 69:0.68 70:0.87 71:0.92 74:0.58 77:0.70 81:0.51 83:0.56 86:0.92 91:0.17 98:0.35 99:0.83 101:0.47 108:0.84 114:0.76 122:0.92 123:0.83 124:0.78 126:0.51 127:0.45 129:0.81 133:0.78 135:0.92 139:0.90 145:0.45 146:0.20 147:0.26 149:0.42 150:0.42 154:0.56 155:0.48 159:0.66 165:0.65 170:0.90 172:0.63 173:0.57 174:0.83 175:0.75 176:0.70 177:0.70 178:0.55 179:0.40 187:0.39 192:0.46 193:0.97 195:0.85 197:0.85 201:0.60 202:0.36 208:0.61 212:0.60 216:0.99 222:0.21 235:0.74 236:0.95 239:0.89 241:0.53 242:0.30 243:0.17 244:0.61 245:0.80 247:0.79 253:0.57 254:0.80 257:0.65 259:0.21 265:0.37 266:0.54 267:0.79 271:0.41 276:0.67 277:0.87 282:0.75 290:0.76 300:0.70 +0 10:0.94 11:0.52 12:0.37 17:0.78 18:0.93 21:0.22 27:0.70 29:0.53 30:0.15 34:0.52 36:0.17 37:0.27 39:0.94 43:0.77 45:0.81 66:0.75 69:0.10 70:0.89 71:0.92 74:0.51 81:0.32 86:0.95 91:0.17 98:0.80 101:0.65 104:0.93 106:0.81 108:0.09 120:0.66 122:0.92 123:0.09 124:0.40 126:0.24 127:0.72 129:0.05 133:0.36 135:0.49 139:0.91 140:0.45 146:0.82 147:0.85 149:0.64 150:0.35 151:0.62 153:0.69 154:0.70 159:0.52 162:0.86 164:0.62 170:0.90 172:0.65 173:0.18 174:0.83 177:0.88 179:0.66 187:0.68 190:0.95 197:0.88 202:0.46 206:0.81 212:0.59 215:0.79 216:0.11 220:0.62 222:0.21 235:0.22 239:0.91 241:0.32 242:0.70 243:0.77 245:0.64 247:0.67 248:0.60 253:0.41 254:0.97 256:0.45 259:0.21 260:0.67 265:0.80 266:0.54 267:0.28 268:0.55 271:0.41 276:0.55 283:0.67 285:0.46 297:0.36 300:0.70 +0 1:0.69 9:0.75 10:0.90 11:0.52 12:0.37 17:0.76 18:0.95 21:0.05 22:0.93 23:0.97 27:0.61 29:0.53 30:0.62 31:0.93 33:0.97 37:0.44 39:0.94 43:0.74 45:0.89 47:0.93 62:0.97 64:0.77 66:0.63 69:0.35 70:0.64 71:0.92 74:0.63 75:0.98 79:0.92 81:0.68 83:0.69 86:0.94 91:0.44 96:0.79 97:0.98 98:0.67 99:0.95 101:0.65 108:0.27 114:0.95 117:0.86 122:0.91 123:0.26 124:0.64 126:0.51 127:0.47 128:0.97 129:0.05 133:0.74 137:0.93 139:0.94 140:0.45 145:0.74 146:0.87 147:0.89 149:0.80 150:0.56 151:0.83 153:0.78 154:0.64 155:0.64 158:0.81 159:0.68 162:0.57 163:0.81 165:0.81 168:0.97 170:0.90 172:0.73 173:0.23 174:0.86 176:0.70 177:0.88 179:0.45 187:0.68 190:0.77 192:0.58 196:0.96 197:0.87 201:0.66 202:0.59 205:0.95 208:0.61 212:0.28 216:0.60 219:0.94 220:0.62 222:0.21 226:0.96 235:0.31 236:0.95 239:0.92 241:0.03 242:0.23 243:0.69 244:0.61 245:0.71 247:0.86 248:0.74 253:0.81 255:0.78 256:0.45 259:0.21 262:0.93 265:0.67 266:0.80 268:0.58 271:0.41 276:0.68 279:0.82 284:0.91 285:0.60 290:0.95 291:0.97 292:0.88 300:0.70 +0 1:0.61 9:0.79 12:0.37 17:0.94 21:0.64 23:0.96 27:0.67 29:0.53 31:0.95 34:0.50 36:0.60 37:0.53 39:0.94 41:0.82 60:0.95 62:0.97 64:0.77 71:0.92 74:0.47 75:0.99 79:0.94 81:0.70 83:0.91 91:0.51 96:0.83 114:0.72 120:0.72 126:0.54 128:0.98 135:0.51 137:0.95 139:0.74 140:0.45 145:0.56 150:0.48 151:0.90 153:0.69 155:0.66 158:0.92 162:0.86 164:0.62 165:0.93 168:0.98 170:0.90 175:0.99 176:0.73 187:0.39 192:0.99 196:0.92 201:0.61 202:0.46 205:0.95 208:0.64 215:0.53 216:0.29 219:0.95 222:0.21 226:0.98 235:0.88 236:0.97 239:0.90 241:0.46 244:0.97 248:0.80 253:0.79 255:0.94 256:0.45 257:0.97 260:0.73 267:0.28 268:0.55 271:0.41 277:0.98 279:0.82 283:0.82 284:0.90 285:0.62 290:0.72 292:0.95 297:0.36 +0 10:0.92 11:0.46 12:0.37 17:0.51 18:0.74 21:0.78 27:0.45 29:0.53 30:0.60 34:0.89 36:0.18 37:0.50 39:0.94 43:0.36 45:0.83 66:0.10 69:0.74 70:0.41 71:0.92 86:0.79 91:0.17 98:0.16 101:0.47 108:0.57 122:0.76 123:0.85 124:0.86 127:0.31 129:0.89 133:0.84 135:0.95 139:0.75 145:0.50 146:0.22 147:0.28 149:0.35 154:0.50 159:0.65 163:0.79 170:0.90 172:0.16 173:0.60 174:0.89 175:0.75 177:0.70 178:0.55 179:0.63 187:0.68 197:0.89 212:0.42 216:0.77 222:0.21 235:0.84 239:0.90 241:0.05 242:0.45 243:0.29 244:0.61 245:0.56 247:0.47 253:0.20 254:0.94 257:0.65 259:0.21 265:0.13 266:0.23 267:0.59 271:0.41 276:0.46 277:0.69 300:0.33 +1 1:0.67 10:0.99 11:0.52 12:0.37 17:0.86 18:1.00 21:0.40 22:0.93 27:0.72 29:0.53 30:0.57 33:0.97 37:0.83 39:0.94 43:0.70 44:0.92 45:0.99 47:0.93 64:0.77 66:0.99 69:0.52 70:0.73 71:0.92 74:0.90 75:0.98 77:0.70 81:0.62 83:0.69 86:1.00 91:0.37 97:0.98 98:0.87 99:0.95 101:0.65 102:0.97 108:0.40 114:0.82 117:0.86 122:0.91 123:0.38 126:0.51 127:0.39 129:0.05 139:1.00 145:0.74 146:0.98 147:0.98 149:0.96 150:0.48 154:0.74 155:0.51 158:0.81 159:0.75 165:0.81 170:0.90 172:0.98 173:0.30 174:0.97 176:0.70 177:0.88 178:0.55 179:0.12 187:0.39 192:0.48 195:0.93 197:0.97 201:0.63 208:0.61 212:0.87 216:0.30 219:0.94 222:0.21 226:0.96 235:0.68 236:0.95 239:0.99 241:0.03 242:0.29 243:0.99 247:0.94 253:0.70 254:0.90 262:0.93 265:0.87 266:0.54 271:0.41 276:0.95 279:0.82 282:0.80 290:0.82 291:0.97 292:0.88 300:0.70 +1 7:0.69 8:0.83 9:0.73 12:0.37 17:0.15 20:0.94 21:0.22 23:0.96 27:0.02 29:0.53 30:0.76 31:0.92 32:0.65 34:0.34 36:0.36 37:0.92 39:0.94 43:0.13 55:0.59 66:0.36 69:0.68 70:0.15 71:0.92 78:0.84 79:0.91 81:0.32 83:0.69 91:0.71 96:0.77 98:0.15 100:0.89 108:0.86 111:0.85 120:0.89 123:0.86 124:0.67 126:0.24 127:0.26 128:0.97 129:0.81 133:0.67 135:0.66 137:0.92 140:0.97 145:0.39 146:0.05 147:0.05 149:0.11 150:0.35 151:0.76 152:0.94 153:0.78 154:0.10 155:0.64 159:0.60 162:0.66 163:0.97 164:0.86 168:0.97 169:0.85 170:0.90 172:0.16 173:0.53 175:0.97 177:0.05 178:0.42 179:0.90 186:0.84 187:0.39 190:0.95 191:0.84 192:0.57 195:0.89 202:0.81 205:0.95 208:0.61 212:0.42 215:0.79 216:0.99 220:0.74 222:0.21 230:0.71 233:0.76 235:0.22 241:0.75 242:0.82 243:0.26 244:0.93 245:0.40 247:0.12 248:0.70 253:0.62 255:0.73 256:0.82 257:0.93 259:0.21 260:0.89 261:0.88 265:0.16 266:0.23 267:0.36 268:0.83 271:0.41 276:0.22 277:0.95 283:0.67 284:0.88 285:0.62 297:0.36 300:0.13 +0 10:0.91 11:0.52 12:0.37 17:0.57 18:0.90 21:0.78 27:0.28 29:0.53 30:0.56 34:0.36 36:0.17 37:0.50 39:0.94 43:0.60 45:0.88 66:0.91 69:0.61 70:0.82 71:0.92 74:0.60 86:0.93 91:0.12 98:0.84 101:0.65 108:0.46 122:0.91 123:0.44 127:0.34 129:0.05 135:0.47 139:0.89 146:0.81 147:0.84 149:0.66 154:0.61 159:0.37 170:0.90 172:0.74 173:0.65 174:0.86 177:0.88 178:0.55 179:0.47 187:0.39 197:0.88 212:0.75 216:0.89 222:0.21 235:0.31 239:0.89 241:0.10 242:0.24 243:0.91 247:0.82 253:0.46 254:0.99 259:0.21 265:0.84 266:0.54 267:0.44 271:0.41 276:0.61 300:0.70 +0 1:0.63 10:0.92 11:0.52 12:0.37 17:0.79 18:0.88 21:0.40 27:0.69 29:0.53 30:0.76 32:0.72 34:0.94 36:0.83 37:0.29 39:0.94 43:0.76 44:0.92 45:0.83 64:0.77 66:0.46 69:0.37 70:0.42 71:0.92 74:0.67 77:0.70 81:0.61 83:0.69 86:0.92 91:0.25 98:0.56 99:0.95 101:0.65 108:0.58 114:0.82 122:0.89 123:0.56 124:0.57 126:0.51 127:0.31 129:0.59 133:0.46 135:0.44 139:0.92 145:0.86 146:0.24 147:0.30 149:0.79 150:0.46 154:0.68 155:0.54 159:0.29 165:0.81 170:0.90 172:0.45 173:0.47 174:0.83 175:0.75 176:0.70 177:0.70 178:0.55 179:0.60 187:0.39 192:0.51 193:0.99 195:0.63 197:0.89 201:0.61 204:0.83 208:0.61 212:0.81 216:0.59 222:0.21 235:0.60 236:0.95 239:0.93 241:0.18 242:0.18 243:0.45 244:0.61 245:0.70 247:0.67 253:0.62 257:0.65 259:0.21 265:0.57 266:0.38 267:0.44 271:0.41 276:0.43 277:0.69 281:0.86 282:0.80 290:0.82 300:0.43 +0 1:0.68 9:0.79 10:0.89 11:0.57 12:0.37 17:0.75 18:0.65 21:0.13 23:0.96 27:0.67 29:0.53 30:0.95 31:0.94 34:0.59 36:0.58 37:0.53 39:0.94 41:0.82 43:0.81 60:0.95 62:0.98 64:0.77 66:0.69 69:0.71 71:0.92 74:0.56 75:0.99 79:0.93 81:0.78 83:0.91 85:0.72 86:0.74 91:0.54 96:0.80 98:0.14 101:0.87 108:0.54 114:0.92 120:0.69 122:0.65 123:0.51 126:0.54 127:0.09 128:0.98 129:0.05 135:0.94 137:0.94 139:0.81 140:0.45 145:0.70 146:0.18 147:0.23 149:0.64 150:0.60 151:0.86 153:0.48 154:0.76 155:0.66 158:0.92 159:0.09 162:0.86 164:0.57 165:0.93 168:0.98 170:0.90 172:0.21 173:0.84 174:0.79 176:0.73 177:0.88 179:0.10 187:0.39 192:0.99 196:0.94 197:0.84 201:0.67 202:0.36 205:0.95 208:0.64 212:0.61 215:0.84 216:0.29 219:0.95 222:0.21 226:0.98 235:0.42 236:0.97 239:0.93 241:0.62 242:0.63 243:0.73 247:0.30 248:0.77 253:0.81 255:0.94 256:0.45 260:0.70 265:0.14 266:0.17 267:0.52 268:0.46 271:0.41 276:0.23 279:0.82 283:0.82 284:0.89 285:0.62 290:0.92 292:0.95 297:0.36 300:0.08 +0 10:0.86 11:0.52 12:0.37 17:0.45 18:0.74 21:0.78 27:0.45 29:0.53 30:0.45 34:0.75 36:0.18 37:0.50 39:0.94 43:0.60 45:0.73 66:0.77 69:0.81 70:0.33 71:0.92 74:0.36 86:0.81 91:0.25 98:0.25 101:0.65 108:0.66 122:0.75 123:0.63 127:0.11 129:0.05 135:0.83 139:0.76 145:0.47 146:0.35 147:0.42 149:0.42 154:0.49 159:0.14 170:0.90 172:0.37 173:0.81 174:0.79 177:0.88 178:0.55 179:0.19 187:0.68 197:0.82 212:0.87 216:0.77 222:0.21 235:0.42 239:0.85 241:0.21 242:0.24 243:0.79 244:0.61 247:0.47 253:0.32 254:0.84 259:0.21 265:0.27 266:0.17 267:0.28 271:0.41 276:0.29 300:0.25 +0 1:0.63 9:0.71 10:0.94 11:0.52 12:0.37 17:0.87 18:0.98 21:0.40 22:0.93 23:0.97 27:0.70 29:0.53 30:0.40 31:0.88 32:0.71 33:0.97 37:0.32 39:0.94 43:0.73 44:0.92 45:0.93 47:0.93 62:0.96 64:0.77 66:0.59 69:0.63 70:0.89 71:0.92 74:0.69 75:0.98 79:0.88 81:0.61 83:0.69 86:0.98 91:0.38 96:0.71 97:0.99 98:0.70 99:0.95 101:0.87 102:0.97 108:0.48 114:0.82 117:0.86 122:0.92 123:0.46 124:0.67 126:0.51 127:0.56 128:0.95 129:0.05 133:0.74 137:0.88 139:0.97 140:0.45 145:0.77 146:0.91 147:0.93 149:0.82 150:0.46 151:0.56 153:0.77 154:0.33 155:0.56 158:0.81 159:0.75 162:0.68 165:0.81 168:0.95 170:0.90 172:0.85 173:0.45 174:0.89 176:0.70 177:0.88 179:0.28 187:0.68 190:0.77 192:0.55 195:0.89 196:0.92 197:0.90 201:0.61 202:0.54 205:0.95 208:0.61 212:0.48 216:0.45 219:0.94 220:0.91 222:0.21 226:0.96 235:0.60 236:0.95 239:0.95 241:0.03 242:0.43 243:0.67 245:0.87 247:0.84 248:0.55 253:0.65 254:0.98 255:0.70 256:0.45 259:0.21 262:0.93 265:0.70 266:0.80 268:0.57 271:0.41 276:0.83 279:0.81 284:0.91 285:0.60 290:0.82 291:0.97 292:0.88 300:0.84 +0 1:0.58 7:0.70 8:0.99 9:0.70 10:0.88 11:0.52 12:0.37 17:0.73 18:0.90 21:0.71 27:0.72 29:0.53 30:0.61 31:0.86 32:0.72 39:0.94 43:0.59 44:0.92 45:0.87 48:0.91 64:0.77 66:0.89 69:0.76 70:0.60 71:0.92 74:0.60 76:0.85 77:0.70 79:0.86 81:0.47 83:0.56 86:0.89 91:0.76 96:0.68 98:0.82 99:0.83 101:0.65 108:0.59 114:0.67 120:0.54 122:0.91 123:0.57 126:0.51 127:0.31 129:0.05 137:0.86 139:0.90 140:0.45 145:0.83 146:0.80 147:0.83 149:0.67 150:0.39 151:0.49 153:0.48 154:0.48 155:0.49 159:0.36 162:0.57 164:0.56 165:0.65 170:0.90 172:0.68 173:0.82 174:0.83 175:0.75 176:0.70 177:0.70 179:0.51 190:0.77 191:0.77 192:0.50 195:0.66 196:0.89 197:0.85 201:0.57 202:0.55 208:0.61 212:0.39 215:0.48 216:0.03 220:0.97 222:0.21 230:0.73 233:0.66 235:0.95 239:0.88 241:0.85 242:0.21 243:0.89 244:0.61 247:0.60 248:0.49 253:0.54 255:0.69 256:0.45 257:0.65 259:0.21 260:0.55 265:0.82 266:0.47 268:0.46 271:0.41 276:0.56 277:0.69 281:0.86 282:0.80 284:0.88 285:0.60 290:0.67 297:0.36 300:0.55 +1 6:0.96 7:0.69 8:0.88 9:0.75 10:0.89 12:0.37 17:0.61 20:0.91 21:0.05 23:0.99 27:0.26 29:0.53 30:0.76 31:0.95 32:0.67 37:0.82 39:0.94 41:0.82 43:0.64 55:0.42 62:0.99 66:0.72 69:0.41 70:0.22 71:0.92 75:0.97 78:0.98 79:0.94 81:0.48 83:0.91 91:0.88 96:0.84 98:0.65 100:0.99 102:0.96 104:0.58 108:0.31 111:0.99 120:0.89 122:0.51 123:0.30 126:0.32 127:0.19 128:0.99 129:0.05 137:0.95 140:0.93 145:0.98 146:0.41 147:0.48 149:0.45 150:0.47 151:0.91 152:0.85 153:0.72 154:0.54 155:0.66 159:0.16 162:0.59 164:0.86 168:0.99 169:1.00 170:0.90 172:0.27 173:0.65 174:0.83 175:0.91 177:0.38 179:0.82 186:0.98 189:0.97 191:0.89 192:0.98 195:0.66 197:0.88 201:0.59 202:0.84 205:1.00 208:0.64 212:0.42 215:0.91 216:0.16 220:0.62 222:0.21 226:0.96 230:0.71 233:0.76 235:0.12 236:0.94 238:0.99 239:0.92 241:0.93 242:0.45 243:0.75 244:0.83 247:0.35 248:0.81 253:0.77 254:0.74 255:0.79 256:0.88 257:0.84 260:0.89 261:0.98 265:0.66 266:0.23 268:0.85 271:0.41 276:0.17 277:0.87 283:0.67 284:0.91 285:0.62 297:0.36 300:0.18 +0 1:0.69 7:0.81 9:0.79 10:0.98 11:0.81 12:0.37 17:0.98 18:0.97 21:0.30 23:0.96 27:0.72 29:0.53 30:0.84 31:0.94 32:0.80 34:0.49 36:0.74 39:0.94 41:0.82 43:0.73 44:0.93 45:0.92 48:0.91 62:0.99 64:0.77 66:0.96 69:0.27 70:0.40 71:0.92 74:0.84 75:0.98 77:0.70 79:0.93 81:0.88 83:0.91 85:0.88 86:0.98 91:0.54 96:0.80 97:0.94 98:0.95 99:0.99 101:1.00 102:0.98 108:0.21 114:0.86 120:0.69 121:0.90 122:0.89 123:0.20 126:0.54 127:0.68 128:0.98 129:0.05 135:0.80 137:0.94 139:0.98 140:0.80 145:0.91 146:0.87 147:0.90 149:0.95 150:0.67 151:0.86 153:0.48 154:0.63 155:0.66 158:0.81 159:0.45 162:0.62 164:0.57 165:1.00 168:0.98 170:0.90 172:0.89 173:0.33 174:0.94 176:0.73 177:0.88 178:0.42 179:0.33 187:0.21 192:0.99 193:1.00 195:0.66 196:0.97 197:0.97 201:0.68 202:0.50 204:0.85 205:0.95 208:0.64 212:0.82 215:0.72 216:0.19 219:0.94 222:0.21 226:0.96 230:0.87 233:0.76 235:0.88 236:0.97 239:0.99 241:0.27 242:0.21 243:0.96 247:0.84 248:0.77 253:0.86 255:0.94 256:0.45 260:0.70 265:0.95 266:0.38 267:0.69 268:0.46 271:0.41 276:0.81 279:0.78 281:0.91 282:0.88 284:0.89 285:0.62 290:0.86 292:0.88 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.83 9:0.80 11:0.88 12:0.06 17:0.57 20:0.98 21:0.30 27:0.05 28:0.66 30:0.56 32:0.80 34:0.77 36:0.94 37:0.94 39:0.47 41:0.92 43:0.87 60:0.87 66:0.09 69:0.55 70:0.85 74:0.88 77:0.70 78:0.95 81:0.74 83:0.87 85:0.93 91:0.11 96:0.87 98:0.19 100:0.97 101:0.76 108:0.94 111:0.96 114:0.86 120:0.79 121:1.00 122:0.57 123:0.71 124:0.92 126:0.54 127:0.44 129:0.93 133:0.91 135:0.72 140:0.45 144:0.85 145:0.79 146:0.25 147:0.31 149:0.85 150:0.84 151:0.95 152:0.97 153:0.84 154:0.85 155:0.67 158:0.92 159:0.87 161:0.84 162:0.71 164:0.76 165:0.84 167:0.53 169:0.94 172:0.27 173:0.24 176:0.73 177:0.38 179:0.52 181:0.64 186:0.95 187:0.68 189:0.93 192:0.59 195:0.97 201:0.74 202:0.67 204:0.89 208:0.64 212:0.16 215:0.72 216:0.82 222:0.60 230:0.87 233:0.76 235:0.42 238:0.94 241:0.55 242:0.40 243:0.26 245:0.63 247:0.47 248:0.86 253:0.90 255:0.79 256:0.68 259:0.21 260:0.80 261:0.95 265:0.15 266:0.92 267:0.52 268:0.70 271:0.62 276:0.60 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.84 +1 8:0.84 11:0.88 12:0.06 17:0.30 21:0.78 27:0.45 28:0.66 30:0.33 34:0.90 36:0.18 37:0.50 39:0.47 43:0.78 66:0.20 69:0.79 70:0.36 74:0.62 85:0.80 91:0.33 98:0.25 101:0.72 108:0.63 122:0.51 123:0.83 124:0.74 127:0.22 129:0.59 133:0.64 135:0.86 144:0.85 145:0.49 146:0.28 147:0.34 149:0.62 154:0.70 159:0.52 161:0.84 163:0.75 167:0.50 172:0.16 173:0.68 177:0.38 178:0.55 179:0.71 181:0.64 187:0.68 212:0.19 216:0.77 222:0.60 235:0.95 241:0.44 242:0.45 243:0.40 245:0.49 247:0.39 253:0.49 259:0.21 265:0.17 266:0.47 267:0.44 271:0.62 276:0.28 277:0.69 300:0.25 +2 1:0.74 6:0.91 7:0.81 9:0.80 11:0.88 12:0.06 17:0.34 20:0.84 21:0.22 27:0.05 28:0.66 30:0.94 32:0.80 34:0.78 36:0.87 37:0.94 39:0.47 41:0.90 43:0.87 60:0.87 66:0.36 69:0.55 70:0.13 74:0.75 77:0.70 78:0.80 81:0.80 83:0.87 85:0.80 91:0.05 96:0.90 98:0.08 100:0.83 101:0.72 108:0.90 111:0.79 114:0.90 120:0.82 121:1.00 122:0.43 123:0.89 124:0.57 126:0.54 127:0.31 129:0.59 133:0.47 135:0.58 140:0.45 144:0.85 145:0.79 146:0.13 147:0.18 149:0.59 150:0.87 151:0.87 152:0.97 153:0.48 154:0.85 155:0.67 158:0.92 159:0.72 161:0.84 162:0.86 163:0.90 164:0.72 165:0.86 167:0.53 169:0.94 172:0.16 173:0.32 176:0.73 177:0.38 179:0.93 181:0.64 186:0.80 187:0.99 192:0.59 195:0.93 201:0.74 202:0.56 204:0.89 208:0.64 212:0.42 215:0.79 216:0.82 222:0.60 230:0.87 233:0.76 235:0.31 241:0.54 242:0.75 243:0.40 245:0.43 247:0.30 248:0.89 253:0.90 255:0.79 256:0.64 259:0.21 260:0.83 261:0.95 265:0.08 266:0.23 267:0.23 268:0.65 271:0.62 276:0.12 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.13 +2 1:0.74 6:0.88 7:0.81 8:0.77 9:0.80 11:0.88 12:0.06 17:0.22 20:0.99 21:0.13 27:0.11 28:0.66 30:0.68 32:0.80 34:0.44 36:0.85 37:0.80 39:0.47 41:0.94 43:0.90 60:0.97 66:0.32 69:0.46 70:0.43 74:0.81 77:0.70 78:0.92 81:0.84 83:0.83 85:0.85 91:0.31 96:0.92 98:0.22 100:0.95 101:0.75 108:0.69 111:0.93 114:0.92 120:0.86 121:0.99 122:0.43 123:0.67 124:0.72 126:0.54 127:0.21 129:0.81 133:0.67 135:0.26 138:0.96 140:0.45 144:0.85 145:0.50 146:0.13 147:0.18 149:0.71 150:0.90 151:0.97 152:0.92 153:0.89 154:0.89 155:0.67 158:0.92 159:0.45 161:0.84 162:0.83 163:0.88 164:0.82 165:0.88 167:0.52 169:0.93 172:0.21 173:0.33 176:0.73 177:0.38 179:0.70 181:0.64 186:0.92 187:0.87 189:0.89 191:0.73 192:0.59 195:0.85 201:0.74 202:0.72 204:0.89 208:0.64 212:0.42 215:0.84 216:0.86 222:0.60 230:0.87 233:0.76 235:0.22 238:0.92 241:0.52 242:0.63 243:0.32 245:0.48 247:0.30 248:0.92 253:0.93 255:0.79 256:0.78 259:0.21 260:0.87 261:0.93 265:0.24 266:0.27 267:0.91 268:0.78 271:0.62 276:0.29 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 7:0.81 9:0.80 11:0.88 12:0.06 17:0.47 27:0.14 28:0.66 30:0.56 32:0.80 34:0.26 36:0.82 37:0.86 39:0.47 41:0.88 43:0.63 60:0.87 66:0.88 69:0.91 70:0.48 74:0.90 76:0.85 77:0.70 81:0.93 83:0.88 85:0.96 91:0.09 96:0.84 98:0.76 101:0.83 108:0.82 114:0.98 120:0.74 121:0.97 122:0.57 123:0.81 124:0.36 126:0.54 127:0.42 129:0.05 133:0.39 135:0.68 140:0.45 144:0.85 145:0.50 146:0.94 147:0.05 149:0.87 150:0.97 151:0.87 153:0.48 154:0.52 155:0.67 158:0.92 159:0.70 161:0.84 162:0.86 164:0.67 165:0.92 167:0.51 172:0.84 173:0.88 176:0.73 177:0.05 179:0.35 181:0.64 187:0.87 192:0.59 195:0.89 201:0.74 202:0.50 204:0.89 208:0.64 212:0.47 215:0.96 216:0.54 220:0.74 222:0.60 230:0.87 233:0.76 235:0.05 241:0.47 242:0.50 243:0.08 245:0.64 247:0.55 248:0.81 253:0.88 255:0.79 256:0.58 257:0.65 259:0.21 260:0.74 265:0.77 266:0.63 267:0.44 268:0.59 271:0.62 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.43 +3 6:0.79 8:0.58 9:0.80 12:0.06 17:0.49 20:0.87 21:0.78 27:0.72 28:0.66 34:0.19 36:0.54 37:0.23 39:0.47 41:0.96 43:0.41 60:0.87 66:0.36 69:0.27 74:0.55 78:0.89 83:0.88 91:0.72 96:0.96 98:0.06 100:0.87 104:0.58 108:0.21 111:0.87 120:0.92 123:0.20 127:0.05 129:0.05 135:0.28 140:0.45 144:0.85 146:0.05 147:0.05 149:0.15 151:0.98 152:0.82 153:0.93 154:0.31 155:0.67 158:0.92 159:0.05 161:0.84 162:0.69 164:0.87 167:0.77 169:0.85 172:0.05 173:0.17 175:0.75 177:0.05 181:0.64 186:0.89 189:0.81 191:0.73 192:0.59 202:0.83 208:0.64 216:0.31 222:0.60 232:0.75 235:0.05 238:0.87 241:0.85 243:0.51 244:0.61 248:0.96 253:0.95 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.86 265:0.06 267:0.79 268:0.86 271:0.62 277:0.69 279:0.86 283:0.82 285:0.62 +0 1:0.74 11:0.88 12:0.06 17:0.53 21:0.78 27:0.45 28:0.66 30:0.45 32:0.80 34:0.79 36:0.20 37:0.50 39:0.47 43:0.81 66:0.27 69:0.72 70:0.25 74:0.61 77:0.70 81:0.41 83:0.71 85:0.72 91:0.10 98:0.15 101:0.71 108:0.55 114:0.63 122:0.51 123:0.83 124:0.61 126:0.54 127:0.36 129:0.05 133:0.39 135:0.47 144:0.85 145:0.63 146:0.13 147:0.18 149:0.48 150:0.61 154:0.76 155:0.67 159:0.52 161:0.84 163:0.90 165:0.63 167:0.50 172:0.10 173:0.68 176:0.73 177:0.38 178:0.55 179:0.96 181:0.64 187:0.68 192:0.59 195:0.77 201:0.74 212:0.61 215:0.43 216:0.77 222:0.60 235:1.00 241:0.46 242:0.63 243:0.46 245:0.40 247:0.30 253:0.54 259:0.21 265:0.09 266:0.17 267:0.19 271:0.62 276:0.13 277:0.69 282:0.88 290:0.63 297:0.36 299:0.88 300:0.18 +1 1:0.74 7:0.81 9:0.80 11:0.88 12:0.06 17:0.28 21:0.22 27:0.05 28:0.66 30:0.94 32:0.80 34:0.73 36:0.83 37:0.94 39:0.47 41:0.82 43:0.87 60:0.87 66:0.36 69:0.55 70:0.13 74:0.75 77:0.70 81:0.80 83:0.85 85:0.80 91:0.03 96:0.80 98:0.08 101:0.72 108:0.90 114:0.90 120:0.69 121:1.00 122:0.43 123:0.89 124:0.57 126:0.54 127:0.31 129:0.59 133:0.47 135:0.56 140:0.45 144:0.85 145:0.79 146:0.13 147:0.18 149:0.59 150:0.87 151:0.87 153:0.48 154:0.85 155:0.67 158:0.92 159:0.72 161:0.84 162:0.86 163:0.90 164:0.57 165:0.86 167:0.51 172:0.16 173:0.32 176:0.73 177:0.38 179:0.93 181:0.64 187:0.99 192:0.59 195:0.93 201:0.74 202:0.36 204:0.89 208:0.64 212:0.42 215:0.79 216:0.82 222:0.60 230:0.87 233:0.76 235:0.31 241:0.49 242:0.75 243:0.40 245:0.43 247:0.30 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 265:0.08 266:0.23 267:0.23 268:0.46 271:0.62 276:0.12 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.13 +2 1:0.74 7:0.76 9:0.80 12:0.06 17:0.49 20:0.96 21:0.13 27:0.72 28:0.66 32:0.80 34:0.26 36:0.71 39:0.47 41:0.94 74:0.55 76:0.85 77:0.70 78:0.80 81:0.84 83:0.83 91:0.91 96:0.94 100:0.83 111:0.80 114:0.92 120:0.89 126:0.54 135:0.37 140:0.45 144:0.85 145:0.50 150:0.90 151:0.96 152:0.89 153:0.87 155:0.67 161:0.84 162:0.82 164:0.81 165:0.88 167:0.79 169:0.81 175:0.91 176:0.73 181:0.64 186:0.81 192:0.59 195:0.53 201:0.74 202:0.71 208:0.64 215:0.84 216:0.14 222:0.60 230:0.79 232:0.74 233:0.76 235:0.22 241:0.94 244:0.83 248:0.94 253:0.92 255:0.79 256:0.75 257:0.84 259:0.21 260:0.89 261:0.83 267:0.28 268:0.77 271:0.62 277:0.87 282:0.88 285:0.62 290:0.92 297:0.36 299:0.88 +1 1:0.74 11:0.88 12:0.51 17:0.26 21:0.40 27:0.45 28:0.91 30:0.31 34:0.68 36:0.17 37:0.50 39:0.75 43:0.82 55:0.71 60:0.87 66:0.10 69:0.69 70:0.81 74:0.91 81:0.71 83:0.84 85:0.93 91:0.04 98:0.32 101:0.76 108:0.82 114:0.82 122:0.67 123:0.63 124:0.93 126:0.54 127:0.71 129:0.96 133:0.93 135:0.78 144:0.85 145:0.49 146:0.13 147:0.18 149:0.83 150:0.81 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 163:0.81 165:0.83 167:0.52 172:0.54 173:0.40 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 201:0.74 208:0.64 212:0.18 215:0.67 216:0.77 222:0.25 235:0.52 241:0.27 242:0.61 243:0.11 245:0.76 247:0.60 253:0.75 259:0.21 265:0.34 266:0.94 267:0.65 271:0.32 276:0.77 279:0.86 283:0.71 290:0.82 297:0.36 300:0.70 +3 1:0.74 7:0.81 11:0.88 12:0.51 17:0.51 21:0.30 27:0.21 28:0.91 30:0.62 34:0.66 36:0.59 37:0.74 39:0.75 43:0.98 66:0.12 69:0.12 70:0.72 74:0.99 81:0.76 83:0.75 85:0.99 91:0.05 98:0.55 101:0.86 108:0.93 114:0.86 121:0.97 122:0.57 123:0.82 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.18 144:0.85 146:0.72 147:0.77 149:0.99 150:0.84 154:0.98 155:0.67 159:0.86 161:0.77 165:0.84 167:0.57 172:0.92 173:0.08 176:0.73 177:0.38 178:0.55 179:0.15 181:0.80 187:0.68 192:0.59 201:0.74 202:0.46 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 233:0.76 235:0.42 241:0.44 242:0.52 243:0.28 245:0.97 247:0.67 253:0.78 259:0.21 265:0.48 266:0.87 267:0.59 271:0.32 276:0.94 290:0.86 297:0.36 300:0.94 +3 1:0.74 8:0.67 9:0.80 11:0.88 12:0.51 17:0.02 27:0.33 28:0.91 30:0.61 34:0.33 36:0.32 37:0.41 39:0.75 41:0.92 43:0.86 66:0.09 69:0.57 70:0.75 74:0.89 81:0.94 83:0.83 85:0.98 91:0.49 96:0.93 98:0.21 101:0.83 104:0.84 106:0.81 108:0.78 114:0.98 117:0.86 120:0.87 122:0.43 123:0.94 124:0.94 126:0.54 127:0.75 129:0.97 133:0.94 135:0.89 138:0.96 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:0.98 153:0.91 154:0.84 155:0.67 159:0.86 161:0.77 162:0.73 164:0.79 165:0.92 167:0.64 172:0.61 173:0.30 176:0.73 177:0.38 179:0.33 181:0.80 187:0.39 192:0.59 201:0.74 202:0.71 206:0.81 208:0.64 212:0.52 215:0.96 216:0.88 222:0.25 232:0.83 235:0.05 241:0.61 242:0.51 243:0.15 245:0.80 247:0.67 248:0.93 253:0.95 255:0.79 256:0.71 259:0.21 260:0.88 265:0.19 266:0.92 267:0.59 268:0.74 271:0.32 276:0.80 285:0.62 290:0.98 297:0.36 300:0.84 +2 6:0.97 8:0.95 9:0.80 12:0.51 17:0.38 20:0.89 21:0.78 27:0.05 28:0.91 34:0.85 36:0.58 37:0.81 39:0.75 41:0.97 60:0.95 74:0.46 78:0.85 83:0.88 91:0.67 96:0.92 100:0.95 111:0.90 120:0.86 135:0.70 138:0.98 140:0.45 144:0.85 151:0.93 152:0.84 153:0.77 155:0.67 158:0.87 161:0.77 162:0.67 164:0.87 167:0.64 169:0.93 175:0.91 181:0.80 186:0.85 187:0.98 189:0.98 192:0.59 202:0.81 208:0.64 216:0.94 220:0.99 222:0.25 232:0.97 235:0.42 238:0.97 241:0.60 244:0.83 248:0.92 253:0.88 255:0.79 256:0.83 257:0.84 259:0.21 260:0.86 261:0.94 267:0.23 268:0.83 271:0.32 277:0.87 279:0.86 283:0.71 285:0.62 +2 6:0.83 8:0.63 9:0.80 11:0.88 12:0.51 17:0.08 20:0.80 21:0.78 27:0.31 28:0.91 30:0.95 34:0.25 36:0.85 37:0.30 39:0.75 41:0.97 43:0.86 66:0.54 69:0.59 74:0.44 78:0.79 83:0.83 85:0.72 91:0.92 96:0.94 98:0.16 100:0.83 101:0.76 104:0.58 108:0.45 111:0.79 120:0.91 123:0.43 127:0.09 129:0.05 135:0.20 140:0.45 144:0.85 146:0.13 147:0.18 149:0.49 151:0.96 152:0.77 153:0.86 154:0.83 155:0.67 159:0.08 161:0.77 162:0.65 164:0.89 167:0.83 169:0.81 172:0.10 173:0.93 177:0.38 179:0.18 181:0.80 186:0.79 189:0.85 191:0.81 192:0.59 202:0.85 208:0.64 216:0.19 220:0.74 222:0.25 232:0.78 235:0.22 238:0.92 241:0.82 243:0.63 248:0.94 253:0.92 255:0.79 256:0.86 259:0.21 260:0.91 261:0.81 265:0.17 267:0.23 268:0.86 271:0.32 285:0.62 300:0.08 +1 1:0.74 6:0.95 8:0.86 9:0.80 11:0.88 12:0.51 17:0.45 20:0.74 21:0.59 27:0.06 28:0.91 30:0.38 32:0.80 34:0.75 36:0.75 37:0.82 39:0.75 41:0.95 43:0.76 55:0.71 60:0.97 66:0.13 69:0.81 70:0.80 74:0.94 77:0.96 78:0.99 81:0.58 83:0.86 85:0.94 91:0.48 96:0.87 98:0.38 100:0.96 101:0.79 108:0.90 111:0.98 114:0.74 120:0.78 122:0.67 123:0.77 124:0.88 126:0.54 127:0.38 129:0.93 133:0.87 135:0.77 140:0.45 144:0.85 145:0.90 146:0.36 147:0.43 149:0.86 150:0.73 151:0.87 152:0.74 153:0.48 154:0.67 155:0.67 158:0.87 159:0.79 161:0.77 162:0.81 164:0.85 165:0.78 167:0.57 169:0.92 172:0.61 173:0.61 176:0.73 177:0.38 179:0.26 181:0.80 186:0.99 187:0.39 189:0.96 192:0.59 195:0.94 201:0.74 202:0.78 208:0.64 212:0.37 215:0.55 216:0.92 220:0.91 222:0.25 235:0.74 238:0.80 241:0.44 242:0.55 243:0.21 245:0.85 247:0.47 248:0.86 253:0.91 255:0.79 256:0.81 259:0.21 260:0.79 261:0.78 265:0.38 266:0.80 267:0.52 268:0.83 271:0.32 276:0.81 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.88 12:0.51 17:0.22 20:0.98 21:0.30 27:0.21 28:0.91 30:0.60 34:0.66 36:0.70 37:0.74 39:0.75 41:0.99 43:0.98 60:0.87 66:0.10 69:0.12 70:0.68 74:0.99 78:0.85 81:0.76 83:0.90 85:0.99 91:0.81 96:0.98 98:0.56 100:0.95 101:0.86 104:0.84 106:0.81 108:0.93 111:0.91 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.82 124:0.71 126:0.54 127:0.61 129:0.81 133:0.67 135:0.17 140:0.45 144:0.85 146:0.90 147:0.92 149:0.99 150:0.84 151:0.99 152:0.97 153:0.97 154:0.98 155:0.67 158:0.92 159:0.85 161:0.77 162:0.64 164:0.94 165:0.84 167:0.61 169:0.91 172:0.91 173:0.08 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.39 189:0.97 191:0.75 192:0.59 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.56 242:0.51 243:0.38 245:0.98 247:0.67 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.96 265:0.48 266:0.80 267:0.59 268:0.92 271:0.32 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.51 17:0.01 20:0.98 27:0.14 28:0.91 30:0.65 34:0.67 36:0.29 37:0.67 39:0.75 41:0.99 43:0.86 60:0.98 66:0.06 69:0.57 70:0.74 74:0.93 78:0.85 81:0.94 83:0.90 85:0.98 91:0.63 96:0.99 98:0.25 100:0.95 101:0.83 108:0.78 111:0.90 114:0.98 120:0.98 122:0.43 123:0.86 124:0.93 126:0.54 127:0.74 129:0.96 133:0.93 135:0.85 138:0.99 140:0.45 144:0.85 145:0.82 146:0.22 147:0.28 149:0.96 150:0.97 151:1.00 152:0.98 153:0.98 154:0.84 155:0.67 158:0.92 159:0.86 161:0.77 162:0.66 164:0.96 165:0.92 167:0.56 169:0.90 172:0.61 173:0.30 176:0.73 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.94 192:0.59 201:0.74 202:0.94 208:0.64 212:0.50 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.41 242:0.53 243:0.15 245:0.83 247:0.67 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.92 267:0.28 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +3 1:0.74 6:0.93 8:0.76 9:0.80 11:0.88 12:0.51 20:0.76 27:0.14 28:0.91 30:0.62 34:0.53 36:0.34 37:0.67 39:0.75 41:0.99 43:0.86 60:0.95 66:0.06 69:0.59 70:0.76 74:0.91 78:0.91 81:0.94 83:0.90 85:0.98 91:0.90 96:0.99 98:0.27 100:0.90 101:0.83 108:0.78 111:0.95 114:0.98 120:0.98 122:0.43 123:0.62 124:0.93 126:0.54 127:0.75 129:0.96 133:0.93 135:0.83 138:0.99 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:1.00 152:0.98 153:0.98 154:0.83 155:0.67 158:0.92 159:0.86 161:0.77 162:0.68 164:0.96 165:0.92 167:0.57 169:0.90 172:0.61 173:0.31 176:0.73 177:0.38 179:0.32 181:0.80 186:0.79 187:0.39 189:0.92 191:0.79 192:0.59 201:0.74 202:0.94 208:0.64 212:0.51 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.46 242:0.52 243:0.15 245:0.82 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.94 267:0.36 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +1 1:0.74 11:0.88 12:0.51 17:0.30 21:0.54 27:0.45 28:0.91 30:0.21 32:0.80 34:0.75 36:0.17 37:0.50 39:0.75 43:0.82 60:0.87 66:0.51 69:0.70 70:0.83 74:0.93 77:0.70 81:0.62 83:0.86 85:0.95 91:0.06 98:0.63 101:0.78 108:0.53 114:0.77 122:0.67 123:0.51 124:0.87 126:0.54 127:0.70 129:0.93 133:0.91 135:0.72 144:0.85 145:0.44 146:0.95 147:0.96 149:0.90 150:0.76 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 165:0.79 167:0.58 172:0.77 173:0.41 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 195:0.96 201:0.74 208:0.64 212:0.15 215:0.58 216:0.77 222:0.25 235:0.68 241:0.50 242:0.45 243:0.61 245:0.73 247:0.67 253:0.74 259:0.21 265:0.63 266:0.94 267:0.44 271:0.32 276:0.77 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.70 +1 1:0.74 8:0.66 9:0.80 11:0.88 12:0.51 17:0.04 21:0.22 27:0.14 28:0.91 30:0.71 32:0.80 34:0.63 36:0.30 37:0.67 39:0.75 41:0.98 43:0.86 60:0.99 66:0.52 69:0.58 70:0.66 74:0.86 77:0.70 78:0.80 81:0.81 83:0.88 85:0.96 91:0.77 96:0.95 98:0.53 101:0.83 108:0.86 111:0.79 114:0.90 120:0.91 122:0.43 123:0.85 124:0.73 126:0.54 127:0.47 129:0.89 133:0.78 135:0.98 140:0.45 144:0.85 145:0.94 146:0.13 147:0.18 149:0.89 150:0.87 151:0.91 153:0.72 154:0.84 155:0.67 158:0.92 159:0.71 161:0.77 162:0.72 164:0.91 165:0.86 167:0.58 169:0.95 172:0.67 173:0.42 176:0.73 177:0.38 179:0.46 181:0.80 187:0.39 191:0.75 192:0.59 195:0.88 201:0.74 202:0.85 208:0.64 212:0.36 215:0.79 216:0.93 220:0.74 222:0.25 235:0.31 238:0.86 241:0.47 242:0.61 243:0.14 245:0.74 247:0.39 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.91 265:0.54 266:0.71 267:0.87 268:0.88 271:0.32 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.88 12:0.51 17:0.26 20:0.97 21:0.30 27:0.25 28:0.91 30:0.75 32:0.80 34:0.31 36:0.90 37:0.43 39:0.75 41:0.97 43:0.83 60:0.87 66:0.60 69:0.65 70:0.59 74:0.90 77:0.89 78:0.78 81:0.76 83:0.89 85:0.97 91:0.40 96:0.94 98:0.32 100:0.83 101:0.83 104:0.93 106:0.81 108:0.90 111:0.79 114:0.86 117:0.86 120:0.90 121:0.99 122:0.43 123:0.90 124:0.58 126:0.54 127:0.44 129:0.81 133:0.67 135:0.93 140:0.45 144:0.85 145:0.72 146:0.54 147:0.60 149:0.90 150:0.84 151:0.96 152:0.90 153:0.85 154:0.80 155:0.67 158:0.92 159:0.75 161:0.77 162:0.85 164:0.89 165:0.84 167:0.55 169:0.81 172:0.74 173:0.46 176:0.73 177:0.38 179:0.40 181:0.80 186:0.79 187:0.39 189:0.94 192:0.59 195:0.92 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.73 215:0.72 216:0.80 220:0.74 222:0.25 230:0.87 232:0.95 233:0.76 235:0.42 238:0.92 241:0.39 242:0.59 243:0.31 245:0.79 247:0.55 248:0.94 253:0.96 255:0.79 256:0.83 259:0.21 260:0.90 261:0.87 265:0.35 266:0.71 267:0.65 268:0.86 271:0.32 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.51 17:0.06 21:0.13 27:0.13 28:0.91 30:0.86 32:0.80 34:0.44 36:0.44 37:0.79 39:0.75 41:0.97 43:0.81 60:0.87 66:0.84 69:0.70 70:0.28 74:0.86 77:0.70 81:0.85 83:0.89 85:0.91 91:0.75 96:0.95 98:0.63 101:0.83 108:0.53 114:0.92 120:0.90 121:0.90 122:0.43 123:0.51 126:0.54 127:0.19 129:0.05 135:0.89 140:0.45 144:0.85 145:0.54 146:0.60 147:0.65 149:0.86 150:0.91 151:0.93 153:0.78 154:0.76 155:0.67 158:0.92 159:0.22 161:0.77 162:0.83 164:0.89 165:0.88 167:0.72 172:0.54 173:0.78 176:0.73 177:0.38 179:0.47 181:0.80 187:0.39 192:0.59 195:0.63 201:0.74 202:0.81 204:0.89 208:0.64 212:0.52 215:0.84 216:0.83 220:0.74 222:0.25 230:0.87 233:0.76 235:0.22 241:0.70 242:0.63 243:0.85 247:0.30 248:0.95 253:0.96 255:0.79 256:0.84 259:0.21 260:0.91 265:0.64 266:0.38 267:0.52 268:0.87 271:0.32 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.93 7:0.72 8:0.77 11:0.93 12:0.51 17:0.32 18:0.86 20:0.80 21:0.59 22:0.93 27:0.41 28:0.73 29:0.71 30:0.76 31:0.86 32:0.80 34:0.97 36:0.29 37:0.44 39:0.75 43:0.88 44:0.93 45:0.93 47:0.93 48:0.91 60:0.95 64:0.77 66:0.80 69:0.54 70:0.58 71:0.65 74:0.91 77:0.89 78:0.84 79:0.86 81:0.47 83:0.91 85:0.91 86:0.94 91:0.42 98:0.83 100:0.83 101:0.97 104:0.96 106:0.81 108:0.42 111:0.83 114:0.58 120:0.53 122:0.75 123:0.40 124:0.40 126:0.54 127:0.65 129:0.05 133:0.43 135:0.78 137:0.86 139:0.96 140:0.80 144:0.85 145:0.82 146:0.93 147:0.94 149:0.95 150:0.71 151:0.47 152:0.85 153:0.48 154:0.86 155:0.67 158:0.91 159:0.69 161:0.63 162:0.58 164:0.53 165:0.93 167:0.45 169:0.86 172:0.90 173:0.42 176:0.73 177:0.70 178:0.42 179:0.29 181:0.82 186:0.84 187:0.68 189:0.91 190:0.77 192:0.87 195:0.83 196:0.88 201:0.93 202:0.53 204:0.85 206:0.81 208:0.64 212:0.71 215:0.39 216:0.75 219:0.95 220:0.96 222:0.25 230:0.75 232:0.98 233:0.76 235:0.80 238:0.83 242:0.30 243:0.82 245:0.88 247:0.86 248:0.47 253:0.48 255:0.74 256:0.45 259:0.21 260:0.53 261:0.88 262:0.93 265:0.83 266:0.80 267:0.95 268:0.46 271:0.32 276:0.82 279:0.86 281:0.91 282:0.88 283:0.78 284:0.87 285:0.56 290:0.58 292:0.91 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.94 7:0.72 8:0.92 9:0.80 11:0.92 12:0.51 17:0.40 18:0.78 20:0.78 21:0.30 22:0.93 27:0.48 28:0.73 29:0.71 30:0.60 31:0.87 32:0.69 34:0.80 36:0.81 37:0.72 39:0.75 41:0.82 43:0.85 45:0.81 47:0.93 48:0.91 60:0.87 64:0.77 66:0.72 69:0.62 70:0.48 71:0.65 74:0.81 77:0.70 78:0.96 79:0.87 81:0.61 83:0.91 85:0.85 86:0.87 91:0.27 96:0.69 98:0.75 100:0.98 101:0.87 104:0.89 106:0.81 108:0.59 111:0.98 114:0.65 117:0.86 120:0.55 122:0.77 123:0.57 124:0.44 126:0.54 127:0.54 129:0.59 133:0.61 135:0.60 137:0.87 139:0.90 140:0.45 144:0.85 145:0.90 146:0.17 147:0.22 149:0.88 150:0.78 151:0.52 152:0.87 153:0.48 154:0.82 155:0.67 158:0.91 159:0.52 161:0.63 162:0.86 164:0.57 165:0.93 167:0.53 169:0.94 172:0.74 173:0.61 176:0.73 177:0.70 179:0.49 181:0.82 186:0.96 187:0.39 189:0.96 192:0.87 195:0.71 196:0.90 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.60 215:0.44 216:0.69 219:0.95 220:0.87 222:0.25 230:0.75 232:0.93 233:0.76 235:0.52 238:0.96 241:0.37 242:0.32 243:0.19 245:0.68 247:0.67 248:0.51 253:0.53 255:0.95 256:0.45 259:0.21 260:0.56 261:0.94 262:0.93 265:0.75 266:0.47 267:0.36 268:0.46 271:0.32 276:0.65 279:0.86 281:0.89 282:0.77 283:0.78 284:0.89 285:0.62 290:0.65 292:0.91 297:0.36 300:0.43 +0 1:0.69 6:0.93 8:0.84 9:0.80 11:0.91 12:0.51 17:0.16 18:0.71 20:0.77 21:0.68 27:0.05 28:0.73 29:0.71 30:0.33 31:0.93 32:0.69 34:0.42 36:0.71 37:0.93 39:0.75 41:0.82 43:0.26 45:0.88 55:0.84 66:0.90 69:0.93 70:0.86 71:0.65 74:0.61 77:0.70 78:0.84 79:0.92 81:0.32 83:0.91 86:0.74 91:0.66 96:0.94 97:0.97 98:0.78 99:0.83 100:0.84 101:0.52 108:0.87 111:0.83 114:0.55 120:0.90 122:0.77 123:0.86 124:0.36 126:0.44 127:0.50 129:0.05 133:0.37 135:0.76 137:0.93 139:0.71 140:0.98 144:0.85 145:0.50 146:0.97 147:0.05 149:0.55 150:0.51 151:0.82 152:0.76 153:0.86 154:0.44 155:0.67 158:0.78 159:0.80 161:0.63 162:0.59 164:0.89 165:0.70 167:0.59 169:0.82 172:0.87 173:0.88 176:0.66 177:0.05 178:0.42 179:0.33 181:0.82 186:0.84 187:0.68 189:0.94 192:0.87 195:0.93 196:0.91 201:0.68 202:0.89 208:0.64 212:0.49 215:0.37 216:0.84 220:0.99 222:0.25 232:0.96 235:0.84 238:0.86 241:0.53 242:0.38 243:0.07 245:0.67 247:0.86 248:0.75 253:0.91 254:0.92 255:0.95 256:0.90 257:0.84 259:0.21 260:0.87 261:0.78 265:0.79 266:0.75 267:0.44 268:0.90 271:0.32 276:0.77 279:0.82 282:0.77 283:0.78 284:0.92 285:0.62 290:0.55 297:0.36 300:0.84 +2 1:0.74 6:0.93 7:0.72 8:0.87 11:0.92 12:0.51 17:0.22 18:0.94 20:0.76 21:0.54 22:0.93 27:0.41 28:0.73 29:0.71 30:0.66 32:0.74 34:0.62 36:0.30 37:0.44 39:0.75 43:0.88 44:0.93 45:0.95 47:0.93 64:0.77 66:0.33 69:0.54 70:0.72 71:0.65 74:0.95 77:0.70 78:0.82 81:0.49 83:0.91 85:0.95 86:0.98 91:0.37 98:0.52 100:0.73 101:0.87 104:0.96 106:0.81 108:0.41 111:0.80 114:0.59 122:0.51 123:0.40 124:0.87 126:0.54 127:0.74 129:0.89 133:0.87 135:0.72 139:0.98 144:0.85 145:0.83 146:0.88 147:0.90 149:0.98 150:0.72 152:0.85 154:0.86 155:0.67 159:0.85 161:0.63 165:0.93 167:0.48 169:0.86 172:0.89 173:0.28 176:0.73 177:0.70 178:0.55 179:0.18 181:0.82 186:0.83 187:0.87 192:0.87 195:0.95 201:0.93 202:0.50 204:0.85 206:0.81 208:0.64 212:0.68 215:0.39 216:0.75 222:0.25 230:0.75 232:0.98 233:0.76 235:0.74 241:0.07 242:0.19 243:0.50 245:0.95 247:0.92 253:0.50 259:0.21 261:0.88 262:0.93 265:0.53 266:0.80 267:0.44 271:0.32 276:0.92 282:0.83 290:0.59 297:0.36 300:0.84 +2 1:0.74 6:0.98 7:0.72 8:0.98 9:0.80 11:0.92 12:0.51 17:0.20 18:0.68 20:0.83 21:0.40 27:0.03 28:0.73 29:0.71 30:0.45 31:0.96 34:0.93 36:0.61 37:0.92 39:0.75 41:0.92 43:0.78 45:0.77 64:0.77 66:0.43 69:0.78 70:0.56 71:0.65 74:0.59 78:0.95 79:0.95 81:0.62 83:0.91 85:0.72 86:0.76 91:0.25 96:0.93 98:0.43 99:0.83 100:0.98 101:0.87 108:0.62 111:0.97 114:0.62 120:0.87 122:0.77 123:0.59 124:0.69 126:0.54 127:0.29 129:0.05 133:0.66 135:0.79 137:0.96 139:0.73 140:0.45 144:0.85 145:0.56 146:0.64 147:0.69 149:0.70 150:0.78 151:0.87 152:0.87 153:0.72 154:0.71 155:0.67 159:0.54 161:0.63 162:0.68 164:0.83 165:0.93 167:0.62 169:0.97 172:0.45 173:0.71 176:0.73 177:0.70 179:0.56 181:0.82 186:0.95 187:0.87 189:0.99 192:0.87 196:0.92 201:0.93 202:0.78 204:0.85 208:0.64 212:0.37 215:0.42 216:0.79 220:0.74 222:0.25 230:0.75 233:0.76 235:0.74 238:0.95 241:0.59 242:0.28 243:0.57 245:0.65 247:0.79 248:0.84 253:0.90 255:0.95 256:0.80 259:0.21 260:0.86 261:0.96 265:0.45 266:0.71 267:0.69 268:0.81 271:0.32 276:0.51 284:0.96 285:0.62 290:0.62 297:0.36 300:0.43 +1 1:0.74 6:0.94 7:0.72 8:0.89 9:0.80 11:0.92 12:0.51 17:0.28 18:0.77 20:0.88 21:0.30 22:0.93 27:0.48 28:0.73 29:0.71 30:0.60 31:0.90 32:0.69 34:0.82 36:0.84 37:0.72 39:0.75 41:0.88 43:0.83 45:0.81 47:0.93 48:0.91 60:0.87 64:0.77 66:0.72 69:0.67 70:0.48 71:0.65 74:0.80 77:0.70 78:0.91 79:0.89 81:0.61 83:0.91 85:0.85 86:0.87 91:0.51 96:0.74 98:0.75 100:0.97 101:0.87 104:0.84 106:0.81 108:0.62 111:0.94 114:0.65 117:0.86 120:0.61 122:0.77 123:0.59 124:0.44 126:0.54 127:0.54 129:0.59 133:0.61 135:0.60 137:0.90 139:0.89 140:0.45 144:0.85 145:0.90 146:0.17 147:0.22 149:0.87 150:0.78 151:0.63 152:0.87 153:0.72 154:0.79 155:0.67 158:0.91 159:0.52 161:0.63 162:0.60 164:0.71 165:0.93 167:0.62 169:0.94 172:0.74 173:0.67 176:0.73 177:0.70 179:0.49 181:0.82 186:0.91 187:0.39 189:0.95 192:0.87 195:0.71 196:0.93 201:0.93 202:0.65 204:0.85 206:0.81 208:0.64 212:0.60 215:0.44 216:0.69 219:0.95 220:0.87 222:0.25 230:0.75 232:0.90 233:0.76 235:0.52 238:0.96 241:0.59 242:0.32 243:0.19 245:0.68 247:0.67 248:0.62 253:0.66 255:0.95 256:0.58 259:0.21 260:0.62 261:0.94 262:0.93 265:0.75 266:0.47 267:0.44 268:0.65 271:0.32 276:0.65 279:0.86 281:0.89 282:0.77 283:0.78 284:0.94 285:0.62 290:0.65 292:0.91 297:0.36 300:0.43 +2 1:0.74 6:0.98 7:0.72 8:0.98 9:0.80 11:0.92 12:0.51 17:0.06 18:0.68 20:0.85 21:0.40 27:0.03 28:0.73 29:0.71 30:0.45 31:0.97 34:0.93 36:0.71 37:0.92 39:0.75 41:0.94 43:0.78 45:0.77 60:0.95 64:0.77 66:0.43 69:0.79 70:0.56 71:0.65 74:0.67 78:0.96 79:0.97 81:0.62 83:0.91 85:0.72 86:0.76 91:0.69 96:0.92 97:0.89 98:0.43 99:0.83 100:0.98 101:0.87 108:0.63 111:0.98 114:0.62 120:0.86 122:0.77 123:0.60 124:0.69 126:0.54 127:0.29 129:0.05 133:0.66 135:0.69 137:0.97 139:0.81 140:0.80 144:0.85 145:0.56 146:0.64 147:0.69 149:0.69 150:0.78 151:0.94 152:0.87 153:0.81 154:0.70 155:0.67 158:0.91 159:0.54 161:0.63 162:0.53 164:0.86 165:0.93 167:0.57 169:0.97 172:0.45 173:0.72 176:0.73 177:0.70 178:0.42 179:0.56 181:0.82 186:0.96 187:0.68 189:0.99 192:0.87 196:0.96 201:0.93 202:0.87 204:0.85 208:0.64 212:0.37 215:0.42 216:0.79 219:0.95 220:0.74 222:0.25 230:0.75 233:0.76 235:0.74 238:0.96 241:0.50 242:0.28 243:0.57 245:0.65 247:0.79 248:0.89 253:0.91 255:0.95 256:0.84 259:0.21 260:0.86 261:0.96 265:0.45 266:0.71 267:0.82 268:0.85 271:0.32 276:0.51 279:0.86 283:0.78 284:0.97 285:0.62 290:0.62 292:0.91 297:0.36 300:0.43 +1 1:0.74 11:0.92 12:0.51 17:0.45 18:0.65 21:0.22 27:0.45 28:0.73 29:0.71 30:0.71 34:0.80 36:0.17 37:0.50 39:0.75 43:0.82 45:0.73 60:0.87 64:0.77 66:0.45 69:0.68 70:0.29 71:0.65 74:0.61 81:0.68 83:0.91 85:0.72 86:0.74 91:0.11 98:0.33 101:0.87 108:0.52 114:0.71 122:0.51 123:0.50 124:0.57 126:0.54 127:0.43 129:0.05 133:0.43 135:0.71 139:0.81 144:0.85 145:0.40 146:0.48 147:0.55 149:0.68 150:0.81 154:0.77 155:0.67 158:0.91 159:0.58 161:0.63 163:0.81 165:0.93 167:0.54 172:0.32 173:0.63 176:0.73 177:0.70 178:0.55 179:0.81 181:0.82 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.51 216:0.77 219:0.95 222:0.25 235:0.42 241:0.41 242:0.14 243:0.58 245:0.59 247:0.67 253:0.52 259:0.21 265:0.36 266:0.54 267:0.28 271:0.32 276:0.25 279:0.86 283:0.78 290:0.71 292:0.91 297:0.36 300:0.25 +2 1:0.74 9:0.80 11:0.92 12:0.51 17:0.13 18:0.77 21:0.22 22:0.93 27:0.16 28:0.73 29:0.71 30:0.62 31:0.90 32:0.80 34:0.92 36:0.76 37:0.53 39:0.75 41:0.82 43:0.84 44:0.93 45:0.73 47:0.93 64:0.77 66:0.90 69:0.64 70:0.53 71:0.65 74:0.75 76:0.85 77:0.70 79:0.90 81:0.68 83:0.91 85:0.85 86:0.88 91:0.07 96:0.74 98:0.74 101:0.87 104:0.98 106:0.81 108:0.49 114:0.71 117:0.86 120:0.62 122:0.57 123:0.47 126:0.54 127:0.23 129:0.05 135:0.71 137:0.90 139:0.89 140:0.45 144:0.85 145:0.63 146:0.82 147:0.85 149:0.87 150:0.81 151:0.69 153:0.46 154:0.80 155:0.67 159:0.37 161:0.63 162:0.62 164:0.54 165:0.93 167:0.49 172:0.71 173:0.62 176:0.73 177:0.70 179:0.38 181:0.82 187:0.99 192:0.87 195:0.75 196:0.93 201:0.93 202:0.49 206:0.81 208:0.64 212:0.49 215:0.51 216:0.90 220:0.62 222:0.25 232:0.99 235:0.42 241:0.15 242:0.18 243:0.90 247:0.76 248:0.64 253:0.69 255:0.95 256:0.45 259:0.21 260:0.63 262:0.93 265:0.74 266:0.54 267:0.44 268:0.45 271:0.32 276:0.59 282:0.88 284:0.87 285:0.62 290:0.71 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.91 7:0.81 8:0.82 11:0.93 12:0.51 17:0.05 18:0.75 20:0.80 21:0.30 22:0.93 27:0.08 28:0.73 29:0.71 30:0.45 32:0.78 34:0.49 36:0.94 37:0.82 39:0.75 43:0.78 44:0.93 45:0.92 47:0.93 64:0.77 66:0.95 69:0.46 70:0.63 71:0.65 74:0.88 77:0.70 78:0.89 81:0.60 83:0.60 85:0.72 86:0.85 91:0.04 97:0.89 98:0.70 99:0.95 100:0.90 101:0.97 104:0.82 106:0.81 108:0.35 111:0.88 114:0.61 117:0.86 121:0.90 122:0.51 123:0.34 126:0.54 127:0.21 129:0.05 135:0.70 139:0.93 144:0.85 145:0.72 146:0.91 147:0.92 149:0.86 150:0.75 152:0.77 154:0.87 155:0.67 158:0.91 159:0.51 161:0.63 165:0.70 167:0.51 169:0.88 172:0.88 173:0.29 176:0.66 177:0.70 178:0.55 179:0.18 181:0.82 186:0.89 187:0.21 189:0.92 192:0.87 195:0.89 201:0.93 202:0.46 204:0.89 206:0.81 208:0.64 212:0.75 215:0.42 216:0.75 219:0.95 222:0.25 230:0.87 232:0.88 233:0.76 235:0.68 238:0.89 241:0.24 242:0.28 243:0.95 247:0.86 253:0.50 259:0.21 261:0.83 262:0.93 265:0.71 266:0.54 267:0.28 271:0.32 276:0.79 279:0.86 281:0.91 282:0.86 283:0.77 290:0.61 292:0.91 297:0.36 300:0.55 +2 1:0.69 6:0.95 7:0.63 8:0.96 9:0.76 11:0.92 12:0.51 17:0.40 18:0.67 20:0.74 21:0.30 27:0.02 28:0.73 29:0.71 30:0.73 32:0.69 34:0.58 36:0.32 37:0.92 39:0.75 43:0.69 45:0.98 66:0.34 69:0.91 70:0.49 71:0.65 74:0.89 76:0.85 77:0.96 78:0.87 81:0.46 83:0.60 85:0.80 86:0.77 91:0.35 96:0.72 98:0.48 99:0.83 100:0.98 101:0.87 108:0.87 111:0.96 114:0.63 120:0.59 122:0.75 123:0.86 124:0.82 126:0.44 127:0.71 129:0.81 133:0.82 135:0.76 139:0.74 140:0.45 144:0.85 145:0.56 146:0.17 147:0.86 149:0.94 150:0.56 151:0.55 152:0.78 153:0.72 154:0.52 155:0.58 159:0.85 161:0.63 162:0.73 164:0.70 165:0.70 167:0.54 169:0.83 172:0.86 173:0.81 176:0.66 177:0.38 179:0.20 181:0.82 186:0.87 187:0.39 190:0.77 192:0.59 195:0.95 201:0.68 202:0.64 208:0.54 212:0.76 215:0.44 216:0.99 220:0.82 222:0.25 230:0.64 233:0.76 235:0.42 241:0.39 242:0.54 243:0.44 245:0.93 247:0.67 248:0.58 253:0.61 255:0.74 256:0.64 257:0.65 259:0.21 260:0.59 261:0.82 265:0.50 266:0.80 267:0.36 268:0.68 271:0.32 276:0.89 282:0.77 285:0.56 290:0.63 297:0.36 300:0.94 +2 1:0.69 6:0.98 7:0.72 8:0.92 9:0.76 11:0.93 12:0.51 17:0.24 18:0.60 20:0.84 21:0.54 27:0.03 28:0.73 29:0.71 30:0.45 34:0.95 36:0.61 37:0.92 39:0.75 43:0.60 45:0.83 55:0.71 66:0.91 69:0.79 70:0.47 71:0.65 74:0.62 78:0.94 81:0.38 83:0.60 86:0.65 91:0.69 96:0.90 97:0.99 98:0.78 99:0.95 100:0.97 101:0.87 108:0.63 111:0.96 114:0.58 120:0.83 122:0.77 123:0.61 126:0.44 127:0.27 129:0.05 135:0.86 139:0.67 140:0.90 144:0.85 145:0.61 146:0.92 147:0.93 149:0.66 150:0.54 151:0.81 152:0.87 153:0.48 154:0.49 155:0.58 158:0.78 159:0.54 161:0.63 162:0.52 164:0.82 165:0.70 167:0.49 169:0.97 172:0.76 173:0.71 176:0.66 177:0.70 178:0.50 179:0.37 181:0.82 186:0.94 187:0.68 189:0.97 190:0.77 192:0.59 201:0.68 202:0.85 204:0.85 208:0.54 212:0.27 215:0.39 216:0.79 219:0.89 220:0.87 222:0.25 230:0.75 233:0.76 235:0.80 238:0.95 241:0.15 242:0.70 243:0.92 244:0.61 247:0.67 248:0.87 253:0.86 255:0.74 256:0.80 259:0.21 260:0.81 261:0.96 265:0.78 266:0.71 267:0.59 268:0.81 271:0.32 276:0.65 279:0.82 283:0.78 285:0.56 290:0.58 292:0.91 297:0.36 300:0.43 +2 1:0.74 7:0.72 8:0.95 11:0.92 12:0.51 17:0.69 18:0.88 21:0.47 22:0.93 27:0.44 28:0.73 29:0.71 30:0.41 32:0.69 34:0.95 36:0.66 37:0.54 39:0.75 43:0.92 45:0.87 47:0.93 48:0.91 60:0.87 64:0.77 66:0.07 69:0.41 70:0.98 71:0.65 74:0.77 77:0.89 81:0.52 83:0.91 85:0.85 86:0.92 91:0.14 98:0.19 101:0.87 104:0.97 106:0.81 108:0.32 114:0.60 117:0.86 122:0.86 123:0.73 124:0.96 126:0.54 127:0.95 129:0.89 133:0.96 135:0.80 139:0.92 144:0.85 145:0.96 146:0.70 147:0.75 149:0.82 150:0.74 154:0.91 155:0.67 158:0.91 159:0.96 161:0.63 165:0.93 167:0.48 172:0.63 173:0.08 176:0.73 177:0.70 178:0.55 179:0.35 181:0.82 187:0.68 192:0.87 195:0.99 201:0.93 204:0.85 206:0.81 208:0.64 212:0.26 215:0.41 216:0.33 219:0.95 222:0.25 230:0.75 232:0.98 233:0.76 235:0.68 241:0.07 242:0.19 243:0.40 245:0.75 247:0.86 253:0.47 259:0.21 262:0.93 265:0.21 266:0.99 267:0.69 271:0.32 276:0.81 277:0.69 279:0.86 281:0.89 282:0.77 283:0.78 290:0.60 292:0.91 297:0.36 300:0.99 +1 1:0.74 6:0.88 7:0.81 8:0.75 11:0.92 12:0.51 17:0.67 18:0.80 20:0.90 21:0.47 22:0.93 27:0.51 28:0.73 29:0.71 30:0.45 31:0.86 32:0.69 34:0.84 36:0.26 37:0.71 39:0.75 43:0.79 45:0.88 47:0.93 60:0.95 64:0.77 66:0.15 69:0.75 70:0.88 71:0.65 74:0.84 77:0.70 78:0.92 79:0.86 81:0.52 83:0.91 85:0.80 86:0.90 91:0.29 98:0.51 100:0.92 101:0.87 104:0.82 106:0.81 108:0.59 111:0.91 114:0.60 117:0.86 121:0.97 122:0.77 123:0.86 124:0.73 126:0.54 127:0.56 129:0.05 133:0.74 135:0.34 137:0.86 139:0.93 140:0.45 144:0.85 145:0.50 146:0.74 147:0.78 149:0.82 150:0.74 151:0.47 152:0.84 153:0.72 154:0.73 155:0.67 158:0.91 159:0.74 161:0.63 162:0.56 165:0.93 167:0.50 169:0.90 172:0.74 173:0.62 176:0.73 177:0.70 179:0.39 181:0.82 186:0.92 187:0.21 189:0.90 190:0.89 192:0.87 195:0.89 196:0.88 201:0.93 202:0.58 204:0.89 206:0.81 208:0.64 212:0.60 215:0.41 216:0.20 219:0.95 220:0.93 222:0.25 230:0.87 232:0.88 233:0.76 235:0.68 238:0.88 241:0.18 242:0.21 243:0.61 245:0.80 247:0.82 248:0.47 253:0.48 256:0.45 259:0.21 261:0.91 262:0.93 265:0.48 266:0.89 267:0.28 268:0.57 271:0.32 276:0.74 277:0.69 279:0.86 281:0.91 282:0.77 283:0.78 284:0.86 285:0.47 290:0.60 292:0.91 297:0.36 300:0.94 +1 1:0.74 7:0.72 9:0.80 11:0.93 12:0.51 17:0.76 18:0.85 21:0.30 22:0.93 27:0.43 28:0.73 29:0.71 30:0.84 31:0.95 32:0.80 34:0.75 36:0.87 39:0.75 41:0.88 43:0.88 44:0.93 45:0.88 47:0.93 48:0.91 60:0.95 64:0.77 66:0.68 69:0.54 70:0.56 71:0.65 74:0.90 77:0.89 78:0.88 79:0.95 81:0.61 83:0.91 85:0.88 86:0.95 91:0.42 96:0.85 98:0.56 101:0.97 104:0.93 106:0.81 108:0.41 111:0.86 114:0.65 117:0.86 120:0.76 122:0.57 123:0.40 124:0.48 126:0.54 127:0.57 129:0.81 133:0.67 135:0.61 137:0.95 139:0.96 140:0.45 144:0.85 145:0.77 146:0.69 147:0.74 149:0.89 150:0.78 151:0.94 153:0.80 154:0.86 155:0.67 158:0.92 159:0.57 161:0.63 162:0.78 164:0.70 165:0.93 167:0.45 169:0.72 172:0.77 173:0.49 176:0.73 177:0.70 179:0.44 181:0.82 187:0.87 192:0.87 195:0.77 196:0.98 201:0.93 202:0.59 204:0.85 206:0.81 208:0.64 212:0.71 215:0.44 216:0.21 219:0.95 222:0.25 230:0.75 232:0.95 233:0.76 235:0.52 242:0.24 243:0.72 245:0.76 247:0.74 248:0.83 253:0.89 255:0.95 256:0.58 259:0.21 260:0.77 262:0.93 265:0.58 266:0.54 267:0.44 268:0.64 271:0.32 276:0.66 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 290:0.65 292:0.95 297:0.36 299:0.88 300:0.84 +3 1:0.69 6:0.95 8:0.96 9:0.80 11:0.92 12:0.51 17:0.04 18:0.66 20:0.82 21:0.22 27:0.06 28:0.73 29:0.71 30:0.41 31:0.91 32:0.80 34:0.59 36:0.98 37:0.85 39:0.75 41:0.88 43:0.73 44:0.93 45:0.92 66:0.54 69:0.66 70:0.85 71:0.65 74:0.82 77:0.89 78:0.91 79:0.92 81:0.58 83:0.91 85:0.72 86:0.72 91:0.75 96:0.95 97:0.99 98:0.42 99:0.95 100:0.93 101:0.87 104:0.96 106:0.81 108:0.50 111:0.90 114:0.63 117:0.86 120:0.92 122:0.51 123:0.48 124:0.76 126:0.44 127:0.60 129:0.59 133:0.81 135:0.90 137:0.91 138:0.96 139:0.79 140:0.98 144:0.85 145:0.56 146:0.81 147:0.84 149:0.87 150:0.60 151:0.52 152:0.80 153:0.89 154:0.62 155:0.67 158:0.79 159:0.85 161:0.63 162:0.58 164:0.90 165:0.70 167:0.60 169:0.93 172:0.83 173:0.41 176:0.61 177:0.70 179:0.30 181:0.82 186:0.90 187:0.39 189:0.97 190:0.77 192:0.87 195:0.95 196:0.91 201:0.68 202:0.91 206:0.81 208:0.64 212:0.68 215:0.44 216:0.88 220:0.82 222:0.25 232:0.98 235:0.52 238:0.90 241:0.55 242:0.33 243:0.63 245:0.83 247:0.79 248:0.53 253:0.95 255:0.95 256:0.91 259:0.21 260:0.89 261:0.90 265:0.44 266:0.84 267:0.69 268:0.91 271:0.32 276:0.80 279:0.82 282:0.88 283:0.82 284:0.94 285:0.62 290:0.63 297:0.36 300:0.84 +2 1:0.69 6:0.96 7:0.72 8:0.93 9:0.76 11:0.91 12:0.51 17:0.22 18:0.75 20:0.87 21:0.30 27:0.21 28:0.73 29:0.71 30:0.55 31:0.88 34:0.66 36:0.70 37:0.74 39:0.75 43:0.72 45:0.98 66:0.10 69:0.12 70:0.70 71:0.65 74:0.89 78:0.85 79:0.88 81:0.46 83:0.60 86:0.72 91:0.81 96:0.98 97:0.89 98:0.59 99:0.83 100:0.88 101:0.52 104:0.84 106:0.81 108:0.93 111:0.85 114:0.63 117:0.86 120:0.96 122:0.75 123:0.82 124:0.71 126:0.44 127:0.65 129:0.81 133:0.67 135:0.17 137:0.88 139:0.70 140:0.45 144:0.85 146:0.90 147:0.92 149:0.94 150:0.56 151:0.97 152:0.82 153:0.97 154:0.62 155:0.58 158:0.79 159:0.86 161:0.63 162:0.64 164:0.92 165:0.70 167:0.60 169:0.86 172:0.92 173:0.08 176:0.66 177:0.70 179:0.16 181:0.82 186:0.85 187:0.39 189:0.97 190:0.77 191:0.75 192:0.59 196:0.88 201:0.68 202:0.91 204:0.85 206:0.81 208:0.54 212:0.75 215:0.44 216:0.95 222:0.25 230:0.75 232:0.90 233:0.76 235:0.42 238:0.89 241:0.56 242:0.45 243:0.38 244:0.61 245:0.98 247:0.90 248:0.98 253:0.98 255:0.74 256:0.93 259:0.21 260:0.95 261:0.86 265:0.50 266:0.80 267:0.59 268:0.92 271:0.32 276:0.94 279:0.82 283:0.82 284:0.86 285:0.62 290:0.63 297:0.36 300:0.84 +2 1:0.69 7:0.72 11:0.91 12:0.51 17:0.51 18:0.74 21:0.30 27:0.21 28:0.73 29:0.71 30:0.58 34:0.66 36:0.59 37:0.74 39:0.75 43:0.72 45:0.98 66:0.12 69:0.12 70:0.72 71:0.65 74:0.89 81:0.46 83:0.60 86:0.71 91:0.05 98:0.58 99:0.83 101:0.52 108:0.93 114:0.63 122:0.75 123:0.82 124:0.77 126:0.44 127:0.68 129:0.81 133:0.78 135:0.18 139:0.69 144:0.85 146:0.91 147:0.92 149:0.95 150:0.56 154:0.62 155:0.58 159:0.86 161:0.63 165:0.70 167:0.55 172:0.92 173:0.08 176:0.66 177:0.70 178:0.55 179:0.15 181:0.82 187:0.68 192:0.59 201:0.68 202:0.46 204:0.85 208:0.54 212:0.75 215:0.44 216:0.95 222:0.25 230:0.75 233:0.76 235:0.42 241:0.44 242:0.50 243:0.34 244:0.61 245:0.97 247:0.92 253:0.52 259:0.21 265:0.51 266:0.84 267:0.59 271:0.32 276:0.95 290:0.63 297:0.36 300:0.84 +1 1:0.74 7:0.63 9:0.80 11:0.92 12:0.51 17:0.38 18:0.82 21:0.30 27:0.02 28:0.73 29:0.71 30:0.85 31:0.87 32:0.79 34:0.40 36:0.39 37:0.92 39:0.75 41:0.82 43:0.88 44:0.93 45:0.95 48:0.91 64:0.77 66:0.54 69:0.52 70:0.33 71:0.65 74:0.86 76:0.85 77:0.70 79:0.87 81:0.61 83:0.91 85:0.85 86:0.92 91:0.29 96:0.74 98:0.48 101:0.87 108:0.86 114:0.65 120:0.61 122:0.51 123:0.85 124:0.70 126:0.54 127:0.64 129:0.59 133:0.77 135:0.76 137:0.87 139:0.93 140:0.80 144:0.85 145:0.65 146:0.54 147:0.60 149:0.91 150:0.78 151:0.52 153:0.72 154:0.86 155:0.67 159:0.85 161:0.63 162:0.86 164:0.66 165:0.93 167:0.52 172:0.84 173:0.27 176:0.73 177:0.70 179:0.30 181:0.82 187:0.39 190:0.77 192:0.87 195:0.95 196:0.90 201:0.93 202:0.53 208:0.64 212:0.71 215:0.44 216:0.99 220:0.74 222:0.25 230:0.64 233:0.76 235:0.52 241:0.32 242:0.53 243:0.32 245:0.87 247:0.60 248:0.51 253:0.64 255:0.95 256:0.58 259:0.21 260:0.60 265:0.50 266:0.80 267:0.97 268:0.62 271:0.32 276:0.80 281:0.91 282:0.87 284:0.89 285:0.62 290:0.65 297:0.36 300:0.55 +1 1:0.69 8:0.87 11:0.93 12:0.51 17:0.34 18:0.85 21:0.64 27:0.60 28:0.73 29:0.71 30:0.57 32:0.69 34:0.33 36:0.21 37:0.78 39:0.75 43:0.67 45:0.99 66:0.12 69:0.64 70:0.75 71:0.65 74:0.89 77:0.70 81:0.33 83:0.60 86:0.89 91:0.08 97:0.89 98:0.34 99:0.83 101:0.87 108:0.49 114:0.56 122:0.75 123:0.47 124:0.96 126:0.44 127:0.78 129:0.96 133:0.96 135:0.81 139:0.85 144:0.85 145:0.50 146:0.85 147:0.87 149:0.91 150:0.52 154:0.66 155:0.58 158:0.78 159:0.92 161:0.63 165:0.70 167:0.47 172:0.79 173:0.28 176:0.66 177:0.70 178:0.55 179:0.15 181:0.82 187:0.68 192:0.59 195:0.98 201:0.68 208:0.54 212:0.42 215:0.38 216:0.30 222:0.25 235:0.80 241:0.03 242:0.31 243:0.31 245:0.94 247:0.88 253:0.47 254:0.74 259:0.21 265:0.36 266:0.87 267:0.52 271:0.32 276:0.95 279:0.82 282:0.77 283:0.78 290:0.56 297:0.36 300:0.84 +1 1:0.69 11:0.93 12:0.51 17:0.32 18:0.79 21:0.30 27:0.45 28:0.73 29:0.71 30:0.61 32:0.69 34:0.66 36:0.17 37:0.50 39:0.75 43:0.75 45:0.93 66:0.23 69:0.68 70:0.70 71:0.65 74:0.78 77:0.70 81:0.46 83:0.60 85:0.72 86:0.85 91:0.10 97:0.89 98:0.34 99:0.83 101:0.97 108:0.52 114:0.63 122:0.51 123:0.50 124:0.89 126:0.44 127:0.34 129:0.81 133:0.89 135:0.71 139:0.81 144:0.85 145:0.49 146:0.70 147:0.74 149:0.86 150:0.56 154:0.66 155:0.58 158:0.78 159:0.77 161:0.63 165:0.70 167:0.56 172:0.63 173:0.45 176:0.66 177:0.70 178:0.55 179:0.26 181:0.82 187:0.68 192:0.59 195:0.94 201:0.68 208:0.54 212:0.42 215:0.44 216:0.77 222:0.25 235:0.42 241:0.46 242:0.31 243:0.43 245:0.81 247:0.90 253:0.50 259:0.21 265:0.36 266:0.89 267:0.36 271:0.32 276:0.79 279:0.82 282:0.77 283:0.78 290:0.63 297:0.36 300:0.55 +2 1:0.69 7:0.72 8:0.89 9:0.76 11:0.91 12:0.51 17:0.40 18:0.75 21:0.30 27:0.21 28:0.73 29:0.71 30:0.55 34:0.70 36:0.63 37:0.74 39:0.75 43:0.72 45:0.98 66:0.10 69:0.12 70:0.70 71:0.65 74:0.88 81:0.46 83:0.60 86:0.72 91:0.07 96:0.69 98:0.59 99:0.83 101:0.52 108:0.93 114:0.63 120:0.55 122:0.75 123:0.82 124:0.71 126:0.44 127:0.66 129:0.81 133:0.67 135:0.18 139:0.70 140:0.45 144:0.85 146:0.90 147:0.92 149:0.94 150:0.56 151:0.51 153:0.48 154:0.62 155:0.58 159:0.86 161:0.63 162:0.62 164:0.54 165:0.70 167:0.54 172:0.92 173:0.08 176:0.66 177:0.70 179:0.16 181:0.82 187:0.68 190:0.77 192:0.59 201:0.68 202:0.50 204:0.85 208:0.54 212:0.75 215:0.44 216:0.95 220:0.87 222:0.25 230:0.75 233:0.76 235:0.42 241:0.41 242:0.45 243:0.38 244:0.61 245:0.98 247:0.90 248:0.50 253:0.53 255:0.74 256:0.45 259:0.21 260:0.55 265:0.50 266:0.80 267:0.69 268:0.46 271:0.32 276:0.94 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.57 12:0.51 17:0.22 20:0.98 21:0.30 27:0.21 28:0.91 30:0.60 34:0.66 36:0.70 37:0.74 39:0.75 41:0.99 43:0.98 60:0.87 66:0.10 69:0.12 70:0.68 74:0.99 78:0.85 81:0.65 83:0.90 85:0.99 91:0.81 96:0.98 98:0.56 100:0.95 101:0.86 104:0.84 106:0.81 108:0.93 111:0.91 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.82 124:0.71 126:0.54 127:0.61 129:0.81 133:0.67 135:0.17 140:0.45 146:0.90 147:0.92 149:0.99 150:0.79 151:0.99 152:0.97 153:0.97 154:0.98 155:0.67 158:0.92 159:0.85 161:0.77 162:0.64 164:0.94 165:0.84 167:0.61 169:0.91 172:0.91 173:0.08 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.39 189:0.97 191:0.75 192:0.59 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.56 242:0.51 243:0.38 245:0.98 247:0.67 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.96 265:0.48 266:0.80 267:0.59 268:0.92 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:0.93 8:0.76 9:0.80 11:0.57 12:0.51 20:0.76 27:0.14 28:0.91 30:0.62 34:0.53 36:0.34 37:0.67 39:0.75 41:0.99 43:0.86 60:0.95 66:0.06 69:0.59 70:0.76 74:0.92 78:0.91 81:0.87 83:0.90 85:0.98 91:0.90 96:0.99 98:0.27 100:0.90 101:0.83 108:0.78 111:0.95 114:0.98 120:0.98 122:0.43 123:0.62 124:0.93 126:0.54 127:0.75 129:0.96 133:0.93 135:0.83 138:0.99 140:0.45 145:0.82 146:0.29 147:0.36 149:0.95 150:0.92 151:1.00 152:0.98 153:0.98 154:0.83 155:0.67 158:0.92 159:0.86 161:0.77 162:0.68 164:0.96 165:0.92 167:0.57 169:0.90 172:0.61 173:0.31 176:0.73 177:0.38 179:0.32 181:0.80 186:0.79 187:0.39 189:0.92 191:0.79 192:0.59 201:0.74 202:0.94 208:0.64 212:0.51 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.46 242:0.52 243:0.15 245:0.82 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.94 267:0.36 268:0.95 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +0 6:0.84 8:0.92 9:0.80 12:0.51 17:0.01 20:0.80 21:0.78 27:0.05 28:0.91 34:0.19 36:0.67 37:0.94 39:0.75 41:0.98 78:0.75 83:0.84 91:0.99 96:0.95 100:0.80 111:0.75 120:0.91 135:0.30 140:0.99 151:0.97 152:0.77 153:0.90 155:0.67 161:0.77 162:0.45 164:0.89 167:0.86 169:0.81 175:0.91 178:0.55 181:0.80 186:0.75 189:0.90 191:0.98 192:0.59 202:0.99 208:0.64 216:0.82 222:0.25 235:0.12 238:0.95 241:0.92 244:0.83 248:0.95 253:0.93 255:0.79 256:0.88 257:0.84 259:0.21 260:0.92 261:0.80 267:0.79 268:0.86 277:0.87 285:0.62 +1 1:0.74 11:0.57 12:0.51 17:0.30 21:0.54 27:0.45 28:0.91 30:0.21 32:0.80 34:0.75 36:0.17 37:0.50 39:0.75 43:0.82 60:0.87 66:0.51 69:0.70 70:0.83 74:0.94 77:0.70 81:0.54 83:0.86 85:0.95 91:0.06 98:0.63 101:0.78 108:0.53 114:0.77 122:0.67 123:0.51 124:0.87 126:0.54 127:0.70 129:0.93 133:0.91 135:0.72 145:0.44 146:0.95 147:0.96 149:0.90 150:0.71 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 165:0.79 167:0.58 172:0.77 173:0.41 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 195:0.96 201:0.74 208:0.64 212:0.15 215:0.58 216:0.77 222:0.25 235:0.68 241:0.50 242:0.45 243:0.61 245:0.73 247:0.67 253:0.75 259:0.21 265:0.63 266:0.94 267:0.44 276:0.77 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.97 7:0.78 8:0.94 9:0.80 11:0.57 12:0.51 17:0.15 20:0.75 21:0.30 27:0.02 28:0.91 30:0.72 32:0.80 34:0.47 36:0.19 37:0.92 39:0.75 41:0.97 43:0.89 60:0.87 66:0.67 69:0.51 70:0.47 74:0.94 76:0.85 77:0.70 78:0.89 81:0.65 83:0.87 85:0.97 91:0.86 96:0.94 98:0.60 100:0.96 101:0.83 108:0.39 111:0.91 114:0.86 120:0.88 122:0.43 123:0.37 124:0.47 126:0.54 127:0.46 129:0.59 133:0.47 135:0.66 140:0.45 145:0.42 146:0.86 147:0.88 149:0.95 150:0.79 151:0.96 152:0.75 153:0.86 154:0.87 155:0.67 158:0.87 159:0.73 161:0.77 162:0.60 164:0.90 165:0.84 167:0.74 169:0.90 172:0.76 173:0.33 176:0.73 177:0.38 179:0.41 181:0.80 186:0.90 187:0.39 189:1.00 191:0.87 192:0.59 195:0.89 201:0.74 202:0.87 208:0.64 212:0.49 215:0.72 216:0.99 220:0.87 222:0.25 230:0.81 233:0.76 235:0.42 238:0.96 241:0.72 242:0.55 243:0.71 245:0.84 247:0.60 248:0.93 253:0.96 255:0.79 256:0.87 259:0.21 260:0.88 261:0.79 265:0.61 266:0.80 267:0.28 268:0.88 276:0.66 279:0.86 282:0.88 283:0.71 285:0.62 290:0.86 297:0.36 299:0.88 300:0.55 +3 1:0.74 7:0.81 11:0.57 12:0.51 17:0.51 21:0.30 27:0.21 28:0.91 30:0.62 34:0.66 36:0.59 37:0.74 39:0.75 43:0.98 66:0.12 69:0.12 70:0.72 74:0.99 81:0.65 83:0.75 85:0.99 91:0.05 98:0.55 101:0.86 108:0.93 114:0.86 121:0.97 122:0.57 123:0.82 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.18 146:0.72 147:0.77 149:0.99 150:0.79 154:0.98 155:0.67 159:0.86 161:0.77 165:0.84 167:0.57 172:0.92 173:0.08 176:0.73 177:0.38 178:0.55 179:0.15 181:0.80 187:0.68 192:0.59 201:0.74 202:0.46 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 233:0.76 235:0.42 241:0.44 242:0.52 243:0.28 245:0.97 247:0.67 253:0.78 259:0.21 265:0.48 266:0.87 267:0.59 276:0.94 290:0.86 297:0.36 300:0.94 +3 1:0.74 8:0.66 9:0.80 11:0.57 12:0.51 17:0.04 21:0.22 27:0.14 28:0.91 30:0.71 32:0.80 34:0.63 36:0.30 37:0.67 39:0.75 41:0.98 43:0.86 60:0.99 66:0.52 69:0.58 70:0.66 74:0.86 77:0.70 78:0.80 81:0.70 83:0.88 85:0.96 91:0.77 96:0.95 98:0.53 101:0.83 108:0.86 111:0.79 114:0.90 120:0.91 122:0.43 123:0.85 124:0.73 126:0.54 127:0.47 129:0.89 133:0.78 135:0.98 140:0.45 145:0.94 146:0.13 147:0.18 149:0.89 150:0.82 151:0.91 153:0.72 154:0.84 155:0.67 158:0.92 159:0.71 161:0.77 162:0.72 164:0.91 165:0.86 167:0.58 169:0.95 172:0.67 173:0.42 176:0.73 177:0.38 179:0.46 181:0.80 187:0.39 191:0.75 192:0.59 195:0.88 201:0.74 202:0.85 208:0.64 212:0.36 215:0.79 216:0.93 220:0.74 222:0.25 235:0.31 238:0.86 241:0.47 242:0.61 243:0.14 245:0.74 247:0.39 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.91 265:0.54 266:0.71 267:0.87 268:0.88 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.84 +0 1:0.74 6:0.98 8:0.84 9:0.80 11:0.57 12:0.51 17:0.16 20:0.74 21:0.68 27:0.05 28:0.91 30:0.54 32:0.80 34:0.42 36:0.71 37:0.93 39:0.75 41:0.98 43:0.59 55:0.84 60:0.97 66:0.94 69:0.93 70:0.66 74:0.88 77:0.70 78:0.78 81:0.47 83:0.89 85:0.94 91:0.66 96:0.95 98:0.76 100:0.87 101:0.79 108:0.87 111:0.79 114:0.70 120:0.91 122:0.67 123:0.86 126:0.54 127:0.25 129:0.05 135:0.76 140:0.80 145:0.50 146:0.96 147:0.97 149:0.77 150:0.65 151:0.96 152:0.73 153:0.86 154:0.48 155:0.67 158:0.87 159:0.67 161:0.77 162:0.59 164:0.92 165:0.70 167:0.60 169:0.89 172:0.85 173:0.88 176:0.73 177:0.38 178:0.42 179:0.24 181:0.80 186:0.82 187:0.68 192:0.59 195:0.93 201:0.74 202:0.89 208:0.64 212:0.37 215:0.49 216:0.84 220:0.99 222:0.25 232:0.96 235:0.84 241:0.53 242:0.45 243:0.95 247:0.67 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.92 261:0.75 265:0.76 266:0.71 267:0.44 268:0.90 276:0.76 279:0.86 282:0.88 283:0.71 285:0.62 290:0.70 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.51 17:0.06 21:0.13 27:0.13 28:0.91 30:0.86 32:0.80 34:0.44 36:0.44 37:0.79 39:0.75 41:0.97 43:0.81 60:0.87 66:0.84 69:0.70 70:0.28 74:0.86 77:0.70 81:0.76 83:0.89 85:0.91 91:0.75 96:0.95 98:0.63 101:0.83 108:0.53 114:0.92 120:0.90 121:0.90 122:0.43 123:0.51 126:0.54 127:0.19 129:0.05 135:0.89 140:0.45 145:0.54 146:0.60 147:0.65 149:0.86 150:0.85 151:0.93 153:0.78 154:0.76 155:0.67 158:0.92 159:0.22 161:0.77 162:0.83 164:0.89 165:0.88 167:0.72 172:0.54 173:0.78 176:0.73 177:0.38 179:0.47 181:0.80 187:0.39 192:0.59 195:0.63 201:0.74 202:0.81 204:0.89 208:0.64 212:0.52 215:0.84 216:0.83 220:0.74 222:0.25 230:0.87 233:0.76 235:0.22 241:0.70 242:0.63 243:0.85 247:0.30 248:0.95 253:0.96 255:0.79 256:0.84 259:0.21 260:0.91 265:0.64 266:0.38 267:0.52 268:0.87 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.25 +2 6:0.83 8:0.63 9:0.80 11:0.57 12:0.51 17:0.08 20:0.80 21:0.78 27:0.31 28:0.91 30:0.95 34:0.25 36:0.85 37:0.30 39:0.75 41:0.97 43:0.86 66:0.54 69:0.59 74:0.44 78:0.79 83:0.83 85:0.72 91:0.92 96:0.94 98:0.16 100:0.83 101:0.76 104:0.58 108:0.45 111:0.79 120:0.91 123:0.43 127:0.09 129:0.05 135:0.20 140:0.45 146:0.13 147:0.18 149:0.49 151:0.96 152:0.77 153:0.86 154:0.83 155:0.67 159:0.08 161:0.77 162:0.65 164:0.89 167:0.83 169:0.81 172:0.10 173:0.93 177:0.38 179:0.18 181:0.80 186:0.79 189:0.85 191:0.81 192:0.59 202:0.85 208:0.64 216:0.19 220:0.74 222:0.25 232:0.78 235:0.22 238:0.92 241:0.82 243:0.63 248:0.94 253:0.92 255:0.79 256:0.86 259:0.21 260:0.91 261:0.81 265:0.17 267:0.23 268:0.86 285:0.62 300:0.08 +2 1:0.74 6:0.93 8:0.83 9:0.80 11:0.57 12:0.51 17:0.12 20:0.78 21:0.05 27:0.14 28:0.91 30:0.53 32:0.80 34:0.26 36:0.54 37:0.67 39:0.75 41:0.88 43:0.86 66:0.15 69:0.59 70:0.59 74:0.97 77:0.70 78:0.85 81:0.81 83:0.81 85:0.99 91:0.08 96:0.86 98:0.45 100:0.92 101:0.84 108:0.96 111:0.87 114:0.95 120:0.77 122:0.57 123:0.95 124:0.96 126:0.54 127:0.83 129:0.99 133:0.97 135:0.83 140:0.45 145:0.47 146:0.22 147:0.28 149:0.97 150:0.88 151:0.92 152:0.98 153:0.72 154:0.83 155:0.67 159:0.93 161:0.77 162:0.76 164:0.69 165:0.90 167:0.47 169:0.90 172:0.82 173:0.20 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.68 189:0.91 192:0.59 195:0.99 201:0.74 202:0.58 208:0.64 212:0.41 215:0.91 216:0.93 222:0.25 235:0.12 238:0.95 241:0.01 242:0.44 243:0.08 245:0.92 247:0.60 248:0.85 253:0.91 255:0.79 256:0.58 259:0.21 260:0.78 261:0.96 265:0.47 266:0.92 267:0.28 268:0.62 276:0.95 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.99 8:0.96 9:0.80 11:0.57 12:0.51 17:0.04 20:0.79 21:0.22 27:0.06 28:0.91 30:0.40 32:0.80 34:0.59 36:0.98 37:0.85 39:0.75 41:0.99 43:0.83 60:0.99 66:0.60 69:0.66 70:0.80 74:0.97 77:0.89 78:0.85 81:0.75 83:0.89 85:0.97 91:0.75 96:0.96 98:0.42 100:0.92 101:0.81 104:0.96 106:0.81 108:0.50 111:0.87 114:0.69 117:0.86 120:0.93 122:0.57 123:0.48 124:0.67 126:0.54 127:0.56 129:0.81 133:0.76 135:0.90 138:0.96 140:0.45 145:0.56 146:0.81 147:0.84 149:0.96 150:0.84 151:0.97 152:0.77 153:0.89 154:0.79 155:0.67 158:0.92 159:0.84 161:0.77 162:0.57 164:0.93 165:0.88 167:0.61 169:0.90 172:0.82 173:0.41 176:0.56 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.99 192:0.59 195:0.95 201:0.74 202:0.91 206:0.81 208:0.64 212:0.68 215:0.72 216:0.88 220:0.82 222:0.25 232:0.98 235:0.52 238:0.94 241:0.55 242:0.44 243:0.67 245:0.84 247:0.60 248:0.96 253:0.98 255:0.79 256:0.91 259:0.21 260:0.93 261:0.85 265:0.44 266:0.75 267:0.69 268:0.91 276:0.74 279:0.86 282:0.88 283:0.82 285:0.62 290:0.69 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.57 12:0.51 17:0.01 20:0.98 27:0.14 28:0.91 30:0.65 34:0.67 36:0.29 37:0.67 39:0.75 41:0.99 43:0.86 60:0.98 66:0.06 69:0.57 70:0.74 74:0.93 78:0.85 81:0.87 83:0.90 85:0.98 91:0.63 96:0.99 98:0.25 100:0.95 101:0.83 108:0.78 111:0.90 114:0.98 120:0.98 122:0.43 123:0.86 124:0.93 126:0.54 127:0.74 129:0.96 133:0.93 135:0.85 138:0.99 140:0.45 145:0.82 146:0.22 147:0.28 149:0.96 150:0.92 151:1.00 152:0.98 153:0.98 154:0.84 155:0.67 158:0.92 159:0.86 161:0.77 162:0.66 164:0.96 165:0.92 167:0.56 169:0.90 172:0.61 173:0.30 176:0.73 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.94 192:0.59 201:0.74 202:0.94 208:0.64 212:0.50 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.41 242:0.53 243:0.15 245:0.83 247:0.67 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.92 267:0.28 268:0.95 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +2 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.57 12:0.51 17:0.47 20:0.91 21:0.30 27:0.50 28:0.91 30:0.52 32:0.80 34:0.44 36:0.66 37:0.60 39:0.75 41:0.92 43:0.88 60:0.87 66:0.07 69:0.53 70:0.85 74:0.99 77:0.96 78:0.77 81:0.65 83:0.82 85:1.00 91:0.18 96:0.84 98:0.37 100:0.81 101:0.85 104:0.84 106:0.81 108:0.97 111:0.77 114:0.86 117:0.86 120:0.75 121:0.90 122:0.57 123:0.96 124:0.96 126:0.54 127:0.90 129:0.98 133:0.96 135:0.69 140:0.45 145:0.76 146:0.59 147:0.65 149:0.98 150:0.79 151:0.92 152:0.85 153:0.72 154:0.86 155:0.67 158:0.92 159:0.94 161:0.77 162:0.75 164:0.76 165:0.84 167:0.46 169:0.79 172:0.91 173:0.16 176:0.73 177:0.38 179:0.12 181:0.80 186:0.78 187:0.39 189:0.93 192:0.59 195:0.99 201:0.74 202:0.67 204:0.89 206:0.81 208:0.64 212:0.60 215:0.72 216:0.86 220:0.87 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.91 242:0.43 243:0.13 245:0.96 247:0.67 248:0.82 253:0.90 255:0.79 256:0.68 259:0.21 260:0.76 261:0.84 265:0.31 266:0.92 267:0.52 268:0.71 276:0.97 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.94 +1 1:0.74 6:0.94 7:0.81 8:0.65 9:0.80 11:0.57 12:0.51 17:0.26 20:0.77 21:0.30 27:0.11 28:0.91 30:0.74 34:0.53 36:0.86 37:0.79 39:0.75 41:0.88 43:0.87 60:0.97 66:0.43 69:0.56 70:0.42 74:0.95 78:0.78 81:0.65 83:0.86 85:0.99 91:0.20 96:0.87 98:0.52 100:0.82 101:0.86 104:0.89 106:0.81 108:0.79 111:0.78 114:0.86 117:0.86 120:0.78 121:0.90 122:0.57 123:0.77 124:0.82 126:0.54 127:0.55 129:0.93 133:0.84 135:0.60 140:0.45 145:0.52 146:0.13 147:0.18 149:0.97 150:0.79 151:0.93 152:0.75 153:0.77 154:0.85 155:0.67 158:0.92 159:0.76 161:0.77 162:0.86 164:0.70 165:0.84 167:0.51 169:0.80 172:0.79 173:0.37 176:0.73 177:0.38 179:0.27 181:0.80 186:0.79 187:0.39 189:0.94 192:0.59 201:0.74 202:0.55 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.85 222:0.25 230:0.87 232:0.93 233:0.76 235:0.42 238:0.88 241:0.18 242:0.61 243:0.09 245:0.87 247:0.39 248:0.85 253:0.91 255:0.79 256:0.58 259:0.21 260:0.79 261:0.77 265:0.53 266:0.71 267:0.52 268:0.64 276:0.83 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43 +1 1:0.74 11:0.57 12:0.51 17:0.26 21:0.40 27:0.45 28:0.91 30:0.31 34:0.68 36:0.17 37:0.50 39:0.75 43:0.82 55:0.71 60:0.87 66:0.10 69:0.69 70:0.81 74:0.93 81:0.61 83:0.84 85:0.93 91:0.04 98:0.32 101:0.76 108:0.82 114:0.82 122:0.67 123:0.63 124:0.93 126:0.54 127:0.71 129:0.96 133:0.93 135:0.78 145:0.49 146:0.13 147:0.18 149:0.83 150:0.76 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 163:0.81 165:0.83 167:0.52 172:0.54 173:0.40 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 201:0.74 208:0.64 212:0.18 215:0.67 216:0.77 222:0.25 235:0.52 241:0.27 242:0.61 243:0.11 245:0.76 247:0.60 253:0.76 259:0.21 265:0.34 266:0.94 267:0.65 276:0.77 279:0.86 283:0.71 290:0.82 297:0.36 300:0.70 +1 1:0.57 2:1.00 8:0.91 9:0.72 10:0.97 11:0.54 12:0.20 17:0.34 18:0.78 21:0.54 23:0.97 27:0.35 29:0.77 30:0.55 34:0.61 36:0.64 37:0.59 39:0.98 43:0.27 44:0.88 45:0.89 46:0.88 56:0.97 62:0.98 66:0.83 69:0.55 70:0.75 71:0.73 74:0.51 75:0.95 77:0.70 80:0.98 81:0.33 82:0.99 83:0.60 86:0.75 88:0.98 91:0.58 98:0.83 101:0.69 102:0.96 107:0.92 108:0.42 110:0.89 122:0.99 123:0.40 124:0.39 125:0.88 126:0.31 127:0.66 128:0.97 129:0.05 133:0.45 135:0.97 138:0.96 139:0.77 140:0.80 143:0.99 145:0.92 146:0.93 147:0.95 149:0.67 150:0.37 151:0.85 153:0.48 154:0.26 155:0.57 159:0.69 160:0.88 162:0.86 168:0.97 170:0.82 172:0.93 173:0.43 174:0.98 175:0.75 177:0.88 179:0.23 187:0.87 190:0.98 192:0.56 193:0.97 195:0.83 197:0.99 201:0.54 202:0.50 204:0.83 205:0.97 208:0.64 212:0.78 216:0.45 220:0.74 222:0.25 226:0.96 235:0.74 236:0.91 239:0.96 241:0.12 242:0.27 243:0.84 244:0.83 245:0.90 247:0.92 248:0.76 253:0.41 254:0.84 255:0.70 256:0.58 257:0.65 259:0.21 264:0.93 265:0.83 266:0.75 267:0.97 268:0.59 271:0.90 275:0.99 276:0.88 277:0.69 279:0.77 281:0.91 282:0.73 285:0.49 289:0.88 294:0.99 295:0.98 298:0.99 300:0.84 +1 7:0.66 8:0.90 10:0.99 11:0.54 12:0.20 17:0.57 18:0.94 21:0.40 23:0.98 27:0.28 29:0.77 30:0.56 32:0.67 33:0.97 34:0.29 36:0.98 37:0.86 39:0.98 43:0.60 44:0.88 45:0.98 46:0.88 62:0.99 66:0.63 69:0.81 70:0.78 71:0.73 74:0.68 75:0.95 77:0.70 81:0.32 86:0.91 91:0.27 98:0.76 101:0.87 102:0.96 107:0.95 108:0.65 110:0.90 120:0.59 122:0.91 123:0.63 124:0.65 125:0.88 126:0.31 127:0.74 128:0.98 129:0.59 133:0.76 135:1.00 139:0.89 140:0.45 145:0.74 146:0.97 147:0.61 149:0.80 150:0.33 151:0.56 153:0.48 154:0.20 159:0.84 160:0.88 162:0.61 164:0.71 168:0.98 170:0.82 172:0.96 173:0.63 174:0.99 177:0.88 179:0.15 187:0.68 190:0.99 191:0.70 192:0.49 193:0.96 195:0.95 197:0.99 201:0.55 202:0.72 205:0.98 212:0.81 215:0.67 216:0.61 220:0.91 222:0.25 226:0.98 230:0.68 233:0.76 235:0.52 236:0.92 239:0.98 241:0.35 242:0.15 243:0.21 244:0.61 245:0.97 247:0.99 248:0.57 253:0.50 254:0.84 255:0.69 256:0.71 257:0.65 259:0.21 260:0.59 264:0.93 265:0.76 266:0.80 267:0.96 268:0.73 271:0.90 275:0.98 276:0.95 282:0.75 283:0.82 285:0.49 289:0.89 291:0.97 294:0.96 297:0.36 300:0.94 +1 1:0.57 10:0.96 11:0.54 12:0.20 17:0.61 18:0.91 21:0.71 27:0.11 29:0.77 30:0.55 33:0.97 34:0.96 36:0.99 37:0.84 39:0.98 43:0.44 45:0.99 46:0.88 64:0.77 66:0.94 69:0.90 70:0.69 71:0.73 74:0.61 81:0.50 83:0.60 86:0.89 91:0.20 98:0.88 99:0.95 101:0.69 107:0.94 108:0.81 110:0.90 114:0.64 122:0.91 123:0.79 124:0.36 125:0.88 126:0.43 127:0.83 129:0.05 133:0.38 135:0.82 139:0.84 146:0.99 147:0.41 149:0.73 150:0.38 154:0.33 155:0.46 159:0.86 160:0.88 165:0.70 170:0.82 172:0.98 173:0.79 174:0.98 176:0.62 177:0.38 178:0.55 179:0.14 187:0.87 192:0.45 197:0.97 201:0.56 202:0.36 208:0.54 212:0.73 216:0.65 222:0.25 235:0.95 236:0.92 239:0.94 241:0.39 242:0.14 243:0.07 244:0.61 245:0.92 247:0.98 253:0.53 257:0.93 259:0.21 264:0.93 265:0.88 266:0.75 267:0.91 271:0.90 275:0.99 276:0.95 289:0.89 290:0.64 291:0.97 294:0.98 300:0.84 +1 10:0.98 11:0.48 12:0.20 17:0.64 18:0.89 21:0.78 27:0.10 29:0.77 30:0.19 32:0.64 33:0.97 34:0.56 36:0.95 37:0.81 39:0.98 43:0.26 45:0.93 46:0.88 55:0.59 66:0.74 69:0.19 70:0.91 71:0.73 74:0.59 75:0.96 83:0.60 86:0.82 91:0.46 97:0.94 98:0.82 101:0.52 102:0.94 107:0.93 108:0.15 110:0.90 120:0.57 122:0.98 123:0.15 124:0.47 125:0.88 127:0.90 129:0.05 133:0.72 135:0.99 139:0.82 140:0.87 145:0.63 146:0.95 147:0.96 149:0.64 151:0.50 153:0.48 154:0.14 155:0.52 158:0.75 159:0.77 160:0.88 162:0.54 164:0.55 170:0.82 172:0.93 173:0.14 174:0.99 177:0.96 178:0.48 179:0.23 187:0.99 190:0.99 191:0.77 192:0.51 193:0.97 195:0.89 197:0.99 202:0.56 204:0.81 208:0.54 212:0.78 216:0.80 219:0.90 220:0.91 222:0.25 226:0.98 235:0.52 239:0.97 241:0.21 242:0.45 243:0.77 244:0.83 245:0.88 247:0.91 248:0.50 253:0.45 254:0.97 256:0.45 259:0.21 260:0.57 264:0.93 265:0.82 266:0.54 267:1.00 268:0.46 271:0.90 275:0.95 276:0.89 279:0.77 281:0.91 285:0.45 289:0.89 291:0.97 292:0.95 294:0.94 300:0.84 +1 1:0.59 2:1.00 9:0.71 10:0.97 11:0.48 12:0.20 17:0.32 18:0.91 21:0.13 23:0.98 27:0.30 29:0.77 30:0.57 33:0.97 34:0.31 36:0.99 37:0.53 39:0.98 43:0.21 44:0.86 45:0.96 46:0.88 56:0.97 62:0.98 66:0.26 69:0.62 70:0.83 71:0.73 74:0.61 75:0.95 77:0.70 80:0.98 81:0.37 83:0.54 86:0.87 91:0.31 98:0.57 101:0.52 107:0.94 108:0.83 110:0.90 122:0.97 123:0.82 124:0.84 125:0.88 126:0.31 127:0.61 128:0.96 129:0.81 133:0.83 135:0.95 138:0.95 139:0.84 140:0.45 143:0.99 145:0.58 146:0.51 147:0.57 149:0.47 150:0.41 151:0.71 153:0.77 154:0.46 155:0.55 159:0.80 160:0.88 162:0.86 168:0.96 170:0.82 172:0.84 173:0.41 174:0.98 177:0.96 179:0.18 187:0.98 190:0.98 192:0.51 193:0.95 195:0.92 197:0.98 201:0.57 202:0.57 204:0.78 205:0.98 208:0.54 212:0.74 216:0.88 220:0.74 222:0.25 226:0.96 235:0.22 236:0.91 239:0.96 241:0.18 242:0.15 243:0.27 244:0.83 245:0.95 247:0.99 248:0.73 253:0.46 254:0.80 255:0.70 256:0.64 259:0.21 264:0.93 265:0.58 266:0.75 267:0.98 268:0.66 271:0.90 275:0.98 276:0.91 279:0.79 281:0.91 282:0.73 285:0.49 286:0.99 289:0.89 291:0.97 294:0.97 295:0.98 298:0.99 300:0.84 +1 2:0.99 9:0.71 10:0.92 11:0.48 12:0.20 17:0.30 18:0.70 21:0.78 23:0.97 27:0.11 29:0.77 30:0.30 33:0.97 34:0.97 36:0.72 37:0.84 39:0.98 43:0.22 45:0.85 46:0.88 55:0.71 62:0.97 66:0.53 69:0.91 70:0.81 71:0.73 74:0.36 86:0.69 91:0.67 98:0.62 101:0.52 107:0.91 108:0.81 110:0.89 120:0.64 122:0.98 123:0.80 124:0.64 125:0.88 127:0.79 128:0.97 129:0.05 133:0.60 135:0.97 138:0.98 139:0.66 140:0.98 143:0.99 146:0.88 147:0.41 149:0.26 151:0.81 153:0.46 154:0.16 155:0.53 159:0.79 160:0.88 162:0.46 164:0.54 168:0.97 170:0.82 172:0.76 173:0.85 174:0.97 177:0.05 178:0.54 179:0.42 187:0.68 190:0.95 192:0.56 197:0.96 202:0.82 205:0.97 208:0.49 212:0.54 216:0.65 220:0.93 222:0.25 235:0.22 239:0.91 241:0.66 242:0.29 243:0.11 244:0.83 245:0.84 247:0.96 248:0.73 253:0.32 254:0.88 255:0.72 256:0.58 257:0.97 259:0.21 260:0.64 264:0.93 265:0.63 266:0.80 267:0.69 268:0.58 271:0.90 275:0.96 276:0.74 285:0.55 286:0.99 289:0.88 291:0.97 294:0.96 295:0.98 298:0.99 300:0.70 +1 1:0.65 8:0.85 10:0.96 11:0.48 12:0.20 17:0.57 18:0.91 21:0.22 27:0.45 29:0.77 30:0.71 34:0.68 36:0.38 37:0.50 39:0.98 43:0.29 45:0.97 46:0.88 56:0.97 64:0.77 66:0.81 69:0.69 70:0.64 71:0.73 74:0.57 81:0.59 82:0.98 83:0.54 86:0.87 88:0.98 91:0.17 98:0.81 99:0.83 101:0.69 102:0.94 107:0.94 108:0.52 110:0.90 114:0.80 122:0.91 123:0.50 124:0.39 125:0.88 126:0.53 127:0.52 129:0.05 133:0.36 135:0.81 139:0.83 145:0.49 146:0.95 147:0.96 149:0.74 150:0.49 154:0.48 155:0.50 159:0.72 160:0.88 165:0.63 170:0.82 172:0.93 173:0.55 174:0.98 176:0.62 177:0.96 178:0.55 179:0.21 187:0.68 192:0.49 195:0.87 197:0.98 201:0.64 208:0.64 212:0.78 216:0.77 222:0.25 235:0.52 236:0.92 239:0.95 241:0.18 242:0.21 243:0.83 244:0.83 245:0.91 247:0.98 253:0.59 254:0.74 259:0.21 264:0.93 265:0.81 266:0.71 267:0.44 271:0.90 275:0.97 276:0.87 289:0.89 290:0.80 294:0.97 300:0.84 +1 1:0.68 2:0.99 10:0.99 11:0.54 12:0.20 17:0.77 18:0.85 21:0.13 22:0.93 27:0.28 29:0.77 30:0.38 33:0.97 34:0.88 36:0.90 37:0.86 39:0.98 43:0.24 44:0.88 45:0.93 46:0.88 47:0.93 56:0.97 64:0.77 66:0.62 69:0.45 70:0.87 71:0.73 74:0.61 75:0.96 77:0.70 80:0.99 81:0.65 82:0.98 83:0.60 86:0.83 88:0.98 91:0.25 97:0.89 98:0.67 99:0.83 101:0.69 102:0.95 107:0.94 108:0.35 110:0.90 114:0.76 117:0.86 122:0.98 123:0.34 124:0.65 125:0.88 126:0.53 127:0.82 129:0.05 133:0.73 135:0.98 139:0.84 145:0.63 146:0.87 147:0.89 149:0.34 150:0.57 154:0.55 155:0.57 158:0.75 159:0.74 160:0.88 165:0.63 170:0.82 172:0.94 173:0.31 174:0.99 176:0.60 177:0.96 178:0.55 179:0.19 187:0.39 192:0.57 193:0.96 195:0.86 197:0.99 201:0.67 204:0.84 208:0.64 212:0.89 216:0.61 219:0.90 222:0.25 226:0.95 235:0.60 236:0.92 239:0.98 241:0.02 242:0.13 243:0.68 244:0.61 245:0.94 247:0.98 253:0.59 254:0.80 259:0.21 262:0.93 264:0.93 265:0.68 266:0.71 267:0.89 271:0.90 275:0.99 276:0.92 279:0.79 281:0.86 282:0.73 289:0.89 290:0.76 291:0.97 292:0.87 294:0.99 295:0.98 300:0.94 +1 1:0.59 2:0.99 9:0.71 10:0.97 11:0.48 12:0.20 17:0.32 18:0.91 21:0.13 23:0.95 27:0.30 29:0.77 30:0.57 33:0.97 34:0.28 36:0.99 37:0.53 39:0.98 43:0.21 44:0.86 45:0.96 46:0.88 56:0.97 62:0.97 66:0.26 69:0.63 70:0.83 71:0.73 74:0.60 75:0.95 77:0.70 80:0.98 81:0.37 83:0.54 86:0.87 91:0.22 98:0.57 101:0.52 107:0.94 108:0.83 110:0.90 122:0.97 123:0.82 124:0.84 125:0.88 126:0.31 127:0.61 128:0.96 129:0.81 133:0.83 135:0.95 138:0.95 139:0.84 140:0.45 143:0.99 145:0.58 146:0.51 147:0.57 149:0.47 150:0.41 151:0.65 153:0.48 154:0.46 155:0.54 159:0.80 160:0.88 162:0.86 168:0.96 170:0.82 172:0.84 173:0.42 174:0.98 177:0.96 179:0.18 187:0.95 190:0.98 192:0.50 193:0.95 195:0.92 197:0.98 201:0.57 202:0.36 204:0.78 205:0.95 208:0.54 212:0.74 216:0.88 220:0.74 222:0.25 226:0.95 235:0.22 236:0.91 239:0.96 241:0.07 242:0.15 243:0.27 244:0.83 245:0.95 247:0.99 248:0.63 253:0.46 254:0.80 255:0.69 256:0.45 259:0.21 264:0.93 265:0.58 266:0.75 267:0.98 268:0.46 271:0.90 275:0.98 276:0.91 279:0.75 281:0.91 282:0.73 285:0.49 286:0.99 289:0.89 291:0.97 294:0.97 295:0.98 298:0.99 300:0.84 +1 1:0.69 10:0.99 11:0.64 12:0.20 17:0.32 18:0.91 27:0.51 29:0.77 30:0.84 32:0.80 34:0.66 36:0.70 37:0.70 39:0.98 43:0.79 44:0.93 45:0.66 46:0.97 56:0.97 60:0.87 64:0.77 66:0.46 69:0.72 70:0.45 71:0.73 74:0.89 75:0.99 77:0.70 81:0.81 82:0.99 83:0.72 85:0.97 86:0.97 88:0.99 91:0.06 98:0.71 101:0.96 102:0.98 107:0.95 108:0.75 110:0.90 114:0.98 122:0.67 123:0.74 124:0.76 125:0.88 126:0.54 127:0.62 129:0.81 133:0.74 135:0.98 139:0.97 145:0.97 146:0.62 147:0.05 149:0.99 150:0.62 154:0.72 155:0.53 158:0.92 159:0.79 160:0.88 165:0.93 170:0.82 172:0.91 173:0.54 174:0.97 176:0.73 177:0.05 178:0.55 179:0.18 187:0.68 192:0.57 195:0.92 197:0.97 201:0.68 208:0.64 212:0.61 215:0.96 216:0.55 219:0.95 222:0.25 226:0.98 235:0.42 236:0.97 239:0.99 241:0.03 242:0.19 243:0.06 245:0.96 247:0.96 253:0.76 257:0.97 264:0.93 265:0.72 266:0.75 267:0.65 271:0.90 275:0.99 276:0.92 279:0.80 282:0.88 283:0.82 289:0.89 290:0.98 292:0.95 294:1.00 295:0.99 297:0.36 299:0.88 300:0.84 +1 1:0.58 7:0.65 10:0.97 11:0.54 12:0.20 17:0.73 18:0.74 21:0.30 27:0.28 29:0.77 30:0.30 33:0.97 34:0.85 36:0.98 37:0.86 39:0.98 43:0.26 45:0.87 46:0.88 56:0.97 66:0.77 69:0.45 70:0.96 71:0.73 74:0.41 80:0.98 81:0.40 82:0.98 86:0.74 88:0.98 91:0.22 98:0.85 101:0.69 102:0.94 104:0.94 106:0.81 107:0.93 108:0.35 110:0.90 122:0.85 123:0.33 124:0.43 125:0.88 126:0.43 127:0.85 129:0.05 133:0.59 135:0.99 139:0.70 145:0.70 146:0.97 147:0.98 149:0.41 150:0.40 154:0.55 155:0.47 159:0.81 160:0.88 170:0.82 172:0.94 173:0.25 174:0.98 177:0.96 178:0.55 179:0.21 187:0.39 192:0.46 195:0.91 197:0.98 201:0.60 206:0.81 208:0.49 212:0.75 215:0.67 216:0.61 222:0.25 230:0.67 233:0.66 235:0.52 236:0.92 239:0.95 241:0.01 242:0.13 243:0.79 244:0.61 245:0.92 247:0.98 253:0.36 254:0.80 259:0.21 264:0.93 265:0.85 266:0.84 267:0.59 271:0.90 275:0.99 276:0.91 283:0.67 289:0.88 291:0.97 294:0.99 297:0.36 300:0.94 +1 8:0.74 10:0.97 11:0.54 12:0.20 17:0.85 18:0.82 21:0.54 27:0.52 29:0.77 30:0.53 33:0.97 34:0.93 36:0.64 37:0.64 39:0.98 43:0.46 45:0.91 46:0.88 56:0.97 66:0.88 69:0.56 70:0.79 71:0.73 74:0.51 75:0.96 80:0.98 81:0.26 83:0.60 86:0.81 91:0.22 97:0.89 98:0.71 101:0.87 102:0.94 107:0.92 108:0.43 110:0.90 122:0.97 123:0.42 124:0.37 125:0.88 126:0.23 127:0.54 129:0.05 133:0.36 135:0.98 139:0.78 145:0.50 146:0.82 147:0.85 149:0.50 150:0.28 154:0.44 155:0.54 159:0.59 160:0.88 170:0.82 172:0.84 173:0.50 174:0.96 177:0.96 178:0.55 179:0.39 187:0.68 192:0.55 193:0.97 195:0.77 197:0.97 204:0.83 208:0.64 212:0.85 216:0.38 222:0.25 226:0.96 235:0.60 239:0.96 241:0.07 242:0.17 243:0.89 244:0.83 245:0.64 247:0.92 253:0.41 254:0.86 259:0.21 264:0.93 265:0.71 266:0.38 267:0.99 271:0.90 275:0.96 276:0.68 279:0.75 281:0.91 289:0.88 291:0.97 294:0.98 295:0.98 300:0.84 +1 1:0.66 9:0.71 10:0.97 11:0.48 12:0.20 17:0.55 18:0.80 21:0.13 23:0.98 27:0.10 29:0.77 30:0.21 33:0.97 34:0.75 36:0.90 37:0.81 39:0.98 43:0.28 45:0.89 46:0.88 56:0.97 62:0.97 66:0.89 69:0.17 70:0.94 71:0.73 74:0.45 75:0.95 80:0.98 81:0.46 83:0.54 86:0.78 91:0.49 98:0.88 101:0.52 102:0.94 107:0.92 108:0.14 110:0.89 122:0.98 123:0.13 124:0.36 125:0.88 126:0.31 127:0.79 128:0.96 129:0.05 133:0.36 135:0.99 138:0.97 139:0.73 140:0.90 143:0.98 145:0.45 146:0.90 147:0.92 149:0.44 150:0.50 151:0.57 153:0.48 154:0.46 155:0.53 159:0.59 160:0.88 162:0.58 168:0.96 170:0.82 172:0.85 173:0.20 174:0.97 177:0.96 178:0.48 179:0.42 187:1.00 190:0.99 192:0.47 193:0.95 195:0.76 197:0.98 201:0.62 202:0.67 205:0.98 208:0.49 212:0.75 216:0.80 220:0.82 222:0.25 226:0.96 235:0.42 236:0.91 239:0.95 241:0.44 242:0.29 243:0.89 244:0.83 245:0.65 247:0.93 248:0.57 253:0.38 254:0.80 255:0.69 256:0.64 259:0.21 264:0.93 265:0.88 266:0.47 267:1.00 268:0.65 271:0.90 275:0.95 276:0.75 285:0.49 289:0.88 291:0.97 294:0.96 300:0.84 +1 1:0.64 7:0.70 10:0.95 11:0.51 12:0.20 17:0.61 18:0.82 21:0.40 22:0.93 27:0.30 29:0.77 30:0.66 33:0.97 34:0.34 36:0.36 37:0.62 39:0.98 43:0.63 45:0.94 46:0.93 47:0.93 48:0.91 64:0.77 66:0.27 69:0.81 70:0.87 71:0.73 74:0.60 75:0.97 81:0.66 83:0.72 86:0.81 91:0.18 97:0.97 98:0.39 99:0.99 101:0.96 102:0.94 104:0.80 106:0.81 107:0.93 108:0.65 110:0.90 114:0.78 117:0.86 122:0.91 123:0.62 124:0.89 125:0.88 126:0.53 127:0.36 129:0.81 133:0.89 135:0.96 139:0.83 145:0.82 146:0.79 147:0.25 149:0.66 150:0.48 154:0.35 155:0.53 158:0.77 159:0.81 160:0.88 165:0.86 170:0.82 172:0.67 173:0.58 174:0.95 176:0.63 177:0.88 178:0.55 179:0.27 187:0.68 192:0.58 193:0.98 195:0.96 197:0.95 201:0.63 204:0.81 206:0.81 208:0.64 212:0.40 215:0.67 216:0.44 219:0.91 222:0.25 226:0.98 230:0.73 233:0.76 235:0.68 236:0.94 239:0.96 242:0.19 243:0.19 244:0.61 245:0.81 247:0.96 253:0.59 254:0.84 257:0.65 259:0.21 262:0.93 264:0.93 265:0.41 266:0.84 267:0.23 271:0.90 275:0.97 276:0.79 279:0.79 281:0.91 283:0.82 289:0.89 290:0.78 291:0.97 292:0.95 294:0.96 297:0.36 300:0.94 +1 1:0.56 2:0.99 10:0.96 11:0.57 12:0.20 17:0.38 18:0.74 21:0.64 27:0.45 29:0.77 30:0.41 34:0.91 36:0.18 37:0.50 39:0.98 43:0.43 45:0.89 46:0.93 56:0.97 66:0.19 69:0.70 70:0.92 71:0.73 74:0.40 75:0.95 81:0.30 86:0.77 91:0.23 98:0.58 101:1.00 107:0.91 108:0.53 110:0.89 122:0.99 123:0.82 124:0.83 125:0.88 126:0.31 127:0.59 129:0.05 133:0.81 135:0.93 139:0.72 145:0.50 146:0.73 147:0.78 149:0.54 150:0.33 154:0.33 155:0.46 159:0.76 160:0.88 170:0.82 172:0.75 173:0.53 174:0.98 177:0.96 178:0.55 179:0.29 187:0.68 192:0.44 197:0.98 201:0.54 208:0.49 212:0.67 216:0.77 222:0.25 226:0.95 235:0.80 236:0.91 239:0.94 241:0.53 242:0.21 243:0.49 244:0.83 245:0.87 247:0.94 253:0.35 259:0.21 264:0.93 265:0.46 266:0.87 267:0.65 271:0.90 275:0.98 276:0.83 277:0.95 279:0.75 289:0.88 294:0.98 295:0.98 300:0.84 +1 1:0.69 9:0.73 10:0.95 11:0.54 12:0.20 17:0.95 18:0.69 21:0.13 23:0.95 27:0.36 29:0.77 30:0.26 31:0.89 34:0.98 36:0.60 37:0.92 39:0.98 43:0.22 44:0.88 45:0.89 46:0.88 56:0.97 62:0.97 64:0.77 66:0.71 69:0.88 70:0.78 71:0.73 74:0.49 77:0.70 79:0.89 80:0.98 81:0.74 82:0.99 83:0.60 86:0.71 88:0.98 91:0.29 96:0.72 98:0.71 99:0.99 101:0.69 102:0.96 107:0.91 108:0.77 110:0.89 114:0.85 122:0.98 123:0.75 124:0.46 125:0.88 126:0.53 127:0.81 128:0.96 129:0.05 133:0.59 135:0.60 137:0.89 138:0.95 139:0.75 140:0.45 143:0.99 145:0.77 146:0.81 147:0.30 149:0.32 150:0.59 151:0.66 153:0.48 154:0.40 155:0.64 159:0.61 160:0.88 162:0.86 165:0.86 168:0.96 170:0.82 172:0.80 173:0.91 174:0.97 176:0.63 177:0.05 179:0.44 187:0.39 190:0.77 192:0.87 193:0.97 195:0.76 196:0.90 197:0.98 201:0.67 202:0.36 204:0.83 205:0.95 208:0.64 212:0.68 216:0.25 220:0.74 222:0.25 235:0.68 236:0.94 239:0.95 241:0.44 242:0.54 243:0.12 245:0.77 247:0.82 248:0.63 253:0.64 254:0.97 255:0.73 256:0.45 257:0.97 259:0.21 264:0.93 265:0.72 266:0.75 267:0.44 268:0.46 271:0.90 275:0.93 276:0.73 281:0.91 282:0.75 284:0.88 285:0.61 286:0.99 289:0.89 290:0.85 294:0.96 298:0.99 300:0.70 +1 1:0.63 9:0.71 10:0.97 11:0.48 12:0.20 17:0.53 18:0.91 21:0.40 23:0.95 27:0.10 29:0.77 30:0.66 31:0.88 33:0.97 34:0.58 36:0.88 37:0.81 39:0.98 43:0.56 44:0.88 45:0.95 46:0.88 62:0.97 64:0.77 66:0.49 69:0.87 70:0.60 71:0.73 74:0.65 75:0.96 77:0.70 79:0.87 81:0.58 83:0.60 86:0.86 91:0.14 96:0.70 97:0.97 98:0.68 99:0.95 101:0.52 102:0.95 107:0.94 108:0.75 110:0.90 114:0.76 122:0.91 123:0.73 124:0.64 125:0.88 126:0.43 127:0.86 128:0.95 129:0.05 133:0.60 135:0.97 137:0.88 139:0.86 140:0.45 145:0.74 146:0.82 147:0.69 149:0.73 150:0.47 151:0.53 153:0.48 154:0.17 155:0.55 158:0.75 159:0.66 160:0.88 162:0.86 165:0.70 168:0.95 170:0.82 172:0.82 173:0.87 174:0.97 176:0.62 177:0.70 179:0.32 187:1.00 190:0.95 192:0.57 193:0.97 195:0.81 196:0.90 197:0.98 201:0.62 202:0.36 204:0.81 205:0.95 208:0.54 212:0.78 216:0.80 219:0.90 220:0.91 222:0.25 226:0.98 235:0.68 236:0.92 239:0.97 241:0.37 242:0.24 243:0.44 244:0.83 245:0.91 247:0.91 248:0.53 253:0.61 254:0.80 255:0.70 256:0.45 257:0.84 259:0.21 264:0.93 265:0.68 266:0.71 267:1.00 268:0.46 271:0.90 275:0.91 276:0.82 279:0.79 281:0.91 282:0.74 284:0.88 285:0.55 289:0.89 290:0.76 291:0.97 292:0.95 294:0.92 300:0.55 +1 1:0.66 8:0.85 10:0.94 11:0.42 12:0.20 17:0.69 18:0.67 20:0.95 21:0.40 27:0.07 29:0.77 30:0.08 33:0.97 34:0.80 36:0.68 37:0.94 39:0.98 43:0.08 45:0.66 46:0.88 56:0.97 64:0.77 66:0.69 69:0.23 70:0.88 71:0.73 74:0.41 75:0.95 78:0.98 80:0.98 81:0.60 83:0.60 86:0.69 91:0.44 98:0.69 99:0.83 100:0.73 101:0.46 102:0.94 107:0.89 108:0.18 110:0.89 111:0.95 114:0.78 122:0.98 123:0.18 124:0.42 125:0.88 126:0.53 127:0.64 129:0.05 133:0.36 135:0.84 139:0.70 145:0.61 146:0.46 147:0.52 149:0.09 150:0.51 152:0.88 154:0.54 155:0.54 159:0.26 160:0.88 165:0.63 169:0.72 170:0.82 172:0.54 173:0.49 174:0.93 176:0.63 177:0.96 178:0.55 179:0.75 186:0.98 187:0.21 192:0.55 193:0.97 195:0.56 197:0.97 201:0.65 204:0.81 208:0.64 212:0.89 216:0.50 222:0.25 226:0.96 235:0.74 236:0.94 239:0.94 241:0.52 242:0.58 243:0.73 244:0.83 245:0.59 247:0.67 253:0.57 254:0.74 259:0.21 261:0.91 264:0.93 265:0.69 266:0.23 267:0.59 271:0.90 275:0.91 276:0.42 281:0.91 289:0.89 290:0.78 291:0.97 294:0.95 300:0.55 +2 1:0.74 6:0.98 8:0.97 9:0.80 11:0.57 12:0.20 17:0.91 20:0.79 27:0.01 28:0.99 30:0.76 34:0.92 36:0.80 37:0.97 39:0.86 41:0.82 43:0.69 46:0.98 66:0.54 69:0.88 70:0.62 74:0.80 78:0.84 81:0.83 83:0.81 85:0.97 91:0.61 96:0.83 98:0.33 100:0.95 101:0.85 104:0.58 107:0.98 108:0.88 110:0.92 111:0.90 114:0.98 120:0.72 122:0.57 123:0.88 124:0.76 125:1.00 126:0.54 127:0.30 129:0.89 133:0.83 135:0.34 140:0.45 146:0.13 147:0.18 149:0.89 150:0.89 151:0.90 152:1.00 153:0.69 154:0.59 155:0.67 159:0.55 160:0.96 161:0.55 162:0.86 164:0.62 165:0.92 167:0.89 169:0.99 172:0.73 173:0.86 176:0.73 177:0.38 179:0.31 181:0.48 186:0.84 187:0.21 189:0.99 192:0.59 201:0.74 202:0.46 208:0.64 212:0.67 215:0.96 216:0.85 222:0.25 232:0.76 235:0.05 238:0.98 241:0.77 242:0.63 243:0.12 245:0.74 247:0.39 248:0.80 253:0.86 255:0.79 256:0.45 259:0.21 260:0.73 261:0.99 265:0.35 266:0.71 267:0.99 268:0.55 271:0.90 276:0.70 285:0.62 289:0.90 290:0.98 297:0.36 300:0.84 +1 1:0.74 11:0.57 12:0.20 17:0.86 27:0.01 28:0.99 30:0.76 34:0.94 36:0.80 37:0.97 39:0.86 43:0.68 46:0.99 66:0.54 69:0.89 70:0.58 74:0.82 81:0.83 83:0.80 85:0.97 91:0.42 98:0.36 101:0.85 104:0.58 107:0.98 108:0.87 110:0.92 114:0.98 122:0.57 123:0.86 124:0.69 125:0.88 126:0.54 127:0.27 129:0.89 133:0.78 135:0.32 146:0.13 147:0.18 149:0.91 150:0.89 154:0.57 155:0.67 159:0.49 160:0.88 161:0.55 165:0.92 167:0.80 172:0.75 173:0.89 176:0.73 177:0.38 178:0.55 179:0.27 181:0.48 187:0.21 192:0.59 201:0.74 212:0.69 215:0.96 216:0.85 222:0.25 232:0.76 235:0.05 241:0.70 242:0.63 243:0.12 245:0.78 247:0.39 253:0.78 259:0.21 265:0.38 266:0.47 267:0.65 271:0.90 276:0.72 289:0.90 290:0.98 297:0.36 300:0.70 +4 1:0.74 6:0.98 8:0.98 9:0.80 11:0.57 12:0.20 17:0.30 20:0.97 27:0.01 28:0.99 30:0.76 32:0.80 34:0.96 36:0.65 37:0.97 39:0.86 41:1.00 43:0.65 46:0.98 60:0.99 66:0.68 69:0.90 70:0.62 74:0.84 77:0.89 78:0.94 81:0.83 83:0.91 85:0.96 91:1.00 96:1.00 98:0.35 100:1.00 101:0.84 104:0.58 107:0.98 108:0.87 110:0.92 111:0.99 114:0.98 120:1.00 122:0.57 123:0.87 124:0.47 125:0.97 126:0.54 127:0.24 129:0.59 133:0.64 135:0.32 138:0.99 140:0.45 145:0.86 146:0.45 147:0.51 149:0.86 150:0.89 151:1.00 152:1.00 153:1.00 154:0.55 155:0.67 158:0.92 159:0.46 160:1.00 161:0.55 162:0.59 164:0.99 165:0.92 167:0.98 169:0.99 172:0.73 173:0.91 176:0.73 177:0.38 179:0.31 181:0.48 186:0.94 189:0.99 191:0.99 192:0.59 195:0.80 201:0.74 202:0.99 208:0.64 212:0.77 215:0.96 216:0.85 222:0.25 232:0.76 235:0.05 238:1.00 241:0.99 242:0.63 243:0.23 245:0.71 247:0.39 248:1.00 253:1.00 255:0.79 256:0.99 259:0.21 260:1.00 261:0.99 265:0.37 266:0.63 267:0.87 268:0.99 271:0.90 276:0.62 279:0.86 282:0.88 283:0.82 285:0.62 289:0.90 290:0.98 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.83 8:0.70 9:0.80 11:0.57 12:0.20 17:0.45 20:0.80 21:0.22 27:0.54 28:0.99 30:0.73 34:0.37 36:0.27 37:0.49 39:0.86 41:1.00 43:0.86 46:0.96 60:0.97 66:0.05 69:0.58 70:0.66 74:0.93 78:0.74 81:0.66 83:0.90 85:1.00 91:0.63 96:0.99 98:0.15 100:0.77 101:0.80 104:0.93 106:0.81 107:0.98 108:1.00 110:0.92 111:0.74 114:0.90 117:0.86 120:0.97 122:0.43 123:0.43 124:1.00 125:0.97 126:0.54 127:0.99 129:1.00 133:1.00 135:0.28 138:1.00 140:0.45 145:0.39 146:0.75 147:0.79 149:0.97 150:0.80 151:0.99 152:0.77 153:0.95 154:0.83 155:0.67 158:0.92 159:0.99 160:1.00 161:0.55 162:0.60 163:0.81 164:0.97 165:0.86 167:0.72 169:0.75 172:0.57 173:0.08 176:0.73 177:0.38 179:0.12 181:0.48 186:0.75 187:0.87 189:0.85 191:0.80 192:0.59 201:0.74 202:0.96 206:0.81 208:0.64 212:0.21 215:0.79 216:0.50 222:0.25 232:0.94 235:0.31 238:0.84 241:0.56 242:0.59 243:0.12 245:0.90 247:0.60 248:0.99 253:0.99 255:0.79 256:0.97 259:0.21 260:0.97 261:0.75 265:0.14 266:0.99 267:1.00 268:0.97 271:0.90 276:0.97 279:0.86 283:0.82 285:0.62 289:0.90 290:0.90 297:0.36 300:0.98 +1 6:0.98 8:0.90 9:0.80 11:0.57 12:0.20 17:0.88 20:0.74 21:0.78 27:0.01 28:0.99 30:0.76 34:0.97 36:0.50 37:0.97 39:0.86 41:0.82 43:0.69 46:0.98 66:0.74 69:0.88 70:0.62 74:0.76 78:0.79 83:0.76 85:0.96 91:0.49 96:0.80 98:0.38 100:0.84 101:0.84 104:0.58 107:0.98 108:0.84 110:0.92 111:0.80 120:0.69 122:0.57 123:0.84 124:0.42 125:0.99 127:0.24 129:0.59 133:0.64 135:0.81 140:0.80 146:0.13 147:0.18 149:0.86 151:0.87 152:1.00 153:0.48 154:0.59 155:0.67 159:0.41 160:0.96 161:0.55 162:0.53 164:0.57 167:0.89 169:0.99 172:0.73 173:0.89 177:0.38 178:0.42 179:0.33 181:0.48 186:0.80 187:0.21 189:0.99 191:0.90 192:0.59 202:0.58 208:0.64 212:0.81 216:0.85 222:0.25 232:0.76 235:0.05 238:0.92 241:0.77 242:0.63 243:0.15 245:0.64 247:0.39 248:0.78 253:0.82 255:0.79 256:0.45 259:0.21 260:0.70 261:0.99 265:0.40 266:0.54 267:0.65 268:0.46 271:0.90 276:0.58 285:0.62 289:0.90 300:0.84 +1 1:0.74 6:0.95 7:0.81 8:0.95 9:0.80 11:0.57 12:0.20 17:0.94 20:0.82 21:0.05 27:0.08 28:0.99 30:0.87 32:0.80 34:0.53 36:0.35 37:0.93 39:0.86 41:0.93 43:0.93 46:0.95 66:0.24 69:0.29 70:0.32 74:0.72 76:0.85 77:0.89 78:0.74 81:0.77 83:0.82 85:0.90 91:0.51 96:0.92 98:0.18 100:0.79 101:0.77 104:0.58 107:0.96 108:0.92 110:0.90 111:0.75 114:0.95 120:0.87 121:0.97 122:0.43 123:0.92 124:0.73 125:1.00 126:0.54 127:0.89 129:0.81 133:0.67 135:0.68 140:0.45 145:0.87 146:0.13 147:0.18 149:0.68 150:0.86 151:0.96 152:0.92 153:0.86 154:0.93 155:0.67 159:0.81 160:0.98 161:0.55 162:0.70 163:0.81 164:0.79 165:0.90 167:0.86 169:0.80 172:0.21 173:0.15 176:0.73 177:0.38 179:0.87 181:0.48 186:0.75 187:0.39 189:0.98 191:0.75 192:0.59 195:0.92 201:0.74 202:0.72 204:0.89 208:0.64 212:0.30 215:0.91 216:0.62 220:0.62 222:0.25 230:0.87 232:0.76 233:0.76 235:0.12 238:0.97 241:0.75 242:0.63 243:0.28 245:0.54 247:0.30 248:0.92 253:0.92 255:0.79 256:0.71 260:0.87 261:0.81 265:0.19 266:0.54 267:0.98 268:0.75 271:0.90 276:0.19 282:0.88 285:0.62 289:0.90 290:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.86 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.75 20:0.98 21:0.30 27:0.21 28:0.99 30:0.76 34:0.79 36:0.91 37:0.74 39:0.86 41:0.99 43:0.98 46:1.00 60:0.99 66:0.31 69:0.12 70:0.44 74:0.99 78:0.75 81:0.61 83:0.90 85:1.00 91:0.75 96:0.98 98:0.66 100:0.78 101:0.87 104:0.84 106:0.81 107:0.99 108:0.94 110:0.93 111:0.74 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.94 124:0.77 125:0.99 126:0.54 127:0.73 129:0.89 133:0.78 135:0.19 140:0.45 146:0.84 147:0.86 149:1.00 150:0.77 151:0.99 152:0.90 153:0.97 154:0.98 155:0.67 158:0.92 159:0.92 160:1.00 161:0.55 162:0.65 164:0.93 165:0.84 167:0.71 169:0.76 172:0.97 173:0.07 176:0.73 177:0.38 179:0.11 181:0.48 186:0.76 187:0.39 189:0.88 191:0.80 192:0.59 201:0.74 202:0.90 204:0.89 206:0.81 208:0.64 212:0.74 215:0.72 216:0.95 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 238:0.91 241:0.51 242:0.63 243:0.20 245:0.99 247:0.55 248:0.99 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.78 265:0.66 266:0.75 267:0.59 268:0.91 271:0.90 276:0.98 279:0.86 283:0.82 285:0.62 289:0.90 290:0.86 297:0.36 300:0.94 +1 1:0.74 6:0.95 7:0.81 8:0.95 9:0.80 11:0.57 12:0.20 17:0.90 20:0.92 21:0.05 27:0.08 28:0.99 30:0.76 32:0.80 34:0.28 36:0.41 37:0.93 39:0.86 41:0.99 43:0.93 46:0.94 60:0.99 66:0.36 69:0.29 70:0.29 74:0.76 76:0.85 77:0.89 78:0.77 81:0.77 83:0.90 85:0.85 91:0.97 96:0.99 98:0.16 100:0.82 101:0.74 104:0.58 107:0.94 108:0.93 110:0.90 111:0.82 114:0.95 120:0.97 121:0.97 122:0.43 123:0.92 124:0.69 125:0.97 126:0.54 127:0.96 129:0.59 133:0.64 135:0.37 140:0.45 145:0.87 146:0.13 147:0.18 149:0.62 150:0.86 151:1.00 152:0.92 153:0.98 154:0.93 155:0.67 158:0.92 159:0.81 160:1.00 161:0.55 162:0.58 164:0.95 165:0.90 167:0.98 169:0.86 172:0.21 173:0.16 176:0.73 177:0.38 179:0.94 181:0.48 186:0.76 189:0.96 191:0.96 192:0.59 195:0.92 201:0.74 202:0.93 204:0.89 208:0.64 212:0.23 215:0.91 216:0.62 222:0.25 230:0.87 232:0.76 233:0.76 235:0.12 238:0.99 241:0.97 242:0.55 243:0.34 245:0.45 247:0.39 248:0.99 253:0.99 255:0.79 256:0.93 260:0.97 261:0.81 265:0.17 266:0.38 267:0.97 268:0.93 271:0.90 276:0.16 279:0.86 282:0.88 283:0.82 285:0.62 289:0.90 290:0.95 297:0.36 299:0.88 300:0.25 +1 1:0.74 6:0.80 8:0.59 9:0.80 11:0.57 12:0.20 17:0.88 20:0.86 27:0.06 28:0.99 30:0.95 34:0.18 36:0.59 37:0.84 39:0.86 41:0.88 43:0.78 46:0.96 66:0.54 69:0.77 74:0.41 78:0.83 81:0.83 83:0.82 85:0.72 91:0.90 96:0.88 98:0.13 100:0.85 101:0.75 104:0.58 107:0.91 108:0.61 110:0.89 111:0.82 114:0.98 120:0.81 123:0.58 125:1.00 126:0.54 127:0.08 129:0.05 135:0.37 140:0.45 146:0.13 147:0.18 149:0.42 150:0.89 151:0.94 152:0.82 153:0.80 154:0.71 155:0.67 159:0.08 160:0.97 161:0.55 162:0.78 164:0.70 165:0.92 167:0.97 169:0.83 172:0.10 173:1.00 176:0.73 177:0.38 179:0.12 181:0.48 186:0.83 189:0.82 191:0.78 192:0.59 201:0.74 202:0.59 208:0.64 215:0.96 216:0.03 222:0.25 232:0.76 235:0.05 238:0.90 241:0.94 243:0.63 248:0.88 253:0.86 255:0.79 256:0.58 259:0.21 260:0.81 261:0.83 265:0.14 267:0.92 268:0.64 271:0.90 285:0.62 289:0.90 290:0.98 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.20 17:0.51 21:0.22 27:0.45 28:0.99 30:0.17 34:0.92 36:0.18 37:0.50 39:0.86 43:0.82 46:0.94 55:0.59 66:0.69 69:0.69 70:0.80 74:0.82 81:0.66 83:0.76 85:0.80 91:0.08 98:0.52 101:0.73 107:0.97 108:0.52 110:0.91 114:0.90 122:0.67 123:0.50 124:0.42 125:0.88 126:0.54 127:0.29 129:0.59 133:0.47 135:0.71 145:0.47 146:0.72 147:0.76 149:0.74 150:0.80 154:0.77 155:0.67 159:0.53 160:0.88 161:0.55 165:0.86 167:0.62 172:0.54 173:0.61 176:0.73 177:0.38 178:0.55 179:0.61 181:0.48 187:0.68 192:0.59 201:0.74 212:0.30 215:0.79 216:0.77 222:0.25 235:0.31 241:0.15 242:0.58 243:0.73 245:0.59 247:0.47 253:0.74 259:0.21 265:0.54 266:0.63 267:0.59 271:0.90 276:0.44 289:0.90 290:0.90 297:0.36 300:0.55 +2 1:0.74 6:0.85 7:0.81 8:0.69 9:0.80 11:0.57 12:0.20 17:0.95 20:0.79 27:0.33 28:0.99 30:0.85 32:0.80 34:0.58 36:0.56 39:0.86 41:0.96 43:0.93 46:1.00 60:0.87 66:0.75 69:0.25 70:0.27 74:0.92 77:0.70 78:0.77 81:0.83 83:0.89 85:0.95 91:0.87 96:0.95 98:0.76 100:0.80 101:0.86 104:0.58 107:0.98 108:0.57 110:0.92 111:0.77 114:0.98 120:0.90 121:0.99 122:0.43 123:0.54 124:0.40 125:1.00 126:0.54 127:0.76 129:0.59 133:0.47 135:0.39 140:0.45 145:0.95 146:0.13 147:0.18 149:0.93 150:0.89 151:0.98 152:0.77 153:0.91 154:0.93 155:0.67 158:0.87 159:0.32 160:0.99 161:0.55 162:0.82 164:0.85 165:0.92 167:0.97 169:0.78 172:0.67 173:0.44 176:0.73 177:0.38 179:0.65 181:0.48 186:0.78 189:0.87 191:0.70 192:0.59 195:0.60 201:0.74 202:0.76 204:0.89 208:0.64 212:0.89 215:0.96 216:0.35 222:0.25 230:0.87 232:0.76 233:0.76 235:0.05 238:0.90 241:0.90 242:0.63 243:0.18 245:0.64 247:0.30 248:0.95 253:0.96 255:0.79 256:0.81 259:0.21 260:0.91 261:0.76 265:0.76 266:0.27 267:1.00 268:0.81 271:0.90 276:0.54 279:0.86 282:0.88 283:0.71 285:0.62 289:0.90 290:0.98 297:0.36 299:0.88 300:0.25 +3 1:0.74 6:0.86 7:0.81 9:0.80 11:0.57 12:0.20 17:0.83 20:0.74 21:0.30 27:0.21 28:0.99 30:0.76 34:0.66 36:0.88 37:0.74 39:0.86 41:0.90 43:0.98 46:1.00 66:0.26 69:0.12 70:0.46 74:1.00 78:0.72 81:0.61 83:0.78 85:1.00 91:0.12 96:0.83 98:0.62 100:0.73 101:0.87 104:0.84 107:0.99 108:0.95 110:0.93 111:0.72 114:0.86 120:0.73 121:0.97 122:0.57 123:0.95 124:0.87 125:0.98 126:0.54 127:0.76 129:0.95 133:0.87 135:0.23 140:0.45 146:0.83 147:0.85 149:1.00 150:0.77 151:0.87 152:0.90 153:0.48 154:0.98 155:0.67 159:0.93 160:0.98 161:0.55 162:0.59 164:0.73 165:0.84 167:0.67 169:0.76 172:0.97 173:0.07 176:0.73 177:0.38 179:0.10 181:0.48 186:0.73 187:0.39 192:0.59 201:0.74 202:0.67 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 220:0.87 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 241:0.37 242:0.63 243:0.19 245:0.99 247:0.39 248:0.81 253:0.90 255:0.79 256:0.64 259:0.21 260:0.74 261:0.78 265:0.63 266:0.75 267:0.96 268:0.66 271:0.90 276:0.98 285:0.62 289:0.90 290:0.86 297:0.36 300:0.84 +2 1:0.74 6:0.89 7:0.81 8:0.81 9:0.80 11:0.57 12:0.20 17:0.97 20:0.82 21:0.05 27:0.07 28:0.99 30:0.45 32:0.80 34:0.19 36:0.55 37:0.78 39:0.86 41:0.88 43:0.74 46:0.93 66:0.27 69:0.85 70:0.25 74:0.63 77:0.70 78:0.84 81:0.77 83:0.80 85:0.72 91:0.88 96:0.84 98:0.09 100:0.89 101:0.71 104:0.58 107:0.92 108:0.71 110:0.89 111:0.85 114:0.95 120:0.90 121:0.90 122:0.43 123:0.69 124:0.72 125:0.96 126:0.54 127:0.33 129:0.05 133:0.62 135:0.82 140:0.90 145:0.69 146:0.13 147:0.18 149:0.41 150:0.86 151:0.85 152:0.81 153:0.45 154:0.64 155:0.67 159:0.49 160:0.97 161:0.55 162:0.63 163:0.89 164:0.91 165:0.90 167:0.96 169:0.86 172:0.10 173:0.85 176:0.73 177:0.38 179:0.96 181:0.48 186:0.84 189:0.92 191:0.94 192:0.59 195:0.76 201:0.74 202:0.87 204:0.89 208:0.64 212:0.61 215:0.91 216:0.25 220:0.62 222:0.25 230:0.87 232:0.76 233:0.76 235:0.12 238:0.93 241:0.87 242:0.63 243:0.46 245:0.38 247:0.30 248:0.82 253:0.84 255:0.79 256:0.88 259:0.21 260:0.91 261:0.83 265:0.09 266:0.17 267:1.00 268:0.88 271:0.90 276:0.18 282:0.88 283:0.71 285:0.62 289:0.90 290:0.95 297:0.36 299:0.88 300:0.18 +3 1:0.74 7:0.81 11:0.57 12:0.20 17:0.87 21:0.30 27:0.21 28:0.99 30:0.76 34:0.68 36:0.77 37:0.74 39:0.86 43:0.98 46:1.00 66:0.27 69:0.12 70:0.47 74:1.00 81:0.61 83:0.75 85:1.00 91:0.08 98:0.62 101:0.87 104:0.84 106:0.81 107:0.99 108:0.96 110:0.93 114:0.86 121:0.90 122:0.57 123:0.96 124:0.89 125:0.88 126:0.54 127:0.75 129:0.96 133:0.90 135:0.23 146:0.27 147:0.33 149:1.00 150:0.77 154:0.98 155:0.67 159:0.93 160:0.88 161:0.55 165:0.84 167:0.55 172:0.97 173:0.06 176:0.73 177:0.38 178:0.55 179:0.10 181:0.48 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.72 216:0.95 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 241:0.01 242:0.63 243:0.06 245:0.99 247:0.55 253:0.79 259:0.21 265:0.63 266:0.75 267:0.52 271:0.90 276:0.99 283:0.71 289:0.90 290:0.86 297:0.36 300:0.84 +3 1:0.74 7:0.81 9:0.80 11:0.57 12:0.20 17:0.84 21:0.30 27:0.21 28:0.99 30:0.76 34:0.66 36:0.87 37:0.74 39:0.86 41:0.88 43:0.98 46:1.00 66:0.26 69:0.12 70:0.46 74:1.00 81:0.61 83:0.77 85:1.00 91:0.11 96:0.75 98:0.62 101:0.87 104:0.84 106:0.81 107:0.99 108:0.95 110:0.93 114:0.86 117:0.86 120:0.63 121:0.97 122:0.57 123:0.95 124:0.87 125:0.98 126:0.54 127:0.76 129:0.95 133:0.87 135:0.23 140:0.45 146:0.83 147:0.86 149:1.00 150:0.77 151:0.69 153:0.48 154:0.98 155:0.67 159:0.93 160:0.97 161:0.55 162:0.62 164:0.67 165:0.84 167:0.66 172:0.97 173:0.07 176:0.73 177:0.38 179:0.10 181:0.48 187:0.39 192:0.59 201:0.74 202:0.60 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 220:0.87 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 241:0.35 242:0.63 243:0.19 245:0.99 247:0.39 248:0.67 253:0.84 255:0.79 256:0.58 259:0.21 260:0.64 265:0.63 266:0.75 267:0.97 268:0.59 271:0.90 276:0.98 285:0.62 289:0.90 290:0.86 297:0.36 300:0.84 +1 1:0.74 8:0.84 11:0.57 12:0.20 17:0.43 21:0.13 27:0.45 28:0.99 30:0.72 32:0.80 34:0.50 36:0.20 37:0.50 39:0.86 43:0.82 46:0.98 60:0.87 66:0.27 69:0.69 70:0.70 74:0.91 77:0.70 81:0.71 83:0.85 85:0.97 91:0.09 98:0.33 101:0.83 107:0.98 108:0.52 110:0.92 114:0.92 122:0.43 123:0.50 124:0.84 125:0.88 126:0.54 127:0.52 129:0.93 133:0.84 135:0.92 145:0.49 146:0.60 147:0.65 149:0.94 150:0.83 154:0.77 155:0.67 158:0.92 159:0.74 160:0.88 161:0.55 163:0.81 165:0.88 167:0.64 172:0.57 173:0.54 176:0.73 177:0.38 178:0.55 179:0.43 181:0.48 187:0.68 192:0.59 195:0.89 201:0.74 208:0.64 212:0.30 215:0.84 216:0.77 222:0.25 235:0.22 241:0.27 242:0.62 243:0.46 245:0.77 247:0.39 253:0.78 259:0.21 265:0.36 266:0.80 267:0.36 271:0.90 276:0.70 279:0.86 282:0.88 283:0.82 289:0.90 290:0.92 297:0.36 299:0.88 300:0.84 +2 1:0.74 7:0.81 8:0.83 9:0.80 11:0.57 12:0.20 17:0.84 21:0.30 27:0.21 28:0.99 30:0.76 34:0.64 36:0.76 37:0.74 39:0.86 41:0.82 43:0.98 46:1.00 66:0.27 69:0.12 70:0.47 74:1.00 81:0.61 83:0.76 85:1.00 91:0.15 96:0.72 98:0.62 101:0.87 107:0.99 108:0.96 110:0.93 114:0.86 120:0.59 121:0.90 122:0.57 123:0.96 124:0.89 125:0.98 126:0.54 127:0.75 129:0.96 133:0.90 135:0.24 140:0.80 146:0.27 147:0.33 149:1.00 150:0.77 151:0.60 153:0.72 154:0.98 155:0.67 159:0.93 160:0.96 161:0.55 162:0.54 164:0.64 165:0.84 167:0.63 172:0.97 173:0.06 176:0.73 177:0.38 178:0.42 179:0.10 181:0.48 187:0.39 192:0.59 201:0.74 202:0.60 204:0.89 208:0.64 212:0.78 215:0.72 216:0.95 220:0.87 222:0.25 230:0.87 233:0.76 235:0.42 241:0.21 242:0.63 243:0.06 245:0.99 247:0.39 248:0.58 253:0.81 255:0.79 256:0.45 259:0.21 260:0.59 265:0.63 266:0.75 267:0.65 268:0.57 271:0.90 276:0.99 285:0.62 289:0.90 290:0.86 297:0.36 300:0.84 +1 9:0.80 11:0.57 12:0.20 17:0.69 21:0.78 27:0.01 28:0.99 30:0.75 34:0.95 36:0.73 37:0.97 39:0.86 41:0.99 43:0.69 46:0.98 60:0.95 66:0.72 69:0.88 70:0.63 74:0.80 83:0.90 85:0.96 91:0.85 96:0.97 98:0.38 101:0.84 104:0.58 107:0.98 108:0.85 110:0.92 120:0.95 122:0.43 123:0.84 124:0.45 125:0.97 127:0.25 129:0.59 133:0.64 135:0.65 140:0.45 146:0.19 147:0.24 149:0.86 151:0.99 153:0.97 154:0.59 155:0.67 158:0.92 159:0.43 160:1.00 161:0.55 162:0.64 164:0.94 167:0.90 172:0.73 173:0.89 177:0.38 179:0.34 181:0.48 187:0.68 192:0.59 202:0.91 208:0.64 212:0.78 216:0.85 222:0.25 232:0.76 235:0.05 241:0.77 242:0.63 243:0.19 245:0.68 247:0.30 248:0.97 253:0.98 255:0.79 256:0.92 259:0.21 260:0.95 265:0.40 266:0.63 267:0.65 268:0.92 271:0.90 276:0.58 279:0.86 283:0.82 285:0.62 289:0.90 300:0.84 +1 1:0.74 9:0.80 11:0.57 12:0.20 17:0.77 21:0.30 27:0.09 28:0.99 30:0.85 34:0.99 36:0.75 37:0.66 39:0.86 41:0.97 43:0.61 46:0.98 60:0.87 66:0.88 69:0.92 70:0.27 74:0.74 81:0.61 83:0.89 85:0.94 91:0.68 96:0.96 98:0.34 101:0.83 104:0.58 107:0.98 108:0.84 110:0.91 114:0.86 120:0.92 122:0.43 123:0.83 125:0.99 126:0.54 127:0.12 129:0.05 135:0.61 140:0.45 145:0.52 146:0.46 147:0.53 149:0.81 150:0.77 151:0.97 153:0.90 154:0.50 155:0.67 158:0.92 159:0.17 160:0.99 161:0.55 162:0.65 164:0.87 165:0.84 167:0.92 172:0.67 173:0.98 176:0.73 177:0.38 179:0.14 181:0.48 187:0.98 192:0.59 201:0.74 202:0.82 208:0.64 212:0.93 215:0.72 216:0.51 222:0.25 232:0.76 235:0.42 241:0.79 242:0.63 243:0.89 247:0.30 248:0.96 253:0.95 255:0.79 256:0.83 259:0.21 260:0.92 265:0.37 266:0.17 267:0.98 268:0.84 271:0.90 276:0.55 279:0.86 283:0.82 285:0.62 289:0.90 290:0.86 297:0.36 300:0.25 +2 9:0.80 11:0.57 12:0.20 17:0.84 21:0.78 27:0.01 28:0.99 30:0.76 32:0.80 34:0.96 36:0.63 37:0.97 39:0.86 41:0.95 43:0.68 46:0.98 66:0.75 69:0.89 70:0.60 74:0.84 76:0.85 77:0.70 83:0.82 85:0.96 91:0.80 96:0.94 98:0.37 101:0.85 104:0.58 107:0.98 108:0.86 110:0.92 120:0.89 122:0.57 123:0.85 124:0.42 125:0.99 127:0.25 129:0.59 133:0.64 135:0.89 140:0.45 145:0.56 146:0.13 147:0.18 149:0.87 151:0.93 153:0.78 154:0.58 155:0.67 159:0.46 160:0.99 161:0.55 162:0.65 164:0.84 167:0.83 172:0.75 173:0.89 177:0.38 179:0.33 181:0.48 187:0.68 192:0.59 195:0.81 202:0.78 208:0.64 212:0.82 216:0.85 220:0.62 222:0.25 232:0.76 235:0.05 241:0.72 242:0.63 243:0.14 245:0.65 247:0.39 248:0.94 253:0.95 255:0.79 256:0.78 259:0.21 260:0.90 265:0.39 266:0.54 267:0.85 268:0.80 271:0.90 276:0.58 282:0.88 285:0.62 289:0.90 299:0.88 300:0.84 +1 1:0.74 7:0.66 11:0.64 12:0.06 17:0.77 18:0.93 22:0.93 27:0.65 28:0.73 29:0.91 30:0.32 31:0.86 32:0.80 34:0.97 36:0.17 37:0.43 39:0.52 43:0.78 44:0.93 46:0.96 47:0.93 48:0.91 55:0.59 60:0.95 64:0.77 66:0.94 69:0.40 70:0.91 71:0.95 74:0.76 77:0.70 79:0.86 81:0.83 83:0.91 85:0.85 86:0.89 91:0.42 98:0.96 99:0.83 101:0.87 106:0.81 107:0.94 108:0.31 110:1.00 114:0.85 117:0.86 120:0.63 122:0.57 123:0.30 125:0.88 126:0.54 127:0.70 129:0.05 131:0.81 135:0.96 137:0.86 139:0.92 140:0.45 144:0.57 145:0.96 146:0.91 147:0.93 149:0.80 150:0.89 151:0.55 153:0.48 154:0.70 155:0.67 157:0.87 158:0.92 159:0.52 160:0.88 161:0.51 162:0.76 164:0.53 165:0.93 166:0.86 167:0.64 172:0.85 173:0.39 176:0.66 177:0.70 179:0.41 181:0.91 182:0.89 187:0.39 190:0.89 192:0.87 195:0.71 196:0.88 201:0.93 202:0.58 206:0.81 208:0.64 212:0.84 215:0.78 216:0.35 219:0.95 220:0.91 222:0.60 227:0.82 228:0.99 229:0.61 230:0.69 231:0.79 232:0.95 233:0.76 235:0.52 241:0.21 242:0.14 243:0.94 246:0.85 247:0.90 248:0.55 253:0.59 256:0.58 259:0.21 260:0.61 262:0.93 265:0.96 266:0.54 267:0.73 268:0.62 276:0.75 279:0.86 281:0.91 282:0.88 283:0.82 284:0.86 285:0.56 287:0.61 289:0.99 290:0.85 292:0.95 297:0.36 299:0.88 300:0.70 +0 1:0.74 11:0.75 12:0.06 17:0.32 18:0.78 21:0.64 27:0.45 28:0.73 29:0.91 30:0.38 32:0.80 34:0.66 36:0.17 37:0.50 39:0.52 43:0.08 44:0.93 45:0.66 46:0.88 55:0.45 64:0.77 66:0.36 69:0.71 70:0.63 71:0.95 74:0.60 77:0.89 81:0.42 83:0.60 86:0.79 91:0.29 98:0.21 99:0.83 101:0.52 107:0.90 108:0.84 110:0.96 114:0.57 122:0.82 123:0.83 124:0.68 125:0.88 126:0.54 127:0.42 129:0.05 131:0.61 133:0.60 135:0.66 139:0.83 144:0.57 145:0.54 146:0.21 147:0.26 149:0.08 150:0.66 154:0.75 155:0.67 157:0.78 159:0.55 160:0.88 161:0.51 165:0.70 166:0.85 167:0.69 172:0.32 173:0.66 176:0.73 177:0.70 178:0.55 179:0.78 181:0.91 182:0.89 187:0.68 192:0.87 195:0.77 201:0.93 208:0.64 212:0.57 215:0.38 216:0.77 222:0.60 227:0.61 229:0.61 231:0.78 235:0.95 241:0.43 242:0.29 243:0.40 245:0.54 246:0.85 247:0.55 253:0.41 254:0.74 259:0.21 265:0.23 266:0.38 267:0.44 276:0.33 277:0.69 282:0.88 287:0.61 289:0.99 290:0.57 297:0.36 300:0.43 +2 7:0.66 8:0.79 9:0.80 12:0.06 17:0.28 18:0.64 21:0.13 27:0.25 28:0.73 29:0.91 30:0.74 31:0.92 32:0.64 34:0.59 36:0.59 37:0.70 39:0.52 43:0.15 46:0.88 55:0.71 66:0.29 69:0.89 70:0.31 71:0.95 74:0.29 77:0.70 79:0.94 81:0.31 83:0.91 86:0.58 91:0.90 96:0.91 98:0.55 106:0.81 107:0.92 108:0.77 110:0.99 120:0.91 123:0.76 124:0.78 125:0.88 126:0.26 127:0.52 129:0.05 131:0.90 133:0.74 135:0.28 137:0.92 139:0.56 140:0.99 144:0.57 145:0.76 146:0.74 147:0.59 149:0.25 150:0.29 151:0.68 153:0.81 154:0.18 155:0.67 157:0.61 159:0.64 160:0.88 161:0.51 162:0.56 163:0.88 164:0.90 166:0.61 167:0.86 172:0.37 173:0.87 175:0.75 177:0.05 179:0.68 181:0.91 182:0.87 187:0.21 190:0.77 191:0.88 192:0.87 195:0.85 196:0.87 202:0.92 206:0.81 208:0.64 212:0.16 215:0.61 216:0.53 220:0.82 222:0.60 227:0.84 229:0.93 230:0.69 231:0.79 232:0.94 233:0.76 235:0.22 241:0.73 242:0.40 243:0.43 244:0.61 245:0.61 246:0.61 247:0.67 248:0.68 253:0.72 254:0.90 255:0.95 256:0.91 257:0.84 260:0.88 265:0.56 266:0.87 267:0.59 268:0.92 276:0.52 277:0.69 282:0.71 283:0.78 284:0.92 285:0.62 287:0.87 289:0.99 297:0.36 300:0.25 +2 1:0.74 9:0.80 11:0.92 12:0.06 17:0.88 18:0.93 21:0.68 22:0.93 27:0.57 28:0.73 29:0.91 30:0.33 31:0.91 32:0.80 34:0.93 36:0.21 37:0.38 39:0.52 43:0.70 44:0.93 45:0.89 46:0.94 47:0.93 55:0.52 64:0.77 66:0.48 69:0.44 70:0.92 71:0.95 74:0.83 77:0.89 79:0.91 81:0.41 83:0.60 85:0.85 86:0.92 91:0.18 96:0.76 97:0.89 98:0.74 99:0.83 101:0.97 106:0.81 107:0.94 108:0.68 110:1.00 114:0.56 117:0.86 120:0.64 122:0.75 123:0.66 124:0.78 125:0.88 126:0.54 127:0.82 129:0.59 131:0.90 133:0.81 135:0.95 137:0.91 139:0.94 140:0.45 144:0.57 145:0.97 146:0.47 147:0.54 149:0.86 150:0.65 151:0.72 153:0.48 154:0.80 155:0.67 157:0.89 158:0.91 159:0.82 160:0.88 161:0.51 162:0.86 164:0.67 165:0.70 166:0.85 167:0.61 172:0.91 173:0.23 176:0.73 177:0.70 179:0.20 181:0.91 182:0.90 187:0.39 192:0.87 195:0.93 196:0.95 201:0.93 202:0.50 206:0.81 208:0.64 212:0.75 215:0.37 216:0.72 219:0.95 220:0.96 222:0.60 227:0.84 229:0.95 231:0.79 232:0.92 235:0.95 241:0.07 242:0.13 243:0.39 245:0.93 246:0.85 247:0.97 248:0.67 253:0.73 255:0.95 256:0.58 259:0.21 260:0.65 262:0.93 265:0.74 266:0.89 267:0.99 268:0.59 276:0.91 279:0.86 282:0.88 283:0.78 284:0.92 285:0.62 287:0.87 289:0.99 290:0.56 292:0.91 297:0.36 299:0.88 300:0.84 +0 1:0.74 11:0.75 12:0.06 17:0.36 18:0.81 21:0.22 27:0.45 28:0.73 29:0.91 30:0.33 34:0.80 36:0.20 37:0.50 39:0.52 43:0.12 45:0.66 46:0.88 55:0.71 64:0.77 66:0.32 69:0.68 70:0.69 71:0.95 74:0.43 81:0.60 83:0.60 86:0.75 91:0.17 98:0.28 99:0.83 101:0.52 107:0.89 108:0.52 110:0.95 114:0.71 122:0.86 123:0.50 124:0.61 125:0.88 126:0.54 127:0.31 129:0.05 131:0.61 133:0.37 135:0.24 139:0.72 144:0.57 145:0.47 146:0.34 147:0.41 149:0.11 150:0.75 154:0.76 155:0.67 157:0.79 159:0.40 160:0.88 161:0.51 165:0.70 166:0.86 167:0.63 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.82 181:0.91 182:0.89 187:0.68 192:0.87 201:0.93 208:0.64 212:0.19 215:0.51 216:0.77 222:0.60 227:0.61 229:0.61 231:0.78 235:0.52 241:0.15 242:0.75 243:0.49 245:0.54 246:0.85 247:0.35 253:0.52 254:0.74 259:0.21 265:0.30 266:0.47 267:0.36 276:0.24 287:0.61 289:0.99 290:0.71 297:0.36 300:0.43 +1 7:0.72 11:0.85 12:0.06 17:0.78 18:0.98 21:0.13 27:0.64 28:0.73 29:0.91 30:0.38 34:0.26 36:0.37 37:0.27 39:0.52 43:0.63 44:0.85 45:0.77 46:0.94 48:0.98 55:0.45 64:0.77 66:0.45 69:0.07 70:0.87 71:0.95 74:0.70 76:0.85 81:0.34 85:0.80 86:0.91 91:0.33 98:0.71 101:0.97 106:0.81 107:0.94 108:0.86 110:1.00 117:0.86 122:0.86 123:0.86 124:0.78 125:0.88 126:0.26 127:0.93 129:0.81 131:0.61 133:0.81 135:0.63 139:0.88 144:0.57 145:0.52 146:0.37 147:0.43 149:0.66 150:0.31 154:0.85 155:0.58 157:0.90 158:0.72 159:0.79 160:0.88 161:0.51 166:0.73 167:0.55 172:0.88 173:0.09 177:0.70 178:0.55 179:0.23 181:0.91 182:0.79 187:0.68 192:0.59 195:0.90 204:0.85 206:0.81 208:0.54 212:0.82 216:0.42 219:0.87 222:0.60 227:0.61 229:0.61 230:0.74 231:0.76 232:0.99 233:0.73 235:0.12 242:0.70 243:0.15 245:0.92 246:0.61 247:0.79 253:0.41 259:0.21 265:0.72 266:0.80 267:0.36 276:0.89 279:0.82 281:0.91 283:0.78 287:0.61 289:0.99 292:0.91 300:0.84 +2 6:0.99 7:0.66 8:0.95 9:0.80 11:0.85 12:0.06 17:0.13 18:0.70 20:0.98 21:0.71 27:0.17 28:0.73 29:0.91 30:0.21 31:0.99 32:0.68 34:0.28 36:0.64 37:0.55 39:0.52 41:0.92 43:0.59 45:0.73 46:0.93 66:0.47 69:0.76 70:0.92 71:0.95 74:0.62 76:0.85 77:0.70 78:0.91 79:0.99 81:0.23 83:0.91 85:0.72 86:0.81 91:0.99 96:0.99 97:0.89 98:0.26 100:0.99 101:0.97 107:0.92 108:0.85 110:0.99 111:0.99 120:0.99 122:0.57 123:0.85 124:0.67 125:0.96 126:0.26 127:0.81 129:0.59 131:0.90 133:0.61 135:0.65 137:0.99 138:1.00 139:0.84 140:0.95 144:0.57 145:0.65 146:0.19 147:0.30 149:0.38 150:0.23 151:0.97 152:0.97 153:0.96 154:0.71 155:0.67 157:0.87 158:0.91 159:0.63 160:0.98 161:0.51 162:0.69 164:0.98 166:0.61 167:0.99 169:0.98 172:0.41 173:0.74 177:0.05 179:0.80 181:0.91 182:0.90 186:0.91 189:0.99 191:0.95 192:0.87 195:0.80 196:0.98 202:0.98 208:0.64 212:0.58 215:0.37 216:0.71 219:0.95 220:0.74 222:0.60 227:0.84 229:0.94 230:0.69 231:0.79 233:0.73 235:0.88 238:0.99 241:0.95 242:0.38 243:0.23 245:0.58 246:0.61 247:0.47 248:0.95 253:0.96 255:0.95 256:0.98 257:0.84 259:0.21 260:0.98 261:0.98 265:0.28 266:0.54 267:0.85 268:0.99 276:0.31 279:0.86 282:0.71 283:0.78 284:0.99 285:0.62 287:0.87 289:0.99 292:0.91 297:0.36 300:0.70 +1 1:0.74 7:0.72 8:0.65 9:0.80 11:0.64 12:0.06 17:0.66 18:0.96 20:0.81 21:0.54 27:0.40 28:0.73 29:0.91 30:0.15 31:0.87 32:0.80 34:0.99 36:0.32 37:0.31 39:0.52 41:0.82 43:0.96 44:0.93 46:0.96 55:0.59 60:0.87 64:0.77 66:0.94 69:0.27 70:0.79 71:0.95 74:0.78 76:0.94 77:0.96 78:0.85 79:0.87 81:0.48 83:0.91 85:0.88 86:0.90 91:0.40 96:0.69 98:0.99 100:0.73 101:0.87 107:0.93 108:0.21 110:1.00 111:0.82 114:0.59 120:0.55 122:0.86 123:0.20 125:0.98 126:0.54 127:0.89 129:0.05 131:0.90 135:0.39 137:0.87 139:0.93 140:0.45 144:0.57 145:0.84 146:0.92 147:0.94 149:0.87 150:0.71 151:0.52 152:0.77 153:0.48 154:0.96 155:0.67 157:0.90 158:0.92 159:0.54 160:0.96 161:0.51 162:0.86 164:0.57 165:0.93 166:0.85 167:0.75 169:0.72 172:0.85 173:0.29 176:0.73 177:0.70 179:0.44 181:0.91 182:0.90 186:0.85 187:0.21 192:0.87 195:0.71 196:0.90 201:0.93 202:0.36 204:0.85 208:0.64 212:0.71 215:0.39 216:0.24 219:0.95 220:0.87 222:0.60 227:0.84 229:0.95 230:0.74 231:0.79 233:0.76 235:0.88 241:0.62 242:0.16 243:0.94 246:0.85 247:0.91 248:0.51 253:0.49 255:0.95 256:0.45 259:0.21 260:0.56 261:0.80 265:0.99 266:0.63 267:0.65 268:0.46 276:0.75 279:0.86 282:0.88 283:0.82 284:0.89 285:0.62 287:0.87 289:0.99 290:0.59 292:0.95 297:0.36 299:0.99 300:0.55 +1 1:0.74 7:0.81 11:0.75 12:0.06 17:0.78 18:0.98 21:0.59 27:0.61 28:0.73 29:0.91 30:0.21 32:0.64 34:0.99 36:0.17 37:0.42 39:0.52 43:0.24 44:0.85 45:0.93 46:0.88 48:0.91 55:0.71 64:0.77 66:0.71 69:0.35 70:0.76 71:0.95 74:0.83 81:0.42 83:0.60 86:0.96 91:0.40 98:0.76 99:0.83 101:0.52 106:0.81 107:0.94 108:0.75 110:1.00 114:0.58 122:0.86 123:0.73 124:0.44 125:0.88 126:0.54 127:0.43 129:0.59 131:0.61 133:0.46 135:1.00 139:0.96 144:0.57 145:0.92 146:0.76 147:0.80 149:0.37 150:0.66 154:0.70 155:0.67 157:0.95 159:0.73 160:0.88 161:0.51 165:0.70 166:0.86 167:0.63 172:0.93 173:0.20 176:0.73 177:0.70 178:0.55 179:0.17 181:0.91 182:0.89 187:0.39 192:0.87 195:0.91 201:0.93 204:0.89 206:0.81 208:0.64 212:0.80 215:0.39 216:0.24 222:0.60 227:0.61 229:0.61 230:0.86 231:0.78 232:0.99 233:0.73 235:0.84 241:0.15 242:0.23 243:0.28 245:0.96 246:0.85 247:0.93 253:0.46 254:0.74 259:0.21 265:0.76 266:0.63 267:0.65 276:0.90 281:0.91 283:0.78 287:0.61 289:0.99 290:0.58 297:0.36 300:0.70 +1 1:0.74 7:0.81 9:0.80 11:0.85 12:0.06 17:0.73 18:0.98 21:0.54 22:0.93 25:0.88 27:0.63 28:0.73 29:0.91 30:0.17 31:0.95 32:0.80 34:0.97 36:0.24 39:0.52 41:0.82 43:0.74 44:0.93 45:0.95 46:0.95 47:0.93 48:0.91 60:0.87 64:0.77 66:0.68 69:0.23 70:0.92 71:0.95 74:0.95 77:0.89 79:0.94 81:0.47 83:0.91 85:0.94 86:0.99 91:0.25 96:0.84 98:0.86 101:0.97 106:0.81 107:0.94 108:0.62 110:1.00 114:0.59 120:0.74 121:0.97 122:0.57 123:0.59 124:0.56 125:0.98 126:0.54 127:0.90 129:0.89 131:0.90 133:0.78 135:0.99 137:0.95 139:0.99 140:0.45 144:0.57 145:0.99 146:0.29 147:0.35 149:0.90 150:0.71 151:0.86 153:0.48 154:0.86 155:0.67 157:0.96 158:0.92 159:0.87 160:0.96 161:0.51 162:0.86 164:0.67 165:0.93 166:0.86 167:0.67 172:0.97 173:0.11 176:0.73 177:0.70 179:0.15 181:0.91 182:0.90 187:0.98 192:0.87 195:0.95 196:0.98 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.69 215:0.39 216:0.15 219:0.95 222:0.60 227:0.84 229:0.95 230:0.87 231:0.79 232:0.83 233:0.76 235:0.80 241:0.32 242:0.31 243:0.09 245:0.96 246:0.85 247:0.90 248:0.82 253:0.89 255:0.95 256:0.58 259:0.21 260:0.75 262:0.93 265:0.86 266:0.75 267:0.87 268:0.59 276:0.95 279:0.86 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 287:0.87 289:0.99 290:0.59 292:0.95 297:0.36 300:0.84 +1 9:0.80 12:0.06 17:0.15 18:0.79 21:0.54 27:0.72 28:0.73 29:0.91 30:0.76 31:0.97 34:0.40 36:0.71 39:0.52 43:0.16 46:0.88 55:0.84 64:0.77 66:0.36 69:0.85 70:0.15 71:0.95 79:0.99 81:0.24 83:0.60 86:0.60 91:0.98 96:0.88 98:0.52 107:0.89 108:0.70 110:0.93 120:0.82 122:0.86 123:0.68 124:0.67 125:0.88 126:0.26 127:0.58 129:0.05 131:0.90 133:0.62 135:0.75 137:0.97 139:0.59 140:0.98 144:0.57 145:0.39 146:0.49 147:0.05 149:0.14 150:0.24 151:0.72 153:0.48 154:0.11 155:0.67 157:0.61 159:0.39 160:0.88 161:0.51 162:0.59 163:0.97 164:0.84 166:0.73 167:0.99 172:0.16 173:0.95 175:0.75 177:0.05 179:0.96 181:0.91 182:0.61 190:0.89 192:0.87 196:0.87 202:0.94 208:0.64 212:0.42 216:0.52 219:0.87 220:0.62 222:0.60 227:0.84 229:0.61 231:0.79 235:0.60 241:0.96 242:0.75 243:0.26 244:0.83 245:0.40 246:0.61 247:0.30 248:0.84 253:0.81 255:0.95 256:0.94 257:0.84 259:0.21 260:0.81 265:0.53 266:0.23 267:0.73 268:0.94 276:0.26 277:0.69 283:0.78 284:0.98 285:0.62 287:0.61 289:0.99 292:0.91 300:0.13 +2 8:0.92 9:0.80 12:0.06 17:0.15 20:0.82 21:0.78 27:0.51 28:0.73 29:0.91 31:0.95 34:0.61 36:0.32 37:0.97 39:0.52 41:0.82 46:0.88 71:0.95 78:0.87 79:0.94 83:0.91 91:0.93 96:0.92 100:0.96 107:0.88 110:0.88 111:0.92 120:0.91 125:0.96 131:0.90 135:0.82 137:0.95 140:0.98 144:0.57 151:0.86 152:0.78 153:0.82 155:0.67 157:0.61 160:0.96 161:0.51 162:0.72 164:0.89 166:0.61 167:0.99 169:0.94 175:0.97 181:0.91 182:0.89 186:0.87 191:0.80 192:0.87 202:0.88 208:0.64 216:0.33 220:0.74 222:0.60 227:0.84 229:0.94 231:0.79 241:0.95 244:0.93 246:0.61 248:0.80 253:0.80 255:0.95 256:0.89 257:0.93 260:0.89 261:0.86 267:0.65 268:0.90 277:0.95 284:0.93 285:0.62 287:0.87 289:0.99 +2 1:0.74 7:0.81 11:0.85 12:0.06 17:0.66 18:0.98 21:0.47 22:0.93 27:0.34 28:0.73 29:0.91 30:0.15 32:0.80 34:0.98 36:0.23 37:0.66 39:0.52 43:0.74 44:0.93 45:0.95 46:0.95 47:0.93 48:0.91 64:0.77 66:0.69 69:0.23 70:0.93 71:0.95 74:0.93 77:0.89 81:0.51 83:0.91 85:0.94 86:0.99 91:0.23 98:0.87 101:0.97 107:0.94 108:0.62 110:1.00 114:0.60 121:0.97 122:0.57 123:0.60 124:0.49 125:0.88 126:0.54 127:0.93 129:0.81 131:0.61 133:0.67 135:0.99 139:0.99 144:0.57 145:0.99 146:0.29 147:0.35 149:0.89 150:0.73 154:0.86 155:0.67 157:0.96 159:0.87 160:0.88 161:0.51 165:0.93 166:0.86 167:0.68 172:0.97 173:0.11 176:0.73 177:0.70 178:0.55 179:0.15 181:0.91 182:0.89 187:0.98 192:0.87 195:0.95 201:0.93 204:0.89 208:0.64 212:0.70 215:0.41 216:0.46 222:0.60 227:0.61 229:0.61 230:0.87 231:0.79 233:0.76 235:0.80 241:0.37 242:0.29 243:0.09 245:0.97 246:0.85 247:0.90 253:0.50 259:0.21 262:0.93 265:0.87 266:0.75 267:0.69 276:0.95 281:0.91 282:0.88 283:0.78 287:0.61 289:0.99 290:0.60 297:0.36 300:0.84 +1 8:0.90 9:0.80 11:0.85 12:0.06 17:0.13 18:0.65 21:0.40 27:0.17 28:0.73 29:0.91 30:0.11 31:0.96 34:0.56 36:0.55 37:0.55 39:0.52 41:0.82 43:0.51 45:0.73 46:0.93 55:0.84 66:0.44 69:0.94 70:0.97 71:0.95 74:0.47 76:0.99 77:0.98 79:0.96 81:0.34 83:0.91 85:0.72 86:0.75 91:0.90 96:0.95 98:0.64 101:0.97 107:0.93 108:0.67 110:0.99 114:0.58 120:0.94 122:0.77 123:0.65 124:0.82 125:0.96 126:0.26 127:0.77 129:0.59 131:0.90 133:0.82 135:0.94 137:0.96 139:0.72 140:0.97 144:0.57 145:0.88 146:0.19 147:0.37 149:0.43 150:0.30 151:0.93 153:0.89 154:0.46 155:0.67 157:0.86 158:0.72 159:0.72 160:0.96 161:0.51 162:0.75 164:0.94 166:0.78 167:0.88 172:0.65 173:0.97 176:0.55 177:0.05 179:0.49 181:0.91 182:0.90 187:0.21 191:0.88 192:0.87 195:0.87 196:0.92 202:0.94 208:0.64 212:0.37 216:0.71 220:0.74 222:0.60 227:0.84 229:0.94 231:0.79 232:0.80 235:0.52 241:0.75 242:0.60 243:0.13 245:0.75 246:0.61 247:0.76 248:0.85 253:0.85 255:0.95 256:0.95 257:0.84 259:0.21 260:0.91 265:0.65 266:0.89 267:0.96 268:0.96 276:0.70 277:0.69 282:0.71 283:0.78 284:0.95 285:0.62 287:0.87 289:0.99 290:0.58 300:0.84 +0 6:0.86 7:0.81 8:0.91 9:0.80 12:0.06 17:0.59 18:0.66 20:0.98 21:0.13 27:0.13 28:0.48 29:0.97 30:0.91 31:0.95 34:0.22 36:0.47 37:0.91 39:0.67 41:0.82 43:0.16 46:0.88 55:0.59 66:0.69 69:0.33 70:0.13 71:0.85 74:0.57 76:1.00 78:0.88 79:0.95 81:0.38 83:0.91 86:0.70 91:0.97 96:0.94 98:0.79 100:0.95 107:0.89 108:0.26 110:0.89 111:0.91 114:0.72 120:0.86 121:0.90 122:0.77 123:0.25 125:0.96 126:0.26 127:0.28 129:0.05 135:0.18 137:0.95 138:0.98 139:0.78 140:0.98 146:0.28 147:0.35 149:0.10 150:0.33 151:0.87 152:0.90 153:0.78 154:0.54 155:0.67 159:0.12 160:0.96 161:0.84 162:0.74 164:0.84 167:0.92 169:0.93 172:0.21 173:0.85 175:0.75 176:0.61 177:0.38 179:0.94 181:0.74 186:0.88 189:0.88 191:0.94 192:0.87 196:0.94 202:0.91 204:0.89 208:0.64 212:0.95 216:0.48 220:0.74 222:0.48 230:0.87 233:0.76 235:0.12 238:0.95 241:0.94 242:0.63 243:0.73 244:0.61 247:0.30 248:0.77 253:0.90 254:0.93 255:0.95 256:0.93 257:0.65 259:0.21 260:0.83 261:0.94 265:0.79 266:0.12 267:0.96 268:0.94 271:0.26 276:0.23 277:0.69 281:0.91 284:0.93 285:0.62 289:0.95 290:0.72 300:0.13 +3 6:0.88 8:0.76 9:0.76 11:0.64 12:0.06 17:0.18 20:0.86 21:0.40 25:0.88 27:0.41 28:0.48 29:0.97 30:0.56 34:0.56 36:0.59 37:0.63 39:0.67 43:0.65 45:0.73 46:0.88 55:0.52 66:0.84 69:0.15 70:0.46 71:0.85 74:0.50 78:0.84 81:0.32 83:0.60 91:0.85 96:0.76 98:0.93 100:0.91 101:0.52 104:0.58 107:0.93 108:0.12 110:0.94 111:0.86 120:0.72 122:0.41 123:0.12 125:0.88 126:0.26 127:0.57 129:0.05 135:0.47 140:0.87 146:0.58 147:0.64 149:0.51 150:0.29 151:0.65 152:0.82 153:0.46 154:0.54 155:0.58 159:0.21 160:0.88 161:0.84 162:0.83 164:0.70 167:0.91 169:0.89 172:0.54 173:0.48 175:0.75 177:0.38 178:0.42 179:0.79 181:0.74 186:0.84 187:0.21 189:0.89 190:0.89 191:0.84 192:0.59 202:0.64 208:0.54 212:0.81 215:0.42 216:0.24 220:0.91 222:0.48 235:0.42 238:0.95 241:0.89 242:0.42 243:0.85 244:0.61 247:0.55 248:0.66 253:0.57 255:0.74 256:0.68 257:0.65 259:0.21 260:0.68 261:0.88 265:0.93 266:0.27 267:0.28 268:0.70 271:0.26 276:0.43 277:0.69 285:0.56 289:0.93 297:0.36 300:0.33 +1 1:0.74 11:0.83 12:0.06 17:0.16 18:0.77 21:0.40 27:0.45 28:0.48 29:0.97 30:0.45 34:0.79 36:0.18 37:0.50 39:0.67 43:0.82 45:0.77 46:0.93 64:0.77 66:0.18 69:0.69 70:0.87 71:0.85 74:0.66 81:0.56 83:0.91 85:0.80 86:0.87 91:0.22 98:0.27 101:0.97 107:0.94 108:0.52 110:0.94 114:0.62 122:0.57 123:0.50 124:0.85 125:0.88 126:0.54 127:0.57 129:0.59 133:0.82 135:0.92 139:0.83 145:0.50 146:0.52 147:0.58 149:0.71 150:0.75 154:0.77 155:0.67 159:0.78 160:0.88 161:0.84 165:0.93 167:0.63 172:0.37 173:0.51 176:0.73 177:0.70 178:0.55 179:0.58 181:0.74 187:0.68 192:0.87 201:0.93 208:0.64 212:0.48 215:0.42 216:0.77 222:0.48 235:0.60 241:0.43 242:0.17 243:0.39 245:0.68 247:0.79 253:0.48 259:0.21 265:0.29 266:0.80 267:0.65 271:0.26 276:0.58 289:0.94 290:0.62 297:0.36 300:0.70 +1 1:0.74 6:0.82 7:0.66 8:0.60 11:0.64 12:0.06 17:0.32 18:0.86 20:0.86 21:0.40 22:0.93 27:0.62 28:0.48 29:0.97 30:0.36 32:0.68 34:0.39 36:0.85 37:0.35 39:0.67 43:0.66 44:0.93 45:0.83 46:0.88 47:0.93 48:0.91 55:0.52 64:0.77 66:0.54 69:0.23 70:0.69 71:0.85 74:0.89 76:0.85 77:0.89 78:0.78 81:0.54 83:0.60 86:0.93 91:0.11 98:0.33 99:0.83 100:0.80 101:0.52 104:0.98 106:0.81 107:0.94 108:0.18 110:0.94 111:0.77 114:0.62 117:0.86 122:0.77 123:0.18 124:0.63 125:0.88 126:0.54 127:0.21 129:0.05 133:0.60 135:0.79 139:0.93 145:0.84 146:0.49 147:0.55 149:0.51 150:0.71 152:0.84 154:0.89 155:0.67 159:0.41 160:0.88 161:0.84 165:0.70 167:0.61 169:0.79 172:0.65 173:0.19 176:0.73 177:0.70 178:0.55 179:0.30 181:0.74 186:0.78 187:0.39 192:0.87 195:0.81 201:0.93 206:0.81 208:0.64 212:0.88 215:0.42 216:0.40 222:0.48 230:0.69 232:0.70 233:0.76 235:0.60 238:0.87 241:0.35 242:0.50 243:0.63 245:0.74 247:0.74 253:0.51 254:0.86 259:0.21 261:0.82 262:0.93 265:0.35 266:0.38 267:0.36 271:0.26 276:0.60 281:0.91 282:0.77 289:0.94 290:0.62 297:0.36 300:0.55 +0 11:0.64 12:0.06 17:0.51 18:0.63 21:0.78 27:0.45 28:0.48 29:0.97 30:0.76 34:0.63 36:0.19 37:0.50 39:0.67 43:0.78 45:0.66 46:0.88 66:0.64 69:0.77 70:0.15 71:0.85 74:0.42 86:0.73 91:0.06 98:0.11 101:0.52 107:0.89 108:0.61 110:0.90 122:0.57 123:0.58 125:0.88 127:0.07 129:0.05 135:0.61 139:0.70 145:0.42 146:0.18 147:0.23 149:0.53 154:0.71 159:0.09 160:0.88 161:0.84 163:0.97 167:0.55 172:0.16 173:0.78 177:0.70 178:0.55 179:0.09 181:0.74 187:0.68 212:0.95 216:0.77 222:0.48 235:0.97 241:0.12 242:0.82 243:0.69 247:0.12 253:0.32 254:0.74 259:0.21 265:0.12 266:0.12 267:0.23 271:0.26 276:0.18 289:0.88 300:0.13 +1 1:0.74 7:0.66 9:0.80 11:0.64 12:0.06 17:0.22 18:0.83 21:0.30 22:0.93 27:0.62 28:0.48 29:0.97 30:0.27 31:0.87 32:0.74 34:0.28 36:0.81 37:0.35 39:0.67 43:0.42 44:0.93 45:0.81 46:0.88 47:0.93 48:0.91 55:0.52 64:0.77 66:0.53 69:0.21 70:0.83 71:0.85 74:0.91 76:0.85 77:0.70 79:0.87 81:0.59 83:0.60 86:0.92 91:0.08 96:0.69 97:0.89 98:0.41 99:0.83 101:0.52 104:0.81 106:0.81 107:0.95 108:0.17 110:0.95 114:0.65 117:0.86 120:0.56 122:0.77 123:0.16 124:0.64 125:0.88 126:0.54 127:0.23 129:0.05 133:0.60 135:0.73 137:0.87 139:0.94 140:0.45 145:0.79 146:0.59 147:0.65 149:0.26 150:0.74 151:0.52 153:0.69 154:0.88 155:0.67 158:0.86 159:0.44 160:0.88 161:0.84 162:0.86 164:0.62 165:0.70 167:0.61 172:0.63 173:0.18 176:0.73 177:0.70 179:0.33 181:0.74 187:0.39 192:0.87 195:0.81 196:0.91 201:0.93 202:0.46 206:0.81 208:0.64 212:0.86 215:0.44 216:0.40 219:0.95 220:0.87 222:0.48 230:0.69 232:0.70 233:0.76 235:0.52 241:0.35 242:0.45 243:0.62 245:0.73 247:0.79 248:0.52 253:0.55 254:0.86 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.43 266:0.47 267:0.52 268:0.55 271:0.26 276:0.61 279:0.86 281:0.91 282:0.83 283:0.67 284:0.90 285:0.62 289:0.94 290:0.65 292:0.87 297:0.36 300:0.70 +1 7:0.66 11:0.64 12:0.06 17:0.73 18:0.64 20:0.76 21:0.40 27:0.42 28:0.48 29:0.97 30:0.21 32:0.78 34:0.83 36:0.27 37:0.62 39:0.67 43:0.63 44:0.93 45:0.66 46:0.88 66:0.72 69:0.73 70:0.37 71:0.85 74:0.51 77:0.70 78:0.72 81:0.32 86:0.68 91:0.44 98:0.58 100:0.73 101:0.52 107:0.91 108:0.56 110:0.91 111:0.72 120:0.55 123:0.53 125:0.88 126:0.26 127:0.17 129:0.05 135:0.88 139:0.77 140:0.45 145:0.69 146:0.58 147:0.64 149:0.33 150:0.29 151:0.48 152:0.75 153:0.48 154:0.52 159:0.21 160:0.88 161:0.84 162:0.86 163:0.81 164:0.53 167:0.61 169:0.72 172:0.27 173:0.79 177:0.70 179:0.75 181:0.74 186:0.73 187:0.39 190:0.89 195:0.66 202:0.36 212:0.42 215:0.42 216:0.49 220:0.87 222:0.48 230:0.69 233:0.76 235:0.42 241:0.35 242:0.45 243:0.75 244:0.61 247:0.35 248:0.48 253:0.36 256:0.45 259:0.21 260:0.55 261:0.73 265:0.59 266:0.23 267:0.65 268:0.46 271:0.26 276:0.23 282:0.86 285:0.47 289:0.92 297:0.36 300:0.25 +1 6:0.90 8:0.88 9:0.76 12:0.06 17:0.82 20:0.93 21:0.59 27:0.56 28:0.48 29:0.97 30:0.95 34:0.33 36:0.70 37:0.24 39:0.67 43:0.18 44:0.90 46:0.88 55:0.92 66:0.20 69:0.21 71:0.85 74:0.53 76:0.94 77:0.70 78:0.80 81:0.24 83:0.60 91:0.91 96:0.82 98:0.12 100:0.87 104:0.58 107:0.88 108:0.17 110:0.88 111:0.81 114:0.57 120:0.79 123:0.16 124:0.61 125:0.88 126:0.26 127:0.49 129:0.59 133:0.47 135:0.42 139:0.75 140:0.92 145:0.63 146:0.05 147:0.05 149:0.09 150:0.24 151:0.65 152:0.86 153:0.72 154:0.12 155:0.67 159:0.19 160:0.88 161:0.84 162:0.85 164:0.71 167:0.92 169:0.84 172:0.05 173:0.56 175:0.91 176:0.61 177:0.05 179:1.00 181:0.74 186:0.80 189:0.92 190:0.89 191:0.75 192:0.87 195:0.55 202:0.70 204:0.85 208:0.64 216:0.11 222:0.48 235:0.68 238:0.95 241:0.97 243:0.40 244:0.83 245:0.36 248:0.63 253:0.68 255:0.74 256:0.77 257:0.84 259:0.21 260:0.73 261:0.86 265:0.12 267:0.23 268:0.77 271:0.26 277:0.87 281:0.91 282:0.77 285:0.56 289:0.93 290:0.57 300:0.08 +4 8:0.83 9:0.76 12:0.06 17:0.24 21:0.78 27:0.08 28:0.48 29:0.97 34:0.61 36:0.28 37:0.82 39:0.67 46:0.88 71:0.85 83:0.60 91:0.95 96:0.94 107:0.88 110:0.88 120:0.82 125:0.88 135:0.83 138:0.99 140:0.96 151:0.70 153:0.48 155:0.58 160:0.88 161:0.84 162:0.74 164:0.81 167:0.92 175:0.97 181:0.74 190:0.89 191:0.90 192:0.59 202:0.90 208:0.54 216:0.43 220:0.62 222:0.48 232:0.78 241:0.94 244:0.93 248:0.79 253:0.87 255:0.74 256:0.92 257:0.93 259:0.21 260:0.77 267:0.52 268:0.93 271:0.26 277:0.95 285:0.56 289:0.93 +2 6:0.89 8:0.72 11:0.64 12:0.06 17:0.26 18:0.84 20:0.88 21:0.78 27:0.21 28:0.48 29:0.97 30:0.14 34:0.75 36:0.83 37:0.74 39:0.67 43:0.57 45:0.85 46:0.88 66:0.92 69:0.64 70:0.80 71:0.85 74:0.88 76:0.94 78:0.80 86:0.93 91:0.51 98:0.77 100:0.85 101:0.52 107:0.96 108:0.49 110:0.96 111:0.80 120:0.74 122:0.57 123:0.46 125:0.88 127:0.26 129:0.05 135:0.17 139:0.92 140:0.45 146:0.75 147:0.79 149:0.28 151:0.63 152:0.83 153:0.72 154:0.77 155:0.58 159:0.31 160:0.88 161:0.84 162:0.52 164:0.53 167:0.62 169:0.83 172:0.79 173:0.69 177:0.70 179:0.31 181:0.74 186:0.80 187:0.39 189:0.90 190:0.89 192:0.51 202:0.62 204:0.85 208:0.54 212:0.90 216:0.95 222:0.48 235:0.52 238:0.92 241:0.41 242:0.23 243:0.93 247:0.86 248:0.61 253:0.46 254:0.74 256:0.45 259:0.21 260:0.67 261:0.84 265:0.77 266:0.27 267:0.76 268:0.57 271:0.26 276:0.69 281:0.91 285:0.47 289:0.94 300:0.55 +2 1:0.69 6:0.94 8:0.97 9:0.80 11:0.64 12:0.06 17:0.01 18:0.70 20:0.91 21:0.13 27:0.06 28:0.48 29:0.97 30:0.45 31:1.00 34:0.40 36:0.67 37:0.96 39:0.67 41:0.82 43:0.57 44:0.90 45:0.66 46:0.88 64:0.77 66:0.27 69:0.51 70:0.25 71:0.85 74:0.59 76:0.85 77:0.96 78:0.92 79:1.00 81:0.54 83:0.91 86:0.70 91:1.00 96:1.00 97:1.00 98:0.36 99:0.83 100:0.98 101:0.52 107:0.89 108:0.39 110:0.89 111:0.96 114:0.75 120:0.78 122:0.80 123:0.37 124:0.61 125:0.96 126:0.44 127:0.63 129:0.59 133:0.47 135:0.28 137:1.00 138:0.99 139:0.82 140:1.00 145:0.63 146:0.31 147:0.37 149:0.28 150:0.58 151:0.87 152:0.85 153:0.99 154:0.46 155:0.67 158:0.79 159:0.31 160:0.96 161:0.84 162:0.75 163:0.96 164:0.65 165:0.70 167:0.93 169:0.97 172:0.10 173:0.67 175:0.75 176:0.66 177:0.38 179:0.98 181:0.74 186:0.91 189:0.95 191:0.96 192:0.87 195:0.59 196:1.00 201:0.68 202:0.97 204:0.85 208:0.64 212:0.61 216:0.68 219:0.92 222:0.48 235:0.22 238:0.97 241:1.00 242:0.63 243:0.46 244:0.61 245:0.40 247:0.30 248:0.77 253:0.99 255:0.95 256:0.98 257:0.65 259:0.21 260:0.76 261:0.96 265:0.38 266:0.17 267:0.69 268:0.98 271:0.26 276:0.16 277:0.69 279:0.82 281:0.91 282:0.77 284:1.00 285:0.62 289:0.94 290:0.75 292:0.95 300:0.18 +2 6:0.87 8:0.93 12:0.79 17:0.03 20:0.76 21:0.13 27:0.25 28:0.46 30:0.21 34:0.75 36:0.99 37:0.86 43:0.39 55:0.59 66:0.20 69:0.55 70:0.37 78:1.00 81:0.94 91:0.95 98:0.22 100:0.94 104:0.74 108:0.42 111:0.99 120:0.87 123:0.57 124:0.73 126:0.54 127:0.56 129:0.81 133:0.67 135:0.63 140:0.45 146:0.22 147:0.27 149:0.34 150:0.89 151:0.62 152:0.75 153:0.98 154:0.30 159:0.43 161:0.80 162:0.51 163:0.81 164:0.94 167:0.86 169:0.91 172:0.10 173:0.59 177:0.05 179:0.96 181:0.81 186:1.00 189:0.89 191:0.87 202:0.95 212:0.42 215:0.84 216:0.50 220:0.62 235:0.12 238:0.80 241:0.98 242:0.82 243:0.40 245:0.40 247:0.12 248:0.82 256:0.91 259:0.21 260:0.88 261:0.81 265:0.17 266:0.23 267:0.69 268:0.92 271:0.40 276:0.21 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.91 8:0.95 12:0.79 17:0.11 20:0.97 21:0.78 27:0.08 28:0.46 34:0.56 36:0.52 37:0.93 78:0.93 91:0.97 100:0.95 111:0.93 120:0.87 135:0.47 140:0.87 151:0.71 152:0.89 153:0.72 161:0.80 162:0.47 164:0.93 167:0.86 169:0.92 175:0.75 178:0.48 181:0.81 186:0.93 189:0.92 191:0.93 202:0.98 216:0.70 220:0.62 235:0.31 238:0.90 241:0.95 244:0.61 248:0.82 256:0.91 257:0.65 259:0.21 260:0.88 261:0.94 267:0.65 268:0.92 271:0.40 277:0.69 283:0.60 285:0.62 +2 6:0.87 7:0.81 8:0.86 12:0.79 17:0.12 20:0.97 21:0.54 27:0.14 28:0.46 32:0.80 34:0.28 36:0.47 37:0.80 78:0.94 81:0.86 91:0.98 100:0.96 104:0.73 111:0.94 120:0.92 126:0.54 135:0.37 140:0.45 145:0.54 150:0.70 151:0.75 152:0.90 153:0.84 161:0.80 162:0.51 164:0.97 167:0.86 169:0.94 175:0.75 181:0.81 186:0.94 189:0.89 191:0.90 195:0.53 202:0.97 215:0.58 216:0.59 220:0.62 230:0.87 233:0.76 235:0.60 238:0.92 241:0.95 244:0.61 248:0.89 256:0.95 257:0.65 259:0.21 260:0.93 261:0.95 267:0.73 268:0.96 271:0.40 277:0.69 283:0.60 285:0.62 297:0.36 +2 6:0.89 7:0.81 8:0.91 12:0.79 17:0.11 20:0.97 21:0.30 25:0.88 27:0.11 28:0.46 32:0.80 34:0.39 36:0.57 37:0.90 43:0.28 66:0.36 69:0.77 78:0.93 81:0.91 91:0.95 98:0.11 100:0.93 104:0.76 108:0.60 111:0.92 120:0.89 123:0.58 126:0.54 127:0.07 129:0.05 135:0.39 140:0.80 145:0.72 146:0.05 147:0.05 149:0.12 150:0.83 151:0.62 152:0.89 153:0.98 154:0.21 159:0.05 161:0.80 162:0.49 164:0.96 167:0.85 169:0.91 172:0.05 173:1.00 177:0.05 178:0.42 181:0.81 186:0.93 189:0.90 191:0.90 195:0.57 202:0.98 215:0.72 216:0.66 220:0.62 230:0.87 233:0.76 235:0.31 238:0.89 241:0.89 243:0.51 248:0.84 256:0.95 260:0.89 261:0.93 265:0.12 267:0.59 268:0.96 271:0.40 283:0.60 285:0.62 297:0.36 +2 6:0.92 7:0.81 8:0.83 12:0.79 17:0.02 20:0.95 21:0.78 27:0.34 28:0.46 34:0.26 36:0.42 37:0.46 78:0.89 91:0.87 100:0.94 111:0.90 120:0.59 135:0.51 140:0.92 145:0.85 151:0.51 152:0.88 153:0.88 161:0.80 162:0.47 164:0.79 167:0.83 169:0.92 175:0.75 178:0.51 181:0.81 186:0.89 189:0.93 191:0.70 202:0.91 216:0.40 220:0.74 230:0.65 233:0.63 235:0.12 238:0.93 241:0.84 244:0.61 248:0.52 256:0.71 257:0.65 259:0.21 260:0.59 261:0.92 267:1.00 268:0.74 271:0.40 277:0.69 285:0.62 +1 6:0.88 8:0.86 12:0.79 17:0.03 20:0.92 21:0.78 27:0.17 28:0.46 34:0.20 36:0.35 37:0.65 78:0.91 91:0.99 100:0.90 111:0.89 120:0.88 135:0.51 140:0.80 151:0.80 152:0.86 153:0.93 161:0.80 162:0.48 164:0.93 167:0.85 169:0.88 175:0.75 178:0.42 181:0.81 186:0.90 189:0.89 191:0.91 202:0.96 216:0.56 220:0.62 238:0.88 241:0.90 244:0.61 248:0.84 256:0.90 257:0.65 259:0.21 260:0.89 261:0.91 267:0.76 268:0.91 271:0.40 277:0.69 283:0.60 285:0.62 +2 6:0.87 7:0.81 8:0.90 12:0.79 17:0.45 20:0.78 21:0.74 27:0.34 28:0.46 30:0.33 32:0.80 34:0.34 36:0.65 37:0.83 43:0.33 55:0.52 66:0.35 69:0.68 70:0.92 78:0.97 81:0.73 91:0.84 98:0.59 100:0.94 104:0.58 108:0.71 111:0.95 120:0.75 123:0.69 124:0.70 126:0.54 127:0.44 129:0.81 133:0.67 135:0.54 140:0.45 145:0.79 146:0.29 147:0.36 149:0.65 150:0.54 151:0.59 152:0.76 153:0.91 154:0.25 159:0.66 161:0.80 162:0.66 164:0.87 167:0.85 169:0.92 172:0.51 173:0.57 177:0.05 179:0.54 181:0.81 186:0.97 189:0.88 191:0.83 195:0.87 202:0.81 212:0.39 215:0.46 216:0.27 220:0.62 230:0.87 233:0.76 235:0.88 238:0.89 241:0.92 242:0.82 243:0.43 245:0.74 247:0.12 248:0.68 256:0.81 259:0.21 260:0.76 261:0.84 265:0.60 266:0.75 267:1.00 268:0.83 271:0.40 276:0.59 285:0.62 297:0.36 300:0.84 +2 6:0.94 8:0.83 12:0.79 17:0.28 20:0.94 21:0.78 27:0.21 28:0.46 30:0.09 34:0.37 36:0.64 37:0.74 43:0.36 55:0.59 66:0.05 69:0.61 70:0.99 78:0.80 91:0.90 98:0.18 100:0.85 108:0.89 111:0.81 120:0.94 123:0.96 124:0.99 127:0.76 129:1.00 133:0.99 135:0.30 140:0.80 146:0.05 147:0.05 149:0.67 151:0.83 152:0.91 153:0.96 154:0.27 159:0.96 161:0.80 162:0.47 164:0.88 167:0.64 169:0.84 172:0.51 173:0.15 177:0.05 178:0.42 179:0.21 181:0.81 186:0.81 187:0.39 189:0.96 191:0.79 202:0.94 212:0.23 216:0.95 235:0.31 238:0.92 241:0.62 242:0.82 243:0.06 245:0.81 247:0.12 248:0.91 256:0.84 259:0.21 260:0.94 261:0.85 265:0.16 266:0.98 267:0.69 268:0.85 271:0.40 276:0.91 283:0.82 285:0.62 300:0.94 +1 12:0.79 17:0.55 21:0.59 27:0.45 28:0.46 30:0.45 32:0.80 34:0.92 36:0.25 37:0.50 43:0.32 55:0.59 66:0.49 69:0.70 70:0.25 81:0.84 91:0.20 98:0.30 108:0.53 123:0.51 124:0.48 126:0.54 127:0.33 129:0.59 133:0.47 135:0.78 145:0.45 146:0.38 147:0.45 149:0.28 150:0.65 154:0.24 159:0.44 161:0.80 163:0.87 167:0.48 172:0.16 173:0.70 177:0.05 178:0.55 179:0.96 181:0.81 187:0.68 195:0.72 212:0.61 215:0.55 216:0.77 235:0.68 241:0.07 242:0.82 243:0.60 245:0.39 247:0.12 259:0.21 265:0.32 266:0.17 267:0.36 271:0.40 276:0.16 297:0.36 300:0.18 +2 1:0.69 6:0.91 8:0.81 9:0.79 11:0.78 12:0.06 17:0.40 18:0.84 20:0.91 27:0.06 28:0.57 29:0.62 30:0.33 31:0.96 34:0.58 36:0.69 37:0.68 39:0.37 41:0.94 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 78:0.97 79:0.96 81:0.80 83:0.60 85:0.80 86:0.91 91:0.63 96:0.88 98:0.23 100:0.99 101:0.87 104:0.73 108:0.09 111:0.99 114:0.98 120:0.80 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.87 133:0.59 135:0.88 137:0.96 139:0.88 140:0.45 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 151:0.94 152:0.91 153:0.80 154:0.84 155:0.66 157:0.81 159:0.71 161:0.62 162:0.67 164:0.80 165:0.93 166:0.82 167:0.84 169:1.00 172:0.57 173:0.12 176:0.73 177:0.70 179:0.74 181:0.54 182:0.79 186:0.96 187:0.39 189:0.92 191:0.84 192:0.87 196:0.97 201:0.67 202:0.75 208:0.64 212:0.89 215:0.96 216:0.17 220:0.74 222:0.25 227:0.92 229:0.88 231:0.73 232:0.77 235:0.22 238:0.98 241:0.74 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 248:0.87 253:0.87 255:0.78 256:0.82 259:0.21 260:0.80 261:0.97 265:0.25 266:0.23 267:0.19 268:0.77 271:0.93 276:0.31 284:0.96 285:0.62 287:0.91 290:0.98 297:0.36 300:0.70 +1 1:0.69 6:0.79 7:0.81 8:0.60 9:0.79 11:0.78 12:0.06 17:0.75 18:0.94 20:0.75 21:0.47 27:0.40 28:0.57 29:0.62 30:0.60 31:0.95 32:0.80 34:0.25 36:0.56 37:0.25 39:0.37 41:0.82 43:0.86 44:0.93 45:0.92 60:0.87 64:0.77 66:0.53 69:0.39 70:0.68 71:0.80 74:0.97 77:0.96 78:0.88 79:0.94 81:0.76 83:0.60 85:0.98 86:0.99 91:0.69 96:0.83 98:0.77 99:0.83 100:0.86 101:0.87 104:0.90 108:0.80 111:0.85 114:0.80 120:0.72 121:0.90 122:0.57 123:0.78 124:0.55 126:0.54 127:0.76 129:0.59 131:0.87 133:0.46 135:0.87 137:0.95 139:0.99 140:0.45 144:0.57 145:0.83 146:0.79 147:0.82 149:0.99 150:0.60 151:0.90 152:0.74 153:0.69 154:0.83 155:0.66 157:0.88 158:0.92 159:0.72 161:0.62 162:0.86 164:0.62 165:0.93 166:0.81 167:0.64 169:0.78 172:0.92 173:0.27 176:0.73 177:0.70 179:0.19 181:0.54 182:0.79 186:0.97 187:0.39 189:0.82 192:0.87 195:0.85 196:0.98 201:0.66 202:0.46 204:0.85 208:0.64 212:0.73 215:0.63 216:0.41 219:0.95 222:0.25 227:0.92 229:0.88 230:0.87 231:0.73 232:0.91 233:0.76 235:0.84 238:0.81 241:0.32 242:0.32 243:0.43 245:0.98 246:0.89 247:0.74 248:0.80 253:0.89 255:0.78 256:0.45 259:0.21 260:0.73 261:0.76 265:0.77 266:0.71 267:0.44 268:0.55 271:0.93 276:0.91 279:0.80 281:0.91 282:0.88 283:0.82 284:0.90 285:0.62 287:0.91 290:0.80 292:0.95 297:0.36 299:0.97 300:0.84 +4 1:0.65 6:0.91 7:0.70 8:0.82 9:0.80 11:0.50 12:0.06 17:0.70 20:0.97 21:0.40 27:0.06 28:0.57 29:0.62 30:0.95 31:0.98 32:0.67 34:0.23 36:0.30 37:0.73 39:0.37 41:0.95 43:0.27 45:0.66 66:0.54 69:0.91 71:0.80 74:0.48 77:0.70 78:0.94 79:0.98 81:0.59 83:0.60 91:0.92 96:0.94 98:0.10 99:0.83 100:0.98 101:0.52 104:0.58 108:0.82 111:0.97 114:0.78 120:0.89 123:0.81 126:0.44 127:0.07 129:0.05 131:0.87 135:0.42 137:0.98 140:0.45 144:0.57 145:0.52 146:0.13 147:0.18 149:0.18 150:0.51 151:0.96 152:0.90 153:0.86 154:0.19 155:0.67 157:0.76 159:0.08 161:0.62 162:0.59 164:0.83 165:0.70 166:0.82 167:0.94 169:0.98 172:0.10 173:1.00 175:0.75 176:0.63 177:0.38 179:0.09 181:0.54 182:0.79 186:0.94 189:0.92 191:0.96 192:0.87 195:0.67 201:0.61 202:0.79 204:0.84 208:0.64 215:0.67 216:0.26 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.76 235:0.60 238:0.97 241:0.85 243:0.63 244:0.83 246:0.89 248:0.94 253:0.92 255:0.95 256:0.79 257:0.65 259:0.21 260:0.89 261:0.96 265:0.10 267:0.52 268:0.80 271:0.93 277:0.69 282:0.75 284:0.97 285:0.62 287:0.91 290:0.78 297:0.36 300:0.08 +1 1:0.66 6:0.81 7:0.81 8:0.60 11:0.78 12:0.06 17:0.97 18:0.89 20:0.77 21:0.30 22:0.93 27:0.16 28:0.57 29:0.62 30:0.13 32:0.80 34:0.68 36:0.39 37:0.44 39:0.37 43:0.74 44:0.93 45:0.85 47:0.93 60:0.95 64:0.77 66:0.07 69:0.85 70:0.98 71:0.80 74:0.89 77:0.70 78:0.82 81:0.74 83:0.60 85:0.96 86:0.96 91:0.76 98:0.46 100:0.73 101:0.87 104:0.86 106:0.81 108:0.85 111:0.80 114:0.86 117:0.86 121:0.90 123:0.93 124:0.91 126:0.54 127:0.79 129:0.93 131:0.61 133:0.90 135:0.98 139:0.97 144:0.57 145:0.59 146:0.30 147:0.52 149:0.95 150:0.57 152:0.75 154:0.64 155:0.56 157:0.86 158:0.92 159:0.86 161:0.62 165:0.93 166:0.82 167:0.55 169:0.72 172:0.68 173:0.67 176:0.73 177:0.05 178:0.55 179:0.19 181:0.54 182:0.78 186:0.82 187:0.21 189:0.83 192:0.55 195:0.95 201:0.64 204:0.83 206:0.81 208:0.64 212:0.67 215:0.72 216:0.28 219:0.95 222:0.25 227:0.61 229:0.61 230:0.87 231:0.73 232:0.88 233:0.76 235:0.60 238:0.81 241:0.02 242:0.29 243:0.13 245:0.94 246:0.89 247:0.90 253:0.71 257:0.84 259:0.21 261:0.76 262:0.93 265:0.34 266:0.84 267:0.44 271:0.93 276:0.91 277:0.69 279:0.81 281:0.91 282:0.88 283:0.82 287:0.61 290:0.86 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.63 6:0.91 7:0.70 8:0.80 9:0.79 11:0.50 12:0.06 17:0.95 18:0.73 20:0.78 21:0.59 27:0.16 28:0.57 29:0.62 30:0.21 31:0.94 32:0.63 34:0.45 36:0.49 37:0.72 39:0.37 41:0.82 43:0.56 44:0.92 45:0.81 64:0.77 66:0.20 69:0.44 70:0.91 71:0.80 74:0.55 76:0.94 77:0.70 78:0.88 79:0.93 81:0.61 83:0.60 86:0.77 91:0.82 96:0.80 98:0.48 99:0.83 100:0.86 101:0.52 104:0.58 108:0.82 111:0.86 114:0.73 120:0.69 122:0.51 123:0.81 124:0.82 126:0.54 127:0.46 129:0.81 131:0.87 133:0.78 135:0.32 137:0.94 139:0.77 140:0.45 144:0.57 145:0.77 146:0.19 147:0.25 149:0.42 150:0.49 151:0.86 152:0.87 153:0.48 154:0.65 155:0.66 157:0.81 159:0.63 161:0.62 162:0.86 163:0.79 164:0.57 165:0.70 166:0.81 167:0.72 169:0.82 172:0.32 173:0.33 176:0.70 177:0.70 179:0.65 181:0.54 182:0.78 186:0.88 187:0.39 189:0.92 192:0.87 195:0.83 196:0.93 201:0.61 202:0.36 204:0.83 208:0.64 212:0.16 215:0.55 216:0.30 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.66 235:0.80 238:0.86 241:0.59 242:0.15 243:0.26 244:0.61 245:0.64 246:0.89 247:0.79 248:0.77 253:0.79 254:0.84 255:0.78 256:0.45 259:0.21 260:0.70 261:0.87 265:0.50 266:0.84 267:0.28 268:0.46 271:0.93 276:0.53 282:0.72 284:0.89 285:0.62 287:0.91 290:0.73 297:0.36 300:0.70 +1 1:0.69 7:0.70 11:0.78 12:0.06 17:0.90 18:0.90 21:0.22 22:0.93 27:0.15 28:0.57 29:0.62 30:0.45 32:0.80 34:0.31 36:0.40 37:0.87 39:0.37 43:0.94 44:0.93 45:0.99 47:0.93 48:0.91 60:0.87 64:0.77 66:0.51 69:0.19 70:0.79 71:0.80 74:0.98 77:0.89 81:0.81 83:0.60 85:0.95 86:0.96 91:0.51 98:0.77 99:0.83 101:0.87 104:0.58 108:0.87 114:0.90 122:0.51 123:0.87 124:0.64 126:0.54 127:0.93 129:0.59 131:0.61 133:0.66 135:0.26 139:0.97 144:0.57 145:0.45 146:0.92 147:0.94 149:0.99 150:0.71 154:0.94 155:0.57 157:0.88 158:0.92 159:0.84 161:0.62 165:0.93 166:0.82 167:0.55 172:0.98 173:0.11 176:0.73 177:0.70 178:0.55 179:0.11 181:0.54 182:0.78 187:0.21 191:0.78 192:0.56 195:0.94 201:0.68 204:0.83 208:0.64 212:0.91 215:0.79 216:0.13 219:0.95 222:0.25 227:0.61 229:0.61 230:0.73 231:0.73 232:0.75 233:0.76 235:0.68 241:0.02 242:0.28 243:0.43 245:0.99 246:0.89 247:0.86 253:0.76 259:0.21 262:0.93 265:0.77 266:0.54 267:0.52 271:0.93 276:0.98 279:0.80 281:0.86 282:0.88 283:0.82 287:0.61 290:0.90 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.69 12:0.06 17:0.16 18:0.67 20:0.74 25:0.88 27:0.61 28:0.57 29:0.62 34:0.96 36:0.63 37:0.78 39:0.37 43:0.19 64:0.77 66:0.36 69:0.35 71:0.80 78:0.85 81:0.80 83:0.60 86:0.66 87:0.98 91:0.85 98:0.13 100:0.73 104:0.54 108:0.27 111:0.82 114:0.98 123:0.26 126:0.54 127:0.08 129:0.05 131:0.61 135:0.42 139:0.64 144:0.57 146:0.05 147:0.05 149:0.07 150:0.68 152:0.74 154:0.12 155:0.54 157:0.61 159:0.05 161:0.62 165:0.93 166:0.82 167:0.66 169:0.72 172:0.05 173:0.72 175:0.91 176:0.73 177:0.05 178:0.55 181:0.54 182:0.78 186:0.85 187:0.21 192:0.50 201:0.67 208:0.64 215:0.96 216:0.24 222:0.25 227:0.61 229:0.61 231:0.73 232:0.76 235:0.22 241:0.41 243:0.51 244:0.93 246:0.89 253:0.69 257:0.84 259:0.21 261:0.74 265:0.13 267:0.44 271:0.93 277:0.87 287:0.61 290:0.98 297:0.36 +2 1:0.69 6:0.91 8:0.83 11:0.78 12:0.06 17:0.64 18:0.84 20:0.79 27:0.06 28:0.57 29:0.62 30:0.33 34:0.62 36:0.70 37:0.68 39:0.37 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 78:0.97 81:0.80 83:0.60 85:0.80 86:0.91 91:0.53 98:0.23 100:0.99 101:0.87 104:0.73 108:0.09 111:0.99 114:0.98 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.61 133:0.59 135:0.88 139:0.88 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 152:0.92 154:0.84 155:0.54 157:0.81 159:0.71 161:0.62 165:0.93 166:0.82 167:0.72 169:1.00 172:0.57 173:0.12 176:0.73 177:0.70 178:0.55 179:0.74 181:0.54 182:0.78 186:0.98 187:0.39 189:0.93 192:0.50 201:0.67 208:0.64 212:0.89 215:0.96 216:0.17 222:0.25 227:0.61 229:0.61 231:0.73 232:0.77 235:0.22 238:0.97 241:0.57 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 253:0.71 259:0.21 261:0.97 265:0.25 266:0.23 267:0.36 271:0.93 276:0.31 287:0.61 290:0.98 297:0.36 300:0.70 +3 1:0.69 6:0.86 8:0.68 9:0.79 11:0.78 12:0.06 17:0.96 18:0.82 20:0.91 27:0.16 28:0.57 29:0.62 30:0.41 31:0.96 34:0.26 36:0.43 37:0.24 39:0.37 41:0.88 43:0.86 45:0.73 64:0.77 66:0.54 69:0.41 70:0.77 71:0.80 74:0.61 78:0.88 79:0.95 81:0.80 83:0.60 85:0.80 86:0.87 91:0.94 96:0.86 98:0.63 100:0.88 101:0.87 104:0.58 108:0.32 111:0.87 114:0.98 120:0.77 122:0.57 123:0.31 124:0.50 126:0.54 127:0.66 129:0.05 131:0.87 133:0.43 135:0.28 137:0.96 139:0.84 140:0.45 144:0.57 146:0.44 147:0.50 149:0.82 150:0.68 151:0.93 152:0.85 153:0.77 154:0.83 155:0.66 157:0.82 159:0.27 161:0.62 162:0.86 164:0.70 165:0.93 166:0.82 167:0.94 169:0.86 172:0.48 173:0.62 176:0.73 177:0.70 179:0.76 181:0.54 182:0.79 186:0.88 189:0.87 191:0.79 192:0.87 196:0.95 201:0.67 202:0.55 208:0.64 212:0.84 215:0.96 216:0.02 222:0.25 227:0.92 229:0.88 231:0.73 232:0.75 235:0.22 238:0.86 241:0.86 242:0.19 243:0.63 245:0.66 246:0.89 247:0.74 248:0.84 253:0.85 255:0.79 256:0.58 259:0.21 260:0.77 261:0.87 265:0.64 266:0.27 267:0.84 268:0.64 271:0.93 276:0.39 284:0.93 285:0.62 287:0.91 290:0.98 297:0.36 300:0.55 +2 1:0.69 8:0.70 9:0.76 11:0.78 12:0.06 17:0.49 18:0.84 27:0.06 28:0.57 29:0.62 30:0.33 31:0.94 34:0.58 36:0.69 37:0.68 39:0.37 41:0.82 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 79:0.94 81:0.80 83:0.60 85:0.80 86:0.91 91:0.56 96:0.82 98:0.23 101:0.87 104:0.73 108:0.09 114:0.98 120:0.72 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.87 133:0.59 135:0.87 137:0.94 139:0.88 140:0.45 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 151:0.90 153:0.69 154:0.84 155:0.66 157:0.81 159:0.71 161:0.62 162:0.49 164:0.65 165:0.93 166:0.82 167:0.85 172:0.57 173:0.12 176:0.73 177:0.70 179:0.74 181:0.54 182:0.78 187:0.39 191:0.87 192:0.87 196:0.95 201:0.67 202:0.71 208:0.64 212:0.89 215:0.96 216:0.17 222:0.25 227:0.91 229:0.87 231:0.73 232:0.77 235:0.22 241:0.75 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 248:0.80 253:0.83 255:0.78 256:0.75 259:0.21 260:0.73 265:0.25 266:0.23 267:0.23 268:0.58 271:0.93 276:0.31 284:0.90 285:0.62 287:0.91 290:0.98 297:0.36 300:0.70 +2 1:0.69 7:0.81 8:0.85 9:0.75 11:0.50 12:0.06 17:0.90 18:0.73 21:0.30 27:0.16 28:0.57 29:0.62 30:0.30 32:0.80 34:0.67 36:0.55 37:0.72 39:0.37 43:0.60 44:0.92 45:0.77 64:0.77 66:0.84 69:0.43 70:0.48 71:0.80 74:0.69 76:0.94 77:0.98 78:0.85 81:0.81 83:0.60 86:0.75 91:0.84 96:0.86 98:0.75 99:0.95 101:0.52 104:0.58 108:0.33 111:0.83 114:0.82 120:0.78 121:0.90 122:0.51 123:0.32 126:0.54 127:0.25 129:0.05 131:0.87 135:0.37 139:0.85 140:0.45 144:0.57 145:0.79 146:0.44 147:0.50 149:0.46 150:0.73 151:0.81 153:0.77 154:0.66 155:0.65 157:0.81 159:0.16 161:0.62 162:0.61 164:0.73 165:0.93 166:0.81 167:0.86 169:0.84 172:0.54 173:0.72 176:0.70 177:0.70 179:0.61 181:0.54 182:0.79 187:0.39 190:0.89 192:0.86 195:0.54 201:0.68 202:0.67 204:0.85 208:0.64 212:0.92 215:0.67 216:0.30 220:0.62 222:0.25 227:0.92 229:0.88 230:0.87 231:0.73 232:0.75 233:0.76 235:0.84 241:0.76 242:0.24 243:0.85 244:0.61 246:0.89 247:0.74 248:0.85 253:0.86 254:0.80 255:0.73 256:0.68 259:0.21 260:0.78 265:0.76 266:0.17 267:0.52 268:0.68 271:0.93 276:0.44 281:0.91 282:0.88 285:0.56 287:0.91 290:0.82 297:0.36 299:0.88 300:0.33 +2 1:0.69 9:0.79 12:0.06 17:0.64 27:0.16 28:0.57 29:0.62 31:0.94 34:0.67 36:0.67 37:0.31 39:0.37 41:0.82 60:0.87 64:0.77 71:0.80 74:0.46 79:0.93 81:0.80 83:0.60 91:0.62 96:0.80 104:0.73 114:0.98 120:0.69 126:0.54 131:0.87 135:0.32 137:0.94 139:0.74 140:0.45 144:0.57 150:0.68 151:0.86 153:0.48 155:0.66 157:0.61 158:0.86 161:0.62 162:0.50 164:0.57 165:0.93 166:0.82 167:0.75 175:0.97 176:0.73 181:0.54 182:0.78 187:0.68 192:0.87 196:0.92 201:0.67 202:0.62 208:0.64 215:0.96 216:0.30 219:0.95 222:0.25 227:0.92 229:0.88 231:0.73 232:0.77 235:0.22 241:0.65 244:0.97 246:0.89 248:0.77 253:0.81 255:0.78 256:0.45 257:0.93 259:0.21 260:0.70 267:0.23 268:0.46 271:0.93 277:0.95 279:0.80 283:0.67 284:0.89 285:0.62 287:0.91 290:0.98 292:0.88 297:0.36 +1 1:0.69 6:0.91 7:0.70 8:0.77 9:0.76 11:0.50 12:0.06 17:0.88 18:0.73 20:0.82 21:0.54 27:0.16 28:0.57 29:0.62 30:0.30 31:0.90 32:0.71 34:0.55 36:0.49 37:0.72 39:0.37 43:0.60 44:0.92 45:0.77 64:0.77 66:0.84 69:0.45 70:0.48 71:0.80 74:0.65 76:0.94 77:0.96 78:0.87 79:0.89 81:0.76 83:0.60 86:0.75 91:0.92 96:0.80 97:0.89 98:0.84 99:0.99 100:0.84 101:0.52 104:0.58 108:0.35 111:0.85 114:0.76 120:0.68 122:0.51 123:0.34 126:0.54 127:0.34 129:0.05 131:0.87 135:0.26 137:0.90 139:0.78 140:0.80 144:0.57 145:0.87 146:0.44 147:0.50 149:0.46 150:0.70 151:0.59 152:0.87 153:0.72 154:0.66 155:0.66 157:0.81 158:0.76 159:0.16 161:0.62 162:0.70 164:0.76 165:0.70 166:0.81 167:0.94 169:0.82 172:0.54 173:0.79 176:0.70 177:0.70 179:0.70 181:0.54 182:0.79 186:0.87 189:0.88 190:0.89 191:0.93 192:0.87 195:0.55 196:0.91 201:0.68 202:0.68 204:0.82 208:0.64 212:0.92 215:0.58 216:0.30 220:0.62 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.76 235:0.98 238:0.84 241:0.87 242:0.24 243:0.85 244:0.61 246:0.89 247:0.74 248:0.61 253:0.80 254:0.80 255:0.78 256:0.68 259:0.21 260:0.69 261:0.87 265:0.84 266:0.17 267:0.44 268:0.70 271:0.93 276:0.44 279:0.77 282:0.79 283:0.67 284:0.93 285:0.62 287:0.91 290:0.76 297:0.36 300:0.33 +1 1:0.65 6:0.86 7:0.70 8:0.94 9:0.76 12:0.06 17:0.49 20:0.79 21:0.40 27:0.06 28:0.57 29:0.62 34:0.22 36:0.41 37:0.81 39:0.37 71:0.80 74:0.44 78:0.82 81:0.59 83:0.60 91:0.96 96:0.96 97:0.99 99:0.83 100:0.79 104:0.58 111:0.80 114:0.78 120:0.91 126:0.44 131:0.87 135:0.18 140:0.45 144:0.57 150:0.51 151:0.99 152:0.77 153:0.97 155:0.58 157:0.61 158:0.77 161:0.62 162:0.57 164:0.88 165:0.70 166:0.82 167:0.96 169:0.77 175:0.97 176:0.63 181:0.54 182:0.79 186:0.83 189:0.87 190:0.89 191:0.97 192:0.58 201:0.61 202:0.88 204:0.85 208:0.54 215:0.67 216:0.17 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.66 235:0.60 238:0.82 241:0.93 244:0.97 246:0.89 248:0.95 253:0.94 255:0.74 256:0.86 257:0.93 259:0.21 260:0.91 261:0.78 267:0.76 268:0.87 271:0.93 277:0.95 279:0.82 283:0.82 285:0.56 287:0.91 290:0.78 297:0.36 +2 1:0.69 6:0.91 8:0.81 9:0.80 11:0.78 12:0.06 17:0.47 18:0.84 20:0.84 27:0.06 28:0.57 29:0.62 30:0.33 31:0.98 34:0.44 36:0.68 37:0.68 39:0.37 41:0.92 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 78:0.97 79:0.97 81:0.80 83:0.60 85:0.80 86:0.91 91:0.80 96:0.91 98:0.23 100:0.99 101:0.87 104:0.73 108:0.09 111:0.99 114:0.98 120:0.84 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.87 133:0.59 135:0.86 137:0.98 139:0.88 140:0.45 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 151:0.98 152:0.91 153:0.92 154:0.84 155:0.66 157:0.81 159:0.71 161:0.62 162:0.61 164:0.79 165:0.93 166:0.82 167:0.88 169:1.00 172:0.57 173:0.12 176:0.73 177:0.70 179:0.74 181:0.54 182:0.79 186:0.97 187:0.21 189:0.92 191:0.91 192:0.87 196:0.98 201:0.67 202:0.75 208:0.64 212:0.89 215:0.96 216:0.17 222:0.25 227:0.92 229:0.88 231:0.73 232:0.77 235:0.22 238:0.98 241:0.76 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 248:0.90 253:0.90 255:0.94 256:0.77 259:0.21 260:0.85 261:0.97 265:0.25 266:0.23 267:0.19 268:0.76 271:0.93 276:0.31 284:0.97 285:0.62 287:0.91 290:0.98 297:0.36 300:0.70 +1 1:0.69 6:0.94 7:0.70 8:0.75 9:0.79 11:0.78 12:0.06 17:0.96 18:0.86 20:0.75 27:0.72 28:0.57 29:0.62 30:0.11 31:0.96 32:0.71 34:0.17 36:0.33 37:0.45 39:0.37 41:0.88 43:0.96 44:0.92 45:0.81 48:0.91 60:0.95 64:0.77 66:0.93 69:0.08 70:0.92 71:0.80 74:0.80 76:0.85 77:0.89 78:0.89 79:0.95 81:0.80 83:0.60 85:0.80 86:0.92 91:0.86 96:0.86 98:1.00 100:0.93 101:0.87 104:0.54 108:0.07 111:0.87 114:0.98 120:0.78 122:0.82 123:0.07 126:0.54 127:0.97 129:0.05 131:0.87 135:0.30 137:0.96 139:0.94 140:0.45 144:0.57 145:0.92 146:0.89 147:0.91 149:0.77 150:0.68 151:0.90 152:0.74 153:0.69 154:0.96 155:0.66 157:0.83 158:0.92 159:0.48 161:0.62 162:0.86 164:0.66 165:0.93 166:0.82 167:0.91 169:0.81 172:0.81 173:0.20 176:0.73 177:0.70 179:0.52 181:0.54 182:0.79 186:0.98 189:0.95 191:0.73 192:0.87 195:0.67 196:0.98 201:0.67 202:0.50 204:0.82 208:0.64 212:0.68 215:0.96 216:0.05 219:0.95 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.71 233:0.76 235:0.22 238:0.84 241:0.79 242:0.16 243:0.93 246:0.89 247:0.84 248:0.85 253:0.90 255:0.94 256:0.58 259:0.21 260:0.78 261:0.76 265:1.00 266:0.54 267:0.44 268:0.59 271:0.93 276:0.70 279:0.82 281:0.91 282:0.80 283:0.82 284:0.92 285:0.62 287:0.91 290:0.98 292:0.95 297:0.36 300:0.70 +1 8:0.98 9:0.80 12:0.69 17:0.24 21:0.78 27:0.64 28:0.66 34:0.68 36:0.30 37:0.72 39:0.85 41:0.94 83:0.79 91:0.88 96:0.89 104:0.78 120:0.83 131:0.97 135:0.78 140:0.45 144:0.57 151:0.91 153:0.90 155:0.67 157:0.61 161:0.90 162:0.72 164:0.84 166:0.61 167:0.84 175:0.91 181:0.55 182:0.94 192:0.59 202:0.77 208:0.64 216:0.32 220:0.62 222:0.84 227:0.96 229:0.97 231:0.91 232:0.84 241:0.85 244:0.83 246:0.61 248:0.88 253:0.84 255:0.79 256:0.79 257:0.84 259:0.21 260:0.84 267:0.44 268:0.81 271:0.51 277:0.87 285:0.62 287:0.95 +1 1:0.74 6:0.86 8:0.88 9:0.80 11:0.42 12:0.69 17:0.43 20:0.84 21:0.05 27:0.64 28:0.66 30:0.68 34:0.44 36:0.58 37:0.72 39:0.85 41:0.82 43:0.37 55:0.52 66:0.54 69:0.15 70:0.31 74:0.49 78:0.88 81:0.91 83:0.80 91:0.48 96:0.79 98:0.62 100:0.83 104:0.78 108:0.12 111:0.86 114:0.95 120:0.77 123:0.12 124:0.48 126:0.54 127:0.69 129:0.59 131:0.96 133:0.47 135:0.49 140:0.45 144:0.57 146:0.51 147:0.57 149:0.27 150:0.95 151:0.81 152:0.84 153:0.48 154:0.75 155:0.67 157:0.61 159:0.35 161:0.90 162:0.86 164:0.68 165:0.90 166:0.94 167:0.53 169:0.80 172:0.32 173:0.33 176:0.73 177:0.38 179:0.90 181:0.55 182:0.94 186:0.88 187:0.21 190:0.77 192:0.59 201:0.74 202:0.53 208:0.64 212:0.61 215:0.91 216:0.32 220:0.62 222:0.84 227:0.95 229:0.97 231:0.91 232:0.84 235:0.12 241:0.30 242:0.63 243:0.63 245:0.50 246:0.93 247:0.39 248:0.73 253:0.80 254:0.86 255:0.79 256:0.58 259:0.21 260:0.77 261:0.86 265:0.63 266:0.27 267:0.52 268:0.62 271:0.51 276:0.28 283:0.71 285:0.62 287:0.95 290:0.95 297:0.36 300:0.25 +2 1:0.74 6:0.88 7:0.81 8:0.74 9:0.80 11:0.64 12:0.69 17:0.75 20:0.98 21:0.30 27:0.15 28:0.66 30:0.21 32:0.80 34:0.17 36:0.39 37:0.63 39:0.85 41:0.92 43:0.89 55:0.71 60:0.87 66:0.20 69:0.17 70:0.92 74:0.87 77:0.89 78:0.96 81:0.83 83:0.81 85:0.96 91:0.72 96:0.79 98:0.56 100:0.95 101:0.79 104:0.58 108:0.14 111:0.95 114:0.86 120:0.84 121:0.97 123:0.95 124:0.80 126:0.54 127:1.00 129:0.81 131:0.97 133:0.76 135:0.49 140:0.80 144:0.57 145:0.52 146:0.93 147:0.94 149:0.94 150:0.89 151:0.70 152:0.90 153:0.78 154:0.88 155:0.67 157:0.96 158:0.92 159:0.91 161:0.90 162:0.72 164:0.80 165:0.84 166:0.94 167:0.81 169:0.93 172:0.89 173:0.08 176:0.73 177:0.38 179:0.16 181:0.55 182:0.94 186:0.96 189:0.89 190:0.77 191:0.75 192:0.59 195:0.97 201:0.74 202:0.73 204:0.89 208:0.64 212:0.70 215:0.72 216:0.09 220:0.62 222:0.84 227:0.96 229:0.97 230:0.87 231:0.91 233:0.76 235:0.52 238:0.90 241:0.79 242:0.76 243:0.44 245:0.98 246:0.93 247:0.47 248:0.74 253:0.84 255:0.79 256:0.75 259:0.21 260:0.84 261:0.94 265:0.54 266:0.89 267:0.82 268:0.76 271:0.51 276:0.95 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.86 297:0.36 299:0.88 300:0.94 +1 6:0.96 7:0.81 8:0.75 9:0.80 12:0.69 17:0.02 20:0.89 21:0.30 27:0.16 28:0.66 30:0.21 34:0.76 36:0.93 37:0.84 39:0.85 43:0.26 55:0.84 66:0.36 69:0.12 70:0.67 74:0.67 78:0.90 81:0.41 91:0.51 96:0.77 98:0.63 100:0.87 108:0.74 111:0.88 114:0.55 120:0.71 121:0.90 123:0.73 124:0.68 126:0.32 127:0.85 129:0.05 131:0.96 133:0.62 135:0.83 140:0.80 144:0.57 146:0.25 147:0.31 149:0.28 150:0.35 151:0.77 152:0.95 153:0.48 154:0.75 155:0.67 157:0.61 158:0.82 159:0.51 161:0.90 162:0.62 164:0.76 166:0.74 167:0.60 169:0.97 172:0.27 173:0.21 176:0.53 177:0.38 178:0.42 179:0.89 181:0.55 182:0.61 186:0.90 187:0.68 189:0.91 191:0.82 192:0.59 202:0.70 204:0.89 208:0.64 212:0.16 216:0.54 220:0.62 222:0.84 227:0.95 229:0.61 230:0.87 231:0.91 233:0.76 235:0.31 238:0.86 241:0.53 242:0.72 243:0.39 245:0.50 246:0.61 247:0.39 248:0.71 253:0.72 254:0.92 255:0.79 256:0.71 259:0.21 260:0.70 261:0.97 265:0.63 266:0.54 267:0.19 268:0.71 271:0.51 276:0.36 277:0.69 285:0.62 287:0.61 290:0.55 300:0.43 +2 1:0.74 6:0.87 7:0.76 8:0.83 9:0.80 11:0.64 12:0.69 17:0.20 20:0.97 21:0.13 27:0.38 28:0.66 30:0.21 32:0.80 34:0.21 36:0.63 37:0.88 39:0.85 41:0.98 43:0.97 55:0.71 60:0.97 66:0.61 69:0.10 70:0.64 74:0.77 76:0.85 77:0.70 78:0.92 81:0.86 83:0.85 85:0.80 91:0.86 96:0.94 98:0.56 100:0.94 101:0.74 104:0.76 108:0.09 111:0.92 114:0.92 120:0.95 123:0.09 124:0.47 126:0.54 127:0.94 129:0.59 131:0.97 133:0.47 135:0.20 140:0.45 144:0.57 145:0.44 146:0.68 147:0.73 149:0.70 150:0.92 151:0.84 152:0.90 153:0.77 154:0.97 155:0.67 157:0.86 158:0.87 159:0.63 161:0.90 162:0.60 163:0.75 164:0.95 165:0.88 166:0.94 167:0.86 169:0.92 172:0.48 173:0.15 176:0.73 177:0.38 179:0.81 181:0.55 182:0.94 186:0.92 189:0.89 190:0.77 191:0.83 192:0.59 195:0.78 201:0.74 202:0.93 208:0.64 212:0.36 215:0.84 216:0.14 220:0.74 222:0.84 227:0.96 229:0.97 230:0.79 231:0.91 233:0.76 235:0.22 238:0.90 241:0.91 242:0.61 243:0.68 245:0.62 246:0.93 247:0.39 248:0.94 253:0.95 255:0.79 256:0.94 259:0.21 260:0.95 261:0.92 265:0.57 266:0.47 267:0.76 268:0.94 271:0.51 276:0.33 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.43 +0 11:0.64 12:0.69 17:0.26 21:0.54 27:0.45 28:0.66 30:0.11 34:0.75 36:0.17 37:0.50 39:0.85 43:0.78 55:0.84 66:0.20 69:0.69 70:0.95 74:0.41 81:0.47 85:0.72 91:0.23 98:0.29 101:0.71 108:0.53 123:0.51 124:0.85 126:0.32 127:0.43 129:0.05 131:0.61 133:0.82 135:0.88 144:0.57 145:0.38 146:0.47 147:0.53 149:0.58 150:0.37 154:0.70 157:0.79 159:0.65 161:0.90 163:0.88 166:0.88 167:0.56 172:0.21 173:0.59 177:0.38 178:0.55 179:0.78 181:0.55 182:0.73 187:0.68 212:0.28 215:0.58 216:0.77 222:0.84 227:0.61 229:0.61 231:0.85 235:0.60 241:0.43 242:0.75 243:0.40 245:0.49 246:0.61 247:0.39 253:0.36 259:0.21 265:0.31 266:0.63 267:0.36 271:0.51 276:0.41 283:0.71 287:0.61 297:0.36 300:0.70 +2 1:0.74 7:0.76 9:0.80 11:0.64 12:0.69 17:0.85 21:0.13 27:0.72 28:0.66 30:0.17 32:0.80 34:0.45 36:0.53 39:0.85 41:0.82 43:0.92 55:0.71 60:0.87 66:0.25 69:0.10 70:0.90 74:0.88 77:0.89 81:0.86 83:0.83 85:0.94 91:0.73 96:0.73 98:0.75 101:0.82 104:0.75 108:0.09 114:0.92 120:0.72 123:0.73 124:0.68 126:0.54 127:0.95 129:0.81 131:0.96 133:0.67 135:0.32 140:0.80 144:0.57 145:0.69 146:0.66 147:0.71 149:0.93 150:0.92 151:0.63 153:0.71 154:0.92 155:0.67 157:0.96 158:0.87 159:0.69 161:0.90 162:0.72 164:0.71 165:0.88 166:0.94 167:0.82 172:0.79 173:0.13 176:0.73 177:0.38 179:0.33 181:0.55 182:0.94 190:0.77 192:0.59 195:0.82 201:0.74 202:0.62 208:0.64 212:0.76 215:0.84 216:0.05 220:0.62 222:0.84 227:0.95 229:0.97 230:0.79 231:0.91 233:0.76 235:0.22 241:0.80 242:0.59 243:0.57 245:0.91 246:0.93 247:0.60 248:0.60 253:0.80 255:0.79 256:0.64 259:0.21 260:0.72 265:0.49 266:0.80 267:0.79 268:0.65 271:0.51 276:0.82 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.69 17:0.99 27:0.68 28:0.66 30:0.31 32:0.80 34:0.61 36:0.90 37:0.63 39:0.85 41:0.88 43:0.98 55:0.52 60:0.87 66:0.98 69:0.05 70:0.71 74:0.98 77:0.70 81:0.95 83:0.86 85:0.96 91:0.70 96:0.86 98:0.99 101:0.86 104:0.57 106:0.81 108:0.05 114:0.98 117:0.86 120:0.78 121:0.90 122:0.43 123:0.05 126:0.54 127:0.92 129:0.05 131:0.96 135:0.84 140:0.45 144:0.57 145:0.47 146:0.89 147:0.91 149:0.96 150:0.98 151:0.90 153:0.69 154:0.98 155:0.67 157:0.97 158:0.92 159:0.49 161:0.90 162:0.86 164:0.66 165:0.92 166:0.94 167:0.49 172:0.94 173:0.16 176:0.73 177:0.38 179:0.24 181:0.55 182:0.94 187:0.21 191:0.76 192:0.59 195:0.64 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.92 215:0.96 216:0.10 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 232:0.86 233:0.76 235:0.05 241:0.10 242:0.41 243:0.98 246:0.93 247:0.60 248:0.85 253:0.92 255:0.79 256:0.58 259:0.21 260:0.78 265:0.99 266:0.27 267:0.97 268:0.59 271:0.51 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.98 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.95 7:0.76 8:0.84 9:0.80 11:0.64 12:0.69 17:0.94 20:0.88 21:0.13 27:0.63 28:0.66 30:0.15 32:0.80 34:0.50 36:0.66 37:0.80 39:0.85 41:0.82 43:0.93 55:0.45 66:0.95 69:0.10 70:0.88 74:0.86 77:0.89 78:0.88 81:0.86 83:0.78 85:0.91 91:0.65 96:0.74 98:0.99 100:0.84 101:0.82 104:0.58 108:0.09 111:0.86 114:0.92 120:0.62 123:0.09 126:0.54 127:0.92 129:0.05 131:0.96 135:0.30 140:0.45 144:0.57 145:0.69 146:0.92 147:0.94 149:0.93 150:0.92 151:0.69 152:0.83 153:0.48 154:0.92 155:0.67 157:0.95 159:0.54 161:0.90 162:0.86 164:0.57 165:0.88 166:0.94 167:0.81 169:0.82 172:0.87 173:0.19 176:0.73 177:0.38 179:0.40 181:0.55 182:0.94 186:0.88 187:0.21 189:0.95 192:0.59 195:0.71 201:0.74 202:0.36 208:0.64 212:0.73 215:0.84 216:0.02 220:0.74 222:0.84 227:0.95 229:0.97 230:0.79 231:0.91 233:0.76 235:0.22 238:0.83 241:0.78 242:0.61 243:0.95 246:0.93 247:0.60 248:0.65 253:0.81 255:0.79 256:0.45 259:0.21 260:0.63 261:0.86 265:0.99 266:0.63 267:0.17 268:0.46 271:0.51 276:0.78 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.81 8:0.60 11:0.64 12:0.69 17:0.84 21:0.22 27:0.72 28:0.66 30:0.14 32:0.80 34:0.45 36:0.96 39:0.85 43:0.94 55:0.52 66:0.53 69:0.25 70:0.86 74:0.85 77:0.70 81:0.82 83:0.76 85:0.85 91:0.66 98:0.71 101:0.75 104:0.79 108:0.20 114:0.90 121:0.90 123:0.19 124:0.72 126:0.54 127:0.98 129:0.59 131:0.61 133:0.76 135:0.72 144:0.57 145:0.69 146:0.89 147:0.91 149:0.86 150:0.88 154:0.94 155:0.67 157:0.89 159:0.76 161:0.90 165:0.86 166:0.94 167:0.76 172:0.83 173:0.17 176:0.73 177:0.38 178:0.55 179:0.34 181:0.55 182:0.94 187:0.21 192:0.59 195:0.88 201:0.74 204:0.89 208:0.64 212:0.60 215:0.79 216:0.08 222:0.84 227:0.61 229:0.61 230:0.87 231:0.91 233:0.76 235:0.31 241:0.75 242:0.74 243:0.62 245:0.87 246:0.93 247:0.60 253:0.75 259:0.21 265:0.71 266:0.89 267:0.84 271:0.51 276:0.83 282:0.88 287:0.61 290:0.90 297:0.36 299:0.88 300:0.70 +1 1:0.74 9:0.80 11:0.64 12:0.69 17:0.45 21:0.13 27:0.38 28:0.66 30:0.74 34:0.39 36:0.83 37:0.88 39:0.85 41:0.88 43:0.97 55:0.84 66:0.35 69:0.10 70:0.55 74:0.60 81:0.86 83:0.78 85:0.80 91:0.54 96:0.78 98:0.59 101:0.74 104:0.76 108:0.83 114:0.92 120:0.66 123:0.82 124:0.67 126:0.54 127:0.81 129:0.59 131:0.96 133:0.64 135:0.58 140:0.45 144:0.57 146:0.50 147:0.57 149:0.69 150:0.92 151:0.69 153:0.48 154:0.97 155:0.67 157:0.86 159:0.66 161:0.90 162:0.86 163:0.94 164:0.67 165:0.88 166:0.94 167:0.62 172:0.45 173:0.14 176:0.73 177:0.38 179:0.70 181:0.55 182:0.94 187:0.39 192:0.59 201:0.74 202:0.50 208:0.64 212:0.30 215:0.84 216:0.14 220:0.74 222:0.84 227:0.95 229:0.97 231:0.91 235:0.22 241:0.58 242:0.59 243:0.48 245:0.67 246:0.93 247:0.39 248:0.71 253:0.79 255:0.79 256:0.58 259:0.21 260:0.67 265:0.60 266:0.71 267:0.76 268:0.59 271:0.51 276:0.52 285:0.62 287:0.95 290:0.92 297:0.36 300:0.55 +0 1:0.74 6:0.82 7:0.76 8:0.61 9:0.80 11:0.64 12:0.69 17:0.32 20:0.90 21:0.05 27:0.50 28:0.66 30:0.54 32:0.80 34:0.52 36:0.95 37:0.42 39:0.85 41:0.88 43:0.97 60:0.95 66:0.83 69:0.60 70:0.51 74:0.93 76:0.94 77:0.98 78:0.92 81:0.85 83:0.88 85:0.93 91:0.65 96:0.83 98:0.89 100:0.83 101:0.85 104:0.82 108:0.46 111:0.89 114:0.95 120:0.79 122:0.57 123:0.44 124:0.37 126:0.54 127:0.58 129:0.05 131:0.97 133:0.39 135:0.49 140:0.45 144:0.57 145:0.56 146:0.74 147:0.05 149:0.91 150:0.91 151:0.83 152:0.93 153:0.48 154:0.97 155:0.67 157:0.96 158:0.87 159:0.33 161:0.90 162:0.77 164:0.75 165:0.88 166:0.94 167:0.54 169:0.81 172:0.71 173:0.74 176:0.73 177:0.05 179:0.56 181:0.55 182:0.94 186:0.92 187:0.21 189:0.82 190:0.77 192:0.59 195:0.71 201:0.74 202:0.65 208:0.64 212:0.80 215:0.91 216:0.92 220:0.74 222:0.84 227:0.95 229:0.97 230:0.79 231:0.91 233:0.76 235:0.52 238:0.82 241:0.37 242:0.37 243:0.10 245:0.57 246:0.93 247:0.55 248:0.81 253:0.89 255:0.79 256:0.64 257:0.65 259:0.21 260:0.80 261:0.90 265:0.89 266:0.27 267:0.52 268:0.69 271:0.51 276:0.60 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.95 297:0.99 299:0.97 300:0.43 +1 1:0.74 7:0.81 8:0.80 9:0.80 11:0.64 12:0.69 17:0.76 20:0.86 21:0.22 27:0.72 28:0.66 30:0.14 32:0.80 34:0.33 36:0.96 39:0.85 41:0.88 43:0.94 55:0.52 66:0.53 69:0.25 70:0.86 74:0.85 77:0.70 78:0.87 81:0.82 83:0.78 85:0.85 91:0.69 96:0.77 98:0.71 100:0.86 101:0.75 104:0.79 108:0.20 111:0.85 114:0.90 120:0.65 121:0.90 123:0.19 124:0.72 126:0.54 127:0.98 129:0.59 131:0.96 133:0.76 135:0.74 140:0.45 144:0.57 145:0.69 146:0.89 147:0.91 149:0.86 150:0.88 151:0.69 152:0.81 153:0.48 154:0.94 155:0.67 157:0.89 159:0.76 161:0.90 162:0.78 164:0.70 165:0.86 166:0.94 167:0.83 169:0.84 172:0.83 173:0.17 176:0.73 177:0.38 179:0.34 181:0.55 182:0.94 186:0.87 192:0.59 195:0.88 201:0.74 202:0.59 204:0.89 208:0.64 212:0.60 215:0.79 216:0.08 220:0.82 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 233:0.76 235:0.31 241:0.82 242:0.74 243:0.62 245:0.87 246:0.93 247:0.60 248:0.70 253:0.82 255:0.79 256:0.58 259:0.21 260:0.66 261:0.84 265:0.71 266:0.89 267:0.84 268:0.64 271:0.51 276:0.83 282:0.88 285:0.62 287:0.95 290:0.90 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.82 7:0.81 8:0.65 9:0.80 11:0.64 12:0.69 17:0.69 20:0.82 21:0.30 27:0.41 28:0.66 30:0.09 34:0.59 36:0.57 37:0.63 39:0.85 41:0.82 43:0.89 55:0.59 60:0.87 66:0.13 69:0.17 70:0.94 74:0.82 78:0.94 81:0.83 83:0.84 85:0.96 91:0.20 96:0.80 98:0.39 100:0.85 101:0.78 104:0.54 106:0.81 108:0.97 111:0.91 114:0.86 117:0.86 120:0.69 121:0.90 123:0.94 124:0.95 126:0.54 127:0.98 129:0.93 131:0.96 133:0.96 135:0.32 140:0.80 144:0.57 146:0.63 147:0.68 149:0.92 150:0.89 151:0.87 152:0.79 153:0.48 154:0.88 155:0.67 157:0.96 158:0.92 159:0.93 161:0.90 162:0.62 164:0.57 165:0.84 166:0.94 167:0.46 169:0.82 172:0.86 173:0.07 176:0.73 177:0.38 178:0.42 179:0.16 181:0.55 182:0.94 186:0.94 187:0.39 189:0.84 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.57 215:0.72 216:0.12 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 232:0.94 233:0.76 235:0.52 238:0.84 241:0.01 242:0.75 243:0.15 245:0.93 246:0.93 247:0.47 248:0.78 253:0.85 255:0.79 256:0.45 259:0.21 260:0.70 261:0.85 265:0.34 266:0.92 267:0.79 268:0.46 271:0.51 276:0.95 279:0.86 283:0.82 285:0.62 287:0.95 290:0.86 297:0.36 300:0.94 +2 1:0.74 8:0.91 9:0.80 11:0.64 12:0.69 17:0.59 20:0.79 21:0.22 27:0.49 28:0.66 30:0.30 34:0.58 36:0.93 37:0.42 39:0.85 41:0.88 43:0.96 55:0.84 60:0.87 66:0.24 69:0.15 70:0.80 74:0.70 78:0.93 81:0.82 83:0.83 85:0.80 91:0.65 96:0.79 98:0.56 100:0.89 101:0.77 104:0.89 108:0.73 111:0.91 114:0.90 120:0.67 123:0.43 124:0.71 126:0.54 127:0.81 129:0.81 131:0.96 133:0.67 135:0.74 140:0.45 144:0.57 146:0.13 147:0.18 149:0.72 150:0.88 151:0.73 152:0.81 153:0.77 154:0.96 155:0.67 157:0.87 158:0.87 159:0.43 161:0.90 162:0.86 164:0.70 165:0.86 166:0.94 167:0.53 169:0.80 172:0.32 173:0.27 176:0.73 177:0.38 179:0.79 181:0.55 182:0.94 186:0.93 187:0.21 192:0.59 201:0.74 202:0.55 208:0.64 212:0.14 215:0.79 216:0.94 220:0.74 222:0.84 227:0.95 229:0.97 231:0.91 235:0.31 241:0.30 242:0.78 243:0.23 245:0.60 246:0.93 247:0.35 248:0.73 253:0.81 255:0.79 256:0.58 259:0.21 260:0.68 261:0.84 265:0.51 266:0.71 267:0.36 268:0.64 271:0.51 276:0.41 279:0.86 283:0.71 285:0.62 287:0.95 290:0.90 297:0.36 300:0.55 +2 6:0.96 7:0.81 8:0.87 9:0.80 12:0.69 17:0.09 20:0.98 21:0.22 27:0.16 28:0.66 30:0.76 34:0.20 36:0.87 37:0.84 39:0.85 41:0.97 43:0.31 55:0.84 66:0.36 69:0.80 70:0.15 74:0.62 78:0.97 81:0.73 83:0.84 91:0.98 96:0.97 98:0.27 100:0.99 108:0.64 111:0.98 120:0.98 121:0.90 122:0.43 123:0.61 124:0.52 126:0.32 127:0.19 129:0.05 131:0.97 133:0.39 135:0.74 138:0.99 140:0.92 144:0.57 146:0.30 147:0.05 149:0.23 150:0.54 151:0.97 152:0.95 153:0.96 154:0.23 155:0.67 157:0.61 158:0.86 159:0.23 161:0.90 162:0.69 163:0.96 164:0.98 166:0.88 167:0.87 169:0.97 172:0.10 173:0.88 177:0.05 179:0.94 181:0.55 182:0.94 186:0.97 189:0.97 191:0.93 192:0.59 202:0.96 204:0.89 208:0.64 212:0.95 215:0.79 216:0.54 220:0.82 222:0.84 227:0.96 229:0.97 230:0.87 231:0.91 233:0.76 235:0.22 238:0.97 241:0.97 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 248:0.97 253:0.96 255:0.79 256:0.97 257:0.65 259:0.21 260:0.98 261:0.97 265:0.29 266:0.12 267:0.85 268:0.97 271:0.51 276:0.16 279:0.86 283:0.71 285:0.62 287:0.95 297:0.36 300:0.13 +1 7:0.81 8:0.75 9:0.80 11:0.64 12:0.69 17:0.59 21:0.05 27:0.34 28:0.66 30:0.14 32:0.72 34:0.50 36:0.35 37:0.36 39:0.85 41:0.82 43:0.56 55:0.45 66:0.16 69:0.94 70:0.93 74:0.61 81:0.87 83:0.76 85:0.91 91:0.35 96:0.80 98:0.52 101:0.73 104:0.80 108:0.97 120:0.83 121:0.90 123:0.91 124:0.92 126:0.32 127:0.70 129:0.89 131:0.97 133:0.92 135:0.87 140:0.87 144:0.57 145:1.00 146:0.60 147:0.87 149:0.88 150:0.72 151:0.87 153:0.48 154:0.45 155:0.67 157:0.90 159:0.93 161:0.90 162:0.73 164:0.77 166:0.88 167:0.54 172:0.92 173:0.75 177:0.05 179:0.12 181:0.55 182:0.94 187:0.21 192:0.59 195:0.99 202:0.69 204:0.89 208:0.64 212:0.78 215:0.91 216:0.84 220:0.62 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 233:0.76 235:0.05 241:0.37 242:0.80 243:0.23 245:0.97 246:0.61 247:0.47 248:0.78 253:0.79 255:0.79 256:0.71 257:0.65 259:0.21 260:0.83 265:0.30 266:0.92 267:0.91 268:0.72 271:0.51 276:0.97 283:0.82 285:0.62 287:0.95 297:0.36 300:0.94 +2 1:0.74 7:0.81 8:0.73 9:0.80 11:0.64 12:0.69 17:0.98 20:0.84 27:0.11 28:0.66 30:0.31 32:0.80 34:0.63 36:0.90 37:0.63 39:0.85 41:0.88 43:0.98 55:0.52 60:0.87 66:0.98 69:0.05 70:0.71 74:0.98 77:0.70 78:0.90 81:0.95 83:0.82 85:0.96 91:0.64 96:0.88 98:0.99 100:0.83 101:0.86 104:0.74 106:0.81 108:0.05 111:0.87 114:0.98 117:0.86 120:0.80 121:0.90 122:0.43 123:0.05 126:0.54 127:0.92 129:0.05 131:0.96 135:0.84 140:0.45 144:0.57 145:0.47 146:0.89 147:0.91 149:0.96 150:0.98 151:0.91 152:0.80 153:0.71 154:0.98 155:0.67 157:0.97 158:0.92 159:0.49 161:0.90 162:0.86 164:0.69 165:0.92 166:0.94 167:0.47 169:0.81 172:0.94 173:0.16 176:0.73 177:0.38 179:0.24 181:0.55 182:0.94 186:0.90 187:0.21 191:0.73 192:0.59 195:0.64 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.92 215:0.96 216:0.09 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 232:0.88 233:0.76 235:0.05 241:0.03 242:0.41 243:0.98 246:0.93 247:0.60 248:0.87 253:0.92 255:0.79 256:0.58 259:0.21 260:0.81 261:0.84 265:0.99 266:0.27 267:0.23 268:0.63 271:0.51 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.98 297:0.36 299:0.88 300:0.55 +1 11:0.64 12:0.69 17:0.26 21:0.76 27:0.45 28:0.66 30:0.28 34:0.68 36:0.23 37:0.50 39:0.85 43:0.69 55:0.71 66:0.43 69:0.88 70:0.75 74:0.65 81:0.30 85:0.85 91:0.23 98:0.39 101:0.74 108:0.86 122:0.51 123:0.86 124:0.75 126:0.32 127:0.32 129:0.59 131:0.61 133:0.76 135:0.93 144:0.57 145:0.49 146:0.41 147:0.05 149:0.63 150:0.28 154:0.59 157:0.89 159:0.67 161:0.90 166:0.88 167:0.51 172:0.45 173:0.81 177:0.05 178:0.55 179:0.59 181:0.55 182:0.78 187:0.68 212:0.48 215:0.46 216:0.77 222:0.84 227:0.61 229:0.61 231:0.89 235:0.95 241:0.18 242:0.40 243:0.12 245:0.60 246:0.61 247:0.55 253:0.50 257:0.65 259:0.21 265:0.41 266:0.71 267:0.65 271:0.51 276:0.50 287:0.61 297:0.36 300:0.55 +0 8:0.86 10:0.90 11:0.45 12:0.69 17:0.01 18:0.69 21:0.78 27:0.28 29:0.56 30:0.15 34:0.80 36:0.73 37:0.46 39:0.76 43:0.09 45:0.73 66:0.82 69:0.55 70:0.68 71:1.00 74:0.29 86:0.74 91:0.66 98:0.82 101:0.47 108:0.43 122:0.57 123:0.41 127:0.31 129:0.05 131:0.61 135:0.49 139:0.68 145:0.45 146:0.73 147:0.77 149:0.10 154:0.45 157:0.61 159:0.29 166:0.61 170:0.82 172:0.48 173:0.66 174:0.86 177:0.88 178:0.55 179:0.75 182:0.61 187:0.87 191:0.91 197:0.88 202:0.95 212:0.50 216:0.64 222:0.35 227:0.61 229:0.61 231:0.68 235:0.84 239:0.88 241:0.71 242:0.14 243:0.83 244:0.83 246:0.61 247:0.67 253:0.26 254:0.94 259:0.21 265:0.82 266:0.47 267:0.76 271:0.64 276:0.37 287:0.61 300:0.43 +0 10:0.89 11:0.56 12:0.69 17:0.28 18:0.63 21:0.78 27:0.45 29:0.56 30:0.91 34:0.50 36:0.17 37:0.50 39:0.76 43:0.78 45:0.73 55:0.42 66:0.69 69:0.76 70:0.13 71:1.00 74:0.41 86:0.73 91:0.04 98:0.10 101:0.65 108:0.59 122:0.65 123:0.57 127:0.07 129:0.05 131:0.61 135:0.73 139:0.70 144:0.57 145:0.41 146:0.19 147:0.25 149:0.61 154:0.70 157:0.61 159:0.10 166:0.61 170:0.82 172:0.21 173:0.62 174:0.79 177:0.88 178:0.55 179:0.08 182:0.61 187:0.68 197:0.83 212:0.61 216:0.77 222:0.35 227:0.61 229:0.61 231:0.62 235:0.52 239:0.88 241:0.15 242:0.63 243:0.73 246:0.61 247:0.30 253:0.36 254:0.74 259:0.21 265:0.11 266:0.17 267:0.28 271:0.64 276:0.23 287:0.61 300:0.13 +0 1:0.56 9:0.70 10:0.90 11:0.45 12:0.69 17:0.16 18:0.79 21:0.71 23:0.95 27:0.44 29:0.56 30:0.37 31:0.87 34:0.30 36:1.00 37:0.53 39:0.76 43:0.57 45:0.83 62:0.96 64:0.77 66:0.43 69:0.30 70:0.44 71:1.00 74:0.32 75:0.96 79:0.87 81:0.38 83:0.56 86:0.75 91:0.22 97:0.89 98:0.45 99:0.83 101:0.47 108:0.24 122:0.93 123:0.23 124:0.59 126:0.32 127:0.51 128:0.95 129:0.05 131:0.61 133:0.45 135:1.00 137:0.87 139:0.75 140:0.87 145:0.38 146:0.60 147:0.65 149:0.47 150:0.33 151:0.52 153:0.48 154:0.15 155:0.49 157:0.61 159:0.58 162:0.53 163:0.81 165:0.65 166:0.61 168:0.95 170:0.82 172:0.48 173:0.25 174:0.89 177:0.88 178:0.48 179:0.64 182:0.61 187:0.39 190:0.95 192:0.45 196:0.88 197:0.91 201:0.54 202:0.58 205:0.95 208:0.50 212:0.22 216:0.50 219:0.90 220:0.93 222:0.35 226:0.96 227:0.61 229:0.61 231:0.67 235:0.88 236:0.92 239:0.90 241:0.21 242:0.33 243:0.57 244:0.83 245:0.75 246:0.61 247:0.74 248:0.51 253:0.29 254:0.94 255:0.69 256:0.45 259:0.21 265:0.47 266:0.75 267:0.44 268:0.46 271:0.64 276:0.46 279:0.75 284:0.88 285:0.50 287:0.61 292:0.88 300:0.33 +0 10:0.90 11:0.45 12:0.69 17:0.06 18:0.78 21:0.40 27:0.32 29:0.56 30:0.60 34:0.55 36:0.70 37:0.64 39:0.76 43:0.30 45:0.83 66:0.34 69:0.37 70:0.46 71:1.00 74:0.29 81:0.28 86:0.74 91:0.10 98:0.44 101:0.47 104:0.98 106:0.81 108:0.29 122:0.94 123:0.28 124:0.70 126:0.24 127:0.28 129:0.05 131:0.61 133:0.62 135:0.90 139:0.71 146:0.63 147:0.68 149:0.47 150:0.30 154:0.22 157:0.61 159:0.50 163:0.81 166:0.76 170:0.82 172:0.37 173:0.28 174:0.90 177:0.88 178:0.55 179:0.60 182:0.61 187:0.95 197:0.91 206:0.81 212:0.19 215:0.67 216:0.55 222:0.35 227:0.61 229:0.61 231:0.73 235:0.42 239:0.89 241:0.12 242:0.45 243:0.50 244:0.93 245:0.62 246:0.61 247:0.60 253:0.26 259:0.21 265:0.46 266:0.75 267:0.79 271:0.64 276:0.46 287:0.61 297:0.36 300:0.43 +0 10:0.93 11:0.53 12:0.69 17:0.81 18:0.75 21:0.78 27:0.24 29:0.56 30:0.54 32:0.65 34:0.90 36:0.51 37:0.65 39:0.76 43:0.29 45:0.87 66:0.82 69:0.79 70:0.57 71:1.00 74:0.50 77:0.89 86:0.80 91:0.33 98:0.73 101:0.65 108:0.63 122:0.65 123:0.60 124:0.37 127:0.44 129:0.05 131:0.61 133:0.36 135:0.77 139:0.75 144:0.57 145:0.77 146:0.74 147:0.05 149:0.23 154:0.49 157:0.90 159:0.44 166:0.61 170:0.82 172:0.70 173:0.84 174:0.89 177:0.05 178:0.55 179:0.56 182:0.82 187:0.39 195:0.66 197:0.91 212:0.72 216:0.76 222:0.35 227:0.61 229:0.61 231:0.75 235:0.12 239:0.91 241:0.05 242:0.18 243:0.10 244:0.61 245:0.56 246:0.61 247:0.74 253:0.41 254:0.90 257:0.93 259:0.21 265:0.73 266:0.47 267:0.52 271:0.64 276:0.56 282:0.74 287:0.61 300:0.43 +0 8:0.94 9:0.71 10:0.86 12:0.69 17:0.59 21:0.59 23:0.99 27:0.72 29:0.56 30:0.40 31:0.89 34:0.18 36:0.27 37:0.78 39:0.76 43:0.10 55:0.92 62:0.96 66:0.08 69:0.21 70:0.88 71:1.00 74:0.33 75:0.95 76:0.85 77:0.70 79:0.89 81:0.32 83:0.56 91:0.44 96:0.69 98:0.22 108:0.93 114:0.64 122:0.83 123:0.93 124:0.95 126:0.32 127:0.98 128:0.98 129:0.05 131:0.83 133:0.94 135:0.30 137:0.89 140:0.90 145:0.44 146:0.39 147:0.46 149:0.13 150:0.31 151:0.61 153:0.90 154:0.21 155:0.53 157:0.61 159:0.93 162:0.63 163:0.98 166:0.75 168:0.98 170:0.82 172:0.21 173:0.08 174:0.88 175:0.75 176:0.59 177:0.70 179:0.61 182:0.61 190:0.98 191:0.77 192:0.48 195:0.98 197:0.85 201:0.55 202:0.77 205:0.99 208:0.50 212:0.16 216:0.11 220:0.74 222:0.35 226:0.96 227:0.82 229:0.61 231:0.70 235:0.74 236:0.92 239:0.86 241:0.80 242:0.57 243:0.21 244:0.93 245:0.60 246:0.61 247:0.67 248:0.60 253:0.49 254:0.94 255:0.69 256:0.79 257:0.65 259:0.21 265:0.24 266:0.94 267:0.94 268:0.78 271:0.64 276:0.62 277:0.87 282:0.71 284:0.88 285:0.60 287:0.61 290:0.64 300:0.84 +1 8:0.69 11:0.49 12:0.69 17:0.36 21:0.78 27:0.25 29:0.56 30:0.60 34:0.94 36:0.55 37:0.41 39:0.76 43:0.26 45:0.83 66:0.44 69:0.85 70:0.44 71:1.00 74:0.43 76:0.94 91:0.46 96:0.80 98:0.20 101:0.47 108:0.70 122:0.82 123:0.68 124:0.67 127:0.22 129:0.59 131:0.83 133:0.64 135:0.63 140:0.95 144:0.57 145:0.41 146:0.36 147:0.42 149:0.42 151:0.65 153:0.48 154:0.18 157:0.92 158:0.72 159:0.54 162:0.47 166:0.61 170:0.82 172:0.41 173:0.74 175:0.91 177:0.38 178:0.53 179:0.50 182:0.81 187:0.39 190:0.98 191:0.75 202:0.72 212:0.72 216:0.81 222:0.35 227:0.82 229:0.61 231:0.75 232:0.91 235:0.52 241:0.41 242:0.45 243:0.57 244:0.93 245:0.60 246:0.61 247:0.47 248:0.62 253:0.72 256:0.45 257:0.84 259:0.21 265:0.21 266:0.27 267:0.23 268:0.46 271:0.64 276:0.41 277:0.87 285:0.46 287:0.61 300:0.33 +0 1:0.56 10:0.86 11:0.49 12:0.69 17:0.28 18:0.66 21:0.76 27:0.45 29:0.56 30:0.21 32:0.64 34:0.83 36:0.20 37:0.50 39:0.76 43:0.19 45:0.73 55:0.92 64:0.77 66:0.07 69:0.71 70:0.96 71:1.00 74:0.42 75:0.97 77:0.70 81:0.45 83:0.56 86:0.69 91:0.10 97:0.89 98:0.22 99:0.83 101:0.47 108:0.54 114:0.61 122:0.83 123:0.85 124:0.90 126:0.51 127:0.56 129:0.05 131:0.61 133:0.88 135:0.83 139:0.70 144:0.57 145:0.50 146:0.41 147:0.48 149:0.19 150:0.37 154:0.45 155:0.46 157:0.61 158:0.73 159:0.79 165:0.65 166:0.75 170:0.82 172:0.37 173:0.52 174:0.83 176:0.59 177:0.88 178:0.55 179:0.59 182:0.61 187:0.68 192:0.44 195:0.92 197:0.84 201:0.53 208:0.61 212:0.18 216:0.77 219:0.91 222:0.35 226:0.95 227:0.61 229:0.61 231:0.74 235:0.98 236:0.94 239:0.87 241:0.51 242:0.58 243:0.40 244:0.61 245:0.62 246:0.61 247:0.74 253:0.46 254:0.99 259:0.21 265:0.23 266:0.91 267:0.28 271:0.64 276:0.58 277:0.87 279:0.75 282:0.73 287:0.61 290:0.61 292:0.86 300:0.84 +0 10:0.85 11:0.45 12:0.69 17:0.89 18:0.62 21:0.13 27:0.72 29:0.56 30:0.56 34:0.36 36:0.79 37:0.24 39:0.76 43:0.10 45:0.66 55:0.84 66:0.84 69:0.58 70:0.58 71:1.00 74:0.33 81:0.30 86:0.66 91:0.83 96:0.74 98:0.90 101:0.47 108:0.45 114:0.74 122:0.83 123:0.43 126:0.24 127:0.48 129:0.05 131:0.83 132:0.97 135:0.44 139:0.63 140:0.80 145:0.89 146:0.74 147:0.78 149:0.12 150:0.33 151:0.57 153:0.48 154:0.28 157:0.61 159:0.31 162:0.62 163:0.75 166:0.75 170:0.82 172:0.54 173:0.72 174:0.79 176:0.56 177:0.88 178:0.42 179:0.77 182:0.61 190:0.98 197:0.82 202:0.50 212:0.39 216:0.04 220:0.74 222:0.35 227:0.82 229:0.61 231:0.70 232:0.74 235:0.12 239:0.84 241:0.92 242:0.42 243:0.85 244:0.83 246:0.61 247:0.67 248:0.56 253:0.61 254:0.84 256:0.45 265:0.90 266:0.47 267:0.28 268:0.46 271:0.64 276:0.45 285:0.46 287:0.61 290:0.74 300:0.43 +0 1:0.59 10:0.87 11:0.53 12:0.69 17:0.66 18:0.62 21:0.40 27:0.53 29:0.56 30:0.85 32:0.65 34:0.90 36:0.52 37:0.31 39:0.76 43:0.56 45:0.92 66:0.92 69:0.32 70:0.36 71:1.00 74:0.67 77:0.89 81:0.46 83:0.56 86:0.70 91:0.46 98:0.90 99:0.83 101:0.65 108:0.25 114:0.76 122:0.55 123:0.24 126:0.32 127:0.46 129:0.05 131:0.61 135:0.68 139:0.66 144:0.57 145:0.67 146:0.70 147:0.75 149:0.75 150:0.40 154:0.52 155:0.47 157:0.97 159:0.28 165:0.65 166:0.87 170:0.82 172:0.77 173:0.50 174:0.79 176:0.62 177:0.88 178:0.55 179:0.48 182:0.93 187:0.39 192:0.46 195:0.57 197:0.82 201:0.56 202:0.36 208:0.50 212:0.84 215:0.67 216:0.28 222:0.35 227:0.61 229:0.61 231:0.80 235:0.52 239:0.85 241:0.05 242:0.58 243:0.92 244:0.61 246:0.84 247:0.55 253:0.60 254:0.93 259:0.21 265:0.90 266:0.27 267:0.28 271:0.64 276:0.65 282:0.74 287:0.61 290:0.76 297:0.36 300:0.33 +0 10:0.83 11:0.45 12:0.69 17:0.51 18:0.61 21:0.78 27:0.72 29:0.56 30:0.38 34:0.22 36:0.38 39:0.76 43:0.20 45:0.73 55:0.92 66:0.29 69:0.94 70:0.78 71:1.00 74:0.27 86:0.63 91:0.68 98:0.25 101:0.47 108:0.94 122:0.83 123:0.94 124:0.84 127:0.23 129:0.81 131:0.61 133:0.83 135:0.85 139:0.61 146:0.13 147:0.18 149:0.17 154:0.13 157:0.61 159:0.78 163:0.75 166:0.61 170:0.82 172:0.27 173:0.82 174:0.83 175:0.91 177:0.38 178:0.55 179:0.62 182:0.61 197:0.81 202:0.36 212:0.14 216:0.06 222:0.35 227:0.61 229:0.61 231:0.64 235:0.22 239:0.82 241:0.81 242:0.45 243:0.26 244:0.93 245:0.49 246:0.61 247:0.60 253:0.25 257:0.84 259:0.21 265:0.27 266:0.71 267:0.19 271:0.64 276:0.38 277:0.87 287:0.61 300:0.55 +0 8:0.81 9:0.71 11:0.42 12:0.69 17:0.49 21:0.78 27:0.30 29:0.56 30:0.89 34:0.69 36:0.63 37:0.57 39:0.76 43:0.08 55:0.59 66:0.80 69:0.75 70:0.25 71:1.00 74:0.48 76:0.85 77:0.70 83:0.56 91:0.42 96:0.73 97:0.89 98:0.67 108:0.68 120:0.59 122:0.83 123:0.66 124:0.39 127:0.30 129:0.59 131:0.91 133:0.47 135:0.20 140:0.94 144:0.57 145:0.50 146:0.17 147:0.22 149:0.22 151:0.61 153:0.48 154:0.22 155:0.54 157:0.61 158:0.75 159:0.37 162:0.51 164:0.55 166:0.61 170:0.82 172:0.77 173:0.79 175:0.91 177:0.38 178:0.50 179:0.36 182:0.93 187:0.39 190:0.95 191:0.70 192:0.56 195:0.70 202:0.69 208:0.50 212:0.91 216:0.40 220:0.91 222:0.35 227:0.82 229:0.96 231:0.80 235:0.31 241:0.27 242:0.80 243:0.14 244:0.93 245:0.70 246:0.61 247:0.39 248:0.59 253:0.52 254:0.74 255:0.70 256:0.58 257:0.84 259:0.21 260:0.60 265:0.68 266:0.27 267:0.19 268:0.59 271:0.64 276:0.66 277:0.87 279:0.75 282:0.71 283:0.67 285:0.50 287:0.87 300:0.33 +0 10:0.80 12:0.69 17:0.45 21:0.78 27:0.34 29:0.56 30:0.21 34:0.58 36:0.41 37:0.46 39:0.76 43:0.09 55:0.84 66:0.20 69:0.66 70:0.37 71:1.00 74:0.25 91:0.01 98:0.15 104:0.99 106:0.81 108:0.50 122:0.41 123:0.48 124:0.81 127:0.52 129:0.89 131:0.61 133:0.78 135:0.34 145:0.54 146:0.31 147:0.38 149:0.09 154:0.08 157:0.61 159:0.84 163:0.96 166:0.61 170:0.82 172:0.10 173:0.40 174:0.79 175:0.97 177:0.05 178:0.55 179:0.96 182:0.61 187:0.98 197:0.80 206:0.81 212:0.42 216:0.53 222:0.35 227:0.61 229:0.61 231:0.64 235:0.60 239:0.80 241:0.10 242:0.45 243:0.40 244:0.97 245:0.39 246:0.61 247:0.35 253:0.23 257:0.93 259:0.21 265:0.16 266:0.23 267:0.59 271:0.64 276:0.23 277:0.95 287:0.61 300:0.25 +0 7:0.64 10:0.81 11:0.45 12:0.69 17:0.75 18:0.58 21:0.76 27:0.39 29:0.56 30:0.56 32:0.62 34:0.75 36:0.32 37:0.67 39:0.76 43:0.14 45:0.66 55:0.71 66:0.10 69:0.95 70:0.83 71:1.00 74:0.40 76:0.85 77:0.70 81:0.24 86:0.60 91:0.09 98:0.30 101:0.47 108:0.77 117:0.86 122:0.83 123:0.89 124:0.93 126:0.24 127:0.72 129:0.05 131:0.61 133:0.92 135:0.96 139:0.59 145:0.82 146:0.78 147:0.54 149:0.33 150:0.26 154:0.10 157:0.61 159:0.91 166:0.76 170:0.82 172:0.63 173:0.83 174:0.79 177:0.05 178:0.55 179:0.33 182:0.61 187:0.21 195:0.98 197:0.80 212:0.56 215:0.46 216:0.89 222:0.35 227:0.61 229:0.61 230:0.64 231:0.79 232:1.00 233:0.66 235:0.95 239:0.81 241:0.02 242:0.81 243:0.28 244:0.93 245:0.80 246:0.61 247:0.55 253:0.35 257:0.93 259:0.21 265:0.30 266:0.96 267:0.19 271:0.64 276:0.80 282:0.71 287:0.61 297:0.36 300:0.94 +0 7:0.64 8:0.63 11:0.49 12:0.69 17:0.77 21:0.30 27:0.52 29:0.56 30:0.32 32:0.62 34:0.56 36:0.90 37:0.62 39:0.76 43:0.34 45:0.66 55:0.84 66:0.12 69:0.70 70:0.79 71:1.00 74:0.41 81:0.28 91:0.54 98:0.43 101:0.47 104:0.87 106:0.81 108:0.80 120:0.62 123:0.79 124:0.96 126:0.24 127:0.86 129:0.93 131:0.83 133:0.96 135:0.86 140:0.45 144:0.57 145:0.44 146:0.73 147:0.77 149:0.37 150:0.31 151:0.53 153:0.48 154:0.25 157:0.76 159:0.93 162:0.83 164:0.74 166:0.76 170:0.82 172:0.70 173:0.33 175:0.91 177:0.38 179:0.21 182:0.72 187:0.39 190:0.98 191:0.79 195:0.98 202:0.64 206:0.81 212:0.14 215:0.72 216:0.15 220:0.97 222:0.35 227:0.82 229:0.61 230:0.64 231:0.79 233:0.76 235:0.31 241:0.15 242:0.45 243:0.31 244:0.93 245:0.88 246:0.61 247:0.67 248:0.55 253:0.35 256:0.68 257:0.84 259:0.21 260:0.62 265:0.45 266:0.95 267:0.36 268:0.70 271:0.64 276:0.91 277:0.87 283:0.67 285:0.46 287:0.61 297:0.36 300:0.70 +0 8:0.98 10:0.84 12:0.69 17:0.91 21:0.64 27:0.40 29:0.56 30:0.91 34:0.62 36:0.51 37:0.90 39:0.76 43:0.10 55:0.84 66:0.69 69:0.55 70:0.13 71:1.00 74:0.30 77:0.70 81:0.25 91:0.83 96:0.74 98:0.71 108:0.43 114:0.63 122:0.85 123:0.41 126:0.24 127:0.22 129:0.05 131:0.83 135:0.17 140:0.45 145:0.70 146:0.53 147:0.59 149:0.08 150:0.27 151:0.56 153:0.78 154:0.18 157:0.61 159:0.19 162:0.76 163:0.93 166:0.75 170:0.82 172:0.21 173:0.73 174:0.79 175:0.75 176:0.56 177:0.70 179:0.91 182:0.61 190:0.98 191:0.80 195:0.60 197:0.82 202:0.64 212:0.61 216:0.09 220:0.82 222:0.35 227:0.82 229:0.61 231:0.70 232:0.75 235:0.74 239:0.83 241:0.89 242:0.63 243:0.73 244:0.93 246:0.61 247:0.30 248:0.55 253:0.55 254:0.90 256:0.64 257:0.65 259:0.21 265:0.71 266:0.17 267:0.44 268:0.68 271:0.64 276:0.21 277:0.69 282:0.71 285:0.46 287:0.61 290:0.63 300:0.13 +0 10:0.82 11:0.45 12:0.69 17:0.88 18:0.59 21:0.78 27:0.69 29:0.56 30:0.45 34:0.98 36:0.24 37:0.45 39:0.76 43:0.20 45:0.66 66:0.49 69:0.95 70:0.25 71:1.00 74:0.25 86:0.61 91:0.10 98:0.26 101:0.47 108:0.89 122:0.76 123:0.89 124:0.48 127:0.24 129:0.05 131:0.61 133:0.37 135:0.34 139:0.59 145:0.80 146:0.45 147:0.05 149:0.12 154:0.13 157:0.61 159:0.55 163:0.99 166:0.61 170:0.82 172:0.16 173:0.96 174:0.79 175:0.75 177:0.05 178:0.55 179:0.93 182:0.61 187:0.21 197:0.80 212:0.61 216:0.18 222:0.35 227:0.61 229:0.61 231:0.62 235:0.88 239:0.81 241:0.21 242:0.63 243:0.29 244:0.93 245:0.39 246:0.61 247:0.30 253:0.23 257:0.93 259:0.21 265:0.28 266:0.17 267:0.28 271:0.64 276:0.15 277:0.69 287:0.61 300:0.18 +0 8:0.88 10:0.85 11:0.53 12:0.69 17:0.51 18:0.62 21:0.78 27:0.36 29:0.56 30:0.38 34:0.56 36:0.85 37:0.74 39:0.76 43:0.47 45:0.73 55:0.52 66:0.83 69:0.57 70:0.63 71:1.00 74:0.41 77:0.70 86:0.66 91:0.63 98:0.68 101:0.65 108:0.44 122:0.83 123:0.42 127:0.20 129:0.05 131:0.61 135:0.91 139:0.64 144:0.57 145:0.92 146:0.60 147:0.65 149:0.26 154:0.35 157:0.80 159:0.22 166:0.61 170:0.82 172:0.51 173:0.67 174:0.79 177:0.88 178:0.55 179:0.56 182:0.74 187:0.39 195:0.64 197:0.82 202:0.71 212:0.70 216:0.46 222:0.35 227:0.61 229:0.61 231:0.68 235:0.80 239:0.84 241:0.32 242:0.71 243:0.84 244:0.83 246:0.61 247:0.47 253:0.36 254:0.86 259:0.21 265:0.69 266:0.27 267:0.19 271:0.64 276:0.38 282:0.71 287:0.61 300:0.43 +0 10:0.97 11:0.45 12:0.69 17:0.67 18:0.89 21:0.59 27:0.49 29:0.56 30:0.38 33:0.97 34:0.20 36:0.58 37:0.66 39:0.76 43:0.22 44:0.88 45:0.97 66:0.45 69:0.89 70:0.82 71:1.00 74:0.35 75:0.96 77:0.70 81:0.32 86:0.85 91:0.11 98:0.60 101:0.47 102:0.96 108:0.78 114:0.64 117:0.86 122:0.94 123:0.77 124:0.84 126:0.32 127:0.54 129:0.89 131:0.61 133:0.87 135:0.75 139:0.84 145:0.80 146:0.96 147:0.97 149:0.65 150:0.31 154:0.20 157:0.61 158:0.72 159:0.89 166:0.75 170:0.82 172:0.90 173:0.69 174:0.99 175:0.75 176:0.59 177:0.70 178:0.55 179:0.18 182:0.61 187:0.39 192:0.44 195:0.97 197:0.98 201:0.55 212:0.48 216:0.43 222:0.35 226:0.95 227:0.61 229:0.61 231:0.68 232:0.94 235:0.74 236:0.92 239:0.97 241:0.12 242:0.29 243:0.58 244:0.83 245:0.93 246:0.61 247:0.92 253:0.49 254:0.80 257:0.65 259:0.21 265:0.61 266:0.92 267:0.44 271:0.64 276:0.91 277:0.69 282:0.72 287:0.61 290:0.64 291:0.97 300:0.94 +0 12:0.86 17:0.64 21:0.78 27:0.72 34:0.59 36:0.36 37:0.34 43:0.24 66:0.36 69:0.86 91:0.54 98:0.09 108:0.73 123:0.71 127:0.06 129:0.05 135:0.76 145:0.87 146:0.05 147:0.05 149:0.10 154:0.17 159:0.05 172:0.05 173:0.96 177:0.05 178:0.55 187:0.68 216:0.16 235:0.84 241:0.27 243:0.51 259:0.21 265:0.09 267:0.52 +1 12:0.86 17:0.38 21:0.78 27:0.25 30:0.58 34:0.44 36:0.21 37:0.90 43:0.46 55:0.98 66:0.18 69:0.44 70:0.44 91:0.42 98:0.11 106:0.81 108:0.34 123:0.32 124:0.90 127:0.37 129:0.96 133:0.90 135:0.37 145:0.42 146:0.22 147:0.28 149:0.37 154:0.35 159:0.83 163:0.99 172:0.16 173:0.17 177:0.05 178:0.55 179:0.82 187:0.21 206:0.81 212:0.37 216:0.13 235:0.22 241:0.30 242:0.82 243:0.39 245:0.43 247:0.12 259:0.21 265:0.11 266:0.47 267:0.28 276:0.37 300:0.33 +1 12:0.86 17:0.49 21:0.78 27:0.25 30:0.45 34:0.25 36:0.21 37:0.90 43:0.24 55:0.92 66:0.16 69:0.86 70:0.42 91:0.56 98:0.27 108:0.80 123:0.94 124:0.88 127:0.52 129:0.95 133:0.87 135:0.20 145:0.42 146:0.05 147:0.05 149:0.30 154:0.17 159:0.83 163:0.89 172:0.16 173:0.70 177:0.05 178:0.55 179:0.83 187:0.21 212:0.15 216:0.13 235:0.22 241:0.37 242:0.82 243:0.17 245:0.46 247:0.12 259:0.21 265:0.23 266:0.63 267:0.73 276:0.39 300:0.33 +1 12:0.86 17:0.22 21:0.78 27:0.21 30:0.76 34:0.49 36:0.28 37:0.81 43:0.43 55:0.84 66:0.36 69:0.47 70:0.15 91:0.40 98:0.25 106:0.81 108:0.70 123:0.68 124:0.52 127:0.23 129:0.59 133:0.47 135:0.84 145:0.92 146:0.05 147:0.05 149:0.24 154:0.33 159:0.34 163:0.96 172:0.10 173:0.44 177:0.05 178:0.55 179:0.96 187:0.68 206:0.81 212:0.95 216:0.45 235:0.22 241:0.15 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.27 266:0.12 267:0.59 276:0.15 300:0.13 +0 12:0.86 17:0.22 21:0.78 27:0.02 30:0.76 34:0.73 36:0.27 37:0.92 43:0.36 55:0.96 66:0.13 69:0.62 70:0.15 91:0.51 98:0.07 108:0.47 123:0.46 124:0.75 127:0.40 129:0.81 133:0.67 135:0.68 145:0.87 146:0.05 147:0.05 149:0.23 154:0.27 159:0.54 163:0.97 172:0.05 173:0.56 177:0.05 178:0.55 179:0.99 187:0.39 202:0.49 212:0.95 216:0.99 235:0.42 241:0.62 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.07 266:0.12 267:0.65 276:0.17 300:0.13 +0 12:0.86 17:0.69 21:0.78 27:0.71 34:0.62 36:0.33 37:0.32 43:0.24 66:0.36 69:0.86 91:0.66 98:0.09 108:0.72 123:0.71 127:0.06 129:0.05 135:0.74 145:0.65 146:0.05 147:0.05 149:0.10 154:0.17 159:0.05 172:0.05 173:0.96 177:0.05 178:0.55 187:0.68 216:0.10 235:0.95 241:0.32 243:0.51 259:0.21 265:0.09 267:0.44 +0 12:0.86 17:0.32 21:0.78 27:0.21 30:0.95 34:0.49 36:0.37 37:0.81 43:0.24 55:0.84 66:0.54 69:0.85 91:0.38 98:0.27 108:0.71 123:0.69 127:0.11 129:0.05 135:0.86 145:0.92 146:0.36 147:0.42 149:0.16 154:0.17 159:0.14 172:0.10 173:0.87 177:0.05 178:0.55 179:0.76 187:0.39 216:0.45 235:0.22 241:0.35 243:0.63 259:0.21 265:0.29 267:0.69 300:0.08 +0 12:0.86 17:0.34 21:0.78 27:0.21 34:0.23 36:0.24 37:0.81 43:0.22 66:0.36 69:0.90 91:0.27 98:0.07 108:0.79 123:0.78 127:0.06 129:0.05 135:0.83 145:0.92 146:0.05 147:0.05 149:0.09 154:0.15 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.39 216:0.45 235:0.22 241:0.39 243:0.51 259:0.21 265:0.07 267:0.28 +0 8:0.91 12:0.86 17:0.49 21:0.78 27:0.72 34:0.24 36:0.36 43:0.22 66:0.36 69:0.89 91:0.72 98:0.06 108:0.79 123:0.77 127:0.05 129:0.05 135:0.88 145:0.98 146:0.05 147:0.05 149:0.09 154:0.15 159:0.05 172:0.05 173:0.85 177:0.05 178:0.55 202:0.50 216:0.04 235:0.31 241:0.94 243:0.51 259:0.21 265:0.07 267:0.28 +0 12:0.86 17:0.61 21:0.78 27:0.25 30:0.85 34:0.37 36:0.19 37:0.90 43:0.26 55:0.92 66:0.09 69:0.82 70:0.28 91:0.38 98:0.23 108:0.66 123:0.82 124:0.95 127:0.42 129:0.99 133:0.95 135:0.24 145:0.42 146:0.52 147:0.58 149:0.41 154:0.18 159:0.85 163:0.96 172:0.16 173:0.58 177:0.05 178:0.55 179:0.65 187:0.21 212:0.37 216:0.13 235:0.22 241:0.35 242:0.82 243:0.27 245:0.50 247:0.12 259:0.21 265:0.24 266:0.38 267:0.52 276:0.53 300:0.25 +1 12:0.86 17:0.59 21:0.78 27:0.21 30:0.95 34:0.76 36:0.29 37:0.81 43:0.29 55:0.59 66:0.54 69:0.76 91:0.49 98:0.23 108:0.59 123:0.57 127:0.11 129:0.05 135:0.79 145:0.92 146:0.23 147:0.29 149:0.19 154:0.21 159:0.11 172:0.10 173:0.92 177:0.05 178:0.55 179:0.64 187:0.39 216:0.45 235:0.97 241:0.27 243:0.63 259:0.21 265:0.25 267:0.44 300:0.08 +2 1:0.74 6:0.88 7:0.81 8:0.76 9:0.80 11:0.92 12:0.37 17:0.43 18:0.94 20:0.98 21:0.30 22:0.93 27:0.21 28:0.76 29:0.86 30:0.70 31:0.95 34:0.49 36:0.74 37:0.74 39:0.56 41:0.82 43:0.97 45:0.83 47:0.93 60:0.87 64:0.77 66:0.95 69:0.15 70:0.50 71:0.63 74:0.92 76:0.85 78:0.88 79:0.94 81:0.61 83:0.91 85:0.91 86:0.98 91:0.33 96:0.84 98:0.82 100:0.92 101:0.87 104:0.84 106:0.81 108:0.12 111:0.89 114:0.65 117:0.86 120:0.74 121:0.90 122:0.80 123:0.12 126:0.54 127:0.31 129:0.05 135:0.51 137:0.95 139:0.98 140:0.45 144:0.85 146:0.82 147:0.85 149:0.95 150:0.77 151:0.92 152:0.90 153:0.72 154:0.97 155:0.67 158:0.92 159:0.38 161:0.76 162:0.67 164:0.64 165:0.93 167:0.67 169:0.90 172:0.86 173:0.23 176:0.73 177:0.70 179:0.27 181:0.74 186:0.88 187:0.39 189:0.90 192:0.87 196:0.98 201:0.93 202:0.53 204:0.89 206:0.81 208:0.64 212:0.89 215:0.44 216:0.95 219:0.95 222:0.60 230:0.87 232:0.89 233:0.76 235:0.52 238:0.91 241:0.24 242:0.32 243:0.95 247:0.82 248:0.81 253:0.88 255:0.95 256:0.45 259:0.21 260:0.75 261:0.91 262:0.93 265:0.82 266:0.27 267:0.52 268:0.57 271:0.60 276:0.77 279:0.86 281:0.91 283:0.82 284:0.91 285:0.62 290:0.65 292:0.95 297:0.36 300:0.55 +3 1:0.74 6:0.98 7:0.81 8:0.96 9:0.80 11:0.92 12:0.37 17:0.05 18:0.91 20:0.98 21:0.22 27:0.18 28:0.76 29:0.86 30:0.70 31:0.99 32:0.80 34:0.88 36:0.92 37:0.85 39:0.56 41:0.97 43:0.95 44:0.93 45:0.77 48:0.97 60:0.99 64:0.77 66:0.92 69:0.19 70:0.48 71:0.63 74:0.88 77:0.70 78:0.95 79:0.99 81:0.67 83:0.91 85:0.88 86:0.96 91:0.84 96:0.95 98:0.94 100:0.99 101:0.87 104:0.58 108:0.15 111:0.99 114:0.71 120:0.90 121:0.90 122:0.57 123:0.15 126:0.54 127:0.62 129:0.05 135:0.72 137:0.99 139:0.97 140:0.45 144:0.85 145:0.42 146:0.82 147:0.85 149:0.91 150:0.80 151:0.99 152:0.96 153:0.94 154:0.95 155:0.67 158:0.92 159:0.38 161:0.76 162:0.81 164:0.90 165:0.93 167:0.91 169:0.98 172:0.77 173:0.33 176:0.73 177:0.70 179:0.53 181:0.74 186:0.94 187:0.21 189:0.98 191:0.78 192:0.87 195:0.62 196:1.00 201:0.93 202:0.84 204:0.89 208:0.64 212:0.61 215:0.51 216:0.72 219:0.95 222:0.60 230:0.87 232:0.77 233:0.76 235:0.42 238:0.98 241:0.76 242:0.27 243:0.92 247:0.79 248:0.94 253:0.96 255:0.95 256:0.89 259:0.21 260:0.90 261:0.98 265:0.94 266:0.54 267:0.23 268:0.89 271:0.60 276:0.65 279:0.86 281:0.91 282:0.88 283:0.82 284:0.99 285:0.62 290:0.71 292:0.95 297:0.36 299:0.88 300:0.43 +2 1:0.74 9:0.80 11:0.92 12:0.37 17:0.15 18:0.85 20:0.83 21:0.54 27:0.25 28:0.76 29:0.86 30:0.66 31:0.94 34:0.61 36:0.88 37:0.77 39:0.56 41:0.82 43:0.89 45:0.85 60:0.87 64:0.77 66:0.26 69:0.52 70:0.52 71:0.63 74:0.82 78:0.78 79:0.93 81:0.49 83:0.91 85:0.85 86:0.93 91:0.05 96:0.80 98:0.56 100:0.73 101:0.87 108:0.75 111:0.77 114:0.59 120:0.69 122:0.57 123:0.73 124:0.74 126:0.54 127:0.41 129:0.81 133:0.67 135:0.83 137:0.94 139:0.93 140:0.45 144:0.85 146:0.31 147:0.37 149:0.89 150:0.72 151:0.87 152:0.80 153:0.48 154:0.87 155:0.67 158:0.91 159:0.50 161:0.76 162:0.86 164:0.57 165:0.93 167:0.71 169:0.72 172:0.54 173:0.49 176:0.73 177:0.70 179:0.41 181:0.74 186:0.79 187:0.68 192:0.87 196:0.96 201:0.93 202:0.36 208:0.64 212:0.69 215:0.39 216:0.72 219:0.95 222:0.60 232:0.91 235:0.74 241:0.44 242:0.22 243:0.36 245:0.83 247:0.82 248:0.77 253:0.84 255:0.95 256:0.45 259:0.21 260:0.70 261:0.79 265:0.58 266:0.47 267:0.44 268:0.46 271:0.60 276:0.69 279:0.86 283:0.78 284:0.89 285:0.62 290:0.59 292:0.91 297:0.36 300:0.43 +1 1:0.74 11:0.92 12:0.37 17:0.32 18:0.73 21:0.59 27:0.45 28:0.76 29:0.86 30:0.87 32:0.69 34:0.79 36:0.21 37:0.50 39:0.56 43:0.81 45:0.85 64:0.77 66:0.49 69:0.70 70:0.48 71:0.63 74:0.71 77:0.70 81:0.47 83:0.91 85:0.80 86:0.82 91:0.09 98:0.45 101:0.87 108:0.53 114:0.58 122:0.51 123:0.51 124:0.56 126:0.54 127:0.34 129:0.59 133:0.46 135:0.66 139:0.78 144:0.85 145:0.50 146:0.58 147:0.64 149:0.81 150:0.71 154:0.77 155:0.67 159:0.49 161:0.76 165:0.93 167:0.64 172:0.54 173:0.67 176:0.73 177:0.70 178:0.55 179:0.54 181:0.74 187:0.68 192:0.87 195:0.75 201:0.93 208:0.64 212:0.59 215:0.39 216:0.77 222:0.60 235:0.80 241:0.12 242:0.41 243:0.60 245:0.76 247:0.60 253:0.44 259:0.21 265:0.47 266:0.63 267:0.28 271:0.60 276:0.44 282:0.77 290:0.58 297:0.36 300:0.70 +1 1:0.69 11:0.92 12:0.37 17:0.47 18:0.73 21:0.74 27:0.45 28:0.76 29:0.86 30:0.75 32:0.69 34:0.70 36:0.45 37:0.50 39:0.56 43:0.79 45:0.92 66:0.69 69:0.70 70:0.58 71:0.63 74:0.79 77:0.70 81:0.32 83:0.60 85:0.80 86:0.84 91:0.05 98:0.72 99:0.83 101:0.87 108:0.54 114:0.54 122:0.75 123:0.51 124:0.44 126:0.44 127:0.44 129:0.05 133:0.43 135:0.83 139:0.81 144:0.85 145:0.54 146:0.88 147:0.90 149:0.89 150:0.51 154:0.72 155:0.58 159:0.63 161:0.76 165:0.70 167:0.66 172:0.79 173:0.61 176:0.66 177:0.70 178:0.55 179:0.38 181:0.74 187:0.68 192:0.59 195:0.83 201:0.68 208:0.54 212:0.57 215:0.36 216:0.77 222:0.60 235:0.95 241:0.21 242:0.44 243:0.73 245:0.84 247:0.76 253:0.44 259:0.21 265:0.72 266:0.71 267:0.19 271:0.60 276:0.69 282:0.77 290:0.54 297:0.36 300:0.70 +1 1:0.74 6:0.87 8:0.72 9:0.80 11:0.93 12:0.37 17:0.18 18:0.82 20:0.91 21:0.30 27:0.06 28:0.76 29:0.86 30:0.42 31:0.95 32:0.78 34:0.68 36:0.99 37:0.82 39:0.56 41:0.93 43:0.74 44:0.93 45:0.85 60:0.87 64:0.77 66:0.49 69:0.83 70:0.77 71:0.63 74:0.77 77:0.70 78:0.84 79:0.94 81:0.61 83:0.91 85:0.80 86:0.91 91:0.25 96:0.88 98:0.61 100:0.88 101:0.97 108:0.84 111:0.84 114:0.65 120:0.79 122:0.75 123:0.83 124:0.57 126:0.54 127:0.30 129:0.59 133:0.46 135:0.89 137:0.95 139:0.94 140:0.45 144:0.85 145:0.88 146:0.41 147:0.48 149:0.65 150:0.77 151:0.80 152:0.91 153:0.48 154:0.65 155:0.67 158:0.91 159:0.67 161:0.76 162:0.69 164:0.80 165:0.93 167:0.72 169:0.85 172:0.70 173:0.70 176:0.73 177:0.70 179:0.32 181:0.74 186:0.85 187:0.39 189:0.88 190:0.77 192:0.87 195:0.90 196:0.97 201:0.93 202:0.74 208:0.64 212:0.60 215:0.44 216:0.92 219:0.95 220:0.62 222:0.60 235:0.52 238:0.89 241:0.49 242:0.15 243:0.45 245:0.88 247:0.88 248:0.80 253:0.87 255:0.95 256:0.75 259:0.21 260:0.78 261:0.87 265:0.62 266:0.71 267:0.59 268:0.76 271:0.60 276:0.69 279:0.86 282:0.86 283:0.78 284:0.96 285:0.62 290:0.65 292:0.91 297:0.36 300:0.70 +2 6:0.98 7:0.81 8:0.98 9:0.80 11:0.92 12:0.37 17:0.28 18:0.90 20:0.83 21:0.78 27:0.18 28:0.76 29:0.86 30:0.66 31:0.95 34:0.86 36:0.74 37:0.85 39:0.56 41:0.90 43:0.86 45:0.88 48:0.97 66:0.94 69:0.27 70:0.56 71:0.63 74:0.78 78:0.92 79:0.95 83:0.91 85:0.85 86:0.94 91:0.46 96:0.84 98:0.97 100:0.97 101:0.87 104:0.58 108:0.21 111:0.95 120:0.77 121:0.90 122:0.75 123:0.20 127:0.77 129:0.05 135:0.68 137:0.95 139:0.93 140:0.87 144:0.85 145:0.39 146:0.93 147:0.94 149:0.88 151:0.87 152:0.96 153:0.48 154:0.83 155:0.67 159:0.56 161:0.76 162:0.62 164:0.72 167:0.77 169:0.98 172:0.84 173:0.27 177:0.70 178:0.48 179:0.45 181:0.74 186:0.92 187:0.21 189:1.00 192:0.87 196:0.97 202:0.68 204:0.89 208:0.64 212:0.42 216:0.72 222:0.60 230:0.87 232:0.77 233:0.76 235:0.12 238:0.96 241:0.63 242:0.28 243:0.94 247:0.79 248:0.82 253:0.85 255:0.95 256:0.68 259:0.21 260:0.76 261:0.98 265:0.97 266:0.63 267:0.76 268:0.69 271:0.60 276:0.74 281:0.91 284:0.94 285:0.62 300:0.55 +1 1:0.69 7:0.72 8:0.91 9:0.76 11:0.93 12:0.37 17:0.20 18:0.64 21:0.30 27:0.21 28:0.76 29:0.86 30:0.21 34:0.21 36:0.56 37:0.74 39:0.56 43:0.72 45:0.99 55:0.71 66:0.54 69:0.97 70:0.81 71:0.63 74:0.88 81:0.46 83:0.60 85:0.80 86:0.70 91:0.75 96:0.98 97:0.99 98:0.79 99:0.83 101:0.97 104:0.84 106:0.81 108:0.96 114:0.63 117:0.86 120:0.96 122:0.77 123:0.96 124:0.86 126:0.44 127:0.83 129:0.59 133:0.91 135:0.17 138:0.99 139:0.68 140:0.45 144:0.85 146:0.90 147:0.88 149:0.94 150:0.56 151:0.96 153:0.95 154:0.14 155:0.58 158:0.79 159:0.96 161:0.76 162:0.67 164:0.92 165:0.70 167:0.72 172:0.99 173:0.80 176:0.66 177:0.38 179:0.10 181:0.74 187:0.39 190:0.77 191:0.80 192:0.59 201:0.68 202:0.91 204:0.85 206:0.81 208:0.54 212:0.58 215:0.44 216:0.95 222:0.60 230:0.75 232:0.89 233:0.76 235:0.42 241:0.47 242:0.54 243:0.16 245:0.99 247:0.90 248:0.98 253:0.99 255:0.74 256:0.93 257:0.65 259:0.21 260:0.95 265:0.79 266:0.94 267:0.23 268:0.93 271:0.60 276:0.99 279:0.82 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.69 6:0.98 7:0.72 8:0.93 9:0.80 11:0.91 12:0.37 17:0.01 20:0.89 21:0.22 27:0.18 28:0.76 29:0.86 30:0.76 31:0.97 32:0.69 34:0.50 36:0.35 37:0.85 39:0.56 41:0.90 43:0.70 45:0.81 66:0.80 69:0.45 70:0.37 71:0.63 74:0.69 77:0.70 78:0.89 79:0.96 81:0.53 83:0.91 91:0.99 96:1.00 97:0.98 98:0.77 99:0.83 100:0.94 101:0.52 104:0.58 108:0.35 111:0.91 114:0.67 120:0.99 122:0.75 123:0.34 126:0.44 127:0.26 129:0.05 135:0.44 137:0.97 138:0.99 140:0.98 144:0.85 145:0.41 146:0.48 147:0.54 149:0.67 150:0.58 151:0.95 152:0.96 153:0.99 154:0.59 155:0.67 158:0.78 159:0.17 161:0.76 162:0.66 164:0.98 165:0.70 167:1.00 169:0.98 172:0.45 173:0.72 175:0.75 176:0.66 177:0.38 179:0.73 181:0.74 186:0.88 189:0.94 191:0.92 192:0.87 195:0.54 201:0.68 202:0.98 204:0.85 208:0.64 212:0.55 215:0.51 216:0.72 222:0.60 230:0.75 232:0.77 233:0.76 235:0.31 238:0.94 241:0.98 242:0.58 243:0.82 244:0.61 247:0.39 248:0.87 253:0.99 255:0.95 256:0.99 257:0.65 259:0.21 260:0.99 261:0.98 265:0.77 266:0.27 267:0.79 268:0.99 271:0.60 276:0.32 277:0.69 279:0.82 282:0.77 283:0.78 284:0.93 285:0.62 290:0.67 297:0.36 300:0.33 +1 1:0.69 7:0.72 8:0.84 9:0.76 11:0.93 12:0.37 17:0.30 18:0.85 21:0.30 27:0.50 28:0.76 29:0.86 30:0.21 32:0.69 34:0.53 36:0.79 37:0.60 39:0.56 43:0.69 45:1.00 66:0.34 69:0.23 70:0.85 71:0.63 74:0.93 77:0.70 81:0.46 83:0.60 85:0.80 86:0.92 91:0.04 96:0.87 97:0.99 98:0.68 99:0.83 101:0.97 104:0.84 106:0.81 108:0.98 114:0.63 117:0.86 120:0.78 122:0.77 123:0.98 124:0.95 126:0.44 127:0.98 129:0.97 133:0.96 135:0.26 139:0.89 140:0.45 144:0.85 145:0.67 146:0.71 147:0.75 149:0.90 150:0.56 151:0.84 153:0.71 154:0.78 155:0.58 158:0.79 159:0.98 161:0.76 162:0.78 164:0.74 165:0.70 167:0.63 172:0.99 173:0.06 176:0.66 177:0.70 179:0.09 181:0.74 187:0.39 190:0.77 191:0.83 192:0.59 195:1.00 201:0.68 202:0.67 204:0.85 206:0.81 208:0.54 212:0.55 215:0.44 216:0.86 220:0.62 222:0.60 230:0.75 232:0.89 233:0.76 235:0.42 241:0.10 242:0.50 243:0.08 245:0.99 247:0.94 248:0.82 253:0.88 255:0.74 256:0.71 259:0.21 260:0.75 265:0.69 266:0.94 267:0.28 268:0.72 271:0.60 276:0.99 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.74 6:0.95 8:0.88 9:0.80 11:0.92 12:0.37 17:0.73 18:0.94 20:0.92 21:0.30 22:0.93 27:0.44 28:0.76 29:0.86 30:0.43 31:0.95 34:0.29 36:0.80 37:0.45 39:0.56 41:0.88 43:0.96 45:0.93 47:0.93 60:0.95 64:0.77 66:0.36 69:0.19 70:0.85 71:0.63 74:0.86 78:0.87 79:0.94 81:0.61 83:0.91 85:0.93 86:0.97 91:0.22 96:0.84 98:0.63 100:0.90 101:0.87 104:0.88 106:0.81 108:0.92 111:0.87 114:0.65 117:0.86 120:0.74 122:0.57 123:0.92 124:0.83 126:0.54 127:0.85 129:0.93 133:0.84 135:0.82 137:0.95 139:0.96 140:0.45 144:0.85 146:0.32 147:0.39 149:0.93 150:0.77 151:0.92 152:0.86 153:0.72 154:0.96 155:0.67 158:0.92 159:0.86 161:0.76 162:0.55 164:0.70 165:0.93 167:0.66 169:0.88 172:0.89 173:0.10 176:0.73 177:0.70 179:0.19 181:0.74 186:0.87 187:0.21 189:0.96 191:0.70 192:0.87 196:0.97 201:0.93 202:0.67 206:0.81 208:0.64 212:0.57 215:0.44 216:0.39 219:0.95 222:0.60 232:0.91 235:0.52 238:0.91 241:0.21 242:0.16 243:0.13 245:0.95 247:0.96 248:0.81 253:0.87 255:0.95 256:0.58 259:0.21 260:0.75 261:0.89 262:0.93 265:0.64 266:0.84 267:0.82 268:0.64 271:0.60 276:0.92 279:0.86 283:0.82 284:0.93 285:0.62 290:0.65 292:0.95 297:0.36 300:0.94 +3 1:0.64 6:0.96 7:0.81 8:0.92 9:0.80 11:0.75 12:0.37 17:0.03 18:0.75 20:0.98 21:0.47 26:0.99 27:0.17 28:0.91 29:0.91 30:0.54 31:1.00 32:0.80 34:0.86 36:0.76 37:0.62 39:0.29 41:0.99 43:0.80 44:0.93 45:0.81 58:1.00 60:0.99 64:0.77 66:0.88 69:0.73 70:0.56 71:0.57 74:0.83 77:0.70 78:0.98 79:1.00 81:0.76 83:0.91 85:0.80 86:0.82 91:0.89 96:0.99 97:0.98 98:0.79 100:0.99 101:0.87 108:0.56 111:0.99 114:0.80 120:0.98 121:0.90 122:0.75 123:0.53 126:0.54 127:0.27 129:0.05 135:0.28 137:1.00 138:0.99 139:0.92 140:0.45 141:1.00 145:0.44 146:0.73 147:0.77 149:0.78 150:0.60 151:0.99 152:0.94 153:0.96 154:0.74 155:0.67 158:0.92 159:0.29 161:0.87 162:0.78 164:0.97 165:0.93 167:0.84 169:0.99 172:0.67 173:0.81 176:0.73 177:0.70 179:0.49 181:0.55 186:0.98 187:0.21 189:0.97 191:0.80 192:0.87 195:0.62 196:1.00 199:1.00 201:0.62 202:0.94 204:0.84 208:0.64 212:0.75 215:0.63 216:0.92 219:0.95 220:0.74 222:0.60 223:0.99 224:0.98 230:0.87 233:0.76 234:0.98 235:0.80 238:0.96 241:0.78 242:0.33 243:0.89 247:0.74 248:0.99 253:0.99 255:0.95 256:0.96 259:0.21 260:0.98 261:0.97 265:0.79 266:0.54 267:0.19 268:0.96 271:0.38 274:1.00 276:0.54 279:0.86 281:0.91 282:0.88 283:0.82 284:1.00 285:0.62 290:0.80 292:0.95 297:0.36 299:0.88 300:0.43 +0 11:0.57 12:0.37 17:0.47 18:0.65 21:0.64 26:0.95 27:0.45 28:0.91 29:0.91 30:0.70 34:0.50 36:0.39 37:0.50 39:0.29 43:0.66 55:0.71 58:0.93 66:0.19 69:0.70 70:0.62 71:0.57 74:0.82 77:0.70 81:0.30 85:0.80 86:0.74 91:0.03 98:0.46 101:0.87 108:0.88 114:0.66 122:0.77 123:0.51 124:0.87 126:0.26 127:0.48 129:0.59 133:0.85 135:0.86 139:0.72 141:0.91 145:0.49 146:0.88 147:0.90 149:0.66 150:0.33 154:0.56 158:0.73 159:0.86 161:0.87 167:0.56 172:0.75 173:0.41 176:0.60 177:0.70 178:0.55 179:0.20 181:0.55 187:0.68 195:0.97 199:0.94 212:0.71 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.74 241:0.30 242:0.80 243:0.40 245:0.91 247:0.60 253:0.61 259:0.21 265:0.41 266:0.89 267:0.36 271:0.38 274:0.91 276:0.89 282:0.73 290:0.66 300:0.84 +0 11:0.57 12:0.37 17:0.66 18:0.63 21:0.78 26:0.94 27:0.45 28:0.91 29:0.91 30:0.45 34:0.83 36:0.29 37:0.50 39:0.29 43:0.76 58:0.93 66:0.69 69:0.82 70:0.25 71:0.57 74:0.49 85:0.72 86:0.72 91:0.11 98:0.15 101:0.87 108:0.67 122:0.57 123:0.65 127:0.09 129:0.05 135:0.88 139:0.70 141:0.91 145:0.67 146:0.22 147:0.28 149:0.50 154:0.67 159:0.10 161:0.87 167:0.57 172:0.21 173:0.83 177:0.70 178:0.55 179:0.12 181:0.55 187:0.68 199:0.94 212:0.61 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.98 241:0.35 242:0.63 243:0.73 247:0.30 253:0.40 259:0.21 265:0.16 266:0.17 267:0.28 271:0.38 274:0.91 276:0.20 300:0.18 +2 1:0.66 6:0.90 7:0.81 8:0.80 9:0.79 11:0.57 12:0.37 17:0.57 18:0.87 20:0.93 21:0.30 26:0.99 27:0.35 28:0.91 29:0.91 30:0.45 31:0.95 32:0.80 34:0.52 36:0.43 37:0.43 39:0.29 41:0.92 43:0.95 44:0.93 55:0.45 58:0.99 60:0.95 64:0.77 66:0.74 69:0.19 70:0.69 71:0.57 74:0.90 77:0.89 78:0.96 79:0.95 81:0.79 83:0.60 85:0.93 86:0.96 91:0.62 96:0.87 98:0.81 100:0.96 101:0.87 104:0.54 108:0.15 111:0.95 114:0.86 120:0.78 121:0.90 122:0.57 123:0.15 124:0.41 126:0.54 127:0.79 129:0.05 133:0.37 135:0.23 137:0.95 139:0.97 140:0.45 141:1.00 145:0.69 146:0.75 147:0.79 149:0.95 150:0.67 151:0.86 152:0.86 153:0.48 154:0.95 155:0.66 158:0.86 159:0.43 161:0.87 162:0.85 164:0.77 165:0.93 167:0.86 169:0.94 172:0.73 173:0.31 176:0.73 177:0.70 179:0.57 181:0.55 186:0.96 189:0.92 192:0.87 195:0.62 196:0.98 199:0.98 201:0.64 202:0.65 204:0.84 208:0.64 212:0.60 215:0.72 216:0.05 219:0.95 220:0.87 222:0.60 223:0.99 224:0.98 230:0.87 232:0.74 233:0.76 234:0.98 235:0.68 238:0.90 241:0.80 242:0.43 243:0.77 245:0.73 247:0.67 248:0.82 253:0.91 255:0.79 256:0.71 259:0.21 260:0.79 261:0.93 265:0.81 266:0.54 267:0.18 268:0.72 271:0.38 274:0.98 276:0.62 279:0.81 281:0.91 282:0.88 283:0.67 284:0.95 285:0.62 290:0.86 292:0.88 297:0.36 299:0.97 300:0.70 +1 1:0.69 7:0.81 9:0.74 11:0.75 12:0.37 17:0.47 18:0.97 21:0.30 22:0.93 26:0.99 27:0.39 28:0.91 29:0.91 30:0.53 31:0.90 32:0.80 34:0.93 36:0.59 37:0.67 39:0.29 41:0.82 43:0.88 44:0.93 45:0.92 47:0.93 58:0.98 60:0.99 64:0.77 66:0.51 69:0.54 70:0.62 71:0.57 74:0.98 77:0.89 79:0.90 81:0.85 83:0.60 85:0.99 86:0.99 91:0.10 96:0.74 98:0.73 101:0.87 104:0.95 106:0.81 108:0.80 114:0.86 117:0.86 120:0.62 121:0.99 122:0.57 123:0.79 124:0.74 126:0.54 127:0.76 129:0.89 133:0.78 135:0.77 137:0.90 139:1.00 140:0.45 141:1.00 145:0.87 146:0.75 147:0.79 149:1.00 150:0.85 151:0.68 153:0.48 154:0.86 155:0.65 158:0.86 159:0.82 161:0.87 162:0.86 164:0.57 165:0.93 167:0.51 172:0.96 173:0.32 176:0.73 177:0.70 179:0.14 181:0.55 187:0.21 192:0.87 195:0.93 196:0.95 199:0.99 201:0.67 202:0.36 204:0.85 206:0.81 208:0.64 212:0.86 215:0.72 216:0.89 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.96 233:0.76 234:0.98 235:0.95 241:0.07 242:0.37 243:0.31 245:0.98 247:0.82 248:0.64 253:0.82 255:0.73 256:0.45 259:0.21 260:0.63 262:0.93 265:0.73 266:0.54 267:0.59 268:0.46 271:0.38 274:0.97 276:0.95 279:0.82 281:0.91 282:0.88 283:0.67 284:0.89 285:0.62 290:0.86 292:0.88 297:0.36 299:0.97 300:0.84 +2 1:0.66 9:0.79 11:0.82 12:0.37 17:0.43 18:0.94 21:0.54 22:0.93 26:0.99 27:0.49 28:0.91 29:0.91 30:0.40 31:0.95 32:0.80 34:0.90 36:0.98 37:0.69 39:0.29 41:0.88 43:0.87 44:0.93 45:0.85 47:0.93 55:0.45 58:0.98 60:0.87 64:0.77 66:0.48 69:0.32 70:0.72 71:0.57 74:0.84 77:0.89 79:0.94 81:0.78 83:0.60 85:0.90 86:0.93 91:0.49 96:0.83 98:0.68 101:0.97 104:0.96 106:0.81 108:0.80 114:0.77 117:0.86 120:0.73 122:0.57 123:0.79 124:0.65 126:0.54 127:0.85 129:0.59 133:0.61 135:0.60 137:0.95 139:0.95 140:0.45 141:1.00 145:0.91 146:0.57 147:0.63 149:0.85 150:0.62 151:0.86 153:0.48 154:0.84 155:0.66 158:0.92 159:0.64 161:0.87 162:0.86 164:0.67 165:0.93 167:0.60 172:0.77 173:0.26 176:0.73 177:0.70 179:0.38 181:0.55 187:0.39 192:0.87 195:0.76 196:0.97 199:0.98 201:0.64 202:0.50 206:0.81 208:0.64 212:0.85 215:0.58 216:0.47 219:0.95 222:0.60 223:0.99 224:0.99 232:0.97 234:0.99 235:0.88 241:0.44 242:0.16 243:0.45 245:0.87 247:0.91 248:0.81 253:0.87 255:0.78 256:0.58 259:0.21 260:0.74 262:0.93 265:0.68 266:0.71 267:0.36 268:0.59 271:0.38 274:0.98 276:0.77 279:0.80 282:0.88 283:0.82 284:0.92 285:0.62 290:0.77 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.64 7:0.81 9:0.79 11:0.82 12:0.37 17:0.26 18:0.96 21:0.47 22:0.93 26:0.99 27:0.21 28:0.91 29:0.91 30:0.21 31:0.94 34:0.73 36:0.83 37:0.74 39:0.29 41:0.82 43:0.95 45:0.92 47:0.93 58:0.98 60:0.87 64:0.77 66:0.85 69:0.23 70:0.88 71:0.57 74:0.95 79:0.93 81:0.76 83:0.60 85:0.98 86:0.98 91:0.22 96:0.87 97:0.94 98:0.89 101:0.97 104:0.84 106:0.81 108:0.18 114:0.80 117:0.86 120:0.78 121:0.90 122:0.57 123:0.18 124:0.39 126:0.54 127:0.74 129:0.59 133:0.61 135:0.44 137:0.94 139:0.98 140:0.80 141:1.00 146:0.99 147:0.99 149:0.97 150:0.60 151:0.86 153:0.48 154:0.95 155:0.66 158:0.92 159:0.87 161:0.87 162:0.86 164:0.66 165:0.93 167:0.57 172:0.97 173:0.10 176:0.73 177:0.70 179:0.15 181:0.55 187:0.39 192:0.87 196:0.97 199:0.99 201:0.62 202:0.50 204:0.84 206:0.81 208:0.64 212:0.67 215:0.63 216:0.95 219:0.95 222:0.60 223:0.99 224:0.99 230:0.87 232:0.89 233:0.76 234:0.98 235:0.80 241:0.35 242:0.24 243:0.86 245:0.92 247:0.93 248:0.77 253:0.92 255:0.79 256:0.58 259:0.21 260:0.79 262:0.93 265:0.89 266:0.75 267:0.84 268:0.59 271:0.38 274:0.97 276:0.95 279:0.82 281:0.91 283:0.82 284:0.89 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 +2 1:0.68 6:0.92 7:0.81 8:0.76 9:0.79 11:0.75 12:0.37 17:0.66 18:0.94 20:0.87 21:0.13 26:0.99 27:0.72 28:0.91 29:0.91 30:0.31 31:0.94 32:0.80 34:0.79 36:0.84 39:0.29 41:0.82 43:0.95 44:0.93 45:0.73 58:0.98 60:0.87 64:0.77 66:0.47 69:0.15 70:0.85 71:0.57 74:0.94 77:0.89 78:0.92 79:0.93 81:0.82 83:0.60 85:0.95 86:0.98 91:0.44 96:0.80 98:0.64 100:0.88 101:0.87 104:0.58 108:0.12 111:0.90 114:0.92 120:0.69 121:0.90 122:0.57 123:0.12 124:0.75 126:0.54 127:0.88 129:0.81 133:0.74 135:0.58 137:0.94 139:0.98 140:0.45 141:1.00 145:0.69 146:0.82 147:0.85 149:0.97 150:0.78 151:0.86 152:0.82 153:0.48 154:0.95 155:0.66 158:0.92 159:0.71 161:0.87 162:0.86 164:0.57 165:0.93 167:0.85 169:0.86 172:0.80 173:0.15 176:0.73 177:0.70 179:0.33 181:0.55 186:0.92 189:0.93 192:0.87 195:0.82 196:0.98 199:0.98 201:0.65 202:0.36 204:0.84 208:0.64 212:0.58 215:0.84 216:0.04 219:0.95 222:0.60 223:0.99 224:0.99 230:0.87 232:0.77 233:0.76 234:0.99 235:0.52 238:0.83 241:0.79 242:0.32 243:0.59 245:0.88 247:0.88 248:0.77 253:0.88 255:0.78 256:0.45 259:0.21 260:0.70 261:0.87 265:0.65 266:0.54 267:0.23 268:0.46 271:0.38 274:0.97 276:0.82 279:0.80 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.92 292:0.95 297:0.36 299:0.97 300:0.70 +0 1:0.67 6:0.87 7:0.81 8:0.74 9:0.80 11:0.75 12:0.37 17:0.57 18:0.80 20:0.98 21:0.22 22:0.93 26:0.99 27:0.37 28:0.91 29:0.91 30:0.26 31:0.98 32:0.80 34:0.45 36:0.98 37:0.62 39:0.29 41:0.94 43:0.89 44:0.93 45:0.89 47:0.93 48:0.91 58:0.99 60:0.97 64:0.77 66:0.96 69:0.47 70:0.77 71:0.57 74:0.87 77:0.70 78:0.92 79:0.98 81:0.81 83:0.91 85:0.85 86:0.88 91:0.54 96:0.95 97:0.89 98:0.97 100:0.89 101:0.87 104:0.94 106:0.81 108:0.36 111:0.90 114:0.90 117:0.86 120:0.90 121:0.90 122:0.75 123:0.34 126:0.54 127:0.76 129:0.05 135:0.56 137:0.98 139:0.94 140:0.45 141:1.00 145:0.45 146:0.96 147:0.97 149:0.83 150:0.73 151:0.95 152:0.90 153:0.85 154:0.88 155:0.67 158:0.92 159:0.65 161:0.87 162:0.77 164:0.85 165:0.93 167:0.59 169:0.86 172:0.89 173:0.38 176:0.73 177:0.70 179:0.34 181:0.55 186:0.92 187:0.68 189:0.89 192:0.87 195:0.80 196:0.99 199:0.98 201:0.64 202:0.79 204:0.84 206:0.81 208:0.64 212:0.71 215:0.79 216:0.58 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.96 233:0.76 234:0.98 235:0.60 238:0.84 241:0.39 242:0.24 243:0.96 247:0.90 248:0.91 253:0.96 255:0.95 256:0.82 259:0.21 260:0.90 261:0.90 262:0.93 265:0.97 266:0.63 267:0.23 268:0.83 271:0.38 274:0.99 276:0.81 279:0.82 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 290:0.90 292:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.64 6:0.91 7:0.81 8:0.74 9:0.80 11:0.82 12:0.37 17:0.18 18:0.95 20:0.98 21:0.47 22:0.93 26:0.99 27:0.21 28:0.91 29:0.91 30:0.21 31:0.98 34:0.75 36:0.87 37:0.74 39:0.29 41:0.94 43:0.95 45:0.92 47:0.93 58:0.99 60:0.98 64:0.77 66:0.85 69:0.23 70:0.89 71:0.57 74:0.94 78:0.92 79:0.98 81:0.76 83:0.60 85:0.97 86:0.98 91:0.46 96:0.93 97:0.89 98:0.89 100:0.92 101:0.97 104:0.84 106:0.81 108:0.18 111:0.91 114:0.80 117:0.86 120:0.89 121:0.97 122:0.57 123:0.18 124:0.39 126:0.54 127:0.73 129:0.59 133:0.61 135:0.47 137:0.98 139:0.98 140:0.45 141:1.00 146:0.99 147:0.99 149:0.96 150:0.60 151:0.96 152:0.90 153:0.88 154:0.95 155:0.67 158:0.92 159:0.87 161:0.87 162:0.70 164:0.85 165:0.93 167:0.57 169:0.90 172:0.97 173:0.10 176:0.73 177:0.70 179:0.15 181:0.55 186:0.92 187:0.39 189:0.92 192:0.87 196:0.99 199:0.99 201:0.62 202:0.78 204:0.85 206:0.81 208:0.64 212:0.67 215:0.63 216:0.95 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.89 233:0.76 234:0.98 235:0.80 238:0.87 241:0.32 242:0.24 243:0.86 245:0.92 247:0.93 248:0.92 253:0.96 255:0.94 256:0.79 259:0.21 260:0.89 261:0.91 262:0.93 265:0.89 266:0.75 267:0.88 268:0.81 271:0.38 274:0.99 276:0.94 279:0.82 281:0.91 283:0.82 284:0.97 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 +2 1:0.69 6:0.90 8:0.68 9:0.71 11:0.75 12:0.37 17:0.22 18:0.97 20:0.86 26:0.99 27:0.70 28:0.91 29:0.91 30:0.19 34:0.47 36:0.97 37:0.32 39:0.29 43:0.85 45:0.95 58:0.93 60:0.87 64:0.77 66:0.54 69:0.61 70:0.93 71:0.57 74:0.90 78:0.87 81:0.85 83:0.60 85:0.95 86:0.97 91:0.31 96:0.72 98:0.76 100:0.86 101:0.87 104:0.90 106:0.81 108:0.79 111:0.86 114:0.98 117:0.86 120:0.59 122:0.57 123:0.78 124:0.83 126:0.54 127:0.72 129:0.81 133:0.89 135:0.73 139:0.97 140:0.45 141:0.91 146:0.77 147:0.80 149:0.96 150:0.86 151:0.57 152:0.81 153:0.48 154:0.81 155:0.58 158:0.92 159:0.91 161:0.87 162:0.76 164:0.68 165:0.93 167:0.57 169:0.84 172:0.96 173:0.25 176:0.73 177:0.70 179:0.14 181:0.55 186:0.87 187:0.39 189:0.91 190:0.95 192:0.57 199:0.99 201:0.67 202:0.58 206:0.81 208:0.64 212:0.51 215:0.96 216:0.35 219:0.95 220:0.91 222:0.60 223:0.99 224:0.93 232:0.93 234:0.91 235:0.31 238:0.82 241:0.35 242:0.19 243:0.31 245:0.95 247:0.91 248:0.58 253:0.79 255:0.70 256:0.58 259:0.21 260:0.59 261:0.84 265:0.76 266:0.94 267:0.28 268:0.62 271:0.38 274:0.91 276:0.95 279:0.80 283:0.82 285:0.56 290:0.98 292:0.95 297:0.36 300:0.84 +1 1:0.64 7:0.81 11:0.82 12:0.37 17:0.51 18:0.96 21:0.47 22:0.93 26:0.99 27:0.50 28:0.91 29:0.91 30:0.21 32:0.80 34:0.80 36:0.88 37:0.60 39:0.29 43:0.95 44:0.93 45:0.93 47:0.93 58:0.93 60:0.95 64:0.77 66:0.72 69:0.27 70:0.89 71:0.57 74:0.96 77:0.70 81:0.76 83:0.60 85:0.98 86:0.98 91:0.07 97:0.89 98:0.88 101:0.97 104:0.84 106:0.81 108:0.21 114:0.80 117:0.86 121:0.90 122:0.57 123:0.20 124:0.46 126:0.54 127:0.88 129:0.59 133:0.61 135:0.61 139:0.99 141:0.91 145:0.70 146:0.99 147:0.99 149:0.98 150:0.60 154:0.95 155:0.57 158:0.92 159:0.89 161:0.87 165:0.93 167:0.50 172:0.98 173:0.10 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 187:0.39 192:0.56 195:0.97 199:0.99 201:0.62 204:0.84 206:0.81 208:0.64 212:0.60 215:0.63 216:0.86 219:0.95 222:0.60 223:0.99 224:0.93 230:0.87 232:0.89 233:0.76 234:0.91 235:0.80 241:0.03 242:0.24 243:0.75 245:0.98 247:0.93 253:0.72 259:0.21 262:0.93 265:0.88 266:0.80 267:0.52 271:0.38 274:0.91 276:0.96 279:0.82 281:0.91 282:0.88 283:0.82 290:0.80 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.66 6:0.92 7:0.69 8:0.83 9:0.80 11:0.82 12:0.37 17:0.55 18:0.95 20:0.96 21:0.30 26:0.99 27:0.47 28:0.91 29:0.91 30:0.21 31:0.97 32:0.80 34:0.59 36:1.00 37:0.49 39:0.29 41:0.95 43:0.76 44:0.93 45:0.81 48:0.91 55:0.45 58:0.99 60:0.99 64:0.77 66:0.82 69:0.81 70:0.85 71:0.57 74:0.89 77:0.70 78:0.88 79:0.97 81:0.79 83:0.60 85:0.95 86:0.97 91:0.44 96:0.90 97:0.94 98:0.69 100:0.84 101:0.97 104:0.95 106:0.81 108:0.65 111:0.85 114:0.86 120:0.83 122:0.57 123:0.63 124:0.39 126:0.54 127:0.24 129:0.05 133:0.37 135:0.90 137:0.97 139:0.98 140:0.45 141:1.00 145:0.59 146:0.95 147:0.28 149:0.93 150:0.67 151:0.94 152:0.89 153:0.82 154:0.68 155:0.66 158:0.92 159:0.68 161:0.87 162:0.79 164:0.84 165:0.93 167:0.57 169:0.82 172:0.92 173:0.63 176:0.73 177:0.05 179:0.15 181:0.55 186:0.88 187:0.87 189:0.93 192:0.87 195:0.94 196:0.99 199:0.99 201:0.64 202:0.75 204:0.82 206:0.81 208:0.64 212:0.70 215:0.72 216:0.66 219:0.95 222:0.60 223:0.99 224:0.98 230:0.71 233:0.66 234:0.98 235:0.68 238:0.83 241:0.35 242:0.24 243:0.15 245:0.89 247:0.94 248:0.89 253:0.93 255:0.94 256:0.79 257:0.84 259:0.21 260:0.84 261:0.87 265:0.70 266:0.75 267:0.73 268:0.80 271:0.38 274:0.99 276:0.86 279:0.82 281:0.86 282:0.88 283:0.82 284:0.97 285:0.62 290:0.86 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.62 6:0.86 8:0.71 9:0.75 10:0.97 11:0.86 12:0.92 17:0.69 18:0.92 20:0.87 21:0.47 22:0.93 23:0.99 27:0.30 28:0.96 29:0.86 30:0.52 31:0.95 33:0.97 34:0.37 36:0.18 37:0.71 39:0.55 41:0.82 43:0.76 44:0.88 45:0.98 47:0.93 48:0.91 62:0.99 64:0.77 66:0.45 69:0.95 70:0.81 71:0.81 74:0.86 75:0.98 76:0.85 78:0.99 79:0.94 81:0.61 83:0.91 85:0.80 86:0.92 91:0.11 96:0.84 97:0.98 98:0.56 99:0.95 100:0.98 101:1.00 102:0.96 104:0.58 108:0.93 111:0.98 114:0.78 117:0.86 120:0.79 122:0.82 123:0.93 124:0.87 126:0.51 127:0.96 128:0.98 129:0.59 131:0.84 133:0.89 135:0.71 137:0.95 139:0.92 140:0.97 144:0.76 145:0.67 146:0.88 147:0.80 149:0.89 150:0.47 151:0.77 152:0.86 153:0.89 154:0.20 155:0.66 157:0.86 158:0.81 159:0.89 161:0.60 162:0.55 164:0.75 165:0.81 166:0.77 167:0.45 168:0.98 169:0.95 170:0.82 172:0.95 173:0.88 174:0.97 176:0.70 177:0.70 178:0.42 179:0.15 181:0.66 182:0.80 186:0.99 187:0.87 189:0.88 192:0.98 195:0.96 196:0.97 197:0.96 201:0.60 202:0.81 205:0.98 208:0.64 212:0.77 216:0.29 219:0.94 220:0.62 222:0.35 226:0.98 227:0.91 229:0.89 231:0.67 232:0.95 235:0.68 236:0.95 238:0.92 239:0.97 242:0.15 243:0.22 245:0.96 246:0.93 247:0.99 248:0.70 253:0.88 255:0.78 256:0.80 257:0.65 259:0.21 260:0.79 261:0.94 262:0.93 265:0.57 266:0.89 267:0.23 268:0.79 271:0.16 276:0.96 279:0.81 281:0.91 283:0.82 284:0.97 285:0.62 287:0.95 290:0.78 291:0.97 292:0.95 300:0.84 +2 1:0.67 6:0.85 8:0.64 9:0.76 10:0.93 11:0.84 12:0.92 17:0.30 18:0.83 20:0.88 21:0.22 22:0.93 23:0.97 27:0.35 28:0.96 29:0.86 30:0.33 31:0.94 33:0.97 34:0.58 36:0.43 37:0.48 39:0.55 41:0.88 43:0.85 45:0.89 47:0.93 60:0.95 62:0.98 64:0.77 66:0.45 69:0.62 70:0.96 71:0.81 74:0.78 75:0.99 78:0.90 79:0.94 81:0.79 83:0.91 85:0.72 86:0.84 91:0.17 96:0.82 98:0.35 100:0.88 101:0.96 104:0.94 106:0.81 108:0.47 111:0.88 114:0.90 117:0.86 120:0.68 123:0.45 124:0.93 126:0.54 127:0.85 128:0.97 129:0.05 131:0.84 133:0.95 135:0.93 137:0.94 139:0.85 140:0.87 144:0.76 146:0.84 147:0.86 149:0.79 150:0.62 151:0.81 152:0.95 153:0.77 154:0.81 155:0.66 157:0.76 158:0.86 159:0.91 161:0.60 162:0.52 164:0.69 165:0.93 166:0.77 167:0.46 168:0.97 169:0.85 170:0.82 172:0.75 173:0.27 174:0.86 176:0.73 177:0.88 178:0.48 179:0.39 181:0.66 182:0.80 186:0.90 187:0.87 189:0.85 192:0.99 196:0.95 197:0.89 201:0.66 202:0.72 205:0.97 206:0.81 208:0.64 212:0.39 215:0.79 216:0.64 219:0.95 220:0.62 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.97 235:0.52 236:0.97 238:0.83 239:0.95 241:0.03 242:0.14 243:0.58 245:0.71 246:0.94 247:0.96 248:0.75 253:0.85 255:0.79 256:0.64 259:0.21 260:0.69 261:0.90 262:0.93 265:0.37 266:0.97 267:0.69 268:0.67 271:0.16 276:0.78 279:0.81 283:0.67 284:0.94 285:0.62 287:0.95 290:0.90 291:0.97 292:0.88 297:0.36 300:0.94 +2 1:0.64 6:0.87 8:0.74 9:0.75 10:0.94 11:0.84 12:0.92 17:0.45 18:0.80 20:0.95 21:0.54 22:0.93 23:0.99 27:0.35 28:0.96 29:0.86 30:0.15 31:0.92 33:0.97 34:0.36 36:0.33 37:0.49 39:0.55 41:0.93 43:0.84 45:0.88 47:0.93 60:0.95 62:0.98 64:0.77 66:0.35 69:0.63 70:0.97 71:0.81 74:0.80 75:0.99 78:0.97 79:0.91 81:0.74 83:0.91 85:0.72 86:0.84 91:0.51 96:0.77 98:0.30 100:0.96 101:0.96 104:0.90 106:0.81 108:0.48 111:0.95 114:0.77 117:0.86 120:0.65 122:0.55 123:0.46 124:0.93 126:0.54 127:0.84 128:0.97 129:0.05 131:0.84 133:0.94 135:0.89 137:0.92 139:0.85 140:0.80 144:0.76 146:0.79 147:0.83 149:0.84 150:0.55 151:0.66 152:0.96 153:0.87 154:0.81 155:0.65 157:0.76 158:0.86 159:0.92 161:0.60 162:0.55 164:0.80 165:0.93 166:0.77 167:0.48 168:0.97 169:0.94 170:0.82 172:0.78 173:0.27 174:0.88 176:0.73 177:0.88 178:0.42 179:0.30 181:0.66 182:0.80 186:0.96 187:0.87 189:0.90 191:0.70 192:0.98 196:0.94 197:0.88 201:0.63 202:0.77 205:0.98 206:0.81 208:0.64 212:0.37 215:0.58 216:0.84 219:0.95 220:0.93 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.95 235:0.80 236:0.97 238:0.89 239:0.94 241:0.12 242:0.13 243:0.51 245:0.79 246:0.93 247:0.97 248:0.71 253:0.81 255:0.78 256:0.71 259:0.21 260:0.66 261:0.96 262:0.93 265:0.32 266:0.96 267:0.73 268:0.75 271:0.16 276:0.83 279:0.81 283:0.67 284:0.97 285:0.62 287:0.95 290:0.77 291:0.97 292:0.88 297:0.36 300:0.98 +2 1:0.68 6:0.87 8:0.71 9:0.79 10:0.95 11:0.84 12:0.92 17:0.45 18:0.82 20:0.88 21:0.13 22:0.93 23:0.98 27:0.35 28:0.96 29:0.86 30:0.32 31:0.96 33:0.97 34:0.29 36:0.34 37:0.49 39:0.55 41:0.90 43:0.84 45:0.92 47:0.93 60:0.87 62:0.99 64:0.77 66:0.12 69:0.63 70:0.92 71:0.81 74:0.78 75:0.99 78:0.98 79:0.95 81:0.80 83:0.91 85:0.72 86:0.86 91:0.54 96:0.85 98:0.31 100:0.97 101:0.96 104:0.90 106:0.81 108:0.48 111:0.97 114:0.92 117:0.86 120:0.76 122:0.55 123:0.91 124:0.96 126:0.54 127:0.84 128:0.98 129:0.59 131:0.84 133:0.96 135:0.89 137:0.96 139:0.87 140:0.87 144:0.76 146:0.69 147:0.74 149:0.79 150:0.66 151:0.83 152:0.96 153:0.78 154:0.81 155:0.66 157:0.82 158:0.86 159:0.93 161:0.60 162:0.56 164:0.74 165:0.93 166:0.77 167:0.46 168:0.98 169:0.94 170:0.82 172:0.73 173:0.24 174:0.92 176:0.73 177:0.88 178:0.48 179:0.29 181:0.66 182:0.80 186:0.98 187:0.87 189:0.89 192:0.99 196:0.96 197:0.91 201:0.67 202:0.70 205:0.98 206:0.81 208:0.64 212:0.41 215:0.84 216:0.84 219:0.95 220:0.62 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.95 235:0.42 236:0.97 238:0.91 239:0.95 241:0.03 242:0.16 243:0.44 245:0.79 246:0.94 247:0.97 248:0.83 253:0.88 255:0.95 256:0.64 259:0.21 260:0.77 261:0.96 262:0.93 265:0.25 266:0.96 267:0.59 268:0.68 271:0.16 276:0.84 279:0.80 283:0.67 284:0.95 285:0.62 287:0.95 290:0.92 291:0.97 292:0.88 297:0.36 300:0.94 +1 1:0.65 6:0.87 7:0.70 8:0.73 9:0.73 10:0.95 11:0.84 12:0.92 17:0.49 18:0.78 20:0.85 21:0.40 22:0.93 23:0.96 27:0.51 28:0.96 29:0.86 30:0.72 31:0.90 32:0.67 33:0.97 34:0.80 36:0.76 37:0.37 39:0.55 41:0.82 43:0.90 45:0.91 47:0.93 60:0.87 62:0.97 64:0.77 66:0.33 69:0.45 70:0.62 71:0.81 74:0.80 75:0.99 77:0.70 78:0.90 79:0.89 81:0.76 83:0.91 85:0.72 86:0.86 91:0.14 96:0.73 97:0.89 98:0.54 100:0.83 101:0.96 104:0.80 106:0.81 108:0.35 111:0.87 114:0.82 117:0.86 120:0.61 122:0.55 123:0.34 124:0.77 126:0.54 127:0.46 128:0.96 129:0.59 131:0.84 133:0.73 135:0.95 137:0.90 139:0.87 140:0.80 144:0.76 145:0.83 146:0.74 147:0.78 149:0.86 150:0.58 151:0.63 152:0.81 153:0.69 154:0.89 155:0.65 157:0.82 158:0.86 159:0.64 161:0.60 162:0.59 164:0.62 165:0.93 166:0.77 167:0.45 168:0.96 169:0.81 170:0.82 172:0.63 173:0.33 174:0.89 176:0.73 177:0.88 178:0.42 179:0.39 181:0.66 182:0.80 186:0.90 187:0.21 189:0.89 192:0.98 195:0.84 196:0.92 197:0.91 201:0.65 202:0.55 204:0.80 205:0.95 206:0.81 208:0.64 212:0.54 215:0.67 216:0.49 219:0.95 220:0.82 222:0.35 226:0.96 227:0.91 229:0.88 230:0.73 231:0.67 232:0.89 233:0.76 235:0.68 236:0.97 238:0.82 239:0.96 242:0.14 243:0.50 245:0.80 246:0.93 247:0.86 248:0.61 253:0.74 255:0.73 256:0.45 259:0.21 260:0.61 261:0.84 262:0.93 265:0.55 266:0.63 267:0.44 268:0.55 271:0.16 276:0.72 279:0.81 282:0.75 283:0.67 284:0.90 285:0.62 287:0.95 290:0.82 291:0.97 292:0.88 297:0.36 300:0.84 +1 1:0.64 10:0.91 11:0.75 12:0.92 17:0.30 18:0.70 21:0.54 27:0.45 28:0.96 29:0.86 30:0.61 34:0.83 36:0.23 37:0.50 39:0.55 43:0.58 45:0.87 64:0.77 66:0.64 69:0.70 70:0.53 71:0.81 74:0.67 75:0.99 81:0.69 83:0.69 86:0.78 91:0.14 97:0.94 98:0.65 99:0.95 101:0.65 108:0.53 114:0.77 122:0.55 123:0.51 124:0.46 126:0.54 127:0.40 129:0.05 131:0.61 133:0.39 135:0.86 139:0.81 144:0.76 145:0.44 146:0.78 147:0.81 149:0.59 150:0.54 154:0.73 155:0.49 157:0.79 158:0.84 159:0.54 161:0.60 165:0.81 166:0.77 167:0.48 170:0.82 172:0.61 173:0.66 174:0.83 176:0.73 177:0.88 178:0.55 179:0.58 181:0.66 182:0.79 187:0.68 192:0.48 197:0.86 201:0.63 208:0.64 212:0.30 215:0.58 216:0.77 219:0.95 222:0.35 226:0.95 227:0.61 229:0.61 231:0.67 235:0.80 236:0.97 239:0.93 241:0.15 242:0.14 243:0.69 245:0.71 246:0.93 247:0.79 253:0.61 254:0.80 259:0.21 265:0.66 266:0.71 267:0.28 271:0.16 276:0.55 279:0.79 283:0.66 287:0.61 290:0.77 292:0.87 297:0.36 300:0.43 +2 1:0.65 6:0.85 8:0.72 9:0.74 10:0.94 11:0.84 12:0.92 17:0.43 18:0.75 20:0.95 21:0.40 22:0.93 23:0.98 27:0.35 28:0.96 29:0.86 30:0.43 31:0.91 33:0.97 34:0.44 36:0.68 37:0.48 39:0.55 41:0.92 43:0.85 45:0.90 47:0.93 60:0.95 62:0.98 64:0.77 66:0.46 69:0.62 70:0.87 71:0.81 74:0.81 75:0.99 78:0.90 79:0.91 81:0.76 83:0.91 85:0.72 86:0.84 91:0.37 96:0.76 98:0.41 100:0.87 101:0.96 104:0.94 106:0.81 108:0.47 111:0.88 114:0.82 117:0.86 120:0.64 122:0.83 123:0.46 124:0.86 126:0.54 127:0.77 128:0.97 129:0.05 131:0.84 133:0.88 135:0.92 137:0.91 139:0.85 140:0.87 144:0.76 146:0.82 147:0.85 149:0.85 150:0.58 151:0.64 152:0.95 153:0.78 154:0.81 155:0.65 157:0.77 158:0.86 159:0.87 161:0.60 162:0.50 164:0.75 165:0.93 166:0.77 167:0.45 168:0.97 169:0.85 170:0.82 172:0.77 173:0.34 174:0.83 176:0.73 177:0.88 178:0.48 179:0.36 181:0.66 182:0.80 186:0.90 187:0.87 189:0.88 192:0.98 196:0.93 197:0.87 201:0.65 202:0.77 205:0.98 206:0.81 208:0.64 212:0.55 215:0.67 216:0.64 219:0.95 220:0.91 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.97 235:0.68 236:0.97 238:0.83 239:0.94 241:0.01 242:0.19 243:0.59 245:0.79 246:0.93 247:0.93 248:0.68 253:0.80 255:0.77 256:0.68 259:0.21 260:0.65 261:0.90 262:0.93 265:0.43 266:0.91 267:0.65 268:0.70 271:0.16 276:0.77 279:0.81 283:0.67 284:0.95 285:0.62 287:0.95 290:0.82 291:0.97 292:0.88 297:0.36 300:0.94 +1 1:0.67 7:0.70 10:1.00 11:0.86 12:0.92 17:0.40 18:0.95 21:0.22 22:0.93 27:0.41 28:0.96 29:0.86 30:0.85 32:0.80 33:0.97 34:0.83 36:0.18 37:0.44 39:0.55 43:0.82 44:0.93 45:0.96 47:0.93 64:0.77 66:0.88 69:0.50 70:0.48 71:0.81 74:0.97 77:0.70 81:0.75 83:0.69 85:0.95 86:0.99 91:0.12 98:0.83 99:0.95 101:1.00 102:0.98 104:0.96 106:0.81 108:0.38 114:0.90 117:0.86 122:0.93 123:0.37 124:0.37 126:0.54 127:0.74 129:0.05 131:0.61 133:0.45 135:0.87 139:0.99 144:0.76 145:0.42 146:0.95 147:0.96 149:0.98 150:0.61 154:0.85 155:0.54 157:0.88 159:0.74 161:0.60 165:0.81 166:0.77 167:0.48 170:0.82 172:0.97 173:0.34 174:0.96 176:0.73 177:0.88 178:0.55 179:0.17 181:0.66 182:0.79 187:0.87 192:0.56 195:0.86 197:0.98 201:0.66 202:0.36 204:0.81 206:0.81 208:0.64 212:0.85 215:0.79 216:0.75 222:0.35 227:0.61 229:0.61 230:0.73 231:0.67 232:0.98 233:0.66 235:0.52 236:0.97 239:1.00 241:0.15 242:0.17 243:0.89 245:0.93 246:0.94 247:0.92 253:0.75 259:0.21 262:0.93 265:0.83 266:0.54 267:0.44 271:0.16 276:0.93 282:0.88 287:0.61 290:0.90 291:0.97 297:0.36 299:0.88 300:0.70 +2 1:0.65 6:0.89 7:0.69 8:0.75 9:0.73 10:0.92 11:0.75 12:0.92 17:0.32 18:0.75 20:0.96 21:0.40 22:0.93 23:0.98 27:0.26 28:0.96 29:0.86 30:0.17 31:0.90 32:0.74 33:0.97 34:0.64 36:0.33 37:0.70 39:0.55 43:0.23 44:0.93 45:0.93 47:0.93 55:0.71 62:0.97 64:0.77 66:0.33 69:0.93 70:0.85 71:0.81 74:0.72 75:0.99 76:0.85 77:0.70 78:0.98 79:0.89 81:0.72 83:0.69 86:0.77 91:0.33 96:0.74 97:0.98 98:0.82 99:0.95 100:0.96 101:0.65 102:0.98 104:0.79 106:0.81 108:0.93 111:0.96 114:0.82 117:0.86 120:0.61 123:0.92 124:0.83 126:0.54 127:0.98 128:0.96 129:0.59 131:0.84 133:0.81 135:0.85 137:0.90 139:0.88 140:0.45 144:0.76 145:0.54 146:0.94 147:0.73 149:0.71 150:0.57 151:0.62 152:0.96 153:0.48 154:0.27 155:0.65 157:0.82 158:0.85 159:0.94 161:0.60 162:0.66 164:0.73 165:0.81 166:0.77 167:0.48 168:0.96 169:0.92 170:0.82 172:0.96 173:0.71 174:0.99 176:0.73 177:0.05 179:0.12 181:0.66 182:0.80 186:0.98 187:0.21 189:0.90 192:0.98 193:0.98 195:0.99 196:0.93 197:0.96 201:0.65 202:0.65 204:0.80 205:0.98 206:0.81 208:0.64 212:0.61 215:0.67 216:0.52 219:0.95 220:0.91 222:0.35 226:0.95 227:0.91 229:0.89 230:0.71 231:0.67 232:0.88 233:0.76 235:0.68 236:0.97 238:0.87 239:0.96 241:0.15 242:0.54 243:0.07 245:0.99 246:0.93 247:0.99 248:0.62 253:0.73 254:0.74 255:0.73 256:0.64 257:0.93 259:0.21 260:0.62 261:0.95 262:0.93 265:0.82 266:0.80 267:0.98 268:0.67 271:0.16 276:0.97 279:0.81 281:0.91 282:0.83 283:0.66 284:0.94 285:0.62 287:0.95 290:0.82 291:0.97 292:0.87 297:0.36 300:0.84 +1 1:0.61 10:0.89 11:0.75 12:0.92 17:0.45 18:0.67 21:0.68 27:0.45 28:0.96 29:0.86 30:0.17 32:0.74 34:0.96 36:0.18 37:0.50 39:0.55 43:0.12 44:0.93 45:0.73 55:0.52 64:0.77 66:0.49 69:0.70 70:0.80 71:0.81 74:0.55 77:0.70 81:0.65 83:0.69 86:0.75 91:0.09 98:0.61 99:0.95 101:0.65 102:0.98 108:0.74 114:0.70 123:0.72 124:0.55 126:0.54 127:0.33 129:0.05 131:0.61 133:0.45 135:0.74 139:0.79 144:0.76 145:0.52 146:0.41 147:0.47 149:0.11 150:0.49 154:0.68 155:0.48 157:0.76 159:0.46 161:0.60 165:0.81 166:0.77 167:0.48 170:0.82 172:0.45 173:0.69 174:0.79 176:0.73 177:0.88 178:0.55 179:0.65 181:0.66 182:0.79 187:0.68 192:0.46 195:0.73 197:0.83 201:0.57 208:0.64 212:0.30 215:0.49 216:0.77 222:0.35 227:0.61 229:0.61 231:0.67 235:0.95 236:0.97 239:0.92 241:0.15 242:0.33 243:0.40 245:0.68 246:0.93 247:0.76 253:0.55 254:0.90 259:0.21 265:0.62 266:0.63 267:0.23 271:0.16 276:0.46 277:0.69 282:0.82 287:0.61 290:0.70 297:0.36 300:0.55 +2 7:0.69 8:0.70 10:0.87 11:0.75 12:0.92 17:0.55 18:0.61 21:0.40 27:0.26 28:0.96 29:0.86 30:0.19 32:0.71 34:0.64 36:0.37 37:0.70 39:0.55 43:0.23 45:0.91 55:0.45 66:0.33 69:0.93 70:0.85 71:0.81 74:0.64 77:0.70 81:0.33 86:0.67 91:0.44 98:0.66 101:0.65 102:0.94 104:0.91 106:0.81 108:0.85 120:0.58 123:0.84 124:0.90 126:0.24 127:0.97 129:0.59 131:0.61 133:0.91 135:0.71 139:0.65 140:0.96 144:0.76 145:0.58 146:0.98 147:0.73 149:0.71 150:0.36 151:0.51 153:0.45 154:0.24 157:0.76 159:0.92 161:0.60 162:0.47 164:0.64 166:0.61 167:0.50 170:0.82 172:0.95 173:0.76 174:0.96 177:0.38 178:0.54 179:0.13 181:0.66 182:0.72 187:0.21 190:0.95 195:0.98 197:0.91 202:0.77 206:0.81 212:0.77 215:0.67 216:0.52 220:0.93 222:0.35 227:0.61 229:0.61 230:0.71 231:0.63 233:0.76 235:0.42 239:0.87 241:0.24 242:0.72 243:0.10 245:0.97 246:0.61 247:0.98 248:0.51 253:0.48 254:0.80 256:0.58 257:0.84 259:0.21 260:0.58 265:0.67 266:0.80 267:0.98 268:0.57 271:0.16 276:0.97 282:0.79 283:0.67 285:0.46 287:0.61 297:0.36 300:0.84 +1 1:0.68 7:0.70 9:0.73 10:0.99 11:0.86 12:0.92 17:0.51 18:0.92 21:0.13 22:0.93 27:0.24 28:0.96 29:0.86 30:0.45 32:0.80 33:0.97 34:0.83 36:0.40 37:0.47 39:0.55 43:0.85 44:0.93 45:0.96 47:0.93 60:0.87 64:0.77 66:0.34 69:0.15 70:0.74 71:0.81 74:0.91 75:0.99 77:0.70 81:0.80 83:0.91 85:0.85 86:0.97 91:0.06 96:0.80 98:0.72 101:0.96 102:0.98 104:0.91 106:0.81 108:0.12 114:0.92 117:0.86 120:0.69 123:0.75 124:0.67 126:0.54 127:0.84 129:0.59 131:0.61 133:0.64 135:0.96 139:0.97 140:0.45 144:0.76 145:0.42 146:0.77 147:0.81 149:0.95 150:0.66 151:0.85 153:0.48 154:0.81 155:0.63 157:0.86 158:0.86 159:0.73 161:0.60 162:0.86 164:0.56 165:0.93 166:0.77 167:0.46 170:0.82 172:0.90 173:0.14 174:0.93 176:0.73 177:0.88 179:0.17 181:0.66 182:0.79 187:0.68 190:0.89 192:0.86 193:0.98 195:0.84 197:0.95 201:0.67 202:0.36 204:0.83 206:0.81 208:0.64 212:0.87 215:0.84 216:0.14 219:0.95 222:0.35 226:0.96 227:0.61 229:0.61 230:0.73 231:0.67 232:0.99 233:0.76 235:0.42 236:0.97 239:0.99 241:0.05 242:0.52 243:0.50 245:0.97 246:0.94 247:0.94 248:0.77 253:0.88 255:0.72 256:0.45 259:0.21 260:0.70 262:0.93 265:0.56 266:0.84 267:0.18 268:0.46 271:0.16 276:0.93 279:0.80 281:0.91 282:0.88 283:0.67 285:0.50 287:0.61 290:0.92 291:0.97 292:0.88 297:0.36 299:0.88 300:0.70 +1 1:0.67 7:0.69 8:0.76 9:0.72 10:1.00 11:0.86 12:0.92 17:0.36 18:0.95 20:0.75 21:0.22 22:0.93 23:0.96 27:0.41 28:0.96 29:0.86 30:0.86 31:0.89 32:0.77 33:0.97 34:0.74 36:0.39 37:0.44 39:0.55 43:0.81 44:0.93 45:0.97 47:0.93 48:0.91 60:0.87 62:0.98 64:0.77 66:0.92 69:0.52 70:0.49 71:0.81 74:0.98 75:0.99 76:0.85 77:0.89 78:0.95 79:0.89 81:0.75 83:0.91 85:0.95 86:0.99 91:0.29 96:0.72 97:0.98 98:0.83 99:0.95 100:0.73 101:1.00 102:0.98 104:0.96 106:0.81 108:0.40 111:0.92 114:0.90 117:0.86 120:0.59 122:0.89 123:0.38 124:0.37 126:0.54 127:0.59 128:0.96 129:0.05 131:0.84 133:0.45 135:0.70 137:0.89 139:0.99 140:0.45 144:0.76 145:0.69 146:0.94 147:0.95 149:0.98 150:0.61 151:0.62 152:0.74 153:0.48 154:0.84 155:0.65 157:0.88 158:0.86 159:0.69 161:0.60 162:0.86 164:0.57 165:0.81 166:0.77 167:0.46 168:0.96 169:0.72 170:0.82 172:0.97 173:0.39 174:0.98 176:0.73 177:0.88 179:0.16 181:0.66 182:0.80 186:0.95 187:0.68 192:0.98 193:0.95 195:0.85 196:0.94 197:0.99 201:0.66 202:0.36 205:0.95 206:0.81 208:0.64 212:0.87 215:0.79 216:0.75 219:0.95 220:0.82 222:0.35 226:0.96 227:0.91 229:0.88 230:0.71 231:0.67 232:0.98 233:0.76 235:0.52 236:0.97 239:1.00 241:0.02 242:0.19 243:0.92 245:0.90 246:0.94 247:0.96 248:0.60 253:0.80 255:0.72 256:0.45 259:0.21 260:0.60 261:0.76 262:0.93 265:0.83 266:0.54 267:0.91 268:0.46 271:0.16 276:0.93 279:0.82 281:0.91 282:0.85 283:0.67 284:0.89 285:0.62 287:0.95 290:0.90 291:0.97 292:0.88 297:0.36 300:0.84 +2 1:0.60 6:0.89 7:0.69 8:0.79 9:0.74 10:0.85 11:0.46 12:0.92 17:0.59 18:0.61 20:0.77 21:0.30 27:0.26 28:0.96 29:0.86 30:0.08 32:0.67 34:0.42 36:0.32 37:0.70 39:0.55 43:0.23 45:0.97 55:0.71 66:0.23 69:0.93 70:0.95 71:0.81 74:0.76 76:0.85 77:0.70 78:0.93 81:0.53 83:0.56 86:0.60 91:0.38 96:0.88 97:0.94 98:0.49 99:0.83 100:0.91 101:0.47 104:0.79 106:0.81 108:0.85 111:0.91 114:0.82 117:0.86 120:0.78 123:0.84 124:0.96 126:0.32 127:0.99 129:0.59 131:0.61 133:0.96 135:0.54 139:0.58 140:0.45 145:0.44 146:0.98 147:0.73 149:0.81 150:0.48 151:0.93 152:0.96 153:0.78 154:0.15 155:0.56 157:0.61 158:0.76 159:0.96 161:0.60 162:0.70 164:0.78 165:0.65 166:0.61 167:0.50 169:0.92 170:0.82 172:0.96 173:0.64 174:0.99 176:0.63 177:0.05 179:0.11 181:0.66 182:0.61 186:0.93 187:0.21 189:0.91 190:0.89 192:0.58 195:0.99 197:0.92 201:0.57 202:0.73 206:0.81 208:0.50 212:0.60 215:0.72 216:0.52 222:0.35 227:0.61 229:0.61 230:0.71 231:0.64 232:0.88 233:0.76 235:0.42 238:0.86 239:0.84 241:0.30 242:0.45 243:0.07 244:0.61 245:0.98 246:0.61 247:0.99 248:0.85 253:0.90 254:0.88 255:0.72 256:0.75 257:0.93 259:0.21 260:0.78 261:0.95 265:0.50 266:0.94 267:0.69 268:0.76 271:0.16 276:0.98 279:0.78 282:0.75 283:0.67 285:0.50 287:0.61 290:0.82 297:0.36 300:0.94 +2 6:0.80 7:0.81 8:0.59 12:0.37 17:0.64 20:0.94 21:0.68 25:0.88 27:0.25 28:0.76 30:0.62 32:0.80 34:0.34 36:0.45 37:0.23 43:0.33 55:0.59 66:0.47 69:0.69 70:0.34 78:0.77 81:0.88 91:0.92 98:0.31 100:0.78 104:0.58 108:0.70 111:0.76 120:0.85 123:0.68 124:0.52 126:0.54 127:0.38 129:0.59 133:0.47 135:0.54 140:0.45 145:0.76 146:0.19 147:0.24 149:0.35 150:0.75 151:0.73 152:0.95 153:0.78 154:0.24 159:0.28 161:0.50 162:0.66 164:0.87 167:0.99 169:0.76 172:0.21 173:0.84 177:0.05 179:0.91 181:0.63 186:0.77 189:0.82 191:0.82 195:0.62 202:0.81 212:0.30 215:0.49 216:0.07 220:0.82 230:0.87 233:0.76 235:0.80 238:0.84 241:0.93 242:0.82 243:0.37 245:0.46 247:0.12 248:0.79 256:0.81 259:0.21 260:0.86 261:0.80 265:0.33 266:0.27 267:0.52 268:0.83 271:0.71 276:0.17 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.85 8:0.78 12:0.37 17:0.62 20:0.76 21:0.05 27:0.55 28:0.76 30:0.60 34:0.52 36:0.83 37:0.59 43:0.42 55:0.84 66:0.34 69:0.48 70:0.41 78:0.76 81:0.99 91:0.63 98:0.76 100:0.79 108:0.84 111:0.76 120:0.93 123:0.83 124:0.68 126:0.54 127:0.81 129:0.81 133:0.67 135:0.61 140:0.45 146:0.87 147:0.89 149:0.75 150:0.98 151:0.79 152:0.75 153:0.90 154:0.32 159:0.78 161:0.50 162:0.86 163:0.75 164:0.86 167:0.66 169:0.77 172:0.68 173:0.30 177:0.05 179:0.40 181:0.63 186:0.77 187:0.68 189:0.87 202:0.75 212:0.22 215:0.91 216:0.34 235:0.05 238:0.90 241:0.27 242:0.82 243:0.49 245:0.87 247:0.12 248:0.90 256:0.79 259:0.21 260:0.93 261:0.75 265:0.76 266:0.63 267:0.91 268:0.82 271:0.71 276:0.78 285:0.62 297:0.36 300:0.55 +1 12:0.37 17:0.78 25:0.88 27:0.72 28:0.76 30:0.95 34:0.18 36:0.36 43:0.29 55:0.98 66:0.20 69:0.75 81:0.99 91:0.81 98:0.09 104:0.54 108:0.58 123:0.56 124:0.61 126:0.54 127:0.33 129:0.59 133:0.47 135:0.56 145:0.69 146:0.05 147:0.05 149:0.17 150:0.99 154:0.21 159:0.30 161:0.50 167:0.95 172:0.05 173:0.86 177:0.05 178:0.55 179:1.00 181:0.63 215:0.96 235:0.05 241:0.80 243:0.40 245:0.36 259:0.21 265:0.09 267:0.44 271:0.71 297:0.36 300:0.08 +0 12:0.37 17:0.47 21:0.59 27:0.45 28:0.76 34:0.85 36:0.22 37:0.50 43:0.32 66:0.36 69:0.70 81:0.91 91:0.20 98:0.06 108:0.53 123:0.51 126:0.54 127:0.05 129:0.05 135:0.58 145:0.50 146:0.05 147:0.05 149:0.13 150:0.83 154:0.23 159:0.05 161:0.50 167:0.72 172:0.05 173:0.51 177:0.05 178:0.55 181:0.63 187:0.68 215:0.55 216:0.77 235:0.68 241:0.51 243:0.51 259:0.21 265:0.06 267:0.73 271:0.71 283:0.60 297:0.36 +2 6:0.98 8:0.94 12:0.37 17:0.20 20:0.75 21:0.13 25:0.88 27:0.03 28:0.76 30:0.95 34:0.31 36:0.30 37:0.95 43:0.27 55:0.99 66:0.20 69:0.79 78:0.94 81:0.98 91:0.86 98:0.07 100:0.96 104:0.58 108:0.62 111:0.95 120:0.89 123:0.60 124:0.61 126:0.54 127:0.41 129:0.59 133:0.47 135:0.42 140:0.87 145:0.76 146:0.05 147:0.05 149:0.15 150:0.97 151:0.75 152:1.00 153:0.84 154:0.20 159:0.53 161:0.50 162:0.69 164:0.85 167:0.86 169:0.96 172:0.05 173:0.77 177:0.05 178:0.48 179:1.00 181:0.63 186:0.93 187:0.21 189:0.98 191:0.98 202:0.79 215:0.84 216:0.65 220:0.74 235:0.12 238:0.97 241:0.74 243:0.40 245:0.36 248:0.84 256:0.80 259:0.21 260:0.89 261:1.00 265:0.07 267:0.69 268:0.82 271:0.71 283:0.60 285:0.62 297:0.36 300:0.08 +0 12:0.37 17:0.22 21:0.68 27:0.45 28:0.76 30:0.21 32:0.80 34:0.76 36:0.19 37:0.50 43:0.32 55:0.92 66:0.10 69:0.70 70:0.67 81:0.88 91:0.10 98:0.21 108:0.53 123:0.90 124:0.86 126:0.54 127:0.37 129:0.93 133:0.84 135:0.65 145:0.49 146:0.36 147:0.43 149:0.38 150:0.75 154:0.24 159:0.73 161:0.50 163:0.86 167:0.70 172:0.16 173:0.52 177:0.05 178:0.55 179:0.82 181:0.63 187:0.68 195:0.91 212:0.16 215:0.49 216:0.77 235:0.84 241:0.44 242:0.82 243:0.39 245:0.46 247:0.12 259:0.21 265:0.21 266:0.54 267:0.28 271:0.71 276:0.36 297:0.36 300:0.43 +3 12:0.37 17:0.47 21:0.13 25:0.88 27:0.03 28:0.76 30:0.95 34:0.29 36:0.26 37:0.95 43:0.25 55:0.99 66:0.20 69:0.83 81:0.98 91:0.46 98:0.07 104:0.58 106:0.81 108:0.68 120:0.57 123:0.66 124:0.61 126:0.54 127:0.33 129:0.59 133:0.47 135:0.76 140:0.80 145:0.76 146:0.05 147:0.05 149:0.14 150:0.97 151:0.49 153:0.48 154:0.18 159:0.53 161:0.50 162:0.62 164:0.67 167:0.88 172:0.05 173:0.81 177:0.05 178:0.42 179:1.00 181:0.63 187:0.39 202:0.60 206:0.81 215:0.84 216:0.65 220:0.74 235:0.12 241:0.75 243:0.40 245:0.36 248:0.50 256:0.58 259:0.21 260:0.58 265:0.07 267:0.89 268:0.59 271:0.71 285:0.62 297:0.36 300:0.08 +2 6:0.98 8:0.66 12:0.37 17:0.32 20:0.74 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.28 37:0.95 43:0.31 55:0.52 66:0.27 69:0.70 70:0.25 78:0.89 81:0.98 91:0.48 98:0.07 100:0.91 104:0.58 108:0.92 111:0.88 120:0.95 123:0.92 124:0.72 126:0.54 127:0.93 129:0.81 133:0.67 135:0.94 140:0.45 145:0.84 146:0.05 147:0.05 149:0.18 150:0.97 151:0.80 152:1.00 153:0.93 154:0.23 159:0.96 161:0.50 162:0.85 163:0.97 164:0.90 167:0.69 169:0.96 172:0.10 173:0.22 177:0.05 179:0.98 181:0.63 186:0.85 187:0.21 189:0.87 191:0.84 202:0.81 212:0.61 215:0.84 216:0.65 235:0.12 238:0.87 241:0.41 242:0.82 243:0.29 245:0.38 247:0.12 248:0.92 256:0.86 259:0.21 260:0.95 261:1.00 265:0.07 266:0.17 267:0.91 268:0.87 271:0.71 276:0.17 285:0.62 297:0.36 300:0.18 +2 7:0.81 8:0.80 12:0.37 17:0.78 20:0.75 21:0.30 27:0.43 28:0.76 30:0.68 32:0.80 34:0.64 36:0.47 37:0.63 43:0.51 55:0.52 66:0.77 69:0.19 70:0.61 78:0.78 81:0.97 91:0.51 98:0.75 100:0.80 106:0.81 108:0.58 111:0.77 120:0.69 123:0.56 124:0.38 126:0.54 127:0.45 129:0.59 133:0.47 135:0.58 140:0.45 145:0.65 146:0.05 147:0.05 149:0.59 150:0.95 151:0.66 152:0.74 153:0.48 154:0.40 159:0.41 161:0.50 162:0.86 164:0.57 167:0.64 169:0.78 172:0.59 173:0.28 177:0.05 179:0.68 181:0.63 186:0.79 187:0.21 195:0.67 202:0.36 206:0.81 212:0.69 215:0.72 216:0.69 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.13 245:0.52 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.74 265:0.75 266:0.47 267:0.23 268:0.46 271:0.71 276:0.48 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.81 7:0.81 8:0.61 12:0.37 17:0.45 20:0.90 21:0.30 27:0.21 28:0.76 30:0.58 34:0.47 36:0.67 37:0.74 43:0.52 55:0.84 66:0.36 69:0.10 70:0.33 78:0.76 81:0.97 91:0.63 98:0.57 100:0.78 104:0.84 106:0.81 108:0.09 111:0.75 120:0.82 123:0.09 124:0.59 126:0.54 127:0.32 129:0.59 133:0.47 135:0.19 140:0.45 146:0.61 147:0.66 149:0.47 150:0.95 151:0.78 152:0.87 153:0.89 154:0.41 159:0.39 161:0.50 162:0.83 163:0.86 164:0.82 167:0.75 169:0.76 172:0.27 173:0.20 177:0.05 179:0.80 181:0.63 186:0.77 187:0.39 189:0.83 191:0.76 202:0.71 206:0.81 212:0.37 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.85 241:0.61 242:0.82 243:0.51 245:0.56 247:0.12 248:0.74 256:0.78 259:0.21 260:0.83 261:0.78 265:0.59 266:0.38 267:0.89 268:0.77 271:0.71 276:0.37 283:0.82 285:0.62 297:0.36 300:0.25 +2 6:0.85 8:0.72 12:0.37 17:0.47 20:0.88 21:0.74 28:0.76 30:0.19 32:0.80 34:0.44 36:0.36 37:0.97 43:0.31 55:0.59 66:0.20 69:0.72 70:0.87 78:0.76 81:0.83 91:0.56 98:0.31 100:0.79 104:0.80 108:0.78 111:0.75 120:0.76 123:0.76 124:0.93 126:0.54 127:0.63 129:0.98 133:0.94 135:0.88 140:0.45 145:0.67 146:0.68 147:0.73 149:0.65 150:0.64 151:0.73 152:0.91 153:0.78 154:0.23 159:0.90 161:0.50 162:0.71 163:0.75 164:0.81 167:0.63 169:0.76 172:0.54 173:0.39 177:0.05 179:0.42 181:0.63 186:0.77 187:0.21 189:0.88 191:0.73 195:0.98 202:0.73 212:0.15 215:0.46 216:0.98 220:0.96 235:0.95 238:0.89 241:0.15 242:0.82 243:0.29 245:0.70 247:0.12 248:0.68 256:0.77 259:0.21 260:0.77 261:0.79 265:0.33 266:0.97 267:0.18 268:0.76 271:0.71 276:0.73 283:0.82 285:0.62 297:0.36 300:0.70 +3 6:0.98 8:0.97 12:0.37 17:0.36 20:0.97 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.73 37:0.95 43:0.51 55:0.71 66:0.27 69:0.10 70:0.25 78:0.99 81:0.98 91:0.99 98:0.21 100:0.99 104:0.58 108:0.09 111:1.00 120:0.98 123:0.09 124:0.72 126:0.54 127:0.65 129:0.81 133:0.67 135:0.82 140:0.45 145:0.76 146:0.28 147:0.34 149:0.29 150:0.97 151:0.85 152:1.00 153:0.98 154:0.40 159:0.50 161:0.50 162:0.61 163:0.79 164:0.97 167:0.99 169:0.96 172:0.10 173:0.19 177:0.05 179:0.98 181:0.63 186:0.99 189:0.99 191:1.00 202:0.96 212:0.61 215:0.84 216:0.65 220:0.62 235:0.12 238:1.00 241:0.93 242:0.82 243:0.46 245:0.38 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:1.00 265:0.23 266:0.17 267:0.95 268:0.97 271:0.71 276:0.20 283:0.82 285:0.62 297:0.36 300:0.18 +4 12:0.37 17:0.88 21:0.13 27:0.53 28:0.76 30:0.21 34:0.53 36:0.38 37:0.90 43:0.51 55:0.71 66:0.16 69:0.62 70:0.37 81:0.98 91:0.37 98:0.35 104:0.95 106:0.81 108:0.53 123:0.82 124:0.81 126:0.54 127:0.65 129:0.89 133:0.78 135:0.96 145:0.76 146:0.05 147:0.05 149:0.36 150:0.97 154:0.40 159:0.56 161:0.50 163:0.89 167:0.85 172:0.10 173:0.60 177:0.05 178:0.55 179:0.96 181:0.63 187:0.39 202:0.36 206:0.81 212:0.42 215:0.84 216:0.27 235:0.12 241:0.73 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.21 266:0.23 267:0.82 271:0.71 276:0.24 297:0.36 300:0.25 +2 6:0.81 8:0.60 12:0.37 17:0.93 20:0.84 21:0.59 25:0.88 27:0.72 28:0.76 30:0.76 32:0.80 34:0.39 36:0.36 43:0.49 55:0.52 66:0.64 69:0.37 70:0.15 78:0.74 81:0.98 91:0.65 98:0.65 100:0.77 104:0.54 108:0.29 111:0.74 120:0.68 123:0.28 126:0.54 127:0.19 129:0.05 135:0.69 140:0.45 145:0.54 146:0.29 147:0.35 149:0.29 150:0.97 151:0.64 152:0.80 153:0.46 154:0.37 159:0.12 161:0.50 162:0.80 164:0.79 167:0.97 169:0.75 172:0.16 173:0.79 177:0.05 179:0.94 181:0.63 186:0.75 189:0.83 195:0.53 202:0.68 212:0.95 215:0.55 216:0.04 220:0.62 235:0.80 238:0.83 241:0.86 242:0.82 243:0.69 247:0.12 248:0.63 256:0.73 259:0.21 260:0.70 261:0.75 265:0.66 266:0.12 267:0.28 268:0.74 271:0.71 276:0.16 285:0.62 297:0.36 300:0.13 +0 12:0.69 17:0.53 21:0.78 27:0.45 28:0.87 30:0.75 34:0.84 36:0.22 37:0.50 39:0.49 43:0.26 55:0.52 66:0.67 69:0.79 70:0.22 74:0.57 91:0.10 98:0.28 108:0.62 123:0.60 124:0.46 127:0.32 129:0.59 133:0.47 135:0.79 145:0.47 146:0.40 147:0.47 149:0.70 154:0.19 159:0.51 161:0.60 167:0.65 172:0.73 173:0.75 175:0.75 177:0.05 178:0.55 179:0.38 181:0.81 187:0.68 212:0.89 216:0.77 222:0.21 235:0.84 241:0.18 242:0.78 243:0.71 244:0.61 245:0.81 247:0.39 253:0.46 257:0.65 259:0.21 265:0.30 266:0.27 267:0.28 276:0.39 277:0.69 300:0.25 +2 1:0.74 7:0.78 8:0.75 9:0.80 12:0.69 17:0.36 21:0.30 25:0.88 27:0.17 28:0.87 30:0.15 32:0.77 34:0.56 36:0.21 37:0.88 39:0.49 43:0.38 55:0.71 66:0.82 69:0.48 70:0.68 74:0.83 76:0.94 77:0.89 81:0.68 91:0.51 96:0.71 98:0.91 104:0.54 106:0.81 108:0.37 114:0.86 117:0.86 120:0.58 123:0.35 126:0.54 127:0.51 129:0.05 135:0.94 140:0.80 145:0.77 146:0.76 147:0.80 149:0.49 150:0.73 151:0.58 153:0.48 154:0.28 155:0.67 158:0.84 159:0.32 161:0.60 162:0.62 163:0.81 164:0.57 167:0.69 172:0.48 173:0.61 176:0.73 177:0.38 178:0.42 179:0.83 181:0.81 187:0.68 192:0.59 195:0.63 201:0.74 202:0.50 206:0.81 208:0.64 212:0.30 215:0.72 216:0.65 220:0.87 222:0.21 230:0.81 232:0.93 233:0.76 235:0.42 241:0.37 242:0.33 243:0.83 244:0.61 247:0.47 248:0.57 253:0.75 255:0.79 256:0.45 259:0.21 260:0.59 265:0.91 266:0.47 267:0.44 268:0.46 276:0.40 282:0.85 283:0.71 285:0.62 290:0.86 297:0.36 300:0.43 +0 1:0.74 6:0.79 8:0.59 12:0.69 17:0.40 20:0.85 21:0.71 27:0.45 28:0.87 30:0.42 34:0.98 36:0.17 37:0.50 39:0.49 43:0.33 55:0.59 66:0.26 69:0.63 70:0.81 74:0.89 76:0.85 78:0.81 81:0.44 91:0.17 98:0.50 100:0.82 108:0.81 111:0.80 114:0.68 122:0.67 123:0.46 124:0.58 126:0.54 127:0.38 129:0.05 133:0.39 135:0.88 145:0.52 146:0.67 147:0.72 149:0.49 150:0.61 152:0.82 154:0.72 155:0.67 158:0.85 159:0.55 161:0.60 167:0.71 169:0.79 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.61 181:0.81 186:0.81 187:0.68 189:0.81 192:0.59 201:0.74 208:0.64 212:0.52 215:0.48 216:0.77 222:0.21 235:0.88 238:0.86 241:0.44 242:0.73 243:0.46 245:0.74 247:0.55 253:0.69 254:0.84 259:0.21 261:0.81 265:0.43 266:0.71 267:0.65 276:0.49 279:0.86 283:0.66 290:0.68 297:0.36 300:0.70 +2 7:0.78 8:0.70 12:0.69 17:0.73 21:0.59 25:0.88 27:0.17 28:0.87 30:0.95 32:0.77 34:0.55 36:0.37 37:0.88 39:0.49 43:0.27 55:0.92 66:0.54 69:0.78 74:0.63 76:0.85 77:0.70 81:0.29 91:0.69 98:0.56 104:0.54 108:0.61 120:0.72 123:0.59 126:0.32 127:0.17 129:0.05 135:0.65 140:0.87 145:0.82 146:0.46 147:0.52 149:0.17 150:0.27 151:0.66 153:0.48 154:0.19 159:0.17 161:0.60 162:0.59 164:0.77 167:0.81 172:0.10 173:0.91 177:0.38 178:0.48 179:0.97 181:0.81 187:0.21 190:0.77 191:0.88 195:0.63 202:0.72 215:0.55 216:0.65 220:0.91 222:0.21 230:0.81 233:0.76 235:0.68 241:0.69 243:0.63 244:0.61 248:0.65 253:0.49 256:0.71 259:0.21 260:0.72 265:0.57 267:0.44 268:0.72 282:0.85 285:0.50 297:0.36 300:0.08 +1 1:0.74 6:0.81 8:0.60 12:0.69 17:0.64 20:0.99 21:0.47 28:0.87 30:0.43 32:0.77 34:0.29 36:0.24 37:0.97 39:0.49 43:0.36 55:0.71 66:0.18 69:0.54 70:0.82 74:0.86 77:0.70 78:0.82 81:0.66 87:0.98 91:0.51 96:0.90 98:0.60 100:0.84 104:0.80 108:0.88 111:0.81 114:0.80 123:0.88 124:0.85 126:0.54 127:0.85 129:0.89 133:0.83 135:0.34 140:0.45 145:0.58 146:0.69 147:0.73 149:0.69 150:0.72 151:0.64 152:0.99 153:0.46 154:0.71 155:0.67 159:0.79 161:0.60 162:0.86 167:0.64 169:0.80 172:0.59 173:0.35 176:0.73 177:0.38 179:0.37 181:0.81 186:0.82 187:0.21 189:0.82 190:0.77 191:0.91 192:0.59 195:0.90 201:0.74 202:0.59 212:0.50 215:0.63 216:0.98 220:0.62 222:0.21 232:0.72 235:0.68 238:0.88 241:0.12 242:0.55 243:0.37 245:0.85 247:0.60 248:0.75 253:0.92 254:0.80 256:0.79 259:0.21 261:0.86 265:0.61 266:0.80 267:0.79 268:0.68 276:0.80 282:0.85 283:0.71 285:0.50 290:0.80 297:0.36 300:0.70 +0 8:0.82 12:0.69 17:0.38 21:0.59 28:0.87 30:0.87 34:0.49 36:0.25 37:0.97 39:0.49 43:0.36 55:0.52 66:0.08 69:0.55 70:0.46 74:0.90 77:0.70 81:0.46 87:0.98 91:0.87 96:0.97 98:0.22 108:0.84 114:0.64 122:0.67 123:0.40 124:0.84 126:0.32 127:0.92 129:0.93 133:0.84 135:0.80 140:0.45 145:0.63 146:0.56 147:0.62 149:0.56 150:0.37 151:0.79 153:0.91 154:0.27 158:0.84 159:0.91 161:0.60 162:0.66 167:0.74 172:0.51 173:0.22 175:0.75 176:0.56 177:0.05 179:0.61 181:0.81 187:0.21 190:0.77 191:0.92 195:0.97 202:0.89 212:0.54 216:0.98 222:0.21 232:0.72 235:0.74 241:0.57 242:0.77 243:0.25 244:0.61 245:0.69 247:0.55 248:0.92 253:0.98 256:0.91 257:0.65 259:0.21 265:0.23 266:0.87 267:0.73 268:0.91 276:0.49 277:0.69 282:0.84 285:0.50 290:0.64 300:0.70 +0 7:0.78 12:0.69 17:0.97 25:0.88 27:0.72 28:0.87 30:0.55 32:0.75 34:0.17 36:0.27 39:0.49 43:0.44 55:0.71 66:0.86 69:0.07 70:0.52 74:0.55 76:0.85 81:0.67 91:0.76 98:0.96 104:0.54 108:0.06 120:0.65 123:0.06 126:0.32 127:0.72 129:0.05 135:0.24 140:0.45 145:0.40 146:0.75 147:0.79 149:0.62 150:0.48 151:0.61 153:0.48 154:0.33 159:0.31 161:0.60 162:0.86 164:0.56 167:0.97 172:0.61 173:0.27 177:0.38 179:0.74 181:0.81 190:0.77 195:0.57 202:0.36 212:0.81 215:0.96 216:0.02 220:0.62 222:0.21 230:0.81 233:0.76 235:0.02 241:0.82 242:0.78 243:0.87 244:0.61 247:0.39 248:0.59 253:0.45 256:0.45 259:0.21 260:0.66 265:0.96 266:0.27 267:0.52 268:0.46 276:0.50 285:0.50 297:0.36 300:0.43 +3 1:0.74 12:0.69 17:0.36 21:0.47 27:0.48 28:0.87 30:0.38 32:0.77 34:0.20 36:0.25 37:0.55 39:0.49 43:0.45 55:0.71 66:0.25 69:0.19 70:0.83 74:0.88 77:0.70 81:0.66 87:0.98 91:0.31 98:0.65 108:0.89 114:0.80 120:0.69 123:0.88 124:0.74 126:0.54 127:0.88 129:0.81 133:0.67 135:0.47 140:0.45 145:0.58 146:0.74 147:0.78 149:0.69 150:0.72 151:0.66 153:0.48 154:0.70 155:0.67 159:0.81 161:0.60 162:0.86 164:0.56 167:0.73 172:0.67 173:0.12 176:0.73 177:0.38 179:0.35 181:0.81 187:0.39 190:0.77 192:0.59 195:0.90 201:0.74 202:0.36 212:0.42 215:0.63 216:0.92 222:0.21 232:0.76 235:0.68 241:0.53 242:0.55 243:0.39 245:0.91 247:0.67 248:0.63 253:0.73 254:0.80 256:0.45 259:0.21 260:0.70 265:0.66 266:0.80 267:0.65 268:0.46 276:0.81 282:0.85 283:0.82 285:0.50 290:0.80 297:0.36 300:0.70 +2 6:0.98 7:0.78 8:0.92 12:0.69 17:0.16 20:0.85 21:0.64 25:0.88 27:0.17 28:0.87 30:0.45 32:0.77 34:0.39 36:0.33 37:0.88 39:0.49 43:0.33 55:0.45 66:0.69 69:0.63 70:0.25 74:0.66 76:0.85 77:0.70 78:0.95 81:0.28 91:0.97 96:0.97 98:0.64 100:0.99 104:0.54 108:0.48 111:0.99 120:0.98 123:0.46 126:0.32 127:0.19 129:0.05 135:0.54 140:0.80 145:0.83 146:0.36 147:0.42 149:0.30 150:0.27 151:0.83 152:0.93 153:0.96 154:0.24 158:0.84 159:0.14 161:0.60 162:0.52 163:0.81 164:0.98 167:0.99 169:0.96 172:0.21 173:0.90 175:0.75 177:0.05 178:0.42 179:0.87 181:0.81 186:0.95 189:0.99 190:0.77 191:0.98 195:0.55 202:0.98 212:0.61 215:0.53 216:0.65 220:0.74 222:0.21 230:0.81 233:0.76 235:0.74 238:0.99 241:0.89 242:0.82 243:0.73 244:0.61 247:0.12 248:0.98 253:0.97 256:0.99 257:0.65 259:0.21 260:0.98 261:0.96 265:0.65 266:0.17 267:0.82 268:0.98 276:0.20 277:0.69 282:0.85 283:0.82 285:0.62 297:0.36 300:0.18 +0 1:0.74 6:0.81 8:0.61 12:0.69 17:0.57 20:0.80 21:0.68 28:0.87 30:0.17 32:0.77 34:0.61 36:0.21 37:0.97 39:0.49 43:0.36 55:0.52 66:0.92 69:0.56 70:0.85 74:0.86 77:0.70 78:0.77 81:0.52 87:0.98 91:0.42 98:0.95 100:0.81 104:0.80 108:0.43 111:0.77 114:0.70 123:0.41 126:0.54 127:0.66 129:0.05 135:0.74 145:0.59 146:0.92 147:0.93 149:0.57 150:0.63 152:0.99 154:0.69 155:0.67 159:0.54 161:0.60 167:0.62 169:0.80 172:0.78 173:0.54 176:0.73 177:0.38 178:0.55 179:0.52 181:0.81 186:0.78 187:0.21 192:0.59 195:0.71 201:0.74 212:0.49 215:0.49 216:0.98 222:0.21 232:0.72 235:0.88 241:0.07 242:0.45 243:0.92 247:0.67 253:0.68 254:0.84 259:0.21 261:0.86 265:0.95 266:0.63 267:0.36 276:0.67 282:0.85 283:0.71 290:0.70 297:0.36 300:0.55 +1 1:0.74 6:0.84 8:0.65 9:0.80 12:0.69 17:0.09 20:0.96 21:0.40 27:0.13 28:0.87 30:0.30 32:0.75 34:0.26 36:0.59 37:0.31 39:0.49 43:0.34 55:0.71 66:0.44 69:0.59 70:0.92 74:0.84 76:0.94 77:0.70 78:0.85 81:0.63 91:0.42 96:0.72 98:0.38 100:0.87 104:0.87 106:0.81 108:0.45 111:0.85 114:0.82 120:0.74 123:0.43 124:0.74 126:0.54 127:0.86 129:0.05 133:0.74 135:0.97 140:0.45 145:0.82 146:0.78 147:0.81 149:0.49 150:0.69 151:0.56 152:0.89 153:0.72 154:0.73 155:0.67 158:0.84 159:0.87 161:0.60 162:0.64 164:0.73 167:0.64 169:0.85 172:0.57 173:0.31 176:0.73 177:0.38 179:0.61 181:0.81 186:0.85 187:0.87 189:0.86 190:0.77 191:0.73 192:0.59 195:0.95 201:0.74 202:0.66 206:0.81 208:0.64 212:0.60 215:0.67 216:0.81 220:0.91 222:0.21 235:0.52 238:0.89 241:0.12 242:0.45 243:0.58 245:0.69 247:0.60 248:0.57 253:0.75 254:0.86 255:0.79 256:0.64 259:0.21 260:0.75 261:0.88 265:0.40 266:0.75 267:0.73 268:0.68 276:0.52 279:0.86 282:0.84 283:0.82 285:0.62 290:0.82 297:0.36 300:0.84 +2 6:0.98 7:0.78 8:0.92 12:0.69 17:0.45 20:0.90 21:0.59 25:0.88 27:0.17 28:0.87 30:0.95 32:0.77 34:0.37 36:0.23 37:0.88 39:0.49 43:0.38 55:0.84 66:0.64 69:0.50 74:0.65 76:0.85 77:0.70 78:0.85 81:0.29 91:0.90 98:0.74 100:0.93 104:0.54 106:0.81 108:0.38 111:0.88 120:0.93 122:0.43 123:0.37 126:0.32 127:0.23 129:0.05 135:0.32 140:0.80 145:0.82 146:0.43 147:0.49 149:0.26 150:0.27 151:0.73 152:0.93 153:0.81 154:0.28 159:0.16 161:0.60 162:0.60 163:0.94 164:0.89 167:0.78 169:0.96 172:0.16 173:0.77 177:0.38 178:0.42 179:0.97 181:0.81 186:0.85 187:0.21 189:0.96 190:0.77 191:0.92 195:0.57 202:0.85 206:0.81 212:0.95 215:0.55 216:0.65 220:0.97 222:0.21 230:0.81 233:0.76 235:0.68 238:0.96 241:0.66 242:0.82 243:0.69 244:0.61 247:0.12 248:0.90 253:0.50 256:0.87 259:0.21 260:0.93 261:0.96 265:0.74 266:0.12 267:0.23 268:0.86 276:0.19 282:0.85 283:0.82 285:0.50 297:0.36 300:0.08 +0 1:0.74 11:0.57 12:0.69 17:0.73 21:0.59 27:0.38 28:0.87 30:0.71 34:0.86 36:0.50 37:0.95 39:0.49 43:0.80 55:0.71 66:0.32 69:0.73 70:0.58 74:0.74 81:0.58 83:0.73 85:0.85 91:0.12 98:0.62 101:0.74 108:0.56 114:0.74 123:0.83 124:0.67 126:0.54 127:0.45 129:0.59 133:0.64 135:0.74 146:0.86 147:0.89 149:0.80 150:0.72 154:0.75 155:0.67 159:0.72 161:0.60 165:0.70 167:0.64 172:0.73 173:0.58 176:0.73 177:0.38 178:0.55 179:0.31 181:0.81 187:0.68 192:0.59 201:0.74 212:0.30 215:0.55 216:0.71 222:0.21 235:0.84 241:0.15 242:0.62 243:0.51 245:0.88 247:0.47 253:0.65 259:0.21 265:0.62 266:0.63 267:0.87 276:0.79 290:0.74 297:0.36 300:0.55 +1 6:0.82 7:0.81 8:0.62 12:0.06 17:0.38 20:0.95 21:0.78 27:0.08 28:0.68 34:0.61 36:0.55 37:0.40 78:0.91 91:0.86 100:0.87 111:0.89 135:0.68 145:0.99 152:0.88 161:0.71 167:0.52 169:0.85 175:0.75 178:0.55 181:0.54 186:0.91 187:0.39 189:0.84 191:0.78 202:0.79 216:0.24 230:0.87 233:0.76 235:0.52 238:0.82 241:0.41 244:0.61 257:0.65 261:0.88 267:0.59 271:0.51 277:0.69 +2 6:1.00 8:0.96 12:0.06 17:0.94 20:0.74 21:0.78 27:0.04 28:0.68 34:0.40 36:0.56 37:0.96 43:0.42 66:0.36 69:0.52 78:1.00 91:0.67 98:0.08 100:0.99 104:0.82 106:0.81 108:0.40 111:1.00 120:0.72 123:0.38 127:0.06 129:0.05 135:0.89 140:0.45 146:0.05 147:0.05 149:0.15 151:0.70 152:0.75 153:0.69 154:0.32 159:0.05 161:0.71 162:0.86 164:0.62 167:0.54 169:0.98 172:0.05 173:0.51 177:0.05 181:0.54 186:1.00 187:0.39 189:1.00 191:0.85 202:0.46 206:0.81 216:0.31 235:0.60 238:0.80 241:0.49 243:0.51 248:0.66 256:0.45 259:0.21 260:0.73 261:0.80 265:0.08 267:0.28 268:0.55 271:0.51 283:0.82 285:0.62 +2 6:0.93 8:0.85 12:0.06 17:0.83 20:0.76 21:0.59 27:0.54 28:0.68 30:0.45 32:0.80 34:0.49 36:0.39 37:0.58 43:0.51 55:0.45 66:0.77 69:0.27 70:0.33 78:1.00 81:0.93 91:0.93 98:0.94 100:0.88 104:0.57 108:0.21 111:0.99 120:0.74 123:0.20 126:0.54 127:0.61 129:0.05 135:0.30 140:0.87 145:0.54 146:0.50 147:0.57 149:0.46 150:0.89 151:0.70 152:0.74 153:0.71 154:0.40 159:0.18 161:0.71 162:0.69 163:0.81 164:0.82 167:0.80 169:0.86 172:0.37 173:0.65 177:0.05 178:0.48 179:0.92 181:0.54 186:1.00 189:0.94 191:0.82 195:0.53 202:0.75 212:0.52 215:0.55 216:0.07 220:0.96 235:0.74 238:0.81 241:0.84 242:0.82 243:0.79 247:0.12 248:0.67 256:0.77 259:0.21 260:0.75 261:0.78 265:0.94 266:0.27 267:0.18 268:0.78 271:0.51 276:0.29 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.82 7:0.81 8:0.63 12:0.06 17:0.30 20:0.95 21:0.78 27:0.06 28:0.68 34:0.92 36:0.59 37:0.34 78:0.98 91:0.80 100:0.92 111:0.95 120:0.53 135:0.37 140:0.87 145:0.52 151:0.46 152:0.88 153:0.46 161:0.71 162:0.51 164:0.70 167:0.67 169:0.90 175:0.75 178:0.48 181:0.54 186:0.98 187:0.21 189:0.84 191:0.73 202:0.71 216:0.10 220:0.96 230:0.87 233:0.76 235:0.84 238:0.83 241:0.69 244:0.61 248:0.46 256:0.64 257:0.65 259:0.21 260:0.53 261:0.92 267:0.44 268:0.63 271:0.51 277:0.69 283:0.60 285:0.62 +2 6:0.88 8:0.80 12:0.06 17:0.40 20:0.81 21:0.78 27:0.21 28:0.68 30:0.21 34:0.67 36:0.97 37:0.74 43:0.48 55:0.52 66:0.54 69:0.34 70:0.37 78:0.88 91:0.71 98:0.19 100:0.88 104:0.84 106:0.81 108:0.27 111:0.87 120:0.93 123:0.26 124:0.45 127:0.38 129:0.59 133:0.47 135:0.20 140:0.45 146:0.42 147:0.49 149:0.35 151:0.79 152:0.78 153:0.90 154:0.37 159:0.82 161:0.71 162:0.65 164:0.89 167:0.49 169:0.88 172:0.21 173:0.13 177:0.05 179:0.94 181:0.54 186:0.88 187:0.39 189:0.91 191:0.76 202:0.84 206:0.81 212:0.42 216:0.95 235:0.31 238:0.87 241:0.27 242:0.82 243:0.63 245:0.40 247:0.12 248:0.90 256:0.85 259:0.21 260:0.93 261:0.81 265:0.21 266:0.23 267:0.65 268:0.86 271:0.51 276:0.14 283:0.82 285:0.62 300:0.25 +2 6:0.90 8:0.84 12:0.06 17:0.36 20:0.96 21:0.78 27:0.07 28:0.68 34:0.25 36:0.23 37:0.78 78:0.96 91:0.93 100:0.93 111:0.94 120:0.61 135:0.92 140:0.87 151:0.56 152:0.94 153:0.72 161:0.71 162:0.46 164:0.64 167:0.83 169:0.89 175:0.75 178:0.48 181:0.54 186:0.96 189:0.92 191:0.94 202:0.80 216:0.37 220:0.62 235:0.42 238:0.84 241:0.99 244:0.61 248:0.55 256:0.45 257:0.65 259:0.21 260:0.62 261:0.92 267:0.85 268:0.57 271:0.51 277:0.69 285:0.62 +1 12:0.06 17:0.55 21:0.59 27:0.45 28:0.68 30:0.91 32:0.80 34:0.94 36:0.18 37:0.50 43:0.32 55:0.92 66:0.69 69:0.70 70:0.13 81:0.85 91:0.27 98:0.53 108:0.53 123:0.51 126:0.54 127:0.16 129:0.05 135:0.66 145:0.49 146:0.59 147:0.64 149:0.29 150:0.67 154:0.24 159:0.21 161:0.71 163:0.86 167:0.46 172:0.21 173:0.72 177:0.05 178:0.55 179:0.79 181:0.54 187:0.68 195:0.70 212:0.61 215:0.55 216:0.77 235:0.74 241:0.07 242:0.82 243:0.73 247:0.12 259:0.21 265:0.54 266:0.17 267:0.73 271:0.51 276:0.22 297:0.36 300:0.13 +1 8:0.85 12:0.06 17:0.28 21:0.76 27:0.45 28:0.68 30:0.21 32:0.80 34:0.63 36:0.18 37:0.50 43:0.32 55:0.59 66:0.36 69:0.71 70:0.37 81:0.71 91:0.17 98:0.19 108:0.94 123:0.94 124:0.67 126:0.54 127:0.32 129:0.81 133:0.67 135:0.83 145:0.54 146:0.05 147:0.05 149:0.21 150:0.53 154:0.24 159:0.84 161:0.71 163:0.99 167:0.48 172:0.16 173:0.39 177:0.05 178:0.55 179:0.93 181:0.54 187:0.68 195:0.97 212:0.42 215:0.46 216:0.77 235:0.97 241:0.18 242:0.82 243:0.26 245:0.40 247:0.12 259:0.21 265:0.21 266:0.23 267:0.36 271:0.51 276:0.21 297:0.36 300:0.25 +2 7:0.81 8:0.84 12:0.06 17:0.86 21:0.78 25:0.88 27:0.72 28:0.68 30:0.27 32:0.80 34:0.22 36:0.32 37:0.71 43:0.52 55:0.59 66:0.35 69:0.07 70:0.71 78:0.95 91:0.62 98:0.71 104:0.58 108:0.61 111:0.92 120:0.63 123:0.59 124:0.71 127:0.73 129:0.81 133:0.67 135:0.28 140:0.45 145:0.49 146:0.35 147:0.42 149:0.71 151:0.56 153:0.77 154:0.41 159:0.58 161:0.71 162:0.77 164:0.69 167:0.56 169:0.82 172:0.59 173:0.13 177:0.05 179:0.52 181:0.54 187:0.21 191:0.82 195:0.76 202:0.58 212:0.55 216:0.07 220:0.62 230:0.87 233:0.76 235:0.05 238:0.81 241:0.55 242:0.82 243:0.27 245:0.80 247:0.12 248:0.58 256:0.58 259:0.21 260:0.64 265:0.72 266:0.54 267:0.69 268:0.63 271:0.51 276:0.68 283:0.60 285:0.62 300:0.55 +2 6:0.87 8:0.77 12:0.06 17:0.28 20:0.77 21:0.78 27:0.21 28:0.68 30:0.21 34:0.67 36:0.96 37:0.74 43:0.48 55:0.52 66:0.54 69:0.34 70:0.37 78:0.91 91:0.65 98:0.19 100:0.93 106:0.81 108:0.27 111:0.90 120:0.85 123:0.26 124:0.45 127:0.38 129:0.59 133:0.47 135:0.24 140:0.80 146:0.42 147:0.49 149:0.35 151:0.73 152:0.76 153:0.81 154:0.37 159:0.82 161:0.71 162:0.52 164:0.79 167:0.47 169:0.90 172:0.21 173:0.13 177:0.05 178:0.42 179:0.94 181:0.54 186:0.94 187:0.39 189:0.89 191:0.70 202:0.79 206:0.81 212:0.42 216:0.95 235:0.31 238:0.87 241:0.15 242:0.82 243:0.63 245:0.40 247:0.12 248:0.79 256:0.75 259:0.21 260:0.86 261:0.81 265:0.21 266:0.23 267:0.69 268:0.74 271:0.51 276:0.14 283:0.82 285:0.62 300:0.25 +2 6:0.89 8:0.81 12:0.06 17:0.28 20:0.75 21:0.78 27:0.07 28:0.68 34:0.36 36:0.28 37:0.78 43:0.23 66:0.36 69:0.87 78:0.99 91:0.96 98:0.07 100:0.91 104:0.76 108:0.74 111:0.97 123:0.73 127:0.05 129:0.05 135:0.47 140:0.96 145:0.76 146:0.05 147:0.05 149:0.10 152:0.94 153:0.81 154:0.16 159:0.05 161:0.71 162:0.46 164:0.65 167:0.76 169:0.89 172:0.05 173:0.85 177:0.05 178:0.53 181:0.54 186:0.99 187:0.39 189:0.85 202:0.85 216:0.37 220:0.97 235:0.52 238:0.90 241:0.77 243:0.51 256:0.45 259:0.21 261:0.92 265:0.07 267:0.28 268:0.58 271:0.51 285:0.62 +2 6:0.89 8:0.77 12:0.06 17:0.72 20:0.95 21:0.78 25:0.88 27:0.04 28:0.68 34:0.42 36:0.21 37:0.66 78:0.99 91:0.90 100:0.97 104:0.76 111:0.98 120:0.63 135:0.75 140:0.45 151:0.56 152:0.88 153:0.77 161:0.71 162:0.86 164:0.69 167:0.81 169:0.95 175:0.75 181:0.54 186:0.99 189:0.90 191:0.75 202:0.54 216:0.04 220:0.62 238:0.88 241:0.87 244:0.61 248:0.58 256:0.58 257:0.65 260:0.64 261:0.94 267:0.52 268:0.63 271:0.51 277:0.69 285:0.62 +2 6:0.89 8:0.86 12:0.06 17:0.82 20:0.74 21:0.47 27:0.53 28:0.68 30:0.21 34:0.83 36:0.37 37:0.87 43:0.44 55:0.45 66:0.72 69:0.47 70:0.37 78:0.98 81:0.89 91:0.90 98:0.76 100:0.90 104:0.58 108:0.36 111:0.95 120:0.63 123:0.34 126:0.54 127:0.25 129:0.05 135:0.30 140:0.45 146:0.58 147:0.63 149:0.37 150:0.77 151:0.56 152:0.74 153:0.72 154:0.34 159:0.21 161:0.71 162:0.86 163:0.75 164:0.69 167:0.82 169:0.87 172:0.27 173:0.64 177:0.05 179:0.90 181:0.54 186:1.00 189:0.91 191:0.82 202:0.53 212:0.42 215:0.63 216:0.02 220:0.62 235:0.52 238:0.81 241:0.94 242:0.82 243:0.75 247:0.12 248:0.57 256:0.58 259:0.21 260:0.64 261:0.76 265:0.76 266:0.23 267:0.44 268:0.62 271:0.51 276:0.23 283:0.60 285:0.62 297:0.36 300:0.25 +2 12:0.06 17:0.61 21:0.30 27:0.12 28:0.68 30:0.38 34:0.33 36:0.21 37:0.66 43:0.36 55:0.92 66:0.20 69:0.61 70:0.50 81:0.92 91:0.91 98:0.37 108:0.72 120:0.94 123:0.70 124:0.85 126:0.54 127:0.63 129:0.93 133:0.84 135:0.21 140:0.80 145:0.86 146:0.05 147:0.05 149:0.44 150:0.85 151:0.74 153:0.83 154:0.27 159:0.59 161:0.71 162:0.67 163:0.94 164:0.94 167:0.69 172:0.21 173:0.57 177:0.05 178:0.42 179:0.83 181:0.54 187:0.87 202:0.91 212:0.28 215:0.72 216:0.39 235:0.31 241:0.71 242:0.82 243:0.16 245:0.49 247:0.12 248:0.91 256:0.92 259:0.21 260:0.94 265:0.40 266:0.63 267:0.44 268:0.92 271:0.51 276:0.42 285:0.62 297:0.36 300:0.33 +2 6:0.94 7:0.81 8:0.85 12:0.06 17:0.84 20:0.78 21:0.22 27:0.11 28:0.68 30:0.45 32:0.80 34:0.47 36:0.69 37:0.84 43:0.52 55:0.71 66:0.26 69:0.17 70:0.64 78:0.99 81:0.99 91:0.89 98:0.57 100:0.93 104:0.78 106:0.81 108:0.71 111:0.97 120:0.82 123:0.69 124:0.83 126:0.54 127:0.75 129:0.93 133:0.84 135:0.81 140:0.45 145:0.84 146:0.05 147:0.05 149:0.68 150:0.99 151:0.75 152:0.76 153:0.84 154:0.41 159:0.81 161:0.71 162:0.69 163:0.75 164:0.75 167:0.53 169:0.93 172:0.61 173:0.11 177:0.05 179:0.41 181:0.54 186:0.99 187:0.21 189:0.95 191:0.89 195:0.92 202:0.67 206:0.81 212:0.23 215:0.79 216:0.45 230:0.87 233:0.76 235:0.60 238:0.84 241:0.47 242:0.82 243:0.08 245:0.81 247:0.12 248:0.74 256:0.68 259:0.21 260:0.82 261:0.83 265:0.58 266:0.87 267:0.95 268:0.70 271:0.51 276:0.76 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:1.00 8:0.94 12:0.06 17:0.91 20:0.76 21:0.78 27:0.04 28:0.68 34:0.50 36:0.54 37:0.96 43:0.42 66:0.36 69:0.52 78:1.00 91:0.79 98:0.08 100:0.98 104:0.80 106:0.81 108:0.40 111:0.99 120:0.77 123:0.38 127:0.06 129:0.05 135:0.89 140:0.45 146:0.05 147:0.05 149:0.15 151:0.74 152:0.74 153:0.83 154:0.32 159:0.05 161:0.71 162:0.53 164:0.65 167:0.54 169:0.98 172:0.05 173:0.51 177:0.05 181:0.54 186:1.00 187:0.39 189:1.00 191:0.96 202:0.64 206:0.81 216:0.31 235:0.60 238:0.96 241:0.50 243:0.51 248:0.69 256:0.45 259:0.21 260:0.77 261:0.79 265:0.08 267:0.69 268:0.58 271:0.51 283:0.82 285:0.62 +0 12:0.99 17:0.83 21:0.78 27:0.64 34:0.70 36:0.41 37:0.69 43:0.30 66:0.36 69:0.75 91:0.35 98:0.10 108:0.58 123:0.56 127:0.07 129:0.05 135:0.18 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 172:0.05 173:0.92 177:0.05 178:0.55 187:0.68 202:0.36 216:0.46 235:0.97 241:0.59 243:0.51 259:0.21 265:0.10 267:0.28 +2 12:0.99 17:0.26 21:0.78 25:0.88 27:0.72 34:0.56 36:0.84 43:0.29 66:0.36 69:0.75 91:0.23 98:0.09 108:0.58 123:0.55 127:0.06 129:0.05 135:0.49 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 172:0.05 173:0.83 177:0.05 178:0.55 187:0.21 216:0.05 235:0.52 241:0.30 243:0.51 259:0.21 265:0.09 267:0.23 +1 12:0.99 17:0.66 21:0.78 25:0.88 27:0.72 30:0.85 34:0.67 36:0.93 37:0.94 43:0.39 55:0.52 66:0.87 69:0.58 70:0.15 91:0.06 98:0.82 108:0.44 123:0.42 127:0.30 129:0.05 132:0.97 135:0.56 146:0.49 147:0.55 149:0.66 154:0.30 159:0.17 172:0.63 173:0.85 177:0.05 178:0.55 179:0.57 187:0.39 212:0.93 216:0.16 235:0.95 241:0.15 242:0.82 243:0.88 247:0.12 265:0.82 266:0.17 267:0.73 276:0.54 300:0.13 +3 12:0.99 17:0.78 21:0.78 25:0.88 27:0.72 34:0.52 36:0.71 37:0.94 91:0.35 120:0.56 132:0.97 135:0.18 140:0.80 145:0.52 151:0.49 153:0.48 162:0.62 164:0.57 175:0.75 178:0.42 202:0.50 216:0.08 220:0.74 235:0.95 241:0.79 244:0.61 248:0.49 256:0.45 257:0.65 260:0.56 267:0.28 268:0.46 277:0.69 285:0.62 +1 12:0.99 17:0.67 21:0.78 25:0.88 27:0.72 30:0.86 34:0.58 36:0.71 37:0.94 43:0.48 55:0.52 66:0.84 69:0.41 70:0.15 91:0.08 98:0.86 108:0.32 123:0.31 127:0.38 129:0.05 132:0.97 135:0.30 146:0.52 147:0.58 149:0.60 154:0.37 159:0.19 172:0.54 173:0.70 177:0.05 178:0.55 179:0.73 187:0.39 212:0.95 216:0.16 235:0.95 241:0.32 242:0.82 243:0.85 247:0.12 265:0.86 266:0.12 267:0.65 276:0.45 300:0.13 +0 12:0.99 17:0.57 21:0.78 25:0.88 27:0.72 30:0.56 34:0.33 36:0.37 37:0.94 43:0.35 55:0.92 66:0.33 69:0.65 70:0.46 91:0.15 98:0.60 108:0.49 123:0.47 124:0.72 127:0.56 129:0.81 132:0.97 133:0.67 135:0.66 146:0.74 147:0.78 149:0.47 154:0.26 159:0.59 163:0.79 172:0.32 173:0.59 177:0.05 178:0.55 179:0.79 187:0.39 212:0.13 216:0.16 235:0.60 241:0.30 242:0.82 243:0.50 245:0.58 247:0.12 265:0.61 266:0.75 267:0.44 276:0.44 300:0.33 +0 1:0.69 8:0.59 9:0.76 11:0.75 12:0.20 17:0.43 18:0.84 21:0.05 22:0.93 27:0.72 29:0.77 30:0.66 31:0.92 34:0.63 36:0.97 37:0.72 39:0.74 43:0.70 45:0.83 47:0.93 64:0.77 66:0.68 69:0.35 70:0.40 71:0.91 74:0.60 79:0.92 81:0.64 83:0.60 86:0.83 91:0.61 96:0.79 97:0.89 98:0.57 99:0.83 101:0.52 108:0.27 114:0.85 122:0.80 123:0.26 124:0.43 126:0.44 127:0.84 129:0.59 131:0.88 133:0.47 135:0.65 137:0.92 139:0.83 140:0.80 144:0.57 146:0.78 147:0.82 149:0.67 150:0.62 151:0.70 153:0.72 154:0.60 155:0.58 157:0.87 158:0.79 159:0.73 162:0.57 163:0.92 165:0.70 166:0.82 172:0.51 173:0.24 175:0.75 176:0.66 177:0.38 178:0.42 179:0.80 182:0.86 187:0.21 190:0.77 192:0.51 196:0.93 201:0.68 202:0.64 208:0.54 212:0.36 216:0.26 219:0.92 220:0.62 222:0.76 227:0.87 229:0.92 231:0.74 235:0.12 241:0.55 242:0.38 243:0.72 244:0.61 245:0.58 246:0.86 247:0.55 248:0.72 253:0.76 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.58 266:0.63 267:0.65 268:0.62 271:0.65 276:0.33 277:0.69 279:0.82 284:0.92 285:0.56 287:0.89 290:0.85 292:0.95 300:0.33 +0 1:0.69 8:0.68 9:0.76 11:0.75 12:0.20 17:0.18 18:0.94 21:0.05 27:0.04 29:0.77 30:0.61 31:0.91 34:0.61 36:0.85 37:0.98 39:0.74 43:0.56 45:0.95 64:0.77 66:0.80 69:0.86 70:0.74 71:0.91 74:0.83 79:0.92 81:0.64 83:0.60 86:0.94 91:0.72 96:0.78 97:0.94 98:0.70 99:0.83 101:0.52 108:0.72 114:0.85 122:0.80 123:0.70 124:0.40 126:0.44 127:0.32 129:0.81 131:0.88 133:0.67 135:0.30 137:0.91 139:0.94 140:0.45 144:0.57 146:0.90 147:0.92 149:0.87 150:0.62 151:0.74 153:0.83 154:0.45 155:0.58 157:0.93 158:0.79 159:0.64 162:0.68 165:0.70 166:0.82 172:0.90 173:0.78 175:0.75 176:0.66 177:0.38 179:0.21 182:0.86 187:0.68 190:0.77 191:0.73 192:0.51 196:0.95 201:0.68 202:0.62 204:0.85 208:0.54 212:0.78 216:0.74 219:0.92 220:0.62 222:0.76 227:0.87 229:0.92 231:0.74 235:0.12 241:0.35 242:0.57 243:0.82 244:0.61 245:0.81 246:0.86 247:0.60 248:0.69 253:0.80 255:0.74 256:0.58 257:0.65 259:0.21 265:0.71 266:0.75 267:0.76 268:0.64 271:0.65 276:0.83 277:0.69 279:0.82 281:0.91 284:0.92 285:0.56 287:0.89 290:0.85 292:0.95 300:0.84 +0 8:0.68 12:0.20 17:0.32 18:0.57 21:0.78 27:0.72 29:0.77 30:0.95 34:0.85 36:0.66 37:0.90 39:0.74 43:0.07 55:1.00 66:0.13 69:0.93 71:0.91 74:0.24 86:0.57 91:0.56 98:0.05 108:0.86 122:0.67 123:0.85 124:0.75 127:0.61 129:0.81 131:0.61 133:0.67 135:0.47 139:0.55 145:0.69 146:0.05 147:0.05 149:0.06 154:0.07 157:0.61 159:0.95 163:1.00 166:0.61 172:0.05 173:0.63 175:0.91 177:0.05 178:0.55 179:0.99 182:0.61 191:0.73 202:0.79 212:0.95 216:0.13 222:0.76 227:0.61 229:0.61 231:0.61 235:0.42 241:0.80 242:0.82 243:0.34 244:0.83 245:0.37 246:0.61 247:0.12 253:0.20 257:0.84 259:0.21 265:0.05 266:0.12 267:0.65 271:0.65 276:0.19 277:0.87 287:0.61 300:0.08 +0 12:0.20 17:0.36 18:0.62 21:0.78 27:0.34 29:0.77 30:0.15 34:0.67 36:0.92 37:0.46 39:0.74 43:0.10 55:0.84 66:0.24 69:0.80 70:0.68 71:0.91 74:0.28 86:0.58 91:0.08 98:0.24 108:0.94 122:0.77 123:0.94 124:0.88 127:0.55 129:0.95 131:0.61 133:0.87 135:0.88 139:0.56 145:0.63 146:0.05 147:0.05 149:0.12 154:0.09 157:0.61 159:0.86 163:0.90 166:0.61 172:0.21 173:0.56 175:0.91 177:0.05 178:0.55 179:0.84 182:0.61 187:0.87 212:0.15 216:0.53 222:0.76 227:0.61 229:0.61 231:0.63 235:0.22 241:0.10 242:0.73 243:0.17 244:0.83 245:0.46 246:0.61 247:0.39 253:0.24 257:0.84 259:0.21 265:0.27 266:0.63 267:0.23 271:0.65 276:0.38 277:0.87 287:0.61 300:0.43 +0 7:0.66 8:0.79 11:0.75 12:0.20 17:0.55 18:0.85 21:0.54 27:0.13 29:0.77 30:0.32 32:0.64 34:0.80 36:0.80 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.35 69:0.81 70:0.84 71:0.91 74:0.62 81:0.25 86:0.83 91:0.54 98:0.44 101:0.52 104:0.86 106:0.81 108:0.65 120:0.65 122:0.80 123:0.63 124:0.82 126:0.26 127:0.39 129:0.05 131:0.61 133:0.81 135:0.61 139:0.80 140:0.45 144:0.57 145:0.50 146:0.76 147:0.54 149:0.69 150:0.25 151:0.55 153:0.48 154:0.58 157:0.86 159:0.74 162:0.84 164:0.70 166:0.61 172:0.61 173:0.66 177:0.38 179:0.40 182:0.77 187:0.39 190:0.89 195:0.92 202:0.65 206:0.81 212:0.52 215:0.39 216:0.65 220:0.62 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.63 242:0.22 243:0.38 244:0.61 245:0.74 246:0.61 247:0.79 248:0.56 253:0.39 256:0.71 257:0.65 259:0.21 260:0.62 265:0.46 266:0.80 267:0.96 268:0.71 271:0.65 276:0.68 283:0.78 285:0.47 287:0.61 297:0.36 300:0.70 +0 1:0.69 11:0.75 12:0.20 17:0.55 18:0.69 21:0.59 27:0.45 29:0.77 30:0.76 34:0.85 36:0.17 37:0.50 39:0.74 43:0.64 45:0.73 64:0.77 66:0.36 69:0.70 70:0.22 71:0.91 74:0.49 81:0.33 83:0.60 86:0.72 91:0.18 97:0.89 98:0.27 99:0.83 101:0.52 108:0.53 114:0.57 122:0.57 123:0.51 124:0.57 126:0.44 127:0.36 129:0.05 131:0.61 133:0.43 135:0.77 139:0.75 144:0.57 145:0.42 146:0.39 147:0.45 149:0.40 150:0.52 154:0.54 155:0.58 157:0.81 158:0.78 159:0.52 163:0.88 165:0.70 166:0.82 172:0.16 173:0.66 176:0.66 177:0.70 178:0.55 179:0.94 182:0.85 187:0.68 192:0.51 201:0.68 208:0.54 212:0.42 216:0.77 219:0.92 222:0.76 227:0.61 229:0.61 231:0.73 235:0.74 241:0.37 242:0.45 243:0.51 244:0.61 245:0.43 246:0.86 247:0.35 253:0.40 259:0.21 265:0.30 266:0.23 267:0.44 271:0.65 276:0.15 279:0.82 287:0.61 290:0.57 292:0.91 300:0.18 +0 12:0.20 17:0.47 18:0.96 21:0.78 27:0.52 29:0.77 30:0.93 34:0.83 36:0.27 37:0.43 39:0.74 43:0.14 55:0.42 66:0.88 69:0.61 70:0.19 71:0.91 74:0.26 86:0.66 91:0.10 98:0.30 108:0.95 122:0.86 123:0.95 124:0.38 127:0.73 129:0.89 131:0.61 133:0.78 135:0.82 139:0.64 145:0.67 146:0.05 147:0.05 149:0.35 154:0.10 157:0.61 159:0.89 166:0.61 172:0.97 173:0.29 175:0.91 177:0.05 178:0.55 179:0.17 182:0.61 187:0.68 202:0.50 212:0.93 216:0.79 222:0.76 227:0.61 229:0.61 231:0.67 235:0.74 241:0.15 242:0.82 243:0.05 244:0.83 245:0.84 246:0.61 247:0.55 253:0.23 257:0.84 259:0.21 265:0.32 266:0.47 267:0.44 271:0.65 276:0.90 277:0.87 287:0.61 300:0.55 +0 1:0.69 8:0.83 11:0.75 12:0.20 17:0.28 18:0.77 21:0.30 27:0.04 29:0.77 30:0.29 34:0.68 36:0.58 37:0.98 39:0.74 43:0.56 45:0.83 55:0.52 64:0.77 66:0.11 69:0.86 70:0.76 71:0.91 74:0.63 81:0.42 83:0.60 86:0.79 91:0.48 97:0.94 98:0.35 99:0.83 101:0.52 108:0.72 114:0.63 122:0.77 123:0.81 124:0.74 126:0.44 127:0.30 129:0.59 131:0.61 133:0.64 135:0.37 139:0.83 144:0.57 146:0.46 147:0.52 149:0.61 150:0.55 154:0.45 155:0.58 157:0.88 158:0.78 159:0.54 165:0.70 166:0.82 172:0.51 173:0.83 175:0.75 176:0.66 177:0.38 178:0.55 179:0.38 182:0.85 187:0.68 192:0.51 201:0.68 204:0.85 208:0.54 212:0.68 216:0.74 219:0.92 222:0.76 227:0.61 229:0.61 231:0.73 235:0.42 241:0.30 242:0.70 243:0.46 244:0.61 245:0.80 246:0.86 247:0.47 253:0.49 257:0.65 259:0.21 265:0.32 266:0.80 267:0.59 271:0.65 276:0.64 277:0.69 279:0.82 281:0.89 287:0.61 290:0.63 292:0.91 300:0.55 +0 7:0.66 8:0.83 11:0.75 12:0.20 17:0.61 18:0.85 21:0.54 27:0.13 29:0.77 30:0.26 32:0.64 34:0.79 36:0.79 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.33 69:0.81 70:0.87 71:0.91 74:0.62 81:0.25 86:0.83 91:0.46 98:0.44 101:0.52 104:0.89 106:0.81 108:0.65 120:0.59 123:0.63 124:0.83 126:0.26 127:0.43 129:0.05 131:0.61 133:0.81 135:0.54 139:0.80 140:0.45 144:0.57 145:0.50 146:0.76 147:0.54 149:0.69 150:0.25 151:0.51 153:0.48 154:0.58 157:0.87 159:0.75 162:0.86 164:0.65 166:0.61 172:0.61 173:0.67 177:0.38 179:0.40 182:0.77 187:0.39 190:0.89 191:0.75 195:0.92 202:0.58 206:0.81 212:0.52 215:0.39 216:0.65 220:0.96 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.32 242:0.19 243:0.37 244:0.61 245:0.76 246:0.61 247:0.79 248:0.51 253:0.39 256:0.64 257:0.65 259:0.21 260:0.58 265:0.46 266:0.87 267:0.97 268:0.66 271:0.65 276:0.71 283:0.78 285:0.47 287:0.61 297:0.36 300:0.70 +0 7:0.66 8:0.85 11:0.75 12:0.20 17:0.73 18:0.85 21:0.54 27:0.13 29:0.77 30:0.19 32:0.64 34:0.81 36:0.84 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.30 69:0.81 70:0.72 71:0.91 74:0.62 81:0.25 86:0.83 91:0.22 98:0.48 101:0.52 104:0.83 106:0.81 108:0.65 123:0.63 124:0.80 126:0.26 127:0.41 129:0.05 131:0.61 133:0.74 135:0.49 139:0.80 144:0.57 145:0.50 146:0.76 147:0.54 149:0.71 150:0.25 154:0.58 157:0.87 159:0.71 166:0.61 172:0.65 173:0.69 177:0.38 178:0.55 179:0.33 182:0.77 187:0.39 191:0.75 195:0.90 206:0.81 212:0.54 215:0.39 216:0.65 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.41 242:0.38 243:0.32 244:0.61 245:0.84 246:0.61 247:0.79 253:0.39 257:0.65 259:0.21 265:0.49 266:0.80 267:0.94 271:0.65 276:0.76 283:0.78 287:0.61 297:0.36 300:0.55 +0 1:0.69 7:0.66 8:0.75 11:0.75 12:0.20 17:0.02 18:0.81 21:0.05 22:0.93 27:0.37 29:0.77 30:0.71 34:0.87 36:0.62 37:0.98 39:0.74 43:0.70 45:0.81 47:0.93 64:0.77 66:0.45 69:0.37 70:0.40 71:0.91 74:0.60 81:0.64 83:0.60 86:0.84 91:0.27 97:0.94 98:0.54 99:0.83 101:0.52 108:0.58 114:0.85 117:0.86 122:0.80 123:0.55 124:0.57 126:0.44 127:0.27 129:0.59 131:0.61 133:0.47 135:0.23 139:0.83 144:0.57 145:0.45 146:0.20 147:0.25 149:0.67 150:0.62 154:0.60 155:0.58 157:0.87 158:0.79 159:0.29 165:0.70 166:0.82 172:0.32 173:0.44 175:0.75 176:0.66 177:0.38 178:0.55 179:0.71 182:0.85 187:0.68 192:0.51 201:0.68 208:0.54 212:0.30 216:0.52 219:0.92 222:0.76 227:0.61 229:0.61 230:0.69 231:0.73 232:0.87 233:0.76 235:0.12 241:0.27 242:0.63 243:0.44 244:0.61 245:0.59 246:0.86 247:0.35 253:0.58 257:0.65 259:0.21 262:0.93 265:0.56 266:0.47 267:0.82 271:0.65 276:0.35 277:0.69 279:0.82 287:0.61 290:0.85 292:0.95 300:0.33 +1 8:0.78 11:0.75 12:0.20 17:0.95 18:0.98 21:0.30 22:0.93 27:0.71 29:0.77 30:0.30 31:0.86 34:0.73 36:0.89 37:0.65 39:0.74 43:0.68 44:0.85 45:0.81 47:0.93 48:0.98 55:0.52 64:0.77 66:0.84 69:0.12 70:0.90 71:0.91 74:0.51 79:0.87 81:0.30 86:0.86 91:0.70 98:0.88 101:0.52 108:0.10 122:0.86 123:0.10 124:0.38 126:0.26 127:0.59 129:0.05 131:0.82 133:0.37 135:0.42 137:0.86 139:0.83 140:0.80 144:0.57 145:0.70 146:0.91 147:0.93 149:0.67 150:0.28 151:0.48 153:0.69 154:0.58 157:0.86 159:0.58 162:0.59 166:0.74 172:0.84 173:0.17 177:0.70 178:0.42 179:0.39 182:0.77 187:0.21 190:0.89 191:0.70 195:0.78 196:0.88 202:0.55 212:0.57 216:0.29 219:0.87 220:0.87 222:0.76 227:0.83 229:0.61 231:0.71 235:0.31 241:0.03 242:0.58 243:0.85 244:0.61 245:0.74 246:0.61 247:0.79 248:0.48 253:0.35 256:0.45 259:0.21 262:0.93 265:0.88 266:0.63 267:0.79 268:0.55 271:0.65 276:0.76 281:0.89 284:0.86 285:0.47 287:0.61 292:0.91 300:0.70 +1 7:0.66 8:0.80 11:0.75 12:0.20 17:0.70 18:0.80 21:0.59 27:0.13 29:0.77 30:0.21 32:0.64 34:0.77 36:0.87 37:0.83 39:0.74 43:0.62 45:0.81 55:0.52 66:0.19 69:0.60 70:0.75 71:0.91 74:0.55 81:0.24 86:0.81 91:0.51 98:0.54 101:0.52 104:0.87 106:0.81 108:0.46 120:0.58 123:0.75 124:0.52 126:0.26 127:0.30 129:0.05 131:0.61 133:0.43 135:0.54 139:0.77 140:0.45 144:0.57 145:0.52 146:0.74 147:0.78 149:0.64 150:0.24 151:0.49 153:0.48 154:0.51 157:0.86 159:0.55 162:0.86 164:0.68 166:0.61 172:0.65 173:0.48 177:0.70 179:0.40 182:0.76 187:0.39 190:0.89 195:0.85 202:0.62 206:0.81 212:0.52 215:0.39 216:0.65 220:0.97 222:0.76 227:0.61 229:0.61 230:0.69 231:0.69 233:0.73 235:0.68 241:0.62 242:0.28 243:0.63 244:0.61 245:0.82 246:0.61 247:0.76 248:0.50 253:0.37 256:0.71 259:0.21 260:0.57 265:0.55 266:0.75 267:0.96 268:0.70 271:0.65 276:0.65 277:0.69 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +1 9:0.76 11:0.75 12:0.20 17:0.73 18:0.91 21:0.64 22:0.93 27:0.27 29:0.77 30:0.27 31:0.89 32:0.64 34:0.88 36:0.99 37:0.87 39:0.74 43:0.70 45:0.85 47:0.93 55:0.71 66:0.92 69:0.41 70:0.66 71:0.91 74:0.60 79:0.88 81:0.24 83:0.60 86:0.87 91:0.54 96:0.72 98:0.96 101:0.52 108:0.32 117:0.86 120:0.53 122:0.80 123:0.31 126:0.26 127:0.70 129:0.05 131:0.87 135:0.79 137:0.89 139:0.83 140:0.80 144:0.57 145:0.93 146:0.93 147:0.95 149:0.75 150:0.24 151:0.59 153:0.48 154:0.60 155:0.58 157:0.89 159:0.57 162:0.86 164:0.54 166:0.61 172:0.79 173:0.37 177:0.70 179:0.52 182:0.86 187:0.39 190:0.77 192:0.51 195:0.76 196:0.91 202:0.54 208:0.54 212:0.29 215:0.38 216:0.43 220:0.99 222:0.76 227:0.86 229:0.92 231:0.73 232:0.92 235:0.74 241:0.65 242:0.24 243:0.92 244:0.61 246:0.61 247:0.76 248:0.58 253:0.50 255:0.74 256:0.58 259:0.21 260:0.53 262:0.93 265:0.96 266:0.54 267:0.98 268:0.63 271:0.65 276:0.68 284:0.87 285:0.62 287:0.89 297:0.36 300:0.43 +0 7:0.66 8:0.86 11:0.75 12:0.20 17:0.61 18:0.85 21:0.54 27:0.13 29:0.77 30:0.21 32:0.64 34:0.84 36:0.62 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.12 69:0.81 70:0.77 71:0.91 74:0.62 81:0.25 86:0.83 91:0.40 98:0.44 101:0.52 104:0.86 106:0.81 108:0.65 120:0.54 123:0.85 124:0.80 126:0.26 127:0.32 129:0.05 131:0.61 133:0.74 135:0.49 139:0.80 140:0.45 144:0.57 145:0.50 146:0.75 147:0.54 149:0.70 150:0.25 151:0.46 153:0.48 154:0.58 157:0.86 159:0.72 162:0.86 164:0.53 166:0.61 172:0.63 173:0.66 177:0.38 179:0.29 182:0.77 187:0.39 190:0.89 195:0.93 202:0.36 206:0.81 212:0.51 215:0.39 216:0.65 220:0.93 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.57 242:0.39 243:0.33 244:0.61 245:0.84 246:0.61 247:0.79 248:0.46 253:0.39 256:0.45 257:0.65 259:0.21 260:0.54 265:0.44 266:0.75 267:0.98 268:0.46 271:0.65 276:0.76 277:0.87 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +0 8:0.64 12:0.20 17:0.83 18:0.66 21:0.78 27:0.72 29:0.77 30:0.76 34:0.59 36:0.98 39:0.74 43:0.17 55:0.71 66:0.54 69:0.88 70:0.22 71:0.91 74:0.25 86:0.58 91:0.65 98:0.40 108:0.76 122:0.57 123:0.74 124:0.45 127:0.78 129:0.05 131:0.61 133:0.37 135:0.74 139:0.57 145:0.80 146:0.41 147:0.05 149:0.10 154:0.22 157:0.61 159:0.45 163:0.86 166:0.61 172:0.21 173:0.97 177:0.05 178:0.55 179:0.97 182:0.61 202:0.67 212:0.42 216:0.10 222:0.76 227:0.61 229:0.61 231:0.63 235:0.60 241:0.81 242:0.45 243:0.26 244:0.61 245:0.40 246:0.61 247:0.35 253:0.23 254:0.95 257:0.84 259:0.21 265:0.42 266:0.23 267:0.79 271:0.65 276:0.17 287:0.61 300:0.18 +0 1:0.69 8:0.76 11:0.75 12:0.20 17:0.15 18:0.67 21:0.22 27:0.04 29:0.77 30:0.45 34:0.70 36:0.65 37:0.98 39:0.74 43:0.56 45:0.73 64:0.77 66:0.77 69:0.86 70:0.50 71:0.91 74:0.40 76:0.94 81:0.48 83:0.60 86:0.70 91:0.48 98:0.60 99:0.83 101:0.52 108:0.72 114:0.67 122:0.80 123:0.70 126:0.44 127:0.17 129:0.05 131:0.61 135:0.47 139:0.68 144:0.57 146:0.63 147:0.68 149:0.38 150:0.57 154:0.45 155:0.58 157:0.82 158:0.71 159:0.23 165:0.70 166:0.82 172:0.37 173:0.92 175:0.75 176:0.66 177:0.38 178:0.55 179:0.64 182:0.85 187:0.87 192:0.51 201:0.68 208:0.54 212:0.23 216:0.74 222:0.76 227:0.61 229:0.61 231:0.73 232:0.94 235:0.31 241:0.60 242:0.55 243:0.79 244:0.61 246:0.86 247:0.39 253:0.50 257:0.65 259:0.21 265:0.61 266:0.38 267:0.91 271:0.65 276:0.29 277:0.69 287:0.61 290:0.67 300:0.33 +1 1:0.69 11:0.75 12:0.20 17:0.59 18:0.88 21:0.59 22:0.93 27:0.39 29:0.77 30:0.44 34:0.19 36:0.42 37:0.62 39:0.74 43:0.58 45:0.83 47:0.93 66:0.54 69:0.83 70:0.89 71:0.91 74:0.54 81:0.30 86:0.88 91:0.51 98:0.56 101:0.52 108:0.67 114:0.54 122:0.67 123:0.65 124:0.68 126:0.44 127:0.36 129:0.05 131:0.61 133:0.74 135:0.70 139:0.85 144:0.57 145:0.85 146:0.86 147:0.05 149:0.58 150:0.47 154:0.59 155:0.58 157:0.86 159:0.73 166:0.74 172:0.70 173:0.69 176:0.55 177:0.05 178:0.55 179:0.40 182:0.77 187:0.39 192:0.59 201:0.68 208:0.54 212:0.35 215:0.38 216:0.15 219:0.87 222:0.76 227:0.61 229:0.61 231:0.72 235:0.74 241:0.64 242:0.13 243:0.09 245:0.73 246:0.61 247:0.88 253:0.38 254:0.74 257:0.84 259:0.21 262:0.93 265:0.57 266:0.87 267:0.97 271:0.65 276:0.66 287:0.61 290:0.54 292:0.95 297:0.36 300:0.84 +0 1:0.69 11:0.75 12:0.20 17:0.92 18:0.78 22:0.93 27:0.65 29:0.77 30:0.45 34:0.39 36:0.75 37:0.31 39:0.74 43:0.71 45:0.77 47:0.93 48:0.91 64:0.77 66:0.82 69:0.21 70:0.61 71:0.91 74:0.63 76:1.00 77:0.96 81:0.73 83:0.60 86:0.81 91:0.12 97:0.89 98:0.82 99:0.83 101:0.52 108:0.17 114:0.95 117:0.86 122:0.77 123:0.16 126:0.44 127:0.31 129:0.05 131:0.61 132:0.97 135:0.66 139:0.82 144:0.57 145:0.87 146:0.58 147:0.63 149:0.62 150:0.69 154:0.61 155:0.58 157:0.85 158:0.79 159:0.21 165:0.70 166:0.82 172:0.48 173:0.46 176:0.66 177:0.70 178:0.55 179:0.75 182:0.86 187:0.21 192:0.51 195:0.55 201:0.68 204:0.85 208:0.54 212:0.61 216:0.67 219:0.92 222:0.76 227:0.61 229:0.61 231:0.73 232:0.81 235:0.05 241:0.07 242:0.33 243:0.83 244:0.61 246:0.86 247:0.55 253:0.64 259:0.21 262:0.93 265:0.82 266:0.27 267:0.52 271:0.65 276:0.37 279:0.82 281:0.86 282:0.71 287:0.61 290:0.95 292:0.95 300:0.43 +1 7:0.66 8:0.84 11:0.75 12:0.20 17:0.85 18:0.80 21:0.47 27:0.13 29:0.77 30:0.14 32:0.64 34:0.80 36:0.88 37:0.83 39:0.74 43:0.62 45:0.77 55:0.52 66:0.53 69:0.59 70:0.80 71:0.91 74:0.51 81:0.27 86:0.77 91:0.51 98:0.52 101:0.52 104:0.78 106:0.81 108:0.45 120:0.67 123:0.43 124:0.52 126:0.26 127:0.27 129:0.05 131:0.61 133:0.43 135:0.54 139:0.74 140:0.45 144:0.57 145:0.45 146:0.74 147:0.78 149:0.58 150:0.26 151:0.55 153:0.48 154:0.51 157:0.84 159:0.55 162:0.78 164:0.70 166:0.61 172:0.63 173:0.46 177:0.70 179:0.39 182:0.75 187:0.21 190:0.89 191:0.70 195:0.85 202:0.67 206:0.81 212:0.61 215:0.41 216:0.65 220:0.74 222:0.76 227:0.61 229:0.61 230:0.69 231:0.69 233:0.73 235:0.52 241:0.59 242:0.30 243:0.62 244:0.61 245:0.81 246:0.61 247:0.76 248:0.57 253:0.36 256:0.71 259:0.21 260:0.64 265:0.53 266:0.75 267:0.85 268:0.72 271:0.65 276:0.64 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +1 7:0.72 8:0.79 11:0.75 12:0.20 17:0.95 18:0.94 21:0.22 22:0.93 27:0.71 29:0.77 30:0.14 31:0.88 32:0.68 34:0.73 36:0.95 37:0.65 39:0.74 43:0.70 44:0.85 45:0.66 47:0.93 48:0.97 55:0.42 64:0.77 66:0.53 69:0.10 70:0.99 71:0.91 74:0.43 79:0.90 81:0.33 86:0.82 91:0.48 98:0.38 101:0.52 108:0.09 120:0.63 122:0.86 123:0.09 124:0.80 126:0.26 127:0.69 129:0.05 131:0.82 133:0.86 135:0.32 137:0.88 139:0.79 140:0.45 144:0.57 145:0.69 146:0.59 147:0.65 149:0.51 150:0.30 151:0.64 153:0.46 154:0.74 157:0.77 159:0.71 162:0.86 164:0.57 166:0.74 172:0.63 173:0.12 177:0.70 179:0.57 182:0.72 187:0.21 190:0.77 191:0.70 192:0.51 195:0.85 196:0.89 202:0.49 212:0.69 216:0.29 219:0.87 220:0.87 222:0.76 227:0.83 229:0.61 230:0.75 231:0.69 233:0.76 235:0.22 241:0.10 242:0.22 243:0.62 245:0.62 246:0.61 247:0.84 248:0.62 253:0.33 254:0.97 255:0.74 256:0.58 259:0.21 260:0.62 262:0.93 265:0.40 266:0.71 267:0.52 268:0.58 271:0.65 276:0.61 281:0.91 284:0.86 285:0.56 287:0.61 292:0.91 300:0.94 +0 1:0.69 7:0.63 12:0.20 17:0.72 18:0.60 21:0.59 27:0.28 29:0.62 30:0.21 32:0.62 34:0.47 36:0.91 37:0.47 39:0.54 43:0.17 55:0.96 66:0.16 69:0.41 70:0.79 71:0.68 74:0.27 77:0.70 81:0.31 86:0.62 91:0.08 98:0.27 108:0.32 114:0.57 123:0.31 124:0.92 126:0.44 127:0.92 129:0.05 133:0.91 135:0.95 139:0.60 144:0.85 145:0.90 146:0.62 147:0.67 149:0.18 150:0.48 154:0.47 155:0.58 159:0.87 163:0.98 172:0.27 173:0.17 175:0.75 176:0.66 177:0.38 178:0.55 179:0.73 187:0.39 192:0.59 195:0.96 201:0.68 208:0.54 212:0.16 215:0.39 216:0.44 222:0.76 230:0.63 233:0.73 235:0.74 241:0.18 242:0.75 243:0.37 244:0.61 245:0.55 247:0.55 253:0.39 254:0.97 257:0.65 259:0.21 265:0.29 266:0.89 267:0.65 271:0.65 276:0.54 277:0.69 282:0.71 290:0.57 297:0.36 300:0.55 +0 8:0.92 11:0.92 12:0.20 17:0.59 18:0.60 21:0.78 27:0.58 29:0.62 30:0.74 34:0.34 36:0.94 37:0.47 39:0.54 43:0.13 45:0.83 66:0.20 69:0.99 70:0.39 71:0.68 74:0.31 85:0.80 86:0.66 91:0.04 98:0.14 101:0.97 108:0.98 122:0.41 123:0.98 124:0.74 127:0.12 129:0.81 133:0.67 135:0.88 139:0.64 144:0.85 146:0.41 147:0.48 149:0.25 154:0.10 159:0.62 172:0.41 173:0.98 177:0.70 178:0.55 179:0.13 187:0.39 202:0.46 212:0.70 216:0.68 222:0.76 235:0.68 241:0.18 242:0.19 243:0.45 245:0.73 247:0.55 253:0.26 259:0.21 265:0.14 266:0.63 267:0.28 271:0.65 276:0.52 300:0.43 +0 11:0.92 12:0.20 17:0.53 18:0.72 21:0.78 27:0.72 29:0.62 30:0.21 34:0.70 36:0.51 39:0.54 43:0.78 45:0.66 66:0.47 69:0.44 70:0.77 71:0.68 74:0.41 85:0.72 86:0.79 91:0.06 98:0.38 101:0.97 104:0.79 108:0.34 123:0.32 124:0.74 127:0.66 129:0.05 133:0.73 135:0.54 139:0.76 144:0.85 146:0.68 147:0.72 149:0.62 154:0.70 159:0.78 172:0.41 173:0.25 177:0.70 178:0.55 179:0.78 187:0.21 212:0.36 216:0.12 222:0.76 235:0.60 241:0.44 242:0.38 243:0.59 245:0.53 247:0.74 253:0.32 259:0.21 265:0.40 266:0.71 267:0.59 271:0.65 276:0.43 300:0.55 +0 1:0.69 7:0.63 12:0.20 17:0.66 18:0.69 21:0.64 27:0.31 29:0.62 30:0.33 32:0.62 34:0.59 36:0.92 37:0.91 39:0.54 43:0.15 55:0.52 66:0.60 69:0.54 70:0.74 71:0.68 74:0.30 81:0.30 86:0.75 91:0.35 98:0.62 104:0.58 106:0.81 108:0.41 114:0.56 123:0.39 124:0.58 126:0.44 127:0.62 129:0.05 133:0.59 135:0.98 139:0.71 144:0.85 145:0.79 146:0.72 147:0.76 149:0.20 150:0.47 154:0.64 155:0.58 159:0.56 172:0.63 173:0.50 176:0.66 177:0.70 178:0.55 179:0.59 187:0.39 192:0.59 195:0.77 201:0.68 206:0.81 208:0.54 212:0.58 215:0.38 216:0.46 222:0.76 230:0.63 233:0.73 235:0.80 241:0.10 242:0.42 243:0.67 245:0.70 247:0.82 253:0.38 254:0.97 265:0.63 266:0.63 267:0.87 271:0.65 276:0.58 290:0.56 297:0.36 300:0.55 +2 1:0.69 11:0.92 12:0.20 17:0.47 18:0.86 21:0.68 27:0.53 29:0.62 30:0.57 34:0.61 36:0.95 37:0.32 39:0.54 43:0.70 45:0.88 64:0.77 66:0.23 69:0.62 70:0.64 71:0.68 74:0.41 81:0.32 83:0.60 85:0.72 86:0.92 91:0.11 98:0.51 99:0.83 101:0.97 108:0.74 114:0.54 122:0.57 123:0.45 124:0.79 126:0.44 127:0.58 129:0.81 132:0.97 133:0.78 135:0.61 139:0.88 144:0.85 145:0.69 146:0.68 147:0.72 149:0.75 150:0.51 154:0.70 155:0.58 159:0.62 165:0.70 172:0.51 173:0.55 176:0.55 177:0.70 178:0.55 179:0.49 187:0.21 192:0.51 201:0.68 208:0.54 212:0.36 216:0.75 222:0.76 235:0.84 241:0.44 242:0.31 243:0.43 245:0.77 247:0.60 253:0.35 265:0.35 266:0.71 267:0.99 271:0.65 276:0.67 290:0.54 300:0.55 +1 11:0.88 12:0.20 17:0.12 18:0.63 21:0.78 27:0.33 29:0.62 30:0.95 34:0.87 36:0.68 37:0.47 39:0.54 43:0.77 66:0.64 69:0.80 71:0.68 74:0.41 85:0.72 86:0.72 91:0.49 98:0.12 101:0.87 108:0.64 122:0.57 123:0.61 127:0.08 129:0.05 135:0.70 139:0.70 144:0.85 145:0.93 146:0.17 147:0.22 149:0.52 154:0.69 159:0.09 163:0.97 172:0.16 173:0.90 177:0.70 178:0.55 179:0.09 187:0.68 202:0.36 212:0.95 216:0.51 222:0.76 235:0.42 241:0.05 242:0.82 243:0.69 247:0.12 253:0.32 259:0.21 265:0.13 266:0.12 267:0.65 271:0.65 276:0.19 300:0.08 +1 11:0.64 12:0.20 17:0.82 18:0.82 21:0.78 22:0.93 27:0.72 29:0.62 30:0.71 34:0.92 36:0.86 39:0.54 43:0.71 45:0.81 47:0.93 66:0.24 69:0.79 70:0.40 71:0.68 74:0.32 83:0.60 86:0.84 91:0.17 97:0.89 98:0.54 101:0.52 104:0.79 108:0.31 117:0.86 122:0.80 123:0.60 124:0.57 127:0.43 129:0.05 133:0.43 135:0.63 139:0.84 145:0.99 146:0.44 147:0.30 149:0.68 154:0.60 155:0.58 158:0.72 159:0.30 172:0.32 173:0.92 177:0.38 178:0.55 179:0.81 187:0.39 192:0.51 208:0.54 212:0.61 216:0.15 219:0.92 222:0.76 232:0.85 235:0.68 241:0.18 242:0.33 243:0.58 244:0.61 245:0.59 247:0.55 253:0.27 257:0.65 259:0.21 262:0.93 265:0.24 266:0.38 267:0.19 271:0.65 276:0.31 279:0.82 292:0.95 300:0.33 +1 1:0.69 8:0.83 9:0.76 11:0.64 12:0.20 17:0.47 18:0.93 21:0.30 22:0.93 27:0.21 29:0.62 30:0.73 31:0.95 34:0.49 36:0.70 37:0.74 39:0.54 43:0.72 45:0.94 47:0.93 64:0.77 66:0.71 69:0.76 70:0.54 71:0.68 74:0.36 79:0.96 81:0.43 83:0.60 86:0.95 91:0.17 96:0.87 97:0.89 98:0.69 99:0.83 101:0.52 108:0.59 114:0.60 117:0.86 122:0.80 123:0.57 124:0.46 126:0.44 127:0.44 129:0.05 133:0.66 135:0.21 137:0.95 139:0.95 140:0.45 146:0.88 147:0.31 149:0.87 150:0.56 151:0.91 153:0.84 154:0.50 155:0.58 158:0.72 159:0.67 162:0.73 165:0.70 172:0.80 173:0.66 176:0.55 177:0.38 179:0.36 187:0.39 190:0.77 192:0.51 196:0.97 201:0.68 202:0.64 204:0.85 208:0.54 212:0.59 216:0.95 219:0.92 222:0.76 232:0.85 235:0.42 241:0.50 242:0.54 243:0.19 245:0.77 247:0.67 248:0.83 253:0.59 254:0.95 255:0.74 256:0.64 257:0.65 259:0.21 262:0.93 265:0.70 266:0.63 267:0.36 268:0.68 271:0.65 276:0.72 279:0.82 281:0.91 284:0.94 285:0.56 290:0.60 292:0.95 300:0.70 +0 12:0.20 17:0.32 18:0.70 21:0.78 27:0.45 29:0.62 30:0.45 34:0.71 36:0.17 37:0.50 39:0.54 43:0.11 55:0.59 66:0.49 69:0.77 70:0.25 71:0.68 74:0.27 86:0.71 91:0.22 98:0.16 108:0.60 122:0.80 123:0.58 124:0.48 127:0.13 129:0.05 133:0.39 135:0.44 139:0.68 145:0.42 146:0.31 147:0.38 149:0.08 154:0.46 159:0.29 163:0.79 172:0.16 173:0.55 175:0.75 177:0.38 178:0.55 179:0.57 187:0.68 212:0.61 216:0.77 222:0.76 235:0.80 241:0.61 242:0.63 243:0.60 244:0.61 245:0.39 247:0.30 253:0.24 254:0.95 257:0.65 259:0.21 265:0.17 266:0.17 267:0.36 271:0.65 276:0.16 277:0.69 300:0.18 +0 8:0.85 12:0.20 17:0.62 18:0.56 21:0.78 27:0.72 29:0.62 30:0.76 34:0.24 36:0.61 39:0.54 43:0.06 55:1.00 66:0.13 69:0.98 70:0.15 71:0.68 74:0.24 86:0.57 91:0.27 98:0.05 108:0.96 122:0.57 123:0.96 124:0.75 127:0.40 129:0.81 133:0.67 135:0.92 139:0.56 145:0.91 146:0.05 147:0.05 149:0.05 154:0.06 159:0.99 163:1.00 172:0.05 173:0.66 175:0.91 177:0.05 178:0.55 179:0.99 202:0.64 212:0.95 216:0.02 222:0.76 235:0.42 241:0.78 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.20 257:0.84 259:0.21 265:0.05 266:0.12 267:0.28 271:0.65 276:0.19 277:0.87 300:0.13 +2 1:0.69 9:0.76 11:0.64 12:0.20 17:0.30 18:0.75 21:0.54 27:0.66 29:0.62 30:0.62 31:0.96 34:0.94 36:0.77 37:0.53 39:0.54 43:0.72 45:0.73 64:0.77 66:0.47 69:0.19 70:0.34 71:0.68 74:0.30 79:0.96 81:0.35 83:0.60 86:0.78 91:0.60 96:0.88 97:0.94 98:0.23 99:0.83 101:0.52 108:0.15 114:0.57 122:0.80 123:0.15 124:0.52 126:0.44 127:0.46 129:0.05 132:0.97 133:0.39 135:0.77 137:0.96 139:0.77 140:0.45 146:0.22 147:0.28 149:0.54 150:0.52 151:0.84 153:0.72 154:0.62 155:0.58 158:0.71 159:0.24 162:0.86 165:0.70 172:0.21 173:0.45 175:0.75 176:0.55 177:0.38 179:0.93 187:0.39 190:0.77 192:0.51 196:0.93 201:0.68 202:0.58 208:0.54 212:0.61 216:0.21 219:0.89 222:0.76 232:0.89 235:0.68 241:0.61 242:0.33 243:0.59 244:0.61 245:0.46 247:0.39 248:0.84 253:0.59 255:0.74 256:0.64 257:0.65 265:0.25 266:0.23 267:0.23 268:0.66 271:0.65 276:0.15 277:0.69 279:0.82 284:0.93 285:0.56 290:0.57 292:0.91 300:0.25 +2 7:0.63 8:0.71 11:0.64 12:0.20 17:0.61 18:0.79 21:0.78 27:0.70 29:0.62 30:0.60 34:0.36 36:1.00 37:0.40 39:0.54 43:0.65 45:0.88 66:0.27 69:0.42 70:0.65 71:0.68 74:0.31 83:0.60 86:0.82 91:0.15 97:0.89 98:0.39 101:0.52 104:0.78 108:0.86 122:0.57 123:0.85 124:0.84 127:0.51 129:0.81 133:0.82 135:0.99 139:0.82 146:0.40 147:0.46 149:0.62 154:0.22 155:0.58 158:0.72 159:0.77 163:0.81 172:0.45 173:0.23 177:0.70 178:0.55 179:0.56 187:0.39 192:0.51 208:0.54 212:0.30 216:0.32 219:0.92 222:0.76 230:0.63 232:0.87 233:0.73 241:0.18 242:0.33 243:0.22 245:0.67 247:0.60 253:0.26 254:0.97 259:0.21 265:0.41 266:0.84 267:0.87 271:0.65 276:0.60 279:0.82 292:0.95 300:0.55 +0 1:0.69 7:0.63 12:0.20 17:0.86 18:0.68 21:0.71 27:0.66 29:0.62 30:0.21 32:0.69 34:0.62 36:0.93 37:0.90 39:0.54 43:0.14 55:0.52 66:0.60 69:0.62 70:0.85 71:0.68 74:0.40 76:0.97 77:0.89 81:0.29 86:0.74 91:0.40 98:0.66 104:0.58 106:0.81 108:0.47 114:0.53 123:0.46 124:0.58 126:0.44 127:0.67 129:0.05 133:0.59 135:0.96 139:0.70 144:0.85 145:0.74 146:0.75 147:0.79 149:0.29 150:0.47 154:0.60 155:0.58 159:0.57 172:0.67 173:0.60 176:0.55 177:0.70 178:0.55 179:0.55 187:0.39 192:0.59 195:0.75 201:0.68 206:0.81 208:0.54 212:0.52 215:0.37 216:0.28 222:0.76 230:0.63 233:0.73 235:0.88 241:0.10 242:0.59 243:0.67 245:0.73 247:0.84 253:0.34 254:0.97 265:0.67 266:0.63 267:0.59 271:0.65 276:0.62 282:0.77 290:0.53 297:0.36 300:0.55 +0 12:0.20 17:0.57 18:0.59 21:0.78 27:0.34 29:0.62 30:0.76 34:0.58 36:0.73 37:0.46 39:0.54 43:0.12 55:0.99 66:0.13 69:0.71 70:0.15 71:0.68 74:0.26 86:0.61 91:0.14 98:0.05 108:0.54 122:0.80 123:0.52 124:0.75 127:0.71 129:0.81 133:0.67 135:0.42 139:0.59 145:0.59 146:0.05 147:0.05 149:0.08 154:0.10 159:0.92 163:0.99 172:0.05 173:0.34 175:0.91 177:0.05 178:0.55 179:0.99 187:0.87 212:0.95 216:0.53 222:0.76 235:0.74 241:0.44 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.23 257:0.84 259:0.21 265:0.05 266:0.12 267:0.23 271:0.65 276:0.17 277:0.87 300:0.13 +1 11:0.91 12:0.20 17:0.06 18:0.77 21:0.47 27:0.10 29:0.62 30:0.60 34:0.71 36:0.97 37:0.86 39:0.54 43:0.57 45:0.83 66:0.44 69:0.81 70:0.53 71:0.68 74:0.39 81:0.26 86:0.84 91:0.12 98:0.42 101:0.52 108:0.65 120:0.53 122:0.57 123:0.63 124:0.58 126:0.26 127:0.20 129:0.59 133:0.46 135:0.74 139:0.80 140:0.80 144:0.85 145:0.70 146:0.57 147:0.63 149:0.46 150:0.25 151:0.46 153:0.48 154:0.64 159:0.36 162:0.54 164:0.52 172:0.41 173:0.79 177:0.70 178:0.42 179:0.46 187:0.87 190:0.89 202:0.56 212:0.30 215:0.41 216:0.81 220:0.97 222:0.76 235:0.52 241:0.43 242:0.33 243:0.57 245:0.68 247:0.60 248:0.46 253:0.31 254:0.74 256:0.45 259:0.21 260:0.53 265:0.44 266:0.47 267:0.73 268:0.46 271:0.65 276:0.46 285:0.47 297:0.36 300:0.43 +0 8:0.86 12:0.20 17:0.77 18:0.57 21:0.78 27:0.72 29:0.62 30:0.62 34:0.50 36:0.68 37:0.77 39:0.54 43:0.06 55:0.96 66:0.07 69:0.97 70:0.23 71:0.68 74:0.25 86:0.59 91:0.93 98:0.05 108:0.94 122:0.55 123:0.94 124:0.89 127:0.24 129:0.95 133:0.87 135:0.61 139:0.57 145:0.82 146:0.05 147:0.05 149:0.06 154:0.06 159:0.82 163:0.99 172:0.05 173:0.91 175:0.91 177:0.05 178:0.55 179:0.84 202:0.75 212:0.61 216:0.06 222:0.76 235:0.60 241:0.81 242:0.33 243:0.23 244:0.83 245:0.39 247:0.39 253:0.23 257:0.84 259:0.21 265:0.06 266:0.23 267:0.87 271:0.65 276:0.29 277:0.87 300:0.18 +1 1:0.74 6:0.95 7:0.81 8:0.93 9:0.80 11:0.75 12:0.51 17:0.09 18:0.96 20:0.95 21:0.68 26:0.95 27:0.12 28:0.80 29:0.56 30:0.15 31:0.98 34:0.25 36:0.70 37:0.88 39:0.80 41:0.98 43:0.37 45:0.94 46:0.97 58:1.00 64:0.77 66:0.17 69:0.54 70:0.90 71:0.74 74:0.57 78:0.80 79:0.99 81:0.44 83:0.91 86:0.81 91:0.92 96:0.96 98:0.61 100:0.83 101:0.52 104:0.58 107:1.00 108:0.90 110:0.98 111:0.80 114:0.56 120:0.92 121:0.90 122:0.85 123:0.78 124:0.82 125:0.97 126:0.54 127:0.90 129:0.81 131:0.94 133:0.84 135:0.21 137:0.98 139:0.84 140:0.90 141:0.97 144:0.57 146:0.82 147:0.85 149:0.61 150:0.70 151:0.87 152:0.88 153:0.88 154:0.78 155:0.67 157:0.61 159:0.84 160:1.00 161:0.54 162:0.60 164:0.91 165:0.93 166:0.90 167:0.94 169:0.81 172:0.85 173:0.30 176:0.73 177:0.70 179:0.23 181:0.91 182:0.99 186:0.81 189:0.96 191:0.94 192:0.87 196:0.98 199:1.00 201:0.93 202:0.95 204:0.89 208:0.64 212:0.52 215:0.37 216:0.41 220:0.74 222:0.25 223:0.94 224:0.98 227:0.94 229:0.99 230:0.86 231:0.87 233:0.73 234:0.97 235:0.88 238:0.91 241:0.82 242:0.14 243:0.28 245:0.93 246:0.97 247:0.95 248:0.92 253:0.92 254:0.84 255:0.95 256:0.96 259:0.21 260:0.88 261:0.85 265:0.54 266:0.80 267:0.94 268:0.96 271:0.80 274:1.00 276:0.90 281:0.89 283:0.78 284:0.99 285:0.62 287:0.98 289:0.97 290:0.56 297:0.36 300:0.70 +1 1:0.74 11:0.92 12:0.51 17:0.38 18:0.93 21:0.54 26:0.95 27:0.45 28:0.80 29:0.56 30:0.53 34:0.80 36:0.21 37:0.50 39:0.80 43:0.82 45:0.92 46:0.97 58:0.93 64:0.77 66:0.20 69:0.70 70:0.86 71:0.74 74:0.63 81:0.54 83:0.91 85:0.90 86:0.89 91:0.12 98:0.51 99:0.83 101:0.97 107:1.00 108:0.79 110:0.98 114:0.59 122:0.57 123:0.77 124:0.85 125:0.88 126:0.54 127:0.57 129:0.89 131:0.61 133:0.84 135:0.74 139:0.85 141:0.91 144:0.57 145:0.47 146:0.74 147:0.78 149:0.85 150:0.73 154:0.77 155:0.67 157:0.98 159:0.79 160:0.88 161:0.54 165:1.00 166:0.91 167:0.73 172:0.73 173:0.51 176:0.73 177:0.70 178:0.55 179:0.24 181:0.91 182:0.97 187:0.68 192:0.87 199:0.97 201:0.93 208:0.64 212:0.67 215:0.39 216:0.77 222:0.25 223:0.94 224:0.93 227:0.61 229:0.61 231:0.86 234:0.91 235:0.84 241:0.59 242:0.15 243:0.40 245:0.91 246:0.97 247:0.92 253:0.44 259:0.21 265:0.53 266:0.87 267:0.79 271:0.80 274:0.91 276:0.86 287:0.61 289:0.96 290:0.59 297:0.36 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.85 12:0.51 17:0.12 18:0.99 20:0.75 21:0.05 25:0.88 26:0.95 27:0.16 28:0.80 29:0.56 30:0.42 31:1.00 32:0.80 34:0.30 36:0.98 37:0.53 39:0.80 41:0.99 43:0.69 44:0.93 45:0.98 46:0.99 58:1.00 60:0.87 64:0.77 66:0.91 69:0.53 70:0.60 71:0.74 74:0.76 77:0.89 78:0.93 79:1.00 81:0.84 83:0.91 85:0.72 86:0.83 91:0.82 96:0.99 97:1.00 98:0.96 99:0.83 100:0.96 101:0.97 104:0.58 107:1.00 108:0.41 110:0.98 111:0.92 114:0.75 120:0.97 121:0.90 122:0.85 123:0.39 124:0.37 125:0.97 126:0.54 127:0.90 129:0.05 131:0.94 133:0.37 135:0.85 137:1.00 138:0.96 139:0.93 140:0.87 141:0.97 144:0.57 145:0.69 146:0.99 147:0.99 149:0.94 150:0.89 151:0.98 152:0.89 153:0.95 154:0.54 155:0.67 157:0.81 158:0.92 159:0.85 160:1.00 161:0.54 162:0.75 164:0.96 165:1.00 166:0.91 167:0.82 169:0.90 172:0.98 173:0.29 176:0.66 177:0.70 179:0.14 181:0.91 182:0.99 186:0.87 187:0.68 189:0.99 191:0.96 192:0.87 195:0.94 196:1.00 199:1.00 201:0.93 202:0.96 204:0.89 208:0.64 212:0.82 215:0.61 216:0.90 219:0.95 220:0.62 222:0.25 223:0.94 224:0.98 227:0.94 229:0.99 230:0.86 231:0.87 232:0.91 233:0.73 234:0.98 235:0.52 238:0.90 241:0.72 242:0.31 243:0.92 245:0.95 246:0.97 247:0.91 248:0.98 253:0.98 255:0.95 256:0.98 259:0.21 260:0.95 261:0.92 265:0.96 266:0.80 267:0.88 268:0.97 271:0.80 274:1.00 276:0.96 279:0.86 281:0.89 282:0.88 283:0.82 284:1.00 285:0.62 287:0.98 289:0.97 290:0.75 292:0.95 297:0.36 299:0.88 300:0.55 +1 1:0.74 7:0.63 11:0.92 12:0.51 17:0.66 18:0.96 21:0.54 26:0.95 27:0.45 28:0.80 29:0.56 30:0.62 34:0.67 36:0.63 37:0.50 39:0.80 43:0.82 45:0.96 46:0.99 58:0.93 60:0.87 64:0.77 66:0.35 69:0.62 70:0.62 71:0.74 74:0.73 81:0.48 83:0.91 85:0.88 86:0.92 91:0.04 98:0.65 101:0.97 107:1.00 108:0.81 110:0.98 114:0.59 122:0.85 123:0.80 124:0.71 125:0.88 126:0.54 127:0.61 129:0.81 131:0.61 133:0.67 135:0.88 139:0.92 141:0.91 144:0.57 145:0.50 146:0.84 147:0.87 149:0.92 150:0.72 154:0.77 155:0.67 157:0.97 158:0.91 159:0.78 160:0.88 161:0.54 165:0.93 166:0.91 167:0.62 172:0.86 173:0.43 176:0.73 177:0.70 178:0.55 179:0.20 181:0.91 182:0.97 187:0.68 192:0.87 199:0.98 201:0.93 208:0.64 212:0.55 215:0.39 216:0.77 219:0.95 222:0.25 223:0.94 224:0.93 227:0.61 229:0.61 230:0.63 231:0.86 233:0.76 234:0.91 235:0.74 241:0.18 242:0.30 243:0.40 245:0.96 246:0.97 247:0.90 253:0.45 259:0.21 265:0.66 266:0.54 267:0.19 271:0.80 274:0.91 276:0.90 279:0.86 283:0.78 287:0.61 289:0.97 290:0.59 292:0.91 297:0.36 300:0.70 +3 1:0.74 6:0.92 8:0.87 9:0.80 11:0.85 12:0.51 17:0.55 18:0.96 20:0.97 21:0.47 26:0.95 27:0.12 28:0.80 29:0.56 30:0.87 31:0.99 32:0.80 34:0.96 36:0.35 37:0.78 39:0.80 41:0.99 43:0.77 44:0.93 45:0.90 46:0.99 58:1.00 60:0.99 64:0.77 66:0.45 69:0.80 70:0.44 71:0.74 74:0.89 77:0.70 78:0.78 79:0.99 81:0.51 83:0.91 85:0.97 86:0.97 91:0.78 96:0.97 97:0.89 98:0.42 100:0.81 101:0.97 107:1.00 108:0.84 110:0.98 111:0.77 114:0.60 120:0.95 122:0.57 123:0.83 124:0.76 125:0.98 126:0.54 127:0.40 129:0.89 131:0.94 133:0.78 135:0.23 137:0.99 139:0.98 140:0.45 141:0.97 144:0.57 145:0.56 146:0.44 147:0.50 149:0.97 150:0.73 151:0.99 152:0.90 153:0.97 154:0.69 155:0.67 157:0.99 158:0.92 159:0.68 160:1.00 161:0.54 162:0.77 164:0.92 165:0.93 166:0.91 167:0.77 169:0.79 172:0.87 173:0.70 176:0.73 177:0.70 179:0.18 181:0.91 182:0.99 186:0.78 187:0.68 189:0.93 191:0.93 192:0.87 195:0.87 196:1.00 199:1.00 201:0.93 202:0.90 208:0.64 212:0.85 215:0.41 216:0.81 219:0.95 222:0.25 223:0.94 224:0.99 227:0.94 229:1.00 231:0.87 234:0.98 235:0.68 238:0.89 241:0.66 242:0.27 243:0.22 245:0.93 246:0.97 247:0.86 248:0.96 253:0.98 255:0.95 256:0.93 259:0.21 260:0.93 261:0.83 265:0.44 266:0.71 267:0.84 268:0.93 271:0.80 274:1.00 276:0.88 279:0.86 282:0.88 283:0.82 284:0.99 285:0.62 287:0.98 289:0.97 290:0.60 292:0.95 297:0.36 299:0.88 300:0.84 +3 1:0.69 8:0.86 9:0.80 11:0.64 12:0.51 17:0.03 18:0.96 21:0.30 25:0.88 26:0.95 27:0.09 28:0.80 29:0.56 30:0.74 31:0.93 34:0.53 36:0.86 37:0.84 39:0.80 41:0.92 43:0.70 45:0.96 46:0.99 58:0.99 64:0.77 66:0.34 69:0.46 70:0.53 71:0.74 74:0.37 79:0.97 81:0.45 83:0.91 86:0.69 91:0.51 96:0.90 97:0.94 98:0.56 99:0.83 101:0.52 104:0.54 107:1.00 108:0.79 110:0.98 114:0.60 120:0.65 122:0.85 123:0.77 124:0.69 125:0.99 126:0.44 127:0.47 129:0.81 131:0.94 133:0.67 135:0.47 137:0.93 139:0.67 140:0.93 141:0.97 144:0.57 146:0.26 147:0.32 149:0.92 150:0.56 151:0.69 153:0.81 154:0.59 155:0.67 157:0.61 158:0.72 159:0.57 160:0.99 161:0.54 162:0.79 164:0.74 165:0.70 166:0.84 167:0.87 172:0.74 173:0.39 175:0.75 176:0.55 177:0.38 178:0.42 179:0.29 181:0.91 182:0.99 187:0.68 190:0.77 191:0.89 192:0.87 196:0.90 199:0.96 201:0.68 202:0.76 208:0.64 212:0.77 216:0.67 219:0.87 220:0.62 222:0.25 223:0.94 224:0.98 227:0.94 229:0.99 231:0.87 232:0.75 234:0.97 235:0.42 241:0.75 242:0.61 243:0.19 244:0.61 245:0.90 246:0.61 247:0.47 248:0.69 253:0.73 255:0.95 256:0.79 257:0.65 259:0.21 260:0.65 265:0.57 266:0.54 267:0.18 268:0.80 271:0.80 274:0.98 276:0.79 277:0.69 279:0.82 284:0.96 285:0.62 287:0.98 289:0.97 290:0.60 292:0.95 300:0.55 +3 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.85 12:0.51 17:0.40 18:0.94 20:0.97 21:0.54 26:0.95 27:0.11 28:0.80 29:0.56 30:0.75 31:0.99 32:0.80 34:0.52 36:0.88 37:0.80 39:0.80 41:0.98 43:0.89 44:0.93 45:0.95 46:0.98 58:1.00 60:0.97 64:0.77 66:0.43 69:0.50 70:0.63 71:0.74 74:0.83 77:0.70 78:0.80 79:1.00 81:0.48 83:0.91 85:0.93 86:0.91 91:0.92 96:0.98 97:0.97 98:0.50 100:0.85 101:0.97 107:1.00 108:0.81 110:0.98 111:0.81 114:0.59 120:0.95 121:0.99 122:0.85 123:0.80 124:0.91 125:0.98 126:0.54 127:0.86 129:0.95 131:0.94 133:0.93 135:0.39 137:0.99 139:0.95 140:0.45 141:0.97 144:0.57 145:0.56 146:0.17 147:0.22 149:0.92 150:0.72 151:0.99 152:0.89 153:0.97 154:0.88 155:0.67 157:0.98 158:0.92 159:0.87 160:1.00 161:0.54 162:0.76 164:0.93 165:0.93 166:0.91 167:0.77 169:0.83 172:0.85 173:0.24 176:0.73 177:0.70 179:0.24 181:0.91 182:0.99 186:0.81 187:0.68 189:0.96 191:0.92 192:0.87 195:0.95 196:1.00 199:1.00 201:0.93 202:0.92 204:0.89 208:0.64 212:0.39 215:0.39 216:0.86 219:0.95 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 230:0.87 231:0.87 233:0.76 234:0.98 235:0.74 238:0.94 241:0.68 242:0.38 243:0.10 245:0.87 246:0.97 247:0.76 248:0.97 253:0.98 255:0.95 256:0.94 259:0.21 260:0.94 261:0.86 265:0.52 266:0.84 267:0.76 268:0.94 271:0.80 274:1.00 276:0.88 279:0.86 281:0.91 282:0.88 283:0.82 284:0.99 285:0.62 287:0.98 289:0.97 290:0.59 292:0.95 297:0.36 299:0.88 300:0.84 +4 1:0.74 6:0.98 8:0.95 9:0.80 11:0.64 12:0.51 17:0.04 18:0.98 20:0.85 21:0.59 25:0.88 26:0.95 27:0.09 28:0.80 29:0.56 30:0.74 31:1.00 34:0.63 36:0.81 37:0.84 39:0.80 41:1.00 43:0.70 45:0.98 46:0.99 58:1.00 60:0.97 64:0.77 66:0.23 69:0.92 70:0.59 71:0.74 74:0.53 78:0.93 79:1.00 81:0.46 83:0.91 86:0.71 91:0.95 96:1.00 97:0.98 98:0.59 100:0.99 101:0.52 104:0.54 107:1.00 108:0.91 110:0.98 111:0.98 114:0.58 120:0.99 122:0.85 123:0.82 124:0.79 125:0.97 126:0.54 127:0.71 129:0.05 131:0.94 133:0.77 135:0.60 137:1.00 138:1.00 139:0.79 140:0.45 141:0.97 144:0.57 146:0.91 147:0.30 149:0.95 150:0.71 151:1.00 152:0.81 153:0.99 154:0.59 155:0.67 157:0.61 158:0.92 159:0.84 160:1.00 161:0.54 162:0.65 164:0.98 165:0.93 166:0.91 167:0.97 169:1.00 172:0.87 173:0.83 176:0.73 177:0.38 179:0.18 181:0.91 182:0.99 186:0.93 187:0.39 189:0.99 191:0.98 192:0.87 196:1.00 199:1.00 201:0.93 202:0.99 208:0.64 212:0.72 215:0.39 216:0.67 219:0.95 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 231:0.87 232:0.75 234:0.97 235:0.80 238:0.99 241:0.92 242:0.60 243:0.11 244:0.61 245:0.96 246:0.97 247:0.67 248:1.00 253:0.99 255:0.95 256:0.99 257:0.65 259:0.21 260:0.99 261:0.95 265:0.56 266:0.71 267:0.65 268:0.99 271:0.80 274:1.00 276:0.92 279:0.86 283:0.82 284:1.00 285:0.62 287:0.98 289:0.97 290:0.58 292:0.95 297:0.36 300:0.84 +3 1:0.74 6:0.97 7:0.81 8:0.88 9:0.80 11:0.92 12:0.51 17:0.49 18:0.99 20:0.97 21:0.30 22:0.93 26:0.95 27:0.21 28:0.80 29:0.56 30:0.68 31:1.00 34:0.34 36:0.74 37:0.74 39:0.80 41:0.99 43:0.97 45:1.00 46:0.99 47:0.93 58:1.00 60:0.95 64:0.77 66:0.09 69:0.15 70:0.66 71:0.74 74:0.88 78:0.77 79:1.00 81:0.59 83:0.91 85:0.99 86:0.96 91:0.80 96:0.99 97:0.97 98:0.39 100:0.86 101:0.97 104:0.84 106:0.81 107:1.00 108:0.98 110:0.98 111:0.80 114:0.65 117:0.86 120:0.96 121:0.90 122:0.80 123:0.98 124:0.99 125:0.98 126:0.54 127:0.97 129:1.00 131:0.94 133:0.99 135:0.18 137:1.00 139:0.97 140:0.45 141:0.97 144:0.57 146:0.54 147:0.60 149:0.98 150:0.77 151:1.00 152:0.90 153:0.98 154:0.97 155:0.67 157:0.99 158:0.92 159:0.98 160:1.00 161:0.54 162:0.70 164:0.93 165:0.93 166:0.91 167:0.77 169:0.84 172:0.97 173:0.05 176:0.73 177:0.70 179:0.09 181:0.91 182:1.00 186:0.78 187:0.39 189:0.97 191:0.76 192:0.87 196:1.00 199:1.00 201:0.93 202:0.92 204:0.89 206:0.81 208:0.64 212:0.57 215:0.44 216:0.95 219:0.95 222:0.25 223:0.94 224:0.99 227:0.94 229:1.00 230:0.87 231:0.87 232:0.90 233:0.76 234:0.98 235:0.52 238:0.97 241:0.66 242:0.33 243:0.07 245:0.99 246:0.97 247:0.96 248:0.98 253:0.99 255:0.95 256:0.94 259:0.21 260:0.96 261:0.85 262:0.93 265:0.41 266:0.95 267:0.59 268:0.94 271:0.80 274:1.00 276:1.00 279:0.86 281:0.91 283:0.82 284:0.99 285:0.62 287:0.98 289:0.97 290:0.65 292:0.95 297:0.36 300:0.94 +4 6:0.98 8:0.97 9:0.80 12:0.51 17:0.01 20:0.90 21:0.78 26:0.95 27:0.04 28:0.80 29:0.56 31:1.00 34:0.77 36:0.26 37:0.96 39:0.80 41:1.00 46:0.88 58:1.00 60:0.87 71:0.74 74:0.47 78:0.93 79:1.00 83:0.91 91:0.99 96:1.00 97:0.89 100:0.99 107:0.88 110:0.88 111:0.99 120:0.98 125:0.98 131:0.94 135:0.86 137:1.00 138:1.00 139:0.74 140:0.45 141:0.97 144:0.57 151:1.00 152:0.84 153:0.99 155:0.67 157:0.61 158:0.92 160:1.00 161:0.54 162:0.65 164:0.97 166:0.61 167:0.97 169:1.00 175:0.97 181:0.91 182:0.99 186:0.93 189:0.99 191:0.97 192:0.87 196:0.99 199:1.00 202:0.98 208:0.64 216:0.60 219:0.95 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 231:0.87 232:0.72 234:0.97 238:0.99 241:0.95 244:0.93 246:0.61 248:0.99 253:0.99 255:0.95 256:0.98 257:0.93 259:0.21 260:0.98 261:0.97 267:0.91 268:0.98 271:0.80 274:1.00 277:0.95 279:0.86 283:0.82 284:1.00 285:0.62 287:0.98 289:0.97 292:0.95 +4 1:0.74 6:0.94 8:0.93 9:0.80 11:0.85 12:0.51 17:0.15 18:0.99 20:0.77 21:0.59 26:0.95 27:0.55 28:0.80 29:0.56 30:0.75 31:0.99 34:0.76 36:0.83 37:0.84 39:0.80 41:0.97 43:0.70 45:0.99 46:1.00 58:1.00 64:0.77 66:0.44 69:0.90 70:0.59 71:0.74 74:0.52 78:0.84 79:0.99 81:0.46 83:0.91 85:0.72 86:0.82 91:0.73 96:0.97 98:0.75 100:0.90 101:0.97 107:1.00 108:0.79 110:0.98 111:0.85 114:0.58 120:0.93 122:0.85 123:0.78 124:0.77 125:0.98 126:0.54 127:0.73 129:0.59 131:0.94 133:0.77 135:0.54 137:0.99 139:0.78 140:0.45 141:0.97 144:0.57 146:0.97 147:0.32 149:0.97 150:0.71 151:0.99 152:0.75 153:0.95 154:0.55 155:0.67 157:0.81 159:0.86 160:1.00 161:0.54 162:0.80 164:0.89 165:0.93 166:0.91 167:0.87 169:0.88 172:0.94 173:0.76 176:0.73 177:0.38 179:0.15 181:0.91 182:0.99 186:0.83 187:0.95 189:0.95 191:0.82 192:0.87 196:0.97 199:0.99 201:0.93 202:0.88 208:0.64 212:0.70 215:0.39 216:0.47 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 231:0.87 232:0.70 234:0.98 235:0.80 238:0.95 241:0.75 242:0.60 243:0.09 245:0.98 246:0.97 247:0.67 248:0.95 253:0.94 255:0.95 256:0.91 257:0.65 259:0.21 260:0.92 261:0.78 265:0.75 266:0.71 267:0.73 268:0.92 271:0.80 274:1.00 276:0.95 284:0.99 285:0.62 287:0.98 289:0.97 290:0.58 297:0.36 300:0.84 +3 1:0.74 7:0.81 11:0.85 12:0.51 17:0.53 18:0.82 21:0.30 26:0.95 27:0.04 28:0.80 29:0.56 30:0.75 32:0.80 34:0.71 36:0.36 37:0.99 39:0.80 43:0.59 44:0.93 45:0.88 46:0.96 58:0.93 64:0.77 66:0.54 69:0.93 70:0.67 71:0.74 74:0.70 77:0.70 81:0.59 83:0.91 85:0.93 86:0.82 91:0.11 98:0.47 101:0.97 107:0.99 108:0.94 110:0.98 114:0.65 121:0.99 122:0.57 123:0.94 124:0.76 125:0.88 126:0.54 127:0.36 129:0.81 131:0.61 133:0.82 135:0.81 139:0.89 141:0.91 144:0.57 145:0.49 146:0.54 147:0.60 149:0.75 150:0.77 154:0.49 155:0.67 157:0.98 159:0.83 160:0.88 161:0.54 165:0.93 166:0.91 167:0.65 172:0.81 173:0.81 176:0.73 177:0.70 178:0.55 179:0.26 181:0.91 182:0.97 187:0.87 192:0.87 195:0.96 199:0.98 201:0.93 204:0.89 208:0.64 212:0.59 215:0.44 216:0.57 222:0.25 223:0.94 224:0.93 227:0.61 229:0.61 230:0.87 231:0.86 233:0.76 234:0.91 235:0.52 241:0.32 242:0.24 243:0.28 245:0.82 246:0.97 247:0.82 253:0.50 259:0.21 265:0.49 266:0.91 267:0.52 271:0.80 274:0.91 276:0.78 281:0.91 282:0.88 287:0.61 289:0.96 290:0.65 297:0.36 299:0.88 300:0.84 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.96 21:0.05 27:0.72 30:0.38 34:0.50 36:0.81 37:0.28 39:0.31 41:0.82 43:0.82 55:0.42 66:0.83 69:0.23 70:0.58 74:0.75 81:0.95 83:0.79 85:0.72 91:0.85 96:0.78 98:0.82 101:0.76 104:0.57 108:0.18 114:0.95 120:0.66 122:0.43 123:0.18 126:0.54 127:0.31 129:0.05 131:1.00 135:0.30 140:0.45 144:0.57 146:0.48 147:0.55 149:0.50 150:0.97 151:0.79 153:0.69 154:0.86 155:0.67 157:0.87 159:0.17 162:0.86 164:0.62 165:0.90 166:0.99 172:0.51 173:0.57 176:0.73 177:0.38 179:0.72 182:0.99 192:0.59 201:0.74 202:0.46 208:0.64 212:0.95 215:0.91 216:0.17 220:0.62 222:0.76 227:0.61 229:0.99 231:0.99 235:0.12 241:0.85 242:0.45 243:0.84 246:0.61 247:0.47 248:0.72 253:0.82 255:0.79 256:0.45 259:0.21 260:0.67 265:0.82 266:0.12 267:0.19 268:0.55 271:0.26 276:0.41 285:0.62 287:0.61 290:0.95 297:0.36 300:0.43 +3 1:0.74 7:0.81 8:0.66 12:0.69 17:0.85 21:0.13 27:0.72 30:0.76 32:0.78 34:0.49 36:0.78 37:0.26 39:0.31 43:0.33 55:0.59 66:0.72 69:0.62 70:0.15 74:0.62 76:0.94 77:0.98 81:0.90 91:0.88 98:0.76 104:0.58 108:0.47 114:0.92 122:0.41 123:0.45 126:0.54 127:0.25 129:0.05 131:0.61 135:0.39 144:0.57 145:0.97 146:0.43 147:0.50 149:0.25 150:0.92 154:0.60 155:0.67 157:0.61 159:0.16 166:0.99 172:0.27 173:0.89 176:0.73 177:0.38 178:0.55 179:0.90 182:0.61 192:0.59 195:0.55 201:0.74 202:0.50 204:0.89 208:0.64 212:0.78 215:0.84 216:0.04 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 232:0.85 233:0.69 235:0.22 241:0.83 242:0.45 243:0.75 246:0.61 247:0.35 253:0.71 254:0.98 259:0.21 265:0.76 266:0.17 267:0.18 271:0.26 276:0.26 282:0.86 287:0.61 290:0.92 297:0.36 300:0.13 +2 11:0.42 12:0.69 17:0.53 21:0.77 27:0.45 30:0.42 34:0.87 36:0.18 37:0.50 39:0.31 43:0.30 55:0.92 66:0.26 69:0.72 70:0.72 74:0.73 81:0.28 91:0.15 98:0.47 108:0.55 114:0.53 122:0.67 123:0.79 124:0.67 126:0.32 127:0.29 129:0.05 131:0.61 133:0.62 135:0.71 144:0.57 145:0.50 146:0.71 147:0.76 149:0.47 150:0.27 154:0.69 157:0.61 159:0.62 163:0.86 166:0.93 172:0.45 173:0.58 176:0.53 177:0.38 178:0.55 179:0.54 182:0.61 187:0.68 212:0.23 216:0.77 222:0.76 227:0.61 229:0.61 231:0.98 235:0.98 241:0.24 242:0.55 243:0.51 245:0.66 246:0.61 247:0.67 253:0.54 254:0.74 259:0.21 265:0.46 266:0.80 267:0.23 271:0.26 276:0.54 277:0.69 287:0.61 290:0.53 300:0.55 +2 1:0.74 11:0.64 12:0.69 17:0.91 21:0.05 27:0.72 30:0.11 34:0.66 36:0.85 37:0.28 39:0.31 43:0.79 66:0.75 69:0.19 70:0.54 74:0.59 81:0.95 83:0.79 85:0.72 91:0.53 98:0.85 101:0.75 104:0.74 108:0.15 114:0.95 120:0.65 122:0.41 123:0.15 126:0.54 127:0.35 129:0.05 131:0.96 135:0.60 140:0.45 144:0.57 146:0.60 147:0.65 149:0.43 150:0.97 151:0.61 153:0.48 154:0.84 155:0.67 157:0.87 159:0.22 162:0.86 163:0.81 164:0.55 165:0.90 166:0.99 172:0.32 173:0.45 176:0.73 177:0.38 179:0.91 182:0.98 187:0.21 190:0.77 192:0.59 201:0.74 202:0.36 212:0.61 215:0.91 216:0.15 220:0.62 222:0.76 227:0.61 229:0.61 231:0.99 235:0.12 241:0.27 242:0.33 243:0.77 246:0.61 247:0.39 248:0.59 253:0.72 256:0.45 259:0.21 260:0.65 265:0.85 266:0.23 267:0.28 268:0.46 271:0.26 276:0.26 285:0.50 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.76 8:0.61 11:0.42 12:0.69 17:0.66 21:0.13 27:0.72 30:0.95 32:0.78 34:0.81 36:0.72 37:0.26 39:0.31 43:0.86 55:0.52 66:0.54 69:0.10 74:0.64 76:0.94 77:0.98 81:0.90 91:0.81 98:0.46 104:0.58 108:0.09 114:0.92 123:0.09 126:0.54 127:0.14 129:0.05 131:0.61 135:0.21 144:0.57 145:0.89 146:0.19 147:0.25 149:0.49 150:0.92 154:0.83 155:0.67 157:0.61 159:0.10 166:0.99 172:0.10 173:0.79 176:0.73 177:0.38 178:0.55 179:0.93 182:0.61 187:0.21 192:0.59 195:0.53 201:0.74 215:0.84 216:0.04 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.85 233:0.69 235:0.22 241:0.76 243:0.63 246:0.61 253:0.71 254:0.86 259:0.21 265:0.48 267:0.19 271:0.26 282:0.86 287:0.61 290:0.92 297:0.36 300:0.08 +3 1:0.74 7:0.81 8:0.86 9:0.80 11:0.42 12:0.69 17:0.64 21:0.64 27:0.58 30:0.62 32:0.78 34:0.66 36:0.91 37:0.47 39:0.31 43:0.36 55:0.45 66:0.83 69:0.25 70:0.69 74:0.71 76:0.94 77:0.70 81:0.65 91:0.81 96:0.71 98:0.97 104:0.58 108:0.20 114:0.72 120:0.83 123:0.19 126:0.54 127:0.77 129:0.05 131:1.00 135:0.47 140:0.92 144:0.57 145:0.88 146:0.67 147:0.72 149:0.24 150:0.70 151:0.54 153:0.89 154:0.84 155:0.67 157:0.61 159:0.26 162:0.77 164:0.83 166:0.99 172:0.51 173:0.52 176:0.73 177:0.38 179:0.84 182:0.61 190:0.77 191:0.83 192:0.59 195:0.57 201:0.74 202:0.75 204:0.89 208:0.64 212:0.57 215:0.53 216:0.28 220:0.74 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 233:0.69 235:0.84 241:0.89 242:0.60 243:0.84 246:0.61 247:0.39 248:0.55 253:0.65 254:0.80 255:0.79 256:0.78 259:0.21 260:0.83 265:0.97 266:0.27 267:0.52 268:0.80 271:0.26 276:0.38 282:0.86 283:0.71 285:0.62 287:0.61 290:0.72 297:0.36 300:0.55 +1 1:0.74 7:0.76 11:0.42 12:0.69 17:0.51 21:0.47 27:0.58 30:0.76 32:0.78 34:0.73 36:0.69 37:0.47 39:0.31 43:0.34 55:0.45 66:0.72 69:0.49 70:0.22 74:0.64 76:0.85 77:0.70 81:0.78 91:0.49 98:0.63 104:0.58 108:0.38 114:0.80 122:0.41 123:0.36 126:0.54 127:0.18 129:0.05 131:0.61 135:0.54 144:0.57 145:0.88 146:0.31 147:0.37 149:0.13 150:0.80 154:0.83 155:0.67 157:0.61 159:0.13 166:0.99 172:0.27 173:0.84 176:0.73 177:0.38 178:0.55 179:0.79 182:0.61 187:0.21 192:0.59 195:0.53 201:0.74 212:0.78 215:0.63 216:0.28 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.68 241:0.44 242:0.45 243:0.75 246:0.61 247:0.35 253:0.64 254:0.80 259:0.21 265:0.63 266:0.17 267:0.23 271:0.26 276:0.19 282:0.86 287:0.61 290:0.80 297:0.36 300:0.18 +2 1:0.74 7:0.81 9:0.80 11:0.42 12:0.69 17:0.53 21:0.64 27:0.58 30:0.21 32:0.78 34:0.76 36:0.91 37:0.47 39:0.31 43:0.44 55:0.45 66:0.80 69:0.30 70:0.67 74:0.65 76:0.94 77:0.70 81:0.65 91:0.58 96:0.73 98:0.86 104:0.58 108:0.24 114:0.72 120:0.67 123:0.23 126:0.54 127:0.38 129:0.05 131:1.00 135:0.51 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.33 150:0.70 151:0.62 153:0.48 154:0.75 155:0.67 157:0.61 159:0.19 162:0.86 164:0.66 166:0.99 172:0.45 173:0.61 176:0.73 177:0.38 179:0.82 182:0.61 187:0.21 190:0.77 192:0.59 195:0.55 201:0.74 202:0.50 204:0.89 208:0.64 212:0.71 215:0.53 216:0.28 220:0.82 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 233:0.69 235:0.84 241:0.53 242:0.72 243:0.82 246:0.61 247:0.39 248:0.60 253:0.66 254:0.80 255:0.79 256:0.58 259:0.21 260:0.67 265:0.86 266:0.27 267:0.59 268:0.59 271:0.26 276:0.36 282:0.86 285:0.62 287:0.61 290:0.72 297:0.36 300:0.43 +2 7:0.76 8:0.73 9:0.80 11:0.64 12:0.69 17:0.79 21:0.71 27:0.26 30:0.33 32:0.78 34:0.89 36:0.96 37:0.94 39:0.31 41:0.82 43:0.84 55:0.45 66:0.20 69:0.65 70:0.49 74:0.68 77:0.98 81:0.38 83:0.72 85:0.72 91:0.68 96:0.71 98:0.44 101:0.73 104:0.76 108:0.49 120:0.58 123:0.74 124:0.56 126:0.32 127:0.55 129:0.05 131:1.00 133:0.39 135:0.24 140:0.80 144:0.57 145:1.00 146:0.41 147:0.48 149:0.56 150:0.34 151:0.58 153:0.48 154:0.80 155:0.67 157:0.86 159:0.42 162:0.62 164:0.57 166:0.94 172:0.27 173:0.72 177:0.38 178:0.42 179:0.89 182:0.98 187:0.39 191:0.84 192:0.59 195:0.83 202:0.50 208:0.64 212:0.19 215:0.48 216:0.29 220:0.87 222:0.76 227:0.61 229:0.99 230:0.78 231:0.99 233:0.69 235:0.95 241:0.75 242:0.19 243:0.58 245:0.53 246:0.61 247:0.55 248:0.57 253:0.56 255:0.79 256:0.45 259:0.21 260:0.59 265:0.43 266:0.47 267:0.65 268:0.46 271:0.26 276:0.28 277:0.69 282:0.86 285:0.62 287:0.61 297:1.00 300:0.33 +2 7:0.76 8:0.87 11:0.42 12:0.69 17:0.78 21:0.71 27:0.26 30:0.30 32:0.78 34:0.88 36:0.80 37:0.94 39:0.31 43:0.33 55:0.59 66:0.64 69:0.49 70:0.83 74:0.84 77:0.70 81:0.40 91:0.37 98:0.78 104:0.76 108:0.60 122:0.55 123:0.58 124:0.47 126:0.32 127:0.46 129:0.59 131:0.61 133:0.47 135:0.30 144:0.57 145:0.80 146:0.34 147:0.41 149:0.45 150:0.34 154:0.84 157:0.61 159:0.44 166:0.93 172:0.67 173:0.51 177:0.38 178:0.55 179:0.53 182:0.61 187:0.21 195:0.70 202:0.36 212:0.55 215:0.48 216:0.29 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.88 241:0.39 242:0.24 243:0.31 245:0.77 246:0.61 247:0.67 253:0.60 254:0.74 259:0.21 265:0.78 266:0.54 267:0.44 271:0.26 276:0.62 282:0.86 287:0.61 297:0.36 300:0.55 +3 1:0.74 7:0.76 8:0.61 11:0.42 12:0.69 17:0.66 21:0.13 27:0.72 30:0.95 32:0.78 34:0.81 36:0.72 37:0.26 39:0.31 43:0.86 55:0.52 66:0.54 69:0.10 74:0.64 76:0.94 77:0.98 81:0.90 91:0.81 98:0.46 104:0.58 108:0.09 114:0.92 123:0.09 126:0.54 127:0.14 129:0.05 131:0.61 135:0.21 144:0.57 145:0.89 146:0.19 147:0.25 149:0.49 150:0.92 154:0.83 155:0.67 157:0.61 159:0.10 166:0.99 172:0.10 173:0.79 176:0.73 177:0.38 178:0.55 179:0.93 182:0.61 187:0.21 192:0.59 195:0.53 201:0.74 215:0.84 216:0.04 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.85 233:0.69 235:0.22 241:0.76 243:0.63 246:0.61 253:0.71 254:0.86 259:0.21 265:0.48 267:0.19 271:0.26 282:0.86 287:0.61 290:0.92 297:0.36 300:0.08 +1 1:0.74 7:0.81 8:0.58 9:0.80 11:0.42 12:0.69 17:0.75 21:0.74 27:0.58 30:0.62 32:0.78 34:0.62 36:0.90 37:0.47 39:0.31 43:0.21 55:0.45 66:0.75 69:0.91 70:0.34 74:0.68 76:0.98 77:0.89 81:0.55 91:0.57 96:0.69 98:0.26 104:0.58 108:0.81 114:0.67 120:0.66 123:0.80 126:0.54 127:0.11 129:0.05 131:1.00 135:0.65 140:0.45 144:0.57 145:0.84 146:0.32 147:0.39 149:0.08 150:0.64 151:0.50 153:0.48 154:0.52 155:0.67 157:0.61 158:0.82 159:0.13 162:0.86 164:0.75 166:0.99 172:0.32 173:0.98 176:0.73 177:0.38 179:0.23 182:0.61 187:0.21 190:0.77 192:0.59 195:0.72 201:0.74 202:0.61 204:0.89 208:0.64 212:0.84 215:0.46 216:0.28 220:0.97 222:0.76 227:0.61 229:0.61 230:0.84 231:0.99 233:0.69 235:0.97 241:0.27 242:0.33 243:0.77 246:0.61 247:0.39 248:0.51 253:0.59 254:0.80 255:0.79 256:0.68 259:0.21 260:0.66 265:0.28 266:0.17 267:0.82 268:0.70 271:0.26 276:0.24 282:0.86 283:0.71 285:0.62 287:0.61 290:0.67 297:0.36 300:0.25 +2 1:0.74 7:0.76 8:0.60 9:0.80 11:0.42 12:0.69 17:0.82 21:0.05 27:0.72 30:0.58 32:0.78 34:0.47 36:0.77 37:0.25 39:0.31 43:0.70 55:0.42 66:0.80 69:0.87 70:0.33 74:0.70 76:0.97 77:0.96 81:0.94 91:0.56 96:0.84 98:0.39 104:0.58 108:0.74 114:0.95 120:0.74 122:0.43 123:0.72 126:0.54 127:0.13 129:0.05 131:1.00 135:0.44 140:0.45 144:0.57 145:0.98 146:0.41 147:0.48 149:0.62 150:0.96 151:0.82 153:0.77 154:0.60 155:0.67 157:0.61 159:0.15 162:0.86 164:0.70 166:0.99 172:0.45 173:0.95 176:0.73 177:0.38 179:0.29 182:0.61 187:0.21 192:0.59 195:0.65 201:0.74 202:0.55 208:0.64 212:0.90 215:0.91 216:0.09 220:0.62 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.83 233:0.69 235:0.12 241:0.39 242:0.40 243:0.82 246:0.61 247:0.55 248:0.81 253:0.85 254:0.74 255:0.79 256:0.58 259:0.21 260:0.75 265:0.41 266:0.17 267:0.44 268:0.64 271:0.26 276:0.34 282:0.86 285:0.62 287:0.61 290:0.95 297:0.36 300:0.25 +3 1:0.74 9:0.80 11:0.64 12:0.69 17:0.96 21:0.05 27:0.72 30:0.38 34:0.50 36:0.81 37:0.28 39:0.31 41:0.82 43:0.82 55:0.42 66:0.83 69:0.23 70:0.58 74:0.75 81:0.95 83:0.79 85:0.72 91:0.85 96:0.78 98:0.82 101:0.76 104:0.57 108:0.18 114:0.95 120:0.66 122:0.43 123:0.18 126:0.54 127:0.31 129:0.05 131:1.00 135:0.30 140:0.45 144:0.57 146:0.48 147:0.55 149:0.50 150:0.97 151:0.79 153:0.69 154:0.86 155:0.67 157:0.87 159:0.17 162:0.86 164:0.62 165:0.90 166:0.99 172:0.51 173:0.57 176:0.73 177:0.38 179:0.72 182:0.99 192:0.59 201:0.74 202:0.46 208:0.64 212:0.95 215:0.91 216:0.17 220:0.62 222:0.76 227:0.61 229:0.99 231:0.99 235:0.12 241:0.85 242:0.45 243:0.84 246:0.61 247:0.47 248:0.72 253:0.82 255:0.79 256:0.45 259:0.21 260:0.67 265:0.82 266:0.12 267:0.19 268:0.55 271:0.26 276:0.41 285:0.62 287:0.61 290:0.95 297:0.36 300:0.43 +3 1:0.74 7:0.76 8:0.63 9:0.80 11:0.42 12:0.69 17:0.81 21:0.05 27:0.72 30:0.33 32:0.78 34:0.52 36:0.73 37:0.25 39:0.31 43:0.80 55:0.42 66:0.79 69:0.54 70:0.49 74:0.77 76:0.97 77:0.96 81:0.94 91:0.80 96:0.83 98:0.71 104:0.58 108:0.42 114:0.95 120:0.71 122:0.41 123:0.40 126:0.54 127:0.22 129:0.05 131:1.00 135:0.28 140:0.80 144:0.57 145:0.98 146:0.41 147:0.48 149:0.64 150:0.96 151:0.77 153:0.48 154:0.76 155:0.67 157:0.61 159:0.16 162:0.81 164:0.67 166:0.99 172:0.41 173:0.81 176:0.73 177:0.38 179:0.72 182:0.61 191:0.75 192:0.59 195:0.53 201:0.74 202:0.62 208:0.64 212:0.42 215:0.91 216:0.09 220:0.87 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.83 233:0.69 235:0.12 241:0.88 242:0.19 243:0.80 246:0.61 247:0.55 248:0.79 253:0.85 254:0.74 255:0.79 256:0.64 259:0.21 260:0.72 265:0.72 266:0.27 267:0.28 268:0.67 271:0.26 276:0.29 282:0.86 285:0.62 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 11:0.64 12:0.69 17:0.93 21:0.05 27:0.72 30:0.38 34:0.50 36:0.78 37:0.28 39:0.31 43:0.82 55:0.42 66:0.83 69:0.23 70:0.58 74:0.75 81:0.95 83:0.79 85:0.72 91:0.80 98:0.82 101:0.76 104:0.74 108:0.18 114:0.95 122:0.43 123:0.18 126:0.54 127:0.31 129:0.05 131:0.61 135:0.26 144:0.57 146:0.48 147:0.55 149:0.50 150:0.97 154:0.86 155:0.67 157:0.87 159:0.17 165:0.90 166:0.99 172:0.51 173:0.57 176:0.73 177:0.38 178:0.55 179:0.72 182:0.98 187:0.21 192:0.59 201:0.74 212:0.95 215:0.91 216:0.15 222:0.76 227:0.61 229:0.61 231:0.99 235:0.12 241:0.75 242:0.45 243:0.84 246:0.61 247:0.47 253:0.75 259:0.21 265:0.82 266:0.12 267:0.19 271:0.26 276:0.41 287:0.61 290:0.95 297:0.36 300:0.43 +2 1:0.74 6:0.94 7:0.76 8:0.85 11:0.64 12:0.37 17:0.89 20:0.76 21:0.13 27:0.58 28:0.93 30:0.68 32:0.80 34:0.76 36:0.37 37:0.55 39:0.29 43:0.90 60:0.87 66:0.19 69:0.45 70:0.48 74:0.96 76:0.85 77:0.70 78:0.99 81:0.89 83:0.84 85:0.97 91:0.51 98:0.50 100:0.90 101:0.84 104:0.83 106:0.81 108:0.35 111:0.97 114:0.92 117:0.86 122:0.43 123:0.72 124:0.77 126:0.54 127:0.34 129:0.81 131:0.61 133:0.76 135:0.49 144:0.57 145:0.56 146:0.73 147:0.77 149:0.97 150:0.94 152:0.77 154:0.89 155:0.67 157:0.95 158:0.92 159:0.68 161:0.78 163:0.81 165:0.88 166:0.95 167:0.52 169:0.84 172:0.70 173:0.27 176:0.73 177:0.38 178:0.55 179:0.28 181:0.76 182:0.89 186:0.99 187:0.21 189:0.95 191:0.87 192:0.59 195:0.91 201:0.74 206:0.81 208:0.64 212:0.40 215:0.84 216:0.17 222:0.76 227:0.61 229:0.61 230:0.79 231:0.92 232:0.88 233:0.76 235:0.22 238:0.81 241:0.24 242:0.54 243:0.51 245:0.84 246:0.99 247:0.60 253:0.79 259:0.21 261:0.82 265:0.45 266:0.38 267:0.52 271:0.36 276:0.77 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.43 +1 1:0.74 7:0.81 8:0.59 9:0.80 11:0.64 12:0.37 17:0.69 21:0.13 27:0.41 28:0.93 30:0.84 32:0.80 34:0.95 36:0.42 37:0.35 39:0.29 41:0.93 43:0.89 60:0.99 66:0.35 69:0.47 70:0.49 74:0.94 77:0.70 81:0.89 83:0.87 85:0.97 91:0.23 96:0.92 98:0.42 101:0.85 104:0.98 106:0.81 108:0.36 114:0.92 117:0.86 120:0.85 121:0.90 122:0.43 123:0.35 124:0.83 126:0.54 127:0.82 129:0.93 131:0.97 133:0.84 135:0.81 140:0.45 144:0.57 145:0.79 146:0.64 147:0.69 149:0.96 150:0.94 151:0.93 153:0.78 154:0.88 155:0.67 157:0.95 158:0.92 159:0.73 161:0.78 162:0.86 164:0.78 165:0.88 166:0.95 167:0.51 172:0.63 173:0.34 176:0.73 177:0.38 179:0.49 181:0.76 182:0.90 187:0.99 192:0.59 195:0.87 201:0.74 202:0.66 204:0.89 206:0.81 208:0.64 212:0.49 215:0.84 216:0.75 222:0.76 227:0.99 229:0.95 230:0.84 231:0.92 232:0.99 233:0.69 235:0.22 241:0.21 242:0.63 243:0.51 245:0.76 246:0.99 247:0.30 248:0.91 253:0.95 255:0.79 256:0.73 259:0.21 260:0.86 265:0.44 266:0.71 267:0.52 268:0.75 271:0.36 276:0.68 279:0.86 282:0.88 283:0.82 285:0.62 287:0.99 290:0.92 297:0.36 299:0.88 300:0.70 +1 1:0.74 11:0.64 12:0.37 17:0.78 21:0.22 27:0.45 28:0.93 30:0.75 32:0.80 34:0.74 36:0.19 37:0.50 39:0.29 43:0.82 66:0.84 69:0.68 70:0.47 74:0.99 77:0.70 81:0.85 83:0.76 85:0.99 91:0.07 98:0.75 101:0.87 108:0.52 114:0.90 122:0.43 123:0.50 124:0.38 126:0.54 127:0.44 129:0.59 131:0.61 133:0.47 135:0.63 144:0.57 145:0.49 146:0.89 147:0.91 149:0.99 150:0.91 154:0.77 155:0.67 157:0.96 159:0.62 161:0.78 165:0.86 166:0.95 167:0.49 172:0.95 173:0.60 176:0.73 177:0.38 178:0.55 179:0.17 181:0.76 182:0.89 187:0.68 192:0.59 195:0.82 201:0.74 212:0.91 215:0.79 216:0.77 222:0.76 227:0.61 229:0.61 231:0.92 235:0.31 241:0.10 242:0.60 243:0.85 245:0.93 246:0.99 247:0.55 253:0.79 259:0.21 265:0.76 266:0.54 267:0.19 271:0.36 276:0.90 282:0.88 287:0.61 290:0.90 297:0.36 299:0.88 300:0.84 +1 6:0.87 7:0.81 8:0.79 11:0.64 12:0.37 17:0.97 20:0.78 21:0.78 27:0.72 28:0.93 30:0.95 34:0.39 36:0.68 39:0.29 43:0.86 66:0.54 69:0.60 74:0.57 76:0.94 78:0.92 85:0.72 91:0.75 98:0.17 100:0.89 101:0.77 104:0.54 108:0.45 111:0.90 121:0.90 123:0.44 127:0.10 129:0.05 131:0.61 135:0.30 144:0.57 145:0.70 146:0.13 147:0.18 149:0.49 152:0.76 154:0.83 155:0.67 157:0.80 159:0.08 161:0.78 166:0.61 167:0.77 169:0.86 172:0.10 173:0.98 177:0.38 178:0.55 179:0.23 181:0.76 182:0.74 186:0.91 187:0.21 189:0.89 192:0.59 204:0.89 208:0.64 222:0.76 227:0.61 229:0.61 230:0.87 231:0.69 232:0.74 233:0.76 235:0.42 238:0.84 241:0.76 243:0.63 246:0.61 253:0.46 259:0.21 261:0.80 265:0.19 267:0.28 271:0.36 287:0.61 300:0.08 +2 1:0.74 6:0.89 7:0.81 8:0.75 9:0.80 11:0.64 12:0.37 17:0.38 20:0.98 21:0.30 27:0.21 28:0.93 30:0.70 34:0.55 36:0.88 37:0.74 39:0.29 41:0.96 43:0.98 60:0.97 66:0.61 69:0.12 70:0.57 74:0.99 78:0.89 81:0.81 83:0.84 85:0.99 91:0.60 96:0.95 98:0.81 100:0.93 101:0.86 104:0.84 106:0.81 108:0.70 111:0.90 114:0.86 117:0.86 120:0.90 121:0.90 122:0.57 123:0.68 124:0.66 126:0.54 127:0.64 129:0.89 131:0.97 133:0.78 135:0.18 140:0.45 144:0.57 146:0.48 147:0.54 149:0.99 150:0.87 151:0.98 152:0.98 153:0.91 154:0.98 155:0.67 157:0.96 158:0.92 159:0.84 161:0.78 162:0.65 164:0.85 165:0.84 166:0.95 167:0.50 169:0.89 172:0.94 173:0.09 176:0.73 177:0.38 179:0.17 181:0.76 182:0.90 186:0.89 187:0.39 189:0.92 191:0.70 192:0.59 201:0.74 202:0.81 204:0.89 206:0.81 208:0.64 212:0.58 215:0.72 216:0.95 222:0.76 227:0.99 229:0.95 230:0.87 231:0.92 232:0.89 233:0.76 235:0.42 238:0.91 241:0.18 242:0.58 243:0.21 245:0.95 246:0.99 247:0.60 248:0.94 253:0.97 255:0.79 256:0.83 259:0.21 260:0.90 261:0.92 265:0.81 266:0.54 267:0.85 268:0.83 271:0.36 276:0.93 279:0.86 283:0.82 285:0.62 287:0.99 290:0.86 297:0.36 300:0.70 +2 1:0.74 6:0.89 8:0.69 9:0.80 11:0.64 12:0.37 17:0.78 20:0.79 21:0.05 27:0.19 28:0.93 30:0.62 34:0.19 36:0.31 37:0.64 39:0.29 41:0.82 43:0.79 66:0.61 69:0.75 70:0.23 74:0.62 78:0.91 81:0.93 83:0.79 85:0.80 91:0.85 96:0.74 98:0.66 100:0.73 101:0.78 104:0.58 108:0.59 111:0.88 114:0.95 120:0.62 122:0.57 123:0.56 124:0.43 126:0.54 127:0.37 129:0.05 131:0.97 133:0.39 135:0.73 140:0.45 144:0.57 145:0.44 146:0.55 147:0.05 149:0.63 150:0.97 151:0.69 152:0.98 153:0.48 154:0.72 155:0.67 157:0.84 159:0.29 161:0.78 162:0.86 163:0.93 164:0.57 165:0.90 166:0.95 167:0.60 169:0.95 172:0.27 173:0.88 176:0.73 177:0.05 179:0.91 181:0.76 182:0.89 186:0.91 187:0.21 191:0.82 192:0.59 201:0.74 202:0.36 208:0.64 212:0.61 215:0.91 216:0.17 220:0.74 222:0.76 227:0.99 229:0.94 231:0.92 232:0.77 235:0.12 241:0.54 242:0.63 243:0.23 245:0.42 246:0.99 247:0.35 248:0.65 253:0.78 255:0.79 256:0.45 257:0.65 259:0.21 260:0.63 261:0.97 265:0.67 266:0.23 267:0.23 268:0.46 271:0.36 276:0.27 285:0.62 287:0.99 290:0.95 297:0.36 300:0.18 +2 1:0.74 6:0.82 8:0.61 9:0.80 11:0.64 12:0.37 17:0.49 20:0.98 27:0.53 28:0.93 30:0.40 34:0.24 36:0.90 37:0.37 39:0.29 41:0.88 43:0.98 60:0.87 66:0.60 69:0.05 70:0.93 74:0.92 78:0.93 81:0.97 83:0.87 85:0.96 91:0.68 96:0.86 98:0.65 100:0.85 101:0.82 104:0.81 108:0.72 111:0.90 114:0.98 120:0.77 122:0.57 123:0.70 124:0.74 126:0.54 127:0.91 129:0.89 131:0.97 133:0.83 135:0.89 140:0.45 144:0.57 146:0.19 147:0.25 149:0.94 150:0.99 151:0.94 152:0.90 153:0.80 154:0.98 155:0.67 157:0.94 158:0.87 159:0.82 161:0.78 162:0.64 164:0.70 165:0.92 166:0.95 167:0.54 169:0.83 172:0.81 173:0.08 176:0.73 177:0.38 179:0.38 181:0.76 182:0.90 186:0.92 187:0.21 189:0.84 192:0.59 201:0.74 202:0.63 208:0.64 212:0.51 215:0.96 216:0.21 222:0.76 227:0.99 229:0.94 231:0.92 232:0.86 235:0.05 238:0.82 241:0.35 242:0.40 243:0.17 245:0.80 246:0.99 247:0.67 248:0.84 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 261:0.92 265:0.66 266:0.87 267:0.52 268:0.64 271:0.36 276:0.77 279:0.86 283:0.71 285:0.62 287:0.99 290:0.98 297:0.36 300:0.94 +2 1:0.74 6:0.93 7:0.81 8:0.79 9:0.80 11:0.64 12:0.37 17:0.95 20:0.78 21:0.13 27:0.58 28:0.93 30:0.66 32:0.80 34:0.67 36:0.29 37:0.72 39:0.29 41:0.92 43:0.91 60:0.87 66:0.93 69:0.44 70:0.47 74:0.96 76:0.94 77:1.00 78:0.98 81:0.93 83:0.87 85:0.96 91:0.72 96:0.75 98:0.82 100:0.89 101:0.86 104:0.87 108:0.34 111:0.95 114:0.72 120:0.63 121:0.99 122:0.57 123:0.33 126:0.54 127:0.31 129:0.05 131:0.97 135:0.42 138:0.95 140:0.45 144:0.57 145:0.85 146:0.79 147:0.82 149:0.96 150:0.97 151:0.69 152:0.76 153:0.48 154:0.90 155:0.67 157:0.95 158:0.92 159:0.35 161:0.78 162:0.86 164:0.67 165:0.91 166:0.95 167:0.55 169:0.87 172:0.80 173:0.48 176:0.56 177:0.38 179:0.35 181:0.76 182:0.90 186:0.97 187:0.21 189:0.94 191:0.75 192:0.59 195:0.65 201:0.74 202:0.50 204:0.89 208:0.64 212:0.80 215:0.79 216:0.14 220:0.74 222:0.76 227:0.99 229:0.95 230:0.84 231:0.92 232:0.90 233:0.69 235:0.60 238:0.82 241:0.41 242:0.55 243:0.93 246:0.99 247:0.47 248:0.69 253:0.81 255:0.79 256:0.58 259:0.21 260:0.64 261:0.84 265:0.82 266:0.27 267:0.82 268:0.59 271:0.36 276:0.69 279:0.86 282:0.88 283:0.82 285:0.62 286:0.99 287:0.99 290:0.72 297:0.36 299:0.99 300:0.43 +2 1:0.74 6:0.93 7:0.81 8:0.89 9:0.80 11:0.64 12:0.37 17:0.85 20:0.98 21:0.05 27:0.19 28:0.93 30:0.72 32:0.80 34:0.33 36:0.63 37:0.64 39:0.29 41:0.96 43:0.94 60:0.87 66:0.84 69:0.21 70:0.48 74:0.91 76:0.85 77:0.96 78:0.97 81:0.93 83:0.89 85:0.90 91:0.88 96:0.93 98:0.91 100:0.98 101:0.85 104:0.58 108:0.17 111:0.97 114:0.95 120:0.88 121:0.97 122:0.57 123:0.16 126:0.54 127:0.51 129:0.05 131:0.97 135:0.39 140:0.45 144:0.57 145:0.79 146:0.41 147:0.48 149:0.88 150:0.97 151:0.93 152:0.98 153:0.78 154:0.94 155:0.67 157:0.92 158:0.87 159:0.15 161:0.78 162:0.72 164:0.85 165:0.90 166:0.95 167:0.83 169:0.95 172:0.54 173:0.68 176:0.73 177:0.38 179:0.78 181:0.76 182:0.90 186:0.96 189:0.96 191:0.86 192:0.59 195:0.54 201:0.74 202:0.78 204:0.89 208:0.64 212:0.87 215:0.91 216:0.17 220:0.62 222:0.76 227:0.99 229:0.95 230:0.87 231:0.92 232:0.77 233:0.76 235:0.12 238:0.95 241:0.83 242:0.55 243:0.85 246:0.99 247:0.39 248:0.93 253:0.95 255:0.79 256:0.81 259:0.21 260:0.89 261:0.97 265:0.91 266:0.23 267:0.19 268:0.81 271:0.36 276:0.41 279:0.86 282:0.88 283:0.71 285:0.62 287:0.99 290:0.95 297:0.36 299:0.97 300:0.43 +2 1:0.74 6:0.93 8:0.64 11:0.64 12:0.37 17:0.79 20:0.93 21:0.05 27:0.19 28:0.93 30:0.33 34:0.96 36:0.20 37:0.64 39:0.29 43:0.98 66:0.68 69:0.07 70:0.88 74:0.88 78:0.89 81:0.93 83:0.79 85:0.91 91:0.40 98:0.78 100:0.89 101:0.82 104:0.58 108:0.06 111:0.88 114:0.95 122:0.57 123:0.06 124:0.44 126:0.54 127:0.66 129:0.59 131:0.61 133:0.47 135:0.83 144:0.57 146:0.81 147:0.84 149:0.88 150:0.97 152:0.98 154:0.98 155:0.67 157:0.92 159:0.51 161:0.78 165:0.90 166:0.95 167:0.50 169:0.95 172:0.67 173:0.15 176:0.73 177:0.38 178:0.55 179:0.60 181:0.76 182:0.89 186:0.89 187:0.39 189:0.85 192:0.59 201:0.74 212:0.52 215:0.91 216:0.17 222:0.76 227:0.61 229:0.61 231:0.92 232:0.77 235:0.12 238:0.85 241:0.15 242:0.24 243:0.72 245:0.74 246:0.99 247:0.60 253:0.78 259:0.21 261:0.97 265:0.78 266:0.71 267:0.52 271:0.36 276:0.60 287:0.61 290:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.83 8:0.65 11:0.64 12:0.37 17:0.38 20:0.81 21:0.22 27:0.45 28:0.93 30:0.67 34:0.62 36:0.19 37:0.50 39:0.29 43:0.82 66:0.35 69:0.68 70:0.70 74:0.76 78:0.83 81:0.85 83:0.76 85:0.94 91:0.17 98:0.53 100:0.81 101:0.82 108:0.52 111:0.81 114:0.90 122:0.43 123:0.50 124:0.69 126:0.54 127:0.42 129:0.81 131:0.61 133:0.67 135:0.70 144:0.57 145:0.47 146:0.70 147:0.74 149:0.87 150:0.91 152:0.80 154:0.77 155:0.67 157:0.93 159:0.57 161:0.78 165:0.86 166:0.95 167:0.51 169:0.82 172:0.48 173:0.63 176:0.73 177:0.38 178:0.55 179:0.57 181:0.76 182:0.89 186:0.84 187:0.68 189:0.84 192:0.59 201:0.74 212:0.57 215:0.79 216:0.77 222:0.76 227:0.61 229:0.61 231:0.92 235:0.31 238:0.83 241:0.21 242:0.60 243:0.51 245:0.71 246:0.99 247:0.47 253:0.72 259:0.21 261:0.85 265:0.55 266:0.71 267:0.28 271:0.36 276:0.54 287:0.61 290:0.90 297:0.36 300:0.70 +1 1:0.74 6:0.87 7:0.66 8:0.85 9:0.80 11:0.64 12:0.37 17:0.88 18:0.96 20:0.77 21:0.59 27:0.42 28:0.51 29:0.62 30:0.86 31:0.95 32:0.78 34:0.17 36:0.50 37:0.92 39:0.29 43:0.30 44:0.93 45:0.88 48:0.91 55:0.45 64:0.77 66:0.99 69:0.44 70:0.44 71:0.95 74:0.98 76:0.94 77:0.89 78:0.88 79:0.96 81:0.47 83:0.60 86:0.97 91:0.80 96:0.90 98:1.00 99:0.83 100:0.85 101:0.52 108:0.34 111:0.86 114:0.58 120:0.59 122:0.77 123:0.32 126:0.54 127:0.95 129:0.05 135:0.66 137:0.95 139:0.97 140:0.80 145:0.74 146:0.99 147:0.99 149:0.80 150:0.67 151:0.57 152:0.76 153:0.82 154:0.50 155:0.67 158:0.74 159:0.80 161:0.64 162:0.81 164:0.75 165:0.70 167:0.84 169:0.83 172:0.99 173:0.25 176:0.73 177:0.70 179:0.13 181:0.49 186:0.88 189:0.89 190:0.77 191:0.94 192:0.87 195:0.90 196:0.98 201:0.93 202:0.79 208:0.64 212:0.91 215:0.39 216:0.13 220:0.62 222:0.21 230:0.69 232:0.72 233:0.76 235:0.98 238:0.88 241:0.82 242:0.70 243:1.00 247:0.97 248:0.58 253:0.92 254:0.80 255:0.95 256:0.83 260:0.60 261:0.80 265:1.00 266:0.54 267:0.91 268:0.84 271:0.53 276:0.97 281:0.91 282:0.86 284:0.96 285:0.62 290:0.58 297:0.36 300:0.70 +2 7:0.72 8:0.79 9:0.76 11:0.83 12:0.37 17:0.89 18:0.98 21:0.13 27:0.44 28:0.51 29:0.62 30:0.43 31:0.96 32:0.69 34:0.18 36:0.43 39:0.29 43:0.95 44:0.90 45:0.92 48:0.91 55:0.45 64:0.77 66:0.99 69:0.23 70:0.56 71:0.95 74:0.85 78:0.83 79:0.97 81:0.75 83:0.60 85:0.88 86:0.99 91:0.67 96:0.74 98:1.00 101:0.87 104:0.75 108:0.18 111:0.83 120:0.83 123:0.18 126:0.44 127:0.96 129:0.05 135:0.30 137:0.96 139:0.99 140:0.45 145:0.95 146:0.95 147:0.96 149:0.96 150:0.54 151:0.84 153:0.72 154:0.95 155:0.67 159:0.63 161:0.64 162:0.86 164:0.80 167:0.83 169:0.85 172:0.98 173:0.23 177:0.70 179:0.14 181:0.49 190:0.77 191:0.84 192:0.87 195:0.77 196:0.99 201:0.68 202:0.74 204:0.85 208:0.64 212:0.94 215:0.61 216:0.10 219:0.92 220:0.82 222:0.21 230:0.75 233:0.76 235:0.42 241:0.80 242:0.51 243:0.99 247:0.94 248:0.85 253:0.60 255:0.79 256:0.81 259:0.21 260:0.80 265:1.00 266:0.47 267:0.89 268:0.81 271:0.53 276:0.96 281:0.91 283:0.82 284:0.96 285:0.62 292:0.95 297:0.36 300:0.55 +2 1:0.74 6:0.87 7:0.72 8:0.75 9:0.80 11:0.64 12:0.37 17:0.85 18:1.00 20:0.76 21:0.22 27:0.39 28:0.51 29:0.62 30:0.57 31:0.99 32:0.77 34:0.17 36:0.42 37:0.55 39:0.29 41:0.95 43:0.69 44:0.93 45:0.93 48:0.99 55:0.45 64:0.77 66:0.99 69:0.23 70:0.53 71:0.95 74:0.97 77:0.70 78:0.90 79:1.00 81:0.75 83:0.91 86:1.00 91:0.88 96:0.98 97:1.00 98:1.00 99:0.83 100:0.85 101:0.52 108:0.18 111:0.88 114:0.71 120:0.80 122:0.86 123:0.18 126:0.54 127:0.95 129:0.05 135:0.56 137:0.99 138:0.98 139:1.00 140:0.87 145:0.63 146:0.92 147:0.94 149:0.86 150:0.83 151:0.86 152:0.75 153:0.97 154:0.88 155:0.67 158:0.79 159:0.55 161:0.64 162:0.83 164:0.84 165:0.70 167:0.83 169:0.87 172:0.97 173:0.27 176:0.73 177:0.70 179:0.17 181:0.49 186:0.87 189:0.87 191:0.95 192:0.87 195:0.72 196:1.00 201:0.93 202:0.88 204:0.85 208:0.64 212:0.94 215:0.51 216:0.27 219:0.92 222:0.21 230:0.75 232:0.77 233:0.76 235:0.80 238:0.90 241:0.80 242:0.45 243:0.99 247:0.95 248:0.88 253:0.99 255:0.95 256:0.91 259:0.21 260:0.81 261:0.77 265:1.00 266:0.27 267:0.98 268:0.92 271:0.53 276:0.94 279:0.82 281:0.91 282:0.85 284:0.99 285:0.62 290:0.71 292:0.95 297:0.36 300:0.55 +1 1:0.74 7:0.72 8:0.92 11:0.64 12:0.37 17:0.98 18:0.97 21:0.30 22:0.93 27:0.36 28:0.51 29:0.62 30:0.58 32:0.69 34:0.28 36:0.75 37:0.73 39:0.29 43:0.57 44:0.90 45:0.66 47:0.93 48:0.98 55:0.52 64:0.77 66:0.98 69:0.25 70:0.52 71:0.95 74:0.87 76:0.94 81:0.67 86:0.98 91:0.56 98:0.99 101:0.52 104:0.91 106:0.81 108:0.20 114:0.65 120:0.72 123:0.19 126:0.54 127:0.91 129:0.05 135:0.54 139:0.98 140:0.45 145:0.42 146:0.89 147:0.91 149:0.55 150:0.76 151:0.62 153:0.69 154:0.89 155:0.67 159:0.48 161:0.64 162:0.55 164:0.53 167:0.53 172:0.94 173:0.32 176:0.73 177:0.70 179:0.25 181:0.49 187:0.21 190:0.89 191:0.85 192:0.87 195:0.69 201:0.93 202:0.58 204:0.85 206:0.81 208:0.64 212:0.92 215:0.44 216:0.18 222:0.21 230:0.75 232:0.76 233:0.76 235:0.68 241:0.27 242:0.33 243:0.98 247:0.94 248:0.60 253:0.52 254:0.84 256:0.45 259:0.21 260:0.67 262:0.93 265:0.99 266:0.27 267:0.73 268:0.55 271:0.53 276:0.88 281:0.91 283:0.82 285:0.47 290:0.65 297:0.36 300:0.55 +1 1:0.74 6:0.94 7:0.72 8:0.81 9:0.76 11:0.64 12:0.37 17:0.94 18:0.98 20:0.76 21:0.30 22:0.93 27:0.36 28:0.51 29:0.62 30:0.36 31:0.92 34:0.18 36:0.51 37:0.73 39:0.29 43:0.59 44:0.90 45:0.73 47:0.93 48:0.91 55:0.42 64:0.77 66:0.97 69:0.23 70:0.68 71:0.95 74:0.90 76:0.85 77:0.70 78:0.88 79:0.92 81:0.61 83:0.60 86:0.99 91:0.72 96:0.79 97:0.89 98:0.97 100:0.88 101:0.52 104:0.97 106:0.81 108:0.18 111:0.87 114:0.65 117:0.86 120:0.72 122:0.86 123:0.18 126:0.54 127:0.76 129:0.05 135:0.54 137:0.92 139:0.99 140:0.80 145:0.47 146:0.88 147:0.90 149:0.48 150:0.72 151:0.76 152:0.75 153:0.69 154:0.89 155:0.67 158:0.79 159:0.47 161:0.64 162:0.86 164:0.53 167:0.54 169:0.88 172:0.91 173:0.31 176:0.73 177:0.70 179:0.30 181:0.49 186:0.88 187:0.68 189:0.94 190:0.77 191:0.70 192:0.87 195:0.68 196:0.97 201:0.93 202:0.50 206:0.81 208:0.64 212:0.88 215:0.44 216:0.18 219:0.92 222:0.21 230:0.75 232:0.76 233:0.76 235:0.60 238:0.91 241:0.32 242:0.39 243:0.97 247:0.94 248:0.70 253:0.81 254:0.74 255:0.74 256:0.58 259:0.21 260:0.67 261:0.77 262:0.93 265:0.97 266:0.54 267:0.99 268:0.59 271:0.53 276:0.84 279:0.82 281:0.91 282:0.77 283:0.82 284:0.86 285:0.62 290:0.65 292:0.95 297:0.36 300:0.55 +0 1:0.74 6:0.94 7:0.72 8:0.88 9:0.80 11:0.64 12:0.37 17:0.78 18:0.98 20:0.76 21:0.30 27:0.36 28:0.51 29:0.62 30:0.43 31:0.99 34:0.18 36:0.73 37:0.73 39:0.29 41:0.82 43:0.55 44:0.90 45:0.66 48:0.91 55:0.45 64:0.77 66:0.97 69:0.23 70:0.77 71:0.95 74:0.90 76:0.85 77:0.70 78:0.87 79:0.99 81:0.61 83:0.91 86:0.99 91:0.85 96:0.96 97:0.99 98:1.00 100:0.88 101:0.52 108:0.18 111:0.86 114:0.65 120:0.80 122:0.86 123:0.18 126:0.54 127:0.95 129:0.05 135:0.58 137:0.99 139:0.99 140:0.80 145:0.47 146:0.91 147:0.92 149:0.48 150:0.72 151:0.72 152:0.75 153:0.93 154:0.89 155:0.67 158:0.87 159:0.51 161:0.64 162:0.84 164:0.79 167:0.82 169:0.87 172:0.93 173:0.29 176:0.73 177:0.70 179:0.27 181:0.49 186:0.87 189:0.92 190:0.77 191:0.95 192:0.87 195:0.70 196:1.00 201:0.93 202:0.84 208:0.64 212:0.89 215:0.44 216:0.18 219:0.95 222:0.21 230:0.75 232:0.76 233:0.76 235:0.60 238:0.91 241:0.79 242:0.50 243:0.97 247:0.90 248:0.75 253:0.97 254:0.84 255:0.95 256:0.88 259:0.21 260:0.78 261:0.77 265:1.00 266:0.38 267:0.97 268:0.89 271:0.53 276:0.87 279:0.86 281:0.91 282:0.77 283:0.82 284:0.99 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.90 7:0.72 8:0.80 11:0.64 12:0.37 17:0.96 18:0.98 20:0.80 21:0.64 27:0.21 28:0.51 29:0.62 30:0.21 32:0.78 34:0.56 36:0.48 37:0.88 39:0.29 43:0.67 44:0.93 45:0.83 48:1.00 55:0.42 64:0.77 66:0.96 69:0.38 70:0.64 71:0.95 74:0.87 76:0.85 77:0.89 78:0.91 81:0.45 83:0.60 86:0.99 91:0.40 98:0.93 99:0.83 100:0.89 101:0.52 104:0.94 106:0.81 108:0.30 111:0.89 114:0.57 120:0.69 123:0.28 126:0.54 127:0.58 129:0.05 132:0.97 135:0.42 139:0.99 140:0.45 145:0.69 146:0.77 147:0.80 149:0.70 150:0.67 151:0.60 152:0.85 153:0.48 154:0.88 155:0.67 159:0.33 161:0.64 162:0.86 164:0.53 165:0.70 167:0.56 169:0.86 172:0.88 173:0.52 176:0.73 177:0.70 179:0.32 181:0.49 186:0.91 187:0.68 189:0.91 190:0.89 191:0.87 192:0.87 195:0.60 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.93 215:0.38 216:0.16 222:0.21 230:0.75 232:0.77 233:0.76 235:1.00 238:0.90 241:0.41 242:0.16 243:0.96 247:0.92 248:0.59 253:0.47 254:0.84 256:0.45 260:0.66 261:0.89 265:0.93 266:0.27 267:0.82 268:0.46 271:0.53 276:0.80 281:0.91 282:0.86 283:0.82 285:0.47 290:0.57 297:0.36 300:0.55 +1 1:0.74 6:0.90 7:0.72 8:0.80 9:0.80 11:0.92 12:0.37 17:0.86 18:0.99 20:0.96 21:0.30 27:0.10 28:0.51 29:0.62 30:0.53 31:0.99 34:0.24 36:0.87 37:0.32 39:0.29 41:0.88 43:0.74 44:0.90 45:0.95 48:0.98 55:0.45 64:0.77 66:0.99 69:0.36 70:0.69 71:0.95 74:0.98 76:0.94 77:0.70 78:0.94 79:0.99 81:0.73 83:0.91 85:0.72 86:1.00 91:0.82 96:0.96 97:0.89 98:1.00 99:0.83 100:0.94 101:0.97 108:0.28 111:0.93 114:0.65 120:0.88 123:0.27 126:0.54 127:0.95 129:0.05 135:0.54 137:0.99 139:1.00 140:0.80 145:0.74 146:0.94 147:0.95 149:0.88 150:0.81 151:0.80 152:0.93 153:0.89 154:0.89 155:0.67 158:0.87 159:0.59 161:0.64 162:0.83 164:0.84 165:0.70 167:0.83 169:0.92 172:0.96 173:0.34 176:0.73 177:0.70 179:0.19 181:0.49 186:0.94 189:0.92 191:0.92 192:0.87 195:0.75 196:1.00 201:0.93 202:0.86 204:0.85 208:0.64 212:0.88 215:0.44 216:0.19 219:0.95 220:0.62 222:0.21 230:0.75 232:0.72 233:0.76 235:0.95 238:0.94 241:0.80 242:0.14 243:0.99 247:0.97 248:0.83 253:0.97 255:0.95 256:0.90 259:0.21 260:0.86 261:0.94 265:1.00 266:0.27 267:0.97 268:0.90 271:0.53 276:0.92 279:0.86 281:0.91 282:0.77 283:0.82 284:0.99 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 1:0.69 6:0.85 7:0.66 8:0.69 9:0.80 11:0.64 12:0.37 17:0.83 18:0.98 20:0.80 21:0.54 27:0.39 28:0.51 29:0.62 30:0.13 31:0.98 34:0.17 36:0.73 37:0.84 39:0.29 43:0.63 44:0.90 45:0.87 48:0.91 55:0.45 64:0.77 66:0.97 69:0.35 70:0.74 71:0.95 74:0.87 76:0.94 77:0.70 78:0.87 79:0.98 81:0.33 83:0.60 86:0.98 91:0.88 96:0.87 97:0.89 98:0.99 100:0.86 101:0.52 108:0.27 111:0.86 114:0.58 120:0.84 122:0.86 123:0.26 126:0.44 127:0.93 129:0.05 135:0.24 137:0.98 139:0.98 140:0.95 145:0.59 146:0.93 147:0.94 149:0.67 150:0.48 151:0.68 152:0.77 153:0.88 154:0.55 155:0.67 158:0.79 159:0.56 161:0.64 162:0.80 164:0.73 167:0.84 169:0.83 172:0.93 173:0.35 176:0.66 177:0.70 179:0.27 181:0.49 186:0.87 189:0.87 190:0.77 191:0.88 192:0.87 195:0.72 196:0.99 201:0.68 202:0.74 208:0.64 212:0.93 216:0.12 219:0.92 222:0.21 230:0.69 233:0.76 235:0.74 238:0.86 241:0.82 242:0.23 243:0.97 247:0.93 248:0.71 253:0.88 254:0.80 255:0.95 256:0.79 259:0.21 260:0.82 261:0.81 265:0.99 266:0.38 267:0.91 268:0.80 271:0.53 276:0.87 279:0.82 281:0.89 282:0.77 283:0.82 284:0.97 285:0.62 290:0.58 292:0.95 300:0.55 +0 1:0.74 11:0.83 12:0.37 17:0.40 18:0.91 21:0.59 27:0.45 28:0.51 29:0.62 30:0.21 32:0.80 34:0.74 36:0.22 37:0.50 39:0.29 43:0.81 44:0.93 45:0.73 48:0.91 55:0.84 64:0.77 66:0.33 69:0.61 70:0.95 71:0.95 74:0.62 77:0.70 81:0.48 83:0.91 85:0.72 86:0.89 91:0.14 98:0.55 101:0.97 108:0.74 114:0.58 122:0.86 123:0.45 124:0.60 126:0.54 127:0.36 129:0.59 133:0.46 135:0.75 139:0.91 145:0.49 146:0.66 147:0.71 149:0.71 150:0.71 154:0.76 155:0.67 159:0.47 161:0.64 163:0.81 165:0.93 167:0.52 172:0.51 173:0.58 176:0.73 177:0.70 178:0.55 179:0.51 181:0.49 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.27 215:0.39 216:0.77 222:0.21 235:0.84 241:0.24 242:0.52 243:0.50 245:0.79 247:0.79 253:0.42 259:0.21 265:0.56 266:0.75 267:0.89 271:0.53 276:0.59 281:0.89 282:0.88 290:0.58 297:0.36 299:0.88 300:0.84 +0 1:0.74 7:0.72 8:0.88 9:0.80 11:0.83 12:0.37 17:0.91 18:1.00 21:0.22 22:0.93 27:0.10 28:0.51 29:0.62 30:0.36 31:0.94 32:0.80 34:0.33 36:0.55 37:0.63 39:0.29 41:0.82 43:0.71 44:0.93 45:0.85 47:0.93 48:0.97 55:0.45 64:0.77 66:0.99 69:0.38 70:0.59 71:0.95 74:0.96 76:0.85 77:0.70 79:0.94 81:0.87 83:0.91 85:0.72 86:1.00 91:0.61 96:0.84 97:0.89 98:0.96 99:0.83 101:0.97 104:0.75 106:0.81 108:0.30 114:0.71 117:0.86 120:0.63 122:0.77 123:0.28 126:0.54 127:0.72 129:0.05 135:0.73 137:0.94 139:1.00 140:0.80 145:0.47 146:0.90 147:0.91 149:0.83 150:0.92 151:0.72 153:0.48 154:0.85 155:0.67 158:0.79 159:0.49 161:0.64 162:0.86 164:0.57 165:1.00 167:0.55 172:0.97 173:0.40 176:0.73 177:0.70 179:0.17 181:0.49 187:0.68 190:0.77 191:0.83 192:0.87 195:0.69 196:0.98 201:0.93 202:0.50 204:0.85 206:0.81 208:0.64 212:0.93 215:0.51 216:0.19 219:0.92 222:0.21 230:0.74 232:0.81 233:0.73 235:0.97 241:0.39 242:0.22 243:0.99 247:0.93 248:0.67 253:0.88 255:0.95 256:0.58 259:0.21 260:0.64 262:0.93 265:0.96 266:0.27 267:0.69 268:0.59 271:0.53 276:0.93 279:0.82 281:0.91 282:0.88 284:0.92 285:0.62 290:0.71 292:0.95 297:0.36 300:0.55 +1 1:0.74 6:0.95 7:0.72 8:0.92 9:0.80 11:0.92 12:0.37 17:0.91 18:0.97 20:0.96 21:0.30 22:0.93 27:0.04 28:0.51 29:0.62 30:0.16 31:0.96 32:0.69 34:0.36 36:0.53 37:0.73 39:0.29 43:0.77 44:0.90 45:0.92 47:0.93 48:0.99 55:0.45 64:0.77 66:0.98 69:0.33 70:0.77 71:0.95 74:0.93 77:0.70 78:0.98 79:0.97 81:0.61 83:0.60 85:0.88 86:0.99 91:0.56 96:0.70 98:0.96 100:0.98 101:0.97 104:0.92 106:0.81 108:0.26 111:0.98 114:0.65 120:0.82 123:0.25 126:0.54 127:0.72 129:0.05 135:0.79 137:0.96 138:0.96 139:0.99 140:0.80 145:0.70 146:0.93 147:0.95 149:0.89 150:0.72 151:0.53 152:0.97 153:0.80 154:0.80 155:0.67 159:0.58 161:0.64 162:0.86 164:0.75 167:0.53 169:0.96 172:0.96 173:0.30 176:0.73 177:0.70 179:0.18 181:0.49 186:0.97 187:0.68 189:0.97 190:0.77 191:0.95 192:0.87 195:0.74 196:0.99 201:0.93 202:0.63 204:0.85 206:0.81 208:0.64 212:0.92 215:0.44 216:0.24 219:0.92 222:0.21 230:0.75 232:0.78 233:0.76 235:0.60 238:0.97 241:0.30 242:0.39 243:0.99 247:0.97 248:0.54 253:0.56 255:0.95 256:0.73 259:0.21 260:0.79 261:0.97 262:0.93 265:0.96 266:0.27 267:0.97 268:0.72 271:0.53 276:0.92 281:0.91 282:0.77 283:0.82 284:0.95 285:0.62 290:0.65 292:0.95 297:0.36 300:0.55 +2 1:0.74 7:0.72 11:0.64 12:0.37 17:0.95 18:0.86 22:0.93 27:0.34 28:0.51 29:0.62 30:0.67 32:0.69 34:0.40 36:0.21 39:0.29 43:0.61 44:0.93 45:0.73 47:0.93 48:0.91 55:0.52 64:0.77 66:0.96 69:0.10 70:0.39 71:0.95 74:0.83 76:1.00 77:0.89 81:0.87 83:0.60 86:0.93 91:0.35 98:0.94 99:0.83 101:0.52 104:0.94 106:0.81 108:0.09 114:0.85 117:0.86 123:0.09 126:0.54 127:0.63 129:0.05 132:0.97 135:0.93 139:0.94 145:0.82 146:0.82 147:0.85 149:0.55 150:0.91 154:0.79 155:0.67 159:0.38 161:0.64 165:0.70 167:0.57 172:0.88 173:0.26 176:0.66 177:0.70 178:0.55 179:0.33 181:0.49 187:0.39 191:0.76 192:0.87 195:0.64 201:0.93 204:0.89 206:0.81 208:0.64 212:0.88 215:0.78 216:0.05 222:0.21 230:0.75 232:0.70 233:0.76 235:0.31 241:0.44 242:0.52 243:0.96 247:0.91 253:0.59 254:0.95 259:0.21 262:0.93 265:0.94 266:0.27 267:0.36 271:0.53 276:0.79 281:0.91 282:0.83 283:0.82 290:0.85 297:0.36 300:0.43 +2 1:0.74 7:0.72 8:0.92 11:0.64 12:0.37 17:0.99 18:0.98 21:0.30 22:0.93 27:0.36 28:0.51 29:0.62 30:0.36 32:0.69 34:0.22 36:0.75 37:0.73 39:0.29 43:0.57 44:0.90 45:0.77 47:0.93 48:0.98 55:0.45 64:0.77 66:0.98 69:0.25 70:0.61 71:0.95 74:0.88 76:0.94 81:0.67 86:1.00 91:0.48 98:0.98 101:0.52 104:0.91 106:0.81 108:0.20 114:0.65 120:0.72 122:0.86 123:0.19 126:0.54 127:0.85 129:0.05 135:0.49 139:1.00 140:0.45 145:0.42 146:0.91 147:0.92 149:0.67 150:0.76 151:0.62 153:0.69 154:0.89 155:0.67 159:0.51 161:0.64 162:0.55 164:0.53 167:0.52 172:0.96 173:0.30 176:0.73 177:0.70 179:0.20 181:0.49 187:0.21 190:0.89 191:0.85 192:0.87 195:0.71 201:0.93 202:0.58 204:0.85 206:0.81 208:0.64 212:0.93 215:0.44 216:0.18 222:0.21 230:0.75 232:0.76 233:0.76 235:0.68 241:0.24 242:0.30 243:0.98 247:0.94 248:0.60 253:0.52 254:0.74 256:0.45 259:0.21 260:0.67 262:0.93 265:0.98 266:0.27 267:0.65 268:0.55 271:0.53 276:0.92 281:0.91 283:0.82 285:0.47 290:0.65 297:0.36 300:0.55 +1 6:0.81 7:0.81 8:0.62 9:0.80 11:0.83 12:0.37 17:0.84 18:0.99 20:0.86 22:0.93 27:0.34 28:0.51 29:0.62 30:0.12 31:0.98 32:0.64 34:0.55 36:0.67 37:0.36 39:0.29 41:0.82 43:0.68 45:0.77 47:0.93 48:1.00 55:0.52 64:0.77 66:0.30 69:0.30 70:0.87 71:0.95 74:0.70 77:0.70 78:0.88 79:0.98 81:0.80 83:0.91 85:0.90 86:0.99 91:0.54 96:0.83 98:0.68 100:0.90 101:0.97 104:0.80 106:0.81 108:0.88 111:0.88 120:0.87 121:0.90 123:0.87 124:0.77 126:0.44 127:0.71 129:0.81 133:0.74 135:0.94 137:0.98 139:0.99 140:0.92 145:1.00 146:0.92 147:0.93 149:0.91 150:0.58 151:0.91 152:0.83 153:0.84 154:0.57 155:0.67 159:0.90 161:0.64 162:0.86 164:0.81 167:0.50 169:0.86 172:0.98 173:0.10 177:0.70 179:0.10 181:0.49 186:0.88 187:0.21 189:0.83 191:0.84 192:0.87 195:0.97 196:0.99 201:0.68 202:0.73 204:0.89 206:0.81 208:0.64 212:0.89 215:0.96 216:0.84 219:0.92 220:0.87 222:0.21 230:0.87 233:0.76 235:0.05 238:0.90 241:0.15 242:0.45 243:0.28 245:1.00 247:0.95 248:0.81 253:0.82 255:0.95 256:0.79 259:0.21 260:0.85 261:0.86 262:0.93 265:0.69 266:0.84 267:0.98 268:0.81 271:0.53 276:0.99 281:0.91 282:0.73 283:0.82 284:0.96 285:0.62 292:0.95 297:0.36 300:0.94 +1 1:0.74 6:0.90 7:0.72 8:0.80 9:0.80 11:0.85 12:0.37 17:0.95 18:0.98 20:0.79 21:0.05 22:0.93 27:0.10 28:0.51 29:0.62 30:0.45 31:0.98 34:0.34 36:0.36 37:0.32 39:0.29 41:0.88 43:0.69 44:0.90 45:0.93 47:0.93 48:0.98 55:0.45 64:0.77 66:0.98 69:0.25 70:0.59 71:0.95 74:0.96 76:0.94 77:0.70 78:0.86 79:0.97 81:0.86 83:0.91 86:0.99 91:0.56 96:0.87 98:0.97 100:0.86 101:0.87 104:0.94 106:0.81 108:0.20 111:0.85 114:0.89 120:0.86 123:0.19 126:0.54 127:0.73 129:0.05 135:0.39 137:0.98 139:0.99 140:0.80 145:0.74 146:0.88 147:0.90 149:0.87 150:0.89 151:0.86 152:0.93 153:0.80 154:0.89 155:0.67 159:0.46 161:0.64 162:0.86 164:0.76 167:0.60 169:0.92 172:0.96 173:0.32 176:0.73 177:0.70 179:0.20 181:0.49 186:0.86 187:0.68 189:0.88 191:0.78 192:0.87 195:0.68 196:0.99 201:0.93 202:0.65 204:0.85 206:0.81 208:0.64 212:0.93 215:0.78 216:0.19 219:0.92 222:0.21 230:0.75 232:0.72 233:0.76 235:0.42 238:0.88 241:0.52 242:0.23 243:0.98 247:0.97 248:0.86 253:0.92 254:0.74 255:0.95 256:0.83 259:0.21 260:0.85 261:0.94 262:0.93 265:0.97 266:0.27 267:0.94 268:0.74 271:0.53 276:0.91 281:0.91 282:0.77 283:0.82 284:0.95 285:0.62 290:0.89 292:0.95 297:0.36 300:0.55 +1 1:0.69 7:0.66 8:0.82 11:0.64 12:0.37 17:0.96 18:0.95 21:0.40 27:0.47 28:0.51 29:0.62 30:0.56 31:0.93 32:0.78 34:0.44 36:0.81 37:0.84 39:0.29 43:0.57 44:0.93 45:0.73 48:0.91 55:0.45 64:0.77 66:0.97 69:0.32 70:0.66 71:0.95 74:0.85 76:0.94 77:0.70 79:0.93 81:0.54 86:0.97 91:0.64 98:0.97 101:0.52 104:0.86 106:0.81 108:0.25 114:0.62 120:0.74 123:0.24 126:0.54 127:0.73 129:0.05 135:0.54 137:0.93 139:0.98 140:0.80 145:0.61 146:0.88 147:0.90 149:0.60 150:0.60 151:0.81 153:0.48 154:0.68 155:0.58 159:0.46 161:0.64 162:0.86 164:0.63 167:0.55 172:0.92 173:0.37 176:0.73 177:0.70 179:0.27 181:0.49 187:0.21 190:0.77 191:0.70 192:0.59 195:0.68 196:0.97 201:0.74 202:0.50 206:0.81 208:0.64 212:0.92 215:0.42 216:0.13 219:0.92 220:0.62 222:0.21 230:0.69 233:0.76 235:0.74 241:0.39 242:0.28 243:0.97 247:0.92 248:0.73 253:0.50 254:0.90 255:0.74 256:0.58 259:0.21 260:0.69 265:0.97 266:0.27 267:0.36 268:0.59 271:0.53 276:0.86 281:0.89 282:0.86 283:0.82 284:0.87 285:0.56 290:0.62 292:0.95 297:0.36 300:0.55 +0 1:0.69 8:0.58 11:0.64 12:0.37 17:0.40 18:0.94 21:0.13 27:0.45 28:0.51 29:0.62 30:0.17 34:0.69 36:0.22 37:0.50 39:0.29 43:0.64 44:0.90 45:0.77 48:0.91 55:0.84 64:0.77 66:0.47 69:0.59 70:0.89 71:0.95 74:0.59 77:0.70 81:0.62 83:0.60 86:0.91 91:0.18 98:0.70 99:0.83 101:0.52 108:0.45 114:0.75 122:0.86 123:0.43 124:0.58 126:0.44 127:0.43 129:0.05 133:0.43 135:0.92 139:0.91 145:0.50 146:0.83 147:0.86 149:0.58 150:0.61 154:0.61 155:0.58 159:0.58 161:0.64 165:0.70 167:0.51 172:0.63 173:0.51 176:0.66 177:0.70 178:0.55 179:0.46 181:0.49 187:0.68 192:0.51 195:0.79 201:0.68 208:0.54 212:0.30 216:0.77 222:0.21 235:0.31 241:0.18 242:0.60 243:0.59 245:0.84 247:0.84 253:0.54 254:0.98 259:0.21 265:0.70 266:0.75 267:0.44 271:0.53 276:0.67 281:0.89 282:0.77 290:0.75 300:0.70 +1 1:0.74 6:0.87 7:0.66 8:0.73 11:0.64 12:0.37 17:0.95 18:0.91 20:0.77 21:0.59 27:0.42 28:0.51 29:0.62 30:0.88 31:0.93 32:0.78 34:0.20 36:0.46 37:0.92 39:0.29 43:0.30 44:0.93 45:0.85 48:0.91 55:0.45 64:0.77 66:0.99 69:0.47 70:0.39 71:0.95 74:0.96 76:0.94 77:0.89 78:0.88 79:0.93 81:0.49 83:0.60 86:0.96 91:0.33 98:0.97 99:0.83 100:0.86 101:0.52 104:0.91 106:0.81 108:0.36 111:0.86 114:0.58 120:0.69 122:0.77 123:0.35 126:0.54 127:0.74 129:0.05 135:0.44 137:0.93 139:0.96 140:0.45 145:0.74 146:0.95 147:0.96 149:0.77 150:0.68 151:0.81 152:0.76 153:0.48 154:0.50 155:0.67 159:0.62 161:0.64 162:0.86 164:0.54 165:0.70 167:0.52 169:0.83 172:0.98 173:0.40 176:0.73 177:0.70 179:0.15 181:0.49 186:0.88 187:0.68 189:0.88 190:0.77 192:0.87 195:0.78 196:0.96 201:0.93 202:0.36 206:0.81 208:0.64 212:0.91 215:0.39 216:0.13 219:0.92 222:0.21 230:0.69 232:0.72 233:0.76 235:0.99 238:0.87 241:0.21 242:0.73 243:0.99 247:0.93 248:0.73 253:0.49 254:0.80 255:0.74 256:0.45 260:0.68 261:0.80 265:0.97 266:0.38 267:0.73 268:0.46 271:0.53 276:0.95 281:0.91 282:0.86 283:0.82 284:0.87 285:0.56 290:0.58 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.82 8:0.64 9:0.80 11:0.92 12:0.37 17:0.95 18:0.99 20:0.80 21:0.30 27:0.53 28:0.51 29:0.62 30:0.53 31:0.96 34:0.18 36:0.36 37:0.67 39:0.29 43:0.70 44:0.90 45:0.98 48:0.98 55:0.52 64:0.77 66:0.99 69:0.43 70:0.57 71:0.95 74:0.99 76:0.85 77:0.70 78:0.92 79:0.96 81:0.76 83:0.91 85:0.80 86:1.00 91:0.73 96:0.89 97:0.94 98:1.00 99:0.99 100:0.90 101:0.97 108:0.33 111:0.90 114:0.65 120:0.59 122:0.80 123:0.32 126:0.54 127:0.95 129:0.05 135:0.60 137:0.96 139:1.00 140:0.45 145:0.49 146:0.97 147:0.98 149:0.97 150:0.85 151:0.56 152:0.77 153:0.86 154:0.67 155:0.67 158:0.79 159:0.73 161:0.64 162:0.84 164:0.78 165:0.93 167:0.81 169:0.87 172:0.98 173:0.31 176:0.73 177:0.70 179:0.14 181:0.49 186:0.92 189:0.84 190:0.77 191:0.87 192:0.87 195:0.84 196:0.98 201:0.93 202:0.75 204:0.85 208:0.64 212:0.86 215:0.44 216:0.08 219:0.92 220:0.91 222:0.21 232:0.77 235:0.98 238:0.88 241:0.78 242:0.23 243:0.99 247:0.94 248:0.58 253:0.91 255:0.95 256:0.78 259:0.21 260:0.59 261:0.83 265:1.00 266:0.38 267:0.97 268:0.81 271:0.53 276:0.96 279:0.82 281:0.89 282:0.77 284:0.97 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 6:0.90 7:0.66 8:0.90 12:0.37 17:0.08 20:0.88 21:0.30 25:0.88 27:0.05 28:0.51 29:0.62 30:0.91 31:0.95 34:0.31 36:0.82 37:0.92 39:0.29 43:0.18 44:0.87 55:0.59 66:0.69 69:0.33 70:0.13 71:0.95 78:0.88 79:0.96 81:0.65 91:0.97 98:0.86 100:0.92 104:0.58 108:0.26 111:0.89 120:0.99 123:0.25 126:0.26 127:0.38 129:0.05 135:0.21 137:0.95 139:0.64 140:1.00 145:0.89 146:0.45 147:0.51 149:0.15 150:0.47 151:0.81 152:0.83 153:0.99 154:0.12 159:0.16 161:0.64 162:0.67 163:0.81 164:0.97 167:0.85 169:0.90 172:0.21 173:0.69 175:0.91 177:0.05 179:0.97 181:0.49 186:0.88 189:0.91 190:0.77 191:0.98 192:0.51 195:0.64 196:0.89 202:0.97 212:0.61 215:0.44 216:0.51 222:0.21 230:0.69 233:0.76 235:0.42 238:0.92 241:0.88 242:0.82 243:0.73 244:0.83 247:0.12 248:0.76 253:0.20 255:0.74 256:0.98 257:0.84 259:0.21 260:0.98 261:0.88 265:0.86 266:0.17 267:0.93 268:0.98 271:0.53 276:0.20 277:0.87 283:0.82 284:0.96 285:0.56 297:1.00 300:0.13 +3 1:0.74 6:0.90 7:0.72 8:0.84 9:0.80 11:0.83 12:0.37 17:0.90 18:1.00 20:0.77 21:0.22 27:0.10 28:0.51 29:0.62 30:0.40 31:0.99 32:0.69 34:0.17 36:0.47 37:0.63 39:0.29 41:0.95 43:0.71 44:0.90 45:0.81 48:0.97 55:0.45 64:0.77 66:0.99 69:0.35 70:0.62 71:0.95 74:0.91 76:0.85 78:0.90 79:0.99 81:0.84 83:0.91 85:0.72 86:1.00 91:0.89 96:0.94 98:0.99 100:0.86 101:0.97 104:0.75 108:0.27 111:0.90 114:0.71 120:0.94 122:0.86 123:0.26 126:0.54 127:0.89 129:0.05 135:0.71 137:0.99 138:0.96 139:1.00 140:0.80 145:0.52 146:0.89 147:0.91 149:0.78 150:0.90 151:0.86 152:0.75 153:0.95 154:0.85 155:0.67 159:0.47 161:0.64 162:0.82 164:0.88 165:0.93 167:0.83 169:0.90 172:0.96 173:0.40 176:0.73 177:0.70 179:0.19 181:0.49 186:0.86 189:0.92 191:0.95 192:0.87 195:0.66 196:1.00 201:0.93 202:0.83 208:0.64 212:0.93 215:0.51 216:0.19 219:0.92 222:0.21 230:0.74 232:0.81 233:0.73 235:0.84 238:0.92 241:0.81 242:0.21 243:0.99 247:0.96 248:0.92 253:0.96 255:0.95 256:0.87 259:0.21 260:0.93 261:0.78 265:0.99 266:0.27 267:0.93 268:0.88 271:0.53 276:0.92 281:0.89 283:0.82 284:0.99 285:0.62 290:0.71 292:0.95 297:0.36 300:0.55 +1 1:0.74 6:0.95 8:0.86 9:0.80 11:0.92 12:0.37 17:0.89 18:0.97 20:0.93 21:0.30 22:0.93 27:0.04 28:0.51 29:0.62 30:0.28 31:0.97 32:0.80 34:0.59 36:0.82 37:0.73 39:0.29 41:0.88 43:0.80 44:0.93 45:0.89 47:0.93 48:0.98 60:0.87 64:0.77 66:0.32 69:0.37 70:0.74 71:0.95 74:0.94 76:0.85 77:0.89 78:0.93 79:0.96 81:0.66 83:0.91 85:0.91 86:0.99 91:0.38 96:0.88 98:0.68 99:0.83 100:0.94 101:0.97 104:0.95 106:0.81 108:0.71 111:0.92 114:0.61 117:0.86 120:0.80 122:0.80 123:0.28 124:0.60 126:0.54 127:0.61 129:0.59 133:0.46 135:0.87 137:0.97 139:0.99 140:0.45 145:0.91 146:0.67 147:0.72 149:0.94 150:0.79 151:0.93 152:0.97 153:0.77 154:0.82 155:0.67 158:0.92 159:0.44 161:0.64 162:0.86 164:0.69 165:1.00 167:0.52 169:0.96 172:0.82 173:0.42 176:0.66 177:0.70 179:0.27 181:0.49 186:0.93 187:0.68 189:0.94 191:0.91 192:0.87 195:0.66 196:0.99 201:0.93 202:0.54 204:0.85 206:0.81 208:0.64 212:0.89 215:0.42 216:0.24 219:0.95 222:0.21 235:0.84 238:0.94 241:0.24 242:0.19 243:0.49 245:0.95 247:0.96 248:0.87 253:0.92 255:0.95 256:0.58 259:0.21 260:0.81 261:0.97 262:0.93 265:0.60 266:0.27 267:0.84 268:0.63 271:0.53 276:0.84 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 290:0.61 292:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.79 8:0.58 9:0.80 11:0.57 12:0.37 17:0.32 18:0.82 20:0.77 21:0.05 22:0.93 27:0.30 28:0.51 29:0.62 30:0.38 31:0.95 32:0.80 34:0.36 36:0.88 37:0.31 39:0.29 41:0.82 43:0.77 44:0.93 47:0.93 64:0.77 66:0.64 69:0.80 70:0.63 71:0.95 74:0.62 77:0.89 78:0.83 79:0.94 81:0.86 83:0.91 85:0.80 86:0.87 91:0.56 96:0.83 98:0.26 100:0.80 101:0.87 104:0.90 106:0.81 108:0.64 111:0.81 114:0.89 117:0.86 120:0.72 122:0.57 123:0.62 124:0.44 126:0.54 127:0.17 129:0.05 133:0.37 135:0.58 137:0.95 139:0.89 140:0.45 145:0.91 146:0.33 147:0.24 149:0.71 150:0.91 151:0.90 152:0.75 153:0.69 154:0.69 155:0.67 159:0.23 161:0.64 162:0.86 164:0.62 165:0.93 167:0.50 169:0.79 172:0.45 173:0.83 176:0.73 177:0.05 179:0.42 181:0.49 186:0.83 187:0.39 189:0.81 192:0.87 195:0.71 196:0.96 201:0.93 202:0.46 206:0.81 208:0.64 212:0.78 215:0.78 216:0.89 222:0.21 235:0.31 238:0.83 241:0.15 242:0.29 243:0.26 245:0.55 247:0.55 248:0.80 253:0.82 255:0.95 256:0.45 257:0.84 259:0.21 260:0.73 261:0.76 262:0.93 265:0.28 266:0.27 267:0.95 268:0.55 271:0.53 276:0.30 282:0.88 284:0.90 285:0.62 290:0.89 297:0.36 299:0.88 300:0.43 +2 1:0.74 11:0.64 12:0.37 17:0.45 21:0.54 27:0.28 30:0.62 32:0.80 34:0.56 36:0.23 37:0.58 39:0.39 43:0.79 66:0.36 69:0.77 70:0.28 74:0.78 76:0.85 77:0.89 81:0.70 83:0.74 85:0.88 91:0.29 98:0.50 101:0.79 104:0.93 106:0.81 108:0.60 114:0.77 117:0.86 122:0.43 123:0.58 124:0.60 126:0.54 127:0.27 129:0.59 131:0.61 133:0.47 135:0.94 144:0.57 145:0.65 146:0.61 147:0.66 149:0.75 150:0.80 154:0.72 155:0.67 157:0.96 159:0.42 163:0.81 165:0.79 166:0.97 172:0.32 173:0.77 176:0.73 177:0.38 178:0.55 179:0.68 182:0.96 187:0.39 192:0.59 195:0.74 201:0.74 206:0.81 212:0.57 215:0.58 216:0.90 222:0.76 227:0.61 229:0.61 231:0.96 232:0.95 235:0.74 241:0.18 242:0.45 243:0.51 245:0.62 246:0.88 247:0.47 253:0.68 265:0.52 266:0.23 267:0.44 271:0.38 276:0.41 282:0.88 287:0.61 290:0.77 297:0.36 299:0.88 300:0.25 +2 1:0.74 7:0.78 9:0.80 11:0.42 12:0.37 17:0.69 21:0.22 27:0.59 30:0.20 34:0.66 36:0.28 37:0.86 39:0.39 43:0.94 55:0.52 66:0.63 69:0.25 70:0.94 74:0.80 76:0.98 81:0.80 91:0.46 96:0.75 98:0.57 104:0.84 106:0.81 108:0.70 114:0.69 117:0.86 120:0.63 123:0.68 124:0.51 126:0.54 127:0.36 129:0.59 131:0.99 133:0.64 135:0.18 138:0.95 140:0.45 144:0.57 146:0.29 147:0.35 149:0.77 150:0.82 151:0.77 153:0.48 154:0.94 155:0.67 157:0.61 158:0.84 159:0.46 162:0.86 164:0.66 166:0.97 172:0.65 173:0.26 176:0.56 177:0.38 179:0.50 182:0.61 187:0.21 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.81 215:0.72 216:0.49 220:0.82 222:0.76 227:0.92 229:0.61 230:0.81 231:0.96 232:0.82 233:0.69 235:0.31 241:0.12 242:0.51 243:0.27 245:0.67 246:0.61 247:0.60 248:0.73 253:0.77 255:0.79 256:0.58 259:0.21 260:0.64 265:0.58 266:0.54 267:0.94 268:0.59 271:0.38 276:0.58 279:0.86 283:0.82 285:0.62 286:0.99 287:0.61 290:0.69 297:0.36 298:0.99 300:0.84 +0 11:0.64 12:0.37 17:0.32 21:0.78 27:0.45 30:0.41 34:0.81 36:0.18 37:0.50 39:0.39 43:0.75 55:0.45 66:0.49 69:0.78 70:0.84 74:0.78 85:0.72 91:0.06 98:0.27 101:0.70 108:0.61 122:0.67 123:0.59 124:0.65 127:0.47 129:0.05 131:0.61 133:0.62 135:0.66 144:0.57 145:0.52 146:0.48 147:0.54 149:0.52 154:0.66 157:0.81 159:0.71 166:0.61 172:0.45 173:0.66 177:0.38 178:0.55 179:0.72 182:0.74 187:0.68 212:0.54 216:0.77 222:0.76 227:0.61 229:0.61 231:0.85 235:0.52 241:0.21 242:0.77 243:0.60 245:0.59 246:0.61 247:0.39 253:0.57 259:0.21 265:0.30 266:0.63 267:0.79 271:0.38 276:0.40 287:0.61 300:0.70 +1 1:0.74 7:0.76 8:0.91 11:0.64 12:0.37 17:0.62 20:0.93 21:0.40 27:0.15 30:0.11 32:0.77 34:0.73 36:0.35 37:0.83 39:0.39 43:0.80 55:0.45 66:0.61 69:0.40 70:0.72 74:0.80 76:0.85 77:0.70 78:0.72 81:0.71 85:0.72 91:0.53 98:0.50 100:0.73 101:0.72 104:0.96 106:0.81 108:0.31 111:0.72 114:0.82 117:0.86 122:0.43 123:0.30 124:0.64 126:0.54 127:0.69 129:0.05 131:0.61 132:0.97 133:0.74 135:0.99 144:0.57 145:0.50 146:0.59 147:0.65 149:0.69 150:0.76 152:0.93 154:0.85 155:0.67 157:0.82 159:0.55 166:0.97 169:0.72 172:0.54 173:0.37 176:0.73 177:0.38 178:0.55 179:0.72 182:0.75 186:0.73 187:0.87 191:0.73 192:0.59 195:0.72 201:0.74 206:0.81 212:0.61 215:0.67 216:0.62 222:0.76 227:0.61 229:0.61 230:0.79 231:0.96 232:0.91 233:0.76 235:0.52 241:0.01 242:0.51 243:0.68 245:0.55 246:0.61 247:0.47 253:0.71 261:0.73 265:0.52 266:0.63 267:0.28 271:0.38 276:0.51 282:0.85 283:0.82 287:0.61 290:0.82 297:0.36 300:0.43 +2 1:0.74 6:0.97 7:0.76 8:0.93 9:0.80 11:0.42 12:0.37 17:0.38 20:0.95 21:0.05 27:0.02 30:0.91 34:0.44 36:0.21 37:0.92 39:0.39 41:0.92 43:0.82 55:0.71 66:0.69 69:0.67 70:0.22 74:0.82 76:0.85 78:0.92 81:0.90 83:0.80 91:0.70 96:0.97 98:0.58 100:0.98 108:0.87 111:0.97 114:0.74 120:0.94 122:0.67 123:0.86 124:0.46 126:0.54 127:0.52 129:0.59 131:0.99 133:0.64 135:0.81 138:0.96 140:0.80 144:0.57 145:0.89 146:0.13 147:0.18 149:0.70 150:0.92 151:0.94 152:1.00 153:0.86 154:0.78 155:0.67 157:0.61 158:0.84 159:0.74 162:0.77 164:0.90 166:0.98 169:0.96 172:0.70 173:0.52 176:0.56 177:0.38 179:0.53 182:0.96 186:0.92 187:0.39 189:0.98 191:0.80 192:0.59 201:0.74 202:0.85 208:0.64 212:0.69 215:0.84 216:0.99 222:0.76 227:0.92 229:0.98 230:0.79 231:0.96 233:0.76 235:0.12 238:0.98 241:0.74 242:0.63 243:0.15 245:0.67 246:0.61 247:0.67 248:0.98 253:0.97 255:0.79 256:0.88 259:0.21 260:0.94 261:0.97 265:0.59 266:0.75 267:0.65 268:0.89 271:0.38 276:0.59 279:0.86 283:0.71 285:0.62 287:0.90 290:0.74 297:0.36 300:0.25 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.37 17:0.57 20:0.92 21:0.30 27:0.34 30:0.09 32:0.72 34:0.62 36:0.35 37:0.36 39:0.39 43:0.68 66:0.36 69:0.55 70:0.95 74:0.98 77:0.70 78:0.72 81:0.77 85:0.85 91:0.42 96:0.71 98:0.64 100:0.73 101:0.73 104:0.80 106:0.81 108:0.43 111:0.72 114:0.86 117:0.86 120:0.58 121:0.90 122:0.57 123:0.41 124:0.68 126:0.54 127:0.39 129:0.81 131:0.99 133:0.67 135:0.97 140:0.45 144:0.57 145:0.96 146:0.94 147:0.95 149:0.86 150:0.80 151:0.58 152:0.86 153:0.48 154:0.76 155:0.67 157:0.91 158:0.85 159:0.80 162:0.86 164:0.57 166:0.97 169:0.72 172:0.87 173:0.30 176:0.73 177:0.38 179:0.16 182:0.81 186:0.73 187:0.21 192:0.59 195:0.95 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.71 215:0.72 216:0.84 220:0.87 222:0.76 227:0.92 229:0.61 230:0.87 231:0.96 232:0.85 233:0.76 235:0.42 241:0.07 242:0.39 243:0.51 245:0.96 246:0.61 247:0.67 248:0.56 253:0.80 255:0.79 256:0.45 259:0.21 260:0.59 261:0.73 265:0.65 266:0.80 267:0.69 268:0.46 271:0.38 276:0.90 279:0.86 282:0.81 283:0.66 285:0.62 287:0.61 290:0.86 297:0.36 300:0.84 +1 11:0.42 12:0.37 17:0.55 21:0.47 27:0.45 30:0.93 34:0.75 36:0.30 37:0.50 39:0.39 43:0.30 55:0.59 66:0.83 69:0.70 70:0.24 74:0.99 77:0.70 81:0.47 91:0.07 98:0.77 108:0.53 114:0.55 122:0.67 123:0.51 124:0.39 126:0.32 127:0.51 129:0.05 131:0.61 133:0.62 135:0.88 144:0.57 145:0.50 146:0.94 147:0.95 149:0.83 150:0.38 154:0.55 157:0.61 158:0.82 159:0.74 166:0.87 172:0.93 173:0.54 176:0.53 177:0.38 178:0.55 179:0.22 182:0.61 187:0.68 195:0.89 212:0.92 216:0.77 222:0.76 227:0.61 229:0.61 231:0.88 235:0.52 241:0.12 242:0.82 243:0.84 245:0.83 246:0.61 247:0.39 253:0.71 254:0.80 259:0.21 265:0.77 266:0.63 267:0.28 271:0.38 276:0.87 282:0.81 287:0.61 290:0.55 300:0.55 +1 1:0.74 6:0.97 7:0.76 8:0.95 9:0.80 11:0.64 12:0.37 17:0.28 20:0.93 21:0.05 27:0.02 30:0.91 34:0.34 36:0.28 37:0.92 39:0.39 41:0.90 43:0.82 55:0.71 66:0.77 69:0.67 70:0.21 74:0.83 76:0.85 78:0.91 81:0.90 83:0.81 85:0.85 91:0.76 96:0.95 98:0.54 100:0.97 101:0.74 108:0.90 111:0.94 114:0.74 120:0.94 122:0.57 123:0.90 124:0.41 126:0.54 127:0.60 129:0.59 131:0.99 133:0.64 135:0.90 138:0.96 140:0.80 144:0.57 145:0.45 146:0.13 147:0.18 149:0.78 150:0.92 151:0.96 152:1.00 153:0.88 154:0.78 155:0.67 157:0.92 158:0.84 159:0.79 162:0.78 164:0.88 166:0.98 169:0.96 172:0.79 173:0.49 176:0.56 177:0.38 179:0.46 182:0.96 186:0.91 187:0.39 192:0.59 201:0.74 202:0.81 208:0.64 212:0.87 215:0.84 216:0.99 222:0.76 227:0.92 229:0.98 230:0.79 231:0.96 233:0.76 235:0.12 241:0.67 242:0.39 243:0.13 245:0.67 246:0.61 247:0.47 248:0.96 253:0.95 255:0.79 256:0.85 259:0.21 260:0.94 261:0.97 265:0.55 266:0.54 267:0.65 268:0.86 271:0.38 276:0.62 279:0.86 283:0.71 285:0.62 287:0.90 290:0.74 297:0.36 300:0.33 +1 1:0.74 7:0.76 8:0.84 11:0.42 12:0.37 17:0.93 20:0.89 21:0.05 27:0.36 30:0.30 32:0.77 34:0.62 36:0.22 37:0.79 39:0.39 43:0.67 55:0.84 66:0.54 69:0.07 70:0.95 74:0.86 76:0.85 77:0.70 78:0.72 81:0.90 91:0.57 98:0.74 100:0.96 104:0.93 106:0.81 108:0.06 111:0.91 114:0.74 117:0.86 120:0.82 122:0.43 123:0.06 124:0.52 126:0.54 127:0.80 129:0.59 131:0.89 133:0.47 135:0.99 140:0.45 144:0.57 145:0.41 146:0.80 147:0.83 149:0.48 150:0.92 151:0.66 152:0.83 153:0.48 154:0.90 155:0.67 157:0.61 159:0.56 162:0.86 164:0.72 166:0.98 169:0.95 172:0.63 173:0.14 176:0.56 177:0.38 179:0.61 182:0.61 186:0.73 187:0.87 190:0.77 192:0.59 195:0.72 201:0.74 202:0.59 206:0.81 212:0.37 215:0.84 216:0.62 220:0.62 222:0.76 227:0.86 229:0.61 230:0.79 231:0.96 232:0.97 233:0.76 235:0.12 241:0.10 242:0.32 243:0.63 245:0.79 246:0.61 247:0.60 248:0.73 253:0.70 256:0.64 259:0.21 260:0.81 261:0.91 265:0.75 266:0.75 267:0.65 268:0.67 271:0.38 276:0.61 282:0.85 283:0.82 285:0.50 287:0.61 290:0.74 297:0.36 300:0.84 +1 1:0.74 7:0.76 8:0.88 9:0.80 11:0.42 12:0.37 17:0.95 21:0.05 27:0.36 30:0.62 32:0.77 34:0.79 36:0.31 37:0.81 39:0.39 43:0.85 55:0.84 66:0.47 69:0.59 70:0.23 74:0.66 76:0.85 77:0.70 81:0.90 91:0.66 96:0.74 98:0.70 104:0.91 106:0.81 108:0.59 114:0.74 117:0.86 120:0.80 123:0.57 124:0.52 126:0.54 127:0.35 129:0.59 131:0.99 133:0.47 135:0.84 138:0.95 140:0.45 144:0.57 145:0.40 146:0.13 147:0.18 149:0.57 150:0.92 151:0.77 153:0.48 154:0.83 155:0.67 157:0.61 159:0.31 162:0.80 163:0.94 164:0.71 166:0.98 172:0.21 173:0.69 176:0.56 177:0.38 179:0.91 182:0.61 187:0.68 190:0.77 192:0.59 195:0.61 201:0.74 202:0.60 206:0.81 208:0.64 212:0.61 215:0.84 216:0.64 220:0.62 222:0.76 227:0.92 229:0.61 230:0.79 231:0.96 232:0.85 233:0.76 235:0.12 241:0.03 242:0.63 243:0.37 245:0.46 246:0.61 247:0.35 248:0.71 253:0.72 255:0.79 256:0.64 259:0.21 260:0.80 265:0.70 266:0.23 267:0.69 268:0.65 271:0.38 276:0.28 282:0.85 283:0.82 285:0.62 286:0.99 287:0.61 290:0.74 297:0.36 298:0.99 300:0.18 +2 1:0.74 8:0.82 9:0.80 11:0.42 12:0.37 17:0.28 21:0.40 27:0.49 30:0.42 32:0.75 34:0.31 36:0.21 37:0.38 39:0.39 41:0.82 43:0.85 60:0.87 66:0.26 69:0.60 70:0.76 74:0.90 77:0.98 81:0.69 83:0.76 91:0.63 96:0.95 98:0.30 104:0.90 106:0.81 108:0.86 114:0.66 117:0.86 120:0.92 122:0.43 123:0.44 124:0.74 126:0.54 127:0.43 129:0.59 131:0.99 133:0.76 135:0.85 138:0.95 140:0.45 144:0.57 145:0.99 146:0.48 147:0.54 149:0.90 150:0.74 151:0.97 153:0.90 154:0.82 155:0.67 157:0.61 158:0.84 159:0.63 162:0.83 164:0.89 166:0.97 172:0.63 173:0.48 176:0.56 177:0.38 179:0.42 182:0.96 187:0.87 192:0.59 195:0.82 201:0.74 202:0.81 206:0.81 208:0.64 212:0.84 215:0.63 216:0.88 222:0.76 227:0.92 229:0.98 231:0.96 232:0.83 235:0.52 241:0.44 242:0.33 243:0.46 245:0.76 246:0.61 247:0.67 248:0.97 253:0.97 255:0.79 256:0.86 260:0.92 265:0.32 266:0.63 267:0.69 268:0.86 271:0.38 276:0.65 279:0.86 282:0.83 283:0.82 285:0.62 286:0.99 287:0.90 290:0.66 297:0.36 300:0.70 +1 8:0.89 12:0.37 17:0.49 21:0.78 27:0.21 30:0.30 34:0.29 36:0.74 37:0.74 39:0.39 43:0.38 55:0.92 66:0.06 69:0.92 70:0.79 74:0.84 91:0.80 96:0.96 98:0.21 108:0.88 120:0.67 122:0.67 123:0.98 124:0.98 127:0.87 129:0.05 131:0.98 133:0.97 135:0.54 140:0.90 146:0.59 147:0.05 149:0.55 151:0.66 153:0.48 154:0.28 157:0.61 159:0.95 162:0.52 163:0.94 164:0.54 166:0.61 172:0.27 173:0.64 177:0.05 178:0.50 179:0.37 182:0.61 187:0.39 190:0.77 191:0.73 202:0.91 212:0.16 216:0.95 220:0.62 222:0.76 227:0.92 229:0.61 231:0.96 235:0.31 241:0.15 242:0.80 243:0.07 244:0.61 245:0.70 246:0.61 247:0.39 248:0.89 253:0.96 256:0.89 257:0.65 259:0.21 260:0.68 265:0.20 266:0.98 267:0.44 268:0.88 271:0.38 276:0.80 277:0.69 283:0.82 285:0.62 287:0.61 300:0.70 +1 1:0.74 11:0.88 12:0.37 17:0.40 21:0.54 27:0.45 28:0.78 30:0.87 32:0.80 34:0.79 36:0.17 37:0.50 39:0.72 43:0.82 66:0.24 69:0.70 70:0.27 74:0.73 77:0.70 81:0.58 83:0.74 85:0.90 91:0.31 98:0.27 101:0.80 108:0.53 114:0.77 122:0.43 123:0.75 124:0.73 126:0.54 127:0.23 129:0.81 133:0.67 135:0.73 144:0.85 145:0.44 146:0.38 147:0.45 149:0.80 150:0.74 154:0.77 155:0.67 159:0.40 161:0.88 163:0.81 165:0.79 167:0.47 172:0.21 173:0.65 176:0.73 177:0.38 178:0.55 179:0.65 181:0.89 187:0.68 192:0.59 195:0.77 201:0.74 212:0.50 215:0.58 216:0.77 222:0.48 235:0.68 241:0.12 242:0.63 243:0.44 245:0.54 247:0.30 253:0.66 259:0.21 265:0.29 266:0.38 267:0.19 271:0.53 276:0.36 282:0.88 290:0.77 297:0.36 299:0.88 300:0.25 +3 1:0.74 6:0.85 8:0.92 9:0.80 11:0.88 12:0.37 17:0.59 20:0.81 21:0.68 28:0.78 30:0.55 32:0.80 34:0.45 36:0.22 37:0.97 39:0.72 41:0.82 43:0.80 60:0.95 66:0.35 69:0.59 70:0.85 74:0.94 77:0.70 78:0.86 81:0.50 83:0.87 85:0.98 91:0.33 96:0.80 98:0.46 100:0.86 101:0.83 104:0.80 108:0.78 111:0.85 114:0.70 120:0.69 122:0.43 123:0.77 124:0.87 126:0.54 127:0.76 129:0.81 133:0.89 135:0.51 140:0.45 144:0.85 145:0.59 146:0.78 147:0.81 149:0.96 150:0.67 151:0.87 152:0.84 153:0.48 154:0.74 155:0.67 158:0.92 159:0.86 161:0.88 162:0.86 164:0.57 165:0.70 167:0.45 169:0.82 172:0.76 173:0.32 176:0.73 177:0.38 179:0.32 181:0.89 186:0.86 187:0.21 192:0.59 195:0.95 201:0.74 202:0.36 208:0.64 212:0.51 215:0.49 216:0.98 222:0.48 232:0.85 235:0.84 242:0.52 243:0.43 245:0.82 247:0.60 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 261:0.84 265:0.48 266:0.89 267:0.73 268:0.46 271:0.53 276:0.81 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.88 300:0.94 +2 1:0.74 11:0.88 12:0.37 17:0.38 21:0.59 27:0.45 28:0.78 30:0.76 34:0.74 36:0.19 37:0.50 39:0.72 43:0.82 66:0.72 69:0.70 70:0.22 74:0.52 81:0.55 83:0.73 85:0.80 91:0.11 98:0.44 101:0.74 108:0.53 114:0.74 122:0.43 123:0.51 126:0.54 127:0.14 129:0.05 135:0.88 144:0.85 145:0.52 146:0.56 147:0.62 149:0.65 150:0.71 154:0.77 155:0.67 159:0.20 161:0.88 163:0.92 165:0.78 167:0.49 172:0.27 173:0.67 176:0.73 177:0.38 178:0.55 179:0.58 181:0.89 187:0.68 192:0.59 201:0.74 212:0.78 215:0.55 216:0.77 222:0.48 235:0.74 241:0.24 242:0.75 243:0.75 247:0.30 253:0.59 259:0.21 265:0.46 266:0.17 267:0.36 271:0.53 276:0.22 290:0.74 297:0.36 300:0.18 +3 1:0.74 9:0.80 11:0.88 12:0.37 17:0.02 21:0.05 27:0.33 28:0.78 30:0.74 34:0.86 36:0.26 37:0.41 39:0.72 41:0.82 43:0.86 60:0.87 66:0.35 69:0.57 70:0.53 74:0.87 77:0.89 81:0.86 83:0.86 85:0.94 91:0.37 96:0.80 98:0.28 101:0.82 108:0.86 114:0.95 120:0.69 122:0.57 123:0.85 124:0.71 126:0.54 127:0.51 129:0.81 133:0.67 135:0.60 140:0.45 144:0.85 145:0.84 146:0.30 147:0.37 149:0.89 150:0.92 151:0.87 153:0.48 154:0.84 155:0.67 158:0.92 159:0.64 161:0.88 162:0.86 164:0.57 165:0.90 167:0.50 172:0.48 173:0.47 176:0.73 177:0.38 179:0.60 181:0.89 187:0.39 192:0.59 195:0.82 201:0.74 202:0.36 208:0.64 212:0.51 215:0.91 216:0.88 222:0.48 235:0.12 241:0.27 242:0.60 243:0.37 245:0.72 247:0.47 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 265:0.31 266:0.75 267:0.36 268:0.46 271:0.53 276:0.46 279:0.86 282:0.81 283:0.82 285:0.62 290:0.95 297:0.36 300:0.55 +0 1:0.74 8:0.60 9:0.80 11:0.88 12:0.37 17:0.12 20:0.92 21:0.22 27:0.64 28:0.78 30:0.32 34:0.34 36:0.55 37:0.45 39:0.72 41:0.99 43:0.93 55:0.92 60:1.00 66:0.06 69:0.32 70:0.86 74:0.66 78:0.83 81:0.76 83:0.90 85:0.80 91:0.76 96:0.98 98:0.16 100:0.78 101:0.68 104:0.79 106:0.81 108:0.25 111:0.82 114:0.90 117:0.86 120:0.96 123:0.97 124:0.96 126:0.54 127:0.95 129:0.05 133:0.96 135:0.30 140:0.45 144:0.85 145:0.41 146:0.22 147:0.27 149:0.73 150:0.85 151:0.99 152:0.92 153:0.95 154:0.93 155:0.67 158:0.92 159:0.94 161:0.88 162:0.65 164:0.95 165:0.86 167:0.65 169:0.79 172:0.21 173:0.08 176:0.73 177:0.38 179:0.69 181:0.89 186:0.79 187:0.21 192:0.59 201:0.74 202:0.92 206:0.81 208:0.64 212:0.14 215:0.79 216:0.87 222:0.48 232:0.85 235:0.31 238:0.83 241:0.66 242:0.78 243:0.29 245:0.52 247:0.39 248:0.98 253:0.98 255:0.79 256:0.94 259:0.21 260:0.96 261:0.81 265:0.09 266:0.89 267:0.97 268:0.93 271:0.53 276:0.56 277:0.69 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.70 +0 1:0.74 6:0.79 7:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.20 20:0.92 21:0.22 27:0.57 28:0.78 30:0.45 32:0.80 34:0.86 36:0.40 37:0.23 39:0.72 41:0.98 43:0.75 60:0.99 66:0.82 69:0.82 70:0.42 74:0.77 77:0.70 78:0.91 81:0.76 83:0.83 85:0.85 91:0.89 96:0.93 98:0.70 100:0.90 101:0.79 104:0.58 108:0.67 111:0.90 114:0.90 120:0.88 121:0.90 122:0.43 123:0.65 126:0.54 127:0.21 129:0.05 135:0.34 140:0.45 144:0.85 145:0.88 146:0.64 147:0.69 149:0.68 150:0.85 151:0.98 152:0.86 153:0.93 154:0.66 155:0.67 158:0.92 159:0.24 161:0.88 162:0.60 164:0.89 165:0.86 167:0.83 169:0.87 172:0.48 173:0.92 176:0.73 177:0.38 179:0.62 181:0.89 186:0.89 189:0.82 191:0.78 192:0.59 195:0.64 201:0.74 202:0.86 204:0.89 208:0.64 212:0.61 215:0.79 216:0.28 222:0.48 230:0.87 232:0.77 233:0.76 235:0.31 238:0.85 241:0.91 242:0.53 243:0.83 247:0.47 248:0.93 253:0.94 255:0.79 256:0.88 259:0.21 260:0.89 261:0.90 265:0.70 266:0.38 267:0.92 268:0.87 271:0.53 276:0.36 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.33 +2 1:0.74 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.06 21:0.40 27:0.64 28:0.78 30:0.95 32:0.80 34:0.47 36:0.87 37:0.70 39:0.72 41:0.98 43:0.96 60:0.95 66:0.54 69:0.21 74:0.74 77:0.70 78:0.96 81:0.66 83:0.90 85:0.72 91:0.88 96:0.97 98:0.23 101:0.77 104:0.58 108:0.17 111:0.94 114:0.82 120:0.94 121:0.90 123:0.16 126:0.54 127:0.11 129:0.05 132:0.97 135:0.42 140:0.45 144:0.85 145:0.61 146:0.13 147:0.18 149:0.53 150:0.79 151:0.93 153:0.78 154:0.96 155:0.67 158:0.87 159:0.08 161:0.88 162:0.76 164:0.92 165:0.83 167:0.83 169:0.91 172:0.10 173:0.84 176:0.73 177:0.38 179:0.42 181:0.89 191:0.75 192:0.59 195:0.53 201:0.74 202:0.86 204:0.89 208:0.64 215:0.67 216:0.19 220:0.87 222:0.48 230:0.87 232:0.77 233:0.76 235:0.52 238:0.86 241:0.90 243:0.63 248:0.97 253:0.97 255:0.79 256:0.89 259:0.21 260:0.94 265:0.25 267:0.69 268:0.89 271:0.53 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.08 +2 1:0.74 6:0.86 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.26 20:0.85 21:0.30 27:0.57 28:0.78 30:0.62 32:0.80 34:0.55 36:0.98 37:0.31 39:0.72 41:0.99 43:0.98 55:0.71 60:0.97 66:0.44 69:0.12 70:0.66 74:0.88 77:0.70 78:0.93 81:0.71 83:0.90 85:0.90 91:0.58 96:0.97 98:0.72 100:0.90 101:0.80 108:0.73 111:0.92 114:0.86 120:0.94 121:0.90 123:0.71 124:0.60 126:0.54 127:0.67 129:0.59 133:0.47 135:0.90 138:0.98 140:0.45 144:0.85 145:0.58 146:0.73 147:0.77 149:0.85 150:0.82 151:0.98 152:0.89 153:0.91 154:0.98 155:0.67 158:0.92 159:0.57 161:0.88 162:0.73 164:0.93 165:0.84 167:0.64 169:0.87 172:0.65 173:0.18 176:0.73 177:0.38 179:0.47 181:0.89 186:0.89 187:0.21 189:0.91 191:0.77 192:0.59 195:0.73 201:0.74 202:0.89 204:0.89 208:0.64 212:0.58 215:0.72 216:0.90 220:0.87 222:0.48 230:0.87 233:0.76 235:0.42 238:0.89 241:0.64 242:0.57 243:0.49 245:0.87 247:0.60 248:0.97 253:0.98 255:0.79 256:0.91 259:0.21 260:0.95 261:0.89 265:0.72 266:0.47 267:0.73 268:0.91 271:0.53 276:0.71 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.98 7:0.81 8:0.95 9:0.80 11:0.88 12:0.37 17:0.43 20:0.97 21:0.30 27:0.21 28:0.78 30:0.36 34:0.69 36:0.79 37:0.74 39:0.72 41:0.98 43:0.98 55:0.71 60:0.97 66:0.20 69:0.12 70:0.83 74:0.99 78:0.94 81:0.71 83:0.90 85:0.99 91:0.76 96:0.98 98:0.62 100:0.99 101:0.79 104:0.84 106:0.81 108:0.95 111:0.98 114:0.86 117:0.86 120:0.96 121:0.90 122:0.67 123:0.94 124:0.93 126:0.54 127:0.71 129:0.97 133:0.93 135:0.26 140:0.45 144:0.85 146:0.35 147:0.42 149:0.95 150:0.82 151:0.99 152:0.95 153:0.96 154:0.98 155:0.67 158:0.92 159:0.96 161:0.88 162:0.67 164:0.92 165:0.84 167:0.49 169:0.97 172:0.95 173:0.06 176:0.73 177:0.38 179:0.11 181:0.89 186:0.93 187:0.39 189:0.99 191:0.73 192:0.59 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.48 215:0.72 216:0.95 222:0.48 230:0.87 232:0.89 233:0.76 235:0.42 238:0.98 241:0.24 242:0.70 243:0.11 245:0.98 247:0.60 248:0.98 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.97 265:0.63 266:0.89 267:0.89 268:0.90 271:0.53 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94 +2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.37 17:0.36 20:0.97 21:0.40 27:0.39 28:0.78 30:0.10 34:0.44 36:1.00 37:0.57 39:0.72 41:0.98 43:0.98 55:0.92 60:0.87 66:0.20 69:0.15 70:0.88 74:0.82 78:0.93 81:0.66 83:0.89 85:0.94 91:0.78 96:0.97 98:0.52 100:0.94 101:0.78 108:0.83 111:0.92 114:0.82 120:0.94 123:0.82 124:0.92 126:0.54 127:0.93 129:0.96 133:0.91 135:0.92 140:0.45 144:0.85 146:0.57 147:0.63 149:0.86 150:0.79 151:0.97 152:0.89 153:0.90 154:0.98 155:0.67 158:0.92 159:0.86 161:0.88 162:0.79 164:0.91 165:0.83 167:0.56 169:0.91 172:0.67 173:0.09 176:0.73 177:0.38 179:0.33 181:0.89 186:0.92 187:0.39 189:0.94 192:0.59 201:0.74 202:0.85 208:0.64 212:0.18 215:0.67 216:0.26 220:0.74 222:0.48 232:0.95 235:0.52 238:0.90 241:0.51 242:0.41 243:0.17 245:0.83 247:0.60 248:0.97 253:0.97 255:0.79 256:0.88 259:0.21 260:0.95 261:0.93 265:0.54 266:0.91 267:0.23 268:0.89 271:0.53 276:0.83 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.70 diff --git a/examples/lambdarank/rank.train.query b/examples/lambdarank/rank.train.query new file mode 100644 index 0000000..769e5aa --- /dev/null +++ b/examples/lambdarank/rank.train.query @@ -0,0 +1,201 @@ +1 +13 +5 +8 +19 +12 +18 +5 +14 +13 +8 +9 +16 +11 +21 +14 +21 +9 +14 +11 +20 +18 +13 +20 +22 +22 +13 +17 +10 +13 +12 +13 +13 +23 +18 +13 +20 +12 +22 +14 +13 +23 +13 +14 +14 +5 +13 +15 +14 +14 +16 +16 +15 +21 +22 +10 +22 +18 +25 +16 +12 +12 +15 +15 +25 +13 +9 +12 +8 +16 +25 +19 +24 +12 +16 +10 +16 +9 +17 +15 +7 +9 +15 +14 +16 +17 +8 +17 +12 +18 +23 +10 +12 +12 +4 +14 +12 +15 +27 +16 +20 +13 +19 +13 +17 +17 +16 +12 +15 +14 +14 +19 +12 +23 +18 +16 +9 +23 +11 +15 +8 +10 +10 +16 +11 +15 +22 +16 +17 +23 +16 +22 +17 +14 +12 +14 +20 +15 +17 +15 +15 +22 +9 +21 +9 +17 +16 +15 +13 +13 +15 +14 +18 +21 +14 +17 +15 +14 +16 +12 +17 +19 +16 +11 +18 +11 +13 +14 +9 +16 +15 +16 +25 +9 +13 +22 +16 +18 +20 +14 +11 +9 +16 +19 +19 +11 +11 +13 +14 +14 +13 +16 +6 +21 +16 +12 +16 +11 +24 +12 +10 diff --git a/examples/lambdarank/train.conf b/examples/lambdarank/train.conf new file mode 100644 index 0000000..f007dcd --- /dev/null +++ b/examples/lambdarank/train.conf @@ -0,0 +1,116 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# alias: application, app +objective = lambdarank + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = ndcg + +# evaluation position for ndcg metric, alias : ndcg_at +ndcg_eval_at = 1,3,5 + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# column in data to use as label +label_column = 0 + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "rank.train.weight" +# if existing query file, should name to "rank.train.query" +# alias: train_data, train +data = rank.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "rank.test.weight" +# if existing query file, should name to "rank.test.query" +# alias: valid, test, test_data, +valid_data = rank.test + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 31 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use one CPU, default is set to #cpu. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 1.0 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 1 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.9 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = LightGBM_model.txt + +# support continuous train from trained gbdt model +# input_model= trained_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + + +# number of machines in distributed training, alias: num_machine +num_machines = 1 + +# local listening port in distributed training, alias: local_port +local_listen_port = 12400 + +# machines list file for distributed training, alias: mlist +machine_list_file = mlist.txt diff --git a/examples/multiclass_classification/README.md b/examples/multiclass_classification/README.md new file mode 100644 index 0000000..71b4a61 --- /dev/null +++ b/examples/multiclass_classification/README.md @@ -0,0 +1,27 @@ +Multiclass Classification Example +================================= + +Here is an example for LightGBM to run multiclass classification task. + +***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) +for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** + +Training +-------- + +Run the following command in this folder: + +```bash +"../../lightgbm" config=train.conf +``` + +Prediction +---------- + +You should finish training first. + +Run the following command in this folder: + +```bash +"../../lightgbm" config=predict.conf +``` diff --git a/examples/multiclass_classification/multiclass.test b/examples/multiclass_classification/multiclass.test new file mode 100644 index 0000000..617e37b --- /dev/null +++ b/examples/multiclass_classification/multiclass.test @@ -0,0 +1,500 @@ +1 0.109 1.261 -0.274 2.605 0.472 -0.429 -0.983 1.000 -0.095 -1.219 -0.369 -0.312 -0.840 1.281 -0.618 -0.532 -0.132 0.443 0.028 2.201 0.044 1.671 0.660 -0.114 0.574 0.276 0.680 -0.670 +4 -0.294 -0.659 -0.569 2.351 0.775 -0.339 -2.436 -1.704 0.086 -0.291 -1.152 -0.132 -0.745 -0.641 -0.173 -1.016 -1.386 -1.354 -0.405 -1.583 2.190 0.933 0.720 -0.885 -0.848 -1.416 1.838 1.343 +2 0.194 1.849 -1.486 0.190 0.331 -1.080 -2.175 1.073 0.371 -1.200 0.022 0.784 -1.400 -0.130 -1.631 -0.166 0.156 0.944 0.360 -0.081 -0.736 0.534 -1.664 0.267 -0.813 0.871 0.924 -0.935 +0 0.261 -0.391 -0.138 -0.822 0.774 -0.146 -0.738 -0.121 1.735 0.335 1.076 -1.612 -0.197 -1.000 -0.519 -0.767 1.289 0.717 -0.755 -0.465 0.363 0.891 1.041 0.635 1.814 0.427 -0.445 -0.815 +2 0.547 -0.093 -0.077 -2.957 -0.713 0.933 0.970 -0.735 0.165 -0.189 0.690 2.209 0.708 -0.670 -0.078 1.402 -1.409 -0.615 0.710 1.294 0.609 -0.607 0.537 0.414 0.351 -0.097 0.053 -0.907 +4 -0.544 1.656 0.213 2.566 -1.318 -0.301 -0.304 -0.777 -0.351 -0.480 -0.979 1.795 -0.083 0.927 0.990 1.428 -0.982 1.266 -0.074 2.036 0.525 -1.153 1.258 1.159 -1.292 0.745 1.604 0.479 +2 0.499 0.546 -0.268 0.686 -0.923 -2.454 0.707 0.166 -0.610 -1.471 -2.037 0.052 1.649 -0.139 -0.361 -0.024 2.179 0.752 -0.934 0.028 0.332 -1.056 0.759 -0.818 -1.511 0.076 -0.082 -0.001 +0 -0.651 -0.487 -0.592 -0.864 0.049 -0.831 0.270 -0.050 -0.239 -0.908 -0.577 0.755 0.501 -0.978 0.099 0.751 -1.669 0.543 -0.663 0.571 -0.763 -1.805 -1.628 0.048 0.260 -0.904 0.639 -1.662 +4 -1.463 0.326 1.539 0.616 0.623 -0.292 1.767 0.728 0.818 0.100 0.842 -0.257 1.059 0.309 -0.462 1.705 -0.238 -0.841 2.439 0.076 -0.003 0.519 0.449 -0.589 -2.275 1.666 -0.145 1.938 +2 0.837 0.724 -0.514 0.910 -1.161 1.077 -0.261 1.684 -1.521 1.763 -0.361 -0.379 0.195 -0.697 -0.072 -0.738 -0.701 0.487 -0.902 -0.768 -1.373 -0.504 1.312 -0.846 -0.732 0.194 -0.371 2.228 +1 -0.350 -0.612 0.361 -0.562 -0.930 -1.303 0.705 -0.769 0.447 -0.075 1.981 0.363 -1.274 -0.208 0.410 -0.552 0.526 -0.348 0.143 0.604 0.265 -0.976 -1.465 2.297 -1.661 0.787 0.096 -0.769 +2 -0.852 0.442 0.536 1.362 -0.641 -0.848 1.189 -0.225 -0.899 -0.495 -1.386 -0.028 -2.881 0.266 -1.542 1.269 -0.706 -0.828 0.042 0.393 0.035 -1.621 0.118 -0.079 -0.117 0.579 -0.773 -1.133 +0 -1.667 0.223 -1.121 0.194 0.025 0.773 -1.064 -0.295 -1.464 0.098 -0.681 -0.536 0.098 -1.381 0.109 -1.370 -0.347 -1.482 0.666 0.646 -0.322 0.344 0.915 0.802 -1.206 0.740 -1.173 0.267 +1 0.068 0.818 -1.738 0.085 -0.070 0.725 -0.188 -1.039 0.007 -1.083 -1.925 0.727 0.579 0.467 1.303 -0.665 -1.431 -0.070 -0.348 -0.842 -0.080 -0.612 -1.836 -0.441 -1.136 0.232 -0.656 1.112 +4 0.009 -0.265 -1.209 0.797 -2.475 -0.172 1.974 -0.278 1.191 -1.676 -0.027 -0.851 -1.736 0.834 -0.205 0.103 -1.528 -0.716 0.647 -1.237 -1.428 1.648 -1.814 0.095 1.461 -0.233 -1.034 -0.994 +2 0.726 1.481 1.267 0.788 -1.491 -2.417 0.117 0.229 -2.124 -0.887 1.691 0.393 -0.295 -0.137 0.296 -0.308 0.281 -0.229 0.286 -0.140 -2.183 0.244 1.006 0.018 -0.536 1.063 0.160 -0.036 +4 -0.374 -0.465 -1.333 -0.949 0.981 0.504 0.273 -1.335 0.101 -2.304 0.843 0.259 -1.345 1.616 -1.796 0.530 0.555 -1.240 -0.038 -1.637 -2.846 -1.068 -0.617 0.644 0.067 0.094 -1.014 -0.530 +0 1.231 -0.064 0.033 0.833 -0.892 -0.583 -0.094 -0.657 -1.489 -0.029 0.136 -0.243 1.020 0.129 0.756 0.265 0.854 1.836 0.166 0.881 0.361 1.008 0.637 -0.645 0.166 -0.404 0.274 0.939 +4 -0.647 -0.659 -1.209 -0.148 1.131 1.019 -0.026 1.784 -0.055 -0.341 -1.977 0.199 -0.896 1.452 -1.243 1.287 0.205 -1.149 -1.661 0.370 -0.921 -1.971 1.979 0.868 -1.145 -1.817 1.181 -1.354 +2 0.314 -0.396 -1.002 1.413 0.361 0.356 -0.549 -0.580 -0.445 -0.045 2.310 -0.581 0.389 -2.498 -0.284 -0.588 -0.132 0.580 0.916 1.235 2.289 -1.210 -0.520 0.079 0.005 0.951 0.216 -1.186 +3 -0.653 -0.269 0.167 -1.778 -0.499 0.294 0.597 1.236 0.016 0.421 1.926 0.748 -0.621 -0.607 0.991 -2.071 -0.566 -0.159 -0.548 -0.582 0.196 0.978 0.174 0.773 -1.765 1.876 1.908 -0.310 +3 -0.033 -1.985 -2.104 -0.444 -1.038 -1.375 0.260 -0.999 -1.046 1.118 0.341 -0.548 0.095 0.233 -0.446 -0.502 1.835 -0.209 1.280 -0.305 0.140 -0.627 -0.211 0.256 2.191 0.856 0.963 -1.432 +3 -0.127 -0.265 -0.637 1.229 -1.362 -0.085 -0.844 0.567 0.134 1.182 0.735 0.082 0.744 0.836 -0.913 0.773 0.297 0.489 -0.408 -0.854 -1.034 -3.408 -0.709 2.439 0.822 0.318 0.961 -0.260 +4 -2.755 0.924 -1.861 0.345 0.034 -1.552 1.405 -2.208 1.390 0.034 -0.175 -0.780 -1.042 -1.789 0.008 -0.425 0.499 0.297 0.941 0.205 0.910 0.567 0.908 -1.248 0.338 -1.810 0.097 -0.171 +2 -0.202 0.177 -1.019 0.758 -1.104 -1.135 0.949 1.259 0.287 -0.281 -1.108 -1.077 -0.438 -0.440 -2.299 0.380 -1.768 -1.403 -0.553 0.245 0.105 -0.044 -1.096 -0.056 1.254 0.007 0.647 -2.126 +4 0.175 0.528 0.853 -1.185 -0.018 0.140 3.120 0.901 0.122 -1.231 -0.183 2.311 -1.496 -0.209 1.321 2.220 -0.193 0.623 0.985 0.698 0.314 1.398 1.208 0.499 1.745 0.190 0.233 -0.202 +4 -0.052 -0.478 0.395 -0.274 -2.571 -1.746 -0.255 -0.102 0.447 -1.724 -0.401 -0.156 0.292 1.451 0.229 -1.350 1.062 1.287 -1.010 0.417 2.362 1.323 -0.552 1.960 -0.098 0.590 2.763 0.381 +0 -0.607 0.753 0.541 -0.371 1.879 -0.843 0.458 -1.308 0.302 0.515 -0.197 0.045 -0.477 0.931 0.875 0.438 0.700 0.553 -0.410 0.356 0.754 -0.204 0.570 -1.322 -1.185 0.083 -0.312 1.101 +2 -0.369 -1.453 1.285 -0.066 -0.155 -0.311 0.256 -1.681 -1.971 0.452 0.560 1.672 -0.159 0.085 0.035 -0.307 -0.558 -2.198 0.301 -0.725 1.913 0.997 -0.588 0.871 -0.643 -0.672 -0.416 0.290 +4 -1.068 1.667 -0.093 -1.823 0.985 -1.302 1.507 -1.712 -1.619 -1.039 -0.824 -0.102 -2.205 0.194 -0.502 -1.238 0.153 1.491 1.334 0.329 -0.399 -1.305 -1.170 1.561 0.425 -0.069 0.284 -1.353 +4 -1.824 0.659 0.617 0.814 2.506 -1.299 -1.488 0.706 -0.337 -0.986 -0.283 -0.618 1.725 2.262 -1.636 -1.477 -0.759 -0.690 -0.775 -0.194 -0.447 -1.354 -1.027 1.546 -0.357 -0.409 -1.520 -0.463 +0 -1.368 0.589 -0.055 -0.378 -1.288 -0.300 -0.415 0.318 0.167 0.792 -0.485 -1.860 0.991 -0.936 -1.107 -0.043 0.302 -0.531 -0.539 -0.391 1.417 -0.003 -0.968 -0.958 -0.251 0.238 0.484 0.124 +0 -0.965 -1.354 -0.158 -0.337 -0.611 -1.336 -1.668 -1.137 0.871 0.029 -0.928 -0.098 0.258 0.103 -0.543 -0.539 -0.470 1.209 0.592 1.290 0.117 -0.988 1.295 -0.330 -1.114 -0.967 0.690 0.138 +1 -0.290 -1.035 -0.820 -1.467 0.141 -0.862 -0.495 -0.128 1.044 0.929 -0.055 1.266 -2.012 1.877 0.692 0.103 -0.891 -0.438 0.609 0.632 0.668 -0.728 -0.930 0.195 0.770 0.003 1.481 0.033 +1 -1.215 2.249 1.060 -0.160 1.110 -0.347 0.475 0.592 0.862 -0.460 0.883 -0.442 0.731 -0.018 -1.824 -0.737 0.848 -0.304 1.214 -1.213 0.696 -0.048 -0.247 -1.178 0.210 -1.009 -0.636 -0.141 +1 -1.617 -0.229 -0.249 0.627 0.046 0.623 0.703 2.155 -0.315 -0.941 0.246 -0.802 1.482 -0.283 -0.566 -0.471 0.718 -0.158 -0.642 1.665 -0.856 0.036 0.652 -1.631 0.480 -1.055 0.823 -0.963 +2 1.022 -0.268 0.245 -2.234 0.225 -1.043 -0.351 -1.567 -0.882 -0.164 -0.272 0.885 -1.868 -0.051 -0.099 -0.458 -0.913 1.102 2.048 -1.714 -0.924 -1.004 -0.282 1.256 0.528 -0.154 0.016 -0.319 +3 -0.153 -0.452 0.570 0.579 -0.310 1.099 -1.020 0.408 1.547 1.248 -1.612 1.060 -1.243 -2.433 0.479 0.931 -1.346 1.494 -0.244 -0.874 0.362 0.725 0.883 -0.750 0.366 0.165 -1.291 -1.055 +1 -0.803 1.518 1.136 2.244 0.580 -0.334 -1.220 0.130 0.103 1.263 -0.188 -0.724 -1.792 -0.099 -0.711 -0.397 -0.203 0.096 -0.910 -0.686 1.451 -0.261 0.716 -0.386 -0.257 1.023 -0.531 0.872 +1 -2.040 -0.141 -0.711 0.734 -0.479 0.467 0.625 -0.335 0.490 -0.255 0.343 -0.949 -0.944 -1.166 0.263 1.207 0.961 -0.241 -0.162 0.820 1.318 -2.157 -0.507 0.156 -0.042 -0.072 1.906 -0.838 +3 1.358 0.042 0.877 -0.775 -0.615 -1.600 1.109 -0.576 -1.328 0.501 1.343 -0.371 -1.771 -0.324 0.239 0.595 0.777 0.076 -0.259 0.686 -1.323 -0.969 -0.727 -0.442 -1.383 0.972 -1.212 -2.688 +0 -0.174 -0.956 0.877 -0.937 0.865 0.054 0.284 0.040 0.238 -0.603 1.559 1.015 -0.592 -0.263 0.450 -0.356 -0.501 0.597 0.028 -0.081 -0.037 -1.521 1.920 -0.712 -0.832 -1.960 -0.520 0.920 +4 -0.969 -0.131 -0.226 2.224 -0.489 0.449 -0.080 2.200 0.299 1.428 1.351 -1.764 -0.067 -0.233 -1.742 -1.553 0.529 1.406 -0.126 -1.170 0.624 -0.069 -0.241 -0.352 -2.241 0.544 1.581 -0.561 +4 1.007 0.023 -0.864 1.590 -0.750 -2.162 0.458 -1.105 0.989 -0.381 0.604 1.402 -1.209 -0.566 -0.076 1.538 -0.658 -0.595 0.478 -0.577 0.153 -1.552 2.214 -1.348 -0.322 -0.032 1.913 -1.325 +4 -0.451 2.086 -1.295 1.459 1.215 1.901 -1.588 -0.869 1.492 -1.877 -1.606 -0.488 0.192 1.592 -0.185 -0.608 0.311 0.470 0.441 -0.178 -1.955 -0.918 1.420 0.187 -0.323 1.118 -1.130 -0.225 +4 0.893 1.292 0.102 -0.997 -0.715 0.629 -1.135 -0.582 0.513 -0.651 1.421 -1.038 -3.288 -0.778 0.310 0.863 -0.408 -0.277 -0.031 1.742 0.379 -0.493 2.052 0.011 -2.075 -1.387 0.558 -1.777 +4 0.711 -1.612 0.990 -1.148 -0.725 -0.018 2.103 2.309 -0.713 -0.173 0.996 -0.361 0.703 -0.812 1.110 -1.219 -1.618 1.175 -0.115 -0.129 -0.659 0.937 -0.484 1.344 -2.643 -2.446 1.094 0.657 +4 -1.149 0.533 -0.579 -0.930 0.311 -1.577 -0.037 -0.311 -1.064 0.541 -0.565 0.739 0.314 -0.479 1.761 0.181 -0.320 1.808 -0.348 2.572 0.609 1.802 1.153 -0.581 0.539 1.552 2.205 0.240 +1 -0.943 0.450 -0.116 -1.006 -0.286 -1.356 -0.610 0.605 1.217 -0.690 1.447 -1.912 0.388 -0.305 -0.404 -1.976 -0.577 -0.596 -1.351 -0.276 0.019 0.150 -0.641 -0.054 0.853 1.015 0.805 0.815 +1 0.317 1.162 -0.190 -0.512 1.780 -1.521 0.152 0.321 -0.766 0.305 2.563 -0.297 -0.620 1.460 0.786 0.979 0.276 -0.005 -0.454 0.047 -0.435 -0.575 -0.660 1.185 -0.794 -1.201 -1.058 0.074 +2 1.513 0.658 -0.687 -1.551 -0.822 0.218 -0.471 1.002 -1.489 -0.129 -0.767 0.991 0.543 -0.831 0.854 -1.623 -1.786 -0.885 -1.983 -0.113 -0.081 -0.406 0.539 -0.723 -1.298 -0.797 -1.220 -0.612 +4 2.724 -1.607 1.359 0.200 -0.425 0.996 1.627 0.072 0.284 0.167 -0.074 -1.667 -0.870 1.244 -0.523 -1.031 -0.355 -0.247 -1.431 -0.164 0.093 -0.719 0.320 2.788 -0.311 -1.230 1.053 0.970 +4 0.210 -0.202 -2.281 0.259 0.680 1.205 -0.545 -1.052 -0.390 -1.206 0.548 0.111 -1.198 -0.423 1.561 -0.062 -0.725 -0.527 0.175 1.822 -1.050 1.428 -0.309 1.287 -1.156 1.552 1.781 1.865 +1 0.581 0.369 0.820 -0.318 -0.010 -0.181 0.954 0.717 0.488 -1.349 1.997 -0.472 -0.297 1.673 0.096 -0.373 0.726 -1.012 -1.694 -0.732 1.760 -0.876 -0.356 0.172 0.571 0.366 -0.909 1.561 +3 -0.916 -0.054 -0.935 0.870 1.537 -0.845 -0.031 0.917 0.024 0.562 0.052 -1.057 0.119 1.127 0.314 1.624 -1.772 0.379 -0.648 -0.876 1.334 -0.255 -0.352 0.344 -2.153 1.355 1.904 0.915 +2 1.169 -0.985 -1.313 -2.164 1.301 0.772 2.109 0.483 -0.277 0.238 0.422 -1.071 -1.037 -0.986 0.009 -1.215 1.358 1.521 -0.779 0.083 0.963 0.441 0.090 0.661 0.860 0.505 -0.477 0.763 +0 -0.464 -1.177 1.994 -0.673 -1.187 0.443 0.054 -1.076 0.155 -0.587 -1.245 0.471 0.106 0.634 -0.807 0.852 -0.967 0.045 -0.449 -0.865 0.599 0.515 -0.228 -0.198 0.041 0.083 -0.886 -0.261 +0 -1.167 -1.565 1.211 -0.225 0.120 -0.637 1.098 -0.184 1.276 -0.629 -0.849 -1.016 0.569 0.978 -0.537 0.178 -0.293 1.209 -0.391 0.290 1.182 -0.119 -0.593 -0.257 -0.039 0.425 0.115 0.944 +2 -1.306 -0.606 -1.189 0.098 -0.336 1.372 0.605 -1.201 1.564 -0.857 0.285 -1.133 -2.060 0.924 2.044 -1.211 -0.017 0.032 -0.778 0.807 -0.846 0.396 -1.447 0.144 -0.166 -0.184 0.973 0.053 +4 1.498 0.223 -0.410 -0.960 -0.353 0.013 -1.118 0.640 -0.848 -1.458 -1.320 -0.480 0.548 1.640 -0.240 0.333 -0.071 -2.130 0.378 -0.022 -1.577 1.690 0.600 -1.666 0.524 3.026 0.007 0.691 +1 -0.558 0.511 2.162 0.737 0.548 0.240 -1.228 1.428 -0.039 0.343 1.179 -0.358 -1.330 -0.632 -0.522 0.568 -0.428 -1.935 -1.613 -0.763 -0.346 -0.605 -0.550 0.017 0.377 -1.561 0.994 0.110 +3 1.343 -0.375 0.460 0.207 0.706 1.673 0.676 1.743 0.702 0.132 0.321 -2.625 -0.455 -0.144 0.438 -0.026 0.334 -1.323 -1.581 -1.183 1.571 -1.268 -0.789 0.105 -1.592 -0.872 0.677 -0.438 +0 -0.422 -1.600 -0.317 -0.217 -0.305 -0.305 -0.173 0.508 1.343 -0.169 -1.202 0.082 0.634 1.018 0.406 -1.046 0.358 -0.410 0.476 -0.027 0.531 -0.670 -0.699 -1.242 -1.757 -0.346 -0.751 1.292 +4 -1.489 0.394 0.958 0.051 1.384 0.746 -1.301 1.184 -0.417 -1.163 -0.670 1.780 1.750 -0.351 0.995 0.444 -0.323 0.303 -1.805 -0.538 -0.080 0.334 1.280 2.277 -0.746 0.453 0.530 -2.800 +3 0.580 0.075 -0.623 0.201 1.046 -0.240 -0.955 0.207 -0.642 0.328 1.267 -1.142 -1.846 2.293 0.133 -1.246 -1.001 -0.475 0.302 -2.417 0.699 -0.687 1.483 0.386 0.894 -0.234 0.245 1.384 +4 -1.445 1.318 -0.490 0.708 -0.614 1.684 -0.031 -1.329 0.470 1.301 1.188 0.094 0.812 -0.422 0.591 0.363 -2.895 -1.001 0.208 0.165 -0.029 0.089 1.208 0.354 -2.333 1.948 -1.907 0.958 +1 -0.700 0.155 -0.624 0.155 0.162 -0.578 0.203 -0.458 -0.502 0.302 0.599 0.043 -1.081 -0.948 1.293 -0.227 -0.679 1.975 1.317 1.076 0.008 -0.230 -0.217 -0.319 0.444 0.859 -2.607 -0.921 +3 -0.135 -0.569 1.305 -1.329 -0.483 0.257 2.419 1.955 -0.045 0.349 0.602 -1.012 0.010 1.356 0.142 1.950 -0.446 -0.089 -0.081 -1.301 1.106 -0.334 0.258 -0.598 1.725 1.233 0.058 -1.224 +2 0.218 0.335 0.453 -0.629 1.158 -1.004 1.201 -0.959 0.129 0.677 0.470 -0.935 -0.983 -1.461 -1.294 -1.416 1.288 1.816 0.462 0.576 0.716 -0.223 -0.574 -0.178 -1.730 0.060 2.006 -0.170 +0 -1.154 0.354 -0.134 -0.033 -0.552 0.593 -1.273 0.723 -0.008 -0.108 1.719 0.072 0.016 -0.495 -0.401 1.544 1.352 -0.181 2.132 -0.527 0.518 0.386 -0.677 0.398 -0.759 1.269 0.264 -0.156 +3 0.619 0.044 0.937 -0.276 -0.724 1.190 0.318 -0.684 -0.418 -0.736 2.022 0.004 0.158 0.721 -1.215 1.097 -0.608 1.830 -0.313 1.038 -2.497 -1.175 -0.294 -2.031 0.601 0.313 -0.991 -0.016 +0 0.799 0.006 -0.558 0.994 0.528 -0.768 0.049 -1.352 0.686 -0.262 -0.436 -0.033 0.040 0.895 0.591 -0.156 0.764 0.401 -0.427 -0.125 -0.148 0.505 -0.367 -0.390 1.969 0.940 -0.761 0.571 +2 0.484 0.298 1.362 1.081 1.075 0.475 1.042 -0.300 -0.567 -0.339 2.272 -1.044 0.943 1.980 0.397 -0.318 0.556 -0.493 -0.910 1.346 1.282 -0.319 -0.488 0.216 -0.096 -0.818 -1.524 -1.458 +3 0.378 0.183 -0.012 1.110 -1.286 0.034 -0.403 0.716 0.854 1.470 0.420 -0.215 0.965 -0.037 -0.703 -0.562 -0.443 2.518 1.612 -0.975 1.248 0.540 -1.020 -1.066 0.505 -1.914 2.140 0.026 +1 -0.035 -1.066 -0.423 0.416 -0.065 -0.934 -1.637 -0.302 1.781 -0.458 -0.364 0.110 0.229 0.166 0.418 0.394 -0.060 -0.721 1.270 -1.028 -1.460 0.079 1.066 0.932 0.598 1.899 -1.400 0.435 +3 1.453 1.077 0.711 0.094 1.661 1.259 1.548 0.696 0.123 -0.399 -1.269 0.502 0.585 -0.350 0.614 -0.332 0.867 -0.882 -1.055 -0.241 0.829 -1.159 1.050 -1.643 -0.247 -1.413 -2.610 0.366 +3 0.561 -0.954 -1.195 0.054 0.064 -0.147 -0.289 1.711 0.426 1.971 1.269 1.703 -1.074 0.593 -0.432 -1.663 1.496 -0.294 -1.055 -0.415 -0.796 -0.694 -0.222 1.731 -0.666 1.659 0.849 0.396 +4 -0.772 1.336 1.106 -0.645 -0.987 -0.620 -1.633 -1.216 -1.261 0.322 0.588 2.078 0.956 -0.007 -0.415 1.332 -1.218 0.036 -1.231 0.571 1.337 -1.551 1.903 -0.959 -0.729 0.165 -1.110 2.042 +3 -0.250 -0.677 -0.481 1.093 0.602 0.995 -1.247 0.336 1.728 0.272 0.671 0.769 0.716 -0.187 0.448 -0.785 1.327 0.215 -1.001 1.637 2.163 0.079 -1.510 0.455 -1.857 -1.521 0.975 0.212 +4 1.045 -1.233 1.673 -0.715 0.795 1.235 0.041 -0.080 0.967 1.193 2.945 0.297 1.375 0.925 -2.850 -0.144 -1.271 -1.202 -1.025 0.318 -0.815 0.721 0.382 0.589 0.069 -1.018 -0.525 1.785 +2 0.052 1.354 -2.074 1.193 1.272 0.402 -0.841 -0.668 1.198 0.826 -1.396 0.556 0.912 0.514 -0.052 1.267 0.349 1.343 -2.181 -0.721 -1.196 0.284 -0.541 0.230 1.382 0.178 -0.558 -0.120 +1 0.310 -0.112 -0.420 0.510 0.035 -0.788 0.550 -0.233 -0.829 -0.844 -0.666 -0.287 -1.713 0.183 1.669 -1.482 -1.711 0.765 0.442 -0.859 -1.775 1.745 0.530 -0.429 -0.531 -0.579 0.486 -1.337 +0 -0.188 -0.253 -0.145 -1.117 1.147 -0.263 1.259 1.080 0.470 1.234 -0.207 -0.556 -0.648 0.072 0.697 0.384 1.917 0.506 -0.551 0.161 0.901 -0.473 -0.633 1.148 0.775 0.038 -0.516 -0.288 +0 0.669 0.034 -1.125 -0.072 0.698 -2.146 -0.391 -1.275 -0.055 0.578 -0.203 -0.506 -0.774 -0.766 0.625 0.552 1.691 0.482 0.985 -1.077 -0.250 -1.188 -0.266 -0.573 0.289 -0.061 0.607 1.181 +4 -0.153 -0.458 -0.287 0.978 0.235 -1.311 -1.859 1.466 -1.469 -0.602 1.339 -0.565 -0.453 -0.245 -0.180 -0.579 -0.542 1.140 0.293 -0.945 1.952 1.452 0.592 0.001 -0.407 0.910 2.413 2.252 +4 0.545 -0.071 0.763 0.940 -2.912 -0.434 -1.857 0.239 -0.301 1.356 0.207 -0.486 0.939 -0.318 -0.498 0.394 1.613 0.854 0.567 2.751 -0.732 -0.755 0.146 1.515 -0.942 1.755 -1.439 -0.121 +3 -2.016 -0.390 -0.935 -0.134 -0.120 -1.073 -0.810 -0.916 2.335 -1.307 0.425 -2.114 -0.220 1.253 1.149 -0.045 0.677 -0.064 -0.020 -1.344 -1.210 -0.198 1.179 1.050 -1.173 0.037 1.607 0.789 +0 -0.258 0.055 -1.244 1.200 0.613 -0.085 -1.219 -0.213 -0.273 -1.080 -1.162 0.766 -0.324 0.886 -0.013 0.752 0.718 0.467 1.183 0.100 -1.495 -1.335 -0.653 0.742 -0.400 0.205 0.536 0.164 +3 -3.274 0.889 -0.047 -0.011 -0.267 0.608 0.063 0.804 0.015 0.541 0.779 -0.128 0.766 0.492 0.186 -0.696 -0.842 0.385 0.640 -0.327 0.721 -0.700 -0.120 -0.580 -0.653 -0.809 3.428 1.125 +2 0.370 -1.431 0.804 -1.111 -2.099 0.824 1.011 1.363 0.272 1.475 -1.617 -0.362 0.910 -0.005 0.503 0.479 0.893 1.932 0.494 0.613 -0.909 -0.440 0.163 -0.155 -1.157 1.001 -0.508 -0.022 +0 0.085 0.589 0.199 -2.147 -0.158 0.996 0.882 1.507 -1.779 0.763 -0.697 0.050 0.155 -0.270 0.067 1.197 -0.498 0.078 -0.859 0.594 0.179 0.368 0.923 0.872 0.249 -0.588 0.813 -0.567 +4 0.187 1.742 1.962 -0.832 3.442 0.169 0.632 -2.652 1.008 -2.154 -0.144 -0.553 0.466 0.179 0.587 1.246 -1.004 -0.204 -0.845 -0.921 1.933 -1.269 -0.822 2.194 -0.127 -0.064 0.401 -1.205 +0 -0.752 0.262 -1.275 -0.436 -0.418 -0.522 0.219 0.434 0.014 0.901 0.390 -0.494 -0.108 -0.081 -0.383 0.510 -2.131 0.032 0.309 0.599 1.108 -0.141 0.832 0.611 0.264 -1.391 1.090 0.096 +0 -0.272 -2.046 -0.449 1.161 0.183 0.531 -1.095 -0.900 -0.124 0.350 0.608 0.035 -0.561 -0.518 -0.685 -0.737 -0.861 0.539 0.052 0.531 -0.293 -0.085 -1.840 0.755 0.387 0.127 0.916 1.702 +4 2.101 2.078 -2.316 0.629 -0.282 -1.064 1.455 -0.138 0.923 1.016 0.674 -0.334 -0.324 0.095 1.455 1.037 -0.245 -0.086 1.785 -1.232 -1.875 -0.865 2.116 -1.195 -0.926 0.307 0.182 1.160 +0 -0.553 0.680 0.948 0.586 -0.652 0.643 1.208 0.402 0.835 1.790 0.898 -0.727 0.140 -0.332 0.173 1.705 -0.424 -1.267 -0.128 -0.884 -0.147 -0.149 0.620 -0.942 1.031 -1.169 -1.141 -0.244 +3 1.302 0.276 0.815 0.393 -0.847 0.248 -0.332 -1.063 0.920 -1.305 -0.659 2.217 0.701 0.245 -2.173 -0.118 -1.165 -0.078 -0.190 -0.857 1.172 0.955 -1.126 1.574 -1.150 1.664 -0.211 -0.400 +3 1.898 -2.365 0.365 0.166 0.355 -0.558 0.003 -1.094 1.038 1.007 1.641 -0.774 0.270 2.043 -0.675 -1.009 0.300 1.165 -0.667 1.933 -0.916 0.658 -0.677 -0.466 0.823 -0.209 -1.519 -0.799 +4 -0.415 -2.426 0.880 2.158 1.974 -0.476 -0.287 0.874 0.092 0.129 -2.197 -0.817 -0.204 1.778 1.764 -1.111 -1.231 1.682 1.117 -0.474 -0.333 -1.144 -1.214 -1.605 -0.136 -0.417 -2.040 -0.365 +4 -0.772 -0.327 1.185 -0.448 0.400 0.072 -0.274 0.650 0.375 0.437 1.485 -2.901 0.143 0.372 -1.317 -1.662 -1.742 3.620 -2.126 -1.778 -0.169 1.344 0.730 -0.153 0.789 0.805 -0.096 -1.300 +3 -0.732 0.735 1.397 -0.673 1.475 -0.378 0.225 -0.363 0.250 -1.050 1.029 -1.635 -0.934 1.297 2.263 -0.528 1.534 1.293 0.408 -0.480 1.403 -0.188 0.465 -2.162 -0.403 -0.611 0.126 -0.314 +4 -0.458 0.142 -0.771 0.017 0.079 -0.182 -1.644 -1.051 -0.162 -2.136 1.025 1.300 0.852 0.282 -1.233 0.307 1.405 -1.133 2.554 0.818 -1.061 -1.010 -0.505 -0.054 -0.377 -2.801 -1.302 -0.007 +3 0.777 0.503 -0.640 0.811 -1.283 -2.228 -1.562 -0.922 -0.755 -0.435 -0.390 -0.650 -2.116 0.192 -0.265 -0.696 1.398 -0.242 0.059 0.796 -1.490 -0.023 0.793 -0.091 -1.303 -0.482 0.189 -2.107 +1 1.429 0.321 -0.218 0.096 1.137 0.057 -0.020 0.128 0.872 -0.467 0.189 0.215 0.578 0.993 0.078 -0.471 0.679 -1.569 -1.077 -1.013 -0.716 -1.391 0.957 -0.342 -1.724 -1.203 0.314 -1.634 +3 0.886 -1.530 0.249 -0.936 0.655 1.079 -1.891 0.265 -1.558 -1.717 0.763 -0.098 -0.143 1.559 -0.271 -0.789 -0.506 0.406 -1.060 -0.253 0.964 -1.597 -1.641 -0.339 0.679 1.481 -0.791 -0.476 +2 -0.247 0.291 -0.750 1.254 0.817 0.281 0.714 1.157 -0.525 0.047 -1.185 2.254 -0.510 -2.002 -0.424 -0.455 0.714 0.656 0.839 -0.118 0.436 -2.222 -0.313 1.628 1.647 -0.770 -0.076 -0.251 +0 -1.384 -1.904 0.874 0.258 1.549 0.971 -0.623 0.247 -0.792 -1.613 1.196 -0.194 1.348 0.109 0.278 0.577 -0.184 -0.237 0.242 1.246 0.505 0.017 -0.411 -0.118 -0.864 -0.982 -0.238 0.006 +2 -0.966 -0.107 -0.665 -1.543 -0.182 0.106 1.268 0.937 1.220 0.720 0.333 -1.229 0.150 0.597 1.457 0.686 1.240 -1.032 -0.326 0.429 1.161 -0.145 -0.082 0.687 -1.126 1.607 -0.879 -1.919 +2 -1.283 0.717 -0.453 0.330 -0.624 1.474 -1.434 1.034 -1.913 0.097 -1.387 0.713 0.342 0.525 -0.270 -0.265 -0.135 -1.174 2.576 -0.797 -0.400 0.321 -0.448 0.286 -1.851 -0.036 -0.940 0.402 +0 1.013 -0.161 1.329 -1.660 0.446 0.138 0.834 -0.076 -0.984 -1.092 -0.012 -0.260 0.857 0.954 0.048 0.026 1.065 -0.245 -0.204 -1.323 0.879 -0.090 1.137 -1.207 -0.491 0.946 0.326 -1.183 +4 -1.772 0.941 -0.361 -0.467 1.478 0.170 0.904 -0.623 -0.725 -0.365 0.670 0.051 0.468 -2.603 -1.882 -2.154 -0.379 -1.398 1.690 -0.021 0.217 -1.090 -1.121 1.551 1.782 0.416 -2.032 -0.425 +1 -0.952 -1.032 0.604 0.771 -0.250 -0.597 0.569 -1.482 1.950 0.452 -0.159 0.830 0.631 0.235 -1.890 0.411 1.456 -0.555 0.959 0.849 0.294 0.170 0.560 0.416 1.891 0.967 0.707 1.057 +2 1.566 0.434 1.083 0.387 0.379 0.753 -0.567 -0.556 0.350 1.941 -1.541 -2.029 -0.264 1.031 0.051 -1.169 0.079 -1.733 0.629 -0.309 -0.082 0.427 0.206 -0.541 1.034 0.797 -1.774 -0.230 +3 0.633 -1.243 -2.269 -0.977 -0.243 2.580 0.543 -1.808 -0.690 -0.053 0.254 -1.315 -0.135 -0.718 -1.104 0.260 0.774 0.841 0.574 -0.497 0.715 0.309 0.134 -0.612 -0.497 0.074 2.440 0.301 +0 0.148 -0.643 0.262 -0.331 0.880 0.333 -0.315 0.232 1.315 -0.570 0.403 -0.070 0.427 -0.655 0.110 1.108 -0.937 -1.329 -1.291 -1.171 0.595 -1.406 -0.298 0.618 0.417 -0.386 0.191 0.107 +0 -0.719 -0.234 1.263 -0.775 0.483 0.747 0.077 0.040 -1.025 0.731 -1.254 0.391 -0.518 -0.395 -0.201 0.499 1.562 0.111 -0.720 -1.440 1.160 0.175 -0.232 0.059 0.767 -0.410 -0.666 -1.310 +3 0.265 -0.096 0.818 -1.546 -0.614 -0.791 0.123 -1.861 -1.498 1.854 -0.448 -1.560 0.247 1.735 0.266 -0.284 0.287 0.635 -0.347 -0.244 0.348 0.150 -2.634 0.734 0.967 -0.010 0.872 -0.477 +2 1.473 2.152 -0.709 0.140 -0.400 -0.182 0.930 -0.046 1.316 -0.516 0.741 -0.443 -1.837 -1.021 0.124 1.921 -0.460 -0.169 0.490 -0.599 -0.484 0.072 -0.437 -1.783 -0.650 0.615 0.646 1.336 +0 -1.292 -0.696 -1.591 -0.046 -0.534 -0.960 -0.006 -1.009 0.498 -0.086 -0.285 0.134 0.518 -0.089 -0.320 -0.197 0.765 -1.561 -0.054 1.318 0.849 0.278 0.560 2.260 0.169 -0.446 1.260 0.177 +3 1.464 0.352 -0.991 -0.893 1.218 -1.343 0.237 1.947 -1.703 1.175 0.620 0.328 0.249 0.289 0.459 -0.599 0.438 -0.492 -0.833 -0.407 -0.268 1.740 1.117 0.157 -1.392 -0.842 1.656 -1.280 +3 0.823 -0.220 1.427 -0.086 0.348 -1.596 -0.139 -2.060 -0.117 -0.731 1.831 0.903 -0.306 0.022 1.382 -2.009 0.235 -1.025 1.312 -0.484 0.102 0.682 1.380 0.589 2.116 -0.655 0.186 0.030 +1 0.338 0.107 -0.414 0.721 0.010 -0.087 -0.087 -0.298 -1.037 0.172 0.003 -0.419 -0.691 0.730 0.331 -1.333 -0.739 2.111 -1.194 -0.825 -0.499 1.248 1.471 -1.325 -1.542 -0.523 0.002 1.798 +3 0.255 0.957 1.120 0.412 -2.233 0.148 0.212 -1.087 -1.790 0.462 -1.249 2.061 0.263 1.352 0.031 0.752 0.053 1.006 -2.038 0.888 0.669 -0.154 -0.409 -0.299 0.283 1.214 -0.534 -1.702 +3 -0.292 0.090 -0.670 0.636 0.752 1.028 1.425 -0.143 -1.227 -1.131 1.735 1.615 1.338 -0.586 1.688 -0.170 0.343 0.826 1.261 0.541 1.375 0.013 -0.203 -0.320 -1.149 -2.798 1.041 -0.234 +4 0.382 0.506 0.034 2.083 0.645 1.839 0.293 -0.605 -2.070 0.371 0.641 -1.255 0.042 -1.402 0.831 0.673 -1.836 -1.468 -2.054 0.369 0.381 0.870 -1.096 -0.453 -0.770 -0.920 -0.942 -1.288 +3 -0.450 0.904 1.121 1.078 -2.689 -1.009 -0.282 1.845 0.445 -0.457 -0.555 -0.522 -0.864 0.763 0.732 -1.116 -1.727 -0.498 -0.693 0.240 1.660 -0.850 0.714 0.693 -1.807 -0.652 -1.129 0.787 +1 0.084 -0.634 1.757 0.527 -0.000 -1.106 -1.364 0.496 1.799 0.409 0.175 0.190 0.103 0.203 -0.685 -0.974 0.109 1.493 0.777 -1.233 -0.255 0.729 1.517 1.791 -0.211 0.824 0.249 -0.120 +0 0.107 -0.345 0.688 -0.613 -0.057 1.184 0.342 -0.060 -0.602 -0.633 -0.851 -0.541 0.756 -0.222 -1.350 1.768 0.320 -1.407 -0.442 0.111 1.705 -1.108 0.976 1.399 -0.629 -0.278 -1.036 -0.203 +2 0.185 -0.838 1.456 -0.158 -1.370 -0.174 -0.691 0.938 -0.406 -0.712 -1.245 -0.692 -0.284 0.628 -1.513 -2.158 0.599 -0.714 1.433 -0.786 0.178 -0.572 -0.474 -1.309 1.377 0.112 0.326 1.387 +3 0.572 0.651 -0.118 -1.157 -0.682 1.152 -0.485 0.143 -1.076 0.337 -0.458 1.047 0.774 -0.496 -0.127 -0.551 -1.419 -0.857 0.198 0.325 0.149 -0.302 0.933 0.064 0.703 -1.774 -3.922 0.284 +2 -0.401 0.734 -0.989 -0.473 -0.325 0.808 1.056 -0.972 -0.404 -1.934 -0.001 0.045 1.417 -0.175 -0.121 -0.446 0.529 0.257 0.430 2.141 -1.488 -2.737 0.713 -0.047 -0.573 1.013 0.170 -0.407 +4 -1.013 0.519 -2.715 -0.043 0.613 1.073 -1.502 0.322 1.662 2.279 0.003 -0.952 1.510 0.670 1.980 1.823 -1.650 0.481 0.292 0.933 0.029 2.006 -0.193 -0.200 1.075 -0.116 0.008 -1.053 +1 -0.651 -0.763 -0.956 -1.799 1.047 -0.057 -0.063 1.107 -0.908 1.576 -0.285 -1.556 -0.690 0.357 0.110 -1.227 0.157 -0.101 0.115 1.278 0.533 0.009 -0.589 -0.682 -1.088 0.766 0.722 1.488 +3 1.970 0.176 -1.312 0.783 -1.192 0.546 -0.542 0.209 0.671 0.520 -1.705 0.986 -0.904 -1.145 -1.063 -1.623 2.340 -0.483 -1.467 1.070 -1.099 -0.279 0.729 -0.382 0.442 -0.320 -0.600 0.042 +3 -0.573 0.759 1.786 -1.168 0.858 -0.599 -1.871 0.927 1.819 2.111 0.301 -1.444 -0.207 -0.109 1.096 -0.024 -2.076 0.630 0.072 0.904 0.248 0.689 0.015 0.284 -0.921 0.354 -0.185 0.490 +4 -3.236 0.475 1.848 -0.080 0.386 -1.081 -0.022 -0.548 0.616 2.215 -1.101 -1.121 0.147 -0.735 -0.396 -0.540 -1.354 -0.293 -1.260 0.815 0.999 -1.592 0.416 -1.789 -0.668 0.459 1.209 1.399 +4 -0.904 1.844 0.939 -0.312 2.036 -2.341 1.047 0.043 1.213 -0.483 -1.136 0.238 -0.496 0.271 0.356 -1.594 0.901 -2.835 2.991 0.263 -0.711 -1.636 0.594 0.392 -0.082 -0.366 0.671 -2.259 +1 -1.060 0.042 -0.124 0.954 0.357 -1.058 0.369 -1.109 -1.016 -0.109 -0.663 -1.336 0.981 -1.411 1.087 0.281 -1.286 0.374 0.562 0.016 0.406 -1.440 0.010 -1.647 -0.941 -1.205 -0.090 0.436 +4 -0.291 0.522 1.170 -0.131 -0.057 0.022 -0.261 -0.542 1.764 0.138 2.629 0.273 0.210 0.223 0.932 -0.339 1.191 2.093 -0.519 -0.519 -1.467 -1.086 -1.071 -1.349 -1.943 -0.140 2.413 1.458 +2 -0.365 -1.466 0.778 -0.108 -0.057 -1.018 -0.391 0.237 -1.205 -0.168 -0.435 -0.104 -0.180 -1.346 0.849 -2.465 0.214 1.040 -0.084 -1.378 0.789 -1.613 -0.337 1.138 -0.111 0.021 1.232 -1.519 +2 0.489 0.778 1.150 0.490 0.073 -1.080 0.273 -0.376 1.376 0.091 -0.160 1.416 -0.482 -0.003 2.009 0.266 1.125 1.504 -0.552 0.909 -0.333 2.040 1.444 0.743 1.045 -0.696 1.718 -0.904 +2 0.149 -0.631 0.646 -1.122 -1.457 0.361 -0.042 0.702 1.293 -0.479 0.744 -1.640 -1.255 -0.006 -0.379 -2.216 1.761 -2.163 -0.471 0.925 -0.228 0.587 -0.786 0.120 1.373 -0.484 0.465 -0.270 +4 -0.845 0.892 -1.551 -0.323 -1.359 0.425 1.354 0.851 -0.424 -1.156 3.002 2.757 -1.254 0.639 -1.180 -1.338 0.052 0.054 0.939 -2.148 -0.049 0.307 2.770 -0.879 0.409 1.529 2.173 1.730 +0 1.049 -1.089 -1.805 -0.264 -0.250 0.615 0.253 -0.858 -0.562 -0.023 -0.556 -0.759 -0.139 -0.272 0.298 -0.029 -0.532 0.650 -0.949 1.022 -0.872 -0.621 -1.756 1.133 0.583 -0.696 0.749 -0.456 +0 0.532 1.084 -0.341 -0.269 -0.041 -0.214 -0.482 -0.160 0.431 -1.446 -0.262 -0.182 0.812 1.262 0.551 1.055 -1.049 0.906 0.734 -1.189 -1.041 0.511 -0.254 -0.796 0.955 -0.407 0.520 0.972 +4 0.037 1.815 2.613 -0.464 -0.833 1.130 0.335 1.136 0.399 -0.194 -0.189 0.433 -0.211 1.220 -1.302 1.214 -1.873 0.145 -0.331 -1.516 -2.060 -1.098 0.046 -0.602 -1.041 1.352 -1.232 0.679 +1 0.440 -0.075 1.093 0.970 0.413 0.055 0.657 0.314 1.061 0.900 -1.018 -1.086 -0.701 0.976 -1.576 0.395 0.363 1.163 0.180 -1.343 -0.959 -1.182 -0.538 -0.183 -1.165 1.471 -1.325 1.346 +0 1.090 -0.106 -0.229 -0.751 1.105 -0.227 0.621 0.383 0.231 -0.557 -0.246 0.647 -0.782 -0.512 -0.731 -0.026 -0.319 -0.871 0.650 -0.606 0.061 -1.168 -1.638 -0.691 -1.944 1.400 1.401 -0.073 +2 -0.717 -2.274 0.053 0.290 -0.140 1.137 -0.904 -0.005 0.464 0.778 -0.760 -0.160 0.595 -0.328 1.470 -1.311 0.154 0.184 -1.287 0.012 -0.283 -0.363 -1.247 -0.595 0.947 0.311 -2.764 -0.012 +3 0.841 -1.052 -0.699 -1.477 1.283 -0.766 -0.314 -0.567 1.013 -0.940 1.513 -1.470 -1.749 -0.707 2.047 -1.905 0.396 -0.054 0.021 1.783 -0.586 0.435 0.011 -0.524 -0.639 0.312 -0.367 -0.351 +2 2.074 -0.283 -1.497 -0.536 1.214 -0.339 0.394 0.051 0.572 0.501 -0.297 1.453 0.509 1.182 -0.245 -0.166 2.681 -0.037 1.253 -1.456 0.358 -0.408 1.539 -0.676 -0.429 0.580 -1.051 0.127 +3 -0.567 0.157 -1.000 0.637 -0.546 -0.115 -1.128 -1.240 0.291 1.251 -1.035 1.925 0.678 0.276 -0.045 -0.640 0.866 0.089 -0.625 -1.245 2.704 1.784 0.035 -0.263 -0.163 1.957 0.062 -1.285 +4 0.363 0.343 -0.112 0.301 -0.991 1.263 0.071 0.975 -0.504 1.956 -0.233 -0.794 -2.077 0.162 0.759 0.869 0.037 -1.028 1.615 -0.258 -2.001 -0.150 -1.986 -2.491 -0.491 -1.097 -0.617 -0.119 +3 0.566 -1.067 -1.063 -1.676 -0.546 3.493 0.696 -1.208 1.020 -0.623 -1.035 -0.308 -0.164 0.215 0.683 -0.883 0.051 -0.585 -1.444 -0.173 -1.442 0.658 -1.514 -0.803 -0.572 0.430 0.623 0.401 +3 -0.538 0.027 -1.118 0.342 0.216 -0.463 1.311 0.406 1.418 -2.304 0.059 0.943 -0.204 1.597 -1.826 -0.493 0.908 0.311 -0.927 -1.045 0.451 0.374 -0.253 0.496 -1.837 0.262 -2.087 1.255 +4 1.006 -0.042 0.820 -2.478 -0.529 -0.815 -2.285 0.103 -0.100 -0.467 -0.126 0.103 1.013 0.643 0.329 2.307 -0.708 -1.360 1.174 1.867 -0.635 1.378 -0.533 0.585 0.640 -0.631 1.847 1.962 +4 -0.892 1.578 0.574 0.300 1.013 0.894 -1.959 -1.705 -0.143 -1.074 1.660 -0.991 0.092 0.583 2.444 1.149 -0.661 0.525 0.698 0.622 -0.981 2.032 -1.224 0.680 -0.882 0.391 -0.196 -1.303 +3 1.543 0.143 -2.307 -0.772 -0.552 0.720 -1.476 0.061 0.122 0.437 1.113 -1.330 -0.338 -0.478 -0.322 -0.417 0.101 1.031 -0.961 -0.842 -0.782 1.297 -0.395 -0.381 2.726 -0.533 -1.044 -1.825 +4 -0.187 -0.851 0.922 -0.221 0.608 -1.194 1.734 1.277 -1.910 -0.802 0.478 -1.320 -0.132 0.427 -0.185 0.539 -0.693 -2.244 0.982 0.976 2.504 -2.207 0.751 -1.969 1.756 0.488 -0.952 0.780 +4 -1.750 -0.279 0.514 -0.920 1.158 1.692 0.171 -0.192 -0.313 1.772 1.203 -1.506 -0.031 1.797 0.859 -0.959 -0.244 -0.296 -1.082 2.203 1.230 0.431 0.374 1.349 -0.396 0.836 2.176 0.386 +3 0.518 -0.529 -0.869 -0.011 0.137 2.918 -0.184 -1.130 0.273 -2.075 -0.092 0.016 0.795 0.974 -0.504 -0.151 -0.766 -1.128 0.310 -1.274 -0.415 -0.995 0.550 1.801 -1.302 -1.853 -0.125 0.016 +2 0.179 0.032 -0.778 0.806 0.015 0.065 -0.004 -1.046 -0.817 -1.582 1.139 0.002 0.995 -0.450 1.148 -1.617 0.972 -0.078 -1.386 -2.823 0.502 0.990 0.062 0.603 0.446 0.056 0.959 -1.477 +4 2.589 0.145 0.576 1.400 1.278 0.511 -0.141 0.514 -0.816 0.152 -1.109 -1.781 0.050 1.262 0.926 -0.082 1.747 -0.569 -1.918 -2.182 -1.181 0.817 -0.635 0.461 1.646 -1.100 0.478 0.539 +1 -0.356 1.544 -0.747 0.558 2.328 -0.982 0.949 0.678 -0.198 0.817 -0.111 0.879 0.249 0.397 0.332 -1.242 -1.051 -1.057 -1.199 -0.414 -1.802 0.221 -0.841 0.301 0.187 -0.902 0.203 -0.755 +1 -0.969 -1.248 0.889 -0.935 1.427 -0.746 0.605 1.912 0.603 1.930 -0.945 1.195 -0.707 0.987 -0.057 0.241 -0.022 -0.308 0.128 0.435 -1.501 -0.279 0.655 0.158 0.968 -1.475 0.311 0.765 +4 -0.913 1.858 0.251 0.372 0.424 -0.072 -0.618 -1.621 -0.236 -1.113 -0.435 0.520 -1.182 -2.330 -1.115 -0.146 1.613 1.527 2.460 -1.046 -1.821 0.054 0.002 2.159 -1.217 -0.036 1.673 0.944 +4 -0.446 -2.462 1.944 -1.343 0.241 0.377 2.178 2.313 0.408 -1.025 -0.994 -0.622 -0.029 -0.465 -0.970 0.089 -0.842 1.383 -0.834 -0.684 0.112 -1.306 -0.371 -1.503 -0.060 -0.828 -0.495 0.139 +3 0.983 0.036 -1.637 -0.122 0.676 -0.303 -0.928 0.604 -0.266 -0.085 -1.314 1.460 1.576 -0.432 -0.709 2.396 0.267 -0.815 0.260 0.030 1.182 2.309 -0.033 0.706 -0.446 -1.258 0.065 -1.865 +1 -0.137 -0.309 1.254 -0.581 0.459 -2.130 0.316 -0.331 -1.826 -0.006 -0.473 0.149 -1.130 -0.446 -0.988 -0.356 1.026 -0.319 -1.688 1.294 0.818 -0.187 0.428 -0.590 0.703 0.695 -0.688 -0.475 +0 0.813 1.117 -0.176 0.056 -0.343 0.996 0.392 -0.558 -0.513 -0.015 -0.332 1.442 0.808 0.806 0.234 -0.827 0.696 -2.098 0.174 -0.812 -0.889 -0.463 -0.084 -0.023 0.790 -0.174 -0.102 -1.396 +3 0.244 -0.855 -0.103 0.798 -0.578 0.011 1.035 -0.032 -1.537 -2.274 -1.142 1.183 -0.261 0.347 1.297 -1.246 2.664 -0.105 0.536 -0.705 -0.966 0.025 -0.610 0.091 -2.129 -1.374 -0.517 -1.138 +4 -0.011 0.733 0.531 -0.180 0.558 1.424 -1.787 -2.029 1.298 0.899 1.430 -0.108 1.754 2.893 1.153 0.582 0.734 0.169 0.322 0.449 2.273 -0.761 -0.222 0.003 0.627 0.246 -1.287 0.074 +0 0.864 1.221 -0.126 -0.028 0.607 0.725 -0.892 -1.326 -0.880 -1.655 -0.040 0.223 -0.365 1.268 -0.230 -1.589 -0.173 -1.458 -0.491 0.334 0.367 -0.379 -0.177 -0.766 -0.166 0.555 0.466 -0.599 +0 0.413 0.838 0.127 -0.514 -0.436 0.220 0.049 0.905 -0.480 -1.218 0.359 -0.478 0.784 0.008 0.655 -1.014 0.143 -0.699 -1.220 -0.861 -0.544 1.245 -0.571 -1.272 0.131 0.029 -0.492 -0.161 +2 -1.164 -0.612 -0.260 0.038 -1.052 0.304 -1.683 -0.164 0.143 -0.604 -1.022 2.212 -0.575 -1.317 1.971 0.302 0.146 1.801 0.214 -0.890 -0.859 0.697 -0.071 -0.836 0.809 -0.505 -1.583 -0.353 +0 0.286 -1.688 -0.349 -0.016 -0.055 0.715 0.165 0.445 -0.599 1.707 -0.289 0.329 0.114 0.451 1.200 -0.841 0.826 0.737 0.173 0.227 -1.156 -0.340 -1.907 -0.306 -0.063 -1.399 0.299 1.207 +3 1.092 -0.962 -1.554 1.162 -0.744 -1.188 0.740 0.617 0.928 -1.809 -0.145 -0.450 0.814 0.064 -1.626 0.821 -0.621 0.408 -0.770 -0.889 -0.210 -0.377 1.039 -3.112 0.214 0.133 0.878 1.088 +1 0.029 -1.010 -0.942 1.298 0.045 0.970 -0.165 0.420 -1.744 0.861 -0.029 -0.470 0.629 0.994 -0.406 -1.196 1.666 -0.216 -1.919 -0.422 0.450 -0.082 -0.129 -0.352 -0.421 -1.278 -1.298 0.364 +2 -0.049 -1.292 -0.391 1.243 -0.456 -1.001 -1.657 1.329 0.085 0.676 0.781 -1.049 -0.462 1.476 1.353 0.175 1.396 1.080 1.762 0.539 -1.372 -0.421 1.590 -0.166 -0.915 -0.640 -0.968 0.225 +3 -0.728 -0.054 0.931 2.046 -1.460 0.022 -3.130 -0.429 0.117 0.163 1.638 -0.227 1.562 0.247 -0.510 0.191 0.129 0.199 -0.698 -0.710 1.136 1.055 -0.829 -0.135 0.383 0.815 0.188 1.154 +2 -0.239 0.915 -1.645 0.360 0.440 -0.960 1.862 -1.257 -0.243 2.455 -0.951 -0.621 0.156 -1.328 0.517 -1.781 0.001 1.447 0.641 -0.041 -0.322 -0.085 -0.287 0.398 0.645 0.144 0.701 0.514 +2 -1.489 0.559 0.073 0.091 0.337 -0.350 0.070 0.241 0.746 -1.527 1.291 -1.155 0.762 0.079 -1.281 0.528 -0.945 1.069 -0.711 -1.495 1.534 0.181 0.867 -0.851 -1.892 1.675 -0.006 0.491 +4 1.731 0.573 0.122 0.045 1.283 1.105 -2.333 -2.116 0.784 0.606 -1.329 -0.452 0.735 -0.325 1.125 -2.748 0.346 0.349 0.404 0.527 0.819 0.540 0.128 -0.945 -1.558 1.072 -0.126 -0.848 +2 -1.986 -0.674 1.023 0.251 0.339 -0.655 1.062 0.733 -1.579 0.143 0.110 -0.608 1.052 -0.971 0.594 -1.564 -1.212 -0.494 1.219 -1.214 0.005 -0.301 0.340 -1.220 0.094 1.355 1.670 -1.590 +4 -0.159 1.271 -1.309 1.081 -0.115 0.390 1.597 0.622 0.281 0.420 -0.873 2.010 -0.421 0.400 0.201 0.487 0.631 -0.214 -1.148 0.921 -2.085 0.310 -2.415 -1.228 2.470 -1.816 0.063 -0.679 +3 -0.052 0.769 0.530 -0.276 0.135 0.939 -1.530 0.405 -0.836 0.119 2.106 1.836 -0.166 0.991 -1.073 0.352 -0.372 0.651 -0.937 0.177 0.935 -1.923 1.577 -0.454 -1.823 0.593 -1.139 -1.475 +1 -0.611 -0.782 0.116 -1.516 -0.216 0.382 -0.725 0.041 0.265 -1.762 0.206 -0.459 1.847 0.198 -0.919 -1.817 -0.050 1.306 -0.605 0.315 0.740 0.688 -0.004 -0.658 -0.260 -1.070 -0.943 0.990 +1 0.151 -0.113 0.857 0.526 -0.628 1.745 1.204 -0.867 -0.344 -0.158 0.816 -0.809 -0.669 0.878 0.559 -1.948 0.658 -1.474 -0.635 -0.365 -0.469 -0.649 -1.038 0.512 -1.613 -0.532 0.791 0.397 +2 -1.429 1.286 0.121 -0.203 0.431 0.237 0.767 0.538 0.718 1.351 0.884 -0.160 -1.123 1.668 -0.238 -0.473 -1.737 0.338 0.149 -1.515 0.453 -0.498 2.690 -0.569 -0.435 -0.875 1.278 -0.373 +2 -0.699 1.247 0.314 -1.193 1.339 0.108 0.286 -1.216 1.878 1.293 0.527 0.508 0.599 0.050 -0.563 2.102 0.100 -1.871 0.311 -0.585 -0.159 -1.166 1.209 -0.466 -0.375 -0.557 0.964 0.777 +2 0.347 -0.184 1.339 0.658 -0.317 -1.351 1.346 0.782 1.288 0.964 -1.219 0.461 0.953 0.111 -0.324 -0.465 -0.764 -0.107 -1.252 -1.826 -0.550 -0.585 2.238 1.364 -0.204 -0.030 1.052 -0.014 +0 0.698 1.842 -0.340 -0.117 0.949 0.611 0.944 -0.602 -0.560 -0.839 -1.064 -0.254 0.032 -1.757 0.037 -0.908 0.283 0.520 -0.365 0.543 -0.919 0.058 -1.105 0.706 -0.463 -0.509 -0.886 1.832 +4 -1.450 -1.283 -0.922 1.434 0.541 1.966 -1.912 0.607 0.463 -1.634 0.449 0.493 -0.645 0.527 -1.090 0.733 1.188 -0.836 0.926 -1.460 0.118 0.045 -0.355 -0.375 1.646 -2.578 1.555 -1.306 +1 0.138 -1.072 -1.138 -2.150 0.842 0.490 0.199 0.254 1.126 0.691 0.596 1.034 0.373 0.137 -0.731 -1.910 -0.892 0.655 -0.494 -0.309 0.709 -1.793 0.757 0.807 -0.072 -0.046 -0.184 0.662 +4 1.327 -0.327 -0.250 -0.162 0.275 -0.581 -2.894 1.193 1.795 -0.612 -1.067 -0.137 -0.188 0.996 0.055 -0.793 -1.318 0.866 0.600 1.061 -0.968 1.838 -1.744 0.710 -1.910 -0.790 1.561 0.831 +2 0.366 -0.507 -0.189 0.161 -0.007 0.736 0.487 -1.508 -1.358 -0.370 1.829 -0.852 -0.208 -0.536 -0.554 0.178 1.401 0.456 -0.804 2.753 0.079 -0.577 1.412 -0.469 1.663 -0.871 1.430 0.201 +3 -1.313 0.624 1.626 0.497 -1.879 -0.325 0.890 -0.392 0.682 -1.426 -1.990 0.022 -2.174 1.214 -0.757 0.350 1.265 0.042 -0.837 -0.684 0.282 -0.880 -0.162 0.063 -0.397 -2.039 0.275 0.554 +1 1.155 0.571 -0.154 -0.544 0.771 0.275 -0.861 1.302 0.262 0.653 -0.392 1.131 -1.585 1.675 -1.939 -1.377 0.252 -0.531 -0.328 -0.531 -0.783 0.317 -0.049 0.885 0.080 -0.417 0.774 -1.832 +4 1.116 0.766 -1.191 -0.179 0.540 -2.216 -1.627 -1.394 -1.241 1.364 -0.761 1.809 0.578 -0.726 0.127 -1.594 -0.897 0.344 1.192 -1.437 -1.020 -0.293 2.070 0.593 0.002 -0.619 -0.201 -0.024 +1 1.479 0.664 2.139 -1.457 0.119 -0.612 -0.091 -0.270 -0.086 0.852 0.662 0.177 0.086 0.109 -0.434 1.240 0.273 1.276 1.505 -0.249 -0.563 -0.550 1.856 -0.640 -0.545 0.246 -0.187 -0.047 +0 0.028 -1.116 -0.366 0.203 -1.551 1.923 1.126 -1.264 1.276 0.658 -0.915 -0.478 -1.186 -0.563 0.669 -0.433 0.052 0.228 0.931 0.060 1.666 0.548 -0.244 -0.611 -0.898 0.007 -0.003 -0.430 +3 -0.282 0.325 -2.009 1.822 -0.670 -0.296 0.260 -0.196 0.961 -0.370 -0.580 0.933 -2.732 -1.275 0.243 0.838 0.847 -2.723 -0.484 0.860 -1.027 -0.233 0.451 0.065 0.489 -0.173 0.570 0.593 +1 -1.716 -0.486 0.862 1.005 -1.143 -0.860 0.231 0.248 0.018 -1.470 -0.688 -0.993 -0.831 -0.948 -0.545 0.881 1.075 -0.490 0.097 -0.322 1.239 1.151 -0.477 0.577 1.343 -0.241 -1.088 1.270 +0 0.716 0.805 -1.525 -0.641 -0.801 -0.699 1.255 0.088 -0.862 -0.843 -0.603 0.093 -0.942 -0.509 -0.593 0.117 0.902 0.725 0.375 -0.387 -0.905 -0.380 0.385 0.700 -0.400 0.488 0.178 0.032 +0 0.672 -0.096 0.529 -0.126 0.217 0.575 -0.398 -1.234 0.276 1.189 0.299 0.318 -0.042 0.739 -0.186 -0.454 1.105 -0.531 0.889 1.106 0.567 -1.249 0.027 -0.202 -0.496 0.143 0.769 -0.459 +3 0.497 0.791 1.134 0.659 -0.407 -1.810 0.991 -0.525 -1.447 1.872 -0.061 0.489 0.228 -0.014 -0.387 -0.584 2.225 -1.458 -0.433 -0.890 0.518 -0.414 -0.499 0.642 1.935 -0.830 -1.551 0.063 +1 -0.005 0.140 0.260 -0.162 0.363 -0.375 0.292 -1.201 0.740 -0.944 0.770 0.874 1.145 1.723 0.336 -1.014 1.691 0.944 0.280 -2.281 -0.678 1.068 -0.440 1.824 -0.884 0.183 -0.572 0.182 +3 1.125 -0.957 -2.429 1.806 0.808 -0.305 1.265 0.080 0.162 1.593 0.332 1.110 -0.512 -1.308 -0.314 0.696 1.547 0.116 2.162 -0.678 -0.860 0.185 0.275 -0.285 -0.381 -0.706 0.758 0.186 +3 -1.992 -0.652 2.554 -0.205 1.139 -0.028 -0.330 0.933 1.197 -0.892 -1.098 -1.368 -0.878 1.136 0.031 1.184 -1.033 -1.003 -0.392 0.297 -0.140 0.698 0.414 0.488 0.685 1.096 -2.460 -0.012 +1 0.543 0.374 0.927 1.441 -1.064 0.300 -0.722 1.070 0.724 -0.970 -0.007 -1.501 0.950 -0.536 -1.130 -0.346 0.584 0.287 -0.489 -0.105 -0.292 -0.934 -1.866 -0.006 0.585 -0.016 2.050 -0.596 +3 -0.312 1.000 2.085 -0.398 0.746 0.791 -2.211 0.479 -0.610 -1.343 1.161 0.397 2.040 0.758 -0.109 -0.550 -1.531 1.120 0.776 0.268 1.265 -0.087 -0.454 0.048 0.327 1.500 -0.942 -0.441 +1 0.722 0.397 0.726 -0.603 -1.060 -0.819 -0.750 -0.047 -1.184 1.065 0.516 -0.865 -0.199 -0.887 -0.149 -0.835 2.142 -1.591 -1.270 -0.238 0.094 0.100 0.918 0.443 -0.922 1.062 0.628 1.387 +0 -1.131 -0.498 1.009 0.259 -1.518 -0.040 0.583 0.714 1.046 1.198 0.570 0.487 0.233 0.842 -0.788 -0.204 -0.699 0.434 -0.259 1.013 0.052 1.064 -2.111 -0.720 0.938 0.020 1.056 0.251 +3 -0.436 1.048 -1.069 0.345 -0.384 -0.762 2.586 0.419 0.613 1.138 -1.421 1.752 -2.509 -0.769 -0.459 0.582 0.219 -0.167 -0.012 -0.978 -1.497 0.727 0.694 1.584 -0.790 0.693 -0.284 1.190 +3 -2.405 1.667 -1.732 0.492 -1.516 1.371 0.616 0.513 -0.982 0.400 -0.449 -1.538 -0.357 -0.843 -0.973 0.229 -1.497 1.457 0.826 -0.272 1.543 -0.817 0.200 0.347 -1.251 0.881 -0.866 0.673 +1 1.791 0.555 0.599 0.304 1.028 -1.300 -1.776 0.931 -0.267 -0.466 -0.167 0.359 1.275 0.653 -0.315 0.378 -0.064 -1.013 -1.459 1.522 0.476 -0.539 1.415 0.472 0.602 0.727 0.104 -0.111 +1 0.074 1.183 -0.857 0.213 -0.593 1.477 -0.055 -0.211 -1.136 0.828 0.442 -0.018 0.603 1.071 0.202 0.445 0.542 -1.761 0.471 -1.637 0.007 0.563 -1.827 0.052 -0.354 -0.249 0.777 1.900 +1 -1.096 0.680 2.348 0.543 -0.414 -0.349 1.909 0.307 -0.766 -0.062 0.215 0.469 1.512 1.276 0.198 -0.041 0.396 -0.608 0.980 -0.203 0.491 1.713 0.553 0.231 -0.517 -0.078 -1.136 0.533 +2 0.544 -0.400 -0.081 0.017 -0.542 0.132 0.690 -0.129 1.372 1.563 -0.845 0.409 -0.239 -0.763 -0.037 -2.136 0.778 0.868 0.230 0.966 -0.699 -2.784 -0.481 -1.217 0.011 1.440 0.295 -0.258 +2 0.482 2.405 0.256 -0.911 0.835 0.135 0.040 0.296 -0.396 1.869 -1.628 0.816 0.133 0.455 -0.380 1.514 0.884 -0.770 1.120 0.300 -0.584 -1.693 0.769 -0.724 0.527 1.462 -0.811 1.345 +1 -1.381 -2.147 0.623 0.190 -0.477 -1.506 -0.135 0.374 -0.022 -1.567 0.852 -0.530 -2.097 0.309 0.369 -1.057 -0.979 0.866 0.175 0.673 -0.209 0.413 1.005 0.180 -0.207 -0.263 1.081 -0.028 +1 -1.228 1.040 0.218 0.939 0.109 1.622 -0.877 0.536 -0.694 1.410 -0.165 -0.335 -0.135 1.214 0.589 1.901 -0.946 -0.159 0.487 -0.255 -0.174 0.457 0.172 1.128 1.314 1.719 -0.125 -0.535 +2 2.111 -1.228 0.076 0.155 0.115 0.882 -0.377 -0.297 0.094 -1.672 -0.991 1.242 -0.529 -1.946 -1.564 -0.851 -0.544 -0.485 1.382 0.276 1.174 0.312 0.427 -0.314 1.147 -1.061 -0.173 -1.327 +3 -0.443 -0.249 0.726 -0.144 -0.435 -0.495 -1.986 0.763 -1.007 1.858 -0.288 1.309 -1.809 0.731 0.989 1.304 -1.528 -0.703 0.577 0.148 0.293 -0.360 1.551 2.608 0.104 0.453 0.056 1.284 +3 1.666 -0.246 1.137 -0.912 0.331 1.016 2.029 -0.997 -1.046 1.761 1.675 0.219 0.502 -0.382 -0.148 -1.700 0.028 0.140 2.071 1.504 0.552 -0.939 -0.481 -0.217 1.033 -1.651 -0.198 -0.051 +3 -0.020 -1.327 0.667 -0.245 -2.780 0.778 1.164 1.339 -1.277 -1.917 -0.410 0.518 -0.943 -1.591 0.725 -0.345 0.623 0.016 -0.166 -0.064 -0.358 -0.459 -1.101 0.483 1.248 -0.500 1.866 0.377 +3 2.196 0.976 0.004 2.401 0.811 -0.012 2.281 0.659 0.615 -0.386 0.049 -0.411 1.440 -1.609 1.366 -0.877 -0.058 -0.295 -0.520 0.690 -0.005 -0.668 0.154 -0.696 -0.538 -1.497 -0.450 0.494 +4 1.145 -1.352 -0.410 -0.123 1.048 1.869 0.710 -0.570 -0.371 -2.411 -0.167 1.568 0.456 0.543 1.784 2.361 0.864 1.308 -1.954 0.685 0.186 -0.862 1.090 0.655 -0.706 -0.641 1.166 0.941 +0 0.801 1.137 0.171 0.003 -1.053 0.947 0.123 1.140 0.562 1.417 0.879 0.964 0.283 -0.318 -0.037 -0.404 0.536 -1.529 0.344 0.200 0.155 -0.789 -1.289 -0.758 -0.058 -0.089 -0.181 -0.014 +3 -0.775 -0.066 0.894 -0.635 -0.417 -0.655 -0.321 -1.012 -0.330 0.373 -0.703 -1.807 1.091 1.049 2.107 -0.399 0.588 -1.136 1.180 -0.185 -0.422 1.703 -1.293 -0.214 -1.590 0.469 -1.140 -2.061 +4 -0.610 -1.331 0.663 0.045 1.376 0.317 -0.647 -1.372 0.256 -1.045 -0.714 0.783 0.805 -0.379 2.149 0.257 -1.749 -0.364 -1.825 -0.301 -0.323 -2.602 1.065 1.344 1.746 -0.487 -0.274 1.011 +2 -0.661 -0.905 0.259 -0.895 -0.878 -0.832 2.205 -0.117 0.353 -0.569 -0.334 -1.776 0.024 1.486 0.966 -0.155 0.980 1.435 -1.081 0.742 -1.560 -1.045 1.114 0.887 -0.160 0.286 -0.879 0.545 +3 -0.406 0.808 -0.089 -0.030 0.903 0.764 0.648 -0.529 -3.019 1.169 1.776 -1.546 1.699 0.443 -1.587 -0.141 -0.329 -0.388 0.502 -0.178 -0.684 1.722 0.486 -0.714 1.468 -0.408 0.334 1.237 +4 0.502 -0.583 -1.647 0.159 1.248 1.331 0.319 -0.456 0.923 -1.098 -0.617 -1.431 0.998 -1.636 0.408 2.326 0.402 -0.969 -0.418 -1.510 -0.895 -0.715 -0.833 0.137 -2.781 -1.243 0.574 2.099 +0 0.853 0.029 -0.296 0.300 0.506 -0.871 -0.819 0.094 1.038 -0.256 0.778 0.974 1.060 -1.157 1.104 1.020 0.484 -0.247 -0.096 0.189 0.475 -0.850 -0.918 1.188 0.482 -0.042 0.007 0.736 +1 0.955 -0.656 0.624 -1.226 0.486 -1.190 -1.718 -0.645 -0.255 1.275 1.096 0.119 -0.945 -0.511 1.388 -0.518 0.839 1.637 -0.840 0.263 0.761 0.784 0.536 0.444 0.982 -0.020 -0.183 0.791 +0 0.321 -1.125 -0.138 1.692 -0.507 -0.674 0.773 -0.560 -0.899 -0.688 0.970 -0.191 0.810 1.182 -0.434 -0.159 -0.235 -0.797 1.003 -0.423 -1.038 1.708 -0.228 -0.062 -0.576 -1.150 -1.493 0.865 +4 -1.087 1.813 0.527 1.922 0.864 -1.324 0.645 -0.984 0.007 -0.076 -1.618 -0.813 2.205 0.238 -0.176 1.813 -0.396 -0.091 0.991 -0.401 -0.747 -2.254 -0.936 -0.946 0.159 -0.610 2.158 -1.058 +4 0.749 -0.766 -0.088 -0.306 0.149 -0.888 0.835 -1.453 0.441 2.589 -0.204 1.176 -0.442 1.537 -0.186 0.100 0.107 -1.228 0.557 -0.057 -0.163 -2.000 1.658 2.279 -2.551 0.126 0.150 1.789 +2 0.865 0.236 0.340 -1.398 1.560 -0.114 -2.108 -0.137 0.080 0.537 -0.072 -0.577 -0.529 0.002 -0.183 1.993 -0.327 -0.769 1.086 0.002 0.911 -0.087 0.717 1.263 1.427 0.761 -2.075 -1.514 +1 -1.079 -0.345 1.075 -0.508 0.261 1.125 0.382 1.974 0.068 -0.157 -0.382 1.992 1.219 -1.459 2.110 0.498 0.129 0.518 -0.959 0.197 -0.309 -0.360 0.959 -0.264 0.100 0.328 0.944 1.268 +1 1.694 0.915 2.268 1.801 0.192 1.064 -0.231 1.347 -1.146 -0.432 -0.688 -0.369 -0.329 -0.001 0.657 0.127 0.458 -0.980 -0.650 -0.441 -0.100 1.244 0.072 -0.741 0.614 0.077 -0.239 1.378 +0 -0.343 -0.371 -1.408 -0.778 -1.111 1.752 0.936 1.272 0.722 -1.129 -0.525 0.489 -1.222 0.713 -0.240 -0.375 0.711 0.444 -0.361 1.159 -1.081 0.616 0.593 -0.310 0.326 -1.251 0.924 -0.185 +1 2.413 -0.598 -1.363 -0.205 0.299 0.050 -0.157 -0.129 -1.182 -0.924 1.222 0.738 -1.442 -0.877 -1.188 0.402 1.170 -0.773 0.449 -0.668 -0.741 -0.415 0.697 -0.141 0.068 0.084 -0.361 1.385 +0 -0.237 0.177 0.673 -0.114 -0.122 0.267 -0.399 -0.350 -0.899 -0.042 0.448 -0.269 -0.774 -0.096 0.397 0.128 2.230 -0.656 -0.150 0.779 -0.695 1.928 0.005 0.561 0.019 1.019 -1.063 1.572 +0 1.626 -0.205 -0.792 -1.253 0.181 0.997 0.024 -0.177 -2.095 0.146 -0.321 -0.076 -0.447 1.417 0.187 0.099 1.252 0.252 0.960 0.814 0.526 -0.291 0.283 0.308 1.955 -0.094 0.394 -0.356 +3 0.770 -1.022 0.456 0.215 -1.135 1.156 1.607 0.060 -0.133 -0.367 2.534 0.939 -0.715 -1.592 -0.427 -0.420 0.255 0.045 1.145 -2.708 0.538 0.894 -1.797 -0.867 -0.484 0.506 -0.414 -0.034 +1 -0.931 -0.962 1.229 0.661 -0.130 0.832 0.301 -1.186 -2.398 -0.567 1.085 -0.428 0.460 0.184 -1.694 -0.406 0.359 -0.787 -0.363 0.392 0.073 1.047 0.384 -0.956 -0.478 0.020 1.338 -0.474 +3 -1.099 0.513 0.126 0.689 0.451 -0.668 0.572 1.294 0.556 -0.571 -1.364 -2.152 -1.247 1.795 -0.790 0.620 -0.899 -0.367 -1.178 -0.083 0.347 -0.968 -0.113 1.116 1.301 1.264 0.492 2.126 +1 0.473 -2.150 0.112 -0.792 0.052 0.384 0.669 0.227 -0.258 1.431 -0.853 -1.277 -0.270 -0.352 -0.835 0.545 -0.518 0.701 -1.642 -0.758 1.018 -0.565 -0.758 1.487 1.396 -1.145 -0.229 0.096 +0 -0.325 -0.505 -0.035 -0.110 0.275 -0.201 -0.434 0.778 0.603 -0.032 1.195 0.083 -0.161 -0.069 0.165 -0.917 -1.874 0.281 0.834 -1.001 1.073 -1.646 -0.559 1.706 0.356 -0.302 0.252 1.196 +4 0.639 -0.708 -0.435 -1.478 -2.291 -1.108 -1.570 0.337 -0.518 -0.538 -0.984 -3.233 1.508 -1.000 -0.412 -1.455 0.972 0.143 -0.826 -0.392 1.678 -0.073 -0.182 -0.941 -1.895 -0.160 0.325 1.100 +1 0.496 1.483 0.705 0.810 0.294 -1.503 -1.045 0.455 -0.011 0.440 1.676 0.726 1.259 0.095 -0.807 -0.058 -0.805 1.558 0.790 0.521 1.236 -2.020 0.588 -1.311 -0.007 -0.312 0.662 0.228 +4 0.672 1.168 -1.575 0.452 0.632 -0.012 0.476 -1.548 0.221 -0.638 -1.008 1.585 0.908 -1.654 2.767 0.519 -1.204 -0.194 1.077 0.991 -0.879 -0.107 1.325 0.689 0.623 0.109 -2.913 0.312 +0 -0.200 1.765 -0.296 0.099 -0.076 0.691 0.226 0.718 -1.081 0.573 -0.532 0.040 -0.201 0.999 -1.162 -0.050 0.399 0.898 0.134 -0.524 -0.044 -1.065 0.238 1.851 -0.922 -0.069 -0.786 0.029 +1 1.195 1.920 1.271 0.362 1.120 0.447 -0.196 -0.362 0.082 -0.909 1.128 0.374 -0.595 -0.973 0.956 0.200 -0.465 0.262 0.009 -0.618 1.955 -0.788 -0.825 -1.495 -0.766 -0.424 -0.824 -0.381 +4 -0.285 1.179 -0.262 0.362 0.468 0.161 -1.104 -0.342 -2.077 1.643 -3.360 0.842 0.109 -1.737 0.261 -1.107 0.432 -0.058 0.229 -0.369 1.330 -1.016 -0.674 -0.171 -0.102 -1.132 -1.995 0.356 +4 -0.051 -0.297 -1.806 -0.662 -0.829 -0.679 0.617 0.140 -0.891 0.936 1.791 0.637 2.600 0.053 1.681 0.724 1.674 -0.481 -0.360 0.633 -0.946 -1.479 -1.948 -0.095 1.383 0.499 -0.980 -0.525 +4 -0.483 0.087 -2.005 -0.145 -0.971 0.947 0.175 -1.274 0.652 -1.027 1.203 1.109 -0.810 0.606 -0.649 -0.040 1.218 1.623 -0.072 -2.552 0.664 1.998 -2.004 -0.601 -1.111 -0.269 1.695 0.154 +0 1.048 -0.573 -0.216 -0.069 0.920 -0.428 0.603 -0.001 -1.213 -0.003 1.104 0.006 -0.329 1.424 0.390 -1.382 -0.289 0.268 0.235 0.511 -0.161 -0.212 0.014 0.794 -0.684 -0.072 0.248 -0.538 +4 -0.546 -0.519 0.101 0.514 1.252 -0.456 -1.612 0.157 -0.457 -1.301 0.298 -0.213 0.307 -0.894 0.487 -1.317 -0.828 -0.243 1.742 0.785 1.077 1.417 3.603 0.042 1.420 1.013 0.996 1.774 +2 0.902 -1.393 -0.063 1.367 -1.922 0.614 -0.181 0.554 -1.024 2.348 0.495 0.543 0.853 0.574 -0.573 1.246 -0.414 1.737 0.198 0.737 -0.710 -0.313 1.446 1.035 -0.243 0.703 1.043 -0.327 +1 -1.012 0.789 0.217 0.075 -1.488 1.086 -2.110 0.195 1.226 0.114 -0.247 -1.037 -0.572 2.193 -0.548 0.456 -0.834 -0.303 -1.629 -0.050 -0.362 1.350 -0.845 -0.429 0.905 -0.033 -0.164 -0.173 +4 0.414 1.070 0.386 -1.345 0.892 -1.111 -0.356 0.423 0.743 -0.050 2.047 -1.197 2.038 -1.779 -0.043 -1.543 0.330 -1.543 0.455 -0.624 -1.740 1.512 0.686 0.502 1.827 -0.448 1.208 -1.018 +1 -0.339 0.292 0.446 0.621 -0.878 1.821 0.927 0.953 1.203 0.534 -1.027 -0.139 -1.651 0.415 -0.169 -0.041 1.204 -0.457 -0.252 0.022 -1.840 -0.266 -2.486 -0.586 0.855 0.447 -0.595 0.041 +4 -1.564 1.423 -0.278 0.384 -1.986 0.730 0.416 0.617 0.950 0.179 -0.211 -0.811 -0.443 1.206 -0.838 -1.843 -0.988 0.606 -0.303 0.604 -2.000 -2.983 0.264 1.005 -1.711 0.526 1.190 -0.004 +3 1.168 -1.114 0.352 1.361 0.589 -1.698 -0.037 0.834 1.681 -0.136 -1.666 2.188 -0.122 1.444 -0.258 0.550 -0.685 -0.666 1.810 0.940 -0.028 0.935 -0.678 0.002 0.102 -0.758 -0.889 -1.889 +0 -0.306 0.749 0.383 -0.810 -1.124 -0.483 -1.031 1.354 -1.304 0.345 -0.659 -1.139 0.033 -1.041 0.242 -1.191 0.936 -0.102 -0.574 0.635 0.249 -0.793 0.647 -0.865 -0.133 -1.640 0.307 -0.349 +2 -0.134 -0.735 -0.871 -2.209 -0.167 -0.752 -0.832 1.131 -1.613 0.037 -0.006 0.155 2.701 -2.000 0.344 0.140 0.627 -0.696 1.017 -0.209 0.474 1.160 -0.694 0.508 -0.102 0.594 -0.381 0.022 +1 -1.269 -1.560 -0.289 0.605 -1.449 0.697 0.523 1.103 -0.633 1.373 -0.835 -1.926 0.075 0.827 -0.658 0.254 1.052 1.418 0.792 -0.226 -0.198 -0.369 0.055 0.765 -0.930 -0.583 0.717 -0.153 +2 -0.789 1.300 -0.731 -0.057 -1.705 1.153 -1.913 -1.643 1.051 1.920 -0.291 -0.047 0.908 0.289 -0.373 -0.485 -1.567 0.233 -0.092 -0.438 -1.002 -0.901 0.618 0.882 -0.327 -0.432 -0.854 0.937 +0 -0.343 -1.581 0.587 0.198 0.050 -0.619 -0.945 -0.933 0.177 0.204 -0.350 -1.362 0.747 -0.498 0.125 0.382 0.125 -0.399 0.720 0.636 0.242 -0.175 0.745 -0.699 0.349 0.418 0.917 0.512 +1 1.564 -0.477 0.415 -1.228 -1.278 -0.364 0.677 -0.202 1.313 0.208 -1.047 -0.391 0.722 0.384 0.706 1.198 1.577 -0.302 1.366 -1.345 1.249 -0.306 0.358 0.979 -0.711 0.977 0.014 0.295 +4 -0.653 -1.837 -0.451 -1.149 -0.197 0.617 -0.668 -0.832 -3.434 0.442 0.796 -0.898 -1.021 -0.132 -1.711 1.064 -0.836 0.820 -0.098 -1.772 -0.949 -2.108 0.103 -0.763 -0.355 1.346 0.908 -0.384 +3 -0.315 -0.536 -0.240 0.908 -0.789 -0.507 0.099 1.067 0.280 -1.675 2.170 0.964 0.367 -0.776 0.804 0.830 0.450 -1.076 -1.479 0.476 0.626 0.933 -0.337 -1.990 -0.549 0.115 -2.649 0.909 +4 -0.047 -1.083 0.640 -0.948 1.616 -1.179 0.762 -1.908 -2.258 -0.555 -2.260 0.730 -0.022 1.401 -0.167 0.243 -0.268 -0.874 -0.407 -1.204 0.387 -0.308 -0.100 2.824 -0.403 0.314 -0.054 1.158 +4 -1.868 -1.487 -1.478 -0.112 0.298 -0.185 -0.613 -2.111 0.214 0.626 -0.751 0.513 -0.540 -0.601 -1.532 -2.356 -1.139 -0.974 0.642 0.382 0.517 -1.616 0.013 -0.687 0.900 -0.393 1.401 -1.688 +0 -0.469 1.023 -1.140 1.032 -0.160 -0.652 0.076 -0.514 1.069 1.193 -0.637 -0.267 -0.519 0.037 1.005 -0.811 -0.662 0.224 -0.026 0.369 1.009 0.362 -1.794 0.397 -0.568 0.487 0.902 0.370 +1 0.992 1.067 -0.933 -1.964 1.047 -0.462 2.407 0.864 0.730 -0.222 0.632 -0.650 0.072 0.588 0.088 -0.010 0.296 1.062 -1.432 0.723 0.418 0.012 -0.234 -1.138 -0.505 -0.819 -0.169 1.183 +0 -2.236 0.679 0.648 -0.088 -0.337 0.605 0.132 -0.229 2.172 0.511 -0.531 0.747 0.438 0.380 0.869 -0.473 0.264 -0.847 0.805 0.017 -0.811 -0.049 0.235 0.903 1.221 0.934 0.539 0.740 +1 0.466 0.439 1.380 -1.002 -0.294 -0.069 0.866 0.434 0.519 1.219 -0.222 1.463 -0.088 0.895 -0.496 0.595 1.158 -1.082 0.944 0.179 2.038 -0.264 0.293 -0.156 0.663 -2.183 -1.410 0.669 +0 1.707 1.011 0.398 1.216 -0.476 0.545 -0.129 0.322 0.857 0.602 -0.389 0.101 -0.125 -0.643 1.044 1.311 2.367 -0.726 1.082 0.382 -0.267 0.878 0.062 -0.086 -0.052 -0.703 1.269 0.161 +3 1.253 -0.448 -0.692 -1.969 0.103 2.042 0.277 -0.022 0.322 -0.011 -0.813 0.823 0.226 0.018 -0.975 1.304 0.665 -0.553 1.002 -0.498 -0.782 -0.760 -1.771 0.472 -1.831 -1.228 -2.076 -0.086 +1 1.032 0.517 -0.210 -0.430 -0.225 1.010 0.055 0.195 -0.366 -0.210 0.479 1.297 0.741 1.298 1.464 -0.186 0.878 -0.130 0.781 -2.383 0.137 -1.120 0.132 1.114 -1.633 -1.369 0.761 -0.651 +4 0.258 -1.242 0.334 -0.155 -1.908 -0.860 -0.414 1.888 0.557 -1.335 0.486 -1.547 1.083 -0.471 -0.094 1.326 -1.287 -1.397 -0.584 1.038 -1.519 -2.832 -0.451 0.552 1.200 -0.463 -0.411 1.154 +0 -1.578 -0.209 1.588 -0.135 -0.662 0.024 0.081 0.726 -0.296 -1.134 -0.368 0.419 -1.157 -0.894 1.654 0.098 0.845 -1.886 0.371 -0.769 -0.332 -0.967 -0.571 0.339 1.329 0.412 0.546 -0.498 +4 -0.630 -0.281 -0.852 0.248 1.912 1.518 0.244 0.779 -0.729 1.464 -1.562 0.913 0.496 -0.756 -0.209 -1.134 -1.092 -1.591 0.768 1.205 -2.162 1.838 -0.012 0.290 -1.472 1.321 0.200 1.139 +3 0.845 -1.805 0.731 -0.639 -0.736 0.991 1.162 1.035 2.039 1.267 -0.795 0.142 -0.360 2.029 1.105 -0.189 0.878 -1.232 0.250 -0.938 -1.689 0.785 0.909 0.457 -0.183 -1.266 -1.161 -0.969 +4 -0.606 -0.089 0.181 -0.529 0.523 -1.232 -2.524 -1.818 -0.209 0.258 -0.799 0.305 1.469 0.373 0.679 -0.591 0.718 -0.112 0.052 -1.906 0.081 1.315 -1.891 0.005 1.888 -1.930 2.250 0.827 +4 2.076 -0.754 1.164 0.706 -0.387 0.234 -2.553 -1.345 0.363 -0.900 -0.553 -1.946 3.230 -1.685 0.935 -1.678 0.429 -0.223 -1.029 -0.046 1.682 -0.076 -0.405 -0.143 -0.297 -0.412 2.061 1.197 +2 1.224 -1.265 0.722 -0.116 0.119 0.729 0.743 0.839 0.352 0.542 -0.222 -0.644 0.167 -2.145 0.419 0.870 0.620 -0.611 -2.045 0.656 -0.789 -1.287 0.948 -1.355 -0.172 1.777 -0.667 -1.066 +4 1.325 0.422 -2.051 -1.483 -1.008 -0.398 1.199 2.126 0.343 0.218 -0.661 0.286 1.437 -0.721 -0.286 -2.548 0.905 -0.614 -1.097 -1.562 -0.447 0.965 1.035 -0.168 -1.587 -0.151 1.034 -2.078 +3 -1.752 -0.965 -0.015 0.220 -0.693 -2.752 0.194 0.728 0.766 -0.206 -0.135 0.247 0.409 -0.093 -2.323 1.397 -0.464 -1.224 -0.393 0.112 -0.609 -0.540 -0.325 0.362 -0.799 0.337 -1.380 2.006 +0 1.680 -0.191 -0.643 0.192 0.089 -0.569 0.033 -0.253 -0.481 -0.494 0.534 -0.624 -1.481 -0.135 -0.090 -0.706 2.442 0.755 -0.223 -0.734 -1.301 -1.185 -0.293 0.829 0.858 0.308 0.689 0.253 +1 -0.305 -0.515 0.426 -0.030 -1.368 -0.290 -1.515 -0.620 -0.507 0.127 -0.268 0.824 0.970 0.370 0.899 -0.845 1.546 -1.338 -1.189 0.559 1.710 0.060 1.584 -0.284 -0.396 0.497 0.484 2.182 +4 -1.832 -1.051 1.497 1.858 -0.103 -1.239 2.096 1.594 0.679 -0.812 -0.049 -0.160 0.331 1.451 0.879 -1.077 1.376 0.313 0.687 1.467 -1.112 -0.036 -0.531 -1.568 0.347 2.512 -1.840 -0.032 +3 -1.624 0.368 -0.611 -2.293 0.426 2.628 -0.033 -1.066 0.634 -0.376 0.451 -0.994 -1.535 0.330 -0.571 -0.688 0.965 -0.468 0.436 -0.561 1.203 1.748 0.679 -0.954 0.822 -0.006 0.436 -0.338 +3 -0.028 -0.977 -1.448 -0.292 -0.938 -0.692 2.006 0.425 0.931 0.825 -0.101 -0.440 0.249 0.168 -0.569 0.130 -0.706 -0.171 -0.335 -1.269 -2.351 -0.939 1.804 -0.310 0.776 -2.124 -1.008 -1.354 +3 0.059 -2.216 -2.154 -0.839 -0.698 1.466 -0.041 1.292 -0.380 0.606 -0.725 -0.793 -1.821 0.336 0.262 0.777 0.215 2.026 0.176 0.992 -0.324 -0.908 0.718 -0.089 0.832 0.353 -0.793 1.074 +3 0.975 -0.604 -1.172 -0.145 -1.363 -0.277 -0.281 -2.464 0.531 -0.183 1.182 0.216 0.736 1.190 -0.132 -0.001 -1.574 1.240 -0.146 -1.006 0.740 -2.180 -0.429 0.908 -0.438 -0.718 -0.416 -2.010 +3 0.127 -0.678 -1.095 1.698 2.201 0.713 -0.691 1.031 0.624 -0.844 0.926 0.708 1.656 -1.328 -0.418 -0.526 0.084 -0.046 -1.561 1.687 -0.529 0.256 -1.427 1.195 -0.809 0.244 -0.773 0.337 +0 0.809 0.259 -0.584 0.095 -1.963 -1.572 0.423 0.942 0.905 -1.053 0.882 0.662 0.458 -0.141 -0.524 0.734 0.797 -0.208 0.428 0.464 -0.103 -0.694 0.070 0.866 -0.867 1.158 -0.736 1.818 +0 0.523 -1.024 0.384 -0.200 -0.972 -0.335 0.220 1.453 0.328 0.848 0.205 0.559 -0.005 0.239 -0.882 -0.108 -1.106 0.851 0.959 0.421 -0.629 -0.109 2.332 1.320 -0.082 -0.643 0.148 0.142 +1 -0.683 -0.614 0.055 -0.310 0.296 -0.234 1.147 2.111 -1.501 0.025 1.086 -0.703 0.280 -0.380 -1.003 -1.017 -2.471 0.166 0.232 -0.952 -0.280 -1.442 0.533 -0.401 0.771 -0.322 -0.988 -0.536 +1 -0.854 -0.033 -1.061 0.976 -0.543 -1.712 0.114 1.125 -1.377 -0.125 -0.555 0.991 -2.047 1.135 -0.705 -0.160 0.859 1.479 0.051 -0.725 -0.532 1.166 -0.884 0.329 0.007 0.759 0.034 -0.434 +0 -1.472 -0.599 -0.164 0.195 1.446 0.073 0.320 0.350 -0.328 -0.192 -0.107 0.626 -0.913 -0.507 0.356 -1.076 0.114 -0.546 0.505 1.374 -0.545 0.758 0.201 -0.384 0.007 1.298 -0.172 1.309 +1 1.259 -0.139 -0.607 -0.262 -0.644 1.341 -0.335 0.456 0.790 -0.661 -1.804 -0.481 0.637 -1.695 0.253 0.840 1.392 0.678 0.293 0.186 -1.208 -0.947 -1.732 0.072 0.441 0.118 0.284 0.760 +2 -1.486 -1.459 0.751 0.786 0.380 1.098 -0.914 -1.581 0.433 1.680 -0.920 -1.741 0.046 0.974 0.221 0.945 0.488 0.047 -0.111 -0.805 0.309 -0.460 -1.764 1.255 -1.037 -0.306 0.873 -0.769 +0 0.191 0.206 -0.017 0.441 1.021 -1.064 0.378 1.014 1.080 -1.650 0.820 0.818 0.112 -0.473 -0.987 -0.136 1.195 1.137 0.578 0.497 0.543 -1.753 -0.905 0.521 0.250 0.456 -0.688 -0.579 +1 0.909 -0.522 0.253 -0.577 -0.232 0.092 -2.234 1.608 0.532 0.418 1.032 0.863 1.318 0.040 -0.360 -0.572 -2.087 0.113 0.090 -0.341 1.066 -0.973 -0.779 0.109 1.132 0.301 0.223 0.063 +0 0.935 0.649 -1.080 -0.600 1.333 0.188 0.442 -0.186 1.493 -0.844 0.906 0.976 1.360 0.125 0.934 0.806 -0.239 0.643 0.280 1.514 -0.104 0.812 0.718 -0.839 -0.548 -0.955 0.417 0.917 +4 2.332 0.727 -0.653 -0.779 -0.446 -2.064 0.278 0.694 -0.967 -1.552 -0.911 0.997 0.803 0.215 1.642 -1.689 -0.208 0.560 0.790 0.804 0.724 1.319 0.761 -1.154 -0.455 -1.695 0.996 1.195 +2 0.577 -0.129 1.407 -0.484 0.955 -0.039 -0.608 0.593 0.410 0.357 0.081 -0.038 2.388 1.184 0.723 0.112 1.007 0.518 0.026 -0.304 -0.081 -3.170 0.211 -0.488 1.593 0.019 -1.189 -0.050 +4 -0.454 1.726 -0.887 0.772 0.356 1.262 -0.667 -0.733 1.096 0.425 -1.545 -0.539 0.386 0.838 -0.679 -2.914 0.523 -1.220 -2.017 -2.832 0.814 0.129 -0.226 1.389 0.637 1.616 -1.393 -0.035 +0 -0.324 0.117 0.648 -1.262 1.752 -1.062 -0.940 -0.257 0.552 0.045 0.050 0.760 0.135 0.402 -0.269 0.925 -1.057 0.570 -0.357 0.057 0.159 0.871 -0.082 -1.754 -1.005 -0.098 0.837 0.205 +2 -0.849 -1.309 -0.154 -0.866 0.907 0.406 -0.070 -0.797 0.381 0.691 0.143 1.604 0.460 0.914 0.134 0.676 -0.797 -0.458 0.349 0.342 0.508 1.100 -1.425 -0.974 0.230 -0.724 3.378 0.516 +2 1.118 1.003 0.147 -0.919 -0.483 -0.243 -0.915 1.081 1.028 -1.396 -0.322 2.062 1.163 0.188 1.096 1.649 0.095 -1.357 0.860 0.911 -1.006 0.261 -0.791 -1.645 0.221 1.452 0.321 0.177 +3 -0.911 1.576 0.084 -1.499 -0.308 0.439 0.732 0.199 0.528 -0.140 -0.137 3.293 -0.602 -1.259 -0.438 -0.109 -1.566 0.501 -0.610 -2.313 -0.021 -0.812 1.014 -1.010 -0.541 0.345 -0.369 -0.945 +2 -0.374 -1.496 -0.494 -0.346 -1.837 -0.320 -0.171 -0.851 0.158 1.139 2.159 -0.234 -0.769 -0.862 0.443 -1.860 1.328 -0.949 0.823 -1.358 1.068 0.467 -0.103 0.045 -0.272 0.311 -1.744 0.300 +0 -0.805 0.727 0.083 -1.749 -0.606 -0.268 1.198 1.744 0.771 -0.365 -0.151 0.993 1.576 0.278 0.587 -0.040 1.159 0.159 -0.105 0.838 -1.165 0.579 0.178 -0.343 0.068 0.098 -1.113 -1.560 +1 -1.327 0.406 -0.396 1.087 -0.329 -1.594 -0.583 -0.091 1.246 0.120 -0.194 0.008 1.348 0.704 0.955 0.599 -0.040 0.301 -0.110 -0.060 -1.058 -2.342 -1.239 -0.328 0.198 -0.349 1.663 -0.049 +0 0.404 0.625 0.476 -0.424 -1.091 -1.308 0.114 -0.221 -0.265 -1.507 0.409 -0.519 0.719 -0.018 0.395 -2.069 1.221 0.306 -1.181 0.222 -1.190 0.376 -0.288 -1.127 0.261 -1.432 0.900 1.020 +3 0.590 -1.385 0.661 -1.555 -0.772 1.033 0.984 -0.944 -1.140 0.026 2.003 0.561 2.353 -0.165 0.883 -0.316 -0.800 -0.952 0.354 1.295 -0.327 1.421 1.831 0.868 0.327 -0.670 1.303 -1.141 +2 -0.021 0.323 1.446 0.009 0.510 -3.200 -0.511 0.575 -0.608 -0.955 2.165 0.715 -0.546 1.127 -0.413 -1.364 -0.546 -0.777 -0.631 -1.094 -0.022 0.690 -0.197 -0.234 0.419 -0.249 0.584 -0.183 +2 0.567 0.994 -0.127 0.976 -2.038 -1.056 0.164 0.330 -0.912 0.259 -0.624 -0.277 1.025 0.909 -0.997 -1.057 -0.411 -0.027 -0.658 1.041 0.347 0.406 -1.267 1.167 -2.820 0.834 0.934 -0.432 +1 0.108 0.046 1.468 -1.058 -0.284 -0.870 -0.549 -1.546 -1.195 0.670 -1.420 -2.068 0.775 -0.848 1.242 -0.116 0.018 -1.497 -0.605 -0.415 0.905 0.262 0.174 -0.606 -0.056 1.176 0.488 -0.047 +0 0.422 1.073 0.264 0.638 -0.647 -1.283 -0.819 1.201 -0.289 -0.488 0.007 -0.199 -1.804 0.633 0.080 0.707 0.013 0.066 0.013 1.449 -0.160 0.055 1.630 -0.367 0.233 -0.901 -1.266 -0.576 +2 0.136 -0.970 -0.516 -1.078 0.603 1.513 -0.623 -1.102 -2.147 -1.083 -1.162 1.863 -1.503 0.520 0.395 -0.089 -0.403 1.806 0.383 -1.382 -0.326 -0.117 -0.673 0.053 0.836 0.172 0.679 0.568 +1 0.384 0.518 -0.209 1.203 0.081 -0.550 1.304 0.157 -0.129 -0.616 0.757 0.664 -0.564 0.681 1.392 0.228 0.302 -0.644 -0.826 -1.197 -0.370 -0.467 -0.488 2.411 -0.785 -1.808 0.465 1.059 +3 1.551 2.221 0.102 0.239 2.394 -1.093 -0.522 0.581 1.148 0.038 -0.814 1.288 -0.008 0.001 0.930 1.506 0.240 0.478 1.046 -0.226 -0.548 -0.368 -0.920 -1.088 2.221 -0.895 0.271 -0.086 +2 -0.119 -1.571 -0.745 2.019 -1.508 -0.251 -0.016 0.320 -1.497 0.174 -1.686 0.097 -0.652 -0.866 0.835 -0.236 0.474 0.507 1.270 -1.208 0.919 0.016 -0.378 -1.149 0.607 -0.922 0.518 1.928 +2 0.661 0.173 -1.456 0.259 -0.230 1.137 -1.059 -0.640 0.017 -0.663 -1.531 -0.060 -1.493 -0.855 1.253 -0.392 0.083 -0.237 -1.146 0.723 0.234 -0.511 1.419 -0.261 -1.967 1.528 0.356 -2.046 +3 -0.459 -0.260 1.045 0.524 -1.866 -1.289 -0.135 0.536 -0.229 -0.503 -0.984 0.913 0.100 1.680 -1.300 0.691 0.548 0.847 0.827 -0.507 -0.181 0.166 -1.003 1.032 -2.475 -1.162 -1.678 1.100 +0 -1.947 -0.821 0.820 0.593 -0.160 1.069 0.608 0.344 -0.017 0.896 0.755 -0.273 0.813 -0.654 0.235 0.400 -0.335 0.134 0.608 -1.082 0.184 2.215 -0.826 1.030 -0.096 1.183 -0.723 0.006 +4 1.851 -0.095 -0.931 0.133 -0.622 -0.410 1.629 0.126 -1.279 -0.440 -1.851 -0.759 1.003 -0.723 2.642 -2.613 -0.443 0.113 0.061 0.563 -0.275 1.253 -1.230 -0.238 -1.114 -0.495 -0.257 0.619 +3 -1.171 0.254 -2.459 0.414 -1.409 -0.096 -1.195 0.003 0.578 1.503 -1.541 0.975 0.131 -0.451 0.760 -0.236 0.718 -0.992 -0.633 0.944 1.485 -0.035 -2.787 -0.199 0.032 -0.233 0.336 -0.477 +4 2.025 -0.668 -0.099 1.892 -1.012 0.578 2.092 0.315 2.121 0.094 1.062 -0.119 -0.762 0.218 -2.254 -0.956 -0.177 0.378 1.763 -0.270 -0.731 0.286 -0.648 -1.642 -0.680 -1.103 0.764 -0.719 +0 1.330 -0.121 -1.340 -0.486 -1.488 -1.125 0.389 -1.174 1.113 -0.071 0.086 -0.278 0.773 0.783 0.335 0.565 -0.212 0.542 -0.338 0.002 -0.275 -0.457 -0.689 0.468 1.311 1.171 -1.257 1.289 +0 -0.125 0.235 -0.211 -1.040 0.297 -1.566 0.138 -0.995 -0.081 0.191 -0.353 -1.641 -1.188 0.458 -0.557 0.561 0.010 -0.198 0.348 -0.675 0.400 -0.420 2.536 -1.134 -0.357 -0.317 -1.352 0.726 +0 1.220 0.389 -0.502 -0.606 -0.590 -1.016 1.112 -0.518 1.353 -0.829 -0.599 -0.452 -0.899 -0.586 0.551 1.202 0.299 -1.191 -0.600 -0.579 1.701 0.986 1.214 -0.709 -1.273 0.573 -0.460 0.292 +2 0.548 1.883 -0.804 0.255 0.031 0.788 -0.452 -0.566 -0.064 0.475 -0.457 -2.655 -0.952 1.414 1.547 -0.496 1.797 -0.327 -1.535 -0.306 0.725 -0.565 0.603 0.267 -0.286 0.776 1.229 0.182 +4 0.857 1.525 -2.802 0.434 -1.427 0.499 0.128 0.636 -0.499 -1.873 -0.450 0.407 1.015 -1.172 1.027 -0.648 0.362 -0.510 0.182 0.892 -1.227 -0.680 0.185 2.402 -0.863 2.040 2.004 -0.097 +4 -1.475 -0.866 0.012 -2.449 -0.380 -1.165 -1.499 -0.802 -1.069 0.469 1.197 -0.364 -0.596 -0.414 -1.108 -1.289 -1.646 -1.330 1.632 -0.690 -2.942 -0.587 -1.590 0.059 -0.048 0.056 -0.041 -0.373 +0 -0.993 0.458 -0.038 -0.444 -0.889 1.204 -0.186 -0.648 2.455 -1.334 -0.945 -0.171 -0.147 -0.961 -0.200 -0.550 -0.278 -0.194 0.897 -0.505 -0.789 1.144 -0.479 -0.810 -0.905 1.322 0.547 0.021 +3 -0.197 -1.532 0.016 -0.250 0.512 0.081 -0.549 0.926 -2.221 -0.064 -1.763 -0.912 0.525 -1.274 -1.424 2.382 -1.359 1.651 -0.172 -0.525 0.567 1.337 -1.146 -0.510 0.847 0.288 1.102 0.515 +2 -0.121 -0.434 -1.526 -0.654 -1.748 -2.103 0.166 0.112 -1.681 0.192 -1.309 0.146 0.499 -2.119 -0.043 0.628 -1.160 -0.355 0.799 -0.381 1.024 1.420 1.367 -0.538 -1.074 0.604 -0.151 -0.113 +1 -0.183 0.270 0.003 0.327 0.831 -1.441 0.536 1.804 -1.607 -0.802 -0.621 0.703 -0.293 0.432 0.618 -0.931 0.166 -1.256 0.166 1.289 -0.731 -0.741 0.433 1.125 -0.667 1.510 1.527 0.151 +2 -1.866 0.028 0.979 -0.643 -1.227 -0.186 -0.061 -0.909 2.096 -0.479 -1.154 -0.094 0.908 0.017 0.486 -0.872 1.488 0.562 1.048 0.365 0.825 -0.873 -0.491 1.346 1.560 -1.147 0.250 -0.431 +1 0.056 -0.380 0.346 -1.126 -0.366 1.683 -1.048 1.233 -0.832 -1.053 0.107 0.807 -0.742 -2.019 1.290 -0.747 -1.237 0.253 -0.575 0.227 -0.901 -0.678 -1.065 -0.976 -0.193 1.793 0.159 -0.367 +3 1.474 0.180 0.143 -0.028 -0.596 -0.311 0.102 -0.560 -0.387 0.408 -1.431 0.559 2.352 2.014 0.295 -1.249 1.383 -0.190 1.423 -0.764 -0.588 0.481 -0.687 1.877 0.564 1.773 -1.136 -0.873 +4 1.051 2.023 1.384 0.203 -0.258 3.016 0.487 -1.107 0.278 0.038 0.306 1.413 0.010 -0.452 1.768 1.453 1.511 -0.822 -0.274 1.590 -1.351 -0.192 -1.119 -0.819 1.651 -1.733 1.274 0.751 +3 -1.694 -0.456 -0.737 -0.145 1.161 -0.838 0.089 1.568 -0.751 -0.951 -0.332 -2.227 -1.043 -0.951 -2.232 -0.295 0.079 -0.512 -0.054 -1.514 2.014 -0.285 0.792 1.028 -0.418 -0.142 0.884 -0.579 +1 1.273 -0.063 0.062 0.340 3.187 1.344 0.701 -0.913 0.144 -0.813 0.822 0.140 -1.125 0.081 -0.849 0.151 -0.859 0.027 0.750 -0.053 -0.391 0.203 0.697 -1.107 0.261 1.318 0.574 0.058 +4 -0.456 1.301 0.568 -0.803 0.106 1.749 1.016 -1.383 1.765 1.136 0.602 1.449 0.299 -0.586 1.543 -1.143 -0.731 1.972 -0.795 0.480 0.331 -1.303 -0.444 -1.273 0.166 -0.620 0.945 2.148 +2 0.176 0.679 -0.443 1.029 0.386 -0.880 1.466 -0.106 0.703 0.442 0.044 0.193 -0.145 0.829 -2.284 1.765 0.335 -0.257 -0.520 0.595 1.377 1.751 -1.234 2.076 0.883 -1.035 -0.483 0.571 +2 0.803 -0.702 -0.056 -1.873 -0.825 0.510 -2.571 -0.593 0.106 0.196 0.256 0.273 0.879 0.307 1.591 -0.395 1.256 -0.079 0.914 0.585 -0.363 -0.510 0.307 1.706 0.322 0.885 1.610 0.577 +2 -1.042 -0.640 0.880 -0.312 0.859 -0.157 -0.963 0.480 0.560 -1.230 1.136 1.521 0.627 0.077 2.012 0.539 0.197 1.700 1.603 -1.439 -0.504 0.586 1.227 -1.166 0.908 -0.405 0.717 -0.567 +3 0.042 -0.613 -0.075 -1.052 1.050 -0.781 1.199 -0.061 0.856 -0.767 -1.967 -1.649 2.503 -0.598 1.096 -1.298 0.179 0.348 2.836 -0.318 -0.711 0.351 -0.925 0.410 -0.053 -0.754 0.494 0.108 +4 -1.315 2.003 0.121 -1.236 -0.202 -0.483 -0.795 0.425 2.230 1.869 0.201 -0.781 0.280 0.738 0.204 0.745 0.444 0.285 -1.216 -0.130 1.911 0.490 1.168 2.011 -1.179 2.599 0.973 1.162 +4 -0.279 -1.437 -0.082 1.295 -2.067 0.736 0.158 2.593 2.295 -1.764 1.381 -0.390 -0.194 -0.576 -0.699 1.318 -2.354 -0.665 0.676 -2.039 -0.230 -1.208 0.234 0.050 0.933 -2.319 0.430 -0.750 +0 1.683 -1.699 -0.346 -0.438 -0.009 -1.227 -0.580 0.789 -0.269 -1.070 2.080 0.049 0.613 0.764 -0.071 -0.790 0.199 -0.700 0.372 -0.164 1.029 -1.142 0.719 0.614 -0.239 0.730 0.419 0.667 +1 1.116 -0.430 -0.334 0.958 0.165 0.095 2.135 -1.639 -0.797 0.580 -0.413 -0.660 -2.036 -0.974 -0.830 -0.240 0.664 0.301 1.321 -0.228 0.507 -0.168 -1.089 1.105 -0.958 0.894 -0.032 0.569 +2 0.262 0.110 -0.592 -0.937 -0.697 -0.202 0.591 0.574 0.211 -1.335 -1.695 -0.814 0.815 -0.980 0.162 0.028 0.015 0.243 -1.306 0.830 -1.076 -1.025 -2.107 1.811 0.198 -0.477 -1.215 -1.443 +3 0.970 -0.419 -0.556 1.233 0.243 0.983 -1.411 -0.806 -0.523 1.298 1.260 0.088 0.481 0.168 -0.997 0.060 -0.903 -0.381 -0.225 1.651 0.158 -0.407 0.614 -0.661 2.657 0.430 -2.472 -0.804 +3 2.301 0.739 -0.856 1.212 -1.152 -0.137 -0.247 0.182 -0.942 0.128 -1.748 -0.377 1.335 1.025 -0.696 -1.526 -2.183 0.617 0.278 0.386 1.691 0.153 0.228 -1.397 1.624 -0.164 -0.817 0.208 +1 0.479 0.444 0.060 -2.084 0.440 -0.145 0.547 1.120 0.385 -0.260 -1.776 -0.872 0.696 0.283 0.928 0.470 0.270 -0.346 0.207 -0.192 0.095 -1.162 1.425 -0.293 -2.270 -1.011 -0.317 0.961 +1 1.086 0.708 -1.581 0.187 0.144 0.714 -0.335 -1.429 -0.867 1.080 0.156 -0.261 -1.016 -0.951 -1.340 -0.364 0.791 -2.246 -0.575 -1.397 -0.751 0.866 1.093 0.863 -0.049 -0.636 0.544 -0.734 +0 1.755 -0.331 1.391 -0.316 -0.966 1.746 0.521 0.004 0.687 0.274 -0.069 -1.326 -0.271 0.623 -0.123 0.902 0.384 -0.878 -1.319 -0.157 -0.524 0.336 -0.718 -0.615 1.053 0.148 -0.703 0.018 +1 -0.417 0.425 -0.924 -0.020 2.081 1.126 -0.685 1.866 0.794 0.965 0.466 -0.132 1.584 0.690 0.986 -0.389 -0.028 -0.635 0.331 0.355 0.386 0.018 0.002 1.015 0.750 1.562 0.221 -1.388 +1 -0.356 -0.064 0.370 1.473 -0.039 -1.132 0.626 -0.493 -0.190 1.612 -0.579 -0.525 -0.714 -0.961 -0.615 0.241 -2.332 -0.804 -0.305 0.982 1.968 -0.962 0.581 0.548 -0.070 1.756 -0.123 0.665 +1 -1.096 0.685 -0.491 -2.104 0.261 -0.010 0.886 0.766 -2.511 0.454 -1.142 0.304 0.085 -0.055 -0.965 0.612 1.844 -0.268 0.096 0.898 -0.753 0.585 0.104 1.187 0.922 0.015 0.239 -0.364 +0 0.968 -0.498 -1.309 -0.051 0.223 -0.472 0.278 0.330 -0.910 0.395 -0.267 -0.700 0.787 -0.777 -0.457 0.072 1.374 0.193 0.528 -0.298 -0.078 -0.049 0.293 -0.296 1.340 -0.373 -1.707 2.743 +4 1.286 0.057 0.002 0.635 1.576 1.512 0.593 0.517 0.696 0.986 1.299 -0.237 1.318 0.343 0.651 1.382 0.747 0.916 -0.389 1.498 0.696 0.126 1.101 2.588 -1.169 -0.804 2.685 0.427 +2 0.084 1.327 -1.119 -1.038 0.033 -1.636 0.244 0.016 0.545 2.238 0.059 -0.540 0.962 -0.012 -0.164 1.787 0.751 -0.952 -0.922 -1.335 -1.100 1.128 -0.508 -1.786 0.458 0.169 -0.448 -0.637 +1 -0.596 -0.872 -0.671 1.605 -0.026 1.862 -0.547 1.256 -0.854 1.295 1.000 -0.029 -1.446 0.699 0.158 -0.831 1.241 0.335 0.182 0.010 -0.827 0.147 -0.110 1.143 -0.815 0.678 -0.880 -0.114 +3 1.290 1.299 -1.300 -0.545 0.724 -0.627 0.105 -0.716 -1.089 -0.896 0.956 0.014 1.272 0.520 1.606 0.818 0.759 -0.120 3.009 0.113 -1.973 0.268 -0.982 1.017 0.726 -0.192 -0.888 0.146 +3 -0.661 0.862 -0.040 0.385 2.056 1.173 -2.476 0.309 -0.583 -1.207 -0.648 0.756 0.275 0.184 2.527 -0.137 0.039 -1.805 0.531 0.540 1.108 -1.095 -0.384 -0.490 0.437 0.243 -0.091 -0.145 +0 -0.014 0.433 0.939 -0.741 0.376 0.979 0.976 -0.043 -0.348 -0.012 -0.707 0.536 0.134 -2.442 0.364 -0.489 0.164 1.518 -0.911 -0.395 -0.232 -1.303 0.100 0.434 -0.385 0.551 1.489 -0.690 +2 0.630 -1.334 1.093 0.558 0.734 0.180 -1.066 0.795 0.588 -0.762 -1.238 0.069 0.148 0.731 -0.625 0.385 0.981 -2.079 -2.329 0.178 0.285 -0.630 -1.479 1.822 0.067 0.111 -0.499 -0.151 +4 0.340 -1.662 0.167 0.408 -1.078 -0.127 -2.901 -0.334 -0.436 1.402 -0.603 -0.266 -0.079 -0.590 0.720 -1.992 1.137 -0.012 1.302 -2.747 -2.436 -0.292 0.506 -0.555 0.584 0.981 0.666 0.103 +4 2.334 1.195 -0.594 0.039 -0.540 -1.167 -0.401 0.219 0.187 -1.453 -0.393 -2.240 -0.129 0.475 0.133 1.439 -2.717 -0.132 0.540 2.200 0.220 -0.476 1.149 -1.410 0.587 2.751 0.253 -0.011 +4 -1.058 0.628 -0.458 0.112 0.299 0.853 2.632 1.220 -0.978 1.428 -0.056 -2.512 1.173 -1.608 -0.122 -0.382 2.354 0.739 -0.299 -0.705 -0.018 -1.435 -0.212 -1.517 0.241 0.428 0.775 0.301 +1 1.285 -0.498 0.245 -1.663 -1.112 1.059 0.441 0.138 -1.296 0.174 0.114 0.033 -0.777 0.725 -0.784 -0.251 -2.052 -0.057 -1.166 0.181 0.042 0.001 -1.172 -1.471 -0.066 -1.724 1.237 0.282 +2 1.738 2.027 -0.330 0.615 1.487 1.179 0.180 0.668 -0.593 -0.446 0.490 -0.353 2.014 -0.284 -0.289 -0.438 -0.830 0.813 -1.216 -1.473 -0.950 0.104 -0.074 -1.558 -0.668 -0.297 -0.206 -0.179 +4 0.735 0.025 -1.346 1.368 -0.163 -1.686 -1.156 -1.269 -1.114 1.014 0.198 0.541 -2.801 0.768 0.630 -1.791 -1.245 1.138 -0.596 1.009 0.010 -0.513 -0.140 -1.991 0.381 -1.199 -1.830 -1.482 +4 -2.016 0.872 0.700 0.570 -3.170 -0.267 2.034 1.344 -3.300 0.413 1.085 0.090 0.078 0.430 1.564 0.293 -0.176 1.441 -0.792 0.706 1.482 -0.715 0.482 1.234 1.164 -0.315 0.583 0.005 +0 0.293 -1.045 0.473 0.456 -1.484 -0.322 1.409 -0.633 0.018 0.160 -0.026 0.689 0.935 1.265 -0.313 0.524 0.840 -0.118 -0.719 0.144 -0.606 -0.704 0.220 -0.188 0.176 -1.551 -0.176 -2.260 +0 0.483 -0.167 -0.017 -0.980 0.474 0.287 -0.840 1.051 0.147 1.229 -1.027 1.043 0.046 0.194 1.602 0.182 -0.114 0.201 0.807 -0.437 -1.114 -0.745 -0.133 -0.549 -0.364 0.589 0.421 -1.483 +4 0.506 1.447 0.568 -1.050 1.363 1.641 3.152 -1.123 0.243 -2.082 0.553 -0.548 1.923 -0.775 -1.689 -0.471 -1.975 0.751 -2.065 0.028 -2.078 -0.320 1.643 0.361 -0.863 -0.031 0.018 0.473 +2 0.765 -0.575 0.151 0.223 0.736 1.660 0.607 -0.238 0.797 0.304 -2.238 -1.199 -0.225 -0.585 -0.123 -2.427 0.841 -0.050 1.216 0.026 0.201 -1.036 -1.205 0.133 0.002 -1.642 0.940 0.050 +4 0.390 -1.534 1.230 2.416 -1.071 -0.488 -1.125 0.243 0.534 -0.071 1.181 1.429 -1.370 0.189 0.413 0.298 -0.335 0.307 -1.509 1.484 -0.279 -0.574 -1.653 1.739 1.994 0.142 -1.334 -0.297 +3 -0.429 -0.126 -0.974 0.557 0.957 -1.851 1.784 -0.528 -0.295 0.308 -0.838 -0.591 -0.831 -0.002 -0.581 0.377 -1.316 0.444 -1.127 1.011 0.813 -2.139 0.842 -2.610 -1.172 1.033 -0.541 1.561 +4 0.901 0.190 -0.263 0.821 1.913 -0.558 -0.088 -0.991 0.155 -0.156 1.158 1.420 0.788 1.047 -0.705 -0.955 0.437 0.020 1.999 -1.922 0.620 0.603 -0.762 -0.968 -0.339 -1.642 1.457 2.701 +3 -1.354 1.610 -0.225 0.157 -0.537 1.817 -0.260 1.597 0.212 0.800 0.665 -0.638 1.023 -0.210 0.615 -2.405 -0.341 -0.664 0.604 -1.620 -1.408 -0.191 1.786 1.488 0.145 0.559 0.437 -0.256 +3 -0.495 -0.746 -0.670 1.284 0.000 -0.554 -0.908 -0.321 -0.512 -0.937 -0.353 0.324 3.233 -0.396 0.115 -0.907 1.954 0.125 0.484 0.427 2.759 0.856 0.252 0.072 0.100 -0.769 -0.327 1.133 +1 0.352 -1.473 -0.484 -1.213 -1.426 0.853 -1.479 -1.321 1.978 -0.448 0.658 1.125 0.774 0.150 -0.037 0.538 -0.225 0.695 -0.259 -0.146 1.882 -0.480 0.019 0.431 0.495 0.293 -1.250 0.162 +2 -1.000 0.508 0.006 -0.455 2.043 -0.315 0.169 0.086 1.575 -1.499 2.244 1.519 0.398 0.618 -0.228 0.150 0.420 -0.909 1.519 0.224 0.142 -0.867 1.170 -0.046 -0.525 -1.086 -0.162 -1.170 +0 0.547 -0.202 -0.218 1.099 0.825 0.814 1.305 0.021 0.682 -0.310 0.324 -0.130 0.097 0.595 -0.818 2.092 -1.006 -1.214 1.158 0.792 0.624 0.628 -0.012 -0.897 0.076 -0.677 0.975 -0.147 +3 0.166 -0.734 0.518 0.736 0.726 0.935 -0.568 -0.271 0.247 -0.425 -1.158 -0.502 1.635 -0.743 -0.794 -0.547 0.557 0.348 0.706 2.533 0.821 0.900 2.860 0.490 -0.392 -0.303 1.057 -1.252 +3 -0.119 -1.539 -2.092 0.628 0.790 0.699 0.435 -1.072 0.243 1.574 -2.169 -1.281 -0.477 1.070 -1.000 1.626 0.412 -0.821 0.056 0.382 0.097 0.916 0.499 -2.436 0.518 -0.477 0.283 0.356 +3 -0.565 0.355 -1.179 0.754 -0.429 0.808 1.238 -0.840 1.835 0.671 0.061 -0.068 0.587 -0.117 -0.137 -1.152 -0.729 -0.269 -2.916 -1.555 -0.826 -0.497 -1.221 -0.926 0.696 -1.410 -1.323 -1.318 +2 1.634 -0.333 -0.076 0.073 0.479 0.827 -0.375 1.857 -1.829 -1.194 0.608 -0.164 -0.566 0.959 0.813 1.249 -1.036 0.813 0.802 -1.007 0.039 0.969 1.342 -0.147 -1.099 1.532 0.040 -1.889 +0 0.842 0.959 0.329 -0.079 -0.110 1.276 -0.558 -0.141 -0.963 -0.620 -0.449 -0.177 -0.148 0.100 0.292 0.146 1.498 -0.052 0.059 0.679 -1.161 -0.222 0.370 0.449 -0.439 0.253 -1.350 1.060 +0 0.270 0.308 -0.759 1.058 -0.442 0.450 -0.164 0.376 -0.214 0.394 -0.007 0.814 -0.819 -1.770 -0.973 1.069 -0.155 0.092 -1.054 0.066 -1.023 -0.876 -0.402 0.445 0.552 0.579 -1.588 -0.007 +3 0.352 -1.620 -0.757 -0.126 -1.182 -1.167 -0.962 0.508 -2.526 -0.169 1.520 1.062 1.124 -0.349 1.948 -0.570 0.451 0.528 -1.097 -1.966 0.882 -0.922 -0.092 0.399 -0.022 0.677 0.466 0.540 +4 0.437 -2.170 1.192 -1.287 -0.634 0.105 -0.385 0.430 0.552 -1.337 -0.036 -0.823 0.948 1.845 0.302 -2.070 0.124 -1.358 -0.094 0.958 -1.365 0.507 0.731 -0.864 -3.150 0.858 -0.477 -0.032 +4 0.568 1.086 0.333 -2.493 -0.499 1.197 -0.808 -0.223 -1.045 1.784 1.681 -0.884 0.147 -0.680 0.615 0.510 0.812 0.332 2.935 -1.028 -1.711 -0.393 -1.568 -1.492 -1.001 -0.922 -0.955 0.518 +2 -0.362 -0.124 -0.378 0.317 -0.758 -0.509 -0.826 -0.900 -0.341 0.263 -1.521 0.659 -1.837 -0.142 1.079 1.384 0.574 -1.625 -0.196 1.908 1.331 -0.105 1.264 -0.179 0.592 -1.277 -2.003 -0.835 +4 -0.524 -0.390 0.399 -0.672 -1.256 0.015 -0.777 0.339 -1.454 0.397 0.000 0.607 -1.801 -1.846 -0.071 -1.139 -0.544 1.446 0.194 -0.975 0.211 -0.112 0.046 2.515 0.770 -1.748 2.619 -0.576 +2 -0.526 1.559 -0.415 2.083 0.067 -0.234 -0.504 0.422 0.894 -2.538 0.005 -1.988 0.982 1.000 0.041 0.208 -1.430 -0.547 -0.612 -0.449 0.872 1.282 0.611 -0.323 -0.746 0.091 0.297 -0.221 +1 1.046 1.380 0.059 -0.095 0.705 0.412 -1.145 1.647 0.238 0.817 0.355 0.514 -0.165 0.697 0.678 0.853 -1.368 -1.218 0.123 -0.400 1.237 -0.876 0.892 -1.668 -1.518 -0.023 0.354 0.645 +2 0.393 -1.523 -0.679 0.819 -0.131 -1.465 0.394 -0.582 0.436 2.688 0.382 0.612 -0.254 -1.120 1.024 -0.606 -0.229 1.793 -0.338 1.215 0.387 -0.553 0.341 -0.716 0.905 1.247 0.049 1.342 +4 -0.324 0.103 -1.107 0.163 0.902 1.211 -0.451 -0.963 -1.467 1.217 -0.589 -0.541 -2.243 -0.320 2.784 1.897 0.049 1.359 -0.665 1.530 0.216 0.687 -0.539 0.519 3.062 1.038 -0.550 -2.165 +2 -0.324 -1.541 1.405 1.371 -0.672 0.921 -0.332 1.501 -0.515 1.979 0.786 1.798 -0.464 0.405 -0.969 0.844 0.899 2.397 0.482 0.724 -0.471 0.541 -0.380 0.116 0.175 -0.352 0.063 -0.375 +0 0.601 1.142 -0.438 0.090 0.007 1.544 0.006 0.146 0.939 0.508 0.219 -1.325 -0.424 0.361 0.127 -0.139 0.692 2.162 0.075 0.656 -0.200 -1.123 -0.304 0.194 -0.060 0.094 -1.726 -0.465 +0 1.013 -0.122 -1.042 0.171 0.622 -0.274 0.766 -0.384 -0.013 0.024 -0.849 -0.471 0.142 0.333 1.604 -0.652 0.869 0.567 0.823 -0.637 -0.362 -0.585 -0.245 0.542 0.056 0.870 -0.780 -1.954 +4 0.197 1.667 -0.447 -0.523 -2.958 -1.033 1.509 2.252 0.831 0.559 -1.918 0.510 1.250 -1.058 1.583 -0.539 0.908 0.160 -1.177 -0.200 -0.672 0.033 0.796 -0.140 0.556 -0.539 0.118 1.027 +3 -1.673 0.328 -0.870 0.469 -1.645 -0.088 -0.727 0.488 -1.167 0.536 1.066 0.455 0.309 0.957 0.829 -1.861 1.078 1.535 0.582 -1.379 -1.237 -2.234 1.045 1.298 0.444 0.790 -0.311 0.529 +1 -0.773 -0.072 1.095 0.368 1.944 1.398 -1.039 0.593 0.153 1.187 0.523 -0.500 0.476 -0.369 -0.646 -0.111 0.249 1.300 -0.497 -1.917 -0.197 0.256 0.182 -0.254 0.950 0.312 0.603 -1.837 +0 -0.124 -0.273 -0.260 0.920 -0.512 1.012 2.012 -0.650 0.376 0.242 0.052 -1.617 0.888 -0.375 1.163 -0.015 1.135 0.513 0.764 0.626 -0.138 -0.362 0.450 0.698 -0.007 0.778 -0.738 0.255 +3 -0.756 -1.258 -2.202 0.612 -0.065 0.584 1.599 -0.908 -0.249 -0.861 -1.694 -0.275 1.167 -1.062 0.238 -1.225 -0.757 0.279 1.744 1.329 -0.026 -0.844 0.091 -1.611 0.634 -0.245 1.466 0.568 +2 -0.398 0.003 0.728 2.328 0.820 0.914 -0.545 0.157 -0.096 1.408 -0.146 1.329 -1.302 1.399 -0.861 0.854 -0.864 0.618 -0.963 1.036 0.845 -0.601 0.578 0.947 -0.923 -1.034 -1.071 -1.213 +3 -1.382 1.042 0.224 -0.452 2.272 -1.648 0.976 -0.111 0.785 0.109 -1.872 0.095 0.336 -2.192 1.944 0.311 0.672 -1.166 0.691 1.090 0.254 -0.009 -0.894 -0.216 -0.512 0.572 -0.850 0.565 +0 -0.448 0.264 -0.665 1.627 1.401 0.565 -0.525 -0.848 1.226 -1.159 0.269 -0.113 -0.900 -0.022 -0.446 -0.113 -1.249 -1.652 1.109 0.095 -0.414 0.769 -0.008 -1.290 -1.677 -0.290 0.264 -0.197 +2 1.173 0.674 -1.120 -0.809 -1.434 -0.010 -0.980 -0.806 0.917 -0.808 -0.753 1.141 1.977 0.028 -0.227 -0.223 0.471 -2.221 0.644 -0.305 -0.255 0.024 -1.071 1.481 -1.032 1.394 1.437 0.582 +2 1.030 1.982 0.954 2.172 -0.922 -0.819 -1.189 0.442 0.176 -0.152 0.809 1.232 -1.526 -0.597 0.820 -0.468 -0.798 1.019 -1.290 -0.302 0.329 -0.574 0.783 -0.569 1.409 -0.126 0.671 0.746 +3 -0.911 -1.351 -0.373 1.121 -1.588 0.084 0.441 1.588 0.679 -0.270 0.494 0.415 0.143 0.043 -1.084 -0.832 -0.200 2.109 -0.196 -0.033 -0.878 0.893 1.018 -2.145 -1.939 1.033 1.838 -0.744 +4 -0.845 0.909 -1.261 0.181 0.004 0.060 0.617 -1.262 0.912 -1.200 -0.482 -0.353 0.422 -1.574 1.618 -0.282 0.791 -1.231 -0.531 -2.263 0.836 -1.024 -1.161 0.541 -3.132 -0.224 1.782 -1.006 +1 1.171 -0.014 0.490 -1.808 0.511 -0.348 -0.081 1.480 0.379 0.402 -0.482 -0.189 1.360 0.440 1.664 -0.869 -1.510 -0.231 0.970 0.243 -1.251 -0.486 0.601 0.979 -0.703 1.310 0.697 -0.693 +3 0.493 -0.806 -0.145 1.090 -2.567 -0.407 -1.638 -0.222 -0.033 -0.263 0.884 0.443 -0.506 -0.738 0.735 2.056 1.654 0.452 0.009 0.378 1.600 0.758 -0.321 1.563 -0.358 1.170 -1.095 0.728 +0 -0.811 0.547 0.163 0.308 -0.718 0.151 1.222 0.646 1.384 0.149 0.090 -0.975 -0.270 0.362 -1.330 0.383 -0.804 0.868 -0.590 -1.265 0.566 0.160 0.856 -1.065 1.562 -0.094 -1.330 -1.389 +1 1.004 0.483 -0.162 0.895 0.239 0.404 -1.000 -1.283 -0.331 -0.990 1.349 -1.475 -0.708 -1.730 0.397 -0.632 1.670 0.382 -0.287 0.249 0.441 0.789 0.998 -0.386 -0.853 0.379 1.719 1.272 +0 -0.865 0.892 -0.842 -0.300 -0.254 1.235 -1.972 0.409 0.792 0.441 -0.276 0.400 -0.534 -0.543 -0.361 -0.153 -1.287 -0.490 -0.293 -0.681 -0.514 -1.234 -0.407 -0.019 0.366 0.149 -1.771 -0.410 +1 0.766 -0.478 -0.528 -1.112 0.754 -0.191 -0.318 0.110 -0.170 -2.001 -0.491 0.895 -1.507 -1.090 -0.427 -0.726 -0.028 -1.093 -0.388 -0.048 0.649 1.245 -0.262 1.888 -0.184 0.829 1.932 0.139 +1 1.269 -1.387 -0.817 -0.106 0.098 -0.226 0.434 0.996 1.723 0.579 -0.852 -0.147 -0.075 0.515 0.388 2.050 -0.462 0.385 2.355 -0.585 0.145 -0.945 0.082 -0.780 1.047 0.147 -0.935 0.841 +0 0.851 -0.391 0.107 0.846 0.464 -0.585 0.300 -1.984 0.000 -0.618 1.519 -0.977 0.117 -0.429 -0.558 0.377 -0.418 1.852 -1.975 0.631 -0.085 -0.050 0.302 0.998 0.265 1.001 0.046 -1.034 +2 -1.030 -0.669 -0.304 -0.929 -0.778 0.075 0.215 -1.424 0.396 -0.382 0.720 0.780 -0.545 0.614 -0.837 1.324 -0.799 0.353 -0.188 -1.017 0.901 -0.614 -1.700 -2.882 -0.875 1.376 1.258 -0.719 +4 -0.397 1.512 -0.993 -0.017 1.189 0.846 2.216 0.003 -0.442 -1.094 -0.368 -1.104 -1.796 1.963 0.049 2.591 -0.839 -0.429 -0.775 -0.641 0.874 0.566 0.875 1.342 1.107 -1.008 -0.524 0.121 +3 -0.860 -1.097 -0.389 -0.017 -0.242 -1.877 1.722 -0.274 0.173 1.245 -2.345 -0.512 -0.387 -0.336 -0.173 1.447 0.673 0.958 0.047 -1.399 -2.080 -1.529 0.360 0.032 -0.210 -0.369 2.012 -0.823 +1 -0.553 0.300 0.206 1.744 1.815 -0.597 1.058 0.355 -1.413 -0.696 2.336 0.358 1.000 -0.212 -0.540 0.027 -0.266 -1.493 -1.003 0.708 -0.381 0.026 0.235 0.313 1.147 -1.099 0.570 0.755 +0 -1.446 0.362 -0.797 1.075 0.225 0.607 1.411 -0.310 -0.574 0.872 0.510 -0.187 1.391 -0.212 0.033 0.021 -0.935 0.078 1.397 0.445 0.031 -0.162 0.331 0.750 0.609 -0.332 0.325 0.480 +2 -0.951 -0.065 0.261 0.588 0.961 0.768 1.838 0.106 -0.159 -1.467 0.970 0.610 -0.566 0.243 -0.010 0.175 -0.274 0.113 -1.519 -0.406 0.308 -0.705 0.389 2.065 -1.155 -1.833 1.206 2.093 +1 -0.745 -0.427 0.138 -0.595 -1.376 -1.007 0.940 2.072 0.305 1.079 0.354 0.698 0.741 0.678 0.355 0.143 -0.406 0.526 0.310 -0.907 1.189 0.992 0.193 1.178 0.727 -0.440 -1.815 -1.849 +2 -1.576 0.889 0.578 -0.392 0.844 0.478 1.639 1.012 -0.132 -0.881 1.090 0.041 -0.168 -0.723 1.850 -0.894 0.704 1.556 -0.329 0.084 -1.831 -1.174 1.768 -1.405 0.019 0.564 -0.724 -0.513 +4 -2.259 -0.599 -1.019 0.150 -2.016 -2.046 2.170 0.618 0.121 0.364 0.146 0.997 -0.632 0.187 0.227 -1.047 1.277 -0.446 1.017 0.115 1.634 -1.041 -1.085 -0.906 0.133 1.541 1.593 -2.310 +2 0.200 -0.231 1.418 -1.411 3.602 -0.174 0.738 1.387 -0.196 -0.115 -0.503 -0.885 0.272 -0.135 -0.509 -0.451 0.576 -0.798 -0.247 0.899 0.247 -1.050 -0.024 0.437 0.989 -0.755 -0.559 -0.695 +4 -0.468 1.003 -0.290 -0.307 -1.326 -0.760 0.879 0.597 -0.132 1.370 -1.266 -1.143 -0.169 0.993 1.632 -2.390 -0.655 1.270 -1.348 -2.181 -0.287 1.169 0.200 1.240 1.680 1.214 1.448 -1.581 +3 0.225 -1.277 -0.556 -0.080 -0.328 -0.206 1.621 0.100 0.382 -2.009 0.746 2.852 1.346 -0.440 2.281 0.027 0.307 -0.267 -0.293 -2.248 0.787 -0.297 -0.188 0.273 -0.415 0.264 1.203 0.405 +3 2.569 1.179 1.172 0.632 0.961 0.945 -0.263 -0.279 -0.785 0.710 -1.695 0.393 -0.329 -1.635 0.279 -0.270 0.408 -0.515 -1.444 -0.153 -0.869 -1.863 0.850 -0.607 -0.140 -2.208 -0.425 0.701 +2 -0.077 -0.942 -0.081 2.092 0.999 0.551 0.963 1.541 -1.153 -0.392 -0.529 0.587 0.160 0.756 -0.507 1.372 -0.069 0.695 1.195 1.115 0.361 0.819 1.742 0.514 -0.730 0.583 -0.037 -2.348 +3 0.934 0.022 0.714 -0.820 -0.333 -0.024 -0.631 1.249 -0.764 -0.577 1.359 1.165 1.588 -1.908 0.201 0.090 -0.562 0.324 -0.321 0.162 3.402 0.317 0.359 0.821 0.181 -1.098 -1.647 -0.029 +3 -0.329 0.255 -1.519 0.897 -1.078 1.913 0.374 0.618 -0.673 -0.482 1.326 -1.421 -0.007 0.016 0.456 0.235 0.463 -0.561 2.545 0.011 1.041 1.045 -2.639 -1.565 -0.820 -0.507 0.273 0.295 +3 -0.710 0.642 -1.734 0.579 -0.004 0.038 0.255 -0.856 0.424 1.710 -1.375 0.376 -0.621 -1.629 -1.184 -0.103 0.056 0.153 -0.776 0.410 0.408 1.912 -1.201 1.435 -1.357 0.371 2.126 -1.151 +1 -0.042 -0.570 -0.714 -0.260 -1.256 -0.595 0.308 -1.155 -2.068 -0.462 -1.197 -0.530 0.848 0.840 -0.057 0.578 1.968 -0.254 -0.970 0.670 -0.504 -1.084 -0.249 0.394 0.363 -1.384 0.432 -1.460 +0 0.545 0.887 0.211 -0.418 -0.350 0.026 0.250 -0.147 0.019 -1.517 0.667 0.181 1.002 0.000 -1.738 -0.358 -0.200 0.743 -2.070 0.041 0.514 -0.661 0.075 -0.627 -0.730 -0.408 -0.711 0.684 +1 0.536 -0.572 0.087 -1.804 0.608 -0.537 -0.139 -0.153 -0.348 0.065 0.013 0.219 -1.258 0.772 -0.124 0.340 -0.129 -1.066 2.531 0.126 -1.578 -0.092 -0.392 -0.162 -1.636 0.030 -0.873 1.476 +4 0.838 -0.215 0.633 -0.548 2.392 0.614 -0.484 -0.752 0.270 0.674 -1.308 0.011 0.743 0.334 0.624 1.491 -3.109 0.218 -2.351 -0.045 1.299 -0.129 -0.934 0.826 0.140 0.189 2.157 -1.507 +0 -0.522 -0.747 -1.507 0.992 -0.175 -1.246 -0.826 -0.867 0.043 1.067 -0.348 -2.253 -0.189 -0.156 -0.225 -0.627 0.217 0.066 -0.025 0.413 -0.430 -0.487 1.380 0.559 0.827 1.226 0.526 0.928 +1 -1.245 0.233 -0.608 -1.014 -1.340 0.432 -0.681 0.968 0.994 1.374 -0.852 0.475 0.632 -0.474 -0.772 1.610 0.227 0.636 -0.822 -1.219 -0.569 -0.124 -1.064 -0.010 -1.689 -1.120 0.671 0.604 +2 -0.434 -0.066 0.195 -1.333 0.223 -0.053 0.217 -0.031 1.327 -1.374 0.632 -0.319 0.028 -1.506 0.006 -0.073 -2.104 0.099 0.248 -3.023 0.872 -0.298 0.729 0.074 2.087 -0.034 0.601 0.644 +3 -0.175 -1.863 -0.183 1.162 0.215 0.406 -2.446 -0.610 -1.694 -2.428 0.273 0.058 -0.245 -1.572 -0.286 -0.763 -0.018 0.140 -0.449 0.737 0.094 1.058 -1.888 -0.571 1.352 -0.629 0.288 1.075 +1 -0.232 -0.565 -1.591 -1.889 0.367 -0.228 -0.364 -1.542 -0.966 0.045 0.285 -0.164 1.379 -0.575 1.287 0.217 0.108 0.933 -0.624 -0.329 -1.224 0.935 0.336 1.098 0.205 1.103 -0.747 0.987 +2 1.619 -0.162 0.339 -0.056 0.676 0.334 1.557 1.312 -0.012 -0.472 1.760 -1.064 0.264 0.669 0.050 -0.331 -0.020 0.089 1.809 0.425 2.391 -0.235 -1.628 -0.765 0.213 0.103 -1.509 0.164 +0 0.370 -0.699 -0.101 -0.816 1.551 -1.002 -0.607 0.622 0.016 -0.129 1.228 0.358 -0.193 -0.425 -0.777 -0.698 -0.409 -0.433 -0.742 0.810 0.842 -0.905 1.065 -0.606 -1.555 -1.053 0.957 1.191 +3 0.205 -1.623 1.888 -0.164 0.698 -0.836 0.308 -1.303 0.452 -0.356 0.029 0.698 -1.233 -0.684 0.457 2.098 -0.138 0.591 -0.107 1.942 -1.747 0.095 -1.626 -2.507 0.297 0.139 -0.285 -0.106 +3 -0.416 0.312 1.420 0.813 -1.880 -0.988 -0.558 0.457 0.056 2.853 0.654 0.459 0.572 0.191 1.177 -0.226 -1.119 0.453 1.063 0.170 -0.338 2.583 -1.220 0.003 0.702 -0.107 0.093 -0.286 +4 -0.921 -0.777 -1.711 0.134 -1.785 0.394 1.358 0.530 0.052 1.534 0.327 -0.863 0.125 1.960 -1.093 -1.380 0.615 -0.224 -0.532 -1.259 -1.950 -1.456 1.615 -0.009 -1.962 1.184 1.235 1.229 +3 -0.829 0.322 0.559 0.453 -1.094 0.334 0.359 1.486 1.974 1.240 -0.458 -0.772 -0.201 -0.572 2.094 0.955 0.623 0.803 1.371 0.618 -0.191 0.742 0.734 -1.117 2.401 -0.614 1.542 -0.161 +3 1.442 1.780 0.259 0.071 0.935 -0.483 0.247 -0.652 1.893 1.086 1.783 -0.491 0.908 0.423 0.306 1.656 -0.912 -0.606 -0.534 -1.444 -1.992 -0.170 0.744 -0.223 -0.818 1.214 -1.135 -1.849 +1 1.325 0.748 0.994 -0.890 -0.252 -0.105 -0.447 1.784 -0.699 -0.562 -0.396 1.061 -0.023 -0.849 -0.144 -0.132 -1.515 0.114 0.249 -1.548 0.147 1.011 -0.748 0.310 1.565 1.681 0.672 1.508 +0 -1.505 0.015 -0.808 -0.722 0.700 0.578 1.314 -0.298 -0.795 0.422 0.679 -0.324 -0.754 -0.137 0.392 -0.789 -0.324 0.097 -1.352 -0.607 -0.256 1.365 -0.513 -2.273 -0.531 1.089 -0.936 0.286 +3 0.260 -0.371 2.266 0.695 -0.011 -0.260 1.020 1.328 -0.616 -0.834 0.164 -0.187 -0.998 -0.422 -0.638 -0.509 0.827 0.768 2.668 -1.461 0.729 1.168 -1.226 -2.096 -0.898 -1.047 -0.414 1.231 +3 -0.866 0.564 1.425 0.734 1.879 -1.057 -0.227 -1.525 -0.472 0.512 0.263 -0.551 -0.126 -1.980 0.038 1.555 -1.544 -0.025 1.367 -1.022 -0.199 1.165 2.598 0.262 0.346 -1.172 0.364 -0.316 +0 -0.974 1.010 0.665 -0.231 -0.460 0.661 -0.911 0.202 1.199 1.520 -0.525 -1.587 -0.707 0.816 -0.741 1.083 -0.743 -0.557 -0.173 0.945 -1.584 0.280 0.262 -0.991 0.356 -0.180 -1.131 -0.074 +4 -0.291 1.698 -0.270 1.101 -1.821 -0.350 0.277 -1.081 -1.754 0.442 -0.826 0.350 -1.444 1.406 1.944 -2.082 -1.751 0.934 -1.213 1.036 -0.942 -0.020 -0.645 0.151 -0.848 -0.679 -0.426 -0.015 +2 -1.142 -1.586 -0.808 -0.189 -0.517 0.844 -0.682 0.215 0.175 0.372 -0.433 1.340 -0.059 0.660 2.386 -0.840 -0.442 -1.162 -0.058 0.776 -1.145 0.318 1.413 0.969 1.873 -1.523 0.736 -0.315 +0 0.253 -0.190 -0.797 1.564 -0.516 0.781 -2.029 -1.382 0.889 0.682 -0.187 0.814 -1.414 -0.787 1.108 0.040 0.553 0.226 -0.560 0.126 0.036 -0.177 0.197 0.942 1.057 -0.990 -0.163 -0.777 +2 -1.338 -0.873 -1.331 0.375 1.038 1.136 -0.356 0.179 0.380 -0.569 -0.564 -0.826 -0.139 0.786 -0.938 0.178 -0.985 -1.254 -0.208 -1.031 0.785 -0.157 -0.623 -0.648 -0.156 0.385 -3.086 -1.114 +0 -1.289 0.015 -0.487 0.119 1.136 -0.075 -0.551 0.029 0.913 -0.965 -1.784 -0.623 -0.490 -0.103 -0.456 -0.559 -1.031 -0.913 1.190 -0.486 -1.673 -0.668 1.015 -0.503 -0.078 0.225 -0.178 -0.910 +2 0.201 0.607 -2.182 -1.625 0.151 -0.196 0.503 -0.394 0.343 0.422 0.350 0.893 0.399 -1.689 0.979 3.082 -0.518 -0.802 -0.498 -0.582 -0.315 0.311 1.139 -1.461 -0.100 0.067 -0.895 0.593 +0 0.777 0.507 1.466 -0.544 -0.663 -0.368 -0.930 0.054 0.156 -0.333 0.547 0.576 -0.730 0.575 -1.154 -0.928 1.431 -0.227 -0.202 1.667 0.175 -0.259 -0.900 0.465 1.449 -0.067 1.289 -0.145 +3 -1.876 0.351 1.424 -0.787 0.851 -0.336 1.364 -0.415 0.180 -0.497 0.991 0.606 -0.551 -0.427 -0.051 2.607 0.469 0.306 -1.440 -1.563 -0.164 0.945 2.076 -0.995 -1.076 -0.288 -0.981 1.142 +2 1.061 0.520 0.511 -0.223 0.388 -2.143 0.398 1.658 0.781 0.664 0.773 1.209 -0.199 0.985 0.457 0.064 1.351 -0.688 -0.650 -2.357 -0.929 -0.440 -0.050 0.449 0.881 -1.329 0.110 -0.983 +2 1.180 0.454 1.130 -1.472 -0.552 -0.260 0.543 -1.163 -0.439 0.155 0.182 0.341 1.583 1.291 -0.106 1.614 1.657 -0.995 1.940 -0.903 -0.205 0.363 0.877 1.189 0.277 -0.173 -1.655 0.421 +0 0.236 0.083 0.749 0.570 -0.104 1.005 -0.676 -1.289 0.914 0.414 0.565 -0.691 -0.362 0.695 0.109 1.432 0.663 1.154 1.462 1.590 0.764 1.155 1.350 -0.192 0.423 -1.149 -0.279 -0.089 +4 0.658 2.246 0.702 -0.745 -0.157 -0.507 0.008 1.098 -0.324 -0.569 0.516 -0.815 -0.874 0.382 -1.792 -0.483 0.856 -1.834 -1.761 -1.434 -2.731 1.897 0.120 -0.670 -1.290 2.201 0.655 -0.128 +0 -1.052 0.224 0.280 0.521 -0.069 -0.542 -1.798 0.044 -0.232 -0.832 1.250 0.759 -0.427 -0.488 0.260 0.895 -1.341 -0.860 0.592 -1.029 0.788 -0.852 0.458 -0.103 0.693 -0.054 -1.045 -0.718 +3 0.138 1.292 -0.173 -0.829 -0.892 0.478 -0.213 -0.379 0.815 1.254 -0.275 -1.102 1.148 1.875 -1.218 -0.565 1.919 -0.241 2.028 -0.149 0.388 -1.283 -2.092 -0.249 0.225 0.651 1.466 -0.339 +0 -0.967 -0.161 -0.347 0.410 0.864 -1.448 -0.549 -0.429 -1.154 -0.856 0.741 0.401 -0.673 -0.573 1.188 0.919 0.041 -0.567 1.096 -0.241 -0.667 -0.981 -1.206 -1.037 0.321 0.315 1.866 1.170 +4 -1.707 -1.098 -0.422 -0.491 -0.295 -0.560 -0.959 0.655 -0.988 -0.958 -0.921 1.196 -0.298 -2.335 -1.180 0.628 1.920 0.326 -0.210 -0.542 0.149 -0.962 2.833 -1.278 -1.122 -0.860 -1.374 0.042 +1 0.258 0.532 0.505 -0.672 0.238 -1.295 -0.959 -0.087 -1.061 -1.273 -0.712 1.146 1.557 -0.033 0.582 -0.563 -2.066 -0.894 -0.308 0.141 -0.101 -0.282 -0.575 0.344 -1.364 0.802 -0.586 2.067 +3 0.332 -0.110 0.581 -0.502 -1.101 0.406 0.268 -2.547 -1.181 -0.672 0.525 -1.523 -0.320 1.938 0.067 0.129 -0.061 0.982 0.042 -0.220 0.152 1.326 0.402 0.057 1.178 -0.522 -3.202 0.448 +1 1.600 1.122 0.891 0.269 -0.628 -0.515 0.235 0.439 -0.969 1.246 1.408 0.794 1.504 0.638 0.274 0.051 -0.317 -0.005 -1.041 -0.084 -1.168 -0.801 1.229 1.182 -0.837 0.870 -0.588 -0.664 +0 -0.011 0.936 0.701 0.123 -1.090 -1.435 0.107 -0.543 0.919 0.746 -0.185 -1.158 0.511 -1.018 -0.758 -0.979 -0.139 1.170 -0.005 -0.443 -1.526 0.552 -0.780 0.118 -0.354 0.838 -0.157 -0.059 +4 -0.311 0.154 2.081 -0.282 -0.247 0.150 -0.183 0.791 1.469 -0.247 -0.857 1.975 0.120 -1.705 1.862 1.126 1.601 1.434 2.645 -1.037 -0.796 -0.492 -0.521 0.123 -0.490 -1.190 -0.874 -0.294 +4 -0.942 -1.023 2.026 -0.379 -1.015 1.190 -0.147 -2.027 2.083 0.584 -1.512 1.396 -1.048 -1.108 -0.892 1.940 2.045 -0.548 1.706 -2.287 0.914 1.364 1.506 0.077 0.566 -0.717 0.025 -0.164 +2 -0.360 -1.173 0.666 0.814 -0.539 -0.512 -0.852 0.705 1.295 -0.967 -0.927 -0.148 0.830 0.504 1.183 -2.353 -0.468 1.076 1.681 -0.197 -0.516 -0.856 0.727 1.197 0.339 -2.218 0.408 -0.500 +1 0.453 1.017 1.868 -1.376 0.597 1.034 1.105 -1.139 1.256 0.156 -0.211 0.287 -0.266 -0.259 -0.451 0.107 0.702 -0.297 -1.193 -0.939 0.874 -1.344 -0.801 -0.278 0.265 -1.082 1.048 1.836 +4 0.702 -0.454 -2.021 0.657 1.248 -0.903 0.433 1.218 -0.679 -1.910 1.307 -1.562 0.169 0.553 -0.261 2.156 0.116 0.523 -0.373 0.364 0.432 -0.234 -0.911 -1.357 -0.717 1.679 -1.711 1.624 +3 -0.730 1.274 -0.622 0.031 1.119 -0.737 -0.527 1.498 -0.446 1.457 -1.048 -0.463 0.045 -1.769 -0.424 0.207 -0.157 -0.243 2.189 -1.105 1.988 1.169 0.416 -0.601 -2.108 -0.792 1.057 -1.369 diff --git a/examples/multiclass_classification/multiclass.train b/examples/multiclass_classification/multiclass.train new file mode 100644 index 0000000..e5827e4 --- /dev/null +++ b/examples/multiclass_classification/multiclass.train @@ -0,0 +1,7000 @@ +3 -1.047 0.537 1.186 0.719 0.996 -0.757 -1.422 1.501 -0.323 -0.251 1.328 0.556 0.456 2.165 -0.644 0.928 0.057 0.269 1.528 0.508 0.538 1.073 -0.365 -0.839 -1.045 -1.966 2.056 -1.103 +1 -0.151 -0.221 -0.090 -0.762 -0.231 -0.298 0.602 1.190 0.744 -0.428 -1.867 -0.229 0.640 0.972 -0.236 -0.580 -1.206 0.815 -1.529 0.890 -0.581 -1.168 1.394 -1.243 -1.437 1.976 0.595 0.405 +2 -0.568 -0.449 1.564 0.636 1.070 -0.235 0.452 -0.077 0.614 -0.113 1.778 -0.236 0.426 -1.801 1.191 -1.174 -0.646 -1.238 -1.425 0.972 -0.790 -1.459 -1.520 1.108 -0.363 -0.050 0.922 0.591 +4 -0.361 -1.308 1.115 1.569 0.286 0.303 -1.587 0.800 0.931 2.740 0.274 -1.031 -1.494 -0.239 -1.150 -0.919 -1.307 -0.292 -0.158 2.593 0.315 1.228 -0.039 -0.520 -0.092 -0.283 1.446 0.448 +1 0.387 -1.660 0.684 -0.752 -0.418 0.227 -0.694 -1.487 -0.314 -1.304 -1.520 0.647 0.030 0.268 0.564 0.313 -0.684 -0.197 -0.768 0.333 -1.308 -0.872 0.052 -0.260 -1.063 -2.902 -0.400 -0.063 +2 -0.602 0.140 -0.091 1.836 0.463 -1.306 -0.919 -0.437 -0.700 1.419 -0.558 -0.037 0.601 2.791 -0.146 1.661 -0.431 0.976 -0.837 0.801 -0.372 0.912 0.806 0.626 0.428 -0.477 0.725 -1.602 +4 1.481 0.605 -1.118 -1.205 -0.151 -0.281 2.107 -0.114 1.748 0.379 0.829 -2.947 2.369 -0.755 1.112 -1.579 1.661 -1.564 -0.985 -0.459 -0.389 -0.334 0.377 -1.075 0.327 1.717 -1.068 -0.107 +1 -0.049 -1.430 -0.503 0.431 2.028 0.302 0.305 -0.507 0.457 0.804 -0.409 -0.613 0.143 -0.115 0.312 2.146 1.583 0.103 -1.465 -0.460 0.932 0.332 -2.201 -0.447 0.852 -0.345 -0.588 0.042 +4 -2.193 0.020 -0.238 1.858 0.490 -0.305 -0.197 -0.208 2.217 0.675 -1.407 -0.244 -0.024 -1.108 -1.783 0.596 -0.805 1.249 0.008 -0.474 -2.417 1.222 0.294 -0.448 -0.890 1.599 -1.064 -0.369 +1 -0.214 -0.979 -1.833 0.574 0.650 0.343 -0.215 -0.319 0.778 -0.101 1.125 -0.099 -0.135 0.371 1.392 1.177 -0.048 -0.374 -1.208 0.692 -1.784 1.705 0.843 -0.746 1.231 0.548 0.468 -0.267 +4 2.437 0.225 -1.097 -2.233 0.917 -0.688 0.267 -1.385 1.920 -1.574 -2.109 0.782 -1.726 0.803 0.123 -2.135 1.297 -0.308 -0.525 0.243 -0.025 -0.475 -1.121 -0.374 -1.085 -0.528 0.651 -1.187 +0 0.417 0.836 -0.280 -0.088 -1.083 0.556 0.591 -0.656 -0.272 -0.206 -1.050 0.643 1.227 -0.934 1.340 0.729 1.471 -1.422 -1.523 0.580 0.387 -0.196 0.214 1.175 -0.151 -0.317 -0.301 -1.289 +0 0.254 1.685 -0.312 -0.190 0.283 -1.184 0.688 0.071 0.090 0.543 0.376 0.411 -0.390 1.569 -1.462 -0.686 -0.553 -0.721 -1.747 1.644 0.643 -0.631 0.697 -0.161 -0.291 -0.960 0.017 -0.632 +0 0.753 -1.115 1.510 0.450 0.655 0.446 -1.507 -1.198 -0.510 0.644 -1.380 -0.175 -0.177 0.793 0.675 -1.010 1.447 -1.163 1.092 -0.550 0.164 0.226 0.198 0.674 0.391 0.784 0.465 -1.264 +1 -1.739 -0.166 0.378 0.090 0.481 -0.969 0.241 0.294 -0.022 -1.419 -0.231 1.686 1.732 -0.796 -0.078 1.085 0.245 -0.297 0.427 -0.507 -0.352 1.606 -0.224 1.410 1.044 0.563 -2.057 -0.330 +0 0.593 -0.734 -1.328 -0.434 -0.640 0.732 -0.547 0.023 -0.916 -0.531 -1.010 -0.374 0.521 0.327 1.317 -1.339 -0.135 -0.565 0.087 0.115 -0.911 0.284 -0.144 1.345 -1.442 -0.585 1.303 0.201 +3 -1.905 -1.074 -0.092 0.514 0.182 0.560 -1.079 -1.589 0.085 -1.229 -0.865 0.932 0.189 -0.568 -0.698 0.621 0.212 -1.215 -1.948 1.882 -1.222 0.528 1.511 0.148 -0.600 -1.426 -1.440 0.377 +4 -0.103 -0.490 0.042 2.637 1.154 1.163 0.436 0.166 -2.396 -0.679 -0.481 -1.875 -1.157 -1.981 -0.038 0.819 1.107 0.415 -0.963 -1.932 -1.757 -1.269 -0.889 -0.160 -1.014 0.339 -0.232 0.011 +0 0.746 0.586 0.224 1.729 0.248 0.067 -0.509 -0.263 -0.863 1.197 0.479 -0.170 -0.208 1.272 0.699 -0.778 -1.452 -0.227 0.265 -1.714 -1.747 -0.403 0.758 0.216 0.099 -0.334 0.929 0.705 +4 -0.089 -0.276 2.882 -1.629 -0.977 -2.146 0.516 1.780 1.075 -1.754 0.169 1.032 -0.320 -1.196 0.914 0.331 1.397 0.802 3.286 0.983 -0.254 -1.427 -0.677 -0.057 0.394 1.085 -1.722 1.524 +2 1.448 -0.541 0.847 -1.935 -0.758 -1.373 1.635 1.650 -0.686 -0.057 -0.791 0.488 -0.584 -1.115 -0.807 0.158 -1.145 0.753 -2.377 -0.266 0.202 -0.330 0.387 1.217 0.226 -0.436 0.153 0.392 +4 -1.965 0.188 0.799 -0.490 -1.208 1.057 -0.870 0.951 -0.041 1.619 0.404 -1.218 1.736 0.751 -0.948 0.742 1.215 0.195 -1.226 -0.232 -0.130 -0.678 -1.759 -1.224 0.564 -0.594 -1.281 2.319 +4 -0.299 2.875 1.267 -0.698 1.783 0.633 -1.419 0.493 -0.255 0.039 -1.611 -0.588 0.320 0.236 -1.454 0.480 2.028 0.579 -0.618 1.345 -1.771 -0.953 -0.558 1.351 0.153 -1.218 -0.705 0.567 +2 -0.582 -0.918 0.494 -1.531 -1.419 1.507 0.199 0.154 -0.470 -0.133 1.140 -0.630 -0.799 1.273 -0.886 -0.608 0.990 0.606 0.612 -0.594 -0.751 -0.053 -0.075 -1.134 0.274 -2.292 0.125 -1.843 +2 -0.054 -0.763 0.310 -0.214 -0.848 0.102 0.066 -0.143 1.092 0.376 2.631 -0.444 0.068 -1.159 -0.499 -1.896 0.481 0.942 -0.279 -1.661 2.141 -0.441 -0.482 0.764 -0.980 -0.725 0.678 1.293 +4 0.843 0.506 0.846 2.489 1.612 -1.286 2.852 0.219 -1.278 1.458 1.025 1.102 -0.529 -1.346 1.740 0.203 -0.292 1.472 1.457 0.279 1.821 -1.872 -0.830 0.518 1.476 2.592 -0.791 -0.410 +3 1.232 -1.267 0.572 -0.557 0.352 -0.741 0.145 -0.947 0.970 0.055 1.789 0.305 -0.999 -1.589 1.421 -0.598 -0.986 1.344 -0.435 0.913 0.353 -1.915 0.640 -0.973 -1.233 -2.217 -1.155 -0.357 +1 0.514 -1.073 -0.438 -1.002 0.640 1.023 0.669 0.166 0.378 -0.742 -0.444 0.006 -0.277 0.001 -1.618 0.881 -1.072 -2.108 -0.715 -0.216 2.018 -0.489 0.210 0.321 -1.515 -0.105 -0.264 0.084 +4 -1.709 -1.164 -2.232 -1.414 -0.848 -0.171 0.552 -0.418 0.289 -0.165 2.072 -1.423 2.082 -0.804 0.593 -0.738 -0.648 0.119 -1.087 -0.136 0.209 -2.223 -1.240 -0.810 2.171 -0.891 0.215 1.028 +3 0.773 0.381 -0.271 -0.638 0.946 0.650 -1.458 0.328 0.426 -1.397 0.661 1.687 -0.985 1.097 -2.209 -0.712 2.667 0.853 -0.207 0.084 0.392 0.272 0.149 0.894 0.789 2.017 -0.896 -0.166 +0 0.302 -0.528 -0.550 -0.078 1.455 0.147 -0.111 -0.521 0.031 -0.001 0.603 -0.531 -0.812 -0.443 -0.087 -0.021 -0.010 -0.991 -0.085 0.684 1.272 0.984 -0.061 -0.158 1.243 -0.110 0.867 -0.029 +2 0.972 -0.016 -0.713 0.539 -0.886 -1.847 -1.651 -0.065 -0.308 -0.322 0.340 -2.054 0.534 -0.749 0.046 0.736 1.418 -0.960 0.944 0.569 -0.195 0.535 -0.503 -1.091 1.081 -0.235 1.763 1.519 +3 0.523 -1.447 -1.080 0.217 0.882 -0.331 -0.233 0.787 0.829 -2.075 1.483 -0.327 -0.747 -0.468 -1.365 2.547 -1.305 -1.499 -0.262 -1.380 0.122 1.642 0.165 0.339 -0.291 -1.041 -0.292 1.055 +3 -0.312 -0.619 -0.693 -0.871 1.401 0.680 1.248 1.449 -2.350 0.343 -0.586 1.117 -0.741 -1.054 -1.434 -0.303 0.915 0.256 -1.203 2.153 0.650 0.777 1.285 -1.640 -0.911 0.823 -0.811 -0.275 +3 0.370 1.368 0.141 -0.506 1.524 -0.490 -0.677 -1.394 0.390 -3.246 -0.736 1.549 -0.305 -0.015 0.221 0.031 -0.009 -0.167 -0.005 0.368 -1.754 1.186 1.026 1.436 0.625 -0.880 0.231 0.316 +2 1.220 0.627 1.028 -0.446 0.241 2.210 0.462 -0.856 -0.510 1.816 -0.022 0.742 -0.505 -0.915 -1.079 0.391 2.092 0.332 0.539 -0.190 -0.779 -0.654 -1.059 -0.324 -0.660 0.125 1.746 1.346 +2 -0.069 -0.466 0.420 0.228 -1.282 -0.228 -0.524 -0.852 0.445 0.153 -0.493 0.387 0.428 -0.757 2.677 0.729 -0.594 0.594 -0.956 -0.329 0.266 0.381 0.760 -2.938 -1.082 0.848 -0.206 0.769 +0 -0.511 1.049 0.201 0.242 -1.166 0.843 0.049 0.470 -0.091 0.130 -0.887 -0.153 -0.420 -0.706 0.135 0.363 0.251 -0.102 0.411 0.242 0.098 0.166 1.183 -1.205 -0.319 0.338 -1.583 1.383 +2 0.213 -1.299 1.941 1.065 0.424 0.213 -1.780 0.422 -0.530 -1.640 -0.484 0.241 0.373 -1.178 -1.362 -0.303 0.571 -0.793 -0.444 0.188 -0.820 -2.007 0.311 -1.747 0.330 -0.221 -0.229 -1.103 +3 1.298 0.878 2.254 0.374 -1.148 0.397 -0.034 2.866 0.031 -0.734 0.506 -1.312 0.556 -0.438 0.344 -1.663 0.426 0.654 0.486 -0.463 -0.420 -0.558 0.528 1.501 -0.138 1.336 -0.570 1.205 +3 0.257 0.168 1.690 -0.167 -1.693 0.067 -0.136 -0.456 0.698 -0.155 1.098 -0.484 0.652 -1.803 0.526 0.103 0.895 2.257 1.057 -1.783 -0.441 -0.971 -0.316 1.717 -2.034 0.665 -0.193 -0.892 +2 -0.325 -2.244 1.647 -0.809 -0.184 -0.607 -0.158 0.931 0.097 1.451 -0.141 -1.502 -0.691 -1.583 -0.160 -0.570 0.201 -0.264 -1.793 0.274 -0.284 -0.186 -0.567 1.061 -1.277 0.405 -0.907 1.936 +4 0.431 -0.614 -0.993 -1.063 0.840 -1.295 -0.928 1.822 -1.835 -1.416 2.166 0.089 0.039 -2.192 -1.302 1.289 -2.008 0.468 0.248 0.189 1.144 -0.455 1.863 1.354 0.050 0.122 0.988 0.353 +1 -0.361 1.733 -1.154 -0.497 -0.452 0.299 0.661 -1.337 -0.685 0.448 -0.665 -0.402 0.173 -0.980 0.278 0.613 0.038 1.425 0.725 -0.041 -0.321 1.755 -0.661 -0.272 -0.410 0.398 2.255 0.247 +3 0.172 1.277 1.425 1.621 -2.338 -0.151 0.157 -0.643 0.224 -0.047 1.128 -0.901 1.123 -0.229 -0.338 -1.929 1.314 1.513 0.144 -1.241 1.410 1.227 -0.588 1.133 0.691 0.071 -0.514 1.262 +4 1.204 0.788 1.863 -1.773 -0.486 -0.906 0.777 1.573 -0.372 0.599 1.444 0.326 0.701 -1.983 1.566 -0.299 2.670 -0.851 0.475 0.826 -2.502 1.777 0.743 -1.494 0.370 0.364 -0.134 -0.596 +2 1.094 -0.519 -0.889 1.178 -1.148 -0.502 0.709 -0.348 0.997 -0.745 -0.438 -1.650 0.328 -2.558 -1.181 0.656 0.647 -0.311 -0.637 -0.872 -0.848 0.688 -1.776 0.401 1.191 -0.358 0.767 0.266 +4 -0.573 -1.484 -0.626 0.749 1.370 -0.495 -0.913 -0.522 1.229 1.805 -1.660 2.326 -1.372 0.474 2.029 -0.608 0.368 -1.096 1.659 -2.070 -2.184 -0.056 -0.193 0.710 0.334 -0.261 0.026 -2.769 +0 0.551 -1.128 0.758 -1.441 0.749 -0.313 -0.655 0.299 0.329 -1.614 0.167 0.475 0.028 0.434 0.847 -0.993 -0.996 -0.003 -0.989 -0.546 0.870 -0.220 -0.077 1.608 0.163 1.119 1.020 -0.472 +4 0.924 -3.128 0.273 2.366 -0.446 -0.361 -1.064 0.374 1.294 -0.330 -1.284 1.031 -0.686 0.050 1.687 0.173 1.035 0.868 -0.619 -2.266 1.620 -0.655 1.028 0.468 0.621 -0.861 -1.138 -1.172 +2 0.628 1.538 1.195 0.958 0.237 0.721 -0.411 -0.221 1.721 -0.557 -0.631 -0.362 -0.549 -0.465 0.242 0.275 0.911 2.188 -0.368 -0.066 -1.838 -1.292 -0.146 0.464 0.491 0.617 1.257 1.706 +4 0.057 0.167 0.626 -0.756 2.587 1.332 -0.532 -1.091 -0.088 0.182 1.066 -1.169 0.285 -1.529 0.436 -0.108 1.937 -1.546 1.371 -1.704 -1.327 -1.677 0.250 -1.174 -1.348 0.203 0.452 0.040 +1 0.197 0.311 1.700 1.072 0.191 0.941 -1.033 0.398 1.809 -0.218 -0.848 -0.652 -1.090 -0.785 -0.371 -1.406 0.016 0.902 -0.908 1.519 0.511 1.031 -0.658 0.855 -1.092 0.890 0.172 0.553 +2 1.381 0.078 0.304 -1.233 -0.426 -2.033 -0.581 0.324 1.575 0.404 -1.806 0.893 -1.026 -0.665 -1.216 0.388 0.892 1.043 1.193 -0.414 -0.532 1.602 0.748 0.542 -1.002 -0.815 0.751 0.513 +1 -1.080 1.415 -0.709 0.336 -1.271 -0.122 -0.111 -0.315 0.385 -1.007 -0.884 0.438 1.342 -0.023 0.504 0.758 0.831 -0.954 -0.777 -0.690 1.929 0.508 0.463 0.366 -1.321 1.938 0.479 0.723 +2 -0.650 0.624 1.416 0.461 0.960 2.345 0.225 -0.653 -0.136 0.645 0.632 -1.023 0.151 0.908 0.117 -0.754 -0.539 -0.642 -0.135 1.130 0.665 -1.032 -2.322 -1.998 -0.529 -0.107 -0.353 1.185 +2 0.223 -0.727 -1.880 0.807 -1.299 -0.408 1.674 0.611 0.390 0.944 0.907 1.581 -0.602 -0.377 -0.922 -0.033 -0.132 -1.755 -0.654 1.439 0.244 0.715 -0.121 -1.887 -0.129 1.042 -1.186 0.747 +1 -1.065 -1.213 1.365 0.319 0.353 0.264 0.148 -0.225 -0.709 -0.539 2.166 1.804 0.927 -0.067 0.565 -1.424 0.996 -0.240 0.015 0.098 -0.179 0.804 1.595 0.857 0.066 -1.066 0.040 -0.077 +3 0.983 0.012 0.211 1.046 -0.477 -1.374 0.070 0.308 -0.263 1.474 -2.486 1.120 0.791 0.457 -3.336 1.284 -1.026 -0.508 -0.171 0.407 0.639 0.893 0.816 0.253 0.416 -1.091 -0.751 0.895 +0 0.754 0.070 1.055 -0.516 1.360 0.055 -0.148 0.902 -1.316 -0.479 0.302 0.482 -2.282 -0.803 -0.074 -0.098 0.613 -0.346 -1.025 0.545 -1.098 -0.521 -0.798 -0.483 -1.755 -0.438 0.890 -0.215 +4 -0.621 1.433 -0.673 -2.307 -0.526 0.104 0.163 3.180 -0.109 -0.607 -0.717 1.549 -0.031 0.071 -1.516 -0.208 -0.384 0.382 -2.356 0.542 1.469 -0.448 -1.149 0.997 -1.409 1.039 1.496 0.145 +3 0.546 0.905 1.070 -1.347 2.293 1.000 -0.573 0.163 -1.697 -0.617 0.538 0.063 1.743 -1.479 -0.117 -1.137 1.535 0.265 0.008 -0.133 -0.877 1.198 0.171 0.245 0.582 -0.862 1.995 1.570 +2 -0.962 -1.447 0.535 0.514 1.154 -2.144 1.172 1.113 1.209 0.120 0.222 0.217 -0.913 -0.871 0.226 -0.249 0.716 -0.357 -1.469 -1.422 -0.761 0.726 -0.519 0.298 -1.672 0.162 0.038 1.190 +0 0.679 -0.346 -0.005 1.078 -0.025 -1.036 -0.066 -0.045 0.427 1.432 -1.178 -0.010 -0.471 0.528 0.431 -0.262 -0.713 1.555 -0.090 -0.729 -2.561 0.193 -0.034 0.720 0.326 0.496 -1.105 -0.909 +0 0.382 1.600 0.194 -1.585 -1.172 0.097 -0.549 1.046 -0.411 -0.570 0.058 0.433 0.023 -0.404 -1.386 -1.649 -0.239 -1.138 0.326 -0.656 -0.146 0.720 0.049 0.416 0.304 -0.589 -0.664 1.513 +2 0.532 0.189 0.491 -0.137 -1.183 -2.042 -0.617 0.791 -1.039 1.061 1.391 0.473 -0.166 1.025 1.157 -0.225 0.085 -0.813 0.286 -2.067 -0.293 -0.853 -0.088 -0.795 -1.913 -0.654 -0.476 1.065 +2 -0.605 0.498 0.537 0.642 -1.694 1.007 -0.570 -0.358 2.638 0.302 -0.386 -2.205 1.390 -1.345 -1.003 -0.331 1.416 0.773 -0.158 0.308 -0.600 0.140 0.073 0.969 0.967 0.494 -0.362 0.742 +0 -0.320 -1.176 -0.253 1.290 0.227 0.627 1.069 0.387 -0.368 0.100 -1.073 0.057 0.122 1.317 -0.324 -0.324 -1.516 0.648 -1.469 -0.498 -0.710 0.891 0.488 0.708 0.946 -0.639 -0.868 -1.684 +2 -0.188 1.276 -0.796 -1.977 -0.008 0.504 -0.350 0.221 -0.776 -0.927 0.569 0.615 0.584 0.555 0.418 -0.144 0.698 -0.825 1.245 0.898 -1.484 0.818 0.818 2.408 -1.039 -1.325 1.020 0.447 +1 0.284 -0.389 -1.370 0.373 -0.981 0.737 -0.833 -1.053 -1.004 0.291 0.048 1.282 -1.152 1.374 0.711 1.000 0.589 1.364 1.496 0.103 0.262 1.481 -0.539 -0.354 1.173 -0.718 -0.505 -0.720 +4 1.691 -0.581 0.207 -1.798 2.468 1.997 -0.838 1.120 1.211 -0.519 0.710 -1.257 0.102 0.583 0.274 0.213 -0.415 0.242 0.652 -1.342 0.012 0.899 2.414 2.369 -0.517 0.470 0.389 -2.060 +2 0.193 -0.501 -1.080 0.651 0.688 1.254 -0.764 -1.846 0.413 -1.907 -0.049 -1.139 -0.155 -1.433 -0.643 -0.769 0.141 1.310 0.332 -0.435 1.092 0.351 -2.414 -0.610 -0.553 -0.398 -0.311 -0.303 +3 0.628 -0.902 1.673 -1.927 0.533 0.141 -0.177 0.618 -0.727 -0.322 -1.092 1.335 -0.542 -0.577 1.307 -1.708 1.654 -0.105 1.014 0.518 -1.950 0.669 -1.070 -0.245 1.697 -0.562 1.119 -0.964 +2 -0.844 0.074 -0.062 1.404 0.494 -0.771 -0.761 -0.333 -1.564 -0.230 -0.144 0.467 0.612 0.099 -1.630 -1.221 -0.605 0.652 0.619 -0.249 2.360 1.562 1.240 0.701 0.459 0.386 -1.805 -1.051 +1 1.706 -0.052 -0.352 0.115 1.129 -0.251 1.151 0.205 0.735 0.257 0.673 0.877 0.433 1.595 -0.116 1.184 -0.622 0.406 -0.828 -0.813 1.315 2.088 -0.161 -1.006 -0.011 -0.771 1.298 0.603 +3 -1.480 -0.475 1.167 -0.182 -0.264 1.405 -1.953 -0.178 -0.066 -0.797 1.374 -0.910 -0.898 -2.472 1.111 -0.329 -0.328 -0.213 1.193 1.613 -1.732 -0.463 -1.135 -0.224 -0.091 -1.719 0.369 -0.449 +2 0.560 1.537 0.464 -2.525 2.261 -0.264 0.668 0.064 -0.603 -1.204 0.552 0.954 -0.668 -1.400 -0.301 1.764 0.820 -0.133 -1.146 0.305 1.316 0.176 0.245 0.771 0.013 -0.492 0.402 0.637 +3 1.036 0.249 -0.234 -0.472 0.320 -1.694 -0.120 0.169 -1.274 -0.888 1.967 0.528 -1.705 -0.750 -0.029 2.308 0.890 1.090 0.398 -0.985 1.595 0.854 1.275 1.701 1.556 -0.016 0.273 0.193 +3 1.861 1.204 0.967 -1.232 -0.544 -0.869 0.018 0.775 0.437 -0.908 -0.184 1.043 0.511 0.400 -0.878 0.160 -1.135 -0.826 -1.479 1.729 1.769 -1.004 -0.776 -0.318 1.218 0.608 1.381 1.687 +3 -0.191 -0.047 0.615 -0.385 1.040 -2.021 0.461 -0.021 0.967 -1.033 1.692 1.364 -0.464 0.427 -1.099 0.618 -0.835 0.309 -1.001 1.178 0.570 1.987 0.176 -1.526 0.282 2.546 -0.236 0.495 +2 -0.208 0.538 0.390 -0.682 -1.397 1.115 0.029 -1.628 1.829 0.105 -1.442 -0.932 0.516 -1.365 -0.915 -0.580 -0.009 0.468 -0.002 -1.723 1.055 0.148 -1.407 1.039 0.133 0.799 -0.240 -1.166 +2 -1.157 0.882 -1.425 -1.370 1.964 1.112 2.074 -0.370 0.102 0.527 -0.012 -0.746 -0.896 0.214 -1.067 -1.095 0.394 -0.095 0.937 -0.425 -0.581 0.424 -0.812 -0.658 -0.618 -0.210 1.772 -1.377 +2 0.604 -0.559 1.778 0.063 1.260 -0.761 -1.249 -0.234 0.263 -0.511 0.758 -0.210 -2.081 0.553 -0.883 -0.362 1.553 1.773 -0.575 0.578 -0.552 -0.287 -0.606 -1.724 0.095 0.288 -0.671 1.088 +0 -0.641 0.432 0.800 0.754 1.189 0.708 0.351 1.070 -0.027 -0.882 -0.163 -0.745 -0.675 -0.145 -0.792 -0.308 -1.894 0.213 0.001 -0.817 0.659 0.938 -1.608 -0.763 -0.769 -0.940 0.829 -0.194 +0 -2.013 0.411 1.170 0.895 -0.896 0.624 0.704 1.706 0.366 1.026 -0.437 -0.852 0.864 0.878 -0.635 -1.212 0.123 0.044 0.234 0.886 1.029 0.008 -0.995 -0.726 -0.597 -0.120 -0.515 -0.245 +1 -0.285 0.439 -1.057 -0.802 1.649 0.279 0.829 -0.107 -0.687 -0.781 -0.493 -1.547 0.731 -0.128 -1.261 -0.936 -1.339 1.361 0.906 0.994 0.948 0.373 0.868 -1.356 -0.011 0.764 0.776 -0.211 +1 0.796 -0.649 -0.515 -0.215 2.203 0.524 1.920 0.060 0.734 1.793 0.820 0.412 -1.369 -1.014 0.298 1.012 -0.136 0.675 -0.845 -0.636 0.480 0.058 -0.380 -0.533 -1.500 -0.023 -1.458 -0.309 +3 -0.995 -2.529 -1.407 0.053 -0.717 0.129 0.342 -0.624 -0.920 0.911 0.298 -2.676 -1.537 -1.793 -0.159 0.203 -0.171 -0.628 1.022 0.320 -0.912 -0.847 -0.762 -0.347 0.035 0.413 -0.823 -1.356 +1 -1.011 0.414 1.400 0.283 -0.053 -0.940 -1.680 0.545 0.443 1.183 0.570 0.239 0.605 -0.114 -1.604 -0.302 0.073 -1.070 0.936 -0.067 -1.849 -1.121 1.600 0.626 -0.820 0.749 0.279 1.475 +3 -2.638 -1.029 -1.341 -2.140 0.759 -0.653 0.178 -1.384 1.176 -0.953 1.112 1.072 0.257 0.353 -1.012 -1.620 0.191 0.217 -0.376 -0.822 -0.863 -1.479 0.955 -0.697 -0.946 0.631 -0.526 -0.598 +3 -0.236 0.996 1.346 0.609 0.669 1.576 -1.899 -1.479 -1.102 -1.296 -0.140 0.251 0.261 0.501 0.294 -0.127 -0.533 -1.417 -0.086 0.556 1.119 -0.818 2.290 0.882 0.684 0.220 -2.715 0.385 +1 -0.252 -0.988 0.158 1.416 2.086 -0.876 -0.076 0.810 -0.233 -0.433 0.063 0.841 -0.646 -0.985 0.338 -0.028 -1.334 -0.306 -0.389 2.166 0.826 0.730 0.875 -1.026 1.352 -0.059 -0.380 -0.733 +2 1.109 -0.610 0.688 -0.831 1.983 -0.800 -0.882 0.550 0.080 0.327 0.227 -1.090 1.613 0.894 1.359 -0.528 0.417 0.197 1.128 1.160 0.193 -1.518 1.780 0.556 -0.608 0.407 -1.511 1.055 +3 0.203 -0.286 -0.368 0.752 -0.048 1.054 -1.894 1.523 0.895 -0.051 -0.078 0.641 -1.026 0.038 0.155 -2.044 0.348 -1.352 1.083 1.338 1.511 -0.322 -0.336 -1.976 0.680 -1.509 -1.449 -0.304 +1 -1.066 -1.570 0.190 -0.074 -0.327 -0.395 0.658 -0.840 -1.003 0.519 0.801 -0.804 1.267 -0.267 -0.356 0.182 0.082 0.811 2.373 -1.022 -0.367 -0.348 1.929 -1.673 -0.172 0.528 1.085 -0.325 +0 0.021 1.033 1.134 -0.668 -0.068 -1.001 1.502 0.266 0.994 -0.363 1.021 0.744 1.279 -1.344 0.075 1.098 -0.936 0.522 0.571 0.701 0.334 0.618 0.433 1.185 -0.718 0.340 1.219 -0.784 +3 -1.152 -0.600 1.552 -1.508 -0.569 -1.182 0.247 0.744 3.594 1.002 -1.189 0.584 -0.087 -0.449 0.023 0.895 0.835 -1.349 0.044 -0.996 0.162 -0.440 0.820 -0.274 -0.700 0.060 -0.020 -0.158 +0 -0.205 1.226 -0.684 0.271 1.682 0.099 -0.865 -1.259 -0.810 0.646 -0.554 -0.695 0.781 -0.256 0.888 0.582 -0.279 0.922 -0.590 0.873 0.042 0.505 0.352 0.376 0.134 0.672 -0.504 -0.229 +1 0.859 -0.219 -0.792 1.404 -1.439 0.043 -0.065 -0.833 -1.318 -0.127 0.366 1.048 -0.771 -0.784 1.526 -0.468 -0.334 1.219 -1.646 -1.201 1.392 -0.602 -0.534 -0.450 -0.320 -0.459 0.142 1.253 +3 0.153 -0.102 0.696 1.276 1.360 -0.256 -0.146 -0.137 -0.413 2.025 0.312 0.508 0.680 1.214 -0.229 0.512 0.588 -1.560 -0.488 -0.164 0.990 3.492 -0.437 -0.479 0.442 -1.726 -1.785 -0.137 +3 -0.523 1.147 -0.293 -0.720 -0.584 -0.917 -1.497 0.112 -0.892 1.444 -1.455 -0.278 1.412 1.625 -1.961 1.092 -1.037 -0.288 1.461 -0.063 0.666 -1.159 -1.531 -0.340 -0.396 -0.654 -0.236 1.588 +2 0.295 -0.715 -0.387 2.121 1.172 0.380 -0.319 -0.175 -1.402 -0.076 -0.783 -1.852 0.410 0.993 -0.622 -0.248 -1.159 -0.031 -1.389 -0.544 1.062 -1.136 0.630 -0.546 -0.300 0.876 -1.108 1.662 +2 0.457 0.295 -1.484 0.131 -0.243 -0.354 -0.046 1.088 0.458 -1.043 0.497 -0.044 -0.616 -1.240 0.807 -0.570 0.908 -1.077 1.995 2.634 0.542 1.296 -0.453 -0.552 1.194 0.151 -1.372 0.077 +2 0.490 -1.186 -2.598 0.151 1.466 -1.252 -1.050 0.355 0.483 1.122 1.641 0.236 -0.927 -0.896 -0.544 1.031 -0.500 0.962 -1.525 0.287 -0.751 0.522 -0.256 0.798 -0.435 -0.026 0.808 -0.102 +0 -0.171 -0.815 -1.698 0.889 0.425 -1.235 0.963 0.840 -0.671 -0.292 0.250 0.553 -1.095 0.567 0.059 1.111 0.763 0.058 0.917 -1.509 -0.374 -0.973 -0.555 -0.568 -1.263 0.953 0.751 -0.607 +4 0.276 -0.164 -0.160 0.580 -2.138 -0.859 -0.046 1.661 1.774 0.352 -1.023 1.190 0.098 0.696 2.415 3.331 -0.387 -0.309 0.332 0.998 -0.025 0.626 -0.690 0.447 1.074 0.237 0.567 -1.557 +2 -0.607 1.293 1.530 0.104 -0.286 -0.700 0.787 1.281 -1.619 -1.569 0.006 0.448 1.601 0.855 -1.674 -1.267 -0.563 -1.332 -1.387 -0.751 0.992 0.099 0.365 -1.486 0.766 0.420 0.356 -0.323 +1 0.394 1.268 -3.013 -0.350 -1.130 0.484 0.138 0.705 -0.620 -0.247 0.209 0.256 -1.131 -0.219 -1.105 0.272 0.769 -0.362 0.997 0.834 0.758 -1.038 0.131 1.034 0.089 1.471 0.384 -0.581 +4 -1.066 -1.216 -0.374 0.403 1.836 -0.382 -0.758 1.559 1.712 -1.759 -0.518 0.216 0.188 2.216 -0.551 -1.147 -0.774 1.090 -0.013 1.601 0.646 -0.678 0.863 1.203 1.458 -1.403 0.281 -1.115 +2 0.432 1.319 0.876 1.293 0.783 0.144 -1.230 1.541 -0.210 -0.606 0.637 -1.836 -0.739 -0.629 -0.658 1.935 -0.082 -0.639 -0.048 0.854 -1.906 0.114 -0.453 0.270 -1.146 -0.180 -0.137 -1.271 +0 -0.955 -1.303 0.084 0.243 -1.063 0.124 -0.424 -0.099 -0.628 0.488 1.635 0.774 1.393 -1.403 -0.676 0.565 1.508 0.650 -1.026 -0.435 0.333 -0.165 -0.799 -0.290 -0.014 -1.085 0.985 -0.440 +4 0.120 -0.711 -1.391 -1.290 -0.373 1.925 -0.585 1.447 -0.131 -1.181 -0.473 -1.281 -0.856 -0.886 -1.904 1.513 0.211 -0.686 0.222 -0.835 -2.671 2.957 0.190 -0.589 -1.934 -1.494 1.654 1.502 +0 0.982 -0.401 0.481 -0.081 0.265 -0.037 -0.278 -0.721 0.428 0.644 -0.577 -1.096 -0.107 -0.253 1.003 1.007 -1.245 -0.208 0.320 -0.363 1.403 -1.498 -0.678 0.057 -0.739 -0.407 -1.823 1.213 +0 -1.145 -0.291 -0.803 1.849 0.870 -0.911 -0.169 -0.554 -0.625 -0.367 -0.320 -0.416 0.058 -0.289 1.160 0.036 0.001 -0.430 -0.117 -1.270 -0.254 1.230 -0.700 0.334 1.224 0.282 -0.326 0.648 +4 1.589 0.646 1.430 0.706 -0.877 -1.211 1.032 0.295 -1.092 -0.157 -0.255 1.267 -0.163 0.432 -0.254 -0.668 1.179 -0.526 0.825 1.464 0.544 -0.558 -1.312 2.672 1.003 0.652 2.137 1.417 +4 0.771 -0.948 1.030 2.644 0.311 -0.986 0.254 -0.275 -1.077 -1.877 -0.697 -1.760 0.123 1.262 0.702 1.644 -0.713 1.513 -0.941 0.318 -1.415 -0.596 0.508 -0.317 -0.738 -1.733 0.734 -1.492 +1 0.166 -0.007 -0.650 -0.256 -0.731 0.677 -0.912 0.074 0.560 0.150 -0.896 0.699 -0.085 -2.873 -0.983 1.401 1.484 0.526 0.784 -0.778 -0.260 1.235 0.426 0.065 -0.187 1.174 1.122 0.650 +1 0.495 -0.189 0.215 -0.745 0.693 -0.535 1.609 -0.225 -0.095 0.594 0.812 0.920 1.776 0.662 -0.067 -0.530 -0.134 1.484 -1.387 -0.819 -0.219 -0.832 -0.409 1.806 -0.298 0.412 -1.658 -1.102 +2 -0.426 0.133 1.403 -0.716 2.024 0.440 0.705 0.908 0.314 -0.344 0.846 -0.161 0.469 -1.690 0.436 1.246 1.494 -0.194 -0.276 -1.183 -0.447 -1.336 1.189 1.499 -1.340 -0.149 0.821 -0.764 +0 -0.776 -0.480 0.571 1.067 -0.332 0.351 -0.800 0.172 -0.466 -0.009 1.115 0.542 -1.772 -0.895 0.928 -0.288 1.339 -0.345 -1.550 -0.075 -0.415 -0.737 -0.731 -1.068 0.131 0.724 -0.611 0.362 +3 -1.024 1.192 2.493 0.284 1.930 0.830 1.675 0.819 1.759 1.615 -0.486 -0.460 -0.202 -0.616 0.557 1.470 0.525 -0.312 0.243 -1.957 -1.131 -0.301 0.517 -0.245 0.204 -0.233 -0.394 -1.142 +4 -1.769 0.181 0.673 -0.460 1.198 1.155 -1.762 1.063 -2.170 0.025 0.614 -1.494 -0.239 -1.001 1.207 0.270 0.262 -0.227 2.015 -0.911 -0.152 -0.505 -1.055 -1.069 0.406 -0.833 1.838 1.854 +1 0.133 2.798 0.591 -2.259 -0.038 1.237 0.621 0.398 0.571 -0.174 -0.013 -0.233 -0.062 -0.268 0.333 1.462 -1.499 0.214 0.539 0.555 -0.834 -0.507 0.972 0.197 -0.118 -0.366 0.490 -0.101 +0 -0.640 0.508 -0.713 0.226 -0.100 0.215 -0.460 -0.339 0.096 0.202 -0.004 1.705 -0.991 -0.064 -1.574 0.372 -1.115 -0.278 -0.372 0.281 -0.553 -0.595 1.436 0.285 -0.561 -0.251 1.645 -0.235 +0 2.005 0.584 0.792 -1.365 -0.323 -0.257 -0.101 1.273 0.691 -0.166 0.999 1.183 -0.568 -0.597 2.162 -0.240 -0.643 -0.155 1.074 0.718 -0.126 0.612 0.250 -0.142 -0.300 0.148 -0.416 0.071 +3 0.623 1.165 0.183 0.350 -0.399 -0.158 -0.636 -1.035 -1.205 -0.862 -0.931 0.299 0.448 -0.421 -0.839 2.152 -0.441 -0.593 -1.573 0.517 1.819 -0.187 -0.347 0.277 0.308 -2.496 0.648 1.875 +0 -0.061 -1.691 1.122 -0.216 0.137 -0.981 0.646 -0.143 -0.326 1.411 1.309 0.750 0.209 -0.516 -0.289 -0.335 0.392 -0.032 -0.007 1.541 1.144 -0.781 0.297 -0.776 0.603 1.929 -0.356 0.791 +2 0.258 -0.556 -0.016 -0.073 0.275 -0.976 -0.698 1.349 -0.259 0.476 -1.218 1.188 1.152 0.299 -0.614 -1.436 2.274 -0.405 -1.434 -0.597 0.399 -1.247 -0.590 -1.441 1.443 -0.083 -1.452 -0.900 +3 0.548 0.234 0.519 -1.518 0.904 -0.743 -1.647 1.848 1.410 -0.010 1.070 -0.331 -0.469 -0.594 -0.640 -0.259 -0.714 -2.849 -0.183 -0.969 0.297 -0.240 0.635 0.013 -1.266 -1.266 -1.241 -0.864 +2 0.429 -0.966 1.463 1.681 -0.532 -0.741 -2.026 0.484 -0.242 -1.175 0.163 -0.830 0.443 -0.688 -0.807 0.090 -0.370 -1.448 1.171 -0.380 0.860 0.284 -1.884 0.522 -0.386 -0.229 2.198 0.479 +3 0.520 -1.549 -0.782 1.338 0.884 -1.002 0.805 0.129 -0.854 -1.905 -0.046 0.206 1.116 0.682 -1.335 0.071 3.282 -2.310 -0.421 0.505 0.097 0.019 0.723 -0.156 0.561 -0.091 -0.803 0.036 +4 0.004 -0.889 -1.067 -0.608 -0.747 -0.587 0.088 0.323 0.975 0.565 1.955 2.113 -0.493 0.253 0.020 -1.018 0.917 0.826 0.704 0.938 -0.622 1.312 1.261 -2.149 -0.435 0.500 2.867 0.264 +1 0.227 0.883 1.073 0.623 0.841 1.104 0.247 -0.763 0.089 -0.716 1.049 1.202 1.263 -1.150 1.062 -0.573 0.369 0.827 -1.437 0.862 -1.548 -0.199 0.976 1.327 0.809 -0.565 -0.600 -0.143 +2 -1.372 0.039 -0.272 1.014 1.114 -0.170 -1.959 -1.474 1.124 -0.143 1.749 0.897 0.255 0.129 -1.149 -0.631 0.416 -0.345 0.468 1.029 -0.180 -0.432 -0.896 0.835 0.754 2.187 0.177 -0.097 +4 -2.343 1.009 0.251 -1.531 -0.223 -1.927 0.352 -0.597 -1.950 0.166 0.145 2.107 0.828 -2.387 -0.528 1.105 -1.627 -0.772 0.002 -0.528 1.591 0.183 0.033 -0.917 -1.102 0.104 -0.080 -1.217 +2 -0.105 0.510 1.789 0.288 1.731 0.275 0.879 1.317 -0.217 -0.308 0.665 0.976 -0.332 -0.695 -0.028 -0.603 -0.298 -0.208 -1.241 0.032 -1.224 1.093 2.040 0.046 -0.325 0.142 1.874 1.987 +0 0.990 0.268 0.615 -0.781 -1.308 -0.231 0.988 0.045 0.709 0.037 -1.298 0.288 -0.384 1.085 -1.581 1.532 -1.233 -0.283 -1.484 1.014 -1.114 -0.544 1.039 -0.509 -0.078 0.584 0.352 0.214 +4 1.234 0.385 0.154 0.368 -0.476 0.344 -1.652 0.116 0.610 0.610 1.477 -0.827 1.832 2.953 0.901 1.758 0.403 -1.098 -1.518 -2.173 0.643 0.762 0.786 0.510 -0.003 -1.424 -1.704 -0.365 +0 -0.432 -1.165 0.876 -0.497 0.276 0.774 -0.736 0.644 -0.282 -0.345 0.698 0.077 0.090 0.844 -0.122 0.636 -0.576 0.442 0.006 0.573 -1.767 0.477 0.480 0.553 0.730 -0.535 -0.557 -0.862 +1 -0.078 -1.394 -0.378 -1.513 0.083 -0.305 -0.685 -1.060 1.017 -0.160 0.209 0.177 0.108 -0.327 1.518 2.236 1.448 0.021 -0.047 1.093 -0.758 1.710 0.791 -0.280 0.282 0.627 -0.428 1.034 +0 1.161 0.374 -0.295 0.559 -0.667 -0.299 -0.743 0.908 0.780 -0.035 1.731 0.413 0.356 0.249 -1.262 -0.894 -0.055 -0.046 0.590 -0.668 0.071 0.089 0.128 0.016 -1.404 -2.261 -0.942 -0.531 +4 0.038 0.107 -0.416 0.675 1.464 0.283 2.131 -2.278 -0.736 -2.546 1.491 -0.535 -0.049 0.665 -1.432 -0.665 -0.031 -0.142 -0.511 -1.014 1.757 -0.285 0.581 -0.290 0.137 -1.208 -2.555 1.167 +0 0.546 0.245 -0.729 -0.135 -0.144 -0.486 0.057 0.729 -0.495 0.584 -0.239 -0.491 -0.134 1.561 -0.893 0.566 0.137 0.491 0.354 0.098 0.069 -0.986 -0.383 0.785 0.408 -0.832 -0.238 0.079 +2 1.162 0.311 -0.147 0.701 -1.293 1.120 1.262 0.721 0.805 0.255 -2.003 -1.020 -1.390 -0.035 1.320 -0.719 -1.191 -0.753 -1.461 -0.541 -0.572 -0.741 2.292 0.199 -0.214 -0.010 1.080 0.030 +1 0.090 1.100 -0.096 -0.720 -1.157 -0.188 0.681 0.285 0.843 -1.798 0.302 0.133 -1.516 -1.631 -1.188 0.404 1.096 -0.465 0.286 0.223 0.425 1.347 -0.569 -2.057 -0.315 -1.250 -0.239 0.547 +2 1.439 0.284 0.826 0.175 0.552 -0.476 1.830 -0.482 -0.949 0.160 1.873 -0.683 -0.181 0.256 -1.324 -1.376 1.125 0.076 -1.060 -0.314 -0.559 0.008 0.197 -2.148 1.334 0.074 -0.638 0.884 +3 -0.139 1.765 0.651 -1.369 -1.275 0.152 -1.375 -2.002 0.532 0.321 0.451 0.263 -0.065 -0.662 0.516 1.530 -0.501 -1.021 -1.135 -0.867 0.203 -2.140 0.439 0.644 -1.798 -0.979 0.080 -0.143 +4 1.137 -0.572 1.613 -0.137 0.996 -1.711 -0.568 -0.862 1.382 0.931 -1.247 0.239 -1.171 0.321 2.161 1.791 -1.125 -1.004 -0.408 2.877 0.081 -0.888 -0.864 0.794 0.705 -1.160 -0.140 -1.585 +1 -0.313 -0.080 0.310 0.967 0.178 -0.131 0.925 -0.933 0.690 0.606 0.704 0.903 0.361 2.652 0.354 -0.715 -0.019 -0.520 -0.483 -0.444 0.750 -1.583 -1.488 -1.255 -0.145 -1.518 -0.535 1.285 +0 0.476 0.086 1.287 0.116 -0.083 -0.082 1.535 -0.441 0.790 0.564 0.255 0.206 0.059 1.464 0.371 0.778 -0.031 1.476 -0.222 0.225 -1.918 0.176 -0.640 -0.291 -0.831 -0.234 1.525 0.074 +2 0.657 -1.098 -0.011 -0.950 -2.112 2.694 0.802 0.809 0.909 1.425 0.162 0.868 -0.501 -0.172 -1.535 0.202 -0.530 -0.133 0.829 0.302 1.030 -0.591 0.382 -0.640 -0.176 -0.307 -1.014 -0.413 +1 -0.733 -1.033 0.208 -0.164 -0.966 -1.483 0.134 -1.718 0.932 0.211 -0.254 1.915 -0.972 0.250 0.717 0.310 -1.675 -0.787 -1.812 0.213 0.227 -0.310 -0.378 -0.217 -0.624 -1.097 0.954 -0.736 +4 -1.563 -0.610 0.169 0.670 0.437 1.280 -1.529 1.470 -0.714 -1.229 1.037 -1.112 1.414 -2.182 -0.756 0.877 0.672 -2.634 -0.664 -1.716 0.139 0.051 -0.248 -0.107 -1.102 -0.734 1.067 -0.490 +4 -0.526 1.277 1.645 0.706 -0.779 0.876 0.708 -0.749 -0.009 3.238 0.601 -0.220 -0.940 0.126 0.725 2.609 0.835 0.386 1.118 -1.753 -1.326 0.015 0.286 -2.425 2.128 -0.800 -1.333 0.615 +2 -0.159 0.385 -1.549 -1.497 -0.736 -0.511 1.794 0.071 -0.517 0.057 -2.025 -0.904 -0.748 -1.021 1.234 0.660 -1.211 -1.267 -1.259 -0.187 0.200 0.416 -1.014 -0.178 -0.056 1.197 0.774 -1.435 +2 0.407 -0.714 0.860 1.745 0.452 0.315 -0.114 0.100 -0.336 1.633 0.794 -0.347 0.060 2.916 0.272 1.453 -0.577 0.474 -0.624 -0.535 0.794 -0.896 -1.192 0.685 -0.488 -0.280 -1.801 -1.269 +0 0.233 -1.055 -0.226 -0.178 0.193 -0.292 0.466 -1.608 1.105 -0.022 0.764 1.041 0.185 0.867 0.359 0.318 -0.489 1.208 0.109 1.395 -1.116 -0.594 -1.102 1.087 0.597 -1.538 -0.844 -1.134 +3 0.876 1.034 0.159 1.958 1.046 -0.585 0.260 0.860 1.920 0.621 0.289 1.264 -0.219 -0.990 0.704 -0.378 2.716 0.253 0.710 -0.628 1.265 -0.027 -0.125 -0.101 -1.291 -0.251 0.546 -1.545 +2 1.545 -1.735 1.325 0.156 0.192 0.769 -0.468 0.807 1.071 -0.919 0.436 0.195 -1.102 -0.630 -0.128 -0.115 -0.918 0.728 1.587 0.608 0.630 1.937 0.162 -0.025 2.405 1.319 0.544 -0.218 +3 0.262 -1.789 -2.462 -1.125 1.243 -0.395 0.778 1.305 -1.283 1.476 -1.162 -0.206 -1.080 0.237 -0.162 0.797 1.000 1.580 0.752 0.815 -0.448 -0.178 0.314 -1.241 1.123 -0.134 0.194 -1.518 +3 1.308 -0.977 0.212 -0.133 1.218 -0.709 0.878 -0.987 -0.106 -0.885 -1.965 -0.904 -0.258 1.426 -1.087 -0.606 -1.440 1.155 -0.460 -0.338 -0.348 -1.951 -1.467 -0.952 0.616 -2.499 0.541 0.343 +1 -1.330 0.324 0.102 0.493 0.580 -0.482 1.094 -0.918 -1.056 0.398 0.215 -0.810 -0.833 0.380 0.250 -1.038 -0.643 -0.911 -2.120 1.364 0.238 1.158 -0.306 -0.972 -0.874 -0.875 -0.519 -1.548 +3 0.134 0.005 0.313 -0.603 -0.650 -0.627 -0.461 -0.446 -2.505 0.089 -0.301 -0.718 0.093 -0.005 -0.398 0.009 -0.582 -1.169 1.961 -0.496 0.266 2.596 -0.414 0.460 2.327 -1.650 1.334 0.222 +1 1.446 0.739 -0.291 0.545 2.516 0.075 0.961 -0.429 1.070 0.906 -0.611 0.777 0.825 0.740 -0.041 1.229 -0.935 0.010 0.111 1.171 -0.966 0.241 -0.492 0.109 0.392 -1.278 1.264 0.428 +4 0.565 1.636 -0.221 0.069 0.193 2.392 -2.099 0.683 -0.115 0.567 -0.657 -0.049 0.711 3.113 0.808 -0.848 -0.424 -0.453 -1.796 -0.330 0.733 -1.274 1.048 0.488 -0.734 -0.142 1.598 0.734 +0 0.924 -0.684 -0.785 -0.709 -0.519 0.899 1.265 1.250 0.607 0.401 1.017 -0.003 -0.080 0.177 -0.648 0.648 -0.918 -0.237 1.649 1.288 0.338 -1.364 -0.347 0.908 0.562 -0.177 1.252 -0.030 +1 -0.283 -1.323 -1.070 -0.421 0.562 -1.427 -0.050 -1.581 -0.053 1.409 0.269 1.218 0.027 0.365 -0.844 0.700 -0.626 -1.221 -0.046 -1.506 0.635 1.933 -0.284 -0.238 0.502 0.595 0.130 -1.622 +4 -0.692 1.204 -0.003 -0.149 -1.499 3.061 0.108 -0.152 -1.513 0.175 -0.056 0.128 -1.392 -1.667 1.693 -1.169 0.525 -0.281 0.995 0.191 0.092 0.143 -0.771 -1.161 -0.244 -1.531 -2.120 0.144 +2 -0.541 -1.099 0.035 -0.738 0.455 1.520 -0.075 0.409 1.682 -0.783 1.944 -0.797 -2.405 0.574 -1.241 0.633 0.432 1.011 -0.966 -1.110 -0.531 0.600 -0.517 -1.064 0.409 0.844 0.068 -1.030 +4 -0.609 0.915 2.511 1.116 -0.027 1.260 1.713 -1.392 1.225 0.770 1.188 0.813 -0.228 2.033 0.340 -0.434 0.506 0.972 0.095 0.393 0.088 -0.660 -0.822 -1.403 1.518 -0.223 -3.428 0.397 +2 -1.542 0.417 0.525 0.690 0.485 -0.644 -0.670 0.565 -1.846 0.459 0.200 0.522 -1.513 -0.296 -1.128 0.913 1.389 -0.037 2.586 -0.871 0.329 0.227 -0.069 -0.468 -0.410 0.902 -0.284 -2.140 +2 0.343 -0.650 0.956 0.705 -0.322 0.539 -1.332 0.492 -1.459 0.128 0.689 0.338 1.553 -0.793 0.409 -2.132 -0.512 -0.303 1.567 -0.024 0.913 -0.904 -0.333 -0.837 -0.994 -0.925 -0.614 2.043 +4 -0.296 -0.234 0.282 0.480 -0.604 1.218 1.334 0.186 1.725 -0.079 -1.850 -0.922 -0.444 0.920 -0.981 -0.061 2.479 0.232 1.608 -1.198 -2.022 -1.609 1.084 -0.233 -0.328 1.723 1.088 0.466 +1 -1.553 0.872 -0.328 -0.663 -0.451 0.455 0.422 -0.422 -0.830 -1.196 -2.343 1.240 0.302 1.251 0.997 1.028 -0.479 0.154 -0.988 0.570 0.172 0.489 -0.104 1.145 -0.511 -0.950 1.514 0.547 +1 1.257 -0.351 0.124 -2.108 -1.621 0.319 -1.274 0.080 0.449 0.398 -0.134 -0.586 0.404 0.158 0.913 -0.937 1.517 1.252 -0.604 0.403 -0.327 2.055 -0.039 0.644 -0.376 -0.177 -0.743 0.315 +1 0.909 -1.010 -1.663 -0.941 0.527 -0.457 -1.239 1.269 -1.180 -0.171 0.737 0.670 0.417 -0.668 0.558 0.015 0.407 1.371 -1.194 0.385 0.142 0.944 1.778 1.747 0.835 -0.599 -0.422 0.721 +1 -1.313 0.725 -0.477 -0.599 -1.162 0.148 0.269 -0.186 1.484 -0.479 1.556 1.371 -0.767 -0.973 1.805 -0.104 0.689 -0.446 0.010 0.370 0.453 0.147 1.348 1.275 0.060 -0.125 2.135 -0.150 +3 -1.137 -1.453 -2.290 0.877 1.013 -1.500 1.119 2.103 -0.207 -0.903 -0.073 1.169 1.316 -0.035 -0.919 0.243 -0.098 0.119 -0.387 0.192 -1.864 0.359 1.136 -0.190 0.126 -1.606 0.520 0.927 +0 0.948 -0.785 0.886 0.557 -0.166 -1.892 -1.460 1.185 -1.127 -0.648 0.821 0.042 -0.234 0.855 0.121 0.513 -1.093 1.130 -0.318 0.362 -0.764 0.423 0.183 -0.118 0.943 0.583 1.423 -0.501 +4 0.715 1.486 0.709 -0.350 0.073 0.243 -0.009 -0.977 -0.999 -0.733 0.446 0.413 -0.012 -1.996 2.977 -0.708 -0.859 2.525 1.133 0.339 -2.399 -1.991 -0.220 -1.640 0.270 0.193 -0.327 -1.103 +0 -0.826 1.455 0.322 0.017 0.370 0.198 1.006 -2.080 -0.403 -1.392 0.248 -1.569 -1.231 0.318 -0.294 0.356 -1.202 -0.239 -0.051 -1.369 -0.698 0.176 -0.367 -0.847 0.232 -0.507 0.286 0.550 +3 1.486 1.553 0.339 -0.679 1.073 1.970 2.836 0.115 -0.122 -0.942 0.625 0.015 0.658 -0.421 0.406 0.786 0.087 0.103 1.381 -0.181 1.001 0.585 1.627 0.949 0.910 1.375 -1.009 0.746 +2 -0.410 0.360 1.508 -1.248 0.473 -0.254 2.225 0.657 -0.227 -1.304 0.001 -0.112 -2.455 -0.099 1.484 0.560 -0.043 -0.084 -0.462 -0.681 0.408 -0.459 0.196 0.716 -1.628 -1.456 -0.739 -0.433 +1 1.306 1.009 -0.241 0.274 1.241 0.846 -1.463 0.244 -0.168 1.113 1.031 1.319 -1.118 -0.784 1.114 -0.592 -1.521 0.533 -0.547 1.790 0.296 0.160 -0.369 1.591 0.359 -0.408 -0.794 -0.073 +1 -0.735 0.428 0.022 -0.104 1.157 -0.439 0.674 -2.226 -0.300 0.056 -0.153 -0.732 -1.529 -0.600 -0.517 0.196 1.001 -1.094 0.316 -0.591 0.682 0.994 0.666 0.527 -2.220 -0.500 0.895 1.805 +2 0.616 0.637 0.565 -0.455 -2.701 -1.796 0.013 0.336 -0.420 -0.693 0.297 -0.108 -0.476 -1.616 -0.844 0.933 -0.963 0.664 1.386 -0.791 -0.445 0.987 1.233 -0.645 -1.621 1.255 0.584 0.401 +1 1.768 0.123 0.942 0.648 -0.212 0.224 -1.620 -0.466 -0.103 -0.132 -0.174 -0.656 -0.221 -1.481 1.939 -1.308 -0.360 0.606 1.295 1.321 0.187 -0.939 1.374 -0.132 -1.375 -0.031 -0.327 -0.806 +0 -1.776 0.775 -0.707 0.489 -0.679 -0.498 0.434 -0.349 0.171 0.277 -1.427 0.478 -0.938 -1.111 -0.727 0.814 -1.600 0.651 0.340 0.074 0.247 -0.290 0.682 -0.427 -1.625 -0.077 0.801 0.492 +1 -0.538 0.295 0.593 0.147 0.588 -0.660 -1.083 -0.258 0.426 1.033 2.236 0.645 -2.030 1.172 0.416 -0.692 0.824 0.422 -0.483 0.122 -0.895 -0.199 -1.306 0.426 0.370 1.134 1.003 -0.660 +2 1.874 0.907 0.353 -0.536 0.235 0.042 -0.424 -2.099 -0.835 -0.665 0.010 -0.633 1.269 0.927 -1.000 -0.341 -0.402 0.035 0.659 -0.242 -0.487 1.222 -1.248 1.550 0.750 0.280 -1.083 1.724 +4 -0.939 0.843 -0.764 2.399 -1.969 0.081 2.758 0.443 0.275 0.806 0.957 -0.128 0.496 -0.221 -0.809 0.095 -1.592 -2.434 0.852 0.408 -0.399 0.380 -1.041 -0.299 0.984 -0.513 0.662 -0.104 +4 -0.988 -1.049 2.399 1.922 1.911 -1.216 1.755 -0.713 1.645 0.439 -0.992 1.655 1.615 -1.061 0.326 -1.937 -0.395 -0.388 -0.409 1.612 -1.336 -0.224 0.755 -1.315 -0.883 1.115 1.345 -0.433 +4 2.347 1.604 0.083 2.181 0.632 -0.431 0.906 0.399 0.838 -1.369 -0.810 1.488 1.693 1.415 2.219 0.577 0.507 -0.913 -0.581 -1.988 -0.321 -2.059 1.081 0.388 -2.026 -0.190 1.968 -0.126 +0 0.343 0.128 0.283 0.979 0.546 0.387 -0.594 1.039 -0.577 0.968 -0.393 0.589 1.764 0.661 1.416 -0.371 0.096 -0.355 -1.660 1.069 0.569 0.498 1.232 -1.135 -1.375 1.241 -0.070 0.215 +4 0.355 -0.663 2.003 -0.765 -1.307 0.138 0.691 -0.040 1.412 0.084 -0.372 2.661 0.408 -0.656 -1.053 -1.132 -2.957 1.258 -0.129 0.751 0.212 -2.405 -0.237 -0.156 1.427 0.951 -0.170 -0.036 +3 -1.135 -0.186 -0.889 -0.631 0.780 -0.522 -1.334 -0.924 -0.587 -1.733 -1.342 -0.808 -0.707 0.303 -0.242 1.493 0.344 -0.639 -0.952 0.085 -0.468 -1.608 -0.621 0.206 1.663 2.099 0.956 -2.028 +3 -0.240 -0.421 -2.519 1.377 0.216 0.090 -1.576 0.617 0.127 -0.066 -0.387 -2.739 -0.362 -1.075 0.757 -0.184 -0.729 0.841 -1.070 0.996 0.916 -0.689 0.280 -1.294 1.225 1.125 -0.012 -0.250 +2 -0.543 -0.874 0.357 -0.757 -0.224 -1.282 0.089 -0.268 -0.901 -0.565 -0.312 1.864 -0.479 1.856 0.304 -1.161 0.213 0.283 1.128 -0.301 1.074 0.952 -1.139 -1.104 -1.714 0.919 -1.300 1.163 +0 -0.317 -1.456 -0.425 -0.433 0.575 -0.015 1.083 0.281 -0.504 -0.815 -0.603 0.161 -0.373 -1.076 1.007 0.152 0.275 -0.823 -1.042 -0.162 -2.028 0.780 -0.704 -0.268 0.427 -0.225 -1.168 1.506 +4 2.025 0.662 0.758 1.379 -1.666 -1.032 0.350 0.587 1.050 -0.256 -0.197 1.066 0.929 -0.962 -0.737 -1.134 1.563 0.769 0.897 0.153 0.399 0.286 0.167 -1.264 2.713 0.994 -1.983 0.846 +2 1.279 0.735 0.869 -1.744 1.512 -0.773 -0.509 -0.703 0.947 -0.180 0.482 0.076 -0.095 -0.947 1.812 -1.463 0.412 0.377 1.154 1.388 -0.137 0.530 1.765 -0.065 -1.066 -0.384 -1.305 0.372 +2 0.471 1.059 0.583 0.066 1.609 -0.528 0.566 -0.580 -0.969 1.154 0.031 1.514 1.180 0.204 0.113 2.435 0.059 -2.062 1.278 0.901 0.509 -0.022 0.001 0.280 0.045 1.073 1.363 -0.919 +4 -0.602 -0.705 -0.079 -0.753 -2.199 -0.607 -1.240 0.138 -0.736 -1.073 -0.177 -0.983 -1.849 -0.017 -1.167 0.518 0.447 -0.797 1.250 0.643 -0.921 1.443 -3.293 1.162 0.581 -0.663 -0.712 -0.329 +1 -0.360 -1.083 -0.207 1.349 -0.905 -0.429 -0.864 0.956 0.288 0.926 1.565 -1.512 -0.759 -0.434 0.069 0.506 -0.491 -0.692 0.957 0.546 0.316 -2.146 1.207 0.184 -0.162 -0.819 -0.633 -0.891 +3 -0.414 -1.710 -2.157 0.239 0.656 0.063 -0.071 -0.805 0.655 1.678 -2.217 -0.078 -0.970 -1.490 -1.130 0.380 0.405 1.460 0.009 -1.758 -0.238 -1.386 -1.207 0.581 0.194 0.588 -0.357 0.791 +1 -0.207 -0.320 1.200 0.580 -0.952 -0.601 0.282 0.931 -0.854 0.873 0.781 0.440 -0.842 -0.711 0.224 0.727 0.445 -0.402 0.346 3.007 -1.576 -0.224 -0.920 -0.167 0.652 -1.334 0.559 0.032 +2 -0.241 0.455 1.551 -0.465 -0.096 0.209 0.880 -0.250 -1.739 -1.470 -0.119 -1.405 -1.920 0.568 0.951 0.067 0.478 -0.490 0.693 -1.395 1.572 -0.764 -0.749 -1.101 -0.804 -0.865 -1.180 -0.235 +2 -1.654 -0.064 -0.777 1.410 -0.291 0.833 0.350 -0.532 0.950 -0.097 0.498 0.271 0.408 0.441 0.689 0.501 0.103 0.534 0.160 0.391 -3.353 -1.402 0.795 0.101 -1.665 -0.018 0.178 1.203 +4 -0.345 2.528 -0.107 -1.115 0.398 -0.175 -1.597 -0.765 1.514 0.900 0.334 0.421 -0.727 0.600 -0.802 -0.627 0.914 -0.476 2.513 -0.190 -0.242 1.820 -0.422 -0.861 2.078 1.323 0.750 -0.816 +2 -0.528 1.981 1.531 0.967 0.686 0.398 0.205 0.788 0.254 0.378 0.024 -0.134 1.916 0.211 -0.285 0.291 -1.083 1.066 0.138 -0.202 -0.304 -1.453 1.951 0.325 1.937 -0.125 0.590 0.869 +1 -1.519 -2.631 0.388 -0.095 -0.515 0.244 -0.060 1.128 0.086 -0.747 0.990 -0.217 0.527 -0.477 -0.650 0.084 0.013 0.480 -0.269 -1.836 -0.433 0.794 -0.297 -0.246 0.986 -0.712 -1.543 -0.161 +1 0.826 -1.625 -0.452 1.323 0.106 -0.321 -1.115 1.259 -0.701 0.687 1.921 0.123 1.422 0.467 1.010 -0.251 -0.880 -0.462 -0.355 0.269 0.713 -1.550 0.161 0.316 0.052 -1.043 0.413 0.374 +4 -1.069 0.432 0.168 -1.024 -1.277 0.498 -0.282 -1.465 0.579 0.935 -1.171 -0.002 1.518 -0.911 1.707 -0.324 -0.070 -1.395 -1.358 -0.656 0.886 2.341 2.606 -0.123 -0.356 -0.984 1.115 -0.726 +2 1.812 0.579 -2.491 1.217 1.090 -0.359 0.717 -0.859 -0.470 1.499 -0.272 -1.870 -0.063 -0.079 1.198 0.014 -0.422 1.045 0.121 -0.750 1.155 0.194 0.003 0.178 -1.071 -0.528 0.004 -0.195 +0 0.051 -0.068 -0.291 0.532 -0.662 0.550 1.432 0.113 -0.314 -0.075 1.201 0.139 -0.609 0.809 -0.711 -0.233 0.229 -0.807 -1.786 0.449 -2.192 -0.927 0.778 0.419 -0.158 -0.895 -0.431 0.576 +4 -0.446 2.472 -1.570 -0.182 -0.236 0.048 -1.972 -0.981 0.049 -1.395 0.953 0.580 -1.587 -0.230 0.376 -1.376 -1.577 -0.958 2.687 1.094 -0.609 -1.331 -0.825 1.522 2.034 -0.812 0.311 -0.262 +0 -0.545 0.138 0.296 -0.588 1.451 -1.117 0.262 0.246 -0.054 -1.297 0.871 0.744 -0.676 0.707 0.375 -1.281 -0.854 1.138 0.309 -0.564 -0.858 -0.515 -0.444 -0.846 -1.742 1.140 0.971 -1.273 +1 -0.801 0.592 1.125 -0.415 0.647 -0.756 -0.041 0.248 -0.546 -1.000 -0.237 1.230 0.424 1.373 -1.997 0.133 -0.509 -0.908 -1.347 0.542 -1.124 0.023 0.294 0.314 -0.314 1.186 1.992 0.871 +3 -1.308 -0.345 0.044 -1.836 1.389 0.142 0.381 1.366 -0.630 0.574 -0.110 -0.184 -0.772 1.666 -0.376 0.699 -1.875 1.180 1.052 0.097 -0.227 0.400 2.289 -1.763 0.782 -0.912 -0.020 -0.248 +0 0.457 1.292 1.330 0.588 -0.357 1.594 -1.867 -0.217 0.057 -0.893 0.252 0.641 0.391 -0.493 0.073 -1.449 -0.009 1.333 0.647 0.325 0.610 0.149 0.633 0.319 0.600 -1.197 -0.507 -0.293 +3 -0.088 2.580 -0.804 1.639 1.678 -0.554 0.569 1.628 -0.379 -0.204 -0.582 -1.015 -0.649 -1.224 0.034 -0.770 0.234 -1.556 0.331 0.834 -1.994 0.374 1.228 -1.210 1.673 0.419 -0.705 -0.056 +4 1.595 -0.847 -0.991 -2.153 -0.639 -1.323 1.642 1.010 -0.688 2.252 0.982 -0.325 -2.499 2.291 -1.390 -1.645 1.023 2.440 1.384 0.564 0.595 0.853 0.759 0.281 0.104 -0.063 -0.754 -0.281 +0 1.244 -0.467 0.186 -0.461 0.689 0.979 -0.636 0.416 0.588 0.332 0.535 1.495 -1.045 -0.910 -1.073 -0.354 0.137 -0.600 0.833 -0.770 -0.147 0.663 0.348 -0.797 -0.591 -0.193 -0.072 -0.036 +1 0.408 0.843 0.575 -1.276 -0.094 0.709 0.796 -1.661 0.383 -0.133 0.165 0.538 0.474 0.237 -0.240 -1.872 -0.971 -0.733 -0.177 -1.314 -0.520 0.983 1.649 -0.441 -1.436 -0.460 0.583 -1.386 +3 0.170 1.428 -1.700 0.385 1.038 1.410 -1.571 -2.442 0.686 1.035 -0.098 1.021 1.618 0.102 -1.055 1.181 0.988 0.571 0.036 0.707 0.722 0.360 1.132 0.311 -0.794 1.303 -0.474 1.190 +0 -0.303 0.264 -0.972 -0.291 0.030 0.099 0.922 0.405 -0.090 2.090 -0.982 0.899 0.008 0.488 -1.094 -1.116 -0.286 -0.038 -1.090 -0.524 0.320 -0.636 1.617 -0.588 -0.073 -0.250 -1.257 -0.046 +3 0.657 -1.008 -0.667 0.364 -1.456 -0.847 1.145 0.753 -1.444 -0.281 0.681 -0.075 0.729 -0.256 1.408 -0.815 -0.095 -0.273 0.531 2.587 -2.136 1.468 1.145 0.402 -0.194 -0.730 0.890 0.678 +0 0.517 0.655 0.737 -0.131 -0.438 0.122 0.326 0.940 -1.021 -1.188 -0.193 -0.213 -0.189 2.051 1.610 0.637 -0.593 0.685 -0.357 0.232 0.013 -0.675 -1.836 -1.621 0.655 0.149 0.988 0.602 +3 1.078 0.376 -0.653 -0.907 -0.701 -0.066 -0.025 1.029 -0.254 -2.024 -1.193 -0.976 -0.977 -2.806 1.569 -2.183 -0.574 0.658 0.590 -1.470 1.207 1.061 -0.181 0.027 -0.914 0.252 0.226 -0.487 +0 0.715 -0.346 -0.449 0.203 -0.237 1.169 0.785 0.723 -0.415 0.710 0.503 -1.216 -0.246 -0.171 1.495 -0.420 0.482 1.301 -0.923 0.279 2.032 -1.569 -0.607 0.273 0.364 0.157 0.546 1.261 +1 0.636 -0.092 -0.289 -0.945 -0.177 0.192 -1.333 0.503 0.279 -1.015 -0.349 -1.365 -0.928 0.059 1.422 2.058 -0.447 1.622 -2.032 0.115 -0.671 -0.375 -0.393 0.140 -0.860 0.261 0.005 -0.329 +4 0.048 -0.449 -1.147 0.480 -0.744 0.028 0.322 -1.329 0.785 0.359 1.769 1.400 1.500 -2.277 -1.319 1.947 0.636 -0.358 2.418 -2.000 -0.437 -0.244 -0.179 0.394 0.760 0.406 1.050 -3.128 +3 1.897 0.412 -0.942 1.111 0.642 -0.978 -0.350 0.139 0.554 0.669 -0.224 -1.415 1.168 -0.285 -0.588 -1.628 -0.816 -0.674 -2.001 -1.993 0.185 0.153 -2.083 -1.357 1.410 -0.274 -0.604 0.069 +0 -0.133 -0.975 1.107 -0.120 -2.173 0.847 -0.535 -0.091 0.332 0.190 0.709 -0.435 0.513 -0.260 0.739 0.615 -0.935 1.086 -0.536 0.808 0.367 1.838 -0.223 -0.349 -0.019 -0.303 0.800 -1.616 +4 0.544 0.182 0.587 -0.394 0.492 0.510 -1.419 -0.385 -0.171 -0.326 -1.029 -2.472 1.028 -2.166 1.979 -0.236 -0.667 -1.550 -1.660 -0.892 0.191 1.412 -1.549 0.359 -1.851 -0.906 0.502 -1.017 +1 -0.723 -0.121 0.552 1.740 -0.391 -1.327 0.660 0.813 -0.241 0.339 -0.981 -0.126 0.953 -0.210 -1.781 -1.363 0.026 -0.365 -0.613 0.456 1.093 -1.824 -0.609 -1.219 -0.345 0.610 -0.041 -0.922 +2 0.054 -0.286 2.526 0.483 0.210 -0.183 -0.897 -1.951 1.521 -0.779 -0.190 -0.106 1.433 -0.766 0.257 0.512 -1.384 -1.478 -0.715 0.244 0.116 -0.119 0.652 -0.857 -0.171 -0.114 -0.830 2.302 +2 -0.332 -0.306 1.344 -1.063 -0.482 -0.165 0.935 1.697 1.293 1.806 -2.974 -0.576 0.286 -0.605 -0.223 -0.361 0.204 0.489 -1.572 -0.524 -0.585 0.315 0.447 -0.736 -0.289 -0.661 0.360 -1.103 +3 -1.470 1.284 2.097 -0.161 0.771 0.408 -0.644 -0.223 -0.164 -0.523 -1.163 -2.125 0.260 -0.187 -0.377 -1.160 -0.444 -0.512 0.292 1.289 1.118 2.014 -0.046 0.419 1.350 -1.905 -0.828 0.696 +2 0.372 1.132 0.443 1.780 -1.361 -0.917 -1.794 -0.431 1.680 0.636 0.810 0.327 -0.669 1.486 -0.542 -0.172 1.912 0.551 0.878 -0.460 -1.379 -1.337 -0.622 -0.280 -0.008 0.226 0.113 0.667 +1 1.341 0.114 -1.617 0.032 -1.659 -0.580 -1.549 0.048 0.994 -0.808 -0.665 1.036 -0.160 -0.578 0.622 0.270 -0.737 0.535 -2.001 -0.044 -0.438 -0.792 -1.330 0.310 1.212 -0.401 -1.039 -0.503 +4 2.151 1.638 1.752 0.402 -0.057 -0.768 -1.571 0.964 0.489 -0.110 -0.508 -0.716 -0.768 -0.977 3.151 -1.061 -1.958 -0.331 -0.339 -0.176 0.552 1.935 1.068 2.863 -0.100 1.300 -1.160 1.990 +2 0.334 -0.366 1.189 0.642 1.297 -0.046 -0.581 0.489 0.738 1.307 0.071 0.310 -0.346 0.143 1.105 -1.455 -0.617 -1.745 0.004 0.397 -2.380 -1.347 0.163 -2.073 0.282 0.550 0.386 -0.747 +3 -1.456 -0.075 1.019 1.814 -0.238 -0.130 0.500 1.493 -0.486 0.693 -0.823 0.330 0.971 -1.291 -0.525 0.955 0.616 1.036 0.848 -0.341 2.199 0.137 -3.056 -0.282 -0.469 0.795 -0.451 1.099 +1 0.316 -0.578 -0.524 0.302 2.261 1.032 -0.078 0.074 1.360 1.087 0.383 -1.185 0.098 1.018 0.581 -0.021 -0.163 0.850 -0.119 -1.259 -1.175 -1.214 -0.519 1.893 -0.047 0.803 -0.469 -1.193 +3 1.028 -0.410 1.257 1.289 0.968 0.202 -0.238 -0.750 -0.224 -0.070 0.755 -0.498 1.592 0.618 -0.279 0.588 2.000 0.239 0.615 1.010 0.080 0.360 -1.244 -0.585 -0.269 -1.087 -3.018 1.270 +1 0.393 0.829 1.710 0.405 -0.730 0.611 -0.151 -1.345 -0.048 0.283 1.434 0.071 -0.815 -1.887 -1.229 -0.970 1.666 -0.191 -0.090 1.101 0.603 -0.262 1.146 -0.684 0.284 -1.255 0.260 1.018 +2 -0.517 -0.587 1.289 -0.191 0.165 -1.638 -0.251 1.665 -1.513 -0.166 0.596 0.518 1.475 -1.390 0.976 0.695 0.499 -1.625 1.460 -0.692 -1.986 -0.336 -0.840 -0.067 0.042 1.063 0.599 0.117 +3 -1.358 0.786 1.262 -0.130 0.408 1.633 -0.318 0.729 0.381 -1.309 1.536 -0.302 -1.522 0.367 -2.807 -2.041 0.063 0.469 -0.007 -0.497 0.509 0.999 0.085 0.277 -1.295 -1.292 1.014 0.598 +2 -1.353 -1.188 -0.575 0.867 -0.423 -0.810 -1.188 -1.261 -0.529 0.992 -0.945 -1.230 -0.588 1.015 -0.366 -0.494 0.238 1.390 -0.032 -1.940 -0.200 -1.215 -0.867 0.972 -0.415 -1.329 0.429 -1.196 +1 0.406 -0.815 1.482 -0.036 0.220 0.536 -1.711 0.275 -0.631 -0.871 0.459 1.129 -0.001 1.536 -0.235 -1.722 -1.165 1.526 1.782 0.363 -0.617 -0.307 -0.043 0.014 0.558 0.343 -1.192 -1.128 +0 0.658 -0.070 -1.187 1.058 -0.589 -0.936 -0.178 -0.913 -0.417 0.344 0.348 -0.898 -0.488 1.074 0.496 2.075 1.180 1.144 -1.388 0.764 -0.053 -0.551 0.168 0.945 0.501 -0.878 0.742 -0.638 +1 -0.052 0.281 -0.345 0.227 -0.592 1.274 -0.114 -0.383 -0.925 0.269 0.187 0.388 -1.027 -1.601 0.626 1.485 -1.026 1.589 0.284 -0.007 -0.398 -1.385 0.988 -0.776 -0.780 0.992 -1.883 -1.524 +2 0.876 0.114 1.073 1.419 -1.095 1.686 0.552 0.310 -0.686 0.340 -2.221 -1.103 -1.862 -0.139 1.359 0.260 0.593 -0.232 0.011 -1.793 1.053 0.510 0.783 0.344 -0.459 0.513 -0.297 -0.082 +4 -0.028 -0.739 -0.306 -1.407 -0.718 1.754 0.208 0.630 -1.792 1.861 0.763 -0.294 1.702 -1.734 2.510 -0.189 -1.437 -0.726 -1.325 0.991 -0.431 -2.282 -1.679 -1.800 1.033 0.366 -1.148 -0.441 +3 0.545 0.776 0.654 -0.795 -0.018 -0.418 0.369 -1.627 -0.024 -1.763 -1.085 1.632 1.694 -0.651 0.493 0.482 1.366 0.246 -1.048 -0.820 0.396 -1.827 -1.715 0.845 2.642 -0.624 -0.161 -0.081 +2 2.448 0.486 -0.337 -0.766 -0.305 -0.075 0.238 -0.614 -1.197 1.131 1.171 0.197 1.119 -0.769 0.148 0.686 1.285 -0.263 2.352 0.544 -1.065 0.950 -1.035 -0.926 -0.075 0.131 -0.121 -0.403 +0 0.687 -0.783 -0.989 1.561 0.732 -0.663 -0.255 -0.969 -1.820 0.421 -0.128 -0.922 -2.078 0.465 1.006 -0.312 0.675 0.264 0.274 0.363 0.443 0.361 0.436 0.290 1.487 -0.492 -0.992 -0.419 +3 0.075 -1.248 -0.165 -0.545 -1.861 0.426 0.328 1.262 -1.224 1.081 -2.319 -0.147 1.976 -0.029 -0.735 -0.083 0.848 -0.282 0.392 0.381 0.549 1.036 1.980 0.087 0.323 -1.187 0.945 1.361 +4 1.518 -2.143 0.465 1.218 -2.010 1.366 2.209 0.184 0.541 -0.969 2.047 0.283 -2.559 0.966 -0.907 -0.504 0.289 0.026 0.333 0.447 1.346 -1.353 0.536 0.167 -0.226 -1.259 0.397 -0.952 +2 1.459 0.010 0.071 -1.208 -2.426 0.414 -0.531 0.479 0.733 0.452 0.443 0.680 2.462 -0.173 -0.315 -1.374 0.630 0.558 0.125 1.047 0.051 -0.729 1.514 -0.689 -0.677 -0.723 0.570 0.944 +1 0.584 0.371 -0.302 1.825 -0.245 0.590 -0.409 -0.424 -1.136 0.541 -1.307 -0.410 -1.017 1.647 0.419 1.428 0.353 -0.078 1.395 0.618 0.468 -0.923 0.729 -0.368 -1.469 1.311 -1.100 -0.507 +2 0.117 1.739 -2.409 0.355 -0.384 0.344 0.326 0.835 0.732 0.180 -1.174 0.597 -1.214 -0.869 0.478 0.187 -0.979 -1.304 0.121 -0.215 -0.547 0.655 -0.030 1.020 0.591 -1.543 -1.825 0.827 +2 -0.089 -1.587 -1.601 -0.376 -1.190 -1.619 -0.427 0.797 -0.463 0.330 1.437 0.383 -0.933 -0.025 -0.438 0.503 0.187 -0.283 0.080 -0.320 -1.598 -1.939 0.874 1.028 0.187 0.943 1.715 -0.452 +1 -0.902 0.636 -1.276 1.661 0.306 -1.459 -0.102 0.629 -0.356 0.021 -1.009 -2.223 0.785 -0.252 0.036 -2.226 -0.385 -0.263 -0.158 -0.219 -0.015 0.710 0.943 0.010 0.382 1.385 1.003 0.495 +2 0.612 -0.979 -1.382 -1.798 -2.224 -0.518 -0.301 -0.631 1.243 1.228 -0.335 -1.794 -0.567 -0.600 -0.657 -0.698 -0.653 -0.387 -0.636 0.208 0.360 -0.911 1.271 -0.980 -1.618 0.381 -0.817 0.321 +1 -0.048 -1.172 -0.356 -0.705 0.101 0.885 1.374 0.086 -1.011 1.415 0.391 -0.566 -0.333 -0.668 0.724 0.981 0.332 1.093 -0.064 2.350 -0.127 -1.934 0.914 -0.402 1.074 0.293 -0.684 -0.097 +0 0.444 -0.427 0.799 -0.024 0.494 0.555 1.227 1.225 0.070 0.404 0.399 -0.284 -0.103 0.536 -0.019 0.338 -0.417 1.824 0.801 0.809 -0.218 -1.259 -1.484 0.799 -0.609 0.639 0.483 0.643 +1 -0.732 0.602 -1.380 -1.780 0.983 0.003 -1.165 0.520 -1.114 -0.220 -0.856 -1.843 1.061 -0.731 1.411 0.107 -0.525 -1.096 0.042 0.499 0.969 0.595 -0.251 0.530 -0.398 1.501 -0.338 0.536 +2 0.344 -0.420 0.956 0.514 -0.437 0.971 0.369 -1.661 -0.992 -1.329 1.031 -0.702 0.627 2.172 0.794 -0.419 -0.744 -0.306 -1.028 -1.144 -0.805 0.532 -0.521 -0.225 0.469 0.727 -1.929 -1.723 +4 -2.125 -0.566 0.890 0.723 -0.210 -1.491 -0.976 -1.088 -1.842 1.600 2.041 -0.733 -1.927 0.162 0.048 0.949 0.241 0.132 -1.069 -0.174 1.052 1.702 0.162 0.426 -0.230 0.502 -1.583 0.871 +4 0.106 0.584 1.976 -1.564 1.617 0.104 -0.899 -1.330 -0.189 0.922 -0.128 1.511 -1.451 -0.012 -1.252 0.364 0.887 -0.421 -2.604 0.199 0.437 0.404 1.236 -1.071 0.680 1.193 -1.779 0.320 +1 -0.005 0.911 0.170 0.215 -0.922 -0.596 -0.018 2.581 -0.851 -0.906 0.060 1.894 1.305 -0.627 -0.563 0.292 -0.460 1.772 0.505 -1.115 -0.313 0.451 0.557 0.113 -0.283 0.858 1.450 0.791 +1 -1.044 0.848 1.866 -0.323 0.841 1.229 1.017 0.126 -0.698 -0.081 0.459 0.908 0.334 -0.380 0.555 0.860 0.380 -0.836 -0.181 0.948 -1.383 -2.093 1.508 0.511 0.607 -1.077 0.738 0.275 +0 -0.698 0.867 -0.459 0.410 0.379 -0.052 0.453 0.644 -1.249 -0.480 1.114 0.421 1.117 -1.702 1.146 -0.074 -0.513 0.389 1.577 -1.496 0.363 -1.236 -0.791 -0.689 -0.322 0.118 -0.334 -0.231 +3 -0.711 -0.384 -0.411 1.022 -0.379 2.083 0.431 -1.276 -0.129 -0.652 -2.194 1.599 0.703 0.533 0.165 -1.382 -0.484 0.842 -1.051 -2.532 -0.227 -0.142 0.136 0.729 0.638 0.259 1.196 -1.519 +4 -2.888 1.229 0.439 0.505 -1.093 -1.013 1.150 0.243 -0.117 0.221 1.509 0.724 -0.475 -1.403 0.588 -0.924 -1.182 1.316 0.872 0.386 -1.238 1.789 1.029 -0.304 -0.515 0.078 -0.478 2.212 +1 0.137 1.473 0.123 0.614 -0.780 0.042 0.140 -0.141 -0.022 0.126 0.481 1.169 -2.266 1.114 -1.711 0.309 2.356 -0.043 0.180 -0.310 0.667 0.362 -0.676 -0.114 -1.118 -0.119 -0.419 0.137 +0 -0.362 -1.120 -1.295 1.161 -0.468 0.347 -0.047 0.477 0.077 -1.283 0.996 -0.494 -1.557 -0.428 1.501 0.850 -0.349 -0.349 -0.322 2.077 0.382 0.430 1.030 0.239 -0.259 -0.196 -0.072 -0.037 +0 -0.530 0.555 -0.115 0.756 0.219 -1.890 0.604 0.624 -1.148 -0.414 -0.453 -0.697 -1.385 0.728 0.510 0.886 -0.145 1.167 -1.480 0.413 0.990 -0.922 -0.429 -1.343 0.767 -0.878 0.003 -0.450 +4 -0.140 -1.472 -0.236 0.154 2.270 0.201 -1.704 2.236 0.368 0.017 -0.513 -0.105 -1.236 -0.665 -0.036 1.057 0.426 1.037 -0.352 1.437 -0.724 -1.526 -1.356 2.467 1.070 -0.545 -0.812 -0.033 +3 -1.347 0.455 -0.990 0.244 -1.128 2.783 0.162 0.276 -0.365 -1.071 0.958 0.446 1.217 1.682 0.013 1.179 -1.846 -1.397 0.354 -0.904 1.023 1.156 0.285 0.602 -0.422 -0.791 -0.305 -1.374 +0 -0.221 -0.463 -0.141 -0.809 -2.211 -0.257 -0.458 -1.119 -0.732 0.368 1.902 0.238 -0.031 -0.033 0.252 -0.316 0.013 -0.924 0.171 0.962 -0.714 -2.109 0.001 -0.463 -0.017 -0.774 0.190 -0.871 +3 -1.128 -1.585 -0.023 0.454 0.029 -0.306 0.309 1.715 0.230 2.192 -0.169 -0.113 0.255 -2.426 0.144 -1.004 0.722 -0.577 -0.647 0.144 -0.681 -0.730 -0.052 1.412 -0.198 2.221 1.208 -0.350 +2 0.479 -0.408 1.854 -1.085 -0.477 0.858 -0.635 0.111 -0.532 -0.391 -1.294 0.761 0.783 0.780 -0.429 0.332 1.995 0.290 -1.156 -0.854 -0.901 1.321 0.394 -1.574 1.303 0.567 0.174 1.517 +2 -1.301 -0.107 -0.496 0.122 2.556 -0.748 0.745 -0.328 -0.390 1.256 0.182 1.612 -0.051 1.366 -0.335 -0.685 0.438 0.118 -1.338 0.199 -0.762 0.817 -0.664 -0.364 -1.025 1.004 0.428 2.276 +4 -2.004 -0.085 0.361 1.352 1.017 0.905 0.774 0.745 0.106 -1.130 -1.112 -0.294 -0.113 1.137 0.152 -0.414 2.517 0.780 -0.685 -0.392 -2.458 1.861 1.068 0.922 -0.516 1.700 0.276 -1.899 +0 -0.733 -0.615 -1.695 -0.381 1.064 0.383 0.822 0.053 -0.562 -0.844 0.416 -0.418 0.380 -1.234 0.962 0.359 0.561 -0.733 -0.091 0.107 0.234 0.121 0.276 -0.624 0.269 0.765 -0.498 0.081 +3 -0.202 -1.504 -1.055 -1.799 1.553 0.677 1.149 0.858 1.340 -1.403 0.008 -0.978 -0.548 0.591 0.824 0.004 0.268 -1.589 -0.551 -0.072 -1.546 -1.325 0.952 -0.085 0.174 2.155 0.178 0.225 +4 0.184 0.199 0.169 -0.672 -1.274 -0.631 0.039 0.190 -0.346 -1.965 -2.262 -2.406 0.357 1.800 0.798 -2.163 -0.534 0.768 1.473 -0.151 0.701 -0.223 2.058 -1.072 0.798 -1.552 -0.232 0.333 +0 1.132 0.755 -0.335 -1.856 -0.230 0.611 0.840 -0.527 0.083 0.943 -0.331 -0.798 0.641 0.398 -0.281 0.692 -0.978 -0.072 -0.811 0.640 -0.045 -1.736 1.441 0.721 0.568 0.346 1.365 0.358 +4 -1.147 0.723 -0.349 0.966 0.025 0.862 0.507 1.298 0.962 -1.305 -1.874 -0.077 0.602 -0.725 0.446 0.404 -0.073 -0.635 -1.019 2.479 -2.121 -1.239 2.600 -1.033 -0.563 0.680 0.481 0.443 +4 0.754 -1.977 0.716 -0.374 -0.562 -1.327 -1.074 1.765 0.849 1.295 -0.857 1.799 -0.067 -0.789 -1.078 -1.048 -0.747 -0.894 0.904 2.601 -0.247 -0.228 -1.858 -0.925 -1.621 0.813 1.933 -1.081 +0 0.101 -0.660 -1.277 -0.436 0.290 0.487 0.322 -1.786 0.139 0.339 0.349 0.029 -1.229 -0.922 1.213 -0.776 -0.722 -0.296 -1.217 0.634 -1.268 1.084 -1.345 -0.280 0.344 0.791 -0.225 -0.703 +3 0.245 -1.091 -0.277 -1.680 -1.145 1.175 -2.481 0.746 2.011 0.248 -0.960 0.615 -1.440 -0.804 -1.332 -1.326 0.879 -0.183 0.618 0.230 -1.536 -0.291 -0.953 -0.478 -0.086 -0.305 0.208 0.128 +3 0.576 -1.125 -1.201 -1.277 -0.776 0.286 -0.507 1.278 -1.444 0.513 -0.367 -0.105 0.684 -2.634 -2.138 -0.063 -0.222 -1.428 2.005 -1.159 -0.532 1.150 -0.675 -0.868 0.389 -0.743 0.395 0.513 +4 -2.530 0.772 1.746 -1.377 0.349 -0.411 -0.444 -0.935 1.373 0.863 -0.863 -0.940 -2.076 0.709 0.572 0.615 -1.480 0.928 -0.370 -1.455 -1.075 0.300 0.998 -0.984 -1.893 -0.582 0.230 -0.869 +3 -0.158 -1.519 1.640 1.195 0.992 0.526 1.278 0.448 0.496 -0.519 0.451 1.627 1.650 -0.918 -1.350 0.152 -0.543 0.018 -0.307 -0.563 1.474 -0.344 0.066 -1.526 2.444 -1.591 -0.163 0.898 +2 -0.496 -0.918 1.339 -1.910 -1.608 -0.157 0.247 -0.726 -0.379 0.576 -0.020 -0.440 0.209 -1.608 0.600 -1.160 0.709 -0.357 -0.263 1.168 0.230 1.114 -1.331 2.075 0.293 1.067 -0.010 -0.820 +0 0.113 -1.125 -1.075 0.133 0.658 1.378 0.873 0.248 0.398 0.049 0.110 -1.751 -0.333 1.720 -0.171 0.506 -1.044 0.129 -1.283 -0.002 0.872 0.718 -1.276 -0.731 -0.223 -0.902 -0.986 -1.099 +0 0.914 1.624 0.346 -0.255 -1.067 -0.113 0.203 -1.463 0.921 -0.424 0.861 -0.011 0.297 -0.702 -0.345 0.642 -0.131 -1.105 0.112 0.386 1.373 1.153 -0.065 -0.547 -0.228 -0.524 0.506 1.042 +2 1.136 -0.188 -1.093 -1.616 0.447 0.024 -0.390 0.084 1.056 0.766 -0.343 -0.132 1.083 -0.638 -0.170 0.819 0.953 -0.103 2.008 0.141 -1.301 1.109 -0.530 0.683 0.196 2.711 -0.401 -0.030 +4 0.364 -0.422 -0.006 -0.552 0.972 0.546 0.298 0.062 2.531 -0.157 -0.697 -1.173 0.905 1.631 -0.848 -0.285 -0.359 -0.124 -1.768 0.807 1.190 -1.905 1.758 1.618 -3.070 -2.173 -0.133 1.316 +2 0.997 1.467 -0.810 -0.173 -1.029 0.125 -0.342 0.165 -0.324 2.573 0.425 -0.730 -0.340 0.223 -0.876 -0.353 -1.440 -0.538 -0.896 -1.959 -0.201 -0.803 -2.091 0.859 0.883 -0.758 0.639 -0.993 +0 -0.744 -0.039 0.375 -1.004 -0.360 0.215 0.329 -0.211 -1.140 -1.252 -1.116 1.331 0.225 0.204 -0.712 0.112 1.193 0.453 0.740 0.897 -1.465 -0.470 0.924 -0.357 0.015 -1.713 -0.930 0.028 +1 -1.760 -0.439 -0.269 -0.825 1.526 -1.930 -0.114 1.133 0.763 -0.380 -0.900 1.091 -0.521 -0.831 1.000 0.307 -0.352 0.325 0.445 -0.054 0.154 -2.163 -0.106 -0.640 -0.799 -0.685 -0.497 -0.297 +1 0.302 2.181 0.393 -1.385 -0.710 -0.512 -2.332 0.101 -0.350 -0.304 -0.125 -0.722 0.855 0.184 -0.534 0.445 0.355 -0.955 -0.173 -0.174 -0.585 -1.357 -0.616 1.266 -0.649 0.899 -0.035 0.168 +0 0.949 -1.166 0.459 0.358 0.012 0.009 0.812 -1.415 -0.490 0.120 -0.525 -0.080 -0.332 1.057 0.007 1.736 0.007 1.101 0.566 1.444 -0.313 0.225 -1.300 -1.425 0.865 -0.728 1.006 -0.035 +2 -0.466 -1.175 1.286 -0.580 -2.098 -0.847 0.198 -1.720 0.270 -0.888 1.116 0.455 0.080 -1.549 -1.290 -0.578 -0.229 1.529 0.296 -0.383 1.475 -0.090 0.434 1.554 0.717 0.442 0.581 -0.065 +3 -0.470 1.288 -0.038 0.135 0.411 1.395 0.084 -1.765 0.127 1.440 0.641 0.215 0.354 -1.837 -1.977 0.894 1.286 -0.629 0.193 0.621 1.660 -0.080 0.738 1.993 -0.280 0.065 -1.106 -1.055 +2 -0.449 0.017 -1.112 0.255 -1.430 1.562 1.702 -0.664 0.877 0.855 0.409 -2.454 -0.875 0.846 0.254 1.037 0.231 -1.385 -0.419 0.752 -1.347 -1.218 0.242 -0.211 -1.546 -0.296 -0.747 0.482 +0 0.243 0.674 -0.855 -0.049 -0.307 0.191 0.382 -0.354 -0.208 1.129 -0.917 0.720 0.291 0.749 -0.312 1.033 -0.637 0.501 0.498 0.156 1.026 1.105 0.684 1.776 -0.401 -0.267 0.286 -0.175 +3 1.107 1.877 -1.509 -1.935 1.576 -0.416 -0.252 -0.865 -0.951 -0.020 2.192 -0.114 0.478 -1.044 -0.162 0.164 -0.098 -0.493 1.477 -0.278 -0.186 0.244 -0.444 0.903 -1.803 -0.549 1.107 0.322 +0 -0.325 0.396 -0.130 -0.247 -1.201 -2.081 0.818 0.243 0.902 -0.437 -0.713 0.007 0.854 -0.231 0.802 -1.006 -0.161 -0.070 1.226 0.208 1.746 -1.348 0.130 0.179 0.620 -0.813 1.684 0.013 +3 0.096 1.917 -0.132 1.158 -0.358 -0.236 0.494 -1.702 -2.019 -0.677 -1.300 -2.256 0.743 -0.791 -0.344 0.139 0.505 0.283 0.202 0.469 -0.500 -0.713 -2.317 0.638 -0.686 1.984 0.738 0.950 +3 0.606 0.212 0.345 -0.669 0.485 -1.667 0.606 2.629 0.480 -0.027 0.618 0.071 1.679 1.231 -0.191 -0.941 0.220 0.218 0.410 1.092 1.831 -0.361 -1.372 -1.027 -0.680 1.067 -1.813 0.148 +4 -0.772 -1.946 -0.922 -1.977 0.100 0.192 0.202 -2.113 1.222 -0.051 -0.549 -0.945 1.042 0.557 -0.917 0.970 -1.977 0.208 2.272 -0.744 -2.116 1.758 1.261 -0.085 0.750 -1.054 1.538 0.330 +4 0.257 0.025 1.696 -0.144 -2.812 -1.514 0.574 -1.305 -0.000 0.386 0.769 0.289 -0.689 -2.831 -1.031 -1.100 0.150 -1.763 -0.010 1.048 -0.099 0.803 -1.016 0.522 -0.283 0.083 -1.269 1.241 +4 -0.223 0.991 0.210 0.668 2.499 -1.985 -1.027 -0.928 -0.854 1.423 -2.667 0.482 -1.533 0.026 0.259 0.906 -0.266 -0.173 -2.050 2.280 0.306 0.439 1.073 -0.067 0.424 0.715 -1.475 0.531 +2 0.261 -1.262 1.326 -0.827 1.189 0.329 1.001 0.176 0.567 0.990 1.907 1.075 0.448 -1.349 -2.416 -0.130 -0.714 -0.678 -0.082 0.663 -0.621 0.895 0.033 0.038 1.875 0.178 -0.054 0.524 +0 -0.711 1.436 -0.832 -0.068 1.045 0.512 0.048 0.305 1.109 -0.100 0.354 1.281 0.090 -0.452 0.717 1.213 1.205 0.698 1.575 0.174 -0.464 -1.386 -0.985 0.466 0.467 1.450 0.661 0.424 +1 1.052 -0.168 1.043 -1.803 -0.080 -0.068 0.071 -2.318 -0.618 0.034 0.593 -0.193 -2.037 0.789 -0.131 -0.640 -0.479 -0.656 -0.094 0.351 1.678 0.224 0.361 0.390 -0.139 0.318 -0.808 -0.718 +0 0.855 0.534 0.327 0.865 0.089 0.985 0.614 -0.360 0.896 0.091 -0.106 -0.476 0.166 0.996 0.889 0.353 0.600 1.272 -0.150 -1.115 1.301 -0.530 -0.690 -1.796 0.571 -1.621 -0.750 0.588 +3 -0.283 -0.680 0.538 -1.868 -0.389 0.291 -0.623 -0.429 0.991 -0.057 1.195 1.637 0.531 -0.291 1.582 -2.527 -1.552 0.770 2.474 0.683 0.185 -0.466 0.556 1.158 0.749 0.063 1.034 0.305 +0 -1.520 -0.187 0.553 0.285 -0.323 -0.572 0.976 -1.157 0.177 -0.815 -0.728 -0.514 -0.174 -0.449 0.092 0.646 0.480 0.078 0.013 0.137 0.018 0.243 -1.549 -1.368 0.077 0.373 -1.857 0.357 +1 0.612 -1.008 1.155 -0.653 -1.215 -0.349 -0.329 -0.784 -0.986 0.778 0.466 0.006 0.897 -0.148 -0.908 1.147 -0.230 0.638 -1.750 0.332 1.039 -1.923 0.081 -1.724 0.116 1.249 -1.313 -0.450 +2 -0.287 1.748 0.101 1.288 -0.452 0.125 1.056 1.331 -0.084 -1.378 -0.912 -0.387 0.475 -0.766 0.462 0.293 -0.461 -1.884 0.502 -1.691 0.846 2.160 0.023 -0.513 1.518 -0.273 0.267 0.666 +3 0.006 0.096 -0.906 -0.228 -0.180 -0.248 -0.575 0.265 -0.121 -2.261 2.151 -0.965 -1.173 -2.224 1.779 -1.000 -1.914 -0.592 0.616 -0.497 -1.912 -0.268 -0.928 0.643 -0.325 0.379 0.885 0.101 +1 0.263 0.333 0.945 -0.331 -1.356 -0.274 -0.020 1.126 -1.509 -0.377 0.293 1.546 -0.669 -0.813 0.665 -2.277 -0.567 0.591 0.353 0.202 -1.640 -1.275 1.343 -0.094 -0.218 -0.790 0.019 -0.488 +3 0.582 -0.114 -0.718 1.056 -0.110 -1.036 1.552 2.471 0.157 0.356 1.812 -0.298 1.125 -0.572 0.481 -0.965 -0.256 -1.658 0.491 0.400 -1.021 2.195 0.076 -0.141 -0.978 0.781 -0.897 0.765 +1 1.063 -0.139 0.839 -2.127 -0.091 -0.706 1.121 0.072 -0.329 1.267 -0.153 0.574 0.489 0.388 0.039 2.108 -0.493 -0.465 -0.884 0.207 1.002 1.640 -0.169 0.274 1.522 1.069 -1.199 -0.075 +3 0.258 -3.564 -0.969 -0.150 0.965 0.312 0.590 -0.910 -0.431 -1.797 0.660 -0.551 -0.954 0.311 1.164 0.672 0.093 -1.057 -0.022 0.321 -0.501 0.707 -0.987 -1.249 -0.823 -0.656 0.400 1.334 +3 0.783 1.288 2.530 -0.935 0.702 0.460 -0.386 -0.647 1.652 -1.022 -1.409 -0.563 -1.782 -0.846 -1.053 0.086 1.445 -0.589 0.865 0.751 -2.220 -0.552 -0.376 0.381 0.570 0.331 -0.895 -0.633 +4 0.880 -2.102 1.272 -0.889 -0.911 2.488 0.057 -0.255 -1.153 0.443 0.793 -0.351 -2.148 1.627 -0.289 -0.311 -0.035 -0.805 1.274 0.726 0.488 2.311 -0.697 1.277 -2.224 0.644 2.258 -1.050 +1 0.565 -0.223 1.044 1.347 -1.296 1.116 2.237 0.526 0.984 -0.647 -0.504 -0.210 -0.402 -1.312 -0.173 0.836 -1.299 0.179 0.148 -0.301 0.497 0.403 -0.372 1.061 0.561 1.573 -0.066 -0.322 +0 -0.357 0.217 0.352 -0.031 0.188 0.659 0.613 1.534 1.043 0.018 0.641 0.424 0.164 -0.869 0.162 -2.065 1.072 0.350 -0.159 -0.421 0.655 1.838 0.375 1.902 0.784 -0.805 0.488 0.390 +3 -1.563 -0.529 0.794 -1.254 0.294 -1.357 0.466 -0.036 -1.615 1.165 -0.735 -0.810 0.201 1.149 -1.016 0.062 0.429 0.693 0.176 -0.367 -0.828 0.086 -1.072 -2.921 0.437 0.904 -2.363 -1.010 +1 -0.460 2.452 -0.608 -0.325 -1.255 -2.299 -0.451 -0.066 0.019 -0.818 -0.065 -0.761 -0.488 0.959 1.668 0.448 -0.074 0.102 -0.754 -0.257 0.899 1.157 -0.618 -1.197 0.129 0.719 0.926 0.539 +4 0.950 -1.485 -2.554 0.934 -1.367 -0.225 -1.170 -1.802 0.541 0.759 -0.577 -2.591 -0.546 0.392 -1.479 0.183 -0.015 0.579 0.120 -0.973 1.197 -0.159 -0.027 -0.933 -0.443 -0.885 -0.173 1.712 +4 0.238 1.032 1.537 -0.120 -1.337 -1.303 -0.691 -0.935 -1.682 1.848 0.528 -0.524 -0.033 -1.786 1.157 -1.950 0.510 -1.838 -0.414 1.243 -1.634 -0.149 0.628 0.464 0.070 0.496 -0.611 1.933 +4 -0.833 2.085 -0.220 0.939 0.741 -1.185 1.700 -0.192 0.905 -2.201 -0.492 -0.121 0.829 -0.039 1.761 -1.753 -0.946 -0.772 -1.187 -0.246 -1.760 -1.366 -1.975 0.744 -0.837 -0.213 0.355 -0.394 +0 1.207 -1.129 -1.126 -0.536 0.099 0.048 1.240 1.295 0.006 -0.228 0.517 1.063 0.552 -0.432 -0.612 -1.111 0.193 0.623 0.735 1.152 1.480 -1.375 0.405 0.439 -0.074 -0.828 1.445 -0.589 +3 0.464 -0.242 -0.462 -1.779 0.690 -0.273 -0.218 0.589 1.366 -0.498 0.183 1.676 0.308 -0.271 1.822 -1.298 0.298 -0.469 0.611 0.493 -3.536 1.032 0.333 1.504 -0.992 -0.248 0.803 -0.117 +0 -0.689 -0.685 -0.549 -0.594 -1.280 -1.124 -0.829 0.592 -0.381 -0.207 1.270 1.295 -0.282 -0.631 -0.864 0.693 -0.205 -0.386 -0.814 -0.770 0.320 -1.315 0.995 -0.534 -0.150 -1.337 -0.012 -0.328 +1 -0.432 2.366 -1.415 -0.130 0.273 0.154 -0.561 1.232 0.667 0.740 -0.668 -0.403 0.488 -1.101 0.666 -0.101 1.065 -0.428 1.814 0.411 0.520 -0.693 0.253 -0.549 -1.540 -0.196 1.163 -0.064 +3 -0.800 1.530 -1.119 -2.351 -1.298 0.386 -0.118 0.752 0.502 -0.636 -0.489 -0.385 1.884 0.691 1.892 0.841 -0.064 -1.019 0.202 -0.061 -0.094 -0.209 -2.052 1.261 -0.050 -1.414 0.482 -0.856 +4 -2.291 -1.187 1.766 0.326 -2.700 0.070 1.803 0.274 0.044 -1.828 -0.271 -1.643 -1.222 -1.451 1.739 0.728 1.401 -1.323 0.191 -1.008 -0.901 0.469 0.179 0.286 -1.256 0.128 0.348 -0.675 +4 2.973 -2.141 -1.389 1.175 0.915 0.128 1.120 -1.117 1.955 1.387 1.685 -1.574 1.067 0.002 2.388 0.261 -0.888 0.584 -1.693 1.058 -0.425 -0.179 0.249 -0.313 1.627 -0.282 0.517 0.560 +1 0.786 -0.055 -0.119 -0.314 -1.380 -0.283 0.006 -1.325 -1.102 2.327 1.625 -0.735 -0.736 0.545 -0.415 0.086 -0.046 -0.494 -1.496 -0.828 -0.390 -0.636 -1.254 1.664 0.497 -0.220 0.640 -0.262 +0 0.449 -0.613 0.977 0.407 -0.068 0.313 -0.265 0.687 -0.954 0.855 -1.237 1.826 1.341 -0.557 -1.742 -1.479 0.154 0.276 0.649 -0.838 -0.602 0.612 -0.451 -0.730 -0.215 0.906 0.438 0.987 +3 -1.054 -0.330 -0.331 -2.091 -0.640 -0.861 -0.538 -1.080 0.126 -0.047 0.043 0.306 -0.006 0.965 -1.423 -0.854 0.616 0.636 1.863 -1.634 -1.552 0.161 -2.329 -0.845 -0.043 -0.365 1.052 1.253 +2 0.235 -0.055 0.441 -0.012 0.835 -0.763 0.756 0.472 1.089 1.157 -0.952 0.517 -1.166 1.160 -0.853 -0.380 0.218 0.738 0.371 0.766 0.012 0.244 -1.767 1.026 -0.291 -0.430 1.937 -2.709 +3 1.128 0.390 -0.670 -0.633 0.560 0.761 -1.007 2.046 0.905 -0.410 -1.022 -1.520 1.388 0.812 0.037 0.353 -2.228 -2.300 0.478 0.259 0.140 1.532 0.716 -0.500 -0.493 0.585 0.548 1.865 +1 0.911 -1.504 0.003 1.279 0.262 1.356 -1.508 -0.227 0.120 0.553 -0.851 0.416 0.494 -0.074 0.470 0.316 -0.658 -0.158 -1.249 -1.496 0.254 -1.894 -0.242 0.413 0.016 2.185 0.427 -0.269 +1 0.774 0.935 1.090 0.445 -0.339 0.352 -0.997 -0.172 -0.305 -1.174 1.558 -0.003 -1.153 -1.988 -1.640 -0.606 0.847 -0.740 0.456 1.598 -0.251 1.040 -1.299 0.326 -0.419 0.278 0.762 -0.493 +3 -1.208 0.404 0.676 0.930 -0.312 1.380 -0.928 0.134 -0.138 1.973 1.208 0.032 0.198 1.536 -0.752 -2.033 -0.968 -0.414 1.361 1.036 -0.044 0.467 -0.709 2.089 1.213 0.054 -0.074 0.702 +4 -0.654 -0.908 1.074 -1.879 1.099 -0.451 0.285 -3.421 -1.773 -0.679 -0.912 0.758 0.303 1.934 0.513 -0.650 1.223 -0.463 -0.520 0.002 1.619 1.051 1.020 1.961 0.996 -1.228 0.538 1.623 +1 -2.065 -0.982 -0.834 0.174 0.225 0.587 1.879 0.028 0.845 -0.445 0.290 -0.349 -0.149 0.166 0.984 -0.054 -0.057 0.313 0.879 -1.408 0.244 -1.331 1.309 0.339 1.227 -1.665 -0.169 0.870 +3 0.982 0.512 -0.041 1.178 0.257 0.469 -0.294 -0.459 0.188 -0.893 0.155 0.206 -1.027 1.290 1.456 1.369 -0.170 0.179 -0.339 0.743 -0.168 0.221 0.371 -1.089 -0.030 0.923 3.830 -1.763 +0 -0.241 -0.552 0.977 0.027 -0.980 -1.099 0.277 -0.544 0.673 0.127 -0.943 -0.736 -1.304 0.570 0.187 0.551 2.080 -0.128 -0.033 0.342 -1.259 -1.204 -0.314 -1.638 0.350 0.131 -1.014 -1.363 +1 -1.892 0.158 0.265 -0.178 0.876 -0.331 -1.249 -1.159 0.282 0.551 1.751 -0.623 0.622 0.445 -0.000 -1.141 -0.739 0.089 -0.373 -1.699 0.180 0.724 0.683 -1.061 1.506 -1.322 -0.030 -0.208 +2 -0.188 -0.992 -0.847 -1.723 -0.318 0.173 0.074 -0.151 -1.980 0.697 0.453 -0.145 -0.647 -0.718 -1.073 -0.364 -1.532 1.590 0.185 0.750 0.009 -0.195 0.195 1.974 -0.214 -2.099 0.890 -1.199 +3 1.074 0.258 0.509 0.796 -0.088 -1.567 0.162 1.182 -0.287 -0.506 -0.153 -0.676 -0.931 -0.564 -0.059 0.900 -1.508 -0.019 0.357 -3.870 0.028 -1.162 0.061 -0.543 -0.096 -1.516 -0.952 -0.806 +4 1.227 -1.581 1.245 2.231 -1.321 0.771 0.001 -1.544 0.493 0.834 -0.601 1.166 -1.667 -1.495 0.137 0.025 -0.717 0.810 0.531 0.858 -0.123 0.603 -0.446 2.054 -0.813 2.105 0.035 -1.376 +1 -0.246 -0.843 2.171 -0.176 0.123 0.551 0.044 1.695 -0.623 0.195 -0.742 -1.320 -0.612 -0.037 -0.429 -0.692 -1.406 -0.083 -1.505 0.760 0.082 -1.458 -0.309 -0.752 0.319 1.340 -1.875 0.115 +4 1.570 0.407 -1.337 -1.137 -1.468 -2.618 -1.729 -1.490 1.715 -0.752 -0.019 -0.835 -1.413 -0.366 -1.573 1.185 -1.119 0.484 -0.197 -1.530 0.298 0.000 0.476 1.703 1.876 -1.847 -0.203 1.013 +2 -0.869 -0.008 0.868 -1.258 -0.928 -1.232 -1.107 1.167 0.076 -0.422 -1.115 -0.466 -0.391 -0.857 0.369 -0.907 -1.424 -1.249 -0.500 1.347 1.449 -1.493 -0.952 -2.243 -0.196 -0.711 0.259 -0.925 +1 1.015 -0.369 -1.192 -2.017 -1.960 0.285 0.127 -0.202 -0.582 -0.425 0.259 -0.593 0.049 -0.253 0.098 -2.182 0.810 0.278 -0.475 -0.316 0.527 -0.129 0.605 0.113 0.930 -0.966 0.127 -1.781 +0 -0.562 -0.209 -1.683 -0.806 0.965 1.616 -1.234 -0.592 -0.026 0.280 -0.810 0.424 -0.474 -0.014 0.546 0.006 -0.436 -0.110 -0.088 -0.370 -0.259 1.599 0.561 -0.295 0.697 -0.334 1.173 0.370 +0 0.326 1.954 0.361 -0.910 -0.986 0.517 0.097 -0.146 -0.257 -0.118 -0.506 -0.938 -0.308 -1.158 -0.812 -0.139 -0.163 0.196 1.177 -0.592 0.462 1.833 -1.921 -0.320 0.725 0.654 -1.250 0.134 +1 -0.641 -0.320 1.169 1.097 0.253 1.620 0.471 -0.020 1.184 0.156 0.620 -1.987 0.033 -1.019 -0.745 -0.064 1.163 0.629 -1.077 0.052 -0.015 0.028 1.076 0.826 1.229 -0.137 -1.533 0.043 +1 0.291 0.904 1.346 1.143 -0.453 0.578 -0.156 -0.236 0.501 0.115 -1.221 -1.595 -0.039 2.858 -0.248 -1.090 -0.152 0.272 -0.458 1.017 0.631 1.028 -1.124 0.621 0.199 -0.341 0.147 0.459 +0 -0.084 -0.107 -1.230 1.748 -0.485 0.340 0.254 -1.340 -1.745 0.006 -0.454 -0.089 1.065 0.225 0.759 0.445 -0.316 -1.002 0.265 0.084 0.915 -0.151 -0.297 -0.229 -0.919 1.447 1.140 -1.213 +4 -1.054 -1.068 0.950 1.711 -0.104 -0.169 0.070 1.162 -0.927 0.238 0.975 0.501 0.190 1.001 -2.703 0.678 -0.654 -1.831 0.511 1.374 -0.137 0.953 1.612 1.315 1.640 0.742 0.075 -1.602 +2 -0.798 -0.129 -0.738 0.819 -0.229 1.874 -0.561 0.141 -0.289 0.440 1.066 0.608 -2.031 0.500 -1.011 -0.310 0.689 -1.120 1.156 -0.353 2.171 0.069 1.705 1.198 -0.611 -1.302 0.114 -0.583 +2 0.739 0.860 0.591 -2.197 0.013 -0.187 0.991 0.239 0.557 -0.495 0.939 1.234 -1.627 -1.296 0.762 -0.172 1.029 0.242 0.693 -0.747 0.340 0.343 -0.689 1.002 -1.895 0.208 -1.294 -1.022 +4 -0.129 -0.329 -2.303 1.126 1.525 -1.266 -0.766 -0.333 0.182 0.471 -1.649 2.311 0.488 0.851 0.757 -2.196 0.025 0.892 -0.803 -0.563 0.085 1.458 -0.121 0.189 -2.079 -0.627 -0.220 0.457 +1 0.401 1.855 1.020 0.535 1.030 -0.371 -0.508 -0.748 -0.125 -0.422 1.208 -0.475 -0.803 1.943 -0.451 0.154 0.451 -1.137 1.235 0.637 1.558 -0.679 0.099 0.039 0.264 -1.455 0.111 -1.426 +1 0.173 -0.883 -0.621 0.797 1.442 -2.115 0.180 -0.113 0.107 -0.317 -1.290 1.542 1.159 -0.104 -0.488 -0.609 -2.586 0.354 0.780 0.138 -0.086 0.133 -0.853 -0.500 -0.166 0.697 1.379 -0.229 +1 -0.277 2.984 -1.728 0.310 -0.039 0.853 0.199 1.215 0.015 0.774 0.366 -0.292 -0.434 1.278 0.625 0.996 1.255 -0.518 -0.271 -0.341 1.031 0.091 0.183 -1.055 0.824 0.857 0.561 0.163 +0 -1.040 -0.675 0.887 0.425 -0.388 0.883 -0.650 -0.854 1.003 1.347 -0.498 0.727 0.871 0.139 -0.195 -0.603 0.153 -0.256 1.060 1.091 -0.049 0.529 0.412 1.565 0.002 0.399 -0.133 0.631 +4 1.365 -0.806 0.026 0.903 -3.042 -0.050 -1.572 0.963 -0.792 1.784 -0.955 0.450 -0.876 -0.237 -0.004 0.026 1.655 1.427 -0.749 0.043 -1.164 -1.391 -0.667 1.683 -1.688 0.576 0.460 -0.743 +2 1.120 0.283 -0.339 -0.041 0.020 -0.802 0.579 0.460 1.540 1.255 -0.686 0.820 -0.951 0.696 -0.517 -1.023 0.280 -2.675 -1.751 -0.177 0.082 -0.369 -1.579 -0.818 0.391 0.582 -0.016 1.157 +1 0.478 1.008 1.859 0.121 -0.029 1.425 -0.678 0.475 -0.588 -0.141 -0.722 0.667 -0.226 -1.218 -1.254 0.131 1.076 -1.853 -0.272 -0.607 0.891 -0.963 -0.023 -1.269 1.196 -0.930 1.162 0.055 +1 -0.337 0.560 -1.697 0.818 -0.977 -0.170 0.881 -0.284 -1.136 -0.090 -1.066 -0.356 0.112 0.451 0.674 -0.487 -1.737 -1.921 0.248 -0.813 -0.439 -1.986 -0.042 -0.435 -0.806 0.379 -1.432 0.487 +3 -0.442 -0.575 -1.645 -0.431 0.708 0.408 2.147 -0.049 -0.194 0.707 0.104 0.104 -1.032 0.692 2.080 1.213 2.060 -0.446 -1.159 -0.470 -1.089 1.872 0.128 -1.948 -0.176 0.539 -0.217 -0.347 +2 0.149 0.827 1.577 0.325 -0.784 0.071 -1.085 -0.700 0.245 1.257 1.247 0.525 0.787 -0.382 -0.218 -0.846 0.413 1.773 -1.524 -0.184 -0.103 -0.493 -0.060 0.615 -0.425 0.886 -2.225 -1.709 +3 0.332 -0.344 -0.044 -0.343 -0.586 -0.298 0.524 0.371 2.437 1.247 -2.000 0.808 2.746 1.078 -0.706 0.898 -0.290 -1.078 0.143 1.370 1.211 -0.216 -0.521 1.524 1.259 -0.364 -0.671 0.099 +4 -1.358 -0.501 0.530 -1.013 -0.111 2.147 0.214 -1.127 2.787 0.372 -0.822 -0.101 -1.701 -0.821 0.873 -1.236 -1.414 0.941 2.196 0.551 1.769 -1.148 0.464 1.129 -0.509 -0.951 -0.105 0.352 +0 -0.720 -0.571 -1.070 -1.305 -0.260 -0.328 -0.172 -0.612 -1.413 -0.718 -0.721 -1.479 0.469 -0.804 -0.184 -0.779 -0.609 -0.430 -0.597 -0.268 0.196 0.887 -1.020 -1.973 0.896 0.040 0.252 -0.654 +4 1.008 -0.799 0.500 -0.962 2.198 1.560 2.074 2.191 -0.635 -0.884 2.026 -0.141 0.833 -0.621 -0.604 0.830 -1.970 0.783 0.524 -0.530 0.024 0.181 0.237 0.690 -0.815 2.698 -0.206 -0.131 +2 1.306 1.365 0.154 1.760 -0.083 0.932 1.516 0.098 0.457 1.026 -0.366 -0.814 1.333 0.307 -0.506 0.094 0.509 -1.569 0.716 0.408 1.655 -0.219 1.377 -0.045 0.139 0.281 2.093 -1.239 +1 0.364 0.346 0.554 0.809 -0.329 1.035 -1.084 -0.005 0.836 0.995 0.461 -0.393 0.897 -0.669 1.751 -0.085 -1.540 -1.195 0.044 0.140 1.970 -1.038 0.039 -1.059 -0.493 1.684 -1.136 0.587 +2 0.576 0.350 -1.306 0.911 0.465 -0.898 1.098 -0.021 -2.092 -0.817 -1.469 -0.372 -0.091 -0.257 -1.274 -1.004 -2.109 1.189 -0.231 0.440 0.600 0.713 -0.245 0.896 -1.551 -1.301 -0.825 0.659 +3 -0.274 0.639 -0.751 -1.468 0.641 -0.758 1.828 -0.409 -0.136 -2.304 -1.162 -2.279 0.294 -0.082 0.630 0.807 0.961 -0.225 0.085 0.579 -0.795 -0.938 1.540 -1.806 0.500 0.341 -1.123 -0.668 +2 -1.230 -1.117 1.194 0.828 -0.834 0.491 0.106 0.822 1.278 -1.726 0.421 0.038 1.096 -1.719 -1.284 0.096 -0.275 0.352 -1.640 1.194 0.182 0.181 -0.147 -1.149 -0.777 1.640 0.075 0.008 +0 -0.068 0.230 0.643 0.284 0.701 -0.263 0.127 -0.761 -0.452 0.843 -2.063 1.233 0.202 -1.082 0.747 0.294 0.515 0.181 -0.663 -0.231 0.106 0.609 -0.564 -0.538 1.305 0.237 0.191 2.120 +1 -1.304 -0.872 -0.131 -1.894 0.085 -0.263 0.572 0.650 -1.122 -0.519 -0.928 -0.526 -0.252 1.120 -0.596 0.887 0.260 1.386 -0.353 -0.813 0.144 -1.607 0.158 0.566 1.098 0.406 -0.143 1.941 +4 -2.139 0.033 -1.963 0.129 0.808 -0.859 2.857 0.930 0.829 -0.152 -0.437 1.237 2.151 -0.888 -0.473 0.431 1.427 -0.995 -1.207 -1.456 0.866 0.856 -1.514 1.725 -1.528 -0.824 -1.476 0.731 +3 -0.798 1.290 0.832 -0.191 -1.411 0.822 1.310 1.034 -0.969 -0.865 1.656 -1.209 0.478 -1.778 -0.300 0.585 -0.030 0.524 -1.226 -0.820 0.482 0.697 0.220 0.947 -1.657 -0.466 1.823 1.283 +4 0.036 -0.052 -0.311 -0.326 0.384 1.134 -0.481 2.438 1.961 1.329 0.622 2.008 -0.290 -0.833 -0.526 -1.003 0.122 0.300 0.201 -0.012 0.133 1.736 0.620 0.923 0.191 -1.616 -1.814 2.083 +4 0.751 -2.635 -1.021 -0.110 0.289 2.740 0.968 2.435 -0.296 -0.916 -0.434 -0.081 -1.761 -0.004 0.130 -1.970 0.010 -1.550 -0.283 0.559 -1.707 -0.447 1.046 0.488 0.112 0.386 -1.215 -0.786 +4 0.032 0.459 0.334 0.856 -1.072 -1.193 1.360 0.695 0.447 -1.672 1.542 0.484 0.501 2.779 2.954 0.550 -2.212 -0.306 -0.366 -0.126 0.838 0.388 0.758 0.958 0.656 1.256 1.854 -0.503 +4 1.906 -1.640 -0.464 -1.122 0.683 1.857 0.904 -1.392 0.883 1.685 -1.488 0.460 -0.265 -2.796 0.706 1.095 -1.031 1.464 0.824 3.002 -0.260 -1.189 -0.151 -0.241 -1.127 -0.752 0.631 -0.290 +4 0.801 0.596 -0.013 -1.047 0.558 -1.096 0.040 0.080 0.787 1.040 0.246 0.938 -0.439 1.293 -0.298 -1.856 1.164 2.038 -1.158 1.349 -0.718 -1.110 -2.256 1.381 -0.742 3.482 0.992 0.987 +1 0.411 -0.986 1.162 1.458 0.297 -0.320 1.613 1.546 -0.885 -0.264 -0.054 1.640 0.892 -0.322 -0.005 -0.109 -0.762 0.520 0.453 0.592 0.223 -1.086 -0.635 0.063 0.900 0.281 -1.230 -2.022 +3 -0.043 -1.171 1.735 1.826 0.502 0.121 1.695 0.908 -0.347 0.580 -1.104 -0.469 0.352 0.174 -0.842 -1.013 -0.304 -0.854 -0.581 -1.012 -1.438 1.554 2.240 -0.430 0.344 -0.549 -1.386 -1.052 +0 -0.153 -1.132 -0.358 1.294 -0.228 -0.612 0.326 1.227 -0.247 0.004 0.791 0.628 0.287 -1.258 0.231 1.203 -0.993 -0.233 0.016 0.115 0.788 -0.769 -1.633 -0.452 0.115 0.082 0.791 0.723 +0 1.724 -0.981 -0.249 0.629 0.493 -0.240 -0.247 0.877 0.688 0.225 0.383 0.396 0.100 -0.832 0.275 -1.817 -0.591 0.141 0.133 -0.321 0.977 -0.493 -0.272 -1.565 1.007 -1.619 1.013 -0.235 +1 0.590 -1.344 1.200 0.772 0.182 1.160 0.872 -0.558 -1.813 0.120 0.528 -0.025 -0.651 -0.012 -0.101 0.062 2.176 0.706 1.295 0.744 1.809 0.114 0.846 -1.225 0.385 -0.630 -0.144 1.176 +1 -0.236 0.292 1.009 1.026 -0.869 -1.113 1.778 1.783 0.733 -0.296 -0.293 -0.980 0.217 -0.122 -0.094 -1.150 0.954 0.449 0.456 -1.786 -1.517 -1.099 0.773 -0.489 -1.034 0.654 -0.324 0.373 +1 0.684 -1.260 -0.607 -0.593 0.942 0.528 0.904 1.339 -0.306 -1.187 -1.181 -1.838 -0.459 -0.384 0.788 0.017 -0.490 -0.116 -1.039 1.025 -0.786 -1.140 0.818 0.574 -1.724 0.855 -0.742 -0.374 +3 -2.172 -1.552 -0.075 0.296 -0.282 -0.542 -0.082 0.527 -1.147 -1.114 -0.866 1.320 0.373 0.572 1.591 0.787 0.156 1.558 -0.972 -0.265 1.156 -2.194 -0.797 0.790 0.107 0.514 -1.916 -0.841 +2 0.919 1.316 -0.327 -0.065 0.479 0.660 -0.081 -0.587 -0.160 -0.388 0.237 -1.083 1.167 1.971 -0.724 -0.785 -0.851 -0.475 1.743 -0.576 0.128 -0.469 -2.052 -0.556 -0.047 1.521 -1.750 0.944 +2 1.726 0.662 -0.743 0.335 0.535 0.760 -0.427 -1.953 1.450 -0.099 -2.098 -0.276 0.742 -1.070 -1.083 -0.250 0.106 -0.581 -0.368 0.812 0.014 -1.483 1.187 -0.185 -0.908 -0.188 -1.325 0.010 +1 1.199 0.309 -2.056 1.069 0.048 0.939 -1.347 0.211 -0.106 0.542 -0.098 -1.273 -0.295 0.223 -2.057 -1.625 -0.324 0.807 -0.408 -1.974 -0.275 -0.157 -0.736 -0.178 -0.254 -0.272 0.208 -0.818 +0 0.617 0.091 -0.334 0.471 0.189 0.299 -0.068 0.535 0.918 0.904 0.457 -0.004 0.445 1.195 -0.154 -0.272 0.494 0.738 -0.100 0.451 -0.840 -0.011 1.553 0.608 -0.647 -0.513 -1.244 1.623 +4 -1.424 -1.682 1.826 -0.919 1.684 -1.362 0.489 -0.346 0.379 -1.555 -0.446 1.983 -0.759 -0.489 -1.586 -1.362 0.969 1.466 -0.412 -0.679 0.677 -0.948 -0.018 1.758 0.039 -0.922 0.099 1.461 +3 -0.239 1.307 -1.066 -0.356 1.258 1.709 0.766 1.556 1.171 -1.709 0.563 0.239 -1.034 -1.704 0.828 -0.117 0.136 0.627 1.714 -0.640 -0.356 -0.320 -0.662 1.853 0.979 0.653 0.579 -0.103 +3 -0.732 -0.129 0.148 -1.054 -1.036 1.804 -0.479 1.323 0.628 0.217 1.341 0.026 -0.370 1.828 -0.328 0.581 0.176 -0.418 -0.051 1.600 -2.347 0.921 -0.507 0.859 2.601 -0.670 0.819 0.014 +0 0.792 0.030 -0.460 0.725 1.216 0.700 -0.556 1.067 0.025 0.107 0.960 0.598 0.648 0.141 1.222 -0.431 -0.850 1.361 -0.073 0.488 0.219 -0.240 -1.199 0.451 -0.643 -1.474 0.143 0.504 +2 -0.870 1.553 1.298 1.618 0.683 -0.029 1.615 -0.681 -0.871 -0.602 0.523 -0.762 -0.155 -1.602 0.481 -1.517 -0.932 1.217 1.180 0.374 0.276 -0.531 -0.773 -0.362 -1.514 0.252 0.049 -0.933 +3 -1.533 1.177 0.101 0.598 -1.552 -0.753 1.469 -1.253 0.548 -0.335 0.938 0.917 -1.116 0.522 -1.176 1.054 2.108 -1.071 0.469 -0.445 0.510 -0.356 0.484 0.506 1.272 1.607 -1.324 -0.488 +0 0.292 -1.707 -0.630 -0.303 1.053 0.684 1.272 0.604 -0.638 -0.208 -0.589 -0.073 -1.823 -0.338 0.027 0.795 0.011 1.007 1.211 0.695 -0.337 0.305 -0.178 1.640 0.156 -0.095 1.260 -0.676 +3 -0.552 0.866 1.650 -1.219 -0.507 -1.583 0.403 -0.695 -1.208 -1.441 -0.843 0.040 1.074 -0.758 0.290 -0.708 -1.429 1.575 1.860 2.223 -0.959 -0.017 -0.984 -1.187 0.853 -0.397 -0.333 0.774 +3 1.585 0.499 0.140 -1.098 0.428 -0.291 0.266 0.513 -0.468 0.282 1.847 1.261 0.909 2.381 1.958 1.521 -1.043 -1.187 -1.266 0.855 0.755 0.455 -1.062 0.965 -0.296 -0.213 0.716 -0.165 +2 0.211 0.408 -0.746 -1.553 0.616 0.118 1.302 -0.912 -1.282 0.711 -0.658 -1.551 -0.582 -1.044 -1.531 0.425 0.574 -0.308 1.845 -0.250 0.640 -1.539 -1.152 1.074 1.183 -0.215 0.614 0.318 +3 0.011 -1.298 -0.294 -0.010 1.606 1.013 0.351 -1.348 0.436 -0.645 -2.385 0.956 0.642 -0.759 0.518 0.016 -0.522 0.537 1.167 -0.453 -0.860 1.196 1.622 1.723 0.879 -1.003 -1.339 1.246 +3 0.773 -1.056 -0.706 0.757 -0.859 -1.082 -1.179 0.352 0.309 -0.684 0.005 1.527 -0.974 -1.952 0.455 -0.110 -0.710 0.652 0.839 -2.208 -0.653 2.318 0.583 0.676 1.173 0.017 0.797 0.718 +2 0.001 0.692 1.354 1.475 -0.295 -0.987 -0.319 -1.182 -0.217 -0.342 0.727 -2.206 -0.734 -0.726 1.187 -1.114 0.227 -0.128 1.043 0.596 0.121 -0.555 -1.852 0.247 0.530 -1.575 -1.234 0.252 +1 1.801 0.148 -0.473 0.406 -1.589 -0.972 -1.407 -0.368 1.609 0.597 0.479 -0.660 0.780 -0.776 -0.204 0.755 -0.081 0.915 -0.201 1.358 0.417 -0.882 -0.543 -0.223 0.957 1.837 -1.487 -0.386 +1 0.112 -1.047 1.678 0.413 -0.153 -0.603 0.512 0.302 0.063 -1.189 1.397 -0.137 2.206 -0.843 -1.052 -0.691 -1.039 -0.654 -0.543 -0.828 -0.105 0.418 0.823 1.302 0.722 -0.098 0.974 -0.742 +2 1.613 -0.235 1.409 -1.285 0.127 0.999 -0.175 0.221 0.268 0.777 0.118 1.508 0.386 0.699 -0.378 -0.255 2.000 -2.187 -0.073 -1.170 0.815 0.719 -0.480 -0.019 0.404 1.613 -1.105 -1.032 +3 1.295 -1.894 -0.292 -1.553 -0.241 -1.244 -1.134 1.486 -0.819 -0.278 -0.661 1.592 0.307 -0.367 0.201 -0.186 -0.769 -0.585 1.723 -1.337 0.802 -2.760 -0.249 -0.440 0.114 -0.106 -0.284 -1.292 +0 -0.157 -0.037 -1.223 -0.140 0.066 -0.660 0.633 0.502 -0.290 1.189 -0.275 -1.063 0.117 -0.177 1.178 0.166 1.489 -0.027 -0.636 -0.283 1.523 0.356 -0.451 -0.337 -0.962 -0.752 0.143 -1.250 +1 -0.189 -0.480 -1.481 -0.725 1.446 0.041 -0.593 -0.026 -0.367 0.440 -0.354 -0.051 -2.037 -0.983 0.309 -0.149 0.583 0.250 1.404 -2.905 -0.060 0.641 0.080 -0.259 -0.439 -0.644 -0.440 -0.918 +4 3.452 0.034 2.889 -0.126 -1.053 -0.317 1.101 0.619 0.308 -0.061 0.129 -0.149 1.197 0.916 -0.942 -1.477 -1.226 -0.356 0.221 -0.055 0.630 -0.015 1.596 0.509 0.018 -1.511 0.350 0.499 +4 -0.361 0.101 0.272 2.523 -1.039 -1.308 -0.588 -3.035 -1.892 -1.326 1.133 -1.352 -1.697 -0.844 0.400 -0.513 -0.342 -0.574 -1.324 -0.924 -0.818 0.634 -1.120 1.020 0.684 -1.564 0.386 0.056 +0 -0.286 -0.879 -0.864 -0.039 -0.587 0.127 -0.838 0.707 -0.460 1.637 -0.051 -0.102 1.604 -0.495 -0.407 -0.701 0.204 -0.735 0.777 -0.047 0.839 0.726 -0.512 -0.737 -0.328 0.009 0.334 0.445 +1 1.342 0.232 -0.787 0.306 -0.815 1.196 -0.115 -0.801 -0.555 -0.789 1.537 -1.128 -0.068 0.257 -0.441 -1.349 2.093 -0.325 -1.312 -0.778 0.120 -0.174 -1.403 0.305 -1.170 0.774 0.389 -0.301 +4 -1.670 -1.946 0.439 -1.176 -0.044 0.639 -0.554 -0.238 0.095 0.512 0.071 -0.645 0.481 0.396 1.708 0.506 -1.107 -1.055 -0.579 -2.323 -1.698 0.101 -1.136 1.964 -0.001 1.515 -1.618 -0.685 +1 -0.387 -0.191 1.960 -0.184 -0.825 0.476 0.416 1.269 1.597 1.000 -1.434 0.263 -1.413 -1.605 1.042 0.041 -0.131 0.464 1.364 1.180 0.389 -0.296 0.422 0.493 0.736 -0.041 0.845 0.117 +3 0.720 -0.078 0.298 -0.101 -1.532 0.478 1.029 1.506 -0.019 -1.922 0.016 -0.177 0.697 -1.348 -0.700 -0.908 -0.358 -0.182 -0.814 0.998 1.532 -2.361 0.191 2.219 0.142 -0.940 -0.644 1.216 +3 -0.709 0.284 1.726 0.504 0.173 1.773 1.179 -0.816 -0.648 -1.402 0.625 0.309 0.490 -1.760 -0.245 1.391 -2.168 0.023 -0.052 -1.393 -0.935 -0.300 0.198 1.383 -0.359 0.367 -1.782 0.008 +4 1.227 1.441 0.128 -0.543 -2.194 -0.455 -1.275 0.028 0.812 0.742 0.826 -1.490 -1.473 1.286 -2.024 0.698 -0.190 -0.335 0.196 1.810 -0.794 -0.401 1.123 -0.878 0.848 -0.045 -2.241 0.115 +0 0.752 -0.588 1.089 0.382 -2.342 0.300 -0.406 0.537 0.944 0.195 1.665 -0.179 0.625 0.805 -0.097 0.779 -0.595 -0.423 -1.312 0.241 0.325 1.179 0.791 -0.285 -1.178 0.433 1.111 0.755 +4 0.157 0.443 0.242 -1.140 -1.428 2.503 2.247 -1.738 -0.650 0.297 0.260 0.267 -0.993 -0.357 1.127 0.098 -0.486 -1.012 0.901 0.149 -0.368 0.553 -0.114 -1.822 0.638 3.196 -1.234 0.417 +0 0.297 -0.175 0.227 -0.360 0.495 -0.884 1.074 -0.362 0.832 0.765 0.152 0.691 -0.444 0.644 -1.603 -1.055 -0.180 -1.892 0.088 2.121 -0.712 0.045 -0.959 1.080 -0.223 -0.014 -0.211 -0.259 +3 -0.160 0.671 0.213 -0.752 -0.319 -0.796 1.076 0.021 1.901 -0.061 -0.708 -1.514 -1.803 -1.584 0.267 0.509 -1.581 0.895 -0.483 0.147 1.612 0.897 -0.269 -0.891 -2.152 -0.719 -0.211 -0.987 +1 0.472 1.218 0.907 0.314 0.997 0.170 0.082 -1.473 0.212 1.508 -1.636 -1.312 -0.327 -1.356 0.684 -1.728 0.681 1.004 0.657 0.020 0.534 -1.155 0.260 -0.618 0.891 0.710 -0.810 0.352 +0 -0.259 -0.113 0.551 -1.189 -0.315 0.069 0.132 1.449 0.043 -0.235 -0.628 0.415 -1.122 -0.260 -1.013 0.463 -0.115 0.902 -0.912 0.461 -0.963 0.234 0.033 0.544 -0.328 -0.201 1.516 -0.561 +2 -1.962 -0.418 -0.012 -0.004 -1.013 -0.677 1.169 1.341 1.696 -0.480 -0.979 -0.408 1.312 0.161 -1.646 1.064 1.119 0.102 0.424 1.081 -0.518 2.196 0.925 0.078 0.111 -0.433 0.007 1.113 +0 0.448 1.238 -0.593 -0.713 1.615 0.958 -1.429 0.016 1.023 0.178 -0.550 -0.669 0.064 1.021 -0.995 -2.134 -0.406 0.450 0.672 -1.014 0.270 -0.639 0.736 -0.132 0.838 0.067 0.500 -0.443 +1 0.472 2.081 -0.786 1.114 0.853 0.233 0.272 0.718 0.026 -0.428 1.120 -0.136 0.136 -1.001 -0.068 -1.425 0.145 -1.426 -1.743 -1.452 0.456 -0.969 -0.128 -0.638 0.428 -1.764 -0.053 -0.411 +1 0.289 -1.423 0.131 0.541 0.985 -1.398 1.316 1.695 0.410 -1.038 -0.328 -0.048 0.270 0.022 0.281 -0.091 0.265 -0.461 -0.338 -0.576 1.917 1.464 0.555 1.325 0.798 0.181 -1.506 -0.262 +4 -0.397 2.466 -0.255 1.195 2.679 0.317 -1.742 1.510 -0.512 -0.687 1.779 -1.302 1.435 -0.224 -1.853 0.884 -2.252 -0.046 -1.634 -0.328 0.697 0.423 0.208 1.009 -0.000 -0.216 0.285 -0.662 +1 -2.640 0.164 1.275 -0.215 0.287 -0.287 1.313 1.109 0.508 -0.698 0.082 -0.282 -0.751 -0.637 1.333 -2.069 0.730 0.496 -0.641 -1.122 -0.178 0.131 0.558 0.144 -0.565 0.246 -0.303 -0.579 +1 -0.280 1.832 -1.192 0.543 0.172 -0.365 -0.247 -0.663 0.243 0.834 0.070 -0.021 -0.612 0.565 1.058 0.920 -0.423 0.821 -0.354 0.222 -2.867 0.542 0.309 1.127 -0.776 1.379 0.393 0.473 +2 -1.080 -1.210 0.675 1.591 0.105 1.828 0.028 0.607 -1.582 0.812 -0.637 0.831 1.283 0.305 0.433 0.119 0.640 1.873 0.011 0.165 -1.778 0.422 -0.781 -0.198 0.477 -1.616 0.466 0.251 +2 0.577 -0.382 -0.081 0.281 0.867 -0.296 1.858 -0.227 -2.307 -0.778 -0.103 0.747 1.523 0.352 0.203 0.076 0.858 0.777 -1.475 -1.765 0.198 -0.358 -0.537 0.521 0.515 -0.413 -1.000 -2.124 +4 -0.034 -0.489 1.862 -2.710 3.332 0.908 0.873 -1.486 -0.346 -2.219 -0.727 -0.373 0.326 -0.411 -0.217 0.053 -1.060 -0.399 -0.898 -0.591 -0.330 0.268 -0.248 -0.415 -1.200 -1.419 -0.082 -0.371 +1 -0.056 -0.511 -0.427 -0.878 -0.992 -0.018 0.171 -0.955 -0.077 0.733 -1.060 0.052 0.582 0.195 -0.543 -0.747 -0.824 2.808 -0.136 -0.121 -1.086 1.664 -0.668 -0.786 0.436 -1.467 -0.797 0.654 +4 0.931 -2.649 0.927 -0.555 -0.413 1.715 0.213 0.448 -0.049 0.764 1.618 -2.475 1.085 1.547 0.501 -0.677 -1.152 2.362 -0.905 1.518 0.391 2.046 -0.318 -1.608 0.002 -1.486 2.677 0.629 +0 0.058 1.189 0.208 0.395 0.315 -0.252 -1.246 0.967 1.215 1.272 0.014 -0.063 -1.344 -1.496 -0.192 1.452 -0.990 -1.134 -0.107 -0.399 0.003 -0.557 0.807 -0.763 1.028 -0.997 -0.596 -0.133 +2 -1.230 -0.509 0.269 0.436 -1.268 -0.938 0.725 0.508 0.887 1.368 0.538 -0.415 0.115 1.660 2.764 0.463 0.536 0.726 0.024 1.164 1.008 0.354 -0.318 -1.521 0.732 0.213 -0.738 -1.130 +2 -0.074 -1.750 0.110 -0.228 0.303 1.231 0.463 1.336 1.697 0.268 0.275 0.431 -0.865 -0.689 -0.251 -1.834 -1.996 -0.659 -0.830 0.635 -2.064 -0.084 0.314 0.465 0.855 -0.470 0.157 -0.249 +1 0.021 0.199 -1.215 -0.729 0.553 -1.383 0.134 1.238 0.385 0.335 -2.020 -0.822 2.054 -0.599 1.264 -0.611 -0.278 0.516 0.101 0.382 -0.818 -1.136 -0.886 -0.532 -0.229 1.037 0.087 0.485 +3 0.197 -0.908 1.499 1.139 -0.234 0.457 -0.088 1.504 -1.610 0.195 -0.953 0.423 -1.883 0.329 1.939 -0.546 -1.508 0.660 -0.316 0.944 1.099 1.032 -0.026 1.824 0.794 -0.723 -0.650 1.304 +3 -1.559 0.287 -0.181 0.851 -2.626 -1.739 0.519 0.538 0.775 0.611 0.061 -0.603 -1.874 1.938 -0.975 1.172 -0.609 -0.240 -0.820 0.066 0.489 -1.725 0.504 1.647 0.517 0.803 -0.177 -0.524 +1 -0.755 -0.486 -0.745 1.377 1.131 0.390 -2.371 1.478 -0.381 -0.622 -0.333 0.040 0.430 1.088 0.528 0.029 -0.372 -0.878 0.380 0.530 -0.764 -1.054 0.784 -0.422 -1.168 -0.947 0.516 1.572 +3 -0.546 0.482 0.163 -1.468 0.459 -1.100 0.939 -0.671 -0.161 1.372 0.496 -1.079 -1.015 -0.599 0.395 0.358 -0.843 -1.167 0.282 -0.182 2.157 0.037 1.814 -1.170 -1.573 -1.120 1.189 1.756 +2 0.825 -0.265 -0.385 -1.349 1.024 -0.671 -1.087 0.213 -0.767 -0.526 0.258 -0.326 2.312 -0.075 0.618 -1.942 -0.861 -1.087 -0.954 0.416 1.036 0.345 -0.227 -0.631 1.114 2.071 0.135 0.939 +3 -0.722 0.144 -1.984 -0.053 0.073 1.210 -1.511 -0.283 1.453 1.585 -0.834 -0.686 -1.700 -2.123 -0.389 -0.645 2.062 0.348 -0.209 0.656 1.028 1.214 0.260 -0.024 0.158 -0.058 -0.529 1.043 +1 -0.724 -0.035 1.288 -0.549 -1.748 2.141 -0.606 -0.257 0.032 -1.369 0.241 -0.489 0.236 0.554 1.187 1.753 -0.213 -0.564 -1.226 0.559 0.010 -0.169 -1.235 -0.672 0.608 0.924 0.244 0.077 +2 -0.314 0.403 0.321 0.762 -0.927 -1.069 2.792 0.853 0.260 -0.701 0.690 -0.338 -1.008 -1.682 -0.266 -0.116 -1.139 1.168 -1.605 0.251 -1.653 -0.405 -0.435 0.363 -0.257 1.144 0.214 -0.422 +3 0.159 -1.032 -0.809 2.295 0.844 0.369 0.514 0.306 -0.089 0.757 -0.827 1.596 0.120 0.802 1.573 0.491 -0.810 1.220 1.696 -1.947 -0.102 0.291 -0.264 -1.246 -0.883 -1.849 0.955 1.374 +4 0.181 -0.066 0.367 1.932 -1.746 -0.310 -3.076 0.507 1.087 -0.307 0.653 -1.536 0.479 2.455 -0.382 0.417 -0.590 0.176 1.108 0.599 -0.102 -0.119 -1.202 1.018 0.749 -1.255 0.176 -0.927 +1 0.480 0.610 0.546 -1.563 -1.012 1.181 -0.971 0.290 -0.622 0.989 1.084 -0.922 0.703 0.869 0.587 -0.233 -1.722 1.165 -0.622 -0.226 0.467 -1.012 -1.117 -0.855 -0.926 -1.096 -0.009 0.212 +3 -0.023 -1.214 0.846 -1.097 0.959 -0.425 0.437 -0.742 -0.620 -0.028 -0.509 -0.029 2.072 -0.088 0.573 0.082 -0.986 -2.209 1.050 0.471 -0.362 -0.985 -0.617 1.130 0.666 -3.026 1.120 -0.639 +3 1.038 2.344 -1.290 0.007 1.535 -0.040 -2.227 1.441 0.150 -1.011 0.238 0.539 -0.810 -0.114 0.653 -0.941 0.755 1.702 -1.846 0.196 0.480 0.153 0.814 0.331 0.972 0.833 -1.014 -1.497 +3 -0.576 -1.385 -0.059 -1.909 0.539 -0.285 1.406 0.455 -0.430 -2.532 1.814 -0.288 0.590 -0.030 0.278 -0.887 -0.976 -1.608 0.664 1.170 2.006 -0.795 -0.487 0.633 0.199 -0.006 -0.016 -0.553 +0 -0.658 -0.044 0.947 -0.064 -0.135 0.439 0.818 -1.449 0.956 0.192 -1.503 0.529 1.056 -0.049 0.450 0.352 1.220 0.737 -0.280 -1.404 -0.872 0.792 0.769 -1.126 -0.761 -0.350 1.406 -0.549 +0 -0.890 1.224 0.050 -0.049 -1.206 0.906 0.447 -0.349 0.858 -1.117 0.764 -0.502 -0.147 -1.231 -0.310 -1.236 -0.666 -1.051 1.127 -0.032 0.201 -0.047 0.120 -0.432 1.518 -1.309 0.187 0.401 +1 -0.350 0.553 0.415 -0.067 1.058 -0.433 0.364 -1.423 -0.382 -0.532 -0.874 0.380 -0.122 0.467 -0.672 -0.061 -1.191 -0.308 -0.524 -0.731 1.429 2.879 -1.068 1.048 0.254 -0.488 -1.340 1.052 +3 1.125 -1.744 -1.215 0.776 0.021 0.823 1.450 -0.953 0.071 0.700 1.209 -1.248 -1.509 0.186 1.519 0.182 -0.966 -0.651 -0.321 -1.814 -0.016 -0.777 -0.023 1.745 1.192 0.649 -0.457 0.845 +2 -2.513 -1.079 -0.665 0.320 0.960 -0.964 -0.143 1.396 -0.187 0.519 1.121 -0.930 -1.133 1.621 0.579 -1.327 -1.057 -0.200 1.394 0.982 1.067 -0.436 -0.008 -0.798 -0.416 -1.123 -0.872 -0.726 +1 -0.729 -0.273 -1.275 0.705 0.506 -1.184 -0.315 -1.552 -1.695 -1.314 0.015 -0.515 0.043 -0.541 0.781 -0.359 1.095 -0.483 0.585 -0.074 -1.236 1.546 1.758 -0.077 -0.229 0.936 -0.034 0.692 +2 0.256 1.317 0.875 -0.205 -0.606 0.102 -0.286 -0.704 -1.059 1.925 -1.354 -1.925 0.386 -0.722 -0.298 -1.370 0.407 0.283 0.951 0.552 -0.290 -1.612 -0.797 -0.850 -1.103 1.295 1.391 -0.513 +0 0.960 0.705 1.067 0.508 0.187 -0.134 -0.747 -1.163 -0.135 -0.156 -0.780 0.452 1.196 -0.475 0.177 -0.616 0.759 0.476 -0.630 0.440 -2.219 0.601 -0.724 -1.015 -0.851 -0.745 -1.308 1.283 +1 -0.714 -1.156 1.095 -0.969 0.166 0.030 0.447 -0.961 -0.121 -0.967 0.074 0.504 0.895 1.495 -0.264 -0.281 1.273 0.575 1.237 1.953 -0.844 0.809 -0.599 1.563 -1.188 0.918 -0.554 0.554 +4 1.125 0.241 1.783 -0.356 1.446 0.709 -0.652 -1.730 0.204 -1.620 0.216 -0.973 0.801 -1.331 1.911 0.270 -1.724 0.771 -1.609 0.586 -0.819 2.265 -0.071 -2.160 -0.184 -0.994 1.454 0.316 +4 -2.468 1.274 -0.901 -1.331 -1.072 -1.324 0.167 1.235 0.492 0.946 1.146 0.241 0.572 1.243 0.315 2.434 1.549 -0.756 0.120 -1.586 0.594 -0.568 0.026 1.002 -0.508 -0.112 -1.133 -1.168 +4 0.513 0.391 -0.983 -0.235 0.673 0.486 -0.181 -0.938 1.284 0.720 2.602 0.074 0.614 0.310 -1.459 0.286 -1.840 -0.796 0.295 -0.021 -2.857 0.204 -1.150 0.683 -0.083 1.203 -1.589 -1.831 +2 -1.045 0.135 -0.447 -0.146 -0.325 -0.754 0.124 1.171 -0.717 -0.768 -1.442 -0.893 1.202 1.856 1.330 0.371 0.125 0.863 -0.150 0.114 -1.880 2.151 0.346 1.268 -0.796 0.082 -0.153 -1.545 +4 -2.160 1.241 0.800 0.808 0.096 1.334 -0.448 0.392 0.073 -0.232 0.122 -0.949 -0.664 1.580 2.882 0.520 0.834 0.252 0.455 0.544 1.035 1.537 -0.884 2.828 0.138 0.139 -0.413 -0.409 +1 0.153 1.695 -0.193 -0.405 -0.813 1.236 0.090 -0.026 -0.297 1.485 0.238 -0.565 1.814 -1.153 0.877 -0.287 0.875 -0.138 1.560 -0.842 0.191 -0.958 1.140 -1.112 0.150 0.114 -0.714 0.683 +3 -2.378 0.498 0.330 1.517 0.436 0.639 -1.930 -1.221 0.313 0.324 -0.547 -0.924 -0.337 -1.561 -0.967 0.588 -1.555 1.018 0.024 0.395 -0.203 -0.070 -1.041 1.222 0.261 2.051 0.963 -0.497 +4 0.192 0.285 0.498 -0.390 -0.583 0.810 -0.190 -1.014 -0.739 2.003 1.724 1.829 2.496 -0.467 1.368 -0.322 -0.557 1.250 -0.001 -1.467 -0.518 -2.266 0.151 0.702 1.565 1.221 2.011 -0.172 +2 0.206 -0.296 0.233 1.593 -0.344 -0.211 0.334 1.271 -0.155 -2.812 -1.207 -2.247 0.556 -0.997 -0.875 -1.156 0.340 -0.007 -1.257 -0.194 -1.110 0.611 0.334 0.570 -0.120 -0.236 0.746 -0.406 +2 -0.512 0.537 -0.073 -0.695 0.341 0.668 -0.089 1.650 1.236 0.521 -0.365 -1.820 -1.217 0.890 -0.924 0.410 -0.894 -0.181 -0.480 0.836 0.907 -1.617 1.349 -0.262 1.086 1.315 -0.564 1.620 +2 -1.818 -0.051 -0.334 -0.102 0.260 0.259 0.399 -0.275 -0.350 0.124 -0.959 0.462 1.306 1.354 -0.616 -0.753 0.188 1.433 -0.813 0.072 -1.140 2.697 0.169 1.884 -0.366 1.119 -0.624 0.594 +2 0.224 0.789 -0.044 -1.211 0.678 1.253 0.317 0.489 -1.235 0.707 1.346 0.405 -1.797 1.636 -0.945 0.388 0.953 1.080 -1.800 0.117 0.051 1.439 0.459 1.448 0.540 -0.074 0.194 1.524 +2 0.936 -0.193 -0.773 -0.114 -0.340 1.953 -1.226 0.135 -0.018 -0.117 -0.109 -0.116 0.873 0.609 0.419 -3.034 0.300 -1.656 0.646 1.474 -0.926 -0.262 -0.599 1.235 0.095 1.728 0.414 0.242 +1 -0.636 1.341 1.164 0.279 0.471 0.880 0.930 -0.482 0.559 -0.779 0.559 -0.397 0.052 -0.676 0.341 -0.499 2.619 -0.833 1.544 0.109 0.206 0.848 -0.820 1.246 0.173 -2.084 0.346 0.079 +0 -0.789 0.648 0.036 0.937 -0.124 0.482 -1.812 -1.609 -0.803 0.311 -0.781 0.728 0.387 -0.459 0.224 -0.444 0.315 -0.510 0.396 1.361 -1.236 -0.694 -0.653 0.311 0.838 -1.740 -0.183 0.028 +3 0.875 -0.470 -0.296 0.534 -1.510 -1.089 -0.990 -1.796 1.085 -1.988 0.343 0.812 -0.294 -0.668 0.233 0.002 1.004 -2.629 -0.919 0.562 0.050 0.752 0.262 -0.135 -0.302 2.325 1.535 0.496 +2 0.043 -0.590 1.066 -1.230 -0.036 -0.569 0.842 -0.947 -0.409 0.044 1.488 -0.533 -0.401 -1.348 -0.983 0.413 0.032 0.048 1.741 -1.672 2.318 1.178 -0.326 0.391 -0.683 0.319 2.150 -0.646 +0 -0.051 0.512 0.192 -0.671 0.547 2.257 -0.526 -0.211 -1.835 0.616 0.597 0.566 -0.271 -0.396 0.191 0.521 -0.013 -0.473 -1.587 0.971 0.135 -1.418 0.624 0.663 -0.485 -1.041 1.046 -0.489 +2 0.271 0.029 -0.510 1.251 1.897 1.167 0.558 0.762 1.856 0.760 -0.585 0.392 0.626 -0.995 -0.285 1.100 -1.823 -1.250 -0.176 0.354 -1.358 -0.127 1.154 0.069 0.939 -0.592 -0.810 0.849 +3 -0.130 -0.374 -1.051 -0.339 -0.780 -0.771 0.586 0.971 -0.226 0.502 2.096 -0.841 -1.634 0.833 -0.734 -0.527 0.123 1.239 0.131 -2.120 0.358 0.232 -0.860 -1.702 -0.382 -2.303 -1.787 -1.351 +2 0.121 1.234 0.697 -0.104 -0.687 0.325 0.530 -1.254 -1.019 -0.290 -1.332 -1.811 -1.031 0.383 0.102 -0.445 -0.936 0.369 0.316 1.243 -2.511 -1.746 0.938 0.474 0.934 -0.892 -0.389 -1.278 +3 1.098 1.081 -0.303 -0.120 -1.260 1.372 -0.051 1.636 1.457 -0.184 -1.591 0.076 -0.803 -0.172 0.838 1.541 -0.460 2.687 -1.077 -0.519 1.197 -0.984 1.123 0.213 -0.445 1.304 -0.794 0.353 +1 1.144 0.340 -1.574 0.543 -0.100 0.131 -0.651 -0.412 1.456 -0.237 -0.969 -0.779 1.125 1.310 0.438 0.370 1.158 0.004 -0.571 -2.272 -0.326 -0.109 1.214 -0.402 -0.352 -1.794 0.548 0.645 +2 2.380 0.161 0.227 1.445 -0.149 0.247 -2.421 1.738 0.391 -0.107 0.907 -1.181 -0.551 0.553 0.044 0.447 0.249 0.631 -1.321 1.278 -0.484 0.058 -0.601 -0.287 0.229 0.735 0.901 0.433 +3 1.704 -1.336 1.074 1.594 -1.095 0.650 -1.694 -0.026 -1.039 0.682 -0.050 -0.349 0.610 -0.853 0.020 0.696 -0.332 0.131 -0.255 1.431 1.412 -0.082 -0.117 -1.011 2.257 0.783 0.901 1.426 +2 0.259 -1.860 -0.660 0.137 0.199 -1.626 2.576 0.122 -0.051 -0.058 -0.484 -0.495 -1.363 0.815 -0.304 1.183 -1.064 1.563 -0.408 1.002 -0.436 -1.285 -0.211 -0.494 0.800 0.445 0.232 1.841 +2 -0.107 -1.638 -0.688 -0.021 0.321 0.347 -1.971 -0.630 -1.530 -0.006 -0.431 -0.625 3.167 -1.052 -0.017 0.479 0.358 0.578 0.355 -0.330 0.011 -0.656 -0.742 -1.318 0.788 1.104 -0.587 -0.077 +0 0.655 0.811 -0.994 0.420 -0.856 0.098 -1.264 -1.793 -0.383 -0.124 -0.398 -1.184 -0.400 0.269 -0.045 0.183 -0.670 1.172 -1.176 0.488 1.182 0.481 -0.203 -0.528 1.036 -1.423 -0.970 -0.398 +3 -0.798 0.167 -0.477 -0.627 -2.315 -1.260 -0.725 0.546 0.430 0.374 0.788 0.523 -1.195 -0.242 0.019 0.798 -1.064 -0.543 0.441 -0.160 1.665 -0.987 0.935 -0.111 -0.439 0.789 2.282 -2.639 +2 -0.932 0.074 0.960 -0.878 0.640 0.056 0.047 -0.350 0.773 0.671 -0.133 0.203 0.743 0.542 -0.307 1.166 0.864 2.111 -1.570 0.702 1.152 1.005 -0.253 -1.363 1.540 -0.482 -0.295 2.067 +0 0.795 -1.452 -0.701 -0.493 -0.330 -0.602 -1.019 -0.071 0.806 0.382 1.027 -0.201 1.259 -1.276 1.311 1.092 -1.578 0.468 -0.126 -0.106 0.066 -0.619 0.939 0.004 -1.125 -0.038 -0.474 0.616 +1 0.831 0.029 0.170 0.214 0.257 0.647 -0.095 -1.905 0.688 0.578 -1.295 -1.166 0.883 1.119 -2.664 0.441 -0.221 0.074 0.131 -0.762 0.653 -0.688 0.747 0.969 -0.872 -0.848 0.114 1.504 +4 -0.387 1.149 0.943 -0.473 0.705 -0.880 0.837 -1.872 2.637 0.549 1.726 0.762 -0.795 -3.197 -0.748 0.665 -1.622 1.945 -0.192 2.293 -0.608 0.584 0.546 -1.611 0.403 0.557 0.738 1.270 +4 -0.608 -1.903 1.135 -0.775 -1.212 0.678 -0.519 0.168 -0.634 -0.463 -0.993 -1.214 3.472 -0.828 -1.463 1.130 0.267 0.017 -0.488 -0.407 -0.297 1.137 -0.248 1.144 -0.054 -0.205 -1.021 2.145 +0 0.252 0.653 -1.404 0.299 -0.059 -0.771 -0.302 0.073 0.059 1.467 -0.078 0.894 0.112 -0.678 -0.273 -0.523 0.619 0.619 -1.509 -0.205 0.721 -0.599 0.069 0.823 -0.445 0.961 0.290 0.840 +1 1.085 0.039 -0.484 -0.247 -0.168 0.604 1.375 0.360 1.187 -0.965 -0.415 -0.724 0.314 -0.359 0.730 -0.931 0.043 0.687 -2.065 2.122 -0.283 -2.025 0.158 0.033 -0.082 0.991 0.747 0.298 +3 1.382 2.097 2.217 0.499 0.386 -0.443 0.060 1.066 0.953 0.551 -0.844 1.936 0.473 0.201 0.899 -0.637 -0.733 -0.367 -1.009 0.117 -2.035 -0.478 -1.968 -0.258 -0.214 1.172 0.287 -0.777 +4 -0.279 -1.670 -1.016 -0.424 -1.638 -2.197 0.144 1.582 1.003 -2.984 -0.562 -0.056 1.154 0.098 0.039 0.249 -2.325 -0.865 0.423 0.208 -1.816 -0.498 0.429 -0.552 -0.060 -1.921 0.475 0.369 +1 0.617 -2.241 -0.135 -0.119 0.451 -1.677 0.800 0.737 0.775 -0.662 1.122 -0.813 -2.113 0.311 -0.061 1.220 -1.034 -0.346 -0.436 0.803 -1.084 1.273 -0.735 -0.443 0.042 -1.015 0.392 0.263 +0 -0.377 0.779 1.437 0.529 -0.997 0.946 -1.320 -0.847 0.269 0.342 1.362 -0.417 -0.369 -0.105 0.714 -0.651 -0.513 0.388 -1.764 0.884 0.676 -0.339 1.135 -1.337 0.046 -0.888 -0.748 1.234 +4 0.256 0.528 0.460 -1.930 -0.541 -0.680 2.360 -0.805 -2.036 -0.311 -1.617 -0.180 0.052 -2.518 1.701 -0.326 1.334 0.220 -1.199 1.333 -1.930 1.001 -1.097 0.765 0.995 -0.228 -0.477 -0.057 +2 1.579 -1.224 0.673 -0.497 -0.779 -0.615 -0.602 -0.793 -0.727 1.615 0.591 -0.425 0.076 1.121 0.074 -1.374 0.863 0.010 -0.342 -0.942 3.129 -1.041 -0.389 0.366 -0.302 1.088 0.036 0.596 +3 -0.895 0.608 0.755 1.707 0.561 0.659 0.845 -3.378 0.385 1.008 0.230 -0.508 -0.704 -0.043 -0.133 0.207 0.003 0.632 -1.310 0.801 1.268 -1.676 -0.437 0.845 0.421 -1.150 0.594 -1.874 +1 0.757 0.046 0.713 -1.134 -1.533 0.426 0.767 -1.228 -0.574 -0.333 0.013 -0.061 -1.401 0.116 -0.580 1.125 1.140 -1.459 1.378 0.539 1.366 -1.491 -0.164 0.742 0.493 -0.053 0.530 0.772 +2 -0.171 -0.241 1.164 -0.257 -1.234 -1.573 0.363 -0.761 0.669 -1.331 -0.539 -1.331 0.435 0.649 0.339 -1.086 -0.746 0.234 -1.688 -0.241 1.517 -0.180 -1.489 -0.607 -0.631 2.140 1.666 0.731 +3 -0.768 0.412 -0.043 0.368 1.404 -1.784 -0.418 -0.888 -0.725 0.121 -0.018 -1.697 -1.203 1.109 -1.367 0.688 -1.930 -1.628 0.513 1.493 0.964 -0.523 -0.752 0.426 -1.631 0.443 -0.557 1.625 +4 2.356 1.045 1.001 2.375 1.073 0.745 -0.641 -0.187 -0.617 -0.531 1.546 -0.619 0.501 1.291 0.716 0.818 -1.342 0.599 0.751 0.671 -0.349 -0.872 -1.226 -1.145 0.828 -2.503 0.065 -0.616 +3 -2.163 -1.483 2.161 0.948 -0.125 0.976 0.514 1.437 -0.323 0.298 -0.126 0.716 0.291 -0.306 0.660 0.440 0.399 0.061 -1.380 0.345 -2.222 -1.095 1.325 -1.083 0.452 1.533 0.322 0.319 +2 -0.421 1.293 -0.405 1.099 -0.415 -0.704 1.426 -0.034 -1.347 -0.433 -0.306 -2.530 1.281 -2.101 -1.625 -0.214 -1.006 1.377 -0.036 -0.337 -0.801 -0.380 -0.488 0.805 0.521 -0.901 0.421 0.112 +2 0.054 0.525 0.287 1.514 -1.777 -0.573 1.082 2.061 1.166 0.506 0.877 0.846 -0.576 0.611 -0.966 0.137 -0.486 0.478 -0.528 0.075 -1.004 -0.368 1.164 -1.857 0.061 1.405 0.893 0.085 +2 0.948 -0.024 -1.514 -2.257 -0.607 1.082 0.103 -0.112 0.577 0.152 -0.729 1.003 -1.733 1.323 -1.619 -0.352 -1.298 -0.709 -1.052 0.844 -0.191 0.269 -1.260 -0.585 0.935 0.318 -1.111 -0.373 +3 -0.037 -0.758 0.514 0.946 0.447 0.387 0.760 0.876 -0.778 -1.538 0.278 0.924 -1.581 0.069 1.128 -0.116 1.521 1.334 -0.183 -0.368 -0.349 0.135 -1.174 -1.358 -2.271 -0.021 2.039 -1.991 +2 0.324 0.309 -1.530 1.238 -0.533 0.190 0.423 -0.024 -1.133 0.135 -0.033 -0.045 -0.230 0.674 1.618 1.772 0.758 0.674 -1.541 0.136 0.467 -2.078 1.912 -0.083 -0.863 -1.034 0.248 0.417 +4 -2.114 0.137 1.312 -1.341 0.150 0.985 1.091 1.280 -1.868 0.505 -1.506 -0.998 0.821 0.158 0.726 -0.929 1.474 0.688 0.889 -1.080 1.171 -0.316 -0.611 0.625 0.583 -0.847 2.056 1.814 +0 1.012 -0.267 1.214 -0.776 -0.706 0.014 0.497 0.448 0.273 0.159 -0.171 -0.393 0.003 0.333 -0.798 -1.365 0.090 0.179 -0.347 -0.534 0.170 -0.048 -0.066 1.966 -1.830 -1.280 -0.167 0.709 +3 -2.490 -0.167 -0.525 0.346 -0.807 -0.347 1.885 -0.320 1.426 -1.387 1.507 -0.117 -1.804 0.982 -0.188 -0.285 -1.595 -0.555 1.145 -0.476 -1.354 -0.576 0.781 0.098 0.956 1.542 -0.830 -0.006 +1 -0.030 1.504 0.822 0.105 -1.458 -1.374 0.637 0.740 -1.945 0.700 -0.562 -0.405 0.889 0.732 -0.099 0.688 -0.364 -0.790 1.260 1.326 -0.644 -0.534 0.891 -0.693 0.801 -1.024 -0.287 0.979 +2 1.780 0.513 -1.384 -0.558 -1.930 -0.175 -0.944 0.047 -0.217 0.401 1.418 1.288 1.636 -0.105 -0.295 -0.764 -0.622 0.423 -1.303 -0.371 -1.270 0.030 -1.024 -1.381 -0.124 -1.549 0.257 -0.511 +4 -0.169 0.836 -2.312 -0.450 1.301 -0.621 0.839 0.668 1.314 -0.821 -2.714 -0.560 -1.687 -0.610 -0.282 -0.206 0.978 0.298 -0.800 1.543 -2.274 0.797 -1.048 1.869 -0.825 1.125 0.288 0.640 +4 -1.599 0.387 -0.941 -0.858 0.524 1.293 0.781 0.582 0.338 0.869 1.684 -0.577 -0.544 0.731 -1.705 -0.099 0.261 -0.543 -1.918 1.385 -0.600 -0.555 1.328 -2.953 1.511 2.226 -0.889 -0.489 +0 0.304 1.522 -0.563 -0.456 -0.181 0.760 0.980 0.554 0.163 0.480 0.086 -0.277 2.094 1.384 0.656 -0.164 -1.186 0.010 -1.124 0.570 0.389 0.639 0.632 -1.166 0.420 0.882 0.419 -0.122 +2 -1.151 -0.771 -0.751 0.736 0.259 -0.093 0.303 0.323 0.453 1.074 1.281 0.124 0.696 -2.011 0.574 1.675 -0.432 -0.180 -0.706 -0.110 -2.649 0.183 -0.160 1.815 -1.128 1.038 0.483 -0.975 +4 0.662 -1.536 -2.015 1.255 -0.990 2.123 -0.864 -0.185 0.369 -2.132 2.864 -0.395 -0.839 -1.166 0.362 0.291 -1.133 -1.372 1.055 -0.656 -1.721 -0.148 -0.402 1.950 -0.522 -0.308 0.219 -0.571 +3 -0.054 1.274 0.772 -1.143 -0.276 1.743 0.320 -1.508 -1.437 0.890 0.702 -0.921 0.748 1.599 1.458 1.604 0.555 -0.178 -0.402 -0.017 -0.876 -0.359 1.275 -1.609 -1.541 0.831 -0.350 -1.926 +0 0.604 0.389 -0.772 -1.374 1.021 0.251 0.075 0.892 0.845 0.358 0.636 -0.525 0.534 -1.569 0.020 0.747 -0.719 0.142 0.301 -0.891 1.129 0.320 -1.676 0.091 1.454 -0.004 -0.651 -0.545 +0 0.168 0.048 0.380 -0.541 -0.911 -0.020 0.521 -0.887 -0.628 -1.225 -1.172 -0.547 -0.841 0.800 0.030 1.265 1.275 0.015 -2.004 0.398 1.144 0.632 0.349 -0.248 -1.178 0.626 -0.195 0.312 +2 2.133 -0.798 0.947 0.684 1.979 0.705 0.601 0.851 0.921 1.454 -0.819 -0.392 1.735 -1.959 -0.085 0.150 -0.177 1.581 0.476 -0.387 -0.634 0.282 1.166 0.138 -0.134 -0.695 0.374 0.319 +2 1.476 -1.629 0.034 -1.014 -0.125 -1.816 -0.691 0.923 0.542 0.524 -0.116 -0.597 -0.257 0.428 0.565 -1.581 0.112 0.372 -0.682 0.547 -1.107 0.318 -2.702 -0.534 -0.696 1.333 -0.337 -1.025 +1 -0.572 -0.193 1.982 -0.546 1.325 -1.098 -0.546 0.216 0.996 0.017 -0.185 0.766 0.195 -1.450 1.564 0.356 0.204 0.205 -1.408 -1.483 1.824 -0.322 -0.065 -0.305 0.236 -0.638 -0.388 -0.175 +4 0.520 -1.131 1.115 0.423 -1.171 1.088 1.002 0.263 1.576 0.206 2.100 0.495 -1.019 0.243 -0.436 0.971 -0.371 -0.553 -1.387 -2.179 0.727 -1.081 0.390 -0.598 0.294 2.231 2.290 -0.131 +3 0.694 0.484 -0.225 0.045 -0.423 -0.996 -1.206 -1.069 -1.272 0.939 0.656 -0.258 -2.393 -0.622 0.590 -0.011 -0.961 -0.411 0.772 1.940 1.092 -0.317 -1.039 -0.633 1.244 -0.141 -2.400 -1.965 +0 -0.437 1.002 0.421 0.728 0.091 0.080 -1.649 -1.233 0.494 -1.998 -0.104 -0.294 1.395 0.439 1.018 0.315 1.053 0.702 -0.964 -0.048 -0.671 0.652 0.460 -0.601 0.497 -1.005 0.068 0.088 +1 -1.156 -1.267 1.812 -0.174 -0.400 -0.213 -0.630 -0.107 0.035 0.015 -1.263 0.892 -0.553 -0.659 -0.704 -1.052 0.268 0.895 -0.723 -1.843 -1.027 -0.448 -1.231 -0.599 -0.552 -0.015 -1.455 0.580 +4 -1.169 -0.412 -0.131 1.550 0.692 -2.426 -1.368 1.106 -0.718 0.376 -0.632 0.875 0.362 0.840 -1.843 0.500 -1.392 0.857 2.102 -1.614 0.584 -1.383 -0.603 -0.443 0.620 0.986 0.983 -0.915 +4 0.911 0.155 -0.725 0.065 -0.378 2.503 -0.249 0.740 0.435 -0.151 0.440 0.210 2.487 -0.097 -2.607 2.189 -0.510 0.086 -0.544 -0.599 -1.119 -2.002 -0.717 0.514 -1.646 0.505 2.320 0.287 +0 1.044 0.430 1.813 0.211 0.563 -1.267 -1.509 0.689 -0.131 0.767 -0.506 0.251 -0.758 0.471 -0.929 0.118 -0.584 1.275 -0.712 -0.585 -0.280 0.916 -0.040 -0.173 -0.350 0.234 -0.599 -0.797 +4 0.737 -0.080 -1.576 -0.301 -0.329 0.117 -1.147 -0.436 -0.453 1.469 0.065 -0.251 0.903 1.662 3.544 0.329 -1.226 -0.940 -0.564 -0.862 -1.159 2.217 -2.364 1.491 -1.550 1.915 0.973 -0.462 +3 -1.879 1.885 0.601 0.227 1.249 0.248 -0.799 -0.175 -0.678 -0.500 -1.258 -0.970 0.895 0.332 -0.503 -1.028 -0.405 -1.684 0.127 0.945 2.367 -1.103 0.904 -0.707 -1.128 -1.129 0.141 -0.931 +4 -0.241 0.685 1.004 -1.481 -0.954 -1.676 -0.873 -1.277 0.539 0.229 -0.154 0.026 1.194 -1.042 -1.930 0.711 -0.391 -1.018 0.554 -1.968 -0.858 -2.389 0.259 1.574 -0.573 -0.792 1.785 0.627 +2 -0.051 1.015 -0.079 -0.560 0.768 -0.711 -0.713 -1.432 -0.929 -1.435 0.726 -0.320 0.066 0.644 0.028 0.009 0.095 0.131 -1.947 -1.434 -0.316 0.663 0.487 0.207 1.426 2.246 -0.278 2.365 +1 -1.422 0.223 0.205 0.338 -0.011 -0.567 -0.657 0.159 -0.052 -0.636 -0.287 1.539 1.970 -0.220 -0.734 -0.043 1.136 -0.044 0.355 -0.658 -1.167 0.762 0.195 0.656 -1.711 1.833 0.252 -1.807 +2 -0.863 -0.388 -1.370 1.364 2.197 0.557 0.732 -0.159 -1.619 -0.844 -0.465 -0.385 -0.615 0.048 -0.850 2.168 -1.365 0.887 -0.262 0.463 0.508 0.157 -1.133 -0.332 0.169 0.106 -1.023 1.291 +4 0.357 -0.818 0.385 -0.631 -1.321 0.086 -0.153 -1.152 -1.190 0.716 -0.857 0.706 0.215 -3.112 0.797 0.963 1.725 -0.224 -0.635 -1.515 -1.239 1.510 2.856 0.520 1.645 0.409 0.345 0.215 +3 0.362 0.343 -1.482 -2.108 -0.955 0.690 0.573 -1.895 1.604 2.450 0.737 0.629 -2.109 0.915 0.665 0.428 -0.341 0.134 -0.126 0.874 0.992 -0.128 0.359 0.336 -0.008 -0.640 0.416 0.555 +2 -1.486 0.141 0.164 -0.134 0.489 -0.172 -1.950 -0.677 0.524 -1.065 -0.471 1.171 -0.026 0.615 1.232 -1.103 -0.849 -0.657 0.059 -1.014 1.502 -1.431 0.840 1.325 -2.106 -1.409 -0.346 0.370 +4 2.065 -0.380 -0.510 -1.681 1.412 -0.022 1.100 1.781 1.588 -0.823 1.451 -0.970 -0.956 -1.867 0.386 0.012 -0.863 -0.313 0.042 -0.821 0.718 0.744 -1.734 0.992 0.187 0.541 0.234 -1.609 +2 -1.147 -1.912 0.790 0.246 0.138 0.405 -0.092 -0.461 0.167 -1.061 1.482 -0.652 -1.215 0.388 -0.827 1.195 -1.995 -1.650 -0.372 0.130 -0.739 -0.266 1.729 -0.701 -1.610 -0.233 -1.132 0.575 +0 0.234 0.057 -0.308 1.193 -0.012 -0.238 1.131 -0.151 -0.401 -1.170 0.061 0.932 -0.417 -1.329 1.011 1.050 0.585 0.096 0.420 -1.550 -0.582 0.676 0.338 0.903 1.534 -1.235 0.484 -0.258 +0 -0.584 -0.393 0.435 -0.286 0.255 -1.920 -0.027 0.533 0.663 -0.210 0.319 -0.604 1.414 -0.341 1.275 -0.110 -1.163 -0.233 -0.998 -0.273 -1.029 -0.228 0.984 1.432 -0.614 0.724 1.519 -1.164 +3 0.906 0.387 -0.210 -0.868 2.212 -0.011 -0.306 0.224 -1.961 -0.691 0.675 0.835 0.507 -2.174 -0.187 -0.877 0.461 0.144 0.791 -0.377 0.082 1.441 1.543 0.990 -1.766 -0.460 1.320 -0.041 +1 0.335 0.288 -0.717 1.197 -0.232 0.131 1.165 -0.723 -0.635 1.034 0.814 0.344 -2.178 1.279 -0.610 -0.580 -0.200 -1.198 1.218 0.172 0.699 -0.775 0.987 1.424 -1.275 -1.125 -0.805 0.925 +0 -0.935 0.297 0.964 -0.949 0.641 -0.958 -0.541 -1.738 -2.442 0.836 1.075 0.414 -1.044 0.784 0.237 0.511 0.415 -0.656 0.098 -0.366 0.134 0.758 -0.603 0.738 0.054 -0.142 -0.138 0.745 +0 -1.201 -0.793 0.428 -1.008 0.151 -0.664 -0.453 1.076 -1.165 -0.034 0.479 0.258 -0.592 0.365 -1.030 -0.487 1.591 0.639 0.003 0.450 -0.806 0.281 -0.451 -0.580 -0.124 -0.601 0.141 0.417 +1 -1.221 -0.530 -1.552 1.059 -0.440 0.378 0.146 0.532 -1.318 -0.243 0.618 0.858 0.602 1.920 0.511 0.464 1.317 0.609 1.143 -0.210 -1.679 0.660 -0.168 0.697 -0.974 0.434 -0.103 0.387 +0 1.026 -0.505 -0.229 0.904 -1.565 -0.123 0.205 0.729 -0.443 -1.529 0.206 -0.870 0.546 1.102 0.192 -1.232 0.236 0.215 -0.734 1.336 0.211 0.206 0.032 1.752 1.553 -0.321 0.941 -0.033 +0 0.136 -0.818 -0.982 -0.270 1.336 -0.161 2.259 0.754 0.408 -0.243 -0.901 -0.117 0.548 -1.325 0.136 0.160 -0.274 -0.417 1.056 -0.277 0.586 0.077 0.427 -1.603 -0.799 -1.746 0.077 0.097 +0 -1.381 -0.233 0.246 -0.465 -0.540 0.142 -0.502 0.310 1.114 0.677 0.881 1.086 0.283 -0.159 -0.772 -0.391 -0.524 0.732 0.347 0.697 -1.160 1.432 -0.427 -1.100 -0.439 2.484 -0.388 -0.325 +3 0.708 -0.429 -0.573 1.503 -0.479 -1.860 -0.928 0.123 -0.785 2.390 -0.647 -0.624 -0.985 0.881 -0.279 0.814 1.155 0.363 0.309 -1.201 -1.514 -1.496 0.072 2.304 -0.567 1.025 0.525 0.034 +2 0.591 0.242 0.143 -1.436 0.382 -1.230 -0.751 -1.154 -1.031 0.143 -1.354 -1.232 1.582 1.867 0.995 -0.299 1.140 0.535 -1.948 1.160 -1.251 -0.513 0.303 0.294 0.383 1.197 0.270 0.158 +3 -0.540 -0.778 0.196 -0.978 0.408 -1.703 1.029 0.473 0.256 0.983 1.665 1.014 -1.841 -1.280 -0.625 0.026 0.518 -0.726 0.187 -0.755 -0.612 -1.407 -0.923 -1.352 -0.976 1.054 -0.949 2.632 +2 -0.209 0.981 0.583 -0.041 -0.228 0.430 0.122 0.984 -0.866 1.890 0.900 -1.902 0.169 -1.209 -0.101 -2.347 -1.372 -0.064 -0.505 1.038 -0.409 0.453 1.245 -1.374 -1.112 -0.654 -0.076 0.687 +4 1.442 1.724 0.614 0.056 -1.435 -2.571 0.189 -0.398 -0.547 0.397 -1.338 1.462 1.863 0.690 -0.301 -0.604 -0.308 0.320 1.009 0.450 1.261 0.552 1.033 -0.030 -1.149 1.287 2.185 0.295 +1 0.227 0.024 -0.353 0.064 -0.717 0.670 -0.683 -1.262 -1.089 0.590 -1.240 0.107 1.131 -0.012 1.277 -0.127 -1.995 -0.343 0.581 1.185 0.087 0.353 1.473 0.532 -1.857 -1.343 -0.071 0.155 +0 0.461 0.422 -1.149 -0.425 0.521 0.611 -0.487 1.907 0.498 -0.593 -0.681 -0.804 -1.050 -0.035 0.304 1.546 0.746 1.181 -0.503 0.365 0.007 -1.151 0.638 1.112 -0.560 1.195 0.451 -0.225 +0 0.721 -2.134 -0.470 -0.116 0.553 0.336 0.275 0.342 0.298 -0.630 -0.453 1.170 -0.081 -0.739 1.048 -0.410 0.788 -0.705 -1.625 -0.364 -0.836 -0.257 -0.338 0.313 -0.215 0.028 -1.665 1.753 +1 0.167 1.779 -1.869 -0.386 1.516 1.114 -0.261 -0.561 -0.243 0.745 -1.065 0.814 0.356 0.182 -1.334 -0.214 -0.388 -0.258 -0.245 -1.775 -0.453 -0.550 1.078 0.641 0.281 0.797 -0.169 0.775 +3 0.870 0.284 0.927 -0.781 -0.110 -0.695 -0.108 -0.934 -0.715 0.778 -0.970 0.211 1.247 -0.905 0.873 -0.788 -0.249 1.004 -1.082 2.621 -0.089 2.207 1.694 0.335 -1.364 0.704 -1.045 0.014 +1 -0.572 1.140 -1.179 1.480 1.565 -1.016 0.921 -2.162 0.102 -1.009 0.505 0.315 -0.277 -1.093 0.917 -0.031 -0.707 -0.513 -0.096 -0.099 0.310 0.956 1.047 -0.226 0.425 -0.201 1.213 -0.132 +0 0.309 0.967 0.739 0.480 -0.201 -1.659 -0.047 0.554 -2.159 -0.067 -0.095 -0.915 0.760 0.944 1.814 -0.028 1.292 -0.819 -0.371 0.679 -0.656 -0.162 0.840 -0.006 -0.920 -0.590 -0.273 0.588 +1 -0.874 -0.322 0.436 -1.749 -2.311 1.449 0.598 -0.876 -0.235 -0.421 0.290 -0.507 -1.149 0.489 1.814 0.530 0.075 -0.214 -0.789 -0.408 0.392 1.187 1.232 0.170 0.187 0.052 0.571 0.048 +0 1.611 0.139 0.452 -0.598 0.461 -0.149 -0.258 -0.577 0.212 -0.155 -0.966 -0.910 0.208 0.095 0.629 0.128 -1.094 0.571 -0.860 1.033 0.961 -0.595 -0.831 -1.202 -1.299 1.628 -0.805 -0.396 +1 -0.283 0.849 -0.667 0.906 1.488 -0.629 1.056 0.647 -1.217 0.183 -0.570 -0.268 -0.316 0.708 -0.492 0.361 -1.986 0.497 1.028 0.346 1.240 -0.431 -0.932 0.755 0.578 -0.298 -1.172 1.533 +4 0.619 1.144 -0.771 -1.525 -0.356 1.273 -0.813 1.015 -0.263 1.756 2.245 1.031 -1.647 -2.403 0.061 0.907 0.501 -0.724 -1.486 0.930 1.859 -0.670 0.825 2.603 -0.190 -0.169 0.560 -0.284 +2 0.053 -0.274 -0.028 -0.294 -1.350 -1.492 1.359 -0.823 -0.241 1.190 -0.270 0.877 -0.888 1.555 0.227 -0.027 -0.165 0.765 0.236 -3.197 -0.448 0.322 -0.968 -0.504 -0.388 1.595 0.598 0.329 +1 -0.887 0.578 0.251 0.859 1.915 -0.388 1.397 -1.115 -0.009 -0.748 -0.632 -1.137 -0.437 0.618 -0.175 -0.869 1.663 0.443 0.972 0.029 0.167 0.281 1.729 -0.380 1.512 -0.126 -0.667 -1.763 +0 -0.825 -0.321 0.413 -0.564 -0.822 0.244 0.245 -0.507 -0.471 0.232 -1.448 -1.407 -0.718 -0.213 0.311 1.475 0.858 -0.160 -0.019 -1.003 -0.019 -0.289 0.323 -0.827 0.519 1.533 -0.109 0.402 +3 0.852 0.885 0.666 -0.510 -1.368 0.590 -1.214 0.693 0.693 -0.468 -0.112 0.199 -0.615 -1.382 -0.039 0.065 1.361 -0.782 1.068 -1.147 -1.345 -0.301 -0.941 -1.359 1.944 -0.452 2.311 -0.995 +4 0.608 -1.887 -0.483 -1.974 -0.905 -0.048 0.916 0.546 -2.433 0.737 -2.596 -0.659 -0.924 -0.892 0.246 0.591 0.898 2.082 -0.292 -1.986 -1.571 0.238 0.642 0.645 -1.769 -0.756 0.763 2.139 +3 0.042 -1.585 -0.709 -0.049 0.463 -0.434 2.389 0.786 0.247 -1.445 -0.057 0.511 -0.093 -0.133 -0.157 -0.703 -1.250 2.184 -1.003 -0.467 0.254 -2.056 1.553 1.170 -1.503 -0.323 0.956 -0.023 +2 0.095 -0.780 -0.219 0.445 0.631 -1.801 0.465 -0.895 -1.290 -0.091 -0.993 -0.081 -0.812 0.257 0.793 1.105 -0.788 -0.384 -1.068 -0.623 -0.790 0.377 -0.171 -0.045 0.869 0.860 -0.544 -3.395 +1 0.916 -0.906 0.865 0.829 -0.376 1.358 1.762 0.667 0.136 0.377 -1.103 0.217 0.348 0.415 0.181 -0.565 -0.455 -0.789 -1.596 -0.921 -1.859 0.166 -0.825 -1.534 -0.340 1.611 -0.690 -0.476 +3 0.105 -1.424 -1.627 0.897 -0.948 0.814 1.492 -1.215 0.603 -0.983 1.273 0.787 0.259 1.819 0.918 0.035 0.772 0.486 -0.325 0.684 1.299 -0.431 1.338 0.219 -2.319 -0.905 0.465 0.560 +2 -0.400 -0.283 -0.207 -0.168 0.921 -0.826 0.230 -1.513 -0.522 -1.307 1.618 0.134 -0.426 -1.737 -1.339 0.852 0.355 -1.441 -1.436 1.848 1.355 0.295 0.116 -0.168 -1.257 0.860 -0.195 0.730 +0 -1.417 -0.382 -0.103 1.186 0.212 0.360 0.445 -0.629 0.069 0.356 -0.368 -0.921 0.367 -0.464 0.954 -1.439 0.693 0.269 0.530 -1.211 -0.724 1.148 1.015 2.229 0.113 0.143 0.549 0.193 +4 -0.717 -1.867 -0.083 -0.122 1.513 0.631 -1.024 1.854 1.221 0.582 -0.226 -0.959 -0.372 1.089 1.885 1.543 -0.489 -1.120 0.141 -1.768 0.323 -0.148 -0.466 -1.595 0.514 -0.533 -1.170 -2.872 +0 -0.742 0.581 1.139 -0.265 0.607 -0.794 -0.449 0.271 0.181 0.435 0.042 0.571 -0.576 2.178 1.180 0.396 0.324 0.108 -0.141 1.271 0.630 -0.576 0.116 0.601 0.367 -0.572 -0.769 0.862 +0 -0.698 1.031 0.840 1.040 0.671 0.370 1.677 -0.669 0.325 -0.093 0.332 0.517 -1.009 -0.062 1.892 1.385 -0.525 0.672 0.245 1.090 0.408 -0.091 -0.718 0.820 0.519 0.401 -0.263 0.291 +3 0.555 -0.365 -0.760 -1.045 -0.158 -0.653 -0.541 0.235 1.181 1.196 -2.134 1.081 -1.402 0.607 -1.451 0.155 1.199 -1.478 0.364 -0.121 1.976 -0.815 0.284 1.502 -1.049 0.642 0.957 1.151 +4 -1.598 -1.892 -1.485 -0.369 -0.500 -1.734 1.433 -1.278 -0.486 -0.400 1.072 0.769 -1.799 -1.764 0.248 0.554 0.509 0.616 1.363 1.017 1.306 0.578 2.688 0.976 -0.119 1.034 -0.715 0.448 +1 -0.281 -0.119 -0.514 0.900 -0.720 -0.555 -0.710 1.092 -0.492 0.460 1.976 -0.075 1.112 1.671 1.027 -0.017 0.642 -1.287 -0.780 -0.605 2.241 -0.813 -0.827 -1.044 0.560 0.143 1.087 0.671 +4 -0.484 -0.966 -0.175 0.492 -0.009 0.307 1.813 -0.343 0.278 -0.509 -0.846 -0.585 -0.479 0.359 2.349 -0.716 -0.417 -0.063 0.396 0.264 1.284 -2.426 -2.387 -0.496 1.097 -1.566 -3.008 0.571 +0 1.010 1.093 1.386 -1.258 0.263 -1.241 1.837 0.589 -0.469 -0.315 0.839 -0.368 -1.357 0.450 1.233 -0.073 0.750 -0.592 0.126 0.100 -0.350 -0.945 -0.730 -0.685 -0.460 -0.137 0.144 0.143 +1 -0.338 0.990 -1.245 -0.017 -0.166 -0.631 0.321 -0.100 -0.917 -0.994 -1.284 0.057 0.133 -1.154 0.971 -1.210 -0.698 0.432 1.150 1.180 0.646 -2.019 -0.146 -1.168 0.254 -2.107 -0.577 -0.881 +2 0.619 -0.943 0.720 0.307 0.121 0.184 0.740 0.544 -1.579 1.295 0.555 -0.993 -1.788 -1.210 1.248 -0.112 0.787 2.029 -1.154 -1.194 -0.017 0.298 0.766 -0.929 -0.664 1.381 0.405 0.314 +0 -1.484 1.739 0.467 0.426 -1.225 -0.129 0.660 -0.871 -0.457 1.106 -1.072 -0.432 -0.818 0.339 0.775 1.606 0.396 1.247 0.367 -1.367 0.040 0.055 1.066 -0.094 -0.371 -0.852 -0.261 -0.089 +0 -1.249 -0.149 -0.702 -1.456 1.175 0.981 -0.638 -0.594 -0.099 0.852 1.315 -1.470 0.951 0.118 -0.171 -1.206 -0.159 -0.121 -0.350 -0.171 1.071 0.313 0.960 -1.429 -0.356 -0.497 0.568 0.238 +3 0.018 -0.105 -2.503 -0.383 -0.597 -0.397 -0.247 0.213 -0.085 0.368 2.240 -0.284 -0.371 -1.940 -1.211 0.154 -0.485 -0.268 -0.641 -2.442 1.066 0.216 0.118 -0.249 -0.487 0.767 -1.580 -1.185 +2 -2.090 -0.170 -0.266 -0.400 -0.115 0.264 -0.150 -1.264 0.486 -1.949 -2.050 0.739 0.507 -0.057 -1.395 -0.708 -0.263 -1.596 -0.739 0.652 1.427 -0.419 0.756 -1.914 0.659 -0.804 0.307 -0.248 +2 -1.734 0.166 0.166 0.091 0.632 -1.642 0.947 1.148 -2.209 -1.691 -0.013 -1.337 0.488 0.593 0.670 -1.852 -0.640 -0.064 -1.348 -0.719 0.203 -0.158 0.638 -1.868 0.144 -0.202 -0.105 -0.284 +1 0.391 -0.115 0.068 1.291 1.057 0.713 0.252 -0.316 1.073 -0.911 1.784 0.712 1.182 0.468 -0.122 0.948 -1.407 -0.589 -0.191 -1.381 0.637 1.266 -1.709 -0.415 -0.170 0.323 0.855 -0.279 +3 0.120 0.183 0.328 2.123 1.555 -1.463 0.487 -1.565 1.331 1.138 -0.720 -0.132 0.875 -0.178 -0.558 0.045 1.401 -1.525 -0.849 -0.598 1.864 1.352 -0.889 -0.089 0.874 0.815 0.139 0.040 +4 0.333 -2.128 0.827 -1.307 1.074 1.033 -1.255 0.126 -1.934 -1.427 0.246 1.671 -1.304 1.732 0.634 -0.024 -1.666 -1.346 0.189 -0.961 -0.947 -0.520 -1.699 -0.401 -1.042 0.813 0.264 -0.829 +2 1.202 -0.126 2.379 1.126 -0.245 1.189 0.192 -0.679 0.959 -0.197 1.388 -1.068 0.118 -0.076 -0.033 0.604 1.915 0.709 1.747 1.195 -0.455 -0.603 -0.092 0.070 0.874 0.529 -0.915 0.485 +0 0.328 -0.421 2.310 -0.753 -1.278 -0.818 -0.055 -0.062 0.890 0.031 -0.495 0.483 1.165 0.207 0.869 0.083 -0.260 0.698 0.229 1.004 1.542 -0.113 0.647 1.068 0.410 0.510 0.143 0.255 +0 -1.457 -0.625 0.479 -0.854 0.264 0.241 -0.187 0.832 -0.228 0.232 1.146 0.791 0.786 -1.149 0.525 -0.878 -1.281 -0.072 -0.904 0.791 0.815 -1.903 -0.828 -1.460 -0.463 0.455 -0.242 -0.590 +4 0.381 1.203 0.372 -0.121 1.215 -0.048 0.252 0.320 0.944 0.519 0.583 -2.423 -0.698 -1.498 1.541 -1.151 -0.538 2.187 0.270 2.021 -0.671 -2.252 1.220 0.388 -0.379 -0.928 0.831 -0.272 +1 0.841 -0.799 -1.483 0.588 0.323 0.571 -0.131 0.859 1.600 1.530 -0.819 2.500 0.658 -1.426 0.437 0.230 -0.117 -0.203 0.702 0.706 -0.587 -0.536 -0.575 -0.175 0.886 1.203 -0.214 1.099 +3 1.470 1.063 -1.490 0.864 0.188 1.223 0.322 -0.007 0.234 -1.806 -0.767 0.373 1.139 -0.550 -0.041 -1.601 1.459 1.114 0.652 0.407 1.553 -0.805 -0.913 1.062 0.994 -1.193 -0.531 1.689 +2 -0.310 -1.738 -0.264 0.573 -1.265 0.770 -0.239 -0.902 0.841 -0.149 0.944 0.546 0.149 -1.489 1.329 0.849 -0.230 1.199 -0.726 0.197 0.847 0.175 0.716 -1.377 1.894 -0.367 -1.944 1.657 +2 -0.324 -0.039 1.980 -0.587 -1.142 -1.717 -0.581 -1.767 -0.686 -1.610 -1.588 0.482 0.069 -0.452 0.124 -0.549 0.392 -0.690 0.786 -0.000 0.824 -0.903 -1.225 -1.142 0.262 -0.337 -0.007 1.612 +3 0.341 0.924 0.292 -0.060 -0.589 -1.046 2.027 0.211 1.240 0.197 -1.538 1.635 -1.072 0.356 -3.354 0.876 0.197 -0.606 1.007 -0.066 -0.321 0.540 -0.699 -0.710 0.167 -0.411 0.859 0.954 +3 0.228 -1.169 2.124 0.508 1.605 -1.536 0.270 -0.098 -1.481 -1.381 -0.513 2.897 0.127 0.740 0.347 -0.476 -1.012 -0.755 0.261 -0.313 -0.527 0.075 0.591 -1.292 -1.600 -0.667 -0.960 -0.920 +1 -1.124 -1.007 0.543 -0.478 0.288 -1.621 -0.731 -1.240 0.582 -1.226 -0.408 1.107 -1.671 -1.202 -1.306 -0.353 -1.157 -0.834 1.165 0.617 -1.544 -0.094 0.328 -0.012 -0.627 -0.410 0.728 -0.894 +2 0.795 -0.058 0.635 -0.525 -1.964 0.881 -1.001 -0.600 0.235 0.973 -0.078 -0.500 1.487 -0.304 -0.254 -1.573 0.117 1.448 -1.453 -0.709 -0.037 0.540 0.302 0.040 -0.661 1.367 -2.109 1.137 +4 -0.184 -0.002 -1.371 1.845 0.369 1.856 -0.349 0.472 0.993 0.330 -0.333 -0.475 -0.347 2.670 -0.621 -1.986 0.037 0.025 -0.315 -0.621 -0.274 -1.320 1.779 0.245 0.118 1.051 2.710 -0.402 +1 0.530 -0.331 2.359 1.332 2.220 -0.551 0.609 0.115 -0.338 0.098 -0.897 -0.423 -0.632 0.655 -0.900 0.186 -0.632 0.512 0.070 -0.423 0.867 -1.242 -0.450 -0.868 -2.022 -0.750 -0.619 0.126 +0 -0.181 -0.288 1.143 0.456 0.623 2.561 0.287 -0.943 -0.605 -0.643 -0.253 0.377 -1.794 -1.244 0.903 -0.254 0.142 1.056 -0.948 0.080 0.345 -0.168 0.440 -0.355 0.127 0.450 0.815 -0.530 +1 -1.476 -0.011 0.022 1.211 0.593 0.216 0.061 1.018 1.019 -0.663 0.579 -0.882 1.014 0.651 -0.498 0.310 -0.443 -1.989 1.364 -1.424 -1.426 0.383 -0.100 -0.302 0.255 -0.152 -1.106 0.731 +3 -0.868 -1.411 0.246 1.116 0.348 0.458 0.168 0.683 0.467 -2.715 0.906 0.801 -0.362 -1.974 0.242 0.327 -0.440 0.187 1.072 2.398 0.147 0.334 -0.824 -0.501 -0.136 1.178 0.819 1.929 +3 -0.099 0.652 0.780 -0.666 -0.317 0.024 0.648 -0.445 -0.822 2.347 -0.643 -0.158 0.828 0.036 -1.311 1.410 0.725 1.557 -1.342 -0.546 1.215 -1.730 -0.974 2.038 0.156 -0.004 -0.787 1.344 +3 -0.128 0.215 -1.098 -1.647 -1.544 1.023 -0.340 -0.034 0.221 -0.343 -0.962 -0.643 -0.835 -1.113 1.603 -0.794 0.371 -0.203 -2.064 0.151 0.418 -1.987 -1.086 0.252 0.398 2.280 -0.709 0.576 +1 -0.195 1.468 -1.592 -0.391 0.842 0.318 1.187 0.682 -0.472 0.581 -1.320 -2.017 -0.612 0.080 -0.812 0.174 0.059 -1.860 -0.540 0.941 0.487 -0.696 0.619 1.755 -0.424 0.741 0.584 -0.477 +4 0.819 0.528 0.138 1.087 0.389 0.536 -1.920 1.633 -0.790 -0.739 0.427 -0.832 -0.810 1.751 1.594 -0.193 -0.443 -0.568 -3.026 0.991 0.243 -0.591 2.344 -1.933 1.558 -1.104 1.862 1.465 +1 -0.235 1.016 -0.179 1.608 -0.758 -0.781 -0.904 -0.239 0.003 -1.326 0.097 0.565 -1.465 -0.521 0.567 -1.089 -0.316 1.088 1.795 0.440 -0.169 -0.501 0.911 1.133 -1.156 0.910 -0.607 -0.860 +2 1.017 0.079 1.835 0.848 0.700 0.284 -0.908 -1.329 0.063 -0.793 1.385 -1.824 -1.136 0.333 0.705 -1.615 -1.553 -1.184 1.029 0.147 1.315 -0.034 -0.155 0.699 1.573 -0.688 -0.056 -0.156 +4 1.034 -0.225 -1.571 -0.209 -1.116 0.020 -0.406 0.297 -2.715 1.255 0.698 -1.163 0.151 0.846 -0.592 -0.110 -1.962 -2.115 -1.294 -0.568 1.625 -0.048 -2.098 0.147 0.384 -0.819 0.016 1.117 +0 0.157 0.141 -1.488 0.848 0.311 -0.287 0.013 1.176 0.104 0.573 -1.349 -0.116 -0.455 -0.561 -0.178 0.274 0.208 0.330 -2.407 0.334 0.570 -1.164 0.614 1.047 0.168 -0.231 -0.004 -0.986 +3 -0.670 0.724 -0.801 0.150 0.337 -0.239 1.151 -0.520 -2.012 -0.244 1.254 2.456 1.316 1.067 -0.379 2.303 0.684 0.723 0.628 -1.158 0.384 -1.455 0.827 0.336 -0.632 1.126 -0.358 -0.334 +0 0.235 1.496 -0.668 -0.128 -0.808 0.660 0.267 0.923 -1.006 -0.037 -0.234 -0.170 0.995 -0.571 -0.449 -0.792 2.158 -0.953 0.594 -1.370 0.463 -1.148 0.152 0.311 -0.075 0.706 0.465 -0.683 +1 -0.524 0.055 -0.275 -0.293 0.996 1.964 -0.169 1.419 -1.242 -0.092 -1.296 -0.312 0.716 1.119 -0.779 0.458 1.230 1.088 0.259 -0.589 -0.689 -0.843 -0.032 -0.823 0.678 0.228 0.183 -1.840 +0 -0.481 -0.123 0.838 0.177 1.110 1.536 -0.457 -1.741 1.195 -0.224 -0.265 0.636 -0.125 -0.108 -0.006 -0.945 -0.799 1.029 1.050 0.039 -0.227 0.642 -0.280 0.152 1.883 1.502 -1.240 0.169 +2 -1.103 0.248 -1.038 -0.707 0.354 -2.429 1.642 0.044 -1.293 -1.017 0.992 0.601 1.387 -0.683 0.297 0.427 0.695 1.127 -0.717 -0.114 -0.610 -0.053 0.698 -1.654 1.109 -1.139 -0.307 1.244 +2 -1.097 0.080 0.933 0.721 0.364 1.737 2.134 0.498 1.598 0.349 0.088 0.961 -0.071 -2.360 -0.259 -1.506 -0.388 -0.545 -0.539 -1.052 -1.199 -0.647 -0.371 -0.298 0.259 0.706 -1.010 -0.875 +4 0.436 0.228 -0.982 0.902 -1.331 -0.294 -1.263 0.344 2.217 -0.666 1.114 -0.857 -0.662 0.888 -1.974 -0.427 -1.694 -0.619 0.553 0.084 -0.580 1.890 2.049 1.229 -2.306 -0.097 -1.076 -0.336 +0 0.850 -0.220 0.269 -2.026 2.169 0.317 1.017 -1.378 0.172 -0.179 -1.058 1.285 0.146 -0.054 0.625 -0.855 0.161 0.090 0.134 1.027 -0.424 -0.530 -0.614 -0.656 1.102 -0.071 1.023 0.271 +1 0.840 -2.087 -0.698 -1.678 0.487 1.165 -0.398 0.903 0.418 -0.295 -1.383 0.447 0.045 -0.539 1.653 0.361 1.313 1.133 1.026 -0.489 0.154 -0.624 0.776 0.233 -1.307 -0.819 1.159 -0.014 +3 0.453 -0.492 0.492 -0.662 2.063 -0.188 0.269 -0.959 -0.418 0.494 -0.046 -2.782 0.499 0.609 0.731 1.099 0.181 2.396 0.234 -2.303 0.224 1.121 -1.378 -0.271 1.213 -0.565 -0.238 0.781 +2 -0.856 0.108 0.997 -1.175 -1.532 0.049 -0.768 0.032 0.320 -2.144 0.690 -1.170 -0.022 -0.659 -0.552 1.694 0.319 0.905 0.151 0.620 0.417 0.254 1.107 0.157 2.285 0.961 -1.140 -0.177 +3 -0.775 -0.280 1.570 0.235 -0.904 -0.817 0.144 0.668 -0.850 -0.437 -1.036 0.128 -1.303 2.047 -1.175 -0.288 1.745 2.096 2.040 -0.468 1.416 -0.531 -0.269 -0.013 1.651 -0.733 -1.121 -0.482 +2 0.557 -0.813 -0.774 0.224 -0.694 0.435 -2.161 -0.488 1.797 0.439 0.167 1.985 -2.336 -0.815 -0.401 0.858 0.876 -0.866 0.998 -0.403 1.212 -1.033 -0.123 -0.490 1.056 -0.523 -0.042 0.507 +2 0.854 0.797 0.985 -0.483 -0.427 0.202 1.959 0.228 1.678 1.480 1.487 1.412 -0.949 -1.079 -0.861 1.119 -0.276 -0.601 -0.417 0.478 -0.697 -0.501 -0.304 0.918 -1.659 0.009 -0.357 0.608 +0 -1.656 0.020 -0.925 -1.284 0.520 0.588 -0.260 -0.264 0.250 -1.228 0.559 0.763 1.322 -1.826 0.512 0.583 1.602 -0.195 -0.192 -0.228 -0.400 -0.898 -1.021 -0.688 0.452 -0.897 0.631 -0.033 +4 -1.416 -1.366 -0.051 1.208 0.990 0.031 -1.154 0.966 1.946 0.844 1.746 0.815 1.625 0.389 -1.422 0.177 0.563 -1.126 -2.241 0.134 0.257 0.036 1.447 0.975 1.229 1.065 -0.324 -0.267 +1 2.379 -0.356 0.051 0.414 0.116 -0.761 1.485 -0.461 0.468 -0.217 -0.606 1.048 0.263 -2.184 0.298 -0.627 -1.589 0.957 -0.764 0.262 0.920 -0.242 0.251 -0.681 1.104 -0.182 0.537 1.505 +0 -1.223 0.468 -0.730 -0.030 -0.029 0.497 0.082 -0.389 0.129 -0.178 1.694 -0.576 0.555 0.914 -1.321 -1.113 1.931 -0.677 0.012 -0.174 -0.038 -0.372 0.121 -0.813 -0.224 -1.971 -0.116 0.335 +3 -1.403 0.744 -0.132 1.198 0.833 -1.748 1.316 1.900 1.034 1.074 -0.248 -0.477 0.212 0.920 0.895 -0.438 -0.539 -1.490 0.647 -0.390 -0.301 1.728 0.598 0.323 0.830 1.421 -1.617 -0.566 +0 -0.581 -1.036 -0.849 -0.553 -0.414 -0.087 0.563 -1.903 0.460 1.220 0.682 0.806 -0.441 0.838 1.839 0.456 -0.558 -0.113 -0.295 1.071 -0.643 -0.435 1.331 -0.619 0.588 -0.443 0.130 1.090 +4 -1.925 0.021 -0.617 -0.705 -0.038 -2.099 -0.179 0.204 -1.536 1.354 0.443 1.291 0.434 0.361 1.522 1.315 1.942 -0.124 -0.795 -1.158 -0.977 0.678 2.726 1.509 -0.180 0.766 -0.553 -0.491 +0 0.137 -0.135 -0.591 -0.402 -0.505 -0.086 0.304 -1.173 -0.026 -0.238 -0.721 0.851 0.891 0.280 0.468 1.036 -1.281 -1.300 -0.354 0.375 -0.438 0.709 0.616 0.920 -0.675 -0.164 -1.015 -0.346 +4 0.718 -0.497 -0.839 1.074 1.361 -0.779 -0.292 1.244 0.459 1.599 0.698 0.485 -0.362 -0.977 -1.207 -0.030 1.409 -1.048 -0.123 -1.660 1.384 -0.809 2.268 0.462 1.543 -1.736 -2.477 -0.384 +4 -2.205 0.573 -0.306 0.500 0.372 1.594 -1.206 0.263 -0.483 -3.056 0.883 1.623 0.365 1.072 -1.308 0.245 0.082 0.187 -1.162 1.476 1.612 0.004 0.488 -0.152 -1.536 -1.286 2.433 -1.036 +1 -0.269 -1.499 0.328 0.970 -0.068 0.596 0.816 -0.499 -0.806 -0.342 -2.157 -0.450 0.276 1.139 -0.828 0.488 -0.635 -1.679 0.401 -0.354 1.314 1.553 -0.113 0.141 -1.380 -0.318 0.771 -0.343 +3 1.248 0.817 0.974 -0.800 -2.059 0.325 -0.736 -0.848 -0.848 -1.386 0.592 0.811 -1.546 1.051 -0.346 0.734 -1.111 1.698 -0.895 -0.393 -0.314 -0.287 0.507 -1.972 -0.586 0.776 -0.522 -1.669 +1 -0.121 0.845 -0.819 0.172 0.492 0.398 -0.196 -0.113 1.001 0.665 0.364 -2.587 0.740 -1.620 -0.060 0.173 1.013 0.898 0.616 0.370 1.586 -1.029 1.391 0.869 0.173 -0.247 -0.661 -0.001 +3 -0.500 -0.081 -0.499 1.608 0.319 -0.526 -0.981 1.569 1.303 -1.185 1.065 1.998 0.136 -0.457 1.268 -0.667 -2.259 -0.921 -0.340 0.824 -0.440 -1.304 1.320 -0.079 -0.299 -0.710 0.599 -1.051 +3 -0.905 -0.297 1.111 1.602 1.378 0.610 -0.409 -0.606 0.209 -0.587 -1.450 0.599 0.200 -0.159 -0.142 1.030 1.378 0.840 0.962 -0.932 1.408 -0.151 1.056 -1.827 1.330 -1.564 1.873 -0.813 +0 0.748 0.386 0.464 -1.148 -0.688 0.718 -0.749 -1.363 0.521 0.365 -0.031 -0.104 -0.230 -0.550 1.109 0.066 -1.886 -0.011 -2.246 0.728 -0.678 -0.407 0.930 1.079 -0.644 0.642 -0.144 0.865 +0 0.549 0.642 0.772 0.899 0.776 1.127 0.167 1.072 1.892 0.196 0.481 -0.580 -0.165 0.353 -0.389 0.092 0.341 1.521 -0.864 0.420 -0.604 0.766 0.845 1.677 -0.426 0.146 0.804 0.659 +4 -0.237 -0.270 -1.131 0.791 -0.361 -0.045 1.190 -1.047 -0.087 -1.558 -0.524 1.288 -1.541 0.236 1.604 0.549 -0.584 -1.112 -1.930 1.562 -0.421 -1.802 -1.021 -1.078 -0.495 2.388 0.923 0.484 +1 0.058 -0.967 -0.581 -0.614 -0.277 -1.658 1.090 -0.016 0.202 0.914 0.838 -0.575 0.604 0.594 0.316 1.061 0.800 0.727 1.365 0.029 0.058 0.819 -0.178 0.482 -0.560 -2.279 0.598 -1.549 +2 0.033 -1.120 -1.103 0.368 0.947 -0.194 1.649 0.212 -0.777 -1.087 1.018 0.128 0.676 -2.658 0.245 -0.512 -1.394 -0.063 1.092 2.266 0.242 -0.709 -0.770 0.963 -0.997 0.622 0.294 0.183 +1 1.492 1.246 -1.323 -0.806 1.038 0.198 0.906 -0.109 -1.858 -0.378 0.882 1.189 0.343 -0.872 0.469 0.880 0.738 0.240 -0.972 1.015 -1.103 -0.300 0.855 1.326 -0.602 -0.543 -0.380 0.005 +4 1.738 -0.723 -0.014 0.275 -2.183 0.298 1.228 -0.898 -0.138 0.768 -0.926 -0.043 0.899 -1.968 0.265 -0.545 0.993 1.836 -1.421 -1.484 -0.702 0.590 -0.068 1.559 -1.373 -0.888 1.860 -0.140 +1 0.976 0.671 0.584 -1.184 2.887 1.051 0.517 -1.164 0.948 -0.501 -0.194 -1.681 -1.241 0.439 1.006 0.685 0.651 -0.065 -0.407 0.289 0.821 0.005 1.210 -0.215 -0.980 -0.010 0.106 -0.082 +4 0.485 1.042 0.473 -0.542 3.611 1.199 0.933 -0.353 -0.625 -0.280 1.081 -0.991 -1.068 0.540 0.319 -0.770 -1.091 1.438 0.357 -1.364 -1.294 -0.362 0.733 0.595 1.132 -1.490 0.224 1.426 +4 -0.057 -1.821 1.533 1.169 0.372 -0.420 0.010 -2.644 -1.107 1.767 0.517 -1.949 -0.828 -1.925 -0.780 -0.165 1.602 0.785 -0.302 -0.253 -0.801 1.090 1.210 -1.338 -1.721 -0.319 -0.196 0.259 +1 -0.219 -0.031 0.700 0.270 0.830 1.587 -0.575 -0.130 0.372 1.011 1.093 1.238 -1.006 0.264 -2.401 0.035 1.112 -1.609 0.567 -0.310 0.478 0.062 0.120 -0.396 0.318 0.576 -0.188 -1.463 +1 -0.788 1.391 -0.972 1.181 -0.862 0.294 -0.358 -0.285 -0.748 0.667 0.922 1.185 -0.852 1.159 0.838 -2.040 0.183 0.955 0.244 -0.629 -1.600 -0.988 0.462 -1.339 -0.528 0.323 -0.663 -1.068 +4 -0.877 1.481 2.150 -0.273 -1.171 -1.044 -1.208 -0.282 1.630 -0.126 -0.543 0.503 0.326 -0.437 -0.238 0.258 0.512 -0.544 1.354 1.266 0.528 -0.930 -2.113 -2.249 0.618 0.632 1.277 -1.356 +2 2.665 0.470 0.741 0.652 1.178 0.621 0.467 -0.065 1.614 0.600 -1.166 0.821 0.368 -0.050 0.521 -1.677 -0.625 1.091 0.313 0.113 0.290 -0.210 -0.101 0.414 -0.855 -1.183 1.478 -1.117 +4 -2.508 1.543 0.015 1.539 1.804 -0.155 1.349 -1.060 0.433 -0.473 0.696 0.883 -0.160 0.545 0.945 0.188 2.539 -0.137 1.656 1.145 -1.860 1.493 1.074 0.114 0.369 0.493 1.375 -0.413 +4 -0.564 0.889 -1.445 -1.339 0.875 -0.260 0.821 0.457 -0.066 -0.346 -0.237 0.133 -1.271 0.633 -1.497 2.360 1.102 -0.747 1.507 -0.839 1.288 0.351 1.407 -1.268 1.057 -1.881 -0.091 -2.089 +4 0.016 0.595 1.775 0.081 -1.491 1.182 -1.357 -0.496 1.193 -0.335 -0.572 0.281 0.010 -0.693 0.878 1.228 1.388 0.552 -0.701 -1.255 -2.317 1.033 0.195 -0.419 -1.933 -1.183 -1.765 1.442 +4 0.754 0.508 -0.704 0.885 -1.530 -1.735 -1.276 -0.033 0.675 -2.976 1.323 0.131 -1.568 0.101 1.093 0.599 1.099 -1.366 -0.414 -0.925 1.718 0.639 2.847 -0.468 -0.725 -0.143 1.378 -0.366 +4 -0.727 0.060 -0.130 1.096 -0.231 0.839 -1.472 -0.524 0.695 2.067 -0.142 0.829 1.336 0.953 0.748 -0.354 1.519 -1.318 -1.267 1.867 0.362 -1.238 -1.434 -1.476 2.034 -0.081 -0.517 1.629 +2 -0.401 0.437 -1.038 -1.859 -0.401 -1.788 -0.067 -1.137 -1.125 1.472 0.274 -1.536 -0.048 -0.143 -2.068 0.348 0.953 -0.423 -0.292 -0.557 0.385 -0.249 -0.210 -0.809 2.041 -0.260 0.701 0.265 +0 0.575 0.999 -0.379 -0.609 -0.158 -1.115 0.305 -0.009 -0.936 0.641 -0.633 0.228 -0.303 0.981 0.727 -0.382 -1.180 -0.150 -0.992 -1.127 0.427 0.603 0.117 -0.384 -1.017 -0.199 -0.415 0.568 +2 -1.656 0.738 -1.375 0.432 0.022 1.456 -0.156 -1.349 1.262 -0.996 0.045 -0.421 -0.007 0.999 0.482 -0.123 0.271 -1.925 -0.891 -0.817 -1.089 0.171 -1.307 -0.885 2.290 0.114 -1.112 0.262 +2 1.380 1.226 -0.085 0.214 -1.069 -1.836 0.980 0.520 -0.732 -0.966 0.173 0.072 0.683 -0.618 -0.760 -0.784 2.215 -0.369 0.194 -1.709 -0.559 -0.402 1.832 1.247 -1.449 0.693 -0.478 -0.289 +2 -2.237 1.359 1.235 0.368 -0.006 1.040 0.618 -0.059 -0.237 -0.238 -0.492 -0.102 1.690 0.883 -0.739 2.233 0.149 0.161 -1.602 -0.940 0.991 -1.169 0.067 -1.011 0.771 0.906 -0.886 -0.061 +2 -1.118 1.092 -0.578 0.913 0.291 0.738 -0.784 -1.087 -0.721 0.443 -0.688 0.254 0.578 -1.946 -0.268 -0.617 0.832 1.971 -0.392 1.631 0.414 -0.511 0.038 -0.074 0.925 -1.912 0.217 1.383 +1 1.310 1.014 -0.344 -2.125 0.582 -0.125 -0.978 -0.556 1.434 -1.137 0.468 0.218 -0.357 0.645 1.632 -0.296 -0.243 0.622 0.918 -0.128 -0.305 -2.218 -0.778 -1.139 0.685 -0.244 -0.283 -0.009 +3 -0.541 -1.106 0.763 -1.283 0.736 1.458 1.478 -0.826 2.162 0.428 -0.885 -1.104 -0.847 1.178 -0.314 -0.137 0.214 1.316 -0.183 -0.877 -1.149 1.634 0.298 1.056 0.973 -0.877 0.763 -1.961 +2 0.062 0.945 -0.271 -0.293 -0.733 -0.497 1.593 -0.393 -0.198 0.320 2.022 -0.465 -0.297 -0.031 0.132 -0.729 -0.578 0.513 1.352 2.484 -1.150 -0.000 -1.631 -1.030 -0.025 -0.255 -1.006 1.540 +2 0.586 0.590 0.354 1.385 1.018 -0.552 0.725 1.271 -0.108 1.074 0.275 -1.119 -1.419 -0.510 -0.980 2.284 0.496 -0.973 0.712 -1.186 -1.125 1.367 -1.568 0.495 -0.744 -0.061 -0.794 -1.311 +3 1.341 0.462 -0.219 0.227 -0.502 0.881 1.042 0.666 1.705 0.846 1.581 -0.065 -0.702 0.259 2.827 0.438 -1.961 1.891 -0.012 -1.145 -0.222 -0.110 0.640 -0.454 -0.296 -0.005 -0.423 1.267 +4 -0.495 1.406 1.041 0.101 0.985 0.995 2.709 -0.018 -1.670 -0.722 0.716 -0.532 -0.643 -0.033 1.973 -0.995 -1.851 1.838 0.625 -1.770 -0.420 0.575 -0.180 -0.552 0.135 2.183 -0.223 -0.595 +1 0.391 1.492 1.781 -0.047 0.910 -0.157 0.883 0.828 -1.777 -0.053 -1.401 0.562 0.023 0.344 0.704 0.438 0.509 1.338 -0.106 0.555 -1.148 -0.334 1.695 -1.000 0.783 1.237 0.202 0.344 +3 0.708 -2.081 -0.117 -0.620 0.328 -0.557 0.591 -0.421 1.698 1.650 -1.447 0.323 -0.462 0.252 0.587 -1.279 -0.974 -0.849 -0.551 -0.522 0.797 0.222 -0.871 -0.320 -2.370 2.020 0.018 0.993 +0 0.442 0.133 1.868 0.329 -0.135 0.836 -1.231 -0.219 0.262 0.719 0.241 -1.289 -0.885 -0.431 0.242 -1.297 1.409 0.313 0.286 -0.173 -1.443 0.091 0.748 -0.153 1.069 -0.062 0.696 -0.111 +3 -0.521 -0.483 1.309 -1.251 0.993 -1.615 1.608 -2.390 0.465 -0.659 -0.167 -0.135 0.606 -1.962 -0.031 0.185 -0.661 -0.111 -0.664 0.555 0.364 0.217 0.662 1.247 -0.992 2.222 0.393 -0.501 +1 -1.273 0.934 -1.436 -0.251 -1.065 0.924 0.812 -1.420 -0.761 0.172 -0.016 -0.403 0.124 0.309 -0.892 -0.643 -0.752 -1.260 0.828 0.276 0.622 -0.566 -1.833 0.157 -0.588 -0.543 -1.969 -0.032 +4 -0.192 1.091 -0.209 -0.300 -1.431 0.015 -2.118 0.392 2.451 -1.061 0.222 -0.401 1.991 -3.001 0.265 -0.626 1.199 -1.510 0.066 1.830 -0.817 0.920 -0.123 -1.621 -1.602 -0.119 0.739 1.555 +1 0.572 0.530 -0.575 0.125 -2.334 0.990 1.694 0.191 -1.606 0.743 -0.764 0.182 0.686 0.546 1.081 0.146 0.173 0.737 -0.071 -0.688 0.452 -1.129 -0.081 -1.378 -0.555 -0.513 -0.984 1.101 +3 -1.584 1.424 0.159 -0.396 -0.378 -2.015 0.593 0.262 -0.194 1.278 -1.384 -0.597 -0.850 -0.279 0.067 0.430 -0.347 -0.877 1.686 2.412 0.143 -0.757 1.475 -1.447 -0.335 -0.696 -1.160 -0.280 +3 -0.511 -0.924 -0.725 -0.395 0.262 1.375 -1.654 0.374 -1.719 0.523 -1.826 -1.769 0.518 0.202 -1.433 1.098 -0.268 0.512 1.008 1.745 -0.014 -1.037 1.124 1.369 0.853 0.394 1.166 -0.305 +1 -0.064 -0.455 1.016 0.187 0.052 0.780 -0.375 1.569 -0.788 0.657 1.434 -1.377 -0.343 1.142 1.157 -0.171 -0.735 0.956 1.184 -1.165 -1.204 0.225 0.629 -0.299 0.679 -0.710 -2.115 -0.424 +1 0.514 -1.446 1.141 0.405 0.972 -0.425 0.557 0.481 -0.362 -0.391 0.230 1.205 -0.224 2.248 0.575 1.714 -0.293 -0.520 -0.101 0.808 -0.526 -1.177 0.880 -1.357 -0.978 0.134 -0.316 0.804 +2 0.443 0.528 -0.914 0.008 -0.324 0.955 0.543 0.637 -0.225 -0.410 0.306 1.215 -0.908 -0.471 1.974 -1.033 1.147 -1.671 -0.597 0.813 -0.506 -0.359 -1.696 -0.919 -1.546 -1.286 0.726 -1.296 +3 0.312 -0.970 0.088 -0.772 -0.745 -1.978 1.573 0.431 1.225 -1.528 -0.515 -0.843 -0.282 0.696 -0.167 -0.351 0.777 0.028 -0.682 0.881 0.303 -1.578 0.872 1.187 -0.588 0.046 -2.329 -1.784 +3 1.468 -0.126 0.810 -1.675 -0.975 0.515 2.126 1.376 0.431 2.059 1.062 -0.122 0.016 -1.586 -0.999 0.180 0.295 0.745 -0.500 0.602 0.421 1.443 0.300 1.014 1.037 0.414 -2.109 0.773 +1 0.931 0.311 1.362 -0.194 -0.419 1.388 1.912 0.009 -0.753 0.811 -0.254 0.599 -0.259 1.564 -0.699 0.481 -0.489 -0.282 -0.412 0.739 0.918 0.558 -0.108 0.140 0.898 0.929 -2.690 -0.272 +4 1.354 0.689 0.208 1.342 0.425 -0.387 0.776 0.739 -0.444 1.282 0.614 -0.028 0.423 1.187 0.125 -0.281 -0.847 -2.237 -0.615 -0.521 1.856 -1.603 -0.236 -1.467 -0.499 2.583 -1.823 -0.275 +1 -0.264 -0.312 -0.041 -0.137 -0.394 -0.214 1.175 0.969 -1.172 0.713 -1.398 1.492 -0.954 0.794 0.637 -0.305 -0.383 -0.230 0.489 0.844 2.212 1.739 0.580 0.265 -0.538 0.650 1.375 1.153 +0 0.376 0.606 0.493 0.453 2.701 -0.076 0.936 -0.471 0.791 0.939 0.364 1.669 -0.884 -0.487 0.212 0.881 0.179 1.286 -0.662 -1.141 0.308 -0.077 0.543 -0.027 -0.340 -0.075 0.029 -0.305 +1 -0.277 -0.685 -0.361 1.181 -1.331 -0.718 -1.142 1.071 1.131 0.360 0.308 -1.200 0.199 -1.122 0.041 -0.343 -0.594 0.028 0.677 1.775 0.551 2.278 0.201 1.784 0.436 0.240 -0.168 0.299 +1 -0.296 0.331 1.145 2.006 1.114 0.920 1.402 0.507 0.459 -0.333 -0.123 0.186 -0.235 -0.828 -0.020 -0.203 -1.031 1.414 -0.854 0.400 0.261 -1.115 0.281 0.396 -0.085 -0.062 -0.584 2.609 +0 0.771 0.311 -0.822 -0.172 0.083 0.102 1.230 0.176 0.260 1.165 -0.057 -0.196 0.047 -0.952 -1.245 -0.364 0.283 1.346 -0.641 1.274 -1.636 -0.049 0.623 -0.878 0.898 -1.163 -0.930 -0.782 +3 1.984 0.757 0.417 -0.985 0.571 -0.723 1.267 -0.971 -0.113 -0.555 -0.988 0.741 0.535 1.209 -1.780 1.064 2.505 -0.452 1.895 -0.263 -1.372 -0.306 1.048 -0.838 -0.600 0.594 0.003 0.585 +3 -0.496 0.682 0.125 0.219 0.537 0.887 -0.911 -0.006 1.529 -0.104 0.979 2.752 -0.427 -0.449 0.720 -1.161 1.079 1.945 1.575 -0.073 1.055 0.822 -0.591 -0.460 1.686 -0.449 -0.662 1.364 +2 -0.692 0.322 -1.087 1.281 -0.167 2.570 0.985 0.720 0.022 0.653 1.547 -0.312 0.196 -0.983 0.914 0.106 1.090 -0.981 -0.757 1.304 0.929 0.495 -1.083 0.559 0.655 1.113 -1.141 -0.420 +2 0.411 0.404 0.097 0.992 0.886 0.204 0.066 0.130 -0.148 -2.174 1.135 0.398 -1.216 1.428 -0.590 -0.108 1.024 0.220 1.335 -0.352 1.691 -0.778 1.318 -2.265 -1.100 0.298 -0.400 -0.075 +1 1.686 0.255 -1.845 2.254 -0.680 1.608 -0.260 0.631 0.357 -0.611 0.976 0.522 -0.978 0.939 0.081 0.354 0.429 1.104 0.594 -0.447 -0.114 -0.525 0.971 -0.127 -0.251 0.108 0.378 0.570 +3 2.398 -0.201 0.679 -0.951 0.114 1.422 0.284 1.806 -0.606 0.224 -1.364 1.093 -1.106 0.117 1.980 0.587 -0.803 -2.170 -1.091 0.471 0.847 -0.061 -1.211 -0.311 -0.261 1.165 0.879 0.301 +2 -0.944 -2.928 -0.267 -1.057 -0.793 -1.255 2.031 0.155 -0.225 -0.048 -0.739 -0.248 -0.209 -0.258 0.186 0.415 -0.782 0.630 -0.053 1.516 -0.362 0.941 0.341 0.677 1.501 -1.477 -0.936 0.225 +1 -0.392 -0.569 1.149 1.625 -0.380 -1.095 -1.045 1.154 -0.503 0.039 0.529 0.977 -1.703 -0.172 0.311 -1.679 -0.973 0.973 -1.107 1.324 0.449 1.265 0.104 0.340 -0.915 -0.800 -0.269 0.425 +0 -1.288 -0.892 1.104 0.874 -0.151 0.094 -0.350 0.152 0.622 0.288 -0.731 -0.313 0.185 -0.837 0.267 0.601 -0.026 1.055 0.366 0.655 -0.776 1.243 0.572 -0.174 0.435 0.012 0.867 -0.135 +2 0.748 0.014 1.286 0.567 -1.665 0.296 0.784 -0.289 0.126 -0.613 -1.378 -1.550 -1.537 0.669 -2.490 -0.666 -0.766 -0.282 -1.856 0.404 -1.273 1.132 -0.622 -0.096 0.004 0.244 0.037 -0.427 +0 -0.126 -0.707 0.901 -0.645 -0.663 -0.470 -1.323 0.589 -0.020 0.299 0.090 1.159 0.852 -0.685 0.658 0.712 0.553 -0.515 -0.016 0.164 -0.409 0.392 0.106 0.754 0.021 0.276 1.769 2.679 +1 1.053 0.036 -1.072 0.629 -0.613 -0.845 -0.733 -0.302 -1.456 -1.411 -1.878 -0.296 -0.636 -1.200 -0.113 0.730 -2.152 1.067 0.131 0.281 1.080 0.694 -0.795 -0.732 0.595 -0.025 -0.957 0.107 +0 0.143 -0.282 0.029 0.217 -0.747 -1.390 1.339 0.988 0.378 0.919 1.914 -0.535 -1.120 0.662 -1.467 0.038 -0.298 0.674 -0.482 1.200 -0.747 0.706 -0.666 0.548 -1.015 -0.024 1.322 0.115 +2 0.423 0.851 -0.856 0.483 -0.728 -0.910 1.753 -1.813 -0.517 0.222 -0.599 0.150 -1.343 -1.845 -0.874 0.152 -0.172 0.636 -1.425 -0.576 -0.072 -0.969 0.138 -0.281 0.487 0.289 -1.853 1.473 +1 0.433 0.313 0.066 -2.655 -1.395 0.101 0.616 0.290 -0.892 0.937 1.482 0.938 0.990 -0.559 1.076 0.146 0.512 0.365 -0.121 -0.860 -0.082 0.987 -0.441 0.893 0.737 -0.299 1.389 -0.356 +0 -0.517 -0.535 -0.574 -0.128 1.747 -0.092 0.488 0.032 -0.308 -0.024 0.769 -0.053 -0.161 -0.235 -0.993 1.040 -0.447 1.469 0.245 1.406 -0.200 -0.733 1.043 -0.561 1.286 0.579 1.464 -0.976 +2 0.692 1.347 0.000 -0.165 -1.061 -0.243 1.157 -1.447 1.054 -0.048 -0.121 -0.499 -1.266 -0.731 -2.442 1.518 0.190 1.123 0.719 2.077 0.512 -1.399 1.014 0.531 -0.014 -0.068 -0.819 0.449 +0 -1.461 -0.117 0.786 0.075 0.672 0.197 1.276 1.486 -0.760 0.884 0.508 2.505 -0.798 -0.117 -1.032 -0.973 0.386 0.206 0.541 0.520 -0.236 -0.243 -0.971 -0.701 -0.057 0.197 0.711 0.609 +3 -1.005 1.032 0.899 0.153 -1.061 -1.050 -0.163 -1.420 0.830 0.111 -0.371 -0.626 0.273 -0.148 0.195 -1.206 -1.098 1.916 0.996 -0.441 -0.881 -0.830 -0.595 -0.530 0.450 1.995 2.126 2.027 +2 -1.320 1.831 1.179 -0.469 -1.713 1.354 -0.115 1.238 -1.594 -0.599 0.005 0.047 -0.450 0.623 -1.068 -0.142 0.120 0.514 0.712 -1.125 -1.534 1.278 0.332 -0.748 1.551 0.116 1.179 0.068 +3 0.330 -0.522 1.296 0.330 1.532 -1.187 -1.302 0.432 0.649 -0.639 1.081 -1.082 -0.031 0.840 1.307 -0.083 -1.523 0.177 0.454 0.673 0.547 0.225 1.643 1.551 -1.382 0.108 -1.399 -2.214 +2 1.163 -1.363 0.258 0.305 -0.107 -0.049 -1.652 -0.868 0.937 -2.041 -0.086 -0.313 -1.992 1.692 -1.030 0.549 -0.469 -0.845 -0.236 -0.356 -0.187 -0.787 -1.014 1.004 0.007 -0.060 -0.891 1.574 +2 -0.443 -0.441 0.294 -0.147 -1.743 0.179 -0.192 2.106 0.792 0.001 0.059 1.519 -0.061 0.854 -0.989 0.736 0.024 -0.331 -1.203 -1.470 -1.202 -0.755 0.660 -2.501 0.318 -0.237 1.337 0.768 +3 0.293 -1.148 2.007 0.581 -0.306 0.532 -0.344 -2.032 0.753 0.965 1.359 -0.085 0.595 0.153 1.617 -0.230 -0.189 -0.460 0.034 0.853 1.756 -0.223 -0.415 -2.002 -0.245 -1.217 2.527 0.173 +0 -0.546 0.473 -0.352 0.032 0.085 -0.757 -0.719 -0.072 -0.198 0.570 0.271 0.606 -0.141 -0.807 -1.043 0.989 -0.485 -0.632 1.279 -0.393 -2.180 0.220 -0.549 -0.723 -0.139 -2.048 -1.859 -0.460 +3 -0.217 -0.452 -0.456 1.243 0.251 -0.737 -1.436 0.211 -0.938 -1.768 1.089 -1.232 -1.787 -0.137 -0.300 -0.873 0.219 -0.297 2.199 -0.193 -1.273 0.300 0.383 -0.335 -1.663 1.547 -1.262 0.884 +0 -1.858 0.981 0.616 -1.011 0.362 0.174 0.711 -0.505 -1.024 0.472 0.577 -0.382 1.042 0.412 1.670 -0.335 -0.842 -0.490 -1.364 0.354 0.563 -1.094 1.199 0.666 -0.319 0.230 -0.284 -0.988 +0 -0.767 -1.064 0.582 -0.154 -0.387 0.759 1.168 0.617 -0.260 1.104 -1.234 0.626 -0.439 -1.977 -0.488 0.768 0.140 0.603 -1.167 0.046 0.355 0.174 -1.374 -1.303 1.020 0.209 -0.047 0.472 +3 1.807 -0.152 -0.656 -0.344 -0.387 0.718 -2.516 0.903 1.628 -0.912 -0.792 -1.565 -0.924 1.461 -1.074 0.152 -1.277 0.167 -0.478 1.002 0.493 0.834 -0.135 -0.925 -0.298 1.217 -0.900 -0.544 +0 0.038 0.957 1.464 2.245 0.352 0.405 0.254 -0.832 -0.367 0.459 1.212 1.190 -0.222 -0.784 1.456 -0.607 -1.238 0.220 -0.299 -0.102 -0.226 -0.076 -0.065 0.562 0.128 -0.036 0.436 0.800 +3 -1.173 -1.923 1.096 1.270 -1.726 0.251 -1.126 -0.521 -0.185 -0.550 0.953 2.609 -1.312 0.859 0.521 -0.040 -0.817 -1.965 0.447 0.141 0.099 0.731 0.490 -0.016 0.733 0.094 0.072 1.318 +1 0.948 -0.983 -0.698 -1.196 1.325 0.828 -1.059 0.126 0.037 0.944 -1.381 0.555 0.207 -0.892 1.174 0.244 0.017 0.732 0.749 -0.846 0.640 1.528 0.754 0.770 -0.624 -0.379 -0.765 2.246 +2 -0.705 0.914 1.293 -0.596 -0.680 -1.359 -2.086 0.808 1.129 -0.170 -0.896 -0.482 0.169 0.792 -1.101 0.057 -0.021 -0.830 0.798 0.909 -0.177 0.062 -1.020 0.488 1.948 1.061 -0.304 -1.624 +4 0.729 0.082 -0.954 -2.085 0.944 0.986 -1.654 -0.335 0.690 1.516 1.366 -0.206 0.362 0.093 0.184 1.258 0.330 2.196 1.972 -0.565 -0.754 -0.950 0.407 -1.345 -0.563 -0.639 2.492 -0.551 +2 -0.969 -0.588 0.376 -1.798 0.073 1.161 0.865 -0.050 0.021 1.492 1.371 -0.095 -0.630 0.857 1.131 1.379 0.584 -0.356 -1.670 1.938 1.603 -0.767 0.191 -0.221 0.066 -0.400 0.692 -1.134 +2 -0.740 0.637 -0.801 0.882 -1.646 0.314 1.654 -0.385 1.225 -0.473 -0.478 2.164 -0.281 0.528 0.774 0.973 -0.371 0.318 -1.033 -0.741 -0.592 -0.069 0.102 0.742 1.285 0.336 2.312 0.090 +2 -1.913 -1.083 1.948 0.335 0.332 -0.003 0.007 -1.104 -0.500 -0.048 -1.064 -0.183 0.344 -0.460 1.893 1.054 -0.064 0.662 -0.539 0.613 -1.111 -0.831 -1.360 0.795 -0.757 0.300 -2.065 0.382 +1 -0.378 -0.787 -1.460 0.526 0.770 0.578 -1.345 0.371 -1.269 -0.030 -0.767 0.131 -0.093 0.395 -0.784 -0.972 -0.310 0.718 -0.270 0.242 0.136 -0.322 -2.582 1.914 -0.580 0.522 -0.800 -0.588 +3 0.624 -0.317 -1.637 2.391 -0.597 2.671 -0.470 1.512 0.718 0.764 -0.495 -0.273 -0.259 0.275 -0.085 -0.407 -0.816 -0.717 0.534 -0.702 -1.099 0.141 -2.182 -0.006 0.079 0.563 0.341 -1.278 +4 -0.013 -0.865 -1.904 0.266 1.132 2.320 -0.188 -0.259 0.936 1.712 -1.236 0.665 1.828 -0.249 -1.158 0.785 -1.346 1.190 1.991 -0.500 1.658 0.631 1.386 -0.650 0.623 0.242 -0.786 -0.352 +0 0.774 0.257 0.427 -0.651 -0.336 0.275 0.487 1.185 0.491 0.747 0.947 1.932 0.206 -0.572 -0.699 -0.157 -0.120 1.685 -1.395 -1.002 -0.458 0.545 -1.254 -0.590 0.227 0.042 -0.744 0.310 +1 0.698 1.638 -0.356 0.230 1.409 0.835 0.822 -0.126 0.353 -1.345 -0.600 -0.358 -0.601 0.202 0.041 1.104 1.612 2.134 -0.123 -0.661 -1.219 1.101 -0.150 -0.692 0.636 -1.264 0.106 -0.934 +1 0.340 -0.059 -0.858 0.201 1.825 0.911 0.468 -0.655 -0.478 0.197 0.565 -0.137 0.001 -1.115 -0.309 0.218 0.218 1.319 -0.870 -0.248 1.829 -1.214 -0.202 1.630 1.724 -0.021 -0.887 -0.247 +3 2.195 0.373 1.053 1.375 -0.043 -0.655 0.660 0.967 0.567 1.151 1.070 1.006 0.730 -1.529 0.588 -0.105 -2.336 0.135 -0.041 -0.854 -0.663 -1.023 -0.166 -1.903 -0.875 1.187 0.552 -1.089 +3 -0.377 0.995 0.261 -0.419 0.773 -1.065 -2.325 -0.686 -0.139 2.076 -0.577 0.685 -0.511 -1.117 1.051 -0.435 0.172 0.431 2.775 0.328 1.148 -0.791 -0.317 0.254 -1.292 -0.946 0.149 0.142 +3 -0.327 1.313 1.405 0.945 -0.198 0.287 -1.275 1.990 -0.852 -0.254 0.099 -0.107 -0.657 1.394 0.950 0.322 -0.400 1.064 -0.464 1.570 0.111 1.230 -0.114 0.072 1.225 2.194 1.560 0.877 +0 -0.091 -0.699 -1.148 -0.314 -0.878 0.028 0.992 -0.301 -0.426 1.074 0.734 0.798 -2.521 -0.448 -0.572 -0.826 1.054 -0.543 -1.390 0.246 -0.008 -0.922 1.018 0.531 0.528 0.484 1.090 -0.155 +0 0.918 0.343 -0.544 0.390 -0.290 0.984 -0.717 -0.016 -0.744 -0.971 -0.114 1.396 0.486 -0.147 -0.111 -1.597 -0.852 -0.774 0.159 0.977 -0.158 0.211 0.037 0.146 0.013 0.391 -0.708 0.887 +1 -1.760 -0.850 -0.622 0.836 -0.702 1.013 1.142 0.171 -0.452 -1.009 -0.878 0.680 0.832 -0.338 0.162 -1.509 -0.553 0.382 -0.654 -1.229 -0.902 -1.455 -1.055 0.804 1.419 1.692 -0.203 -0.124 +0 -1.570 1.595 -0.674 0.510 0.554 -0.642 0.632 -0.418 -0.687 -0.044 1.757 0.318 0.930 0.082 -0.520 0.350 -0.133 -0.438 0.768 0.439 -0.621 0.894 -0.007 0.844 -0.660 -0.863 -0.458 0.028 +2 1.719 0.155 0.355 1.060 -0.261 -0.175 -2.945 0.482 -0.250 1.101 0.543 -1.213 0.988 1.277 0.257 -1.503 -0.458 -0.375 1.333 0.500 0.131 0.247 -0.178 -0.434 -0.716 -0.890 -0.712 0.172 +4 0.759 -1.955 -0.239 -0.221 -0.600 1.209 -0.040 -0.414 -2.563 0.397 -0.334 -0.154 0.464 -1.242 -0.213 -0.328 0.331 -0.879 -0.484 1.251 -0.909 1.096 1.090 3.412 0.700 0.239 1.138 -0.386 +4 0.024 0.733 -2.695 -1.192 0.568 -1.694 -0.649 -0.745 0.162 1.297 -1.381 -0.345 -1.108 0.118 -0.484 0.165 0.123 1.043 -1.397 1.159 0.366 -0.341 0.574 -1.207 1.838 -1.052 -2.434 -0.203 +1 0.899 -0.608 -0.136 0.786 1.173 -0.524 1.960 0.313 -1.043 0.369 -0.383 0.434 -0.043 0.074 -0.912 0.990 -1.156 -1.109 0.367 1.603 -0.353 0.701 1.073 0.815 -0.311 -1.091 -2.208 -0.263 +1 -0.366 0.116 -1.446 -1.624 -2.027 0.541 0.290 -0.116 -0.451 -1.614 -0.792 -1.640 1.040 -0.825 1.179 0.537 0.765 -0.079 0.584 -0.831 -1.026 -0.047 0.252 0.033 -0.793 0.008 1.154 -0.552 +3 0.322 -1.130 -1.063 -1.174 0.329 0.101 1.741 0.081 2.016 0.697 0.276 0.698 -0.853 -0.263 -1.319 0.465 -0.252 -1.074 -2.367 -1.422 0.480 -0.315 0.500 -1.552 -1.037 -1.766 1.472 0.465 +2 -0.839 -0.309 0.331 0.976 -0.479 -0.186 -1.106 -1.196 0.813 1.356 -0.072 1.004 0.362 -0.645 0.361 1.538 -0.036 1.565 -2.620 0.822 0.087 -0.299 0.092 -1.988 -0.220 0.357 1.478 -0.518 +4 -0.304 1.848 0.628 -0.041 1.480 0.182 0.229 1.042 -0.677 1.019 0.566 -0.921 1.958 -0.457 -1.634 -0.778 -1.036 0.196 1.623 0.249 1.586 1.067 -2.666 0.899 -0.581 0.513 -0.058 2.786 +4 0.216 -1.229 0.326 -2.901 0.349 0.311 1.488 2.818 0.180 0.140 -0.014 0.954 0.429 -1.090 -0.980 -1.255 0.168 -0.043 0.891 0.455 -0.573 -2.104 -0.182 0.037 0.927 -0.901 1.077 -0.759 +2 -0.789 1.098 0.333 -1.100 0.186 0.428 -0.359 -1.287 -1.089 -1.586 2.140 -1.729 -1.254 -0.023 -1.827 -1.159 0.115 0.203 0.410 0.715 -0.040 -0.689 0.457 1.391 0.618 -0.864 0.614 0.559 +3 0.272 0.432 -0.168 1.272 1.933 -2.293 0.815 -0.895 -2.593 0.517 0.519 0.680 0.371 0.875 -0.603 0.263 0.698 1.353 0.044 -0.214 -0.055 -1.194 0.770 -1.457 0.391 0.085 -1.551 -1.494 +3 0.537 0.413 1.025 1.836 -0.467 -1.445 -0.578 -0.310 0.997 -0.490 0.926 -0.531 0.282 -0.502 -2.164 -1.363 -1.331 -0.508 1.464 0.176 -0.554 0.378 1.632 -2.494 0.080 -1.288 1.342 0.353 +4 -0.957 0.440 0.318 -1.489 -0.746 -0.691 -0.444 -0.956 0.939 1.061 -0.788 -1.154 -0.013 1.330 2.154 -0.675 -2.275 -1.487 -1.782 0.417 0.098 0.217 1.126 1.629 0.843 0.735 -1.474 -0.509 +3 1.310 0.048 0.095 -0.788 -1.884 -0.819 0.570 -0.201 -0.333 -1.699 1.811 -0.059 2.342 0.367 1.551 -0.718 -1.514 -0.186 0.560 0.669 0.395 2.292 0.392 -0.310 -0.234 -0.805 0.293 1.082 +3 1.847 -0.469 -2.419 0.743 0.427 0.109 -0.232 1.351 -1.546 1.532 1.734 0.150 0.284 -1.186 1.004 1.271 0.764 -1.240 -1.609 -0.240 0.079 1.015 -0.241 1.063 1.129 0.474 0.869 -0.416 +0 -0.226 -0.049 -0.424 0.330 0.369 1.765 -0.808 0.902 -0.030 0.615 0.480 -0.053 0.669 -0.926 0.574 -1.470 0.444 0.665 -1.817 -1.220 -0.788 0.990 1.017 -0.398 0.274 -1.094 -0.232 0.872 +3 0.710 0.008 0.395 1.826 -0.196 -1.092 -2.526 1.936 1.981 -0.991 1.286 1.033 0.641 0.503 -0.745 0.129 0.348 0.070 -0.055 1.300 1.016 0.874 0.932 0.229 1.166 -1.198 0.070 0.708 +1 -0.046 0.119 -0.099 -0.370 -0.412 -1.827 -0.277 -0.373 -0.678 -0.626 0.041 -0.381 0.756 -1.454 0.943 1.321 0.784 -2.177 0.215 -1.177 -0.920 -0.374 0.162 0.861 0.209 -0.628 1.554 1.111 +0 -0.435 -0.036 0.527 2.479 0.896 -0.656 -0.764 0.002 -0.075 0.770 0.001 0.217 -0.754 -0.144 0.492 2.179 -0.037 0.276 -0.495 -0.778 0.512 0.477 -1.310 -1.363 0.956 -0.768 0.142 0.292 +0 -0.268 -0.709 -0.526 0.707 -0.138 -0.958 -0.399 0.830 -0.320 -2.570 0.905 -0.245 0.794 -0.409 0.479 0.572 -0.738 -1.332 -1.145 0.203 -0.152 -0.020 0.427 -0.324 -0.453 -0.825 0.374 1.700 +3 -1.660 -1.256 -0.460 -0.240 -0.894 -1.159 1.510 0.263 0.585 -0.137 0.289 0.933 -0.996 0.265 0.696 1.545 1.948 0.070 1.139 -2.670 1.552 -0.726 -0.241 0.441 -0.153 0.196 1.588 -0.384 +2 0.969 0.427 -0.646 1.775 -1.194 0.919 1.001 -0.671 1.392 -0.250 0.289 0.260 -0.134 0.811 0.793 -1.749 1.304 -1.662 1.033 1.127 -1.091 -0.411 -1.106 -0.215 -0.308 0.780 1.310 1.396 +0 0.788 0.754 0.603 -0.034 1.722 0.700 0.222 0.567 -0.399 -0.057 -2.082 -0.387 1.760 0.148 0.107 0.999 0.358 -0.786 0.024 -1.135 0.184 -0.838 0.928 -0.510 -0.825 -0.113 0.365 -1.207 +0 1.113 -0.002 -0.023 0.181 0.004 -0.770 0.208 0.509 -0.545 0.477 -0.737 -0.332 -0.158 1.075 0.311 0.823 0.246 0.503 -0.248 0.023 1.012 -1.499 0.006 0.181 0.519 0.193 0.089 -1.017 +2 1.818 0.498 -1.502 -0.228 0.245 -1.685 -0.536 -1.405 -1.030 0.339 -0.049 -1.095 -0.067 -0.362 0.883 -2.074 -0.741 1.517 -1.164 -1.081 -0.518 1.269 0.402 -1.062 -0.084 -0.352 0.450 -0.042 +2 -0.224 0.191 0.317 -1.978 0.370 0.375 0.012 0.765 0.328 -1.258 1.552 0.444 -1.281 -0.571 -0.489 0.427 0.790 0.144 0.496 0.396 -2.255 -0.953 -2.418 -0.786 -0.235 -1.300 -0.386 0.385 +1 -0.226 0.499 0.719 -1.400 -0.190 -1.072 -0.565 1.273 1.153 0.589 -1.405 0.187 -0.576 0.992 -0.563 -0.828 0.361 1.214 1.068 1.299 1.308 1.633 0.181 -0.445 -0.826 1.195 -0.173 -0.910 +2 1.487 0.815 0.242 -0.459 -0.549 -0.336 0.457 0.836 -1.444 -1.947 -0.951 -0.408 1.196 -0.280 0.255 0.418 -1.024 1.460 -0.665 1.560 -0.126 -0.338 -1.111 0.197 2.254 0.414 0.270 -0.667 +2 0.134 -2.809 -0.589 1.407 0.982 -0.509 -0.433 0.567 0.935 -0.533 -0.234 -0.566 -1.331 -0.220 -0.296 -1.261 0.646 0.483 0.696 -0.768 -1.984 0.467 -0.664 -0.891 -1.257 -0.275 1.577 -0.505 +2 -0.007 0.654 0.300 -0.160 -0.415 0.932 -0.931 0.773 0.808 -0.445 0.303 0.553 -0.111 -0.877 -1.309 -1.049 0.823 -1.359 0.323 -0.419 -1.041 -1.409 -0.156 1.282 0.079 2.771 1.826 -0.969 +4 -0.020 -2.806 0.460 0.553 -0.713 -0.138 0.914 -0.473 -1.644 -1.999 -1.183 2.036 1.204 -1.055 -1.515 0.872 -1.056 -0.363 0.240 0.415 -0.953 -0.321 0.408 -2.044 -0.186 1.377 0.406 -0.823 +0 -1.146 -0.460 1.027 0.773 -0.588 0.467 -0.451 0.384 0.782 -0.718 -0.361 0.100 -0.490 -0.609 0.359 1.080 -0.581 -0.749 -0.838 -0.091 1.344 -0.399 1.324 -0.484 -1.310 1.707 -0.141 0.947 +3 -0.900 1.307 -0.260 -2.423 -0.924 1.963 0.640 -0.396 0.809 -1.438 0.855 1.461 -0.758 0.641 1.130 -2.088 -0.986 -0.244 0.659 0.295 0.071 -0.015 -0.740 -1.605 0.106 -0.224 -0.036 -0.253 +3 -1.542 0.502 1.440 0.053 -0.427 0.805 -0.161 -0.275 0.316 0.753 -1.073 -1.747 -1.189 -0.811 1.953 -0.461 0.520 -0.569 -0.679 1.087 -1.801 1.310 -0.005 0.109 1.083 1.419 -0.888 -1.525 +0 0.220 0.132 0.564 -0.492 -0.214 0.810 0.277 -0.114 -1.401 1.498 -0.661 0.987 0.176 -1.293 1.252 0.255 1.335 -0.182 0.704 -1.763 -0.500 -1.273 -0.375 0.472 -0.605 0.864 -0.857 0.885 +2 -0.303 -0.931 -0.018 0.123 0.157 -1.561 1.262 0.247 -0.227 -1.078 1.245 2.138 2.150 0.231 1.059 0.518 -0.843 0.664 1.188 -1.775 -0.960 -0.884 0.628 -1.403 -0.302 0.042 -0.755 0.493 +4 -1.210 -0.115 -1.503 -0.200 -1.249 0.207 -0.679 0.291 1.082 1.589 -2.528 -0.834 -0.303 0.460 -1.650 -0.982 0.130 0.085 0.378 0.444 0.339 0.194 0.056 0.665 -2.239 1.544 -2.204 -1.079 +3 0.326 -0.491 0.950 -2.250 -0.512 1.331 1.182 -0.106 -1.048 -0.666 -0.558 -1.145 0.728 -0.010 1.477 -1.323 -0.995 0.730 1.870 -1.170 0.122 -0.735 -1.560 -0.775 -0.551 0.049 -1.538 0.337 +4 1.123 0.416 -0.064 1.365 0.390 0.209 1.934 -1.105 -1.498 1.986 -1.772 1.887 0.107 -1.320 0.422 -1.004 -0.613 0.467 -0.858 -0.878 0.839 -0.643 -0.992 -1.991 -0.925 -0.876 -1.000 1.512 +4 -2.078 1.318 -1.470 -0.147 -0.183 0.533 0.168 -0.533 1.713 1.709 0.356 -0.131 1.514 1.056 -1.271 2.227 -0.961 -0.453 -1.484 -0.521 1.179 0.030 0.217 -0.810 0.704 -1.920 -0.333 -0.146 +3 1.520 -2.293 -0.952 -0.323 0.763 -0.647 -0.684 0.156 -0.474 -1.345 1.188 0.609 0.882 0.186 -0.108 1.398 -0.744 0.504 0.181 0.102 -1.497 -0.958 1.846 0.612 -1.169 -1.705 -1.137 -0.891 +3 -0.357 -0.485 -0.723 0.416 -0.502 0.010 -0.889 -0.558 1.914 -0.800 1.495 -1.110 1.585 -0.148 0.359 -0.932 0.261 0.365 0.009 -1.419 -0.872 -1.266 -0.051 -0.987 -1.201 -1.179 -0.900 2.525 +4 1.617 -1.622 -2.033 0.167 0.840 -1.711 -1.300 0.793 -1.152 -0.256 0.210 0.955 -0.306 0.492 -1.240 0.589 -0.909 -1.381 2.065 1.830 0.148 1.547 -0.326 -0.136 -0.177 -0.913 -2.344 -0.309 +2 1.295 0.681 0.815 -0.027 0.785 1.884 0.988 0.670 -2.202 0.640 0.302 -0.530 -0.632 0.764 1.528 -0.133 -0.480 -0.917 -1.548 -0.104 0.713 0.508 -0.975 0.517 -0.208 -1.411 -1.906 0.193 +4 0.396 0.690 0.330 -0.497 1.549 -0.475 0.388 -0.173 -1.084 -0.576 0.634 2.456 0.311 0.372 -1.070 2.409 -0.555 1.027 -0.651 -0.001 -0.599 -0.850 0.840 1.300 -1.804 1.251 1.770 -1.730 +1 -1.338 0.103 -0.269 -0.540 1.291 1.977 0.641 -0.638 1.569 -1.936 -0.236 0.315 -1.408 -0.832 -0.568 0.784 0.094 -0.025 -0.458 0.450 1.113 -0.198 0.828 0.926 -0.380 0.246 0.747 -0.621 +3 2.175 -0.986 -0.064 -0.112 -1.686 0.486 0.313 1.111 0.411 0.193 -0.683 -1.836 0.606 1.550 -1.428 0.718 -0.068 -0.968 1.515 0.300 0.252 -1.516 1.651 -0.701 0.334 0.990 0.363 0.852 +4 1.060 -0.652 1.759 1.896 0.578 -2.229 -0.843 -1.533 -0.148 -0.638 0.443 -1.242 -2.361 0.973 1.049 -2.309 -0.540 -0.408 -0.600 1.281 0.151 -1.779 0.718 0.127 0.067 0.660 -1.929 -0.023 +4 -0.552 -1.177 1.450 0.540 0.989 -0.983 1.334 -2.897 -1.282 0.164 0.907 -0.679 -1.973 -0.897 -1.123 2.796 0.689 -1.021 -1.577 -0.820 0.162 -0.482 2.154 -0.745 0.619 -0.706 0.835 -0.440 +1 -1.213 -0.183 -0.478 -1.750 0.595 -0.954 0.268 1.871 -0.958 0.575 -0.741 -1.561 1.615 0.178 0.380 -1.407 -0.703 -0.471 -0.063 0.861 0.482 -0.458 -0.791 -0.388 -0.179 -0.474 -0.224 1.145 +4 0.010 1.698 0.586 0.751 0.041 -0.152 -1.545 -2.259 0.196 1.313 0.344 0.624 1.584 0.329 -0.123 -0.819 2.022 0.527 -0.944 -1.171 1.253 -1.037 1.854 -2.756 1.423 0.487 0.119 -0.175 +3 -1.854 -0.070 -1.585 -0.378 -1.241 -0.070 -0.915 -0.693 1.064 -0.448 -2.739 -0.166 1.414 0.360 0.924 -0.611 0.637 -0.458 0.319 1.172 -1.075 -1.797 -0.421 0.329 1.647 0.088 0.627 -0.686 +3 0.471 -0.079 0.626 0.798 0.642 -1.549 2.033 2.403 -1.909 0.511 2.046 1.002 1.013 -1.439 0.791 0.239 0.780 -1.036 -1.071 -0.143 -0.379 -0.157 -0.480 -0.201 -0.666 -0.048 -1.424 0.514 +4 -0.243 0.115 1.298 0.646 0.701 0.723 -0.154 0.319 -1.085 1.269 -2.783 -0.087 1.141 -1.364 0.400 -0.681 -0.369 0.946 0.198 -2.034 -0.976 0.975 0.449 1.748 1.683 -0.342 2.503 0.024 +4 -0.107 0.657 -0.300 -1.099 0.851 -2.220 -0.488 -1.099 1.071 0.224 -1.957 0.554 0.051 -0.903 -0.644 0.618 0.889 -0.352 0.534 -0.869 1.709 2.086 -1.427 1.416 1.343 0.675 -2.660 1.202 +2 1.279 -1.467 -1.246 -0.014 -0.479 0.705 0.457 1.576 0.543 0.470 -1.136 -0.254 0.927 2.938 -0.842 1.177 0.016 0.119 -0.819 -0.478 -0.091 0.884 1.509 -0.483 -0.552 0.753 -0.247 1.070 +4 -0.734 -0.496 0.392 1.784 0.604 -0.387 -0.305 -0.149 -1.341 -0.458 -0.502 2.778 0.927 -1.073 -1.569 -1.306 1.414 -1.349 1.610 0.240 -0.246 -1.145 1.264 0.546 0.900 -0.273 0.667 1.482 +0 0.441 0.303 -0.778 -1.144 1.085 0.492 1.286 -1.059 0.075 0.794 1.799 0.458 0.391 -0.683 -0.177 -1.312 -0.638 1.030 -0.761 0.042 -0.496 0.297 -0.601 0.363 0.400 -0.648 -1.198 -1.012 +0 -0.764 0.115 -0.417 0.163 1.187 0.402 0.372 0.044 1.589 -0.983 0.387 -0.614 0.308 -0.499 0.433 0.059 1.341 -0.656 -1.019 0.347 -0.334 0.942 -1.288 0.993 -0.758 2.074 1.055 -0.034 +3 -0.627 1.316 -3.091 0.254 0.330 -0.862 1.070 0.681 0.273 -1.904 -1.051 1.955 0.004 1.411 -1.437 -0.983 -0.481 0.330 1.006 -0.516 1.816 0.579 -0.379 -0.013 -0.075 0.005 -0.642 -0.386 +0 0.605 -0.327 -0.905 0.724 0.131 -0.554 0.141 0.364 1.256 -0.146 0.459 0.117 -0.201 1.093 -0.103 -0.637 -1.062 -0.440 -0.102 0.478 -0.732 0.974 -0.320 0.449 -1.299 -0.642 -1.329 1.050 +1 -0.771 1.059 0.209 -0.810 0.412 -0.659 -0.753 0.186 0.306 -0.450 -0.472 -0.384 0.582 0.653 0.558 -0.650 2.132 -2.259 -0.923 0.509 1.522 0.325 -0.318 1.179 -0.493 -0.815 0.804 1.456 +0 -0.818 -0.339 0.398 0.004 -0.909 0.573 2.284 -0.422 0.488 0.228 -0.371 0.806 0.793 -0.550 1.433 0.011 1.076 0.344 0.040 -0.008 0.676 -1.100 0.371 1.310 0.511 -1.747 -1.383 0.021 +2 -0.977 -0.361 1.187 0.342 -1.219 0.223 -0.153 0.052 0.947 0.241 -0.093 -0.142 1.077 -0.720 -2.168 -0.225 0.378 -0.416 -0.525 -0.364 -0.025 -1.298 0.661 -2.662 0.540 0.313 0.497 -1.928 +0 0.934 -0.702 -1.118 -1.040 0.587 -1.010 -0.249 -1.691 0.541 0.579 -0.185 0.194 0.796 0.167 0.781 -1.168 0.379 0.193 1.202 0.030 -0.097 -0.557 1.491 -2.356 0.511 -0.020 -0.184 -0.329 +4 -0.790 -2.032 1.623 0.882 0.662 0.487 0.630 -1.505 -1.562 0.630 -2.168 0.427 0.780 1.752 -0.313 -1.090 1.052 -0.242 -1.702 -3.109 0.581 0.793 -0.010 -0.045 0.018 -0.182 -1.596 -0.362 +2 -0.032 -0.058 -1.111 -0.955 1.787 1.604 0.957 -0.632 -0.450 -0.470 2.346 0.436 -0.107 -1.088 1.182 -0.471 -0.746 -0.670 0.064 0.329 -1.482 -0.014 -0.343 -0.687 0.655 -0.019 -1.783 -0.793 +2 0.112 0.754 -0.893 -1.904 0.328 0.759 -1.743 1.006 -0.541 -0.262 -0.384 -0.365 -0.261 0.189 -0.343 1.520 0.107 0.473 2.010 0.816 2.782 -0.243 0.807 0.484 0.229 0.513 0.088 0.831 +2 1.897 -0.935 0.050 0.584 0.834 0.221 -0.113 -1.565 -0.403 0.608 1.350 -1.121 1.260 1.005 0.190 -0.786 1.399 -0.089 0.475 -0.808 -1.436 0.673 0.393 0.460 -1.070 0.785 0.725 2.486 +3 0.548 -0.166 -0.540 1.001 -1.218 -1.374 -1.241 0.346 -0.723 -0.111 1.385 -1.177 -1.702 0.830 -0.834 -1.172 -0.108 -0.191 -0.868 0.562 -0.779 -0.825 -1.866 -2.455 0.236 -1.321 -0.153 1.928 +0 -0.885 0.437 1.277 -0.896 -1.432 -0.382 0.469 -0.524 1.248 0.176 -0.362 -0.425 0.724 -0.758 -1.348 -0.453 0.236 -0.964 -1.693 0.326 -0.437 -0.202 0.730 0.142 -1.131 -1.855 0.475 0.679 +3 -1.205 -0.086 -1.198 0.341 0.453 1.191 -0.209 1.032 1.612 0.074 2.235 1.437 -0.479 2.944 -0.068 1.400 0.629 0.303 1.309 0.151 -0.076 -0.046 -1.144 1.067 -0.660 0.210 0.020 0.607 +3 1.272 2.038 2.403 -0.075 1.197 -0.044 -1.159 -0.039 0.389 -0.236 -0.942 0.543 2.298 -0.262 0.287 0.017 0.783 0.025 0.319 1.116 1.182 1.847 -0.276 -0.566 -1.927 -0.249 -0.616 0.202 +4 1.139 -1.661 0.620 0.525 -2.274 -0.894 -0.014 -1.510 -0.851 1.320 0.344 0.020 -1.289 -0.621 -0.278 1.588 -0.995 1.450 -0.105 -0.968 2.402 0.821 -0.876 0.535 0.004 0.332 1.778 -2.347 +0 -0.473 0.233 0.252 -0.082 -0.205 -1.364 -1.091 -0.614 -1.009 -0.573 -0.076 0.088 0.562 0.157 0.119 0.536 -0.917 -0.833 -0.373 -0.697 -1.753 0.576 -0.998 1.812 0.165 -1.502 0.667 0.033 +2 3.031 -0.447 0.214 -0.631 -0.167 -0.974 -1.098 -1.805 -1.083 0.233 -0.313 -0.220 -0.450 0.154 0.093 0.031 -0.326 0.730 -2.007 -0.764 -0.798 0.646 -0.539 -0.973 0.173 0.800 0.739 0.929 +4 1.508 -0.409 0.590 -0.102 -1.289 0.735 -1.095 0.022 -0.342 0.893 -1.058 0.391 -0.840 -0.511 -3.635 2.092 0.551 -0.767 -1.125 -0.496 0.717 -1.892 -0.142 1.771 -1.134 0.080 2.203 0.632 +0 -0.248 1.041 0.106 0.374 0.063 1.631 -0.817 -0.138 0.188 -0.996 0.126 0.030 0.114 0.118 -0.672 -1.625 0.183 -0.893 -0.092 -0.748 -1.481 0.361 0.700 1.103 -0.447 0.040 0.341 1.021 +2 -0.353 -0.112 0.688 -0.696 -1.057 -0.536 2.464 0.157 0.400 -0.255 1.733 0.860 -1.041 0.158 -0.387 0.411 0.997 -1.223 0.359 -0.807 1.416 0.966 -1.990 -1.127 -1.344 -0.062 0.301 0.791 +3 -1.093 1.384 -0.093 0.395 0.322 -0.773 0.955 1.285 1.368 -1.988 -0.114 -0.879 1.086 1.128 1.226 -0.427 1.295 0.569 -0.127 1.389 0.779 -2.074 -1.487 -0.656 0.224 1.595 1.405 -0.216 +3 -0.273 0.810 -1.009 1.489 -2.060 0.598 0.817 0.692 1.096 -0.212 1.253 -0.691 -0.607 2.069 0.177 0.952 0.496 0.723 -2.005 0.376 0.016 0.973 -0.796 1.193 -1.206 -0.452 -1.578 -1.081 +2 2.064 0.991 0.668 0.854 1.081 0.910 0.328 0.345 0.232 -0.838 -0.439 1.932 -0.261 0.553 -1.844 -0.554 -0.288 0.718 -0.330 0.954 0.017 0.867 -0.620 0.802 -0.211 0.730 -2.119 -0.895 +0 -0.668 0.268 -1.219 -0.873 -0.296 0.059 -0.633 0.997 -0.003 -0.942 0.276 1.156 -0.657 -0.526 0.872 0.738 -0.217 -0.536 -1.575 0.078 0.812 -1.522 0.477 -1.512 1.351 -0.375 -0.119 -0.786 +0 -0.970 -0.711 0.956 0.570 1.011 -0.097 0.791 -0.719 0.870 0.678 -0.863 -0.495 -0.279 0.602 -0.075 -0.188 -1.216 0.518 -0.298 -0.608 -0.843 0.174 1.051 -1.566 -0.657 -0.645 1.637 0.259 +2 0.302 -1.458 -1.085 0.899 -0.664 -0.568 -0.395 1.213 1.263 2.337 0.856 1.578 -0.705 -1.409 0.606 -0.192 1.668 -0.383 -0.613 -0.296 -0.042 0.033 0.150 1.347 -1.151 0.885 0.699 0.319 +2 1.541 -1.093 -0.138 -0.974 -0.688 0.691 0.168 -0.477 1.936 -0.183 0.456 -1.565 -0.751 -0.749 2.009 -1.032 0.588 0.549 1.221 -0.456 0.945 -1.173 0.163 1.309 -0.961 -0.063 1.134 -0.577 +4 -0.309 -2.105 0.138 -1.886 0.053 0.865 -0.477 1.677 -1.461 1.191 0.675 -1.716 2.711 -0.064 -0.877 -1.407 -0.220 -0.127 0.103 -0.298 0.633 -0.388 -1.935 -0.149 -2.367 0.354 -0.979 0.163 +1 -1.105 -0.291 0.326 1.261 0.361 0.132 -0.928 0.902 -0.278 -1.410 -0.885 0.684 0.716 -2.681 -0.092 -1.293 0.396 0.588 -1.074 0.010 0.836 1.024 -0.845 1.079 0.258 -0.493 -0.091 0.800 +1 -0.636 -0.822 0.802 -0.018 0.225 2.313 -0.976 0.409 0.179 1.225 -2.080 -0.434 0.586 0.044 0.136 -0.683 0.597 1.193 1.676 0.119 -0.029 0.272 -0.811 -0.295 -0.872 0.406 -0.692 0.083 +4 0.204 -0.797 0.723 -3.145 -0.122 1.874 0.047 1.286 -1.749 1.917 -0.223 -0.166 -2.014 -0.893 -0.418 -1.386 0.158 0.477 -0.484 -1.267 -0.340 0.616 -0.984 0.324 1.152 0.128 1.223 -0.373 +2 0.802 -1.794 0.754 0.789 -0.227 -1.219 0.043 -0.211 -0.255 -1.795 0.093 -1.687 1.070 -1.564 0.119 0.148 -0.984 -0.440 -0.620 0.112 -0.866 2.344 -1.051 -0.420 -0.406 -1.082 -0.581 -0.365 +2 0.674 1.404 1.039 -2.410 -0.706 0.149 0.763 -0.357 -0.395 0.223 -0.867 -0.652 0.160 -0.039 -1.163 0.262 0.416 1.377 -0.043 1.135 -2.027 -0.056 -0.500 -0.494 0.428 -1.826 -1.689 0.591 +3 -0.157 0.638 2.436 0.028 0.083 -0.699 0.090 1.602 -0.061 -1.411 -0.270 -1.651 0.217 -0.556 1.153 0.301 0.397 0.060 -0.845 1.009 0.507 -2.077 -0.317 0.189 -2.880 -0.156 0.327 0.884 +0 -0.499 -0.404 0.737 -0.264 -1.163 -0.012 0.215 -0.530 1.182 -1.089 0.002 0.462 0.923 -1.102 0.476 0.298 -0.969 0.780 -0.343 -0.329 0.358 0.250 -0.732 -1.780 0.105 -0.549 -0.021 -0.326 +3 -0.080 0.763 1.063 0.062 -0.848 -1.003 0.377 0.877 -0.458 -0.765 -0.516 0.150 0.779 0.373 0.007 1.390 2.723 0.012 1.607 -0.687 1.143 -0.823 -0.354 -0.040 -1.923 -1.823 -0.851 1.324 +0 -1.568 -0.357 -1.458 -0.588 0.917 1.231 0.214 -0.570 1.588 -0.584 -1.136 0.696 0.458 -0.543 0.100 0.442 0.204 0.459 -0.506 0.526 0.018 -0.472 -0.246 -0.098 -1.445 -0.637 0.065 -1.351 +1 0.504 1.369 0.265 -0.076 0.868 -1.945 -0.856 0.299 -1.001 0.554 -0.495 0.706 -0.724 0.336 -1.214 0.134 1.941 -0.830 -0.226 -0.977 -0.226 -1.544 -1.433 0.032 -1.006 0.103 0.745 -0.281 +2 -0.229 -1.171 -0.115 1.036 1.206 -1.461 -0.767 0.223 -0.034 -1.600 -1.438 -1.068 -1.771 -0.458 1.194 -0.666 1.082 0.030 -0.183 -1.666 1.170 0.116 -1.600 0.382 -0.588 0.061 0.240 1.216 +4 1.081 -0.216 0.816 1.468 0.244 0.810 2.064 -0.010 0.685 0.644 -1.084 1.002 -2.414 2.154 -1.102 1.121 -0.248 -0.608 -0.980 0.603 1.326 -0.462 0.223 0.789 0.228 -0.247 -0.178 -2.215 +2 0.223 1.254 0.803 -0.799 0.129 0.734 1.167 2.725 -0.889 0.704 0.936 0.570 0.169 -0.216 0.077 0.338 0.338 1.134 0.150 0.510 1.282 0.594 1.140 -0.030 -0.020 -2.228 0.394 -1.078 +4 1.394 0.971 -0.368 -0.208 -1.976 -0.607 0.044 -0.082 -1.766 0.744 3.977 -2.220 1.165 1.331 0.147 0.310 0.581 1.249 1.017 -0.262 -0.835 0.758 1.377 1.207 -1.663 1.194 0.307 0.573 +2 -0.142 -0.057 -1.150 0.529 0.131 -0.422 0.856 -1.343 -0.741 0.011 -1.603 -0.981 0.218 0.828 -2.626 -1.475 0.142 0.175 -1.072 2.172 1.322 -0.130 -0.102 0.326 0.919 -0.615 0.977 -0.406 +4 1.265 0.628 0.066 2.395 0.502 2.715 -0.468 -0.036 -0.790 0.076 -0.239 3.070 -0.272 1.086 -0.056 0.612 0.312 0.419 -0.023 -1.719 0.880 -1.900 1.434 0.515 0.257 0.022 1.295 1.322 +4 1.293 -1.666 1.805 1.397 -0.631 -1.117 1.053 -1.370 -0.668 -1.315 0.006 1.465 0.375 1.018 -0.352 0.097 -1.226 -0.097 -0.746 -0.160 -0.229 -0.314 2.108 1.768 1.416 1.795 1.419 -0.022 +2 0.118 -0.213 1.142 0.546 -0.630 0.173 -0.275 1.561 -1.002 -0.985 -2.470 -0.601 -0.247 -1.377 -1.083 0.949 -1.182 -0.060 0.131 1.188 0.039 1.099 0.997 -0.552 -0.175 1.164 -1.381 -0.115 +2 -1.275 1.622 0.888 -1.878 0.329 -0.555 0.166 0.380 -0.540 -0.285 -0.638 0.259 -0.182 0.326 -0.297 -1.700 -0.143 0.578 0.972 -1.790 -0.013 1.108 -0.562 1.230 -2.174 -0.966 -1.243 0.047 +1 1.654 1.299 -1.060 -0.018 0.329 -0.607 -0.907 0.237 0.660 0.031 -0.171 -0.184 -1.132 0.702 0.206 0.125 0.478 2.632 1.884 0.451 -0.617 0.862 -0.222 0.131 0.849 0.513 0.192 0.583 +0 -0.529 0.922 2.180 1.523 1.102 -1.091 1.020 0.945 0.131 -1.437 -1.184 0.355 -0.774 -0.485 -0.315 -0.314 0.609 0.012 -0.307 -0.755 -0.981 -0.072 -0.840 0.299 -0.817 0.567 -0.421 -0.430 +0 -0.510 -0.381 2.037 -0.952 -0.603 -0.342 0.527 -0.069 -0.667 0.624 1.356 -1.623 -0.682 0.512 -1.233 -0.155 1.271 0.623 0.521 0.747 0.325 -0.337 0.965 -0.476 -0.162 -1.656 -0.478 0.079 +2 -0.019 -1.929 -0.143 -1.344 0.659 -1.561 1.013 -0.409 -0.543 0.294 -0.866 0.279 -0.732 -0.557 0.405 -1.139 -1.264 0.248 -0.027 0.169 0.328 0.057 0.608 0.097 -2.007 -1.748 1.928 0.000 +0 0.390 0.159 0.405 0.285 1.974 0.015 1.174 0.781 -0.447 -0.923 -0.318 -0.106 -0.431 -0.103 -0.839 -0.355 0.050 -0.135 0.237 -0.463 -0.547 0.118 0.807 -0.054 0.231 0.249 -0.087 0.659 +4 0.498 -1.472 -0.836 -0.387 0.562 -0.553 -0.792 -0.404 1.414 1.927 0.198 -2.535 0.867 -1.071 0.423 -1.411 -1.135 -0.064 0.397 -2.486 -0.946 0.868 -0.804 -1.103 -0.414 1.609 0.851 0.351 +1 -0.516 1.167 1.385 0.693 0.408 1.001 -0.415 0.693 -0.797 1.618 1.043 0.658 -0.342 0.693 -0.019 -0.556 0.585 -1.902 0.887 -1.030 0.733 -1.490 -0.414 0.454 0.871 -0.691 -0.925 1.022 +2 -0.509 0.202 0.147 -0.572 1.202 -0.646 -1.486 1.387 -1.068 -1.218 1.358 1.055 0.252 -0.694 -1.850 1.501 0.735 -0.540 0.315 -1.358 -1.345 -1.234 -0.566 -0.655 -0.550 0.774 -0.423 -0.355 +3 -0.847 0.650 -1.981 1.562 -0.892 0.169 0.811 0.588 -0.638 -0.725 -1.889 -0.707 -1.182 0.436 0.827 0.734 -0.784 0.651 -0.040 -1.745 -1.792 0.478 0.470 -1.944 -1.537 -1.219 1.066 -0.408 +1 0.016 -0.868 -0.611 -0.631 -0.552 0.668 1.176 -1.236 -0.005 0.294 0.013 2.122 -0.225 -1.293 -0.594 0.176 0.414 -0.434 0.160 1.454 1.919 1.310 1.069 0.728 -0.159 -0.099 0.915 0.894 +0 0.497 -0.133 0.378 -1.069 0.944 -0.567 1.679 -0.323 -0.450 -0.197 0.586 1.446 0.358 2.137 -0.822 -0.204 -1.108 -0.504 -0.660 -1.467 0.935 0.592 -0.400 0.321 -0.413 -0.224 0.506 -0.017 +2 1.073 0.618 1.867 -2.095 -0.998 -0.701 -1.376 0.113 0.160 1.975 0.904 -0.508 0.494 -1.827 0.142 0.089 -0.902 -0.201 -0.693 -0.933 0.674 1.034 0.510 -0.438 -0.663 -0.407 -0.220 -0.872 +0 -0.705 0.242 0.852 -1.263 -0.007 0.172 -0.253 -1.011 0.015 -1.922 -0.032 1.094 -1.815 0.150 0.355 -0.010 0.091 -0.457 1.509 0.124 0.453 -0.574 -0.599 -0.683 0.491 0.348 -0.590 0.403 +3 -1.688 0.240 0.880 -0.080 1.915 -1.082 -1.208 -0.664 -0.702 0.526 -0.422 -1.548 0.892 -1.760 -1.071 -1.623 0.207 0.186 1.916 0.914 1.442 0.289 -0.977 0.128 0.592 0.704 -0.472 0.489 +2 -0.269 0.094 0.144 -1.952 -0.230 0.210 -0.908 1.727 -1.126 -1.015 0.692 0.279 -0.452 1.007 0.244 1.849 -1.778 -0.522 1.654 0.480 -0.105 -0.403 0.350 -1.145 -1.434 0.563 0.855 0.941 +0 0.496 0.796 0.403 -0.003 -1.290 -2.011 0.027 1.226 -1.125 0.362 0.512 0.407 -0.963 -1.721 -0.945 0.108 -0.735 -0.204 -0.250 -0.340 0.157 -0.993 0.572 -0.994 0.203 -0.588 -0.662 -0.065 +2 0.455 -0.253 0.297 1.382 0.784 -1.163 0.587 0.454 1.164 0.033 -0.369 -1.368 -0.695 1.208 0.198 -0.100 1.296 1.090 -0.736 1.201 -1.659 0.460 0.341 1.150 0.381 -0.245 -0.166 2.693 +1 -0.449 1.103 -0.587 -0.962 -1.690 -0.657 1.205 0.276 -0.023 1.881 0.989 0.487 -1.281 0.569 -1.031 0.081 -1.367 0.073 0.290 -1.478 -1.140 0.367 1.071 0.073 -0.536 -1.176 -1.276 -0.146 +2 0.857 -1.054 0.835 1.644 0.618 -1.234 0.821 -0.490 -0.523 0.135 -0.247 -0.464 0.686 0.116 -1.912 1.023 1.199 0.601 0.660 -0.222 -1.196 0.178 1.548 0.273 1.662 1.710 -0.036 -0.464 +1 -1.334 0.173 -1.268 -0.350 -0.349 0.770 0.040 0.706 -1.196 -0.108 0.530 1.242 -1.139 -0.841 0.505 1.612 0.286 -1.110 -1.090 0.135 0.358 -0.221 -1.079 1.101 -0.349 0.527 -1.773 1.201 +4 0.754 -0.182 -0.290 0.113 0.972 -0.096 -1.625 -0.607 -2.902 -0.471 -0.473 -0.307 0.517 1.752 -0.827 0.406 -1.614 0.432 0.143 2.493 -0.363 -0.080 -1.269 1.395 -0.938 0.952 1.519 -1.512 +3 1.580 -0.390 0.853 0.167 -1.075 -0.164 -0.032 -0.636 -1.607 -0.412 -1.495 0.542 0.549 -1.275 0.082 -1.344 -0.362 -0.669 0.480 -1.619 0.983 1.228 -0.144 -1.345 -2.093 1.095 1.984 0.044 +4 0.563 1.166 -0.506 3.223 1.420 0.272 -0.182 -0.555 1.661 0.210 -1.439 -1.712 0.112 0.134 -0.917 -1.514 -1.963 -0.759 -0.363 0.692 -0.130 1.064 0.605 0.513 0.979 -1.387 -0.536 2.401 +1 -0.735 -0.903 1.768 0.961 -1.862 -0.765 0.242 0.324 -1.490 1.159 0.825 0.482 1.357 0.452 0.449 1.558 1.101 -0.842 0.633 -0.664 0.504 0.477 -1.383 -0.122 0.265 -0.436 0.315 0.806 +2 -0.085 0.502 -0.565 -0.095 0.033 -2.966 0.509 0.678 0.367 0.297 1.511 -0.129 -0.095 -0.737 -1.999 0.373 -0.629 -2.010 -0.160 -0.188 -0.315 0.051 -1.646 0.907 0.349 1.158 -0.640 0.345 +0 1.032 -1.426 0.282 0.502 -1.813 -0.424 0.303 0.322 -0.062 -1.750 -0.887 0.286 0.890 0.383 0.466 -0.902 0.129 -1.693 0.373 -0.886 -0.339 -0.596 0.867 1.051 0.186 0.883 1.160 -0.148 +3 -1.171 1.032 0.511 -1.609 -0.038 0.386 -0.590 -1.480 0.278 2.211 -0.387 1.569 -0.752 0.216 0.772 -0.008 -0.298 0.726 1.891 0.301 -0.516 -0.190 2.520 0.135 -0.594 -1.145 -1.826 0.364 +0 -0.104 -1.306 -0.605 -0.364 0.856 -0.847 -1.154 -0.312 0.744 -0.201 0.456 -0.737 0.340 0.537 -0.912 -0.760 -2.026 0.497 -0.775 -0.253 -0.181 0.483 -0.574 -0.167 1.218 1.580 0.555 1.234 +0 -0.134 0.741 -0.881 -1.440 -0.234 -0.653 -1.456 0.031 -0.541 -0.953 -0.016 -0.948 1.161 1.780 0.702 -0.428 0.410 -0.669 0.666 0.722 0.787 -0.369 0.707 0.492 0.264 0.090 -1.072 0.416 +2 0.916 -0.159 0.022 0.000 -1.129 -0.403 -0.260 0.304 -2.049 1.479 0.890 0.375 -0.154 -1.537 -0.104 0.396 -1.327 0.774 -1.770 1.803 0.057 -0.115 0.464 -1.264 1.373 0.745 1.307 -0.063 +4 -2.045 -0.579 1.056 0.788 0.914 -0.987 1.616 -0.965 0.213 -0.010 -0.174 1.339 -0.705 -1.350 0.020 -0.400 3.377 0.677 -0.409 -1.056 -1.880 -1.025 0.115 1.427 1.122 0.229 -0.323 0.269 +0 0.342 -0.547 0.175 0.718 0.892 -0.095 -0.043 0.735 -0.695 -0.254 -1.361 -0.021 0.557 -1.545 -0.886 -0.664 0.575 0.252 0.501 1.681 0.724 -1.777 -0.758 -0.980 -0.367 -0.311 0.053 0.224 +0 0.193 -0.178 0.152 0.768 1.370 -0.108 -1.083 1.392 -0.113 -0.533 0.888 0.548 0.123 -0.595 -0.256 0.476 -0.338 -0.246 -0.800 0.814 -0.528 0.489 0.028 -0.557 1.887 0.126 -1.178 1.971 +1 -1.549 -1.002 -0.932 -0.580 -0.987 0.465 -0.325 -0.348 -0.004 0.129 2.056 0.609 -0.153 1.098 0.108 -0.971 0.063 0.572 0.553 -0.182 -1.277 0.005 -0.709 1.277 0.857 -0.432 -1.748 -1.127 +3 -1.166 -0.685 0.850 0.660 -1.421 1.399 -0.861 0.358 2.734 -0.418 0.168 -0.559 0.175 1.425 -0.195 -0.294 1.104 -1.263 0.083 -0.728 -0.238 -0.887 -1.429 -2.209 -0.453 0.228 0.813 -1.335 +0 0.219 -1.088 0.322 1.277 0.025 -0.599 -0.051 -1.488 -1.081 -0.658 -0.657 -0.334 -1.025 0.736 0.240 -0.316 0.008 -0.419 -1.458 -0.544 -1.371 -0.515 -0.192 -1.015 0.214 0.918 1.904 0.659 +1 0.234 0.506 0.812 -0.244 -0.803 0.717 0.478 -1.553 0.148 -1.858 -0.632 0.373 0.507 1.864 0.687 -1.450 -0.225 0.479 -1.328 0.624 2.129 0.274 0.496 1.024 0.707 -0.376 0.027 -1.042 +4 0.571 1.611 1.285 -0.606 -0.302 -0.084 0.177 0.422 1.523 -0.112 -0.370 0.177 -0.555 1.536 -1.117 -2.089 -0.854 -2.078 0.338 -1.266 2.087 0.215 -0.310 -1.210 0.192 1.769 -1.905 0.807 +4 -0.188 2.157 -0.436 1.514 0.055 -2.678 0.640 1.266 -0.049 1.233 -1.668 -1.386 0.205 0.477 1.357 0.639 -1.194 0.213 0.601 -1.874 -0.044 -0.336 -0.655 0.499 -1.150 0.417 0.559 1.084 +4 0.909 0.810 -2.433 -0.287 -1.043 -3.125 -0.674 -1.641 -0.035 -0.757 0.508 0.089 0.913 0.860 0.841 0.308 -0.343 -0.491 -0.686 -0.199 0.879 0.969 -1.528 0.194 0.067 -1.052 0.760 1.927 +4 -0.622 1.536 1.299 0.614 1.829 2.232 -0.175 -0.809 -1.864 2.245 -0.440 1.135 -0.741 1.538 -0.700 -1.192 -0.209 -0.532 0.596 -0.051 0.628 -0.291 -0.665 -0.584 2.045 0.225 -1.984 -0.520 +3 -1.124 0.103 -1.687 0.831 0.872 0.327 -1.121 1.380 2.037 0.147 0.511 0.202 0.530 -0.120 -0.035 0.906 2.346 -1.400 -0.104 -1.180 1.592 -0.588 -1.443 0.638 1.744 0.664 0.205 0.409 +4 1.849 -1.308 0.274 1.567 0.599 0.176 -0.968 -1.382 0.770 1.506 -0.439 -1.139 -2.458 -0.981 -0.114 1.599 0.673 0.305 -0.400 -0.583 1.406 0.201 -2.103 -0.674 -0.858 -0.546 0.563 -0.107 +2 1.249 -1.920 0.112 -2.196 0.850 -0.059 2.074 0.436 -0.944 -1.396 0.066 0.289 -0.423 -0.197 -0.750 0.193 -0.453 -0.540 -1.172 1.708 -0.875 0.724 -0.242 0.324 -0.052 -0.501 -0.200 -1.011 +3 0.939 0.742 0.454 0.243 -1.018 0.504 0.129 2.002 -1.293 -0.723 -1.587 -0.218 -1.741 1.186 1.092 -0.160 -1.014 -1.524 -0.704 0.625 -0.787 1.909 1.491 0.001 -0.462 -0.569 1.598 -0.657 +3 0.382 0.943 -1.117 -0.486 0.939 -2.055 -0.050 1.508 -0.512 -0.790 0.697 0.861 2.511 -0.080 -0.065 -2.119 -0.132 -1.266 0.374 1.191 -1.276 0.033 -0.573 -1.369 0.194 -0.493 0.642 0.048 +4 -2.912 -1.445 0.775 2.019 0.903 0.324 -0.021 1.683 2.148 -0.587 -2.000 -2.098 1.322 -0.047 0.183 -1.791 -0.753 -0.295 0.208 -0.399 -1.037 -0.785 1.208 0.245 0.612 -0.899 -2.954 -0.523 +1 -1.082 1.275 -0.126 -1.106 -0.755 1.738 -0.196 -1.450 1.211 -0.172 0.235 -0.369 -0.463 -1.270 -0.141 0.691 -1.030 -1.120 -0.196 0.299 0.069 -1.246 -0.261 -0.782 1.617 -1.143 -0.791 0.023 +0 0.127 0.753 -1.336 1.848 0.309 0.466 -0.097 0.019 -0.148 1.018 -0.008 0.767 -0.434 0.196 -0.804 1.217 -0.834 -0.325 -0.315 -0.354 0.722 -0.317 1.394 -0.818 0.169 -0.776 0.421 -0.016 +4 0.192 -0.931 1.819 -1.927 0.284 0.421 -0.802 -0.834 -1.800 3.009 -1.030 -0.613 0.755 0.756 -1.548 -1.496 -0.517 -0.569 -0.083 0.050 -0.378 2.205 -0.228 1.570 -0.932 -0.609 -0.014 -0.366 +2 1.863 0.136 -0.235 -0.138 0.420 0.835 -0.270 0.463 -1.782 1.747 -1.230 0.307 0.560 1.491 -0.638 0.470 -0.126 -0.576 0.859 -1.623 1.642 -0.657 0.792 -0.240 0.479 1.323 0.633 -0.296 +1 -0.664 -0.311 -0.394 -0.948 0.070 0.340 -1.699 0.133 -0.851 -0.724 1.075 -2.385 -0.256 1.192 -0.666 0.831 0.205 -1.899 0.540 -0.460 -0.146 -0.219 0.148 1.787 -0.046 -0.459 -0.236 -1.190 +1 0.243 -1.690 0.848 -0.647 -0.071 0.213 -0.471 0.707 1.114 -0.889 1.415 1.470 1.566 -1.056 -0.621 0.116 -0.611 1.453 -1.377 0.426 0.823 0.641 0.067 1.189 1.279 -0.280 -0.851 -0.850 +2 0.175 -0.700 -1.001 -0.046 -1.067 1.401 -0.086 1.441 1.325 0.397 -0.881 -2.478 1.054 1.014 1.191 -0.128 -1.247 -0.987 0.533 0.843 0.712 1.194 0.917 0.240 0.637 0.129 -0.950 -0.333 +3 -0.050 0.381 1.609 -2.376 -0.622 -1.397 -0.335 -1.144 0.769 -1.278 -0.900 -0.223 1.792 0.446 0.049 0.996 1.197 -0.617 0.094 0.392 -1.379 1.111 1.823 -0.615 -0.021 -0.069 1.770 0.515 +2 0.615 0.527 0.097 0.935 -1.174 0.372 -0.817 -1.443 1.159 -0.733 0.403 0.789 0.095 -0.859 1.516 -1.638 0.209 1.985 1.593 -0.686 1.265 -0.956 -0.766 0.575 -1.035 -0.766 -0.265 0.219 +0 -0.659 -0.685 -0.002 0.408 -0.805 0.714 -0.360 -0.600 -0.897 -0.605 -0.220 1.459 -2.164 0.179 0.964 0.703 0.455 0.168 1.743 0.596 1.370 0.039 1.347 0.549 1.243 -0.061 0.225 -0.147 +2 -0.650 -1.608 -1.087 -0.446 -2.373 -0.475 -0.202 -1.294 -1.040 -0.221 0.356 -0.891 0.292 0.137 0.047 -0.652 -1.333 1.696 0.630 1.006 -0.084 -0.164 0.491 -1.835 -1.115 -0.798 -0.455 -0.880 +0 1.136 -0.555 0.837 -0.427 -1.418 0.729 0.125 -0.107 0.020 0.177 -0.853 -0.862 -0.022 0.451 0.538 0.624 1.238 -1.151 0.698 0.663 0.063 0.651 -0.551 -0.538 1.079 0.454 0.595 -0.455 +0 -2.474 0.226 -0.204 -1.448 -0.313 0.313 -1.523 1.010 0.194 0.164 0.880 0.220 -0.967 -1.344 -0.178 -0.605 -0.521 0.683 0.711 1.336 -0.082 -1.110 -0.341 -0.395 0.744 -0.553 0.423 0.353 +2 0.102 0.770 -1.097 1.440 -0.758 0.007 0.607 -0.774 1.230 -1.716 1.578 -0.329 -1.839 -0.772 -1.184 0.503 -1.237 -0.531 1.014 -0.234 0.511 1.396 -0.424 -0.127 -0.309 -0.944 -1.300 -0.021 +1 0.668 -0.452 -0.383 1.288 0.210 -0.056 -1.663 -0.722 0.961 0.273 0.864 0.273 0.188 0.145 0.496 1.359 -0.163 -0.517 1.125 0.428 -1.588 1.302 1.892 1.806 0.180 -0.986 -1.131 0.715 +0 -1.510 -0.607 -0.124 -0.045 1.120 -1.170 -0.443 0.524 0.834 0.864 -1.183 0.581 -0.097 0.374 0.446 -1.619 -0.071 -0.795 0.525 -0.713 1.382 -1.281 -0.089 -0.098 0.286 -1.685 1.131 0.238 +2 0.206 0.162 -0.781 1.299 2.058 -0.748 0.711 -0.988 1.321 1.265 1.009 -1.656 -0.543 -0.529 -0.596 1.329 -1.811 0.390 0.744 -0.702 0.505 0.442 -0.272 1.087 -0.543 1.089 0.132 -0.312 +3 1.069 0.345 -0.181 -1.208 -0.986 1.958 1.251 -1.608 0.630 -0.779 0.362 0.950 -0.996 -0.278 0.094 0.689 -1.435 -0.924 1.107 -1.312 -0.163 -2.294 0.857 -0.545 0.237 -0.097 -1.964 0.012 +4 -0.742 0.642 -1.050 0.407 1.937 0.219 0.403 1.454 -1.704 1.330 -0.110 1.109 -2.104 1.166 -0.291 0.900 -1.587 0.282 -2.527 0.902 0.649 -0.618 -2.132 0.749 -0.855 -0.657 0.095 0.682 +3 -1.495 -0.668 -1.506 -1.401 1.361 -0.504 0.463 0.165 0.708 0.148 0.107 -1.483 0.035 0.024 -0.942 -2.069 0.245 1.224 -1.770 -0.168 -2.233 0.650 1.184 -0.098 0.625 0.064 -0.971 -0.134 +2 -0.098 -0.426 -0.756 -0.268 0.329 1.581 0.729 -0.771 -1.044 0.569 0.902 1.227 -0.269 0.470 0.856 -1.351 0.278 0.904 0.510 -0.081 2.610 1.931 0.009 -0.190 0.700 -0.058 1.272 -1.267 +2 1.743 0.398 -0.251 -0.179 -0.053 1.506 0.456 1.537 1.455 1.069 -1.685 0.855 -0.075 -0.798 -0.369 -0.253 0.939 -1.039 -1.293 -0.687 0.718 -1.134 -1.964 -0.451 -0.534 0.360 0.678 0.276 +3 -0.054 0.914 -1.419 -1.722 0.966 -0.330 -0.766 0.916 1.477 -0.841 -0.262 0.946 -0.430 -0.735 -1.514 -0.530 0.114 -1.842 -0.839 0.747 -0.542 1.237 0.035 2.490 -0.700 1.620 0.130 -0.348 +1 0.487 0.904 -1.644 0.205 -0.865 1.748 0.563 -0.352 -0.135 1.297 0.057 -1.789 1.517 0.813 -0.537 0.601 -0.613 -0.640 -0.121 0.600 0.470 0.938 0.475 1.453 1.724 0.302 -0.336 -0.179 +2 0.587 -1.773 -0.263 -0.895 -0.607 -0.226 0.083 -0.673 0.909 1.242 0.631 -2.768 -0.197 -0.204 -0.273 1.471 -0.928 -0.232 0.178 1.643 -1.482 0.934 0.505 -1.086 -0.587 0.054 0.393 -0.157 +4 -1.463 -1.005 -0.263 -3.534 0.371 3.073 -1.951 -1.431 -0.244 1.012 -0.571 -0.148 -0.328 -0.469 0.435 0.209 1.896 1.422 -1.216 -0.087 0.593 -0.217 -1.524 -0.680 -0.077 -1.191 0.434 -1.778 +2 0.281 0.367 0.869 -0.956 -0.655 -0.343 1.331 -0.194 0.029 1.249 0.103 1.708 -0.624 -1.496 -0.549 0.185 0.196 -2.119 -0.147 0.712 2.346 -1.662 -0.222 -1.351 -0.148 -0.658 -0.194 0.984 +0 0.359 1.150 0.640 0.350 1.587 0.955 0.934 1.906 -0.656 0.744 0.941 -0.414 0.477 -0.156 -1.096 0.245 0.171 -0.517 -1.271 0.609 -1.206 0.385 -0.082 -0.167 0.498 0.817 0.296 0.185 +1 0.356 0.085 -1.627 0.987 -0.277 0.241 2.202 1.286 0.211 -1.373 -1.356 1.162 -0.402 -0.168 -1.145 0.446 1.141 1.524 -0.448 0.645 -0.095 0.880 -0.004 -0.824 1.320 0.099 -0.351 0.178 +2 0.511 -0.583 -0.455 -0.449 0.883 -1.093 -0.205 -0.489 1.435 0.120 -0.024 -2.097 -1.363 0.723 -0.195 0.608 0.804 1.190 -1.131 1.751 0.341 0.439 -1.824 0.624 -0.841 0.120 1.855 0.389 +0 0.124 0.097 0.039 -0.652 -0.137 -0.180 -0.008 0.944 1.761 -0.840 -0.392 -0.825 0.187 -0.538 0.240 1.104 -2.526 0.492 -0.460 0.460 -0.896 1.196 0.913 0.568 0.793 -0.746 -0.620 0.752 +1 0.846 -0.792 -0.928 0.416 0.607 -0.835 -0.373 -0.251 -0.694 0.480 -0.249 0.477 -0.175 -1.036 -0.689 0.697 -1.479 -0.516 0.666 2.983 -1.162 -0.028 0.552 -0.642 -0.368 0.039 0.760 -0.829 +4 0.898 -0.448 -1.633 -1.219 -1.204 -1.162 -0.048 1.407 0.590 1.451 1.173 -0.719 -0.348 0.243 0.054 -0.449 1.118 -0.692 0.817 -2.457 -0.349 -0.357 0.001 -1.588 0.266 -1.400 2.717 -0.544 +0 -1.129 -0.370 -0.745 0.066 -0.418 1.284 1.101 0.052 0.179 -1.297 -0.409 -0.236 0.470 0.297 0.341 -0.335 -0.138 -0.447 -0.285 1.008 -1.777 -0.178 -0.875 0.550 0.504 0.347 -0.256 0.176 +0 -0.294 -0.851 1.529 0.875 0.017 0.491 0.922 -0.585 -0.655 -0.893 -0.243 -0.409 -0.738 1.394 0.605 0.068 -0.069 0.139 0.502 -0.044 -2.007 -0.196 -0.590 -0.097 1.344 1.283 -0.655 0.094 +4 -1.174 0.494 -0.191 -0.628 2.643 1.038 1.056 2.202 0.802 -0.771 -0.888 0.965 -2.059 -0.078 -2.171 0.099 0.854 0.279 0.938 1.965 -0.144 2.808 0.213 0.949 -0.611 1.619 1.042 1.471 +1 0.553 -1.417 0.063 -0.680 -0.288 -0.689 -0.638 1.288 -1.433 0.409 -1.131 1.322 0.428 -1.405 0.335 -0.696 0.227 -1.328 1.231 -1.997 -0.808 -1.082 0.379 -0.493 -0.184 -0.642 -1.368 -0.229 +0 -0.645 -0.257 -0.207 0.968 -1.914 -1.476 -1.408 -0.123 0.586 0.355 -0.966 0.943 1.129 0.758 -0.466 -0.010 1.010 -0.039 0.140 0.386 -0.184 -0.488 0.421 0.696 -1.248 -0.032 -0.905 -0.036 +1 -1.237 -0.233 0.567 1.161 -0.647 -1.457 0.509 -0.248 0.240 0.190 0.149 -0.414 0.907 -0.849 0.265 0.390 -0.196 0.793 -0.465 2.297 0.237 1.238 0.888 1.424 0.088 1.127 1.456 0.288 +0 -0.757 0.028 -0.295 2.627 -0.995 -0.512 0.089 1.506 -1.178 1.026 0.912 0.414 -0.722 -0.725 -0.218 0.426 0.984 -0.379 -1.114 -0.776 -0.304 0.594 -0.785 -1.001 -0.208 0.252 0.410 -0.383 +3 -1.115 -2.813 0.053 -0.734 -0.903 -1.242 0.557 -0.378 0.225 -0.390 -0.726 -0.067 -1.791 -0.140 1.003 0.026 -1.668 -0.515 -1.656 -1.151 0.026 -1.554 0.054 -0.949 -0.157 1.018 0.518 -0.229 +2 -1.335 -0.093 -0.566 -1.602 -1.415 -1.266 -0.058 -0.458 -0.959 -0.392 0.748 0.357 1.002 -0.228 1.603 0.348 -0.489 0.093 -1.248 -0.306 0.430 0.612 -0.410 2.734 -0.172 -1.678 1.213 0.607 +2 1.191 0.197 0.344 1.181 -0.096 0.754 0.684 -1.286 1.473 -1.420 0.917 -1.517 0.601 1.862 -0.213 -0.256 0.610 -0.329 1.818 0.192 -0.320 -0.540 -0.933 -0.628 -0.351 -0.488 -0.658 2.099 +2 -1.281 1.011 -0.591 0.510 0.455 -1.573 -0.044 -0.753 0.087 1.198 0.532 0.190 0.632 -0.858 0.782 -0.095 -2.365 1.475 -0.146 -0.655 2.146 0.353 -1.227 -0.757 0.116 1.401 -0.175 0.416 +2 -0.605 0.956 -0.163 0.597 -0.653 0.804 0.197 0.405 -0.179 1.630 1.870 -2.204 0.926 -1.080 0.510 -1.778 0.403 -0.836 -0.459 -0.432 -0.735 -0.707 -0.398 -0.154 0.323 1.861 -0.947 0.127 +3 0.564 0.887 0.627 -0.252 0.816 0.571 0.009 -1.400 0.522 1.316 1.023 1.078 -0.870 1.067 -2.474 0.141 0.742 0.011 -2.024 -0.238 0.790 0.982 1.328 -0.427 1.162 1.362 -0.798 0.779 +1 0.241 0.168 -0.294 1.065 -1.600 -0.152 -1.007 -0.738 -1.637 1.621 1.480 0.133 -0.068 0.031 -0.406 0.258 0.953 0.454 0.813 -0.512 0.782 -0.022 0.365 -2.314 -0.336 0.220 -0.123 1.304 +0 -1.701 -0.735 -0.194 0.666 0.189 1.608 -1.036 0.881 0.047 0.695 -0.437 1.072 0.295 -0.679 -0.012 -0.240 0.489 -1.180 -0.029 0.395 0.033 1.347 0.774 -0.007 0.217 0.296 0.403 -1.826 +2 -0.934 1.793 -0.663 -0.746 0.820 -0.911 -1.293 -0.768 0.820 2.140 0.005 -0.724 1.235 -0.615 -0.444 0.417 1.141 0.071 -0.355 2.321 -0.671 -0.560 -0.388 0.551 -0.467 0.817 -0.052 -0.319 +0 -0.756 0.020 -0.059 -0.529 0.432 1.049 -1.206 1.179 0.510 0.068 0.670 -1.744 1.225 -1.260 0.439 0.329 0.793 0.351 1.514 2.194 0.542 -0.038 0.507 0.172 0.192 0.164 -0.579 -0.240 +4 2.210 0.772 0.560 0.867 -0.488 0.004 0.863 -0.213 -1.881 1.239 0.711 -1.155 -1.167 0.326 3.600 0.059 -0.568 -1.808 0.359 0.676 -0.349 -0.163 -0.300 -1.000 -0.967 -2.752 -1.078 0.394 +2 1.661 1.429 -0.584 0.669 0.550 -0.044 0.177 0.683 0.883 -0.812 0.748 1.666 0.882 1.536 -2.584 0.596 0.264 1.027 -1.126 -0.506 0.901 -1.060 0.242 -0.017 -0.196 0.573 -1.116 -0.780 +0 0.132 1.743 -0.571 -0.809 0.938 -1.239 0.013 -0.524 -1.653 -0.143 -0.628 0.507 0.200 0.175 -0.949 -0.566 0.251 -0.889 -0.038 0.350 0.749 -0.540 -0.392 0.196 -0.733 -1.383 0.353 -0.262 +4 0.202 -0.525 -1.726 -0.519 0.777 -0.381 -0.687 0.397 -1.492 2.768 1.756 0.888 0.511 -1.387 -1.820 0.177 1.349 -0.105 -0.672 -0.306 -0.608 0.865 -0.998 0.398 -0.096 2.683 -0.809 -0.044 +1 0.499 -1.692 -0.170 -0.813 0.828 1.071 -2.074 -0.022 1.122 -0.187 0.729 0.351 0.342 -0.271 -1.217 -1.122 0.709 0.019 -0.139 -0.424 -0.305 1.047 -0.335 -1.341 0.744 -0.205 0.031 1.608 +0 0.700 -1.046 -0.642 0.121 0.411 0.529 -0.945 1.631 -0.418 -0.719 -1.272 -1.371 -1.400 0.314 0.604 -0.864 -1.142 -0.212 0.097 0.263 -0.964 -0.167 1.567 -1.195 -0.029 0.500 -0.968 0.611 +1 -0.171 0.226 -1.634 -0.277 0.506 -0.392 -0.332 0.164 0.431 1.522 0.440 1.352 -0.097 -2.369 -0.428 -0.810 -0.063 -0.321 0.263 -0.670 0.216 0.370 0.035 -1.463 0.894 0.620 -0.191 -1.811 +0 0.441 -1.017 -0.239 0.407 1.138 -0.586 -0.598 1.541 -0.552 -0.057 1.713 0.067 -0.248 -0.173 -0.740 0.451 -0.924 0.779 0.015 0.235 1.730 -1.449 0.024 0.638 -0.616 0.951 -0.545 0.292 +4 -1.222 -0.185 1.049 1.459 0.276 1.338 -0.460 1.701 -0.723 -0.809 -0.461 -0.747 0.517 1.252 0.016 0.311 -1.382 0.096 1.771 1.413 -1.095 -0.438 3.407 0.418 1.515 1.594 -1.116 0.724 +1 1.230 -0.276 1.386 1.099 -0.132 0.638 0.489 -0.505 -2.158 0.139 0.701 -0.554 2.007 1.032 -1.473 -0.195 -0.840 -0.367 0.654 -0.252 -0.570 0.094 0.760 0.750 0.615 1.370 -0.090 -0.361 +2 0.002 -0.125 0.932 -0.379 0.761 0.387 1.182 0.467 -1.593 -1.082 0.467 -2.276 0.338 -0.053 -1.625 0.099 0.436 0.223 2.090 -0.761 -1.377 -0.829 -1.017 -1.033 -0.034 1.030 -0.623 1.422 +1 0.947 1.024 0.105 0.136 -0.496 0.410 -1.319 1.306 1.418 0.440 1.361 0.206 0.099 0.215 -0.329 1.537 1.049 0.988 0.926 0.389 -1.319 -1.480 -0.251 -1.099 0.329 1.142 -0.180 -0.788 +2 -0.426 0.888 -0.872 0.855 1.328 0.370 -1.789 0.612 -0.644 -0.528 0.362 1.105 -1.661 0.058 1.992 -1.099 -0.200 -0.616 0.703 -1.250 -1.570 0.832 0.239 -1.186 -0.068 -1.282 0.922 0.278 +2 1.271 -0.029 -1.614 0.213 0.126 -0.469 0.268 -0.044 0.634 0.575 0.365 -1.299 2.119 -1.556 -0.129 -1.094 0.396 0.138 -0.169 1.518 1.108 -0.327 0.804 1.565 1.101 -0.136 -1.590 0.325 +0 -0.998 -1.786 -1.756 0.536 -0.434 -0.351 -0.430 -0.140 -1.227 0.316 -1.423 -0.756 1.011 0.376 0.686 2.150 -0.060 0.021 -0.264 -0.443 -1.011 0.084 0.355 0.411 0.064 0.926 0.029 -0.409 +3 -0.564 0.789 -0.920 0.599 0.037 -1.655 -0.518 0.834 0.476 -1.216 -1.056 -1.697 -0.821 0.182 0.748 -1.722 0.136 -1.115 0.004 1.824 -1.214 0.015 -2.959 0.545 -0.460 1.174 -0.199 0.525 +3 -1.927 -0.012 -0.493 -1.620 -0.852 -1.078 0.094 -0.344 0.346 1.098 0.575 0.191 -0.305 1.778 0.461 0.920 0.067 1.773 1.160 1.103 -0.973 -0.965 1.432 1.205 1.028 -2.258 -1.062 -0.308 +4 -0.317 -0.179 0.329 0.062 -0.610 -0.029 1.910 0.706 -0.412 -0.273 1.288 -2.175 -0.897 -0.032 -0.986 1.516 1.149 1.753 -0.034 2.791 0.050 0.217 -0.091 0.605 1.502 -1.916 -1.155 0.713 +1 0.429 -0.804 -0.151 -0.676 -0.139 1.215 -0.176 -0.795 1.041 1.277 -0.785 1.480 0.963 -0.552 1.219 -1.232 -0.482 -1.095 -0.977 0.851 -0.015 -1.111 -0.590 -0.665 0.251 2.273 0.206 0.272 +3 -0.996 -1.732 -1.514 1.011 1.392 -1.346 0.699 2.703 0.226 -0.530 -0.132 0.080 -0.248 -0.380 -2.027 0.122 0.905 0.864 -1.764 0.683 0.193 -0.710 -0.029 -0.654 0.801 -0.087 0.601 0.577 +4 0.249 0.399 2.492 -0.275 -0.318 0.117 1.072 -1.507 -1.966 -0.587 -1.634 -0.425 0.502 -0.891 -1.427 0.069 0.863 -1.695 0.820 0.817 0.954 1.218 2.202 -0.356 0.110 -0.416 0.002 1.533 +3 -0.987 0.715 3.132 -1.170 0.626 0.688 -1.121 0.806 2.036 0.955 -0.367 -1.361 0.448 -0.145 0.658 -0.299 -0.915 0.073 1.165 0.814 -0.282 -1.179 -0.290 -0.316 -0.050 1.980 -0.310 0.132 +2 -0.309 1.624 0.342 1.766 -0.329 0.945 -0.263 0.573 1.393 -0.359 -1.267 1.056 -0.478 -0.128 -0.017 -0.366 -1.986 1.392 -0.390 0.213 0.794 -0.450 -1.466 -0.110 -0.580 -1.797 1.056 -1.453 +2 -0.963 -0.634 -2.257 1.607 1.595 -1.016 -0.228 0.096 -0.960 -1.755 -0.210 -0.321 -0.388 0.223 1.430 -0.303 0.170 0.727 0.554 -0.614 0.096 0.548 -1.128 1.264 1.432 -0.723 0.194 0.642 +4 1.140 -0.429 -0.279 -0.448 0.871 0.125 1.092 -0.218 -0.562 -1.538 2.136 -1.387 -1.457 -2.170 0.315 -0.598 1.807 -0.937 -0.092 -1.252 0.787 -1.998 -0.757 -1.716 -0.011 -1.292 -0.455 -0.382 +0 -1.625 -0.312 0.516 0.466 1.572 -0.373 -1.845 1.197 0.195 0.282 0.767 1.264 -0.023 1.177 0.552 -0.656 -0.233 0.617 -0.709 0.886 0.158 0.382 1.441 0.357 0.071 -0.083 -0.135 0.312 +0 1.338 -0.549 -0.392 1.275 -0.221 0.630 -1.569 -0.511 -0.020 1.392 -0.344 1.094 0.135 1.249 -0.565 -0.704 -0.458 -0.553 0.321 0.421 0.497 -1.768 0.104 0.265 -1.086 1.029 0.362 -1.102 +2 -0.582 -0.462 0.706 0.959 -0.098 -0.081 0.464 0.568 1.176 -1.284 0.020 -1.068 -0.051 -0.226 -0.047 -0.800 -1.487 -2.094 -0.131 -0.431 -0.676 1.323 -0.859 1.417 1.624 -1.951 0.092 -0.400 +2 -0.125 1.233 -0.400 0.587 0.182 -1.480 1.050 0.183 -2.166 -0.919 0.160 -1.664 -0.833 -0.996 -0.903 -0.076 0.078 0.084 0.384 0.933 0.781 -1.160 0.494 -1.871 1.158 0.663 -0.157 -1.009 +4 -0.285 -0.637 0.088 -0.316 1.132 -0.879 -1.731 -1.357 0.994 -0.970 0.398 -0.295 0.824 -0.231 2.764 1.898 -0.315 -0.452 3.132 -1.079 -0.055 0.022 -0.002 -1.739 0.497 -0.970 1.436 -1.456 +4 1.409 -0.297 -0.053 -0.642 0.968 -1.235 -0.722 -4.158 -1.179 -0.591 -0.511 -1.295 -1.130 1.262 -0.780 -1.809 -1.043 -1.030 -0.070 0.360 -1.354 0.300 1.400 1.016 0.579 0.408 -0.317 1.419 +4 0.201 0.986 -0.249 0.076 -1.212 -1.537 -0.525 -0.915 0.429 -1.384 1.121 0.765 1.845 -0.361 0.529 1.189 1.085 0.145 0.510 2.231 -1.829 1.086 -2.120 1.422 1.855 -0.229 1.346 0.894 +2 -1.999 0.034 0.614 0.375 0.168 1.162 -0.540 0.895 -1.632 -0.905 -1.807 -0.275 -0.046 0.114 0.062 -0.928 -0.190 -1.497 1.363 -0.304 -0.489 1.589 -1.242 -1.126 -1.096 -0.068 0.432 0.740 +4 -0.808 -0.082 -0.711 1.529 -0.981 -1.221 0.045 -0.469 0.670 0.547 -2.450 1.385 1.061 1.060 0.683 0.325 -1.487 0.502 0.561 1.339 0.594 1.923 -1.054 2.650 -1.072 -0.679 0.837 -0.212 +4 -0.602 0.282 0.647 -0.711 2.709 1.857 1.138 0.549 0.016 -0.330 0.340 0.966 2.713 2.239 0.093 -0.849 -0.479 0.355 -0.096 0.700 0.034 -1.187 -0.753 0.799 1.244 0.305 0.333 1.763 +4 -1.688 0.258 1.310 -0.454 1.396 -1.641 -1.377 -2.722 -0.759 -0.116 -0.635 -0.329 0.364 1.040 -0.386 1.588 -0.959 -0.524 1.228 -2.114 0.314 -1.361 -0.922 -1.260 -1.038 -0.672 -0.363 -0.213 +4 -1.392 -0.018 0.071 -1.066 0.539 -0.829 1.880 0.385 -0.028 -2.320 -1.734 1.353 -0.382 -0.241 -0.295 -0.198 1.645 -0.679 -1.677 2.374 0.324 -0.273 1.834 1.429 -1.674 0.901 0.579 -0.992 +1 0.183 2.078 -0.683 0.655 -0.792 0.321 0.202 0.791 1.208 -0.580 1.417 1.487 -0.250 -0.007 -0.494 -0.305 -1.203 0.778 -2.050 -0.697 -0.046 1.300 -0.545 0.428 -0.081 0.945 0.376 0.830 +3 0.695 -1.141 -0.112 -0.805 -0.388 -0.414 -0.478 -1.255 -0.128 -0.561 -2.929 2.053 1.089 -0.376 0.019 -1.172 1.696 1.897 0.157 1.024 0.175 -1.337 -0.412 0.132 -0.455 -0.219 -0.091 -0.081 +0 0.399 2.177 -1.500 -0.843 0.006 0.468 0.044 -0.966 0.483 0.329 0.243 -0.569 0.477 1.189 0.841 -1.136 -0.961 0.745 -0.604 -1.241 -0.584 -0.685 -0.271 -0.189 -1.777 0.232 0.476 0.144 +0 0.565 -0.377 -0.359 -1.036 -0.402 1.151 0.311 -0.882 0.615 -0.840 -0.446 -1.127 -1.356 0.788 -0.081 -0.551 0.695 -0.664 1.526 -0.808 -1.026 -0.511 -1.097 0.504 -0.281 -0.575 -0.651 -2.066 +2 0.680 -0.849 -0.374 -0.149 1.347 1.837 0.341 0.614 0.311 -0.467 -1.656 1.397 0.378 0.291 1.210 -1.066 -0.834 0.115 0.651 0.524 0.892 1.232 -0.607 2.699 -0.653 0.382 -0.017 -0.167 +1 1.863 -0.142 0.364 -1.066 1.327 -0.343 -0.847 0.215 -0.504 -0.702 -0.855 -1.894 -1.154 1.393 0.927 -0.095 -0.069 -1.389 1.032 -0.581 0.707 -0.394 -0.739 1.682 -0.073 0.846 0.422 -0.755 +0 1.345 0.651 0.473 0.616 -0.400 -0.055 -0.902 -0.333 -0.367 -0.635 0.137 -0.324 0.502 -1.622 -0.113 0.132 -0.311 -0.172 -1.585 0.356 0.648 0.494 1.039 0.202 0.479 0.156 0.688 -1.577 +3 -2.268 0.331 0.842 1.700 -1.464 1.206 1.644 -2.017 0.479 0.618 -0.327 -0.490 0.668 -0.615 -0.217 0.812 1.754 0.538 0.133 -0.516 -1.011 1.110 -0.153 1.151 -0.330 -0.649 0.421 -0.077 +1 -1.364 1.704 0.474 -1.050 0.337 -0.832 -1.920 -0.345 -0.009 -1.567 -1.257 -1.002 -0.204 0.129 -1.443 -0.582 -0.471 -1.738 0.225 0.597 0.312 -1.322 0.598 -0.101 0.983 0.478 0.312 -0.282 +1 -1.774 0.029 0.131 0.651 -0.403 -0.431 0.533 -0.370 -0.672 1.107 -1.021 -0.715 0.354 0.733 -0.239 0.470 0.206 -0.290 -0.976 0.987 -0.990 2.683 -0.519 1.602 1.093 -0.291 1.034 0.976 +2 0.509 0.341 -0.349 0.274 0.428 1.435 2.500 -1.062 -1.297 0.103 -2.079 0.649 0.113 0.367 -0.304 0.255 0.472 0.200 -1.345 0.172 1.854 0.241 0.137 1.560 -0.746 0.890 -0.530 1.087 +4 0.649 0.042 0.165 -0.707 1.741 2.843 -0.279 0.353 0.097 -0.260 -0.832 -1.085 -1.136 0.405 -0.520 -2.578 0.445 -0.038 1.347 0.373 -1.479 -2.230 -0.719 -0.625 0.943 -0.009 1.134 -1.325 +4 -1.927 -0.827 1.018 -1.329 0.848 0.870 0.777 2.347 -0.501 0.556 -1.481 -0.211 -1.296 -1.290 1.143 0.028 0.910 -0.238 0.397 2.487 1.286 -1.017 0.797 -0.629 -0.051 1.945 -0.762 0.052 +2 -0.354 0.753 -0.862 -0.505 1.436 -0.379 0.486 0.185 -0.086 1.014 0.830 -0.782 1.014 -0.630 1.202 -1.701 -0.967 -0.681 1.136 -1.522 -0.716 -0.672 2.692 -0.645 0.186 -0.378 -0.610 -1.313 +3 -0.708 1.326 -1.230 -0.521 0.588 -1.569 0.388 1.139 1.677 -0.390 -0.440 0.715 -0.618 -0.309 0.322 0.372 -1.907 -1.166 -1.833 -1.668 -1.119 -0.222 -0.125 -2.152 -0.933 -1.518 -0.200 -0.656 +1 -0.250 0.331 -0.927 -1.966 -0.383 -1.431 0.712 -1.154 1.485 -0.006 0.059 -1.981 0.145 -0.080 -1.299 0.087 -0.398 -0.217 0.026 -0.471 0.246 -0.087 0.473 -0.529 -1.065 -0.709 1.443 0.956 +1 0.812 0.851 -0.116 -1.726 0.752 -1.266 0.858 -0.248 -0.021 -0.070 -0.453 0.287 -1.197 0.542 -1.622 0.307 -1.317 -1.350 -1.241 -0.790 0.131 0.599 -1.213 -0.838 -1.638 1.109 -0.516 -0.221 +1 1.426 -0.003 0.400 0.529 0.863 -0.182 0.063 1.163 0.922 0.073 -1.026 0.771 -1.035 0.618 1.202 0.515 0.090 -0.383 1.064 0.588 2.799 -0.267 -0.403 -0.782 0.117 0.870 -0.504 -1.894 +2 -0.043 0.494 -0.534 -0.247 -1.035 1.428 0.682 0.674 -0.358 1.789 -1.557 0.326 -0.219 -0.004 -0.158 -1.583 -0.810 1.659 -0.880 0.258 -0.670 -0.230 -0.174 -1.467 1.087 1.911 0.844 -1.051 +4 -0.429 -0.469 -0.003 -0.993 0.664 -0.296 -0.067 -0.175 0.514 0.286 1.906 -0.384 1.793 -1.812 0.490 -1.586 -1.024 -0.975 -1.052 -0.608 2.201 -0.878 -1.614 0.494 0.242 2.216 -0.041 -2.287 +3 -0.757 0.351 0.205 -0.729 -0.761 1.943 -0.246 -0.118 -0.531 -1.769 -0.264 -1.796 -2.491 -0.273 0.239 -0.520 0.011 -1.087 0.827 0.872 0.438 -1.075 1.726 0.882 1.785 0.796 -0.135 -1.499 +3 -0.309 -1.097 -0.114 -0.813 -1.074 1.132 0.672 -0.882 0.945 0.309 -0.315 1.591 0.305 1.778 0.566 0.652 -1.076 -0.557 1.457 1.162 -0.358 -1.353 1.660 1.067 -1.144 -1.145 1.834 -0.508 +4 1.703 0.171 0.070 -1.348 -0.051 -2.180 0.442 -1.157 -2.171 -0.305 -0.045 -1.267 0.769 -2.645 2.301 0.412 -1.156 -0.927 -0.112 -0.374 -0.602 0.547 1.313 -1.104 -0.780 -0.345 0.826 0.134 +1 0.882 -0.452 -0.470 0.266 -0.437 -0.066 2.100 -0.247 -0.358 -0.648 0.744 -0.181 -0.649 1.321 1.420 -0.600 -1.867 1.008 -0.685 0.791 -1.970 0.893 -1.211 0.731 0.014 -0.954 -0.407 0.686 +4 -1.618 -1.384 1.038 -0.158 0.765 1.422 1.014 0.788 -0.560 2.198 -0.946 0.815 0.337 0.559 -1.319 1.888 -2.322 -2.504 -1.805 1.047 -1.883 -0.895 -0.023 0.470 -0.449 1.572 -0.460 0.877 +3 0.957 -0.743 -2.396 0.896 -0.006 0.962 0.092 0.021 1.498 0.118 -0.476 -0.088 0.158 -2.845 0.491 1.359 -0.049 -0.421 0.388 1.744 0.469 0.214 -0.391 -1.069 0.023 0.042 -1.932 -0.727 +4 0.091 1.872 -1.043 0.085 0.689 -1.141 -1.559 0.320 -2.120 0.748 -1.048 0.176 -1.672 -0.196 -0.099 -1.761 1.537 -0.633 -0.065 -1.216 -0.044 -0.701 -0.153 1.207 1.172 -1.156 2.082 1.429 +1 1.126 -0.496 0.003 -0.111 -1.039 -0.379 -1.007 0.920 -1.661 -0.650 -0.989 1.774 0.794 0.502 1.099 0.042 -0.151 -0.127 -0.468 0.695 0.960 0.681 -0.445 -0.565 2.237 -0.363 1.405 -0.394 +4 -0.594 -1.992 -1.709 -1.976 -0.695 -0.310 0.347 0.978 -1.449 1.006 -0.323 0.650 0.646 0.232 0.402 0.697 2.523 2.817 -0.334 1.424 0.059 0.627 -0.697 1.185 0.598 1.231 -0.669 -2.708 +2 -1.022 -2.521 -0.933 0.893 0.018 -0.463 0.131 0.070 2.602 0.753 -0.214 0.909 0.843 -0.273 0.898 1.222 -0.259 -1.195 0.772 -0.191 0.233 0.706 1.466 -0.405 -0.362 0.370 -1.312 -0.419 +1 -0.409 -0.528 -0.394 0.713 -0.628 -0.380 0.643 0.444 -0.687 0.966 0.909 0.839 0.303 -0.174 -1.074 -1.384 -0.085 0.014 -0.261 -0.111 1.364 1.627 -1.106 0.289 0.052 0.675 1.734 2.188 +1 -1.076 -2.579 -1.109 0.276 1.412 -0.871 0.618 -0.194 -0.554 -0.376 -0.983 -0.847 -0.551 -0.009 -1.162 0.127 -0.660 0.414 0.958 0.219 -2.372 -0.547 -0.601 -0.051 -0.536 -0.152 -0.025 0.813 +3 -0.632 -0.595 -1.240 1.963 -0.034 -0.161 0.633 -0.582 0.739 -0.574 1.392 0.140 1.391 0.620 -2.332 -1.212 1.648 -0.309 -1.766 -0.489 -0.351 1.279 -0.822 -0.104 1.108 -0.459 -0.837 0.381 +0 -1.385 -0.355 -0.093 -1.443 -0.918 -0.717 -1.860 0.128 0.237 -0.544 -1.369 0.407 0.546 0.099 0.042 0.594 -0.095 -0.918 -0.070 -0.392 -0.070 0.884 -1.202 0.177 -1.253 -0.918 1.574 0.457 +1 0.614 0.192 -1.534 -0.150 -0.798 -1.329 0.395 1.651 -2.262 -0.924 -0.019 0.490 -0.318 0.538 1.230 0.275 0.402 -1.570 -2.243 -0.594 0.062 0.241 0.225 -0.575 0.348 -0.056 0.106 0.158 +1 0.384 0.419 -0.762 1.187 0.862 1.975 -0.459 0.156 0.233 -0.327 0.403 0.478 0.479 -0.326 -0.209 -0.567 -0.751 -0.064 1.517 2.039 1.314 -0.464 0.408 -2.047 -0.155 -0.389 1.874 0.260 +4 0.824 -0.645 -0.888 0.298 0.920 0.153 0.966 -1.276 0.474 -1.282 2.667 1.341 0.385 -1.604 -1.375 1.895 -1.158 0.500 1.823 0.106 -1.195 -1.775 0.383 -1.104 0.002 -1.065 -0.601 -0.569 +3 0.012 -0.103 0.846 -0.945 -2.136 0.938 0.199 -0.527 0.301 0.748 0.146 -1.445 -1.050 1.174 -0.064 0.637 -0.606 -1.180 -0.107 -0.177 2.045 -1.075 -0.541 -2.031 0.517 1.143 2.027 0.616 +4 0.975 1.345 1.032 0.467 0.211 2.038 -0.474 0.698 1.174 1.044 -0.762 2.815 1.671 1.060 -1.006 0.980 -2.311 -0.232 0.107 -0.449 1.583 0.104 -0.961 0.753 0.398 -1.753 1.338 0.101 +3 -0.072 1.361 0.672 0.429 -0.605 -0.623 -0.753 1.299 0.860 -1.535 0.964 1.213 0.233 1.602 -1.047 0.224 1.393 0.658 1.888 -0.640 -0.954 -0.649 -0.272 2.199 -0.643 -0.254 -1.179 -0.616 +4 0.252 2.556 -0.085 0.899 2.136 1.417 0.270 0.219 0.566 -1.096 -1.010 -0.818 0.255 -0.226 1.114 2.111 -0.313 2.075 0.703 -1.374 1.512 0.449 -1.328 -0.200 -0.192 -0.896 0.590 2.167 +4 0.659 -1.873 1.641 0.429 1.204 0.080 -0.175 -0.551 -0.481 0.273 0.273 -1.210 -2.488 -0.273 1.424 -0.358 -2.972 0.482 -0.264 -0.890 -2.100 0.384 0.775 -0.268 -1.056 -2.298 2.537 -0.062 +0 0.055 -1.255 0.460 -1.697 -0.336 0.191 2.377 0.319 0.140 -0.063 -1.342 0.086 0.103 -0.969 -1.187 -0.482 0.065 -0.458 -1.051 0.289 0.539 0.121 -0.563 0.595 -0.191 -1.189 1.036 0.354 +2 -0.749 -0.195 0.044 -1.172 -0.390 1.639 1.348 1.227 0.537 2.678 -0.561 -0.422 -0.185 0.840 -0.660 0.954 -1.116 0.292 -2.240 -0.233 -0.712 -0.419 0.805 0.692 0.907 -1.255 0.183 -0.592 +0 0.614 -0.580 -0.168 0.735 -0.530 -0.913 -0.642 -0.608 0.031 -0.027 -0.167 -0.800 -0.932 0.649 0.343 -0.435 -0.057 1.212 -0.072 -0.006 1.527 1.070 -0.641 1.070 -0.170 -1.051 1.304 -0.710 +2 0.010 0.960 0.538 0.083 1.008 -1.265 0.739 0.011 1.268 0.701 -1.884 -1.236 -0.545 -0.766 -1.332 -0.878 -1.500 0.500 0.340 -0.395 2.031 -1.506 0.997 0.246 -1.623 0.529 0.410 0.724 +1 2.048 -0.298 0.342 -1.594 -2.117 -0.677 0.873 0.028 -0.540 0.430 -0.550 1.377 1.418 -0.577 -0.777 -0.073 0.427 -0.274 1.046 0.266 1.088 -1.122 -0.593 0.533 0.053 -0.188 -0.519 0.087 +4 -1.163 -1.095 1.182 0.169 -0.969 2.299 1.045 0.549 -0.558 1.239 0.882 -0.106 1.088 1.046 0.274 -1.147 -1.021 -1.585 1.325 -1.461 0.034 -1.591 1.684 -1.163 0.323 -1.568 0.871 1.085 +0 1.261 -0.423 0.386 0.215 -0.791 0.542 0.131 0.332 -0.624 1.250 -1.051 -0.825 2.025 -0.419 0.075 -0.179 -1.156 -1.333 -0.480 -0.357 -0.843 0.432 0.924 0.309 -0.412 1.019 0.605 1.537 +0 0.014 -1.318 1.036 0.377 -0.163 -0.495 0.624 0.530 0.408 2.347 0.746 0.001 1.448 -1.687 1.142 0.368 0.213 -0.032 0.117 -0.384 -0.105 -1.486 -0.208 -0.123 0.163 -0.453 1.220 0.048 +4 -0.710 -0.284 0.617 2.524 1.260 1.520 -0.105 -0.537 -2.204 -0.598 -1.379 0.769 -0.197 -1.320 0.547 0.317 -1.134 1.513 -0.753 0.597 -0.125 1.647 0.921 -1.098 1.820 -1.267 -1.059 1.623 +3 -1.617 -0.318 0.647 -1.211 0.490 0.166 -0.125 -0.156 -0.517 -1.313 -2.922 1.131 0.234 0.543 0.419 0.364 2.349 0.363 1.097 -0.764 0.427 2.081 0.462 -0.315 -0.007 -1.800 -0.871 0.565 +4 -0.727 1.045 1.215 -0.925 0.307 1.834 0.415 1.422 0.999 -0.910 -0.190 0.035 0.104 0.916 0.409 -2.936 -1.313 -2.187 0.175 0.240 0.283 -0.959 0.674 0.716 -1.255 -1.466 -0.000 1.792 +3 0.641 -0.222 2.318 0.231 1.399 -0.422 1.068 -0.546 0.323 -0.607 1.901 -0.221 -1.108 0.095 0.537 -0.027 -0.813 1.037 1.259 -1.648 -0.877 -0.159 -0.531 0.526 1.839 -0.381 1.577 -1.468 +1 0.391 0.294 -0.502 0.717 -0.363 -1.081 -0.773 -1.705 -0.240 -1.085 -0.621 -1.457 -1.573 1.958 0.095 -0.320 -0.626 0.346 0.033 -0.297 0.390 0.575 -2.196 0.028 0.388 -0.244 -1.498 -0.356 +3 -0.649 0.324 0.262 -1.517 -1.247 -0.262 1.699 -1.319 0.615 1.071 -2.243 0.319 2.132 1.031 1.583 1.305 -1.893 -0.107 0.015 0.370 -0.863 -0.430 0.883 0.068 1.293 0.140 -0.081 1.059 +2 -0.854 0.800 -0.239 -0.985 0.956 0.310 -0.840 1.129 -0.475 -0.667 0.364 -1.108 -0.507 -2.001 0.172 -2.403 1.358 0.287 -0.147 -0.128 -1.268 1.367 1.115 -1.072 -0.288 1.233 -0.254 -0.344 +4 1.196 -0.083 1.974 1.583 -1.984 -1.030 -2.255 -0.325 -1.211 -0.814 1.091 -1.755 0.130 0.124 -0.082 0.131 -2.232 -0.810 1.543 -1.197 1.667 -1.407 -1.527 -0.952 1.129 -0.546 0.345 0.074 +1 0.689 0.137 1.238 0.727 -0.327 -0.053 0.901 0.239 0.426 -1.253 -0.526 -0.057 1.132 1.891 -0.900 -0.621 0.447 2.127 -0.996 -1.963 0.164 -1.349 0.568 0.781 -0.220 -0.450 -0.712 0.065 +3 0.770 1.207 0.234 0.916 -0.056 -0.785 -0.098 1.557 0.387 -0.722 0.907 0.794 1.305 -1.100 -0.691 1.449 -0.237 -2.662 -1.553 0.871 -0.496 -0.660 1.651 0.233 1.066 0.217 -0.401 -1.637 +0 0.138 -0.441 0.101 -0.188 -1.399 0.181 -1.328 0.166 1.374 -0.281 -1.171 -0.127 -0.832 0.595 -0.148 1.710 1.163 -0.153 0.716 -0.393 0.034 -0.361 0.766 1.433 0.799 0.922 0.923 0.551 +0 -0.749 0.231 -0.987 -0.510 0.705 2.352 -0.898 1.249 1.340 0.638 0.767 -0.707 -0.969 -0.473 0.267 -0.301 -0.747 -0.703 -0.304 0.860 -1.079 0.566 0.525 -0.522 0.296 1.186 -0.324 -0.772 +3 0.779 -1.574 2.242 0.629 0.982 1.961 0.503 -0.252 -0.905 -0.015 0.653 0.434 -0.305 -0.149 1.323 -0.596 1.249 0.956 -0.768 1.510 1.314 0.029 2.025 -1.408 0.291 -0.789 -1.288 0.460 +4 -0.604 0.988 -0.240 1.662 3.003 1.374 -1.058 1.405 -0.573 0.499 1.383 -1.121 0.121 0.118 -0.872 -1.814 0.717 -1.415 0.651 -0.192 1.340 -1.131 -0.076 2.095 -0.709 1.639 -0.784 0.161 +3 0.267 0.451 -1.027 0.486 -1.560 -2.285 0.456 0.132 1.383 -1.784 0.821 -0.227 0.703 -1.420 0.468 -1.557 1.378 -0.577 -0.208 0.357 -0.910 0.737 1.157 -1.573 -0.462 -0.590 -1.036 -0.059 +2 1.379 0.294 0.593 -0.752 1.026 2.318 -0.791 -1.182 0.176 0.876 -0.091 -1.053 -0.109 0.598 -0.407 0.607 1.165 0.240 -0.028 0.829 -0.856 -0.955 -0.267 -0.492 -0.803 1.830 2.172 -0.354 +4 -1.050 -0.972 -0.450 -0.756 0.224 -1.947 -1.118 1.539 -0.928 1.243 0.355 -1.809 0.635 -0.118 -0.455 0.245 0.475 0.708 1.178 0.408 -1.376 -0.474 2.943 0.724 2.369 1.395 0.242 0.301 +0 -0.127 -0.275 0.308 -0.224 -0.891 -1.490 -0.783 -0.481 0.478 0.289 -0.205 -0.097 -0.091 -0.071 1.323 0.532 -1.516 0.870 1.032 0.675 0.308 -0.196 0.516 -0.730 -0.503 1.317 0.160 -1.354 +4 1.031 1.019 1.708 0.082 0.336 -0.217 0.592 1.755 -2.238 1.042 1.687 1.294 -1.337 -1.524 0.404 -1.465 -3.097 0.706 1.592 1.186 1.299 -0.435 -0.173 -0.734 -1.087 0.303 0.582 -1.312 +2 -1.618 0.676 -0.058 -0.834 1.193 1.350 -0.956 -1.283 -0.143 0.139 -0.179 0.146 -0.741 -0.881 1.004 1.418 -1.043 -1.191 0.124 -0.829 1.535 -1.074 -0.061 -1.347 -0.585 -0.156 -2.159 -0.711 +1 0.143 0.569 1.791 -1.014 -1.121 -0.417 1.050 0.066 1.262 -0.888 -0.148 -1.072 -0.379 -0.673 -1.253 0.481 1.824 -0.266 -1.703 -0.832 -0.129 -0.254 0.889 -1.443 0.190 -1.049 -0.738 -0.856 +2 0.401 -1.325 1.395 -0.440 0.082 1.089 -0.781 0.180 -1.155 0.083 0.288 -2.136 0.712 -1.105 -0.360 -0.256 -1.298 0.354 0.055 -1.329 1.460 2.649 -0.401 -0.707 -0.715 -0.137 0.532 -0.857 +4 0.760 -0.581 0.412 -0.468 -0.030 -0.045 2.448 -2.284 2.588 -2.788 0.364 1.316 0.265 2.536 -0.831 -0.753 -0.148 2.612 0.244 1.017 -2.688 2.741 -0.506 -0.786 -0.498 0.447 -1.375 -0.915 +2 0.325 -0.205 -1.366 -2.055 0.172 -1.693 -0.524 0.684 0.416 -0.597 0.895 0.526 -0.488 1.521 0.487 -0.780 -2.053 0.224 -0.505 0.021 -0.237 -2.198 -0.292 -1.103 -0.508 -0.579 0.064 0.300 +2 -0.345 1.922 0.535 0.492 0.176 0.421 -0.376 -1.125 0.918 -0.627 1.425 -0.533 1.158 0.413 -0.559 0.054 0.560 0.220 -0.261 1.351 0.228 -0.878 2.667 0.985 1.531 0.880 0.016 -0.645 +4 -1.005 -1.673 -0.515 -0.510 0.074 1.695 -0.895 0.116 -0.262 1.346 0.231 1.203 -1.000 1.895 -0.651 1.792 -2.733 0.982 1.117 0.543 0.706 -1.365 0.624 -1.110 -0.538 0.277 0.214 0.404 +0 1.183 -0.524 -0.493 1.241 -0.886 -0.977 0.609 1.739 1.649 -0.022 1.190 -0.934 -0.894 0.156 0.243 0.364 -0.171 0.291 -0.454 -0.637 0.151 0.026 -0.378 0.639 1.516 0.810 0.374 -0.942 +3 -0.142 -0.347 -0.224 -0.871 -0.231 1.056 -0.107 -0.279 -0.184 1.812 0.869 0.860 0.539 0.558 0.868 1.747 0.201 0.214 1.387 0.154 1.034 -0.350 -1.375 -1.292 1.723 2.839 0.051 -0.536 +3 -2.249 -0.549 1.008 1.289 -0.741 -0.547 0.457 1.988 1.072 1.206 -1.327 1.634 -0.572 -0.592 0.910 0.745 0.622 0.830 -0.127 1.114 1.158 1.506 1.012 -1.529 -0.318 -0.210 0.196 0.510 +4 0.319 -1.659 0.759 -1.112 1.559 -0.049 -1.246 -1.142 0.348 -0.391 -0.097 1.613 -0.699 0.060 1.420 0.271 -1.607 0.505 -1.163 1.407 -0.823 2.970 1.467 -0.797 0.020 0.754 0.784 0.442 +3 0.124 -1.856 -0.763 0.620 -0.573 0.865 0.296 -1.361 -0.365 0.408 0.187 -0.325 -0.218 -0.716 0.516 1.058 2.334 -0.687 -0.083 -3.082 -0.370 -0.944 0.785 -1.966 0.249 -0.138 0.398 0.316 +2 -1.233 0.553 -1.032 -0.517 0.356 1.633 -0.561 -0.559 0.775 -0.046 0.005 0.261 -0.689 0.178 2.114 -0.139 2.071 -1.528 -0.256 -0.008 -1.399 -0.302 -0.724 -0.735 0.050 1.619 0.614 0.832 +4 -0.341 0.895 -0.650 -0.495 -2.862 -0.797 -1.730 -0.024 -2.138 -0.437 0.159 -0.193 0.641 0.548 -1.518 -0.019 -2.123 -0.185 -0.846 -0.353 -0.315 0.469 -0.057 -0.629 -2.099 0.639 -1.305 -1.617 +3 0.987 -0.381 0.092 -1.237 -0.067 -1.615 -0.722 0.862 0.913 -1.495 -2.417 -0.901 2.051 0.066 0.541 -1.417 1.337 -1.539 0.777 -0.075 -0.708 -0.174 -1.035 1.657 0.619 -0.562 0.759 0.825 +4 -2.516 -1.048 -0.942 0.501 -1.479 1.871 1.629 1.052 -0.510 -2.079 0.832 -0.748 0.788 0.601 -0.440 -1.220 0.187 -1.570 0.281 0.470 -0.660 -1.503 1.628 -0.535 -0.207 1.091 -0.568 -0.762 +1 -1.139 1.701 1.804 -0.234 -0.371 0.612 0.551 -0.202 -1.250 0.306 0.551 0.726 0.349 -0.271 0.994 0.066 -0.465 -0.181 -1.746 -0.182 0.688 0.340 1.521 -0.263 0.487 0.793 2.343 0.145 +4 -1.142 -0.569 -0.833 1.211 0.863 -2.030 0.179 -1.786 -1.084 0.832 -1.724 -1.364 -1.024 -1.585 -1.420 -0.056 -1.685 -0.249 0.009 0.752 0.929 -1.376 -1.324 -1.380 0.791 0.346 -0.353 -1.192 +4 0.270 2.386 1.193 -1.848 0.240 1.273 -0.615 -0.645 1.682 -0.981 0.592 -0.104 0.698 -0.575 -0.666 1.111 1.592 -0.178 0.436 0.847 0.197 0.694 1.720 -1.316 1.002 2.141 1.070 -1.239 +1 0.376 1.616 -0.298 1.315 -0.546 -0.387 -0.383 -1.005 0.867 0.967 -2.078 -0.936 1.257 -0.447 0.183 -0.437 -1.070 -0.056 -1.742 -0.497 0.945 -0.110 -0.621 -0.915 0.407 1.165 0.677 -0.018 +1 0.041 0.433 0.898 0.143 0.365 0.176 0.440 -0.019 2.116 0.578 0.197 -0.738 -0.342 1.510 -2.485 0.057 -0.025 0.500 0.265 1.516 0.098 -0.064 0.952 1.533 0.687 1.012 -0.217 0.125 +4 0.311 1.012 1.869 -0.118 1.908 -1.217 -1.323 0.058 -1.482 -0.634 -0.628 0.506 0.177 -0.225 0.690 -0.236 1.746 0.064 1.458 0.269 0.537 -3.135 -0.561 0.217 -0.558 0.424 -1.623 -0.649 +4 0.310 1.537 -0.099 0.202 -1.200 -1.443 -1.390 1.202 -0.937 -0.568 1.311 -0.953 1.136 -0.921 1.656 -0.624 -1.267 -1.492 0.205 2.758 0.142 0.774 -1.160 1.364 0.764 -1.076 -0.157 0.195 +0 0.548 -0.573 -0.307 -0.592 0.726 -0.244 -0.583 0.145 -0.278 -0.140 -0.805 0.621 -0.404 -0.295 0.187 1.962 0.835 -0.671 -1.254 0.323 -0.586 -0.230 -0.183 -0.111 -0.216 -0.478 -2.143 0.186 +2 1.150 0.297 1.268 0.289 -1.157 -1.333 -1.552 0.662 1.189 1.849 0.843 1.163 -0.580 -1.156 0.850 0.418 0.107 0.387 0.323 1.284 1.295 0.544 0.846 0.514 -0.461 0.757 -1.394 1.054 +3 -0.596 -0.426 1.128 -0.684 -0.194 0.917 -0.307 0.593 -1.526 -1.000 -0.550 -1.437 -1.129 -0.645 2.118 0.396 0.826 0.677 -2.274 0.127 -0.523 -1.582 0.456 -1.838 -0.016 -0.303 -0.886 0.963 +0 -0.404 0.688 0.665 1.101 -1.259 0.401 -0.727 -1.220 -0.429 1.069 -0.372 1.565 0.549 -0.394 1.160 0.445 -0.933 1.859 0.259 0.019 -0.856 1.180 0.517 0.629 -0.508 0.834 0.912 -0.669 +0 1.038 0.764 1.728 -0.469 -1.286 -0.505 0.209 0.058 -0.393 -1.909 0.470 1.972 0.464 0.609 0.572 0.397 0.932 0.395 -0.313 -0.796 -0.249 0.314 -0.316 0.314 0.439 0.833 1.283 0.078 +0 -0.504 -0.082 0.348 -0.487 -0.676 0.034 -1.087 -1.086 0.679 -1.149 0.666 0.463 -1.726 -0.678 1.194 -0.981 -0.464 0.462 0.783 -0.252 -0.598 1.422 1.739 0.979 0.085 -0.808 -0.830 0.523 +1 -1.282 -0.012 -0.972 -0.268 0.237 1.060 0.312 -0.565 1.197 0.615 -1.942 0.838 0.065 -0.033 1.190 -0.204 0.894 1.277 0.062 0.447 0.694 0.462 0.889 -1.044 -0.641 -1.260 1.686 -1.676 +1 -0.257 0.983 -0.608 0.265 0.194 2.806 -0.182 1.498 0.419 0.219 -0.439 0.421 1.088 1.090 0.008 0.378 1.773 -0.404 0.340 1.282 0.717 0.350 -0.429 0.348 0.100 1.102 0.344 0.166 +1 0.742 1.953 0.333 0.625 0.436 0.257 -0.107 -0.819 -1.184 0.369 -0.837 0.517 1.746 -0.555 -1.208 -0.361 0.371 0.619 -0.784 -0.549 -0.859 1.511 -1.471 0.480 -0.434 0.416 -0.520 -1.310 +0 -0.270 -1.542 1.221 -0.341 -0.964 -1.475 0.095 0.121 -0.651 0.814 0.282 -1.020 -1.626 -0.174 -0.522 0.408 0.834 1.174 -1.031 -0.465 -0.644 -1.187 0.880 -0.093 0.855 -0.139 0.702 -0.579 +3 -0.042 -2.118 0.502 -1.571 0.982 1.048 -1.670 0.737 -0.187 -1.235 0.302 -0.814 -2.504 1.243 0.371 0.715 1.642 0.274 0.902 0.879 -0.462 -0.346 0.012 1.040 -0.317 -1.272 0.529 1.505 +3 3.250 -0.748 -0.505 0.244 0.388 -1.062 0.344 1.262 -0.223 1.785 1.885 -0.259 -0.689 0.650 -0.148 1.198 0.099 0.048 0.840 -0.513 -0.292 -0.147 -0.510 -0.340 1.077 0.187 1.499 -0.974 +4 0.073 0.157 -1.479 -1.542 -1.839 -1.637 0.828 -1.967 0.385 1.317 0.833 -1.031 2.366 0.297 -0.330 1.831 1.010 -0.277 0.426 0.734 1.222 -0.924 1.775 -0.610 0.260 -0.969 -1.897 -0.929 +2 -0.926 1.178 0.567 -1.363 -1.427 0.119 1.143 -0.825 0.667 2.041 1.229 1.750 0.932 -1.179 0.438 -0.268 0.122 -0.112 1.876 0.132 -0.386 0.736 0.234 0.190 -0.518 -0.058 0.964 -1.102 +0 -0.184 -0.214 -0.152 1.257 -0.888 -1.946 -0.534 -0.668 -2.231 0.245 -1.186 -0.083 -0.363 -1.074 0.891 -0.281 0.054 0.586 0.289 0.145 -0.824 -1.112 0.158 -0.962 0.794 -0.130 0.649 -0.986 +0 -0.699 1.830 0.019 0.079 0.270 -0.783 0.340 0.348 -0.010 -0.067 -0.222 -0.380 0.727 0.290 0.931 -0.521 1.128 0.891 -0.243 -0.666 0.595 -1.586 -0.415 0.275 -0.150 -1.071 0.090 -0.999 +2 -0.193 0.480 0.397 -0.608 0.843 -0.938 -0.591 -0.342 -0.644 1.450 0.459 0.508 -0.570 2.604 -1.293 0.873 1.956 0.422 0.204 -0.324 0.639 -0.038 1.654 -0.567 -0.726 -0.955 1.579 0.511 +1 0.985 -0.199 -1.975 -0.311 0.673 -1.452 0.559 0.230 -1.378 -0.455 -1.100 0.066 -1.358 1.752 -0.121 0.088 0.829 -0.670 0.137 0.153 -0.806 -0.639 0.741 -0.323 -0.347 1.686 1.586 -0.768 +0 0.760 1.652 -0.502 -0.412 -1.346 0.521 -0.645 0.346 1.165 -0.684 -0.474 1.015 0.707 0.601 -0.391 -1.436 -0.712 -1.009 0.582 0.285 -0.599 -0.428 0.476 -0.319 -0.581 1.979 -0.550 0.669 +1 -0.619 0.200 -0.096 0.636 0.757 -0.240 -0.962 1.548 -1.355 -0.595 -0.850 0.746 1.317 -0.174 0.230 0.429 1.804 -1.774 0.744 0.377 -0.371 0.326 0.741 0.583 2.159 0.127 0.027 -0.181 +4 0.670 -0.269 -1.218 -1.521 0.435 -1.017 2.225 -1.000 0.354 0.169 -1.398 1.530 -0.964 2.518 -1.041 1.649 -0.658 1.170 0.445 -1.766 1.885 -1.251 0.004 -0.865 0.082 0.750 -0.293 -1.330 +3 -1.578 -0.704 -0.200 -0.867 0.667 0.813 1.263 -0.620 -1.625 -0.396 -0.495 -2.179 0.477 -1.740 0.249 0.956 -0.306 -0.604 1.214 -0.687 1.434 -0.972 0.081 1.357 1.406 -0.120 -0.974 1.473 +4 1.166 0.531 -2.178 -0.529 -0.562 0.479 -2.625 2.549 0.531 0.149 1.182 2.344 0.871 0.949 0.131 -0.142 -0.476 -0.826 -0.687 -1.351 -0.076 -1.685 0.191 -0.036 0.147 -0.522 3.077 1.360 +3 -0.266 0.388 -0.851 0.475 -0.584 -1.119 1.136 0.084 0.640 -0.781 -0.675 1.823 -0.597 1.294 -0.210 0.122 1.837 -0.130 0.156 -2.177 0.926 0.281 -0.723 2.598 -0.627 -0.825 -0.101 -1.273 +0 -0.058 -0.206 1.291 0.717 1.009 -1.394 0.842 0.751 0.219 -0.327 0.327 -0.856 0.455 0.302 1.172 -0.032 -1.438 -0.262 1.925 -0.127 0.110 0.722 -1.524 0.403 -0.777 0.769 -1.413 0.105 +2 -0.685 1.345 0.613 -1.429 0.246 1.657 -0.598 1.658 0.386 1.340 0.159 -0.124 1.837 0.721 -0.235 0.424 -0.565 0.108 1.196 -0.162 -0.875 -0.967 0.074 -0.808 -1.433 -1.199 1.175 -0.142 +0 -0.839 -1.253 -0.102 -0.167 0.011 -0.048 -0.973 0.791 -0.447 1.908 0.581 0.933 0.188 1.887 0.770 1.315 0.686 0.192 -0.099 0.287 -0.122 0.952 0.281 0.796 0.558 -0.318 -0.678 0.946 +1 0.643 -1.098 -0.384 0.422 0.760 1.213 -0.496 1.034 -0.232 0.702 -1.856 1.643 0.853 -0.467 0.640 0.762 1.615 -0.322 -0.909 0.533 -0.358 1.618 -0.298 0.289 -0.559 0.207 0.063 -1.209 +4 0.664 0.222 -1.901 0.083 0.318 -0.329 -0.061 0.190 0.765 -1.575 -0.244 1.508 1.417 0.851 0.537 1.655 1.918 -0.129 -0.969 -0.270 3.097 -1.569 -0.471 -1.413 0.125 -1.203 -0.109 -1.950 +4 0.767 0.550 0.680 -1.402 0.586 -0.274 1.899 1.412 -0.790 -2.343 -0.281 0.983 0.548 0.099 -1.175 -0.386 1.309 1.196 -0.499 0.147 -2.189 2.171 0.365 0.359 1.099 -2.069 0.404 2.033 +2 0.374 0.246 -0.213 -1.928 1.728 0.149 -1.858 -1.434 1.290 0.759 -0.107 0.721 -0.050 0.373 -0.215 -0.476 1.253 -0.017 -1.644 0.249 0.195 0.447 -1.184 0.930 0.587 1.733 -0.387 -0.088 +3 -0.897 -0.794 -1.517 0.110 -0.239 -0.779 0.811 0.492 0.857 0.053 -0.789 -0.718 0.591 -1.279 -0.429 0.286 -0.837 -0.223 -1.071 -0.521 1.783 -1.399 0.801 0.109 -3.681 -1.382 -0.661 -0.863 +1 -0.409 -0.141 2.362 -0.384 -0.276 -0.436 0.809 0.758 -0.075 0.059 -1.493 0.260 -0.337 0.500 -2.195 -0.794 0.439 -0.223 0.533 -1.269 -0.496 -0.085 1.810 -0.372 0.668 0.610 -1.457 0.988 +2 0.130 0.246 0.556 -1.351 1.415 0.497 -1.055 0.239 -0.752 -1.240 1.086 -0.698 1.311 -2.313 -0.750 -0.821 -0.547 -1.233 0.545 1.254 1.436 -0.002 -0.392 0.984 1.109 -0.899 -0.507 -0.514 +1 -0.633 -0.885 -0.527 0.252 1.492 1.618 -0.353 -0.702 0.620 -0.788 -1.545 -0.202 -1.677 1.061 -0.054 -0.605 0.763 0.024 -1.611 -0.391 -1.415 0.149 0.363 1.597 0.115 -0.949 -0.878 -0.612 +4 -0.253 -1.274 -1.057 0.971 -0.074 -1.135 0.075 0.434 0.067 0.087 -0.555 -0.745 -3.298 2.111 1.414 -0.444 -1.616 -0.346 -0.871 -0.254 0.112 -0.834 -0.558 0.933 2.006 0.424 -0.601 0.911 +3 -0.617 1.345 -1.792 1.405 1.073 0.541 -2.004 -0.362 1.008 -1.687 -0.522 0.396 -1.159 1.301 1.103 0.242 0.434 -0.540 0.590 -0.598 -0.197 -0.492 1.436 1.207 -1.003 1.338 -0.798 1.853 +0 -0.344 1.218 -0.326 0.959 -0.994 -0.787 -0.957 -0.626 -1.648 -1.016 -0.979 1.936 -0.138 -0.401 0.744 -0.459 0.300 0.659 0.604 -0.034 0.105 0.356 0.528 0.341 0.156 -0.941 0.560 -1.211 +0 -0.941 -0.507 1.562 -0.158 0.242 0.273 1.588 -0.576 -0.223 -0.106 1.137 1.344 0.475 1.233 0.178 -0.173 1.179 0.488 -0.736 0.997 -0.208 -0.331 0.893 -0.089 0.389 0.662 -0.783 0.385 +4 0.843 2.524 -0.100 -2.198 1.425 -1.218 0.461 0.538 0.126 -2.134 -0.363 -1.570 0.454 -0.885 -2.124 0.712 -1.267 0.135 -0.737 0.868 0.826 0.238 -0.107 -0.441 -1.205 -0.588 0.478 1.955 +1 0.418 -1.620 -0.373 -0.382 0.962 0.693 -1.683 -0.693 -0.690 -0.217 -1.098 0.452 -0.694 0.589 -0.003 -0.973 1.388 1.418 0.290 -0.239 -1.056 1.071 0.823 -0.814 -0.687 0.457 -1.175 -0.626 +1 0.148 1.311 -0.486 -0.987 0.051 1.320 2.005 -0.616 0.132 0.275 -1.421 -1.476 0.324 -0.376 -1.401 0.687 0.061 0.012 -1.095 -1.706 -0.598 0.049 -0.709 -1.097 -0.372 0.035 1.415 0.150 +0 0.745 -0.837 -0.581 0.310 0.661 -1.809 -0.028 1.129 0.791 0.277 0.331 -0.596 -0.013 0.163 1.461 0.596 0.471 -0.812 0.065 -0.625 -0.353 0.582 0.590 -0.364 -0.225 -0.408 -0.095 0.070 +0 -0.621 1.963 0.138 -0.257 -1.477 -0.932 -0.642 0.439 -0.356 0.015 -1.433 1.404 -0.737 0.235 -0.531 -0.318 -0.794 0.296 -0.980 0.314 -0.119 0.630 0.729 -0.318 0.366 -0.448 1.316 -0.359 +0 0.968 -0.179 0.891 -0.722 -0.413 -0.714 0.249 0.825 0.009 -2.141 1.042 -0.630 -0.974 1.221 -0.085 -0.292 0.327 0.088 -0.210 -0.477 -0.675 -1.382 -0.139 0.759 -0.313 -0.185 1.000 0.017 +0 -1.312 -0.213 -0.027 -0.679 -0.562 0.843 0.470 0.798 0.337 -0.873 0.423 -0.952 -0.312 -1.036 -0.611 0.090 -0.457 -0.883 1.138 -0.120 -0.528 -1.082 -1.872 -0.222 -1.378 0.712 0.029 1.558 +2 0.012 -0.656 0.305 -0.719 -0.543 -0.195 0.388 -1.667 -2.030 -0.860 0.522 -1.265 1.834 0.852 0.669 0.855 0.427 0.507 1.242 1.000 0.300 1.924 0.077 -0.455 -1.483 -0.722 1.202 -0.034 +0 -0.004 0.003 -0.739 -0.976 0.900 0.016 0.480 0.425 -1.293 -1.217 0.786 0.432 -0.119 0.399 1.450 0.923 0.263 0.448 1.474 -0.411 0.230 -0.024 -0.635 -0.201 -1.017 1.063 -0.436 0.212 +4 1.428 -2.009 -0.152 -0.108 -1.087 -0.156 -1.639 -0.584 -0.268 -0.005 1.875 -0.816 -1.325 0.992 -4.463 -0.619 -0.326 -0.941 -0.095 -0.649 -1.741 0.618 0.657 -0.987 -0.541 1.856 0.448 0.000 +2 0.730 -0.711 -1.963 -0.429 -1.290 2.009 -0.769 0.122 0.340 -0.227 -0.109 -0.545 -1.486 1.763 -0.461 -1.213 -0.129 -0.314 1.504 -0.792 0.473 1.225 -0.848 -0.124 -0.856 -0.564 -1.272 -0.341 +2 1.006 0.691 0.051 -0.261 -1.773 1.292 -0.447 1.555 0.196 2.025 0.606 -1.432 -0.614 -0.169 -0.874 -0.377 -1.367 0.006 0.237 -1.004 1.940 0.064 -0.817 -1.630 0.060 -0.260 0.783 -0.143 +0 0.251 0.360 -0.265 -0.893 -0.139 0.259 -0.026 -0.177 -0.112 0.655 -0.189 -0.922 -0.523 1.010 0.186 -1.102 0.363 -0.784 0.062 1.026 -0.744 -1.076 0.438 -0.112 0.143 1.123 -0.638 0.291 +4 0.768 -1.961 -2.204 -2.005 -2.143 1.357 0.787 -1.199 -0.106 1.466 0.500 2.114 -0.114 -0.655 -1.081 0.024 0.936 0.099 -0.358 1.257 0.529 -0.113 -0.767 -0.254 0.473 -0.780 -1.234 -0.480 +4 -0.990 -0.572 0.835 -1.388 0.207 0.574 0.182 -0.438 0.172 0.321 -0.131 -0.836 1.040 -1.109 1.239 1.816 -1.502 -0.054 1.564 2.059 0.582 0.663 -1.010 1.932 1.720 -0.346 3.523 1.546 +4 -1.658 1.877 2.256 -1.158 -1.406 -0.550 0.490 0.768 -1.113 1.636 -0.740 -0.041 -0.336 -0.540 1.817 -2.232 0.052 1.337 -1.998 -0.294 0.589 1.050 0.127 -1.320 0.493 -0.814 0.257 -1.941 +1 0.055 0.435 -0.828 -0.755 0.489 2.385 1.003 1.266 -0.192 -0.600 -0.781 -0.488 -2.053 -0.725 -0.560 -0.175 0.132 0.362 -0.015 -0.581 -1.963 -0.575 -0.972 0.466 0.909 0.939 0.757 -0.134 +4 -0.177 0.170 1.284 0.009 1.273 1.169 0.149 -1.560 0.894 0.696 -0.224 -1.034 -0.065 0.494 -1.214 -1.198 3.111 -1.142 -0.707 1.615 -0.970 -1.033 0.937 -1.854 -0.516 0.664 1.514 0.507 +4 -1.155 0.725 1.527 1.903 2.865 -0.902 2.824 -0.354 -0.592 0.327 -0.582 0.399 -1.320 -0.239 1.286 -0.057 0.142 0.475 1.171 -1.655 0.585 -0.268 0.167 1.060 -1.194 -1.013 -1.935 -0.549 +2 0.125 -0.032 -0.344 0.020 -0.842 0.954 0.246 1.041 -1.376 0.068 1.760 -0.654 1.054 0.145 1.081 -0.496 1.337 0.560 -0.957 0.848 1.140 1.287 -1.289 0.210 2.242 0.346 -1.964 0.605 +1 0.083 -1.687 -1.778 -0.297 -0.046 1.103 0.674 1.658 -0.830 -0.854 1.147 1.113 0.526 0.080 -0.078 1.367 0.559 0.470 1.977 0.874 -0.693 -0.273 0.263 0.222 0.035 0.613 -0.658 -1.328 +1 0.932 1.098 -0.683 -2.116 -0.136 -0.677 0.831 -0.957 0.714 0.107 1.304 0.558 -0.392 0.374 0.009 -1.524 0.612 -0.046 -2.129 -1.441 -0.564 0.876 -0.637 0.748 0.792 -0.182 0.764 -0.048 +2 -2.355 -0.070 0.142 0.111 -0.716 0.058 0.704 -2.135 0.544 0.849 -0.340 -1.340 0.900 1.289 -1.120 -0.170 -0.984 -0.623 -0.047 0.928 0.615 0.140 2.209 -1.299 -0.568 0.106 -0.312 -0.207 +0 0.220 -0.376 -0.228 -0.159 -0.800 -0.041 1.443 1.329 -0.754 1.276 0.761 0.633 0.432 -0.930 -0.406 0.833 -1.207 -1.041 0.306 -0.790 0.824 -0.754 0.244 0.328 -0.482 -0.240 -0.680 -0.019 +2 -0.004 0.346 0.436 0.417 0.630 -0.060 1.290 -1.612 -2.552 0.741 -0.331 1.717 1.043 0.589 -0.487 0.676 -0.396 0.060 -0.326 0.259 -0.251 1.001 0.453 -0.509 -0.447 -1.270 -1.952 0.933 +2 -1.730 -0.215 0.453 -1.607 -0.885 -1.231 0.787 1.673 -0.210 0.532 0.506 1.554 -0.653 -0.220 -0.341 0.075 2.504 0.031 -0.855 -1.154 1.253 -0.859 0.664 -0.680 -1.267 0.605 -0.631 0.425 +0 -0.567 -1.866 1.133 -0.019 -0.729 0.284 0.153 0.168 -0.217 1.622 -0.134 1.619 0.564 0.351 -0.113 0.974 0.113 -0.932 -0.965 0.422 -0.907 -0.062 1.057 0.304 -0.722 1.410 0.548 1.018 +3 0.697 -0.448 0.895 2.189 0.479 -0.110 -0.687 0.203 -0.978 -0.238 -0.896 0.841 -0.530 -0.032 -1.722 -0.342 -0.370 -0.918 -0.294 -2.489 -1.154 -1.671 0.761 -0.027 -2.573 0.992 0.426 -0.107 +3 -0.626 -0.137 -1.243 -0.705 0.834 1.083 1.128 -1.441 0.328 0.312 -0.585 1.171 2.180 -0.282 1.510 -1.131 -0.209 0.144 1.660 -1.379 1.705 -1.553 0.957 0.141 1.160 0.106 0.233 -0.888 +4 -2.543 -1.406 -2.886 -0.344 1.074 -1.891 -1.322 -0.164 -1.064 -1.069 0.542 -0.495 -1.487 -2.088 2.208 1.128 -0.452 0.088 -0.489 0.510 -0.854 -0.543 0.455 1.454 0.741 1.234 -0.312 -0.623 +4 1.820 1.166 0.150 -0.102 -1.004 1.818 -1.065 -1.869 0.900 0.087 0.264 0.187 -1.058 0.369 -0.323 -0.463 1.423 0.791 -2.301 0.216 1.066 -1.246 -0.864 1.000 -0.454 -1.978 1.200 -0.174 +1 1.405 -0.274 1.598 -0.048 0.828 -1.427 -0.335 -0.486 -0.008 -1.524 0.202 0.998 1.097 0.672 -0.789 1.329 -0.579 1.009 -0.223 2.188 -0.135 -0.057 -0.350 -0.467 -0.315 -0.176 0.254 -1.144 +3 0.259 -0.372 -0.016 -0.844 -0.296 0.414 0.191 -0.357 0.518 0.138 -0.310 0.625 -0.057 1.261 -2.073 0.386 -0.407 0.259 0.779 0.830 1.388 -2.333 -1.945 -0.620 -0.793 -0.440 1.332 -2.458 +0 0.275 -0.183 -1.244 0.637 0.464 -0.360 -0.291 0.470 1.190 -0.283 0.655 0.565 0.879 0.039 -0.912 -0.462 0.343 1.818 -0.484 0.805 -0.328 0.110 1.515 0.764 -1.241 -0.572 1.256 0.142 +0 0.237 -0.905 0.394 0.042 -0.225 0.235 0.002 1.755 1.304 -1.036 0.762 -0.156 0.990 -0.273 -0.202 -1.054 0.049 0.305 -0.769 -1.171 0.078 0.470 1.111 -0.716 0.286 1.464 0.748 0.657 +0 0.594 0.048 0.932 1.034 0.658 0.838 1.038 0.400 -0.242 -0.108 -0.133 -1.051 0.557 -0.197 1.006 -0.841 1.793 -0.777 -1.212 0.352 0.990 0.032 0.107 -0.462 -0.833 -0.111 -0.610 0.777 +2 -1.147 0.219 1.295 0.975 0.507 -0.081 -1.500 -0.439 -0.226 -0.298 -1.534 0.434 -1.181 1.546 0.148 0.979 0.959 2.078 0.182 -0.390 -0.371 1.496 -1.320 -0.878 0.237 -0.153 0.968 -1.339 +0 -1.069 -0.561 0.276 0.013 -0.638 0.126 0.847 0.082 0.006 -0.004 0.476 1.856 0.674 -1.300 -0.186 -0.282 -0.186 0.281 -1.154 -0.535 -1.444 -1.068 -1.187 -0.219 -0.288 0.426 -0.522 -0.574 +3 -2.757 0.370 0.823 -0.411 -1.019 0.917 0.038 -1.693 -1.220 0.318 -0.695 0.517 0.827 1.229 -1.313 0.079 -0.176 -0.440 2.128 -0.612 -0.493 -0.516 -0.915 0.620 1.662 -0.041 -1.917 0.068 +3 -0.118 -0.435 0.150 0.279 1.855 -1.444 0.708 1.154 -0.915 1.301 -1.005 -1.484 -0.231 0.766 -1.906 -1.737 1.074 -0.154 0.376 0.585 -1.469 -0.582 -0.040 -0.894 -0.395 0.930 -2.058 0.162 +4 -1.050 0.210 -0.345 -0.127 -0.363 -0.429 -0.290 -0.383 -1.288 -1.157 -0.733 -1.155 0.633 -2.555 2.551 -0.718 0.589 0.695 -1.805 0.665 1.812 2.408 -1.205 0.332 0.794 -0.361 -0.530 0.532 +2 0.143 0.872 -1.251 -1.533 1.579 -0.612 1.136 -0.154 -0.162 0.438 -0.884 0.132 -0.381 -1.320 0.337 -0.263 -0.157 -1.210 1.591 0.877 0.964 -0.305 -1.729 1.953 -0.061 -0.749 0.417 1.167 +1 -0.407 1.299 1.178 0.632 0.895 1.099 1.231 0.935 -0.995 0.199 1.145 -2.156 0.347 -0.255 -1.024 0.196 -0.070 -0.850 0.202 -0.590 0.489 1.148 0.137 1.361 0.892 -0.066 0.662 -1.163 +3 -0.357 -0.877 0.881 0.618 -0.802 1.731 -0.393 0.897 -0.704 -0.920 0.547 0.466 1.931 1.433 -0.313 -0.902 1.423 0.718 0.470 -0.661 -0.179 1.815 0.159 -0.919 2.112 -1.662 -0.686 0.124 +1 0.443 -0.976 -0.678 0.409 0.587 -0.974 1.277 0.170 0.520 0.344 -1.341 1.536 1.476 -0.309 1.973 0.164 0.198 -0.195 -0.165 1.926 0.850 0.143 -0.474 -0.463 -0.205 -1.017 0.218 1.491 +2 1.554 -1.337 2.331 1.726 0.141 -1.648 -0.055 -0.387 -1.274 0.291 -0.824 -0.715 -0.204 -0.575 2.078 -0.536 0.441 0.146 0.353 0.429 0.881 0.266 0.225 0.058 -0.138 -0.912 -0.061 0.241 +4 -1.473 0.953 -2.296 -0.217 -0.037 1.435 0.698 -1.814 -0.916 -1.526 -0.104 -0.765 -0.214 -1.104 -0.720 0.750 0.755 -0.968 -2.391 1.112 -0.042 -0.634 -1.153 -0.312 -0.151 0.696 2.256 -0.621 +3 -0.334 0.978 1.229 0.868 -2.489 0.396 1.257 2.027 -1.143 -1.258 0.306 -1.356 -0.182 0.500 0.987 0.524 0.990 0.929 0.264 0.346 1.648 0.747 0.816 0.229 -1.308 -0.200 0.331 1.254 +0 -0.497 -0.183 -0.990 -0.035 -0.811 -1.114 0.259 0.213 0.783 -0.431 0.408 0.551 0.445 -0.826 0.149 0.364 0.007 -2.086 0.160 2.213 -0.360 -0.728 0.373 0.312 1.193 -1.258 0.731 0.590 +3 1.068 0.528 1.536 0.978 -1.648 0.623 -0.087 0.967 -0.941 0.002 0.935 0.390 -0.239 -0.790 0.868 1.549 1.092 0.047 -0.159 -1.459 -0.414 -1.007 -1.951 -1.309 -1.904 1.072 1.360 0.352 +3 0.162 1.281 -1.874 1.922 0.461 0.251 -0.817 0.641 -0.293 1.005 1.151 -0.348 -0.857 -0.819 -0.204 0.838 -0.108 -1.678 -1.574 -0.033 0.594 -0.648 -1.389 0.931 -2.130 1.103 1.232 1.182 +0 -0.718 0.796 0.654 0.290 1.316 -0.539 -0.187 0.095 -1.030 0.227 -1.719 1.047 0.047 -0.521 -0.834 -0.235 0.688 0.548 -0.706 -0.846 0.657 -0.722 0.060 0.124 0.312 0.858 -1.263 -1.911 +1 -0.857 -0.085 0.158 0.194 1.344 -0.324 -0.847 0.642 0.511 -0.589 -1.801 2.010 1.413 -0.839 -0.141 -0.028 0.610 -0.282 -0.411 -0.117 -0.929 0.952 0.618 -0.988 0.338 -0.640 0.139 -1.998 +3 -0.220 0.405 -0.923 -0.902 1.130 -0.136 -0.313 -2.203 -1.458 1.153 0.925 -1.905 2.062 0.307 -0.583 0.183 -1.370 -0.069 0.587 -1.584 0.641 0.973 -1.111 0.541 -1.280 -0.474 -1.037 0.920 +4 1.007 -0.001 0.601 -0.748 0.613 -0.795 1.880 -0.986 -1.638 -0.165 0.592 2.318 -0.724 0.063 -0.001 -1.053 -1.001 -1.886 1.527 -0.794 -2.086 -0.387 1.219 0.516 0.511 1.619 1.042 -2.617 +4 -1.713 -1.382 -0.048 -2.175 1.148 -2.234 -0.010 0.321 0.692 1.129 1.641 0.393 -0.594 0.189 0.397 0.241 -0.478 1.208 -0.389 -0.119 -0.841 0.927 2.195 1.200 -1.074 -0.808 -1.561 0.305 +3 1.807 -0.920 -1.265 -1.295 0.699 -0.292 -0.685 0.825 -0.880 1.016 -1.004 -0.348 1.387 0.125 -1.032 1.515 1.135 1.439 -1.709 0.299 -0.704 -0.605 0.643 -1.750 0.158 1.533 0.145 1.094 +3 -1.158 -0.083 1.140 -0.015 -0.306 2.156 1.112 -0.040 -1.369 -0.158 -0.344 0.166 -0.658 -0.026 1.905 1.637 -0.373 0.764 0.067 -1.730 -0.537 0.657 -1.795 1.506 1.469 -0.779 -1.173 -0.020 +0 -1.611 -0.365 0.192 0.135 0.560 1.389 -0.913 -0.227 0.044 -0.180 -0.477 -0.478 -1.136 0.752 -0.318 -0.757 0.788 -1.503 0.497 -0.856 0.744 -0.339 1.060 -1.009 -0.333 0.187 -0.547 0.300 +2 2.474 0.331 0.898 0.415 -0.090 1.830 -0.615 0.238 -0.014 1.499 1.489 0.973 0.463 1.407 -0.129 1.501 1.350 0.226 0.483 -0.522 -0.318 0.493 0.588 0.202 -1.123 0.608 0.490 0.067 +2 -0.092 -2.707 -0.245 0.293 0.229 -0.535 0.644 0.398 -1.496 1.255 -0.084 -2.485 0.214 -0.198 2.200 -0.017 -0.254 0.486 -0.121 -0.569 0.837 1.025 -0.229 0.449 0.927 -0.131 -0.850 0.690 +4 -0.853 0.520 -1.263 -0.537 -0.506 -0.714 1.469 -0.771 0.122 1.857 0.249 -2.486 2.028 1.116 1.288 1.345 -0.095 -1.472 0.986 -1.703 -1.430 -0.008 1.037 -0.680 -0.608 2.553 0.929 0.449 +1 -0.065 0.340 -1.434 1.082 -1.347 -1.647 1.772 0.020 -1.287 0.064 -0.830 1.698 -0.386 -0.106 -0.275 -0.529 -0.711 -0.519 0.132 0.509 0.546 -1.813 -0.708 0.631 0.814 0.203 -0.520 -1.242 +3 -1.375 0.962 0.596 -1.323 -1.389 0.992 0.129 0.758 -0.970 0.654 -0.850 0.866 2.026 -0.325 0.118 -0.623 1.760 -1.307 -0.074 -0.305 0.889 0.505 -1.012 -0.345 -2.070 1.235 -0.779 0.249 +0 -0.822 1.121 0.000 -0.009 -0.328 0.155 0.825 -0.867 -0.658 -0.304 -1.346 -0.819 -0.476 0.874 0.263 0.194 0.851 -0.137 0.390 -0.103 0.265 -0.583 -2.439 -0.134 1.423 0.926 0.965 1.236 +1 0.658 1.676 -0.983 -0.895 -1.180 -0.254 0.052 0.022 0.898 0.393 -0.059 0.041 0.086 -0.058 -0.677 -1.203 0.142 -0.499 0.525 0.072 0.015 0.984 -1.405 1.769 -2.185 1.451 0.201 -0.524 +4 -0.580 -0.948 -1.689 0.696 -1.524 0.349 0.109 -1.687 1.217 -1.688 0.817 -2.505 -0.119 0.271 -0.237 1.662 1.901 0.784 1.341 -1.719 -0.963 0.337 -0.878 -1.682 1.461 -0.114 -2.182 0.915 +1 -0.401 0.549 -0.457 -1.130 0.441 -1.947 1.467 -1.297 -0.333 0.786 0.368 0.269 1.528 0.981 0.906 0.111 -1.990 -0.404 0.217 -0.503 0.011 1.121 -0.108 0.585 -0.660 -0.158 -1.926 -0.392 +1 0.712 -0.309 -0.498 1.558 0.036 -0.025 -0.125 0.425 0.467 1.940 -0.388 -1.010 -0.096 1.376 -0.768 -0.265 1.490 1.081 -0.427 -0.116 -0.784 -0.371 1.433 -0.737 -0.971 -0.520 1.808 1.579 +3 -1.533 -1.129 0.972 0.873 0.363 -1.721 -0.358 -1.225 1.177 -0.584 -1.086 0.030 -0.640 1.484 -0.735 -1.399 -2.153 -0.031 -0.562 -1.429 1.189 0.667 -0.028 1.364 0.663 -0.823 -1.039 0.818 +3 -0.287 -0.041 -0.684 0.784 0.676 -0.544 -1.045 0.772 0.509 -0.664 0.694 0.639 1.230 0.847 -0.339 -0.762 2.015 -0.514 -1.834 -1.208 -2.027 1.221 1.821 0.250 -1.604 1.202 0.466 1.231 +3 -1.552 0.811 -0.115 -1.539 0.292 1.125 0.197 -0.432 -0.032 1.711 -0.068 0.090 -0.631 1.396 -1.559 0.834 -1.480 0.319 -0.449 -1.266 1.529 -0.114 -1.731 -0.195 -2.095 -0.765 0.923 -0.479 +3 -0.296 -0.855 -1.685 -1.352 0.404 -1.750 0.879 -1.574 1.368 1.480 -1.134 -1.624 0.644 0.726 -0.081 0.433 -0.293 -1.341 0.475 0.594 -0.612 -1.142 -0.081 -1.027 -1.726 0.126 0.038 0.756 +4 -2.374 0.998 -0.056 0.964 -1.773 -0.615 0.450 -0.620 0.396 1.093 1.040 -1.839 -1.075 -1.103 0.499 1.202 -1.035 -0.679 1.230 0.816 0.098 2.275 0.151 -0.394 -1.861 -3.142 1.573 -1.741 +1 -0.167 -0.860 -0.202 0.905 0.327 0.262 -0.226 -0.121 0.530 0.345 0.891 0.299 -0.424 1.281 -2.332 0.083 2.132 0.271 0.909 -1.659 1.441 1.848 0.536 0.115 -0.793 0.077 0.248 -0.460 +0 -0.009 1.102 0.070 1.335 0.568 -0.410 1.374 0.263 -0.646 0.189 -0.191 0.042 -0.877 0.983 -1.078 -0.144 0.391 -0.751 -0.020 -0.860 -1.272 1.673 0.876 0.021 0.865 0.795 -0.340 1.030 +2 -2.560 -0.371 -1.153 0.977 -1.266 0.495 0.996 0.280 0.004 -0.396 -0.124 -1.340 0.178 1.051 -0.872 0.018 -0.103 0.313 -0.536 -1.509 -1.120 -2.318 0.776 0.398 -0.015 -0.697 -0.162 -0.029 +0 -1.053 0.411 0.262 0.377 -1.019 0.957 0.048 0.439 -0.010 1.626 0.160 -0.192 -1.451 -0.226 1.828 -0.608 0.641 -0.533 -0.306 -0.840 -0.716 0.294 -1.354 0.627 -0.135 -0.628 1.205 0.179 +0 -0.963 0.420 -0.047 0.157 -0.763 -0.032 -0.366 0.539 0.938 -1.503 1.267 0.028 0.948 -0.175 1.088 0.876 0.413 0.045 0.519 1.931 1.106 0.658 -0.551 -0.621 -0.742 -1.235 0.845 0.136 +3 -1.088 -0.184 0.955 -0.764 0.024 -0.653 2.268 0.137 1.280 0.495 -1.136 -2.699 0.256 -1.328 0.238 -0.675 -1.416 -0.019 0.229 -0.662 0.134 -0.094 -0.739 1.223 1.488 -1.319 0.617 0.390 +3 -0.503 -1.031 0.712 -0.510 -0.927 0.465 0.802 -1.148 1.034 -0.232 -1.198 0.266 1.084 0.073 -0.801 -1.489 -0.993 -0.151 1.780 -0.139 0.970 0.462 1.332 2.559 0.998 0.314 1.290 1.071 +0 -0.885 -0.303 -1.067 -0.179 -1.771 0.826 0.461 -0.525 -0.046 0.487 0.116 -0.931 -0.022 0.056 -0.013 -1.100 0.426 -0.619 -0.018 -1.618 0.483 -0.711 0.576 1.459 0.635 -1.941 -0.410 0.455 +4 1.049 -1.371 -0.880 -1.287 -2.686 0.842 1.430 0.556 0.380 0.085 0.055 1.695 1.549 -0.585 1.109 1.229 0.650 0.388 0.353 -1.482 -1.571 0.783 -1.100 -1.389 -2.075 -0.476 0.374 0.455 +4 1.129 -0.528 1.009 -1.447 0.681 2.780 0.440 -1.342 0.887 1.571 0.540 -0.389 -0.919 0.085 0.580 -1.815 1.357 -2.950 0.993 0.786 1.078 -1.339 1.038 2.397 -0.139 0.267 -1.414 -0.776 +4 -1.610 -1.017 -0.166 1.115 1.915 -0.664 -1.310 0.816 2.614 1.107 -0.749 2.861 0.687 -0.791 1.934 -0.870 -0.621 0.727 -0.025 0.037 0.039 1.114 -2.324 0.285 -0.290 -0.752 -0.755 -2.637 +2 1.038 -0.101 0.927 0.360 -0.015 0.253 -0.554 -1.840 -1.877 0.917 -1.026 -1.530 -1.550 -0.476 1.516 -0.145 -1.431 1.482 0.327 -0.347 0.485 0.012 -0.717 -0.547 -1.237 -0.470 0.930 0.903 +3 1.027 0.260 2.112 -0.862 -0.519 1.122 -1.279 1.320 -1.207 0.610 -1.843 0.045 -1.370 1.926 -1.072 -0.829 0.535 -0.601 0.700 -0.711 -0.788 -0.028 -1.217 0.396 0.877 0.553 0.643 -1.925 +1 0.392 0.115 0.876 1.424 -0.677 0.770 -0.624 0.219 1.070 -0.274 -0.952 -0.459 -0.953 -1.074 1.207 0.532 -0.720 -0.984 -0.915 -0.720 1.153 -0.110 2.132 -0.040 -0.576 0.928 -1.670 0.188 +2 -1.718 0.566 -2.034 -0.400 -0.571 0.360 -0.666 0.492 -1.067 -1.242 0.019 0.105 0.295 1.480 1.281 0.452 0.809 0.262 1.180 0.364 -0.552 -0.752 1.151 -0.183 1.917 -1.104 1.251 -0.426 +0 -0.290 -1.049 -0.871 -0.055 -0.588 0.124 0.837 1.183 -0.580 -0.218 0.610 0.896 -1.352 -0.972 0.444 -0.083 -1.205 -0.559 0.766 0.996 0.347 -0.375 -1.657 0.744 1.112 -0.555 1.197 -1.101 +2 0.669 -0.721 -0.358 -0.064 0.820 -0.223 1.315 0.362 1.363 -1.147 1.500 1.170 0.075 -0.525 1.302 -0.458 -0.005 0.505 -1.136 -0.183 -0.922 -1.556 2.084 -0.655 0.624 2.094 -0.633 -1.260 +3 0.776 -0.446 -0.196 0.228 0.458 -0.017 -1.374 -0.034 0.165 2.189 -0.877 1.768 0.994 0.153 1.733 0.004 -3.058 -0.102 0.791 0.370 -0.628 -1.177 1.307 -1.780 -0.083 0.424 -0.856 0.606 +2 0.044 -1.984 1.049 -0.008 -0.202 0.244 -1.069 -1.117 0.838 0.517 -0.950 0.745 3.088 -0.456 1.390 0.131 -0.245 -0.732 0.950 0.485 1.086 0.874 1.161 1.258 -0.340 0.382 0.177 -0.628 +0 0.751 -0.509 0.571 1.229 -0.826 0.938 0.861 1.846 0.030 -0.526 0.216 1.281 1.396 -1.005 -0.365 0.095 -0.612 -0.422 -0.260 1.459 -0.610 1.509 -0.046 -0.277 0.199 -0.923 -0.404 0.418 +2 -0.169 0.392 -0.989 0.032 -0.121 -0.738 -0.877 0.919 -1.697 0.478 0.798 -0.219 0.052 1.163 -0.705 -0.602 -0.495 1.165 1.292 -1.396 1.006 0.819 1.554 0.385 -0.044 1.145 0.699 2.245 +4 1.141 1.173 0.592 0.028 -0.082 1.628 2.283 -1.945 0.728 -0.289 2.194 0.268 -0.712 -0.861 -0.058 -2.140 -1.152 -1.446 -2.637 0.642 0.731 0.910 -1.067 0.473 -0.024 -1.041 -0.402 -1.338 +0 -0.507 1.544 1.364 0.286 -0.535 -1.517 0.378 0.208 0.858 -1.466 0.142 0.429 -0.902 0.117 -0.727 -0.825 -0.531 -0.266 1.239 -0.322 -0.846 0.813 1.028 0.819 1.369 0.192 0.156 -0.704 +1 0.471 0.109 -0.751 -0.552 -0.059 -0.778 -2.025 0.744 -0.385 -0.092 0.026 1.302 -2.197 -0.560 0.674 0.676 -0.985 0.546 -1.731 0.785 -0.580 1.481 1.008 0.875 0.754 -1.107 -0.178 0.525 +3 0.753 0.810 -1.066 -0.735 1.642 -0.323 -1.349 -1.665 0.553 -0.916 -0.835 -0.698 -1.875 0.758 2.049 -1.771 0.228 -0.172 1.177 0.725 0.824 -1.354 0.088 -0.624 -0.681 0.050 0.280 1.205 +2 -0.304 1.010 -0.901 -0.720 0.582 0.801 1.429 0.469 0.130 0.575 -0.267 -0.275 -1.774 0.893 0.211 0.777 -2.816 -0.047 -0.856 1.501 0.614 0.234 -0.432 0.397 -0.928 -0.100 -0.578 2.266 +3 -1.974 -0.124 0.367 -1.120 1.499 0.434 -0.318 0.424 -0.593 1.251 1.015 0.373 0.612 -1.477 1.467 -0.099 1.025 0.750 1.010 -1.048 -0.920 2.248 -1.519 -1.098 0.043 0.045 -0.540 0.616 +1 -0.145 -1.791 0.268 -0.137 0.350 -0.363 -0.476 -1.781 0.935 0.053 0.623 -1.158 0.855 0.555 -0.552 1.294 -1.053 0.955 0.459 -0.087 0.697 0.924 0.478 1.043 -1.821 -0.215 -0.209 -0.892 +4 -1.722 -0.054 -0.921 -1.575 -0.490 0.133 -0.225 0.503 1.940 -0.050 1.402 -1.419 -3.321 -0.849 -0.409 -1.202 0.616 -0.621 -0.176 -0.039 1.215 -0.616 -0.495 1.228 1.469 1.465 1.415 -2.334 +3 -1.940 -2.689 -0.280 0.075 0.640 0.455 -0.914 0.049 0.026 -0.409 1.843 -1.148 0.288 -1.012 -1.470 -1.300 0.227 -1.498 -1.273 0.874 0.268 1.064 -0.368 0.217 -0.026 -0.203 0.591 -1.200 +3 0.815 -1.296 -1.003 0.905 -2.454 -0.980 1.101 -1.363 0.267 -1.344 -1.780 -0.049 0.432 -0.721 -1.467 0.682 0.922 0.820 -0.347 0.561 0.720 0.233 -0.922 0.263 -0.215 0.945 -1.231 -1.097 +0 0.083 0.703 -0.823 0.124 0.022 0.424 0.449 0.838 1.958 -0.132 -1.119 -0.524 -0.346 0.668 0.654 0.257 0.148 -0.310 -0.004 -1.248 -0.089 0.201 1.001 -1.520 0.351 1.783 0.253 -0.173 +4 2.852 -0.295 -0.440 -0.657 0.379 0.987 0.886 -2.149 -0.486 -0.428 0.981 -0.361 0.935 1.895 0.126 -0.959 -1.871 1.046 0.125 -0.648 2.097 1.249 0.729 -1.106 -0.424 -2.455 0.487 0.762 +2 0.922 0.185 -1.623 -0.799 -1.661 -0.179 -1.328 -1.761 -2.055 1.984 -1.365 -0.316 0.059 0.119 0.621 -0.051 -0.454 0.220 0.209 0.051 0.641 -0.626 -0.201 -1.326 -0.245 -0.638 1.055 -1.462 +2 0.806 0.923 -2.009 1.246 -1.318 0.610 -1.454 -1.437 -0.199 -1.273 0.232 0.245 -0.811 -1.297 -0.633 0.858 0.405 0.409 0.835 -0.543 -1.722 -1.554 -1.245 0.032 -0.148 1.120 -0.707 -0.179 +0 -0.604 -0.680 -0.496 1.179 -0.327 -0.302 -0.742 -0.260 0.216 -0.215 -0.150 1.016 0.274 -1.151 -0.085 -0.538 0.521 0.103 1.130 -0.740 -0.192 -1.064 -0.042 -1.537 0.321 -0.055 0.155 0.788 +0 0.023 0.268 -0.102 0.919 0.181 0.364 0.137 0.911 0.870 -0.163 -0.722 0.750 -0.165 -2.032 1.043 0.905 -0.324 0.787 -0.528 -0.557 0.228 -1.297 -1.136 -0.612 0.787 -0.588 0.463 2.201 +3 0.071 -0.109 1.033 -1.310 -2.283 -0.213 0.469 0.152 1.232 0.437 -0.564 -2.325 -0.237 1.028 -0.392 0.028 0.421 -1.015 0.623 1.637 -0.173 -1.129 -0.067 -2.224 0.337 0.452 1.676 -0.194 +3 -0.156 0.371 1.550 -1.956 -0.169 0.321 -0.426 1.288 -0.271 -0.087 -1.077 0.473 -0.155 1.097 -0.808 0.205 0.381 0.614 0.423 1.826 -2.880 -0.073 -0.756 -1.281 0.523 0.783 2.095 1.057 +4 0.157 -0.432 -2.096 2.801 0.080 0.483 0.198 0.456 0.252 -1.359 0.848 0.848 1.286 -0.456 0.333 0.461 2.969 3.258 0.339 -1.001 -0.605 -0.226 1.200 1.359 0.997 0.781 1.070 -1.449 +0 0.406 1.109 0.891 0.621 0.275 -0.581 -1.033 -1.191 0.394 0.498 1.094 0.562 1.051 -0.131 0.771 -1.001 -0.060 -1.831 0.032 1.225 0.070 0.722 0.273 -0.570 -0.187 1.180 0.717 -0.618 +1 1.417 1.612 -0.691 -0.496 -1.982 1.264 -0.486 0.478 0.619 -0.318 -0.705 0.296 0.197 0.621 -1.156 0.850 -0.165 -0.545 -1.193 -0.846 -1.935 1.032 0.341 0.830 0.421 0.103 0.561 1.065 +3 -0.493 1.444 -1.257 0.813 -0.279 -0.280 0.790 0.340 0.571 0.968 -0.331 -0.612 -1.085 -0.825 2.949 1.245 -1.351 -1.322 0.482 0.547 0.549 -0.255 -0.125 0.328 0.086 -2.219 -0.230 -0.851 +2 -1.900 -0.128 -1.383 0.902 0.784 -0.898 -0.766 -0.461 -2.253 0.336 -0.273 0.970 -1.098 -0.619 -1.507 -1.004 -1.182 0.996 -0.698 0.124 -0.381 -1.269 -0.168 0.599 -1.208 0.809 -0.850 -0.483 +1 0.742 -1.050 -0.354 0.449 0.954 0.598 -0.220 2.106 1.193 -0.952 0.038 2.055 0.405 0.030 -0.402 -1.093 -0.079 -0.137 -1.490 -0.866 0.953 -1.189 -0.193 0.464 0.520 1.570 -0.316 0.018 +1 -0.704 -0.384 -0.302 -0.777 0.167 -0.036 -1.079 -0.679 -0.197 -0.919 -1.354 -1.050 -0.183 -0.616 0.406 1.036 -0.843 -0.883 0.615 0.065 1.119 -0.225 0.209 0.933 -0.488 -2.114 2.183 1.021 +3 1.814 -0.253 -0.161 -0.233 -0.776 -0.473 -1.039 -2.294 -0.580 0.023 0.631 -0.576 -0.564 1.031 -1.331 -0.163 0.957 -0.708 -1.534 1.707 0.799 0.830 -0.089 0.641 -2.490 -0.437 0.948 -1.396 +4 0.444 1.221 -0.121 -1.387 1.057 1.436 0.898 -0.164 1.044 3.429 0.115 -1.312 -0.586 0.625 -1.605 0.169 0.380 1.535 1.310 0.865 -2.163 0.921 0.706 0.256 -1.730 -0.777 -1.167 -2.015 +3 0.167 0.529 0.855 -2.412 0.209 0.427 -1.322 0.689 -0.505 -0.168 1.883 1.021 0.608 0.428 -0.875 -0.555 0.114 -1.016 -0.823 -0.631 2.110 1.179 0.076 -1.141 0.077 -0.109 1.727 1.549 +3 0.170 -0.752 -2.425 1.777 0.560 -0.683 1.326 0.077 -0.127 2.051 0.552 1.452 1.434 -0.395 -0.665 0.968 0.868 0.242 0.483 -0.295 -0.561 -0.717 1.378 2.460 -0.311 0.453 0.092 0.514 +3 -0.295 -0.210 -1.680 -0.241 0.244 -0.492 -0.617 -0.192 1.168 1.648 1.027 2.677 2.109 0.004 -0.360 -2.039 -1.465 -1.369 -0.084 -0.645 -0.070 0.904 0.790 -0.614 0.771 0.424 0.189 0.992 +2 -0.042 -0.138 0.891 -0.276 0.883 -0.668 0.027 -0.993 -1.702 -0.914 0.505 1.365 -0.142 0.460 -0.328 -0.471 1.110 1.300 1.412 0.840 -0.316 -0.589 -1.662 1.934 -1.489 0.038 -1.601 1.071 +4 0.137 -0.623 -0.582 1.828 0.589 0.899 -0.436 -2.140 0.779 -0.376 0.682 -0.177 0.120 0.970 -0.410 -0.156 -1.452 0.847 -1.866 -0.936 -0.360 -2.896 -1.375 1.209 -0.030 -0.546 -1.530 0.333 +0 -0.844 0.087 -1.810 0.988 -0.979 0.546 -1.906 0.552 -0.088 0.376 -0.109 0.473 -0.079 0.128 0.081 0.208 -0.701 -0.098 -2.103 -0.758 0.228 -0.250 0.849 1.111 1.084 -1.414 0.104 -0.108 +0 0.123 -1.281 -1.289 -0.715 0.062 -0.637 0.195 -0.368 -1.722 1.229 1.056 1.686 0.749 0.016 -0.269 1.067 -0.073 0.528 -0.612 0.409 0.714 0.461 -0.164 -0.305 0.872 0.395 -0.052 0.942 +4 1.099 1.882 1.833 -1.479 -1.110 -1.004 -0.944 0.323 -0.567 2.073 1.905 -0.006 1.955 1.488 -0.451 -2.009 -0.218 -0.645 0.207 0.089 0.063 0.524 -1.213 0.411 -0.440 0.200 0.957 -1.224 +4 -2.248 -1.125 -0.581 -0.003 -0.145 -0.122 0.408 -0.903 0.934 0.433 -0.320 1.601 1.556 0.933 -3.019 1.579 0.795 2.006 1.237 1.578 0.879 1.349 1.994 0.217 -1.572 -0.307 0.390 0.075 +3 0.794 1.498 1.228 -1.119 1.152 1.686 -0.575 -0.894 -0.024 0.179 -0.350 -1.187 1.472 -0.860 -0.507 0.109 -1.472 -1.549 -0.432 0.826 0.188 -2.059 1.389 0.797 0.191 0.164 0.673 2.326 +0 0.607 -0.648 0.175 1.321 0.577 -0.985 -0.542 -0.462 -0.653 0.275 -0.719 1.087 1.150 -0.013 -0.718 0.172 -1.195 -0.529 -0.277 0.008 -0.618 0.117 0.499 -0.078 0.173 -0.096 0.808 1.750 +3 0.886 -1.496 0.977 0.206 -0.115 1.454 -0.871 0.497 -0.600 0.082 -0.993 -0.470 0.643 -0.829 -0.710 -0.217 1.457 -1.558 -2.858 -0.356 -1.672 0.201 -0.386 -0.709 1.235 -0.014 1.354 0.862 +2 1.392 0.111 1.318 -1.358 -0.443 -0.289 -0.115 0.084 0.691 -0.445 0.139 -0.045 -0.926 -0.114 -0.144 -0.165 0.032 0.076 -0.432 1.273 -1.169 -0.721 2.780 -1.038 -1.354 0.734 -2.077 -0.235 +0 1.161 -0.855 -0.609 0.369 -0.296 -0.384 0.094 -0.150 1.388 0.187 0.544 -1.108 -0.995 1.070 -0.284 -0.199 0.530 0.494 0.305 -0.182 -0.061 1.173 -0.615 -0.108 2.016 -1.643 -1.238 0.422 +0 -0.423 -0.269 0.138 1.401 -0.555 -0.754 0.696 1.209 -0.053 0.670 0.112 -0.251 -0.829 -1.154 -0.615 -0.976 0.283 1.218 0.204 -0.921 0.855 0.297 0.495 -0.519 1.133 1.128 -0.184 0.718 +4 -2.747 1.783 0.997 1.529 0.579 0.689 -0.255 0.014 0.790 1.300 -0.441 -0.073 -2.294 0.403 -1.128 1.621 0.359 -0.859 0.617 -1.933 -0.419 1.508 -0.709 0.428 -0.028 -0.118 -0.307 1.849 +3 -1.169 -0.828 0.139 -0.057 0.779 -1.573 -1.073 -0.017 -0.063 -1.145 1.891 0.478 -0.286 0.860 -0.665 2.215 -0.688 -0.321 1.800 -0.680 -0.127 -0.193 -1.441 0.410 0.861 0.638 0.046 -2.201 +2 -0.375 -0.318 1.282 0.558 -1.111 0.247 0.498 1.140 1.581 -1.015 -0.811 -1.258 -0.234 0.466 0.987 -0.076 -0.320 0.152 -0.835 2.090 -1.608 0.185 2.024 0.007 -0.190 -0.357 -0.180 1.373 +2 -2.272 -0.991 0.107 2.883 0.139 -0.440 0.873 -0.589 0.433 0.052 1.072 0.662 -0.479 -1.502 -1.069 0.296 -0.052 0.027 0.254 -0.819 -0.104 -0.010 -1.438 -0.612 0.391 0.699 -0.808 0.499 +1 0.758 1.720 -1.983 -0.126 -2.040 0.491 -0.167 -0.471 0.267 -1.053 1.848 -0.244 0.077 -0.129 0.922 -1.202 0.828 -0.519 -0.510 0.879 -0.268 0.614 0.632 -0.349 0.233 -0.484 -0.020 0.474 +3 -1.555 -2.311 0.952 0.484 1.683 -1.905 -0.096 0.315 0.489 0.844 -1.539 0.046 -0.228 0.871 -0.367 1.133 -1.598 0.968 -1.017 0.855 -1.067 0.234 -0.157 -0.333 -0.150 0.912 -1.209 0.464 +4 0.865 -0.884 1.776 -0.913 -0.740 1.138 1.215 -0.324 1.244 -0.907 -1.908 0.991 -1.513 -0.014 1.904 -0.946 -1.295 -0.953 -1.229 -1.106 -1.538 0.971 -1.682 0.651 -1.650 0.153 0.888 -0.036 +0 -0.720 -0.924 -1.254 -0.215 0.386 0.703 0.241 -0.666 0.369 0.124 0.936 0.777 0.030 -0.897 -0.653 -0.550 -0.315 0.268 0.715 -0.051 0.016 0.143 -0.516 -0.925 -1.755 -0.695 0.473 -0.703 +0 0.412 -0.179 0.238 1.219 -0.653 0.094 -1.338 1.164 1.096 0.362 0.120 -0.509 -1.396 0.306 0.756 2.019 -0.394 0.270 1.114 1.145 -0.622 -0.279 -0.525 -0.902 1.472 0.063 -0.074 0.248 +4 0.127 -1.249 1.009 1.400 1.540 1.126 -0.320 0.894 1.461 0.945 0.853 0.191 -0.906 1.855 -0.037 1.214 2.478 0.908 1.175 -0.936 -0.727 1.265 -0.550 -1.341 -1.661 -0.217 -0.229 0.998 +1 0.497 -1.727 0.076 0.090 -1.297 1.244 1.281 -0.419 -0.114 -0.586 1.042 0.721 1.232 1.281 0.148 -0.622 1.641 -0.817 1.159 -0.054 -0.764 0.425 1.284 -0.610 0.794 0.815 0.486 0.742 +4 2.037 -0.130 -0.266 -0.514 0.076 -0.640 0.447 -0.076 -1.085 -0.950 -0.296 -2.190 -1.578 1.254 1.214 0.937 0.677 0.474 0.465 -0.654 1.044 -0.942 2.532 -0.507 -0.942 3.284 -1.065 0.800 +1 0.769 0.911 0.414 -1.302 -0.059 -0.278 -0.537 1.412 -1.047 0.316 1.194 0.381 1.241 0.564 0.429 0.367 0.613 -0.386 -1.302 0.954 1.162 0.252 -0.548 -1.558 0.086 -1.349 -1.489 -0.021 +0 1.070 -0.975 -0.148 -0.308 1.222 -0.992 -0.468 -0.193 -1.876 -0.303 0.086 1.559 -0.321 -0.139 -0.570 -0.488 -0.305 -0.100 0.323 0.604 -0.392 0.206 -0.458 -0.733 1.204 0.001 -0.564 0.263 +1 0.755 0.024 -0.783 -1.584 0.765 0.373 0.206 -0.873 0.760 -0.717 0.908 -0.936 -1.240 -0.020 0.664 0.787 0.192 1.151 0.270 0.093 -2.337 -1.042 1.058 -0.943 -0.283 -0.403 -1.036 -0.623 +4 3.259 -0.563 -0.120 1.564 0.796 -0.898 0.387 1.005 -2.639 -0.394 1.824 -0.685 1.485 -0.754 0.217 0.082 -0.729 -1.753 0.827 -0.590 2.032 -0.292 -0.139 0.472 -0.757 -0.031 -1.644 0.891 +1 0.137 0.656 -0.551 -1.367 1.484 0.648 0.041 -1.046 -1.015 0.167 -0.465 0.219 0.373 -0.451 1.694 -0.755 -0.949 0.420 0.482 -1.002 -0.343 -1.540 -0.565 0.803 -1.294 -1.091 -0.829 1.141 +2 -1.143 -0.429 -0.048 0.233 0.093 0.362 0.129 -0.950 -0.696 -0.628 -0.621 0.568 1.277 -2.736 0.315 0.835 -1.483 1.776 -0.259 0.821 -0.863 0.705 0.339 -1.507 -1.260 -0.460 0.809 -0.171 +4 0.907 -1.452 -0.665 -0.121 1.366 -0.046 0.450 -0.590 0.487 1.677 0.996 -0.081 0.675 0.578 0.507 1.112 1.104 -0.127 0.642 -0.383 -0.923 -0.269 -0.076 -1.662 0.860 -2.767 2.715 1.502 +3 -0.588 0.140 -1.062 0.073 0.796 -1.121 -0.462 -1.120 0.090 -1.864 -0.403 0.528 -2.139 0.705 2.353 -0.286 -0.098 -1.586 -0.495 0.391 0.497 -0.207 -0.347 -0.802 -0.304 -0.320 -0.581 -2.839 +4 -0.243 1.087 -1.154 0.462 -0.640 0.047 -2.175 -1.558 -0.072 0.610 -2.468 -0.801 -1.080 -0.079 1.227 0.138 -0.279 -0.825 0.092 2.242 0.561 -0.211 -1.638 1.594 0.704 0.377 0.908 1.716 +0 0.572 0.717 0.437 0.985 -0.289 1.392 0.357 0.735 0.364 0.491 -1.001 -0.942 -0.541 0.514 -0.589 -0.296 -0.073 1.418 0.603 -1.461 0.160 -0.303 -0.228 0.001 1.126 -0.814 0.204 0.291 +3 -1.426 0.624 -1.147 -1.744 -1.474 -2.449 0.870 -0.070 -0.038 -0.827 -0.103 0.773 0.253 -1.211 -1.032 0.069 -0.226 0.170 -0.129 0.639 -1.161 -1.245 -1.948 0.170 0.089 -1.085 -1.115 1.179 +3 -0.470 -0.504 0.902 -0.964 1.615 -0.152 2.862 -0.693 0.250 -0.352 -0.793 1.064 -1.106 -0.676 0.586 0.745 -1.392 0.582 0.129 -0.918 -0.789 -2.033 1.717 1.015 -1.289 0.799 -0.574 0.915 +3 0.959 0.589 -1.382 -1.791 -0.558 -1.115 1.617 -1.299 -0.525 0.676 -0.969 -0.985 0.270 2.027 -0.491 1.201 -0.728 1.668 1.127 -1.213 1.204 0.504 -0.375 -0.586 -0.407 0.900 1.111 0.723 +4 -0.266 -0.705 -1.422 -1.165 -0.370 0.210 0.440 -0.271 1.258 -0.678 0.696 -2.930 -0.360 0.351 2.346 0.847 -2.047 0.878 0.709 0.892 1.153 -0.088 -0.016 -1.753 0.533 -0.541 -0.761 -0.976 +4 0.146 -0.242 1.603 -0.650 1.998 -0.448 -1.389 -0.871 -0.850 1.658 -1.345 0.323 1.468 2.771 -0.110 0.112 -1.166 -0.381 2.186 0.871 1.215 0.648 1.068 1.939 -0.457 0.738 -0.691 -2.036 +2 -0.409 -0.935 -0.962 1.592 -1.825 0.508 -0.825 1.772 -0.273 -0.364 1.570 0.877 0.554 0.806 -1.083 0.742 -0.199 0.361 0.485 -1.111 0.594 -0.186 -1.240 -0.147 0.020 0.594 -2.388 0.388 +0 -1.557 -0.718 0.157 -0.295 0.410 -1.858 -1.453 0.589 1.292 -1.905 -0.649 0.614 -0.231 -0.324 -0.206 0.134 0.738 -0.362 0.610 0.261 0.513 0.538 -0.059 -0.128 -1.167 0.780 -0.390 -1.034 +2 -1.056 -0.803 -0.493 -0.749 0.701 -0.369 0.746 -0.869 1.709 -0.654 -0.333 1.179 0.193 -0.368 -0.070 0.137 -0.483 -0.788 0.176 -1.722 -2.035 -1.019 0.502 -0.114 1.942 1.711 -0.965 -1.364 +4 0.666 -0.269 -0.582 0.175 0.773 -1.006 3.198 -1.186 0.379 -0.352 0.027 1.674 1.056 -1.760 -0.101 -1.014 -0.707 -1.415 -1.456 0.454 0.598 -0.296 -0.437 0.480 -0.858 -2.400 1.178 0.807 +3 -0.597 -0.103 -1.492 0.859 -1.319 0.916 -1.063 1.015 0.010 -0.559 1.600 -0.069 -0.362 0.909 -1.480 -2.527 1.158 -0.836 0.865 0.046 -1.164 0.607 -0.410 -1.077 -1.051 -1.733 0.403 -0.935 +4 -3.385 0.071 0.061 0.198 -2.144 -0.750 0.661 2.306 0.572 1.961 1.405 0.288 0.988 1.815 0.083 -1.109 0.450 -0.226 -0.472 0.061 -0.719 -0.473 -0.561 -0.452 -0.478 1.384 -1.206 1.082 +2 -0.992 -1.303 0.326 0.533 1.541 1.409 -1.585 -1.998 0.565 0.539 1.482 0.393 -0.563 1.039 -1.045 2.007 0.272 -0.737 -0.887 0.195 -0.080 -0.137 -1.310 -0.162 0.827 -0.755 0.256 1.119 +0 0.461 0.337 0.680 -0.519 -0.138 0.085 -0.885 0.481 -0.235 -0.182 0.188 0.098 0.043 -1.860 -0.238 -0.002 -1.029 -0.687 1.802 0.584 -0.119 -0.863 -0.074 2.102 -0.344 -0.145 -1.711 -1.008 +0 -0.006 1.026 0.793 -0.162 -1.534 0.830 0.170 0.022 -0.466 0.745 0.348 -0.237 -1.225 1.068 0.605 0.255 -2.657 -0.038 -0.241 -0.618 0.754 0.715 0.437 1.038 -1.022 -0.454 0.075 -0.622 +4 -1.363 -3.153 -0.245 0.638 -0.420 0.125 -0.344 -0.320 -1.672 -2.381 0.143 -0.586 -1.380 -0.276 -1.362 -0.109 -2.705 1.213 0.981 -0.958 0.416 -0.633 2.255 -1.523 1.118 0.913 -1.168 1.097 +0 -1.523 -0.924 0.412 -0.113 -0.230 0.040 -0.240 -0.334 0.591 0.795 0.080 1.303 -0.581 0.079 -0.202 -1.467 0.560 1.148 0.842 0.259 -1.527 0.688 1.835 -0.110 0.119 1.270 0.093 -0.178 +3 -0.274 1.373 2.483 -0.789 1.092 1.612 -0.828 -0.562 -1.195 -0.593 -0.867 -0.220 -1.276 0.602 0.842 -0.433 -1.070 0.203 0.270 -0.710 -0.417 -0.464 0.794 0.197 -1.827 2.388 -0.434 0.754 +1 -0.512 0.810 -0.002 -0.333 -0.178 0.333 0.908 -0.891 -0.344 1.469 -0.361 0.367 -1.234 -1.715 -1.040 -0.690 1.040 1.150 -1.925 -0.541 0.698 -0.105 1.505 0.674 -0.268 -0.179 -0.057 -0.846 +4 0.920 -0.323 0.718 1.076 0.783 -1.699 1.615 -1.424 -0.926 1.010 -0.230 -0.335 -1.386 -0.611 0.545 1.073 -1.353 -1.257 -0.834 -0.662 -3.235 0.807 -1.803 -0.792 -0.482 -1.030 0.368 -0.243 +4 -0.226 2.137 -0.710 -0.534 1.080 1.328 1.385 -0.763 -0.343 1.040 -0.778 0.169 -2.471 -0.045 -0.249 -2.116 -0.260 0.041 3.194 -1.020 -0.223 0.567 -1.549 0.853 -1.462 0.508 -1.180 -1.427 +3 1.928 0.010 -1.011 0.112 -0.729 0.849 0.747 0.655 -0.660 -0.172 -1.643 -0.958 -0.652 -1.137 -0.499 0.186 -1.129 -1.026 0.201 -0.147 0.402 0.925 0.608 0.900 0.527 -3.104 -0.865 -1.437 +0 -0.307 -0.421 0.097 -1.372 -1.550 1.298 -0.785 -0.132 -0.327 -1.150 0.125 0.431 0.276 1.697 -1.260 -0.173 -1.386 -0.397 -0.888 1.488 -0.606 -0.815 0.129 -0.376 0.409 -0.082 0.086 0.644 +0 0.255 -0.058 0.898 -0.209 0.608 -0.086 -0.667 1.254 1.409 0.195 -0.872 -0.178 -0.097 0.637 1.568 1.771 -0.392 0.599 0.971 0.195 0.332 1.328 -0.607 0.447 0.867 -1.214 -1.604 0.409 +0 -0.074 -0.601 1.436 0.817 -0.487 1.912 -1.544 -0.511 0.796 -0.509 -0.607 0.698 -0.965 0.327 -1.031 -0.297 0.172 0.509 -1.209 -0.914 0.075 -0.340 1.595 1.009 0.330 -0.424 0.532 0.947 +4 -0.096 -0.353 2.557 -0.581 -0.222 -0.893 -1.315 0.480 1.551 1.987 0.735 0.018 1.405 -0.088 1.319 -0.628 1.668 -0.127 1.350 1.781 0.224 0.047 1.376 0.758 1.329 0.833 0.373 0.841 +3 1.713 0.595 -1.745 -1.430 -1.094 -0.823 -0.071 0.504 -1.245 -1.382 -1.100 -0.468 0.610 -1.088 0.490 -0.558 -0.886 -0.381 -0.261 1.576 0.393 -1.161 1.635 1.350 -1.092 -1.132 -0.638 -1.279 +3 1.082 0.331 0.120 0.599 -0.615 0.235 0.421 0.101 -0.017 0.685 -0.985 -0.836 -0.208 1.074 -1.420 1.014 1.416 0.215 -1.049 -1.658 2.110 -1.209 -0.507 -1.829 -2.067 0.680 -0.584 0.852 +1 -0.485 0.150 -0.874 0.198 1.115 1.299 -1.391 0.705 -0.891 1.759 1.943 0.382 0.099 -0.189 1.484 0.160 0.296 -0.231 0.044 0.528 0.434 0.898 0.237 -0.145 0.323 0.230 -0.713 -2.109 +1 0.902 -0.018 -0.268 -0.022 -0.497 -1.698 0.301 0.766 -0.154 -2.265 -0.572 0.906 -1.438 0.696 -0.445 -0.019 -0.686 -1.136 1.434 0.816 0.645 0.909 0.503 0.944 -0.664 1.378 1.288 0.380 +4 -2.352 -0.051 0.010 0.093 -1.819 0.354 0.513 -0.938 -1.399 -0.979 0.129 -1.238 -1.354 2.099 -0.005 -0.311 -0.670 -0.423 1.802 0.816 2.387 0.392 -0.956 0.271 0.549 0.614 -0.442 1.804 +1 -0.857 -1.768 -1.016 -0.509 0.249 0.791 0.821 -0.460 -0.519 -0.375 0.165 -0.897 0.156 -0.422 1.689 -2.395 0.316 -2.390 -0.144 -0.417 -0.570 0.413 0.021 -0.211 -0.181 -0.502 0.244 -1.290 +4 1.010 -0.044 1.378 -0.672 -0.055 -0.922 0.007 -1.479 -0.488 -1.436 0.694 0.717 -1.730 1.334 2.497 0.046 -0.641 2.824 -1.319 0.020 0.180 1.345 0.360 -0.499 -0.951 -0.342 -1.007 0.048 +1 -1.389 0.403 0.958 1.304 0.188 0.604 0.850 0.602 0.318 -0.639 0.954 -0.536 2.602 -0.640 0.803 -1.278 -0.477 -0.589 -1.367 -0.574 0.360 -0.247 1.193 -0.931 0.697 -0.593 1.129 0.783 +2 1.194 0.597 1.137 1.353 -1.227 0.507 -0.383 1.438 -1.156 0.565 -0.811 1.651 -0.136 0.421 0.682 -1.083 0.274 1.418 0.477 -1.233 -0.626 1.757 1.086 0.947 -0.767 -0.026 -0.481 -0.886 +2 0.774 0.645 1.472 1.186 0.018 -0.601 -0.120 0.153 -0.392 0.994 1.427 0.474 -1.197 -0.384 0.230 1.747 -0.740 -0.249 0.701 0.827 -0.259 -1.266 -0.050 -2.342 -0.042 -2.098 -0.005 -0.556 +0 -0.701 -0.321 -0.723 -0.123 0.953 1.270 1.790 -0.253 0.610 0.223 0.141 1.031 0.956 0.121 -0.467 0.091 -0.503 0.125 -0.389 1.485 0.512 0.778 -0.529 0.333 0.163 -0.016 -1.129 1.788 +4 0.758 -0.681 0.328 0.569 -0.680 -0.998 0.761 -0.862 -1.440 -0.853 -0.674 1.071 -1.928 1.466 -0.798 0.254 0.812 -0.232 -0.979 1.760 -0.280 -0.406 2.011 -0.456 -2.094 1.600 -1.089 1.959 +3 -1.794 1.083 -1.839 0.898 -0.368 0.703 -1.332 1.171 -1.465 -0.668 -0.998 -1.754 0.095 1.806 2.667 0.097 1.117 0.232 0.499 -0.342 0.195 0.537 -0.578 -0.077 -0.058 0.826 -1.066 -0.052 +3 0.703 -0.638 1.113 0.305 -0.710 -1.369 -0.541 0.871 0.446 -2.146 -0.333 0.125 1.909 1.288 -0.179 0.181 -1.363 -2.627 -0.397 1.003 0.181 -0.749 0.912 1.793 -1.489 -0.053 -0.533 0.044 +0 0.221 0.108 -0.270 -1.855 0.545 -0.035 -1.176 -0.680 -0.833 1.327 0.917 1.535 0.254 0.874 0.514 -1.242 -0.251 0.064 1.221 -0.683 -0.754 -0.486 1.153 -0.013 0.646 1.169 0.051 -0.068 +3 0.568 1.689 -1.762 -0.714 0.285 -1.733 2.210 0.005 1.188 1.252 -0.197 0.928 -0.233 1.262 0.343 2.180 -0.442 -1.180 -0.524 -0.690 0.122 0.136 1.208 0.025 0.741 0.470 0.099 -1.154 +1 -1.199 -0.393 1.592 -0.681 -0.991 -0.883 -0.986 -0.304 -0.001 0.260 0.189 0.324 0.092 0.131 -0.124 -0.498 2.659 -0.286 0.029 -0.017 1.766 0.143 1.383 -0.082 -0.967 1.041 -0.845 1.518 +0 -0.007 -0.472 -0.283 0.464 -0.451 -0.532 -1.482 0.175 -0.454 -1.336 -0.425 0.059 0.364 0.618 0.151 -0.828 0.396 -0.790 -0.604 -0.697 -0.475 0.865 0.498 0.646 1.166 -1.109 0.021 -0.143 +4 0.863 0.686 0.691 0.427 0.597 0.505 0.565 3.562 -0.107 -2.241 -1.076 1.123 0.396 -0.288 0.422 0.933 0.549 1.430 1.887 0.925 -0.058 1.351 -0.735 -1.166 -1.174 -0.251 -1.074 0.333 +3 -0.378 -1.004 -2.370 1.062 0.123 -1.135 -0.512 -0.755 -0.982 1.299 -0.376 -1.607 -0.040 0.572 -1.048 1.025 0.797 -0.654 -1.633 0.773 1.150 -0.962 -0.541 0.456 1.188 1.704 -0.891 0.661 +0 -0.112 0.248 0.291 0.725 -1.145 0.340 0.017 1.631 -1.310 -0.932 1.307 -0.770 1.062 -0.143 -1.793 1.249 -0.940 0.441 -0.221 -1.225 -0.087 1.181 0.112 -0.192 -0.121 0.233 0.857 -0.547 +2 -0.866 0.287 0.490 -0.717 0.856 1.634 -0.490 0.402 0.502 0.016 1.609 0.714 0.728 -0.404 0.929 -0.279 0.681 -1.605 0.668 0.664 0.284 -2.293 0.912 1.173 -1.193 0.957 -1.103 -0.608 +4 -2.748 -0.500 -0.526 1.388 -0.385 0.383 0.141 -2.131 0.768 0.215 0.508 3.926 -2.084 1.725 -0.287 0.287 -0.046 -0.424 -0.570 0.330 -1.517 0.751 -0.416 -1.130 -0.450 1.257 -0.535 0.358 +3 -0.144 -0.397 -0.153 -1.538 0.041 -1.363 -1.635 0.007 1.733 -0.666 0.679 0.627 -1.095 2.046 2.582 1.064 0.808 -0.432 0.494 -0.408 1.158 -0.072 1.146 0.879 -0.510 -0.681 1.403 -1.375 +4 -2.226 -1.321 -0.142 -2.495 0.151 1.204 -0.480 0.498 1.787 0.390 1.182 -0.472 -0.418 0.405 0.442 0.658 1.020 1.087 1.609 1.822 -0.238 0.189 0.302 1.025 0.059 -0.823 0.409 2.239 +0 -0.905 -0.128 -0.768 0.009 0.478 0.995 -0.189 -1.116 -0.182 -1.629 -0.790 0.468 1.474 0.135 -0.367 -0.379 0.103 -0.686 0.920 1.552 0.090 -0.396 1.521 -0.718 0.365 0.708 -1.208 0.338 +2 1.513 1.730 0.990 0.928 0.046 -0.101 -1.062 2.053 0.094 -0.635 -0.078 -1.236 0.309 -0.386 -0.575 -0.425 -1.494 0.295 0.055 0.229 0.782 1.431 -1.757 0.082 -0.924 -0.424 1.426 1.019 +4 -0.306 0.690 0.491 -0.020 0.722 1.820 1.814 1.275 0.734 -0.629 1.988 1.224 0.216 -1.066 0.736 0.941 0.060 -1.472 -0.448 0.431 1.227 0.045 -0.737 -2.177 1.962 0.031 -0.728 -1.837 +3 1.178 -1.124 0.700 -0.950 0.106 0.708 -0.659 0.170 -0.415 0.705 0.235 0.057 1.558 0.662 -0.690 -0.086 -0.076 0.080 2.525 0.371 -2.036 -0.543 -1.932 -0.330 -1.987 -0.872 -1.521 0.384 +4 -0.634 -1.906 -0.088 2.211 0.805 2.190 0.882 1.027 1.974 0.377 -0.187 1.033 1.454 -0.480 -0.722 0.139 0.831 0.798 -1.424 1.912 -0.641 -0.265 -0.903 2.698 0.242 -0.851 0.864 0.769 +0 -0.175 -0.583 -0.007 -0.703 -0.212 0.116 0.598 0.199 0.180 1.243 0.612 -0.626 -0.362 -1.892 1.276 -0.351 -0.876 -0.517 0.693 0.153 -1.336 -1.459 1.378 -0.780 -0.394 0.524 -1.219 -0.735 +4 2.146 -1.545 -1.982 -1.499 0.898 1.125 -0.513 0.683 1.390 1.787 -0.246 -2.301 -0.821 -0.427 -0.259 -0.188 1.119 1.274 -0.172 1.098 0.358 0.065 0.907 0.072 2.058 -1.438 -0.515 -0.127 +3 0.841 -0.342 -0.798 -1.514 -0.423 -0.703 -0.086 0.476 -0.282 0.185 0.472 -1.230 1.886 -0.741 0.517 -1.136 0.110 0.147 1.659 -0.233 1.299 0.290 1.852 -1.780 0.482 -2.673 -0.058 1.021 +0 -1.200 1.242 -0.159 -0.849 -0.779 -0.439 0.725 0.379 -0.596 -0.381 2.063 -1.438 1.603 -0.512 1.469 -0.567 -0.556 0.501 -0.026 0.435 1.001 -0.405 -0.217 0.305 -0.803 1.072 0.416 -0.231 +0 0.429 1.716 -0.506 0.551 2.236 0.813 0.151 -0.270 -0.968 -0.290 -0.819 -0.230 -0.110 0.989 -0.409 -0.597 0.887 0.697 -0.243 0.601 -0.262 -0.213 1.554 -0.138 -0.406 0.018 -1.965 -0.168 +1 -1.344 1.441 1.031 -1.414 -0.181 0.162 0.719 -0.012 0.786 -0.071 0.612 -1.368 -0.026 1.150 1.411 0.173 -0.099 1.368 -0.101 1.145 -0.107 0.040 -1.105 -1.249 0.978 1.723 0.106 -0.166 +0 0.247 -1.682 -0.797 -0.799 0.053 1.466 1.075 -0.360 -0.829 -1.558 -0.379 -0.899 -0.036 0.002 -0.476 -0.455 -0.694 -0.512 1.436 0.518 0.531 1.012 0.924 1.425 0.588 0.205 -0.591 -0.824 +0 0.836 0.405 0.629 -1.116 -0.057 -0.050 -0.347 -0.203 1.267 0.149 0.509 -0.368 1.888 1.437 0.385 1.173 -0.356 0.072 -0.627 1.308 1.251 1.842 0.222 0.422 0.089 -0.547 -0.202 -0.600 +1 1.441 -0.015 0.203 -0.953 0.208 -0.114 -0.515 0.846 0.067 0.533 -1.394 0.794 0.043 -0.094 -0.064 -0.995 1.352 -0.125 -0.668 -0.455 0.646 1.767 -1.594 1.603 1.031 -0.886 0.829 0.469 +3 1.385 -0.264 0.727 -0.012 0.534 0.158 0.536 0.227 0.482 -2.058 1.051 -0.006 0.272 -1.311 0.969 1.190 -0.637 0.191 -2.135 -0.480 -0.991 1.518 0.113 2.075 -0.137 -0.702 -0.486 -1.749 +3 -0.020 -0.064 2.192 -0.333 -1.203 0.021 0.196 0.562 0.756 1.956 -0.596 -0.687 -1.854 0.662 -0.887 0.892 -0.492 1.726 -1.434 1.023 0.904 0.527 -1.117 0.531 0.127 -1.830 1.558 -0.346 +2 -0.320 0.876 -0.615 -0.421 0.925 0.486 -0.514 2.158 0.858 -0.970 -0.617 -0.499 0.370 0.999 1.404 -1.795 0.146 -0.089 0.817 0.819 0.962 -0.178 1.510 -0.748 1.594 1.077 -1.040 -1.773 +2 -0.980 1.019 -1.632 1.870 0.452 -0.911 -0.691 1.144 -1.342 0.050 -0.031 -0.058 0.545 0.645 0.413 1.121 0.258 -0.502 -1.131 0.062 0.390 -1.767 0.443 -0.872 -0.901 0.151 -2.099 1.447 +0 0.374 -0.519 0.261 -2.222 0.071 0.503 0.400 -1.306 -0.201 0.598 0.723 0.477 -0.159 -1.238 -1.195 0.765 0.966 -1.089 -0.454 0.287 0.294 0.419 0.120 -0.893 0.650 0.663 -0.835 1.024 +0 -1.242 1.032 0.555 0.740 -0.379 0.179 0.063 -0.148 0.171 1.256 1.075 -1.182 0.985 1.191 -0.151 0.508 0.494 -0.850 -0.382 0.751 -0.496 0.975 1.305 1.144 1.980 -0.091 0.177 0.321 +0 0.708 -1.082 -0.128 -0.253 -0.141 0.830 -1.408 1.199 -0.461 0.113 0.369 -0.214 -0.356 -0.383 1.409 -0.041 0.387 -0.310 0.044 -0.914 -0.891 -0.504 0.888 -0.724 -0.966 1.101 -1.347 -1.709 +1 0.725 1.806 -0.451 1.678 -0.592 -0.146 1.244 -0.260 -1.372 0.692 -1.263 0.932 0.781 1.481 -0.887 -0.870 -0.008 -0.421 -1.621 -0.288 0.046 0.510 0.206 -0.208 1.574 -0.345 -0.628 -0.203 +0 0.886 2.044 2.297 -0.341 -0.052 -0.351 -0.337 -0.014 0.136 -1.139 0.287 0.189 -0.158 -0.807 1.000 0.862 -0.225 -0.637 0.192 -0.929 -0.186 0.013 -1.462 -0.750 -1.378 -0.607 -0.327 -0.130 +4 -2.638 0.088 1.261 -1.096 -0.645 1.325 0.556 -0.329 0.296 -0.626 2.471 0.887 1.709 0.092 -1.274 0.503 1.255 1.297 -1.688 0.211 -1.598 -0.586 0.585 1.331 0.222 -1.148 0.599 -0.851 +2 0.078 -1.274 1.197 -0.651 0.562 1.141 1.539 -2.264 -0.108 1.446 0.449 -0.423 -0.451 -0.350 1.501 -1.870 -0.355 0.349 -0.430 1.425 -0.690 0.481 -0.201 -0.749 -0.532 0.632 0.200 -0.983 +1 -0.446 0.252 0.104 0.311 0.181 0.364 -0.623 -0.179 0.040 -0.177 2.706 0.093 -1.380 -1.088 0.246 -0.121 -0.175 -1.560 -1.368 -1.120 -0.980 -1.337 0.699 0.174 0.855 1.340 0.240 -0.783 +4 -1.779 -0.673 -1.098 0.055 1.669 -0.395 -1.453 -0.196 -0.441 1.261 -1.885 -0.992 0.761 -1.570 0.574 -0.665 1.026 -0.049 -0.160 0.599 0.019 -1.236 -0.521 -1.627 -0.864 -2.139 2.338 -0.026 +1 0.691 -0.841 -1.133 1.155 1.185 -0.676 1.915 -0.401 0.671 0.671 0.183 -1.046 -0.128 -2.588 0.159 0.473 0.131 1.413 0.609 -0.226 -0.132 -0.252 -0.406 0.554 0.093 0.169 0.072 -0.953 +2 1.059 0.377 1.082 0.519 0.382 -0.035 1.931 0.451 -0.366 -1.284 1.291 -0.134 0.236 1.518 0.529 0.519 0.464 0.167 -0.381 0.481 -0.851 -0.353 -1.840 -0.873 0.626 0.584 0.696 -3.038 +4 0.449 -0.503 0.061 1.897 -0.384 -1.887 0.873 0.314 -1.709 0.428 -0.998 0.441 1.907 -0.177 1.083 -1.228 -1.519 2.017 0.308 0.637 1.152 -0.528 0.825 0.674 -1.295 1.513 1.307 -1.284 +1 1.252 -0.123 -0.367 -0.347 0.858 0.365 0.291 0.795 -2.051 0.310 0.219 1.669 -1.529 1.557 0.075 0.489 1.676 0.561 0.400 0.242 -0.483 -0.028 -0.716 -0.045 -2.336 0.429 -0.122 -0.336 +2 -0.251 -1.953 -1.638 0.271 -0.095 -1.270 0.868 -0.206 0.440 0.931 -0.845 -1.428 -0.560 -0.786 0.035 0.330 -0.578 0.813 1.706 -1.549 -0.935 -0.283 -0.799 0.391 -1.024 -0.683 1.401 1.299 +1 -0.190 1.258 -0.018 0.230 -0.664 0.197 -0.825 0.088 0.938 -2.083 1.341 1.396 0.368 -0.435 0.368 -0.862 0.661 -2.508 -1.194 -0.433 0.507 0.737 0.308 0.068 -0.851 -0.747 0.873 0.061 +0 -0.078 -0.515 -0.480 0.030 0.112 -0.457 -0.273 1.344 1.385 0.252 -1.341 0.539 1.554 1.370 0.734 1.422 0.053 0.628 -0.681 0.292 1.330 -0.589 -0.861 0.097 -0.227 0.649 -0.453 1.718 +4 0.765 1.047 -0.529 -2.241 -2.633 0.638 -0.354 2.017 -1.411 0.342 1.662 0.206 -1.592 2.773 1.581 -0.227 0.819 0.830 0.642 -1.382 0.324 1.461 0.505 0.397 -0.537 -0.904 -1.573 0.757 +3 0.446 -1.722 -0.720 0.411 0.410 1.215 -0.145 0.406 0.016 -0.987 -2.256 0.431 0.174 -0.618 -1.619 0.630 -0.266 0.557 1.864 -2.141 -0.700 -0.209 1.455 -1.636 1.936 0.136 -0.096 -0.198 +4 1.971 1.667 0.865 -0.587 0.678 0.265 0.241 -1.558 1.062 0.108 -0.101 -0.600 -0.794 0.317 -1.749 0.789 -1.054 -2.059 1.822 -0.858 -0.722 1.135 1.854 -1.533 -0.956 -0.883 0.935 1.639 +3 1.363 -0.612 -0.687 1.285 -0.566 0.216 -0.592 -1.679 0.198 1.115 -0.062 -1.482 0.109 -0.756 -2.620 -0.081 0.518 1.178 1.757 0.651 0.934 -1.285 1.622 1.267 -0.367 0.682 0.581 1.470 +3 -2.132 -0.679 -0.319 0.096 -0.811 -0.134 1.006 0.487 0.414 -3.037 -0.505 1.856 0.403 -0.824 -0.577 0.694 -0.614 1.178 -0.694 -0.020 1.420 0.847 1.083 0.278 -0.211 0.789 1.746 -0.118 +1 0.017 -1.912 0.821 1.303 1.026 -0.533 0.977 0.432 -0.865 -0.302 -0.100 -2.331 0.195 -1.123 -1.056 0.156 -0.345 -1.000 0.152 -0.858 -0.861 1.177 0.160 0.331 1.568 -0.233 -0.634 -0.618 +3 0.373 -1.455 0.027 0.480 2.293 0.405 -0.176 0.671 -0.676 1.416 -0.085 -0.232 -1.439 -0.467 -0.082 1.727 -0.400 -0.075 0.696 -0.831 -0.480 1.294 -1.510 -0.691 0.367 0.394 2.632 0.168 +2 0.454 -0.661 1.781 1.088 0.708 1.172 -0.030 -0.664 0.370 1.196 -0.282 -0.297 0.118 -1.978 -1.037 -0.573 1.178 0.407 -1.049 0.339 0.102 -0.512 -0.615 2.367 -1.781 -0.555 0.814 -0.867 +3 -0.620 0.544 2.373 -1.819 0.194 1.264 1.830 -0.200 1.296 0.740 1.777 -0.198 0.098 -0.619 -0.294 -0.300 -0.815 1.797 0.063 -0.480 -0.401 -1.547 0.611 -0.864 -0.613 1.137 -0.537 0.460 +2 1.256 -0.274 1.129 0.620 -1.419 -0.464 -1.549 -0.678 2.325 0.199 -1.350 -0.171 0.066 -1.785 -0.213 -0.648 -0.456 0.446 -0.374 0.720 0.128 2.091 -0.079 0.427 -0.720 -1.047 0.738 0.991 +0 -0.990 -0.433 0.080 -1.162 -1.063 1.216 0.501 0.302 -0.544 0.221 -2.403 -1.207 0.625 -0.675 -0.210 0.359 -0.752 -0.620 -0.359 0.151 -1.630 0.302 -0.024 0.109 -0.741 -0.051 0.956 0.681 +4 -0.894 1.587 -2.460 -0.140 1.172 -0.093 -0.405 0.129 -0.353 0.031 -0.683 1.109 1.423 -0.133 -1.386 -0.524 -0.184 0.506 -1.008 -0.769 0.511 0.939 -0.622 -1.333 3.284 0.783 -0.267 0.869 +3 1.896 -0.150 0.200 -0.067 -0.822 1.624 -0.528 -1.127 -0.451 -2.002 -0.651 -0.968 0.617 -0.673 0.888 1.398 0.781 -0.215 0.141 -1.525 -0.134 -0.860 0.694 2.573 0.500 -1.991 -0.361 0.122 +3 0.799 -1.339 -0.554 -0.226 0.188 1.185 -0.880 -1.350 1.537 1.010 3.006 -1.536 0.153 -1.461 1.645 -1.860 -0.955 0.296 0.139 -0.037 0.452 1.053 0.428 -0.404 -0.832 0.300 0.450 0.604 +2 0.424 -1.923 0.762 0.110 -0.372 -1.354 -0.441 -0.493 -0.152 -1.475 0.126 0.632 0.057 1.132 -1.547 1.709 0.902 0.220 -0.130 -0.195 1.365 0.702 -0.067 -2.392 -0.205 -0.786 -0.085 0.241 +0 -0.405 -0.330 -1.399 0.254 0.464 -0.033 -0.384 -0.851 -0.519 1.361 -0.485 0.068 0.105 -0.938 -0.528 0.253 -0.671 -0.855 -1.808 0.655 -0.705 -0.994 -0.079 0.314 -0.565 1.668 1.290 -0.211 +0 -0.057 0.480 0.626 -0.219 0.574 -0.074 1.127 -0.835 -0.663 0.462 0.646 0.502 -0.426 -0.410 -0.012 -1.102 -0.817 1.461 -0.166 -0.524 -0.579 0.291 2.108 1.150 -1.310 1.283 -0.834 -0.648 +1 -1.790 -0.801 -0.139 0.996 -0.105 -0.315 0.580 0.140 -1.505 -0.437 -0.575 -0.150 -0.475 -0.806 1.465 -0.045 0.477 -0.117 0.815 -0.091 0.972 -2.870 0.100 0.261 0.305 -1.084 -0.723 -1.043 +0 2.167 0.223 1.040 -0.208 -0.662 0.263 1.296 -0.089 0.447 0.258 -0.098 -0.047 -0.998 -0.011 0.653 0.296 0.184 0.725 0.195 0.014 0.735 0.137 -0.237 0.468 -0.035 -0.282 0.155 0.167 +3 0.284 0.912 0.494 -0.074 1.124 -0.084 -1.645 -0.439 0.547 -1.215 0.268 -0.516 0.874 0.514 -0.376 0.165 -1.590 2.126 -0.882 -0.403 1.959 -0.320 0.689 -0.426 2.846 0.392 -0.825 -0.474 +0 -0.064 -0.285 0.275 0.514 -0.008 -0.718 1.039 -0.156 1.423 -1.046 -0.494 -1.394 0.709 0.416 0.556 -0.366 0.774 0.556 -0.543 1.916 0.032 0.197 0.883 -0.756 -0.013 -0.685 -1.220 0.539 +1 1.206 -0.129 -0.560 0.114 1.648 -0.755 0.458 -0.363 0.201 0.639 1.374 -0.055 1.134 0.405 -1.296 -0.196 2.324 -0.114 -0.315 -0.317 -0.466 -1.376 -2.298 -0.774 -0.351 0.551 -0.370 0.127 +1 -0.930 -1.225 -0.594 0.075 -0.573 -0.324 0.695 -0.501 -1.334 0.563 0.266 0.329 0.626 0.457 0.029 2.391 -1.285 -0.586 0.967 0.143 0.758 0.170 1.374 -1.008 1.342 1.171 -1.210 0.631 +0 -0.816 -0.461 -0.776 0.121 0.849 -0.791 -0.704 -0.606 0.330 -1.010 -1.485 -0.244 0.075 -0.134 -0.110 0.836 1.176 -0.061 0.123 1.837 -0.252 -0.419 -1.349 0.681 -1.441 -0.634 -1.014 -1.283 +1 -2.149 0.186 -0.871 1.049 0.641 0.895 0.301 -0.239 -0.036 -0.978 1.205 0.566 0.233 -0.615 0.680 0.520 0.282 -0.846 0.999 -0.759 0.085 -1.435 1.243 0.610 -1.812 -1.463 -0.259 0.370 +1 -0.542 1.117 -0.373 -1.306 -1.018 1.188 -0.730 -0.345 -0.303 -0.582 0.305 1.772 0.552 0.821 -0.964 0.372 1.368 -0.232 -0.004 0.764 1.530 1.187 -1.268 0.337 -0.606 -0.321 -1.303 -0.266 +3 -0.538 0.538 2.122 -1.149 -1.515 0.566 0.069 0.196 0.387 -0.651 0.939 0.379 1.801 -1.516 0.990 0.724 -1.066 -1.027 1.678 -0.266 -1.509 0.993 -0.764 -1.260 -0.496 0.426 -0.347 -0.842 +2 1.113 1.295 2.542 0.317 0.271 0.323 0.761 0.038 -1.898 -0.392 -0.452 -0.561 0.777 1.791 -0.065 -1.097 -0.305 -1.109 -0.072 -1.629 -0.418 0.750 -0.001 0.474 0.509 -0.994 -0.064 1.109 +0 0.245 0.333 0.558 -1.145 -0.236 -0.713 0.587 0.081 -0.683 1.260 0.044 1.274 -0.060 0.685 -0.758 -1.914 0.232 -0.240 -0.656 0.241 1.376 -0.348 -1.359 0.307 -1.307 -0.000 -0.590 0.231 +0 -0.944 0.420 -1.717 0.373 -0.613 -0.656 1.308 -2.341 0.552 0.790 -0.321 -1.415 0.069 -0.462 1.034 0.870 -0.000 -1.069 -0.121 0.147 -0.135 -0.241 -0.608 -1.266 -0.236 0.135 0.077 -0.044 +4 -1.770 -0.521 -0.985 -0.206 0.171 0.537 0.566 -0.600 0.629 -1.528 0.137 0.213 0.126 -1.036 -0.928 0.647 -0.418 0.590 -0.609 2.853 -0.279 1.194 2.377 1.111 -0.353 -0.098 -3.307 -2.341 +4 -1.203 -2.541 0.087 -0.012 -2.057 -1.941 -0.451 0.604 0.432 0.537 -1.903 -0.673 -0.166 0.938 -1.592 1.013 -0.552 -1.307 -0.268 0.528 1.741 -1.336 -0.377 0.699 -0.646 -0.427 1.357 -0.847 +4 0.066 0.491 0.362 0.612 1.060 -0.047 2.821 0.449 0.398 0.391 0.405 0.399 1.046 -2.506 -0.244 -1.647 1.364 -1.409 -0.790 -0.666 -1.212 -0.304 -2.097 1.526 0.422 -0.762 0.573 0.324 +4 -1.295 -0.863 -0.046 -0.700 -0.295 0.519 -0.365 3.187 -0.319 2.399 -0.734 -0.746 1.912 1.008 -0.136 -0.893 1.033 0.141 1.467 -1.023 -0.239 0.652 0.472 -0.794 0.338 0.067 -1.137 1.379 +1 0.227 1.307 -1.607 0.185 0.260 0.782 -1.237 -1.320 0.522 0.297 0.250 0.346 -0.680 0.232 0.293 -0.714 1.866 0.474 -1.191 0.657 -0.975 0.787 1.159 -0.821 0.963 0.413 0.822 1.897 +0 1.419 -0.702 0.145 -0.106 -0.817 -1.556 0.543 -0.742 0.778 0.480 1.240 0.957 -0.781 0.276 -0.791 0.045 -0.247 -0.254 -0.926 0.399 0.760 0.019 0.679 0.250 0.308 0.672 0.905 -0.094 +2 -2.522 0.400 0.139 -1.370 0.436 0.434 -0.302 1.959 1.269 -0.399 -0.380 -1.458 1.225 0.247 0.823 -2.134 0.778 -0.510 0.584 0.521 -0.021 -0.697 0.399 -0.836 1.051 -0.722 0.023 -0.746 +4 -0.408 1.645 -2.059 2.435 0.501 3.043 1.365 -0.215 -1.269 1.553 -1.651 0.476 -0.943 0.965 -1.606 0.773 0.493 0.229 0.386 0.214 -0.756 -1.224 -0.125 2.032 0.572 -0.717 0.728 -0.287 +3 1.822 0.432 0.531 -1.076 -0.385 -0.733 -1.065 0.664 0.865 0.399 -0.828 0.331 -1.913 1.323 -1.298 -1.120 0.115 0.204 -0.073 -1.110 1.133 -1.686 -1.483 -0.107 -1.264 1.223 -1.178 1.557 +3 -0.771 -0.225 -0.600 1.102 -0.775 -1.301 -1.354 -0.405 -1.022 -0.609 1.695 -1.502 0.291 1.030 -0.790 0.462 -0.422 0.732 -1.174 0.164 -1.436 0.813 -0.460 2.970 -1.247 -0.437 0.404 -0.173 +1 -0.434 -0.477 1.304 -0.392 0.079 -2.264 0.426 0.481 -1.083 1.688 0.049 0.794 -1.060 0.661 -0.610 -0.489 -0.692 0.298 1.038 0.853 -0.375 -1.128 -1.126 0.282 -0.986 -0.213 0.927 -0.969 +2 1.957 -1.030 -0.099 -0.749 -0.017 -1.931 1.498 0.024 0.917 -0.232 0.191 1.129 0.132 0.168 -0.794 -0.534 -1.013 -0.738 1.251 1.809 -1.247 0.395 -0.318 1.397 0.135 -0.007 -0.745 -1.532 +4 0.291 -0.498 -0.931 -1.461 -1.282 -1.368 -0.357 -0.052 -1.547 0.903 0.142 2.427 -0.405 2.233 0.746 -1.959 -1.191 0.318 -1.748 -0.896 -1.592 2.139 1.099 -0.339 1.512 1.258 1.310 -0.559 +4 -2.821 0.322 -0.249 -1.619 0.202 -0.409 1.437 -0.037 -0.485 -1.357 0.048 0.836 -0.194 0.781 -0.012 0.783 0.046 -1.410 1.233 1.014 1.105 3.575 -0.099 -0.062 -0.152 -0.572 0.550 -0.308 +3 -0.389 1.996 -0.812 0.211 0.115 1.009 1.103 -0.032 0.291 0.329 -2.272 -1.589 -0.605 -0.562 -1.199 0.750 0.002 -0.215 -1.256 -0.045 -2.627 -0.163 0.063 0.242 -1.564 0.225 -1.041 -0.812 +0 0.257 0.569 0.472 0.458 0.164 -0.540 -0.116 1.009 0.435 0.577 0.792 1.489 0.576 -0.596 0.519 -1.977 0.291 -0.538 0.377 0.678 -0.505 -0.366 0.994 0.340 -1.069 1.479 0.212 -0.105 +0 -0.127 0.937 -0.944 -0.237 0.502 0.006 -1.780 0.618 -0.153 1.204 1.453 -0.286 0.735 -0.054 -1.606 -0.642 0.470 -0.583 0.906 0.925 1.197 0.582 1.077 -1.041 0.387 0.646 0.521 -0.809 +2 1.564 0.650 0.104 1.042 0.845 -0.323 0.707 -0.614 0.318 -1.643 0.948 -0.949 0.829 -0.491 -0.889 -1.188 0.034 0.767 -1.395 -0.590 0.584 -0.444 2.354 -1.194 0.273 0.919 -1.000 1.517 +1 0.536 -0.554 -0.454 2.333 -0.566 0.747 0.516 1.120 0.107 0.586 -1.108 -0.274 0.255 2.226 -0.213 0.140 -0.108 2.034 0.757 0.250 -0.660 0.387 0.566 -0.301 -1.072 -1.103 -0.961 0.495 +4 -0.127 0.468 -0.089 1.515 0.016 1.676 -2.298 -0.637 -0.342 2.277 -1.383 0.222 0.449 1.116 -0.622 -1.630 1.020 1.153 -0.169 -0.014 -1.904 1.866 1.130 -0.966 1.014 -2.223 -0.262 -0.237 +4 -0.176 -0.112 1.609 0.833 1.186 1.641 0.252 2.363 0.487 -1.320 -0.522 -1.605 1.166 0.220 -0.493 0.180 -0.183 -0.871 2.194 0.151 0.088 -0.098 -1.168 -1.738 1.263 -0.463 -2.226 -1.472 +4 0.432 -0.130 -2.289 -0.563 0.720 0.567 1.293 -2.286 -0.279 1.195 -0.665 -1.520 -2.365 -0.882 -0.009 -3.142 -0.460 -1.206 -0.106 -0.397 1.374 -0.516 -1.570 1.885 -0.156 -1.166 1.487 1.739 +0 -0.184 1.149 -0.026 0.351 -0.421 -0.120 -0.730 -1.060 0.930 1.075 0.931 1.109 0.216 -0.440 -0.699 -1.755 0.536 0.128 0.377 0.602 1.387 -0.328 0.478 -0.497 -1.706 -0.177 1.460 -0.184 +3 1.041 0.443 0.131 -1.322 -0.643 -1.422 -1.157 0.359 -0.788 1.141 2.208 -1.270 1.480 0.263 0.116 -1.307 -1.913 -0.212 1.172 -0.204 -1.128 -0.036 1.065 0.348 -0.089 0.612 -0.081 1.522 +0 -0.107 0.719 0.884 0.446 0.162 1.098 -0.622 0.748 -0.416 0.091 -1.210 -1.304 -0.076 1.072 -0.203 1.690 0.397 -0.168 1.481 1.309 -1.362 -0.038 0.513 0.721 0.609 -0.165 -0.311 -0.105 +4 0.064 -0.827 0.974 0.865 -1.117 -0.694 -0.990 -3.701 2.114 -1.409 -0.828 0.495 0.340 0.658 0.288 1.528 -0.579 1.758 -0.405 -1.266 -0.383 0.511 1.266 0.684 -1.189 -0.867 -0.567 0.263 +1 0.098 -0.700 -1.034 -0.147 -0.010 1.149 -2.252 -1.259 0.196 -0.139 0.110 -0.458 0.249 -1.589 2.098 0.418 0.457 -1.066 -0.589 0.511 -0.350 0.268 -0.779 -1.338 1.111 -0.896 0.910 -0.839 +1 -0.501 0.063 0.675 -0.221 1.779 -0.000 1.191 0.279 2.131 1.058 0.043 0.629 -0.261 -0.271 -1.303 -0.808 2.061 -1.204 -0.655 -0.094 -0.060 0.384 -0.469 0.445 1.142 1.267 -0.268 0.611 +0 0.645 0.640 -0.037 -0.150 -0.246 -0.844 0.165 -0.505 0.162 -0.610 -0.657 -0.545 1.269 -1.377 1.773 0.768 -1.563 -0.578 -1.623 0.647 1.277 0.229 1.219 0.184 0.971 0.292 0.014 0.774 +1 0.675 1.083 1.274 0.495 -1.669 -0.265 0.482 0.044 -0.129 0.970 -0.709 -0.261 -0.084 2.261 -1.988 1.379 -1.106 -0.007 0.581 0.390 -0.093 1.035 -0.112 -0.358 0.236 -0.292 -0.270 1.056 +0 -0.585 -0.246 -0.472 1.562 -1.140 0.835 -0.587 0.326 0.647 1.530 0.393 -0.396 0.066 1.918 0.163 -1.308 -0.383 -0.409 0.730 -1.222 -0.508 0.214 -0.280 0.091 0.148 0.214 -0.927 0.798 +1 -0.211 0.104 -0.585 2.268 -1.055 0.500 0.303 0.761 -2.501 -1.687 -0.548 0.920 -0.264 -0.305 0.016 -0.841 0.470 0.192 0.502 1.516 -0.230 0.110 -0.985 -1.253 -0.007 0.103 0.689 0.575 +2 0.322 1.404 0.593 -1.035 0.719 1.193 -0.961 -0.594 1.413 1.253 0.619 -0.965 -0.550 0.949 -0.329 1.519 1.398 1.102 -0.163 -0.503 -0.124 -1.409 -0.505 0.456 0.071 1.089 1.812 0.607 +1 -0.876 -1.348 -1.226 -1.533 -0.852 0.451 1.158 -1.212 0.123 -0.592 -1.182 0.371 0.311 0.767 -0.183 -0.956 0.727 -0.470 1.181 0.081 0.493 0.465 -0.936 0.166 0.653 -0.359 2.007 -0.408 +4 1.259 -0.887 0.990 1.525 1.533 0.988 0.968 -0.303 -0.837 -0.352 -1.665 1.343 -0.283 1.291 -1.231 1.083 -0.495 -1.284 1.627 1.107 0.212 0.034 0.440 0.195 -0.792 -0.412 1.961 2.672 +3 1.607 0.741 0.462 0.142 -1.745 2.061 -1.023 -0.547 0.709 2.042 0.734 0.183 -0.362 0.807 -0.248 -0.869 -1.596 0.621 0.969 -0.669 0.906 1.081 -2.237 -1.707 -0.008 -0.334 0.043 -0.388 +0 0.317 0.219 -1.057 1.246 1.335 0.897 0.273 -0.273 0.154 1.698 0.175 -0.200 -1.072 -0.498 -0.409 0.330 1.433 0.725 -0.723 -0.463 -0.811 0.913 1.393 0.445 0.082 -1.042 -0.570 0.429 +4 -0.042 -0.547 0.824 -0.788 2.049 0.330 1.377 -1.316 -0.487 -2.107 -0.663 1.088 0.321 1.564 1.182 1.410 -0.621 -1.535 -0.850 -1.728 -0.954 -1.910 -0.450 0.789 2.444 0.100 0.962 1.927 +4 -1.469 -0.037 0.795 1.505 1.414 0.838 0.326 -1.410 1.963 0.727 0.246 0.173 1.298 -1.204 -0.403 -2.242 -1.196 1.881 0.158 -0.839 0.037 1.019 0.164 0.842 -1.215 1.124 1.815 -1.388 +2 -1.815 -0.146 0.446 -2.602 -0.801 0.510 0.984 -0.360 -1.092 0.101 -0.134 -0.615 -1.447 -0.481 -1.580 -0.748 -0.078 -0.666 1.901 -0.151 0.903 0.113 -1.197 -0.641 -0.171 -0.651 -0.025 -0.657 +1 -0.196 0.039 -0.020 -0.842 1.174 -1.957 -1.190 -0.746 0.367 -2.422 -0.002 0.471 -0.506 0.103 1.571 1.026 0.214 0.687 -1.168 0.473 0.071 -0.692 -0.248 -0.737 0.950 0.474 0.047 -1.809 +3 1.284 -0.689 0.248 1.132 1.843 -0.431 0.143 -2.010 0.328 -0.585 0.464 -1.083 0.405 0.604 1.528 -1.950 1.262 -1.513 -1.188 -1.358 1.586 -0.509 0.352 -0.815 0.769 1.065 1.225 0.471 +4 2.081 0.308 0.738 -1.770 0.327 -0.405 -0.403 -0.087 1.895 0.595 2.080 -1.461 0.033 1.666 -0.027 0.036 1.568 1.821 1.259 0.924 0.693 0.802 -0.556 -1.100 -0.443 -0.555 0.617 -1.433 +3 -1.545 -0.376 -0.258 -0.131 -1.440 0.721 -0.859 0.383 0.798 0.014 0.366 -0.926 1.325 0.621 1.762 0.705 -1.603 -1.850 0.285 0.512 -1.098 2.258 0.591 -0.551 0.074 0.967 1.588 1.055 +4 -0.031 1.162 0.833 0.819 0.119 -0.490 0.396 1.283 1.807 2.406 1.530 0.044 -1.202 0.602 1.097 -2.553 -1.479 -0.457 -0.769 -1.609 0.380 1.241 -0.188 -1.660 0.206 -1.110 -1.009 0.960 +1 -1.284 -1.260 -1.091 0.926 1.143 -0.119 0.514 0.470 -0.373 1.445 -1.234 0.459 0.645 -0.729 1.206 -1.020 0.173 -0.163 -1.231 0.911 0.196 0.944 -1.583 -0.322 -0.383 0.397 -0.369 -1.529 +0 0.217 0.222 -0.906 -1.839 0.619 -0.526 -0.351 -0.006 -2.473 0.373 -0.254 -0.594 0.633 0.697 0.096 -0.160 2.109 -0.595 0.154 0.357 -0.111 -0.081 -1.103 -0.308 0.190 0.392 -1.020 0.539 +3 1.101 1.061 0.532 0.362 1.761 -0.000 -1.182 0.449 2.086 -1.015 -0.361 0.416 -0.054 -0.982 1.122 2.320 0.196 -0.904 -1.549 0.258 1.104 0.475 -0.002 -0.589 -1.092 0.835 0.914 -1.546 +2 1.387 -1.108 -0.203 -0.549 -0.944 2.003 0.189 1.486 -1.072 -0.088 -1.248 -1.559 0.401 0.095 0.817 2.476 -1.238 -0.458 0.573 -0.804 0.294 -0.491 -0.410 -1.123 0.370 0.290 -0.248 0.641 +0 -0.707 1.582 0.412 0.675 0.882 0.637 -0.106 -0.179 -0.899 -0.909 0.252 -1.521 0.870 0.996 0.222 0.418 -1.127 1.326 -0.277 0.972 0.836 -1.540 0.182 0.639 0.020 -0.366 -1.029 -1.253 +0 0.500 1.386 -1.453 0.216 1.015 -1.142 0.431 0.609 1.728 -0.340 0.211 -0.556 -0.141 1.619 -0.029 -0.709 -0.183 -0.519 -0.441 -0.539 -0.500 -1.187 0.069 -0.979 1.141 -0.312 0.409 0.411 +3 0.517 -0.519 0.038 -0.088 1.347 -1.860 1.614 0.706 0.152 -1.434 -1.672 -0.561 0.635 -0.006 0.578 -1.463 0.044 1.095 1.452 -1.254 -0.352 -0.341 -0.626 0.908 2.871 -1.114 0.425 0.296 +1 1.908 0.033 -0.714 0.356 -0.286 1.292 0.102 -0.651 0.201 0.316 0.224 -0.284 0.093 -0.565 0.245 -0.922 -0.782 1.964 -0.521 0.397 -1.624 -0.346 0.890 0.390 -1.639 -0.646 0.938 -1.149 +4 -1.845 -0.475 -0.504 0.606 -0.993 2.011 3.225 0.433 1.244 0.496 -0.540 -0.938 0.343 -0.675 1.013 -0.643 0.221 -0.857 -0.925 1.444 -0.461 -0.406 -0.647 -0.502 -0.107 -2.024 0.813 0.394 +0 0.006 0.676 -1.299 -0.781 0.531 0.161 -0.720 0.648 0.303 0.506 0.390 -0.681 -0.076 -1.857 -0.511 -0.245 0.627 0.512 -0.752 -0.774 1.307 -0.294 0.114 0.785 -0.080 -0.152 -0.315 -0.509 +3 1.209 -0.666 -0.453 2.312 -0.704 0.742 0.562 0.390 0.282 0.376 0.272 -0.052 1.511 -1.313 1.021 0.822 0.698 1.117 -0.093 -1.869 -0.072 -0.991 -0.732 -1.242 -0.477 -0.597 -2.378 0.700 +1 0.881 -0.483 -0.859 0.915 -0.428 1.612 -0.838 -0.193 0.076 0.304 0.196 2.004 -1.056 -0.022 0.898 0.284 -0.436 -0.794 -0.846 -0.135 0.069 -1.559 -1.561 -0.235 0.130 0.265 1.971 0.746 +4 0.566 0.275 0.231 0.310 -1.933 -0.250 1.162 3.942 -1.204 0.423 -0.571 2.048 -1.354 0.625 -1.407 -0.319 -0.034 1.070 -1.077 0.663 0.019 0.273 0.958 -1.283 0.811 -0.569 1.096 0.335 +2 1.675 0.295 -1.338 -1.215 -0.610 1.976 -0.341 -0.085 -0.323 -1.315 -1.459 -1.108 0.083 -0.459 -0.331 0.601 1.058 -1.195 -1.140 -1.211 -1.549 0.518 -0.610 -1.178 -0.449 -0.386 0.642 0.532 +2 -0.114 -0.452 -0.934 1.109 -1.034 -1.303 1.099 0.200 -1.181 -0.077 -0.624 1.489 -0.242 0.532 -0.023 1.437 0.203 1.158 0.541 -0.553 0.002 -0.213 -0.573 -1.077 1.360 -2.061 1.332 -1.338 +0 0.470 0.792 0.605 -2.271 -0.927 0.076 -1.072 0.005 0.206 -0.079 -0.628 -0.860 0.338 -1.355 0.370 0.492 -0.379 0.353 -0.499 0.360 -0.874 -0.334 0.261 -0.135 -0.148 1.251 -0.952 -1.064 +3 -1.719 -0.551 2.228 0.194 2.381 -1.823 -0.189 -1.091 -0.585 -0.010 -0.454 -1.106 -0.542 -0.968 0.298 0.905 1.191 0.171 -0.041 -1.519 -0.082 0.229 -0.326 -1.171 0.765 -0.822 0.884 0.807 +0 -1.601 -0.774 -1.019 0.124 1.008 0.054 -0.762 -0.770 -0.395 1.393 -1.089 -0.001 -0.459 0.304 -0.436 -0.002 -1.555 -1.365 -0.246 -0.433 -0.122 -0.039 0.607 -0.636 -0.855 1.521 0.867 0.350 +1 0.032 -1.087 -0.131 -1.469 0.335 0.597 0.834 1.152 0.159 1.103 -0.467 -0.018 1.576 0.346 0.798 1.326 -0.302 0.427 1.654 2.147 -0.212 -0.969 1.033 -1.116 0.774 -0.378 -0.020 0.113 +3 -0.009 -1.059 -0.034 -1.344 -1.234 0.242 -1.326 -0.617 -1.169 0.140 -0.712 0.532 0.451 -1.304 0.921 -0.172 -1.224 0.373 1.761 -0.196 -1.648 0.923 1.575 -1.080 -0.796 0.035 -1.934 2.082 +2 0.742 0.356 -0.065 -1.029 0.685 0.304 -1.528 -0.965 -2.769 -1.366 -0.313 -1.056 -0.832 1.391 1.029 -0.695 0.794 1.215 0.619 0.299 1.098 0.770 -1.213 -0.189 -0.552 1.152 -0.580 -0.752 +1 -0.997 -1.934 0.444 0.072 -0.748 0.263 -0.780 -1.759 -0.364 1.645 0.972 0.455 0.244 -0.363 0.736 0.565 -0.083 0.199 -1.606 -0.480 -1.193 -1.212 0.470 -1.265 0.768 1.032 -0.062 -0.606 +4 1.378 2.149 0.521 -2.084 -0.558 0.613 -0.421 1.272 0.588 -0.706 0.032 -0.894 -0.183 -0.555 1.175 2.116 -1.431 -1.923 -0.245 -1.518 0.046 0.749 1.494 0.201 -0.193 -1.978 0.026 0.071 +0 -0.462 0.389 -0.300 1.070 -0.224 -0.777 0.007 -0.176 0.030 -0.247 -2.009 -0.647 0.873 -0.529 -1.846 0.096 0.901 1.645 0.712 1.035 0.811 0.150 -1.138 -0.103 0.688 0.057 1.167 0.238 +3 -0.361 0.494 -1.442 0.273 -0.919 2.240 0.553 0.651 0.073 -1.595 0.019 0.391 -0.571 -0.077 0.631 0.112 0.272 0.525 1.235 -0.834 1.940 1.937 -0.554 0.672 1.822 1.441 -1.718 -0.154 +2 0.996 2.735 -2.405 0.288 0.288 -0.892 1.670 0.723 -0.331 -0.661 0.218 0.033 -0.454 0.498 -1.020 -0.435 -1.428 0.061 0.474 -1.489 0.125 -1.289 0.028 -0.270 -0.252 0.091 0.155 -1.163 +1 -0.175 -0.772 -0.039 0.721 1.767 -0.244 -0.437 0.716 -0.663 0.209 -1.188 -1.948 0.049 0.546 -0.468 1.112 0.382 -1.961 -0.356 1.284 -0.708 -0.096 0.471 -0.720 1.404 -0.415 -1.052 -1.104 +4 -0.157 0.330 1.443 -1.748 -1.012 -0.085 -1.140 -1.489 1.310 -1.048 -1.309 -1.148 1.086 -0.695 -1.386 -0.840 -0.048 0.892 0.808 -0.778 -0.531 2.103 0.996 -0.252 -2.295 -0.459 -1.327 -0.863 +0 -0.284 -0.615 1.267 0.502 0.970 -0.432 -1.191 -0.421 1.151 1.451 0.441 0.473 -1.569 0.252 -0.440 -0.568 0.137 0.160 -0.362 -0.258 -0.352 0.135 1.350 1.389 -1.302 -1.229 -0.260 -0.798 +1 0.963 -2.233 -1.048 -0.865 -0.199 0.459 0.063 -1.808 0.888 0.274 -0.849 0.068 0.090 -1.441 -0.347 0.299 -1.048 0.197 -0.058 0.357 -0.092 -1.236 -1.556 1.347 0.968 -0.881 1.114 0.256 +0 0.096 -0.615 -0.055 -1.024 -0.785 0.514 -0.463 -0.738 0.449 0.380 -0.215 -0.149 0.416 0.969 0.201 -1.192 -0.221 -0.407 -1.053 0.498 0.575 0.726 -0.991 0.157 -0.006 0.307 0.130 -0.013 +2 1.637 0.018 -1.637 0.950 0.828 -0.962 -1.912 -0.529 -0.534 0.934 0.611 1.099 -0.203 0.043 -0.121 0.542 -0.028 1.277 1.247 -0.992 0.519 -0.084 -0.812 -0.849 -0.396 1.298 -0.232 1.933 +2 -0.441 -1.620 0.180 -0.321 -0.295 -1.710 -1.509 -0.548 -0.928 -0.569 0.833 -1.064 -0.906 -0.527 -0.963 -0.215 1.028 -0.287 0.258 -2.321 0.082 1.476 0.098 0.128 0.286 1.568 1.354 -1.022 +2 -0.051 -0.442 0.293 -1.996 -0.826 0.475 0.184 -0.664 0.103 1.723 1.089 0.972 0.550 0.181 -0.684 0.056 1.967 0.920 -0.358 -2.096 0.757 0.936 -0.543 -0.668 -1.949 1.256 -0.415 0.261 +0 -0.224 0.671 1.510 -0.906 -0.836 1.111 0.525 0.475 -0.041 -0.609 -1.219 0.780 -1.018 0.511 0.028 -0.170 -0.082 -0.736 0.594 -0.087 0.002 -1.051 -0.265 0.192 0.382 0.118 -0.022 -0.334 +4 -1.029 -2.905 0.867 2.266 0.920 1.010 0.946 -1.243 -0.180 0.026 1.732 -0.688 0.686 -0.754 -1.202 -1.126 -1.657 -0.118 0.768 2.550 0.755 -0.844 1.052 0.826 -0.159 -1.040 -1.734 -0.826 +4 -1.051 -1.518 0.447 -1.291 0.432 1.228 0.502 -1.830 0.870 -1.261 -1.548 -1.204 0.886 1.045 1.076 0.844 -1.294 0.580 1.041 -0.686 0.132 0.722 2.548 0.882 -1.291 -1.119 -0.687 -0.297 +2 -0.854 0.576 -1.656 1.601 1.001 -1.618 0.244 -0.012 0.555 -1.393 0.502 -0.558 -1.738 -0.472 1.332 0.470 0.236 -0.968 -0.960 1.239 -1.490 0.074 -1.008 0.369 -1.477 -0.279 0.469 0.488 +4 0.396 1.561 0.340 -0.263 -1.296 -0.099 -1.332 -0.310 -0.063 -2.361 1.388 -1.239 -0.186 0.534 1.309 -0.792 -2.509 1.079 -1.249 0.359 0.422 -3.252 -1.615 -0.461 -2.584 -1.338 0.123 -0.088 +1 0.783 -0.101 1.258 0.866 -0.062 0.043 -1.842 1.210 -0.330 -0.400 -0.550 0.457 -1.129 -0.984 -0.053 -0.627 0.335 -0.590 1.595 0.693 0.681 -0.605 2.406 -0.439 -0.714 0.707 0.195 -0.818 +2 0.313 -0.228 -2.120 1.850 -0.835 -1.341 -0.031 1.184 0.513 -1.316 -1.481 -2.192 -0.292 -0.473 0.514 -0.686 0.974 0.655 -0.729 0.391 1.329 -0.961 -0.213 -0.233 -0.358 0.802 0.881 0.407 +0 -1.195 -0.457 0.687 0.439 -0.344 0.544 -1.181 0.065 -0.016 1.323 0.381 -2.001 -1.261 0.385 -1.940 -0.057 -0.536 -0.046 -0.660 0.897 -0.388 0.459 0.438 -1.222 0.284 -0.570 0.394 -1.090 +4 0.596 -2.007 -0.027 -1.548 -2.827 2.345 -0.034 0.500 -0.749 -0.304 -0.440 1.275 -0.015 1.211 -1.405 0.307 -0.051 0.481 -1.024 -0.664 -0.161 0.316 0.066 0.225 -2.076 -0.843 -1.188 -0.158 +1 -0.517 0.521 -2.187 0.233 -0.143 1.158 0.349 -0.242 1.226 0.156 0.457 -0.117 0.239 0.098 0.675 -1.530 0.449 -0.668 0.382 -2.221 -0.713 -0.071 -0.335 -1.749 0.057 -0.436 -0.822 0.658 +2 3.024 -1.208 -0.710 0.482 -1.552 -0.492 -0.374 0.936 0.237 1.713 0.778 -0.517 0.699 0.028 0.677 -0.070 -2.001 0.156 0.467 -0.404 -0.756 0.517 -0.794 -0.174 1.216 -0.296 0.455 0.959 +4 0.317 1.333 0.373 -2.136 -1.437 -1.067 -0.749 -0.140 -0.215 -1.823 -1.609 -2.119 -1.554 0.991 -0.270 -0.953 -1.918 -1.013 0.607 0.476 -0.252 -0.267 -1.771 -0.776 0.556 -0.675 1.623 1.206 +3 -2.246 0.195 1.919 -0.353 -0.225 0.296 -0.559 -0.312 -0.338 -0.627 1.229 0.461 0.251 0.809 -0.264 -0.774 -0.603 0.002 -1.059 -0.500 -0.516 2.125 -2.774 -0.180 1.726 0.122 0.753 0.100 +2 0.452 -0.464 -1.427 0.748 -1.228 -0.262 0.364 -0.678 1.570 1.972 -0.810 -0.770 -0.439 1.153 0.486 0.751 1.315 -0.906 0.503 0.051 0.430 1.149 -0.152 -0.697 -1.051 1.754 -0.042 -1.368 +0 0.946 0.417 0.803 1.426 0.041 -0.331 0.326 -0.092 -0.696 -0.305 0.439 -0.817 -1.744 -1.476 0.438 -0.115 0.149 -0.154 0.408 0.914 0.727 -1.419 -0.883 0.414 -0.702 1.047 1.314 1.297 +3 0.785 0.168 1.295 -0.223 -0.128 1.217 -2.090 -0.143 -1.661 -0.029 -0.420 0.602 0.466 -0.286 -0.962 0.078 1.305 -0.980 -1.503 -1.843 -1.535 -0.007 -1.405 -0.375 0.462 -2.129 0.987 1.427 +1 0.249 0.438 0.851 0.379 -0.519 0.716 0.407 1.167 -0.816 1.645 -0.374 -0.746 0.494 1.186 -0.823 0.524 -0.572 0.205 1.567 -1.023 -0.596 0.430 0.383 2.065 -0.589 0.930 1.992 0.167 +3 0.524 0.781 -0.476 -0.911 0.848 1.025 -1.837 0.344 0.254 0.012 0.633 -2.456 1.704 2.355 0.254 0.510 -0.751 0.738 -0.027 -0.220 -0.199 -0.919 1.303 1.629 -0.809 0.428 0.804 -1.048 +0 0.423 -0.512 0.332 0.178 -0.427 -1.396 -1.029 -0.872 -1.004 0.455 2.129 -0.511 -0.384 -0.585 0.858 0.426 -0.017 -1.213 -0.968 0.175 0.223 -0.016 0.665 -1.223 0.304 0.248 -0.958 -0.605 +2 -1.549 0.209 -0.656 -1.797 0.470 1.018 0.453 0.871 1.286 0.241 0.977 0.009 -0.536 0.214 2.261 -0.593 0.939 -1.335 -0.641 1.024 1.609 0.122 -0.346 -0.046 -0.364 0.118 -0.640 1.203 +3 0.604 -1.314 -0.416 0.010 0.234 -0.279 -1.172 -0.054 -1.652 -0.233 -0.441 -0.134 -0.559 -1.508 -0.366 0.931 1.261 2.439 1.692 -1.197 -0.408 -0.023 -0.086 0.070 2.408 -1.082 -0.714 1.644 +1 -1.884 -0.111 -0.637 -0.961 -0.756 0.580 -1.504 1.848 -0.718 0.633 -0.669 -1.518 -0.146 -1.254 0.147 -0.025 -0.695 0.151 1.039 -0.110 -0.729 0.400 0.442 -0.285 1.373 -0.300 1.488 -0.655 +4 1.705 -0.548 0.759 0.288 -0.496 0.088 1.237 1.230 -1.936 -0.640 -1.190 0.852 -1.030 1.980 0.612 1.443 -0.549 0.707 0.965 0.038 -2.885 -1.385 -0.466 0.137 -1.812 0.426 -1.623 0.315 +1 -0.036 0.262 0.231 0.676 -0.909 -1.230 1.438 1.071 0.639 0.072 -1.003 0.461 -0.912 1.035 -1.257 0.000 0.647 0.417 0.961 -0.933 0.602 -0.319 0.146 -1.867 1.055 -1.000 0.317 1.349 +2 -0.174 -0.467 -0.488 0.586 0.275 -1.663 -0.477 0.465 -1.008 -0.957 1.365 2.303 0.838 -0.278 0.135 1.605 -1.598 0.788 1.095 0.436 -1.067 0.419 1.719 0.426 -0.237 1.658 -0.193 -0.772 +3 0.039 0.426 2.035 0.047 2.222 0.063 -0.189 -0.117 -1.307 -0.384 -0.018 0.876 -1.595 0.975 1.642 -0.464 -1.581 -1.779 -1.791 0.876 0.235 0.182 -1.198 0.820 -0.647 -0.214 0.992 0.362 +4 -0.608 1.382 0.814 -0.950 0.485 0.489 -0.709 0.427 0.002 -1.531 -3.196 2.049 -0.124 0.108 -1.027 -0.598 2.168 2.418 -1.855 0.057 1.868 0.198 0.684 -0.170 0.515 1.446 0.522 -1.216 +0 -0.066 -1.211 -0.652 0.047 -0.860 -0.385 1.006 -0.577 0.836 -1.130 0.530 1.442 -2.472 -0.797 0.577 -0.203 0.371 -0.604 0.087 -0.156 1.168 0.254 0.338 -0.412 -0.488 -0.433 0.394 -0.421 +0 -0.876 0.435 -0.194 0.784 -1.301 0.457 -0.981 -0.647 0.580 0.047 0.373 0.752 0.166 -0.137 -1.001 2.196 0.547 0.001 -0.320 0.207 -0.005 0.529 -0.574 1.680 0.690 -1.807 0.288 0.805 +3 1.595 0.660 -0.373 0.341 0.688 1.043 -1.676 0.252 -0.672 1.370 -0.256 0.804 1.862 -1.504 0.225 1.401 0.662 1.510 -0.111 0.871 1.894 0.341 2.186 -0.152 0.050 -0.237 0.833 -0.522 +4 0.207 0.335 1.290 -0.557 1.644 1.646 0.226 -0.828 2.491 1.725 0.190 -1.362 0.621 -0.590 0.874 0.230 -1.596 -1.180 -0.762 1.641 -0.650 -2.274 -0.202 -0.261 1.590 1.515 -1.309 0.393 +0 -0.639 -0.414 0.324 -0.332 0.003 0.807 -0.518 -1.332 -1.526 1.438 0.931 0.656 -0.436 -1.333 -0.282 -0.093 -1.132 -1.470 -1.730 0.657 -1.205 0.309 -0.398 0.590 0.336 -0.232 0.130 -0.110 +1 -1.055 1.284 -0.430 -0.247 0.219 -0.916 0.211 -0.503 -0.477 -0.162 -0.735 -1.565 0.174 -2.290 -2.253 0.045 -0.182 0.259 0.664 -1.977 -0.615 -0.448 0.312 0.228 -0.405 0.044 -0.275 -0.913 +2 0.121 -0.026 0.461 0.631 1.385 1.211 0.043 -0.043 -0.225 1.008 0.245 2.462 -0.064 1.521 1.530 1.504 -0.330 -0.084 -0.162 1.474 -0.450 -0.578 0.694 -1.328 0.768 -1.141 -0.412 1.155 +1 0.987 0.516 -0.061 0.468 0.109 1.164 -0.768 1.735 0.948 -0.606 -0.766 0.146 -1.124 -0.205 1.346 0.182 2.094 -0.770 0.452 -0.532 -0.207 0.500 -0.734 -0.862 -0.466 -0.538 -1.010 1.445 +2 -0.401 -0.326 0.075 -0.059 0.287 -1.321 0.042 -0.883 0.321 1.765 0.117 1.577 0.267 -0.644 0.237 -2.043 0.255 1.276 -0.994 1.090 -0.900 -0.134 -1.055 0.016 2.981 -0.580 -0.155 0.024 +1 -0.496 -0.203 -0.227 -1.637 -0.297 -1.766 0.247 -0.708 -0.261 0.158 0.639 0.933 -0.771 -0.661 -0.438 -0.748 -0.005 -0.343 1.247 1.996 -0.634 -0.163 -0.009 0.027 -1.137 1.029 -0.215 2.196 +1 -0.521 0.105 1.338 -2.529 0.615 -0.384 -0.495 -0.681 -0.364 0.621 -0.537 1.659 0.624 0.198 -0.166 1.190 1.336 -1.161 -0.702 0.635 0.937 -0.300 -0.742 0.079 1.477 0.041 -1.264 0.110 +1 0.101 -1.211 0.574 -0.235 0.590 0.030 2.022 -0.814 1.506 -0.521 -1.252 1.203 0.567 -0.786 1.255 -0.161 0.722 0.455 2.395 -0.162 0.875 0.311 -0.034 0.328 0.120 0.411 0.078 0.908 +1 -0.153 -0.294 1.774 0.573 1.071 -0.187 1.798 1.003 -0.895 -1.193 0.057 0.524 -0.366 -0.733 0.968 0.363 -0.078 -0.732 -0.066 -0.755 0.919 0.746 -0.946 -0.917 1.307 0.804 0.694 1.142 +3 0.455 0.641 -0.493 -0.369 0.004 -1.289 0.575 -0.358 -2.120 1.887 0.265 0.326 -0.691 0.332 0.736 -1.454 0.604 -1.059 -1.286 -0.880 2.101 0.783 -0.018 1.702 -0.923 -1.027 -0.916 1.878 +2 0.671 -0.264 0.443 0.288 -0.145 1.209 -1.275 0.117 -0.580 1.642 -0.174 1.923 -0.422 0.109 1.088 -0.956 1.509 0.026 0.412 0.546 -0.076 -0.810 0.094 -2.011 -1.311 -0.140 1.547 1.517 +4 1.273 2.163 1.878 -1.085 -0.714 -0.369 0.411 -1.277 -0.545 0.441 -0.869 0.452 0.975 1.117 0.404 0.870 -1.149 0.609 1.844 -1.531 0.921 0.454 1.368 -1.114 -0.887 1.422 -1.762 -1.746 +4 1.077 0.467 -1.273 -2.380 -1.428 0.193 0.121 0.856 1.091 -1.961 -0.065 1.019 -0.186 2.830 -1.688 -0.332 0.814 0.385 -0.225 -0.471 -1.546 -0.069 0.494 -0.499 0.366 0.040 0.961 -1.445 +4 0.523 1.334 -0.252 -2.157 -0.225 -0.487 1.252 0.890 0.814 0.319 -0.322 -0.695 0.708 1.558 -1.881 -1.300 0.929 -1.290 -0.306 -0.948 -0.558 1.709 -2.408 -2.225 1.840 0.367 0.471 1.531 +3 -0.902 1.151 0.212 0.343 0.690 -0.042 1.013 -1.197 -1.086 -1.141 0.422 0.506 -0.473 -0.232 1.268 -0.694 0.295 -0.686 1.096 0.628 -1.399 -0.261 -2.079 0.900 2.047 -2.180 -1.427 0.167 +3 0.542 0.292 0.429 0.093 -0.084 0.982 -0.328 -0.511 0.355 -0.555 0.998 -2.161 1.759 -1.740 1.416 -0.913 0.769 -0.412 0.770 -1.203 0.191 -0.812 -2.465 0.369 -0.351 -0.653 -1.432 -0.162 +3 0.199 -1.058 0.449 -0.462 -0.795 -0.270 -1.165 -3.101 0.710 -0.569 0.285 -0.157 1.019 -0.468 -1.261 0.852 -2.376 0.222 0.210 0.647 2.409 -0.380 0.470 0.644 0.093 -0.825 -1.544 -0.692 +3 -1.178 -0.056 1.090 0.726 0.466 0.985 -0.925 -0.457 0.615 -0.475 -0.420 1.862 0.095 1.764 -1.823 -0.631 0.409 0.094 -2.056 -0.230 2.473 0.247 0.604 -0.644 0.243 -0.012 1.119 1.087 +0 0.484 -0.981 -1.127 -0.459 -0.439 -0.666 -0.305 -0.417 -0.536 -1.530 0.959 -1.176 -0.415 -1.188 -0.122 0.052 0.592 0.188 -1.365 0.453 0.570 0.543 0.164 -0.628 1.375 1.356 -0.340 0.552 +3 0.704 -0.160 1.057 -1.495 -0.063 -1.188 0.257 -0.762 -0.312 1.051 -0.117 -3.010 -1.127 0.339 2.224 0.181 -0.700 -0.624 -0.550 0.862 -0.984 -0.528 0.372 0.999 0.258 -0.169 -1.271 -1.325 +2 -0.312 0.390 0.324 -0.918 0.284 0.410 -1.413 -1.683 0.600 -1.181 0.098 -0.163 -0.523 1.562 -1.032 -0.820 0.653 0.315 0.135 0.279 -0.575 -1.062 1.564 -1.137 -1.989 -0.729 0.807 -2.017 +0 0.929 0.332 0.145 -0.106 -0.149 0.301 1.298 -0.952 0.073 0.345 1.656 -0.432 -0.780 1.510 0.510 -0.423 1.056 -1.877 -0.054 0.294 0.590 -0.826 -0.843 1.540 0.016 0.159 -0.729 -0.917 +3 -0.864 -0.550 -0.095 1.593 -1.961 0.129 -0.816 -1.608 -0.674 -0.003 0.030 -0.628 0.561 -1.748 -0.515 0.809 0.606 2.150 0.747 -1.355 0.999 -0.461 1.521 1.263 -1.448 0.275 -0.255 -0.316 +0 0.588 -2.018 0.995 -0.225 -0.387 0.141 -0.404 -0.028 -1.017 -0.368 -0.719 0.473 -0.114 -0.077 -0.972 0.682 0.445 1.171 0.342 -0.080 0.618 -0.714 -0.431 -0.539 1.246 -0.094 0.119 -0.626 +2 -0.000 0.631 0.838 -0.799 1.039 -0.534 -0.164 -0.433 -1.136 1.570 0.048 1.087 1.045 -0.968 1.216 0.503 -1.041 0.449 -1.023 1.606 0.814 -0.071 1.265 -1.582 -0.749 -0.252 2.236 -1.070 +1 -0.391 -0.609 1.134 -1.500 0.543 1.387 -0.579 1.472 1.157 -0.349 0.819 -0.827 -1.102 -0.464 1.350 -0.458 0.042 -1.004 0.867 0.831 -0.704 -2.317 -0.362 0.443 -0.013 0.080 0.350 -0.511 +1 -0.626 -0.993 0.684 -0.280 -1.495 0.860 0.483 -0.084 -0.017 0.940 -0.479 -1.478 -1.232 1.006 0.001 0.311 -0.316 -1.317 -0.547 1.381 -0.024 -0.377 1.989 -0.820 -0.486 -1.409 0.368 -1.386 +0 -0.036 -0.301 0.640 1.120 1.750 0.645 0.137 0.137 -1.881 -0.430 0.361 -2.258 0.180 -0.812 1.577 -0.285 -0.264 -0.137 -0.005 0.793 -0.344 0.023 0.799 0.009 0.655 -0.446 0.351 1.309 +4 0.137 -0.208 1.383 -1.835 0.604 -0.430 2.027 -0.455 0.628 -0.577 -0.529 2.021 -0.250 0.631 -1.072 1.676 -0.594 -1.049 -1.744 0.765 2.038 0.155 -0.141 1.225 -0.882 1.273 -1.521 0.822 +1 -0.710 -0.151 0.943 -0.052 1.067 -1.060 -0.245 0.011 1.915 0.011 -0.497 -1.463 0.088 1.128 0.382 -0.967 -1.061 -0.336 -1.260 -0.573 0.296 0.039 -0.765 -0.598 -0.475 -0.944 -0.446 -1.995 +3 -0.894 1.269 -0.123 -0.662 0.170 0.098 1.007 2.109 -0.576 -2.418 1.661 1.016 0.684 0.227 0.996 -0.709 0.783 -0.589 2.047 -0.554 -1.318 0.711 0.872 0.257 -0.531 -0.843 1.944 -0.174 +2 -0.270 0.972 -0.844 -1.222 0.379 -0.788 2.423 0.192 1.025 -1.315 0.457 -1.006 -0.179 -0.752 1.040 0.321 0.117 1.361 0.016 1.030 1.801 0.140 0.260 0.955 1.437 0.128 -0.637 0.867 +1 -0.510 -0.680 -0.518 1.314 0.671 0.793 1.061 -0.396 -0.337 0.084 -0.570 -0.589 0.841 0.582 -2.080 -0.189 1.176 0.783 0.109 1.530 -0.407 -0.238 -2.062 -0.012 1.870 -0.013 0.210 -0.180 +2 1.065 -0.305 0.887 0.700 -0.738 -2.574 0.023 1.240 1.130 0.307 -0.574 0.706 -1.339 0.398 0.994 -0.080 -1.073 -0.065 0.339 -1.006 -0.420 0.839 0.961 0.122 -0.820 -2.162 0.279 -1.521 +3 -0.754 -2.016 0.528 -0.294 -1.114 -0.375 -0.807 0.770 -0.982 -0.876 -0.067 0.005 -0.273 -2.041 -1.640 0.134 -1.155 0.499 -2.343 -0.514 -1.249 -1.495 -1.583 -0.332 -0.766 -0.657 -1.203 0.814 +2 1.185 0.092 0.859 -0.954 -1.928 0.293 0.529 -0.427 -0.439 -0.040 1.268 0.698 0.024 -0.323 -1.662 -0.980 1.149 0.319 -1.012 0.170 -0.464 1.410 -1.606 -0.023 1.371 -1.223 -1.525 -1.275 +0 0.320 -0.491 0.296 -0.642 0.509 -0.192 -0.373 0.976 -0.477 -0.565 1.601 1.624 -0.851 1.219 0.413 -0.840 -0.220 -0.649 0.320 -1.034 -0.196 -0.754 -0.402 -0.824 -1.409 0.394 0.399 -0.269 +1 0.663 -1.671 -1.669 0.554 0.250 -0.362 -0.580 -0.996 -0.489 0.293 -1.413 -1.452 1.144 -0.854 0.474 -0.511 -0.476 -1.785 0.360 -0.624 0.343 0.846 0.777 1.027 0.354 0.614 -1.215 0.548 +1 -1.695 -1.137 0.109 0.948 1.472 0.992 1.552 0.182 -0.622 1.184 1.364 0.931 0.613 1.392 0.342 -0.021 0.726 0.478 0.885 -0.132 0.909 0.858 0.213 -0.750 1.241 -1.210 -0.958 0.168 +1 0.236 -1.051 0.991 -0.952 -0.239 -0.329 -0.360 0.263 1.576 1.583 0.808 1.543 1.107 0.227 0.376 0.238 0.895 -1.076 -0.120 0.887 -0.760 0.970 -0.037 0.510 -0.865 1.798 -0.135 2.040 +2 -0.340 0.637 2.231 -0.277 0.351 0.612 0.571 2.098 1.158 -0.979 -0.951 -0.115 0.586 1.028 1.022 -0.016 0.603 1.593 0.535 0.387 0.986 0.235 0.705 1.882 -1.149 -0.169 -0.587 -0.657 +0 -0.928 -0.268 0.707 0.757 0.572 -0.400 -0.607 2.535 -0.039 -0.162 -0.714 0.245 0.198 -0.016 0.382 1.764 -0.595 0.043 -0.648 0.295 -0.568 -0.525 -1.460 0.098 0.430 1.860 0.470 -0.450 +0 0.592 -0.183 0.224 -1.687 0.661 -0.396 -0.807 1.314 0.274 -0.665 -0.604 0.366 0.288 0.029 -1.203 -1.055 -0.748 -0.196 -0.041 -0.160 -0.114 1.881 0.849 1.188 0.464 0.072 0.410 0.226 +4 0.166 3.376 -0.877 -0.618 -1.670 0.954 0.196 -0.825 -1.057 -0.574 0.697 0.357 -0.863 -0.611 0.947 -1.907 -1.109 0.137 0.108 0.670 0.828 0.656 -1.889 0.802 0.402 0.034 0.994 1.251 +4 -1.076 -0.595 -0.235 0.264 1.972 1.031 0.478 -1.925 -0.972 0.070 1.218 -0.224 0.481 -1.301 -0.885 -0.263 -2.031 0.602 0.948 0.722 -1.633 -0.123 -0.856 -0.645 -2.692 0.697 1.195 -2.473 +2 0.863 0.071 0.716 -0.177 0.499 0.665 -2.019 -1.461 -0.558 -0.131 1.275 0.454 0.523 1.542 1.376 -1.009 1.233 0.105 -0.515 -0.293 -0.625 0.141 -0.083 1.236 1.933 -0.405 -1.296 0.668 +2 -0.541 -1.509 -0.325 -0.575 0.127 -0.306 1.337 -0.558 -0.129 0.240 -0.051 -0.536 0.051 0.525 0.899 -2.118 0.507 -0.674 1.158 0.932 0.926 -0.584 -1.393 1.271 1.169 -2.004 1.375 0.355 +3 1.095 0.535 -0.896 -0.776 0.397 0.399 -0.376 -1.582 0.803 -2.925 0.348 1.133 -1.849 0.160 -0.384 0.195 0.978 -1.346 -0.223 -0.164 -1.949 0.574 1.629 0.671 0.130 0.886 -0.595 -0.881 +4 -0.028 0.312 0.423 -1.077 1.881 2.194 -1.868 -0.450 0.680 -0.112 -0.543 0.562 0.660 1.692 -1.734 -1.008 -0.754 2.397 1.510 0.275 -0.084 0.441 1.175 -0.253 0.274 -0.337 -1.395 -1.790 +4 1.088 -0.002 -1.024 1.512 0.681 -1.469 1.734 -0.519 0.449 -0.996 -0.902 -1.462 1.103 0.365 -1.416 -1.580 1.757 -0.533 -1.308 1.263 -0.423 -0.400 0.112 0.110 0.732 -1.780 1.486 -1.325 +1 0.064 1.598 0.594 -0.001 -0.710 0.667 -1.205 -1.214 -0.188 1.529 -1.241 1.118 0.660 -1.132 0.299 -0.721 -0.719 1.182 -0.125 -0.174 -0.013 1.298 -0.251 0.783 0.791 -1.657 1.113 1.234 +0 0.109 -0.098 -0.594 -0.703 1.022 0.452 -0.042 -0.346 -0.148 -1.117 -0.892 0.917 -0.712 -0.138 0.359 0.460 -1.385 -0.771 0.221 1.076 -0.866 1.781 -0.601 -0.563 -0.623 -0.778 -0.224 0.437 +0 -0.882 -0.655 -1.539 0.564 -0.733 -0.747 -0.473 -0.486 -0.895 0.799 0.044 -0.680 -0.070 -0.191 -0.180 0.585 0.374 1.918 -0.975 1.685 0.343 -0.088 -0.908 -0.585 0.128 -1.076 -0.094 1.875 +1 1.891 0.707 -0.903 -1.719 0.177 -0.001 -1.520 -0.055 -0.574 0.009 0.726 1.493 0.909 -0.721 0.369 0.063 -1.332 0.398 1.857 0.032 -1.594 0.030 -1.214 0.864 0.525 -0.171 -0.331 -0.212 +0 -0.235 1.140 1.371 0.710 -0.388 0.585 -0.127 -0.282 -0.738 0.905 -0.738 1.251 -1.119 0.879 0.169 -0.278 0.926 0.783 0.191 -0.103 0.481 1.612 1.763 -0.015 -0.742 0.398 0.750 0.642 +1 -0.019 -0.097 -0.137 -0.572 -0.737 -0.536 1.069 -1.346 -0.669 1.996 -0.203 0.685 -0.787 -1.269 0.418 -0.370 1.081 -0.583 -0.002 1.474 0.773 -0.046 0.303 0.851 -0.170 -1.655 -1.667 0.022 +4 -0.473 0.305 0.585 -2.121 -1.928 0.544 1.903 -1.446 -0.222 0.434 -0.573 -0.200 -0.124 -0.588 0.274 1.926 -0.538 -0.677 -0.906 -2.565 -0.917 0.361 -0.734 -0.195 -1.799 -0.855 0.639 -1.631 +1 -0.303 0.386 -0.665 -0.816 0.729 2.632 0.540 -0.914 0.871 1.744 1.063 -1.544 0.528 -0.459 0.053 -0.374 0.743 -0.353 1.418 -0.003 -0.223 0.572 0.357 0.600 -0.133 1.334 0.990 -0.012 +4 -0.916 -1.189 -0.924 1.277 0.211 0.313 0.049 -0.701 -2.634 -1.646 1.234 0.759 1.609 0.262 0.702 -1.283 -1.589 1.073 0.289 0.002 -0.501 1.809 -0.844 1.499 0.025 -2.007 0.622 -0.071 +0 -0.404 0.363 -0.480 0.070 0.797 -0.535 2.020 -0.578 0.136 1.382 1.450 0.467 0.269 0.069 1.356 0.957 1.090 1.634 0.458 -0.407 0.351 0.056 -0.189 -0.404 0.548 0.638 -0.696 0.316 +0 0.985 0.411 -0.337 0.424 -0.101 0.291 0.601 0.147 0.465 -0.372 -0.571 0.775 1.450 -0.517 0.315 -1.409 1.805 1.146 -0.584 -0.353 1.209 0.176 0.633 -0.986 0.577 -0.149 0.299 -0.398 +0 1.298 -0.665 1.705 0.494 0.608 1.636 0.210 -0.393 -0.592 1.566 -0.721 0.491 0.294 0.203 0.342 0.702 -0.228 0.131 0.003 -0.241 0.226 1.635 0.536 -1.837 -0.081 -0.397 -0.695 0.615 +2 1.777 0.317 0.287 -1.102 0.026 -0.286 -1.393 0.383 1.856 0.768 -0.960 0.000 -0.642 1.393 0.797 1.410 0.339 -0.134 -1.230 0.458 -0.771 -0.838 0.527 0.933 -0.055 -0.935 0.171 1.992 +1 -0.859 -0.341 1.494 0.416 -0.965 -0.784 0.625 0.745 0.363 -0.291 0.810 -0.312 -0.855 1.587 0.489 -0.785 0.134 0.027 -0.350 0.234 -0.929 0.874 -0.006 -1.609 -0.228 -0.616 -2.990 -0.156 +1 -0.587 1.999 0.239 -0.380 -1.136 -1.399 0.621 -0.582 -0.450 -0.543 -1.092 -0.290 0.056 -1.007 0.332 -1.224 -0.441 0.365 -0.014 -1.641 -2.786 0.567 0.690 0.437 -0.227 -0.248 -0.107 0.585 +4 1.267 -0.102 -2.009 2.034 -0.652 0.195 0.727 1.409 1.806 -0.364 1.190 0.198 2.743 1.100 -0.177 -0.793 -2.627 0.675 1.008 0.745 -2.353 -1.258 0.481 1.786 -0.056 1.092 -1.034 0.207 +2 0.264 2.368 0.313 -0.811 -1.460 -0.276 -0.119 -2.211 -0.714 0.297 -0.532 0.969 -1.558 0.677 0.914 0.956 -1.566 0.315 -0.669 -1.406 -0.414 -0.905 0.921 0.513 -1.152 -0.724 -0.381 -0.072 +2 -1.120 -1.160 0.929 -0.753 -0.084 0.976 0.085 -0.266 -0.068 -0.759 -0.745 -0.282 -0.867 0.824 2.127 -1.993 -0.746 -0.946 -0.840 0.692 -0.391 -0.770 0.676 2.191 0.229 -0.790 -0.377 0.469 +1 0.229 -1.858 0.603 0.298 0.639 1.058 0.368 0.148 -0.881 -0.712 1.187 1.436 -0.238 0.046 -0.905 1.173 0.665 1.941 -0.878 -0.378 0.231 0.646 -0.216 -0.873 0.881 0.721 -0.916 1.355 +3 0.759 -1.262 -0.805 -0.688 1.366 -0.496 1.207 -0.699 -1.058 1.700 0.190 -0.697 -1.712 -0.583 1.833 -0.367 -2.531 -0.413 0.799 -0.278 0.561 0.407 -0.258 -0.755 -0.538 -0.477 -1.590 0.475 +1 -0.797 -2.040 1.083 0.193 0.251 0.832 -0.762 -0.918 0.549 1.158 0.532 1.603 0.564 0.510 0.822 -0.232 -1.242 0.019 0.714 -0.859 -1.406 -0.726 1.335 0.496 -0.126 -0.968 0.275 0.185 +4 -0.360 1.896 -0.493 -1.609 0.782 -1.424 0.077 1.340 0.498 -0.247 -0.351 1.265 -1.220 0.775 2.194 0.543 -0.238 -0.160 -0.480 -0.651 0.118 0.679 1.611 -1.957 -2.439 1.630 0.057 0.110 +1 0.596 0.006 0.129 -0.491 -1.089 -0.333 0.369 -0.023 -2.553 1.133 2.343 -1.627 -0.596 0.439 0.069 -0.663 -0.060 0.274 -0.164 -0.379 1.933 0.069 0.074 -0.459 -0.040 1.046 -0.639 0.343 +2 1.056 1.591 1.307 0.046 -1.174 0.188 1.047 -0.073 -0.429 -0.305 1.277 -0.351 0.689 0.909 0.589 -0.640 -0.275 -1.588 -0.379 -1.732 1.031 -1.283 -1.069 -0.294 -0.535 1.949 1.181 -0.239 +4 0.196 -0.322 1.322 -0.043 -0.626 1.061 0.591 -0.910 -3.940 0.540 -0.436 0.042 1.182 -0.953 -1.125 -1.396 2.203 1.192 0.672 -1.927 -1.424 0.703 0.402 -0.272 -1.352 1.055 1.792 0.439 +0 -0.331 0.663 0.970 1.048 0.511 0.246 0.794 -1.963 0.211 0.052 0.213 -0.088 0.432 -0.575 1.130 0.474 0.151 -0.698 0.815 0.207 -0.755 -0.057 0.629 0.552 0.487 0.093 1.026 1.238 +3 -1.142 0.262 -2.218 -1.386 -0.880 -0.546 0.389 -1.655 -0.641 -0.616 0.688 -0.192 -0.967 -1.079 -0.553 -1.648 1.626 -0.004 -0.914 -1.054 -1.207 0.371 -1.653 -0.178 0.574 -0.586 -1.012 0.838 +2 -1.007 0.168 -2.161 0.483 0.660 0.748 -0.395 0.309 1.424 1.414 -0.029 0.139 1.111 -1.501 -0.323 1.336 0.975 0.369 -1.506 -0.050 -0.590 0.331 -0.809 -1.026 -2.065 1.222 -0.400 0.157 +2 -0.622 0.659 0.297 -0.266 0.480 0.966 0.130 -0.258 -0.233 -0.886 0.811 2.078 -1.261 -0.541 0.310 -0.305 0.629 -0.863 -2.065 0.941 0.770 0.858 1.963 -0.479 1.855 1.521 -0.295 -1.164 +4 0.848 -0.677 0.067 -0.627 -0.973 -1.899 0.339 -3.394 1.298 1.032 -0.910 1.588 2.061 -0.273 0.411 0.136 1.514 -1.393 -0.227 0.919 0.814 -0.043 1.392 0.233 0.114 0.697 -0.061 0.050 +4 0.728 0.052 0.733 -0.081 0.079 -1.998 0.916 0.346 0.998 -2.896 2.088 -0.140 1.108 -1.040 0.613 -1.053 -0.624 1.914 -0.191 0.217 0.870 0.496 0.150 0.365 2.403 -0.058 0.201 1.051 +1 -0.532 -0.805 0.063 0.007 -1.534 -1.138 1.096 -0.429 0.702 0.269 0.321 -0.498 1.177 -0.245 -0.503 -0.362 -0.891 0.853 -1.575 -0.058 0.506 2.061 1.822 0.389 -0.122 -0.929 -1.264 -0.856 +1 -0.158 -0.323 -0.952 -1.938 -1.135 1.220 0.232 0.280 0.967 -0.274 0.753 0.771 -0.773 0.777 0.311 -0.850 1.808 0.882 -0.990 -0.060 -0.647 1.497 -0.740 0.576 -0.904 1.689 0.632 0.598 +3 0.084 -2.184 -1.101 0.397 0.196 -0.484 -0.147 1.688 -0.395 1.187 0.676 1.478 -1.472 -0.178 0.977 0.735 -1.952 -2.003 -0.673 1.155 0.310 -0.051 0.175 -1.094 1.270 -0.105 1.164 1.490 +1 -0.282 0.120 -0.001 -2.108 -0.433 -0.979 -0.584 0.182 0.337 -1.724 -0.966 1.383 0.244 0.377 -0.263 0.974 0.429 1.843 -0.196 1.200 -0.939 0.285 -0.122 0.013 -1.746 -0.790 1.103 -0.408 +0 0.946 1.196 0.915 -0.765 -0.573 -1.126 0.734 -1.252 1.741 -0.062 -0.297 -0.480 -0.692 -0.023 -0.923 0.980 0.838 0.217 0.848 -0.848 -1.116 0.465 -0.136 0.073 -1.038 0.051 -1.135 1.081 +0 0.154 0.763 1.946 0.040 1.534 0.124 -0.534 -0.109 -0.507 -0.471 -0.573 -0.303 0.711 -1.701 0.287 0.120 1.384 -0.366 1.132 -0.860 -0.455 0.417 0.608 -0.265 0.223 -0.164 0.791 -0.147 +3 0.720 0.040 -0.363 2.487 -0.767 0.823 -0.264 -1.339 1.716 -1.742 -2.149 0.700 -0.304 0.432 -0.007 0.601 -0.296 0.902 0.931 1.549 1.264 -0.106 0.696 0.417 0.421 -1.324 0.753 0.609 +2 0.735 -1.296 -1.200 -1.151 1.417 1.315 -0.788 1.634 1.605 0.126 2.101 1.302 -0.119 0.585 -0.656 -0.319 -0.132 -0.889 0.197 -1.968 0.153 -0.373 0.746 -0.440 0.282 -0.301 -1.068 0.159 +2 0.486 1.936 0.819 -0.100 -1.337 -0.143 -1.268 0.665 0.813 1.140 -1.200 -2.432 -1.009 0.968 0.532 0.313 -1.550 -0.253 0.376 0.391 0.526 -0.179 -0.686 0.265 -1.547 -0.562 0.220 -0.391 +0 -0.198 0.977 -1.476 0.992 0.763 -0.580 -0.766 -0.247 -0.758 -0.351 -1.831 -0.324 -0.407 0.152 1.265 -0.186 -0.981 -1.732 0.563 0.229 -0.101 0.161 0.837 0.309 0.311 1.429 0.053 -0.681 +2 -0.561 1.183 2.421 -0.318 -0.737 -0.674 1.647 0.758 0.085 0.569 -1.606 1.718 0.704 0.848 -0.450 -1.206 0.200 0.526 0.171 0.917 -0.869 -0.881 -0.791 -1.101 -0.416 -0.342 0.167 0.897 +4 -1.532 -0.635 2.541 0.329 -1.818 -1.400 0.226 0.101 -0.587 2.193 2.619 -0.264 0.873 0.923 1.056 -0.640 1.939 -0.069 0.422 0.002 -0.140 0.278 -1.186 -1.971 -0.213 -0.880 0.548 0.434 +4 -0.325 -2.449 0.786 0.645 -1.066 -1.133 1.351 -0.241 0.754 -0.694 0.655 -0.923 0.422 0.357 -2.065 0.124 -0.719 1.093 0.235 -0.247 1.519 -1.903 1.927 1.875 -0.390 0.888 0.485 -2.485 +0 0.307 0.802 0.691 0.905 0.918 -0.411 1.195 -0.112 0.281 0.615 -0.711 0.434 0.620 -1.893 1.402 -0.819 0.273 1.529 0.654 1.040 1.110 -0.492 -1.037 0.415 0.713 1.104 0.781 -0.492 +2 1.409 0.394 0.702 0.233 -0.099 -1.203 1.192 -0.423 0.825 -0.035 -0.956 -0.827 -0.578 0.111 1.182 0.709 -0.794 -1.314 0.673 1.937 0.959 2.034 -0.499 -0.002 1.329 -1.636 0.382 0.316 +4 1.962 1.166 -0.123 -0.732 0.593 -0.234 0.757 -1.361 -0.018 -0.927 -0.445 -1.091 0.530 -1.132 1.616 0.053 -0.952 -0.480 -0.015 2.284 1.083 -2.750 -0.245 -0.960 -1.770 2.116 -0.112 -0.549 +3 -0.568 0.546 0.266 1.132 1.292 2.084 0.763 -0.601 -0.991 0.252 0.186 0.496 1.343 -0.322 -1.448 -1.931 -0.393 -0.762 0.224 1.194 -1.120 0.500 -0.591 1.541 -0.777 -0.672 2.667 0.027 +4 -1.249 0.728 -3.063 -0.718 -1.342 0.142 -2.189 0.548 0.656 -1.633 0.191 0.516 0.940 -1.469 -1.610 1.503 -0.822 -0.527 0.633 -1.169 -0.266 0.892 -1.421 -0.500 -1.127 -0.774 1.186 1.405 +3 0.018 0.073 0.402 -1.749 -1.098 -0.448 -1.334 0.477 0.675 -0.607 0.663 0.227 0.233 -2.878 -0.195 -0.599 -0.141 -0.426 -1.199 -0.335 -0.415 0.902 -0.141 -0.571 -1.247 2.727 0.995 0.165 +4 -1.212 -2.182 -0.118 0.835 0.214 0.094 -2.599 -0.155 -0.145 0.621 -1.058 0.830 -0.509 -0.299 0.528 -1.189 0.824 -1.355 0.655 -0.235 -0.154 -3.065 -0.591 -0.210 1.549 -0.036 -0.653 0.834 +4 0.494 1.152 -0.817 -1.058 -0.592 1.212 0.377 -0.759 1.198 -0.288 -0.108 3.590 1.697 0.237 0.411 0.020 0.216 -1.929 -0.399 0.433 -0.655 -0.944 0.912 -2.074 0.010 -1.195 1.286 -0.344 +1 -0.840 -0.116 1.530 -0.111 0.727 -1.295 0.505 2.406 1.344 1.337 1.138 0.032 -1.314 -1.066 -0.094 0.579 -0.527 1.043 0.073 0.182 0.563 -1.137 -0.901 -0.416 -0.099 -0.186 0.115 0.360 +4 -1.454 -0.938 1.696 -0.359 0.233 2.928 1.844 0.155 1.768 0.468 1.955 0.027 0.723 0.582 -0.823 0.756 -1.048 -0.525 0.813 0.389 0.028 0.317 -0.045 1.050 2.317 1.419 -0.349 0.504 +1 0.427 -0.245 -1.464 -0.492 0.209 -0.355 1.306 -1.422 0.230 -1.462 0.194 0.557 1.018 -0.440 0.572 0.036 0.016 1.359 1.185 0.447 -0.717 -0.493 0.189 -0.698 1.925 -1.112 1.783 -0.852 +4 0.646 0.332 3.213 -0.902 1.333 -0.852 0.527 -2.599 -0.512 0.472 0.240 -2.005 -0.412 1.477 1.029 0.960 2.352 -1.745 -0.394 0.031 -0.731 0.125 1.301 0.110 -2.204 0.677 -0.015 -2.179 +1 0.057 0.788 -1.682 -1.438 -0.052 0.857 0.000 -0.588 -0.186 0.639 -0.475 1.447 -1.983 -0.191 0.197 -0.906 -0.535 0.789 -1.517 -0.357 -0.030 0.474 0.055 -0.156 -1.523 -0.961 0.867 0.446 +4 0.603 -1.024 -1.588 -0.124 -0.761 1.719 1.469 0.528 -0.940 0.154 -0.380 -0.498 1.946 -0.662 -2.490 -2.292 0.837 -0.450 0.365 -1.249 0.493 -2.255 -0.801 0.136 0.387 -1.737 1.691 0.289 +0 0.373 0.166 1.543 0.243 -0.404 0.002 0.118 -1.672 2.710 0.520 0.210 0.615 0.709 -0.141 -0.631 0.599 0.386 -0.810 1.419 0.293 0.730 1.392 0.022 0.317 0.810 -0.725 0.054 0.126 +4 -0.841 1.161 0.113 -0.158 -0.343 1.099 0.070 -1.081 0.811 0.728 -0.902 -0.333 2.118 0.308 3.766 1.339 2.508 -0.727 2.452 -1.225 -0.182 -0.721 0.057 0.617 -0.095 0.303 -0.285 0.554 +3 -1.045 0.036 2.011 -0.958 0.886 1.336 -0.360 -0.701 0.575 0.353 0.695 2.074 0.167 -1.192 0.067 0.242 1.267 -0.197 -0.409 -1.371 0.547 3.348 -0.223 0.452 -0.548 -1.281 0.434 0.028 +4 0.364 -1.763 -0.053 0.468 -0.317 0.983 -0.489 -0.797 0.325 2.369 -0.330 -0.504 -0.672 -1.294 -1.146 -0.104 -1.642 0.814 1.787 1.507 -0.248 0.436 -0.466 -2.661 0.513 -0.668 2.395 -0.686 +3 -0.921 -0.102 -0.136 -0.956 1.067 -0.135 1.219 -1.169 -1.080 -1.106 0.519 1.747 -0.304 -2.314 0.475 0.665 -1.034 0.566 0.811 -2.049 1.116 1.179 -0.308 0.645 1.976 -0.657 1.161 0.245 +3 0.069 0.786 0.638 0.417 1.142 -1.296 -1.067 0.741 0.199 0.419 0.386 1.813 -0.731 1.086 -0.917 -0.742 -1.387 0.865 -1.017 -0.623 2.742 -1.788 -0.009 0.453 0.121 1.899 -0.295 0.701 +0 0.069 0.276 -0.059 -0.446 0.591 0.422 0.575 1.209 -0.144 -0.102 0.771 -1.919 -1.498 1.403 -0.967 0.108 0.309 1.074 -0.569 0.663 -0.819 -0.608 -0.466 -0.360 -0.712 -0.168 0.273 0.985 +4 0.437 0.458 0.053 1.266 1.627 -1.151 -2.275 -0.489 -0.983 -0.437 1.820 2.697 -0.948 0.224 0.202 0.346 -1.370 1.236 0.902 -0.480 1.231 -1.442 -1.693 0.265 0.431 -0.164 1.107 -0.657 +0 0.765 1.744 0.598 0.458 2.140 -0.693 -1.072 1.000 -0.313 0.350 0.235 -0.380 -0.479 0.158 -0.479 0.531 0.224 1.358 -0.050 0.751 0.654 0.903 0.792 -0.215 -0.874 0.157 0.703 -0.993 +0 -0.311 0.063 -0.851 -0.925 0.124 -0.187 0.389 -1.443 0.121 -0.603 1.111 -1.272 0.076 0.514 -1.076 0.711 -0.149 1.028 0.756 -1.559 -0.344 1.145 0.598 0.236 -0.407 0.986 -1.158 0.410 +2 -0.722 -1.654 -2.001 -0.476 0.711 -1.390 -1.236 -0.494 0.808 0.933 0.140 0.983 0.688 -0.417 0.910 -1.284 0.486 -1.166 0.143 1.219 0.213 -0.013 0.226 0.663 2.134 0.652 -0.503 -0.209 +3 -1.358 0.201 1.879 -1.148 0.719 -0.582 1.681 0.842 0.477 -0.742 -0.144 -0.360 -0.874 -0.150 -1.948 0.725 -0.414 -0.704 -1.434 0.419 1.929 -0.301 1.375 0.580 -0.158 1.947 -1.726 0.144 +1 0.281 0.271 0.408 0.620 -0.150 0.351 0.060 1.462 -0.766 -0.928 -0.619 -0.848 -0.382 -1.096 1.100 0.224 0.348 -0.883 0.188 0.163 -2.447 -1.322 -0.745 0.696 -0.424 0.828 0.576 -1.839 +0 0.941 0.388 -1.236 -0.365 -0.625 -0.693 -0.597 1.227 -0.800 1.181 0.862 0.963 0.526 0.532 0.211 -0.520 1.581 -0.412 -0.107 0.669 0.466 -1.277 -0.176 -0.196 1.306 0.474 1.765 -0.087 +4 -2.632 -0.130 0.328 -1.289 0.513 0.792 0.329 -0.193 0.901 1.064 1.278 1.057 1.995 1.484 1.218 1.379 0.288 -3.289 0.338 0.288 0.199 1.432 -0.119 0.574 1.686 0.472 -0.056 2.247 +3 1.387 0.277 1.354 1.399 0.009 -1.313 -0.283 1.290 0.550 -0.122 -0.535 -0.149 0.899 0.092 -0.922 0.367 -0.148 -1.263 -2.305 -3.416 -0.001 0.646 0.923 0.446 0.542 0.930 -0.097 0.240 +0 0.514 1.678 0.158 -1.959 0.049 -0.095 -0.126 -1.596 0.276 -0.532 -0.106 0.948 -0.812 -0.102 -0.202 -0.166 -0.444 -0.390 1.675 -0.423 0.271 0.105 0.073 0.480 -0.633 1.401 -0.920 -0.165 +2 -0.486 -0.901 -0.410 -0.036 -1.861 1.308 -0.922 -1.117 -0.088 0.917 0.195 1.699 1.234 0.586 -0.329 -0.894 -0.080 0.347 0.470 -0.206 -0.171 -0.541 0.594 0.439 0.133 1.057 3.095 -0.363 +1 -0.680 1.496 0.502 1.556 0.440 0.995 0.510 -0.622 0.342 0.516 1.531 0.536 -0.964 -0.358 0.885 -0.398 -0.102 0.135 -1.694 0.332 1.416 -2.250 -0.197 -1.301 0.171 0.196 0.334 0.614 +4 2.901 0.877 -0.127 1.243 -1.796 -1.051 -1.883 -1.583 0.014 -1.379 -1.006 -1.716 0.465 -1.226 0.872 -0.137 1.395 -0.357 0.867 0.206 0.608 0.948 0.184 0.380 0.561 -1.612 0.370 0.837 +3 -0.948 1.200 -0.535 -1.202 -0.252 -0.146 0.575 -1.284 1.699 -0.542 1.727 0.203 0.567 0.826 -0.414 -1.357 0.717 2.170 0.011 0.057 0.333 -1.460 1.177 -1.242 1.496 0.261 1.280 -1.784 +2 -0.630 0.576 -1.042 -1.463 -0.691 0.113 1.462 1.008 0.451 1.611 -1.053 0.475 1.044 -1.364 -0.406 0.419 -0.405 -0.315 0.846 1.492 -1.308 1.355 -0.889 -0.210 0.839 0.848 -1.399 1.527 +3 -1.018 0.090 0.574 1.294 1.063 -0.392 -1.466 0.048 -0.055 0.822 0.994 -0.373 -0.916 0.977 -0.711 -0.906 -1.173 -1.179 2.493 0.075 -0.413 -1.606 -0.512 1.918 -0.652 0.101 -0.407 1.276 +4 -0.189 -0.920 1.795 -0.853 0.448 0.906 -0.136 0.191 -0.180 3.018 1.680 0.917 0.464 -0.608 -1.175 0.411 0.662 -2.267 -1.129 0.179 -0.574 0.362 -0.315 0.506 1.551 -1.269 0.141 2.202 +3 0.945 -0.553 -1.873 2.044 -0.997 -1.059 1.104 -0.384 -0.063 0.483 1.084 -0.333 1.520 -2.136 0.760 -0.840 1.372 0.142 -0.673 0.663 0.893 0.642 -1.030 -0.484 -0.004 -0.041 0.750 -2.015 +4 -0.348 0.595 -0.849 -0.867 -0.003 -0.922 0.259 -2.039 1.176 -2.785 0.905 -0.581 -1.450 0.170 0.886 1.159 1.126 -0.858 0.205 -1.778 0.079 0.338 -0.375 -1.836 -0.594 1.907 0.391 -0.485 +1 1.399 -0.092 -0.304 -0.393 0.637 0.084 0.190 -0.325 -0.198 -0.148 1.422 -1.801 0.448 1.088 -1.839 -0.071 0.982 -0.753 0.269 -0.932 0.033 1.278 1.696 0.255 -1.279 -0.310 -1.813 -0.294 +0 -1.017 -0.522 -0.279 0.493 -1.628 0.247 -0.985 0.447 -1.491 -0.278 -0.921 0.103 1.573 0.594 0.625 -0.071 -0.196 -0.140 -0.252 -0.625 -0.734 -0.752 -0.287 -0.953 0.205 -1.516 0.355 -0.024 +2 -0.049 -1.818 2.228 -1.316 -0.707 0.696 0.246 1.643 0.720 -0.081 1.044 1.161 -0.660 0.963 -0.222 -0.103 0.143 -0.381 -0.435 0.985 -1.529 -1.062 0.675 -0.073 0.992 0.358 -1.334 0.063 +3 1.709 0.736 -1.018 -1.883 -1.744 0.869 -0.758 0.818 -0.455 0.388 0.701 -0.783 0.823 -0.660 -0.702 0.821 0.632 0.460 -1.103 -0.384 0.689 2.284 1.811 -0.468 -0.659 1.730 0.601 -0.468 +2 -0.454 -0.015 1.190 -0.569 1.365 1.494 -0.849 1.567 -1.098 -2.009 -0.112 -0.230 -0.618 -0.802 0.464 -1.645 0.464 -0.193 -1.231 -0.722 -1.233 0.768 -0.417 -1.371 0.607 0.489 -0.613 0.680 +2 -1.318 0.601 0.139 0.782 0.669 -0.421 -0.070 -0.511 -0.210 -0.967 0.390 1.075 -0.817 1.281 0.114 0.241 -1.699 -1.807 0.144 0.790 -0.671 1.670 0.735 -0.908 0.031 2.477 -1.243 0.528 +0 0.583 0.077 0.460 -0.336 -0.965 -0.823 -0.784 -0.003 -1.556 -1.247 0.136 -0.128 0.190 -0.045 2.519 0.127 -0.379 0.101 1.020 -0.050 0.685 0.396 -0.290 0.111 1.599 0.243 -0.986 -0.320 +4 -1.114 1.201 -1.397 -0.020 0.276 -1.268 0.061 -1.306 -0.934 -0.888 1.083 0.712 -1.699 -0.067 0.298 0.589 1.142 1.462 0.165 2.510 -1.066 -0.006 0.174 -1.293 0.237 -1.372 1.547 -2.008 +1 -1.495 -0.283 0.951 -0.209 0.993 0.565 1.097 0.519 -0.935 0.071 0.188 0.007 -0.738 -0.239 0.589 0.045 -2.365 1.297 -0.446 -1.632 0.001 -0.616 -0.891 0.175 1.392 0.465 -1.364 -0.264 +2 0.289 0.485 0.087 -0.653 0.385 1.865 -0.520 0.867 0.181 -0.590 0.895 0.779 0.580 1.749 1.836 0.054 -0.106 -1.427 1.533 -0.073 -0.609 -0.933 -0.941 1.759 0.898 -1.488 -1.448 0.122 +0 0.074 -1.457 -0.353 -0.241 0.293 1.839 0.125 -0.251 0.159 -1.471 0.563 -0.989 0.159 -0.888 0.136 0.245 0.751 0.627 0.913 0.106 -0.844 -0.481 0.258 1.052 0.525 1.006 0.520 -0.029 +1 -0.227 0.673 -2.995 -0.028 0.792 0.995 -0.697 -0.301 -0.616 -0.393 -1.032 -0.889 0.434 0.132 0.492 0.214 0.704 0.878 -1.742 -0.854 -0.231 -0.023 -0.670 0.912 1.980 0.186 0.084 0.849 +2 -0.551 -0.717 0.254 -0.867 0.686 0.929 0.182 1.037 0.871 -0.894 -2.188 -2.466 0.608 -0.741 -0.239 1.047 -0.430 -0.348 -0.363 -0.792 -0.488 1.219 -0.943 0.349 0.767 1.173 1.614 -0.214 +1 -0.386 -1.115 0.855 -0.464 1.798 -0.488 0.309 -0.667 0.488 0.510 0.888 -0.134 1.270 0.300 -0.643 -0.636 0.729 1.264 -1.122 0.751 -1.486 -0.617 0.490 -0.848 1.466 0.609 -1.182 0.655 +1 -1.149 -0.819 0.703 0.994 0.018 2.475 0.824 -0.243 -0.816 -0.975 0.335 -0.489 1.674 1.466 0.633 -1.179 -0.672 1.256 -0.563 1.122 0.475 0.052 -0.703 -0.094 0.190 1.033 -0.886 -0.263 +1 0.239 -0.079 0.601 -0.702 -0.137 0.702 0.757 -0.027 -0.579 0.866 1.019 0.034 1.575 -0.977 -1.212 -0.480 -1.089 -1.106 -1.545 0.585 0.712 0.978 1.763 -0.188 -0.025 0.390 -1.539 1.416 +3 0.431 0.699 -0.775 0.964 0.471 2.233 0.806 -0.598 -1.350 0.218 -1.029 -0.735 -0.966 -0.581 1.456 -0.967 -0.425 -1.431 -1.706 -0.279 0.207 -1.150 -1.519 -1.871 0.145 0.935 -1.428 -0.648 +2 -0.202 -0.469 -0.733 2.034 0.486 0.068 -0.476 1.113 -0.546 0.121 2.203 -0.957 0.540 0.289 0.528 -0.285 -0.592 0.226 -0.456 -2.332 -0.824 -0.317 -1.078 0.151 -0.758 -0.043 2.168 1.227 +3 -0.069 -0.046 0.674 -0.806 0.596 0.109 -1.344 -0.638 0.583 -0.258 -0.051 0.120 0.162 1.427 1.310 -1.345 0.546 0.483 -0.301 -1.212 0.184 -0.725 -2.252 3.248 -0.331 -1.858 -0.842 0.435 +1 0.586 0.643 -0.301 -0.140 0.011 0.626 0.456 -1.205 0.949 -0.322 -3.065 -0.233 0.321 -0.231 0.139 -0.041 -0.720 2.284 -0.879 -0.488 -0.279 -0.020 0.792 -0.399 -1.539 -0.889 -0.100 0.541 +4 -0.898 0.128 0.282 1.234 -1.635 0.126 -0.464 -0.342 2.044 1.035 1.158 0.272 0.124 -1.152 1.712 -0.959 0.425 -0.387 -0.345 -1.430 0.285 -1.831 -2.464 -0.569 0.434 -1.160 -1.972 1.832 +4 -1.099 -1.448 1.496 -0.389 -0.750 2.902 0.985 -0.721 0.737 -1.605 -0.319 -0.838 0.418 1.657 -1.694 1.857 0.750 -0.999 -0.980 -0.715 -0.866 -0.835 -1.814 -0.195 0.747 -0.574 0.297 1.125 +3 -0.434 0.927 0.905 1.636 -0.958 1.120 -1.827 -0.310 -0.666 0.673 -1.932 0.427 -1.834 0.192 0.245 -0.286 1.144 -0.832 0.388 0.101 1.552 -1.608 1.584 1.227 0.385 0.057 -0.531 -0.309 +0 -1.471 -0.221 0.244 1.039 0.107 0.635 -0.028 -0.032 0.085 -0.189 -1.687 -0.768 0.245 -0.249 -0.156 -0.323 -0.864 -0.493 -0.307 0.005 0.393 -0.181 -1.030 -1.398 2.597 -1.130 -0.519 0.517 +4 -1.817 1.730 -1.334 1.889 -0.839 -0.849 -0.398 -0.439 -0.605 -0.371 0.092 -0.897 0.841 1.412 -1.945 1.855 0.773 0.258 -0.574 0.167 0.897 2.610 2.192 -1.529 -0.208 0.078 1.344 -0.084 +3 -0.427 -0.842 0.522 0.644 -0.556 1.432 -0.002 0.913 0.909 -1.324 1.158 1.023 0.335 -2.412 0.234 -0.909 -1.365 -0.157 -2.180 -0.104 1.092 0.549 1.172 -0.318 0.453 -1.088 -1.946 -1.111 +2 2.511 -0.020 -0.313 -0.522 0.376 0.387 0.951 -0.926 -0.312 2.031 0.225 -0.173 -0.942 0.506 1.031 0.875 -0.259 0.361 1.672 1.071 -0.784 -2.546 0.297 0.799 -1.089 -0.009 -0.264 0.104 +2 -0.388 -0.284 1.269 0.770 0.533 0.222 -0.434 -0.854 -1.197 -0.695 -0.043 -1.155 0.170 -0.194 -0.397 1.162 -2.461 -0.384 -2.352 0.239 0.762 -1.351 -1.223 -1.698 0.064 -0.341 -0.279 -0.368 +4 -0.050 -1.451 -0.529 -0.258 1.860 -0.160 0.273 2.151 -1.579 -1.534 -0.778 -0.333 -0.232 -0.511 -1.234 0.974 -0.454 1.184 -1.017 1.960 -1.666 -0.014 -0.998 0.036 -0.980 -2.145 -0.781 1.596 +0 0.970 0.812 -0.035 -1.442 -0.992 -1.122 0.146 0.542 -0.669 -0.876 1.003 -0.431 0.133 0.772 0.324 0.821 0.545 -0.137 0.637 -0.125 -0.003 -1.672 0.152 0.768 -0.375 1.053 1.665 -0.871 +1 0.581 1.541 0.939 0.273 -1.810 -0.285 0.262 -0.106 0.018 -0.558 0.704 0.702 1.305 -1.019 0.855 -1.165 -0.819 2.308 -0.753 -0.985 0.444 0.505 -0.755 -0.690 -0.959 0.380 0.440 -0.479 +0 -0.098 0.229 -0.066 -0.320 -0.888 0.077 -1.372 -1.445 -1.026 -0.691 0.086 1.689 -0.005 0.148 -0.379 0.396 -0.415 -1.010 -0.644 -0.490 1.168 0.184 1.248 -0.331 0.451 0.227 -1.232 0.450 +1 0.883 -1.743 -1.035 0.362 -0.588 -0.130 0.746 -0.357 -0.353 -1.038 0.369 0.636 1.431 -1.409 -1.078 -1.187 -1.234 1.165 -1.354 -0.881 -0.106 -0.785 0.982 0.267 -0.392 1.134 0.765 1.345 +4 0.009 -0.238 0.077 -0.512 -2.105 2.042 0.155 0.398 -0.058 -0.735 -0.186 -1.599 2.277 -1.235 -1.032 -0.440 1.288 0.015 -1.236 1.254 0.731 0.538 0.716 2.394 2.185 -0.687 1.624 0.383 +2 0.063 -0.728 -0.913 0.701 0.845 0.604 1.515 -0.542 1.674 -0.901 -1.013 -1.760 -0.446 -0.504 0.526 0.244 -1.193 -0.393 -0.371 -1.776 -0.981 -0.771 1.434 0.191 0.662 -1.499 1.194 1.301 +0 0.742 -1.439 -1.026 -0.773 -0.861 1.088 -0.067 1.158 0.059 -0.794 -1.400 -0.373 -0.626 0.494 0.992 1.395 0.465 -0.959 0.678 0.689 -0.827 0.212 -0.533 -0.866 0.961 0.121 1.004 -0.163 +0 -0.933 -1.043 -1.116 0.025 -1.178 -0.425 0.903 -1.029 -0.504 0.072 0.247 -0.499 0.787 0.590 1.140 -0.082 -0.513 -0.683 0.307 -0.377 -0.569 -0.448 -0.684 0.199 -1.413 0.303 0.677 -0.581 +0 0.949 1.339 -1.119 -1.080 -0.330 -1.199 0.993 -0.052 0.345 1.696 -0.659 0.008 -1.013 0.025 -0.920 0.466 -0.233 1.155 0.064 1.061 -0.639 -0.507 -0.193 -0.275 -0.344 0.194 -0.429 -0.376 +2 -2.134 0.968 0.732 0.098 -0.533 -0.016 -1.774 0.824 -0.164 1.515 0.392 0.231 0.401 -0.976 -0.451 0.133 -0.303 -0.577 -0.282 0.044 2.319 -0.325 -0.677 -0.153 1.502 1.573 1.949 0.061 +4 -1.767 -0.397 2.203 0.127 0.417 -1.860 1.405 1.521 0.215 0.614 -0.669 -0.276 2.694 -0.468 1.830 2.054 0.442 -0.120 -1.325 0.539 0.015 -0.106 -0.477 0.301 0.467 -0.033 1.177 -0.019 +1 -0.349 0.453 -0.771 -0.676 1.723 0.699 0.760 -1.123 -1.473 1.055 0.422 -0.298 1.736 0.593 -0.850 -1.938 0.598 -0.469 -0.139 -0.222 1.431 0.843 -0.096 -0.123 0.288 1.318 0.083 -0.575 +0 0.426 0.185 -0.039 -0.448 1.737 -0.015 -0.615 1.429 -0.905 -0.099 -0.159 -0.301 0.099 0.593 -0.625 -0.292 -0.289 1.505 -0.030 -0.092 -0.148 1.180 0.415 -0.439 1.550 0.594 1.751 -0.145 +3 -1.205 0.333 -0.370 1.261 -0.893 2.223 0.855 1.288 -1.029 1.788 -1.213 -0.210 1.305 0.507 -1.239 1.135 0.340 -0.416 -1.542 0.435 -0.163 0.156 -1.585 -0.419 0.480 1.876 -0.205 1.443 +2 1.859 -0.139 1.187 -0.379 -0.339 0.328 -0.205 -0.465 0.550 -0.558 2.073 0.426 0.546 0.385 1.879 1.432 1.066 1.495 -0.017 1.101 0.315 1.209 0.127 0.215 -1.027 -1.378 1.007 0.207 +4 0.365 -0.909 0.496 -0.249 -2.844 0.954 -0.388 1.036 0.064 2.765 -1.227 0.966 0.514 -0.928 -1.092 1.199 -1.493 0.674 1.059 0.765 -1.513 -0.718 -1.095 1.001 0.696 -0.718 -0.957 1.256 +0 0.715 -0.123 -0.272 -0.269 0.014 0.653 -0.052 -1.362 0.576 0.499 0.206 1.075 0.030 1.111 1.407 0.582 -0.748 -0.131 1.239 -1.498 -0.392 -0.343 -0.865 0.873 0.377 -0.711 0.606 -0.158 +3 0.113 -0.314 0.448 0.871 0.093 0.218 -1.118 -0.837 -0.772 0.247 -1.132 -2.011 -1.397 0.533 1.676 0.949 -1.082 -0.857 -0.630 -1.548 -0.203 0.455 -0.985 -1.197 0.779 -0.907 -0.792 -2.396 +0 -0.201 1.328 0.293 -0.075 -0.036 -1.030 -0.857 -0.195 0.271 0.948 0.093 1.353 1.605 0.795 1.225 -0.991 -1.149 0.518 -1.341 -0.290 0.771 1.413 -0.254 -0.650 0.320 -1.074 -0.173 -0.008 +3 -0.073 -0.751 -1.671 -0.642 0.237 -0.631 1.477 0.624 1.054 3.106 -0.572 -1.433 -0.785 0.066 0.392 0.185 -0.447 0.413 1.142 -0.753 -0.930 1.964 -1.126 0.847 -0.197 1.486 -0.289 0.398 +3 1.415 0.769 0.159 -1.093 2.229 -0.666 -0.015 -0.959 1.607 -1.463 1.021 -0.901 0.785 0.288 0.231 -0.608 -0.330 -1.059 0.558 1.367 1.012 0.453 1.146 1.092 -0.054 -1.254 2.175 0.084 +0 -1.711 0.856 -1.047 0.012 -1.510 -0.328 0.214 -2.060 0.054 0.109 -0.469 -1.100 -0.377 -0.375 1.925 -0.121 -0.464 -0.123 0.424 0.387 0.450 0.603 -1.460 0.516 0.184 -0.623 0.106 -0.110 +4 0.668 1.033 1.608 1.415 -1.246 1.468 0.446 -1.001 -1.231 -2.053 -2.147 -1.127 -2.067 -0.048 -0.722 0.740 1.483 -0.270 -1.674 -0.802 -0.287 1.888 -1.852 0.882 1.439 1.163 -1.285 -0.236 +1 0.502 1.562 0.020 -0.199 1.798 -0.839 -0.283 0.475 -0.896 0.447 0.004 1.135 -0.418 -0.639 1.919 0.851 1.086 0.836 -0.674 -0.960 -0.949 -0.575 1.200 -0.026 0.940 0.823 1.602 0.801 +2 0.155 0.713 0.914 -0.387 -0.843 1.488 -0.581 -1.601 -0.058 0.252 0.479 -1.014 0.854 -1.047 -0.833 -0.906 -1.211 2.933 0.533 0.232 1.328 -0.665 -0.981 0.189 -0.297 0.565 1.294 0.698 +2 0.745 0.195 -1.219 -0.755 0.785 -0.332 -0.207 -1.082 0.405 0.104 0.713 1.238 0.449 -1.173 -1.512 0.731 0.120 1.751 0.025 -1.727 -1.266 -0.750 1.741 1.169 -0.536 -0.194 -1.362 -1.397 +1 0.147 0.430 -0.441 0.929 0.361 0.315 -0.624 -1.564 -0.501 0.283 -0.445 0.292 -0.232 0.431 -0.977 0.193 -0.641 -1.538 -0.165 1.536 -1.459 0.119 0.798 2.300 -0.493 -0.632 -1.167 1.141 +3 -2.494 -0.250 -0.921 0.182 1.017 -1.407 2.354 0.202 0.382 -1.440 -0.047 0.972 0.210 0.140 -0.052 -1.378 -1.384 1.169 0.882 1.030 -0.823 -0.158 1.232 -1.229 1.130 -0.587 -0.855 -0.032 +4 0.443 0.553 0.848 -0.375 0.001 -2.453 -0.670 0.176 1.471 1.846 -0.342 0.874 -0.147 -1.616 0.605 1.831 -0.381 -0.907 0.067 -0.123 2.610 1.006 0.129 -2.895 -0.143 1.852 0.080 0.822 +1 1.451 -0.443 -0.817 -0.550 0.566 0.696 -0.025 -0.978 -1.185 0.677 0.524 0.411 1.583 -0.793 1.978 0.337 -0.505 -0.054 -1.192 -1.021 -0.113 0.777 -0.704 2.149 -0.043 -0.602 -1.284 -0.520 +1 0.215 -0.005 -0.525 0.372 -1.016 -2.412 1.580 0.961 -1.099 -1.446 0.106 0.064 0.816 -0.335 1.155 -0.217 1.213 -0.181 -0.864 -0.374 0.654 0.354 0.053 -1.044 -0.060 -0.653 -0.679 0.995 +0 -0.073 1.035 -0.125 0.980 -1.029 0.433 0.839 -0.830 -0.178 -0.158 0.210 0.475 -0.911 -0.060 1.591 1.054 0.794 -0.193 2.129 1.030 0.037 0.220 -0.459 0.393 0.593 0.703 -0.181 -1.474 +2 0.270 -0.564 -1.109 -0.290 0.183 0.250 1.056 1.307 0.336 0.517 0.095 0.767 0.487 -0.054 2.152 -0.546 1.454 -1.354 0.814 1.437 -1.907 0.139 0.715 0.252 0.378 0.136 -1.094 2.368 +2 0.577 0.585 -2.514 0.383 0.632 -1.824 -0.790 1.493 0.125 0.434 -1.067 1.642 0.202 1.105 -0.731 -0.668 0.399 1.311 0.492 -0.652 -0.901 -0.095 0.148 0.004 -0.779 -1.013 -0.079 -1.269 +1 -0.169 0.480 1.571 0.705 -0.129 1.676 1.580 1.140 0.282 -0.771 -2.366 0.211 0.774 -1.337 0.415 -0.445 0.510 -0.617 -0.726 -0.061 -1.176 0.962 0.020 -1.145 -0.616 0.590 -0.301 0.710 +4 0.882 -0.111 -1.360 -2.093 2.148 -0.986 -0.686 1.228 -1.209 0.531 1.822 2.114 -0.447 -0.012 -0.396 -0.018 1.298 -1.399 0.648 -0.195 1.369 1.315 -0.407 -0.111 0.893 0.983 -2.472 0.890 +3 -1.994 1.478 0.419 0.021 0.019 0.975 2.185 0.903 0.368 0.286 0.364 -1.004 0.585 -1.311 0.442 -0.472 0.866 0.381 -0.351 -1.011 1.476 -0.086 1.740 -1.231 -0.253 -2.251 0.756 0.838 +2 -0.690 0.671 -1.489 0.272 0.981 0.570 -0.592 0.798 -1.753 -1.158 -0.955 -1.825 1.046 1.479 -0.215 1.214 -1.018 -0.446 -0.383 -1.126 0.487 -0.091 -0.495 0.081 0.882 -0.399 2.243 0.697 +2 0.062 0.075 1.175 -1.404 0.468 1.539 1.432 0.569 -0.757 0.777 1.325 -0.515 0.358 -0.205 -0.614 -0.315 0.129 -0.918 -1.082 -0.890 0.328 -1.466 -2.217 1.373 -1.013 -0.194 0.922 -1.438 +2 1.061 0.040 1.245 0.144 -1.457 0.481 -0.593 -0.998 -0.645 -1.532 0.773 0.419 0.237 -1.191 0.506 2.034 0.187 -0.580 -1.133 -0.750 1.750 -0.454 -1.626 0.317 -0.456 -1.009 0.634 -0.342 +0 -1.645 -0.821 -0.404 1.707 1.111 0.355 0.294 0.251 1.284 -1.642 0.678 -0.233 0.598 0.176 -0.174 0.000 -0.007 -0.688 -0.136 -1.456 -0.600 -0.273 -0.250 0.268 -0.858 -0.548 0.530 1.297 +3 0.242 1.416 0.405 0.262 -1.064 0.516 -0.629 -0.796 -1.654 -0.482 -1.410 -0.742 -0.261 1.168 -1.735 -0.010 0.421 -0.785 -0.715 1.419 -1.101 1.681 0.471 1.722 0.673 -2.018 0.599 -1.068 +0 0.693 1.106 0.861 0.251 -0.231 -0.576 1.471 -0.699 -1.037 0.665 -0.796 -0.950 -1.444 -0.483 -0.083 0.243 0.951 0.527 -0.191 -0.198 0.510 1.273 0.126 -0.469 -0.993 -1.445 0.666 0.578 +1 0.282 -1.417 -0.741 0.580 -0.444 -1.670 -1.355 1.128 -0.353 -0.330 -0.611 -1.520 -0.268 1.237 -0.778 -1.888 0.774 0.084 0.190 0.379 -1.745 -0.113 0.242 -0.043 -0.050 1.157 0.440 0.628 +2 -0.887 -1.174 -0.154 -0.966 1.394 -1.376 2.337 0.302 0.069 -0.540 0.657 -0.204 0.300 0.842 0.669 1.240 0.538 -0.951 -2.080 0.211 0.225 1.926 -0.116 0.374 0.777 0.513 0.844 -0.818 +0 1.002 -0.154 -0.431 -0.422 -0.494 0.606 -0.623 0.616 0.042 0.529 -1.022 -0.333 -0.874 2.121 -0.286 -1.163 -0.898 -0.618 0.457 0.009 0.958 0.591 1.237 -0.863 -1.076 0.485 1.548 0.701 +0 -0.093 2.176 0.150 -0.650 0.406 -1.248 0.647 0.951 -0.443 -0.021 0.270 0.089 0.943 0.902 -0.270 -0.700 -1.063 -0.744 -1.176 1.261 0.395 -0.878 0.116 -0.102 0.687 0.884 1.284 -1.071 +0 0.432 -1.417 0.700 -1.484 0.701 0.599 1.421 -0.862 -0.987 1.107 -1.075 -1.173 0.209 -0.047 0.155 0.538 0.543 -0.078 0.048 -0.067 0.035 -1.387 0.756 1.049 0.637 0.561 -0.991 -1.415 +1 0.916 -0.129 -0.712 1.156 -1.234 0.189 -1.486 -1.026 0.745 -0.679 -1.877 -0.723 -0.830 -1.262 0.550 -1.547 -0.588 0.133 -0.343 -0.404 -0.723 -1.219 -0.637 0.658 -0.434 0.167 1.117 0.600 +1 -0.037 0.660 1.090 -0.748 -1.967 -0.021 0.294 0.249 -0.327 0.125 1.515 0.382 -0.373 -0.500 -0.170 1.637 -0.183 -0.020 -0.482 1.099 1.960 0.451 0.083 0.037 -2.384 0.745 0.134 0.428 +4 0.097 1.845 1.029 -0.922 2.418 -0.031 1.246 -1.155 -2.398 -1.408 -3.250 2.112 0.215 -0.925 0.453 0.189 0.621 1.210 -0.290 -0.880 0.828 0.802 0.101 2.178 -0.438 -0.900 0.418 -0.675 +3 0.437 1.611 -0.780 -2.302 0.125 2.461 -0.759 2.061 0.911 -0.222 -0.238 0.543 -1.871 -0.104 0.571 0.291 0.393 0.255 0.270 -0.125 -1.458 0.787 -0.181 -0.023 0.472 -1.151 0.047 0.776 +0 2.387 1.779 0.158 0.114 0.347 -0.304 -0.790 -0.591 -0.495 -0.364 0.658 0.913 0.274 0.205 0.225 -1.153 1.973 1.668 -0.274 -0.032 -0.256 0.135 0.411 -0.610 -0.205 0.264 -0.531 0.433 +1 -0.956 -0.237 0.598 1.461 -1.080 -0.446 1.382 0.464 0.671 -1.051 -0.379 -0.168 -0.858 -1.675 -0.510 1.354 0.059 1.374 0.179 -0.765 1.278 0.212 0.962 -1.068 1.039 1.069 0.290 -0.807 +1 1.419 0.181 -0.171 -1.819 0.192 -0.005 -1.356 -0.781 0.093 0.467 -1.018 -1.009 0.116 -0.841 -0.201 1.775 -0.043 1.023 -1.246 -0.768 -0.789 -1.104 -1.768 0.193 0.387 -0.946 -0.745 0.078 +1 -1.169 0.320 -0.244 -0.476 -0.885 0.480 1.609 -0.140 -1.952 0.100 0.515 0.090 1.037 -0.993 0.125 -0.671 -0.453 -0.741 -1.244 -1.412 -0.685 0.150 -0.615 -0.526 -0.693 -0.223 -1.262 1.933 +2 0.332 1.395 -1.176 0.171 -0.699 1.628 2.250 0.555 0.878 1.366 0.765 -0.823 -0.445 -0.452 -1.733 0.978 -1.252 0.829 -0.727 -0.222 0.558 1.528 0.869 -0.118 -0.034 -1.177 0.637 -0.082 +4 1.560 -1.528 -1.061 0.789 -0.770 1.996 1.503 -0.268 1.722 1.054 1.885 -0.767 -0.463 -0.358 -0.032 0.745 1.472 -0.852 -0.135 0.264 -0.856 1.311 -2.823 1.641 -2.668 1.923 0.694 1.386 +3 -0.594 -0.866 0.274 1.430 -0.316 1.606 1.070 -0.556 -0.110 1.956 0.490 -0.809 0.873 -0.092 0.215 1.359 0.361 0.119 -0.144 0.554 -1.246 -0.914 1.097 2.814 0.442 1.479 -0.595 0.403 +3 -0.428 0.418 0.863 0.790 -2.237 1.365 -1.826 1.089 -0.600 -0.342 -0.286 0.100 1.263 1.299 0.190 0.834 -0.228 -0.351 0.865 1.040 -0.591 1.630 1.322 -1.767 -0.844 -0.635 -0.366 -1.087 +0 -0.884 0.699 0.034 0.494 -0.434 -0.076 -0.570 -0.236 -1.451 -0.776 -0.168 0.340 0.766 0.578 -0.145 -0.417 -0.414 -0.422 -0.153 -0.504 0.134 -0.844 -0.996 -2.173 0.224 -0.646 -0.485 0.858 +2 0.524 -1.954 -1.057 -0.158 0.147 0.861 0.043 0.051 0.238 -0.255 0.087 1.126 -0.084 -0.030 -0.034 1.298 -1.108 1.776 -1.176 -0.654 0.745 1.030 -1.403 -1.093 0.278 2.344 1.246 -0.248 +2 1.291 1.902 -1.133 1.276 -1.409 -1.047 -0.803 0.504 0.033 -0.167 0.146 0.591 0.668 1.094 1.820 -0.021 -0.521 0.614 -1.144 -0.679 0.999 -0.124 -1.208 -1.490 0.049 1.142 1.129 0.111 +4 -1.794 1.269 2.333 1.344 1.162 -0.485 0.485 -1.495 2.262 -1.760 0.584 0.842 0.329 0.254 -0.937 -1.816 0.733 0.990 0.444 -1.693 -0.312 -0.457 -2.586 -0.192 -0.096 1.890 0.449 -1.525 +2 -0.021 0.289 -1.179 -0.220 0.610 -1.555 0.201 0.101 0.041 1.037 0.334 1.478 -0.994 0.136 0.084 -1.069 0.956 2.325 -0.017 0.505 0.085 -0.714 -1.821 -0.706 -1.392 1.704 -0.115 -0.346 +1 0.504 0.386 -0.110 0.049 -1.404 -0.584 0.050 -0.736 0.860 -0.659 0.216 0.117 0.746 0.901 0.694 -2.106 1.194 1.527 -0.482 0.237 -0.891 -1.948 -0.739 -0.540 -0.355 -0.959 -0.733 -0.011 +4 1.004 -0.800 1.462 0.051 0.509 1.488 0.769 -0.000 -0.672 2.709 -0.789 0.318 0.510 0.557 2.128 0.048 -0.095 1.552 -0.136 0.876 0.194 0.921 0.725 -0.403 -1.844 -1.674 -1.840 -0.091 +0 0.226 -0.762 -1.142 -0.200 -1.375 -0.800 0.464 1.076 0.862 -0.662 -0.399 -0.473 0.197 -0.871 0.050 -0.112 0.592 -1.124 -0.959 0.302 0.126 -1.152 -1.867 0.523 0.120 -0.247 0.515 -0.809 +4 1.003 0.211 0.481 -0.061 -1.000 0.453 1.106 1.613 -3.353 -0.472 -0.931 -0.552 -0.438 0.384 -0.815 -0.610 1.693 0.197 0.151 -1.015 0.872 1.158 -1.105 2.422 0.549 -0.396 -0.382 -0.870 +3 -0.924 -0.484 1.663 0.273 -0.202 -1.015 2.002 0.017 1.168 -1.122 1.927 -1.338 1.230 0.192 0.276 0.272 1.720 0.670 -1.000 -1.021 -0.770 -0.705 1.806 0.376 0.019 0.399 0.745 1.253 +3 -0.853 2.403 0.009 0.799 0.140 0.297 -0.504 1.233 2.347 -0.629 1.938 0.597 -1.008 -1.398 1.040 -0.554 0.444 0.723 1.503 -0.338 -0.264 0.965 0.364 0.339 0.182 -0.335 0.919 0.739 +0 -0.676 -1.184 0.585 0.687 -0.343 -0.152 0.288 1.194 -0.692 -0.227 0.763 -0.794 0.215 0.126 0.320 0.114 -0.442 0.731 -1.132 0.679 -1.451 -0.178 1.204 1.381 0.970 0.548 0.143 -0.840 +1 0.877 1.295 0.146 0.104 0.597 -1.316 -0.358 0.979 0.864 2.199 -0.076 1.217 -0.059 -1.100 0.177 -0.219 -0.421 -1.074 -0.259 -0.839 1.984 0.907 -1.168 -0.597 -0.395 -0.208 -0.838 -0.111 +1 -0.293 -0.998 0.895 -0.391 -0.726 -1.690 1.682 -1.554 0.287 0.662 -0.388 0.419 0.496 0.841 0.604 -0.313 -0.400 2.049 1.094 -0.698 0.330 0.395 -0.018 1.756 -0.103 -0.082 0.823 -0.945 +2 0.543 -0.586 -1.667 1.145 -2.399 0.796 0.130 1.051 0.626 0.613 0.898 0.838 0.451 -0.335 -0.229 0.107 -0.266 2.186 -0.087 -1.150 -0.121 -0.838 1.958 0.098 0.635 0.515 0.946 0.238 +2 -0.116 -0.393 1.343 -0.264 0.353 0.278 0.489 -1.100 0.440 -0.882 -0.599 0.929 -0.503 -0.638 0.204 1.347 2.405 1.142 1.129 -0.024 2.560 0.199 -0.036 0.383 0.264 -1.314 -0.697 0.381 +2 0.546 -0.481 1.001 -0.022 -1.243 -0.254 2.318 -0.190 0.237 0.288 -0.043 1.532 0.733 0.653 -0.269 0.695 -0.308 0.427 2.065 1.562 -0.117 0.224 -1.098 -0.243 -2.224 0.914 -0.748 -0.096 +0 -0.064 1.579 -0.310 -1.074 0.817 -0.235 -0.173 -0.225 -0.155 0.497 -0.305 0.308 1.044 1.478 -0.903 1.021 1.133 1.261 0.049 -0.239 -1.545 0.099 1.782 0.314 -0.216 0.580 -1.186 -0.273 +1 -0.786 0.537 -0.489 -0.707 0.824 -0.828 2.482 1.594 -0.213 1.250 0.187 -0.416 1.978 0.390 -0.894 0.460 1.677 0.002 0.210 -0.300 0.352 -0.978 -0.796 -0.342 0.168 1.112 -0.147 0.827 +1 0.400 -0.879 -0.272 1.513 0.718 0.238 0.281 -0.014 1.025 -1.280 0.211 -0.810 -1.048 -0.050 -0.882 1.619 0.128 -1.189 -0.720 1.031 0.647 0.676 1.427 0.406 0.886 1.208 1.181 0.759 +0 -0.289 -0.351 -2.022 0.128 1.055 -0.558 -1.642 0.095 1.061 0.157 -0.727 -1.049 0.706 -0.232 -0.519 -0.607 0.220 0.117 0.054 -0.965 -1.025 -0.651 0.290 0.550 1.467 -0.837 0.726 -1.720 +3 0.495 0.087 1.009 -0.786 -0.224 0.114 -1.434 -1.806 -0.021 1.338 -0.352 -0.215 1.151 -0.149 0.777 2.666 -0.577 -0.177 0.396 1.492 -0.115 0.729 0.348 -0.072 -0.420 -0.609 3.066 -1.541 +3 -0.013 -1.651 2.740 2.158 0.175 -1.111 0.381 -1.192 -2.035 -0.227 0.132 -0.101 -0.908 0.417 -0.425 0.869 0.499 2.180 -0.218 -0.039 0.545 0.590 0.478 0.124 0.386 -1.135 0.754 1.444 +0 -0.677 -0.418 0.261 0.151 -0.506 0.582 1.058 0.593 0.997 1.409 -0.742 0.284 -0.436 -0.446 -0.742 0.629 -0.561 -1.075 0.272 -1.381 -0.761 -0.186 -0.147 -1.551 -0.498 1.600 -0.434 -0.423 +3 0.640 1.837 -0.346 1.139 0.266 0.706 1.863 0.823 -0.755 -1.112 -1.456 -1.761 -0.434 0.212 0.028 -0.616 -1.865 1.301 -0.699 0.521 -1.026 -1.620 -0.466 0.561 -1.693 -0.316 -0.122 -0.422 +1 0.389 0.434 -2.474 -0.573 0.477 1.099 -0.634 -0.415 -0.238 -0.135 0.854 -0.738 0.865 1.419 -0.394 -0.430 0.158 -0.586 -0.088 -1.470 0.856 1.663 -0.673 -1.646 0.921 -0.418 -0.668 -0.122 +2 0.295 2.323 0.387 -1.435 0.058 0.395 0.516 -1.839 0.500 0.412 1.402 -0.897 -0.267 0.546 -1.341 -0.051 -0.615 -0.641 -1.998 -0.461 1.346 1.452 0.687 1.068 0.631 -0.762 0.242 -0.090 +0 0.728 0.782 -0.742 0.028 -0.043 0.169 -0.372 0.815 1.106 -0.960 -1.194 -0.796 0.049 0.356 0.788 0.971 0.362 0.230 1.649 0.005 0.590 0.320 -0.173 -0.167 1.322 -0.090 -0.353 -0.737 +3 -0.335 -1.133 -1.486 0.493 0.325 -1.167 -0.746 1.161 -0.878 0.171 -0.190 0.506 -0.856 0.090 0.169 0.230 0.079 0.345 2.192 2.561 -0.639 -2.318 0.595 0.186 1.061 0.285 0.059 1.129 +0 -0.246 -0.770 1.455 2.318 0.376 0.305 0.226 -0.003 -1.460 -0.740 -0.037 0.906 -0.340 1.585 1.093 -0.034 -0.191 0.291 0.580 0.084 0.217 0.984 -0.026 0.938 -0.320 -0.776 -0.515 -0.323 +2 -0.470 0.945 1.449 0.104 0.964 1.375 -1.982 1.155 1.412 1.269 1.807 0.084 -0.349 0.267 -1.016 -0.353 0.548 -0.415 0.115 -0.721 0.243 0.541 0.053 0.161 -1.243 -0.652 1.600 0.616 +1 -1.243 -0.593 -0.109 -0.502 0.750 0.801 0.509 -1.020 1.770 0.339 -1.593 -1.204 -0.759 1.652 -0.873 -1.060 1.095 0.895 -0.559 0.508 -0.253 0.620 -0.305 0.529 0.023 -0.590 0.931 0.070 +0 -0.483 -0.640 -0.172 -0.333 1.057 1.328 0.426 -0.236 0.258 0.805 1.002 0.370 1.528 -0.617 1.140 0.704 -0.610 0.233 0.301 -0.142 -0.153 -1.525 -0.689 -0.238 -0.573 -0.856 1.061 -0.104 +1 0.614 -1.291 -0.750 0.199 -1.083 -0.627 0.755 -0.606 0.374 -0.827 0.601 -1.121 0.594 2.163 -0.535 -0.661 1.139 0.065 -1.170 0.339 0.217 -0.633 -0.671 0.869 -0.988 -1.371 -0.775 0.866 +4 0.784 -1.998 2.018 1.254 -1.793 0.730 -2.124 -0.008 -0.235 0.618 1.418 0.133 0.448 0.280 -1.201 -0.538 1.831 -0.655 0.864 -1.157 -0.800 0.091 0.227 -0.958 -2.123 1.355 -0.831 0.219 +4 1.529 1.892 -0.452 -2.508 0.328 -0.373 -0.938 0.258 0.210 0.715 -0.507 -0.602 -0.629 -0.678 0.676 -0.798 -1.432 0.491 0.447 1.913 2.482 1.519 0.671 -0.020 -0.761 2.225 0.391 1.452 +1 0.667 0.648 0.808 1.095 0.030 -0.796 -0.064 0.621 1.085 -1.148 0.593 -1.218 0.371 1.112 0.349 0.298 -1.426 0.218 -1.953 -1.016 -0.030 1.371 -0.265 -0.740 -0.893 -0.469 1.759 -0.496 +1 0.359 -1.271 0.669 0.370 -0.685 -0.456 -0.861 -1.307 -1.327 -1.530 -0.497 0.063 0.164 0.783 -1.737 0.784 1.243 0.334 0.894 -1.156 0.296 1.218 1.689 0.485 -0.076 -0.102 -0.239 -0.050 +1 -1.321 -1.158 -0.516 -0.835 0.536 1.234 -0.151 0.430 0.221 -0.891 -0.461 -0.869 0.626 -1.685 0.314 -0.818 0.630 0.827 1.810 -0.014 -1.577 -0.902 0.402 -0.156 -0.424 1.027 -1.011 0.111 +1 -1.182 -1.073 -1.266 0.506 -0.092 1.185 -1.164 -0.636 1.086 0.006 0.613 -0.511 -0.014 -2.223 1.358 0.757 -1.985 -0.185 0.624 0.446 0.256 0.650 -0.182 -0.451 0.412 -0.565 0.548 0.103 +2 -2.063 -1.571 1.263 1.094 0.431 -0.330 -0.314 0.141 -0.186 -0.716 0.265 0.776 -0.190 0.852 -0.209 -0.434 0.139 1.558 0.530 1.408 -0.305 0.344 0.980 -0.640 -1.521 -1.249 0.896 1.986 +0 0.505 0.532 -0.774 0.600 -0.360 0.273 -0.859 1.469 -0.295 -0.067 -1.307 0.356 -0.525 0.557 0.503 -0.859 1.056 0.323 0.150 0.552 0.513 -0.015 1.107 0.745 0.797 0.461 -1.753 0.146 +0 0.977 -1.670 -0.215 0.541 -0.253 1.178 -1.415 0.815 0.053 -0.853 0.439 -0.070 1.480 0.583 0.413 -0.673 0.147 -0.575 -0.062 1.245 -0.286 -1.446 0.495 0.787 1.589 0.864 -0.272 0.261 +1 1.159 0.610 0.287 0.801 0.409 1.085 2.857 0.505 -0.860 1.039 -0.146 0.337 0.367 -0.582 -0.371 -2.308 -0.101 0.561 0.015 -0.460 0.377 -0.943 -1.058 0.526 0.581 0.511 -0.301 1.094 +3 -0.867 -0.371 -1.849 0.884 1.158 0.846 -1.605 0.607 -1.673 0.058 0.205 0.310 0.535 -0.008 -1.962 0.535 0.098 -0.903 -1.569 1.540 1.231 0.706 -0.676 -2.517 0.765 0.367 -0.375 1.125 +2 -1.673 -0.900 0.865 -0.499 0.415 0.417 0.890 -0.647 -2.552 0.397 -1.569 0.935 -0.042 -0.296 0.691 -1.742 -0.200 0.448 0.253 -0.489 -0.479 1.543 0.913 -0.664 1.348 0.755 0.488 0.527 +1 0.376 -0.908 -0.535 1.499 -0.360 0.009 -0.234 0.126 1.044 -0.113 1.005 0.576 -1.224 0.435 1.235 0.052 0.262 0.520 1.107 1.468 0.953 0.164 0.763 0.942 -0.999 -2.354 -1.723 -0.213 +1 -0.658 0.059 0.818 1.574 0.445 -0.410 0.287 0.819 0.036 0.686 -0.157 -0.609 0.030 0.713 1.868 0.356 1.265 0.935 0.390 0.961 0.893 -0.786 -2.276 -0.880 0.684 -0.849 0.902 0.157 +1 -0.187 0.237 0.438 -0.159 -0.747 0.904 -0.374 -0.981 -0.769 -0.337 -0.295 -0.541 0.312 -0.088 0.884 0.079 -0.356 -0.506 -0.399 -1.077 -0.236 0.479 0.778 0.179 3.384 0.324 -1.734 0.414 +0 0.234 0.137 0.260 0.791 -0.083 0.548 -1.799 -0.962 -0.342 -1.005 0.007 -0.344 -0.455 0.884 0.084 -1.084 -0.603 0.753 0.200 1.196 0.847 -0.389 1.436 1.518 -1.785 0.032 -0.550 -0.260 +2 0.445 -0.012 0.044 -0.603 -0.346 -2.047 -0.121 1.023 0.857 0.686 -0.896 0.416 -1.018 0.286 -1.156 -2.007 -1.829 0.935 0.864 -0.190 -1.309 -1.096 -0.684 1.377 -0.477 0.010 0.855 -0.502 +0 0.023 0.601 0.085 -0.351 1.867 -1.607 -0.586 0.872 -1.175 0.912 0.384 -0.646 0.000 1.222 -0.363 0.385 -0.835 -1.398 0.078 -0.085 0.315 -0.037 -0.096 0.362 1.760 -0.543 0.606 1.164 +2 -1.321 -0.959 -1.472 0.915 -0.658 -0.160 1.499 -0.370 0.750 -0.273 0.971 -0.372 1.129 1.207 -1.340 0.644 1.284 -0.375 -0.749 -1.459 0.253 -0.214 -0.117 -0.130 0.509 0.596 2.359 0.701 +0 0.513 1.886 -0.288 -0.499 0.732 -0.878 -0.167 -0.386 -0.033 -0.450 0.150 0.514 0.444 0.729 1.030 1.190 -0.193 -1.248 -0.203 -0.552 -1.030 0.999 0.305 -1.015 1.923 1.133 0.603 0.239 +2 1.353 0.292 -0.382 -0.198 0.770 -0.342 2.391 -0.790 0.699 0.941 -1.642 0.209 -0.306 -0.321 0.999 -0.218 1.438 -1.258 -1.168 -0.309 -0.624 -0.826 -2.297 1.040 0.364 0.514 -0.774 0.549 +4 0.208 0.986 1.491 -0.301 0.267 1.679 -2.124 -0.690 1.466 1.093 0.522 -0.449 -0.882 -0.591 1.548 -0.628 0.101 -0.754 0.843 -0.539 -0.098 -1.683 -1.613 0.008 -1.325 2.105 -2.062 -0.800 +3 0.564 0.435 0.177 -0.894 -0.162 -1.947 -0.381 0.784 -1.107 -0.833 -1.311 2.206 1.179 -0.443 2.470 1.240 1.039 -0.068 0.736 0.809 -1.338 0.318 -0.081 0.025 0.875 1.338 -1.208 -0.836 +2 -0.341 -0.639 -1.311 -1.096 -0.589 1.209 3.193 -0.723 -0.902 -1.168 0.451 0.344 0.101 -0.444 1.359 0.764 0.721 0.370 -0.189 -0.715 0.520 0.423 0.567 -1.564 0.339 -0.309 -0.879 0.728 +1 0.568 -0.332 0.180 0.351 -0.183 1.350 0.177 0.306 -0.162 -0.060 1.219 0.146 0.169 0.182 -0.365 -1.408 1.573 1.068 1.249 -1.073 1.445 1.967 1.045 1.648 0.798 -0.847 0.508 -1.020 +1 0.087 -0.731 0.245 -1.243 -0.945 0.646 -1.016 0.538 -1.369 1.765 -0.774 -0.224 0.331 -0.565 0.143 0.575 0.961 0.791 -0.172 -0.147 2.349 0.950 -0.922 0.100 -0.335 -0.135 -1.229 -0.412 +4 0.323 1.158 0.721 -0.359 -1.043 -0.479 -0.828 0.118 0.489 -0.406 -0.366 -2.651 -0.570 3.137 1.122 1.331 0.486 0.927 -0.344 -0.813 1.487 -1.092 0.490 1.782 0.967 -0.257 1.278 0.217 +4 -1.434 -1.159 2.028 0.119 -1.125 -0.658 2.195 -0.760 -0.078 1.651 1.107 0.961 0.260 -1.384 2.115 1.102 -0.361 -1.228 -0.686 0.137 0.180 0.181 -2.689 0.191 1.885 -1.247 0.203 0.683 +0 0.271 0.066 1.374 0.128 1.478 -0.083 0.829 1.703 0.976 -0.154 0.218 -1.203 -0.994 -0.441 1.904 -0.292 0.021 -0.181 -0.229 1.166 0.084 -1.621 0.477 0.169 -0.366 0.003 -0.352 0.285 +3 -1.391 0.116 -0.123 1.548 -1.527 0.504 0.523 -0.025 -0.428 1.336 -0.231 -0.350 -0.917 -1.635 1.077 0.708 -0.147 -0.842 1.076 2.097 0.147 2.704 -0.245 -0.894 0.763 -0.263 0.847 1.116 +0 0.580 0.399 -0.512 -0.181 -0.772 0.225 0.866 -0.791 -0.505 0.565 2.528 0.684 -0.181 -1.042 1.062 -0.818 -0.207 -0.388 -0.089 -0.951 0.953 0.438 -0.305 -0.241 -0.340 0.119 -0.781 -1.047 +1 -0.336 0.379 2.500 -0.565 -1.629 0.289 0.684 -0.434 -0.594 -1.271 0.337 1.944 1.712 0.301 -0.416 0.382 -0.325 -0.641 0.163 1.278 -0.189 -0.201 -0.265 0.793 0.852 -1.076 -0.177 1.106 +3 -0.023 0.138 -0.811 0.211 1.449 0.813 0.542 0.364 -1.752 -1.119 1.444 -0.080 0.714 0.113 -1.316 0.152 0.133 0.719 1.904 1.557 0.416 -0.090 -0.203 -0.586 1.685 2.464 1.268 0.232 +2 -1.805 0.211 0.544 -0.939 2.026 -0.624 -0.511 0.952 0.631 0.190 0.451 -0.365 -0.484 -1.105 1.939 -1.351 -0.047 -1.318 -0.166 0.454 0.033 1.883 0.431 0.026 0.658 -0.493 -0.168 -1.280 +1 1.360 -0.886 0.504 0.085 0.818 -0.234 0.473 1.102 -0.651 0.242 -1.888 0.703 1.955 -1.244 -0.091 0.880 0.031 0.165 -0.391 -0.428 -0.087 0.042 -1.181 -0.066 1.569 -0.419 -1.237 -0.151 +1 0.893 0.752 0.651 -1.272 -0.822 0.294 0.472 1.404 -0.575 -0.003 1.171 -0.139 0.166 -0.044 0.570 0.505 -1.607 0.646 1.677 -1.387 0.340 0.578 -1.320 -0.762 -0.682 0.217 -1.364 -0.686 +0 1.145 -0.748 -0.056 0.891 -0.276 0.549 -0.912 0.485 0.735 -0.567 0.132 0.259 -0.245 -0.593 -0.308 -0.113 0.031 0.295 0.138 -0.354 -0.396 0.598 0.237 0.184 -0.250 -1.054 -0.175 0.111 +1 1.540 -0.290 0.499 -0.122 1.199 -0.438 -0.497 0.406 0.472 0.794 -0.521 -0.681 1.700 0.880 -1.438 -1.110 -0.871 1.148 0.098 -0.886 0.440 1.328 0.987 0.717 -1.749 -0.466 0.603 -1.137 +2 2.412 -1.169 -1.258 0.693 0.065 1.194 0.738 -0.005 0.329 0.667 -0.970 0.106 -0.636 -0.204 0.760 -0.834 -0.068 -0.888 -0.439 1.348 -0.161 -0.236 -2.046 -0.931 -0.815 1.029 -0.231 -1.456 +2 -1.164 -0.102 0.061 0.799 1.846 0.498 2.303 0.191 -0.794 -0.710 -0.074 -0.727 0.881 -0.149 0.322 -0.968 0.741 -0.613 1.164 -0.612 -0.663 -0.909 -1.312 1.335 1.323 0.824 -0.326 -1.574 +2 -0.290 0.848 -0.167 0.554 -1.126 1.918 0.197 -0.218 -1.890 0.053 0.258 -0.203 0.496 0.265 -0.924 1.094 -0.133 0.235 1.395 -1.028 -0.017 -0.198 -1.787 0.278 2.212 0.709 -0.063 -1.481 +4 -0.584 1.401 -0.486 -0.654 -0.589 -0.307 0.613 -0.135 1.621 -0.085 -1.033 0.218 2.027 -0.014 -1.733 -0.863 0.329 -1.888 1.620 -0.408 -2.900 2.460 1.472 -0.591 -0.895 -0.094 1.644 0.014 +0 1.773 0.394 0.525 0.317 -0.376 0.601 -0.386 0.407 -1.437 1.151 -0.647 -0.979 0.352 0.092 -0.385 -0.576 -0.956 0.605 0.142 0.838 0.540 0.114 -0.648 1.619 -0.751 0.307 1.320 0.951 +1 -0.353 0.318 -1.156 0.805 -1.627 0.644 -0.503 0.462 -0.706 -0.852 1.588 1.702 -1.144 0.159 0.774 -0.066 -0.032 0.723 0.547 -0.449 0.527 -0.264 -0.355 -0.737 -0.734 0.259 -1.530 -1.558 +1 -0.300 0.118 0.091 0.006 -1.016 -0.590 0.367 0.651 0.234 0.239 2.039 1.381 -0.447 1.934 0.862 1.681 0.685 -0.697 0.625 -0.422 0.825 -1.480 0.376 0.392 1.127 0.962 -0.406 -0.544 +0 0.366 -0.480 0.828 -0.127 0.270 -0.079 0.770 0.164 -0.928 0.967 0.836 0.462 0.245 -0.802 0.041 -0.672 0.502 0.707 1.123 0.153 0.506 -0.344 -1.153 -0.618 0.624 -0.272 -1.559 -0.068 +0 0.809 -0.129 0.065 -0.224 0.423 0.685 -0.123 -1.498 0.297 -0.340 0.857 -0.896 -0.314 -1.482 0.783 1.225 -0.488 -0.194 -0.810 -0.713 0.694 -0.504 0.475 -0.015 0.751 -0.392 1.085 1.875 +0 0.727 -1.129 0.011 0.427 -0.981 -0.460 0.759 -0.059 -2.017 0.774 0.089 -1.606 -0.878 -0.126 0.312 1.029 -0.901 -0.451 0.884 1.045 -0.323 -0.533 0.500 -2.071 -0.014 -0.027 -0.468 0.472 +3 -1.462 0.015 -0.409 -1.498 -0.293 0.134 0.443 -1.349 -1.460 1.011 1.863 -1.055 -0.944 -0.675 -1.020 -0.414 -0.816 0.381 -0.198 0.243 1.533 1.066 -0.316 0.605 2.275 -1.750 -0.234 -0.342 +3 -0.085 -0.104 0.013 0.294 0.714 -0.483 -0.214 1.301 0.955 -1.167 1.073 0.521 1.797 -1.197 0.400 0.302 2.039 0.217 0.429 1.231 1.723 -0.755 -0.541 0.300 1.131 -1.524 -0.440 2.152 +3 0.999 1.288 0.029 -0.593 -2.198 0.431 -0.301 -2.100 0.645 -0.615 0.369 -0.078 0.341 -1.480 -0.303 -2.530 0.361 1.968 1.610 -0.292 -0.424 -1.113 -0.698 1.041 0.646 -0.261 0.345 0.981 +2 0.563 -0.138 0.780 -1.174 -0.356 1.648 0.068 0.126 2.306 -1.399 0.796 1.664 -0.325 0.860 -0.982 -0.687 0.385 0.799 1.121 1.570 -0.067 1.194 -0.342 0.902 0.207 0.446 0.424 -1.605 +4 0.369 -1.186 -0.801 1.565 -0.925 0.934 -0.478 -0.003 0.295 -2.101 -1.463 0.086 -0.724 0.997 -1.606 0.565 -1.376 -0.352 -1.064 1.902 -1.339 -0.122 -1.727 1.600 0.401 0.137 1.474 0.147 +1 2.545 -0.067 0.029 0.771 -1.276 -0.978 -1.597 0.837 -0.861 1.002 -0.462 0.004 -0.171 0.190 -0.489 1.040 -0.168 0.364 0.110 -1.014 1.349 -0.652 0.809 0.729 -1.499 -0.180 0.580 -0.290 +1 -0.192 -0.037 -0.321 0.540 -0.102 -0.607 -0.396 1.218 0.317 -1.676 1.511 0.434 0.639 0.929 0.981 -0.919 -1.070 2.396 -0.698 0.379 1.631 -0.444 -0.360 0.926 0.213 0.495 -0.080 -0.526 +1 -0.554 -1.721 -0.041 -0.419 0.116 -1.080 0.161 2.415 -1.249 0.065 0.320 0.066 -0.025 0.078 -0.430 1.680 0.414 -0.623 1.211 -0.724 -1.135 -0.421 1.435 -1.118 -0.286 -0.584 0.857 -1.075 +0 0.367 0.767 0.018 -0.347 0.462 -1.083 -0.422 0.587 2.135 1.305 -0.129 1.882 -0.101 1.236 0.689 -0.558 -0.216 0.406 -1.076 -0.256 -0.234 -0.668 0.033 1.008 -0.908 -0.434 0.785 1.008 +0 -0.397 0.726 -0.818 -1.514 -0.351 -0.422 0.347 0.100 -0.396 0.716 -0.289 1.303 0.455 0.546 0.678 -0.016 -1.115 0.417 -0.548 -0.105 -0.548 -0.078 -0.117 0.014 0.449 -0.111 0.396 0.871 +3 0.441 0.377 0.045 -1.654 0.136 0.449 0.823 -1.668 1.993 -1.098 0.945 0.269 1.499 -2.075 -1.095 0.562 0.188 -0.392 -0.780 0.530 -1.816 2.326 0.900 -0.497 0.465 -0.135 -1.088 -0.283 +3 0.884 1.089 0.749 1.137 -0.187 2.265 -1.546 -0.275 1.731 -0.171 0.663 -0.472 0.446 -0.360 1.169 0.291 -1.750 -0.813 -0.975 1.406 -1.241 -0.268 2.111 1.248 -0.855 -0.884 -0.981 0.206 +4 0.938 -0.786 2.542 -1.533 -0.852 0.234 2.247 -0.563 -1.066 0.549 -0.249 0.665 -0.071 -0.857 2.349 -2.954 1.820 0.933 -0.746 1.872 -1.371 1.008 -2.083 -1.146 -1.148 -0.751 0.012 -0.826 +3 1.525 -0.445 1.601 0.892 -0.155 -1.813 -0.019 0.102 1.167 1.588 -0.685 0.801 0.765 1.073 0.499 -1.942 -0.155 -1.156 -0.203 0.980 0.305 1.148 0.342 -1.298 -2.521 0.149 -0.214 -0.977 +2 0.583 0.617 0.893 0.583 -1.161 0.044 -0.795 0.730 -0.166 -1.150 1.248 2.176 -0.824 -0.948 -1.243 -1.764 0.434 0.307 -0.866 0.349 -1.148 0.716 1.223 0.511 0.295 -0.241 0.694 2.019 +2 0.277 0.009 -1.571 1.356 -0.611 0.504 -1.563 -1.179 1.435 -0.347 1.453 -0.078 -1.842 0.892 -0.590 0.699 0.025 -1.543 0.164 2.196 0.493 -0.058 1.075 0.211 0.210 -1.092 0.234 1.035 +3 1.249 1.078 0.409 -0.606 0.536 0.152 -1.207 -0.207 0.553 1.513 -0.765 0.859 -0.595 -0.057 -0.402 1.937 0.649 0.803 -0.703 0.531 -1.194 -2.534 -0.449 1.357 -0.410 0.666 2.627 -0.087 +1 0.308 0.277 1.419 0.610 -0.168 -1.554 -1.524 0.141 -0.466 1.335 0.256 -0.529 -1.118 0.691 1.123 -1.009 -0.257 2.151 0.603 0.940 0.137 -0.354 -0.203 -0.154 -1.199 -0.142 -0.308 0.636 +1 -1.158 0.405 0.228 -1.344 0.609 -1.064 0.878 0.208 0.456 -0.109 0.534 -0.730 0.407 1.408 -0.185 0.004 -0.587 1.421 -1.611 -0.548 1.698 1.188 0.029 -1.246 1.140 -0.176 0.580 1.486 +3 -0.498 0.635 0.431 -0.143 0.440 -0.056 -1.061 -0.598 0.298 -0.645 1.400 -0.413 -1.030 1.792 0.392 1.808 0.410 -0.671 -0.034 -0.461 1.193 0.182 3.024 0.266 2.684 -0.418 -0.896 0.444 +1 0.193 0.060 0.106 -0.638 -0.058 1.128 0.114 -0.793 -0.925 -0.450 0.074 1.194 -1.340 -0.320 0.144 -1.834 -0.834 -0.680 -0.796 0.192 -0.161 -0.555 0.428 1.723 1.639 -1.923 0.577 -0.178 +1 0.944 0.921 0.728 -1.898 0.241 -0.578 -0.924 0.657 0.282 0.244 -0.891 1.218 1.530 -1.192 -0.549 -0.559 -0.236 0.242 -0.943 1.462 1.225 -0.739 -0.901 1.353 -0.357 -0.948 0.333 0.855 +2 0.497 -1.415 -0.839 -0.973 0.574 -0.462 0.425 1.140 1.780 0.231 -1.102 1.251 1.511 -0.060 1.319 0.333 -1.659 -0.631 -0.505 -0.364 -0.933 -0.965 1.346 -1.737 -0.724 0.621 -0.557 0.400 +2 -2.290 -0.808 0.756 -1.128 0.625 -1.179 0.760 0.565 -0.989 -1.038 1.447 -1.236 -1.260 0.217 0.869 -0.527 -0.069 -0.048 -0.292 0.185 1.837 0.876 -0.258 -0.470 0.403 1.056 -0.276 1.124 +3 0.708 1.459 0.257 1.571 -1.069 -2.017 -1.226 0.756 -0.757 0.271 -0.041 0.306 1.157 -0.156 1.284 -1.520 0.155 2.097 1.153 0.710 -0.300 0.537 1.020 -0.099 1.481 0.018 0.776 1.306 +3 2.368 2.901 1.695 1.001 -0.379 -1.927 1.333 -0.496 0.024 0.425 0.590 0.353 -0.775 -0.159 -0.315 1.076 -0.329 -0.270 0.094 -0.296 -0.006 -1.065 -1.128 -0.580 0.328 -0.886 0.261 0.968 +4 -0.884 0.154 0.058 -1.143 0.358 0.561 1.083 1.054 -1.378 -0.938 0.515 0.514 0.515 3.853 0.571 1.136 0.954 0.651 -0.315 0.759 -0.773 -0.237 -0.485 0.082 2.315 -1.867 0.686 -1.613 +3 2.007 -0.405 0.581 0.807 0.885 -0.873 1.669 0.775 -0.209 -1.120 -1.608 -0.167 1.048 -1.321 -0.221 -0.693 -1.475 0.072 1.119 0.083 -1.008 1.243 -1.220 0.478 -0.874 0.180 1.794 -1.389 +1 -0.497 -0.953 0.363 -1.174 -0.994 1.153 1.901 0.393 1.106 -0.246 -1.338 0.612 0.569 0.072 -0.242 0.962 -0.986 -0.774 -1.008 0.085 -1.088 -0.684 -0.706 -0.261 -1.487 0.087 1.247 0.135 +4 -0.770 -0.676 2.302 -0.618 1.470 -0.850 0.961 -1.597 -0.554 0.975 0.506 -1.007 0.113 -0.696 -0.930 2.086 -0.342 0.459 -0.858 1.247 0.269 0.265 0.702 1.409 0.882 0.922 -2.788 1.026 +1 0.066 -1.573 0.911 0.892 -0.107 1.197 -0.514 0.001 1.295 -0.315 -0.406 0.099 0.851 -1.191 1.109 1.070 1.360 -1.711 1.154 0.131 -0.275 -0.328 -0.264 0.550 0.141 0.096 -2.358 -0.603 +4 -0.757 -1.882 -0.808 0.059 0.783 0.098 1.341 0.365 0.525 -1.021 -0.061 -0.907 0.202 -1.742 -0.871 -0.053 -0.015 0.183 -2.532 -0.631 0.326 -0.212 0.921 -1.018 0.575 -0.217 -2.869 1.923 +4 1.088 -0.175 1.533 2.876 -2.514 0.660 1.388 1.698 -1.112 -1.058 -0.757 0.530 -0.133 1.464 0.685 0.517 0.047 0.506 0.085 -0.129 -0.545 0.346 1.343 -0.668 -0.614 0.194 1.213 -1.720 +4 0.698 -0.625 0.376 -1.011 -1.555 0.877 -0.981 -0.181 0.206 -0.182 -1.028 0.061 0.148 0.871 2.445 -1.976 -0.799 0.223 1.011 0.346 1.123 -1.480 -0.318 1.462 -0.860 -2.899 -0.817 0.437 +2 0.359 0.415 -2.383 -0.240 -0.038 1.281 0.048 -1.647 0.430 -0.804 -1.217 -0.834 0.543 1.259 0.483 0.632 0.473 0.101 0.697 -2.419 0.111 -1.045 1.163 -0.147 0.031 -1.505 1.017 0.627 +1 0.285 0.683 -0.384 -0.530 -0.320 -1.531 -0.509 -1.015 -0.705 -1.258 -2.108 -0.027 1.084 -1.294 -1.258 -0.920 -0.016 0.549 -0.592 -1.519 0.652 -0.405 0.701 0.680 -0.484 -0.305 -0.930 -0.813 +1 1.076 -0.762 -0.443 -0.386 2.079 0.283 0.054 -1.691 -1.020 -0.159 -0.885 0.755 -0.017 -1.086 -1.155 -0.265 -1.427 0.425 -0.872 0.011 0.397 1.394 -0.870 0.537 0.059 -0.738 -1.252 0.483 +2 -0.340 1.896 -0.565 0.682 -1.580 -1.015 1.029 1.421 -0.150 2.280 1.309 -0.696 -0.378 -1.386 -0.443 0.239 0.040 0.031 0.997 0.167 0.685 0.447 0.287 0.922 0.802 -1.126 1.819 0.120 +4 -1.439 -0.633 0.961 -0.022 -0.008 -0.295 0.562 1.432 2.984 -0.148 1.526 0.999 0.267 -2.210 -0.378 -0.338 0.359 -0.197 -0.563 2.030 1.308 -2.023 -0.281 0.631 1.331 -0.420 -0.215 0.095 +3 -0.117 1.911 0.512 0.461 -0.430 -0.207 0.106 -2.049 0.552 0.322 -0.668 -0.356 1.085 -2.634 -0.916 -0.394 0.416 -0.386 -0.803 0.485 -1.760 0.216 -1.226 -1.392 1.287 -0.549 -0.457 1.075 +2 0.234 -0.296 0.847 0.340 1.732 1.886 -1.605 1.657 0.032 0.119 -1.356 -0.312 0.844 -0.749 -1.798 -0.865 0.369 0.854 0.685 -0.428 -0.797 -0.832 0.231 0.352 -0.794 0.522 1.196 1.180 +0 0.275 -0.640 0.699 1.918 -1.590 -0.088 -0.388 0.012 -1.171 -0.278 0.729 0.065 1.191 1.001 -0.391 0.220 -1.356 -0.295 1.552 -0.554 0.081 -1.012 -1.144 1.151 -0.114 0.026 -0.831 -0.209 +2 1.185 -0.154 0.178 -0.330 0.504 -0.145 -0.167 -0.463 -0.821 -0.301 -2.847 -1.317 -1.627 0.697 1.456 1.060 -0.319 1.028 -0.931 1.595 -0.578 -0.476 0.961 -0.654 -1.300 -0.615 0.187 -0.345 +4 -0.715 -1.338 0.139 -0.710 -0.625 0.153 -1.533 -1.742 0.671 -1.931 -0.977 -1.800 -0.486 0.428 0.112 2.392 1.087 -0.931 -1.197 2.707 1.063 0.589 0.075 0.274 -1.482 0.157 0.071 -0.904 +1 -1.111 2.029 -1.175 1.024 -1.273 0.879 -0.942 0.784 0.046 0.481 0.620 0.917 0.077 0.085 -0.147 -0.316 1.234 0.214 -0.258 0.967 0.734 1.291 -0.757 -1.223 0.141 -1.776 -0.189 -0.589 +0 -1.610 0.328 -0.777 -0.248 0.309 -0.213 0.230 0.673 0.295 -0.647 -0.629 -1.440 -0.854 1.962 -0.792 -1.193 0.320 -0.110 -0.196 1.409 0.549 -0.530 -0.424 -0.503 0.875 -0.308 -1.793 -0.048 +3 1.047 0.491 0.995 0.844 -0.772 2.823 -2.261 -0.864 0.681 0.291 0.134 0.716 -0.843 0.840 -0.129 1.401 -0.305 0.468 0.637 0.905 0.037 -0.287 -0.775 0.486 -1.897 0.309 0.383 1.874 +3 -0.847 -1.362 -2.517 1.040 -0.251 2.034 0.028 -0.630 -0.279 1.045 -0.546 0.213 1.006 0.136 1.124 -0.978 -0.271 -0.562 -2.467 -0.138 1.129 0.227 -0.545 -0.231 -0.838 0.423 -0.112 -0.992 +2 0.170 -0.173 0.221 0.553 -0.294 -0.737 -2.006 0.771 0.079 -0.176 1.172 -1.289 0.056 -0.964 0.656 -2.226 0.804 0.763 -0.923 0.066 -0.902 0.428 0.820 -1.005 -1.297 1.096 1.941 0.494 +2 1.533 1.630 -0.933 0.753 -1.383 -0.463 -1.174 -1.153 0.242 -0.692 0.287 -1.209 0.484 -0.430 -0.292 -1.300 1.019 0.140 0.540 -1.277 0.676 0.041 -0.496 1.377 0.409 0.453 -1.965 1.081 +4 -0.607 -0.440 -0.740 -0.813 -2.429 0.087 0.068 -0.797 0.360 1.397 0.024 1.330 2.238 2.531 -1.188 -1.183 -0.444 0.129 1.587 0.627 -1.203 -0.612 -0.042 -1.489 0.014 -1.188 -1.651 -0.947 +0 0.614 -0.919 -0.453 0.271 0.274 -0.472 0.311 -0.002 0.096 1.367 -1.080 0.481 1.225 0.237 0.112 -0.255 0.993 0.614 1.010 1.799 -0.158 -0.687 -0.286 1.048 1.482 -0.429 0.465 -1.848 +4 -0.503 0.447 1.320 -0.676 1.755 -0.931 0.555 -1.111 0.526 -0.656 -2.306 1.395 0.207 -0.538 -0.234 -0.192 -0.357 -0.389 -0.139 1.989 -2.279 -2.013 0.449 -2.584 -0.500 -0.785 -0.039 -0.626 +3 0.876 0.935 0.172 -0.281 -1.347 -0.472 -1.855 -0.353 0.047 1.086 -0.528 0.252 1.142 -1.138 0.566 -1.659 -0.954 -0.861 -0.691 -0.717 0.339 -0.209 -1.035 -1.520 0.679 0.958 -0.010 -2.798 +3 -0.979 -0.337 -0.351 -0.690 0.307 0.613 0.612 0.893 -0.095 1.362 0.103 -1.677 -0.405 -0.333 -1.331 0.770 -0.102 -2.255 -2.178 -1.842 0.034 0.631 0.287 0.161 -1.079 -1.642 1.887 0.035 +3 -0.867 0.106 1.621 -0.980 1.001 -0.107 -1.239 0.157 -0.181 -0.580 0.701 1.791 0.014 -2.270 0.144 -1.045 -2.014 -0.561 -1.217 0.977 1.330 2.315 0.198 0.391 0.031 -0.392 0.097 -0.976 +1 0.595 -0.794 -0.106 -0.796 0.558 -0.701 -0.039 0.547 -1.711 1.003 1.825 -1.199 -1.911 0.371 0.639 -1.405 -0.538 0.231 -0.248 0.852 0.457 -0.896 0.847 -0.143 -1.025 -0.494 0.595 -1.369 +0 -0.651 -0.758 -0.229 0.168 -0.316 1.159 0.508 -1.591 0.524 0.606 -0.435 -1.495 0.008 -0.008 -1.122 -1.451 -0.088 -0.233 -0.417 -0.083 -1.103 -0.435 0.533 -0.350 -1.672 -0.473 -1.070 0.267 +4 -0.829 2.281 0.058 -0.271 1.393 1.966 -0.744 -1.350 -0.174 -0.713 0.019 2.092 0.128 -0.855 -1.491 -0.569 -0.137 -1.486 -1.030 -1.930 0.735 0.097 1.049 -1.798 0.222 -0.012 -2.797 -0.439 +1 -0.364 0.863 0.419 0.830 -0.337 0.129 -0.104 0.664 0.261 -0.383 -2.560 0.171 -1.678 0.689 0.832 -2.284 0.300 0.275 0.376 -1.232 0.477 -1.259 0.690 0.434 -0.330 0.425 -0.947 -0.559 +1 0.819 -1.384 0.057 1.624 -0.540 0.740 1.337 0.834 -1.146 -0.396 0.837 1.505 -1.262 1.021 -0.018 -0.727 -0.333 -1.456 1.112 -0.281 -0.537 1.025 0.116 0.450 1.312 0.742 0.023 -0.070 +1 -0.427 0.527 -1.923 0.587 -0.925 -0.570 0.918 0.337 0.542 0.971 -0.278 -0.995 0.260 0.827 0.066 1.323 -0.325 -0.207 -1.140 -0.945 0.214 -1.279 -0.058 -0.039 0.585 0.960 0.498 2.166 +0 0.071 0.950 -0.599 -0.129 -1.370 0.776 1.070 0.603 -0.234 -0.044 0.147 1.095 1.036 -1.435 -0.878 0.434 -1.338 0.919 -0.275 0.707 0.034 -0.099 -1.087 -0.547 -0.115 -0.148 0.724 -0.643 +2 1.122 0.441 0.812 -1.892 0.728 0.940 -2.240 0.765 -1.424 -1.970 1.326 -1.007 -0.716 0.990 -0.305 -0.180 -0.202 0.348 -0.226 0.114 -0.504 0.352 0.314 1.898 -0.381 -0.184 0.239 0.964 +0 1.986 0.823 1.434 -0.322 0.230 -1.072 -0.278 -0.552 0.489 -0.707 -0.837 0.396 -0.054 -0.701 -0.847 -0.701 0.815 0.418 -0.755 -0.715 -1.147 -0.393 0.123 -0.879 2.187 0.624 -0.032 0.085 +4 0.988 -0.114 1.718 -1.401 -0.491 -0.357 0.651 0.767 -0.312 2.479 -0.420 -2.572 0.781 -0.564 1.251 -1.966 -1.068 0.417 -0.079 0.151 -1.372 -0.758 -0.456 0.607 2.398 1.852 1.669 0.131 +0 0.224 0.507 -0.258 -1.216 0.156 0.173 -0.161 0.646 1.469 -0.362 0.662 1.193 0.358 1.060 -1.057 -0.073 -0.079 -0.725 0.269 0.853 0.077 -0.839 1.315 -0.088 -1.170 -0.290 -0.943 0.182 +0 -0.434 0.829 0.633 0.162 -0.600 -0.391 -0.132 -0.895 -0.111 -0.947 0.366 0.478 1.117 0.260 0.229 -0.836 -0.048 -1.023 -1.704 -0.265 -0.504 0.611 -0.962 2.813 -0.480 -0.070 -0.567 0.778 +2 0.031 -0.476 -1.187 0.323 0.362 0.610 0.377 0.735 0.335 2.023 1.473 0.237 -1.726 1.870 -0.267 0.626 0.472 -0.117 -0.480 -0.345 -2.681 -1.168 -0.491 0.050 0.592 -0.575 0.682 -0.229 +1 0.411 0.241 0.642 -0.996 -0.680 0.576 1.233 -2.265 -0.297 0.461 0.665 0.494 -0.119 -2.112 -1.516 0.791 -1.444 1.042 -0.303 -0.226 -0.450 0.607 -0.761 -0.979 0.914 -0.382 0.305 0.855 +0 0.005 -0.270 -0.013 1.160 -0.632 -0.420 0.718 -0.312 -1.065 -0.044 -0.934 0.083 -0.359 -0.117 -0.620 0.805 0.280 -0.124 -0.620 -0.430 -0.369 -0.306 0.678 -0.837 -0.140 -0.114 0.849 1.004 +1 -1.329 1.393 1.026 -0.634 -0.078 -2.203 -0.701 -0.211 0.309 -0.116 0.139 -0.968 0.324 1.683 0.439 0.034 -0.741 -0.357 -1.312 -1.446 -0.070 0.554 -0.856 1.196 -0.561 -0.090 0.945 -0.313 +2 -0.505 1.032 0.261 -0.900 0.938 0.601 0.896 -0.182 0.601 1.920 0.171 -0.399 1.544 0.667 -0.450 1.735 -1.203 0.529 1.146 1.072 -0.900 -1.241 0.230 0.705 -1.078 0.302 -1.457 1.061 +3 1.446 -0.224 1.007 -0.373 0.470 -0.352 -2.403 -0.178 1.760 -0.597 0.765 -1.230 -0.184 0.024 -0.314 -0.564 -1.109 0.879 -0.014 -1.895 -2.093 0.961 0.083 -0.653 1.389 -0.393 0.143 1.071 +0 0.189 1.485 0.608 -0.434 -0.277 0.243 0.895 -0.332 0.172 0.126 0.219 -1.336 -1.470 -0.125 -1.728 0.014 0.574 0.868 -1.152 0.234 -0.031 0.315 -0.303 -0.636 0.667 -0.961 0.910 1.150 +1 0.216 -0.533 0.096 0.264 -1.240 0.146 1.389 1.565 0.853 0.258 0.925 0.706 -1.124 -0.914 0.635 -0.669 -1.025 1.289 0.063 -0.065 -1.203 -0.449 -0.343 -0.905 -1.215 1.748 1.409 -0.105 +4 1.164 1.125 -1.098 -0.619 -0.555 0.062 -0.134 1.292 1.534 1.803 2.159 -1.580 -0.189 -0.694 -0.898 1.409 0.421 -1.589 2.594 -1.815 -0.354 0.088 -1.131 0.550 -0.618 0.823 0.399 -2.504 +3 0.385 0.349 -0.270 1.450 -2.010 -0.587 0.689 1.408 0.197 1.497 1.711 -0.357 -0.843 -0.863 -1.426 -1.229 -0.947 -1.166 1.025 -0.446 -1.388 -0.389 0.250 -0.261 -0.217 -1.829 0.910 -0.352 +4 -1.915 1.962 1.191 -0.363 1.486 1.475 -1.275 -1.717 -0.431 0.069 -2.039 -1.465 0.662 0.720 1.139 -1.639 -1.240 0.694 -0.475 0.079 0.338 0.440 0.618 -1.570 -0.989 0.798 -0.480 2.422 +2 -0.056 0.479 -0.795 0.135 0.351 -0.954 0.504 0.359 -0.799 -1.188 -0.812 0.135 -0.076 1.394 -0.367 -0.889 0.657 -0.980 -0.791 -0.778 -2.997 0.567 0.940 0.917 1.885 1.507 -0.334 1.109 +4 -1.195 -0.763 -0.211 0.183 0.408 0.269 1.023 -1.352 0.209 -1.982 -0.648 -1.631 1.064 1.680 -0.049 -0.642 -0.327 -0.817 0.427 -1.678 0.939 -1.620 -2.615 -1.190 -0.581 0.480 1.084 1.279 +3 -0.888 -1.139 0.767 -0.693 0.561 -1.808 0.657 0.615 0.617 1.743 -0.367 -1.254 -0.420 -0.555 -0.097 -1.076 1.696 -0.542 1.113 0.154 -1.195 -2.574 -0.433 -0.911 -0.857 1.389 0.048 0.206 +3 -0.056 1.018 -2.046 -0.139 -2.086 0.161 -0.236 1.361 0.712 -0.526 -0.406 0.381 -0.573 -0.032 1.317 -0.829 -0.448 -0.307 3.158 0.608 1.132 0.768 0.835 0.474 0.365 -1.451 0.358 -0.108 +3 -0.696 0.058 -2.743 0.293 0.067 1.091 0.374 0.542 -0.701 -1.757 -0.016 -1.538 0.057 0.484 -0.107 -0.979 1.731 -0.290 1.002 1.545 1.290 0.676 1.745 0.359 1.007 0.212 -0.851 0.184 +2 -1.141 0.445 0.675 0.721 -0.945 -0.259 0.305 -0.198 2.043 -0.128 -0.919 -0.114 -1.524 0.096 -0.925 -0.830 0.675 -1.814 -0.323 1.251 -1.078 -0.046 1.928 1.218 -0.413 -0.817 0.623 -0.349 +0 0.769 0.312 0.542 -0.731 -0.487 0.011 0.076 0.606 0.381 -0.740 -0.808 0.062 -0.084 -2.629 0.130 0.416 0.731 0.247 -0.059 -0.268 2.206 -0.538 -0.122 -0.467 -1.103 0.595 -1.094 0.203 +4 -0.079 -0.990 -0.121 0.582 -0.387 -0.670 -0.056 -1.320 -3.064 -0.379 0.501 -3.058 -0.444 1.950 -1.192 -1.199 1.847 -1.378 -0.302 0.021 -0.048 0.205 0.471 -0.469 0.342 0.649 -0.562 1.350 +2 0.782 -1.138 -0.261 -0.753 0.800 -0.471 2.201 0.218 1.182 -1.015 0.012 0.616 -1.033 0.020 1.052 -0.327 -0.852 1.803 -1.172 0.156 -0.708 -0.937 0.742 -0.206 0.406 -2.255 0.503 -0.721 +0 -0.597 0.316 0.182 0.463 -1.792 -0.154 0.549 0.748 0.814 0.251 -0.379 0.501 -0.300 0.901 0.427 -0.062 -0.769 1.119 0.559 2.103 0.526 0.610 -1.465 0.310 -0.317 0.826 0.335 0.985 +4 1.344 -2.121 1.685 1.357 0.712 2.010 2.049 1.585 0.038 0.006 0.968 -0.685 -0.192 -1.598 -0.523 0.219 -0.151 0.433 2.373 0.273 -0.718 -0.948 1.304 -0.826 0.604 0.752 0.793 -0.963 +2 -1.266 0.319 0.151 0.453 1.138 -0.035 -0.133 0.468 -1.020 0.042 -0.102 0.877 -0.459 0.091 -0.156 -2.487 -0.528 -0.008 -0.891 -0.122 1.339 0.963 -1.866 0.760 0.562 -0.093 -2.345 0.783 +4 0.054 -0.074 0.595 1.990 0.600 1.039 -1.573 -0.180 -1.214 0.015 1.427 -1.292 1.737 -0.005 0.893 1.029 1.996 0.581 -0.872 1.012 -2.671 -0.907 -0.581 -2.104 1.684 1.329 0.451 0.980 +1 -0.470 -1.448 1.559 0.393 1.127 -1.152 0.850 0.325 -0.356 1.529 -0.068 -0.151 0.178 -0.488 -0.425 -0.557 -0.335 -0.327 0.544 -0.756 -0.103 -0.843 1.995 1.540 1.234 -0.324 -1.147 -0.035 +1 0.750 0.279 -0.951 0.319 -0.992 -0.633 0.189 0.297 -1.189 1.054 0.572 -0.705 -1.538 -0.145 -0.446 0.113 -0.363 -2.007 -0.388 -0.306 0.634 -0.698 1.679 -1.912 1.311 -0.224 0.860 0.080 +0 0.115 -0.710 0.925 0.029 -0.582 0.257 1.551 -0.457 0.266 0.756 -0.090 -0.209 -1.278 0.889 -0.901 0.586 0.503 -0.273 0.571 1.092 2.172 -0.571 -1.287 0.335 -0.133 -0.397 -0.754 0.082 +1 1.562 1.041 -0.009 -1.485 0.304 0.172 0.584 -0.043 1.340 0.299 -0.622 -0.050 -1.006 -0.583 -1.155 -1.026 1.218 -0.335 0.034 1.337 -0.175 -1.713 0.767 0.444 1.262 -0.897 -1.470 -0.513 +1 0.154 -0.284 0.049 -1.742 -0.359 0.996 0.199 -1.438 -0.350 0.312 -0.651 0.541 1.544 -0.610 0.670 -0.886 0.160 0.493 -1.250 0.714 -1.151 0.629 0.503 -0.835 -1.041 0.642 -2.088 0.477 +0 0.378 1.125 -1.685 0.354 0.210 -1.417 -0.266 0.394 0.050 -0.025 -0.014 -0.465 2.006 0.328 0.310 -1.327 -0.316 -0.529 -0.031 -0.322 -0.617 -0.228 -1.083 0.595 0.805 0.498 0.856 0.786 +2 -0.005 -1.273 0.762 0.605 -0.105 1.763 -0.437 -1.730 1.902 1.011 0.626 -0.339 0.342 -1.260 -0.225 -0.904 0.262 0.923 -1.048 0.725 0.200 0.684 -0.242 0.704 -0.696 -1.682 -1.454 -0.503 +3 0.066 1.426 1.743 0.627 1.478 0.361 -1.574 1.276 0.079 -1.247 -1.361 -0.389 -0.204 0.425 0.220 1.041 1.105 1.372 0.014 -1.126 -0.698 -1.628 -1.642 -0.215 -1.136 1.312 1.115 0.248 +2 -0.072 0.702 0.301 -0.688 -0.145 0.930 0.683 -0.553 -0.617 -1.716 -0.429 -1.736 2.067 -0.855 1.109 1.818 0.216 -0.100 -1.322 -0.505 1.604 0.351 0.028 1.312 0.696 -0.148 0.216 -0.768 +1 -0.857 1.220 0.716 -0.022 1.493 0.499 0.224 -1.785 1.292 -1.553 0.175 0.307 -0.508 -1.091 0.131 0.618 -0.675 -0.575 -1.140 -1.635 -0.151 -0.735 0.664 0.672 0.597 -0.714 0.950 -0.957 +1 -0.387 1.432 -1.090 2.138 0.420 -1.296 -1.036 0.832 0.738 0.430 0.116 -0.104 -0.779 0.389 1.618 -0.030 0.262 1.026 -0.521 0.412 0.800 -0.734 -1.299 -0.226 0.855 -1.469 0.242 -0.186 +1 -0.262 -0.964 -0.468 -0.423 -0.647 -0.689 1.122 1.408 0.392 -0.647 -0.584 -2.073 0.380 -0.683 1.471 -1.378 -0.592 -0.096 0.465 1.803 -1.370 -0.302 -1.428 0.790 -0.647 -0.103 -0.172 0.116 +3 0.050 0.689 -1.758 0.307 0.694 0.099 -0.958 0.711 -0.638 1.705 1.060 0.254 -0.265 -1.454 1.823 -0.594 1.657 -2.208 -0.071 -0.572 -0.977 0.324 1.635 -0.398 0.222 -0.760 -0.912 -2.159 +4 0.516 0.533 -2.021 1.250 -0.560 -0.724 -0.695 1.230 1.474 0.472 -0.481 2.909 -0.018 -0.653 1.025 -0.121 2.091 -0.846 -0.181 1.533 0.488 -0.296 -0.923 -1.136 -1.295 0.972 0.786 0.521 +1 0.348 -1.223 -0.334 -0.092 0.987 -0.204 -0.729 0.335 -0.658 0.697 -0.998 -0.149 0.331 0.223 -0.325 0.463 2.309 0.039 -0.626 -0.841 0.033 -0.211 1.213 -2.190 0.874 0.781 -1.531 1.118 +1 0.576 -0.785 0.056 -1.664 0.162 0.203 -0.432 0.862 1.029 -0.959 1.096 0.061 -0.785 1.270 0.100 -1.022 -0.669 0.106 0.438 1.043 0.400 -1.191 -0.571 2.237 1.930 0.253 0.756 0.443 +1 -1.075 -0.824 -0.179 -0.596 -0.143 -0.273 1.065 -1.151 -0.097 1.775 0.864 -0.817 1.132 0.921 -0.041 0.765 0.025 0.210 1.912 0.866 0.423 0.289 -1.795 -0.526 -1.549 0.469 1.214 0.697 +3 0.604 -1.059 0.619 0.725 0.489 2.478 0.726 0.968 1.876 0.850 -0.551 1.243 -0.401 -0.983 -0.897 1.231 0.620 1.061 0.625 0.189 0.318 0.591 -0.602 -1.290 -0.485 0.544 -0.648 2.130 +4 1.024 -0.236 -1.298 1.401 1.549 1.998 1.967 -0.264 -0.365 -1.870 -0.951 0.363 -1.547 0.585 0.442 -0.762 0.581 0.654 -1.085 -0.474 0.254 -0.960 -2.899 0.214 -0.862 -1.627 -1.052 -1.518 +1 -1.320 1.101 0.630 -1.309 -0.173 -1.143 0.837 -1.018 -0.934 0.082 2.071 1.221 -0.989 -1.225 -0.591 -1.402 0.787 0.659 0.914 0.049 0.120 -1.066 0.275 1.264 -0.430 0.407 0.068 -0.533 +1 -0.601 -0.292 -0.602 1.852 -0.013 -1.058 0.823 -1.221 0.209 -1.960 -1.328 0.197 0.738 0.171 -0.116 -0.301 -1.479 -0.720 -0.461 1.057 0.344 -1.763 0.324 -0.385 -0.677 0.612 1.031 0.931 +2 -0.974 -0.975 -0.660 -0.239 1.488 0.278 0.395 0.234 1.195 0.055 -0.378 -0.300 0.920 2.237 -0.278 -0.106 -0.250 -0.478 1.243 0.840 0.113 0.954 0.969 -0.230 -0.486 -2.381 1.658 -0.830 +1 0.832 -0.015 1.616 1.460 -1.463 -0.154 0.867 -0.860 0.203 -1.269 -0.642 -0.391 -1.587 0.518 0.264 -1.440 1.062 0.058 -1.379 -0.339 1.196 0.566 -0.378 -0.378 0.082 1.025 0.446 0.455 +3 0.999 -0.181 -0.714 -0.000 -0.573 -2.734 -0.147 0.022 -0.868 -2.057 0.041 0.095 -0.864 1.960 -0.038 -0.859 1.164 -0.990 0.944 0.704 -1.139 1.387 -0.290 -0.506 -0.728 -1.257 0.383 0.237 +2 0.522 -0.608 -1.698 -0.088 -0.169 1.620 0.296 0.562 -0.412 -0.596 -0.835 -1.260 0.726 0.307 1.477 0.550 0.243 -1.981 1.908 0.039 -2.084 0.226 -0.811 -0.847 0.267 -0.587 1.027 -0.115 +4 -1.209 -0.284 0.795 -0.980 -2.154 0.530 -0.942 -0.639 0.131 0.077 1.022 -0.242 0.068 -1.693 0.628 -0.691 3.231 -0.214 0.424 0.638 -0.089 0.437 2.255 -0.754 -2.243 -1.303 0.997 -0.479 +4 1.064 -0.121 -1.621 1.022 -1.776 -1.546 -0.178 0.252 0.422 -0.066 1.760 0.065 1.035 0.787 -1.293 -0.170 2.123 -0.504 -0.675 0.171 -0.469 -0.794 -0.483 1.432 -0.137 -1.328 -1.512 2.207 +2 2.133 1.161 -1.261 0.333 0.323 -1.068 1.203 1.202 -1.327 0.522 -0.667 -0.285 -0.345 -0.325 1.221 0.915 1.064 0.425 -0.284 1.256 1.073 -1.727 1.039 -0.011 0.087 -0.790 -0.784 1.278 +4 2.333 1.296 0.013 0.867 -1.637 2.523 -0.590 -0.120 -1.856 0.486 -1.106 0.102 -0.544 -1.078 -0.804 0.226 -1.281 -0.749 0.663 2.602 0.478 -0.917 -0.367 0.032 -0.349 0.903 0.217 0.908 +2 -0.143 -0.095 1.612 0.890 0.791 -1.578 -0.656 1.189 -1.171 0.084 -1.065 -0.965 1.681 0.719 0.983 0.784 -0.139 -0.692 -0.402 -0.192 1.776 -1.336 -0.554 -0.913 1.057 1.854 0.309 0.391 +4 -0.081 -1.034 -0.612 -0.061 -0.678 0.278 -1.537 1.604 -2.389 -1.359 -0.358 -1.986 -1.394 1.752 0.091 -1.286 -0.523 0.222 -1.908 -2.336 0.546 -0.399 -0.113 -0.209 0.387 -0.274 -0.014 -0.465 +1 -0.858 -0.385 0.556 2.054 0.605 0.473 0.531 -1.073 0.116 -0.030 0.322 0.117 0.380 -0.311 0.059 -1.228 -0.320 1.391 0.319 -0.021 1.896 -1.361 -1.109 1.713 0.046 -0.243 -1.199 0.448 +3 0.185 -2.108 -1.517 0.070 -0.625 -0.142 1.642 0.091 -1.540 0.833 -0.206 1.436 0.166 0.676 0.780 -0.340 -1.447 1.550 0.732 0.435 -0.698 -1.082 -0.283 1.178 0.886 -1.825 0.794 -1.246 +1 1.049 0.116 1.387 -0.129 0.245 0.751 0.199 0.527 0.284 -1.414 0.766 1.178 -0.130 -0.205 0.293 1.946 -0.285 0.622 -0.175 0.598 -1.276 1.796 0.367 -0.279 -0.252 0.278 2.003 1.547 +1 -1.034 -0.968 0.555 1.483 1.041 1.458 0.784 -0.034 -0.526 0.577 -1.680 -0.605 -0.788 1.244 -1.378 0.930 0.387 0.400 -0.468 -1.463 0.500 0.886 0.497 -0.144 -1.138 -0.623 0.450 -0.790 +2 -1.957 -0.557 -0.476 0.906 0.197 -0.418 -1.478 2.285 -0.450 -0.949 -0.414 0.011 0.127 -0.937 -1.232 -1.183 -1.478 -0.169 -0.428 -0.392 0.671 -0.286 -2.661 -0.610 0.346 0.067 -0.209 -0.247 +4 0.781 1.516 -0.766 -1.685 0.683 2.052 0.134 -1.073 0.458 -1.618 0.135 -1.890 -0.605 2.248 -1.272 0.471 0.642 -0.999 1.635 -0.551 0.041 -0.059 0.762 2.526 -0.886 0.367 1.215 -1.033 +4 0.030 -1.878 -0.719 0.930 -0.993 -0.100 -0.687 0.217 -0.498 0.635 1.976 -0.628 1.424 1.162 -0.233 0.338 -0.007 -1.515 0.364 1.230 -1.315 2.733 0.021 0.954 -2.040 0.706 1.755 -0.235 +4 -0.901 0.972 -1.032 0.504 0.576 0.055 -0.495 0.141 0.267 1.790 -0.872 -0.556 0.285 -0.256 -0.100 1.070 -2.820 -0.598 -1.742 -1.196 2.405 0.598 0.952 -0.081 0.759 -1.767 1.038 -0.820 +3 1.160 0.557 -0.013 0.030 0.755 -0.261 1.621 0.812 0.781 -0.449 0.414 -0.754 2.254 1.497 -0.040 -0.326 1.175 1.502 -0.451 0.130 0.639 0.303 1.076 2.679 0.491 1.444 -0.793 1.242 +2 -0.905 0.824 0.166 -1.091 0.155 -1.000 -0.364 0.181 1.065 1.479 0.558 -0.905 -0.448 1.863 -1.966 -1.585 0.864 -0.432 1.258 -0.830 -0.123 0.804 -0.616 -0.982 -1.559 -0.705 1.104 0.250 +4 1.193 -1.494 -0.018 -0.700 -0.569 0.961 1.367 -1.331 -0.176 -0.652 0.668 1.112 2.266 1.197 1.146 -1.001 -0.676 0.056 2.568 0.076 1.431 -0.419 -0.024 -0.021 -0.493 0.687 -1.294 -1.828 +0 0.251 -0.327 1.061 0.598 0.600 -0.270 -0.895 -1.010 0.103 0.008 0.483 -0.425 1.280 -1.326 0.418 -0.909 -0.243 -0.921 -0.368 -1.070 -1.001 0.964 -1.389 0.650 0.852 0.730 -0.027 -1.092 +2 -0.803 1.300 -0.754 1.424 -0.251 0.432 -0.866 -1.600 0.345 -0.120 -1.592 0.588 -0.776 -2.118 -0.811 1.761 0.735 0.497 0.778 -0.734 -1.676 -0.487 0.405 0.097 -0.301 0.283 0.738 -0.589 +1 1.476 1.572 1.277 0.235 -0.338 1.276 -0.791 0.271 -0.044 -0.891 0.278 0.742 -0.931 0.460 -2.229 -1.049 -0.457 0.357 0.869 0.083 0.567 -0.161 0.405 0.352 -0.509 -0.355 0.600 1.812 +2 0.274 -0.601 1.069 -0.208 -0.788 0.226 1.964 0.335 -0.363 -1.527 -0.219 0.235 -0.388 -1.665 -0.851 1.455 1.434 -0.887 0.673 -1.179 1.404 0.245 0.950 -1.715 0.678 -0.008 0.852 0.915 +4 1.087 -0.571 0.369 0.554 0.170 -0.006 1.817 -0.340 -0.063 -0.417 0.385 -1.590 1.087 -1.316 0.430 -0.583 1.612 0.293 -2.374 -0.227 0.434 -0.296 -1.020 -2.636 1.935 2.659 -1.041 0.248 +0 -1.004 -0.409 0.210 -0.632 0.577 0.720 0.536 1.025 -0.905 0.933 -0.696 -0.941 0.532 1.216 0.488 0.021 0.310 0.136 -1.126 -1.243 -0.079 0.080 0.011 0.676 -0.215 0.563 -0.145 1.823 +2 0.600 0.545 0.617 -0.835 -1.237 -0.009 -0.861 0.867 -1.038 -1.242 0.946 0.159 -0.145 0.911 -1.299 -0.527 0.690 0.799 0.088 -1.151 0.830 -1.000 -0.837 -0.168 -1.377 -2.821 0.881 -0.253 +2 0.765 1.606 0.228 -0.034 -0.258 -1.745 -1.250 -1.622 -0.539 -0.393 1.728 0.100 0.153 -0.751 -0.142 -2.207 0.640 -0.412 -1.217 -0.171 0.667 0.772 1.797 -0.122 -1.141 0.347 0.978 0.492 +3 0.075 1.722 -1.775 -1.660 1.521 0.328 -0.019 0.907 1.758 0.272 -0.011 -1.886 -0.891 -1.857 0.032 1.419 -0.418 0.455 0.875 0.395 -0.089 -1.362 0.714 1.194 0.028 -0.337 0.813 0.347 +1 1.243 0.018 0.150 -1.165 1.321 -0.988 0.845 1.352 0.048 0.968 1.848 0.268 -0.175 -0.067 -0.743 -0.766 0.442 -0.164 0.328 -1.490 -0.093 1.030 -1.119 0.017 -1.855 -1.305 0.808 -0.309 +3 0.467 -1.210 1.406 0.066 0.276 0.117 0.210 1.647 1.240 1.604 -0.195 -2.125 0.747 -1.116 -0.362 -0.062 0.002 0.644 -0.923 0.627 -0.190 2.602 -1.590 0.127 0.615 -0.411 -0.574 1.325 +1 -0.385 0.448 1.702 -1.321 -0.457 -0.853 -0.741 1.223 -0.158 0.723 -0.667 -0.645 -1.018 1.445 0.775 -1.051 1.142 -0.260 0.453 0.047 -1.437 -0.828 1.354 0.939 -1.022 0.208 -0.215 1.142 +0 -0.340 -0.407 0.200 0.346 0.307 0.365 -0.179 -0.199 -1.449 -0.689 0.183 -0.446 -0.820 2.831 0.711 0.631 -1.061 -0.292 -1.013 -0.357 0.121 0.350 0.379 -0.110 -0.368 -0.548 0.014 0.188 +4 -0.941 -1.061 -0.087 -1.636 2.013 -0.938 0.878 0.599 2.153 -0.858 0.714 -1.022 -0.262 -2.318 -0.025 -1.074 -2.683 -0.813 -0.154 -0.029 -0.208 -0.293 1.583 -1.249 0.192 -0.263 -0.976 -0.113 +1 -0.921 -0.383 -0.371 -2.061 0.287 1.405 0.960 -0.376 1.055 -1.734 0.832 -0.731 0.968 0.036 -1.204 0.247 0.001 0.238 -0.802 -1.171 0.614 -0.067 0.573 0.555 0.708 1.619 0.199 -0.997 +2 0.073 1.781 -0.199 0.340 1.102 -0.691 1.100 1.912 -1.775 1.137 -1.483 -0.775 1.131 0.322 -0.459 2.234 -0.330 1.096 -0.306 0.458 0.492 0.080 -1.419 0.277 -0.580 -0.458 0.593 0.525 +1 0.431 -0.469 0.164 0.377 0.593 -1.009 -0.951 -1.014 -0.516 -2.983 0.881 0.672 -1.240 -0.336 0.586 0.685 -0.080 1.531 0.886 0.685 1.313 -0.568 -1.055 -0.552 -0.929 0.153 -0.611 0.225 +4 -1.104 -1.039 -0.904 -0.713 -1.399 0.606 -0.189 0.534 -2.297 1.072 -0.082 0.500 1.521 0.368 2.112 -1.880 -0.318 -1.803 0.314 0.077 -1.092 2.174 -0.359 -2.206 0.680 0.696 -0.479 0.788 +3 1.876 -0.186 0.406 1.712 0.347 0.343 0.223 0.839 -0.058 1.584 0.097 0.962 -1.208 -2.036 -0.230 -0.900 -0.193 1.404 -0.419 0.941 1.726 0.219 -0.352 0.203 0.244 -1.959 0.045 -1.518 +0 0.786 0.425 -0.967 -0.048 -0.004 -1.158 1.503 0.877 -0.221 0.027 0.208 -2.042 -0.247 -0.682 -1.002 -0.281 1.798 0.641 -0.571 0.573 1.399 0.925 0.060 -0.647 0.698 0.393 0.895 0.635 +4 1.193 -1.079 -2.078 -2.444 0.013 1.886 0.110 0.750 -0.755 -1.902 -0.908 -1.105 0.844 -0.133 0.045 1.118 -1.807 0.074 -0.301 0.164 2.651 0.151 0.749 -0.675 -0.009 0.407 0.804 0.897 +3 -0.230 0.940 -0.264 -0.039 0.396 1.213 -0.559 0.951 0.544 -0.588 -2.344 -1.494 0.183 0.496 1.884 0.519 -0.400 -2.184 0.642 -0.703 0.547 -1.356 0.684 1.764 0.694 -0.258 -0.080 1.484 +4 -0.462 0.318 -0.967 -1.578 -1.221 -0.416 -1.001 2.223 1.982 1.166 0.376 0.378 1.322 0.131 -0.823 2.123 0.575 1.453 1.812 -1.772 -0.864 -0.203 0.501 0.917 -0.063 1.293 0.609 0.597 +4 2.480 0.157 0.549 -0.218 0.845 -1.298 -1.746 -0.030 2.054 1.208 -0.808 0.516 -0.636 0.426 0.504 0.926 -1.566 -1.266 -0.076 -1.032 -1.110 0.272 -0.916 2.258 -0.533 -0.270 0.298 -1.508 +3 -0.102 1.803 0.853 -1.487 -1.658 1.054 0.095 -1.963 -1.120 -1.301 -0.767 -0.865 1.487 -1.454 -1.257 -0.755 0.333 0.140 -0.664 -0.165 0.719 -1.143 0.615 0.591 -1.588 0.926 0.594 0.709 +3 -0.337 1.480 -1.281 -0.918 -0.370 0.693 -1.058 1.073 -0.491 -1.514 -0.847 -1.367 -1.292 1.927 0.138 1.062 -0.411 0.609 -0.806 1.712 -0.253 -0.845 -1.276 -1.146 -1.274 1.726 -0.217 -0.291 +0 0.049 0.006 -0.771 -0.623 1.003 -1.858 -0.185 -0.704 0.192 -0.528 0.174 -0.058 0.035 -0.106 0.095 0.206 0.064 -0.190 1.010 -0.930 2.165 -0.876 -0.483 -1.826 -0.303 0.881 -0.961 1.366 +4 -0.759 1.864 1.366 -0.341 2.115 -2.431 0.868 3.695 -0.245 0.888 0.890 -0.219 0.566 1.706 -0.856 0.520 0.970 -0.669 0.129 0.526 -0.676 1.501 0.308 -0.670 -1.102 -0.234 -0.273 0.437 +3 1.173 0.043 0.171 -0.400 0.223 0.767 0.239 1.132 0.141 0.838 0.425 -2.084 -0.882 -0.825 -0.799 -1.078 1.132 1.513 -1.641 0.792 0.597 2.224 -0.077 -1.546 -1.550 1.118 -0.849 0.720 +2 -0.026 -0.912 -0.110 0.756 0.842 0.479 -0.531 -1.770 1.771 0.432 -0.309 -1.248 0.323 1.921 -0.783 1.136 0.918 -0.559 -1.337 -0.027 0.600 0.845 -1.623 1.474 0.745 -0.017 0.794 0.482 +3 0.280 -0.918 -2.721 -1.413 -0.455 0.490 2.159 -0.593 -0.654 0.337 0.222 0.256 -0.256 -1.981 0.197 -0.048 -1.589 0.581 0.730 0.558 -0.407 0.221 -0.898 -1.385 -0.099 -1.502 -0.133 -0.052 +1 0.428 0.998 0.123 -1.066 -1.200 -0.555 -1.209 1.576 0.468 1.445 -0.289 -0.765 -0.703 -0.462 -0.494 1.222 0.914 0.271 -1.660 0.295 -0.311 1.406 0.895 0.142 -2.098 -0.136 -0.653 0.405 +0 1.226 -0.056 -0.156 0.575 -1.347 0.607 0.306 0.197 1.081 -0.069 0.040 0.181 -2.048 1.048 0.594 0.540 0.340 1.359 0.213 -0.214 -1.734 -1.270 -0.305 0.100 -0.590 -0.230 -0.904 0.944 +1 0.190 0.902 -0.666 0.150 0.009 -0.837 0.621 0.481 2.318 1.067 -1.545 0.933 -1.432 1.474 -0.295 0.317 1.314 0.471 0.813 -0.648 -0.932 0.028 0.659 -0.101 1.642 0.568 -0.304 -0.155 +3 -1.914 -1.627 0.967 -0.524 0.351 -1.126 1.633 -0.621 -0.204 -0.029 1.030 0.986 0.062 1.642 1.007 -0.706 -0.832 -0.650 -0.907 -0.859 -0.149 -0.951 -1.468 -0.290 0.722 0.791 2.478 -1.041 +3 1.088 -0.444 -1.069 -0.755 1.717 -0.657 -0.123 -0.447 -1.233 -0.743 -1.301 0.956 0.101 2.483 -0.709 -0.859 1.534 1.058 0.385 -1.114 1.745 -0.072 -0.431 1.143 -0.022 -1.166 0.040 -0.781 +2 1.451 -0.434 -0.443 -0.421 -0.696 -0.725 -1.725 -1.552 -0.193 0.456 -1.676 0.182 1.283 -0.472 -0.989 -0.060 0.519 -0.559 0.953 0.469 1.731 -1.681 -1.412 -0.418 -1.339 1.574 0.233 -0.330 +4 -0.274 0.976 0.844 0.517 0.502 -0.792 0.474 -0.611 0.582 2.755 -2.404 0.736 0.073 -0.621 0.749 -1.262 2.054 1.061 -0.555 -1.928 -0.625 1.431 1.921 -0.237 0.712 -0.411 -0.288 -0.874 +0 -0.462 -0.457 0.550 -0.661 0.396 -0.380 -1.018 -0.059 0.031 0.682 0.360 1.082 1.032 -0.934 0.713 -0.030 1.017 -0.586 -0.780 -1.205 0.734 -0.445 -0.452 1.217 0.249 0.471 -2.046 0.707 +4 0.775 -0.578 0.873 0.575 1.823 1.679 -1.543 0.444 -0.103 -0.644 1.198 0.542 -0.739 0.020 0.693 0.521 -2.413 2.226 -0.679 -0.438 0.380 -0.722 -0.261 -1.905 -1.295 1.195 -2.175 -0.343 +4 -0.638 0.671 0.711 0.005 -1.826 1.715 -0.213 2.709 0.653 -0.210 0.448 -2.019 1.026 2.567 -2.089 -0.287 -0.105 0.875 2.106 0.383 2.142 0.170 0.451 0.328 1.220 -0.422 0.818 -1.231 +4 -0.691 0.524 1.382 -0.134 0.096 0.364 -0.487 2.194 -0.925 0.090 0.106 -2.188 -0.842 -1.352 0.720 -1.693 0.942 -1.180 0.962 -1.396 0.424 0.935 0.573 -1.221 1.130 0.546 -2.358 0.111 +0 0.232 0.658 -0.738 0.337 -0.968 1.176 -1.290 0.154 -0.388 -1.563 -0.935 0.450 -0.897 -0.669 0.373 0.915 0.661 -0.047 0.218 0.119 -1.036 0.279 -1.045 0.614 0.135 -2.164 0.979 -0.522 +0 -1.271 0.337 1.509 0.754 0.453 0.651 0.139 -0.731 -0.400 -0.682 0.396 1.274 1.115 -1.143 -0.857 -0.578 0.204 -0.888 0.869 -1.275 0.792 0.562 -1.279 -0.100 -0.494 -1.074 0.661 0.277 +2 -1.294 0.484 -1.007 -1.302 0.828 0.652 -1.216 -0.282 1.138 -0.211 -0.122 1.575 0.556 0.554 -0.508 -0.971 0.689 -1.087 0.508 0.598 -0.714 -0.955 0.105 -1.382 2.246 1.494 1.330 -0.024 +1 -0.566 -2.111 -0.818 -0.285 -0.742 0.497 -0.422 0.858 0.865 -1.004 -0.730 1.056 0.284 0.696 0.699 -0.681 2.496 0.985 -0.424 1.035 -0.114 0.121 -0.620 -0.887 0.774 -0.697 -0.776 -0.319 +4 0.276 -2.607 -1.629 -0.564 -1.082 1.072 1.302 0.054 0.147 -0.029 -1.580 -1.208 -1.963 -0.775 1.162 0.715 -0.666 -0.403 -1.141 0.610 1.700 1.768 1.118 -1.220 -0.134 0.001 -1.508 1.015 +0 -0.046 0.611 0.235 0.601 1.012 -0.648 -1.155 -0.038 -0.330 0.786 -1.074 1.020 1.354 0.000 0.061 -0.768 -0.543 -0.408 1.443 -0.072 -1.322 0.255 0.826 -0.025 1.469 0.778 -1.049 -0.551 +0 2.151 -0.563 -1.781 0.300 -0.601 -0.017 0.884 1.234 -0.095 0.342 1.319 -0.775 -1.262 0.494 -1.026 -0.434 0.006 0.206 -0.288 0.996 -0.077 -1.634 0.229 0.151 -0.292 -0.241 -0.496 -0.854 +0 -0.044 -0.052 0.599 -0.074 -0.569 1.284 -0.350 0.467 -0.910 0.950 -0.218 -0.004 -1.553 -0.465 0.082 1.923 1.454 -0.218 -0.250 -1.498 1.376 -0.188 0.160 -0.351 0.340 -0.545 0.204 -0.454 +3 1.085 0.373 -0.987 -1.078 0.158 -0.238 0.205 -1.646 2.232 -1.708 -0.214 -0.736 -1.234 0.627 -0.201 -1.153 -0.457 0.660 0.978 1.578 0.330 -0.508 -0.630 0.513 1.320 -1.974 -0.019 -1.267 +4 1.287 -0.289 -0.760 1.956 -0.158 -0.436 1.104 0.580 1.191 0.270 1.316 -0.486 -0.702 -1.114 2.034 -1.003 -0.514 0.367 0.595 1.430 -0.802 -2.225 0.517 2.329 0.179 -0.225 1.702 0.352 +1 0.274 -0.358 -1.486 -0.606 -0.592 -1.813 0.635 2.088 -0.915 0.153 0.354 -1.011 1.246 0.806 1.658 -0.457 0.293 0.770 -0.384 1.458 0.070 0.628 -0.834 -0.310 0.081 1.362 0.220 -0.365 +0 0.519 -0.940 -0.319 -0.053 0.090 0.429 0.179 0.042 -0.435 1.057 0.348 -0.112 -0.390 0.280 0.030 -1.524 -0.991 1.305 0.370 -0.640 -0.373 -0.862 -0.525 1.025 -0.181 0.833 1.628 -0.395 +1 0.544 -1.094 -0.140 -0.336 0.256 -1.119 1.252 0.916 -0.363 -0.216 -0.136 -0.778 -0.443 -0.270 1.613 0.947 0.935 0.370 -1.117 -0.041 0.970 -1.258 0.637 2.323 1.062 1.414 0.218 0.968 +3 0.534 -0.016 0.233 1.032 -0.738 -0.823 -0.233 -0.063 0.642 1.942 -0.043 0.036 -0.652 -0.773 0.677 0.039 1.247 1.138 0.869 -1.461 0.683 -0.935 -1.884 0.221 1.358 0.712 -0.882 2.796 +3 -0.545 0.371 0.136 -1.920 -0.788 0.909 -0.688 1.355 -0.950 1.747 -0.340 0.756 0.705 1.568 0.220 0.678 -0.448 -0.196 -0.565 -0.474 -2.817 -0.170 -0.005 -1.556 0.964 -0.783 0.046 0.778 +4 1.401 0.366 -0.854 1.767 1.059 -1.481 -1.757 -1.425 -0.494 0.902 0.878 0.341 -0.140 2.371 0.034 -0.087 -0.508 2.212 0.721 -1.034 -1.153 -1.680 -0.097 -1.498 -1.168 1.068 0.891 1.491 +1 1.291 1.117 0.277 0.595 -0.957 -0.078 0.343 -1.583 0.429 -0.959 -0.850 -0.529 -0.031 -1.328 0.857 0.177 1.925 0.300 0.571 -0.854 -1.182 0.619 -1.093 0.642 1.707 0.946 0.570 0.269 +0 1.068 -1.437 0.328 0.016 1.955 0.447 -0.693 1.042 0.054 0.534 0.094 -0.423 0.367 -0.295 1.737 -1.873 0.187 -1.062 -0.327 -0.794 -0.077 -1.050 -0.091 -0.301 -0.163 0.935 -0.384 0.412 +3 2.479 1.154 1.025 0.669 1.041 -2.354 -0.131 -0.686 -1.571 -1.463 -0.163 -1.406 -0.224 -0.452 1.617 0.648 0.866 -1.085 1.281 0.594 -0.094 -1.449 0.392 -0.306 -0.678 0.238 -0.381 -0.111 +1 -0.133 -1.619 -0.480 -0.764 -0.191 1.549 0.759 -0.518 -0.242 -0.786 0.708 -1.180 0.946 1.311 -0.554 -0.323 0.417 -1.261 -0.331 0.279 -1.302 -0.768 -0.011 -1.328 1.827 -0.802 0.787 -0.001 +3 -0.962 -0.224 0.748 -1.137 -0.487 0.518 -2.078 1.856 1.045 -1.538 -2.104 0.030 -1.120 0.961 0.394 0.123 -0.570 1.600 0.753 0.603 1.200 -1.019 1.285 -0.265 0.335 -0.156 -1.878 0.957 +0 -0.240 1.133 -1.219 0.117 -0.327 -0.037 -0.436 0.503 -0.223 0.657 -0.368 1.119 0.013 -1.207 -0.856 -1.158 -1.101 -1.313 -1.086 -0.005 0.738 -1.353 -0.805 0.803 -0.158 -1.711 0.038 0.362 +2 -0.932 -1.667 -1.797 -0.540 -0.808 -0.853 0.983 -0.732 -1.966 0.565 1.336 0.450 -0.670 0.172 -0.803 -0.972 -0.198 -0.057 -0.203 1.672 0.043 0.960 2.232 -0.474 0.149 0.817 0.122 -0.277 +4 0.132 -0.822 1.014 -0.460 -0.465 -2.136 -0.744 -0.992 -2.077 1.559 -0.125 -1.426 -0.964 -0.581 -0.596 0.801 -0.269 0.099 -0.265 1.163 1.378 -0.134 -0.946 -0.110 -3.110 0.314 -1.359 -0.221 +3 0.786 0.043 2.444 -0.143 -0.603 0.081 0.346 -2.218 -0.154 1.806 0.354 0.166 0.494 -0.698 0.627 1.370 1.067 -0.332 -0.769 -0.945 -0.540 0.930 0.463 -0.134 -1.368 1.596 1.746 1.539 +0 0.114 0.662 1.586 -1.238 2.133 -1.952 -0.152 0.588 0.281 -0.623 -0.208 -0.493 -0.589 0.850 0.357 -0.693 0.900 0.307 0.813 0.630 -0.829 -0.560 0.747 0.610 -0.021 0.117 1.278 -0.592 +1 0.532 -0.161 1.578 -1.294 0.880 -0.738 1.038 -0.368 -0.519 0.321 -0.820 -0.938 1.033 -0.762 0.619 -1.983 0.248 0.324 0.551 -0.479 0.117 0.391 -2.778 -0.205 -0.341 0.879 -0.268 -0.322 +1 1.269 -0.834 -0.462 -1.775 0.825 -0.898 -0.205 1.464 -0.969 1.168 1.052 -0.113 -0.058 -0.608 -0.098 -0.643 1.628 0.135 1.014 -0.697 1.358 0.875 0.467 0.608 0.052 0.743 0.766 0.597 +1 -0.041 -0.240 1.451 -1.560 -1.929 -0.775 0.124 1.942 -0.285 -1.118 0.077 0.671 -1.085 -0.785 0.956 -0.654 -1.278 0.330 -0.344 -0.320 0.622 0.633 -0.898 -1.228 0.396 0.314 -0.807 -0.869 +0 -1.234 1.660 -0.661 0.140 0.959 -0.091 0.798 0.665 -0.625 -1.162 0.892 0.469 -0.555 1.056 -0.151 0.296 0.310 -0.422 -0.881 1.421 1.591 -0.801 -0.165 0.695 0.428 1.585 0.547 0.272 +1 0.548 1.185 -0.804 -1.970 0.487 -1.478 -0.248 0.681 1.249 -1.614 0.720 -1.896 -0.452 0.164 -1.067 0.354 -0.402 -0.014 0.055 -0.022 1.394 -0.511 -0.695 1.459 -0.425 0.164 0.843 0.408 +4 0.166 -1.228 0.172 -0.498 2.120 -0.891 -0.239 -0.050 1.493 -0.407 -2.025 0.639 -2.215 1.463 0.658 -1.234 0.121 0.596 -1.059 1.494 1.574 -0.725 0.389 0.578 -1.992 -0.981 0.121 -0.718 +4 0.773 -0.709 -2.257 -1.909 -2.220 1.540 0.614 0.756 0.981 -0.575 2.303 0.335 -0.073 1.110 0.667 1.335 1.394 -0.353 0.187 -0.909 -1.628 0.837 0.957 -1.982 -0.717 0.056 0.151 2.045 +2 -1.366 -2.797 -0.774 0.237 -1.221 0.614 -0.446 0.992 -1.074 -0.058 -1.208 -1.909 -0.263 -1.200 -0.059 0.420 -1.455 0.830 -1.204 -0.715 -0.666 0.215 0.567 0.521 -0.696 0.531 0.856 -0.596 +1 -0.535 -0.012 -1.212 0.040 0.190 0.036 -0.780 1.128 0.583 -0.139 -0.148 -0.738 -0.623 1.457 -0.358 2.764 -1.160 0.369 0.226 0.036 -1.592 -1.371 0.196 -1.024 1.209 -0.224 0.255 -0.040 +3 -0.123 0.162 0.074 -0.032 -0.473 -0.665 0.316 0.053 -0.692 0.343 -0.463 1.555 2.489 1.389 1.333 0.814 -0.210 -1.899 -1.025 -0.677 1.208 0.256 -0.660 -1.845 2.318 0.533 0.909 -0.536 +2 -0.139 -0.344 0.064 1.846 -1.204 -0.041 0.361 -0.117 0.995 -1.736 1.884 -1.371 -1.130 -0.449 -0.648 0.425 0.309 0.114 -1.418 -0.139 0.020 1.232 0.199 0.383 1.962 -1.160 -0.388 -1.136 +2 -0.210 -0.709 -0.361 -1.529 1.660 -1.287 -1.208 1.795 0.323 -1.254 -0.129 -0.236 -1.688 -0.758 -0.026 1.266 -0.189 -0.114 -0.815 1.096 0.902 0.789 0.112 -1.599 1.087 0.057 -0.309 0.448 +3 2.163 1.215 0.161 0.011 0.377 1.573 0.742 1.771 0.816 0.198 -0.780 1.198 -0.631 1.889 -1.290 -1.312 0.193 0.235 1.406 1.161 0.582 0.360 0.157 0.545 0.018 -1.159 -2.193 0.006 +1 -0.976 -0.281 -2.174 -0.040 -0.660 0.322 -0.265 -1.015 1.311 0.083 0.863 0.184 0.464 -1.361 1.278 0.879 -0.019 0.288 1.003 -0.372 -1.583 -0.353 0.152 -0.032 1.318 -0.960 -0.833 -1.058 +4 1.044 1.603 -0.219 1.825 0.877 -1.232 -1.261 -0.577 1.445 -0.501 -1.892 -0.229 -1.981 -1.055 -0.587 0.150 1.024 0.694 -1.179 -0.372 0.076 -0.262 -0.269 -1.723 -1.153 -0.403 -3.093 0.641 +3 1.359 1.066 1.068 0.404 1.123 0.801 -1.553 0.315 -0.851 -0.854 0.155 -0.006 -0.231 -1.279 -0.162 -1.189 0.310 -0.032 0.626 -0.993 1.183 0.204 3.384 -0.857 1.639 -0.767 1.038 0.504 +3 0.285 -0.029 1.370 -0.113 0.665 -0.963 0.085 0.360 -0.937 0.235 -1.100 -2.203 0.186 1.000 -2.110 0.746 1.106 -2.025 -0.116 -1.246 1.983 0.078 -1.691 0.928 -0.388 0.025 -1.069 0.748 +4 0.163 1.495 -1.313 0.394 -0.802 -0.505 -0.803 -0.732 2.722 0.844 1.102 0.492 -1.606 0.395 1.073 -0.853 0.965 0.704 1.852 -0.418 -1.098 -0.362 1.436 -1.828 -2.068 0.089 -0.443 0.008 +4 0.056 0.223 0.833 -2.785 -0.055 -0.494 0.348 0.337 0.256 0.126 -0.072 0.122 -1.851 3.054 0.652 1.666 0.005 0.547 -1.610 1.668 1.346 -0.948 -0.605 -0.177 0.401 -0.217 0.693 0.244 +1 -0.266 1.300 -0.217 -0.006 -1.845 -0.460 0.555 -0.800 -1.751 -0.961 1.264 0.367 0.892 0.599 2.490 1.019 -0.036 -0.255 0.483 1.214 -0.829 0.452 0.190 0.316 -0.304 0.571 1.309 0.364 +0 -0.018 -1.173 2.311 0.153 1.572 0.817 0.610 0.611 -0.555 -0.590 -0.100 -0.163 -2.452 -0.016 0.571 -1.302 -1.069 0.065 0.411 -0.167 0.346 0.133 -0.385 -0.035 -0.031 0.023 -0.149 0.341 +4 -1.417 0.581 -1.573 0.152 -0.447 1.219 -0.393 1.107 -0.514 1.000 2.060 -1.068 0.292 2.749 0.067 0.062 0.788 0.041 -1.707 1.132 -2.003 -0.417 1.207 -0.357 -0.694 0.879 -0.851 -1.222 +3 1.190 -0.437 0.870 -1.621 -0.206 1.324 0.078 0.317 0.568 0.883 1.455 -2.165 -0.339 -2.893 0.839 1.334 -0.356 0.473 -0.713 1.349 0.936 -0.054 -0.992 -0.142 1.527 0.623 0.530 -0.087 +1 -0.375 0.518 -0.086 -0.324 0.381 2.058 -0.317 0.408 0.496 -0.758 -0.685 0.687 1.221 1.994 1.118 -0.621 0.658 -0.072 -1.069 -0.362 0.131 1.531 0.578 -0.849 0.802 1.784 0.305 0.291 +2 0.535 0.234 -0.468 -0.274 -0.560 -1.325 -1.463 0.390 -0.871 0.576 -0.595 -1.313 -0.408 2.094 -0.096 0.646 0.169 -1.405 1.449 -0.248 -1.311 -0.512 -0.206 0.749 -0.681 2.620 -0.937 0.612 +2 0.568 -0.382 -0.546 0.010 0.288 -1.348 -0.125 -2.049 -0.025 1.457 -1.301 -0.140 -0.217 -1.124 -0.453 -1.321 -0.968 0.164 0.387 -0.329 1.342 0.732 1.968 -0.118 -1.049 -0.189 2.016 -1.002 +1 1.696 -0.640 1.186 -1.660 -0.834 0.347 -0.317 0.826 0.263 1.443 -0.499 -1.094 -0.225 -0.409 -0.671 -0.868 -1.433 1.293 -1.485 -0.084 0.129 0.359 -0.046 -1.437 -0.792 -0.054 -0.007 -1.198 +0 -2.111 0.388 -0.679 -0.266 -0.465 -0.430 -0.294 1.668 1.098 0.608 -0.312 0.305 -0.684 0.347 -0.100 0.493 -0.463 0.982 0.050 -0.191 0.760 1.610 0.017 -0.186 0.412 -0.465 -1.100 -0.667 +3 -0.822 0.080 0.083 0.344 -0.679 0.598 1.608 0.973 -1.936 -0.643 0.151 0.059 1.459 1.455 -1.050 -0.617 0.839 1.734 0.797 0.444 -0.145 1.461 -1.466 -1.593 1.811 -0.104 -0.523 1.320 +0 -1.742 -0.099 -0.877 -0.062 -1.095 0.050 -0.297 -0.626 0.289 0.375 -0.049 0.569 -0.413 0.400 -0.042 -0.999 -0.801 1.165 1.576 0.713 -0.583 1.428 0.613 0.336 1.842 -0.860 0.773 -0.815 +1 -0.068 -0.907 -1.198 0.529 1.362 1.269 0.370 0.871 -0.928 1.947 0.811 0.785 -0.312 0.130 1.363 -1.096 0.341 -1.623 -0.483 0.404 0.322 0.621 -0.609 0.476 -0.166 0.949 0.780 -0.658 +3 0.997 -0.466 0.640 -0.191 0.324 -1.148 0.085 -2.991 -0.187 -1.630 1.206 0.777 0.467 1.521 -0.949 1.747 0.932 -0.237 1.136 -1.106 -0.825 -0.609 -0.529 -1.057 1.223 -0.259 0.353 -0.570 +1 -1.051 1.258 0.665 -0.860 0.827 1.612 0.288 0.955 1.345 -0.784 0.599 -1.364 -0.080 -0.507 -0.459 -0.818 -0.881 -1.122 -1.658 -0.398 -1.413 -0.748 -0.953 -1.448 0.305 0.084 0.244 0.044 +2 -0.206 0.719 0.317 0.619 0.232 -0.466 0.274 -0.525 1.646 0.813 2.964 -0.157 0.957 1.906 0.266 0.062 -0.327 -0.838 0.918 0.530 1.085 1.143 -1.448 0.189 0.210 0.525 -0.958 0.467 +0 -0.458 -0.383 -1.209 -1.052 -0.690 1.429 -0.424 -0.353 0.665 -0.006 -0.481 -0.089 -0.908 -1.198 0.965 0.313 -1.937 0.799 -0.211 -0.262 0.923 -0.984 0.054 1.432 -0.294 -0.422 1.644 -0.544 +4 0.595 -1.056 0.520 0.755 1.677 0.585 1.102 0.616 -1.355 0.829 0.120 1.997 -1.439 -1.132 0.725 1.512 -0.120 -0.115 0.207 -0.484 0.988 -0.483 0.700 -1.323 1.719 -2.120 -1.063 2.253 +4 0.686 0.973 0.669 0.443 -0.460 1.170 -0.369 -0.711 -0.424 -0.491 -2.219 0.340 -0.596 0.282 1.979 -3.357 -1.163 0.429 0.699 -1.274 -1.481 -0.362 -0.835 -0.773 -1.339 1.046 0.721 -0.799 +4 0.727 -0.522 1.298 -0.931 2.132 2.029 -0.095 1.146 -0.400 1.013 -0.176 -1.285 -3.533 -0.269 0.279 1.330 0.037 1.003 -0.454 -0.337 1.054 -2.465 0.013 -0.400 1.361 -0.528 0.517 0.922 +3 -1.838 1.565 0.348 0.681 0.063 1.178 0.536 1.305 0.846 1.118 -0.715 -0.381 0.016 0.003 0.575 -0.750 -0.006 0.627 -1.915 1.057 2.045 -0.915 1.480 0.227 -0.886 0.828 1.940 -0.403 +2 0.206 0.833 -0.275 -0.707 -0.187 -0.496 0.656 -0.543 1.994 -0.351 -0.646 -0.270 2.391 0.519 0.709 -0.332 -1.164 -1.151 -0.471 0.625 -1.140 -0.734 -0.474 2.142 1.041 -0.836 -0.353 -0.571 +3 -1.374 0.091 -0.612 -0.274 1.985 -1.223 0.300 -0.878 -0.308 0.745 0.318 -0.225 -0.650 0.316 1.708 -0.901 1.912 1.199 1.369 0.308 1.171 -1.009 -0.668 0.766 0.296 -0.148 -2.503 -0.435 +1 0.097 0.692 1.907 0.549 -0.257 0.265 -1.128 -0.057 0.264 -0.245 -1.081 -0.393 -0.399 -0.953 -1.297 -1.273 0.632 -0.771 -0.792 1.605 -0.750 1.359 1.394 -0.266 0.435 -1.189 -1.090 0.728 +4 2.059 2.092 -0.119 1.423 -0.161 0.433 1.536 -0.233 -1.000 0.471 -0.808 0.036 0.328 0.681 0.693 0.307 0.860 1.562 0.853 1.058 -0.889 0.228 0.421 2.329 -2.190 0.892 0.555 0.740 +0 -0.387 -0.575 0.873 0.537 -0.383 0.476 -1.095 -1.263 -0.094 0.102 -0.140 1.356 0.687 1.035 -0.553 0.333 -1.043 -0.731 0.331 -0.890 -0.791 0.431 -0.764 0.962 -0.214 -0.173 0.251 -0.135 +3 0.797 0.152 1.430 0.652 -0.557 0.596 0.432 -0.395 1.243 -1.265 0.372 -0.432 -1.606 0.117 -0.414 -0.584 -1.060 2.010 -2.460 -2.162 0.523 0.463 -1.389 -0.598 0.627 -0.276 0.796 -0.046 +1 1.054 -0.900 -0.214 -0.794 -0.566 2.111 -1.230 2.235 -1.096 -1.374 -0.296 -1.129 0.713 0.740 -0.986 -1.008 0.577 -0.201 -0.645 -0.331 0.380 0.627 -0.378 0.586 0.155 -0.579 0.430 -0.059 +1 0.194 -0.353 0.338 -0.295 0.168 1.318 -1.007 1.140 1.317 -0.118 -2.122 -0.608 1.297 -0.023 -0.999 -0.505 0.841 0.547 -0.239 -0.367 -0.392 -0.922 1.615 -0.322 1.217 1.521 0.998 -0.432 +2 0.466 0.063 -0.038 0.137 0.698 0.940 -0.498 -0.406 1.451 0.502 -0.824 -0.256 1.258 -1.553 0.244 -0.458 0.997 -0.608 -1.792 -0.927 1.087 -2.789 -0.189 -1.470 -0.154 0.446 0.339 0.020 +1 -0.027 -0.145 0.371 0.133 -0.194 -0.641 0.500 0.430 0.177 -0.829 -1.131 0.676 -0.892 -0.338 -1.556 0.443 -1.240 -1.291 -0.989 0.429 -0.216 -0.634 1.653 0.522 -1.992 -0.873 -1.978 -0.478 +3 0.896 -1.717 1.614 -0.337 -0.799 -0.541 -0.865 1.515 0.672 0.097 -1.203 -0.055 -0.771 -2.631 -0.694 0.605 -0.426 -0.145 1.041 0.862 1.961 -2.194 -0.842 0.908 0.431 0.099 -0.605 0.226 +4 0.119 0.281 -0.259 -0.326 -0.974 0.693 -1.797 -1.307 1.867 -0.116 0.140 -1.212 0.485 1.374 1.451 -1.517 1.169 0.010 -2.527 -1.540 -1.881 0.602 -0.187 -0.651 -0.095 1.756 -0.295 0.329 +4 1.961 -0.491 -2.881 0.286 -0.564 -0.188 -0.922 0.537 -0.549 0.987 1.517 -0.277 -1.977 0.428 -1.817 -0.482 0.916 0.094 -1.342 -0.709 -0.551 -0.788 0.445 0.078 -1.341 -2.144 0.515 -0.114 +1 -1.283 0.186 -1.182 -0.110 -1.440 1.094 -0.395 0.906 -0.076 -1.844 -0.484 -0.531 -0.015 1.508 1.674 -0.231 -1.260 -0.744 0.480 -0.108 -1.575 -0.504 1.312 0.292 0.047 -0.389 -0.460 0.544 +4 -0.616 -1.736 -0.288 -0.504 -1.525 0.251 -1.083 1.324 0.168 -0.261 1.254 0.260 0.347 -0.065 -0.314 0.404 1.524 -3.420 1.868 -1.008 0.192 -1.717 1.491 -1.383 1.122 -0.241 -0.163 -1.259 +4 1.734 -0.637 -0.024 0.117 -0.139 0.994 -2.388 -0.474 0.715 -0.904 -0.807 -0.395 1.473 0.220 -0.145 -1.162 0.211 -1.393 -2.146 -0.409 -2.356 -0.291 1.110 1.565 0.867 1.001 -0.324 -0.752 +2 -0.619 -0.455 0.488 -0.516 0.547 0.095 -0.084 -1.573 -0.820 0.603 -0.682 1.205 2.346 0.656 1.493 0.721 -0.534 0.200 2.013 -0.524 -1.039 -1.134 -0.218 -1.003 1.674 0.216 0.972 -0.299 +0 0.066 1.330 0.065 -0.727 -0.218 -1.020 -0.198 -0.818 -1.333 -0.478 -1.505 0.749 0.637 1.016 -0.532 0.453 -1.141 -0.571 1.025 1.175 0.123 0.679 -0.233 1.006 -0.910 0.120 1.184 -0.506 +4 -0.989 1.377 -1.213 -1.186 0.138 0.053 0.580 -1.176 0.353 -1.678 -2.195 1.828 1.047 -0.004 -2.023 -0.458 0.449 -0.460 1.561 0.629 -0.923 -1.596 0.875 0.841 0.756 1.685 0.458 0.094 +1 -0.817 0.445 -1.156 1.962 0.327 1.047 -0.262 0.768 0.537 -0.591 -0.275 1.492 1.144 -1.413 1.019 -0.303 -0.246 -0.345 0.864 1.213 0.763 -0.087 0.075 -0.317 -1.258 0.215 1.247 0.986 +4 1.197 0.667 -2.353 -1.333 2.159 1.180 -0.583 -1.488 -1.250 0.509 1.977 1.913 -1.576 -0.296 -0.857 0.137 -1.331 -0.546 -0.867 0.524 -0.515 -0.371 0.511 1.643 2.116 0.456 -0.089 1.316 +0 0.995 0.880 -1.232 -0.831 -0.226 0.953 0.608 0.955 -0.440 -0.426 0.568 -0.296 -0.378 -0.609 0.662 0.613 1.810 -0.676 0.335 0.645 -0.638 0.299 -0.648 1.167 -0.183 0.899 -0.563 -0.981 +2 0.662 -0.470 -0.833 -0.082 -1.761 0.424 -1.253 0.358 -0.400 0.398 -0.477 -0.082 -0.713 0.260 0.922 1.208 -0.366 -2.659 -0.503 0.695 1.019 1.903 -0.665 1.555 -0.312 0.451 0.424 1.261 +2 0.071 -1.294 -0.696 -0.918 1.240 -0.396 1.068 0.604 2.304 -1.479 1.259 1.146 -0.973 1.007 0.336 -0.227 -0.824 -0.719 2.050 0.001 0.782 -0.791 -0.780 1.082 -1.350 -0.415 0.034 0.111 +4 -0.317 0.872 -1.221 -1.918 -0.544 -0.248 -0.542 -1.482 0.265 -0.886 0.760 0.502 -0.186 0.342 0.967 2.239 -0.783 0.607 0.953 -2.086 1.005 -1.621 -0.021 1.879 0.255 -0.039 -0.779 2.323 +2 -0.963 -1.352 -1.364 1.466 -0.853 0.874 0.356 -1.028 -0.617 -1.121 0.272 -0.318 -0.093 -1.249 2.404 -1.233 0.140 -0.834 0.886 0.820 0.774 -0.850 -0.175 -0.302 -0.091 -0.226 -1.557 -0.051 +1 -0.983 2.050 0.566 1.762 0.900 0.522 0.556 -0.153 0.039 -1.126 -0.551 -0.436 0.374 0.727 0.944 1.906 0.946 0.791 -1.328 -1.611 0.363 -0.303 0.147 -1.095 0.009 -0.495 0.462 0.602 +2 1.370 2.165 0.627 0.767 -0.036 -1.483 -1.158 -0.543 0.577 -0.452 -0.550 1.741 -0.727 -1.181 0.969 1.003 1.026 -0.593 -0.568 1.505 -0.331 0.203 1.525 -0.689 -0.650 -0.282 0.331 -1.146 +2 -0.998 0.222 2.459 0.976 0.620 1.991 0.468 1.075 0.214 0.452 -1.409 -0.310 -0.562 -0.615 -0.674 1.431 -0.369 -0.144 2.265 0.775 0.008 1.058 -0.669 -0.425 -0.362 0.250 0.467 -0.949 +3 -2.039 2.288 0.831 -0.947 1.024 -0.808 0.068 0.063 -0.446 -0.813 1.209 0.084 1.682 -1.041 -0.473 -0.046 1.103 -0.789 0.571 0.343 1.428 0.498 -1.796 -1.014 -0.963 -0.452 1.324 -0.265 +0 0.460 -0.140 0.257 -0.228 0.667 -1.545 -0.363 0.297 0.534 1.493 -0.391 1.479 -0.659 1.329 0.817 -0.913 0.307 -0.051 0.626 -0.223 -0.307 1.369 -0.099 0.826 -0.225 1.667 -0.678 -0.614 +0 -1.073 0.012 0.580 1.484 0.778 1.094 0.864 -1.209 0.360 0.849 -0.957 -0.580 -0.803 0.773 -0.069 -0.321 -1.282 -0.067 -0.568 0.243 -1.143 0.178 -0.439 -1.249 -1.327 -1.177 -0.206 0.121 +3 -1.158 1.181 0.012 1.478 -0.508 -0.877 -1.012 2.136 -1.894 0.420 -0.483 -2.578 -0.937 -0.848 -0.332 0.001 -1.328 -0.454 -0.980 0.189 0.469 -0.203 1.025 -0.576 -0.940 0.986 -0.402 -1.393 +0 -0.345 -1.827 0.758 -1.248 -0.068 0.153 0.020 1.169 -0.053 0.427 -0.056 0.613 1.393 0.138 0.782 1.455 1.357 -0.385 1.036 0.249 1.117 0.569 0.276 0.434 0.255 0.294 -0.159 -0.252 +0 0.153 0.395 0.765 -0.483 0.879 -0.396 -0.435 -0.462 -2.253 0.424 0.043 0.091 0.512 -0.012 0.713 -0.609 -1.145 -0.222 0.184 0.320 0.297 2.371 -2.124 0.313 -0.390 0.669 0.462 -0.235 +0 0.888 -0.460 1.499 0.110 0.677 -0.410 -0.939 -0.887 -0.381 0.513 -0.298 -0.791 -0.951 1.266 -0.122 -1.958 -0.008 -0.415 0.204 -0.567 1.287 -0.153 -0.210 -0.316 0.474 0.395 -0.391 0.024 +2 0.445 0.279 0.730 -0.179 0.202 -0.398 -0.584 1.338 -2.044 0.877 -0.621 -0.750 0.262 -1.490 -0.816 -0.849 -0.221 -0.804 1.601 1.797 -2.022 0.544 0.859 0.543 -0.658 -0.244 -0.748 -0.420 +4 0.381 -1.651 -0.652 0.225 1.860 1.413 -1.209 -1.052 1.754 -1.140 -1.346 -1.791 -0.540 1.860 0.865 -0.013 1.606 1.885 -0.523 -1.600 -1.314 -0.830 -0.077 1.694 0.902 -0.862 0.589 0.629 +3 0.418 1.402 0.650 -1.503 1.052 -0.998 -0.384 0.250 1.996 3.110 0.607 -0.183 0.535 0.888 -0.321 1.795 0.230 0.498 0.666 0.422 0.839 -0.617 -0.558 -1.100 0.440 0.779 0.458 1.674 +3 0.743 -1.605 -1.917 1.411 -1.023 0.106 -0.268 0.344 0.051 1.043 -1.429 0.671 -0.051 -1.532 -0.530 -1.604 -1.673 -0.437 1.186 0.313 1.299 0.076 1.550 1.447 -0.950 0.071 -0.722 -0.145 +0 -0.397 0.152 2.247 -0.414 -0.454 -0.171 0.683 -0.490 0.471 -0.190 -0.442 0.094 -0.610 0.097 -0.280 0.402 -0.402 2.193 -2.190 0.982 -0.632 0.034 0.141 0.505 0.979 -0.447 -0.353 -0.845 +0 -0.246 0.231 0.204 -0.587 -0.080 -0.555 -1.643 -0.525 0.727 -0.384 0.515 -0.008 0.989 1.161 0.184 0.695 -0.352 -1.541 -1.878 -0.005 -1.319 1.591 -0.885 0.199 -0.231 -1.447 -0.702 0.018 +4 -0.462 -0.227 1.454 -0.581 -2.302 -1.004 -0.661 -2.156 0.969 -1.570 0.446 -1.092 1.507 -0.276 -0.479 0.528 -1.007 0.576 -0.482 -0.595 -0.088 -1.090 -0.191 0.339 -2.309 1.091 0.816 -1.855 +3 0.161 -2.524 0.189 -1.377 0.162 0.471 -0.930 0.251 0.514 -0.082 1.625 0.116 -0.785 -1.234 0.445 0.066 0.550 1.014 0.638 -0.840 -0.103 -0.667 1.931 0.771 0.080 -0.275 -2.590 1.185 +3 2.927 -0.335 -0.667 -1.701 0.960 0.730 1.552 -0.917 0.716 -0.522 1.344 0.536 -1.556 -0.131 -1.948 -1.163 -1.103 -0.641 -0.263 0.165 0.115 -0.024 0.332 -1.250 0.073 1.434 -0.166 0.495 +2 -2.407 0.593 -0.931 -0.205 -1.231 -0.436 0.052 0.344 0.337 -0.066 0.296 0.563 0.305 -0.316 -0.275 0.002 2.527 -1.407 0.233 0.034 -0.211 -1.671 0.936 1.845 1.044 -0.875 -0.917 0.914 +2 -2.488 -0.687 0.570 -0.210 0.811 1.042 -0.213 0.601 1.456 1.019 -0.771 -0.185 0.027 -0.935 1.070 -0.645 -0.101 1.072 -0.451 -0.337 0.338 -0.409 1.533 -0.915 -2.295 0.843 -0.358 -0.808 +0 0.071 -1.488 -0.133 -0.840 -0.440 -0.518 1.171 0.142 1.821 -0.218 0.123 0.093 1.122 1.185 -0.612 -0.749 -0.298 -0.535 -1.633 0.080 0.511 0.254 -0.170 0.441 1.745 0.340 -0.459 0.554 +3 -0.595 -0.141 1.584 0.615 0.689 -0.044 -0.451 1.950 0.094 -0.673 0.009 -1.230 1.915 0.663 -1.745 -1.845 -0.553 0.027 -0.922 0.673 -0.773 0.129 0.061 1.634 0.228 0.158 -0.155 -2.443 +0 -0.602 -0.230 -0.679 -0.305 -0.508 0.053 0.362 -0.555 0.170 0.852 1.822 0.181 0.565 0.768 0.325 -0.875 -0.852 -0.027 0.478 -0.598 0.448 -0.178 0.582 0.200 0.068 -1.995 0.713 0.820 +2 1.464 0.997 0.281 1.759 0.372 0.389 -0.053 1.224 1.096 -0.626 -1.319 -0.203 -0.800 -0.064 1.237 -0.457 -0.043 0.058 0.848 -2.247 -0.607 0.211 1.200 -0.492 -1.877 0.620 -0.635 -1.190 +4 -0.930 0.266 0.940 -1.324 3.152 -0.099 -0.086 -0.579 -0.772 -0.779 1.507 0.284 -0.916 0.705 0.798 1.473 -1.886 0.882 1.553 -0.565 2.690 0.462 -0.911 -0.070 0.272 0.833 -1.970 0.703 +4 0.475 -0.494 1.229 0.004 -0.559 0.070 0.739 -0.714 -0.421 0.718 0.825 -1.238 0.262 1.113 -2.680 1.045 1.826 0.323 -0.926 0.198 -0.034 -1.544 -2.104 -1.065 -2.060 -0.576 -0.889 -0.761 +3 -0.764 1.326 -0.801 0.607 -1.760 1.116 0.073 1.291 -0.717 0.213 1.519 -0.026 0.430 0.699 -1.552 -0.521 1.138 -2.048 1.118 -0.589 2.055 0.437 -0.801 0.979 1.433 0.604 -0.446 1.438 +1 -0.394 0.874 -1.974 0.309 -0.548 1.615 0.329 -0.787 1.452 0.022 0.131 0.887 -0.606 1.447 0.577 -0.641 -0.432 -1.256 0.308 -0.178 0.313 -1.702 -0.524 -0.982 0.493 -0.602 0.470 -0.533 +2 0.811 -0.338 1.091 0.726 0.864 1.661 -1.145 -2.102 1.451 -0.171 -0.228 0.078 -1.605 -0.644 -0.824 -0.744 -1.108 -0.009 -0.464 2.135 0.133 0.410 -0.304 0.240 1.265 0.469 -0.110 -1.316 +4 -0.248 1.556 -0.909 2.441 1.838 0.462 1.902 0.401 1.955 0.169 -2.293 0.408 -0.043 -0.828 0.665 1.397 -1.604 -0.683 -1.074 -0.307 1.817 0.372 0.140 -0.749 0.294 -0.130 0.066 -0.410 +4 -0.428 -0.010 -1.134 1.029 -1.794 0.540 -0.327 -1.171 0.202 -1.557 0.040 -1.423 0.461 -0.924 -0.451 0.294 -0.773 -0.058 -1.514 0.631 1.765 2.792 0.091 0.788 -1.781 1.503 0.776 -0.484 +1 0.193 1.423 -0.143 -0.624 2.205 0.229 -0.061 0.676 -0.500 -0.436 -0.061 1.146 1.377 -0.227 -1.058 -0.213 -0.298 -0.708 0.649 1.498 1.281 -0.081 -0.949 1.001 -0.625 -1.576 1.372 -0.820 +2 -0.087 -1.896 0.276 -0.764 0.911 1.038 0.923 -0.461 -0.113 0.249 0.893 -1.038 0.093 1.868 -0.535 -0.026 2.613 -0.241 -1.539 -0.057 1.673 1.057 -0.980 0.415 -0.176 0.812 -0.615 0.495 +1 -1.402 1.550 -0.963 0.324 -0.135 2.065 0.467 0.582 -0.030 0.073 0.290 0.334 0.220 -0.630 0.450 -0.878 0.057 -1.398 0.507 -1.187 0.931 -0.778 0.645 1.546 1.466 -0.080 0.050 -0.543 +3 -0.620 0.285 0.313 0.571 1.101 0.417 0.485 -1.103 -1.534 -0.553 0.327 0.415 -2.526 0.132 1.126 2.260 1.441 -0.521 -0.580 0.492 0.809 1.123 0.598 -1.225 -1.392 1.459 -0.165 -0.969 +1 0.806 2.230 1.207 0.285 0.073 -0.418 -2.323 0.046 -0.514 0.322 -0.516 0.221 1.246 0.157 -1.086 -0.831 0.691 0.753 -0.872 -0.029 0.905 1.721 0.176 -0.199 -0.370 0.143 1.389 0.145 +4 -0.804 -0.402 0.905 -0.614 0.978 -0.274 -0.789 1.371 0.133 -2.749 -2.085 -0.813 -0.470 0.263 0.116 -0.965 -0.085 -0.390 -0.580 -0.612 2.929 -1.246 -1.039 -0.246 1.418 -0.140 0.209 2.019 +2 0.468 0.154 1.306 1.302 0.439 0.177 -0.971 -0.784 -1.918 -0.199 -1.124 -0.805 1.191 0.871 -0.632 -0.398 2.223 -0.843 0.701 1.546 0.232 0.410 -1.698 -0.407 0.182 1.443 -0.549 0.148 +0 0.269 -0.014 -0.238 -0.246 -0.134 -0.126 1.554 -0.041 -1.033 1.849 -0.055 -0.165 -0.909 -0.768 1.587 -0.905 -0.762 0.598 -1.554 1.014 0.611 -0.140 0.389 1.566 0.804 0.884 0.534 -0.196 +2 -0.443 0.480 1.249 0.650 0.368 -0.338 -0.420 -0.489 -1.724 0.870 1.581 -0.682 -0.510 -0.548 0.585 -1.661 0.689 -1.604 0.269 2.244 0.440 1.239 -0.524 -0.007 0.569 -0.973 -0.243 -0.947 +1 -0.248 -0.273 1.635 0.848 0.018 0.437 1.321 0.400 -1.588 1.433 1.102 0.766 -0.185 0.447 1.452 -0.275 -0.186 0.619 0.101 1.709 0.067 2.063 -0.489 -0.839 0.338 -1.104 0.221 0.012 +2 -0.119 1.310 -0.407 -0.382 -0.664 -1.569 -0.860 0.055 0.229 -0.968 -0.510 -1.893 2.187 1.467 -0.716 0.138 -0.880 1.024 0.643 -1.314 -0.974 -0.070 1.384 -0.379 1.308 0.311 -0.833 -0.418 +1 -0.406 0.591 -0.576 -1.124 -0.856 1.420 -0.474 -1.372 0.688 0.291 -0.702 0.518 0.707 -1.181 0.979 -0.308 -1.656 -1.592 -0.684 0.261 0.746 -0.252 -1.701 1.227 1.241 0.230 -0.469 -0.377 +2 0.640 0.222 1.757 0.216 0.734 -1.466 0.735 1.017 -0.395 1.258 -0.985 -0.954 0.399 0.342 -0.540 -1.229 1.176 -1.576 0.443 -0.843 0.270 -0.126 1.647 1.394 -0.172 1.167 -1.857 -0.135 +0 0.187 -0.122 -0.206 -0.130 -0.748 -0.680 -0.239 0.698 -0.855 0.603 -2.018 -0.754 0.452 1.405 0.539 -0.588 1.203 0.292 -0.198 -0.225 0.400 0.438 -1.192 -0.357 -0.190 -0.618 1.264 0.617 +2 0.386 1.458 0.882 -0.407 -0.109 0.395 0.151 -0.903 0.450 0.515 -0.942 0.123 -0.222 0.474 -0.658 1.929 -1.395 1.814 -0.835 0.555 -1.085 -0.754 -0.899 -0.520 -0.952 2.162 1.030 -0.289 +3 0.208 0.650 0.665 0.775 -0.368 -0.480 -1.594 0.523 1.082 1.616 0.305 -0.766 -1.697 1.042 -1.796 -0.341 -0.224 -0.069 0.036 0.138 -2.415 0.015 -2.230 0.732 -0.555 -2.157 -0.814 -0.570 +4 -1.468 0.424 0.152 1.306 0.106 -0.247 -0.657 0.634 2.132 -0.729 -0.409 -2.113 0.104 0.274 2.009 0.649 -0.562 1.989 -0.423 0.445 -2.007 0.249 0.259 -0.953 -1.539 0.722 1.292 -1.784 +1 0.474 -0.595 0.144 1.349 -0.390 -0.303 -0.281 0.958 -0.152 -0.445 0.179 1.063 -1.304 0.748 0.353 0.397 0.019 0.031 0.704 -1.550 -0.808 -1.688 1.812 1.259 -2.172 0.362 -0.219 0.594 +2 -2.123 0.837 -0.196 -1.655 -0.607 0.980 0.004 -0.647 0.101 -0.142 -1.207 -1.230 0.209 0.630 -0.407 0.242 -0.781 1.871 -0.018 -1.835 -1.028 -0.309 1.034 0.652 -0.660 -1.577 -0.231 -0.683 +3 0.622 -0.145 -0.077 1.281 0.117 2.378 -0.041 0.805 0.484 -0.574 -1.031 0.037 1.921 -0.394 0.519 -0.257 0.222 1.142 0.115 -0.615 1.606 -0.605 0.677 -1.491 1.285 2.605 -0.027 -1.026 +4 1.504 0.151 0.673 -1.520 -0.442 -0.147 -1.023 0.132 1.696 0.309 1.337 0.709 1.721 -0.062 -0.669 0.783 -1.568 0.597 -2.208 -0.872 0.350 -1.273 -0.410 -1.313 0.711 1.136 0.003 2.197 +0 -0.015 1.982 -0.652 -1.075 0.034 0.599 -0.142 0.411 -0.146 0.117 -0.187 -0.568 -0.909 -0.571 -0.436 1.395 -1.048 -0.526 0.314 0.843 0.577 -1.181 1.013 0.688 -0.790 -0.546 -0.304 0.255 +3 -0.172 -0.734 -1.600 0.707 -0.171 0.171 -0.901 -0.319 -0.487 -0.101 -0.670 -1.534 2.728 -1.363 0.181 -1.772 -1.997 0.098 0.174 1.261 0.400 -0.150 -0.950 1.499 0.929 -1.246 0.038 0.144 +3 -0.898 0.195 -1.903 1.166 -0.332 1.481 -0.162 0.412 1.704 0.316 -0.695 0.958 0.310 -1.140 -1.394 -0.755 1.527 0.149 -0.057 1.231 1.821 0.051 0.681 0.697 1.133 -0.094 1.229 -1.617 +3 -1.270 0.339 -0.292 -0.777 0.247 1.257 0.404 0.273 -2.020 0.921 -0.406 0.534 0.920 0.193 -2.001 1.417 1.805 0.813 -0.726 1.230 -0.282 -0.067 1.198 0.647 -2.353 0.208 1.185 -1.364 +0 -0.739 -0.148 0.615 0.096 -0.011 1.348 -0.170 -0.036 0.277 0.085 -0.034 -0.788 -1.138 0.400 -0.699 -0.620 -0.642 0.719 -0.844 -1.618 -0.042 -0.502 -0.203 0.492 0.807 -1.011 -0.892 -1.502 +3 0.462 -0.482 -1.714 0.913 -1.591 -0.058 1.299 2.478 1.411 -2.230 0.171 -1.068 -0.500 0.951 0.534 -1.714 -0.644 0.543 0.861 -0.996 -0.333 -0.059 -0.493 0.144 -0.370 -1.134 0.016 0.165 +3 0.135 0.496 1.033 -0.149 0.398 -0.008 -0.186 1.577 -0.419 0.503 1.011 -1.694 1.802 -0.320 -2.792 0.298 0.374 0.448 -1.307 1.485 1.466 -0.689 0.034 -0.996 1.259 0.766 -0.390 0.759 +3 0.320 -0.080 0.423 1.025 1.336 0.157 -0.293 -1.468 -0.534 0.923 -0.238 -0.107 0.115 0.074 1.105 0.572 -0.235 0.522 -0.261 -2.254 -2.147 -1.894 -2.086 -0.308 0.045 0.029 -2.429 -0.307 +0 0.180 -0.154 -0.093 -0.497 -0.585 0.029 -1.231 1.036 -0.763 0.112 -0.324 0.650 0.595 0.507 -1.169 -0.192 1.602 -0.342 1.049 -0.010 -0.310 0.187 -0.832 0.900 -0.854 0.554 1.079 0.426 +3 -0.647 0.847 0.019 -0.521 0.618 -0.727 0.638 0.213 -0.596 0.529 0.547 1.158 0.966 0.491 -1.128 2.449 -1.404 0.949 0.431 -1.174 -0.124 -0.079 1.306 -0.713 -0.262 -2.336 1.735 -1.070 +2 0.108 -0.298 -0.430 -0.364 -0.007 -0.405 0.556 1.469 0.103 1.106 -1.323 0.357 0.086 -1.395 -0.255 0.856 -2.691 -0.200 -0.453 0.269 0.906 0.699 0.972 1.181 -0.363 -2.427 0.672 -1.008 +1 0.669 -0.770 0.023 -0.083 1.388 1.854 1.175 -0.086 1.148 0.649 -0.075 2.092 -0.738 0.604 0.840 -0.287 0.559 0.945 0.245 0.472 0.105 0.798 -0.229 -0.312 0.021 1.184 -1.747 -0.909 +3 -0.602 -0.200 1.006 -0.526 -0.927 0.163 -0.214 -0.467 0.216 -1.204 -1.722 2.139 0.104 0.174 -0.782 0.898 -0.533 -2.786 -0.567 -0.281 -0.170 -0.199 -0.645 -0.742 -0.298 -1.645 -1.958 0.098 +2 0.099 0.438 -0.514 -0.977 -1.902 0.459 2.381 -1.147 -0.997 0.775 -2.438 -0.790 0.209 -0.828 0.518 0.413 0.115 -0.759 -0.410 0.078 0.211 1.427 0.729 -0.377 0.565 0.633 -0.656 0.582 +1 -0.028 0.089 -0.835 -0.727 -0.747 -1.620 0.342 0.895 0.796 -0.262 -0.811 0.744 0.639 0.217 1.744 -0.026 -0.257 -0.400 -1.561 0.661 -0.105 0.510 1.842 -0.391 -2.338 0.481 0.167 -0.732 +3 -1.524 1.563 -0.203 1.272 1.220 -1.933 0.062 0.773 -1.075 -1.289 0.503 -1.196 0.287 0.753 0.888 0.807 1.282 -1.007 1.688 -0.513 -1.080 1.075 0.621 -1.704 0.874 0.284 0.033 -0.475 +4 -0.477 0.435 0.189 0.556 2.683 -0.970 -2.250 -2.433 0.922 -0.160 -1.489 -0.235 -0.480 -0.354 -1.913 0.593 0.718 0.129 -0.076 0.179 0.049 -1.776 -0.838 0.214 -0.735 0.884 -1.352 2.349 +0 0.104 0.467 -0.027 0.195 0.688 0.253 -0.571 2.330 0.094 -0.199 -0.345 0.341 -0.243 -2.029 -0.254 -1.083 0.267 -0.030 -0.664 0.941 -0.071 -0.377 -0.362 -0.384 0.507 -0.779 0.070 -0.504 +1 -0.528 0.194 1.041 -1.296 -0.748 -0.731 0.575 -0.569 0.765 0.918 -0.212 0.185 -0.135 2.034 -0.956 -1.505 -2.604 0.216 0.238 -1.134 -0.760 0.989 0.823 -0.240 -0.453 -0.495 0.357 0.372 +0 0.139 -0.633 -0.617 0.847 0.985 0.784 0.793 0.569 0.132 -0.175 1.210 -0.374 0.370 -0.804 -0.659 -0.236 -0.804 0.798 -1.348 0.099 0.133 -1.811 -0.441 0.256 0.429 -0.204 0.677 -0.109 +0 0.786 -0.547 -1.000 -0.495 1.220 -1.205 1.138 -0.938 0.130 0.299 -0.090 0.929 0.190 0.370 -0.763 -2.401 1.445 0.752 0.014 -0.260 0.822 -0.017 -0.713 -0.395 0.894 -0.268 0.691 0.786 +2 0.872 0.324 0.824 -0.164 1.202 0.009 0.058 -0.266 0.684 -0.421 0.976 -1.132 -1.099 0.687 -0.089 -0.454 0.890 -0.607 -0.200 1.399 1.539 1.572 -0.282 1.231 -0.949 -0.907 -2.350 1.673 +0 1.135 0.479 0.927 0.612 -0.348 0.560 0.181 0.228 0.428 -0.102 -0.276 -0.536 0.446 -0.508 0.521 0.706 2.120 0.762 -1.533 0.505 1.174 -0.425 0.884 0.491 1.359 0.608 0.053 0.194 +3 1.093 -1.371 -0.701 -0.330 0.713 0.219 2.143 -0.806 -1.395 1.789 -1.431 0.683 -0.979 -2.069 -0.375 0.899 -1.222 0.576 0.763 -1.272 -0.228 1.577 0.651 -0.261 0.415 -0.685 1.114 1.113 +0 0.106 -0.114 1.238 0.032 -1.657 -0.053 -0.035 -0.567 -2.405 0.862 -1.210 -0.521 -0.471 0.683 0.520 0.199 -0.259 1.277 0.183 0.618 0.581 -0.902 0.644 0.310 -0.513 1.123 1.427 0.035 +3 -0.146 -0.761 0.355 0.753 0.001 -1.593 -0.459 -0.358 -0.375 1.433 -0.186 0.517 1.623 -0.027 0.001 0.122 1.564 2.790 0.100 1.308 1.280 1.314 0.793 0.057 1.606 -1.720 -0.596 1.207 +4 -2.140 -1.219 -0.756 1.962 -0.307 -0.335 -1.385 -0.646 -0.641 1.092 -2.346 -1.745 0.179 0.746 -0.982 1.139 -0.155 -1.156 -1.302 0.237 0.511 -2.493 -0.640 -1.631 -0.038 -0.183 -0.396 2.249 +0 -0.285 0.430 -0.567 -0.361 0.529 0.047 -0.640 -0.482 1.042 0.310 -0.361 0.866 0.436 1.355 0.886 1.166 -0.340 -0.889 0.772 0.781 -0.105 -1.043 0.307 0.388 -1.358 0.374 1.151 -1.314 +1 -1.369 -0.605 -2.231 -0.442 0.496 -0.792 -0.252 0.391 2.723 -0.082 0.719 -0.187 0.356 -0.028 -0.422 -0.203 1.247 0.289 -1.197 -0.424 -0.892 0.927 -0.759 0.862 0.977 0.144 0.544 -0.501 +4 1.385 0.397 -0.833 1.066 0.018 0.038 -1.203 -0.990 -0.825 0.046 0.078 1.544 0.385 -2.708 0.263 -1.450 -0.966 -0.812 -0.663 -1.375 0.203 -1.411 0.357 2.237 -1.329 -0.285 -2.523 -0.301 +4 -0.496 0.816 3.051 0.439 0.005 0.620 1.310 1.150 1.782 1.207 -0.187 0.891 -1.182 0.788 -1.143 1.701 -1.552 -1.555 -0.319 0.136 -0.702 -0.177 -0.368 -0.907 0.422 2.290 -0.415 -0.004 +4 0.886 3.206 0.269 0.808 0.310 -1.146 -0.301 0.139 -1.304 -0.005 -1.298 3.148 1.161 0.534 0.951 1.417 1.464 0.271 -0.166 0.387 0.118 -1.282 -1.814 1.010 0.953 -0.068 -1.004 -0.571 +0 -0.562 -0.025 0.447 -0.035 0.233 -0.775 -0.716 -0.005 0.120 0.048 -0.590 0.073 0.025 -0.121 -0.652 0.519 -0.012 -0.750 -0.125 -0.583 0.003 1.428 0.148 0.201 1.424 0.271 0.434 0.144 +4 -1.316 -1.709 -0.553 -2.792 0.016 -1.600 -1.915 -0.348 1.245 -1.434 -0.055 2.137 0.353 -0.913 0.878 0.577 -1.051 0.119 -1.275 1.030 0.069 0.854 -0.343 0.454 0.740 0.372 -0.018 -1.011 +0 -0.817 1.941 -0.599 0.212 -0.191 0.939 -0.335 -1.267 0.415 -1.503 -0.848 -0.510 -0.473 -0.346 -0.426 0.126 -0.680 0.435 -0.842 -1.269 -1.131 -0.411 0.971 -0.028 -0.461 -0.040 -1.210 -1.706 +4 0.871 0.902 2.888 -0.268 -0.904 0.456 -0.880 -1.133 -0.252 1.980 0.689 0.380 -1.673 -0.623 1.433 -0.270 2.195 0.448 -0.150 -0.314 -0.065 -1.452 0.973 0.442 -0.256 -0.112 0.810 2.030 +4 -1.856 2.013 -0.465 0.985 0.325 -1.043 -1.510 -0.596 2.222 -1.584 -0.694 -0.909 1.090 0.031 1.634 1.402 2.394 1.639 -1.129 1.652 -0.596 0.092 0.704 1.025 0.271 1.631 -1.014 0.075 +0 -0.489 -0.426 -1.251 0.887 1.224 -0.032 1.371 0.818 0.383 0.604 -1.193 0.763 1.433 -0.001 -0.701 -0.490 1.117 -0.000 -0.433 0.338 0.163 -0.094 0.643 -1.394 1.921 -0.475 -0.603 1.035 +0 1.009 -0.519 0.267 0.228 -0.213 -0.264 0.363 -1.106 0.840 -0.609 0.441 -0.030 1.408 0.271 1.660 0.244 0.467 -0.948 0.603 0.804 -1.094 -1.246 1.937 0.171 0.361 1.271 -0.046 -0.797 +2 -0.508 -0.230 -0.561 0.545 -0.349 -0.855 -0.790 0.534 -2.215 -0.087 -1.448 -0.071 -0.327 -0.226 0.860 -1.444 0.215 0.111 0.257 1.643 -1.904 0.402 1.796 -0.262 -0.294 1.555 1.044 1.023 +2 -0.502 -0.456 2.535 -0.294 -0.385 -0.789 0.576 0.212 -0.566 -0.382 0.967 -1.053 -0.598 0.152 0.193 0.568 -0.856 -2.044 -0.136 -0.595 -0.668 0.164 -2.741 -0.435 0.238 0.061 -0.919 1.563 +0 2.540 0.144 -0.238 0.729 0.194 -1.598 0.279 1.286 -0.228 0.260 0.172 1.054 -0.487 -0.258 0.226 -0.548 -0.806 0.640 1.002 -0.624 -0.449 0.001 0.560 -0.349 -0.471 0.477 -1.048 -0.283 +0 -0.363 -1.521 1.113 0.054 1.320 -0.103 1.190 1.250 0.099 0.172 0.496 -1.057 -0.803 -0.216 0.137 1.526 -0.630 -0.406 0.677 0.786 0.501 -1.646 -0.654 0.001 -0.556 0.511 -0.322 -0.712 +4 1.245 -0.451 -0.219 1.544 -0.823 -0.225 -1.595 0.950 3.092 -1.553 -2.275 0.738 -0.809 -0.232 1.251 -0.549 -0.563 -1.937 1.554 -0.272 -1.084 -0.434 -2.117 -1.799 -0.366 -0.145 0.841 0.735 +1 0.738 -1.786 0.961 -1.019 -0.825 1.440 -1.654 0.995 -0.263 1.002 0.303 -0.393 -0.860 -1.646 -1.030 -0.860 -1.094 0.737 1.018 0.246 0.452 0.251 0.759 -0.156 0.708 0.354 0.397 0.333 +2 0.259 -0.486 1.000 -0.431 0.973 0.118 -1.908 0.588 -0.053 0.555 0.216 0.222 0.043 2.883 0.526 -1.768 -1.508 0.433 -0.572 1.073 0.323 0.377 -0.920 0.575 0.172 -0.936 -0.932 1.656 +2 1.307 -0.368 0.259 0.237 1.663 0.741 -0.458 -0.272 -0.276 0.018 -0.945 -0.081 1.368 1.324 -1.565 -1.723 -0.593 -1.779 -1.263 0.351 -0.038 -1.721 1.170 -0.422 -0.518 1.017 0.411 -0.042 +3 0.454 -1.545 0.886 -2.189 0.940 0.490 -0.755 0.115 -0.863 -0.483 -1.008 -0.538 -0.219 -0.167 -0.029 -1.768 -0.890 -1.420 2.246 -0.059 0.259 1.352 0.792 1.786 -0.150 1.739 0.263 -0.681 +2 1.029 0.029 -0.134 -0.730 0.025 -0.255 1.217 -0.736 -0.086 -0.763 -0.820 -1.397 -1.104 0.831 -0.574 0.666 1.544 1.271 -0.578 -1.824 -0.229 -1.025 -0.557 1.992 -1.885 0.088 -0.269 0.458 +0 0.298 -0.674 -1.076 -1.273 0.168 0.082 1.195 -0.805 -0.288 0.017 -0.550 0.839 1.238 -1.106 -0.458 1.325 0.323 0.117 0.341 0.656 0.389 0.214 0.010 1.348 -0.266 0.001 1.552 0.326 +3 -0.429 -1.945 -0.177 -0.639 0.025 -0.251 0.496 -2.712 -0.080 0.522 -2.234 -0.602 -0.160 1.115 0.111 0.966 -0.231 0.003 0.759 1.370 1.245 1.789 -0.110 0.136 0.352 -1.411 0.007 1.030 +3 -1.711 -0.139 -1.275 1.276 0.897 0.599 2.038 -0.066 1.183 -0.475 -1.798 0.135 1.251 -1.887 0.845 0.255 -0.208 -0.582 -0.354 -0.236 -0.491 -0.978 1.463 0.712 -0.888 -0.007 0.092 1.554 +0 -0.086 -0.296 1.654 -0.861 -0.092 0.890 -0.618 1.625 -0.253 -1.243 0.239 -1.385 -0.927 -1.165 -0.955 -0.656 -0.569 -1.423 -0.050 1.106 0.960 0.917 0.800 0.003 0.590 0.124 -0.141 0.108 +3 -0.459 0.230 -0.295 0.849 -0.362 -0.994 0.094 -0.665 0.758 0.294 1.775 0.420 -0.591 -2.375 -1.537 -0.982 1.218 -1.680 -1.694 0.624 -0.494 -2.076 -0.126 -0.965 -0.048 0.853 0.095 1.005 +2 -1.162 -0.632 0.675 0.247 -0.018 0.440 -1.872 -0.922 -0.602 0.763 -2.154 0.181 -0.377 0.651 0.808 1.698 -1.174 -0.821 0.049 1.410 0.437 0.105 1.224 -0.397 1.170 1.735 -0.153 -1.550 +4 1.262 -0.028 -0.370 -0.660 0.064 0.915 -0.812 0.095 2.658 1.815 -2.232 -1.815 1.806 0.896 0.051 0.528 -0.316 -0.666 0.730 -0.097 0.865 -0.549 0.713 0.312 -1.335 1.474 -1.408 1.331 +3 0.467 -0.288 -0.885 0.089 0.458 0.524 0.444 1.064 0.521 0.271 -1.266 0.246 -0.171 -1.122 0.501 0.518 0.425 0.556 0.124 -0.247 1.770 0.148 -2.543 -2.428 1.205 -1.834 -0.544 -1.216 +4 -1.142 -1.081 0.667 1.886 -0.470 2.601 -1.896 -0.508 0.183 -2.552 1.522 -0.692 0.034 0.448 -1.591 2.432 0.134 -0.341 -1.111 0.205 -1.576 0.139 1.397 1.165 -0.556 -0.278 0.541 1.182 +1 0.302 -1.135 -0.126 -0.079 0.657 -0.082 1.159 0.657 -1.778 -0.233 1.161 -0.930 0.270 0.376 0.143 -0.381 0.049 -0.562 -0.720 0.925 -0.305 2.361 -1.459 0.127 -0.812 0.774 -1.140 1.123 +4 2.047 0.529 -0.512 0.451 1.756 -0.454 0.276 1.375 -0.774 1.191 0.605 1.716 -1.932 -1.064 -0.944 -0.672 0.256 2.404 -1.183 -0.641 1.307 0.244 1.868 -0.229 -0.277 1.081 -0.582 -0.792 +2 1.061 -1.376 2.444 -0.067 -1.711 0.035 0.435 1.245 0.069 0.159 0.078 -1.206 0.030 -0.029 -0.700 -1.893 -0.385 0.607 1.472 -0.207 -0.145 -0.283 0.307 -0.488 0.561 -2.301 0.549 -0.443 +2 0.156 -2.365 -1.293 0.608 -0.512 -0.495 0.015 0.377 -0.470 1.347 0.171 -0.936 -1.205 0.892 -0.201 1.749 -0.102 -0.009 -0.511 2.247 0.465 -0.210 -0.394 -0.006 1.002 1.337 -0.797 0.933 +2 1.321 0.472 -0.876 -1.271 -0.038 -0.224 0.395 -2.199 -0.189 -0.877 1.277 0.409 -0.508 1.421 -0.436 -0.104 0.271 0.909 0.012 1.420 -0.402 -0.981 1.593 -0.313 0.593 0.768 2.083 -0.297 +3 -0.682 0.709 -1.435 -0.553 0.891 0.685 -0.802 -0.900 1.575 0.375 -0.813 -0.041 1.086 -0.894 -0.915 0.221 -1.352 -0.673 -0.435 -2.657 0.083 0.613 1.950 0.597 -1.026 -0.452 1.861 0.454 +0 -0.591 -0.400 1.781 -0.297 -0.129 -0.115 -1.346 0.572 -0.653 -0.030 0.444 1.943 -1.318 0.865 0.869 -0.638 0.600 1.643 -0.765 0.241 0.405 0.534 0.769 0.166 0.513 -0.911 0.164 -0.476 +3 -0.856 -1.340 -0.179 1.023 0.335 -1.287 -0.920 0.769 0.051 -0.537 2.416 0.289 -1.313 0.545 -0.063 1.991 -0.240 -0.705 1.263 1.127 0.644 2.134 1.733 -0.575 0.366 -0.380 -0.902 0.041 +1 0.382 -0.904 1.012 -0.320 -1.689 0.080 -1.159 -0.605 0.707 -0.120 -0.037 1.168 -2.256 1.125 0.147 -0.069 -0.038 0.640 1.026 -0.236 -0.134 0.609 -2.098 -0.099 0.498 -0.493 -0.799 -0.152 +2 0.479 1.257 -1.522 -0.479 -1.101 0.672 -0.173 0.555 0.729 0.010 -0.313 -0.224 -0.876 2.623 -0.098 1.219 1.079 1.792 0.531 -0.544 0.598 0.584 -0.873 -0.265 0.743 0.066 0.318 2.339 +4 -0.171 1.216 0.465 -1.180 -1.809 -1.018 -0.433 -0.149 -0.635 0.483 -2.057 0.308 0.981 2.650 0.179 1.251 -0.026 0.324 -0.031 0.530 0.503 2.627 -0.708 1.055 0.871 -1.825 0.587 0.547 +0 0.377 0.514 -0.459 1.935 -1.747 0.058 -0.219 -0.551 -0.698 -0.007 -0.226 -0.392 -1.299 -1.618 -0.289 -0.402 -0.197 -0.109 -0.523 -0.212 -0.854 1.524 -0.800 -0.371 -0.724 -0.741 0.025 -0.102 +0 -0.630 -0.499 0.298 0.638 -0.474 -1.039 0.137 0.518 1.614 1.146 -1.259 -0.141 0.456 -0.850 -0.351 0.674 0.250 0.002 -0.654 -1.446 -0.312 -0.091 -1.468 0.048 0.392 -0.692 0.403 0.276 +0 2.525 -0.789 0.384 0.556 -0.289 -1.072 -0.095 0.554 -0.665 -0.243 -0.127 -0.792 1.292 0.161 -0.201 0.815 0.153 1.203 -0.877 0.602 -0.024 1.343 0.966 0.290 0.299 -0.083 1.133 -0.534 +0 0.237 0.362 -0.688 0.601 -0.556 -0.133 -0.546 0.513 1.192 -0.085 -0.702 1.235 -1.568 1.193 0.853 -0.329 -1.386 -0.995 0.541 -0.484 0.368 -0.882 -1.129 -0.805 -0.561 0.473 1.433 -1.170 +2 -0.927 0.286 -1.188 0.604 0.484 -1.927 0.272 -0.199 -1.185 -0.292 2.062 0.573 -0.827 -0.331 -1.935 -1.080 1.684 0.917 0.451 0.390 1.417 0.320 -0.283 -0.092 -0.262 -0.275 1.696 -1.057 +1 0.520 -0.141 1.265 -0.321 -0.983 -1.063 0.735 0.030 0.634 0.613 -2.020 -1.140 -1.021 -0.816 -0.420 0.999 1.198 -0.293 -0.251 -0.705 0.089 0.203 0.806 0.798 -2.302 -0.153 1.384 -0.438 +3 -0.272 -1.645 -2.156 2.008 0.205 -0.359 0.798 -0.870 0.110 1.106 -1.102 -0.492 -1.381 0.835 0.205 1.172 0.843 -0.261 0.411 0.421 0.733 1.466 -1.090 -1.876 -1.051 0.473 0.414 -0.569 +2 -0.283 -0.611 1.985 1.949 0.824 0.074 -1.231 -0.208 0.021 -0.678 1.369 1.866 -0.151 1.033 0.303 -0.745 1.170 1.582 0.564 -0.432 0.825 -0.238 0.901 1.488 -0.635 0.600 -0.274 1.179 +3 0.075 -0.802 0.416 -0.152 0.437 -2.028 1.333 0.077 1.960 -1.292 -1.580 -0.084 -0.198 -1.024 -0.593 1.866 1.269 0.228 -0.166 -0.783 0.841 -0.329 0.702 0.529 -1.799 1.516 0.101 -1.318 +3 1.027 -0.470 0.049 -0.145 2.274 -0.753 -0.385 -0.957 1.304 1.378 -0.217 1.186 0.328 0.702 -0.995 0.161 1.872 1.477 0.805 0.951 2.433 0.786 -0.662 1.213 -0.074 0.489 0.118 -0.709 +0 -1.180 -0.256 -1.186 0.618 -0.391 -1.974 0.078 -0.611 0.981 -0.382 -0.090 -0.902 0.958 -0.324 -0.321 0.716 -0.366 0.217 -0.161 0.903 -0.654 -1.438 0.996 0.426 -0.441 0.577 -1.363 0.434 +0 -0.546 -0.518 -0.894 -0.733 -0.231 -0.078 0.705 -0.011 -0.402 0.558 0.109 1.135 0.740 -2.287 0.077 0.869 -0.137 0.455 -1.152 0.499 1.375 -0.944 0.295 -0.839 0.196 -1.641 -0.555 0.781 +0 -0.344 0.810 1.505 -0.909 0.557 -0.826 -0.405 -0.202 0.052 0.946 -0.602 0.338 -0.053 -1.876 0.931 -0.113 -0.355 -0.759 -0.251 1.490 0.378 -0.334 0.371 -1.661 -0.114 0.986 0.478 0.811 +2 -1.035 0.522 -1.994 0.386 -0.902 0.279 -1.345 0.231 1.377 -0.595 -0.617 0.432 0.211 -1.382 1.108 0.809 1.282 0.546 1.888 -1.873 1.524 -0.390 -0.544 -0.714 0.509 0.800 -0.466 0.274 +3 0.855 0.701 1.787 -0.653 -1.317 0.310 0.211 -0.005 -0.746 -2.653 -1.007 0.557 0.284 0.965 0.345 0.736 0.765 0.020 0.027 -2.909 -0.657 -0.613 -0.109 -0.930 0.938 -0.882 1.102 -0.961 +4 0.407 -1.302 -1.838 1.480 -0.439 0.985 -0.656 0.987 -0.429 -1.210 0.646 0.945 0.640 -0.145 -1.749 -1.069 -0.702 -0.258 1.131 -0.117 -2.107 -2.591 -0.695 -0.513 0.356 -1.452 -1.383 0.454 +1 -0.800 -1.029 0.700 2.891 0.834 0.469 1.470 -1.328 -0.133 0.528 1.109 -0.724 -0.335 0.131 1.985 0.307 0.282 -1.191 -0.159 -0.438 0.127 -0.072 0.876 -0.160 0.156 0.386 0.404 -0.785 +0 -1.585 -0.354 -1.474 -0.911 0.635 1.659 0.858 -0.546 -0.252 -0.081 -0.634 1.326 -1.409 1.633 0.330 -0.159 0.287 -0.115 -0.281 0.539 -0.588 -0.006 -0.729 0.693 1.045 -0.516 -0.708 0.200 +3 0.546 -2.172 -0.008 0.158 -0.278 1.291 0.422 1.310 -0.216 0.937 1.635 1.312 -1.092 -0.254 -0.345 0.489 0.686 -0.839 0.068 -2.020 1.442 -0.512 0.056 -0.310 1.146 -2.838 -0.017 -0.106 +2 1.399 -0.708 0.396 0.174 2.619 0.032 -0.505 -0.730 0.914 2.023 1.809 -0.490 0.812 -1.040 -0.023 -1.373 0.045 -1.012 -0.067 1.326 -0.849 -0.404 0.957 -0.378 -1.203 -0.416 0.026 0.618 +3 0.045 0.663 -0.908 -1.317 0.674 -1.518 1.285 -0.156 -1.548 -0.480 0.379 -0.533 0.020 0.740 -0.202 -0.937 -0.299 1.415 1.685 -2.020 1.091 0.250 0.924 -0.550 -1.768 0.584 -0.941 -1.388 +4 1.150 0.850 -0.916 -1.122 -0.874 -0.958 -0.882 -2.026 -2.443 -2.560 -1.551 1.166 3.606 2.020 0.249 -0.776 -0.431 0.140 2.067 -0.992 0.003 0.097 -0.088 -2.009 -0.276 -1.352 2.288 0.571 +2 1.070 -0.247 1.369 0.366 -2.406 0.883 -1.272 0.859 0.902 0.330 0.544 1.099 -0.496 0.793 1.163 1.760 -1.106 0.409 -0.961 -1.004 0.359 0.077 1.145 -0.346 -0.134 0.212 -0.862 -0.723 +4 -0.023 -0.718 -0.410 -2.851 -0.200 -0.421 -0.456 -0.759 0.614 0.424 0.282 -1.048 -0.654 -2.808 0.423 1.329 -2.079 0.991 1.169 -0.903 2.631 -0.938 -0.476 -0.250 0.526 -0.841 -0.490 -1.346 +4 -1.001 0.576 2.211 0.413 1.179 -0.548 0.080 -1.112 -2.392 -0.730 -0.433 -1.155 -0.219 -1.235 1.882 -1.273 -0.858 -0.637 0.927 0.695 0.033 -0.654 -2.718 -0.877 0.037 -0.411 1.099 0.474 +1 0.614 0.624 -0.158 0.790 1.280 0.761 -0.453 0.780 0.173 -0.932 -0.273 -1.239 -1.792 -0.585 0.585 1.045 0.623 -0.481 0.271 2.339 1.514 0.049 0.093 -0.871 -0.967 -0.130 0.094 0.940 +4 0.780 -0.434 1.447 -1.288 1.217 1.017 2.529 -0.882 0.546 0.084 -0.296 0.155 2.246 -0.320 -1.755 -0.202 -0.650 -1.144 -1.696 1.277 0.028 -0.871 -0.740 2.808 0.884 -0.624 -0.576 1.967 +0 -0.038 -0.458 0.547 0.085 0.658 0.346 -0.193 -0.974 -0.725 0.170 -0.237 -0.717 0.110 1.422 1.235 -0.392 0.060 -0.865 1.394 1.930 0.590 -0.141 -0.584 -0.757 -0.061 -0.722 -2.370 -0.799 +1 1.105 1.796 -0.198 1.158 -0.152 -1.337 -0.675 0.441 -0.301 -0.017 -1.252 1.751 0.431 0.082 0.851 1.268 0.029 0.494 -1.390 0.378 2.262 0.530 -0.078 0.220 0.983 0.180 -0.360 -0.292 +3 -0.077 1.313 -0.696 1.864 -0.671 0.089 -0.281 0.123 0.445 0.434 -0.753 -1.123 2.503 -0.484 0.699 -1.608 -0.280 -0.127 0.473 -2.722 0.166 -1.150 0.324 -1.036 -0.855 -0.274 -1.052 -0.476 +0 -0.823 0.783 -0.369 0.894 -0.529 1.284 -1.643 0.604 0.191 -0.292 -1.629 -0.499 -0.109 0.509 0.446 -0.779 0.060 -0.537 -0.447 -0.684 0.574 0.468 -0.294 -1.608 1.435 0.801 0.207 1.156 +1 -1.154 -1.752 -0.390 0.158 -0.097 -0.416 -0.946 0.608 -1.317 0.776 -1.002 -0.752 -1.467 -0.501 0.975 0.516 0.978 0.522 -1.104 -0.331 -0.780 1.331 -1.197 0.894 0.893 1.830 -0.410 0.712 +1 -1.533 -2.237 0.875 -1.459 1.253 0.377 0.283 -0.569 -0.825 -0.376 -0.877 1.208 -0.743 0.506 1.005 -0.881 0.377 -0.894 -0.174 -0.459 -0.695 0.158 -1.214 0.929 -1.082 -1.267 -0.252 -0.373 +3 -0.652 -0.725 1.092 0.253 0.635 -0.138 0.853 -0.373 0.233 0.487 -0.623 -2.097 -1.693 2.312 0.651 0.698 1.421 -1.125 -0.724 -1.105 2.357 -0.998 1.254 -0.659 0.085 1.270 0.286 -0.132 +3 0.098 -0.031 -2.347 0.358 -1.708 0.721 -0.386 -0.369 -0.501 -0.610 -0.229 0.927 -1.084 0.011 2.111 -0.672 -0.016 0.842 2.185 1.552 -0.140 0.393 -0.208 1.092 -0.119 1.702 -1.930 0.613 +3 0.060 0.790 0.730 0.389 -1.421 -1.327 0.981 1.082 -1.349 -0.461 -1.635 -0.434 -0.803 2.677 0.504 -0.134 -1.237 -1.735 0.554 0.764 0.027 0.801 -0.730 0.303 0.595 0.042 1.416 1.912 +0 2.637 -0.344 1.649 -1.178 0.975 0.481 -0.600 -0.012 0.341 -0.383 -0.454 -0.171 0.207 -0.763 -0.205 0.081 0.098 0.427 0.089 -0.863 -0.792 0.726 0.148 0.440 0.332 -0.249 0.526 -0.777 +1 -0.593 1.277 0.149 0.776 -1.801 -0.419 -1.646 -0.484 0.244 0.461 0.075 0.487 -0.494 -1.362 0.143 -0.109 1.737 1.162 1.184 -0.613 0.021 0.374 -1.004 -1.137 -0.753 -0.795 0.523 0.334 +1 0.261 -0.415 1.127 -0.974 -0.187 -1.405 -0.347 -1.680 0.746 0.767 0.784 0.029 -0.880 -0.800 1.530 1.436 0.123 -0.516 1.369 -0.695 0.229 1.352 -0.836 0.829 0.521 -0.040 -0.993 -0.724 +3 1.715 -0.513 0.328 -0.669 0.214 -0.415 -0.399 1.509 1.238 0.467 0.816 2.075 -0.855 0.908 -0.462 -1.065 0.896 2.441 -0.247 0.645 0.807 0.683 1.128 0.623 -1.560 -0.133 0.988 -1.085 +2 -0.247 -0.422 -0.640 1.024 -1.019 0.229 0.936 -1.102 -0.117 -0.300 0.695 -1.392 1.579 0.701 -1.428 -0.629 0.619 1.093 -0.759 -0.629 0.573 -0.496 -0.749 0.717 0.226 2.588 -1.171 -1.610 +1 1.299 0.406 0.262 0.331 0.041 0.177 -1.850 1.481 -0.074 -0.151 0.755 1.823 -0.692 -0.026 -0.276 0.422 0.583 0.991 -1.946 1.312 0.613 0.517 -0.777 -1.105 -0.285 0.202 -1.151 0.683 +0 0.404 -0.024 -0.904 0.324 -1.179 1.188 -0.465 0.201 0.283 -0.259 0.587 -0.475 0.871 -1.346 0.126 1.939 -1.000 -0.678 0.514 0.180 0.351 0.489 0.635 1.110 0.410 -0.241 0.673 1.900 +3 -1.123 -1.505 0.279 -0.286 2.903 0.458 0.058 -0.173 -1.218 0.564 -0.079 -0.078 0.094 0.221 -1.739 0.837 -0.846 -0.138 1.286 1.779 -1.700 0.030 0.273 -1.508 0.803 0.116 0.194 -0.918 +1 -1.624 0.698 1.806 0.334 0.554 -1.065 -0.619 -1.018 0.396 -0.518 -0.580 0.704 1.670 -0.088 -1.575 0.040 0.192 0.831 -0.324 0.881 0.728 -0.298 0.820 0.930 -1.091 0.960 -0.120 -0.247 +3 0.188 0.995 1.816 -1.182 0.140 -1.030 -0.899 -0.983 -0.334 -0.882 2.479 -0.460 0.988 -0.660 2.233 0.339 0.837 -0.591 0.976 0.335 -0.379 -1.080 -0.238 -0.323 0.869 -0.232 -1.922 0.646 +1 0.769 -0.789 0.103 -1.667 0.769 -0.249 2.042 0.261 -0.755 -0.274 -0.631 -0.582 -0.022 -0.987 -0.058 -1.974 0.161 -0.760 -1.348 0.724 -1.769 0.625 0.469 -0.892 -0.275 0.266 0.232 0.914 +2 0.013 1.482 -0.006 -1.458 -0.224 1.171 -1.884 -0.094 0.575 2.652 1.208 0.133 0.521 -1.898 -1.050 0.651 0.161 0.289 0.034 -0.005 0.103 -1.056 -0.229 -1.244 -0.620 1.196 -0.181 0.596 +1 1.229 0.040 -0.091 0.797 -1.030 -1.263 -1.625 0.568 -0.164 0.826 0.051 -1.371 -0.356 0.897 -0.262 1.187 -0.454 0.719 -1.783 -0.226 -0.387 -1.398 1.112 -0.640 -0.743 1.301 0.161 0.780 +2 -0.878 -0.817 0.955 -1.380 -1.040 1.273 -0.392 1.376 -1.167 0.074 0.772 -0.906 1.674 -0.390 0.509 -0.678 1.492 0.303 2.097 1.179 0.837 -0.955 0.084 -0.317 0.026 0.148 -0.901 -0.157 +1 1.161 1.607 -0.587 1.973 -0.582 0.300 1.144 0.627 -0.924 -1.181 -0.616 0.643 -0.354 -0.399 0.503 -0.373 -0.665 1.254 -0.466 -1.289 0.199 0.962 -1.081 -0.158 -0.530 0.623 -0.344 -1.501 +1 -0.361 -0.196 0.072 -0.054 -0.507 0.417 -0.805 0.350 1.349 -0.595 0.134 0.267 0.705 -0.679 -1.596 1.971 0.710 -0.847 -1.694 -0.533 0.363 -0.991 0.313 0.066 0.213 1.814 1.302 -0.932 +0 1.111 1.692 0.444 -0.648 0.123 0.156 -0.189 1.289 0.845 -1.154 -1.263 -0.534 -0.994 -0.468 -0.001 -0.178 0.171 -0.334 -1.396 -0.966 -1.055 -0.794 0.406 -0.398 -0.930 -0.090 0.291 0.257 +0 -1.167 0.311 -0.177 0.874 0.331 -0.647 0.010 0.146 0.784 0.822 -0.707 0.943 1.978 0.016 -1.918 -1.029 0.236 1.103 0.312 0.347 0.221 -1.052 0.324 -0.826 1.455 -0.258 0.640 -0.514 +4 -0.998 -0.315 -1.663 0.985 -0.682 -0.704 0.464 -0.958 0.950 0.241 -0.186 -1.950 2.741 -2.528 -1.248 0.403 -0.294 1.995 0.572 -0.963 1.818 0.467 0.352 0.381 1.704 0.310 -0.574 -0.105 +2 -0.456 0.388 1.343 -0.302 -0.891 1.369 -1.462 -0.485 0.840 -0.419 0.924 0.689 -0.427 -2.268 -0.350 0.329 -0.612 -0.016 1.943 0.367 0.498 -1.451 0.372 0.089 -0.294 0.351 1.605 -1.802 +0 0.912 0.756 -0.455 0.386 0.748 -0.064 1.224 0.693 0.853 -0.653 -0.024 0.618 -0.257 -0.686 0.213 -0.676 1.286 0.199 0.655 -1.229 0.127 -2.067 0.400 -0.529 -1.923 -0.369 0.255 0.607 +0 0.530 -0.402 0.101 -1.715 0.274 1.002 0.470 1.503 -0.800 -0.868 0.208 0.999 -0.130 -0.915 0.725 0.500 0.732 -0.696 -1.056 -0.078 0.200 -0.729 -1.338 0.970 1.198 0.338 -0.530 -1.146 +4 -0.156 -0.972 0.085 -0.794 0.815 0.057 -1.040 2.407 1.659 2.443 -0.343 -0.835 0.806 1.108 0.197 -2.104 -0.981 0.423 -0.660 -1.365 -0.258 -3.247 -0.603 -0.426 0.310 0.548 0.378 0.897 +2 1.383 -0.680 0.182 0.294 0.374 -0.263 1.281 -1.890 -0.421 0.448 0.457 1.353 1.198 -0.286 1.300 -0.714 0.931 1.811 1.215 0.106 -0.332 -1.529 0.311 -1.115 -0.793 -1.191 -0.460 -0.028 +2 0.941 0.192 1.948 1.025 -0.654 0.027 -0.047 -1.582 -0.940 -0.561 -2.036 0.047 -0.249 -1.673 0.404 0.716 0.227 -0.324 0.591 0.680 0.307 -0.825 0.277 1.195 -0.597 2.098 -0.526 0.032 +0 -1.195 0.622 -0.372 -1.467 0.148 -0.833 -1.578 -0.782 -1.028 -0.647 -1.326 -0.395 -0.713 0.376 0.594 0.260 -0.235 -0.262 -0.837 -0.813 0.704 -1.760 -0.081 -0.324 0.599 -1.425 0.012 0.343 +1 -1.938 0.490 -0.136 0.116 0.173 -2.062 -0.097 1.019 1.087 -0.705 -0.639 0.426 0.029 -1.261 -0.437 0.709 -1.031 1.925 -0.027 -0.430 0.933 0.346 0.690 -1.618 0.169 0.706 0.188 0.826 +4 0.476 -0.388 -0.160 -1.369 2.048 -0.494 -1.888 -1.229 -0.029 -0.226 -1.076 -0.306 -0.678 0.359 0.150 1.330 -0.944 -2.213 -1.026 2.581 1.369 1.660 -0.371 0.406 -1.361 -1.162 -0.463 1.151 +3 0.211 -0.051 -1.526 0.955 1.665 0.278 0.317 0.377 0.188 -1.155 -0.908 0.072 -1.519 0.958 -1.276 1.453 0.352 -1.099 -1.689 1.026 -0.221 -1.070 2.467 0.567 0.174 -1.011 0.722 0.027 +3 -1.824 -0.967 -0.453 -1.954 -1.101 0.245 -0.347 -0.264 -0.915 0.046 -0.325 -1.641 -0.201 -1.242 0.898 -0.392 2.522 0.967 -0.130 0.579 -2.090 0.962 -0.820 0.090 -0.728 -0.621 -0.181 0.077 +1 -0.028 1.772 1.661 -0.457 -0.602 0.469 -0.998 0.302 0.766 1.227 -0.100 -0.204 -0.878 -0.827 -0.226 0.367 0.914 -0.803 1.493 -0.271 -0.021 -0.747 -2.424 0.884 0.737 -0.281 0.067 0.516 +1 0.879 -0.945 -0.777 0.018 0.784 0.675 0.831 -1.427 -0.151 0.447 -1.599 -0.068 0.006 -1.210 0.280 0.721 -0.321 1.500 0.824 -0.508 0.544 -1.827 1.651 0.497 0.352 -0.758 -1.200 0.929 +0 0.811 -1.148 0.819 1.538 -1.123 -0.918 1.018 0.271 0.551 0.341 0.391 -1.326 1.047 1.170 -0.229 -0.043 -1.531 0.514 0.572 -0.062 1.124 -0.334 0.565 -1.020 -0.024 -0.174 0.225 -0.370 +3 0.633 -1.260 0.259 0.705 0.727 -0.399 -0.929 -0.743 -0.247 0.113 -1.336 1.052 0.877 2.497 0.300 -0.182 -1.518 -1.522 -1.043 -1.285 -0.483 -0.753 1.105 0.531 0.643 -1.608 0.034 -1.644 +2 -0.242 0.013 1.754 0.855 -1.235 -1.476 -0.353 1.528 1.150 -0.787 -0.782 0.234 -0.044 -1.535 -0.150 -0.913 -1.197 -0.732 -1.275 -0.092 -1.083 -0.235 0.259 0.721 -0.266 1.608 0.671 1.167 +0 -0.403 -0.055 0.802 0.531 0.029 1.320 -0.328 1.090 -0.440 -1.109 0.075 -1.806 -0.294 0.806 0.791 1.339 -0.704 -1.068 0.655 -0.745 0.217 -1.025 -0.454 -0.494 -0.014 -0.114 0.222 0.224 +2 -0.510 -0.888 1.783 -1.205 0.883 -0.201 -1.215 -0.377 -0.226 -1.630 0.428 0.327 -0.348 0.473 0.204 1.294 -1.078 -0.293 1.001 0.541 -2.302 -1.669 0.001 1.640 -0.432 0.649 0.083 0.579 +3 0.657 -0.335 0.226 1.594 -0.032 0.942 -1.991 1.124 -1.522 1.009 0.391 -0.096 -0.100 -0.253 -0.821 -0.206 2.208 1.302 -0.871 0.268 0.096 -0.142 -1.003 0.446 0.846 -0.834 1.956 1.522 +1 1.098 -0.232 0.097 0.293 -0.391 0.550 -0.199 1.705 -0.722 -0.224 -0.476 -0.440 -1.776 0.343 0.991 -0.675 -0.421 -0.672 -0.128 0.221 -0.172 -0.330 0.586 -2.952 0.074 -1.376 -1.288 0.659 +0 0.172 -0.628 -0.550 -0.058 0.831 1.278 0.207 0.329 -0.317 0.275 -0.080 0.117 -0.731 -0.522 0.879 -1.383 0.255 0.652 -0.621 -1.098 -1.286 0.006 1.002 0.861 2.400 0.746 -0.887 -1.007 +1 0.842 0.126 1.573 -1.451 0.317 -0.702 2.315 0.187 -1.546 -0.286 -1.094 0.618 0.579 -0.862 -0.287 -0.883 0.235 -0.774 -0.161 0.339 0.106 -0.350 0.360 -2.053 -0.215 -0.717 -0.431 0.770 +3 -0.482 0.029 -1.463 0.432 -1.288 -1.087 -1.135 -1.641 1.268 0.522 0.922 0.831 -0.168 -0.030 1.014 1.638 -1.258 2.520 -1.316 -0.462 0.074 0.166 1.964 1.012 0.413 1.035 0.346 -0.675 +2 0.136 -0.902 -0.262 -0.345 -0.428 2.003 -0.440 -0.808 -0.763 1.044 -1.652 -0.763 1.447 0.244 0.192 2.132 0.246 -0.708 -0.282 -1.135 1.256 0.575 1.124 1.213 -0.414 -0.945 -0.052 -0.873 +4 0.158 -0.261 -0.131 -0.031 -2.895 -0.964 -0.913 0.579 0.679 1.938 0.067 -0.928 -0.292 1.153 -2.322 -0.923 -0.686 1.723 1.359 1.041 1.061 -0.701 0.468 1.159 0.013 0.598 0.761 -2.240 +2 1.435 1.461 0.144 -1.159 -1.053 -2.087 -1.084 0.069 -0.560 -0.180 0.493 -0.304 -1.122 -0.053 -0.898 -0.271 1.343 -0.271 1.771 1.432 -0.579 -1.111 -0.053 1.153 -0.490 0.908 1.208 -0.774 +1 -1.561 -0.572 0.529 -0.626 1.677 -1.058 -1.098 0.746 -0.381 -0.866 -0.156 0.689 1.813 0.827 1.371 0.117 0.375 1.031 0.777 1.003 0.005 -1.159 -1.163 -0.108 -1.402 -0.183 -0.472 0.326 +1 0.682 1.083 0.789 -0.160 -1.201 -0.698 -0.508 -0.669 -1.819 -0.685 -0.425 -1.991 -0.400 -0.422 0.116 -0.153 1.226 1.032 -0.204 0.132 0.173 -1.154 -0.398 -0.949 -0.288 0.084 -2.067 0.482 +0 0.113 0.043 0.110 -0.662 -0.056 2.482 -0.524 1.053 0.668 -0.247 0.507 0.090 0.293 0.730 0.446 -0.529 0.611 0.178 -0.175 0.052 1.000 -0.996 -1.459 -0.266 -0.771 0.188 -1.297 -0.998 +3 0.285 0.833 -1.915 -0.671 -0.964 1.251 -0.208 -0.513 0.538 -0.593 -0.015 1.814 1.786 -0.407 -0.182 -0.619 -1.926 1.036 2.421 -0.443 -0.817 -0.273 0.085 -1.136 -0.458 1.015 0.127 1.270 +3 -0.278 1.343 -0.876 -0.892 1.072 -1.548 -0.002 1.250 1.317 -0.433 1.009 -1.123 -0.667 0.230 0.378 -1.048 1.862 -1.877 0.054 0.196 -1.747 1.324 -0.740 0.098 0.328 -1.454 -1.072 1.563 +4 0.714 -1.612 -0.024 0.334 0.287 -0.670 -0.333 1.017 -3.200 0.750 0.094 -0.412 -1.841 -1.130 0.626 -2.140 1.792 -1.898 -0.249 0.738 2.680 0.941 -1.009 0.776 -0.845 -0.362 0.046 0.656 +2 0.593 0.133 0.339 -0.893 -0.529 -0.621 0.200 -1.364 0.239 0.684 0.668 -0.664 -0.908 0.773 -0.771 -0.237 1.934 -0.063 0.476 0.567 -1.798 0.355 2.435 1.229 0.868 1.060 1.115 -0.988 +0 -0.861 1.354 -0.288 -0.384 2.112 0.523 0.925 1.355 -0.650 1.250 -0.502 -0.545 -0.272 -0.622 0.822 0.267 -0.491 0.675 -0.275 -0.328 0.049 0.463 0.390 -0.363 0.847 -0.222 -0.655 -1.468 +3 0.122 2.061 -0.734 -0.066 0.226 -0.956 0.399 0.722 1.208 0.396 0.155 1.952 -0.422 0.968 0.809 -0.397 -0.126 -0.314 -1.203 -1.281 -1.065 -0.541 -2.288 -0.751 0.765 2.603 -0.955 -1.014 +4 -0.409 1.826 0.674 0.573 -0.942 1.805 2.815 2.011 -1.003 -0.190 -1.189 -0.610 0.284 0.730 0.533 -1.721 -0.036 0.518 0.949 0.428 0.489 -0.238 0.272 2.585 1.207 0.365 0.729 0.076 +1 1.175 -1.425 0.483 -0.611 0.803 0.159 0.405 0.065 1.328 0.347 -0.705 0.099 -0.369 0.162 -0.420 -0.677 0.199 0.056 1.064 -0.415 -1.981 -0.364 -1.626 0.416 -1.674 -0.988 -0.362 1.544 +2 -0.454 -0.265 2.223 0.847 0.070 0.186 0.149 -0.017 -1.071 -0.402 -1.436 0.725 -0.132 0.775 -1.274 1.422 1.298 -1.574 -0.506 -0.397 -1.438 -0.260 0.558 -0.469 -1.713 0.772 -0.792 -0.686 +2 -0.938 -0.981 0.106 -0.799 0.393 0.237 1.058 -0.613 -1.752 -0.793 0.124 0.345 0.769 -1.473 0.469 -0.114 0.961 -0.952 0.031 0.048 -1.726 1.215 -0.071 -0.080 -2.968 0.725 -0.965 -0.547 +1 0.223 -1.198 1.084 0.210 0.198 -0.009 -0.518 0.218 0.476 -1.747 -0.539 -0.255 0.256 1.193 -0.772 -0.498 -0.513 -0.802 -0.347 0.771 -1.054 0.428 -1.702 -0.561 -0.329 1.675 -0.275 -2.201 +4 0.365 -1.089 -0.576 0.248 -1.123 -1.252 -1.192 0.516 -0.366 1.182 -0.896 -2.457 -0.131 1.648 0.035 -1.372 -1.624 1.198 -0.807 1.542 0.654 -0.770 1.167 -0.924 -1.447 -0.867 0.769 1.138 +1 -0.628 1.321 -0.530 -1.022 0.650 -0.028 0.855 0.171 -1.389 0.304 -0.908 0.146 -0.598 -0.205 0.043 0.734 -0.551 -2.790 -0.957 0.134 1.182 0.300 0.996 0.158 0.218 -0.242 1.049 1.646 +3 -1.153 -0.370 -3.321 -0.590 -0.082 0.564 -0.269 0.348 -2.180 -0.057 -1.680 -1.208 -0.187 1.276 0.371 0.018 -0.020 -0.714 -0.544 0.427 -1.385 0.593 -0.389 0.826 0.961 -1.673 -0.582 -0.856 +3 -1.151 0.794 -1.961 0.648 0.774 -0.900 0.866 -0.126 -2.188 0.298 -0.805 0.931 1.310 -0.413 -1.089 -0.026 0.733 -0.609 0.458 0.896 1.124 0.382 -1.724 -0.296 -0.210 0.088 -2.575 -0.798 +1 -0.923 -0.099 1.689 0.946 -1.877 -1.232 0.201 0.100 -1.796 -1.136 -0.012 -0.993 -0.711 1.550 -0.289 -0.828 0.987 -0.002 -0.494 1.113 0.129 -1.543 0.386 -0.489 -0.687 -0.498 0.109 -0.241 +1 1.510 0.434 -0.016 -0.888 -0.088 -0.204 -1.258 -0.218 -0.804 0.586 1.306 -1.227 1.712 -0.129 -0.975 0.758 -0.446 -0.082 -0.404 -0.817 0.724 -0.286 -1.437 0.551 -1.206 -1.153 1.193 -1.196 +2 -0.372 -0.450 0.732 0.395 2.156 -0.059 -1.564 0.493 0.138 0.150 -0.191 -0.041 0.599 0.546 -0.738 -0.240 1.455 1.195 0.232 -0.006 -2.515 -0.068 -0.608 -1.207 -1.415 0.548 -1.498 -0.590 +4 -0.043 -0.879 0.969 -1.516 1.294 -0.187 -1.193 1.006 0.746 -0.063 1.875 1.054 -0.162 0.051 1.839 0.861 -1.530 1.892 1.481 0.410 -0.555 -0.002 -1.385 -0.521 -1.056 1.698 0.002 -2.534 +1 -0.938 0.481 0.657 -2.070 -0.354 0.955 -0.791 -0.699 -0.389 1.474 0.318 -0.364 -0.065 -0.228 0.696 -0.952 0.129 1.440 -0.100 0.868 -0.376 -0.464 0.940 1.415 -0.835 1.630 -0.934 -0.690 +2 -0.423 -0.069 -0.992 -1.056 -0.444 0.308 -0.760 -0.700 -0.099 0.895 1.439 -0.906 1.457 -0.484 1.845 1.029 -0.340 -0.817 -0.814 -0.963 1.852 -0.133 -1.395 -1.483 0.167 -0.485 1.341 -0.349 +0 -0.359 0.151 -0.197 -1.467 1.006 0.046 -0.582 -0.291 -0.522 0.439 0.700 -1.859 0.697 -0.128 -0.582 0.623 0.026 -1.418 -0.260 -0.080 -0.118 -0.366 0.093 -1.041 -1.766 0.283 -1.416 1.150 +4 -0.032 1.455 -2.795 -0.288 0.675 -1.823 -0.213 -1.139 -1.007 -0.402 -1.123 -1.206 -0.059 2.227 0.758 1.011 0.797 -0.698 0.311 1.098 0.828 0.195 0.520 2.463 0.839 -0.315 -0.074 -1.054 +1 0.080 0.562 1.294 -1.405 1.050 0.916 0.135 -0.310 -1.608 0.175 -1.396 0.183 1.365 -0.492 1.087 -0.640 0.322 0.194 0.626 1.547 -1.102 0.971 1.038 1.448 0.068 -1.264 -0.471 0.273 +2 -0.875 0.688 2.111 -0.429 2.276 0.830 -1.393 0.214 0.864 -2.425 0.644 1.173 0.399 -0.062 -0.173 -0.231 -1.176 -0.688 -0.417 0.113 -0.557 -0.869 0.760 0.371 -0.539 0.506 -0.453 0.063 +2 0.299 -0.031 1.188 1.274 1.447 -1.135 -0.008 -0.036 0.681 -1.541 -0.219 0.497 0.851 -0.244 0.112 1.833 -0.209 0.496 0.502 0.175 1.882 -0.489 0.231 1.305 -0.460 -2.359 -1.607 0.308 +0 0.133 -0.949 0.287 0.836 0.754 -0.517 -0.990 -0.034 -0.166 0.534 0.667 0.764 -1.431 1.219 0.269 0.188 -0.100 0.281 1.406 0.312 -0.017 0.242 0.257 -0.188 0.879 -1.590 0.361 0.427 +4 1.694 -0.122 -0.538 0.088 0.223 -0.038 -0.464 0.899 0.864 0.107 -1.467 1.436 -1.120 -1.063 -1.951 0.407 -1.178 -1.641 0.911 -0.095 2.280 1.759 -0.709 0.496 1.928 -0.884 0.436 0.694 +2 0.525 -1.081 0.159 -1.843 0.145 -0.019 0.398 -0.524 0.539 0.949 -0.198 -1.496 0.451 -0.468 0.310 0.029 -1.670 0.244 0.890 -0.537 -2.085 0.409 -0.251 1.820 -1.201 2.041 0.899 -0.001 +1 0.146 1.644 0.494 -1.376 1.270 0.795 1.000 1.133 -0.543 1.059 -0.646 -1.182 -0.829 0.693 -0.409 1.476 1.114 0.674 1.380 -1.175 0.957 0.574 -1.273 -0.720 -0.159 -0.187 -0.041 -0.619 +2 1.383 0.407 -0.555 1.332 0.552 0.484 1.399 -0.370 0.664 -0.493 0.001 -1.048 0.282 0.758 -0.080 0.350 -2.222 0.452 -1.313 1.031 0.928 -0.887 -0.541 1.376 1.341 0.445 -0.515 -1.829 +2 -0.442 1.484 0.972 0.334 -0.379 -0.936 -0.530 0.637 -1.983 1.795 1.239 1.216 0.615 0.108 0.378 -0.366 2.118 -0.138 -0.476 -0.265 -0.723 0.809 -0.519 -0.126 1.336 1.071 -1.024 1.181 +2 -1.308 1.635 -0.797 0.724 -1.129 -1.233 0.427 -0.480 -0.573 -0.321 -0.245 1.724 0.597 0.696 1.868 1.689 -0.265 -0.204 0.254 0.132 0.983 1.753 0.349 -0.659 -0.040 0.547 -0.640 1.873 +3 -1.690 -2.087 -1.357 -0.485 -1.780 -1.897 0.780 -0.673 -1.535 -0.337 -0.976 0.439 0.343 0.766 0.831 -1.132 -1.150 -0.473 -0.814 -0.837 -0.829 -0.698 0.005 0.822 0.992 -0.925 1.068 1.462 +4 -1.989 -0.368 0.259 1.708 0.785 2.393 -0.482 0.471 -0.107 2.502 0.606 -2.349 -0.617 -1.500 1.308 -0.808 0.426 -0.150 -0.398 -0.960 -0.489 0.052 -0.326 0.513 -1.736 -1.455 0.757 -1.030 +1 -1.077 0.160 -0.136 -0.506 -1.266 0.889 -0.469 -2.935 0.356 0.381 0.933 0.413 0.138 -0.487 -0.484 0.676 -0.157 -1.437 -0.344 -0.054 -1.991 0.747 0.001 -1.363 -0.297 -0.102 0.946 0.917 +0 -1.709 -0.377 -0.803 0.815 -0.739 0.426 0.122 -0.307 -0.645 0.089 0.070 -0.767 -0.258 -0.290 0.484 0.025 0.222 0.096 -0.368 -0.525 1.263 -0.794 1.672 1.699 -0.663 0.575 0.557 0.754 +4 -0.274 0.519 -1.921 -0.006 -1.302 0.135 -0.901 0.402 -0.125 2.689 1.061 1.535 -1.737 0.333 -0.094 1.295 -0.646 -1.272 -2.396 0.621 -1.502 0.674 -0.526 0.119 -1.864 0.163 0.246 -1.472 +4 -0.073 -3.397 0.118 -0.710 -1.743 0.225 1.067 0.015 1.032 2.138 -0.052 0.226 0.321 0.836 -0.719 1.052 1.066 0.339 1.490 1.064 -1.082 0.032 0.833 1.066 -2.560 -0.107 1.240 1.034 +4 -0.351 0.734 -2.001 0.440 0.778 -2.613 -0.527 0.344 1.303 -1.379 1.387 0.439 -1.003 0.993 2.303 -0.335 -0.418 -0.952 1.838 -0.730 -0.969 -0.624 -0.612 -0.578 -0.789 -0.716 0.143 1.350 +3 -1.766 0.780 -0.356 -1.323 -0.954 -1.265 -1.282 0.061 -0.387 0.334 1.676 -0.130 -2.245 -0.242 1.193 -0.086 1.529 0.453 -1.838 0.110 0.297 1.080 1.820 0.908 0.440 0.676 -0.168 -1.463 +2 1.110 0.855 -0.556 0.281 -1.494 -0.770 1.669 -1.154 0.010 -1.396 0.338 0.563 0.724 0.468 1.427 -0.554 -1.058 -0.587 -1.346 0.079 -0.140 0.478 -1.616 -0.013 1.214 0.572 -1.896 -0.012 +0 0.232 1.071 -0.226 0.791 1.074 -1.431 0.541 -0.519 0.081 -0.702 -0.075 -0.879 -0.686 -1.137 0.402 1.096 -1.106 1.743 -0.647 -0.141 -0.765 0.885 0.199 0.189 -0.778 0.228 0.623 -0.222 +4 2.124 -0.464 -2.665 1.312 -0.245 0.203 0.078 1.056 -1.111 -0.486 -0.875 -0.120 1.985 -0.827 0.419 0.606 0.308 0.141 -0.052 0.455 0.880 -0.975 1.777 -0.980 1.613 1.277 1.288 0.799 +3 1.161 1.147 0.106 1.331 0.617 -0.293 0.385 -1.565 0.432 -0.633 0.589 -2.360 0.733 0.040 -0.857 -0.344 -0.143 0.039 -1.527 0.387 -1.966 -0.474 -1.613 0.362 -0.474 -0.934 -2.750 0.483 +0 0.402 0.463 -1.097 -1.083 -0.393 -0.693 -0.597 -0.095 -0.092 -2.560 0.096 -0.252 0.226 -0.641 -0.635 -0.391 1.069 -0.417 1.111 0.977 -0.338 -0.511 -0.476 -0.144 -0.559 0.736 -0.646 0.863 +0 0.486 2.107 0.888 -0.089 0.003 -0.525 -0.950 -0.553 -0.772 0.653 -1.071 1.333 0.752 0.021 0.000 -0.557 0.270 1.088 -0.703 -1.533 0.703 0.324 -1.120 -0.733 -0.859 -0.725 -0.324 -0.267 +3 0.177 0.641 -2.557 -0.433 -2.710 -2.767 0.945 0.221 0.401 0.997 -0.511 0.476 0.060 -0.998 -1.326 1.030 -0.682 0.619 1.393 -0.807 -0.175 0.296 0.139 0.518 0.550 -0.124 -0.156 0.714 +3 0.038 1.657 -0.066 0.631 0.902 1.600 1.429 -0.712 -0.077 -0.286 -0.908 -0.278 -1.342 0.126 -2.733 0.594 -1.101 -1.174 0.805 -0.191 -0.397 -0.976 -0.751 1.210 0.241 0.699 -1.507 1.420 +3 -0.943 0.103 -0.667 -0.372 0.621 0.944 0.537 1.149 -2.662 2.016 0.817 -0.356 -1.477 -0.176 -0.797 1.211 0.679 -0.645 -1.400 -0.248 1.004 0.546 -1.403 -0.525 0.980 -0.132 -0.052 1.890 +4 -0.027 -0.002 -1.626 2.332 0.232 0.438 -0.340 -0.874 0.151 0.289 1.302 2.078 1.477 -1.767 -0.460 0.336 -0.400 -0.044 -2.145 0.914 -1.875 -1.572 -1.015 -0.098 -1.623 -1.060 1.149 -0.112 +0 1.817 -0.442 -0.955 0.889 0.710 0.149 0.033 -0.496 -0.735 -0.606 0.313 -0.207 -1.491 -0.421 -0.213 0.510 0.292 1.755 -0.980 0.553 0.635 0.279 -0.297 -0.684 -0.041 -1.788 -0.924 -0.975 +2 0.297 -1.261 0.118 0.474 0.838 -2.247 -0.644 1.529 -1.290 -0.298 0.859 0.611 0.757 -0.508 -1.219 1.201 -0.741 0.858 1.313 -0.767 -0.279 1.614 0.988 0.526 0.451 -0.561 1.441 -0.758 +4 -2.198 0.571 -0.455 -0.188 -0.300 0.768 1.402 1.799 1.385 -0.197 0.189 -1.962 0.699 -0.487 -1.510 1.346 0.724 1.535 -0.650 -1.695 1.448 -0.656 -0.113 1.461 -1.146 -0.427 -0.972 -0.057 +3 0.251 -0.825 -2.018 -1.364 -0.448 0.898 0.347 0.140 -2.010 1.270 0.224 -0.119 1.132 1.247 1.155 -0.247 -1.003 0.989 -1.365 1.947 0.028 -1.061 -1.232 -1.278 0.140 -0.006 -1.236 -0.356 +2 0.464 2.028 0.239 0.282 -0.795 -1.697 -0.886 -0.819 0.565 0.230 -0.399 0.519 -0.086 0.722 -1.073 -0.616 -0.641 1.964 -1.678 0.058 -0.765 0.343 0.229 0.882 -1.262 -0.165 -1.738 0.465 +1 -0.024 1.422 0.135 -0.595 -1.060 -1.279 -0.263 -0.199 0.552 -0.357 1.389 -1.294 -1.124 0.410 -0.489 -0.560 -0.782 -1.385 -2.103 -0.671 -1.136 0.296 -0.056 -0.648 -0.512 1.092 -0.819 0.939 +1 0.195 -0.056 -1.315 0.563 -1.020 0.554 0.111 1.326 -0.865 -1.285 -0.615 0.347 1.455 -0.478 -0.209 0.147 -0.875 -1.309 -0.078 0.847 -0.308 0.876 -1.591 -0.094 -2.179 0.334 -0.950 0.790 +1 2.362 -0.182 0.750 -1.321 0.925 1.161 -0.683 -1.598 1.732 -0.516 -0.030 -0.165 -0.099 -0.204 0.051 -0.700 -0.293 0.547 -0.060 0.476 0.412 0.935 0.354 -1.360 -0.626 0.695 1.693 0.437 +1 -0.006 1.256 -0.972 -1.039 -0.874 -0.309 -1.467 -0.081 0.573 0.985 0.959 -0.228 0.919 1.128 -0.059 0.754 -0.149 1.576 -1.558 0.456 0.074 0.403 1.917 -1.106 0.296 -0.871 0.082 1.154 +2 1.570 -0.125 1.148 -2.044 -1.849 -0.750 0.285 0.375 0.594 -0.452 1.174 -2.001 -0.176 -1.663 0.053 0.143 1.071 -0.978 -0.624 -0.554 0.133 0.362 0.003 -0.665 0.628 -0.921 -1.612 -0.417 +0 0.545 0.701 -0.277 1.175 1.151 -1.177 -0.697 -0.330 1.024 1.299 0.299 1.423 -0.348 1.502 0.949 0.175 0.655 1.196 -0.442 -0.629 0.548 -0.192 0.003 1.550 -0.803 0.878 0.275 -1.109 +2 -0.674 2.291 -2.030 0.029 0.103 -0.209 0.526 -0.202 -1.495 -0.089 0.165 0.912 1.418 -0.855 1.672 -0.428 -0.511 -0.411 -1.951 -0.423 1.311 0.477 0.228 0.351 1.261 -0.878 0.904 0.067 +3 0.180 1.779 -0.032 0.474 -1.335 -0.475 -0.222 1.104 2.062 0.343 -1.727 -1.179 1.159 -0.699 0.778 0.358 0.045 0.278 0.183 -1.324 -0.285 2.136 -1.101 -1.130 -0.451 1.895 -0.906 -0.486 +3 -0.454 -0.487 0.637 -1.503 1.506 1.163 -1.339 -0.508 -0.508 0.539 -0.752 -2.379 1.227 -2.289 1.689 -0.387 -0.016 -0.265 -0.445 0.071 -0.267 -0.845 0.612 -0.230 1.974 -0.884 0.494 -0.532 +2 -1.803 -1.312 -1.107 -1.020 -0.325 2.109 -0.266 -1.746 -0.411 -0.652 0.614 1.100 0.911 0.347 -0.252 0.300 -0.209 -0.284 1.002 -0.009 0.454 -1.153 1.942 -0.031 0.286 -0.715 -0.517 1.741 +0 0.338 -0.993 0.020 -1.466 -1.769 0.367 -0.384 0.202 -0.018 -0.976 -0.045 -1.233 1.391 -1.356 -0.196 -1.042 0.570 -0.331 -0.519 0.853 0.029 0.517 -0.981 -1.405 0.107 -0.319 0.626 -0.080 +2 0.976 -2.327 0.400 -1.885 0.306 -0.150 2.014 -1.239 -0.341 0.315 -1.519 -0.928 1.438 -0.060 -0.873 0.291 -0.373 -0.301 1.514 0.309 0.994 0.311 -0.641 0.043 1.293 -0.916 -0.227 0.128 +2 0.590 -0.656 -1.808 0.038 0.937 -0.771 1.704 -0.602 2.007 -0.102 1.528 -0.106 -0.097 -0.463 -0.271 -0.527 -0.247 1.329 0.641 -1.626 -0.173 1.457 0.926 -0.801 -0.457 0.628 -0.694 -1.184 +3 1.113 -0.709 -1.504 1.121 0.260 -0.758 -0.939 0.362 1.093 0.911 -0.334 -2.430 -1.264 -0.850 0.872 -0.850 0.161 0.629 -0.287 1.476 1.221 -1.516 -1.016 -0.160 -1.957 0.876 0.540 0.289 +2 -1.141 1.679 -0.149 0.913 -1.412 -0.283 1.302 -1.565 2.275 0.203 -0.819 0.352 1.109 -0.826 -0.682 0.249 0.463 -0.198 0.402 0.854 0.173 -0.691 -0.021 1.756 0.333 1.240 -0.514 0.263 +4 1.344 0.447 -0.053 -0.721 1.110 0.952 0.796 -1.327 -0.151 -0.436 -0.071 0.738 2.223 -0.891 -1.797 0.074 0.936 0.441 -0.532 -1.939 1.842 -1.919 2.296 0.253 -2.028 -0.723 1.423 -1.477 +1 -1.926 -1.031 1.108 0.769 0.211 0.801 0.075 -1.709 -1.112 0.490 -1.743 -0.804 -0.713 0.255 -0.203 0.186 0.477 0.358 0.862 0.166 -0.991 -1.248 0.402 -0.108 -0.016 -0.662 -0.070 -1.522 +0 -1.046 0.473 0.291 0.958 -0.791 0.034 -1.365 0.701 0.519 0.496 0.002 0.415 1.604 -0.285 0.139 -1.086 -0.868 0.098 1.104 -0.256 -0.236 -0.407 -0.535 0.295 -1.497 1.281 -0.665 0.722 +2 0.690 -1.642 -0.828 0.012 1.248 0.736 -0.459 -0.582 -0.138 1.442 -1.196 -1.187 -0.080 0.429 -0.921 0.793 0.801 -0.049 -0.447 0.168 -2.127 -1.882 0.410 -1.487 -0.751 0.209 0.886 0.385 +0 -0.273 0.613 -0.219 -1.145 -0.661 0.021 0.646 0.813 -1.362 0.051 0.774 -0.873 1.800 -0.494 1.020 0.691 -0.688 0.016 1.364 -0.537 0.177 -1.506 0.360 -0.405 0.524 -0.844 0.320 -0.350 +3 0.951 -0.367 -1.704 -0.924 1.563 -0.274 -0.243 -0.300 1.904 1.626 2.219 -0.159 0.296 -1.517 1.477 -1.168 0.217 -1.097 -0.589 -0.837 -0.608 -0.539 -0.548 0.833 -1.105 0.221 1.218 -0.513 +4 -1.216 1.001 0.007 0.231 1.454 1.287 0.042 0.255 -0.361 1.143 2.188 -0.791 -0.910 -1.415 -0.974 1.352 -0.738 -0.236 0.714 0.349 1.694 -0.338 0.005 -0.360 0.813 0.483 -0.557 -3.178 +3 -0.616 -1.890 1.712 1.086 -0.848 -2.275 1.050 0.336 0.829 -1.998 -1.271 0.112 -0.665 -0.500 -1.999 -0.435 0.207 0.671 0.400 -0.435 0.796 -0.417 1.902 0.351 -0.057 0.027 -0.866 0.764 +2 1.363 1.362 0.622 1.960 1.219 0.830 -0.885 -1.192 0.108 0.137 -1.859 -0.749 0.621 -1.376 0.776 -0.378 0.023 -0.660 0.627 0.906 -0.073 0.583 -0.351 2.100 -0.599 0.170 0.916 0.872 +1 0.026 0.145 -1.465 -0.733 1.282 -0.820 0.580 -1.193 -1.391 0.748 1.115 0.013 0.390 1.338 -0.205 0.074 -0.228 -0.746 -1.514 -0.871 1.156 1.627 -1.078 0.739 -0.240 1.523 -0.034 -0.664 +3 -1.548 -0.058 -0.400 1.597 2.417 -0.838 0.125 0.847 0.450 0.399 1.289 1.389 0.347 2.239 0.858 -0.713 0.985 -0.716 0.145 0.830 -0.628 0.796 0.230 -1.172 -1.784 -1.739 -0.324 -0.609 +2 1.031 -0.181 -0.618 -2.086 0.409 -0.994 0.575 -1.395 -0.926 0.886 0.047 -0.371 -1.197 -1.326 0.024 -1.147 0.985 1.872 -0.403 1.165 -0.016 -1.608 0.315 -0.642 1.086 -0.165 0.025 -0.302 +1 0.273 1.919 1.543 0.334 -0.990 -0.957 -1.617 1.018 -0.069 0.116 -0.575 -0.320 -0.641 0.281 -0.506 -0.698 0.356 -0.416 -0.493 0.890 1.183 1.244 0.204 1.176 1.155 1.052 -0.140 0.186 +1 0.273 -0.454 -0.215 0.793 1.147 1.078 -2.016 0.328 -0.743 -0.203 -0.772 0.795 -0.278 0.367 -0.629 -0.094 0.668 1.045 0.666 1.986 -1.422 -1.976 0.357 -0.162 0.911 0.547 1.151 0.526 +0 0.349 -1.246 -0.572 0.775 0.969 -0.652 0.986 0.215 -0.646 0.191 1.799 1.152 0.061 1.418 0.938 -1.591 -1.020 -1.228 -0.913 -1.166 -0.345 0.371 -0.432 -0.409 0.412 0.540 -0.126 0.200 +0 0.701 -0.846 -0.353 1.050 -1.139 0.175 -0.397 1.061 0.832 1.128 -0.096 -0.135 -0.039 0.208 -1.279 1.373 -1.053 0.695 -0.313 0.516 0.260 0.665 0.360 0.559 1.609 -0.158 2.041 0.440 +0 -0.006 0.669 -1.092 -0.387 0.696 0.849 -0.294 -0.072 -1.518 -0.357 0.890 0.575 0.501 0.050 0.007 -0.660 0.699 0.421 0.492 -0.526 -2.153 1.097 -0.479 -0.863 0.693 -0.392 1.060 0.617 +2 -1.339 0.635 0.043 -1.419 0.688 -0.896 -0.153 0.630 -0.964 -0.528 1.126 -2.070 1.419 -1.464 -0.699 0.282 0.603 0.298 0.047 -0.436 1.561 0.773 1.176 -0.421 -2.328 1.020 0.356 0.183 +2 0.193 1.271 -1.287 -0.308 0.907 1.845 1.955 -1.196 0.074 0.492 -0.164 0.695 2.260 -0.900 -1.255 0.637 -0.465 0.676 0.194 -0.186 -0.691 0.096 -0.391 1.116 0.105 -1.275 1.400 -0.138 +0 1.320 -0.874 0.628 -0.280 -0.158 -0.184 -0.846 -0.014 -0.063 0.241 0.085 0.798 0.924 -1.051 0.869 -1.186 0.588 0.620 -1.911 0.093 -0.468 -0.759 1.351 -0.654 -0.806 0.076 0.686 -0.142 +4 0.238 -0.331 -0.217 0.271 0.137 -0.260 2.395 -0.493 -2.258 -2.831 -0.965 -2.659 1.025 1.154 1.233 0.347 0.401 0.496 -0.772 -0.408 -0.864 -0.585 0.419 -1.302 0.400 -0.163 0.569 0.473 +3 -0.889 -0.239 -0.290 -0.344 -0.201 0.099 -0.138 -1.453 -1.032 0.395 -0.574 -0.594 0.275 0.575 2.633 -0.413 1.451 1.104 -1.853 1.421 -0.141 -1.470 0.805 2.245 -0.858 0.146 0.658 -1.143 +0 1.404 0.350 -0.428 -0.270 -1.042 1.463 0.651 -0.179 0.544 -0.110 -0.301 -0.755 -0.738 0.381 1.048 -0.336 -0.793 0.952 0.079 -2.232 -0.705 0.469 -0.790 0.051 -1.244 -0.411 -1.087 -1.232 +4 0.058 0.760 -0.077 -1.585 -0.571 -1.923 -0.250 0.471 0.125 1.395 1.283 0.257 -2.392 0.671 -0.772 0.881 0.313 0.656 0.502 2.390 0.521 -1.280 1.071 -0.430 1.345 1.543 0.622 -1.278 +2 1.153 -0.484 0.090 0.851 0.518 0.962 0.118 -0.408 0.132 -1.063 -0.251 0.049 0.403 1.725 -0.981 1.584 0.437 -2.378 1.254 -0.095 1.534 -0.606 -0.385 0.785 -1.679 -0.707 -1.513 -0.976 +2 -0.533 0.999 0.679 -0.611 -0.003 0.121 0.487 -0.405 -1.507 -0.700 1.166 -0.031 -0.421 -1.475 1.237 -0.207 1.087 0.682 0.795 1.659 0.121 -0.133 1.564 0.834 -0.705 1.043 1.515 -2.300 +0 1.308 -0.399 1.817 -0.959 0.992 0.251 -0.650 -1.048 -0.090 -1.142 -0.300 -0.003 1.520 -0.388 -0.070 -0.773 0.178 0.152 0.049 -0.769 0.348 -0.433 -0.020 0.754 -0.533 -0.843 -1.360 -1.019 +1 -1.435 -1.242 0.055 -1.467 -0.694 -0.781 -0.378 1.550 -0.694 -0.979 -0.606 -0.715 0.629 0.440 -0.680 -0.146 -0.677 0.162 1.084 1.048 0.431 -0.579 -0.712 -1.419 -0.107 -0.681 0.987 -1.401 +1 1.518 -0.049 0.693 0.377 -1.385 -1.290 1.154 -0.879 0.369 -1.024 0.746 0.247 -0.298 0.667 0.260 -0.722 0.012 0.669 1.108 -0.129 1.786 -0.745 -1.557 -1.411 -0.010 0.012 0.126 -0.668 +0 -0.643 0.473 -0.935 0.521 0.310 1.327 -1.759 1.019 -0.303 -1.788 -0.659 0.512 -0.086 1.348 -0.338 -0.033 -0.185 -1.022 -0.029 -0.219 0.088 0.059 0.002 0.096 1.100 -0.978 -0.724 1.417 +4 -0.645 0.023 -0.912 -0.365 -0.637 -1.437 -0.915 -0.772 -1.219 -1.443 -0.595 0.865 -0.148 -3.183 1.010 -0.693 1.438 0.807 -0.307 -0.195 -1.141 0.652 1.617 -0.727 -0.603 1.974 0.086 -1.524 +0 -1.125 0.466 -0.085 -0.312 0.376 -0.963 0.133 -0.022 1.230 0.776 -0.362 0.689 0.956 -0.306 1.712 1.735 -0.603 -0.120 0.965 0.645 0.751 0.607 0.639 -0.207 0.610 0.616 -0.303 -1.462 +2 0.334 -0.815 -1.738 0.917 -1.158 0.398 0.742 0.391 -0.618 0.954 -0.340 -1.238 -0.871 -0.960 0.588 0.611 -0.462 1.819 -1.831 -1.483 -0.333 -0.816 -0.253 0.540 2.089 -0.470 -0.463 -0.021 +4 1.157 -1.885 -0.907 -0.685 -0.586 0.655 -0.551 -0.525 -0.304 2.481 2.390 1.252 -0.130 -1.030 -1.000 0.487 -1.014 -1.097 -0.120 0.055 0.568 1.018 0.893 -1.176 -0.748 1.560 0.228 1.365 +3 -0.203 -0.248 0.683 0.105 2.114 -0.749 0.900 -0.595 -1.602 1.427 1.873 0.410 -0.630 -1.139 0.561 -2.127 0.872 -0.418 -0.439 -1.073 -1.437 -0.058 0.196 -0.092 1.095 0.060 0.716 -1.469 +0 -0.516 -0.323 0.863 0.039 -0.958 -1.079 0.707 0.699 -0.773 -1.260 -0.463 -0.304 -0.179 0.240 0.848 -0.831 -1.431 -0.316 0.654 -0.837 0.614 -0.069 -0.997 1.045 -0.458 0.889 -1.622 -0.168 +0 -0.978 1.029 -1.808 -0.140 0.610 0.342 -0.717 0.660 -0.886 -0.022 0.652 0.463 -0.675 1.197 0.087 0.743 1.529 1.118 1.120 0.193 0.794 0.214 -1.209 0.303 0.256 0.584 -0.128 -0.761 +2 -0.778 1.889 0.067 -0.001 0.590 0.317 -0.447 0.819 -1.520 -0.905 -0.230 -0.479 0.527 -0.620 -1.506 -0.090 0.040 -1.073 -1.183 -0.407 0.757 -0.707 1.305 -1.034 2.095 1.219 -1.226 -1.733 +0 -0.747 -0.064 0.758 0.293 1.892 0.172 -0.177 1.509 0.595 -0.414 0.239 0.218 -0.205 0.182 -1.288 -0.356 0.431 -0.528 -0.892 -0.041 -0.473 -1.322 -1.297 0.408 0.091 0.314 -0.299 -1.513 +3 0.393 -0.024 0.519 -1.160 1.656 1.925 0.047 2.423 -0.399 0.400 1.432 -0.459 -1.382 -1.612 2.061 -0.529 -0.186 -1.113 0.948 -0.782 -0.491 0.300 -1.064 -0.179 1.402 0.428 0.131 0.215 +0 -0.173 -0.833 0.271 0.275 -0.221 -0.036 1.812 1.508 0.182 0.192 0.512 0.395 0.418 -1.562 0.053 0.744 -0.211 -0.862 -0.115 0.067 -1.394 -0.550 0.717 0.400 0.284 1.534 0.151 0.145 +0 -1.840 0.678 -0.344 0.617 -0.227 -0.659 0.178 -1.181 1.699 -0.307 -0.478 1.603 0.119 -0.967 1.147 -0.023 -0.180 -0.958 0.257 -0.011 0.626 -0.633 1.107 -0.318 1.871 -0.411 -0.297 0.063 +3 0.442 0.149 0.892 0.306 0.098 -0.795 -0.335 1.024 -0.305 0.802 -1.755 -0.828 -1.613 -2.277 -1.829 -0.164 1.454 -1.603 0.022 -0.226 0.050 -0.223 -0.676 -2.242 0.568 1.024 -0.031 0.052 +4 -0.335 -0.215 1.204 0.259 0.339 -1.467 -0.205 -0.991 1.758 -0.940 0.434 2.723 0.452 -3.217 1.052 -0.092 -1.528 0.122 0.664 -1.277 0.455 0.375 1.321 0.031 0.456 -1.571 0.423 0.326 +4 0.237 -1.186 -2.504 -0.100 -0.534 -1.263 0.828 -1.576 0.617 -1.410 0.134 0.934 -0.338 1.690 1.918 0.237 0.541 1.061 0.544 0.437 -0.524 -1.687 -1.390 2.382 0.322 -0.246 1.263 -0.749 +2 0.652 -0.105 -2.050 -1.227 -0.915 -0.793 -1.232 -0.210 0.676 0.509 -0.375 0.674 2.721 0.164 -1.244 0.042 0.128 -1.533 0.580 -0.495 0.993 -0.061 -0.074 1.001 -0.587 -1.232 -0.044 -1.612 +2 0.710 -0.693 0.180 -1.855 0.290 -1.691 0.155 -0.457 -0.814 0.095 -1.075 -0.955 0.334 0.530 -1.465 -0.283 2.043 -0.190 0.689 -0.131 -0.998 -0.595 -1.053 1.523 -0.058 -0.132 -1.671 1.045 +4 0.788 -0.016 -1.863 -2.434 0.225 -1.181 -0.529 -0.227 2.076 0.122 0.720 0.505 -2.422 -0.399 -1.220 1.727 -1.819 -0.421 -0.261 -0.986 0.101 -0.042 0.163 0.502 0.237 0.380 1.112 -1.650 +1 0.826 0.101 -0.451 0.254 0.232 -0.696 0.090 -0.258 0.338 1.280 -0.921 -0.553 0.007 1.110 -1.028 0.610 -1.755 0.856 1.264 -0.181 -0.385 -0.217 0.739 -1.566 1.217 -0.661 1.999 -0.367 +2 -0.056 -0.416 0.597 -0.007 -0.817 0.272 1.615 -0.321 0.424 1.114 0.080 0.953 -0.276 -2.372 -1.378 0.526 1.708 -0.791 1.537 -1.116 -0.437 -0.069 0.430 0.517 -0.109 2.449 0.286 -0.666 +0 1.001 0.340 0.343 0.393 -0.315 -0.040 0.728 0.189 1.524 0.630 -1.441 1.550 1.155 -1.276 1.410 0.346 -1.498 0.613 -0.219 -0.591 -0.984 0.159 0.923 0.922 0.014 -0.011 0.579 1.092 +0 -0.102 -0.419 1.821 -1.099 -1.216 0.156 -0.167 -0.891 -0.891 -0.132 -0.325 -1.481 0.204 0.186 1.639 0.309 0.593 0.563 1.053 -1.075 -0.066 -0.237 0.512 -0.498 1.763 0.448 0.754 -0.761 +1 -0.877 -0.464 0.657 -0.271 0.203 0.070 0.496 -0.428 -0.726 -1.647 0.332 -2.993 0.164 0.889 -0.149 0.365 -0.431 -0.006 -0.162 0.695 -0.284 -1.188 0.327 -0.127 -0.697 0.646 1.997 0.471 +2 -1.019 -0.892 0.339 0.772 -1.101 -0.290 -0.745 -1.262 0.052 -0.760 0.739 1.096 0.920 0.684 -1.633 0.847 1.180 1.292 -1.072 0.087 0.136 1.553 1.491 -1.069 -0.049 -1.108 -0.511 -1.117 +2 1.078 -0.221 -0.444 -0.280 -0.253 -0.991 1.677 -0.549 0.784 -1.018 0.550 0.169 0.025 0.175 0.416 -0.227 -0.177 0.577 -1.142 -1.296 0.179 -2.010 -1.683 1.805 1.549 -0.571 0.999 1.034 +3 0.101 0.052 0.211 -0.132 1.140 -1.784 -0.562 0.091 0.091 -0.265 -0.970 -2.781 0.796 0.171 0.913 -2.014 -0.991 -0.194 -1.515 -0.372 0.880 -0.827 1.236 -0.806 0.773 -0.809 1.362 0.344 +2 1.272 -0.623 0.090 -0.131 -0.975 1.241 0.474 2.271 0.152 -0.108 -0.331 -0.284 1.520 -0.564 0.457 -0.347 -1.298 0.326 -1.547 0.247 -0.456 -0.391 -0.126 0.023 -0.205 0.525 0.007 2.804 +4 2.167 0.481 -0.953 -1.925 0.618 0.075 -1.097 2.398 -0.323 -0.640 1.594 0.561 -0.119 1.137 -0.021 0.718 -0.780 -1.094 -1.366 1.364 -1.546 -0.168 0.653 -1.507 0.727 1.525 0.916 0.117 +2 1.306 0.328 1.491 -1.346 1.192 0.897 0.133 -0.306 -1.696 0.554 1.152 -1.449 0.858 0.018 0.079 0.384 1.914 0.005 0.202 -0.136 -1.032 -0.687 -0.166 0.319 2.334 -0.757 0.783 1.137 +0 0.677 -0.776 1.135 -0.404 -0.430 1.030 -0.466 0.520 0.011 0.764 0.004 -0.747 -1.259 -0.003 0.163 0.920 0.730 0.042 -0.513 -1.050 -0.437 0.660 1.215 -2.036 -0.538 -1.166 0.869 0.193 +2 -0.522 0.441 -2.051 -1.062 -1.312 0.785 0.739 0.963 -0.557 -0.053 0.973 -0.572 1.400 -0.608 1.045 1.326 1.001 0.815 1.159 0.418 -1.252 -0.797 -0.723 -0.293 -0.761 1.220 -1.783 -0.800 +4 2.044 -1.353 -0.401 -0.288 0.848 0.995 0.986 -0.134 0.525 -0.766 -0.479 2.161 0.454 0.860 -1.151 2.106 -1.622 2.354 -0.838 -1.001 -0.010 0.331 0.548 -1.110 0.595 -0.992 0.637 1.274 +3 0.069 0.209 -0.640 -1.219 -2.050 1.267 0.496 0.363 1.265 -1.255 -1.736 0.384 0.293 -1.731 0.360 -0.400 -0.807 -0.333 -2.442 -0.591 -0.704 0.557 -0.158 0.639 -2.550 -0.839 0.556 -0.309 +0 -0.132 -1.720 1.511 -0.473 -0.953 0.251 1.427 0.405 -0.365 0.248 0.118 -0.468 0.067 -0.824 -0.989 0.553 -0.394 -0.026 -0.772 -0.567 -0.483 -0.587 -0.410 0.197 1.390 0.761 -0.335 -0.633 +3 1.260 1.677 -0.922 0.938 -1.523 0.425 -0.071 1.243 1.172 0.570 0.298 0.508 0.062 -0.061 1.443 1.563 -0.142 0.238 0.082 -1.612 -0.336 1.600 -0.750 2.115 1.172 0.401 0.561 -0.917 +4 1.244 -0.294 0.566 -0.043 0.788 -0.584 0.245 2.003 -1.771 2.722 1.003 -0.344 -1.466 -1.770 -1.234 0.428 -0.431 0.360 0.702 0.353 -1.547 0.146 0.103 -1.474 -2.356 0.137 0.106 -0.304 +1 -0.097 -0.450 1.860 -0.151 -0.008 0.928 0.383 -0.296 0.940 -1.580 -0.089 0.030 -0.147 0.831 -0.835 -0.118 2.509 0.719 0.991 -0.110 1.383 -1.581 1.125 -0.244 -0.677 -0.748 -0.080 0.978 +3 0.794 2.303 0.639 -0.540 0.599 1.088 1.159 -0.841 -0.313 -0.397 1.463 -0.901 -1.492 0.215 1.769 1.202 1.510 -0.242 0.512 -0.677 0.013 -0.292 1.044 0.820 -0.727 -0.341 -1.775 1.141 +0 -1.109 0.006 2.306 -0.870 -1.011 0.234 -0.082 0.291 0.214 -0.133 -0.516 -0.382 -1.088 -0.232 -0.127 -0.328 0.447 -0.215 0.163 -0.025 0.088 -1.053 -0.965 1.112 0.335 -1.033 -1.790 0.893 +4 0.973 0.405 -0.813 -0.236 1.197 0.292 -0.542 -1.385 -0.069 -0.805 1.129 -1.289 1.566 -0.835 0.537 -0.644 -0.408 -2.401 0.689 -0.783 0.588 -0.309 -0.309 -2.478 2.310 -0.844 1.475 1.880 +2 1.201 0.140 -1.967 -1.117 -0.186 0.310 -0.057 1.219 -1.951 0.144 -1.818 0.760 -0.094 0.420 -0.864 1.279 1.042 0.584 -0.130 0.580 -0.707 0.856 1.649 1.071 -0.730 0.361 -1.293 0.572 +2 0.600 0.126 -0.183 -0.043 1.320 -0.696 1.363 -0.506 0.481 0.198 0.680 -2.812 0.562 -0.178 0.629 0.291 0.926 -0.984 -1.822 -1.618 -0.504 -0.809 0.512 1.227 0.722 -0.596 -0.100 0.592 +2 -0.766 -0.823 0.314 -0.916 -0.554 1.926 -0.167 0.676 2.515 0.199 0.525 0.313 -0.325 0.126 -0.179 -0.911 0.833 -1.809 0.008 -0.279 -1.036 0.623 -1.022 -0.408 0.728 -1.925 1.078 -0.036 +4 0.387 0.564 -0.692 -0.383 -1.235 0.764 1.343 0.282 -2.679 1.357 0.119 -2.654 -1.703 -1.054 -0.000 -1.021 -0.890 0.914 1.187 0.949 -1.636 0.943 -0.479 1.963 0.260 1.216 0.556 0.433 +4 0.003 0.745 -0.609 0.500 1.017 0.771 0.476 -1.639 -0.253 0.178 0.534 -1.588 -0.129 0.851 1.569 -0.811 -0.982 -0.213 3.206 1.318 1.342 -0.631 0.293 0.509 0.461 -1.351 -1.767 1.056 +2 -0.240 -0.109 0.616 -1.351 0.196 2.535 -0.358 -1.603 0.411 -0.901 -0.107 1.088 0.558 -0.489 0.838 -1.668 0.197 -0.384 -2.360 -1.211 -0.015 0.747 0.022 -0.296 0.810 0.274 0.997 0.574 +1 0.400 0.567 0.252 0.214 0.142 1.494 0.844 -0.691 -1.112 -0.794 0.428 -1.104 -0.653 -1.343 0.932 0.663 0.879 -0.475 -1.759 -0.835 -1.347 -1.358 1.137 -0.247 0.289 1.103 -1.020 0.614 +3 1.195 -1.523 -0.559 0.377 1.566 -0.066 -0.555 1.881 -1.448 -2.199 0.440 -0.502 -1.021 0.708 0.244 -0.564 -1.280 0.872 0.650 -0.099 1.847 -1.070 -1.526 -0.692 -0.046 0.243 -0.241 0.352 +2 -0.954 0.651 -0.342 1.831 -0.105 0.976 -0.213 1.626 -0.864 0.239 0.056 1.696 -0.571 0.718 1.156 -1.093 0.405 -1.805 0.678 -0.218 1.138 0.873 -1.071 1.743 -0.369 -1.261 -1.000 0.549 +4 -0.385 0.213 0.764 1.196 0.409 0.092 1.319 -1.103 -1.922 1.256 -0.449 1.337 1.920 1.854 -3.012 0.801 -0.509 -0.652 0.372 -0.627 0.749 1.321 -0.202 0.359 -0.458 0.468 1.417 0.410 +2 1.065 -0.984 0.924 0.679 0.756 -0.320 -1.531 -0.854 -0.904 -2.262 -0.528 0.755 -0.505 -0.741 0.401 0.041 -0.171 -1.849 0.801 -0.244 -0.793 1.085 1.369 -0.574 0.827 0.727 0.624 1.248 +1 -0.782 -0.461 -1.169 -1.812 0.718 0.223 0.010 -1.400 1.640 -0.503 0.534 -1.455 -0.191 0.291 0.327 0.155 0.495 -1.131 0.197 0.114 0.232 0.072 -1.273 -1.318 -2.214 0.818 0.828 0.844 +0 -0.891 -0.865 1.143 -0.259 0.764 0.386 0.200 -0.383 0.496 -0.426 0.156 1.106 1.317 -0.436 0.372 0.584 0.237 0.764 0.775 1.838 2.156 -0.165 1.660 0.439 -0.177 0.607 -0.233 -0.043 +2 1.158 -0.010 -0.786 1.109 0.090 0.790 0.291 1.257 0.368 0.483 0.416 0.533 0.564 -0.209 -0.417 0.165 0.014 -2.678 1.528 -0.479 -0.350 -0.997 -1.212 0.174 -2.053 1.555 0.086 1.005 +3 -0.506 -0.870 0.756 -0.696 0.236 -0.168 -0.530 -1.298 1.301 -1.005 -0.862 1.339 -0.102 0.320 1.514 1.490 -0.549 1.152 -1.603 -1.758 0.044 -0.084 0.648 0.543 0.692 1.602 1.963 0.878 +1 0.539 -1.441 0.159 -0.420 0.667 0.112 0.873 -0.441 1.229 -0.905 0.103 1.077 -0.213 0.706 1.079 0.491 0.664 -0.814 0.052 -0.086 -0.605 -0.120 0.259 1.102 0.866 1.547 -0.235 -3.096 +2 -0.139 -1.201 1.231 -0.799 0.786 -0.876 0.704 -1.609 1.409 1.135 -0.553 -0.826 0.458 -1.526 0.035 -0.808 -0.254 -0.644 -0.798 1.705 0.781 -1.480 -0.876 0.857 0.969 -1.385 0.699 0.265 +4 1.496 0.018 1.331 -1.103 -0.073 -1.168 0.196 0.328 1.131 0.039 0.464 -3.056 -1.643 1.503 0.873 -1.916 -2.246 -0.207 -0.844 -1.407 0.339 1.608 0.193 -0.654 0.495 1.390 0.319 -0.842 +4 -1.906 -0.298 0.476 -1.877 -0.753 1.996 0.170 -0.574 0.210 -0.576 1.713 -0.991 -0.523 -1.492 -0.295 -0.175 1.196 -1.017 0.484 0.087 -1.893 -0.725 -0.765 1.683 0.546 0.145 -4.295 -1.908 +4 -1.416 -1.501 -0.327 0.293 -0.225 0.439 -0.377 -2.059 -0.191 -1.521 0.003 -1.253 0.517 1.355 -1.729 -2.042 -0.300 1.129 0.156 -0.427 -1.076 0.723 1.537 -1.869 0.294 2.545 -0.606 -0.191 +0 -1.224 1.497 -1.005 -0.353 -0.556 0.043 -0.651 -0.580 -1.215 0.131 -1.338 -0.295 -0.999 -1.075 -0.748 -0.861 0.441 -0.558 1.313 -0.684 -0.181 -0.541 -0.123 -0.026 -0.050 0.637 0.058 0.245 +4 0.883 -2.263 0.681 1.430 2.001 0.136 -0.861 0.439 -0.859 0.130 1.131 -2.071 -0.606 0.514 -2.050 -0.917 2.921 0.490 0.270 0.534 0.066 2.542 1.189 -0.372 -0.745 0.463 -1.062 0.570 +3 1.671 -0.901 0.506 -0.087 1.258 0.406 -0.235 -0.894 0.798 -1.873 0.646 -1.099 1.252 0.161 0.644 0.082 -1.752 -0.861 0.672 0.167 1.181 0.403 -0.967 -0.796 0.103 -2.314 1.673 0.442 +3 1.316 -0.199 -2.061 0.430 1.275 0.925 -0.663 0.087 -0.533 -0.819 0.955 -1.198 -0.894 -1.104 -1.490 0.146 1.162 -0.185 -1.506 0.840 1.686 -0.646 -0.479 0.516 1.622 -0.520 -1.332 0.631 +1 0.523 -0.653 -1.019 -0.815 -0.455 -0.354 2.166 1.055 1.025 1.169 -0.999 -1.222 -1.107 1.335 -0.547 -1.251 -0.431 -0.239 0.268 0.251 -0.828 0.686 -0.639 -0.156 0.687 -0.642 -0.569 0.592 +3 1.191 -0.819 1.092 -0.548 -1.336 0.724 1.291 2.588 -1.200 0.699 -0.410 0.783 -1.470 -1.263 -0.776 0.079 -0.641 0.659 1.027 0.600 1.619 0.416 -1.217 1.363 -0.605 0.910 1.574 0.457 +3 0.925 -0.315 0.876 -2.484 0.036 0.810 -1.237 -0.038 1.100 -0.184 -0.523 -0.911 -0.244 -0.031 0.245 2.382 -0.048 -0.673 1.446 -0.722 -0.832 -1.451 -0.860 -2.314 -0.357 -1.464 -1.056 -0.360 +1 0.121 -0.585 0.014 -0.444 -0.406 -0.015 -0.713 -0.239 0.281 1.575 0.332 -0.015 0.887 0.660 0.816 1.446 -0.121 0.375 0.635 0.901 -2.021 -0.718 1.529 1.219 -0.091 -0.170 1.733 1.459 +1 2.018 -1.188 1.191 1.071 0.854 1.292 0.281 -0.583 1.095 1.133 1.105 0.045 -0.716 0.191 0.942 1.024 -0.430 0.315 -1.309 0.985 -0.783 0.785 -1.149 1.031 0.322 -0.781 0.691 -0.590 +3 1.321 -1.614 1.492 -0.254 -1.149 -0.247 -0.723 0.441 -0.648 -1.912 -0.221 -1.357 0.204 -1.187 -0.488 0.183 -0.212 0.250 0.680 -1.158 -0.064 -3.330 0.247 -0.301 -0.983 0.316 1.173 -0.476 +2 -0.054 1.370 -2.002 -1.315 1.810 -0.398 0.296 1.253 0.343 0.913 0.616 0.983 0.493 0.731 -0.580 0.523 -0.040 -0.087 -1.679 -0.422 -0.134 1.556 -0.279 0.283 1.375 -1.238 -0.383 0.880 +4 1.100 -0.322 -1.347 -1.861 -0.231 -1.299 -0.041 -1.364 -1.317 -1.119 -0.398 1.264 1.363 -0.079 -2.913 0.003 0.200 3.006 1.094 -0.930 -2.209 1.027 -0.613 -1.223 -0.096 0.202 1.681 1.572 +0 -0.078 1.547 2.552 0.073 0.375 -0.940 -0.171 0.179 0.698 -0.205 0.746 -0.375 1.009 0.415 0.370 -0.460 -0.084 -0.374 0.869 -1.355 -0.706 1.207 0.188 1.223 0.830 0.933 0.421 -0.658 +4 1.459 -0.600 0.107 1.594 -0.129 -1.005 0.046 0.686 -1.240 -0.466 -0.042 0.130 -0.762 -1.439 -0.581 0.167 1.014 -1.969 -0.133 1.647 2.745 -2.197 0.513 -0.473 -0.625 0.779 -0.395 1.707 +2 -1.449 0.298 -1.801 -1.701 0.533 -0.925 -0.424 0.498 -0.549 0.415 1.199 -0.841 -0.557 0.635 -0.435 0.629 0.515 -0.329 1.328 -1.199 0.611 -2.115 0.094 0.061 -1.205 1.534 -1.096 -0.338 +0 -0.940 -0.312 0.317 0.098 -1.310 0.389 0.341 1.070 -1.111 1.049 -0.798 -1.434 0.593 0.616 0.875 -0.812 -0.789 -0.554 -0.145 0.844 -1.032 0.375 0.283 -0.454 0.767 1.199 1.108 -0.162 +0 -0.264 -0.544 -0.160 -0.551 -0.130 0.016 -0.898 0.631 -0.370 -0.842 -0.352 0.242 0.083 0.422 0.898 1.305 0.285 -1.069 -0.269 -1.004 1.697 1.987 0.650 0.858 0.251 0.366 -1.597 -0.074 +2 1.368 0.126 0.851 1.224 0.338 -0.592 0.976 0.001 0.686 -2.154 0.447 -0.639 1.653 0.623 0.240 0.486 -0.998 1.623 1.991 0.393 1.590 -0.569 -0.797 0.041 0.435 -0.394 0.538 0.306 +1 1.051 0.197 0.329 -0.273 -1.175 1.882 0.624 -1.368 0.264 -1.087 -1.362 -0.241 -0.112 -0.724 0.046 -0.243 -0.906 -0.610 -1.810 0.333 0.695 -0.211 -2.130 0.300 -1.312 -0.310 -0.078 -0.166 +0 -0.702 0.639 -1.250 0.554 -0.300 0.424 0.211 0.536 -0.460 -0.166 0.918 -0.351 -1.397 0.213 -0.251 -0.478 0.449 0.471 1.193 0.844 0.764 -0.333 -1.018 -0.591 -1.376 -0.030 -1.769 1.267 +2 -0.253 0.850 -1.289 -0.341 -0.196 1.395 1.175 0.392 -0.994 0.057 1.880 -0.149 0.129 0.186 -0.850 -0.809 -0.810 -0.298 0.442 0.170 0.507 2.160 2.595 0.094 0.923 0.659 1.077 1.042 +3 0.374 -1.960 0.782 0.004 0.196 -0.100 -0.882 -1.739 1.521 -0.998 0.290 -2.092 0.141 -0.240 1.619 -0.848 0.729 1.209 -1.133 -1.442 1.684 0.025 -0.110 1.233 0.580 0.835 0.577 -0.509 +3 -0.406 0.089 0.547 -1.102 0.359 0.035 -0.304 1.173 -0.904 0.275 1.455 -0.707 -0.297 2.345 0.474 -0.506 0.803 -0.568 -1.565 1.372 -1.103 2.452 -0.489 2.133 -1.369 1.025 0.500 0.838 +1 0.376 -1.867 -0.832 -0.876 -0.742 1.213 -0.149 -0.427 1.122 0.495 -1.741 -0.617 0.322 -0.934 2.030 0.113 -0.753 -1.123 0.928 -0.648 0.380 -1.152 0.070 -0.636 -0.315 -0.796 1.026 -0.491 +0 1.117 -0.764 0.759 0.859 -0.011 1.498 1.643 -0.565 0.470 0.532 0.897 0.538 0.953 0.344 2.373 0.040 -0.896 0.886 -0.258 -0.479 0.004 0.419 -0.621 0.780 -0.444 0.834 0.068 0.423 +2 -0.269 -1.230 -0.185 -0.109 -0.372 0.178 -2.336 0.882 1.773 -0.181 0.079 -0.304 0.510 0.837 -1.137 -0.478 -0.887 1.382 -0.005 -0.302 0.650 -0.378 -0.641 -1.576 1.273 0.401 -0.463 2.284 +0 1.486 0.527 -0.242 0.033 1.037 1.126 -0.842 0.363 -0.008 -0.066 1.330 -0.238 -0.576 -0.815 -0.175 -0.735 -0.865 -0.890 -0.831 -0.761 0.399 -1.960 -0.702 -0.100 1.149 -0.246 -0.153 -0.088 +3 0.817 -0.889 0.257 -0.767 0.864 -1.617 2.312 0.373 0.506 -0.888 0.567 -0.429 -1.543 0.781 -0.191 -1.754 2.433 -1.173 0.044 -0.321 -1.127 0.306 -1.624 0.586 -0.598 -1.087 -0.588 -0.015 +4 2.204 -1.678 -0.820 0.807 2.628 -0.301 -2.825 -1.643 1.478 -1.546 -2.861 -0.936 0.333 0.257 1.700 -0.530 -0.043 -0.528 -0.684 0.229 -0.260 -0.565 -1.585 -1.425 1.549 0.998 -0.179 0.595 +1 -1.282 0.836 -0.448 -1.280 -0.492 0.062 0.240 -0.749 -0.216 -1.393 -0.708 -1.362 -0.079 -0.838 2.075 0.383 -1.626 -0.746 0.702 0.245 -1.571 -0.787 0.643 0.371 0.576 -0.280 -0.857 -1.276 +4 1.455 0.819 -0.037 0.319 2.454 0.243 0.466 -0.398 -0.751 0.814 0.928 1.100 0.751 1.981 -1.925 -0.238 0.811 -0.644 1.271 -3.281 1.026 -0.818 -0.969 -2.072 -0.364 1.036 0.842 0.163 +2 -1.347 0.019 0.186 -0.176 -1.202 -0.545 -0.502 -2.070 0.712 -1.309 2.066 0.435 -0.907 1.061 0.072 -0.776 1.308 0.332 -1.068 1.571 -0.525 0.022 0.613 -0.200 0.609 -0.235 -1.333 0.107 +2 -0.365 -0.741 -1.115 -0.972 -0.988 -0.216 -1.259 -0.347 0.617 -0.690 0.773 -0.550 0.237 0.161 0.998 -0.017 1.106 -0.610 0.538 1.087 2.375 0.928 -0.141 1.158 -1.270 1.425 0.143 -1.688 +4 -1.150 -1.217 -0.951 0.095 0.549 -0.466 1.355 0.423 0.368 0.935 0.977 0.524 -1.340 2.442 -0.764 1.539 -2.184 -0.461 -0.193 1.079 0.612 0.665 1.991 1.872 0.995 -0.482 -0.066 -1.092 +4 0.840 -0.749 0.909 0.846 -1.471 0.949 0.724 1.435 -1.085 -0.775 -0.296 0.006 -3.295 0.779 -1.259 -0.786 -0.718 -0.976 0.600 -2.322 -0.301 0.082 0.907 1.005 1.195 -0.307 -1.032 -0.298 +4 0.995 -1.642 -1.595 -0.189 1.367 -1.820 0.044 -1.007 -2.303 1.276 0.757 1.434 -1.354 -1.109 -0.086 0.310 0.286 -1.322 0.744 -1.266 0.275 -0.430 1.173 1.931 0.326 1.134 -1.807 -0.062 +0 -1.030 0.147 0.593 0.630 -0.059 -0.241 0.452 0.267 -0.259 -1.562 -0.468 -1.147 -0.667 0.033 0.359 1.780 -0.537 0.775 0.306 1.296 0.576 -1.008 -1.723 0.311 -0.528 -0.980 -1.268 0.161 +3 1.623 0.610 -0.101 1.177 1.036 -1.376 0.084 -0.415 0.328 -0.506 1.135 -0.618 -1.023 -0.056 -2.331 -0.546 -0.333 3.019 -0.136 2.144 0.289 0.136 1.160 -0.739 -0.748 -0.137 -0.715 -0.396 +3 -0.531 0.476 -1.761 0.505 0.260 0.024 0.258 0.282 -0.915 0.432 1.726 1.440 1.104 -0.386 -0.143 -1.939 -2.921 1.034 0.059 -0.941 0.172 -0.832 -0.504 1.685 -0.108 -0.076 0.291 -0.688 +1 0.833 -0.687 -0.941 -0.200 0.265 0.600 0.445 0.979 0.116 -1.048 -1.355 0.758 1.006 1.971 2.424 0.972 0.367 -0.420 -0.874 0.067 -0.105 1.905 0.586 -0.230 -0.882 0.534 0.138 -0.451 +1 0.333 0.081 1.393 2.474 -1.263 0.147 0.146 -0.014 -1.085 -0.917 0.240 -0.945 0.159 -0.246 0.142 -0.953 0.157 -0.881 0.161 1.339 0.261 0.322 0.817 -1.404 1.719 -1.056 0.880 0.747 +3 1.107 1.022 -1.023 -0.538 -0.900 0.511 0.108 1.437 -0.096 -0.303 0.215 0.655 0.401 0.589 -0.337 0.151 -2.272 0.638 -2.077 0.507 -0.276 0.367 0.803 0.455 1.304 0.508 -1.221 2.920 +3 1.389 2.216 0.361 -0.322 -1.231 -0.400 0.197 -0.075 -0.687 -0.897 -0.442 -0.313 1.184 0.195 -0.783 1.026 -0.092 1.025 -0.456 0.946 1.441 0.952 -0.117 -1.418 2.609 -1.486 -0.510 1.964 +2 -0.158 0.209 1.727 1.076 2.373 -1.297 0.130 -0.142 -0.126 -0.575 -0.444 -0.398 0.733 0.329 -0.676 0.939 -1.554 0.813 0.497 -1.010 0.219 -1.057 0.456 -1.595 0.469 -2.102 0.470 -1.208 +0 -0.067 -0.187 -1.072 0.879 -0.092 -0.134 -0.825 -0.370 -1.022 0.864 1.033 1.680 0.434 -0.339 -0.469 0.491 0.201 -0.174 -0.262 1.171 -0.432 1.347 0.182 -0.368 -0.785 -0.155 0.503 -0.377 +0 0.180 0.612 -0.121 1.665 -1.485 -0.740 -0.635 0.009 -0.646 -1.241 -0.356 1.015 0.754 -0.695 -0.373 -0.778 -1.261 0.427 -1.281 0.663 1.819 0.376 0.623 0.594 -0.489 0.370 0.312 -0.626 +4 -1.149 1.483 1.821 0.900 0.438 0.890 -0.337 -1.328 -0.740 2.288 -1.164 0.959 -0.111 -0.357 1.689 0.093 0.933 2.399 -0.209 0.474 0.705 -0.322 0.492 1.128 0.783 0.117 -2.883 -1.446 +3 -1.696 0.455 -2.432 0.209 0.163 -1.535 0.065 -0.293 -0.154 -0.144 -0.589 0.076 0.866 -0.239 -0.050 0.649 2.270 -0.193 -1.702 -0.897 0.602 1.513 -0.946 -0.043 0.019 -0.769 1.535 1.164 +3 1.994 0.158 0.108 0.021 -0.395 0.314 0.775 -0.332 -2.491 0.466 2.133 -0.376 -1.471 0.677 1.744 0.529 1.725 0.794 0.707 -0.518 0.285 -0.029 -0.064 -0.254 0.226 -1.296 0.812 -0.878 +3 -0.285 0.336 0.697 -1.102 1.001 -1.164 -0.575 0.676 -1.307 -2.007 0.581 0.377 -0.321 -0.712 0.484 2.463 1.026 0.925 0.316 -1.470 -0.762 0.181 -0.193 0.939 0.704 1.456 -1.050 2.366 +4 1.465 0.452 0.378 0.970 -0.235 1.378 -0.222 -3.150 1.608 -2.949 -0.267 0.753 -2.350 -0.145 -0.163 0.918 0.483 -1.340 -0.649 -1.072 -1.857 -0.651 1.483 0.445 0.426 -1.210 -1.950 0.568 +1 -0.122 -1.575 0.012 -0.393 0.355 -1.131 0.757 -0.581 0.616 1.848 -1.686 -1.087 -0.764 0.109 0.174 -0.677 0.926 0.853 1.156 0.810 1.248 -0.644 -0.701 -1.065 0.491 0.219 0.772 -0.360 +3 -0.639 -0.570 -0.416 1.993 -0.260 1.577 -0.350 0.063 -0.239 -0.298 -0.140 -0.139 0.745 -1.774 2.418 0.843 -1.722 -0.362 0.284 -1.031 -2.120 0.115 0.731 -0.176 1.374 0.462 -1.321 -1.446 +3 -0.266 -0.834 1.097 -0.861 1.573 0.954 1.179 -0.031 -1.135 -1.700 0.839 -0.097 -0.448 0.656 2.634 0.039 1.107 -0.156 0.966 0.101 0.907 0.621 -0.642 -1.385 -2.253 0.415 1.351 0.589 +0 0.553 0.383 -0.176 -0.391 -0.447 1.220 0.438 -0.823 -0.326 0.002 -0.036 -0.233 -0.071 0.184 0.899 -1.264 0.179 0.108 1.273 0.589 0.997 0.633 -0.407 -0.181 0.557 1.599 0.571 0.415 +3 0.079 -0.732 0.888 0.938 0.171 0.962 0.919 0.928 0.264 -1.172 0.680 0.736 -0.658 -0.080 2.073 1.066 0.929 0.385 -1.706 0.482 -1.059 -0.209 0.899 1.638 -2.424 1.082 -1.161 -1.913 +4 -0.305 -1.551 1.133 -1.330 -1.010 -0.336 0.163 0.381 0.961 -2.575 -0.941 1.058 -0.895 -0.051 1.347 -0.171 -0.262 -0.160 1.288 -1.603 1.787 -0.226 -0.341 -0.811 -1.135 -2.339 1.218 -2.216 +0 1.272 0.217 0.855 0.009 1.056 0.398 0.013 -0.013 -0.954 0.507 -0.040 -0.502 1.106 -1.250 -0.422 1.028 0.316 -0.048 0.251 0.179 -1.517 -0.349 1.617 -0.659 -0.318 -0.542 -0.087 -0.583 +0 -1.625 -0.056 0.258 -1.583 0.041 0.275 -0.439 -1.183 1.275 0.234 0.309 0.462 0.007 -1.638 -0.504 -0.927 -0.283 -0.409 -0.108 -0.168 -1.034 0.178 -0.185 -0.341 -0.135 -0.810 0.165 0.173 +4 -0.114 -0.918 2.149 1.545 0.246 0.592 -0.745 0.209 2.655 1.240 -1.056 0.404 0.082 0.521 1.782 -0.910 -0.202 -1.124 0.215 0.108 2.083 -0.851 0.906 -2.621 1.933 0.029 0.139 -0.223 +1 -1.095 -0.774 1.219 -1.637 0.949 -0.997 1.586 0.843 0.786 -0.063 1.369 -1.240 0.785 0.061 0.303 -0.013 0.763 -0.464 1.378 -0.938 0.280 0.451 0.127 -0.966 -0.767 -0.283 -1.060 1.579 +2 0.089 0.215 0.692 0.594 0.005 1.144 -0.373 0.290 -0.874 -2.060 -0.782 0.100 -1.605 -0.891 1.724 -0.286 0.454 -0.867 -1.375 -1.016 1.062 -0.292 0.122 -0.711 0.744 1.406 2.248 0.651 +0 0.328 0.340 -1.181 -1.044 0.941 -0.589 -0.495 0.964 -1.050 0.269 0.043 -0.003 -0.518 -0.580 -1.078 0.483 -0.668 0.969 -0.225 -1.058 -0.238 -0.411 1.009 -0.369 -0.276 1.412 0.508 1.663 +4 -0.826 0.484 -0.924 1.968 -0.811 1.064 -0.581 0.226 0.856 -1.632 1.869 1.612 2.336 -0.766 -0.421 -1.732 -0.427 -1.003 0.786 1.909 -1.664 1.466 -0.625 0.140 0.605 1.424 0.914 0.340 +2 -0.388 -1.213 -0.111 -0.091 1.765 -1.664 -0.696 -0.486 -0.719 0.707 0.767 1.176 1.867 -0.643 0.007 0.276 -0.794 0.853 0.041 0.170 1.234 -2.074 -0.116 -0.011 -0.646 0.460 -1.610 -0.452 +4 0.206 -0.218 0.836 0.284 0.129 1.602 0.969 2.654 -1.954 1.295 1.485 -0.682 -0.116 0.086 -0.449 -0.341 3.306 0.002 0.590 0.375 0.093 -1.124 0.965 -0.047 -1.557 0.162 0.308 1.160 +4 1.408 0.319 -0.590 -0.922 -1.800 1.034 0.335 -0.260 1.402 0.172 0.890 -0.431 1.118 -0.627 1.421 -0.330 3.613 0.660 -0.550 -1.662 -0.334 1.462 0.303 1.849 1.443 1.441 -1.355 -1.512 +2 0.904 0.548 -1.096 -0.298 -0.221 0.690 0.192 1.070 1.072 0.731 -2.451 -0.891 -0.904 0.600 0.727 1.291 1.100 0.206 -0.470 1.027 -1.222 -0.949 -1.746 0.474 -1.604 -1.136 0.239 -0.176 +1 0.921 -1.652 -0.278 -0.518 1.012 -0.459 0.438 0.333 -1.835 -0.066 -1.743 0.351 -0.730 1.187 -0.181 -0.770 1.200 -0.872 0.895 0.468 -0.365 -1.336 1.242 0.500 -0.772 0.245 1.006 0.112 +1 0.001 0.303 -0.105 -1.394 0.514 1.922 0.409 -0.426 -1.428 -0.908 0.453 1.073 0.261 0.800 0.708 0.507 -0.658 0.174 -0.848 0.017 0.331 1.679 -1.310 0.482 1.481 0.759 0.833 0.588 +0 -0.172 -0.175 -0.991 -1.240 0.407 -1.159 -0.029 -0.255 -0.644 0.198 -0.259 0.735 -0.082 0.801 -0.306 -1.014 0.617 -0.154 -0.251 -0.569 -0.005 -0.331 -0.372 -0.383 1.067 0.288 -0.590 -0.806 +0 1.644 -1.383 2.078 -0.461 -0.741 -0.536 0.032 -0.152 -0.052 0.316 0.240 0.006 -0.442 0.428 0.376 0.329 -0.002 -0.693 0.074 -0.278 -2.066 0.999 0.260 -0.020 0.525 -0.755 1.359 1.207 +4 -1.112 -2.349 1.162 0.674 -0.127 1.075 -0.731 -0.705 -0.844 0.568 -1.583 -0.767 0.290 -1.204 -0.660 -2.212 0.606 -0.688 -1.099 -1.295 -0.418 -1.580 1.942 -2.385 -0.451 0.107 -0.775 0.212 +4 0.498 -0.780 1.178 0.168 -1.637 0.914 1.364 -1.277 -0.803 0.502 0.682 2.353 0.891 2.401 0.019 0.384 -0.568 -0.523 0.147 -0.583 -2.564 -0.667 1.077 0.073 0.078 -0.763 0.333 1.659 +4 0.442 1.370 1.338 0.112 -0.709 -0.252 -0.143 1.398 -0.078 0.933 1.689 0.212 0.536 -0.332 0.604 0.751 0.410 -1.320 0.852 1.643 -0.026 -1.484 0.980 2.416 0.488 -2.241 1.722 -0.713 +4 -0.664 -0.916 1.059 -2.675 -0.669 -1.395 1.061 1.365 -0.724 2.765 1.377 0.197 1.500 -0.642 -0.277 0.463 -0.692 -1.284 -0.938 -0.197 0.469 -0.338 -1.640 0.470 1.273 -0.660 1.663 0.869 +0 -0.114 -1.136 -1.194 -0.302 0.959 0.146 -0.784 0.174 -0.597 0.497 -0.858 -0.652 0.005 0.682 0.815 0.639 2.092 0.322 0.359 -0.127 -0.773 -0.036 -0.009 -0.187 -0.237 1.415 -1.115 -0.602 +1 0.593 -0.594 0.356 0.015 -0.460 1.262 -0.303 -0.210 -0.485 1.480 1.391 1.147 0.869 -0.884 -0.450 -0.771 1.030 2.336 0.679 1.676 0.457 0.317 -0.580 0.613 0.913 1.054 0.103 0.198 +4 0.218 1.129 1.058 -1.749 -1.739 -0.615 3.057 -0.012 -0.472 1.927 1.176 0.543 1.446 0.665 0.206 0.345 0.105 0.104 0.478 -1.192 0.464 -1.189 -0.147 -1.254 1.920 0.175 0.884 0.928 +0 -0.493 -0.461 -0.739 -1.250 1.391 0.684 -0.414 -0.603 1.462 0.519 -0.422 1.162 1.904 -0.578 -0.418 -0.222 0.297 -0.035 -1.460 -0.915 0.127 1.510 0.215 0.927 0.342 -0.160 -0.875 0.375 +4 -0.446 -1.211 0.739 -1.316 -0.633 -1.766 -2.550 0.187 -1.035 -0.261 0.138 -0.182 1.137 0.717 0.308 -1.144 1.103 -0.124 -0.077 1.369 -1.830 -1.660 0.447 2.627 1.508 0.089 0.837 0.137 +0 -1.304 -1.801 1.199 0.400 -0.548 1.233 0.515 0.291 0.026 0.158 -1.241 -0.094 -2.267 0.115 -0.033 -0.193 0.981 0.538 0.911 -0.151 -0.186 -0.236 0.140 -0.252 0.345 -0.616 0.208 0.537 +3 1.058 1.642 -0.014 0.178 -0.323 -1.316 1.440 -0.392 0.214 -1.723 -1.100 -2.249 0.146 -0.905 -0.472 0.140 -1.536 -0.551 0.232 0.129 -2.186 0.427 -1.136 0.188 0.204 1.373 -0.002 -0.460 +0 0.517 -0.234 -0.087 -0.313 -0.422 -0.412 0.338 -0.094 1.046 -1.009 0.522 -1.234 -0.878 -0.138 -0.195 0.347 1.258 1.174 -0.046 1.838 -0.584 0.002 -0.169 0.638 -0.199 0.022 0.354 1.405 +1 0.428 0.654 -1.449 0.564 0.977 -1.352 0.672 -0.789 -0.580 0.251 0.610 -0.592 2.046 0.916 0.450 -0.610 1.054 -1.650 -1.201 -0.379 -0.296 -1.006 0.979 -1.190 1.627 0.509 0.039 0.034 +4 -2.894 -0.350 -0.123 -0.970 0.805 0.244 0.373 0.471 -0.947 -0.115 2.061 -1.035 -0.635 -0.381 -0.562 -0.003 -1.831 0.217 1.258 0.237 1.111 0.507 -0.766 -2.103 -0.077 0.238 0.083 2.415 +3 1.628 2.212 0.411 -1.908 0.877 -0.261 -1.691 0.691 0.439 -0.806 -1.186 1.396 0.506 -0.791 0.663 -0.420 0.545 0.314 -2.564 0.108 -0.167 -0.740 -1.264 -0.218 -0.353 0.651 -0.145 0.089 +3 0.006 -0.984 0.264 1.092 0.715 0.579 -0.357 -1.732 1.566 0.899 0.530 -0.026 -1.744 2.586 0.989 1.509 0.195 -0.317 -0.019 -0.733 -1.628 -1.396 -0.554 0.735 0.166 0.936 -0.199 -1.122 +3 2.213 0.205 0.149 -0.336 -0.934 1.375 1.874 -0.893 -0.083 -0.450 -0.420 1.186 -1.312 0.466 0.728 -0.142 0.893 0.148 1.217 -1.522 -0.349 -0.072 -2.672 -1.086 -0.814 0.948 -0.707 -0.818 +2 -0.147 0.013 1.300 -0.756 0.030 -0.002 -0.289 0.545 0.238 1.376 0.152 -1.935 -1.016 -0.530 -2.471 -1.066 0.979 -1.524 0.505 -1.291 -0.022 -0.004 0.257 -1.444 1.007 -0.452 -0.916 0.461 +3 0.869 0.887 -0.763 0.038 0.683 -0.209 1.073 2.364 -0.786 -1.381 0.304 0.722 -0.231 1.453 -1.339 0.693 -0.606 1.719 1.993 -0.767 -0.550 0.860 -0.387 -0.045 0.025 -1.920 -0.014 -0.690 +0 -0.733 0.013 -0.381 1.739 0.086 0.317 -0.044 0.474 -0.217 0.774 -0.392 1.376 0.496 -0.153 0.842 0.493 0.827 0.086 -0.241 0.135 -0.500 0.979 1.469 0.579 1.418 1.127 -0.268 0.836 +3 0.767 0.185 -0.152 0.003 -0.046 -0.209 0.517 0.443 -1.572 -0.132 -1.506 0.636 0.768 -0.611 -0.117 -2.111 0.973 0.710 1.150 1.487 -1.224 0.888 1.479 -1.495 -0.171 -0.363 0.855 -2.394 +4 -1.525 0.562 1.918 -1.232 1.044 0.726 -0.677 -0.021 2.638 0.493 2.059 -0.195 0.576 -0.300 0.447 -0.818 -1.528 -0.524 1.887 -2.076 0.186 -0.118 0.144 -0.944 1.126 -0.058 2.204 -1.132 +2 -2.486 0.043 -0.054 -1.152 -0.597 0.264 -0.218 0.932 1.761 0.945 0.447 -0.149 0.796 -0.623 -0.547 -1.232 -1.550 1.281 0.270 0.531 -0.126 1.890 1.759 -0.017 -0.028 -0.220 -1.115 -0.409 +1 -1.507 0.146 -0.605 0.630 -0.885 -0.773 -2.021 0.170 -0.161 -1.188 0.194 1.027 -0.216 0.268 0.273 -1.204 2.057 -0.471 0.656 0.769 -0.140 0.607 0.246 -1.418 -0.332 -0.613 -0.462 -1.304 +2 -1.290 1.254 -0.765 -1.796 0.238 -0.255 -1.110 -1.681 -0.008 0.976 0.585 0.057 0.479 -0.027 -1.430 -1.421 -0.203 -0.161 0.851 0.757 0.605 -0.280 -1.381 0.412 0.525 -2.389 -0.834 -0.691 +2 -1.298 0.768 0.416 -1.548 -0.507 -1.424 -0.112 -0.290 -0.503 -0.551 -0.889 -0.135 0.582 0.480 -0.419 -1.803 0.199 -0.061 -0.478 0.727 1.098 1.515 1.823 -0.149 2.121 -0.431 0.067 1.719 +4 0.182 0.331 1.464 -1.786 0.011 -0.643 -0.533 -0.850 0.539 -2.451 -0.138 -1.822 -0.693 -0.214 -0.406 0.681 1.250 1.958 -0.008 1.161 1.464 -0.476 1.233 -2.176 0.298 1.605 0.099 -0.190 +4 0.740 -0.284 2.807 0.809 0.030 -0.675 -0.911 0.437 -0.527 0.991 -0.082 -0.079 0.115 -0.152 0.277 2.070 0.541 0.423 0.787 -0.109 -0.964 -3.019 -2.051 0.748 0.369 -0.148 2.362 -0.034 +1 -1.311 -1.397 0.558 0.765 -0.393 0.028 -0.450 1.097 -0.203 -0.499 0.966 -0.774 0.157 -1.048 0.665 0.815 -1.289 -1.445 0.289 1.941 0.884 -0.212 -0.854 -1.216 0.717 0.923 -0.631 -0.247 +3 0.334 -0.290 -0.839 -0.742 -0.215 -3.601 -0.701 -0.876 0.533 -0.806 -0.215 1.542 0.994 0.902 -0.500 0.543 -0.234 -0.196 0.410 -0.597 1.493 -0.160 0.618 1.456 1.032 -1.409 0.445 0.851 +3 -0.413 0.724 -0.829 1.201 1.612 1.377 0.208 -0.711 0.563 -0.340 0.300 0.586 -0.482 0.570 0.456 -1.272 0.421 -2.764 1.951 0.681 -0.339 -0.429 0.888 -0.467 -0.843 -0.333 -2.076 -1.324 +2 -0.547 0.014 2.156 0.101 0.479 -0.168 -1.759 -0.636 -0.543 0.580 0.384 0.318 0.897 -1.155 2.049 -1.679 -1.325 0.373 -1.088 0.050 -0.725 0.507 -0.038 -0.577 -0.026 2.025 -0.085 -1.394 +3 1.898 -1.069 -0.176 -1.462 -0.154 -1.859 -0.343 -1.972 1.904 -1.813 -0.414 1.019 0.760 1.199 0.499 0.969 0.276 -0.692 -1.019 1.078 -0.426 0.482 -0.627 -0.732 -0.164 0.611 -0.148 -0.733 +4 -1.698 0.258 0.058 -1.305 -0.336 -0.323 0.753 -0.808 1.297 -0.313 -0.362 -2.020 -3.237 -0.438 -1.667 -1.132 -0.297 1.113 -0.265 -0.319 -0.585 0.253 2.389 0.505 -1.191 0.254 -0.808 0.884 +3 0.669 -1.448 -0.204 0.716 -2.934 1.335 0.354 0.717 -0.413 -1.723 0.534 -0.083 0.032 1.088 0.762 1.921 0.154 -0.691 -0.188 0.416 1.016 -1.467 0.009 1.610 -0.240 0.931 0.493 0.956 +4 -0.889 -1.620 -0.260 -0.316 1.549 -1.116 -1.900 -1.475 -0.030 -0.990 -1.285 -0.295 -0.529 0.821 0.817 -0.473 -0.168 2.112 2.629 2.627 0.165 -0.320 -1.315 1.007 0.408 -0.636 0.361 -0.557 +2 0.042 -0.789 -2.279 1.107 -1.037 2.010 -0.619 1.269 0.130 1.583 0.379 0.480 -1.156 0.760 0.929 0.174 1.100 0.411 1.215 0.119 1.110 0.264 2.058 -0.336 -0.093 -0.663 -0.387 -0.518 +0 -0.042 0.486 0.441 -0.256 -0.054 -0.083 0.683 -0.695 -2.434 -0.343 1.688 0.801 -1.569 0.675 0.208 -0.375 -1.078 -0.814 -0.560 1.835 0.640 -0.710 -0.542 -0.037 -0.433 0.016 -0.146 0.097 +0 -1.123 0.959 1.126 -0.207 0.771 -0.691 0.403 0.503 0.002 -1.151 0.140 0.437 -0.974 0.991 -0.554 -0.234 -1.200 0.529 2.652 -0.364 -0.735 -0.352 0.116 0.486 1.062 0.829 -0.455 0.467 +2 0.436 0.584 1.078 -1.489 1.083 -0.216 -0.691 0.820 -1.488 0.939 -0.157 -0.100 -2.244 0.900 0.248 -0.467 -0.927 0.782 0.348 0.005 -0.988 0.303 0.243 1.316 -1.197 -0.716 1.944 0.192 +2 -1.380 -1.471 0.250 -1.185 0.131 1.082 -0.204 0.696 0.457 0.352 1.794 -0.695 -1.102 1.798 -0.427 -0.230 0.021 1.634 -0.184 -1.851 -0.231 1.348 -0.310 0.875 1.369 -1.170 -0.634 -0.226 +3 2.632 0.301 0.676 1.700 -0.433 0.118 1.896 -0.146 0.055 0.982 -1.061 0.029 -0.066 1.565 1.027 0.087 -1.289 0.678 -0.463 1.552 1.034 -0.088 0.277 -1.312 1.884 0.012 -0.511 0.002 +4 2.344 1.198 -2.102 1.357 1.600 1.195 0.751 1.551 0.883 0.579 0.134 -1.072 -1.373 0.773 0.755 -0.043 -0.857 -1.130 -0.362 0.702 2.238 -0.025 0.288 1.126 -0.064 0.369 0.822 1.102 +3 -0.058 -1.194 -0.678 -0.742 1.261 0.610 1.018 0.858 1.238 -1.441 0.342 -0.191 0.992 -0.833 0.455 0.145 1.091 1.187 -2.148 0.578 1.111 -2.185 -1.067 1.303 -0.496 0.673 0.138 1.224 +4 1.835 -2.729 -2.690 0.767 0.236 0.672 -0.526 -2.142 -0.832 1.560 -0.452 -0.443 -2.629 -1.804 1.110 -0.259 0.991 -0.754 -1.468 -0.261 -0.573 -0.833 -0.807 -0.135 -0.492 -0.932 1.805 0.458 +4 1.571 2.193 0.293 -0.280 -0.303 0.375 -1.014 -2.410 -0.400 0.041 0.556 0.397 0.141 1.169 -0.873 -0.672 2.109 -3.184 1.119 0.563 -0.001 0.896 0.718 -1.459 -0.974 -0.481 -2.121 -0.009 +4 1.169 -0.054 -0.447 1.523 -0.059 1.181 2.459 0.871 -1.437 -0.859 -0.899 0.469 -2.014 0.008 -0.721 -2.239 -0.051 -1.443 -0.878 0.778 -0.211 1.131 0.834 1.136 1.258 -0.375 -0.257 -1.034 +1 -0.047 -0.651 -0.196 0.680 -0.419 1.183 -0.266 0.545 0.827 -0.623 1.643 0.105 -1.318 -0.911 -0.819 -0.329 -0.409 -1.589 0.044 1.265 0.570 0.916 -1.046 -0.828 -0.986 0.507 -1.767 1.447 +0 1.526 0.843 -0.063 -0.091 1.084 -1.640 -0.627 -1.822 -0.210 -0.403 -0.539 0.357 -0.818 -0.622 0.544 -0.928 -0.535 -0.099 0.478 0.821 -0.143 1.467 -0.492 0.127 0.767 -1.105 1.424 -0.663 +1 -0.615 0.980 -0.423 -0.087 -1.415 0.274 -0.611 -0.477 -0.840 -0.124 -2.006 0.682 -0.039 -0.467 -1.548 0.433 -0.634 1.306 0.486 1.535 0.137 1.078 0.463 -0.451 0.187 -0.834 2.069 -0.101 +1 0.051 -0.914 -0.726 -1.160 -0.894 0.352 -0.833 -0.904 -0.338 -1.185 -0.818 -0.962 -0.679 2.594 0.033 0.853 -0.080 -1.481 0.659 -0.350 -0.399 0.088 0.218 1.386 -0.152 -1.259 -0.031 1.115 +1 0.284 1.664 1.165 -0.530 -1.264 0.838 -0.653 -0.336 -1.060 0.679 -0.646 -0.035 -0.020 0.184 0.662 -0.243 -0.741 -1.860 0.103 -0.157 -0.710 0.063 0.856 1.112 -1.535 -0.361 0.961 1.596 +2 -0.889 -1.123 -0.604 0.620 -1.371 0.287 -0.961 -0.781 0.401 2.173 -0.911 0.720 -0.313 -0.475 1.071 -0.844 -1.154 0.460 1.595 0.750 -1.407 -0.615 0.980 1.581 0.229 -0.579 1.646 -0.075 +3 1.281 0.805 -1.431 1.534 -2.741 2.151 -0.287 -0.190 -0.060 0.385 -0.000 -0.133 1.309 0.566 -0.959 0.741 0.524 -0.545 0.792 -0.158 -0.222 -0.249 -2.428 -0.527 0.713 -0.022 -1.285 0.965 +0 1.489 -0.164 1.098 -1.301 0.028 0.475 1.196 0.712 0.045 0.474 -0.728 0.604 0.478 -0.778 0.123 0.771 2.444 1.186 0.225 0.285 -0.145 0.111 0.776 0.752 -0.085 -1.099 -0.214 -0.836 +2 -0.196 1.060 -0.052 0.503 -0.319 -1.030 -0.123 0.309 -0.656 -0.236 -0.939 1.119 -0.294 1.453 1.529 -0.081 -1.008 -2.086 -1.472 -1.374 1.378 0.116 0.390 -2.220 -1.198 0.887 0.287 -0.147 +0 -0.744 1.559 0.386 0.429 0.568 -0.815 0.359 0.417 1.055 0.011 -0.695 -0.433 0.727 -1.631 -0.281 1.497 -0.146 1.740 1.235 0.547 0.096 1.276 -0.070 -1.279 0.523 0.185 0.604 0.843 +2 0.268 -1.480 -1.146 1.132 -2.007 0.616 -0.374 0.160 1.558 -0.131 0.625 0.059 -0.109 -0.233 0.701 -0.732 -1.132 0.074 0.193 -1.824 -0.010 -1.227 0.627 -1.804 1.925 -0.338 -0.865 -0.283 +4 -1.104 0.333 1.847 -0.980 -0.503 2.780 -0.822 1.286 -0.229 0.060 -0.131 0.426 -1.580 0.186 -0.403 -1.863 1.121 -2.108 2.148 0.617 -1.361 1.338 -0.750 -0.705 -1.801 -0.864 -0.746 0.818 +1 1.059 0.337 -0.728 0.880 0.393 1.624 -0.109 0.798 -1.912 -1.159 -0.341 0.322 -0.191 -0.427 -0.064 0.673 -0.311 -0.134 0.428 2.051 -0.429 1.331 -0.748 0.569 -1.012 0.862 0.485 1.117 +1 -0.570 -2.000 -1.399 -0.075 -0.930 -0.319 -0.787 -0.278 -1.202 -0.476 -0.268 0.030 -1.825 0.051 -1.112 -1.366 1.231 0.730 0.551 -0.221 -0.581 1.487 0.135 -0.899 -0.995 0.311 -0.348 1.033 +3 1.365 1.684 -0.184 0.368 0.102 -1.612 -1.552 0.049 -0.425 1.892 -0.237 1.951 -0.476 1.912 0.506 0.563 -0.512 0.577 1.693 -0.145 -0.339 -0.247 1.620 0.855 1.448 0.314 0.039 0.424 +4 0.342 -0.371 -0.360 -1.017 0.292 -1.970 -0.705 -0.991 -0.630 -0.575 -0.420 -1.370 0.706 -1.386 0.775 -1.444 -2.265 2.487 0.887 -0.520 1.807 -0.324 1.167 0.041 -1.839 0.149 -1.068 -0.052 +2 -0.346 0.618 0.055 -1.262 -0.189 1.793 -1.551 0.377 0.788 -0.555 -0.322 -1.702 1.359 0.326 1.468 0.080 -1.423 0.600 1.121 -0.215 1.740 -0.078 0.312 1.308 -0.561 0.080 -1.693 0.216 +1 -0.092 1.248 -1.365 -0.584 -0.103 -0.200 -0.076 1.334 0.026 -0.603 0.902 0.825 0.459 0.733 -0.015 -0.088 -0.543 -2.002 -0.928 1.701 0.667 -0.015 0.271 -1.198 1.429 1.491 -0.531 0.383 +3 -0.864 -0.127 -0.910 0.695 -0.376 -1.305 0.442 -1.434 -0.837 -2.107 -1.424 0.179 -1.965 -1.481 -0.002 -0.544 1.992 1.834 -0.868 0.557 0.749 -0.597 -1.496 -0.012 -0.173 -0.436 0.442 -0.751 +1 1.507 0.700 1.471 0.519 0.368 0.068 1.127 0.725 -1.204 -0.663 1.891 -0.094 0.283 -0.625 1.212 0.498 -0.031 0.543 -0.231 -0.401 -1.208 0.536 1.213 0.413 -0.611 0.052 1.774 0.643 +1 0.487 -1.162 -1.200 0.123 -1.054 -0.133 -0.700 -0.862 -2.102 0.140 -1.247 -0.168 -0.512 -0.207 -0.150 0.194 -0.728 1.243 -1.847 -0.227 0.836 0.477 -0.975 -0.808 -1.007 0.599 -0.740 0.255 +2 -0.483 -0.169 0.166 2.061 -0.221 0.470 -0.793 1.012 -0.663 2.042 1.202 -0.223 0.403 -0.766 -1.844 -1.006 0.571 -0.992 1.320 -0.075 -0.235 0.122 -0.719 -0.183 0.680 -0.283 1.687 -0.984 +2 -0.007 1.064 -1.362 -1.250 -0.122 -0.213 0.535 -1.201 0.524 -0.870 -0.323 2.144 -0.009 -0.036 0.196 -0.247 1.676 -0.151 0.393 -1.904 -0.313 1.214 -1.626 -1.187 1.146 0.386 0.089 0.306 +0 -0.308 -0.611 1.203 0.139 0.795 -0.581 -0.229 -0.764 -0.479 -0.838 -0.381 0.932 -0.339 1.429 -0.480 0.229 0.959 -0.984 0.004 1.367 -0.294 0.338 -1.221 0.019 0.183 -0.259 1.222 -0.770 +2 0.785 -1.778 0.715 -0.234 0.707 0.777 1.608 1.353 1.347 1.444 0.291 0.032 0.873 0.732 -0.309 0.405 -0.035 -0.854 1.290 0.055 2.049 -1.625 -0.557 0.033 1.137 0.399 0.310 -1.127 +2 -2.362 -0.698 1.010 1.136 -1.629 1.489 -1.585 -0.350 0.530 -0.389 0.521 -0.424 0.176 -1.305 1.584 -0.145 -0.315 0.496 -1.315 -0.186 -1.223 -0.764 -0.066 0.488 -0.514 -0.299 0.733 -0.277 +2 -0.237 0.497 0.451 -0.611 -0.095 -0.584 -0.832 -1.025 -1.472 0.362 -0.241 -1.136 -1.228 -1.244 -0.612 1.126 -0.681 0.558 1.561 0.168 -0.765 -0.089 -0.043 1.728 -1.732 1.116 -1.743 0.812 +2 -0.473 2.071 -0.447 0.030 1.853 -0.072 -0.301 1.032 -1.476 0.344 0.756 0.208 1.059 -0.745 1.911 -0.733 0.877 0.215 0.989 0.194 -1.579 -0.305 -0.757 1.297 -0.935 0.692 -0.240 -0.863 +0 -1.096 -0.654 -1.459 0.181 0.069 0.555 -0.709 1.721 0.641 -1.230 -1.318 -0.506 -1.293 0.156 0.163 -0.761 -0.633 -0.638 0.191 0.098 -0.664 -0.325 -1.581 -0.692 0.596 -0.246 -1.617 -0.153 +4 0.028 0.586 0.217 0.097 -2.262 -1.208 0.992 -1.162 1.790 0.136 1.323 -0.911 -1.615 -0.182 -0.915 0.818 1.155 2.250 1.587 -0.328 1.081 0.621 0.227 -1.682 -0.149 1.006 -1.380 0.325 +1 0.064 -0.860 -0.884 -0.691 0.240 -0.306 2.582 0.388 1.571 1.485 0.425 -0.041 -0.899 -0.602 0.840 0.270 -0.642 -0.207 0.329 0.025 -0.238 -0.055 -2.416 0.018 -1.058 0.363 0.134 0.748 +2 0.649 0.774 1.431 0.431 -1.279 1.962 2.567 1.583 -0.010 0.346 0.499 1.415 1.019 -0.075 0.954 0.445 0.252 0.524 0.409 -0.345 -0.615 -0.093 0.983 0.718 1.119 -0.235 -0.507 0.342 +4 -0.405 0.519 2.198 0.116 -1.983 0.954 -0.232 0.070 0.558 -2.225 0.007 -0.410 1.222 0.727 -1.627 1.263 0.450 -1.547 0.056 1.035 -1.214 1.378 -1.513 -0.791 -0.943 1.377 -0.491 -1.163 +0 0.344 0.213 -1.296 -0.159 -0.706 -0.410 -0.083 0.214 -0.853 -0.660 1.330 -1.273 -1.009 -0.518 -1.293 -0.408 -0.013 -0.462 -2.069 0.836 -0.529 -0.606 -1.932 0.457 0.492 0.006 0.448 -0.408 +1 -0.760 -0.710 0.130 -1.063 0.421 0.847 0.534 -1.768 0.995 0.937 0.830 0.373 0.220 1.032 1.924 -0.953 1.078 -0.086 -0.130 -1.537 -0.285 -0.997 -0.464 -1.098 -0.449 -0.406 0.097 -1.615 +4 -0.127 -0.413 0.114 -1.239 2.308 -2.114 -1.754 -0.268 -0.011 -0.711 1.605 -0.115 0.893 0.825 -1.381 -0.064 -0.870 -1.650 -0.324 -0.960 1.774 1.326 -1.216 0.276 0.977 0.402 -1.155 0.789 +2 -1.206 1.817 -0.627 0.928 0.842 -1.259 -1.002 -0.302 -0.866 1.726 -0.200 0.890 0.514 0.264 0.040 1.237 -0.825 1.726 -1.287 0.782 -1.405 -0.316 0.093 -0.487 -0.182 -1.387 -0.987 0.498 +1 2.764 2.341 -0.075 -1.107 -0.978 0.234 0.640 -1.842 -0.946 0.142 -1.404 -0.297 0.431 -0.162 -0.734 0.087 0.507 -0.921 0.344 0.325 0.519 0.263 0.130 -0.156 -0.299 0.723 -0.095 0.535 +4 0.898 -0.518 2.515 -0.748 -2.567 0.829 -0.176 1.761 -1.223 -0.582 0.341 0.813 0.491 -0.002 0.361 -1.250 -0.901 -2.568 0.347 -0.790 -1.336 -2.597 0.144 -1.308 -1.772 -0.644 0.573 1.819 +1 -0.142 0.339 1.029 0.872 -1.143 0.820 -0.065 0.073 0.073 -0.150 1.192 -0.084 -1.120 -0.394 0.179 1.229 0.603 -0.851 2.485 0.265 1.275 -0.152 0.533 0.839 1.218 1.057 0.872 -0.315 +3 -1.021 0.201 0.915 -1.883 0.026 -1.653 0.667 0.086 -0.374 -1.567 -0.599 0.519 -0.041 1.193 1.128 1.085 -0.538 0.295 -0.965 -1.260 1.182 -1.187 -1.294 0.105 -0.271 -0.184 2.306 -0.994 +4 2.029 -0.449 1.528 -1.103 -1.651 0.393 0.502 -0.798 0.130 0.312 3.608 -0.340 0.644 -1.433 -0.689 0.972 1.091 -1.153 -0.007 0.553 0.650 -1.549 0.637 0.063 0.709 -0.613 0.580 -0.061 +3 2.037 -0.236 1.316 1.268 0.820 -1.776 -1.420 -0.506 -0.499 -0.913 0.556 0.612 1.791 -0.607 -0.651 0.187 -1.288 1.870 -0.358 -0.079 0.136 -0.148 -0.276 2.355 0.086 0.482 -0.494 0.616 +2 -0.479 -1.738 -0.635 0.334 1.116 0.757 0.397 1.575 -0.302 0.714 0.620 0.975 -1.069 -0.610 0.002 0.933 0.147 -1.777 0.991 -0.687 0.213 0.639 -1.599 -2.297 -0.184 0.903 0.052 -0.307 +0 -1.358 0.551 -0.079 -0.093 -0.472 -0.620 -0.253 0.116 -0.014 -0.767 -0.145 -0.658 1.834 0.929 1.514 -0.608 -0.376 0.655 -1.116 -0.089 1.875 1.186 -0.283 0.155 -0.341 1.487 0.096 1.093 +4 1.206 1.076 -1.221 -2.799 2.723 -0.726 -1.411 -2.904 -0.508 0.551 1.917 -0.379 -1.818 0.433 0.291 -0.698 -0.318 0.154 0.806 0.556 0.265 0.049 1.942 0.544 1.109 0.876 0.353 1.490 +3 -0.755 -0.498 0.044 -0.168 0.580 1.698 2.604 1.342 -1.340 0.283 0.589 0.952 -0.069 -1.315 -1.679 -0.481 0.908 0.312 -1.352 0.984 1.185 -1.671 -0.071 0.710 -1.396 0.725 -0.079 0.965 +2 0.343 0.756 -1.165 -0.488 0.395 1.212 -0.818 -2.033 -0.498 1.010 -1.584 -1.177 0.156 -0.116 0.594 -1.229 -0.325 1.195 -1.193 1.477 -1.067 0.849 -0.373 -1.145 -0.497 0.545 -0.007 1.118 +4 0.733 1.261 -0.686 0.320 0.736 -0.154 0.383 0.853 -0.502 0.108 -0.087 1.675 -1.257 -0.940 0.616 -1.121 -0.251 -0.492 1.828 0.589 -1.007 1.037 0.991 -0.645 -0.191 1.406 0.108 -3.657 +1 1.131 -0.257 0.352 -1.027 -0.598 -1.322 0.269 -0.400 1.250 0.235 0.353 0.011 1.580 1.452 -0.172 0.298 1.415 -1.176 0.302 -0.350 1.473 0.531 -1.738 -0.416 1.404 0.892 -1.042 -0.979 +2 0.312 -0.070 -0.082 1.056 -1.016 0.273 -0.396 0.563 0.838 -0.083 2.658 -0.166 -0.761 2.051 -0.051 -1.054 -0.154 -1.069 0.593 0.056 0.729 -0.146 -0.244 0.021 -2.085 0.973 -1.110 1.757 +4 -0.090 -1.121 1.832 -0.979 1.353 1.377 -2.436 1.295 0.287 -1.245 -0.172 -0.625 -1.000 0.774 -0.589 0.692 -0.058 -0.148 -1.500 -0.557 -2.197 2.193 -0.772 -1.746 0.201 0.085 -0.172 1.011 +0 0.186 -0.039 0.013 0.062 0.130 0.060 -1.104 -0.152 0.265 0.097 0.954 0.620 -0.870 -0.167 -0.311 -0.004 -0.003 0.612 0.177 1.231 0.241 -0.831 -0.467 0.604 -1.614 -1.346 0.011 0.951 +1 0.266 0.413 0.478 -2.614 0.275 -1.763 -0.019 -0.188 0.646 -1.920 -1.116 -0.433 -0.389 0.263 -1.188 0.229 -0.132 1.415 -1.079 0.875 -0.234 -0.656 1.522 -0.434 0.396 -0.218 -0.228 0.599 +1 -0.162 0.886 0.518 0.188 -0.114 0.152 -1.074 1.081 1.217 1.187 0.522 -0.660 -0.757 -1.010 -2.307 -0.509 1.590 0.956 -0.158 1.068 -0.303 0.288 -0.177 0.413 0.689 -2.173 0.262 -0.875 +1 1.368 1.528 -0.149 0.721 -0.359 0.843 -0.354 1.411 0.689 0.027 -0.319 -0.868 -2.217 1.638 0.431 -1.111 -0.175 0.644 -0.387 1.566 -0.339 -0.547 -0.067 0.512 0.241 0.490 0.570 -0.604 +3 1.334 1.581 -0.630 -0.151 -1.009 0.720 -1.363 0.545 -0.990 0.396 -1.893 -0.369 -1.211 -0.979 -0.047 -1.790 -1.260 -0.758 0.696 -0.675 -0.265 -0.853 -0.693 0.573 0.759 0.689 2.037 0.794 +0 -0.292 -0.500 0.699 1.274 -0.456 1.270 -1.759 0.082 0.698 0.238 0.328 -0.481 0.582 0.547 -0.434 0.726 0.847 -0.978 0.248 -0.047 0.019 0.372 0.130 -0.467 -0.923 -0.996 2.197 0.775 +4 -0.901 -1.239 -2.432 -0.462 -1.404 0.097 -0.509 -1.086 -2.104 -0.349 -1.752 0.706 0.255 -0.227 1.451 0.010 0.386 0.687 -0.517 -0.511 0.041 -1.347 1.785 0.161 -0.201 2.086 -1.101 1.536 +0 1.994 0.203 -0.112 -1.027 0.188 0.467 -0.232 0.409 0.124 1.435 0.034 0.026 0.442 1.761 0.035 -0.515 0.285 -0.242 -1.265 0.735 -1.612 -0.951 -0.468 0.946 0.496 1.480 0.487 -0.206 +3 -1.256 1.608 -0.684 -0.635 -2.042 0.499 0.360 -0.074 -0.653 0.198 0.587 -0.759 -1.116 -0.538 0.141 -0.276 -2.055 -1.470 -0.078 0.240 -0.910 -1.561 -0.518 -0.870 0.821 0.637 2.181 -1.345 +1 -0.647 -0.697 -1.110 0.122 -0.759 -1.004 0.549 -1.457 -1.398 -0.487 -1.232 -1.980 -0.651 0.042 -1.078 0.807 1.271 0.778 0.240 -0.840 -0.644 -0.707 -0.583 0.091 1.598 -1.386 -0.257 -0.044 +0 -0.980 -0.715 -0.081 1.592 -0.047 0.374 0.873 -0.359 -0.415 1.052 0.568 -0.564 -0.412 0.085 0.330 1.244 0.532 -0.984 -0.786 0.310 0.143 -0.844 1.053 -2.248 0.336 -1.547 0.881 -0.265 +3 -0.499 0.669 1.869 -0.754 -1.665 -2.002 -0.150 -0.663 0.208 0.133 -1.603 -1.815 0.296 0.135 -0.426 -1.657 0.471 1.272 0.278 -0.847 0.045 -1.366 1.058 0.082 -1.768 -0.301 0.463 -0.333 +3 0.983 2.062 -1.509 0.701 -2.100 0.397 1.310 1.278 -1.114 -2.064 -1.359 -1.336 -0.631 0.985 -0.342 -0.098 0.731 -0.037 0.921 -0.151 -0.805 0.080 0.077 0.648 0.142 -0.663 -1.209 0.390 +4 -0.572 0.333 0.933 -0.223 1.065 1.453 -0.916 -0.837 -0.140 0.308 -0.525 1.352 0.424 0.039 -1.436 -1.316 0.281 -2.133 1.013 -0.158 3.243 2.308 -0.181 -0.106 0.996 1.703 -1.638 -1.786 +0 0.162 -1.029 0.982 0.610 -0.138 -0.463 0.335 -0.244 0.064 0.205 -0.830 2.148 -0.607 -0.593 -1.104 -0.223 1.489 -0.988 0.140 1.002 -1.374 0.172 0.359 1.283 0.125 0.382 -1.604 0.733 +3 -0.029 -0.652 -1.511 -1.272 0.668 -0.557 -1.475 -0.636 0.289 -0.674 -1.210 -0.920 0.884 -2.078 -0.268 -0.115 -0.756 -0.958 0.983 0.980 0.105 -0.543 0.113 0.404 -0.112 0.431 -0.354 -3.151 +1 -0.594 -1.025 -0.406 -1.079 -0.970 -0.503 -0.964 -0.481 0.811 0.908 -0.192 0.624 -0.197 -0.710 -1.413 0.310 1.265 0.876 0.993 -0.743 -0.517 0.714 -0.488 0.032 0.551 0.421 -1.753 2.097 +3 1.022 0.211 -0.340 0.961 0.647 -0.684 -1.328 -0.289 1.515 1.146 -1.112 -1.168 1.275 -1.136 0.509 -0.616 1.617 -0.770 1.143 0.185 0.252 -1.970 -2.203 -0.221 0.247 -1.125 -1.089 0.561 +4 -1.966 0.443 1.723 -3.011 0.689 -0.159 -0.850 0.801 1.710 0.775 -0.371 -0.943 0.145 0.873 -0.387 -0.290 2.822 1.079 1.150 -1.460 -0.707 0.075 -1.001 1.600 1.410 -0.534 1.784 -0.004 +4 1.905 -1.230 -0.230 1.064 -0.907 -0.176 -1.764 1.997 -0.422 -0.112 -0.587 0.137 0.143 -1.824 -1.713 1.056 1.011 0.702 -1.656 1.074 -1.410 -0.411 -0.839 2.952 0.080 -0.056 0.321 0.644 +1 0.136 1.002 -0.703 1.397 0.971 -0.429 -0.028 1.308 -1.555 -1.218 1.136 -1.382 -0.100 0.307 -0.731 -1.368 1.139 -1.473 -0.064 0.214 2.078 -0.454 0.555 0.700 0.005 0.253 -0.025 0.053 +0 -0.569 0.186 -0.734 -0.211 -0.523 -0.761 0.631 0.984 1.053 0.242 1.469 -0.677 1.285 0.910 0.040 0.223 1.036 0.662 0.261 -0.450 -0.924 0.108 -1.144 0.732 -1.774 0.307 -0.277 -0.439 +3 0.817 -0.842 -2.046 -0.205 -1.964 -1.030 -1.699 -0.754 -0.534 -0.874 2.300 -0.187 1.328 0.569 -0.207 -0.478 1.351 -1.036 -0.627 -0.642 0.075 -0.909 0.937 1.075 -0.718 0.527 -0.840 0.179 +0 -0.245 -0.754 -0.890 -0.816 -0.077 0.341 0.277 0.827 0.013 1.454 -0.265 2.720 0.626 -0.857 -1.071 0.482 -0.223 0.714 0.473 -0.073 -0.847 -1.515 -0.447 0.856 0.214 -1.246 0.173 0.385 +3 1.286 -2.203 0.278 0.857 -0.332 1.036 1.774 1.032 0.429 -0.880 1.724 -0.933 0.557 -0.617 -1.026 1.336 1.398 -1.081 0.489 -0.143 -1.174 -0.883 -0.386 0.255 2.019 1.028 0.259 -0.110 +4 -1.475 2.307 0.344 -0.904 1.073 -0.585 2.487 -0.631 -0.572 0.992 0.567 -1.081 -0.066 0.483 -0.075 2.243 -0.750 0.578 2.661 -0.057 -0.871 0.460 -1.095 -1.070 -1.836 -2.506 1.165 0.196 +0 0.234 0.305 1.197 0.655 -0.162 -2.026 -0.446 -0.112 -0.020 -1.643 0.162 0.711 -0.398 0.421 -0.697 0.212 1.847 -0.443 -1.177 0.713 -0.301 -0.810 -0.779 0.210 0.014 1.255 0.388 0.932 +4 -1.874 -1.004 0.101 0.320 1.820 0.798 0.589 0.387 -1.477 -1.824 1.502 -0.947 1.450 -0.975 1.159 0.403 1.509 -1.988 0.943 -1.855 0.356 -0.364 1.006 -0.215 -0.756 -0.041 -0.670 -0.277 +4 1.404 -2.768 -0.578 -0.880 -0.334 0.286 -1.039 0.326 0.485 1.589 -0.346 0.369 0.133 0.605 -2.000 1.368 -1.365 -0.374 0.138 1.241 -0.160 -0.575 0.827 1.909 -0.264 -0.976 -1.501 -1.456 +2 -0.067 0.631 0.227 -0.739 0.245 -0.809 1.827 0.239 -0.938 -0.393 0.760 -1.091 2.720 -1.455 0.926 -0.487 -0.809 0.538 -0.454 1.592 -1.401 0.562 -0.275 0.296 0.514 -1.732 0.234 1.089 +1 1.472 -0.118 0.441 -0.096 0.023 -1.355 -0.511 -0.724 -1.309 0.078 1.403 -0.892 1.674 -0.139 -0.453 -0.269 2.247 0.374 0.376 0.612 -1.318 1.103 -0.829 -0.950 -0.467 -0.242 0.002 -0.306 +3 -0.484 1.044 -0.087 -0.600 -0.991 0.873 0.981 1.923 -0.322 -1.013 -0.126 -0.698 -0.629 0.474 1.092 0.413 0.113 0.821 -0.227 1.450 2.999 -0.630 1.209 1.211 0.527 1.286 0.355 0.649 +4 0.577 0.311 3.079 1.120 -0.128 -0.956 -1.606 0.203 -0.756 -1.422 -0.647 -1.082 1.687 0.882 -0.008 1.480 0.077 -0.861 1.523 0.539 -1.037 -0.190 -0.876 -1.383 0.926 1.909 -1.399 0.563 +4 -1.383 -1.265 0.055 -1.309 0.611 -1.124 0.827 -0.962 0.690 0.198 0.571 -0.414 -0.487 1.745 -1.004 -0.800 -1.958 -0.728 -1.478 1.663 0.689 -0.625 0.804 -2.797 -1.103 0.728 -1.158 -0.650 +3 0.986 -0.006 1.977 -0.230 -0.779 1.204 1.327 0.342 -0.102 0.486 -0.338 -0.337 0.284 -0.518 0.515 -0.505 2.201 -0.168 -1.022 -0.238 0.181 0.605 -2.186 -0.351 -2.638 0.609 -0.575 0.798 +2 -1.181 -0.623 -1.789 1.007 1.271 -0.327 -0.298 0.453 1.474 0.409 -0.698 -1.619 -1.115 0.073 -0.655 0.195 -0.553 -1.058 -1.177 1.562 0.889 1.731 0.540 -0.430 1.323 -0.033 0.221 -0.043 +2 -0.800 -1.908 -0.393 1.002 1.393 0.711 0.429 0.380 -0.556 -0.130 1.669 -0.943 1.615 -0.322 1.325 -1.407 0.586 -0.737 -1.415 0.219 1.009 -0.845 -1.499 -0.092 -0.087 0.548 0.950 -0.059 +2 0.275 0.484 1.686 -0.761 0.890 1.425 0.950 -0.498 -0.309 1.518 -0.186 -0.030 -0.487 0.774 -2.461 -0.810 -0.980 1.588 0.232 -0.202 -0.204 -1.382 1.356 -0.351 0.593 -0.657 -0.060 -0.919 +1 0.435 -0.795 0.314 0.382 1.395 1.150 1.620 -0.201 0.884 -0.742 0.034 1.132 -0.356 0.903 -0.024 -0.108 0.114 -0.419 -0.143 -1.270 0.429 1.139 -1.522 1.620 0.655 -0.378 -0.743 1.331 +4 1.268 -0.644 -1.468 -0.259 -0.387 -1.284 -2.009 1.217 1.492 -0.602 -0.293 0.808 0.823 1.779 0.622 -1.025 0.791 -0.698 -0.066 1.113 -0.905 -0.073 -1.226 -0.989 -0.547 1.630 0.033 2.644 +0 -0.239 -0.257 -0.713 0.941 0.535 -0.606 0.329 -0.455 -0.245 -1.314 0.311 0.099 -1.712 -0.349 0.576 0.683 0.445 -0.315 -1.128 -0.738 -0.783 0.114 0.045 1.301 -0.664 1.081 0.184 0.240 +3 -0.687 -2.142 0.493 -0.798 1.331 0.911 -1.858 -0.122 0.191 -0.669 0.823 -2.002 -0.566 -0.159 1.967 -0.044 -0.862 1.010 -1.423 -0.920 0.489 0.425 -1.066 1.169 0.029 2.104 0.466 0.087 +1 -0.943 -0.273 0.175 0.901 2.178 1.405 -0.849 -0.088 0.664 0.048 0.857 0.043 0.529 1.432 1.107 0.458 -0.335 -1.088 0.589 0.670 -0.303 0.722 1.289 -1.140 -1.001 -1.632 0.812 -0.881 +1 0.433 -0.325 0.470 0.216 0.206 1.435 -0.645 0.175 -0.550 0.156 1.135 0.041 -0.094 -1.552 -0.220 -0.450 -1.387 -0.427 0.232 0.592 1.630 0.166 2.519 -0.456 -0.472 1.842 -0.853 -0.203 +2 -1.998 -0.818 -1.599 -0.060 -1.151 0.730 -1.664 1.195 0.837 -1.066 -0.094 -0.854 0.579 1.800 1.181 -0.688 0.115 1.522 0.524 1.010 0.651 0.076 -0.069 -1.211 -0.766 -0.902 -0.351 0.100 +0 -1.223 0.530 -1.407 -0.505 -1.006 -0.311 -1.463 0.194 0.519 0.822 0.060 0.117 -0.507 -0.055 0.103 1.101 0.015 -2.501 -0.301 0.647 -1.045 -0.519 -0.924 -0.467 0.385 -0.049 0.480 0.664 +2 -0.257 -0.939 -1.493 0.481 0.013 -0.541 -1.050 -0.795 -1.073 0.162 0.084 -0.596 -0.463 -0.428 2.054 0.013 0.538 -1.930 -1.769 -2.336 -0.524 0.071 0.684 -0.142 -1.075 -0.310 -0.228 -1.370 +1 1.445 0.810 0.921 -0.058 -0.426 -0.180 0.151 0.204 -1.366 -0.538 -0.017 -0.478 -0.595 1.715 2.148 2.012 -0.249 -0.370 -0.866 0.337 -0.360 0.912 -0.691 -0.091 1.258 -0.856 -0.564 -1.282 +4 -1.572 0.402 -2.809 1.945 -0.622 -1.800 0.522 -0.135 -0.948 -0.574 -0.089 1.084 -2.645 0.318 1.646 -0.815 0.748 0.671 1.881 0.235 0.704 1.065 0.617 -0.893 -0.729 0.267 0.868 0.338 +1 0.208 0.594 1.655 -1.303 0.674 0.219 -0.510 0.806 -0.111 -0.080 0.314 -0.458 0.654 -0.397 1.690 0.228 -0.670 0.309 0.990 -1.575 1.267 0.340 -0.030 -0.686 0.347 -0.884 -1.989 -0.713 +4 -1.003 0.106 1.738 -0.546 -1.464 -0.250 0.794 0.650 0.371 1.583 1.449 1.131 -0.269 2.811 -0.668 0.894 -0.655 0.766 0.582 -0.328 -2.081 2.176 1.609 1.489 -1.455 0.994 -0.146 -0.418 +4 -0.507 2.424 0.943 -0.525 1.213 0.730 0.634 -0.122 0.420 -2.103 1.317 -3.436 -0.442 -1.596 -0.501 -0.074 -1.197 -2.774 0.207 2.086 -0.351 0.959 1.504 -1.900 -0.457 -1.276 -0.806 1.702 +2 0.362 -1.247 -0.409 -0.866 0.345 0.431 -0.601 -1.640 2.812 0.323 0.320 -1.874 0.724 1.064 0.338 0.134 0.136 -0.888 -0.697 -0.230 1.100 0.267 -0.741 -0.647 -2.162 0.450 -0.210 0.187 +1 -0.148 -0.522 -0.935 1.196 1.464 1.185 0.492 0.290 -1.216 -0.246 -0.684 -1.826 1.057 -0.696 1.196 0.882 0.557 0.613 -0.615 1.858 0.043 0.302 -0.649 -0.931 0.477 0.218 -0.789 -1.438 +1 -1.680 0.000 -0.804 0.101 0.036 0.145 -1.542 -1.177 1.779 0.837 0.295 -1.540 0.310 0.356 -0.437 -1.181 -0.661 1.007 0.761 0.978 0.570 -0.918 -1.670 -0.065 0.965 1.006 -1.030 0.331 +1 0.516 1.593 -0.069 1.135 0.469 -1.114 1.021 0.809 -0.650 0.290 2.108 -0.483 0.726 -1.095 -0.570 0.811 0.257 -0.201 1.702 -0.922 -0.146 0.492 -0.872 0.509 1.075 -0.115 -0.685 -0.312 +4 0.431 -0.838 0.850 1.094 -0.752 -0.860 1.613 -1.080 1.753 0.562 1.284 1.014 1.046 -1.145 0.724 1.269 -0.240 -1.988 -1.311 -1.577 -0.383 -1.400 -0.628 2.025 0.843 0.618 1.134 -1.285 +2 -1.291 -0.143 1.097 -2.191 1.534 1.122 -0.460 0.543 0.003 1.230 0.983 -1.400 0.587 -0.206 0.083 -0.728 1.748 1.040 -0.740 -1.335 -0.386 0.435 0.240 1.253 0.790 0.970 0.327 -1.389 +1 1.256 -0.900 -0.745 -0.701 -0.152 -0.623 -0.175 -0.872 0.784 -0.948 -0.998 0.520 0.722 -0.225 0.869 0.797 1.899 0.733 1.707 0.062 1.400 1.421 -0.143 -0.689 -0.513 0.415 -1.150 -0.454 +2 -0.920 -0.811 -0.412 1.076 -0.603 1.563 1.733 -1.747 -0.966 0.977 -0.206 1.080 -1.298 -0.772 1.046 -0.658 -1.064 -0.427 1.437 0.486 -0.413 -0.999 -0.615 -0.412 1.012 -0.894 -1.040 0.348 +1 1.152 0.351 -0.585 0.156 -0.571 1.345 1.001 -1.582 -0.443 0.837 -1.226 0.027 -1.759 -0.402 -0.033 -1.174 0.129 0.525 -0.926 0.722 1.028 -0.198 -1.783 -0.642 0.277 -1.155 -0.161 -0.857 +1 1.032 0.249 -1.497 1.321 1.227 -0.362 -0.314 0.290 -0.843 0.208 0.324 -0.070 0.194 -0.296 -0.664 -0.143 2.059 -0.756 -0.173 -1.464 -0.439 0.668 -1.679 0.100 -1.229 0.586 -0.965 0.394 +3 0.983 0.746 -0.205 0.142 -0.055 -0.363 -1.651 -0.485 0.590 0.265 0.954 0.611 1.895 -0.564 -0.159 -2.268 -0.593 0.285 0.656 -0.170 -0.817 1.948 -0.659 -1.953 -1.753 1.351 0.686 0.549 +2 -0.365 1.629 1.012 0.303 -0.474 0.244 0.753 1.159 -0.764 -1.997 1.905 1.286 0.003 -0.534 -0.591 -0.653 -1.633 -0.312 -0.180 -1.143 0.023 1.024 -0.569 1.130 0.339 0.282 -0.967 1.440 +0 0.677 -1.042 0.607 -0.761 -0.697 -0.600 1.008 1.222 1.717 -0.675 0.245 -0.634 -1.173 0.537 0.386 -0.760 -0.162 0.158 0.057 0.765 -0.558 -0.232 1.978 -0.263 -1.280 -0.690 -0.583 -0.268 +3 1.523 0.377 -0.202 0.921 -0.904 -1.587 1.213 0.465 -1.302 -0.700 -0.915 0.971 0.297 1.789 1.909 1.975 1.258 0.862 0.818 0.378 1.173 0.710 0.375 -0.107 -1.070 0.712 -0.810 -0.698 +3 -0.443 -0.219 -0.205 1.405 -0.792 -1.809 -0.248 0.106 -0.144 2.322 1.204 2.104 0.236 -1.120 0.734 0.518 -0.836 -0.745 1.407 -0.281 1.470 -1.787 0.842 0.530 0.348 -0.389 -0.168 0.388 +3 0.933 1.668 0.221 -0.306 0.496 -2.597 0.229 -1.046 0.794 -2.192 -0.628 -0.041 -0.762 -0.375 0.904 -0.024 -0.931 0.940 0.251 0.292 -0.893 -2.548 -0.906 -1.008 -0.613 -0.842 0.113 0.881 +2 -0.261 -0.851 1.481 -0.884 -0.215 0.053 0.620 0.435 -0.583 0.204 -1.552 2.199 -0.804 0.901 -0.975 0.641 -1.017 0.893 1.904 0.073 0.566 0.974 -0.578 0.948 -0.454 2.163 -1.211 0.161 +0 -0.188 0.488 -1.012 -0.345 -1.365 0.299 0.597 0.314 0.097 0.528 0.593 -0.609 0.208 -0.432 -1.415 0.951 -0.316 0.327 1.193 -0.062 0.284 -0.099 -2.422 0.253 -0.845 -0.164 -1.237 -0.851 +0 -0.066 0.078 0.963 1.158 -1.049 -0.403 -0.134 0.216 0.832 -0.382 -0.184 0.446 0.492 -1.039 -0.913 2.326 1.193 -1.058 -0.955 0.767 0.744 0.328 0.024 0.926 1.055 -1.420 0.067 -0.103 +4 1.127 -2.155 -0.812 0.880 -1.275 1.185 0.306 1.354 1.236 -1.032 -1.750 -1.143 -1.255 1.251 -2.121 2.649 1.787 0.032 0.757 2.141 -0.029 -1.523 1.073 -1.976 -0.097 -0.144 -0.200 0.785 +0 1.238 0.380 -0.968 -0.310 -0.614 -0.710 0.979 -1.372 1.609 0.828 1.091 -0.463 -0.150 -1.433 -0.525 0.494 1.296 -1.107 -0.366 -0.151 -1.396 0.591 -0.724 -0.195 -0.452 -0.789 -0.007 -0.216 +1 0.818 0.703 -1.039 -0.675 0.924 -0.783 0.492 0.839 -1.642 -0.406 0.815 -0.147 -0.404 0.831 1.104 -0.678 1.767 -1.784 -1.507 -0.454 -0.335 -0.289 -0.535 -0.726 -0.220 -0.590 0.682 0.031 +0 0.263 0.302 0.312 1.521 -0.603 0.562 -0.011 0.290 -0.331 0.515 -0.071 0.372 0.229 -0.129 0.265 0.081 0.333 -0.423 -0.194 -1.513 -0.842 -1.666 -0.728 1.323 -0.708 1.923 -0.809 -1.233 +1 -0.356 -1.217 0.444 -0.721 -0.069 -1.292 0.649 0.088 0.500 -0.428 0.247 -1.455 2.275 0.415 1.707 -0.266 -1.050 -0.492 -0.044 -0.117 1.862 -0.856 -0.605 0.713 -1.587 -0.041 0.142 0.433 +4 2.221 1.836 -1.563 -0.217 -0.920 -0.090 -0.585 -2.979 -0.725 0.506 -1.808 -0.872 0.242 -1.409 1.070 -0.823 1.311 1.332 0.049 0.344 0.713 0.310 2.022 0.750 1.574 0.560 -0.254 -1.692 +2 1.867 -0.396 0.706 1.009 0.471 -1.671 0.154 0.569 -0.002 -0.859 -0.201 0.901 0.394 0.465 -0.249 0.426 -2.106 -0.044 -0.381 0.027 -1.173 0.370 1.922 0.081 0.047 2.508 -0.948 0.495 +1 1.213 0.533 -0.617 1.202 1.207 0.269 -0.900 -0.413 0.286 -0.189 0.939 -1.530 -1.494 0.181 -0.492 -0.627 -1.768 0.020 -0.053 0.653 0.069 -0.133 1.177 -1.312 0.536 -1.671 -0.838 -1.213 +3 0.208 -0.036 0.331 1.877 1.045 -1.040 2.039 -0.232 -0.421 0.294 -1.036 0.412 0.682 -0.270 0.201 -0.146 -3.032 -0.374 -0.693 -0.185 -1.306 -1.433 -0.861 0.477 -0.077 -1.029 -1.362 1.016 +1 2.049 -1.102 -1.160 -0.594 0.593 0.604 -0.548 1.017 -0.435 0.852 -1.393 -0.964 -0.415 -0.061 -0.839 -0.855 0.451 0.818 -0.777 -1.342 0.097 0.464 -1.050 1.674 -0.878 -0.265 -0.249 -0.813 +3 0.684 -0.768 -0.028 -1.149 1.826 -0.908 -0.523 -0.730 0.080 1.370 1.789 -0.399 0.378 0.082 -0.982 2.423 1.363 0.189 1.582 1.212 0.082 0.326 1.512 -0.946 1.674 -0.225 1.525 -0.005 +3 0.117 0.244 -1.243 0.080 1.322 -1.799 0.827 1.375 -0.561 1.694 -0.690 0.441 -0.307 -0.307 -0.617 0.699 0.849 -0.064 -1.360 0.403 0.422 -0.217 1.867 -1.564 -2.217 -0.731 0.710 -1.056 +4 -1.159 -0.344 -0.943 -0.852 -0.053 -0.607 2.629 0.836 1.085 -0.111 0.106 -0.430 2.624 -0.109 -0.604 -0.754 0.335 1.421 1.087 -0.756 -0.781 1.246 2.101 -0.145 1.207 -1.466 -0.844 -1.010 +3 0.681 -1.327 -0.309 -3.309 0.725 0.528 -0.821 -0.058 -1.200 1.246 0.691 1.166 -1.720 -1.151 -0.218 1.369 -0.504 0.362 -0.248 -1.201 0.959 0.919 0.300 1.175 -0.472 0.921 0.367 0.772 +4 1.715 0.870 1.564 0.364 -1.368 -1.618 -0.563 -0.705 1.713 1.993 0.679 0.626 -0.888 0.511 -1.425 -1.115 1.505 0.304 0.712 -2.462 -0.455 -0.520 1.087 0.948 0.885 0.127 1.411 -0.797 +1 -0.368 0.312 -1.429 -0.705 0.794 -1.587 -0.198 -1.226 0.913 0.343 -1.468 -1.042 0.014 -2.307 -1.100 -0.824 1.285 -0.577 0.659 -0.473 0.745 -1.552 -0.723 -0.109 0.178 0.324 0.193 0.506 +3 2.002 0.435 0.998 0.712 0.834 0.328 0.853 -0.508 1.341 1.559 0.395 -0.360 -1.206 0.384 -0.019 -0.646 -0.984 -1.052 0.062 0.483 0.523 3.209 0.728 1.113 0.006 -1.391 -0.422 1.790 +4 -1.146 -0.722 -1.174 -0.046 -0.859 -1.521 -0.386 -1.729 1.327 -0.558 0.985 -0.492 -1.195 1.176 0.089 1.887 -0.467 -0.445 1.005 1.012 0.153 3.335 -0.281 -1.269 -1.003 -0.275 1.519 -0.186 +2 0.677 -0.632 0.605 0.160 0.179 -1.087 0.370 -0.041 -0.195 -0.802 -0.443 -1.023 -0.337 0.455 -1.450 -0.029 -0.594 1.265 -0.384 -0.084 0.378 1.705 0.378 2.393 1.153 -0.112 -2.742 -0.745 +0 2.300 -0.415 -1.211 -0.510 -0.558 1.085 -0.113 0.100 0.064 1.166 -0.323 -0.205 -0.689 -0.421 0.525 0.729 1.700 -0.104 -0.264 0.397 -0.469 -0.641 0.324 -0.392 -0.290 0.606 0.310 2.001 +0 0.656 0.242 -0.764 0.053 -0.896 1.811 -0.824 -0.158 -0.694 0.324 1.283 -0.058 -0.802 0.677 0.095 0.080 -1.483 1.104 1.644 -0.424 -0.209 0.323 0.547 -0.705 0.426 0.543 0.357 -0.513 +0 0.537 0.028 -0.226 -1.367 -0.317 -0.177 -1.016 0.826 0.675 -0.506 0.684 1.398 0.192 0.739 -0.225 0.374 -0.078 -1.075 -1.250 0.850 0.914 0.701 0.967 1.089 0.147 0.785 -1.904 -1.375 +1 -0.937 0.821 1.458 0.899 0.621 1.010 0.068 -1.988 1.140 -1.045 0.029 1.816 -0.024 1.637 0.153 -0.837 -0.078 -0.535 1.090 -0.180 0.718 0.390 -1.840 -0.742 0.047 -0.356 0.159 0.562 +2 1.597 -1.206 0.713 -1.397 0.768 -0.684 -0.089 0.703 0.086 0.242 0.099 1.850 -1.278 -0.142 0.010 -0.889 0.799 -0.436 0.814 0.085 -0.365 1.723 -1.832 0.620 1.399 -1.491 -0.028 0.474 +3 -0.520 1.213 1.132 0.743 1.778 0.774 -0.656 0.079 0.940 -1.052 -0.273 -1.583 2.012 0.407 0.596 -1.509 1.112 0.311 -1.798 0.457 -0.978 0.057 0.753 0.195 -1.844 0.265 0.852 -0.200 +2 -0.929 0.937 0.451 0.587 0.867 -0.968 -0.047 -1.061 -0.756 0.498 0.324 0.523 -2.422 0.151 -0.570 2.132 -0.534 0.304 -0.118 -0.568 -1.708 -0.623 1.412 0.282 -0.625 -0.829 -0.602 -1.322 +0 0.346 -0.777 0.746 0.265 -0.340 -0.712 -0.185 -0.817 0.058 0.085 0.262 1.766 -0.132 1.005 0.230 -0.238 -0.119 -0.059 0.280 -0.959 -1.465 0.654 0.898 0.746 0.155 0.767 0.713 0.788 +2 -1.914 -0.660 1.157 -0.263 -0.631 -1.345 1.091 0.875 0.446 -0.072 0.671 -0.709 -1.314 0.126 2.145 -0.145 1.172 -1.443 0.716 -0.450 -1.432 1.313 0.327 -0.624 0.875 -0.549 -0.155 -0.207 +1 -1.256 -0.408 1.205 -0.126 -2.019 -0.510 0.794 1.276 0.051 0.484 -0.369 0.903 1.425 0.768 -0.118 0.434 0.952 0.028 0.801 0.370 1.419 0.700 -0.499 1.404 0.460 -0.140 -1.354 0.231 +4 -1.050 0.757 1.371 0.695 0.284 -0.988 -0.847 1.250 0.779 -0.039 -0.418 -2.026 -1.071 1.911 -1.280 0.189 1.007 -1.255 0.185 0.938 0.012 2.868 -1.669 1.059 -0.173 0.772 0.441 -0.733 +1 0.492 -0.269 -0.307 -0.242 -1.073 -1.178 -0.957 0.435 0.847 -1.582 -0.747 -1.553 -0.157 0.087 -0.078 1.497 -1.223 -1.437 -0.837 -0.397 1.379 0.565 -0.143 1.084 -0.148 -0.467 0.156 -1.215 +3 2.025 1.447 0.241 0.186 -2.165 -0.638 -0.213 -0.443 0.262 -0.253 0.292 0.091 0.672 0.305 1.778 -0.054 1.210 -1.212 -0.684 -1.305 1.889 0.066 -1.661 -1.007 0.399 -0.480 -0.672 -1.291 +0 0.037 0.601 -0.554 0.039 1.398 -0.294 0.608 0.633 -0.758 0.688 -0.607 0.429 -0.623 0.281 0.129 0.112 0.272 -0.531 -1.166 0.082 1.103 0.583 0.447 -0.511 0.186 -0.830 -0.424 0.357 +2 0.676 0.553 0.994 -1.323 -0.048 -0.510 1.701 1.425 1.446 2.360 1.370 -1.592 -0.073 1.399 0.264 -0.903 0.163 -0.632 0.027 0.054 -0.200 -0.671 -0.198 -0.961 0.135 -0.045 0.489 0.373 +1 -0.242 0.629 -0.269 -0.162 -0.459 0.250 -0.770 -0.288 0.449 1.449 -2.263 0.039 0.063 -1.027 -0.809 -1.198 1.073 -1.789 -1.106 -0.854 -0.736 -0.156 -0.651 -0.181 -1.286 -1.717 0.119 1.079 +0 0.677 -0.282 1.584 -0.136 0.943 -0.914 -0.874 -0.519 0.263 1.012 1.749 -0.284 0.034 -0.263 -0.041 -0.546 -1.276 0.031 -0.419 0.777 -0.610 -0.664 0.216 -0.896 0.511 0.938 -0.569 -1.778 +0 0.324 0.603 -0.038 -0.684 0.321 1.689 -0.545 -0.228 -0.514 1.074 -0.042 -2.011 -0.637 -0.532 0.316 0.044 -0.840 -0.115 -0.158 0.634 0.163 -0.875 -1.277 -0.154 -1.497 0.518 0.498 1.563 +1 1.315 0.077 0.379 0.120 -0.203 -0.382 0.805 -0.811 2.533 -0.198 -1.588 -0.188 1.358 -1.132 0.540 1.356 0.590 0.862 0.615 -0.243 -1.200 0.723 0.311 0.229 1.389 -0.978 -0.427 0.280 +3 -1.685 -0.097 0.013 0.843 -0.470 0.182 0.176 2.054 0.658 -0.855 0.943 -0.143 0.456 -0.938 0.642 -1.724 0.918 0.857 -0.971 1.408 -0.654 -0.922 -2.891 -0.079 -0.592 0.268 0.148 0.711 +1 -0.048 1.400 0.176 -0.752 0.782 -1.134 -0.296 -0.171 -2.274 1.458 0.540 -1.427 0.320 -1.123 0.312 -0.918 0.070 0.402 0.255 -0.319 -0.665 -0.275 1.013 0.477 0.764 -0.031 1.022 1.489 +0 -0.449 -0.837 -0.269 2.326 0.570 -1.205 -0.617 0.112 -0.552 0.028 -1.086 -0.166 -0.278 1.536 0.848 -0.136 0.331 0.798 -0.479 0.040 -0.146 2.089 -0.532 -0.373 0.289 -0.266 0.559 0.302 +4 -0.030 0.332 0.921 -0.963 1.305 1.244 -0.632 1.759 -0.173 0.567 -0.462 0.819 -0.445 0.160 -2.619 -0.472 -0.565 -1.891 0.768 -0.386 -1.818 0.602 1.089 -0.134 -1.510 -1.884 -1.225 -0.830 +3 0.481 -0.675 -0.553 -1.902 -0.112 -0.697 -0.687 2.289 0.668 -0.028 0.224 -0.504 0.969 -0.273 -1.086 1.631 -0.787 2.052 0.907 0.680 -0.961 1.471 2.391 -1.014 -0.082 -0.897 -0.345 -0.171 +1 -0.205 -0.067 0.268 -0.148 -2.163 0.256 0.431 -0.585 -1.398 0.532 -0.281 -0.822 0.109 1.049 -0.510 -0.258 -0.325 -1.249 -0.094 1.625 -1.779 -0.232 0.801 -1.165 0.188 0.879 -1.511 1.547 +0 0.078 0.742 0.017 -0.329 0.693 -0.019 -0.650 0.855 1.112 0.734 0.764 0.287 0.771 -0.831 0.420 0.093 -0.660 1.821 0.303 -0.852 -0.107 0.292 1.144 0.630 0.335 0.473 -0.089 -0.015 +0 -0.401 -0.610 0.305 1.628 1.161 -0.435 -1.144 1.761 0.893 1.066 0.386 -0.133 0.261 -0.034 0.100 0.457 0.834 0.039 -0.524 -0.134 -0.015 -0.783 -0.767 -0.843 2.207 0.641 0.220 0.165 +4 0.054 -0.481 0.204 -1.697 0.294 0.867 1.698 0.925 -2.566 1.392 1.467 -0.921 -0.401 1.439 0.056 -0.588 -0.751 2.635 0.787 -0.279 1.429 -0.090 1.211 0.581 -0.339 0.498 0.539 0.801 +4 1.371 -1.673 -1.077 0.438 2.054 -1.677 0.060 0.554 1.470 0.447 -1.842 -1.286 1.372 0.440 -1.126 0.493 0.307 -2.494 -2.099 0.311 2.035 0.264 -2.449 -1.612 0.626 0.735 -0.194 -0.484 +2 0.776 0.361 0.293 0.351 0.973 1.091 -0.234 0.627 -2.118 -1.659 -0.351 -0.637 1.753 -0.296 0.372 0.211 -1.406 0.290 0.033 1.565 -0.554 0.622 0.580 -0.846 -2.346 0.036 1.248 0.310 +0 0.864 0.663 -0.184 -0.616 0.462 -0.984 -1.061 0.386 -0.369 0.594 0.800 0.844 -0.295 0.444 -0.654 0.965 -0.765 0.178 0.780 -0.312 1.039 -0.931 0.022 0.513 -0.413 -0.390 0.535 0.213 +1 2.254 1.233 0.769 -0.660 -0.238 -0.002 1.314 0.403 0.526 -0.168 0.793 0.302 0.044 -0.743 -0.114 -1.080 -0.040 0.089 -2.405 -0.230 -1.176 0.499 0.637 -0.426 0.549 0.591 -0.230 1.534 +3 1.714 -1.522 0.413 0.483 -1.271 1.240 0.183 -0.338 -1.069 -0.346 0.257 0.549 -0.532 1.598 0.725 0.726 0.539 -0.764 -2.324 0.191 -1.082 0.366 0.818 -2.056 0.066 0.785 1.842 0.327 +0 -0.831 -0.850 -1.302 1.075 0.318 -0.522 0.492 -0.755 -0.063 -1.729 0.099 -0.163 -0.827 1.950 0.212 -0.371 0.484 0.387 0.955 1.133 0.189 0.165 -1.445 -1.296 -0.845 -0.839 0.056 -0.199 +3 0.148 1.972 -0.003 -1.172 -0.808 1.024 -1.134 0.558 -0.357 -0.930 1.387 -0.589 -0.828 -0.079 -0.508 2.215 1.060 -0.591 -0.475 -2.019 0.137 -0.747 0.088 -1.966 -0.027 1.699 0.054 -0.775 +0 0.739 1.096 1.018 0.175 -0.694 0.621 -0.904 0.885 0.193 -0.615 1.335 0.646 0.569 0.423 -0.034 0.062 0.211 -1.330 -0.394 -0.118 -0.550 0.631 1.512 -0.273 0.327 0.056 0.313 0.314 +0 1.412 0.978 -0.097 0.068 -0.083 0.149 0.523 -0.241 -0.658 0.733 -1.734 0.771 1.782 -0.502 -0.188 -0.111 -0.016 0.108 -0.065 -0.533 0.348 0.849 -0.451 -0.986 1.178 0.107 -1.592 1.002 +2 -1.120 0.213 1.188 -0.008 0.643 -1.711 -0.224 -0.223 0.589 0.136 0.859 -1.129 -0.703 0.826 0.721 -0.614 -0.208 -0.512 -1.743 0.178 -1.232 -0.175 -0.685 -0.303 1.893 -1.810 2.173 -0.038 +0 -1.770 -0.252 -1.268 0.753 -0.036 -0.924 0.811 0.459 -0.236 -1.246 -0.842 -0.857 0.746 -1.681 -0.047 -0.250 1.036 0.233 0.535 -0.525 -1.231 -0.325 0.777 0.775 -0.431 -0.861 -1.057 -0.137 +4 -0.056 0.985 2.639 1.892 -0.887 -0.496 -1.471 -0.842 -0.213 -0.367 2.055 -1.315 -0.045 -2.063 0.331 -1.342 1.103 -0.291 -1.402 -2.103 -0.979 0.239 0.422 -0.498 0.926 -1.136 -2.927 0.283 +0 0.445 0.661 0.668 -0.271 0.255 -0.650 1.675 1.144 1.083 0.794 0.520 -1.244 0.633 0.114 1.263 0.807 0.550 -1.684 -0.089 -0.558 0.252 -0.416 -0.276 0.459 -0.432 0.122 -1.519 -0.832 +3 0.288 -0.614 -1.830 0.999 0.333 0.598 -1.107 -0.664 1.474 0.173 1.350 1.404 -1.267 -0.620 -0.272 -1.803 0.824 -1.466 -0.106 -2.521 0.417 1.138 0.210 -0.461 0.713 0.468 -0.600 1.425 +3 0.838 -1.336 -0.745 -0.086 1.490 0.521 0.311 1.352 -0.264 1.998 0.504 0.134 -1.797 0.976 0.537 -0.223 -0.505 2.013 -0.797 0.349 0.035 0.688 -1.922 0.673 1.600 -1.902 -0.948 0.565 +3 -1.470 -1.002 -0.629 1.640 0.063 1.822 0.627 -0.112 0.574 -1.729 -2.054 0.259 0.014 1.934 -1.379 0.372 0.695 0.343 1.099 -0.632 0.832 0.562 1.166 -0.511 -1.186 -0.629 -1.063 -0.565 +0 -0.698 -1.187 -0.176 -0.180 -1.717 0.422 -0.153 0.509 0.449 -2.247 -0.036 -0.110 -0.464 0.279 -0.337 -0.706 -0.245 -0.825 0.096 -0.559 -0.309 -1.489 -1.135 0.363 1.261 0.001 1.671 0.564 +4 -0.096 -1.010 0.800 0.222 0.813 0.487 -1.714 -0.833 1.653 0.785 -1.622 0.470 -0.832 -2.096 1.657 0.075 0.217 -1.257 -1.153 -1.592 1.417 -1.643 1.617 -0.987 -1.228 -0.055 -0.684 0.097 +0 -0.686 -0.230 -1.434 1.879 0.497 0.727 -1.480 0.448 1.530 -0.080 0.346 -0.252 0.442 0.008 -0.282 0.404 -0.007 0.337 -0.071 1.090 1.887 0.262 0.526 -0.405 0.644 -1.560 0.527 -0.924 +0 -0.438 -0.152 0.865 0.340 -1.177 1.417 0.056 0.585 -1.139 -0.048 -0.339 -1.004 -1.213 0.415 -0.109 0.492 -0.719 -0.596 0.106 0.690 0.055 -0.446 1.350 -0.790 -1.016 0.944 1.860 0.911 +3 -0.913 -0.987 0.207 -0.413 0.145 2.500 1.622 0.633 0.780 0.168 0.615 -1.325 0.353 -0.240 0.292 0.466 -1.162 -1.265 -0.371 -0.003 -0.662 2.874 1.139 -1.231 -0.042 -0.124 0.512 -0.995 +1 0.798 -0.412 -0.266 0.978 -1.855 -0.442 0.620 -0.308 0.135 -2.041 -0.854 0.679 -0.751 -1.302 1.062 0.516 -1.089 0.139 -1.056 -0.152 0.176 -0.808 0.742 0.897 0.264 0.414 -1.453 1.362 +4 1.197 1.834 0.528 1.441 -0.641 -0.863 -1.032 0.294 -2.630 1.325 -0.494 0.826 -1.392 1.111 1.383 0.103 0.528 -1.026 0.046 0.972 -0.303 1.526 -1.783 -0.329 -1.921 0.316 -0.213 -0.176 +3 -1.624 -1.782 1.138 2.629 1.461 -0.387 0.662 0.874 -0.018 1.174 -1.183 1.719 -0.346 0.708 -0.532 -0.379 -1.072 0.410 -0.130 0.297 -0.713 0.726 -0.608 0.757 0.048 0.980 -0.251 -1.296 +4 -0.641 -0.848 -1.192 1.143 -0.223 0.272 -1.544 -1.755 -0.902 1.623 -1.091 -0.626 0.013 -0.625 0.478 -0.831 -1.101 2.718 -0.937 0.795 -0.942 -1.541 -0.090 -1.341 -0.393 -0.796 1.252 -1.542 +1 -0.221 -0.277 0.307 0.816 0.860 -0.583 -0.167 0.283 -0.249 1.607 0.491 0.735 0.663 1.173 0.181 -1.297 0.400 -0.651 -0.529 0.586 1.238 0.021 0.309 1.702 0.241 2.602 0.566 -1.761 +0 0.638 1.166 0.078 -0.750 0.224 -0.079 0.785 -0.045 0.889 -0.261 1.290 0.178 -0.598 -0.333 0.727 -1.271 -0.130 1.069 0.517 -1.151 0.311 -1.342 0.187 1.380 0.073 -1.132 -0.191 0.194 +0 -0.610 -0.329 0.039 1.340 1.642 0.156 -0.727 0.678 0.005 0.038 -1.032 -1.617 -0.019 -1.069 -0.530 1.483 -0.070 1.107 0.296 0.584 -0.550 1.153 0.311 -0.264 0.310 -0.715 -0.294 -0.055 +2 -0.696 -0.595 2.365 0.118 1.364 -0.218 0.340 0.060 -0.519 -0.912 -0.001 -0.781 0.145 -0.431 0.827 -0.811 -0.103 -0.290 -0.362 0.803 -1.740 -1.884 -0.322 1.933 0.355 1.457 1.072 -1.406 +2 -0.302 -1.716 0.625 -0.515 0.152 0.070 -1.345 -0.026 0.339 0.809 -1.883 -1.543 -0.568 -0.314 -1.673 -0.651 -1.294 -1.149 0.045 -0.569 -1.200 0.764 -0.767 1.352 -1.836 0.642 -0.037 -0.307 +1 -0.208 1.178 -0.149 -0.088 1.961 -1.344 0.357 -0.061 -0.251 -0.953 -0.664 -0.408 0.496 0.852 -1.779 -1.202 0.548 0.632 -1.464 0.874 -0.014 0.747 -0.852 -1.390 -0.309 -0.264 -0.320 -0.390 +0 -0.303 0.070 1.337 -0.568 0.576 0.909 0.666 -0.839 0.771 0.434 1.191 0.728 -0.096 1.033 -1.261 -0.520 0.319 -1.690 0.298 -0.369 -0.453 -1.088 0.134 -0.133 -0.546 1.114 0.940 -0.173 +1 -0.186 0.653 0.418 -0.782 0.416 0.915 -0.976 -1.960 1.774 0.315 -0.457 -0.492 0.147 1.041 -1.510 -0.401 1.654 0.825 0.739 0.408 0.310 -0.468 -0.558 0.806 -0.540 0.284 0.152 2.192 +2 0.472 -0.570 -0.438 0.258 -0.558 0.924 0.773 0.567 -0.630 0.573 -0.743 -0.087 -2.636 1.157 1.727 0.225 -1.582 -0.218 0.071 0.016 2.541 -0.245 -0.577 0.163 -0.143 -0.749 -1.762 0.732 +2 -0.037 0.797 -2.961 -0.065 -0.386 1.690 0.357 -1.342 1.291 0.802 0.801 0.417 -0.602 -1.683 -0.005 -1.208 0.004 -0.964 0.711 0.848 -0.735 -0.851 -0.328 0.379 0.360 0.811 0.903 0.255 +4 -1.117 -1.897 0.076 0.534 1.215 0.607 1.965 1.361 0.738 0.469 1.230 -2.213 -0.480 -1.858 0.663 -0.743 3.020 -0.518 0.106 -0.402 -0.489 -1.247 -1.280 -2.179 0.545 -0.425 1.934 -1.244 +0 -0.275 -2.113 -0.217 -0.140 -0.487 0.936 0.837 0.571 -0.197 0.117 0.687 -0.631 -0.668 -0.292 0.767 -0.874 0.540 -0.119 1.091 -0.212 -1.856 0.367 0.131 -0.773 0.009 -0.662 0.031 -0.735 +1 0.605 1.620 -0.443 0.380 0.467 -0.586 -0.656 0.083 0.620 0.570 0.075 0.501 -0.776 0.217 1.507 -0.670 -1.002 -0.009 1.318 1.571 -1.677 -0.890 -1.633 0.880 1.627 -0.399 0.359 0.941 +4 -1.087 -0.016 -0.313 2.783 1.050 0.050 -0.112 0.951 0.999 -2.912 0.643 0.028 -1.311 0.880 2.246 0.557 1.100 1.804 -0.830 -1.858 0.657 -0.248 -1.374 -0.078 0.728 -0.149 -0.787 -0.224 +1 0.071 0.592 -0.420 -0.508 -1.725 -1.216 0.587 -0.439 0.668 -1.585 0.811 -0.040 -1.471 1.129 -1.407 1.077 -0.032 -0.894 1.848 0.405 1.215 0.433 -0.216 -0.871 -0.181 0.163 0.817 -0.393 +1 -1.103 -2.153 0.389 2.493 -0.006 0.838 0.082 -0.099 0.919 -0.290 0.267 0.322 -0.668 0.992 -0.175 -0.756 0.537 -0.898 0.028 -0.009 1.086 0.475 -0.025 0.818 1.390 0.558 0.010 -1.312 +0 -0.755 -0.906 -0.752 -0.204 0.112 -0.279 0.342 -0.438 0.693 1.968 1.251 -1.030 -0.576 1.259 -1.634 -0.295 0.045 -0.274 -0.223 0.324 0.057 -0.813 1.747 -0.011 0.368 -0.308 0.037 -0.757 +2 0.587 1.640 -0.361 0.598 0.292 -0.883 -1.031 0.241 -0.150 -0.954 0.209 0.898 -0.705 -0.647 -1.406 0.032 0.857 -0.843 -0.023 -1.170 0.846 -0.969 -0.936 0.964 0.845 1.736 2.625 -1.023 +3 0.619 2.057 0.021 -0.728 -0.183 1.375 -0.646 -0.799 -0.483 -0.953 0.123 1.625 0.323 -0.252 -0.292 -1.563 0.883 -0.078 -0.180 3.193 0.299 -0.752 -0.426 1.148 0.113 -1.438 0.919 -0.668 +2 0.961 -0.084 0.722 -0.007 -1.156 0.264 0.977 0.909 1.531 -0.895 0.401 -1.848 0.344 0.475 -1.303 -1.648 0.452 -0.816 -0.501 -0.282 -0.597 0.302 0.798 -1.327 -0.102 2.690 0.732 0.809 +1 0.994 -0.715 -1.709 0.429 1.450 -1.469 -0.444 0.042 -1.607 -0.316 -0.230 0.981 1.234 1.233 -0.501 -0.953 -1.679 -0.444 -1.042 -0.729 0.718 0.673 -0.262 1.191 -0.401 -0.202 -0.303 -0.600 +1 1.014 1.013 -0.546 -1.518 0.338 -0.340 0.837 -1.251 -0.312 0.707 0.937 1.403 1.417 -0.158 0.946 0.998 -1.234 0.602 -0.222 0.641 0.485 1.057 0.632 -0.290 0.199 -0.829 1.289 0.904 +2 0.194 -0.527 -0.339 0.226 0.451 1.429 1.131 0.943 0.432 -0.488 0.622 1.194 1.195 0.016 -0.344 0.792 -2.436 1.597 1.228 -0.280 -0.331 2.076 -1.035 -0.262 1.752 -1.073 0.162 -0.239 +3 0.467 0.114 -1.121 -0.997 0.634 2.054 0.448 -0.448 -2.071 0.894 0.705 0.730 0.324 0.714 -0.660 2.044 0.599 2.067 0.140 -1.426 -0.345 0.241 1.178 0.351 -0.959 -1.393 -0.928 1.131 +0 -0.163 -0.182 0.083 1.118 -0.895 1.451 0.421 0.029 1.213 -1.182 0.522 0.430 -0.935 0.461 0.283 -0.179 0.863 -0.572 0.270 -0.132 -0.615 0.503 -0.208 -1.473 -1.801 0.789 -1.221 0.279 +4 2.646 -0.820 1.014 0.036 0.215 0.898 -2.348 0.580 -1.019 0.682 0.375 -1.688 1.540 0.734 0.133 2.265 -0.790 -0.209 1.066 0.897 0.503 -0.862 0.357 2.946 1.175 0.726 3.389 1.136 +3 -0.993 -0.701 0.382 0.445 -1.225 0.731 0.718 -0.682 1.632 -0.856 1.227 -0.803 1.506 -2.776 1.245 1.717 0.680 -1.114 0.293 -1.220 0.077 0.085 0.497 -0.284 -1.570 0.389 -0.535 -1.536 +0 -0.584 1.028 0.590 -0.105 0.179 -1.341 0.658 -0.567 -1.327 1.388 -0.425 -0.977 -1.756 0.900 -0.176 1.564 0.378 -0.247 -0.350 0.275 1.678 -1.005 0.194 0.184 -0.465 -0.230 0.218 0.177 +1 1.365 0.951 -0.020 -0.199 -1.768 -0.084 -1.191 0.159 1.185 -0.980 0.239 0.998 0.220 -0.524 0.010 0.957 0.263 -0.003 1.156 0.515 -1.203 0.631 0.950 -1.237 -2.269 -1.042 -0.102 1.010 +2 -1.329 -0.556 0.516 -0.365 1.422 0.523 0.949 0.231 -1.033 -0.143 -1.019 1.697 0.596 -0.420 -0.059 -1.326 1.063 1.854 0.064 0.983 -0.420 0.462 -1.612 0.917 1.308 0.178 -0.144 -1.228 +0 0.326 -0.802 2.066 -1.049 0.582 0.276 -1.015 -0.943 -1.109 -0.756 0.008 0.336 0.247 0.252 1.003 0.174 0.750 0.591 0.592 0.692 0.204 -1.299 0.881 1.602 -1.088 0.883 0.029 0.392 +3 1.803 -0.770 0.258 1.365 0.141 0.147 -0.827 0.105 -0.982 1.500 -2.279 -1.217 0.223 -1.333 -1.923 2.601 0.596 -0.242 0.508 0.422 -0.693 -0.304 -0.270 -0.393 -0.445 0.799 -0.198 0.933 +1 -1.276 0.487 -0.499 -0.687 0.951 -0.370 0.323 -0.739 -0.284 -0.868 1.550 -0.734 0.289 0.813 -0.238 0.788 0.440 1.525 0.762 1.109 0.648 -2.055 -1.047 0.722 -1.638 0.392 0.823 0.889 +3 1.651 -1.680 -0.084 1.744 -0.112 -1.060 2.015 0.907 0.130 0.168 -0.961 0.388 1.895 -0.488 0.363 1.021 0.003 -0.873 -0.490 -0.312 -0.654 0.621 -2.466 0.539 -0.731 -0.756 -0.414 -1.809 +4 -3.221 0.396 0.034 -1.681 0.310 0.476 0.590 -2.652 1.366 -0.738 0.687 0.741 -0.877 0.660 -0.385 -2.071 1.168 -0.775 -0.854 -0.403 0.272 -0.057 0.548 -1.339 -0.742 -0.116 1.898 -0.114 +3 -0.677 -0.521 -1.360 0.731 -1.067 -1.330 1.495 1.314 0.334 -1.147 0.710 -0.661 -0.430 0.369 2.195 1.176 1.487 -0.340 0.243 1.173 0.074 -1.610 -1.730 -0.834 0.695 -0.909 0.832 1.584 +1 -2.947 1.605 0.491 0.330 -0.062 0.163 -0.597 0.617 -1.307 0.186 0.562 -0.199 0.371 -0.991 -0.966 0.237 0.679 -1.875 0.522 -0.543 -0.193 -1.340 -0.852 0.966 0.033 -0.383 -0.116 0.489 +0 0.957 0.998 0.459 0.498 0.039 1.037 0.124 -0.785 -0.273 0.241 0.857 0.329 -1.227 0.844 -0.367 0.524 -1.574 0.578 0.970 -0.511 0.403 0.199 -0.707 0.953 -0.212 -0.391 0.599 0.204 +2 -1.366 -0.507 -1.998 0.499 -0.299 0.013 1.051 1.154 0.963 0.963 -0.047 -0.099 1.467 -0.397 -1.497 -1.900 0.939 -0.125 1.106 -1.244 -0.287 0.282 1.867 -0.707 1.091 -0.179 -0.403 1.066 +1 -0.267 0.722 0.900 -2.731 0.009 0.412 -0.225 -0.022 0.158 -0.248 0.229 -1.500 -0.753 -0.237 -0.526 -0.818 0.621 -0.790 1.694 -0.690 -0.308 -0.894 -1.045 0.397 -1.001 0.107 0.819 -1.058 +3 -0.250 0.837 0.944 -0.284 1.097 0.529 0.016 0.902 0.765 -1.047 1.484 0.345 1.376 0.052 0.248 0.399 0.547 1.528 0.466 -0.028 1.010 0.430 2.956 0.291 1.238 0.882 -2.050 -0.737 +3 0.817 -1.618 -2.071 -0.362 -1.448 -1.410 0.573 0.876 0.789 1.109 1.241 -2.129 0.450 -0.530 -0.747 0.560 -1.730 -1.751 0.634 0.488 0.137 -0.880 0.744 0.680 0.085 -1.177 -0.425 0.838 +3 0.510 -0.436 0.479 1.082 0.183 0.980 0.351 1.352 0.812 1.064 -0.125 -0.531 0.356 -0.556 1.465 0.381 0.677 -0.071 0.569 -2.734 1.170 -2.596 -0.771 0.086 0.514 1.732 -0.059 0.675 +3 -0.980 -0.128 -0.593 0.389 -0.279 -0.230 -0.705 -0.461 -0.010 -2.021 -2.302 0.059 -2.769 -0.193 -0.377 0.542 -0.672 1.570 0.848 -1.512 -0.284 1.527 -0.698 -0.053 1.404 -0.541 1.123 -0.910 +1 0.925 -1.476 1.521 0.807 0.133 -0.601 -0.155 -0.467 1.045 -0.773 0.576 1.398 -2.261 1.393 1.442 -0.367 0.158 1.044 -0.734 0.414 0.999 -0.545 -0.151 0.001 0.040 1.521 0.204 0.164 +4 0.896 0.749 -0.374 1.523 0.284 -1.148 -1.596 -0.297 0.225 -0.288 1.994 0.404 -0.973 -2.270 0.235 0.989 1.864 -0.202 1.722 -1.359 -0.344 -1.014 0.792 -1.301 0.402 -1.242 1.758 -0.374 +2 1.089 0.695 0.811 0.595 -0.759 -0.740 0.452 1.543 0.386 0.978 -0.526 1.098 -0.419 0.240 -0.366 0.889 0.903 -1.477 1.325 2.376 -0.355 -0.127 1.239 0.067 -0.004 1.897 -1.354 -0.789 +4 0.232 -0.465 0.393 1.619 0.202 -1.470 -3.453 0.385 -0.986 -0.100 -0.809 -0.881 -0.765 -0.675 0.776 0.574 -1.208 -1.072 0.379 1.053 1.037 -1.003 0.989 -1.776 -0.203 0.487 -2.079 2.093 +3 -0.388 0.498 -0.821 -0.167 0.510 2.166 -0.074 0.839 -1.172 1.695 -0.808 -0.132 0.511 -0.780 1.081 -0.662 0.831 1.068 0.578 0.708 2.531 0.417 0.110 0.336 1.271 2.329 -0.166 -0.912 +3 -1.026 0.062 0.107 -1.079 -0.175 0.399 0.360 0.504 0.291 -0.507 -0.514 -1.199 1.785 0.013 -0.225 -0.929 2.936 -1.073 -0.429 0.416 0.600 0.958 1.808 0.506 -1.056 -0.257 0.308 2.068 +3 -0.238 0.466 0.521 -1.080 0.997 1.127 -1.130 -0.824 1.191 -0.147 -2.220 -1.400 -0.249 -1.163 -0.413 0.032 1.462 -0.290 1.073 1.429 1.434 0.285 -1.963 0.461 1.048 -0.081 1.745 0.860 +0 1.614 0.899 -0.929 1.165 -0.901 0.711 0.363 0.828 -0.418 1.339 0.328 -0.745 0.705 0.414 0.878 -0.575 -0.257 0.215 -0.592 -0.710 -1.278 0.009 -0.641 -0.661 -0.592 -1.416 -0.558 0.172 +2 -1.275 -2.747 1.211 -0.350 0.053 -0.217 0.008 0.244 -0.618 0.505 0.622 1.314 0.355 -0.480 -1.630 0.541 0.335 -0.702 -0.487 -0.778 0.744 -1.578 1.168 1.433 -0.257 0.013 1.432 -0.036 +3 -1.216 1.978 0.004 1.276 0.160 0.474 -0.823 -0.694 0.083 2.786 -0.872 0.064 1.357 0.251 -0.872 0.168 -0.495 0.278 -0.375 1.477 0.745 0.844 -0.554 -1.354 -2.225 -0.994 0.480 -0.139 +3 -0.060 -0.164 0.253 0.319 -1.448 -1.937 2.356 1.409 1.116 1.216 0.316 0.578 -0.932 0.133 0.367 1.491 -2.227 2.027 0.763 0.415 0.048 -0.265 -1.353 -0.209 0.384 -0.178 0.417 0.240 +4 -1.282 0.423 -0.677 2.248 -0.530 -0.194 0.521 -1.818 0.060 1.119 -0.081 -1.148 -0.434 -1.915 -0.291 1.110 -0.624 0.699 -1.482 1.879 1.131 -0.492 -1.323 -0.508 0.282 1.086 -2.110 1.205 +2 -0.120 -1.412 -0.147 -1.490 -0.318 -0.356 1.817 1.609 0.085 1.211 -0.398 -1.427 0.528 0.361 -0.938 -0.893 0.940 -1.684 0.558 -0.314 0.560 0.988 -0.874 -1.140 1.466 -0.057 0.328 0.911 +2 -2.225 -1.952 -0.470 -1.059 -0.792 0.362 0.801 0.374 1.422 1.827 -0.824 1.528 0.146 0.485 0.130 0.357 -0.400 -0.189 -0.306 -1.007 -0.431 0.673 1.405 0.961 -0.045 -1.372 0.669 -0.618 +1 -0.154 0.992 0.309 0.282 0.352 0.468 -0.114 0.632 -0.660 0.651 0.857 0.699 0.751 0.465 1.173 -2.272 0.854 1.975 -0.622 -0.734 1.286 -0.927 -0.667 0.170 -0.799 0.875 -0.791 0.361 +1 -0.959 0.010 -0.079 -0.399 1.220 -1.385 -1.859 1.257 -0.810 1.363 -0.472 -0.604 0.933 0.866 -0.293 1.097 -0.309 0.909 0.041 0.357 0.228 -1.367 -0.311 -0.585 1.419 -1.138 0.354 -1.011 +1 0.227 -0.427 -1.106 -0.642 0.020 -0.374 0.310 -1.249 2.385 0.222 -0.482 -0.322 -1.441 -1.538 -0.190 0.213 -1.289 -0.899 0.124 -1.060 0.899 0.302 -0.443 0.146 -0.845 -0.838 1.461 1.667 +0 -0.614 -0.879 1.910 -0.415 -1.475 -0.438 -0.266 0.913 -0.359 0.378 1.063 -0.583 0.291 -0.063 0.225 0.101 0.146 -0.286 0.025 -0.273 2.331 0.605 0.002 -0.338 0.979 -0.815 -1.283 -1.358 +2 -1.890 -0.417 -1.366 0.254 -0.704 1.694 1.378 -1.059 -0.607 0.298 0.344 0.777 -0.068 -2.112 -0.392 -0.359 0.163 0.905 0.990 0.500 -0.221 0.172 -0.680 1.159 -0.394 -0.040 -1.886 -0.212 +4 0.674 -1.224 2.614 -0.781 -2.686 -0.082 -1.148 -0.053 0.071 0.445 1.006 -1.007 -0.363 0.679 -0.657 -1.877 0.619 -0.565 -0.632 0.120 0.004 0.997 -1.249 2.262 -1.414 -0.388 -0.869 0.056 +0 -0.231 0.193 0.528 0.460 1.074 -0.379 0.513 -0.537 -0.249 -0.484 -1.513 0.289 2.340 -0.127 -1.160 0.411 1.451 0.266 0.240 1.167 -0.767 1.039 -0.188 -0.787 0.158 -0.627 -0.248 0.696 +0 -0.365 -1.671 2.036 0.469 -0.492 1.328 -0.973 0.407 -0.173 0.639 -0.064 -1.451 0.076 1.049 -0.029 0.775 -1.046 -0.026 -0.625 0.747 0.031 0.690 1.088 0.692 0.273 -0.998 -0.003 -0.533 +3 2.111 -0.342 -0.346 0.064 -0.731 0.515 0.167 -0.420 0.103 1.790 0.796 -1.419 0.311 -0.238 0.012 1.021 -0.271 0.141 0.043 0.228 -1.027 -2.537 -0.964 1.016 -1.515 0.235 2.403 0.146 +0 -0.627 -0.726 0.183 0.907 0.197 -2.191 0.760 -1.349 -0.715 0.198 -0.084 0.873 -0.611 -0.967 -0.312 0.977 0.630 0.087 1.326 1.145 0.788 -1.178 0.783 -0.202 -0.395 -0.683 -0.172 -0.626 +0 -0.216 -1.196 -0.082 -0.545 -0.419 0.604 0.308 1.149 1.749 0.208 -0.142 0.373 -0.876 -0.826 -0.595 -0.229 -0.642 1.055 0.588 -0.528 -2.355 -0.142 0.863 0.400 -0.155 -0.404 -0.197 -1.309 +3 -0.008 0.113 1.737 -0.747 -0.141 -0.435 -1.403 2.912 -0.936 0.369 1.700 -1.239 0.876 -1.539 -0.802 0.561 -0.273 1.455 0.129 0.521 -0.690 -1.039 -1.156 -0.935 -0.695 1.157 0.403 -1.278 +1 -0.335 1.392 0.686 1.756 -0.311 0.702 0.733 1.077 0.353 1.113 -0.631 -0.056 -1.488 -0.270 -0.851 0.331 0.986 0.565 0.780 0.096 -0.141 -1.173 -1.925 -1.508 -0.663 0.912 -1.229 -0.822 +3 0.270 -1.225 -0.114 2.100 1.232 0.615 0.025 -1.467 1.309 2.325 1.497 0.853 -0.521 -0.780 -0.701 -0.987 0.716 1.099 -0.670 -0.494 0.079 -1.539 0.025 0.499 0.668 0.485 -0.896 -0.707 +2 -0.458 -0.797 0.260 -0.235 0.116 -0.700 0.001 1.020 -1.043 0.337 -0.654 -0.675 2.417 1.536 -0.616 -0.564 -0.811 0.815 -1.354 0.388 2.375 0.185 1.034 0.907 -0.469 -1.383 1.025 1.013 +0 0.151 -1.184 -0.389 -0.772 0.124 -0.467 0.903 -0.848 2.183 -1.472 0.306 -1.611 1.048 0.047 0.206 -0.897 1.086 0.643 -0.545 -0.017 -0.890 -0.969 -0.615 -0.274 0.002 -0.044 -0.865 0.759 +4 0.875 1.194 1.164 -1.033 -0.206 0.478 -2.276 0.457 1.880 1.137 -2.410 0.397 0.216 0.272 0.107 -0.326 -2.325 0.311 0.627 -0.555 1.259 -1.640 -1.725 0.085 -0.682 0.972 -0.428 1.358 +0 -1.187 0.897 -0.921 0.756 0.265 0.176 1.116 -0.605 1.467 -0.384 0.432 -0.193 0.617 -1.003 0.107 0.018 0.436 0.405 -0.861 1.306 -0.058 0.730 -1.261 -0.415 1.079 1.635 0.424 -0.088 +1 0.768 -1.424 -0.345 0.534 1.298 -0.588 -0.855 -1.631 0.751 0.495 -0.482 -0.799 -0.240 -1.354 -0.853 -1.884 -1.020 0.177 1.708 -0.615 0.642 -0.838 -0.174 0.328 0.869 -0.126 0.348 -0.389 +4 -0.561 -0.206 0.557 -1.247 0.076 -0.390 -0.901 -0.384 -2.321 -0.758 -1.099 -0.280 -2.280 0.191 0.909 -0.117 0.499 -0.513 -1.555 0.002 0.680 -1.324 -0.465 -0.247 2.851 0.223 -1.906 -2.153 +2 0.610 0.822 -0.511 -0.509 0.326 -3.108 0.785 0.313 0.194 0.185 -1.703 -0.238 0.245 0.207 2.134 -0.481 0.429 -1.467 -1.050 1.440 -0.380 0.101 0.276 1.441 -0.062 -0.631 -0.731 0.178 +1 -0.147 -0.377 1.445 0.538 0.973 0.734 -0.494 -0.910 0.497 1.475 0.331 -1.187 0.565 -0.520 -1.752 1.613 -0.823 1.291 1.379 0.408 -0.852 1.179 -0.807 -0.046 -0.242 -0.384 0.243 -1.682 +0 -0.579 -0.776 1.047 0.145 -1.022 -0.195 -0.970 -1.181 -0.545 -0.205 -0.617 0.542 0.651 -0.708 -0.176 -0.471 -0.989 0.157 -1.880 -0.959 -1.163 -0.904 -1.086 -1.616 -0.292 -0.371 -0.010 0.236 +2 -1.347 0.371 -0.612 0.228 0.848 -0.950 0.092 -1.810 1.533 -1.359 -0.515 1.338 -0.235 -1.197 0.713 0.691 -0.633 -0.525 -0.435 -0.207 -1.893 -0.181 1.784 -0.896 -0.583 -1.184 0.612 1.203 +2 -0.032 -0.084 -0.220 0.949 -0.193 2.056 2.149 -0.378 -0.361 -0.049 -0.642 0.851 1.184 1.284 -0.208 -0.220 -1.991 -0.657 -0.932 -1.107 -0.487 -0.307 -0.025 1.360 1.166 -1.125 0.428 -0.800 +1 0.164 -0.239 1.347 -1.474 0.426 -0.244 1.529 -0.829 0.415 -0.838 0.188 -0.369 -0.385 -1.987 0.415 -1.082 -1.117 -1.555 -1.056 0.500 0.451 0.664 -1.027 0.215 -0.615 0.955 0.426 -0.657 +1 -0.181 1.610 -0.048 0.471 -1.418 -1.338 0.213 -0.906 -0.768 -1.708 -1.396 1.089 -0.540 -0.311 -0.100 0.178 1.698 -0.200 0.905 0.599 -1.493 0.309 -0.495 -0.186 -0.464 1.395 0.483 0.241 +3 -1.562 -0.342 1.142 1.734 -2.135 0.265 0.368 1.462 0.167 0.185 -0.355 -0.954 -0.567 0.392 -1.172 1.208 0.265 -1.558 0.267 -0.565 1.659 -1.580 -0.125 -0.841 0.698 0.948 -1.372 0.171 +2 -0.315 -1.322 -0.119 -1.440 0.915 0.729 -0.395 2.404 -0.100 1.075 1.315 -0.069 -1.154 -0.183 -0.638 0.537 0.065 -0.682 1.193 1.069 -0.419 -0.630 -1.148 -0.658 -0.771 -0.139 -1.736 -0.812 +1 0.761 1.282 0.774 0.863 -2.078 -0.307 0.965 -1.168 0.092 0.519 0.443 0.369 0.599 0.181 -1.463 0.507 0.285 -1.450 1.380 0.534 -1.380 -0.817 -0.564 -0.565 0.293 -0.976 1.128 1.046 +2 -0.646 -0.498 -1.223 -0.270 2.297 -1.719 -0.310 -0.062 -0.724 -1.978 0.751 -0.631 -0.623 0.596 -0.773 -0.186 0.290 1.388 0.030 0.496 -2.256 1.163 -0.600 1.177 0.010 -0.106 -0.516 -0.170 +2 -0.962 0.256 -1.012 1.103 -1.095 -0.225 0.597 -1.320 -0.814 2.235 0.941 0.335 0.645 -1.704 0.628 1.955 -0.494 1.824 -0.463 0.121 -1.339 0.436 0.149 -0.223 -0.861 0.225 -0.628 1.106 +0 0.224 -0.680 -0.502 1.734 -0.091 -0.660 1.337 -0.746 0.469 1.382 -0.183 -2.180 0.883 1.278 0.021 -0.218 -0.017 0.092 -0.345 1.759 0.201 0.019 0.780 -0.419 -0.244 0.501 -0.039 0.751 +3 -0.160 1.168 0.707 -0.117 -1.244 -0.053 0.899 -1.225 0.836 1.343 0.096 0.914 0.140 0.571 1.494 -1.554 -0.765 1.251 1.435 -1.408 1.562 -2.572 0.626 0.219 -0.733 -0.672 -0.635 -0.204 +3 0.609 -0.703 -1.600 -3.627 -0.127 0.321 1.338 -1.735 1.009 -0.487 -0.062 0.006 -0.010 -2.033 0.716 0.835 0.957 0.487 -0.207 -1.468 0.449 0.039 -0.002 0.441 -0.045 -0.444 -1.180 -0.006 +1 -0.764 0.345 -0.159 -0.607 -1.705 -0.853 -0.816 -1.225 -0.329 -1.147 -0.164 0.240 -0.826 0.425 -1.361 -1.112 -0.113 0.687 0.867 1.612 -0.377 -0.128 -1.033 -1.254 0.229 -0.776 1.232 -1.462 +0 0.455 0.051 -0.653 -2.447 -0.281 -0.200 -0.491 1.336 -0.264 1.446 0.504 0.552 0.854 0.364 -0.394 0.482 0.868 0.511 -1.482 0.810 0.742 0.845 -0.315 -0.742 -0.039 0.939 -1.447 0.034 +1 -1.399 1.372 0.019 -1.161 1.565 1.297 0.946 0.102 0.611 -1.767 -0.660 -0.597 -1.773 -0.145 0.279 -0.893 -0.222 -0.519 -1.263 0.562 0.950 0.812 -0.008 -0.964 -0.366 0.815 0.353 -1.145 +1 -0.516 0.394 0.257 -0.032 -0.661 -0.230 0.234 0.816 1.012 -0.032 0.596 0.184 -0.588 -1.294 1.287 0.377 -1.118 1.453 0.666 2.818 1.173 0.002 -0.855 -1.163 0.508 0.478 -0.067 0.471 +0 0.217 -0.303 -0.219 0.766 -1.040 1.945 -1.821 -0.598 0.370 0.584 -0.100 -0.057 0.411 -0.231 1.393 0.839 0.873 -0.966 -0.079 -0.300 -1.006 0.360 -0.538 0.655 -1.070 1.719 -0.896 0.219 +0 -1.476 0.191 -0.131 -0.343 0.354 0.190 0.457 0.139 0.418 0.093 -0.659 0.004 -0.714 -0.780 -0.336 -0.119 0.035 0.916 -0.498 0.197 0.118 -0.609 0.074 1.003 0.187 0.227 -0.705 1.969 +2 0.294 0.166 0.548 -1.020 -0.175 -1.002 0.667 0.541 -0.453 -1.615 -1.966 0.480 0.640 -0.421 -0.712 -1.885 -2.505 1.334 -0.361 0.467 0.517 -0.900 -0.368 1.399 -0.557 0.050 0.393 1.050 +3 0.447 -0.554 -0.731 -0.964 0.423 0.865 0.316 0.396 -1.222 -0.020 1.621 -1.451 0.013 0.357 -1.066 1.883 2.058 -0.382 -1.357 -0.600 -0.067 -0.058 2.705 -0.978 0.419 0.808 1.874 -0.082 +4 0.314 2.246 -0.753 1.100 -0.024 0.468 -0.651 -0.673 -1.251 -1.468 0.671 -0.536 2.545 -0.474 0.673 0.055 -1.825 -1.346 -1.511 1.469 1.299 1.928 -2.220 -1.369 0.605 -0.768 0.694 -0.445 +4 1.058 -1.314 -0.243 2.492 2.662 1.991 0.709 0.302 -0.765 -1.682 0.935 -0.422 -1.436 0.478 2.949 0.469 -0.440 0.665 -0.676 -0.438 0.459 -1.590 0.283 -0.157 -0.092 -0.565 1.540 -1.054 +2 0.361 0.085 -1.094 0.112 0.928 -0.353 0.120 0.348 0.553 0.566 -0.379 -2.233 -0.631 -0.468 1.355 -1.150 1.476 0.550 0.796 -0.711 -1.039 1.664 -0.966 -0.944 -1.347 -1.383 0.135 1.344 +1 -1.377 0.893 -1.283 0.534 -0.898 0.828 0.224 0.953 0.389 -0.157 0.112 -0.606 0.655 0.753 -1.153 -1.108 1.197 1.451 -1.229 -0.901 -1.087 -1.004 0.984 1.273 0.313 1.562 0.834 -0.416 +1 -0.210 -0.101 -0.604 1.037 -1.210 0.199 -1.710 0.239 0.170 1.908 -0.615 -0.169 -0.240 1.681 -0.576 0.225 -0.293 -0.152 0.011 1.098 1.454 -1.388 0.781 1.255 0.340 1.184 -1.249 0.508 +2 -0.751 1.132 1.122 0.601 0.486 -1.552 2.291 -0.282 0.794 0.833 -1.041 -0.628 0.398 -1.546 -0.089 -2.395 0.010 1.375 -0.002 -1.463 -0.553 0.615 0.167 0.452 -0.172 -0.766 0.125 0.068 +3 -1.624 0.334 -1.460 -0.491 0.959 -0.334 -1.061 1.250 0.896 1.454 -1.244 -1.786 1.511 -1.295 -0.008 -0.118 -0.661 -1.229 -0.365 0.927 -0.573 -1.231 0.089 -2.018 0.300 0.610 -0.460 0.809 +4 1.697 -0.344 -0.154 1.171 0.819 -0.017 -1.043 2.626 -0.273 0.071 -1.926 -0.560 0.889 -0.144 2.652 0.291 -0.592 1.712 2.454 1.348 0.135 -0.183 -1.640 0.355 0.396 -1.140 0.077 1.120 +2 0.655 -0.151 -0.363 0.312 -1.428 -1.535 -0.057 0.729 -1.254 0.034 -0.909 -0.971 -0.329 1.122 0.906 -2.042 -2.158 0.823 2.057 -0.438 -0.379 0.806 0.311 0.878 0.090 0.691 -0.146 0.162 +4 1.198 -0.153 0.349 -1.212 -0.764 -0.267 -0.026 0.987 1.238 2.859 2.301 1.145 -1.488 1.432 1.891 0.140 -1.695 0.206 0.894 0.246 -0.405 -0.075 1.292 0.475 1.473 1.089 -0.194 0.840 +0 -0.884 0.938 0.327 -0.059 0.196 0.695 0.447 0.289 0.415 0.058 -0.755 -0.535 -2.563 0.913 -0.921 -0.053 0.057 0.460 0.448 0.857 -0.332 -1.241 -0.003 -0.579 0.621 0.369 -1.087 0.430 +4 1.042 -0.707 -0.588 -0.386 0.685 -1.075 1.550 0.938 0.662 -0.294 0.829 -2.917 0.441 0.160 -1.886 0.406 0.591 -1.854 -1.187 2.064 -0.186 0.976 1.127 0.394 1.017 -2.113 -0.231 -3.081 +4 -1.182 -0.573 1.883 0.455 -0.209 2.621 0.681 0.336 1.106 1.234 -0.628 -1.073 -0.985 0.064 -0.078 -0.620 -0.933 1.240 -0.590 -1.130 -0.350 -0.894 -0.798 -0.013 0.565 2.989 1.767 -1.292 +0 -0.228 0.874 1.199 -0.454 -0.023 -0.235 0.762 0.469 -0.090 1.010 0.518 -0.771 0.008 -0.177 -0.988 -0.225 1.139 1.095 0.913 0.779 0.208 -0.601 0.564 -1.646 -0.672 1.105 -0.492 0.640 +4 -0.950 -0.417 0.394 0.924 -0.224 -0.482 -0.041 -2.046 -0.280 -1.981 -0.842 -1.422 0.528 -0.731 1.238 -1.847 -0.244 0.085 0.490 2.154 -1.967 -0.758 1.997 -0.921 0.442 -0.940 -1.394 -0.816 +2 -0.188 -0.019 1.950 -0.941 0.167 -0.412 -0.431 -1.224 -0.535 0.171 -0.849 -1.703 0.622 -1.166 1.892 1.110 0.890 0.153 -0.263 0.993 0.577 -0.236 -1.475 -0.530 1.576 1.729 0.103 -0.459 +3 0.340 1.850 0.068 0.010 0.428 -0.949 -1.726 -0.312 1.072 1.234 -1.879 0.666 -2.185 0.459 0.399 -1.724 1.000 1.744 -0.062 0.141 1.479 -1.126 -1.101 -0.839 0.037 0.034 -0.553 -0.300 +1 -1.359 -1.058 1.080 1.725 -1.107 -1.361 -0.757 -0.487 0.152 0.774 -0.184 1.730 -0.047 -0.687 -0.954 -0.433 0.189 -0.126 -0.392 -0.313 -0.789 0.228 1.994 -0.312 0.375 -0.484 1.702 -0.573 +1 -0.836 -0.709 1.021 -0.166 -1.090 -0.224 0.882 0.583 1.286 1.030 -1.255 0.535 1.019 -1.274 0.286 -0.873 -1.191 -0.574 -0.503 -0.336 0.212 -0.862 -0.368 1.521 -1.632 -0.305 1.043 0.679 +3 0.531 -0.663 0.038 -0.315 0.129 -2.381 -1.830 0.616 1.037 -0.910 -0.244 0.295 0.693 -2.028 -0.886 -0.333 0.703 2.498 -0.228 -1.407 0.402 1.321 0.108 -0.907 0.958 0.664 -1.026 0.381 +4 1.351 -0.856 1.025 2.029 0.322 0.867 -0.043 0.697 1.459 -1.456 -1.296 -2.475 1.311 -0.907 0.849 2.116 2.032 -0.700 0.239 0.373 0.866 -1.233 1.924 0.430 2.795 -0.199 0.623 0.379 +2 -1.079 -0.123 0.044 0.146 -0.986 -0.127 -0.868 0.221 0.616 1.954 -1.365 0.081 -1.751 -1.762 -0.331 -0.890 -0.430 -1.165 -0.787 1.685 -0.227 0.328 0.134 -1.045 0.899 1.400 0.900 -0.740 +1 -1.546 -0.405 0.103 0.468 -2.042 -0.479 -2.054 -0.508 -0.430 0.629 0.973 -1.022 0.294 0.655 -0.747 0.773 0.136 -0.273 0.142 -1.580 0.140 -0.846 0.029 -0.406 0.288 1.192 0.127 1.000 +2 0.171 -0.942 -0.051 0.528 1.731 -2.304 -0.389 -0.547 -0.771 -1.722 0.921 0.708 0.636 -0.227 -0.527 -0.965 0.415 -0.862 0.686 -1.067 1.534 -0.100 -0.437 -0.520 1.197 0.415 2.044 1.088 +3 -0.221 -0.263 0.327 0.280 -1.250 0.158 -1.858 0.548 -1.591 0.632 -0.736 0.540 0.939 -0.978 1.241 -1.048 0.130 -0.810 -1.699 0.257 0.958 -0.418 0.755 -1.061 0.572 -2.966 1.238 -0.570 +4 0.673 0.156 0.416 -0.067 -0.662 -0.714 -0.667 -0.513 3.076 0.549 -0.712 1.018 1.880 -0.518 -0.458 -1.798 -0.280 0.802 -0.336 -1.036 -1.488 0.311 -0.623 2.027 0.053 -0.778 0.881 2.041 +2 -1.092 -1.053 -1.092 0.978 -0.183 -0.747 -1.004 -1.344 -0.718 -1.437 -0.435 -0.708 -0.779 1.238 -0.244 -0.182 1.282 -1.359 -0.487 -1.549 0.070 0.607 -2.425 0.860 0.311 0.036 -1.242 0.772 +2 0.783 0.908 -0.673 0.224 -0.240 0.212 -0.507 1.587 -0.018 -0.139 0.444 1.109 0.045 -0.312 0.890 1.742 1.421 -0.984 0.773 -0.070 1.274 1.833 -2.374 0.014 -0.457 -0.711 -0.923 -0.447 +2 -2.938 -0.441 1.467 1.583 0.456 0.577 0.534 0.115 0.604 -0.035 0.786 -0.606 0.332 -1.204 -0.990 0.343 -0.750 0.345 -1.840 -1.382 0.112 -1.481 -0.373 0.052 0.334 0.048 -0.063 0.528 +0 0.639 0.532 0.409 0.519 -1.006 0.467 -0.572 -0.500 -0.196 -0.108 -0.238 1.272 0.558 0.666 0.376 -0.395 1.403 2.370 -0.987 1.467 1.026 0.645 0.063 -0.379 -0.495 0.085 0.963 -0.673 +4 -0.597 0.729 -0.346 -1.803 -0.038 -0.442 -0.699 1.390 -1.265 0.710 1.937 2.539 2.081 -0.402 0.144 -0.712 1.204 1.718 -0.829 1.292 0.949 1.208 -0.045 -1.245 -0.057 1.351 0.517 0.008 +2 0.202 -1.484 0.146 -0.303 -0.301 -0.631 -0.792 0.293 0.078 -0.214 0.370 -0.094 -0.242 -0.690 1.077 2.269 -2.400 -0.246 -0.175 0.162 2.779 0.055 -0.346 0.611 0.831 -0.607 -0.087 -0.044 +1 -0.903 0.553 1.188 -0.369 -0.197 1.378 -0.090 -0.341 -1.103 -0.584 -0.391 -0.788 1.577 -0.616 1.770 1.902 1.110 -0.326 -0.536 0.627 0.040 0.201 1.276 -1.247 -0.070 -0.051 0.132 0.909 +3 -0.926 -0.317 1.553 0.418 -1.838 -0.234 0.870 -0.493 0.560 1.003 -1.403 0.441 0.275 1.097 -0.320 0.389 1.470 1.034 -0.468 0.012 1.791 -2.287 -1.590 1.126 0.275 1.095 0.137 0.642 +3 -0.396 0.070 0.699 -1.154 -0.086 -0.327 -0.220 -0.819 -0.047 0.494 -1.392 1.728 0.942 -1.575 1.447 1.515 -0.503 0.206 0.090 -1.377 1.027 -1.881 0.289 1.568 -1.689 -0.293 1.140 0.402 +3 1.383 -1.710 -1.550 -0.316 -0.547 0.312 -1.926 0.326 1.278 -0.982 -0.378 -0.802 0.320 -1.075 -1.201 -0.151 0.337 0.150 -0.440 0.668 0.230 -1.169 -2.676 0.522 -0.257 -1.110 1.625 1.615 +1 -0.472 1.089 0.064 -1.078 -0.715 0.680 -0.730 0.216 0.046 -0.652 2.144 0.634 -2.025 0.186 -0.662 0.852 -0.793 -0.115 0.505 0.866 -1.200 -0.335 -0.475 -0.653 1.765 0.405 -1.261 0.918 +0 0.381 1.343 0.265 -0.649 0.884 -1.366 -1.081 0.336 0.516 -1.245 -0.383 -0.606 -0.487 0.944 0.707 -0.019 -0.546 -1.350 1.420 0.592 -0.707 0.720 -0.070 -0.287 0.435 -0.741 -0.344 0.121 +4 -0.269 -1.964 1.629 0.107 1.439 -2.232 1.458 -0.485 1.259 0.215 0.453 0.455 -0.425 -0.647 -0.361 -1.472 -0.481 -0.697 -1.496 0.459 0.008 0.246 0.697 -0.238 -0.939 0.304 -2.922 -0.108 +0 -1.196 2.375 -0.411 -1.199 0.813 -0.461 -0.480 0.058 -0.515 -0.548 -0.148 1.399 -0.193 -0.177 1.081 -1.972 0.630 0.676 0.112 -0.346 0.043 -0.296 0.261 -0.234 1.082 -0.369 -0.187 0.774 +3 -0.258 -0.919 -2.593 -1.220 1.747 0.963 -0.749 -0.535 -0.502 1.090 0.909 0.964 -1.013 1.786 -0.291 0.838 0.292 -1.710 -0.515 -0.040 -0.824 -1.481 -0.459 1.308 0.552 -0.555 0.220 -1.362 +0 0.100 0.823 0.316 0.159 0.617 0.755 0.598 -1.282 0.521 0.408 0.544 -0.764 -0.883 -1.324 0.132 -0.453 0.185 0.249 0.382 0.126 -0.265 -0.460 0.366 -0.160 -1.190 2.140 -0.022 -1.256 +0 0.112 0.506 -0.190 0.795 1.185 1.701 -1.330 -0.371 -0.113 -0.559 -0.138 -0.168 0.301 0.355 1.462 -0.151 0.500 -0.047 0.434 -0.335 1.227 -0.140 1.168 0.706 -0.799 0.940 0.391 -1.173 +1 -1.937 0.695 -0.312 0.346 -0.521 0.565 1.357 1.428 -0.648 -0.422 0.170 1.871 -1.153 -1.146 0.869 -0.666 0.659 -0.585 0.352 -0.510 -1.005 0.146 -1.213 0.456 -0.234 -1.361 0.300 0.279 +4 -1.303 -1.832 0.626 -1.239 1.457 1.418 0.886 -1.136 2.122 -2.352 -0.883 0.833 0.755 -0.725 -0.786 -0.215 -0.087 -0.469 1.658 -0.684 1.466 0.406 1.019 -1.495 1.028 0.993 -0.666 0.848 +3 0.928 0.177 0.301 -1.557 -0.582 -1.337 0.174 -1.841 1.188 2.480 -0.155 -0.603 -0.707 -1.869 0.658 -0.044 0.337 0.069 0.027 -1.477 0.503 1.699 -0.458 -0.788 1.289 -1.221 0.636 1.170 +2 -0.175 -1.122 -1.041 0.262 0.507 -0.361 -0.626 0.373 -1.449 0.372 -0.754 -0.546 0.913 0.335 1.839 -0.670 -1.096 0.924 0.868 -1.586 -0.520 -2.807 0.196 0.632 -0.452 1.333 0.594 0.590 +1 0.905 -0.316 0.142 -0.280 0.196 -1.688 0.082 0.284 -0.126 0.533 -1.174 0.470 0.478 0.427 0.721 -0.191 0.429 -0.021 -0.466 -0.759 -0.433 -1.115 0.827 -1.035 -2.518 1.785 1.307 1.093 +2 -0.568 1.196 -0.622 -0.236 1.613 2.137 -1.467 -2.126 -0.713 -0.978 0.477 -0.014 -0.129 0.877 -2.128 -0.618 -0.116 1.032 -0.059 0.411 -0.326 -0.043 -0.293 -0.230 0.076 1.446 0.414 -0.206 +2 0.566 -0.024 -1.867 -1.492 2.717 0.721 0.814 -0.296 -1.005 -1.856 1.216 -0.396 -0.561 0.076 -0.610 -1.197 -0.217 -1.431 0.403 -0.651 -0.701 1.011 0.440 0.360 0.039 0.224 0.365 1.093 +1 0.829 -1.066 -0.613 -1.009 2.695 0.635 -0.596 -0.676 0.805 0.905 0.798 -0.426 0.306 0.111 -0.936 -0.926 0.412 0.196 0.553 0.791 1.499 -0.776 0.630 0.749 -0.312 0.122 -0.287 2.109 +1 -0.482 0.307 0.313 0.489 2.556 0.326 -0.530 0.202 -0.270 0.096 -1.364 -0.528 -1.280 -0.722 -0.344 -1.847 0.724 -1.474 -0.640 -0.686 -1.224 1.166 0.745 0.520 0.272 0.270 0.024 1.339 +1 -0.720 1.056 1.187 -0.154 -0.709 -0.170 1.629 -1.853 0.042 -0.251 -0.768 0.455 -0.109 -0.923 1.745 -1.039 0.044 0.080 0.541 -0.309 -0.685 1.117 -0.728 -1.979 -0.177 1.196 0.335 -0.843 +0 -1.135 -0.806 0.281 -0.006 0.008 -0.204 0.419 -0.172 0.834 -0.183 0.389 0.976 0.374 -0.386 0.010 0.386 -0.650 -0.394 0.131 -0.337 3.178 -0.803 -0.847 -0.514 1.372 0.656 0.136 0.066 +3 -0.620 2.385 -1.129 -0.384 -0.834 -0.117 -0.815 -0.243 -0.719 1.055 0.082 -1.003 0.841 -0.958 -1.564 -0.003 1.907 -1.828 -1.331 0.753 -0.642 0.612 -0.837 0.142 1.744 -1.195 1.024 0.680 +3 0.136 1.885 -0.619 0.985 0.324 0.938 0.488 0.909 -0.048 1.149 -0.286 0.032 -0.377 0.265 -1.030 2.115 -0.054 0.159 0.899 0.708 -0.593 1.232 0.851 -2.275 0.667 -2.340 -0.974 -0.028 +3 3.292 1.526 -1.253 1.346 -0.357 -0.130 -0.593 -0.151 -0.603 -0.339 -0.419 0.469 0.503 0.136 1.482 1.067 1.318 -0.744 -1.157 0.279 0.416 -0.998 0.332 -1.720 -0.341 -1.302 0.416 0.791 +2 -1.078 1.291 -0.451 -0.777 0.194 -0.129 2.051 0.101 0.007 1.020 0.040 0.651 0.353 0.986 1.068 -0.870 -1.091 -1.157 0.386 1.463 -1.765 1.859 0.893 -1.191 -0.361 -0.921 1.233 0.745 +2 1.005 0.053 0.285 -0.651 1.409 -0.340 -0.307 0.395 0.493 2.362 1.102 0.241 -1.047 -1.064 0.051 1.825 1.363 0.501 -1.476 -1.031 -0.377 -0.068 -0.413 1.178 1.383 -0.400 -0.451 -0.380 +1 -1.566 -1.027 -0.440 -1.455 -0.380 -1.098 -0.039 -0.777 1.042 0.425 -1.059 0.751 -1.319 0.709 1.861 0.431 -1.117 -0.199 -0.376 -0.705 -0.198 -0.113 0.680 0.597 0.796 -1.386 -1.176 1.127 +0 0.955 0.982 -1.094 -0.153 -1.143 -0.310 -0.457 -0.042 0.826 -0.262 0.844 -1.466 0.448 -0.436 0.253 0.863 -1.442 1.469 -0.194 1.862 0.071 1.321 0.060 0.267 0.116 -0.220 -0.257 0.226 +2 0.950 -1.896 2.094 0.144 0.110 -1.394 -0.571 1.359 0.069 0.416 -0.461 1.971 -0.153 1.131 -0.151 0.348 0.352 0.925 0.739 -0.474 1.159 -0.990 -1.184 -0.291 0.393 -1.022 -0.078 -1.385 +4 -0.734 -2.321 -1.770 -1.004 0.265 2.096 0.269 2.192 0.833 0.158 0.634 -1.917 -0.477 -1.450 0.308 1.358 -0.091 0.651 -0.565 2.357 0.659 0.369 0.807 0.075 -2.068 0.745 0.335 -0.754 +0 -0.033 0.350 1.214 0.236 0.479 -0.368 1.571 0.640 0.078 -0.123 0.978 -0.606 -1.783 0.072 1.190 1.591 -0.107 0.044 0.520 -0.264 0.516 -0.085 1.011 0.723 0.810 -0.205 1.926 0.484 +2 -0.622 0.583 0.497 1.070 -1.200 -2.316 0.858 -0.828 -2.018 0.145 0.793 -0.122 -0.458 -0.159 -0.371 -1.348 -0.715 0.521 0.322 0.168 -0.995 -0.182 -2.177 0.178 1.429 -1.474 -0.586 0.329 +1 -0.248 -0.562 -0.716 0.737 1.231 2.260 -0.567 0.351 0.934 1.523 -0.910 -0.181 -0.012 -0.688 -0.526 -1.138 1.187 -0.248 -0.569 -1.016 0.333 1.724 -0.150 0.148 0.383 -0.005 -1.628 -0.210 +1 0.808 -1.537 0.378 -1.153 0.170 -0.662 0.122 -1.446 -0.950 -0.005 0.369 0.114 -1.648 0.975 0.698 -1.188 0.081 -0.842 -2.439 0.160 1.610 -0.523 0.784 0.646 0.768 -0.581 0.611 0.010 +1 -1.206 -0.603 0.577 0.843 -0.148 -0.669 0.216 -1.059 0.443 -1.113 -1.066 0.266 0.787 -0.519 1.398 0.958 -0.301 -0.542 0.148 -0.980 1.912 0.387 1.443 -0.854 1.585 -0.032 1.066 1.070 +4 1.283 -0.777 -0.465 1.783 1.022 1.758 1.275 0.693 -0.802 -1.473 0.798 -0.545 -2.347 0.437 0.232 -1.302 1.397 -0.871 2.228 1.314 1.011 -0.607 0.173 0.268 -0.290 0.016 1.733 -0.699 +0 1.377 0.268 -0.404 0.018 1.610 -0.027 0.087 0.357 0.538 0.273 0.599 0.484 0.322 1.756 -0.741 0.425 -0.250 -0.888 -0.248 -1.014 0.116 0.166 0.323 -0.168 0.026 -0.774 -0.470 -0.959 +1 -0.792 -1.088 0.476 -0.361 -1.548 -0.243 -0.233 0.330 -0.102 -1.026 -0.704 -0.643 0.264 -1.342 -1.533 2.060 0.267 0.104 -2.226 0.306 0.177 0.938 0.520 1.004 0.494 -0.935 -0.039 -0.337 +4 -0.700 1.785 2.284 -0.606 -2.187 0.352 0.954 -2.507 0.454 -1.204 0.978 -1.411 0.433 0.305 -0.155 -3.119 -0.644 -0.833 -1.502 -0.513 0.143 0.785 1.488 1.026 1.163 0.127 1.009 -2.246 +4 -0.560 0.477 -0.596 -0.126 -1.716 2.806 0.648 1.079 1.730 -2.496 0.265 -0.320 -0.172 -0.400 -0.035 -0.318 0.862 -0.442 -0.540 -1.821 0.617 -0.102 -1.122 1.177 -2.133 2.039 -0.666 0.997 +1 0.089 0.197 -0.618 -0.316 0.616 1.204 -0.139 -0.450 0.001 0.601 -1.444 -2.296 -0.551 -1.221 -0.508 -0.148 -0.453 1.452 0.327 0.300 0.622 -1.139 1.039 -0.076 0.670 -1.072 -1.554 0.818 +1 -0.439 0.233 0.266 -0.094 -0.672 0.246 0.736 -2.502 -0.717 -0.910 0.592 0.326 0.648 -0.781 -0.028 -0.058 -1.043 -0.535 -0.612 -1.104 -1.556 0.377 0.831 0.660 -0.457 2.449 -0.628 0.180 +4 -0.402 -0.363 -0.159 -0.526 0.663 -0.809 0.910 0.616 1.360 1.734 1.740 -1.186 0.344 -0.718 3.280 0.614 1.416 0.435 -2.635 -0.784 -0.686 -0.390 0.360 0.501 -2.627 -0.237 -0.486 1.381 +3 0.217 1.509 -0.555 0.510 1.250 0.603 1.658 0.526 -0.178 -1.118 -0.445 0.859 1.014 -2.015 -0.554 0.464 0.190 -0.768 1.513 0.895 2.193 -0.351 -1.566 0.142 2.013 0.189 0.602 0.483 +4 -1.701 -0.707 0.384 -0.811 1.161 1.085 0.668 1.031 -0.515 1.487 1.076 -0.129 -1.626 -2.367 0.990 -1.611 -0.921 0.477 -1.300 2.434 0.237 1.478 1.043 -2.538 0.744 -0.016 -0.062 -0.025 +2 0.144 -0.107 -1.056 0.294 -0.403 0.244 -0.721 0.032 2.653 -1.546 -2.899 0.132 -0.324 -0.203 -1.040 0.381 1.425 -0.720 0.969 0.277 0.415 0.171 -0.449 0.667 -0.559 0.979 1.138 -0.201 +2 0.049 0.041 -0.702 -0.663 -1.403 1.750 -1.244 -0.693 -0.718 0.895 -0.295 1.248 -0.673 0.279 -0.835 2.145 -1.188 0.310 0.634 0.414 -0.185 -0.130 0.044 -0.147 0.964 2.211 -0.557 -1.370 +4 1.125 -1.869 -1.190 -1.565 -1.092 -0.993 -0.278 0.500 0.881 -1.036 0.698 1.885 1.080 0.626 0.644 0.152 0.222 0.727 -1.533 -1.008 -0.624 -0.228 -1.091 1.490 0.986 2.741 -0.426 -0.526 +0 -0.331 1.311 -0.064 0.567 0.505 -0.177 -1.483 -1.357 1.160 0.187 0.972 -0.501 -0.347 -0.358 -1.670 -0.346 -0.314 -0.010 -0.251 1.362 -0.581 0.837 0.301 0.031 0.752 -0.338 -0.524 -0.361 +2 1.011 0.482 -1.187 -0.976 0.005 -0.643 -2.161 -0.745 -0.279 -0.400 0.721 -2.235 -0.772 0.796 -0.668 -1.211 -0.681 0.446 -0.253 -0.725 0.482 0.375 -1.263 0.095 -1.299 1.170 1.231 -0.288 +3 -0.968 1.828 -1.777 -0.035 0.153 -0.250 0.874 -0.275 0.953 0.549 -1.353 1.162 -0.314 -1.791 0.471 -0.287 1.629 1.096 -0.902 0.949 -0.696 -1.657 -0.615 -1.329 -0.014 1.931 0.079 0.461 +1 1.244 -0.065 0.354 0.764 0.080 -0.023 -0.134 0.566 0.552 -0.564 1.103 1.404 -0.551 0.613 1.955 -0.268 -0.002 -1.846 0.553 -1.385 -0.204 -0.199 -0.889 1.637 -0.390 0.905 -1.223 -0.600 +0 0.240 1.168 -0.677 -0.615 -0.372 -0.083 -1.775 -0.021 -0.034 -0.613 -0.769 0.272 0.589 0.144 0.143 -0.037 0.015 0.681 1.621 1.493 -1.084 -2.111 0.530 0.021 -0.060 0.381 -0.244 -1.620 +1 -1.579 0.204 -0.183 1.678 0.928 -0.630 0.373 0.168 0.454 -1.019 0.360 -0.228 0.282 -0.724 -0.585 1.174 -1.052 -0.978 -0.052 -0.772 -0.885 0.105 -1.505 -1.008 2.054 0.459 -0.747 0.418 +4 -0.842 -1.604 1.632 -0.289 2.710 0.773 1.829 0.993 0.159 -0.598 -0.977 1.120 0.265 0.617 0.815 0.356 -0.472 1.013 -0.198 0.091 0.717 -0.059 -1.818 1.041 1.255 -1.831 -1.043 2.024 +4 2.250 1.167 1.155 1.471 -2.203 0.104 -1.506 -1.084 -0.169 1.512 -0.311 -0.570 -0.118 -0.048 -1.541 1.215 -0.177 -0.831 0.891 -0.984 0.702 0.021 -1.505 0.574 -1.505 -0.153 -1.465 0.783 +2 -2.578 -1.181 -0.285 0.575 0.202 0.673 0.120 -0.903 0.540 -0.040 0.538 0.038 0.992 1.050 -0.585 -0.324 -1.302 2.340 -0.452 -0.438 -0.507 -0.903 1.107 0.118 -0.183 -1.579 0.385 -0.863 +1 0.604 -0.613 0.476 -0.956 0.317 2.211 -0.371 -1.334 0.897 0.286 0.458 1.272 -0.711 -0.681 0.545 1.040 0.237 -0.071 0.323 0.734 -1.448 -0.990 -0.089 0.690 2.172 -0.113 0.245 -0.665 +1 1.358 1.630 0.679 0.239 0.008 -0.014 1.863 -1.102 1.048 -1.103 1.179 0.037 -0.078 0.340 1.402 1.032 0.411 -0.529 -0.654 0.285 -0.779 -0.231 -1.542 0.213 0.002 -0.701 -0.941 -0.227 +2 0.767 0.702 -1.868 1.123 0.420 0.804 1.830 0.794 -1.183 0.653 -1.504 -0.063 -0.503 0.010 -1.510 -1.394 -0.449 -0.223 -0.426 1.450 0.424 0.396 -0.382 -1.392 -0.004 1.807 0.224 1.113 +3 -0.953 -0.465 -0.780 -0.190 -1.049 -1.483 -0.441 -0.270 1.116 0.066 -1.172 -0.732 0.713 -0.424 -1.093 0.082 -0.790 0.478 0.363 -1.147 1.785 -0.123 -2.664 0.381 -2.598 1.040 0.057 -0.146 +2 1.358 0.059 -1.950 -1.325 0.993 -0.301 1.855 -1.029 1.060 1.011 -1.567 -0.581 1.313 1.302 -0.108 -0.034 -1.136 1.304 0.615 -1.440 -0.052 1.034 -0.149 0.600 -0.383 0.045 0.436 0.286 +1 -0.252 -1.157 -2.444 1.902 0.307 -1.107 -1.261 -0.279 0.808 0.631 0.682 -0.887 -0.179 0.127 -0.057 0.546 1.574 0.210 -0.585 -1.453 0.176 -0.660 0.429 0.365 1.564 0.214 0.456 -0.655 +2 -0.076 0.576 -0.446 1.066 -0.426 -0.678 -1.807 0.647 -0.904 -1.832 0.280 1.340 0.860 -0.743 0.603 -0.816 0.747 -0.872 0.189 0.840 -0.163 -0.624 -2.676 0.055 0.678 1.184 1.336 -0.167 +0 0.945 0.071 -0.247 0.033 -1.049 -0.095 -0.923 -0.644 0.172 -0.277 1.265 -0.245 0.549 -0.599 0.561 -0.758 -0.547 1.344 0.250 -1.742 1.476 -0.495 -0.187 0.615 0.213 -0.713 -1.367 -1.568 +4 -0.776 -1.441 0.670 -1.326 0.907 0.468 -0.075 0.940 3.103 0.478 -1.120 1.489 0.186 2.111 -0.506 1.282 -0.000 2.657 -0.282 3.002 0.440 0.847 1.700 -0.788 0.969 -0.293 0.151 0.083 +4 -0.607 2.602 0.502 1.849 0.544 0.402 -0.626 0.978 0.025 0.080 -0.261 -0.101 0.025 0.123 -0.051 -1.557 0.542 -0.123 -3.317 -1.588 -0.473 1.279 0.608 -0.687 -0.191 1.105 -0.616 -1.086 +3 -0.759 2.015 -0.680 0.865 -1.491 -1.494 0.409 0.704 -0.886 1.094 0.395 0.215 0.364 0.172 -1.026 1.634 0.809 -0.123 1.068 1.564 0.963 0.461 -0.696 -0.662 -1.902 0.242 -0.059 -2.313 +0 -0.017 -0.279 -1.461 0.977 -0.431 1.067 0.464 0.243 0.197 -1.009 0.912 0.282 -0.353 0.853 0.162 -0.440 -0.385 -0.802 0.250 -1.253 -0.279 0.273 -0.306 0.306 0.662 -1.719 -0.151 -1.663 +4 2.020 0.854 -0.456 -1.453 -1.728 1.012 -0.372 -0.103 -0.064 -1.737 2.140 -1.862 -3.418 0.521 0.180 -0.839 -0.137 0.076 0.106 1.941 -0.006 -0.575 0.490 0.179 1.186 1.777 -0.976 0.541 +4 0.994 0.529 -0.414 0.533 -0.607 0.345 1.607 -1.993 -0.697 -1.138 2.337 0.571 -1.153 -0.025 -0.315 -0.820 -0.903 -2.189 -0.941 -1.501 1.699 -0.580 -0.806 0.505 -0.377 -1.799 -0.697 -0.232 +1 -0.537 0.385 1.712 0.101 -1.035 0.405 0.104 0.033 -1.339 0.396 -0.469 -0.478 0.900 0.581 -1.137 1.503 1.048 -0.843 -2.187 0.356 0.267 -0.931 1.823 -0.109 -0.035 0.340 1.226 0.289 +3 0.942 0.380 -1.539 -0.792 0.147 -0.616 -0.998 2.144 -1.000 1.207 0.413 0.608 1.138 -0.689 -0.300 0.473 -1.849 1.650 0.116 -0.942 1.094 0.092 -0.721 0.946 -2.326 0.420 1.310 -0.833 +2 0.757 0.612 -1.017 -0.244 -0.039 -0.134 0.334 1.431 1.082 -1.312 0.622 1.329 0.387 1.091 2.012 1.024 0.249 1.045 0.145 0.024 -0.351 1.563 -0.818 1.532 0.500 -1.399 0.368 -2.100 +1 -0.645 -0.374 -0.494 -0.708 -1.220 -0.074 1.133 0.085 -0.298 0.806 -0.492 0.193 -1.556 -0.004 -0.333 -1.862 -0.448 -2.349 -0.590 0.494 -0.907 -0.711 -1.520 0.289 -0.443 -0.041 1.149 -1.000 +3 0.383 0.547 0.497 0.200 -1.012 2.841 -0.192 0.408 -0.006 1.433 -0.570 -0.281 0.649 0.127 0.147 0.145 1.092 0.601 0.185 -0.320 1.646 0.689 -0.236 1.421 -2.104 -0.194 -1.182 -1.891 +2 0.668 0.971 -0.649 -0.678 -1.267 -0.084 -0.516 -0.904 -1.730 -0.173 -0.316 0.354 -1.054 -1.422 -0.143 1.208 2.031 -0.422 -0.507 -1.895 0.405 -0.681 -0.622 -0.251 1.006 0.225 0.633 1.681 +3 1.273 -0.804 -1.198 -0.966 0.468 -1.080 -2.225 -0.339 0.161 -0.450 -1.131 1.166 0.810 0.340 1.301 -0.901 -0.211 1.170 0.161 1.701 -1.750 -1.818 -0.496 0.583 0.711 -0.239 1.031 0.416 +4 0.341 0.661 0.005 -0.039 -0.522 -0.914 0.683 -0.861 -0.972 0.181 -0.955 -0.039 1.431 0.028 -1.400 -0.005 -1.660 -2.504 -1.162 1.451 -0.737 1.755 0.910 1.687 -2.333 0.848 -0.265 0.866 +3 1.344 1.680 -0.503 0.780 1.430 0.401 1.285 -0.036 0.394 -0.622 -0.165 0.434 -0.697 -0.224 0.761 2.053 -2.049 1.977 -0.260 0.965 1.161 1.564 -0.728 -0.225 -0.054 -0.357 1.319 -0.077 +3 0.901 2.344 1.365 -1.426 -0.312 0.360 -0.064 1.313 -0.020 -0.070 1.085 -1.409 0.489 -0.632 0.776 -0.366 0.513 -0.546 -1.980 0.361 -1.299 1.497 0.564 0.725 -0.283 -0.019 -2.366 0.393 +4 -1.023 0.498 1.905 -0.844 -1.809 -2.126 1.316 -1.663 1.880 -1.039 0.140 0.862 0.142 -0.150 -0.779 0.560 0.743 0.227 -0.104 -0.148 -2.282 0.448 0.976 -0.546 0.949 -0.840 0.998 -1.509 +3 1.183 -0.026 -0.505 0.172 0.618 -1.906 0.522 -0.940 -1.188 0.539 1.339 0.216 -1.355 -0.671 -0.418 0.196 -0.072 0.495 1.000 0.345 0.898 -0.270 3.296 0.553 0.684 1.758 0.088 -0.646 +3 -1.325 -0.868 -2.070 1.542 0.187 -0.990 -0.060 -2.081 -0.951 0.543 1.560 -0.489 1.379 0.245 0.357 -1.404 0.041 1.236 -0.352 0.493 -0.140 -0.754 0.707 -2.008 0.661 0.586 0.186 -1.297 +3 -1.255 0.664 -0.221 0.599 -2.736 -0.760 0.869 -0.060 2.142 0.311 0.883 0.693 -0.684 -0.279 0.675 0.441 -1.741 1.004 0.154 -1.912 -0.623 0.676 -0.622 -0.554 0.741 -0.691 0.674 -1.212 +4 -0.842 -0.424 0.734 -1.146 -0.466 0.597 0.875 -0.193 -0.411 -1.322 0.526 1.319 2.565 0.386 -0.051 -0.920 -1.879 0.177 2.350 1.271 1.117 0.246 0.360 -2.424 0.763 -0.579 -0.702 -0.323 +1 -0.462 0.289 0.304 0.091 -1.374 0.038 0.630 -1.639 -0.735 -0.915 -0.172 0.770 -0.214 1.450 0.319 0.075 0.039 -1.795 -0.107 0.410 1.861 -0.941 1.279 0.614 -0.591 -0.601 1.325 0.533 +1 0.182 0.698 0.191 -0.643 1.274 0.655 -0.446 -0.318 0.492 -1.170 0.701 -0.532 -0.756 -1.945 -0.103 -0.956 -0.362 -0.028 -1.337 -2.049 -1.273 -1.650 0.137 0.175 0.578 1.480 -0.063 0.412 +3 -1.466 -2.257 1.139 -0.121 0.578 0.889 -1.297 0.899 -2.092 0.340 0.809 0.151 -1.294 -0.003 1.627 0.551 -0.595 -0.021 0.965 1.571 -1.763 1.145 1.006 -0.932 0.502 -0.253 -0.884 -0.134 +3 -0.679 0.236 0.899 -0.819 -2.424 1.490 -1.135 -2.560 -1.037 0.773 -0.808 0.359 1.086 -1.370 -0.092 0.220 -0.259 -1.914 -0.619 -0.395 -1.493 0.110 0.944 -0.323 0.824 -0.226 -0.478 -1.444 +4 -0.465 -0.106 2.644 -1.503 0.254 0.468 1.086 0.098 0.308 -0.392 0.269 -0.343 0.622 -0.370 0.377 -0.029 1.126 -0.051 -1.773 1.262 -0.906 -0.654 -0.596 1.374 -2.136 3.138 1.056 0.223 +1 -1.908 -0.041 0.665 0.685 -0.710 -1.023 -1.092 0.466 1.007 0.142 -1.070 0.180 -0.952 -0.737 -0.369 -0.799 -1.130 1.549 -1.888 -0.592 -0.216 1.228 0.295 -0.282 -0.741 1.147 -0.807 0.211 +3 0.646 -1.263 -1.939 0.260 -1.597 0.085 -0.198 -1.501 1.892 -0.676 0.978 1.292 -0.760 2.078 -0.858 -0.112 0.295 -1.412 -0.178 0.646 0.340 1.318 -1.187 -0.244 -0.521 -0.910 -0.302 0.219 +2 -0.140 -0.383 -0.580 -0.488 2.274 1.040 -0.590 0.193 -0.000 -0.137 1.013 -0.001 -0.658 1.613 -0.329 -0.123 0.118 0.200 -0.660 -0.164 1.113 0.862 -1.947 -1.595 -0.958 -1.841 -1.296 0.324 +1 -0.183 0.412 -1.320 0.533 0.165 -0.073 0.139 -0.211 0.257 0.976 1.400 -0.528 0.303 -0.948 2.422 0.813 -0.910 1.490 -1.616 -0.857 -0.605 -0.228 0.605 -0.889 -0.459 -0.155 0.653 -0.345 +1 -0.881 0.281 -0.060 1.947 0.917 0.285 -0.267 1.676 -0.030 0.332 0.042 -0.404 0.037 -1.962 -1.548 0.350 1.568 0.348 -0.720 -1.443 0.785 -0.567 0.643 0.390 0.478 0.034 0.384 -0.119 +2 0.302 -1.269 -0.545 0.743 -1.823 -1.411 -0.373 0.927 -0.217 0.461 0.005 -0.141 -0.654 0.840 -1.254 -0.234 1.146 -0.764 -1.556 -2.117 0.666 0.734 -0.344 -1.271 -0.146 1.178 -1.269 1.058 +0 -0.854 -0.247 0.042 1.809 1.435 -0.388 2.009 0.951 0.471 -0.281 -0.577 0.543 0.384 -0.391 1.114 0.163 0.418 1.073 0.827 -1.620 0.762 0.028 -0.447 0.052 -0.306 0.127 -0.021 0.688 +3 -0.797 1.321 0.150 0.562 -0.249 0.201 -0.891 -1.178 0.015 0.570 -1.946 -0.499 1.842 0.479 1.538 -0.634 -0.458 0.967 0.624 -0.094 -1.263 1.157 2.510 -1.572 1.404 -0.468 -0.333 0.270 +3 1.495 -1.098 -0.149 0.563 1.287 -1.404 1.779 -2.162 -0.032 0.188 -1.384 0.585 0.039 -0.232 -0.422 0.291 0.027 0.678 -1.031 -0.565 1.605 -2.200 0.305 -1.446 -0.268 -0.070 1.123 -1.339 +4 1.344 0.509 0.580 -2.222 -0.020 -0.557 -0.815 -1.027 -1.300 1.233 -0.661 0.623 -1.452 -2.914 0.187 -2.064 -0.079 -1.350 -0.752 1.648 0.620 -0.837 0.590 0.091 0.195 -0.934 -1.251 0.168 +1 -0.545 1.086 0.618 2.162 -1.404 -0.619 0.970 0.921 -1.168 1.278 0.990 -0.514 0.332 0.983 1.584 -0.978 0.469 0.131 0.956 0.577 -0.150 0.127 -0.749 -0.054 0.984 1.016 -0.077 0.040 +4 0.905 -0.469 1.216 -0.747 0.600 -1.305 1.031 0.876 0.468 -0.393 -1.402 -0.391 1.110 0.130 -1.553 -1.125 1.253 0.488 -1.234 -1.766 -0.625 -1.851 -0.156 2.452 -1.597 -0.186 1.312 0.749 +0 0.504 0.342 -0.155 -1.360 0.099 0.127 -0.546 0.031 -0.235 -1.118 0.466 -1.358 0.076 0.157 -0.389 0.081 0.313 1.157 0.742 -0.946 0.865 1.375 -0.098 2.280 -0.466 -0.036 0.146 0.592 +3 -0.823 0.300 0.261 0.122 -2.479 -0.663 2.050 0.304 0.907 0.183 1.275 0.706 0.536 -2.791 0.915 -0.062 0.768 1.305 0.804 -0.341 1.014 0.935 -0.128 1.013 -0.453 1.491 -0.189 0.589 +4 0.172 0.014 -1.869 -1.116 0.519 -1.089 0.367 -2.824 0.562 -1.719 0.317 0.705 0.880 -0.057 -0.009 -2.467 -1.537 -0.993 -0.513 -0.837 0.377 0.553 0.119 1.820 0.948 -0.580 -1.508 -0.114 +3 0.562 -0.272 2.671 1.444 -0.966 -0.267 -0.140 -1.501 -0.921 0.658 0.072 -1.216 -0.179 -1.048 -0.549 2.857 0.246 0.787 0.995 0.210 0.177 0.645 0.085 0.249 -0.100 -0.485 -1.673 -0.933 +0 -0.199 -1.075 -0.906 0.300 -0.262 -0.103 -0.619 -0.951 -0.244 1.070 0.091 -0.235 0.424 0.326 -0.625 -0.683 -0.706 -1.377 0.759 -0.297 1.969 -0.521 0.916 -0.302 -0.001 -0.315 -0.387 1.198 +3 -1.232 -0.875 0.200 -0.530 1.025 2.320 0.212 1.754 -1.249 0.701 -1.353 -0.431 0.969 -0.826 1.521 0.560 -1.341 1.567 -0.468 -0.256 -0.178 -2.059 -0.048 0.312 -1.542 -0.145 0.768 0.334 +4 0.121 0.231 -0.999 -1.352 -0.377 -0.087 1.063 0.922 0.185 1.191 -0.943 1.077 -0.790 1.240 -0.013 -1.998 0.730 0.810 -0.457 -0.371 -0.160 -1.925 -2.365 1.302 -0.824 1.855 1.370 1.567 +0 -1.792 0.337 0.495 1.664 0.590 -0.035 0.474 -0.373 -0.853 0.648 0.314 -1.277 -0.038 -0.832 0.920 -0.427 1.708 1.179 -1.091 -0.667 0.672 0.056 -0.280 0.055 -0.125 1.007 -0.285 0.469 +2 0.953 1.388 -0.604 -1.328 1.180 -0.095 -0.218 -0.877 0.277 -0.643 -1.625 -0.719 -0.691 -1.672 -0.228 -1.103 -0.269 0.456 0.599 -1.548 0.950 1.699 0.502 1.614 0.423 -0.048 -0.502 0.709 +4 -1.227 -2.525 0.394 -0.406 -0.384 0.731 -1.160 1.717 1.154 -0.470 1.180 -0.070 -1.785 1.390 1.625 1.386 0.314 1.101 0.025 -0.583 -1.270 -0.139 1.091 -0.265 -0.983 -0.602 -1.507 1.093 +4 1.170 0.103 -0.105 2.288 0.360 -0.843 -0.921 2.279 -0.741 -0.701 0.027 0.732 -1.241 -0.279 -1.341 -0.153 0.822 -0.251 1.969 -0.847 0.218 1.307 1.009 2.643 0.324 -0.895 0.084 -0.838 +0 0.038 -0.670 -0.949 0.031 -1.000 0.301 -0.716 1.398 0.875 -1.163 -1.225 -0.495 1.822 -0.168 -0.561 0.014 -0.264 0.159 1.062 -1.395 -1.079 1.737 0.561 0.374 0.105 0.050 -0.159 -0.413 +3 0.799 1.986 0.524 -0.975 -1.388 -0.920 -0.454 -0.677 0.054 -0.181 0.075 2.442 0.876 0.270 1.062 -0.807 -0.850 0.916 -0.000 0.123 -1.143 -0.474 -0.410 -0.481 2.145 2.121 1.138 0.097 +1 -1.537 2.184 -0.966 -0.162 0.258 -0.289 -0.290 0.422 1.526 0.768 1.479 -1.047 0.317 0.155 0.351 1.376 -0.221 -0.406 0.534 -1.778 -0.237 -0.134 -0.920 1.057 -1.062 -0.136 0.293 1.353 +2 -0.577 -0.037 1.073 0.140 -0.292 0.673 1.476 0.314 0.191 -1.660 -1.362 -1.490 -0.191 -0.834 -0.496 -0.846 -0.758 -1.854 0.220 0.700 0.070 0.365 0.465 0.425 0.847 1.191 1.578 -1.926 +4 0.393 -0.423 -0.658 0.899 1.954 -0.365 -0.774 -0.139 0.108 -1.794 -1.014 -1.506 -1.152 1.452 -2.287 0.373 -0.370 0.243 -1.169 -0.460 1.224 -0.454 1.410 -2.725 0.539 0.042 -1.438 0.916 +1 0.265 0.124 -0.075 -0.639 0.137 0.423 1.962 0.535 0.587 -0.749 -0.955 -0.981 -0.296 0.060 0.216 -0.818 -2.241 -0.420 0.937 -0.376 -0.074 -1.594 0.124 0.409 -0.570 0.964 1.199 2.116 +3 -0.521 -0.549 0.284 1.073 -0.696 -1.561 0.337 -1.189 -0.467 -0.888 0.066 0.458 0.598 -0.214 1.400 -0.809 -1.219 -1.058 2.610 -1.218 1.216 -0.257 0.786 -2.227 -0.405 0.657 0.548 1.496 +1 2.106 0.338 -1.395 0.513 -0.603 0.268 -0.627 0.930 0.110 0.443 0.339 0.820 -0.947 1.038 -1.414 -0.683 1.365 -0.867 0.372 -1.280 -0.338 -0.810 0.734 -1.177 -1.618 0.700 -0.662 0.104 +0 -1.084 1.394 0.758 -0.765 -0.970 -0.191 -1.454 1.134 -0.543 0.670 -0.126 -0.491 0.319 0.140 -0.291 -0.164 0.791 -0.848 1.889 0.163 0.006 0.385 -0.236 -0.933 0.438 0.004 0.688 0.041 +2 1.885 -1.147 0.488 -0.996 0.454 -1.064 0.369 1.892 0.370 -0.250 0.329 -1.827 0.611 -0.431 1.078 0.118 -1.467 -0.261 -1.628 0.685 -0.773 0.775 -1.239 0.317 0.193 0.942 -0.843 0.511 +2 0.007 1.368 0.035 -1.284 -2.111 0.674 -0.657 -0.524 1.077 -0.135 -0.299 0.162 1.482 0.254 -2.159 -0.818 -0.285 0.863 -1.243 0.149 -0.082 1.017 -0.997 0.167 1.533 0.899 0.599 -0.311 +1 0.328 0.007 0.484 0.217 -0.675 -0.585 0.649 -0.637 0.840 -0.137 1.024 2.004 1.787 -0.913 0.480 -1.634 0.705 -0.483 -1.117 -0.035 -0.797 -2.096 -0.984 1.293 -0.096 -0.687 -0.165 -0.406 +1 0.525 -0.749 0.330 0.450 0.169 -1.029 -1.439 -1.099 -0.097 1.438 -0.299 1.104 -1.936 0.478 1.632 -0.889 -0.172 0.143 0.719 -1.125 -1.228 0.687 0.528 -0.963 0.627 -0.130 -0.897 -0.825 +1 -0.168 0.273 1.087 0.892 -0.440 -0.130 0.905 0.939 -0.088 -0.054 0.268 1.367 1.068 0.693 -1.079 -0.275 -0.222 1.418 -0.239 1.253 -0.571 -0.491 0.481 1.568 -0.212 -2.563 -1.564 0.298 +3 -0.018 -0.411 -1.738 0.323 0.888 2.847 1.307 -1.299 -0.012 0.592 -0.688 0.780 0.703 1.237 0.802 0.285 -0.638 -1.293 0.219 -0.404 -0.307 1.122 1.369 -1.235 -0.210 -0.433 0.315 -2.266 +3 -2.323 -1.128 -0.347 -0.546 0.071 -0.789 -0.733 1.646 0.226 -0.427 1.539 -0.740 1.992 -0.208 -0.714 0.395 0.720 0.308 -0.475 0.669 2.155 -1.019 -0.498 -1.711 0.063 0.735 1.486 -1.146 +3 -0.270 -0.027 1.953 0.687 1.536 0.598 -0.711 0.117 0.236 -0.939 -0.384 -0.206 1.606 -2.081 1.206 -0.937 -1.372 1.180 0.732 1.255 0.801 2.060 0.559 -0.156 0.343 1.243 -0.779 -0.652 +0 -0.012 -1.121 -0.057 -0.051 0.161 0.662 -1.039 1.688 0.245 0.727 -0.628 1.286 -0.257 0.278 -0.933 -0.997 0.688 -0.444 -0.854 -0.643 0.699 -0.629 -0.118 0.015 -1.917 -1.471 0.336 -0.737 +4 0.546 -1.453 -0.016 0.984 -0.823 0.716 -1.647 0.088 -0.416 -0.214 -1.278 -0.768 0.514 -0.346 0.078 0.911 -0.715 2.978 -2.082 -0.438 -0.359 -1.949 -0.223 2.002 0.896 -1.262 0.839 0.449 +0 -0.120 -0.645 -0.245 1.236 0.842 0.783 1.680 -0.072 0.104 0.572 -1.382 1.126 -0.705 0.282 0.725 -0.551 -0.893 -0.032 0.632 0.682 0.198 -1.036 -1.110 0.813 0.091 0.899 -0.214 -0.048 +0 -0.220 -0.137 -1.201 -1.002 -0.186 -0.982 0.491 0.467 0.256 -0.503 -1.082 0.390 -0.219 -0.042 0.634 0.173 -0.354 2.196 -0.714 -1.315 -0.053 -0.274 0.306 -1.074 -1.691 1.153 0.642 0.572 +0 -1.278 -0.895 -1.337 1.337 1.549 0.320 1.221 0.134 -0.095 -0.333 -0.310 -1.548 0.264 -0.040 0.745 0.094 -0.163 -0.871 0.072 0.738 -1.143 0.087 0.233 -0.718 -0.588 0.231 -1.133 0.350 +0 -1.187 0.046 0.111 -0.908 0.375 0.541 -1.462 -0.633 0.250 0.354 -0.182 0.914 0.071 -0.111 0.654 1.956 0.201 0.056 -0.656 -0.248 -1.521 -0.028 -0.535 -0.312 0.648 0.231 1.197 1.284 +2 2.102 0.594 0.698 -0.364 -0.800 -1.790 0.144 0.567 2.210 0.412 0.221 0.617 1.083 1.003 -1.377 -1.707 0.777 -0.204 0.178 -0.349 0.095 -1.430 0.516 -0.918 -1.433 -0.405 -0.505 0.203 +1 -0.159 1.438 0.358 0.335 -0.091 0.258 0.331 -1.746 0.646 -0.019 1.211 -0.907 1.680 -0.840 1.169 -0.464 0.634 1.331 0.541 -0.251 -0.502 -0.295 -1.166 0.215 1.277 1.114 1.281 0.773 +2 0.008 -0.772 -1.388 1.139 -0.385 0.167 0.285 0.322 -1.174 -1.676 -0.589 0.396 -0.728 -0.355 2.127 -1.845 -0.755 1.897 -0.045 -0.706 1.169 0.055 -0.348 0.192 -0.658 -1.017 -0.114 -1.011 +3 0.639 -1.510 1.161 -0.069 -0.537 -0.626 -2.039 -1.003 -0.949 -1.019 0.974 0.340 1.361 -1.624 -0.898 0.298 -0.673 -0.643 0.320 -0.763 -2.203 -0.679 1.223 -0.035 -0.253 0.554 -0.450 -2.487 +2 0.289 -1.703 0.554 0.191 -2.033 -0.437 -1.585 0.798 -1.353 -1.682 -1.062 -0.226 -0.201 -0.905 -0.088 -0.810 0.115 0.619 -0.006 -0.293 -0.084 -0.986 0.766 -0.138 1.298 1.844 -1.567 0.541 +2 -1.398 1.203 0.180 0.838 -0.102 -0.842 0.751 0.654 0.972 -0.013 -0.600 -1.559 0.819 0.424 -0.966 -0.539 -0.467 -1.483 -0.818 -1.134 -0.412 -0.739 -1.627 2.023 1.253 -0.779 0.272 0.417 +2 -0.421 0.200 1.020 -0.950 0.711 -0.050 -0.928 0.383 -0.155 0.561 0.411 -1.348 -1.589 -3.106 1.934 0.585 0.401 -1.384 -0.372 -1.310 0.043 -0.152 -0.532 0.453 0.092 -0.146 0.031 -0.134 +0 -0.344 0.209 0.585 -0.398 -0.726 -1.279 1.252 1.233 0.314 0.054 -2.235 0.411 -1.434 -0.134 0.592 -0.791 -0.011 -1.117 1.000 0.649 0.251 -0.140 0.942 0.795 0.224 -1.146 -0.784 -0.873 +3 -0.236 -0.047 0.148 1.275 -0.151 1.217 -0.314 0.080 -0.235 -1.750 -0.804 1.567 -0.864 0.794 -2.170 -0.405 0.427 -0.059 0.370 -2.495 -1.030 -0.631 -1.019 -0.128 -1.818 -0.344 1.937 -0.370 +1 1.779 -0.857 0.277 -1.572 -1.212 0.808 0.049 -1.054 -0.200 -0.923 -0.580 0.611 0.380 -1.204 0.009 1.774 1.169 0.491 0.113 0.035 0.266 1.356 0.132 -0.329 0.632 1.929 -0.094 -0.901 +0 -0.406 0.139 -0.150 -1.304 0.547 -0.737 1.392 -0.340 0.122 0.467 0.409 0.523 0.586 -0.257 -0.465 0.616 0.018 -0.805 -0.037 -1.436 -0.242 -0.262 -0.097 -0.040 0.642 1.243 -0.560 -0.656 +0 -0.384 -1.236 -0.469 -0.359 0.526 -0.374 0.440 -0.790 -0.295 -0.785 -0.847 1.275 -0.715 -0.348 1.045 0.750 1.641 0.483 -0.600 2.057 0.582 -0.155 0.057 0.179 -0.237 -1.376 1.627 -0.022 +3 -2.010 0.842 0.765 0.623 -1.427 1.829 -0.755 0.239 0.252 1.593 0.125 0.416 -0.077 -1.197 -2.319 0.033 -1.412 0.350 0.149 0.124 -1.053 1.037 0.475 -1.182 2.069 -0.211 -1.334 -0.758 +1 1.193 1.302 0.270 -0.877 -0.175 0.677 1.099 1.066 -0.176 0.225 0.520 1.461 -0.755 0.460 -0.514 1.168 -0.033 0.238 -0.838 -0.428 1.985 0.615 0.187 1.371 1.509 -0.040 -0.764 -0.152 +0 0.560 -0.040 -0.427 0.220 2.078 -0.203 -1.626 -0.459 -0.678 0.330 -0.932 0.507 -0.913 -0.040 0.499 0.676 -0.922 -0.584 -0.700 0.694 -1.096 -0.620 -0.259 -1.825 0.568 0.691 -0.006 -1.242 +0 -0.193 -1.014 0.021 0.717 -1.456 -0.269 -0.802 2.183 0.092 1.552 0.244 0.333 -0.061 -0.223 -0.074 -0.657 -0.011 -1.624 0.173 -1.205 0.094 0.321 1.417 0.317 -0.266 -0.629 0.716 -0.099 +2 0.793 -0.036 -2.382 -0.558 0.354 0.061 1.040 -0.002 1.842 -1.060 0.518 -1.029 0.914 -0.753 -1.757 -1.486 -0.472 1.559 0.741 0.447 -0.065 -1.126 -0.933 -0.552 -0.642 -0.657 -0.951 0.006 +0 -0.454 -2.262 0.681 0.045 -0.768 -0.968 0.106 -0.448 1.873 0.883 0.212 1.008 0.160 -0.552 -1.051 1.065 -0.676 1.103 0.694 0.264 0.463 -0.417 -0.028 -0.271 0.004 -1.001 0.465 1.159 +2 0.438 -0.449 -1.256 -0.392 0.222 -0.023 0.950 -0.878 0.241 -0.529 -0.220 1.348 0.672 0.558 1.760 -1.277 0.209 -0.866 -2.262 -0.196 0.706 -1.584 0.122 0.671 -0.360 -1.478 1.482 -1.858 +3 -0.693 -2.290 -0.560 2.116 1.325 -0.480 0.301 0.614 0.131 -1.270 0.105 0.020 -1.932 0.157 0.852 0.868 0.582 -0.402 -2.396 -1.079 -0.456 0.838 -0.424 -0.512 0.619 -0.511 -0.292 -1.599 +2 1.270 -1.035 0.807 1.586 -0.739 -0.757 1.957 -0.448 0.267 -1.687 0.453 -0.374 0.448 -0.124 0.957 0.868 0.132 -0.690 -1.952 -0.389 0.708 -0.166 0.173 -0.100 -2.103 -0.072 0.035 -0.386 +3 -0.974 1.132 0.272 -1.185 -0.701 -0.872 1.406 0.711 -0.126 1.707 1.452 -0.231 -0.411 -0.362 -0.953 1.734 -1.006 0.868 0.691 -0.305 1.810 -1.559 -0.080 -0.489 0.961 0.354 -1.727 1.239 +2 -0.439 -1.500 -0.395 -1.371 -1.109 0.242 1.919 -1.362 -1.215 -0.533 -0.147 -1.542 -0.699 -0.791 0.318 0.426 -0.153 -0.378 -0.710 0.052 -0.976 1.547 -1.498 -0.656 0.868 -0.399 0.672 1.717 +2 0.047 -0.803 0.144 0.383 1.505 -1.575 2.083 -0.570 2.153 -0.423 -0.223 1.030 -1.665 -1.026 -1.506 1.184 0.500 1.222 0.105 -1.249 -0.054 -0.261 -0.974 0.183 0.233 0.255 0.612 -0.242 +0 -0.643 -0.081 -1.101 2.013 0.656 -0.013 1.416 -0.205 -0.033 -0.704 -0.941 0.211 1.064 -0.878 -0.239 -0.204 0.772 -0.431 0.753 0.715 -0.662 -0.293 0.080 -0.437 -0.895 1.482 -1.783 0.233 +2 -0.526 1.486 -0.624 1.823 -0.189 1.031 -0.330 -1.517 0.720 -0.250 1.601 0.627 -0.411 0.864 -1.647 0.155 0.958 -0.447 -0.508 -1.854 1.439 0.018 0.359 0.553 0.500 0.689 1.105 0.518 +2 -0.676 0.713 0.152 -0.686 -0.352 -0.071 -1.565 0.766 -0.479 -2.784 -0.024 -0.742 1.689 -0.585 -1.098 0.508 -0.660 -0.779 0.510 -1.040 -1.457 1.726 0.579 -0.264 -0.003 0.506 0.973 -0.756 +1 0.238 0.480 -0.432 0.232 -0.927 1.288 -0.634 1.129 -0.234 0.202 -0.495 -1.016 -1.745 -1.055 -0.324 0.212 1.010 -0.446 -0.882 -1.443 0.480 1.383 1.266 0.710 0.639 1.698 0.332 0.646 +4 2.122 1.032 -1.519 -0.484 1.267 -0.708 0.444 0.775 -0.927 -0.060 -3.241 -1.024 -0.253 -1.248 1.632 -1.430 -0.440 0.131 1.441 -1.436 1.163 0.010 -0.982 0.462 0.199 -0.600 0.070 -0.385 +3 -1.104 -0.706 -0.002 1.219 -0.180 -0.868 0.050 -0.191 0.025 1.600 -0.383 -0.229 -0.243 -1.045 1.085 1.482 1.655 -2.084 -0.370 -0.962 -0.022 0.269 0.550 -2.566 -1.150 -0.100 -0.356 -1.483 +4 -2.241 0.465 0.136 0.512 -0.861 -0.701 -0.865 1.602 -0.972 -0.757 -0.075 -1.176 -0.430 -2.093 -0.482 -2.785 -0.228 -1.280 -1.246 -0.562 -0.813 1.179 -0.986 0.285 -1.748 -0.374 -0.448 0.349 +1 0.835 -1.381 0.947 0.056 0.937 1.680 0.801 -0.413 -1.088 0.536 -0.286 0.664 -0.507 -0.929 -0.242 0.501 0.122 -0.813 1.801 -0.139 -2.262 0.932 -0.011 -0.171 -0.190 -0.185 1.669 0.465 +4 1.393 0.821 -0.148 0.559 -0.415 -0.234 0.279 -1.727 1.064 -0.615 -1.386 -0.344 0.942 -0.047 -1.668 1.784 -0.331 -1.490 -0.995 -1.817 0.832 -1.192 -1.055 2.601 -0.414 -0.550 0.402 -2.025 +2 -1.327 1.156 -1.671 1.793 -1.142 0.190 -1.629 -1.350 0.898 1.028 0.239 0.741 1.045 -0.684 -1.399 -0.014 -0.577 0.389 0.548 -0.682 0.704 0.654 0.723 -0.408 0.780 0.520 0.640 1.678 +3 -1.251 1.442 0.903 -0.892 1.135 0.649 -0.782 -1.029 1.521 -0.627 0.845 0.365 -2.041 0.220 -0.004 1.260 0.836 0.041 1.289 -0.532 0.010 0.266 -1.452 -0.180 -1.270 -0.478 -2.035 -1.580 +2 -2.036 0.463 0.049 0.409 0.172 0.954 -0.075 1.013 -0.776 -0.719 1.376 1.356 0.574 -0.889 -0.970 1.328 1.273 -0.064 2.342 0.459 -1.107 -0.276 0.005 0.086 1.250 -0.956 -1.332 0.602 +2 1.515 -1.009 0.768 -0.957 0.143 -1.193 -1.142 1.950 0.672 -0.390 1.028 1.341 1.103 -0.152 -0.592 0.510 -0.781 -0.845 1.336 -1.643 1.771 -0.253 1.232 0.346 0.376 0.577 -0.304 0.239 +2 -0.105 0.894 -0.330 -0.607 -1.236 -0.012 -1.300 1.109 -0.051 -0.075 -0.267 -0.740 0.450 0.277 2.028 0.286 1.766 -0.998 -0.410 0.692 1.580 -0.482 -0.931 1.519 1.573 -1.788 0.930 0.167 +2 1.024 -0.552 0.298 -0.730 0.230 1.494 0.773 0.922 1.114 -0.070 -0.873 -1.854 -1.084 0.117 -2.033 0.177 0.494 -1.564 0.879 -0.421 -1.635 -1.693 -0.056 0.085 -0.015 1.037 0.062 -0.580 +0 -0.145 -0.834 0.565 -0.977 0.705 0.388 1.402 -0.464 1.337 0.212 -0.075 -0.853 0.396 -0.476 -0.028 0.777 -0.031 0.317 1.514 0.563 -0.679 -0.405 0.141 2.122 -1.335 0.190 1.364 0.615 +3 0.562 0.882 1.020 -0.161 -0.414 -0.626 -0.723 -0.017 -0.209 -0.825 -1.144 -0.109 -0.434 -1.141 0.831 1.772 0.963 -0.720 -0.050 1.540 -1.174 0.783 -1.448 1.684 -0.430 2.229 1.728 -0.106 +2 -0.632 0.777 -0.918 0.685 -1.194 1.381 -1.388 0.460 -0.435 -0.643 0.876 0.567 -0.021 0.450 1.758 -1.813 -2.629 -0.434 -0.308 0.516 0.178 0.072 -0.942 0.259 0.899 0.319 0.102 0.888 +2 1.100 0.839 1.273 -0.022 -0.459 -1.161 0.009 2.219 -1.142 0.580 1.688 0.667 1.389 1.304 -0.228 0.133 1.265 0.015 -0.507 0.628 0.604 -0.192 0.438 -1.368 -1.343 -0.137 -0.655 -0.243 +4 -0.841 1.143 -0.775 1.523 0.594 -1.019 -0.945 -0.220 -1.054 -1.606 1.232 -1.938 1.617 -0.425 0.456 2.307 0.683 -0.647 1.784 -1.845 0.223 1.287 -0.469 -1.043 -1.244 -0.099 -1.994 -1.916 +1 0.194 -0.580 0.367 0.451 0.330 0.065 0.192 -0.472 -0.287 1.022 -0.077 -0.410 -0.998 0.837 0.732 0.544 -1.034 0.357 -0.244 0.478 0.991 -1.646 -0.184 0.944 2.997 -0.562 -1.065 -1.950 +4 -0.995 -0.280 -0.072 2.227 -0.728 0.627 0.471 1.748 1.360 -0.463 -1.049 -1.416 0.236 1.014 1.144 -0.261 0.044 1.426 -0.763 -2.475 1.153 -1.160 -0.291 0.457 -0.462 1.080 2.671 -0.807 +1 -1.246 -2.335 0.172 -0.450 0.203 1.672 -0.268 0.311 -0.845 0.492 -0.781 1.001 0.123 -0.146 -1.638 -1.373 0.580 1.163 0.241 -0.228 0.048 -0.143 -0.703 -1.351 -1.341 -0.687 0.092 -1.329 +2 3.080 1.100 0.322 0.056 1.662 0.078 0.573 -0.998 -0.506 0.499 1.188 -0.188 0.483 -1.022 0.424 1.101 0.049 -0.150 -0.458 -0.454 1.356 -0.540 -0.031 -0.929 -0.045 0.998 -1.121 -0.954 +0 0.668 0.802 -0.653 0.746 1.278 -1.415 -0.415 -0.365 0.007 -0.285 1.123 -0.346 1.481 -0.473 0.431 0.914 1.566 -0.820 -0.188 -0.387 0.460 -1.310 -0.281 0.368 0.040 0.704 -0.254 1.340 +0 0.392 -0.686 -0.607 -0.409 -0.465 0.966 0.139 0.316 0.773 -0.692 0.193 0.292 -0.378 -0.681 -0.053 -0.824 1.263 -0.425 -0.254 0.135 -0.973 2.132 -0.903 -0.619 1.233 0.149 0.561 -0.280 +0 0.636 0.219 1.139 -1.428 -0.643 0.614 -0.724 1.433 -0.491 0.819 0.107 1.947 0.739 0.590 -0.356 -0.568 0.071 0.560 0.340 -1.451 -0.336 0.325 -0.962 0.407 -0.440 0.592 -0.221 0.048 +1 -0.794 -1.166 1.397 -0.464 1.989 -0.385 -0.714 0.602 -0.079 0.278 -0.539 -0.539 -0.489 -1.042 0.966 0.206 0.153 0.666 0.357 2.264 0.427 -2.052 -0.494 -0.932 0.468 -0.413 0.501 -0.684 +3 0.585 -0.390 1.510 0.070 0.290 -1.316 -0.645 0.163 0.869 1.413 -1.061 -0.827 -1.230 0.494 1.413 -0.158 0.058 0.133 -0.469 -1.221 1.060 0.154 -0.548 3.005 -0.003 -1.712 0.655 -1.842 +4 0.402 0.929 -2.256 2.526 -0.551 -1.786 0.058 -0.082 -0.417 -0.020 1.485 -1.118 1.913 1.055 0.758 -0.846 0.413 1.942 0.717 -1.235 -1.570 -1.599 1.347 -1.066 0.149 1.031 0.350 -1.786 +4 2.141 0.014 0.049 -0.667 0.497 -1.158 2.165 1.317 0.984 -1.390 0.205 1.209 0.593 -1.002 -0.650 -0.452 -0.603 1.529 -1.921 0.656 -1.292 -0.961 -2.010 -0.549 1.511 0.265 -1.198 1.017 +1 0.865 -1.977 0.152 1.817 0.964 0.389 0.666 0.922 -0.961 -0.211 -1.386 0.375 -0.379 0.816 1.201 0.273 0.563 0.228 0.101 -1.840 0.100 -0.564 -0.931 -0.858 -0.238 0.026 0.816 -0.173 +0 2.194 -0.570 -0.072 -0.685 -0.956 1.296 0.196 0.315 -1.280 -0.299 -0.622 -0.826 0.467 -0.338 0.693 0.721 -0.435 0.454 -0.462 -0.788 -1.060 0.086 -0.742 1.378 1.626 0.475 0.244 0.019 +2 1.449 -1.749 -0.908 0.731 2.055 -0.864 1.012 -1.150 -0.389 0.010 -0.691 -0.419 -1.364 0.674 -0.898 -0.664 -0.782 -0.729 0.145 0.063 2.519 -0.245 -0.100 1.337 0.225 -0.099 -0.134 0.194 +1 0.940 -2.233 -1.196 1.959 -1.227 -0.763 1.251 0.050 0.059 1.591 -0.575 -0.536 0.761 0.160 1.028 -0.645 0.199 -0.073 -0.391 -1.282 -0.421 -0.539 1.253 0.567 0.444 -0.182 -0.892 0.223 +0 -0.052 -0.993 0.281 0.962 -1.569 0.906 0.082 -0.455 0.606 -0.173 -0.398 -1.513 -0.172 0.703 -0.710 0.369 -1.321 -0.941 -0.753 -1.626 -0.187 0.682 0.552 -0.082 -0.231 -0.791 1.981 -0.535 +2 -0.735 0.559 1.636 -0.723 -1.069 -0.206 -1.652 1.140 -0.017 -0.932 0.578 -0.914 -1.925 0.035 0.176 -1.092 -1.095 -1.129 1.428 1.593 -0.511 -0.376 -1.102 0.650 0.297 -0.936 1.001 1.372 +4 -0.465 -1.019 -0.669 -0.289 0.233 0.894 -1.242 -1.707 0.838 -0.321 -2.836 -1.117 -0.932 -0.934 0.105 -0.731 1.266 1.336 0.422 -1.755 0.432 -2.181 -0.948 0.842 0.514 1.108 0.273 -0.699 +4 0.905 1.968 0.673 -1.548 -0.689 -0.608 -1.023 1.624 -2.393 0.352 0.028 -0.694 -0.828 -1.919 1.456 -0.127 -1.576 -0.590 1.219 -0.433 0.628 0.641 0.168 -2.036 -0.270 0.915 -2.773 0.085 +0 1.148 0.259 -0.834 1.449 0.878 1.287 0.501 0.793 0.086 -0.445 0.409 0.957 0.459 2.178 0.469 -1.318 0.385 0.715 1.056 -0.430 -0.751 0.666 -0.084 0.299 0.737 0.321 0.798 0.056 +0 -0.475 -0.524 0.671 -1.276 0.004 0.079 0.924 -0.855 -1.307 0.475 -1.395 -0.523 -0.239 0.616 -1.542 -0.296 0.026 -0.861 -0.358 0.889 -0.507 -0.364 0.886 -0.218 1.338 -1.638 -0.273 0.776 +1 0.946 0.338 0.474 -1.904 -0.629 -0.413 -1.371 -0.550 0.218 -1.342 -0.455 -0.807 0.608 -1.538 0.354 -0.174 1.502 1.222 -0.993 -0.233 -0.091 -1.769 -0.869 0.114 -0.853 -0.180 -0.102 -0.321 +0 -0.470 0.965 1.085 0.628 -0.493 -0.367 -0.260 -1.627 -0.066 -0.322 -0.252 -0.739 -0.213 1.111 0.742 2.036 -0.335 -0.691 -1.067 0.405 0.086 1.005 -1.587 -0.711 -0.593 -0.106 -0.385 0.471 +1 -1.193 -1.833 1.018 -1.296 -0.179 0.753 -0.615 -0.825 0.226 1.118 -1.169 -0.150 -1.732 -0.393 0.034 -0.855 -0.457 -1.408 0.077 1.056 -1.097 -0.623 0.503 1.212 -1.493 -0.169 0.283 -0.102 +2 1.289 0.327 -1.361 -1.006 1.161 2.888 1.140 -0.990 0.194 0.967 -0.949 -1.472 0.991 -0.034 0.483 0.803 -0.087 -0.857 0.521 0.652 -0.675 0.891 0.911 -0.146 0.108 -0.008 1.505 -0.870 +0 -1.263 -0.277 0.413 0.457 -1.977 0.575 0.413 -0.424 -1.268 -0.408 1.611 0.597 0.973 -0.989 -0.533 -1.297 -0.665 1.358 -0.449 -0.689 0.239 -0.575 0.093 -0.569 0.191 -0.380 -1.118 1.054 +2 -0.462 0.868 -0.247 0.024 -0.078 -0.892 -0.171 -0.458 -0.692 -1.058 -0.101 0.618 1.121 -1.394 1.606 0.293 -1.127 -0.404 -1.877 -1.622 1.721 -0.699 0.537 -0.111 0.018 0.368 -2.084 1.319 +1 -0.246 -0.282 0.284 3.150 -1.515 -1.879 -0.536 -0.719 -0.738 -0.012 -1.170 -0.595 -0.295 -0.271 -0.141 -0.150 -0.807 -0.255 -0.005 0.960 -1.481 0.024 0.165 0.184 0.231 -1.313 0.161 0.106 +4 -0.422 -1.204 2.211 0.057 0.512 0.059 1.150 -0.459 -0.615 -2.068 2.339 -0.560 0.548 -0.505 1.790 0.803 1.830 0.092 1.099 -0.058 0.961 1.434 -0.004 0.235 -0.721 0.504 0.633 -1.926 +3 -1.148 -1.727 0.991 -0.437 0.070 0.655 -0.719 0.137 0.648 1.643 -0.511 -0.463 0.563 0.209 -0.137 1.593 -1.682 -1.422 -1.671 1.202 0.515 -1.371 -0.123 -0.028 -2.209 0.188 -1.178 0.707 +3 -0.194 2.463 -0.307 0.587 -1.930 0.432 1.142 -0.528 -2.510 -0.039 0.017 0.724 2.011 -0.380 -0.856 -1.167 0.198 0.479 -0.546 0.779 0.465 0.043 -0.902 -0.482 -2.430 0.236 0.026 0.188 +3 0.382 -0.868 1.067 1.936 0.003 1.204 0.299 1.697 -0.468 -0.356 1.246 -1.756 -0.194 2.232 -0.591 0.186 -0.912 -0.338 -0.322 1.512 -0.719 1.319 -0.931 -0.219 -0.013 -0.414 0.838 1.713 +4 1.779 -0.052 -1.333 1.585 -2.370 -2.202 -0.006 -0.822 0.056 -0.262 -0.182 0.594 0.089 0.574 0.717 0.738 -0.905 1.590 -0.879 -0.119 -0.399 1.549 -1.745 -1.433 0.346 -0.268 0.740 1.308 +1 1.305 1.667 0.311 0.807 0.620 -0.651 0.118 0.360 1.371 -0.152 -1.179 -0.147 -0.184 -0.751 1.014 1.092 -2.498 1.275 0.301 0.106 0.502 -1.743 -0.476 0.683 0.540 -0.340 0.055 0.137 +0 1.144 0.796 -0.696 0.008 0.376 0.313 -0.499 1.501 -0.234 -0.204 0.964 0.722 0.900 1.881 -0.677 0.140 -0.810 -0.535 -0.802 -0.086 0.803 -0.361 0.145 -0.895 -0.747 -1.377 -0.354 -0.162 +0 -0.613 0.274 0.285 -0.977 2.174 -0.314 0.460 -0.720 1.251 -1.523 0.965 0.155 0.632 -0.034 -0.064 -0.208 -0.540 0.012 -1.083 0.788 0.478 -0.057 -0.397 -0.621 -0.115 -2.087 0.160 0.201 +2 0.165 1.132 -0.168 0.150 -0.094 0.803 -0.668 -2.196 0.471 0.988 0.446 -0.640 -1.135 0.494 0.141 -0.126 1.822 -0.947 1.775 0.032 -1.836 -0.179 0.959 -0.365 -1.475 -0.165 -0.857 0.492 +3 -1.377 0.812 -1.724 -2.557 -1.262 1.299 -0.327 0.717 0.563 -0.406 -0.264 0.622 1.403 -0.112 -1.016 -0.493 1.354 -0.792 0.857 -1.443 1.189 -0.026 1.271 -1.491 -0.827 -0.009 -0.985 -0.403 +3 -2.090 -1.266 -1.462 -0.902 0.740 0.284 -0.758 0.648 -0.008 -0.480 0.330 0.227 -0.469 -2.142 -0.859 1.087 -2.031 0.249 0.478 -0.404 -1.200 1.899 0.955 -0.655 -1.635 0.760 -0.358 0.867 +0 1.341 0.092 0.135 -1.447 -0.309 -0.241 -0.829 0.022 -0.429 0.080 0.122 -0.956 -0.399 0.077 1.184 0.257 -0.662 0.399 1.708 0.917 0.548 0.726 1.440 1.076 0.567 1.069 -1.082 -1.502 +3 -0.712 0.430 1.412 1.186 -0.433 1.255 1.172 -0.189 -0.009 1.542 1.058 1.225 2.585 0.547 0.326 1.358 1.988 -0.611 -0.202 -1.019 0.343 0.889 -0.949 1.134 -0.453 -0.535 1.130 -0.625 +2 0.865 -0.440 0.404 -1.547 0.152 1.113 -1.760 0.069 -0.815 0.072 -1.201 -1.450 -0.604 -0.745 -1.553 1.209 -0.627 1.060 -1.161 0.979 -0.671 1.949 0.175 1.173 -0.491 -1.217 -0.657 -0.437 +2 1.691 1.335 -2.001 -0.770 0.943 -0.022 -0.018 -1.568 -0.745 -1.358 -1.653 -1.258 -0.928 -0.761 -0.842 1.020 -0.907 1.426 -0.484 0.439 0.471 -0.898 -0.328 -0.503 0.372 -0.129 -0.748 0.119 +4 0.494 -1.676 1.176 2.124 0.824 -1.003 -1.989 1.364 -0.147 -0.391 1.165 -0.590 0.762 0.024 0.247 1.629 -0.489 -0.580 -0.202 -1.203 0.936 0.804 -0.009 -0.924 -2.745 -0.834 -0.231 -1.899 +4 0.142 0.334 1.438 0.722 -0.786 -1.092 0.277 0.707 2.196 -1.342 0.872 -0.500 1.757 -0.369 1.178 2.514 0.489 -0.751 -0.599 -0.952 -0.660 -0.712 2.415 -0.776 0.147 0.434 0.400 1.821 +1 -0.369 -0.598 0.234 1.347 -0.332 -0.688 1.053 -2.114 0.027 0.055 1.335 1.687 0.643 1.678 1.613 -0.443 0.074 -1.168 -1.160 -0.549 0.254 0.279 -0.924 -0.402 0.137 -0.378 -0.402 0.644 +2 2.358 0.192 -0.528 -0.708 -0.083 0.117 0.059 -1.582 -2.264 -0.969 0.059 0.063 0.455 0.363 -0.500 -1.021 0.521 0.226 -1.093 0.625 0.957 0.789 0.964 -0.751 0.852 -0.158 2.045 0.894 +4 -0.138 2.219 -1.543 -1.489 -2.084 -0.300 0.040 -1.649 0.930 1.529 0.483 0.321 -3.041 -0.289 -0.130 1.132 -0.701 1.103 0.884 0.750 0.228 -2.386 0.322 1.599 0.805 1.188 -1.295 -0.946 +4 1.583 0.241 -0.850 1.854 0.533 0.858 -2.380 1.644 0.492 0.065 -0.181 0.107 -1.057 0.319 -1.794 2.319 -1.309 0.787 0.544 -0.485 -0.030 0.044 0.147 0.053 1.190 -2.158 1.339 -1.454 +0 -1.066 -0.199 -1.010 -0.570 0.975 -0.045 0.829 -0.211 0.735 0.818 0.713 0.951 0.488 -0.428 -1.416 -0.761 0.392 1.113 0.489 -0.312 -0.426 0.035 0.607 0.036 -0.500 -0.849 -0.716 -0.695 +3 -1.039 0.040 0.956 -1.134 0.227 -0.590 0.701 -0.798 0.772 -0.158 -0.937 0.064 -1.938 -0.559 0.337 -1.772 -2.571 -2.339 1.327 -0.077 -0.532 0.945 -0.404 0.268 0.026 -0.471 0.294 0.773 +2 1.164 0.188 0.939 -0.531 0.499 -1.919 -0.455 1.177 -0.562 -0.223 1.237 0.354 -1.517 1.041 1.229 -0.997 1.310 0.221 0.078 -1.545 0.904 0.618 0.792 0.606 0.182 -0.567 1.126 -1.971 +1 -0.967 -0.020 -1.061 -0.184 0.843 -1.278 -0.255 1.495 0.040 0.769 -0.269 -1.209 -1.726 -0.450 2.169 -0.150 -0.579 0.212 -0.647 0.030 0.245 -1.374 1.374 0.074 1.511 -0.670 -0.755 -0.535 +0 0.597 -1.238 -0.121 0.244 -0.259 0.309 -1.747 -0.007 1.516 -0.365 0.835 -0.406 -0.521 -0.650 -1.466 0.332 -0.185 -1.078 -0.685 -1.763 0.329 -0.452 -0.305 0.556 0.988 1.206 0.509 -0.427 +2 1.919 0.188 -0.390 -1.252 1.275 -1.347 2.079 0.890 -0.580 0.419 -0.984 1.033 -0.855 -1.300 -1.231 0.405 0.100 0.116 -0.630 -0.287 0.201 1.323 -0.335 0.397 1.533 0.622 -0.497 -0.040 +0 1.762 -0.932 -0.411 -0.570 0.155 -1.821 0.622 0.465 0.865 0.845 0.454 -0.306 0.059 -0.652 0.194 -0.790 0.137 -0.440 -0.818 1.050 0.793 0.057 -1.743 -0.879 -0.077 0.473 0.490 0.232 +0 1.627 1.238 0.096 0.507 1.024 1.694 -0.875 -0.381 1.598 0.075 0.127 -0.066 0.531 0.187 -0.709 -0.611 0.518 -0.129 0.681 0.573 0.105 0.663 0.289 0.217 -1.378 -0.337 -0.622 0.177 +3 1.025 0.581 -0.434 0.956 0.739 -0.361 1.485 2.445 -0.355 0.160 0.833 -0.320 -0.248 0.697 1.493 -1.783 -0.035 0.349 -0.233 1.447 0.256 -0.815 0.824 -1.672 -0.692 -1.427 -2.133 0.208 +1 0.833 -0.765 -0.486 0.924 -0.122 0.511 0.121 0.732 -0.248 -1.185 1.106 -0.600 0.488 0.430 -0.082 0.122 0.066 -2.353 0.139 0.366 1.093 -1.380 0.508 -1.234 1.348 -0.466 0.727 1.434 +2 0.514 1.055 -1.850 0.356 -1.278 -0.256 -0.031 -0.347 -0.806 0.841 0.568 1.228 -0.316 -0.119 2.620 -0.344 -1.553 0.866 0.967 1.149 1.076 -0.897 0.682 0.501 0.696 -0.118 1.046 0.508 +2 -0.999 -0.973 -1.146 -0.254 -0.233 -1.044 0.173 -0.358 1.605 1.655 0.374 0.156 -0.744 -0.030 -0.573 1.342 -1.179 -2.491 0.254 0.397 1.617 -0.593 0.561 0.130 1.029 0.736 0.768 -0.915 +4 0.286 0.344 -0.405 1.895 0.863 -1.690 1.943 -1.186 0.527 -0.507 2.561 0.240 -0.645 0.757 -0.891 -0.334 1.987 -0.632 1.687 -0.552 0.467 0.084 -0.223 1.885 -1.361 -0.226 -1.062 -1.179 +1 -0.540 -0.492 -0.769 -0.760 -2.001 -0.224 -1.956 0.751 -0.560 -0.119 -0.373 -0.244 1.263 -1.351 -1.623 0.119 -0.496 0.360 0.717 -1.106 0.603 -1.022 0.075 -1.237 0.132 -0.021 -1.103 -1.358 +4 2.572 -2.249 0.609 -0.557 -0.586 -0.620 -0.909 1.685 0.842 0.023 1.351 -1.513 -1.331 -2.178 -0.699 0.162 0.103 0.149 -1.411 0.803 0.240 0.733 -1.893 0.311 -1.188 -0.397 0.534 0.091 +4 -0.377 -2.338 -0.144 -0.768 0.080 -0.659 2.603 -0.028 -0.643 1.299 -0.214 -0.545 0.300 1.144 0.201 -0.114 -0.489 -1.009 -2.165 -0.195 -1.338 -0.205 -1.613 2.447 2.412 0.148 0.688 -1.406 +2 0.222 2.543 -1.416 -0.237 -1.370 -0.122 -0.127 -0.458 0.054 -0.409 -0.110 -0.756 1.443 -1.109 0.212 -1.193 -0.835 -0.728 -0.132 0.175 -1.107 -0.414 -0.991 -1.035 1.134 -1.392 0.075 1.698 +2 -0.346 0.423 -0.767 0.067 -0.277 -1.049 0.735 -0.939 0.267 -0.373 0.317 0.039 -0.594 -0.038 0.074 1.263 1.847 -0.986 -0.669 -2.322 -1.167 -0.937 -2.123 -1.608 -0.286 -0.520 -0.447 -0.870 +1 0.392 -0.487 1.691 0.189 -0.707 0.022 1.428 -1.479 0.270 0.074 -1.540 0.767 0.256 0.879 1.138 -0.056 -0.556 -1.425 -0.644 0.011 -0.964 0.583 -0.903 -1.763 -0.578 0.184 -1.259 -0.888 +0 1.026 0.441 -0.326 -0.268 0.844 -0.590 -0.628 -0.744 0.788 0.637 -1.141 0.152 0.291 1.185 -0.407 -0.525 0.523 -0.531 0.665 0.232 -1.732 -0.253 0.211 0.708 -0.502 -0.821 -0.854 0.191 +3 0.310 -1.033 1.311 0.019 -0.343 -1.069 0.057 -0.944 -0.085 -0.304 1.101 -1.442 -0.891 0.595 -0.583 -0.855 0.062 -0.397 -1.468 -2.277 1.416 0.618 1.539 -1.620 2.100 -0.135 -1.856 0.727 +3 -0.183 -0.667 0.053 -0.168 2.474 0.360 -0.799 -1.035 0.968 -1.141 0.709 1.515 0.734 0.954 0.421 0.504 -1.235 1.815 1.930 -1.019 0.195 0.893 0.699 1.691 0.795 0.451 0.044 0.181 +1 0.176 -0.662 -0.413 0.395 1.674 0.603 -1.221 -0.780 -1.136 0.419 0.801 -1.766 -0.072 -0.565 -0.576 -0.278 -0.312 -0.772 0.359 -0.787 -0.350 -0.465 1.264 -0.387 1.977 0.576 -1.580 -1.080 +4 0.750 -0.736 0.734 1.231 0.236 1.004 -1.015 -1.159 0.781 2.472 0.091 0.885 -2.937 0.612 -0.491 2.002 0.775 0.084 -0.546 1.283 1.307 -0.807 1.172 1.016 0.276 0.829 0.567 -1.038 +1 0.808 0.098 -1.443 -0.358 -0.022 -0.805 -0.386 0.317 0.347 0.498 0.402 -0.937 -1.589 0.291 -0.196 0.024 0.082 -0.492 0.334 -1.483 2.299 0.221 -0.065 -0.856 0.420 -1.683 -0.191 1.654 +1 -1.622 -0.619 1.141 0.951 -1.271 -0.291 -1.333 0.699 0.401 -1.429 0.604 0.879 0.762 -0.239 0.596 0.963 -2.136 0.639 -1.870 -0.497 -0.235 0.577 -0.091 0.701 -0.498 0.676 0.492 0.234 +2 0.743 0.598 0.158 0.944 1.072 -0.952 1.533 0.864 -0.876 -0.649 -1.243 0.628 -0.177 -2.493 -0.554 -0.381 0.106 -1.539 0.117 1.501 0.187 0.589 1.563 -0.095 -0.127 0.345 -0.885 -0.695 +4 -2.612 -1.536 0.752 -1.518 -0.604 0.259 0.505 0.511 0.677 -1.063 0.508 -0.473 -2.678 1.642 -0.447 0.001 -0.806 0.524 -0.418 -0.762 2.520 -2.824 -0.530 0.192 -1.200 -0.758 0.240 -2.546 +0 -0.596 0.369 0.240 0.059 0.534 -1.276 -2.027 1.190 -0.131 0.416 -0.868 1.262 0.350 -0.180 0.286 -1.742 0.373 1.487 0.315 0.186 -0.317 1.083 1.231 -0.432 0.359 0.232 0.402 -0.773 +4 0.183 -0.749 -0.059 0.991 0.761 -0.198 0.319 1.235 1.445 1.227 0.557 -1.024 -2.077 0.140 2.615 -0.287 -1.776 -1.118 1.364 -1.542 -0.713 1.661 1.454 -0.558 1.700 0.518 1.142 -0.772 +1 -0.857 0.147 0.043 0.504 1.389 -0.407 0.932 0.142 1.024 1.232 -0.757 1.979 0.181 -1.147 -1.139 1.473 1.314 1.097 -0.541 -0.374 -1.576 -0.292 0.171 1.500 -0.275 -0.334 0.289 0.851 +3 -1.518 -2.995 -0.043 1.407 0.124 0.081 -0.061 0.449 -1.482 -0.463 0.818 0.881 -0.379 1.216 -0.929 -0.373 1.845 1.311 1.048 -1.726 -0.285 -1.340 0.767 0.226 -0.705 -0.587 -0.048 0.173 +0 0.160 -1.098 -1.135 -0.216 -0.331 -0.535 0.751 1.436 -0.175 -1.178 0.493 -0.278 -0.182 -0.397 -0.533 -1.426 -0.188 -0.287 -0.773 -1.568 0.736 -1.333 0.032 0.465 -1.584 -0.554 0.856 -0.463 +2 0.696 -0.286 -1.462 -0.174 -0.518 -0.530 -0.750 -0.390 -0.245 1.928 -1.114 1.145 1.231 -2.279 -0.819 -0.109 0.144 -1.513 -0.210 0.422 -1.496 -0.060 -0.916 -0.209 -0.815 -0.391 -0.396 1.672 +1 -0.225 1.154 -0.974 0.239 -2.029 0.090 0.349 -0.208 1.130 -0.401 0.524 0.639 -1.069 1.406 0.462 -0.027 0.623 -1.154 -0.689 0.506 -1.150 -0.011 2.501 0.978 0.006 -1.266 0.712 0.121 +1 -0.970 0.204 0.670 1.004 -0.243 0.166 1.045 0.721 0.471 -0.136 -0.419 0.169 -0.379 -1.118 1.211 -0.931 0.999 -0.715 0.391 -1.713 0.567 0.173 0.639 -0.989 2.376 -1.936 0.119 -0.799 +4 0.888 -0.885 -0.829 1.479 1.376 -0.244 0.806 0.096 -2.973 2.167 -0.176 0.958 1.926 0.176 0.987 -0.646 0.594 0.509 -1.130 0.638 -1.271 0.176 1.204 0.621 0.434 -1.438 -0.029 -1.777 +3 0.282 2.590 0.167 1.150 -0.759 -1.052 0.971 -0.135 -0.299 0.055 -0.179 0.217 1.195 0.956 0.868 0.109 1.250 1.914 0.935 1.604 -0.869 0.911 -0.262 -2.216 1.120 -0.275 1.356 0.539 +4 0.956 -1.143 -1.264 2.742 -1.943 -1.191 0.804 -1.627 0.736 1.227 1.346 0.067 -1.173 -0.914 1.265 -1.866 0.823 -1.608 -0.095 -0.623 -0.443 1.341 -0.539 1.043 -0.720 0.262 -0.127 -1.373 +1 0.030 0.680 -0.075 1.134 0.261 1.012 -0.721 -1.228 -0.340 -1.346 0.409 -2.017 -0.453 -0.286 -1.047 -0.306 0.004 1.269 -1.041 0.357 0.556 -0.021 -0.498 -1.139 1.899 0.073 -0.316 2.122 +2 -0.107 1.153 -0.177 0.375 -0.425 -2.294 -0.597 1.462 0.335 0.894 0.930 0.779 1.595 -0.318 1.485 0.671 0.445 -2.056 0.402 0.918 0.233 1.251 1.296 -0.279 0.109 -0.265 -0.422 0.099 +1 0.548 -1.232 0.401 0.282 0.513 1.590 -0.176 2.048 0.407 -0.290 2.399 0.830 -0.455 0.084 0.635 -0.151 0.134 -0.817 -1.685 0.106 0.140 0.527 -0.576 -1.341 -0.965 1.056 -0.667 -0.442 +3 -0.545 1.291 0.148 -0.879 0.917 -1.264 1.284 0.991 1.390 -0.158 -0.496 0.084 -0.084 -0.653 -0.554 -0.366 -1.170 0.678 -0.052 -0.308 -1.110 -1.160 1.936 -0.627 0.611 0.163 -2.775 1.792 +3 -0.788 1.215 -1.317 1.386 -0.550 1.928 0.969 -0.282 -0.558 -1.055 0.539 1.714 -0.764 -1.294 1.021 0.541 0.740 -0.260 1.380 -1.857 0.577 0.423 0.151 -1.776 0.641 1.357 -0.305 0.345 +1 1.275 0.858 0.852 -0.785 2.210 1.551 0.425 -0.650 -0.913 0.435 0.267 -0.235 -1.051 0.359 -0.164 -1.486 0.011 0.938 0.354 -0.325 1.707 -0.723 0.375 -0.516 0.079 -0.424 1.272 0.313 +2 -0.332 0.263 0.016 0.787 -1.201 1.724 1.345 0.935 1.265 -1.553 0.699 0.740 0.967 0.847 0.445 0.404 1.192 -0.623 0.367 -0.553 -0.152 -2.672 -0.871 -0.436 -0.423 -0.333 -1.185 -0.332 +2 0.207 -0.239 1.478 -1.482 -0.751 1.520 0.131 0.885 2.051 -0.694 -0.099 -0.272 -1.955 -0.310 0.881 0.541 0.619 -0.376 -0.656 1.269 -0.497 0.294 0.163 1.462 0.971 -1.295 -1.188 0.579 +2 0.172 0.420 -1.067 1.041 0.842 0.916 -2.317 -0.661 0.994 -0.821 0.517 -0.149 0.764 1.810 -1.032 0.682 0.223 -0.496 0.795 0.380 -0.413 0.160 -0.935 1.107 -0.615 -0.955 1.313 -1.650 +2 -0.448 -1.153 0.446 1.150 0.537 -2.454 -1.115 0.047 -0.244 -0.327 -0.983 -1.406 2.008 0.027 0.642 0.006 0.046 -0.062 -1.120 -1.514 0.386 0.188 0.938 0.735 -0.809 1.133 0.765 0.691 +4 -1.223 1.770 -0.205 0.516 1.583 -0.152 -0.396 -0.208 3.224 0.853 0.533 1.619 0.294 0.194 0.816 0.610 -0.491 -0.473 -0.955 -0.281 2.419 0.705 0.689 0.743 -1.238 0.654 -0.481 -0.426 +4 2.036 -0.417 -1.041 0.583 -0.144 -1.415 2.231 -0.352 -0.378 0.160 1.730 -1.680 0.276 2.087 0.129 -1.235 -0.618 0.207 1.331 -0.339 0.827 -1.435 -1.444 -0.483 1.438 0.069 0.290 0.278 +1 0.088 1.695 1.391 0.050 1.237 -0.783 -0.192 -0.536 -0.158 -2.004 1.799 -0.305 0.787 0.245 -0.272 -1.277 -0.914 -0.236 -0.475 -0.974 -0.687 0.461 0.877 -1.166 -0.880 0.215 -0.163 -0.366 +3 1.873 1.080 -0.447 1.281 0.068 0.853 0.485 -0.846 -0.644 1.030 -0.335 -0.404 -0.955 0.424 2.063 -1.068 0.024 1.412 -0.080 0.452 -1.062 0.428 -0.187 0.986 1.187 2.590 0.580 0.326 +2 1.187 0.757 -0.809 -0.005 -1.701 0.811 1.541 -0.340 -0.006 0.422 -0.227 -2.067 1.078 1.206 0.686 1.397 -0.132 0.443 -0.700 -0.875 -1.302 0.345 0.088 1.099 -1.741 0.264 1.726 0.118 +1 0.732 1.035 1.150 0.114 -0.291 -0.121 -0.342 1.448 -0.758 1.134 -0.482 1.105 0.727 -0.039 0.259 -1.110 0.577 0.947 1.338 -0.325 2.200 -0.318 -0.450 -0.167 -0.158 -1.434 -1.131 -1.222 +1 0.108 -0.751 0.192 1.109 0.204 0.154 0.157 -0.746 0.449 1.042 1.352 0.430 0.015 1.877 0.120 -0.484 -0.689 -0.804 -0.503 1.927 -0.757 1.527 -0.205 1.042 -1.079 -0.018 -1.616 0.022 +1 -1.301 0.438 -0.731 0.880 -1.441 -0.977 -0.367 0.789 -0.862 0.423 1.096 0.153 -0.160 0.668 -0.801 -0.739 -1.279 1.590 0.584 -1.346 -0.632 0.192 -0.041 0.412 -0.576 1.073 -1.039 1.297 +1 -0.727 0.569 0.110 0.274 0.150 -1.352 0.225 -1.403 0.138 0.312 0.542 1.096 -0.407 0.098 2.272 0.019 0.231 -1.396 -0.952 0.241 -1.872 -0.306 1.857 -0.289 -0.328 0.824 -0.446 0.925 +4 -1.254 -0.335 0.308 1.080 0.025 -0.550 0.629 -1.400 -0.151 0.508 0.187 -1.195 -0.104 -4.374 0.184 -1.430 -1.360 0.082 0.853 -1.359 -1.169 0.267 -0.165 -0.259 0.853 -0.928 -0.562 -0.808 +0 -1.239 -0.164 -0.931 -0.921 -0.517 -0.714 1.138 0.586 -0.002 -0.342 0.124 0.449 -1.515 0.139 0.245 -0.286 -1.398 -0.960 0.730 0.067 0.207 -0.673 1.082 -0.321 -1.075 0.287 -1.310 0.189 +1 -0.891 0.930 -1.191 1.424 1.092 0.519 -0.967 -0.098 -0.274 0.088 0.310 -0.531 -0.539 0.724 2.322 -1.221 0.751 0.529 0.469 0.238 -0.549 0.144 -0.295 0.554 0.459 -2.410 0.183 0.232 +1 -1.724 -0.535 1.107 -1.032 0.762 -0.547 -0.584 -0.191 0.312 -0.436 -0.525 1.381 0.125 -1.401 -2.107 1.444 0.924 0.179 0.145 -0.010 0.097 0.212 1.044 0.947 0.197 0.215 0.841 0.584 +2 -0.774 0.450 -1.978 0.499 0.861 -1.155 0.252 0.758 -0.285 0.880 -0.699 0.287 0.060 0.129 1.102 0.093 -0.450 1.758 1.703 1.976 1.183 0.728 1.354 -0.586 -0.799 -1.458 -0.699 -0.212 +4 -0.922 -0.065 -1.907 0.630 -0.866 0.034 0.161 -0.292 -2.572 -0.316 -2.500 1.564 -0.312 -0.908 -2.097 0.589 0.422 -0.194 -0.068 -0.834 -0.791 1.212 -0.388 0.342 -0.394 -0.189 2.096 1.356 +1 0.099 0.691 -0.458 0.693 2.299 -0.818 -0.887 0.584 0.079 0.184 2.372 0.526 -0.199 -0.621 -0.842 -0.737 0.018 -0.889 1.114 -0.946 -0.229 1.386 -0.659 0.367 0.023 -1.416 0.272 -0.071 +3 0.754 0.889 1.368 0.242 0.280 -0.364 -0.929 -0.975 -0.144 0.344 -1.455 2.046 -0.696 -0.857 -0.028 -2.370 0.362 -0.990 -1.922 0.143 0.142 1.945 0.180 0.172 0.041 -2.578 0.077 -0.048 +3 1.299 1.236 0.874 -0.676 -0.844 -0.725 2.632 0.164 0.921 0.475 -0.339 1.078 -1.040 -0.814 -0.848 0.972 1.194 -0.193 -1.059 -1.855 -0.943 -0.969 -0.203 0.937 -0.202 -1.262 -1.258 0.374 +1 0.066 1.127 -1.382 1.854 0.334 -0.680 -0.571 0.194 -0.122 0.632 -0.521 -0.550 0.260 -0.675 -0.288 0.917 -1.685 0.215 0.648 1.199 0.099 -0.103 -1.204 -0.447 0.998 -1.020 -2.059 1.241 +3 -1.992 0.131 -0.405 0.272 -0.604 1.199 -0.001 0.240 2.817 -0.436 -0.033 -0.709 0.782 -0.248 -0.154 1.051 -0.259 1.451 0.218 0.545 1.138 0.976 -1.237 2.504 0.527 -0.615 -0.302 0.015 +1 -0.363 -0.912 -0.243 -0.843 1.001 -0.553 1.311 0.669 -1.002 -1.063 -0.221 -0.732 -0.739 0.840 0.274 1.230 -1.072 -1.833 -0.731 1.077 -0.627 1.637 -0.533 0.584 -0.026 -1.960 -0.660 0.480 +3 0.750 -0.274 -1.721 -0.982 -0.430 1.368 0.901 1.624 1.654 -0.535 -0.188 -0.411 -0.672 -1.841 -0.015 -1.286 -0.034 2.116 -0.509 0.195 0.907 0.280 0.292 -0.362 -0.232 -0.960 0.846 -1.741 +2 -0.175 -1.413 -0.834 0.470 -0.533 1.609 -0.694 -0.112 -0.635 0.918 -1.494 -2.062 -0.538 -0.216 -1.695 0.429 0.708 -0.354 -0.682 1.462 -1.113 0.531 -0.478 0.183 0.789 0.320 -1.811 0.914 +2 0.571 0.715 -0.108 0.526 -2.148 -0.722 0.704 0.722 -0.543 -1.308 -0.071 -0.434 0.941 0.703 1.549 1.110 -0.274 0.349 2.034 -0.400 0.175 -0.004 0.017 -0.874 1.308 -0.917 1.858 1.639 +1 -0.188 -0.104 -0.214 0.970 0.419 0.221 -1.271 -0.379 0.899 0.038 0.660 1.767 1.039 0.093 -0.511 -2.554 -0.168 -0.326 0.390 -0.154 -0.320 -1.779 -0.940 0.477 1.716 -0.098 0.328 0.952 +4 -0.089 2.082 1.450 -1.116 0.553 0.033 -0.195 -1.229 2.054 1.790 -0.851 1.369 1.013 -0.142 1.252 -0.188 0.288 -1.404 -1.164 0.679 0.872 -0.476 1.194 -1.674 -0.961 1.352 0.518 -0.439 +4 0.621 0.353 -2.494 1.214 0.497 0.096 1.646 -0.795 0.918 -0.277 -0.565 -0.690 0.672 0.911 1.468 -1.793 -0.089 -0.893 -0.333 0.369 -2.472 0.621 -2.289 0.620 -1.046 -0.510 0.606 -0.591 +2 1.802 -0.570 -1.062 0.075 1.699 0.275 1.116 0.639 1.173 -0.639 1.499 0.622 -1.373 -0.198 0.174 0.109 -0.069 0.255 0.255 -0.535 0.414 0.785 1.777 2.143 -0.356 -0.448 -0.327 -1.177 +4 1.797 -0.047 0.440 -0.108 -0.103 -0.490 -0.560 1.696 -0.554 -1.541 0.125 1.018 1.243 -1.194 1.113 2.059 -0.684 -0.174 -1.232 1.854 -1.492 -0.400 0.785 -0.684 -0.989 -0.052 2.390 -0.256 +0 0.806 -0.444 0.519 0.489 0.947 -0.540 -0.017 0.353 -0.532 -0.694 0.952 -0.478 0.430 -1.694 0.477 0.118 -0.568 1.484 0.240 -0.524 -0.401 -0.724 -0.381 -0.429 -0.931 -0.215 -0.096 -0.855 +2 -0.541 -1.888 0.645 0.752 0.773 0.537 0.747 1.100 0.576 -0.280 2.164 -0.289 0.469 -0.213 0.243 0.169 -1.591 -0.903 -0.762 0.747 -1.014 -1.778 0.236 -0.758 0.020 1.059 0.400 1.560 +0 -0.842 -1.267 0.826 0.322 0.399 -0.370 -0.279 -0.371 -0.081 0.835 0.336 0.500 0.500 0.884 0.288 0.359 1.189 -0.665 -0.856 -1.186 0.183 -1.854 0.462 -0.109 1.135 -0.672 -0.203 1.122 +3 0.155 -2.221 -0.197 -0.896 0.223 1.140 -0.438 0.139 -0.493 0.962 -0.252 1.045 0.220 1.583 0.508 0.082 0.947 1.722 1.581 2.351 -0.094 0.280 -0.304 -0.896 0.588 -2.468 0.396 1.378 +2 0.934 0.503 2.306 -1.016 0.823 -1.312 0.157 0.977 -2.072 -0.205 -0.563 -0.702 0.385 1.121 -0.133 0.205 0.114 0.002 -1.089 1.108 0.295 0.395 -0.807 -0.459 -0.176 -1.534 -1.688 -0.601 +1 -1.149 2.071 1.242 -0.243 -1.280 0.459 -1.624 0.814 -0.466 0.126 -0.291 0.305 0.162 0.575 0.523 -1.660 0.217 -0.269 0.005 -0.237 -0.841 -1.589 1.028 -0.876 -0.360 0.498 -0.854 -0.416 +3 -0.727 -1.679 -0.400 -2.085 -1.890 -0.303 -0.785 -0.138 -0.812 -0.795 1.199 -0.917 0.238 -0.080 0.155 -1.097 0.149 0.637 -2.576 -0.825 -0.959 -1.211 -1.167 1.359 0.012 -1.361 0.298 1.243 +2 1.630 -0.323 -0.043 -1.609 0.023 -1.468 -0.036 -0.898 -0.274 -0.736 0.327 0.881 -1.103 -0.544 1.625 -0.419 0.872 -0.404 -1.304 -1.561 -0.132 1.157 -0.422 -0.744 -1.265 1.250 -1.832 0.562 +3 -1.040 1.583 -2.297 -0.105 -0.130 0.326 0.347 -1.677 -0.346 -0.597 1.170 -0.280 0.133 0.726 -1.890 -2.089 -0.135 0.456 0.300 -1.124 0.333 -0.609 -0.397 1.309 0.771 1.005 -0.889 1.753 +0 0.259 -1.066 0.595 -0.613 -0.325 0.238 0.103 0.024 -0.751 -0.300 0.002 -1.375 -0.463 0.388 -1.277 1.157 1.270 -1.245 1.358 -0.144 0.419 -0.497 0.923 0.406 -0.814 -1.165 -1.460 0.188 +3 -0.512 0.350 0.368 0.300 0.303 -2.205 -0.006 -1.501 -0.758 0.066 0.261 -0.119 -0.879 0.095 1.990 0.667 1.343 -0.382 -0.810 0.812 2.494 -0.739 -0.528 1.354 -1.217 -0.155 1.606 0.139 +3 0.724 -1.234 -0.490 -2.538 0.200 -0.825 -0.146 1.158 -0.792 1.172 -0.899 0.218 -0.089 0.843 -0.046 -1.034 1.664 1.899 -1.213 0.734 -0.145 -0.727 -1.294 0.901 0.813 0.682 0.704 -1.443 +2 -1.865 -0.082 -0.372 0.101 0.238 -0.312 0.919 1.165 -1.259 -1.345 1.656 -0.394 -1.640 -0.164 0.395 0.624 0.539 -0.049 -1.371 -0.007 0.650 1.120 0.383 1.291 -1.076 0.066 -0.262 1.843 +0 -0.445 0.303 -1.511 0.103 0.302 -0.248 0.040 1.739 -0.289 0.161 0.157 0.655 -0.046 -0.188 0.990 -0.234 -0.414 0.226 -0.039 -0.105 0.783 -0.637 -0.875 0.477 0.652 0.115 -1.103 2.259 +3 -0.535 0.982 -0.186 -0.548 1.184 2.359 0.454 0.833 1.522 -0.359 0.030 -1.246 0.613 -0.980 1.053 -0.641 0.325 -0.206 1.603 1.914 0.599 -0.449 1.155 1.294 -1.506 0.532 0.599 -0.369 +3 -0.948 0.952 -1.939 1.288 -1.085 -0.397 -0.895 -0.999 0.403 0.742 0.521 -2.041 0.655 -0.978 1.335 1.391 -0.814 -2.460 -0.137 -0.305 0.602 0.305 0.657 -0.398 1.381 0.570 -0.927 0.899 +0 0.696 -0.360 0.251 0.066 0.096 0.508 0.196 -0.407 2.642 -1.332 0.322 0.646 1.256 -0.134 0.685 -0.316 1.009 0.313 0.812 0.339 -0.275 -1.310 -1.146 -0.634 -0.171 0.624 0.008 0.622 +4 -1.078 0.177 -0.719 0.607 0.300 -0.360 -0.335 -2.522 -0.393 0.425 1.121 -1.047 2.050 0.485 2.815 -1.761 0.162 0.532 0.456 -0.914 -0.194 -1.500 -1.136 -0.490 0.222 0.314 1.005 -1.555 +3 -1.049 0.586 -0.232 1.159 -0.284 2.351 0.029 1.242 -0.433 -1.616 -0.816 0.481 1.589 -0.375 -1.391 0.661 -1.971 1.596 -0.669 0.952 -0.273 -0.706 -0.314 0.871 1.138 0.418 0.573 0.919 +2 -0.341 1.022 0.733 1.378 -0.991 -0.343 0.759 0.448 1.532 0.421 1.848 0.540 1.062 -0.324 0.014 0.911 1.613 0.729 -0.083 1.377 -2.192 -1.422 0.682 0.014 0.606 0.064 0.571 -0.814 +4 0.201 0.976 -1.192 -2.613 -1.427 -0.515 -1.222 -1.872 -1.114 1.159 1.451 -1.454 0.506 0.428 -0.977 2.286 -0.169 0.058 -1.818 0.575 -1.851 0.168 -0.560 -0.695 -0.709 1.383 0.935 0.836 +4 1.667 -1.058 -1.125 2.999 1.039 -0.907 0.815 0.446 0.913 -0.215 -1.015 -0.327 0.190 -0.007 0.011 -0.450 0.077 -0.572 -0.757 1.240 1.808 -0.380 0.070 0.003 1.556 0.470 0.371 -3.133 +0 0.275 -0.038 1.240 1.484 -0.918 -0.406 -0.477 1.098 0.408 -0.729 -0.076 -1.470 0.616 -0.918 1.103 0.826 -0.163 -0.288 -0.890 -0.260 -1.631 1.298 1.353 -0.043 0.464 -0.457 0.487 -0.631 +1 -1.169 -0.237 1.127 -0.707 1.177 -0.896 -0.228 -0.605 1.593 0.059 -1.209 -0.775 0.984 -1.335 0.459 -0.427 0.013 1.909 0.061 2.138 -1.098 -0.003 -0.917 -0.155 0.064 -0.399 -0.699 -0.468 +4 1.181 -0.627 0.045 0.051 -0.502 -1.372 0.323 -0.061 0.500 -0.534 1.221 -0.877 1.712 -1.748 0.435 0.475 -0.796 0.424 1.280 -2.217 0.503 -2.493 -0.966 1.629 -0.582 -1.174 -0.198 2.068 +0 0.142 0.771 0.547 0.020 0.384 -0.593 -0.048 1.341 0.011 -0.217 0.210 -1.166 0.221 1.696 -0.809 0.267 -0.023 -0.049 -0.067 0.714 -1.098 0.689 0.069 -1.763 0.380 0.114 -0.872 0.161 +3 2.042 0.719 -0.035 -0.894 0.280 2.115 -0.075 -0.668 -0.802 -1.430 -1.008 -0.998 -0.754 0.822 -0.420 -0.070 -1.906 -0.931 0.549 -0.878 -0.677 -1.143 1.404 2.149 1.460 -0.394 0.778 -0.332 +1 1.569 0.607 0.692 -0.487 -0.001 0.627 0.153 1.602 -0.204 -0.505 0.634 -1.265 0.924 -1.301 0.639 -0.648 0.477 0.907 0.153 -1.200 -0.897 -0.729 -1.895 1.127 0.110 -1.136 1.456 -0.701 +0 -0.026 0.222 0.287 -1.164 -0.141 0.545 0.851 -0.225 1.474 0.595 0.844 0.401 1.042 0.536 -1.063 -0.111 -0.009 1.084 -0.163 0.386 -0.904 0.448 0.697 -0.162 0.942 1.689 -1.196 -1.078 +4 -0.968 -0.215 1.896 1.519 0.586 0.209 0.553 -0.073 -0.696 -0.808 2.184 -0.585 1.046 -2.155 -0.521 -0.113 -0.548 -0.770 0.939 0.347 -0.546 -0.634 -1.356 -1.108 -1.066 -0.448 3.067 -0.548 +1 -0.188 -1.106 1.341 -0.014 -0.748 1.143 1.257 -1.887 1.107 0.158 0.971 -0.068 0.379 -0.304 0.992 -0.671 1.026 0.179 0.757 -1.009 0.423 0.533 1.854 0.698 -0.588 -0.630 0.761 -0.540 +2 0.053 0.059 0.700 -1.535 -0.265 0.499 -0.767 -0.152 -1.355 -1.690 -1.898 0.118 -0.628 0.170 1.370 -0.309 0.406 -1.335 -1.130 -2.374 -1.462 -0.849 -0.404 -0.666 -0.322 0.343 -0.792 -0.202 +3 -0.050 -0.074 -0.485 0.105 -0.071 -2.404 0.026 -0.138 0.347 -2.007 0.512 0.029 0.495 -1.629 -1.235 -1.875 0.146 -0.345 -0.144 -1.392 -1.210 -2.496 -1.346 -0.650 -0.598 -0.068 0.762 -0.689 +3 2.998 -0.048 -0.018 1.660 -0.226 -0.238 0.503 0.563 0.041 -0.338 -0.913 -0.216 0.713 -0.009 2.362 -0.552 -1.539 -0.378 -0.291 1.099 -0.027 0.396 0.990 1.944 -1.438 0.847 -0.137 -0.049 +2 0.461 0.421 0.919 -0.064 0.104 0.581 -1.992 0.192 -0.731 -0.439 -0.487 0.129 0.960 0.636 1.101 -0.342 2.084 1.510 0.168 -0.495 -0.477 -1.667 -0.274 -0.264 0.791 -1.805 0.933 1.871 +4 -1.056 2.050 -1.904 -1.458 1.024 0.505 -0.663 1.889 -0.208 -0.283 0.335 0.596 0.575 -0.044 0.585 -1.788 -2.603 0.073 -0.893 -0.433 -0.297 -0.023 1.495 -1.006 -0.557 -0.462 -1.506 -0.203 +4 -0.987 0.794 0.081 1.095 -1.302 0.024 -0.028 1.543 0.288 -2.358 -0.556 -1.024 2.016 0.050 0.543 2.127 1.224 0.408 0.333 0.381 0.652 -2.014 0.090 0.739 -1.304 -1.471 -0.314 1.235 +4 0.554 -1.007 2.574 3.260 -0.618 -0.131 0.791 0.956 -0.503 0.730 -0.163 -1.145 -0.311 0.303 0.415 -0.818 -0.275 0.836 0.595 -0.010 -0.074 1.207 1.182 -0.430 -2.911 -1.669 -1.472 -0.123 +3 -0.821 0.239 1.521 -0.970 -0.005 0.496 -1.568 2.022 -0.826 -0.526 -0.169 -0.982 0.477 1.352 0.621 0.457 0.800 -0.514 -0.636 1.618 0.758 -1.446 1.704 1.439 0.693 1.290 1.511 -0.057 +2 1.435 -0.735 -0.779 -2.208 0.627 1.529 0.503 -0.532 -1.865 0.472 0.323 -0.635 -0.141 0.649 0.760 0.612 1.331 0.253 0.666 -0.373 -0.983 0.509 0.437 -0.518 0.979 -0.239 -1.946 0.143 +0 -0.720 0.098 0.846 -0.950 0.062 0.664 0.929 1.142 -0.869 0.288 -1.315 -0.724 -0.174 1.144 0.528 0.040 0.449 0.521 0.369 -0.575 -0.957 -0.223 -0.794 0.621 1.529 0.564 -1.810 -0.633 +0 0.865 0.791 1.334 0.082 0.537 0.116 0.825 -0.507 1.380 0.592 1.712 -0.562 0.711 1.131 1.012 0.383 0.134 -0.808 0.299 -0.780 0.492 -1.041 -0.094 0.919 0.053 0.575 0.552 0.234 +3 0.879 0.189 -0.250 1.147 0.778 0.178 -2.165 -0.321 0.906 -0.626 0.304 0.651 1.258 0.222 -0.922 1.237 -0.241 0.515 -0.639 -1.299 -1.249 1.421 -0.040 0.727 2.062 0.936 1.899 -1.052 +2 -1.520 -0.989 1.211 -0.913 0.049 -0.978 1.884 1.534 -0.547 0.908 -0.519 1.226 0.175 -0.868 0.928 0.570 -0.410 -1.072 -0.162 0.003 -0.785 -1.066 -0.233 -1.993 0.203 -0.309 -1.568 -0.804 +3 0.148 -0.755 1.515 -1.200 1.030 -0.323 -0.399 0.094 0.704 -1.079 0.003 -1.114 1.495 0.381 -0.070 -3.402 -0.289 -0.693 0.688 -0.196 1.246 -1.573 -0.256 0.283 1.020 0.972 0.714 -0.907 +0 -0.504 0.299 -0.251 -0.133 -0.322 0.471 0.082 0.395 -0.018 -1.604 0.031 0.064 0.243 -0.594 -1.195 0.625 -0.274 0.171 -0.281 -0.006 -0.449 -0.968 0.474 -0.603 0.164 0.410 -0.587 -1.204 +3 1.280 1.541 -0.856 0.111 0.674 1.516 0.718 0.291 0.667 0.674 -0.928 0.193 1.459 -0.207 0.129 -0.243 -2.314 0.021 0.302 -0.512 -1.155 -0.881 0.764 1.973 1.382 -1.344 0.855 -0.663 +4 -0.023 1.279 1.083 -1.076 -0.674 0.958 -2.713 -0.149 -1.431 -0.477 -0.299 -0.358 -0.552 -1.097 2.292 1.867 -1.162 1.582 -0.399 0.129 -0.063 -0.660 0.984 0.126 -0.477 -1.305 -0.894 -1.641 +2 0.083 0.597 -2.021 -0.827 0.519 -1.615 0.549 -0.725 -1.034 -0.729 1.528 0.333 -0.910 0.775 0.830 -0.381 0.959 1.007 0.851 -0.674 -1.179 -1.693 -0.656 -0.368 -0.286 -0.047 1.733 -0.340 +1 -1.513 1.724 0.882 0.037 -1.189 0.627 -1.015 -0.605 -0.368 -0.158 0.608 -0.279 0.400 -1.844 1.289 1.305 0.450 0.424 -0.134 0.315 -0.672 -0.174 0.149 -1.628 1.446 0.225 0.667 -0.052 +2 -0.321 -1.362 -0.684 -0.009 0.061 1.926 0.081 1.179 -0.435 -0.312 -1.329 -0.742 0.527 0.008 0.943 0.181 -0.352 2.148 -0.094 1.163 -0.401 0.710 0.928 0.208 -2.354 0.018 1.230 0.527 +0 0.799 -1.251 1.454 1.547 -0.069 1.125 0.130 0.518 1.791 0.660 -0.641 -0.152 -0.412 -0.504 0.221 -0.234 0.565 0.460 0.517 -0.174 0.471 0.019 0.220 -0.266 -0.615 -0.540 1.155 1.636 +2 1.503 0.679 -0.591 1.431 0.222 1.339 -0.181 -1.836 -2.191 0.532 -0.527 -1.824 0.510 0.808 0.093 -0.146 0.773 -0.174 -0.349 -1.156 -1.348 0.029 -0.797 1.694 0.078 -1.081 0.258 -0.125 +3 -2.211 0.463 1.915 -0.358 -0.701 0.181 0.710 0.547 -0.659 1.922 -1.526 0.073 0.874 0.233 -0.255 -0.873 0.432 1.179 -0.116 0.313 1.159 1.361 1.376 -1.234 1.158 -0.013 0.870 2.116 +1 -0.270 -0.194 -0.407 0.399 0.055 -2.037 0.110 0.374 -0.041 1.647 0.348 0.024 0.909 2.266 -0.143 1.054 1.839 -0.231 -1.173 0.438 1.347 -0.285 1.096 -0.859 -0.247 -0.092 -0.139 -0.860 +0 1.653 0.076 -0.893 0.802 -0.658 -0.674 -0.876 0.650 1.028 -0.314 1.221 -0.401 -0.119 -0.938 -0.514 1.035 -0.532 -0.821 -0.947 -1.835 -0.976 -1.451 -0.688 -0.242 -0.288 0.201 0.417 -1.040 +4 1.835 -0.955 1.168 -1.226 -0.963 0.680 1.554 0.651 0.357 -1.361 -2.128 -0.432 0.155 -0.052 2.211 -1.542 -0.305 1.670 0.651 -1.094 -2.215 -0.393 -1.012 -0.445 2.287 -1.013 -0.647 0.914 +2 2.025 0.741 -0.590 0.288 -1.160 -0.956 0.540 0.583 0.368 -1.194 2.350 0.571 -0.117 0.083 -0.610 -0.861 2.472 -0.848 -0.661 -0.330 -0.711 0.012 -0.427 -0.128 -0.322 -0.232 -1.539 -0.312 +3 -1.194 1.671 0.436 -0.254 0.165 0.474 -1.429 0.158 -0.450 0.507 -0.740 -0.605 1.663 1.231 -3.296 1.157 -0.074 -0.708 -1.007 -0.949 -0.680 -1.127 -1.355 -1.040 0.323 0.050 0.556 -0.066 +3 0.919 -1.539 0.480 -0.751 1.218 -0.526 -1.761 -0.040 -0.840 0.811 0.330 -0.629 -1.003 -1.098 -0.586 1.364 -0.144 0.947 -1.481 -1.921 -0.945 0.657 -0.289 0.890 1.264 -0.957 0.834 1.388 +3 0.429 -2.404 1.164 -0.824 1.293 -1.202 -1.260 -0.446 0.398 1.155 -0.177 -0.648 1.236 -0.261 -0.211 0.926 0.612 0.700 0.978 -1.083 0.259 -0.796 -0.741 0.943 1.105 2.446 0.087 -1.332 +0 -0.185 -0.461 -0.343 -2.298 0.283 -1.209 0.630 -0.090 0.105 0.700 -0.927 0.369 0.268 -0.185 -0.928 0.595 0.703 0.114 -1.070 -0.527 -0.060 0.688 -1.583 -0.564 -0.403 -0.083 -1.301 -0.218 +2 -1.370 -0.007 1.191 1.762 -0.130 0.042 2.211 1.135 -0.617 -0.619 0.957 -1.011 1.088 0.334 0.518 -0.257 -0.630 1.462 -0.247 -0.411 -0.080 0.193 -1.031 -0.565 -0.618 0.845 -0.886 -1.756 +4 1.486 0.387 1.023 0.296 -1.878 0.454 -0.193 1.229 1.652 1.804 -0.172 -0.472 -0.320 -0.117 0.166 -0.921 0.021 -2.033 2.151 -0.230 -0.426 0.545 0.839 -2.029 -0.686 1.422 0.874 -1.171 +2 -1.687 1.927 0.333 0.226 -1.645 1.012 1.246 -0.229 -0.436 -0.376 1.727 -0.098 0.165 0.950 -2.439 -0.082 -0.282 0.152 0.807 0.974 -0.236 -0.145 0.024 0.758 1.148 -1.405 0.375 -0.200 +4 -0.355 1.056 -0.184 0.002 -0.269 -0.253 -0.540 -1.273 -1.863 1.043 1.020 0.094 0.377 -0.133 0.645 3.008 -3.486 0.173 1.257 0.861 1.190 0.490 -0.232 -0.206 -0.244 0.857 -0.986 -0.971 +1 -0.048 -0.268 1.491 -0.541 -0.181 0.311 -1.083 0.802 0.550 -0.541 1.165 -1.173 -0.370 1.494 -0.132 -1.483 0.243 -0.281 0.524 1.097 -0.067 -2.372 0.559 -1.010 0.510 0.866 1.063 0.849 +2 -0.323 0.707 1.267 -0.386 1.119 2.221 0.066 0.785 -0.590 0.019 -0.177 0.797 1.205 -0.110 1.534 1.530 0.847 -0.516 -0.612 -0.812 0.949 -1.212 0.677 -1.081 0.824 0.017 1.334 1.141 +2 1.134 2.481 -1.914 1.289 0.642 -0.742 -0.363 0.882 0.051 -0.049 -0.687 -0.759 -0.966 -0.510 -0.129 0.354 -0.823 0.756 -0.908 0.769 -0.866 2.017 0.648 0.841 -0.172 0.607 -1.252 -0.976 +2 0.387 0.421 -0.800 0.907 -0.316 -0.646 -0.168 -1.686 -1.770 0.583 -0.663 -1.223 -1.626 -1.271 -0.769 -1.993 0.657 0.415 -0.797 1.306 -0.713 -0.038 1.331 -0.347 0.800 0.696 -0.487 -0.278 +4 1.700 0.563 -0.730 0.887 -0.452 -0.768 -2.144 0.102 -0.630 0.096 -2.299 0.170 -2.431 1.088 0.522 -0.279 0.563 -0.978 0.072 0.795 -3.094 -0.447 -0.023 -0.196 1.457 -1.124 -0.621 -1.916 +4 0.406 -0.604 0.433 -0.531 -2.603 0.767 0.632 -0.600 -0.088 2.489 0.713 -0.617 0.351 0.583 -0.320 -0.167 -1.742 -0.158 -1.285 -1.193 -0.433 0.471 -1.971 -0.338 -1.460 2.126 -1.470 -0.093 +2 -0.145 -1.171 0.775 0.163 0.872 -0.571 0.406 -0.091 -0.422 0.350 0.686 -0.605 1.578 -0.248 2.187 -0.747 -0.340 -2.084 -0.004 1.207 -1.009 0.645 -0.656 0.312 0.869 2.230 1.220 -0.301 +2 -0.061 -0.204 -0.431 0.686 -1.333 0.013 -0.211 0.038 -1.732 -0.523 0.020 0.541 1.846 0.806 0.461 1.271 -0.304 1.554 1.119 -0.446 1.924 0.447 2.193 0.303 -0.562 -0.768 -0.356 -0.571 +1 -0.770 0.592 0.921 -0.627 0.086 1.047 -1.179 -0.168 0.821 -1.022 0.126 -0.539 -0.711 -0.699 -1.343 -2.226 -0.961 0.409 -0.984 -0.454 -1.380 0.136 1.735 0.562 -0.262 0.409 1.179 1.001 +0 0.486 0.068 -0.843 1.263 -0.054 -1.121 -0.963 0.535 1.049 0.482 0.486 -0.810 0.093 -0.330 -0.195 -0.112 -0.660 0.752 -1.009 -1.299 -0.785 0.883 0.339 -1.783 0.597 -0.049 -0.417 -0.242 +2 0.030 -0.695 0.447 0.206 -0.582 -0.579 1.734 -2.150 -1.530 -0.209 0.984 -0.869 0.579 -1.763 -0.257 0.301 0.861 0.600 -1.883 0.010 -0.718 -0.107 -0.315 0.692 -0.030 0.243 0.061 -2.218 +0 0.296 0.515 -1.576 0.323 0.299 -1.127 -0.136 -1.068 -1.832 1.034 0.904 -0.177 0.444 -1.121 0.860 0.173 -1.245 0.313 0.376 -0.505 -1.519 -0.486 1.131 0.691 0.316 -0.692 -0.578 -0.908 +4 1.070 1.179 3.183 0.426 -0.788 -0.479 -0.158 1.506 0.733 -1.347 -0.057 -1.471 0.592 -0.707 -0.611 -1.879 0.757 1.209 -0.693 -0.162 -0.479 -0.218 -0.820 -1.100 1.369 -2.640 0.504 0.283 +2 0.580 -0.833 -0.046 0.415 -1.268 -0.772 -0.013 -0.909 -0.328 2.496 -1.507 1.425 0.342 -1.224 0.679 0.043 1.205 0.660 0.196 -0.306 1.169 -0.392 -1.400 -1.896 -1.236 0.803 -0.363 -0.772 +4 -0.667 -2.242 0.205 -0.776 -0.800 -0.499 1.745 2.405 0.217 -0.465 -1.177 0.469 1.783 -0.247 -2.379 0.670 0.985 -1.586 0.176 0.451 -0.073 -0.791 1.016 -0.403 -1.485 -2.049 1.261 0.617 +2 -0.027 0.881 -0.171 -0.953 -1.154 -1.126 1.654 -1.635 -0.904 0.555 -0.957 -0.765 -0.833 0.694 -2.444 -0.536 0.346 -0.033 -0.845 1.408 -0.235 -0.500 0.101 -0.148 1.179 -1.131 -0.298 -1.124 +1 -1.353 1.974 0.485 1.483 0.866 0.394 -0.309 0.381 1.450 0.826 0.167 -0.172 0.498 -0.532 0.498 -0.698 -0.354 -1.028 -0.440 -0.684 1.692 -0.894 0.385 -0.947 0.047 0.290 -1.851 -1.398 +4 -0.979 0.684 -1.052 0.232 -0.217 0.260 0.917 0.406 0.870 1.922 -1.916 0.278 -0.043 0.011 -2.051 0.217 -0.505 0.704 2.569 -1.100 0.365 -1.789 0.148 0.950 0.484 1.363 0.669 -1.746 +0 0.636 -0.137 0.516 0.673 0.487 -0.087 -0.559 -0.432 -0.139 0.569 1.305 -1.060 -0.425 0.120 -1.386 -0.795 1.186 0.905 -0.157 -0.988 0.007 -0.616 0.662 1.310 0.092 0.414 0.039 0.325 +4 0.428 -0.279 -1.545 2.874 0.266 0.112 -1.098 0.261 -0.164 -1.889 -1.070 0.795 1.944 0.367 1.103 -0.156 -0.087 1.091 1.663 1.301 -1.066 -0.067 0.278 -0.260 0.388 0.821 1.003 1.686 +1 0.880 0.328 -0.363 -1.024 1.242 -0.279 -0.910 -0.862 -0.173 -0.615 -1.085 -0.756 0.018 -1.214 -0.352 -0.868 -0.130 -0.205 -0.830 0.785 -1.088 -0.035 1.308 0.962 -2.488 -0.835 -0.280 0.265 +1 -0.567 -0.362 0.100 0.218 0.435 -0.439 0.529 0.450 -0.395 -1.719 -0.885 1.568 -1.694 1.318 -1.729 -0.773 -0.375 1.782 0.026 -0.214 -0.935 0.522 -0.513 -0.229 -0.358 0.720 0.762 -0.890 +3 -1.270 1.196 -0.613 -0.339 1.450 -1.122 -0.727 -0.769 1.618 1.993 0.752 0.783 -0.580 1.947 0.268 0.853 -0.106 -0.268 1.663 0.540 1.451 0.255 -0.848 -0.583 0.103 -0.627 -1.107 1.201 +4 -1.285 -2.664 1.382 -2.303 1.900 0.199 0.943 -1.452 0.062 0.507 1.092 0.468 -0.141 -0.159 -0.193 -0.118 -0.607 0.681 -0.886 0.414 -0.249 -2.374 1.295 -1.509 0.199 1.114 -0.220 3.015 +3 -0.162 0.019 2.765 -1.440 1.084 -0.998 -0.469 -0.351 -0.589 -0.023 0.729 -1.139 -0.843 -0.461 0.098 0.327 1.951 1.069 0.761 -1.850 -0.044 1.317 1.527 -0.623 -1.126 -0.634 1.467 -0.096 +2 -0.475 0.946 0.605 -0.213 -1.655 1.232 -0.021 0.883 -1.764 0.949 -1.614 -0.055 -0.696 -0.434 -0.405 -0.909 -0.465 0.049 0.158 0.088 0.891 0.468 0.530 1.160 0.131 2.809 0.777 -0.340 +2 -0.718 -1.417 -0.265 0.090 -0.339 -0.073 -0.549 0.180 -0.070 -1.071 2.477 0.327 0.915 -0.807 -0.427 -0.287 1.182 -0.695 -0.572 -1.387 1.086 -1.611 -2.177 0.390 -0.827 0.569 0.091 -1.144 +3 -0.310 -1.189 1.263 0.829 -0.749 -0.779 -2.098 0.642 0.200 -1.894 0.052 1.267 0.588 -1.307 0.346 1.342 1.300 -0.948 -1.305 0.064 -0.063 -0.740 -1.932 1.010 -1.040 -1.317 1.458 -0.638 +3 1.357 2.133 1.754 0.836 -0.682 1.542 0.922 -0.678 -0.172 -0.013 -1.164 -0.695 1.099 0.640 2.446 -0.973 -0.007 -0.222 0.638 -0.248 -1.103 -0.608 1.285 -0.561 0.265 -0.065 -0.699 0.542 +1 1.180 0.226 -0.387 -0.599 0.021 -0.389 0.116 -0.647 0.887 -0.632 -1.491 2.817 1.002 -0.673 -0.884 0.687 -0.646 1.039 0.519 0.290 0.190 1.461 -0.200 0.141 -0.757 0.605 -0.864 1.221 +0 -0.007 0.013 0.172 0.142 -0.061 0.025 1.555 0.472 0.105 0.659 0.659 0.946 -0.757 -0.481 -0.814 0.767 0.718 -1.273 0.391 1.372 -0.235 0.314 0.730 -1.321 0.152 1.272 -0.593 0.832 +0 -1.068 1.124 -0.424 -0.367 0.185 0.317 0.612 -0.159 0.676 0.137 -0.549 -1.243 0.634 0.305 -0.663 -1.417 -0.586 -1.955 0.975 -0.110 0.263 -0.405 1.770 0.701 -0.371 -0.342 -0.625 -0.148 +4 -0.904 -0.727 0.155 -0.327 0.827 1.566 0.836 0.913 -0.768 0.607 1.949 -0.995 -1.303 0.492 2.676 -1.506 -0.828 -1.121 -0.174 2.369 0.581 -0.438 0.117 0.021 1.690 -2.114 -0.304 -0.696 +3 -0.294 -1.587 0.800 0.356 1.087 -0.348 1.134 0.039 0.113 2.114 -0.485 1.227 0.480 -0.572 -1.316 -1.033 1.131 1.734 0.091 0.144 0.776 0.073 1.100 0.292 0.136 -0.699 -0.945 -3.159 +2 -0.448 0.386 0.475 1.677 -0.873 1.236 -0.566 -1.555 -1.008 0.109 -0.695 1.426 1.457 0.974 -1.587 0.146 -0.245 -0.148 -0.002 0.222 1.088 0.617 0.254 -0.028 -2.293 0.741 -0.390 -1.045 +1 2.174 0.218 1.644 0.471 1.334 0.043 1.236 -0.984 1.036 -0.716 -0.163 -1.072 -0.649 -0.292 0.614 0.366 -0.372 0.597 0.485 -0.336 -0.386 -0.339 2.037 -0.631 1.105 -0.749 -0.427 0.481 +4 -1.819 -0.794 -1.357 0.459 2.084 -0.893 -1.483 -1.029 1.764 1.262 1.069 -1.346 0.925 0.056 0.857 -1.064 -0.025 -0.588 0.690 2.202 -1.302 -0.211 1.038 -0.410 -0.823 0.096 -0.403 1.751 +3 2.258 0.892 1.856 -1.178 -0.052 1.485 0.241 0.179 0.625 -1.416 1.370 -0.981 -0.643 -0.865 0.086 0.379 -0.450 -1.267 -1.249 -0.663 0.763 -2.146 1.601 -0.733 0.425 0.262 -0.565 0.542 +1 0.889 -1.122 -0.767 0.068 1.029 0.502 0.248 0.078 0.400 0.202 -1.030 0.046 -1.074 0.081 -0.481 2.539 -0.040 -1.404 -0.134 0.519 1.673 -0.589 -1.582 0.817 -1.381 -0.436 -0.258 -0.070 +1 -0.565 0.410 -0.139 -0.662 -0.488 0.600 -1.287 0.311 -1.100 0.549 1.361 -0.845 -0.874 1.961 0.792 -0.300 -0.079 -1.176 -2.097 -0.039 -0.484 0.647 0.013 -0.347 -0.392 -1.683 -1.132 -1.094 +0 -0.941 0.198 0.265 1.422 1.401 -0.610 1.207 0.276 1.222 0.807 1.200 -0.428 -0.086 0.155 -0.068 1.186 0.247 1.136 0.676 -0.394 1.100 -1.220 0.292 0.374 0.569 0.420 0.620 0.877 +1 2.189 1.786 1.138 1.123 0.832 -0.047 -0.737 -0.021 0.422 -1.454 -0.153 -0.826 1.014 -0.550 0.328 -0.700 1.598 -1.701 0.476 -1.277 -0.509 0.206 0.255 0.229 0.152 -0.642 -0.164 0.032 +1 0.350 -1.176 0.104 0.035 0.003 0.160 0.117 1.950 0.105 -0.124 -1.556 -1.387 1.339 0.885 0.934 1.418 0.467 1.801 -1.043 -1.643 -0.118 0.320 0.415 1.135 0.014 0.790 -0.115 -0.846 +4 0.418 0.522 -1.090 -0.649 -0.677 -0.914 1.304 0.216 0.553 3.458 -1.287 0.612 -1.737 -0.838 0.701 -0.738 1.128 -0.871 0.934 1.574 -0.007 0.589 0.912 1.663 -0.425 0.393 3.314 0.303 +2 1.847 0.972 -0.731 0.840 -0.305 1.217 -0.486 -1.052 -0.796 -0.745 -0.840 -0.573 0.857 1.796 -1.330 1.264 -0.478 0.348 -1.654 0.524 -2.005 0.314 0.257 0.191 -0.735 0.550 0.391 -0.210 +0 -1.001 0.836 0.442 1.396 1.170 0.132 0.117 0.493 -1.024 -0.719 -0.994 -1.485 -0.302 0.853 -1.733 0.030 0.530 -0.030 0.491 -1.615 -0.132 -0.314 0.229 0.369 -0.161 0.692 0.354 0.781 +1 -1.545 -1.180 -1.174 0.275 -1.416 -0.207 0.040 -0.303 -0.271 1.276 0.500 0.703 -0.372 0.785 0.304 0.210 -0.391 0.237 2.108 0.604 1.709 -1.887 0.072 0.821 0.387 0.515 -0.389 1.211 +4 -1.598 1.490 2.055 1.543 -0.336 -1.155 -1.066 -1.684 -0.107 1.606 -0.142 0.302 1.376 -0.404 2.036 1.992 -0.131 -0.578 0.697 -0.348 -1.157 1.281 1.216 0.156 -1.263 -1.230 0.107 -0.500 +3 1.422 -0.010 -0.670 1.282 1.126 -1.253 1.416 -1.020 0.379 -0.816 0.164 0.116 2.232 -1.622 -0.771 -0.465 0.329 -0.312 -2.198 0.737 1.335 0.433 -0.456 0.205 -1.217 1.061 0.498 -0.533 +1 -0.002 -1.067 -1.259 -1.005 -0.723 0.367 -0.796 1.746 1.027 0.808 -0.120 1.514 -0.218 -0.031 0.170 0.702 0.637 -0.475 -0.533 0.640 0.715 -0.393 -1.587 -0.207 0.624 1.805 -1.198 -0.427 +3 0.948 0.435 -2.309 -1.387 0.592 -0.815 1.174 -1.478 0.825 -0.875 1.123 -0.739 -0.967 0.081 -1.467 -0.948 0.868 1.452 -0.227 -0.495 -1.523 0.635 -0.442 -1.210 0.278 -1.319 0.152 -0.392 +0 -0.232 -0.978 -0.265 -1.551 0.922 -0.518 0.201 0.051 0.557 -2.461 0.450 -0.275 0.407 -1.179 0.414 1.299 0.190 -0.717 -0.674 0.576 0.570 0.357 -0.038 0.203 -0.065 -0.381 0.757 0.310 +3 -1.604 -0.648 0.327 -0.280 -1.628 -0.852 -1.494 -0.719 1.661 -2.181 1.142 0.911 -0.051 0.521 0.649 -1.058 -0.112 -0.836 0.423 -0.848 1.716 1.195 -0.050 -1.107 -0.337 0.526 2.040 -0.447 +4 0.346 1.603 1.571 -0.528 -0.928 -2.666 0.142 0.473 -0.594 -0.840 1.439 -0.436 0.052 1.203 -2.271 1.737 -0.833 -0.858 -0.279 1.138 0.807 0.423 -0.401 1.155 -0.842 2.009 1.088 0.451 +3 -1.127 0.572 1.328 2.520 1.050 0.361 0.275 1.552 -0.286 0.796 2.194 -0.109 -2.014 -0.861 0.293 -0.837 -0.347 -0.295 0.561 -0.946 -0.584 1.407 -0.482 0.726 0.014 -0.023 0.738 -1.862 +3 -0.738 -0.434 -0.721 -1.039 1.737 1.386 -1.863 0.318 -0.065 0.145 -1.451 -1.847 -0.055 -0.370 -0.549 -0.207 0.600 0.881 -0.981 -0.887 0.150 1.223 -0.214 0.145 -1.364 -2.196 2.313 0.277 +2 -0.054 1.518 0.641 -0.647 -1.682 0.042 0.251 -0.073 1.806 -0.132 -2.282 -0.407 0.455 0.387 -0.177 -0.342 -2.000 0.086 -0.339 0.249 0.491 1.241 -0.956 -1.294 -0.574 -0.736 -0.825 1.445 +3 -1.467 1.472 0.671 -0.576 2.086 -1.363 -0.329 0.987 1.153 -1.493 -0.283 0.746 1.473 1.389 0.700 0.090 -0.179 0.498 1.434 -0.214 -0.273 1.145 1.062 -0.500 -1.319 -1.713 0.130 -1.105 +1 -0.092 -1.318 -0.088 1.515 0.693 -0.359 -2.632 0.001 0.085 -1.152 0.864 0.397 -0.808 -1.575 1.423 0.583 -0.971 -1.193 -0.115 0.501 -0.707 -0.038 0.892 0.542 0.047 -0.122 -0.960 -0.516 +0 1.199 0.200 0.761 0.354 -0.220 0.929 0.345 -0.961 -0.989 0.149 0.907 1.639 -1.777 0.026 0.490 0.296 1.009 0.656 -0.986 -1.015 0.312 0.991 0.143 -1.578 -1.047 0.692 -0.349 0.118 +4 -0.329 0.554 1.033 -0.604 -0.359 2.250 -1.758 1.176 2.477 -1.541 1.565 0.814 1.004 0.251 -0.619 -0.865 -0.293 1.149 1.576 1.682 -0.271 -1.006 -0.029 1.893 -0.018 -0.463 1.589 -1.290 +2 -0.163 0.222 0.045 0.036 -3.141 0.165 -1.328 0.476 1.057 0.560 -1.148 -0.834 -0.067 0.853 0.995 0.361 -0.964 1.174 -0.026 -0.398 -0.487 -1.522 1.100 -0.125 -0.692 0.607 0.787 -1.733 +3 -0.439 -0.881 0.129 0.352 1.605 1.397 -0.346 0.941 -0.326 0.140 -1.057 1.147 -1.163 -0.721 -0.821 0.694 -0.749 -1.572 1.457 -0.123 1.389 0.100 -1.081 -1.993 1.599 0.226 1.686 -0.463 +1 0.959 -0.712 -2.112 0.656 1.549 0.692 0.316 -0.848 0.440 0.653 0.069 0.915 1.158 -0.490 -0.368 -0.141 1.512 1.485 1.048 -0.142 0.487 -0.544 -1.263 -0.238 0.956 0.273 0.255 -0.405 +2 1.392 0.170 0.369 0.625 0.589 -0.021 -0.901 2.684 -0.293 0.331 0.440 -0.533 -0.426 1.488 -0.739 0.412 0.896 -0.214 -0.094 0.957 -0.137 -2.077 1.504 -0.975 0.215 -1.318 -0.511 -0.436 +2 0.085 0.251 1.530 -1.428 1.255 0.982 -0.813 1.156 -0.953 0.479 -0.793 -0.100 0.622 0.213 -0.203 0.173 -0.554 -0.042 0.429 0.717 -2.244 0.713 -1.494 -0.155 -0.186 -2.090 1.857 -0.664 +2 -0.104 0.197 0.941 -0.261 0.025 -1.860 2.084 -0.026 -0.515 -0.027 -0.043 1.394 -0.701 0.192 0.101 0.768 -0.894 -0.958 1.829 0.585 0.691 -1.016 0.066 0.725 1.956 1.285 1.119 0.423 +0 -0.346 -0.820 -0.536 0.635 -1.076 0.065 1.474 0.489 0.503 1.147 0.464 0.076 -0.568 -0.577 0.934 0.061 -0.892 0.395 1.683 -1.601 -0.022 -0.206 1.406 -0.051 0.117 0.210 0.760 0.821 +1 0.318 0.103 0.528 1.439 -0.127 -2.363 -0.577 -0.901 0.612 -0.722 0.678 0.786 1.258 0.765 0.823 -0.609 1.440 0.160 -0.147 1.836 0.294 0.736 -0.515 0.024 0.694 1.088 -0.411 0.938 +1 0.997 -0.887 -0.301 1.285 0.052 -1.400 1.679 0.270 0.670 -0.779 -0.538 -0.274 -0.827 -0.402 -1.011 -1.870 -1.370 -0.872 -0.449 -0.470 0.305 1.460 -0.364 -0.661 1.220 0.010 -0.067 0.865 +1 2.079 0.009 0.875 0.447 0.258 0.830 -1.443 -0.089 0.281 -0.963 -0.047 0.094 -0.169 -0.888 -0.754 -0.321 0.385 0.095 0.199 -0.231 -0.840 -0.307 -0.833 -3.028 -0.014 -0.260 1.237 0.689 +0 0.520 -0.973 0.914 0.414 -1.882 0.914 -1.015 -0.089 1.017 1.965 1.148 0.403 -0.664 0.482 0.983 -0.582 -0.894 -0.865 -0.117 0.230 -0.081 0.186 -1.254 -0.362 -0.004 -0.426 -0.047 -1.235 +2 -0.648 -1.668 0.860 -0.115 -0.303 -0.546 1.872 0.355 0.550 0.163 0.270 1.399 -0.251 -1.457 0.868 0.497 0.108 0.471 -0.371 0.021 -0.132 -0.591 1.189 -1.412 2.361 0.877 -1.778 -0.224 +3 0.368 -0.114 1.442 -0.183 0.419 -0.630 0.132 2.065 0.860 0.426 -1.374 2.358 -1.221 0.071 0.462 0.631 -0.706 0.671 -1.227 1.658 -1.284 -0.519 0.571 -0.171 1.516 1.259 0.494 -0.719 +3 -0.148 -0.780 -1.258 -0.266 0.511 0.630 0.180 1.275 0.086 0.823 -0.264 0.457 1.959 0.178 1.197 0.010 -1.254 0.158 0.534 0.849 -0.681 3.766 1.080 1.257 0.880 -0.841 -0.419 0.563 +1 -0.220 -0.641 -0.438 2.883 -0.180 1.753 0.193 0.647 0.764 -0.073 1.489 0.165 0.150 0.017 -0.727 -0.554 0.139 0.899 1.851 0.948 0.392 0.195 -0.326 -0.191 -0.183 -0.011 0.012 0.400 +0 -0.495 0.570 -0.433 -0.228 -1.176 1.143 1.553 0.212 -0.486 0.935 0.589 -0.444 1.151 -0.424 -1.286 -0.066 1.084 0.103 -1.173 -0.403 1.196 0.274 -0.019 -0.130 1.080 1.015 -1.401 0.940 +2 -1.402 -0.075 -0.041 -0.127 -0.943 -1.673 1.429 0.019 -0.313 -0.067 -1.341 1.288 -0.715 -0.113 0.486 0.717 -0.024 -1.031 0.599 0.850 1.173 0.543 2.095 0.149 0.200 1.229 1.611 1.389 +2 -0.658 0.648 -1.432 -0.119 -0.304 -0.571 -0.163 0.650 0.311 -0.054 0.218 1.580 1.299 -1.192 -0.501 -1.021 1.425 -0.259 -0.636 -1.588 0.159 1.611 1.238 -1.489 0.452 -0.546 -0.507 1.710 +1 1.005 0.723 0.583 0.810 -0.070 0.890 0.559 -0.119 -0.808 -0.387 0.628 -0.985 1.833 -0.579 -0.822 -0.329 1.118 -1.242 1.462 1.044 1.798 0.608 0.294 -1.703 0.145 -1.276 0.939 -0.143 +0 1.085 -0.299 1.298 1.233 -0.587 0.569 -1.313 -1.054 0.112 0.509 1.613 -0.163 0.176 0.098 1.794 1.342 0.385 0.794 0.350 1.075 0.862 -0.302 0.099 0.162 0.152 -0.508 0.337 -0.509 +4 -0.210 0.407 -2.131 0.736 -0.294 -0.968 -0.658 0.329 -0.689 -0.381 1.727 -0.856 -1.635 -0.415 -0.868 1.159 -0.820 -1.216 0.975 -0.493 1.432 -0.540 -1.848 -1.196 -2.565 -2.722 -0.015 0.666 +3 -1.229 -0.626 0.791 -0.306 -0.979 -1.424 -0.408 0.082 0.765 1.054 -0.329 0.439 0.303 -0.012 1.488 -3.159 -1.927 -0.327 0.412 1.849 -0.030 0.416 -0.020 0.878 -0.715 -0.071 -1.223 -0.596 +3 -1.550 0.849 0.404 -1.026 -0.669 -0.088 -2.025 2.272 -0.980 0.031 1.671 -0.184 0.322 -0.957 -0.548 -0.925 1.280 -0.667 -0.743 -0.861 0.382 0.581 -1.998 0.068 0.301 1.405 -1.224 -1.121 +1 0.298 -0.072 0.176 1.841 -0.812 0.032 -0.985 -0.329 0.574 0.363 0.073 -1.497 0.808 1.312 -1.577 0.601 -1.213 0.991 0.840 1.362 -0.513 -0.608 1.888 -0.172 -0.489 -0.782 -1.453 -0.367 +4 -1.276 0.448 1.048 -0.582 -1.586 -0.441 0.188 0.592 -0.521 -2.035 -1.276 0.360 1.991 -0.022 1.313 -1.057 -0.648 -2.340 0.370 -0.894 0.599 -0.502 2.022 1.793 -2.303 0.591 -0.743 0.647 +3 1.962 -0.940 -0.606 0.428 -0.701 -0.351 0.143 0.908 0.157 1.254 -1.434 0.355 -1.510 -2.088 -0.251 0.339 0.136 -0.382 -0.182 -0.261 -0.874 -1.776 -0.406 -0.095 0.471 -0.125 2.865 -0.459 +4 -0.858 -0.802 -0.845 -0.736 0.631 -0.547 0.909 1.130 0.538 -1.426 -0.788 0.677 1.629 -1.845 0.846 -0.918 -1.720 0.708 -1.367 -1.398 2.469 -1.686 -1.197 -0.073 -0.722 0.301 0.470 -1.009 +3 0.269 -0.133 0.812 1.034 -1.295 0.098 -0.902 0.199 -1.387 -0.061 0.283 -2.288 -0.268 -1.497 0.830 -1.942 -0.353 1.314 -1.047 -0.734 -0.075 0.053 -2.324 -0.262 0.694 2.151 -0.762 -0.296 +0 -0.719 0.195 -0.180 -0.402 -1.658 -0.515 -1.110 0.552 0.991 0.741 0.342 -0.853 0.205 0.793 -0.586 0.119 -0.145 1.662 0.852 0.609 -1.717 0.173 -0.155 -0.164 -1.563 -1.088 -0.068 1.114 +2 -0.272 -0.834 -0.107 -0.694 1.637 1.323 0.724 0.031 1.139 0.099 0.161 -1.728 0.261 -1.291 -0.403 0.169 -0.899 0.479 1.052 -0.884 0.117 -1.246 -1.167 0.310 0.713 -1.509 2.049 -0.497 +2 0.018 -1.435 0.031 0.469 1.409 1.819 -1.192 -0.117 -0.104 -0.713 0.099 -0.080 0.614 1.357 0.132 -0.490 -1.026 2.042 0.907 0.400 -0.166 1.612 -0.160 -1.712 0.265 -1.228 0.269 1.344 +4 -1.140 -1.821 0.722 -1.306 0.968 -2.185 0.149 -0.320 1.677 0.226 0.613 -0.431 -0.179 1.052 0.016 -0.919 1.140 1.686 1.503 1.290 -0.283 -0.981 -1.186 -1.307 -0.094 1.771 -1.654 0.017 +3 0.863 -1.975 0.514 0.281 0.267 -1.163 -1.704 2.244 0.133 1.936 0.903 0.112 0.232 1.080 1.193 0.476 1.093 1.140 0.330 -0.604 0.497 -0.748 -0.380 0.398 -0.079 1.316 -0.660 -2.128 +0 1.012 -0.806 -0.031 -0.136 0.342 0.472 -0.942 0.644 0.267 0.112 -0.276 0.774 1.149 0.744 -1.291 1.625 0.619 -0.396 0.556 -0.186 0.491 1.463 -0.067 1.048 -0.268 1.029 0.444 0.339 +1 -1.665 0.327 0.981 -1.657 0.904 1.348 0.992 0.311 -1.376 1.696 -0.251 -0.529 0.828 -0.203 -0.800 -0.360 -0.136 1.370 0.216 1.141 -0.476 -0.905 1.037 -0.259 -0.118 1.032 -0.863 0.364 +2 -0.144 -0.982 -0.220 0.543 0.685 0.240 0.244 -1.525 1.213 0.304 1.312 0.507 0.954 -1.077 2.924 0.251 0.920 0.575 -0.754 -0.817 0.031 -0.993 1.770 0.385 1.296 0.702 -0.999 0.458 +3 -0.889 0.160 0.330 -0.252 -1.712 -0.398 1.519 2.296 2.807 -0.008 1.018 -0.102 0.204 0.659 0.630 0.306 0.352 0.298 1.126 -0.818 -0.493 -0.461 0.380 0.768 -0.742 -2.065 1.734 0.081 +2 0.882 0.495 1.947 -0.351 -0.002 0.165 0.754 -1.970 -0.733 -1.528 -0.840 0.912 0.668 0.037 1.113 0.404 0.748 -0.387 -1.660 2.058 -0.730 -1.239 -0.164 -0.596 0.348 -0.724 -0.375 -1.089 +0 -0.675 1.449 -1.155 0.478 -0.492 0.410 0.050 -0.283 0.389 -0.493 -1.784 -0.928 -1.048 -1.604 0.369 -0.173 0.354 0.521 0.756 0.407 -0.980 0.580 0.222 2.190 0.008 0.383 -0.095 0.049 +2 -0.968 -1.876 1.320 -1.848 -0.072 1.017 1.682 -0.899 0.159 -0.122 -0.727 1.244 0.570 -1.191 -0.199 -0.378 0.435 1.907 0.336 -0.280 -0.097 0.403 -1.732 1.112 0.168 -0.124 -0.409 -0.068 +1 0.632 0.841 0.932 0.214 0.446 2.257 2.407 0.047 -1.768 0.129 0.116 -1.262 0.125 -1.045 0.377 0.595 0.527 0.989 0.469 0.789 1.292 -0.442 -0.144 -0.355 -0.745 -0.298 0.835 0.727 +3 -0.806 -0.408 -0.428 0.100 -0.280 0.269 0.431 1.268 0.549 -0.702 2.111 -0.695 -0.388 0.597 2.895 0.402 -0.642 -1.639 -1.706 0.011 0.545 -1.420 0.222 -1.314 -0.924 0.632 -0.748 1.310 +4 2.157 -0.157 -0.764 1.249 -0.079 0.775 1.882 1.478 0.373 0.727 1.347 -0.956 -1.306 2.014 -1.251 2.268 -0.695 -0.903 0.794 -1.606 2.572 1.924 1.540 -1.525 1.295 1.189 0.166 0.841 +4 0.041 1.322 2.697 0.808 -0.870 -1.153 -1.956 0.601 0.330 1.635 -1.699 -0.805 -0.862 1.499 0.362 -2.057 -0.675 -0.609 1.734 0.914 -0.079 0.333 -1.332 0.171 -0.159 1.753 -0.049 -0.043 +2 -0.924 0.547 -0.007 1.283 0.030 1.325 0.268 0.414 1.280 0.502 0.051 0.239 -0.333 0.739 0.795 -0.810 -0.172 -0.857 0.162 -2.115 -2.444 0.322 -0.520 2.156 1.091 0.905 -0.155 -1.200 +2 0.302 -1.589 -0.584 1.783 -1.591 -0.439 -0.165 0.004 -0.413 0.082 -0.036 0.710 0.845 0.440 0.237 -1.564 0.116 1.114 -1.611 -1.317 1.492 -1.498 -0.103 -0.552 1.432 0.303 0.791 0.056 +4 0.633 0.668 0.581 0.141 -1.153 -1.255 -0.642 2.242 -1.205 1.136 0.138 1.345 0.880 0.816 0.561 -2.251 1.303 -0.311 1.913 0.739 -2.031 -0.606 -1.699 -1.290 0.487 1.338 0.791 -1.026 +0 1.053 2.130 0.751 0.295 -0.340 0.182 1.360 -0.045 -0.886 -1.620 0.513 0.247 -0.646 1.026 -0.422 -0.135 -0.017 -0.351 0.236 1.152 -1.142 0.489 0.172 0.154 0.872 0.049 0.480 0.716 +3 2.383 0.612 0.558 -0.192 1.181 0.166 -0.619 0.739 -0.025 -1.500 -0.965 1.271 -0.307 0.751 -1.297 1.023 -1.909 -1.575 -1.498 0.262 0.241 -0.886 -1.827 1.170 0.331 0.471 0.226 -1.457 +1 -0.132 -1.598 0.829 0.583 0.311 -1.468 0.176 0.450 1.860 0.384 -1.745 0.518 1.176 -0.329 -1.542 1.875 0.121 -0.537 0.798 -0.932 -0.938 -0.381 0.239 -0.608 0.560 0.691 -0.026 -0.198 +0 0.137 -0.728 -0.111 0.417 -0.734 0.041 -0.257 1.108 -0.563 -0.480 1.172 0.886 -0.746 -0.418 0.299 0.643 0.018 -0.530 -0.886 -0.030 -0.446 1.328 -1.321 1.683 0.060 -1.157 -0.437 -1.092 +2 1.538 1.234 -0.792 -0.605 -0.780 1.296 -0.029 0.385 -0.702 -0.555 -0.064 1.033 0.647 0.411 -0.592 0.220 -0.397 1.416 -1.314 0.649 0.122 -0.505 1.856 -2.104 -0.102 0.679 1.328 1.266 +2 1.615 -0.456 0.749 0.423 0.049 -0.592 0.756 1.598 -1.996 0.161 0.587 -0.846 0.446 0.360 0.725 -0.340 -1.318 0.027 1.302 0.885 -1.311 -0.162 -0.110 0.592 -0.101 -0.167 -0.582 2.419 +4 1.065 -1.202 -0.414 -0.523 0.355 0.773 -1.480 -0.006 0.869 -1.855 -0.064 -1.216 -0.642 -2.505 1.393 -0.356 -0.807 0.328 -1.042 -1.137 1.510 1.809 -0.592 1.067 0.356 -1.414 -0.928 1.551 +3 0.348 0.893 -0.282 -1.110 1.246 0.439 0.195 -0.664 0.194 -0.751 -0.218 -1.618 1.278 -0.015 0.653 1.781 -0.383 0.543 1.730 1.193 0.870 0.368 -0.566 2.869 -1.750 -0.353 0.041 1.394 +4 -1.822 -0.354 -0.669 -0.389 -1.376 1.415 -0.171 -0.444 -0.665 0.288 -2.111 0.275 0.481 0.727 0.999 -0.714 -2.289 -0.704 -0.996 -1.915 -1.399 -0.132 -0.334 -1.644 -1.379 0.614 -1.780 1.198 +0 0.122 0.054 0.946 1.089 0.696 0.353 -1.049 -1.069 -0.846 0.752 0.073 0.869 1.003 -0.726 0.032 -0.013 -0.328 -0.535 1.185 0.052 0.013 -0.803 0.675 -1.005 0.455 0.482 -0.413 0.318 +1 0.683 -0.137 0.023 0.838 0.158 0.606 -0.257 1.740 -1.377 1.602 -0.419 -0.997 1.354 -0.528 -0.660 -2.434 0.217 1.590 0.267 0.156 1.288 0.051 -0.456 -1.032 -0.261 -0.463 0.841 -0.041 +3 -1.238 -0.045 -1.151 0.804 0.897 -0.990 -0.599 -0.964 0.225 -0.101 0.038 -0.327 1.833 0.960 -0.144 -1.530 -1.695 -1.618 0.438 -0.164 1.232 0.717 -2.230 -0.821 -1.678 -0.402 0.310 0.075 +3 1.115 1.366 -0.848 -0.282 0.672 1.694 1.323 -0.276 -0.020 0.686 -0.669 1.490 0.888 -0.655 0.470 1.371 0.769 -0.102 -0.519 -0.851 1.400 -2.408 -2.038 1.181 -0.631 -1.481 0.445 0.116 +1 0.964 0.633 -1.553 -0.499 -0.325 -0.960 0.310 0.972 0.309 -0.982 0.581 0.729 0.457 0.795 0.140 1.852 -0.325 -1.451 -1.043 1.672 0.982 -0.523 0.584 -0.560 -1.341 0.650 0.749 0.435 +1 0.963 1.374 -1.362 0.115 -1.680 -0.276 0.120 0.319 -0.182 -0.418 -0.574 -1.432 1.229 1.885 1.276 1.070 0.452 0.449 1.352 -1.119 0.804 0.078 -0.566 -0.601 0.119 0.051 0.612 -0.241 +0 -1.397 -1.318 -0.213 -0.133 -0.591 -1.182 -1.221 -1.217 -0.270 -0.649 -0.241 0.890 0.183 0.012 1.240 -0.516 0.532 0.236 1.318 0.054 1.142 -1.750 -0.055 -0.593 1.065 -0.117 -0.996 -0.090 +4 -0.852 0.014 -1.494 1.465 0.416 0.585 2.102 -0.344 0.856 0.582 1.539 0.691 -0.509 0.424 -0.647 -0.467 -0.014 -0.186 -0.894 -2.269 0.296 0.368 0.364 1.906 -1.536 1.553 2.492 1.764 +3 -0.504 0.434 -0.647 -2.276 -0.925 -1.448 1.092 -0.607 -0.514 -1.064 0.094 -0.716 0.992 0.302 -0.572 0.850 0.008 0.021 -1.463 -0.071 0.322 1.334 -0.752 2.278 0.557 -1.519 -0.845 1.421 +3 0.267 -1.436 0.279 -2.204 -0.469 0.630 -0.401 -1.663 0.045 0.776 -1.006 0.148 -0.639 0.999 2.628 -0.720 -1.787 0.106 -0.538 1.088 -1.065 0.114 0.357 0.242 0.267 1.204 1.289 0.102 +2 -1.195 0.286 -0.845 0.553 -0.034 -1.595 0.421 -0.578 0.881 0.001 0.395 -0.958 -0.589 -0.459 0.857 1.677 -0.805 1.077 -2.170 -1.194 -0.959 -1.353 -1.584 0.413 -0.214 0.379 0.690 0.266 +1 -0.265 1.441 -0.615 1.061 -1.377 -1.198 1.901 -0.012 -0.538 1.363 1.246 0.921 -0.570 -0.287 0.312 0.694 -0.508 0.171 0.267 1.287 0.754 -1.507 -0.214 -0.929 0.568 0.923 0.102 -0.268 +4 2.373 -0.567 -0.271 -0.298 -0.047 -1.602 -0.053 2.277 2.050 0.085 1.031 -0.044 0.087 0.849 -0.099 1.572 -1.836 -0.796 0.508 -1.332 -1.004 -0.033 -2.183 -1.516 -1.410 0.128 -0.596 0.096 +1 -0.309 -0.117 0.427 0.815 -0.989 0.921 -1.816 -2.087 -1.652 0.521 0.622 0.202 -0.564 0.473 -0.671 -0.326 -0.837 0.647 -0.581 0.387 0.088 1.601 0.069 1.244 0.809 -1.123 -0.557 -0.272 +2 -0.052 0.717 -0.299 0.714 -0.076 0.867 1.151 -1.585 0.358 1.830 0.618 2.438 -0.928 0.258 0.488 -0.527 -1.236 2.170 1.122 0.558 0.048 0.140 0.628 1.442 -0.707 0.106 -0.370 -1.057 +3 1.013 0.158 1.651 -0.548 0.487 -0.252 1.382 -0.204 0.854 -1.078 -0.415 -0.084 1.263 -2.197 -0.333 -0.366 -2.077 -0.464 -1.541 -2.331 -0.262 -0.296 -0.245 0.253 -0.879 1.791 -0.033 1.286 +3 1.786 -1.406 -0.905 -1.164 -1.053 -1.182 0.658 -1.329 -0.717 1.900 -0.352 1.114 0.188 -0.980 -1.032 -0.637 -0.447 2.110 1.058 0.116 -0.960 -0.118 0.024 0.804 -0.588 0.781 1.497 0.192 +0 0.721 0.589 0.222 -0.537 -0.990 -1.383 -0.626 0.912 -1.002 1.209 -1.026 -0.164 0.451 1.027 -0.367 0.481 0.213 -0.250 -1.039 0.440 0.028 -0.320 0.115 0.571 -1.680 -0.546 -0.974 -0.899 +0 0.014 0.252 0.927 -0.676 -0.487 -0.669 -0.332 1.426 0.573 -1.131 -0.600 0.373 -0.907 -0.725 1.345 -0.613 0.955 1.255 -0.733 -0.890 -0.276 -1.089 0.121 -0.206 0.361 1.397 0.817 0.489 +2 0.608 -1.312 1.000 -0.505 1.107 0.400 0.626 -0.554 -1.145 -1.162 -1.716 -0.383 -0.587 1.012 -1.034 2.194 -1.002 -0.907 -1.480 0.010 -0.322 -0.108 0.407 0.983 -1.170 0.020 0.855 -1.094 +0 -0.627 -0.197 -1.461 0.628 -0.626 1.224 0.600 1.938 0.420 0.401 -0.183 -0.385 -0.259 0.999 -1.349 0.063 -1.666 0.373 -0.459 0.504 -0.742 -0.257 0.632 0.126 1.543 -0.482 -0.568 1.237 +4 -1.139 0.411 -0.817 3.086 0.752 -1.165 0.958 -0.409 0.092 -0.142 -1.827 -0.604 -1.127 -2.413 -0.530 -0.261 -1.470 1.079 0.372 0.103 0.514 0.546 0.267 1.545 -2.095 0.532 -0.374 1.400 +0 -1.156 1.475 -0.091 -0.195 -0.507 -0.729 0.367 -0.229 -0.917 -0.821 0.896 1.004 -0.353 0.035 0.452 -0.040 -0.298 0.019 0.991 -0.499 -0.246 1.404 0.522 1.145 0.150 0.945 2.480 0.480 +4 1.865 0.638 1.663 0.614 0.111 0.829 -0.605 1.207 -0.880 0.584 -1.297 -1.449 0.756 -1.645 0.210 -3.426 1.049 -0.861 0.423 0.365 0.849 0.184 0.323 -0.996 -1.805 1.973 -0.728 0.082 +0 0.393 -0.474 0.022 0.512 -0.457 -0.886 0.228 1.483 -0.153 -0.352 -0.793 1.150 0.875 -0.146 -0.787 -0.797 -0.855 -0.493 -0.561 -0.738 1.115 1.585 -1.089 -0.571 -0.948 0.318 -0.340 -1.031 +4 0.423 0.926 1.244 -1.041 -0.691 0.908 0.148 -2.227 -1.666 0.663 0.839 -1.349 1.099 -0.197 -1.022 -0.560 -1.223 -0.616 -1.869 -0.074 0.587 -1.347 0.017 -0.805 0.669 -2.314 0.712 -1.818 +0 1.333 0.290 -1.329 -0.882 0.125 -0.696 0.438 0.304 -1.450 -0.082 0.546 0.836 -0.385 0.255 -0.301 -0.018 -0.025 0.739 0.268 -0.954 -0.522 0.531 -0.725 0.356 1.296 1.011 0.701 0.169 +1 0.280 1.747 -1.475 0.446 -0.349 1.792 -0.080 1.160 -1.070 0.548 -0.302 0.917 -0.043 0.301 1.114 -1.040 -0.230 -0.271 0.697 1.926 -0.037 1.213 0.397 -1.000 0.279 0.638 -0.487 1.197 +2 -0.368 1.207 0.931 -0.331 -0.440 -0.712 0.818 0.236 0.288 -0.361 1.458 -1.489 0.782 0.982 -0.317 -1.450 0.383 1.196 1.772 0.657 -0.614 0.693 0.501 0.322 -0.885 0.598 0.470 2.428 +4 2.302 -0.763 1.005 -0.393 -1.087 0.103 0.649 0.921 1.562 2.945 0.251 -0.663 0.453 -0.266 0.926 -1.297 2.368 0.856 -1.684 -0.709 0.816 1.176 -1.272 0.713 -1.947 -0.089 1.112 -0.552 +4 -0.787 0.203 0.682 0.963 -1.226 2.197 0.767 -0.002 1.090 2.365 -1.439 -0.225 -2.378 0.193 0.365 -0.039 -0.655 -2.050 0.374 1.455 -0.591 0.440 -1.568 0.457 -0.393 -0.762 -0.760 -0.394 +0 -0.022 -0.070 0.060 0.018 0.523 -1.051 0.502 -1.128 2.104 -0.105 0.179 1.771 -0.775 -0.011 -1.410 -0.970 -1.424 0.798 0.569 -0.437 -0.507 0.971 -0.005 -0.867 1.073 -0.141 0.677 0.072 +0 -0.118 -0.233 -0.841 0.319 1.137 -0.499 -0.391 -0.522 -1.151 0.134 0.170 -0.041 1.839 0.400 -1.116 0.632 -0.339 -0.329 -0.173 -0.285 0.270 2.115 -0.368 0.089 0.234 -0.513 -0.401 0.994 +4 1.470 -0.073 -1.397 -1.999 1.668 0.479 0.544 1.490 -1.652 -0.359 -0.236 0.742 -0.693 -1.201 -1.210 1.149 -0.743 -0.552 0.634 -0.699 -1.705 -1.582 -0.057 0.497 -0.202 0.430 -1.613 -1.545 +0 -0.765 -0.625 1.128 0.441 -0.957 -1.951 -0.914 0.157 -0.632 -0.710 -0.589 -0.073 0.785 -0.000 0.062 -0.225 -0.499 0.894 0.742 2.045 0.611 -0.498 -0.905 0.953 -0.815 0.810 -0.760 0.349 +4 -1.999 0.004 -0.063 -3.035 0.933 0.446 -0.415 -1.286 -0.300 1.709 1.511 -0.385 1.740 1.854 0.242 0.931 0.380 -0.833 -1.085 0.546 1.569 0.618 1.003 1.457 0.258 -0.548 -1.670 -0.127 +4 1.880 -1.481 1.848 0.679 0.365 1.724 0.583 0.975 0.194 -0.626 -1.043 2.109 1.525 -0.125 -0.620 1.607 0.948 1.082 1.325 1.205 1.963 0.851 -1.319 -0.335 0.505 0.337 0.810 0.044 +3 0.393 0.719 -0.133 -1.036 -1.601 -0.763 0.225 -1.383 1.074 -1.051 -0.113 1.476 -0.182 -1.092 -2.510 0.406 0.310 0.576 0.113 -0.536 -0.786 0.042 0.990 -2.959 0.939 -0.290 -0.167 0.695 +2 -0.514 0.336 0.271 1.265 1.580 0.904 1.264 -0.020 -0.699 -0.031 -0.132 -0.200 -0.010 -0.171 -0.458 -0.131 0.802 -0.865 1.345 1.082 0.087 -2.383 -2.178 0.112 0.678 1.773 -0.405 -0.514 +3 -0.528 -0.847 1.660 -1.208 1.198 -0.415 0.751 0.779 -0.353 -0.003 -1.355 0.039 1.250 -0.196 -0.081 0.781 -0.500 0.130 1.386 -0.037 -0.266 -0.710 2.965 0.604 2.499 0.179 0.326 0.265 +3 0.290 0.193 -0.873 0.623 -1.098 0.794 0.885 -0.064 -1.464 0.802 -0.584 0.274 0.691 -0.810 2.146 -0.044 -0.671 0.999 1.550 -2.067 -1.381 -0.776 2.113 -0.479 1.521 0.220 -1.450 0.881 +1 1.010 -0.734 -0.358 -0.106 -0.178 -0.401 1.204 -1.134 0.989 -0.763 -1.832 0.505 -0.835 -1.154 0.800 -1.683 -0.671 0.688 -1.094 -1.320 0.052 -0.589 0.156 -0.459 -1.051 0.508 0.856 -0.701 +3 0.385 0.100 2.009 1.129 0.122 0.723 -0.249 -1.604 -1.022 -0.461 -0.525 -0.723 -0.678 1.306 1.833 1.312 -2.157 1.078 0.737 -0.037 -0.497 -0.305 0.990 0.386 1.220 0.815 1.441 0.634 +0 -0.372 -0.074 0.357 -0.130 0.196 -1.351 1.016 0.633 0.241 -0.813 -0.737 -0.212 1.125 -0.984 -1.309 -0.851 0.754 0.303 0.686 -0.745 -0.002 0.833 -0.726 1.659 0.788 -0.306 1.515 -0.450 +2 -0.627 -0.164 -2.072 -0.088 -0.809 -0.271 1.185 1.525 -2.141 -1.106 0.809 -0.586 -0.035 -0.328 0.573 -1.510 1.333 -0.655 -0.701 -1.002 1.198 -1.522 0.832 0.192 -0.080 -0.376 -0.172 -0.069 +1 0.628 0.450 -0.693 0.236 1.418 -1.612 -0.255 -0.386 0.303 -1.052 -0.878 0.576 -0.431 0.731 0.385 0.878 -1.006 1.381 0.341 0.978 -1.390 -0.144 0.046 0.474 1.257 -0.331 -1.411 -1.982 +0 0.114 -0.109 0.827 -0.104 -1.758 -0.251 -0.972 0.080 1.434 -1.739 -0.800 -0.553 -0.382 0.740 -0.174 0.416 -0.626 0.230 0.778 -0.732 0.781 0.052 1.045 -0.201 0.381 -0.041 -0.096 0.259 +2 0.563 -0.070 -1.109 0.009 0.879 -1.447 1.747 0.573 0.593 -0.001 1.747 0.962 -0.368 0.757 -0.733 -0.280 1.467 0.388 0.662 1.170 0.548 -0.811 1.661 0.516 0.587 0.056 1.111 1.915 +0 1.009 0.163 0.760 -0.334 0.056 -0.722 0.386 -0.186 0.606 0.782 -0.353 0.514 -0.696 1.796 1.412 0.295 -0.759 0.245 -1.213 0.409 -2.131 -1.078 0.368 1.393 0.367 -0.319 -0.964 -0.512 +0 -1.223 1.760 -0.113 1.227 -0.293 0.285 -0.330 0.837 0.550 0.908 -0.396 -1.441 0.474 -0.579 1.034 0.668 1.107 -0.399 0.206 -0.454 -0.990 1.697 0.273 0.890 -0.336 -0.680 -0.388 0.688 +4 -2.102 -0.879 0.105 0.842 1.526 -0.637 2.338 -0.049 -0.224 0.562 0.311 0.255 1.996 0.247 -0.984 -0.840 1.473 0.684 -1.556 -1.606 0.025 0.616 -0.678 -1.680 -1.125 1.646 0.497 1.000 +0 -0.659 0.615 0.714 -1.312 -0.384 0.778 0.989 0.065 0.365 0.253 0.954 -1.109 0.268 -0.889 1.088 -1.058 0.408 -0.884 0.752 0.439 0.240 1.961 -0.703 0.926 1.172 0.926 -0.677 -0.235 +0 1.710 -0.204 -1.273 -0.615 1.071 0.555 0.047 -0.169 0.004 0.156 -0.194 0.102 -0.301 -0.856 -0.264 0.330 -0.728 0.421 0.830 0.341 -0.562 0.096 0.153 0.199 1.490 -1.158 0.207 -0.400 +4 1.466 -2.120 0.541 0.766 0.131 -0.281 -1.252 0.272 2.100 1.458 0.098 -0.937 0.893 0.456 -2.031 -1.435 2.356 -1.156 -0.154 0.814 -0.066 -0.539 1.016 0.539 0.458 -1.004 0.002 1.210 +4 1.272 0.007 -0.324 1.241 -0.982 1.697 -0.386 -0.050 -0.533 0.035 -0.911 -0.372 0.154 -0.106 -2.489 2.474 1.582 -1.165 -0.622 -0.172 0.665 0.472 -1.099 2.342 -0.397 1.805 -0.604 1.057 +1 0.187 0.587 0.445 -0.050 -0.425 0.412 -0.118 1.805 0.586 -0.923 -0.478 0.048 1.299 1.411 -0.396 -0.934 -1.061 0.252 2.304 0.539 -1.594 -0.726 -0.624 -0.822 -0.388 0.369 0.682 1.092 +3 -0.891 0.415 -1.573 1.211 1.411 -0.159 -0.052 0.522 0.275 0.687 -0.731 1.140 1.037 0.862 -1.009 -1.264 0.140 0.759 -0.411 -0.474 2.006 -0.084 -0.574 -2.141 1.152 1.541 -1.457 -1.242 +3 0.056 -0.109 0.795 0.676 0.173 0.773 0.820 -1.173 -1.149 -1.291 -1.514 0.811 0.813 0.448 1.662 -0.308 -0.690 0.123 1.142 -0.591 1.942 -0.072 0.513 0.163 -0.397 0.910 -3.475 -0.339 +1 0.986 -1.100 -1.142 0.580 -1.111 1.018 0.645 -0.778 0.980 -1.939 0.472 -1.246 -0.523 -0.459 1.281 -0.501 -0.079 -0.438 -0.983 -1.030 0.309 -0.026 0.763 -0.242 -0.210 0.766 -0.372 -1.440 +3 -2.060 -1.360 0.478 -0.944 0.261 0.031 -1.648 0.657 0.299 0.039 -0.648 0.390 -1.294 -1.008 0.321 1.030 -0.477 0.457 0.205 0.414 -0.692 -0.149 -0.038 1.498 -1.104 -1.404 2.999 0.309 +4 -1.532 0.184 -0.029 2.252 -1.957 0.922 0.433 0.766 -0.447 0.319 0.145 3.536 -0.309 -0.785 2.001 0.344 0.514 1.127 0.077 -0.419 0.132 -0.841 -1.452 -0.843 -2.362 -0.364 -0.500 0.604 +4 -0.027 0.888 -1.548 0.068 0.788 0.691 0.963 -1.682 -0.116 -0.028 1.467 2.745 1.438 0.896 0.448 0.033 1.264 0.200 -0.473 -0.062 -0.698 -1.858 -1.711 0.043 -2.049 -1.155 0.468 -0.750 +0 -0.109 0.168 -0.754 -1.325 0.463 -0.356 0.575 -0.671 1.416 1.001 0.297 -0.213 0.852 0.393 0.239 1.043 1.427 -1.252 -1.050 -0.780 -1.502 -0.063 0.600 -0.021 -0.534 1.126 0.141 0.343 +1 -1.124 -0.297 0.748 0.860 -0.772 2.143 -0.131 0.088 0.978 1.552 0.062 1.173 -0.456 1.668 0.039 0.314 -0.347 0.216 -0.730 -0.062 0.017 -1.056 -0.571 -2.100 -1.241 -0.014 0.959 -0.357 +4 -0.110 -0.869 0.196 -1.113 -0.093 3.125 -0.190 -0.609 0.111 -3.029 -0.396 -0.065 0.196 1.060 0.395 -1.147 -1.882 1.004 -0.613 0.290 1.502 0.275 -1.550 0.327 -0.245 -1.402 0.967 0.346 +2 0.460 -0.148 1.716 -0.738 -0.333 0.339 2.826 -0.926 -1.134 0.226 1.490 0.903 -0.673 -1.149 -0.655 0.104 0.314 -0.242 -0.267 -0.275 0.605 0.349 0.622 0.342 -1.870 0.011 -0.670 1.582 +2 -1.162 -0.306 1.844 -1.203 2.573 1.407 -1.250 1.197 -1.077 -0.805 0.321 1.063 -0.058 -0.157 -0.635 0.167 0.146 0.286 0.976 -0.834 0.579 0.135 0.335 1.754 -0.117 -0.352 -0.668 -0.553 +0 1.233 -0.797 1.183 0.912 -0.582 -0.853 -0.118 0.095 -0.334 -1.762 0.591 -0.176 -1.147 -1.579 0.532 0.155 -1.117 -0.399 0.315 -0.213 0.907 -0.658 1.280 -0.390 1.207 -0.501 0.031 -0.979 +1 -1.537 1.585 -1.216 0.160 -0.835 -0.388 -0.980 1.676 -0.140 0.226 -1.072 -1.415 -0.444 0.521 -0.121 0.975 0.124 0.006 0.535 0.100 0.944 0.555 0.911 0.716 1.360 0.829 1.748 -0.541 +0 -0.055 0.286 0.521 0.645 0.556 0.090 -0.197 -0.151 -0.195 1.134 0.594 -2.940 0.656 0.195 -0.019 -0.389 1.124 0.948 -0.773 0.407 -0.972 -1.380 -0.627 0.862 0.953 0.513 0.725 0.516 +4 -0.981 0.951 0.463 0.377 0.781 -0.425 -0.379 0.810 -1.345 -2.143 -0.433 0.931 0.256 0.857 -2.016 0.315 0.654 -1.351 0.862 -0.112 3.071 -2.177 1.369 -0.394 -1.617 -0.527 0.277 1.037 +3 -1.248 -1.358 -0.862 0.430 1.909 -0.292 -0.825 0.584 -1.375 -1.040 0.123 1.617 0.880 2.371 -0.587 0.279 -2.282 -0.019 -0.269 1.137 -0.143 0.451 -0.630 1.213 0.507 0.696 0.024 -0.572 +3 -1.047 1.350 0.949 0.674 -1.686 -0.324 -0.305 -0.005 0.374 -1.495 0.970 0.808 1.461 -0.641 0.400 1.223 -0.608 -0.430 -1.657 -0.903 -2.768 -0.683 0.115 -1.316 0.843 -0.338 -0.054 1.084 +1 0.320 0.864 -0.367 -0.381 1.019 1.903 0.979 0.072 -1.591 0.416 0.212 -1.109 -0.426 0.932 -1.028 0.271 -1.587 -0.498 0.361 1.250 -1.876 -1.062 -1.145 -0.959 -0.918 -0.172 -0.562 -0.110 +1 -0.551 -1.014 0.367 -0.488 0.030 0.317 1.242 0.787 -0.681 -1.145 1.707 -1.583 -0.453 0.645 1.639 -0.190 0.027 -0.875 0.127 -1.150 -0.750 0.764 -0.434 0.917 -0.154 -1.369 1.168 -1.315 +2 0.686 -1.804 0.980 -1.994 0.811 0.169 0.713 -1.061 -1.752 0.640 -1.525 0.306 1.277 -0.136 -0.054 -0.599 1.073 -0.177 0.477 -1.849 -0.523 -0.284 -0.636 -0.346 -0.066 0.011 -1.035 0.888 +0 -0.441 -0.690 1.473 0.548 0.644 -0.392 -0.186 -0.249 0.337 0.642 -0.030 -0.793 0.761 0.033 -1.441 -2.135 0.298 -0.829 0.431 -0.776 0.133 -0.378 1.752 0.950 1.347 0.024 0.822 0.092 +1 -0.736 -0.522 0.216 -0.093 -0.162 -0.258 -0.877 -0.461 2.434 0.699 0.020 -1.500 -0.990 -0.042 1.360 0.229 0.216 1.110 -0.971 1.406 -0.383 1.158 1.670 -0.907 0.000 0.789 -0.287 -0.259 +0 1.101 -0.465 -0.081 0.080 -1.098 0.442 1.023 -0.144 0.340 1.399 -0.405 0.360 -0.570 -0.597 0.342 0.241 0.555 0.076 0.465 1.666 1.060 0.338 0.505 -0.299 -0.159 0.492 0.088 0.793 +1 -0.227 0.208 -1.252 0.299 0.758 0.276 -0.780 -1.613 0.095 -0.572 1.620 1.419 -1.867 -0.264 -0.758 -1.187 -0.169 -0.790 -0.913 -1.018 1.327 -0.198 -0.062 1.261 -0.316 0.320 -0.724 -0.497 +3 0.008 -0.488 -0.205 -0.358 -0.634 -0.018 -0.228 0.691 2.239 -1.638 -1.491 0.064 -0.287 -0.315 1.427 -0.870 1.052 1.620 0.503 -0.545 0.653 1.047 1.048 0.480 -2.548 0.602 1.160 1.001 +0 -1.364 0.102 -1.176 -0.675 1.155 0.323 -1.621 0.106 0.411 0.917 0.612 0.501 0.052 0.464 0.917 -0.137 -0.741 -0.143 -0.416 -0.127 -1.976 1.046 0.223 1.120 -0.989 0.494 -0.202 0.117 +2 -0.907 -1.915 -0.531 -0.183 -0.110 -1.831 2.480 2.403 0.445 0.025 -0.565 0.579 -0.117 1.252 0.914 -0.851 -0.688 0.970 0.695 -1.113 -0.457 -0.154 -0.037 -0.072 0.899 0.146 0.081 -0.757 +3 0.418 1.500 -1.373 0.660 1.127 -1.487 -0.181 -1.065 0.431 -0.358 -0.130 -0.411 0.720 0.229 0.231 0.161 0.572 -0.262 1.701 -1.235 -1.726 -1.809 -2.080 0.673 0.551 1.208 -1.057 0.745 +4 2.764 -0.670 -0.394 1.420 -1.576 0.245 -2.415 -1.380 -1.190 -0.877 -1.247 0.201 1.400 2.039 -1.029 0.728 1.209 0.121 0.708 0.460 0.459 -0.737 -1.994 -0.349 -0.767 -0.278 0.961 1.436 +2 -0.224 0.669 -0.067 0.719 -1.079 0.260 0.900 -1.474 2.514 1.191 0.001 -0.828 -0.175 0.084 0.441 1.501 -0.476 0.563 -1.292 -0.414 0.377 0.691 -0.753 -0.441 1.461 1.162 0.208 -2.237 +4 0.049 -0.856 0.053 0.168 0.708 -1.888 -0.696 0.482 0.779 3.270 1.378 -0.689 1.774 0.580 1.366 0.467 1.437 1.083 0.552 -0.438 -0.442 -0.950 -0.074 1.107 0.581 -0.775 0.968 -1.176 +0 -1.532 0.591 0.263 -0.140 0.055 -0.564 -0.937 0.559 0.917 0.074 0.205 0.167 1.814 -1.302 0.297 -0.256 -0.500 1.109 0.438 -0.155 0.850 -0.741 1.495 0.302 0.759 -0.696 -0.543 0.305 +2 0.224 1.260 1.044 0.010 -1.219 -1.221 1.214 0.116 -0.663 -0.941 0.575 1.788 -0.241 0.986 -1.118 0.768 1.534 -0.735 0.817 0.217 -0.050 -1.459 -1.203 0.395 -0.147 0.320 1.390 -1.435 +3 -0.242 0.932 -0.614 0.833 -1.940 0.876 1.441 1.837 -0.586 -1.425 2.518 -0.547 0.321 -0.096 0.919 1.399 1.150 0.589 -0.468 -0.408 -0.640 0.736 -0.925 1.097 -0.203 0.517 0.511 -0.151 +1 0.764 -0.443 1.149 2.127 0.018 1.271 2.106 0.736 -0.283 0.543 -0.913 -0.655 -0.395 0.037 0.289 -0.962 1.685 -0.102 -0.433 1.602 0.847 0.413 0.412 0.085 0.006 0.223 0.826 -0.687 +2 2.130 -0.927 1.052 0.599 -1.179 1.707 -0.732 -0.881 0.354 0.711 -1.519 -0.774 1.058 -0.015 0.017 -0.674 -0.054 0.843 -0.547 0.684 0.705 -0.117 -0.925 -0.732 1.649 0.647 1.373 0.623 +1 -0.750 -1.283 -0.250 -2.366 0.492 -0.557 -0.410 0.587 0.372 -0.630 0.452 1.549 -1.012 0.754 -1.113 1.175 -0.712 -0.750 1.149 0.722 -0.679 0.127 -0.710 -1.593 0.189 -0.356 -0.946 1.305 +4 1.167 0.882 -1.579 1.208 0.358 -0.894 -0.672 0.582 -2.512 -0.125 -0.655 -0.757 -0.457 -1.479 1.052 -2.128 0.169 -0.848 1.984 0.023 -2.123 0.547 -1.093 0.985 0.618 1.473 0.187 0.819 +1 -1.967 -1.470 1.362 0.016 0.501 0.536 0.056 -0.075 1.045 0.146 1.399 0.621 -0.618 -0.497 -0.727 1.218 -0.405 -0.798 0.320 -0.097 0.295 1.890 0.913 0.027 -1.408 -0.500 0.711 -1.171 +2 -0.735 -0.767 -0.091 1.187 -1.207 -0.149 -0.801 -0.380 0.759 -0.071 0.468 -0.041 -1.587 -2.470 -0.709 1.343 1.234 -0.503 0.682 0.809 1.104 0.214 -0.826 2.204 0.563 0.027 0.042 0.052 +3 -0.842 -0.220 -0.025 -1.989 1.729 1.653 0.145 0.250 0.278 -0.909 0.539 -0.790 -1.865 -0.059 -0.159 1.661 -0.656 -0.211 -0.350 -0.583 -0.864 -0.687 0.244 -0.829 2.282 -0.150 2.412 -0.592 +2 0.397 -0.747 -0.281 -1.403 -0.108 1.112 0.685 1.402 -0.303 1.346 -0.193 1.472 -0.022 0.707 1.475 -0.193 0.218 1.289 -0.133 -1.321 2.143 -0.470 0.159 1.156 -0.608 -1.008 0.254 -1.793 +3 -1.403 0.080 -0.459 -0.269 -0.310 -0.179 -0.736 -2.534 1.958 1.727 0.731 -0.391 0.786 -1.747 0.925 -0.525 -1.328 1.161 0.507 -0.485 -0.049 -0.929 0.521 0.475 0.371 -2.147 0.238 -1.122 +4 0.279 1.188 -1.202 -1.927 -0.024 -2.838 -0.575 0.033 0.395 -1.298 2.243 0.224 -0.991 0.914 -0.604 1.014 1.473 -0.424 0.078 1.019 0.213 0.867 -0.486 2.636 -0.678 0.218 -0.828 -0.491 +1 1.864 0.415 0.921 -0.354 0.215 -1.202 -0.640 -0.899 0.297 1.707 0.903 1.812 0.794 0.420 -0.922 0.323 -0.474 0.832 -1.359 -0.108 0.017 1.787 -1.023 1.168 0.823 -0.256 0.410 -0.250 +3 0.102 -1.867 1.333 -1.161 1.789 -0.433 1.301 0.954 -0.451 0.691 -0.320 -0.231 0.743 -1.555 -0.591 0.458 0.909 0.172 -0.674 0.475 -0.331 2.203 0.183 -1.651 -0.487 -0.898 -1.388 -1.191 +4 2.282 -0.618 -1.535 -1.880 0.713 -1.883 -0.372 0.437 0.185 0.425 0.222 1.279 -0.952 -0.677 -0.773 0.830 0.900 0.451 1.183 -1.178 1.667 1.523 0.736 1.782 -1.657 -0.524 -0.735 0.721 +3 0.979 -1.539 0.301 -0.861 -0.067 0.955 0.658 1.283 -1.409 -1.023 1.506 1.522 -0.016 -0.023 1.173 0.532 0.606 -0.925 1.149 -0.200 -1.392 -0.781 -0.597 1.478 -0.697 0.385 -2.110 0.937 +3 -0.234 1.076 1.060 -1.649 -0.693 -0.720 -2.532 0.566 -0.246 -0.113 1.346 0.088 -0.431 0.572 1.059 0.296 0.689 -0.146 -0.082 0.113 -0.753 -0.568 1.378 2.008 1.592 -1.323 -1.420 -1.191 +0 0.022 -0.987 0.627 -0.049 -0.989 2.241 0.476 -0.153 1.415 1.842 -0.145 0.347 -0.988 -0.147 -0.199 0.185 0.333 0.560 0.238 -1.211 -0.938 -0.546 -0.282 1.051 -1.163 -0.357 0.953 -0.300 +3 -1.707 -1.444 0.906 -0.594 -0.159 0.763 -0.158 0.204 0.535 -0.431 -0.166 0.242 0.868 -0.792 -3.021 0.680 0.928 0.200 0.268 -0.564 0.500 0.853 0.056 2.491 -1.564 0.163 -1.174 0.880 +3 -1.090 0.428 -1.377 -0.763 -0.275 1.010 -1.538 1.138 -0.369 0.408 1.499 0.816 -1.446 0.253 0.992 -0.380 0.035 -0.050 -2.170 -0.512 1.105 -0.827 -0.877 1.381 -0.439 0.833 0.530 1.964 +3 -1.637 0.001 0.605 0.357 -1.666 0.750 1.036 2.374 -0.732 0.631 1.597 0.601 3.046 -0.261 -0.458 -1.831 -0.051 0.044 0.298 0.538 -0.177 0.313 -0.266 0.582 -1.349 -0.694 0.328 0.731 +1 0.028 -0.386 -0.856 0.065 -0.036 -0.148 1.371 -0.026 -0.918 0.447 -1.235 1.330 -0.226 -0.718 -0.311 0.385 -2.192 -0.123 0.425 1.289 -1.430 -1.993 -0.244 0.398 -0.634 -0.032 0.244 0.772 +4 0.407 0.604 -0.468 -0.166 -1.137 -1.509 -1.147 0.721 0.523 1.928 0.038 0.052 0.337 -1.067 0.903 0.382 -0.947 0.372 0.758 -1.331 1.057 -0.450 -3.070 0.972 1.343 -1.124 -1.671 1.328 +4 0.414 0.064 0.260 -2.060 0.835 0.733 0.413 0.611 0.078 -0.151 0.052 -2.311 1.917 -1.702 1.141 0.863 0.198 -2.407 0.265 1.693 0.089 -0.327 -0.191 0.260 -2.772 0.566 -0.962 0.379 +2 -1.080 -1.268 0.535 -0.419 -0.143 -0.670 0.370 0.678 2.170 -0.181 -1.448 -0.504 0.847 -0.773 -1.482 -0.752 -0.533 -1.083 0.217 -0.079 0.790 -0.446 0.558 -0.492 -1.130 -0.543 -1.618 1.788 +2 -0.223 0.435 -0.771 0.321 1.459 -0.652 0.459 -1.406 2.023 0.269 -0.413 -0.298 -0.510 -0.459 -2.447 -0.098 -0.140 0.489 -0.523 -0.771 -0.008 -1.612 0.143 -1.862 1.121 0.684 -0.926 0.661 +0 0.753 0.381 1.290 0.673 -0.138 -1.224 -0.209 -0.851 -0.581 0.589 1.670 0.395 -1.196 0.445 1.197 -0.610 -0.134 0.015 -0.785 0.648 -0.121 0.420 -0.887 -0.437 0.722 -0.373 1.727 -0.400 +2 -0.202 -0.089 -0.247 -0.090 -1.283 0.527 -1.730 -0.266 -1.279 -1.678 -0.419 -1.313 -0.671 -0.196 0.356 0.104 0.364 0.262 1.263 0.849 0.110 -0.205 -0.039 -1.212 0.251 -1.027 1.373 -2.764 +1 -0.954 1.301 1.640 -1.106 -0.411 -0.648 0.124 -0.690 0.957 -0.570 0.885 -0.526 1.227 0.796 -0.548 0.620 -0.977 0.823 -0.313 -0.087 -0.640 0.999 1.078 -0.842 -1.478 -0.676 0.161 -1.175 +0 -1.817 -0.677 0.244 1.455 0.890 -0.781 -1.336 -0.207 -0.112 -0.403 0.640 -0.943 -0.186 0.286 -1.567 0.320 0.962 0.768 -0.026 -1.104 -0.870 -0.210 0.151 -0.994 0.342 -0.630 -0.336 0.656 +3 0.927 -2.004 -0.403 -0.748 -0.086 1.375 -2.588 0.310 0.343 -0.061 0.291 0.514 0.158 -2.035 -0.889 1.598 -1.167 0.081 -0.721 0.595 -0.203 0.655 0.383 -0.390 -2.229 -0.866 -1.206 -1.214 +4 0.036 -1.038 1.740 -0.022 -0.928 -0.733 -0.815 0.840 0.162 1.950 -3.437 1.154 -2.445 -1.007 -0.181 -0.845 -0.796 0.668 1.171 0.808 0.251 0.065 -1.810 -0.581 -1.040 1.098 1.111 -1.019 +4 -1.023 0.356 -1.469 -0.762 -2.986 -1.501 0.932 -0.872 -0.336 -0.941 1.029 -0.389 0.030 -0.794 1.991 -1.363 -0.545 1.408 -0.167 0.439 0.626 0.532 1.097 0.781 -1.945 0.728 -0.381 0.497 +4 -0.097 -1.041 -2.593 0.249 -0.031 1.485 -0.136 -1.937 1.275 1.312 -1.382 0.523 1.426 1.643 1.336 0.301 -0.057 -0.600 -0.645 -1.281 0.465 -0.183 -0.415 -0.149 1.104 0.699 -1.989 2.442 +2 0.830 -0.856 0.072 -0.478 0.479 0.334 1.038 -0.510 -0.270 -0.979 -0.444 0.377 0.757 -0.922 0.870 1.356 0.413 1.877 -0.774 -1.245 -1.779 1.496 0.654 -0.056 0.280 -1.125 2.446 0.129 +0 -0.067 0.457 -0.342 -1.011 -0.039 -0.631 0.790 -0.229 0.084 -1.901 0.556 0.906 -0.079 0.897 0.768 0.804 0.031 -0.556 -1.551 -0.401 0.145 -0.548 -0.357 -0.057 -0.244 0.533 1.001 0.616 +0 -1.300 -0.622 -0.308 -0.616 -0.606 0.697 0.256 0.550 -0.154 -0.223 -0.749 0.479 1.019 -1.147 0.786 0.275 -1.157 -1.443 -0.277 -0.517 1.043 -0.391 -0.542 -0.442 -0.235 0.279 1.066 -1.233 +0 0.574 0.299 1.365 -0.029 1.485 0.839 -0.418 -0.921 -0.420 -0.301 -1.185 0.065 -0.096 -1.456 -1.541 0.461 0.370 -0.523 -0.883 0.791 -0.882 0.734 1.463 0.911 -1.272 -0.832 -0.490 0.090 +3 -0.993 2.328 -1.891 -1.517 -1.122 -1.089 0.521 1.800 1.155 0.234 0.010 -0.606 0.391 -0.209 0.189 -0.741 -1.202 0.025 0.003 -0.596 -0.767 0.558 0.455 1.877 1.381 -1.194 -0.191 -1.365 +0 1.314 -0.949 1.061 -0.433 1.753 -0.365 -0.840 0.687 -0.436 -0.841 -0.478 -0.045 1.342 -1.934 0.023 0.625 0.193 1.310 -0.074 -0.185 0.592 0.099 -0.390 0.516 -0.653 -0.611 -0.528 1.353 +1 -1.390 0.254 0.069 0.136 -0.226 -0.615 0.662 0.201 -1.848 -0.441 -1.523 0.437 0.617 -1.598 -0.505 -0.581 0.116 1.370 -0.541 1.470 -0.107 -1.006 -0.189 0.790 -1.397 0.595 -1.019 0.966 +3 -2.648 -0.417 -1.584 0.199 0.704 0.763 -0.590 1.154 -0.862 -0.325 -0.175 2.612 -0.334 -0.407 0.326 -0.925 0.513 -1.082 -1.491 -0.064 0.989 -1.694 0.034 -0.875 0.595 0.369 -0.190 0.174 +4 -0.314 1.326 -0.166 -0.971 2.177 -1.825 -0.739 -0.627 2.508 1.807 1.494 -0.331 2.426 0.368 0.977 0.180 -0.049 -0.252 -0.210 0.608 0.367 -0.031 1.510 -2.967 0.510 0.718 1.147 -0.065 +0 -1.013 -0.278 0.021 0.144 -1.111 0.234 1.167 1.155 -0.278 0.520 -0.162 1.065 0.678 -1.160 -0.153 -0.657 -0.095 -1.153 -0.119 -0.154 -0.127 0.254 -1.542 0.781 0.840 1.406 -0.097 0.260 +3 -0.631 -0.426 -0.530 -1.008 -0.155 -0.117 -1.124 -2.680 0.237 -0.035 -0.789 1.894 -0.517 -0.667 1.269 -0.476 1.422 -0.582 -0.347 0.838 -0.739 -0.639 1.748 0.639 0.040 0.284 2.115 0.360 +2 -1.954 -0.918 -1.457 0.175 -1.189 -0.522 -0.089 -0.492 -1.401 1.561 0.030 1.570 -1.117 -0.461 0.002 1.447 1.563 -0.519 -0.815 -0.090 1.115 0.051 0.233 1.106 0.945 0.983 0.622 1.352 +2 0.469 -0.437 -0.371 -0.641 -0.864 1.034 -1.011 0.293 -0.876 0.175 0.049 -0.865 0.483 -0.913 0.054 -2.364 0.681 0.656 1.586 0.660 -1.401 -1.468 0.986 1.597 -1.947 -0.266 0.027 -0.450 +0 0.381 0.214 -0.603 1.135 -0.730 -0.534 -0.932 1.470 -1.423 0.026 1.305 0.473 -0.577 -1.098 -0.283 0.048 -0.643 0.106 1.092 -0.565 0.333 -0.023 0.604 1.394 -0.301 -0.185 -0.979 1.566 +1 0.246 1.282 0.804 -1.319 -0.114 -0.014 2.152 -0.330 -0.982 0.438 0.497 -0.074 -0.220 0.949 -0.854 -0.341 -0.522 1.094 0.285 -1.065 -0.965 1.873 1.083 0.842 0.012 1.508 -1.103 0.753 +3 1.912 1.164 1.349 0.024 0.321 1.002 1.560 -1.952 -1.315 1.309 -0.277 -0.672 -1.254 -0.555 -1.402 2.091 -0.022 -0.058 0.588 0.962 0.946 1.074 -0.631 -0.298 0.769 -0.527 -0.005 0.399 +2 -0.323 0.920 0.580 0.453 -1.596 0.499 -0.974 -0.649 -2.617 -1.409 -0.503 0.916 0.900 -0.170 -0.897 -0.190 -0.994 0.154 0.194 1.788 0.998 -0.312 -0.002 1.944 0.626 1.100 -0.273 0.255 +3 -2.690 -0.398 -0.239 -0.558 -0.710 1.789 -0.724 -0.411 1.268 -0.594 -0.302 -2.608 -0.620 0.426 1.614 -0.671 -0.866 -0.015 -0.115 -0.114 0.622 1.459 0.423 0.651 0.954 1.455 0.473 -0.742 +4 -1.407 2.929 -2.159 -1.045 -1.177 -1.177 -0.468 -1.317 0.697 0.077 0.448 0.246 -0.706 -0.790 1.315 0.113 0.121 0.098 0.087 -1.451 0.374 2.563 0.070 1.732 0.144 0.303 0.323 -1.940 +2 -1.459 0.146 -0.481 -1.156 -1.606 0.913 -1.664 0.533 0.883 -0.543 0.512 -1.607 0.714 0.668 0.133 0.438 0.061 -1.227 0.320 0.287 0.499 -0.056 -0.177 0.642 -3.258 0.795 -0.463 0.018 +3 -2.119 -0.018 0.782 -0.841 -0.868 -0.879 -0.160 1.295 0.820 1.527 -1.289 -0.696 0.430 -0.858 0.050 0.537 0.557 -0.097 -0.966 -1.098 -0.712 1.433 1.492 1.509 -0.325 -2.209 -0.224 -0.552 +4 -0.515 1.822 0.583 -0.368 2.151 -0.231 2.501 1.385 1.309 0.034 0.916 -0.946 1.015 -0.992 -1.248 -1.664 0.123 -0.368 0.880 -1.125 -0.534 0.203 -0.475 -1.538 1.617 1.853 1.529 -1.862 +2 0.928 0.002 -0.061 1.177 1.463 -0.240 0.799 0.364 0.007 0.204 -0.688 -0.306 -0.581 0.901 -0.095 -1.298 -0.723 -3.199 -0.494 0.038 -0.136 1.980 0.575 0.530 -1.643 -0.365 -0.487 -0.786 +1 -1.746 -0.546 -0.665 0.041 -0.021 0.134 0.175 0.333 -0.959 -0.725 -0.037 -0.021 -1.082 1.082 0.725 -0.860 -0.208 2.010 0.161 0.396 -0.609 -0.313 1.511 0.113 -0.071 0.008 2.545 0.040 +3 0.306 1.527 1.444 -0.216 -1.457 0.444 -0.326 -1.262 -0.176 -0.084 -1.156 -0.749 0.866 0.247 -2.017 1.965 -0.504 0.501 1.539 -0.252 0.803 -1.226 -2.476 0.676 0.460 -0.185 -0.995 -0.680 +1 -1.870 -0.389 0.190 0.449 -0.510 0.034 -2.488 -0.658 0.454 -0.982 0.059 0.447 -0.343 0.170 -0.963 -0.207 0.610 0.157 -0.587 0.224 0.715 -2.050 1.159 -0.336 0.425 1.197 -1.372 -0.709 +1 0.012 -1.197 -0.266 1.370 1.199 -0.032 -0.087 1.880 0.521 -0.328 -1.426 0.394 0.829 -0.632 0.291 -0.364 -0.184 -2.488 -0.222 0.969 -0.631 0.053 -0.756 0.360 -0.217 -0.939 1.333 0.929 +4 -0.182 2.140 -0.958 0.148 1.807 -0.812 -1.214 1.131 1.181 -1.068 0.311 1.197 -0.312 0.719 1.058 1.002 -0.039 -2.249 -2.028 0.360 0.523 1.370 -0.092 -0.756 1.270 0.751 -0.150 -1.694 +1 1.139 -0.306 0.175 -1.137 -1.099 0.725 0.882 0.026 -1.228 0.044 1.257 1.743 -0.317 -1.660 0.840 -0.276 0.943 0.281 -0.883 0.058 1.308 -0.149 -1.035 -1.589 -0.772 -0.410 0.820 -1.121 +2 -0.531 0.505 0.316 0.085 -1.363 0.100 0.961 -0.532 1.315 -0.873 0.007 1.535 -0.681 1.054 1.975 0.171 -0.527 -0.773 1.305 1.277 1.449 -0.033 1.038 -0.049 -0.883 1.124 1.634 -1.021 +1 -0.460 1.341 1.999 0.062 -0.275 -1.180 -1.167 0.233 0.701 -1.487 0.829 1.372 0.631 1.653 0.355 0.365 0.346 1.359 0.538 -0.530 0.423 0.433 -0.212 1.023 0.091 0.705 0.510 -0.726 +1 -0.815 -0.540 -1.729 -0.438 1.590 -0.727 -1.357 -0.528 0.220 -0.746 0.072 0.251 0.002 -0.089 1.764 0.177 -0.791 0.755 -0.060 0.071 1.013 1.341 -1.139 1.732 -0.065 -0.058 -1.010 0.747 +2 -0.386 -1.068 1.588 -0.569 0.786 0.675 -0.059 -0.973 -0.048 -1.170 -0.024 0.773 1.718 -1.088 -0.250 -0.525 0.274 -1.522 -0.929 -1.692 -1.438 -0.660 1.671 0.348 0.065 1.347 0.384 0.478 +0 0.112 -0.790 0.299 0.865 -0.168 0.316 -0.865 0.556 -0.122 -0.702 1.031 1.242 -1.883 -0.427 -0.613 -0.201 0.525 -1.577 1.000 -0.744 -0.824 0.433 1.347 0.404 0.037 -0.749 -0.026 -0.521 +0 1.321 -1.135 -0.516 1.012 -0.205 0.547 1.215 0.562 -0.611 -0.351 1.223 0.155 -0.204 0.384 -1.284 -0.237 -0.079 -0.095 -0.394 -0.309 -0.114 1.460 -2.203 0.699 -0.087 -0.062 -0.406 0.502 +4 -0.407 -0.349 -1.409 0.748 -0.152 -0.291 -0.306 -0.410 0.904 0.508 -2.014 -1.500 -1.856 -2.438 1.133 0.697 1.546 -0.342 0.936 0.525 -1.962 1.121 -0.477 0.432 -0.488 -1.533 0.323 1.608 +0 0.108 -0.843 -0.476 1.373 2.083 0.320 -0.567 -0.125 -0.762 -0.617 -0.715 -0.079 0.121 -0.148 -0.221 0.760 -0.229 -0.165 -0.177 1.543 -0.491 0.596 -0.748 0.350 0.546 1.084 -0.384 -1.114 +4 0.391 -0.844 1.619 -0.010 0.138 0.195 -1.413 -1.577 1.207 1.308 0.962 -0.453 -0.755 -0.555 -2.079 -0.304 -0.103 -0.157 -0.706 0.217 1.023 -1.011 -0.534 -3.783 1.248 -0.557 0.099 -0.521 +0 1.239 -0.026 -0.628 0.930 0.331 0.884 -0.120 -1.309 2.244 -0.603 -1.161 0.717 -0.945 -0.392 0.125 -0.143 -0.363 1.590 -0.111 -0.899 0.977 -0.723 -0.109 -0.905 0.211 0.040 -0.029 0.150 +1 -1.067 -0.146 -0.653 -0.495 -1.316 -0.604 0.608 -0.141 1.699 0.130 0.908 -0.649 -1.550 0.319 0.753 0.082 0.135 1.244 -0.061 -0.784 1.718 1.953 1.544 0.967 -0.148 0.637 0.062 -0.443 +2 0.697 2.119 0.608 -0.662 -0.423 1.488 -3.085 0.330 0.164 -1.554 0.148 -0.577 0.182 1.033 -0.851 0.199 -0.447 0.519 -0.487 -0.589 -0.325 0.671 0.476 1.528 0.048 0.348 0.200 -1.215 +4 -0.174 -0.012 2.221 2.831 0.233 -1.157 -1.535 0.757 1.762 1.500 0.984 -0.013 -1.206 1.314 -1.130 -0.469 -1.226 -1.857 -1.375 -1.327 0.015 -0.687 1.960 -1.035 -1.410 1.681 1.617 -0.683 +0 -0.017 -0.224 -0.046 0.485 -0.275 1.658 1.409 0.444 0.481 -0.553 0.371 0.540 0.559 1.047 0.404 0.320 1.273 0.100 -0.601 0.571 0.660 -0.372 -0.373 0.131 1.276 -0.496 -1.388 -1.470 +4 1.341 1.283 0.643 0.652 0.487 -0.436 0.248 -1.952 -1.661 2.460 0.147 1.524 -1.735 0.005 0.904 -1.517 -1.465 -1.210 1.482 -0.745 0.286 -0.376 -1.182 -0.766 -0.824 -0.478 -0.102 0.231 +4 0.562 -0.183 -0.585 -0.926 -3.428 0.735 -1.638 -0.880 0.292 -0.322 2.149 -1.509 2.239 1.381 0.909 1.439 0.405 -1.703 0.515 -1.481 -0.528 -0.078 -0.911 0.946 -0.214 0.409 -0.009 0.126 +2 -1.083 0.599 0.052 -0.342 0.956 -0.269 1.334 1.321 -0.636 1.113 0.113 -0.394 0.395 -1.587 -0.538 0.418 -0.294 1.078 -0.398 0.121 -0.120 -1.528 0.392 0.317 -1.933 -2.470 -0.453 1.231 +3 -1.150 -0.991 -0.182 -0.288 0.595 1.250 -0.241 -1.210 -0.334 0.956 -0.989 -1.067 -1.432 1.404 -2.275 -1.085 -1.107 -0.381 -0.636 0.132 -0.540 -0.997 -0.469 -1.226 1.028 1.349 -1.843 0.464 +3 -0.803 -1.288 -0.498 -0.898 -1.109 -0.349 0.966 -1.142 0.361 0.896 -1.408 -1.141 1.819 0.258 0.563 -0.500 -0.472 -2.050 -0.483 0.943 -0.429 2.009 -0.392 -1.706 0.755 1.164 -0.801 1.095 +1 -0.991 -0.874 -0.569 0.820 1.038 0.509 0.633 0.656 -2.497 0.010 -1.796 0.509 -2.093 -0.180 0.548 -1.118 0.962 0.507 0.381 0.698 -0.359 0.152 0.010 -0.013 -0.672 -1.243 -0.337 -0.392 +0 -0.167 0.147 1.207 -0.817 0.369 -0.393 0.029 1.278 0.191 0.046 -1.360 0.746 0.645 2.163 -0.308 0.219 0.249 1.577 -0.095 0.279 0.608 0.187 -0.446 0.194 1.074 -1.027 0.133 -0.700 +1 0.126 0.049 -0.356 -0.496 0.521 -0.418 -1.393 0.605 0.941 0.009 -1.357 0.416 -0.314 1.035 0.853 0.882 0.114 -0.213 -0.049 -0.709 0.261 -2.303 -1.710 0.170 0.165 1.795 0.409 -1.039 +0 -0.089 1.153 -0.411 -1.083 0.619 0.337 -0.771 -0.233 -0.182 0.006 0.647 -0.405 0.698 -0.533 1.666 1.688 -0.856 -1.226 0.978 0.258 0.552 -0.655 -0.132 -0.345 0.304 -1.246 -0.099 -1.256 +2 -2.315 -0.505 -0.049 1.357 -0.564 0.978 -0.505 0.569 0.808 0.578 -0.957 -0.364 0.587 -1.580 0.790 -1.213 -1.352 -0.533 -0.685 0.182 0.267 0.070 -0.983 1.209 0.065 0.931 -2.294 0.535 +4 -1.016 -0.793 -1.506 1.630 -0.961 1.080 0.984 -0.295 1.318 -0.226 -0.948 -2.803 0.222 0.585 -2.003 -0.269 -0.459 0.317 0.274 2.002 1.245 -2.181 1.504 0.765 -1.161 -1.646 1.298 -0.472 +3 1.185 1.062 1.008 0.094 -0.668 -1.395 -0.040 1.533 -0.310 0.785 0.844 -0.284 1.316 -2.909 -1.223 -0.916 -0.820 0.336 0.290 0.394 0.596 -0.950 -1.218 0.396 -0.630 -1.570 0.971 1.395 +4 -1.564 1.108 0.655 0.816 1.559 1.263 0.711 -0.949 1.848 -1.046 0.136 -1.108 0.300 0.675 -0.567 -1.981 -0.197 1.810 1.921 0.299 -0.079 2.055 1.107 1.020 0.909 0.612 -1.407 -0.431 +0 -1.039 0.176 0.135 -0.140 -0.070 -1.333 -0.926 0.592 -1.452 -0.258 0.967 0.220 -0.676 0.725 -0.715 0.061 -0.771 -1.480 -0.115 1.214 1.145 0.739 0.721 0.763 -0.199 -1.242 0.169 0.942 +2 0.125 0.129 -0.437 -0.913 1.727 -0.833 1.656 0.495 0.203 0.543 1.523 -2.624 0.359 0.149 0.186 0.941 -0.610 -1.151 -1.384 -0.907 0.687 0.505 0.883 -0.460 -0.415 1.654 1.020 0.752 +4 -2.070 -0.356 -2.745 -0.639 0.245 0.884 -1.710 -1.460 -0.283 0.851 1.178 0.060 -1.061 -0.780 -1.458 1.027 0.107 1.325 -0.835 -1.172 0.997 -0.501 -0.444 1.393 0.182 1.080 0.417 0.729 +2 1.167 -0.501 -0.034 0.817 1.239 -2.365 -0.737 -0.998 -0.124 -0.923 0.664 0.172 0.889 0.953 0.679 -0.336 0.104 -0.461 -1.365 0.954 -0.084 -1.503 0.537 -0.224 -2.060 -1.374 -0.701 0.211 +1 1.595 0.134 0.077 -1.063 -0.043 0.389 -0.675 -1.248 0.388 -0.312 0.148 1.096 -0.465 -1.946 0.383 1.182 -0.481 0.300 -1.347 -2.122 0.842 -1.163 -0.665 0.197 0.391 -0.090 0.837 -1.636 +2 0.066 0.887 0.167 -0.064 0.335 2.239 1.127 -2.224 1.356 -0.018 0.175 0.243 1.013 -1.593 -0.270 0.581 0.751 2.012 0.243 -0.015 -1.201 -0.681 -0.830 0.118 -0.760 -0.838 -0.264 -0.483 +4 1.043 -0.049 1.420 0.715 -1.262 -0.134 0.325 -0.697 1.080 -1.976 0.804 0.690 -1.987 0.104 -0.435 1.418 -0.837 0.799 0.998 -0.393 1.298 -0.416 1.333 2.604 0.930 0.612 -0.473 1.857 +3 0.031 0.249 0.478 0.883 0.700 -0.023 0.006 -1.112 -0.175 0.108 0.917 -2.368 0.309 -0.773 0.776 1.329 -0.048 1.435 0.868 0.139 -1.603 0.385 -0.829 -2.859 -1.100 1.348 0.129 1.227 +1 -0.514 -1.059 -0.063 0.955 -0.986 0.504 -0.530 -0.793 -0.107 -1.035 -0.554 -1.198 1.965 0.035 -0.700 0.214 -0.112 -0.221 0.614 0.758 -0.531 -0.576 -0.275 -2.302 -1.515 1.367 1.645 -0.249 +1 0.704 0.989 0.221 -0.750 0.382 0.723 -1.972 0.116 -0.190 0.954 0.386 0.484 -0.482 0.002 0.944 -0.065 0.175 -1.536 -0.069 -2.012 -0.516 0.064 0.767 -0.999 0.374 1.468 -0.641 -1.547 +4 1.452 -0.427 2.860 1.640 -1.133 1.252 -0.125 0.566 1.577 -0.775 -0.463 0.753 -0.367 -2.225 0.964 0.369 -0.023 0.125 2.238 1.013 -0.861 0.123 1.186 1.165 -1.502 -0.501 -1.134 0.640 +4 1.334 -1.964 -0.769 1.111 0.933 -0.926 1.160 0.045 0.660 -0.704 0.292 -1.166 -0.547 -0.297 0.140 2.407 2.560 -0.388 1.480 -1.109 0.399 -0.686 -0.451 2.661 -0.387 -0.650 -1.000 -0.769 +4 2.061 1.755 -0.249 0.972 0.645 1.369 -0.965 0.686 1.058 -1.759 -1.183 -2.039 -0.269 0.718 1.502 0.074 1.629 -1.380 -1.703 -0.056 0.384 -0.033 -2.067 -0.089 -1.304 0.670 0.367 -0.940 +3 -0.614 0.216 -0.299 0.027 0.725 1.155 2.654 0.771 -0.617 1.103 -0.318 -0.699 0.623 1.414 -0.606 0.315 0.456 -0.949 -1.431 -1.403 1.279 -0.464 -0.630 0.318 1.301 -1.165 -1.888 1.476 +2 0.735 0.607 -2.142 1.174 1.034 0.459 -1.391 0.511 0.700 -0.319 0.944 1.686 -0.393 -0.893 -0.207 0.529 -0.015 -0.959 1.164 -0.171 0.605 0.644 -0.181 0.623 1.732 0.914 -1.619 -0.176 +2 -1.973 1.414 -0.595 -0.370 0.016 -0.164 -0.338 0.457 -0.657 2.266 -0.579 -0.501 1.293 1.147 0.550 0.287 0.483 0.457 -0.566 -0.058 1.929 0.576 -0.522 0.677 -1.211 -1.097 0.377 1.259 +1 -0.368 -0.172 -0.934 0.306 1.322 -1.419 0.493 1.930 0.315 -0.532 0.582 -1.062 -0.567 1.011 1.080 -0.487 0.731 1.771 0.536 -1.828 -0.225 0.132 -1.285 0.001 -0.007 0.068 -0.548 -1.162 +2 -1.002 -0.246 -0.027 1.669 -0.629 0.735 -1.045 0.148 1.431 1.052 0.043 -1.365 -0.458 -0.867 -0.364 0.901 -0.506 1.579 -1.302 0.658 -0.919 0.014 -0.800 2.192 0.181 -1.356 -1.041 -0.976 +3 1.629 0.323 0.446 -1.037 -0.643 -0.664 0.072 -1.351 0.714 0.570 0.013 0.143 1.346 0.867 1.131 0.916 0.174 0.826 0.992 0.450 -2.081 -1.349 1.062 -1.998 -0.936 -1.314 -2.280 0.365 +2 0.572 0.857 0.699 0.207 0.265 0.702 -1.450 1.330 0.450 -0.846 0.185 1.319 0.290 -0.732 -1.274 2.465 -1.739 0.337 -0.587 1.042 -1.216 1.608 -0.125 -0.220 -0.313 -0.593 -0.369 0.718 +0 -1.122 0.852 0.647 1.589 0.536 0.875 -1.421 -0.235 -0.099 0.376 0.114 1.004 0.138 -0.870 -1.017 1.516 0.285 0.127 0.260 0.199 -0.306 0.605 1.015 0.850 -0.043 -1.053 -1.293 0.962 +4 1.055 1.263 -0.127 -0.098 -0.877 0.626 -0.405 -0.282 0.414 -0.824 -1.333 0.543 0.264 -1.819 -2.025 -0.804 1.834 -1.444 0.284 -0.159 -1.642 -2.067 -0.831 0.006 2.403 2.337 0.412 0.364 +4 -1.018 -2.143 1.446 -0.944 -0.945 -0.717 0.137 0.639 -0.300 -1.049 2.298 -0.492 0.011 -1.501 0.579 1.092 -0.860 -1.840 -0.382 1.896 -0.641 2.533 -0.561 -1.825 -0.510 -1.871 -0.719 0.382 +3 -0.557 -0.241 0.657 -1.881 -1.158 -0.309 -0.873 0.138 1.520 -1.252 -1.406 -0.081 -0.673 -0.109 1.022 0.678 -0.896 -0.422 -1.425 -1.801 -1.843 -1.925 -0.661 0.144 0.273 -0.145 0.477 -1.795 +0 -0.133 0.374 1.314 -0.967 -0.185 -0.858 -0.393 -0.101 -0.880 -1.624 -1.307 0.444 -0.668 -0.219 0.049 0.907 -0.284 0.246 -1.132 -0.161 -0.978 -0.141 0.128 -0.234 -0.423 0.269 -0.403 -0.414 +1 -0.334 -0.911 0.008 -1.179 -0.073 -0.495 -0.121 0.393 -0.418 -0.778 -0.655 1.023 1.276 -0.515 -0.155 -1.704 -0.182 -1.452 -0.488 1.077 -1.502 -0.902 -0.897 -1.669 0.863 0.277 1.809 -0.995 +3 0.002 0.510 0.505 -0.919 -0.060 0.036 0.272 0.289 2.587 -1.176 0.980 1.183 0.493 -0.650 1.665 -2.192 -0.683 1.172 -0.370 1.596 -1.512 -1.112 -0.532 0.417 -0.222 -0.490 1.420 1.513 +2 -0.800 -1.808 -1.276 0.874 -0.143 -0.506 -0.005 1.167 0.004 0.540 -0.847 -0.619 0.354 1.893 1.430 0.583 0.151 1.401 0.795 -2.294 -1.054 0.338 -1.787 -0.898 -0.700 -0.256 -0.287 -0.289 +0 -0.471 -0.126 0.889 -0.708 -1.122 0.141 0.771 -0.205 0.604 -0.107 -0.183 0.745 1.171 0.605 0.125 -1.324 0.008 -0.961 0.333 -0.451 1.558 1.224 0.605 -1.416 -0.830 1.089 -0.146 0.529 +0 0.155 -0.398 0.118 0.151 -0.579 -0.896 -0.449 0.234 0.599 0.853 -0.494 0.284 0.963 1.166 -0.059 -0.221 0.191 -1.150 -0.194 0.745 0.642 -0.270 0.218 -0.825 0.703 -0.078 -0.276 -0.362 +0 -0.299 0.333 1.254 -1.237 -0.825 0.606 0.991 0.432 0.869 -0.430 0.473 -0.369 0.034 0.492 0.250 1.014 -0.009 0.856 -1.425 0.451 0.581 1.562 -0.649 -0.288 -0.663 1.146 -0.356 -0.729 +3 0.438 0.725 -1.708 0.282 -0.794 0.397 -0.095 -0.946 1.110 -0.418 0.270 1.314 -0.917 0.008 -0.495 -0.272 2.156 0.457 0.314 0.524 0.430 -1.826 2.297 -1.464 1.733 0.618 0.181 -0.305 +4 -0.371 0.024 1.581 1.422 0.919 1.682 -2.318 -0.672 0.669 -0.018 1.158 -1.166 1.227 0.550 0.149 -1.616 2.243 -1.156 -1.362 0.652 1.141 -0.093 -0.824 -0.439 0.442 -0.561 1.254 -0.788 +4 0.169 -1.852 0.214 -0.517 0.841 0.345 1.956 -0.000 -1.091 -2.489 -0.938 0.377 0.633 -0.605 -0.430 0.070 0.380 1.020 -0.588 -0.743 2.025 1.783 -0.529 1.143 2.205 1.317 -0.763 0.105 +4 -0.742 -0.191 1.287 1.173 0.202 -0.256 -0.711 -0.994 1.172 -0.007 -1.902 -1.314 -1.023 -2.959 -0.738 -2.256 0.267 -0.011 0.988 0.363 2.624 -0.351 -0.583 1.083 0.254 0.030 0.998 1.071 +4 -0.847 0.383 2.246 0.321 -0.296 0.536 0.596 0.585 1.373 0.386 1.325 0.230 1.512 1.207 0.922 0.789 2.302 1.077 -0.849 0.246 -0.265 2.064 1.251 2.032 -0.760 1.102 -1.365 -1.592 +2 -0.181 0.848 0.117 -0.037 0.432 0.400 1.284 -0.655 0.074 0.355 -0.670 1.014 1.208 -2.756 0.795 -0.061 -0.558 2.155 -1.634 -0.840 -0.282 0.710 0.406 -0.248 0.882 1.364 -0.875 -0.665 +0 0.903 0.565 0.006 -1.133 1.309 0.665 0.567 1.699 0.695 -0.049 -0.511 -0.550 0.276 0.967 0.978 -1.380 0.925 -0.296 -0.588 0.366 0.788 -1.233 0.217 -1.621 0.792 -1.110 0.191 -0.366 +4 -0.633 -0.097 1.011 3.529 -1.244 0.450 -0.708 -0.539 -0.720 -1.126 -0.041 -1.475 0.947 0.055 0.422 -0.466 -0.367 0.288 -1.164 -0.358 -0.860 -0.891 -2.110 1.339 -0.687 1.163 1.069 -1.286 +1 -0.046 -1.313 0.226 1.026 1.489 -0.791 0.561 1.774 0.830 0.190 0.841 -0.545 -0.473 2.004 -1.844 0.112 1.341 0.969 -0.626 -0.248 0.252 0.017 0.232 0.148 -0.087 -0.267 -0.358 -0.083 +2 0.642 -1.050 -0.353 -1.115 0.473 0.038 0.226 -0.846 0.851 -2.407 -0.252 -0.107 -2.022 0.384 -0.173 0.561 2.000 0.325 0.973 1.381 0.492 -0.866 -1.640 0.374 -0.400 1.284 -0.475 -0.014 +3 0.787 0.234 -1.012 -0.970 0.618 -0.526 -0.615 -0.181 -0.164 0.901 -0.505 0.801 0.440 -1.674 0.295 -2.668 -0.895 0.463 -0.011 -1.768 -1.119 -2.609 -1.343 0.579 -0.557 0.170 0.014 0.033 +1 0.513 -1.200 0.179 -0.265 1.073 0.892 0.091 0.281 0.599 -0.009 -0.818 -0.483 -1.221 0.925 0.398 -0.423 -0.725 0.236 -1.413 -1.069 -0.020 2.441 0.452 0.185 -1.171 -0.933 1.356 -0.462 +1 0.553 -0.032 0.013 0.018 -0.135 -0.609 0.418 -0.285 0.614 0.111 -0.387 1.565 -0.102 0.623 -0.700 -2.026 -0.567 -2.137 -0.237 1.469 -0.905 -2.106 1.500 -0.475 -0.016 0.788 0.523 0.751 +1 0.976 -1.137 0.938 -0.840 -0.493 0.682 -0.089 0.759 0.203 -0.287 0.157 0.537 -0.330 0.547 -0.169 -0.114 0.444 -0.741 -1.520 0.362 -1.888 0.647 1.822 0.657 1.798 -0.753 -1.710 0.160 +1 0.935 -1.513 0.053 0.228 -0.438 1.628 -1.016 0.057 -0.922 -0.905 1.756 -0.443 0.320 -1.127 -0.240 0.624 -1.047 -0.361 0.544 2.035 -0.784 0.563 0.640 0.514 -0.471 -0.878 1.249 -1.356 +4 1.463 0.773 0.055 -0.426 0.574 -0.588 -1.431 -0.243 -0.149 -0.582 0.545 -2.211 -0.714 -1.128 -1.790 -0.255 0.690 1.712 1.732 0.401 0.627 -0.030 -1.473 -0.411 -2.507 -1.854 0.422 0.460 +3 0.180 0.953 -1.148 0.369 0.773 -0.423 -1.259 -0.042 1.228 1.126 -0.428 -1.283 -0.431 -0.468 0.446 -0.105 -0.753 1.104 0.140 0.435 -1.725 -2.424 -1.438 -0.481 0.576 -2.342 1.593 0.171 +0 -0.131 -0.145 -1.380 0.477 1.032 -0.362 0.409 -0.407 0.594 0.494 -0.384 -2.127 -0.799 0.345 0.884 -0.605 1.345 1.494 0.698 -1.136 0.711 -0.538 -0.593 0.453 0.429 0.456 0.657 -0.057 +2 -0.329 1.618 2.172 -0.367 -0.654 -1.032 -1.247 -0.280 0.063 -0.563 1.267 -0.161 1.267 0.388 1.355 -1.333 1.132 0.241 -0.249 0.079 2.034 0.752 0.960 1.169 -0.086 0.712 -0.295 -0.183 +3 -1.088 2.561 1.751 -0.032 0.257 -0.631 0.432 -0.717 -2.032 -1.322 0.021 -1.131 -0.067 -1.075 -0.782 0.853 0.247 -0.010 0.879 0.481 1.447 -0.460 -0.430 -1.301 -0.429 0.965 1.300 0.960 +0 -0.056 -1.224 -0.889 1.048 0.114 -0.910 -1.207 -2.218 -0.342 1.190 0.985 0.218 1.318 0.823 -1.253 1.554 -0.259 -0.878 -0.361 0.082 0.154 0.122 0.057 -0.477 -0.044 0.686 -0.239 0.294 +2 0.225 1.000 -1.266 -1.493 0.426 1.441 1.693 -1.224 -0.403 -1.479 -0.551 1.123 -0.013 -0.374 0.545 -0.795 1.593 0.007 -0.402 0.587 0.939 1.596 -0.210 1.086 0.101 -0.611 -0.777 -1.097 +3 1.012 0.542 1.805 2.051 -0.290 -1.153 -0.289 0.118 2.226 -0.375 0.278 -0.681 -0.347 -1.214 -1.463 1.361 1.066 -0.370 1.063 -1.307 -0.173 0.174 1.266 0.198 0.545 -0.470 0.264 1.264 +1 1.063 -0.237 2.132 -1.006 -1.321 -0.172 -1.485 -0.691 -0.521 1.429 -1.054 1.261 0.589 -0.880 -0.229 -0.392 0.221 -1.098 -0.307 0.913 -0.588 -0.080 1.277 0.358 0.045 -1.075 0.772 0.759 +1 -1.050 0.082 0.149 0.724 -0.222 -0.778 0.602 1.347 0.365 -1.454 0.276 -1.931 -0.041 -2.010 -0.430 0.511 -0.285 1.149 0.219 -1.124 0.901 0.689 1.274 0.710 0.470 -0.993 0.813 -0.146 +1 -0.150 -1.124 -0.210 -0.065 -2.458 0.513 0.916 2.200 -0.930 -0.108 0.454 -0.055 -0.749 -1.469 0.072 1.230 0.005 -0.062 0.386 0.754 0.328 -1.237 0.459 -0.627 -0.720 -1.080 -0.087 -0.538 +3 1.205 -1.175 0.964 -0.077 -0.127 -0.146 -1.761 -0.458 0.354 0.490 -1.152 -0.776 -1.062 -0.428 -0.360 1.371 -0.696 0.968 0.848 0.144 0.288 -0.866 0.560 -3.720 0.100 0.167 -0.982 0.937 +3 1.787 -0.434 1.496 1.278 -1.001 1.033 -0.651 -1.867 -0.425 0.398 -0.463 -0.307 1.144 0.106 -0.843 0.702 0.198 -2.522 0.428 0.392 0.020 0.892 -0.672 0.336 -1.567 0.407 1.953 -0.995 +0 0.449 0.537 -0.800 0.892 -0.738 0.213 0.833 -0.032 -0.527 0.013 0.727 0.387 -0.450 -0.398 -0.024 0.880 0.362 -0.189 -0.339 -1.335 2.254 -0.306 -0.655 1.323 -0.450 0.307 -0.122 -0.275 +3 0.607 -1.289 -1.057 -0.061 0.704 1.005 0.004 -0.418 -0.790 0.700 0.661 0.178 0.302 1.602 1.017 -1.415 0.888 1.381 -0.820 0.948 3.006 0.482 -0.236 -0.101 0.131 -1.577 -1.623 0.270 +2 0.683 0.341 0.710 0.648 0.629 -0.521 -0.310 0.005 1.130 0.687 0.107 0.182 -1.065 -0.244 -0.496 0.884 0.211 1.230 2.322 0.835 -0.935 -0.192 -1.629 1.784 2.291 -0.933 -0.410 -0.564 +2 2.060 -0.543 -1.357 0.242 -0.543 0.597 0.434 -1.170 1.840 -0.611 -0.504 0.578 1.633 -1.286 -0.462 0.957 -0.573 0.157 -0.414 0.495 -1.248 0.317 0.486 0.681 -0.524 1.448 -2.099 -0.663 +4 -0.780 0.729 0.499 -0.353 -0.460 -0.498 0.030 1.676 -1.354 1.568 0.084 1.627 3.069 -1.033 0.492 -2.200 -0.391 -0.527 1.371 1.437 -0.586 1.369 0.767 -1.691 -1.799 1.498 -0.888 -0.292 +3 1.929 -0.032 -0.424 0.529 -0.035 0.544 0.945 0.720 -2.267 0.196 1.353 -0.776 1.346 1.105 -0.794 -0.120 -0.970 0.853 1.675 0.248 0.759 -1.343 -0.105 -1.519 0.022 1.250 1.039 1.267 +4 0.811 -1.148 0.676 -0.799 -0.627 -1.045 0.139 -0.235 2.008 1.483 -0.609 -1.152 -0.339 0.424 -0.124 0.625 -1.822 1.339 2.039 -1.771 0.622 0.142 0.495 -2.200 -2.001 -0.020 -0.657 -0.598 +2 -0.895 1.063 2.338 -0.861 0.534 -0.727 1.041 -1.265 0.481 -0.873 -0.207 -1.031 0.822 -0.837 0.552 -0.344 -0.114 0.148 -0.762 0.210 1.530 -0.778 1.172 1.498 -0.816 -1.455 0.302 -0.547 +2 0.408 0.071 -1.679 -1.714 0.082 0.875 1.332 -0.890 -1.509 0.933 -0.815 -1.577 -0.494 -0.511 1.320 0.866 -0.737 1.398 0.125 1.061 1.327 -0.263 0.571 -0.407 0.930 0.090 0.960 1.525 +0 -0.099 -0.504 0.468 -0.721 0.208 -0.165 0.711 -0.214 -1.030 0.362 -0.271 -1.821 0.198 -0.537 0.346 -0.855 -1.206 1.180 0.843 -0.007 0.438 0.810 -0.010 -1.209 0.299 1.002 0.593 0.059 +3 -0.596 -1.255 -0.445 -0.273 0.749 -1.780 -0.770 -0.783 1.308 -0.295 1.074 0.430 -0.086 -1.790 -1.019 0.975 0.263 -0.188 -1.236 -2.056 1.300 -0.786 -0.575 -1.079 -1.080 0.072 -1.849 0.385 +2 -0.262 -1.429 0.934 -2.349 0.488 -1.340 -0.367 0.306 -0.976 -0.457 -0.442 -0.119 0.166 -0.002 0.025 1.072 1.440 0.468 0.284 -1.532 -2.387 -0.347 1.535 -0.252 0.712 -0.203 0.157 -0.639 +3 1.766 0.681 0.674 -0.897 1.769 1.377 -0.844 -0.475 1.255 1.796 -1.265 -1.004 -0.144 -2.171 0.019 1.186 -1.439 0.127 -0.582 1.640 -0.570 1.519 -0.597 0.771 0.252 -0.138 -0.791 0.323 +2 -0.232 -0.757 -0.596 -0.403 -1.095 -0.264 0.054 -0.403 -2.299 -0.154 -0.047 1.898 0.027 0.936 -0.426 1.298 -0.945 0.505 0.840 0.694 2.164 -0.584 1.414 1.156 0.049 1.839 -0.147 0.022 +1 0.431 0.722 0.792 -0.006 -1.576 -0.074 -1.318 1.070 -0.470 0.015 -0.075 -1.954 -0.289 -0.079 1.729 1.475 -0.566 -0.383 -1.432 -0.514 0.692 -0.908 -0.704 -0.279 -1.282 0.435 -0.235 0.776 +3 0.034 0.545 -0.207 2.004 -1.399 0.089 -0.829 -1.242 -0.629 -0.278 -0.441 0.208 -2.210 1.159 1.976 -0.199 0.758 0.179 -2.035 -0.912 -1.381 -0.347 2.061 -0.882 -0.532 0.251 -0.437 0.713 +3 0.659 -0.229 0.425 1.677 1.006 2.340 -0.657 1.676 -0.791 0.354 0.732 -0.511 0.088 0.910 -0.710 1.669 -1.385 1.940 1.001 -0.090 -0.403 -0.507 -0.136 -0.048 1.255 1.969 0.199 -0.846 +3 -1.152 0.713 -0.997 1.588 -1.226 -1.061 -1.303 -0.626 0.390 1.048 -0.860 0.767 -0.346 -1.392 0.959 -0.350 -0.568 1.688 0.775 2.459 -0.248 0.289 -1.093 0.830 -0.603 -1.191 -0.746 0.056 +3 0.568 -0.528 0.552 -0.024 -0.846 -0.761 1.249 -0.996 -1.168 0.344 -0.031 -1.312 -0.519 -0.817 -0.642 2.525 -0.303 -0.881 2.022 0.888 0.725 -0.833 1.191 0.816 0.147 -1.716 -0.020 1.730 +0 0.806 -2.103 -1.273 -0.335 0.024 -0.059 0.889 0.477 0.697 -1.390 0.145 -0.307 1.462 0.174 -0.669 0.853 0.563 -0.822 -0.348 -0.331 1.227 0.203 0.727 -0.493 0.335 1.303 -0.361 0.115 +2 -1.220 0.212 1.256 -2.530 -1.005 -0.004 -0.459 -0.644 -1.239 0.079 -0.907 -1.271 -1.210 1.230 -0.817 0.463 -0.591 -1.663 -0.786 -0.583 0.044 0.134 -1.231 1.354 1.429 0.045 0.309 -0.840 +3 2.684 -0.109 -2.651 1.358 0.446 0.563 -0.320 -0.884 0.524 -0.798 -0.225 -1.611 1.022 -1.319 1.194 -0.156 0.086 0.296 0.761 0.808 -0.028 0.602 -1.104 -0.975 0.001 -0.621 -1.655 0.737 +4 -1.839 2.195 1.480 -0.630 1.243 1.392 -1.920 0.851 -0.492 0.383 3.010 1.585 -0.128 1.422 -1.922 0.504 0.527 0.706 -0.508 0.382 0.856 -2.323 -0.316 -0.903 0.662 1.378 -0.612 -1.089 +4 1.505 0.625 0.679 -0.219 -0.891 -3.031 1.143 2.064 -1.354 -1.967 0.655 1.792 -0.753 1.957 0.492 -2.494 1.153 -0.248 -0.130 -0.271 0.082 -0.855 -1.168 0.604 -1.221 -1.071 -1.981 0.937 +0 1.129 0.382 -0.217 0.111 0.118 -1.438 0.893 -1.098 0.651 -2.061 1.156 -0.734 -0.053 -0.680 1.219 -0.431 -0.274 0.658 -1.157 -0.407 1.180 1.321 0.391 -0.131 0.548 0.396 -0.547 -0.238 +0 -0.059 -0.635 0.600 -1.804 -0.530 0.784 -0.781 0.527 1.186 0.872 0.805 -0.462 -0.346 0.510 -1.182 -0.666 -0.077 -0.132 1.174 0.661 -0.010 0.600 -0.584 -0.656 -0.575 0.092 -0.935 -0.969 +0 -0.401 0.123 0.026 0.242 0.131 -0.122 -0.942 1.248 0.126 -0.391 -1.421 1.029 -1.047 0.458 -0.207 0.653 1.566 -0.992 -1.120 0.399 1.205 -0.312 -0.016 -1.397 1.024 0.065 0.994 0.293 +0 -0.443 -1.190 0.440 -0.258 -0.813 -0.918 0.054 -0.982 -0.081 -0.581 0.916 0.980 -0.376 -0.857 -0.462 0.375 -0.593 -0.544 0.976 1.021 0.865 -1.398 -1.966 -0.430 0.032 -0.792 1.761 0.079 +1 0.448 1.518 -0.840 -0.996 1.316 0.380 -0.252 -0.584 0.814 -0.386 0.779 1.267 0.104 -1.358 0.569 1.107 1.200 0.165 -1.716 -0.454 -0.497 0.854 0.412 0.899 -0.680 0.213 -2.294 -0.409 +2 -2.299 -2.099 1.069 -0.367 -0.774 -0.633 0.128 -0.476 -1.172 0.492 -0.337 -1.774 -0.121 -0.914 -0.655 -0.875 1.011 -0.057 -0.299 0.049 -0.532 -1.067 -0.153 -1.293 0.412 0.411 -1.272 -1.176 +3 0.567 -1.088 -0.847 0.975 -0.120 -0.800 0.154 1.258 1.151 0.408 0.960 0.420 -0.882 -1.096 1.968 -1.643 0.447 -1.038 -0.162 -1.758 2.104 0.795 1.077 0.840 -0.446 0.685 -1.145 -0.781 +2 0.041 0.365 -1.204 1.327 1.005 -1.372 1.366 -0.308 -0.778 1.079 -0.051 1.899 -0.853 -1.732 -0.280 0.789 -0.927 -1.330 0.229 -1.464 0.307 -0.341 0.494 -1.384 1.298 -0.183 0.764 0.960 +3 0.200 0.568 -0.740 -0.725 -0.721 -0.439 1.896 0.129 0.405 -2.064 0.162 -0.625 0.476 1.840 -2.502 -0.197 0.282 -1.299 0.295 -0.664 -1.099 0.714 -0.987 -0.447 0.937 2.052 -0.685 0.545 +4 0.331 0.534 0.690 -0.121 -0.898 -1.074 -0.733 -1.809 -0.601 -0.154 -1.885 -0.694 -0.157 1.086 -0.102 2.006 1.144 -0.226 -1.130 -1.299 -0.313 -0.813 0.805 1.442 -1.704 1.546 2.756 0.225 +0 -1.265 0.287 0.631 -0.260 0.933 -1.408 0.399 -0.852 -0.931 -0.765 -0.081 0.439 0.363 -0.290 0.632 1.687 -0.119 0.095 -0.612 -0.392 1.248 1.259 -0.201 0.490 0.920 -0.447 -1.306 0.689 +1 -0.410 -0.154 -1.328 -0.202 -1.900 -1.069 -0.083 0.044 -0.392 0.940 1.178 0.614 -0.265 0.238 0.151 -2.884 -0.668 -0.369 -0.414 1.011 1.101 -1.062 0.055 -1.073 -0.010 1.137 -0.150 0.520 +4 -0.065 0.739 0.746 -0.303 -1.256 0.068 0.624 -0.630 -0.493 0.485 1.205 2.573 0.455 0.169 -0.647 2.159 2.158 0.321 -0.930 -1.965 -0.131 -0.927 1.643 -0.389 0.755 0.597 -1.777 0.041 +1 -0.508 0.518 1.727 -0.539 -0.346 0.613 0.349 -1.056 1.600 -1.216 -0.024 1.214 -0.470 0.908 -0.757 -0.142 -0.689 1.032 0.427 -0.450 -0.396 1.028 0.803 -1.241 0.080 -1.876 1.270 1.540 +1 -0.380 1.077 0.175 0.347 -1.457 0.423 0.530 -0.398 2.085 -2.289 0.784 0.713 0.542 0.176 0.551 0.868 -0.726 0.299 -0.183 -1.094 0.717 -0.564 -1.498 -0.539 -0.799 1.028 0.153 1.382 +3 1.430 -1.315 0.098 -0.156 -1.119 -0.650 -0.307 0.151 -2.389 -0.282 0.764 1.326 0.317 0.547 -1.264 -0.339 -1.244 -0.186 2.296 1.462 0.792 0.318 -0.251 0.652 -0.602 2.166 0.830 0.969 +2 1.175 -0.458 -0.266 0.591 0.058 -0.637 0.661 1.621 1.058 1.357 -0.139 -1.083 -2.000 -2.072 -0.203 0.507 0.843 0.243 -0.722 0.654 -1.198 -1.104 0.613 -1.233 -0.765 -0.978 0.169 0.588 +4 0.205 1.909 1.137 2.172 0.514 -0.303 0.766 1.951 -0.979 -1.739 -2.064 1.708 0.897 0.299 -0.572 0.684 0.382 -0.099 -0.886 -0.573 0.868 -0.137 1.453 0.988 -0.642 -0.450 0.992 1.166 +2 1.513 0.136 -1.591 0.316 1.007 0.472 1.133 0.563 0.787 -0.094 1.507 0.814 0.280 -0.845 1.055 1.192 2.147 -0.051 0.405 0.387 -1.947 0.863 0.481 -0.437 -0.814 -1.139 0.637 -0.678 +2 -0.215 0.634 -0.747 0.036 2.392 1.037 0.944 -0.965 -1.182 1.537 -0.851 -2.711 0.352 -0.624 -0.158 -0.557 -1.669 -0.708 0.370 -0.472 -0.785 0.150 0.015 0.474 -0.429 0.664 0.685 0.775 +1 -1.061 0.935 -0.429 -0.744 -1.833 1.534 -0.713 0.492 1.015 -0.343 -0.972 -1.338 0.384 0.978 -0.303 0.043 -0.397 -0.472 1.369 0.334 -0.407 -0.329 -0.477 -0.738 0.107 -1.589 -1.219 -0.722 +1 1.264 -0.255 1.051 -0.113 -1.635 -1.242 -0.261 -1.663 0.530 0.820 -1.483 -0.285 -0.093 0.689 0.410 -0.414 -1.414 0.001 -0.129 -0.820 2.118 0.173 -0.650 -0.560 0.832 1.189 0.617 1.016 +1 -0.166 1.017 0.174 -1.312 0.863 1.394 -0.727 -0.082 2.009 -0.331 -1.048 -0.591 -0.288 1.142 -0.429 0.779 0.358 0.064 0.017 -2.256 0.502 1.159 -0.593 1.188 -0.411 -0.076 0.251 1.372 +3 0.529 1.310 1.373 -1.874 1.088 -0.314 -0.430 2.512 0.220 -0.383 0.799 1.686 0.173 -0.534 0.091 1.091 -0.311 1.092 -0.140 -1.855 1.520 0.335 1.054 0.302 0.037 0.737 -0.154 1.813 +2 0.817 1.210 0.155 0.400 -0.653 -1.399 -1.198 0.692 -0.242 -2.538 0.518 -2.007 -0.493 -1.748 -0.011 -0.810 0.023 -0.565 1.913 0.966 -0.199 0.571 0.645 0.624 -0.631 -0.416 0.418 -0.996 +1 1.535 -0.063 -0.257 -1.181 0.316 1.144 -1.672 -0.247 -0.994 0.842 0.940 0.449 -1.993 -1.413 -0.974 -1.039 -0.628 0.444 0.256 -1.036 -0.590 0.636 -0.232 -0.224 0.810 -1.167 1.223 0.322 +4 0.393 1.338 0.917 1.440 0.201 1.813 -0.591 2.526 -0.329 -0.087 1.470 -0.100 -1.009 0.651 0.464 0.317 1.535 -1.391 0.785 0.132 -0.939 2.127 -0.206 -0.996 0.540 -0.244 -2.273 -0.268 +4 -0.871 0.625 1.282 -0.385 1.156 0.398 0.852 -0.837 0.214 -1.408 1.711 -2.014 -0.355 -0.932 0.675 1.924 0.655 2.961 -1.378 -1.301 -0.893 0.121 -1.027 -0.752 0.620 -1.239 -1.133 -0.399 +1 -0.168 -1.456 0.044 -0.876 -1.357 0.579 -1.413 0.680 -1.292 -0.291 -0.564 1.137 0.594 -0.824 -0.158 1.329 1.493 -1.369 1.316 1.240 0.179 -0.091 -0.550 -1.253 0.339 -0.170 0.435 0.459 +0 0.207 1.057 -0.611 0.301 -0.112 -0.167 0.283 -2.582 0.854 0.870 -0.558 0.470 1.453 -0.463 0.156 -0.571 -1.534 -0.314 -0.542 0.481 1.226 0.307 -0.297 -0.609 1.117 1.061 -0.327 0.281 +3 0.551 0.520 1.262 -1.188 -0.930 1.215 1.138 -2.125 0.461 1.962 -0.272 -0.984 -0.631 -2.113 0.666 -0.364 -0.883 0.588 0.770 -1.176 -1.187 1.274 -0.062 0.007 1.546 -0.533 0.976 1.362 +1 0.434 -1.236 -0.054 -0.136 -0.795 -0.051 -1.892 0.439 -0.399 -0.257 0.852 0.432 -0.318 -1.117 1.329 -1.340 0.629 -1.597 0.918 1.011 -1.913 0.449 0.367 0.703 0.365 -0.962 -0.217 1.665 +4 0.108 -0.706 0.274 0.282 -0.063 -1.448 -0.824 -0.114 -0.599 -1.049 -0.253 2.503 -0.010 -1.112 1.127 -1.659 0.563 -0.775 1.722 1.470 0.439 1.078 -0.429 1.922 1.806 -0.132 -0.813 -2.053 +4 0.997 -0.945 0.280 -0.849 -0.066 0.643 -0.289 -3.076 -1.345 -2.158 0.068 -0.005 -0.351 -0.626 0.221 -1.447 0.956 0.075 -0.945 -0.418 -0.131 -1.381 0.671 0.965 0.117 0.941 2.650 -1.878 +3 -0.608 -1.227 -0.406 -0.240 -0.432 0.055 -0.079 0.231 0.943 0.593 0.955 -0.848 -0.509 0.654 0.671 -0.679 0.612 -0.944 0.481 -0.026 -1.384 -0.434 2.182 0.203 2.300 -1.444 0.751 -2.534 +0 1.029 0.111 -0.917 0.524 -0.319 1.111 -0.968 0.568 0.160 0.183 0.278 -0.192 0.501 0.281 0.115 1.206 -1.343 0.587 0.221 0.502 0.602 0.660 -0.385 -0.652 0.472 1.476 -0.207 -1.013 +0 -1.176 -0.895 0.597 -0.948 0.463 -1.367 0.848 -1.233 0.552 0.626 -0.697 0.582 0.260 -0.539 -1.009 -1.963 0.350 -1.565 0.095 -0.263 0.679 -0.302 -0.329 0.732 0.335 0.316 0.469 -1.536 +0 0.106 0.703 0.450 0.840 -0.553 0.982 0.679 -0.150 0.235 -0.237 -0.833 1.219 0.617 0.535 -0.603 -0.425 1.213 -0.985 -0.536 0.392 0.582 -0.198 0.169 0.272 0.657 0.406 0.024 -0.751 +0 1.143 -0.076 -0.444 0.941 -0.129 -0.992 1.247 0.263 -1.280 -0.771 -1.004 -1.184 -0.136 -0.076 0.424 0.560 0.846 1.019 0.487 0.356 0.968 0.413 -0.953 -1.393 -1.026 0.315 -0.306 0.227 +4 0.718 2.014 0.051 -0.817 0.189 0.939 0.621 2.743 -0.715 0.252 1.549 1.486 -1.950 -0.003 1.485 2.891 0.704 -0.368 -1.009 2.219 0.729 -0.848 -0.203 0.310 0.544 -0.900 -0.614 -1.653 +0 -0.532 -0.617 0.246 0.550 0.677 1.774 1.501 -0.811 0.969 -0.384 -0.138 -1.094 -1.693 -0.032 -0.956 0.928 -0.281 0.309 1.368 -1.485 -0.725 0.181 0.313 1.103 0.867 0.097 0.303 0.331 +3 0.339 -0.991 -0.574 -1.836 1.377 -1.300 0.542 -0.035 1.290 -0.792 0.061 -1.698 -1.939 0.596 -1.785 -0.228 1.011 0.747 -0.665 -1.589 0.509 1.342 -0.662 -0.803 -0.525 0.572 1.229 -1.240 +3 -0.398 -1.861 1.057 -1.010 -2.289 1.047 0.485 -1.318 1.086 0.665 0.000 -0.350 -0.122 0.206 -0.789 1.118 0.556 -0.135 1.743 -0.302 1.549 0.293 0.758 -0.367 -1.870 0.479 -1.188 -0.613 +2 0.035 0.755 1.242 0.580 -0.676 1.158 0.835 -0.353 -0.086 0.005 2.502 0.124 0.341 -0.379 -0.461 -0.928 0.965 1.208 1.127 0.689 0.042 0.111 1.260 1.834 1.058 1.207 0.743 0.944 +2 1.416 -0.311 -1.417 1.057 0.901 -0.685 -0.980 -0.684 -0.225 0.119 0.488 -0.404 2.065 2.041 0.297 0.340 -0.603 1.191 0.212 -1.388 -0.595 0.072 1.113 -2.387 0.373 -0.011 -0.509 -0.428 +3 0.759 1.073 -1.040 -0.011 -0.441 -0.013 2.301 -0.192 -0.980 -0.256 0.845 0.485 1.040 1.848 1.806 -0.803 0.749 -1.749 1.092 -1.548 -1.544 -0.190 0.931 -0.939 -0.529 -0.835 0.148 0.922 +4 -0.514 -0.791 -0.931 0.095 1.197 -1.905 -0.376 0.390 2.527 1.324 0.190 0.932 0.392 -1.290 -0.716 -2.036 0.639 -0.952 -0.576 -0.992 -0.023 0.140 -0.062 1.340 -0.511 0.847 2.814 0.725 +1 -0.516 -0.097 0.507 0.590 0.761 -1.483 1.264 -0.645 -0.572 0.269 -0.337 0.362 0.547 -0.763 -0.184 -0.154 0.958 -0.465 -0.509 -1.022 -0.910 -1.351 -0.703 1.594 0.691 1.277 -0.950 -1.948 +4 0.115 0.879 -0.256 -1.194 -1.940 -0.890 0.423 1.441 0.054 -1.214 -0.880 -0.849 -0.406 1.121 1.199 -0.172 0.183 -0.798 -1.710 1.788 -2.322 -1.836 1.796 0.017 -1.126 0.683 0.773 -1.333 +1 1.495 -0.958 0.175 -0.054 -0.879 0.104 0.560 0.585 1.069 -0.040 -1.007 -1.384 -0.461 -1.353 -0.711 -1.182 -0.655 1.783 -0.123 1.572 0.418 -0.622 -0.123 -1.651 -0.331 -1.311 0.599 -0.435 +2 -0.473 0.477 1.352 0.730 -1.692 0.752 -0.504 0.716 -0.588 0.932 0.717 -0.081 -1.086 1.254 0.210 -0.114 0.196 -1.213 0.682 -0.288 0.025 0.229 -1.708 -2.004 -1.673 -0.772 -0.241 -1.660 +0 0.618 -0.137 -1.202 1.271 0.858 -0.889 0.917 1.119 0.097 -0.480 0.430 0.088 -0.610 1.227 0.822 0.289 -0.273 1.304 -0.637 0.528 -0.583 1.479 -0.467 -0.310 -0.211 -0.868 1.868 0.651 +2 -0.059 1.320 -0.514 -1.411 -0.989 0.306 1.104 0.025 0.992 -0.008 0.631 1.719 0.547 -0.732 0.266 0.101 -0.360 -0.798 2.233 -0.264 -2.340 -0.635 -0.769 -0.248 -1.499 -0.548 0.252 -1.340 +3 0.052 -1.061 -0.717 -1.285 0.978 -2.106 1.154 1.386 -0.302 -2.603 -0.361 -0.064 -1.011 -0.515 1.530 0.665 -0.925 -1.598 -0.327 -0.213 0.496 -0.535 0.511 1.935 0.816 -0.048 -0.183 -0.357 +0 -0.680 0.128 -0.994 0.767 0.857 0.691 0.977 0.945 0.324 1.343 0.483 -0.584 -0.061 0.892 0.470 -0.526 0.050 -0.766 0.818 1.339 -1.572 0.414 -1.068 -1.389 1.304 1.128 0.216 -0.503 +0 -0.347 0.227 -0.437 0.031 0.884 0.845 -0.023 0.903 -1.019 -1.386 -1.504 -0.038 -0.174 0.142 -0.064 -0.686 -0.214 -1.282 1.332 0.077 -1.558 0.838 0.420 -0.017 -0.710 -0.832 -1.950 0.591 +1 1.451 -0.395 -0.556 0.697 -0.686 0.274 0.042 -0.409 -0.016 1.763 0.605 1.093 -0.919 1.096 0.639 -1.500 0.540 -0.513 -1.546 0.130 0.539 0.152 -0.109 -0.331 2.467 -1.183 0.873 0.015 +4 -1.310 -0.954 -0.562 -0.273 0.987 -0.767 -2.450 -0.867 -0.458 1.343 -0.817 -3.314 -0.953 0.015 -2.343 0.583 0.455 0.016 0.500 -0.467 -0.663 -0.628 -0.323 2.013 -0.758 0.762 -0.973 -0.023 +1 0.501 0.251 -1.640 0.712 0.771 -0.090 0.268 -0.730 -0.260 0.208 1.037 0.458 -0.399 -0.321 0.123 -0.233 -0.019 -2.653 -0.873 1.734 0.439 0.521 0.452 0.358 0.147 1.185 0.512 -1.729 +2 -0.318 -1.249 -0.473 -1.606 -0.660 -0.576 -0.054 -0.417 -0.351 0.492 -0.269 -1.234 -0.264 0.732 0.896 -0.247 2.187 -0.847 -0.009 -1.654 -1.221 1.729 0.224 0.844 -0.093 0.132 1.693 1.042 +4 -0.725 0.707 -1.380 0.625 1.781 0.567 0.638 0.870 1.710 1.210 0.440 0.904 -0.886 0.776 -1.006 -0.367 -3.083 0.401 -0.644 1.111 0.390 1.124 -1.084 1.038 2.106 0.764 0.790 0.170 +4 -0.171 -0.066 -1.709 0.392 -0.089 1.610 -0.940 -1.808 0.618 0.347 -1.008 -1.530 1.537 1.414 -2.487 -0.458 1.664 -0.476 -0.491 -0.283 -1.121 -1.705 1.053 -1.843 0.247 0.887 -1.451 -0.924 +1 0.201 -0.711 0.966 -2.437 0.358 -0.501 -0.788 -0.756 -0.811 -0.545 -0.839 -0.249 0.026 -1.153 -1.011 -0.298 0.507 -1.340 -1.127 0.287 -0.284 -0.846 -0.825 -0.799 -1.046 1.106 -0.716 -0.582 +0 0.644 0.650 -1.042 -0.163 -1.067 -1.015 -0.189 -0.062 -1.509 0.447 -0.874 0.583 -0.878 1.543 -0.984 -0.675 -0.481 0.486 1.092 -1.183 -0.566 0.306 -0.346 -1.023 -0.314 0.977 -0.043 0.006 +3 -0.155 -0.809 -0.833 0.739 0.831 -2.646 0.940 -0.411 -0.632 -0.327 -1.262 -0.168 1.915 -1.548 0.618 0.975 0.582 1.534 0.982 0.671 1.038 0.992 -0.349 0.631 -0.273 -1.334 1.837 -0.553 +0 -0.806 -0.372 0.407 1.034 -0.382 -0.596 0.983 0.613 0.149 1.181 1.947 0.366 -0.590 0.497 -0.820 1.242 -0.702 -0.193 -0.838 0.182 0.217 0.743 0.120 0.046 -0.574 -0.286 -1.393 1.394 +2 0.278 -0.443 1.039 -0.615 -1.596 0.939 1.324 -0.822 1.961 0.583 0.434 -0.517 1.017 -1.524 -0.340 -0.733 -1.309 0.863 0.341 0.805 -0.278 0.985 0.077 -0.472 -0.639 -1.983 1.164 1.051 +4 -2.384 1.430 0.339 1.586 2.361 1.029 -0.634 -0.057 0.147 0.936 0.297 0.227 0.422 0.411 -1.443 0.588 1.049 -0.392 0.950 2.266 -1.608 -0.145 -0.545 -0.667 0.699 -0.397 -1.808 0.483 +2 -1.055 0.798 0.212 1.224 -0.910 -0.445 -0.783 0.567 0.791 0.814 0.241 -0.018 1.204 -1.062 -1.230 0.847 -1.645 1.797 0.082 2.086 -0.746 -0.511 0.508 -1.468 -0.564 -0.727 -0.757 0.627 +2 0.502 -0.478 0.592 0.869 0.241 0.244 1.797 0.781 -0.803 -0.319 1.652 2.665 -0.571 0.547 0.394 0.474 -0.807 0.581 -0.798 -1.757 0.841 1.837 -0.400 0.015 -0.909 0.189 -0.125 -0.853 +4 1.553 -0.703 -1.282 0.128 -0.860 1.482 1.702 0.040 0.470 0.039 -1.880 1.759 -1.160 -0.579 1.253 -1.652 -0.053 -0.665 0.398 -0.174 1.200 1.081 1.301 1.014 0.923 -1.312 2.258 -0.726 +3 0.693 -1.139 2.464 0.556 -0.057 -0.252 0.788 -0.411 -0.370 1.453 -0.472 0.136 -0.760 0.259 0.581 1.683 -0.937 -1.448 -0.267 -0.617 -0.015 0.522 2.265 -0.451 -2.210 -0.238 0.351 -0.970 +2 0.128 1.331 -0.019 -1.076 -0.204 0.567 -1.110 0.759 -1.353 1.291 -0.181 -0.960 -0.125 -0.935 -0.075 -0.610 -0.259 -2.353 -0.228 0.429 0.143 0.117 -1.860 -0.069 2.069 0.392 1.165 0.796 +2 0.225 -0.102 -1.046 1.226 0.331 -0.048 -0.107 0.926 -0.863 1.423 2.190 -0.311 1.289 -0.591 -1.139 -0.589 0.405 -0.246 0.397 0.253 -0.435 -1.234 -0.039 -1.383 0.062 0.327 -2.337 1.261 +2 -0.852 -0.775 -0.093 1.317 -0.174 -0.963 0.515 2.516 0.073 0.610 1.196 -0.344 -0.143 1.404 -0.478 -0.372 -1.115 -1.281 0.896 1.165 -0.902 -0.246 0.776 -1.096 1.752 0.878 -0.730 -0.243 +3 0.035 -1.318 0.925 1.887 0.256 1.202 -1.657 0.514 -1.629 -0.262 0.436 -1.069 -0.529 -0.681 0.782 -0.312 -0.311 0.564 0.788 -1.000 2.012 1.278 1.703 -0.248 0.664 0.142 1.502 -1.199 +1 -0.374 -1.424 -0.804 -0.644 1.136 1.993 0.049 0.989 -1.621 0.345 -0.796 -0.027 1.051 0.465 -0.393 0.605 1.814 0.461 -0.580 -1.314 0.876 -0.721 0.056 1.146 -0.417 1.155 -0.504 -0.906 +1 -0.150 0.828 -0.706 -1.219 -1.323 -0.343 0.322 -0.153 -1.036 1.084 -1.125 -1.012 -0.650 -0.080 -0.130 -0.752 -0.114 -0.582 -0.305 1.075 -0.704 -1.197 -0.632 0.069 2.250 -1.414 0.518 1.052 +4 1.180 0.555 -0.136 0.152 1.496 -1.530 0.915 0.336 2.832 1.144 0.403 -0.124 0.696 -0.883 -1.733 0.171 0.373 -1.313 -1.823 0.571 0.739 -0.749 -1.437 1.251 0.971 0.175 2.596 0.426 +3 -0.291 0.920 0.298 -1.287 0.911 -1.366 -0.179 -0.892 1.228 -1.000 -2.280 1.372 -0.776 -0.001 -0.086 -1.169 0.555 -0.071 -0.306 -0.324 -1.830 0.124 0.671 -0.964 0.932 -2.542 0.471 -0.066 +1 1.385 -0.963 1.260 -0.284 -1.243 0.841 -0.853 -1.490 0.343 -1.596 0.364 -0.464 -1.020 -0.847 0.242 0.455 -1.489 -1.270 0.401 -0.387 0.171 0.268 -0.357 -0.727 -1.216 -0.465 1.022 0.135 +4 1.777 0.217 1.275 -1.637 0.153 -0.314 -0.534 0.179 1.129 -0.154 1.422 -1.237 0.316 0.529 -2.372 0.481 -0.254 0.411 1.188 0.229 -0.270 -1.510 -0.081 1.445 -3.092 1.165 0.630 -0.056 +2 0.768 0.091 2.141 -0.599 -1.337 0.304 0.583 -0.769 0.960 0.056 -0.271 0.545 -0.288 -0.735 -1.614 -0.617 2.495 0.036 -1.017 1.267 -0.047 0.292 0.274 1.485 -1.329 1.459 0.745 0.077 +4 0.311 0.672 1.385 0.924 1.321 -0.511 1.012 -1.743 -0.307 2.046 0.241 -0.893 2.320 0.424 1.170 -0.261 -1.342 -0.140 0.504 -2.095 -3.497 1.456 1.832 -0.435 0.085 -0.935 0.389 1.183 +3 1.034 -1.594 -1.855 -0.449 -0.411 0.843 -0.968 0.416 -1.083 0.345 -0.457 0.359 -0.364 -2.207 0.509 1.170 -0.836 0.810 -0.613 -1.350 -0.611 -0.184 -1.320 1.350 -1.531 -0.541 -0.922 1.354 +0 -1.709 1.291 -0.727 0.139 -1.356 -0.038 -1.372 -0.074 -0.474 0.230 0.626 0.241 0.451 -0.109 -0.435 0.470 -0.111 -0.073 0.398 -1.033 1.430 -0.249 0.124 -0.045 0.653 0.339 -0.050 1.449 +1 0.341 0.334 0.196 -0.883 -0.442 0.252 -0.414 0.985 -1.775 1.271 2.098 0.376 -0.748 1.596 -0.314 -0.779 0.314 1.170 1.943 -0.598 0.129 0.107 -0.466 0.252 0.558 0.318 -0.937 0.072 +4 -0.381 0.774 0.917 0.055 0.919 1.000 0.361 0.218 -1.962 0.551 -2.453 0.098 0.206 0.963 -0.092 -2.109 -0.690 1.202 -1.269 -0.832 1.061 1.991 -0.171 1.156 -0.041 -3.182 0.013 0.244 +2 -0.099 1.005 -0.997 -0.137 -0.763 0.856 -0.477 -1.148 0.306 0.695 3.574 -1.242 -1.586 0.103 -0.837 -0.634 0.223 -0.366 0.600 -0.275 0.331 -0.219 -1.186 -0.457 -0.091 0.297 -1.350 0.356 +1 0.850 0.628 0.780 0.644 0.749 0.381 0.582 1.637 -0.237 2.281 -0.361 -0.478 0.308 -0.411 -2.182 -0.898 -0.594 -0.400 -1.772 -0.722 -0.626 -0.733 -0.369 0.831 0.910 0.254 1.048 0.054 +2 -0.750 -0.412 -0.266 0.506 -0.240 -1.332 -0.328 -0.354 -1.675 0.444 -0.222 1.560 -0.744 -2.505 -0.013 -0.860 -0.663 0.122 -2.171 0.945 -1.044 0.067 -0.487 0.693 0.303 -1.641 -0.816 0.749 +1 -0.579 0.268 0.948 -1.374 1.651 1.002 0.044 -1.068 1.529 0.462 0.461 -0.678 0.194 -0.626 1.215 1.240 -0.154 -0.585 0.791 0.652 -1.166 -0.292 2.193 -0.008 0.235 0.545 -0.559 0.766 +4 1.274 0.226 -3.164 1.175 -0.921 -0.559 0.899 2.032 1.541 0.173 0.739 -1.106 -1.623 1.080 0.323 -1.448 -0.048 0.617 0.907 0.628 -0.363 -0.848 0.117 0.802 0.905 1.580 0.035 -1.110 +2 -0.098 -0.779 1.476 -0.094 0.033 -0.491 -0.782 -0.972 2.450 -0.077 -0.648 0.110 0.400 -1.095 -0.716 0.477 -0.565 -1.261 -0.558 -0.987 1.335 0.448 -2.573 -0.411 0.913 0.613 -0.541 0.955 +3 -0.235 -0.463 0.734 1.614 0.537 -1.663 -1.025 -0.704 -2.363 1.258 -1.149 0.299 -0.253 -0.022 -0.396 -1.286 1.770 -1.187 -0.712 1.381 -0.164 0.218 0.595 1.410 0.201 -0.923 -1.620 0.383 +1 -0.472 1.164 0.411 1.492 -1.540 1.513 -1.418 1.239 -0.955 0.203 0.035 -0.167 0.696 0.176 -0.081 -0.439 -1.589 -0.621 0.491 0.622 0.165 1.188 1.803 0.861 -0.641 1.285 -0.709 -0.091 +2 -0.527 1.470 -0.029 -0.804 0.175 0.143 1.337 1.088 0.205 1.658 1.276 0.358 -0.847 1.791 1.268 -1.211 -0.595 -1.334 -1.074 1.269 0.090 -0.665 0.756 0.841 1.336 0.610 0.640 -0.823 +2 -0.280 -1.283 -0.707 0.431 -0.482 -0.573 -2.079 1.091 -1.103 0.060 -0.469 0.265 0.248 -0.205 0.279 0.019 -1.373 1.207 1.520 -0.880 -1.548 1.253 -1.072 1.294 1.165 -0.158 -0.176 -0.993 +2 -0.368 0.434 1.530 0.387 -0.596 2.404 0.233 -0.233 0.621 -1.039 0.811 -1.043 0.660 1.594 -0.882 0.122 0.355 0.954 0.517 0.087 1.356 0.226 -0.656 -1.097 1.185 1.461 0.174 -1.473 +0 2.125 -1.568 -0.301 -0.155 0.032 0.714 0.737 -0.069 1.078 -1.415 0.196 0.910 -0.005 -0.292 -0.108 0.202 0.204 0.766 0.731 -0.235 0.222 -0.383 -1.724 1.328 1.578 0.217 -0.342 -0.556 +4 1.708 -0.306 1.610 0.478 -0.176 -1.194 -1.053 0.482 1.160 0.954 -0.049 -0.435 -1.647 -2.823 -1.456 -0.567 1.488 -1.630 0.193 0.635 0.923 -1.476 -0.250 -2.756 -2.417 -0.597 -1.306 1.217 +3 1.167 -1.241 0.271 -1.840 0.274 2.162 -1.780 -1.015 0.727 0.845 -0.941 1.474 -0.816 1.673 0.469 1.271 0.371 0.657 0.710 1.145 -0.033 -0.900 -0.079 -0.667 -0.602 1.655 0.623 0.577 +0 -0.277 0.316 0.309 0.963 -1.317 0.702 -1.646 -0.853 0.608 -0.838 0.027 -0.921 0.599 0.047 -0.025 1.100 0.417 0.494 1.109 1.212 -0.950 0.737 0.433 1.312 -1.287 -0.691 -0.197 1.403 +1 -0.049 0.675 -1.123 0.382 0.166 0.492 0.289 2.455 -0.638 -0.531 -0.623 -0.555 -0.637 1.189 1.421 -0.571 -0.832 0.471 -0.552 0.633 0.203 -1.516 1.548 1.796 -0.613 -0.388 0.286 0.334 +2 -0.305 -1.605 0.675 -1.491 -0.032 0.839 0.106 -0.165 0.493 0.392 0.902 0.209 0.889 0.833 0.940 -1.990 -0.585 0.914 0.547 0.176 0.581 0.838 -0.095 -1.521 2.094 -0.248 0.183 -2.062 +2 0.293 -0.543 1.602 0.362 -1.177 -1.691 -0.615 0.812 1.152 0.181 0.080 -0.332 -0.021 1.216 -0.347 0.659 -0.528 1.013 0.579 0.972 1.882 -2.582 0.754 0.469 0.447 0.241 0.240 0.336 +1 0.518 1.349 -0.014 -1.196 0.009 0.161 -0.224 -0.913 1.978 0.791 0.629 -1.799 -1.223 -1.533 -0.290 1.175 -0.198 -0.325 -1.661 0.302 -0.489 1.100 -0.790 -0.184 -0.593 -0.358 0.703 -1.186 +0 -0.622 -1.211 0.694 0.963 1.513 -0.114 0.852 -1.782 -0.115 -0.234 0.302 -0.920 -0.458 -0.774 0.907 -0.105 0.039 -0.796 0.875 0.476 0.033 -0.864 0.139 0.605 1.341 0.802 -0.466 -1.943 +4 0.278 -2.001 1.795 -1.803 0.890 -0.316 -1.804 -1.321 -0.478 -0.289 0.931 -0.381 0.475 -2.824 -0.305 0.667 -0.721 -1.123 -0.875 -1.087 -1.295 2.562 -0.441 -0.038 -1.058 2.867 0.935 0.938 +0 -0.361 0.052 0.734 -0.234 0.365 0.746 0.545 -0.668 -0.480 0.584 -0.135 -0.779 0.044 0.778 0.914 1.021 0.032 0.382 1.105 -0.638 0.978 0.698 0.702 -1.689 -0.549 -0.617 1.423 -0.884 +1 -0.620 -1.692 0.205 -1.187 1.184 -0.149 0.102 0.791 0.308 0.109 -0.371 -0.205 -0.707 0.923 0.506 2.048 0.520 0.167 0.275 -0.693 1.202 -0.160 -2.077 -1.374 -0.172 -1.512 -0.984 0.788 +4 -0.426 1.113 -0.126 -1.203 -1.080 -0.793 2.146 -0.231 0.181 0.682 0.505 0.865 -0.525 -1.184 -0.945 -1.532 -0.520 -3.726 0.725 0.572 -0.975 -1.377 1.528 0.966 -0.257 -0.675 0.038 -1.441 +0 0.750 0.966 0.299 -0.869 0.633 -0.096 0.190 -0.365 -1.790 0.059 -0.598 0.300 -0.036 -0.526 2.133 -0.312 0.080 1.878 -1.008 0.840 -0.747 -0.231 0.396 1.146 -1.023 0.256 -0.743 0.645 +3 0.316 1.854 -0.485 0.102 -1.570 0.028 -0.125 -2.093 -1.037 -1.117 -1.580 -0.725 0.509 -0.633 1.148 -0.363 0.326 0.497 1.534 -0.716 0.442 -0.947 1.722 -1.040 0.384 1.559 -1.197 0.595 +0 -1.463 -0.763 0.326 0.761 0.792 -0.622 1.070 0.025 0.589 1.074 0.246 -0.923 1.181 0.435 0.416 -1.197 1.194 0.428 -1.353 -0.216 -0.078 0.054 -0.204 0.014 0.954 -0.068 -0.554 0.150 +2 -0.246 -1.624 0.288 1.127 1.246 -0.507 0.192 1.199 -0.061 1.532 -0.981 -0.401 -1.495 0.622 -0.938 0.857 0.710 1.726 0.427 0.105 -1.440 0.446 1.392 1.530 -0.301 0.081 1.072 0.157 +1 0.813 -1.878 0.251 -0.021 -0.591 -0.602 -0.323 1.265 1.936 -0.513 -0.171 -0.166 0.467 1.668 -0.440 0.425 -0.047 -1.396 1.015 0.665 1.165 -1.123 0.419 0.881 0.455 0.624 1.386 -1.113 +1 -1.140 -1.309 -0.908 0.348 0.520 0.603 -1.489 -0.000 -1.026 0.401 0.479 -1.172 0.968 2.339 0.835 -0.163 -0.317 -0.209 -0.761 0.252 -1.138 -1.890 0.220 0.021 -0.571 -1.283 0.290 -0.990 +4 1.109 -0.105 -0.513 1.343 -0.654 0.629 -0.058 -1.842 1.129 -2.758 -1.984 0.527 0.527 -0.638 0.148 -2.531 0.707 0.250 -0.046 0.021 0.493 -0.029 -1.874 2.196 1.201 -1.285 -0.345 -0.236 +3 2.194 0.313 0.587 0.247 -0.556 0.754 -0.776 1.360 -0.601 -0.479 0.696 -1.643 0.640 -0.877 -0.673 1.747 -0.722 1.395 0.142 1.491 -1.546 -1.386 1.061 1.776 -0.051 -0.459 0.144 -1.102 +2 -1.149 0.805 -0.927 0.443 0.344 0.138 0.260 -0.257 -0.872 0.547 2.001 1.012 0.693 -0.458 2.795 0.554 0.319 0.797 -0.995 0.239 0.853 0.086 -1.535 0.759 -0.247 1.239 -0.033 -0.828 +0 -0.712 -0.236 0.859 -1.903 0.584 0.113 0.119 0.801 -0.336 -1.110 0.483 0.516 0.879 0.601 -0.555 0.338 0.251 1.173 -0.817 -1.281 0.047 -0.288 1.475 1.901 0.928 0.348 -0.151 -0.073 +0 1.305 -0.568 -1.219 0.415 0.647 -0.795 0.842 -0.010 -0.469 0.032 -0.615 -0.014 1.672 -0.333 -0.370 -0.859 -0.447 -1.263 -0.461 -0.709 -0.249 -1.039 2.477 -0.809 -0.403 0.261 0.398 0.736 +3 0.082 1.031 1.164 -0.074 2.147 1.368 0.528 -0.395 -0.365 0.201 -0.110 1.700 -0.937 0.506 -2.002 0.377 0.872 1.440 -0.591 -0.712 1.023 1.195 -0.227 -1.959 -1.059 -0.092 -1.418 0.729 +3 0.126 -1.014 1.702 0.269 1.001 -0.945 0.817 0.844 1.368 -1.078 -1.220 0.609 -0.599 1.105 2.100 -0.363 -0.426 -0.770 -0.127 0.688 -0.246 1.841 1.168 0.384 -0.058 -1.948 1.472 -0.260 +2 -0.594 -1.156 -0.751 -0.279 2.308 1.299 -1.356 0.351 0.091 -0.343 1.506 -0.808 -0.521 0.154 0.957 -1.709 -0.071 1.018 0.862 1.449 -1.143 -0.194 -0.049 1.226 -0.061 0.663 0.312 1.509 +4 -1.498 2.340 -0.177 0.253 -0.692 0.490 0.960 0.300 1.741 -0.327 -0.263 0.231 0.465 0.228 -1.284 0.313 -0.374 -0.581 0.032 -0.962 -1.135 -2.615 1.747 0.556 -0.117 -0.667 -2.876 -0.729 +4 0.233 -0.003 -1.663 -0.158 0.174 -0.465 1.092 -1.246 2.151 -0.004 -0.192 0.915 -1.496 2.277 -0.682 0.887 0.002 -1.018 1.019 0.529 3.050 0.387 -0.634 -1.855 -0.744 0.256 -0.374 0.763 +0 0.881 1.116 -0.605 0.488 -0.041 -0.591 1.169 -0.664 0.756 0.068 -0.172 -0.555 -0.987 -0.250 -0.512 0.093 -1.197 -0.152 0.406 -0.673 0.741 0.850 -1.207 -0.758 -1.340 0.423 0.128 -1.601 +2 0.576 1.306 0.176 0.004 1.425 -0.809 1.125 0.630 1.683 -0.648 -0.504 0.265 -0.744 -0.791 1.637 0.488 0.558 -1.284 -0.114 1.178 0.986 0.888 2.111 -1.588 0.833 0.280 -0.918 -0.355 +4 0.184 -0.157 0.394 1.383 -1.603 1.146 0.497 0.544 1.344 0.180 -2.078 1.729 -0.721 -0.901 1.210 0.819 0.429 -1.038 -0.063 1.345 -1.931 1.280 0.438 -0.829 2.201 -1.248 -0.944 -0.830 +3 0.058 1.260 -0.340 0.739 0.645 -0.242 -0.947 -0.636 -0.566 -2.580 -0.120 0.714 -0.166 -0.218 -1.440 -0.485 1.238 -0.999 -1.579 1.165 -0.294 0.017 2.012 0.418 0.616 -1.314 1.248 -1.182 +1 -0.431 1.814 0.056 0.182 1.790 1.185 1.054 1.229 0.203 -0.723 -0.943 -1.391 0.089 -0.143 -0.839 -1.566 1.137 -0.776 -1.261 -1.231 0.838 -0.986 -0.511 0.017 -0.043 -0.608 -0.153 -0.110 +0 0.524 -0.255 -0.604 0.213 1.014 0.270 -0.443 2.132 -0.636 -1.410 0.100 -0.256 0.947 -0.526 -0.165 -0.866 0.808 0.144 -0.275 -0.150 -0.021 -1.437 1.163 1.104 0.041 -0.066 -0.997 1.727 +4 0.943 -0.713 1.001 -0.317 -0.592 0.513 1.877 1.085 -0.320 2.527 0.742 0.900 -0.151 -0.692 -0.454 0.575 1.337 0.175 0.302 1.028 -2.315 -2.430 -0.231 -1.875 -1.129 0.049 -0.924 -0.985 +3 0.136 0.366 -0.291 -0.466 -0.771 -0.009 -0.367 1.266 1.144 -0.197 -0.669 0.605 1.451 -1.805 -0.763 -0.864 2.526 0.445 0.413 -0.123 -0.464 -0.961 0.597 1.319 -0.335 -0.599 -3.018 -0.464 +2 -0.075 -0.867 0.560 1.615 -0.396 1.077 0.339 0.752 1.608 0.318 -0.176 -0.300 1.396 -1.546 -0.092 0.642 0.718 -2.154 0.014 -0.590 1.267 0.037 0.172 -0.703 -0.321 1.214 1.959 0.741 +2 -1.290 -1.284 0.843 1.444 0.095 1.138 -0.696 -0.608 1.400 -0.214 1.308 1.836 0.458 -0.435 0.167 -0.237 1.237 1.090 0.811 0.612 -0.664 2.269 0.413 0.136 0.851 -0.133 0.568 -1.004 +2 0.619 -0.651 0.049 1.887 -0.660 0.085 1.001 0.855 -0.625 1.953 0.041 -0.133 -0.658 1.341 -1.190 -0.327 1.218 0.637 1.687 1.167 -0.940 0.505 -1.178 0.631 -1.045 0.017 -1.242 -0.476 +2 -1.070 -0.631 -1.240 -0.061 1.729 -0.402 -1.025 0.194 -0.026 0.694 -0.137 0.160 0.597 -0.074 1.259 -0.111 0.623 0.735 -0.784 0.497 -0.918 -0.981 1.762 1.305 -0.196 1.278 1.375 -2.393 +4 -2.701 0.492 0.259 -0.424 -1.686 -0.207 -0.581 1.334 1.118 -0.802 0.731 -1.117 1.268 0.181 2.077 -0.704 1.245 1.463 -0.468 -1.373 0.750 -0.994 -0.354 -0.819 1.901 0.047 0.344 -0.003 +1 0.412 0.787 -1.159 0.657 -1.420 -1.980 0.727 1.434 -0.254 -0.547 1.711 -0.195 -0.066 1.694 -0.354 -0.654 0.816 -0.712 -0.145 -1.319 0.185 0.068 0.968 0.857 0.552 0.394 -0.073 0.610 +1 -1.167 0.768 -0.593 -0.816 -0.471 -0.886 1.663 -0.619 1.008 0.430 0.825 -2.063 0.239 0.509 -0.520 0.711 0.549 -1.402 -0.949 1.503 -0.886 1.329 -0.149 -0.078 1.189 -0.784 -0.689 0.405 +1 0.252 0.146 0.793 0.195 1.833 0.172 -0.442 -1.255 0.103 -0.219 -0.917 0.128 0.497 0.138 0.622 0.085 1.519 -0.864 -0.731 -0.457 -0.067 0.158 -1.040 -1.481 2.441 0.772 -0.077 -1.614 +4 -0.017 -0.877 -1.037 -1.692 -0.930 0.480 0.642 0.350 -0.879 -0.444 -1.021 1.349 0.636 -1.565 0.281 -1.076 0.380 0.754 -2.923 2.237 1.108 -0.776 -0.044 0.741 -2.015 0.413 0.474 0.890 +1 0.934 1.939 1.051 -0.346 0.266 0.360 1.300 1.437 -0.416 -0.066 -1.320 0.640 -0.671 -0.903 -1.073 -0.537 0.021 0.074 -0.537 -0.117 -0.408 1.023 1.304 0.626 1.417 0.895 -0.643 -0.065 +2 0.277 0.649 -0.435 1.856 0.970 -0.045 2.392 -0.207 -0.030 0.344 0.562 -0.020 -0.396 0.299 -0.445 -0.774 -0.172 -0.695 -0.908 -0.467 0.409 0.774 -1.215 -0.217 -0.566 -0.544 2.827 0.954 +2 0.667 0.012 0.872 -0.580 -0.493 -0.390 0.553 2.730 -0.008 -0.236 -0.963 1.740 -0.387 -0.618 -0.210 1.823 -0.561 -0.025 -2.516 0.980 -0.286 0.912 0.634 0.159 -0.450 0.063 -1.341 0.225 +1 0.451 -1.870 -1.162 -0.283 -0.301 -1.209 0.389 0.251 -0.194 -0.756 1.049 1.655 -0.484 -0.612 0.491 -0.358 -0.139 0.740 -1.909 1.318 0.073 -0.411 -0.089 -0.038 -1.731 1.495 0.041 0.443 +0 0.618 -0.294 -1.208 0.311 -0.619 0.824 0.298 1.041 -0.592 1.343 -1.119 -0.270 -1.015 -1.289 -0.901 -1.000 2.130 -0.306 0.258 -0.413 0.163 0.218 -0.974 0.017 -1.513 -0.458 0.354 0.920 +1 0.428 0.439 -0.745 0.264 2.017 -0.741 -0.416 -1.285 1.265 -0.069 -1.532 -1.214 -0.888 1.257 0.141 -0.198 -0.741 0.012 -0.622 1.338 0.156 -1.030 -0.711 1.677 -0.205 1.545 0.220 0.437 +0 0.467 1.158 -0.697 -1.676 0.288 0.834 0.107 -1.237 0.589 -0.802 -0.280 0.944 -0.271 -0.693 -0.286 1.736 -1.108 1.193 0.434 0.590 0.310 1.316 -1.328 0.309 0.191 -0.406 0.437 -0.181 +4 0.826 -1.340 1.022 -2.622 -0.207 -2.078 0.269 -1.543 -0.665 -0.054 1.625 2.402 -0.628 1.144 -1.561 -0.452 0.312 -2.456 1.384 -0.764 -0.434 0.685 1.064 -0.275 0.163 0.861 0.230 -1.890 +1 0.580 -1.105 -1.035 -0.748 0.607 0.262 0.212 -0.121 -1.325 -0.600 0.211 -0.170 0.179 -1.557 -0.013 -2.087 -1.304 -0.971 -1.843 -0.802 0.277 1.166 -0.535 -0.657 -0.120 -0.268 -1.013 0.542 +0 0.077 0.527 0.621 -0.567 -0.644 -0.152 1.094 -0.514 -0.684 -1.101 0.508 -0.291 -1.005 -0.797 0.669 0.144 -0.669 0.574 1.035 0.656 0.207 1.677 1.324 0.063 1.009 0.651 -0.534 1.600 +3 -1.307 0.494 -1.005 0.647 -1.166 -0.687 -0.361 0.674 0.176 0.020 1.886 -1.837 -1.038 -1.081 -1.771 -0.720 0.688 -0.592 -0.333 -1.723 -0.797 -0.462 0.642 -1.675 -0.455 1.087 -0.445 -2.116 +0 0.759 -0.920 0.801 0.466 0.941 -2.068 -0.639 1.383 -1.364 0.227 0.381 -0.188 0.145 -1.098 0.133 -0.307 0.763 1.077 -0.418 0.511 0.081 -1.121 -0.351 -0.814 0.412 -0.966 0.077 0.694 +1 0.637 1.010 -0.216 0.382 -0.091 0.390 -0.219 0.862 0.591 -0.294 -0.951 0.280 -0.914 -1.642 1.582 -0.007 -0.792 -0.066 2.127 0.559 -0.269 1.235 -1.103 0.103 1.151 -0.151 0.822 1.348 +0 0.171 0.218 1.135 -1.492 -0.224 -0.667 0.048 0.667 -0.683 0.717 0.575 -0.313 0.944 0.487 -0.684 -0.756 0.859 -0.548 0.665 -1.105 0.996 -1.587 -1.589 -0.472 1.265 1.059 0.687 0.548 +4 -0.448 -0.534 1.124 -0.304 -2.289 -1.742 -0.718 0.887 0.576 -0.527 0.051 0.366 -2.084 -1.889 0.037 -0.102 0.765 -2.264 0.357 -1.019 0.263 -1.180 1.060 0.932 -0.486 -1.891 1.105 0.210 +0 -1.132 -0.278 0.115 -0.604 -1.198 -0.559 0.340 -0.729 0.455 -0.255 -0.605 -0.725 0.232 1.127 0.122 -0.965 1.093 -0.885 1.729 -0.161 -0.878 0.559 -0.311 0.443 0.092 -1.379 -0.070 0.501 +2 -0.691 -0.373 0.437 0.269 -1.391 0.009 -0.546 -0.249 0.080 1.281 -0.460 1.678 -1.300 -0.877 -0.096 -1.169 -1.320 0.074 -1.325 -0.960 -0.082 1.703 -0.192 0.130 -1.150 -1.048 -2.292 -0.984 +3 -0.301 -0.271 -0.084 -0.537 0.437 0.912 -0.466 -1.408 1.714 1.369 1.244 1.063 -0.604 -0.784 1.321 -0.781 -1.967 0.488 0.404 2.093 -0.898 -0.371 -0.399 -0.837 0.775 -0.166 -1.623 -1.878 +3 -0.942 0.517 0.002 1.051 0.133 -1.437 -0.497 -1.157 0.563 1.084 -1.544 -0.124 1.153 -1.559 -0.435 -0.000 1.067 0.545 0.749 0.602 -0.373 1.643 -0.671 -2.778 -1.801 -0.117 -0.332 -0.536 +4 -1.813 -1.033 2.441 0.610 1.917 0.829 -0.742 -0.382 0.588 0.438 -0.577 -1.510 3.288 -0.141 -0.326 0.113 -0.243 0.123 1.287 -0.125 -1.163 -0.162 -0.464 1.424 1.171 0.108 -1.279 -1.226 +1 1.740 0.658 0.864 -0.142 -0.253 0.901 -2.924 0.783 -0.409 -0.753 -1.418 0.632 -1.857 -0.076 -0.095 0.702 -0.600 0.098 -0.586 0.249 -1.250 -0.685 -0.004 -0.076 -0.486 0.288 -0.077 0.548 +4 -0.267 2.942 -0.056 -0.500 1.964 -1.008 1.151 0.352 0.790 -0.563 0.043 0.642 0.308 0.197 1.839 -2.636 0.241 1.752 0.463 -0.870 0.362 2.134 -1.447 -0.627 0.135 1.170 0.435 1.616 +2 1.500 -0.480 2.070 -0.612 -0.180 0.812 0.548 1.276 -2.163 -0.356 -0.221 1.783 0.526 -1.571 0.535 1.185 -0.248 -0.778 0.183 -0.543 0.312 0.260 0.832 -0.161 -1.967 0.271 -0.090 0.157 +0 0.883 0.050 -0.526 1.074 -0.948 -0.553 0.158 0.446 -1.125 0.862 -1.042 -0.875 -1.438 -0.616 -1.065 -0.078 -0.292 -0.126 0.328 -0.129 -1.105 -1.036 -0.987 -1.553 -0.935 -0.026 -0.007 0.136 +4 -1.421 1.886 0.100 -0.180 0.953 -2.067 1.380 1.858 1.088 -2.102 0.764 1.646 1.543 -1.408 1.016 1.796 0.029 -1.453 0.829 1.054 -0.139 -1.191 0.982 -0.769 0.397 1.558 -1.659 0.908 +4 0.323 -0.530 0.781 0.020 -0.419 1.440 1.024 1.320 -0.931 3.569 -1.140 0.236 -0.486 0.388 1.387 -0.491 -1.634 -0.330 -0.451 0.350 -1.844 1.086 -0.342 0.186 1.184 -0.820 -1.130 -0.184 +0 -1.354 -0.113 0.669 0.076 0.473 1.510 0.918 0.167 -0.786 1.492 0.868 -0.241 -0.538 0.234 -0.492 0.742 -1.045 0.130 -1.105 -1.221 0.192 0.844 0.974 -1.139 -0.681 0.076 0.506 -0.173 +4 0.514 1.429 1.375 -0.863 1.128 -0.568 0.367 1.275 -2.370 -1.407 -0.593 -0.674 -1.189 -0.068 -0.561 0.222 -0.576 1.947 -0.318 0.468 1.543 -1.118 -0.249 -1.219 -1.410 -1.655 -1.197 1.444 +4 1.231 1.077 0.331 -0.777 0.238 0.155 1.163 0.702 -0.068 0.766 1.295 0.651 -1.024 -0.134 1.434 1.349 -1.169 -1.516 -0.245 -1.285 1.128 0.112 3.237 -2.283 0.480 -0.771 0.487 0.500 +4 1.555 0.544 -1.289 -0.188 -1.200 -0.645 -1.547 2.449 -0.384 0.470 -0.348 0.053 0.277 1.380 -0.108 -0.686 -0.843 -1.083 -0.530 2.954 0.740 -0.280 1.336 -0.512 1.268 0.340 1.273 0.465 +2 1.156 0.415 -1.487 -0.501 1.560 -0.936 0.196 1.509 1.181 -0.378 -0.421 -1.315 0.823 -0.247 0.264 2.691 0.146 0.701 0.898 -0.458 -0.227 -0.945 -0.765 0.013 -0.193 -0.811 -0.363 -1.053 +1 -1.101 1.765 -0.240 0.588 0.180 0.106 0.557 1.318 0.777 -0.243 -0.084 -0.563 0.715 0.590 -0.342 1.327 1.074 1.824 -0.790 -0.100 -0.197 2.604 0.010 1.265 1.035 -0.084 0.056 0.523 +4 3.728 0.772 0.107 0.673 -0.603 0.262 0.955 -0.029 -0.757 -0.078 -0.320 -1.357 -0.572 -1.393 -1.237 -1.141 0.953 1.489 -1.536 1.580 -1.127 0.747 0.259 0.636 -1.166 -1.812 -0.803 0.427 +0 -0.243 -0.430 -0.449 0.605 0.861 -0.424 -0.438 -0.582 1.000 0.879 1.476 -0.812 -0.618 1.654 1.324 -0.991 -0.895 0.253 1.242 -0.904 0.087 1.089 -0.203 -0.076 1.421 -0.418 1.268 -0.150 +3 -0.755 2.233 -0.693 1.026 0.844 0.316 -0.671 -0.151 -2.154 0.224 0.350 0.133 -2.973 -1.266 -0.748 0.292 0.289 -0.170 0.635 -1.026 0.610 0.475 0.378 -0.182 1.831 0.493 -0.968 0.248 +3 -0.163 0.335 0.944 0.925 0.233 -0.161 2.024 -0.100 -0.442 -0.646 -1.727 0.822 -2.348 -0.006 -0.205 2.240 -1.832 -0.901 -0.136 -0.198 -0.550 -0.081 -1.260 0.357 -2.321 -0.026 -0.600 1.016 +4 -0.224 -0.211 2.235 0.809 2.481 -0.497 -0.085 -1.332 -0.810 1.902 -0.522 1.919 -1.243 -1.053 -0.341 -0.064 0.923 0.451 -0.416 -0.133 -0.319 -0.915 -0.888 1.028 -1.828 1.617 -0.343 -0.773 +4 0.672 -0.121 -0.388 0.506 1.598 0.643 -1.113 -0.766 0.468 -0.864 -0.471 1.510 -0.314 -1.576 2.886 1.088 -0.064 -0.007 -2.127 -0.393 -0.744 0.098 1.628 0.440 2.030 -1.396 -1.542 1.242 +0 0.698 0.583 0.294 -1.640 -0.424 -0.517 -1.490 -1.736 -0.374 0.399 -0.061 0.883 1.420 -1.206 1.156 -0.334 -0.484 0.693 1.284 -1.316 0.467 0.541 0.337 -0.011 0.411 -0.799 -0.215 -0.025 +2 -1.665 -0.664 1.032 0.581 -0.125 -0.474 0.789 -1.980 0.792 0.091 -0.489 0.502 -0.386 0.029 -0.046 0.745 0.159 -1.909 -1.392 -1.084 0.457 0.557 1.670 -1.014 1.309 -1.938 0.018 0.245 +3 0.641 0.123 -0.113 -1.299 0.233 -0.757 -2.190 1.195 0.958 0.052 0.229 1.074 0.224 0.904 -0.297 1.312 0.321 0.194 -1.270 0.287 -0.832 -0.638 -0.815 -1.066 2.125 1.331 1.920 -1.223 +3 0.986 -1.413 0.255 1.716 -0.099 0.483 0.780 -1.025 1.834 0.500 0.520 -1.662 0.795 0.506 1.624 -0.311 -0.988 -0.682 -0.505 -1.119 1.290 1.328 -0.196 -0.393 -1.815 0.696 1.653 -0.637 +3 0.565 -0.136 0.815 -2.370 -1.449 -1.967 -1.381 1.586 -0.162 -2.429 0.882 -0.182 0.729 0.774 -0.201 0.342 -0.250 -1.330 0.036 -0.026 -1.464 1.033 0.040 -0.800 0.957 0.721 -0.310 0.186 +1 0.765 1.478 0.245 -0.255 -1.705 -0.083 0.823 0.946 0.504 -0.541 -1.977 -0.495 -0.304 -0.313 0.619 1.986 0.124 -0.217 -0.259 0.124 -0.828 0.120 0.451 0.210 0.458 0.434 -1.772 0.637 +3 -1.576 -0.009 0.287 0.545 -0.978 1.828 1.206 1.056 -2.415 -0.095 1.099 0.556 1.366 0.383 -0.033 -0.631 -0.032 -0.477 0.252 -0.462 -1.458 -0.906 -0.445 -0.721 1.487 -0.397 -2.001 0.643 +2 0.133 0.734 -0.347 0.064 0.271 -2.644 -0.666 -1.261 -1.391 -1.407 0.103 0.536 -1.137 -1.084 0.406 0.994 0.398 0.973 -0.183 0.779 -0.333 0.352 -1.144 1.422 1.714 0.181 1.655 0.464 +0 0.638 -1.070 -1.295 -0.504 0.034 -1.188 0.489 1.097 -1.307 -0.690 0.902 0.670 0.636 -0.108 0.344 1.260 1.246 -0.435 0.095 0.796 0.464 -0.029 0.765 -0.286 0.488 0.106 -0.757 -1.266 +2 -2.360 0.260 0.200 -1.161 -0.666 1.028 -1.321 -0.015 0.870 -1.093 0.095 0.395 0.878 0.303 0.470 -0.477 0.430 0.796 -1.385 0.463 2.758 -0.535 0.168 -0.758 1.242 0.505 -0.072 0.808 +0 1.256 0.314 0.250 -1.125 0.323 -0.402 0.054 -1.120 0.473 0.491 -1.470 -0.787 -0.931 0.834 1.034 -1.848 -0.099 -0.406 -0.551 0.572 0.905 0.742 0.945 1.220 -0.099 0.916 0.713 0.720 +4 0.993 2.262 1.055 1.517 1.256 1.453 0.987 -1.033 1.535 0.057 0.321 -2.065 -0.769 1.245 0.996 0.804 -1.438 0.240 -0.049 0.729 -0.134 -0.764 -0.625 -2.544 -0.277 -0.162 -1.392 -0.503 +0 -0.851 -0.160 0.493 0.283 0.885 -0.487 -1.689 1.597 0.548 0.594 0.539 0.258 0.992 -0.656 0.789 0.356 0.132 -0.490 0.251 -0.208 0.856 -0.462 -1.031 0.565 2.107 0.256 -0.145 1.789 +1 1.140 -1.180 0.419 -0.014 -0.333 0.885 0.879 -0.946 -0.182 0.281 -0.273 0.585 0.281 1.833 -1.530 -0.122 -0.909 0.604 -1.617 0.346 -0.630 -0.734 -1.446 -0.465 1.085 -0.025 0.628 1.665 +1 0.746 -0.035 -0.803 -0.922 1.586 0.956 1.944 0.055 0.288 -0.267 -0.287 -1.042 0.084 -0.789 0.442 0.170 0.288 1.675 -0.038 0.757 2.052 1.307 1.153 -0.307 0.230 -1.318 0.499 0.274 +4 -0.244 -1.690 -0.041 -1.500 0.633 -0.190 0.478 2.443 -0.799 -0.105 -1.354 0.082 -1.190 -1.204 -0.047 0.206 -2.073 -1.157 -1.546 0.084 -0.785 1.052 0.180 0.963 0.367 1.995 -0.363 -1.423 +0 -1.146 1.505 0.952 0.245 0.244 -0.091 -0.569 0.535 -0.795 0.151 0.097 0.385 0.941 0.403 0.694 -1.940 0.460 0.075 0.372 -0.529 1.151 0.250 -0.745 -0.568 0.903 -0.085 -0.005 -0.198 +0 -1.081 0.775 -1.437 -0.052 0.605 0.400 0.854 1.281 -0.393 -0.662 0.970 -0.547 -0.908 0.056 0.403 -0.839 -0.031 -0.622 0.574 -2.668 0.287 -0.686 0.390 0.706 0.113 0.655 -0.073 1.256 +3 -0.118 0.273 1.601 0.115 -0.559 -0.265 -0.064 -0.565 0.988 -1.946 1.066 0.586 -0.073 -1.040 -1.803 0.951 0.641 0.177 1.166 0.569 1.361 -2.648 0.148 0.181 -1.311 -1.367 -0.401 0.081 +3 0.514 0.229 0.314 -0.859 -1.505 -1.039 -0.853 -2.041 0.475 -1.289 -1.345 -0.262 -0.906 -1.578 -1.467 0.536 -0.336 -1.514 1.456 -0.711 0.530 -0.108 0.098 0.021 -1.379 2.244 -0.761 0.825 +2 1.520 -0.730 0.425 -1.423 1.362 0.623 -0.733 0.235 -1.212 -0.701 -1.753 -0.813 0.692 0.523 0.391 -0.317 0.566 -1.635 -0.896 -0.637 -1.014 0.512 -1.108 -2.287 0.604 0.212 -0.468 0.566 +2 1.216 1.331 1.675 0.703 0.664 0.359 0.068 1.781 0.190 -1.946 0.229 0.153 0.413 0.227 1.379 0.664 -0.209 0.749 -1.009 -1.004 1.372 -0.368 -1.094 -0.483 1.122 2.034 -0.161 0.362 +3 0.408 2.461 1.354 -0.227 0.731 -0.602 0.774 -0.823 0.183 0.216 -1.052 -0.589 -2.174 -0.379 1.116 1.999 1.492 -0.386 -0.007 -0.071 -0.210 0.974 -0.551 1.452 0.139 -1.166 -0.097 0.479 +2 -0.451 1.487 0.556 -0.841 0.782 -0.701 -0.952 -0.447 -0.861 -0.905 -1.399 -0.699 -0.533 -1.351 0.540 -1.766 -0.381 0.661 1.132 -0.575 1.013 -0.367 2.255 -1.460 -1.007 0.161 -1.261 -0.708 +1 -1.084 0.016 -0.423 0.121 -0.240 0.626 0.576 0.881 0.666 0.816 0.682 0.586 0.259 -1.280 -0.815 0.241 -0.368 0.222 0.831 -0.931 0.893 -0.196 -0.245 1.927 -2.241 0.747 1.580 0.803 +1 -0.027 1.258 -0.488 -0.053 -0.917 0.341 0.871 1.384 0.387 0.260 -0.769 -2.122 -0.557 -0.239 -0.117 -1.601 -0.278 -2.142 -1.289 1.091 1.315 -0.150 0.240 0.392 0.391 -0.896 0.159 0.259 +4 -1.984 -0.042 -0.372 0.213 0.363 1.509 -2.423 0.430 -1.489 -0.477 -1.433 0.259 -1.497 -0.283 0.736 0.232 -0.116 -0.041 0.548 1.190 -1.174 2.272 0.234 -1.000 -0.271 0.099 -2.516 -0.410 +3 -0.831 0.181 2.079 -0.557 -0.351 -0.792 1.036 0.918 1.289 1.693 -1.421 -0.046 -0.349 -0.964 0.693 -0.611 1.582 0.737 -1.016 -0.009 0.581 1.387 -1.501 0.764 0.876 0.160 1.290 -1.821 +2 0.038 -0.039 0.487 0.464 -1.804 -1.088 0.080 0.542 -0.315 0.509 1.958 0.501 1.391 -0.273 0.621 1.023 1.043 -2.037 -0.577 0.860 -1.457 -0.820 0.578 -0.739 -1.152 1.371 -0.497 -1.114 +3 0.221 -2.057 0.470 -1.471 1.424 -2.075 1.618 -0.883 -0.969 -0.585 -0.734 0.821 -0.532 0.183 -0.672 0.731 1.631 -0.993 -0.222 -0.292 -1.570 -0.872 -1.317 0.154 0.722 -1.708 -0.213 0.021 +0 0.060 0.251 -0.286 -1.198 0.846 -0.406 0.092 -1.047 -1.344 -0.832 0.424 -0.007 -0.023 0.892 -0.938 0.835 1.541 1.462 -0.535 -0.445 1.376 0.334 0.058 0.062 0.099 0.130 -1.079 -0.178 +2 -1.019 -0.753 0.216 -1.893 0.260 0.612 -0.163 0.431 1.909 -0.079 -1.126 -0.866 0.366 -0.312 -0.179 1.516 -0.350 -0.746 0.390 0.885 1.168 2.135 1.726 -0.278 0.095 -0.553 -1.334 1.410 +3 0.353 -0.997 2.610 0.753 -1.270 -2.478 -0.688 0.684 0.490 -0.111 -0.601 -0.904 -0.403 -1.012 -0.399 1.331 -0.771 0.611 0.763 -1.080 -0.843 1.069 -0.926 -0.057 2.040 0.980 0.085 0.841 +2 0.419 1.188 -1.352 1.099 0.042 -0.310 0.415 0.281 -1.825 -0.632 0.317 -1.960 0.384 -1.895 -0.140 0.079 2.142 -0.133 0.059 0.084 1.372 0.676 -0.761 -0.222 0.852 1.888 -0.375 0.395 +4 -1.490 -1.324 1.102 1.449 0.033 -4.466 -0.744 0.335 -0.899 0.185 1.899 1.426 0.809 -0.064 -0.495 0.758 0.025 0.060 -1.551 -0.715 0.752 -1.101 0.266 0.413 0.471 0.560 0.628 -0.624 +0 0.689 -0.684 -0.274 -0.014 0.150 0.098 0.436 0.844 0.393 0.454 -1.582 0.050 0.480 0.729 -0.692 -0.659 -0.574 0.536 1.034 -0.407 0.908 0.123 -0.124 -0.211 0.747 0.326 2.276 -0.852 +0 0.369 -0.076 -0.390 0.520 0.708 -0.073 0.383 -0.190 -1.489 -1.516 0.576 -0.248 0.696 -1.254 1.230 -0.194 0.208 1.230 -0.394 0.670 0.486 0.119 0.931 -0.316 0.944 2.249 0.998 0.147 +1 -0.306 0.058 1.467 -0.776 -0.473 0.422 0.451 -1.845 -0.607 -0.522 1.264 0.126 -1.632 1.114 0.860 -0.990 0.656 -0.195 0.668 1.808 0.134 -1.644 -0.904 -0.385 -0.084 -0.800 -0.090 -1.300 +2 0.234 0.672 1.959 -2.017 0.540 0.044 -0.177 -2.363 0.580 -0.125 -0.834 0.042 -0.293 -1.742 0.088 0.580 -0.549 0.262 0.108 1.045 -0.104 0.801 -0.313 1.967 -0.437 -0.685 -0.100 -0.728 +2 0.787 0.967 -0.005 -0.358 0.087 -0.572 -0.573 -0.408 0.228 1.436 1.299 -0.319 -0.815 -0.733 -1.479 0.728 -1.394 -2.258 0.729 0.685 -1.081 0.303 -1.319 -0.097 0.359 0.694 0.396 -1.894 +3 -1.790 -0.564 0.159 -3.066 -1.189 0.035 -0.727 -0.027 2.007 -0.480 -0.257 -0.930 1.813 -0.425 0.839 -0.378 0.141 0.867 -0.923 0.068 -0.014 -0.718 0.073 2.046 0.903 -0.712 0.767 0.710 +4 -0.357 1.143 -0.472 -0.474 -1.073 1.489 -0.127 0.408 0.693 -1.117 -0.527 -1.406 0.752 -1.225 -0.900 -1.030 1.408 -1.098 -0.225 -0.776 -0.221 -1.358 -0.747 -3.028 0.210 -1.778 -0.089 -2.060 +4 0.322 -0.020 0.964 3.181 1.689 0.259 0.773 -0.235 -2.168 1.535 -0.693 -1.321 1.069 2.392 0.490 -0.230 1.057 0.346 -1.300 -1.019 -0.488 0.381 0.801 -0.381 1.040 1.436 -0.156 -1.410 +3 1.071 -0.055 -0.469 1.532 -1.030 -0.878 -1.477 1.301 -1.832 -0.149 -0.454 0.033 1.202 -0.399 -0.361 -1.356 0.478 -0.042 0.066 1.276 0.264 -1.389 -0.737 2.423 0.905 -0.604 -1.041 -1.672 +2 -0.648 1.178 1.698 0.042 0.404 -1.474 0.303 -0.259 1.359 -0.227 -0.821 -1.343 0.181 -0.543 1.217 0.633 -0.101 0.627 1.743 -1.160 -0.635 1.942 -0.754 0.662 1.187 -0.016 0.689 -0.294 +4 -0.647 0.042 0.351 0.223 -0.188 -0.569 -0.895 -0.543 -0.236 -0.855 -2.534 -0.819 0.360 -3.111 0.625 -1.223 -0.373 -1.270 0.393 -0.853 0.034 -1.580 1.735 -1.003 -1.283 -0.456 1.209 0.354 +4 -1.221 0.482 -1.265 -1.184 -1.198 0.164 -1.814 2.088 -0.376 -2.116 -1.061 0.599 1.348 0.163 0.086 -1.530 1.489 0.556 0.321 0.017 0.531 -0.800 0.939 0.803 -0.098 -0.272 -2.158 -1.732 +4 0.049 0.263 1.325 0.421 0.434 0.956 -0.049 0.258 1.965 -0.636 -0.570 -0.859 0.316 0.376 -1.917 0.964 -1.082 0.450 0.127 -2.833 -2.367 -1.122 1.178 -1.874 1.112 -1.766 -2.186 0.297 +1 -1.453 0.252 1.068 -1.139 0.829 0.225 -0.454 -1.200 0.242 0.494 -0.317 -0.981 0.762 1.185 0.954 1.589 -0.652 -1.415 0.411 -0.403 0.988 -1.037 -0.964 0.068 0.175 0.675 -0.098 -1.917 +2 -0.566 0.649 0.533 1.941 -0.669 -2.142 -0.227 -1.650 0.632 -0.294 -0.746 0.837 0.890 1.103 0.635 -1.268 -1.320 0.832 -0.271 1.386 -0.177 0.688 -0.687 0.164 -0.276 -0.746 0.214 1.992 +3 0.977 0.754 -0.316 -0.518 1.730 0.027 -1.134 -0.585 -1.962 -1.629 -0.530 -0.796 -0.687 0.770 0.874 -1.165 1.892 0.101 0.008 -0.792 -0.300 2.029 -1.081 0.267 -0.542 -1.810 -1.536 0.611 +3 -0.538 0.810 -1.197 -0.747 0.351 -0.291 0.036 0.531 -0.038 0.088 1.847 -1.996 -0.485 -0.189 0.944 0.759 -2.503 1.119 0.106 0.727 -0.540 0.613 0.409 -0.113 1.629 2.308 -0.045 -1.622 +4 0.849 1.548 -1.853 -0.865 -0.062 0.912 -0.136 0.297 -0.867 0.446 0.173 -1.958 -1.684 0.388 -0.919 0.623 1.983 1.250 0.807 2.397 0.618 -0.167 -0.452 0.423 -0.666 1.438 0.584 1.477 +3 -1.703 0.576 0.204 0.394 0.767 -0.418 -0.197 0.812 0.446 2.047 0.294 0.257 0.881 2.066 0.715 -0.287 -0.661 0.162 -1.100 0.646 0.298 0.518 -1.778 1.347 -1.121 -1.259 -2.018 -0.605 +0 -0.333 0.856 1.204 0.151 0.139 -0.068 -0.008 -0.188 -1.485 0.022 0.984 1.292 0.551 1.194 0.772 0.431 0.644 -1.708 -0.329 -0.936 -0.129 -0.751 0.388 -0.183 -0.571 -0.285 -0.142 -0.324 +0 -0.694 -1.334 -0.487 -1.079 -0.395 0.542 0.037 -0.480 -1.791 0.832 -0.352 0.108 -0.912 -0.409 -0.580 -1.334 -0.908 -0.015 0.299 -0.684 -1.203 -0.356 0.780 -0.939 0.013 -0.660 1.129 -0.507 +3 -0.038 0.410 0.792 1.753 1.124 0.617 -0.538 1.443 -1.083 1.095 1.885 1.400 -0.389 -1.645 -0.749 0.046 -0.279 0.241 -1.813 0.794 -0.623 -0.001 -0.478 0.749 -0.566 -1.714 0.560 -1.591 +2 -0.251 1.527 -0.801 0.363 0.202 -2.396 -0.277 1.738 -0.485 -0.517 0.539 -2.239 0.781 -0.399 -1.052 0.903 0.430 1.487 0.374 0.157 0.233 -0.141 -0.958 0.670 -0.126 0.865 -0.671 -0.843 +0 0.809 -0.106 -0.485 -1.015 0.181 -1.450 -0.710 -0.502 0.595 -0.762 -0.009 -1.344 0.102 0.955 -0.328 -1.328 0.231 1.880 -0.300 0.198 -0.963 -0.452 -0.562 0.544 -0.735 -0.401 0.239 -2.025 +1 0.509 -0.479 0.092 -2.425 0.264 -0.999 0.016 -0.214 -0.190 0.179 0.247 -1.317 -0.271 0.156 -0.587 0.061 -1.441 -2.113 -0.371 -0.029 -0.479 0.176 -1.824 -0.730 1.306 0.631 0.561 -0.967 +2 -0.036 -0.284 0.528 0.604 -1.660 0.089 2.436 -1.335 -0.085 1.942 1.104 -0.513 0.206 -0.002 -0.748 -0.074 -0.280 0.113 -0.748 -1.277 -0.041 1.715 0.082 -0.332 0.754 -0.158 1.868 -0.004 +3 0.904 -1.349 0.525 -0.664 0.328 0.811 -1.088 -0.171 0.881 2.964 -0.313 -1.439 -0.894 1.340 0.258 0.034 1.746 -0.509 0.665 -0.599 -0.374 -0.082 -1.202 -1.227 -0.234 -0.466 0.940 -1.168 +1 0.790 0.261 1.777 -1.483 -1.069 -1.350 0.691 2.554 -1.408 0.374 0.163 1.093 -0.448 1.041 -0.164 -0.646 0.007 0.009 -0.958 -0.567 -0.538 -0.241 -0.358 -0.384 -0.362 -1.222 0.413 -0.649 +3 1.323 0.549 0.804 -2.848 -0.253 1.241 -0.413 0.163 -0.067 -0.721 -1.369 0.488 -0.834 0.634 -1.543 1.060 -0.448 -0.398 -0.015 -1.461 0.349 0.972 0.350 0.734 0.403 1.537 -1.944 -1.459 +1 -1.041 -1.008 -0.720 0.906 -1.755 0.433 0.258 0.968 0.257 -0.379 -0.815 0.990 0.497 -0.493 0.909 -0.446 -0.001 -0.604 0.534 -0.994 -0.299 -0.655 0.264 0.388 -1.104 0.406 -1.477 2.192 +4 -0.597 -1.674 -0.109 0.034 0.161 1.362 -0.236 -0.120 -1.103 0.821 0.184 -0.420 -1.905 1.444 -1.228 -0.591 0.358 -1.197 -1.494 -2.124 0.721 -0.545 2.927 -0.994 -0.921 -0.565 0.243 -0.470 +4 -0.346 0.903 -3.795 0.619 1.260 -0.366 -1.974 0.595 0.371 0.693 0.186 0.283 -1.206 0.748 1.713 -1.156 -0.081 -0.729 -0.506 0.233 0.294 -0.403 1.468 0.840 0.606 1.862 -0.074 -0.521 +3 1.061 -1.149 -1.353 -0.983 -0.696 1.146 -0.338 -0.608 0.336 -0.480 -0.175 -2.043 -0.771 0.100 -0.700 -0.989 2.076 -1.407 0.862 0.249 -0.792 -0.328 -1.122 1.475 1.997 0.175 1.549 0.066 +4 0.274 -0.266 -0.103 1.364 -0.438 0.612 0.038 -0.152 2.300 -1.441 1.289 0.656 -0.480 1.629 -1.848 -0.522 -1.208 0.412 -1.113 0.433 -0.186 -0.479 0.389 -0.269 -1.089 2.801 -0.487 -2.218 +3 0.579 -0.704 -1.339 1.568 0.208 -0.312 -0.192 -0.359 0.493 -1.547 -1.805 0.113 -1.743 -1.907 0.903 0.167 0.180 -1.119 1.390 1.006 -1.359 -0.109 -0.410 -0.045 1.088 -0.381 0.984 -1.731 +1 0.834 -1.723 0.327 -1.404 -0.787 -0.256 1.208 0.828 0.274 0.710 -0.364 1.294 -1.547 1.205 0.336 0.330 0.674 -1.766 0.160 0.420 -0.395 0.651 -0.170 -1.052 0.114 -0.580 -0.835 0.316 +0 0.740 1.485 1.118 0.502 1.087 -1.716 -0.522 -0.911 1.225 -0.383 0.845 0.536 -0.862 -0.587 1.294 -0.775 0.125 -0.783 -0.314 -0.046 -0.783 -0.960 0.286 -0.845 -0.811 -0.939 -0.078 0.944 +2 0.246 -0.376 -0.154 0.712 0.729 0.251 0.730 1.384 0.575 -1.846 0.233 1.999 0.973 0.003 0.743 -0.295 0.557 0.745 1.492 -1.603 -1.147 -0.397 0.723 1.002 0.102 -1.921 1.272 0.401 +3 -0.051 0.041 0.357 -0.258 -0.515 -0.941 -1.178 0.406 1.353 0.950 -0.289 -0.193 1.618 -0.799 -1.668 0.251 0.853 -2.488 0.398 -1.128 -1.419 -0.289 -1.155 -1.770 0.108 -0.044 -1.718 -1.407 +4 -0.171 -0.461 -1.000 -1.165 0.163 0.940 -0.520 0.754 2.108 1.918 -0.459 1.534 -1.343 -0.194 -0.393 -0.706 0.964 1.223 0.619 -0.913 2.428 1.858 0.173 0.382 1.206 -0.676 1.252 0.110 +2 0.328 -0.607 -1.110 1.128 0.357 1.670 -2.030 0.911 -0.261 -0.354 -0.765 0.900 0.614 -1.142 0.165 0.706 0.178 1.108 -0.290 -2.254 0.187 0.181 0.311 1.107 -1.952 -0.238 -0.923 -1.208 +1 0.849 -1.240 -0.463 -0.308 0.986 1.705 -0.455 -0.097 -1.181 -0.083 1.101 -1.796 0.018 0.718 0.057 -0.189 0.876 -0.809 -0.779 -1.846 0.325 0.221 -0.573 -0.894 -0.047 0.096 -1.198 -1.946 +1 -1.316 -0.685 0.037 -1.686 -1.151 -1.002 0.079 0.441 -0.893 -0.949 0.138 0.111 -0.700 1.829 0.184 -0.170 -0.386 1.009 0.545 -1.738 -0.696 -0.447 -0.012 0.627 -1.070 -0.079 1.699 0.029 +3 -0.987 -0.406 -0.147 -1.881 1.243 -0.687 -0.984 -0.194 -0.934 2.526 0.582 -1.443 0.868 -1.355 0.253 1.288 -0.290 -1.137 -0.153 -1.118 -2.284 0.960 0.474 -1.023 0.693 -0.017 -0.723 0.404 +0 -0.363 0.713 0.115 -0.006 -0.433 -0.268 -1.509 0.510 -0.639 0.109 -0.064 0.198 0.033 0.456 -0.878 -0.072 0.028 -0.860 -0.981 -0.568 0.175 -1.087 0.942 -0.612 -0.008 0.670 -1.475 1.365 +1 0.792 0.892 -0.136 -0.609 -2.086 -0.383 -0.076 0.043 0.986 -2.391 -1.208 -0.485 0.947 -0.352 1.052 0.422 0.131 -0.188 0.738 -0.334 -0.574 -0.386 -0.986 1.788 0.959 0.656 -1.058 0.723 +0 -0.059 -1.511 0.116 -0.808 0.374 -0.209 -0.131 0.554 -1.321 0.449 0.659 -1.184 0.149 0.648 1.403 -0.461 -0.336 0.962 0.860 -0.496 -0.685 0.543 0.251 -1.133 0.736 -1.745 -0.289 1.401 +1 -0.018 0.293 0.485 -0.302 2.530 -0.228 -0.231 -0.797 0.124 0.615 1.204 -1.489 -0.246 0.447 0.587 0.007 0.542 -0.809 1.558 0.993 -1.345 -1.075 -1.047 0.678 -1.285 -0.331 0.455 -0.066 +0 0.738 0.716 0.257 -0.318 0.272 -0.342 -0.417 -0.168 0.176 0.445 -1.055 -0.172 0.031 -0.059 -1.886 -0.144 -0.421 -0.854 0.595 0.831 0.316 -1.170 -0.987 1.362 1.302 0.621 -0.928 0.687 +1 0.717 -1.030 -0.858 -0.032 1.131 -0.913 0.365 1.390 -0.743 -0.341 2.448 -0.109 -0.106 0.088 -0.112 0.784 0.247 -0.510 -0.569 0.312 -1.192 0.850 2.220 0.211 -1.454 -0.125 -0.461 0.106 +0 0.570 0.994 1.221 2.014 0.244 0.274 -1.523 -0.531 -0.193 1.050 -0.611 0.796 -0.002 -0.177 0.249 -0.391 -0.429 -1.705 -0.248 -0.480 -0.516 0.417 0.659 0.232 0.995 0.576 -0.295 -0.463 +1 -1.668 0.991 -0.878 -0.985 -0.114 0.433 0.164 -0.743 -0.506 -1.133 -0.225 1.075 -0.004 0.859 1.455 -0.588 -0.187 2.171 1.444 -1.672 0.387 0.001 0.826 -0.745 -0.840 -0.676 -0.572 0.815 +3 1.772 -2.107 -0.586 0.495 -0.134 0.558 -0.987 2.060 1.541 0.681 -1.004 0.019 1.058 -0.579 1.526 0.412 -0.153 0.970 -0.805 -0.305 -0.551 -1.611 -2.080 -0.893 -0.729 0.658 -0.364 0.638 +2 -1.111 0.424 1.254 1.818 -0.273 -1.072 -1.507 -0.493 1.186 1.160 1.406 -0.686 -0.834 0.374 1.131 -1.213 0.247 -0.564 -1.167 1.033 -0.265 0.168 -0.509 1.100 -1.135 -1.276 -0.587 0.031 +0 0.588 0.871 0.643 -0.128 -0.034 0.778 0.776 1.168 0.551 0.597 -0.446 -0.581 0.781 -0.574 -0.021 1.379 1.221 0.302 -0.296 -0.123 0.868 -0.036 -0.558 1.216 0.182 -0.577 0.479 0.971 +1 1.361 -0.818 0.654 0.796 -1.745 1.309 -0.734 0.469 -0.221 -0.191 -0.805 -0.593 0.450 1.417 -1.218 -0.531 0.261 0.208 0.501 1.161 -1.715 -0.571 1.044 -0.179 -1.150 -1.265 -0.013 -0.414 +0 -0.756 0.218 0.020 -0.862 -0.800 -0.784 -0.011 -0.304 0.209 -0.880 -0.173 -0.098 0.326 1.470 1.074 -0.074 -1.595 -0.513 0.921 -0.055 -1.258 -0.206 -0.471 1.261 -0.582 0.414 0.688 -1.239 +2 0.205 0.692 -1.514 0.393 -1.479 0.100 1.568 0.149 -1.493 0.877 -0.991 1.341 -0.720 -1.243 1.739 -0.022 -0.343 0.168 -1.333 0.227 0.449 -2.044 1.086 -0.659 -0.104 -0.453 1.192 0.183 +4 -0.762 -2.309 0.099 -0.788 -0.784 -0.355 -0.134 -0.009 -0.884 -0.220 -1.757 0.311 -2.856 -0.289 0.919 -1.059 0.716 1.156 -3.308 -0.151 -2.876 -0.118 -0.007 -2.483 1.925 0.286 -0.849 1.465 +3 -1.832 1.743 -1.178 1.458 -1.649 0.297 -1.001 0.517 -0.462 0.353 -1.132 -0.906 -1.784 1.537 -1.351 0.407 -0.405 -1.762 -1.280 -0.488 0.011 -0.273 1.074 -0.061 0.287 -0.517 -0.468 -0.342 +1 1.276 -0.219 -1.131 0.924 -0.902 1.377 1.198 0.052 -0.856 0.116 -0.169 -0.391 1.534 0.457 0.724 -1.278 1.406 -0.915 -0.194 0.039 0.700 1.177 0.413 0.658 -2.276 1.066 0.246 -0.134 +2 0.306 -0.262 1.145 -1.235 -0.859 -0.917 0.006 -0.108 -0.657 -1.129 1.728 0.292 0.616 -1.369 -0.729 2.123 1.215 0.076 -2.179 0.832 -0.240 0.018 -0.055 -0.400 -1.236 0.457 -1.094 -1.130 +2 -0.550 0.156 -1.722 -1.190 -0.049 0.544 0.373 -0.114 0.407 -1.024 1.306 -0.819 0.338 1.660 0.619 2.079 -0.671 -2.000 1.346 -0.048 -0.150 0.335 0.981 0.250 -0.570 0.541 -0.967 -1.574 +3 -0.051 -2.615 -0.215 1.369 0.761 0.041 -0.999 -0.200 -1.717 0.932 0.664 -0.256 1.223 0.950 0.850 1.439 1.973 -0.433 -0.436 -0.609 -0.954 -1.484 -0.450 -0.160 -1.147 -0.464 -0.228 -0.387 +0 0.798 0.201 0.395 -2.597 -1.235 0.268 -0.177 -0.324 -1.137 -0.541 -0.153 -1.015 -0.201 0.651 0.324 0.140 -0.655 1.193 0.967 0.365 -1.539 1.199 -0.315 -0.423 1.192 0.374 -0.252 -0.085 +3 -0.907 1.555 1.295 -0.247 -1.265 -0.789 0.451 0.852 -0.038 -2.568 0.680 -2.020 -2.119 0.998 -1.186 0.251 0.092 -0.805 0.206 -0.550 0.048 -0.386 -0.501 -0.660 -0.898 0.853 -0.453 -1.132 +3 0.286 1.804 -0.098 1.343 0.874 0.735 -2.011 0.929 -0.128 -0.444 -1.483 -0.006 0.034 -1.303 -1.473 0.487 0.872 0.190 1.151 -0.086 -0.054 -0.917 -1.560 1.855 1.131 -1.393 0.156 -0.100 +4 1.122 -0.121 1.430 -0.449 -0.298 -1.133 -0.311 -1.240 0.403 0.108 1.475 -1.458 1.216 -0.978 0.344 -1.145 0.203 -2.511 -0.671 0.106 1.755 -2.128 -0.002 -0.519 -1.916 -1.125 -0.471 0.467 +1 0.265 -0.795 0.592 1.039 -0.044 0.027 -0.079 -0.531 -0.666 0.973 -0.245 0.136 0.600 0.220 -0.684 -0.061 0.604 0.348 -1.351 -1.448 -0.720 -0.310 -0.680 -1.746 -1.355 -0.376 1.661 2.276 +1 -0.209 1.367 -0.326 0.638 -0.499 -0.507 0.725 0.938 1.575 0.334 0.209 1.503 -0.675 1.327 0.787 0.988 -0.371 1.356 -0.697 0.287 1.179 0.600 1.315 -1.379 -0.787 -0.609 -0.098 1.370 +2 0.022 -2.042 -0.292 0.487 1.283 -0.754 -0.017 -0.170 -0.302 0.795 -0.146 0.077 2.132 0.154 -0.615 -1.483 1.529 -1.276 -0.119 0.473 -1.472 -0.216 0.268 0.842 -1.279 -0.417 -0.572 -2.223 +2 -0.938 -0.171 0.780 -0.824 -0.808 -0.331 0.272 -0.766 2.069 -0.683 -2.038 2.161 -0.321 0.426 0.039 -0.310 0.963 0.324 0.465 0.207 0.551 -1.434 0.145 1.188 0.565 -0.503 1.606 1.644 +0 1.331 0.194 -1.193 -0.762 -0.439 1.476 -0.209 0.637 1.478 0.497 -0.565 -0.188 -1.015 -0.316 -1.227 -1.044 0.479 -0.803 -0.000 -1.227 1.221 0.118 1.300 -0.169 -0.885 -0.629 -0.239 0.310 +3 -0.202 -0.404 2.402 0.436 0.510 0.071 -0.298 -1.498 -1.463 0.005 0.740 -1.255 -0.264 1.070 0.260 0.048 -0.353 0.252 -0.466 -0.595 0.925 0.473 2.660 0.765 0.458 -1.233 -1.526 2.004 +2 -2.007 -0.917 1.188 -1.269 -2.735 1.831 0.297 -0.683 -0.848 0.629 0.478 0.327 -0.189 -1.281 -0.036 1.065 -0.542 0.601 -0.405 0.801 -0.980 0.014 0.456 -0.675 0.363 -0.030 -0.044 -0.678 +3 -0.522 -0.339 0.249 1.833 0.310 0.693 -0.887 -1.245 -0.875 -0.175 0.227 0.480 0.891 1.258 -2.080 -0.981 1.663 0.349 -0.107 -2.228 -1.295 0.885 -0.468 0.545 -0.400 2.182 -0.044 -1.490 +4 -2.502 1.826 0.001 0.363 1.151 -2.048 0.138 0.938 -0.451 -0.571 0.298 0.659 -0.049 -1.078 1.339 1.274 0.349 -0.773 2.195 1.459 -0.593 -1.952 -0.021 -0.525 -1.277 0.409 0.517 0.419 +4 2.976 1.182 2.003 0.634 -0.334 1.218 -1.398 1.148 0.255 1.542 1.066 0.556 0.041 -0.324 -0.588 0.165 1.474 0.116 0.283 -1.232 -1.961 1.913 -0.012 0.217 -0.645 1.769 -0.844 -1.438 +4 0.171 0.767 -0.373 1.545 -0.340 1.044 -2.192 -0.163 1.269 0.238 2.726 2.044 -1.381 3.121 0.865 0.513 -1.513 1.027 1.139 -0.417 1.522 -1.316 0.044 1.459 -0.665 -0.355 -0.572 -0.350 +4 -0.291 -1.907 -0.320 1.107 1.655 1.090 -0.935 1.423 -0.586 0.512 -0.429 1.022 0.552 0.277 1.254 -0.720 0.786 0.645 2.718 -0.913 0.101 -0.316 -1.690 0.181 -2.662 -0.668 -1.394 -1.096 +1 -0.086 -2.804 0.693 1.116 -0.262 -1.111 0.937 0.952 -0.856 0.475 -0.456 0.344 1.395 0.472 -0.623 -0.539 1.981 1.278 -0.436 -0.104 -0.343 0.188 0.792 0.518 0.703 -0.704 0.119 0.849 +2 -0.496 -0.584 0.685 0.672 0.902 1.114 -0.365 -1.568 1.970 -2.258 -0.342 -1.427 0.403 -0.957 1.132 0.232 1.511 0.102 1.159 -1.086 0.985 -0.135 0.238 0.928 0.387 -0.791 -0.489 -1.058 +2 -1.795 0.568 0.197 -2.103 -0.268 0.109 -0.870 -1.053 -0.053 -0.026 -2.131 -0.696 -0.813 -1.341 0.857 0.942 -1.456 -0.179 0.432 0.991 0.872 -0.356 0.087 1.094 -0.413 -0.920 1.127 -1.046 +4 0.659 2.272 -0.302 -0.509 -1.107 -1.936 -0.691 0.796 0.289 2.160 0.007 0.861 0.118 -1.242 0.452 0.186 -0.305 -1.476 0.700 -2.668 1.802 0.363 -0.889 1.186 1.053 -0.248 1.254 -0.358 +1 0.090 -1.804 1.178 -0.425 0.805 -1.290 1.135 0.043 -1.102 -0.930 -0.833 -1.600 -1.644 -0.644 0.558 0.058 0.278 0.908 -0.192 0.969 1.057 1.201 -0.084 -1.409 -1.057 -0.176 -0.126 -0.751 +4 -0.062 1.050 -0.575 1.424 -1.676 1.407 -0.609 -0.111 -0.015 0.722 0.545 2.721 0.957 0.132 0.475 0.083 0.426 0.855 -0.773 0.066 0.023 -0.535 -0.998 2.123 2.520 -0.299 0.726 1.457 +1 0.940 -0.659 -1.046 -0.299 0.520 0.688 -1.635 -0.051 -0.851 -1.165 0.676 -1.173 1.254 0.277 -0.760 1.955 0.846 0.671 0.835 0.056 1.495 -0.119 0.246 0.872 0.126 0.935 0.152 0.589 +2 -0.272 0.437 0.096 -0.385 0.126 0.541 1.417 0.020 -1.530 -0.936 0.056 0.772 -0.958 -1.689 -1.000 1.498 -1.456 -0.670 0.367 1.320 -1.787 -1.687 0.284 0.734 0.020 0.142 1.125 -0.528 +3 0.906 0.826 -0.025 -0.187 0.020 -0.413 1.780 -1.271 0.750 0.744 -0.530 0.296 1.263 -0.330 1.312 1.118 -0.710 -1.108 0.165 -1.240 -2.905 -0.489 2.174 -0.447 0.380 0.700 -0.041 -0.334 +1 0.643 0.721 0.077 0.889 2.546 1.712 -0.523 -1.451 -0.406 -0.947 0.451 -1.181 -0.073 0.838 -0.101 -0.946 1.175 -1.879 -0.328 -0.042 0.016 0.180 0.610 -0.035 -0.368 -0.408 -0.612 0.788 +2 -0.690 1.115 1.288 -0.417 -0.036 -0.539 -1.538 -0.583 -0.003 0.551 1.808 -0.624 -0.867 0.889 -2.751 -1.001 1.121 -0.951 -0.542 -0.009 0.676 -0.046 -0.059 -0.348 -0.281 -0.843 -1.040 1.255 +0 -0.504 -0.523 -1.379 0.506 0.210 -1.466 -0.406 -0.276 -0.585 -0.120 -0.107 0.894 -0.005 0.594 -1.391 -1.062 -1.374 -0.396 0.056 1.668 -0.830 0.849 -0.832 0.024 -0.152 -0.924 -0.846 1.597 +2 -1.549 1.504 1.193 -0.281 0.307 -0.015 1.973 -0.796 0.146 -0.428 -1.433 2.192 -1.182 0.220 -1.741 -0.399 0.422 -0.825 0.587 -0.269 1.160 1.362 -0.587 0.077 0.183 0.619 0.014 0.190 +2 -0.299 -1.222 2.206 -1.053 0.879 -0.769 0.136 -0.902 -1.239 0.522 0.405 1.145 -0.034 2.220 0.268 0.567 -1.358 -0.614 0.520 -0.084 0.188 -0.816 0.122 0.119 -1.180 0.643 -1.700 0.184 +1 -0.411 -0.943 0.924 0.996 0.179 -1.002 0.122 -0.612 1.286 -1.196 0.658 0.636 0.896 0.196 -0.330 2.393 -0.877 0.718 0.018 0.343 0.338 -1.082 -1.342 -1.277 -0.446 0.248 -0.687 -1.436 +2 1.671 -0.106 0.196 -0.022 0.386 -1.532 1.089 -0.979 0.043 1.003 -0.177 -0.272 -0.344 -1.080 0.316 0.310 1.138 -0.828 -1.092 0.682 0.863 -1.188 2.030 0.704 0.596 0.103 0.936 2.001 +2 1.872 0.820 0.963 -0.530 -0.967 1.326 0.330 0.777 1.193 0.827 0.877 -1.076 -1.063 -0.841 -0.937 -0.656 0.067 -0.806 -1.046 0.986 -1.015 -0.575 -0.269 -2.114 -0.791 -0.562 -0.677 -1.234 +0 -1.192 0.167 0.023 0.942 -0.178 -0.523 0.289 -0.253 0.876 -0.670 0.994 -1.246 0.490 0.029 0.751 -0.598 -0.020 0.212 1.017 1.142 -0.637 -1.405 -0.623 -0.804 -1.473 -0.549 -0.364 0.327 +1 1.627 1.931 1.326 0.252 -1.016 0.569 0.389 1.835 -0.352 -0.737 1.821 -0.230 0.021 -0.640 0.194 0.002 -0.008 0.145 0.035 -1.247 1.151 0.856 0.573 -0.715 1.119 -0.069 -0.405 -0.182 +4 -1.750 -1.469 1.388 -1.685 0.917 1.331 0.803 -1.003 -0.892 -0.058 1.751 1.743 0.299 -1.594 0.162 -0.208 0.023 -1.656 0.244 -1.503 -2.530 0.224 1.919 -0.836 -0.473 0.896 1.163 0.106 +0 0.143 1.056 1.222 0.573 0.598 0.514 -0.928 -0.746 0.673 0.805 -0.333 -0.322 -0.292 -0.525 0.712 0.404 -1.196 -0.539 -0.597 -0.328 -0.980 -0.198 -1.670 -0.336 0.672 0.286 -0.743 -0.057 +2 0.359 -0.128 -1.087 -0.784 -1.659 -1.739 -0.577 1.108 -1.291 -1.201 1.916 -0.951 0.030 0.502 -0.197 -0.045 -0.804 -0.648 0.323 -1.202 1.431 -1.791 0.214 -1.063 0.472 -0.971 0.591 0.815 +1 -0.007 0.140 1.668 0.332 0.181 0.533 0.055 -1.185 0.178 -0.313 0.799 1.058 -1.224 0.650 -1.088 -1.107 1.026 -0.857 1.312 0.130 -1.626 0.173 0.116 0.449 -0.335 0.372 -1.260 -1.902 +0 0.269 -0.054 1.098 -0.427 -0.439 0.516 0.240 0.493 -1.211 -0.351 0.408 0.538 1.162 0.379 0.650 -1.310 -0.884 0.874 0.672 -1.499 -0.667 1.251 0.676 -0.236 0.418 0.094 -0.517 -0.627 +2 0.395 -0.215 -0.344 0.503 -1.032 0.989 0.610 -0.532 -0.452 -1.152 -0.284 -0.211 0.587 -0.137 1.641 -3.032 0.079 0.441 -1.241 0.266 -0.381 1.529 -0.635 -0.056 0.087 -1.831 1.004 -0.701 +1 0.402 0.180 -0.233 -0.057 -0.554 0.313 -0.221 0.181 1.001 -0.709 0.597 0.033 2.021 -0.216 0.695 0.015 -1.388 -0.521 0.880 -0.433 -2.220 -0.031 -1.092 0.132 1.551 0.769 1.537 -0.562 +3 0.684 0.910 -0.897 -0.631 1.201 -1.585 -0.784 0.660 -0.763 0.331 -0.289 -0.591 -0.969 -0.544 2.289 -0.635 0.206 0.434 2.682 1.549 -1.687 0.592 0.786 1.254 -1.491 -0.060 -0.365 0.423 +2 -1.328 0.587 -0.484 0.729 -1.015 -0.101 -0.510 1.623 -0.400 1.065 0.288 -0.924 0.399 0.298 -1.382 -0.376 0.032 1.479 0.817 -0.430 -1.549 1.246 0.572 -1.579 -1.447 -0.705 1.471 -0.876 +3 0.485 -0.437 -0.665 -0.469 -0.835 0.104 -0.624 -0.449 0.674 -0.777 2.178 0.237 -0.076 -1.465 0.660 2.361 -0.761 1.244 0.740 -1.203 0.812 1.911 2.634 0.444 -0.356 -0.082 0.354 0.935 +4 1.844 -0.119 0.831 0.171 0.269 1.906 0.875 -1.746 -0.746 -0.601 0.538 0.336 0.044 0.879 2.137 -1.129 1.989 -0.945 -2.755 0.829 0.403 -0.592 1.439 -2.684 0.906 -1.248 1.177 -1.143 +1 1.127 -0.555 0.888 -1.669 -0.622 -1.224 1.908 -0.408 -0.650 -0.258 0.007 -0.371 -1.191 1.349 1.088 0.333 0.280 0.215 -0.457 -0.565 1.594 0.625 -1.330 -0.452 0.488 0.368 0.528 -1.486 +3 -1.269 -0.008 0.265 1.359 0.909 0.123 -0.644 -2.761 -1.632 -0.212 0.807 -0.810 0.149 -0.232 -0.407 -0.034 0.483 -0.388 0.204 0.186 -1.200 1.378 1.550 -1.850 -0.058 -0.467 0.335 1.961 +3 -0.900 1.445 -0.408 -1.397 0.985 -1.007 -0.340 0.743 0.299 -0.747 -0.162 -0.861 1.178 0.328 -2.052 -2.394 0.579 0.093 0.843 -1.318 -0.054 0.533 -1.189 -1.005 -0.416 2.119 -0.484 0.681 +4 0.944 2.233 -0.035 -0.726 0.619 -2.222 -0.210 1.447 -1.633 1.400 -0.656 0.237 0.300 -0.883 1.509 1.232 0.132 -0.454 -0.584 2.045 1.013 -0.012 -1.944 0.301 -1.099 -0.950 0.021 -0.172 +2 -0.229 1.362 -2.210 -0.733 0.792 1.678 -1.218 -0.418 0.166 -0.939 -0.263 1.582 -1.031 -0.240 0.273 0.465 -0.865 -0.178 -0.290 0.304 -1.176 1.271 0.258 1.067 -0.571 -1.220 -0.955 0.701 +2 -0.074 -2.308 0.348 0.792 -0.082 0.555 1.098 -0.047 -0.845 1.068 1.798 -0.697 0.291 -1.024 0.635 -1.068 -0.962 -0.310 -1.342 -1.021 -1.575 -0.765 -0.505 0.626 -1.416 0.804 -0.429 -0.536 +0 0.329 0.826 0.589 -0.314 0.310 -0.530 1.364 -0.282 0.621 -1.459 -0.475 -1.228 0.252 -0.604 0.895 1.229 0.697 -0.883 0.218 -0.968 -1.104 -1.014 -0.248 -0.453 -0.490 -0.119 1.666 1.350 +3 2.276 0.275 -0.407 0.795 -0.785 -2.103 0.355 -0.731 0.101 0.329 0.142 -1.709 0.026 0.445 -1.400 -1.519 -1.069 0.747 -1.998 0.830 0.392 -0.258 -1.481 -0.796 0.782 1.257 -1.031 1.142 +2 1.126 -0.071 0.633 -0.872 -1.847 -1.642 -1.426 1.027 0.948 -1.398 1.810 -0.096 -0.991 -0.590 -0.230 0.080 -0.933 -0.143 0.112 1.408 1.281 -0.236 1.048 0.249 0.121 -0.873 0.794 0.680 +3 -1.356 -1.191 -0.111 1.811 -1.811 0.426 0.241 0.574 -0.113 0.008 -0.038 -1.067 -2.142 2.000 -0.252 -1.089 1.093 -0.496 0.271 0.471 -0.693 0.624 -0.245 0.927 1.060 0.727 -2.693 0.367 +0 -0.965 0.225 -1.192 1.671 0.171 -0.020 -1.139 -0.225 -0.076 -1.074 0.151 -0.859 0.435 -0.986 0.157 -0.194 0.301 0.166 0.095 -0.740 0.554 0.287 0.637 -0.025 -0.180 -1.015 -0.661 -0.473 +4 -0.658 1.236 -1.563 0.879 -0.023 1.036 0.789 1.219 0.157 1.898 -2.289 0.138 1.290 -0.713 0.185 -2.615 -1.563 -0.798 0.880 0.109 -0.708 -0.310 1.228 0.000 0.483 1.742 -2.151 -1.402 +2 -1.803 0.679 0.302 -1.373 0.626 1.115 0.569 0.997 0.148 -0.330 -1.697 0.177 -0.290 0.152 0.573 0.256 -0.900 -1.526 1.133 -0.695 1.189 1.417 0.212 -0.327 -0.200 0.997 -0.010 1.991 +4 -1.526 0.027 -0.980 -2.117 -2.559 -1.191 -0.585 1.195 -1.125 2.244 0.691 -0.057 -0.817 -0.386 -0.961 -0.312 -0.547 0.659 -1.351 0.278 -0.156 -0.390 -2.319 -0.524 -0.056 0.400 0.474 0.865 +1 1.249 0.599 -1.084 -1.179 0.019 -0.512 -0.724 2.067 1.016 -0.636 0.317 0.097 0.722 -0.190 1.451 1.641 -0.059 0.480 0.771 -0.037 -0.255 0.532 0.199 1.534 0.361 1.835 -1.179 -0.473 +2 -0.222 -1.441 0.959 1.128 -1.947 0.978 -0.811 0.857 -0.068 0.029 -0.185 1.212 -1.240 0.608 1.101 0.716 2.325 -0.529 0.957 -0.106 1.233 -0.008 -0.388 1.306 -0.492 -1.413 -0.517 0.619 +4 -0.098 0.212 -0.519 0.308 0.568 0.230 -1.433 -1.122 -0.206 1.411 0.674 -0.615 -1.702 -2.136 -1.835 -1.058 0.961 0.717 0.493 -1.284 1.267 1.401 0.571 0.004 2.578 -1.398 -0.825 0.630 +0 0.361 0.521 -0.443 0.239 0.061 -0.312 0.245 1.055 -0.022 -0.016 1.068 0.961 1.584 -1.756 0.905 0.602 -0.886 0.252 1.051 -0.829 0.443 0.871 -1.071 -0.142 0.301 -0.796 -0.879 -0.135 +2 -0.399 0.047 -1.227 -0.150 1.794 0.735 -0.106 2.012 -0.609 0.343 -0.303 -0.205 0.394 0.576 -0.203 -0.836 1.832 0.101 -1.037 -1.022 1.991 2.464 0.337 -0.614 0.333 -0.306 0.574 0.350 +3 0.184 2.693 0.350 -1.004 -0.095 -1.776 -0.081 -0.833 0.915 -0.550 -0.117 -0.636 1.739 -0.321 1.833 0.814 0.482 0.369 0.394 -1.928 -0.279 0.845 -0.049 -1.405 -0.052 1.704 1.247 -0.062 +4 -0.410 -0.664 1.326 0.992 -0.579 -0.216 -0.951 0.520 -0.775 0.939 -1.514 -0.266 0.338 1.727 1.177 1.093 0.026 0.433 -1.611 0.320 0.076 0.892 -3.346 0.386 -1.613 1.721 0.959 -0.693 +1 0.077 1.182 -0.517 1.432 -1.320 0.089 -0.349 0.229 0.514 0.034 -0.268 -0.598 0.139 -1.324 0.798 -0.884 0.262 0.518 2.002 -0.657 -1.033 0.410 -0.305 -0.006 -2.394 -0.576 -0.495 0.119 +4 -0.031 -1.410 -2.736 -2.617 -0.147 0.494 0.231 1.132 0.446 -0.368 -0.593 1.298 2.253 0.268 0.671 -1.228 -1.614 1.388 -1.567 0.790 1.914 1.625 -0.203 -0.408 0.214 -1.097 -0.103 1.058 +0 -0.839 -0.262 0.616 -0.704 0.651 0.553 -0.271 1.162 0.078 1.102 1.793 -0.241 0.645 -0.638 -1.033 -0.195 -1.363 -0.938 0.591 -0.514 0.711 0.299 1.851 0.524 1.114 0.402 0.311 1.165 +0 0.718 -0.557 0.849 0.533 0.158 1.059 0.772 -1.103 0.021 0.918 -1.122 1.035 0.422 0.636 0.489 1.143 -1.514 0.201 -0.123 -1.219 1.526 -0.577 0.489 -1.063 -0.817 -0.566 1.309 -0.229 +1 -0.726 0.547 -0.557 -1.309 0.604 0.163 0.642 0.059 -0.926 0.347 -0.372 1.037 0.738 -0.980 0.651 -0.883 1.650 0.058 -0.732 -1.306 1.342 -1.400 0.711 -0.110 0.515 1.360 -1.497 0.181 +0 0.775 -1.315 0.609 -1.115 1.356 0.567 -1.315 0.853 -0.613 -0.965 0.722 1.207 0.476 -0.372 0.170 -0.582 -0.174 0.408 -1.426 0.446 -1.412 -1.274 0.654 -0.844 -0.991 -0.296 -0.591 0.231 +2 -0.533 0.876 -0.817 0.282 1.425 -2.392 -0.341 0.262 -1.792 -0.530 1.044 -0.704 -1.484 1.131 0.445 -0.069 -0.098 1.028 0.295 0.646 -0.693 -1.917 0.622 1.101 -0.225 0.888 -0.335 -0.091 +4 0.748 -2.519 -1.635 1.315 0.519 0.792 1.679 -0.346 1.671 2.926 1.266 0.433 -0.056 0.210 0.175 0.027 -1.102 0.426 0.233 1.366 0.571 -0.731 -1.096 -0.006 -0.868 0.145 -0.677 1.278 +3 -0.224 0.302 -1.696 1.191 -0.891 0.363 0.703 1.461 -0.034 -1.462 -1.591 1.275 -0.559 -0.378 -1.129 -0.385 1.744 0.033 -0.808 1.049 -2.493 -0.010 -0.356 -0.275 -1.437 -0.147 -0.400 -0.956 +0 0.380 -0.765 1.298 0.350 0.393 -1.573 0.304 -0.595 -0.808 0.072 0.385 1.498 0.304 1.419 -1.181 0.361 0.566 -0.419 -1.349 0.703 0.992 0.420 -0.098 -0.600 0.211 1.238 0.023 -0.008 +4 0.015 -0.336 0.669 1.719 -1.254 0.596 -1.533 0.435 -1.173 -1.347 2.551 2.148 -0.708 1.902 -0.040 -1.371 0.649 0.949 0.752 -1.690 -1.143 0.937 -1.152 1.018 -0.363 -1.645 -1.319 0.454 +3 0.695 0.943 1.103 -0.391 -2.247 -0.261 -0.079 0.480 -2.037 -0.293 -1.327 0.001 1.511 0.533 -1.158 -0.408 -0.037 -1.279 0.224 1.247 0.187 0.676 -1.759 0.533 -1.219 -0.728 -1.628 -0.845 +4 0.833 1.064 2.132 1.617 0.466 -0.442 0.366 -1.926 0.829 -1.060 1.122 1.980 -1.451 -0.050 -1.064 -1.728 -0.106 0.635 0.754 0.154 -1.664 0.123 -1.493 -1.427 -0.997 1.050 0.545 0.323 +1 -1.356 -0.910 -0.333 1.033 -0.766 0.159 -1.248 0.761 0.556 1.315 -0.084 0.933 -1.605 -1.014 -0.533 -1.019 -0.008 1.302 -0.170 -0.427 1.399 -1.336 -0.220 -0.755 0.098 -0.473 -1.466 -0.225 +0 -0.150 -0.327 -1.043 -1.172 0.464 -0.551 0.316 -0.885 0.181 1.303 0.586 -0.412 0.257 -0.241 0.008 -0.326 -0.164 0.212 -0.993 -1.016 -1.389 -1.600 -1.217 0.159 -0.893 0.514 0.035 -1.969 +1 0.959 0.154 -1.174 -0.581 -1.436 -0.215 0.926 -0.004 1.022 0.338 -0.403 0.088 0.472 0.139 0.151 -0.769 -0.433 2.258 1.228 -1.474 -2.367 0.076 0.893 -0.520 1.022 0.060 -0.163 0.756 +4 0.894 -2.237 -0.449 0.063 0.909 0.537 0.378 -1.064 0.171 0.729 -0.642 -0.967 0.206 -2.178 -0.996 0.466 2.058 0.722 -0.488 -2.448 0.742 0.575 -1.577 -0.591 -0.499 3.385 -1.723 1.160 +1 -1.350 -0.263 -0.394 0.677 -1.855 -0.829 0.431 -0.249 -1.455 0.360 0.849 -0.998 0.967 -0.596 -0.356 -0.533 1.014 -0.163 0.773 -0.486 0.359 -1.087 1.001 -0.307 1.249 -0.052 0.096 2.286 +0 -0.400 1.200 0.336 -0.237 0.402 1.102 -0.498 -0.521 -0.410 -0.627 1.199 -0.764 -0.588 0.311 0.230 -0.410 1.684 -0.631 -0.171 -1.927 -0.305 -1.323 -0.916 -1.956 -0.893 -0.075 -0.244 -0.415 +2 0.601 -1.180 0.700 -0.637 0.148 -0.608 0.785 -2.864 -0.011 -0.937 0.142 0.420 -0.348 -1.067 -0.261 -0.105 -0.855 -0.745 -0.095 -1.094 -0.601 -0.985 0.596 0.868 1.426 1.794 -1.053 -0.618 +0 -0.089 -0.019 -0.415 0.009 -0.117 0.877 -0.019 -0.379 0.855 0.161 0.385 -1.453 -0.974 0.083 0.891 0.701 -0.714 0.355 1.117 -0.414 -0.548 0.692 1.314 0.157 -1.295 0.510 0.501 0.451 +3 -1.991 0.834 0.689 -1.461 0.472 0.774 0.269 -1.992 -1.131 -0.136 -0.963 -1.646 0.940 0.039 1.104 1.507 -0.777 -2.247 0.393 -1.265 0.030 0.488 -0.244 0.805 0.538 -0.747 -1.232 -0.284 +0 0.749 -1.120 0.612 -0.108 1.054 0.715 -0.069 -0.572 -0.164 0.197 0.590 0.479 -0.313 -1.200 0.423 0.612 0.110 -2.259 -0.244 -1.570 0.401 -1.466 0.162 0.142 1.006 -0.049 0.309 0.191 +4 -0.945 0.165 -1.336 -0.660 1.222 0.062 -1.879 -1.797 0.162 0.862 -1.618 0.450 1.330 1.888 0.331 0.507 -2.133 2.100 -0.575 -0.933 -0.255 1.512 2.208 0.125 0.998 0.043 -1.038 1.477 +1 -0.938 0.357 -0.896 0.271 -0.736 -2.123 0.307 0.148 0.518 -0.673 -0.780 0.401 1.519 0.011 0.425 -0.322 -1.544 0.967 0.908 0.711 -0.182 0.597 0.894 1.620 0.989 0.064 -1.308 0.156 +0 -0.883 1.552 0.375 -0.169 1.021 0.548 0.204 -0.181 0.480 0.461 -1.727 -0.112 1.204 -0.369 1.306 0.093 1.029 0.313 0.469 -0.715 -0.932 -1.083 -0.800 -0.242 -0.315 -0.074 1.599 0.622 +4 1.747 0.427 -0.079 -0.995 0.541 1.036 1.154 2.565 -0.980 -2.003 0.999 -1.200 -1.157 0.557 0.313 -0.641 -0.822 0.032 -0.480 0.124 -1.000 -0.878 -1.547 0.366 -0.648 0.729 -2.201 -1.386 +2 0.314 -0.797 -0.048 1.508 0.275 0.381 -0.396 0.974 -1.466 0.131 -1.069 0.480 -1.672 1.237 -0.337 -0.469 -1.104 -0.104 -0.577 0.467 1.151 0.810 -2.443 0.502 0.384 -1.741 0.790 1.102 +3 -0.229 0.109 -0.736 1.136 -1.942 -0.788 -0.391 -2.216 -1.814 0.412 0.435 0.192 0.500 -1.485 0.581 -2.093 -0.117 -1.497 -1.076 -0.204 -0.821 0.685 1.295 -1.350 -1.250 -0.503 0.353 1.001 +4 -0.925 1.539 0.860 -0.606 -0.562 -0.239 2.205 -0.238 0.585 0.186 -0.555 -1.835 0.634 0.422 0.294 1.811 -1.010 0.555 -0.229 1.762 0.092 1.613 -1.654 -1.209 -1.091 -0.606 -1.352 2.796 +0 0.533 -0.835 0.526 0.774 0.936 -0.083 1.015 -0.608 0.374 0.048 -1.175 -1.830 -0.299 -0.295 -0.270 1.050 1.376 0.657 0.587 -0.080 0.177 0.430 0.140 0.593 0.046 -0.495 0.261 -0.236 +0 -0.230 -0.186 0.756 -0.628 1.342 -1.234 -0.323 -0.116 2.330 -0.917 0.080 0.028 1.131 -0.452 0.652 0.754 0.832 0.084 0.082 -1.160 0.302 -0.346 -0.933 -0.287 -1.440 -0.610 -0.699 1.458 +4 -1.447 -0.126 1.313 -0.610 -0.103 -2.325 0.689 0.373 1.805 -0.882 -0.703 1.230 -0.031 0.821 -1.263 -1.841 -1.739 -0.652 0.601 0.210 2.426 -0.685 1.233 -0.014 -0.093 -1.574 2.159 1.500 +4 -0.240 -0.607 -0.478 -0.270 1.712 1.732 0.903 -0.072 2.373 1.676 0.074 -1.328 1.568 0.909 -1.168 0.961 2.598 0.848 -0.466 0.855 0.447 -0.937 1.700 -0.243 -1.118 0.014 0.213 0.709 +1 0.290 0.021 0.574 -0.808 1.277 -0.104 -1.633 -0.795 0.369 -0.290 -0.393 0.203 0.359 -1.230 1.223 -0.131 0.420 0.097 -0.410 0.912 0.081 0.073 -2.489 0.820 -0.171 0.755 1.645 0.823 +0 -0.467 1.266 -1.606 1.283 -0.347 0.921 0.201 0.850 -0.118 0.238 1.098 0.096 1.330 0.352 -0.733 -0.355 0.888 -1.240 0.793 -0.366 -1.290 0.207 -0.700 -0.572 -0.626 0.115 -0.390 0.391 +0 1.128 0.595 -0.842 1.438 0.534 1.028 0.697 -0.412 0.062 -0.201 1.991 0.361 0.565 0.031 -1.257 -0.909 -0.054 -1.795 -0.601 0.235 -0.219 0.383 0.735 -1.183 0.101 -0.401 -0.836 -0.642 +4 1.112 0.857 -0.575 1.280 1.449 -0.113 -0.451 2.260 0.721 0.256 1.364 0.302 -0.506 0.080 1.039 -1.992 -0.288 1.383 -0.512 1.546 1.415 -0.066 -0.569 -0.287 2.665 1.874 0.125 -0.608 +3 0.602 -1.871 0.555 -1.154 -0.925 0.492 -0.970 -1.110 -0.003 -0.613 -1.630 0.555 0.985 0.905 -0.898 0.411 -0.494 -1.267 -0.747 -2.819 -1.057 -1.467 0.203 0.291 -1.690 0.828 -0.608 -1.069 +2 -1.134 0.030 1.340 -1.625 0.629 -1.249 -0.320 -0.211 -1.178 -0.911 -0.073 1.377 0.481 -1.639 0.466 0.041 -0.633 -0.437 0.498 -1.572 -0.647 -1.145 -0.799 0.329 0.049 0.671 -2.254 -0.270 +1 0.794 1.105 0.694 -0.954 0.899 -0.353 -0.602 -0.641 0.021 0.570 -2.021 -0.731 -0.461 1.036 -0.867 -0.331 0.810 0.040 1.533 -0.150 1.883 2.063 0.458 0.418 0.559 0.817 0.124 1.169 +1 -0.208 -0.247 -0.593 -0.309 -0.486 0.461 -1.138 -0.903 0.161 1.771 0.044 -1.876 -0.746 -1.617 -0.557 -0.022 -0.657 -2.082 -1.212 0.272 0.137 -1.688 -0.202 1.501 -0.342 -0.036 0.280 0.398 +0 -0.616 -0.836 -0.747 -1.576 -0.323 0.713 2.297 -0.370 0.032 -0.452 -0.633 -0.688 0.068 1.194 0.152 -0.173 0.440 -0.323 -0.100 1.016 1.180 -0.585 0.812 -0.373 -1.476 0.973 -0.364 0.680 +3 -1.512 0.216 0.779 1.071 -0.878 1.023 -0.628 -0.221 1.228 0.069 -0.221 -0.107 -0.323 0.669 -1.151 0.421 -1.565 2.314 1.120 0.437 0.217 -0.919 0.980 0.590 -3.036 0.446 0.585 0.281 +1 -2.116 -0.995 -1.193 0.975 -0.097 0.722 -0.932 -0.264 0.484 -0.656 1.293 0.734 -0.013 0.343 0.160 1.390 1.291 0.734 0.578 -1.076 0.232 -1.240 -0.789 -0.006 -0.439 0.032 1.449 -0.976 +1 -0.141 0.476 0.034 0.382 -0.718 -0.600 -0.148 -1.130 -0.045 -0.389 -0.470 0.321 -1.821 -0.229 2.317 -1.086 1.286 0.055 -0.603 -1.321 0.140 1.453 -0.389 -0.081 1.010 0.341 -1.572 0.099 +1 -0.045 -0.166 -0.716 0.537 -1.548 0.776 0.588 0.585 0.138 1.533 0.872 -0.607 -0.589 -0.880 0.551 -1.000 1.083 1.323 -0.879 -0.186 0.017 0.884 1.609 -1.685 -0.318 -0.266 -1.401 -0.485 +1 -1.181 -0.961 -0.629 0.946 -1.369 0.727 -0.336 -0.790 -0.361 -1.401 -1.132 0.959 -0.426 1.000 0.540 1.162 -0.923 0.949 0.397 -0.027 -1.867 -0.281 0.348 -0.533 0.266 -0.176 -0.693 1.214 +2 0.142 -1.308 -0.754 1.553 0.054 0.930 0.232 1.314 -1.999 -0.188 1.645 0.956 1.325 -0.007 -0.537 -1.027 -1.097 0.807 0.513 -0.067 0.508 0.142 0.962 0.953 -1.066 0.274 -1.669 0.133 +2 1.862 -0.526 -0.262 0.272 1.219 -1.212 -0.288 1.319 -0.398 1.614 1.748 -0.284 -0.133 0.547 -0.400 -0.191 -0.961 -0.748 1.867 0.432 -0.636 0.176 1.012 0.713 1.826 -0.118 -0.851 1.081 +3 1.989 0.174 1.037 1.322 -1.623 0.375 1.352 1.083 0.403 -0.478 1.104 0.101 0.149 -1.778 0.818 -0.728 0.003 -0.098 0.824 -1.117 -0.396 -0.266 -1.227 1.380 0.049 -1.544 -2.234 0.419 +1 -0.852 0.006 -1.532 1.150 -0.205 1.119 -0.527 -0.524 -0.764 0.876 -0.709 0.645 -0.382 -1.976 -0.570 -1.179 0.034 -1.190 0.926 -0.091 -0.178 0.656 0.964 -0.020 -0.771 -1.774 -0.686 -0.617 +2 -0.349 -1.055 -0.040 -0.592 0.771 0.673 -0.923 -0.002 0.639 -0.175 -0.728 -1.092 0.854 0.903 -0.208 0.766 -2.965 1.328 -0.157 0.142 -0.321 1.396 -1.061 1.474 0.396 0.623 -0.329 -1.308 +2 0.922 -0.767 -0.400 -1.373 -0.973 1.262 -0.278 -1.386 -1.947 -1.472 -0.803 0.004 -0.397 0.698 -0.936 1.226 -0.136 0.458 -0.520 1.017 -1.327 -1.234 -0.970 1.176 -0.237 -0.948 -0.196 0.172 +3 0.297 0.732 1.037 0.175 -1.206 1.447 1.182 0.976 1.368 0.822 0.116 -1.097 -0.778 -0.386 0.779 1.588 1.384 -0.454 -0.128 -1.352 1.668 -1.455 -0.174 1.385 0.393 0.566 0.248 -1.734 +2 0.376 -0.902 -0.870 1.125 -1.189 1.643 -0.901 0.638 -0.329 0.603 -0.544 -0.163 0.041 -1.002 0.741 -0.513 -0.229 -0.994 -2.562 -0.191 2.413 0.785 -0.019 -0.263 0.022 0.547 -1.181 1.114 +2 0.152 -1.228 -1.070 1.224 0.223 -1.054 -0.093 0.492 -0.527 0.207 1.033 1.746 -1.801 0.907 0.498 0.326 -0.879 0.436 -0.520 0.431 0.454 1.263 0.808 -1.909 0.048 1.319 -1.477 -0.121 +0 0.176 -0.283 -0.348 0.641 0.315 -0.685 0.849 -1.187 1.169 -0.473 -0.760 1.035 -0.425 -1.462 -0.090 1.187 0.139 1.081 -0.430 0.213 0.853 -1.052 0.483 -0.324 0.173 0.922 1.879 -0.850 +3 -0.789 1.168 -0.407 -0.068 0.398 1.190 1.402 -1.012 0.809 0.205 0.343 -1.093 1.180 0.522 -0.768 -1.735 -0.237 0.547 -1.363 -2.530 -0.073 -0.660 -1.320 -0.615 0.111 -1.119 2.009 0.483 +3 0.217 -0.067 0.596 -0.811 -1.116 -0.373 0.833 1.610 0.032 0.903 0.344 0.536 -0.400 1.066 -0.373 -2.116 -0.402 -1.625 2.208 1.034 0.094 -0.500 1.368 -0.743 2.233 -0.592 0.077 0.844 +2 0.059 -0.762 0.720 -0.618 1.185 -0.332 -0.353 -0.542 -0.664 0.674 1.611 -0.014 -0.103 -0.110 2.391 -1.503 0.600 -0.657 1.473 1.215 1.239 -0.027 -0.143 -1.036 0.508 -0.461 -1.320 -1.305 +4 0.112 -1.231 1.697 0.676 -0.922 2.455 -0.188 0.241 -0.287 1.888 0.352 -0.639 -2.673 0.655 1.118 -0.618 1.649 0.496 -0.847 -0.706 -0.740 -1.405 0.892 -1.350 -0.109 0.012 -0.198 -1.164 +4 0.829 -1.931 1.993 -0.338 1.140 0.960 0.040 -0.781 0.209 2.124 -2.291 1.099 -0.512 -2.798 -1.838 -1.001 -1.174 -0.196 0.155 0.745 0.909 -0.549 1.567 0.773 1.763 0.631 -0.668 -0.696 +3 -2.074 -0.181 -1.295 0.991 -0.628 -1.132 2.078 0.547 0.878 -0.417 -0.913 0.529 0.029 -1.778 0.891 -0.407 -0.898 0.022 0.518 -0.054 1.689 -0.178 -1.285 -0.104 -1.698 0.775 -1.093 0.051 +2 2.175 0.580 0.005 0.102 1.233 -1.149 -0.111 0.033 0.373 -0.730 -1.990 0.059 2.366 0.427 -1.166 -0.495 -0.268 1.580 -0.132 1.097 0.198 0.082 -1.003 0.796 -0.543 1.126 0.028 -0.094 +1 -0.914 -2.048 -1.041 1.403 -0.295 0.274 -0.274 0.562 0.071 -0.069 0.614 -0.466 0.372 -0.175 -0.107 -0.819 -0.216 -0.541 1.130 -1.684 0.889 -1.744 0.537 -0.838 0.566 -0.121 -1.432 1.359 +1 1.085 -1.104 0.756 -0.441 1.488 0.853 -0.861 0.386 -0.172 0.084 -0.040 1.688 -0.589 0.386 -0.500 0.051 0.873 -0.554 0.546 0.200 0.062 0.333 1.494 -1.614 1.547 1.423 -0.566 -0.698 +3 0.404 -2.413 1.056 -1.510 0.231 -0.775 2.143 1.440 -0.742 -0.127 0.245 0.863 0.029 -1.025 -0.718 -0.004 1.234 0.428 -0.773 0.648 -0.387 0.890 2.527 -0.359 0.802 -0.745 -0.112 0.030 +0 -0.112 -0.889 -0.480 0.095 0.490 -0.823 -0.640 0.263 -0.083 0.146 -2.614 -0.295 0.853 -0.616 0.487 -1.209 -1.563 0.418 -0.054 -0.336 0.846 -0.312 -0.633 -0.741 -0.952 0.467 -0.312 1.143 +3 0.505 0.627 -1.240 -1.503 -0.746 -0.662 0.753 -0.615 -0.856 -0.612 1.119 0.013 -0.179 -0.110 1.292 -0.749 1.316 0.210 -0.160 2.501 -1.056 -0.354 -1.232 0.952 1.689 1.670 1.224 -0.198 +4 1.098 0.743 -2.316 -1.302 -1.799 0.429 1.091 -0.055 0.126 -0.980 1.099 -0.770 1.096 -0.605 1.623 -1.411 -1.051 1.175 1.151 -0.271 -1.358 -2.499 1.397 0.418 0.159 -0.693 -0.386 0.865 +2 0.386 -0.006 0.457 -1.402 -0.428 1.177 -1.347 1.033 -0.275 -1.406 0.333 1.497 -0.508 0.829 0.667 -0.191 0.938 0.594 -1.523 -1.201 -0.169 1.045 -0.263 2.006 0.954 -0.518 -1.721 -0.077 +4 1.979 0.176 1.196 0.215 -0.107 1.369 -0.875 -1.851 0.401 1.299 0.546 -0.062 -1.102 0.425 0.276 1.632 0.087 -2.646 -0.857 2.438 -0.641 -0.640 -1.769 0.019 0.008 -0.515 -0.307 0.411 +4 0.290 2.075 0.871 -0.326 1.201 -0.408 -2.038 -1.008 -1.871 -0.352 0.018 1.676 0.327 -0.219 0.829 -2.211 0.236 0.771 -1.479 1.144 0.338 -0.415 0.633 2.271 0.182 0.248 -0.459 -0.850 +1 0.101 -0.029 -0.825 -0.922 1.402 1.362 0.249 -0.655 0.563 -0.030 -0.969 0.439 2.155 -0.885 0.662 0.601 1.680 -1.592 -0.836 -0.158 1.274 -0.912 -0.711 -0.158 -1.064 -0.222 -0.473 -1.164 +0 -0.302 -1.484 1.278 -0.061 -0.193 0.897 0.422 -1.129 -0.071 -0.335 -1.745 -0.690 -0.184 -0.115 -1.482 1.152 -0.102 0.038 0.401 -0.788 0.456 -0.367 -0.897 0.573 1.492 0.725 0.923 -0.521 +1 0.101 -0.890 0.902 -0.044 0.387 -1.187 -0.751 0.290 2.094 0.542 1.706 2.161 -0.202 1.284 0.020 -0.718 1.292 -0.003 -0.215 1.083 -0.521 -0.475 0.746 0.659 0.350 0.660 1.389 -0.679 +2 -0.400 -1.840 -0.711 0.165 -1.217 -1.658 0.736 0.181 0.376 0.992 -0.848 -0.047 -1.627 0.421 1.515 -0.477 -0.637 0.924 -0.905 0.100 -0.441 -0.017 -1.234 -1.868 0.163 -1.516 0.361 0.717 +0 -0.154 -0.101 1.205 0.395 -0.148 -0.471 -0.591 0.211 -0.273 -0.398 0.285 1.044 0.646 -0.371 1.618 -0.626 -0.520 0.942 1.071 -1.164 -0.141 -0.185 -0.429 -0.070 0.420 -2.105 0.131 -0.336 +1 -0.057 -0.635 -1.381 0.060 0.804 -2.269 -0.568 -0.210 1.211 0.362 0.830 0.039 0.047 -0.185 -0.311 -1.763 -0.161 0.754 -0.631 -0.752 1.279 0.855 0.552 -1.537 0.795 -1.119 -1.169 0.282 +4 -0.954 0.541 0.671 -0.334 0.061 -0.577 -1.095 -0.497 0.074 -1.475 -0.424 0.088 1.195 -0.313 -1.246 -0.210 -0.567 0.552 -0.338 -2.711 3.692 0.142 0.089 -1.541 -1.465 -0.092 0.336 0.702 +2 -0.152 -0.567 0.764 -0.045 -1.319 -1.931 -1.129 2.393 0.723 0.189 -0.182 -0.247 1.167 -0.491 0.577 -0.348 1.940 0.742 1.246 -0.584 1.058 -0.313 0.110 1.635 -1.043 0.203 0.438 0.520 +1 -1.772 0.509 -0.627 -0.856 -1.667 -0.313 1.299 0.176 -0.119 -0.294 1.447 -0.210 -0.933 -0.243 0.130 -0.519 -2.208 -0.301 0.653 -0.528 -0.520 0.867 0.752 -0.323 -0.474 -1.531 0.200 -0.354 +0 0.789 -1.029 -1.090 0.741 -0.222 -0.465 1.059 -0.259 -0.202 0.556 0.909 1.099 1.024 -0.131 0.186 0.130 -0.967 0.051 -0.507 -0.925 0.308 -0.252 -0.876 0.024 -2.095 0.963 -0.770 0.388 +3 -0.079 -0.509 -2.247 2.056 0.028 0.853 0.318 -1.580 0.269 -0.265 0.470 1.216 0.516 -1.182 -0.401 -1.025 -0.282 -0.516 0.076 -1.213 -0.963 0.496 1.346 1.469 0.124 -0.319 -2.246 -0.307 +1 0.126 0.507 -0.401 1.300 -0.399 -1.959 -0.391 -0.967 -0.281 -0.259 1.197 0.695 0.263 -0.003 -1.059 -2.181 0.113 0.795 -0.108 -0.535 0.465 -0.083 1.686 1.233 0.960 0.758 -1.290 0.663 +0 -0.578 0.656 -1.260 2.005 -1.379 0.329 -0.652 -0.714 0.779 1.321 -0.280 0.665 0.632 -0.900 0.503 -1.223 0.973 0.002 0.565 -0.655 -0.186 0.482 -0.777 -0.241 0.701 -0.034 0.033 -0.925 +0 -1.496 -0.918 1.323 -0.169 0.080 0.759 0.696 0.279 -0.209 -0.313 0.858 0.554 -0.257 1.082 -0.129 -0.852 -0.759 -0.979 -0.272 1.234 -0.805 0.188 -0.657 -0.260 -1.510 -0.221 -0.194 0.540 +1 -0.245 -0.250 0.073 -1.181 0.893 1.715 -0.022 0.316 -2.115 -0.435 -0.696 -0.858 -0.658 -0.558 -1.455 -0.487 -1.042 0.788 0.330 0.332 0.977 -0.826 0.270 1.070 -0.896 -2.029 -0.691 0.957 +3 -1.619 -0.633 -1.578 -0.228 0.673 -0.654 -0.403 -0.275 0.141 0.516 0.807 2.256 0.123 0.736 -1.588 -0.379 -0.270 -1.017 -0.536 -0.107 -0.505 2.602 0.512 2.001 1.493 -0.639 0.038 -0.435 +2 -1.297 0.906 -0.628 -0.842 1.023 -0.883 0.377 -0.489 -2.357 0.478 0.126 -1.910 -0.699 -1.807 0.732 1.388 0.849 0.989 -0.518 0.547 -0.009 0.185 0.957 1.175 -0.390 -1.408 -0.187 -0.219 +3 0.020 -0.667 -0.106 -0.971 0.490 0.428 -1.332 -2.508 0.872 0.236 -0.206 1.575 0.287 0.789 -0.627 -1.495 0.374 -1.056 -2.217 -1.162 0.564 -0.438 -1.360 -0.735 -0.676 1.317 0.676 0.390 +0 0.476 -1.000 1.020 0.141 -0.357 -0.783 -0.655 -0.257 -1.027 0.038 -0.165 0.687 -0.149 -1.821 -0.310 -1.129 -0.597 -0.944 2.090 -0.340 0.718 -0.306 -0.227 -0.475 -0.696 -0.675 0.374 -0.060 +4 1.631 -0.971 -0.742 -2.817 -0.644 -3.084 0.294 0.776 0.442 -1.521 -0.066 0.441 -0.388 -1.293 0.947 -1.209 0.473 0.020 1.477 2.302 -0.568 -1.179 0.142 0.035 0.613 -0.759 -0.756 -0.094 +1 -1.219 0.909 0.339 -0.013 1.883 -0.726 -0.848 1.431 0.577 0.642 -0.553 -0.539 -1.136 0.372 1.468 -1.201 0.761 0.409 0.131 0.683 -1.070 0.567 0.123 0.268 -0.127 1.807 -0.177 -0.797 +0 0.927 0.522 -0.385 0.976 0.979 -0.650 -0.283 -0.688 -0.093 -1.072 -0.535 -1.455 -0.062 0.373 -0.858 0.179 -1.337 -1.030 0.552 -0.174 1.216 -0.042 0.128 0.416 0.124 0.030 -0.330 -2.179 +2 -1.493 -0.139 -0.432 0.946 0.675 -0.233 -1.047 1.038 -0.068 -1.618 -1.532 0.438 -1.276 1.860 -0.937 -0.000 0.159 -0.729 -2.240 0.191 -0.753 0.117 -0.122 0.263 1.562 -0.546 -0.469 -1.666 +2 0.651 -2.390 0.443 2.592 -0.271 -0.063 -0.655 0.059 1.346 -1.012 -0.399 -1.151 0.246 -0.327 -0.863 -0.357 -0.766 1.034 -0.793 0.514 -0.753 1.677 0.525 0.100 -0.187 -0.939 1.363 -0.250 +3 1.181 -0.947 -1.957 -0.111 0.907 -0.648 -0.547 -1.313 -1.955 0.873 2.214 1.324 0.392 -0.588 1.205 0.511 -1.096 -1.498 -1.218 -0.259 0.504 0.618 -0.686 0.001 0.872 0.268 -0.727 -0.956 +4 0.260 0.005 1.902 -0.453 0.762 -0.574 -0.611 -2.266 -2.003 -1.262 2.238 -0.750 0.311 0.048 -1.129 0.311 -1.013 -1.957 0.076 1.204 -0.078 -0.348 -2.748 -1.216 -0.340 0.089 -0.459 -0.411 +2 2.834 1.116 -0.499 0.544 -0.393 1.015 -0.814 1.209 0.920 0.643 2.071 1.006 1.022 1.126 0.714 0.464 -0.677 -0.664 0.432 0.609 0.018 -0.482 -0.136 -0.053 0.275 -0.222 1.166 -0.111 +4 -1.702 0.174 1.738 0.971 0.376 1.283 -0.668 0.518 -0.113 -0.133 0.191 1.175 1.567 -0.267 -1.089 -0.820 1.948 1.481 -1.091 1.661 -0.712 0.665 -0.712 2.163 -0.570 -1.050 -0.457 2.627 +0 0.878 0.582 0.092 -0.449 1.515 0.526 -0.202 0.299 0.267 0.418 0.754 -1.206 -1.192 1.196 -0.742 -0.220 0.407 0.022 0.145 -0.586 1.680 0.704 0.042 2.452 -0.621 0.507 0.191 0.380 +0 1.358 -2.133 0.191 -0.370 0.549 -0.685 1.142 -0.222 1.451 0.219 -1.050 0.543 0.418 -0.107 -0.748 0.861 -0.965 -0.860 0.527 -0.211 -1.965 -0.962 0.406 -0.270 -0.101 0.262 0.456 0.116 +3 -0.226 0.051 1.160 -0.058 0.325 1.166 0.796 2.464 0.616 -0.639 -1.232 -0.671 -0.964 -0.652 -1.806 -2.273 -1.538 0.081 0.095 0.652 0.081 0.483 -0.986 0.162 0.799 0.462 -1.870 0.838 +0 -1.564 -0.238 -0.007 1.630 -1.761 -0.904 0.219 0.314 1.251 -1.964 -0.714 0.843 -0.062 0.709 -0.131 1.037 -0.662 -0.251 -0.054 -0.725 0.116 -0.436 0.316 0.265 -0.744 0.550 0.243 -0.589 +3 -0.740 -0.780 1.228 -0.151 2.143 1.525 -1.009 1.026 1.047 -0.360 0.020 -0.659 2.384 1.616 -0.291 1.562 -0.445 0.951 0.256 -0.089 -1.019 -0.581 0.609 0.070 -0.333 1.455 0.129 0.002 +3 -0.896 0.757 -1.201 -0.838 -0.115 -0.553 0.890 0.286 1.766 -0.247 -0.122 1.210 -0.091 1.494 0.541 -2.353 1.178 0.913 0.951 -1.911 -0.663 -0.589 -0.841 1.742 -0.012 -0.037 0.552 0.569 +1 -1.509 1.359 1.080 0.739 0.551 1.248 -0.242 0.423 -0.812 -0.153 0.450 -3.081 -0.620 0.165 -0.078 -0.452 0.589 -0.708 -1.222 1.184 1.043 -0.599 -0.069 0.846 0.044 0.530 0.476 -0.319 +3 2.120 -0.741 2.208 -0.895 1.247 0.402 -1.467 1.028 0.760 0.714 -0.213 0.025 -0.569 -0.785 -0.551 -0.810 0.327 0.818 0.376 1.009 0.008 1.147 0.593 -2.365 0.893 -1.440 1.112 -0.104 +4 -0.927 -1.549 1.460 -0.699 -0.926 -0.418 -0.278 1.639 -0.328 2.633 0.064 2.057 0.894 0.361 1.578 1.122 0.635 2.318 -0.498 -0.995 -0.676 1.752 1.451 -0.525 -2.355 -0.206 -1.013 -0.494 +1 -1.791 -0.517 -0.982 -0.526 -1.470 0.810 -0.326 -0.801 -0.168 2.020 -0.146 -0.689 0.060 -1.584 -0.083 2.001 -0.487 0.274 0.745 0.212 0.951 0.189 -0.006 -0.486 0.009 0.143 -0.399 0.662 +0 0.618 1.222 -0.384 -0.135 -0.163 0.181 -0.038 0.768 1.233 -0.814 -0.309 0.902 -1.422 -1.971 0.226 0.496 -0.722 -0.579 -0.760 -0.867 0.693 -1.180 -0.266 -0.905 -1.455 0.870 -0.520 0.188 +4 1.106 0.445 -3.217 -0.241 -2.313 -0.114 -0.034 -0.017 1.970 0.198 -0.998 -0.083 -0.398 -1.000 1.104 1.073 -0.026 1.818 -0.378 0.518 0.321 0.959 1.397 -0.406 0.263 1.938 -1.080 1.229 +3 0.294 1.499 0.819 -0.690 0.515 0.116 -1.760 2.162 -1.060 -0.108 -0.472 0.117 0.193 0.579 -0.476 1.082 -0.679 -0.532 -1.720 0.230 1.520 -0.248 1.765 -1.090 -1.358 1.389 -0.169 -0.974 +1 -0.818 0.913 -0.542 -0.170 0.291 -0.977 -1.282 -0.013 -0.371 0.228 -1.432 -0.535 0.689 2.946 0.119 0.671 0.488 0.293 0.682 -0.959 -0.357 0.250 -0.966 0.433 -1.916 0.812 -0.150 0.694 +4 1.319 -1.803 -1.441 0.750 -0.270 -0.387 -0.327 -1.127 -0.410 -1.320 1.309 0.133 0.107 0.667 2.790 -1.052 -1.842 -0.031 -1.827 -0.339 0.874 1.528 -1.566 -0.943 0.363 0.754 0.811 1.475 +2 -0.676 0.772 -0.334 0.529 -0.721 0.083 -0.260 -0.209 -0.097 -0.218 0.148 -1.254 0.221 0.128 1.653 -0.265 -1.913 1.138 1.736 0.881 0.687 -2.028 -0.716 0.308 1.780 0.494 -1.278 -0.286 +4 1.362 -1.006 0.753 -0.458 0.147 1.075 0.445 1.387 1.414 1.952 -1.089 1.981 -1.547 0.968 -0.547 1.698 1.040 0.254 0.031 0.960 0.016 2.035 -0.472 0.384 1.163 -1.085 0.691 0.569 +4 -0.244 1.202 -0.658 -0.854 -0.719 1.228 0.367 -2.580 1.790 0.238 -1.053 -0.317 0.396 -0.186 -0.856 -0.013 -0.537 -0.982 -1.080 -0.302 -2.726 -0.299 1.147 -0.047 -0.781 -1.321 -1.841 -1.609 +3 1.624 1.720 1.752 -0.737 1.218 -0.021 -0.017 0.821 -0.354 -1.666 0.025 1.892 -0.349 -0.076 0.395 -0.366 -0.199 0.127 -0.414 1.310 2.118 0.460 -0.489 0.509 -1.141 -1.382 -0.084 1.261 +2 -0.310 -1.038 -0.118 -0.541 0.496 -0.885 0.090 -0.808 -0.600 0.297 0.235 -0.417 -0.269 -1.454 1.403 0.687 0.651 -1.595 -0.595 -0.335 -1.931 0.890 1.454 2.062 -1.046 0.117 1.323 -0.254 +0 0.561 -0.263 -0.307 -0.002 0.890 0.039 -0.051 -0.556 -0.389 0.179 1.315 0.238 -0.814 -0.301 1.108 0.078 -1.383 0.434 -0.644 -1.149 0.085 -0.841 0.813 0.090 1.344 0.347 -0.497 -0.510 +4 0.642 -1.133 -1.387 0.150 0.231 -1.468 0.621 -0.020 -1.000 -0.214 0.589 -1.274 -1.023 2.364 0.606 -0.010 -1.982 -0.967 0.574 -2.338 1.040 -1.525 0.861 1.608 0.903 -0.552 0.402 0.757 +0 -0.224 -1.389 -0.362 -1.122 0.265 0.901 0.457 -0.713 -0.265 0.277 0.329 1.637 0.623 0.148 0.104 -0.348 0.000 0.717 -0.707 0.410 -0.109 1.718 0.295 0.328 -0.856 1.464 0.507 1.364 +1 -1.183 1.223 0.498 0.638 0.481 0.763 1.050 -0.094 -1.023 1.179 -0.941 -0.233 -0.119 0.415 0.551 -0.775 1.714 1.451 0.223 -0.034 -0.810 -0.030 -0.001 -1.787 0.843 2.241 -0.439 0.017 +3 -0.229 1.049 -0.697 -1.246 0.422 1.141 1.082 -0.706 -1.086 -0.251 0.026 -0.019 -0.578 1.173 1.333 -1.364 -0.959 -0.240 -0.114 0.484 -0.385 -0.217 -2.356 1.213 -1.792 -1.790 -1.595 -0.876 +4 -1.020 -1.638 -0.185 1.463 0.747 -3.842 0.712 0.439 -0.779 1.223 -0.175 1.092 -0.732 0.167 -0.554 -0.232 0.523 0.631 0.177 -0.568 -0.459 1.270 0.085 -0.486 2.407 0.596 -1.170 -2.378 +2 0.232 2.125 0.131 1.591 -0.854 -0.296 -1.968 0.157 -0.258 0.409 -0.167 1.417 -0.246 0.536 1.628 1.762 0.400 0.142 0.414 1.626 0.100 0.150 -0.836 -0.778 0.571 -0.902 0.321 -0.285 +2 -0.824 0.316 0.963 -0.987 0.717 0.121 -1.221 0.584 1.062 -0.196 0.121 -0.383 -2.962 -0.090 -0.191 -0.686 -1.883 -0.330 -0.665 0.629 0.273 -2.327 -1.190 0.123 0.335 -0.865 0.909 0.682 +3 -0.119 0.579 0.166 -1.124 -1.639 -1.085 -1.360 -1.910 0.820 1.548 -0.542 0.507 1.655 0.594 -0.937 -0.092 -1.259 -0.899 -0.736 -2.317 0.014 -1.763 -0.095 0.362 0.491 0.199 0.458 1.439 +1 -0.197 1.042 0.065 -0.496 0.877 -1.171 0.995 -1.058 -0.933 0.066 0.724 -0.578 1.014 2.656 0.273 -0.733 1.149 0.635 1.271 -0.347 2.013 0.089 -0.369 -0.215 -1.087 0.223 0.083 -0.568 +1 0.170 -1.514 0.968 -1.262 0.771 0.128 -0.455 -1.105 0.329 -0.873 1.208 1.244 1.642 0.281 -0.404 -0.297 -0.370 0.248 0.437 1.232 0.020 -1.403 -0.407 1.556 -1.557 0.739 -0.391 -0.883 +3 -0.547 -0.165 -1.186 -0.241 0.334 -0.242 0.895 0.031 1.423 -0.867 -0.354 -1.744 0.917 -0.864 1.509 -1.860 -0.687 1.458 0.886 -2.585 0.392 -0.897 0.797 -0.451 0.727 -1.347 -0.208 0.322 +0 -1.213 -0.046 -0.538 -0.188 -1.289 0.359 -0.278 0.762 1.036 0.349 -0.322 -0.769 -0.873 0.332 0.518 -0.135 -0.013 -1.386 0.143 -0.461 1.160 0.322 -0.304 0.393 1.879 0.872 -0.570 0.510 +2 -1.085 -2.074 0.998 -1.394 -0.274 -0.605 0.535 -1.654 -1.956 1.399 -1.128 0.276 0.204 -1.160 0.765 1.655 -0.425 -0.260 -0.104 0.768 0.227 -0.401 0.273 -0.533 0.102 0.931 -0.094 -0.540 +2 1.013 1.950 1.025 -0.381 0.043 -0.008 0.127 -0.290 0.173 -2.995 -0.974 0.736 2.031 0.240 -0.385 -1.163 -0.760 -0.048 0.312 -0.727 -0.025 0.533 0.968 0.212 -0.434 -1.444 -0.303 0.827 +2 0.011 -0.904 -2.939 1.131 -1.739 1.571 -0.008 -1.300 -0.420 1.025 -0.036 -0.540 -0.358 -0.611 -0.178 -1.167 0.011 -0.124 0.537 -0.263 -0.402 -0.759 0.916 0.318 2.079 0.110 0.062 0.648 +0 -1.348 -0.116 -0.681 -0.833 0.225 -0.445 0.504 -0.893 -0.142 0.287 0.418 0.188 -0.042 -0.578 1.588 0.214 -1.135 -1.964 1.197 -1.289 0.186 -1.194 0.143 0.312 0.801 -0.091 -0.088 -1.068 +3 1.563 0.478 -0.338 -0.798 -0.732 -0.553 2.151 1.806 0.746 -0.961 0.893 -0.239 -2.153 0.287 -0.842 -0.808 -0.250 -0.498 -0.034 -0.082 -1.876 1.520 -0.983 -0.952 -0.329 -0.108 1.459 0.269 +4 -0.629 1.567 -2.838 0.109 -0.503 0.532 0.449 -0.257 -0.390 1.944 0.051 -0.300 -0.847 0.010 -1.487 -0.208 -0.979 -2.604 0.324 -2.780 -1.020 0.750 0.746 0.253 -1.254 -1.223 0.607 -0.535 +2 -0.238 -0.618 -1.006 0.758 -0.603 -0.402 -1.109 -0.433 -0.707 -1.982 -0.839 0.746 1.349 -0.107 0.034 0.286 1.963 -1.010 -1.262 -0.052 0.394 -0.033 -1.365 0.885 -0.138 0.554 1.553 -1.463 +0 1.321 -0.682 1.812 -0.249 0.495 0.126 -1.801 0.536 -0.769 -0.120 0.047 -1.029 -0.604 1.256 -1.317 0.591 -0.379 1.000 0.706 -0.695 0.685 -0.570 1.711 0.120 -0.338 -0.420 -0.356 -0.293 +4 -1.573 1.030 0.618 -0.169 -0.128 -0.933 2.291 1.298 -0.397 -1.295 1.137 -1.770 -1.633 -0.576 -0.763 0.285 1.430 -0.018 0.871 -0.726 0.683 1.068 0.792 -0.255 0.019 -2.338 -0.354 1.655 +2 -1.046 0.713 -0.860 0.144 1.381 0.458 1.402 -0.708 1.199 -1.253 1.198 1.214 -0.577 0.285 -1.790 -0.081 1.043 -0.149 1.073 0.441 -0.020 -0.968 0.357 -0.807 -1.815 -0.713 1.615 -0.683 +0 0.449 -0.448 -1.392 0.382 -0.476 0.581 -0.320 0.854 1.061 0.923 1.253 -0.014 1.001 -0.370 -0.545 0.083 1.312 -0.203 0.903 -1.001 -1.875 -0.484 -0.051 0.996 -0.231 1.615 0.502 -0.233 +2 -1.986 0.697 -0.366 1.856 -0.019 0.166 -0.510 0.129 0.976 1.505 1.028 -2.374 -0.594 -1.045 0.559 -0.978 0.059 -1.032 0.733 0.610 -0.031 0.830 1.084 0.035 0.970 1.461 0.434 -0.188 +1 0.052 -1.343 1.561 0.246 -0.698 -0.260 1.336 0.452 0.579 1.488 0.954 1.124 -0.126 -2.254 0.104 -0.664 0.789 -1.770 0.828 0.189 0.354 0.619 -0.028 -0.333 1.135 0.593 0.689 -0.083 +1 0.126 1.087 1.247 1.145 0.303 0.414 -0.673 0.845 -0.667 0.681 0.042 0.015 1.001 0.561 0.283 -0.498 1.602 0.333 0.747 0.081 -0.694 0.355 -0.596 0.438 -3.265 -0.401 -0.014 -0.954 +2 1.974 -0.521 0.750 -0.521 -0.848 0.883 0.832 -1.565 0.402 0.293 0.421 0.027 -1.080 -0.054 -1.851 -0.038 -0.118 -1.086 -0.525 -1.498 1.300 1.435 0.497 1.345 0.005 0.849 -0.905 0.738 +3 -1.053 0.506 -0.042 -0.480 -1.039 2.337 -3.017 -0.758 -0.482 0.921 -0.315 0.182 -0.441 1.844 0.395 -0.650 0.704 -0.247 1.827 1.351 0.294 -0.237 -0.635 0.882 1.452 -0.292 0.449 0.505 +0 0.063 -0.276 -1.159 -1.816 -0.514 0.898 1.256 1.188 -0.816 -1.154 0.497 0.635 0.400 -2.014 -0.867 -0.030 0.487 0.767 0.144 0.331 0.085 0.686 0.313 0.066 -0.268 0.081 0.485 0.267 +3 1.025 0.870 1.811 0.748 0.478 -0.911 -1.481 0.673 1.610 1.635 -0.586 -1.198 -0.214 -1.771 -1.239 0.011 0.411 -0.210 -1.351 1.388 -0.115 0.161 -0.827 0.144 1.541 -0.517 0.753 0.547 +4 1.929 -1.464 0.052 -1.202 1.254 -0.062 -0.469 -1.835 1.576 -1.328 0.487 0.932 2.174 -0.551 -0.855 -0.427 -0.588 -1.884 1.635 -0.418 -0.160 0.627 1.386 -1.161 0.358 -1.216 0.334 0.292 +2 -1.201 0.343 -0.954 1.100 0.908 0.272 -1.391 -0.053 -0.304 0.830 -0.902 1.155 0.967 -1.374 -0.220 -0.051 1.006 -0.687 0.208 1.099 0.251 -1.857 -0.495 0.904 0.779 -2.249 0.201 0.946 +2 -1.453 -0.255 -1.187 -0.255 -0.440 -0.673 -0.097 -1.062 0.537 0.435 0.066 0.541 1.034 -1.406 1.608 1.672 0.859 -0.326 2.127 1.119 -1.496 -0.376 0.605 -1.268 1.103 -0.196 -0.987 -0.176 +4 0.453 0.191 -0.035 -0.025 -2.461 0.149 -0.636 -0.370 -1.590 0.673 0.552 -1.165 0.179 1.646 0.140 -2.014 -0.570 0.127 2.116 -1.209 -0.147 -1.067 0.879 -0.366 -2.732 -0.393 -0.112 0.388 +4 0.450 -0.104 1.775 -1.479 0.140 0.065 0.813 -0.844 -1.744 0.157 0.281 1.490 -1.270 1.155 2.367 -0.378 -1.291 -0.407 0.014 0.564 -0.591 1.555 -1.115 -1.739 1.107 -2.275 -0.204 3.118 +1 1.375 -0.497 -0.059 -1.379 -0.190 -0.503 1.763 0.697 1.719 1.033 -0.165 1.132 -0.393 -0.433 1.030 -0.730 -0.131 -1.592 0.104 0.626 -1.767 -0.716 -0.238 0.892 0.071 0.202 -0.802 1.326 +1 0.066 0.158 -0.905 -0.281 0.239 0.562 -0.943 1.350 0.250 0.883 -1.256 -0.184 -1.107 -0.989 0.528 -0.629 -0.827 0.747 -1.195 2.648 -0.207 -1.177 -0.276 -0.225 -1.288 0.620 -0.003 0.364 +2 -0.563 2.428 1.917 0.137 -0.229 0.314 -0.323 -0.135 0.968 -1.006 -0.377 0.339 -0.721 0.083 -0.546 0.745 -0.366 0.708 -0.872 -0.800 1.536 -1.587 -0.571 -0.207 -1.956 -0.290 -2.067 0.153 +4 -0.912 -1.611 -2.326 -0.946 0.396 0.237 -1.703 0.278 -0.324 2.522 -0.555 -2.098 1.446 0.643 1.430 -0.386 2.175 0.368 -0.682 0.484 -0.981 -0.634 -1.400 -0.448 0.849 -0.365 0.559 0.607 +1 0.830 1.313 -1.289 -0.600 1.346 0.067 0.436 0.070 0.891 0.513 0.106 -2.102 0.640 0.729 -1.240 -1.172 0.518 0.194 -1.975 -0.822 0.314 0.677 -0.584 -0.147 -0.879 1.324 0.175 -0.668 +2 1.404 0.150 -0.946 -1.316 -2.192 -0.021 0.441 -1.116 -0.115 0.523 -0.816 -0.033 0.030 -2.354 -1.733 1.286 -0.317 -0.114 -0.106 -1.432 0.377 0.436 -1.699 0.464 -0.199 -0.048 0.470 0.339 +0 -0.174 -0.139 1.684 -0.106 -0.660 -0.719 0.979 -0.580 -0.853 0.917 0.392 -0.236 -1.328 0.554 0.827 -0.953 0.365 -0.101 -0.884 -0.444 -0.047 -1.318 0.571 -0.710 -0.670 0.506 -0.453 -0.216 +1 0.294 -0.092 -0.470 -0.734 -1.211 -1.383 -0.297 -0.535 0.121 -0.559 -0.107 -0.222 -0.676 1.926 0.722 -0.568 -0.874 -0.678 0.872 0.555 -0.619 -0.453 -2.297 1.120 -0.327 0.616 -1.477 0.390 +2 0.299 2.767 0.389 0.465 -0.861 -0.421 0.499 -0.289 0.661 -0.177 -0.648 -1.671 1.305 -0.311 -0.223 0.860 -0.596 1.655 0.435 1.202 0.924 0.790 -0.473 -1.060 0.973 -1.547 -1.284 -0.245 +2 -1.216 0.608 0.345 -0.919 -0.325 0.307 0.258 0.439 -0.762 0.358 -0.048 0.772 1.075 3.029 1.310 0.126 -0.038 -0.936 -0.911 0.272 0.303 2.676 -0.728 -0.832 0.250 -0.504 0.389 -0.474 +0 -0.858 -0.206 -1.886 1.074 0.448 0.341 1.107 -0.535 1.381 0.341 -1.730 -0.166 -0.733 0.034 0.516 -0.974 -0.541 -0.489 0.999 -0.536 0.522 1.355 -0.341 -0.242 -0.155 -0.676 0.452 -1.070 +2 -2.072 -0.346 1.687 0.364 -2.169 -0.216 0.246 0.805 1.376 1.709 0.405 -0.131 0.081 0.176 -1.548 -0.738 -0.098 0.382 0.438 -0.158 -0.928 -1.006 0.586 -0.127 0.377 -1.094 1.389 0.791 +3 1.510 -0.246 -0.438 -0.052 0.636 0.347 2.648 0.454 -0.465 -0.561 -0.393 -1.502 0.040 0.783 -0.613 -0.807 1.516 -1.426 -2.068 -1.153 -1.540 0.386 -0.361 -0.220 1.153 -0.691 0.775 1.511 +2 0.680 0.541 -0.283 -1.292 0.923 1.943 0.238 1.150 0.583 -0.493 0.730 -0.188 0.484 -0.731 0.073 1.049 1.073 0.954 -2.532 -1.529 0.250 0.223 -0.300 -1.248 -0.596 0.964 -0.408 -1.084 +4 -1.297 -1.185 -0.527 -0.520 0.496 1.261 0.885 -0.710 -0.597 2.480 -1.513 0.914 -0.855 0.782 1.184 0.195 1.076 3.318 -1.350 1.073 1.173 -1.433 0.369 -0.151 0.653 0.238 0.858 -0.869 +2 -2.333 -1.771 -1.126 -0.592 -0.007 -0.518 -0.047 -0.496 -0.005 0.931 -0.015 0.541 0.714 -0.158 -1.791 0.253 -0.674 0.862 1.366 -0.258 0.164 0.668 -0.813 0.009 -1.548 1.044 1.686 0.100 +2 -0.061 -0.565 0.881 -1.895 -1.672 -0.547 -1.274 1.086 -0.443 0.877 1.305 0.061 0.327 1.227 0.999 -2.071 -0.412 0.513 0.216 -0.993 -1.708 0.611 -0.843 -0.134 0.145 -0.163 -0.932 -0.083 +2 0.182 0.657 -0.124 0.085 0.012 -0.415 0.568 0.847 -0.044 -0.492 -1.083 0.355 -1.004 0.159 0.131 1.067 1.387 -3.038 -0.202 -2.101 1.162 -0.023 1.152 0.258 -1.350 -0.968 -0.134 0.570 +1 1.662 -0.870 -1.225 -0.617 1.894 0.161 -0.105 0.341 -0.793 -1.523 -0.136 0.448 0.849 0.587 -1.044 -1.040 -0.376 -0.723 -0.081 -1.817 -0.659 1.084 -0.023 0.124 1.239 -0.077 0.652 0.557 +1 0.630 -1.214 0.565 0.920 1.000 -0.146 0.937 0.491 0.068 0.916 1.744 0.923 -0.074 -0.488 0.527 0.154 1.123 -0.951 1.037 -1.396 -0.175 0.583 -0.112 0.439 1.386 -0.567 -1.404 -1.135 +3 0.450 0.625 -0.018 -1.807 0.974 0.650 -1.372 -0.087 -0.302 1.134 0.652 -1.181 2.047 -0.004 0.734 -0.151 -0.844 -0.501 -1.829 -1.937 -0.262 -0.664 -0.288 -0.919 2.284 0.344 0.050 1.331 +0 -0.030 0.525 -0.336 0.269 -1.511 -0.688 -0.677 -0.374 -0.927 -0.534 -1.514 0.549 -0.083 -1.705 -0.835 0.337 -0.543 -0.697 -0.075 0.344 -1.436 0.550 -0.800 -0.018 -0.035 -0.527 -1.024 -0.286 +0 -0.413 1.117 0.345 -0.207 0.212 -0.018 1.596 0.245 -0.002 -0.238 0.843 0.708 -0.274 -1.156 -0.902 -0.184 1.349 -0.250 -0.594 -1.761 1.514 0.569 0.796 0.962 -0.165 0.085 1.489 0.180 +3 0.640 -1.408 -1.617 0.931 -0.703 2.389 -1.159 1.216 1.480 -0.590 0.607 -0.182 1.005 -0.618 0.423 -0.599 2.152 0.954 -0.149 1.258 -0.530 0.271 0.012 0.485 0.392 -0.917 1.080 -0.771 +4 -1.351 -1.753 -0.436 -1.709 -1.127 0.374 0.268 -1.321 0.150 1.391 0.150 -0.710 -1.551 -0.045 -1.084 -1.042 -2.570 0.390 -1.536 0.775 0.241 -0.345 0.492 1.921 -1.209 -0.434 -2.267 -0.999 +3 -0.752 -0.848 -0.134 -0.665 0.044 1.792 1.231 1.721 -1.796 1.841 -0.399 0.483 2.321 0.146 -1.520 -1.755 -0.001 -1.153 -0.263 0.126 0.460 -0.195 1.441 -0.061 0.110 -0.220 0.197 0.400 +3 -1.590 1.430 -1.385 0.473 -1.319 0.298 0.665 -0.557 -1.462 -0.763 -0.679 -0.702 -3.193 -1.314 -0.746 -0.113 -0.806 0.375 -0.683 0.506 1.153 0.288 0.628 -0.736 0.798 -1.220 -0.184 0.415 +1 -0.501 -0.622 0.417 0.278 0.261 1.170 -2.581 1.678 1.034 -0.303 -0.264 -1.363 -0.264 0.410 0.183 0.821 -1.031 -0.591 0.150 2.183 0.512 -0.349 0.894 0.842 0.804 -0.506 0.531 -0.065 +3 0.109 0.726 0.481 0.224 -0.790 0.471 1.882 1.345 1.593 -0.511 -0.990 -0.126 0.056 1.094 -1.692 1.530 -0.158 -0.427 -1.012 -1.655 0.823 0.073 -1.290 -1.295 -0.336 1.669 -0.260 -1.503 +1 1.527 -0.538 0.223 1.417 0.267 1.351 -0.597 -0.220 1.103 1.192 0.939 0.874 0.211 -1.131 -0.615 -0.898 1.050 -1.014 -2.490 0.233 0.104 0.621 0.932 0.010 -0.391 -0.041 0.294 -0.459 +0 -0.408 -1.592 -1.873 -0.216 -0.557 0.503 0.525 0.198 0.483 0.618 0.415 -0.573 -0.818 -0.259 -0.395 0.180 1.000 -0.330 0.183 1.973 0.329 0.505 1.658 -0.203 0.645 -0.855 -0.360 -0.356 +3 0.504 0.686 -1.023 -1.522 0.503 -1.273 -1.414 -1.539 1.837 -0.732 -0.638 0.757 -0.418 -1.361 2.314 0.919 0.638 -0.744 0.454 -0.906 -0.530 0.255 -0.792 1.313 0.613 0.464 -1.311 0.662 +1 -1.160 -0.457 0.417 0.921 -0.092 -1.979 0.127 0.294 0.037 0.486 -0.057 -0.293 0.624 0.283 0.270 1.031 0.615 1.267 0.138 2.011 0.744 -0.454 -1.719 0.125 1.419 0.401 0.807 -1.676 +2 -1.335 1.483 1.056 0.998 0.792 -0.062 1.630 -0.766 0.721 0.317 -1.152 -0.669 0.243 1.006 1.683 0.181 -1.317 1.410 -0.827 -0.171 0.562 0.485 -0.552 0.196 -0.150 1.869 1.399 -0.965 +3 0.165 0.087 0.917 -0.142 -1.074 1.039 -1.290 -1.236 -1.106 -0.646 -0.086 0.509 0.196 0.770 0.702 1.967 -0.277 0.654 1.979 0.814 -0.595 -0.880 0.996 0.569 -0.190 2.805 -1.011 -1.532 +1 0.005 1.043 1.129 -0.451 -0.695 0.535 0.035 -0.128 -1.446 1.114 -1.401 -0.260 -0.719 -1.292 2.563 -0.357 -0.019 1.684 0.391 -0.125 -0.265 -0.239 1.238 0.787 -0.601 -0.673 1.305 -0.382 +2 0.562 0.529 -1.242 -0.022 0.316 0.306 1.412 0.408 0.069 0.862 0.910 0.064 1.316 0.183 -0.510 0.931 -0.708 1.453 -0.474 -0.993 -0.765 0.779 -0.647 -0.768 -0.362 -2.652 1.255 -1.449 +4 1.870 -0.952 0.084 -0.633 -0.708 0.333 0.946 0.665 -0.810 1.227 0.477 -2.407 0.772 1.160 0.143 0.575 1.895 -2.561 0.591 -0.913 1.146 -0.306 0.385 -1.607 0.808 -1.216 -0.329 1.229 +4 0.292 -2.512 -2.517 1.183 -0.586 -1.419 -2.583 -1.197 1.000 -0.094 -0.594 -1.232 0.235 0.478 -0.941 1.574 -0.889 -0.677 1.592 -0.064 -0.435 0.236 0.080 0.666 -0.993 -0.194 -1.463 0.198 +4 1.980 -0.990 -1.119 0.324 1.987 1.611 0.938 -0.047 0.180 -2.152 2.113 0.783 2.091 0.181 0.566 1.015 -2.011 -0.289 0.359 2.066 1.307 0.865 0.209 -0.128 -1.223 0.877 -0.254 -0.253 +0 1.094 -1.318 0.548 -0.899 0.441 -1.179 0.033 0.127 -0.092 0.663 -0.316 -0.241 -0.084 -0.663 -0.452 0.722 0.769 -0.784 0.676 -1.466 -0.320 -1.135 0.077 0.007 1.401 1.317 -0.446 0.048 +0 0.210 -1.313 0.664 0.457 -0.183 0.965 -1.856 -0.493 0.658 -0.636 -0.954 0.366 -0.932 0.612 0.914 -0.203 -0.176 -0.596 -0.046 -0.405 1.264 1.330 -0.124 0.876 0.478 -0.896 -1.145 -0.402 +4 -0.362 -0.442 -0.028 2.210 -0.647 1.636 -1.067 -0.671 -1.037 -0.796 1.246 -0.017 -1.703 -0.860 -2.341 -0.170 -0.122 -0.349 1.217 -1.007 0.233 -0.814 1.075 -1.640 -0.612 1.957 -0.564 -1.443 +2 0.687 -0.226 2.316 1.149 0.786 -0.041 -1.724 1.829 -0.465 0.279 1.167 0.285 0.909 -1.211 -0.557 0.593 0.042 0.180 -1.151 1.146 0.351 0.619 -0.368 0.946 -1.033 0.378 -0.664 -1.284 +0 0.911 0.049 -0.941 -0.452 0.346 0.888 -1.201 -0.665 0.833 -1.158 -0.102 -0.094 0.877 0.311 -0.095 0.064 0.212 0.148 1.036 0.756 0.080 1.047 2.265 0.900 -0.326 -1.388 -0.596 0.040 +1 -0.756 1.524 0.169 1.630 -0.358 2.062 -0.448 -0.661 0.829 -0.073 0.387 -1.018 -1.001 -0.381 0.358 -0.778 1.069 -0.107 1.707 -1.052 0.342 0.459 0.271 -0.049 0.847 -1.236 0.283 -0.986 +3 0.359 -0.562 0.331 -1.285 2.479 1.350 0.957 0.408 0.409 -1.690 -1.259 -1.134 0.058 -0.282 0.811 -0.966 1.027 -0.820 -0.418 0.895 0.505 -1.251 1.064 0.089 -2.142 -0.843 -0.410 -1.169 +1 0.134 1.139 0.716 -0.938 -0.126 -1.408 1.491 -0.697 -0.761 1.825 -0.596 -0.950 0.659 -0.009 -0.894 0.337 0.627 0.863 0.254 0.887 0.847 -0.463 1.708 0.651 1.395 -0.139 0.847 0.272 +0 -1.047 1.037 -0.268 -0.206 0.401 -1.419 -0.800 -1.241 -0.380 -0.909 1.317 -0.583 -2.378 -0.334 0.807 1.630 -0.229 -0.517 -0.014 0.415 -1.129 -0.244 -0.050 0.046 -0.145 -0.936 0.339 -0.045 +1 -0.275 -0.857 -0.068 0.882 -1.812 -0.583 -1.078 0.023 1.155 0.373 -0.800 1.546 1.591 0.250 -0.539 -0.627 0.038 -0.099 -1.280 -0.184 -0.532 0.622 0.006 0.599 0.284 -1.443 -1.617 -0.214 +3 0.011 1.259 -1.666 0.892 0.426 0.256 -0.243 1.801 -0.003 1.263 1.921 -0.706 -0.141 -1.557 -1.076 0.606 -0.115 -0.566 2.439 -0.263 -1.298 -0.913 0.173 0.339 -0.717 -0.792 -0.722 1.546 +0 0.462 0.117 1.156 -0.750 -1.506 0.169 0.380 0.094 0.021 -1.069 -0.139 -0.348 1.073 0.379 -0.278 1.724 -0.269 0.558 -0.167 1.631 0.087 0.116 -0.140 0.197 1.200 0.846 0.028 1.701 +2 -0.089 -1.084 0.958 -0.255 -0.570 -0.940 -0.201 -0.506 0.174 1.413 0.570 0.077 -0.363 0.733 -1.010 -0.489 -0.159 -0.726 -0.156 -1.718 0.216 3.234 0.353 0.162 -1.153 -0.900 -0.807 -1.070 +4 1.901 -0.910 -2.615 1.521 -0.147 -0.905 0.058 1.945 0.658 0.781 0.162 0.477 1.697 -0.903 0.760 -0.035 -0.063 -1.144 0.421 0.582 -0.187 1.447 0.083 -2.123 0.550 -1.163 1.428 1.876 +4 0.805 -1.501 1.686 0.376 -2.044 1.348 0.989 -0.190 1.396 -0.639 -0.130 2.344 0.521 1.084 0.817 -1.386 0.299 0.207 1.186 1.501 1.290 -0.473 -1.437 0.500 -0.089 -1.052 -0.696 -0.059 +0 -0.912 0.144 1.080 -0.256 -0.734 0.298 0.456 0.877 1.871 -0.457 0.196 -1.269 0.666 0.184 -0.179 -0.618 -0.348 0.236 -0.966 -0.307 0.017 -1.540 -1.051 -0.660 1.568 -0.831 -0.056 -1.041 +4 -0.994 -1.431 1.677 -1.681 1.154 0.955 0.939 0.510 0.281 -0.283 1.049 -0.375 -1.592 -0.509 -0.484 0.650 0.338 -1.779 -2.162 0.379 -1.352 0.459 -0.797 -1.271 2.130 -0.293 0.912 -2.267 +1 -0.165 -0.084 0.694 -0.988 -0.169 0.972 1.439 -0.409 1.791 1.693 -0.597 0.152 0.634 -1.002 -1.131 0.298 0.495 -0.171 -1.006 0.707 -0.185 0.113 -1.078 0.456 0.096 -0.412 -1.772 1.728 +2 -0.378 0.231 -0.666 -0.378 1.440 -0.517 -1.064 0.761 -2.710 1.612 -0.162 0.092 -0.802 0.655 -0.705 0.280 0.869 1.034 0.503 -0.292 2.400 -0.629 0.127 -1.734 -0.335 -0.395 -0.263 -0.548 +0 1.360 -0.030 0.272 1.070 -0.233 -0.949 -0.571 0.176 -0.714 0.473 -1.355 -0.243 0.406 0.133 -0.735 0.735 0.262 -2.089 0.455 -0.422 -0.525 -0.830 -1.003 0.207 0.546 0.153 0.949 0.581 +4 -0.265 -2.004 0.635 -1.239 0.060 0.277 1.361 -1.309 -3.020 0.184 1.801 1.239 0.210 -0.492 0.807 -0.974 0.476 0.505 1.060 2.760 0.392 -0.509 -0.026 -1.769 -0.695 -0.409 -0.524 0.152 +2 -0.937 1.698 0.168 -0.723 1.105 0.495 -1.772 -0.526 0.279 -2.160 0.085 0.158 -1.183 0.271 0.007 -1.191 0.920 0.838 -0.290 -0.199 0.726 -0.201 -1.931 0.487 -0.813 -0.390 1.458 -0.134 +2 -1.432 0.515 1.044 -0.465 0.351 0.360 -1.734 1.381 0.530 0.656 -0.144 -2.452 0.675 0.364 -1.054 0.315 -0.064 -0.985 -0.273 0.635 -0.126 0.225 -0.093 1.353 -1.484 -1.232 1.799 -0.256 +3 0.811 0.430 -0.376 -1.857 1.499 -0.686 0.358 -1.917 -0.011 0.100 0.149 3.014 -0.631 0.821 0.523 -0.679 0.806 -0.164 0.875 1.961 0.151 -0.281 1.052 1.397 -0.883 -0.453 0.675 -0.132 +2 -0.505 -1.825 1.011 0.343 1.738 -0.640 -0.795 -1.165 0.456 -0.350 0.300 -0.424 1.439 1.265 0.086 1.053 -0.132 -0.046 -2.164 0.507 0.023 0.880 0.714 -0.749 -0.192 -0.478 1.045 1.573 +1 -0.672 0.223 -1.742 -0.546 -1.092 0.135 0.994 0.646 -2.372 2.042 0.295 -0.776 0.157 0.176 -0.667 0.198 -0.607 -0.404 -0.686 -1.433 0.146 0.585 0.515 0.820 0.324 -0.533 0.840 -1.358 +3 -0.114 -1.202 -0.502 -0.488 -1.248 -1.492 -0.587 -0.392 2.693 0.813 0.868 -0.384 2.032 0.408 0.490 0.413 0.908 0.270 0.247 0.984 -0.064 -2.177 0.281 -2.063 -0.346 0.354 0.330 -0.605 +2 -1.188 1.483 -0.612 1.674 -0.456 0.509 0.611 0.277 -1.890 -1.552 0.899 0.383 -0.383 1.797 1.182 -0.132 -1.193 -0.088 1.090 -0.486 0.005 -0.462 -1.043 -1.623 0.946 -0.877 -0.162 0.484 +0 0.258 0.655 0.975 -0.455 0.555 1.047 -0.379 0.005 -0.767 -0.493 -0.350 -0.904 -0.063 0.331 0.428 -0.808 -0.606 0.851 -0.609 0.824 -0.732 0.377 0.752 -2.650 0.868 1.304 -0.309 0.346 +2 0.157 -0.179 0.904 -0.147 1.473 1.366 -0.238 0.724 -0.325 -1.225 -1.577 -0.253 -0.252 0.188 0.832 2.269 -2.003 -1.151 -1.381 -0.305 0.799 -0.182 0.696 -0.490 0.080 -1.159 -0.671 0.650 +1 -0.052 -0.477 -0.652 -0.658 1.883 -0.523 1.742 -0.404 0.688 1.165 0.046 -0.423 0.709 -0.410 -0.169 0.862 -0.665 1.382 0.372 -0.011 0.373 -1.167 1.014 0.406 2.220 0.614 -0.395 -1.257 +3 -1.047 -1.943 -0.587 1.184 0.450 0.264 1.040 1.743 -1.240 -2.048 -0.598 2.166 -0.024 0.294 -0.291 0.484 0.158 0.056 -0.043 0.147 -1.034 -0.398 -0.205 -0.353 2.265 0.265 0.223 -0.729 +3 0.323 1.249 0.435 -1.088 1.037 -0.771 0.573 -1.157 -1.662 -0.273 1.665 -0.999 -1.090 -0.712 -1.936 1.325 1.874 -0.173 -0.761 -0.479 -1.014 0.099 0.886 1.708 -0.777 -0.895 0.599 -0.265 +3 1.145 0.254 -0.112 0.909 1.577 0.596 -1.580 -0.768 1.021 -0.475 0.088 -0.513 0.870 0.530 -0.634 0.859 0.915 0.601 0.243 3.705 -1.410 -0.284 0.009 1.273 -0.257 1.150 -0.562 0.474 +3 0.176 -0.147 -0.796 1.451 -0.525 -0.300 0.236 -1.181 0.322 -0.720 0.803 2.842 1.061 -1.542 0.304 -0.600 0.310 0.681 -0.511 0.556 0.915 -0.336 0.693 -1.491 0.852 2.491 0.995 1.167 +3 0.015 -0.378 1.904 -2.580 0.867 -0.144 0.571 0.041 1.281 0.419 -1.421 -1.466 -0.215 0.608 1.307 -0.669 -2.211 0.431 0.042 0.143 -1.101 1.193 -0.816 0.308 0.276 0.550 -0.745 -0.923 +1 0.609 -0.153 0.863 1.088 -1.850 -0.837 -0.125 -1.024 -0.473 -1.279 -0.439 -0.966 0.345 0.902 0.409 2.598 0.939 0.901 -0.082 -0.307 1.273 -0.253 0.035 -0.172 0.540 0.892 1.064 -0.304 +1 0.171 0.134 -1.696 0.359 1.157 1.149 0.668 -1.129 0.313 0.668 -1.794 -1.121 0.888 0.876 -1.549 0.795 0.095 0.781 -0.635 0.645 0.844 -0.351 0.018 -0.649 1.652 -0.120 -0.922 -0.716 +1 0.112 1.343 -0.209 0.684 -0.358 1.509 0.071 -1.061 0.095 -0.215 -0.237 0.394 -1.556 -0.188 1.077 1.305 -0.730 -1.644 1.504 -0.910 -0.424 -0.822 -0.080 -0.594 0.841 0.702 -1.307 -1.530 +1 -0.220 0.658 0.732 -0.530 -0.852 -1.262 0.033 -1.423 0.099 1.217 -0.353 -0.536 1.998 1.510 -0.765 0.139 0.329 0.204 1.815 0.087 0.237 0.689 1.190 0.937 0.538 -1.559 -0.488 0.819 +0 -0.573 -0.401 1.911 0.767 -0.245 0.107 -0.329 0.170 0.177 -0.629 0.367 0.192 -0.549 0.819 -0.630 -0.972 0.807 -0.948 0.542 1.795 1.084 -0.518 -0.647 0.946 0.228 -1.629 0.657 -0.223 +0 1.417 -0.946 -0.144 1.507 -0.278 1.987 -0.234 -0.694 -0.039 0.260 -0.412 0.398 -0.149 1.269 0.508 0.011 -0.317 0.803 0.031 -0.452 -0.723 0.699 -0.472 -0.300 0.553 1.071 0.595 0.853 +4 -0.956 1.417 -1.215 0.139 0.299 -1.222 -0.435 0.389 -1.961 1.884 0.064 -1.362 -1.095 1.005 0.676 0.440 -0.755 0.058 0.032 0.232 1.247 1.297 -0.923 -0.359 0.869 0.351 3.291 0.559 +0 1.383 0.015 0.463 -0.147 -0.543 0.500 0.183 -1.691 1.114 -1.526 -0.147 -0.264 -0.634 -0.817 0.044 0.047 -0.137 -1.350 -0.521 -0.389 -0.635 0.928 -1.429 -0.433 -1.342 -1.042 -0.093 0.830 +0 -0.711 -0.959 0.158 0.050 1.149 0.148 0.448 -1.732 1.265 0.000 -1.714 0.404 -0.714 -0.961 0.306 -1.030 0.542 -0.382 -0.098 0.413 -1.339 0.071 1.433 0.430 0.656 0.508 -1.540 0.408 +1 1.465 1.485 1.097 0.574 -0.493 0.313 -0.427 0.996 -0.504 1.483 0.014 -0.154 -1.619 -1.228 1.462 -0.709 1.219 -0.498 -0.103 -0.594 0.821 0.368 0.337 0.432 -0.398 0.284 -1.869 -0.820 +2 0.619 1.128 -0.136 0.928 -0.405 -0.062 2.086 -0.371 0.540 -0.389 0.280 0.696 -0.901 -0.705 0.568 0.162 1.172 0.270 1.860 0.697 1.879 1.690 -0.642 1.480 0.940 0.870 1.185 -0.309 +4 0.338 -0.034 -0.532 -0.270 1.379 -2.314 -1.239 -0.725 -1.078 1.510 1.258 0.592 -0.638 1.335 -1.588 2.142 -0.685 -0.262 -0.429 -1.073 1.925 0.789 1.215 -1.224 0.181 0.521 0.271 -1.160 +3 -0.072 -1.414 -0.792 -0.547 -0.823 -0.937 0.933 0.832 0.930 -1.188 0.252 0.308 -0.812 -0.423 -0.799 -0.791 -1.882 -1.532 0.082 -0.439 1.737 1.473 1.129 1.946 0.604 -1.184 -0.439 0.728 +1 -1.376 1.241 -0.281 0.647 0.538 0.465 -1.200 -0.587 1.055 0.508 -1.081 0.376 -1.046 -1.265 -0.621 -0.096 -1.202 0.891 -0.709 -0.032 0.448 1.846 0.593 -0.305 -0.987 -1.252 -0.982 -0.149 +4 -0.307 1.518 1.007 -2.237 -0.986 -0.087 1.256 0.244 -2.537 -1.173 0.720 0.204 0.135 1.112 -0.346 0.699 1.642 0.224 -0.894 -0.427 -0.903 -2.241 -0.791 0.636 0.009 1.359 0.144 0.884 +4 -0.635 -0.963 -2.385 -0.181 1.519 0.730 0.228 -0.734 0.997 -0.119 1.575 -0.981 -0.750 -0.847 -1.482 -0.937 0.103 -0.712 -3.856 1.074 -1.684 0.589 0.031 2.117 0.997 -0.505 -0.702 1.503 +1 -0.261 -1.709 -0.438 -0.393 0.113 0.669 -0.549 1.058 0.003 -2.127 -0.045 -0.624 0.133 1.047 -1.172 -0.166 1.638 -0.387 -0.638 1.868 0.501 1.423 -0.680 -0.424 -0.997 0.513 -0.687 -0.895 +4 -1.499 -0.965 -0.493 0.475 -0.768 0.411 -0.589 -0.195 1.081 -1.048 -0.997 1.590 -0.130 -0.686 -0.295 0.283 2.267 -2.126 1.038 -1.407 -1.292 0.389 -0.908 0.611 -1.388 -0.606 -1.876 2.408 +2 1.558 -2.389 -0.713 0.587 0.058 -0.051 -0.356 -0.916 0.709 0.011 0.801 -1.565 0.749 -1.094 -2.298 -1.024 0.788 -0.476 -1.536 0.330 0.180 0.778 -0.142 0.218 -0.450 -0.410 0.843 0.493 +0 -0.357 -0.540 -1.081 -2.298 0.428 -0.037 0.038 -0.176 -0.110 0.028 0.069 1.940 0.180 0.259 -0.880 0.710 -1.202 0.346 1.330 0.876 -0.465 -0.571 1.384 -0.136 0.463 0.690 0.038 -0.412 +3 0.113 0.837 0.482 0.443 -0.814 0.192 1.036 -0.155 1.744 -1.136 0.329 -0.231 -1.583 0.175 -0.125 -1.142 -1.628 0.193 0.011 0.069 1.026 0.196 -0.715 2.568 1.822 -0.202 0.154 -2.182 +4 -0.246 0.087 -1.515 0.318 0.382 -0.504 0.607 0.873 1.400 -1.020 0.669 1.338 -0.351 1.395 -0.726 0.070 -1.727 -1.867 -1.553 1.419 -0.776 -0.773 0.408 -0.744 1.205 1.864 -2.222 0.520 +2 -0.956 0.540 0.498 0.317 0.141 -0.276 -1.660 1.740 0.731 0.463 0.513 1.382 -1.233 -1.304 -1.906 -0.401 0.129 -0.013 -1.642 -1.142 1.813 -0.262 -0.333 0.929 1.118 0.296 0.384 -1.206 +3 -0.288 0.245 -0.270 -1.543 1.594 0.104 1.431 -0.260 -0.880 0.269 -0.216 -1.178 1.470 -0.622 -1.840 1.328 -0.504 -0.608 -1.765 1.741 -1.289 0.058 0.913 0.422 -0.060 -1.702 -0.810 0.188 +3 1.156 0.110 -0.264 1.518 -1.465 0.449 0.652 0.277 -0.546 2.191 0.724 -0.925 -0.635 -1.820 -0.160 2.067 -1.417 0.310 -0.349 0.320 0.371 0.178 -1.430 -0.837 -0.408 0.115 1.355 2.166 +1 0.961 -0.009 1.356 -0.774 -1.401 0.723 1.170 0.105 1.822 0.508 0.398 -0.430 -1.303 -0.267 1.581 0.191 -0.391 -0.905 0.539 -1.125 0.357 -0.461 -0.261 0.642 -0.950 0.007 -1.527 -0.113 +4 0.647 2.138 0.712 -0.788 -1.082 -0.953 2.003 -0.327 -0.663 -0.158 -0.317 1.568 -1.014 1.181 0.122 1.207 1.583 0.085 -0.315 0.066 -0.095 -1.657 1.552 0.246 -1.163 -2.068 -2.328 0.770 +0 0.060 -0.308 -1.041 0.711 0.091 -0.962 -0.519 0.159 0.941 -0.650 1.300 0.418 -0.100 -0.743 0.982 1.013 0.662 0.326 -0.748 -0.475 -0.444 -1.324 0.392 -0.078 0.201 1.301 -1.775 -0.158 +3 0.189 -0.731 1.299 1.024 0.346 -1.395 1.339 -1.897 -1.116 0.811 -1.495 1.378 1.927 0.637 -0.738 -1.298 -1.523 0.301 0.103 0.202 1.400 0.784 0.304 0.096 -1.020 0.497 0.239 1.085 +4 -0.279 -0.479 3.298 -1.068 -1.457 1.725 -0.124 -0.872 0.822 -1.389 1.443 -0.477 -0.745 -1.534 -1.115 0.645 0.207 -0.616 -0.627 -0.011 -1.562 -0.384 -0.432 -1.240 0.586 0.058 -0.860 -1.654 +2 -0.995 -0.000 -0.732 -0.558 -2.152 -1.461 -1.431 -0.378 -0.366 0.351 0.320 0.211 1.322 -1.096 -0.669 1.265 -0.359 -0.028 0.130 -1.473 -0.784 -0.852 1.366 -1.311 -1.061 0.093 -1.172 -0.092 +4 -3.328 -0.055 -0.516 0.031 1.068 -1.072 0.070 0.477 -0.310 -1.309 -1.204 -0.035 -0.620 -1.928 -0.798 -1.301 0.748 -1.484 1.420 2.160 -0.741 -0.614 0.944 1.084 -0.492 -0.440 -0.432 -0.852 +4 -0.066 -2.697 -0.302 -0.033 0.219 -1.999 -0.029 -0.833 0.221 0.506 0.400 1.404 2.191 -1.134 -0.382 0.743 -0.127 -2.723 -0.193 1.234 -1.395 0.221 -1.907 0.870 0.242 -0.568 -0.418 -1.499 +4 -1.153 -1.687 -1.794 -1.355 -0.709 1.953 -0.526 0.178 0.400 0.131 -0.077 -1.195 1.451 1.807 -1.683 -1.024 -0.280 -0.965 0.506 -0.728 2.165 1.191 0.213 1.027 1.106 -0.564 -0.816 0.078 +4 -1.009 2.124 0.286 -0.405 -0.788 1.252 -1.274 -0.877 -0.489 1.179 1.185 -0.255 2.316 0.985 -0.806 -1.223 -1.673 -0.602 -1.251 0.093 -0.808 -2.776 0.742 0.222 -0.690 0.911 0.219 0.257 +3 0.191 -0.115 1.208 2.611 -0.665 -0.097 -0.351 0.733 1.222 1.621 0.767 2.519 -0.575 -0.835 1.469 0.123 -1.376 -0.316 0.256 -0.646 -1.015 -0.208 -1.065 0.635 -1.944 0.090 0.520 -0.091 +3 0.016 -1.455 0.291 2.335 0.956 0.376 -0.236 -0.756 3.399 0.785 0.035 -0.221 -0.878 1.558 0.257 0.589 -1.212 0.536 -0.027 0.378 -0.304 -0.109 0.504 -0.940 -0.987 -1.762 0.137 0.426 +3 0.970 0.750 -0.285 -1.630 0.276 0.550 -1.621 -0.386 0.010 0.163 -0.315 -1.743 -0.959 1.068 -0.994 -0.542 -2.019 0.781 -0.097 -1.032 -0.289 2.085 0.291 1.746 -1.295 -0.143 -1.096 -0.319 +4 0.028 -0.459 2.166 -1.280 -0.205 0.383 -0.973 1.928 1.949 -0.858 -0.592 2.147 1.395 -0.369 0.431 -1.387 2.197 0.073 0.586 -0.935 -1.136 0.451 1.916 -0.540 -0.979 0.305 1.637 -0.639 +1 -0.548 -1.816 -0.577 0.371 -0.988 0.069 -1.047 -0.774 -0.705 0.465 0.858 1.838 0.779 0.364 1.775 0.275 0.614 0.956 0.174 -0.068 -0.167 -0.573 -0.496 1.062 -1.097 0.138 -0.024 -1.425 +3 -0.159 1.013 -0.180 2.344 0.134 1.062 0.805 -0.036 -0.587 0.332 0.510 -0.573 0.166 1.236 2.070 0.854 1.863 -0.629 0.702 1.339 -0.896 -0.486 1.738 -0.975 -1.341 1.528 1.197 0.432 +3 -0.973 -1.352 -1.540 -0.089 -1.279 1.428 -0.479 0.077 0.075 -1.175 0.232 2.045 -0.791 1.247 -1.447 0.918 1.340 -1.270 0.373 -1.584 -0.188 -1.171 0.638 0.291 -0.074 -0.771 -1.049 -1.489 +3 -1.804 0.331 0.365 -0.680 -1.284 1.476 0.491 -0.229 -1.137 -0.822 1.303 0.138 -0.551 0.510 -0.814 1.501 1.353 -0.467 -0.629 -2.618 -1.482 -1.339 0.135 -0.326 -1.345 -0.281 0.505 0.528 +0 -0.755 0.179 0.619 -0.049 0.607 -0.026 0.088 0.890 0.423 -0.385 1.347 -1.041 0.630 0.218 0.424 -0.949 -1.236 0.589 0.127 -0.545 0.039 0.571 -0.456 -1.730 0.353 -1.185 0.295 -0.590 +4 1.055 1.645 -1.192 0.077 1.402 -0.207 0.203 0.217 2.115 0.748 2.071 1.744 -0.227 0.301 2.052 -1.142 0.761 0.771 -0.276 -0.380 -0.243 0.702 1.473 0.739 -1.258 1.506 1.193 -0.246 +1 0.238 0.564 1.127 0.558 0.317 -0.574 -0.660 0.909 1.189 0.783 -0.845 0.374 -1.324 1.330 -1.267 -0.769 1.495 0.545 -0.768 0.059 2.110 -0.179 -0.202 -1.111 -0.125 -0.013 0.947 0.181 +3 -1.140 -1.001 1.445 -0.834 -0.214 -0.182 0.600 -1.601 -1.056 -0.578 1.073 -1.813 0.578 0.108 -0.247 1.628 -0.522 1.421 -0.708 -1.906 0.855 -1.055 -0.100 2.121 1.534 -0.535 -0.600 1.083 +0 0.369 0.284 1.186 -0.637 0.378 0.003 -0.443 0.223 -0.103 -1.834 1.619 -0.407 0.866 -1.734 -0.385 -0.177 0.022 0.619 -0.556 0.323 0.816 0.192 0.312 0.230 0.129 0.033 -2.162 1.127 +1 -1.101 -1.125 -0.385 -0.864 1.199 1.749 -0.401 0.515 0.410 -0.456 0.618 0.830 -1.668 -1.180 -0.292 -0.321 0.078 -0.919 -1.040 -0.361 -1.167 1.196 1.040 0.610 1.020 -0.429 -0.439 0.402 +3 -0.074 -0.094 1.879 -0.926 -2.334 0.317 1.201 0.220 0.502 0.752 0.111 0.556 0.540 -1.959 0.114 -1.573 -0.038 -0.495 1.034 0.244 0.473 -0.191 1.052 -1.597 -0.896 1.286 -0.436 -2.146 +1 -0.159 -0.387 -0.210 0.758 1.632 -0.192 0.121 -0.269 0.087 -0.718 -1.036 0.325 0.223 1.136 0.049 -0.047 0.518 0.985 -0.158 -2.575 -1.050 1.070 1.706 1.333 -0.439 -0.328 0.740 0.600 +0 -1.396 -0.529 0.267 0.070 0.597 1.174 0.474 -0.802 0.923 -0.216 -0.270 -0.836 -0.942 0.426 1.036 -0.254 2.461 0.459 -0.417 -1.467 0.397 0.573 0.799 -0.351 -1.246 -0.750 -0.189 -0.387 +2 -1.017 2.042 -0.275 -0.105 -2.178 -1.347 0.484 -0.465 -1.109 1.297 -0.701 -1.301 0.040 -0.815 -0.816 -0.061 -0.644 -1.667 0.559 0.866 -0.251 0.620 -0.369 -1.910 0.896 -0.099 0.151 -0.106 +2 0.426 -0.518 -0.045 0.498 1.171 0.908 -0.791 0.009 0.491 -1.144 -0.934 -0.089 -0.756 2.427 -0.605 0.929 0.673 0.490 -1.048 0.201 2.823 0.313 -0.085 -0.428 -0.864 0.546 0.828 1.208 +0 -0.714 -1.471 1.325 1.143 -0.973 0.204 -0.195 -1.024 -1.411 0.276 -0.162 -0.453 -0.105 -0.071 0.318 1.219 0.934 0.794 -0.464 -0.702 -0.679 0.884 0.766 -1.709 1.000 0.260 0.021 1.104 +2 -0.339 0.806 0.410 0.838 -1.533 -0.829 -1.018 -0.645 1.484 -0.612 -1.007 -0.547 -1.130 -1.651 0.161 0.588 -0.372 1.000 -1.402 -0.847 -0.496 1.071 -0.170 1.138 -0.855 -2.072 -0.952 0.370 +1 0.377 -1.406 -1.350 0.069 0.869 0.506 -1.069 -0.985 0.392 -0.735 -0.810 -0.180 1.855 -0.444 -0.246 -0.061 1.205 0.315 0.134 0.186 1.801 -0.863 -0.241 0.285 0.634 0.278 -1.937 -0.122 +1 -1.018 -0.027 1.335 1.240 -0.793 -0.276 0.812 0.089 0.353 1.384 0.218 -0.594 1.256 -0.823 -0.023 -0.579 -1.193 -0.102 0.060 -0.251 1.000 -0.760 0.362 -1.212 2.398 -0.656 -1.620 -0.233 +3 2.605 0.604 0.018 -0.535 -0.655 0.051 -0.931 0.680 -0.646 1.005 0.983 -0.055 0.580 0.630 -1.462 1.732 -0.256 0.696 0.724 -2.490 -0.657 0.840 0.062 0.689 0.092 1.229 0.114 1.424 +2 0.087 0.556 -1.324 1.355 -2.221 -1.315 -0.270 -0.593 0.026 -0.685 -0.357 -0.184 -0.223 0.149 0.896 -0.754 1.348 0.049 1.261 -0.343 -0.866 0.080 2.564 -1.365 0.677 0.177 0.404 -1.206 +0 1.479 -1.234 -1.205 -0.943 -0.635 -0.713 1.680 -0.659 -0.122 -1.262 -0.469 0.601 0.680 0.984 -0.998 -0.673 0.818 -0.024 -0.060 0.590 0.997 -0.699 -0.104 -0.120 -0.969 0.300 -0.539 -0.005 +2 0.700 -0.553 0.055 -1.648 -1.765 -0.773 0.009 0.458 0.710 -0.388 -0.186 0.200 0.016 0.046 1.231 1.607 -2.058 0.258 -0.313 -0.483 -0.410 1.531 0.445 1.633 1.007 1.473 0.082 -0.741 +1 -1.008 -0.561 -0.128 -0.019 -0.365 1.561 0.010 0.387 -0.252 0.403 -0.584 0.486 -1.379 -0.291 0.100 0.037 -0.906 -0.156 0.995 -1.812 0.091 -0.693 2.174 0.369 1.121 -0.014 -1.509 1.105 +4 0.384 0.107 -0.611 -2.385 1.223 -0.053 1.388 -0.950 2.429 -0.249 -0.692 -0.215 0.170 -0.070 -0.515 -1.803 -0.804 1.074 -0.880 -1.717 -1.055 -0.089 -0.759 -0.280 -0.798 1.010 0.491 2.218 +3 1.436 -1.183 -1.256 -1.305 -0.899 1.104 0.560 -0.545 -0.011 1.404 1.908 0.259 -2.659 0.297 0.935 0.225 0.727 0.357 -1.235 -0.886 -0.321 1.065 -0.766 0.456 0.304 1.586 -0.042 -1.755 +1 0.026 1.166 0.768 -0.269 0.599 0.235 -2.036 -0.328 0.496 0.877 -0.258 0.288 -1.332 0.781 1.344 0.671 0.591 0.767 1.523 1.645 -0.635 0.507 -0.375 1.128 0.085 0.522 -0.447 0.915 +1 -0.268 0.554 -0.131 -0.076 2.577 0.679 0.666 -0.406 0.197 -0.097 0.486 -0.480 -0.904 2.368 0.293 -0.670 -0.694 -0.616 -0.784 -0.854 -0.020 1.436 -1.896 -0.731 -0.796 -0.341 -0.570 0.135 +0 -0.193 0.685 0.031 0.344 -0.271 -0.193 0.171 1.944 0.842 0.710 0.205 -1.332 1.226 0.907 0.366 -0.044 0.546 -0.231 0.728 -0.838 -1.910 -1.201 0.141 -0.302 -0.148 -1.296 0.302 -1.037 +1 0.431 0.693 -0.240 0.382 -0.364 2.595 -0.440 0.644 -1.124 1.958 -0.023 -0.675 -1.000 0.972 -0.855 -1.810 0.528 -0.105 -0.379 1.019 -0.676 0.736 -0.610 0.026 0.993 0.406 0.519 0.164 +0 -1.416 -0.176 -0.384 -0.782 0.313 0.110 -0.410 0.203 -0.085 -0.932 0.081 0.601 -0.221 2.479 -0.030 0.436 0.208 0.629 0.590 -0.593 0.251 -0.296 -0.569 -0.184 -0.200 1.311 -0.821 0.245 +4 0.989 -2.519 -1.170 -1.060 0.014 -0.884 -0.876 -0.179 -0.128 -1.153 -1.343 -0.851 1.002 2.637 1.460 -0.357 0.416 -0.002 -0.143 0.110 0.277 -1.746 -0.619 -0.043 2.384 0.637 1.102 -0.110 +1 0.288 2.355 0.721 0.205 0.238 0.171 -1.022 -0.126 0.554 1.873 0.498 -0.948 -0.277 1.095 -1.172 -0.321 -0.388 1.192 -0.584 1.110 -0.215 -0.127 0.287 -0.228 0.679 -1.999 -1.306 -0.087 +2 2.085 -0.270 -0.799 -0.299 -0.101 -0.257 -0.633 -0.456 1.650 -0.252 2.146 1.045 -0.063 0.658 -0.530 1.066 1.330 0.597 -0.506 0.595 -0.696 -1.385 0.261 -1.630 -0.415 0.368 1.564 -0.639 +4 -0.153 -0.242 -0.372 1.166 2.376 -0.513 -0.458 -0.090 -1.377 0.528 0.187 0.150 -1.853 -2.262 -0.368 0.662 -1.778 0.895 -0.303 -0.829 0.083 -1.298 -2.538 1.206 -0.049 -0.721 -0.578 -0.775 +3 -0.061 -1.194 -0.611 -0.204 0.321 -0.044 2.141 1.402 -0.209 -0.469 -1.687 1.728 1.213 -0.286 0.471 1.239 1.577 -0.657 -1.140 0.882 0.802 0.146 -0.321 -0.112 1.437 0.775 -1.943 0.296 +1 -0.910 -0.221 -1.076 -0.210 -0.654 0.315 -0.145 0.583 1.343 -1.174 0.145 -1.584 0.054 0.551 -0.328 0.392 0.318 0.583 -1.202 1.086 -0.320 -1.653 -0.918 -1.259 -0.536 -0.860 -1.531 -1.672 +2 -1.117 -1.013 -0.162 -1.801 0.842 1.202 0.480 -0.186 -0.285 0.865 0.072 -0.454 1.313 -1.324 -0.394 0.131 0.289 -1.114 0.797 -2.467 2.405 -0.106 0.459 0.529 0.255 0.997 -0.451 -0.845 +4 -1.285 0.731 2.554 -0.670 0.844 -0.695 -0.494 0.345 -1.254 0.865 1.397 -0.072 -0.021 -2.767 -0.071 -0.327 -1.292 0.969 -1.676 0.801 -0.275 1.773 1.808 0.943 1.535 0.363 1.900 -0.375 +1 -1.581 -1.334 -0.633 0.266 -0.595 -0.585 -0.181 -0.242 1.487 0.686 -0.325 2.097 0.492 -0.969 0.257 0.536 -0.688 -1.053 0.086 -0.360 1.479 0.741 -0.641 0.426 1.267 -1.394 -1.023 -0.011 +0 -1.387 -0.132 -0.763 -0.620 0.442 0.631 -1.134 -0.460 -1.775 -0.551 0.338 -0.518 0.751 0.973 1.443 1.306 0.766 0.271 0.862 -0.512 -1.201 -0.250 0.967 -1.274 0.002 0.317 -0.177 0.885 +2 0.971 -1.404 0.963 -0.354 -1.232 0.579 0.929 -0.438 0.721 -2.285 -1.321 1.292 -0.224 -1.862 -0.699 -0.923 -0.166 -0.371 0.179 -0.069 1.360 0.425 0.458 1.165 -0.389 -1.262 1.207 0.136 +4 -0.579 -0.691 0.154 0.685 0.620 0.175 -2.787 0.492 0.087 0.800 0.806 -2.492 0.176 -2.102 2.532 -1.192 0.879 0.124 0.631 -1.100 0.120 1.194 -0.527 -0.659 -0.009 1.284 0.670 0.847 +3 0.856 -0.014 -0.865 -0.768 -0.518 -1.786 -1.003 -1.482 1.346 0.618 0.786 0.589 0.167 1.461 -0.611 0.107 0.747 -1.321 3.097 0.029 0.142 1.257 0.915 0.038 0.130 -1.504 -0.556 0.517 +1 -1.403 -0.948 -0.295 1.823 -1.557 0.045 -0.811 -1.246 1.644 -0.546 -0.138 0.763 -0.450 -1.553 1.329 -0.752 0.430 -0.427 -0.055 0.379 0.411 0.321 -0.567 -1.190 -1.423 0.106 0.177 -0.318 +3 -0.397 -0.753 -0.063 -0.189 -0.940 -0.813 -0.680 -1.132 0.098 0.203 0.320 -1.310 -1.914 0.304 0.969 -2.279 0.837 -0.041 1.968 0.361 -2.107 1.252 -0.675 0.300 0.083 0.384 1.541 0.080 +2 0.343 0.466 -0.765 0.426 1.192 0.706 0.932 0.172 -0.936 -0.810 0.320 0.943 2.786 0.982 -1.170 -0.767 2.279 -0.214 0.048 -0.960 1.565 0.500 -0.775 -0.994 0.307 0.044 0.832 0.547 +4 0.784 -0.064 -0.196 0.649 1.169 2.807 -0.063 2.122 1.038 -0.167 -0.270 -0.645 -1.293 -1.622 -0.668 -1.879 -1.168 1.875 0.704 -0.166 -0.294 1.029 -0.668 0.830 -2.047 0.178 0.897 0.249 +0 0.369 -0.373 0.353 0.151 0.787 -0.846 0.424 0.081 -0.217 1.935 0.609 -0.561 -0.199 -0.719 0.008 0.423 1.749 0.235 -0.116 -0.051 0.637 -0.646 -0.896 -0.322 0.351 2.259 -1.524 -0.298 +4 0.862 0.139 -1.577 -0.803 -0.074 -0.076 1.973 -1.386 0.506 1.489 2.271 -0.404 0.491 0.570 0.195 -0.099 0.436 -2.532 0.682 0.126 -0.222 2.047 -0.675 -0.403 1.992 -0.832 -0.550 -0.147 +3 0.222 0.395 1.019 1.150 -2.231 0.705 0.487 0.296 0.473 -0.316 -2.397 -0.606 -1.828 -1.652 1.070 -0.545 0.269 0.004 1.339 0.075 -0.598 1.481 -0.499 0.417 0.498 -1.242 0.920 1.354 +0 1.219 1.810 1.191 0.662 -0.448 0.282 -1.346 -0.781 -0.711 0.062 -0.392 0.262 0.878 -0.710 0.149 -0.416 0.190 0.686 0.921 0.670 0.414 -1.420 -1.081 -0.824 0.359 -1.291 0.338 0.264 +0 0.858 1.557 -0.407 0.792 -0.423 -1.353 0.004 -0.244 -0.927 -0.339 -0.227 0.279 1.040 1.041 -0.087 -0.547 1.340 -0.100 -0.597 0.170 -1.826 -0.470 -0.537 -0.790 0.380 1.078 1.693 -0.496 +2 -1.207 1.189 -0.545 0.682 0.213 -0.425 1.141 1.175 0.384 0.755 -0.773 1.613 -0.064 0.147 1.134 -0.328 -1.414 -1.295 -0.734 1.301 1.980 1.039 -0.483 0.125 -0.357 -1.442 -0.685 -0.890 +0 0.956 0.665 0.920 0.213 -0.729 0.801 -0.381 0.694 -1.255 -0.356 -0.691 0.392 -0.577 -0.015 -0.145 1.027 0.471 0.290 0.020 -0.767 -0.877 0.277 -1.414 -0.038 0.107 -1.411 -0.056 0.006 +0 0.903 -0.487 1.506 0.055 -0.042 0.552 -1.634 -0.228 0.364 -0.583 -0.038 1.673 -0.800 0.802 -0.625 0.081 0.535 0.825 0.374 0.745 -0.177 -0.431 1.034 -1.840 -0.403 -0.248 0.179 0.902 +4 -0.377 2.924 -0.463 0.851 -0.235 0.937 -0.544 0.256 0.629 -0.722 2.758 0.681 -0.226 -0.697 0.092 -1.216 1.782 0.431 -0.088 1.046 -0.722 -1.156 -0.130 0.128 0.531 -1.476 -0.456 -3.083 +2 0.269 0.789 -0.038 0.333 0.169 -0.726 -0.297 0.598 0.023 -0.517 1.981 0.679 -1.208 -0.507 -0.052 -0.412 1.879 1.886 -0.538 0.458 -0.752 0.937 -0.058 0.035 1.439 -1.687 1.763 0.915 +3 0.019 0.002 0.675 -0.778 0.244 0.480 0.855 1.966 -0.961 -0.373 -1.980 -1.911 0.462 0.344 1.455 1.580 -1.471 -0.673 0.515 0.859 -1.036 -1.346 -0.054 -1.303 0.285 0.240 -0.479 -1.555 +4 0.667 -0.636 1.217 -0.537 -1.843 -0.463 1.341 -1.200 0.699 0.226 1.294 0.623 1.111 1.603 -0.578 0.915 -0.019 -0.200 1.899 2.329 0.040 -0.255 2.273 -0.076 -1.003 0.855 -0.870 -1.660 +1 2.426 -0.528 0.623 1.939 0.790 -0.300 -0.067 -0.833 0.882 0.566 -0.514 -1.042 -1.321 0.987 0.120 0.215 -0.823 -1.464 0.963 0.042 0.907 -0.386 -0.237 0.713 0.732 0.798 -0.295 0.615 +1 1.554 0.293 -0.209 -0.782 0.556 0.522 0.682 0.806 1.451 -0.828 2.237 1.800 1.717 -0.206 -0.714 0.105 0.283 0.857 -0.164 0.921 -0.347 0.048 0.723 0.122 -0.676 -0.902 1.065 0.496 +2 0.848 0.176 0.035 0.051 0.680 -0.857 0.426 -0.921 1.337 0.532 -0.688 0.081 -0.854 1.504 1.623 0.696 1.557 0.152 0.764 1.225 -0.006 0.071 0.934 0.646 -0.755 -2.187 -0.176 -2.097 +1 2.219 -0.144 1.857 -0.253 -0.224 -0.247 -0.207 -1.074 1.311 0.544 -0.064 0.199 -0.864 -1.341 -0.853 -2.299 -0.423 -0.915 0.152 -0.046 -0.194 1.401 0.024 0.402 -0.484 -0.149 0.636 0.449 +0 -0.162 1.829 0.146 0.806 0.143 -1.098 0.550 -0.656 -0.091 0.305 -1.941 -1.610 0.909 -0.664 -0.616 0.166 -0.386 0.418 -0.051 0.199 -0.702 0.194 1.105 -0.568 0.915 -0.052 0.734 0.129 +1 0.482 0.963 0.198 -0.478 0.631 -0.182 0.621 -0.256 0.065 0.618 -1.929 -1.522 -0.843 -0.709 0.253 -1.089 1.371 -0.668 0.391 -0.793 0.559 -2.260 0.555 -0.345 0.796 -0.159 0.749 -1.102 +2 0.863 -0.828 1.533 -0.196 1.270 -0.099 1.038 1.507 0.378 -0.480 -0.717 0.457 0.093 -0.745 1.123 -0.167 -1.518 -0.095 -0.244 0.535 -2.784 -1.689 1.523 -0.035 -0.196 0.513 -1.040 -0.284 +1 -0.032 -0.681 -0.587 0.518 -0.650 0.560 0.593 1.534 -0.481 -0.325 -2.313 -0.943 -0.956 0.506 1.807 0.581 0.991 -0.711 -1.198 -0.242 -0.624 -0.861 0.218 -1.186 0.826 -0.399 -0.045 0.100 +3 1.942 0.979 0.673 0.457 -1.215 -0.324 -0.598 -0.486 1.593 -0.766 1.488 0.518 -2.236 -0.674 0.267 -0.475 -0.831 -0.166 -0.092 -0.359 -1.838 -1.577 -0.694 0.559 0.829 1.200 1.101 0.944 +3 1.925 1.710 -0.493 -0.307 -0.220 -0.796 -0.434 0.097 1.314 2.535 -0.694 -2.828 -0.215 0.173 -0.601 0.322 -0.800 -0.249 0.322 0.126 -1.190 -0.193 -0.661 0.989 0.959 1.691 -0.301 0.211 +4 -0.677 -0.181 -0.461 -1.232 1.363 2.037 -2.044 1.801 -0.037 0.580 -1.055 -0.077 1.415 -1.289 0.651 1.045 -0.487 -1.104 -0.187 1.766 0.731 1.184 -2.044 -0.417 -0.209 -1.201 -0.524 0.213 +1 -1.425 -0.212 -0.302 1.587 0.400 0.166 0.837 0.368 -0.458 1.071 -1.110 0.323 -1.548 0.204 0.193 1.676 -1.565 -0.601 0.823 0.335 0.180 -0.956 -0.591 1.245 -0.052 0.375 -0.115 -1.183 +3 0.946 -0.448 0.664 0.280 -0.743 0.763 -1.078 1.544 0.048 2.189 0.941 -0.510 1.514 0.297 0.358 -0.033 1.678 0.315 1.419 -0.231 0.586 1.517 1.874 0.283 1.272 0.110 1.324 -0.436 +0 -0.673 0.345 -0.075 -1.138 -0.773 0.209 0.633 -0.816 -1.152 0.366 -0.719 -1.060 -0.697 1.311 1.165 0.970 1.576 0.298 0.168 -1.103 -0.227 0.553 -0.339 -0.714 0.644 -0.275 0.149 0.477 +0 -0.131 0.077 -0.225 -0.650 0.169 0.442 -1.090 1.411 -0.099 0.019 0.708 0.233 0.953 0.287 -0.612 0.362 -1.144 0.109 -0.033 -0.208 -0.129 -1.882 -0.549 0.093 0.160 -1.028 1.266 -0.866 +0 0.118 -0.803 -0.038 -0.211 -0.159 -0.716 -0.639 -1.080 0.380 0.571 -0.771 1.521 -0.315 1.530 -0.642 -2.027 0.402 0.249 0.034 -0.642 -0.035 -0.511 -0.302 0.842 0.184 0.610 -0.232 -0.342 +3 -0.933 0.591 -0.694 -0.394 0.022 1.641 0.657 -0.773 0.152 1.651 0.615 -1.598 -0.915 -0.992 1.441 1.256 -0.625 -0.647 0.045 0.075 -0.962 -0.249 -1.454 1.272 -1.760 -1.771 0.435 -1.237 +2 -0.496 -0.363 -0.288 -0.657 1.399 0.899 0.110 1.025 -1.627 -1.228 0.475 0.736 0.279 -1.883 -1.062 0.417 2.426 1.218 1.577 0.340 0.332 -1.033 0.952 -0.073 0.693 0.031 0.279 -0.088 +3 -1.008 -0.200 -0.247 0.452 0.632 -0.551 -1.493 1.032 -1.120 0.271 -0.132 0.162 1.651 3.038 -1.633 0.984 -0.156 1.048 0.957 1.410 0.285 -0.890 0.928 0.118 -0.234 -0.476 1.006 1.603 +0 -0.652 -0.370 0.391 0.340 -1.363 0.691 -0.004 0.939 0.316 0.386 -0.395 0.754 0.700 0.258 0.410 0.425 0.056 -0.883 -1.061 -0.526 1.535 1.169 0.906 -1.749 0.594 0.884 0.343 0.482 +1 -0.768 1.446 -0.324 0.502 2.099 -0.706 0.069 1.054 0.951 -0.328 -0.517 -0.233 1.448 -0.279 0.078 -0.427 0.706 0.156 -1.003 -0.969 -1.999 -0.831 0.794 1.324 0.686 0.555 -0.127 0.775 +2 0.548 -0.165 -0.476 1.358 0.743 -0.059 1.745 0.980 -0.805 -2.260 0.107 1.383 0.611 1.574 -0.598 -0.117 -0.093 0.241 0.767 1.632 1.843 1.667 0.669 -0.342 0.226 0.655 0.135 -0.914 +2 -0.715 -0.580 -0.459 -1.075 1.340 1.163 -0.401 -0.315 -1.123 -0.161 0.489 1.181 -0.896 -1.685 -1.007 0.556 -0.044 0.992 0.042 1.616 -1.586 -2.490 1.353 -0.796 0.138 0.134 0.327 -0.474 +0 -0.125 -1.461 -0.842 -0.243 0.879 0.425 -0.270 -0.759 0.764 -0.578 0.680 0.518 1.067 -0.856 -0.444 -0.002 -0.308 -0.674 -0.682 -1.280 -0.772 0.699 0.891 0.294 -0.246 -0.107 1.410 0.855 +3 -0.638 1.295 0.013 -1.269 -0.384 0.128 0.898 -0.605 -0.538 0.023 0.873 -1.369 0.505 -0.464 -0.324 2.002 -0.196 0.755 0.719 -1.180 -1.845 0.821 -1.084 -1.664 1.026 -1.019 -0.773 1.974 +1 0.085 1.021 -1.641 -0.444 -0.822 -0.779 -0.823 0.068 0.523 0.348 0.186 0.622 2.380 0.136 1.021 0.964 1.778 0.561 -0.844 0.305 0.251 0.815 -0.873 1.266 0.005 0.839 0.310 0.123 +0 0.744 -0.735 0.948 1.907 0.001 1.312 0.751 -1.433 0.111 0.501 0.598 0.178 0.165 0.005 0.133 -1.224 -0.236 -1.453 -0.498 -0.591 -0.655 -1.052 0.269 -0.318 -0.420 -1.408 -0.298 1.146 +0 -0.213 0.656 -0.281 0.521 -0.135 -0.720 -0.397 -0.168 0.611 -0.172 -1.072 1.665 0.311 1.361 -1.093 -0.254 0.191 0.658 -1.887 -1.858 -0.661 0.547 -0.673 -0.127 0.052 -0.828 1.464 -0.795 +0 -0.802 -1.046 -0.213 -1.311 0.787 1.219 0.584 1.398 0.690 -0.416 0.112 0.130 1.189 0.087 -1.090 -1.330 -0.184 0.058 -1.331 1.120 -0.197 0.895 -0.848 0.927 -1.016 -0.552 -0.417 -0.275 +3 -0.160 -1.212 1.402 0.924 1.077 -1.504 0.129 1.078 -0.947 -0.072 -1.099 0.834 -0.215 -0.092 -1.129 1.548 0.051 0.792 0.384 -0.909 -1.641 -0.191 1.106 -1.970 1.948 0.834 0.191 -0.116 +2 -1.434 0.138 0.999 -0.285 1.723 -0.434 -0.901 2.332 0.084 -0.275 0.094 0.104 -0.550 0.057 -0.543 -1.287 -0.636 -0.963 -0.136 -0.662 1.771 0.225 -2.110 -1.377 0.031 0.578 0.337 0.481 +3 0.688 0.150 -0.313 -0.194 2.818 0.980 0.460 -0.065 1.509 -2.070 0.790 0.156 0.252 1.271 1.596 0.009 -0.029 -0.930 -0.105 1.544 -0.320 -1.879 -1.018 -0.446 -0.744 0.631 0.458 1.040 +1 -0.645 -0.804 -0.420 -0.248 -1.660 -1.236 -1.270 -1.312 0.637 -0.334 -0.193 0.383 0.823 0.456 1.638 0.040 0.197 -0.605 -1.894 0.079 0.864 -0.742 -0.511 0.960 -0.049 -0.923 -0.958 0.656 +2 -0.532 -0.287 0.670 -0.031 1.909 0.694 0.629 -0.050 0.875 -0.323 0.282 -0.916 1.039 0.291 1.983 2.392 0.763 0.068 -0.946 0.905 0.210 0.538 1.730 -0.824 -0.924 -0.236 0.821 0.555 +2 1.024 0.155 -1.632 1.356 -0.631 1.269 -0.334 -0.201 0.349 0.905 2.179 -0.401 -0.167 -0.895 -0.834 -0.200 1.001 -0.567 0.987 -0.380 1.109 -2.467 -0.634 1.017 0.460 -0.126 0.311 0.457 +2 -0.168 0.004 -1.049 1.751 -0.746 -0.655 -0.738 0.950 1.696 -0.966 0.840 0.266 -0.762 0.263 -1.319 -0.250 -1.001 -0.336 0.069 1.495 0.053 -0.996 0.016 -2.074 0.855 -0.811 1.900 0.914 +1 -1.796 0.403 -0.384 -0.193 -2.341 1.701 0.105 1.335 -1.393 0.099 0.408 0.134 0.233 -1.390 0.317 -0.843 -0.618 0.017 -0.352 -0.294 1.124 -0.156 -0.397 -0.123 -1.111 0.163 0.722 0.314 +4 1.299 -0.527 -0.463 -1.881 -1.667 -1.910 1.244 0.553 1.595 -1.348 -0.239 -0.346 -0.088 -1.320 0.898 -1.117 -0.472 1.370 0.211 0.953 -0.097 1.053 0.465 -2.077 -1.555 -0.985 -0.069 -0.972 +4 -1.469 1.465 0.507 -1.066 -0.529 -1.597 -0.418 -1.096 -1.728 0.563 -1.731 -0.922 0.980 -1.529 -1.720 0.937 1.754 -0.317 -0.120 0.881 0.553 0.649 1.831 -0.038 0.411 -1.657 -1.722 -0.151 +1 -0.336 -0.878 0.838 1.174 -0.786 -0.301 0.134 -1.807 1.725 0.166 1.423 0.430 -1.912 -0.619 -0.448 -0.604 0.030 -0.123 -0.132 -1.039 0.144 1.100 -1.152 0.412 -0.608 -0.066 0.874 0.019 +3 -0.692 0.250 -0.748 -0.340 1.038 -1.155 -0.562 0.418 -0.319 0.189 0.002 -1.507 1.399 -0.099 1.595 0.338 1.317 1.022 1.229 -0.223 1.787 -0.472 0.363 0.863 3.172 1.094 -0.526 0.269 +2 0.662 1.280 1.558 0.940 -0.623 -0.499 1.637 -0.676 0.294 0.852 0.167 1.575 -0.943 0.749 -0.122 1.052 0.572 -1.215 1.448 0.419 -1.748 -0.146 1.026 -0.731 -1.693 -0.866 0.579 -1.088 +4 0.965 0.721 -2.018 1.323 0.093 0.392 -0.865 1.275 -1.186 0.832 0.821 -0.974 1.983 0.232 0.654 -1.527 2.118 -1.225 1.025 -0.995 -0.619 0.789 1.028 -1.271 1.090 1.045 -0.938 1.599 +0 -0.113 -1.565 0.099 0.827 0.663 0.222 0.003 -0.563 -0.271 1.153 0.610 -0.014 0.990 1.523 0.667 0.636 -0.714 0.753 0.011 -0.096 0.950 -0.221 -0.929 1.144 0.225 -0.700 0.043 0.376 +1 -0.297 -0.997 -0.065 0.205 -1.202 -1.337 0.447 -0.406 -0.166 -0.360 1.521 1.262 -1.237 -0.533 0.850 0.602 -0.041 -1.138 -0.683 -0.517 0.277 1.704 -0.687 -1.026 -0.109 1.032 -1.447 1.454 +4 0.761 0.080 1.643 1.642 0.725 -0.234 -0.480 -0.523 1.976 -0.112 1.544 0.717 -0.941 0.975 0.044 -0.190 -0.193 -0.139 -0.799 -0.795 0.360 0.409 -0.096 -3.463 -0.498 0.349 1.409 1.707 +4 0.984 -0.364 -0.983 -2.818 -1.226 -0.670 0.815 -1.026 -1.649 -0.577 -0.529 0.008 2.552 0.323 0.171 0.394 2.191 -1.755 1.238 0.852 -1.483 -0.698 -0.922 -1.437 -0.529 -1.334 0.543 1.443 +0 -1.687 -0.194 0.593 -0.721 -1.855 -0.026 1.199 -0.722 -1.368 -1.602 -0.713 0.026 -0.471 0.253 0.108 0.999 -0.137 0.619 0.041 0.181 0.171 -1.025 -0.085 -0.632 -0.834 0.395 0.492 -0.630 +0 0.733 0.773 0.944 0.105 0.718 -1.601 -0.300 1.101 0.201 0.330 -0.887 -0.889 -0.499 0.261 0.112 -0.564 -0.486 0.031 -0.362 0.731 2.337 0.124 -0.334 -0.358 -0.409 -1.158 1.808 0.123 +2 -0.750 -1.364 0.517 0.576 1.756 -0.591 0.366 -0.254 -0.146 -0.631 0.048 1.384 -0.431 0.405 -0.596 -1.463 -0.156 1.914 -0.816 -0.966 -0.876 1.807 1.105 0.634 1.354 0.907 -0.108 -0.450 +2 0.341 0.738 0.987 1.811 -0.465 1.460 0.117 -1.095 -0.650 0.466 0.724 -1.016 -0.764 -0.533 -0.863 -1.880 -2.219 -0.382 0.252 0.656 -1.362 -1.345 -0.528 0.003 1.009 0.718 -0.368 0.360 +2 -0.429 0.211 -0.968 -0.742 0.936 1.623 -0.427 1.513 -0.429 -0.441 -1.418 0.497 -0.195 -0.139 -0.380 -0.992 0.626 -0.061 0.808 -0.944 0.239 2.137 -0.711 -0.485 -0.496 -1.043 1.658 -2.304 +4 -0.068 0.660 1.500 1.932 -0.498 -1.090 -0.926 -1.176 3.183 2.244 0.217 -0.827 -0.062 0.274 -1.616 0.166 0.577 -0.706 -0.143 0.840 0.186 -0.641 0.158 1.581 1.602 -0.972 1.502 -0.147 +2 -1.449 2.083 -0.391 -0.713 -1.215 0.152 0.036 -0.120 1.498 0.659 -1.018 2.074 -1.515 -0.116 -0.730 0.370 0.636 -0.413 1.050 0.199 1.461 0.929 -0.471 1.055 -0.645 0.627 0.526 -0.517 +4 0.996 0.100 0.586 -0.360 1.316 -1.836 0.814 -0.508 -0.682 -1.751 -1.060 1.464 -0.763 1.321 0.312 -2.047 -0.389 0.006 0.263 0.801 0.784 -2.185 1.306 1.441 0.617 0.914 1.326 -0.391 +4 1.662 -0.149 -0.488 -0.833 1.340 -0.176 -2.095 1.263 -1.910 1.245 0.013 -1.761 0.423 -0.204 -1.787 -0.844 -1.754 -1.803 -1.366 1.352 1.329 -2.959 -1.536 -0.268 -0.071 -0.469 -0.612 1.131 +2 -0.962 -0.845 0.791 -0.057 -1.602 1.255 -0.181 0.480 0.602 -0.834 -1.230 -0.967 -0.195 0.438 1.863 0.898 -1.965 -0.153 -2.275 0.960 -0.199 0.035 -0.236 0.076 -0.686 0.098 -0.950 0.291 +1 1.394 0.625 0.292 0.779 1.206 0.323 1.106 0.965 -1.499 -1.197 -1.298 1.587 0.012 0.076 -0.567 -1.573 -0.259 1.120 0.629 -0.843 0.677 -0.652 -0.225 -0.596 -1.581 0.374 0.682 0.383 +1 -0.252 0.289 -1.411 0.240 -1.594 -1.306 -0.505 1.623 -0.634 1.454 -1.447 0.017 -0.337 0.321 0.412 1.011 -0.285 0.284 0.442 0.072 0.828 -0.329 -0.268 -0.522 0.987 1.528 -1.818 1.463 +1 0.493 0.185 -0.858 0.700 -0.576 0.122 2.560 -0.096 1.149 -0.703 -0.035 1.771 -0.627 1.812 0.708 -0.562 0.632 0.973 0.622 -1.570 -0.727 -0.248 -0.074 0.621 0.178 -1.335 0.380 0.611 +4 -0.275 1.188 -0.540 1.677 -0.308 -1.070 -1.036 -0.622 0.371 -0.610 -0.820 0.890 -3.211 -0.430 -1.334 -0.869 0.244 -0.205 -0.296 1.190 -0.014 1.806 0.712 -0.967 1.934 0.278 1.872 -0.266 +4 1.021 2.088 -1.586 -1.880 1.871 0.390 -0.868 0.535 -2.636 0.003 0.328 0.924 -1.014 0.086 -0.925 0.255 -0.895 -0.408 -0.996 0.651 0.858 -0.235 0.038 -1.449 -0.300 -0.050 2.621 -1.112 +0 -1.467 -1.898 -0.165 0.484 0.018 0.235 -0.434 0.984 0.728 0.126 0.205 -0.466 1.945 0.808 -0.103 -0.173 -0.390 -0.299 0.246 -1.707 -0.249 -0.131 -0.008 0.175 0.032 0.405 -0.135 0.988 +1 0.929 0.604 -0.073 0.317 -0.085 -2.126 0.460 -0.616 -1.377 0.387 0.321 0.981 -0.261 0.859 0.021 0.345 0.428 -1.331 0.377 0.104 -0.271 0.114 2.550 -1.919 -0.132 0.812 -1.246 0.371 +0 -0.734 -0.251 -0.050 -1.210 0.620 -0.473 0.992 -0.460 0.795 1.190 -0.419 -1.022 -1.463 1.269 0.208 1.448 -0.334 -2.070 -0.121 -0.295 -0.171 -0.556 0.686 -0.994 0.013 0.366 0.470 1.136 +3 -0.578 -0.426 0.399 -1.966 0.975 0.733 -1.476 0.272 0.370 -1.016 -0.521 -0.483 -1.998 -0.686 0.928 -0.973 0.068 0.415 -0.931 1.048 -0.388 -0.393 0.097 3.511 0.898 0.049 -0.593 1.174 +1 0.927 0.927 0.084 1.009 -1.265 -0.413 -0.168 0.038 1.764 -0.846 -0.617 -0.462 0.906 0.397 1.819 -0.165 0.492 0.684 -1.110 -0.078 -0.228 0.677 2.153 -0.582 0.568 -0.254 -0.867 -0.481 +4 -0.338 -1.281 0.195 0.437 -0.929 0.399 2.083 -0.038 -1.041 0.196 0.298 0.496 -0.284 -0.123 -1.048 0.421 -1.097 1.389 -0.020 1.311 0.430 2.434 -2.523 -2.190 1.252 -0.968 0.546 0.326 +2 0.661 -1.601 0.300 0.844 0.550 -0.391 -1.249 0.587 1.201 -0.573 0.507 1.582 0.339 -0.397 1.041 -0.738 -1.298 0.812 -0.406 -0.317 -0.627 -0.473 1.403 1.662 1.795 1.090 0.466 -1.432 +3 0.352 0.334 1.343 0.946 0.234 -0.900 0.341 -0.266 2.620 2.811 1.067 0.981 -0.270 1.002 0.672 -0.594 0.595 0.091 -0.032 0.669 1.081 1.485 0.328 -1.673 -1.046 -0.149 -0.530 0.022 +4 1.918 0.496 0.066 -0.614 0.821 0.298 0.526 1.109 0.379 1.311 -1.631 1.089 -1.111 0.026 -2.254 -0.005 0.853 -0.781 1.530 -1.636 -0.694 -1.777 -0.199 -1.503 -0.994 1.737 -2.292 -1.182 +4 -0.969 -1.585 1.731 -1.143 0.804 0.680 0.027 -0.234 -0.982 1.190 0.736 2.700 -0.106 -0.053 -1.143 0.663 -2.189 -1.763 0.218 0.497 -0.619 -1.141 0.035 -0.713 -0.738 0.499 2.928 -0.623 +3 -0.101 -0.197 0.386 -0.270 0.811 1.979 2.191 0.097 0.337 1.579 -0.150 0.679 -1.135 -0.311 1.156 -0.718 -0.128 -1.574 2.006 1.263 0.188 0.508 0.396 1.384 -0.519 0.732 1.077 1.215 +1 1.492 -2.261 -1.118 0.025 -0.701 -0.159 -0.891 -0.400 -0.432 0.164 -0.856 -0.682 -0.040 0.233 0.468 0.031 -0.164 0.106 -0.988 0.464 -0.546 0.851 0.101 -1.357 -1.813 -0.157 -0.514 1.622 +1 1.273 -0.263 -0.476 -0.105 -1.157 -1.668 -0.433 -0.960 1.771 -0.456 0.456 -0.628 1.782 -0.194 -1.244 0.670 0.259 1.203 -0.154 0.108 -1.507 0.835 0.063 -0.124 -0.229 -0.282 0.849 -0.926 +0 0.141 0.137 -1.097 -1.790 -0.419 1.462 -0.278 -0.006 -0.062 0.991 -1.186 0.814 -0.185 -0.022 0.434 -0.066 0.289 -0.621 1.184 0.112 0.999 0.543 0.919 0.922 0.259 0.925 0.429 2.017 +0 1.158 0.916 -0.665 -0.803 -0.600 -0.445 0.071 -0.251 -0.459 -0.636 0.140 -0.917 0.730 0.133 -1.674 0.431 0.670 1.222 -0.797 -0.983 0.601 -0.370 0.385 0.792 -0.073 0.144 -1.018 0.373 +2 1.617 -1.956 -0.385 1.105 0.214 -0.236 1.726 -0.649 0.703 0.556 -1.210 -0.253 0.091 1.232 1.791 -0.285 0.553 0.768 0.460 -0.746 0.054 0.798 -0.637 -1.125 -1.397 0.230 -1.787 -0.796 +0 -0.788 0.064 -0.631 -0.878 0.141 0.864 0.262 -0.225 1.009 -0.459 -0.080 1.170 -0.431 -0.541 0.744 -0.998 1.201 -1.938 0.075 -0.651 0.340 1.464 -1.157 -1.405 1.150 -0.230 -0.083 -0.266 +4 -0.620 0.968 1.465 -0.571 -0.550 0.899 0.310 -1.463 -0.828 0.153 1.956 0.969 2.529 0.027 -2.371 2.492 -0.946 -1.418 0.881 1.418 1.254 0.441 -1.871 0.352 1.381 0.466 -0.018 1.037 +3 1.234 -0.147 -0.400 -1.952 -0.640 -0.772 -0.795 0.333 0.122 -1.361 -1.298 -1.300 1.811 1.493 -0.852 -1.977 0.128 0.047 0.317 0.741 0.452 -1.116 0.294 0.854 -0.724 0.300 -0.553 1.996 +4 1.116 -0.885 -0.449 2.042 1.024 0.422 1.344 1.961 1.518 1.050 1.146 -0.329 0.196 0.681 0.318 0.503 -0.141 -2.731 0.677 0.108 0.127 2.201 0.357 -0.805 0.688 0.086 1.116 -0.597 +4 -0.196 -1.287 0.465 -0.401 1.030 0.157 -0.220 -0.450 0.091 1.291 -0.654 -0.735 0.423 2.051 1.039 -1.367 -0.801 -0.907 3.564 0.378 1.238 -1.022 -0.447 -0.250 0.386 0.889 1.510 -0.793 +2 -1.289 0.292 1.588 -0.413 -0.839 -0.645 -0.771 0.183 0.820 1.690 -0.994 -0.675 1.357 0.785 0.235 0.458 -0.345 0.716 -0.081 -0.756 0.207 1.313 0.500 2.378 -0.052 -0.159 -1.403 1.220 +4 0.701 -0.315 0.352 -1.365 0.562 -1.902 -2.723 1.563 -2.121 -1.738 -1.072 -0.266 -2.354 -1.579 -1.359 -0.470 1.742 0.534 -0.025 -0.901 -0.313 -0.300 0.327 0.182 -0.271 0.509 0.055 -1.706 +1 1.018 1.314 -0.188 -0.434 0.804 0.128 0.124 -2.544 0.757 0.624 -0.005 0.816 0.602 -0.697 -0.220 -1.008 1.462 0.977 -0.814 0.619 0.279 -0.686 0.124 0.858 0.940 2.332 0.094 -0.238 +4 -2.477 0.501 1.519 0.117 -1.031 -0.513 1.770 0.910 0.640 0.356 -0.661 -0.201 -0.078 0.550 1.509 1.118 -1.419 0.224 0.737 0.619 -0.629 2.914 -1.277 -1.083 0.234 2.117 -0.836 -0.266 +2 0.439 2.236 0.096 1.530 0.026 0.452 -0.553 -1.885 -1.827 -0.084 -0.237 -1.180 -0.277 0.450 1.291 -0.692 0.393 1.739 0.750 -0.769 0.101 0.185 0.637 0.039 0.484 1.020 -1.052 -1.321 +4 -0.848 -2.188 -0.754 0.939 -0.536 0.565 -0.716 2.306 0.458 2.654 0.160 0.365 0.395 -1.415 1.099 0.997 1.691 -0.142 0.211 0.095 1.073 -1.240 -1.056 1.299 0.049 -1.519 -0.289 0.054 +3 0.027 -2.003 -0.634 -0.393 -0.485 0.932 1.256 -2.238 -0.119 0.114 -1.482 1.253 0.371 0.851 -0.446 -0.779 -0.569 -0.110 -0.426 0.906 2.666 0.583 -1.947 0.155 -1.188 -0.408 -0.036 -0.259 +4 -0.349 -1.257 -1.692 0.498 0.225 -1.464 -1.108 -3.323 0.249 -0.919 0.998 1.245 -0.168 0.572 -1.489 -0.506 0.747 -0.183 0.750 0.879 0.560 -1.668 0.066 -1.733 -1.759 -2.321 0.713 1.577 +0 1.069 -0.004 0.185 -0.248 0.570 -0.676 0.994 -0.253 -0.420 0.022 -0.591 0.329 -0.537 0.469 1.216 -0.719 -0.131 0.461 -0.436 0.464 0.555 1.083 -0.606 -0.943 -0.308 -0.968 0.564 -1.162 +3 -1.554 0.902 -1.505 0.023 1.259 -0.316 1.020 0.648 -0.144 0.304 -0.273 0.375 -2.331 0.046 1.556 0.405 1.183 -0.404 1.042 -1.774 0.984 -1.010 -2.000 -0.120 -0.470 0.490 -1.110 -0.106 +4 -1.912 0.689 0.315 0.594 -0.110 -0.313 2.074 -0.618 0.022 2.054 0.959 -1.665 -1.149 0.999 0.085 2.566 0.200 0.769 0.400 1.293 2.169 0.325 1.584 0.768 0.307 0.077 -1.235 1.815 +4 0.250 -3.213 0.795 1.118 -0.040 0.084 0.074 -0.784 0.432 -0.231 0.930 -0.114 -0.861 -1.536 -1.864 1.313 -0.458 0.339 1.783 -0.563 -0.627 -0.132 0.029 1.888 1.566 0.621 1.449 -0.178 +0 -0.035 -0.408 1.097 1.802 -1.705 -0.934 1.246 0.600 0.437 0.697 0.096 0.481 -0.325 -0.228 -0.095 -0.407 0.469 0.298 -1.866 -0.203 0.021 1.530 -0.287 0.096 -0.077 1.322 -1.195 -0.785 +1 -1.865 -0.035 -0.847 0.283 0.026 1.300 -0.749 -0.036 0.411 0.474 -0.519 0.643 1.267 0.213 0.717 -0.403 -0.986 1.951 1.285 -0.462 -0.009 -0.837 1.132 0.153 0.894 1.189 -0.151 -1.410 +3 -0.082 -0.367 0.185 0.632 -0.904 -0.816 0.650 -1.707 -0.584 3.041 -0.968 1.148 -0.586 0.509 0.654 1.129 -0.200 1.989 -0.589 0.234 0.245 -1.122 -0.286 0.162 -0.432 2.441 -0.839 1.179 +2 2.376 -1.338 -0.731 -0.012 0.822 -0.073 1.646 -0.748 0.004 0.038 -1.754 -0.126 0.310 -1.508 -0.745 1.819 -0.749 0.126 -0.304 -0.620 -1.198 0.363 -0.932 -0.412 1.231 -0.886 1.146 -0.457 +0 0.792 -0.121 0.176 1.106 0.293 0.670 1.407 -0.447 0.796 -0.444 1.074 -0.621 0.435 -0.228 0.587 -0.845 -0.278 -0.053 -1.075 -0.608 -1.640 0.377 0.794 -1.391 -1.171 -0.959 -1.273 0.399 +2 -0.260 0.043 -0.344 1.149 1.013 0.778 1.456 -0.117 1.270 0.470 -0.907 0.494 -0.438 -1.508 -1.508 -0.964 1.562 -2.722 -0.265 0.544 -0.145 -0.347 -0.807 1.476 -0.452 -1.228 -0.494 0.267 +2 -0.479 -0.580 -1.252 -1.657 0.459 1.357 0.937 -0.887 1.070 1.461 -0.535 1.802 0.127 0.258 -0.329 -1.912 -0.205 -0.094 -0.280 -1.145 0.685 -0.585 -1.408 -0.534 -0.208 1.423 -1.005 -1.282 +4 -0.584 0.461 1.238 -1.989 0.072 -0.162 -1.244 -2.508 -0.912 -1.676 0.210 -0.721 -0.793 0.677 1.926 -0.303 0.529 0.923 -1.320 -1.592 -0.259 0.471 -0.791 -1.798 1.404 0.685 -0.284 -1.031 +1 -1.788 -1.036 -0.418 -1.033 -1.446 1.026 -0.447 0.078 0.677 1.090 1.552 -0.873 1.547 0.162 0.168 -0.279 0.309 1.544 0.657 1.099 -0.870 -0.432 0.083 0.504 0.346 1.798 -0.666 0.466 +2 0.881 0.016 -0.234 0.937 -2.094 -0.556 -0.361 0.746 -1.713 -1.297 0.084 -0.026 -0.558 -0.456 -1.187 1.389 1.046 -1.738 1.179 -0.489 -0.705 0.818 -0.611 1.642 0.649 -1.478 0.799 -0.294 +4 -1.518 -0.015 -1.068 0.128 1.135 1.343 -0.184 0.661 -1.504 -1.748 1.120 -3.026 -0.687 0.677 0.221 -2.836 0.526 -0.861 0.121 -0.624 -0.692 -1.800 -0.359 1.236 -0.423 -0.377 -0.705 -1.286 +1 0.699 0.126 -0.284 0.374 0.153 0.604 -1.135 -1.648 0.782 0.609 -0.008 0.409 -1.035 -0.696 0.398 -0.799 -0.798 -3.116 -0.450 -0.523 0.278 -0.032 -0.369 -0.261 -0.743 0.631 0.232 -1.139 +1 1.159 0.054 -1.443 1.069 1.473 -0.007 0.858 -0.693 0.063 1.109 0.382 0.512 1.027 1.687 1.132 0.332 -0.015 0.192 -1.284 0.268 -1.145 -0.374 0.750 -0.394 1.409 -0.040 1.204 -0.276 +0 0.000 0.794 0.924 -2.131 0.676 0.157 -0.790 1.431 0.654 -1.308 0.739 1.170 -0.773 0.216 -0.083 0.017 -0.319 -0.442 0.620 0.114 0.774 1.074 1.609 -0.760 1.308 0.461 0.186 0.328 +4 -0.741 -0.468 -0.459 0.045 1.586 -1.443 0.132 1.776 -1.178 -0.415 0.859 -1.207 1.516 1.258 0.269 -2.957 -0.482 0.991 -0.367 0.009 2.028 0.320 0.233 -0.377 1.574 1.029 -0.476 -0.723 +3 0.288 1.677 1.355 2.036 1.055 -0.100 0.357 -0.029 -0.923 -0.219 0.566 -1.158 0.013 -0.653 0.461 -1.041 -0.106 -2.147 -1.183 -0.144 -0.269 -0.885 0.360 -1.124 -0.776 0.296 -0.776 2.628 +4 -0.218 -0.780 -2.161 -1.590 0.268 -0.294 2.111 0.715 -0.237 -1.207 -0.312 1.828 0.057 -0.592 -0.003 -0.774 0.563 -0.253 1.486 -0.446 -0.892 -1.554 2.446 -0.894 -0.128 -1.453 -0.552 -1.845 +4 2.112 1.069 -1.825 -0.287 0.259 0.424 -1.321 -2.829 -2.567 2.212 0.956 -0.254 -0.927 -0.892 -0.557 0.018 0.348 0.312 0.072 -0.522 -0.090 1.161 -1.503 1.740 -1.329 -1.422 0.577 0.067 +2 -0.068 -0.294 -0.093 1.660 -0.039 1.395 -0.370 -0.248 0.511 0.650 0.051 -0.489 -0.939 -0.572 0.265 2.842 -0.369 -0.481 -1.849 0.261 1.354 0.930 0.267 -0.826 -0.837 0.223 1.653 -0.008 +3 0.825 1.107 -0.953 0.109 0.203 0.593 0.193 1.323 0.209 0.195 -0.844 2.738 -0.835 0.138 1.025 1.495 0.280 -0.126 -0.041 1.440 1.428 0.361 0.351 -1.249 0.940 -0.107 -0.837 -2.675 +2 -1.725 0.290 0.717 1.218 0.664 0.788 -1.118 0.380 0.575 -0.611 0.007 1.165 0.880 1.201 -0.522 1.529 -0.543 -1.459 -0.122 -0.097 -0.850 0.388 2.437 0.502 0.862 0.737 -0.597 -1.045 +0 1.984 1.088 0.288 -0.335 0.042 -0.219 1.241 -0.878 0.581 -0.796 1.287 0.500 0.481 0.527 -0.467 0.121 -0.457 -0.676 0.457 -2.036 -0.947 0.488 -1.029 0.892 -0.346 0.409 0.865 -0.832 +4 1.159 -0.258 -2.456 1.601 -0.892 -0.470 1.062 -0.742 -1.970 0.218 0.797 -0.469 -0.123 -1.180 -0.995 -0.322 0.649 0.098 1.020 1.654 0.887 -1.590 -0.284 0.877 -0.120 -1.619 -0.552 -2.272 +0 2.038 0.669 -0.387 0.515 0.651 0.559 0.744 0.572 -0.351 0.057 0.776 0.419 0.893 -0.864 0.305 -0.809 0.072 -0.676 -0.296 0.920 0.567 -0.994 -0.474 0.830 1.862 0.846 -0.654 -0.166 +4 1.457 -0.932 1.510 -1.435 -1.241 -1.129 1.196 -0.583 -0.296 -1.391 -0.516 -0.242 0.588 0.349 -0.665 -1.352 -0.254 1.806 0.310 -1.111 -1.549 -0.993 -0.308 1.209 -2.653 0.282 -0.971 0.694 +3 -0.793 -0.217 -0.985 -1.268 -1.183 1.730 -1.446 0.739 0.441 0.948 2.013 0.736 0.029 -0.891 0.182 1.000 0.892 -1.686 1.187 -0.080 1.573 0.896 2.057 -0.212 0.956 0.047 0.382 -1.560 +0 -0.895 0.304 0.097 0.863 -0.340 0.519 -0.036 -1.268 0.130 -0.368 -0.339 -1.070 1.770 0.316 0.340 -0.432 -1.264 -1.379 -0.368 0.586 0.891 -0.960 -2.305 0.639 -0.676 0.515 0.579 0.490 +4 -0.105 -0.009 0.840 1.196 -0.840 1.929 -1.497 -0.711 -0.222 0.704 -1.729 2.216 -1.797 1.550 -0.414 0.095 -2.221 0.743 -0.154 1.173 -1.391 1.430 -1.716 0.253 -1.459 1.961 0.951 1.595 +0 0.172 -0.296 -0.844 0.013 0.680 -0.347 -0.726 1.556 -1.000 0.888 -1.088 -0.646 0.937 0.254 -0.352 1.550 -1.182 0.539 1.247 0.039 0.122 0.227 -1.480 -0.689 1.134 -0.811 -0.404 -1.266 +2 0.402 0.862 0.287 0.845 0.507 0.657 0.442 -1.007 -1.171 -0.353 1.552 -0.172 0.310 -1.967 0.194 0.086 -2.673 0.291 -1.651 -0.725 0.994 0.157 -0.448 -0.534 -0.418 0.161 -1.201 1.124 +3 0.429 -1.526 0.744 -0.933 -0.773 1.770 1.233 0.119 -1.311 2.014 -0.196 0.248 0.912 -1.963 -0.640 -0.068 0.131 -1.304 0.326 0.034 1.667 1.000 -0.269 -0.196 1.014 0.284 -1.371 2.094 +2 0.129 -0.372 0.896 -0.396 2.117 -0.701 2.171 0.365 1.922 1.664 -0.340 0.136 -0.467 -0.231 1.299 -1.313 0.124 -1.165 0.274 0.144 0.193 -0.719 0.719 0.719 1.136 -0.155 1.263 -0.446 +4 0.215 1.717 1.327 1.002 2.566 -0.146 -0.465 0.860 -0.253 0.012 -0.986 0.607 -1.471 -0.096 -0.015 -0.436 -1.757 -1.765 -0.746 0.539 0.289 -0.016 -0.885 0.341 -1.799 -2.397 -0.243 -0.546 +4 1.125 -0.490 -1.094 0.009 0.327 0.516 -1.701 0.316 0.260 -0.440 1.136 0.452 -0.857 0.469 -1.887 -0.478 -0.409 0.661 3.040 -0.293 1.465 1.358 -1.330 1.339 -0.705 0.009 2.297 0.517 +2 0.373 -0.071 2.104 -0.730 0.736 -0.693 -0.014 -1.301 1.756 -1.873 0.482 0.834 0.772 1.641 0.098 0.638 0.315 0.740 0.754 -1.323 0.385 0.926 0.355 -0.087 1.879 1.084 -0.183 -1.044 +2 0.515 -0.259 0.144 1.415 1.311 -0.899 -0.294 0.733 1.433 -0.950 1.539 0.866 -0.902 0.330 0.302 -0.777 0.623 -0.274 -0.918 1.186 -1.053 -0.579 1.828 -0.673 1.868 0.884 -0.639 0.699 +1 0.607 -1.279 0.298 -1.439 0.242 0.329 -1.170 1.062 0.687 -0.488 -0.487 -0.106 -1.095 -0.467 1.069 1.220 1.552 0.713 0.067 -0.505 -0.770 -0.173 0.612 -0.018 -0.352 2.066 -1.457 0.516 +2 0.582 -0.535 0.353 0.945 -1.362 1.562 -1.130 1.246 -0.099 1.904 -0.454 -0.445 0.630 -0.997 -1.271 0.732 0.963 1.116 0.683 -0.654 0.185 -0.973 1.680 0.506 0.754 0.463 -0.335 1.694 +4 0.726 1.682 0.112 0.560 1.687 0.744 -0.437 -1.057 -1.231 0.961 2.326 1.261 -0.288 2.086 -1.012 0.523 -0.626 -2.300 1.243 0.811 -1.191 0.632 -0.172 -0.088 -0.223 1.754 0.619 0.291 +1 1.649 0.649 0.010 0.143 -0.738 0.726 -1.307 0.523 1.081 0.128 0.037 -2.317 0.521 -1.892 0.825 0.289 0.524 0.484 0.630 0.387 -0.986 0.440 0.590 0.904 -0.430 0.226 -0.294 0.895 +2 0.697 -0.092 0.793 -1.773 -0.074 -0.751 0.695 0.801 1.155 -0.072 -0.089 0.610 1.273 -1.426 -0.162 -2.290 0.978 -0.701 0.415 -1.342 -0.086 -0.522 -0.772 0.965 -0.899 1.191 1.569 -0.522 +0 0.487 -1.441 -1.199 -0.571 -0.569 0.099 0.891 -0.847 0.195 0.061 0.781 1.310 0.128 -1.531 0.119 -1.736 -0.817 1.000 -0.517 -1.039 0.115 -0.300 -0.855 -0.421 -0.919 -0.711 -0.291 -0.504 +3 0.058 -0.001 0.298 0.818 0.766 0.068 0.694 0.318 -2.174 -1.746 0.965 0.097 -0.204 -0.752 0.766 -0.345 -2.274 -2.132 0.240 -0.888 -1.204 -0.103 0.388 1.211 0.010 0.719 -1.461 1.258 +1 0.294 -0.466 1.815 0.406 -0.535 0.869 0.003 0.325 -2.384 -1.152 0.231 -1.282 -0.594 0.927 0.516 1.167 0.013 -0.682 1.412 0.046 -1.070 1.634 0.068 0.979 0.296 0.977 0.645 -0.303 +3 -0.346 0.772 0.832 -0.190 -0.281 1.499 0.933 0.072 -1.629 0.344 0.698 -2.755 0.252 -0.417 -0.221 -0.907 -0.148 1.392 -0.446 0.966 -0.806 0.759 0.002 0.352 0.007 0.642 -2.568 -1.420 +4 1.131 0.031 1.541 -0.526 -0.435 0.816 -1.623 -1.213 1.097 -1.172 -1.819 1.869 -1.394 0.184 0.069 -2.811 -0.267 0.448 0.354 0.904 -0.387 0.713 -0.689 -1.410 -1.298 -1.462 1.010 -1.515 +0 -0.738 1.333 0.147 -0.764 0.089 -0.121 0.261 -0.071 0.036 0.941 -0.836 -0.309 0.743 0.536 -1.180 -0.932 -1.346 -0.319 0.863 0.544 -0.146 -1.661 1.102 -0.455 -0.334 -0.378 -1.491 1.502 +4 -1.234 -1.602 1.629 -2.353 -1.178 -0.415 -1.204 -0.004 1.542 1.204 0.901 -1.342 0.354 0.465 -0.968 -0.919 -0.054 0.583 -1.939 -0.219 0.213 -1.820 -1.189 0.229 -0.960 -0.556 0.733 0.694 +1 0.447 0.442 -0.180 -0.118 0.502 2.446 -0.989 0.042 -1.196 -0.021 -0.674 -0.120 0.155 -0.489 -1.784 1.426 0.839 -0.131 -0.895 0.085 0.361 0.770 0.862 1.051 -1.741 -0.305 0.993 -0.924 +2 0.305 0.481 0.237 0.629 0.540 0.852 0.746 -0.127 -0.390 0.279 -0.938 1.399 2.011 -0.173 1.051 -0.547 -1.389 -1.223 -0.605 -1.126 1.347 0.530 0.448 1.077 0.391 -0.398 -1.228 -2.364 +1 1.082 -0.214 -0.100 0.380 -0.443 -0.778 0.307 -1.381 -1.612 -0.692 0.712 1.162 -0.475 0.830 1.382 0.038 0.636 1.867 -0.705 -0.242 1.601 -0.940 0.156 -0.628 1.364 -1.185 -0.067 -1.047 +0 0.722 -0.694 -1.180 0.454 1.108 0.027 -0.536 0.186 0.737 2.307 0.949 -0.471 0.100 1.403 -0.319 -0.434 0.220 -0.739 -0.088 -1.526 -0.777 0.145 0.161 -0.128 0.680 0.281 -0.037 0.307 +4 -0.535 1.763 0.775 -0.452 2.292 -1.197 -0.813 -0.111 -1.531 1.790 0.183 -0.790 0.448 0.146 -0.305 -1.209 -1.436 2.187 -1.353 1.698 -0.845 0.790 0.465 0.665 0.732 -1.612 -0.676 -1.368 +4 1.084 -0.483 1.282 -0.086 -1.148 -0.325 0.719 0.601 -0.837 1.652 0.644 1.457 0.304 1.343 -0.343 0.790 -0.706 1.498 -2.245 1.656 -1.718 -2.095 -1.180 0.147 0.342 0.575 -0.683 -0.096 +3 1.385 -0.023 0.333 0.140 -0.533 0.748 -1.665 0.377 1.106 -0.844 -0.431 -2.372 -0.885 1.748 -1.929 -0.821 1.447 -0.231 -0.678 0.148 -0.623 -0.669 0.208 -0.469 0.274 1.887 0.796 0.296 +2 0.140 1.239 0.380 -0.237 -1.626 -1.363 -1.492 0.563 -0.876 -0.788 -0.392 -0.967 0.054 -1.596 0.575 0.485 -0.206 -0.618 0.257 1.078 -1.971 0.581 -0.221 -0.525 -0.345 -1.242 -1.775 0.602 +0 -0.228 -0.952 0.080 0.375 0.976 -0.201 -1.580 0.674 -0.569 1.027 0.705 -0.418 -0.648 -0.652 0.250 0.416 -0.107 0.167 1.834 1.747 -0.699 1.159 0.008 -0.769 1.005 -0.623 -0.626 -1.535 +0 1.036 -0.625 0.041 0.321 -0.231 -0.116 -0.842 0.349 1.143 -1.491 0.723 -0.345 0.197 -0.324 0.002 -1.119 1.895 -1.453 0.009 -1.192 0.421 -1.723 0.360 -0.550 -0.325 0.302 0.590 0.817 +1 0.255 -0.941 0.942 0.212 0.137 -1.853 0.683 -0.682 -1.152 -0.079 0.513 -0.602 -0.045 -0.880 0.284 -0.160 -0.626 -0.503 1.948 -0.554 1.046 1.361 0.720 0.283 -0.869 -0.455 0.505 -1.812 +4 -0.179 0.299 -0.843 -0.264 -0.079 -0.287 -0.634 -1.848 -0.662 1.633 -0.365 0.887 -0.754 0.808 -2.860 0.267 -0.294 -1.704 -1.219 2.141 1.166 -1.098 0.441 -0.077 0.032 2.282 -1.078 -0.275 +0 -0.333 0.320 -0.784 0.007 0.386 0.302 -0.401 -0.829 0.396 0.339 0.290 0.114 0.340 0.877 0.310 0.098 -0.527 1.386 -1.123 -0.203 -0.509 -1.827 0.354 -0.203 0.032 -0.179 0.753 0.881 +0 0.438 -0.002 -0.153 0.483 -0.414 -0.853 0.610 -0.144 0.034 1.089 -0.114 0.015 -0.600 0.431 0.350 0.901 -0.579 -0.458 0.792 0.252 -0.906 1.307 -1.698 0.784 -1.265 1.886 -1.545 1.152 +1 0.920 -0.166 1.013 0.448 -0.270 0.163 -0.343 0.522 1.031 0.847 0.289 1.244 -0.143 0.767 -0.939 0.585 0.558 -0.286 0.491 -0.031 1.749 0.254 -2.704 -1.223 -0.001 -1.508 -0.158 0.851 +4 0.153 -2.634 -0.833 0.745 0.655 -0.952 -1.578 0.127 -2.907 -1.149 1.760 1.445 0.487 0.307 -1.950 -0.741 -0.295 -0.719 -0.350 1.299 -0.542 -0.341 0.740 -1.419 -0.840 -0.738 0.056 -0.345 +3 -0.153 -0.516 0.545 -0.394 -0.130 0.950 -1.383 0.065 -0.167 -0.305 0.511 -1.514 -0.853 -0.465 -1.332 1.164 -0.651 1.994 -0.485 -0.802 0.805 -0.908 -2.890 0.515 1.732 -1.637 0.421 1.102 +0 -0.535 -0.706 -0.708 0.351 -1.205 0.756 2.116 0.003 0.435 0.265 -0.934 0.316 0.222 1.772 0.248 1.242 0.429 -0.509 -1.185 0.308 0.760 -0.089 -0.633 1.407 -0.441 -1.152 -0.065 0.078 +4 -1.650 -0.397 -0.980 -0.703 -0.421 2.150 0.732 0.986 0.465 0.052 0.955 -0.274 -1.524 -0.127 -0.092 2.270 0.937 -0.433 -0.194 -0.476 -0.790 0.569 -2.270 -0.066 -0.873 -1.756 -1.888 0.389 +3 -0.707 -0.090 -0.953 -1.840 -0.932 1.484 0.609 -0.004 -0.024 0.040 0.004 0.644 2.549 -2.198 -1.416 0.437 -0.174 0.150 -2.067 -1.285 0.120 -0.581 -0.530 -1.083 -0.220 -1.250 -1.241 0.546 +4 0.886 -1.436 -0.097 0.037 -1.231 -1.382 -0.136 1.037 -1.033 -0.501 -0.034 1.568 0.696 1.539 0.834 1.016 -1.577 -2.567 0.890 0.372 -2.154 -0.811 -0.455 0.764 0.081 0.845 -1.217 -1.687 +1 -0.953 -1.143 0.880 2.306 -0.881 -0.235 -0.693 0.221 -0.608 0.283 0.665 -1.224 0.272 -1.794 -0.257 -0.324 -0.229 -0.312 -0.495 -0.266 0.238 0.266 0.115 -0.574 1.080 1.525 -1.288 0.164 +4 1.310 0.402 0.286 -0.979 -1.097 0.557 -1.352 0.988 0.263 1.149 -0.265 -1.015 1.097 0.035 -0.132 1.498 3.229 -1.999 -0.445 -0.405 -1.336 0.511 -0.783 -0.411 -0.819 -0.283 -1.945 -1.652 +0 0.496 0.371 0.938 0.285 -0.983 -0.091 0.618 -0.687 0.565 -0.275 0.010 -0.593 0.572 -0.387 -0.348 -0.622 0.795 0.681 0.049 0.289 0.137 -1.122 -0.377 0.735 -0.783 -0.251 0.546 -1.577 +1 -0.591 1.751 2.301 0.476 0.499 -0.144 0.121 0.141 -0.300 -0.636 0.128 -0.865 -0.548 -0.937 0.644 1.344 0.771 -0.672 -0.630 -0.903 -0.478 -0.198 1.807 -0.163 -0.105 -0.963 1.447 0.032 +4 0.601 1.298 0.512 -1.404 -1.316 -2.710 0.202 -1.060 -0.570 -0.320 -0.608 -0.034 -0.283 -0.383 -1.548 0.242 2.176 0.276 2.269 -0.568 -0.253 -0.020 -0.170 1.165 -2.191 -0.455 1.663 -0.497 +0 0.847 0.596 -1.071 1.222 0.771 -0.537 -1.642 0.290 0.228 0.264 1.380 -0.029 0.387 0.563 -0.445 0.778 1.361 -0.414 -1.826 -0.793 0.066 -1.575 -0.522 0.032 0.642 0.984 0.352 -0.171 +4 1.042 0.521 -1.246 -0.497 0.648 -2.155 0.324 0.267 2.727 0.432 -0.108 1.164 -0.344 0.042 0.089 0.600 -0.923 0.834 -0.526 2.059 -1.513 -0.757 -2.029 2.965 -1.227 0.760 0.727 -0.906 +2 0.690 -0.401 0.224 0.013 0.098 -0.773 0.025 0.498 1.451 0.959 2.153 -0.767 0.872 0.183 2.190 -0.808 -0.840 -0.599 -2.124 -0.526 -0.759 0.150 0.342 1.876 0.950 -0.577 -0.898 0.492 +4 0.372 0.890 0.853 -0.863 -3.011 1.366 -2.136 -0.463 -1.405 -0.855 1.408 0.947 -0.889 1.787 0.072 0.381 0.580 -1.200 0.405 2.271 -0.722 0.251 1.726 1.038 -0.167 -1.044 0.033 -0.368 +3 0.847 1.899 -1.263 2.572 0.047 0.292 0.696 1.876 1.261 1.727 1.622 -0.899 0.814 -0.567 -0.189 -1.649 -0.113 0.412 -1.098 0.482 0.517 0.327 0.002 -1.578 -0.417 0.055 0.077 -0.049 +0 -0.135 -1.817 -0.344 0.022 1.509 -1.011 -0.230 -0.521 -0.582 -0.541 -1.989 0.067 -0.676 0.365 1.151 -0.552 -0.425 0.166 -0.325 0.687 1.091 1.845 -0.648 -0.786 0.887 -0.189 -0.113 -0.159 +1 -1.520 -0.243 0.884 0.274 1.470 0.462 -0.260 -0.490 0.955 0.748 0.378 -2.855 0.542 -0.825 -0.356 -1.233 1.546 -0.232 0.447 0.702 -0.414 -0.852 -0.279 0.192 1.255 -0.327 -0.615 -0.221 +1 -0.438 -0.928 3.068 -1.084 0.541 -0.880 0.140 0.101 0.610 -0.049 -0.018 0.349 0.479 0.945 -0.425 -1.302 -0.772 0.488 -1.166 -1.209 -0.981 -0.669 -0.889 0.057 -0.137 0.564 -0.731 0.719 +1 0.487 -0.719 0.163 0.658 0.809 -0.190 0.596 1.740 0.353 0.870 -0.205 -0.352 -0.293 -1.029 -1.494 0.816 0.641 0.321 -1.185 2.248 -1.220 -0.616 -0.494 -0.434 0.930 -0.094 -1.024 0.645 +3 0.626 0.885 -0.592 0.124 1.954 -0.506 -1.059 1.482 1.963 0.004 1.011 1.341 -0.742 -0.485 1.231 1.685 0.563 -0.880 1.987 -0.531 -0.335 0.342 1.554 0.854 0.415 0.463 0.044 0.558 +4 -0.649 -2.452 0.546 -0.003 1.178 0.848 1.362 0.772 1.581 0.425 -0.073 -0.341 2.588 -0.815 -0.989 -0.533 0.403 -0.496 0.046 0.965 -0.324 2.007 -0.985 -1.012 0.667 -1.399 -1.406 -0.804 +4 -0.412 -0.030 2.933 0.009 0.454 -0.992 0.721 1.380 -0.151 0.615 0.575 0.069 1.291 -0.799 -0.872 0.004 -0.373 -2.911 -0.731 -2.130 -0.321 0.713 0.054 0.133 -0.156 0.212 2.825 -0.352 +1 -0.108 0.216 0.664 -0.416 0.738 1.138 0.415 -0.545 0.947 0.483 0.619 1.535 0.709 1.574 -0.148 -0.431 1.149 -0.986 1.888 1.302 0.088 1.201 0.662 -1.290 -0.816 0.286 0.160 1.308 +1 -0.984 0.189 -2.187 0.050 0.196 -0.958 0.748 -0.638 -0.070 -0.301 1.211 -1.442 -0.129 -0.686 1.780 -0.050 -1.162 -0.480 1.176 0.440 -0.776 -0.569 1.083 -1.549 0.671 0.139 -0.984 0.391 +0 -0.798 1.260 0.208 0.804 0.268 0.558 -0.050 0.147 0.250 0.468 -0.528 -0.272 0.414 0.937 1.844 -0.440 -0.508 -1.753 0.633 0.599 -1.222 0.061 1.286 -0.666 0.104 -0.263 0.239 1.406 +1 -0.696 -0.096 -1.219 1.619 -1.157 0.215 -1.039 0.575 0.838 1.051 -0.068 -1.504 -1.064 -0.863 0.192 -1.508 0.258 -0.540 -0.870 -0.152 1.908 -0.027 0.045 1.154 0.258 0.675 1.255 0.466 +1 0.011 0.354 0.853 1.128 -0.406 0.499 0.493 0.328 -0.479 -1.028 0.502 -1.707 -0.268 -2.554 -1.336 -0.877 1.097 0.688 -0.554 0.729 0.888 -0.161 -1.384 -0.208 -0.098 -1.232 0.367 0.280 +2 -1.838 -0.452 -1.782 1.432 -0.003 0.696 0.273 -0.626 1.462 -1.096 -1.645 0.875 0.950 0.242 0.939 -0.939 -0.725 -0.347 -1.440 1.424 -0.011 -1.041 -1.574 -0.600 0.273 -0.942 0.233 -0.281 +0 -0.766 0.238 0.295 1.421 0.281 0.008 -0.447 0.517 -0.184 -0.918 -1.183 -0.807 -0.872 0.595 0.093 -0.749 -2.167 -0.841 0.033 0.434 -1.087 0.435 0.182 -0.118 0.389 0.941 0.340 -0.558 +4 0.592 -0.387 -1.145 0.779 -0.622 -0.291 -0.541 -1.720 0.019 -2.096 0.622 0.650 -0.112 -1.233 0.546 -3.705 -0.011 1.292 0.303 -0.636 -1.195 2.473 -0.415 -0.208 -0.522 -0.954 -2.497 -0.598 +1 0.076 -0.642 -0.032 0.200 -1.474 0.274 -0.621 -1.068 -2.001 -1.554 0.024 0.042 0.518 1.716 1.356 0.996 1.186 0.181 0.163 1.537 0.216 0.238 1.166 -0.277 -0.135 -1.361 -0.463 -0.330 +1 0.125 -0.180 1.060 0.424 0.618 -1.782 0.468 0.229 -0.379 -1.254 0.720 -1.516 1.055 -0.079 -0.094 -0.506 -0.862 -0.873 -1.593 0.193 -1.118 0.068 -1.208 -0.020 0.158 -0.409 2.365 -1.261 +1 -0.043 -0.607 -1.889 -1.011 0.708 0.814 -0.211 0.817 0.945 0.155 -0.722 0.441 0.487 0.674 0.865 -1.362 -0.510 -1.899 0.747 -0.212 0.431 -1.476 1.401 -1.194 0.775 0.118 -0.189 -0.270 +4 0.586 0.993 -0.165 0.198 0.918 0.366 0.708 -0.681 0.858 -0.585 2.086 0.195 1.105 -0.442 -0.214 -0.264 -1.760 -2.637 -0.288 -1.888 0.286 -0.159 -2.818 -0.532 -0.025 1.411 -0.457 -1.642 +2 0.089 0.519 0.601 -0.691 1.242 -2.032 -1.704 -0.715 0.336 0.133 0.073 -0.071 -0.321 -1.275 -1.377 -1.284 -1.093 -1.415 -1.436 -0.274 1.056 0.440 -0.926 -0.690 1.897 -0.499 -1.087 -0.288 +4 -1.422 1.313 -0.352 0.302 -1.697 0.675 -2.313 1.580 -0.456 0.102 -0.243 -0.007 -0.350 0.410 2.437 -0.149 1.982 1.060 0.422 1.054 1.891 -1.906 -0.174 1.659 -0.176 -0.350 -0.477 -0.231 +1 -0.881 0.090 1.164 0.926 0.034 -0.362 0.948 0.675 -0.618 -0.795 -2.072 0.154 -0.083 1.136 0.359 1.334 -0.697 1.138 -0.529 0.778 -1.459 0.235 -1.985 -0.267 0.440 -0.521 -1.050 0.846 +0 0.151 -0.340 0.645 -0.717 -0.109 0.923 0.513 -2.110 -0.241 1.235 -0.285 -0.115 -0.718 0.158 -0.403 0.933 0.595 1.245 0.665 0.113 0.453 -0.846 1.209 0.751 -0.664 -0.856 -0.084 0.674 +4 -0.600 -0.065 -0.880 -1.273 0.169 -1.033 0.220 2.539 -0.054 0.267 -0.019 -1.988 -0.655 -2.486 0.639 -0.841 -0.427 -0.388 -0.166 1.243 -1.575 -0.352 -2.031 0.658 -1.633 0.608 0.175 -0.704 +1 -0.089 1.246 -0.314 2.156 -1.271 -1.749 0.234 -0.567 -0.753 0.021 -1.232 0.545 0.196 -0.358 0.211 1.912 -0.246 -0.491 0.672 -0.663 0.481 0.821 1.400 -0.321 -1.320 -0.257 0.177 -0.618 +4 1.772 2.106 -1.215 0.712 -0.078 0.550 2.819 1.137 1.584 -0.885 -2.100 -0.294 2.059 1.384 -0.342 0.610 0.300 -1.268 0.345 1.403 0.785 -1.349 -1.834 1.641 0.702 1.349 1.944 -1.144 +1 1.195 -1.404 0.085 0.645 0.067 -0.266 0.159 0.819 1.476 -1.452 -0.470 1.487 1.566 0.856 -1.001 -0.834 0.027 0.363 0.618 -1.360 0.592 0.154 0.095 0.553 0.173 -0.837 1.269 -1.294 +3 -1.836 -0.266 1.718 -0.776 2.271 0.544 0.517 0.239 1.046 1.101 -0.019 -0.430 -1.425 0.814 0.066 -0.844 -0.580 -0.068 -0.981 0.049 -1.620 -0.532 -1.162 0.200 -0.978 -0.559 2.260 0.865 +4 1.143 0.565 1.202 -1.115 -1.057 2.059 1.147 -0.815 0.868 -0.466 -1.699 -2.100 -0.022 1.561 1.521 1.260 0.830 0.317 1.152 -2.011 0.990 -1.121 -0.913 0.696 0.929 -0.610 0.646 0.563 +3 0.161 -0.300 0.707 -1.966 -1.483 -0.896 0.499 1.027 0.166 -1.518 0.114 -0.708 0.674 0.955 1.219 0.274 -0.246 -1.948 -0.754 -1.546 -2.330 0.238 -0.460 0.632 0.012 -1.360 0.828 0.021 +3 -0.645 -0.729 -0.524 -1.366 -0.785 1.949 0.736 0.926 1.177 1.834 -1.771 -1.230 -0.804 -0.470 -0.301 -0.447 0.219 0.035 -0.723 -2.176 -0.993 -0.549 -0.247 -1.130 -0.491 -1.131 -0.126 -1.073 +1 0.273 -0.501 -1.585 0.500 -0.675 1.474 -1.434 1.437 1.510 1.274 0.660 -1.621 0.332 -0.418 -0.706 -0.721 0.256 0.825 -0.150 -0.567 -0.368 -0.601 -0.138 -0.559 -1.379 -1.612 0.253 0.496 +1 0.493 1.348 0.999 0.957 -0.587 0.968 0.002 1.475 1.653 0.890 0.235 0.363 0.230 -0.797 -0.954 0.648 -0.679 0.262 -0.369 -0.957 -0.688 2.240 -0.683 -0.657 -0.902 0.214 -0.259 -1.709 +3 0.462 0.349 -2.069 0.034 1.417 1.496 0.682 1.270 -0.153 0.570 0.164 0.669 1.845 1.002 -0.342 1.580 0.248 0.652 -0.236 0.737 0.399 1.132 -0.502 0.951 -2.439 -0.616 -1.345 0.337 +4 0.247 0.164 -0.651 -0.610 1.051 1.447 0.161 1.274 -0.778 0.534 -0.255 0.024 -0.240 -0.215 -2.614 -1.289 -1.762 -0.481 -1.063 -1.735 -1.023 -0.216 2.976 -0.147 -0.375 -1.396 -1.093 0.867 +0 -0.131 1.606 1.650 1.552 0.403 -0.946 -1.483 0.595 0.669 0.262 -1.329 -0.798 0.083 -0.613 -0.776 -0.967 -0.135 -0.207 -0.827 0.010 0.249 0.353 -0.957 -0.307 0.016 0.125 -1.335 -0.341 +1 1.892 0.382 -0.707 -0.826 -0.490 -1.539 1.483 1.484 0.927 0.064 -1.038 -0.563 0.515 -0.388 -0.002 -1.288 -0.984 -0.554 1.112 0.226 -0.750 -0.356 1.399 0.718 -1.000 0.154 0.140 0.765 +0 0.001 0.029 0.208 -1.236 0.625 -1.255 -0.940 -1.049 -0.175 -0.352 -0.001 -0.905 1.005 0.834 -0.174 -0.478 -0.579 -0.310 -1.400 -0.960 0.860 0.302 1.634 -0.299 -0.362 -0.408 -0.319 -0.660 +0 -0.436 -0.637 0.527 -1.543 0.907 -0.402 0.989 1.288 0.640 -0.812 -1.251 -1.434 1.103 -0.135 -0.599 -0.132 1.081 0.779 0.439 0.638 0.180 0.302 0.221 -0.000 0.753 0.361 -0.881 0.529 +3 0.755 -1.278 -0.938 1.289 -0.984 -1.341 -1.570 -0.669 0.706 0.253 -1.266 -0.287 0.626 -0.859 -0.177 1.727 0.067 1.612 -0.784 -0.424 1.258 0.060 -1.227 2.033 0.279 1.878 -0.676 -0.174 +3 -0.432 0.011 -0.424 1.130 1.946 -0.592 -1.241 0.907 0.706 1.282 0.216 -2.062 -1.188 1.823 -0.057 -0.871 -1.902 0.480 0.065 -1.232 -0.936 -0.797 0.451 1.696 -0.349 1.635 -0.015 -0.820 +2 -1.219 -0.021 -0.906 1.713 -1.156 -0.415 1.529 1.151 1.125 -1.626 -0.209 -0.989 0.434 0.703 1.242 0.055 -0.622 -0.686 0.006 0.444 0.715 0.262 -1.169 0.145 0.354 -2.388 0.201 -0.010 +3 0.189 -1.323 -1.981 -0.237 -2.209 0.259 -1.258 -0.841 -2.280 1.650 -1.463 -0.364 -1.792 -0.836 1.752 0.297 -0.724 -0.034 -0.561 -0.092 0.744 -0.331 0.220 0.236 -0.326 -0.736 -0.525 0.699 +0 -1.092 0.104 -2.508 -0.272 1.073 -0.726 0.177 -0.529 -0.859 0.971 -1.045 0.293 0.308 -0.852 0.156 -0.935 0.522 -1.315 -0.340 -0.253 1.113 0.140 0.955 0.555 0.548 -0.332 1.021 -0.356 +1 0.605 0.430 0.330 -1.550 0.113 0.100 -0.576 -0.309 -2.381 -1.097 0.922 1.731 -0.943 0.183 1.098 -0.520 0.676 -0.034 1.190 -1.282 -1.105 0.280 0.096 -0.056 0.576 -0.931 -1.369 -0.500 +0 -1.243 0.389 0.040 -0.696 0.220 -1.246 0.161 0.760 -0.175 -0.907 -0.097 0.992 -0.124 0.667 0.823 -0.904 -0.623 1.355 0.713 -0.272 0.889 1.414 -1.061 -0.434 -0.552 -0.136 1.245 1.355 +2 -0.651 1.843 0.995 -0.077 0.635 1.598 0.142 0.393 -0.535 -0.120 0.701 -0.180 -0.503 1.151 0.483 -0.480 1.753 2.114 1.015 -1.195 0.407 0.589 -1.585 1.192 0.209 1.123 -0.306 0.484 +1 -0.524 0.421 -0.135 -1.717 0.044 1.415 1.040 0.045 0.386 2.880 -0.260 0.336 0.346 -0.888 0.867 -1.274 -1.014 -0.376 -0.908 0.031 -0.828 0.520 -0.042 0.111 -0.318 0.734 -1.673 -0.771 +4 -0.175 -2.127 -1.553 -0.767 -0.252 2.296 0.018 0.069 -0.435 -0.722 0.637 1.186 2.048 0.572 -0.392 0.320 0.871 0.738 0.204 0.321 0.457 -0.719 -2.043 0.064 0.820 -1.441 -0.670 -2.291 +2 -1.278 0.550 0.434 -1.041 -0.306 -0.660 -0.359 -0.024 -0.366 1.164 -0.686 -0.777 -0.575 1.027 0.911 0.760 -0.312 -0.490 0.571 -0.553 0.585 0.472 0.031 3.665 0.016 1.730 1.222 0.033 +2 -1.515 -0.716 0.029 1.156 -0.625 1.113 0.962 -0.628 0.252 0.449 0.479 -0.175 0.649 1.424 2.261 0.322 0.120 -0.362 -0.705 -1.606 -1.708 0.680 -0.586 -0.611 0.095 0.960 -0.413 -2.276 +4 -1.181 0.808 -0.378 -0.625 0.327 -0.092 1.752 0.702 -0.074 0.630 -0.901 1.557 0.388 2.274 -0.729 -2.056 -0.232 -2.145 2.304 -0.063 -1.305 -0.596 -1.630 -0.942 1.657 0.499 0.582 0.322 +3 -0.566 -0.904 0.073 -1.771 -0.311 1.166 -0.718 -1.024 -0.696 -0.475 -0.668 -2.515 1.206 1.719 -1.231 -0.465 0.615 1.418 -1.274 -0.566 1.084 1.195 -1.913 -0.630 0.196 0.390 -0.579 -0.872 +1 -0.218 -0.949 -0.090 -1.261 -0.453 -0.235 -0.238 0.208 1.016 1.001 0.994 -0.437 0.720 -0.819 0.528 -0.112 2.151 -1.163 0.266 0.364 1.089 -1.021 0.872 0.403 1.948 0.348 1.655 -0.019 +1 -1.134 -0.285 0.393 0.123 1.480 0.946 1.241 -2.032 1.579 0.267 0.409 0.896 0.021 -1.070 1.447 -0.768 -0.986 -0.234 -0.386 0.004 -0.098 1.347 0.459 -0.858 -0.721 -0.388 -0.384 -0.515 +0 0.054 0.875 -0.064 -0.462 -0.359 1.136 0.056 1.524 0.951 -0.523 -0.449 -0.108 -0.137 1.156 -0.540 0.112 0.335 0.706 0.472 0.396 -0.478 -0.379 0.248 -0.189 -0.932 -0.440 0.171 1.188 +3 -1.592 -0.638 -1.033 -1.126 -0.383 -0.310 -0.720 0.256 0.271 -1.458 0.840 -1.218 -2.325 -0.162 -0.224 0.658 1.133 -0.352 -1.368 -1.149 -1.272 -1.094 1.248 1.177 -0.735 0.295 0.805 -1.226 +3 0.243 0.217 0.046 -0.737 -2.723 2.130 0.333 -0.263 -0.168 -1.024 -1.451 0.561 0.954 -0.896 0.614 0.915 0.111 -1.772 -0.907 -0.383 -0.726 0.848 1.402 0.938 1.452 -0.776 0.816 0.701 +2 0.163 -1.431 0.462 1.500 -0.671 -0.639 -0.080 -0.501 -1.512 -0.385 -0.587 1.367 1.155 1.397 0.802 0.206 -0.874 1.326 0.348 -1.248 -0.914 -1.264 -2.052 0.389 -1.238 -0.756 0.039 -0.188 +4 0.338 0.483 -0.229 1.297 0.547 -0.723 -1.231 1.450 1.113 0.280 -1.298 -0.299 0.522 -0.926 1.884 -1.205 -1.178 0.223 1.261 -2.532 -0.845 -2.735 1.310 -0.842 1.664 -1.712 -1.068 -0.369 +3 -0.319 -0.027 -1.135 1.248 0.205 -1.371 0.887 0.463 -1.206 -0.844 0.850 0.496 0.180 -0.613 2.638 -1.145 -1.183 1.469 0.924 -1.297 0.577 1.025 0.053 0.837 -1.396 -0.494 0.651 -0.891 +2 0.098 0.960 0.808 0.035 1.529 1.552 0.204 0.405 -0.651 -0.077 -0.070 -0.857 1.022 -0.060 -0.669 -1.031 1.510 -0.896 -0.390 1.170 -0.793 -0.063 0.860 -0.625 -1.764 0.056 -2.447 -1.607 +4 0.357 0.660 0.738 -0.129 -0.218 0.494 0.864 1.038 -0.458 -0.583 1.650 0.648 -0.659 1.204 -2.716 0.045 -0.678 -0.857 1.213 0.496 -0.582 2.399 -0.402 0.252 -0.802 2.694 -0.672 -0.940 +3 0.730 2.485 -0.281 -0.341 0.431 -0.798 -2.544 1.037 -0.613 1.050 -0.761 0.122 0.855 1.070 0.233 0.183 0.620 0.464 -1.291 -1.272 -2.109 0.767 -0.396 0.820 0.404 -1.002 -0.120 -0.455 +4 2.444 0.029 1.125 0.190 1.738 -0.192 0.224 0.875 1.533 -1.845 0.518 -0.853 -0.055 -0.536 -2.241 1.058 -1.312 -0.069 -0.107 -0.795 0.639 -0.365 -0.715 -1.455 -0.925 0.423 -1.999 1.678 +4 -1.694 1.715 0.847 0.228 -1.381 -1.185 1.503 1.004 1.129 -0.133 1.307 -1.370 -1.233 1.009 -0.918 -0.040 -1.775 -2.844 -1.022 -0.379 -0.925 0.620 1.011 0.610 -1.157 0.525 -1.228 1.863 +4 1.028 1.208 -0.468 -2.069 -1.981 1.311 1.235 1.558 -0.125 -2.057 0.491 -0.488 1.771 0.686 0.200 -0.396 1.343 -0.716 -1.387 0.081 1.089 1.642 -1.575 1.272 -0.062 0.809 -1.428 -0.384 +0 -1.066 -0.401 -0.662 -1.011 0.699 -0.235 -1.238 -0.676 -1.165 -0.409 0.781 -0.026 0.476 0.303 -0.060 0.248 0.508 -1.062 1.170 1.366 -0.702 0.630 0.000 -0.325 1.952 -0.443 -0.497 0.040 +2 -0.205 -0.768 0.737 1.189 0.394 0.572 -1.195 -1.278 -1.350 1.051 -0.387 0.826 -0.600 -0.015 -0.806 -0.328 -1.710 -0.318 -0.406 -1.695 0.425 0.574 -0.662 -1.205 1.730 0.256 -2.380 -0.428 +3 -1.171 0.365 1.214 0.650 -0.267 1.070 -0.743 -1.379 1.879 1.183 0.278 0.873 0.285 0.724 0.792 -1.346 0.905 2.171 -0.897 -0.271 0.516 -1.157 0.793 0.218 -0.067 -2.450 -0.294 -0.325 +0 0.117 -1.153 -0.226 -0.332 0.580 0.315 0.161 1.637 0.270 -0.418 -0.295 -0.764 -1.019 -1.389 1.134 -0.718 0.316 -0.300 -0.029 1.154 0.177 -0.923 -0.564 0.860 0.483 0.922 -0.239 -1.396 +0 0.376 -0.856 0.588 0.183 0.922 -0.776 -0.084 0.809 -1.828 -1.154 0.040 0.180 -0.096 0.798 -0.100 1.023 0.323 -1.118 0.162 -0.078 -0.671 -1.517 -1.180 -0.848 1.059 -0.325 1.222 -1.022 +2 -0.409 0.245 1.889 0.295 -0.217 -0.682 -0.347 1.554 -0.496 -0.418 -1.141 0.739 0.579 1.352 0.755 -0.130 1.502 1.313 0.133 0.400 -1.640 0.290 0.104 -0.069 0.468 -0.924 2.316 0.756 +4 0.946 3.114 0.647 2.013 0.555 -0.269 -2.045 0.321 0.188 -0.009 -1.105 -0.136 0.343 -0.690 -0.813 -1.266 -0.839 0.196 1.393 0.176 -1.387 -0.221 -1.053 -1.253 0.372 -0.182 -0.032 -1.758 +0 0.251 -0.073 -0.153 -0.410 0.216 0.214 1.891 1.267 0.570 0.374 -1.167 -1.522 -0.696 0.944 0.929 0.966 0.177 0.450 -0.378 -0.070 -1.392 0.298 -0.088 -0.178 -0.706 1.434 -0.321 -0.964 +0 0.115 -0.385 -1.009 -0.400 0.183 0.580 0.122 0.649 -1.520 0.867 -1.128 0.984 0.502 -1.870 0.737 0.995 1.258 -0.769 0.179 0.694 0.736 0.410 -0.427 0.988 0.994 -0.154 -0.784 -0.728 +0 -1.217 0.599 0.007 -0.477 0.023 -0.094 0.968 -0.427 -0.003 0.849 -0.264 -0.741 -1.281 -1.041 -0.643 0.961 -0.737 -0.215 -1.029 0.054 0.287 0.355 1.756 -0.014 0.293 -0.564 2.172 0.847 +1 1.309 1.583 0.648 -1.248 -1.223 -0.826 0.963 -0.324 0.621 -0.594 1.401 -1.154 -0.222 -0.237 0.686 0.360 1.482 1.297 -0.700 0.044 -0.008 -0.541 -1.541 -0.020 0.874 0.193 0.338 1.434 +4 -0.584 1.109 2.265 -0.746 2.466 0.013 -0.633 -0.296 -0.403 1.402 1.318 1.306 -1.299 0.189 -1.550 1.011 -0.484 0.503 -0.502 -1.756 0.885 0.962 0.139 -2.111 0.750 -0.738 0.517 -0.491 +0 -0.357 1.164 0.405 0.186 -0.426 0.586 -1.411 0.946 0.839 0.513 -0.942 -0.968 -0.659 -0.905 0.303 -0.513 -0.383 -0.468 -1.164 -0.541 1.105 0.834 0.487 1.494 -0.532 -0.346 1.001 -0.031 +3 -1.282 -0.090 1.698 -0.694 -0.363 -0.824 -0.160 1.298 -2.301 -0.310 -1.234 0.964 0.354 -0.632 1.614 -0.022 0.794 1.404 -0.198 -0.387 -0.379 -0.057 -0.041 1.908 1.007 -0.938 1.930 0.738 +0 0.500 0.586 0.772 -0.213 0.486 -0.487 -1.971 0.530 0.298 1.013 -0.025 -1.603 -0.092 0.887 0.476 -0.147 -1.664 -1.026 0.477 -1.665 -0.318 0.038 1.150 0.614 0.963 0.302 -0.845 -0.614 +0 0.920 0.368 0.599 -1.232 0.112 -0.929 -1.389 0.411 1.158 -0.383 -0.212 -1.138 0.007 0.760 0.792 0.545 -0.618 -0.650 0.795 0.812 -0.172 -0.426 1.167 -0.360 0.746 -0.137 -1.048 1.612 +1 -0.222 1.229 0.441 -0.362 0.444 -1.363 2.018 -0.487 -1.256 -1.147 0.263 0.067 -0.322 0.053 1.090 0.968 0.663 -1.007 -2.277 0.260 0.167 0.628 0.919 0.525 0.535 0.641 0.885 0.675 +0 0.539 0.654 2.062 -0.043 0.305 -0.939 -0.635 -0.332 0.850 -0.748 1.235 -1.093 0.640 0.552 0.257 0.985 -0.743 -0.466 -0.963 0.009 -0.120 0.856 0.387 0.689 -0.197 0.406 1.813 -1.346 +4 -0.891 -0.109 -0.577 0.497 0.079 1.236 1.025 2.138 0.804 -0.791 1.627 -1.183 1.669 1.906 1.346 0.762 -0.221 1.815 -0.146 -0.940 0.491 0.252 -2.050 -0.358 -0.933 0.379 -1.542 -0.251 +0 2.252 -0.361 1.568 0.285 -0.533 -1.055 -0.181 -0.742 -0.799 -1.311 -0.571 -0.812 -0.160 -0.035 -0.889 -0.459 -0.180 0.595 0.903 -0.247 -0.109 -1.006 -0.201 -1.136 0.555 -1.196 0.250 -0.600 +4 -0.650 -0.838 -0.799 0.017 -2.241 -0.165 2.861 0.823 -0.430 1.079 1.433 0.291 -1.182 -0.810 -0.894 -1.601 1.024 2.100 0.042 -0.696 0.689 1.164 -0.696 0.263 -1.721 0.128 -1.001 0.300 +3 -0.510 1.784 -0.915 1.643 -1.196 1.159 -0.315 -1.214 2.382 -0.417 0.278 1.461 -0.063 0.100 1.589 0.369 -0.338 -2.306 -0.143 -0.132 0.080 -0.185 0.779 1.615 -1.365 -0.330 0.228 0.495 +2 0.219 -0.446 -0.201 0.368 1.040 -1.955 -0.369 0.214 -0.918 1.731 -0.635 -0.041 0.556 -0.591 -1.847 -0.429 1.029 -0.337 -0.846 0.926 -0.331 -0.510 0.149 0.771 2.382 0.827 1.160 -0.506 +4 1.274 -1.980 2.123 0.962 0.809 1.524 -0.501 -0.637 2.309 0.565 -0.154 0.158 0.392 -0.822 -0.563 -0.294 -0.405 2.049 -1.502 0.180 -1.754 -0.840 1.788 -0.407 -0.146 -1.535 -0.435 -1.050 +3 -0.315 -2.217 -2.278 -0.064 0.832 -0.770 1.750 -0.281 -0.847 -1.424 0.098 -2.010 -0.750 0.045 -1.397 -0.523 0.025 -1.218 0.651 0.942 -1.011 -0.200 -0.125 0.347 -0.298 -0.722 -1.057 0.836 +0 -0.561 -0.517 -0.374 0.183 -1.071 0.013 -0.447 0.330 0.603 0.135 0.553 -1.015 -0.023 0.115 -0.162 -0.463 -0.140 0.646 -0.670 0.121 0.782 0.782 -0.896 0.193 0.071 0.255 0.723 -0.648 +0 -1.048 0.115 0.239 0.236 -0.840 -0.496 0.127 -0.727 0.271 -0.614 1.000 1.563 -0.297 0.920 -0.212 -0.936 -0.426 0.086 -1.787 0.626 0.086 1.001 0.608 0.120 -0.356 1.523 2.170 -0.339 +2 0.332 0.236 -2.155 0.428 0.169 -0.407 -0.137 -0.068 -0.180 2.090 -0.371 0.169 -0.404 0.783 0.177 -0.280 -1.926 0.243 -0.310 -0.061 -1.182 1.433 0.857 1.431 1.147 0.592 -1.142 1.843 +2 0.221 -0.234 0.909 -0.900 -0.166 0.046 -0.204 0.598 -1.086 0.512 1.139 -0.823 0.401 0.357 0.589 0.001 1.418 2.110 0.625 0.314 0.014 -1.314 -1.427 -1.154 0.266 -0.193 1.527 -2.505 +4 0.442 -2.342 1.461 -0.176 1.215 0.801 -0.636 0.411 1.103 -1.726 0.986 -1.308 -0.873 -1.137 2.281 0.197 -1.950 -0.716 -0.695 0.103 -0.739 -1.471 1.389 -0.091 -0.178 1.632 1.900 -0.073 +3 0.192 -1.453 0.198 -1.777 0.271 -0.089 -1.874 -0.013 0.698 -0.444 -1.301 -0.429 -2.267 0.316 0.752 -0.469 -0.258 1.113 1.538 0.731 -1.014 1.237 0.617 0.238 0.310 1.068 -1.860 -0.152 +0 -1.014 -0.724 -0.301 -0.339 0.142 0.703 -0.368 0.396 0.015 0.935 -0.189 -0.079 -0.374 -0.162 -0.783 -0.883 0.776 -1.083 -0.319 1.015 -0.353 0.829 -0.591 -1.051 -1.595 1.313 -1.137 0.747 +3 -0.157 -2.559 -0.326 -0.024 -0.194 -0.075 0.214 0.950 -0.857 -0.205 1.537 -1.621 -0.277 -2.182 -0.258 0.102 -1.817 0.210 -0.678 1.080 0.475 -0.542 -0.208 1.550 -0.115 -1.346 -1.142 -0.188 +1 -0.953 0.155 0.820 -0.226 0.584 -1.996 -1.128 0.481 -1.488 0.447 -0.761 -0.975 0.347 0.573 1.207 0.630 0.357 1.309 -0.940 -1.251 -1.500 0.826 -0.732 -0.800 -0.103 -0.828 -0.665 -0.453 +4 -0.545 -2.695 1.295 1.086 0.065 0.747 1.774 -0.391 -1.803 0.881 -1.484 0.096 -0.040 2.120 1.066 1.395 1.496 -0.022 -0.630 1.419 1.499 0.432 -0.038 1.268 -0.546 0.635 -0.259 0.366 +2 0.827 -0.220 0.302 0.563 0.047 -1.337 -2.267 -0.812 -1.705 0.252 -1.021 -0.656 1.771 -0.394 1.187 0.039 -1.437 -0.041 -2.318 0.220 -0.935 -0.019 -0.656 1.184 0.043 -0.314 -0.493 0.212 +1 -0.289 -0.784 1.735 -0.857 -0.556 0.204 -1.202 -0.396 0.317 -0.333 -0.093 -0.529 -1.514 0.322 1.755 0.018 0.225 0.693 -1.269 1.703 0.202 1.632 -0.733 1.818 0.775 0.553 0.234 -0.249 +0 -0.175 1.090 0.575 0.028 0.069 -1.050 0.409 0.493 0.212 0.010 -0.143 0.791 -0.614 -1.342 0.905 -1.320 -0.742 0.424 0.332 -0.162 1.408 0.975 -0.106 -0.562 0.266 0.189 1.188 -0.198 +1 -0.447 0.394 0.949 0.852 0.885 -0.313 0.609 -1.775 0.548 1.550 -1.563 -0.037 1.219 0.739 1.280 0.277 1.470 0.588 -0.138 0.263 0.880 -0.112 -0.236 -0.007 -1.728 -1.350 -1.271 0.523 +1 -1.247 -0.924 0.164 -1.698 1.275 0.796 -0.410 -0.594 -0.267 -0.081 -0.747 0.465 0.460 -0.104 0.952 1.609 -1.388 1.067 -1.015 -1.257 -0.987 0.411 -1.126 -0.489 0.150 -1.330 -0.488 -0.483 +3 -0.455 0.333 0.190 0.549 0.406 0.084 0.512 1.938 -0.249 1.104 0.023 -0.273 -0.271 0.056 -1.926 -0.813 1.493 -0.818 1.174 0.439 -0.708 -0.402 -1.948 2.799 -0.808 1.482 -0.218 -1.398 +4 1.910 -2.612 0.593 1.319 -1.556 -0.721 -0.394 -0.731 -0.806 -0.375 0.852 1.488 1.303 0.994 1.832 2.031 0.166 1.431 0.424 -0.216 -0.050 1.013 1.466 0.580 -0.396 -0.489 -1.853 0.361 +3 0.498 -1.249 1.334 0.859 -1.761 0.406 -1.031 0.361 0.521 0.133 0.911 2.792 0.442 1.865 1.047 0.879 0.229 -0.353 0.476 0.895 -1.208 -0.665 -0.323 0.632 1.623 -0.815 -0.605 -1.034 +2 0.402 -0.309 -0.561 -0.401 -0.624 -2.674 0.609 -0.204 1.185 0.471 0.741 -0.442 1.227 -1.556 1.073 1.201 -1.568 0.053 -0.695 -1.032 0.852 -0.055 -0.390 0.166 -1.013 0.100 -0.279 1.441 +3 0.980 1.391 -0.719 0.763 -1.144 -0.233 1.741 -0.608 1.111 1.016 1.075 -1.267 0.525 -0.157 1.088 -1.655 0.368 0.226 -1.990 -0.885 -1.830 -0.726 0.549 0.195 0.169 -1.040 1.120 -0.019 +3 1.351 -0.069 0.917 -0.211 -1.135 1.602 1.863 -0.421 0.937 -0.145 2.181 -1.493 -0.178 -1.447 -1.587 -0.116 -0.450 -1.011 0.837 1.299 0.513 -1.877 0.339 0.330 -0.842 0.346 -0.507 -0.820 +0 1.380 -0.736 0.927 -0.261 -0.704 -0.540 1.309 0.781 0.116 0.912 1.489 -0.156 -0.982 0.433 -0.380 1.150 -0.761 -1.305 0.333 1.086 -0.377 -0.745 -0.729 -0.732 -0.694 -0.325 -0.775 0.055 +1 0.661 -0.452 0.086 0.196 -0.900 0.002 -0.095 -0.957 0.203 0.301 -1.086 0.869 -0.574 -0.674 0.742 0.704 -0.832 0.437 0.428 -0.399 0.581 -1.895 -0.616 0.862 -0.478 -1.067 -2.676 1.025 +2 1.856 0.224 -0.346 -1.612 -0.263 -0.336 0.960 0.462 -1.179 0.172 0.672 1.528 0.888 0.761 0.060 0.388 1.250 -1.333 -0.349 -0.773 0.379 1.230 -0.597 -2.390 -0.412 0.913 0.538 0.428 +2 1.049 -0.792 1.489 -0.268 -0.312 0.482 -0.392 -1.521 0.132 -0.318 -0.768 1.241 2.392 -0.919 0.197 -0.398 2.288 0.492 0.927 1.182 0.130 -0.186 1.519 0.778 -0.846 -0.724 -0.390 1.159 +0 0.774 -1.182 1.001 -0.173 0.586 1.658 0.251 -0.274 -0.149 0.492 -1.039 0.273 0.974 -2.361 -0.390 0.391 0.130 -0.581 -0.146 0.368 -0.435 0.827 0.504 1.435 -0.186 0.205 0.896 0.043 +0 0.582 0.148 -0.188 0.251 0.724 -0.577 -2.592 0.380 -0.780 0.223 -0.277 0.030 -0.446 0.163 0.149 -0.292 -0.849 0.651 0.106 -0.152 -0.679 0.616 -0.966 -0.709 2.065 0.681 -1.009 0.074 +0 -0.710 -0.214 0.009 0.078 0.274 -0.174 -0.787 0.082 -0.179 1.025 1.151 -1.372 0.617 0.818 -1.346 0.579 0.896 -0.950 -0.236 0.186 0.570 -0.988 1.157 -2.029 1.324 0.550 -0.622 0.864 +2 0.941 0.675 -0.385 1.147 -1.736 -0.693 0.175 -0.264 -0.215 0.754 0.610 0.247 -1.596 -0.242 -1.177 0.580 0.012 0.897 1.540 1.465 2.071 -1.047 0.654 -0.448 1.565 0.651 1.069 -0.267 +2 0.926 0.110 -0.325 0.647 -1.445 -0.262 0.005 0.031 -0.145 -0.553 -0.591 -2.258 -0.809 -0.748 1.202 0.752 0.090 -1.424 -0.057 -0.561 -0.787 -2.324 -0.080 -0.823 0.485 1.249 2.047 -0.458 +3 1.219 0.322 1.443 -0.051 1.127 -0.060 0.261 -0.103 0.061 -0.601 2.256 0.481 2.275 0.303 -0.121 0.244 0.516 1.657 -0.759 2.287 -1.360 -1.625 0.826 -0.230 -0.476 -0.785 0.740 -0.807 +1 2.412 0.068 0.233 0.537 0.082 0.613 -1.340 1.625 -0.767 -1.343 -0.658 1.744 -0.605 -0.930 -1.351 -0.963 -0.300 -0.581 0.752 -0.266 -0.904 1.253 -0.411 0.626 0.673 -0.340 -0.153 -0.010 +3 0.936 -1.428 -0.543 -0.176 0.506 0.851 1.629 -0.954 0.218 -1.763 0.410 0.493 0.020 0.654 -0.861 -1.320 -0.951 0.370 1.311 -0.859 -0.683 0.115 3.446 -0.448 0.964 -0.179 -0.540 -1.329 +1 0.764 1.235 0.687 0.031 0.968 0.096 -0.488 0.121 -0.319 -0.086 -0.846 0.999 0.214 0.711 0.482 -0.902 1.323 -0.225 -0.361 0.214 0.234 2.601 0.596 -0.486 1.898 -1.630 -0.482 -0.556 +2 -0.222 0.128 -0.124 0.272 -2.103 0.506 0.062 -0.187 0.927 -1.046 -0.237 0.284 -0.853 -2.003 -1.153 -0.065 -0.249 -0.018 -0.659 -1.312 0.137 -0.167 0.638 -0.748 -2.021 -2.604 -0.401 0.128 +4 -1.152 0.109 0.386 -1.177 0.928 -1.770 -0.578 -0.258 -0.636 -0.565 1.871 -0.851 2.441 0.437 0.092 0.476 2.530 -0.994 -1.120 -0.755 0.032 -1.726 0.022 1.694 0.151 -0.293 -0.618 0.899 +4 -1.002 -2.636 1.921 0.367 0.607 0.347 -1.169 -0.526 -0.046 -2.296 1.220 0.173 0.474 -0.751 -1.729 2.274 0.133 1.024 0.516 0.613 1.131 -1.387 -1.322 -1.733 -0.319 0.759 -0.720 0.205 +1 -0.570 0.005 0.687 1.812 -1.312 1.595 0.813 0.049 -0.654 -0.640 -0.495 0.689 0.219 1.182 -0.241 -0.207 -1.073 0.734 -0.459 -1.501 -0.781 -1.148 0.712 -0.229 0.101 1.032 -1.070 1.200 +4 1.387 1.145 -0.282 1.007 0.352 -0.114 2.650 2.346 0.650 -0.850 -0.478 -0.542 0.814 0.384 -0.312 2.066 -0.430 1.864 1.171 0.644 -0.779 1.604 1.973 0.236 1.248 0.098 1.505 -0.948 +1 0.142 -1.115 0.017 0.131 0.760 -1.069 -2.040 0.176 0.354 -0.179 0.028 0.879 -0.459 -0.279 0.079 -1.220 -0.152 -0.064 1.637 -0.298 0.200 0.919 -0.837 1.376 1.764 -0.029 -0.539 1.627 +2 -0.706 0.762 -1.722 -0.267 -0.228 -0.372 0.071 -0.826 1.141 0.992 0.860 0.853 2.521 0.192 -0.034 2.039 -0.336 -0.579 0.770 0.132 2.003 0.384 -0.327 0.914 -1.182 -0.480 0.883 -1.011 +3 -0.035 -0.182 -0.942 -1.523 2.534 -0.049 1.823 1.165 -0.321 0.181 0.152 1.844 0.830 -0.807 0.352 0.804 -1.175 0.494 1.345 -0.857 -0.719 1.160 -0.354 -0.982 0.882 0.577 0.094 1.125 +4 0.947 0.861 -0.728 0.281 1.653 1.687 -1.135 -0.371 -1.743 -0.477 -0.628 -2.566 1.045 1.424 1.241 0.964 -1.714 1.882 0.048 0.445 1.056 0.503 1.651 -0.735 1.443 -1.476 0.415 -0.536 +1 -2.076 -0.091 -0.071 0.503 -0.476 -1.169 0.880 -1.861 -1.267 0.818 -0.859 0.113 1.228 -0.800 -1.153 -0.044 -0.305 0.065 -0.070 0.577 -0.667 0.070 -0.689 -1.822 -1.285 0.335 -0.723 -0.320 +0 0.539 -0.322 0.172 -0.588 0.013 -1.168 -0.791 -0.282 -0.944 -0.505 -0.164 0.402 0.949 -0.464 1.335 0.939 0.504 0.154 0.319 1.187 1.224 1.335 0.041 0.555 1.344 -0.624 -0.318 0.016 +3 -0.718 1.412 -0.921 0.249 0.074 0.612 -1.459 -0.985 1.148 -1.223 -1.670 0.562 0.085 -0.736 -0.120 0.069 0.113 2.657 1.244 0.602 -0.896 0.950 -1.313 -0.155 -0.187 -0.340 -2.256 -0.982 +1 -0.318 1.173 0.846 -1.224 0.524 0.325 1.111 1.068 0.103 -0.722 -0.311 0.440 0.167 0.542 1.003 0.247 -0.065 -0.651 -2.658 -0.302 0.008 -0.913 0.672 -0.723 -0.034 -0.357 1.656 1.533 +0 0.413 -0.798 1.179 0.133 0.195 -0.048 -0.573 0.580 0.228 2.061 -0.742 1.404 -0.017 -0.073 -0.874 0.378 -0.180 0.482 -0.923 0.213 0.022 -0.446 0.108 -0.006 0.548 0.877 0.501 0.158 +3 -0.215 -0.123 1.915 0.187 0.714 1.155 -0.259 -0.643 0.189 -0.774 -0.509 -2.067 1.021 -0.887 1.222 -0.957 -1.363 -1.734 -2.226 1.555 0.773 0.168 1.385 -0.911 -1.007 0.153 -0.556 1.239 +3 0.637 0.377 0.925 -0.746 -1.839 0.066 -0.446 -0.028 -0.797 2.006 -1.743 -0.462 -1.510 2.149 -0.545 1.665 1.493 -0.199 1.013 0.752 0.765 -0.224 0.202 1.573 -0.274 0.085 -0.414 -1.169 +1 0.602 -0.358 -0.127 -1.258 -0.526 1.168 -2.279 -0.074 -0.095 -0.574 -1.808 -0.173 0.752 0.594 -0.132 1.310 0.354 -1.907 0.416 1.252 0.018 -0.525 0.440 -0.643 -1.177 -0.775 -0.206 0.384 +0 -0.456 0.812 0.990 0.711 -0.271 0.461 -0.773 0.753 -0.018 -0.475 -0.729 0.678 -0.137 -0.490 -0.493 -1.243 0.167 0.287 -1.482 -1.076 -1.648 1.205 0.618 1.104 1.620 0.439 -0.060 0.195 +1 0.497 -0.138 0.648 1.523 -0.234 -0.234 1.579 0.767 -0.469 0.543 -0.463 -0.466 0.242 -1.913 -1.725 -0.562 -1.013 0.314 -0.908 -1.412 1.466 -0.226 0.068 -1.425 -0.544 0.111 -1.151 0.376 +2 0.224 -1.069 -0.321 -1.904 0.086 1.330 -1.796 0.129 0.668 0.816 -0.746 -1.102 -0.587 1.191 -1.710 -1.216 -0.459 -0.976 -0.042 1.415 -1.204 -0.146 -0.008 -0.335 -1.578 -0.638 -0.635 0.634 +3 2.363 -0.025 1.213 -2.514 -0.265 -1.461 -0.861 0.051 -0.335 1.314 1.769 -0.967 -0.488 -1.031 1.156 0.159 0.032 0.312 -0.311 -0.856 0.051 -0.267 0.347 -1.632 0.186 -0.689 0.151 0.905 +0 -0.188 -1.247 0.076 -1.818 0.361 0.576 -1.634 -0.295 0.113 -0.637 0.390 -1.756 -0.589 0.933 -0.648 0.918 0.704 0.178 -0.200 1.296 -1.169 -0.155 -0.013 -0.752 0.598 -0.247 1.467 0.087 +4 -1.000 1.317 2.207 -1.687 -1.085 0.773 0.513 0.777 -0.574 1.988 0.118 -1.217 -0.253 -1.204 1.591 -0.660 0.492 -0.471 -0.615 -1.910 0.598 1.299 -0.916 -1.545 0.803 -1.646 1.220 -0.904 +3 -0.433 0.392 -0.926 -1.209 1.243 0.727 0.066 1.665 -0.268 1.323 0.176 -1.025 1.848 0.991 -0.121 0.727 -0.404 0.829 -2.391 -1.020 0.979 -0.250 0.208 -1.347 1.848 1.191 -0.201 -0.405 +2 -0.730 -1.166 -0.465 0.202 1.586 0.457 0.778 -1.134 0.029 1.694 1.027 1.996 1.336 0.857 0.014 -0.246 0.992 -0.970 0.057 -0.086 1.176 -0.801 0.846 2.060 0.641 -1.011 -0.562 0.527 +4 1.773 -1.165 -0.090 -0.078 0.174 2.147 -0.449 -1.158 -0.680 0.350 -0.168 0.687 2.259 -0.063 1.250 0.504 0.085 0.886 -0.891 0.376 2.682 -2.371 1.462 -1.226 -0.154 -0.069 -1.646 -0.705 +4 -0.611 -0.832 -0.492 2.177 -0.399 0.907 -0.376 0.261 0.792 -0.572 -0.641 1.659 0.985 0.791 0.787 0.460 2.018 0.044 0.577 -1.451 -0.825 -1.201 1.274 -2.553 -0.402 -2.994 -0.831 -0.491 +3 1.599 0.412 0.037 0.764 -0.250 -0.183 0.756 -0.205 1.699 -0.486 -0.492 -1.221 0.096 1.943 1.669 0.495 0.181 -1.493 0.628 -0.678 0.741 0.950 3.191 -0.268 0.635 0.657 -0.249 0.660 +2 0.341 -0.203 0.701 -0.554 -0.966 -0.861 -1.053 0.232 0.084 -2.350 -0.257 0.862 1.546 0.997 -1.402 0.224 -0.148 1.329 -1.307 0.388 1.283 -1.898 -0.357 -1.103 0.657 0.286 0.853 -0.800 +0 0.570 1.451 -0.216 -0.735 -0.107 0.904 1.129 -1.375 0.115 -1.434 0.884 0.422 -0.921 1.534 0.335 0.225 -0.179 -0.008 -0.381 -0.741 1.229 -0.493 -0.526 -0.129 1.425 1.131 -0.395 1.227 +4 1.814 -1.198 0.339 1.951 0.395 -0.144 -0.072 0.527 2.014 -0.548 0.402 1.603 0.883 1.901 -1.505 -0.431 -1.028 0.236 0.580 -0.611 -1.148 -1.505 -1.020 1.433 -1.025 1.743 0.997 -2.328 +1 1.092 1.083 -1.089 -0.324 0.394 -0.605 0.498 -1.348 0.261 1.027 0.054 0.176 0.844 -0.444 0.622 -0.031 -0.908 0.902 1.401 1.159 0.902 0.511 0.363 -0.094 -0.663 -2.066 -1.567 0.015 +4 -0.699 0.820 0.832 1.175 -2.020 0.296 -0.258 0.988 1.476 -1.309 0.016 0.833 -0.338 1.746 -0.401 -0.300 -1.073 0.154 -3.018 -0.248 1.269 -1.397 -1.618 1.116 1.252 -1.356 1.733 0.432 +2 -0.304 0.849 -0.870 0.096 -0.854 -0.418 -1.514 2.318 0.306 -0.326 1.291 -0.090 0.459 1.562 1.474 -1.144 0.013 1.277 -1.081 -0.258 -0.294 0.501 0.803 1.150 0.083 -1.041 -0.355 -1.058 +3 -0.319 -0.235 -0.934 -0.286 1.912 1.101 1.017 0.149 0.467 0.335 0.711 -0.673 -2.788 0.922 0.325 -0.919 -0.183 1.577 1.181 1.700 0.280 -2.075 1.044 0.480 0.066 0.286 1.324 0.522 +4 1.270 -1.094 0.979 -1.340 0.948 -0.851 -0.314 -0.645 1.961 0.994 -0.411 -0.825 1.314 -3.398 0.479 -0.959 2.254 2.731 0.031 -0.365 0.068 -0.622 -0.794 0.235 0.624 0.133 0.290 -0.633 +1 0.746 0.119 0.330 -0.051 1.634 0.180 0.272 -0.287 -1.408 1.598 -0.389 0.580 1.202 -0.142 1.513 -0.629 -1.952 -0.762 0.341 0.090 0.510 -0.477 -2.387 0.227 0.366 0.338 -0.312 0.155 +1 -1.446 0.245 0.203 0.426 -0.875 -1.026 -0.285 0.908 -1.018 0.151 -0.512 -1.524 -1.281 0.910 -0.604 1.244 -0.037 -1.368 0.477 -0.801 -0.377 1.133 -1.290 -0.450 -1.084 -1.190 -0.473 -0.858 +4 0.646 0.387 -1.503 1.828 -0.693 -1.332 0.673 -0.302 0.771 -0.887 0.143 -0.113 1.022 -0.375 0.803 -1.208 1.232 1.421 0.733 -0.016 2.055 -0.472 1.185 1.373 -1.243 0.715 0.719 -2.495 +2 0.502 1.347 -0.027 0.805 0.333 -0.750 -0.566 -0.311 -0.247 1.166 1.489 0.867 1.478 0.278 -2.671 0.004 -0.775 0.671 -0.142 0.313 0.051 -0.551 -0.839 -0.488 0.439 1.375 0.438 2.549 +4 -1.231 0.371 -2.281 1.416 2.439 1.144 0.120 -0.401 -1.020 -1.274 -0.633 0.544 -0.532 -0.199 0.262 0.961 1.875 -1.625 1.972 1.372 0.783 -0.328 -0.434 1.055 -0.359 -1.961 -0.148 -0.611 +3 -1.186 -0.325 0.661 0.510 0.262 0.933 -0.362 0.179 -3.376 -0.450 0.764 -0.390 -0.466 -1.041 1.003 0.548 0.825 -1.726 -0.160 -0.280 -0.281 -0.616 -0.346 0.850 -1.913 1.777 0.484 -0.042 +0 -0.615 -0.402 -0.541 0.024 0.158 0.230 0.886 0.733 -1.502 0.150 -0.612 0.820 1.296 0.842 1.906 0.753 0.999 -0.794 -0.025 0.690 0.289 -0.914 -0.924 -1.810 0.372 -1.030 0.672 0.724 +4 0.963 -0.549 -0.188 1.710 0.172 0.898 -0.097 1.323 1.597 1.877 1.110 -1.270 -0.917 0.806 -2.651 0.013 -0.414 0.566 -0.689 1.121 1.422 -1.392 -1.660 -1.235 -1.034 -2.276 1.110 0.935 +2 -0.270 -1.390 1.352 2.099 -0.041 2.159 0.091 -0.380 -0.630 1.637 -0.780 0.735 -0.559 -0.460 1.030 -0.380 -0.405 1.133 1.597 0.645 -0.655 -0.511 0.080 0.321 -0.988 0.723 0.086 -1.716 +4 0.080 -0.973 -0.270 -0.037 -0.074 1.743 0.468 -1.235 -1.871 0.397 0.597 2.094 1.046 0.307 -0.028 0.427 1.116 -0.374 0.012 -2.105 -2.059 0.545 -0.594 0.291 1.622 0.885 -0.697 -2.435 +3 1.532 0.186 -0.823 -1.533 0.123 -0.783 0.155 0.291 0.717 -1.172 -0.887 -0.314 0.261 0.202 2.578 -2.259 1.110 1.059 -0.915 -0.472 1.762 -0.622 -0.606 0.120 0.512 0.998 1.041 0.782 +0 1.982 -0.585 0.454 0.212 0.156 -0.837 0.102 0.251 0.096 1.020 -0.699 -0.562 -0.659 -0.672 0.823 1.645 0.166 0.074 -1.615 0.872 -0.627 0.762 0.421 0.604 -0.916 -0.685 0.555 -1.775 +1 0.144 -0.482 0.592 1.196 1.738 -0.431 0.047 -0.332 0.336 0.022 -0.757 1.387 -0.902 -0.073 0.504 -0.745 -0.848 1.093 0.950 1.922 0.766 -0.963 -1.456 -1.576 -1.329 0.699 0.609 -0.651 +2 0.516 -0.965 0.394 -0.074 -0.361 -1.247 0.132 0.039 -0.840 -1.597 -1.644 -0.254 0.306 1.850 -0.045 -1.625 -0.207 1.251 -0.683 1.119 0.176 0.647 1.859 -1.837 0.897 -0.095 -0.949 -0.101 +4 -2.292 -1.492 -1.099 -0.248 -1.719 0.461 1.585 1.094 0.584 0.897 1.341 0.577 -1.244 1.283 0.307 -0.468 0.459 -0.358 -0.475 1.826 -1.902 -0.173 -0.901 -0.885 -1.365 -0.173 0.808 0.519 +2 1.612 0.169 -0.170 1.793 -0.920 -0.249 -0.535 -2.226 -0.160 0.831 -2.171 1.392 0.449 -1.564 -0.703 0.264 0.328 0.036 -1.286 -0.798 -0.680 0.314 -0.492 0.474 -0.971 -0.227 -0.091 -0.921 +4 0.142 -0.795 -0.378 -2.450 1.434 1.337 0.108 0.319 -1.268 -1.195 -1.697 -0.161 1.061 -0.383 0.348 0.979 -1.624 1.846 1.157 0.514 0.924 0.971 0.293 1.428 -0.453 0.462 1.322 -2.691 +0 -0.616 -0.962 -1.585 -0.664 -0.954 -0.294 0.869 -0.682 0.836 0.929 -0.097 0.494 0.833 -0.358 -0.212 0.366 0.458 -0.057 0.753 -0.182 0.548 0.384 -0.630 1.370 0.938 -1.048 0.669 -0.319 +0 0.700 -0.389 1.299 -0.087 1.250 1.220 -0.237 -0.030 -0.330 0.543 0.359 -0.028 -0.042 0.026 1.199 0.786 1.284 -0.311 -0.734 -1.057 -0.227 0.110 0.193 -0.362 -0.239 -0.432 1.700 0.242 +2 -1.220 -0.380 -1.755 -0.121 -0.827 -1.168 -0.025 1.882 0.795 -0.252 -0.049 1.878 1.259 -0.311 0.501 -0.999 0.025 0.764 1.020 -0.640 1.002 0.148 -0.289 1.909 -0.360 -1.108 -0.672 0.726 +4 -0.276 0.650 2.828 1.970 -0.455 1.124 -0.219 -1.788 -0.868 -0.280 0.255 -0.585 0.877 -1.072 0.811 -0.711 -0.844 -1.232 -0.198 -0.217 1.796 -0.160 2.115 -0.321 0.128 -1.004 -0.225 -2.270 +2 -1.733 0.441 -0.756 -1.285 0.019 0.973 -0.574 0.497 0.682 0.792 -0.732 -1.121 -0.985 0.791 0.667 -0.957 2.133 -1.076 0.999 1.520 0.110 -0.770 -0.748 -0.262 1.081 -1.200 -1.839 0.362 +3 -0.864 0.013 0.027 -0.621 -0.371 -0.231 0.810 -0.148 0.825 0.586 0.572 1.388 -1.119 -0.473 1.763 -0.257 -2.815 -0.203 0.135 -2.617 1.334 -0.714 0.329 -0.226 -1.288 -1.071 -0.935 1.086 +2 0.327 -0.464 -0.996 2.213 -0.078 -0.210 1.413 1.887 1.446 -0.390 0.347 0.585 2.237 0.420 0.465 -1.013 -1.255 1.354 0.346 0.447 1.074 0.852 0.722 0.636 0.139 0.209 0.916 -0.938 +1 0.581 -0.382 -1.073 -0.181 -0.563 0.454 2.167 0.442 0.063 1.109 0.125 -0.342 -1.201 -1.155 -0.303 -1.117 0.609 0.086 -0.676 1.286 0.357 0.732 0.540 -0.531 -0.146 1.808 -1.583 0.793 +1 -0.261 -0.740 0.285 0.997 -0.360 0.261 -0.935 -1.567 0.402 0.880 -1.914 -0.091 0.432 0.289 -0.590 0.048 0.926 -0.860 1.555 -1.388 0.898 0.054 -0.515 -1.842 1.190 -0.934 -0.030 0.471 +4 -1.664 -1.636 -0.983 -0.537 0.099 0.606 -0.968 0.538 2.325 -0.628 0.090 0.613 1.230 -2.032 -0.063 2.103 -2.451 -0.336 0.746 -0.654 1.377 -0.540 0.986 0.909 -0.326 0.772 -0.745 1.447 +0 0.346 0.774 -0.393 0.236 1.220 -0.173 -0.105 0.006 -0.441 -0.352 -0.769 0.429 -0.072 0.599 1.022 -1.051 -0.837 0.692 -0.254 0.081 0.084 -0.948 0.506 0.249 -1.008 -0.165 -0.109 0.748 +2 -0.363 0.550 -1.540 1.272 -1.116 -1.362 1.218 -0.003 -0.306 1.568 0.731 -0.450 -0.134 1.074 -0.964 1.443 -0.443 -0.871 1.087 0.735 -1.777 -0.107 -1.105 0.798 1.720 0.538 -0.760 0.890 +3 -1.918 0.663 0.452 -0.285 0.463 -1.114 -1.334 0.553 -0.334 1.493 -0.363 -1.023 2.641 -1.424 0.150 0.107 0.714 -0.337 -0.274 -0.149 1.801 0.865 -0.853 -0.529 -0.131 -0.896 -1.607 -0.681 +1 0.772 1.086 0.548 1.180 0.569 -0.416 -2.103 -1.156 -0.600 0.487 -1.275 -1.387 -0.443 -0.329 0.076 1.246 -0.531 -0.175 0.852 -0.939 1.132 -1.016 -0.455 -0.744 1.538 0.089 -0.209 -1.212 +4 -1.186 -0.582 -0.719 0.061 0.567 -0.274 -0.179 0.886 1.780 -1.566 -0.220 -0.750 0.556 0.010 -0.588 0.448 -0.167 1.441 0.470 2.608 -0.361 1.825 0.425 -1.210 0.179 -2.196 2.129 0.353 +2 0.072 -1.201 1.861 -1.021 -1.222 -0.084 0.466 -0.899 0.696 -0.493 2.445 0.333 -1.085 -0.704 0.493 -0.812 -0.076 1.970 0.640 0.595 1.984 -0.759 -0.017 -0.303 -0.626 -0.255 -0.456 -0.658 +3 -0.672 -0.216 0.960 -2.072 -1.503 -0.036 -0.003 -1.572 -1.848 -1.501 0.289 0.773 0.181 -1.818 -1.284 -0.214 -0.015 -0.589 -0.375 0.292 2.012 1.022 -0.368 -0.173 -0.116 -0.576 1.270 1.244 +3 -0.929 -0.504 -0.073 1.889 0.238 -0.841 0.221 -0.352 0.325 0.581 1.209 -0.024 1.639 0.908 -0.707 2.210 0.327 -0.835 1.653 2.078 -0.033 -0.504 -0.172 0.715 1.278 0.570 0.102 1.498 +4 -0.202 2.422 0.460 1.696 -0.254 1.365 2.073 -0.109 -0.539 -1.318 -0.309 0.082 2.031 -1.295 0.060 -1.814 -0.427 0.311 0.513 0.589 0.397 0.580 0.046 -1.048 -1.852 -0.547 -0.135 1.254 +0 -1.588 -0.497 -0.326 -0.228 0.297 -1.348 1.025 0.887 0.674 -0.208 -0.738 -0.325 0.061 0.666 -0.081 0.360 -0.434 0.286 -0.249 0.268 -1.767 0.487 0.540 0.529 -0.987 -0.710 -0.869 0.330 +4 -0.457 -1.355 0.069 -0.818 -1.055 -0.720 2.485 0.136 0.362 -1.222 -1.844 -0.819 1.438 0.334 0.599 0.739 -0.591 1.388 -0.633 1.406 0.442 -1.789 -0.748 -1.785 1.163 -0.025 -2.228 0.376 +3 0.174 -0.877 1.983 0.025 0.881 -0.009 0.997 -2.101 -0.705 -0.522 -0.530 0.519 0.587 -0.074 -0.049 2.441 0.325 -2.225 0.770 0.559 1.159 0.598 -0.905 0.890 0.099 1.046 1.548 -0.122 +0 0.958 -0.267 -0.892 0.726 0.375 -0.313 -0.360 -0.915 0.725 -0.360 -0.770 -1.286 0.392 -0.509 -2.013 -1.203 -0.234 -0.206 -0.244 -0.383 -1.476 -0.875 1.184 1.036 0.578 0.490 0.905 -1.121 +3 -0.517 -0.854 -0.355 0.152 -0.440 -0.211 1.391 0.887 -0.789 0.354 1.485 -0.443 -0.953 -0.929 -1.201 0.337 -0.719 0.434 0.593 0.291 1.519 -0.592 -1.075 0.388 -2.913 -0.771 -0.637 -2.030 +4 1.665 0.757 -0.776 -1.253 -0.498 -0.253 0.682 -2.302 -0.965 1.800 0.016 0.062 -0.564 0.789 -1.804 -0.425 -2.081 -0.020 -0.446 -1.630 1.011 0.690 1.030 -1.507 -1.004 1.202 0.811 1.404 +3 0.737 -0.587 -1.058 -0.544 1.954 0.282 2.126 -0.120 -0.259 -0.074 -0.092 -0.774 -1.149 0.501 1.055 2.445 -0.434 -0.353 -0.771 1.126 0.815 -0.877 -0.902 0.953 0.383 0.758 -0.883 -1.878 +1 -1.243 0.357 -0.797 0.032 0.248 0.980 0.892 -0.003 -1.136 0.205 0.118 -0.542 -1.916 0.048 0.052 0.656 0.324 -0.757 -0.514 1.360 -0.294 -0.083 -0.409 0.713 -1.336 -0.313 2.669 0.201 +1 -2.217 0.491 0.384 1.934 -0.435 1.636 0.043 1.524 0.338 -0.561 1.422 -0.351 -0.360 0.266 0.029 0.552 0.255 0.568 0.553 -1.232 -1.981 0.279 -0.691 -0.805 -0.272 0.267 -0.125 0.725 +3 -0.539 1.515 -0.125 2.071 -1.741 -0.522 -1.547 -1.209 -0.828 0.315 0.267 -0.679 -2.228 1.478 -0.286 -1.116 0.275 0.204 -0.323 0.149 -1.155 0.760 -0.997 -0.606 0.649 1.204 1.586 1.056 +1 -0.701 0.097 -0.572 0.129 -0.181 1.024 -1.821 -0.112 0.411 -2.546 -1.026 1.191 -0.069 0.469 -0.835 -1.160 -0.019 1.421 -0.432 -0.058 0.383 0.926 0.724 0.952 -1.326 0.126 -0.138 -0.554 +3 0.420 0.758 -1.137 1.163 -0.149 1.183 0.124 0.214 -0.288 -0.332 0.410 -0.322 -1.671 -0.124 -2.587 -1.124 -1.000 0.578 2.551 -0.450 0.323 1.238 0.240 -1.667 -0.655 0.496 0.575 2.071 +0 -0.149 -0.146 -1.156 -0.268 0.243 1.105 -0.620 -0.190 -0.949 0.225 0.784 0.630 -1.170 0.147 -0.677 1.011 -0.161 -1.520 -0.220 0.458 0.244 -0.512 -0.585 -0.988 -0.395 -0.856 0.970 -0.901 +0 0.649 -0.009 0.458 -0.044 0.915 0.731 0.679 0.308 0.487 0.898 2.350 1.022 -1.303 -0.087 -0.575 -0.012 -0.117 -0.149 0.491 -0.062 0.819 -0.315 -0.832 -0.063 -0.523 1.009 0.756 1.174 +3 -0.628 -2.053 0.361 0.072 0.556 -0.227 -0.430 2.008 -1.472 0.734 -0.575 -0.336 -0.317 -0.803 0.571 1.391 0.470 0.103 -0.986 0.517 2.156 -0.381 -0.376 -0.561 -0.489 -2.745 0.405 -0.846 +3 1.097 -1.425 1.825 0.142 -0.541 0.027 0.116 0.433 0.214 2.029 -0.547 -0.030 -2.056 2.859 0.111 -0.052 0.823 0.712 -1.011 1.042 -0.622 -1.511 -0.687 -0.048 -0.040 -0.909 -0.437 0.048 +0 -0.624 -0.185 -0.605 -2.206 0.897 1.275 0.652 -1.138 -0.201 -0.007 0.599 0.668 -0.734 0.082 0.457 1.456 0.705 0.789 0.084 1.410 0.410 -0.861 1.403 0.698 -0.443 -0.479 0.297 0.463 +0 0.096 0.092 -0.508 -0.707 0.941 0.960 0.114 -1.592 0.065 -0.408 -0.064 0.126 -0.113 0.986 -0.665 0.977 -0.351 0.805 0.903 0.419 -0.587 -1.819 0.474 -0.425 0.095 0.972 -0.696 0.132 +1 -0.335 -0.019 0.796 -0.261 -0.820 -0.114 0.307 -1.045 -0.303 -0.054 0.019 1.266 0.563 0.487 -1.109 -0.492 0.271 -0.070 0.859 0.658 -2.060 0.243 0.482 -2.067 0.348 2.755 -0.510 -0.474 +4 1.600 0.960 0.568 0.233 0.215 0.076 2.055 -1.267 -2.765 0.443 1.403 0.186 -1.980 0.746 -1.065 -1.284 -0.978 -1.609 0.203 -0.648 -1.330 2.380 -0.531 0.052 1.554 0.677 1.908 -0.696 +4 -1.699 -0.396 0.974 -1.420 -2.715 -0.070 -1.403 -1.727 -0.106 1.532 -0.590 -1.640 -2.376 0.188 0.211 -0.157 0.945 0.824 -1.143 1.101 0.080 -0.386 -1.212 -0.915 -1.115 1.366 -0.444 0.049 +3 0.781 0.550 0.349 0.155 2.131 -0.666 -2.133 0.601 -0.950 -1.927 0.088 -0.355 -0.874 -0.893 0.450 1.113 -2.097 0.872 1.335 -0.146 -1.482 -0.904 -1.506 -1.114 0.088 -0.294 0.584 -0.039 +1 -1.182 -0.394 0.403 -0.495 -0.545 1.945 -1.273 -0.325 0.525 0.889 -1.251 0.419 -0.396 1.449 0.372 0.241 1.667 -0.002 -0.804 -1.327 -0.685 1.170 -0.850 0.072 -0.533 0.278 -0.311 -0.776 +2 -0.698 0.065 0.181 -0.016 -0.464 -0.666 -0.422 -0.746 1.121 -1.025 0.946 -0.564 -0.416 0.361 0.757 -0.273 0.152 2.261 0.272 1.358 -1.399 -1.737 0.280 0.653 -1.913 1.284 0.956 -0.727 +2 0.258 -0.741 -0.021 -1.040 1.024 -0.837 0.017 0.595 -0.080 -0.423 1.398 1.016 1.953 -0.886 -0.651 0.298 1.110 -0.728 2.084 -0.267 0.395 1.193 -0.484 -1.186 1.836 0.480 0.952 0.158 +3 0.350 -1.512 -1.225 0.753 0.153 -0.317 -1.394 0.369 0.087 0.723 -1.737 0.620 -0.542 0.291 -0.063 -0.851 -0.412 -0.877 -0.525 -0.909 0.402 -0.860 0.558 3.266 -0.257 -1.582 2.104 0.349 +0 0.386 0.543 0.156 0.207 -0.111 0.047 -0.255 -0.517 -0.770 0.424 0.140 -0.825 1.210 -0.125 1.489 0.435 -2.140 -0.134 -1.449 -0.459 -0.240 1.069 0.430 -0.143 -0.211 -1.050 -0.101 0.416 +3 1.022 -0.275 1.194 -0.037 0.556 -0.666 0.028 -2.632 1.905 0.563 -0.283 -0.449 -1.507 -1.077 0.377 -0.004 0.744 -1.067 1.330 -0.693 -1.038 -0.514 1.528 0.796 -1.001 -0.768 -2.344 0.480 +4 -0.528 1.529 -0.054 0.465 -2.384 -0.912 0.230 1.313 1.368 0.388 1.544 1.959 -0.110 0.750 -1.328 1.543 0.887 0.714 -0.000 1.061 0.999 -0.927 0.207 0.416 -0.305 1.244 1.957 1.208 +0 1.670 0.808 0.421 0.800 -0.006 0.739 -1.127 0.432 0.647 -0.117 -0.654 -0.043 0.015 0.172 -0.231 1.770 -1.121 -0.294 0.714 -0.876 -0.133 -0.192 1.199 0.952 -0.042 -0.600 -0.312 0.670 +2 0.584 -0.707 1.001 -1.156 -0.855 -0.801 1.328 -0.179 -0.629 0.164 0.542 1.589 -1.497 1.263 -2.067 0.906 1.163 1.215 0.569 -1.065 0.747 -1.068 -0.939 -0.456 -0.366 0.241 -0.695 0.330 +2 -0.523 1.049 -0.704 -1.408 -1.557 0.606 -1.280 1.755 -2.082 1.696 0.211 -0.097 -0.545 0.399 -0.038 1.103 0.114 0.150 -0.364 -0.057 0.308 -1.710 -1.348 0.743 0.171 -0.184 0.018 0.348 +1 0.427 -0.685 -1.006 0.038 1.161 1.608 0.795 0.029 0.270 0.012 -1.026 0.025 -1.335 -0.124 1.636 0.823 -0.923 -0.072 -0.834 -0.063 1.041 0.653 2.484 1.192 0.204 0.677 -0.202 -1.128 +0 0.080 0.204 0.485 0.474 -0.032 -0.072 -1.079 0.338 2.052 0.439 0.109 -2.180 -0.504 0.789 0.148 0.501 0.433 0.321 -0.192 0.609 -1.066 -1.206 -0.183 1.324 0.010 -0.756 -0.046 -1.866 +1 0.970 0.668 -0.805 -1.103 -0.172 -1.227 0.514 1.413 -0.787 0.523 -0.792 1.256 1.012 -0.080 0.765 0.837 -1.192 0.495 -0.622 0.124 -0.344 0.029 0.920 -1.144 -1.749 -0.448 1.105 -0.898 +4 0.128 1.067 0.460 -0.451 -0.013 0.008 -0.462 0.888 2.511 0.116 0.717 0.529 -2.610 0.492 0.442 -1.249 -1.789 0.427 1.645 -0.369 -0.332 2.308 0.896 -1.214 -0.713 2.179 -0.663 -0.533 +3 -0.041 -1.552 1.596 0.438 1.731 -0.622 -1.626 0.810 1.842 -1.130 -0.731 0.716 0.120 -0.231 0.235 -0.184 1.316 -0.608 0.716 -1.469 -0.335 -1.415 0.858 0.620 1.297 1.777 -0.589 -0.548 +1 -0.154 0.702 0.750 -0.635 -0.798 -0.243 0.939 0.877 -0.358 -0.733 0.833 -0.300 1.037 0.035 0.564 -0.638 1.486 -1.107 0.675 -0.928 1.358 -1.255 1.829 1.787 0.887 0.791 0.706 -0.604 +1 -0.854 -0.344 1.480 -0.111 0.546 1.267 1.073 -0.007 -0.706 0.499 -0.686 1.815 0.868 0.209 0.513 0.178 -0.368 0.499 -0.243 1.937 -0.693 0.509 -0.783 -0.793 -1.786 0.411 -0.532 1.083 +0 -0.496 0.230 0.309 0.047 0.148 0.086 -0.772 -1.446 0.054 -0.782 0.976 -1.250 -0.357 0.155 1.261 -0.393 0.184 0.617 0.763 0.367 1.808 -0.397 1.731 0.690 0.245 0.323 0.938 -0.719 +2 0.884 -0.896 -0.371 1.454 0.277 1.238 -1.191 1.678 0.711 0.836 -0.890 1.102 -1.381 0.291 -0.062 0.847 0.284 1.164 0.918 1.543 0.181 -0.731 0.501 -0.993 0.684 1.435 -1.147 1.447 +3 0.260 0.536 0.157 -0.320 1.281 -0.159 0.110 0.016 1.151 -0.922 -0.093 -2.071 0.099 1.120 1.277 0.588 0.692 0.542 1.293 2.245 -0.145 -1.068 -0.946 -1.134 0.770 2.396 0.732 -0.679 +3 0.054 -2.144 0.245 1.412 -0.347 2.062 0.603 1.458 0.386 0.552 0.143 -1.568 -1.965 -0.241 -0.603 -0.571 -1.264 1.109 -1.031 -1.096 0.038 1.083 0.848 -0.650 -0.212 1.640 1.106 0.877 +1 0.464 1.243 -0.071 0.157 0.374 -0.649 -0.158 -0.909 0.687 1.567 -1.909 0.998 -0.044 1.262 0.260 0.971 -0.105 0.048 0.963 2.453 -0.527 0.116 -0.725 -0.239 -0.513 -0.998 0.508 0.204 +2 -0.730 1.154 -1.422 -1.098 -1.022 0.510 -1.755 -1.441 0.381 0.795 0.018 1.272 0.669 -1.239 -0.708 -0.802 0.430 -1.462 -0.556 0.739 0.512 -0.253 -1.054 -0.258 -1.315 -1.820 0.261 0.582 +1 -0.492 0.419 -0.133 -2.702 0.716 -0.426 0.659 0.169 -0.989 -0.174 -0.031 0.184 0.512 0.031 1.746 0.841 0.996 0.825 0.076 0.732 1.249 0.126 1.051 -1.302 1.099 -0.771 -1.304 -0.279 +0 0.408 -0.861 -0.356 -1.525 -0.195 -0.305 0.299 -0.624 0.083 -0.333 -0.331 0.080 0.041 0.761 -0.121 -0.350 -0.181 -0.715 -0.360 2.031 -0.913 -0.279 -0.387 -0.247 -0.331 0.279 -1.710 1.037 +3 0.225 1.036 0.917 -0.810 0.328 -0.371 -0.582 0.861 2.422 -0.214 -0.599 -1.093 1.129 2.175 -1.500 -0.334 -2.201 0.635 -0.659 -0.974 -0.620 -1.344 1.213 0.796 1.354 -0.527 0.380 0.444 +3 1.815 -0.934 1.737 -0.301 -0.299 -0.649 0.682 1.983 -0.564 -0.302 1.463 -1.118 -0.781 1.369 0.422 -0.865 0.622 -1.541 1.085 -1.967 -1.022 0.375 -1.262 0.011 0.566 -0.739 -0.119 0.838 +3 1.553 -0.699 -0.171 0.137 -0.266 -0.620 -0.257 -1.218 -0.606 -1.233 0.012 -0.392 -0.545 0.016 -2.473 -0.317 0.195 0.975 0.609 1.270 -1.433 1.046 0.299 -0.537 0.108 3.125 0.145 0.080 +1 -1.268 0.018 0.571 -0.889 1.303 -0.876 0.414 -0.606 0.556 -0.168 -0.541 1.052 0.991 -0.361 1.813 0.126 1.072 -0.690 -0.588 -0.708 0.406 -0.628 0.025 -1.279 0.020 0.637 -1.374 1.925 +2 1.165 -0.626 -0.373 -0.168 0.417 1.090 1.870 0.814 0.822 1.927 -0.205 1.189 0.578 0.615 -0.215 -1.394 0.345 0.349 -0.575 -0.097 -0.882 -0.873 -0.481 -1.328 -0.583 0.714 1.456 2.075 +0 -0.989 -1.679 1.189 -0.706 -0.602 0.742 -1.023 0.199 0.018 -0.082 1.195 0.466 0.979 -0.250 -0.724 1.039 -0.571 0.124 0.424 -0.770 -0.766 -0.272 0.744 0.346 -1.353 1.953 0.380 -0.424 +3 -1.148 -0.657 0.580 0.316 1.581 -0.670 -1.088 -0.032 -1.744 0.088 -0.587 1.954 -0.214 2.020 0.429 0.745 0.422 -1.542 1.370 0.786 0.077 -1.396 -1.691 -0.268 0.882 0.084 0.325 1.511 +1 -1.363 -0.766 -0.334 -1.061 1.322 -0.838 0.787 0.187 0.031 1.486 -2.261 -0.196 -1.080 -1.961 -1.195 0.017 -0.725 0.897 -0.214 -0.325 -0.320 -0.098 -0.418 -0.851 0.989 -0.624 -0.384 0.429 +2 -0.195 -0.164 -1.237 -0.052 1.863 0.984 -0.544 0.030 -1.296 -0.969 0.149 -0.494 -1.045 0.490 -0.082 -1.568 -2.075 -0.570 -1.588 -1.869 1.129 -0.098 -0.265 -0.188 -1.138 0.411 -0.131 -0.757 +0 -0.261 -0.564 -0.068 0.487 1.535 0.351 1.960 0.731 -0.298 0.891 0.637 1.162 0.737 0.114 -0.481 0.135 -1.482 -0.163 -1.180 0.458 0.834 0.316 -0.163 -0.285 1.201 0.987 -1.061 0.255 +0 0.669 0.210 0.206 0.538 -0.671 -0.714 1.425 1.864 -0.929 -0.669 1.143 0.534 0.676 0.897 -0.241 0.330 0.252 -0.858 0.315 1.238 -0.334 0.153 0.207 -0.285 0.677 -0.680 1.333 0.626 +0 0.596 1.003 2.211 -0.453 -0.105 0.513 1.193 -0.261 -1.175 -0.278 0.843 -0.522 -0.311 0.943 0.205 0.141 -1.652 0.719 -0.455 -0.408 -0.385 0.547 -1.044 1.325 0.105 -0.173 1.111 -0.148 +0 -0.218 -0.421 0.300 0.532 -0.557 -0.872 -1.112 -0.525 -0.147 0.972 -0.026 0.124 -0.817 0.303 0.516 0.549 -0.792 0.172 0.490 -0.651 0.243 -1.531 -0.422 -0.934 -0.845 0.745 1.634 -0.437 +0 0.239 -1.274 0.015 0.386 -0.155 0.714 0.099 -0.522 -0.567 0.811 -0.654 0.915 1.223 -1.669 1.133 2.060 0.252 0.599 1.037 -0.275 0.182 -1.314 0.139 0.498 0.516 1.065 -0.775 -0.533 +4 0.157 0.069 -0.544 0.826 -1.863 -0.535 -0.485 -1.636 1.495 -1.359 -0.588 0.908 0.646 -1.108 0.682 0.290 2.333 0.404 -0.141 -0.857 -0.744 -0.826 1.371 0.018 0.565 1.514 -2.358 1.279 +0 -0.911 1.589 0.485 0.692 -0.402 -0.175 1.424 -0.901 0.243 1.063 -0.985 -0.804 -0.719 -0.037 -0.148 -0.664 0.215 -0.374 -0.304 1.124 0.147 0.539 0.174 -0.660 -0.029 -0.730 0.031 1.021 +0 0.444 -0.453 0.137 -0.631 0.590 0.111 0.270 -0.762 -0.819 0.314 -1.422 0.868 -0.110 -0.975 -1.500 1.010 -2.250 0.471 0.139 0.636 -1.085 -0.672 0.325 0.052 0.878 -0.045 1.397 0.139 +2 2.495 0.416 0.544 -0.150 0.738 -0.301 -0.162 1.405 1.098 -0.011 2.022 -0.082 -0.506 0.127 0.301 -1.192 0.487 2.115 0.950 0.664 0.480 -0.998 -0.996 0.163 -0.428 -1.447 -0.351 0.451 +1 0.277 1.290 -1.578 0.374 0.905 -0.009 -0.982 0.136 -1.363 0.092 0.063 0.861 -0.118 0.063 1.636 -1.599 0.679 0.034 -1.173 -0.011 2.310 0.245 1.444 0.691 -0.605 0.169 0.448 0.021 +0 0.633 -0.182 -0.120 -0.194 0.439 0.773 0.537 1.009 0.333 -0.058 0.020 -0.197 0.835 -1.864 0.419 -0.176 -0.014 0.321 0.294 0.738 -0.068 -0.224 -0.362 -0.403 -0.923 1.582 -0.425 -2.066 +1 0.773 0.500 -0.955 -0.565 -0.869 0.144 1.530 0.314 0.683 1.894 0.654 -1.049 -1.015 -0.826 -0.758 -1.443 -0.106 -0.717 1.280 1.538 -0.183 -0.564 -1.010 -0.097 1.393 0.748 1.041 0.532 +1 -1.087 -0.297 -0.258 -0.988 -1.430 -0.600 0.550 0.871 0.287 -1.695 -0.248 1.966 -0.162 -1.067 -0.734 -0.538 0.708 -0.599 1.230 -0.415 0.205 -1.025 0.944 0.228 2.307 -0.059 -0.784 0.132 +1 -0.286 1.764 -0.922 0.152 -0.850 0.372 -1.621 -0.904 0.576 1.753 0.740 1.353 -1.217 -0.256 0.197 -0.892 0.325 -0.712 -0.742 0.885 0.586 0.299 0.174 0.698 -0.132 0.567 -0.658 1.195 +3 -0.255 0.038 0.041 -0.168 -1.074 -1.296 1.508 0.589 2.359 0.401 0.666 -0.905 1.469 -1.847 0.646 0.111 1.459 -0.131 0.038 -1.932 -1.893 0.369 1.139 0.290 -0.135 0.308 0.198 0.662 +1 0.397 0.044 0.687 0.797 -1.360 1.281 -0.497 -0.596 -0.599 0.809 -0.267 -0.664 -0.424 -1.795 -0.842 -1.240 -0.269 0.664 0.554 1.257 1.034 -0.529 0.019 -0.033 -2.151 -1.217 -0.167 -0.173 +4 -0.049 0.114 0.104 -0.825 0.587 1.879 2.081 -2.149 -0.028 -1.019 -0.621 -0.433 0.199 -1.442 0.613 -0.019 1.327 -0.841 1.717 -0.246 0.486 -1.278 0.950 0.851 2.118 -1.212 -0.895 -1.394 +1 1.016 -1.878 -0.096 0.816 -1.476 1.619 -1.802 0.605 -0.351 0.218 0.086 -0.875 0.536 -0.227 0.583 -0.650 -0.244 0.557 -0.723 1.171 0.295 1.983 -0.164 0.273 -0.226 0.006 0.556 1.618 +1 -1.175 -1.176 0.372 0.124 -0.457 -1.269 -0.268 0.217 -1.308 -0.410 0.382 0.788 -0.286 -0.801 0.115 -0.207 -1.465 -0.451 -0.590 0.023 -0.734 -0.548 -0.182 0.638 0.093 2.665 -1.265 0.912 +3 -0.976 -1.277 -0.079 0.653 -1.680 -0.468 1.119 -0.777 -0.584 0.712 -0.173 0.418 0.840 1.486 1.125 1.860 1.964 0.621 0.145 -0.235 -0.696 -1.729 -0.505 1.162 -0.787 1.411 -0.029 1.105 +3 -0.022 -0.206 -0.014 -0.574 0.924 1.041 -0.634 -0.165 -1.769 -0.091 0.039 -1.107 -0.528 -1.153 -1.574 1.482 -1.572 0.114 0.976 1.422 -1.061 1.278 0.161 0.573 1.760 0.611 -0.928 -2.055 +4 -1.328 -1.725 -0.452 -2.254 -0.453 -2.021 -0.094 0.196 -0.187 2.172 -0.206 1.730 0.296 2.013 -1.756 -1.082 -0.287 1.603 -0.239 -0.680 0.607 0.906 -0.767 -0.689 1.695 1.249 1.516 0.941 +4 -0.305 -0.416 -0.332 -0.856 0.345 -0.845 -0.781 0.497 -0.242 0.062 -1.712 -0.523 1.681 -1.781 1.750 0.261 -0.533 -0.204 1.231 -2.277 1.415 0.266 1.012 -1.748 -0.227 -2.328 1.198 -0.983 +4 0.255 1.981 0.092 0.438 0.346 2.269 1.003 -1.451 1.229 -0.945 -0.654 -1.300 1.822 1.186 1.231 1.036 0.691 -0.320 0.206 -0.516 -0.851 -0.640 2.354 -0.382 1.001 2.077 0.781 -0.788 +1 0.392 0.661 -0.639 -0.327 -0.297 0.079 -0.058 -0.522 -0.843 1.302 0.807 -0.517 -1.273 1.986 1.352 -0.489 1.887 -1.103 -1.234 1.596 -0.366 0.449 -0.505 0.359 -0.253 0.920 0.376 -0.132 +0 0.823 0.497 0.688 -1.398 1.246 0.167 -0.028 -0.951 -0.595 -0.210 -0.204 1.664 -0.068 -0.304 -0.233 0.384 0.230 -1.045 1.847 -0.238 -1.317 -0.648 -0.042 1.667 -0.247 -0.849 -0.342 -0.130 +3 0.507 0.327 -1.657 0.053 1.178 1.400 -0.260 0.496 0.542 1.008 -0.091 1.295 -0.730 0.780 -1.002 -1.790 0.612 2.955 0.320 -0.209 1.242 -0.445 -1.765 0.943 1.555 0.129 0.313 -0.609 +2 0.423 -1.968 -0.074 0.350 -0.570 -0.479 -0.450 -0.479 0.006 0.129 -2.101 0.653 -0.290 1.933 -1.192 -0.867 -0.203 0.413 -1.951 -0.829 0.591 -0.948 1.370 -0.454 -0.899 0.610 1.608 0.416 +1 1.106 1.187 0.639 -1.143 1.633 -1.146 0.303 -0.754 -0.064 0.329 0.321 0.422 1.614 0.454 -0.244 0.964 1.189 -1.228 0.597 0.701 -0.298 1.376 -0.150 0.126 -0.173 0.016 -1.096 -1.440 +1 -0.050 1.472 0.178 -0.795 -0.711 0.595 1.299 0.282 0.689 0.610 1.419 -1.950 0.667 0.984 -1.339 -0.886 -0.851 -0.729 -0.482 0.243 0.866 1.081 -0.838 -0.845 1.316 0.647 0.083 0.473 +0 0.143 -1.822 -0.361 0.540 0.424 1.411 0.837 0.408 0.029 -0.457 0.503 0.323 -0.374 -0.867 -0.053 1.156 0.110 0.028 0.423 0.481 0.249 -0.042 -0.508 1.005 -0.427 1.572 0.154 0.053 +2 -0.356 0.904 0.423 0.209 -0.259 -1.787 -1.050 0.575 0.224 0.046 0.043 0.131 -1.369 -0.481 0.437 0.916 0.166 0.131 1.866 -1.371 -1.014 2.051 -0.637 -0.986 -0.823 1.994 0.032 0.095 +0 -1.183 0.834 1.293 -0.141 0.985 1.526 0.131 -0.077 0.470 -0.838 0.547 0.151 -1.307 -1.295 -0.985 0.513 -0.337 -0.025 1.366 0.443 -0.643 -0.987 0.305 -0.971 1.253 0.246 0.129 -0.955 +1 -0.697 0.285 1.141 0.452 -0.320 0.515 -0.360 0.412 1.396 -2.077 0.797 -1.539 1.228 -0.637 -1.218 0.695 0.364 -0.999 -1.253 -0.187 -1.204 -1.061 0.116 -0.966 0.427 1.490 -0.149 0.130 +4 -3.661 0.680 -0.754 1.254 0.434 -0.970 -1.334 -0.667 -1.549 -0.112 -0.142 -0.053 -0.756 0.268 -0.191 1.497 0.856 0.619 -1.367 -1.294 -1.135 0.481 -1.102 -0.543 -0.005 2.004 -0.033 -2.317 +4 0.035 1.272 1.188 -1.328 1.170 -0.406 0.677 -0.772 -0.302 2.343 -0.643 -0.624 -0.165 -3.048 0.599 -0.622 -1.816 -2.088 0.578 -0.345 1.964 2.422 -1.187 -1.826 0.821 -0.713 -0.732 -0.044 +1 0.553 0.082 0.036 -0.606 -0.161 -1.097 -0.546 2.065 0.089 0.485 -0.026 0.066 0.134 -0.241 0.593 -0.716 -1.483 0.197 1.118 -0.649 0.784 0.672 1.560 1.864 0.580 -0.120 -1.616 0.977 +2 0.886 -1.387 0.630 1.712 -0.507 -1.315 0.318 0.170 -0.779 0.238 -0.602 1.002 0.005 -1.724 -1.317 -0.041 -0.657 -0.838 -0.101 -0.619 -1.315 -0.985 -0.253 0.552 -1.185 -1.760 1.237 -1.264 +0 -1.503 0.066 -0.367 0.533 0.697 -0.334 0.282 -0.216 -1.113 -0.800 -2.269 -0.396 -0.594 -1.095 0.819 0.526 0.554 0.692 -0.002 -0.012 0.830 0.667 1.027 -0.044 -0.986 0.237 -0.578 0.654 +0 0.748 -0.391 1.326 -1.866 0.879 -0.608 0.375 0.417 -1.392 0.997 0.394 0.117 0.385 0.125 0.835 -0.471 0.836 0.572 -0.172 1.846 0.452 0.195 0.574 -0.105 1.201 -0.021 0.819 -1.068 +1 0.669 0.422 0.240 0.370 -0.605 -0.025 0.149 0.641 -1.590 0.211 1.062 -0.603 -0.109 -1.863 0.066 -1.489 -0.553 -0.049 -0.607 -0.164 0.373 -0.038 0.141 -1.870 -0.158 -2.220 -0.396 1.841 +0 -0.667 0.057 0.543 -1.007 -2.873 -0.185 -1.232 -0.030 -0.338 -0.023 0.198 0.775 -0.035 1.341 0.789 0.814 -0.053 0.139 -0.570 0.838 -0.114 -0.624 -0.608 -0.813 -0.120 -0.109 -0.862 1.463 +3 -2.522 -0.372 -0.317 -0.482 1.486 0.688 -0.398 0.065 0.911 1.280 -0.604 -0.483 -0.585 -0.684 -1.516 -0.257 1.320 1.330 0.443 1.561 0.588 0.591 1.108 0.573 -2.298 1.081 0.494 -0.782 +4 0.578 -0.250 0.585 0.861 -2.027 -0.070 1.129 -0.110 2.816 -0.785 0.632 -0.046 -1.656 0.632 0.408 -0.158 1.195 -1.464 -0.562 -0.489 -0.077 1.069 0.255 1.514 -1.427 -1.094 -1.806 1.001 +1 1.315 1.002 0.037 0.430 0.855 0.936 0.953 -1.047 0.042 -0.204 -0.632 2.024 -0.613 1.777 0.925 0.990 -0.682 -0.218 0.646 0.107 -0.501 -0.349 0.340 -1.670 0.212 0.107 0.238 -1.341 +3 -0.440 -0.420 -1.069 0.375 -0.651 -0.325 0.041 -2.507 -1.600 1.587 -1.558 0.920 0.887 -0.018 1.492 0.822 0.197 0.389 0.954 0.397 0.853 1.609 1.255 -0.311 -0.852 0.047 2.075 -1.178 +3 1.096 1.933 0.160 -0.157 -1.123 -1.444 -1.774 -0.796 -2.091 1.079 0.423 -0.252 1.109 -1.071 -0.820 -1.934 0.774 -0.810 0.016 1.003 1.371 -0.029 0.384 -0.904 1.683 -0.166 0.080 -0.407 +0 -0.132 0.196 0.533 0.571 -0.427 -0.108 -0.099 -1.266 -0.967 -0.072 -0.838 -1.463 -1.519 0.695 0.355 0.148 -0.327 -0.582 -2.016 0.594 -0.407 0.622 -0.938 1.648 0.187 -0.121 -1.019 0.165 +4 -1.142 0.316 -1.096 0.615 0.182 -1.315 1.351 -0.196 -0.882 1.036 -0.550 -0.170 1.531 0.398 0.618 -0.112 -0.663 -0.330 -2.364 0.672 0.592 -1.487 0.379 -2.991 2.136 -0.572 -0.515 0.745 +4 -0.226 -0.973 0.604 0.813 -0.776 -3.442 1.655 -0.498 0.909 -1.136 -0.221 0.418 -0.475 1.402 -1.504 2.185 0.146 -1.705 1.437 -2.668 -0.559 -1.061 0.103 -2.223 -1.800 -0.251 0.030 -0.131 +3 -1.863 0.589 0.987 -1.276 -1.275 0.344 1.714 -0.877 0.087 -1.899 -0.199 -0.114 1.468 -0.707 0.686 -0.772 0.090 2.077 1.793 1.961 0.398 -0.210 0.285 0.466 0.389 -0.711 -0.752 -0.596 +0 0.311 0.652 0.769 0.395 0.807 -1.141 1.219 0.129 0.158 0.677 0.153 0.026 0.327 -1.431 -1.045 -0.153 -0.380 0.535 0.185 -0.712 1.755 1.539 -1.401 1.215 0.194 1.157 -0.648 0.603 +2 0.582 -0.158 1.083 1.414 0.101 0.926 0.220 -0.008 0.857 0.152 0.310 1.527 2.322 0.516 -0.110 1.557 0.900 0.709 -0.329 0.115 -0.565 0.984 0.511 -1.226 -1.104 1.542 0.239 2.299 +1 1.504 -0.532 0.849 -0.475 -0.760 2.249 -0.196 0.667 -0.120 -0.185 1.259 -0.374 -0.961 -0.282 0.058 -0.472 -0.651 -1.259 -0.532 -1.453 -0.970 0.784 1.061 0.041 -1.507 -0.481 1.144 0.453 +3 -0.063 -0.448 0.658 -0.395 1.593 1.465 -0.892 0.070 -0.651 -0.327 2.002 -0.308 0.360 0.541 -1.001 -0.855 -1.206 -0.886 -2.850 0.545 2.054 0.148 -1.416 -0.643 -0.459 -1.018 0.860 0.810 +0 -0.194 -0.584 -0.475 -2.099 1.313 0.018 -0.013 -0.816 -0.460 -1.133 -0.879 -0.247 0.126 0.490 0.798 1.198 0.634 -0.075 0.434 -0.160 -0.946 2.053 0.943 0.503 0.184 0.681 0.302 0.119 +3 -1.156 1.706 -0.977 1.278 -1.423 0.289 0.983 -1.122 -0.822 1.150 1.954 0.730 -0.115 0.005 -1.117 -2.032 0.734 -1.830 -0.522 -0.113 -0.829 0.711 -0.016 -0.456 0.558 0.333 0.837 -0.424 +1 -0.460 0.905 -1.454 0.013 -0.484 0.526 0.761 1.324 0.139 0.670 -1.146 -0.763 1.308 -1.051 2.298 -0.469 0.039 0.482 -0.956 0.459 1.009 -0.571 -0.746 1.141 -0.968 -0.688 0.991 0.739 +0 0.628 0.573 -0.674 -0.647 1.222 1.320 0.626 -0.868 0.868 -0.278 -1.269 -0.637 -0.224 1.087 -0.803 -0.966 -0.483 -0.122 0.673 0.570 -2.250 -0.356 0.124 -0.870 -0.780 0.625 0.683 -0.177 +0 -0.152 -0.868 0.595 -0.418 0.890 -1.063 -0.990 0.596 1.198 0.195 0.231 -0.834 -0.516 -0.657 1.295 1.391 0.520 2.011 0.618 1.183 -0.219 -0.157 -0.156 -0.936 -0.441 0.329 -0.599 0.556 +4 1.426 -0.223 -0.956 0.648 -2.313 1.383 -0.432 0.563 0.906 -1.655 1.274 -0.160 0.878 1.252 -0.462 0.756 1.818 -0.848 1.134 1.110 1.045 -0.920 0.353 4.562 1.032 -1.000 0.458 -1.973 +1 -1.575 1.655 0.906 0.823 0.863 0.650 0.452 -0.246 -1.258 -0.904 0.273 0.094 -0.766 -0.602 -0.044 -0.649 -0.744 0.814 -1.025 -0.033 0.500 0.084 -0.376 1.585 0.874 1.853 -0.482 0.171 +3 0.833 -1.202 -1.154 -0.187 0.443 1.206 -0.785 -0.485 -0.251 -0.271 1.496 -0.104 0.153 0.911 1.591 -0.035 1.259 1.828 -0.110 -0.564 2.187 1.121 0.012 0.650 -1.045 1.109 -1.800 1.071 +3 0.158 -0.067 0.666 0.532 -1.034 -1.532 -2.274 1.792 -0.445 0.592 0.202 0.913 -0.065 -1.328 -1.037 -0.351 -1.585 -0.854 -1.199 -0.329 -0.496 0.119 0.854 -0.243 -1.502 -2.048 0.162 -0.665 +1 0.462 -1.208 -2.145 0.163 0.988 0.615 0.783 1.127 -0.159 0.121 -0.470 -0.823 -0.279 -0.280 0.601 -1.652 -1.365 0.999 0.055 0.078 -1.536 0.026 -0.405 0.104 -0.232 -1.249 -0.199 1.359 +1 0.977 0.428 0.748 -0.243 -0.213 -1.071 1.005 -0.347 -0.033 -0.605 2.041 1.004 0.166 -0.073 -0.063 0.837 -0.288 0.969 -0.739 0.862 0.007 -1.663 2.121 -0.707 -0.027 -0.478 -1.488 -0.480 +3 2.083 2.407 0.454 -0.286 -1.431 0.144 1.043 0.405 -1.317 -0.421 1.500 0.167 1.233 0.063 -0.455 -1.168 0.300 1.145 0.591 0.866 -1.100 -0.151 1.842 -0.139 -0.635 -1.631 0.716 1.147 +3 -1.931 1.955 -0.023 2.299 -1.625 -0.318 1.170 0.718 0.567 -0.172 -0.709 0.151 -0.002 0.315 1.134 -0.243 -0.443 -1.297 -1.677 -1.210 1.009 1.198 -1.328 -0.522 0.399 0.387 0.978 0.804 +2 -0.871 -0.938 -0.521 -0.125 0.318 1.374 -0.871 -1.389 0.831 1.332 -0.490 1.041 0.770 -1.708 -0.637 -0.477 1.989 0.509 -0.503 -0.697 -1.570 1.066 0.396 0.619 -1.286 0.736 -0.238 -0.727 +3 -0.834 -1.328 2.887 0.542 1.242 -0.158 -0.406 -0.331 -0.516 -0.160 -0.086 1.432 0.464 0.687 1.441 -1.579 -0.409 1.071 -0.833 -1.496 -1.212 0.940 1.341 -0.155 0.376 -0.053 0.200 -0.862 +0 1.487 -0.275 -1.314 -0.546 -0.874 -0.576 -0.594 -0.634 -0.308 -0.065 0.764 -0.245 0.326 0.008 0.541 0.047 -0.647 -0.918 0.210 1.273 -0.229 -0.701 -0.256 -0.561 -0.667 -1.444 -0.244 0.236 +3 0.384 1.319 0.212 0.699 0.352 1.270 0.352 0.991 2.020 -0.804 -0.733 1.245 0.248 0.273 0.660 0.239 -1.587 0.259 -1.094 0.542 0.468 0.323 -0.121 2.097 2.247 -1.789 -0.474 1.721 +2 -0.022 -1.152 -0.562 -1.799 1.231 -0.973 -0.756 1.169 0.146 -0.127 -0.385 0.142 -1.306 1.774 -1.208 -0.228 -1.468 1.677 -1.258 -0.893 0.773 -0.347 -1.809 0.077 0.653 -0.124 -0.849 -0.326 +0 -0.346 -0.209 -0.050 -0.743 -0.066 0.296 0.045 -1.045 -0.263 0.069 -0.431 0.206 -0.509 0.173 0.671 -0.351 0.372 1.192 -0.164 0.637 -0.561 0.251 -0.339 -1.883 1.462 -0.358 0.480 -0.236 +3 1.661 0.614 -1.459 0.029 0.487 1.136 -0.475 1.028 -0.308 -0.714 -1.230 -0.899 0.154 -0.015 -0.493 0.023 0.804 2.128 -0.147 1.696 0.059 -0.668 1.474 -0.663 -0.129 -0.399 2.920 -0.628 +4 1.978 -1.167 -0.298 -0.255 0.916 1.129 0.108 -3.190 -0.889 -1.198 1.159 -0.921 1.683 0.408 1.135 2.256 -0.944 -1.620 0.187 0.954 -0.665 0.036 1.217 1.118 -0.582 0.271 -0.936 -1.450 +2 -0.419 -0.499 0.320 -0.183 0.319 1.563 -0.478 -0.305 0.174 -0.228 -1.914 -0.580 0.687 1.438 -1.155 -1.159 0.030 -2.076 -0.445 -1.736 1.369 -0.769 1.653 1.114 -0.646 -1.072 -0.194 -0.105 +3 1.865 1.474 2.413 0.230 0.806 -0.251 0.663 0.137 -1.663 -1.504 1.809 0.155 1.420 -0.567 -0.892 -0.086 0.785 1.392 0.186 0.654 -0.144 1.456 1.471 -0.785 0.383 -0.996 0.816 -0.394 +4 0.016 1.506 0.546 -0.416 -0.906 -1.157 -0.446 0.374 -2.110 -0.135 0.802 -1.035 -0.178 -0.992 -2.708 -0.800 -1.064 0.775 0.481 -1.042 1.293 0.725 -0.256 0.330 -0.945 -2.462 0.091 -1.364 +4 2.521 -0.878 -0.017 1.891 0.839 -1.005 1.672 0.781 1.554 -1.217 -1.131 -0.139 0.545 -1.128 1.026 2.294 -0.603 -0.652 1.789 -0.535 -1.354 1.485 0.747 1.505 0.035 0.111 -0.268 -0.358 +0 -0.512 0.105 -1.435 -0.264 -0.140 -1.785 -0.197 0.762 -0.391 -0.718 -0.030 0.988 0.341 0.910 -0.339 0.023 0.587 0.145 -1.245 -0.524 -1.644 0.315 0.779 0.731 -0.550 -0.852 0.206 -0.165 +0 -0.773 1.445 1.121 1.828 0.018 -0.382 -0.968 0.950 -1.110 0.302 -0.388 0.845 -0.192 0.705 -0.069 -0.180 0.583 1.314 0.636 1.069 -1.397 0.705 0.597 0.168 -0.657 0.462 1.337 -0.143 +2 -0.782 -0.917 -1.423 0.347 -0.554 -0.475 -0.226 -0.319 0.723 0.417 1.051 1.339 0.416 -1.760 -0.323 -0.693 -0.281 -0.800 2.670 -2.171 0.330 0.032 1.035 0.739 0.165 0.941 -0.917 -0.080 +3 1.683 -0.407 -0.317 0.023 -0.399 -0.178 1.028 -0.066 0.290 2.033 1.464 -1.047 1.744 1.377 1.494 -0.625 0.212 -0.392 0.121 1.752 0.140 -1.720 -0.373 -0.655 1.790 -0.813 -0.407 -0.373 +4 -0.356 0.362 0.054 0.763 -1.518 1.652 0.525 1.360 0.874 -0.012 -1.284 -0.564 1.590 1.385 -0.620 -2.818 -0.213 -0.288 -0.250 0.529 0.876 0.990 0.446 -1.801 -0.341 2.304 0.374 0.368 +1 -1.054 -1.610 1.883 -0.684 1.413 -0.221 -1.131 0.509 -0.391 -0.207 0.145 -0.812 -1.621 -0.051 0.964 -0.902 0.154 1.506 -0.090 0.086 0.055 -0.682 -0.993 -0.989 0.368 0.758 0.805 -0.379 +2 -1.408 -1.148 -1.242 -0.333 -0.016 -1.563 0.050 -0.804 -0.067 0.932 -0.261 0.207 0.099 0.136 -0.970 0.404 1.486 -0.698 -0.958 1.977 -1.640 -0.450 -0.430 0.978 -0.225 -0.158 2.359 0.376 +1 0.015 0.753 2.545 0.964 0.248 1.424 -1.421 -0.405 0.067 -1.039 -0.541 -1.656 -1.279 -0.245 -0.941 0.421 0.268 0.357 -0.504 1.082 0.501 0.225 0.233 -0.208 1.319 1.142 -0.365 0.165 +2 -0.497 -0.163 -0.135 0.166 0.361 -0.450 -0.239 0.114 -2.957 0.553 -0.954 -0.289 -0.677 0.748 1.025 -0.112 -0.099 -0.039 -0.016 1.001 3.010 -0.172 0.568 -0.847 -0.755 -1.020 1.339 0.704 +0 -0.649 -0.846 -0.361 -0.318 0.096 -0.308 -1.254 0.209 -0.636 -0.335 0.971 0.351 -0.608 0.543 0.016 -0.505 -1.413 0.376 0.061 -1.914 0.651 -0.045 0.775 -0.203 0.672 -0.251 0.989 -0.309 +0 2.086 0.472 -1.363 0.758 -0.127 0.509 0.227 1.386 0.460 0.007 -0.062 -0.383 0.320 0.658 0.779 0.205 -0.680 0.506 0.789 0.727 0.032 -0.910 0.881 -0.209 1.293 1.446 -0.043 -0.534 +4 -0.956 2.318 -1.407 -1.298 1.314 0.713 -1.026 0.607 1.468 -0.030 2.599 -1.724 0.694 1.167 0.891 -0.204 -0.290 0.444 0.816 0.602 1.040 -1.019 -0.228 -0.940 -0.386 1.015 0.108 2.607 +3 -0.067 -0.938 -0.594 -0.687 0.326 0.316 0.012 0.941 -0.273 0.476 -1.260 1.938 0.975 -0.788 0.893 0.845 -1.682 1.016 -1.057 0.608 1.773 0.361 3.058 0.814 -0.111 -0.155 0.399 -0.043 +2 -2.484 0.283 0.048 -0.017 -0.323 -2.014 -0.736 -0.307 0.551 1.534 0.354 -1.388 0.622 2.116 0.207 -1.172 0.820 -0.325 0.154 -0.256 0.294 0.986 0.089 -0.001 -0.078 1.460 0.487 -0.251 +4 -0.182 0.124 0.897 -0.763 -1.453 1.243 -0.038 -1.289 0.745 0.535 1.822 -1.233 -0.684 0.363 1.474 1.550 0.967 -0.705 -0.696 0.310 -2.116 1.618 0.693 1.785 -0.986 -0.862 1.595 -0.258 +4 0.283 -0.110 -2.279 1.996 0.176 0.001 1.310 0.989 -0.298 -1.953 0.564 -0.963 1.239 -0.776 -0.452 -1.343 -1.262 -0.109 -0.202 -0.476 2.053 0.604 0.010 -2.757 0.826 2.995 -0.188 -0.811 +4 0.283 -1.823 2.127 0.685 0.587 1.124 -1.078 0.727 -0.353 -1.476 0.957 -2.496 0.502 0.287 2.216 -1.125 -0.140 -0.002 0.545 0.755 -0.116 0.419 0.569 0.801 0.286 -0.300 2.424 -0.634 +0 0.390 -2.281 1.726 -0.802 0.299 -0.003 0.199 -0.270 0.943 0.357 -0.716 -1.393 -0.008 0.585 -0.723 0.645 0.124 -0.792 1.200 0.425 0.429 -0.894 1.056 -0.618 -0.000 -0.522 -0.417 0.074 +4 1.296 0.738 1.567 3.137 1.431 0.926 -1.312 -1.186 0.360 -1.698 0.615 -2.135 1.152 -0.322 0.127 -0.748 -1.899 1.235 1.336 -0.351 0.662 -0.391 0.288 0.759 0.555 1.341 0.519 0.028 +1 0.716 -0.696 -0.188 -1.594 -0.008 -0.865 -1.327 0.113 -1.073 -0.653 1.932 -0.981 -0.306 -1.038 1.406 0.220 1.135 0.429 0.218 0.571 -1.780 -1.144 0.489 0.313 -1.098 0.100 -0.267 1.011 +0 0.163 -1.128 -0.130 0.942 -0.547 0.890 0.232 0.455 -0.781 1.287 -0.215 -0.466 0.849 0.095 -0.180 1.098 0.181 0.484 -0.070 -0.848 -0.177 -1.093 1.007 -0.784 1.275 -0.167 -0.426 0.993 +3 0.835 -0.449 0.508 -0.572 -0.134 -1.208 2.472 2.720 0.729 0.541 0.010 -0.976 -0.655 0.494 0.974 -0.452 0.934 -0.794 0.265 0.710 2.089 0.818 -0.669 -0.058 -1.076 0.980 -0.070 -0.406 +1 -0.023 0.549 0.305 0.372 2.114 -0.159 0.186 -0.567 1.097 1.527 0.237 0.389 1.380 -0.391 0.314 -1.611 0.135 -0.205 1.202 -0.171 -0.315 1.607 -0.199 0.642 1.421 -0.583 0.864 -0.742 +2 0.309 -1.014 -1.314 1.299 1.433 -0.289 -0.284 0.392 -1.186 -0.231 0.781 -0.928 0.798 0.897 0.530 2.087 1.693 -0.938 -0.239 0.361 -0.770 0.741 1.384 -0.960 -0.540 -1.032 -0.642 -0.459 +1 -0.357 -0.198 -0.651 0.857 -0.436 0.080 -0.707 -1.095 -0.114 0.677 1.037 0.715 -0.944 0.055 -0.084 0.727 0.509 -0.625 0.348 0.571 0.329 -0.037 -0.657 -0.402 2.164 -1.184 1.882 1.875 +4 -0.278 -1.712 -1.091 -0.478 -0.344 0.148 -0.939 1.086 -0.645 1.173 -0.166 2.689 -1.566 -0.215 1.865 -0.654 -1.134 0.291 -0.112 1.405 1.023 0.646 -0.709 -1.783 -1.406 -0.338 1.038 -1.123 +1 0.670 -0.780 1.167 0.160 1.569 -0.060 0.948 -1.639 1.282 0.689 -0.645 0.865 -1.267 -0.497 -0.851 0.920 1.248 -0.449 -1.271 -0.977 0.092 0.029 -0.340 -0.830 1.628 -0.620 -0.833 1.147 +2 0.336 -0.238 -0.109 1.323 0.037 -1.085 -1.525 0.215 -1.041 -0.756 0.874 0.212 -0.598 0.323 -1.352 2.773 -1.125 -0.743 0.348 0.932 -1.109 0.127 -0.490 -1.495 -0.969 -0.558 0.100 -0.581 +0 0.799 -0.441 -1.354 -0.550 -0.305 -0.330 -0.672 0.034 -0.090 -0.788 1.057 1.123 0.015 0.947 0.641 -0.602 0.434 -0.881 -0.939 0.609 0.513 0.450 0.142 -1.713 -1.498 0.375 0.735 -0.711 +4 -0.278 -0.572 0.171 0.808 -0.495 0.128 3.381 0.476 0.966 -0.958 -1.357 1.465 1.179 1.714 -0.080 1.254 -2.554 0.541 1.033 -0.688 1.056 -0.180 -0.080 -0.259 1.581 -0.454 -0.577 -0.857 +1 0.473 -0.339 0.487 1.031 -1.163 -0.996 0.706 0.790 -0.326 0.603 -0.383 -0.021 -0.022 0.119 1.389 2.480 -1.367 -0.567 -0.694 1.379 1.190 0.891 -0.394 0.213 -1.588 0.730 0.391 -0.463 +4 -2.479 0.106 -0.560 -0.221 -0.824 1.359 -1.883 0.935 -1.571 -0.743 0.365 1.950 0.779 -2.582 0.348 -0.808 2.398 0.311 0.953 -0.872 -0.934 -0.050 -0.751 0.329 -0.447 -0.027 0.968 0.031 +1 -0.197 0.697 0.144 -0.323 1.580 0.727 -1.679 -0.152 -0.153 0.459 -0.810 -0.628 -1.803 0.508 -1.154 -0.294 1.126 0.344 -1.512 1.045 -1.521 0.611 0.341 1.364 -1.596 0.546 0.262 -0.215 +4 -0.775 2.524 -0.762 0.466 -1.687 -1.202 -1.326 1.343 -0.260 -0.540 1.843 -1.605 1.688 -0.207 1.209 -1.010 0.965 0.233 -2.095 -2.371 1.612 -2.380 0.348 -1.017 -1.126 1.505 -0.167 -0.804 +4 0.778 1.523 0.396 -1.637 -0.419 -0.125 0.581 -0.891 -1.596 -0.026 -0.087 -0.627 -1.037 -1.500 -0.580 0.972 2.392 1.354 0.669 -0.699 0.304 0.989 0.253 1.034 0.653 2.730 1.196 -2.094 +0 0.123 0.701 1.602 1.142 0.413 0.579 0.393 0.481 0.438 0.342 0.115 0.530 0.229 -0.221 -0.282 0.897 -0.200 -0.463 -0.517 -0.517 -1.483 -1.527 -0.648 -0.914 1.140 0.200 -1.437 -0.632 +2 -1.435 1.521 -0.365 0.820 0.714 1.608 -0.991 0.323 0.700 -1.247 -0.384 0.198 -0.438 -2.189 -1.720 2.163 1.204 0.643 -0.120 -0.661 0.404 -0.941 1.011 0.552 0.092 -0.267 -0.282 0.326 +1 0.396 -1.338 1.046 -1.555 1.224 0.944 -0.785 -0.534 -1.896 -1.761 -0.299 0.191 1.040 -0.282 -0.135 -1.257 -0.147 0.097 0.587 -0.047 0.939 0.021 -0.135 0.800 0.807 -0.322 1.801 0.262 +3 1.862 -0.405 2.388 -1.788 2.000 0.852 -1.123 -0.243 1.010 -0.307 0.600 -1.725 0.239 -0.479 -1.188 -0.394 -0.520 -0.276 0.631 -0.764 -0.778 -0.600 -0.588 1.287 0.515 -0.050 -1.452 0.778 +2 0.705 1.097 0.512 -0.103 1.207 0.500 1.877 0.383 0.255 0.908 1.533 0.059 -0.163 0.483 0.338 0.610 -1.051 -1.185 -1.368 -1.401 0.851 2.154 -1.020 -0.875 1.457 0.749 -0.587 -0.849 +3 1.391 -0.518 0.407 2.656 0.997 0.196 -2.268 1.859 1.343 -0.516 0.203 -0.546 0.135 0.592 0.973 -0.369 1.206 -1.283 -0.233 -0.376 -1.116 0.477 -0.678 -0.312 -0.617 -0.112 -0.556 -1.168 +2 -0.193 0.039 0.140 -0.698 0.826 0.259 -1.373 -1.124 0.707 -0.073 1.086 1.234 0.738 0.942 -0.015 -0.487 0.282 -2.504 0.803 -0.342 1.479 -0.160 1.478 -1.221 -1.335 0.457 0.900 1.001 +3 0.045 0.943 0.362 0.701 2.226 -0.119 1.321 0.585 -0.229 0.425 -0.822 0.510 1.220 -0.232 -1.359 -0.983 -0.566 0.676 0.684 -0.126 0.163 -0.400 0.899 -0.054 -3.484 -0.980 0.728 0.155 +4 0.008 -1.629 1.865 -0.237 -0.756 1.912 0.643 -0.762 -0.563 0.208 2.452 -0.811 0.721 -0.300 0.957 -0.304 -0.156 -1.631 -0.149 2.632 -1.116 -0.217 0.026 1.012 0.040 1.275 -0.841 -0.005 +0 -1.111 -0.215 1.363 0.484 1.068 -0.919 -0.199 -0.258 -0.501 -1.043 0.974 -2.209 0.274 0.541 -0.002 1.393 0.029 0.176 -0.465 -0.556 -1.308 -0.535 0.451 -0.055 0.026 -0.960 -0.084 -0.092 +4 -0.974 0.270 -3.599 -0.911 1.284 -0.256 -0.235 0.575 -0.344 0.437 -1.232 -0.941 1.639 -0.124 2.092 0.045 -0.731 -1.315 1.251 0.232 -0.677 0.840 1.155 -0.574 2.420 0.053 -0.123 2.101 +2 -0.888 0.385 0.890 0.512 1.355 -0.935 1.389 -2.053 -2.031 -1.463 0.480 -1.509 -0.638 -0.423 0.746 1.278 0.902 -1.431 0.179 -0.370 -0.164 -0.066 -0.464 -0.726 -0.448 -0.691 1.016 -0.064 +1 -0.467 -0.360 -1.153 0.732 -0.751 0.114 -0.507 -0.878 -1.458 -0.623 -0.232 0.118 -0.442 0.052 0.245 0.765 1.149 1.051 -0.993 0.094 1.532 -0.494 1.327 0.324 -1.439 1.972 0.833 0.404 +0 -1.065 -0.305 -0.610 -0.187 0.057 0.530 -0.070 0.487 0.064 -1.975 -0.939 -0.144 -1.210 0.600 1.531 1.219 -0.213 1.491 0.149 -0.337 -0.613 -0.302 -0.388 0.170 0.161 0.003 0.437 1.191 +1 -0.790 -0.524 1.236 -1.861 -0.481 -1.286 0.053 0.644 0.211 -0.923 -0.740 -0.427 0.095 1.786 -0.516 0.949 -0.529 -1.609 1.447 0.273 0.966 0.365 1.103 0.676 0.720 -0.572 0.263 0.551 +1 -0.597 -0.013 0.166 0.259 0.588 -1.138 0.357 0.315 0.329 0.425 -0.743 0.214 0.233 -1.692 1.413 -0.886 0.230 -2.111 1.083 -0.598 -0.415 -0.988 -0.495 0.125 1.555 0.878 0.846 -1.196 +0 -0.780 0.171 0.809 0.581 0.011 -0.334 -0.581 0.229 0.078 -0.111 0.859 0.761 0.170 -0.402 -1.402 -0.508 -0.798 -0.206 -0.354 -0.806 0.496 -1.799 -0.202 -0.710 1.368 -0.056 -0.364 -0.389 +2 0.374 -0.049 1.121 -1.099 0.664 1.319 -1.349 -0.740 1.645 1.081 -0.819 -1.215 0.283 0.330 -0.059 2.333 0.647 -0.840 1.058 0.058 -0.677 0.901 1.476 0.216 -0.977 0.113 -0.956 0.227 +0 -0.819 0.266 0.268 0.842 -0.266 -0.014 2.665 0.039 -0.829 0.531 0.155 -0.876 0.419 1.015 -0.667 1.262 -0.642 -0.818 0.133 0.733 0.844 0.315 -1.133 -0.886 -0.335 0.544 -0.392 0.985 +2 0.066 0.571 0.600 1.685 0.250 -1.241 -1.065 1.256 1.611 -0.035 0.382 0.093 -0.298 -1.491 0.477 1.782 0.170 -0.302 -0.432 0.497 1.151 0.533 -1.870 1.742 0.833 -0.180 -0.530 -0.447 +3 -2.729 0.005 -0.460 -1.063 -0.553 -0.574 -0.048 -1.367 0.316 1.366 0.281 0.809 1.095 1.236 1.870 -0.499 -0.718 0.783 0.708 0.776 1.889 0.011 0.426 -0.980 2.135 -0.337 -0.308 -0.347 +1 1.134 -1.038 -0.593 -1.063 -0.163 0.057 -1.170 -0.799 -0.158 1.074 -0.941 0.799 -0.781 0.476 1.214 1.144 0.021 0.046 -1.697 1.198 -0.829 -0.502 0.542 -1.393 0.106 1.715 1.133 -0.408 +1 -0.760 -0.741 0.771 0.786 2.306 0.353 -1.290 -0.237 -1.222 -0.275 0.626 0.239 -0.750 -1.627 -0.951 -0.661 0.705 -0.617 0.923 -0.760 0.835 0.245 0.316 0.818 -0.623 0.554 1.879 -1.124 +4 -0.515 -1.744 -1.052 0.240 -1.245 -0.849 1.005 0.577 -0.502 2.387 0.364 -0.778 1.979 0.852 2.865 1.468 0.806 -0.679 1.828 -1.974 -2.067 2.443 -1.108 -1.138 -1.222 -0.683 0.891 0.142 +0 -0.066 -1.269 -1.037 0.738 0.116 -0.776 0.120 -0.607 -0.784 1.164 0.783 -0.021 0.144 -0.008 0.487 -0.800 0.863 0.354 -0.263 -0.488 0.841 -0.150 -0.699 -0.385 0.769 -1.611 -0.355 1.071 +4 -0.341 -1.152 2.472 -0.994 -1.187 -0.636 1.260 -0.179 1.495 -0.562 -0.007 0.473 -0.993 -1.063 1.216 -1.717 -0.757 0.762 0.880 0.193 -0.289 2.314 0.539 1.561 0.422 -0.433 1.498 -1.268 +2 -0.542 -0.951 -0.382 0.989 0.832 -0.231 1.244 -1.335 2.293 0.766 1.572 -0.412 -0.797 1.463 0.155 -0.545 0.095 -0.319 0.312 1.282 0.044 -2.094 -0.818 -0.296 -0.138 -2.000 -0.465 -0.085 +0 1.194 -1.282 -0.834 0.882 1.410 0.665 0.125 -0.256 0.116 1.182 -0.256 0.763 0.105 0.383 0.667 0.246 -0.042 0.902 -0.447 0.128 0.434 -0.969 1.456 -0.449 -0.063 -1.551 0.995 1.806 +4 0.872 -1.704 0.608 1.874 -1.306 0.611 -1.178 0.093 -0.268 -1.065 0.265 2.750 -1.589 -1.606 2.274 0.199 0.969 0.473 -2.287 0.029 -1.309 0.675 -0.030 0.790 0.648 1.323 -0.276 -0.204 +3 -1.313 -0.678 0.246 -0.666 -1.286 2.124 -0.020 1.146 1.907 -0.523 -0.085 0.077 0.471 1.117 0.976 0.720 -0.321 -0.008 -1.253 0.713 2.311 0.262 1.397 0.838 -0.545 -0.885 -1.138 0.121 +2 -0.485 1.546 -1.237 0.902 -0.921 0.049 0.913 -1.289 0.880 0.318 0.316 -0.044 0.623 -0.157 -1.523 -0.096 1.910 -0.167 -0.114 -1.195 2.659 -1.769 -0.097 -0.521 -0.456 -0.887 0.014 -0.783 +1 -0.712 0.671 0.961 1.400 -0.527 -0.363 0.880 0.842 -0.510 -0.672 -0.433 0.079 -0.888 0.420 -1.727 -0.832 0.184 -0.919 -0.418 -0.127 2.121 -1.306 0.236 -1.025 -1.062 -0.875 -0.157 1.434 +4 2.227 -1.146 0.701 0.808 0.783 2.419 0.151 0.057 0.707 2.813 1.379 -0.377 2.703 0.118 0.248 1.990 0.025 -0.636 0.675 1.579 -0.579 0.751 -0.357 -0.608 -0.662 0.986 0.595 0.678 +3 0.161 -1.658 -0.236 -2.073 0.037 -1.247 0.558 0.161 -1.216 0.382 2.093 -0.203 0.880 -2.103 0.468 -0.569 -0.269 0.595 -0.243 -1.617 1.527 -1.450 -0.840 0.128 0.137 0.428 0.133 1.148 +2 0.080 -1.446 -0.019 1.344 -0.667 0.131 -3.083 1.425 -0.106 -0.163 0.520 0.297 0.418 -1.165 1.326 1.159 0.338 1.557 0.029 -0.465 0.276 1.203 -0.413 -0.481 -1.057 -0.011 0.210 -1.032 +4 0.852 -0.571 -1.421 1.200 0.059 0.443 0.713 -0.364 -0.707 0.056 -0.451 -1.554 -1.254 -0.590 1.241 -1.035 1.822 -1.009 -0.416 -2.189 1.643 -2.544 -0.723 -0.721 0.692 0.324 1.062 0.732 +1 -1.249 0.680 -0.912 -0.291 1.783 1.286 -0.770 0.429 -1.427 1.285 0.088 1.168 0.280 -0.417 -0.277 -0.387 0.445 -0.766 0.011 0.118 -1.585 -1.109 -1.677 -0.293 -0.091 0.731 -0.874 -1.334 +0 -0.516 -0.883 1.847 -1.435 -1.246 -0.878 -0.238 1.316 1.175 1.238 -0.050 -0.118 0.253 0.437 0.177 0.788 0.491 0.185 -0.573 0.952 -0.539 0.122 -0.888 -0.804 -1.647 -0.744 -0.161 -0.356 +3 0.820 0.360 0.632 0.726 -0.396 -0.434 2.227 1.326 -0.048 0.683 -0.938 -0.599 -1.247 0.286 0.297 1.084 -0.212 1.067 0.653 2.459 1.667 1.492 0.836 -0.894 0.293 2.248 0.507 -0.822 +1 -0.181 0.993 -0.488 -0.927 0.323 -2.121 0.083 2.302 0.455 -0.790 0.100 -0.740 -1.281 -0.378 -0.802 1.383 1.909 -0.116 -0.357 0.354 -0.727 -0.184 0.061 -0.739 -1.154 0.322 0.425 -0.940 +4 -1.274 -0.324 -0.788 0.014 1.659 -0.899 1.766 -0.108 -0.952 0.327 0.434 0.996 1.896 0.010 0.537 0.401 -1.956 0.182 1.834 0.412 -2.075 0.796 2.320 -0.869 -0.054 -0.683 -1.855 -0.977 +0 1.035 1.235 1.887 0.081 -1.013 -0.103 -0.567 -0.709 0.151 -0.312 -0.752 -0.245 0.549 0.956 -1.603 -0.303 -0.141 -1.183 -0.120 0.148 0.519 0.077 -0.881 2.012 0.829 0.553 1.005 0.245 +1 -0.343 1.201 -1.133 1.077 0.928 -0.151 1.139 -0.447 -1.265 0.978 -0.085 0.602 1.821 -0.722 -0.143 1.570 0.669 -0.700 -0.955 -0.528 -0.738 0.237 0.078 0.279 -0.841 -0.071 -2.056 0.145 +1 -1.055 0.007 0.207 -0.917 -0.811 1.058 0.277 1.140 0.644 1.364 -0.277 0.101 -1.609 -1.912 0.725 -0.759 -1.105 -1.450 -1.357 0.576 -0.037 -0.274 1.076 0.615 1.195 0.106 0.011 0.904 +1 -0.896 -0.915 -0.422 1.948 -0.534 -0.569 -0.876 -0.016 -0.384 0.184 -0.470 1.582 -0.508 -1.659 0.351 1.086 -1.436 0.449 -0.344 -0.229 -1.443 0.646 0.464 -1.395 -1.503 -1.147 -0.733 0.210 +0 0.529 -0.630 -1.488 -0.108 -0.424 0.382 -0.290 -0.795 0.464 -0.394 0.984 -0.029 0.668 0.384 0.295 -1.526 -1.113 0.021 0.374 -0.467 -0.767 -0.214 1.546 -0.017 -0.926 -0.336 1.657 -0.509 +1 0.015 0.336 -0.375 -0.480 -0.593 -0.023 -2.383 -1.101 -0.627 1.208 -0.814 0.180 0.227 1.298 1.263 -0.058 -0.475 -0.664 -0.241 -0.434 0.554 0.420 0.592 1.259 1.863 0.125 1.099 -0.352 +0 -0.481 -0.776 -0.187 -0.496 1.305 0.227 0.246 1.140 -0.693 0.448 0.046 0.447 1.768 -0.718 0.210 0.710 -1.209 -0.675 -0.499 -0.547 -0.284 -0.396 -0.979 2.380 0.112 0.237 -0.904 -1.155 +2 -2.399 0.664 0.966 -0.328 -1.157 1.522 -0.146 -0.344 0.449 2.736 0.484 0.178 0.070 1.654 0.527 0.864 0.304 0.536 -0.278 0.169 -0.686 -0.508 0.490 -0.704 0.131 -0.368 -1.124 0.228 +2 -1.508 -0.742 0.756 2.587 -1.976 -0.201 0.412 -0.616 0.850 -0.867 0.154 -0.522 1.219 -0.676 -0.352 0.946 -1.028 0.069 -1.823 -1.088 0.382 -0.991 -0.545 0.493 -0.579 0.823 0.216 0.436 +4 0.915 -0.404 -1.246 -0.777 -3.532 -1.265 0.687 -0.333 0.065 0.774 1.321 1.105 1.015 -1.192 -1.493 -1.823 0.158 1.033 0.123 0.231 1.792 0.460 -0.445 -2.015 1.266 -0.201 0.557 -1.894 +1 0.698 -1.403 0.778 0.193 -0.425 0.211 2.064 -1.241 0.026 -0.384 0.126 0.219 1.079 -1.148 1.380 0.023 -1.820 -0.622 0.287 0.507 -0.161 -0.453 0.861 0.742 -0.368 -0.829 -1.296 -0.176 +3 1.557 0.187 0.626 1.370 -0.641 1.111 0.275 0.914 0.186 1.296 0.224 -1.484 1.452 -0.948 1.253 -0.090 1.176 1.135 0.430 0.509 0.423 -2.014 0.118 -1.116 0.171 -1.143 -1.185 1.605 +3 -0.554 0.187 0.519 0.542 -0.497 0.616 1.027 1.090 0.045 -1.920 -0.159 -1.399 0.488 -0.571 -0.260 -0.565 1.616 0.875 -0.148 1.169 0.774 0.969 -2.432 2.555 1.227 -0.136 0.921 -0.135 +1 -0.624 1.031 -0.653 -0.191 -0.769 -0.140 -1.128 1.298 0.975 -1.338 1.035 1.403 0.577 1.204 0.096 0.642 0.488 -0.928 0.865 0.365 -1.060 -1.629 0.268 -0.146 -1.250 1.217 0.461 0.462 +0 -0.334 -1.718 -1.511 0.593 -0.019 0.184 1.005 -0.374 0.750 -0.626 0.092 0.184 -1.068 -0.006 0.854 0.511 1.456 -0.468 1.512 0.219 -0.476 0.163 -0.718 0.311 -1.642 1.241 -0.643 0.036 +3 -2.316 0.623 -0.304 -0.218 -0.315 0.232 -0.753 1.476 -1.476 0.445 0.642 -0.760 -1.024 1.017 -0.991 -0.607 0.887 0.035 -1.797 1.747 -0.530 -0.403 0.256 0.625 1.961 -1.660 0.826 0.743 +2 -0.079 0.226 -0.010 0.681 -0.705 -0.207 -1.608 0.890 -0.733 -0.129 0.169 -1.755 1.068 1.429 -0.762 -0.120 1.754 0.336 -0.319 -0.506 -1.825 0.588 2.033 0.814 0.534 -0.966 -0.932 1.395 +4 0.815 0.559 -0.500 1.784 -1.460 0.313 0.847 1.235 0.122 0.766 0.004 1.668 0.323 1.160 -1.156 -0.114 0.539 0.531 -0.568 -1.861 -1.760 -0.947 2.787 -3.036 -0.168 -0.294 0.042 -0.082 +4 -1.601 -0.150 0.124 -0.573 0.028 -1.965 -1.296 1.270 0.243 -0.379 0.980 1.387 1.810 -1.887 1.383 -1.161 0.302 0.611 1.609 0.469 0.279 -0.838 -1.688 -0.864 1.407 0.033 1.661 -0.717 +1 0.149 -1.222 0.306 -1.992 -0.863 0.110 1.194 1.431 -0.237 0.156 0.984 -1.329 0.405 -0.828 -0.172 -0.459 0.435 0.550 0.891 0.348 0.172 1.854 -1.524 1.696 -0.290 0.571 -0.326 -0.703 +0 0.027 0.045 0.444 -1.562 0.113 -0.808 -0.366 -1.277 -1.311 0.325 -0.135 1.644 -0.381 0.749 1.181 0.179 0.245 1.230 1.332 0.605 1.395 0.197 -1.084 0.316 -0.666 1.252 -0.269 0.900 +0 -1.307 -1.260 -0.554 0.118 0.846 -1.582 -0.254 1.852 0.721 0.772 1.245 0.241 -0.902 0.049 -0.526 -0.544 -0.609 1.216 1.035 0.146 -0.398 0.663 0.766 0.019 0.280 -0.889 -0.693 -0.233 +2 -0.698 1.709 -1.733 -0.211 1.300 -1.293 -1.078 -0.781 0.705 -0.986 -0.408 -1.772 0.162 0.241 0.969 -0.077 1.416 -1.802 -0.510 0.469 -0.709 -0.894 -1.074 0.213 0.534 0.431 -0.176 0.200 +0 1.150 -1.020 -0.995 0.348 0.530 0.191 1.083 0.003 0.124 1.262 -0.832 0.310 0.365 -0.630 0.013 -0.026 -0.622 -1.303 -0.842 0.008 -0.465 -1.386 1.674 0.068 -0.065 -1.472 0.997 1.559 +0 -0.065 -0.234 -1.211 -1.279 -0.335 0.027 -0.427 -0.394 0.654 -0.454 -1.450 0.616 1.086 0.555 -0.419 -0.155 -0.386 0.439 1.187 0.398 -0.033 0.061 0.404 1.819 0.781 -0.969 -0.965 1.799 +1 0.091 1.392 -0.806 0.132 0.521 -0.692 -1.251 -0.019 -1.068 -1.544 1.521 -0.600 0.475 0.274 -1.227 0.250 0.318 0.142 0.023 0.524 0.450 0.611 -1.214 -1.742 -0.552 -0.107 0.786 1.964 +0 -0.539 -0.599 1.182 0.910 0.698 -0.391 0.342 1.148 1.450 -0.781 -2.079 -1.143 -0.518 0.263 0.525 -0.066 0.371 0.653 -0.602 -0.417 0.552 0.406 -1.315 0.767 0.123 -1.041 -0.217 -0.226 +4 2.627 -1.343 -2.268 -0.775 -0.531 -0.958 -2.034 -0.106 0.367 1.214 1.777 -0.492 -1.086 0.280 0.874 -1.248 1.183 1.392 -0.531 -0.241 -0.250 -0.377 -1.459 1.508 0.771 1.005 -0.147 0.349 +3 -0.693 -1.541 0.366 1.595 0.077 -1.974 -0.774 0.992 0.214 -1.327 2.542 -0.573 -0.236 -0.326 0.880 -0.231 -1.867 -1.859 -0.589 0.204 -0.517 0.606 1.439 -1.253 0.204 -0.503 -0.771 -0.539 +4 0.405 -0.593 0.776 -1.141 -0.305 0.742 0.849 -0.276 0.472 0.368 -2.310 0.148 -1.132 0.802 1.851 0.825 2.351 -0.319 -0.367 0.530 1.384 1.757 1.671 0.767 1.421 0.246 1.817 -1.574 +2 -0.037 -1.738 0.018 1.211 -0.600 0.412 0.852 -0.910 -0.451 -1.018 -0.822 -1.288 -0.900 1.419 -0.915 0.126 -0.503 1.179 1.311 0.864 0.306 -0.231 -1.192 1.944 -0.760 0.664 1.060 -0.555 +1 0.404 -0.839 -0.522 0.972 0.766 1.093 -0.023 0.883 -0.586 -0.258 -0.326 0.024 -1.286 -0.225 1.452 -1.093 -1.329 -1.249 2.662 0.465 -0.270 0.068 0.228 -0.640 -0.517 -0.158 -0.725 -0.397 +0 -0.877 -0.313 -0.758 0.861 0.513 0.248 1.054 0.263 0.179 -0.120 -0.716 -0.714 0.147 1.018 0.570 0.565 -1.106 0.523 -0.399 0.176 0.552 -1.156 0.157 0.236 0.999 -0.517 -0.061 0.170 +1 2.553 0.644 0.542 0.472 -0.744 -0.596 0.241 -0.487 -1.185 -0.080 0.351 -0.012 1.052 0.163 1.586 2.306 -0.133 -1.115 -1.144 -0.049 0.802 -0.348 0.026 -0.520 -0.317 0.109 0.976 0.933 +0 -1.182 -1.715 0.348 0.651 0.982 1.204 1.319 0.687 -0.571 0.283 -0.322 -0.230 0.392 1.266 0.761 -0.008 0.099 0.360 1.178 1.856 0.130 -0.203 0.158 1.032 0.521 -0.511 0.012 0.016 +4 -0.445 -0.488 -0.291 1.499 2.164 -0.451 0.336 -0.841 0.768 -0.476 -0.711 2.002 -1.356 -2.453 0.735 -0.368 -0.646 2.732 0.730 -0.059 -0.708 0.446 -1.733 -0.516 -0.037 0.213 -0.142 -0.693 +1 0.485 -1.035 -0.350 -1.300 -0.171 0.150 -0.335 -0.450 -1.613 0.337 -1.924 0.031 -1.799 0.051 -0.006 0.403 0.862 1.048 -1.610 -0.058 2.011 0.119 -0.831 -0.080 -0.384 -1.176 0.869 0.584 +4 -0.537 -1.484 1.678 0.107 -0.293 -0.399 -1.513 -1.305 1.285 -0.474 0.022 -2.044 -1.318 0.610 0.378 1.753 1.343 -0.231 0.473 -0.317 2.055 1.149 0.170 0.200 1.445 0.580 1.559 -2.194 +3 -0.423 0.202 -0.614 -0.367 0.342 0.305 0.428 0.134 -0.987 1.315 -0.477 -0.844 -1.190 0.035 -2.064 -2.478 1.813 -1.218 -0.832 -0.356 0.143 -0.043 0.039 -2.442 1.578 -0.725 0.962 -1.378 +3 1.687 1.198 0.630 0.946 -0.335 1.096 -1.290 -0.326 -2.508 0.400 1.030 1.649 -0.215 0.232 -0.014 1.809 0.332 1.854 1.245 0.753 -0.557 -1.084 0.089 0.097 -1.164 1.083 0.184 0.587 +2 1.422 -0.925 0.163 2.556 1.891 0.247 -0.956 -1.008 -2.140 -0.019 1.751 -0.698 -0.284 -0.076 0.013 -0.908 -0.153 0.311 0.466 0.341 -0.800 -0.064 0.313 1.043 0.526 -1.049 0.068 0.404 +3 0.597 -0.423 0.632 -0.808 -1.614 -0.676 2.155 -1.034 -0.812 2.016 0.315 2.600 -0.169 0.696 0.985 -0.123 1.003 0.328 -1.618 1.124 0.028 0.779 1.528 0.301 -0.143 0.152 -1.461 -0.148 +0 -0.827 1.101 -0.099 0.774 0.400 1.059 -0.935 0.906 -1.863 0.043 -0.546 -1.405 -0.894 -0.641 -0.197 -0.525 0.080 -0.830 -0.477 -0.289 0.182 0.849 0.854 0.237 0.430 0.312 -0.315 0.901 +2 -0.553 -1.273 -0.226 -1.800 0.402 -0.339 -1.101 -1.260 1.574 -0.246 -1.334 1.109 -0.307 0.100 -1.290 0.613 -0.331 -1.243 -0.952 -1.026 0.250 1.723 1.062 1.555 -1.193 -0.401 -0.068 -0.392 +2 -1.825 1.958 -0.092 0.173 0.448 0.165 -0.172 0.145 0.636 0.849 1.220 -1.098 -0.546 0.853 -1.570 0.162 -0.786 0.551 -0.845 -0.360 -0.290 0.246 0.958 1.032 0.205 -1.110 2.580 -0.085 +0 0.596 -0.815 2.340 -0.904 -1.279 0.510 -1.278 0.026 -0.693 0.652 0.155 1.142 0.424 0.048 0.205 -0.969 -0.699 0.219 0.595 -0.325 -0.213 -0.394 0.376 0.004 0.619 -0.618 -0.054 1.511 +0 -1.507 0.825 0.197 -1.248 -0.352 -0.183 0.585 -0.645 0.631 0.049 0.451 -0.881 -0.241 0.110 0.082 -0.040 2.092 0.574 0.727 0.247 0.533 0.199 1.818 -0.060 0.831 2.085 0.653 -0.285 +3 -1.675 0.570 0.574 -1.434 0.233 0.053 0.352 -1.761 -0.503 -1.050 -0.344 2.783 -0.437 1.099 -0.159 0.158 -0.753 1.135 -0.754 0.969 -0.122 -0.466 0.448 0.027 0.646 1.483 -1.965 0.384 +1 -0.260 0.661 -0.424 -0.737 -0.279 -1.336 -0.980 0.838 -0.316 0.895 -0.361 0.817 1.051 -0.283 1.808 0.672 0.198 0.035 0.013 0.161 -0.558 -2.992 0.872 0.357 0.509 -0.035 -1.683 -0.534 +2 -0.335 -1.967 0.693 0.036 1.150 -0.090 -1.798 0.319 -0.662 1.069 -0.212 2.015 -0.894 0.246 0.599 -0.330 -1.333 -0.200 -1.819 -0.698 -0.178 1.001 0.280 -0.404 -0.466 -0.551 0.920 -1.485 +4 -1.023 1.434 -1.013 -1.253 -0.275 -0.444 -1.582 -1.128 -3.594 -1.351 -0.660 1.490 0.713 0.323 2.165 -1.792 -0.068 2.035 -0.507 -0.828 0.817 -0.557 1.259 0.831 -0.190 0.981 -0.236 0.133 +4 0.556 0.609 -1.136 1.233 -0.554 0.123 -0.643 -1.183 -1.261 0.711 -0.011 -1.100 -0.621 1.423 0.882 0.614 2.110 1.503 0.036 -0.543 2.585 1.206 -0.545 -1.331 -2.247 -1.280 -0.481 1.339 +2 1.191 -0.824 0.689 1.040 -1.064 -2.014 0.620 -1.917 -0.704 -1.697 0.030 1.945 0.737 1.503 0.525 -1.614 -0.093 0.295 0.521 0.986 -0.793 0.176 -0.257 0.831 -0.042 0.054 0.095 0.362 +4 -0.090 0.520 -0.073 0.366 1.664 -0.306 -2.128 0.111 2.626 2.326 0.121 -1.521 -0.167 -0.130 -1.254 1.045 -0.478 -1.678 0.601 -1.496 -0.750 1.096 0.392 2.431 -1.533 -1.110 1.722 0.376 +3 0.657 -0.754 -0.963 -1.847 -0.443 1.667 -0.934 -0.208 0.974 -0.540 -1.586 0.580 2.349 -1.192 0.795 -0.814 0.489 -0.714 0.147 -0.231 2.152 -0.931 -0.789 0.879 0.651 -0.690 0.985 -0.059 +3 1.086 0.083 -1.283 0.624 1.412 0.383 1.424 -0.485 -0.728 0.855 -0.353 1.205 0.171 1.219 0.631 -0.900 -1.992 0.782 0.565 -1.018 0.799 1.391 -0.707 -1.934 0.971 1.272 -0.734 0.774 +4 0.062 -0.502 0.956 1.806 -1.119 -0.783 1.547 2.069 0.374 1.350 -0.473 -1.667 -0.068 1.429 -0.721 1.621 1.249 0.174 2.872 -0.152 -1.300 -0.327 0.377 -0.666 -0.686 0.481 0.854 0.297 +0 -0.179 -1.105 1.372 1.704 0.782 1.271 0.224 0.167 0.459 0.089 0.838 -0.809 0.891 1.669 -0.273 -0.926 1.213 0.534 0.328 -0.448 0.457 0.082 0.228 0.477 -0.404 -0.691 -1.200 -0.458 +0 -0.074 0.729 0.456 0.128 0.167 1.119 -0.190 -1.224 1.709 0.171 0.323 0.542 1.085 -1.300 0.788 -0.098 -0.379 1.752 0.652 -0.327 -0.320 0.509 0.313 -1.051 -1.065 0.350 0.200 1.281 +0 0.451 -0.175 -0.364 0.517 1.336 1.023 -0.802 -1.579 0.186 -1.900 0.090 -0.093 0.784 0.463 0.439 0.329 -0.262 0.092 0.525 0.161 -0.395 0.741 0.407 -0.239 -0.566 -0.505 -0.905 -0.299 +2 1.398 -0.969 -0.225 -0.681 -0.108 -1.227 0.529 0.168 -0.275 0.282 0.440 1.486 -0.097 1.332 -0.813 -0.621 1.601 1.219 -1.696 -2.190 1.508 -0.130 -0.629 0.259 -0.584 0.005 -0.292 -0.374 +3 1.288 1.986 -0.022 1.739 -0.042 1.522 -0.931 0.125 0.352 -1.249 2.132 0.012 -0.332 -0.792 -0.006 -0.172 1.144 1.455 0.898 0.787 0.218 -2.218 0.472 0.047 0.307 0.433 -0.023 1.223 +2 -0.836 -2.068 -0.878 -0.434 1.014 0.695 1.008 -0.252 0.986 1.620 -0.847 1.129 0.863 -0.408 -0.094 -1.000 -1.531 -0.867 0.046 -0.015 -0.829 -1.172 0.149 -0.508 -2.567 -0.240 0.673 0.384 +2 -0.406 0.640 0.055 0.051 -0.507 0.146 0.232 -0.764 -0.885 0.839 0.454 0.472 -1.969 -0.777 -1.215 1.440 -2.109 -0.141 -0.012 0.774 0.636 2.232 1.030 0.609 1.392 0.524 -0.574 -0.709 +4 -1.839 0.637 -0.849 -1.382 0.534 -1.004 1.539 0.156 -0.203 -0.822 0.215 -1.591 -2.468 -1.554 1.137 -0.924 -1.435 -0.089 2.225 1.092 -0.800 -0.818 -0.796 -0.910 0.544 -0.336 -1.113 -1.336 +2 0.290 -1.192 -0.819 0.923 1.877 -1.001 -0.737 -0.440 -0.548 1.933 -0.531 -0.388 1.616 0.577 0.986 -0.906 -0.530 -1.040 0.801 1.225 -0.600 -0.364 -0.320 -1.547 -0.674 0.773 -0.875 0.093 +0 0.388 0.905 0.710 0.505 -0.725 -0.973 0.674 0.523 0.232 0.292 -0.772 -1.178 -0.733 -0.251 -0.595 1.568 1.476 0.853 1.221 -1.294 0.718 -0.036 -0.461 -0.283 -0.270 -0.379 -0.615 -1.341 +3 2.039 -0.772 1.829 -0.470 0.685 0.726 3.657 0.472 -0.126 -0.863 -0.895 -0.513 -0.278 -0.236 0.214 0.532 -0.466 0.366 0.998 0.592 0.211 -0.614 -0.810 0.724 0.005 -1.356 -1.407 0.774 +3 -1.685 -0.145 0.195 -0.965 -0.595 1.412 -0.935 -1.444 2.099 0.580 -0.735 1.414 -0.148 0.203 -0.698 2.660 1.536 -0.885 -0.175 0.076 -1.111 -0.671 -0.274 0.446 -1.189 -1.121 -0.874 0.899 +2 -0.547 1.426 -2.888 -0.581 -0.511 -0.744 -0.137 1.445 0.504 0.993 -0.435 0.222 -0.137 0.910 0.808 -1.211 1.265 -0.621 0.677 0.630 -0.768 -0.877 -0.675 1.766 -1.441 -0.465 -0.539 -0.517 +1 -0.667 1.239 1.607 -1.114 0.367 1.862 -0.120 -0.654 1.059 -0.590 -0.142 -0.427 0.105 -2.103 0.456 -0.293 0.080 -1.300 -0.237 -0.024 0.212 -0.001 -0.059 0.701 1.063 2.006 0.191 -0.660 +0 0.093 -1.008 -0.805 0.896 -0.086 -0.129 -1.004 -1.306 -0.202 -0.384 -0.165 1.015 -0.170 -1.576 -0.283 -0.727 -0.333 -0.642 1.359 -1.091 0.008 -0.737 -1.063 1.366 0.722 -0.652 -1.443 0.434 +0 -0.214 -0.135 -0.848 0.119 -0.572 -0.052 -0.168 0.033 -0.963 -0.508 -0.158 -0.034 0.008 0.591 -1.098 -0.267 -0.929 -1.032 0.349 0.080 -0.542 -0.593 -1.309 1.052 -0.137 -1.617 0.070 -0.762 +1 -1.470 -0.156 0.248 0.459 -0.924 -1.269 -0.235 1.967 -0.819 1.174 1.585 0.045 -0.680 0.513 0.195 -0.859 -0.659 0.454 0.933 -0.323 -0.069 0.669 -0.172 -0.299 -0.755 -0.773 2.669 -0.196 +4 -0.465 1.101 -0.536 -1.492 0.148 -0.148 -0.024 -1.273 0.268 1.389 -1.314 -1.910 -1.544 -0.029 -1.608 0.326 0.357 0.367 2.589 -1.866 -1.707 1.279 0.533 1.148 -2.041 -1.360 0.979 -0.167 +0 -0.300 -0.142 -0.048 0.510 0.438 1.438 -1.978 -0.621 0.295 -0.042 0.589 0.502 0.157 0.270 0.384 0.558 0.515 -0.625 0.420 -0.642 0.243 -0.700 1.117 0.317 1.093 -0.841 -0.138 -1.434 +2 -0.787 1.009 -1.190 -0.878 -0.445 0.764 1.355 1.490 0.786 -0.531 -1.715 0.849 1.239 -0.547 1.553 -0.102 -0.454 -0.019 -0.785 -1.392 -1.155 0.257 -0.871 1.401 0.185 -0.408 -1.318 -0.996 +3 0.657 -1.313 1.459 -0.231 0.246 0.559 0.591 -1.409 0.824 0.303 -0.635 -0.750 -0.334 -1.299 1.722 1.486 0.452 -0.223 -0.197 0.758 1.758 -0.570 -1.118 1.548 -1.787 -1.009 -0.247 -1.560 +0 -0.201 0.138 -1.761 0.919 0.625 1.094 -0.611 -0.654 0.612 1.105 0.601 -0.210 -0.852 -0.242 -0.676 -0.810 -0.048 0.629 -0.906 0.382 1.160 0.065 -1.828 -1.802 -0.248 0.935 -0.409 0.507 +4 -1.808 1.400 0.177 0.652 1.115 0.183 1.190 -0.224 -0.992 -0.280 0.386 -0.068 1.623 0.589 -1.904 0.562 1.963 0.880 1.675 2.163 -2.130 1.664 0.632 -0.294 1.030 2.061 0.744 0.149 +4 2.111 -0.089 -0.121 0.296 1.233 1.516 0.298 -1.183 -1.145 -1.089 -0.339 -0.777 2.192 0.990 1.715 0.189 -0.155 -0.353 -0.008 -0.028 0.274 0.364 1.103 1.329 -0.284 -2.253 -1.820 0.810 +1 0.366 -1.069 -0.044 -0.294 -0.737 1.003 -1.136 -2.354 -1.219 1.527 -0.269 1.085 -0.015 1.203 -0.822 0.285 0.796 -1.266 -1.029 -0.251 0.477 -0.794 1.139 0.953 -0.063 0.447 0.730 0.852 +1 -1.291 1.340 -1.721 -2.405 0.439 -0.600 -0.392 1.486 1.066 0.935 0.028 0.513 -0.484 -0.608 -0.006 0.607 1.552 1.073 0.672 -0.116 -0.262 0.228 0.177 -1.070 -0.376 0.028 0.643 -0.064 +2 -0.192 -0.149 -0.781 0.143 1.076 0.314 -1.111 -0.344 -0.552 0.053 1.330 -0.149 -0.088 1.778 0.876 0.574 1.958 0.490 -1.987 0.833 -1.592 0.452 -1.714 -0.467 -0.364 -0.770 -0.349 1.033 +1 -1.558 0.259 -0.447 1.278 -1.271 -0.559 1.346 0.991 -0.436 1.720 -0.141 0.654 -0.434 -0.998 1.306 -1.245 0.077 -0.910 0.033 0.292 0.873 0.248 -0.792 -1.454 -0.237 -0.124 0.242 -0.311 +1 0.420 2.342 -0.806 -0.301 0.005 0.357 -0.460 -0.254 1.491 1.131 0.484 0.957 0.575 -0.502 0.719 1.356 0.020 0.087 1.617 1.408 0.589 0.058 0.299 0.551 -0.352 1.173 -0.012 0.649 +2 0.873 -0.438 -1.380 -0.415 0.413 0.597 1.359 -1.095 -0.439 -1.178 -1.339 -0.628 0.367 0.771 -0.948 -0.809 -1.780 0.214 -0.732 1.663 0.410 -0.171 -0.641 -1.495 -1.368 -0.622 -0.699 -0.999 +1 0.319 -0.893 0.755 0.538 0.178 -0.431 -0.407 -0.202 -1.232 -0.039 0.892 0.876 -0.381 1.064 1.906 -0.316 0.692 -2.263 -1.258 0.091 -1.007 2.039 0.598 0.966 -0.029 -1.003 -0.519 0.254 +0 0.683 1.024 -0.351 -1.243 0.691 0.470 -0.188 0.179 0.066 0.046 0.496 -1.687 -0.336 -0.002 -0.627 1.261 -0.565 -1.062 0.117 -0.668 0.335 -1.136 -0.316 1.123 0.341 1.437 1.355 -0.408 +0 -0.834 0.951 -0.152 0.724 1.061 -0.907 -0.160 -0.074 -0.839 -0.146 -0.128 0.091 1.100 1.011 1.277 -1.195 -1.084 -0.687 0.876 -0.557 -0.129 0.333 -1.972 -0.282 -0.226 -0.159 0.965 -1.838 +2 -0.400 -0.027 -0.061 -0.744 2.024 -0.169 -1.522 0.737 -1.580 -0.243 -0.936 0.026 1.727 0.412 -1.387 -1.323 0.633 0.905 -0.601 0.789 -1.305 0.642 1.181 1.021 0.677 -1.851 -0.500 -0.305 +4 -1.512 -0.178 1.731 -0.462 0.520 -0.935 0.948 -1.472 1.081 -0.040 -0.187 0.691 1.350 -1.560 0.687 -1.656 -0.670 -2.520 -0.269 0.201 0.959 0.318 0.053 0.929 1.346 1.049 2.084 -0.534 +1 -0.170 0.631 -0.535 1.160 -1.377 -0.153 -1.511 0.279 0.563 0.620 -1.058 0.615 -0.446 -0.587 -1.167 -1.342 0.448 -0.383 -0.374 -0.201 0.092 -1.078 0.075 -0.490 1.684 0.581 0.127 -2.291 +1 -0.694 0.331 -0.658 -0.005 0.614 1.146 -0.203 0.354 0.193 -2.049 -1.257 -0.833 0.734 -0.246 -1.049 0.275 -2.236 0.233 1.185 -1.282 0.984 0.464 -0.112 -1.687 0.780 0.674 0.041 -0.943 +4 -2.556 -2.253 0.137 2.018 0.234 0.196 1.175 0.176 0.148 -1.371 -0.023 -0.592 -0.136 -0.519 -0.514 -0.209 0.160 -0.960 -0.277 -0.106 0.149 1.164 1.050 1.088 0.749 2.806 0.157 -1.010 +3 0.778 0.779 -0.694 -0.132 0.110 1.944 -1.157 -0.729 -0.363 -0.674 -1.459 -2.705 -1.827 1.099 -0.008 0.836 -0.583 -0.098 1.613 1.553 0.942 0.202 0.518 0.371 -0.167 -0.404 1.122 -1.296 +2 -1.152 0.347 -1.019 -1.028 0.072 -0.094 0.082 0.492 -0.287 -0.991 1.889 -0.951 1.180 -2.155 0.188 -0.909 -0.583 -0.012 1.009 0.385 -0.714 1.006 0.099 2.209 1.046 0.289 0.276 0.985 +2 0.637 -0.591 0.924 0.498 -0.226 0.465 -0.990 -0.405 0.787 -0.270 0.451 -1.455 -0.061 0.244 -0.622 0.112 0.224 -1.026 -2.835 -0.908 -2.105 0.726 1.132 -1.173 -1.063 -0.374 -0.668 -0.752 +0 -0.172 0.624 -0.485 0.906 0.569 1.732 0.213 -0.022 -0.351 0.282 -0.028 0.433 -0.861 0.309 -0.015 0.311 -1.856 -0.366 1.365 -0.584 -0.264 -0.485 -1.231 -0.777 -0.542 0.608 -0.230 0.862 +1 2.243 -1.299 -0.684 0.583 -1.333 -0.544 0.114 -1.003 -0.350 -1.573 0.142 0.436 -0.086 0.948 1.268 -1.120 0.705 0.675 1.611 -0.314 0.333 0.077 0.123 -0.826 -0.850 -0.073 1.379 -0.295 +2 0.619 -0.293 -0.956 -0.022 -1.343 -1.626 -1.016 -0.335 1.672 1.143 -0.262 0.461 -0.998 0.379 0.592 -1.655 0.508 0.363 -1.078 1.681 0.567 -0.482 0.015 -0.680 -0.890 -0.502 1.887 -1.328 +0 0.167 1.102 -0.086 -0.683 1.339 -0.361 0.579 -0.518 0.654 0.713 1.650 -0.366 1.078 0.708 -0.190 -0.987 -1.603 -0.225 -0.528 0.088 -0.694 0.673 -0.247 0.140 -1.530 -0.016 0.907 -0.258 +0 -0.325 -0.169 0.434 2.071 -1.130 1.486 0.095 1.118 0.413 0.041 0.437 -1.445 -0.495 0.233 -1.319 0.061 -0.877 0.451 0.476 1.330 0.372 -1.090 -0.556 -1.284 0.382 -0.470 0.021 -0.158 +4 -0.079 -0.269 -1.033 0.954 -0.477 -0.262 0.674 -0.844 -0.759 0.014 -1.604 0.374 -0.033 -1.668 -2.158 -0.550 -0.404 1.250 2.104 -0.847 -0.853 1.429 0.376 3.549 -0.215 0.985 2.357 -1.230 +4 -0.806 -1.489 2.383 -0.802 0.362 -0.506 -1.167 1.047 0.655 -0.060 2.382 0.235 -0.456 1.915 0.404 1.628 -0.039 -3.014 -1.559 1.083 -0.589 0.253 1.134 0.179 -0.859 -0.011 0.107 -0.408 +0 -1.199 -1.101 0.483 0.389 -1.192 -0.647 0.546 1.003 0.776 -0.171 0.133 0.238 -0.185 0.550 0.229 1.293 -0.132 -1.146 -0.668 0.174 -1.186 0.053 0.443 -0.011 -1.300 -0.251 0.836 -0.456 +0 -0.177 -1.549 -0.491 -0.285 -0.256 -0.241 -0.062 0.479 0.875 -0.650 -1.203 -1.042 -0.487 -0.352 -0.770 -1.296 -0.456 0.181 0.595 -0.557 -0.413 -0.927 -0.031 -0.848 0.573 -1.786 -0.360 0.301 +4 0.412 -0.395 1.004 -1.460 0.109 0.678 1.702 0.135 1.257 1.192 2.337 0.324 2.052 0.810 -1.534 0.029 -0.917 1.530 0.169 -1.320 0.883 1.322 -0.403 -0.447 0.202 -1.811 0.710 1.691 +1 -0.571 2.194 1.617 -0.132 -1.064 -0.569 0.681 0.481 0.605 0.352 0.886 -0.699 -0.795 0.224 -1.058 -1.545 0.186 -0.659 0.111 0.245 -0.775 0.029 -0.562 -1.499 0.470 1.225 0.241 1.392 +3 0.821 -1.382 -0.342 -0.397 -1.329 2.455 -2.000 0.148 -0.458 -0.881 0.012 1.105 2.073 0.254 -0.330 -0.601 -1.151 1.516 0.911 -0.403 0.376 -0.537 1.432 0.570 1.036 0.357 0.029 0.362 +0 -0.524 0.332 0.791 -0.805 -0.035 1.281 -0.590 -0.093 0.521 -0.303 0.888 0.591 0.243 -0.762 -0.283 -1.260 -0.622 -0.493 -1.831 1.043 -0.511 -0.744 -0.459 0.495 -1.382 0.323 0.060 0.668 +0 0.864 -0.652 -0.175 0.300 -0.113 -0.485 -0.088 -0.854 -0.194 -1.275 -0.370 -0.186 -0.089 -0.648 -0.797 -0.190 -0.079 0.064 -0.063 -0.380 -0.600 1.080 1.306 0.066 0.308 1.192 -0.725 -1.038 +1 1.637 1.325 0.911 0.829 0.328 0.231 -0.459 0.099 0.540 -0.036 -2.278 -0.800 0.250 -0.173 0.829 -0.825 0.322 1.230 -0.029 -0.189 1.020 -1.261 -0.358 -1.546 -0.078 1.040 -0.072 -1.148 +4 0.615 -1.949 0.283 -1.212 1.402 -1.455 -1.434 2.330 1.263 -0.807 -1.236 -0.926 0.360 0.284 -0.399 1.716 -0.786 -1.890 2.670 0.915 -0.205 0.209 0.121 -1.623 -0.017 0.432 0.383 -2.384 +3 1.604 0.679 -1.551 1.505 1.576 0.019 1.323 -0.552 -1.866 -0.760 1.729 -1.491 -0.000 0.034 -0.513 -0.444 0.072 -0.562 -1.422 0.215 1.073 0.870 -0.810 -1.251 -0.284 1.568 0.646 0.704 +4 0.500 -1.422 1.594 1.337 -0.296 -0.104 1.179 2.945 -1.685 -0.511 -0.394 1.041 0.354 -0.512 -1.416 1.011 -0.200 -1.158 -0.682 -0.425 -0.300 -1.045 -1.032 2.370 0.330 -0.411 0.378 0.161 +3 0.031 0.368 0.358 -0.308 -1.165 -0.996 1.128 -0.726 -0.410 -1.134 -1.216 -0.769 1.688 0.658 -0.527 1.083 -0.152 1.354 2.212 1.028 0.095 0.699 0.015 -1.836 -0.655 -0.952 -0.677 1.794 +2 -0.133 1.107 -0.862 -0.173 1.081 -0.811 -0.760 0.734 -1.424 0.661 -0.490 0.410 -1.221 1.185 -1.217 0.725 -1.175 1.808 -0.071 1.951 -0.819 -0.443 1.211 -1.426 -0.699 0.495 -1.019 0.522 +0 0.700 -0.755 0.503 0.275 -0.057 -0.502 -0.638 0.282 0.629 -0.127 0.287 -0.038 0.099 -0.200 0.018 2.698 0.312 0.645 0.438 -0.263 1.294 -0.291 1.055 -0.334 -0.960 0.365 -1.105 0.536 +4 1.860 -0.042 1.646 0.840 1.289 0.832 -1.448 -0.418 -0.894 -0.076 -1.435 1.996 1.217 -2.245 0.036 1.288 2.012 -0.898 -0.314 0.850 0.062 -0.323 0.162 -0.553 -1.687 0.765 -0.152 -0.540 +3 0.500 -1.215 0.102 1.056 -0.761 -0.070 -2.896 -1.284 1.220 -0.835 0.714 1.356 -0.473 -0.853 1.586 -0.065 0.509 -0.445 0.487 1.225 -1.064 0.667 0.957 -1.073 -0.030 -0.698 -2.037 -0.184 +3 1.191 -0.300 1.282 0.164 -2.686 0.191 1.411 0.595 -0.116 -0.541 1.633 0.083 -1.174 0.119 -0.852 -2.349 0.012 0.056 -0.345 -0.339 0.137 -0.677 -1.121 -1.293 -0.395 0.261 -2.248 0.490 +0 -2.530 -0.297 0.241 -1.151 0.386 -0.204 1.755 1.573 -0.466 -0.151 -0.074 -0.452 0.195 -0.758 -1.131 0.623 0.630 -0.804 0.895 -0.632 0.253 0.820 -0.034 0.455 -0.516 -0.196 -0.206 -0.747 +3 -0.359 1.362 1.666 0.965 0.787 -0.296 -1.142 -0.469 0.328 -0.197 -2.060 0.052 1.076 2.444 -0.902 0.329 -0.696 0.411 -1.377 -1.370 -0.241 0.588 -1.170 -1.947 1.276 -0.305 -0.946 -0.108 +2 0.772 0.220 1.574 -1.084 -0.346 -0.677 -0.358 1.194 -1.055 0.509 0.068 -2.989 -0.225 -1.004 -1.871 0.419 0.349 1.514 -0.707 -0.978 0.495 0.208 0.481 -0.361 -0.570 -0.717 0.143 0.841 +3 -0.648 -0.447 0.319 -0.328 -0.689 1.257 0.355 1.858 -0.018 -1.645 -0.696 -1.192 0.133 0.160 -1.230 -0.230 0.439 -0.992 3.228 -0.503 -0.194 -0.681 -1.392 -0.780 -0.552 0.385 0.092 -1.214 +3 2.076 -0.086 -0.967 -0.338 0.807 -1.087 0.145 0.815 -0.947 -0.318 -0.401 -1.112 -1.897 1.176 0.720 1.232 -0.223 0.400 0.299 0.135 -3.349 0.148 -0.161 -1.376 -1.628 0.321 0.095 0.301 +3 -0.636 1.613 0.731 0.896 -0.103 0.511 -0.896 0.666 -0.204 -1.022 -0.023 -0.292 2.323 0.435 1.580 1.131 0.038 -0.444 -1.740 -0.871 0.663 -0.302 0.376 -2.030 0.454 1.096 -1.776 0.372 +0 0.105 0.452 -0.264 -0.480 -1.302 1.100 0.768 -0.301 -0.900 0.180 -0.498 -0.222 -1.264 0.217 0.237 1.095 -0.151 -0.334 -1.557 1.101 0.893 0.029 -1.329 -1.215 -0.547 0.965 0.290 -1.493 +0 -0.197 -1.089 0.026 -0.600 -0.967 -0.597 -1.510 0.946 -0.093 -0.127 1.282 -0.571 -1.093 0.553 -1.113 -2.132 0.594 0.210 -0.391 -1.256 0.526 -0.483 0.755 0.548 -0.817 -1.306 0.415 0.357 +3 0.175 2.985 0.367 -0.314 0.922 0.483 0.420 0.607 2.057 -1.131 0.474 -0.926 0.556 -0.919 -0.417 -0.295 0.976 0.918 -1.246 0.055 -0.710 -1.259 -0.217 -0.308 2.427 0.433 -1.378 -0.565 +2 -1.680 -0.835 0.686 -0.227 -1.073 -1.839 0.733 0.652 0.243 -0.496 -1.113 -0.053 -1.315 -0.752 1.261 -1.851 0.678 1.494 0.816 0.398 -0.464 -0.646 -2.248 0.037 -0.402 -0.698 -0.020 0.062 +2 -0.762 -0.959 -0.154 -1.272 -0.545 0.644 -0.370 -0.540 0.685 -2.703 0.251 1.104 1.369 0.014 0.398 1.041 -1.032 1.493 1.070 -1.023 0.596 -1.173 0.920 -0.100 0.296 1.209 0.014 -1.018 +3 -1.084 -0.490 -0.297 -1.163 0.983 1.353 -0.728 -1.801 -0.570 -1.504 0.607 -0.717 -1.592 -1.219 -1.397 0.735 -0.793 -0.166 0.071 -1.130 -1.542 -0.249 1.209 -0.547 1.919 -0.373 -1.061 -0.591 +4 -0.707 -0.798 2.425 -2.273 1.202 0.509 0.073 0.135 0.203 0.133 0.473 -1.780 0.231 0.830 -1.036 0.777 0.196 -0.748 -0.989 -1.096 -1.319 -1.224 -0.171 0.584 2.242 0.107 -1.620 0.826 +4 -1.759 -0.142 0.221 -1.121 2.227 -1.830 0.926 -0.047 -0.814 0.676 -0.432 2.272 0.514 0.509 -0.009 -1.511 -2.064 0.529 0.924 -1.013 -0.319 0.510 -1.321 1.112 0.467 -0.001 1.882 0.397 +1 -0.998 0.519 0.864 0.171 1.153 -1.217 0.468 -1.170 -1.114 -0.631 -0.942 -0.548 -0.214 0.837 -0.321 -1.586 1.140 -0.837 -0.059 0.447 0.200 1.094 0.479 -0.861 2.179 0.194 -0.147 0.964 +2 -0.741 -0.787 0.013 -2.042 1.079 0.706 0.816 0.744 -0.115 0.244 0.390 -1.451 0.202 -0.377 1.847 -0.192 -0.126 -1.221 -0.391 -2.114 1.859 0.584 -0.367 0.504 -0.961 -0.182 -1.108 -1.531 +1 0.043 -1.336 -1.132 0.016 -0.404 -1.313 -1.694 -0.847 0.194 -0.006 -0.501 0.202 1.040 0.394 -0.121 0.928 -0.960 -0.225 -0.201 -1.237 -0.420 -2.075 1.912 -0.937 1.586 0.038 0.565 -0.489 +3 -0.319 0.736 1.088 -1.840 0.785 0.246 0.971 1.178 0.023 -1.048 -0.928 -0.839 0.571 -0.911 -2.590 1.522 0.032 1.620 -0.061 1.966 0.732 -0.133 -1.395 0.848 -1.066 0.543 -0.371 -0.365 +0 0.277 0.998 0.435 0.143 -0.669 0.545 -1.273 1.251 1.014 -0.257 -0.397 0.956 -0.058 0.898 1.059 -0.756 -1.495 -0.102 0.891 -0.050 1.024 -1.132 -1.835 0.075 -1.037 -0.461 -0.176 1.050 +0 -0.227 0.411 -1.944 0.207 0.826 -2.079 0.705 1.899 -0.255 0.740 -1.005 0.920 0.510 0.218 0.161 0.394 -0.064 -0.973 -0.148 -0.048 -1.174 -0.110 -0.062 -0.521 0.352 0.590 -0.664 0.149 +0 -0.808 -0.502 0.915 0.329 -0.530 0.513 0.097 0.969 -0.702 -0.328 -0.392 -1.464 0.296 0.261 0.005 -0.235 -1.415 -0.421 -0.343 -0.802 -0.161 0.404 1.886 0.175 0.258 -0.074 -1.919 -0.027 +0 0.174 0.678 -0.356 0.080 0.402 -0.616 1.226 1.128 1.361 -0.595 0.949 -0.925 -0.656 -0.579 -0.076 -0.142 0.292 0.001 -0.369 0.325 -0.006 1.460 -0.913 -0.527 0.284 0.216 -1.563 0.139 +2 1.370 0.836 -0.139 0.618 1.909 -0.094 0.332 0.498 0.574 -0.310 0.844 0.016 0.189 -0.557 1.068 -0.879 -1.469 -2.082 0.813 -0.484 -0.604 0.029 -1.217 0.369 2.269 0.725 0.405 0.902 +4 1.156 2.202 1.006 -2.311 -1.860 -0.004 -1.461 0.322 -0.149 1.487 0.680 -0.846 0.659 0.435 -0.017 -0.533 0.790 -0.117 0.801 -0.196 -0.287 1.637 0.995 0.438 1.296 -0.333 1.978 1.139 +4 -1.815 -0.317 1.164 -1.071 -0.552 2.761 0.326 -0.258 -0.477 0.373 0.685 -2.156 -2.234 -0.998 -0.154 0.076 -1.703 -0.205 -0.072 -0.850 -0.534 -0.727 0.464 0.007 -1.142 0.955 -1.367 -1.713 +4 -2.307 0.946 0.511 0.455 -0.524 -0.021 -1.282 0.942 -0.461 -0.471 -0.676 0.001 1.030 -1.641 -0.456 3.782 -0.855 0.664 0.706 -0.303 0.528 -1.235 1.375 0.074 -0.606 1.171 -1.424 -0.486 +2 1.046 -0.702 0.492 -0.737 0.517 0.393 -0.896 0.511 -0.438 -0.162 0.815 1.077 0.415 0.013 0.705 -0.325 0.895 -0.617 -0.218 -0.929 -0.084 1.516 1.524 1.628 -0.641 2.708 1.183 1.019 +2 0.221 -0.020 -0.362 0.726 0.058 2.589 0.208 -2.747 -1.112 0.532 -0.643 -0.581 -1.239 -0.477 0.194 0.386 -1.020 0.805 -1.191 0.438 0.337 -0.934 -1.053 0.382 -0.560 -0.190 0.687 0.479 +3 -1.060 0.780 -1.234 0.273 -0.198 0.102 -0.396 -0.419 0.155 -1.002 -1.008 -0.359 -0.397 -1.005 0.924 -0.334 -0.227 -1.328 2.072 2.427 -0.780 1.226 -0.092 1.604 0.336 0.100 -2.499 -0.879 +2 -1.152 -0.360 -1.471 2.219 1.128 0.615 0.144 0.367 0.170 0.180 -1.134 0.810 -1.684 0.318 -0.527 1.198 0.635 1.063 -1.168 0.048 0.363 0.353 0.411 -0.984 -0.839 -2.066 -0.951 -0.141 +0 -0.329 -0.044 -0.259 -0.615 0.281 0.823 2.302 0.740 -1.483 0.650 -0.959 0.145 -0.199 -0.415 0.884 -0.691 1.705 -0.611 1.043 0.410 1.084 -0.146 0.808 0.602 -0.791 -0.897 0.532 0.407 +3 0.849 -1.029 -0.366 -0.500 -0.917 0.780 1.440 0.902 0.027 -1.487 0.130 0.625 -0.092 1.328 -0.197 -0.599 0.987 0.867 1.498 0.523 -0.805 -2.591 -1.723 -0.581 1.327 1.224 -0.404 0.814 +2 -0.473 -0.637 -1.311 -0.601 -0.281 0.345 -0.037 -0.365 -0.912 0.399 -0.498 0.354 -1.913 0.336 0.747 0.620 -0.197 0.915 -1.273 -0.089 -0.193 1.036 -2.020 -1.058 1.747 -2.431 0.814 -0.700 +3 0.382 -1.312 -0.397 -0.041 -1.099 1.797 -0.853 -0.669 -0.978 1.798 0.553 -0.937 1.106 0.201 0.257 -0.344 -0.749 0.338 -0.545 1.817 -1.269 1.536 -0.111 0.299 0.833 -0.630 0.864 -2.708 +1 -1.101 1.253 0.327 1.319 -0.253 0.091 -0.902 0.920 -1.032 1.836 -1.117 -1.341 0.346 -0.760 -0.507 1.050 1.495 0.083 -0.301 -0.335 0.430 -2.327 -0.096 -1.071 0.265 -0.212 0.097 0.178 +1 0.580 -0.058 -0.738 -0.059 -0.603 0.312 -1.055 0.009 1.605 2.025 0.960 1.238 -0.871 -0.576 0.416 1.979 1.506 -0.983 0.206 0.885 1.417 -0.236 -0.118 0.279 -1.135 -0.093 0.647 -0.350 +2 0.676 0.285 -0.229 -0.758 0.172 0.225 0.394 -0.269 -1.145 0.878 -0.003 -0.613 -1.002 0.922 1.384 0.575 0.642 1.172 -0.362 2.134 -1.060 -0.243 -0.772 0.064 1.731 -0.238 0.422 -2.908 +3 -0.556 1.197 -1.741 1.544 1.238 -0.887 -0.878 -1.495 0.921 -0.330 -0.404 -1.123 1.812 -0.973 0.024 0.216 -0.399 -0.601 -0.369 0.565 -0.347 1.626 -0.777 0.754 -1.756 -0.777 -0.816 -1.104 +3 -1.506 -0.503 0.137 1.782 0.232 -0.545 1.092 -0.275 -1.343 -0.744 0.652 -0.811 1.020 1.876 1.430 -0.465 -1.192 -2.173 -0.390 -2.056 0.391 1.574 0.366 -0.492 1.296 -0.555 -0.336 -0.137 +0 -0.505 0.048 -0.362 1.576 -0.016 0.698 -0.529 0.292 -0.383 -1.420 1.308 0.348 0.771 -0.728 1.185 0.468 0.150 1.029 -0.329 1.295 -0.662 0.033 -0.937 -1.148 -0.466 -0.161 0.552 -0.643 +2 -0.854 0.370 -2.720 0.881 -0.479 1.983 -0.335 1.226 0.914 0.789 0.801 0.496 0.647 0.742 -0.262 -0.644 -0.489 -0.311 0.202 0.760 0.078 0.951 0.014 2.180 0.076 -0.051 -0.667 1.134 +0 -1.009 -0.721 0.313 0.484 -0.605 0.666 -0.730 -0.061 0.298 -0.592 0.771 -1.409 0.435 0.965 -0.346 -0.535 0.152 1.247 0.089 -0.483 0.654 1.231 0.045 0.341 -1.802 -0.010 0.235 0.037 +0 -0.068 -0.343 0.563 0.722 1.199 0.764 -0.163 -0.472 0.305 -0.507 1.212 0.114 0.268 -0.422 0.256 0.153 -0.100 0.878 -0.082 -1.115 0.014 0.020 1.066 0.775 -0.221 -0.223 0.154 0.600 +1 0.086 0.917 -0.860 0.488 0.455 -0.575 0.546 -1.363 0.487 1.742 0.787 0.057 -0.565 1.745 -2.287 -0.531 -0.164 -0.595 -0.405 1.164 -0.412 0.457 -0.090 -0.560 1.375 -0.404 1.210 0.972 +4 1.255 2.234 0.008 -0.712 -0.834 -2.109 -1.370 -0.358 1.596 -0.055 0.006 1.453 0.385 1.170 1.191 -0.294 -2.530 3.644 -0.180 1.299 -0.091 -1.185 -1.210 0.708 1.928 -1.590 -1.144 -1.943 +0 -0.501 -0.284 -0.342 -0.100 -0.369 -2.165 1.240 -0.650 -0.374 -0.193 0.524 -0.493 1.066 1.875 1.636 0.253 -0.577 -0.603 -0.370 -0.851 0.416 -0.226 -0.016 -0.308 0.451 0.256 -0.353 -0.148 +1 0.615 -0.279 -0.467 -1.193 -0.071 -0.564 -0.155 0.252 -0.553 0.946 -0.182 -0.477 0.747 0.106 -1.095 -1.490 -0.398 1.605 0.507 0.611 -0.201 0.361 0.823 -0.167 -1.392 -0.800 -0.799 2.506 +3 1.538 1.071 -0.844 0.142 -0.432 1.163 -0.071 -0.000 1.686 -0.294 -1.928 0.411 0.704 0.772 0.382 1.257 0.763 0.553 -0.422 -2.539 -0.251 2.324 -0.227 0.367 -0.684 0.864 -0.078 0.724 +1 0.641 0.118 -1.011 0.178 0.204 1.477 -0.469 0.380 0.770 -0.274 -0.951 0.243 0.048 -0.546 -0.533 1.913 -0.831 -0.015 1.270 -1.168 -1.409 -0.468 -1.144 -0.038 1.651 -1.735 0.266 -0.217 +0 -0.403 -0.767 0.865 -0.039 1.143 -0.568 0.602 -0.415 0.150 -0.113 1.314 0.106 -1.328 -1.070 0.296 -0.865 0.433 -0.982 -0.256 -0.894 -0.054 -1.127 -0.184 0.472 -0.441 0.309 -1.288 -0.961 +2 -0.836 0.199 0.658 -0.084 0.462 -2.362 0.049 0.653 -1.192 -0.415 0.617 0.105 1.161 -0.770 0.250 -0.299 0.977 -0.440 -0.184 0.536 -1.282 1.169 -1.814 -1.053 -0.256 -2.019 -1.119 0.822 +0 -0.222 0.864 0.276 -0.411 0.780 -1.267 0.072 0.073 1.048 0.917 0.038 -0.061 0.409 -0.705 -0.856 0.191 1.660 -0.104 0.712 -0.175 -0.919 0.400 -0.594 -0.859 0.351 0.407 1.290 0.836 +2 0.068 -0.734 -2.561 -0.026 -0.227 -1.495 -0.680 0.891 -0.196 0.187 -1.070 0.594 2.224 0.523 0.438 0.252 -0.660 0.960 -0.122 1.724 1.787 -0.538 0.766 -0.588 1.163 -0.881 0.237 0.519 +2 0.950 -0.346 -0.058 -0.453 2.466 0.253 -0.535 -1.652 1.974 -0.163 0.744 0.856 -0.463 0.672 -0.604 -0.990 1.164 -0.158 0.519 0.411 0.638 -1.557 -0.585 -0.324 -0.831 0.429 1.187 1.345 +3 -0.616 -1.055 0.809 0.932 0.030 -0.602 -1.901 1.221 0.869 -0.440 0.060 -0.227 1.629 -1.038 -0.620 -0.717 0.310 0.056 0.200 -0.881 1.485 -0.454 1.188 3.139 -0.873 0.342 -0.132 2.031 +0 -0.456 -0.910 -0.219 0.842 -0.820 -1.491 -1.662 -0.519 -1.357 -0.140 0.011 0.308 -0.382 0.761 -0.417 0.900 0.150 -1.288 -0.043 0.741 0.096 1.577 -0.342 0.589 -0.244 0.345 -0.571 1.790 +0 0.324 0.172 -0.270 0.909 -2.031 0.263 -0.001 -0.534 -0.916 0.186 0.652 0.069 0.238 0.397 1.965 0.074 -1.667 0.097 -1.567 0.273 -0.226 -0.271 0.724 0.514 1.433 -0.682 -0.130 0.015 +1 -1.085 0.557 -0.616 -1.171 1.965 0.419 -0.319 -0.301 1.531 1.605 -0.545 0.265 -0.367 0.768 -0.354 -1.932 -1.031 1.042 -0.720 0.785 0.371 -0.712 -0.155 -0.625 -0.380 -1.785 -0.115 -0.579 +1 -0.145 0.421 1.666 -0.366 0.588 -0.179 0.963 -1.255 -0.536 0.663 0.582 0.600 1.409 -1.734 -1.254 0.561 0.813 0.655 2.709 -0.916 0.369 -0.071 0.621 0.376 -0.518 0.388 0.083 0.128 +4 -1.327 0.063 0.247 -0.469 -2.126 -1.024 -0.508 0.150 0.033 1.923 0.944 -0.023 0.121 1.271 1.309 0.334 1.733 -0.898 -0.029 3.166 0.710 -0.141 0.104 0.180 1.670 0.804 -0.079 -1.676 +2 0.673 -0.426 2.006 1.911 -0.672 -1.631 -0.591 -0.837 0.044 0.491 0.098 0.923 -0.168 -0.345 0.347 0.711 1.378 -0.310 1.645 -0.843 0.182 -0.803 -0.717 -0.307 -0.099 -1.532 2.353 -0.174 +1 -1.291 1.217 0.400 -0.725 0.203 -0.238 -1.562 0.274 1.640 1.412 0.103 -0.436 0.563 0.267 -0.139 1.860 -0.396 0.957 0.144 0.149 -1.651 1.101 0.837 0.188 1.035 0.416 -1.297 -0.971 +3 -2.084 -3.100 1.584 -1.172 -0.550 -1.052 1.163 0.942 -0.344 0.680 0.280 0.305 0.629 -0.009 -0.517 0.891 0.160 -0.013 0.111 -0.622 -1.319 -0.633 0.222 -0.087 -1.092 -1.410 -1.217 -0.175 +3 1.041 0.507 -1.387 -1.672 -1.348 1.649 -1.015 1.876 -1.067 -1.174 0.880 0.926 0.556 0.676 -0.802 0.833 0.240 -0.365 0.095 1.061 0.392 -2.158 0.891 0.815 -0.379 0.117 0.113 -1.979 +2 0.900 0.915 -0.231 -1.509 0.246 -0.918 -1.490 -1.264 0.326 1.361 0.039 0.211 1.183 -1.544 0.520 -0.012 0.566 -0.483 -0.818 0.141 1.649 0.861 -0.508 -1.341 -1.682 1.316 -0.955 -0.326 +1 -1.424 -1.007 -0.753 0.039 -0.224 -0.624 -0.979 -0.796 0.645 -0.770 -0.696 0.255 -1.161 0.092 0.041 -0.145 -1.151 -1.495 -0.033 -0.724 -2.214 1.035 1.030 -0.564 0.423 -0.757 -1.348 1.242 +4 2.602 0.820 -0.314 0.470 2.237 -1.783 -1.056 -0.773 2.301 0.576 -0.492 -0.542 0.790 0.760 -0.213 0.582 -1.583 1.114 1.876 0.498 0.287 1.006 -0.131 1.091 -0.039 0.326 0.448 -0.941 +2 -1.244 -1.026 1.357 -0.331 -1.217 -0.896 0.386 0.422 0.666 0.804 0.334 0.608 -0.476 -1.118 1.358 0.880 -0.281 -0.664 2.286 -0.904 0.227 -0.670 -0.411 -0.750 -2.164 0.390 0.657 -0.665 +4 0.470 -1.036 -1.144 -0.153 1.177 0.858 0.463 2.076 -0.598 1.979 -0.834 -0.199 0.186 0.242 0.664 -1.365 0.700 -2.018 -0.383 -1.019 -0.192 -0.640 -0.220 0.990 -1.394 0.393 1.404 2.770 +0 0.147 1.201 1.322 -0.922 0.330 0.282 0.212 -0.977 -0.742 0.345 -0.385 0.091 -1.014 -0.490 -1.334 0.264 -0.348 0.266 -0.367 0.236 1.805 -0.524 0.491 -0.659 -0.132 0.473 0.283 0.691 +1 -1.373 0.687 0.599 -0.544 1.001 0.743 -0.309 -0.422 1.097 0.232 1.045 -2.389 0.098 -1.577 -0.026 0.714 1.739 -0.938 -0.526 -0.096 -0.975 0.979 -0.070 0.122 0.620 -0.072 -0.818 -0.063 +2 1.620 -0.155 0.400 -0.632 0.414 0.843 0.215 1.498 0.246 0.156 1.642 0.161 0.243 2.248 0.844 -0.300 -0.612 1.357 0.741 -2.671 -0.200 -0.019 -1.269 -0.220 -0.257 -0.106 0.276 -0.250 +1 -0.208 -0.411 -0.443 -0.515 -0.663 1.425 0.840 -0.522 -0.013 -0.925 -1.236 0.181 0.496 0.337 -1.212 0.040 -1.026 -0.662 -1.067 0.421 -0.426 0.832 0.134 1.378 -2.787 0.248 1.598 0.642 +2 -0.238 -1.661 0.365 -1.044 0.840 -1.719 -0.350 -1.257 -0.401 0.217 1.694 0.589 1.971 -0.713 0.028 0.989 0.250 -0.490 -1.343 -1.361 0.235 -0.181 -0.314 -1.072 -0.807 0.174 -1.590 -1.094 +1 -0.522 -1.458 -0.065 -0.641 0.995 0.955 -0.245 -0.036 -0.371 -0.531 -1.070 0.068 -0.290 1.490 -0.353 2.406 1.807 -0.720 -0.075 -1.553 -0.420 0.374 0.206 -0.808 -0.076 0.501 -0.128 1.349 +3 -3.203 1.243 0.920 -1.836 0.929 1.058 0.562 -1.559 0.683 1.007 -0.572 0.441 -0.353 0.885 -0.314 -0.580 -0.039 -1.342 -0.139 0.362 0.870 0.249 -1.433 1.352 1.436 0.113 -0.752 -0.532 +1 -0.722 1.132 -1.131 -0.242 1.561 -0.487 -0.744 -1.412 -0.429 0.565 -0.529 0.026 -0.527 0.890 0.974 -0.221 1.484 0.157 0.167 -1.653 -0.268 -0.029 0.318 -0.679 -0.868 1.404 0.736 1.625 +1 -0.335 0.689 -1.227 0.199 0.714 1.371 -0.136 0.711 -0.793 -0.052 0.475 0.843 -0.394 0.862 -0.984 0.113 -1.367 -0.634 -0.339 1.753 1.629 -0.960 -0.808 -0.908 -0.107 -0.781 -1.716 -1.649 +4 3.098 -0.258 0.510 -0.649 0.295 -0.238 -0.411 1.819 1.177 0.137 -2.304 -0.361 2.703 -0.231 1.103 1.442 0.219 -0.842 -2.435 0.684 -0.815 0.176 -1.250 -0.265 0.034 0.269 -1.115 1.197 +2 0.548 -0.431 -0.388 2.390 0.810 0.257 -0.280 -0.344 0.237 -0.965 -1.511 0.664 1.532 1.560 0.653 -0.402 1.426 1.578 0.293 1.254 -1.984 0.402 0.401 -0.530 -0.338 1.047 0.370 0.647 +4 2.075 1.747 1.532 1.911 -1.260 -0.525 -0.911 3.495 0.244 -1.414 -1.102 0.107 -1.363 1.869 0.791 0.313 -1.054 -1.321 0.349 0.137 -1.283 -0.054 -0.051 0.738 2.106 -2.080 0.801 -0.696 +0 -0.261 0.009 0.303 -0.589 0.193 -0.144 -0.094 0.181 -1.314 0.040 0.163 0.129 -0.216 1.138 -0.354 0.227 1.297 -0.079 0.214 -0.096 1.237 -0.258 1.283 -0.104 -0.859 0.679 -0.305 0.621 +1 -0.643 0.598 -0.783 -0.893 -0.068 -1.226 1.602 1.927 -0.454 -1.387 0.337 -0.482 1.175 -0.787 0.545 0.114 -0.676 0.240 0.879 0.087 0.097 -0.873 1.144 1.045 -0.411 -0.124 1.414 -1.134 +0 0.439 1.268 -0.024 -1.422 0.843 0.755 -1.942 -0.004 -1.590 -0.665 -0.903 0.809 0.117 -1.286 0.880 -1.434 0.330 -0.814 -0.366 0.504 0.521 1.337 -0.082 0.306 -0.438 0.182 0.439 -0.098 +1 0.774 -0.980 0.812 0.608 1.439 0.174 0.306 -0.923 -0.001 0.611 0.706 1.713 0.461 -0.093 0.557 -0.557 1.325 -0.694 -0.405 -1.075 0.083 0.645 1.653 -0.057 -0.224 -2.669 0.223 0.028 +0 -0.657 -1.356 -0.648 -0.330 -2.369 -0.197 0.924 0.567 -0.496 0.047 -0.556 0.149 1.540 0.968 0.521 -0.493 0.750 -1.687 -0.252 0.584 0.740 0.670 0.295 -0.196 0.538 -0.953 -0.845 0.062 +1 -0.828 -0.248 1.112 -1.390 -0.078 -0.741 -0.827 -0.279 1.141 1.086 -0.059 1.002 0.740 -0.025 -0.094 0.287 0.791 0.280 -0.014 1.969 -1.048 -0.456 -0.483 -0.210 0.613 1.878 -1.207 1.083 +1 -0.964 -0.957 0.344 -0.049 0.033 -0.758 -0.230 -0.924 0.890 1.035 -1.846 -0.930 -1.497 -0.650 -0.083 -1.450 -0.922 -1.004 0.207 0.069 -0.722 0.177 -0.547 -0.272 1.673 1.340 -1.300 0.830 +2 1.512 -0.037 -2.741 0.388 -0.198 -1.829 -1.679 0.975 -0.732 -0.907 -0.036 -0.644 0.100 -0.203 -1.200 -0.511 0.443 0.042 -0.082 1.937 0.731 0.852 -0.166 0.220 -0.431 0.778 -0.110 0.222 +3 0.871 1.609 -1.005 -0.211 -0.600 0.139 0.268 -0.239 -0.044 -0.094 2.203 -0.455 2.870 0.484 0.118 1.696 0.736 0.557 -1.015 -0.488 -0.896 -1.543 0.105 1.203 -0.159 -0.216 -0.522 -1.668 +2 -0.564 -1.494 1.255 -0.498 -1.268 -0.168 0.068 -1.746 0.332 -1.047 1.711 0.524 -0.400 -0.745 -0.105 0.613 -0.025 -0.217 0.058 -0.604 -0.343 -2.086 0.974 0.299 -0.773 1.899 0.087 -1.036 +1 -0.941 -1.101 0.760 -0.409 -0.847 -0.019 -0.899 2.204 -0.774 0.190 0.518 0.948 -1.078 0.454 -1.642 0.309 -0.058 0.752 0.260 -0.111 -0.035 -1.455 -0.814 1.521 0.205 0.682 -0.617 0.871 +3 0.052 -0.983 -2.196 0.927 -1.252 1.954 -0.815 1.516 0.192 1.068 2.017 -0.531 1.116 -0.722 1.254 0.140 0.775 0.731 0.298 0.866 -0.884 -1.202 0.524 0.500 0.682 0.725 0.941 0.300 +3 -1.070 -0.114 -0.832 -0.735 0.020 0.833 0.652 0.340 -0.562 0.091 -1.173 -0.611 -0.175 -0.714 0.317 0.524 1.439 0.309 0.546 -0.222 2.870 2.042 0.848 -1.948 1.948 -1.243 1.143 -0.821 +4 -1.090 0.652 0.173 -2.119 0.662 2.128 -2.601 -1.236 -2.849 -0.075 0.990 0.106 1.392 0.831 1.702 1.160 -0.403 0.067 -0.869 -0.231 0.062 -0.085 1.342 -0.330 -0.932 -0.870 0.655 -1.403 +0 0.027 -0.675 -0.097 -0.526 -0.888 -0.254 0.068 -0.166 -0.265 0.226 0.531 -1.144 -0.252 -1.974 -0.162 0.151 0.570 0.154 0.548 0.065 -0.676 -1.116 -0.263 -0.766 -0.120 -0.492 0.419 0.049 +4 -0.821 -1.363 -0.876 -0.821 -0.185 1.083 -0.036 -0.742 -0.518 0.272 -0.117 -0.465 0.127 -1.341 -0.902 -0.284 1.752 -2.346 3.063 1.621 0.137 -2.499 2.861 -0.850 0.032 0.250 0.090 0.450 +1 0.194 0.121 0.958 0.465 -0.725 0.570 0.917 -0.787 0.198 -1.068 1.452 1.012 -0.433 -0.502 -0.042 -0.048 -0.206 -0.042 -1.062 -1.092 -1.046 0.210 1.076 2.183 1.599 -0.802 1.200 0.152 +3 -1.715 -0.300 0.178 -0.266 -0.231 -0.203 -1.193 0.818 -1.330 -1.215 1.688 -0.016 -2.159 2.036 0.972 -0.523 0.748 -1.067 -0.894 -0.646 0.704 1.230 -0.101 0.417 0.298 -1.477 -2.189 0.280 +0 -0.182 0.441 2.258 0.659 -0.997 -0.831 -0.681 -0.391 0.226 -0.944 -0.219 -0.199 0.338 -0.407 -0.766 -0.704 0.392 -0.725 0.699 0.366 -1.089 1.239 0.080 -0.666 1.830 -0.012 0.358 0.016 +1 -1.263 -0.485 -0.330 1.555 0.925 1.148 0.820 0.946 -0.688 -0.199 -0.990 -0.210 0.684 0.135 1.311 -1.141 0.500 -1.248 0.094 0.740 -0.215 0.551 -0.960 -1.664 0.557 1.744 -0.996 1.222 +1 0.806 -0.984 0.323 0.096 -0.935 0.163 -0.772 -1.553 1.060 -0.832 -0.109 1.298 -1.745 1.589 -1.031 -0.534 0.828 -0.296 -0.373 1.357 0.508 0.449 -0.067 0.095 -1.537 -0.049 1.333 -1.375 +4 2.414 -0.637 0.544 0.927 0.101 1.386 1.995 -1.110 0.116 0.916 -1.633 1.064 1.707 1.284 0.438 1.539 0.357 -1.320 -0.522 -0.256 -0.064 -0.094 -0.338 0.219 1.504 0.214 0.629 -2.194 +4 -0.901 0.021 0.660 -1.924 0.519 -1.427 -0.986 0.514 -1.128 -1.090 2.072 -1.523 1.384 0.597 0.767 2.173 -0.313 1.250 -2.389 1.336 -0.274 0.092 -0.331 -0.232 -1.917 1.260 0.070 -2.111 +2 0.128 -0.782 0.451 1.160 0.930 0.121 0.078 -0.009 2.125 -0.582 0.079 -0.181 -0.783 -0.380 0.028 -1.185 1.649 0.726 1.092 -0.161 -0.313 -0.214 -0.696 1.300 1.397 0.550 1.698 2.396 +2 0.083 0.536 -0.669 0.452 -1.776 0.718 -0.871 0.567 0.482 2.686 0.889 1.211 0.277 0.158 -1.907 1.388 1.952 -0.012 -0.700 -0.595 -0.191 -0.796 0.251 0.948 -0.136 0.461 0.968 -0.377 +4 1.653 0.488 -0.979 1.507 -1.147 0.614 2.418 -0.131 -0.288 0.168 2.225 1.476 -0.443 0.305 0.923 -0.429 -1.132 1.084 0.510 0.319 1.107 -1.145 -0.232 2.638 0.568 0.628 0.106 -1.841 +3 -0.643 0.445 -1.529 1.062 -0.644 -2.459 -0.360 -0.514 0.499 -1.153 0.349 -0.681 -0.070 1.066 1.099 1.276 -0.762 0.297 0.340 1.252 -0.328 -1.489 0.849 1.341 0.317 -1.093 2.166 0.120 +3 -0.671 0.961 -0.440 -0.883 0.044 1.493 0.809 -1.900 -0.156 -1.508 1.626 0.940 1.533 0.526 1.134 -1.177 0.176 -0.721 -1.648 -0.814 1.143 1.017 0.016 -0.796 0.398 0.070 1.200 1.365 +4 -0.519 -0.482 0.476 -0.567 1.003 -2.387 -0.618 0.633 -0.026 -1.414 0.201 0.902 -1.675 1.315 -0.739 -0.248 -1.274 0.060 3.732 -0.311 0.053 -0.836 -1.172 0.148 -0.554 0.250 -0.769 0.113 +3 -0.545 0.960 1.199 3.055 -0.305 -0.278 -0.330 0.174 -0.645 1.212 1.008 -0.257 -0.711 -0.436 -0.469 0.494 0.193 0.822 1.487 0.299 0.593 2.456 0.571 -0.490 -0.068 -1.666 0.545 -0.550 +4 0.642 -0.155 -0.580 -1.044 -0.612 1.759 -0.648 0.312 1.028 0.007 -1.932 -2.461 0.643 0.376 1.549 2.217 -0.584 -0.115 -0.874 -1.780 0.379 1.695 -0.225 1.049 -0.804 -1.239 0.520 0.119 +3 -1.686 -1.237 -2.261 0.317 0.835 -1.122 -0.774 1.199 -0.530 0.080 0.606 -1.135 -1.685 -1.027 -0.940 0.034 0.992 -1.825 -1.424 -0.630 0.047 1.685 -0.273 0.159 0.388 -0.709 0.392 0.162 +4 -0.577 -0.246 -0.343 -1.689 1.229 -0.013 -0.190 -0.087 -0.014 -0.379 -1.597 0.345 0.121 0.263 -1.035 0.343 -2.812 2.185 0.827 -0.721 2.339 -0.251 -1.652 -1.722 -1.204 0.847 0.248 1.433 +4 -1.961 0.601 1.172 1.825 0.884 -0.081 -1.133 0.481 0.142 -0.237 0.224 -0.120 1.384 -0.964 1.259 -0.606 1.897 0.426 2.275 -0.464 -0.286 0.467 1.655 2.276 -0.289 0.599 0.104 -1.210 +0 -1.146 -1.719 -0.690 0.269 -0.525 -0.763 -0.186 0.649 1.015 -0.032 1.070 -0.631 0.209 -0.386 -0.132 -0.616 0.162 1.050 -0.870 0.167 -0.812 -0.508 1.674 -0.031 0.618 -0.478 -0.351 -1.781 +0 1.062 1.376 -1.542 -0.300 1.570 -0.605 0.133 0.935 0.785 0.550 -0.355 0.677 -1.196 1.741 0.593 -0.263 -0.098 -1.362 0.333 -0.218 1.231 0.248 -0.141 1.250 0.062 0.210 -0.242 -0.466 +0 -0.652 0.308 0.113 1.210 -1.152 -0.009 -1.002 0.110 -0.181 0.272 -0.409 0.762 -0.715 -1.015 0.225 1.854 -1.521 -1.145 0.232 1.127 0.306 -0.608 0.420 0.516 -0.680 0.825 -0.035 -1.750 +4 0.469 0.467 2.181 -0.370 -1.350 0.791 -0.246 -1.301 1.519 1.066 1.377 -0.491 -0.461 1.076 -0.559 -0.290 0.526 1.666 -2.373 -0.356 -0.982 0.937 -1.295 0.467 -2.063 0.796 0.889 0.074 +3 -1.219 0.917 -1.198 1.485 -0.313 -1.807 -0.226 1.071 -2.442 0.055 1.364 -0.256 1.215 -1.215 -0.873 -1.268 1.281 0.427 -1.283 0.098 0.368 0.275 -0.056 -0.554 0.378 -1.065 -0.623 1.630 +0 -0.085 1.834 -0.223 0.924 0.443 1.982 1.173 1.116 -1.116 0.217 -0.917 0.594 0.572 0.843 1.401 0.583 -1.032 0.251 -0.279 0.419 -0.393 0.035 0.811 -0.239 0.679 0.913 0.155 -0.778 +3 -1.276 0.414 1.747 -3.225 -0.972 2.186 0.834 -0.112 -0.532 0.586 0.979 -1.269 1.492 0.398 0.938 1.024 0.312 0.445 0.518 -0.357 0.648 -0.260 0.001 1.564 -0.036 0.414 -0.175 -0.366 +1 -0.627 -0.018 0.646 0.801 -0.161 1.574 0.833 0.288 0.691 -1.004 1.141 -1.299 0.436 -0.082 -1.433 -1.222 -0.031 -0.100 -0.751 -0.390 -1.470 -1.442 -0.316 -1.831 -0.217 0.453 -0.202 0.482 +0 -0.285 -0.593 -0.976 0.491 0.186 -0.010 -0.130 0.907 1.559 -0.169 0.487 -0.489 -0.275 0.027 -1.008 -0.309 -0.159 0.857 1.050 0.214 0.358 -0.043 -0.137 0.033 -1.720 -0.321 1.051 -0.794 +4 0.372 -1.031 0.128 -0.906 1.708 -1.229 0.559 -2.036 -0.916 1.272 0.677 -1.003 0.154 -0.176 -1.131 0.179 -2.151 -0.508 2.014 -0.700 -0.325 0.754 0.286 -1.905 -1.107 0.209 -1.484 1.552 +2 -0.571 -0.164 0.864 0.771 0.474 1.029 0.409 1.448 -1.711 -0.060 -0.102 -0.219 0.497 0.214 0.825 -1.184 2.292 -0.391 0.287 -0.206 0.081 -0.535 0.206 0.580 -2.402 -0.329 -0.367 2.321 +0 -0.587 -1.035 0.342 1.274 0.778 0.102 0.668 -0.019 -0.278 -1.014 -0.329 0.304 -0.417 -1.143 0.115 0.304 -0.369 -0.022 -2.531 0.742 0.288 1.415 -0.213 0.734 1.178 0.025 0.344 0.143 +4 -0.277 -2.882 -0.442 -0.868 -0.855 3.121 -1.389 0.396 -0.796 -0.912 -1.347 1.128 1.204 -0.620 0.912 -1.036 -0.401 0.679 -1.001 -0.739 0.025 0.037 -1.078 2.233 -0.844 0.953 0.596 -0.805 +3 -0.433 -1.527 1.500 1.406 0.433 -1.751 0.954 -1.161 -1.026 -0.146 0.676 -0.532 -0.931 0.016 0.363 -1.867 -0.770 1.580 0.434 1.681 0.626 0.833 2.036 -0.386 -0.473 0.708 -0.399 -1.517 +4 0.092 -0.188 1.107 1.306 -1.777 0.476 -1.307 1.758 0.440 -0.823 -0.241 -0.289 0.525 1.042 -1.148 1.839 1.606 -0.875 0.073 1.711 1.233 -0.337 0.814 2.997 0.105 -0.633 0.172 0.247 +0 -0.031 0.225 1.383 0.473 0.201 -0.958 0.671 0.330 1.284 0.047 -1.816 -1.700 0.736 -0.570 0.437 0.114 -0.167 -0.096 -0.865 -0.016 -0.584 -0.372 -0.324 -0.227 0.922 -0.130 1.024 1.538 +4 1.023 -0.203 0.911 -2.477 1.111 -1.290 -1.073 -0.705 1.084 -0.889 -0.656 -0.962 0.255 0.744 1.704 2.400 0.650 0.366 -1.217 1.909 0.215 -0.289 0.388 -0.636 -2.338 0.572 1.342 0.557 +4 1.171 -0.712 -0.525 1.021 -2.642 -0.586 -1.098 0.294 0.557 0.573 1.508 0.554 0.249 2.433 -2.128 2.201 1.742 -0.148 -0.707 -0.658 -0.289 1.196 -2.467 2.010 -0.001 1.177 -1.230 0.724 +2 -0.203 -0.404 0.857 0.194 2.257 0.161 -0.551 2.671 0.414 0.990 0.592 0.623 0.536 -1.102 0.368 -1.325 -0.174 -0.726 0.357 0.334 0.481 0.287 -0.607 -0.261 0.920 1.068 -1.147 -1.531 +1 1.636 -1.863 0.358 -0.099 -0.183 0.552 -0.769 0.755 0.866 0.956 0.702 -0.795 -0.284 -0.953 -0.419 -0.413 0.719 -0.051 -1.705 0.322 -0.291 0.870 0.505 -0.610 0.232 1.548 -1.791 0.618 +0 1.204 -0.514 0.132 1.565 0.396 -0.088 1.462 -0.470 -1.696 -0.779 1.136 0.521 0.068 1.061 0.167 -0.222 0.835 -0.382 0.490 -0.795 -1.055 -0.866 -0.026 -0.648 -0.620 -0.153 -0.072 -1.551 +4 0.005 -0.237 0.849 1.744 -0.011 0.503 -0.851 -0.808 -0.501 0.396 1.647 -1.577 -0.473 1.247 1.512 1.124 -0.025 0.819 -0.986 0.414 -1.336 -0.184 -1.385 2.068 -0.009 1.235 -2.701 -0.033 +1 -0.573 1.412 -1.522 -0.994 -0.316 0.704 0.046 0.059 2.861 0.115 -0.761 1.065 0.548 0.567 -0.180 -0.574 -0.330 -0.892 0.760 0.312 0.601 -0.883 -0.149 1.344 -1.199 0.140 -0.205 -0.007 +1 -1.553 -1.404 -0.084 2.254 -0.202 -0.183 -1.442 0.946 0.953 0.789 -0.629 -0.707 -1.293 0.084 -1.080 -1.177 -0.424 -0.489 -0.538 0.466 0.079 -0.288 0.183 -0.992 0.105 0.151 -1.503 0.996 +2 -0.552 -0.510 -0.315 -0.877 -1.620 1.180 -0.515 -2.120 -0.286 0.445 -1.596 0.619 0.315 -0.870 0.286 1.626 0.056 -0.499 -0.823 1.814 -0.209 -0.238 0.880 0.572 -1.378 0.204 -0.489 1.178 +1 0.845 -1.684 0.310 1.225 1.034 0.873 0.831 -0.990 0.269 1.917 -0.559 0.827 0.411 -1.928 -0.322 0.277 0.060 1.501 -0.550 0.975 0.583 -0.089 0.076 0.881 -0.379 -0.660 0.209 -0.609 +4 1.485 0.394 0.820 1.230 1.027 -0.856 -0.311 0.468 0.203 0.753 0.644 0.247 -2.604 1.173 0.839 2.069 -0.477 -1.843 -0.180 0.457 -0.442 1.372 -0.660 -0.101 -0.246 2.774 -0.906 0.584 +2 -0.972 0.956 -0.822 1.405 -0.715 -1.503 0.525 -0.447 -1.581 0.255 -1.306 -1.989 0.677 1.489 -0.395 1.212 0.802 0.667 -0.170 -0.596 0.344 0.648 -1.261 -0.324 -0.189 -0.282 -0.675 -1.333 +3 -0.782 0.460 1.568 1.446 -2.130 0.437 0.031 0.015 0.039 -0.284 -2.424 0.583 0.822 0.507 -0.175 -1.431 0.160 -0.239 2.339 -0.512 1.534 -0.188 -1.365 -0.269 0.013 -0.439 -0.291 0.301 +0 -0.208 0.462 -1.411 0.722 -0.997 0.180 -1.172 0.366 -1.395 -0.564 -0.550 0.511 1.442 -0.463 -0.350 -0.723 1.561 -0.281 0.466 0.376 -0.811 0.545 1.239 -0.447 0.959 0.283 -1.189 1.548 +3 -0.636 -1.617 -0.814 1.935 -0.704 -2.284 -1.188 0.954 0.877 -0.474 0.598 0.540 -0.426 1.213 -0.453 0.005 -1.183 0.277 0.923 -0.342 -0.299 -2.258 0.975 -0.557 -0.771 -0.618 -1.608 -0.260 +1 0.344 0.475 0.971 -0.937 0.832 -0.438 -0.461 0.069 -0.495 -0.620 -0.673 -0.176 -1.334 -0.745 0.428 -1.000 0.398 1.668 1.663 -1.702 0.228 -1.028 -1.164 0.038 1.056 0.954 -1.605 0.288 +3 2.626 -0.093 -0.684 0.707 -0.051 -1.086 0.483 -0.853 1.576 0.495 -0.277 0.109 0.903 0.186 2.122 1.136 0.488 -0.937 -0.147 0.479 -0.823 1.293 -0.974 -0.637 -0.562 0.744 -0.805 -1.800 +4 1.313 0.542 -0.169 2.071 -1.684 -0.548 0.156 -0.993 -1.434 1.017 1.600 -0.616 2.277 -2.398 0.115 -1.129 -0.024 0.070 -0.098 -1.271 0.127 0.005 0.221 -1.463 1.366 -0.702 0.361 0.513 +1 -1.515 -0.983 -0.147 0.157 0.258 0.759 1.418 0.673 -1.655 -0.131 -0.982 -0.837 0.239 0.011 0.912 -0.637 0.802 -0.458 -0.856 0.829 1.997 0.073 -0.929 0.594 -0.245 0.481 0.166 1.495 +4 0.160 0.915 2.951 2.475 -0.183 0.294 -1.054 0.799 0.389 -0.774 1.187 -0.266 -0.521 0.399 -0.465 -0.032 1.746 -0.615 0.675 -0.583 -0.973 -0.082 2.684 0.782 0.462 -0.525 -0.385 -1.483 +4 0.202 -1.044 -0.493 -1.867 -1.142 -1.615 -0.920 -2.183 -0.490 0.036 0.289 1.148 -1.025 -1.024 -0.864 0.489 0.267 -0.458 1.589 -0.114 -0.486 -0.397 -0.152 -0.681 -3.159 0.418 0.699 1.304 +3 -1.821 0.270 -1.912 -0.069 -1.368 1.987 0.911 0.106 1.264 -0.846 0.543 0.200 0.264 1.272 0.732 0.289 -1.655 -0.960 -0.123 0.093 -1.130 2.412 1.516 0.602 0.072 -0.212 -0.952 0.077 +1 0.887 1.901 0.246 -0.839 -0.067 2.179 -1.172 1.176 -1.014 0.029 1.046 0.290 0.823 1.570 0.073 -0.869 0.560 -0.041 -0.231 0.198 -1.188 0.129 0.053 -0.040 0.384 -1.152 -1.088 1.255 +0 0.379 -1.210 -1.839 1.170 0.356 0.151 0.451 0.382 -0.163 0.533 -0.197 0.007 -0.439 -1.412 0.786 0.559 -0.429 -0.165 -1.392 -0.050 -1.454 -0.429 -0.860 -0.193 0.338 0.982 0.426 -1.415 +3 -2.212 1.533 -1.424 -0.267 -0.429 0.589 -1.598 0.462 2.024 -1.363 0.190 -0.662 0.426 0.019 -0.641 0.488 1.804 -0.191 0.720 -1.293 -0.956 0.472 1.484 0.356 -0.313 -0.001 -1.250 0.605 +3 -0.866 0.806 0.447 0.770 0.635 -0.887 1.001 -0.298 0.929 -0.125 0.313 0.343 0.813 -1.635 -1.576 0.672 0.356 -0.776 0.364 -1.408 3.264 1.312 0.622 0.076 -0.619 -1.310 -0.114 -0.860 +3 -1.531 0.895 -1.388 -0.438 -0.759 0.186 0.899 -0.604 1.836 0.020 -0.200 -0.736 -0.065 0.926 1.891 0.198 -1.074 -0.170 -2.772 0.909 0.336 0.345 -1.083 -1.789 -0.679 -0.853 -0.490 0.852 +2 -0.769 -0.800 0.123 -0.007 -0.498 -1.957 -1.103 0.336 -0.339 -0.299 -1.254 1.260 -2.482 1.566 0.432 -0.590 -0.355 -0.217 0.575 -0.159 1.354 -1.233 0.725 0.870 -1.033 0.222 0.020 0.479 +1 1.180 -1.248 0.500 1.434 0.510 0.478 -0.472 0.220 1.236 0.328 -1.248 -0.456 1.209 0.325 0.119 -0.554 1.189 1.662 1.263 1.443 -0.359 -0.500 -0.156 0.039 1.431 1.166 0.538 -1.431 +1 0.393 -0.774 -0.199 -0.029 -0.802 -0.034 -0.358 0.080 -2.069 -0.046 -0.599 -0.859 0.451 -0.400 -1.061 1.419 0.500 -0.057 1.466 -0.645 0.304 1.095 0.156 2.524 0.295 0.330 1.271 0.521 +2 -1.422 0.538 1.651 -0.315 1.082 0.082 -1.392 0.120 -0.185 0.896 -0.363 0.499 -1.056 0.429 0.024 -0.128 0.031 -0.292 0.142 -0.197 -0.973 -1.296 -0.494 -0.317 -3.005 0.019 2.203 0.690 +2 -1.242 -0.207 -1.656 -1.062 1.619 -1.101 -1.174 0.670 0.863 -1.666 0.249 -0.548 0.331 -0.303 -1.366 -0.481 0.626 1.944 0.390 -0.391 -1.180 -0.158 -0.378 0.306 -0.060 0.480 -0.360 1.709 +4 -0.693 -0.332 1.312 0.008 0.751 -1.678 -0.542 0.762 -0.139 -0.206 -0.415 0.866 0.018 -1.323 -0.718 2.250 -0.090 -2.040 0.268 0.733 1.296 -0.282 2.074 0.501 2.187 -0.108 1.168 2.015 +2 0.019 0.166 0.261 1.306 0.157 1.388 -1.750 -2.184 0.127 0.327 -0.534 -0.010 1.075 0.670 -0.330 0.198 0.759 -1.230 -0.805 0.923 -1.730 -1.090 -1.213 0.918 -1.085 0.792 -0.353 1.779 +0 -0.056 0.027 -0.260 0.679 -0.574 -1.054 -0.911 1.247 -0.319 0.902 -0.430 -0.801 0.523 0.778 -0.651 1.013 0.293 -0.480 0.108 -0.792 -0.827 1.572 1.577 0.730 0.063 -0.166 0.486 -0.135 +4 2.035 0.735 -1.195 0.855 -0.205 -0.014 -1.641 0.076 -0.757 -0.776 2.752 -1.696 2.788 -0.260 0.365 0.613 0.510 -2.150 -1.586 0.108 -0.033 1.097 -2.863 -0.068 -0.897 0.025 0.250 0.492 +4 -0.403 -1.938 2.176 -3.427 -0.148 -0.166 -0.569 -0.239 0.635 -1.258 -1.775 -0.533 -2.720 -0.876 -0.541 0.092 1.291 0.298 0.343 -0.726 -0.611 -0.587 -0.128 -0.443 0.322 0.619 1.279 0.276 +4 -0.236 -0.490 -2.550 0.386 0.544 1.602 -0.788 -0.870 -2.680 -0.294 0.901 -0.612 2.143 -0.257 0.147 -1.363 0.287 -1.979 0.694 0.724 1.359 -1.020 0.161 0.564 0.442 -0.865 -2.392 0.347 +2 -1.361 -0.470 -0.243 0.734 -1.525 -1.382 1.374 1.101 -1.522 0.043 1.956 0.700 0.580 -0.186 0.513 0.332 -0.366 0.542 -0.170 0.326 0.736 1.062 1.000 -0.451 -0.367 -2.112 -0.019 1.793 +0 0.589 0.624 1.493 0.843 -0.306 -0.383 0.194 -1.307 0.794 0.059 -0.676 0.544 -0.519 -0.699 -1.496 -0.989 0.836 0.619 -0.469 0.637 0.097 0.912 0.175 -0.704 0.109 -0.951 2.150 0.518 +2 -0.256 0.543 -0.246 0.432 -1.456 1.125 -0.684 -1.214 2.114 0.931 -0.279 -1.285 0.811 -0.142 0.669 2.124 -0.337 0.388 -0.642 -0.864 0.721 -0.669 0.286 0.576 0.326 -1.729 -1.881 -0.396 +2 1.625 0.014 -0.165 -1.370 -0.364 -0.715 0.027 -1.513 -0.329 -1.009 0.940 -0.859 -0.572 0.282 -0.454 2.359 0.931 0.017 0.368 -2.355 1.232 0.512 -0.073 0.566 0.688 -0.416 0.380 1.049 +0 0.716 0.942 0.642 0.158 1.304 -0.329 1.085 -0.385 -0.881 -1.006 -1.064 0.659 -0.743 -1.033 0.714 0.328 0.963 0.513 -0.753 0.076 0.125 -0.919 -1.105 -0.681 2.037 -0.413 1.233 -0.469 +3 -0.304 -0.278 -0.473 0.649 -1.369 0.280 -1.794 -0.066 -2.256 -0.890 -0.022 -1.425 -1.419 0.982 -1.596 -0.119 1.476 0.142 0.029 -0.714 -0.710 -0.168 -1.997 1.013 0.208 -1.748 1.026 0.679 +3 -1.796 -1.229 -2.299 1.801 0.332 0.659 0.301 0.574 0.078 0.271 0.108 -0.609 -0.047 0.274 0.111 -1.478 -1.345 1.083 -0.307 0.585 -1.092 -1.473 -0.844 -0.223 -0.665 1.294 0.180 1.849 +2 -0.796 1.215 0.013 0.802 0.335 -2.526 -0.019 0.274 0.424 -0.908 -1.305 0.210 0.114 -0.424 -0.540 1.240 -0.760 -0.569 -2.267 0.808 0.152 1.562 -0.888 -1.550 -0.804 0.896 1.104 0.111 +0 -0.731 -0.316 -0.097 1.009 0.955 -0.665 -1.362 -1.025 -1.508 1.093 1.438 -0.072 0.105 -0.838 -0.326 -0.342 0.842 -0.401 -0.040 0.174 1.431 -1.619 -1.668 -0.128 -0.291 -0.306 -0.779 0.046 +4 -0.140 -0.252 -1.767 -1.964 -2.176 -0.450 -0.993 -0.932 -0.421 0.004 -0.313 -0.705 1.931 -0.201 0.998 -1.237 1.335 -0.548 2.802 -1.312 -1.067 -0.189 -0.355 -1.173 1.474 1.369 -0.340 1.010 +4 3.245 -1.999 -0.171 -1.109 -0.932 0.556 -0.302 0.820 0.574 -1.420 -1.533 0.772 -1.679 -0.868 0.239 0.896 -0.092 1.171 -0.650 0.552 -0.432 0.772 -0.317 -0.265 0.880 0.353 -1.070 2.486 +4 0.606 -1.682 -0.084 -2.056 0.017 -0.791 2.528 -0.781 0.035 -1.855 -1.050 -1.647 -0.175 2.132 -1.421 -2.317 1.022 0.700 -0.279 -0.633 0.188 1.546 -1.015 0.599 0.225 0.692 -0.491 0.635 +3 -0.304 0.830 0.758 1.025 -1.336 -0.349 0.687 -0.615 1.488 -1.228 0.820 -0.446 1.051 0.270 -0.845 0.820 -1.728 -0.348 -0.456 0.432 -2.688 0.233 0.373 -0.794 -0.304 0.035 0.946 -2.165 +2 1.090 -1.014 1.431 0.286 -1.245 -0.038 0.926 0.458 -0.121 1.242 0.869 0.682 -0.372 -0.295 0.147 0.112 2.198 -1.400 -0.110 -0.739 0.720 0.329 0.466 -2.015 0.370 -0.904 -1.636 1.139 +4 0.927 -1.943 0.010 1.569 0.720 0.006 -0.737 0.668 2.400 -0.217 1.094 -1.937 0.310 -0.676 2.437 -0.997 0.635 1.659 -0.801 0.823 2.307 0.846 1.095 0.853 -0.231 0.910 2.149 -0.788 +4 0.225 0.933 -1.418 -1.761 -1.526 1.263 -0.552 2.558 -0.564 0.185 1.542 2.006 2.062 1.208 1.024 0.593 0.778 -0.551 -0.818 -0.003 -0.170 -0.453 0.696 0.955 0.088 1.478 -1.142 -0.194 +1 1.641 -2.375 0.529 -1.740 0.391 -0.554 0.501 0.431 0.083 -0.456 0.162 0.939 0.267 0.791 -1.389 0.064 -0.484 0.777 0.531 -0.514 0.154 -0.140 0.392 0.349 0.068 -1.338 1.485 1.056 +4 -0.294 -0.307 2.242 0.608 -1.590 -0.543 -0.401 -0.721 0.064 -0.574 -0.513 0.154 -1.541 -2.151 1.243 0.837 0.369 0.305 -0.189 3.016 0.193 2.608 2.063 0.462 -0.210 0.016 -0.401 0.461 +2 0.250 0.826 1.887 -1.177 1.138 -0.330 0.935 -0.041 2.127 0.130 -0.641 0.420 0.324 1.138 0.448 2.475 -0.111 -0.100 -0.604 0.086 0.231 0.281 0.067 -0.610 -0.882 -0.375 1.142 1.357 +4 -0.923 -0.892 0.183 -0.153 0.961 -0.277 -0.519 -1.272 2.337 -1.481 3.116 0.670 0.450 -1.507 0.546 -1.552 -0.165 1.099 -1.443 0.654 -0.362 0.564 -1.343 1.326 0.694 -0.841 -0.777 1.270 +0 -0.459 -0.283 -0.720 0.402 2.055 -0.875 0.394 0.045 0.669 0.697 0.205 -0.040 -0.434 -0.765 -0.097 -1.285 -0.594 1.107 0.099 0.893 1.470 -0.177 0.207 -0.302 -0.813 -0.953 0.843 0.209 +0 -0.752 0.204 -0.950 -0.368 0.450 -0.507 -0.228 -0.273 -0.433 -1.096 1.012 1.370 0.952 0.943 -0.416 0.175 -0.779 -0.135 -0.046 -1.385 0.132 -0.531 0.265 -0.235 0.346 0.698 -1.253 -0.320 +3 1.750 0.711 0.576 -0.468 1.599 -1.174 -1.483 1.327 -0.697 0.332 -1.013 1.535 -0.030 -0.971 -0.862 0.034 -0.408 -0.635 1.182 0.504 0.983 0.894 -0.678 0.605 -0.236 -1.758 -1.531 -1.259 +0 -0.636 0.376 1.219 -0.960 -0.411 -0.068 -0.152 0.697 -0.050 0.650 -1.203 0.145 0.035 0.844 2.520 0.956 -0.704 0.633 -0.050 -0.339 -0.529 -0.582 -1.144 -1.295 0.655 -0.356 0.182 -0.870 +2 0.501 -0.187 1.975 0.492 -0.448 -0.358 -0.144 -0.777 1.689 0.124 -1.171 0.405 1.439 1.061 -0.760 -1.109 1.427 -1.624 0.371 -0.569 1.314 -1.055 -0.796 0.617 0.796 1.360 0.473 0.349 +4 2.189 -1.493 0.991 1.063 0.834 0.064 -0.424 -0.475 0.957 0.878 -1.568 -0.830 -0.125 -0.500 0.673 -0.508 2.811 -0.453 -0.866 1.043 -0.244 0.899 1.323 1.071 -0.432 -1.534 1.649 -1.321 +2 -0.868 -0.010 0.130 -1.088 -0.158 0.683 -0.084 1.602 0.701 0.099 -0.272 2.119 -0.753 0.100 -1.464 1.125 -0.482 0.968 -1.010 -2.358 1.333 0.126 1.313 0.656 0.007 -0.425 -0.047 -0.017 +3 1.857 -0.540 -0.357 0.855 -0.270 1.538 1.129 0.406 0.840 -0.499 -0.358 -1.362 0.375 2.173 -0.581 -1.881 0.724 0.327 0.548 -0.347 -1.740 -0.941 -1.930 0.919 0.567 -0.297 -1.101 -0.299 +1 -0.333 1.194 -0.296 0.429 -0.969 2.040 0.319 0.340 0.926 -0.481 1.194 -1.516 -0.614 -0.801 -1.881 -0.529 -0.052 -1.090 -1.234 0.041 -0.080 -0.266 -1.266 -0.395 -0.666 0.086 -0.559 -0.039 +4 -0.071 -1.285 -0.722 -1.331 -1.213 -0.879 0.105 -2.388 1.366 -0.104 -1.107 -0.974 -0.635 0.006 0.151 -0.194 -0.755 -0.011 -2.074 -1.215 -0.676 -1.342 -0.219 -1.338 1.713 -1.458 1.511 -2.005 +0 -0.491 -0.822 -0.313 0.614 0.405 -0.326 0.083 -0.723 -0.444 -0.192 -2.254 -0.455 -1.046 -0.655 0.867 0.257 -0.521 -0.076 -1.535 -0.023 -0.202 -0.321 -0.067 0.962 -0.072 0.907 0.893 -1.215 +4 -1.613 1.550 -0.083 0.919 0.787 0.006 3.537 2.928 1.392 -0.905 1.433 -1.749 1.397 -2.556 -0.319 1.209 0.385 -1.479 0.170 0.386 0.205 -0.407 0.855 1.145 -0.154 1.520 -0.156 -0.490 +4 -2.381 -0.859 1.121 1.222 -1.248 0.036 -0.360 -0.208 0.169 -0.685 0.231 -1.557 0.676 1.329 -2.193 -0.860 -0.665 1.406 -1.891 -1.039 -0.430 0.754 1.730 0.341 -1.379 -0.376 1.778 1.208 +2 0.393 0.441 1.531 -0.809 -0.758 0.655 -0.132 1.125 0.562 1.918 -0.091 -1.482 0.341 -0.072 0.534 0.663 -0.535 -1.009 -0.146 -2.381 0.081 -0.407 0.862 -1.986 0.006 1.576 0.675 0.981 +3 -1.318 -0.461 -0.730 0.276 0.348 1.410 -0.980 -0.346 0.936 0.474 -0.660 -1.323 1.850 -1.593 -1.248 0.187 0.961 -0.877 0.682 -1.009 0.801 0.813 -0.878 0.878 -0.981 -2.481 -0.147 0.386 +1 -1.811 -0.353 0.851 -0.766 0.391 -0.973 0.064 -0.428 1.625 -0.532 -0.890 0.615 -0.680 0.249 -0.279 -1.102 -0.750 0.711 -0.620 -0.455 0.805 0.040 0.437 -0.102 -0.400 1.241 -1.330 -2.086 +4 -1.040 0.177 -1.376 0.049 -0.178 0.401 1.211 -0.371 0.000 -1.981 1.972 -1.861 -0.083 -2.549 -0.826 1.735 1.145 0.985 -0.193 1.651 0.257 0.690 2.647 1.090 -0.065 0.510 1.404 0.622 +4 -0.448 0.825 1.307 1.310 1.521 1.672 -1.945 1.473 -0.582 0.740 -0.115 -0.294 0.753 0.162 -0.753 1.308 0.060 -0.010 1.114 -0.244 -0.799 -1.290 0.942 -0.918 2.303 1.413 0.699 -1.897 +4 0.189 2.010 -0.042 -0.699 1.285 0.425 -0.012 -0.505 1.092 1.550 1.573 -1.022 0.189 -0.199 -1.056 -1.555 -0.807 1.341 1.895 -0.842 -3.287 0.388 1.991 0.213 -1.170 -0.343 -1.797 1.077 +2 -0.228 0.642 1.536 0.183 0.813 0.814 0.322 0.459 -0.393 -0.731 -0.956 -0.189 0.265 -0.349 -0.047 -1.487 2.265 1.602 -0.171 0.289 1.662 0.792 -0.571 0.512 -1.377 0.273 0.293 -1.867 +0 0.066 1.166 0.540 0.168 -1.036 -0.123 0.995 -0.387 0.079 -1.076 0.372 0.464 2.344 0.233 0.302 -0.154 -0.086 -0.301 1.359 -0.516 1.149 -0.087 -0.837 -0.560 -0.691 -0.534 -1.208 -0.747 +2 -0.394 0.585 -0.460 0.246 0.187 -0.282 -1.323 -0.129 -0.078 0.424 0.408 1.215 -0.663 0.616 0.058 -0.529 0.672 -0.452 -0.540 -3.432 1.948 -0.695 0.953 -0.851 -0.766 -0.233 0.752 0.987 +2 -1.258 -0.519 -0.825 0.773 -0.357 -0.695 1.858 1.095 1.222 -0.757 -0.160 -1.199 0.128 0.901 0.527 -0.884 0.172 -0.180 0.445 0.564 -1.322 -2.813 -1.517 0.873 0.549 0.260 -0.997 -0.345 +0 -0.146 -0.424 1.956 -0.009 1.023 0.344 0.161 1.022 0.244 -0.019 0.519 -1.143 0.461 0.121 -0.498 0.355 -0.626 -0.849 1.144 0.816 0.819 1.273 0.697 -1.542 0.969 -0.407 -0.523 0.595 +3 -1.151 -2.839 -0.809 0.957 0.517 -0.222 -0.022 -0.689 0.205 0.766 -1.616 0.300 0.932 -0.415 0.701 2.815 -0.104 0.393 -0.738 0.181 0.460 0.195 -1.020 0.206 1.462 0.449 0.962 -0.237 +1 0.333 0.196 -1.279 0.070 0.057 -0.864 0.857 0.346 0.279 0.543 -0.886 -1.469 0.582 -1.478 2.056 -0.115 -0.209 0.905 0.328 1.741 0.987 0.092 -0.518 1.249 -1.128 -0.083 -0.065 -0.378 +1 -0.586 0.234 -0.220 0.647 0.287 -1.906 0.187 -0.300 -0.980 1.272 1.418 -0.325 0.213 1.113 2.235 0.236 -0.084 -0.318 -1.255 -0.466 0.365 0.804 0.060 1.451 0.284 -0.433 -1.068 -0.132 +0 -0.151 -1.030 -2.184 -0.097 0.180 0.038 0.737 -0.706 0.265 0.363 0.298 1.811 0.167 -1.245 -0.967 1.062 0.537 -0.086 -0.533 0.335 0.171 0.458 -1.883 -0.263 0.335 -0.394 0.849 0.768 +1 -1.513 -0.359 1.728 -1.593 -0.001 0.738 -1.141 0.191 -0.913 -1.226 0.623 1.823 -0.387 0.581 0.484 0.174 -0.689 -1.013 -0.797 -0.179 -0.419 -1.149 -0.205 -0.636 0.557 -0.391 0.844 0.230 +2 0.703 -0.254 0.961 0.292 1.355 1.729 -0.948 -0.753 0.586 0.744 -0.148 0.089 -0.955 -0.751 -0.110 1.575 -0.707 0.858 0.079 1.935 -0.242 0.390 -0.250 0.158 1.170 -2.080 1.544 -0.333 +4 1.203 1.376 -1.710 0.026 0.526 -2.800 -0.680 -0.716 -0.645 1.045 -0.057 0.421 0.089 2.468 0.996 -0.018 -0.264 -0.697 -0.973 -1.197 0.982 -0.895 0.642 -0.504 -0.751 -0.330 1.792 3.038 +0 -0.154 -0.283 -0.491 -0.019 0.597 -0.664 0.478 -0.095 0.741 1.316 -0.067 1.014 1.490 0.957 -0.145 -0.127 -0.097 -1.755 -0.163 -0.460 -0.476 -0.660 0.251 -1.620 -0.547 -0.539 0.166 1.064 +1 -0.674 -0.111 -1.743 0.429 0.641 -1.302 -1.695 1.014 -0.149 0.640 0.110 -0.088 1.035 0.840 0.385 0.453 -0.434 0.554 1.553 1.024 -1.872 0.452 0.534 1.133 1.238 0.496 -1.239 -0.113 +1 0.537 0.909 0.507 -1.357 0.178 0.508 1.061 0.475 0.917 0.616 0.231 -1.858 0.340 0.891 -1.213 -0.947 0.762 1.056 -1.421 -0.045 0.663 -0.358 0.205 -2.132 1.064 -0.664 0.486 -0.445 +4 -1.273 0.819 -0.754 -0.776 0.168 0.676 0.950 1.081 -0.707 -1.144 -0.343 0.347 -0.206 -3.632 -1.827 -0.240 0.855 -0.905 0.733 -1.496 0.256 0.263 0.469 -1.008 -1.548 0.726 -1.526 -0.771 +0 0.018 -0.066 0.811 -0.610 1.089 0.297 -1.573 1.847 0.184 -0.338 0.165 -0.718 -0.153 0.789 0.688 0.620 -0.601 -0.861 0.388 0.771 -0.733 -0.228 0.392 -0.096 1.157 -1.308 -1.453 1.096 +3 0.655 -0.397 -0.015 -1.170 0.133 0.481 -0.535 1.690 -0.837 0.283 0.848 -0.077 -0.188 -1.429 1.357 -0.170 0.537 0.673 -0.628 -0.198 1.334 -1.117 2.128 -0.745 -1.180 0.132 2.208 -1.911 +1 0.203 1.444 1.224 0.747 -0.935 -1.700 0.061 -0.870 -1.122 -0.220 1.231 0.843 0.781 -0.678 -1.715 -0.791 -0.048 0.939 0.272 -0.696 -0.618 0.809 0.865 0.925 0.898 -1.546 -0.434 0.175 +2 -0.072 -0.945 0.324 0.462 -0.768 -1.247 0.665 -0.855 2.013 1.641 0.777 1.684 0.583 -0.219 1.614 0.542 -0.403 -1.007 -0.680 1.150 -0.069 1.781 0.649 0.804 -0.641 -0.453 -1.369 -0.758 +2 -2.067 0.559 -0.271 -0.592 0.185 0.714 0.567 -0.582 0.129 0.881 -1.949 -0.938 -0.087 -1.502 -1.041 1.299 0.219 -0.032 -0.984 -0.416 -1.360 -0.293 -1.777 1.307 -0.901 -1.025 -0.109 0.408 +0 0.261 -2.017 -0.011 1.662 0.275 1.604 -0.560 0.966 1.315 -0.073 -0.411 1.100 0.540 0.954 0.413 -0.080 -0.089 -0.355 0.400 -0.177 0.253 -0.793 -0.852 0.584 0.388 -0.430 1.377 -0.821 +0 0.882 -0.155 -0.733 1.151 -0.028 -1.472 -0.438 -0.697 -0.201 0.559 -0.490 -0.114 0.439 1.879 0.297 0.363 0.144 0.355 -0.201 0.965 -0.401 -0.383 -1.757 -1.050 -0.631 0.440 0.634 -1.842 +1 0.646 -0.478 -1.966 -0.357 -0.135 1.334 -0.828 1.150 0.193 -1.489 -1.113 0.888 -0.503 -0.587 -0.490 0.901 -0.392 0.000 0.155 -1.640 0.830 -0.334 1.675 0.271 0.315 0.077 -1.675 -0.194 +1 1.654 -0.016 0.600 1.041 1.463 -0.819 0.064 0.287 -0.868 0.786 -0.257 0.086 -0.490 0.581 -0.586 -0.039 0.012 -0.537 -1.822 0.686 -0.029 -0.349 -0.031 0.840 2.156 -1.718 0.426 -0.472 +2 0.785 -0.333 -0.447 0.559 0.776 0.162 0.283 1.060 -1.010 1.234 1.763 -0.796 -1.844 1.145 -1.873 -1.144 -2.048 0.050 -1.339 0.274 0.873 0.457 0.603 0.066 0.977 0.074 0.491 -0.405 +4 0.889 0.902 0.953 0.388 1.377 0.378 1.714 -1.620 0.348 0.283 -0.937 0.580 -1.490 -0.654 -1.999 1.559 -0.232 2.167 0.803 0.448 -0.586 -1.046 0.523 -0.072 -1.339 -1.517 -0.959 1.821 +3 -1.401 0.932 -0.715 -0.379 0.099 0.339 -0.693 1.288 0.545 1.567 0.742 0.776 1.294 1.010 1.594 -2.150 -0.571 -1.072 -1.682 0.697 1.989 -0.362 0.049 0.384 -0.666 0.191 1.856 -0.612 +4 1.658 -0.642 -1.186 -1.120 -0.395 3.745 1.473 -2.873 -0.036 -0.629 0.986 -1.179 -1.036 -0.571 -0.692 0.652 -0.335 1.003 -0.810 0.866 -1.157 0.615 -0.500 0.892 -2.441 -0.783 1.168 -0.202 +0 -1.175 0.238 -1.341 -0.894 -0.577 0.170 1.950 -1.238 0.383 0.703 0.605 0.393 0.506 0.281 0.366 -0.595 0.918 0.722 0.139 -0.061 -1.274 0.495 1.229 0.848 -0.504 0.296 0.024 1.519 +0 0.808 -0.975 0.725 0.167 -1.807 0.086 -1.306 -0.535 -0.159 -1.507 -0.054 0.068 0.755 -0.454 0.274 -0.095 -0.418 -1.671 -0.112 0.745 -0.934 -0.218 -0.070 1.102 0.030 0.288 -0.009 -0.492 +4 -0.107 0.448 -1.571 -1.127 -1.194 0.143 1.733 2.231 0.638 0.501 -1.801 -0.543 -0.788 -0.621 -0.168 -0.472 -1.979 0.748 -1.073 0.239 2.074 -0.919 -2.530 -0.286 1.101 1.958 -1.230 0.497 +0 0.916 -1.185 0.391 -0.285 0.603 0.799 0.977 0.599 0.251 0.284 0.367 -0.624 -0.370 0.402 0.650 -0.651 -1.077 -1.675 -1.261 -0.540 -0.447 -0.283 -1.205 -1.813 0.127 0.127 0.621 0.815 +1 1.752 -0.292 -0.351 -1.870 0.690 -0.710 0.147 -0.315 -0.446 -0.118 -0.041 1.176 1.520 0.651 1.272 0.854 0.495 1.455 -1.308 -0.684 -0.821 -0.301 0.686 -1.115 0.503 -0.587 -0.347 0.200 +1 -0.690 -1.649 0.678 1.937 1.456 0.031 0.493 0.247 0.909 1.742 -0.570 -0.532 -0.287 -0.033 0.728 -0.067 -0.853 -0.840 -1.175 -0.107 1.034 0.420 0.563 -1.084 -1.173 0.178 1.246 0.845 +1 -0.738 0.721 -0.408 -1.034 0.094 1.224 0.660 -0.830 -1.115 0.193 -0.128 -1.263 0.763 -0.626 -0.514 1.190 0.048 0.929 0.382 1.381 -1.583 1.032 0.861 0.051 -0.169 -0.000 -1.932 1.600 +2 0.655 -0.543 1.307 0.011 -0.549 -1.830 -1.092 -1.566 -0.965 0.396 -0.040 -0.596 1.210 -0.353 0.968 1.231 -0.934 -0.727 2.038 1.004 -0.391 1.002 -1.236 -1.226 -1.116 0.448 1.223 -0.302 +2 0.952 -1.021 0.473 -0.268 0.847 -2.127 -0.099 -0.603 0.432 0.470 -0.708 -0.712 -0.111 -0.897 0.842 -0.369 -2.907 -0.375 -1.039 -1.631 -1.237 0.109 1.329 0.313 -0.607 0.456 -0.459 -0.695 +3 1.339 -0.404 -0.031 -1.442 -0.236 -1.008 0.346 -1.664 0.980 -0.871 1.784 1.371 0.536 -1.538 -0.628 -0.896 -0.771 1.577 -0.153 -0.658 -0.748 -0.532 0.533 -1.627 0.098 0.424 -1.735 -1.418 +2 -0.556 0.307 1.209 -1.139 0.173 0.647 -1.420 -1.038 1.616 -1.146 -0.350 -2.016 -0.786 0.776 0.959 0.794 -0.035 -1.075 0.414 -0.442 -1.608 0.029 1.270 1.110 1.178 0.303 -0.930 0.587 +0 0.150 1.200 0.454 0.649 -1.080 -0.519 0.700 0.108 0.477 0.614 0.166 1.582 -0.532 -0.819 -1.765 0.749 -0.570 1.266 0.248 -0.339 -1.055 0.847 -0.054 0.268 0.604 0.266 -0.850 0.521 +3 -1.486 0.522 1.569 0.154 0.925 1.452 1.774 -2.780 -0.124 -0.263 0.491 -1.019 0.598 1.066 0.235 0.431 -1.063 0.808 -0.330 0.639 -0.577 1.157 -0.916 0.340 -0.024 1.630 0.533 0.368 +0 0.503 -0.454 0.224 1.374 -0.487 -1.705 -0.554 -0.270 -1.079 0.638 0.364 0.848 0.313 -0.380 1.219 0.345 1.122 -0.581 0.838 0.228 0.396 0.612 -1.616 -0.249 -0.367 0.087 -0.050 0.514 +1 0.155 1.369 1.304 -1.473 1.071 -1.486 -0.402 -0.520 0.266 -1.002 0.103 -1.081 0.172 -1.317 -0.398 0.004 -0.250 0.845 -2.259 0.175 -0.830 0.591 0.031 1.088 -0.233 1.234 0.157 -1.293 +4 -0.547 1.346 0.550 -1.499 0.503 -0.051 0.886 -0.438 1.329 -0.248 -2.959 -0.621 0.252 0.136 -0.484 -1.557 1.152 1.631 -0.946 0.871 0.622 -1.251 0.373 2.846 -1.324 1.352 -0.040 -0.279 +3 1.973 -0.138 -1.590 0.166 0.301 -0.104 -1.250 0.381 -0.971 0.001 -0.474 1.000 -1.560 -1.162 0.068 0.005 -0.233 0.345 0.185 0.894 0.155 1.745 -1.086 1.615 0.028 -1.822 -2.414 -0.568 +0 -1.419 1.295 0.500 -1.391 -0.693 -0.171 -0.156 0.924 1.100 0.377 1.085 0.468 -1.011 0.046 -0.518 1.156 -0.688 0.344 -0.635 -1.621 0.088 0.298 1.026 0.714 -1.220 -0.025 -1.068 -0.815 +2 -0.440 -1.201 -0.446 0.815 0.772 -1.066 -0.065 -0.494 -0.753 2.406 -0.390 1.392 0.053 -0.234 1.453 1.627 -0.245 1.299 -0.157 0.045 0.350 0.916 1.752 -0.836 0.208 -1.510 -0.398 0.223 +2 -0.703 -0.094 -0.433 0.676 0.778 0.372 2.154 0.553 1.255 -0.885 -0.698 1.980 -1.354 0.768 1.287 1.176 -1.595 -0.351 0.622 0.742 -0.009 -0.696 -0.273 -0.618 -1.243 -0.299 1.849 -0.279 +0 0.884 0.155 -1.279 0.720 0.895 -0.900 -0.361 -1.758 0.928 -0.969 0.630 0.826 0.623 0.109 0.993 -0.093 1.039 -0.429 1.241 0.240 -0.025 0.024 1.209 -0.435 0.269 -0.988 -0.471 0.530 +0 0.187 -0.437 -0.103 -1.353 0.768 0.209 -1.062 -0.332 -0.370 1.226 0.314 0.759 0.266 -0.426 -1.112 -0.836 0.384 -1.331 -0.850 0.273 0.457 -1.590 -0.536 -0.511 0.235 0.021 -2.026 0.686 +3 0.465 0.028 0.424 2.157 -1.886 -0.136 1.475 0.308 1.205 -0.626 -1.250 1.911 0.075 0.826 0.888 1.280 0.294 0.338 0.497 -0.642 1.050 -0.571 1.106 0.795 -2.206 0.807 0.234 1.334 +2 1.307 -0.217 1.061 0.072 -1.531 -0.008 -0.619 0.918 -0.026 -1.278 -1.500 -0.021 -2.420 -0.518 0.231 -1.057 0.994 1.191 -1.017 -0.298 0.995 -0.001 -1.394 -0.143 -0.853 1.108 0.606 -0.314 +4 -0.137 0.787 0.934 -1.382 1.750 -1.559 -1.135 -0.262 1.925 -0.308 -0.032 1.432 -0.300 -1.178 -0.417 1.371 0.071 1.931 0.738 -0.645 0.051 -0.853 -0.668 4.479 1.019 0.861 -0.623 1.570 +1 -1.083 1.144 0.219 0.566 0.100 0.596 0.258 0.964 -1.631 0.546 -1.503 0.630 -0.277 -0.476 -0.055 0.896 -0.217 -0.476 1.952 -1.539 -0.104 0.082 -0.110 -0.306 -1.453 0.706 0.035 -1.700 +2 0.672 0.978 0.498 0.308 0.475 1.776 0.878 -0.626 0.715 0.057 -0.431 0.670 0.218 1.371 -1.272 0.579 -1.582 -0.531 -1.163 -0.205 -0.326 -2.357 -1.437 0.106 1.712 0.713 -0.919 0.131 +1 -0.641 0.788 0.556 2.936 -0.445 -0.303 -0.155 -0.111 1.027 -0.276 1.972 0.582 -0.486 -0.741 1.041 0.029 0.009 -0.598 -0.061 -0.639 1.362 0.717 -0.175 -0.189 0.092 -0.006 -0.957 -1.901 +1 0.764 -0.105 -1.729 -0.828 1.305 -0.724 0.622 -0.736 0.244 0.524 -0.661 -0.134 0.308 -0.568 -0.209 -0.944 0.105 1.076 -1.163 -0.706 -2.044 0.512 -1.599 0.502 0.570 0.910 0.369 -1.509 +3 0.220 -1.434 1.929 -0.103 -0.146 -0.965 0.024 1.387 1.036 0.854 0.546 -0.359 -1.450 -0.201 -0.236 1.049 0.309 1.612 1.197 -0.456 -1.795 1.386 1.359 -1.366 1.509 0.382 0.161 1.615 +2 -1.279 -0.765 -0.710 0.210 2.287 0.342 -0.180 0.621 -0.871 -0.293 1.069 1.382 0.438 -1.829 -0.372 -1.431 0.061 -0.496 0.410 0.149 1.380 0.786 1.018 -0.919 -0.965 -0.696 -1.655 -0.242 +0 0.344 -0.362 -0.201 1.058 0.704 0.689 1.196 -0.297 -0.454 -0.431 -1.905 -0.254 0.576 -1.373 0.437 1.139 0.710 -1.245 0.384 0.734 -0.201 -1.027 0.314 -0.939 1.094 1.092 -0.131 0.722 +3 -0.398 -0.447 0.612 -1.559 -1.078 0.343 0.623 0.642 -0.787 -0.978 -0.507 0.240 1.485 -2.047 -0.221 -1.333 -1.652 0.967 0.545 0.449 0.921 -0.480 -0.433 2.186 -0.247 0.735 0.006 -1.848 +1 0.150 1.513 1.169 1.515 -0.370 -1.150 1.092 1.050 0.273 -1.363 0.213 -0.335 0.914 0.757 -0.375 -0.767 -0.908 -0.513 0.084 -0.211 -1.702 1.394 -1.397 -0.934 -0.347 -0.672 -0.294 0.045 +2 0.110 -0.013 -1.744 -1.381 -0.123 -1.877 -0.110 -0.894 -0.076 0.170 -1.858 0.325 -0.450 -2.323 -0.461 0.380 -0.406 -0.467 0.154 -0.479 -0.267 -1.326 -0.518 1.884 0.178 0.269 -0.643 -0.395 +0 -0.123 -0.145 0.798 0.081 -0.192 1.307 -0.843 -0.052 -0.958 -0.366 0.479 -0.205 1.415 -1.194 0.174 0.408 0.253 1.369 0.931 -1.509 1.162 0.483 0.316 -0.750 -0.318 -0.150 -0.672 -0.666 +3 -2.335 0.140 1.416 -0.168 -0.716 -1.120 1.114 0.081 0.121 -1.444 -0.917 0.735 -0.526 -0.652 2.510 0.070 -0.172 -0.136 -0.678 -0.073 1.940 0.413 -1.595 -0.437 0.104 -1.506 -0.468 -0.961 +4 2.226 1.511 -1.144 -0.102 0.049 0.686 -1.524 -1.714 -1.219 1.504 -0.655 0.809 0.589 -0.180 0.827 -1.228 -0.471 -2.217 0.098 -0.792 2.190 0.059 0.819 0.653 -0.078 0.992 0.033 0.476 +0 1.298 -1.087 -1.386 -0.006 -2.161 1.231 1.221 -0.433 -0.371 -0.072 0.052 0.127 0.558 -0.173 0.943 0.446 -0.607 1.335 -1.358 -0.092 0.967 -0.723 0.310 0.543 -0.861 -0.072 -0.157 -0.552 +2 0.034 -1.536 1.586 -1.211 1.182 1.590 -0.819 0.791 0.700 -0.070 1.440 0.649 0.423 1.680 0.770 0.034 -1.109 -0.002 -1.493 1.271 -0.131 -0.387 0.960 -0.119 1.623 -0.640 -0.389 0.693 +1 0.000 -1.500 -1.188 -0.518 0.678 0.905 0.939 -0.131 -0.376 -0.940 0.522 -1.113 1.533 -0.156 -1.364 -0.083 -0.404 -1.304 1.743 0.379 -0.943 -1.075 0.534 1.017 0.845 0.680 0.245 1.515 +0 0.992 -0.501 -0.068 0.285 -0.667 -0.701 0.842 1.158 -0.078 -0.110 -0.179 -0.297 -0.693 -2.054 -1.728 0.967 1.582 -0.957 0.021 0.009 -0.275 -0.243 0.593 1.603 0.404 0.599 0.013 0.949 +0 0.574 -0.392 0.919 0.798 -0.217 0.651 0.826 -0.505 -0.527 0.211 -0.379 1.898 0.704 0.296 -0.558 1.704 -0.461 0.400 1.073 0.451 -0.700 0.213 -0.207 -0.578 1.319 0.344 0.789 -0.495 +0 0.327 -0.101 -0.098 0.810 1.671 0.251 0.655 0.329 1.018 -0.463 1.075 1.110 1.083 -0.660 -0.168 0.239 -1.371 1.595 -0.012 0.284 -0.577 -1.327 -0.604 0.219 0.673 0.162 0.347 -0.337 +0 -0.638 0.658 -0.518 -0.893 0.286 -1.878 0.142 -0.203 1.312 0.101 0.800 -0.001 0.300 -0.181 -0.166 0.997 0.189 -0.928 -0.139 1.360 -0.472 -0.195 -0.372 -0.331 0.839 0.150 1.072 -2.013 +4 -0.036 -0.298 0.922 0.874 1.048 2.112 0.316 2.126 1.612 1.652 0.490 1.002 1.706 -1.066 -0.026 0.287 1.214 0.731 0.934 0.517 0.286 -1.584 -0.421 0.891 0.034 -1.480 -0.491 2.020 +1 -1.684 -0.128 -1.581 -2.054 0.128 -1.576 -0.272 -0.760 1.110 0.032 0.063 -0.507 0.106 -0.057 -1.460 -1.383 -1.085 -0.831 -0.588 -0.703 -0.084 -0.269 0.134 -0.514 -0.472 0.406 1.159 -0.250 +4 -0.990 2.479 0.428 -0.635 -2.367 0.777 -1.550 0.357 1.934 -1.138 0.738 -0.812 1.133 0.066 -0.019 1.207 0.625 -0.805 1.787 -1.536 0.276 -0.823 -1.487 -1.134 -0.049 0.350 1.059 1.476 +0 0.650 0.867 -0.550 -2.268 -1.057 0.155 -1.066 -0.411 0.511 -0.285 0.081 -0.665 1.882 -0.722 -0.049 0.540 -1.045 0.015 -0.209 0.772 0.038 -0.585 0.297 1.125 -0.298 0.061 -0.079 0.397 +4 0.512 1.093 0.362 0.268 0.265 0.370 -2.002 1.698 -1.257 1.474 -0.691 2.472 -1.077 -0.287 0.706 -1.159 1.223 -2.703 -0.034 -1.214 1.175 -0.658 1.018 0.028 -1.464 -0.130 -1.194 -0.985 +3 -0.954 -1.629 0.532 -0.746 0.225 -0.198 1.293 0.475 1.103 0.760 1.762 -1.565 1.467 -0.790 -1.090 -1.250 -0.414 0.424 1.384 -1.907 0.303 -0.985 -1.255 -0.554 0.094 -0.660 -1.235 -0.413 +2 1.808 0.179 0.222 0.322 -0.142 0.848 0.237 0.788 0.944 0.428 0.502 -1.010 -0.550 0.843 1.424 -1.940 -0.568 0.024 -0.879 -0.823 0.475 1.581 -2.032 -0.170 0.463 0.780 0.319 1.761 +3 -0.397 0.811 0.099 -0.925 -0.789 1.885 0.407 0.323 -0.250 0.438 -0.319 1.104 0.326 0.742 -1.814 1.630 0.426 -0.765 2.971 -0.946 1.413 0.157 -0.210 -0.530 -1.483 0.512 0.855 0.421 +2 0.076 -0.646 0.045 -1.767 -1.881 -1.358 -0.670 1.599 -0.501 -1.043 -0.679 -0.515 -0.028 1.079 -0.255 -1.163 -0.631 0.073 -1.524 -0.813 0.522 1.846 -0.165 0.150 -1.360 -0.696 -1.163 0.223 +2 0.881 0.296 -1.227 0.165 0.773 -0.115 -0.523 0.554 -0.988 0.868 -0.436 -2.122 -0.455 -0.057 0.552 0.437 -1.290 0.122 -0.534 -0.360 -0.998 -0.051 3.132 1.856 -0.999 0.066 -0.314 0.142 +2 0.487 0.197 0.774 -1.051 -0.340 -0.226 0.456 -0.793 0.916 1.186 -0.989 -0.491 -0.532 2.632 -0.430 1.678 -1.267 -0.990 -0.687 -0.846 -0.268 -0.709 -0.819 1.158 1.224 0.949 0.524 0.093 +2 0.684 -1.366 1.212 0.261 -0.369 0.143 -1.776 0.409 -1.029 -1.353 -1.522 1.113 -0.629 1.534 -0.536 -1.707 -1.117 1.236 -0.156 -0.548 0.160 0.502 1.117 1.448 -0.360 -1.326 -0.413 0.260 +3 -0.810 -0.805 0.020 -1.532 -0.270 0.253 1.785 0.403 0.625 1.164 -0.965 -0.165 2.425 -0.079 1.116 0.964 -1.478 0.310 0.147 0.330 -0.754 -1.117 -0.476 -0.098 1.185 1.817 1.397 0.860 +4 0.349 1.988 -0.885 -0.644 0.609 0.226 0.351 -1.405 2.181 0.452 -1.582 0.611 -1.103 0.929 -1.316 1.006 -1.221 -0.974 0.138 -0.612 -0.554 -1.530 -0.330 0.884 1.878 0.828 0.685 -1.689 +0 0.078 -0.054 -0.257 -0.403 -0.588 2.255 -0.366 -0.659 0.506 -0.101 -0.922 -0.250 -0.060 0.430 0.313 -1.015 0.609 0.892 -1.076 -0.777 -0.176 0.220 0.258 -0.043 -1.432 1.012 -0.992 1.351 +4 1.456 -2.386 0.109 1.743 0.322 -0.030 0.283 -1.381 -0.314 2.685 0.230 -0.139 0.619 -0.888 -1.118 -1.502 -1.145 -1.474 1.256 0.562 0.103 -0.113 -1.704 -1.448 0.890 -0.428 -0.723 -0.295 +4 0.413 0.148 -1.410 0.585 -0.662 -1.433 0.431 -1.258 -2.091 1.297 -0.561 -1.744 -0.963 -0.721 1.144 -1.663 -1.339 0.980 -0.450 0.228 -0.491 2.021 -1.290 0.282 1.503 -0.375 -0.754 0.767 +0 0.642 -0.061 1.379 1.101 0.279 -0.066 -0.157 -0.030 0.061 1.421 -2.037 -0.419 0.205 -0.603 -0.286 -0.441 1.303 -0.423 -0.721 0.781 -0.535 -0.168 -1.107 -0.406 -0.035 -1.265 -0.100 0.513 +4 1.924 0.576 0.623 -0.600 -0.286 1.698 -1.837 -0.563 0.602 0.071 0.885 -0.547 -1.907 -1.692 0.484 -0.129 2.724 -0.817 -1.727 1.897 0.799 -0.560 2.535 -0.452 2.316 -0.545 0.957 -0.262 +2 -1.045 0.431 2.442 -0.019 0.050 0.201 2.004 0.393 0.390 0.067 -1.734 -0.154 0.149 1.756 1.562 -1.037 0.687 -0.127 1.312 -0.079 -0.345 -0.790 -0.489 -0.065 -1.244 -0.146 -1.239 0.580 +1 0.250 -2.441 -0.184 -0.206 -1.364 -0.928 0.810 0.231 0.102 0.306 0.093 1.243 0.123 -1.458 1.630 0.370 -0.589 -1.253 0.396 -0.117 1.574 -0.346 -0.837 0.896 0.925 0.169 0.676 -0.038 +0 0.248 -1.026 0.368 0.129 -0.542 0.708 -1.396 0.151 1.557 -0.200 0.769 0.736 -0.360 -1.370 -0.606 0.312 1.055 -1.614 -0.793 0.025 -0.835 1.442 -0.140 0.269 -0.836 0.720 -0.919 0.448 +2 -0.021 0.174 -1.678 0.263 0.650 0.244 -0.725 -0.863 0.182 0.803 1.309 0.765 0.512 -2.543 -0.614 0.313 -0.680 -0.930 -1.062 1.765 -1.300 -0.212 -0.728 -0.864 0.411 0.823 -1.177 0.053 +2 -0.953 2.022 -1.438 0.034 0.383 0.759 -1.758 -1.195 -0.142 -0.597 0.572 -0.457 0.680 -1.684 1.200 1.589 1.268 0.532 -1.182 -0.198 -0.341 -0.004 -0.480 0.270 -0.901 -1.328 0.413 0.543 +1 -0.085 -1.958 -0.106 1.326 -0.819 0.225 -1.128 -0.015 1.753 1.504 -0.177 1.648 -0.965 -0.623 0.036 0.495 -0.549 0.133 0.260 -0.419 -1.002 1.462 0.842 -0.402 0.057 0.089 -1.240 -0.690 +0 -1.253 0.879 -0.151 0.349 0.208 -1.539 -0.564 -0.228 -0.133 0.479 0.925 0.370 1.762 0.439 -0.534 1.284 -1.220 0.534 -0.150 -0.858 0.209 -0.285 -1.846 0.184 -1.032 0.122 -0.127 0.431 +1 0.195 -0.344 -0.211 0.099 0.784 -0.544 0.016 -0.568 0.394 1.189 -1.079 0.103 2.175 -0.100 -0.254 -0.806 -0.155 -0.251 -1.601 -1.007 0.634 -1.402 -0.485 -1.820 -0.835 -0.941 0.380 1.326 +4 -0.680 -0.217 1.145 2.068 -0.235 -0.922 0.739 1.721 -1.333 0.533 2.150 0.024 0.520 0.331 -0.163 2.761 0.511 0.787 -1.701 -0.193 0.828 -0.457 1.198 -0.538 0.148 1.560 -0.045 1.137 +3 -1.226 -1.464 0.224 1.047 1.684 -0.459 1.079 -0.039 -0.173 0.884 0.652 -1.576 1.477 1.380 -0.626 0.396 0.494 0.261 -0.550 -0.672 -0.026 1.173 0.544 -0.371 0.772 -2.849 1.149 -1.740 +2 0.011 -1.450 1.090 0.946 0.066 -0.732 1.322 0.128 -1.286 0.252 -0.919 -1.212 -1.119 1.038 -0.939 -1.498 -0.596 2.467 0.219 1.136 1.246 1.462 0.751 0.502 -0.627 0.009 -0.262 -0.412 +0 0.177 2.354 0.484 -0.297 -0.108 -1.172 1.231 0.315 0.332 -1.064 0.779 -0.935 -0.554 0.125 -0.581 -0.792 -0.050 1.010 0.895 0.707 0.486 -0.476 1.121 -1.736 0.434 0.841 -0.090 -0.722 +2 1.399 1.522 0.359 -1.122 2.200 -1.065 -0.498 0.715 -0.479 1.037 -0.204 -0.637 1.699 -0.498 -1.298 0.366 -0.483 0.217 0.318 1.361 -0.366 -0.776 -0.613 -1.337 0.970 -1.050 -0.661 -0.917 +2 -0.003 2.170 -0.657 -1.050 -1.231 0.271 -0.282 0.150 -0.322 -0.909 -0.079 -0.754 2.252 0.806 0.955 -0.142 -1.006 1.328 -0.628 0.096 0.941 0.151 -1.297 -1.681 -0.141 0.019 0.404 1.108 +0 0.189 0.601 -0.787 -1.416 -0.311 -0.179 -0.235 -0.916 0.806 -0.042 1.773 -0.374 1.062 1.377 -0.590 0.473 0.485 -1.426 0.843 0.325 -0.690 -0.401 0.005 0.647 -0.116 -1.266 0.333 -0.264 +1 -1.166 0.410 -0.119 -1.060 0.955 0.858 1.368 0.216 -1.656 1.874 -1.406 -0.891 -0.524 0.599 -0.301 0.627 -1.127 -0.236 0.905 1.119 0.799 0.462 -0.657 -0.805 0.769 0.085 -0.886 0.888 +2 0.794 0.678 -0.782 0.115 0.476 -0.534 -0.013 -0.657 -0.856 -1.244 -1.154 -0.395 -0.623 0.886 -0.734 -0.158 0.202 -0.846 -0.383 -2.707 1.071 0.300 2.804 -0.131 0.647 0.960 0.182 -1.491 +3 0.074 0.755 0.124 1.106 0.823 -1.248 0.869 0.141 -0.092 0.671 0.494 -0.563 0.454 -1.456 0.909 0.439 0.515 -0.450 3.113 1.092 -0.167 0.613 0.568 0.433 -3.281 0.314 1.048 -0.466 +1 1.878 -0.905 0.955 0.154 2.139 0.099 0.350 -0.753 -0.103 1.539 -0.225 0.183 0.286 -0.017 0.563 1.004 1.154 0.320 -0.358 -0.003 1.790 -1.115 0.778 -0.316 -1.212 -0.870 0.060 0.910 +2 -0.082 -1.353 0.500 -1.403 -1.682 1.163 0.522 -0.800 0.423 -0.198 0.806 3.053 -0.886 -0.351 -1.180 1.045 -0.230 -0.222 -0.427 -0.585 -0.524 -0.388 0.786 0.929 0.157 1.442 0.629 -0.629 +3 -1.246 -1.258 0.877 -1.404 0.299 0.131 -0.858 0.836 -1.182 -0.549 -1.091 0.765 1.184 0.249 2.539 0.446 0.632 1.007 -0.672 0.408 -0.967 -1.318 1.092 -0.850 0.183 1.632 -1.811 0.651 +4 -0.660 1.097 1.665 -1.607 0.538 1.297 -0.104 0.148 -0.130 1.535 -1.104 -0.258 2.149 -0.621 -1.541 -0.011 1.080 -0.038 -0.486 0.501 2.490 -0.256 2.072 0.413 -0.865 1.663 -0.349 -1.558 +3 0.843 0.736 -1.814 0.849 0.235 2.360 -0.525 0.743 -0.589 -0.666 0.742 -1.504 -1.045 0.183 -0.503 0.244 0.170 -0.244 -2.064 0.160 0.843 0.463 -1.817 -0.624 0.846 1.357 -0.007 -1.697 +0 -0.784 1.416 1.263 -0.414 -1.092 0.281 1.131 -1.259 -1.247 0.586 0.138 0.855 0.292 0.369 0.162 0.040 0.657 -1.352 -0.046 -0.440 -0.498 0.627 0.816 0.786 0.812 0.278 -1.429 -0.249 +1 -0.288 -1.344 -0.389 0.966 1.277 0.954 1.989 -0.721 1.445 0.961 -0.041 -2.269 -0.271 -0.047 -1.253 -0.108 -0.086 0.290 -0.976 -0.232 -0.943 -0.036 -1.307 -0.386 0.276 -0.549 -0.153 0.022 +4 -0.688 -0.544 -2.055 0.671 -0.364 1.561 -0.621 -1.341 -0.844 0.704 -1.917 1.056 -0.722 0.252 -2.164 -1.624 0.625 -0.572 0.832 -0.660 -0.622 -1.438 -0.043 -0.765 0.577 1.624 0.401 1.616 +2 -1.396 -0.942 0.764 -0.342 -0.509 -0.389 -0.409 -0.553 -0.218 1.147 -0.018 1.743 0.431 -0.267 -0.330 1.357 0.070 -0.665 0.771 0.143 -1.678 -1.796 -0.851 -1.418 -0.920 -1.644 -1.384 -0.546 +2 -0.143 -1.044 -0.684 -0.927 -1.307 1.366 -2.092 1.370 1.246 0.500 -1.115 -1.582 -0.548 0.625 0.003 -1.043 1.148 1.684 -0.227 0.109 -1.509 -0.414 0.510 0.355 -0.508 -0.274 -0.447 -0.278 +3 -0.539 1.185 -0.320 -1.134 -0.462 0.054 -1.336 1.626 0.868 1.430 0.392 -2.094 1.535 -0.864 -0.782 0.156 1.378 1.053 0.906 -0.312 -0.328 0.346 -0.130 0.889 0.830 0.591 -2.526 0.128 +4 -0.627 -0.171 0.650 -0.350 -0.505 -0.587 0.233 -0.176 0.682 0.357 -2.915 -1.752 -0.501 0.980 -0.652 0.375 0.598 0.540 -0.547 -1.148 -1.312 2.971 0.455 1.841 -0.833 1.248 -0.477 -0.029 +3 1.445 -1.506 -0.351 -2.124 0.008 0.466 0.383 -0.602 -2.144 -1.242 1.256 0.879 -0.091 0.162 -1.028 1.291 -0.719 0.437 -1.328 -0.178 1.248 -0.947 0.749 -1.081 -0.902 -0.009 0.972 -0.298 +0 0.701 0.775 0.552 -0.084 -1.211 -1.438 -0.959 -0.453 -0.448 -0.037 0.713 1.093 0.322 0.843 -0.254 0.793 -0.139 -0.887 -1.237 -0.550 1.157 -0.827 1.127 0.918 0.326 0.229 0.302 -0.411 +1 0.661 0.896 0.416 0.144 -1.269 -0.286 0.136 2.526 -1.191 1.214 0.570 -1.847 0.729 -0.287 1.249 -0.065 1.030 -1.042 0.232 0.295 -0.657 -0.230 -0.364 1.017 -0.203 0.041 1.111 0.533 +0 -1.288 0.308 0.854 -0.721 -0.313 0.714 -0.733 -0.108 0.435 0.957 0.162 -0.758 0.276 -0.451 -0.098 -1.094 -0.875 0.575 0.239 -0.373 -0.844 1.229 0.504 2.140 -0.814 -1.373 0.369 -0.132 +1 0.125 0.258 -1.194 0.996 0.380 0.567 1.688 -0.606 -0.126 0.327 -0.935 -0.188 0.493 -0.325 1.309 -1.511 1.123 0.661 -0.569 -0.924 -2.080 -0.795 1.047 -0.056 1.163 -0.620 0.707 -0.717 +0 0.365 1.999 0.094 0.380 -0.783 1.836 -0.601 0.244 -0.045 -1.075 -0.928 0.636 0.196 0.129 0.633 0.321 0.362 -0.018 -0.207 1.215 -0.322 1.623 -0.672 -0.340 0.360 -0.894 -0.021 -0.265 +0 0.238 -0.271 2.293 -0.860 -0.194 0.916 -0.818 0.766 -0.479 1.003 0.977 -0.090 -1.000 -0.391 -0.015 0.888 0.321 0.538 0.349 0.846 -0.200 0.165 0.028 -0.438 1.012 0.991 1.206 -0.764 +4 0.311 0.956 -2.134 -2.447 -0.142 -0.762 0.853 -0.094 0.263 -1.434 -0.392 0.836 0.331 -1.988 -0.319 2.655 1.121 -1.451 -0.811 -0.276 -0.335 0.440 0.156 0.037 -0.095 -1.672 0.592 0.235 +0 -0.809 0.828 -0.141 0.786 -0.754 0.603 -0.856 -1.763 -0.223 0.909 0.685 -0.740 0.372 0.203 -0.412 -0.089 1.036 -0.355 -0.779 0.255 -1.167 -0.192 0.430 0.148 -0.277 0.190 0.726 1.156 +0 -0.124 1.000 -0.773 -0.432 0.473 -0.671 -0.691 -1.498 1.333 0.483 0.830 -0.533 1.832 0.315 0.647 0.317 0.174 0.149 -0.778 0.357 0.329 -0.351 -0.327 0.088 -0.975 0.161 0.998 -1.275 +1 0.189 0.211 -0.673 -0.036 0.553 1.743 -1.171 0.534 0.479 -1.612 -1.843 0.496 -0.887 -0.157 0.066 1.591 1.465 0.915 0.007 1.389 -1.562 0.610 -1.118 -0.184 0.863 -0.090 -0.060 -0.370 +0 -0.300 -0.233 -0.084 0.477 0.645 -1.387 -0.282 1.227 -1.316 0.074 -0.171 -0.567 1.591 0.037 1.511 0.006 0.460 -0.577 -0.566 0.409 0.428 0.598 0.377 -1.336 -0.806 0.850 -1.958 0.293 +3 -1.593 -1.927 0.753 1.111 -1.294 0.953 -0.765 -0.327 -0.573 1.773 -1.248 -0.696 0.526 -0.838 1.662 0.055 0.959 0.246 0.253 1.068 0.623 1.318 -0.291 0.324 -1.857 -1.161 -0.863 -0.839 +4 -1.276 -2.330 -0.442 -1.074 0.040 0.183 -1.710 -2.085 0.552 -0.408 -0.857 -0.274 0.442 0.968 -0.016 0.244 0.921 0.899 1.135 -1.290 -2.803 -0.790 0.936 -0.059 1.261 -1.253 -0.773 0.423 +3 -0.440 0.589 1.199 -0.115 0.351 -0.586 0.586 -2.801 -1.626 0.573 -1.636 -0.489 1.403 0.864 2.395 0.075 -0.083 0.010 -0.925 -0.213 -1.261 1.173 0.738 -1.160 -0.665 0.391 0.271 -1.050 +3 0.161 -0.018 2.938 -0.667 -0.676 0.694 -0.595 -0.291 -1.086 0.492 0.504 -0.315 0.715 -1.858 -0.707 -0.170 -1.172 -1.749 -0.308 0.694 1.282 -0.547 0.733 0.637 1.219 -0.350 -1.799 0.070 +0 -0.309 -1.379 0.090 0.025 0.547 -0.470 -0.975 0.314 -0.578 0.504 -1.031 0.794 1.490 -0.167 1.349 0.753 0.337 -0.013 -0.544 1.198 0.996 -1.045 0.115 -0.814 0.475 0.749 -1.563 -0.084 +4 0.177 0.640 -0.146 -0.839 1.630 0.395 0.951 -0.220 0.309 1.213 0.329 0.047 -1.025 -2.016 1.829 0.021 2.444 0.586 -0.611 -0.164 1.579 1.424 0.937 -1.515 -1.533 1.293 0.607 1.565 +4 -0.914 1.080 0.121 -0.037 -0.807 2.486 -0.743 -1.338 -0.532 -0.557 -0.206 0.865 1.780 1.486 -2.043 0.060 0.231 -0.078 0.305 1.222 -0.121 -0.867 0.041 -2.769 1.067 -0.487 0.897 0.023 +3 0.134 -0.589 -0.818 -0.791 2.057 -0.018 -0.465 0.073 -0.881 0.997 0.928 0.190 -1.253 0.663 -0.447 0.207 -0.831 -0.513 2.368 -2.386 -0.019 -0.708 -2.113 0.376 -1.457 -0.038 0.789 0.909 +0 -0.361 0.742 0.148 0.985 0.256 -0.762 0.168 -0.155 0.881 0.344 -0.179 0.700 -0.698 0.787 -0.158 0.403 -0.880 -1.268 0.648 1.369 -0.805 -1.060 1.023 -0.224 1.928 1.135 0.342 0.498 +0 -0.824 -0.323 1.467 -0.043 -0.795 0.170 0.009 1.056 -0.044 -0.010 -0.052 0.168 1.517 0.725 -1.042 0.282 0.346 0.377 -1.480 0.146 -0.568 -1.497 1.209 -0.282 -0.267 0.882 1.024 -0.249 +0 -0.707 -0.728 0.329 1.220 1.106 0.478 -1.148 -0.980 -0.615 -0.493 0.249 -0.641 0.409 -1.028 1.129 -0.130 0.502 0.059 -1.235 0.707 -0.836 -0.488 -1.828 -0.783 -0.766 -0.639 0.305 0.302 +1 -2.419 0.082 0.362 -0.383 -0.851 0.159 -0.942 1.872 1.517 0.658 0.090 -0.660 -0.021 1.639 0.869 0.204 0.924 0.546 0.396 0.981 0.517 0.859 0.161 -0.254 -0.548 -0.667 -1.605 -0.912 +2 2.220 0.606 -0.203 -0.055 -0.421 0.090 -0.037 -0.841 1.683 -1.487 0.718 -0.101 1.901 0.341 -0.360 -0.248 0.040 -1.143 -0.767 -1.785 -0.923 0.094 0.300 -1.979 -0.137 0.563 -0.163 -0.634 +3 -1.736 -0.892 0.117 1.981 -0.579 -0.551 0.573 -0.792 -0.370 -0.086 0.033 0.417 -1.467 0.671 0.350 0.683 0.133 -0.765 -2.331 -0.615 -0.773 -0.238 -0.613 -2.252 0.833 0.500 -2.308 -0.518 +4 -0.569 -0.856 0.035 1.267 -1.320 0.388 0.988 0.451 1.035 0.537 -0.697 1.796 0.520 2.284 -1.862 2.390 0.864 0.470 -0.922 0.741 1.198 -1.757 0.570 -0.646 0.578 -1.030 1.271 -0.080 +0 0.303 0.075 1.871 0.321 -0.120 -0.903 0.323 0.573 1.717 -0.205 0.233 -0.287 0.586 0.638 -0.529 0.055 0.100 0.325 1.041 1.519 -1.433 0.943 -0.943 0.571 0.307 -0.628 1.532 -0.459 +1 -0.243 -0.802 0.655 0.934 0.136 -0.179 -0.504 -0.042 0.690 -0.668 -0.801 0.600 -0.848 0.151 0.098 0.000 -0.643 1.734 0.398 -0.514 0.331 1.832 0.090 1.533 -1.148 -0.851 -2.140 1.976 +0 -0.978 0.359 -0.226 0.253 -0.930 -1.637 -1.276 -0.249 -0.849 1.714 1.036 -0.565 0.241 -0.656 1.022 -0.977 -0.852 -1.100 -0.665 -0.408 0.220 0.786 0.480 0.031 1.631 0.288 0.119 0.586 +4 0.030 -0.053 0.337 0.548 -0.829 -1.872 0.397 -1.852 0.322 0.733 1.126 -0.130 2.008 -1.391 1.456 -0.767 0.036 0.157 1.348 -1.098 2.136 -2.144 -0.080 0.574 1.033 -1.428 -1.970 1.311 +0 0.392 -0.455 -0.255 0.505 2.026 0.211 -0.163 1.063 -0.497 0.391 1.806 0.415 1.216 -0.212 -0.423 0.129 -0.126 -0.045 -0.704 -0.490 -0.385 0.750 0.650 -0.347 -1.379 0.363 -0.546 -0.285 +4 0.446 0.847 -2.005 -0.730 0.487 -0.512 0.567 0.018 2.114 -1.409 -1.084 1.081 1.664 0.934 0.354 -0.255 -0.947 0.265 0.283 -1.761 1.105 -1.187 1.976 -1.529 1.812 -1.204 -0.416 -2.308 +1 0.253 0.966 1.812 -1.162 2.182 -0.044 0.264 0.060 1.498 0.958 -0.145 -1.127 -0.243 -1.337 0.891 -0.678 -0.102 1.173 0.807 0.383 -0.796 0.485 -0.795 0.494 -1.307 1.207 0.200 -0.013 +3 -0.611 -0.277 -0.968 1.294 -1.152 -0.944 0.441 -0.890 0.648 -1.238 0.707 1.516 0.344 -0.370 -0.381 1.324 0.952 0.188 -1.830 -2.185 1.967 0.051 -0.719 -0.620 0.649 -0.669 1.810 0.341 +2 -0.045 -1.627 1.218 -1.721 0.083 0.037 -1.708 1.088 -0.606 0.534 0.366 -0.520 -1.109 -1.441 -0.391 1.638 0.404 1.160 0.078 -0.564 0.062 -0.076 -1.773 0.256 -1.091 -1.324 0.054 0.416 +0 0.099 0.315 -1.475 -0.050 -0.225 -0.600 0.774 0.846 0.120 1.260 0.684 -0.119 0.130 -1.683 0.537 0.688 1.187 -0.424 0.352 0.223 -0.877 0.302 -0.263 1.695 0.241 1.484 -0.468 -0.683 +2 0.050 -0.012 1.459 -0.625 0.492 0.004 0.152 0.258 -0.529 1.010 0.595 0.588 -0.780 1.651 0.231 -0.365 -0.893 -0.618 -0.157 -3.115 1.670 0.692 1.233 -0.118 1.106 0.719 0.351 -0.300 +4 2.537 -1.399 -1.208 0.175 0.707 0.864 0.288 -1.519 -0.220 -1.766 0.395 0.456 -0.126 -1.183 -0.819 -1.623 -0.051 3.168 -0.102 -0.420 0.217 -1.150 -1.116 -0.619 0.885 0.334 -0.159 0.749 +1 0.788 0.261 2.078 0.222 1.542 0.051 0.330 0.220 0.042 0.224 1.019 1.235 0.403 0.860 -0.476 1.108 0.055 1.090 0.890 -0.886 -0.183 0.229 -1.525 -1.484 0.343 -0.692 -0.799 1.075 +0 -0.208 0.811 -1.428 0.754 0.596 1.787 -0.170 -0.169 0.087 0.350 -0.023 0.607 -0.534 0.560 0.840 0.989 0.270 0.356 -0.217 -0.203 1.296 -0.906 -0.979 0.420 -1.578 0.255 -0.383 -0.698 +4 1.255 1.436 -0.528 1.456 -2.627 -2.537 -0.319 -1.456 0.009 -1.843 -0.951 -0.207 0.282 1.326 -0.637 0.640 -0.266 0.867 -0.844 0.447 -1.044 0.038 -0.785 0.408 0.431 -0.110 0.821 1.029 +2 -0.608 0.551 -1.196 0.379 -0.167 0.056 0.517 0.342 -0.653 0.207 0.759 2.294 2.788 -0.770 -0.172 0.628 0.960 0.398 -0.161 0.369 1.957 -0.593 0.352 -0.974 -0.478 0.248 0.567 -1.553 +0 0.648 0.001 -0.121 -0.339 0.006 0.023 -1.131 -0.557 0.594 -0.132 0.494 -0.166 -0.571 1.023 0.643 -0.108 0.776 0.621 -0.572 0.165 0.377 0.488 1.760 0.645 0.657 -1.086 2.654 -1.288 +4 -1.302 -0.677 0.041 -0.185 -2.022 0.556 -2.475 -1.226 -1.843 0.949 -1.119 -2.027 1.244 1.988 1.060 0.630 -1.292 -1.871 -1.324 -0.395 0.925 -2.030 -1.081 -0.490 -1.621 0.205 -0.762 -0.762 +0 0.446 -0.189 -0.036 -0.149 0.657 -0.272 -0.535 -0.502 -1.213 0.034 -0.170 0.195 -0.593 0.022 -0.366 -0.266 -0.248 0.087 -0.982 0.028 -0.945 0.475 1.590 2.000 0.526 0.281 -0.064 1.340 +3 1.097 -0.256 -0.511 2.351 -1.371 -0.264 -0.595 -1.151 0.136 -3.035 -0.924 0.669 0.292 -0.796 -0.316 1.802 1.572 0.747 -0.761 0.660 0.769 -0.490 1.081 -0.760 0.704 -0.039 0.409 0.685 +1 -1.198 -0.282 0.534 1.388 -0.268 -0.089 0.488 -0.869 -2.039 -0.692 0.489 -0.351 0.102 -1.154 -0.098 2.233 0.752 1.264 0.572 -1.011 0.736 0.115 -1.198 -0.809 -0.354 0.854 -0.561 1.457 +3 -0.922 -0.617 1.042 0.672 -0.630 -0.134 0.779 -2.385 0.302 0.758 -0.918 0.650 0.015 1.811 -0.523 0.591 -1.470 0.075 0.815 -0.525 1.120 0.920 0.144 -0.271 -3.084 -0.649 1.101 -0.273 +1 0.232 0.004 -0.426 -1.031 -0.194 0.974 -0.016 0.610 1.766 -0.580 0.303 1.004 -0.933 0.178 1.872 -0.340 1.068 -0.141 1.800 -1.645 0.065 -1.479 -0.701 -0.707 -0.267 -1.188 -0.072 0.460 +3 1.026 -1.332 0.352 -0.709 0.006 -0.220 -1.110 -0.735 -0.463 1.745 -0.534 -1.896 -0.741 -1.763 -1.044 -2.438 -0.621 0.373 -0.413 -0.360 2.553 -0.602 0.218 0.106 0.254 -0.608 0.766 1.188 +1 0.307 -2.133 -1.027 0.170 0.136 -0.223 0.369 0.012 0.121 -0.639 0.982 -0.431 -1.331 -0.771 -0.348 0.994 -0.042 -0.186 -1.249 0.785 -0.652 -0.100 1.480 0.509 -1.628 -1.770 -0.064 -1.213 +0 -0.646 0.405 0.594 1.051 -0.091 -1.405 0.669 0.159 0.060 -1.283 -0.196 -1.194 0.482 -0.430 -0.520 -1.537 1.027 -0.259 0.087 -0.735 -1.651 -0.532 -0.372 0.293 -0.355 -0.718 -1.503 0.048 +1 -0.262 0.550 -0.760 2.326 -0.002 0.077 -1.002 0.629 1.025 0.199 0.314 0.243 -0.903 1.441 -0.312 1.727 -1.207 0.907 0.186 0.092 -0.591 0.670 0.536 1.787 0.669 -0.408 -0.431 0.324 +0 1.681 -0.712 -0.080 -1.078 -0.722 -1.087 0.245 0.425 0.687 -0.754 0.508 0.113 -0.058 -1.241 0.153 0.185 0.102 0.400 0.695 -1.098 -0.217 0.516 -0.268 0.959 0.090 -1.955 0.839 -0.117 +3 -2.081 0.433 0.892 -1.262 -0.278 0.855 1.351 0.550 0.734 -1.093 -1.911 -0.069 -2.268 0.159 -0.259 -0.492 0.690 -0.659 -1.369 0.288 1.756 0.703 1.083 0.423 0.796 0.805 0.196 -0.749 +4 0.201 1.066 0.161 -1.011 2.001 -1.763 1.637 -0.150 -0.485 0.389 -1.134 0.523 3.152 2.210 -1.361 0.404 -0.732 0.684 -0.787 1.721 0.429 -0.737 -0.367 -0.578 -0.201 -1.170 -0.599 1.336 +0 -1.431 0.104 -0.373 0.885 1.355 -1.076 0.398 0.413 1.326 0.899 1.121 0.391 -1.211 -0.718 0.431 -1.262 -0.507 -0.360 -0.102 0.388 0.318 0.612 -0.924 -0.391 0.462 -1.141 0.751 0.110 +4 -2.678 1.006 0.265 -0.176 -1.667 1.109 -0.438 0.565 -0.011 -1.615 1.269 1.079 0.764 0.315 -1.197 0.869 2.205 0.116 0.136 -1.102 -2.070 -0.359 1.482 1.344 -0.065 0.430 -1.218 0.111 +0 0.446 0.617 -0.563 0.747 -0.463 1.375 -0.042 0.566 0.297 0.723 0.109 0.717 0.709 -1.223 0.654 -0.453 -0.157 -0.250 -0.405 1.394 -1.374 -2.725 -0.559 -0.190 -0.808 -0.949 0.693 0.113 +1 -0.901 -1.133 0.350 -0.844 0.102 -0.483 -1.781 -2.230 0.227 -0.562 -0.553 0.696 1.173 -0.273 -0.341 0.500 -1.357 0.223 0.552 0.680 -1.937 -0.761 -0.878 0.100 0.481 1.497 -0.495 -0.498 +2 -0.444 -0.673 -0.202 -0.227 1.921 0.154 -0.429 2.290 -0.619 -1.496 -0.487 -1.740 0.351 -0.046 0.369 -0.500 0.197 1.583 0.777 0.974 0.630 1.540 0.182 0.057 -0.004 -0.726 -0.178 1.406 +3 0.533 -1.097 -1.976 1.699 1.146 1.685 0.026 0.849 -0.959 0.349 0.013 0.287 -0.034 1.013 -0.943 -0.410 0.041 0.780 0.537 1.296 1.723 0.706 0.250 -1.144 -1.545 -1.454 -1.030 0.627 +0 0.528 -0.783 -0.481 -0.758 0.847 0.920 0.369 -0.776 -0.651 -0.702 0.357 -1.581 -0.397 -1.339 0.506 -0.991 1.958 1.045 -0.089 0.413 0.468 -0.960 1.238 -0.096 -0.768 0.542 -1.123 0.656 +2 0.235 -0.443 -0.510 0.609 0.222 -0.781 0.644 -0.335 0.559 -1.232 0.625 -2.623 -0.293 1.231 -1.485 0.279 -1.089 -0.414 1.424 -0.536 -1.679 0.006 0.548 -0.485 -0.344 1.786 -1.601 -0.199 +3 1.219 -0.147 0.718 -2.066 -0.891 0.251 -1.578 0.361 -0.662 -0.778 0.513 0.171 -1.124 1.123 0.505 -2.014 -0.303 0.373 -0.111 -3.323 0.064 0.758 -0.081 0.055 1.020 -1.584 0.736 0.340 +1 -0.064 -0.809 -0.560 0.686 0.587 1.696 0.341 -1.407 -1.541 0.127 -0.048 -0.250 -1.181 -0.961 1.076 1.020 0.265 0.623 -1.147 0.317 -0.650 1.245 0.484 -1.548 -1.222 -0.082 -0.908 1.116 +1 1.431 0.781 -1.067 -0.368 0.584 -0.796 -0.016 0.160 1.820 -0.157 -1.573 0.771 1.045 2.279 -0.440 -1.373 0.353 0.524 0.578 -0.403 0.428 0.107 -0.563 -1.097 -0.324 -0.068 0.687 -0.031 +1 0.715 0.718 0.438 0.020 0.673 0.592 -0.354 -0.574 0.102 1.549 -1.239 -1.468 0.165 0.051 0.173 0.244 -0.223 1.490 -1.601 -0.828 -0.103 -1.643 -0.176 1.661 0.021 0.231 -1.260 -0.616 +2 1.675 -1.165 0.882 1.149 0.363 1.713 -0.059 0.659 -0.439 0.583 0.523 1.202 0.285 0.845 -1.063 0.818 0.597 0.058 -2.218 1.450 -0.307 -1.247 -0.353 -0.683 -0.137 -0.773 0.867 -0.389 +0 0.501 -0.617 -1.625 0.944 0.280 -0.225 0.243 -1.055 1.032 0.319 -0.148 -0.340 0.071 0.483 -1.743 -0.783 -0.331 -0.371 -0.712 0.778 -0.690 -1.069 -0.468 1.506 0.013 -0.482 -0.330 -1.752 +3 -0.124 0.867 0.159 0.091 -0.114 -1.447 1.938 -0.942 -1.360 -0.213 -0.961 -0.052 0.688 0.995 0.815 -0.306 0.050 0.408 -1.758 0.571 -1.457 2.040 -0.405 -0.185 -0.770 -0.630 2.274 0.940 +2 1.541 -0.543 1.333 -0.425 -0.360 -0.330 -0.107 -0.175 0.358 -0.424 0.100 -1.612 0.782 1.132 0.222 0.748 -0.837 1.518 0.190 -1.605 1.215 0.718 1.872 1.664 1.342 0.474 0.713 -1.079 +1 -0.263 0.943 -1.032 -0.030 -0.532 0.393 0.140 0.713 -0.224 1.053 -0.712 0.906 1.245 0.270 -0.869 -1.920 -0.244 -1.095 -0.533 2.209 -0.219 -1.428 -0.602 0.970 0.248 0.694 1.868 -0.069 +3 0.026 0.390 -1.087 1.321 -1.025 -1.251 -0.167 -1.279 1.562 -0.231 -3.221 0.200 0.554 -0.421 -1.356 -0.103 -0.482 -0.976 -1.064 0.156 0.903 0.462 -1.728 0.615 0.772 1.394 -0.950 -0.057 +4 -0.670 1.219 -1.574 1.100 1.438 0.346 -0.196 0.312 -0.604 2.625 -0.587 1.317 -0.741 1.597 2.012 0.386 0.287 -0.957 -1.076 0.298 1.458 -0.548 2.208 -0.610 0.229 0.232 -0.102 -2.044 +0 -0.288 -1.224 0.229 1.685 -1.161 1.018 -0.471 -0.386 -1.175 -0.752 0.647 0.771 0.236 -0.501 -1.104 0.062 -0.365 0.211 -0.321 -0.296 0.159 0.352 1.224 -0.337 0.545 1.804 0.572 1.164 +4 0.095 -2.240 0.340 -0.699 -2.934 1.089 -1.290 0.949 -0.975 -0.464 -2.284 0.425 -0.574 -0.367 -0.095 -1.648 0.691 -0.564 2.295 -0.480 -0.390 -1.357 -1.614 0.878 1.061 -0.565 0.121 1.242 +3 -0.276 0.484 -0.690 -0.444 -0.375 -0.623 0.445 -0.221 -0.250 -0.753 -2.078 -0.803 0.627 1.605 1.251 0.478 1.017 2.611 0.456 -0.659 0.360 2.227 -0.709 0.362 -0.513 0.284 1.523 0.824 +1 -0.058 -1.353 -0.524 1.017 -0.702 1.488 1.030 -0.194 -0.656 -0.951 0.192 0.415 1.340 0.357 -0.352 1.374 0.819 -0.688 -0.083 0.660 0.305 -1.008 0.176 -0.676 0.063 -1.300 -0.891 2.099 +0 0.114 -0.480 -1.229 -0.009 -0.544 0.252 -0.192 -0.701 0.386 -0.238 1.659 0.487 -0.434 -1.051 -0.744 1.523 -0.618 1.655 -0.326 0.057 -0.636 0.323 -2.071 -0.038 -1.191 -1.137 -0.022 0.300 +0 0.552 -0.305 -1.350 1.242 -1.553 1.058 -0.312 1.623 0.381 0.433 0.028 -0.542 0.669 -0.261 -0.461 -0.381 -0.123 0.917 -0.727 -1.028 0.280 -0.055 -0.018 0.968 -0.382 0.535 1.497 0.143 +3 0.396 0.170 -0.846 0.608 -0.755 -0.102 1.447 -0.661 -1.573 -1.053 0.994 0.255 -0.129 -0.509 0.485 -0.849 0.306 -0.731 -0.898 -0.637 -0.375 -0.049 0.235 -3.688 -0.808 -0.392 1.505 -1.080 +0 0.078 -2.348 -0.295 -0.311 -0.449 -0.931 0.500 -1.197 0.594 1.051 -0.564 -0.766 0.061 1.065 -1.730 -0.372 -0.144 0.494 -0.047 0.333 0.151 -0.111 0.535 -0.013 0.192 -0.018 -0.434 0.892 +2 1.064 2.081 -1.597 1.146 -0.707 -0.302 -0.581 0.968 0.382 -0.375 -1.401 0.583 0.774 -0.343 0.833 1.342 0.656 -0.317 0.652 0.633 -0.734 1.264 -0.813 -0.336 -0.234 -1.255 0.549 1.633 +1 -1.427 -0.324 1.215 -0.214 -0.246 0.609 0.120 -1.521 0.323 -1.845 1.228 0.517 0.467 2.105 -1.106 0.697 0.191 0.557 -0.424 0.519 -0.094 1.005 0.281 0.953 -0.680 -0.481 -1.124 1.277 +3 0.789 -0.306 1.418 -0.168 -1.476 -0.893 1.999 -2.597 -0.908 -0.213 0.536 -0.282 -0.064 -0.199 -0.415 0.290 0.814 -0.935 1.132 -1.203 1.470 0.699 -1.406 -0.499 1.447 0.319 -1.754 0.364 +0 0.854 -0.788 0.430 0.153 -0.282 -0.486 0.663 0.555 -0.758 0.470 -1.124 -0.693 0.379 0.255 -1.081 0.355 -0.185 1.062 0.420 -1.134 0.183 -0.768 0.077 0.389 0.490 -0.959 -0.610 0.046 +2 -0.343 -1.627 -0.427 -2.182 -0.931 -0.462 0.649 -0.020 -0.866 1.192 -1.082 -2.000 0.877 -0.454 0.734 -0.770 -0.758 -1.214 0.189 0.685 0.967 1.474 0.521 -1.012 1.282 0.153 0.883 -0.409 +2 0.067 0.600 0.373 0.542 0.346 -0.674 0.466 0.155 -0.707 -1.128 -0.429 0.696 -0.049 1.254 -0.062 -0.922 -0.126 0.917 1.194 -1.429 0.067 0.460 -2.128 2.263 -1.653 -1.742 -1.044 -0.062 +4 -0.639 -1.446 -2.587 -0.946 -0.499 -0.416 -0.487 -0.121 1.690 -0.602 1.479 1.669 -0.412 -1.287 0.377 1.595 -2.578 -0.265 0.137 1.720 -1.817 0.385 0.721 -0.857 0.252 0.317 1.209 1.320 +3 -1.554 -1.550 0.983 0.185 -0.414 -0.050 -0.877 0.123 0.226 1.220 0.262 -1.635 -0.452 0.859 0.096 0.277 -0.568 1.108 -0.287 -1.463 -0.980 2.048 0.191 -2.266 1.685 -1.387 -0.042 -0.537 +3 0.466 -0.369 -1.414 0.508 1.876 0.662 1.158 0.933 -0.393 0.090 0.869 -1.580 0.652 1.259 -0.051 0.814 -0.554 0.038 -0.273 0.460 -0.105 -1.676 -0.718 1.943 0.568 -2.098 1.208 -1.153 +0 -0.990 -0.387 -0.056 -0.235 -1.647 -0.356 0.801 -0.496 0.410 0.817 1.022 0.745 0.943 0.500 -0.703 0.295 -0.416 0.049 1.988 1.851 -1.293 -0.399 0.047 0.194 -0.072 1.461 -0.873 0.334 +3 -0.737 0.415 -0.196 -0.795 2.377 -1.007 0.353 0.057 -1.026 -0.337 -1.343 2.066 -0.268 -0.183 1.945 -0.824 1.294 0.593 -1.979 -0.371 0.437 0.036 0.038 -0.607 -0.713 -0.308 1.961 -0.908 +1 -0.204 0.279 -0.304 -0.858 1.023 0.257 -1.052 0.291 0.162 -0.718 1.195 0.135 -0.217 -0.083 0.444 -0.072 -0.081 1.066 -1.418 -1.252 0.777 2.416 0.876 1.864 -0.434 -0.637 0.457 0.954 +0 -0.461 -1.361 -0.495 -0.168 0.880 0.751 -0.761 -0.510 -0.375 -0.099 0.645 0.541 -0.029 -0.457 0.771 0.033 -0.055 0.012 1.466 0.662 -0.209 0.218 -0.353 -0.705 0.266 0.818 -0.146 -0.122 +1 -0.661 0.188 0.159 -1.106 -0.006 -1.342 0.963 0.591 0.432 -0.637 1.419 -1.085 0.050 -1.169 -0.183 1.271 0.472 -0.921 -0.522 -0.379 -0.834 0.273 2.184 -0.559 1.930 -0.004 0.322 0.261 +1 -0.713 1.519 -1.527 -1.483 0.458 -0.626 -0.665 -1.079 0.338 0.226 0.119 -0.583 -0.751 -0.163 0.967 1.568 1.111 0.506 0.982 -1.182 -0.714 0.524 -0.118 0.338 0.979 0.824 -1.091 0.012 +0 -0.061 -1.107 -0.005 -0.491 -1.125 0.755 -1.575 0.524 0.587 -0.527 1.030 -0.548 -0.611 -1.200 -0.008 -0.959 -0.792 -0.065 -0.024 1.256 0.931 0.551 -0.147 -0.404 -0.027 -0.556 -0.701 -0.079 +4 0.658 -0.697 1.316 1.302 0.138 0.371 -1.680 -0.529 -1.252 -0.910 0.515 0.756 0.798 0.242 0.523 -1.589 -1.290 -0.209 0.597 -0.447 -1.361 -0.692 -0.933 -1.724 2.224 1.233 -1.976 -1.922 +2 -0.718 1.543 -0.229 1.764 -1.001 -1.045 0.073 0.166 -0.022 -0.808 -0.220 -0.498 -1.649 -0.532 -0.309 -1.507 0.887 1.691 0.039 -1.350 -0.222 -0.141 0.218 1.808 -1.079 0.320 0.809 0.757 +2 1.544 0.755 0.496 -0.558 -0.867 -0.457 0.250 0.249 -0.819 -0.726 -1.548 2.280 -0.898 -1.684 -2.488 -0.672 0.406 -0.105 -0.189 0.730 0.584 -0.898 0.261 0.069 -0.592 1.429 -0.777 -0.577 +1 0.035 -0.225 0.539 -0.722 -0.171 -0.183 -0.071 -0.566 -1.335 0.933 -0.116 1.129 0.479 0.515 1.131 2.598 1.304 0.886 0.292 1.259 -0.747 0.585 1.277 0.952 -0.076 0.455 -0.329 -0.768 +4 0.351 1.092 1.503 -1.509 -0.003 -0.189 1.591 -2.209 -1.047 1.433 -0.470 -1.301 1.512 -1.432 1.022 -1.560 -1.443 0.200 -0.848 0.272 1.802 -0.155 1.628 0.324 0.956 -2.353 0.636 0.483 +1 1.662 0.089 -0.335 -1.957 1.656 0.207 0.760 0.325 1.184 -0.383 -1.206 1.094 0.828 0.011 -0.911 -0.679 -1.008 -0.902 0.105 -0.128 -0.861 -0.380 -1.321 0.054 -0.805 1.471 -0.269 -0.712 +2 0.021 0.278 -0.386 0.429 0.572 0.144 0.645 -0.223 -0.698 -1.950 0.574 2.028 -0.499 -0.648 -1.013 0.494 -0.538 -0.430 -0.332 0.693 -2.150 1.563 1.206 0.917 0.193 -0.665 -0.399 2.216 +2 1.477 1.011 1.274 -0.950 -1.525 0.133 0.335 0.365 1.478 -1.734 0.860 -1.097 0.856 -0.475 -0.814 -0.610 -1.446 0.040 -0.773 0.786 0.640 1.688 0.366 0.521 1.400 -0.628 -1.488 0.200 +4 0.204 -0.338 1.377 1.108 1.910 0.137 -0.139 0.796 0.001 0.090 0.771 -0.816 -0.553 1.987 1.087 -2.783 -0.753 1.632 -0.599 -0.214 -0.732 0.483 -1.332 -0.549 0.401 2.345 -0.357 -0.279 +0 -0.019 -1.978 0.220 -0.657 -1.651 0.361 -0.083 -0.406 -0.769 -0.345 0.983 0.998 -1.685 0.338 -0.255 -0.834 -0.613 0.806 -0.021 0.470 -0.590 -0.831 -1.124 0.232 0.155 -0.869 0.495 0.412 +0 -0.752 0.153 -1.857 0.080 -0.780 -0.326 1.994 -0.485 0.790 1.049 0.854 0.371 -1.108 -0.651 -1.166 -0.352 -0.340 -0.914 0.547 -0.493 -0.363 -1.569 0.069 -0.094 -0.599 0.381 0.119 0.588 +1 0.589 -0.507 0.295 -0.913 0.376 0.440 -1.961 0.039 0.415 -0.084 0.358 -1.342 -0.776 1.035 0.001 0.340 -0.547 0.019 -2.302 0.846 1.808 -1.065 -0.940 0.441 1.474 -0.002 -1.229 -0.209 +1 -0.519 -1.335 1.410 -0.695 -0.121 -0.380 -1.822 0.406 0.562 -1.323 0.977 0.881 -0.448 -0.800 0.016 -0.290 0.819 -1.442 -1.068 1.126 1.040 -0.069 -1.029 -1.337 -0.139 -1.001 -0.173 -0.135 +2 -0.693 0.670 0.330 -1.629 1.286 -0.204 -0.831 0.504 1.752 -2.016 -1.015 0.293 0.142 1.147 0.394 -1.364 -1.194 -0.815 0.983 -0.304 -0.042 0.385 1.782 -0.789 -1.067 -0.123 -0.758 -0.788 +4 -0.074 -0.713 0.185 -0.121 -0.724 -0.693 2.113 1.377 1.168 0.009 0.202 1.512 -0.194 -1.189 -0.971 0.464 1.937 2.183 1.149 0.434 1.621 0.227 0.836 3.140 -0.698 -0.571 -1.053 0.556 +4 -0.271 -0.121 -1.183 -2.000 -0.603 -1.503 -1.027 0.472 0.618 -0.737 -0.105 -2.042 0.004 0.500 -1.383 -1.836 1.764 0.550 1.248 1.288 -0.529 -1.902 -1.525 1.499 -0.487 0.589 -0.415 1.425 +1 -0.389 1.356 -0.035 -0.907 0.559 -0.405 -1.224 -0.893 1.734 0.384 0.108 -0.250 0.292 0.280 0.292 0.115 0.869 0.023 -0.978 -0.612 -1.979 2.027 0.863 -0.830 -0.282 -0.637 1.543 -1.420 +4 -1.270 0.669 0.672 0.384 -0.994 0.250 -1.183 -0.253 1.029 0.797 0.015 -0.975 0.646 0.019 -2.320 0.783 -0.024 1.202 -1.095 -3.177 0.279 0.292 -1.691 -0.978 2.755 -0.306 0.731 -0.651 +2 -1.155 -0.165 -0.389 0.360 0.334 -0.458 -0.677 -0.088 0.069 0.217 0.031 1.243 -0.227 1.866 2.255 -0.657 0.642 0.365 0.581 0.294 -0.762 -0.859 -0.361 -0.243 -1.757 1.438 0.552 2.416 +0 -0.832 0.285 -1.081 1.369 0.199 0.411 -1.124 -1.486 -0.248 0.271 1.197 0.859 -0.357 0.913 0.165 -0.152 -1.607 0.146 -0.176 0.357 -0.252 -0.503 0.234 0.298 0.037 0.211 -0.428 -0.293 +3 1.096 2.067 -0.163 0.105 0.796 -0.199 0.189 -0.929 -0.948 -0.445 1.413 1.214 -0.345 1.609 1.231 -1.471 0.861 -0.223 0.515 1.520 -0.534 -0.943 -1.077 -1.139 -0.104 -0.318 1.012 -2.330 +4 0.729 1.143 -2.086 2.819 -0.772 -0.503 1.383 -2.269 1.039 1.038 0.467 0.485 0.072 -0.203 0.194 -0.228 -1.318 -2.150 -0.152 -2.697 0.133 0.600 -0.916 -0.566 1.442 -1.278 1.245 -0.418 +2 -0.932 -0.068 2.207 -0.512 0.651 0.714 -0.493 0.698 -1.404 0.888 0.125 -2.006 -0.323 -1.468 -0.887 -0.385 0.667 -1.126 -0.422 -1.204 -0.845 1.460 0.267 -1.382 1.159 1.092 -0.286 0.829 +3 -1.367 0.593 -2.704 -0.630 -0.488 0.633 0.023 -1.406 -0.985 0.199 -0.079 -0.579 -0.481 0.696 -0.518 -0.088 2.170 -1.424 -1.852 0.784 -0.679 -0.318 -0.781 -0.260 1.599 0.802 0.862 -1.416 +4 2.739 -2.245 -1.713 -0.303 -0.283 0.114 0.928 -1.256 -1.358 -0.694 1.031 0.809 -0.070 -0.509 -0.153 0.362 0.099 -1.408 -1.478 -0.259 0.725 -1.061 0.407 -1.789 0.071 -0.782 -2.090 -0.546 +2 0.175 -0.539 -0.633 -1.023 0.287 -0.544 0.153 0.676 0.546 1.261 -1.178 -0.251 -1.431 0.151 0.832 -0.606 0.239 2.467 -0.654 -0.004 -2.086 0.472 -0.784 0.397 0.208 -1.100 1.157 1.736 +1 0.729 -0.120 1.062 0.070 0.275 -0.494 0.157 -1.630 1.035 -1.009 -0.888 -2.327 -0.823 -0.958 0.217 -1.295 -0.444 -0.680 -1.760 0.238 -0.219 0.040 0.830 0.008 -0.333 0.923 -0.326 -0.505 +0 0.505 0.388 0.989 -0.018 0.674 -0.226 -0.047 0.172 0.667 -0.939 -1.875 -0.871 -0.075 0.743 1.482 1.308 0.393 0.034 0.171 0.675 0.210 -0.736 0.994 0.673 1.083 1.393 -0.069 -1.379 +1 0.608 0.899 0.269 1.538 0.690 -0.689 1.520 -1.386 0.818 -0.272 -0.867 -0.568 0.351 -0.186 -1.110 0.629 0.496 0.298 -0.892 1.142 -1.051 -1.310 0.384 -0.253 -0.036 0.948 -1.898 -0.919 +2 -1.076 0.896 0.727 0.424 0.192 -1.105 0.178 0.472 0.699 1.051 -0.411 -0.174 -0.751 -0.726 -0.019 1.863 0.006 1.934 -0.230 -1.503 -1.029 -2.046 -0.808 0.253 0.709 0.876 -0.112 1.614 +3 -1.118 0.308 0.769 0.612 0.472 0.019 1.133 -0.753 -0.995 0.555 -0.561 -0.896 0.639 0.997 0.154 -1.679 2.173 1.031 -2.231 -1.675 0.219 0.527 -0.689 0.135 0.734 0.674 -0.989 2.058 +4 -0.134 0.680 0.744 0.225 1.763 0.752 -1.326 0.701 -0.033 0.548 1.168 1.615 0.084 0.838 -1.080 -0.464 -0.563 1.034 1.727 3.189 0.861 1.124 -1.730 -1.788 -0.869 -1.254 0.512 1.736 +2 2.268 -1.834 -0.944 -0.310 1.044 -0.504 0.968 -1.722 1.148 0.894 -1.075 -0.034 -0.269 0.450 1.161 -0.683 0.369 0.246 0.125 1.455 0.161 -1.215 0.738 1.601 0.140 1.352 -0.419 0.414 +1 -0.606 -0.297 -0.841 -0.363 1.468 -0.319 0.106 -0.725 -0.015 0.827 0.489 1.418 -1.210 -0.756 -0.413 1.525 0.299 -1.417 1.092 0.110 0.890 1.584 -0.978 1.583 -0.074 -0.414 0.155 -1.193 +1 -0.737 0.119 2.257 1.424 0.537 -0.204 0.379 0.189 0.183 -0.875 -0.894 0.329 -1.160 -0.625 0.030 0.331 1.116 -1.253 -1.250 1.306 1.676 -1.044 -0.845 0.546 0.049 0.057 -0.781 0.720 +2 0.336 -0.507 -0.039 2.383 -0.463 0.085 0.891 -1.141 -0.131 -0.035 -0.029 -0.840 0.507 -0.924 1.331 -1.131 0.864 -2.004 -1.339 1.302 -0.482 -0.419 0.576 -0.991 -1.308 -0.332 -0.776 0.350 +4 -2.022 1.982 -0.091 -1.274 -2.392 -1.186 -0.659 -0.925 -1.357 0.468 -0.625 -1.157 3.057 -1.194 -0.188 -0.938 2.821 1.266 0.836 -0.997 1.433 -2.820 2.252 -0.496 -0.280 -0.482 0.844 -0.876 +0 -0.025 0.708 0.230 0.035 0.227 0.796 0.179 -0.406 -0.329 0.151 -1.106 0.491 2.510 -0.185 -0.619 0.437 -0.015 -0.392 0.896 0.623 -1.022 -1.275 -1.480 -0.055 0.615 -1.787 0.852 0.404 +4 0.976 0.321 -0.085 -0.796 -1.771 0.686 -0.883 0.144 0.867 3.012 -0.075 -1.112 0.153 -1.820 -0.166 0.021 0.155 1.208 0.715 -0.692 1.346 1.277 -0.845 -1.644 -2.207 0.613 1.020 -0.404 +1 -0.558 -1.060 0.506 0.657 -2.079 0.692 0.591 -0.051 1.061 -0.878 -1.308 0.072 0.669 0.903 0.336 0.400 -0.496 0.940 -0.026 -0.018 -2.239 -0.272 -0.336 0.080 -1.349 -1.259 -0.297 0.932 +4 -0.060 0.542 -1.711 0.205 -1.377 -0.963 0.733 -0.199 0.698 -0.221 1.106 -0.239 -1.955 0.225 -2.169 -1.453 -0.883 -1.008 -0.545 -1.042 -1.406 1.542 -0.678 -2.231 -0.361 -1.125 -0.885 0.710 +4 0.461 -2.398 1.421 -1.632 -0.013 -0.829 0.296 -1.829 1.549 -0.359 0.501 -0.801 1.661 -1.372 -2.631 1.363 -1.176 -0.070 1.558 -0.405 0.260 0.815 0.030 1.301 0.705 -0.894 0.732 -0.034 +1 0.299 -0.302 -1.290 1.204 0.532 0.018 -0.002 -1.506 -0.977 0.336 -0.529 -0.218 -0.105 -0.682 -0.968 -1.612 -0.147 0.619 -1.644 -0.024 0.281 -0.990 1.422 -0.386 -0.101 -0.161 -1.771 1.009 +0 -1.034 -0.314 -1.217 0.744 1.194 0.384 -0.189 -1.276 -0.457 0.232 -0.574 -0.455 -1.122 -0.743 -1.467 0.277 0.050 0.095 -0.361 -0.591 -0.298 -0.426 0.153 1.833 -0.189 -0.592 -0.249 -0.492 +0 0.401 0.632 0.976 -0.549 1.037 0.962 -0.435 0.853 -0.829 -1.451 -0.468 0.215 -0.461 -1.556 0.439 -0.143 -1.072 -0.197 -0.658 0.016 -0.221 0.461 -0.235 -2.031 -0.855 -0.884 0.194 0.008 +0 0.523 -0.073 0.284 1.321 0.530 0.438 -0.533 0.508 -0.024 -1.299 -0.155 1.221 -0.883 0.351 -0.103 0.020 -0.197 0.277 -1.014 0.560 -0.438 0.182 0.238 0.263 -0.229 -1.252 -0.545 -0.355 +4 -0.754 -0.839 1.688 0.384 0.630 1.574 0.178 0.270 1.418 -2.831 -1.332 2.517 -0.830 0.193 -1.151 -0.510 -0.520 0.523 0.056 -0.469 -0.281 -1.656 0.816 -0.131 -1.104 -1.220 1.433 -0.110 +0 0.069 0.868 1.224 -0.358 0.138 -0.352 -0.413 -0.532 -1.364 0.863 0.102 1.526 0.824 -0.400 -1.141 1.867 -1.699 -0.342 -0.326 -0.647 -0.851 -1.221 -0.726 -0.051 0.306 -0.525 -0.530 -1.042 +1 0.412 -0.217 -1.075 1.194 0.916 0.450 0.334 0.301 -0.526 2.501 -0.286 -0.427 1.121 -0.029 -0.019 1.415 0.679 0.729 -1.418 0.401 -1.006 -0.455 0.176 -0.036 0.654 0.771 0.846 -1.344 +0 0.282 0.066 -2.235 0.575 0.113 -1.295 0.211 -1.185 1.735 0.069 0.003 0.589 -0.450 -0.160 -0.386 -0.796 -0.722 -0.028 -0.331 0.324 -1.276 -0.925 0.095 0.719 -0.266 -0.276 -0.003 0.094 +2 0.827 0.767 -1.833 1.661 -0.380 0.558 -0.437 -0.177 0.796 1.232 -0.234 0.170 -1.130 1.063 -2.127 -1.419 -2.335 -0.308 -0.124 0.376 1.069 -0.340 0.472 -1.449 0.589 -0.379 -0.340 -0.043 +3 -0.595 -1.515 -1.343 -0.168 0.349 0.204 -0.674 -0.037 -0.558 -1.286 -1.852 -0.373 0.749 -0.110 -1.471 1.065 -0.790 -2.370 -0.149 1.057 0.129 -1.125 -1.041 1.070 -0.684 -1.355 -1.209 -0.402 +2 -0.156 -1.121 0.132 0.411 0.078 -0.825 0.365 1.459 -1.496 0.401 -0.493 -0.625 2.431 -0.029 0.992 0.906 -1.062 -0.185 -2.058 0.110 -0.892 0.469 1.333 -0.961 -0.728 -1.133 0.693 1.052 +1 0.427 -0.170 0.951 -0.476 -0.372 0.158 0.684 1.151 -0.041 0.429 0.033 -0.294 -1.922 0.206 -0.911 0.871 -0.868 -0.568 -0.822 0.533 -0.303 0.568 -2.670 1.994 -1.010 0.769 0.500 0.712 +2 0.914 -0.633 0.770 -1.801 0.578 -1.659 0.597 -0.559 0.847 -0.039 1.098 0.435 0.089 1.185 0.448 1.739 -0.033 -0.210 -0.898 -0.557 -0.239 -0.691 0.639 -1.256 -0.866 -2.133 -0.690 1.217 +1 -0.547 -1.185 0.252 0.366 -1.082 1.444 -0.964 0.456 -1.283 0.554 0.516 -0.730 0.377 0.346 -0.228 0.828 -0.890 1.241 0.545 -1.274 0.557 -0.214 -0.585 0.769 1.046 -2.329 -0.055 -0.132 +0 0.912 -1.189 -0.189 0.153 -0.749 -0.497 -0.123 -0.011 -0.133 0.056 0.375 -0.336 0.433 1.169 0.452 1.741 -1.403 -0.583 0.364 0.243 -0.386 0.613 0.208 1.176 -1.060 0.056 -0.445 0.857 +0 0.987 -0.373 0.031 -0.478 -0.098 -0.048 -1.670 0.866 0.113 0.446 0.816 0.121 0.331 -1.470 -0.830 0.085 0.186 0.401 1.406 -0.094 0.363 0.059 -0.250 -0.211 -0.784 -0.117 -0.219 -0.152 +0 0.117 -0.243 0.889 0.727 -0.535 0.194 -1.164 0.624 1.690 0.883 -0.082 -0.966 0.685 -0.594 0.029 0.767 -0.803 0.105 -1.038 -0.324 -0.447 1.226 0.217 -0.662 -0.410 1.027 -1.560 -0.803 +1 -0.753 0.284 -0.762 0.654 1.055 2.200 0.244 -0.941 -0.997 0.135 0.891 -0.528 0.605 -0.054 -0.541 0.356 1.458 -0.243 -0.622 -2.490 -0.837 0.544 0.108 1.560 -0.705 -0.242 0.297 -0.008 +3 1.119 0.941 -0.276 1.843 0.434 0.212 0.218 -1.062 0.944 0.952 0.241 -1.118 1.089 -1.800 -0.774 0.580 -0.408 -0.990 1.384 1.133 1.066 0.332 0.035 1.738 1.435 -0.084 2.711 -0.125 +3 -0.039 1.889 -0.780 0.225 -0.109 0.731 0.800 1.189 -0.124 1.258 0.345 1.360 1.500 0.018 -0.674 0.204 0.991 -1.505 -0.495 0.683 -2.588 -1.034 -0.613 -0.083 -0.322 -0.303 -1.331 1.770 +4 1.573 2.015 0.579 -1.025 -2.053 0.458 -0.226 2.246 -2.057 -1.121 -0.195 -1.056 -1.631 -0.434 -0.488 0.083 0.854 -0.631 1.384 1.010 0.275 1.981 -1.719 0.806 -1.335 0.741 1.110 1.787 +4 0.482 -0.026 -0.283 -1.895 -0.877 -0.897 -1.299 1.533 -0.846 0.821 -1.067 -1.752 1.222 -0.902 -0.971 -2.389 1.694 -1.051 1.192 0.914 -0.092 -1.042 0.619 1.784 2.144 0.754 0.494 -0.161 +1 0.334 -2.260 0.344 -0.624 0.167 0.227 -0.255 -0.007 -1.047 -0.122 -0.575 0.373 0.037 -1.062 -0.789 -0.791 -0.079 0.124 -0.098 0.131 -0.551 -0.806 -1.373 -0.369 -0.629 2.301 2.026 0.478 +3 0.625 -0.229 1.574 -1.357 -1.487 -2.124 -1.870 -0.460 -0.260 0.957 -0.297 1.539 0.254 0.520 0.884 -0.388 1.678 0.398 1.665 1.033 0.564 -0.357 -0.081 0.342 -1.141 -2.248 -0.396 -0.331 +0 -0.934 -0.944 0.772 0.080 0.202 0.341 -0.058 0.108 1.296 0.777 -0.172 -0.472 1.027 0.333 -0.308 -1.291 1.694 -1.127 0.284 1.825 -0.341 -0.045 0.854 -1.193 -1.670 -0.357 0.081 0.069 +0 1.427 -0.851 0.052 0.390 -0.230 1.317 0.370 0.203 0.208 1.141 -0.393 -0.669 0.470 0.605 0.554 -2.326 -0.016 0.069 0.696 -1.083 0.615 -0.616 -0.068 1.394 0.208 0.198 0.577 0.705 +1 -1.900 -0.055 -0.863 0.566 -0.144 0.192 -1.626 -0.262 1.184 -0.467 0.067 0.352 -0.634 -1.180 2.549 0.645 -0.124 1.339 0.237 0.750 0.608 0.996 -1.096 -0.136 0.930 -0.001 -0.914 -0.234 +2 0.882 1.332 -1.057 0.197 -0.052 -0.889 -0.118 0.348 1.255 0.909 -0.810 0.729 -2.640 0.419 0.860 -0.382 0.450 -0.946 -0.221 -0.173 -0.308 2.203 1.186 1.950 0.635 -0.362 0.348 0.584 +3 0.323 -0.568 -0.316 0.328 -0.815 1.542 0.528 -0.615 -0.817 1.009 2.235 -0.416 -2.952 -0.192 1.021 -0.676 0.497 -1.068 1.149 -0.252 0.480 0.864 0.630 1.142 0.112 -0.634 -0.045 -2.258 +4 1.899 -0.641 -0.872 -2.357 -1.248 -0.541 0.254 -0.004 0.124 -1.100 0.064 -1.228 -2.211 -0.337 -0.950 0.072 -0.056 -1.202 -1.089 1.332 1.446 -0.852 -0.848 -1.633 -0.714 1.835 0.085 0.471 +3 0.138 -0.791 0.698 -0.511 1.131 1.772 0.085 -0.319 0.802 0.701 0.022 0.719 0.614 0.291 1.050 -1.414 1.124 -1.586 -0.187 0.506 0.542 -0.502 2.675 2.400 -0.339 0.193 -0.425 -1.116 +0 1.869 0.035 -0.802 -1.147 1.090 -0.571 0.044 0.956 -0.484 -0.046 -0.017 0.683 0.591 -0.220 0.064 -0.372 -1.478 0.258 1.892 -0.884 -0.773 -0.871 0.725 0.448 -0.159 -0.918 0.092 -1.565 +0 0.447 -0.475 0.305 -0.734 -0.891 1.237 0.209 -0.110 -0.297 -0.214 -0.033 -0.754 0.029 0.391 -1.013 0.650 -0.169 -0.901 -1.328 0.001 0.117 0.782 0.948 1.175 -0.224 -0.371 -1.116 0.335 +2 -1.838 -1.323 -1.627 0.717 -0.738 1.169 -0.460 0.184 -1.149 0.067 0.295 -0.296 1.135 -0.561 -0.635 -0.114 1.302 -0.541 0.316 0.728 1.548 -1.070 -0.409 1.135 -0.811 1.265 1.573 1.148 +2 -0.211 0.915 -1.290 0.451 0.107 -1.260 -1.068 -0.619 0.013 1.177 -0.947 -0.947 1.359 -0.717 0.267 1.259 1.362 -2.364 0.664 -1.552 0.447 -0.531 -1.351 -0.731 0.185 -0.257 -0.671 -0.254 +1 1.076 0.122 -0.955 -1.855 0.607 2.450 -1.015 0.170 1.110 -1.270 -0.480 -0.860 0.985 1.326 0.159 -0.842 -0.443 -0.630 -0.977 -0.102 0.575 0.490 -0.008 0.057 -0.082 -1.297 0.160 -0.584 +2 1.154 -0.123 2.060 -0.448 1.998 -0.975 0.517 -0.643 1.120 -0.478 0.291 -0.651 0.937 -0.656 1.683 -0.996 -1.346 0.952 -0.592 -0.007 -0.322 -1.313 -0.077 1.265 1.003 0.232 1.112 -1.060 +2 -0.373 -0.673 -0.537 0.669 0.523 1.580 -0.841 -0.355 -0.473 1.660 -1.072 2.129 -0.338 0.145 -0.329 1.347 -1.437 0.663 -0.840 -1.242 0.386 0.449 0.184 1.721 1.376 -0.761 1.132 -0.687 +0 1.084 -0.212 -0.467 -0.737 -0.056 -0.079 0.207 -0.129 -0.134 -0.777 0.942 0.077 -0.174 -0.747 -1.332 1.308 0.555 -1.624 0.763 0.922 0.124 1.598 0.217 -1.417 0.365 0.245 -0.603 0.574 +1 0.106 0.028 -2.587 -0.494 0.331 -0.212 -0.474 0.156 0.989 0.538 -0.756 -1.459 0.519 -1.178 -0.528 0.201 1.147 1.286 0.061 -0.509 -1.201 1.959 -0.290 0.678 0.197 -0.496 -0.538 -1.383 +0 -0.419 0.023 0.364 0.416 -0.391 -0.164 0.860 -0.198 -1.395 -0.050 0.274 0.969 0.289 0.537 -0.607 -0.806 0.337 1.953 1.360 1.132 0.539 -0.127 1.234 1.203 -0.192 -0.776 -0.110 0.248 +2 1.510 0.763 0.054 1.663 1.346 1.014 -0.483 -0.844 -0.263 0.398 -0.499 0.154 0.345 1.214 1.707 0.034 -0.779 0.806 -0.649 -1.085 1.266 -0.558 1.233 -1.210 0.798 1.912 -1.315 0.581 +2 1.626 0.052 0.916 -0.113 0.923 -0.773 -0.042 -0.465 1.869 -1.189 2.070 -1.374 0.736 -0.622 0.255 -0.171 -0.830 0.081 -0.417 0.571 -1.005 0.472 0.864 -0.108 -1.323 1.732 0.467 -0.721 +4 -0.050 -0.748 -2.417 0.721 2.585 -2.782 -0.701 0.747 -0.287 -1.519 0.280 1.382 0.701 0.596 -0.550 0.522 0.718 0.254 1.314 0.667 -1.981 -0.507 -0.708 -1.941 -0.206 0.178 -0.974 0.840 +1 -1.004 0.118 1.448 -0.859 0.121 -1.043 0.056 0.079 -0.588 -1.008 -0.984 1.136 -2.171 -0.406 -1.542 -0.010 0.092 0.094 -0.806 -0.322 1.949 0.830 -0.181 -0.287 -0.705 0.448 -0.654 1.103 +2 1.654 -0.236 -0.371 -0.808 0.356 -0.708 -1.129 -0.737 -0.947 -1.006 0.085 -0.195 0.550 0.147 -1.203 -2.123 -1.056 0.764 0.741 0.985 0.838 1.087 0.406 1.803 0.294 1.999 -0.626 -0.652 +3 -1.316 -0.326 0.086 0.612 -1.098 -0.360 -0.732 -0.709 0.428 0.594 1.142 0.024 -1.527 0.898 -3.062 -0.273 1.100 -0.713 -2.180 0.657 0.166 1.204 0.309 -0.388 0.141 1.835 -0.289 0.730 +0 2.698 0.367 0.773 -0.121 -0.460 0.950 0.316 -1.161 0.711 0.254 1.338 0.178 -0.525 -0.228 0.063 -0.660 -1.192 -1.292 -1.214 0.166 0.253 0.428 -0.051 0.321 0.184 0.910 -0.018 0.442 +2 1.800 -0.549 -0.852 0.296 0.716 0.912 -0.962 0.435 -0.686 -1.489 -0.929 -0.751 0.522 1.152 -1.473 0.352 -1.177 0.629 -0.623 -1.051 0.800 -0.587 -0.316 -2.220 0.947 0.821 0.005 -1.528 +2 -2.010 -0.297 -0.934 -1.424 0.182 0.312 -0.502 -0.121 -0.277 -0.122 0.953 -0.421 -0.137 -0.300 -0.177 -0.505 0.360 -2.414 1.839 0.707 -1.661 0.637 0.989 0.354 0.772 -0.439 1.665 0.738 +3 -0.658 0.347 1.588 0.024 0.266 -0.682 -0.564 1.121 -1.455 -0.762 -0.239 0.109 -0.090 0.791 2.956 -1.057 0.273 -0.672 0.929 1.841 -0.056 1.004 -0.967 1.267 1.725 -0.131 0.671 -1.247 +4 0.617 -1.211 -0.437 -0.302 -1.517 0.754 1.129 0.453 1.874 0.348 0.021 -0.552 0.276 -2.695 1.763 0.392 1.134 1.146 0.736 -1.021 -3.361 2.183 0.018 -0.338 -0.264 -0.462 -0.015 -0.075 +4 0.174 -1.041 -1.800 -0.538 1.525 0.556 1.484 -0.034 -0.038 -2.694 -1.914 0.461 0.191 -2.267 0.132 1.005 0.314 -0.533 -2.490 0.405 -0.890 0.511 0.698 0.921 0.381 -0.346 0.586 -1.014 +2 -0.923 0.043 -0.525 -0.391 -0.429 1.105 -0.945 -0.711 0.187 1.889 1.467 0.527 -0.858 0.427 1.032 1.341 1.389 1.458 0.893 0.501 0.675 1.538 0.691 -0.729 0.812 -1.128 0.031 -1.119 +4 -1.024 2.383 0.636 -0.490 -1.868 0.874 0.419 -0.925 0.109 -1.400 -1.367 -0.297 -0.223 1.493 0.003 -0.456 -0.733 0.268 -2.498 0.111 1.728 1.002 2.103 -0.698 0.460 -0.165 1.219 -0.098 +4 -0.356 0.769 1.008 -0.138 -1.188 0.641 0.698 -2.051 0.731 -0.198 -0.687 0.073 -0.936 0.590 -0.424 3.247 1.709 -0.861 1.251 -0.643 -1.252 -1.309 1.148 -1.307 0.555 -1.107 0.194 -0.847 +1 -0.443 -0.502 0.777 -0.761 -1.019 -0.315 -1.315 1.095 -0.683 0.249 0.154 0.206 -0.493 -0.807 0.484 -1.022 0.179 -0.952 0.073 0.543 -1.974 -0.872 -0.134 1.096 -0.399 -1.536 -1.085 -1.780 +0 0.556 0.890 -0.274 -1.439 -1.212 0.286 0.987 1.117 0.168 0.967 1.912 1.190 0.922 -0.807 0.963 0.461 0.651 1.261 0.359 -0.103 -1.070 -0.297 0.213 0.278 0.244 0.960 0.865 -0.062 +2 -0.759 0.873 0.417 2.052 -0.598 1.019 1.858 1.118 1.136 0.121 0.231 -0.921 -2.672 0.204 -0.960 -0.425 -0.826 0.324 -0.200 1.257 -0.723 0.181 -0.191 -0.294 -0.077 1.520 0.843 -0.456 +4 0.719 -0.861 -0.208 -0.213 -0.777 -2.295 0.694 0.796 0.543 1.193 0.247 1.284 -0.094 1.188 -0.224 1.079 0.134 0.797 -1.017 1.726 1.840 -0.211 -2.191 -0.221 -2.659 -0.169 0.231 -0.130 +0 0.760 -0.653 0.574 0.115 0.411 -0.684 -0.693 -1.833 -0.457 -0.510 -0.667 -0.115 0.064 0.500 0.566 0.650 0.692 0.275 0.055 0.929 0.693 -1.482 -0.173 -1.803 -1.823 -0.509 -0.128 -0.424 +2 -0.156 -0.898 -0.959 -0.953 -0.041 1.587 1.269 0.540 -0.912 -1.020 -0.289 0.409 0.558 0.240 -0.093 0.214 -0.551 -1.442 -0.289 0.183 -0.128 -1.525 -0.212 -0.613 1.336 0.562 -3.012 0.051 +2 -0.567 -0.348 -0.599 0.045 -1.136 0.763 2.037 -0.598 -1.593 -0.102 0.315 0.453 1.603 0.816 -1.812 -1.236 0.406 -0.454 1.515 -0.937 0.961 -0.649 0.262 -0.828 0.559 1.073 0.253 1.909 +0 -0.478 -0.632 -1.509 -0.941 -1.615 -0.999 0.384 0.563 -1.175 -1.392 -0.715 0.739 -0.689 -0.042 0.281 -0.415 -0.596 -0.457 0.144 0.338 1.838 -0.742 0.135 1.244 -0.037 0.451 0.496 0.190 +2 -0.132 -1.235 -0.880 -0.435 0.367 0.105 -1.246 1.347 -0.743 0.441 -0.947 2.482 1.325 -0.774 0.088 -1.029 -0.356 0.423 0.845 -1.238 -0.749 0.942 1.324 -0.773 0.957 -1.236 -0.298 -1.144 +2 0.780 0.030 -1.716 0.615 -1.340 -1.231 -0.317 -0.285 -1.753 -0.148 -0.089 -0.128 -1.688 0.144 0.620 -1.347 -1.076 0.787 1.580 -0.166 -0.450 0.198 1.356 -0.850 -0.484 -0.510 -1.417 0.853 +0 -1.077 0.051 -0.518 0.999 1.028 -0.015 -0.836 -0.204 -0.369 1.582 -0.334 0.228 0.646 1.452 -0.353 -1.468 0.123 1.134 0.383 -0.549 -0.337 -0.858 -0.228 -0.870 0.390 0.500 0.085 0.877 +2 0.246 0.032 -2.036 -0.652 0.527 -1.006 1.470 1.825 -0.639 0.082 1.922 0.461 0.518 -1.010 0.545 -0.615 0.798 -0.425 -0.153 -0.732 -1.593 0.268 -0.590 -0.223 0.972 -1.177 0.098 2.184 +4 -2.425 -0.348 0.408 -2.238 -0.770 -0.542 -1.733 -0.721 1.301 0.425 1.674 -0.681 -0.399 0.271 -0.269 0.303 0.789 0.571 -1.656 -1.245 0.271 -1.858 -0.756 -1.150 -1.508 -1.579 -0.611 0.562 +0 -1.166 -0.032 0.674 1.369 0.980 -0.394 -0.377 -1.273 0.537 -0.446 -0.389 1.179 0.277 -0.546 -1.556 0.213 -1.338 -1.235 -0.061 0.493 -0.238 0.173 1.360 -0.112 -0.088 0.364 1.731 1.183 +4 -0.324 -0.689 0.837 1.252 -0.100 -2.658 1.912 0.998 1.812 -0.150 0.336 -0.532 -2.149 -0.411 -0.898 -0.092 -1.586 0.639 0.343 -2.127 -2.438 -0.851 0.817 2.135 -1.032 -0.560 -1.272 -0.215 +1 0.008 -0.177 -0.155 -0.477 1.267 1.399 1.176 1.614 0.625 0.236 -0.285 0.155 0.073 1.316 0.565 1.551 -0.928 2.060 -0.231 1.659 -0.342 0.518 -0.143 0.287 0.381 -0.244 -0.947 0.609 +0 0.857 0.398 0.395 -0.426 0.450 -0.262 0.556 -1.905 -0.434 1.722 -0.767 -0.604 -0.448 -1.215 -2.208 -0.231 0.136 -0.053 -0.145 0.043 1.023 -0.190 -0.574 -0.281 0.579 1.412 -0.628 0.099 +1 0.077 -0.415 -0.115 1.101 -0.832 -0.162 -0.084 1.387 0.728 -0.642 -1.135 -0.166 -1.279 0.709 1.545 0.032 0.215 0.905 -0.124 0.868 -0.972 1.613 0.922 1.023 1.132 0.166 0.239 -1.691 +1 -1.908 -0.794 1.193 0.700 -0.161 0.070 -1.117 -1.072 -0.695 -0.315 -0.539 0.137 0.052 0.210 -1.928 2.561 0.291 -0.361 -0.403 -0.004 -0.473 -0.769 -0.083 0.851 -0.092 0.181 0.984 0.812 +4 -0.222 1.135 0.215 1.693 0.248 2.191 1.227 -0.087 0.175 -0.840 0.160 -0.983 1.330 -0.344 0.356 -2.281 -0.267 -0.372 0.921 0.522 1.018 0.443 1.231 0.419 -1.376 -0.090 -0.039 -3.265 +4 0.041 0.625 1.540 -0.309 -0.425 -0.227 0.366 -1.242 -1.796 0.325 -1.351 1.142 -2.209 -0.785 -1.352 -0.673 -0.880 1.125 2.809 -1.226 -0.693 -0.145 -1.060 0.559 -1.386 0.610 0.089 0.755 +0 -0.276 -0.558 -1.030 0.366 -0.288 -1.310 0.298 -0.876 0.509 1.337 1.211 -0.283 -0.301 0.041 -0.136 1.775 0.225 -0.406 -1.765 -1.136 -0.610 0.904 -0.899 -0.788 0.406 -1.589 0.284 -0.020 +4 -1.580 -1.422 -1.430 3.140 -0.180 -0.407 -1.324 -0.450 1.557 1.152 -1.818 -0.546 1.864 -0.037 -1.320 -0.419 0.192 2.159 -0.762 0.342 -1.124 -0.224 0.642 -1.252 0.602 1.943 0.320 1.107 +4 -0.246 -0.273 -2.697 -0.054 -0.231 0.696 1.849 1.127 -0.269 -1.107 2.573 0.059 0.014 -0.024 0.198 -0.144 -0.574 -0.547 -0.033 -0.543 -0.713 0.106 -0.255 1.504 -2.651 1.092 1.246 -2.073 +2 -0.722 2.502 -1.762 -0.093 0.325 -0.074 -1.489 -0.711 0.322 0.929 1.549 -0.092 0.868 1.028 1.177 -0.092 0.337 0.411 0.407 1.146 -0.099 0.419 0.297 -1.247 -0.087 0.779 0.328 -1.423 +0 0.703 0.565 -0.756 -0.936 -0.800 0.482 -0.125 0.692 -1.607 0.082 -0.218 -0.414 0.289 0.816 -0.576 -0.015 -0.654 -0.567 0.997 0.438 -2.088 0.759 0.014 -0.436 0.200 -0.133 -0.348 0.513 +1 -0.448 -0.556 0.453 -1.276 -1.007 1.096 0.265 0.172 0.890 0.040 -1.192 0.495 0.564 -1.519 0.306 0.651 0.696 0.837 0.412 0.997 -0.998 1.547 -1.888 0.859 -0.955 1.246 0.721 -0.551 +1 -1.693 -0.098 -0.989 -1.104 0.180 1.392 0.918 -1.571 -0.990 0.941 -0.982 -0.225 0.550 -0.968 0.105 -1.334 -0.601 0.320 -1.593 0.440 -0.020 0.552 0.224 1.364 0.125 -0.429 0.122 0.543 +0 -0.547 1.704 0.483 0.212 0.195 -0.712 0.042 1.409 -1.145 1.044 0.580 -0.769 0.573 0.258 0.005 1.698 0.125 0.113 0.639 1.778 0.269 -0.629 -1.002 0.648 -0.055 -0.997 -1.482 0.384 +4 -0.508 -0.004 -0.775 -1.656 -0.447 -0.227 0.751 -0.206 0.390 -0.724 -0.018 -1.731 -0.628 -2.722 -1.191 -1.274 0.615 0.352 1.068 -1.320 0.822 -0.577 1.794 -0.179 1.605 2.876 0.423 1.782 +0 1.327 0.647 -0.965 -0.068 0.790 -1.123 -0.180 0.910 -1.495 0.332 -1.755 -0.944 -0.505 0.514 -0.701 0.126 -0.687 -0.531 1.251 -1.150 0.759 -0.001 -0.576 -1.218 -1.146 -0.739 -0.150 0.604 +1 -0.804 -0.465 -0.236 1.006 -0.379 -0.333 -0.292 -0.048 1.698 -0.379 0.561 0.350 0.062 -0.958 0.524 -1.926 -1.021 -0.542 -0.225 0.084 1.391 0.630 0.169 -0.336 -0.325 -1.135 -2.107 -1.275 +3 -1.385 -1.930 -1.089 -2.178 0.092 -0.460 -0.383 0.510 0.874 0.238 -0.223 -0.702 -0.351 0.207 0.925 -1.390 -1.128 -0.006 -2.111 0.285 1.056 2.358 0.082 0.859 0.022 1.620 -0.423 0.329 +0 0.885 -0.597 0.793 1.457 -0.075 -0.960 -0.283 -0.063 0.987 -0.870 0.669 0.587 0.503 1.235 0.292 -0.532 -0.035 0.071 -0.476 -1.435 0.132 0.257 -0.037 0.054 -0.893 -2.395 -0.254 -1.335 +2 1.048 -0.418 1.009 -0.424 -0.016 1.001 -0.025 0.680 -0.570 -2.534 0.726 0.206 -1.540 1.063 0.104 -0.187 0.050 0.576 -0.655 -1.030 -0.935 0.922 -2.125 0.299 -1.099 0.867 -0.748 -1.258 +3 2.464 -0.359 -0.753 0.896 0.406 -0.214 -1.890 -1.758 -0.712 0.761 0.452 -0.451 1.728 -1.670 -0.020 -0.800 -0.151 -2.016 -1.083 0.524 1.256 -0.138 -0.873 -0.423 -0.780 -0.036 -0.280 -0.338 +4 1.381 1.671 0.511 -2.230 -1.167 -0.678 -1.385 -1.651 -0.038 -1.166 -0.795 0.275 -0.870 0.066 -0.275 1.074 -0.646 -1.355 -0.316 2.150 1.536 1.647 0.420 0.190 -1.006 0.459 -1.358 -0.192 +2 -0.804 -0.591 0.032 1.572 -0.295 -0.899 -2.019 -0.761 -0.868 -1.192 0.070 -0.263 1.426 0.215 1.009 -0.171 -0.878 0.619 -1.129 1.157 -0.748 0.740 0.376 -0.971 1.211 1.920 -0.749 -0.655 +3 0.216 0.084 0.093 0.336 0.751 -0.889 1.037 -0.617 -1.015 -0.754 -0.272 0.124 3.708 -0.048 0.806 1.106 -1.390 0.877 1.980 -1.371 0.931 -0.257 0.019 0.880 -0.316 -0.467 -0.797 1.432 +2 0.090 0.961 0.821 0.337 0.153 -1.091 1.106 -1.800 -0.342 -0.902 0.605 1.721 -0.353 1.634 -0.503 -0.615 -0.675 1.222 1.174 -0.327 0.615 -0.551 1.084 -0.489 0.923 0.784 0.463 -2.185 +3 0.993 2.671 -0.621 -0.182 -1.129 -0.233 1.370 -0.972 0.990 1.507 -1.946 -0.679 1.519 0.507 -0.941 -0.444 0.337 0.997 2.024 -0.152 0.735 -0.196 0.270 0.870 -0.784 0.698 -1.413 0.551 +3 -1.564 0.138 -0.881 1.026 0.297 2.099 1.710 0.008 0.473 1.587 -0.252 -1.023 -0.796 -0.807 -0.625 -1.459 1.243 -0.040 -0.851 -0.856 0.483 -1.095 2.193 -0.548 0.045 -1.642 -0.808 0.582 +1 1.827 0.635 1.145 -0.934 0.385 0.046 -0.530 -0.392 -2.011 1.206 -1.691 0.097 0.638 1.251 1.655 0.651 0.339 1.559 -0.408 -0.626 0.056 -0.817 -0.764 0.450 -0.059 -0.604 0.083 0.387 +4 -0.506 -0.180 0.894 -0.766 -0.265 0.366 -1.942 -2.385 0.346 0.492 2.064 0.158 0.820 -0.195 0.297 1.667 -2.124 0.414 2.136 0.282 1.651 -0.629 -0.032 0.034 -2.218 -1.168 -1.097 0.822 +2 0.152 0.559 0.360 0.258 -2.162 -0.120 -0.082 0.545 1.387 -0.259 -0.345 0.131 0.022 0.432 0.529 -0.176 0.499 -0.219 -1.450 -1.156 -2.335 1.663 1.285 1.035 -0.216 0.977 0.898 -1.454 +0 0.622 -0.971 0.033 0.403 0.005 -0.161 0.037 -0.105 0.564 1.177 -0.287 0.205 0.881 1.070 -0.886 -0.811 0.324 -0.925 -0.130 -0.049 0.983 -0.557 1.259 0.217 1.373 -0.991 -0.053 -2.419 +2 -1.196 0.600 0.499 -0.585 0.943 1.085 0.604 -0.612 1.702 -0.685 -0.258 -0.019 0.416 -0.346 -0.268 -1.518 -2.054 0.796 0.910 2.606 0.192 -1.051 -1.329 -0.755 0.404 -0.871 0.841 0.579 +2 -0.049 0.265 -0.357 -1.012 -0.764 -1.244 0.203 0.587 -1.365 0.301 1.140 1.109 2.072 -0.937 -0.342 -0.382 0.246 0.439 -1.859 0.802 0.082 0.803 0.998 -1.462 0.231 -1.811 1.372 0.930 +0 -0.931 0.984 0.086 -0.371 1.038 0.226 0.092 -0.607 0.700 0.747 0.285 0.097 1.878 -0.845 -0.328 -0.859 0.176 -0.990 -0.312 0.842 -1.062 0.838 -1.134 0.180 0.405 1.054 0.903 -0.862 +3 0.839 -1.167 1.237 1.550 -0.913 -0.914 0.411 -1.187 -0.493 1.103 0.738 1.980 0.171 -0.611 0.051 -0.151 -0.137 -0.024 -0.932 0.282 -1.409 -0.717 -0.119 -2.260 0.769 -2.053 0.670 -1.293 +3 1.703 -0.189 -1.568 1.053 0.617 -0.176 2.611 0.942 -0.941 -1.698 -0.424 2.073 -0.367 -0.082 -1.162 -0.492 -0.099 0.176 -0.166 -0.193 -0.745 0.720 1.116 1.368 -1.100 -1.465 -0.045 0.932 +2 1.722 1.422 -0.965 1.130 0.391 -0.655 -0.212 -0.710 0.037 -0.730 -1.028 0.479 0.629 -0.188 1.368 -0.260 -0.303 0.660 -0.132 0.085 -2.520 0.017 -1.827 -1.221 -1.335 -1.043 -0.549 -0.486 +2 0.001 -0.048 -1.837 0.399 -1.436 2.305 1.142 1.847 -0.579 -1.002 1.073 0.691 0.737 1.132 1.019 0.355 0.185 -0.726 -0.646 0.618 -0.010 0.574 1.778 0.766 -0.198 -0.933 -0.348 -0.965 +3 -0.847 -1.489 0.586 1.514 0.574 0.646 -0.525 0.488 -0.751 -0.575 -0.038 0.042 1.792 0.867 0.116 1.709 2.403 0.020 0.383 -1.239 0.165 -0.678 -1.886 0.990 0.106 -0.869 -1.827 -0.609 +4 -0.775 1.368 1.967 -0.039 1.250 -1.056 1.075 1.047 0.863 -0.016 -2.954 -0.642 -0.626 -0.978 0.122 -0.419 -0.825 -1.059 -0.716 1.121 -0.661 -0.515 -0.457 0.339 0.456 -3.045 1.022 1.436 +1 0.223 0.321 -1.337 -1.026 0.866 -0.431 -0.219 -0.231 0.220 -0.620 -0.181 1.809 -0.760 0.495 0.383 -0.850 0.776 -0.462 -0.431 0.106 0.845 2.241 0.071 1.270 1.382 1.295 0.509 -0.183 +4 1.314 -0.417 -0.212 2.290 -1.041 0.741 -1.567 -0.866 0.737 -1.415 0.876 -0.221 -0.975 -0.107 -0.586 -0.712 -0.495 -1.393 -1.763 -2.127 0.503 0.432 -0.794 -0.130 1.385 -0.626 -0.267 1.939 +0 0.626 0.656 0.163 0.163 1.135 0.814 -0.589 0.493 -0.619 -0.313 0.568 -0.634 0.148 0.380 -0.339 0.720 -0.439 0.943 0.131 0.173 -0.899 -0.235 0.195 1.024 -0.322 1.021 -0.756 0.334 +3 0.328 1.703 1.252 0.066 -0.675 1.128 -0.849 0.893 -1.435 1.966 -1.068 -0.990 -1.907 -0.397 0.436 -0.598 -0.889 -1.449 0.280 0.695 0.164 0.692 -1.215 -0.633 -0.200 1.278 1.272 0.793 +4 -0.929 1.072 -2.408 0.118 -0.558 -1.372 1.305 -2.147 1.384 -1.533 1.053 -0.779 -1.954 -0.453 0.564 -0.898 0.260 0.690 1.493 0.063 1.166 0.849 -0.092 1.147 0.894 -0.166 0.713 0.272 +3 0.608 -0.406 -0.731 0.537 -1.199 -1.401 -1.925 0.474 0.043 -0.564 1.262 2.420 -0.147 0.498 -0.105 1.450 -0.368 -0.936 -1.287 2.231 -0.197 -0.304 0.030 -1.561 1.784 0.142 0.400 1.241 +2 0.026 0.924 -0.438 -0.398 -0.541 0.241 1.546 -2.310 -0.488 -0.351 -2.132 -0.124 -0.353 -0.439 -1.103 -0.087 -1.166 -0.842 -0.199 1.022 0.410 0.614 1.962 0.440 -1.047 -0.563 -0.857 -0.136 +3 0.859 -1.221 0.442 0.308 1.795 -0.365 0.045 -0.067 -0.517 1.478 -0.975 -1.518 0.244 -0.725 0.254 0.633 0.146 0.024 -2.062 0.560 0.196 1.874 1.137 -1.482 1.061 -1.300 0.834 1.454 +2 0.088 -0.440 -0.529 -1.076 -0.431 -2.120 -0.736 -0.311 0.610 -0.040 0.795 1.349 1.058 0.471 0.012 -0.981 0.740 1.680 0.342 1.917 -1.526 -1.154 0.855 0.264 1.314 -0.015 -0.868 -0.214 +1 -1.570 -0.420 -0.901 0.381 0.169 1.292 -0.054 0.338 -0.558 -1.103 0.835 1.243 0.706 0.131 -0.974 1.142 0.580 2.025 -1.154 0.863 0.549 -0.342 1.364 0.402 -0.937 -0.118 -0.949 -0.469 +1 -1.389 1.597 -0.050 -1.734 1.303 -1.157 0.255 1.814 -0.258 0.210 -0.887 1.644 0.782 -0.099 -1.213 0.115 0.667 0.367 -0.166 0.720 0.594 0.810 0.275 0.453 -0.271 1.128 -1.458 -0.076 +0 -0.017 -0.582 -1.118 0.034 -0.589 -0.971 1.004 -1.289 0.697 0.240 1.366 -0.474 -1.346 -0.635 -0.041 0.988 0.805 -0.217 -0.077 0.697 1.055 0.327 0.288 0.238 0.269 0.270 -0.997 -1.618 +4 1.170 0.134 0.080 0.554 -0.862 0.030 -2.152 0.876 -1.561 1.503 -0.330 -0.212 -0.628 -0.288 1.419 -2.488 1.277 0.338 -1.207 -1.075 1.676 -0.946 -1.153 1.137 0.339 -0.938 0.217 -1.026 +3 -0.047 -0.438 -0.169 0.151 -2.722 -0.558 -0.809 0.692 0.758 0.170 0.113 -2.012 -0.909 -0.353 -0.109 -0.005 -1.948 0.975 -1.023 -2.267 0.783 -0.912 0.692 -1.744 -0.405 -0.964 0.393 -0.890 +1 0.085 1.062 0.725 -0.079 0.660 -1.007 0.984 -0.246 -0.838 -0.041 -0.631 -1.182 -0.218 0.278 0.796 0.465 2.400 0.268 1.578 0.239 0.693 0.036 0.395 0.577 1.012 -1.645 -0.274 -0.982 +3 -0.100 -0.065 -0.221 0.555 -0.032 0.779 0.942 0.832 -0.427 -1.863 0.151 0.213 -1.972 1.447 0.460 1.447 0.145 0.254 0.379 -0.466 -1.169 -1.281 2.278 -0.197 -1.184 1.783 1.303 1.120 +4 1.623 -0.747 1.369 -0.875 0.361 -1.781 0.640 -0.221 -1.745 -0.471 1.766 0.570 0.113 0.530 1.010 1.524 -0.291 0.593 0.475 0.107 -0.186 1.604 0.263 -1.217 -2.905 0.954 -0.430 -0.152 +1 -0.424 -0.766 0.734 -1.538 0.565 0.684 -0.880 -0.820 -0.223 0.282 -0.153 0.434 1.763 -0.508 -1.715 0.826 1.683 0.393 -1.088 -0.124 -1.119 0.643 -1.351 -0.300 -1.004 0.127 -0.366 0.177 +1 -0.141 -0.375 0.039 -1.089 0.839 0.908 1.583 0.440 -0.131 0.114 0.578 -0.022 1.164 1.036 -2.167 -0.129 -0.290 -1.479 0.780 0.080 -0.347 -0.327 0.407 0.146 1.512 1.744 -1.418 -0.008 +0 0.040 0.848 0.428 1.039 -1.620 -0.193 0.854 -0.096 0.748 -0.438 1.381 -0.347 -0.786 -0.244 0.869 -0.243 -2.255 -0.691 0.008 0.133 0.842 -0.459 -0.632 -0.035 -0.373 -0.301 -0.718 0.800 +3 0.773 -1.069 -0.814 -1.562 0.670 -1.959 0.393 -0.124 1.743 0.033 0.848 0.511 -0.103 1.115 0.380 0.899 -0.928 0.528 0.802 -1.089 -0.074 -1.738 -1.587 -0.060 1.800 1.878 -0.305 -0.951 +2 0.119 0.090 0.042 1.684 0.055 -2.048 0.843 -2.131 0.703 0.423 0.514 0.156 1.055 1.448 -2.167 0.457 -0.407 0.484 -0.177 -1.231 0.312 -0.036 -1.457 0.567 0.887 1.318 -0.542 -0.190 +3 -0.495 -1.234 1.972 0.987 -1.285 -0.261 -0.777 0.100 0.047 1.647 0.501 0.380 1.254 1.566 0.185 -1.746 0.794 -1.932 -0.516 1.224 0.448 -1.924 0.609 0.084 -0.199 0.763 -0.074 -0.944 +3 -0.679 -0.298 0.776 -0.311 1.109 -0.611 -0.081 1.422 0.340 1.402 1.119 -2.558 -0.685 1.846 -0.681 0.429 1.057 1.438 -0.408 0.766 0.906 -1.147 1.005 0.992 0.113 0.823 1.203 -0.590 +3 0.129 -0.139 1.178 -0.417 -1.110 -2.528 0.025 -0.053 1.269 -0.868 0.750 -2.483 -1.299 0.384 0.689 -1.659 -0.795 0.045 -0.095 0.548 0.913 -1.278 0.444 -0.720 0.336 -0.467 1.077 0.587 +0 -0.702 -0.421 0.565 -0.969 0.604 -0.635 0.596 -0.087 1.831 -1.073 0.680 0.328 -0.270 0.429 -0.567 0.125 -0.576 0.190 0.352 -0.129 0.614 -0.305 0.268 -1.163 -0.437 -0.125 1.474 0.217 +4 -2.475 0.886 -0.559 0.139 1.800 0.624 -2.898 -1.225 -1.841 -0.834 -0.271 2.555 1.007 0.356 0.444 0.475 -1.752 -1.409 0.455 0.005 0.302 1.605 -0.692 -0.370 0.247 -1.702 -1.473 1.363 +1 0.234 1.165 0.625 -0.169 1.352 -2.365 2.052 -1.706 -0.627 -0.041 -0.146 1.006 -1.058 0.261 -0.578 0.295 -0.065 0.093 -0.550 -0.510 0.287 0.944 -0.598 -0.869 0.613 -0.033 0.830 -0.906 +4 0.239 0.660 0.660 0.150 0.242 -1.624 -0.016 1.861 -0.833 -0.795 -1.154 0.354 0.565 -3.307 0.480 -0.003 0.681 -0.688 0.261 0.755 0.463 1.453 1.968 0.642 -0.783 -1.577 0.855 -1.566 +1 0.502 0.646 -0.134 -0.507 0.545 -0.194 -0.434 1.905 1.211 1.020 1.095 0.724 -1.339 -2.340 0.926 -1.066 -0.767 -0.096 0.946 -0.154 0.445 0.759 0.194 -0.570 0.768 -1.080 0.993 0.481 +2 -0.551 -0.509 -0.629 -0.183 -1.389 0.762 2.809 0.595 -0.923 0.676 -1.544 -0.769 -0.438 1.045 0.698 -1.428 0.814 -0.555 0.608 -0.759 -0.106 -0.825 -0.103 -1.186 -0.853 -0.636 -0.970 -0.379 +0 0.317 -0.862 -0.661 -1.050 -0.625 0.658 -0.904 0.084 0.314 -0.055 -1.616 0.749 -1.165 -0.575 -0.460 -0.032 0.096 0.686 -0.312 -1.217 1.087 -0.401 -0.185 -1.001 -0.866 1.442 -1.375 -1.227 +2 1.295 -1.424 0.720 0.759 -0.917 0.024 -0.574 0.122 1.850 -0.872 0.297 -0.921 0.726 0.322 0.595 -0.528 -1.141 -1.589 0.009 0.537 0.395 0.736 -0.539 -0.096 -2.346 -0.136 1.741 0.186 +2 -0.902 1.009 0.498 1.890 0.193 -0.109 -0.515 -1.743 -0.318 -0.276 0.262 -1.423 -0.239 0.576 0.676 -1.860 0.706 -0.247 -0.185 -2.365 1.494 0.177 -1.531 0.375 0.344 1.223 -0.310 0.012 +4 0.293 2.058 -0.064 1.726 -2.107 -1.162 -0.668 0.584 1.774 -0.591 0.978 2.237 1.565 -0.404 0.869 0.339 -0.659 0.427 -0.474 0.124 -1.378 -1.763 -0.287 0.318 -1.128 0.526 0.043 0.650 +0 -0.453 1.030 1.369 1.411 -1.277 0.654 -0.872 0.149 0.963 -0.261 0.221 -0.619 1.092 -0.217 -0.534 0.340 0.920 -1.395 0.458 -0.498 0.667 -0.518 -0.863 -0.710 0.166 0.536 -0.625 0.055 +4 -0.446 0.198 1.310 3.277 -2.081 -0.440 0.269 -0.536 0.145 0.523 -0.432 0.409 -1.183 -0.852 0.814 0.642 1.466 0.735 1.193 -0.123 -1.412 -0.993 0.997 0.873 1.364 0.885 0.350 1.414 +3 0.031 -0.215 -0.628 1.656 1.033 0.583 -1.134 -1.183 -0.032 -0.513 -1.139 0.104 -1.884 -0.091 0.509 0.477 -1.805 -1.853 -0.213 -0.631 -1.217 -2.152 -0.882 -0.102 -0.236 -1.203 -1.003 -0.341 +3 -0.068 0.861 0.821 0.008 0.229 -0.127 -0.973 -0.057 -0.735 -0.996 -2.419 -0.409 0.785 1.223 0.446 -1.296 -0.155 -1.155 1.909 -0.387 -0.494 0.923 -0.039 1.072 -1.123 0.089 0.507 -2.618 +2 -0.162 -0.976 -0.442 0.360 0.333 0.253 -0.060 0.861 -0.257 1.392 -1.848 0.657 0.488 -0.675 -0.355 0.465 0.916 -1.749 0.628 -0.458 1.125 -1.839 2.445 -0.886 1.430 -0.069 -0.819 0.265 +4 0.504 0.625 -1.072 -1.308 -1.463 0.978 0.915 0.649 1.503 1.778 -0.993 -0.394 0.780 -2.761 -0.251 0.963 -0.349 0.732 -2.000 0.284 -0.461 -0.225 -0.948 1.599 0.069 0.146 2.062 -0.925 +3 0.734 2.364 -0.676 -2.348 -0.057 -0.291 -1.156 0.832 -0.654 0.224 0.443 0.033 -0.376 -0.022 -0.852 0.164 0.455 -1.326 -0.935 -0.273 -0.641 -1.041 0.023 0.528 -2.463 0.238 1.019 -1.302 +1 1.557 -0.973 -0.233 -0.885 1.277 -0.502 1.155 1.453 0.413 0.293 0.039 -0.317 -0.437 -0.147 1.824 -1.042 0.205 0.711 -0.284 0.416 -1.505 1.325 1.131 -0.098 -0.880 0.793 -1.148 -1.005 +4 0.906 -0.700 0.947 0.448 -1.573 1.247 0.707 -0.238 0.936 0.457 -0.726 0.641 -0.838 -0.092 0.214 1.215 0.480 -0.619 -1.711 -0.213 2.560 1.493 -0.841 1.794 1.317 -0.662 2.700 -0.320 +3 0.822 0.645 -0.228 0.426 -0.242 -1.393 -0.364 0.800 1.065 1.069 -0.016 1.268 -0.977 2.660 -0.029 0.094 -1.201 -0.470 0.710 0.517 -1.432 0.311 -1.670 1.230 1.244 -1.031 0.214 -1.412 +1 0.548 -0.841 0.382 1.325 1.361 1.110 -1.280 -1.127 0.303 0.483 1.677 -0.113 -1.035 0.200 0.606 -0.534 -1.018 0.337 -1.716 -0.534 1.020 0.142 0.281 -0.541 -0.950 -1.955 -0.854 0.675 +1 -0.594 -0.265 -1.290 1.153 -0.324 -0.083 0.077 0.246 2.009 -1.292 0.600 1.375 0.413 -0.756 -1.191 -1.421 -0.450 1.062 -0.180 0.196 -0.965 -0.232 0.868 0.864 -0.718 0.645 0.144 1.399 +2 -0.722 -1.183 0.365 0.923 0.575 -1.643 0.937 0.015 1.211 -1.404 1.166 -0.058 2.165 0.354 0.677 -0.540 -0.310 -0.167 0.118 -0.241 0.640 1.110 0.593 0.238 -0.326 -2.949 0.722 -0.721 +4 1.864 0.621 0.746 -1.215 1.305 0.592 0.829 0.359 2.148 2.042 1.526 1.861 -0.648 -0.313 0.564 2.389 -1.619 -0.830 0.206 -1.181 0.985 1.445 0.310 1.480 1.162 1.222 -0.730 0.748 +2 2.179 -1.287 0.360 -0.399 0.582 0.182 -0.844 1.000 -0.065 0.356 0.853 0.844 -0.531 -0.293 -0.326 -1.123 -0.120 0.467 -0.988 0.095 0.955 1.096 -1.726 -2.592 -0.128 -0.423 -1.248 -0.273 +1 0.420 -1.552 -0.387 -0.500 1.955 0.292 -1.077 -0.777 -0.379 0.362 0.485 0.200 -0.210 -0.136 -0.129 -1.783 -0.061 -0.577 -1.653 2.418 0.311 0.013 -0.371 0.730 -1.162 -0.313 -0.952 0.722 +3 -0.436 -0.481 1.856 -1.199 -0.102 0.339 -0.980 0.487 -0.400 0.374 -0.108 -2.051 1.306 2.070 -0.177 -0.898 1.506 1.890 -0.169 -0.219 -0.080 -1.675 -0.291 1.715 -0.169 -1.040 0.497 -0.667 +4 -3.040 0.398 -0.894 1.430 0.936 0.185 1.080 2.396 -0.050 0.345 -1.246 -0.866 -0.916 0.053 0.376 -0.677 0.285 0.999 0.181 1.092 -1.753 -1.581 0.635 0.246 0.081 0.116 -1.133 -1.705 +2 -0.282 0.512 -0.875 0.506 0.735 -1.116 -0.534 -0.905 -0.528 1.601 0.257 1.948 1.483 1.131 1.454 1.160 1.705 0.545 0.686 0.658 -0.439 0.846 1.258 -0.600 -0.857 -0.551 1.357 -0.237 +0 0.323 1.279 1.583 0.036 1.822 -1.258 0.614 -0.162 0.705 0.392 1.007 0.070 1.583 0.325 -0.349 0.208 -0.596 -1.200 -0.255 1.259 0.151 0.662 0.657 -1.533 0.321 -0.515 0.189 0.595 +4 2.082 1.327 -0.675 0.149 -0.213 2.319 -1.497 0.177 -0.761 -0.784 1.223 -1.349 -0.598 0.372 1.173 1.256 1.516 0.677 -0.896 1.195 -0.073 2.220 0.845 0.422 0.013 -1.042 -0.292 1.347 +3 -0.918 0.725 -0.791 0.208 -0.110 -0.085 0.169 -0.619 0.906 -0.812 0.838 -0.371 -1.988 -0.781 1.309 1.158 -1.954 -0.740 -1.942 0.100 -0.678 0.355 0.674 1.512 1.725 1.923 0.929 -0.147 +2 -1.468 -0.874 0.730 -1.201 -1.041 -1.046 -1.846 -1.147 1.000 -0.717 -1.749 -0.512 0.511 -1.831 0.953 -1.441 -0.395 -0.408 -0.561 -0.065 0.779 -0.041 -0.606 0.457 0.207 -0.670 0.042 -0.752 +2 -2.127 -1.707 -0.110 0.181 0.510 -0.299 -1.758 -1.360 -0.611 -0.152 0.642 -0.219 -0.916 -1.068 0.278 0.253 -0.410 -0.553 -0.240 1.791 -0.895 -1.616 -0.415 0.303 -0.710 0.871 -1.491 -0.265 +0 -0.631 0.773 -0.068 -0.545 -1.090 -0.997 -0.715 0.457 -0.517 0.178 0.158 0.769 -0.822 1.616 -1.759 -0.326 -0.425 -0.790 -2.204 1.213 0.829 0.499 -0.726 -0.506 -0.336 -0.240 0.795 -0.495 +3 -0.300 2.846 0.216 -0.818 -0.793 0.627 -0.072 -0.041 -0.446 -1.201 0.093 2.077 -0.115 -0.093 0.608 -0.006 -0.877 -0.473 -0.542 0.831 -0.618 -1.161 1.143 0.864 0.301 -0.694 -2.491 2.226 +1 -0.149 -1.757 -1.002 0.814 0.396 0.345 -0.600 -1.501 0.096 0.415 1.829 -0.832 0.648 0.564 -1.845 0.312 -0.868 0.568 0.738 -0.665 0.408 1.871 0.332 -0.318 0.209 -0.567 -0.844 1.002 +0 0.889 -0.038 0.025 0.293 -0.727 0.339 -0.585 -0.883 -2.098 -0.481 -1.105 -0.861 -1.072 -0.501 1.344 -0.058 -0.195 0.049 0.900 -1.373 0.129 -0.526 0.293 0.241 -0.169 -0.955 -0.165 -1.256 +0 -0.281 0.249 0.227 -0.100 0.755 0.093 0.482 0.214 -0.605 -0.269 0.555 0.551 1.245 1.859 -0.521 0.135 -0.193 -1.107 0.565 0.778 -1.077 -0.374 -0.472 -1.508 -0.036 -1.145 0.159 0.269 +3 -0.764 2.053 1.855 1.141 -0.676 1.477 -0.088 0.173 -0.820 0.003 -0.102 0.333 -1.860 -0.643 -1.248 0.452 1.378 -0.424 -0.069 -1.238 -0.142 -0.305 -0.593 2.694 -0.767 -0.533 -0.796 -0.440 +2 0.151 -0.257 0.667 0.457 -0.620 -0.666 -1.153 0.257 0.907 -0.591 -1.066 0.503 1.579 1.990 1.515 -0.049 -0.901 0.608 -0.071 -0.199 1.901 1.385 0.637 -0.096 0.008 -1.544 1.046 1.297 +2 -0.465 -0.034 1.147 0.308 0.640 -1.205 -0.220 -0.369 0.236 0.071 -0.089 0.351 1.452 -0.924 -0.019 -0.723 2.482 -2.130 -1.146 -1.116 1.005 1.655 0.359 0.191 -0.035 -0.068 1.151 -0.980 +3 -2.684 -0.770 1.219 -0.986 -0.783 0.856 -0.013 0.279 0.284 1.178 0.128 0.392 1.059 1.382 0.372 -1.129 1.564 -0.671 1.221 -0.771 2.113 0.495 1.276 -0.372 0.099 1.025 1.680 -0.361 +1 -0.264 -1.092 -0.665 0.048 -0.203 -0.245 0.272 0.992 0.283 -0.484 -1.215 -0.156 -2.044 1.090 0.097 1.770 1.339 0.551 -0.500 -0.371 1.558 0.482 -1.314 -0.333 1.411 1.102 0.408 1.364 +2 0.337 0.456 1.396 -0.860 1.963 0.657 -0.740 -0.717 -0.267 -0.145 -1.818 -0.186 1.598 0.836 -0.567 -0.006 -1.506 0.809 -0.414 -0.559 -0.197 -2.026 -1.022 0.330 -0.897 0.677 -0.436 -0.855 +2 -0.674 -0.370 1.399 1.507 -0.241 -0.067 1.052 1.068 0.366 0.663 0.250 -2.303 0.865 -1.315 -1.051 -0.244 -0.563 -0.618 2.062 1.119 0.384 1.021 1.716 -0.930 -0.427 0.421 0.047 -0.394 +0 0.501 0.174 -1.436 0.277 -0.278 -0.330 -1.578 0.160 -0.074 -0.123 -0.993 0.619 -0.728 0.073 -0.469 0.467 0.410 1.174 0.254 -0.416 1.172 -1.306 0.191 1.340 1.647 -0.119 0.728 -1.149 +3 -0.152 1.349 -1.215 -1.516 -1.904 -0.420 0.620 -0.735 -0.725 -0.393 -1.505 -0.192 2.335 -1.222 0.497 0.549 -0.235 0.302 0.358 -0.399 -0.004 1.057 0.616 -0.874 0.339 -0.778 -0.511 2.240 +0 -0.658 -0.895 -0.115 0.771 -1.171 0.793 -0.656 -0.175 0.228 0.608 0.125 -0.142 -1.339 -0.055 1.309 0.034 -0.732 -0.611 -1.296 -0.552 1.017 0.312 -1.886 0.270 0.596 0.215 -0.371 0.367 +0 0.192 0.836 -0.623 0.119 -1.316 -0.339 -0.562 1.058 1.706 -1.176 0.908 0.681 -0.549 -0.752 -0.764 -0.377 -0.359 -0.153 -0.510 0.806 0.509 -0.173 -0.539 -0.566 0.439 0.056 0.199 0.858 +3 0.932 0.444 -0.717 -0.618 -2.126 0.078 -0.141 1.318 -0.205 -0.436 -0.428 -0.912 1.360 1.677 -0.758 -0.609 -0.468 -0.631 0.387 0.686 -1.680 1.880 0.342 -0.968 -0.960 -0.684 0.169 2.493 +3 -0.592 -0.261 -0.602 -0.617 0.078 1.173 -0.729 0.487 1.328 -0.488 1.529 -1.623 -0.598 1.389 -0.046 0.361 0.272 2.445 -0.412 -1.733 -2.373 0.038 1.035 -0.884 0.490 -0.777 -1.342 -0.501 +2 -0.637 -0.844 2.410 0.543 -1.373 2.494 0.149 -0.719 -0.201 1.608 -0.711 -0.416 0.562 -0.230 1.373 -0.767 -0.685 -0.369 -0.267 0.347 -0.199 -0.718 0.503 -0.784 0.841 -1.922 0.247 -0.067 +2 -0.744 -0.220 -0.779 0.814 0.474 1.546 -0.062 0.396 0.307 0.542 0.448 -0.748 0.142 0.041 2.217 -1.715 0.795 1.541 -0.907 2.172 0.192 -0.187 0.118 0.213 0.269 -1.155 0.478 -1.703 +3 -0.108 1.241 1.238 0.476 0.270 -0.104 2.698 -0.685 -0.447 0.302 -0.907 1.049 -1.471 -0.714 -0.402 -1.005 1.041 -1.315 -0.963 -0.677 0.843 1.452 0.522 -1.314 -0.620 1.572 0.029 -1.446 +3 1.592 1.010 -1.241 -0.984 0.108 -0.034 -0.508 -0.108 -0.053 1.026 -0.467 -0.495 2.543 -0.779 -2.117 -0.286 -0.488 1.427 0.286 1.373 -0.642 -0.288 0.903 0.808 -1.413 -0.427 0.483 -1.367 +0 -0.110 0.225 0.889 0.974 -0.249 0.355 -0.114 0.901 0.770 0.674 -0.182 1.222 0.577 -0.388 0.117 -0.680 0.464 0.328 -0.094 -1.038 0.582 0.274 -1.310 0.656 -0.263 -1.045 0.530 -0.865 +1 -0.106 -0.775 -0.518 -0.018 -0.015 0.527 0.571 1.219 -1.506 0.913 -0.341 0.658 0.339 -0.577 1.520 0.761 0.421 2.190 -0.974 0.025 0.562 -1.067 -0.664 0.997 -0.496 0.872 -0.630 2.020 +4 -1.206 -0.037 -1.611 0.476 2.234 -0.640 -0.169 -0.119 0.986 0.406 0.920 -0.506 0.434 -3.368 0.667 -1.218 -0.328 0.745 -1.536 1.089 -0.124 1.453 -0.903 -1.813 -0.029 0.587 0.671 -1.135 +4 0.291 -1.143 -0.926 0.316 -1.875 2.203 1.749 0.601 0.568 2.109 1.459 0.166 0.293 1.049 0.801 0.183 2.006 -1.453 0.183 0.990 -1.814 -0.441 -0.243 -0.720 2.133 -0.167 0.509 1.794 +1 0.662 -0.106 -0.290 0.417 0.057 0.521 -0.068 -0.859 0.498 -0.111 1.004 0.704 -0.981 1.613 -0.092 -1.138 -0.593 -0.186 0.949 -2.145 -1.523 -0.931 1.574 0.987 -0.822 -0.700 0.908 0.231 +4 -0.283 -0.385 1.348 -2.921 -0.494 -0.565 0.620 1.015 1.070 -0.618 0.259 -0.524 1.041 0.276 -0.065 0.193 0.931 0.324 -0.297 -0.196 0.732 -1.024 0.953 -1.937 0.073 -0.665 2.851 -2.324 +3 0.525 0.482 -0.721 1.578 1.294 0.767 -0.515 -1.864 -0.928 0.116 -0.716 -0.020 -0.209 0.123 -0.847 1.407 1.487 0.841 0.849 1.495 -1.647 -2.223 0.737 1.777 -0.302 0.177 -0.469 0.155 +3 -0.046 -0.763 1.319 0.706 0.484 0.829 0.176 0.659 -2.491 -1.226 -0.697 0.716 0.197 1.740 -2.464 0.523 0.366 -2.000 0.868 0.125 -1.615 -1.117 -0.562 -0.228 1.346 0.394 0.360 0.711 +4 1.481 0.119 -2.674 0.408 1.081 -2.256 1.621 0.820 0.674 -0.294 -0.390 -0.908 1.297 0.883 -1.160 -0.099 1.632 -0.186 0.123 -1.747 1.221 -1.367 -0.768 0.845 0.001 -0.876 -0.808 -0.144 +1 0.640 0.452 0.754 -2.024 -1.553 1.005 -1.505 0.402 -0.352 -0.996 0.350 0.201 2.110 -0.625 1.440 0.352 -0.661 0.799 0.566 0.036 0.266 -0.597 -0.826 0.759 -0.224 -0.080 0.280 0.265 +4 0.320 1.115 -1.505 1.740 0.330 0.110 -0.952 -0.637 -0.378 -1.426 -0.315 -0.892 0.683 -3.046 -0.247 -0.968 0.003 2.354 1.674 -0.495 -0.185 -0.838 -0.151 0.736 -1.314 1.567 0.914 0.871 +3 -0.985 -1.514 1.187 0.649 0.868 -1.772 0.886 -1.636 -0.122 -0.744 -0.457 -1.628 -0.008 0.293 -0.407 0.617 -2.083 0.293 0.982 0.115 0.411 -1.417 -1.393 -1.412 1.576 -0.857 -0.495 -0.473 +1 0.934 -0.172 -0.290 0.161 -0.429 0.311 0.532 0.191 -0.643 0.645 0.952 0.659 -1.210 -1.371 -1.265 1.453 0.989 -0.488 -1.306 0.485 -1.816 -1.975 0.165 -0.395 0.561 0.841 -0.383 -0.908 +4 0.633 0.715 1.669 0.129 -0.946 -0.430 -0.403 -0.920 -2.513 0.231 0.034 1.914 1.850 0.680 0.563 -0.830 0.189 -0.117 0.146 -0.769 0.419 -1.369 3.035 1.447 1.327 -1.275 -1.621 0.275 +3 -1.018 -1.993 1.273 1.879 -0.147 0.932 0.697 0.847 -0.728 1.731 0.051 0.472 -0.027 -0.669 -0.444 2.723 -0.321 0.282 -0.466 0.516 0.518 -0.169 -1.587 0.588 1.208 -0.533 -0.124 -1.634 +0 0.405 -1.356 -0.162 0.067 0.339 -0.806 -0.357 0.524 -0.083 0.120 0.690 0.714 -0.530 -0.048 -1.626 0.666 0.076 2.119 -0.685 -1.746 -1.528 -0.093 0.558 0.001 0.698 0.542 0.033 -0.221 +4 -0.060 0.634 -0.741 -1.014 0.908 -1.436 0.931 -0.336 -1.211 1.366 -1.799 1.118 2.888 1.704 -0.308 1.537 1.138 -0.825 -0.975 0.250 0.544 1.041 0.996 0.222 -0.501 0.911 1.128 0.141 +1 -1.653 1.047 0.224 -0.440 0.120 0.007 -0.385 0.019 -0.095 -0.765 0.904 0.326 -0.432 0.310 1.378 1.529 1.755 1.151 -0.332 -0.819 -0.641 -0.051 0.348 -1.697 1.145 -1.526 0.933 -1.111 +4 2.718 -1.821 1.626 1.162 -0.583 1.883 0.467 -0.227 -1.780 0.102 0.041 2.239 -0.946 -0.886 -0.511 2.656 -1.150 1.308 -1.558 0.432 0.098 -0.700 0.718 -0.800 -1.615 -0.312 -0.917 0.259 +3 0.206 -1.834 -0.167 0.693 -0.024 0.118 0.461 0.088 -1.623 1.458 2.614 1.525 -0.214 -0.886 1.514 0.143 0.930 -0.992 0.511 0.387 1.385 0.818 0.167 1.892 -0.269 1.534 0.565 0.742 +1 0.456 2.504 0.494 -0.694 -1.385 -0.560 -0.457 0.829 -0.618 -0.992 0.538 -0.619 0.577 -0.846 -0.472 -0.582 0.411 -0.144 -1.222 -1.305 -0.316 -1.415 -0.414 0.256 -1.182 0.848 1.384 1.083 +3 0.257 -1.163 -2.549 0.137 0.220 -0.616 0.912 2.034 -0.399 -0.747 1.717 -0.221 0.659 -0.607 1.140 -1.244 0.145 0.296 -0.183 -0.636 1.793 0.809 -0.865 -1.884 1.479 -0.926 0.062 0.092 +3 2.133 -0.212 -0.355 0.149 1.672 0.854 0.899 -0.215 1.517 -0.229 -1.372 -0.067 -0.008 -0.982 -0.595 -0.700 1.258 -0.430 0.637 0.747 0.914 0.661 0.315 -2.394 -1.790 -0.857 -0.592 -0.665 +2 -0.901 -0.085 0.267 -2.193 -0.030 -1.330 -0.336 -0.857 -0.098 -0.448 0.207 0.748 0.561 1.855 -0.469 1.398 -1.012 -1.757 0.531 1.504 -0.810 0.527 -0.790 1.092 1.049 0.362 -1.134 0.398 +1 -1.638 0.487 -0.011 0.834 0.213 -0.813 -1.037 -0.402 -0.121 0.194 -0.571 1.706 0.535 0.874 -0.139 -1.038 0.883 -0.993 -2.023 0.566 -0.756 -0.334 0.694 -0.930 -1.602 1.219 -0.058 1.257 +1 -0.383 -2.124 -0.157 0.892 1.076 0.302 0.664 -1.092 -1.117 -1.419 -0.116 -0.546 -0.427 0.907 -0.076 0.368 -0.431 0.982 -0.790 -1.706 -0.173 -0.554 -1.016 0.669 0.519 0.474 -0.172 -1.692 +0 0.115 -0.532 0.559 -1.085 -1.306 -0.780 0.795 0.858 0.106 -0.207 -0.040 0.670 -2.067 0.411 0.764 0.762 0.319 0.559 1.585 -0.655 -0.045 -0.191 1.181 -0.327 -0.878 -0.697 -1.529 -0.211 +0 -0.676 -0.531 2.118 0.616 -0.560 0.192 -1.990 -1.047 -0.397 -0.077 -0.138 0.442 -0.153 0.236 -0.341 -0.324 0.466 -0.560 0.859 0.107 0.050 2.244 -1.102 1.470 -0.261 -0.066 0.252 0.027 +4 0.235 -0.254 1.144 0.527 -1.609 1.484 2.381 1.993 0.473 -1.094 0.165 -1.289 1.021 0.564 2.218 0.840 -0.822 1.363 -0.063 -0.140 0.317 -0.465 -0.569 2.438 1.235 0.639 0.070 -1.420 +2 1.126 1.602 0.978 1.271 -0.345 -0.636 -0.779 0.644 0.227 -0.077 1.295 0.405 -0.839 -0.681 0.682 1.042 0.552 1.897 1.635 0.441 -0.839 -1.310 -0.387 0.753 1.827 0.417 1.169 -0.245 +1 -0.724 2.929 0.979 -0.036 0.529 0.098 -0.157 1.715 0.235 -1.002 0.249 0.106 -0.070 0.381 -0.244 -1.880 0.549 -0.494 -0.167 -0.179 0.586 0.640 -0.620 0.619 -0.742 1.554 -1.125 0.582 +2 0.799 -0.644 -0.760 0.742 1.036 1.516 0.132 0.433 0.403 -2.134 1.185 0.827 0.355 0.285 0.136 2.123 -1.809 -0.458 0.037 1.195 0.367 -0.677 -0.846 0.192 -0.354 2.090 0.231 -0.701 +0 -1.380 -0.807 1.365 -0.203 1.188 -0.150 -0.968 -0.303 -0.968 -0.566 0.201 0.512 -1.590 -0.017 -0.587 -0.648 0.133 -0.550 1.382 1.188 1.182 -0.495 -0.962 0.082 -0.150 -0.609 -1.176 -0.042 +1 0.487 -2.143 -0.205 0.468 -0.668 0.636 -1.668 1.115 -0.924 -0.787 -0.183 0.173 0.070 1.216 0.334 -0.388 -0.026 -1.693 0.196 0.881 -1.886 0.985 0.022 0.559 -0.998 0.601 -1.351 0.532 +0 0.242 -0.310 0.118 -1.235 0.865 0.250 0.362 -0.279 -0.328 0.525 1.590 -1.394 -1.444 0.266 -0.332 0.124 -0.024 0.622 -0.286 -1.208 -0.790 -0.160 -1.255 -0.037 0.061 -0.358 0.209 -1.614 +0 -0.121 -0.894 -1.556 0.986 1.019 0.560 0.876 0.954 0.209 -0.888 0.572 0.737 -0.193 -0.119 -0.002 -0.408 0.117 -0.931 0.286 -0.956 -1.275 1.024 -1.327 0.752 -0.313 -1.863 -0.663 -0.448 +3 -1.625 0.173 -0.789 0.230 1.462 1.136 -0.313 -0.122 -1.889 -0.998 1.053 1.098 1.909 -0.810 -0.109 -1.181 0.023 1.235 0.600 -0.505 -0.005 -1.398 -0.830 0.412 1.354 0.025 1.757 1.421 +3 -0.836 0.460 0.230 2.014 -1.097 0.991 0.130 -0.974 -2.675 -0.636 -0.509 -0.512 0.902 -0.458 0.273 -1.394 0.389 -1.299 0.676 0.967 -1.499 1.923 -0.455 0.362 0.765 -0.674 -0.701 -0.424 +3 0.068 -0.332 0.254 -1.548 1.013 -1.507 -0.664 -0.453 0.249 1.620 1.031 -1.472 -0.886 -1.399 -1.300 -0.590 1.310 -0.246 -0.116 0.229 -0.940 -0.071 0.247 2.459 0.924 1.646 0.029 0.528 +4 2.332 -0.084 0.670 0.272 -0.597 -0.201 1.188 -0.212 1.034 0.873 1.092 0.566 0.620 -1.391 0.879 -1.212 1.547 1.271 0.607 1.122 -1.637 -0.542 -0.183 1.150 -1.316 -0.573 -1.689 -2.141 +2 -0.090 0.342 1.503 -0.276 0.981 -0.363 0.907 -1.711 0.157 0.853 -0.085 0.614 -0.938 -1.058 0.126 -0.568 1.044 -0.306 1.950 -0.827 -1.438 -1.041 0.209 0.112 0.293 -0.057 -2.761 -0.375 +1 1.500 -0.036 -0.630 -1.551 0.466 -0.806 -0.032 1.726 0.797 1.310 0.144 0.713 1.330 -0.829 -0.037 -0.689 -0.609 -0.148 -0.954 -0.370 0.686 -0.096 -0.776 0.786 -0.916 -1.104 1.201 0.845 +3 0.111 1.893 -0.046 1.736 1.151 0.581 0.168 -0.844 -0.636 -1.447 0.833 -2.895 -0.312 0.773 -0.430 -0.330 0.809 -1.231 -0.144 0.170 -0.705 -2.151 0.962 -1.444 -0.042 0.041 -1.202 0.193 +3 2.414 -0.369 -1.943 -0.453 0.490 0.390 0.838 -0.594 -0.280 -2.452 0.614 -1.310 0.200 -0.871 -0.087 0.958 -0.437 -0.740 0.003 -0.642 -0.942 1.867 -1.855 0.654 -0.136 -0.826 0.347 0.034 +3 -0.013 -0.268 0.415 0.329 -1.062 -0.110 1.270 2.457 -1.708 0.828 0.106 -0.592 -0.428 -0.940 -1.362 0.385 1.783 0.353 0.172 -0.624 -0.930 0.022 -1.029 1.123 -0.578 -0.843 2.251 1.299 +2 -1.182 0.715 0.961 -1.142 -0.325 0.026 1.078 1.529 0.153 -0.261 -1.444 0.108 2.075 1.290 0.175 0.303 -0.689 -0.357 0.148 0.151 1.011 0.962 0.521 -0.999 -0.961 -0.888 0.236 -2.026 +0 -0.755 0.054 -0.385 0.987 2.388 0.911 0.951 1.236 0.455 -0.399 -0.300 1.096 0.289 -0.982 1.309 -0.774 -1.127 -0.877 0.306 0.973 0.255 -0.557 -0.808 0.771 -0.093 0.341 -0.140 0.299 +2 0.732 -0.890 0.234 -0.980 -0.854 0.106 -1.743 -1.978 2.265 0.063 0.457 0.909 0.235 0.722 0.316 1.088 0.703 0.724 0.652 -1.240 -0.305 -0.440 0.866 1.056 0.594 -1.132 -0.159 -0.914 +1 -1.722 -0.749 1.495 -0.090 -0.572 -1.275 -1.170 0.982 -0.823 0.805 -0.981 -0.126 -0.069 -0.754 0.077 -2.116 -0.089 0.445 -0.835 0.213 0.153 0.194 -1.345 0.095 -1.722 -0.629 -0.004 0.208 +0 1.207 -0.243 -0.445 0.547 0.309 -1.605 0.540 -0.274 0.588 -0.519 -1.435 0.294 1.532 0.296 0.132 0.600 -0.081 -0.182 0.069 0.863 -0.870 1.098 0.420 -1.296 -0.137 -0.157 -1.384 -0.220 +0 1.260 -0.441 -1.551 -0.628 -0.157 -1.802 -0.849 1.016 -0.859 -0.874 0.518 -0.542 0.012 0.392 -0.619 0.772 0.691 0.325 1.511 0.879 -0.272 -0.762 0.155 -0.069 -0.059 -0.559 0.041 1.562 +1 -0.223 1.307 0.903 -0.442 0.648 -2.366 1.004 -1.532 -0.044 0.013 0.390 -0.929 0.167 0.161 0.836 -0.598 0.747 -1.585 0.923 -0.798 -1.188 0.993 -1.318 0.316 0.016 1.193 0.070 0.000 +0 0.223 0.686 -0.732 -0.850 0.830 -0.279 0.015 0.285 -0.329 0.489 0.167 -0.498 -0.959 0.831 -0.819 0.640 -0.647 -1.113 -1.107 -0.177 0.270 0.659 -0.800 0.174 0.904 0.475 1.565 0.789 +4 1.023 0.337 -2.398 0.994 -1.337 -0.012 -0.441 -0.641 0.047 0.894 0.031 1.410 1.534 1.525 -0.056 -0.478 -0.034 1.125 1.065 -2.595 0.306 -2.368 -0.019 -0.529 -0.837 0.849 -0.647 -0.010 +0 -0.221 -1.341 0.976 -0.587 -0.858 -0.496 -0.193 0.458 -0.875 1.010 0.484 -0.515 -0.160 -0.423 0.066 1.520 -0.433 0.189 -0.699 0.532 0.276 1.330 -0.829 0.691 1.264 -0.438 -0.127 0.191 +3 -2.071 0.678 -0.937 0.210 -0.330 -1.085 0.440 -1.268 0.407 0.039 -2.018 0.140 0.409 -1.036 -0.978 -0.004 1.593 -1.320 1.644 -0.361 0.852 0.064 -0.014 -1.119 1.410 -1.689 -0.870 0.376 +1 -0.117 0.434 1.724 0.200 0.195 0.179 -1.425 -1.447 -0.808 1.013 -0.335 -0.530 -0.547 -0.138 1.702 -0.151 -0.786 0.025 1.755 -0.503 -1.181 -0.501 0.129 0.688 1.007 -1.046 -0.951 -0.323 +1 0.528 1.176 0.014 -2.205 0.374 -1.133 -0.685 0.257 0.205 -0.338 -1.316 -1.910 0.476 0.238 1.313 -0.586 -0.469 -1.444 -0.174 0.545 -0.249 0.113 1.382 -0.080 0.699 -0.370 -0.099 -0.475 +1 -0.683 -0.903 0.548 0.706 -0.200 0.617 -0.067 -0.291 0.325 0.213 -1.061 0.937 -1.433 -1.309 -1.026 -0.022 1.566 1.288 -0.915 -0.107 -1.239 -0.694 1.929 -0.204 1.180 0.812 -0.681 -0.858 +4 -0.796 -2.548 -0.193 2.082 0.840 0.405 -0.599 -1.578 0.760 -0.218 0.481 -1.915 0.584 1.989 -0.770 -2.669 1.213 -3.149 1.256 -0.066 2.522 -1.368 0.388 -1.893 1.472 0.655 0.418 1.000 +0 -0.179 0.396 0.693 0.735 -0.986 -0.284 1.195 0.895 -1.373 0.351 -1.978 0.047 1.898 -0.948 -0.834 0.764 -1.543 -0.633 0.596 -0.634 -0.236 0.744 0.422 0.267 -0.339 0.370 -0.124 0.458 +2 -1.979 0.055 -0.804 0.132 0.638 -1.089 0.237 -1.167 -0.020 0.353 -0.582 0.365 1.237 0.808 -0.029 -0.903 -0.505 0.340 -0.969 2.082 0.955 -0.755 -0.505 0.886 0.212 0.643 2.826 0.828 +3 -0.151 0.764 1.815 1.220 1.771 0.679 0.022 -0.341 1.659 0.700 -1.129 0.463 -1.151 -1.846 1.193 1.246 0.771 1.489 -1.470 -0.638 -0.484 -1.061 1.388 -1.210 -0.763 -0.490 -0.118 0.665 +1 -1.990 0.332 -0.376 0.316 0.584 1.284 -1.507 -0.557 0.429 0.770 2.193 0.956 -0.057 -0.890 -0.564 -0.593 0.655 0.257 -1.324 -0.436 0.878 0.352 0.569 0.383 1.626 0.236 -0.386 -0.916 +2 -1.063 0.315 -1.645 0.023 0.446 2.443 -0.904 0.740 -1.218 1.149 -0.051 0.573 -0.905 0.538 -0.591 -0.126 -1.111 -0.355 0.275 -0.018 1.237 -0.577 1.597 -0.341 1.632 0.332 2.065 0.159 +0 -0.295 0.213 -0.223 0.303 0.675 0.543 0.364 0.286 0.289 -0.147 -0.660 -0.093 0.653 0.647 0.508 -0.475 -0.216 -0.213 -0.607 0.894 -1.841 2.022 2.074 0.050 0.405 0.849 -0.758 0.568 +4 1.271 0.259 2.024 0.489 1.874 1.460 1.628 -1.149 0.668 -0.083 -1.338 1.336 1.194 -0.145 -0.618 0.058 -1.814 -1.347 -0.300 0.616 -0.185 -0.556 -0.828 2.439 -1.074 -1.828 -0.576 0.518 +1 0.797 0.288 -2.222 -2.003 0.493 -1.101 0.240 0.490 0.196 -1.048 -0.081 -0.996 -0.154 -0.280 -0.663 0.652 1.658 0.214 -0.937 -0.856 -0.871 0.094 -0.842 0.439 -0.121 -0.542 1.069 -0.606 +2 0.141 -0.479 0.789 -1.161 -1.231 -1.798 -0.358 1.147 0.515 0.573 0.326 -1.638 -1.805 -0.561 1.015 0.395 -0.160 -0.421 0.616 -0.242 1.249 0.471 1.269 0.771 0.503 1.238 -0.061 -2.131 +0 -0.643 -0.772 -1.076 -0.557 -0.353 0.092 0.537 0.218 -0.124 0.172 0.228 0.239 0.291 -0.417 -1.371 0.205 0.031 0.845 -0.442 1.579 0.974 0.816 -1.176 0.712 -0.971 -0.018 0.456 -1.060 +4 0.270 -0.281 0.774 -0.364 -2.009 -0.301 -0.353 0.163 -0.439 -0.216 -0.775 -1.760 1.055 -1.210 -0.975 2.131 -0.511 -0.845 1.095 -1.981 -2.714 -0.464 0.381 1.672 0.739 1.132 0.682 -0.009 +0 0.371 0.006 -0.645 0.539 -0.015 0.849 -1.713 0.327 -0.834 0.947 -1.145 0.630 -0.582 -0.435 0.850 -0.796 -1.501 0.073 -0.661 -1.371 0.350 0.290 0.117 0.508 -1.995 -0.763 0.278 1.036 +0 0.061 1.299 0.556 -0.348 -0.922 -0.057 0.512 1.161 0.188 -0.008 -1.851 0.873 1.051 -0.330 0.725 -0.312 -0.163 0.062 -0.867 1.843 -0.933 -1.035 0.774 0.211 -0.197 -0.446 -0.173 -0.837 +2 0.841 0.208 -1.293 -0.532 -0.607 -0.077 0.426 0.418 -1.776 1.064 0.253 1.385 0.444 1.101 0.467 1.346 0.522 -0.098 2.272 0.889 0.574 -1.276 -1.288 0.293 0.146 -0.614 0.141 1.589 +3 0.671 -1.572 1.818 0.730 -0.659 0.664 -0.229 0.235 1.180 0.702 0.888 -0.968 2.114 -0.539 -0.050 0.764 -1.510 -0.139 -0.138 -1.009 1.401 0.227 -0.332 1.314 1.713 -2.186 0.150 -1.019 +2 1.611 0.528 -0.630 0.247 0.173 -1.577 -2.358 0.723 -0.949 1.157 0.361 -0.084 -0.115 -0.361 -1.350 -0.444 -1.424 0.879 0.467 -0.203 0.313 1.036 -2.182 -0.206 -0.246 0.007 -0.591 0.628 +3 0.034 -1.416 0.073 -0.847 0.560 1.398 -1.740 0.614 -0.057 -0.836 1.026 0.209 0.303 0.728 0.409 2.452 1.410 -1.773 1.248 -0.234 -0.613 -1.188 1.464 -1.254 0.048 1.117 0.261 -0.839 +0 -1.909 -1.691 0.990 -1.179 0.195 0.495 -0.177 -0.479 -0.323 -1.303 0.810 1.098 0.204 -0.858 -0.059 -0.202 -0.597 0.023 -0.286 1.135 -0.911 -0.220 -0.867 -0.052 0.256 -0.266 0.877 1.664 +4 0.132 2.050 0.179 1.457 -0.103 0.955 1.844 -0.929 -1.383 0.400 1.077 -0.048 -0.456 -0.149 0.151 0.322 -1.492 -2.858 0.983 2.059 -1.858 -1.464 0.479 0.633 0.384 -0.714 -1.198 1.601 +2 0.909 -0.645 -0.962 1.099 1.831 0.062 -1.354 -0.016 -1.106 -0.449 0.052 0.818 0.188 -0.750 1.538 0.682 1.605 0.019 0.466 -1.268 0.829 -1.041 -0.533 -0.444 0.287 -0.996 2.451 -1.168 +4 0.458 0.584 1.396 1.243 1.231 0.874 0.797 -0.403 -0.554 -1.543 1.399 0.048 0.334 -2.813 -0.658 0.974 0.331 -0.662 -0.117 1.979 -0.236 0.121 -0.007 0.208 1.204 2.482 1.477 0.233 +2 0.092 1.114 -2.334 -0.648 -1.267 0.022 -0.270 -0.808 -0.361 -1.438 -0.719 1.119 0.916 -0.441 -0.817 1.196 -0.273 0.860 0.449 -1.816 1.134 1.548 0.738 0.707 -1.168 -0.376 0.377 -0.047 +3 0.720 1.483 1.217 0.882 0.896 -0.344 -0.376 1.425 -0.633 -0.390 1.060 -1.230 1.266 -1.970 -0.146 -1.089 0.871 -1.761 -1.406 1.073 0.615 -1.411 0.826 0.900 -0.580 0.386 -0.337 0.094 +3 -0.068 1.308 0.922 1.654 -1.577 -0.497 -1.132 -0.045 0.295 1.537 1.473 -1.474 -0.310 1.453 0.129 -1.802 1.654 0.239 -0.609 0.643 -0.117 -0.573 -0.896 0.073 -0.941 0.121 -1.636 -1.453 +0 0.630 -1.210 0.321 1.465 0.497 1.180 0.980 -1.138 1.780 1.254 -0.283 1.296 -0.128 -0.089 -0.894 -1.035 0.149 -0.253 -1.487 0.350 0.511 0.299 0.290 0.408 0.167 0.349 0.506 -0.585 +1 1.134 -0.744 -1.387 0.096 0.497 -0.520 -0.677 -2.188 0.436 1.476 -1.249 -1.283 -1.304 -0.641 0.525 -0.405 0.481 -1.510 -1.080 -0.789 -0.339 -0.316 0.302 -0.212 0.598 0.614 -0.745 -0.711 +1 0.431 2.188 -0.478 1.671 -0.860 0.489 0.178 -0.126 -0.006 -0.623 0.904 -0.843 0.318 0.756 1.384 0.972 0.572 -0.638 -0.786 -1.139 -1.234 -0.001 -0.727 1.199 -0.579 0.094 -0.265 -1.404 +3 -0.688 0.992 0.245 -1.537 -1.666 1.439 -0.149 -0.079 -0.226 0.240 0.679 -0.518 1.174 -1.401 1.370 0.396 -1.210 1.573 0.415 1.000 -2.103 -0.778 -0.244 0.054 0.839 0.893 -2.324 -0.619 +4 0.207 -0.266 -2.221 0.508 -1.329 1.216 -0.304 1.399 0.283 -0.047 -0.944 -0.397 0.578 1.479 0.663 1.580 -2.123 1.433 -2.495 -1.984 -0.039 -0.569 -1.235 1.380 -0.134 1.071 1.889 -0.145 +3 0.524 0.708 0.387 -1.516 0.229 -0.609 -0.963 0.500 1.659 1.493 0.622 -0.030 -0.216 -0.905 -0.270 0.909 1.823 -0.816 -1.995 -0.531 0.904 2.072 1.310 1.285 -0.850 -0.821 -1.324 -0.665 +4 1.728 -0.294 0.624 0.715 0.502 0.507 -1.216 -1.699 -1.843 -1.008 0.470 2.640 1.874 1.415 -1.100 1.039 0.129 -1.369 1.708 -0.549 1.779 0.638 -0.524 -1.385 0.296 1.225 0.114 1.026 +1 -0.614 -0.099 -0.618 -1.380 0.252 -0.342 -1.453 1.959 0.554 1.288 -1.491 1.130 -0.650 0.759 -0.145 -0.315 -0.796 0.076 0.465 -0.619 -0.947 0.301 0.837 -0.490 0.499 0.265 -1.603 -0.828 +1 0.840 -1.076 1.537 -0.880 -0.634 0.100 0.940 0.677 -0.049 0.284 1.073 0.008 -0.812 -0.603 0.909 -0.811 -1.568 -0.304 -0.075 0.860 -1.458 0.346 1.151 -0.817 0.082 -2.151 0.873 0.796 +1 2.127 -0.588 -0.072 0.136 -0.423 1.423 1.209 0.818 0.961 -0.685 -1.148 -0.677 0.280 -0.325 0.569 0.108 -1.339 1.241 -0.511 0.786 -0.848 -0.881 -0.929 -1.432 -1.408 0.381 -0.928 0.346 +0 0.473 0.066 -0.219 0.812 -0.112 0.717 -0.287 -0.466 -0.638 -1.214 0.395 0.244 -1.585 -1.897 0.301 -0.828 -0.589 -0.206 -0.551 -0.352 1.052 -0.401 -1.613 -0.325 0.240 1.264 0.557 -1.655 +1 1.490 1.013 -1.460 1.073 0.737 -1.128 -0.350 -0.009 0.162 0.253 0.180 0.459 -0.573 0.859 -0.501 0.483 -0.483 -0.213 0.118 0.659 -0.639 -2.068 0.337 -0.764 0.304 0.653 -0.747 2.730 +0 -0.381 -1.007 -0.205 -0.417 0.169 -0.279 0.447 0.174 1.274 -0.534 1.780 1.067 1.487 0.253 -0.281 -0.538 1.752 1.097 -1.008 0.978 -0.825 0.324 0.826 1.458 0.238 -0.134 0.044 1.067 +0 0.042 -1.191 -1.116 0.232 -0.666 1.341 -0.192 0.719 0.443 -0.601 -1.980 1.185 -0.979 -0.404 -0.151 -0.051 0.962 0.006 -0.127 1.018 0.601 -0.736 -1.147 -0.824 -0.049 -1.246 0.091 -0.129 +1 1.095 0.516 -1.581 1.279 1.129 -0.497 -0.463 -0.028 -0.449 0.392 1.168 0.496 -1.691 1.416 -0.332 -0.160 -0.606 -0.394 -1.269 0.607 0.136 -1.105 1.256 -1.465 -0.663 0.200 -0.550 -0.430 +3 -0.229 -0.838 -0.399 0.307 -0.913 -0.621 -1.753 0.279 -1.276 0.028 0.706 1.271 -0.321 0.625 -0.007 -1.872 -1.486 0.847 1.158 -2.119 0.334 0.269 -0.803 -0.792 -1.056 -1.921 -1.054 0.696 +3 -1.284 1.493 0.869 -1.909 -0.760 -1.233 0.434 -0.534 0.024 1.735 1.444 -0.958 0.458 1.827 -0.915 0.968 -0.168 -0.941 1.514 -0.428 -0.150 -0.355 1.053 0.983 -0.742 0.116 0.369 0.975 +1 0.529 1.693 0.353 0.103 1.825 0.085 -1.568 0.663 -1.093 -1.221 0.158 0.561 -0.924 -0.688 0.825 0.369 -1.079 -0.265 0.583 -1.468 -0.870 0.213 -0.725 1.513 -1.464 -0.518 0.007 0.657 +2 0.030 -0.691 1.324 -1.220 -0.362 -0.506 0.615 0.048 -0.108 -1.844 0.865 -0.218 0.116 -0.749 -0.906 1.499 -1.773 -1.054 1.480 0.641 -0.325 1.247 0.447 0.233 0.226 0.465 0.506 2.196 +0 -0.058 0.937 0.773 -0.204 -0.100 0.363 0.263 0.744 1.063 0.170 1.530 0.271 -1.134 0.329 0.348 0.064 0.569 -0.964 -0.361 -0.712 0.914 -0.962 0.214 -0.210 -0.459 1.334 2.351 -0.475 +1 -0.486 0.618 -1.202 -1.539 0.087 -1.470 -0.618 -0.772 -0.250 -0.670 -1.031 -0.205 -1.559 0.511 1.040 0.525 -0.518 0.714 0.987 -0.009 1.130 -0.433 0.061 -0.936 1.250 -1.562 -1.014 -0.472 +4 0.685 0.813 -0.566 0.075 -0.748 2.914 -1.827 -0.102 2.084 -0.874 1.781 -0.891 -1.241 1.617 -0.387 -0.712 -1.299 1.852 1.302 -0.530 0.745 -0.379 -0.920 0.678 -0.317 -0.071 1.095 0.063 +2 -0.699 0.300 -0.426 1.402 -0.284 -1.395 1.470 -1.373 -0.231 -2.497 1.008 0.259 -0.575 0.402 0.736 -0.250 -1.031 1.051 0.565 0.300 -0.126 -1.370 0.221 0.322 0.419 -1.215 -1.527 1.296 +1 -0.718 -0.231 1.180 0.194 -0.531 0.484 -1.102 0.681 0.409 -0.308 -0.839 -0.887 0.535 1.229 -0.637 0.458 -2.087 -0.585 -0.031 -0.910 -0.937 -0.668 0.292 -0.187 -2.238 -2.121 -0.607 0.458 +2 -1.207 -1.485 -0.396 -0.229 -1.643 0.491 -0.781 -1.309 0.650 -0.429 1.058 0.459 -0.198 1.098 0.420 -0.020 0.512 0.544 -1.094 2.358 0.068 0.088 -1.757 0.129 0.172 -1.150 -1.040 -0.899 +4 -1.265 3.713 1.180 0.069 -0.844 0.562 -1.217 0.325 0.837 -0.161 -0.600 -0.515 0.969 -0.048 1.655 -1.827 -1.268 0.084 0.452 -0.067 1.402 -0.945 0.017 1.084 1.782 -0.204 2.685 0.092 +2 1.507 -0.726 2.150 -1.005 1.084 -0.722 -0.871 -0.745 -0.052 0.580 0.670 -0.944 -0.990 -0.917 -0.111 -0.210 -0.950 0.270 2.263 -0.505 -0.434 0.560 -0.559 0.669 -0.719 1.832 0.345 -0.620 +3 0.997 1.182 -0.066 -0.358 1.227 -1.210 0.439 -1.370 1.065 -0.258 1.269 0.491 -1.223 1.425 1.551 0.882 -0.762 -0.306 0.145 -0.704 1.692 0.144 -2.703 0.339 -0.348 -0.075 -0.369 0.807 +4 0.255 0.372 -0.730 -0.329 -0.838 0.019 -0.005 0.698 1.226 -0.061 -2.235 1.354 0.658 -0.849 4.202 0.083 -0.626 -0.431 0.997 -0.600 -1.475 1.263 -0.021 -0.500 0.903 0.129 -0.318 -0.250 +4 -0.295 -0.063 -0.850 -0.374 0.305 -0.787 2.086 -1.580 -0.760 -0.493 -2.595 0.733 0.253 1.352 -2.218 -0.793 -1.319 1.083 0.273 -0.639 -0.214 0.899 -0.730 -1.878 -0.938 -0.594 -1.362 0.413 +3 1.286 -0.316 -2.130 -1.441 -0.607 -2.040 0.306 1.124 -0.469 0.117 1.872 -0.631 -0.614 -0.506 0.728 -0.877 -0.069 1.578 -1.217 0.673 1.092 0.841 1.001 -0.707 0.925 -0.846 -0.063 -1.310 +0 1.447 0.445 0.768 -1.030 0.246 0.810 -0.674 0.628 -0.204 -0.645 1.013 1.138 0.129 0.451 1.325 -0.395 0.349 -0.386 -1.018 -0.395 -1.125 0.426 -0.772 -1.397 0.743 0.678 -0.330 0.703 +2 0.173 -1.197 1.818 -0.086 1.386 0.449 0.306 0.399 0.310 -1.500 -1.505 1.036 0.087 0.092 -1.278 -1.341 1.377 -0.406 -0.983 1.328 -0.641 0.369 -0.152 0.719 1.659 1.011 -1.166 -0.610 +4 0.992 0.030 -2.225 -1.676 0.944 1.603 -0.511 0.321 0.873 0.710 0.131 0.372 0.918 1.688 0.529 1.293 0.383 -2.102 2.774 -0.413 -1.296 0.693 1.446 0.632 0.684 -1.110 -0.743 -0.169 +0 0.697 -0.145 1.210 -0.027 0.655 -0.237 -0.055 -1.025 0.205 0.948 2.205 -1.178 -1.485 0.320 -0.622 1.274 -0.228 -0.371 -0.159 0.753 -0.183 -0.528 -0.984 0.644 0.706 -0.775 -0.894 0.285 +1 0.379 -0.604 -0.871 -0.235 1.513 1.251 0.720 -0.945 -0.067 1.323 0.024 0.977 -0.939 -1.048 -0.952 -1.656 -0.854 -0.114 -0.007 -0.815 -0.387 -1.642 1.079 -0.346 -0.088 -1.731 0.719 0.254 +4 -0.643 0.837 -0.108 1.798 1.674 -0.252 -1.296 -0.826 2.080 0.255 2.126 1.814 1.633 -0.470 1.237 0.693 1.438 -0.829 0.082 0.951 1.704 -1.633 2.221 -0.181 -1.412 -1.351 -3.622 -1.096 +4 -0.726 0.166 -0.321 -0.629 -0.082 -1.900 -0.080 1.774 0.803 0.138 0.631 -0.671 -2.634 -0.286 -0.193 0.421 -2.652 0.418 2.858 0.227 2.009 0.302 -0.586 -0.239 0.647 1.331 1.063 -0.684 +1 1.415 -0.874 -0.112 -0.453 -1.168 -0.328 1.108 0.567 0.644 0.146 0.523 -0.801 -0.310 0.192 0.278 0.320 -0.138 -0.377 3.158 0.907 0.413 -0.233 -0.451 -0.131 0.293 -1.880 -1.569 -0.637 +3 -0.257 -1.107 0.484 -0.892 0.708 0.294 -1.362 -0.551 1.413 -0.973 -0.728 -0.029 0.913 -0.163 -1.431 1.147 2.450 -0.299 0.792 -0.823 1.245 1.300 0.776 0.874 1.131 0.076 -2.095 -0.085 +4 -1.998 0.800 -0.560 0.907 2.196 -1.453 0.098 -0.420 0.395 -2.107 -0.358 -0.819 1.627 1.859 -0.286 -1.147 -0.839 0.039 -1.401 0.519 -0.196 0.356 0.394 1.379 0.635 0.293 0.249 -1.978 +3 -0.092 -1.421 -1.515 1.024 0.322 -0.921 0.348 0.026 -1.222 -0.110 0.270 -1.684 0.889 0.210 1.702 1.271 1.613 -0.705 0.592 0.073 1.546 1.370 1.683 -0.241 -1.229 -0.899 0.417 -1.221 +0 -1.611 -1.357 0.115 -1.075 0.089 0.902 1.432 -0.915 -1.084 0.493 0.826 -0.663 -0.822 0.786 1.055 0.017 0.564 -0.807 -0.544 -0.191 -0.600 -1.637 -0.566 0.372 -0.422 0.851 -0.376 -1.017 +0 0.397 0.786 -0.947 -0.301 1.477 -0.722 0.067 -2.624 -1.482 0.158 1.228 -1.362 0.075 -0.376 0.953 -1.218 0.430 -0.654 0.223 0.069 -0.252 0.478 0.044 -0.133 -0.356 0.078 0.222 -0.989 +4 -1.260 -0.711 -1.528 -1.698 1.389 1.406 0.623 0.369 -1.740 0.383 -1.468 -1.876 -2.149 -1.183 -0.925 0.490 -0.618 -0.245 0.510 -2.478 -0.962 -0.103 -1.083 0.950 -1.008 0.500 0.402 -0.166 +4 2.137 0.336 1.390 0.427 -2.228 -0.836 -1.262 -1.860 0.232 0.161 -1.080 0.352 -1.312 0.980 1.721 1.782 0.252 -0.585 -0.401 -0.362 1.733 0.767 -1.189 1.464 -0.509 0.231 1.160 0.028 +0 1.113 1.145 1.393 0.596 -1.338 -0.461 0.104 0.980 -0.246 -1.518 -0.226 0.242 -0.235 0.204 0.445 0.272 -0.473 0.053 -0.687 -0.357 0.680 0.787 -0.359 -0.878 -0.106 -1.233 1.646 0.087 +2 -1.375 -0.655 -0.823 -0.988 0.641 -1.664 -1.281 0.793 -0.251 -1.081 0.480 -0.232 0.258 0.200 -0.247 -0.836 -0.299 0.321 0.418 0.314 -0.334 0.339 -1.469 0.402 0.808 -3.087 1.050 1.563 +2 -1.419 -1.518 1.671 1.611 0.275 -0.810 -0.549 -0.869 2.078 -0.204 0.812 0.953 -1.285 0.079 -0.281 1.288 0.603 -0.054 0.147 -0.686 -0.949 -0.756 0.495 0.919 0.632 -1.566 0.324 0.613 +0 0.718 -0.306 -0.664 -1.406 -1.720 -0.703 0.888 -0.557 0.400 -0.603 0.639 -0.352 -1.232 -0.269 1.834 1.790 0.487 -0.137 0.304 0.317 -0.040 0.441 0.208 -0.316 -1.195 -0.460 0.800 -0.031 +4 -0.402 -0.481 -0.779 -1.537 0.755 -0.735 0.370 -3.382 -0.179 0.026 -1.078 0.263 1.136 1.105 1.278 0.535 -0.715 1.318 -0.388 -0.816 -0.917 2.025 0.502 1.197 -0.911 -0.790 -0.028 -1.955 +0 -0.770 0.544 0.682 2.380 1.126 0.243 -0.620 0.294 0.327 -0.513 0.784 -1.398 -0.244 0.556 -0.288 -0.207 -0.138 -0.836 1.174 -1.018 0.318 0.414 -0.949 0.073 -0.910 1.391 -1.139 -0.654 +1 -1.201 -1.040 0.212 -0.804 -0.778 -1.254 -1.235 -1.262 -0.080 1.042 1.502 0.053 1.061 -0.783 0.568 -0.872 0.435 -0.820 -0.739 -0.053 1.619 0.854 -0.730 -1.054 -0.818 0.644 0.218 0.652 +2 -0.644 0.511 0.814 -0.767 0.565 0.251 -0.829 0.004 -0.547 1.240 -0.649 1.509 0.131 -1.746 -2.173 -0.209 1.334 1.231 0.166 -0.737 -0.417 0.207 1.087 0.569 0.968 0.644 0.939 -2.261 +2 -0.280 -1.279 0.515 -0.834 2.184 0.571 -0.581 -0.611 -0.093 -0.240 1.117 0.186 -0.732 1.890 0.049 0.769 -0.608 0.363 0.311 -1.890 2.015 1.291 -0.397 -1.102 0.348 0.087 0.356 0.192 +0 -0.369 0.195 0.639 -0.753 0.373 0.612 -0.933 0.063 -0.085 0.074 -0.530 -0.135 0.031 1.864 0.045 1.338 1.414 -1.319 -0.291 1.416 0.181 -0.116 0.750 1.068 -0.029 -0.278 -2.364 0.047 +4 -0.154 -0.624 -0.608 -1.783 0.306 -1.270 1.122 -1.046 -0.964 -1.172 0.977 -0.012 -0.106 -0.147 0.996 1.505 -0.290 -1.585 0.628 0.485 -1.092 -2.692 -0.854 1.966 -1.363 0.205 1.406 -1.101 +2 2.658 0.882 0.020 -1.347 -1.262 1.158 0.383 -1.585 -0.489 -1.139 1.116 1.060 0.093 -0.046 1.097 1.449 -0.296 0.549 0.168 -0.725 -0.523 -0.841 0.192 -0.066 0.281 1.178 -0.097 1.365 +1 0.640 0.407 -0.795 0.183 -1.355 0.360 -1.340 1.210 2.101 0.289 0.388 0.588 0.277 -1.096 1.413 0.450 0.560 1.009 0.965 1.234 0.454 -0.859 0.417 -0.025 -0.875 1.452 -0.150 0.228 +2 0.622 -0.169 0.034 0.087 0.278 -0.136 -0.458 1.707 -1.235 0.918 -1.781 0.435 -1.645 1.871 -0.751 0.147 0.944 -1.087 0.101 -0.288 0.993 1.029 0.351 -1.339 -0.501 -0.088 1.258 -1.388 +4 0.634 -0.155 1.035 0.429 -1.517 0.996 -0.866 0.760 -1.616 1.599 -0.275 -1.109 -0.689 -0.337 -0.006 1.318 0.070 -2.171 -1.230 2.758 0.135 0.951 -0.308 -1.005 -0.481 0.128 -0.460 1.918 +4 1.978 -1.432 -0.724 -0.980 -0.811 1.901 1.550 -0.792 -0.591 -2.716 -0.005 -0.830 -0.530 0.266 -0.311 -0.601 0.951 0.414 1.420 -0.824 1.467 1.655 -0.381 -0.779 0.069 -0.444 2.095 0.138 +3 -0.726 1.473 -1.179 0.984 -1.611 -0.290 0.780 1.302 -2.319 0.364 0.942 -0.582 -0.522 -2.405 0.214 -0.192 -0.678 -0.521 -0.961 0.327 0.048 0.383 0.082 0.738 1.363 -1.012 -0.991 -0.354 +3 -0.090 0.234 2.071 -0.990 -0.308 2.413 0.303 -1.366 -2.221 0.323 -0.559 -0.675 0.526 -0.769 0.032 0.994 0.222 1.318 -0.966 -0.447 0.453 -0.630 0.413 0.393 1.367 1.208 0.989 -0.818 +3 -0.606 -0.775 -1.195 2.834 1.421 -0.656 1.732 0.533 0.474 -0.718 -0.514 -0.042 -1.966 -0.487 0.004 1.310 -0.617 0.812 -2.060 1.167 -0.900 0.130 0.406 0.210 -0.107 -1.242 -0.890 0.996 +0 -1.320 0.004 0.877 0.709 -0.086 -0.344 -1.158 0.183 0.804 2.065 -0.456 0.847 0.764 -0.476 0.721 -0.816 0.257 -0.211 0.444 0.523 -0.529 -0.010 -0.129 -1.089 -0.370 -0.579 -1.127 -0.804 +2 1.131 -0.266 -1.464 -1.810 -0.096 -1.014 -0.210 -0.375 1.468 0.837 -0.564 -0.416 0.930 0.119 0.317 1.181 -0.772 0.952 -0.394 -2.245 -1.168 -0.928 1.340 -0.125 -0.304 1.495 -0.778 -0.395 +0 -0.307 -1.330 -0.578 0.894 0.198 0.209 1.044 0.377 -1.077 0.540 0.026 -0.133 0.478 -1.275 -0.414 -0.313 1.536 -0.488 0.614 -0.479 -0.296 0.476 -0.779 -1.308 -0.663 0.373 0.083 1.451 +1 -0.942 0.707 -0.213 1.134 -0.485 -1.351 0.976 -0.230 -0.025 0.266 -0.982 -0.292 -0.535 -0.395 1.250 -0.776 -0.826 1.399 -0.985 0.064 0.685 0.678 -1.405 -0.715 -1.744 0.833 1.049 1.007 +4 0.909 0.073 -0.059 -1.135 -0.283 -1.952 -0.872 0.320 0.050 1.653 -1.427 1.957 0.265 0.656 1.562 0.663 -2.822 2.059 -0.497 -0.539 0.904 -0.924 0.446 -2.091 -0.081 0.389 -0.833 -0.939 +1 1.426 -1.539 1.496 -1.001 -0.947 1.081 -0.406 0.450 0.133 1.521 -1.564 -0.174 -0.281 0.684 0.661 -0.304 0.592 1.448 -0.218 0.271 0.524 0.972 1.754 -0.096 0.472 0.078 -1.364 0.563 +1 -1.163 0.526 -1.212 -0.779 0.234 -0.438 0.823 -0.661 -1.475 -0.231 -2.589 -0.319 -1.253 0.727 0.181 0.556 0.841 0.695 0.637 0.774 0.934 -1.254 0.746 0.193 0.392 1.611 0.432 -0.211 +3 -0.427 -0.816 -1.901 0.646 -0.266 -0.388 0.182 -1.578 1.771 -1.821 1.776 -0.015 -0.386 0.671 -0.346 0.932 0.308 0.713 1.040 -0.174 0.965 0.518 1.334 -0.367 1.366 -0.773 -2.007 -1.365 +4 -1.750 -2.410 0.737 -1.122 0.329 0.874 -0.466 1.343 1.250 1.289 1.711 1.478 1.045 -2.086 -0.178 0.696 -0.103 -0.979 0.687 2.129 0.161 -0.501 1.175 0.032 0.889 -1.891 -0.530 -0.246 +0 0.023 1.027 -1.208 1.635 0.902 -0.071 -0.149 0.481 1.521 0.102 0.144 0.018 1.782 -0.369 -0.030 1.463 0.147 1.137 -0.911 -0.205 1.484 -0.676 -0.151 0.257 -0.989 -0.414 0.113 0.047 +4 -1.250 -1.756 -0.829 -1.412 -1.333 1.383 0.569 -1.290 1.130 0.257 0.349 -1.186 -0.580 0.139 -2.189 0.058 -1.116 0.241 0.226 1.119 1.483 1.059 -1.681 -1.969 -1.569 -0.737 -0.373 -0.550 +1 1.561 0.443 -0.032 -0.916 -0.602 2.185 0.472 -0.238 0.077 0.138 -0.052 -2.459 0.848 1.782 0.287 0.450 -0.824 -0.382 0.379 0.177 -0.175 1.052 0.178 0.568 -0.527 0.867 -1.035 0.000 +3 2.606 0.283 0.167 0.387 -0.593 -0.978 -0.761 -0.545 0.561 -0.378 0.619 -1.262 -0.644 -0.712 -3.817 -0.458 -1.038 0.457 0.733 -1.400 -0.245 -1.280 0.324 -0.687 -0.534 -0.217 0.286 0.055 +2 0.686 -0.007 -0.738 -0.581 1.823 0.178 1.330 0.242 -0.453 0.584 2.158 -0.644 0.914 0.191 -1.450 -1.472 -0.452 0.347 0.878 -1.571 0.653 -0.118 0.003 0.589 1.746 0.304 -1.357 -0.187 +1 0.943 1.658 -0.045 0.014 1.252 -1.707 -0.930 -0.152 -0.191 0.612 -0.458 0.196 1.681 0.736 2.478 -0.316 -0.120 0.589 0.012 -0.209 -0.625 0.221 -0.495 0.176 -0.628 0.613 -0.683 -0.575 +0 1.345 0.312 1.321 0.702 0.657 -0.770 -1.263 0.539 0.814 -0.807 0.117 0.493 1.579 0.971 -1.269 1.411 0.241 -0.052 -0.828 0.603 0.696 -0.922 -0.267 -0.765 -0.508 -0.061 -0.109 1.058 +4 -2.952 0.923 0.360 -0.091 -1.291 -1.660 0.751 0.242 0.223 -0.102 0.850 -0.586 -0.336 -0.177 -0.214 1.337 -1.254 -1.955 -1.454 0.684 -0.101 -1.599 0.100 -0.324 0.559 0.794 2.518 -1.182 +2 -0.357 -0.718 0.014 -0.558 0.100 -1.487 -0.214 -1.096 -0.641 0.143 -1.604 -0.822 0.569 -1.413 1.778 1.204 -0.495 -0.787 0.202 -1.447 -2.002 -0.459 -0.134 0.094 -0.615 1.274 -1.495 -1.138 +1 -0.062 -1.013 1.037 -0.427 1.201 1.310 1.266 0.569 0.679 0.705 0.351 -0.991 -1.882 1.166 0.105 -1.065 0.049 0.197 -1.014 -0.993 0.246 -0.273 -0.075 -0.361 -1.444 -0.453 -1.497 0.841 +2 -0.511 0.638 -1.687 0.293 -0.728 -0.565 1.620 -0.811 0.475 0.008 0.895 -1.103 -1.490 -1.232 -1.525 -0.399 -0.175 0.626 0.581 -1.549 0.511 -0.747 0.602 -2.496 -0.235 0.220 -0.453 0.085 +3 -0.228 -0.546 0.507 -1.413 -1.037 0.304 -0.032 1.498 0.290 1.086 2.213 -0.235 -0.711 0.964 -0.906 1.910 1.259 -1.299 -1.959 1.494 -0.904 -0.526 -1.315 0.296 -0.824 -0.901 0.861 -0.426 +1 -0.995 1.014 -1.274 -0.180 -1.385 -0.097 -1.061 0.664 -0.244 -0.809 -0.956 0.134 0.051 -0.552 -0.318 -0.493 -2.179 -0.039 -1.556 -0.887 0.321 -0.240 1.528 -0.797 -0.411 0.162 -0.037 1.048 +3 -0.793 0.496 0.588 -0.099 0.886 0.559 -0.797 -2.413 0.161 0.536 -0.064 0.606 0.293 -0.948 -1.456 -0.881 0.257 0.977 -0.515 -1.260 -1.989 1.417 -0.636 1.518 1.839 0.713 -0.782 -1.016 +4 0.950 1.944 0.277 0.936 -0.799 -0.021 -0.063 0.385 0.078 2.777 0.800 1.299 0.271 -2.126 0.448 1.095 1.393 -0.067 -1.435 -0.289 -1.450 1.287 0.454 -0.512 0.742 1.469 0.784 2.000 +0 -0.761 1.785 1.110 -0.001 -0.769 -0.611 -0.262 0.432 -0.619 0.003 0.539 1.227 0.051 1.142 -0.258 -1.473 -0.543 -0.304 0.898 -0.403 -0.460 -0.440 1.590 0.395 0.136 -0.816 -0.173 -2.125 +2 -0.796 -0.879 0.643 -0.141 1.295 0.273 0.434 -1.006 0.698 0.528 1.407 1.004 0.065 0.524 0.490 1.224 1.130 -1.666 1.219 -0.915 -0.539 -1.999 1.782 0.473 -0.738 0.699 0.436 0.247 +1 0.290 -0.912 -0.175 0.025 -0.242 -0.291 0.537 0.967 1.375 -0.075 1.346 -0.111 0.124 0.410 0.964 1.978 -1.270 -0.266 0.546 -1.100 -1.530 -0.183 -0.970 0.591 -0.692 0.522 1.474 1.955 +2 -0.742 1.116 0.707 -1.241 -1.746 -1.112 -0.777 -0.303 1.173 -0.548 1.680 -1.680 -0.349 0.786 0.847 -0.336 1.002 0.812 -0.682 0.315 0.704 -0.611 1.694 -0.835 0.144 -1.250 0.103 -1.067 +4 0.327 -1.167 -0.336 0.068 1.461 -0.197 2.109 2.065 0.430 1.707 -0.529 0.169 -1.266 0.619 -0.307 1.423 0.631 -0.407 -0.582 2.000 -1.226 -1.177 -0.336 -0.065 -1.350 1.268 0.338 2.991 +1 -0.276 -1.224 0.567 -0.648 -2.156 -0.371 0.981 -0.387 1.738 0.429 0.339 -0.128 -0.068 -0.263 -0.302 0.531 -0.936 -1.076 -1.123 2.046 0.530 0.304 -1.058 0.623 -0.117 0.214 -0.977 0.160 +4 -1.224 -1.358 1.591 0.543 0.140 -0.657 0.908 -0.824 0.794 -0.873 -0.645 0.758 0.864 0.597 -1.050 -0.447 0.157 -0.849 1.371 -2.402 -0.779 -0.510 -2.844 -1.396 0.625 0.030 1.573 1.545 +2 -0.816 -0.941 0.901 -1.168 0.093 -1.349 1.269 0.030 0.745 0.195 -0.253 0.068 1.507 -0.897 -2.549 -0.787 -1.209 0.968 -0.176 0.827 0.032 0.009 0.164 1.480 0.187 1.109 1.045 -0.163 +0 -0.298 -0.692 -0.068 -1.407 -0.076 1.497 -0.611 -0.241 -1.701 0.374 0.264 0.064 -0.217 -0.293 0.502 -0.029 0.315 0.215 1.123 0.677 0.193 1.518 0.420 1.305 0.607 -1.842 1.305 0.135 +4 1.209 1.100 -0.887 -1.006 0.179 -0.391 -0.844 -0.719 -1.147 -0.737 -1.374 -0.726 -1.917 0.419 1.057 -0.663 -0.800 1.755 1.700 -0.527 1.333 -0.067 0.597 -0.092 -2.706 1.251 0.706 0.352 +4 -2.404 1.392 0.672 -1.500 -1.479 1.672 -0.737 -0.834 -2.266 -0.351 -0.228 0.715 -1.399 -1.520 0.451 1.007 -1.571 1.082 0.038 1.092 -0.070 -2.522 -0.152 1.060 -0.894 -0.388 -0.176 -2.900 +0 0.120 -0.853 1.930 -0.192 -1.260 -1.387 -0.186 0.874 -0.011 -1.497 0.084 1.091 -0.127 1.457 -0.836 0.898 0.484 0.528 0.445 -0.329 0.300 -0.427 0.274 0.292 -0.233 -0.142 -1.429 0.052 +3 0.488 -0.014 0.266 -0.695 1.313 1.747 -1.570 0.650 0.738 0.469 -0.917 -0.555 -2.228 -1.115 1.868 -1.421 1.246 0.433 -0.334 -0.098 1.130 -1.193 -0.426 0.696 -0.503 1.682 0.534 -0.848 +0 -0.621 -0.720 -0.159 -0.053 1.071 -0.730 -0.089 1.075 0.216 -0.011 0.238 -1.128 -1.144 -1.588 0.207 0.919 0.766 1.144 1.454 0.145 -0.065 1.476 -0.701 -1.179 -0.339 -0.456 0.048 -0.850 +2 -0.920 0.701 -0.577 -0.964 1.232 0.302 2.729 -0.265 -0.934 -0.731 0.058 -1.153 -0.418 -1.171 -0.708 -0.693 0.479 -0.315 -0.245 -0.574 -1.531 -0.734 1.502 -1.388 -0.367 -0.946 0.421 -0.661 +1 -0.025 -0.231 -0.562 -1.523 0.520 -0.970 -2.046 1.315 -0.617 0.244 -0.145 1.132 -1.472 0.763 -0.047 0.454 -0.405 -0.373 0.919 -0.749 -0.527 -0.242 -1.449 -0.269 -0.207 0.687 -0.527 -1.885 +4 -2.037 -0.576 -0.972 -1.965 1.620 -0.716 0.465 2.814 -0.112 -1.285 1.867 -0.147 0.849 -2.720 0.297 -0.062 -0.367 0.968 -0.674 -0.278 0.104 0.867 -0.248 -0.157 -0.166 -0.171 -1.318 0.758 +1 1.000 1.527 -0.829 0.467 -1.204 -0.465 0.069 0.146 -0.850 0.596 -1.468 -0.881 1.072 -0.799 0.272 1.635 -0.018 1.344 -1.354 -0.590 0.221 -1.031 -0.463 0.144 0.168 -0.313 -1.832 -0.647 +2 -3.122 1.049 -1.475 -0.571 -0.947 0.724 0.224 0.830 0.390 -0.489 -0.982 0.575 0.526 -0.701 -0.970 0.312 1.305 0.010 -0.235 -0.274 -1.447 1.176 -0.228 -0.869 -1.686 0.756 0.819 0.165 +2 0.264 -0.559 0.467 -0.544 -0.504 -1.520 0.926 -0.386 0.182 -1.036 0.436 -0.762 -0.142 0.878 0.791 -0.022 -1.768 2.993 1.746 0.723 -0.753 -0.114 -0.543 1.264 0.263 -0.784 -0.471 1.121 +4 0.055 -0.369 1.957 0.180 0.756 0.285 -0.232 -0.019 1.459 1.088 1.952 0.222 -0.877 -0.441 -1.188 0.767 -0.586 -3.055 2.464 -0.571 -0.577 1.672 -1.208 1.137 -0.587 -1.324 1.706 0.923 +0 -0.731 0.758 0.686 1.849 -0.176 0.669 0.098 1.296 -0.719 0.745 -0.195 -0.064 0.092 0.252 -0.116 0.215 1.578 0.985 0.869 -0.456 -0.889 0.955 0.876 1.473 -0.604 -0.230 -1.639 -0.392 +0 -0.633 -0.807 -0.633 0.928 0.285 -1.326 -0.263 1.190 -0.540 1.113 0.981 0.170 -0.039 0.872 -1.348 1.022 -0.621 0.126 -0.286 0.625 -1.250 -1.039 0.576 0.502 -0.186 -0.141 2.168 -0.808 +1 -0.760 -0.980 0.020 0.463 -0.603 0.425 0.954 -0.755 1.099 0.929 1.354 -0.278 0.743 1.492 -0.304 1.646 0.129 -0.110 -1.293 0.684 1.855 -1.388 0.490 0.879 -0.635 -0.116 0.481 -1.567 +1 -0.818 0.737 -1.658 0.431 -0.646 1.271 -0.311 -0.096 0.158 -0.300 -1.150 0.253 0.032 0.608 -0.359 -1.182 -1.086 0.016 -0.865 -0.007 1.298 -1.330 -0.742 0.949 -2.107 0.448 1.428 0.721 +0 -0.231 0.793 -0.738 -0.528 0.592 1.098 0.968 0.849 -1.076 -0.773 -0.359 -1.056 -0.045 -0.435 -0.504 -0.077 -1.143 1.505 0.140 0.874 0.149 -1.024 1.230 0.581 -0.129 -0.271 0.069 -0.836 +3 -0.149 0.671 -0.778 -0.465 -1.900 -1.075 -0.236 -0.437 -1.962 0.525 -0.881 -1.620 0.364 0.834 -2.437 0.682 -0.216 -0.256 -1.120 -0.155 -0.091 2.253 0.522 -0.002 1.182 0.885 -1.454 0.662 +0 -0.488 -0.878 -0.704 0.014 -0.720 0.071 -0.380 0.168 0.878 0.839 1.288 0.838 0.239 -0.433 -0.388 -0.878 0.005 0.697 -0.726 -0.037 -0.677 0.209 -0.549 -0.740 -0.680 0.503 -0.097 -0.428 +0 -0.376 1.678 0.083 -1.175 -0.126 -0.215 0.273 0.211 -0.757 0.582 1.434 1.607 -0.902 0.090 -0.647 -1.285 -0.467 1.153 -0.932 0.262 1.156 0.034 -0.297 1.249 -0.909 0.531 -0.004 -0.086 +2 0.678 0.764 -1.280 0.037 -0.813 0.469 0.916 0.379 0.463 -0.748 -0.413 1.566 0.184 0.517 0.456 0.269 -0.560 -0.330 0.241 -1.638 0.161 -0.693 0.679 1.082 1.969 2.688 0.903 -0.646 +0 0.300 -0.451 0.700 0.263 0.719 -0.170 -0.532 0.879 -0.846 0.319 0.291 0.796 -0.203 1.095 0.013 -1.343 0.299 -0.769 -0.143 -1.674 -1.057 -1.022 1.555 -1.239 -1.294 0.537 -0.706 0.647 +2 -1.320 -0.949 -0.073 -0.385 -0.367 -1.848 -0.469 1.933 -0.406 1.749 -0.838 0.684 -1.255 0.122 0.284 -0.787 -0.028 -1.352 -2.065 -1.482 -0.122 -1.258 -0.376 0.135 -0.002 0.143 -1.264 -0.057 +1 0.874 -1.942 -0.656 0.380 1.520 0.382 -0.014 -1.227 0.440 0.248 1.172 0.681 -0.555 1.818 -0.029 -0.121 1.076 0.169 -0.065 -0.303 -0.917 0.386 -0.576 -0.796 0.173 -0.224 1.087 1.762 +2 0.456 0.076 1.219 2.024 0.652 0.692 -0.771 -0.140 -2.485 -0.478 0.504 1.604 -0.255 -0.285 -0.427 -0.911 -0.964 -0.879 1.257 -0.195 -1.135 -1.028 -1.937 -0.360 -0.654 0.167 0.744 -0.806 +0 -0.005 0.585 1.142 -0.306 -0.817 0.351 0.294 0.317 -0.738 1.191 -2.115 -0.362 -0.281 -0.442 -0.396 -0.333 -0.620 -1.131 -0.314 0.001 -0.886 -0.321 1.563 -0.640 0.230 1.396 -0.505 0.212 +1 -0.854 -0.048 -1.393 -0.595 -0.047 -1.551 0.506 -0.964 -1.452 -1.080 2.176 -1.978 0.436 -0.752 -0.350 0.604 0.555 -0.681 -1.358 0.622 0.552 -0.285 0.442 -0.446 -0.827 0.004 0.519 0.309 +3 0.791 1.568 0.512 -1.759 1.227 -0.659 -1.010 -0.807 -0.028 -0.333 -0.861 -1.065 -0.516 0.003 1.968 1.413 -0.490 1.664 -0.056 2.260 0.249 0.372 0.309 -0.651 1.720 -0.388 -0.385 0.793 +1 0.467 -1.530 0.047 0.427 -0.836 0.789 -1.274 0.601 -1.494 -0.424 -1.629 0.801 1.250 1.706 -0.413 -0.773 1.014 0.061 -0.639 -0.651 0.029 1.221 -1.055 1.318 -0.180 1.217 -0.207 0.838 +4 -0.674 2.252 1.213 1.105 1.021 -2.403 1.093 -1.353 0.559 -0.476 -2.155 1.532 0.266 -1.288 1.902 0.637 -1.897 0.714 -0.842 0.126 -1.106 0.484 -0.245 -0.020 0.714 0.241 0.055 0.444 +4 -2.362 0.750 1.161 0.720 1.176 -1.761 0.095 -0.830 0.523 1.520 -0.368 0.652 -0.876 -0.018 -0.514 1.801 -1.080 0.088 1.369 -1.945 0.431 0.111 -0.681 1.216 1.716 0.658 -0.297 -1.447 +3 0.975 1.119 0.423 0.033 0.822 -0.849 -0.631 0.586 0.080 2.087 0.940 -1.061 -0.838 -0.196 1.635 0.199 -0.925 0.578 0.170 1.914 -0.572 -0.357 -2.057 -1.426 -1.028 -1.386 0.688 -0.222 +4 0.504 2.025 -2.465 -1.506 0.232 -0.253 0.453 -0.047 1.318 0.570 0.877 1.018 -1.193 0.587 0.643 -0.342 -0.348 -0.320 -0.879 -1.380 2.146 1.080 0.026 -1.467 0.546 -0.364 -0.157 2.248 +1 0.558 0.076 0.539 -0.921 0.169 -1.414 -0.111 -0.904 -0.736 1.236 1.091 0.609 -1.092 -0.316 1.213 0.142 2.319 0.393 0.192 -0.309 0.134 -0.152 0.708 0.957 -0.786 -1.331 -1.836 0.508 +2 -0.792 0.391 0.390 0.633 0.332 1.915 0.209 2.249 0.638 0.275 0.892 0.197 0.449 0.347 0.248 2.119 2.272 0.327 1.259 1.201 1.093 0.545 0.663 0.571 -0.719 -0.065 -1.197 0.576 +0 -0.295 -0.654 0.324 -0.955 1.017 0.726 0.446 -0.459 0.044 -0.125 0.027 -1.339 0.105 0.325 1.444 -1.007 -1.185 0.983 -1.512 1.456 1.183 0.581 -1.517 -0.159 -0.052 0.289 0.554 -0.749 +1 -0.096 0.484 -0.216 -0.470 1.463 -0.539 1.310 -1.254 -0.589 -0.928 1.454 -0.036 1.859 0.947 0.254 0.451 0.366 -0.909 -1.605 1.343 0.774 -0.287 -0.173 -0.966 0.779 0.897 -0.050 -1.334 +2 1.909 0.460 1.475 0.452 -0.011 0.042 0.712 0.341 0.862 -1.844 -0.711 -1.115 0.185 1.323 0.820 -1.380 1.513 -0.727 0.223 -0.418 -0.144 -0.305 0.253 -1.631 -1.644 0.832 1.346 -0.755 +0 0.013 2.350 -0.351 -0.896 -0.591 0.359 -0.598 -0.805 -0.812 -0.391 0.215 -0.266 0.452 -0.443 0.963 -1.053 1.428 0.702 -0.530 -0.198 -0.751 0.017 -0.044 -0.122 -1.028 0.730 2.036 -1.001 +3 0.537 -1.250 1.434 -1.860 -0.685 -0.094 0.412 1.576 -1.858 -0.508 -2.127 0.032 0.272 1.591 0.121 1.044 0.584 -0.435 -1.409 0.275 -1.492 1.295 -0.162 1.195 -0.480 0.269 0.209 -0.045 +4 0.379 1.179 -0.386 -0.504 -0.086 -2.830 0.214 -0.330 0.382 -0.896 1.345 -0.208 0.836 -0.507 0.467 -1.555 -2.197 -0.207 -0.020 -0.096 -0.289 -0.402 0.675 1.784 2.587 1.094 -2.288 1.051 +4 -0.874 -1.507 -1.452 -0.891 1.020 1.301 0.093 0.927 -0.555 -0.628 0.771 0.166 0.733 0.051 -0.375 -0.216 0.307 0.240 -0.932 -0.122 -1.521 -0.349 -0.921 -3.046 2.210 -0.772 -0.941 1.623 +3 0.314 -0.243 -1.349 0.810 0.466 0.137 0.458 -1.140 -0.002 0.110 0.772 0.988 -2.735 -0.227 0.896 1.689 -1.044 -0.058 0.663 0.613 -1.234 -1.104 -2.130 -0.454 -0.835 -2.142 0.437 -0.269 +4 -0.313 1.031 -0.288 0.431 -0.114 -0.056 -0.377 1.345 -0.733 -0.740 2.121 0.978 -2.385 -0.565 -1.812 -0.951 -3.139 -0.137 -1.142 0.060 -1.625 0.083 -0.415 1.502 1.411 0.785 -0.327 1.985 +0 0.305 -0.896 1.385 -0.645 -0.225 0.698 -0.616 1.297 -0.150 0.309 -0.235 -0.002 -0.605 -0.449 -0.358 0.179 -1.244 0.403 0.654 1.146 -0.208 0.154 -0.650 1.455 -0.652 0.121 1.826 0.777 +3 -0.550 -0.656 0.036 -1.765 0.026 1.272 0.309 0.740 1.379 0.626 -0.580 -0.497 0.113 -1.233 2.526 0.005 1.499 -0.165 0.070 -0.045 1.276 2.486 -1.461 -0.243 0.424 -1.746 -0.629 0.424 +3 -0.515 -0.839 -0.746 0.350 1.278 0.874 -1.323 1.394 1.017 0.472 -1.132 -0.904 -0.767 -0.536 0.192 -2.729 -1.283 -0.230 0.985 -0.306 0.187 1.099 -0.797 -1.009 -1.105 -0.278 -0.652 1.661 +0 -1.086 -0.102 0.783 -0.041 0.010 -0.105 0.925 -0.609 -0.193 0.293 -1.934 0.742 0.708 0.365 -0.008 -0.403 -0.342 -1.504 -0.558 -1.377 -0.812 -1.037 -0.607 -1.051 -0.531 -0.333 -0.370 0.269 +1 2.053 -1.152 -0.170 -0.034 -1.103 0.687 0.441 1.612 -0.625 0.510 0.031 -0.428 1.501 -0.372 1.107 -0.625 1.474 -0.161 -0.980 1.921 0.480 0.967 -0.943 -0.194 -0.545 -0.165 -0.679 -0.025 +2 -0.826 1.846 1.933 0.257 -0.012 1.031 1.095 -0.806 -0.527 -1.322 0.409 0.667 -1.350 -0.455 -1.347 -0.899 0.551 -0.733 -0.301 -1.627 0.187 -0.044 -0.729 -0.189 0.180 -0.815 -1.476 1.806 +1 -0.215 -1.978 -0.532 1.031 0.083 -1.529 0.248 0.479 0.261 0.325 1.529 1.166 -0.763 -0.588 -0.820 0.141 0.632 -1.440 0.243 -0.765 -1.122 0.103 1.846 -0.008 0.976 0.174 -0.147 0.485 +3 -1.360 -1.221 -1.296 1.878 0.288 -0.583 -0.960 -0.856 -0.609 1.316 -0.030 -1.447 2.310 -1.140 -0.591 1.310 0.415 -0.470 -1.231 -0.967 -0.906 0.007 0.191 -0.149 -0.039 -1.051 0.293 1.281 +4 0.364 0.861 0.031 -1.051 -0.273 0.203 1.522 0.425 2.086 0.827 1.215 -0.651 0.761 0.180 2.449 -0.347 1.781 1.718 -0.574 0.311 -1.432 1.813 -2.148 -1.612 -0.154 -0.941 -0.998 -0.341 +2 -0.392 -0.290 -0.736 -0.654 -0.016 0.064 1.326 1.025 -1.486 -0.294 -1.106 -2.608 0.087 0.577 0.071 0.437 -0.335 -1.028 -0.367 1.369 0.375 -1.521 0.130 1.274 0.761 1.287 -1.489 0.126 +3 0.657 0.705 -2.029 1.771 0.280 0.209 1.324 1.896 -0.796 0.439 0.603 -0.346 -0.949 -0.212 0.373 -1.444 0.342 2.147 1.076 0.095 -0.837 0.876 -0.238 0.825 -0.477 -2.408 -0.691 0.207 +2 -0.625 -1.057 -0.371 0.365 -1.942 -0.658 -0.238 0.444 0.779 0.447 0.051 0.065 -0.854 -0.241 -0.067 -0.062 0.978 -0.510 0.511 -2.072 2.850 0.294 0.217 -0.881 0.097 0.311 -2.253 0.333 +3 1.116 -0.549 1.182 0.607 -1.548 0.530 0.522 0.423 -0.142 -0.940 1.346 -0.102 -0.883 0.457 -0.743 -0.949 -0.375 -0.665 -0.912 0.806 -0.114 1.252 -0.855 -1.728 0.149 1.856 0.531 3.215 +3 -1.045 -0.089 1.213 0.537 0.090 -0.377 -0.251 1.306 -1.106 -0.726 1.067 -0.344 1.939 0.691 1.155 -1.626 1.059 -0.849 0.716 -0.364 0.223 -1.106 -1.721 0.151 -0.294 -0.894 -1.682 -2.675 +0 -0.877 0.599 0.262 1.727 -0.105 1.169 0.552 -0.808 -1.313 -0.735 1.133 0.895 -0.749 -1.201 0.284 0.828 0.269 -0.238 -0.589 -1.305 0.178 -1.371 -1.103 0.109 1.034 0.344 -0.664 -0.084 +4 -0.055 -0.583 0.017 -0.772 0.784 -1.299 -1.548 -1.003 0.066 0.441 -0.656 0.547 -2.737 -0.388 -0.542 0.339 1.029 2.679 -0.114 -3.999 0.083 -0.252 0.392 0.460 -0.591 -1.250 0.631 1.178 +2 -0.403 2.015 0.795 -1.196 -1.110 1.061 1.079 0.192 0.109 0.639 0.687 -0.557 -0.126 -0.303 -0.518 -0.170 0.366 1.431 -0.832 -0.119 1.336 -0.527 -2.269 0.841 1.691 -0.380 0.904 0.332 +4 1.330 2.221 -1.059 2.137 -1.896 1.451 -0.464 0.670 -1.118 1.085 -0.881 -0.905 1.264 -1.355 -0.776 1.428 0.383 0.671 -0.498 -1.348 -0.146 -0.540 -0.530 -0.201 0.346 -0.720 0.454 1.205 +2 -0.490 -1.175 -0.515 -2.080 -0.934 0.875 -0.307 0.774 1.155 0.470 1.734 1.438 1.260 0.706 1.379 -0.016 1.591 0.579 -0.485 -0.150 -0.887 -0.097 -0.623 0.048 1.229 -0.776 0.388 -0.839 +2 0.796 0.082 1.427 1.321 -0.336 0.249 0.577 0.029 -1.391 -0.938 -1.358 0.493 -1.184 0.192 1.832 1.428 -1.837 -1.279 -0.777 -1.169 -1.654 -0.011 0.705 0.954 -0.474 -0.272 0.356 -0.108 +4 1.294 -0.933 0.988 -0.793 -2.148 -0.246 1.761 0.015 0.749 0.434 0.287 -0.662 0.144 1.460 -2.151 -0.903 0.697 0.666 1.874 -0.274 2.361 -0.844 -0.556 -1.632 -1.251 -0.637 0.023 -0.292 +3 0.020 -0.029 1.171 -1.229 -0.538 -2.148 1.375 0.251 -0.463 -1.322 -1.230 0.414 -1.107 0.104 2.043 -0.348 -0.961 0.382 -1.226 1.720 1.404 -0.164 -0.897 -0.426 -0.296 0.384 1.040 -1.037 +4 -0.993 -0.801 -2.459 2.429 -2.504 -0.541 0.741 1.468 1.804 -0.100 0.502 -0.305 -1.556 -1.045 -1.717 0.316 -0.342 -0.187 0.672 0.247 0.662 1.812 -0.794 -1.873 -0.580 -0.207 0.436 0.724 +0 0.490 -0.350 -0.212 0.284 0.522 -0.075 0.508 -0.185 -0.007 -0.546 0.578 1.054 -0.764 -0.298 -0.968 1.372 -1.008 -0.013 -0.684 -0.482 -0.346 0.030 1.508 0.068 -0.280 0.198 0.917 0.508 +2 0.424 0.091 1.784 -1.573 1.355 -0.884 -2.143 0.320 -0.126 0.092 0.521 0.550 -0.531 0.812 -2.017 -0.095 1.129 0.286 0.050 -0.344 0.347 -0.281 0.627 -0.519 1.526 0.547 1.813 -0.388 +3 -0.515 0.464 0.820 -1.134 -0.853 -0.632 -0.006 -1.469 -0.818 -1.434 -0.912 -0.003 -0.355 1.867 0.108 0.339 0.465 -0.491 1.148 -0.376 2.772 -0.539 0.672 0.101 -0.949 -1.744 0.196 1.571 +4 -0.346 2.035 -0.061 -0.025 -1.340 -0.643 1.108 -0.828 1.914 0.883 -0.060 0.524 0.907 2.033 0.062 -0.614 -0.669 -0.123 -2.120 -1.099 0.415 -0.020 -0.741 0.010 1.389 1.267 3.464 -0.272 +1 -1.125 -1.566 0.531 2.035 -0.693 1.847 0.504 -0.428 -0.285 0.504 1.562 0.544 0.020 0.428 -0.973 -0.610 0.360 0.242 -0.004 0.223 -0.164 -1.091 -0.880 0.637 1.687 0.465 -0.879 1.056 +2 1.077 -0.018 -0.335 0.566 0.273 1.030 1.291 0.307 0.292 0.214 0.489 0.055 -0.001 1.197 1.516 -1.508 0.010 -0.689 -0.311 0.403 0.669 -0.112 -1.913 -0.965 0.896 -2.740 1.187 0.521 +2 -1.617 -0.184 -1.339 -1.433 1.555 -1.125 -1.956 0.422 1.791 -0.224 -0.076 -0.023 1.320 -0.555 0.027 -1.064 -0.112 0.882 0.033 1.413 0.455 1.170 -1.176 -1.033 0.413 -0.771 0.487 0.548 +2 1.206 0.425 -0.016 0.737 0.369 1.423 -0.134 1.001 -0.326 1.893 -0.162 -1.619 0.078 0.765 1.629 -0.996 -1.744 0.497 0.466 -1.411 -0.330 -0.285 0.823 -0.414 -0.436 0.115 -1.378 1.901 +1 0.961 -1.245 -1.339 -0.071 -0.766 -0.382 -0.440 -0.631 -0.763 -0.932 -0.369 -1.462 0.606 -0.884 0.453 -0.659 1.037 0.040 -1.077 2.498 -0.418 -0.557 -0.488 0.398 -1.453 0.136 -1.685 -0.074 +2 -0.845 -0.033 1.097 0.047 -1.348 0.154 0.464 -0.344 -0.128 1.200 -0.288 0.348 0.248 -1.568 -1.142 -0.115 0.662 1.717 -1.568 -1.455 -0.079 0.594 -0.926 0.361 0.870 0.815 2.239 -0.607 +4 -0.322 0.189 -1.313 -0.188 1.704 0.662 1.231 1.013 -0.709 1.912 -1.776 0.332 -2.060 -1.713 0.936 -0.691 1.245 -2.174 -0.089 -1.216 -0.329 1.548 1.792 0.513 -1.595 -1.207 0.160 -0.806 +3 -1.016 -0.252 1.553 1.069 -1.711 -1.011 0.246 1.130 -0.822 0.369 -0.455 1.752 0.622 -0.193 -1.495 -1.061 -2.525 0.272 0.666 0.716 -0.614 1.280 1.584 -0.665 1.182 0.140 -0.527 1.231 +2 -1.176 0.766 -0.436 -0.471 0.852 -0.558 0.005 0.137 0.253 -0.323 -0.333 -0.499 0.874 0.149 0.965 0.754 0.670 -0.497 0.082 -1.934 -1.513 -3.124 -0.327 1.286 0.638 -0.703 1.362 -0.237 +4 -0.396 -1.868 -1.557 -1.068 -1.294 0.265 1.938 0.796 -1.128 -0.694 -0.499 0.389 1.571 -0.467 1.539 2.104 -0.350 -0.376 -0.361 0.062 -0.358 0.884 -1.549 -0.535 0.329 0.870 2.195 -1.289 +4 0.461 -0.515 1.756 1.546 0.033 1.536 1.783 0.074 2.472 -0.031 -0.215 0.866 -0.354 0.085 1.409 0.019 -1.695 0.367 -0.696 0.865 0.696 1.471 0.908 1.090 -0.669 0.804 1.968 -0.689 +4 0.102 0.544 -2.949 -0.513 1.688 0.697 -0.785 0.186 0.640 -0.014 2.006 -1.556 -0.366 -0.046 -0.375 -0.573 -0.840 2.311 0.057 0.959 -0.051 1.028 -0.451 1.784 -1.345 -0.338 -0.493 1.175 +4 -0.104 0.397 0.944 1.987 -0.669 -2.077 -1.688 -0.398 0.504 -0.179 -0.026 -1.293 0.189 0.285 -1.981 1.983 0.804 0.250 2.172 -0.505 0.288 -1.518 1.733 0.666 -0.700 -0.625 0.968 1.551 +1 -0.234 -1.334 -1.428 -0.145 0.323 2.498 -1.070 -0.632 0.478 0.097 -0.602 -0.043 -1.134 -0.447 -1.111 1.043 0.742 -0.052 0.016 0.224 -0.455 -1.689 -0.340 0.488 0.952 0.072 -0.603 0.560 +3 -1.377 -0.166 -0.233 -0.126 -1.557 1.239 -0.854 -2.050 -0.325 0.364 0.338 0.147 -0.135 -0.673 0.746 2.377 0.112 -0.784 1.229 -1.007 -2.065 1.892 0.294 1.582 -0.212 -0.015 0.755 0.446 +0 1.601 0.826 0.192 -0.784 -0.714 0.443 0.143 0.908 -0.043 0.535 0.100 0.648 -0.306 -1.462 0.995 -0.137 1.358 0.105 0.031 1.332 -0.650 -0.435 0.439 -0.847 1.793 -0.315 -0.190 1.361 +4 -2.306 2.156 -0.296 0.866 1.029 -0.119 2.163 1.122 -0.683 0.936 1.209 0.005 0.686 -0.150 -0.431 0.172 0.175 -0.211 -2.058 1.598 1.328 -0.801 -0.265 -0.957 -1.543 -0.997 0.138 0.412 +4 1.498 -1.344 1.215 1.196 0.172 -0.859 -1.452 0.754 -1.202 -0.362 1.915 -0.890 0.157 0.623 0.849 0.032 -0.070 -2.535 -2.197 -0.807 0.882 0.405 0.096 0.735 0.538 1.324 1.029 -0.535 +1 0.376 -0.974 -0.472 0.342 -1.189 -1.885 -0.499 0.262 0.757 -1.279 -0.896 0.750 -1.201 -0.967 1.437 -0.434 -1.152 0.143 0.603 0.463 -0.107 -0.799 1.633 1.033 -0.645 -0.014 -0.825 0.184 +0 1.297 1.246 0.160 -0.560 -0.549 2.042 -0.091 0.343 -0.440 0.090 0.378 1.806 -0.087 0.517 -0.357 0.186 0.342 -0.205 0.237 -0.709 0.503 0.056 -0.552 -0.117 1.307 -0.105 0.428 0.425 +4 -0.462 -0.194 -1.270 0.474 -0.494 1.927 -0.938 1.972 -1.100 -0.866 1.067 0.988 -0.269 -0.229 -0.658 -0.500 1.046 1.238 1.219 0.934 0.865 0.716 -1.706 -1.258 -0.786 -0.727 2.678 2.566 +1 0.247 0.611 -0.104 0.812 -0.061 0.724 -0.456 -0.387 0.602 -0.332 2.206 0.566 0.025 0.820 -0.481 0.923 0.895 1.254 -0.441 -1.223 -0.610 -0.282 1.667 2.195 0.345 0.009 -0.350 0.825 +3 -0.112 -1.265 -1.356 -0.286 -0.252 1.216 -0.752 -0.395 1.367 -0.184 -0.513 0.857 0.531 0.100 0.924 -1.248 -0.582 1.998 -3.034 -0.673 -1.183 0.071 0.605 1.107 -0.410 -0.923 -1.451 0.048 +0 -0.351 0.001 -0.419 -0.112 -0.395 0.231 -0.530 0.546 0.658 0.175 -0.071 -1.498 0.744 -0.008 -0.131 1.168 -0.188 1.786 -0.515 -0.538 0.989 0.093 0.627 -0.408 -0.619 1.648 -0.364 0.287 +0 0.335 -0.142 -0.061 1.018 -0.595 -0.384 0.647 0.856 0.293 -0.481 -1.671 -1.630 -0.204 0.166 -0.353 -1.051 -1.206 0.163 1.357 -0.745 -0.025 -0.711 0.602 -0.125 0.988 0.118 0.120 -0.853 +0 0.159 1.126 -0.224 0.600 -0.871 0.533 -0.273 0.428 -0.865 0.449 -0.047 1.565 0.259 0.493 -0.442 0.312 0.474 -0.354 -0.432 -0.807 -0.076 -0.653 0.292 -0.790 -0.122 -0.946 1.144 0.025 +2 -1.036 -0.660 0.522 0.321 0.369 0.061 1.731 -0.254 -0.158 -0.706 -0.202 -1.310 -2.692 1.002 -0.369 0.723 -0.147 0.868 0.324 -0.310 1.611 0.132 1.423 0.142 -1.549 0.948 -1.380 -0.534 +1 -1.892 -0.541 1.006 0.808 -0.565 2.615 -0.243 0.547 1.534 -0.004 -0.108 -0.189 0.467 0.034 -1.105 0.479 -0.629 0.051 0.830 -0.095 -0.337 1.344 -0.716 0.943 0.288 -0.449 0.057 0.060 +2 -0.645 -0.069 1.447 0.387 1.081 -0.853 0.175 -0.566 1.466 1.016 0.796 -0.169 1.533 -0.282 0.939 1.783 1.020 0.351 -1.502 1.184 -1.456 -0.525 0.181 -0.080 1.131 -1.759 -0.052 -0.344 +4 1.090 0.914 0.706 -2.503 -0.998 -1.488 -1.773 1.144 1.695 0.008 0.471 -0.359 1.170 0.280 -0.521 0.562 -0.182 0.196 0.693 3.760 1.786 0.150 -0.940 -2.254 1.284 1.120 -0.628 -0.088 +2 -0.945 1.991 0.683 1.030 -1.464 -1.399 -0.638 0.453 0.779 0.106 -0.946 1.184 0.473 -1.150 -1.703 -0.517 -0.875 0.065 0.319 -0.395 0.489 -0.002 -1.218 -0.407 0.088 -1.013 0.288 2.460 +0 0.242 0.707 -0.979 -0.091 1.341 -1.203 -0.284 1.487 0.158 -0.526 -0.007 0.826 -0.204 0.598 0.235 0.317 0.662 -0.295 -0.945 0.474 -0.132 -0.448 0.526 1.277 -0.155 0.008 -1.182 -0.223 +2 0.029 1.312 -0.646 0.058 -1.213 0.901 -0.120 0.358 -0.313 0.586 0.397 -0.995 -0.937 -0.028 0.660 1.863 0.303 1.008 -1.613 2.013 0.606 -0.050 -0.165 -1.107 -0.821 1.910 -0.989 -1.008 +0 -0.229 1.194 0.863 0.492 -1.580 0.364 -1.700 0.529 0.403 0.455 0.507 1.707 0.338 0.217 0.228 -1.007 0.607 -0.538 1.754 -0.577 -0.634 0.355 -0.900 -0.455 0.597 0.857 0.432 -0.724 +2 -0.882 1.328 0.521 -1.805 -0.152 -0.783 1.641 0.210 -1.938 1.262 0.549 0.415 0.023 -1.420 1.581 -0.240 1.439 0.014 -0.190 -1.729 -0.708 1.322 0.721 0.525 0.592 0.003 -0.470 -0.799 +1 -0.425 -0.031 0.263 -0.614 0.100 -0.545 0.943 -0.826 0.993 0.068 -2.030 1.714 -0.382 -0.702 0.363 -0.298 -1.420 0.153 -0.422 -0.147 -0.722 0.123 2.022 0.760 -1.459 0.983 0.596 -0.164 +3 0.920 -0.243 -0.556 -1.830 0.676 -2.207 0.997 2.317 -1.202 -0.319 0.431 0.149 -0.136 0.012 -1.064 -0.662 -0.029 -1.931 1.021 -0.296 -0.313 0.284 -1.226 -0.245 1.623 0.621 1.520 0.971 +4 -2.727 0.867 1.260 0.127 -1.810 -0.708 -1.168 0.055 0.137 1.133 1.405 2.540 -0.692 0.004 -0.016 -0.674 0.279 0.746 2.309 0.630 -1.077 -1.335 -0.818 0.674 0.074 -1.290 -0.867 1.082 +1 1.047 -0.986 -1.848 -0.225 -0.740 0.122 -0.914 0.421 -0.973 -0.984 0.616 -0.046 -1.187 -0.952 -0.373 -1.143 -0.288 -0.253 -0.917 0.650 -1.020 1.246 -0.218 -0.660 0.539 0.034 0.760 -2.001 +2 -0.006 1.409 0.667 -0.351 -0.698 0.806 0.306 -0.246 -0.589 0.076 -0.303 0.784 1.241 -1.367 -1.226 1.348 0.166 -1.132 0.983 0.342 2.203 -0.235 1.109 -1.449 1.396 0.944 1.604 1.111 +2 1.817 0.161 1.701 -0.163 1.584 2.427 -1.427 -0.858 0.774 0.977 0.136 0.191 -0.767 1.243 1.247 0.685 -1.360 0.238 -0.116 -0.067 -1.574 -0.552 0.171 0.159 -0.062 -0.541 0.143 -0.932 +1 1.429 -0.036 0.672 2.092 -0.486 0.525 -1.443 0.696 1.331 0.642 -0.504 1.241 1.306 0.734 -0.682 -0.493 0.188 1.738 -0.059 0.138 -0.939 -0.226 -0.855 0.860 -0.071 0.490 -0.102 -0.517 +2 1.199 -0.628 0.677 1.157 0.142 0.391 1.307 -1.156 1.195 -1.645 -0.520 1.982 0.966 0.017 2.058 -1.099 0.526 -0.103 0.023 0.753 0.642 0.107 0.756 1.377 0.344 0.822 0.220 -0.475 +0 0.620 -0.322 -1.159 -0.229 -0.525 0.309 -1.442 0.443 -0.497 0.216 0.950 -1.490 1.127 0.086 -0.114 -0.264 1.469 -0.351 -0.032 -0.531 0.029 0.186 0.399 0.376 -0.959 -1.954 0.114 1.802 +4 2.563 0.395 -0.918 -1.924 0.097 0.582 -1.182 1.172 -2.108 1.128 0.356 -1.639 0.245 1.525 -0.151 1.540 -2.520 -1.036 -0.857 -0.013 0.129 1.435 1.044 -0.075 0.303 0.077 1.658 -1.497 +2 -0.383 0.685 -0.019 -0.468 -1.236 -0.244 -0.233 -0.795 1.137 0.140 -2.134 -1.795 1.652 -0.123 -0.106 1.268 0.815 -0.525 0.705 1.676 0.840 -0.033 -0.564 -0.044 1.258 0.233 1.183 -0.897 +4 0.722 -0.652 -0.134 -0.873 -0.548 0.266 -0.502 -0.244 -1.465 1.133 0.510 0.793 1.290 -0.825 -0.358 -1.895 3.140 -0.150 1.039 -1.238 0.996 1.858 -0.060 -1.011 -0.139 -2.227 -0.038 0.821 +4 1.000 -2.098 -1.787 0.088 -1.608 1.207 -2.199 -0.752 0.208 1.473 -0.004 0.316 0.177 0.941 0.308 0.738 0.758 1.090 -2.173 -1.521 0.677 1.964 0.325 -0.258 0.985 1.321 0.674 -0.036 +3 -0.546 -0.183 -1.030 -1.179 2.097 0.303 -0.833 0.817 -1.178 -1.096 -0.803 1.655 -0.253 -2.442 1.210 -0.306 -1.849 -0.317 0.521 0.404 1.798 -0.853 0.047 -0.700 -1.542 0.886 0.463 0.284 +3 0.199 -1.266 -1.587 0.216 0.349 1.425 -0.296 -0.376 -1.119 -1.502 -2.095 0.379 0.813 0.877 0.282 -0.409 -0.340 -0.801 1.216 -0.665 -2.524 -0.401 0.552 0.136 -0.820 -0.466 -1.515 0.663 +4 -2.613 -0.852 -0.519 1.531 0.632 -1.439 0.695 2.872 0.824 -0.869 1.502 0.335 -0.674 0.049 -0.242 -0.701 -1.218 -0.602 -0.050 0.795 -1.276 -1.379 0.105 -0.329 -0.394 -0.919 -0.870 -1.122 +3 0.649 1.385 -0.943 -1.244 -0.577 -1.333 0.072 -1.208 -1.286 -0.870 0.525 -1.093 0.064 -0.860 0.481 0.005 0.210 0.723 0.864 0.350 -2.480 -2.122 -0.981 -0.085 -0.276 0.522 1.110 -1.712 +0 0.053 1.309 -0.617 0.165 -0.236 -0.375 -0.653 0.688 0.307 1.593 -0.012 0.776 -0.563 -0.121 -1.100 0.261 0.831 1.465 -0.129 0.122 1.003 1.466 -0.420 1.051 0.423 0.314 1.614 -1.793 +2 -0.128 0.217 -0.376 0.357 0.043 -0.197 -0.971 0.406 -2.409 0.262 0.015 -0.813 -1.389 0.418 -0.247 0.139 -1.837 0.477 0.940 -0.153 -0.897 0.434 -0.284 0.863 0.021 -2.142 -2.349 -0.710 +2 0.712 0.330 0.787 -2.384 -0.507 0.256 0.198 -0.173 -1.888 0.265 0.215 -1.217 0.845 0.047 -0.813 1.398 1.665 0.010 -0.599 1.315 -0.746 0.877 -1.262 0.060 -0.775 -1.035 -1.111 -1.019 +4 -1.423 2.454 1.455 2.164 0.062 0.551 -0.459 0.883 -1.216 -2.543 -0.791 -1.455 1.478 1.816 -1.403 1.292 0.951 -1.570 -0.390 -0.580 0.748 -1.229 -0.099 -0.686 0.473 -0.217 -0.535 -0.213 +2 0.326 -0.545 1.291 -1.940 -0.521 -0.661 1.752 0.056 0.390 -0.138 -0.696 0.657 0.751 1.808 -1.600 -1.206 -0.672 1.464 -0.758 1.278 -1.519 0.336 0.072 -1.197 0.392 -0.424 0.676 0.701 +1 1.392 -1.094 -1.787 -0.666 -0.602 -0.373 -1.721 0.351 1.896 0.302 -0.221 0.668 0.075 0.057 -0.082 0.218 -0.411 -1.628 1.849 0.530 -0.702 0.351 -1.130 0.380 0.743 1.033 0.569 -0.190 +3 -1.811 -0.351 0.535 -0.440 -0.318 0.232 0.048 0.686 -0.769 -2.534 -1.057 1.309 0.108 -0.300 1.330 -1.430 1.051 0.471 0.868 0.483 0.890 0.526 0.384 0.460 -1.113 -0.776 2.649 -0.710 +1 -0.938 0.199 1.524 -0.540 -0.300 0.383 1.513 0.673 -0.854 0.107 0.103 0.651 -1.377 -1.347 -0.108 -0.020 -0.849 0.073 0.024 1.834 -0.396 -1.165 1.457 -0.551 0.418 1.129 1.690 0.153 +1 1.225 1.441 1.119 1.450 0.306 -0.304 -2.018 -0.827 -0.061 0.528 -0.286 -0.064 -1.157 1.499 0.003 -0.401 -1.360 1.536 -2.004 0.272 -0.610 -0.043 -0.084 0.499 0.024 0.352 -0.357 -0.275 +4 0.018 1.590 0.270 -2.248 -0.250 0.978 -0.353 0.651 0.641 0.662 -1.509 0.138 2.401 0.054 1.411 1.569 -0.399 1.512 0.622 0.533 -0.299 -0.020 0.749 -1.670 1.888 -0.898 1.531 1.582 +1 1.147 0.370 0.442 0.519 0.251 0.615 0.679 -0.193 -0.229 -1.369 -1.469 -0.396 -1.351 0.128 0.908 -1.275 -0.006 0.075 -2.113 -0.842 1.824 -0.418 0.015 -0.026 0.081 0.125 0.928 -1.332 +0 -0.353 -0.097 0.089 -0.445 -0.173 -0.258 -0.137 -1.186 -1.559 -0.383 0.434 -0.213 1.137 -0.744 -0.450 0.309 -2.257 1.097 0.674 0.983 -0.656 0.757 -0.118 0.481 0.890 -1.406 -1.437 0.322 +4 0.293 -1.202 -2.391 -0.814 -1.015 1.807 1.675 -1.116 -0.100 -0.656 1.838 -1.820 0.465 1.278 -0.546 0.024 1.053 -1.107 0.637 -1.548 -1.595 0.851 -0.967 0.179 -1.178 0.281 -1.659 1.296 +0 0.313 0.574 1.398 1.191 -0.541 -0.102 0.671 1.068 0.939 -0.064 -1.254 -0.999 0.273 0.342 -1.099 0.045 0.631 -1.151 1.105 -0.378 1.258 -0.533 -1.027 -0.458 -0.095 -0.388 -0.216 0.334 +3 0.959 1.148 -0.377 0.841 -1.151 -0.517 1.621 0.182 0.233 -0.178 1.869 0.531 1.485 -0.592 -0.972 -0.765 0.629 1.647 0.750 0.877 0.173 -1.273 2.023 0.664 0.523 0.874 -1.714 -0.501 +4 -0.318 0.601 -0.906 0.142 0.314 -1.485 0.608 1.346 -0.175 1.360 -0.190 0.850 -2.219 2.419 1.495 -0.285 -1.972 0.288 -1.444 0.445 0.412 -0.941 1.121 0.806 0.976 0.009 -1.416 0.978 +0 0.255 1.414 0.293 1.435 -0.492 -0.360 0.787 -0.439 0.597 1.184 0.868 -1.579 -0.341 0.282 -0.244 0.079 0.295 -0.527 -0.193 -1.181 -0.478 -1.527 0.116 -0.193 -0.916 1.785 -0.245 0.287 +2 -0.143 -0.582 0.076 1.661 0.380 -0.241 -0.789 -0.314 -0.475 0.067 -0.413 1.303 0.322 -0.524 -0.857 1.185 0.744 1.892 -1.275 1.751 -0.623 -0.612 -0.198 0.100 0.418 0.562 -2.627 -0.366 +1 -0.540 -0.542 0.349 0.027 -0.752 0.596 0.521 -1.326 0.130 -2.021 0.243 1.857 -1.450 0.729 -0.363 0.228 -0.347 0.167 0.501 -0.682 -0.372 0.662 0.854 -0.250 2.036 1.899 0.634 0.958 +2 0.798 1.357 -0.624 -0.570 -1.965 -0.935 1.666 0.717 0.143 -0.067 1.677 0.555 -0.132 1.193 -0.179 -0.468 0.513 -0.264 -0.927 1.347 -0.004 0.463 -0.036 1.439 1.413 -1.151 1.822 0.510 +1 -1.076 0.925 0.558 -1.696 -0.510 -1.158 0.862 -0.450 -1.544 0.170 0.606 -0.954 -0.265 -0.902 1.244 -0.431 -0.722 -0.051 -0.208 0.688 1.216 1.224 -0.382 0.305 1.488 1.527 -0.876 0.592 +2 -0.142 0.982 -1.293 0.114 -1.272 -0.237 -1.095 -0.917 -0.004 -0.566 1.467 1.179 0.805 0.785 0.130 1.305 -0.294 2.119 -0.942 -1.129 -1.395 -1.221 -1.629 0.214 0.854 0.034 -1.385 -0.470 +2 -1.675 -0.716 -0.082 -0.779 0.858 1.869 -0.539 0.889 -0.357 -0.930 -0.712 0.769 -0.642 -0.446 -1.144 0.634 0.458 1.455 0.596 0.413 0.583 -0.256 0.110 0.931 -0.820 1.702 0.638 2.021 +0 -1.785 -0.940 1.723 -1.194 -1.257 -0.587 -0.767 0.013 0.463 0.413 -0.479 -0.531 -0.323 1.245 -0.447 0.367 -0.124 -0.543 0.818 -0.600 0.237 0.644 0.435 0.326 0.057 0.117 -0.023 -0.508 +3 0.328 0.916 -1.229 -1.491 -0.180 -0.691 -0.496 1.429 -1.328 -0.208 0.474 -0.798 1.025 1.500 -0.181 1.538 0.129 -1.049 0.851 -1.041 -1.238 0.780 -0.748 1.741 -1.678 0.988 -0.139 -2.146 +0 0.038 -0.083 -0.282 0.778 -0.212 0.092 -1.430 0.275 -1.203 0.478 -0.796 -0.690 -0.524 0.076 0.475 1.574 0.796 0.112 0.763 -0.978 0.139 1.992 -1.279 0.830 0.241 -1.040 -0.257 1.092 +3 0.003 -0.056 -1.889 -0.591 -1.005 0.271 0.677 -0.563 -0.733 0.429 1.818 0.761 -0.029 0.826 -1.124 1.363 -1.500 0.876 2.346 -0.476 0.251 0.351 0.024 1.129 -1.978 0.453 2.151 -0.313 +0 -0.172 1.454 -0.232 0.836 -0.917 -0.439 -0.597 0.274 1.456 0.906 1.228 0.026 -0.773 1.047 -0.485 -0.057 0.565 -0.317 1.658 1.460 1.078 0.270 -0.390 0.890 -0.328 0.065 -0.626 0.446 +4 1.733 0.111 0.357 -1.283 -0.168 1.656 0.000 0.927 -0.022 -0.117 0.575 -2.912 0.372 -2.249 -0.083 -0.835 -1.155 1.478 1.120 -0.356 2.378 0.994 -0.158 -1.888 -0.099 -0.632 -0.462 0.414 +1 0.512 0.464 -1.771 0.519 0.179 0.623 1.067 -0.143 1.246 -0.889 -0.946 0.429 -0.369 0.262 1.993 0.393 0.212 -0.869 -1.700 -0.073 1.067 -0.323 0.401 1.426 1.402 0.429 0.170 -1.519 +3 0.509 -0.761 1.582 -0.342 -0.119 -1.319 -0.194 0.532 0.763 1.562 0.520 0.277 -0.446 -0.662 0.970 0.951 -1.077 0.723 0.650 -1.114 0.540 -1.050 -2.429 -0.160 0.258 -0.115 2.867 0.379 +0 -0.519 -0.859 0.133 0.219 -0.326 1.695 0.571 1.758 0.131 -0.733 -0.995 -0.123 0.753 -0.297 0.857 1.403 -0.807 -1.099 0.948 0.269 0.325 0.697 -1.526 0.441 0.180 0.462 -0.955 0.081 +0 -0.839 -0.034 1.311 -0.280 -0.202 -0.368 0.136 -0.148 -0.654 1.225 0.009 0.257 0.020 1.180 -0.809 0.597 0.647 1.323 0.626 -0.295 1.784 0.828 1.572 0.910 -0.358 0.472 -0.516 -0.453 +2 0.363 1.323 0.097 -0.283 1.422 2.716 -0.486 0.698 -0.478 0.778 0.366 1.188 0.263 1.847 -0.277 0.001 -1.579 -1.391 -0.260 0.719 -0.878 -0.942 -0.641 -0.089 0.517 -0.379 -1.121 0.743 +4 -1.485 -0.952 -1.367 -1.403 -2.297 0.184 -0.898 0.196 0.951 -0.966 0.773 1.133 -0.669 -1.979 -1.208 2.449 -0.253 1.016 0.018 -1.753 0.505 -0.620 1.967 -0.743 1.701 1.683 -1.445 0.691 +0 1.591 0.691 0.806 -1.079 -0.408 -0.120 0.374 1.428 0.773 -0.562 0.586 0.582 0.305 -0.750 -1.153 -0.078 0.088 0.101 1.283 -0.472 1.088 0.198 -0.891 -0.929 -1.334 -0.369 -1.232 0.580 +1 -1.137 -1.600 0.646 0.601 -0.617 0.247 -0.454 -0.493 -1.433 0.045 -0.517 0.378 -1.753 0.602 -0.515 -1.067 -0.440 -1.598 -0.291 0.123 0.073 0.460 -0.568 0.779 0.266 0.816 1.659 -1.551 +4 2.469 -1.985 -0.117 0.628 0.841 2.246 -0.645 -0.235 -1.628 0.128 0.511 2.092 1.704 1.516 -0.695 0.426 0.389 0.467 -0.043 1.131 -0.356 0.329 1.453 2.154 -0.829 0.551 0.454 1.220 +2 -0.456 0.737 -0.242 0.924 -1.571 -1.646 -0.391 -0.889 -0.517 -0.220 0.720 -0.408 -0.824 -0.352 -0.769 -0.934 -1.613 0.069 -0.569 0.966 -1.447 -0.542 1.813 -0.940 -0.237 -1.216 -1.348 1.547 +2 0.553 0.827 -1.952 -0.347 0.243 0.992 -1.470 -1.595 -1.611 -0.505 0.339 -1.487 -0.344 0.449 -0.107 -1.449 0.470 0.750 -0.945 -0.430 -1.016 2.181 -0.067 -0.625 0.251 -1.126 0.258 0.094 +0 -0.320 1.082 -1.121 0.159 0.205 1.023 0.112 -0.957 0.192 0.719 0.140 1.096 -1.307 -0.158 0.006 0.772 0.899 0.024 -0.361 0.188 -1.141 -0.489 0.570 0.354 -0.662 -0.782 -0.799 -0.731 +3 -0.406 1.909 -0.055 -0.282 -0.565 -0.300 -0.809 0.137 -0.033 -0.457 -0.545 0.183 -0.729 -0.240 -0.451 -2.206 0.860 -1.833 -1.768 0.895 -1.108 -2.076 1.337 -0.638 1.215 -0.915 -1.643 -0.322 +2 -0.049 0.663 -0.282 0.083 0.951 -2.276 -0.916 -0.524 -0.159 -1.417 -0.909 0.699 0.465 -2.793 -0.225 -0.142 -0.913 -0.142 0.393 0.715 0.101 0.647 0.011 -2.285 -0.134 -1.229 0.113 -0.220 +1 -0.020 -0.243 -0.938 -0.646 1.138 -0.178 -0.981 0.448 0.767 -0.002 -1.234 -2.547 -0.901 1.054 0.963 -0.796 -0.236 0.698 -0.138 0.936 -0.097 0.482 -0.323 -0.784 1.166 -1.502 -0.600 -0.094 +3 -0.873 -0.795 -0.429 1.078 0.896 0.265 1.429 -0.927 -0.423 0.363 -0.204 0.847 1.728 -0.092 0.410 0.928 0.765 1.771 0.417 0.054 -0.971 0.350 -0.918 -0.958 0.414 -0.331 -2.420 2.321 +3 -1.108 0.323 1.151 -0.991 -1.417 -1.266 0.395 -1.275 -0.135 1.371 -0.051 -0.778 0.125 -0.069 0.453 1.619 0.397 2.034 0.455 -0.767 2.288 -0.125 -1.055 2.324 -0.733 -0.772 -0.551 0.165 +1 0.243 1.140 0.739 1.206 -1.003 -0.996 0.328 1.133 -0.200 -0.740 -1.601 0.363 -0.113 -0.434 0.905 -0.087 0.001 0.900 2.191 -0.444 -0.876 -0.989 0.477 -1.091 -0.373 -0.285 1.556 1.193 +0 0.772 0.072 -1.157 -1.125 0.359 0.422 0.023 0.530 0.530 0.021 0.080 -1.627 0.033 -0.157 -0.810 0.589 -0.063 0.292 -1.981 -0.177 0.643 -0.380 -0.175 0.236 -0.008 1.314 0.536 0.465 +2 0.813 -0.601 -1.863 -0.205 -0.358 -0.544 1.051 1.214 0.428 1.491 -2.803 -0.304 -0.352 0.842 -0.318 0.555 -0.482 -0.674 0.801 -0.002 0.307 -1.240 0.678 -1.077 1.713 -0.256 -0.414 0.892 +4 -0.933 0.321 -0.503 -1.749 -0.564 -1.788 0.184 0.736 2.828 1.382 1.210 -1.759 0.297 0.468 -0.511 -1.483 0.435 -1.842 -0.339 0.537 0.323 0.279 -0.209 -0.739 -0.515 -2.676 -1.123 0.788 +2 1.450 -0.717 0.337 -0.694 0.513 -0.487 0.613 -0.557 -0.726 1.012 -0.873 -0.875 -0.491 -0.599 0.707 -0.647 2.037 -0.817 0.109 -0.274 -1.587 -0.038 -1.118 -2.148 0.292 -2.251 -0.270 0.953 +3 -0.724 1.026 1.706 0.049 0.302 -1.105 0.737 0.464 -0.326 -0.743 1.848 0.383 -0.918 0.856 0.230 0.059 0.979 -2.613 -1.807 1.379 -0.129 -0.427 -1.338 0.194 0.317 1.441 0.372 -0.303 +4 -1.346 1.470 -3.127 -0.172 2.196 0.760 0.490 -1.192 1.331 -0.914 -0.906 1.329 -0.948 -1.463 -0.964 0.189 -0.502 1.817 0.034 -1.240 -1.868 0.900 0.300 0.608 -0.427 0.154 1.191 0.006 +3 1.329 -0.220 -0.276 0.041 0.606 0.116 1.346 2.578 0.819 0.376 -2.001 1.385 1.227 1.535 0.796 -0.949 -0.965 -1.090 0.080 -0.701 0.859 -0.572 -0.326 0.264 -0.478 -0.908 -1.813 0.931 +1 -0.134 1.144 -0.620 0.330 -0.385 1.610 0.260 0.147 0.725 1.457 1.024 0.881 1.262 0.008 2.067 0.850 0.574 -0.097 -1.487 0.078 -0.858 -0.442 0.134 1.358 -0.116 -0.973 -1.435 -1.072 +0 -0.137 0.381 0.718 2.027 -1.759 -0.562 0.124 -0.170 -0.552 -1.853 -0.634 0.310 0.875 -0.110 0.360 0.003 0.392 -1.247 0.539 -0.566 -0.511 -0.081 -0.188 -0.615 1.109 0.262 -0.559 -0.452 +2 1.383 0.595 -0.630 -0.563 0.642 -0.366 0.897 -0.410 0.137 -0.586 -0.433 0.632 2.178 -0.514 -1.201 -1.617 1.247 0.700 -0.198 -1.447 1.117 1.177 -0.556 0.033 0.876 -1.473 -1.287 0.371 +3 1.119 0.640 -0.284 1.220 -0.195 -0.188 0.802 -0.404 -0.257 0.562 -0.374 1.299 1.045 -0.787 -1.060 -0.522 2.146 0.601 -0.947 1.538 1.972 0.416 -1.919 0.103 0.411 1.918 0.482 0.340 +1 1.140 -0.387 -1.099 1.421 -0.113 0.222 1.235 -1.032 0.595 -0.590 -0.132 0.072 -1.500 -0.779 1.351 0.886 -0.063 0.243 -0.294 1.357 -0.200 0.232 1.146 -0.777 1.559 -1.530 1.630 -0.373 +0 0.730 -0.448 -0.022 -0.868 -0.179 -0.573 0.095 0.028 2.114 -0.696 -1.812 0.374 0.608 0.253 0.201 1.759 0.738 1.143 -0.220 1.003 -0.507 0.892 0.040 1.319 -0.701 -0.065 -1.181 -0.053 +1 -0.817 1.378 -1.350 1.148 -0.282 1.563 -0.464 -0.310 -0.133 -1.537 -1.762 0.510 0.988 0.608 -1.254 0.480 -0.990 0.408 1.131 0.367 1.469 -0.150 -0.217 0.067 -1.343 -0.403 -0.315 -0.284 +2 -0.813 -0.587 0.115 0.900 0.611 0.665 -0.746 0.882 0.104 0.795 -1.477 0.452 -0.611 -1.265 0.647 -0.429 0.530 -0.978 2.032 -0.119 -0.469 0.990 -0.086 3.166 1.165 -0.473 0.074 0.066 +3 0.015 1.793 -0.275 -0.033 -0.972 0.366 0.599 1.147 0.597 0.421 -1.969 0.729 -1.201 -0.506 -1.494 -0.218 0.782 1.523 -0.605 -0.439 -0.701 0.575 -1.142 -1.454 1.361 -2.434 0.040 -1.202 +0 -0.764 -0.628 0.975 -0.324 1.136 -0.782 -0.312 0.519 -0.324 -0.382 -0.404 0.695 0.963 0.493 -0.698 1.368 0.255 1.051 -1.290 1.168 -0.372 -0.442 0.405 -2.051 0.350 -0.277 1.414 0.271 +4 -1.530 -2.545 0.021 0.196 -0.264 0.175 -1.533 0.704 0.768 -0.386 -2.237 -0.735 0.403 -0.887 2.268 0.655 0.450 0.508 -1.064 0.072 -2.011 -2.148 -0.271 -0.168 0.948 -1.152 -0.001 -0.533 +1 0.263 0.651 -0.197 -0.360 -0.271 1.829 -0.657 -0.785 -1.840 -0.476 -0.255 1.051 1.898 -0.159 -1.741 -0.632 0.914 -0.348 0.831 -0.299 0.730 1.862 -0.083 -0.276 -0.088 -0.482 -0.312 -0.289 +3 1.074 0.512 0.384 0.538 -1.417 0.977 0.452 0.690 -0.026 0.235 1.079 -1.590 -2.360 1.072 -0.060 -1.989 -0.598 0.410 -0.022 0.014 0.439 -0.773 -1.376 0.820 1.379 0.084 -2.294 -0.202 +3 -0.101 2.437 1.234 1.022 0.531 -0.692 0.261 0.583 0.199 0.095 -0.397 0.273 -0.526 0.703 -2.016 1.359 -1.312 -1.067 0.409 0.540 2.078 0.296 -0.040 0.191 1.590 2.424 -0.185 0.543 +2 0.990 -0.477 0.235 -0.199 1.374 0.126 0.721 -0.805 -0.144 0.533 -1.592 -0.392 -0.702 -1.385 -1.962 0.311 -0.767 -2.071 0.712 -0.613 -0.614 1.204 -1.061 0.230 0.532 0.674 -2.088 0.895 +3 -1.372 -1.614 1.471 -0.209 -0.669 1.040 -0.606 1.826 0.678 -0.488 2.157 -0.606 0.742 0.299 1.302 1.562 0.032 -0.753 0.460 -0.678 2.013 0.137 -0.365 0.185 -1.347 -0.972 1.200 -0.657 +3 -1.546 0.011 0.226 -1.203 -0.198 -0.781 0.258 0.478 0.423 -0.759 0.629 -0.655 -0.399 2.255 -0.238 -1.597 1.632 -0.092 -1.440 -2.568 -1.000 0.479 0.589 -1.125 1.172 0.139 -0.319 -0.903 +4 1.677 -1.156 -0.587 -1.797 1.706 0.381 -0.328 -1.725 1.363 -0.761 0.893 -1.569 0.029 -1.084 1.138 -1.014 -0.984 -0.326 -1.251 0.767 0.658 -0.240 0.078 2.156 -0.550 -1.997 0.571 -1.265 +3 -1.391 -1.199 1.323 1.635 -2.215 0.470 1.382 -0.342 1.519 0.767 0.451 -0.245 0.092 -0.447 0.296 0.253 -0.434 -1.947 -0.195 -1.018 -1.121 -0.690 0.827 -1.111 -0.887 0.707 -0.766 -1.127 +4 -0.648 -1.457 0.929 -0.284 -0.149 -0.908 -0.890 0.120 -2.024 -1.879 0.440 0.321 -0.911 -0.372 -0.620 -1.318 -0.275 -0.334 1.070 -1.243 2.606 -1.143 0.499 0.050 2.538 0.782 0.666 1.252 +3 -0.241 -0.351 -1.124 0.387 -0.529 0.953 1.143 -0.145 0.692 -2.417 -0.090 -0.312 1.130 -0.448 0.936 0.832 -2.142 0.317 1.678 0.690 -0.083 -1.138 -0.915 0.891 -2.598 0.517 0.133 -0.178 +3 0.278 -0.888 -0.757 0.222 0.182 0.616 2.308 1.254 -0.893 0.062 -0.290 0.265 -0.995 -1.440 -1.349 -2.475 -0.734 2.119 -0.463 0.303 -1.093 -0.806 -1.000 -0.624 -0.025 1.093 1.361 0.663 +0 -0.737 0.852 -1.172 -0.172 1.166 0.909 1.216 -0.591 0.784 -1.063 -0.276 -0.182 -0.576 -0.362 -0.493 -0.982 0.856 -0.182 0.201 0.164 0.055 0.093 -0.305 1.418 0.761 0.018 -0.447 -0.119 +0 -1.037 1.552 0.748 1.787 0.526 -0.177 -0.298 -0.419 -0.163 -0.125 0.681 -1.237 -0.611 -1.505 0.435 0.737 0.716 -0.865 -0.901 -0.734 -1.115 0.573 -0.773 0.327 1.103 -0.222 -0.142 -0.779 +3 0.523 -0.867 -1.065 -0.209 0.897 0.111 2.326 -0.458 -0.770 1.244 1.146 -2.078 -0.946 -0.890 -1.001 -0.307 1.359 -0.555 1.709 0.524 1.096 -0.132 1.563 0.533 -0.366 1.211 0.106 1.105 +0 2.249 2.044 0.690 0.565 1.082 -0.584 -0.570 0.673 -0.856 0.593 0.157 -0.856 0.026 1.305 0.082 0.087 0.329 0.459 -0.331 0.350 -1.049 -0.799 -0.671 0.470 0.160 -0.134 0.691 0.379 +2 -0.852 -0.760 0.573 0.780 1.278 -0.858 0.815 1.335 -0.394 -0.002 2.413 -1.077 -1.111 0.682 0.927 -0.553 1.202 0.579 0.297 1.000 0.151 0.423 -1.293 0.576 -1.664 -1.365 -0.077 -0.603 +0 -0.014 -0.820 -0.600 -0.277 0.605 0.711 0.528 1.079 -0.705 0.113 0.752 -0.347 0.025 0.054 0.480 1.320 0.113 0.696 -0.255 1.850 0.664 -0.909 -0.461 -0.320 -0.108 1.182 0.656 -1.433 +0 -0.725 1.471 -0.225 -0.843 -0.352 -1.099 0.785 -1.603 1.390 -0.936 0.837 0.024 0.271 -0.590 0.353 0.135 -1.458 -0.963 -0.541 -0.755 1.253 0.007 -0.779 -0.491 -0.504 -0.288 -1.075 -0.814 +0 0.570 -0.473 0.532 0.544 -0.648 -0.476 1.880 0.122 -2.597 -0.737 0.602 -0.049 -0.000 0.710 -0.800 0.151 -0.183 0.228 -0.364 -0.326 0.460 -0.928 -0.589 1.592 0.521 0.875 0.050 0.148 +4 -0.791 -1.105 1.369 1.557 0.454 0.807 -1.254 0.659 0.649 0.439 1.647 -2.280 -0.179 1.482 -1.112 0.650 -0.486 0.088 -1.057 2.263 -1.147 -0.130 -0.259 1.074 0.070 -1.577 -0.643 -2.162 +3 -2.020 0.432 -0.955 1.326 1.490 -0.300 -1.743 -0.616 -2.022 1.335 0.019 -1.081 -1.247 -0.665 0.647 -0.765 1.294 0.178 0.612 -0.477 1.880 0.765 -0.405 0.291 -1.858 -0.494 -0.343 0.902 +3 0.044 0.219 0.834 0.688 -1.153 -0.314 1.848 -0.851 -0.111 -0.104 0.318 1.429 -0.372 1.594 0.432 0.090 -0.172 0.661 0.439 -1.191 -1.173 1.341 1.619 -2.788 -0.472 1.009 -1.183 0.311 +4 -0.432 0.110 -2.584 0.193 0.098 1.856 2.335 -0.280 0.987 1.344 0.096 0.248 -0.375 -1.310 1.112 -0.978 -0.256 1.054 -1.382 -0.299 1.424 -1.419 -0.436 -1.280 0.978 1.940 1.205 0.367 +2 -0.983 -1.387 0.398 0.546 0.406 -0.284 1.520 -0.152 1.666 0.791 -1.452 -0.508 -0.543 -0.500 0.537 0.248 -0.563 -1.735 0.607 -1.500 -1.093 0.974 0.812 2.390 -0.344 0.609 -0.337 -0.034 +4 2.236 0.089 -1.328 -1.142 0.747 -2.095 -0.514 0.156 2.404 2.256 0.049 -0.938 1.253 -0.287 0.277 0.649 1.278 0.167 0.749 -0.597 -0.907 -0.247 -0.758 0.854 2.365 -0.125 -0.051 1.752 +0 -0.936 0.651 -0.307 0.664 0.586 -0.243 1.711 -0.665 1.349 1.175 1.771 0.174 -0.789 2.077 -0.335 -0.814 -0.190 -0.982 0.526 -0.606 -0.287 0.968 0.624 0.016 0.171 0.012 -0.431 -0.003 +3 -1.457 0.190 1.341 -0.369 1.486 0.241 -0.420 -0.242 -0.410 -0.197 1.155 -1.500 -0.970 -0.556 -0.173 -0.650 0.081 0.244 0.562 1.133 0.717 1.710 1.240 2.485 1.516 -1.447 0.526 1.152 +0 -1.106 -0.962 0.047 0.881 0.539 0.189 1.147 0.128 -0.527 -0.770 0.552 -1.101 0.349 -0.428 -0.838 -0.855 0.965 -0.858 -0.279 -0.469 1.059 1.538 0.909 -0.376 0.342 -0.910 0.210 -1.757 +3 0.017 -0.621 1.534 1.018 -0.783 -0.893 -0.406 0.980 -2.591 1.310 -0.358 -1.224 0.142 0.281 0.871 1.147 0.346 0.506 0.064 0.665 -0.610 -0.392 -1.567 1.651 -0.631 1.076 1.259 -1.546 +4 -0.088 0.797 -1.884 -0.813 1.149 2.104 -0.455 -0.190 0.379 -0.390 -0.138 -2.574 -1.360 0.772 0.679 -0.056 -1.166 0.616 -1.881 0.455 -1.084 -0.650 -0.557 1.705 -2.433 1.835 1.866 0.870 +4 0.892 0.778 3.083 1.401 -0.346 0.447 0.304 2.383 0.926 -0.202 -0.134 -0.426 -0.354 1.710 -0.034 1.481 0.387 -0.990 -0.952 -0.462 1.072 0.353 -1.775 -0.583 0.080 0.192 -1.503 -0.891 +1 1.783 1.570 2.484 -0.238 -0.103 -0.394 0.450 -1.167 -0.255 0.812 -0.807 -0.259 0.519 -0.189 -0.575 0.676 -0.016 0.205 -0.686 -0.595 0.715 1.460 -0.508 0.723 0.565 0.788 -0.086 0.955 +2 1.586 -1.084 -1.760 0.593 1.259 -0.180 -2.264 -1.491 0.436 -0.010 1.284 0.073 -0.741 0.309 1.016 1.351 -0.111 0.067 0.779 -0.749 0.737 -0.232 -0.076 0.003 0.130 1.914 -0.469 -0.417 +1 -0.279 -0.168 -0.323 -1.290 0.491 -0.732 -0.856 -0.250 1.075 -0.117 -1.022 0.117 1.278 0.819 -1.792 0.210 1.247 0.135 -1.562 -1.599 -0.898 -1.390 -1.207 -1.417 -0.226 -0.531 -0.155 0.568 +4 1.092 -0.976 -0.728 0.666 -1.452 -0.532 2.168 0.189 -0.284 -1.724 -2.407 1.063 -1.163 0.045 1.274 -0.498 -0.460 0.696 1.355 0.847 1.470 -2.308 0.421 1.284 0.450 -0.144 -1.225 -0.734 +1 0.484 -0.396 -1.123 -1.523 -0.161 0.919 -1.051 0.566 0.193 0.145 2.652 0.274 0.218 -0.582 -0.659 -1.102 -1.315 -0.243 0.519 -1.867 -0.137 -0.792 0.778 -0.393 0.837 -0.589 0.474 0.000 +3 1.625 -1.362 1.692 -0.260 0.035 -1.087 -0.771 -0.503 -1.046 -0.209 -0.407 1.851 -1.166 0.103 0.248 -1.070 0.687 -0.398 -2.492 0.660 -1.117 -1.048 0.688 -0.347 0.918 1.210 -0.645 0.793 +4 1.185 0.898 -0.694 0.342 -1.277 0.911 -0.074 -0.409 -1.224 -2.478 -1.039 1.113 -0.403 -0.169 0.656 1.806 1.325 -0.972 -1.943 2.360 2.269 0.188 -1.270 0.963 -0.108 -0.113 0.894 0.334 +0 -0.585 -0.119 0.269 1.226 0.710 1.110 1.773 -0.585 0.711 0.751 0.256 0.893 0.088 0.671 -1.416 0.002 0.708 1.028 -0.861 -0.082 -1.063 -0.388 0.316 1.035 0.904 0.223 -0.651 -0.139 +2 0.456 -0.595 0.477 -1.757 -1.285 0.475 -0.630 -0.323 1.836 0.138 0.902 -0.759 -1.341 -0.081 -1.490 -0.699 0.238 -0.111 0.335 0.600 -1.196 -0.942 -2.179 -0.371 0.737 1.491 0.190 1.373 +0 -0.795 1.580 -0.493 0.254 0.592 -0.709 -2.043 -0.217 -0.203 -0.760 -0.771 -0.403 -1.191 0.053 0.414 -0.015 -2.150 -1.228 -0.060 0.592 -0.294 1.376 0.484 0.231 0.219 0.070 -0.171 0.091 +3 2.688 0.492 1.301 0.225 -0.047 -0.670 -0.787 0.213 -1.009 -0.554 1.169 0.904 -0.254 -1.008 1.891 0.374 0.213 -1.011 -0.869 0.859 0.546 1.386 -0.246 -1.620 -0.743 0.004 0.068 1.774 +0 -0.182 0.142 0.161 1.264 0.438 1.177 1.507 -1.611 1.277 0.653 0.488 0.990 -0.265 0.603 -1.058 -1.412 0.532 -0.069 -0.003 -0.798 0.391 -0.426 -0.047 -0.487 -0.802 -0.207 1.186 -0.473 +1 1.072 -0.275 0.335 -1.168 0.562 0.845 0.266 0.902 -0.007 -0.101 -0.660 0.556 -0.837 0.414 0.303 1.344 0.584 -1.757 -0.664 -0.689 -0.732 0.967 -2.203 1.424 0.999 0.186 0.349 0.075 +1 -0.839 0.363 2.461 -2.197 -0.501 0.797 1.262 -1.382 -0.638 -0.580 -0.653 -0.116 -1.429 -0.255 -0.936 -0.069 -0.705 -0.169 -1.036 0.953 -0.326 0.014 -0.146 0.007 0.002 0.805 -0.286 -0.720 +1 0.496 -0.833 -0.400 1.891 -0.406 -0.799 1.171 -1.051 -0.693 0.577 0.938 1.220 -0.747 0.596 -0.466 -0.606 1.203 -0.550 0.278 0.156 -1.071 0.966 0.560 -1.986 0.277 -0.773 1.097 -1.445 +1 0.105 -0.031 -0.148 -0.009 -1.101 -0.528 -1.276 -0.771 -0.654 -0.607 -0.369 0.276 -0.136 -0.396 -0.889 -0.879 0.313 -0.345 -0.164 1.431 -1.165 -0.655 1.362 -0.181 -0.055 1.940 2.413 1.237 +2 1.037 -0.108 0.708 0.008 -0.456 -0.732 -1.283 0.926 0.731 -0.278 -2.024 0.472 -0.905 0.144 -1.541 0.092 -0.131 -2.410 1.618 -0.326 -0.259 0.535 -0.051 0.364 -1.144 -0.142 -1.909 -0.238 +1 1.681 -0.766 -0.314 0.804 -0.676 -0.217 0.849 -0.819 1.204 0.109 0.775 -0.480 -0.761 -1.005 1.774 -0.005 -0.964 0.646 -1.693 0.557 1.786 -0.314 1.314 0.085 -0.145 -1.088 0.660 0.211 +4 -0.928 -1.231 0.761 -0.041 -0.221 -1.381 -1.093 -0.895 -0.340 2.109 -1.519 1.572 0.531 -0.835 0.953 -2.136 -1.723 -1.211 0.042 0.322 -0.194 0.242 -0.995 2.948 -0.200 -2.452 0.745 -0.656 +1 0.706 0.531 0.476 0.569 0.175 -0.616 -1.179 0.423 1.292 -0.125 0.151 -1.318 -0.454 -1.229 -1.159 0.099 -0.058 0.788 0.766 -0.874 -1.936 -0.948 -0.711 0.552 0.612 -0.047 0.740 2.562 +2 -0.549 -2.117 -0.426 1.084 -1.229 -1.717 -0.569 -0.767 0.850 1.749 2.227 -0.735 0.604 -0.021 0.419 0.574 -0.063 -0.943 -0.767 0.211 0.373 -0.616 -0.221 1.169 0.014 0.469 1.504 0.143 +3 -1.224 1.164 2.060 -0.698 1.304 -1.269 -0.054 -1.172 0.377 -0.204 -0.051 1.312 1.244 -0.172 1.107 1.150 1.109 0.148 0.331 -0.284 2.334 0.399 -0.082 0.503 0.272 2.062 -0.022 0.766 +3 -1.055 0.366 0.983 -0.325 1.504 -0.248 0.321 0.209 -1.763 0.904 0.755 -1.335 -0.637 1.291 -0.464 1.678 1.612 -0.786 -0.049 -1.461 -0.711 -1.774 -1.495 -0.188 0.230 -0.004 -1.212 1.461 +4 0.509 -0.401 -0.065 -0.663 0.295 -1.319 1.692 -0.244 -0.713 -1.737 -0.354 -0.237 0.306 -0.210 2.896 -1.270 0.614 0.406 -1.348 -0.326 2.349 -0.523 2.021 -0.111 0.032 -0.375 -0.993 -1.214 +1 -1.244 -0.810 0.723 0.789 0.520 0.876 -1.108 -1.206 1.253 0.104 1.209 -0.699 -1.056 -1.271 0.801 1.132 0.542 -0.483 0.637 0.610 -2.025 -0.350 0.279 1.108 -0.085 0.149 -0.067 0.265 +3 0.385 0.732 1.539 -1.173 0.845 0.337 1.157 -0.801 -0.016 0.306 -1.385 -0.444 1.192 -0.465 0.223 -1.733 0.860 -2.063 -0.534 0.684 1.362 -1.198 -0.561 0.589 -2.051 0.462 -0.972 0.461 +0 -0.008 0.460 -0.078 1.781 0.439 -1.480 -0.216 0.201 -0.041 0.807 -0.508 0.005 0.892 -1.417 -0.973 1.908 -0.379 0.836 1.072 -1.298 -0.860 0.009 -0.676 0.402 0.527 0.205 -0.050 0.576 +4 -0.652 0.296 -0.300 1.457 -0.335 0.542 0.534 -0.216 -0.032 -0.266 1.907 1.087 2.279 -0.125 -1.817 -2.212 -1.330 0.551 0.816 0.785 2.734 -1.249 -0.136 0.986 0.016 -1.335 0.825 -0.242 +2 0.737 -0.735 0.808 0.677 1.474 0.787 0.508 1.251 -0.108 -1.539 0.802 -1.208 0.944 -1.814 1.106 2.015 0.255 -0.596 -0.033 0.679 0.560 -1.055 1.624 -0.876 -1.011 -0.680 0.276 0.244 +0 0.792 -0.571 0.852 -1.229 1.452 0.014 -0.596 -0.299 0.809 -0.584 -0.749 -0.222 -1.177 1.821 -0.566 0.832 0.209 -0.985 -0.006 0.784 -0.188 1.193 1.022 0.919 -0.460 -1.080 -0.614 0.196 +3 -0.058 -1.234 -0.338 -1.782 0.470 0.242 0.107 0.339 0.119 0.031 -0.509 -0.685 0.184 1.466 1.806 -1.269 -1.269 -1.668 -0.500 -1.565 -0.481 -0.316 1.806 0.950 0.273 1.272 -0.497 -2.246 +1 0.195 -1.170 2.317 -0.351 0.171 -0.591 0.795 1.377 0.672 1.369 0.564 0.746 0.265 0.340 0.995 0.937 -1.137 1.143 0.761 -0.641 -0.427 1.309 1.364 -0.420 1.057 0.003 0.637 1.093 +3 -0.050 0.724 0.524 1.868 -1.756 0.307 -0.312 0.364 0.546 0.176 0.952 1.486 -0.425 -1.987 0.226 1.631 0.451 0.641 -0.239 -1.520 -0.990 -1.171 -0.069 0.757 0.264 -1.525 -0.031 1.768 +4 0.187 0.093 1.480 0.438 0.508 -0.982 -1.627 2.591 0.733 -1.058 1.131 0.055 -0.304 -0.662 0.266 1.335 1.044 2.152 -0.210 0.039 0.954 0.675 2.239 2.272 -0.315 -2.022 -2.760 0.443 +0 1.054 -0.202 0.765 -1.400 -0.688 0.859 0.874 -1.203 0.303 0.329 -0.708 0.357 -1.580 -0.927 0.599 0.030 0.081 -0.175 0.537 0.585 0.791 0.126 -0.388 -0.557 -1.057 -0.403 -0.606 1.602 +2 0.316 -0.561 -0.608 -0.207 1.153 1.284 0.091 -0.357 2.196 -0.978 1.305 1.095 -0.444 0.456 0.319 -0.970 0.046 0.457 0.124 -1.261 -1.856 -0.942 0.148 -0.231 -0.739 0.898 -2.289 0.977 +3 -0.124 0.396 -1.421 0.008 0.321 -0.982 -0.478 -0.395 0.561 -0.806 -0.481 -0.113 -0.014 1.012 0.359 0.252 -1.615 0.541 -3.429 -0.324 -1.494 0.349 -0.674 -0.283 0.430 0.863 -2.267 0.050 +0 -1.195 -0.882 -0.195 -0.161 -0.459 1.337 0.211 -0.037 -0.608 -1.052 -0.625 -0.876 -1.259 1.078 0.930 -0.375 1.318 0.590 -1.080 -1.458 0.306 0.476 -1.293 -0.526 0.853 1.571 0.084 0.056 +2 -0.702 -1.198 0.015 1.089 -0.610 -1.373 0.292 -0.056 1.003 -0.455 -0.260 -0.150 0.089 -0.393 -0.555 -1.850 -0.565 0.715 0.850 -0.471 1.299 -0.029 -1.338 -1.652 1.218 -0.373 -1.214 -2.259 +3 1.928 -1.580 0.434 1.149 1.458 0.905 -0.223 -0.053 -1.127 0.512 0.547 -0.084 -0.327 -1.038 0.910 -0.365 0.861 -0.836 -0.031 -1.608 0.090 -0.931 -1.104 1.518 2.326 0.628 -0.875 1.983 +3 -1.159 -0.379 -1.407 -0.801 -0.438 1.162 -0.295 0.305 -0.964 -0.159 -0.109 -2.263 -0.463 1.361 -0.609 1.247 1.111 -1.337 -0.513 -1.646 -0.050 1.935 1.423 0.014 -0.126 2.059 -0.954 0.630 +3 -1.071 -0.359 -0.254 -0.583 0.756 1.502 0.936 0.571 -0.397 2.331 0.457 1.149 -0.590 1.761 -2.017 -1.913 0.817 -1.482 -0.597 -0.079 0.030 1.916 0.048 -1.389 0.167 -0.627 -0.284 0.211 +2 0.370 -0.417 0.043 0.683 0.699 -0.022 -0.406 0.424 2.105 0.580 1.696 0.220 0.321 -2.248 -0.166 0.989 0.277 -0.083 0.053 1.752 -0.631 1.032 1.628 0.689 -0.883 0.627 0.543 1.535 +3 -0.192 0.224 0.678 0.816 0.113 -0.898 -0.099 0.758 -0.611 0.467 -0.645 0.816 -0.083 -1.059 2.606 0.139 -2.514 -0.925 1.362 0.830 1.217 -0.340 0.797 -0.272 0.351 -1.288 -0.268 -2.559 +2 -0.366 1.028 -2.590 -0.178 -0.363 -0.009 -0.544 1.024 -0.258 0.133 0.892 0.052 -1.269 -0.427 -0.124 -1.647 -0.712 -0.068 2.012 0.371 0.158 1.691 0.588 0.729 -0.407 -1.265 0.262 1.822 +2 -0.030 1.819 0.605 -2.381 -0.990 0.850 -0.918 -1.356 1.284 -1.588 0.778 -0.363 0.702 0.267 -2.073 -0.315 -0.244 0.409 0.319 -0.245 -1.134 0.481 0.871 0.331 -0.219 -0.370 -0.918 0.248 +0 -0.468 0.926 -0.013 0.053 0.495 -0.298 1.677 0.510 0.932 -0.221 -0.123 0.812 0.711 -1.249 0.128 0.474 -0.130 0.379 -0.088 -2.247 -1.311 0.975 -0.244 0.190 -0.004 -0.825 -0.467 0.329 +2 -0.027 0.881 0.319 -1.594 -0.539 0.243 1.060 0.798 0.920 -0.160 -0.638 -0.573 -0.700 2.194 1.834 -1.192 0.412 -0.121 -0.295 0.623 -0.491 -0.469 0.506 1.348 2.057 0.540 -0.853 0.742 +4 0.391 0.745 -1.550 0.286 -1.925 1.236 0.225 -0.015 -0.289 1.452 -1.286 0.011 1.426 -0.309 2.082 1.566 1.142 0.060 1.815 1.795 -0.945 0.896 0.012 0.281 -0.551 1.196 1.931 -0.497 +3 -1.427 2.281 -0.571 1.581 -0.306 0.800 1.395 1.390 -0.584 -0.857 -0.081 -1.300 0.239 -0.821 1.083 -0.570 -1.742 1.401 0.183 0.029 1.094 0.872 -0.762 -0.450 -1.113 -1.464 0.579 0.392 +3 -0.275 0.371 0.553 -0.643 -0.766 -0.609 -1.401 -1.590 0.297 0.939 0.871 -2.382 -0.245 -1.102 0.144 -0.418 0.592 -0.019 -1.896 -0.967 0.006 0.090 -1.406 -1.536 0.056 -1.404 -1.669 -0.445 +2 0.383 -0.242 1.869 -0.930 -0.928 -0.303 -1.695 0.454 2.156 -0.565 -0.025 0.162 0.719 -0.807 -1.793 1.086 1.666 0.407 -0.267 -0.717 -0.042 -0.352 1.621 0.228 0.273 -0.248 -0.634 -1.786 +3 0.659 2.010 -0.177 -0.798 -1.379 -0.731 -0.033 1.795 -0.518 0.224 -0.016 1.188 2.527 -0.531 -0.489 1.044 0.682 1.847 0.584 -0.359 0.591 1.109 0.820 0.507 1.067 1.169 1.382 0.649 +1 -1.787 -1.814 -0.616 -0.418 0.149 0.919 -0.093 -0.514 0.610 -0.317 0.418 -1.016 0.375 -0.526 0.853 -0.226 -0.181 -0.734 1.125 0.291 1.633 0.230 -1.439 0.873 1.118 -0.314 -1.126 1.866 +3 0.200 -2.374 0.761 -0.341 -0.445 -0.018 -1.175 -1.219 0.732 0.657 -0.550 0.818 0.991 -1.992 0.799 -0.041 1.036 1.007 0.076 0.397 0.599 1.262 0.391 -0.438 -0.454 -0.474 -0.035 2.822 +2 0.122 0.200 0.746 1.071 -0.053 1.080 -0.823 0.706 -0.735 -0.700 1.031 -2.233 0.663 0.198 0.168 1.618 -1.073 -1.177 1.110 -0.211 1.213 0.659 0.330 -0.208 -0.253 1.986 -0.612 1.040 +1 -0.207 -1.116 0.385 0.884 -0.606 0.536 -0.599 -1.894 0.457 2.074 -1.196 0.179 1.310 -0.102 -1.427 0.045 1.490 -0.507 0.232 -1.054 -0.099 0.387 0.126 -0.431 0.443 0.669 0.736 -0.293 +1 -1.611 1.162 0.189 0.617 0.795 0.556 -0.811 -0.286 -1.452 -0.298 0.323 0.607 0.391 -1.755 -0.562 -0.662 -1.310 -0.836 0.001 1.414 -0.953 -1.129 -0.599 0.337 1.224 -0.087 -1.446 -1.233 +0 -0.617 0.576 -0.710 0.601 -0.664 -0.759 -0.086 -0.208 -0.557 0.604 0.775 -0.912 -0.646 1.524 -0.410 1.040 -0.720 -0.768 -0.711 -0.533 0.732 -0.739 -0.163 -1.110 -0.267 -1.091 -0.453 -0.871 +4 0.575 -2.549 -2.062 0.164 0.125 0.078 -0.454 0.329 -0.398 0.739 -2.965 -0.015 0.152 -1.656 -2.208 -0.073 -1.238 -0.877 0.582 0.350 0.898 2.329 0.489 1.554 -0.618 -1.802 -1.577 -1.755 +4 0.900 -1.746 -1.414 1.171 -0.782 -0.673 -1.263 -0.405 -0.349 -0.937 -1.053 0.319 -0.808 -0.606 -2.186 1.938 0.942 -0.495 -0.899 0.011 -0.017 -1.276 -2.551 -0.526 -1.563 0.171 0.178 -0.973 +4 0.959 -0.371 0.197 -1.984 1.928 0.079 -0.169 -1.383 -0.644 2.455 -0.189 -0.625 -0.551 -2.913 -0.062 0.379 -0.911 0.264 0.268 -3.465 0.230 0.519 -0.957 0.745 -1.171 0.153 0.149 0.997 +1 -1.082 -0.878 -0.948 -1.387 -0.956 -0.198 -1.342 -0.138 -0.703 -0.929 0.377 -0.640 -0.470 1.298 -0.920 1.566 -0.348 -1.136 -0.554 0.069 1.255 0.799 0.064 1.260 -1.881 0.249 0.310 0.322 +0 -0.886 0.973 -1.459 1.894 0.728 -0.373 -1.156 -0.540 -0.259 0.216 -0.017 -0.634 1.157 -1.699 0.573 0.186 -0.007 0.630 -0.668 -0.770 -0.290 -0.527 -0.237 0.720 0.110 0.030 0.231 -0.330 +0 -0.826 0.083 0.049 1.545 0.546 0.610 -0.146 -2.236 -0.392 -0.021 0.173 -0.681 -1.407 0.767 -0.319 -0.008 -1.733 -0.239 0.716 0.316 -0.411 -0.538 -0.186 0.362 0.352 0.559 0.821 0.114 +4 1.816 -0.113 0.916 -0.060 0.492 -2.321 -0.385 0.848 0.289 -1.637 0.061 -0.819 -0.905 -1.521 0.619 1.113 2.120 -1.474 0.767 0.833 -0.581 -0.918 -1.086 -0.527 0.203 1.360 1.105 -1.615 +4 -0.429 -2.380 0.695 -0.636 -0.105 0.677 -0.365 -0.373 2.465 -2.154 -1.286 -1.092 1.789 -0.645 0.240 -0.474 0.603 -0.013 0.253 1.287 0.440 -0.735 0.312 2.419 0.565 0.152 -1.371 0.724 +1 0.285 0.727 -1.611 -1.300 0.575 0.434 0.587 -0.486 1.258 1.093 -0.227 -1.055 -0.060 1.164 -0.100 0.947 0.340 1.262 -1.720 0.117 1.535 -1.432 1.684 0.076 -0.767 -0.747 -0.423 -0.020 +2 0.580 -1.439 1.189 -1.661 -0.671 0.920 1.635 1.716 0.978 0.371 2.416 0.315 -0.637 -0.575 -0.718 0.072 0.590 -0.991 -1.140 -0.217 0.225 -0.210 -0.683 -0.329 0.135 -0.995 0.648 0.938 +4 1.224 0.592 -2.107 0.828 -0.024 0.681 1.271 1.043 0.542 0.402 -0.199 -0.886 -0.124 -1.937 -1.135 -0.127 -2.416 -1.908 -1.337 -0.202 0.594 -0.149 -1.269 0.604 2.085 -1.342 -1.292 0.106 +1 -1.991 -1.414 1.087 0.265 -1.122 0.641 -0.615 0.418 0.438 0.671 0.903 -0.199 -0.229 1.344 -0.877 0.513 -0.663 0.051 1.772 -1.232 -1.434 1.411 0.241 -0.704 -0.128 -0.200 -0.283 1.354 +4 1.693 -1.700 -0.845 -0.419 0.024 0.711 0.865 1.077 0.481 -1.730 -1.664 1.147 1.083 0.636 -0.820 -0.323 -0.399 -1.025 0.259 2.155 -0.496 1.297 -0.885 -1.537 0.987 -0.219 1.271 1.471 +4 -0.344 -0.375 -0.980 -1.957 -0.668 0.193 -1.348 0.016 1.900 1.023 1.644 0.391 1.168 -0.724 -0.644 -2.739 1.186 1.540 -0.553 -0.254 -0.754 0.383 1.125 -1.743 0.409 -0.277 0.113 0.459 +4 2.564 1.679 0.874 2.679 1.213 0.271 -0.373 -1.054 -0.711 0.082 -1.218 -1.539 1.133 -1.418 -0.051 0.879 -0.073 -0.890 0.854 -1.022 1.050 -0.355 0.605 0.266 1.296 -0.507 0.598 -0.782 +4 0.260 -0.263 1.728 0.843 0.989 0.278 1.000 0.393 -0.371 0.369 2.097 0.204 1.270 1.191 0.563 1.657 -2.173 -1.836 1.728 -0.647 0.112 1.751 0.200 1.690 -0.388 0.616 0.058 1.491 +1 0.423 -0.025 -1.412 -0.523 0.600 -0.440 0.483 -1.801 -1.134 -0.736 0.594 -0.841 -0.924 0.785 -0.695 -0.502 -0.757 -0.995 0.443 -0.226 -1.118 0.149 0.730 -1.858 -0.788 -0.703 1.726 0.425 +3 -0.703 0.919 0.186 -1.861 -0.876 -0.037 -1.004 0.081 -0.839 -0.134 -0.569 0.112 1.441 0.893 -1.081 2.511 1.181 -0.686 -0.866 0.340 0.704 1.767 0.508 -0.664 -0.507 0.627 -1.340 1.495 +4 0.997 1.410 0.269 -1.733 -1.761 -1.265 0.720 -1.409 -0.234 -0.422 0.361 0.715 0.095 0.802 -1.101 1.021 0.131 0.760 -0.517 -2.051 0.666 -0.143 1.202 0.422 2.952 1.271 -0.249 -1.585 +1 -0.744 0.820 -0.866 -0.676 -0.048 -1.429 -1.219 0.139 -0.007 0.193 0.969 -0.406 -0.569 0.672 -0.488 0.773 -2.107 0.272 0.213 1.573 0.703 1.048 -0.013 0.336 -1.037 -0.527 0.511 2.057 +0 0.823 -0.263 0.570 0.825 0.250 -0.820 -1.193 0.293 -0.724 1.054 -1.509 1.317 0.051 -0.854 -0.451 -0.204 0.483 -1.225 -0.959 -0.324 -0.031 0.716 0.269 -0.389 0.236 0.165 -0.252 0.116 +3 -1.252 1.444 -0.082 1.117 0.343 0.457 0.570 0.448 0.643 1.329 0.197 0.709 -0.090 1.440 -0.676 1.801 -0.040 -1.431 0.128 -0.681 0.841 -0.653 -0.446 -1.890 -0.452 -2.424 -1.584 0.760 +0 0.234 -2.090 -0.039 0.405 0.130 -0.071 -1.011 -1.306 -0.532 -1.812 -0.043 -0.842 0.202 -0.561 -1.945 -0.981 0.186 0.780 -0.273 0.252 -0.183 -0.216 -0.811 -0.032 -0.613 1.378 -0.675 0.176 +4 1.015 -0.005 -2.445 -0.061 0.551 -0.669 -1.749 1.189 -0.466 0.222 -1.941 1.187 -1.257 1.334 -0.643 0.397 0.850 -1.255 0.579 0.678 -1.833 -0.471 -1.430 -2.116 -0.231 -0.306 -0.703 -0.874 +0 0.203 0.218 -0.161 -0.116 1.665 -0.108 1.085 -0.038 1.097 -0.103 -0.208 -0.110 0.816 -1.490 -0.417 0.663 -0.125 -0.784 -0.112 0.735 1.175 -1.088 1.693 -0.705 -0.115 -1.030 0.235 -0.500 +0 0.636 -1.306 1.635 -0.722 0.349 0.352 -0.094 -0.303 0.034 -0.280 0.249 0.289 -0.524 1.101 -0.837 0.390 -0.339 -0.869 -0.277 -0.661 0.659 -0.095 1.369 -0.951 -2.197 0.395 1.723 0.489 +3 -0.535 0.142 -1.372 0.035 -0.661 -0.277 -1.221 -1.134 0.398 -0.833 1.306 0.848 -1.737 0.523 -1.155 0.307 1.223 0.942 0.805 -2.287 -1.416 -0.702 0.963 -0.302 -2.049 0.598 -1.425 0.506 +0 -0.327 1.806 0.535 0.680 -0.804 1.009 1.131 0.561 -1.104 0.193 0.685 0.500 -0.099 -1.017 0.257 -1.232 -0.518 -1.161 -0.211 -0.304 -0.346 -1.422 1.506 -0.448 -0.396 -1.315 -1.070 0.394 +4 -1.243 0.665 0.874 -3.106 2.251 0.058 0.649 -0.766 0.304 -0.214 0.257 -0.640 1.177 -1.269 0.910 0.291 -0.196 -0.211 -0.770 -1.038 0.152 1.851 -1.817 0.301 -0.259 1.673 -1.597 -0.190 +0 0.275 0.032 1.381 0.847 -0.368 0.920 -0.867 -0.540 0.881 0.571 0.259 0.472 1.641 -0.055 0.002 1.374 -1.054 -0.463 0.678 -0.630 0.051 -1.175 0.263 -1.590 -1.319 0.304 0.879 0.041 +0 -0.500 1.121 0.348 -0.641 -0.516 0.156 -1.604 0.301 -0.059 -0.071 -0.510 -0.435 0.160 -1.325 -0.054 -0.121 0.265 -0.852 0.245 0.366 0.636 0.063 -0.385 0.290 0.579 -0.401 -2.107 0.463 +4 -0.493 -1.238 1.267 0.128 0.519 1.150 0.849 -1.795 -2.224 -1.343 1.441 -1.987 0.129 0.112 0.259 2.744 -0.863 0.572 -1.980 1.111 1.446 0.102 0.861 0.127 0.466 -0.035 -0.175 1.487 +1 -0.049 1.363 -1.160 0.227 -0.278 -0.228 1.539 0.081 -0.914 0.889 0.815 -1.001 -1.193 0.411 -0.033 -0.471 1.056 -0.592 0.422 1.175 -0.339 -1.829 -0.403 0.189 1.003 -0.521 1.723 -1.299 +3 0.735 -0.383 2.076 -0.762 -0.066 -0.402 1.005 -0.443 0.167 0.937 -1.064 0.604 0.344 -0.813 -0.562 -0.115 0.214 1.439 1.796 -0.748 2.554 -0.457 1.835 1.437 -1.007 -1.405 0.120 0.905 +0 0.147 -0.336 -1.260 -0.938 -1.199 -0.541 1.090 0.074 0.107 0.604 0.326 -1.724 0.009 -0.083 -0.452 0.206 -1.386 0.834 -0.926 -1.134 -0.637 0.313 -1.150 -1.428 -0.213 -1.606 0.829 0.107 +1 -0.266 -0.858 0.447 -1.015 1.134 1.389 -0.969 0.409 -0.932 -1.771 -0.621 -0.289 -1.176 0.263 0.323 1.562 0.613 1.233 -0.226 0.494 0.391 -0.851 0.179 0.663 0.133 0.114 -0.853 1.979 +1 0.738 -0.763 -0.914 0.078 -0.262 -1.246 0.451 0.323 -1.102 1.055 2.291 0.216 -1.806 -0.293 -0.251 -0.834 0.822 -1.621 -2.058 -0.023 0.205 0.470 -0.237 -0.307 0.261 -0.169 0.341 0.791 +0 0.399 -0.418 1.633 -0.074 -1.414 0.762 -0.219 1.020 0.305 -1.215 -0.884 0.266 -1.512 -0.259 0.026 0.767 0.261 -1.094 0.220 0.317 -0.470 -0.074 -0.197 -1.163 -0.014 0.526 -0.052 0.166 +2 -0.151 -0.746 -0.490 0.897 -1.920 -2.997 0.894 0.275 0.018 -1.306 -1.710 -0.635 0.362 -0.586 0.900 -0.954 -0.519 0.703 -0.377 -0.327 -0.643 -0.829 0.354 0.484 0.227 -0.616 -0.201 1.596 +2 -0.960 0.613 -1.386 0.602 1.980 0.179 0.953 2.021 0.434 -0.018 -0.229 -0.532 -0.776 -0.691 -0.391 0.109 -0.632 -0.604 -0.939 1.006 -0.825 0.077 -0.300 1.683 0.266 0.245 0.764 2.100 +2 -0.858 -1.318 -0.568 0.029 -1.236 -1.095 1.360 0.873 -0.740 -0.975 0.899 1.752 -1.186 0.181 0.056 0.196 0.219 -0.887 -0.745 -1.209 0.397 -0.377 0.567 -1.376 -0.018 0.864 1.983 -1.745 +1 1.240 -0.412 -0.512 -0.420 -1.932 -1.203 -0.035 0.379 0.491 -0.780 -0.491 -0.270 -0.381 0.688 -1.308 -1.149 -0.298 -1.366 -1.900 -1.493 -0.243 0.631 0.651 1.588 0.102 0.236 -0.250 -0.202 +0 -0.757 -0.626 -0.103 -0.431 0.160 0.591 -1.639 0.005 -0.836 -1.176 0.221 -0.511 0.366 -0.183 1.040 -0.509 0.457 -0.147 -0.496 0.539 0.220 -1.349 -0.260 0.965 0.745 1.180 -1.272 0.108 +3 -0.801 -1.417 0.250 -0.377 0.535 0.457 0.229 2.659 0.824 -1.261 0.459 -0.054 -1.342 -1.422 0.453 0.698 -1.046 0.970 0.285 1.196 1.090 -0.158 1.636 -1.124 2.390 -0.879 -0.048 -0.567 +1 -0.445 1.316 1.061 -0.030 -1.665 0.940 0.534 0.091 1.171 0.455 -0.298 1.001 0.220 0.922 0.141 0.561 1.478 1.397 -0.534 0.928 -0.609 0.795 -0.133 -1.508 0.290 1.051 0.603 -0.863 +0 -0.123 -1.604 -0.031 0.525 1.130 0.011 0.324 0.619 -0.757 1.289 -1.357 0.812 -1.132 -1.386 -0.839 0.595 -1.042 0.860 0.034 -1.158 -0.251 0.776 1.291 0.736 0.958 0.011 -0.423 -0.205 +1 1.221 1.335 -0.648 -0.208 -0.612 0.895 0.333 1.069 1.113 -1.727 1.351 0.951 -0.369 0.350 -0.124 0.616 -0.371 0.479 0.067 -0.432 0.190 2.706 -0.686 -0.250 -0.668 -0.563 0.816 0.716 +0 -0.166 0.349 -0.391 -1.066 0.105 -0.241 0.693 -1.259 0.243 1.230 -0.584 -0.741 -0.033 -0.090 -0.019 -0.373 -0.415 1.315 -1.030 0.763 0.996 -0.861 -0.794 -0.077 0.101 -0.671 -0.504 -0.647 +4 0.662 1.015 1.541 -1.353 -1.398 1.165 1.001 -0.617 -3.237 0.081 -1.517 -0.828 0.181 0.316 1.581 -0.373 -0.976 0.917 0.377 -0.363 0.032 -0.378 0.692 1.688 -0.725 0.421 -1.530 -1.073 +2 -1.723 -1.235 0.822 0.701 0.059 -1.178 -1.182 -1.324 -0.134 0.641 -0.424 0.885 1.610 0.391 -1.253 0.086 1.354 -0.631 -0.658 1.140 -1.436 0.198 -0.618 -0.433 1.703 -0.481 0.122 1.031 +1 -1.148 -0.906 -0.624 0.881 -0.292 -0.328 1.393 0.218 0.522 -1.397 -0.051 -0.490 -1.262 -0.300 -0.734 0.635 -0.016 -0.064 -1.501 0.839 -1.708 -0.615 -0.313 0.473 -0.314 -0.921 0.890 -1.717 +4 2.542 1.045 0.319 -1.774 0.104 -1.415 0.777 1.210 0.044 -1.637 -1.538 -1.644 -0.800 -1.194 -0.614 1.473 -0.622 -0.671 0.117 0.647 1.107 1.864 -0.701 -0.191 -2.005 -1.194 -0.275 -0.745 +4 1.526 -0.290 -0.045 0.252 1.338 0.478 0.015 1.347 -1.585 0.226 -0.817 -1.577 0.892 -1.879 -0.644 -2.125 -1.237 -0.330 -0.517 0.983 3.088 -1.857 -0.708 -0.786 -0.592 1.405 0.002 0.657 +1 0.872 0.313 0.114 2.833 0.855 1.240 0.067 -0.132 0.632 -0.133 -0.050 0.463 -0.513 0.220 0.080 0.967 -0.611 -2.000 -0.311 0.397 0.964 -0.693 0.542 1.135 0.751 -0.791 -0.683 0.435 +2 1.390 0.221 1.487 2.972 0.881 0.052 0.790 0.155 0.251 1.675 -1.290 0.423 0.288 -0.216 0.021 1.350 -1.397 -1.224 -0.456 0.461 0.724 -0.634 -0.854 -0.349 -0.722 0.969 0.669 0.157 +3 1.498 -2.037 2.538 1.649 1.352 0.215 -0.257 -0.687 -0.133 0.813 -1.038 -0.817 2.044 0.984 -0.423 -0.499 -0.897 -0.200 0.941 -0.223 -0.270 0.648 0.755 0.051 -0.329 -1.453 1.234 0.865 +2 -2.467 -0.044 -0.233 -1.062 1.872 -1.059 0.783 0.689 0.376 -1.110 0.732 -0.717 -2.235 -0.094 0.130 0.751 -0.735 1.829 -0.030 0.939 0.699 0.483 1.190 -0.244 0.571 -0.078 -0.087 -0.650 +1 -0.382 -0.285 1.184 0.391 -0.942 0.556 0.397 -1.794 -0.651 0.190 1.446 0.698 -0.564 -0.708 0.798 -2.426 -0.808 1.409 0.297 -0.759 -1.202 0.840 0.078 -0.219 0.374 0.165 0.088 -1.324 +2 0.465 0.846 1.475 0.068 -0.447 -0.333 0.792 -0.144 0.752 0.071 1.324 0.296 -0.695 1.292 -0.649 -0.791 0.512 -0.855 0.241 0.083 0.315 2.161 0.573 -0.199 2.883 0.914 -0.295 1.600 +0 -0.671 -1.096 -1.104 0.434 -0.223 -1.682 0.478 -1.440 0.140 0.238 0.886 1.782 -1.365 -0.052 -0.276 0.434 0.216 -0.353 -0.441 -1.116 0.988 0.459 0.918 -0.310 -0.657 -1.078 0.359 -1.040 +4 0.727 -0.854 -2.079 -0.507 1.763 0.559 0.191 -0.224 -1.636 0.270 -0.697 0.178 2.171 -0.423 -1.978 -0.200 -0.760 -0.890 0.830 0.319 0.768 -1.794 -0.031 1.075 -0.757 -1.484 -2.045 -1.717 +0 0.231 -0.996 -0.236 -0.895 1.503 0.146 -0.236 -0.634 -2.125 -0.417 0.013 -0.889 0.791 -1.181 0.174 0.558 -0.226 -1.046 -0.132 -1.065 -0.122 0.276 -0.074 -0.779 -0.570 1.543 1.546 -0.288 +1 -0.155 -2.143 -0.966 -0.186 0.015 0.807 -0.095 -0.539 -0.815 -0.157 0.177 0.258 0.682 0.288 -1.504 -1.331 -0.392 -0.355 1.430 -0.871 0.539 1.610 0.690 0.968 1.468 0.438 0.823 0.089 +4 -0.582 -2.110 -1.352 -2.086 -0.047 1.255 -1.051 0.095 0.855 -1.509 0.571 0.238 -0.495 1.492 0.724 0.762 -0.176 1.168 -1.919 -0.322 -0.386 -0.534 -0.704 0.845 -0.565 -0.068 2.650 0.038 +2 -0.484 1.055 -0.089 0.796 -0.871 -0.748 -1.088 1.834 1.491 -0.084 0.578 1.107 1.682 -1.974 1.537 0.152 -0.351 -0.970 0.301 1.483 -0.876 0.445 0.343 1.468 0.178 -0.262 -0.645 -0.856 +4 -0.913 -0.554 -0.754 0.007 -0.751 -1.917 1.010 -0.294 -1.624 -0.606 -1.894 -1.117 -1.394 -0.182 2.279 -2.098 -1.675 -1.078 1.024 1.212 0.531 -1.862 -0.042 -1.130 -1.386 -0.006 -0.347 -0.299 +2 -0.774 -0.290 0.211 1.379 -0.140 1.679 1.768 0.431 0.630 -0.459 1.681 -0.637 1.202 -1.032 0.640 1.114 0.908 -0.467 -0.270 1.538 -0.506 -0.472 1.504 -0.063 -0.055 1.839 -0.645 -0.731 +3 -0.335 1.627 1.359 -0.117 0.704 1.647 0.778 -0.094 1.077 0.661 0.955 0.085 0.131 1.600 0.490 -1.002 -1.423 -1.643 -0.829 0.924 -0.435 0.444 1.651 -2.516 -0.776 -0.301 -0.340 -0.193 +2 -0.738 1.327 0.942 -0.880 -0.245 -1.507 -0.502 1.265 0.584 -0.718 1.039 -0.010 0.555 -0.637 -1.703 -1.022 -1.849 0.968 1.529 0.377 -0.381 1.099 0.162 -0.632 0.223 -0.666 0.661 -2.086 +2 1.241 0.320 1.539 0.911 -0.523 1.293 0.264 -0.395 0.278 1.112 0.455 1.822 1.087 0.523 -1.026 -0.180 0.072 0.697 0.637 0.170 0.120 0.720 -0.859 1.696 -2.047 1.455 -0.045 1.003 +3 0.923 -1.305 0.828 -0.980 1.211 0.113 0.344 -0.081 0.088 0.083 0.706 -0.063 1.462 -0.736 0.861 0.564 1.233 0.121 0.329 0.318 -1.216 0.394 3.270 1.609 -0.780 -1.801 0.052 1.083 +3 0.941 0.088 -0.603 0.231 -1.263 -0.255 -0.175 -0.605 2.533 1.302 0.200 0.428 0.517 0.625 1.562 0.927 -1.986 0.834 -0.837 -0.554 0.981 -1.863 1.713 0.817 0.334 0.834 1.148 0.223 +4 -0.229 0.848 -0.960 0.699 1.294 -0.400 -1.670 1.333 -2.351 0.621 1.629 -0.938 -0.747 0.562 0.250 -0.248 0.900 -1.504 0.738 0.698 -0.685 1.303 0.122 0.555 -2.175 -2.375 -0.172 -0.345 +3 -0.712 0.409 -0.900 -0.085 0.972 -0.304 -0.377 0.214 0.076 -0.001 -0.051 1.447 -0.782 0.677 0.856 -0.634 0.921 1.266 -0.710 -1.417 0.260 -0.274 0.927 -2.063 2.879 -2.461 -0.993 0.286 +1 0.510 0.971 -0.934 -0.022 0.114 -1.201 -1.090 -0.626 1.042 -0.196 0.130 0.308 0.631 0.501 0.067 -0.138 1.095 -0.493 1.360 -0.571 -0.284 0.708 2.365 -1.936 0.866 0.473 -0.453 0.359 +0 1.160 -1.129 0.942 -0.311 -0.916 -0.469 0.166 -0.026 -0.351 0.482 0.681 -0.065 1.196 1.629 -0.695 0.037 -0.535 -0.495 0.533 -0.089 -0.383 -0.300 0.656 -1.150 -0.584 0.265 0.790 -0.188 +3 0.558 -1.000 -0.140 -0.776 0.185 -0.781 0.074 -0.272 -0.152 2.051 -0.360 -1.934 1.016 1.008 -1.979 -0.701 0.436 0.524 -0.005 0.018 -0.939 -2.400 2.050 -0.969 0.177 -0.159 0.897 0.115 +2 -1.103 1.384 0.862 -0.565 -0.227 -0.068 -0.346 -1.358 0.200 -1.445 0.980 1.264 -0.011 -0.105 -1.472 -0.334 -1.502 0.593 -0.724 -0.207 -1.000 0.377 0.056 -1.838 -2.428 -0.316 0.303 -0.107 +3 -0.518 0.501 -0.508 -1.206 1.204 0.491 -1.029 -1.287 0.358 0.110 0.677 -2.748 -0.226 1.711 -1.596 -1.354 0.873 1.311 -0.024 0.570 -0.682 0.297 0.775 -1.126 -1.544 -0.585 0.671 0.762 +2 -0.184 -1.131 -1.496 -0.333 -0.999 -0.700 -0.415 -0.096 -0.276 -1.495 -1.575 2.003 1.077 0.008 1.721 -0.123 -0.993 1.375 1.045 1.438 -0.822 -0.679 -0.113 -0.752 -0.569 0.022 -0.810 -0.195 +1 -1.172 0.988 -0.316 0.695 0.140 -0.304 -0.216 0.719 -0.209 -1.582 0.317 1.031 0.608 0.054 -1.484 -0.045 -0.136 -1.886 -1.580 -1.816 0.486 -0.144 0.341 1.558 -0.412 -1.002 0.779 -0.223 +2 0.088 -0.181 1.565 0.870 0.593 0.901 -0.533 -0.755 0.591 -0.885 -0.649 -0.146 -2.900 -0.750 0.836 0.126 0.598 0.712 -0.895 -2.037 0.085 -0.263 -0.639 -0.584 1.081 -0.853 0.488 0.720 +0 2.294 0.387 0.624 0.641 1.857 0.651 0.179 0.475 0.655 -0.346 -0.394 0.088 0.803 -0.708 -0.678 1.514 1.027 1.042 -0.323 -0.485 0.637 -0.550 -0.629 0.383 0.170 -1.564 0.125 -0.003 +1 0.702 -0.784 0.522 -1.569 -0.747 -1.220 -0.059 0.747 -1.075 0.689 -0.100 1.875 0.099 -1.028 0.800 -0.851 -0.911 -0.455 -0.252 0.109 -0.972 -0.501 -0.603 0.337 -0.980 -1.594 -1.095 -0.453 +1 1.197 -0.796 0.828 -1.502 -0.455 0.430 -0.277 1.182 0.601 0.331 -0.190 0.601 0.027 0.273 -0.453 1.429 1.199 -1.181 -1.326 -0.598 0.672 -2.080 0.010 -0.573 -1.623 -0.319 0.123 -0.569 +1 0.187 -0.640 -1.025 -1.169 -0.120 0.084 0.058 -0.865 -1.400 0.525 1.370 -0.359 -0.115 -1.685 -1.050 1.388 -0.023 -0.173 0.944 2.278 1.383 -0.773 0.618 0.240 0.245 -0.074 -0.032 0.289 +2 0.050 0.259 -0.135 -0.529 -0.621 1.153 -0.847 -0.044 2.281 -0.748 -0.099 -0.269 -0.393 -0.511 0.738 -0.527 -2.028 -0.113 0.134 -1.191 -2.443 -1.168 1.038 -0.745 0.742 0.211 0.489 -1.990 +0 0.178 -1.468 0.948 1.340 -0.686 0.866 -0.104 0.594 0.262 -0.456 -1.243 1.407 1.394 -0.040 -0.541 0.334 -0.282 -1.248 0.673 0.021 -0.312 -0.613 0.092 -0.007 0.530 0.359 0.765 -1.997 +3 2.801 0.373 -0.049 1.282 -0.179 -1.389 -0.755 1.600 -0.347 -1.758 0.768 0.332 -0.598 0.181 1.321 0.718 -0.222 1.175 1.512 -0.981 2.194 0.107 0.673 0.108 0.590 0.539 -0.512 0.240 +4 1.945 -1.829 1.934 0.093 -0.530 -1.120 -1.269 -0.342 0.278 0.622 0.266 -0.510 1.340 2.674 1.495 -0.359 0.502 0.566 1.604 -0.338 -0.965 1.134 0.406 -1.109 0.706 1.838 -0.694 -0.891 +4 -0.780 -0.338 -2.042 -1.971 -0.920 -1.194 0.650 1.260 0.325 -1.897 -1.410 0.350 -0.172 -1.857 -0.857 -1.075 1.457 0.705 -0.287 0.229 0.146 0.079 1.827 1.409 1.525 0.331 -0.338 -1.241 +1 -0.969 -0.701 -0.822 0.722 -0.548 -0.909 1.618 0.456 -0.115 2.228 0.854 0.436 0.524 -0.451 0.406 -0.311 0.050 -0.055 -0.611 -0.593 -0.252 0.890 2.440 -0.191 -0.216 0.157 -0.983 -0.624 +0 -0.168 1.430 -1.052 0.479 0.173 -1.055 0.063 -1.323 0.061 -0.217 0.888 -0.714 -2.222 -0.578 0.622 0.147 -0.573 1.210 -0.280 0.784 0.024 0.673 0.769 1.340 -0.781 0.571 -1.231 0.560 +4 -1.163 -1.514 0.169 -0.709 -1.113 1.962 0.486 0.336 0.286 1.619 -0.167 0.618 0.903 1.858 -0.001 1.235 -1.828 2.117 0.423 0.755 -1.284 -1.349 -0.646 0.020 -0.255 -0.410 -0.197 -2.335 +3 2.238 -2.021 0.678 -2.086 0.213 -2.362 -0.943 0.223 1.007 0.866 0.854 -0.244 -0.358 0.426 -0.239 0.047 0.057 1.035 -0.115 0.346 -0.096 -2.102 0.460 -0.897 0.014 -0.174 0.470 -1.340 +0 1.587 0.174 -2.293 0.262 0.801 -0.187 -0.060 -0.293 0.194 -0.794 1.017 -0.393 -0.991 1.102 0.569 0.515 0.306 0.226 1.337 -0.248 -0.936 -1.341 -0.251 -0.053 0.768 -1.265 -0.765 -0.371 +0 -0.399 -0.189 -0.797 -0.842 0.330 -0.054 0.027 0.114 0.805 0.561 0.523 -0.219 -0.933 -1.652 1.132 0.534 -0.311 -0.755 1.555 0.593 0.068 0.585 0.065 -1.737 -0.068 0.102 1.437 0.270 +4 0.329 0.482 1.465 0.610 -1.608 0.072 1.003 0.286 1.974 -0.866 2.050 -0.146 -0.725 0.080 -2.248 -0.086 -0.017 0.186 -1.733 0.999 0.102 2.148 1.664 0.641 0.921 -0.911 -0.147 -0.584 +0 -0.761 -0.479 0.810 -1.502 1.106 -0.281 -0.819 -1.099 0.674 -0.015 1.589 -1.263 -0.578 -0.092 -0.752 0.006 0.320 0.484 1.506 -1.303 0.338 -0.647 0.430 0.431 -0.987 -0.901 0.512 0.023 +0 -0.174 -0.677 -1.081 -0.280 -0.286 0.871 0.266 1.126 1.535 -0.634 1.102 0.874 -0.041 0.525 0.193 -0.058 0.259 0.326 1.021 -1.044 0.762 -1.151 -0.084 0.261 -2.512 -0.291 0.446 -0.109 +2 -1.134 -0.484 -1.120 -0.380 -2.506 0.162 -1.216 -0.634 -0.641 -0.091 1.193 0.461 -0.057 0.021 -0.390 -0.927 0.810 0.901 1.790 -0.556 -0.202 -1.198 -1.117 1.108 0.762 0.524 1.318 1.290 +0 -0.078 -1.277 -0.361 0.915 0.729 1.113 1.010 0.406 0.327 0.253 -0.627 -1.030 -0.690 -0.326 -0.656 0.414 0.202 0.309 0.351 1.196 -0.009 1.008 0.517 1.130 1.615 0.577 0.104 0.097 +1 -0.845 0.311 0.183 0.262 0.372 -0.348 0.097 1.210 0.919 -1.318 0.555 0.282 1.872 0.915 1.077 0.905 -1.954 -0.858 -1.226 0.194 0.240 0.811 -0.088 0.578 -0.971 0.172 1.120 1.059 +1 -0.703 0.412 -0.561 0.577 -1.869 0.140 -0.085 0.431 -0.697 -0.145 1.325 -0.501 1.381 1.618 1.552 -0.178 0.065 -0.669 -0.912 0.717 0.288 0.513 -1.499 -1.101 -0.123 0.023 -1.395 -0.662 +2 0.984 0.756 -0.045 0.673 2.099 -0.549 -0.214 0.626 0.104 -0.126 0.645 0.014 0.279 1.193 -0.176 0.426 1.252 -0.065 -0.403 -0.700 -0.326 2.384 2.310 1.709 0.813 -0.832 0.164 0.619 +0 0.462 0.881 -1.457 0.357 1.323 0.214 0.370 0.960 -0.235 0.094 0.400 0.511 -0.493 1.120 -0.650 -0.646 0.049 0.897 -1.055 -0.241 -0.914 -0.363 -0.731 0.071 -1.694 0.641 0.889 -1.229 +0 -0.170 -0.153 -0.866 -1.156 1.420 0.939 0.308 -0.352 1.728 0.314 -0.682 -0.215 -0.632 0.052 -0.340 -0.222 0.839 1.000 0.780 -0.158 0.008 -0.824 -1.419 -1.613 -0.762 -0.349 -0.538 -1.432 +3 -0.455 -0.196 2.274 -1.512 1.565 -0.299 0.065 0.799 0.290 -1.362 1.636 0.441 -1.908 0.412 0.099 -0.364 -1.691 1.893 -0.843 0.311 0.831 -0.775 0.186 -0.179 -1.275 -0.319 -0.675 0.079 +0 -0.476 0.927 1.087 1.350 0.774 -0.100 0.961 0.375 -0.124 -0.464 -0.172 -0.477 0.415 -0.213 0.488 0.507 0.007 -0.451 -0.908 -0.108 0.293 -1.365 -1.778 -0.122 -0.895 1.009 1.305 -1.011 +2 0.417 -2.044 -0.531 -0.054 -0.828 -0.692 0.272 1.009 -0.958 -0.575 -0.731 -2.421 0.142 0.935 1.017 -0.871 -1.252 0.022 0.829 0.155 0.289 1.142 -0.739 -0.220 1.635 -0.197 -1.267 -0.669 +0 -0.048 1.441 1.203 -0.608 1.409 -0.320 -0.328 0.094 0.839 -0.609 -0.398 -1.248 0.351 0.933 0.995 -1.367 -0.383 1.245 0.232 0.034 0.011 0.541 0.286 -0.575 0.881 -1.203 1.322 -0.896 +0 0.182 -1.228 0.493 0.384 1.287 0.301 0.576 -0.374 -0.565 0.477 0.258 0.274 0.137 -2.062 0.598 0.678 -0.595 0.684 -0.163 0.120 -0.687 -0.025 -1.595 -0.294 1.836 0.164 -0.322 -0.356 +1 1.333 0.359 2.348 0.609 1.043 0.044 -0.781 -0.875 0.182 -0.212 -1.769 -1.026 -0.318 0.727 -0.016 -0.072 -0.987 0.023 1.319 0.156 -1.507 -1.252 0.589 -0.224 -0.075 -0.613 0.503 -0.301 +2 -0.616 0.394 1.035 0.486 0.192 0.939 -0.893 -0.496 0.536 -2.069 1.417 0.303 0.153 -1.631 -1.287 -0.399 0.807 -2.077 0.788 -0.360 -0.223 0.254 0.269 -0.712 0.625 -2.319 -0.450 0.154 +1 0.549 -0.181 -0.096 0.069 -1.029 -0.107 0.438 -1.234 -0.898 0.153 1.015 -2.108 1.592 2.136 1.194 -0.724 1.166 -0.044 -0.214 -0.350 -0.137 -0.283 -0.347 1.176 0.647 0.356 -0.474 0.315 +4 -0.891 0.555 -2.892 0.180 -0.407 2.099 -1.892 -0.015 -1.898 -0.550 -1.701 2.301 -0.356 -0.211 -0.043 -1.132 1.055 0.640 0.918 -1.218 1.135 1.020 -0.232 0.895 0.273 -1.594 -0.183 -0.482 +1 -0.591 1.220 -1.045 -2.018 -0.700 -1.294 0.805 -0.153 0.730 -0.709 -0.899 1.302 0.189 -0.067 -0.323 -0.992 -0.444 0.301 1.583 0.403 -1.580 0.180 0.408 1.462 0.669 -0.368 0.101 -0.562 +3 2.485 0.672 -0.391 1.189 0.934 0.492 -0.477 0.667 1.090 -1.720 0.224 0.298 0.266 0.046 1.186 -0.211 -0.003 0.316 -0.178 -1.386 0.214 0.225 -0.365 -0.132 -3.180 1.208 0.901 -1.338 +0 0.271 0.478 -0.099 1.039 1.819 -1.534 -1.267 -0.171 -0.452 0.475 0.475 0.300 -1.660 0.196 1.852 0.516 -0.066 0.618 -0.865 0.418 -0.021 -1.144 0.246 0.044 0.807 0.133 0.206 0.149 +3 1.699 -0.776 -2.083 -0.586 1.730 0.008 -0.190 -0.517 -0.006 -0.571 -0.492 1.046 2.591 -0.843 -0.536 -0.558 1.591 0.758 -0.257 -0.502 1.563 0.041 -0.039 -2.161 0.796 -0.007 0.107 0.103 +2 0.047 1.813 0.065 -0.981 -1.157 -0.911 1.264 0.513 -0.345 -2.925 -0.853 0.344 0.352 0.343 -1.347 0.177 -0.976 0.002 -0.768 1.070 0.337 -0.143 1.380 -0.372 -1.415 1.056 0.252 -0.319 +4 0.529 -0.508 0.645 0.399 2.133 -2.582 0.176 1.125 1.970 -0.784 -1.107 -1.961 -1.190 0.555 1.710 0.943 2.091 1.386 -2.032 -0.347 -0.154 0.886 -0.722 -0.869 -0.481 0.182 0.161 -0.824 +4 -0.153 -0.123 0.201 -0.832 0.208 0.685 0.088 -0.424 -0.287 0.652 -1.100 -0.816 -1.607 0.034 0.862 1.331 1.549 -0.690 0.250 -0.402 1.648 1.303 0.168 -0.619 -3.448 -1.434 -1.852 -1.446 +0 -0.176 -0.165 -1.033 0.396 -1.383 0.832 0.419 -0.265 0.435 -0.341 0.189 0.215 1.167 0.489 1.049 0.393 -0.057 0.515 0.345 -0.361 -1.408 -0.522 -0.530 0.555 -0.065 -0.813 -0.165 -0.321 +3 1.925 -0.611 0.244 -0.123 0.415 0.009 -0.309 0.556 -0.818 -1.314 0.960 0.022 1.507 0.007 -1.452 0.423 -1.793 -1.607 0.021 0.203 0.134 -0.760 -0.794 2.697 -1.129 -0.803 1.452 -0.804 +2 0.623 0.823 -1.980 -0.413 0.950 0.274 -1.197 -2.430 -0.366 -0.072 -0.884 0.143 0.506 0.671 -2.356 -1.314 -0.305 0.572 0.489 0.472 -0.383 -0.328 -0.728 0.768 -0.920 0.850 0.208 -0.045 +3 -1.702 0.020 0.545 -0.312 2.472 -0.493 -0.578 0.204 -0.755 -1.441 -1.098 0.758 -0.305 1.702 -0.441 0.711 0.511 -0.456 -0.879 -1.732 0.553 1.198 -1.590 -0.370 -0.667 0.551 -0.329 1.323 +1 -2.152 0.449 -0.314 -0.783 1.563 -0.577 0.788 -0.146 0.877 0.313 0.516 -0.484 0.215 -0.388 -1.238 -1.033 -0.537 0.200 0.560 0.986 1.586 -0.664 1.384 -1.174 0.054 -1.189 -0.717 -1.424 +1 0.054 -0.221 -0.762 1.565 -0.570 -0.931 -0.081 2.158 -0.822 0.710 -0.246 0.245 0.922 -1.720 -0.896 0.908 0.097 0.212 0.711 -0.890 1.055 0.820 0.238 0.468 1.165 -0.594 0.661 1.744 +3 -2.353 -0.160 0.134 0.856 0.504 0.902 0.414 -0.324 -0.106 -0.852 0.228 -1.584 1.066 -0.957 0.154 0.321 1.163 -1.681 -0.325 0.550 1.751 -0.039 -0.594 2.027 0.803 1.374 -1.210 0.521 +0 -1.931 -1.171 -0.720 1.315 0.062 -0.467 -0.534 -0.363 -0.247 -0.168 -0.276 0.333 0.364 -1.435 0.915 0.585 -0.387 -0.267 -0.068 1.341 0.251 1.849 -0.510 -0.489 0.029 -0.846 -0.248 -0.686 +4 1.807 0.635 -0.442 -0.686 0.860 -0.024 1.815 0.215 0.032 0.152 1.238 -0.773 1.662 -0.636 1.733 0.442 -0.089 0.300 1.649 0.095 1.040 0.976 0.143 -0.859 0.171 1.492 -2.610 -1.756 +0 0.931 0.206 0.321 0.261 0.329 1.121 -0.002 1.139 1.375 0.462 0.979 -0.870 0.303 -1.136 0.604 0.397 1.184 -0.985 -1.118 -0.798 0.729 -0.149 0.657 -0.419 -0.718 -0.123 1.152 1.360 +3 -0.068 1.521 1.084 0.154 0.675 1.173 -0.598 -1.555 0.553 1.246 -1.715 -0.606 -0.241 -0.982 -0.145 -1.113 -0.319 0.699 -2.069 -1.051 1.401 -0.824 -2.599 0.171 -0.075 0.148 0.057 1.273 +1 -0.390 1.186 0.061 0.292 -1.097 0.851 -0.059 -1.753 0.701 0.292 -0.344 -0.345 -1.070 1.159 0.727 -1.058 0.051 -0.709 -1.275 1.555 -0.354 -0.841 -1.190 -0.241 -2.120 -0.152 1.211 0.927 +2 1.633 1.030 -0.281 -0.134 -0.004 1.991 1.514 -0.763 -0.868 1.167 -0.219 0.449 0.355 1.469 -1.055 0.430 1.473 2.279 0.561 -0.746 -0.102 -0.326 1.128 0.566 0.795 0.128 0.345 -0.098 +3 -0.836 1.424 -0.092 -0.888 0.998 -0.287 0.760 -0.292 -0.060 0.494 0.291 -1.996 -1.121 0.929 -0.132 -0.524 -1.359 -1.080 -2.022 -0.101 -0.598 -0.967 -0.068 0.364 2.093 -1.512 1.159 0.973 +4 -1.334 0.003 -0.997 -0.273 0.626 -0.225 -1.463 -0.588 1.194 1.973 -1.396 1.061 -1.935 1.900 1.075 0.684 -0.944 -1.492 -0.950 -1.774 -0.894 -0.377 0.762 0.953 1.407 0.172 -0.420 -0.199 +1 1.076 0.837 -0.898 0.059 1.565 -1.585 1.348 -0.996 0.294 0.413 0.190 2.228 -0.301 -1.639 0.356 -0.170 0.555 0.882 -0.034 0.436 0.144 1.792 -0.371 -0.687 0.881 -0.929 0.392 -0.170 +1 0.098 0.683 -0.446 0.256 -1.177 0.076 -0.721 -0.517 0.572 -1.762 -1.091 0.725 -0.157 -0.735 0.450 -1.107 -1.140 -0.208 -0.138 -0.743 -0.264 1.333 -0.036 -1.150 0.024 -2.119 1.612 -1.187 +3 -0.735 -0.091 -1.695 0.424 -1.050 -0.382 -0.677 1.402 -0.303 -0.843 2.090 1.795 0.836 0.562 -0.865 1.087 0.365 -0.637 0.141 -0.464 -1.522 1.365 -1.814 2.157 -0.214 0.452 0.534 -0.990 +2 0.436 -1.091 -0.536 0.172 -0.239 1.160 -0.073 -1.596 0.035 -1.339 0.287 -0.002 -2.846 0.434 0.129 0.889 0.482 -1.193 -0.813 1.055 1.050 0.785 -0.251 -0.147 0.109 0.171 -1.230 1.716 +1 0.890 -0.779 0.444 2.244 0.096 0.454 0.379 0.554 -1.157 2.048 0.100 0.545 0.088 0.943 -0.502 -1.307 -0.118 0.479 -0.347 -0.671 0.348 -0.928 0.979 -0.312 0.497 -1.161 1.353 0.469 +4 -0.705 -1.278 -1.473 -0.310 1.328 -1.227 2.064 0.367 -1.817 -0.645 0.492 0.809 -2.027 1.593 0.569 0.292 2.666 -0.564 -0.528 2.070 -0.589 -0.004 -1.844 1.699 -0.809 0.373 0.935 -0.294 +4 -0.744 0.924 0.914 -1.443 -1.370 -1.218 1.095 -0.510 -0.185 -0.939 1.337 -1.209 0.936 -1.173 1.133 0.652 -1.671 -0.196 -0.786 0.490 -0.118 0.710 3.285 -0.073 1.925 -0.534 0.153 -1.097 +1 0.859 -1.352 -0.810 1.372 -0.276 -1.201 -1.485 0.829 -0.846 -1.197 -0.397 0.290 0.335 0.317 0.246 0.755 -0.810 1.157 2.321 -0.142 0.275 0.337 1.085 -0.150 -1.289 -0.697 -0.530 -0.549 +1 -0.073 0.245 -1.080 -0.021 -1.839 0.091 -1.368 0.450 -0.662 0.223 0.196 -0.751 -2.204 0.168 -0.547 -0.118 0.890 0.715 -1.275 0.387 -0.455 -0.116 -0.010 0.882 1.829 -0.627 0.966 0.076 +1 -0.123 -1.391 -0.325 1.758 0.833 -0.058 1.058 -0.576 -1.243 -0.441 1.608 -0.366 -0.216 1.222 0.028 -0.365 -1.781 0.243 1.199 -0.115 0.754 0.030 1.123 -1.196 0.531 -0.537 -0.109 -0.559 +2 -1.642 0.465 0.133 2.722 -0.884 -1.520 0.869 0.069 -0.445 -1.540 -0.432 -0.536 1.139 0.670 0.467 0.915 0.022 0.024 -0.533 -0.915 0.505 1.493 -0.322 -0.085 0.221 0.860 0.917 0.777 +1 1.383 0.154 -0.566 -0.704 0.086 0.044 -1.095 1.016 -0.619 0.146 -0.917 -1.342 -1.950 0.194 -0.117 -1.016 -0.063 -0.209 0.748 -1.383 0.520 -0.855 -0.040 -2.630 -0.978 -0.367 -0.132 -0.147 +3 0.880 -1.505 -2.025 -0.735 -1.537 -1.285 -2.376 0.233 -1.158 -1.168 -0.664 -0.643 -0.262 -1.401 0.985 0.279 0.414 -0.440 1.074 0.489 0.190 0.998 -1.670 0.194 -0.669 -0.726 0.799 1.372 +3 0.791 -0.834 0.313 -1.002 -0.288 -0.012 -0.296 0.233 0.320 -0.511 -0.550 1.263 -0.237 0.296 -1.372 1.796 0.457 -2.023 -1.011 -0.720 1.710 2.524 0.532 1.096 1.249 -1.101 -0.073 -0.088 +1 0.016 -1.256 1.030 0.695 -0.548 0.169 -2.750 0.591 0.583 -0.441 1.757 0.403 0.281 1.356 1.047 -0.331 0.497 1.292 0.970 0.377 -0.263 0.287 -0.526 -0.059 -0.385 -0.385 -0.167 1.337 +2 -1.617 1.136 -0.210 -1.406 -0.267 1.290 -0.482 0.299 -1.613 -1.826 -1.077 1.013 -0.051 1.100 1.158 -0.758 1.567 -0.544 1.000 0.959 0.065 0.067 0.320 0.115 -0.390 -0.476 -0.382 1.218 +3 -0.786 -1.324 -0.300 -0.522 -0.532 1.156 -0.811 -0.056 1.522 -1.923 1.621 0.594 -0.522 0.971 0.767 0.757 0.285 0.362 0.510 -0.211 0.706 1.567 1.242 -3.055 0.773 -0.424 0.271 -0.250 +3 0.830 -0.198 0.752 -0.076 -1.562 1.364 0.034 0.247 0.674 1.038 1.723 0.364 0.099 1.224 1.721 -0.835 -0.592 -0.903 0.205 -0.407 0.436 -0.007 -0.250 -0.788 -0.073 -1.728 -2.757 1.361 +1 0.587 -0.320 -0.494 0.629 -0.544 1.825 0.432 -1.368 -0.119 -0.647 -1.641 1.058 1.081 -0.725 0.711 0.877 -0.013 0.952 0.295 1.145 -1.172 0.148 0.202 -1.156 -1.061 -1.584 0.045 -0.440 +4 1.424 -0.550 -2.010 0.877 1.269 -1.230 0.450 0.173 -0.948 -2.378 -0.797 -0.907 -1.524 0.025 -1.600 0.397 2.522 0.475 2.112 1.397 -0.870 0.494 0.870 -1.181 -1.174 1.077 -2.296 0.610 +4 2.374 1.962 -0.205 -0.199 0.327 -1.180 -1.800 -1.338 -0.302 -0.945 1.455 -1.469 -1.335 -1.834 -0.645 -0.398 -0.080 -0.035 -0.622 -1.052 0.419 -0.900 0.781 -1.482 -0.748 2.042 2.179 -1.361 +2 0.168 -0.029 -0.480 -1.151 1.277 1.780 0.245 1.435 0.230 -1.387 0.300 -0.340 0.476 1.571 -0.507 0.388 0.861 1.354 0.986 -1.115 -1.116 0.765 -0.385 -0.984 1.188 -0.370 1.630 -0.440 +2 0.690 -1.282 0.793 -0.553 0.913 -0.550 0.524 -1.925 -0.110 0.080 -0.988 0.035 2.672 -1.018 0.453 -0.591 1.205 0.265 -0.500 0.126 1.693 -0.478 0.892 -1.073 0.414 -0.094 1.193 -0.054 +4 -0.310 0.416 0.807 1.019 -1.982 0.301 0.845 -0.963 1.076 0.210 -0.432 0.427 0.823 -2.154 1.619 -1.949 -1.104 1.426 0.924 -0.451 1.029 0.343 0.235 -2.888 -1.029 -0.367 -2.354 0.775 +0 0.060 -1.659 -0.067 0.175 -0.454 0.168 -0.627 -1.055 -0.521 1.418 0.793 1.380 -0.679 -0.167 -0.730 0.297 -1.999 -0.114 -0.936 0.172 0.725 -1.179 -1.254 1.056 0.082 0.242 0.593 0.144 +4 0.480 0.398 0.139 0.653 0.479 -0.471 1.317 0.202 -0.385 2.443 -0.116 0.255 0.175 -0.130 0.449 -0.845 0.597 -1.758 -1.298 1.826 -1.256 -1.960 -1.231 1.740 -1.792 -0.956 -1.458 -0.274 +0 0.153 0.470 -0.266 -1.327 -2.022 0.498 -0.159 -0.658 -0.307 -1.514 1.575 0.464 0.208 -0.352 0.546 -0.687 -1.155 1.179 -0.413 -0.292 -0.827 -1.416 0.520 0.075 0.005 -0.435 -1.093 1.016 +0 -0.286 1.564 -0.319 0.276 -1.162 -0.427 -0.912 1.185 -0.758 -0.650 0.299 -0.714 1.410 1.958 -0.897 0.846 -0.845 -0.357 0.690 0.140 0.166 1.098 1.091 0.051 -0.634 0.001 -1.009 0.786 +1 0.088 -1.312 -1.834 0.418 -0.195 1.611 -0.353 1.143 0.544 0.822 0.432 0.454 -2.292 -0.237 -0.664 1.271 -1.002 -0.280 0.801 0.301 0.167 1.109 0.260 -0.545 -1.296 0.487 -0.770 -0.241 +2 0.813 1.254 -2.093 0.536 1.029 -0.280 -1.739 -0.734 0.318 -0.421 1.477 -1.328 -0.043 -1.068 0.254 -0.528 1.170 1.071 1.621 -0.496 0.510 -1.032 0.906 -0.704 -0.271 1.126 0.076 -0.424 +0 -0.594 -0.008 0.208 -0.007 -0.415 0.031 -1.021 -0.359 -2.228 0.638 0.714 0.094 0.833 -0.008 0.007 -0.028 0.038 0.145 -0.978 -0.699 0.085 0.998 0.634 -0.309 2.497 0.677 0.296 1.713 +2 1.291 -1.191 2.162 -1.767 0.727 0.141 0.111 0.033 -0.240 0.897 -0.987 0.465 -0.565 0.361 -0.482 0.561 -1.146 -0.282 -0.110 2.571 0.448 -0.109 0.466 -0.611 1.207 0.457 0.779 0.229 +2 -1.186 0.895 -2.815 -0.174 -2.517 -0.212 0.497 -0.235 0.259 -0.565 0.462 0.483 -0.650 0.894 0.102 0.016 0.359 0.485 0.114 0.678 1.460 -0.330 -1.017 1.238 -0.455 0.929 1.533 0.930 +4 0.066 -0.357 0.693 -1.102 -0.895 -1.451 1.235 -0.721 0.404 -0.126 2.898 0.442 -1.862 1.175 -0.387 -0.201 -0.673 0.196 0.183 -0.645 -0.508 0.101 -2.343 -1.441 1.670 0.328 -0.525 -1.696 +1 1.231 0.104 2.035 0.822 0.683 -0.803 0.627 0.217 0.520 -0.685 0.128 -0.817 -2.147 0.998 -1.502 0.107 0.333 0.327 -0.283 0.271 0.471 0.735 1.733 0.234 0.064 -0.907 0.436 0.667 +4 1.922 -0.168 -0.723 -3.250 -1.100 -0.312 0.895 1.385 0.091 -0.413 -0.530 0.358 -0.787 0.861 1.163 1.222 -1.459 0.346 -0.212 -1.762 -0.069 -0.390 -0.464 1.606 -1.529 -0.072 0.183 -2.646 +3 0.849 -1.715 -1.296 0.536 -0.342 0.219 0.414 -1.672 1.742 -0.106 -0.070 -0.191 0.554 0.755 -2.923 0.802 -1.271 -0.531 -1.079 -0.559 -0.361 -0.230 -1.667 0.006 0.459 -0.095 -0.581 -0.949 +2 1.551 -0.768 2.033 0.831 -0.096 -1.425 0.630 -0.245 0.341 -1.330 -0.442 0.895 -1.948 -1.061 -1.582 0.273 0.901 -0.562 0.135 2.085 0.302 -0.196 0.285 0.033 0.506 0.350 0.119 0.555 +2 -0.477 -0.321 1.003 1.159 -1.807 -1.852 1.447 0.957 1.844 -0.257 -0.076 -0.567 0.184 0.233 -1.553 1.145 0.985 0.082 0.639 -0.620 0.490 -0.418 0.095 0.913 0.481 0.713 -1.571 -0.254 +0 0.507 0.253 0.279 -1.382 -0.640 -0.938 0.399 0.434 -0.043 0.968 1.491 0.353 0.671 -0.542 -0.725 1.010 -0.875 0.697 1.719 0.653 -0.081 -1.340 0.163 1.009 -0.179 1.021 0.365 0.291 +1 -0.457 0.028 0.509 -0.007 1.680 -1.593 1.516 -1.371 0.851 -0.582 -0.063 -1.113 0.319 1.019 -0.284 -0.600 0.041 0.056 0.375 -0.582 -0.468 1.488 0.312 2.524 -0.375 0.480 0.331 -0.593 +2 -1.066 -2.235 -0.145 0.163 1.814 1.861 -0.663 0.808 -1.151 0.073 -0.130 -0.876 -0.120 0.843 1.096 -0.145 -0.087 -1.839 0.274 -0.265 -0.090 -0.481 -0.949 -0.873 -0.677 -1.327 0.291 0.560 +3 0.965 -1.363 -0.207 -0.816 1.794 0.399 0.850 0.293 0.761 1.081 0.038 -0.531 -0.935 1.759 0.821 -2.223 -0.245 0.416 1.672 -1.103 0.426 -0.400 -1.197 1.345 -0.964 0.971 -1.595 -0.490 +2 -0.332 -0.879 2.848 -0.804 1.039 -0.565 0.243 0.008 -0.165 -0.197 0.247 1.376 -1.131 -1.768 -0.830 0.128 -0.296 1.542 1.420 -0.455 1.545 0.421 -0.729 -0.120 -0.193 -0.736 -0.634 0.451 +2 -0.052 -0.082 0.454 2.743 0.105 -0.889 0.328 -0.670 -0.549 0.193 1.821 -0.288 0.302 -0.020 0.671 -2.285 -0.282 -0.127 0.251 0.770 1.370 0.102 1.537 0.807 1.142 0.427 0.339 1.382 +3 -1.036 -0.245 -1.387 0.588 1.979 0.527 -1.955 0.105 -0.015 -0.530 -2.045 0.479 0.948 -0.884 -0.894 -0.317 1.743 -0.283 -0.766 -0.399 0.579 -2.023 -0.232 -0.348 -0.108 -0.696 -1.331 0.932 +3 -0.759 0.826 -1.316 0.066 -1.930 -1.259 -0.423 1.820 -0.165 0.735 1.509 -0.222 1.723 -0.039 -1.027 1.102 0.628 -0.298 -0.956 1.008 -1.109 -2.168 1.164 0.883 0.681 -0.280 0.685 -0.754 +3 -0.901 -0.964 1.080 0.943 0.487 1.072 -0.879 0.349 -0.125 0.847 -0.115 -0.385 0.541 0.876 -0.668 -0.061 -1.058 -2.498 0.200 0.609 0.262 -1.950 -0.351 -2.218 1.370 -1.086 1.255 0.946 +0 -0.003 -0.798 2.625 -0.169 1.294 -1.569 -0.530 0.097 -0.908 -0.422 -0.218 -0.234 1.855 0.378 0.480 0.540 0.613 -0.083 -0.321 0.167 1.157 1.126 -0.240 0.768 -0.195 0.034 -0.474 -0.182 +2 0.390 2.425 0.402 -0.387 0.176 -0.950 -0.958 1.250 0.304 1.119 -1.625 -0.976 0.298 -0.824 -1.424 -1.079 -0.321 0.413 1.715 -1.053 -1.001 -1.451 0.573 0.125 -0.965 -0.831 -0.124 -0.067 +1 0.486 1.809 -0.922 0.360 0.619 0.765 0.181 -0.839 1.056 -0.940 1.118 0.512 -0.585 -0.560 -1.273 0.184 -1.867 -1.318 1.177 -0.087 1.076 -0.794 0.625 -0.186 -0.984 -0.625 -0.533 0.855 +3 0.491 -0.702 -1.033 0.207 -0.477 -0.740 1.257 -0.184 -0.415 0.760 -0.119 0.560 -1.990 -2.082 -0.966 -0.195 1.153 -1.514 -1.345 1.046 -0.953 -1.076 0.951 -2.891 0.397 1.102 -0.078 -0.248 +1 1.614 -0.136 0.494 1.715 -0.936 -1.037 -1.244 0.755 -1.013 0.560 1.360 -0.662 -0.762 0.670 0.048 -2.363 1.532 0.552 -0.380 -0.421 0.700 -0.045 0.057 0.151 -0.819 -0.361 -0.188 -0.761 +3 -0.758 1.171 0.070 -0.625 0.385 0.367 0.270 1.378 -0.583 0.357 -1.737 -1.534 -0.097 -0.795 1.239 0.819 0.455 -0.127 -1.026 -2.993 -0.822 0.347 -1.154 1.422 0.114 0.669 1.408 -0.981 +2 0.965 0.106 -0.993 -1.816 1.000 0.576 -0.333 1.826 -1.877 0.382 -1.111 0.477 0.506 0.796 -1.651 0.884 -0.306 -0.276 1.014 1.494 -0.102 -0.735 -1.148 -0.880 0.039 -0.710 1.008 0.506 +1 -0.091 -0.935 0.146 0.745 0.512 -1.085 0.758 -0.061 0.501 -0.802 1.046 0.183 1.189 -1.535 -1.401 -0.240 1.468 1.963 -0.888 0.053 0.155 0.718 0.885 -1.114 0.065 -2.009 0.811 -0.339 +1 0.320 0.500 -0.865 0.137 -0.065 0.686 0.665 -0.563 0.129 0.974 -0.620 1.905 -1.173 -0.257 -0.898 0.510 0.012 -1.345 2.083 -0.583 -1.974 0.237 0.127 -0.630 -0.839 -0.022 -1.052 0.855 +1 -1.083 -1.305 0.582 0.652 -0.482 1.432 -0.369 -0.270 -0.036 -0.243 1.277 -1.042 -0.754 -1.191 1.884 -0.325 -0.769 -0.287 0.849 0.664 -0.575 -0.400 -1.455 0.139 1.282 -0.582 0.636 -0.582 +2 -1.102 0.290 -0.456 -0.952 0.485 0.848 -0.246 1.781 2.216 0.497 -0.469 0.269 -1.224 -0.872 -1.683 -0.628 -1.233 0.624 -0.749 0.277 0.748 1.171 -0.353 0.311 0.343 1.272 0.784 1.303 +4 0.710 -1.589 0.628 0.550 -0.917 2.256 -1.788 1.483 -0.414 -0.245 0.400 -1.738 2.769 0.228 -1.387 -0.301 -0.061 0.154 -0.832 1.165 1.238 1.993 0.352 1.191 1.750 -1.816 2.268 0.770 +3 1.887 -0.072 0.275 -1.383 -0.631 -0.719 -0.183 -1.219 -1.721 0.107 -1.468 0.728 1.970 0.227 0.263 -2.221 0.624 -0.075 0.479 -1.171 -0.268 1.231 -0.762 -0.324 0.902 0.696 1.057 -0.714 +0 0.251 0.006 -0.777 0.071 1.080 -0.684 1.468 1.025 -0.106 0.796 -0.913 -0.103 2.217 1.194 0.498 0.069 -0.074 -0.029 -0.774 0.652 1.044 -0.252 0.467 -0.728 -0.067 -0.846 -0.227 0.119 +1 -0.714 0.545 -0.966 -0.189 0.324 -0.997 -1.312 0.492 1.123 -0.079 -0.457 0.156 0.055 0.243 -2.528 -1.069 -0.845 0.151 0.186 0.636 0.258 -1.301 1.447 1.272 -1.258 0.333 -0.595 0.139 +2 0.039 1.796 -1.370 -0.965 0.665 -1.384 1.322 -0.172 0.011 -0.665 -0.580 -0.593 0.788 1.122 1.517 0.105 -0.347 -0.567 -1.548 -1.767 -0.146 0.028 -0.325 0.155 2.357 0.028 0.827 0.221 +3 0.652 1.086 0.449 -2.111 -0.865 1.378 0.384 0.778 -0.125 1.722 1.731 0.618 0.103 -0.216 -0.089 0.014 0.805 0.331 -1.516 0.821 -0.786 -1.074 0.038 1.876 1.535 1.173 -1.467 0.755 +4 1.714 0.011 0.487 -1.061 -1.654 0.230 1.539 -0.151 0.519 1.872 0.116 0.690 -0.270 -0.371 1.306 -0.599 1.296 -1.746 0.065 -0.795 0.678 1.224 0.935 1.749 -3.034 -0.967 -0.682 -1.409 +0 -0.851 -0.184 0.525 0.488 0.532 -0.764 -0.413 -0.876 0.483 -1.534 1.613 -0.801 -0.740 0.420 0.872 1.148 0.692 -0.266 0.912 -0.838 -1.591 0.409 -0.165 -0.734 -0.123 0.353 0.270 -0.514 +4 0.688 -1.386 -0.405 0.051 -0.857 2.145 0.410 0.932 0.947 0.112 -1.201 0.789 2.011 -0.378 -0.567 0.839 0.924 1.145 -0.141 -0.144 -2.628 0.050 -0.564 -2.207 1.894 0.242 -1.783 0.377 +2 -0.777 -0.742 0.564 0.520 -1.385 -0.151 2.188 -0.049 -0.586 -0.322 -2.466 -0.501 -0.017 0.826 -2.192 1.280 -0.037 -0.661 -0.103 0.661 1.042 -0.167 0.789 0.587 -0.963 -0.545 -0.070 0.683 +3 0.409 -1.231 -0.114 -0.557 0.520 -1.178 0.838 0.573 1.533 0.069 0.259 -0.605 -1.950 -2.106 0.855 0.466 0.070 -0.739 -0.441 -2.356 -0.276 1.855 -0.465 -0.211 -1.000 0.576 -0.641 1.019 +4 -1.315 1.710 0.154 2.521 -0.292 -0.134 0.104 -0.059 1.891 -1.778 2.089 -0.061 1.221 -2.377 -1.264 0.227 -1.274 0.380 -1.431 -1.382 0.050 0.198 -0.623 -0.056 0.350 -1.799 0.046 1.526 +4 0.804 -1.845 0.985 -0.696 0.360 -1.930 -1.199 0.469 -0.701 -0.840 1.396 2.211 0.937 -1.092 1.764 -1.561 -0.304 0.055 1.924 0.913 -0.464 -0.945 1.002 -0.533 0.217 -1.370 0.569 0.489 +0 -0.755 -0.463 -1.117 0.995 0.089 1.689 0.279 0.090 -1.091 -0.500 -1.662 -0.002 0.480 0.200 -0.447 -1.816 -0.402 0.219 0.923 0.178 -0.734 -0.920 0.656 0.567 -0.408 0.608 -0.973 -0.310 +4 2.000 2.135 -0.970 1.175 0.336 0.988 0.277 0.615 1.321 -1.749 0.360 -1.170 -0.464 0.001 -1.008 -0.581 -1.401 -0.593 0.512 0.865 -0.769 1.328 0.577 1.433 -1.771 0.311 0.611 -1.719 +4 0.326 -0.753 -0.037 0.875 0.096 2.044 -0.149 -0.979 0.737 -0.976 -1.499 -0.190 1.217 -1.551 -0.351 0.057 0.570 -0.433 -0.257 1.459 0.475 1.216 1.232 -1.183 -0.216 -4.067 1.339 1.685 +2 1.057 0.967 0.719 1.820 -0.083 0.649 -0.584 2.452 1.984 0.012 0.387 1.359 -0.147 -0.657 0.874 0.078 0.183 -0.834 -1.718 0.236 0.905 -0.226 1.436 -0.704 -1.055 -0.067 -0.083 0.770 +1 0.102 0.880 -1.018 1.061 -0.906 -2.742 0.190 -0.875 1.500 0.301 -0.085 0.202 -0.497 1.684 -0.548 0.463 -0.819 0.002 -1.237 0.229 0.065 0.549 0.468 0.029 2.039 -0.018 0.039 -0.085 +2 -0.165 0.043 0.355 0.151 0.572 -1.552 1.959 -1.198 1.025 -0.771 0.070 1.502 0.095 1.109 -0.202 -1.300 -0.226 1.081 1.651 -2.478 0.547 -0.535 0.303 -0.732 -0.312 0.054 -0.793 0.608 +2 -0.046 -0.439 0.282 1.580 0.239 2.167 -0.898 -0.020 -0.373 -1.085 -0.153 0.417 0.524 2.170 0.464 1.150 0.066 1.678 2.398 0.531 -0.993 -0.549 0.282 0.456 0.698 0.460 0.007 -0.636 +2 -0.242 -1.101 -0.289 -0.481 -0.014 -0.964 -1.183 -0.751 -1.835 -0.585 -2.283 -0.633 0.553 0.428 0.066 -0.793 0.793 -1.545 -0.574 -0.703 0.050 -0.548 -0.235 -0.622 1.519 -0.248 -2.021 1.357 +1 -0.666 0.502 1.135 -0.860 0.341 1.117 0.685 0.896 1.295 0.481 -0.008 -1.384 -0.833 -1.098 0.273 -0.767 -1.145 -1.034 -0.191 0.546 -0.259 -1.598 -1.582 -0.808 0.750 -0.391 -1.412 1.549 +1 1.806 1.632 1.639 -0.037 0.362 0.087 -0.163 -0.703 1.917 -0.084 -1.452 -0.187 0.119 0.309 0.173 -0.818 1.895 -0.177 0.755 0.365 -1.056 -0.188 0.873 -0.381 0.004 0.189 -0.347 0.506 +2 1.608 -1.317 0.531 1.743 -0.816 1.071 0.164 -0.683 0.936 0.484 1.085 -0.153 -0.217 -2.794 0.184 1.089 -1.028 -0.774 0.524 0.716 0.850 -0.003 -0.595 -0.878 -0.822 -0.885 -0.931 -0.047 +2 0.173 -1.826 0.455 0.773 -0.077 2.374 -0.683 -0.361 -0.395 -0.076 -0.329 -1.407 -0.547 -0.098 1.229 2.202 1.335 -0.775 -0.696 -0.159 1.785 -0.165 -1.328 -0.501 0.757 0.690 -0.200 0.683 +0 1.575 0.277 -0.577 -1.055 -0.063 -0.892 -1.133 -0.646 -0.268 -0.240 -0.089 0.425 -0.181 0.754 0.714 0.999 -0.117 -1.240 -0.241 -0.968 0.797 -1.478 0.341 0.202 0.733 0.273 -0.047 -1.093 +0 -0.790 -0.088 1.303 0.038 -0.072 -0.556 -0.486 1.070 -0.062 0.255 -0.698 0.777 1.223 -0.928 -0.902 -0.844 1.036 -0.961 -0.344 -0.984 1.692 -0.532 1.663 -0.027 -0.493 0.799 -0.039 1.410 +3 0.210 -0.878 1.082 -1.756 0.466 0.610 -1.270 -0.294 -2.495 1.477 -0.760 0.948 0.362 1.403 0.293 -0.364 -2.304 0.245 -0.543 -0.617 1.208 0.652 -0.946 -0.033 0.018 0.977 1.255 -0.316 +3 0.406 -0.805 0.062 -0.262 -0.108 -0.457 -0.802 -0.245 -0.476 0.299 0.945 -0.941 -0.537 2.319 -1.821 -1.610 1.053 1.913 1.453 0.314 -0.431 -0.274 0.401 0.935 0.537 -2.015 -1.177 0.278 +4 2.550 0.748 1.769 0.806 0.166 -0.879 -0.591 -1.819 -1.293 0.204 -1.952 -1.096 -0.770 -0.189 0.252 -0.334 -0.896 -0.664 0.404 -1.352 1.523 -0.861 1.312 -2.015 -0.099 0.838 0.885 -0.760 +1 -1.998 -0.705 0.496 0.644 -0.678 -0.305 -0.597 0.110 1.197 -0.771 1.001 -0.782 -0.848 0.819 0.922 0.851 -1.316 -0.466 0.823 0.042 -1.074 0.458 -0.715 1.795 1.545 0.604 1.361 0.065 +3 -0.706 -0.562 -0.291 0.240 0.627 1.256 -2.199 2.024 -1.061 0.100 0.961 0.642 1.120 1.018 0.417 0.750 -1.238 -0.710 -0.974 -2.370 -0.939 -1.035 -0.931 0.668 -0.767 0.845 -1.567 -0.653 +1 0.091 0.437 0.773 0.426 0.499 0.775 -1.778 0.002 0.305 0.416 -0.171 0.161 -0.720 -1.775 0.983 -1.134 -0.160 -0.749 1.587 -0.080 -0.310 0.748 2.156 0.406 -0.377 0.697 -1.573 0.046 +1 0.074 -0.643 0.322 1.828 -0.167 -0.682 0.307 -0.795 -0.147 -0.678 -1.070 -0.879 -0.661 -1.042 1.279 0.431 0.942 -1.358 -0.294 0.419 1.653 -0.753 -1.286 0.191 -0.674 1.706 0.542 -0.286 +0 1.646 0.256 0.950 1.279 0.222 0.814 0.013 -0.052 -0.662 0.173 1.887 -0.902 0.617 0.321 1.475 1.663 -0.924 -0.574 -0.520 -0.484 -0.398 0.347 -0.851 -0.711 -0.791 0.493 -0.064 -0.241 +0 -0.407 0.220 -0.902 0.302 0.630 -1.461 -0.924 0.058 0.203 -1.461 0.087 0.649 0.290 -0.019 -0.298 0.611 1.554 0.676 -0.307 0.018 -1.550 1.326 -0.468 -0.346 1.214 -0.960 -0.532 -1.930 +3 0.789 1.609 -0.893 2.502 0.099 0.442 0.791 -0.895 -0.803 -0.254 0.124 -0.806 -1.377 1.155 -1.094 0.915 -1.190 -0.679 -0.176 2.284 0.191 0.267 -0.139 -0.887 0.682 0.029 1.406 -1.150 +3 -0.909 0.585 -0.799 0.752 0.885 0.965 1.838 0.471 0.581 -0.643 -2.313 0.323 -1.368 -1.398 -1.165 0.648 0.966 0.161 -1.108 -0.283 1.411 -0.265 -0.502 1.352 2.281 -0.328 -1.629 0.077 +3 -0.113 1.178 -1.153 0.456 0.597 -0.780 0.725 -1.412 -1.025 0.704 -1.474 0.714 -1.076 0.759 -1.767 -0.307 0.526 1.880 -0.786 -1.818 -1.150 0.617 1.949 -0.669 0.957 0.088 0.449 -0.231 +1 -0.231 0.454 1.027 0.022 0.835 -0.054 2.227 -0.369 -0.246 0.554 0.433 -0.455 0.064 0.150 1.486 -0.952 0.515 1.058 -0.353 1.531 -0.699 1.241 1.955 1.938 -0.154 0.022 -0.348 0.642 +3 -1.345 -0.861 1.377 1.020 -0.363 0.854 0.341 1.500 0.075 1.026 2.642 0.598 0.049 0.722 -0.365 2.388 0.180 -0.273 -0.836 1.097 0.867 -0.497 0.576 -1.509 0.185 0.453 1.707 -0.771 +3 0.514 0.750 -0.759 0.622 1.126 -0.099 2.002 0.044 1.141 0.293 0.586 -1.671 0.371 -0.735 -0.846 1.744 -0.214 -1.003 -0.359 -1.300 1.434 -0.696 0.529 1.356 -0.500 0.532 -1.030 2.446 +3 0.074 -0.369 0.255 -2.282 -0.620 0.037 -0.103 -0.950 1.370 -0.529 0.747 -2.254 -1.407 -1.845 -1.104 -0.511 -0.317 -0.885 -1.617 0.104 -0.079 0.835 -0.166 -0.261 1.317 -0.873 -1.462 1.193 +2 0.710 0.708 0.993 0.129 -0.574 -1.554 1.068 0.571 -1.231 -1.298 -1.814 -0.299 1.158 -1.470 0.081 -1.516 -1.453 -0.686 1.809 -0.802 -1.403 -0.545 -0.529 -0.249 -0.668 -0.566 0.400 -0.032 +2 0.118 0.392 0.137 1.190 0.346 -0.062 1.042 0.233 0.121 0.314 -0.094 0.009 -0.587 -0.751 -0.999 -1.392 -0.880 -0.174 0.103 -0.271 -2.356 1.575 1.086 0.446 1.907 -0.977 1.888 -0.965 +3 -0.895 -1.165 0.868 -0.494 2.243 -0.084 0.044 -0.227 -1.410 -1.413 1.387 1.338 -0.984 0.498 1.333 -1.326 -0.011 -0.525 0.165 1.233 0.398 0.590 -2.219 0.700 -0.036 2.159 0.599 -0.327 +0 0.853 0.553 0.335 1.126 0.342 -0.370 0.518 0.450 0.476 -0.883 -0.979 0.880 -0.148 -1.943 -1.371 -1.319 1.067 -0.048 0.035 -0.353 -1.059 0.760 0.652 -0.705 1.402 0.745 -0.854 -0.287 +1 0.175 -0.986 -1.711 -0.203 1.101 -0.066 -0.348 1.730 0.753 -1.728 -0.279 0.223 -1.068 -0.293 0.021 -0.224 -1.211 1.468 0.267 -0.859 0.876 -0.081 0.992 -0.104 -1.476 -1.067 1.141 1.033 +1 -0.784 0.965 2.082 0.834 -1.262 0.801 -0.944 -0.349 0.187 0.355 0.065 -0.601 1.060 -0.528 1.255 -1.359 0.216 -1.226 -0.834 -0.513 1.521 1.308 0.874 0.033 0.188 1.266 0.701 0.018 +0 -0.178 -1.397 -0.517 0.442 -0.368 -0.199 -0.398 -0.582 -0.595 -0.625 -0.753 -1.017 -0.380 -0.640 -1.072 0.766 1.233 -0.000 0.066 -0.693 1.067 1.738 -0.164 -0.521 0.341 0.129 -0.500 0.471 +3 -0.188 0.841 -0.825 -0.493 -2.010 -1.324 1.808 1.641 0.066 1.663 -0.160 -0.243 0.057 -0.125 -0.215 1.143 0.150 0.712 1.031 0.388 -0.970 -1.353 0.149 -0.171 0.087 -0.016 2.584 -1.330 +0 0.321 0.526 0.383 -0.663 0.562 0.611 -1.063 0.212 -0.034 0.605 2.395 -0.154 1.352 -1.214 0.346 0.138 0.083 -1.246 1.582 0.663 0.010 -0.641 0.398 0.938 -0.271 0.019 0.255 -1.015 +3 1.780 -0.582 1.520 1.027 0.564 0.386 0.897 0.035 -0.690 -1.642 -0.527 0.786 -0.498 -2.023 -0.350 0.736 -1.288 0.273 -0.492 0.294 0.942 1.331 1.685 0.850 0.626 -1.485 -0.895 2.060 +2 0.515 1.817 1.375 -0.841 0.119 0.731 0.083 -1.285 -0.757 0.627 1.929 -0.647 -0.953 1.128 0.234 -0.389 -0.193 -0.142 0.751 0.228 -1.530 -0.127 1.836 1.109 1.254 -0.835 0.361 0.949 +2 1.071 0.944 0.909 -0.615 -0.151 0.519 0.482 0.595 -1.769 1.197 -1.703 -0.751 0.195 -0.029 1.419 -1.171 -0.364 -1.969 0.132 -0.208 -1.240 1.309 1.080 0.072 1.138 -0.092 0.714 0.863 +3 -1.387 0.207 -0.582 -0.177 -1.454 1.041 -0.097 0.055 -0.833 -0.277 0.122 -0.613 -0.216 1.386 -1.750 0.185 -0.785 1.336 -0.181 -1.047 -0.216 0.350 2.776 -2.110 -1.199 1.305 -1.509 0.010 +2 -0.332 -0.849 0.101 0.023 -1.638 -0.787 0.726 -0.450 0.461 -1.051 0.125 0.124 0.299 -0.377 0.159 1.895 0.419 1.472 -0.173 -2.591 0.751 1.965 0.138 -0.571 0.089 1.166 0.136 -0.675 +3 -0.567 1.802 2.022 1.831 -0.193 -1.782 -2.343 -0.614 -0.198 1.301 0.868 0.227 -0.890 -0.961 0.254 0.697 0.392 -1.035 0.651 0.426 -1.071 -0.784 0.688 -0.235 1.589 0.501 -0.487 -0.010 +3 -0.234 0.964 -0.100 3.521 -1.906 -0.668 1.077 -1.570 0.197 -0.332 -0.117 1.211 0.146 -0.751 1.019 0.234 -1.381 -0.669 -0.084 0.769 -0.128 0.956 0.071 -0.678 0.057 0.730 -0.184 -1.102 +4 1.008 0.307 -1.714 2.878 -0.621 -1.037 -0.184 0.693 -0.962 -0.082 -2.204 1.117 1.008 -0.391 -1.221 -0.583 0.797 -0.136 -1.816 0.701 -1.776 -0.267 -0.287 -0.630 -1.013 0.455 1.014 0.031 +2 -0.422 0.093 -0.293 0.165 0.609 0.711 -0.126 -0.585 1.333 -0.920 0.773 -2.053 -1.304 -0.810 -0.231 0.415 0.095 0.100 0.688 1.949 2.339 -0.957 -0.384 -1.366 1.061 -0.715 1.082 0.207 +3 -0.929 0.262 0.691 -0.025 0.648 -0.595 1.413 0.483 -1.241 0.703 -0.026 -0.906 -1.364 -1.776 1.458 0.432 -0.179 1.765 2.459 0.945 0.479 0.324 1.123 0.083 -2.018 -0.405 -0.429 -0.652 +1 0.436 -0.622 -0.269 1.258 0.454 1.475 0.269 -0.154 0.708 0.368 0.439 -0.230 -0.572 -0.534 0.520 0.649 -2.629 -0.733 -1.591 -0.498 -0.603 0.148 0.186 2.130 0.373 0.639 -1.422 -0.335 +1 -1.024 -1.332 -0.141 -1.287 0.308 -0.784 0.190 -0.173 0.637 0.326 -0.404 -1.368 0.163 -1.742 1.246 0.529 0.843 -0.969 0.356 -0.667 0.149 -0.998 -0.083 -1.379 -0.160 -1.644 1.747 0.160 +0 0.761 0.353 0.377 -0.599 -0.146 0.791 0.982 -1.178 -0.633 -1.390 0.201 0.884 -0.279 -0.663 -0.335 2.306 1.192 0.731 0.320 -0.013 -0.907 -1.062 -0.568 -0.954 -0.608 -0.102 -0.025 -0.620 +3 1.357 1.320 -0.620 -0.765 1.764 -0.103 0.003 -0.252 1.322 2.144 0.070 2.418 -0.555 -0.283 0.673 -0.817 0.660 -0.518 -1.544 -1.743 -0.324 0.418 -1.361 0.179 0.181 -0.168 -0.926 0.240 +0 -0.532 -0.159 0.164 -0.240 0.784 -0.715 0.618 -0.571 -0.307 1.825 -0.258 -0.457 0.046 1.281 2.048 0.427 1.063 1.525 -0.567 0.892 1.131 0.291 0.323 0.812 0.270 -1.020 -0.617 -0.127 +4 0.207 -0.534 -0.284 1.518 1.505 -1.650 -0.564 2.951 -0.433 0.423 1.136 0.853 -2.818 0.334 1.749 0.029 -1.041 -1.022 -0.199 0.675 2.890 -0.290 0.562 -1.545 -1.685 -0.497 0.445 -0.086 +2 1.233 -0.310 0.880 0.967 -1.072 1.471 0.329 -2.165 0.198 -0.559 0.446 -0.640 -0.634 -1.036 0.225 0.520 -0.003 -0.294 0.097 1.057 -1.040 -1.337 -1.201 -1.639 -1.014 1.141 -1.699 -0.251 +2 0.967 1.506 -1.548 -0.351 1.034 -0.192 -0.910 -1.550 -0.949 -1.689 0.058 0.896 -1.261 0.258 0.845 -0.003 -1.611 0.386 -0.055 -0.157 -0.547 0.087 0.510 2.391 0.112 1.511 0.358 -0.100 +0 0.996 -0.846 -0.024 -0.112 -0.723 -0.009 0.008 -0.182 -1.312 -0.542 -0.810 -0.494 0.189 -0.846 -0.119 -0.022 -1.187 -0.309 -1.042 0.864 0.410 -1.262 -1.014 0.220 0.589 -0.741 0.154 -1.433 +2 0.171 -0.019 -1.122 0.447 1.117 0.850 -0.032 -0.230 -0.648 -0.980 0.651 0.253 -0.336 0.494 0.651 1.842 -1.476 -0.922 2.819 -0.069 0.260 0.416 -0.729 -0.650 -0.747 -1.627 1.034 1.667 +2 -1.309 0.923 0.243 -0.390 -0.363 0.071 0.921 -1.414 0.669 0.885 1.301 -0.227 -0.772 1.159 1.273 -1.222 1.614 -0.852 1.203 0.321 0.165 -0.956 0.659 0.836 -0.233 -1.157 -1.848 0.894 +3 -0.152 -0.112 -2.104 -0.174 -1.364 1.154 -1.357 0.806 1.736 0.498 -1.149 -0.277 -0.130 0.659 -1.843 -0.991 2.131 1.376 0.072 -1.012 -0.299 0.843 -0.046 -0.104 1.600 0.384 -0.561 1.031 +3 -0.192 0.545 -0.293 0.671 0.913 0.899 0.118 -1.040 -0.612 0.595 2.449 -0.341 0.456 -0.556 -0.974 -0.676 -2.082 1.353 -2.230 -0.264 -1.178 -0.884 1.377 0.201 -0.965 -0.590 -0.765 -0.044 +3 -1.936 -1.088 1.044 0.697 -0.850 -1.035 -2.450 1.471 0.041 1.884 -0.192 0.283 0.891 1.639 0.501 0.002 0.028 -1.046 -0.382 0.051 1.382 -0.430 1.063 -0.082 1.244 0.677 0.609 0.979 +1 1.607 1.675 1.529 0.547 0.381 -0.458 0.769 -1.281 -1.285 -0.420 1.887 -0.513 -0.378 0.288 0.899 1.252 -1.120 -0.097 0.225 0.325 0.866 0.820 0.025 1.152 0.969 0.888 -0.529 0.713 +0 0.066 -0.601 0.780 -0.060 -0.873 -0.920 -1.477 -0.510 1.103 0.178 -0.530 0.805 -0.832 0.095 -0.180 0.779 -1.577 -0.850 -1.638 -0.260 1.211 -0.505 -0.511 0.872 1.069 -0.089 0.685 -1.077 +1 -0.784 0.464 0.084 -0.807 -1.070 -0.435 -0.511 1.221 -0.214 -0.373 1.129 1.350 -0.831 -1.244 0.515 -0.207 -0.545 1.038 -0.261 1.918 0.048 0.164 2.240 0.310 -0.706 -1.020 -0.462 0.236 +3 -0.108 -0.819 0.644 2.073 -0.858 0.355 -0.840 0.881 0.600 0.510 -1.527 -2.249 0.410 -1.385 -0.239 -0.478 1.035 0.827 -0.412 0.329 0.348 0.864 -1.989 -0.874 0.891 -1.361 -1.272 -0.192 +0 0.802 0.644 -0.861 0.652 -0.836 -1.610 -0.367 1.123 0.656 -0.002 -0.417 -0.390 0.109 -1.068 -0.348 -0.216 -1.351 -0.838 0.411 0.010 -0.309 -0.911 1.577 -0.838 0.252 -0.488 0.036 0.458 +0 0.700 0.022 0.351 -0.322 0.058 0.581 -0.262 -1.362 -1.557 0.519 0.115 0.838 -0.767 -1.109 0.810 0.305 -0.406 -0.134 -0.780 0.813 0.202 0.127 0.706 -0.592 1.600 -1.261 -0.227 1.575 +4 0.656 -1.230 1.246 0.974 -0.268 2.999 1.243 -0.949 0.746 0.066 -0.777 -1.070 0.995 -0.598 -0.102 -0.921 -0.748 -0.127 0.388 -2.588 0.017 -0.746 1.402 2.943 -0.359 -1.722 0.117 -0.205 +4 -1.182 1.399 0.248 0.793 0.005 0.603 -2.245 -0.108 -0.793 -0.360 1.011 -1.600 0.740 0.461 1.280 -2.074 0.589 0.253 -0.053 1.444 -2.108 0.125 1.022 -1.625 0.594 0.325 -2.130 1.097 +4 1.600 1.528 0.757 -0.253 -3.242 0.297 -0.002 0.140 0.054 -1.326 0.557 0.632 0.682 0.947 0.604 -0.410 -0.071 0.091 0.108 0.998 0.644 2.077 2.206 0.080 -3.837 1.237 -0.540 -0.801 +3 0.463 -0.276 -0.228 -1.221 0.145 0.481 0.972 -1.185 -0.821 0.577 0.579 0.844 2.461 -1.190 -0.704 0.208 0.089 0.376 1.629 2.278 -0.753 0.726 1.495 -0.597 0.253 -0.320 -1.558 -0.736 +0 -0.927 0.521 -0.218 -0.600 -1.418 -0.198 -0.504 -1.187 0.093 0.804 0.796 -1.195 -0.722 -0.485 0.250 -0.622 1.284 0.767 0.003 -0.729 0.882 0.167 0.760 0.384 0.990 0.585 0.278 -0.920 +2 -1.364 1.628 1.776 -0.784 0.135 1.451 -0.474 0.261 0.994 -0.467 1.053 -1.387 -0.504 0.256 -0.056 -0.101 -0.407 -0.068 -1.775 0.433 0.826 1.553 1.428 0.849 -0.953 0.693 -0.113 -0.176 +0 -0.494 0.024 0.500 -0.522 -0.957 -0.986 -2.241 0.540 -0.163 1.210 0.645 0.077 0.358 -1.731 0.360 -0.208 -0.719 -0.425 0.664 -0.569 -0.242 0.572 -0.255 0.178 -0.105 1.105 0.171 -1.639 +4 1.266 -1.204 0.387 0.542 -0.348 -0.738 1.226 -0.507 -2.044 -0.702 0.234 -0.040 -0.578 -1.536 0.187 -0.171 0.702 -2.046 0.855 -0.891 -0.848 0.073 -1.585 -1.707 -2.324 0.449 1.423 -1.327 +1 1.091 0.470 -0.430 1.152 -0.303 -0.285 -0.114 0.379 -1.635 1.868 0.214 0.365 -0.304 1.751 0.774 0.634 -1.028 0.594 -1.313 0.681 0.043 -0.069 -1.413 -0.609 -0.482 1.347 0.584 -1.267 +3 1.923 -0.207 0.196 0.048 0.854 -0.790 -0.055 -0.802 -0.736 1.553 -1.285 1.042 -1.556 -1.534 -1.197 -2.681 -0.482 1.540 0.245 -0.758 -0.039 -1.743 0.151 -0.653 -1.000 -0.447 0.852 0.034 +1 0.803 -0.170 1.693 0.300 1.505 -1.209 -0.420 -1.294 -0.368 -1.105 -0.581 -0.859 1.114 -0.690 0.537 0.374 -1.097 -1.402 -0.143 1.172 1.275 -0.267 -0.407 1.300 0.973 -1.478 0.546 0.101 +0 -0.195 0.410 -0.483 0.416 -0.178 -1.302 0.312 -0.206 0.123 -0.567 -0.655 0.157 -0.231 1.724 0.799 0.545 -0.177 -2.026 -0.808 -0.647 -0.275 -0.033 0.656 0.273 1.159 -0.312 0.257 0.344 +4 -2.037 1.194 0.386 -0.290 -0.808 0.412 -0.885 -1.013 -1.295 -0.729 0.042 1.819 -1.046 3.626 0.716 -0.596 0.565 0.378 -0.843 -0.230 1.183 0.107 -0.405 0.214 1.756 -0.898 0.334 -1.146 +2 -0.019 -0.435 0.177 0.622 -0.420 0.836 0.714 -0.880 -0.964 0.448 -0.588 -1.046 1.717 -0.966 -1.524 -0.784 0.232 0.074 1.887 -0.768 0.236 1.128 -0.659 0.704 -1.832 1.759 0.995 -0.681 +1 1.678 -0.189 -1.282 -1.308 0.586 -0.717 1.152 -0.590 -0.237 -1.143 0.674 0.032 1.019 -1.570 -0.972 -0.101 -0.173 0.976 -0.464 0.197 -0.253 0.349 -0.641 -0.129 1.511 0.852 1.069 -1.123 +4 0.274 0.242 0.354 -0.847 0.439 1.822 -2.009 0.032 -1.122 0.989 1.141 -0.128 0.061 -0.881 -2.358 -0.270 -0.214 0.043 -3.279 -1.725 -1.483 0.083 0.585 -1.163 -0.276 -0.579 2.032 -0.385 +0 -0.446 -1.020 -0.682 -1.141 -0.064 -0.892 -0.776 1.582 -1.075 -0.482 0.280 0.437 0.068 -0.517 -1.580 0.036 1.223 -1.588 -0.200 -1.883 0.654 0.644 -0.693 0.026 0.482 0.403 0.339 0.155 +3 -0.751 0.895 0.693 0.291 -0.832 -0.916 -0.424 1.949 -1.134 -1.845 0.198 1.075 0.466 -0.109 0.677 0.231 0.566 -1.025 -0.111 2.192 1.310 1.962 -1.772 0.243 -1.049 1.403 0.617 -0.719 +2 0.739 -0.085 1.495 0.227 0.172 0.114 1.227 0.539 -1.482 1.638 -1.548 2.231 -0.090 -0.548 0.603 -1.202 -0.396 0.006 0.236 -0.263 -1.414 -0.675 -0.238 0.305 0.268 0.707 1.877 -0.716 +3 -1.548 0.547 -0.295 0.932 0.124 0.337 0.362 0.041 1.084 2.609 1.310 -1.648 -0.331 -0.463 0.484 1.101 -1.759 0.114 1.411 -0.768 2.336 -1.233 0.292 0.569 -0.062 -1.103 0.968 -0.621 +1 -0.221 -0.638 -0.755 -0.421 0.738 -0.358 -0.099 -1.157 -0.474 0.229 -1.667 1.045 -0.593 1.763 0.420 -1.835 0.278 -0.318 0.290 1.617 0.705 -0.226 -1.397 0.873 0.510 1.158 -0.143 0.295 +0 0.457 -0.564 -1.574 1.061 0.404 0.984 -0.958 -0.622 0.394 0.773 1.193 -0.101 0.504 1.788 -0.383 0.281 -0.146 0.210 1.386 -0.595 -0.842 0.876 1.510 -1.458 -0.338 0.315 0.045 0.139 +3 2.420 1.380 0.635 0.246 -0.047 -2.249 -1.213 1.161 0.742 0.604 -0.427 1.228 -0.084 -1.020 0.905 0.267 0.376 0.193 -0.292 1.005 2.955 0.229 -0.685 -0.383 -0.060 -0.560 1.005 0.694 +1 0.265 -1.053 1.251 -0.060 -0.525 -0.466 0.260 -0.133 0.198 1.634 0.704 -1.282 1.209 -0.831 0.048 -0.532 0.943 1.869 -1.236 0.034 -0.526 1.088 -0.659 0.821 1.176 -0.512 0.435 1.615 +3 -1.062 0.797 0.307 -2.353 -1.733 0.350 -0.832 -1.040 -0.104 0.343 1.430 -0.817 0.436 -1.027 -0.495 0.189 -0.148 -1.366 0.513 0.741 2.359 0.222 0.174 0.303 -1.364 -1.296 -0.083 -2.297 +2 0.669 1.214 0.879 -1.534 -0.412 2.390 0.093 0.861 -0.769 0.334 -1.114 -0.619 -1.336 -1.324 -1.646 -1.078 -1.364 -1.056 0.145 0.352 -0.679 -0.771 0.325 -0.211 0.380 0.690 0.074 -0.112 +0 -0.131 0.826 -0.437 -1.607 1.750 1.381 -1.292 0.690 -0.503 0.263 0.294 -0.234 -0.784 -0.691 -0.916 -0.832 -0.067 -0.716 0.682 1.487 -0.580 0.239 0.500 0.472 0.076 0.743 0.482 -1.238 +1 1.693 -1.334 0.830 -0.379 -0.387 0.327 -0.321 0.350 1.077 0.146 0.088 0.931 -0.366 1.079 1.655 1.021 0.423 0.967 -0.512 0.852 -0.231 -0.315 -0.440 1.296 1.234 1.276 0.432 -2.116 +4 -2.152 0.933 0.548 1.498 -0.811 0.571 -1.905 -1.766 -0.237 -0.579 1.047 1.178 1.284 0.598 0.641 -1.282 1.400 1.252 -1.380 0.468 -0.547 1.850 -0.975 0.093 0.186 -0.053 2.148 1.691 +1 0.433 0.667 -1.519 -1.077 1.107 -0.061 -0.424 -0.263 -0.264 0.558 -1.108 0.025 0.246 0.396 0.223 0.516 -0.573 -0.942 2.457 -0.301 -0.184 0.678 1.596 -0.545 -0.999 -1.242 1.593 -1.038 +1 -0.071 1.206 -0.377 2.022 -0.172 1.301 0.886 0.503 1.354 1.293 -0.204 -0.156 0.457 1.634 0.564 1.583 0.373 -0.668 -1.399 0.636 -0.539 0.742 0.200 -0.446 -0.935 -0.483 -1.086 -0.949 +4 -1.345 2.034 -0.356 -0.406 0.781 2.264 -0.481 0.598 -0.990 -0.042 1.745 1.487 -0.444 0.910 0.176 -0.078 -1.780 -0.421 -1.041 0.310 -0.808 -2.242 0.346 0.585 -0.740 -2.315 -0.313 -0.847 +4 2.406 -0.594 0.374 1.067 1.565 -0.251 -1.798 -0.308 0.355 2.643 -1.388 -0.055 -0.679 0.915 -0.617 -1.532 -2.058 0.259 0.078 0.521 0.135 0.306 0.463 -0.833 -0.000 0.508 -0.953 -1.760 +4 -0.445 -1.681 -0.651 -1.677 -1.091 -2.230 2.609 0.635 0.148 1.945 1.105 1.771 0.276 0.359 0.338 -0.592 -0.391 1.821 -0.724 2.416 2.381 -0.217 0.925 1.067 1.280 -0.969 -0.625 0.330 +3 -0.068 -1.181 1.190 -0.569 0.265 1.698 -0.227 -2.377 -1.946 -0.886 -0.703 -0.350 0.290 1.896 0.129 0.625 0.357 -1.177 -0.077 -1.512 -0.255 -0.011 -0.022 0.752 1.757 -1.034 -0.801 -0.804 +0 -0.247 1.270 -0.041 1.197 -0.683 -0.343 -0.360 0.494 0.939 -0.126 0.868 0.268 -1.464 0.897 -1.342 -1.970 -0.579 -1.135 0.391 0.552 0.701 -0.158 -0.630 -0.611 -0.089 0.252 1.040 -1.568 +1 0.366 0.864 1.262 -1.446 0.915 1.129 -0.591 -0.231 -0.775 0.311 0.391 0.196 -0.766 1.372 -0.582 0.553 0.788 -1.712 0.335 1.254 -0.014 0.899 -0.678 1.054 -1.137 -0.022 1.235 1.180 +0 -0.284 -0.662 0.259 0.229 -0.141 -1.501 0.871 0.222 -0.708 0.199 -0.513 -1.024 1.035 -1.254 0.903 0.092 -0.328 -0.031 -0.391 0.066 -1.984 0.785 1.896 -0.320 -0.303 -0.107 0.058 -0.830 +3 -0.153 1.139 -0.070 -1.033 -1.269 0.617 0.335 1.285 1.529 0.447 -0.719 0.555 1.064 -1.366 -0.356 -2.818 0.544 1.554 0.760 -1.664 -1.070 0.126 0.639 1.449 0.353 -1.387 -0.292 -0.394 +0 -1.006 -0.240 0.853 0.384 -0.639 0.601 -0.741 -0.271 -0.953 0.122 0.748 0.842 0.209 2.409 0.391 -0.178 -0.290 1.034 0.365 -0.447 -0.519 -0.188 -0.408 0.308 -0.413 0.451 1.257 -1.454 +2 0.592 1.000 -0.499 -0.468 0.297 -0.053 1.842 -0.863 -0.231 0.813 -1.347 1.224 -1.605 -2.539 0.946 -0.308 -0.429 0.020 -0.005 -1.955 0.103 0.638 0.840 -0.120 -0.228 -0.164 -0.596 -0.476 +3 -0.119 -1.687 0.001 0.853 0.136 -1.156 1.585 -1.190 -0.773 -1.125 -1.036 0.045 0.611 -0.587 0.564 0.769 -0.329 -1.514 1.514 -0.671 -2.210 -0.389 0.584 0.734 -1.137 -1.849 0.306 0.517 +2 0.249 -1.562 -0.626 -0.417 -0.149 -0.427 -0.028 0.478 0.076 -0.200 -0.261 1.417 -0.386 0.779 0.022 -1.865 2.320 1.232 2.264 0.884 0.055 0.575 -0.477 -0.851 2.095 -0.285 0.139 0.471 +0 -1.666 -1.132 0.701 0.333 -0.941 -0.760 2.065 0.050 -0.100 -0.550 -1.051 -0.510 0.126 0.373 -1.133 -0.033 -0.321 -0.116 -0.540 -0.827 -1.423 0.194 -0.960 -0.407 -0.664 1.103 -0.037 0.696 +4 1.401 0.220 0.807 0.746 -2.218 -0.012 -0.262 -1.133 0.460 1.717 -2.673 -0.534 -1.246 0.072 -0.326 -0.606 0.690 1.636 2.661 -0.740 -1.821 0.559 1.610 0.814 1.606 -0.225 -0.610 0.115 +3 0.922 0.641 -0.963 0.039 -0.169 0.466 -1.372 0.372 0.118 0.548 2.244 -1.311 -0.303 0.319 1.994 -0.185 1.408 -0.088 1.437 -0.937 -2.386 -0.193 -0.463 -0.691 -0.768 0.130 -0.266 1.323 +4 -0.369 -1.758 1.977 1.733 1.046 -0.130 1.352 -0.537 -1.318 -0.926 1.312 -1.143 -1.213 -0.469 -1.149 0.231 1.478 2.033 0.183 0.182 -0.078 -0.049 -0.560 -1.200 1.980 -0.892 1.447 0.598 +0 -0.895 1.095 -0.568 0.285 -2.370 0.691 -0.874 -0.608 0.949 0.187 -0.138 0.120 0.583 -0.111 0.169 -1.333 -0.900 -0.803 -1.372 0.268 0.924 0.301 0.585 0.827 -0.283 -0.425 1.212 -1.266 +1 0.608 1.914 -0.376 0.213 -0.951 -0.124 1.041 -0.319 0.760 0.005 0.688 -0.855 -0.992 -0.726 0.013 -0.018 -0.914 0.723 1.006 -0.352 1.704 0.712 1.284 -0.062 -1.478 0.866 0.463 -1.411 +1 1.207 1.614 1.063 -0.067 -1.930 0.085 0.412 0.238 0.477 -0.812 -0.484 -2.394 0.890 -0.898 -0.249 0.253 -0.561 -1.073 0.256 -1.783 0.767 0.660 -0.550 -0.494 -0.988 -0.382 -0.201 -0.318 +0 -1.942 -0.403 -0.136 0.048 -1.321 -0.763 -0.623 -0.536 -0.519 -0.133 0.310 1.774 0.871 1.317 -0.481 -1.004 0.801 -0.016 0.730 -0.882 0.649 -0.175 -0.461 -0.367 0.490 0.774 -0.366 0.344 +2 1.590 0.574 1.400 -1.342 -1.366 -0.149 0.503 1.796 0.706 -0.243 -1.026 1.230 -0.965 1.628 -0.284 1.591 0.679 -0.137 -0.519 -0.341 0.428 0.077 -0.594 -0.166 0.079 -2.129 0.458 -0.980 +3 0.060 2.463 -0.192 0.302 -0.035 -1.169 1.143 0.752 0.791 -0.909 1.403 -1.402 0.587 2.190 -0.991 -0.566 0.100 -0.503 -1.551 0.069 -1.062 0.474 -0.919 1.550 -0.783 -0.322 0.814 -1.231 +4 1.144 -0.575 0.810 -1.321 -3.199 -0.133 -0.610 -0.882 -0.999 -0.708 -0.987 -0.897 -1.359 -2.129 -2.209 2.461 -0.063 1.525 -0.178 -0.144 0.307 0.640 -1.247 -0.380 0.305 -1.934 1.297 1.063 +4 -0.608 -1.870 0.762 0.053 -0.220 -0.428 -0.159 0.408 -1.190 -0.736 -1.018 -2.571 1.781 0.679 -0.812 0.239 -1.165 0.501 2.402 0.953 -1.981 -1.535 -1.487 -0.419 -0.066 1.411 -1.357 0.022 +3 0.963 -0.508 1.578 -0.096 -0.231 -0.024 0.011 -0.666 0.394 -1.368 0.022 1.458 0.149 0.510 -1.030 0.666 1.812 1.589 -0.046 1.551 -0.909 1.515 2.759 0.060 0.775 -0.940 -0.559 -0.327 +1 -0.867 0.574 -2.293 -0.119 0.666 0.190 -0.693 -2.586 0.342 0.944 -0.132 0.844 0.217 -0.019 0.466 0.015 1.257 0.641 0.055 1.808 -0.529 0.493 0.537 1.032 0.360 -0.216 -0.041 0.484 +1 0.539 -0.756 1.831 -0.256 -1.180 0.406 -0.097 -0.548 -1.230 1.290 1.082 -1.620 0.970 -0.008 -1.217 -0.310 0.612 -1.312 -0.228 -1.544 -0.130 -0.388 1.027 0.363 -0.506 -0.511 1.638 0.052 +3 -0.605 1.204 0.164 -0.117 -0.583 -1.657 -0.175 -1.475 1.268 2.779 1.195 -0.031 0.316 0.421 -0.894 0.930 -0.241 -0.393 -0.234 0.540 1.139 2.299 -0.445 -0.135 -0.315 -1.413 -0.412 0.988 +0 1.696 -1.521 -0.918 -0.861 -1.752 -0.779 0.666 1.371 0.388 1.886 -0.825 0.792 0.667 0.193 -0.102 -0.273 0.830 0.478 0.333 0.539 -0.067 0.791 -0.686 0.110 -0.035 -0.205 0.080 -0.584 +1 1.239 0.560 0.220 -0.988 -0.208 -0.361 0.330 -1.414 -0.537 0.404 -0.702 -0.916 -0.791 -0.272 -2.381 -0.746 0.953 0.324 0.420 -1.828 -0.414 -0.116 0.464 1.111 0.955 -0.585 -0.562 1.412 +0 1.445 0.580 -0.383 -0.394 -0.018 1.006 -0.526 -0.281 0.618 1.064 -0.232 -0.459 -0.519 0.836 0.215 -0.742 1.822 0.593 0.199 -0.002 -0.522 -1.507 0.932 -0.554 -0.503 -0.180 -0.499 1.201 +1 -0.377 -2.313 -0.544 0.003 -0.755 0.247 0.107 -2.194 -0.710 1.323 0.535 0.629 0.974 -0.245 -0.563 0.845 1.114 1.234 -0.639 0.657 -0.115 0.707 -0.092 -0.026 0.799 -0.329 -0.373 1.062 +2 -0.134 -0.878 0.522 -2.190 -0.193 -1.006 -0.403 -0.759 1.705 0.211 -0.125 0.782 1.693 0.339 -0.111 1.482 -0.077 -0.011 -0.364 1.483 -0.437 0.321 1.859 1.325 0.406 0.606 0.394 -1.520 +2 -1.132 -0.543 1.594 -0.243 0.723 0.350 -0.058 -1.974 0.837 -0.743 -1.656 -1.847 0.589 0.806 -2.285 -0.522 0.364 0.155 -0.514 -0.084 0.947 0.437 0.647 -0.437 -1.272 0.243 0.800 -0.678 +1 -0.779 0.203 -0.458 0.186 1.509 0.606 0.509 -0.430 -1.484 -0.208 1.207 -1.527 -1.055 0.126 -0.985 -0.793 -0.424 -1.366 -0.974 0.703 -1.307 -1.598 -0.630 0.732 -0.197 -0.465 0.044 -1.746 +2 -0.315 0.888 0.609 0.297 0.378 -1.141 0.547 -0.457 2.021 -0.380 2.226 -0.161 -0.095 -0.294 -1.258 1.326 0.097 0.627 1.615 -0.131 -0.093 0.145 -1.283 -1.829 -1.504 1.241 -0.326 0.784 +2 -0.778 -0.858 0.149 1.019 2.063 0.095 0.062 -0.526 0.553 0.500 -0.668 0.403 -1.494 0.875 0.024 -0.817 -0.125 0.183 1.583 1.499 0.093 0.966 -2.447 0.592 0.269 -0.111 -1.354 1.027 +0 -0.294 -0.316 -0.809 0.371 0.524 0.423 -0.243 -0.432 -1.226 -0.092 0.729 -0.144 0.111 0.234 -0.133 -0.682 -0.025 0.270 0.552 2.125 0.106 0.445 -0.703 0.099 -1.020 -1.393 -1.063 0.011 +3 -0.294 -0.604 2.471 0.120 -0.954 -0.437 -0.725 -0.443 0.693 2.076 -0.060 -1.231 -1.641 0.523 0.370 -0.342 -0.745 -1.709 -0.012 0.108 -1.527 -1.092 0.633 -1.239 -0.799 0.640 1.654 0.500 +4 2.518 1.197 0.547 -2.137 0.392 1.582 -0.427 2.191 0.544 0.295 -0.991 1.504 -0.831 -0.564 -1.881 0.896 0.588 0.010 0.740 0.646 -0.366 -0.785 -1.715 -0.178 -1.308 0.163 -0.160 -0.402 +1 0.082 0.140 0.636 0.421 -1.047 0.509 -0.246 -0.556 0.970 -0.934 -0.271 -1.066 -0.402 2.393 -0.765 -0.151 1.392 0.439 -0.139 -0.693 0.674 -0.324 2.175 -0.283 1.690 0.878 0.869 -0.158 +4 0.591 1.809 -0.073 -0.062 -1.056 1.278 1.957 0.907 1.423 -2.737 2.340 -0.104 2.356 1.732 0.772 -0.343 0.158 -1.910 0.931 -2.555 -0.299 1.543 0.721 -1.530 -1.340 -0.597 0.078 0.877 +3 -1.715 -0.714 -0.736 -0.114 -1.857 0.792 -1.035 0.631 1.328 2.383 -1.672 0.866 -0.331 0.480 -0.374 -0.856 0.055 0.383 1.652 -1.463 -0.106 -1.172 -0.542 0.512 -0.338 0.308 1.521 -0.646 +0 -0.396 0.405 -0.698 -1.053 -0.549 1.243 -1.393 -0.642 1.211 0.494 0.179 -0.648 0.031 -0.538 -0.859 -1.080 0.784 -0.060 0.315 0.152 0.441 -0.255 -0.482 -0.815 -1.414 -1.122 -0.365 -1.603 +1 -0.190 -0.519 0.681 -0.325 0.143 0.165 0.493 -0.476 1.205 1.037 1.878 -0.902 0.183 -1.586 -1.123 0.745 -0.117 -0.966 0.778 0.336 -2.393 1.105 0.061 -0.546 -1.344 0.462 -0.482 -0.148 +2 1.533 -0.080 -0.115 0.448 2.000 -0.688 -1.716 -0.010 1.397 0.585 -0.515 0.007 -0.983 -1.783 -0.284 -0.996 -0.313 -1.488 1.722 0.948 -0.533 0.046 1.062 0.308 -0.347 0.471 -0.263 -0.143 +4 -0.546 -1.401 -0.256 -0.802 0.014 -0.157 -0.691 0.131 1.098 1.201 -0.065 0.854 -1.812 -0.670 -3.105 -1.234 0.411 -0.462 -1.456 0.422 -0.532 1.263 -2.329 0.713 2.580 2.483 0.746 1.185 +2 0.613 -1.924 0.901 0.142 -1.171 -1.124 0.162 0.012 0.071 -0.061 1.184 -0.002 0.634 0.972 0.006 -0.279 2.773 1.165 -0.431 0.421 0.059 0.487 0.387 -2.290 -1.585 0.179 0.509 -0.537 +4 -0.403 1.230 2.045 2.115 -0.375 -0.274 -1.029 0.118 -1.074 -0.527 0.701 0.390 -1.811 1.570 -1.087 -0.961 -1.239 -0.661 -0.897 -0.872 -2.464 0.130 0.232 2.244 0.486 -0.497 -1.201 -0.096 +1 0.422 0.131 0.500 -1.243 0.970 0.041 1.392 1.592 0.237 0.990 0.903 1.339 1.266 -1.164 0.278 -1.388 -0.522 -0.481 0.585 -0.230 -0.160 0.999 0.879 0.276 0.749 0.646 1.965 -0.209 +1 0.261 -1.028 -0.788 -1.748 0.540 -1.251 1.106 0.790 -0.100 1.236 0.926 0.157 -0.768 0.414 0.408 0.491 0.079 0.327 0.302 1.362 -1.274 -1.002 0.075 -0.760 0.211 -1.113 -0.955 2.060 +0 0.370 -0.569 1.228 -1.195 -0.139 -0.335 0.231 0.927 0.266 -0.129 0.691 0.843 0.269 0.388 0.647 -1.644 -0.351 0.376 -0.292 -0.288 0.375 0.398 0.467 -0.792 -1.418 0.804 -0.742 -0.812 +4 0.743 -0.755 -0.682 -3.416 -0.338 1.395 1.548 -0.003 1.548 1.154 0.646 -1.278 -0.268 -0.983 -1.064 0.325 0.593 -2.205 -0.461 -1.063 0.015 -0.732 0.292 1.177 0.068 0.785 1.309 0.148 +2 0.577 -0.916 -0.664 1.244 1.418 0.832 1.708 1.365 -0.088 1.989 1.560 -0.072 0.251 -0.662 -0.096 -1.554 -0.586 0.794 -1.024 -0.313 0.559 -0.290 1.151 1.197 -0.618 -0.437 0.045 -0.665 +4 0.379 0.868 -1.080 0.647 1.054 2.190 0.071 -2.099 -1.781 -0.887 1.282 2.197 -0.406 0.053 -2.581 0.651 0.134 -0.250 -0.964 -0.352 -1.345 -0.861 -1.271 -1.017 -1.155 0.577 -1.150 -0.052 +0 -0.073 0.274 2.017 0.136 -0.413 -1.340 1.058 0.244 1.264 0.333 1.381 0.486 -0.567 0.248 0.460 1.074 0.065 -1.569 -0.384 0.423 0.316 0.098 0.274 0.038 -0.295 0.984 0.351 -0.481 +1 0.300 1.307 0.103 -0.792 -0.202 0.067 1.803 1.277 1.090 0.321 1.682 0.039 -0.530 1.035 0.676 -0.002 0.028 0.398 -0.981 1.576 -0.723 -0.766 0.628 0.410 -0.913 0.417 -0.126 -1.536 +3 -0.652 -2.266 -1.110 -1.769 -0.521 -0.228 0.469 -0.299 0.470 0.114 -1.033 -1.686 0.584 1.647 2.535 0.935 1.458 0.461 0.302 0.111 -0.106 0.607 -0.521 -0.147 -1.178 0.475 -0.959 0.034 +3 -0.273 -2.852 -0.869 -1.082 0.370 -0.730 -0.501 0.840 -0.781 0.203 -0.618 -0.471 -1.164 -0.521 0.508 -0.965 -0.716 2.190 0.257 0.338 -0.626 1.042 1.205 -0.605 1.069 1.340 -2.069 0.860 +0 1.922 0.018 -1.561 -0.281 0.063 0.449 -0.257 -1.012 -1.095 -0.654 -0.558 -1.135 -0.733 0.659 1.648 0.755 1.306 0.974 0.833 -0.338 0.141 -0.421 -0.219 0.068 0.714 0.423 -0.162 0.056 +1 -0.094 1.641 -0.121 -1.902 -0.450 -0.003 0.065 0.677 0.174 -0.337 -0.915 -0.406 0.605 0.858 -0.674 1.007 0.569 -0.887 0.247 0.370 -1.064 -1.359 0.474 -0.454 1.512 1.721 0.472 0.952 +4 0.895 -0.065 2.188 -0.558 -1.404 -0.849 0.010 -1.087 -2.386 0.640 2.076 1.387 -0.357 -0.573 -0.053 1.141 -0.577 -0.206 2.242 0.138 1.421 0.326 -0.048 -0.023 -1.458 0.431 0.507 -1.078 +4 0.596 -1.756 -0.340 0.958 1.072 -0.682 2.213 -1.052 -0.228 -1.170 0.433 2.801 1.844 0.846 -0.607 -0.168 1.116 0.750 0.083 0.142 1.056 1.303 -1.501 0.227 0.578 -0.532 1.279 -0.289 +4 0.247 -0.761 -0.097 0.114 -0.788 -0.292 -0.166 0.056 -1.763 -0.135 -0.377 -0.021 1.035 2.052 0.678 -1.234 -0.345 0.596 0.483 0.987 -0.097 -0.364 1.752 0.059 -1.017 1.475 -1.496 -3.495 +3 -0.215 0.235 2.152 -0.452 -0.129 0.366 -0.197 0.477 1.131 1.768 -2.359 -1.250 -0.349 0.288 -0.546 -0.344 0.402 -1.846 -1.121 0.930 -0.699 1.688 0.250 1.661 1.321 -0.318 -1.500 0.451 +2 -2.386 1.610 0.416 0.074 -0.422 0.405 0.451 0.401 1.068 1.295 2.006 -0.837 -0.573 0.361 -0.779 -0.618 0.475 0.901 1.276 -0.193 0.463 0.243 0.018 -0.816 -0.943 0.667 -0.833 -1.777 +3 -0.329 -1.364 0.358 1.884 -0.477 0.687 0.696 0.191 0.007 -0.923 -2.282 -1.112 -0.390 1.097 -0.103 -0.519 -0.891 0.657 1.358 1.063 0.606 -1.476 -0.352 1.947 1.122 -1.538 -0.056 0.363 +1 -0.605 -0.654 -0.388 -0.240 -0.559 1.081 -0.469 -1.896 -0.255 -0.512 -1.308 0.189 -1.926 0.381 -0.033 -0.563 -1.146 -1.463 0.882 -0.299 -1.679 -0.012 -0.273 1.413 -0.412 0.979 -0.219 -1.182 +0 -1.542 -2.359 0.577 -0.009 -0.618 0.112 -1.002 0.485 -0.842 -0.169 0.467 -0.629 -0.466 -0.137 0.255 1.350 -0.448 0.721 0.546 0.266 0.859 1.542 0.071 0.678 -0.572 -0.357 -1.030 -0.309 +0 -1.230 0.180 -0.260 -1.488 -1.839 -0.167 -0.303 -1.318 0.828 -0.522 0.557 -0.840 -0.376 -0.606 -0.383 0.224 -0.274 0.625 -1.160 -0.725 0.339 0.899 0.087 0.744 -0.891 -0.486 0.572 -0.363 +2 -2.007 0.453 -0.664 -0.574 -0.483 -0.597 -1.498 -2.048 0.900 -0.221 0.528 0.529 -0.594 -1.365 0.779 -0.941 0.113 1.052 -0.940 1.695 0.792 -0.514 -1.331 -0.325 -0.942 -0.249 -0.133 -1.379 +3 0.288 0.051 -0.392 1.001 0.924 -0.560 -1.228 0.179 1.186 -1.262 0.470 -0.821 -1.017 0.931 0.650 -0.152 0.789 0.684 -1.486 0.084 1.413 0.474 -2.616 -1.071 0.996 -0.557 1.121 2.046 +1 -0.606 1.010 0.058 -0.146 -0.008 1.573 1.563 -0.037 1.984 0.526 -0.070 0.480 -0.113 -0.986 0.608 0.894 -0.983 -0.532 -0.744 1.062 -0.803 1.720 0.279 0.300 0.202 -0.671 -0.900 -1.167 +3 -0.864 -1.006 -0.811 -1.462 0.656 0.522 0.190 0.710 0.432 0.588 1.824 0.982 0.176 -0.934 -1.596 -1.759 0.946 -0.558 0.079 -0.953 2.206 -1.796 0.139 -0.359 0.018 -0.321 -0.891 -1.118 +1 -0.273 0.418 -1.732 -1.604 0.320 0.152 -0.218 0.541 0.546 -0.180 -0.196 -0.659 -0.317 0.095 0.277 -2.122 -0.273 0.415 -0.261 1.512 -0.267 -0.003 0.668 -0.331 2.335 -0.297 -1.584 -1.349 +3 -0.210 -0.590 0.823 1.261 -0.533 -1.133 0.694 -1.449 -1.306 -1.063 -0.415 -1.336 -1.939 -1.135 0.642 -1.272 -0.340 1.947 -0.049 0.343 -0.946 2.163 -0.541 -0.228 1.317 -0.182 0.048 -0.282 +0 -0.754 1.722 -0.277 -0.869 0.160 1.591 -1.113 0.121 -0.196 0.313 -0.916 0.428 0.335 0.824 0.439 0.914 0.412 0.954 0.753 0.480 0.585 0.345 -0.670 0.107 -0.546 0.521 -2.299 -0.314 +4 1.258 -0.172 -0.509 0.932 -1.147 0.253 1.060 0.430 -0.567 -1.001 1.187 -0.176 -0.901 1.139 0.801 -1.052 2.514 -0.541 0.784 1.987 0.888 0.259 -1.432 2.041 0.865 -1.415 1.545 0.061 +4 0.258 -1.755 -1.327 0.404 -0.049 1.341 0.647 1.546 1.357 0.258 1.206 -1.068 0.621 -1.053 1.685 -0.017 -0.075 -0.897 -3.227 -0.050 -1.209 2.147 -0.023 1.046 1.250 1.555 1.453 0.991 +1 -0.207 -0.181 0.514 -0.771 -1.175 -1.784 1.232 -0.372 0.570 -0.485 -1.162 0.652 0.328 -0.237 0.302 -0.960 1.061 1.292 0.759 -0.372 0.059 1.569 1.281 1.994 -0.781 -0.870 0.134 0.407 +2 0.378 -1.789 -1.495 -0.773 0.851 0.659 1.085 -1.510 0.507 -0.560 -0.808 -0.537 -0.116 0.661 1.123 0.521 0.633 1.823 0.697 -0.214 0.581 1.708 -0.870 -0.431 1.450 -0.290 0.764 1.289 +2 1.385 -0.550 1.106 0.123 0.783 -0.872 0.763 -0.639 0.231 -0.029 1.192 0.832 -1.766 -0.810 -0.636 0.692 -0.495 -1.089 -1.365 0.727 -2.074 1.055 -1.287 -0.448 -1.169 -0.919 -0.223 -0.137 +3 1.384 0.477 1.344 0.377 2.426 0.275 0.514 -0.720 0.956 -0.135 0.702 1.445 0.063 0.695 1.412 -0.988 0.143 -1.626 -0.142 0.940 -1.836 -0.853 -1.261 0.847 0.500 1.165 0.953 -0.537 +4 1.795 0.269 0.182 0.204 2.090 0.300 -0.193 1.799 -0.130 -1.692 -1.527 -0.192 0.292 0.822 -0.918 -0.225 -0.810 1.114 1.282 1.958 0.352 1.865 -0.693 0.216 -1.061 0.573 0.285 -2.454 +0 -0.384 0.737 -0.578 1.653 -1.269 -0.517 -0.514 0.533 -0.484 -0.400 1.214 1.094 0.349 -1.062 0.412 0.826 -0.123 -0.777 -0.521 0.270 1.370 -0.902 0.426 1.041 0.453 0.991 -0.541 -0.187 +4 2.320 0.146 0.165 -0.784 -2.298 -0.247 -2.074 0.786 0.343 -0.833 0.810 1.165 0.014 -0.232 -0.071 1.789 0.077 0.579 1.943 -0.973 -2.933 0.096 0.691 -0.853 0.511 1.307 -1.757 -1.015 +3 0.354 0.868 1.010 1.279 0.355 0.493 -1.722 -0.615 -1.629 -1.349 -1.232 0.332 -1.156 -1.728 0.557 -0.706 -0.350 -1.009 -0.586 -0.100 0.694 -0.161 1.281 -1.101 0.576 1.553 -0.984 -1.615 +1 0.991 2.015 -0.547 0.361 0.568 0.368 0.566 -0.904 0.915 1.314 -0.716 -0.972 -0.080 0.317 -0.021 0.052 -0.289 0.380 -1.847 -0.798 -0.277 0.530 1.074 1.295 -0.977 -1.554 -1.353 -1.236 +1 0.441 0.119 -0.606 0.916 -1.212 0.316 -0.436 0.121 0.486 -1.146 -1.671 -0.932 0.879 0.373 -0.403 1.968 1.285 1.043 -0.724 1.032 0.341 -0.722 -0.104 -1.038 -0.713 -1.144 0.957 -0.071 +0 -1.026 0.748 0.559 0.382 -0.587 0.765 -0.371 1.302 -0.747 -0.525 -0.151 0.847 -0.035 0.038 -0.392 0.268 -0.071 -0.784 0.127 0.151 2.350 -0.611 -0.839 0.279 -0.993 -1.073 -1.437 -0.775 +2 1.207 -0.445 -0.566 1.897 -1.037 -0.057 0.219 1.215 -0.080 -1.090 0.428 -0.875 -1.473 -1.057 1.060 0.841 1.712 -1.703 -0.620 0.581 0.137 -0.825 -1.001 0.573 -0.523 0.888 -0.745 -0.225 +2 -0.525 0.761 -1.506 -0.418 -1.771 -0.704 0.642 0.505 0.051 0.170 -1.886 -0.373 -0.681 -0.659 0.600 -0.861 -0.605 -2.087 -0.294 -1.088 0.079 -0.951 0.105 -0.145 1.672 0.497 -1.840 -1.265 +0 -0.788 0.446 -0.791 0.391 -0.593 -0.558 1.370 -1.070 0.003 1.112 0.615 0.999 -0.667 -0.471 -0.159 0.650 0.318 1.992 -0.251 0.643 0.234 -0.101 1.002 0.677 0.284 0.757 0.531 -1.623 +3 1.093 0.307 0.071 -3.339 -0.817 -1.521 0.627 0.095 -1.072 0.304 -1.423 -1.044 -0.710 0.540 1.399 -0.586 1.322 0.047 0.236 -0.064 -0.197 0.439 0.740 -0.885 -0.189 1.669 0.177 0.287 +0 0.461 -1.294 -0.122 -2.050 0.596 0.620 0.136 0.353 -0.455 0.318 0.611 0.342 0.415 1.308 -0.144 -0.524 -0.356 -0.964 -0.759 0.462 -0.655 1.227 -2.098 -0.181 -0.049 0.159 -0.421 -1.313 +1 1.906 1.623 -1.146 -0.040 -0.116 0.115 0.182 1.095 -1.710 0.797 0.594 0.600 -0.597 0.607 -0.513 0.265 -0.160 -0.741 -1.649 0.858 -0.470 -0.215 0.450 -1.241 0.618 0.275 -1.326 -0.630 +2 1.447 0.197 1.032 -1.486 0.267 0.890 0.082 1.065 -0.517 1.409 2.299 -0.363 -0.446 1.453 1.580 -0.523 -0.420 -0.282 -1.344 -0.919 -1.004 -0.768 -0.035 0.234 1.551 -0.998 0.984 -0.214 +4 0.510 -0.324 0.115 0.084 0.231 0.649 -0.224 1.163 -0.306 -2.773 0.887 -1.622 1.891 2.013 -1.260 -1.092 1.825 0.503 0.168 2.657 -0.229 1.186 1.020 0.056 0.252 -1.706 0.136 0.144 +0 -0.092 -1.082 -0.403 0.085 0.910 -0.184 -1.209 0.057 0.113 0.186 0.920 1.019 -0.594 -0.616 -1.040 0.699 1.449 -0.315 -0.241 1.467 0.506 0.436 -1.191 0.679 -0.929 -1.134 -0.347 -0.586 +3 -1.610 0.753 -1.082 -0.339 1.466 -1.409 -1.533 0.269 1.149 -1.574 0.408 0.581 -2.059 0.799 0.877 -0.312 1.252 1.136 0.103 -0.451 -0.973 -0.038 0.570 -0.324 -2.198 1.269 0.190 -0.380 +3 -2.555 -0.093 0.887 -0.686 -0.280 1.064 -1.015 0.554 -1.997 1.178 -0.259 0.053 0.543 -2.411 -1.331 0.019 -1.003 -1.366 0.736 -0.820 -0.071 -0.664 -0.425 0.309 -0.953 0.303 -0.848 0.085 +3 2.081 0.261 0.348 -1.145 0.410 0.658 1.110 0.630 -0.127 -0.169 -0.072 0.107 1.268 1.073 0.753 2.029 -2.656 -0.460 -0.011 -0.672 -0.930 -1.717 -0.311 0.894 0.774 -0.752 0.220 1.358 +4 0.113 1.256 0.312 -0.242 0.210 -0.492 0.360 0.359 -1.239 0.773 2.233 0.546 -2.030 -0.427 -1.279 -0.292 0.686 -0.718 1.885 -2.881 -0.248 0.379 -1.219 -1.088 0.756 1.928 2.046 -0.347 +0 -0.030 0.297 -1.064 -0.321 -1.645 0.596 0.034 -0.785 0.172 0.491 -0.509 -1.397 -0.668 0.088 -0.752 0.700 1.257 0.215 1.314 -0.449 0.779 0.620 0.982 1.312 -1.462 -0.054 -1.227 -0.740 +1 1.681 0.397 -0.809 -1.189 0.966 0.897 -0.507 -1.259 0.386 0.594 -0.373 -1.103 0.025 0.462 -0.487 0.262 0.361 1.145 -0.421 -1.139 -0.067 1.688 -1.763 -2.313 -0.108 -0.379 -0.208 -0.246 +2 -0.413 1.992 0.018 -1.680 -1.578 0.881 0.853 -0.117 -0.350 0.777 -0.586 -0.662 -0.934 1.011 0.952 1.073 1.754 1.383 0.021 0.197 -1.554 -0.389 -0.010 -0.335 0.926 -0.123 -1.104 -0.127 +1 -0.243 -1.890 -0.455 -1.040 -0.608 -0.137 0.958 0.208 0.308 -1.096 -0.881 1.799 -0.735 1.656 -0.243 0.002 1.070 1.272 -0.111 -0.388 -0.056 0.338 0.891 0.004 0.619 1.638 0.381 0.312 +3 0.257 0.709 1.354 -0.095 2.101 -1.759 0.419 -0.799 -1.907 -0.202 -1.457 -0.161 0.035 1.119 -1.508 0.908 0.098 1.029 -0.432 -1.332 -1.217 -1.292 0.478 1.477 -0.565 0.629 -1.523 0.180 +0 -0.293 -0.147 0.561 -0.056 -0.792 -1.247 0.040 0.267 -0.812 0.497 1.818 0.209 0.188 -0.386 1.933 -0.358 -0.196 0.380 -0.747 -0.033 1.021 0.645 -1.218 -0.142 -0.729 1.488 0.556 -1.150 +0 -0.035 -1.760 -1.202 0.305 -0.371 0.621 -0.336 0.987 -1.147 0.588 -1.268 0.308 0.640 -1.063 0.378 0.564 0.500 -0.746 -0.364 1.709 -0.885 0.140 -0.207 0.338 1.404 -0.592 0.365 0.709 +2 0.649 0.269 -0.887 0.204 0.139 -0.561 -0.863 0.154 -1.397 -1.068 -1.283 -1.212 -0.479 1.062 0.618 1.123 -0.611 -2.011 -0.597 1.128 -0.394 -0.022 -0.215 0.697 0.830 -1.549 -2.430 0.655 +3 0.137 0.343 -0.070 0.117 1.555 1.040 -0.047 0.635 0.865 0.327 -0.759 -1.841 1.824 0.038 0.476 -0.879 -0.230 -0.795 -1.002 0.330 0.335 0.124 0.100 0.532 -2.720 0.791 2.263 -1.475 +3 -0.611 -1.481 0.147 -0.461 0.642 -1.627 -1.175 -0.513 -1.068 -2.203 -0.218 0.447 -0.210 1.585 -0.075 0.519 0.072 0.338 0.384 0.952 -0.480 -0.157 -0.085 -0.139 -0.824 -3.310 -1.318 0.682 +0 -0.221 0.451 -0.711 -1.408 0.583 0.378 -0.046 0.557 0.172 -0.765 0.059 0.334 -1.625 0.261 0.322 1.112 -0.275 -0.504 -0.814 -0.681 0.211 0.225 -0.700 1.173 0.827 0.399 0.941 0.235 +1 0.102 0.365 0.777 0.923 0.032 -1.354 -1.122 -0.673 0.527 -0.882 -0.749 -0.041 0.554 0.050 -0.291 -0.847 -1.165 0.782 -1.465 -1.665 0.201 0.598 -0.814 -1.118 1.134 -1.462 -1.071 0.488 +3 0.389 -0.654 -0.619 -0.965 -0.040 -2.208 0.037 -0.420 -1.041 0.785 0.461 -0.945 0.718 0.343 -0.288 -2.366 -0.054 0.482 -1.091 0.512 -1.512 1.339 -1.571 -1.712 -0.376 -0.635 0.336 -2.076 +1 0.950 0.535 -1.708 -1.673 0.554 -0.031 1.243 -1.063 -0.467 0.568 -0.575 -1.486 -0.760 -0.974 -0.273 0.489 -0.153 0.234 1.769 -0.337 -1.238 0.899 -0.383 -0.610 0.786 -0.927 0.019 -0.650 +2 0.051 1.090 -2.723 -0.486 -0.913 0.702 -0.740 0.976 0.787 -0.607 0.239 -0.214 0.116 0.750 -0.326 -1.656 0.596 1.072 -1.700 -0.037 -0.932 1.127 1.182 -0.810 -1.316 0.245 -1.109 0.858 +2 -1.572 0.535 -1.222 -1.022 -1.101 0.222 0.679 -0.301 -0.039 -1.575 0.330 -0.498 -1.076 -2.533 -1.034 -0.263 0.551 -1.033 -0.347 1.494 1.155 -0.543 -0.493 1.848 0.165 -0.342 -0.102 -0.909 +0 1.213 1.283 -0.466 0.296 0.076 0.660 -0.289 0.093 0.072 0.532 0.656 0.094 0.124 0.226 1.647 -1.730 -0.970 0.674 0.270 0.263 0.667 0.214 -0.559 0.629 0.021 -0.448 0.690 0.651 +3 1.249 -0.630 -1.288 1.142 -0.423 -0.578 -1.880 -0.514 -0.699 -1.803 -0.585 1.185 0.342 -1.637 0.390 0.727 0.104 -0.035 -0.793 0.418 0.585 0.281 1.745 -0.205 -1.303 1.674 -1.890 -0.399 +4 1.847 -0.032 1.528 -0.030 -0.384 -0.680 -0.360 0.539 -0.046 -0.460 -1.169 -1.464 -1.280 -0.731 3.465 1.130 1.373 0.070 -1.055 -1.649 0.469 -0.128 -0.155 -0.778 -0.285 -1.728 -0.790 -0.325 +4 -1.046 -0.282 -0.254 0.109 -0.074 0.301 0.617 -1.570 -0.349 -0.788 -1.734 -0.985 1.021 -0.254 0.514 0.432 -0.905 0.538 -1.103 0.725 -0.595 -1.334 0.140 -0.706 0.629 0.711 -2.987 3.444 +4 -1.172 -1.451 -0.205 -0.495 -0.639 -0.917 -0.189 1.636 -1.611 0.841 1.534 -0.626 0.376 -0.122 -1.719 1.219 0.035 -0.521 -1.522 0.521 0.152 0.606 0.555 -0.813 -3.420 -0.110 -0.177 0.164 +3 -0.811 0.367 -0.740 0.865 0.151 0.298 -0.783 0.707 2.085 -1.008 -0.340 0.232 0.786 -0.560 -2.833 1.359 0.761 -1.743 -0.608 1.568 1.448 0.939 -0.176 0.083 -0.131 1.128 -0.876 -0.806 +1 0.946 1.344 0.603 -0.295 0.189 2.329 0.985 0.072 -0.151 -0.598 -1.187 1.708 0.478 -1.269 -0.125 1.270 -0.447 -0.814 0.357 0.658 -0.446 -0.600 0.461 -1.125 -0.538 -0.120 0.739 -0.428 +1 1.670 0.154 -1.602 -1.785 -0.544 -0.134 1.222 -0.408 -1.413 -0.431 -1.156 -0.009 -0.514 0.012 0.668 0.342 0.568 1.603 1.122 -0.921 0.091 0.391 -0.120 -1.116 0.655 -0.338 0.992 -0.304 +2 -1.846 0.767 -1.181 -1.201 0.087 0.338 0.364 -0.059 -2.071 -0.138 -0.607 -2.373 -1.077 0.805 -0.461 0.953 -0.413 -0.190 1.246 0.361 0.029 0.053 -0.712 0.957 0.654 1.473 0.163 0.209 +2 -0.229 0.563 2.244 -0.037 1.427 -0.848 0.675 0.873 -0.507 0.354 -1.087 -0.659 0.567 0.518 -0.274 -0.191 2.135 0.676 -0.636 -1.178 1.480 -0.379 0.718 -0.340 1.291 1.295 0.728 0.034 +1 0.166 -0.248 0.643 1.192 -0.001 -1.390 -1.132 1.416 -0.782 0.950 -1.342 -1.255 0.435 -0.722 1.321 -0.023 -0.898 -1.256 1.865 -0.193 -0.090 -0.219 0.214 0.227 0.031 -1.735 0.736 -0.255 +3 -0.142 -0.390 1.267 -0.091 -1.215 -1.100 0.247 3.003 -0.976 -0.315 2.405 -0.484 0.455 1.876 -0.951 -0.309 -0.484 -1.518 1.569 -0.585 -0.876 -0.444 0.929 0.113 -0.792 0.409 0.813 -0.024 +1 -0.551 0.475 -0.392 0.200 1.531 0.456 -0.051 -0.221 1.321 0.391 0.758 -0.580 -1.329 1.283 1.066 -1.058 0.837 1.369 -0.389 -0.045 1.914 1.262 -0.733 -0.642 -0.231 0.262 0.530 -1.251 +4 -0.867 0.349 3.286 0.695 2.736 0.569 -0.630 0.348 1.781 1.830 -0.575 0.253 -1.429 0.825 1.553 0.040 -0.667 0.360 0.417 0.081 0.179 -0.174 0.996 0.736 -0.983 -1.011 -0.254 0.737 +3 0.961 0.596 0.811 -1.204 -1.220 1.133 0.826 -0.258 0.853 -1.203 -1.244 -1.617 0.361 0.217 -0.522 -0.780 2.415 -0.259 1.388 -0.146 -0.501 1.417 0.790 -0.024 1.134 2.520 -0.732 0.472 +2 -0.023 -0.627 0.232 1.630 0.832 0.291 -0.700 1.491 0.783 -0.209 1.242 1.450 -1.301 -1.559 -0.125 0.714 0.162 1.283 1.421 -0.953 -0.494 -0.694 -0.573 -0.132 -0.440 -0.096 -1.343 2.044 +1 -0.333 -0.608 -0.961 -0.995 0.030 0.613 -0.149 0.582 1.147 0.548 0.910 -1.090 -0.556 -1.536 -0.373 -2.310 0.642 0.701 -1.120 0.150 0.823 0.314 -1.288 -0.115 0.798 -1.413 -0.840 0.334 +1 -0.615 0.128 -0.963 0.788 -1.057 0.578 -0.628 2.425 0.573 0.949 -0.417 0.880 -0.004 -0.170 0.489 -1.300 -0.508 -0.690 1.994 1.426 -0.705 0.735 -0.372 0.746 1.006 -0.232 -0.773 0.168 +1 0.890 0.421 0.121 0.662 -0.546 -1.414 0.723 1.238 -1.118 0.822 1.681 -0.002 -0.818 0.123 -0.551 -0.617 0.433 -1.604 -1.444 0.019 -0.022 0.344 1.875 0.105 0.598 -0.976 0.768 -0.526 +3 -0.890 -0.713 0.436 0.939 -0.487 0.544 1.582 -1.259 0.356 0.831 -0.095 1.238 2.199 0.343 -0.234 0.977 0.842 -2.519 -0.003 -0.395 0.397 -0.194 -0.085 -0.863 0.735 1.655 -0.848 -1.397 +3 0.517 -0.966 1.656 2.291 -0.353 -0.575 1.795 -1.215 0.478 2.753 0.358 0.162 0.117 0.815 -1.443 0.935 -1.214 -1.050 -0.849 -0.203 -1.050 -0.180 0.160 1.262 -0.799 0.706 -0.620 -0.221 +0 0.577 -0.590 0.608 0.773 -0.265 -0.561 0.184 -0.811 0.615 0.911 0.076 -0.631 0.167 -1.288 0.127 1.474 0.421 -1.249 0.689 -0.933 -0.655 -0.813 -0.504 -0.428 -0.721 -0.491 0.511 0.197 +4 0.017 -0.365 -1.104 -0.958 -0.991 -0.287 1.952 0.078 1.183 0.507 1.114 -2.800 -0.304 1.307 -0.572 1.762 1.484 -0.469 -0.503 -1.014 -0.630 -1.233 0.429 -1.456 0.604 0.628 -1.661 1.550 +2 1.634 -0.412 0.911 -1.497 -0.773 -1.085 -1.244 -0.516 0.367 1.533 -0.202 0.082 0.339 -0.728 -0.024 2.088 0.388 2.087 0.024 -0.620 1.044 1.264 -0.497 0.364 -0.064 -1.381 -1.323 -0.205 +2 -0.283 -0.127 -1.825 0.042 -0.487 0.633 -0.898 0.683 0.543 0.160 1.254 -1.885 0.444 -0.045 -0.074 -0.982 -1.420 0.892 0.788 1.799 1.963 -1.548 -0.155 0.251 0.962 1.427 -1.029 -0.541 +3 1.995 -0.744 0.344 1.257 1.484 -0.268 -2.025 -0.384 1.744 -0.763 0.291 -0.307 -0.072 1.312 0.254 2.042 0.976 0.131 -0.094 0.332 -1.233 -1.058 0.757 0.920 -1.323 0.860 -0.176 -0.831 +1 -0.805 -0.869 1.174 -1.774 0.040 -0.726 0.963 -0.530 -0.977 -0.852 1.487 0.359 1.637 -0.218 0.240 -1.491 -0.084 0.087 1.563 0.627 -0.971 -0.661 -1.232 -0.072 0.465 1.017 0.082 -0.969 +0 1.011 0.618 -0.508 -1.087 0.116 -1.060 1.024 -1.124 0.548 -0.081 -0.561 -0.103 1.080 -1.192 -0.412 -0.752 -0.039 -0.685 -1.768 -1.055 1.638 0.247 0.212 -0.283 -0.052 -0.430 0.527 0.101 +4 -0.089 -1.530 -0.270 -0.498 -0.392 1.145 0.049 1.425 1.066 -1.421 0.659 -0.800 -0.325 0.514 -0.430 1.473 1.674 -0.090 -0.299 0.538 -1.857 -0.991 -0.974 -1.639 -1.174 2.129 -1.205 2.039 +1 -0.554 -0.543 -0.912 0.349 -0.931 -0.575 -1.735 1.074 -0.418 0.342 1.336 1.187 -0.395 -0.524 -0.112 -0.861 0.912 -0.133 -0.766 0.655 0.371 -1.416 0.454 2.113 0.141 0.719 1.430 1.007 +0 0.150 0.986 -0.114 -1.345 1.416 -1.431 0.829 0.466 0.979 -0.401 1.260 0.920 -0.370 0.321 1.216 -1.509 1.348 -0.233 -0.232 0.858 0.322 -0.096 -0.916 -0.556 0.436 0.961 -0.541 0.024 +4 -1.286 1.938 1.474 2.058 -0.065 -0.979 -0.097 -0.584 1.646 -0.261 3.261 0.659 1.403 -1.616 2.047 -0.021 -0.269 -0.984 -1.329 0.367 -1.030 1.270 -0.314 -0.168 -1.057 0.681 -0.359 -0.696 +4 -0.068 -1.301 -1.880 -1.674 -0.364 1.629 0.451 -0.033 -0.524 -0.271 1.188 2.823 -0.203 -0.090 -0.403 -0.338 -0.169 0.485 -1.107 0.994 0.255 1.082 -0.666 2.648 1.813 -0.136 -0.536 -0.982 +2 0.341 -0.130 -0.354 0.082 0.058 -0.817 0.744 -0.795 -0.303 0.848 1.085 0.944 -1.694 -0.188 -1.160 -1.046 -0.640 -0.290 -0.556 -2.641 0.256 -0.093 1.232 -0.089 -1.890 -1.362 -1.700 -0.270 +1 -1.247 -1.631 -0.741 0.808 1.580 0.917 1.057 -0.081 -1.742 -0.455 -0.259 -0.497 0.055 0.441 0.853 -0.278 -1.904 -0.483 0.044 1.223 -0.568 0.513 0.290 0.044 0.931 -0.661 -0.575 -0.012 +1 0.407 0.337 -0.988 0.524 1.341 1.285 1.462 1.770 1.677 -1.278 -0.482 0.171 0.194 -0.966 -0.409 0.729 0.972 -0.528 -0.248 0.683 -0.160 -1.849 -0.165 -0.608 -0.168 1.556 0.579 -0.582 +4 -1.432 1.074 1.207 -0.331 -1.934 1.361 1.188 -0.117 1.069 -0.041 0.217 -1.626 1.222 -1.753 1.721 1.823 -2.555 1.557 -0.239 0.422 -1.411 -0.323 -1.181 -0.789 1.222 0.999 0.820 -0.539 +0 -0.103 0.015 -0.857 0.191 -1.566 -1.011 -1.040 -0.571 0.289 0.174 0.273 0.809 0.181 -1.007 0.030 -0.933 -0.335 0.398 -0.169 0.643 0.008 -1.514 -0.395 0.635 0.457 -1.072 -0.460 0.207 +4 -0.780 -1.172 1.162 -1.188 -1.585 -0.670 0.742 0.236 1.092 -1.034 0.926 -0.207 -1.858 1.373 0.089 1.433 1.225 1.051 2.548 -1.118 2.105 -0.233 0.920 0.125 0.931 -0.098 1.597 0.807 +1 0.298 -0.642 -0.025 1.502 -0.145 -0.660 1.074 -0.923 0.818 1.410 -1.570 -0.298 0.455 0.547 0.699 0.336 -0.317 -0.695 0.808 -0.097 -0.038 -0.444 0.661 2.052 0.226 -0.230 1.087 1.746 +4 -0.987 0.829 -0.853 -0.075 2.210 -0.883 -2.020 1.367 -1.400 -0.812 0.729 0.689 -2.374 -0.343 0.852 2.029 -1.239 1.068 -0.662 1.042 -1.040 -0.230 0.387 0.954 0.570 -0.429 -0.502 1.779 +3 0.516 -0.614 1.473 -0.298 -0.412 -1.832 -0.468 0.980 1.505 -1.859 -1.121 0.511 -0.395 2.405 0.602 1.149 1.148 -0.002 -0.743 2.386 -0.566 -0.587 0.295 -0.322 -0.933 0.219 0.238 0.197 +0 0.300 -0.283 -1.060 -0.239 -1.022 -1.086 1.206 -0.420 -0.741 1.618 0.815 -0.372 0.428 -0.236 -0.693 0.475 -0.324 0.075 1.021 0.911 -0.550 0.486 -1.128 0.450 0.148 0.379 -0.297 2.084 +3 -1.463 -1.200 -1.319 0.563 0.099 0.208 0.144 1.275 -0.742 0.703 -1.095 0.004 -0.352 -0.990 2.448 0.657 -1.560 -1.238 0.989 1.447 -0.375 0.179 1.683 -1.745 -0.689 0.093 0.962 -0.058 +3 0.805 -0.910 0.185 -1.285 0.364 -0.608 1.047 -0.524 -0.662 -0.417 -2.038 1.640 -0.764 -0.422 0.122 1.793 -1.350 1.089 -0.299 2.009 1.178 0.480 -1.066 0.261 -1.043 0.830 -0.246 1.421 +1 -1.520 -0.159 0.286 0.010 0.851 -1.498 -1.006 0.682 -1.702 0.812 0.450 0.742 -0.469 0.510 -1.172 -0.415 0.036 -0.899 0.218 -1.144 0.269 1.742 -1.257 -0.023 -1.618 -0.833 0.702 -0.431 +2 1.560 -0.771 0.211 0.016 -0.799 -0.114 -0.053 0.353 -0.853 -1.028 0.914 -0.699 -0.311 -0.392 0.793 0.444 0.456 -1.628 1.204 -0.255 0.780 -3.058 0.857 -0.585 1.159 1.054 0.491 0.970 +1 -0.961 0.902 0.640 1.470 1.453 0.063 0.753 0.045 0.145 -1.129 -0.224 -1.795 -0.137 1.320 0.778 0.112 -0.202 -0.540 -0.919 0.819 -1.535 -1.558 0.904 0.208 0.045 0.207 2.010 0.056 +1 0.402 -1.392 -1.290 0.370 0.949 -0.404 -0.498 0.139 0.135 0.205 2.364 -0.898 -0.880 -0.426 0.063 -0.685 -0.489 -1.080 -0.931 -1.474 0.487 -0.567 0.781 0.573 1.889 -0.118 0.255 -1.035 +4 -2.582 -1.588 -0.344 0.391 -2.671 -0.012 -0.355 0.137 -1.908 2.615 0.033 -1.217 0.129 1.495 -1.287 -1.495 -0.118 -0.033 0.709 1.475 0.780 -1.388 -1.071 -0.798 0.778 0.607 1.262 0.558 +3 0.312 -0.746 1.370 0.260 0.269 0.529 -1.062 -1.209 0.794 -0.411 0.586 -0.021 1.100 1.657 0.197 0.875 -0.005 1.323 -1.496 0.613 -0.373 0.633 -0.103 0.419 -0.351 -2.833 1.227 -1.904 +4 -1.599 0.937 -0.217 -0.598 -1.243 1.063 -1.905 1.334 -0.991 -1.729 -0.374 0.546 0.069 -1.260 -0.262 0.425 -0.114 -1.349 0.811 -0.813 -1.948 0.286 2.652 -0.261 0.370 1.427 -0.748 0.190 +3 0.585 -0.728 1.912 -0.159 -0.356 -0.042 0.432 -0.398 1.808 -0.246 -0.383 -1.098 0.119 -1.994 -1.021 0.353 0.156 0.280 -0.089 -0.712 -3.094 0.047 -1.869 -1.222 0.352 0.433 0.556 -0.613 +0 0.779 -0.541 -0.282 0.187 0.918 -0.985 0.261 -0.390 1.188 -0.124 0.515 0.802 -0.310 -0.328 0.882 -0.129 0.499 -1.776 -0.831 -0.408 0.980 1.596 -0.008 -0.140 -0.326 0.275 0.638 -0.374 +4 -1.561 -0.314 1.737 1.308 0.477 -0.151 0.068 0.454 -1.890 1.675 -1.557 -1.171 -2.004 0.703 1.957 -1.295 -0.592 -0.550 -0.774 -0.152 -0.167 0.880 -0.021 -1.447 1.313 1.417 -0.536 0.892 +2 -1.824 0.634 -0.797 -0.877 -0.340 0.169 -0.071 -0.548 1.266 -0.514 -0.127 1.237 -0.672 -0.945 -0.381 0.260 0.887 -0.020 -2.104 -0.176 0.794 0.506 -0.775 -1.076 0.075 -0.317 -0.509 2.809 +2 0.590 -1.456 1.392 -0.533 -0.862 0.887 -0.384 -0.694 -0.294 -1.216 -0.705 1.022 1.166 0.065 -0.679 0.414 -1.896 1.171 -0.070 -1.034 1.487 0.043 -0.426 2.050 -0.525 0.749 0.693 -0.847 +1 -1.200 1.304 1.590 1.096 0.055 -0.159 0.030 -0.054 -0.555 0.417 -1.033 0.298 -0.042 1.142 1.011 -0.731 -0.572 1.176 0.270 0.733 -0.207 -1.648 0.839 -0.104 0.960 0.319 1.593 1.585 +3 -0.012 0.174 -1.013 -1.618 -1.118 -1.687 0.224 0.939 -0.948 1.009 0.452 -0.554 -0.349 1.115 -2.324 1.111 0.896 -0.589 -0.093 1.627 -1.310 -0.182 0.647 0.210 0.052 1.817 -1.108 -1.063 +4 0.755 -1.310 -1.323 -0.625 -1.376 0.806 2.149 -0.848 -3.347 -0.016 -0.087 2.106 -0.521 0.617 -0.475 -0.298 -0.523 0.242 -0.239 1.179 0.987 -1.722 0.987 -1.444 0.310 0.102 -0.665 0.971 +3 0.055 -0.930 -2.968 1.350 0.720 0.521 -0.607 0.428 -0.704 0.205 -0.921 1.516 -1.570 1.232 0.039 -0.489 0.167 0.733 1.367 0.313 -0.891 0.056 1.817 1.348 0.909 0.796 0.699 1.159 +3 0.627 -2.647 1.056 -1.015 1.037 0.423 -0.030 0.176 -0.233 1.419 -0.488 -0.694 0.890 -2.129 -0.056 0.181 -0.590 -1.384 -0.862 0.586 -1.492 -1.395 -0.967 0.336 -1.158 0.231 0.932 0.366 +2 0.479 0.081 -1.203 -0.288 -0.834 -0.435 -0.888 0.482 0.812 0.035 -1.990 -1.353 0.646 -0.756 -0.755 0.069 -0.787 -2.121 -0.924 -0.404 -0.555 0.701 0.089 0.018 1.157 -1.560 1.565 1.651 +0 0.143 0.791 1.127 -0.766 0.075 0.470 -0.523 1.285 -0.620 0.065 -0.099 1.090 1.482 -1.215 0.372 -0.473 1.938 0.553 -0.295 -0.025 1.292 0.711 -1.200 0.183 -1.196 0.208 -0.301 0.113 +0 0.684 -0.045 -0.515 0.137 1.382 2.019 1.348 -0.885 -0.825 -0.111 0.394 0.793 0.204 0.099 0.666 -0.580 0.685 -0.106 0.801 -1.804 -0.292 0.104 1.370 1.019 -0.508 0.966 0.827 0.565 +3 -0.456 2.792 -0.119 0.300 0.168 -1.253 0.204 -1.472 1.803 0.903 -0.577 -0.167 1.026 0.273 -1.652 0.168 1.085 -2.334 0.138 -0.836 1.267 -0.453 -0.556 0.828 -0.898 0.084 0.281 -0.194 +3 0.850 -0.427 -0.125 -1.287 0.559 -0.267 1.579 0.705 0.084 1.225 0.405 1.008 -0.862 0.461 0.503 -1.545 1.507 -1.160 -1.299 -2.849 0.474 -1.643 -0.517 0.838 0.192 -0.624 -1.591 -0.108 +3 -1.040 0.476 0.486 0.997 0.455 1.202 0.804 -1.629 0.160 -0.582 1.780 -0.624 0.804 -1.468 -0.155 0.116 -1.555 -0.315 0.476 1.013 0.901 0.132 0.449 -1.108 -1.699 -0.004 -1.334 -2.233 +1 -1.432 -0.394 -2.085 0.242 0.336 -0.197 -0.017 0.931 -0.323 0.760 0.038 0.622 -0.109 1.858 -1.106 1.110 -0.279 0.165 0.278 -0.041 1.379 -0.858 0.811 1.150 0.631 1.319 -1.309 0.222 +4 -0.727 -0.734 2.701 0.448 -0.182 0.451 -1.904 -0.962 -0.813 1.318 -1.980 -0.946 0.041 -0.461 -0.981 -0.280 1.625 0.864 -1.655 0.078 -1.768 0.269 0.920 -1.370 0.376 1.516 -2.184 0.100 +0 0.284 -0.598 0.139 0.727 1.188 0.237 0.955 1.063 -0.290 -0.576 1.695 0.715 0.561 -0.541 -2.053 0.480 0.486 0.687 0.001 -0.561 0.270 1.259 0.550 -1.343 -0.181 -0.648 1.596 -0.217 +1 -1.270 -0.448 0.514 0.946 0.406 0.346 0.345 0.390 -1.104 0.876 -0.768 -1.040 1.389 -0.670 0.605 -1.410 0.936 -0.508 1.382 1.293 -0.043 -0.108 -1.260 0.501 -1.388 -0.331 1.345 -0.468 +4 1.879 -1.295 -1.061 -0.921 2.599 -0.062 0.994 0.175 -1.984 1.533 0.401 -0.122 1.760 -0.347 -0.144 -0.345 1.076 0.814 0.279 -1.052 -0.370 -1.052 1.160 -1.575 1.594 1.288 -0.919 -0.416 +2 1.481 -0.068 0.982 -0.860 0.214 0.144 -0.324 -0.869 -3.067 -0.846 0.701 -1.395 -1.188 -1.435 0.209 -0.893 1.220 0.735 0.200 0.304 -0.763 -0.763 0.711 0.913 0.702 -0.471 -0.391 -0.932 +4 -0.023 -1.946 -1.192 0.794 -0.607 -0.467 -0.112 1.529 -3.688 -0.947 2.358 1.137 0.496 0.236 -0.277 -1.402 -0.450 -0.151 1.116 0.320 0.036 -0.684 -0.501 -1.775 0.199 -1.271 -0.379 0.995 +0 0.308 -1.170 0.816 -0.692 -1.175 -1.210 0.619 -1.578 -0.841 1.017 0.521 1.219 -0.224 1.529 -0.459 0.812 -0.624 -1.087 -0.250 0.214 -1.753 0.269 -0.562 0.607 -0.215 0.587 0.482 0.167 +0 0.057 0.178 -0.357 1.130 0.146 0.048 0.295 -0.415 -0.350 0.481 0.776 0.881 -0.195 0.123 -0.606 0.787 -0.945 0.222 0.879 0.126 0.300 -0.273 -1.854 -0.674 0.044 -0.689 0.485 -1.464 +3 1.810 -0.230 -0.391 0.213 -1.393 -0.947 1.204 -1.486 0.419 -0.809 1.913 0.363 -1.097 0.705 -2.321 -2.307 -0.847 1.181 1.057 0.444 1.076 -0.409 -0.044 0.301 0.349 0.994 0.769 -0.283 +3 -1.157 0.323 -1.364 -0.532 0.501 -0.982 1.605 1.224 -1.571 -0.932 -2.286 -0.783 0.766 -0.414 0.461 -1.522 0.798 -0.555 0.012 -0.093 -1.272 -1.153 0.968 0.399 1.050 0.325 0.143 -1.726 +1 1.051 0.144 -0.507 -0.445 -0.832 -0.715 -0.104 -2.476 1.404 0.398 -0.865 1.438 0.632 0.773 -0.345 0.526 -1.674 0.315 -0.351 0.954 -0.380 -0.557 1.180 1.116 0.782 0.444 0.686 -0.244 +2 -1.700 -0.092 -0.093 -0.716 0.408 1.121 -1.974 -2.050 1.657 -0.240 0.089 -1.068 0.044 1.369 0.496 0.193 -1.770 0.743 0.010 -0.608 0.643 -0.848 0.561 1.217 0.154 -0.424 -0.572 0.383 +1 0.015 -0.001 1.859 0.417 0.527 0.060 -0.774 1.217 -1.416 -0.977 -0.777 0.251 -0.016 -1.863 2.317 0.760 -0.491 0.433 0.447 -0.330 0.394 0.406 1.091 0.069 0.322 -0.209 -0.433 -0.770 +3 0.210 0.488 0.809 1.253 0.593 0.422 -2.554 0.573 1.866 -1.375 1.048 1.133 0.309 -1.515 -1.422 0.175 1.543 -1.667 -0.285 0.737 -0.388 -0.235 1.457 0.639 0.479 -1.231 -0.641 0.147 +1 -0.524 0.222 0.019 0.267 0.246 -1.285 -0.801 -0.891 0.822 -1.044 1.093 1.021 0.414 0.005 0.245 2.066 -0.242 -1.751 -0.144 0.023 1.480 1.196 -0.163 -0.772 1.626 -0.286 -0.676 1.369 +4 -3.745 0.826 -0.111 0.221 1.829 -0.374 1.218 -1.237 1.615 -0.004 0.634 0.238 0.127 0.714 -0.715 -0.373 1.375 -0.998 -0.279 -0.059 -1.560 1.712 -0.784 -1.235 -0.712 -0.182 0.994 -0.346 +1 0.809 0.039 -0.405 -1.438 2.158 -0.752 0.569 -0.606 1.161 -0.968 1.664 -0.983 0.210 0.111 -0.337 -0.921 -0.306 0.236 -1.051 -0.802 -1.190 0.021 2.120 0.135 -1.032 -0.031 0.709 0.389 +4 -0.490 -0.681 0.355 -1.400 0.238 -1.407 -1.400 -1.366 -1.327 0.722 0.723 -0.163 -0.632 1.382 0.695 -1.179 0.351 0.299 0.718 2.749 -0.818 0.554 1.496 1.718 0.181 -1.308 -1.795 0.826 +4 -0.195 2.209 -1.911 0.231 -0.006 1.914 0.633 -0.635 -0.324 0.962 0.743 0.702 -2.043 -1.070 1.752 -0.416 0.084 -1.633 -2.230 -2.075 0.275 -1.091 1.074 0.066 0.734 0.403 0.369 -0.419 +4 -0.052 1.411 0.389 -2.289 0.778 -0.073 -0.610 0.872 -0.236 -0.952 0.860 -0.598 -1.677 -1.195 -0.677 -0.080 2.279 2.450 1.208 2.010 -0.856 -1.174 -0.960 -0.435 1.109 0.957 0.399 0.425 +0 -0.242 0.428 -0.279 0.710 -0.219 -0.122 -0.407 0.514 0.837 -0.709 0.779 0.307 1.053 -0.246 0.239 0.893 -0.779 -0.144 -1.683 -1.421 -0.952 -0.377 -0.381 1.276 1.230 -0.765 -0.133 0.637 +2 1.134 1.009 -0.478 0.377 1.077 -1.473 0.115 0.116 -1.686 -0.961 -1.225 -0.129 0.214 -1.418 -0.716 -0.191 1.838 -0.564 -0.057 0.317 1.737 0.154 1.781 -1.512 0.723 -0.807 -0.249 -0.445 +3 0.302 0.009 0.588 0.702 -1.499 0.908 0.122 2.654 -0.121 -1.191 -0.290 1.032 -0.623 -1.713 -2.530 0.894 -0.865 0.318 0.031 1.074 0.419 1.495 -0.570 -0.295 -1.010 -0.739 -0.407 1.572 +1 0.019 0.439 0.551 0.405 0.381 0.411 0.639 -1.510 0.302 0.434 -0.149 -0.834 -0.872 -1.545 -1.802 1.409 1.897 -1.356 0.320 0.309 0.557 -0.021 1.410 0.298 1.223 0.355 -0.697 0.587 +4 0.782 -0.065 -0.014 -1.107 -0.300 -0.763 0.383 -0.991 1.330 -1.676 -0.807 1.677 -0.435 -1.315 0.219 -0.475 -0.836 -1.096 2.768 -0.252 -1.926 1.494 0.611 -0.766 -0.933 0.673 -0.247 -1.926 +3 -1.075 -0.002 2.930 1.846 0.053 0.160 -0.980 -0.802 0.362 -0.525 0.904 1.570 0.333 0.102 -1.681 0.748 -1.144 -0.418 0.960 1.030 -0.467 -0.409 -0.699 -1.198 -0.635 -1.613 0.683 0.395 +3 0.079 0.151 0.883 -1.701 -2.081 2.482 -0.057 0.534 -1.439 0.682 1.048 -1.909 0.488 0.470 -2.494 0.182 -0.652 0.461 0.699 0.469 -0.307 -0.433 0.212 -0.259 -1.479 0.194 -0.182 -0.184 +3 -0.014 -2.277 -0.668 -0.443 0.744 -1.303 -0.805 -0.521 -0.064 1.771 -0.740 -1.312 -0.325 -0.817 -0.536 0.542 1.005 1.480 0.009 -0.187 -0.782 -1.304 -0.899 -1.215 -1.609 0.853 -1.493 -1.721 +3 -0.346 0.503 -0.659 -0.414 1.600 -1.359 -0.517 0.045 -2.475 -0.934 0.808 -1.287 0.153 0.335 0.502 -1.061 0.157 -1.175 -0.300 0.865 -0.853 0.529 -0.079 0.757 -1.971 1.176 -1.805 -1.229 +3 1.388 -1.175 2.522 1.486 -1.060 -1.120 -0.825 -0.705 1.502 1.779 0.161 -0.655 0.560 0.501 0.582 -0.657 0.606 0.667 -0.955 -0.111 0.199 -0.111 0.834 0.585 0.757 1.699 1.583 -0.341 +4 -1.253 1.649 -0.150 0.466 0.355 -0.982 -0.388 1.203 0.297 -1.280 0.962 -1.198 0.948 1.538 -1.030 1.124 0.966 1.781 -1.199 -1.314 -0.618 -1.143 2.689 0.058 0.086 1.637 -0.572 0.471 +4 -1.333 0.628 -0.554 -0.778 0.882 -1.216 -0.212 -1.148 -0.299 -2.192 0.542 0.390 2.177 0.242 -0.902 0.992 0.969 1.307 -1.961 -0.701 -0.908 1.529 0.806 1.130 -1.085 0.133 -1.627 -1.102 +4 -1.226 -1.558 1.456 -1.206 -1.160 -0.612 0.841 -0.660 -0.161 -0.914 1.150 -2.104 0.228 1.922 -0.281 -0.925 -2.174 0.355 0.918 1.125 1.475 0.769 -1.624 1.207 -0.845 -0.406 0.721 0.341 +1 -0.112 0.850 0.417 -0.664 -0.709 -0.523 0.580 -2.302 0.157 -0.883 0.191 0.344 -1.662 -0.227 0.156 0.088 -0.122 -1.221 -1.069 -1.378 0.554 -0.116 -0.052 -1.705 -0.034 0.634 -1.842 -0.535 +3 -0.259 -1.578 0.866 -0.889 0.580 0.924 -0.044 0.474 -1.423 2.925 0.440 0.781 0.934 -0.139 0.078 1.085 -0.368 0.391 0.468 -0.735 0.444 -1.694 0.918 1.570 0.906 1.497 -0.762 -0.225 +3 0.188 0.465 -0.051 -1.215 1.311 -0.033 -0.214 -0.156 -1.465 1.955 1.130 -0.690 -1.924 0.527 0.748 0.065 -0.336 -1.666 0.599 -1.683 0.276 0.727 -0.308 -0.254 -2.521 -0.927 -0.479 -1.615 +1 -0.990 3.019 1.119 0.557 1.758 -0.386 -0.151 -0.603 1.079 0.184 0.046 -0.829 -0.805 -0.508 0.392 0.297 0.670 0.072 -1.056 0.385 0.237 0.228 -1.014 0.563 -0.202 -0.180 0.206 1.502 +2 1.473 -1.103 1.003 0.715 -1.712 0.609 0.018 -0.083 1.996 0.941 -0.928 0.640 -0.917 -0.875 0.160 1.174 -1.487 -0.504 1.176 -0.941 0.031 -0.311 1.277 0.319 -0.299 -0.837 2.105 -0.076 +2 0.486 -1.006 -0.820 0.539 -0.676 -0.516 -0.812 1.785 -0.617 0.052 -0.202 0.221 -0.615 -0.201 0.394 -0.863 1.089 1.185 0.606 0.070 -2.991 0.424 -0.391 -0.246 -1.897 0.494 -1.449 0.118 +3 -1.255 0.739 -0.885 -1.202 0.500 -1.106 -0.818 1.581 1.589 1.191 0.759 1.156 1.963 1.094 0.251 0.873 -0.465 0.146 0.191 -0.138 -0.315 1.288 -0.953 0.148 -1.289 -2.008 1.195 1.286 +1 -1.117 0.849 -0.158 0.588 -1.110 -0.253 1.887 -0.336 -1.068 -0.561 0.503 1.418 -2.416 -0.231 -0.623 -0.348 -0.911 -0.944 -1.232 -0.889 -0.215 -0.175 0.492 0.145 0.095 0.801 -1.177 -0.644 +0 0.483 1.111 -0.266 -1.077 -0.078 -0.302 -0.209 -0.255 0.592 0.716 0.189 0.616 -0.076 -1.051 0.411 -0.674 -0.546 -0.502 -0.561 -1.884 -0.912 -0.751 0.310 -0.779 0.603 0.121 0.207 0.729 +0 1.345 -0.602 0.709 0.162 -1.635 -0.919 -0.150 1.112 0.464 -1.168 -0.140 2.018 0.111 -0.327 0.492 0.763 -0.456 0.503 -0.329 0.719 0.170 0.482 -0.759 -0.821 0.945 -0.079 0.285 -0.306 +3 1.763 0.478 -0.573 -0.277 1.056 -0.736 -1.482 -1.408 -1.729 -1.299 -1.181 1.385 -0.046 -0.363 -1.443 -0.143 -1.737 -0.369 2.064 0.664 -0.210 0.698 -0.522 -0.801 1.704 -1.142 -0.354 0.306 +1 0.675 0.744 0.735 -0.696 -0.148 0.553 -1.537 -0.081 -1.089 0.286 -0.797 -0.542 0.330 1.478 0.276 -2.163 0.660 -1.397 -0.736 0.361 0.019 -0.144 0.147 -0.027 0.880 0.195 2.309 -0.562 +3 1.300 -2.305 1.237 -0.544 -0.078 -0.527 1.543 -1.231 0.012 0.527 -1.611 -0.920 -0.148 -0.887 -0.816 -0.350 0.650 -0.343 1.078 -0.998 -1.252 1.926 -0.658 -1.311 -1.742 1.075 -0.341 -0.254 +4 -0.799 0.168 -2.182 2.161 -1.067 0.355 0.431 -0.464 -0.385 1.096 -1.437 0.448 -0.932 0.541 -0.068 1.380 -0.621 0.314 -1.211 1.492 0.685 0.472 2.019 -1.793 0.361 0.414 -2.138 -0.975 +3 -0.026 1.465 -0.111 -0.725 -1.567 0.377 0.466 -0.037 0.184 -1.736 0.314 1.353 -1.723 0.815 -0.341 1.856 -0.649 0.689 0.847 0.630 0.392 -1.464 -1.020 0.582 0.629 0.009 0.370 2.949 +1 1.031 0.552 1.195 -1.082 -0.260 -0.219 0.209 0.625 0.138 1.335 -0.375 -1.049 -0.666 -0.790 -0.456 -0.269 -0.227 0.232 -0.461 1.049 -1.938 -2.227 1.001 0.923 1.239 0.637 0.003 -0.258 +3 -1.363 0.659 -0.315 -1.122 0.838 -0.696 0.735 0.658 1.115 0.657 1.260 0.482 0.360 -0.817 -1.001 0.085 -0.529 1.137 -1.619 0.898 -1.313 -1.804 0.444 -1.365 -1.451 -1.158 -1.943 1.138 +1 -1.938 0.072 0.379 -0.300 -0.777 1.243 0.632 1.313 -0.954 -0.708 1.203 -0.047 -0.569 -0.500 -0.011 1.003 -0.161 0.791 0.467 -0.878 -0.879 -0.450 -1.337 -0.514 1.513 -0.031 -0.766 1.916 +1 -0.281 0.023 -0.127 0.118 1.344 0.409 -0.160 -1.055 -0.489 -1.063 -0.787 0.911 0.428 1.787 -0.341 0.039 0.082 -0.063 -1.358 -0.411 0.830 0.423 0.287 1.457 1.566 0.826 2.272 0.781 +3 2.473 -0.391 -0.385 -0.552 -1.479 0.152 0.031 0.312 -1.031 -1.900 -1.962 0.313 0.362 1.742 1.173 -1.326 -1.095 0.518 0.035 0.981 1.376 0.382 0.063 -1.017 -0.979 0.647 -1.227 -0.378 +1 -0.714 0.042 1.309 -1.554 0.128 0.759 -0.551 1.266 -0.090 1.281 0.113 -0.962 0.122 0.234 -1.153 1.476 0.811 -0.658 -0.322 0.518 1.386 -0.048 0.643 0.019 1.736 -0.186 -0.958 0.921 +2 -0.372 -0.728 0.320 1.654 0.140 0.996 -1.390 0.158 -1.097 -1.477 -0.739 1.192 0.950 0.243 -1.533 -0.196 0.302 0.176 -1.844 -0.890 -0.064 0.536 -2.196 -0.162 0.143 0.850 0.693 0.583 +2 -1.463 -0.192 0.497 1.132 0.296 1.151 0.264 -0.519 -1.354 -1.209 -0.044 0.025 -2.251 -1.541 0.281 -0.660 -0.210 -0.199 -1.300 -0.514 0.293 0.421 0.243 1.601 1.608 0.009 -0.792 1.116 +3 0.740 -1.838 0.479 -1.099 -0.570 -1.542 1.551 0.193 1.176 -1.317 -0.113 0.478 -0.139 0.111 -1.260 -0.297 -0.059 -0.236 -0.675 0.156 2.231 -1.024 -1.296 1.415 -0.248 0.829 -0.041 -2.014 +3 0.447 2.293 1.188 -0.401 0.269 0.027 0.030 0.386 3.617 0.853 -0.274 -0.235 -0.006 -0.136 -0.420 1.068 -0.430 -1.876 -0.620 0.836 -0.343 0.558 -1.428 -1.593 -0.818 0.345 -0.605 -0.368 +3 0.173 1.896 -1.916 -0.035 -0.316 0.947 0.355 0.590 0.345 -0.932 1.211 0.799 0.916 1.264 0.418 1.481 0.378 -0.219 -0.007 -0.024 -0.519 1.632 0.296 0.095 0.388 2.980 -1.132 -0.845 +4 -0.001 0.671 1.219 -1.349 -1.118 -0.313 3.159 -0.261 -0.122 0.618 0.977 0.170 1.164 -1.194 -0.766 1.162 0.011 -1.262 -1.323 -0.811 0.002 0.467 -0.329 0.385 -0.437 1.224 2.552 -0.393 +1 0.303 0.449 -1.682 -1.285 0.761 1.524 -0.967 0.259 0.807 0.238 -0.249 -0.641 1.985 0.096 0.389 0.324 -0.812 0.051 -0.322 -0.340 0.317 -0.916 -1.205 0.503 -0.532 -0.345 -1.929 0.963 +2 -1.738 0.808 -0.934 0.554 -1.427 0.032 0.558 -0.182 1.369 -1.209 -0.088 1.506 -0.256 0.469 -0.562 0.123 -2.312 0.473 -1.305 0.506 -0.721 -0.647 -0.187 0.311 0.247 2.087 -0.791 -0.038 +2 -1.172 0.884 -0.886 -0.786 0.595 1.651 0.855 0.109 1.152 1.685 -0.164 -0.911 -0.684 -0.098 -0.410 0.335 -1.048 0.287 -0.445 0.036 1.400 2.042 0.052 1.442 0.248 -1.440 0.200 -1.260 +2 1.419 -0.696 -0.321 -0.670 -1.695 0.850 -0.340 0.188 -1.107 -0.649 0.807 -1.007 -0.796 -0.617 0.455 0.788 -1.696 0.802 1.028 1.102 -1.343 1.308 -1.089 -0.459 1.989 0.995 0.846 0.514 +1 -1.029 1.252 0.796 -1.151 1.353 0.251 0.553 0.949 -0.628 0.505 -0.123 -0.231 0.341 -0.171 0.741 -0.289 -0.263 -0.559 -1.211 1.479 0.379 2.229 0.449 -1.228 0.698 0.005 0.799 -0.682 +0 -0.919 1.467 -0.186 -0.792 -1.662 0.087 0.518 -0.652 0.140 -1.091 -0.229 1.085 -0.233 -0.644 1.342 0.198 -0.503 -0.866 -1.149 0.427 0.767 0.000 -0.198 0.588 -1.418 0.076 0.017 0.415 +2 1.660 0.076 -0.428 2.039 0.562 0.187 -0.516 0.736 0.168 -1.252 0.022 -1.005 -0.729 -0.060 -1.562 -1.394 0.475 1.454 0.269 0.564 0.233 1.017 -0.102 2.294 0.685 0.076 -0.584 -0.498 +2 -1.574 0.912 -1.627 -0.630 -0.438 -0.406 1.441 -0.226 0.037 0.494 -0.097 0.926 0.478 1.270 0.393 0.172 -2.008 -1.025 0.927 -0.668 -0.034 0.134 -0.289 1.692 -2.221 1.116 0.231 -0.551 +1 1.131 -1.151 -1.094 -1.892 -0.026 -0.589 1.861 -0.592 0.650 -0.871 0.816 1.452 -0.524 -0.168 -0.216 0.270 0.989 -0.623 1.050 0.203 1.577 0.338 -1.405 0.713 0.009 -0.282 0.520 -0.778 +3 0.349 0.655 1.159 0.803 -1.529 0.343 0.312 0.775 -0.006 -0.829 -0.892 -0.115 -1.207 -0.555 -0.478 -0.259 2.377 -0.956 -2.737 0.342 -0.825 1.144 -0.163 0.095 -1.338 0.062 1.280 0.883 +1 0.385 1.390 0.372 -0.264 0.382 -0.005 -2.131 1.003 -0.622 -1.151 1.205 0.378 0.680 -0.126 0.224 -0.301 -0.861 0.685 0.238 1.722 -2.236 1.179 -0.388 -0.483 0.584 -0.888 -0.450 0.595 +0 -0.830 0.423 0.652 -1.860 1.637 -1.453 1.083 -0.494 0.769 0.425 -0.346 -0.886 -0.246 -0.481 0.368 0.086 -0.796 -1.227 -0.413 -0.081 -0.753 0.380 -0.221 0.983 0.374 0.597 -0.572 1.283 +1 -1.398 1.127 0.069 1.840 -0.869 0.652 -0.137 2.165 0.507 0.801 1.266 1.684 -1.074 0.602 0.217 0.850 -0.643 -0.215 -1.152 0.236 -1.098 -0.304 -0.309 0.192 -0.359 1.070 -0.720 0.408 +4 -0.156 1.457 -0.315 0.567 -0.707 -1.573 0.358 -1.804 2.149 -0.354 -0.078 -0.310 0.141 0.340 -0.840 -2.554 -1.101 -0.485 0.280 0.184 0.753 -1.643 -0.893 -0.441 1.524 2.721 -1.348 1.092 +1 1.167 1.763 -0.651 -0.070 0.959 -0.717 1.421 0.611 -1.810 -0.026 -0.719 0.808 1.294 1.547 -0.854 -0.274 0.003 0.271 -0.200 1.408 0.745 0.599 -0.453 -1.152 0.497 -1.002 0.494 0.244 +0 -1.287 -0.233 -0.738 -1.177 0.248 0.790 0.600 0.118 -0.352 -0.525 0.871 -0.512 -0.540 0.411 -0.981 0.085 -0.529 -0.671 -0.250 0.754 0.177 0.293 0.759 0.624 -0.179 0.557 -0.168 0.438 +3 -0.020 -0.248 -0.747 0.830 0.850 -0.185 1.234 0.268 -2.339 1.518 0.877 -1.130 -0.801 1.619 -1.816 0.221 -0.638 1.428 -2.084 -0.041 0.569 -0.658 -0.534 1.944 0.264 1.099 -0.658 0.750 +4 -0.678 0.903 0.581 -0.762 -1.405 0.974 -1.617 0.545 0.248 -0.889 -1.884 0.829 0.096 -1.805 -1.007 -0.586 2.482 2.710 -0.278 -1.045 -1.038 0.429 1.048 -1.143 0.522 0.707 0.882 -0.136 +3 -1.234 -1.205 0.185 0.368 -1.550 0.099 0.544 -0.252 1.816 2.135 -0.819 -0.306 0.704 0.011 -1.169 1.360 0.122 0.094 -1.004 -1.104 0.343 2.099 -1.282 -0.448 -1.029 -0.958 1.747 0.226 +4 -3.009 -0.882 -0.528 -1.156 0.575 -0.777 0.478 -1.233 0.909 -1.276 -2.342 1.237 -0.856 1.757 0.565 0.790 1.045 0.113 0.124 1.281 0.111 -0.728 0.586 -2.114 1.320 1.340 1.198 1.160 +3 2.211 -0.173 0.633 0.595 -2.029 0.446 -1.238 0.633 0.118 -1.115 -1.190 -0.073 2.481 1.623 0.360 0.515 0.602 -0.534 -0.693 0.851 -0.602 1.441 -1.353 -0.902 1.416 -0.287 -0.123 0.645 +3 1.541 1.334 0.778 0.075 0.023 0.764 -0.392 -1.883 0.492 -1.131 -0.962 0.000 -0.095 -1.412 0.165 0.966 -1.128 1.272 -1.560 2.117 -0.411 0.146 -1.892 -0.062 0.228 -0.086 -0.563 -0.947 +2 0.250 0.173 0.522 -0.293 -1.572 0.277 -0.001 0.806 0.236 -0.524 0.296 0.431 0.872 1.896 -1.310 0.822 -1.336 -0.295 -0.626 -0.633 -0.183 1.399 -2.245 -0.803 -0.065 -0.208 -2.775 0.147 +1 -0.315 -0.480 -0.563 -0.470 0.607 -0.042 0.987 0.464 0.031 0.373 1.196 0.657 0.281 0.423 -1.153 2.572 0.528 0.628 -0.029 -2.298 -0.265 0.076 -0.823 -0.832 -0.339 0.772 1.256 0.171 +3 1.318 -1.661 1.026 -1.242 -0.432 -0.022 -0.451 -0.356 0.810 1.070 -0.114 -0.003 0.631 -1.053 0.038 -0.224 0.282 -1.166 0.365 0.284 1.979 -1.293 2.315 1.924 1.283 -0.505 0.984 -1.727 +0 0.860 1.500 0.558 0.082 0.364 1.123 1.750 0.302 -0.623 0.925 -0.853 0.390 0.681 -0.108 -0.905 -0.455 -0.316 -1.054 -0.386 -1.452 0.591 0.333 0.266 -0.386 -0.252 -0.829 0.505 -1.358 +1 0.226 -1.666 0.336 -1.762 1.023 0.907 0.505 -0.676 0.170 -0.513 -1.031 -0.339 0.698 0.044 1.451 0.766 0.659 -1.609 -0.285 -1.064 -0.183 -0.693 -0.378 1.061 0.475 1.007 -1.601 0.058 +1 1.349 -0.956 0.450 -0.829 1.021 1.012 -0.354 2.210 1.107 0.707 0.651 0.984 -0.996 0.195 0.383 -0.400 0.465 1.232 -0.422 -0.054 -0.821 -1.787 0.779 -0.026 0.048 -0.987 -0.186 1.097 +2 1.580 -0.247 -0.300 -1.396 0.419 -1.174 0.442 0.580 -0.750 0.957 -0.275 -1.471 1.545 -0.923 0.507 0.426 0.389 0.372 -0.970 1.691 0.956 0.475 -1.333 -0.978 0.657 -2.004 0.339 -1.331 +0 0.282 -0.781 0.201 -0.109 0.221 1.226 -0.551 1.329 1.811 -0.227 0.133 0.605 0.275 0.410 -1.741 -0.003 -0.933 -0.426 1.165 0.466 0.697 -0.050 -0.228 1.194 -0.149 0.927 1.229 0.917 +1 0.955 -0.107 -0.863 0.884 1.522 -0.793 0.743 -1.097 -1.890 0.807 0.474 0.210 0.081 0.901 -0.717 1.504 0.696 -1.211 -0.975 -0.966 0.585 0.357 -0.610 0.018 0.979 0.822 0.080 0.708 +2 0.744 1.083 -0.927 -0.143 0.077 1.087 -0.151 0.962 -0.773 -1.055 1.832 -1.312 -0.751 0.785 -0.531 -0.557 -0.198 -2.156 1.121 0.176 -0.811 -1.716 -1.365 -1.594 0.227 0.237 0.612 0.733 +1 -0.666 -1.916 0.353 0.172 -0.114 0.963 0.153 0.167 0.725 0.206 -0.218 -0.366 -1.126 -0.061 1.087 0.413 -0.463 0.812 0.416 -0.524 1.347 -1.274 -0.021 -0.829 1.044 -1.181 -0.919 2.201 +3 0.929 0.142 -0.015 1.558 -1.147 1.083 -1.807 -1.208 0.021 1.624 0.099 -0.166 -0.649 1.224 0.433 0.007 1.696 -1.396 1.594 -1.366 -0.648 0.830 0.725 0.593 -0.217 1.138 0.748 1.382 +2 0.027 -0.184 0.140 -0.106 -0.269 1.884 -1.471 2.287 -0.879 -0.385 -0.394 1.467 0.488 0.706 1.492 -0.924 0.213 1.975 -0.986 1.096 -0.462 -0.098 0.618 -0.794 0.585 0.500 0.592 -0.066 +1 -0.830 -0.655 0.781 -1.428 0.451 -0.724 -1.859 -0.980 0.028 1.395 0.913 -0.294 0.972 -0.357 -1.467 0.021 -1.506 0.791 0.806 -0.880 0.146 0.136 0.872 -1.245 0.599 -1.317 0.777 1.063 +2 1.560 -0.766 -0.896 -2.445 0.364 0.973 0.298 -0.435 0.191 0.534 0.040 -0.779 0.474 0.114 -0.894 1.503 -0.483 -0.927 -1.685 1.385 1.721 -0.030 0.569 0.418 -1.365 0.821 0.365 -0.362 +0 1.054 0.010 -0.413 0.936 -0.745 -1.526 0.624 1.306 -0.357 -0.749 1.207 0.513 -0.273 0.252 -0.988 0.330 -1.975 -0.861 -0.161 0.159 -0.398 0.971 0.178 -0.475 -0.209 0.642 0.387 -0.313 +4 0.596 0.694 -0.458 1.130 -1.400 1.601 -1.662 0.537 0.245 0.824 -1.763 -0.274 1.049 -1.088 1.434 -1.992 -1.707 -0.866 0.639 1.409 -0.675 0.222 1.252 0.611 2.132 -0.644 1.282 -0.757 +3 0.882 -0.896 0.730 0.293 1.774 2.194 0.796 -0.260 0.491 0.581 0.527 0.420 0.534 1.050 2.198 -1.809 1.300 0.142 -0.790 -1.351 0.306 0.036 -1.079 -0.031 1.037 1.552 0.127 0.124 +1 0.447 -0.571 0.104 -0.749 0.467 -1.605 0.339 -0.302 -0.728 -1.070 -0.811 2.089 -0.378 -0.097 -0.002 0.338 -0.152 0.184 1.132 0.693 2.610 0.214 -0.800 0.245 -0.990 0.989 -0.049 0.418 +2 -1.003 -0.540 0.545 -0.069 -0.721 1.003 0.140 2.055 0.659 -1.135 0.116 -1.204 2.005 -0.989 1.114 0.954 -0.683 0.328 0.256 0.833 0.177 -0.668 0.561 0.564 -0.931 -2.305 0.592 0.373 +1 -0.997 -0.639 0.496 -0.545 0.513 0.803 0.587 1.161 0.988 0.885 -0.263 -2.123 -1.293 -0.183 0.454 0.980 -0.654 0.693 0.034 0.184 0.594 0.905 -0.673 -0.348 1.817 -1.768 1.422 -0.538 +0 -1.545 0.636 -0.207 0.794 1.372 -1.328 0.011 1.170 0.093 0.158 -0.922 -0.300 0.446 0.946 -0.854 0.533 -0.022 0.368 -0.825 0.368 0.824 0.362 -1.239 1.279 -0.581 -0.284 0.229 0.297 +4 -0.593 -0.403 0.321 0.657 0.527 0.032 -0.306 0.332 -3.201 1.869 -0.186 0.182 2.099 -0.741 2.332 0.740 0.447 0.644 -0.841 -1.649 -1.180 1.087 -0.936 -1.654 -0.079 0.575 0.039 0.606 +2 -0.283 0.619 -1.545 0.363 1.185 0.581 0.276 -1.443 -0.108 -1.320 -0.077 0.029 -0.770 1.302 -1.387 2.422 0.720 0.829 0.084 -1.609 -0.688 -0.358 -0.201 -0.740 1.613 -0.989 -0.320 -1.019 +4 2.155 -1.635 -1.439 -0.532 1.395 0.195 1.094 -0.187 0.251 0.756 -0.350 0.041 0.610 2.495 -0.533 -0.501 0.692 -1.064 1.215 -0.003 -0.379 0.103 2.482 -0.798 -1.663 1.181 -0.262 -0.798 +4 0.081 2.341 0.352 -0.214 -0.899 -1.443 -0.991 -0.880 0.395 -0.526 0.187 1.282 -0.088 2.079 -1.176 -0.151 -0.539 0.937 -2.240 -1.443 -0.107 1.551 0.226 -1.352 -1.197 1.673 -0.621 -0.943 +3 2.100 1.532 0.474 -1.516 0.400 0.129 0.525 1.359 -0.417 -0.469 1.032 1.164 0.191 1.399 -1.936 0.440 0.285 1.315 -1.057 1.126 -0.607 -0.163 -0.787 -1.349 0.728 -0.440 -0.863 -0.880 +2 1.237 -0.254 -0.458 0.475 -1.063 1.022 -1.056 -1.050 -0.910 0.760 -0.617 -1.348 -0.606 -0.032 -1.181 -1.152 2.111 -0.861 -0.323 -0.367 -0.421 0.621 0.948 -0.306 -1.236 1.274 -0.095 1.730 +0 0.408 -0.542 -0.177 -0.187 1.483 -0.651 0.593 -0.021 -0.990 1.415 -0.724 -1.046 0.337 0.874 -0.983 1.197 -0.712 -1.639 -0.730 -0.509 1.137 0.907 -0.583 -0.879 0.214 0.266 0.119 -0.915 +2 -0.063 -0.388 0.874 -1.713 -1.376 -0.551 0.497 -0.390 0.629 -0.189 0.022 0.195 -2.180 0.498 -0.305 0.578 1.749 1.291 -0.106 0.630 -0.772 -1.365 -1.013 0.756 -0.621 -0.007 -0.074 2.463 +3 -0.787 2.933 -0.473 0.729 1.909 -1.343 0.611 0.023 1.795 -0.909 -0.752 -0.050 -0.494 -0.459 2.028 0.127 -0.785 -0.390 0.405 0.337 -0.577 -1.628 -0.051 -0.078 -0.646 1.280 0.575 0.135 +0 -0.109 0.211 0.627 -0.424 0.520 -0.242 0.239 0.083 0.391 0.363 -0.002 -0.824 1.369 -0.455 1.174 -1.177 -1.105 1.097 1.746 0.584 -1.411 1.138 -0.934 -1.103 0.155 -0.497 0.813 0.514 +3 -0.667 0.874 2.840 0.356 0.862 0.694 -1.422 0.586 0.306 -0.260 -0.884 0.694 -0.972 2.134 0.114 -1.223 -0.514 -0.278 2.412 -0.885 -0.116 -0.538 0.499 -0.522 -1.139 -0.195 -0.299 -0.592 +4 1.080 -1.530 0.045 -0.489 -1.328 0.089 0.666 0.311 2.529 2.082 0.092 -0.059 -0.114 1.197 -1.643 0.188 -2.029 0.283 0.160 1.574 0.326 0.671 1.334 0.537 -0.230 -1.320 -0.671 1.781 +4 -0.375 1.065 0.034 -1.998 -1.404 0.424 1.010 0.609 0.769 2.590 0.528 -0.265 -0.127 1.635 -1.269 1.238 0.016 -0.832 -1.450 1.481 -0.857 0.062 -0.975 1.966 0.098 -1.346 -0.429 1.393 +4 0.859 -0.879 0.127 -1.387 0.440 1.824 -1.960 -0.727 0.837 2.944 0.588 -0.729 -0.149 -1.227 0.805 1.046 1.406 -0.330 -0.808 1.495 1.381 -0.988 -0.376 0.790 -0.252 0.032 -1.469 -1.122 +3 0.831 0.304 0.707 0.949 0.758 0.001 0.315 0.948 -0.982 0.234 -2.068 -0.186 -2.363 -0.734 -1.789 1.059 -0.630 -1.017 0.226 -0.742 -1.281 -0.276 -1.542 1.956 0.131 0.658 1.271 1.324 +0 1.065 -0.756 -0.140 -0.585 -0.025 0.066 -0.003 -0.090 0.127 1.255 -2.011 1.921 -0.351 -0.078 1.329 0.607 0.535 1.246 -0.165 0.919 -1.260 0.418 0.562 1.146 0.708 0.204 -0.318 -0.329 +0 -0.571 0.565 -0.761 0.618 -1.430 0.368 0.760 -0.261 0.618 -0.496 -0.676 -0.945 0.190 0.269 -0.237 -0.387 -0.651 1.432 -0.366 0.335 -2.177 1.108 -0.346 0.781 0.815 0.270 0.089 1.320 +4 2.010 -0.733 1.217 0.153 0.132 -0.507 -0.200 1.787 0.855 -1.363 -0.991 0.383 0.639 0.767 -0.077 -0.849 -0.810 -2.301 1.634 -2.629 -0.241 -1.772 -0.584 0.576 -2.287 0.353 0.871 -0.417 +0 0.463 -0.841 0.071 0.824 -0.558 0.411 -0.576 0.176 0.106 -0.367 -1.831 0.781 1.990 0.140 -0.613 0.208 1.146 1.280 0.912 0.445 0.675 0.118 -0.956 -0.379 -0.323 0.293 -1.764 0.511 +1 1.173 -0.008 -1.681 0.726 2.198 0.718 -0.273 0.401 0.800 0.113 1.315 -1.132 -0.404 0.459 0.698 -0.547 -0.453 -0.667 -0.745 0.519 -0.038 1.297 -1.094 -0.157 0.736 0.344 -1.098 1.152 +3 0.649 -0.902 0.825 0.287 -1.172 0.346 0.749 0.361 0.537 -1.729 2.255 2.821 1.429 0.116 -0.405 -0.384 0.784 1.069 -0.650 -0.022 1.257 -0.103 0.112 1.377 1.216 0.805 -1.069 -1.057 +2 -1.270 -0.082 -0.008 -1.217 -0.737 -0.462 -0.568 0.757 0.390 -0.895 -1.336 -3.284 0.275 1.331 -0.651 -0.079 -0.589 -0.430 -0.063 -0.453 0.077 0.305 0.425 1.320 -1.114 -0.202 -0.199 -1.664 +0 0.234 -0.248 -1.247 0.456 0.385 0.961 -0.226 -0.521 -0.793 0.388 0.403 -0.508 -0.175 0.312 -0.027 0.056 1.412 -1.271 -0.332 1.504 0.270 -0.839 0.306 0.315 0.962 0.814 -0.675 0.120 +0 -0.200 1.246 0.238 2.027 0.035 0.870 -0.636 -0.512 -0.492 0.996 -0.699 -0.730 -0.052 -0.386 -0.008 -0.782 -0.244 -1.486 -0.035 -0.319 1.435 0.374 0.347 -0.053 -0.732 0.608 -0.706 0.163 +1 0.170 0.841 0.510 1.308 -0.733 0.414 -0.918 2.010 -0.465 -0.301 0.654 -0.754 -1.318 1.597 1.213 0.481 -0.384 0.874 -1.539 -1.512 -1.188 -0.356 0.404 -0.600 -0.426 -0.438 0.638 -0.143 +0 1.200 -0.289 0.731 -1.074 0.529 -0.384 -1.326 -0.907 0.410 -1.481 -0.282 1.068 -0.493 -1.541 -0.277 0.678 -1.298 1.187 0.763 0.350 0.577 0.127 0.334 0.071 0.883 1.293 -0.821 -0.702 +3 -0.304 -0.418 0.898 -0.890 -0.956 0.343 1.086 0.331 2.187 -0.934 -2.167 0.487 -0.153 0.472 0.856 -1.525 -0.287 0.586 0.255 -0.176 0.738 0.997 -2.051 1.576 -1.223 0.595 -0.331 1.255 +0 -2.108 0.037 0.133 -0.536 0.165 -1.355 0.770 0.020 -0.142 1.052 1.124 0.663 -0.138 -1.697 0.474 -0.672 1.528 0.620 -0.270 0.158 0.431 0.543 -0.123 -1.758 0.602 -0.131 0.269 0.982 +0 -0.274 -0.409 0.381 0.818 0.402 -0.121 -0.157 0.937 0.463 0.073 -0.036 0.345 -0.468 0.822 -1.076 -0.411 0.611 -1.133 -1.573 0.063 -1.696 -1.299 0.757 -0.205 1.285 -1.205 -0.094 0.400 +1 0.591 0.727 -0.907 -0.576 -0.115 -0.122 -0.490 2.556 0.314 -1.092 -0.006 -0.433 -0.278 -0.890 -0.843 -1.933 -0.240 0.026 -0.577 0.900 -0.043 0.042 0.238 -0.034 0.054 -1.394 1.118 -1.353 +0 0.259 0.303 0.080 -0.899 -1.001 1.264 -0.641 -0.361 -0.508 0.078 -0.415 -0.192 0.670 -0.955 0.129 1.396 0.714 -1.276 -0.253 0.393 -0.942 1.153 -0.125 1.498 0.420 -0.659 -0.273 0.083 +0 -0.843 -1.460 0.852 -1.072 -0.002 -0.781 -1.708 -0.622 -1.429 0.003 0.603 0.580 0.254 0.649 -0.938 -0.313 -0.313 -1.456 -0.816 -1.107 -0.146 0.570 -0.426 -0.960 0.108 -0.428 -0.685 1.566 +1 0.465 0.528 -2.646 -1.264 -0.083 0.423 0.411 0.603 -0.720 0.178 -0.394 -0.560 -1.512 0.297 -1.649 -1.171 0.686 -0.166 1.052 -0.336 -0.555 1.147 -0.400 0.281 -0.162 0.120 -0.221 0.878 +2 -0.114 -0.636 -1.783 -0.075 -0.123 -0.728 -0.845 -0.447 1.538 2.388 0.855 -0.247 -1.044 -0.253 1.851 0.654 0.778 -0.509 0.361 0.768 -0.249 -1.503 -0.198 -1.734 0.733 -0.453 0.686 0.018 +0 0.560 1.081 0.834 0.459 -0.070 -1.661 0.430 0.208 0.272 -1.277 -1.081 1.053 -0.040 0.682 0.028 0.030 0.938 -0.516 0.096 -0.462 -0.434 -0.309 0.222 -0.479 1.256 -0.895 -0.187 -0.440 +1 -0.867 0.326 -1.725 1.329 -0.096 -0.162 -0.935 1.310 0.710 0.252 0.551 0.110 0.835 -0.348 0.021 0.548 -1.405 -1.285 1.840 -0.867 1.529 -0.357 -1.099 1.197 0.609 0.722 -1.186 0.447 +1 -0.127 1.370 1.305 -0.802 1.192 -0.827 -0.266 1.380 0.902 -1.341 0.553 -0.015 1.213 0.817 0.966 -0.326 0.568 -0.696 -0.515 -0.970 -0.486 0.910 0.598 1.156 0.065 -1.818 0.269 1.684 +3 -0.803 0.618 0.682 0.526 0.508 -0.314 0.143 -0.867 -0.458 0.268 0.168 -0.312 -2.666 -0.040 -1.385 0.330 1.388 2.948 -1.019 1.166 0.178 0.223 -0.296 0.958 -1.851 0.390 0.069 0.649 +1 -0.427 -0.447 -1.233 -0.563 -0.150 -0.061 -1.380 0.609 1.636 0.281 1.106 0.181 -1.046 0.271 -0.503 -1.109 -0.078 0.820 -0.341 1.940 0.951 -0.548 -0.578 2.145 -0.336 0.364 0.422 -1.724 +4 -1.309 0.843 0.235 1.810 1.143 -1.622 -0.269 0.154 0.275 -0.929 0.085 -0.279 -0.849 -0.496 -0.475 1.842 1.171 0.291 -1.684 1.409 -2.026 -1.286 -1.519 -0.938 1.681 0.781 0.258 0.009 +3 1.519 -0.474 -1.177 1.185 1.119 0.440 -0.385 -0.102 0.352 -0.102 -1.137 -0.033 1.533 0.446 0.871 1.791 2.463 -0.350 0.325 -0.851 -0.015 -0.883 1.263 -1.449 1.660 -1.158 0.496 -0.630 +4 2.009 -0.105 -0.139 -2.567 -0.447 0.852 1.564 -0.830 0.462 1.047 0.011 -2.283 0.247 0.761 -0.947 -0.254 -1.155 -2.570 1.027 -0.004 1.598 0.857 -0.354 -0.083 0.566 0.967 0.323 1.106 +4 -0.221 -0.072 1.114 -0.483 -1.273 -0.977 -0.233 0.482 0.880 1.649 0.739 -0.883 -0.203 -2.164 -0.312 -0.605 -2.313 2.108 -0.378 -2.107 -0.274 1.651 1.907 0.058 0.096 1.226 -0.151 0.460 +3 1.508 2.096 -1.507 0.906 -1.108 -0.760 0.209 0.489 -0.728 0.610 1.184 0.728 -0.966 -0.445 0.875 1.551 -1.271 0.303 0.734 -1.397 1.019 2.115 0.664 0.566 -0.751 -0.490 -0.647 -0.884 +1 0.055 1.530 2.495 -0.784 0.339 1.219 -0.179 0.658 -0.366 -1.542 0.751 0.765 -0.781 -0.340 -0.161 0.709 1.005 0.530 -1.085 -0.289 -0.181 -0.266 0.982 1.317 0.981 1.416 0.080 -0.487 +2 -0.217 -0.171 -2.226 -1.639 -0.099 -1.351 0.364 -1.673 -0.832 -1.595 -0.166 0.465 -0.867 1.794 0.492 -0.936 -0.510 0.125 0.024 -0.952 -0.282 -0.938 -0.054 -0.128 -0.248 -1.646 0.242 1.240 +2 -0.926 -0.256 0.305 -0.615 0.522 -0.930 2.052 -0.588 -0.300 -1.045 -2.029 0.412 -1.216 0.236 -0.560 1.229 -0.029 -1.532 0.135 2.509 -0.192 -0.499 0.625 -0.600 0.071 0.196 -1.007 0.839 +4 2.078 -0.207 -2.008 -0.228 0.266 0.460 0.809 -0.581 1.564 0.965 0.131 -0.694 1.928 0.729 -0.492 1.402 0.220 1.509 -0.843 -0.200 1.382 -0.669 -0.253 -2.738 0.412 1.154 -0.921 0.448 +4 -0.498 0.411 -0.471 -0.928 1.006 -0.864 -0.380 -2.030 1.390 -0.028 0.957 -0.073 -1.870 1.192 0.136 -0.810 2.058 -0.608 -0.834 -2.385 0.269 0.110 -0.122 2.004 -0.559 0.916 -1.311 0.862 +0 -0.384 -0.115 -0.565 -1.315 0.035 1.108 -1.289 1.398 1.217 0.397 0.405 -0.186 0.042 -0.464 0.032 0.140 1.881 0.566 -0.209 -0.099 -0.713 -0.548 0.911 -0.494 -0.618 -0.593 -0.729 1.127 +3 1.061 -1.637 -0.582 -1.227 -1.247 1.279 -0.518 0.430 0.534 0.283 -0.427 -2.020 -1.448 -0.516 0.291 -1.603 -2.470 1.059 0.093 0.068 0.247 -0.106 1.109 -0.669 -0.393 1.138 -1.622 0.508 +4 0.446 2.303 -1.147 0.274 0.092 1.073 1.600 -2.247 0.552 1.636 -0.612 0.277 0.177 -1.912 0.784 1.450 1.347 0.033 0.653 1.932 -0.222 -1.134 0.170 -0.656 0.577 2.142 1.014 0.496 +2 0.243 -1.905 -0.509 -0.739 1.134 -0.362 -0.183 -0.256 1.671 0.908 0.691 0.202 0.192 -0.909 0.871 -0.376 -1.281 -0.147 0.013 1.346 0.857 -0.988 1.039 -2.596 0.417 -0.688 -1.404 -1.180 +4 2.055 0.782 0.171 0.612 -1.787 1.255 0.332 -2.005 0.038 -0.243 1.135 -0.686 0.420 -0.684 -2.128 -1.445 -0.129 -0.375 0.375 -0.468 -1.085 -0.289 -1.741 -0.093 -2.286 0.337 0.391 1.227 +2 -2.181 0.121 1.549 0.553 0.307 -0.203 0.251 0.284 -0.491 -1.260 1.444 -1.397 -1.243 0.012 0.488 0.415 0.940 0.146 1.092 -1.088 1.561 -1.351 -1.322 -0.155 0.528 0.880 -1.674 0.154 +1 0.492 0.227 0.957 -1.452 0.112 0.096 -0.372 1.059 -0.710 0.712 -0.724 1.326 0.406 -1.557 1.137 0.562 0.091 -0.640 -0.854 0.505 -1.541 -0.677 0.698 -1.407 -1.361 0.521 -0.266 1.665 +0 0.034 0.940 -0.991 0.229 0.423 -0.546 0.652 0.477 -0.101 -0.132 0.524 0.788 0.063 1.120 0.659 -1.788 -1.405 1.182 -0.247 0.164 1.139 -0.006 0.037 -0.869 0.370 0.587 -0.578 -0.922 +1 0.124 0.461 -1.237 -1.082 -0.634 0.673 0.941 1.713 -0.406 -0.556 0.118 -1.107 0.955 -0.960 -1.136 -1.383 -0.673 -0.325 -0.773 -1.056 1.485 -0.318 0.326 0.458 -0.573 -1.302 -0.571 0.371 +2 -0.294 -0.490 0.241 -1.669 -0.307 -0.575 0.367 -2.110 -0.801 -0.461 -0.485 1.052 -0.787 3.076 0.747 -0.487 0.605 0.727 -0.310 1.152 -0.976 0.586 -0.105 0.290 0.229 0.018 1.977 -0.585 +1 1.336 -0.875 -0.053 -0.172 -0.439 -0.748 1.957 0.877 0.099 0.423 0.969 -0.699 -0.930 -0.273 0.942 2.227 -0.205 -1.112 0.274 -0.115 1.213 0.110 0.502 -0.407 0.214 -1.258 -0.308 -0.735 +2 -0.033 0.290 -0.375 -0.120 -0.022 -1.101 -0.544 1.631 1.019 0.057 -0.009 -1.020 -1.037 -3.046 -1.199 1.033 -1.973 -0.561 -0.128 0.289 -0.738 -0.573 -1.557 -0.621 -1.317 -0.179 0.062 0.234 +3 -1.022 -1.722 0.247 0.203 -0.009 -0.133 -0.112 -0.754 0.519 -0.660 0.923 0.330 -1.063 -0.829 -1.765 1.591 -1.274 -0.779 0.208 2.056 0.104 -0.456 0.464 -0.883 -1.925 2.864 -0.180 -0.299 +2 1.050 -0.535 1.317 0.198 2.075 -0.689 1.736 0.198 -0.651 -0.484 -0.320 0.424 0.523 -0.574 -0.024 2.142 1.728 0.436 0.038 0.120 0.614 -1.023 -0.257 -1.669 0.399 0.647 -0.483 1.574 +0 -0.175 0.589 -0.018 0.494 -1.852 -0.448 -0.562 0.053 0.546 -1.237 -0.223 -0.206 -1.176 -1.761 0.570 1.003 -0.012 0.970 2.342 -0.142 -0.625 0.084 0.347 0.071 -0.264 0.049 0.284 -0.046 +4 -1.725 -1.454 -1.206 -0.084 0.085 -0.168 0.359 0.401 0.330 0.089 -1.056 -1.335 -0.562 1.135 2.344 0.557 0.358 1.067 1.513 -0.922 2.903 1.588 -1.366 -0.998 2.817 1.037 -0.597 0.079 +3 -0.383 -0.763 2.138 0.597 0.287 1.196 0.012 0.467 -0.456 -1.364 0.897 -0.960 0.749 1.084 -2.244 1.045 0.255 -0.970 -1.740 -0.071 -0.179 1.092 0.065 -1.088 -1.554 0.146 0.004 1.320 +0 0.124 0.222 0.393 -0.650 0.425 0.005 1.570 -0.427 -1.835 0.617 0.336 -0.890 -0.327 0.194 -0.379 -1.110 -0.200 0.884 -0.362 0.212 -0.181 -1.235 -0.106 -0.445 0.759 1.235 0.882 -0.735 +4 1.968 1.150 2.452 -1.867 1.658 -1.083 0.709 -0.953 -0.633 -0.374 1.190 0.472 0.589 -2.225 -0.085 0.360 -0.097 -0.131 -0.151 0.476 0.053 1.800 -1.130 0.079 -0.601 -0.856 0.579 -1.095 +0 -0.138 0.177 -0.230 1.142 0.054 0.278 0.804 -0.024 1.393 -0.665 1.726 -0.197 -0.653 -1.569 0.872 -0.365 0.144 0.261 -0.488 1.073 0.455 -0.852 -1.344 0.781 -0.241 0.698 -0.250 0.212 +2 0.892 -1.113 -0.657 0.995 -0.762 -0.300 1.576 -0.048 -0.727 -0.878 0.333 -1.333 -0.247 0.333 1.299 0.668 -0.714 1.178 -0.300 -1.236 -0.172 1.438 0.165 0.013 1.637 -1.431 -1.286 -1.616 +0 0.195 -0.066 0.730 0.093 0.201 -0.383 -1.052 0.154 1.207 0.444 -0.817 0.815 -0.335 0.715 -0.369 0.353 -0.518 -0.493 -1.566 0.512 0.370 0.426 0.275 -0.762 0.966 0.993 -0.788 0.532 +2 -1.062 -0.021 1.118 -0.499 0.131 -0.236 -0.387 1.651 0.919 -0.790 -2.032 0.466 -0.846 0.248 -0.230 1.276 -0.167 0.214 2.186 1.179 -1.778 -0.284 -0.546 0.578 -0.566 -1.545 0.108 0.956 +0 0.336 -0.990 -0.856 0.413 0.740 -0.798 -0.560 -1.426 -0.129 -1.946 -0.299 0.080 -1.756 -1.007 -0.060 -0.197 -0.150 0.288 -1.672 0.709 -0.319 -0.596 -1.865 -0.268 0.512 0.142 0.171 -0.339 +2 -0.406 1.311 -2.403 -0.122 0.422 0.413 0.702 -0.533 0.111 -0.923 -0.540 0.590 1.006 -1.092 0.168 0.981 -1.064 0.384 -0.510 -0.226 0.661 -1.001 -0.964 0.069 2.338 -0.436 1.486 0.582 +0 -0.930 0.778 1.419 -0.178 -0.562 -0.578 0.402 -1.192 0.076 -0.406 0.505 0.582 -1.123 0.668 0.458 -0.191 -0.431 -0.425 -0.171 -0.931 -0.684 0.204 -0.572 0.878 -0.131 -0.475 0.332 -0.406 +0 -0.382 -1.286 0.692 0.977 1.322 0.261 0.284 0.675 1.065 -1.536 0.798 -1.310 -0.156 0.273 1.009 0.408 0.595 0.043 -0.735 -1.241 0.512 -0.748 0.732 0.152 0.081 -0.244 -0.091 -0.764 +4 -0.301 -0.579 -0.605 -1.057 0.633 -2.508 0.601 0.012 -0.313 0.866 -0.694 -0.645 -1.069 -1.311 -0.037 2.006 -0.501 0.321 -1.420 0.037 0.217 0.731 -1.225 0.978 -1.249 2.385 -0.089 2.066 +4 0.167 -0.893 -1.071 -0.250 -0.242 -0.573 -0.169 -2.113 -1.263 2.068 2.191 -1.375 1.244 0.411 -1.222 -0.579 1.070 0.595 0.590 -0.359 -2.144 0.644 0.144 0.867 0.997 -2.097 1.671 0.036 +3 0.592 0.158 -0.063 0.495 -1.816 -0.101 -1.197 0.029 -0.724 -0.656 -1.333 1.116 -0.678 -0.624 1.531 0.568 -2.035 1.385 1.648 1.554 0.668 -1.102 -0.049 -1.448 -0.866 -1.111 -0.627 0.164 +3 -1.295 0.433 -0.820 0.001 0.169 1.857 0.323 -0.457 0.626 0.815 1.205 -0.923 1.291 2.286 1.269 1.451 -0.435 1.689 -0.476 -0.039 -0.465 -0.581 -0.005 -1.154 -0.687 -0.973 1.534 1.620 +1 -0.239 -1.099 0.009 -1.088 0.235 -0.046 1.553 1.421 1.120 0.175 0.118 0.067 -1.159 0.867 1.519 0.391 -1.521 -0.311 -0.867 -0.311 0.053 1.245 -1.784 -1.211 -1.174 -1.108 -0.057 0.642 +0 -0.965 -0.231 0.070 -0.732 -1.278 -0.179 -0.498 -1.898 0.608 -0.729 -0.269 -0.220 -0.015 0.146 -1.305 0.312 -0.344 1.084 0.815 1.323 -0.938 1.287 0.094 0.690 0.674 0.459 -1.088 0.955 +3 -0.074 -0.378 1.283 -0.941 -0.723 -0.276 -1.148 -0.591 1.136 0.651 -0.421 0.803 2.082 0.313 1.420 0.852 -0.294 0.197 0.526 -0.745 -2.609 -0.834 -2.158 -0.751 -1.707 0.880 -1.183 0.544 +2 1.079 -2.159 -0.499 -1.761 -0.285 -0.473 0.080 -0.605 -0.751 0.620 0.455 0.551 -0.177 -0.572 -0.119 0.501 -2.177 0.394 0.309 0.062 -0.149 -2.566 -0.440 1.789 0.013 -0.307 0.017 0.355 +4 1.014 -0.435 -0.957 -0.035 -1.034 -0.670 -0.778 -2.238 0.643 0.848 0.095 0.775 -0.794 -1.931 1.556 -0.200 0.219 -0.702 -1.570 -1.448 0.327 0.866 -0.882 1.321 -1.684 -2.192 0.330 -0.172 +4 -1.511 0.103 1.268 -0.563 0.583 -0.685 -0.886 2.373 -2.391 -0.751 1.719 -0.188 -2.003 -0.723 -0.232 1.161 -0.692 0.794 1.202 -0.863 -0.153 0.362 -0.154 -0.356 -0.412 -0.195 -2.393 -0.395 +0 -0.367 -0.413 0.383 -0.164 -0.312 -0.693 1.362 -0.151 -0.562 -0.297 0.355 1.461 -0.169 -0.863 1.919 0.136 -0.106 0.072 -1.245 -0.045 0.760 1.220 -0.358 0.188 1.238 0.038 0.719 0.424 +0 0.970 -1.537 0.037 0.220 0.614 -1.348 0.237 -1.511 0.673 -1.299 -1.499 -0.882 -0.924 -1.167 0.399 0.369 -0.238 -0.251 -1.639 0.097 0.302 0.197 -0.379 1.250 -0.565 -0.053 0.266 -0.785 +4 0.520 0.421 -0.603 1.204 -0.151 0.714 -0.377 1.054 1.556 0.880 0.138 2.012 -2.064 -1.522 -1.608 -0.070 -0.604 0.639 -2.256 1.199 0.879 1.829 -0.180 1.629 -0.330 -1.691 -0.051 -0.141 +2 -0.479 2.478 1.005 0.883 -1.548 -0.075 -0.700 -0.428 0.270 -0.251 -0.219 1.202 -0.753 -0.006 -0.783 -0.774 -0.227 1.190 -1.256 -0.186 -0.048 -1.603 -0.754 0.756 0.404 0.384 0.271 2.063 +2 -0.578 0.881 0.172 0.429 0.267 0.838 -1.582 0.057 -0.871 -0.585 -0.883 0.392 1.454 1.338 0.759 1.623 -0.001 -0.948 -0.177 0.454 1.125 1.299 -1.746 -2.056 -0.257 0.959 -0.444 -0.488 +2 -0.810 0.427 -0.304 -1.225 -0.720 -1.506 0.059 -1.545 0.460 -2.224 -0.373 -0.648 -0.046 0.581 -0.414 -1.556 0.053 0.625 0.415 -1.100 0.348 0.732 0.342 1.046 2.040 -1.789 0.694 0.261 +1 -1.399 1.908 -0.185 -0.660 1.059 -1.210 1.104 0.635 0.977 0.145 -1.544 -0.623 -0.579 0.970 -0.175 0.211 0.065 0.562 -0.736 -0.735 -1.320 -1.848 -0.013 -1.057 1.125 -0.719 0.041 0.693 +0 -0.313 -0.665 -1.071 0.609 -0.338 0.619 -1.112 -0.665 -0.221 -0.128 0.754 -0.431 0.747 0.375 -0.107 -0.951 0.757 -0.926 -0.021 -0.054 -0.207 -0.389 -0.822 -1.617 1.404 -0.492 -0.128 -0.134 +1 0.495 0.383 -0.409 1.006 -1.127 1.128 0.154 -0.337 -0.020 -0.107 1.220 -1.077 -0.763 1.036 -0.596 0.901 1.699 -0.402 0.681 -1.154 0.755 0.199 0.473 -0.646 -1.661 0.503 -2.063 -0.317 +0 -0.353 0.507 -0.637 0.066 0.598 -1.076 -0.138 -0.549 1.955 -0.086 -0.226 1.165 -0.965 0.646 -0.186 0.641 -0.339 -1.011 -0.498 -1.004 -0.328 0.227 -1.109 -0.222 0.742 1.129 0.976 -2.108 +1 0.751 -0.453 1.012 -0.543 1.302 -0.948 1.473 0.174 0.400 -1.004 0.729 0.764 -0.324 -2.134 -1.113 -1.113 0.449 -0.465 -0.446 -0.566 1.469 0.045 0.198 -0.619 -0.030 -0.976 0.344 -1.147 +1 0.105 -1.404 -0.193 -0.477 0.110 -0.283 0.354 -2.419 -0.939 0.445 -0.495 1.493 0.674 0.221 -1.799 1.551 -0.668 0.374 -1.289 0.886 0.102 0.644 0.913 0.692 -0.472 -0.331 -0.470 0.849 +0 0.702 1.272 0.140 -0.372 -0.686 -0.629 0.196 -0.337 -0.419 1.616 -0.309 -0.683 -0.471 1.863 -0.554 -0.729 -2.160 0.646 1.252 0.069 -0.008 -0.292 -0.585 0.653 -0.664 -1.156 0.478 0.121 +4 -1.133 1.651 1.089 -0.278 1.214 0.555 1.206 -1.793 -0.408 1.573 2.652 -0.390 -0.407 0.683 0.344 0.533 -2.162 -0.239 0.805 -2.354 -1.617 -1.774 0.651 -0.930 -1.201 0.055 -1.241 0.452 +3 -1.703 -0.799 2.086 0.854 -1.073 0.225 -0.996 1.394 -2.270 0.275 0.260 -1.113 -0.273 0.376 -1.778 -1.280 -0.155 -0.833 -0.463 -0.020 -0.910 -0.027 0.298 0.779 -1.246 -0.628 0.589 -1.482 +2 -0.186 0.774 0.654 -1.255 -1.105 -1.563 0.242 -0.274 0.605 1.232 0.343 -0.087 1.287 -1.527 0.063 -0.214 -0.155 -0.522 1.303 -1.741 0.675 -1.007 -2.037 0.442 0.518 1.371 0.047 0.952 +3 1.418 -0.858 0.454 -0.453 -0.006 -0.156 0.450 -0.156 -0.503 -1.577 0.220 0.611 0.063 -0.073 1.487 1.621 1.072 0.335 0.097 -1.054 -1.906 1.371 0.114 3.392 -0.584 0.427 1.022 -0.653 +2 -0.242 0.309 -0.858 -0.347 0.319 -2.117 0.949 0.213 0.227 1.156 0.938 0.607 0.280 -0.010 -0.581 -0.961 0.387 0.394 0.141 -1.344 -2.655 0.056 -1.834 0.318 1.949 0.331 -0.437 -1.046 +2 -0.155 1.188 -0.654 0.587 -0.769 0.113 -1.196 -0.663 -0.082 1.815 0.101 1.655 0.220 -0.387 1.539 0.437 -0.643 0.471 -0.293 -1.989 0.936 1.095 1.838 0.422 -0.249 0.835 0.850 1.638 +1 -0.840 -0.977 0.847 -1.439 1.491 -1.104 1.535 -1.386 -1.287 0.202 -0.037 0.795 -0.961 -0.374 -0.042 0.537 0.455 0.790 1.278 -0.123 1.475 -0.155 0.129 0.361 0.058 0.736 0.352 -1.483 +4 0.780 -0.200 -1.849 1.278 1.733 -1.366 0.267 1.491 -1.322 -2.121 -0.237 0.089 1.288 2.283 0.674 -1.747 0.453 -0.141 0.957 -1.890 0.926 1.583 0.221 1.168 0.784 -2.161 0.163 0.337 +1 -0.602 -0.020 -0.886 -0.057 0.323 1.789 -0.545 0.486 1.981 0.007 -2.135 -1.571 0.266 -0.309 -0.519 2.123 0.282 0.489 0.591 -0.564 0.485 -0.821 0.008 -0.391 -1.248 -0.264 -0.228 -0.656 +0 -0.392 -1.018 -1.027 -0.373 0.645 0.928 -0.497 -1.153 0.268 -0.824 -0.518 1.325 0.827 0.158 -1.272 0.211 1.063 -0.376 1.351 1.293 1.664 0.305 0.587 1.414 0.447 -0.501 -0.449 0.239 +0 -1.594 -0.791 -0.845 0.931 -0.744 0.830 0.168 -0.060 -1.407 -0.621 -0.708 -1.275 -0.596 0.886 -0.127 0.599 -0.450 -0.406 0.880 -0.082 -1.688 -0.113 -0.540 0.152 1.086 0.699 -0.727 -0.689 +0 0.604 0.050 -0.596 -0.608 -0.215 0.843 0.632 1.006 1.106 -0.217 1.962 -0.104 -0.494 0.333 0.484 -0.742 -0.159 -0.447 0.142 0.796 -0.266 0.573 -1.389 0.890 -0.453 -0.471 0.400 0.453 +0 -1.329 -0.711 1.452 -0.629 -0.172 -0.428 1.112 -0.101 0.157 -0.610 1.357 0.423 0.622 0.120 -0.557 -0.643 1.138 1.322 -0.351 0.488 -0.733 0.022 -1.430 -0.566 -0.349 1.031 0.629 -0.941 +3 1.254 0.326 3.260 0.391 1.622 0.960 0.149 0.953 -0.010 -0.149 0.546 0.903 -0.630 0.196 -1.106 -0.031 -0.436 -0.102 -0.859 -1.213 1.140 1.296 -0.276 -1.840 -0.607 0.216 1.557 -1.120 +1 0.078 0.538 0.827 0.419 -0.751 1.785 0.246 2.014 0.187 -0.246 1.384 0.301 0.133 -1.200 0.660 -0.493 -0.448 -0.491 0.439 -1.535 -0.211 0.980 -1.246 0.173 -0.631 -0.404 1.655 -1.650 +3 -1.644 1.004 -0.598 -1.554 -0.204 -0.554 0.114 0.023 0.510 0.151 2.262 -0.689 -1.372 -0.944 1.122 -1.693 0.944 2.131 1.367 -0.449 0.655 0.878 -0.439 -1.096 -0.071 0.879 1.326 0.092 +1 0.495 0.042 -0.029 0.583 1.106 0.532 1.235 -0.251 -0.049 -1.001 1.983 -0.187 -0.297 0.607 1.107 -0.692 1.187 -0.303 1.431 -0.541 0.735 0.043 -1.126 -1.416 -0.743 0.993 -1.943 0.334 +1 -1.259 0.837 -0.068 0.099 0.821 1.092 -1.542 -1.485 -0.237 -0.460 -0.431 0.386 -0.988 0.390 0.333 -2.074 -0.794 -1.751 -0.870 0.487 -1.017 0.163 -0.268 -1.213 -0.092 -0.552 0.119 -1.351 +0 0.351 0.081 0.355 -1.109 -0.543 -0.940 -0.635 -0.094 -0.993 -0.736 0.584 0.322 1.704 0.144 1.756 -1.512 -1.871 -0.366 -0.453 -0.768 -0.425 0.740 -0.435 0.692 1.241 0.755 -0.213 -0.428 +2 0.815 -0.778 -0.342 -0.288 0.266 -0.212 -0.352 -0.069 0.751 1.155 0.118 -0.007 -0.045 -2.101 0.367 -2.058 0.347 1.721 -0.895 -1.170 0.129 -1.329 -0.950 1.523 -0.017 -1.578 1.133 -0.650 +4 -0.519 1.718 -0.189 -0.086 -2.216 -0.195 0.836 -1.068 -0.751 0.793 -1.477 1.058 -0.532 -0.389 -1.124 0.378 2.460 0.781 -0.864 1.136 -0.317 1.374 0.807 -2.943 -1.004 0.944 0.152 -1.024 +4 -1.851 -0.685 -0.173 0.128 0.615 1.221 -0.745 -0.202 1.122 0.338 -1.701 -1.783 -0.489 0.552 0.533 -0.893 0.664 0.252 -1.081 -0.601 -1.945 -1.191 -1.884 -1.547 1.679 -0.400 -1.774 0.419 +1 -1.779 -0.533 -0.766 -0.079 -0.637 0.741 -0.288 0.150 0.158 0.195 -0.024 0.511 -0.796 1.399 -0.237 -1.312 0.188 -0.851 0.665 2.210 -0.787 0.332 -1.756 0.454 -0.886 -0.625 -1.101 0.732 +1 -0.162 -0.741 0.509 -2.088 0.316 -1.561 0.184 1.039 0.789 1.284 -0.227 0.208 0.028 0.500 -1.324 -0.083 0.117 0.301 -0.818 1.191 -0.012 0.402 -1.161 -0.726 -0.255 1.642 0.702 1.581 +0 -1.337 -0.131 -0.268 -1.944 0.641 0.784 -0.442 0.557 0.975 -0.936 -1.583 0.386 0.430 1.602 -0.146 0.445 -0.480 -0.860 0.909 0.749 -1.403 0.105 -0.570 0.703 -0.214 -0.036 0.828 0.532 +4 0.358 -2.129 -1.852 0.819 -0.547 -0.988 1.271 0.328 0.872 -0.166 2.680 -0.605 1.744 -0.061 -1.655 0.483 0.315 -1.051 -0.922 -0.025 0.350 -2.471 1.053 -0.494 -1.332 0.941 -0.024 1.216 +3 -0.589 -0.514 1.373 -1.269 -0.668 -0.562 -0.016 -0.271 -1.491 -0.400 -0.100 -0.628 0.687 0.919 -0.078 0.231 -2.161 -0.421 1.005 -0.423 -0.737 2.278 1.819 0.468 1.139 -2.226 -0.788 -0.470 +2 -0.301 -0.593 1.470 1.161 0.534 -1.035 1.199 -1.032 -0.306 0.240 -2.103 0.246 -0.837 -0.464 -1.188 1.063 -0.350 0.004 1.625 0.956 0.054 -0.720 -0.729 0.436 -0.933 -1.885 -0.780 0.403 +2 -0.485 0.051 -0.289 -0.288 -0.488 0.460 -0.381 0.415 1.668 0.390 0.125 -0.571 -0.014 -1.670 1.540 -0.958 -1.919 -0.105 0.354 1.325 0.245 0.925 0.930 0.832 -1.134 0.511 -1.385 1.994 +1 0.033 0.073 0.043 -0.345 -0.377 0.153 0.557 -0.314 -1.872 0.049 -0.236 0.031 -0.311 -0.563 1.323 -0.866 1.914 -0.749 -0.335 0.683 1.465 0.785 1.478 0.734 -0.627 1.103 -2.018 0.720 +3 1.398 -1.398 1.061 -1.381 -0.623 -0.346 0.568 0.668 1.552 0.918 -0.250 1.173 -0.869 0.004 0.027 0.155 1.048 2.408 1.361 -0.191 -0.275 -0.564 -1.245 2.408 0.728 -0.551 1.313 0.660 +3 0.582 1.968 -1.382 -0.135 1.162 0.121 -0.195 0.790 -0.599 0.851 -1.254 -1.519 -0.274 -2.563 0.085 -1.733 0.282 0.185 -0.068 0.436 0.204 -0.293 1.831 0.393 0.772 1.073 0.668 2.063 +2 0.349 1.220 -1.134 0.040 -0.796 0.160 -0.270 0.595 -1.478 0.660 -0.828 -1.009 -0.462 1.302 0.164 0.156 0.949 -2.865 -1.293 -0.164 0.748 0.371 0.304 0.135 1.850 1.199 0.088 0.339 +2 -1.194 -0.319 -1.432 2.824 -1.654 0.010 1.143 1.036 0.851 -0.317 1.351 0.913 0.212 0.241 -0.304 -0.910 1.237 1.312 -0.884 -0.154 -0.143 -0.033 0.064 0.947 -0.747 -0.846 1.237 -0.463 +0 -1.173 -0.395 -0.548 -0.003 0.009 0.392 0.487 0.501 -0.263 0.417 -0.411 -1.251 0.963 -0.351 0.431 -1.941 -0.348 0.343 -0.889 0.026 0.810 1.190 0.470 -1.539 -0.832 -1.145 1.021 0.802 +1 1.056 0.724 -0.285 -2.174 -0.825 -0.151 0.691 0.633 0.586 -1.637 -0.549 -0.649 -0.240 0.096 1.443 -0.416 0.307 1.065 -1.326 0.209 0.675 -1.152 1.196 0.314 1.277 1.177 0.263 -0.502 +4 0.353 0.978 1.012 -0.711 -0.617 0.381 0.751 -1.148 -0.299 0.810 -1.260 -0.183 -1.763 0.828 -1.540 -0.212 0.879 -0.659 -0.893 2.224 -0.397 1.540 1.913 0.140 -1.162 -0.746 -0.114 -2.462 +0 0.151 -1.012 1.444 0.229 -0.731 1.313 0.242 -0.446 -0.007 1.068 -1.045 -0.345 0.420 -0.911 0.604 -0.236 0.266 -0.237 -0.507 1.235 -1.480 0.075 0.834 1.007 0.069 -0.247 -0.635 0.585 +4 -0.686 0.549 -0.407 -0.323 -0.485 2.834 0.158 -0.823 -2.573 -1.641 -0.293 1.695 2.287 -0.238 0.364 -0.165 0.455 -0.240 -0.557 -0.657 0.710 1.303 -0.449 0.727 0.157 -0.382 0.678 1.348 +0 -0.503 0.856 0.946 -1.182 -0.707 -1.575 -0.088 -0.288 0.465 1.805 0.268 -0.299 0.804 -0.351 -0.180 0.174 -1.191 -0.308 -0.799 0.807 -1.030 -0.940 -0.710 -0.783 1.674 1.085 0.490 -0.407 +2 -0.787 0.890 -0.474 0.551 1.939 0.023 -1.012 0.396 0.283 0.250 1.368 -1.186 0.999 -0.606 1.381 -2.122 2.231 -0.271 -1.210 0.908 0.512 0.453 0.930 0.036 -0.293 -1.159 -0.114 -0.511 +4 1.323 -0.523 1.477 -2.783 0.323 0.457 0.570 1.200 0.846 0.873 -2.114 -0.667 -0.284 0.741 0.887 -0.874 0.431 -0.996 0.630 0.925 1.229 -1.232 -0.802 -2.686 0.848 1.339 -0.998 -0.820 +0 -0.800 0.505 0.837 -0.136 0.494 -0.827 1.336 0.837 -1.428 0.215 1.101 -0.040 0.202 -0.141 -1.072 0.901 -0.044 0.372 0.555 -0.956 -0.737 -0.033 1.435 -0.892 0.609 -0.187 -0.661 -0.549 +3 -2.981 -0.729 -0.637 -1.030 -0.786 -0.140 0.594 -1.041 0.054 -0.190 0.913 0.135 0.218 -2.038 0.286 -0.370 -1.038 0.394 -1.983 0.277 0.949 0.015 -0.321 0.737 1.652 -1.312 -0.057 0.811 +2 0.656 1.502 1.888 0.186 1.111 1.603 0.059 0.518 -1.735 -0.726 0.027 -1.547 -0.563 1.280 -0.010 1.322 0.620 -1.389 -0.778 0.015 0.459 0.327 -0.316 -0.277 0.542 -1.455 -0.955 0.447 +4 -0.412 -0.541 -0.637 0.174 2.308 -0.744 -0.318 -0.052 -0.687 1.098 -2.371 -0.531 0.818 -0.294 -0.821 0.441 0.906 0.211 -0.801 0.456 -0.402 0.745 1.483 -2.606 0.111 -1.736 2.442 -0.468 +2 0.366 1.373 -0.524 0.720 -1.591 1.057 -0.996 1.030 -0.500 -0.317 0.379 0.166 1.743 0.341 -0.507 -0.386 0.470 -0.417 -0.106 1.359 -0.477 -1.657 -0.061 -0.019 -1.429 -0.095 2.255 -1.725 +0 1.916 0.625 0.687 1.203 0.377 -0.755 -2.541 -0.622 0.649 -0.428 -0.187 -0.224 -0.548 0.663 0.563 -0.120 0.707 0.050 0.136 -0.352 -0.417 -0.474 -0.134 0.518 -0.509 0.047 1.132 1.091 +2 0.340 -0.253 -0.077 1.542 0.112 1.495 -0.212 0.417 -1.804 -0.572 -0.303 -0.044 -0.105 -0.150 1.257 -0.449 0.653 0.045 -0.308 2.144 0.624 -0.118 0.627 0.588 -0.090 -0.978 -0.380 -3.088 +0 0.262 0.999 -0.910 -0.018 1.120 1.421 1.299 -0.385 0.451 -0.231 -0.693 0.302 -0.667 1.644 0.821 -0.773 1.146 0.082 1.209 -0.010 0.840 -1.038 0.240 -0.684 -1.153 0.029 0.778 0.256 +1 -0.099 -1.171 -0.602 -1.761 -0.921 0.426 -0.048 -0.224 -0.793 -1.772 0.182 0.379 -0.992 -0.533 1.542 1.159 -1.484 -0.085 1.455 -0.216 -0.269 1.093 0.987 0.935 -0.262 0.663 1.495 -0.050 +2 0.783 -1.265 -0.969 0.959 0.886 0.676 0.130 -0.718 1.453 0.072 1.293 1.130 0.708 -0.043 -0.235 -0.074 1.418 -0.411 -0.103 0.563 1.882 0.523 0.163 -0.353 2.558 -0.152 -0.924 0.985 +1 -0.741 1.276 0.686 -1.682 -0.466 0.808 1.498 -1.103 -0.745 -0.596 -1.200 1.490 -0.793 -1.045 1.203 -0.119 -0.828 0.254 0.275 -1.889 -0.537 0.321 -0.041 0.034 0.507 0.152 -0.709 0.584 +3 -0.101 1.761 1.355 -0.821 0.554 2.280 -1.200 1.382 -0.685 0.292 0.519 0.817 0.596 -1.049 -0.030 -0.799 0.151 0.443 1.165 -1.367 -0.120 1.744 -1.355 -0.118 -0.856 1.139 0.405 -1.278 +0 0.219 0.875 -0.730 0.585 -0.189 2.161 0.183 1.557 -1.332 0.579 0.688 0.486 -0.485 0.141 0.519 -0.045 -0.233 0.016 -0.143 0.675 -0.107 -1.292 -1.546 0.195 -0.151 -1.147 1.575 -0.243 +1 -1.610 0.304 -0.017 -0.189 -1.512 -0.157 1.457 -0.547 0.103 0.192 -1.528 -1.503 -1.423 0.386 1.229 1.747 -0.139 1.094 -0.400 -1.209 -0.866 -0.433 -0.037 -0.073 0.614 -0.195 0.937 -0.648 +1 0.915 0.310 -0.375 -0.145 0.987 0.665 -1.404 -0.619 -0.960 -0.948 -0.788 0.074 1.302 0.738 0.259 -0.191 0.081 0.052 0.629 0.742 0.966 -0.021 -1.127 -0.319 0.083 -2.036 -2.007 -0.962 +4 0.249 2.144 0.608 -0.950 -1.506 1.865 -0.605 0.011 -1.236 -0.137 0.990 2.704 -1.156 -0.388 -0.214 2.564 -1.267 0.650 -2.025 -1.625 0.173 0.651 0.338 0.866 0.801 -0.182 -0.700 -0.253 +1 0.869 -0.816 -0.692 0.283 -1.134 -0.178 -0.993 -0.069 0.816 1.743 -1.565 0.600 0.678 -0.232 -0.593 -0.594 -1.853 0.779 -0.764 -1.669 0.323 0.313 0.847 1.817 0.040 -0.299 -0.391 -0.294 +3 0.457 0.057 2.275 -0.481 0.789 1.048 -0.115 0.184 0.062 1.016 0.574 -0.860 -0.434 0.223 1.502 -0.088 -0.986 1.547 1.312 0.593 -0.374 2.220 1.178 -0.560 1.135 -1.030 0.240 1.895 +4 3.040 0.183 0.267 -1.092 -0.697 -0.504 1.147 -1.418 -0.343 0.441 -2.311 -1.662 -1.404 -0.185 -0.456 0.930 -2.220 -0.415 1.175 -1.574 -0.116 0.751 1.407 -1.254 0.372 0.041 -1.038 -1.232 +1 -0.499 0.638 -0.229 2.161 -2.029 0.483 0.395 1.914 0.756 -1.810 -0.348 0.493 -0.709 -0.160 -0.360 -0.506 -0.222 -0.070 0.815 -0.649 -0.150 0.735 0.560 -1.658 -0.847 -0.621 -0.598 -0.206 +1 0.277 -0.475 0.538 0.623 -1.353 0.486 -1.506 -0.645 -0.986 -0.129 -0.437 -0.200 -1.895 -0.076 -1.101 2.284 -0.400 -0.258 0.695 1.322 -1.178 0.573 0.966 0.185 0.165 -0.512 -0.322 -0.108 +4 1.989 3.065 -0.402 -1.386 -0.736 -0.191 0.224 0.369 0.787 -0.846 1.058 -0.754 -0.231 1.387 2.075 -0.519 0.686 -0.321 1.317 -0.940 -1.589 0.717 1.494 1.835 0.234 -1.003 1.528 1.307 +0 -0.893 -1.105 -1.200 0.006 -1.447 -0.944 -0.099 -1.604 -0.486 0.639 -1.225 0.796 -0.806 0.272 -1.106 0.453 -0.304 0.466 -0.545 0.556 0.847 0.120 -0.480 -0.234 -1.264 0.203 0.623 1.566 +1 1.040 -0.342 0.466 1.422 0.220 -0.113 0.194 0.063 1.246 0.311 -0.544 1.048 1.230 -1.338 1.533 0.365 0.283 1.007 -0.567 0.954 -0.377 -1.475 -0.998 0.567 -0.945 1.610 1.189 1.333 +2 -1.300 -0.214 -0.599 1.180 -0.074 0.148 0.482 -1.214 0.299 -1.010 -0.490 -0.443 0.484 -0.793 -0.475 0.324 1.385 0.438 1.131 -2.124 1.435 -0.857 -0.609 1.941 0.170 -0.750 -0.612 -1.383 +2 -0.272 -0.445 0.732 -1.408 -0.235 0.484 -1.904 -1.294 1.207 -1.893 -0.969 1.645 0.370 1.289 -0.789 -1.402 0.098 0.443 -1.651 0.005 -0.834 0.066 1.012 -0.276 1.178 -0.235 0.905 0.864 +2 0.706 0.084 -0.519 1.909 -0.359 0.347 -0.095 -1.536 -0.305 -0.578 -0.437 2.006 -0.621 0.088 0.819 -0.766 -1.006 0.279 0.029 0.505 -1.653 1.625 0.061 1.026 2.422 -0.139 -0.874 -0.375 +3 1.029 -1.553 0.702 -0.012 0.577 0.011 -0.572 -1.113 0.673 0.667 0.662 0.428 0.945 1.406 -1.876 -2.342 1.718 -1.341 -1.086 -0.288 -1.654 -0.382 -0.948 -0.233 0.280 -0.344 -0.394 0.258 +1 -0.525 0.442 -1.254 -0.666 0.758 -1.755 0.903 -2.012 -1.731 -0.499 0.287 1.325 0.058 -0.247 0.580 -0.871 0.139 -1.199 0.083 -0.130 -0.804 0.165 0.159 1.175 -1.606 -0.076 -0.760 0.870 +3 2.002 1.448 -0.938 -0.938 -0.087 -1.571 -0.142 1.155 0.761 0.054 1.055 -0.672 2.084 -0.285 -0.472 -1.059 -0.101 1.643 1.165 -1.041 2.484 0.063 -0.801 0.751 0.786 -0.095 -0.561 0.332 +3 -2.273 0.104 0.861 -0.833 -0.836 -1.866 1.590 -0.720 0.241 0.604 1.512 -0.154 -0.539 0.173 1.284 -0.170 -0.955 -1.541 -1.221 0.864 -0.321 -0.608 -0.716 0.085 -0.239 -0.273 1.349 1.538 +1 1.162 -0.166 -1.180 -1.204 -0.237 -0.151 0.950 -0.502 0.060 0.041 -1.361 0.407 -0.911 -0.727 -0.005 -0.689 0.986 0.930 -0.369 0.255 1.700 -0.984 0.024 -1.206 0.253 -2.111 0.160 0.682 +1 -1.071 1.100 -0.993 -0.653 -0.236 -0.675 0.291 -1.890 -0.905 -0.760 -0.867 1.375 -0.437 -0.488 0.176 1.018 -0.648 0.253 -1.281 0.320 -0.041 -1.053 -1.722 1.193 0.913 0.528 1.293 0.537 +2 -1.078 0.262 0.158 0.610 -1.511 1.909 2.121 0.637 -0.466 -0.309 -0.726 -0.524 0.910 -1.720 -0.153 0.142 -1.106 -0.456 1.580 0.606 -0.252 -0.017 -0.834 -1.823 -0.757 -0.754 0.073 0.725 +0 1.155 0.751 0.062 1.017 0.311 0.095 -0.588 -1.374 -0.037 -0.842 0.624 0.735 0.032 0.072 0.954 0.181 0.144 0.759 -0.153 -0.679 0.257 -0.027 0.664 1.378 -0.277 -0.052 -1.074 1.285 +3 0.645 0.506 0.099 -0.831 0.706 -0.640 1.034 -0.173 -0.825 0.307 -0.576 -1.470 -1.467 -1.607 -0.091 0.911 -0.271 -0.573 -1.973 -0.765 -0.457 -1.485 -0.195 0.929 -1.022 -1.848 -1.259 -2.103 +4 -3.335 -0.646 -1.413 0.400 -0.289 0.015 -2.079 -0.310 1.115 1.409 0.719 -1.335 1.259 0.724 -0.117 -2.025 0.954 -0.592 0.346 0.068 1.538 -0.125 -0.015 1.579 -0.658 0.814 -0.199 -1.003 +0 -0.527 1.051 0.068 0.311 -0.468 1.558 -0.449 0.155 0.882 -0.356 0.338 0.614 0.346 0.368 1.163 0.714 0.087 -0.265 1.348 0.742 1.484 -0.222 1.240 -1.128 0.441 0.842 -0.053 2.081 +2 -0.159 1.117 0.705 -0.289 -0.076 -0.092 -2.078 -1.787 -0.420 -0.723 1.385 -0.807 -1.230 1.047 -2.128 1.377 -1.122 -0.440 -0.051 -0.481 0.333 -0.587 1.074 0.079 0.801 -0.549 -0.173 0.462 +2 1.456 0.055 0.582 0.443 0.587 -0.919 1.280 0.484 0.513 0.471 -2.364 0.106 0.085 0.265 1.016 0.914 0.988 -0.967 0.125 -1.564 0.084 -1.120 -0.264 0.806 2.135 -0.959 0.254 -0.047 +0 0.740 -0.591 0.644 0.826 -0.213 0.214 1.315 -0.225 -0.570 0.409 -0.211 0.120 1.031 -1.155 0.575 -0.619 -0.327 0.048 -0.119 -1.676 1.382 1.134 -0.146 0.934 -0.398 -0.982 0.054 -1.169 +4 -1.602 0.148 -1.437 2.466 -1.032 -0.075 1.506 -0.366 -0.359 -2.587 -1.310 0.562 1.673 -0.891 0.197 2.708 0.567 0.976 -0.766 1.552 -0.692 -0.868 1.479 -0.637 -0.004 1.626 -0.914 -0.237 +4 -1.308 -0.144 -0.563 -1.222 0.790 0.847 -0.970 -0.216 0.044 -1.815 -1.397 1.286 -1.065 1.258 -1.345 0.659 -0.311 -0.171 1.856 -1.254 0.595 0.553 3.355 -1.077 -0.626 0.816 -0.200 -0.463 +4 0.244 -1.090 -0.986 -0.970 0.111 -0.626 0.494 -2.855 -0.857 -1.126 -0.256 -0.538 0.798 -1.859 0.045 0.558 1.857 -0.482 0.324 -1.804 -2.392 0.845 -0.006 1.179 -1.373 0.520 1.869 0.420 +1 1.130 0.869 0.125 0.739 0.573 0.226 -0.593 -0.910 -0.351 0.264 0.091 -0.756 2.130 0.886 -0.292 0.186 -0.514 2.603 -0.752 -0.250 -0.527 0.034 -0.309 0.592 -0.573 -0.064 0.018 1.615 +1 -0.102 0.911 -1.744 -0.368 0.093 1.186 0.843 0.892 -2.239 1.188 0.779 0.640 0.379 0.425 0.395 0.393 -0.290 1.052 0.052 -0.478 0.080 -0.496 0.329 0.432 -0.721 0.514 -2.149 0.873 +4 0.640 1.388 -1.616 0.786 -0.981 0.445 -0.244 -0.689 1.174 -1.677 1.813 0.631 0.169 -0.149 -0.371 0.468 -1.394 -3.073 0.086 -0.746 -0.916 -1.576 1.204 1.821 -0.052 -0.156 0.423 0.363 +1 0.140 -0.681 -0.730 0.308 -0.282 1.978 1.782 -0.162 -1.541 -0.291 1.048 -0.887 1.605 0.581 -1.171 -0.660 -0.519 -0.806 -1.145 -0.575 -0.381 1.299 0.439 0.498 0.350 -0.796 1.147 -0.434 +2 -1.150 0.525 -1.666 -1.154 -0.403 0.675 0.333 0.416 0.230 1.683 1.649 -1.101 1.173 1.138 0.397 -0.708 -0.152 0.051 -1.314 -0.541 0.409 -1.089 -0.414 -2.247 0.371 -1.314 0.474 0.235 +4 0.112 -1.009 -2.023 0.316 -0.794 1.095 0.014 -0.700 1.798 0.576 -3.655 -0.486 -1.384 -0.800 -0.145 0.354 0.261 1.078 0.637 0.575 0.201 1.287 -0.316 -0.290 -1.906 1.056 -0.077 2.934 +4 -1.467 -2.022 -2.454 0.340 -1.052 0.498 1.180 0.590 -0.227 1.848 -0.461 -0.903 0.334 0.773 -2.585 1.111 -1.625 -2.052 -0.344 -1.063 0.416 0.356 0.308 1.224 -0.315 0.367 -1.669 0.961 +1 -0.476 0.602 -0.589 -2.749 0.997 -0.346 -0.225 0.698 0.216 0.362 -0.786 1.991 0.588 1.395 -0.589 -0.294 -0.462 -0.718 -0.772 1.074 -0.413 0.535 -1.132 -0.791 1.406 0.638 -0.063 -0.523 +3 0.488 0.251 0.985 0.498 0.830 3.439 -1.594 1.342 1.479 -1.098 -0.799 0.502 -1.250 -0.614 0.239 -0.684 -0.228 -0.867 -0.942 0.472 -0.101 0.123 0.704 0.184 -0.335 -0.880 0.449 1.994 +4 -0.838 -1.070 0.411 0.148 -0.741 2.958 1.587 0.018 -0.692 0.642 -0.830 -0.461 -0.467 -0.395 0.750 0.329 -0.502 -1.139 -0.838 -1.092 -0.671 2.342 -2.050 0.391 1.602 2.309 -1.359 -1.060 +0 -0.320 -1.298 1.743 0.999 0.255 0.815 0.753 -0.993 0.637 1.270 -0.678 -1.534 -1.162 -0.096 -1.220 -0.172 -0.655 -0.001 -0.510 -0.036 0.603 -0.958 0.281 -0.852 -0.006 -0.082 0.356 0.185 +2 -1.054 -0.648 -0.313 -0.185 -2.438 -0.753 -0.554 1.891 0.227 -0.798 -0.439 0.425 -0.378 -0.585 -0.553 0.480 -1.382 -1.087 -0.928 -1.104 -0.824 0.897 1.036 0.272 0.215 -0.670 -2.081 0.144 +3 -0.366 -0.458 0.955 0.153 -0.653 0.491 0.768 -0.240 -0.212 1.715 -0.821 -0.540 -0.463 1.699 -1.977 0.822 -0.029 1.813 0.356 -2.151 1.611 -0.236 -2.041 -0.405 0.036 -1.049 -1.531 0.297 +1 -0.667 0.409 0.347 -0.646 0.840 0.636 0.611 1.529 0.616 -0.121 -0.084 -1.738 -0.779 1.293 1.202 0.457 0.118 -1.107 -1.467 0.129 0.376 -1.018 -0.107 1.273 0.387 0.097 -0.726 1.867 +3 0.288 -0.115 -1.091 -1.181 0.583 0.659 1.190 -0.169 -0.471 -1.831 -0.013 -1.395 -0.574 -0.336 -1.027 1.381 2.484 1.461 1.420 0.475 0.957 0.803 -0.412 1.226 -0.567 1.180 0.867 -0.181 +3 1.227 -0.900 0.683 -0.624 -2.269 0.932 0.772 1.485 -1.463 0.164 -0.223 1.144 -1.389 -0.253 0.094 0.742 0.415 -0.032 -0.311 -0.089 0.752 -0.826 -0.234 -0.234 -2.086 -1.956 -1.491 -0.213 +0 0.491 -0.126 0.420 -0.837 -0.604 -0.734 -0.565 1.287 0.726 -1.077 0.437 -0.391 -0.682 -1.486 0.972 -1.364 -0.052 -0.946 0.776 -0.334 -0.134 -0.101 0.303 0.013 -0.043 -0.518 -1.575 -1.119 +2 -0.316 1.946 0.839 -1.289 -1.518 1.871 0.366 0.986 1.648 -0.735 0.454 0.343 -0.768 -0.742 0.548 -1.308 -0.535 0.398 0.057 1.000 0.078 0.469 -0.653 0.051 0.513 -0.186 -1.679 1.098 +2 -0.010 -0.555 0.570 2.541 -1.295 1.338 -0.411 -0.683 0.818 -0.256 -1.286 -0.024 0.137 -0.040 0.086 -0.396 0.922 0.266 -1.657 -1.771 1.562 0.832 0.263 -0.503 0.389 0.995 0.194 0.654 diff --git a/examples/multiclass_classification/predict.conf b/examples/multiclass_classification/predict.conf new file mode 100644 index 0000000..57a6019 --- /dev/null +++ b/examples/multiclass_classification/predict.conf @@ -0,0 +1,5 @@ +task = predict + +data = multiclass.test + +input_model= LightGBM_model.txt diff --git a/examples/multiclass_classification/train.conf b/examples/multiclass_classification/train.conf new file mode 100644 index 0000000..b432756 --- /dev/null +++ b/examples/multiclass_classification/train.conf @@ -0,0 +1,68 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# multiclass +# alias: application, app +objective = multiclass + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +# multi_logloss +# multi_error +# auc_mu +metric = multi_logloss,auc_mu + +# AUC-mu weights; the matrix of loss weights below is passed in parameter auc_mu_weights as a list +# 0 1 2 3 4 +# 5 0 6 7 8 +# 9 10 0 11 12 +# 13 14 15 0 16 +# 17 18 19 20 0 +auc_mu_weights = 0,1,2,3,4,5,0,6,7,8,9,10,0,11,12,13,14,15,0,16,17,18,19,20,0 + +# number of class, for multiclass classification +num_class = 5 + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# column in data to use as label +label_column = 0 + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "regression.train.weight" +# alias: train_data, train +data = multiclass.train + +# valid data +valid_data = multiclass.test + +# round for early stopping +early_stopping = 10 + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.05 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 31 diff --git a/examples/parallel_learning/README.md b/examples/parallel_learning/README.md new file mode 100644 index 0000000..952c35a --- /dev/null +++ b/examples/parallel_learning/README.md @@ -0,0 +1,22 @@ +Distributed Learning Example +============================ + + +Here is an example for LightGBM to perform distributed learning for 2 machines. + +1. Edit [mlist.txt](./mlist.txt): write the ip of these 2 machines that you want to run application on. + + ``` + machine1_ip 12400 + machine2_ip 12400 + ``` + +2. Copy this folder and executable file to these 2 machines that you want to run application on. + +3. Run command in this folder on both 2 machines: + + ```"./lightgbm" config=train.conf``` + +This distributed learning example is based on socket. LightGBM also supports distributed learning based on MPI. + +For more details about the usage of distributed learning, please refer to [this](https://github.com/lightgbm-org/LightGBM/blob/main/docs/Parallel-Learning-Guide.rst). diff --git a/examples/parallel_learning/binary.test b/examples/parallel_learning/binary.test new file mode 100644 index 0000000..c9674d6 --- /dev/null +++ b/examples/parallel_learning/binary.test @@ -0,0 +1,500 @@ +1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796 +0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116 +0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696 +1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 1.231 1.234 +0 0.897 -1.703 -1.306 1.022 -0.729 0.836 0.859 -0.333 2.173 1.336 -0.965 0.972 2.215 0.671 1.021 -1.439 0.000 0.493 -2.019 -0.289 0.000 0.805 0.930 0.984 1.430 2.198 1.934 1.684 +0 0.756 1.126 -0.945 2.355 -0.555 0.889 0.800 1.440 0.000 0.585 0.271 0.631 2.215 0.722 1.744 1.051 0.000 0.618 0.924 0.698 1.551 0.976 0.864 0.988 0.803 0.234 0.822 0.911 +0 1.141 -0.741 0.953 1.478 -0.524 1.197 -0.871 1.689 2.173 0.875 1.321 -0.518 1.107 0.540 0.037 -0.987 0.000 0.879 1.187 0.245 0.000 0.888 0.701 1.747 1.358 2.479 1.491 1.223 +1 0.606 -0.936 -0.384 1.257 -1.162 2.719 -0.600 0.100 2.173 3.303 -0.284 1.561 1.107 0.689 1.786 -0.326 0.000 0.780 -0.532 1.216 0.000 0.936 2.022 0.985 1.574 4.323 2.263 1.742 +1 0.603 0.429 -0.279 1.448 1.301 1.008 2.423 -1.295 0.000 0.452 1.305 0.533 0.000 1.076 1.011 1.256 2.548 2.021 1.260 -0.343 0.000 0.890 0.969 1.281 0.763 0.652 0.827 0.785 +0 1.171 -0.962 0.521 0.841 -0.315 1.196 -0.744 -0.882 2.173 0.726 -1.305 1.377 1.107 0.643 -1.790 -1.264 0.000 1.257 0.222 0.817 0.000 0.862 0.911 0.987 0.846 1.293 0.899 0.756 +1 1.392 -0.358 0.235 1.494 -0.461 0.895 -0.848 1.549 2.173 0.841 -0.384 0.666 1.107 1.199 2.509 -0.891 0.000 1.109 -0.364 -0.945 0.000 0.693 2.135 1.170 1.362 0.959 2.056 1.842 +1 1.024 1.076 -0.886 0.851 1.530 0.673 -0.449 0.187 1.087 0.628 -0.895 1.176 2.215 0.696 -0.232 -0.875 0.000 0.411 1.501 0.048 0.000 0.842 0.919 1.063 1.193 0.777 0.964 0.807 +1 0.890 -0.760 1.182 1.369 0.751 0.696 -0.959 -0.710 1.087 0.775 -0.130 -1.409 2.215 0.701 -0.110 -0.739 0.000 0.508 -0.451 0.390 0.000 0.762 0.738 0.998 1.126 0.788 0.940 0.790 +1 0.460 0.537 0.636 1.442 -0.269 0.585 0.323 -1.731 2.173 0.503 1.034 -0.927 0.000 0.928 -1.024 1.006 2.548 0.513 -0.618 -1.336 0.000 0.802 0.831 0.992 1.019 0.925 1.056 0.833 +1 0.364 1.648 0.560 1.720 0.829 1.110 0.811 -0.588 0.000 0.408 1.045 1.054 2.215 0.319 -1.138 1.545 0.000 0.423 1.025 -1.265 3.102 1.656 0.928 1.003 0.544 0.327 0.670 0.746 +1 0.525 -0.096 1.206 0.948 -1.103 1.519 -0.582 0.606 2.173 1.274 -0.572 -0.934 0.000 0.855 -1.028 -1.222 0.000 0.578 -1.000 -1.725 3.102 0.896 0.878 0.981 0.498 0.909 0.772 0.668 +0 0.536 -0.821 -1.029 0.703 1.113 0.363 -0.711 0.022 1.087 0.325 1.503 1.249 2.215 0.673 1.041 -0.401 0.000 0.480 2.127 1.681 0.000 0.767 1.034 0.990 0.671 0.836 0.669 0.663 +1 1.789 -0.583 1.641 0.897 0.799 0.515 -0.100 -1.483 0.000 1.101 0.031 -0.326 2.215 1.195 0.001 0.126 2.548 0.768 -0.148 0.601 0.000 0.916 0.921 1.207 1.069 0.483 0.934 0.795 +1 1.332 -0.571 0.986 0.580 1.508 0.582 0.634 -0.746 1.087 1.084 -0.964 -0.489 0.000 0.785 0.274 0.343 2.548 0.779 0.721 1.489 0.000 1.733 1.145 0.990 1.270 0.715 0.897 0.915 +0 1.123 0.629 -1.708 0.597 -0.882 0.752 0.195 1.522 2.173 1.671 1.515 -0.003 0.000 0.778 0.514 0.139 1.274 0.801 1.260 1.600 0.000 1.495 0.976 0.988 0.676 0.921 1.010 0.943 +0 1.816 -0.515 0.171 0.980 -0.454 0.870 0.202 -1.399 2.173 1.130 1.066 -1.593 0.000 0.844 0.735 1.275 2.548 1.125 -1.133 0.348 0.000 0.837 0.693 0.988 1.112 0.784 1.009 0.974 +1 0.364 0.694 0.445 1.862 0.159 0.963 -1.356 1.260 1.087 0.887 -0.540 -1.533 2.215 0.658 -2.544 -1.236 0.000 0.516 -0.807 0.039 0.000 0.891 1.004 0.991 1.092 0.976 1.000 0.953 +1 0.790 -1.175 0.475 1.846 0.094 0.999 -1.090 0.257 0.000 1.422 0.854 1.112 2.215 1.302 1.004 -1.702 1.274 2.557 -0.787 -1.048 0.000 0.890 1.429 0.993 2.807 0.840 2.248 1.821 +1 0.765 -0.500 -0.603 1.843 -0.560 1.068 0.007 0.746 2.173 1.154 -0.017 1.329 0.000 1.165 1.791 -1.585 0.000 1.116 0.441 -0.886 0.000 0.774 0.982 0.989 1.102 0.633 1.178 1.021 +1 1.407 1.293 -1.418 0.502 -1.527 2.005 -2.122 0.622 0.000 1.699 1.508 -0.649 2.215 1.665 0.748 -0.755 0.000 2.555 0.811 1.423 1.551 7.531 5.520 0.985 1.115 1.881 4.487 3.379 +1 0.772 -0.186 -1.372 0.823 -0.140 0.781 0.763 0.046 2.173 1.128 0.516 1.380 0.000 0.797 -0.640 -0.134 2.548 2.019 -0.972 -1.670 0.000 2.022 1.466 0.989 0.856 0.808 1.230 0.991 +1 0.546 -0.954 0.715 1.335 -1.689 0.783 -0.443 -1.735 2.173 1.081 0.185 -0.435 0.000 1.433 -0.662 -0.389 0.000 0.969 0.924 1.099 0.000 0.910 0.879 0.988 0.683 0.753 0.878 0.865 +1 0.596 0.276 -1.054 1.358 1.355 1.444 1.813 -0.208 0.000 1.175 -0.949 -1.573 0.000 0.855 -1.228 -0.925 2.548 1.837 -0.400 0.913 0.000 0.637 0.901 1.028 0.553 0.790 0.679 0.677 +0 0.458 2.292 1.530 0.291 1.283 0.749 -0.930 -0.198 0.000 0.300 -1.560 0.990 0.000 0.811 -0.176 0.995 2.548 1.085 -0.178 -1.213 3.102 0.891 0.648 0.999 0.732 0.655 0.619 0.620 +0 0.638 -0.575 -1.048 0.125 0.178 0.846 -0.753 -0.339 1.087 0.799 -0.727 1.182 0.000 0.888 0.283 0.717 0.000 1.051 -1.046 -1.557 3.102 0.889 0.871 0.989 0.884 0.923 0.836 0.779 +1 0.434 -1.119 -0.313 2.427 0.461 0.497 0.261 -1.177 2.173 0.618 -0.737 -0.688 0.000 1.150 -1.237 -1.652 2.548 0.757 -0.054 1.700 0.000 0.809 0.741 0.982 1.450 0.936 1.086 0.910 +1 0.431 -1.144 -1.030 0.778 -0.655 0.490 0.047 -1.546 0.000 1.583 -0.014 0.891 2.215 0.516 0.956 0.567 2.548 0.935 -1.123 -0.082 0.000 0.707 0.995 0.995 0.700 0.602 0.770 0.685 +1 1.894 0.222 1.224 1.578 1.715 0.966 2.890 -0.013 0.000 0.922 -0.703 -0.844 0.000 0.691 2.056 1.039 0.000 0.900 -0.733 -1.240 3.102 1.292 1.992 1.026 0.881 0.684 1.759 1.755 +0 0.985 -0.316 0.141 1.067 -0.946 0.819 -1.177 1.307 2.173 1.080 -0.429 0.557 1.107 1.726 1.435 -1.075 0.000 1.100 1.547 -0.647 0.000 0.873 1.696 1.179 1.146 1.015 1.538 1.270 +0 0.998 -0.187 -0.236 0.882 0.755 0.468 0.950 -0.439 2.173 0.579 -0.550 -0.624 0.000 1.847 1.196 1.384 1.274 0.846 1.273 -1.072 0.000 1.194 0.797 1.013 1.319 1.174 0.963 0.898 +0 0.515 0.246 -0.593 1.082 1.591 0.912 -0.623 -0.957 2.173 0.858 0.418 0.844 0.000 0.948 2.519 1.599 0.000 1.158 1.385 -0.095 3.102 0.973 1.033 0.988 0.998 1.716 1.054 0.901 +0 0.919 -1.001 1.506 1.389 0.653 0.507 -0.616 -0.689 2.173 0.808 0.536 -0.467 2.215 0.496 2.187 -0.859 0.000 0.822 0.807 1.163 0.000 0.876 0.861 1.088 0.947 0.614 0.911 1.087 +0 0.794 0.051 1.477 1.504 -1.695 0.716 0.315 0.264 1.087 0.879 -0.135 -1.094 2.215 1.433 -0.741 0.201 0.000 1.566 0.534 -0.989 0.000 0.627 0.882 0.974 0.807 1.130 0.929 0.925 +1 0.455 -0.946 -1.175 1.453 -0.580 0.763 -0.856 0.840 0.000 0.829 1.223 1.174 2.215 0.714 0.638 -0.466 0.000 1.182 0.223 -1.333 0.000 0.977 0.938 0.986 0.713 0.714 0.796 0.843 +1 0.662 -0.296 -1.287 1.212 -0.707 0.641 1.457 0.222 0.000 0.600 0.525 -1.700 2.215 0.784 -0.835 -0.961 2.548 0.865 1.131 1.162 0.000 0.854 0.877 0.978 0.740 0.734 0.888 0.811 +0 0.390 0.698 -1.629 1.888 0.298 0.990 1.614 -1.572 0.000 1.666 0.170 0.719 2.215 1.590 1.064 -0.886 1.274 0.952 0.305 -1.216 0.000 1.048 0.897 1.173 0.891 1.936 1.273 1.102 +0 1.014 0.117 1.384 0.686 -1.047 0.609 -1.245 -0.850 0.000 1.076 -1.158 0.814 1.107 1.598 -0.389 -0.111 0.000 0.907 1.688 -1.673 0.000 1.333 0.866 0.989 0.975 0.442 0.797 0.788 +0 1.530 -1.408 -0.207 0.440 -1.357 0.902 -0.647 1.325 1.087 1.320 -0.819 0.246 1.107 0.503 1.407 -1.683 0.000 1.189 -0.972 -0.925 0.000 0.386 1.273 0.988 0.829 1.335 1.173 1.149 +1 1.689 -0.590 0.915 2.076 1.202 0.644 -0.478 -0.238 0.000 0.809 -1.660 -1.184 0.000 1.227 -0.224 -0.808 2.548 1.655 1.047 -0.623 0.000 0.621 1.192 0.988 1.309 0.866 0.924 1.012 +0 1.102 0.402 -1.622 1.262 1.022 0.576 0.271 -0.269 0.000 0.591 0.495 -1.278 0.000 1.271 0.209 0.575 2.548 0.941 0.964 -0.685 3.102 0.989 0.963 1.124 0.857 0.858 0.716 0.718 +0 2.491 0.825 0.581 1.593 0.205 0.782 -0.815 1.499 0.000 1.179 -0.999 -1.509 0.000 0.926 0.920 -0.522 2.548 2.068 -1.021 -1.050 3.102 0.874 0.943 0.980 0.945 1.525 1.570 1.652 +0 0.666 0.254 1.601 1.303 -0.250 1.236 -1.929 0.793 0.000 1.074 0.447 -0.871 0.000 0.991 1.059 -0.342 0.000 1.703 -0.393 -1.419 3.102 0.921 0.945 1.285 0.931 0.462 0.770 0.729 +0 0.937 -1.126 1.424 1.395 1.743 0.760 0.428 -0.238 2.173 0.846 0.494 1.320 2.215 0.872 -1.826 -0.507 0.000 0.612 1.860 1.403 0.000 3.402 2.109 0.985 1.298 1.165 1.404 1.240 +1 0.881 -1.086 -0.870 0.513 0.266 2.049 -1.870 1.160 0.000 2.259 -0.428 -0.935 2.215 1.321 -0.655 -0.449 2.548 1.350 -1.766 -0.108 0.000 0.911 1.852 0.987 1.167 0.820 1.903 1.443 +0 0.410 0.835 -0.819 1.257 1.112 0.871 -1.737 -0.401 0.000 0.927 0.158 1.253 0.000 1.183 0.405 -1.570 0.000 0.807 -0.704 -0.438 3.102 0.932 0.962 0.987 0.653 0.315 0.616 0.648 +1 0.634 0.196 -1.679 1.379 -0.967 2.260 -0.273 1.114 0.000 1.458 1.070 -0.278 1.107 1.195 0.110 -0.688 2.548 0.907 0.298 -1.359 0.000 0.949 1.129 0.984 0.675 0.877 0.938 0.824 +1 0.632 -1.254 1.201 0.496 -0.106 0.235 2.731 -0.955 0.000 0.615 -0.805 0.600 0.000 0.633 -0.934 1.641 0.000 1.407 -0.483 -0.962 1.551 0.778 0.797 0.989 0.578 0.722 0.576 0.539 +0 0.714 1.122 1.566 2.399 -1.431 1.665 0.299 0.323 0.000 1.489 1.087 -0.861 2.215 1.174 0.140 1.083 2.548 0.404 -0.968 1.105 0.000 0.867 0.969 0.981 1.039 1.552 1.157 1.173 +1 0.477 -0.321 -0.471 1.966 1.034 2.282 1.359 -0.874 0.000 1.672 -0.258 1.109 0.000 1.537 0.604 0.231 2.548 1.534 -0.640 0.827 0.000 0.746 1.337 1.311 0.653 0.721 0.795 0.742 +1 1.351 0.460 0.031 1.194 -1.185 0.670 -1.157 -1.637 2.173 0.599 -0.823 0.680 0.000 0.478 0.373 1.716 0.000 0.809 -0.919 0.010 1.551 0.859 0.839 1.564 0.994 0.777 0.971 0.826 +1 0.520 -1.442 -0.348 0.840 1.654 1.273 -0.760 1.317 0.000 0.861 2.579 -0.791 0.000 1.779 0.257 -0.703 0.000 2.154 1.928 0.457 0.000 1.629 3.194 0.992 0.730 1.107 2.447 2.747 +0 0.700 -0.308 0.920 0.438 -0.879 0.516 1.409 1.101 0.000 0.960 0.701 -0.049 2.215 1.442 -0.416 -1.439 2.548 0.628 1.009 -0.364 0.000 0.848 0.817 0.987 0.759 1.421 0.937 0.920 +1 0.720 1.061 -0.546 0.798 -1.521 1.066 0.173 0.271 1.087 1.453 0.114 1.336 1.107 0.702 0.616 -0.367 0.000 0.543 -0.386 -1.301 0.000 0.653 0.948 0.989 1.031 1.500 0.965 0.790 +1 0.735 -0.416 0.588 1.308 -0.382 1.042 0.344 1.609 0.000 0.926 0.163 -0.520 1.107 1.050 -0.427 1.159 2.548 0.834 0.613 0.948 0.000 0.848 1.189 1.042 0.844 1.099 0.829 0.843 +1 0.777 -0.396 1.540 1.608 0.638 0.955 0.040 0.918 2.173 1.315 1.116 -0.823 0.000 0.781 -0.762 0.564 2.548 0.945 -0.573 1.379 0.000 0.679 0.706 1.124 0.608 0.593 0.515 0.493 +1 0.934 0.319 -0.257 0.970 -0.980 0.726 0.774 0.731 0.000 0.896 0.038 -1.465 1.107 0.773 -0.055 -0.831 2.548 1.439 -0.229 0.698 0.000 0.964 1.031 0.995 0.845 0.480 0.810 0.762 +0 0.461 0.771 0.019 2.055 -1.288 1.043 0.147 0.261 2.173 0.833 -0.156 1.425 0.000 0.832 0.805 -0.491 2.548 0.589 1.252 1.414 0.000 0.850 0.906 1.245 1.364 0.850 0.908 0.863 +1 0.858 -0.116 -0.937 0.966 1.167 0.825 -0.108 1.111 1.087 0.733 1.163 -0.634 0.000 0.894 0.771 0.020 0.000 0.846 -1.124 -1.195 3.102 0.724 1.194 1.195 0.813 0.969 0.985 0.856 +0 0.720 -0.335 -0.307 1.445 0.540 1.108 -0.034 -1.691 1.087 0.883 -1.356 -0.678 2.215 0.440 1.093 0.253 0.000 0.389 -1.582 -1.097 0.000 1.113 1.034 0.988 1.256 1.572 1.062 0.904 +1 0.750 -0.811 -0.542 0.985 0.408 0.471 0.477 0.355 0.000 1.347 -0.875 -1.556 2.215 0.564 1.082 -0.724 0.000 0.793 -0.958 -0.020 3.102 0.836 0.825 0.986 1.066 0.924 0.927 0.883 +0 0.392 -0.468 -0.216 0.680 1.565 1.086 -0.765 -0.581 1.087 1.264 -1.035 1.189 2.215 0.986 -0.338 0.747 0.000 0.884 -1.328 -0.965 0.000 1.228 0.988 0.982 1.135 1.741 1.108 0.956 +1 0.434 -1.269 0.643 0.713 0.608 0.597 0.832 1.627 0.000 0.708 -0.422 0.079 2.215 1.533 -0.823 -1.127 2.548 0.408 -1.357 -0.828 0.000 1.331 1.087 0.999 1.075 1.015 0.875 0.809 +0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184 +1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822 +1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948 +0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885 +1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871 +1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794 +0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679 +1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903 +1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840 +0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909 +1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892 +1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836 +1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886 +1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661 +1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883 +1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783 +1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838 +0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106 +0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895 +0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352 +1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836 +1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645 +1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866 +0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706 +0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750 +0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720 +0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006 +1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826 +1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908 +0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041 +0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898 +1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823 +0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498 +0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062 +0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863 +1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358 +0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655 +0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089 +1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806 +0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779 +1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832 +1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713 +1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977 +0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749 +1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926 +0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150 +0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107 +1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791 +1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781 +0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692 +1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824 +0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754 +0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969 +0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786 +1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791 +1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798 +1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777 +0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687 +0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806 +1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694 +1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906 +1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770 +1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656 +1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785 +0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827 +0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745 +0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650 +1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489 +1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876 +0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041 +1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242 +0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769 +1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078 +1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623 +1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137 +1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589 +0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816 +1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638 +0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057 +0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774 +1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526 +1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958 +0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902 +0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707 +1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740 +1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949 +0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815 +1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474 +0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183 +1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715 +0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094 +1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777 +0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008 +1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807 +1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879 +1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798 +0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661 +0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870 +1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963 +1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705 +1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866 +0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280 +1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866 +1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878 +0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676 +0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608 +1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726 +0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578 +0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059 +1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939 +0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035 +0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145 +0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276 +0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466 +1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776 +0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278 +0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934 +0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430 +1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841 +1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906 +0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325 +1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786 +0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950 +0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762 +0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861 +0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851 +0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079 +1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724 +1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744 +0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912 +0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012 +0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665 +1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034 +0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095 +0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221 +1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777 +1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917 +1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771 +0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395 +1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865 +1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890 +1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815 +0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131 +0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154 +1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717 +0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775 +1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893 +0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901 +0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769 +1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828 +0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748 +1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599 +0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120 +1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833 +0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051 +1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126 +1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881 +1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135 +1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797 +0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612 +0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556 +0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789 +1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708 +0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878 +0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087 +0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846 +1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028 +0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994 +0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868 +1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913 +0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291 +0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123 +1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878 +0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511 +0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076 +0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112 +1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046 +1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009 +1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891 +1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140 +0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744 +1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238 +0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306 +0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758 +1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172 +1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992 +1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786 +1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922 +0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609 +1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351 +1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836 +0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252 +1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122 +0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042 +0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662 +1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626 +1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920 +1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059 +1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722 +1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941 +0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072 +1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053 +0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310 +0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847 +0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873 +0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183 +1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807 +0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936 +1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013 +1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822 +1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803 +0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176 +0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714 +0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815 +0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918 +0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056 +0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513 +0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773 +0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799 +1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813 +1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978 +1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743 +1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817 +1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875 +0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078 +1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619 +1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726 +0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950 +0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860 +0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034 +0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846 +0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920 +1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668 +0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313 +0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671 +1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224 +1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779 +0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851 +1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860 +0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958 +1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671 +1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127 +1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807 +1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823 +1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872 +1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061 +1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833 +1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780 +0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143 +1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709 +1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858 +1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091 +0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838 +0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661 +0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729 +0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878 +1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838 +1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792 +0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702 +0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068 +1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931 +0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522 +0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807 +1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054 +1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606 +1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961 +0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308 +0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146 +1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709 +0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180 +1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868 +1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779 +0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213 +1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168 +1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238 +0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891 +0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561 +1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911 +0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303 +1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094 +1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825 +1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995 +1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151 +1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280 +1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893 +1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043 +0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226 +1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851 +0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733 +0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764 +0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028 +1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774 +1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661 +1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776 +0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675 +1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931 +1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715 +0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889 +1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886 +0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087 +0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760 +0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006 +1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811 +0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176 +0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096 +0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721 +1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732 +1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874 +1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753 +0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883 +0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018 +1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806 +0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472 +0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827 +1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835 +1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826 +1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640 +1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764 +1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025 +1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223 +1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169 +1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003 +1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908 +1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836 +0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833 +0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294 +1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845 +1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510 +0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445 +1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926 +1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933 +1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853 +0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678 +1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038 +1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197 +1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746 +0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772 +1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983 +1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759 +1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825 +1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776 +0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892 +0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110 +1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762 +0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756 +1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831 +1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748 +0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756 +1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993 +1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881 +1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786 +0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983 +0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983 +1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867 +0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937 +0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293 +1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105 +0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949 +1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895 +0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722 +1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809 +0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007 +1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717 +0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656 +0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856 +1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832 +1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753 +0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954 +1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874 +0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848 +1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518 +1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787 +1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834 +0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722 +1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040 +0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758 +1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854 +1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828 +1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745 +0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701 +1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215 +1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837 +1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867 +0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119 +1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914 +0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373 +1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623 +1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133 +0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079 +0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919 +1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651 +0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207 +0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660 +1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951 +1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910 +0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715 +1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026 +0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239 +1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696 +0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662 +1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910 +0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050 +0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258 +1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884 +0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026 +1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119 +1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926 +0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079 +1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674 +0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733 +0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795 +0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329 +0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161 +0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200 +1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875 +0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071 +1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993 +1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866 +1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978 +1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982 +1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853 +1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122 +1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719 +1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739 +0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690 +1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643 +0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142 +1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758 +1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823 +0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258 +1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836 +0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349 +0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961 diff --git a/examples/parallel_learning/binary.train b/examples/parallel_learning/binary.train new file mode 100644 index 0000000..6ce09b8 --- /dev/null +++ b/examples/parallel_learning/binary.train @@ -0,0 +1,7000 @@ +1 0.869 -0.635 0.226 0.327 -0.690 0.754 -0.249 -1.092 0.000 1.375 -0.654 0.930 1.107 1.139 -1.578 -1.047 0.000 0.658 -0.010 -0.046 3.102 1.354 0.980 0.978 0.920 0.722 0.989 0.877 +1 0.908 0.329 0.359 1.498 -0.313 1.096 -0.558 -1.588 2.173 0.813 -0.214 1.271 2.215 0.500 -1.261 0.732 0.000 0.399 -1.139 -0.001 0.000 0.302 0.833 0.986 0.978 0.780 0.992 0.798 +1 0.799 1.471 -1.636 0.454 0.426 1.105 1.282 1.382 0.000 0.852 1.541 -0.820 2.215 0.993 0.356 -0.209 2.548 1.257 1.129 0.900 0.000 0.910 1.108 0.986 0.951 0.803 0.866 0.780 +0 1.344 -0.877 0.936 1.992 0.882 1.786 -1.647 -0.942 0.000 2.423 -0.676 0.736 2.215 1.299 -1.431 -0.365 0.000 0.745 -0.678 -1.360 0.000 0.947 1.029 0.999 0.728 0.869 1.027 0.958 +1 1.105 0.321 1.522 0.883 -1.205 0.681 -1.070 -0.922 0.000 0.801 1.021 0.971 2.215 0.597 -0.350 0.631 0.000 0.480 -0.374 0.113 0.000 0.756 1.361 0.987 0.838 1.133 0.872 0.808 +0 1.596 -0.608 0.007 1.818 -0.112 0.848 -0.566 1.581 2.173 0.755 0.643 1.426 0.000 0.922 -1.190 -1.616 0.000 0.651 -0.654 -1.274 3.102 0.824 0.938 0.972 0.789 0.431 0.961 0.958 +1 0.409 -1.885 -1.027 1.672 -1.605 1.338 0.055 0.013 2.173 0.510 -1.038 0.708 0.000 0.747 -0.358 -1.647 0.000 0.367 0.069 1.377 3.102 0.869 1.222 1.001 0.545 0.699 0.977 0.829 +1 0.934 0.629 0.528 0.238 -0.967 0.548 -0.059 -1.707 2.173 0.941 -2.654 -0.157 0.000 1.030 -0.176 0.523 2.548 1.374 1.291 -1.467 0.000 0.902 1.084 0.980 0.783 0.849 0.894 0.775 +1 1.405 0.537 0.690 1.180 -0.110 3.202 -1.527 -1.576 0.000 2.932 0.567 -0.130 2.215 1.787 0.899 0.585 2.548 0.402 -0.151 1.163 0.000 1.667 4.039 1.176 1.045 1.543 3.535 2.741 +1 1.177 0.104 1.397 0.480 0.266 1.136 1.535 -0.253 0.000 1.027 0.534 1.180 0.000 2.406 0.088 -0.977 2.548 1.250 0.269 0.530 0.000 0.833 0.774 0.986 1.104 0.849 0.937 0.812 +1 0.946 1.111 1.218 0.908 0.822 1.153 -0.365 -1.566 0.000 0.745 0.721 -0.376 2.215 0.609 0.308 -1.282 0.000 1.598 -0.451 0.064 3.102 0.829 0.981 0.994 0.908 0.776 0.783 0.725 +0 0.739 -0.178 0.830 0.505 -0.130 0.961 -0.356 -1.717 2.173 0.621 -0.482 -1.199 0.000 0.983 0.081 -0.290 0.000 1.065 0.774 0.399 3.102 0.945 1.026 0.982 0.542 1.251 0.830 0.761 +1 1.384 0.117 -1.180 0.763 -0.080 1.020 0.877 1.277 2.173 0.331 1.410 -1.474 0.000 1.283 0.737 -0.225 0.000 1.560 0.847 0.505 3.102 0.959 0.807 1.192 1.221 0.861 0.929 0.838 +1 1.384 0.889 0.619 1.082 0.345 0.956 0.855 -1.129 2.173 0.546 -0.308 -0.623 2.215 0.348 1.024 0.184 0.000 0.781 -1.636 1.144 0.000 0.522 0.738 0.986 1.350 0.813 0.953 0.780 +1 1.344 0.839 -1.061 2.472 -0.573 1.513 1.144 0.856 0.000 0.884 1.475 -1.361 1.107 1.587 2.235 0.078 0.000 1.609 2.396 0.757 0.000 0.934 0.845 1.078 1.400 0.948 1.008 0.901 +0 0.547 -0.350 -0.647 2.040 0.276 0.545 0.839 1.729 0.000 0.653 1.472 1.243 0.000 0.786 -0.044 -1.020 2.548 0.419 -0.629 1.571 3.102 0.689 0.867 1.082 0.664 0.354 0.580 0.817 +1 1.484 1.700 -1.059 2.700 -1.056 2.409 0.457 0.345 0.000 1.415 1.114 -1.449 0.000 1.013 -2.057 1.131 0.000 0.905 2.182 1.043 0.000 1.654 0.994 0.983 0.741 0.163 0.592 0.745 +0 1.058 -0.161 -0.195 2.705 -0.751 1.910 -1.032 0.865 0.000 1.301 0.147 -1.119 1.107 0.967 -0.367 1.108 0.000 0.555 -0.714 1.505 3.102 0.954 0.651 1.125 0.894 0.672 1.182 1.316 +0 0.675 1.121 -0.280 1.540 0.735 0.615 -0.507 0.795 2.173 0.219 -1.894 -0.581 0.000 1.246 -0.348 -0.856 2.548 0.753 -1.146 -1.375 0.000 0.907 0.898 1.120 1.269 1.089 1.015 0.915 +1 0.643 -1.430 1.519 0.941 0.887 1.615 -1.337 -0.267 1.087 1.667 0.656 -1.588 0.000 0.828 1.836 0.408 0.000 1.709 -0.347 -1.183 3.102 0.921 1.373 0.985 1.423 1.547 1.783 1.438 +1 1.102 0.427 1.717 0.934 0.776 1.279 -0.250 -0.926 2.173 1.067 0.434 0.681 0.000 1.054 0.004 0.255 0.000 0.743 1.208 -1.151 0.000 0.709 0.522 1.054 1.273 0.835 0.935 0.865 +1 1.330 0.202 1.173 0.135 -1.083 0.728 1.109 -0.540 1.087 0.462 0.133 -0.561 0.000 0.479 1.187 0.658 0.000 0.670 1.007 0.055 3.102 0.782 0.672 0.990 0.734 0.379 0.765 0.643 +0 1.290 -1.423 -0.687 0.131 -1.136 0.821 0.296 0.168 2.173 0.696 -0.469 -1.151 1.107 0.940 0.273 1.641 0.000 0.720 1.106 0.727 0.000 1.007 0.868 0.999 1.110 1.125 0.883 0.859 +1 1.048 -1.119 -0.957 0.996 -1.550 0.733 0.283 0.919 2.173 1.050 -0.041 0.109 2.215 0.943 0.320 -0.858 0.000 0.628 -0.325 1.217 0.000 0.873 0.873 0.976 1.373 0.888 1.207 0.999 +0 0.488 1.698 0.791 0.894 -0.709 1.563 -0.076 1.739 2.173 0.624 2.395 0.523 0.000 1.661 0.266 -0.218 2.548 0.947 -0.077 0.285 0.000 1.675 1.414 0.988 1.333 2.004 1.551 1.217 +0 1.413 -0.852 0.310 1.128 -1.510 0.820 1.153 -1.670 2.173 1.170 0.100 0.266 0.000 0.852 0.401 -1.334 0.000 1.370 0.960 -0.632 0.000 0.890 0.938 1.745 0.974 0.677 1.136 0.973 +1 0.770 -0.449 -0.986 0.966 -1.301 0.739 -1.033 0.875 1.087 1.369 -1.181 0.167 1.107 1.257 -0.122 -1.588 0.000 0.600 0.611 0.116 0.000 1.048 1.106 0.993 1.132 0.892 0.974 0.951 +0 2.468 0.664 1.024 0.317 1.407 0.996 -0.453 -0.500 0.000 0.348 1.016 -0.161 0.000 0.978 -2.634 -0.285 0.000 1.245 -0.472 1.464 3.102 1.006 0.795 0.996 0.945 0.322 0.735 1.470 +1 1.014 0.013 -0.485 0.695 1.701 0.597 0.076 0.143 2.173 0.917 0.685 1.713 2.215 0.531 -0.987 -1.654 0.000 0.963 1.295 0.264 0.000 1.576 1.067 1.072 0.806 1.130 0.838 0.752 +0 1.251 -0.750 1.090 0.462 -0.381 0.677 0.340 -0.711 0.000 0.601 -0.461 -1.247 0.000 0.822 0.985 -1.653 0.000 0.754 -0.907 0.279 3.102 0.848 0.842 1.021 0.666 0.411 0.607 0.638 +1 1.114 1.782 1.450 0.653 1.513 0.825 1.851 -0.480 0.000 0.846 1.158 0.514 2.215 0.520 2.685 1.542 0.000 1.042 0.549 -0.463 1.551 1.321 1.037 0.997 0.824 0.692 0.804 0.831 +1 0.657 -0.901 -0.855 1.176 1.487 0.745 -1.236 1.649 2.173 0.661 -2.099 0.137 0.000 1.780 -1.036 -0.213 0.000 1.236 -0.185 0.784 3.102 0.861 1.016 1.045 0.759 0.898 0.849 0.765 +0 1.009 -0.660 -1.539 1.316 -1.693 1.146 2.025 0.137 0.000 1.063 -0.539 1.052 2.215 1.124 0.548 -0.887 2.548 1.017 -0.057 0.172 0.000 1.076 0.939 0.974 0.932 1.346 0.854 0.822 +0 2.122 0.792 0.723 2.438 1.064 2.692 0.361 -0.993 2.173 1.725 1.204 0.488 2.215 0.267 -0.767 -1.134 0.000 1.372 0.601 -0.568 0.000 0.727 0.981 0.989 2.837 3.398 2.152 1.568 +1 0.304 -1.425 -1.646 1.166 -1.469 1.458 -0.472 0.510 2.173 0.867 -0.309 -1.605 0.000 1.317 0.136 -0.332 2.548 0.853 0.744 -1.365 0.000 0.760 0.980 0.986 1.376 1.309 1.081 0.957 +1 1.167 0.556 -0.911 0.908 0.051 1.078 0.387 1.253 0.000 1.213 0.155 -0.673 2.215 0.489 -1.384 0.704 0.000 1.348 0.692 -1.502 3.102 0.868 0.829 1.087 0.782 0.878 0.642 0.621 +1 0.880 0.617 -0.649 1.724 1.104 1.213 -0.576 1.216 2.173 0.782 -0.913 -0.102 0.000 1.183 -0.576 -0.783 0.000 0.432 1.286 -0.204 0.000 0.879 0.616 1.706 1.435 0.598 0.911 1.007 +0 0.313 1.256 -0.904 1.002 1.290 1.383 1.295 -1.528 2.173 1.160 -0.765 0.080 1.107 1.060 2.309 -0.340 0.000 0.852 1.129 0.378 0.000 0.911 1.480 0.988 1.000 2.976 1.837 1.444 +0 1.263 0.596 0.460 1.063 1.060 0.709 -0.613 -0.688 0.000 1.464 1.079 1.174 2.215 1.411 0.369 -0.596 1.274 0.611 0.293 -0.894 0.000 1.175 1.244 0.988 0.905 1.623 1.442 1.222 +1 1.121 -0.379 1.363 1.451 0.782 1.088 -0.803 -0.793 1.087 0.515 0.368 -0.665 0.000 0.708 -1.372 1.449 0.000 0.579 0.441 0.238 3.102 1.336 0.869 0.984 1.459 0.905 0.950 0.863 +0 1.205 0.916 -1.209 0.354 -0.706 1.124 1.045 0.787 0.000 0.489 -0.457 -1.033 2.215 0.388 1.276 0.000 0.000 0.443 -0.889 1.403 0.000 0.842 0.653 0.986 0.500 0.532 0.580 0.589 +1 0.420 -0.722 0.732 0.885 -0.724 0.741 1.244 1.619 0.000 1.248 0.281 0.076 2.215 1.085 0.331 1.242 0.000 1.025 0.086 -0.955 1.551 0.919 0.927 0.989 0.744 0.824 0.923 0.798 +0 1.380 1.427 1.105 1.788 0.982 1.955 -0.205 -0.852 1.087 0.901 -0.193 0.854 0.000 1.172 0.352 -0.512 1.274 0.445 -0.158 1.421 0.000 0.403 0.882 1.000 2.450 0.804 1.608 1.272 +1 0.704 0.369 -0.230 1.167 -1.430 0.721 0.012 1.508 2.173 0.683 0.028 0.688 2.215 1.013 -0.764 -0.222 0.000 0.930 0.082 -0.753 0.000 0.865 0.748 1.107 0.835 0.696 0.681 0.604 +1 0.695 0.420 1.203 0.769 -0.911 0.830 1.168 0.076 0.000 0.394 0.392 0.510 2.215 0.747 1.559 0.835 0.000 1.090 -0.422 -1.161 3.102 0.973 0.654 0.987 0.688 0.652 0.784 0.703 +1 0.312 1.722 1.411 1.133 1.163 0.756 1.210 -0.700 2.173 0.755 -0.053 -0.139 2.215 0.812 -0.193 1.153 0.000 0.847 1.298 1.682 0.000 1.010 1.000 0.996 1.118 0.931 0.860 0.794 +0 0.431 0.572 -0.684 2.262 0.155 1.178 0.178 -1.429 2.173 0.463 0.649 0.544 2.215 0.757 0.955 1.552 0.000 0.658 1.073 1.064 0.000 0.344 0.840 0.986 0.580 1.096 0.957 0.821 +0 0.309 -1.951 -1.229 1.592 0.770 0.633 -0.197 -1.568 1.087 0.898 -1.885 -0.257 0.000 0.897 -0.933 0.931 2.548 1.280 -0.431 -0.799 0.000 0.921 0.862 0.990 0.812 0.831 1.026 0.895 +1 0.458 0.129 -0.519 1.195 0.737 0.534 -1.316 -1.729 0.000 0.687 0.351 1.103 2.215 0.911 1.049 -0.219 2.548 0.808 -1.014 -0.367 0.000 0.888 1.371 0.984 0.871 0.852 1.238 1.006 +0 0.637 -0.037 -1.732 1.254 -0.425 0.486 0.090 0.024 2.173 0.675 -1.119 1.644 0.000 0.494 -2.085 0.544 0.000 0.386 -0.239 1.092 0.000 0.913 0.912 1.144 0.698 0.525 0.741 0.726 +1 0.976 0.291 -1.128 0.668 -0.540 0.950 2.026 1.060 0.000 0.678 -0.571 1.307 2.215 1.199 1.293 -0.273 0.000 0.602 1.124 0.825 3.102 1.891 1.026 0.990 0.814 0.693 1.131 1.181 +1 0.535 -1.391 -0.825 1.343 -1.449 1.111 -0.852 -0.484 0.000 1.677 -0.700 1.069 2.215 0.623 0.018 -1.653 0.000 0.925 0.350 0.169 0.000 0.852 1.025 0.986 1.447 0.755 1.273 1.138 +0 2.638 1.289 -0.280 0.991 0.872 1.152 -0.702 1.551 2.173 0.643 -0.767 -1.689 0.000 0.747 -2.603 0.907 0.000 1.259 0.986 -0.759 0.000 0.889 0.937 1.931 2.569 0.709 1.666 1.322 +0 1.541 0.058 1.227 1.217 0.660 0.524 1.040 -0.640 0.000 0.709 -0.226 -0.727 2.215 0.543 1.360 1.720 0.000 0.981 0.326 -0.429 3.102 0.842 0.839 0.988 0.882 0.311 0.754 0.792 +0 2.559 -0.021 -1.615 2.095 -1.335 1.720 -0.641 0.033 2.173 0.737 -0.414 -0.379 0.000 1.158 -0.598 -1.608 2.548 0.847 1.549 0.847 0.000 0.980 0.951 1.004 0.748 1.751 1.606 1.295 +1 1.925 -0.859 1.353 1.769 -1.452 0.756 -0.342 -0.809 2.173 1.734 -0.850 0.151 0.000 0.944 -0.376 0.932 0.000 0.606 0.624 -1.039 0.000 0.964 0.931 1.474 1.062 0.530 0.907 0.819 +1 1.545 0.059 -1.732 1.034 0.807 2.467 -1.237 -0.565 0.000 1.933 2.370 -1.639 0.000 3.921 -0.645 0.727 2.548 1.843 -0.219 -0.527 3.102 2.292 2.692 1.319 1.447 1.914 3.176 2.387 +0 1.200 -1.018 -1.173 0.845 -0.439 0.601 -0.814 1.627 0.000 0.706 -1.103 0.845 0.000 1.111 -0.536 0.424 2.548 1.038 -0.456 -0.630 3.102 0.923 0.890 0.990 0.887 0.667 0.658 0.694 +0 0.609 -0.521 0.287 0.650 0.198 0.511 1.237 -0.670 2.173 0.648 -1.193 -1.686 2.215 0.364 1.444 0.064 0.000 0.451 1.152 0.677 0.000 0.433 0.925 0.983 0.770 1.497 0.925 0.731 +0 0.318 -1.381 -0.250 2.482 0.957 1.383 0.001 -0.222 2.173 1.045 -1.565 1.525 2.215 0.904 2.253 1.645 0.000 1.349 -0.541 -1.383 0.000 0.992 2.146 1.091 0.821 2.375 2.313 2.267 +1 0.947 -0.329 -0.033 0.020 -1.381 1.245 0.865 0.799 2.173 1.130 -0.013 -1.688 0.000 1.371 0.681 -0.931 0.000 0.982 0.958 0.019 0.000 1.001 0.587 0.525 0.860 0.892 0.820 0.697 +0 1.147 0.502 -1.131 1.237 -1.061 0.869 0.812 0.520 0.000 1.011 0.808 1.346 2.215 0.635 1.284 -0.138 0.000 0.538 0.612 0.124 3.102 0.848 0.987 0.993 0.677 0.595 0.704 0.778 +1 1.028 -0.732 1.243 1.198 -0.032 0.756 -1.491 1.404 0.000 1.343 -1.475 -0.263 2.215 0.483 -2.591 1.686 0.000 0.707 -0.687 -1.342 1.551 0.831 0.686 1.402 1.093 0.791 0.829 0.856 +1 0.303 1.225 0.629 1.256 -0.602 0.897 0.529 0.974 2.173 0.913 -0.667 -0.299 2.215 0.991 0.560 1.376 0.000 0.534 -1.176 -0.672 0.000 0.771 1.006 0.988 0.700 1.491 0.876 0.757 +0 0.534 -0.766 -0.533 0.974 -1.501 0.797 -1.574 0.323 2.173 1.137 0.271 -0.998 2.215 2.434 2.003 1.210 0.000 1.956 0.216 -0.272 0.000 3.588 2.573 0.989 1.251 1.990 2.742 2.023 +0 0.459 -1.448 -0.858 0.262 -0.304 0.760 1.090 -0.338 2.173 1.076 -1.079 1.151 2.215 0.357 -0.614 1.522 0.000 0.506 1.609 -1.293 0.000 0.842 0.866 0.988 0.935 2.209 1.120 0.920 +0 1.076 1.912 -0.667 0.618 -0.665 0.496 -1.524 1.127 0.000 0.944 -0.870 0.103 2.215 0.935 1.243 1.271 2.548 1.235 -0.512 -1.578 0.000 0.961 1.036 0.975 0.872 1.634 1.178 1.285 +1 0.442 1.823 -1.466 0.988 -1.565 1.444 -2.428 0.846 0.000 2.252 0.525 -0.141 1.107 2.366 0.328 -1.663 0.000 1.064 -0.091 -0.788 0.000 0.657 0.900 0.991 0.834 1.460 1.053 0.845 +1 0.575 -0.588 1.555 0.501 0.137 0.407 -1.782 1.262 0.000 0.348 -1.980 0.111 0.000 0.942 -0.695 -1.028 2.548 0.607 0.406 -0.667 3.102 0.695 0.884 0.987 0.705 0.428 0.634 0.590 +0 0.999 1.633 1.532 1.019 -0.793 0.613 -0.171 1.109 1.087 0.817 0.619 0.904 0.000 1.225 0.506 -0.244 0.000 1.189 1.033 0.553 0.000 0.992 0.948 1.211 1.278 0.973 1.015 0.924 +1 1.175 -0.643 0.099 1.273 -0.627 0.584 -0.133 -1.130 0.000 0.561 0.226 1.221 0.000 1.565 1.090 1.382 2.548 0.522 0.666 0.624 0.000 0.936 1.043 1.030 0.500 1.077 1.064 0.882 +0 0.733 -0.490 1.685 2.278 1.609 1.372 -1.278 -0.212 0.000 1.102 0.960 1.197 2.215 1.219 -0.308 -0.175 2.548 0.483 -0.242 -0.916 0.000 0.982 0.782 0.988 1.978 1.458 1.476 1.445 +1 1.792 -0.344 0.136 0.841 -0.813 1.685 0.625 1.499 0.000 0.548 0.587 -1.315 0.000 0.806 2.248 -0.160 0.000 1.011 1.329 -0.285 3.102 1.160 0.878 1.283 1.102 0.299 0.793 1.010 +1 0.641 1.633 0.001 1.118 1.010 1.013 0.750 1.516 0.000 1.438 0.526 0.358 2.215 1.649 0.175 -0.915 0.000 1.605 -0.493 -0.864 1.551 0.845 0.645 0.987 0.815 1.472 1.009 0.965 +0 0.442 0.276 0.929 1.638 -1.072 1.752 0.460 -0.802 2.173 1.436 -2.551 0.752 0.000 1.424 0.493 0.587 0.000 1.545 0.634 1.463 3.102 0.521 0.675 1.148 0.917 1.574 1.078 0.926 +1 1.152 0.873 -1.400 0.290 -0.264 0.831 0.373 -0.288 0.000 1.157 0.599 0.723 2.215 1.550 0.878 1.527 1.274 1.283 0.871 -0.714 0.000 0.798 1.181 0.988 0.758 0.975 0.987 0.872 +0 0.546 0.444 -0.292 1.429 -1.480 1.474 0.659 -1.104 2.173 2.622 0.481 0.538 0.000 0.685 -0.777 1.058 2.548 0.564 -1.013 -1.035 0.000 0.413 1.265 1.073 0.854 1.565 0.917 0.799 +1 1.274 -0.150 -0.628 1.824 -0.101 2.833 1.929 -1.628 0.000 1.361 0.040 0.111 2.215 2.690 0.230 0.574 1.274 0.776 0.382 -1.153 0.000 2.074 3.255 0.990 1.344 0.851 2.496 2.299 +1 0.625 -0.506 1.263 0.814 -1.314 1.228 -0.925 -0.091 0.000 1.217 0.430 1.588 2.215 0.976 0.010 -0.291 2.548 0.518 -1.251 0.127 0.000 0.921 0.750 0.986 0.647 1.177 1.064 0.929 +0 0.667 1.941 -0.188 0.446 0.506 1.049 0.577 1.737 1.087 1.508 0.766 -0.323 2.215 0.930 0.075 1.093 0.000 0.677 -0.442 -0.886 0.000 0.930 1.235 0.988 0.754 1.785 1.221 1.047 +1 1.864 0.056 -0.290 0.550 0.224 0.604 0.555 0.877 0.000 1.060 -0.375 1.727 2.215 0.824 -1.420 -0.485 0.000 0.817 0.925 1.318 0.000 0.510 0.916 0.990 0.821 0.441 0.842 0.785 +0 0.732 -0.712 -0.454 0.451 -0.392 1.167 0.448 0.949 2.173 0.920 0.120 1.609 0.000 0.926 1.528 -0.666 2.548 0.615 0.689 -0.687 0.000 0.930 0.983 0.987 1.117 1.539 0.967 0.852 +1 1.065 -0.611 -0.375 1.116 0.990 0.582 -1.434 -0.946 0.000 0.986 -0.550 -1.030 0.000 1.145 1.286 0.130 0.000 1.169 0.648 1.056 3.102 0.936 0.946 1.424 0.845 0.724 0.728 0.717 +1 0.910 -1.631 -0.125 1.964 -0.646 1.310 -0.927 1.357 2.173 0.445 -0.372 0.368 0.000 1.188 -1.481 0.595 0.000 1.407 -0.139 -1.529 3.102 0.984 0.993 0.996 1.619 0.930 1.159 0.979 +0 0.512 0.589 -1.486 0.552 -0.637 0.439 -0.923 -0.210 2.173 1.266 0.445 1.368 2.215 0.366 0.425 -0.052 0.000 0.641 -0.054 0.686 0.000 0.360 0.633 0.983 0.645 1.362 0.814 0.639 +1 1.377 -0.587 -0.869 1.735 -1.399 0.433 -0.277 0.236 2.173 0.921 0.321 1.152 1.107 0.330 -0.051 1.366 0.000 1.935 -2.212 0.028 0.000 0.635 0.758 0.988 0.980 0.740 0.923 0.794 +1 1.825 0.661 -0.885 1.030 0.833 1.565 2.020 -0.009 0.000 1.341 0.817 1.398 1.107 1.286 0.089 -1.706 0.000 1.295 1.032 -1.295 0.000 1.000 0.904 1.900 1.043 0.663 0.883 0.810 +1 1.477 0.870 0.367 0.643 0.024 0.425 0.141 0.632 0.000 1.340 0.221 -1.515 0.000 0.334 0.049 -1.312 2.548 1.172 1.080 -1.022 3.102 1.499 1.109 0.984 0.654 0.340 0.633 0.750 +1 1.074 -0.203 0.943 1.242 -1.727 0.952 -0.813 -0.239 2.173 0.629 -1.616 1.494 0.000 0.759 -0.793 -1.276 2.548 0.668 -0.085 -0.832 0.000 0.921 0.765 1.075 0.735 0.852 0.866 0.765 +1 0.652 0.084 -0.285 0.344 -0.839 1.105 0.260 1.644 2.173 0.700 0.765 -0.311 1.107 0.762 1.143 0.745 0.000 0.977 1.361 0.130 0.000 0.532 1.219 0.991 0.562 1.316 0.871 0.769 +1 1.748 -1.259 -1.568 1.159 -1.308 2.531 -0.895 -0.116 2.173 1.097 -0.529 1.515 1.107 1.602 0.505 1.042 0.000 0.954 -0.732 -1.359 0.000 1.553 1.095 0.985 2.288 2.479 1.717 1.644 +1 0.653 0.816 1.491 1.173 0.353 0.999 0.795 0.099 2.173 1.032 1.716 -0.995 0.000 1.052 0.893 -1.388 0.000 1.044 -0.757 -1.378 0.000 0.849 1.122 1.037 0.773 1.037 1.016 0.879 +1 0.603 -1.305 -0.295 1.986 -0.397 1.038 0.458 1.221 2.173 0.430 0.015 1.719 2.215 0.470 0.031 -0.543 0.000 0.524 -1.371 0.515 0.000 0.682 1.045 0.984 1.363 0.480 1.875 1.364 +0 0.510 -0.400 1.364 1.352 -0.990 0.630 -0.448 0.685 2.173 0.594 -0.795 -0.770 2.215 0.600 0.602 0.801 0.000 0.456 -0.936 1.413 0.000 0.659 0.725 0.988 0.901 0.886 0.668 0.599 +1 0.664 -0.216 0.435 1.156 1.437 1.839 -2.034 0.306 0.000 2.575 0.989 -1.165 2.215 1.506 1.083 -1.623 0.000 0.631 0.661 0.674 3.102 0.839 0.945 0.988 0.541 1.154 0.998 0.837 +1 1.436 1.090 0.733 0.278 -0.823 2.421 1.483 0.320 0.000 2.447 -1.403 -1.503 2.215 2.000 2.287 -1.506 0.000 2.205 1.306 -0.221 0.000 1.660 2.246 0.983 2.974 1.665 3.841 2.825 +1 0.709 0.850 0.672 0.949 -1.138 1.241 0.417 1.582 2.173 0.957 0.470 -0.037 2.215 0.877 0.102 0.661 0.000 1.705 1.461 -0.759 0.000 0.972 0.856 1.134 0.950 1.595 1.049 0.923 +0 1.135 0.285 -1.109 1.089 -0.896 1.103 0.127 0.964 0.000 0.731 -0.489 0.048 2.215 0.754 0.464 0.380 0.000 0.715 -1.183 -0.956 1.551 0.883 0.926 0.987 1.058 0.600 0.887 0.971 +1 1.124 0.354 0.040 1.132 1.620 0.956 1.375 0.416 0.000 1.543 0.437 -0.805 2.215 1.724 1.678 -1.636 0.000 2.128 -0.175 1.562 0.000 0.852 1.251 1.546 0.743 0.139 0.718 0.746 +1 0.341 -1.223 -1.373 0.994 0.692 1.086 0.319 -1.186 0.000 1.213 1.562 0.163 2.215 1.057 0.491 1.657 2.548 0.565 1.305 0.426 0.000 1.430 0.975 0.988 1.257 1.353 1.040 0.963 +0 1.218 -0.308 -1.602 1.532 -1.007 0.556 -0.059 0.820 2.173 0.840 -1.431 0.502 0.000 0.463 -0.801 -0.215 2.548 0.407 -1.488 0.811 0.000 0.627 0.812 0.989 0.704 0.573 0.709 0.765 +1 0.352 -1.440 0.063 0.644 1.519 1.138 0.660 1.460 2.173 1.127 -0.034 -0.520 0.000 0.931 0.095 0.137 0.000 0.496 0.796 -0.591 3.102 0.883 0.568 0.995 0.906 0.773 0.907 0.786 +1 0.637 0.994 1.198 0.755 1.183 0.646 -1.285 0.844 0.000 1.288 0.139 -1.166 2.215 0.826 -1.664 -0.400 0.000 0.702 0.662 -0.712 0.000 0.925 0.712 1.001 0.989 0.705 0.810 0.732 +0 0.802 -0.507 0.519 1.249 -1.030 1.596 0.474 0.732 0.000 1.052 -0.734 -1.455 0.000 1.868 0.130 -0.997 2.548 0.906 -0.195 -0.393 3.102 0.782 0.968 1.366 0.968 0.548 1.045 0.989 +0 1.178 -1.468 -0.931 1.113 0.177 0.710 -1.096 0.586 2.173 0.659 0.755 1.437 0.000 0.792 -1.703 -0.403 0.000 1.131 -0.379 -1.623 3.102 0.736 0.788 1.333 0.960 0.921 0.798 0.689 +1 0.775 -1.014 -0.295 0.804 -1.630 0.743 -0.163 1.621 2.173 0.559 -2.107 -1.004 0.000 0.450 -1.627 0.467 0.000 0.984 0.124 0.343 3.102 0.761 0.978 1.020 0.841 0.839 0.853 0.737 +0 0.928 -0.440 -0.658 1.570 -1.652 1.047 0.218 -0.527 2.173 0.696 -1.161 1.125 0.000 1.395 -0.149 0.858 0.000 0.653 0.972 0.477 1.551 0.890 0.900 1.304 1.137 0.811 0.990 0.961 +0 0.812 -0.292 0.794 0.639 -0.177 1.133 -0.240 -0.137 1.087 1.422 -0.659 1.663 2.215 1.218 0.176 -1.177 0.000 1.184 0.265 1.585 0.000 0.903 0.981 0.995 0.817 1.910 1.048 0.878 +1 1.263 -0.147 -1.176 1.318 -1.711 1.417 0.788 -0.284 2.173 1.114 -2.680 0.734 0.000 0.449 1.895 1.423 0.000 0.646 -0.064 1.098 1.551 6.084 3.149 0.989 1.753 1.062 2.739 2.117 +0 1.277 -0.164 0.024 0.660 -1.226 0.841 -0.443 1.136 2.173 0.788 0.152 -1.739 2.215 0.597 -0.002 -0.640 0.000 0.701 2.486 -1.042 0.000 1.528 1.286 1.148 0.997 0.725 1.191 1.061 +0 1.447 -1.048 1.596 1.251 -1.497 1.650 -0.449 -0.184 0.000 1.313 -0.575 1.523 2.215 0.635 -1.123 0.221 2.548 0.422 0.784 1.142 0.000 1.526 0.981 0.986 0.612 0.949 1.060 1.045 +1 0.923 0.825 -1.263 0.812 -0.403 0.916 0.648 1.435 2.173 0.737 1.715 -0.442 0.000 1.649 1.150 0.529 0.000 1.227 -1.071 -1.294 0.000 0.923 0.931 0.989 0.829 0.607 0.739 0.695 +0 0.863 0.002 -0.218 1.329 -1.344 0.364 1.067 -0.937 0.000 0.431 -0.404 1.713 0.000 0.611 -0.045 0.202 2.548 1.152 -0.293 1.027 0.000 0.897 0.744 1.260 0.757 0.171 0.557 0.563 +0 0.621 0.645 0.474 0.697 0.467 1.350 2.065 -1.640 0.000 1.298 0.237 -0.505 2.215 1.846 0.267 0.252 0.000 2.003 -0.829 1.240 0.000 1.049 1.014 0.994 1.248 0.588 0.902 1.115 +1 0.664 0.845 1.330 0.592 -0.368 1.532 0.473 -1.002 0.000 1.232 1.295 1.302 1.107 1.087 2.506 0.963 0.000 2.042 0.245 0.262 0.000 0.706 1.086 0.987 0.617 1.044 0.747 0.678 +1 1.302 -0.364 0.124 0.147 1.571 0.709 -1.391 0.877 0.000 0.720 0.189 -0.711 0.000 0.985 -0.285 -1.221 1.274 1.175 0.418 1.662 3.102 2.017 1.317 0.990 0.784 0.547 0.909 0.824 +0 1.073 1.932 -1.676 0.803 0.434 1.031 0.949 -1.336 2.173 1.064 0.704 0.255 2.215 0.560 -0.953 -0.640 0.000 1.275 -0.461 0.544 0.000 0.848 0.988 1.216 1.111 1.537 1.088 1.172 +1 0.415 1.473 -0.724 0.829 1.331 0.606 0.368 -0.583 0.000 0.600 -0.576 0.055 1.107 0.394 1.805 0.495 0.000 1.022 -0.054 -0.292 1.551 0.837 0.722 0.991 1.183 0.288 1.121 0.948 +1 1.565 0.915 -1.256 0.485 1.501 0.549 1.244 -0.414 0.000 1.305 1.168 0.520 2.215 0.468 -0.242 -0.108 2.548 0.459 0.355 0.876 0.000 0.920 0.893 0.986 0.723 0.801 0.836 0.726 +0 0.684 0.813 -1.557 0.770 0.439 1.436 0.458 0.763 2.173 0.908 -0.660 -1.353 2.215 1.145 2.230 -1.641 0.000 1.706 -0.599 -0.662 0.000 0.705 0.853 0.988 0.890 1.882 1.347 1.173 +1 0.818 -0.868 0.519 0.705 0.776 1.202 0.951 -1.058 2.173 0.606 0.685 1.517 2.215 0.989 0.391 0.207 0.000 1.054 0.289 0.975 0.000 0.720 0.695 0.993 1.427 0.933 0.971 0.842 +1 0.847 -0.389 0.605 0.414 -0.117 0.884 0.391 1.665 2.173 0.436 -0.325 -0.601 0.000 0.765 -1.419 0.035 0.000 0.418 0.291 1.276 0.000 0.889 0.796 0.978 1.225 0.682 0.863 0.774 +0 0.823 1.163 1.226 1.047 0.010 0.742 1.415 -1.439 2.173 0.798 -1.929 1.047 0.000 0.402 1.814 -1.194 0.000 1.063 -0.775 -0.249 3.102 0.841 0.844 1.144 0.947 1.613 1.691 1.591 +0 0.964 0.029 -0.104 0.350 0.913 0.730 0.093 -1.261 2.173 0.442 1.332 -1.143 2.215 0.661 2.238 0.373 0.000 2.534 1.524 1.156 0.000 1.033 0.915 0.987 0.883 0.578 1.050 0.935 +1 1.240 0.152 0.496 0.770 -0.247 0.771 2.734 -0.009 0.000 0.775 1.245 1.534 0.000 1.190 -0.475 1.513 2.548 1.941 0.445 -1.070 3.102 2.091 1.879 0.990 0.922 1.061 1.663 1.504 +1 0.617 1.431 -1.119 0.798 -0.421 1.166 0.250 -0.175 2.173 1.347 0.837 1.512 0.000 0.416 1.295 1.737 0.000 0.500 0.890 -0.447 0.000 0.801 0.700 0.993 0.812 1.091 1.019 0.885 +1 0.445 -1.120 -1.676 0.837 0.217 1.127 -0.084 -0.095 2.173 0.933 0.224 1.700 0.000 0.526 -0.430 1.275 0.000 0.767 0.662 1.526 0.000 0.557 1.286 0.983 0.931 0.728 0.952 0.939 +1 1.349 -1.565 -0.602 1.033 -1.097 1.380 -0.398 0.928 2.173 0.385 0.596 1.480 0.000 0.600 -0.880 -1.298 0.000 1.038 -0.545 -0.047 0.000 0.982 1.003 0.977 0.661 0.848 1.050 0.904 +1 0.524 -1.114 1.010 0.394 1.008 0.602 -1.107 0.556 2.173 1.212 -0.314 -1.062 2.215 0.666 -0.798 -0.794 0.000 0.464 -1.154 -1.287 0.000 0.307 0.675 0.975 0.940 1.351 0.836 0.680 +1 1.215 0.744 0.537 0.534 -1.342 1.031 0.398 -1.499 2.173 0.964 1.233 0.606 0.000 0.837 1.197 -1.060 2.548 0.521 -2.332 -0.709 0.000 0.617 1.543 1.108 0.994 0.706 1.188 1.068 +0 0.348 -1.520 0.332 0.486 -1.136 1.203 -0.862 -1.639 0.000 0.895 -1.623 -1.204 2.215 1.856 -0.118 0.433 2.548 0.685 -1.014 0.913 0.000 0.935 0.794 0.985 0.818 1.792 1.106 0.912 +1 1.599 -1.367 -1.364 0.376 0.956 0.414 -0.288 0.354 0.000 0.710 0.691 -0.085 2.215 0.572 -0.480 1.130 0.000 0.617 0.713 -1.179 3.102 0.573 0.700 0.988 0.850 0.500 0.873 0.743 +1 1.038 1.267 -0.809 1.920 -1.217 2.402 -1.935 0.666 0.000 1.667 0.815 -1.069 0.000 1.486 2.160 -1.727 0.000 2.174 0.159 -0.459 1.551 0.702 0.889 0.989 1.392 0.525 1.130 1.038 +1 1.040 -0.629 -1.578 0.873 1.250 1.270 -0.737 -0.440 1.087 0.587 -1.312 -1.565 0.000 1.391 -0.061 0.561 2.548 0.701 -1.555 1.379 0.000 0.449 1.047 0.985 1.287 1.419 1.071 0.910 +0 0.488 -1.191 1.315 0.574 -0.503 0.440 0.489 1.144 1.087 0.723 0.911 1.517 0.000 1.571 0.206 -0.050 2.548 0.838 0.142 -0.965 0.000 0.892 1.049 0.988 1.256 0.923 1.148 1.103 +1 0.395 1.580 -0.387 1.730 1.123 0.801 -0.316 -0.692 2.173 0.687 0.675 1.167 1.107 0.557 -0.132 0.231 0.000 0.462 0.820 -1.510 0.000 0.884 0.829 1.120 1.598 1.230 1.081 0.927 +0 0.368 -0.881 0.232 1.259 1.729 0.726 0.981 -0.303 0.000 0.540 2.865 0.353 0.000 1.324 -0.082 1.687 1.274 0.819 1.872 -0.461 0.000 0.807 0.757 0.987 0.882 0.625 0.870 1.198 +1 0.932 0.790 1.624 0.789 -0.636 0.646 -0.122 1.068 1.087 0.691 0.694 -1.189 2.215 0.854 2.067 -0.058 0.000 1.282 0.913 0.298 0.000 0.805 0.995 1.062 0.888 0.975 0.943 0.820 +1 0.876 -0.059 -0.512 0.623 1.056 0.787 0.152 -0.092 0.000 1.525 0.151 -1.709 2.215 0.951 0.673 0.986 1.274 0.770 1.160 -0.496 0.000 0.892 0.926 1.011 0.912 0.918 0.946 0.803 +0 1.861 -0.667 -0.277 1.997 -0.758 0.721 -1.048 1.412 0.000 0.659 -1.964 1.670 0.000 1.140 0.067 0.124 2.548 1.639 -1.440 1.187 0.000 0.807 0.936 1.121 0.968 0.639 0.911 1.058 +0 0.331 1.639 1.172 0.843 1.560 0.537 -0.534 -1.733 0.000 0.811 -0.430 -0.969 0.000 2.548 0.670 0.310 2.548 1.293 0.781 -0.813 3.102 0.895 0.947 0.999 1.144 1.184 1.131 0.959 +1 0.378 -1.116 -0.289 1.450 0.283 0.884 0.966 -1.630 2.173 0.369 1.390 0.267 0.000 0.825 0.734 -0.766 2.548 0.383 0.864 1.276 0.000 0.399 0.669 0.986 1.920 0.753 2.084 1.657 +0 1.399 0.506 1.676 1.639 1.095 1.759 1.332 -1.423 2.173 1.874 0.352 -0.292 2.215 2.006 0.346 0.377 0.000 1.793 0.421 0.028 0.000 0.659 0.947 1.048 1.462 2.654 1.669 1.487 +0 1.763 -1.140 0.063 0.717 -0.207 1.013 -0.222 -1.663 0.000 0.689 -1.374 1.153 1.107 0.649 0.861 -1.090 1.274 0.903 -0.892 -1.149 0.000 0.913 0.969 0.992 1.084 1.228 0.926 0.942 +0 0.598 -1.099 -0.297 0.887 1.681 1.774 -0.119 -0.024 2.173 1.206 -0.239 -1.702 0.000 1.209 0.951 -1.688 1.274 0.810 0.557 1.213 0.000 0.879 0.810 0.987 1.298 2.139 1.309 1.136 +1 0.685 0.643 -1.637 0.794 0.241 0.645 -1.297 -0.422 2.173 0.442 -0.656 1.553 0.000 0.661 -0.569 1.007 2.548 1.208 -0.553 0.371 0.000 0.859 0.709 1.014 0.691 0.831 0.802 0.688 +0 0.381 -0.498 -0.126 0.379 -0.311 0.544 0.665 1.639 2.173 0.913 -0.448 -0.753 2.215 0.277 -0.388 -1.183 0.000 0.723 0.095 0.255 0.000 0.494 0.590 0.984 0.889 1.063 0.753 0.598 +1 0.490 -2.359 -0.439 0.467 -0.508 1.020 -1.376 -1.350 0.000 0.414 0.153 -0.294 0.000 1.583 0.262 0.810 2.548 1.155 -0.548 0.533 1.551 1.628 1.221 0.985 1.198 0.568 1.080 0.939 +0 1.974 -0.520 -0.274 1.046 -0.317 1.240 0.975 1.141 1.087 1.082 0.029 -1.722 0.000 0.638 0.247 0.819 0.000 0.732 0.334 -1.273 3.102 0.971 1.057 1.002 0.898 0.881 1.491 1.227 +1 0.464 -2.190 0.863 1.148 -0.355 0.479 0.768 0.208 2.173 0.599 -1.474 -1.680 0.000 0.808 -0.366 -1.517 2.548 0.430 -0.155 0.625 0.000 0.747 1.047 0.990 0.857 0.913 0.925 0.783 +0 3.524 0.413 1.358 0.502 1.397 1.182 0.445 -0.197 2.173 2.001 0.849 -0.630 0.000 1.133 -0.419 1.143 2.548 0.681 0.639 -0.851 0.000 0.311 0.811 0.987 0.708 1.509 1.232 1.297 +1 1.032 -0.978 0.678 0.604 -1.027 0.743 -1.006 -0.794 1.087 1.060 -0.218 1.430 2.215 1.286 0.382 0.398 0.000 0.456 1.251 -0.015 0.000 0.570 1.009 1.093 0.820 1.298 0.992 0.883 +1 0.796 -0.391 0.418 1.192 -1.625 0.721 0.456 -0.743 0.000 0.782 0.667 0.955 2.215 0.757 1.490 -0.856 0.000 1.124 -0.781 0.166 3.102 0.838 1.091 1.301 0.825 0.944 0.923 0.897 +1 0.583 1.687 -0.278 0.980 1.532 0.654 2.241 0.179 0.000 0.916 0.640 -1.499 2.215 0.730 0.337 -0.457 2.548 0.398 1.153 0.935 0.000 0.604 0.858 1.045 0.820 0.713 0.803 0.722 +0 1.212 0.266 0.342 0.968 0.934 0.798 -0.127 -1.372 2.173 0.661 -0.117 -0.256 0.000 0.689 -0.790 -0.920 1.274 0.748 -0.763 1.364 0.000 0.985 0.905 0.993 0.855 0.506 0.797 0.703 +0 2.110 0.969 0.013 0.566 -1.518 2.075 -1.121 1.561 0.000 1.281 0.153 -0.573 2.215 0.664 -0.617 1.216 0.000 0.524 -2.282 0.079 0.000 0.971 0.762 1.487 1.092 0.546 0.840 1.086 +0 1.006 0.503 -0.135 0.709 0.959 0.661 1.545 -0.028 1.087 0.637 2.118 -1.141 0.000 0.605 1.485 1.235 1.274 1.753 -0.224 -1.704 0.000 2.243 1.345 0.988 0.800 0.716 1.039 0.913 +0 0.628 -0.317 -1.678 0.587 -0.446 1.237 0.947 1.659 2.173 1.313 0.604 0.225 0.000 1.009 -2.638 -1.139 0.000 0.412 1.205 0.642 3.102 5.249 3.002 0.979 1.526 0.629 2.510 1.855 +1 0.464 -1.107 -0.410 0.525 1.402 0.962 -1.204 -0.155 2.173 0.769 -0.136 -1.737 0.000 0.774 0.955 -1.494 0.000 0.918 2.343 0.106 0.000 0.789 0.724 0.984 0.914 1.006 1.024 1.087 +1 1.031 1.057 -0.080 1.233 -0.598 0.590 -0.332 -1.222 2.173 0.506 -0.231 0.536 0.000 1.498 0.105 1.593 0.000 1.397 1.207 0.338 0.000 0.963 0.824 0.990 1.114 0.507 0.998 1.061 +0 0.990 0.753 1.671 0.664 -0.394 0.730 -0.244 -0.925 2.173 0.966 0.689 0.358 2.215 0.345 2.219 1.238 0.000 0.618 0.003 0.968 0.000 0.795 1.088 1.076 0.836 1.287 0.824 0.731 +0 2.066 0.020 0.385 1.332 0.659 1.167 0.934 -1.575 1.087 0.818 0.447 -1.135 1.107 0.788 -2.658 -0.248 0.000 0.462 1.233 -0.762 0.000 2.981 2.199 0.974 1.680 0.658 1.993 1.799 +0 0.872 0.717 1.067 0.425 -1.153 0.856 -1.057 0.204 0.000 1.407 -1.190 -1.741 2.215 1.543 -1.330 -0.421 2.548 0.617 0.194 0.694 0.000 0.907 0.986 0.987 1.742 1.465 1.549 1.312 +0 0.625 0.276 0.272 2.293 0.929 0.991 0.450 1.653 2.173 0.437 -0.252 -1.166 0.000 0.645 -1.489 -0.402 0.000 2.805 0.934 -0.404 3.102 0.901 0.978 0.990 1.051 1.797 1.183 0.965 +1 1.067 -0.284 0.666 0.562 -0.683 0.824 -0.122 -0.604 0.000 1.345 0.099 1.294 2.215 0.612 -1.449 1.524 2.548 1.019 -1.009 -0.562 0.000 0.802 0.958 1.006 0.869 0.939 0.980 0.830 +1 0.471 0.532 1.703 0.764 -1.341 0.834 -0.732 -0.231 0.000 1.166 -0.564 1.506 1.107 1.265 -1.159 0.236 2.548 0.599 -2.162 -1.436 0.000 1.498 1.029 0.996 0.689 1.261 0.980 0.866 +0 0.923 1.977 0.963 0.348 -1.165 0.964 1.353 0.241 2.173 0.877 0.460 -1.350 0.000 0.577 1.670 -0.859 0.000 0.755 -0.619 1.732 3.102 0.928 0.879 0.991 0.829 1.439 0.958 0.842 +1 0.735 1.285 -0.067 0.604 0.201 0.385 -0.484 -1.267 2.173 0.689 0.040 -1.695 0.000 0.531 0.878 -1.477 2.548 0.480 1.001 -0.409 0.000 0.889 0.662 0.985 0.687 0.458 0.590 0.566 +1 0.470 0.019 -0.417 1.425 0.555 1.138 -0.645 -1.303 2.173 0.393 -1.135 0.464 1.107 0.640 -1.579 -0.002 0.000 0.969 -0.878 1.387 0.000 0.878 1.064 0.982 0.778 1.016 1.005 0.918 +1 0.690 -0.866 -1.684 0.493 0.323 0.739 1.529 -0.242 0.000 1.197 0.469 -1.467 2.215 0.924 1.691 0.195 0.000 0.929 -0.031 1.188 3.102 0.818 0.932 0.985 1.281 0.695 0.857 1.106 +1 1.052 0.423 -0.511 1.364 1.328 1.202 -0.309 -1.602 2.173 0.932 0.014 -0.157 0.000 0.499 0.470 1.229 2.548 1.654 -0.738 0.246 0.000 0.947 0.864 1.654 1.248 0.676 0.973 0.956 +1 0.675 -0.414 0.782 0.170 1.003 1.559 1.024 1.680 0.000 2.118 0.665 -0.359 1.107 0.864 0.753 1.272 0.000 0.828 -1.272 -0.183 0.000 0.803 0.914 0.989 1.107 1.024 0.875 0.802 +1 1.645 -0.421 -0.496 0.371 -0.958 1.178 -0.788 1.118 2.173 0.568 -0.388 -1.170 0.000 0.632 -0.688 0.177 1.274 0.485 -0.146 -1.556 0.000 0.245 0.880 0.979 0.657 0.807 0.925 0.743 +1 0.353 1.209 0.993 0.777 0.593 0.734 -0.475 0.589 1.087 0.994 0.301 -1.078 0.000 0.716 1.057 -0.503 0.000 0.625 0.274 -1.448 0.000 0.950 0.804 0.996 0.760 0.903 0.613 0.587 +1 0.424 0.476 1.372 2.397 -1.284 0.818 -0.252 0.083 2.173 1.090 0.366 0.771 2.215 0.430 0.300 -0.351 0.000 0.523 -1.048 -1.537 0.000 0.650 0.811 0.989 1.260 0.922 1.038 0.816 +0 1.330 1.084 0.755 0.416 0.198 0.500 0.181 0.414 0.000 0.819 -0.310 -1.592 2.215 1.121 0.477 -0.994 2.548 1.493 0.929 -0.537 0.000 0.728 0.752 0.990 1.000 0.684 0.822 0.842 +1 1.274 -1.011 -0.817 0.246 -0.339 0.754 -0.031 0.790 2.173 0.961 0.020 -1.485 2.215 0.323 0.139 0.422 0.000 0.435 -2.003 1.055 0.000 0.721 0.887 0.989 1.027 1.111 0.829 0.731 +0 1.045 -1.383 -0.047 0.145 0.425 1.032 -0.769 1.020 0.000 1.052 -1.520 -0.524 2.215 1.971 0.564 -1.104 2.548 1.216 0.284 1.074 0.000 0.969 1.347 0.980 1.303 2.163 1.358 1.110 +1 0.965 0.728 -0.924 0.797 -1.577 0.774 -0.019 0.805 0.000 0.989 -0.081 0.423 2.215 1.061 0.606 -1.266 2.548 0.889 -1.991 0.894 0.000 1.884 1.313 0.997 0.564 1.165 1.148 1.047 +0 1.842 -0.780 -0.583 1.509 -1.214 0.591 -1.562 0.471 0.000 0.575 -0.674 0.889 0.000 0.701 -1.324 0.855 0.000 1.099 -0.173 -1.407 3.102 1.034 0.858 1.243 0.798 0.324 0.578 0.770 +1 0.832 1.305 1.098 1.187 0.502 0.977 2.932 0.745 0.000 1.699 0.550 -1.112 0.000 1.104 0.231 -0.695 0.000 0.945 -0.453 -1.724 3.102 0.839 0.901 0.983 1.166 0.555 1.017 1.085 +1 0.399 0.185 -1.260 1.759 -0.670 0.969 1.055 0.589 0.000 1.884 -0.363 1.360 0.000 1.489 -1.122 -0.162 2.548 1.935 -0.867 0.686 3.102 2.311 1.624 0.994 0.778 0.906 1.241 1.116 +1 0.694 -0.023 0.247 0.844 0.997 0.852 -0.371 1.057 1.087 0.578 -0.083 -0.531 0.000 1.575 -0.947 -0.407 0.000 1.338 -0.710 -1.604 3.102 0.745 0.934 0.982 0.675 0.811 0.897 0.792 +1 0.351 -1.542 -1.212 1.485 1.533 0.972 -0.071 0.116 0.000 1.430 0.320 -1.621 2.215 0.872 -0.511 -0.508 1.274 0.505 1.521 0.384 0.000 0.788 0.976 0.987 1.314 1.140 1.776 1.579 +1 0.878 -0.497 0.312 1.068 0.529 1.362 -0.567 -1.019 2.173 0.496 -1.036 0.853 0.000 0.619 -0.368 1.506 2.548 0.800 0.672 0.349 0.000 0.956 0.676 0.978 1.514 0.876 1.003 0.872 +0 0.701 -0.652 0.516 2.586 0.100 0.739 -0.411 -1.204 2.173 0.664 0.324 -1.287 0.000 0.553 -0.857 1.365 0.000 0.451 1.125 -1.626 0.000 0.897 0.681 0.974 0.923 0.659 0.969 0.906 +0 3.291 0.586 -0.841 0.297 -0.460 1.598 -1.229 1.031 0.000 0.792 -0.490 0.765 2.215 0.757 -0.612 -0.369 2.548 0.775 -0.974 1.610 0.000 0.848 1.126 0.988 1.354 0.705 0.929 1.339 +1 1.037 -0.375 -0.433 1.485 -1.244 0.969 0.344 0.853 0.000 0.798 2.616 1.576 0.000 1.285 0.920 0.123 2.548 1.324 -0.055 -0.813 3.102 0.811 1.015 1.146 1.247 0.933 0.831 0.890 +0 0.465 -0.020 0.313 1.280 -1.013 0.857 0.325 1.216 2.173 1.457 -0.428 -0.829 1.107 1.949 -1.452 0.568 0.000 0.747 -1.525 -0.120 0.000 0.789 1.608 0.994 0.965 1.711 1.409 1.169 +0 1.149 0.542 -0.779 0.412 1.454 0.634 0.510 1.730 2.173 0.806 -0.062 0.175 2.215 0.538 -0.076 1.196 0.000 0.484 -0.450 0.622 0.000 0.307 0.525 0.983 0.769 1.082 0.688 0.567 +1 1.224 -1.015 1.297 0.718 0.394 1.455 -0.730 1.726 0.000 2.353 -0.191 0.087 2.215 0.833 0.346 -0.651 2.548 0.856 -1.437 -1.468 0.000 0.982 1.277 0.987 1.243 1.017 1.457 1.187 +0 0.940 0.183 1.100 1.314 0.247 0.533 -0.899 -0.670 0.000 1.232 0.200 1.522 2.215 1.625 0.124 -0.710 2.548 0.554 -0.782 0.893 0.000 0.819 1.086 1.070 1.061 1.361 0.942 0.861 +1 1.126 -0.754 1.556 1.080 1.743 2.352 -0.618 -0.337 0.000 1.093 -1.694 0.298 0.000 1.396 -1.219 -1.700 0.000 2.313 0.810 1.721 1.551 0.863 0.848 0.985 1.841 0.872 1.406 1.290 +1 0.380 -1.178 0.873 1.237 -1.067 0.855 0.226 0.970 0.000 1.376 -0.159 -0.635 2.215 0.499 1.295 -0.745 0.000 0.804 -0.397 0.357 1.551 1.389 0.915 0.988 0.786 0.756 0.881 0.820 +1 0.793 1.258 -1.374 0.374 0.317 0.564 0.302 1.099 2.173 0.461 2.217 0.043 0.000 0.656 1.539 1.448 0.000 0.836 -0.529 -0.186 3.102 0.950 0.880 0.989 0.700 0.754 0.655 0.600 +1 0.738 -0.628 -1.413 1.892 -0.735 0.694 -0.090 1.403 0.000 1.238 -0.707 0.465 1.107 0.466 0.374 0.372 0.000 0.553 0.863 0.929 3.102 0.860 0.954 0.985 0.983 0.806 0.928 0.860 +1 0.730 -0.971 0.273 1.771 1.435 0.726 -0.923 -0.457 0.000 0.966 0.787 -0.450 0.000 0.788 -0.719 0.811 2.548 0.994 -0.349 -1.088 0.000 0.911 0.946 1.364 0.714 0.529 0.641 0.662 +0 0.669 0.758 -0.462 0.211 0.475 1.541 -0.041 0.031 2.173 2.259 0.322 -1.715 0.000 1.302 -1.141 -1.197 2.548 1.192 2.222 -0.355 0.000 0.899 0.959 0.978 0.815 1.945 1.363 1.107 +1 1.337 0.073 -1.373 0.259 0.715 1.040 0.171 1.256 0.000 1.326 -0.272 0.207 2.215 0.615 -2.401 -0.242 0.000 1.529 0.254 -0.705 3.102 0.934 1.114 0.985 1.032 1.015 0.992 0.864 +0 0.299 -1.604 -0.905 1.220 -0.732 1.313 2.107 1.474 0.000 1.890 -0.639 -0.258 2.215 1.279 1.384 1.245 2.548 1.910 0.672 0.574 0.000 0.975 0.890 0.990 0.905 2.710 1.452 1.214 +0 0.617 -0.042 -1.005 0.378 1.614 0.672 0.984 0.245 1.087 0.383 -0.813 -0.481 0.000 0.847 -1.144 1.130 2.548 0.873 0.749 -1.374 0.000 0.917 0.964 0.989 0.662 1.460 0.958 0.828 +0 0.940 -0.703 1.332 1.113 -0.912 0.504 -0.749 -1.210 2.173 0.601 -0.754 0.402 2.215 0.513 0.242 -0.533 0.000 0.650 -0.944 0.979 0.000 0.789 0.657 1.275 0.839 0.804 0.628 0.563 +0 0.892 1.239 0.822 0.256 -1.493 0.848 0.461 -0.262 1.087 0.330 0.790 0.309 0.000 1.024 1.600 -1.499 0.000 1.017 0.236 1.575 3.102 0.991 1.084 0.978 0.579 0.983 0.758 0.684 +1 0.892 0.780 -1.602 0.373 -0.768 0.603 1.175 -1.077 2.173 0.462 -0.122 1.005 2.215 0.526 -1.449 1.257 0.000 1.674 0.657 0.181 0.000 1.053 0.973 0.985 0.725 0.921 0.780 0.763 +1 0.481 1.083 -1.015 1.993 -0.354 1.192 0.755 1.679 0.000 1.114 -0.428 -0.413 2.215 0.921 0.227 0.266 2.548 2.434 -1.435 0.893 0.000 0.890 1.097 0.983 1.730 0.725 1.186 1.202 +0 1.577 2.017 -1.364 0.560 0.205 0.470 1.358 0.410 0.000 0.607 -0.958 0.699 0.000 1.002 1.173 -0.519 2.548 0.582 0.038 0.567 0.000 0.580 0.687 1.286 0.753 0.661 0.631 0.668 +0 0.517 1.836 0.705 2.437 0.849 0.604 -1.041 -0.422 1.087 0.586 0.199 -0.563 0.000 1.296 -0.246 -1.353 2.548 0.862 1.006 -1.193 0.000 0.694 0.983 0.997 1.394 0.932 1.306 1.065 +1 0.871 0.166 -0.658 1.662 0.113 0.883 -0.065 -1.450 0.000 0.552 0.324 -0.054 2.215 1.552 0.404 0.809 0.000 2.551 -0.779 -1.530 0.000 0.880 1.017 1.068 1.031 0.837 0.759 0.837 +0 1.033 -0.488 -1.260 0.761 0.365 1.177 -1.283 -1.533 2.173 1.050 -0.422 0.232 2.215 0.597 -0.926 1.075 0.000 0.724 -2.056 -0.037 0.000 0.838 0.909 1.222 1.057 1.790 0.996 0.872 +0 2.931 0.181 -0.816 1.290 -1.261 1.699 1.014 1.091 0.000 1.008 1.223 -0.743 1.107 0.968 -0.651 0.752 2.548 1.141 0.309 0.233 0.000 0.856 1.289 1.048 0.972 1.587 1.212 1.367 +1 0.866 -1.386 1.434 0.824 0.737 0.823 0.116 -1.009 2.173 0.318 -1.644 0.333 0.000 0.422 -0.399 0.623 2.548 0.727 -0.996 -0.526 0.000 0.477 0.749 0.978 0.667 0.759 1.033 0.774 +1 0.526 -0.454 -0.254 1.847 0.634 0.835 0.982 -1.610 2.173 0.623 1.186 -0.714 0.000 0.570 0.919 0.517 0.000 0.857 -0.324 -1.116 3.102 0.824 0.886 0.988 0.791 0.772 1.004 0.914 +0 0.697 -1.126 0.719 0.884 -0.586 0.677 0.291 0.103 0.000 1.430 -0.933 1.128 1.107 0.658 0.649 -0.868 0.000 1.880 0.246 -1.217 1.551 0.955 0.936 1.003 0.926 1.610 1.102 0.977 +1 0.877 0.080 0.958 1.354 -0.028 0.608 -0.044 1.594 0.000 0.897 0.333 0.204 0.000 0.878 2.132 -1.621 0.000 2.168 0.305 -1.249 0.000 1.024 0.774 1.171 0.851 0.682 0.813 0.818 +1 0.838 -0.008 -0.861 0.536 1.464 1.230 0.532 -0.581 2.173 1.309 0.320 1.087 1.107 0.717 0.807 1.713 0.000 0.375 2.376 1.015 0.000 0.739 0.920 0.984 0.842 1.873 1.057 0.870 +1 0.693 0.460 1.485 0.579 0.584 1.160 1.208 -0.611 2.173 0.821 2.256 1.369 0.000 1.488 2.077 0.782 0.000 0.801 -0.015 -0.918 0.000 0.855 1.276 0.993 1.405 0.778 1.178 1.257 +0 0.753 -0.642 0.580 0.686 -0.609 0.760 0.133 -1.106 2.173 0.846 0.890 1.018 1.107 0.564 0.343 0.032 0.000 0.795 -0.282 1.402 0.000 0.748 0.774 0.986 1.185 1.207 0.973 0.776 +1 0.493 -1.340 -1.280 1.537 1.615 1.157 -0.847 -0.157 2.173 0.739 0.345 0.179 2.215 1.269 0.123 0.981 0.000 0.876 -2.150 -1.471 0.000 1.185 0.839 0.996 1.672 0.963 1.468 1.594 +0 0.438 -0.827 1.361 0.702 -1.052 0.949 0.046 0.781 2.173 1.777 -0.999 -1.409 0.000 1.263 -0.628 0.189 2.548 1.393 -0.299 0.000 0.000 2.079 1.526 0.990 0.905 0.864 1.213 1.022 +0 0.585 0.462 -0.588 1.346 0.294 1.039 -0.231 -1.064 2.173 1.147 1.579 1.041 2.215 1.686 -0.438 1.187 0.000 1.818 -0.603 -0.517 0.000 0.910 0.941 0.989 1.323 2.294 1.598 1.284 +1 0.307 -1.864 0.730 0.868 -0.308 0.897 -0.021 -0.367 0.000 1.445 0.238 1.102 2.215 0.716 -0.256 -0.948 0.000 1.022 0.253 -1.404 1.551 0.744 0.726 0.981 1.047 0.848 0.894 0.797 +1 0.687 0.401 -1.718 0.229 -0.528 1.048 -0.493 0.765 1.087 0.918 0.993 -1.088 0.000 0.291 -0.495 -0.631 0.000 0.498 0.513 -0.293 3.102 0.756 1.447 0.988 0.579 0.769 0.844 0.743 +0 2.058 0.191 1.474 0.653 -0.064 0.814 1.760 -0.065 0.000 0.554 1.753 -0.800 0.000 0.467 0.595 -1.692 1.274 0.703 1.335 0.524 3.102 0.878 0.839 1.579 0.960 0.453 0.626 0.859 +1 0.647 1.255 -1.699 1.302 0.762 0.717 1.893 0.460 0.000 0.972 -0.472 -0.198 2.215 1.854 -0.757 -1.187 1.274 0.898 0.444 -1.050 0.000 1.507 1.657 1.014 1.754 1.137 1.526 1.340 +0 0.580 -0.676 1.715 1.587 1.682 1.179 1.109 -0.145 2.173 0.661 1.213 0.764 0.000 0.937 0.093 0.099 0.000 1.402 -0.245 -0.345 3.102 0.962 1.289 0.972 3.017 1.073 1.935 1.784 +1 0.288 -2.154 -0.071 1.130 -1.398 0.907 -0.507 -0.392 2.173 1.303 -1.171 0.840 0.000 1.030 -0.936 1.407 0.000 0.973 -0.773 -0.690 1.551 0.883 0.976 0.986 0.873 0.336 0.918 0.806 +1 1.423 1.049 -1.648 1.105 -0.791 0.488 1.094 -0.311 2.173 1.218 0.504 0.584 2.215 0.277 0.555 0.241 0.000 0.693 -0.047 -0.201 0.000 0.903 0.884 1.213 1.267 0.886 0.895 0.821 +0 0.759 0.896 0.919 1.366 -1.734 0.565 0.676 0.154 0.000 0.579 -0.872 -1.190 2.215 0.695 0.123 -0.635 2.548 0.961 -0.907 0.064 0.000 1.136 1.035 0.990 0.784 0.487 0.700 0.781 +1 1.109 -0.751 0.057 0.535 1.697 0.348 -2.114 0.209 0.000 0.646 -0.625 -1.280 1.107 0.476 1.509 1.123 0.000 1.036 -1.012 1.392 0.000 0.914 0.857 1.062 0.854 0.749 0.859 0.747 +1 0.624 -0.150 1.539 0.554 1.434 0.528 0.985 -1.187 0.000 1.411 0.536 -0.142 1.107 1.313 0.494 1.293 0.000 1.824 0.571 -0.790 3.102 1.135 1.180 0.990 1.515 0.804 1.167 1.044 +0 0.569 2.386 1.580 1.233 -0.223 0.426 0.838 0.922 2.173 0.451 -1.694 -1.301 0.000 0.707 -0.124 -0.199 2.548 0.863 -1.071 0.854 0.000 0.780 1.113 1.159 1.264 0.676 0.917 1.449 +0 0.863 -1.453 0.273 1.565 0.306 1.009 -0.750 -0.070 2.173 3.236 0.454 -1.606 2.215 0.696 -2.400 1.145 0.000 0.854 0.322 0.265 0.000 0.915 1.116 0.986 4.261 3.133 2.832 2.157 +1 0.586 -1.273 1.394 0.040 0.717 0.992 -0.297 0.132 2.173 1.030 0.176 -1.342 0.000 0.988 0.370 -0.731 0.000 1.313 0.129 0.964 3.102 0.857 0.844 0.907 0.846 0.868 0.890 0.756 +1 1.674 -0.130 0.319 1.216 0.949 0.840 -0.960 -0.382 2.173 0.754 -0.284 1.270 1.107 1.872 -0.409 -1.069 0.000 0.998 0.776 1.455 0.000 1.609 1.159 1.063 1.175 1.235 1.016 1.016 +0 0.551 1.170 -0.672 1.159 0.359 0.734 0.593 1.340 0.000 0.813 -0.633 0.108 2.215 1.183 -0.654 -1.264 2.548 0.548 -1.177 -0.394 0.000 1.502 1.153 0.984 0.813 0.985 0.874 0.850 +0 0.389 -0.963 1.296 0.386 -0.569 0.804 -1.083 0.729 0.000 0.708 0.570 -0.022 2.215 0.308 2.066 -0.021 0.000 0.629 -0.614 -1.068 0.000 0.905 1.042 0.986 0.581 0.736 0.666 0.634 +1 0.782 -0.170 -0.049 0.863 1.490 1.023 0.352 -1.291 2.173 0.891 0.106 0.827 2.215 1.264 1.808 -0.002 0.000 1.432 1.041 1.642 0.000 1.143 0.971 1.119 0.956 1.337 0.819 0.759 +1 0.632 -1.118 -1.367 0.996 0.795 1.022 -0.367 -1.664 0.000 1.673 -0.159 0.051 2.215 0.512 0.009 1.316 0.000 0.430 -0.703 1.390 3.102 1.112 0.617 1.021 1.129 0.763 0.877 0.796 +1 0.680 1.366 -0.416 1.004 1.071 1.351 -0.737 0.691 0.000 2.494 1.615 -0.978 0.000 1.081 0.734 0.653 2.548 0.867 0.945 -1.476 3.102 0.671 0.595 1.114 0.697 0.706 0.873 0.810 +0 1.369 0.372 1.691 0.342 -0.556 0.510 2.220 0.416 0.000 0.746 1.225 -0.587 2.215 0.764 1.067 -1.405 2.548 1.085 0.682 0.606 0.000 0.899 0.906 0.990 0.559 0.541 0.675 0.705 +0 0.794 1.949 0.703 0.324 -1.319 0.979 -0.166 1.632 1.087 1.493 0.478 -0.283 2.215 0.972 0.729 1.614 0.000 0.518 -0.186 0.244 0.000 0.849 1.047 0.992 1.153 1.856 1.106 0.890 +0 0.534 -0.706 -1.284 1.574 -0.386 0.963 -1.186 0.795 0.000 1.090 -1.384 1.648 0.000 0.758 -0.493 1.047 0.000 1.181 0.782 -1.090 1.551 0.959 1.039 0.991 0.788 1.021 0.976 0.889 +1 1.950 -0.143 -0.222 0.999 -0.740 0.557 -0.133 1.610 0.000 1.007 0.326 0.946 2.215 1.021 0.549 -1.434 2.548 0.412 0.964 0.419 0.000 0.925 0.978 0.996 0.953 0.916 0.914 0.878 +0 0.415 1.655 -1.666 1.441 -0.762 0.630 1.022 0.873 0.000 0.365 -1.530 1.388 0.000 0.450 1.452 0.366 0.000 0.871 -0.061 -0.898 3.102 0.837 0.820 0.996 0.562 0.429 0.529 0.596 +0 0.301 -0.435 1.176 2.128 -1.580 1.009 -0.016 -1.030 2.173 0.990 -0.748 0.609 0.000 0.370 -2.559 1.214 0.000 1.733 -0.414 -0.050 0.000 0.982 0.637 0.981 1.359 0.477 0.878 0.985 +0 0.630 0.685 0.468 0.919 -1.254 0.516 2.425 0.838 0.000 1.141 -0.024 1.388 2.215 1.271 1.927 -0.691 0.000 1.019 1.328 0.085 0.000 0.874 0.501 1.054 0.854 0.701 0.980 0.822 +1 0.454 -1.220 1.122 0.662 1.024 0.894 -0.380 -0.425 0.000 0.683 -0.914 0.068 0.000 1.402 -0.409 1.181 2.548 1.717 -0.250 -1.428 3.102 0.853 1.096 0.987 0.618 0.848 0.914 0.821 +0 1.904 -0.093 -1.471 1.313 -1.123 3.175 -0.233 0.654 0.000 2.244 0.018 -1.012 2.215 1.520 -0.705 -0.819 0.000 1.227 0.523 0.844 3.102 3.983 2.262 0.989 0.780 1.560 2.031 1.754 +1 0.787 -0.891 0.909 0.873 -1.643 2.202 -0.040 -0.601 0.000 1.164 0.923 0.477 0.000 1.368 0.684 1.033 0.000 1.596 0.457 1.509 3.102 0.949 0.918 0.988 0.586 0.731 0.901 1.072 +1 1.474 0.491 0.744 0.392 -0.420 1.040 1.308 0.470 0.000 1.285 -0.710 -1.403 1.107 0.690 -0.517 -0.637 2.548 0.987 0.084 -0.828 0.000 0.626 0.692 0.987 0.819 0.643 0.904 0.738 +0 0.463 1.242 -1.314 2.791 1.720 0.748 1.157 0.372 0.000 0.601 1.056 -0.308 0.000 0.898 0.007 0.125 1.274 0.934 -1.066 -0.693 3.102 0.822 0.723 0.979 1.207 0.674 0.957 1.029 +0 1.079 -0.575 0.495 0.313 0.078 0.524 0.421 -0.528 0.000 0.869 -0.779 -0.583 0.000 2.022 -1.058 1.205 1.274 1.743 -0.434 -1.414 3.102 0.937 0.942 0.985 1.118 1.114 1.064 0.958 +1 0.920 0.674 0.591 0.417 -1.276 0.574 1.485 1.671 0.000 1.258 1.206 -1.256 0.000 0.849 1.905 0.081 0.000 1.387 0.827 -0.189 3.102 0.899 0.980 0.989 0.654 0.569 0.770 0.688 +1 0.697 0.436 1.342 0.199 -1.430 1.067 -1.009 0.838 2.173 0.982 -1.186 -0.471 0.000 0.841 -0.398 -0.803 0.000 0.644 -0.439 -1.181 3.102 0.695 1.354 0.977 0.536 0.878 0.821 0.744 +1 1.002 0.348 -1.265 1.269 1.351 1.317 0.496 -0.267 2.173 0.646 -0.247 -1.579 0.000 0.580 0.400 0.681 0.000 0.854 -0.422 0.340 3.102 0.930 0.915 1.103 1.352 0.830 0.933 0.883 +1 0.401 2.052 -1.082 1.207 0.977 0.463 1.540 0.750 0.000 1.100 1.368 -0.910 1.107 1.013 0.296 -0.526 2.548 0.414 1.733 -0.760 0.000 0.678 0.831 0.988 0.913 0.742 0.773 0.675 +0 0.645 -1.241 0.485 2.162 1.506 1.458 -1.382 -0.385 0.000 0.868 -1.593 -1.584 2.215 1.103 0.572 0.574 2.548 0.775 -0.624 -0.384 0.000 0.533 1.190 1.302 1.419 1.742 1.274 1.213 +1 0.934 -0.787 1.514 0.149 1.322 0.499 0.464 -0.702 0.000 1.009 1.198 -0.343 2.215 0.447 0.787 0.885 2.548 0.900 -1.064 0.468 0.000 1.348 0.904 0.994 1.143 0.652 0.808 0.786 +1 0.801 0.080 -0.663 0.347 -0.820 0.847 -0.042 0.308 0.000 1.145 1.244 -1.143 2.215 1.157 0.661 0.984 0.000 1.263 0.118 -1.674 0.000 0.987 1.133 0.992 0.606 0.600 0.672 0.660 +0 1.440 -0.196 -0.599 1.265 -0.270 0.918 0.980 1.698 0.000 0.608 -0.048 0.489 2.215 0.658 0.829 0.983 0.000 0.470 1.057 -0.584 3.102 0.843 0.956 0.986 0.546 0.523 0.617 0.790 +1 0.928 0.249 -1.091 0.799 1.270 1.109 -0.534 -0.220 0.000 0.883 -0.368 1.682 2.215 0.734 0.227 0.242 0.000 1.005 0.853 1.170 1.551 0.927 1.173 1.012 0.657 0.749 0.936 0.826 +1 0.417 -0.870 -1.429 0.710 -0.594 1.157 -0.427 -1.612 2.173 1.013 0.668 0.813 0.000 1.154 -1.378 -0.811 0.000 1.113 0.527 0.270 0.000 0.820 1.207 0.979 0.831 0.948 0.944 0.823 +1 0.943 -0.893 -0.091 0.943 -1.307 0.968 -0.080 0.616 0.000 0.773 -1.127 -0.775 0.000 1.554 -0.724 -1.512 1.274 0.710 0.497 1.221 1.551 0.892 0.701 1.162 0.834 0.787 0.794 0.780 +0 0.671 -1.476 -1.489 1.633 -0.633 0.479 -1.200 0.092 0.000 0.804 -0.665 1.629 1.107 0.875 -1.185 1.122 1.274 0.666 -1.675 -0.445 0.000 0.536 0.897 1.011 0.882 0.483 0.727 0.671 +1 0.932 0.036 -1.741 1.614 -1.197 0.845 -0.520 0.913 2.173 0.668 0.388 -0.294 2.215 0.953 0.397 0.185 0.000 0.831 0.852 1.054 0.000 0.751 0.900 0.986 0.855 1.113 0.957 0.838 +1 0.810 1.075 -0.641 1.238 1.640 0.436 2.313 -0.797 0.000 0.875 0.733 0.239 2.215 0.535 2.417 0.696 0.000 0.846 0.050 1.516 3.102 0.858 1.056 1.227 0.729 0.761 0.827 0.787 +0 1.256 0.559 0.839 0.545 1.684 0.596 0.949 -0.895 2.173 0.445 0.967 -0.212 0.000 0.640 0.068 0.987 0.000 1.251 -0.582 -1.032 1.551 0.821 0.849 0.982 0.888 0.854 0.827 0.710 +0 1.305 0.256 0.817 0.433 -1.628 0.698 1.286 -0.805 0.000 1.097 -0.728 -0.882 0.000 2.087 -0.792 0.778 2.548 0.716 -1.157 -0.155 0.000 0.798 0.783 0.989 0.840 1.194 0.905 0.829 +1 0.742 0.523 1.054 1.258 -1.253 0.981 -0.809 1.417 2.173 1.091 0.634 -0.318 0.000 0.806 -0.074 0.290 2.548 0.472 2.198 -0.358 0.000 1.090 0.941 1.170 1.160 1.030 1.250 1.058 +0 0.777 2.260 1.450 1.346 -1.116 0.701 1.864 0.312 0.000 0.772 0.291 1.113 0.000 1.467 1.356 -0.605 2.548 0.846 0.553 0.381 0.000 0.765 0.939 1.046 0.941 0.914 0.801 0.841 +0 0.451 -2.143 -0.834 1.143 0.252 1.105 -0.367 0.946 1.087 1.451 -1.128 -0.730 0.000 0.413 1.078 1.694 0.000 1.474 -0.703 1.739 1.551 1.988 1.327 0.990 1.117 0.941 1.146 1.027 +0 0.818 -0.663 1.109 1.043 1.611 0.778 -2.913 -0.636 0.000 1.510 -0.061 0.308 0.000 1.067 -0.136 -0.453 2.548 2.652 0.039 -1.536 3.102 0.792 0.866 0.976 0.760 1.072 0.975 0.878 +1 1.788 0.170 -0.595 0.320 -0.650 0.803 0.713 1.321 2.173 1.119 -0.299 0.652 1.107 0.937 0.698 -0.704 0.000 0.525 1.308 1.318 0.000 0.815 0.864 0.986 1.087 1.090 1.023 0.894 +1 0.558 -0.627 0.091 1.325 -1.043 0.614 -0.332 0.386 0.000 0.691 0.335 -0.040 0.000 0.921 2.498 1.066 0.000 1.627 0.759 1.491 3.102 0.706 0.938 1.015 1.115 0.793 0.811 0.799 +0 0.741 0.706 1.546 0.985 -0.670 0.879 1.520 -0.497 0.000 1.305 1.542 1.493 0.000 0.511 -0.274 0.652 1.274 0.966 1.260 -0.069 3.102 2.219 1.318 1.079 0.708 0.645 0.957 0.840 +1 0.744 -0.659 0.502 0.939 -0.808 1.124 0.855 0.982 0.000 1.157 -0.776 -0.976 0.000 0.662 -0.139 -0.561 2.548 0.894 -0.604 1.446 0.000 1.085 0.966 1.071 0.588 0.305 0.600 0.589 +1 1.780 0.235 -0.147 0.885 0.597 0.338 0.551 1.357 0.000 1.500 1.307 1.716 2.215 0.715 1.376 -0.480 0.000 1.220 0.869 0.342 0.000 0.983 0.940 1.080 0.761 0.960 1.047 0.871 +1 0.721 -1.425 0.859 0.327 -1.571 1.067 -1.671 -0.301 0.000 0.890 -0.866 -1.106 1.107 0.827 -1.035 1.543 2.548 0.669 -1.794 0.511 0.000 0.915 1.037 0.989 0.770 0.634 0.800 0.758 +0 1.954 -0.564 -1.073 0.619 1.349 2.851 0.887 0.839 0.000 1.725 0.325 -0.817 2.215 1.199 1.304 -0.860 0.000 0.647 0.368 -0.191 3.102 1.596 0.900 1.247 1.124 0.512 0.765 0.842 +0 1.815 -1.727 1.495 1.537 -1.659 1.308 0.802 0.074 0.000 1.014 0.513 -0.236 0.000 1.117 -1.150 0.905 2.548 1.721 0.944 -0.701 1.551 0.760 0.933 1.003 0.847 1.912 1.599 1.859 +0 0.439 1.898 0.440 0.923 -1.309 0.589 -0.361 1.084 0.000 0.874 1.102 -0.024 1.107 0.672 0.243 -0.408 2.548 0.398 0.327 0.021 0.000 0.670 0.945 0.984 1.030 0.458 0.807 0.947 +1 0.597 -1.663 -0.154 0.568 -0.946 1.420 -0.949 1.455 0.000 1.441 -0.599 -0.582 0.000 1.014 -1.290 1.064 2.548 1.675 -1.080 0.650 3.102 0.887 0.965 0.985 0.811 0.368 0.724 0.668 +0 1.971 0.855 0.367 0.062 -1.284 0.873 -0.975 -0.945 0.000 0.922 0.088 0.865 2.215 1.025 -1.606 -1.251 0.000 0.987 -0.066 -1.730 3.102 0.822 0.884 0.986 0.719 0.624 0.954 1.118 +0 2.475 -1.448 -0.413 0.593 -1.307 0.890 -0.545 1.193 2.173 0.741 -1.699 0.980 0.000 0.614 -0.074 -1.439 2.548 0.477 -0.576 -0.653 0.000 0.881 0.856 1.210 0.968 0.676 1.024 0.865 +1 0.939 -0.172 1.040 0.513 1.625 1.879 0.740 -1.057 0.000 1.489 -0.058 0.641 2.215 0.848 0.914 0.063 0.000 0.919 0.074 -0.271 0.000 0.744 1.445 0.994 0.913 0.625 1.322 1.195 +1 1.530 0.627 1.239 0.681 -1.076 0.681 -0.039 -0.386 2.173 0.470 0.094 0.035 0.000 0.452 1.011 -0.274 0.000 0.971 -0.738 1.140 3.102 0.607 0.655 1.231 1.063 0.924 0.820 0.664 +1 0.729 0.312 0.462 1.264 1.574 1.043 -0.424 -1.411 0.000 1.822 -0.677 0.286 2.215 0.552 -0.622 -0.767 0.000 0.864 -0.678 1.218 3.102 0.868 0.718 1.121 1.283 0.846 0.929 0.891 +1 0.879 0.320 -0.376 2.413 -0.045 2.062 -0.411 1.571 0.000 1.145 0.065 -0.994 2.215 1.274 -0.835 -0.179 0.000 2.082 -0.950 -1.092 3.102 0.950 1.329 0.994 1.210 0.902 1.353 1.574 +1 0.669 0.353 -1.717 0.385 -1.529 0.786 0.237 0.715 2.173 0.652 2.350 -1.499 0.000 1.169 0.929 -0.239 1.274 1.153 1.550 -0.582 0.000 0.890 0.946 0.984 0.941 1.025 1.024 1.148 +0 1.539 -0.825 -1.022 0.991 -0.049 1.007 0.232 0.363 2.173 0.778 0.959 0.655 0.000 1.578 -0.855 -1.510 1.274 1.366 0.533 1.276 0.000 0.975 0.989 1.316 1.338 1.840 1.142 1.011 +1 2.168 0.073 -0.688 0.725 -0.384 1.384 1.738 0.897 0.000 1.365 0.620 0.905 2.215 0.614 0.677 -0.853 1.274 0.715 -0.493 -1.424 0.000 0.676 0.960 0.998 0.513 0.974 0.922 0.810 +1 1.896 -0.414 -0.963 0.636 0.168 1.093 0.303 0.748 2.173 0.290 -0.549 -1.624 2.215 0.332 0.902 1.217 0.000 0.544 0.131 -0.511 0.000 0.510 0.645 1.297 0.680 0.791 0.895 0.694 +1 0.855 -1.294 0.416 1.312 1.036 0.257 -2.689 -0.308 0.000 0.920 -1.012 -0.838 2.215 0.568 1.189 -0.558 0.000 0.543 -1.191 1.299 0.000 0.688 0.758 0.995 1.534 1.142 1.158 0.954 +1 4.335 -0.902 -0.530 0.835 -1.042 0.836 -1.622 1.248 0.000 1.913 -0.266 0.005 2.215 4.189 1.139 1.302 0.000 1.276 -0.454 -0.891 0.000 0.822 1.007 1.172 0.755 0.774 0.945 0.807 +0 0.656 0.761 -1.076 0.990 -0.311 0.513 -1.057 0.361 0.000 0.794 0.382 1.265 2.215 1.490 -0.942 -0.820 2.548 0.754 -0.484 1.629 0.000 0.894 0.932 0.990 1.005 1.421 1.246 1.129 +0 2.683 -0.605 0.494 0.890 -0.386 1.066 -0.617 -0.904 2.173 0.939 0.447 0.950 2.215 1.416 -0.399 -1.565 0.000 1.330 -1.534 1.240 0.000 1.067 0.832 1.524 1.198 1.687 1.216 1.095 +1 0.565 -0.095 0.713 0.188 -1.350 0.733 -1.734 -0.394 0.000 0.755 -0.288 -1.514 2.215 0.799 -1.464 1.537 2.548 1.071 -0.961 0.253 0.000 0.850 0.913 0.985 0.736 0.661 0.800 0.695 +1 0.621 -1.016 0.577 0.776 -0.343 0.619 0.017 1.555 0.000 0.560 1.141 -0.607 2.215 1.421 -0.690 -1.669 0.000 1.291 -0.424 0.013 3.102 0.776 1.069 0.993 0.798 0.832 0.896 0.798 +0 1.013 -0.552 1.710 0.265 -0.847 0.588 1.633 1.525 1.087 0.975 -0.568 0.404 2.215 1.554 0.756 -0.158 0.000 1.127 0.070 -1.269 0.000 1.350 1.276 0.990 1.616 1.789 1.246 1.156 +0 2.391 -1.293 0.948 1.210 0.949 1.694 -0.775 -0.618 2.173 0.754 -1.064 -1.365 1.107 0.359 -0.875 -1.729 0.000 0.637 0.448 0.233 0.000 0.675 1.017 0.996 1.130 1.068 1.480 1.157 +1 0.948 -0.014 0.759 0.496 -1.070 0.728 0.192 -0.249 1.087 0.995 0.341 -0.718 0.000 1.259 -0.109 1.494 0.000 0.670 0.740 1.408 1.551 1.617 0.975 0.988 0.770 0.782 0.777 0.699 +0 0.950 -1.377 1.522 1.153 0.926 0.463 -0.557 -0.747 0.000 0.866 0.338 -1.272 1.107 0.635 -2.558 -0.132 0.000 1.089 -0.297 0.784 3.102 1.301 1.048 0.984 1.088 0.899 0.988 0.897 +1 0.529 -0.590 -0.818 1.141 1.610 0.849 0.508 0.943 0.000 1.363 -0.856 -0.392 2.215 0.734 -0.058 1.704 0.000 0.747 0.771 -0.168 3.102 0.989 0.835 0.984 1.085 0.957 1.006 0.871 +1 0.865 -0.429 -0.818 0.711 0.995 0.885 -0.933 -0.535 0.000 0.687 -2.766 0.626 0.000 2.097 -1.141 1.208 2.548 1.836 0.759 -0.514 0.000 0.841 0.931 1.084 0.985 0.854 0.914 0.792 +0 2.584 -0.257 -0.364 2.446 -0.585 1.610 0.245 1.426 2.173 0.801 -0.660 1.577 0.000 0.482 -1.194 1.077 0.000 0.539 0.287 0.313 3.102 0.516 0.953 1.000 0.700 0.833 1.426 1.217 +1 0.626 1.159 -0.123 1.036 -1.551 0.809 0.047 0.559 0.000 0.826 -0.442 1.379 2.215 0.758 0.461 -1.064 2.548 0.590 1.088 -1.052 0.000 1.264 0.946 1.071 1.037 0.798 0.738 0.752 +1 0.685 0.490 0.258 2.455 0.996 1.325 1.383 -1.287 0.000 0.740 2.909 1.030 0.000 1.233 0.078 -0.595 2.548 1.876 0.885 -0.234 3.102 2.610 1.936 1.109 1.200 0.698 1.478 1.467 +1 1.567 0.410 -0.444 0.838 0.329 0.804 -2.841 -0.263 0.000 1.123 -0.900 -1.702 2.215 1.271 -0.449 1.271 0.000 1.168 0.468 1.398 1.551 3.238 2.198 1.020 0.882 0.914 1.662 1.689 +0 0.983 1.151 -0.541 1.881 -1.054 0.766 0.843 1.393 2.173 0.876 2.034 0.671 0.000 0.931 1.825 0.212 0.000 1.074 0.867 -1.294 0.000 0.908 1.005 0.981 0.906 0.452 0.823 0.794 +1 0.749 -0.875 -1.195 0.766 -0.553 0.394 1.605 0.657 0.000 0.879 -0.696 1.599 2.215 0.811 -0.739 0.055 2.548 1.469 -1.674 0.157 0.000 0.816 1.087 0.983 0.899 0.884 0.896 1.080 +0 0.697 0.026 -1.442 0.788 0.333 0.893 -0.052 0.780 0.000 1.273 0.831 -1.102 2.215 0.696 -0.198 -0.021 0.000 1.165 -0.198 1.723 0.000 0.945 0.850 1.026 0.909 0.570 0.903 0.766 +0 1.791 -0.412 1.684 0.433 0.714 0.749 -0.996 -0.356 1.087 0.330 1.009 0.025 2.215 0.539 -0.604 1.012 0.000 0.429 -0.283 -1.057 0.000 0.515 0.657 0.989 0.806 0.919 0.837 0.647 +1 0.880 -1.111 -1.408 2.535 -1.146 1.650 2.276 0.851 0.000 1.416 -1.525 -0.564 0.000 1.318 0.221 0.246 2.548 0.906 -0.210 1.615 3.102 1.712 1.206 0.987 1.966 0.816 1.292 1.182 +0 1.210 0.383 -1.246 0.442 -1.513 0.971 -0.784 0.149 1.087 1.459 -0.110 1.579 2.215 0.592 -0.193 -0.015 0.000 0.932 -0.639 -0.294 0.000 0.916 1.085 0.993 1.196 1.788 1.045 0.890 +1 1.166 0.383 -0.953 0.770 -0.463 1.174 1.892 0.787 0.000 1.117 -0.070 -0.848 2.215 1.190 1.062 1.516 0.000 0.919 1.712 -0.028 0.000 1.071 0.958 0.980 0.771 0.548 1.222 1.051 +0 0.662 0.429 1.475 1.126 0.524 1.216 0.360 -1.648 1.087 1.672 0.175 0.058 2.215 0.735 -0.270 -1.222 0.000 0.488 0.375 0.582 0.000 0.706 0.899 0.989 1.065 2.106 1.136 0.894 +1 1.842 -1.461 0.324 0.521 -1.223 0.971 -0.175 1.405 2.173 0.540 -0.776 -1.251 0.000 0.760 0.386 -0.453 2.548 0.868 0.281 -1.269 0.000 0.535 0.790 1.336 1.140 1.113 1.085 0.911 +0 1.781 0.188 1.399 0.115 0.478 0.706 0.339 -0.364 2.173 0.883 0.814 0.372 2.215 0.694 1.483 -1.025 0.000 1.101 1.350 0.928 0.000 0.931 1.001 0.983 1.035 0.772 0.897 0.898 +1 1.542 0.725 -1.055 0.750 0.365 1.171 1.251 1.177 2.173 1.266 -0.753 -0.372 0.000 0.615 0.711 0.725 0.000 1.246 1.012 -1.536 3.102 0.788 0.900 1.428 0.838 0.820 0.878 0.761 +1 1.283 -1.669 0.700 0.582 -0.312 1.563 1.064 -1.179 0.000 2.011 -0.751 0.245 2.215 1.446 -1.420 -1.636 0.000 1.081 -1.181 1.058 0.000 0.908 0.720 0.986 0.894 0.831 0.959 0.830 +1 1.462 -0.491 1.483 0.307 -1.384 0.931 -1.114 -1.440 0.000 1.163 0.044 0.734 2.215 1.423 -1.297 -0.229 0.000 1.308 -0.330 -0.400 3.102 0.961 0.936 0.986 0.997 0.982 0.867 0.826 +1 0.705 -1.570 0.177 1.320 0.677 0.990 -0.449 -1.302 0.000 0.498 0.136 0.772 1.107 0.751 -0.535 -0.324 0.000 0.685 -0.037 -1.726 0.000 0.785 0.637 0.991 1.543 0.443 1.153 0.967 +1 1.210 -1.128 -0.022 0.570 -1.092 0.738 -1.743 1.661 0.000 0.603 -1.304 1.010 0.000 1.044 -0.185 -0.078 2.548 0.883 -0.846 -1.243 3.102 0.841 0.700 0.987 0.728 0.706 0.782 0.729 +1 1.165 -0.590 1.582 0.222 -0.160 0.930 -1.602 -0.033 0.000 1.069 -0.254 0.951 1.107 1.406 -0.435 -1.094 2.548 1.433 -1.433 -1.366 0.000 1.646 1.404 0.990 0.782 1.263 1.175 0.959 +1 1.449 1.070 0.521 0.371 1.673 2.089 -0.175 -0.979 0.000 2.539 0.602 0.127 2.215 2.081 -0.017 -1.639 0.000 1.040 1.750 1.238 0.000 0.935 0.767 0.982 1.070 0.915 1.183 1.015 +1 0.717 -0.863 1.272 1.338 -0.299 0.845 -0.658 0.194 0.000 1.566 0.017 -1.366 2.215 0.439 -1.509 0.879 0.000 0.841 -0.450 1.129 3.102 0.851 0.660 1.340 1.258 0.858 0.927 0.857 +0 0.837 -0.180 1.222 0.715 -1.527 0.643 -0.963 -0.231 2.173 0.678 -2.048 0.504 0.000 1.049 -0.427 -0.928 2.548 0.375 -2.198 1.707 0.000 0.607 1.020 0.990 1.125 0.656 0.814 0.950 +1 1.432 -0.539 -0.390 0.872 0.266 1.837 -0.474 1.632 0.000 1.443 -0.817 0.128 0.000 1.037 0.585 -0.899 2.548 0.887 -0.981 0.743 0.000 0.816 0.914 0.985 0.851 0.646 0.876 0.774 +0 0.371 -1.945 -1.516 1.786 -1.677 0.495 1.134 0.234 1.087 0.884 0.065 0.222 0.000 0.611 0.361 1.535 1.274 0.737 -0.802 0.224 0.000 0.598 0.664 0.975 0.662 0.684 0.899 0.745 +0 0.510 -0.044 0.945 0.957 -0.555 0.859 0.636 0.131 1.087 0.792 -0.678 -1.614 0.000 0.586 -1.731 1.503 0.000 0.462 0.642 -1.662 3.102 0.750 0.694 0.989 0.716 0.668 0.957 0.824 +1 0.731 0.709 -1.671 1.584 0.953 0.627 2.045 -1.655 0.000 1.078 -0.650 -0.547 0.000 0.685 -0.592 0.772 0.000 1.178 0.537 -0.631 3.102 1.223 0.969 1.045 0.649 0.283 0.613 0.791 +0 1.076 0.028 0.125 1.631 -0.604 0.931 0.793 1.415 0.000 1.008 0.624 -1.311 0.000 1.294 1.626 0.248 0.000 1.117 0.707 1.128 1.551 1.311 0.867 1.120 0.911 0.566 0.762 0.871 +1 0.785 -1.019 0.790 0.688 -1.245 0.669 -2.245 -1.196 0.000 0.560 -0.492 -0.182 2.215 1.150 -0.404 1.195 2.548 0.745 -1.913 0.074 0.000 0.982 1.033 0.988 0.653 0.808 0.911 0.772 +1 0.680 0.577 0.657 0.565 -0.732 0.987 1.558 1.467 0.000 0.910 -0.330 -0.488 0.000 0.750 0.910 0.481 2.548 0.982 0.541 -1.308 3.102 0.544 0.791 0.984 0.637 0.666 0.609 0.626 +0 1.612 0.907 -0.118 1.128 0.649 0.525 -1.080 -1.293 0.000 0.386 -1.181 0.488 0.000 2.023 -0.913 1.520 2.548 1.865 0.752 -0.686 3.102 0.957 0.878 1.192 0.937 2.127 1.493 1.302 +0 1.324 0.518 -1.578 0.692 -0.548 0.973 0.664 -0.698 0.000 1.513 -0.837 0.978 0.000 1.712 -0.406 0.401 2.548 1.711 -0.303 -0.530 0.000 0.972 0.829 1.062 1.201 1.117 0.910 0.822 +0 0.952 -1.832 0.426 1.383 1.005 0.655 1.308 -0.956 0.000 0.503 -0.889 -0.591 1.107 0.277 1.172 1.346 0.000 0.759 1.305 -1.325 0.000 0.974 0.676 0.985 1.044 0.617 0.837 0.857 +1 1.674 0.081 -1.698 1.277 -1.290 0.903 0.286 0.439 2.173 0.641 0.513 1.006 0.000 1.194 -0.183 -0.444 2.548 0.431 0.327 -0.186 0.000 0.604 0.753 0.978 1.386 0.978 1.029 0.833 +0 1.436 0.264 -0.970 1.420 -0.439 1.127 -0.676 0.638 2.173 1.465 -0.458 1.314 2.215 0.629 -1.381 -1.115 0.000 0.377 0.698 -0.052 0.000 0.903 1.000 0.988 1.641 1.099 1.355 1.086 +0 1.763 1.187 0.443 0.876 0.808 0.564 -0.542 -1.087 0.000 0.703 0.665 -1.075 2.215 0.799 1.860 1.442 0.000 0.586 -2.287 -0.446 0.000 1.079 0.913 0.991 1.029 0.717 0.739 0.812 +1 0.951 -0.172 0.304 0.352 0.219 1.295 -0.804 0.925 2.173 1.121 -0.104 -0.956 0.000 1.316 -0.407 -0.269 0.000 1.345 -2.407 -1.548 0.000 0.904 0.980 0.989 0.873 0.951 0.867 0.782 +0 1.690 1.438 0.949 0.772 -1.109 0.720 1.100 0.316 1.087 0.717 -1.314 -1.479 2.215 0.553 -1.301 -0.125 0.000 0.915 -0.059 -1.219 0.000 0.881 0.807 1.520 1.002 1.966 1.437 1.233 +0 1.552 0.228 0.776 1.065 -1.439 0.885 0.266 -1.087 2.173 1.055 -0.966 1.285 0.000 0.881 -0.021 -0.509 0.000 1.628 -0.524 0.302 3.102 0.826 1.113 1.623 1.170 1.343 0.984 0.922 +1 0.730 0.076 -0.712 0.749 1.059 0.626 0.958 0.557 1.087 0.663 -0.371 1.699 0.000 0.785 -1.320 -1.239 2.548 1.186 -0.283 0.285 0.000 1.105 1.040 1.024 0.813 1.593 0.902 0.761 +1 0.562 0.301 -1.472 0.944 -0.477 1.072 0.134 -1.675 0.000 1.040 0.045 -0.025 0.000 1.083 -0.337 -0.560 0.000 0.955 1.001 0.855 3.102 0.968 0.935 0.984 0.894 1.024 0.759 0.709 +1 0.667 -0.357 -1.584 1.287 -0.108 1.336 0.390 -0.162 2.173 1.069 0.453 1.255 2.215 1.344 0.321 -1.683 0.000 0.588 1.082 -1.729 0.000 0.484 0.578 1.247 1.018 1.684 1.022 0.942 +1 0.934 0.111 -1.263 2.339 1.684 0.759 1.012 -0.094 2.173 1.016 -0.570 -0.576 0.000 0.911 0.696 0.805 0.000 1.817 2.067 0.498 0.000 0.958 1.122 0.985 0.746 0.420 0.897 0.874 +0 0.463 0.022 -1.153 0.431 -0.343 0.612 -1.019 -0.760 0.000 1.267 -0.497 0.817 2.215 1.331 0.114 -0.745 0.000 2.139 0.837 1.193 0.000 0.990 0.927 0.984 1.293 0.680 0.923 0.939 +0 0.610 -0.721 0.047 1.126 0.749 0.809 0.640 0.567 1.087 0.703 1.973 0.077 0.000 1.261 -0.972 -1.063 0.000 1.639 -1.359 -1.232 0.000 0.787 0.881 0.993 0.981 1.326 0.937 0.881 +1 1.468 0.169 0.736 0.469 1.395 0.628 -0.368 -1.245 2.173 1.108 -0.791 1.408 2.215 1.204 -0.358 -0.170 0.000 0.588 -0.948 -0.524 0.000 1.009 0.908 0.988 1.031 0.881 0.876 0.898 +1 0.380 1.267 1.541 0.874 0.535 0.950 -0.452 1.424 2.173 1.049 -2.570 -1.208 0.000 1.162 -0.563 0.083 0.000 0.815 -0.269 -0.378 0.000 1.057 0.836 0.988 2.190 0.355 1.564 1.363 +1 1.131 -0.147 0.081 2.441 -1.303 1.351 -0.135 -0.277 2.173 0.607 0.242 1.464 0.000 1.307 0.486 0.137 2.548 2.348 -0.362 1.247 0.000 0.988 1.726 2.182 1.525 0.826 1.352 1.312 +0 0.518 2.315 0.329 1.341 -1.002 1.079 0.369 0.688 0.000 0.643 0.470 -1.616 0.000 0.734 1.039 -0.324 2.548 0.863 0.178 -0.994 3.102 0.742 0.831 1.076 0.991 0.452 0.694 0.875 +0 1.107 1.540 -1.021 0.954 -0.132 0.880 0.266 -1.645 0.000 1.130 0.018 -0.303 0.000 0.966 1.813 1.514 0.000 2.144 2.210 0.483 0.000 0.816 0.776 1.023 0.717 0.523 0.722 0.775 +0 2.144 -0.219 -1.342 0.564 -1.422 0.759 0.172 0.152 2.173 1.137 -0.799 -0.749 2.215 1.417 -0.633 0.593 0.000 2.100 0.169 0.967 0.000 1.084 0.984 0.999 1.002 1.221 1.034 1.091 +1 1.197 -0.081 0.919 0.618 -0.264 0.433 -1.525 0.096 2.173 0.618 -0.334 -0.438 0.000 0.632 -0.709 1.717 2.548 1.101 -2.163 -1.515 0.000 0.813 0.821 1.043 0.688 0.696 0.625 0.593 +1 1.061 -0.736 1.441 0.997 0.986 1.499 -0.812 -0.800 2.173 0.569 -0.875 0.247 2.215 0.916 0.182 0.750 0.000 0.448 -0.348 -1.589 0.000 0.646 1.264 0.997 0.748 1.102 1.033 0.900 +1 0.796 2.221 1.734 0.569 0.178 0.432 0.667 0.774 2.173 0.356 0.846 -1.658 0.000 0.576 0.075 1.243 0.000 1.217 -0.336 -0.537 3.102 0.449 0.686 0.991 0.735 0.833 0.823 0.679 +1 0.586 -1.090 -0.937 1.280 0.093 1.070 -0.254 -1.575 0.000 0.740 -1.108 1.684 0.000 0.939 0.358 0.158 1.274 1.243 -0.716 -0.168 0.000 0.944 0.821 0.988 0.960 0.821 0.848 0.895 +0 0.953 -0.957 0.828 1.468 1.565 0.998 -0.700 -0.762 2.173 0.474 -0.139 0.643 0.000 0.426 -0.026 -0.852 0.000 0.810 0.079 0.122 3.102 0.673 0.808 1.011 0.822 0.785 0.877 0.722 +0 1.127 1.349 -0.949 1.410 -0.310 0.641 -0.372 1.146 1.087 0.442 2.154 0.874 0.000 1.092 0.622 1.157 0.000 1.001 0.087 -0.881 3.102 0.939 0.999 0.985 1.408 0.845 0.935 0.925 +1 0.571 -0.457 -1.127 1.556 -0.934 0.586 0.263 1.002 0.000 0.785 0.834 -1.295 0.000 0.699 -0.105 0.709 2.548 1.227 1.004 1.116 3.102 0.860 0.821 0.992 0.887 0.562 0.747 0.742 +1 1.883 -0.760 1.158 1.173 1.232 0.765 -1.050 -0.035 2.173 1.043 -0.192 -0.977 2.215 0.648 -0.252 1.740 0.000 0.871 -0.488 -0.458 0.000 0.771 0.810 0.991 1.391 1.147 1.126 0.902 +1 0.609 -0.069 -1.094 0.776 0.386 1.228 -0.194 0.991 0.000 1.191 -0.244 -0.561 1.107 1.030 1.151 -1.008 0.000 1.297 -0.224 0.443 0.000 0.919 0.697 0.990 0.772 0.577 0.856 0.752 +0 0.805 1.150 1.305 1.911 0.250 0.471 -1.387 1.229 0.000 0.545 -1.035 -0.475 2.215 0.600 -0.439 -1.436 2.548 1.271 1.168 -0.627 0.000 0.907 0.885 1.399 1.137 0.497 1.029 1.465 +0 0.640 0.104 -1.140 1.011 1.345 0.592 0.328 -0.640 2.173 0.910 -1.596 0.378 2.215 0.384 0.133 0.787 0.000 0.865 -0.529 -0.620 0.000 0.658 0.784 0.988 0.845 1.514 0.929 0.731 +1 0.462 0.106 -0.497 0.753 -0.976 0.819 1.280 0.529 0.000 1.223 1.442 -1.138 2.215 0.528 1.678 0.819 0.000 1.265 0.419 1.570 3.102 0.449 0.833 0.985 0.659 0.924 0.855 0.766 +1 1.193 -0.881 -0.713 0.668 1.275 2.249 1.337 0.157 0.000 1.605 -0.887 1.577 1.107 0.660 -0.415 -1.709 2.548 2.670 -1.459 1.743 0.000 0.672 1.197 1.207 0.679 0.319 0.738 0.692 +0 1.116 1.277 0.465 0.612 -1.667 0.891 0.205 1.204 2.173 0.766 1.342 -0.306 2.215 0.514 -0.063 -1.553 0.000 1.328 0.772 -0.698 0.000 0.792 1.010 1.076 0.751 1.409 0.835 0.764 +0 0.566 -0.800 -0.360 1.095 1.021 0.571 0.507 -1.188 2.173 0.518 -1.229 -1.066 0.000 0.839 0.201 0.979 1.274 0.405 -1.411 0.356 0.000 0.586 0.874 1.033 0.677 0.809 0.708 0.661 +0 0.654 -0.280 -1.240 1.077 -0.091 0.792 0.294 0.708 2.173 0.412 -0.304 1.168 0.000 0.541 0.954 1.561 2.548 0.793 1.879 -1.109 0.000 1.407 0.813 1.000 0.903 0.647 0.710 0.766 +0 0.780 -0.675 1.168 0.783 -1.458 1.448 -0.427 0.471 1.087 0.770 0.130 -1.340 0.000 0.678 -1.095 -1.018 0.000 1.022 -1.272 -0.559 0.000 0.858 0.514 0.987 1.255 0.714 0.869 0.837 +1 0.651 -0.058 -0.599 1.620 0.657 1.273 -1.425 -0.619 0.000 1.827 -0.251 0.944 2.215 3.026 2.116 -1.487 0.000 1.516 -0.575 -0.180 0.000 0.886 1.854 1.287 0.963 0.858 1.446 1.203 +1 0.998 0.385 -0.033 1.496 1.038 0.935 1.078 1.641 2.173 0.683 0.925 0.017 2.215 1.047 0.565 -0.977 0.000 0.876 -2.352 -0.508 0.000 0.497 0.773 1.392 0.846 1.172 0.883 0.805 +0 1.818 1.089 1.715 1.050 -0.959 2.660 0.985 0.185 2.173 2.121 0.205 -1.620 1.107 0.620 0.966 0.474 0.000 0.857 1.063 -0.285 0.000 0.516 1.318 1.280 2.142 3.762 1.971 1.439 +1 1.116 0.725 0.711 1.180 1.478 1.636 -2.434 0.743 0.000 1.676 2.760 -1.144 0.000 0.990 0.595 -1.250 0.000 2.721 0.667 -0.725 3.102 0.700 0.636 1.014 0.843 0.451 0.848 0.696 +1 0.488 0.183 -1.314 1.377 -0.839 1.020 0.497 1.353 2.173 1.009 -0.135 -0.349 0.000 0.720 0.280 0.475 2.548 0.483 0.630 -1.636 0.000 0.934 1.218 0.990 0.864 0.767 0.870 0.835 +0 0.963 0.638 1.729 0.933 0.178 1.512 -0.013 -0.216 2.173 2.022 0.660 1.005 2.215 0.805 0.215 -0.809 0.000 1.309 -0.408 1.688 0.000 0.976 1.275 1.293 1.019 2.466 1.348 1.111 +1 1.042 -0.348 -0.758 1.613 -1.110 1.710 -0.231 -0.539 2.173 1.172 -0.332 1.671 1.107 0.596 -2.224 0.319 0.000 1.442 2.169 0.928 0.000 0.930 1.995 0.999 1.087 1.904 2.008 1.918 +0 0.408 -2.071 1.086 1.695 0.551 0.520 -2.288 -1.098 0.000 0.653 -0.978 -1.530 2.215 0.623 -1.358 -0.614 2.548 0.658 -2.475 -0.044 0.000 0.787 0.879 0.979 0.762 0.525 0.660 0.747 +0 1.003 0.687 -1.158 0.648 0.335 1.169 0.570 0.912 1.087 1.743 -0.246 -0.998 2.215 0.345 2.216 0.807 0.000 0.553 -0.369 0.554 0.000 0.965 0.847 1.088 1.015 2.261 1.201 0.983 +0 0.950 -0.796 0.636 0.391 -1.445 0.262 1.049 -1.691 0.000 0.740 -0.349 -0.902 2.215 1.093 1.759 0.988 0.000 1.162 1.066 -0.425 3.102 0.771 0.813 0.988 0.743 0.841 0.857 0.862 +1 1.089 -0.397 -1.419 0.292 0.574 1.074 -0.134 1.248 0.000 0.926 -0.930 -1.088 1.107 2.335 0.291 0.078 0.000 1.343 -1.071 -0.056 0.000 1.760 1.430 0.983 0.746 0.563 1.071 0.910 +1 0.302 1.168 0.252 0.226 0.045 0.722 -0.160 -0.810 2.173 0.751 -2.118 1.693 0.000 0.544 -1.115 0.888 0.000 1.003 0.038 0.725 3.102 0.812 0.986 0.984 0.745 0.889 0.915 0.789 +1 0.652 0.543 -1.645 0.683 0.227 0.712 -0.044 1.111 2.173 0.948 1.066 -0.356 0.000 0.927 1.266 -1.276 0.000 0.785 0.993 0.411 0.000 0.944 1.058 0.986 0.568 0.737 0.724 0.642 +1 1.559 0.828 -1.298 1.116 -0.771 0.923 0.179 0.519 2.173 0.798 -0.285 -0.051 2.215 2.043 0.635 1.316 0.000 0.597 0.107 0.879 0.000 0.713 0.746 0.979 1.015 0.690 0.974 0.801 +0 0.451 1.024 -1.245 0.855 0.187 0.461 0.596 0.217 2.173 0.689 0.160 -1.070 0.000 1.022 0.988 1.031 0.000 0.826 -1.129 -1.409 1.551 1.006 1.009 0.991 0.723 0.989 0.981 0.863 +0 1.579 0.002 -1.705 0.922 1.466 0.838 -0.732 0.980 2.173 1.163 -0.666 -0.354 0.000 1.051 -1.594 -0.423 0.000 1.539 -0.131 0.212 3.102 0.952 0.972 0.987 1.113 0.845 0.928 1.150 +1 0.809 -0.003 0.685 0.716 -1.615 0.763 -0.973 -1.510 0.000 1.279 0.281 0.131 2.215 0.856 -0.457 -0.792 2.548 0.680 -0.836 1.116 0.000 0.770 0.727 0.988 0.915 0.938 0.905 0.777 +0 0.595 1.803 0.639 0.851 -0.324 0.872 -0.593 0.349 2.173 0.891 -0.767 -1.686 0.000 1.360 1.389 -1.475 2.548 0.639 -0.289 -0.446 0.000 0.910 1.059 0.979 0.889 2.196 1.241 1.098 +0 0.775 0.764 -1.566 0.592 1.375 1.671 -0.304 -0.108 2.173 1.266 -0.480 1.463 2.215 0.785 -1.017 -0.154 0.000 0.886 0.808 0.761 0.000 0.873 0.962 0.991 1.371 2.123 1.172 0.959 +1 0.544 -1.350 -0.754 0.636 0.688 1.030 -1.729 0.976 0.000 1.353 -0.723 -0.407 2.215 1.588 0.296 -1.119 0.000 0.981 -0.656 0.865 3.102 0.769 0.606 0.988 1.053 0.948 0.939 0.837 +0 1.037 0.854 0.339 0.543 0.675 1.020 0.228 1.326 2.173 0.529 -0.171 -0.546 0.000 0.470 0.172 0.575 0.000 0.968 -1.496 -1.171 0.000 0.977 1.221 0.989 1.214 1.363 1.034 1.150 +1 0.669 -0.383 -1.047 0.892 0.760 1.403 -1.426 -1.164 0.000 1.645 -1.142 0.322 2.215 0.390 -0.969 -0.689 0.000 0.408 0.074 0.606 0.000 0.912 0.690 1.069 0.972 0.307 0.755 0.685 +0 0.552 1.123 0.951 1.039 -1.464 0.667 0.616 0.033 2.173 0.576 0.449 1.545 0.000 1.125 1.337 0.336 0.000 1.127 -0.101 -1.002 3.102 1.272 0.967 0.990 0.966 0.816 0.836 0.797 +1 0.933 -0.060 -1.544 0.469 -0.324 1.087 -1.162 0.156 2.173 1.023 -1.258 -1.330 0.000 0.733 -0.558 1.640 0.000 1.179 -0.261 0.827 3.102 0.754 0.896 0.985 1.039 0.863 0.916 0.800 +1 0.648 0.439 0.041 1.678 0.075 0.943 0.638 -0.162 0.000 0.774 0.645 1.562 2.215 1.878 -2.326 1.112 0.000 0.857 -0.085 -1.281 0.000 0.588 0.798 1.007 1.556 0.979 1.139 0.996 +0 1.038 -1.641 0.223 0.790 -0.829 1.051 1.667 1.001 0.000 0.937 0.075 -0.693 0.000 0.521 1.437 0.175 0.000 0.743 -0.313 -1.107 3.102 0.906 1.031 1.018 0.715 0.328 0.826 1.448 +1 1.106 0.303 -0.341 1.223 -0.646 0.469 -0.722 1.392 0.000 1.034 -0.496 0.599 1.107 1.321 0.236 1.545 2.548 0.371 -0.723 -0.797 0.000 0.588 0.677 0.996 1.128 1.056 0.942 0.773 +1 0.299 0.479 1.727 1.487 1.049 1.138 -0.706 -0.896 0.000 0.886 -1.420 -1.493 0.000 1.033 0.265 0.616 2.548 1.431 -1.125 0.675 0.000 1.363 1.149 0.981 0.942 0.733 1.075 1.601 +0 0.907 0.334 -0.775 0.580 -1.343 0.339 0.326 0.912 0.000 0.690 2.388 0.191 0.000 0.878 0.636 1.686 2.548 0.453 1.925 -0.183 0.000 0.855 0.695 0.986 0.562 0.219 0.503 0.591 +1 0.558 0.003 1.133 1.205 0.211 0.695 -1.330 0.741 1.087 1.294 -0.261 -1.009 2.215 0.388 -0.428 -0.165 0.000 1.152 0.337 1.688 0.000 0.881 1.172 0.990 1.224 1.602 1.251 1.090 +1 0.696 0.696 0.731 0.731 -1.701 1.863 0.404 -0.606 2.173 2.216 0.396 0.907 0.000 0.865 1.314 1.335 0.000 0.806 1.042 -0.025 0.000 0.871 0.753 0.992 1.298 0.788 0.895 0.843 +1 0.440 -0.663 0.928 1.982 -0.061 0.484 0.416 -1.585 2.173 1.092 1.215 1.643 1.107 0.512 1.192 0.099 0.000 0.416 0.329 1.392 0.000 0.526 0.644 1.006 1.067 0.519 1.164 0.895 +1 1.078 0.892 0.670 1.059 -0.855 0.948 1.322 -1.627 2.173 1.076 1.386 0.246 0.000 0.837 0.249 0.669 0.000 1.508 0.025 -1.268 1.551 0.836 0.914 1.451 1.094 0.974 0.858 0.788 +1 1.242 0.567 -0.166 1.531 1.725 0.626 1.722 -0.065 0.000 0.978 0.245 1.551 2.215 0.365 0.165 0.746 0.000 0.683 -0.294 -0.744 3.102 0.936 0.855 1.893 1.110 0.687 0.782 0.822 +1 0.538 1.149 1.727 0.914 1.430 0.532 -0.276 0.474 0.000 0.610 -0.806 -0.154 0.000 1.149 0.860 -0.788 2.548 0.721 1.107 -0.643 3.102 0.732 0.885 0.984 0.915 0.162 0.736 0.758 +1 1.693 0.392 1.691 1.251 1.241 1.189 0.564 -0.424 2.173 0.647 0.382 0.145 0.000 0.443 -2.715 -1.276 0.000 0.615 0.647 0.578 3.102 2.285 1.470 1.001 1.530 0.717 1.326 1.373 +0 0.917 -0.250 0.669 0.803 1.350 1.759 -2.767 -0.384 0.000 0.872 -0.621 1.370 0.000 1.085 -0.883 -1.464 1.274 1.234 -0.208 -1.544 3.102 1.104 0.949 0.992 0.783 0.331 0.660 0.634 +1 1.305 1.039 1.573 1.512 -1.052 0.812 0.768 0.675 2.173 0.775 1.466 -0.012 2.215 0.637 0.386 -0.719 0.000 0.454 1.074 0.389 0.000 0.564 0.690 1.365 1.095 0.807 0.948 0.746 +1 0.911 0.276 -0.320 1.795 -0.442 0.603 0.832 1.302 0.000 0.613 -0.271 0.813 2.215 0.752 -0.004 1.535 2.548 0.517 1.076 -1.603 0.000 0.783 0.635 0.976 0.954 0.448 0.750 0.757 +1 0.686 -0.500 0.939 1.350 -1.333 0.745 -0.845 -0.527 2.173 0.846 1.423 -1.663 0.000 0.998 -1.130 0.519 2.548 0.946 1.231 0.935 0.000 0.941 1.519 1.185 0.933 0.894 1.173 1.002 +0 1.154 -0.132 -1.172 1.205 -0.379 0.479 -1.096 -0.309 2.173 1.043 -0.651 0.596 1.107 0.975 -0.205 1.443 0.000 0.816 -1.059 1.388 0.000 1.048 0.959 1.072 1.092 0.791 0.781 0.764 +1 0.895 -1.061 0.742 0.840 -1.061 1.095 0.918 -0.389 2.173 0.839 0.630 -1.238 0.000 0.934 0.439 -1.651 0.000 0.673 0.306 0.526 1.551 0.715 1.312 1.200 0.758 0.715 1.035 0.956 +1 1.776 1.181 -1.527 0.775 -1.263 0.720 -0.094 -0.055 2.173 0.604 0.422 0.387 0.000 0.604 1.440 0.657 2.548 0.448 1.799 1.255 0.000 0.815 0.870 0.984 0.858 0.915 0.924 0.795 +1 0.999 -2.310 0.531 0.385 -0.514 0.448 1.585 -1.032 0.000 0.688 -0.803 -1.696 0.000 0.773 -0.943 0.955 0.000 0.621 -2.074 1.410 0.000 0.843 0.813 0.979 0.653 0.223 0.535 0.582 +1 0.879 1.530 -0.715 0.825 0.221 0.556 1.070 1.175 2.173 0.480 1.791 0.360 0.000 1.072 0.322 -1.236 2.548 0.379 -1.707 -1.581 0.000 1.984 1.301 0.986 0.890 0.868 0.910 0.978 +1 2.368 0.796 0.542 1.064 1.237 0.410 1.171 -1.301 0.000 0.462 -0.457 1.680 0.000 1.201 0.601 -0.696 2.548 0.616 -0.965 -1.238 3.102 0.945 0.820 1.291 1.188 0.750 0.978 0.874 +1 1.663 -0.508 0.134 0.601 0.287 0.795 -0.459 1.634 0.000 1.157 0.111 -0.902 2.215 0.776 0.324 0.912 2.548 0.449 0.664 -1.671 0.000 0.631 0.923 1.002 0.701 1.013 0.797 0.802 +0 0.413 0.203 1.624 1.527 1.027 0.677 -0.037 -0.241 2.173 1.438 0.046 -1.649 2.215 1.186 1.332 -0.158 0.000 0.626 1.445 -0.770 0.000 0.923 0.949 0.983 1.040 1.387 0.999 1.078 +1 1.084 0.762 1.222 0.897 -1.221 0.584 0.973 0.305 0.000 0.424 -0.638 -0.397 0.000 0.739 0.724 -0.945 2.548 1.455 0.211 -0.150 0.000 0.886 0.859 1.104 0.672 0.797 0.789 0.882 +1 0.952 -0.757 1.033 0.522 -0.883 0.821 -0.303 1.364 2.173 0.752 -0.788 -0.168 0.000 1.113 0.237 -0.274 0.000 0.976 -1.176 -1.303 3.102 0.799 0.935 0.987 0.686 0.842 0.872 0.742 +1 1.502 1.209 0.702 0.759 1.018 0.895 1.605 -0.734 0.000 0.429 0.898 1.597 0.000 0.429 2.044 -1.154 0.000 0.453 2.215 0.697 0.000 1.074 0.777 0.983 0.613 0.241 0.480 0.681 +0 0.510 -0.845 -0.825 0.923 1.316 0.627 0.712 0.285 2.173 0.923 -1.091 -1.590 2.215 0.531 -2.126 -0.594 0.000 0.983 -0.569 0.180 0.000 0.896 0.926 0.987 0.875 1.622 1.032 0.863 +0 0.937 0.030 -0.446 0.814 1.215 0.240 0.395 1.715 0.000 0.525 -0.233 1.012 2.215 0.735 0.064 0.077 2.548 1.730 -0.389 -0.968 0.000 0.838 0.707 1.207 0.692 0.503 0.514 0.522 +1 1.154 -0.586 -0.910 1.279 -0.295 1.508 -0.209 1.276 2.173 0.619 -0.247 0.484 0.000 0.289 2.100 -0.482 0.000 0.553 -0.039 -0.364 3.102 1.186 0.709 0.984 1.547 0.965 0.977 0.926 +1 1.046 -0.419 -0.646 0.379 -0.816 0.884 -0.646 1.732 0.000 2.073 -1.193 -0.000 0.000 1.723 -0.541 1.309 2.548 0.396 -2.305 -0.722 0.000 1.230 1.440 0.988 1.139 0.682 1.164 1.099 +0 1.434 -0.903 -0.793 0.328 0.160 0.625 -0.750 -1.657 0.000 0.810 -0.641 1.026 2.215 0.901 0.306 0.206 2.548 0.445 -0.961 -0.075 0.000 0.813 0.902 0.988 0.900 0.772 0.714 0.667 +0 1.759 1.471 -1.264 1.191 1.650 0.904 -0.670 0.183 2.173 0.427 1.040 0.708 2.215 0.819 -1.790 -0.173 0.000 0.375 -1.798 1.201 0.000 0.581 0.807 0.988 0.815 0.996 1.383 1.565 +0 1.088 -0.611 1.523 0.421 -0.635 1.282 0.556 -1.466 2.173 1.269 -0.915 -0.515 0.000 2.838 -0.261 0.369 2.548 0.996 -0.471 1.077 0.000 0.872 0.920 0.993 1.208 2.586 1.363 1.051 +0 3.865 -1.225 -0.169 0.486 0.408 2.683 -0.363 1.606 1.087 1.958 -1.265 0.264 2.215 1.240 -2.103 -1.370 0.000 0.837 -0.928 -1.044 0.000 0.784 1.563 0.989 2.964 3.554 2.220 1.832 +0 0.928 0.878 -0.710 2.000 -0.880 0.622 -0.708 1.700 2.173 1.604 -0.162 0.867 0.000 0.733 -0.312 0.269 1.274 0.526 1.809 1.632 0.000 1.113 1.023 0.976 0.885 0.823 0.850 0.960 +0 1.040 0.987 1.700 1.159 -0.835 0.590 -0.071 0.455 0.000 0.777 0.459 0.033 2.215 0.614 1.220 0.784 0.000 1.232 0.106 -1.287 3.102 0.888 0.942 1.151 0.934 0.834 0.690 0.737 +0 2.217 -0.194 0.202 0.677 -0.312 1.576 -0.361 1.544 2.173 0.499 -0.504 -0.912 2.215 0.954 0.392 -0.859 0.000 0.374 0.289 -1.345 0.000 0.281 1.065 0.999 0.800 1.048 1.146 0.916 +0 3.530 -1.640 -0.277 1.379 1.114 3.102 -0.125 1.149 1.087 1.597 -0.746 1.243 1.107 0.826 1.343 -1.556 0.000 1.646 -0.667 -0.759 0.000 1.990 1.895 2.903 3.876 1.104 2.520 2.291 +1 0.656 -0.074 -0.380 0.778 1.296 0.957 -0.518 -0.730 2.173 0.808 0.910 -1.476 0.000 1.184 0.268 1.486 0.000 0.786 1.260 0.584 0.000 0.840 0.850 0.988 0.846 0.838 0.894 0.760 +1 1.246 0.206 -1.344 0.424 -0.901 1.368 0.872 1.430 2.173 1.848 0.918 -0.372 0.000 1.497 0.392 0.981 2.548 1.154 -0.863 0.132 0.000 1.020 1.389 0.984 1.313 0.813 1.255 1.152 +0 0.607 0.109 1.623 0.180 -0.713 0.492 0.652 0.752 0.000 1.722 0.905 -0.711 2.215 0.814 0.403 -1.359 0.000 1.841 1.442 0.404 0.000 0.934 0.907 0.985 1.423 1.404 1.113 1.140 +0 0.686 -1.481 1.477 1.200 -1.048 0.755 -0.235 -1.156 2.173 0.328 0.687 0.285 0.000 1.227 -1.036 0.522 2.548 0.487 -0.995 -0.596 0.000 0.684 0.880 0.988 0.957 1.321 0.916 0.831 +1 0.899 -0.758 0.336 0.892 -1.491 0.954 -0.567 -1.630 0.000 1.337 -0.688 0.071 2.215 1.042 -0.162 -0.929 2.548 0.810 -0.912 1.466 0.000 1.103 0.837 1.237 0.928 1.039 0.878 0.787 +1 1.036 1.431 -0.434 0.735 -1.208 0.853 -0.021 0.667 2.173 0.657 0.803 -1.566 0.000 0.813 0.116 -0.345 0.000 0.803 1.398 1.111 0.000 1.077 1.105 0.986 0.465 0.651 0.733 0.685 +0 1.920 -0.262 -0.501 0.125 -1.455 1.435 1.362 0.847 0.000 0.709 -0.778 -1.399 0.000 0.803 1.271 -0.271 1.274 0.543 1.654 1.534 0.000 0.886 0.980 0.985 0.768 0.448 0.714 1.073 +1 0.935 -0.677 1.227 1.685 0.671 1.040 1.335 -0.993 1.087 0.531 1.327 1.499 0.000 1.149 0.405 -0.811 0.000 0.990 0.792 0.209 3.102 0.915 0.868 0.986 0.828 0.973 1.213 0.983 +1 0.289 -1.962 -0.349 0.403 -0.806 1.102 -0.800 0.764 2.173 0.793 -1.894 -1.180 0.000 0.611 -0.043 -1.295 2.548 0.590 -0.553 0.267 0.000 1.056 0.843 0.978 0.976 1.058 0.859 0.770 +1 0.929 -0.410 -1.322 0.358 0.787 0.907 -0.861 1.236 2.173 1.703 1.927 -0.007 0.000 0.589 -2.041 -0.842 0.000 1.586 -1.496 -1.547 0.000 0.676 1.083 0.991 0.591 0.601 0.731 0.779 +0 0.604 -0.716 -0.886 0.424 0.105 0.489 -0.521 -0.287 2.173 0.617 -0.564 0.672 1.107 1.298 -1.322 1.363 0.000 0.868 -1.460 -1.005 0.000 1.006 1.013 0.982 0.710 0.615 0.708 0.738 +0 1.520 0.177 -0.041 0.385 -1.280 0.763 -0.247 -1.729 0.000 1.466 -0.945 0.210 2.215 0.832 -0.771 -1.434 0.000 1.637 -1.318 1.574 3.102 0.576 0.761 0.986 0.943 1.387 1.006 0.971 +1 1.015 0.441 0.518 0.486 -0.352 0.638 -1.062 1.286 2.173 0.600 0.017 -0.315 0.000 1.295 -0.793 -1.104 1.274 0.554 0.676 0.687 0.000 0.665 0.964 0.987 0.947 0.951 0.808 0.722 +1 0.930 -0.507 -0.821 1.348 1.604 0.279 -0.406 -0.183 0.000 0.620 0.573 -0.425 2.215 0.952 0.460 1.508 1.274 0.958 -1.824 0.475 0.000 0.912 1.111 1.267 0.839 0.805 0.898 0.846 +0 1.520 -0.439 1.458 0.798 1.134 0.687 -0.530 -0.491 0.000 0.494 0.426 0.417 2.215 0.316 1.026 -0.442 2.548 0.606 0.683 0.074 0.000 0.866 0.694 0.986 0.731 0.329 0.565 0.661 +1 0.802 -0.480 0.089 0.713 1.225 2.125 0.779 0.466 0.000 2.200 0.875 -1.341 2.215 1.891 0.187 -1.631 2.548 1.643 1.121 -0.408 0.000 0.889 1.109 0.991 0.931 0.951 0.994 0.924 +0 0.547 -1.312 -0.111 1.370 1.409 1.299 2.403 -0.697 0.000 1.469 0.111 -1.584 2.215 2.116 0.956 0.545 0.000 0.945 0.363 0.680 3.102 3.481 2.120 1.175 1.273 0.964 1.864 2.242 +0 0.485 0.296 1.047 0.483 0.932 0.500 -0.995 -0.115 2.173 0.750 0.038 -1.036 2.215 0.655 1.879 -1.513 0.000 0.578 0.760 -1.625 0.000 0.697 0.729 0.987 0.874 0.827 0.685 0.607 +0 0.806 -0.819 -0.676 0.840 -1.487 1.184 -0.611 -0.043 2.173 1.369 -0.494 1.612 2.215 1.187 -0.002 0.484 0.000 0.483 -0.102 1.476 0.000 0.653 0.913 0.989 1.132 1.870 1.092 0.940 +0 0.819 0.204 0.528 0.681 0.399 0.364 -0.645 -0.344 1.087 1.127 -0.122 -0.919 2.215 0.855 -0.996 1.038 0.000 1.343 -0.409 -1.585 0.000 0.904 1.033 0.990 0.902 0.530 0.859 0.903 +1 0.808 0.326 -1.705 0.460 0.211 0.703 -0.672 0.739 2.173 0.907 0.208 -0.624 0.000 1.145 -0.488 -1.145 0.000 0.995 -0.784 0.255 0.000 0.927 0.718 0.989 0.740 0.531 0.760 0.669 +1 0.794 -1.488 -0.678 0.386 -0.735 0.667 0.554 1.703 2.173 0.698 0.322 0.659 0.000 0.555 -0.807 0.380 0.000 0.616 -0.439 1.695 3.102 0.662 0.928 0.979 0.584 0.387 0.652 0.665 +1 0.299 1.251 -1.641 0.713 -0.331 0.875 -0.458 -1.640 0.000 1.323 -0.835 0.371 0.000 0.711 -1.060 1.310 2.548 1.053 -0.014 -0.401 3.102 2.266 1.302 0.991 0.546 0.773 0.889 0.779 +1 0.770 0.238 -1.724 0.857 -0.297 1.253 -0.041 0.474 1.087 1.185 1.875 1.483 0.000 1.244 -0.300 -0.849 0.000 1.078 0.390 -0.298 1.551 0.632 0.565 1.080 1.016 0.850 0.877 0.765 +1 0.729 -0.410 -0.092 0.613 -1.276 1.212 0.098 1.722 2.173 0.845 0.853 0.273 0.000 0.736 0.321 -0.329 0.000 0.539 -0.419 0.514 3.102 0.703 0.553 0.993 0.951 0.803 0.840 0.733 +1 1.380 1.749 1.237 0.261 -0.646 1.317 0.820 0.146 2.173 1.013 2.789 -1.036 0.000 0.890 1.811 -1.211 0.000 0.919 0.822 1.088 1.551 0.702 1.069 0.992 1.161 0.878 1.293 1.062 +0 1.212 -0.170 -0.364 0.344 -0.602 0.649 -1.609 0.900 0.000 1.054 -0.528 -0.579 2.215 1.369 -0.245 -1.438 0.000 2.053 0.221 0.656 3.102 1.092 1.030 0.986 1.039 1.313 0.977 0.859 +0 2.060 0.441 0.342 0.723 -0.460 1.045 0.925 -1.156 0.000 1.269 -0.149 0.971 2.215 0.617 0.134 -1.540 0.000 0.745 -0.585 -0.977 3.102 0.761 0.753 1.117 1.061 0.897 0.920 0.955 +1 0.897 2.121 0.592 1.129 1.328 1.486 0.322 -1.045 0.000 1.498 1.112 0.683 1.107 0.730 -1.578 -1.656 0.000 0.646 -1.104 -0.347 3.102 1.277 1.218 0.984 0.790 1.568 1.331 1.300 +1 0.911 1.825 1.033 0.276 0.217 0.779 0.936 -0.498 0.000 0.897 0.972 -1.069 0.000 1.146 1.040 1.347 2.548 1.413 0.870 0.427 1.551 0.875 1.004 0.999 0.625 0.719 0.818 0.741 +0 0.568 0.545 -1.368 3.152 -0.728 1.429 0.490 0.741 2.173 0.530 0.157 0.252 0.000 1.888 1.186 1.644 0.000 1.628 1.345 -0.333 3.102 0.962 1.251 1.012 1.860 1.635 1.343 1.246 +0 0.495 -0.522 0.831 0.778 -1.338 0.416 -1.958 -0.059 0.000 0.588 0.936 -1.432 1.107 0.561 0.428 -0.195 1.274 0.465 1.257 0.828 0.000 1.922 1.177 0.986 1.067 0.568 0.925 0.873 +1 2.287 -0.206 -0.855 1.496 0.296 1.100 0.074 0.650 0.000 0.942 0.181 -1.407 0.000 0.991 -0.509 1.578 2.548 0.374 -0.678 -1.486 0.000 1.023 0.933 2.208 1.345 1.049 0.985 0.934 +1 0.487 1.476 -0.178 0.848 -1.075 0.872 0.103 0.873 0.000 1.173 0.796 -0.840 2.215 1.040 0.547 1.395 0.000 1.247 0.393 0.039 3.102 0.886 0.907 0.981 0.624 0.800 0.918 0.798 +1 1.847 -0.658 -0.506 0.289 -1.092 1.252 -0.533 1.050 2.173 0.590 -1.709 1.563 0.000 0.356 -1.737 -1.208 0.000 0.644 -1.067 -1.351 3.102 0.743 0.921 0.989 0.586 0.867 0.917 0.763 +1 0.916 -1.572 1.325 0.251 -0.371 0.624 -1.203 -0.810 0.000 1.074 -0.130 1.187 2.215 1.870 -0.493 0.160 2.548 0.854 -1.943 -1.216 0.000 0.768 1.293 0.992 0.778 1.242 1.104 0.905 +1 0.632 1.011 -1.116 1.253 1.276 0.877 0.580 0.117 2.173 1.001 0.446 -0.349 0.000 1.070 0.747 -1.429 2.548 1.088 -0.787 1.509 0.000 1.703 1.239 1.028 1.034 1.196 0.965 0.906 +1 0.496 -0.645 -0.882 0.955 1.070 0.602 0.041 -1.731 0.000 1.136 -0.089 -0.037 2.215 0.682 1.005 0.572 2.548 0.638 0.757 -1.152 0.000 0.916 1.059 0.986 0.949 0.768 0.880 0.814 +0 1.619 0.557 -1.233 1.899 -0.925 2.482 0.130 0.869 1.087 1.486 0.724 -0.510 2.215 0.758 0.402 -0.067 0.000 0.550 1.708 -1.484 0.000 0.933 0.821 0.976 2.565 2.818 1.887 1.428 +1 0.610 -1.762 -0.635 0.832 0.164 0.988 -0.941 1.129 0.000 0.933 -0.445 -0.569 2.215 0.874 0.405 -1.210 0.000 0.700 2.340 -0.587 0.000 1.158 0.946 0.996 0.661 0.712 0.628 0.639 +0 0.951 1.868 -0.565 1.429 -1.374 1.180 0.888 0.859 2.173 0.590 -0.433 0.306 2.215 0.705 0.993 -1.305 0.000 0.960 1.256 0.216 0.000 0.910 1.003 1.075 1.473 1.065 1.261 0.995 +0 1.031 0.730 0.885 1.183 -0.781 0.457 1.358 -0.144 2.173 0.907 0.032 -1.634 2.215 0.618 0.518 -0.807 0.000 0.673 1.537 1.003 0.000 0.860 0.852 1.527 0.875 1.147 0.810 0.694 +1 0.801 -0.320 -0.450 0.124 -1.296 0.862 0.546 0.511 1.087 1.553 0.375 1.646 0.000 1.094 0.281 0.029 0.000 0.956 1.089 1.405 3.102 0.861 0.879 0.993 0.728 0.785 0.690 0.643 +1 0.748 1.009 -0.466 1.164 1.214 0.684 1.051 1.639 2.173 0.649 0.309 0.300 0.000 0.696 -0.611 -0.612 1.274 0.600 -2.133 0.054 0.000 1.630 1.009 1.290 0.807 1.141 1.121 1.083 +1 1.193 -1.403 -0.085 0.816 -1.139 0.949 -0.738 0.008 2.173 1.086 -1.325 -1.530 0.000 1.686 -2.134 0.954 0.000 0.816 -0.341 -0.807 3.102 1.944 1.422 1.111 0.870 0.643 1.204 1.008 +1 1.839 -1.074 1.122 0.806 1.498 1.029 -1.038 -0.306 2.173 0.822 1.729 -0.471 0.000 1.135 -0.985 -1.399 0.000 1.271 -1.652 0.831 0.000 1.349 0.839 1.001 1.404 0.705 0.919 0.855 +1 1.063 1.222 0.546 1.183 1.151 1.248 -0.357 0.025 0.000 1.352 0.337 -1.118 2.215 0.570 0.225 1.504 2.548 0.389 1.878 0.147 0.000 1.827 1.229 0.981 1.259 0.657 1.032 1.005 +1 1.111 -1.797 0.036 0.529 -0.940 0.751 -1.832 0.580 0.000 0.509 -1.418 1.428 2.215 1.410 -0.030 -1.366 2.548 0.694 -0.463 1.197 0.000 0.961 0.685 0.986 1.125 0.874 0.877 0.822 +1 0.436 1.636 1.156 0.631 -0.584 0.744 0.446 1.137 1.087 0.955 0.376 -1.404 1.107 0.816 0.640 -0.252 0.000 0.622 0.678 0.130 0.000 0.558 0.922 0.981 0.762 0.933 0.806 0.689 +1 1.699 0.749 0.180 0.391 1.545 0.379 -1.137 -0.713 2.173 1.090 0.368 -1.426 2.215 0.672 -0.754 0.404 0.000 0.488 -0.996 1.387 0.000 0.503 0.933 1.064 1.051 0.976 0.902 0.789 +1 0.712 1.437 -0.315 0.527 -1.501 0.490 -1.566 -0.437 1.087 0.450 -0.086 0.472 0.000 0.632 -0.536 1.711 2.548 0.972 0.194 1.352 0.000 0.630 0.962 0.992 1.195 0.740 1.485 1.142 +1 1.067 0.978 0.296 0.821 1.644 1.242 0.979 1.709 2.173 0.908 0.877 -0.734 0.000 0.726 1.093 0.830 2.548 1.254 -0.110 -0.301 0.000 0.919 0.947 1.216 1.010 0.853 0.951 0.848 +1 2.015 -0.442 -0.686 0.868 0.486 0.329 1.213 -1.220 0.000 1.093 0.182 1.307 2.215 0.429 -0.187 0.882 0.000 0.395 0.669 -0.501 0.000 0.828 0.740 1.596 0.855 0.595 0.861 0.756 +1 0.369 -1.946 -1.683 0.662 0.100 0.696 -1.520 -0.623 0.000 1.639 -0.705 1.190 0.000 1.087 -2.389 -0.667 0.000 1.033 -0.630 -1.279 3.102 0.897 0.920 0.980 0.587 0.720 0.815 0.735 +1 1.133 0.679 -1.641 1.875 -1.571 1.967 1.177 0.287 0.000 0.867 2.400 1.151 0.000 1.338 -0.202 -1.070 2.548 1.564 -1.309 -0.843 3.102 1.791 1.956 1.001 1.346 0.836 1.803 1.601 +1 1.279 -0.508 -1.397 0.827 -0.518 0.955 -0.014 -0.854 1.087 0.733 -1.671 0.266 0.000 0.906 -0.311 1.592 2.548 0.952 1.784 0.625 0.000 0.826 0.916 1.015 0.691 0.953 0.943 0.818 +0 0.593 1.520 0.195 0.986 -1.719 0.901 0.099 0.068 2.173 0.869 1.364 -0.191 0.000 1.478 -0.479 1.319 2.548 1.914 -0.832 -1.567 0.000 0.769 1.118 1.047 1.300 1.376 1.167 1.046 +1 0.806 0.343 0.691 1.028 -1.710 0.876 -0.303 0.237 2.173 0.912 1.447 -0.795 0.000 0.597 -0.293 -1.563 1.274 0.397 0.279 -1.466 0.000 0.653 0.749 1.047 0.979 0.900 0.866 0.789 +1 0.645 -1.552 0.848 0.814 -0.964 1.174 -1.072 -1.720 2.173 1.398 -0.661 -1.308 2.215 2.026 -0.015 0.443 0.000 2.718 0.270 -0.077 0.000 0.935 1.633 1.002 0.872 0.784 1.289 1.105 +1 0.862 -0.460 0.781 1.114 -0.301 0.960 -0.170 -1.666 0.000 1.435 -1.251 0.024 2.215 0.969 -1.776 1.203 0.000 0.823 -0.013 -0.717 3.102 1.978 1.305 1.123 0.867 0.902 1.168 0.998 +1 0.799 -0.496 1.631 0.505 1.055 2.129 -0.470 0.093 0.000 1.757 -0.395 -1.518 0.000 1.672 0.337 -1.476 2.548 1.597 0.961 0.797 0.000 0.937 0.959 0.980 0.732 0.636 1.127 1.001 +1 1.883 0.739 -1.420 1.654 0.888 0.328 0.103 -1.184 0.000 0.646 0.844 -0.333 0.000 1.210 -0.167 -0.383 1.274 1.231 -0.634 0.686 3.102 0.779 0.938 2.134 1.481 0.813 1.119 0.928 +0 1.042 -1.796 -1.740 0.744 -0.539 1.752 1.248 0.027 0.000 2.031 -1.125 1.676 2.215 0.516 1.901 -0.225 0.000 0.620 0.533 1.352 3.102 0.824 0.944 1.077 1.012 1.079 2.281 2.187 +1 1.763 1.357 -1.215 0.237 -0.008 0.816 1.278 0.268 2.173 0.394 0.314 0.241 0.000 0.540 2.243 1.471 0.000 0.847 0.376 1.437 1.551 1.109 0.881 0.989 0.683 0.854 0.753 0.699 +0 1.048 0.167 1.301 0.879 -0.834 0.753 0.035 -0.849 0.000 0.995 -0.663 -0.531 0.000 2.804 -1.293 1.050 0.000 1.763 -0.006 0.176 3.102 0.852 0.989 1.248 0.719 1.032 0.749 0.749 +0 0.772 -1.315 0.988 0.614 -0.389 1.371 -1.362 -0.232 2.173 0.884 -2.188 1.679 0.000 1.084 -2.520 -1.257 0.000 1.347 -0.988 1.448 3.102 0.815 0.912 0.983 0.891 1.442 1.169 0.942 +1 0.830 1.685 -0.317 1.009 0.743 0.922 -0.428 1.680 0.000 1.237 0.735 0.026 2.215 1.311 1.807 1.113 0.000 1.210 1.379 -1.370 0.000 0.807 0.943 1.035 0.855 0.754 0.954 1.042 +0 0.438 -0.581 -1.598 1.106 0.362 0.900 -0.164 -1.394 2.173 0.636 0.517 0.367 0.000 0.803 0.589 -1.005 2.548 0.736 0.008 1.362 0.000 0.735 0.980 0.989 0.747 0.572 0.683 0.647 +1 1.208 -1.938 0.301 0.617 1.694 0.651 0.087 0.301 1.087 0.737 -1.155 -1.454 0.000 0.841 -0.535 -1.140 2.548 0.465 -1.413 1.263 0.000 0.525 1.045 1.137 0.941 0.943 0.948 0.797 +0 1.009 0.489 -0.344 0.186 1.110 1.015 0.395 -1.268 1.087 0.755 0.124 0.525 1.107 0.407 -0.249 -0.562 0.000 0.779 -0.857 1.550 0.000 0.635 0.807 0.988 0.750 1.298 0.814 0.732 +0 0.332 -0.778 -0.149 1.561 1.368 0.991 -0.288 0.515 2.173 0.895 -0.173 -0.683 1.107 0.672 -0.894 -0.348 0.000 1.190 -2.350 -1.613 0.000 0.744 0.926 0.988 0.926 1.224 0.836 0.741 +1 0.363 1.356 1.085 1.256 0.275 1.007 0.365 -1.242 0.000 1.293 0.655 0.687 2.215 0.992 -0.142 -0.724 0.000 0.774 1.065 -1.575 3.102 0.941 0.766 0.986 0.672 0.853 0.945 0.859 +0 1.176 0.484 -1.026 1.331 -1.547 0.532 0.422 0.827 2.173 1.039 -1.007 1.358 1.107 1.281 -0.297 -0.239 0.000 1.843 0.669 -0.376 0.000 0.990 0.872 0.996 1.120 1.008 0.909 0.888 +1 1.259 -1.345 1.469 0.534 -0.900 1.459 -2.898 0.759 0.000 2.024 -1.099 -0.817 1.107 0.855 -0.624 1.630 0.000 2.036 -0.473 -0.282 3.102 3.096 2.726 0.990 1.076 0.998 2.072 1.571 +0 1.235 0.497 -0.416 0.199 0.552 0.635 1.995 -1.100 0.000 0.891 0.123 0.941 1.107 1.358 -1.445 0.873 0.000 0.998 0.956 -0.477 0.000 0.846 0.884 0.989 0.847 0.636 0.878 0.827 +1 0.910 1.750 -0.856 1.089 -1.368 0.842 0.083 1.142 2.173 0.472 0.358 0.473 2.215 0.292 1.583 -0.477 0.000 0.414 2.071 -0.488 0.000 0.914 0.811 0.976 1.151 0.543 1.279 1.083 +0 2.233 0.447 1.075 1.085 -0.234 2.101 -0.109 -0.154 0.000 1.976 -0.249 1.414 1.107 1.123 0.113 -0.847 0.000 2.001 0.377 -1.424 3.102 0.843 0.785 1.993 1.519 1.184 1.154 1.033 +1 0.355 -2.258 -0.727 0.916 1.169 1.029 -1.102 -0.624 2.173 0.416 -2.371 1.191 0.000 0.560 -0.656 -0.320 0.000 0.681 1.521 1.707 0.000 1.020 0.995 0.991 0.572 0.749 0.660 0.634 +0 0.312 1.518 -0.017 1.033 1.697 1.179 0.583 0.073 2.173 0.873 -0.603 -1.708 2.215 0.631 -1.421 1.234 0.000 0.601 0.526 -0.828 0.000 1.113 0.799 0.989 1.036 1.773 1.041 0.892 +0 1.134 0.540 0.211 0.764 0.174 1.170 -0.832 -1.057 2.173 1.225 -0.467 1.597 0.000 1.099 0.245 0.907 2.548 0.786 -0.703 0.014 0.000 1.286 0.988 0.983 1.345 1.611 1.048 0.962 +1 1.043 -0.499 1.052 0.746 0.285 0.667 -0.071 -1.052 2.173 0.662 2.284 -1.676 0.000 0.873 -0.518 0.084 1.274 1.043 -2.102 0.883 0.000 0.970 0.943 0.989 0.947 0.847 0.902 0.899 +1 0.409 1.029 -0.046 1.288 0.841 0.940 0.643 -1.218 0.000 1.298 0.371 0.025 2.215 0.804 1.194 -1.713 0.000 0.495 -0.927 1.021 3.102 0.855 0.979 0.987 0.797 0.820 0.941 0.859 +0 1.599 -0.018 0.014 0.778 -1.280 0.849 -0.839 1.739 2.173 1.350 0.311 -0.859 1.107 1.771 -0.740 0.658 0.000 0.551 -0.125 1.633 0.000 0.916 1.015 1.420 0.977 1.491 1.081 0.995 +0 1.974 -0.448 -0.526 1.002 -0.852 0.876 -0.634 1.166 2.173 0.415 -1.481 0.271 2.215 0.901 0.037 1.547 0.000 0.607 -0.573 0.268 0.000 0.804 0.710 0.987 1.401 0.759 0.983 0.820 +0 1.430 0.120 0.450 1.019 -1.307 0.893 -0.345 -1.481 1.087 1.210 0.138 -0.590 2.215 0.716 1.340 1.072 0.000 0.855 -0.465 1.272 0.000 1.047 1.110 1.673 1.103 1.165 0.946 0.904 +0 1.815 0.926 -0.662 0.132 -0.394 1.203 1.021 0.705 0.000 0.851 0.540 1.095 0.000 1.735 0.749 -1.074 2.548 0.630 0.138 1.638 3.102 0.900 0.753 0.979 0.686 0.579 0.938 0.915 +0 0.841 0.636 1.168 1.073 0.417 0.836 0.149 -1.182 0.000 0.829 -0.105 -1.723 2.215 0.891 1.469 0.202 0.000 0.968 0.309 -0.344 3.102 1.924 1.125 0.986 0.824 0.790 0.852 0.823 +1 1.313 -0.893 -1.258 0.920 -0.460 0.413 0.123 1.358 0.000 1.160 0.363 0.554 2.215 0.324 0.818 0.149 2.548 0.417 0.767 -0.727 0.000 0.684 0.812 1.003 0.767 0.289 0.863 0.698 +1 0.986 -0.412 -1.647 0.678 0.520 0.910 -0.711 0.528 0.000 1.273 0.507 -1.145 2.215 0.798 -0.469 -0.136 2.548 0.373 -0.080 -1.123 0.000 0.930 0.668 1.051 0.975 1.025 0.889 0.781 +1 0.382 -1.484 -0.178 0.268 -0.258 0.791 0.137 -1.506 1.087 1.132 -0.921 0.772 2.215 1.268 0.047 -0.749 0.000 1.064 0.386 0.755 0.000 1.279 1.041 0.983 0.876 1.466 0.989 0.828 +0 1.480 -1.407 -1.710 0.685 -0.874 0.381 0.035 -0.047 2.173 0.554 -1.361 0.097 2.215 0.509 0.565 1.204 0.000 0.724 -0.637 0.939 0.000 0.526 0.704 0.986 1.012 0.536 0.742 0.704 +1 1.057 -0.750 -1.238 1.709 1.552 0.723 1.124 0.143 2.173 0.590 1.808 0.371 0.000 0.488 -0.659 1.666 0.000 0.995 -0.540 -0.599 3.102 0.931 1.075 1.094 1.717 1.068 1.143 1.120 +1 0.673 -0.030 -0.051 1.008 -1.395 0.732 -0.725 0.806 0.000 0.882 0.367 -1.089 2.215 1.510 0.104 0.578 0.000 0.791 -0.843 -0.812 3.102 0.910 0.978 1.067 0.661 0.598 0.895 0.789 +0 0.509 2.377 -1.043 0.824 1.685 1.132 0.635 -0.696 2.173 1.184 0.222 0.997 2.215 1.313 -0.632 0.780 0.000 0.709 0.377 -0.125 0.000 1.008 0.850 0.997 1.015 1.738 1.069 1.045 +0 0.674 0.092 0.431 0.362 1.076 1.068 0.104 0.043 2.173 1.022 -1.504 -1.682 0.000 0.626 -0.494 0.535 2.548 1.326 -1.342 -0.858 0.000 1.027 0.969 0.986 0.922 0.551 1.123 0.919 +1 0.557 -0.410 -0.034 0.825 0.782 1.324 0.211 -0.554 2.173 0.481 -0.954 0.556 0.000 1.195 -0.279 1.252 0.000 1.071 -0.192 -1.481 1.551 0.792 0.722 0.987 1.453 0.976 1.008 0.919 +1 0.301 2.128 1.126 1.421 0.364 0.580 -0.401 -0.190 2.173 0.538 0.145 -1.080 0.000 1.145 -1.000 -1.047 2.548 0.936 -2.157 0.859 0.000 0.868 0.839 0.981 1.411 0.795 0.996 0.851 +0 1.192 -0.449 -1.693 1.264 -1.267 1.166 1.036 0.275 0.000 0.867 0.355 -0.236 0.000 1.510 0.639 1.220 1.274 0.895 0.680 -0.886 3.102 0.814 1.119 0.988 0.665 0.843 0.724 0.749 +0 0.750 1.800 -1.700 1.218 0.896 0.631 -0.063 0.765 2.173 0.960 0.526 -1.076 2.215 0.805 -0.068 0.109 0.000 0.411 0.958 -1.465 0.000 0.751 0.737 0.988 1.041 1.194 0.937 0.787 +1 0.705 -0.898 1.526 0.626 0.688 0.850 0.157 0.353 0.000 1.342 0.490 -1.238 2.215 0.843 -1.412 -0.721 0.000 0.681 0.196 1.248 3.102 0.678 0.604 0.995 0.993 0.686 0.826 0.735 +1 0.661 -2.283 -0.198 1.152 -0.090 1.541 -1.150 -1.728 2.173 0.944 -1.168 0.323 0.000 0.884 -0.314 0.595 2.548 0.903 -0.209 -1.041 0.000 1.127 0.778 0.996 1.496 1.399 1.094 0.936 +0 2.199 1.614 -0.241 1.571 -0.502 0.936 -0.010 1.277 2.173 0.913 1.460 1.511 0.000 0.798 1.263 -1.424 2.548 0.822 0.400 0.860 0.000 0.869 0.968 0.993 0.934 1.073 1.298 1.108 +1 1.191 0.989 0.518 1.176 1.150 0.413 -0.268 1.063 0.000 1.446 -0.464 -0.603 2.215 0.375 -0.248 -0.992 2.548 0.940 0.615 -1.255 0.000 0.966 1.125 0.990 0.825 0.281 1.133 0.941 +1 1.050 -1.317 -0.205 0.725 0.486 0.455 -0.260 0.756 0.000 1.160 -0.952 1.435 1.107 0.314 -0.962 0.007 0.000 0.669 1.544 -0.734 0.000 0.813 1.333 0.992 1.220 1.060 1.010 1.092 +0 0.580 -1.170 0.179 3.078 -0.363 1.778 -0.790 1.458 1.087 0.988 -1.800 1.164 0.000 0.672 -0.779 -0.365 1.274 0.875 -1.647 -0.785 0.000 1.193 0.938 0.985 2.092 1.358 1.323 1.211 +1 1.011 -1.861 -0.189 1.571 -0.818 1.393 -0.656 -1.475 2.173 1.303 1.281 0.233 2.215 1.160 -2.356 0.562 0.000 2.296 -0.989 1.227 0.000 0.947 1.736 0.989 2.881 3.037 2.301 1.989 +0 0.720 0.723 -0.954 0.482 -0.483 0.471 0.969 0.947 2.173 0.605 0.106 -1.387 2.215 0.400 -0.362 0.493 0.000 0.714 0.703 0.242 0.000 0.591 0.630 0.992 0.815 0.763 0.696 0.642 +1 0.561 -0.420 1.258 0.147 -0.322 0.740 0.494 -1.164 0.000 1.141 0.799 -0.712 0.000 0.679 0.136 0.328 1.274 0.663 -1.141 1.570 3.102 0.836 0.937 0.991 0.754 0.629 0.806 0.724 +1 1.002 0.494 0.151 1.539 0.876 1.007 1.178 -0.989 1.087 0.755 0.040 1.274 0.000 0.646 2.427 -0.058 0.000 1.261 0.179 -1.550 3.102 2.097 1.403 1.047 1.371 0.836 1.051 1.032 +1 0.883 0.562 1.699 1.298 1.200 1.130 -0.247 -0.472 2.173 0.774 -0.236 0.480 0.000 0.961 0.332 -1.219 1.274 0.435 -0.170 -1.701 0.000 0.698 0.934 0.989 0.838 0.905 1.117 0.937 +0 0.903 1.260 -1.738 0.151 1.045 0.684 -0.091 0.408 0.000 0.776 -0.095 -0.429 2.215 0.458 -1.085 0.819 0.000 0.377 -2.003 -1.166 0.000 0.686 0.829 0.991 0.842 0.623 0.636 0.686 +1 2.631 1.183 0.485 0.401 -0.144 0.981 0.545 1.675 2.173 1.456 -1.294 -1.231 0.000 0.747 0.623 -0.938 0.000 0.892 0.712 0.207 3.102 0.855 1.026 0.994 0.488 0.972 0.887 0.779 +0 0.402 -1.692 0.078 0.632 -1.720 0.785 -1.067 -0.747 0.000 0.898 0.773 0.695 2.215 1.291 0.563 1.532 2.548 0.542 2.256 0.179 0.000 0.893 0.899 0.995 0.940 0.790 0.758 0.807 +1 0.828 -0.961 -0.164 1.830 -1.374 1.110 -0.991 0.660 1.087 1.310 0.073 -1.368 2.215 0.786 -1.699 0.160 0.000 0.438 1.429 0.048 0.000 1.888 1.441 1.511 1.143 1.987 1.280 1.174 +0 1.285 -1.618 -0.050 0.488 -1.005 0.748 -1.306 -0.923 0.000 1.028 1.738 1.213 0.000 1.776 1.136 1.594 2.548 1.381 -0.005 0.220 3.102 4.556 2.569 0.988 2.740 1.386 1.788 2.032 +1 0.982 1.026 0.656 1.963 -1.379 0.553 0.280 0.942 2.173 0.898 2.721 -0.860 0.000 0.676 -0.067 -0.614 0.000 0.800 -0.612 -1.471 3.102 2.389 1.892 1.858 1.169 0.689 1.346 1.199 +1 1.091 1.280 1.624 1.175 -1.139 0.951 0.962 0.185 2.173 0.955 2.604 0.895 0.000 0.707 0.810 -1.679 0.000 1.522 0.267 -0.433 3.102 1.007 1.012 0.989 1.234 0.795 0.944 0.833 +1 0.966 0.273 0.402 1.043 1.419 1.141 1.634 1.460 2.173 1.050 -0.243 -0.726 0.000 1.049 1.112 0.145 0.000 0.630 1.141 -0.288 3.102 1.044 1.177 1.103 0.724 0.906 0.847 0.822 +0 0.448 1.679 1.504 1.756 -0.115 0.888 -0.269 -1.070 0.000 0.901 -0.260 0.855 2.215 0.306 0.844 0.935 2.548 0.443 -1.456 -1.329 0.000 0.804 1.111 1.221 0.656 0.356 0.872 1.086 +0 0.879 -0.948 0.250 0.758 1.048 1.244 0.589 -1.733 1.087 1.253 -0.218 -0.373 0.000 0.380 0.778 -0.974 0.000 0.706 -0.042 0.226 3.102 0.811 0.576 0.981 1.862 1.026 1.188 1.077 +0 1.412 -0.519 0.589 0.874 -0.316 0.785 0.400 -0.827 0.000 1.766 -0.403 1.612 2.215 0.455 0.529 -0.173 1.274 0.594 1.253 0.268 0.000 1.059 0.620 1.120 1.295 1.073 0.940 0.938 +1 1.307 0.127 0.223 0.472 1.399 1.694 -2.066 0.511 0.000 2.344 0.364 -1.028 2.215 2.016 0.062 -1.515 2.548 0.932 -0.255 1.629 0.000 0.659 0.908 0.985 1.061 1.043 1.034 0.810 +1 0.994 0.562 1.612 0.748 0.186 0.504 0.049 -0.551 2.173 1.022 -0.060 -1.419 0.000 0.479 2.088 -0.185 0.000 1.839 0.059 0.436 3.102 0.768 0.965 1.146 0.792 0.791 0.793 0.721 +0 0.398 0.529 -0.313 5.039 -0.763 1.475 0.033 0.790 0.000 1.848 -0.333 1.128 0.000 1.571 1.478 -1.056 2.548 2.096 0.345 1.138 3.102 1.190 0.909 0.982 0.811 1.555 1.276 1.516 +1 1.436 0.204 0.466 1.121 1.171 1.136 0.153 -1.441 1.087 1.030 1.459 0.235 0.000 1.047 -0.051 -0.516 0.000 0.736 -0.404 -1.238 3.102 0.994 1.067 1.043 0.797 0.361 0.845 0.823 +0 0.423 -0.607 -0.638 0.273 1.480 0.721 -0.608 0.209 2.173 0.977 -0.514 -1.727 2.215 0.690 0.439 0.899 0.000 0.966 1.936 1.378 0.000 1.024 1.432 0.988 0.909 1.217 1.192 0.957 +1 1.279 1.695 0.754 0.976 -1.491 0.699 -1.401 1.398 0.000 0.778 -0.389 -0.673 2.215 0.887 0.547 -1.413 0.000 2.165 0.802 0.136 1.551 0.527 0.912 1.393 1.513 1.162 1.125 0.906 +0 1.500 -1.729 -0.283 0.019 -0.582 0.464 -0.285 -1.665 0.000 0.895 0.072 1.251 2.215 0.572 -1.110 0.721 0.000 1.075 0.147 -0.737 3.102 0.895 0.792 0.998 1.217 0.865 0.892 0.774 +0 1.405 -0.187 1.623 1.148 0.628 0.562 1.633 -1.131 0.000 0.764 1.009 0.257 2.215 0.606 0.696 1.296 0.000 1.730 -0.361 -0.255 1.551 0.981 0.928 1.374 1.078 0.954 0.903 0.960 +0 0.960 -0.237 1.295 0.514 0.684 0.414 0.725 1.453 0.000 0.805 -1.280 -0.623 0.000 1.114 -0.283 -0.315 2.548 0.631 -0.389 0.667 1.551 0.952 0.890 0.979 0.502 0.498 0.568 0.597 +1 0.524 0.870 1.305 1.065 -0.394 2.409 -0.012 -1.642 1.087 3.340 1.512 0.178 0.000 0.686 0.325 -0.480 0.000 1.006 -0.100 0.677 0.000 0.822 0.713 1.034 1.519 0.829 0.971 0.888 +1 1.047 -0.444 -0.637 0.346 0.503 0.951 -0.460 0.555 0.000 0.835 -0.422 1.266 0.000 0.888 0.164 -0.336 2.548 2.631 0.261 -1.469 3.102 1.130 1.077 0.988 1.110 0.998 1.022 0.918 +1 0.649 -1.028 0.278 0.624 -1.366 0.968 -0.061 0.972 0.000 0.731 0.831 -0.275 2.215 0.751 -0.307 -1.193 0.000 0.589 -0.952 -1.349 0.000 1.196 0.874 0.990 1.242 0.697 0.790 0.825 +0 1.609 -0.811 -1.614 0.527 -0.440 0.894 -0.232 1.216 2.173 0.788 -0.564 -0.418 0.000 0.465 0.111 0.677 1.274 1.448 -1.666 -0.272 0.000 1.089 0.938 1.112 0.960 0.403 0.947 0.865 +0 0.633 -0.569 -1.082 0.861 -0.066 0.607 1.311 -1.466 2.173 0.436 1.565 -0.016 0.000 1.002 -0.084 1.031 1.274 0.724 -1.265 -0.342 0.000 1.079 0.804 0.991 1.530 1.062 1.078 0.923 +1 2.545 1.411 -1.622 0.611 1.254 2.032 -0.686 0.063 0.000 1.202 -0.022 -1.354 2.215 0.902 1.518 1.417 0.000 1.004 0.650 -0.342 3.102 0.856 1.004 0.984 0.973 0.884 0.985 0.828 +1 2.268 1.403 -0.518 0.437 -0.299 1.423 -1.183 1.099 0.000 0.682 1.323 -0.856 0.000 0.839 -0.139 -0.911 2.548 0.770 -0.330 0.449 3.102 0.926 0.833 0.992 0.877 0.583 0.746 0.689 +1 0.522 -1.186 0.970 1.017 -1.117 1.086 2.217 -0.099 0.000 2.026 -0.854 1.623 1.107 0.789 0.406 0.328 0.000 0.914 -0.982 0.442 0.000 0.853 0.752 0.986 0.936 0.843 0.881 0.803 +0 0.844 0.260 -0.758 0.919 1.612 0.726 2.215 -0.165 0.000 0.581 -0.146 0.499 0.000 0.656 1.367 0.538 0.000 1.443 -0.281 1.558 3.102 0.875 0.943 1.030 0.574 0.665 0.667 0.636 +0 1.722 -0.697 1.243 0.834 1.258 0.765 -0.485 0.585 2.173 1.837 0.156 -0.913 2.215 0.891 0.788 -0.280 0.000 0.482 -1.043 -0.238 0.000 0.917 0.956 0.993 0.898 1.797 1.348 1.145 +0 0.772 0.305 0.990 1.195 -0.510 1.097 -1.251 0.818 2.173 0.745 -0.892 -0.527 0.000 0.881 0.495 1.720 0.000 1.018 0.602 -1.080 0.000 0.693 0.892 1.299 1.456 1.639 1.078 0.953 +0 0.688 0.216 1.378 0.392 1.109 0.543 -0.854 -0.680 0.000 0.930 -0.549 1.728 2.215 0.844 -0.523 -0.175 2.548 0.655 -1.196 0.413 0.000 0.804 0.893 0.989 0.743 0.932 0.627 0.617 +1 1.047 -0.745 -0.368 1.504 -1.445 1.159 -0.826 1.220 2.173 1.018 -0.507 0.464 0.000 0.524 -2.457 -1.289 0.000 0.509 -1.017 -0.665 0.000 0.865 1.024 1.434 0.858 0.593 0.854 0.785 +0 0.849 0.632 0.655 0.427 -0.225 0.812 -0.749 0.041 0.000 1.112 1.177 -1.698 2.215 1.222 -1.500 -0.706 0.000 0.610 -1.398 0.818 3.102 1.132 0.754 0.994 1.087 1.596 1.430 1.162 +0 0.629 0.004 1.257 1.422 0.356 0.708 -0.664 -1.682 2.173 0.909 1.207 -1.668 0.000 1.006 0.713 -0.157 2.548 0.768 1.084 0.757 0.000 0.889 0.918 0.987 0.949 1.326 0.951 0.915 +1 0.473 -1.476 -1.269 1.489 0.857 0.538 -0.103 -1.303 0.000 0.854 0.777 -0.176 2.215 0.549 0.883 1.105 2.548 0.531 -0.308 0.855 0.000 0.767 0.890 1.094 1.096 0.668 1.092 0.891 +1 1.677 0.129 0.400 0.886 1.061 0.808 -0.882 -1.385 2.173 0.703 -0.689 0.385 0.000 0.917 1.251 -0.693 0.000 1.389 -0.369 -0.570 0.000 0.991 1.077 0.986 0.976 0.949 1.002 0.871 +1 0.403 -1.524 -0.423 0.179 0.134 0.722 -0.265 -1.027 1.087 0.855 -1.027 0.768 0.000 1.299 -0.509 1.611 2.548 1.569 -0.866 0.115 0.000 0.837 1.048 0.984 0.708 0.852 0.893 0.771 +0 0.766 1.705 1.333 1.245 0.465 1.304 0.965 -0.952 2.173 0.759 0.528 0.963 0.000 0.747 -0.319 0.129 2.548 0.500 0.209 1.525 0.000 0.406 1.184 0.991 1.213 1.343 1.194 0.983 +0 1.471 -0.052 0.010 0.417 -1.663 0.795 -0.381 0.576 0.000 1.033 0.571 -1.097 0.000 0.881 -0.509 -1.680 1.274 0.745 -0.209 1.351 3.102 0.903 0.816 1.082 0.668 0.265 0.561 0.609 +0 0.924 0.804 0.949 2.844 0.574 1.006 0.319 -1.077 0.000 0.756 -0.525 -1.423 2.215 1.035 0.808 -0.667 0.000 0.542 1.319 1.316 3.102 0.847 0.921 0.991 0.594 0.819 1.064 1.133 +0 2.039 -0.500 1.510 2.119 1.485 2.993 -0.704 1.712 2.173 3.991 -0.281 -0.192 0.000 1.751 -1.763 0.714 0.000 0.574 0.619 -1.358 0.000 2.015 1.163 0.974 1.157 1.955 2.211 1.913 +0 0.559 -1.689 -0.223 1.257 -0.142 0.458 0.998 0.525 0.000 0.379 0.721 0.878 2.215 1.139 -0.842 -1.510 0.000 0.514 1.216 -1.728 0.000 0.685 0.622 0.984 0.823 0.506 0.651 0.691 +1 1.779 -0.989 -0.983 0.410 -0.168 0.828 -0.107 1.078 2.173 0.347 -1.834 0.879 0.000 0.378 0.032 -0.703 2.548 0.626 -1.652 -0.074 0.000 0.460 0.963 0.991 0.512 0.699 0.758 0.711 +1 0.531 1.348 1.628 1.125 0.808 0.575 1.047 -0.075 0.000 1.004 0.596 -0.642 0.000 1.638 0.311 -1.616 2.548 1.627 0.952 1.172 3.102 0.868 1.144 0.985 0.835 0.895 0.936 0.832 +1 2.287 -0.327 1.010 0.560 0.648 0.492 -0.601 -0.038 0.000 0.697 -1.186 -0.752 2.215 0.822 2.214 -1.395 0.000 1.644 -0.489 -1.352 3.102 0.746 0.715 0.986 1.206 0.587 0.933 0.770 +0 0.757 -1.171 1.622 1.045 0.412 0.508 -0.373 -1.306 0.000 0.857 -1.222 0.699 2.215 0.973 0.306 -0.470 1.274 0.946 -1.543 -1.245 0.000 0.857 0.992 1.092 0.631 1.203 0.860 0.789 +0 1.840 0.130 0.400 0.929 0.338 1.292 -0.661 -1.251 2.173 0.771 -0.334 -0.309 0.000 0.740 -1.042 -1.620 0.000 1.245 -0.175 1.460 3.102 1.172 1.039 0.984 0.888 0.916 1.112 0.955 +1 0.529 -0.260 -0.615 0.935 0.768 0.845 -0.527 0.597 0.000 0.934 0.364 -1.099 0.000 0.620 -1.135 0.846 0.000 0.724 1.184 -1.373 3.102 0.556 1.163 0.990 0.499 0.325 0.696 0.625 +1 0.670 -1.182 -1.325 0.915 1.046 0.571 -0.138 1.443 2.173 0.856 -2.268 -0.118 0.000 0.819 -0.315 -0.330 2.548 0.486 -1.855 -0.628 0.000 0.954 1.052 0.986 0.624 0.856 0.972 0.838 +0 0.413 0.291 0.041 1.193 -0.366 0.434 -0.542 0.367 1.087 0.707 -0.193 1.132 0.000 0.737 -0.859 -1.639 2.548 1.140 0.025 -1.675 0.000 0.685 0.731 0.992 0.765 0.699 0.586 0.652 +1 0.801 1.721 0.108 0.865 -1.604 0.636 -0.492 1.591 2.173 0.638 0.819 -0.945 0.000 0.720 -0.154 0.643 0.000 0.770 0.664 0.238 3.102 0.879 0.680 1.153 1.368 0.865 0.905 0.837 +1 0.725 -1.768 0.455 0.344 -0.315 1.074 -1.423 -0.064 2.173 1.658 -0.936 -1.447 2.215 0.963 -2.331 1.379 0.000 0.517 -0.513 0.694 0.000 0.990 1.196 0.982 1.066 1.919 1.141 0.947 +1 0.554 0.461 -0.626 1.569 1.021 0.508 0.505 -0.987 0.000 0.594 -0.149 -0.880 0.000 0.738 -0.986 0.728 2.548 1.126 0.326 1.213 3.102 0.923 0.957 1.287 0.669 0.634 0.666 0.670 +0 1.347 0.691 -0.587 0.128 -1.020 0.494 -0.152 0.568 1.087 1.015 -0.255 -0.891 0.000 1.171 0.323 -1.648 2.548 2.220 0.885 1.045 0.000 1.224 1.012 0.979 0.797 0.896 0.796 0.710 +1 0.619 -0.955 1.499 2.304 0.646 1.729 -1.755 -0.690 0.000 1.276 0.434 1.140 2.215 0.368 0.144 -1.174 2.548 0.711 0.580 -0.391 0.000 0.183 0.823 1.150 0.833 0.643 0.875 0.788 +0 3.324 -0.673 -0.990 0.444 0.717 2.085 -0.158 1.118 0.000 0.854 2.388 0.184 0.000 0.963 -0.667 -0.377 0.000 0.930 0.078 -1.483 0.000 0.982 0.738 1.683 0.935 0.325 0.728 0.655 +1 0.506 -0.169 0.456 1.688 1.549 2.896 -1.339 1.687 0.000 3.969 -0.389 -0.054 1.107 0.818 -1.375 -0.081 0.000 1.053 -0.051 -0.597 1.551 2.775 2.042 1.067 1.908 0.923 2.424 1.903 +0 1.529 0.612 1.162 0.925 -1.310 1.713 -0.112 1.467 2.173 2.798 -1.190 -0.305 0.000 1.462 -0.076 -0.480 2.548 1.831 -1.483 -0.042 0.000 0.873 1.127 1.306 1.092 1.938 1.782 1.668 +1 0.609 0.022 -0.221 1.247 -1.435 0.511 1.060 0.783 1.087 0.284 -0.889 0.960 0.000 0.551 1.712 -0.446 0.000 0.523 -1.789 0.760 0.000 1.290 0.907 1.072 0.607 0.793 0.661 0.667 +0 0.754 -0.922 -0.779 1.369 1.696 1.165 1.732 -0.227 0.000 0.892 -0.422 1.617 2.215 1.055 -1.325 0.714 2.548 0.899 -0.145 0.297 0.000 0.998 1.901 1.113 0.902 0.930 1.754 1.595 +1 0.801 -1.643 -1.022 0.428 -0.115 0.465 -2.446 -0.140 0.000 0.826 -1.072 1.146 2.215 0.969 -1.933 1.421 0.000 0.889 -2.385 -1.282 0.000 0.864 0.947 0.992 0.813 0.298 0.757 0.724 +1 0.558 1.595 -0.901 1.174 -0.242 0.695 0.478 0.127 0.000 1.061 -0.591 1.006 2.215 0.662 0.612 -1.430 1.274 1.482 0.055 0.898 0.000 0.909 1.069 0.982 0.965 0.946 1.571 1.237 +1 1.571 0.860 -1.020 1.754 1.663 1.193 0.637 1.047 2.173 0.600 -0.436 0.128 0.000 0.953 0.845 -0.632 0.000 0.860 0.923 -0.204 3.102 0.805 0.820 1.522 0.964 1.001 0.980 0.833 +1 0.401 2.244 -1.036 0.387 -0.595 1.097 1.629 1.600 2.173 1.537 0.234 0.146 2.215 1.315 0.016 -1.606 0.000 0.947 1.132 -0.032 0.000 0.371 0.944 0.979 0.978 2.350 1.169 0.944 +0 2.150 -0.120 -0.014 0.601 -0.461 0.843 0.469 1.338 0.000 0.786 0.436 -0.754 2.215 1.344 0.833 -1.252 2.548 1.946 1.240 1.607 0.000 1.137 1.043 0.975 0.834 0.539 0.890 1.135 +0 0.921 0.714 0.347 1.167 1.464 0.900 0.951 -1.423 2.173 0.911 1.700 -0.067 0.000 0.506 2.066 1.493 0.000 0.694 0.645 0.102 3.102 1.062 1.204 1.214 0.666 0.824 0.759 0.766 +0 0.816 -0.033 1.174 0.643 1.055 1.187 -1.315 -0.455 0.000 1.308 -1.020 -1.624 2.215 1.318 -0.503 -0.418 1.274 0.671 -1.267 0.975 0.000 0.809 0.927 0.989 1.174 1.284 1.154 0.928 +0 1.738 -0.994 -1.384 2.604 -1.554 1.229 -1.332 0.369 2.173 0.791 -1.036 0.747 0.000 1.634 -1.641 0.059 0.000 1.970 -0.542 -0.926 3.102 1.207 0.827 0.975 1.002 1.623 1.374 1.288 +0 0.720 2.012 -1.469 0.695 -1.509 1.025 0.928 0.025 2.173 0.618 0.574 -0.727 2.215 1.040 0.832 1.399 0.000 1.053 1.524 1.587 0.000 1.011 1.048 0.983 0.685 0.762 0.784 0.743 +1 0.861 -0.006 -1.528 2.018 1.669 1.027 -0.340 0.471 1.087 0.341 -0.258 -1.085 0.000 0.662 0.621 -0.535 2.548 1.117 -1.014 0.134 0.000 0.819 0.845 0.977 0.835 0.978 1.058 0.958 +0 0.882 0.672 -0.704 0.539 1.024 0.396 2.133 1.030 0.000 0.648 -1.635 -0.751 0.000 0.641 0.322 -0.130 2.548 0.936 -1.002 1.455 0.000 0.909 0.751 0.986 0.705 0.443 0.512 0.570 +1 1.666 -0.272 -1.441 0.830 1.219 0.494 -1.346 0.426 2.173 0.650 -1.086 -1.006 0.000 0.799 0.184 0.125 2.548 0.689 -0.563 0.202 0.000 0.796 0.736 1.103 1.043 0.707 0.805 0.712 +0 0.630 -1.064 -0.570 0.612 1.687 0.523 -0.144 1.378 2.173 0.782 0.246 -0.064 0.000 0.782 0.693 0.677 0.000 1.022 0.964 -1.021 3.102 0.804 0.871 0.989 0.762 0.839 0.677 0.665 +0 0.517 -0.170 0.380 0.472 -1.000 1.124 -0.353 1.526 2.173 1.623 0.311 0.243 2.215 1.601 -0.342 -0.053 0.000 1.413 -1.872 -1.250 0.000 1.045 0.879 0.984 0.983 1.945 1.118 0.957 +0 0.385 0.705 1.303 1.534 0.256 1.033 0.751 1.573 2.173 1.228 0.430 -0.227 2.215 1.601 0.740 -1.213 0.000 1.317 -0.229 1.231 0.000 1.584 1.153 0.986 0.941 1.675 1.085 1.044 +1 0.794 0.715 1.444 0.500 -0.958 0.912 0.779 -0.365 1.087 1.066 0.237 0.747 2.215 1.990 -1.218 -1.018 0.000 1.854 1.217 0.921 0.000 0.817 1.023 0.988 0.949 1.286 0.911 0.803 +1 2.172 0.379 0.295 0.618 0.826 1.011 1.058 -1.160 1.087 0.997 0.915 1.040 2.215 1.002 -1.083 -0.797 0.000 1.070 0.184 -1.576 0.000 1.144 1.435 0.985 1.511 1.357 1.164 1.138 +1 1.744 -1.480 0.661 0.889 -0.443 1.301 -2.118 -1.511 0.000 1.756 1.506 0.675 0.000 1.648 -1.363 -1.076 2.548 1.030 -0.538 -0.445 0.000 1.097 0.926 1.447 0.719 0.761 0.789 0.855 +1 0.748 1.087 -1.668 0.021 0.322 0.524 1.945 0.958 0.000 0.776 0.106 -1.249 2.215 0.834 0.624 -0.197 1.274 1.300 1.421 0.122 0.000 0.878 0.840 0.690 0.490 0.738 0.848 0.714 +1 1.617 0.979 -1.741 1.253 -1.586 3.405 -1.444 0.365 2.173 1.975 1.107 -1.166 0.000 1.295 -0.045 -0.804 2.548 1.548 1.527 1.626 0.000 1.558 1.539 1.006 5.880 3.028 3.975 3.496 +0 1.477 -0.002 0.347 0.880 -1.315 1.873 0.939 -1.655 2.173 1.982 0.217 -0.102 2.215 0.516 0.721 0.250 0.000 0.672 0.521 0.894 0.000 0.576 1.008 1.575 1.154 2.985 1.591 1.149 +1 0.982 -2.058 -0.774 0.973 -1.436 0.958 -0.267 1.530 0.000 0.853 -1.245 0.380 0.000 1.048 0.263 -0.489 2.548 0.706 -1.503 -1.146 0.000 1.027 0.880 0.993 1.183 0.931 1.002 0.879 +1 1.477 0.231 1.666 0.759 -1.208 0.697 -0.795 0.604 2.173 0.620 -0.667 -0.180 0.000 0.873 -1.018 -0.486 2.548 0.614 -1.329 1.188 0.000 0.847 0.684 0.984 1.114 0.823 0.989 0.858 +1 0.690 -0.281 -1.113 0.448 -0.466 1.039 1.168 0.555 2.173 0.968 0.857 1.148 2.215 1.002 0.729 -1.465 0.000 0.865 -1.735 -0.748 0.000 0.974 1.028 0.994 0.881 0.783 0.819 0.741 +1 1.136 0.062 -0.641 0.160 0.421 1.112 0.948 1.643 0.000 0.890 -1.225 1.085 1.107 0.821 0.140 0.256 0.000 1.796 0.486 -0.225 0.000 0.633 0.678 0.986 1.235 0.737 0.834 0.783 +1 0.691 0.721 -1.738 0.830 -0.919 2.469 0.751 0.581 0.000 1.873 0.557 -1.002 0.000 1.120 0.660 -1.378 1.274 1.560 1.375 1.727 3.102 1.700 1.740 0.989 0.621 0.592 1.268 1.167 +0 0.849 -1.216 1.586 2.412 -1.307 1.565 -0.936 0.083 0.000 0.956 -0.382 1.445 2.215 0.693 2.635 -0.690 0.000 0.983 -1.383 0.564 0.000 1.055 0.932 1.011 0.947 0.484 0.911 1.086 +0 1.220 -0.315 0.096 2.332 0.648 2.569 0.952 -1.323 2.173 0.862 -0.556 1.176 2.215 0.453 1.568 -0.142 0.000 0.464 -1.440 0.170 0.000 1.384 1.116 1.117 2.930 2.512 1.994 1.579 +1 1.451 -0.627 1.115 1.625 1.591 1.349 -0.182 -0.080 1.087 0.888 0.364 -0.970 2.215 0.417 -0.787 -1.001 0.000 0.517 -0.209 -1.525 0.000 0.282 0.818 0.986 1.293 1.244 1.310 0.953 +0 0.664 1.185 -0.422 1.481 -1.207 1.031 1.249 0.369 2.173 0.544 1.675 1.094 0.000 0.516 1.502 -0.633 0.000 1.680 -0.411 1.696 3.102 0.813 0.825 0.990 1.006 1.896 1.135 0.960 +0 1.043 0.372 -0.267 0.310 0.072 0.619 -0.301 0.649 0.000 0.553 0.387 -0.545 2.215 0.678 2.457 1.286 0.000 1.260 0.880 -1.509 3.102 0.882 0.834 0.996 0.579 0.628 0.596 0.650 +1 0.391 1.039 1.704 0.533 -1.271 1.051 0.932 0.418 0.000 1.285 -0.727 0.382 0.000 1.198 0.517 1.507 2.548 2.418 1.054 -1.001 1.551 0.778 1.044 0.977 1.084 1.106 0.957 0.917 +1 0.927 -1.161 1.473 1.308 -1.306 1.304 -1.396 0.051 2.173 1.694 -0.536 -1.449 0.000 2.041 -0.964 0.478 2.548 0.552 -1.847 0.517 0.000 1.013 1.733 0.990 1.380 0.843 1.414 1.241 +0 2.127 -0.129 0.684 2.429 0.732 3.401 -0.736 -0.819 1.087 4.316 -0.465 0.838 0.000 0.704 -2.230 1.401 0.000 0.506 -0.891 1.672 1.551 3.346 1.795 0.998 3.206 1.108 2.564 2.174 +0 1.605 0.629 -1.432 0.182 -0.363 0.458 1.947 1.285 0.000 1.246 -0.400 -0.050 1.107 0.748 0.302 0.453 0.000 0.686 -0.031 -1.728 3.102 1.197 0.863 0.986 1.305 0.848 0.952 0.896 +0 0.469 -0.238 -1.226 1.624 1.343 0.399 -0.451 0.614 1.087 0.544 0.182 0.110 0.000 1.182 0.726 -0.970 0.000 0.735 -0.546 -0.170 3.102 1.086 0.932 0.987 0.785 0.377 0.597 0.671 +1 0.656 0.643 1.658 0.385 -0.191 1.029 -0.732 -0.989 1.087 1.102 -0.426 0.943 0.000 0.771 -0.553 0.397 0.000 0.592 -1.219 0.749 0.000 0.679 0.604 0.991 1.483 0.653 0.952 0.968 +0 0.383 1.191 1.183 1.646 -1.455 0.734 0.222 0.737 2.173 0.790 -0.841 0.192 2.215 1.241 -0.794 -0.722 0.000 0.551 -1.286 -1.725 0.000 0.814 0.881 0.984 2.127 0.832 1.514 1.317 +1 2.841 0.463 -1.484 0.259 0.488 0.834 1.575 -0.038 2.173 0.608 1.416 0.777 0.000 0.376 0.169 -0.359 0.000 0.703 0.060 0.977 3.102 1.013 0.990 1.164 0.751 0.933 0.990 0.875 +0 1.423 -0.578 1.443 0.463 -0.952 1.136 1.436 0.083 0.000 0.708 -1.394 -1.078 0.000 0.667 0.009 0.782 2.548 0.780 1.126 -1.093 0.000 0.808 0.631 0.986 0.675 0.531 0.522 0.550 +1 0.736 -0.884 -1.632 0.530 0.101 1.294 -0.024 -0.928 2.173 0.860 0.057 0.589 0.000 1.158 -0.599 1.088 1.274 0.486 1.858 0.227 0.000 1.138 1.106 0.989 0.895 1.558 1.134 0.926 +1 0.793 -0.120 0.990 2.447 0.409 0.750 0.916 -0.711 2.173 0.486 1.452 -1.697 0.000 0.620 0.689 1.494 2.548 1.253 2.151 -1.475 0.000 0.869 0.729 0.988 0.881 0.780 1.000 0.880 +1 1.013 -0.437 -1.047 0.905 0.585 1.257 0.083 1.734 2.173 1.329 -0.690 -0.305 0.000 0.878 -0.125 0.820 0.000 0.971 -0.693 0.338 3.102 1.486 0.871 1.320 1.122 1.244 1.064 0.913 +1 1.119 -0.804 0.620 1.092 -0.100 0.924 0.228 -1.687 1.087 0.561 1.888 -1.183 0.000 0.314 -1.223 -0.359 0.000 1.009 0.563 -0.729 0.000 0.896 1.057 0.981 1.400 0.801 0.951 1.195 +1 1.152 -0.448 -0.949 1.240 -1.623 0.702 -0.169 0.634 2.173 0.838 -1.033 -0.071 2.215 0.580 -0.581 -0.467 0.000 0.802 0.094 1.236 0.000 0.953 1.009 0.986 0.965 0.848 0.874 0.788 +0 1.299 -1.578 0.576 0.506 -0.744 0.747 -0.239 -0.193 0.000 0.988 -1.110 -1.409 2.215 1.467 -0.578 0.895 2.548 0.820 0.937 -1.522 0.000 0.870 0.939 1.042 0.933 1.166 0.816 0.757 +0 1.631 0.635 -0.306 0.215 -0.452 0.550 0.697 -1.597 2.173 0.520 0.087 1.156 0.000 0.705 -1.460 1.293 0.000 1.216 -0.483 0.347 3.102 0.891 1.011 1.001 0.976 1.039 0.831 0.928 +0 1.011 -0.372 -0.521 0.203 -1.241 1.022 0.620 -1.302 0.000 1.172 -1.167 -0.005 2.215 1.998 0.005 0.858 2.548 1.364 0.729 1.280 0.000 1.308 1.061 0.985 1.041 1.544 1.072 0.953 +0 1.698 0.254 0.090 1.329 -0.614 0.651 0.267 -1.228 0.000 1.012 -0.919 0.686 2.215 1.434 2.257 1.730 0.000 0.419 1.549 -1.509 0.000 0.730 0.624 1.232 1.267 0.703 0.897 0.890 +0 1.687 -1.122 -0.905 0.405 0.100 1.399 -0.955 -0.228 2.173 1.835 -0.159 1.432 0.000 1.108 0.552 0.820 2.548 1.018 0.322 1.717 0.000 0.671 0.879 0.989 0.883 1.842 1.399 1.270 +1 0.704 -0.200 -0.918 1.037 1.565 0.544 -1.831 0.627 0.000 0.828 -0.151 -0.573 0.000 1.228 -0.568 0.795 2.548 1.541 -0.004 -1.162 3.102 1.095 1.039 0.988 0.598 1.084 0.779 0.714 +1 0.785 -1.415 0.797 0.675 -1.088 0.358 0.167 0.618 2.173 0.638 -1.648 -1.198 0.000 0.819 -0.841 -0.793 0.000 1.364 -0.596 -1.383 3.102 0.857 0.996 1.001 0.783 0.795 0.644 0.663 +0 0.629 0.532 -1.103 0.638 0.875 0.655 -0.656 -0.131 1.087 0.517 -1.705 0.400 0.000 0.967 -0.912 1.497 2.548 0.687 -1.628 -1.119 0.000 0.765 0.780 0.985 1.048 1.001 0.916 0.946 +0 0.580 -0.427 -0.570 1.226 0.234 0.888 0.334 1.126 0.000 0.700 0.935 -1.360 2.215 0.427 -0.247 0.210 0.000 1.429 -0.134 -0.890 3.102 0.883 0.948 0.990 0.847 0.650 0.909 0.897 +1 0.406 -0.898 -0.210 0.259 0.314 1.769 -0.435 0.680 0.000 1.905 -0.717 -1.166 1.107 1.270 0.100 -1.704 2.548 0.649 0.367 -0.290 0.000 1.460 1.442 0.990 1.229 1.056 1.388 1.138 +1 0.554 0.356 -0.511 2.467 0.625 1.100 -0.547 -1.426 1.087 0.500 0.123 1.641 1.107 0.884 -0.084 0.054 0.000 1.244 0.883 -0.908 0.000 1.128 0.865 1.384 1.641 0.552 1.057 0.964 +1 0.906 0.291 -0.761 1.419 -1.296 0.613 -0.392 0.068 0.000 0.579 0.050 0.867 2.215 0.936 -0.955 0.972 2.548 0.452 -0.751 1.326 0.000 0.756 0.657 1.000 0.892 0.458 0.758 0.682 +1 0.295 1.668 -0.353 0.459 0.955 1.234 -0.381 -1.072 2.173 1.093 -0.698 1.074 0.000 1.028 -0.634 0.497 0.000 0.981 0.007 -0.667 0.000 0.808 0.688 0.990 0.995 0.900 0.950 0.814 +0 0.703 -0.281 0.924 1.084 -0.744 1.216 -0.799 -1.426 2.173 1.182 -1.027 0.247 2.215 2.000 0.072 0.222 0.000 0.627 -0.109 1.175 0.000 0.943 0.960 1.206 1.016 1.775 1.196 0.991 +0 0.837 -1.376 -0.253 0.859 -0.871 0.699 1.501 0.691 0.000 0.699 0.106 -1.391 0.000 0.772 -0.047 0.981 2.548 0.777 -1.102 -0.759 0.000 0.910 0.818 0.992 0.680 0.193 0.747 0.753 +0 0.425 1.198 -0.499 1.453 0.767 0.893 0.060 -0.704 2.173 1.395 -0.141 0.785 1.107 1.660 -0.318 -1.193 0.000 0.720 -1.126 -1.608 0.000 0.769 0.893 0.990 1.053 1.609 1.079 1.113 +1 0.591 1.441 -1.402 1.680 0.797 0.913 -1.437 -1.301 0.000 1.172 0.257 -0.222 1.107 0.610 -0.734 0.554 0.000 0.534 0.646 0.663 3.102 0.859 1.021 1.266 1.292 0.543 0.953 1.323 +1 0.703 -0.798 -1.426 0.580 -1.334 0.634 0.633 -1.243 2.173 1.110 1.030 0.336 0.000 0.552 -0.812 -0.805 2.548 0.543 -0.363 0.821 0.000 0.933 0.997 0.988 0.607 0.683 0.799 0.740 +1 0.430 1.295 -1.078 0.335 -0.265 1.109 -0.608 -1.155 2.173 1.182 0.003 0.553 2.215 0.616 1.083 0.512 0.000 0.427 -0.350 1.000 0.000 0.561 1.178 0.999 0.842 1.765 0.946 0.795 +1 0.319 0.162 -0.815 1.435 1.057 0.997 -0.224 -0.995 0.000 1.139 -1.030 -0.785 0.000 2.209 0.224 0.815 0.000 1.389 -0.245 0.181 1.551 1.061 1.152 0.987 1.067 0.905 0.818 0.964 +0 0.645 1.492 1.414 1.287 0.782 0.345 -0.197 -0.207 2.173 0.390 -0.162 0.309 2.215 0.672 1.796 -1.656 0.000 1.027 1.113 -0.733 0.000 0.748 0.837 0.988 0.649 0.242 0.596 0.635 +1 0.976 -0.919 0.680 0.233 -0.734 1.077 -0.738 1.454 2.173 1.119 -0.310 -1.023 2.215 1.600 -0.531 -0.202 0.000 0.580 0.222 1.051 0.000 1.067 1.004 0.984 0.934 1.320 0.977 0.893 +0 0.340 -0.962 0.132 1.928 -0.755 0.965 -0.793 -0.103 2.173 1.168 -0.821 1.158 0.000 1.510 -0.867 1.656 2.548 1.138 -0.579 0.490 0.000 0.858 0.865 0.989 0.878 1.508 0.976 0.962 +1 1.072 0.289 1.245 0.781 -1.440 0.679 -0.312 1.664 2.173 0.852 1.236 0.084 0.000 0.616 -0.362 0.255 0.000 0.784 1.013 -0.987 0.000 0.876 1.088 0.988 0.725 0.849 0.922 0.832 +0 0.471 -0.701 -0.227 1.114 1.618 0.811 -0.490 0.476 0.000 1.142 -0.170 -1.430 2.215 1.490 0.476 -0.090 0.000 0.979 0.443 1.637 0.000 0.965 1.163 0.999 0.646 0.698 0.734 0.673 +0 0.706 1.344 -0.790 0.354 1.581 1.100 0.724 1.582 2.173 0.982 1.139 -0.201 1.107 1.000 2.159 0.824 0.000 1.262 1.888 -0.437 0.000 1.126 1.031 0.993 0.839 1.565 1.147 0.974 +0 0.692 0.556 0.290 1.077 0.173 0.700 0.330 -1.043 2.173 0.490 -0.794 0.859 0.000 0.725 0.636 1.539 0.000 0.752 -0.782 -1.427 3.102 0.924 0.959 0.976 1.218 0.583 0.965 0.896 +1 1.444 -0.011 0.918 0.786 0.033 2.253 -0.561 -0.087 0.000 2.255 -0.565 -1.542 2.215 1.410 0.507 -1.294 2.548 1.322 0.260 1.190 0.000 2.687 2.199 1.056 1.477 1.213 1.804 1.458 +0 0.282 0.276 -0.532 0.852 0.820 0.330 -0.286 -0.258 2.173 0.777 0.804 1.500 0.000 0.662 0.426 -1.512 2.548 0.907 1.056 -0.308 0.000 1.118 0.889 0.989 0.736 0.573 0.659 0.632 +1 0.664 -0.406 -0.535 1.312 -0.146 0.978 0.558 0.113 0.000 1.107 -1.199 1.502 0.000 1.071 0.003 1.405 0.000 0.534 1.065 1.111 0.000 0.953 0.858 0.982 1.001 0.699 0.820 0.751 +0 3.356 -0.497 0.920 2.241 1.150 1.484 -2.553 -0.746 0.000 0.621 -1.832 -0.397 0.000 0.563 -0.851 -1.497 2.548 0.824 -0.435 -1.207 3.102 0.958 1.094 0.981 1.045 0.173 0.851 1.768 +1 0.691 -1.415 1.141 0.553 0.480 1.007 -0.459 -0.737 1.087 0.853 -1.377 0.648 0.000 0.865 -0.344 0.946 0.000 1.649 -0.685 -1.487 3.102 0.797 0.999 0.977 1.031 0.887 0.943 0.814 +1 1.190 -0.865 -1.286 0.551 1.221 1.078 -1.198 0.458 0.000 0.979 -2.270 -0.730 0.000 1.116 -0.884 1.405 2.548 1.137 -0.885 -1.046 3.102 1.008 1.103 0.985 0.572 0.693 0.713 0.656 +1 0.603 -0.301 1.575 0.651 0.827 2.186 -1.834 1.026 0.000 3.067 0.263 -0.854 2.215 1.088 -0.333 -0.127 0.000 0.686 -0.094 -1.218 1.551 0.609 1.094 0.986 0.664 0.492 1.166 0.963 +1 1.447 -1.325 -0.344 0.917 0.129 1.137 -0.756 1.313 1.087 0.330 -1.542 -0.826 0.000 0.715 -1.161 -1.284 2.548 0.594 -1.498 -1.708 0.000 0.416 0.799 0.987 0.792 0.857 1.014 0.774 +0 2.057 1.335 1.524 1.329 0.278 2.270 0.973 -0.001 0.000 1.256 0.942 -1.229 0.000 1.114 -1.910 1.428 0.000 1.918 0.188 -1.473 3.102 1.383 2.253 2.064 1.208 1.569 2.755 2.870 +1 1.539 -0.726 0.540 0.785 0.528 1.167 0.037 -1.166 0.000 0.684 -1.443 1.193 0.000 1.417 0.120 0.092 2.548 0.679 -0.367 -1.647 3.102 2.263 1.198 0.974 1.036 0.781 1.015 1.053 +1 0.841 0.120 -0.965 1.057 1.716 0.914 -0.341 -1.639 1.087 0.830 -0.454 1.237 0.000 1.051 0.813 -0.410 2.548 1.269 -0.804 -0.324 0.000 0.949 0.806 0.992 0.612 1.356 0.914 0.835 +0 0.555 -0.876 -1.496 1.017 1.122 0.737 -1.337 1.015 0.000 0.506 0.767 -0.742 2.215 0.795 -0.222 0.380 0.000 1.143 -0.254 -1.058 3.102 0.872 0.843 0.988 0.694 0.440 0.578 0.598 +0 3.968 -0.884 1.743 1.228 1.674 1.135 1.306 -0.194 0.000 1.021 0.732 0.053 1.107 1.950 -0.837 1.245 2.548 2.057 -1.085 -0.549 0.000 0.824 0.666 0.962 0.915 1.928 1.559 1.909 +1 1.083 -0.136 1.574 0.804 0.047 0.779 0.070 0.346 0.000 1.254 0.487 -1.579 2.215 1.179 0.845 -0.805 1.274 1.028 1.132 0.305 0.000 0.944 1.068 1.268 0.968 0.875 0.957 0.883 +0 0.789 1.014 0.446 0.338 -1.201 0.794 1.247 -1.205 2.173 0.778 -0.132 -1.351 0.000 2.059 0.149 0.616 2.548 0.640 0.209 0.231 0.000 0.927 0.967 0.985 0.758 1.835 1.005 0.824 +0 1.148 0.037 0.824 1.506 0.223 1.823 -0.662 -1.529 0.000 1.535 -0.879 -0.542 0.000 0.947 -0.956 0.725 0.000 1.128 1.048 -0.077 3.102 1.108 1.061 0.988 0.784 0.973 0.745 0.704 +1 0.442 0.931 -1.322 1.243 0.125 1.017 0.422 1.651 0.000 1.439 0.039 0.491 1.107 1.271 -1.064 -0.772 0.000 0.740 2.471 -0.838 0.000 0.900 1.068 0.991 0.899 0.626 0.953 0.911 +0 1.705 -0.324 -1.051 0.503 1.617 0.646 -1.234 0.642 2.173 0.729 1.340 -0.247 0.000 0.545 -2.303 -0.655 0.000 1.065 -1.820 1.396 0.000 0.821 0.872 0.987 0.856 0.760 0.849 0.952 +1 0.365 -0.571 -0.945 0.094 -1.114 0.731 0.746 -0.469 0.000 0.673 0.163 0.140 0.000 1.116 0.286 1.000 2.548 1.431 -0.532 -1.733 3.102 0.895 1.010 0.749 0.680 0.772 0.858 0.704 +1 0.815 0.519 0.070 1.116 -0.367 0.338 -1.486 -1.500 0.000 1.138 -1.056 1.444 2.215 0.707 -1.203 0.435 2.548 1.267 -1.382 -0.943 0.000 0.750 0.804 0.993 1.368 0.760 1.396 1.211 +0 0.950 -0.274 0.300 1.403 -0.356 0.601 -2.283 -0.202 0.000 1.082 -0.716 0.791 2.215 0.636 -0.696 -1.691 0.000 0.739 0.165 -0.373 1.551 1.435 1.133 0.989 1.009 0.802 0.893 0.945 +0 0.919 0.236 -0.612 1.227 0.866 0.665 1.327 0.908 2.173 0.907 1.335 -1.274 2.215 0.726 1.873 -0.761 0.000 0.777 1.537 0.308 0.000 0.686 0.792 1.429 1.128 1.054 0.902 0.822 +0 0.770 0.759 0.736 0.736 -1.027 0.330 -0.940 -0.272 2.173 0.295 0.326 1.407 0.000 0.530 1.248 -0.710 2.548 0.974 1.705 1.241 0.000 0.681 1.113 1.042 0.589 0.775 0.718 0.638 +0 1.045 0.237 -1.715 1.000 0.388 1.114 0.780 -1.023 2.173 1.398 -1.402 0.506 0.000 1.070 2.054 -1.315 0.000 0.955 -1.307 -0.241 0.000 0.943 0.807 1.342 1.162 1.755 1.551 1.295 +1 0.737 0.111 -1.599 1.047 -1.551 0.383 0.519 -1.677 0.000 0.917 -0.411 0.415 0.000 0.505 1.139 0.488 1.274 0.466 -0.195 -1.269 3.102 1.347 0.900 0.991 0.569 0.478 0.567 0.675 +1 0.430 -0.795 0.753 0.833 1.400 0.561 -0.519 1.514 1.087 0.685 1.162 -1.049 0.000 1.267 -1.348 0.172 0.000 1.024 -0.045 -0.336 3.102 0.804 0.872 0.984 0.862 0.820 0.819 1.297 +0 1.267 1.312 -0.193 0.674 1.350 0.788 2.235 1.519 0.000 0.603 1.016 -1.227 2.215 1.217 0.161 0.022 0.000 0.542 0.396 -0.487 0.000 0.956 0.902 1.259 0.752 0.347 0.568 0.654 +0 0.411 -2.314 1.243 0.286 -1.179 1.442 -0.166 0.026 0.000 1.954 -1.225 -1.559 1.107 0.416 0.637 1.702 2.548 0.632 1.717 -0.888 0.000 0.953 0.987 0.997 0.839 1.114 1.345 1.065 +0 0.568 -0.484 -0.507 0.522 -0.660 0.847 -0.634 0.902 2.173 0.953 0.712 1.556 0.000 1.203 -0.088 -0.938 2.548 1.134 -0.078 0.125 0.000 1.423 1.120 0.983 1.069 1.299 0.941 1.019 +1 0.746 0.534 -1.727 0.757 0.116 0.757 -0.347 -0.058 2.173 0.942 -0.169 0.665 1.107 1.354 0.284 -1.322 0.000 0.712 1.602 1.640 0.000 1.088 1.261 1.037 0.841 0.761 1.000 0.830 +1 0.603 1.177 -0.435 1.395 0.143 0.510 0.599 -1.483 2.173 0.647 0.824 -1.093 0.000 0.675 -0.530 0.131 0.000 2.241 -0.716 1.488 3.102 1.200 0.870 0.993 2.475 1.037 1.606 1.301 +0 0.564 -0.290 0.604 0.391 -0.364 1.268 0.363 -1.401 2.173 0.705 -1.356 0.811 0.000 0.652 -2.372 0.147 0.000 1.087 -0.631 -0.013 3.102 0.888 0.773 0.979 1.441 1.391 1.387 1.108 +1 1.091 -0.295 0.673 0.296 0.463 0.853 -0.749 -0.960 1.087 0.495 -0.678 1.098 0.000 0.962 -0.557 0.059 0.000 1.003 -1.329 1.602 0.000 0.853 0.997 0.996 0.625 0.539 0.691 0.640 +0 1.251 -0.903 -1.037 0.290 1.112 0.733 -0.671 0.212 1.087 0.796 -0.140 1.510 0.000 0.835 -0.034 0.612 0.000 0.819 1.161 -1.006 3.102 0.908 0.939 0.981 0.858 1.246 0.818 0.770 +1 0.380 2.223 -1.632 1.200 1.213 0.662 1.012 -1.054 2.173 2.048 0.629 -0.434 2.215 0.956 0.456 0.782 0.000 1.391 1.210 1.111 0.000 0.730 1.054 0.992 1.387 0.965 1.036 0.940 +1 1.337 -0.088 -1.053 0.569 -0.497 1.002 -0.563 1.051 2.173 0.578 -0.901 -1.281 0.000 1.331 -1.320 0.302 2.548 0.389 2.295 1.080 0.000 1.971 1.675 0.987 1.367 1.113 1.350 1.199 +0 2.183 -0.597 0.334 0.133 0.096 1.253 -1.244 -1.085 0.000 0.697 0.675 0.368 0.000 1.001 -1.381 -1.556 0.000 0.939 0.149 1.238 3.102 0.869 0.848 0.987 0.738 0.581 0.777 0.922 +0 1.576 1.712 -1.165 0.511 1.131 1.031 0.142 0.366 0.000 0.437 -0.409 -1.272 2.215 0.393 0.616 -1.734 0.000 0.920 0.091 0.686 0.000 1.128 0.933 1.092 0.682 0.182 0.659 0.818 +0 1.148 -0.158 0.480 0.532 -0.813 0.656 -0.504 -1.600 2.173 0.892 -0.495 -0.264 0.000 0.808 0.685 1.512 2.548 0.452 -0.881 0.842 0.000 0.732 0.928 0.994 0.850 0.680 0.709 0.659 +1 1.229 -1.187 0.889 0.634 1.715 1.019 -0.305 -0.382 0.000 0.850 0.536 1.684 2.215 0.640 0.079 -0.059 2.548 0.883 -1.634 -1.333 0.000 0.717 0.812 0.994 0.957 0.805 0.819 0.747 +0 0.898 -0.837 -0.229 0.972 0.753 2.051 0.304 1.445 1.087 1.007 -1.240 -0.751 0.000 1.833 -0.029 -0.390 1.274 0.563 -1.022 0.596 0.000 0.920 1.031 1.002 1.647 2.442 1.546 1.273 +1 1.808 -1.086 -1.533 0.992 0.029 2.164 -1.137 1.155 2.173 2.107 1.035 -0.170 0.000 1.183 -1.559 -0.868 0.000 1.322 -0.453 -0.846 3.102 4.772 2.620 1.831 1.637 1.826 2.599 2.091 +0 1.280 -0.733 -1.638 1.119 1.336 0.774 -0.951 -0.297 0.000 0.628 -0.707 0.770 2.215 0.986 -0.025 -0.015 0.000 1.391 -0.084 -1.051 3.102 0.855 0.886 0.976 0.737 0.883 0.701 0.809 +0 1.132 1.830 1.010 5.547 1.284 2.728 0.134 -0.603 1.087 1.193 0.327 -0.146 0.000 1.829 2.140 -0.235 0.000 2.644 0.982 1.489 3.102 2.693 2.274 0.980 4.509 3.117 2.898 2.586 +0 0.283 -0.993 1.421 1.151 -1.511 0.728 -0.930 0.526 0.000 0.875 0.909 1.584 2.215 1.163 -0.685 -0.131 0.000 0.888 0.136 -0.668 1.551 0.938 0.861 0.986 0.614 0.780 0.983 0.910 +0 2.298 0.328 -0.186 0.289 0.452 0.705 0.860 0.475 2.173 1.522 0.658 1.728 2.215 0.492 0.228 1.577 0.000 1.233 1.768 -1.530 0.000 0.947 0.910 0.991 1.459 1.385 1.082 0.952 +1 0.824 -1.436 -0.667 0.897 0.891 0.973 -0.852 -1.125 0.000 1.406 0.101 0.925 2.215 0.461 -0.331 -0.634 2.548 0.453 -1.172 -0.297 0.000 0.744 0.490 1.175 1.270 0.868 0.882 0.850 +0 1.644 -1.171 -1.352 0.854 1.194 1.289 -0.954 -0.691 1.087 1.059 -0.819 1.127 2.215 1.345 0.220 0.448 0.000 1.245 -0.709 0.359 0.000 0.827 0.950 1.231 1.197 1.718 1.131 1.077 +0 2.114 -0.490 -1.230 0.143 -0.704 1.362 -0.346 0.591 1.087 0.794 -1.431 0.319 0.000 1.385 -1.097 -0.813 2.548 0.760 -0.571 1.403 0.000 0.933 0.978 0.989 0.812 1.798 1.174 1.010 +1 2.173 0.688 1.385 0.494 0.071 0.449 -0.254 -1.673 0.000 0.778 -0.364 -0.812 2.215 1.611 0.560 0.121 2.548 0.611 -0.493 0.713 0.000 0.683 0.985 1.329 1.166 1.078 0.971 0.825 +0 0.362 -0.623 -0.094 0.652 -0.853 0.726 1.193 1.301 0.000 0.446 0.007 -1.514 1.107 0.989 0.706 -0.233 2.548 0.490 -0.058 0.537 0.000 0.849 0.903 0.989 0.626 0.703 0.631 0.605 +0 0.722 -1.687 1.609 0.718 -0.895 0.614 -0.123 -1.434 2.173 0.972 -0.556 1.142 0.000 0.773 1.853 -0.171 0.000 0.712 0.842 -0.137 3.102 2.581 1.443 0.988 1.239 0.768 1.148 1.561 +0 0.501 2.095 -0.656 3.431 -1.148 0.667 0.755 0.373 2.173 0.720 1.580 1.359 0.000 1.383 1.522 0.393 0.000 0.930 0.064 1.347 3.102 1.165 1.122 0.985 1.450 0.703 1.160 2.031 +1 0.774 -1.179 0.285 0.495 -1.259 0.698 0.033 -1.026 1.087 0.933 -0.441 1.151 2.215 0.910 -1.478 -0.938 0.000 0.706 1.990 -0.314 0.000 3.264 1.910 0.989 0.897 1.136 1.327 1.259 +1 0.974 0.339 -1.483 0.262 0.377 1.363 -1.036 0.863 0.000 2.333 -0.469 -0.472 2.215 1.337 -0.766 -1.492 2.548 1.009 -0.055 0.706 0.000 0.948 1.278 0.982 1.408 1.530 1.396 1.282 +1 0.471 -1.274 -1.286 1.530 -1.608 1.679 0.282 0.426 2.173 0.470 0.560 -0.707 0.000 1.042 1.839 -1.662 0.000 0.683 -0.353 -0.568 1.551 1.173 1.002 0.982 3.275 0.975 2.001 2.262 +1 1.987 0.656 -0.793 0.319 0.549 0.832 1.639 0.155 0.000 1.178 -0.323 -1.722 1.107 1.029 0.371 0.657 2.548 0.947 1.495 -0.278 0.000 0.974 0.893 1.032 1.123 1.079 1.102 0.992 +1 1.098 1.986 0.505 1.088 -0.151 1.104 1.758 -1.154 0.000 1.076 1.207 0.607 0.000 0.975 1.459 -0.352 2.548 1.103 -0.872 -1.398 0.000 0.826 0.885 0.985 0.701 0.779 0.738 0.801 +1 1.879 -0.505 -1.528 0.267 0.397 1.926 -2.132 0.358 0.000 1.979 -0.797 -1.689 2.215 1.056 -0.679 -0.355 0.000 1.159 -0.324 0.376 0.000 0.778 0.788 0.987 0.691 0.862 0.911 0.783 +1 0.675 0.805 1.619 1.846 -1.444 0.391 0.688 -0.920 0.000 1.352 1.136 0.148 2.215 1.016 1.491 -0.337 0.000 1.519 0.426 0.619 3.102 0.794 0.942 0.976 1.538 0.686 1.078 0.964 +0 0.554 0.619 1.040 3.079 1.734 1.070 1.061 0.049 2.173 0.653 -1.762 0.307 0.000 0.731 0.096 -0.934 2.548 0.599 1.712 0.434 0.000 0.970 0.898 1.059 1.652 1.017 1.211 1.324 +0 0.749 -0.649 -0.470 1.787 -0.549 1.492 -0.448 1.201 2.173 0.956 -1.578 -1.133 2.215 0.861 -1.604 0.803 0.000 1.030 -0.922 0.180 0.000 0.905 1.030 0.994 1.383 1.866 1.457 1.195 +1 2.447 -0.507 1.655 0.301 -1.339 0.716 -0.085 -0.443 2.173 0.818 -0.895 0.367 2.215 0.834 -0.837 -0.174 0.000 0.639 -0.099 0.999 0.000 0.776 0.700 0.986 1.098 0.893 0.952 0.789 +1 1.113 -0.430 -1.397 0.714 1.551 0.729 0.332 -0.191 2.173 0.382 -0.755 -0.532 0.000 0.760 -1.037 0.908 0.000 0.726 -0.057 1.149 3.102 0.811 0.929 0.980 0.630 0.737 0.805 0.701 +1 0.336 -1.917 0.471 0.572 -1.013 0.950 -0.600 1.223 0.000 1.313 0.083 -0.504 2.215 0.561 -0.328 0.454 0.000 0.645 0.151 -1.081 3.102 0.856 0.774 0.990 0.812 0.415 0.837 0.722 +1 0.892 0.137 -0.084 0.509 1.530 0.793 1.255 0.144 0.000 0.916 -0.066 -1.342 2.215 1.625 0.534 1.311 2.548 0.379 2.063 -0.849 0.000 0.846 1.186 0.987 0.770 0.984 0.990 0.825 +1 0.395 0.578 0.747 1.939 -0.228 0.831 0.010 -1.220 2.173 0.745 1.928 1.187 0.000 0.387 -1.868 0.355 0.000 0.587 -1.185 0.884 3.102 0.562 0.880 0.991 0.798 0.906 0.790 0.713 +0 0.424 -0.714 0.029 2.095 -0.111 0.609 -0.530 1.204 0.000 0.799 0.599 1.294 0.000 0.592 -0.348 -0.872 2.548 0.813 0.645 -1.435 3.102 0.909 0.867 0.990 0.821 0.414 0.606 0.781 +0 0.582 0.167 1.192 1.301 -0.880 0.395 0.450 -0.209 2.173 0.561 0.658 1.575 0.000 0.830 -0.414 0.728 2.548 0.610 1.342 0.048 0.000 0.834 0.808 1.154 0.695 0.630 0.608 0.607 +0 1.225 -0.893 -1.589 2.172 -1.489 1.795 0.438 0.813 2.173 2.186 -1.190 -1.400 2.215 2.578 1.482 -0.119 0.000 1.288 0.683 0.403 0.000 0.633 0.867 0.990 0.642 3.801 2.230 1.798 +1 2.054 1.082 0.131 0.331 -0.898 0.790 -0.398 0.630 0.000 0.641 0.015 -1.563 0.000 1.588 0.730 -1.139 2.548 1.213 0.538 -1.737 3.102 1.425 1.032 0.986 1.064 0.550 0.886 0.968 +0 1.319 0.241 1.057 1.784 1.604 0.810 -0.905 -0.576 0.000 0.593 -0.062 -0.008 2.215 1.083 -0.516 0.509 0.000 1.332 0.431 -1.042 1.551 1.428 0.897 1.006 0.921 0.686 0.782 0.952 +1 1.471 -1.212 -1.277 1.476 -0.918 0.295 -1.307 0.596 0.000 0.699 0.250 1.208 2.215 1.025 -0.877 0.145 0.000 0.636 0.254 0.223 3.102 0.429 0.843 0.982 1.077 0.467 1.039 0.894 +0 1.424 1.123 -1.552 0.217 -1.560 1.198 1.323 -0.368 0.000 0.558 0.936 -0.906 0.000 1.132 0.134 0.672 2.548 1.592 -0.588 1.259 0.000 0.872 0.785 0.998 0.924 0.255 0.784 0.807 +0 0.440 -0.386 -0.493 1.082 -1.375 0.707 -1.804 -0.421 0.000 1.262 0.290 0.561 2.215 1.206 -0.315 1.091 0.000 1.411 -0.188 -1.576 3.102 2.106 1.459 0.993 1.048 1.173 1.219 1.131 +0 1.237 -0.208 -0.792 0.863 -1.743 1.641 -0.479 0.974 2.173 2.302 0.330 -0.681 2.215 1.387 -0.328 0.530 0.000 0.665 0.754 0.998 0.000 0.837 0.885 1.081 1.017 3.098 1.510 1.241 +0 0.376 -1.987 -1.713 0.334 -1.229 1.472 0.897 -0.083 1.087 1.631 0.038 1.210 1.107 1.729 -0.566 -1.217 0.000 0.508 2.080 0.333 0.000 2.562 1.945 0.979 1.494 2.330 1.661 1.353 +1 0.728 -0.776 1.739 0.607 -0.097 1.218 -1.424 1.078 0.000 1.531 0.614 -1.048 2.215 2.581 -0.009 0.154 1.274 1.906 -0.212 -1.437 0.000 2.305 2.340 0.984 1.302 1.989 1.899 1.457 +1 0.424 -2.146 -0.253 0.266 -1.690 0.755 -0.358 0.744 0.000 0.992 -0.434 -1.321 1.107 0.685 0.315 0.167 1.274 0.404 -0.654 0.898 0.000 1.013 1.008 0.995 0.712 0.924 0.692 0.651 +0 1.152 1.118 0.030 0.354 1.742 0.860 -0.251 1.570 0.000 0.899 0.847 -0.980 2.215 0.921 0.027 0.827 0.000 1.090 0.388 -0.132 3.102 1.020 1.010 0.986 0.775 0.645 0.850 0.842 +0 0.425 0.551 0.925 1.083 -0.025 1.386 0.275 1.186 2.173 1.158 0.717 -0.674 2.215 0.748 -0.445 -1.391 0.000 0.766 0.004 0.735 0.000 0.872 1.113 0.991 1.008 1.903 1.113 0.917 +1 0.892 -0.333 0.294 1.400 0.912 0.369 -0.190 -0.816 0.000 0.570 -0.783 1.161 0.000 1.675 -1.180 -1.046 1.274 0.860 -0.189 -1.234 3.102 0.999 0.948 0.984 0.796 0.537 0.967 0.809 +0 1.548 -1.045 -0.035 0.455 0.117 0.941 -0.445 1.621 0.000 0.460 -0.491 0.883 0.000 0.614 -0.933 -0.659 2.548 1.069 -0.628 -1.110 1.551 0.865 0.853 0.987 0.769 0.259 0.582 0.703 +0 2.321 -0.783 -0.100 1.382 0.565 1.046 -0.550 -0.727 1.087 1.487 -0.711 1.577 0.000 0.640 -0.143 0.451 0.000 0.974 -0.551 -1.675 0.000 0.931 0.941 1.401 1.443 1.536 1.213 0.988 +0 1.642 -0.917 -0.783 0.243 -0.061 1.615 -0.349 1.021 2.173 0.927 -0.055 -0.550 2.215 0.888 0.197 1.673 0.000 0.610 1.555 -0.138 0.000 1.104 0.997 0.987 1.497 1.799 1.134 1.061 +0 0.900 0.096 1.066 1.948 0.350 0.672 0.443 -1.085 2.173 0.918 -2.441 -1.246 0.000 0.405 -1.067 1.671 0.000 0.569 -0.688 0.607 0.000 0.816 1.816 1.103 0.743 0.672 1.390 1.360 +0 0.712 -0.227 0.050 1.660 1.078 1.040 0.764 -1.236 2.173 1.019 -2.099 0.095 0.000 1.103 1.036 -0.736 0.000 1.793 -0.762 1.545 3.102 0.956 0.967 1.205 0.886 1.616 1.102 1.043 +1 1.075 0.719 -0.168 0.938 1.391 1.006 0.266 -0.973 0.000 1.000 0.634 0.852 0.000 1.129 -0.088 1.684 2.548 1.466 0.073 0.307 3.102 2.163 1.391 1.372 0.842 0.935 0.962 0.875 +0 2.243 -1.695 -0.020 1.125 0.059 1.318 -1.387 -1.604 0.000 1.218 -1.428 -1.245 0.000 1.487 -1.289 0.735 1.274 0.626 -0.393 0.823 3.102 0.863 0.977 0.988 1.024 0.366 0.942 1.171 +1 0.531 0.554 1.743 1.299 -0.321 0.922 1.827 -0.462 0.000 0.771 1.311 1.488 0.000 1.145 1.275 1.023 2.548 0.749 0.937 0.134 0.000 0.936 0.783 1.103 0.934 0.810 0.696 0.650 +0 0.399 1.909 -1.123 1.100 1.127 0.595 0.555 1.011 2.173 0.449 2.066 -0.598 0.000 0.805 -0.167 -0.519 2.548 1.426 -0.024 -1.391 0.000 0.857 0.844 0.983 0.876 0.908 0.673 0.646 +1 0.331 0.758 1.528 1.575 -0.447 0.653 0.506 1.115 0.000 0.925 -0.416 0.914 0.000 0.893 0.449 -0.995 2.548 1.245 -0.567 -0.702 3.102 0.858 1.065 0.989 0.899 0.543 0.824 0.850 +0 0.561 -1.671 0.303 1.009 -0.794 1.524 -0.881 0.071 2.173 1.164 -0.005 -1.721 1.107 0.723 1.929 1.327 0.000 0.713 0.784 -1.152 0.000 0.796 1.070 0.986 0.892 2.153 1.618 1.377 +0 0.513 1.738 1.001 1.013 -0.600 0.654 0.783 -1.149 2.173 0.650 -0.163 1.734 0.000 2.189 0.679 0.651 2.548 1.772 0.919 -0.430 0.000 0.820 1.046 0.990 0.795 1.489 0.931 0.827 +1 0.757 0.022 -0.775 1.862 -0.070 1.395 -0.516 1.154 1.087 1.058 1.919 -1.489 0.000 0.757 0.364 -0.395 0.000 1.589 -0.477 -1.284 0.000 0.804 1.225 0.988 0.670 0.467 0.980 0.825 +0 1.177 1.021 0.804 1.538 0.044 1.845 0.051 -1.582 1.087 0.436 0.056 -0.534 1.107 0.564 2.532 0.159 0.000 0.811 -0.892 0.122 0.000 0.523 0.544 1.179 1.986 1.070 1.280 1.171 +0 3.211 -1.053 -1.274 0.813 -1.617 3.254 0.430 0.376 0.000 3.664 -0.912 -1.600 2.215 3.134 0.888 0.385 0.000 1.896 1.036 -1.472 3.102 1.334 1.170 0.988 0.858 3.156 2.371 2.063 +0 1.917 -0.638 1.101 0.687 1.539 0.906 0.047 -0.920 0.000 1.027 0.515 -0.205 2.215 0.544 0.615 -1.685 0.000 1.233 -0.315 0.328 3.102 0.899 0.984 0.991 1.480 0.670 0.985 0.983 +1 0.573 1.144 -0.806 1.199 1.644 0.869 0.572 -0.790 2.173 1.125 0.577 -0.033 2.215 1.109 -0.573 1.332 0.000 0.737 1.324 0.771 0.000 0.774 1.101 0.992 0.976 0.918 0.925 0.979 +0 1.536 -0.761 -0.482 2.613 -0.144 1.034 -1.478 1.349 0.000 0.870 -2.741 1.663 0.000 0.733 0.152 0.849 2.548 0.573 0.084 1.225 0.000 1.047 0.943 0.990 0.936 0.433 0.861 1.078 +0 0.687 1.583 -0.708 1.385 -1.613 0.889 1.434 0.248 0.000 0.404 0.328 -0.463 2.215 0.641 -0.559 -1.331 2.548 1.164 -0.127 0.942 0.000 1.653 1.051 0.986 0.996 0.465 0.842 0.891 +0 1.155 -0.234 -0.225 1.264 -0.759 0.906 -1.571 1.637 2.173 0.784 -0.632 -1.240 0.000 1.311 -0.049 0.522 2.548 1.510 -1.779 0.649 0.000 1.817 1.301 0.983 0.985 1.620 1.135 1.086 +0 0.670 -0.980 -0.184 0.480 1.275 1.140 0.324 -0.626 2.173 0.711 0.551 1.229 0.000 0.758 1.302 0.612 0.000 0.680 -0.862 1.331 0.000 0.811 1.296 0.983 0.583 0.844 0.798 0.706 +1 0.380 -0.978 0.445 0.734 -1.036 1.221 0.040 -0.264 0.000 1.085 -0.433 0.985 0.000 0.999 -0.838 1.426 2.548 1.754 0.886 -1.355 3.102 1.062 0.736 0.982 0.776 1.321 1.042 0.876 +0 0.347 -2.251 0.510 1.561 -1.457 1.496 0.562 1.067 2.173 1.687 -1.045 -0.144 2.215 1.103 0.672 -0.846 0.000 0.902 -0.972 -1.094 0.000 0.678 0.987 1.000 2.922 2.984 2.181 1.584 +1 1.528 0.425 -0.742 0.329 -1.267 1.570 -0.698 1.034 2.173 0.489 -0.703 -0.528 0.000 0.273 2.610 -0.776 0.000 0.453 -0.663 0.060 3.102 1.168 0.741 0.989 1.915 0.688 1.188 1.038 +1 0.766 0.317 -0.285 0.773 -1.326 0.778 -0.231 1.325 2.173 0.446 1.290 -0.571 0.000 0.532 -1.283 -1.319 1.274 1.118 0.325 0.276 0.000 0.778 0.999 0.985 0.874 0.743 0.807 0.721 +0 0.514 -0.069 -0.630 0.477 -1.693 0.998 0.226 0.548 0.000 0.788 1.281 0.044 2.215 1.669 0.616 -1.390 0.000 0.448 0.797 -1.073 3.102 0.759 0.859 0.991 0.473 0.464 0.572 0.598 +0 0.940 -0.694 1.330 0.895 -0.610 0.658 0.214 -0.862 0.000 0.426 -0.015 1.434 0.000 0.898 -0.959 0.407 2.548 0.840 1.838 -0.224 0.000 0.996 1.011 1.251 0.674 0.142 0.655 0.640 +1 0.407 2.138 1.044 0.743 0.819 2.112 0.373 -1.540 0.000 2.009 1.203 0.381 0.000 1.791 0.603 -0.251 1.274 1.107 0.137 -1.202 3.102 0.841 1.017 0.995 0.778 0.857 0.883 0.806 +1 0.787 1.108 -1.695 0.872 1.703 0.854 0.754 -0.130 2.173 0.644 0.076 1.677 2.215 0.548 -0.565 -0.584 0.000 0.744 -1.106 1.149 0.000 0.897 0.779 0.995 1.313 1.154 1.033 0.842 +0 1.365 1.113 1.709 0.438 -0.509 0.784 1.249 1.034 2.173 1.186 1.278 -0.682 2.215 0.377 1.205 -0.290 0.000 1.466 0.056 0.292 0.000 0.687 0.933 0.987 0.789 1.419 0.857 0.781 +1 0.717 -0.525 -0.352 1.629 -0.280 1.262 0.201 1.026 0.000 0.930 -1.141 -0.897 0.000 0.927 -0.576 1.247 2.548 1.330 -0.767 -1.323 1.551 0.909 1.119 0.993 1.078 0.636 0.875 0.922 +1 0.908 0.115 1.149 1.646 1.724 1.225 -0.293 0.400 2.173 1.041 -0.484 -0.481 2.215 0.557 0.108 -1.742 0.000 1.101 -0.088 -1.007 0.000 0.540 1.114 0.993 1.156 1.197 1.073 0.878 +0 0.326 -1.718 -0.762 1.476 -1.081 1.009 -0.526 0.085 2.173 0.807 0.924 -1.573 1.107 0.401 2.102 0.554 0.000 0.737 -2.058 1.475 0.000 0.859 0.825 1.000 1.087 1.706 1.022 0.976 +1 0.410 -0.630 -0.621 0.106 -1.494 0.960 0.757 1.360 0.000 1.260 -0.039 -0.345 2.215 1.099 0.236 0.839 2.548 0.997 1.162 -1.681 0.000 1.058 1.246 0.894 0.728 1.111 1.100 0.872 +1 0.815 0.465 1.062 1.180 -1.456 0.486 -2.050 -0.383 0.000 1.334 0.492 -1.611 1.107 0.702 1.159 -0.504 0.000 0.596 -1.142 -0.013 0.000 1.281 0.743 1.042 0.652 0.855 0.767 0.713 +0 0.677 -1.636 -0.558 0.793 -1.238 1.291 0.166 0.707 0.000 0.964 0.674 1.077 0.000 1.057 -0.317 -0.370 2.548 1.123 -0.462 -1.004 1.551 0.998 1.314 0.981 0.522 0.458 0.976 0.969 +1 0.716 -0.196 -1.450 0.547 0.205 1.360 0.703 1.173 2.173 1.445 1.036 -0.685 2.215 0.664 1.985 -0.341 0.000 0.908 1.186 0.695 0.000 0.979 0.779 0.987 0.947 2.084 1.108 0.918 +1 0.821 -1.303 -0.662 0.255 0.587 0.949 -1.584 0.074 0.000 1.314 -1.036 1.250 1.107 1.682 -0.910 -1.430 2.548 0.405 -2.460 1.057 0.000 1.007 1.273 0.995 0.816 1.050 1.046 0.907 +1 3.017 -0.240 -0.278 0.742 -0.499 1.309 -1.166 1.318 0.000 0.456 -0.627 -1.579 0.000 1.095 -0.002 0.621 2.548 1.182 0.617 -1.491 1.551 0.939 1.133 0.991 1.091 0.886 0.948 1.190 +1 0.525 0.090 -1.129 1.475 1.199 1.179 -0.271 0.808 1.087 1.038 -1.332 -0.208 0.000 1.146 -1.507 -0.625 0.000 1.108 -0.720 -0.937 0.000 0.924 0.923 1.053 0.872 0.798 0.960 0.900 +0 0.541 1.873 0.034 0.982 -1.227 0.820 0.544 -1.361 2.173 0.948 -1.400 0.751 0.000 1.107 1.254 -0.361 0.000 1.776 0.724 0.680 3.102 0.987 0.990 0.988 0.801 1.248 0.832 0.729 +1 0.861 1.019 -0.607 0.939 -1.434 1.869 0.577 0.981 0.000 0.782 0.873 -1.518 0.000 1.714 0.141 -0.476 2.548 1.681 1.314 -1.022 0.000 0.867 0.985 0.988 1.053 0.880 0.885 0.774 +1 0.382 -1.725 1.542 2.375 -0.686 0.412 -0.814 0.416 0.000 0.622 0.718 0.890 1.107 0.843 -1.152 1.337 0.000 0.614 -1.151 1.673 3.102 0.817 0.930 1.197 0.727 0.796 1.106 0.926 +0 1.356 -0.852 0.561 0.841 0.171 1.106 -1.225 -1.355 0.000 0.685 -0.324 -0.375 1.107 0.518 -1.494 0.743 0.000 1.197 -0.464 -1.234 3.102 1.321 0.862 0.977 0.850 0.579 0.741 0.830 +1 1.890 -0.933 0.683 0.671 1.065 0.655 0.224 -1.491 2.173 0.613 -0.680 -1.113 0.000 0.688 -1.341 -0.954 0.000 0.841 -0.399 0.284 0.000 0.896 0.827 1.000 0.760 0.741 0.914 0.781 +1 0.809 -0.053 -0.717 1.257 0.552 0.864 -0.223 1.572 0.000 0.650 0.470 -1.404 0.000 0.960 0.132 -0.166 2.548 1.513 -0.573 0.553 3.102 0.910 1.073 1.272 0.775 0.682 0.831 0.781 +0 0.655 1.421 -0.148 1.282 0.766 0.921 0.374 1.032 2.173 1.782 -0.174 -0.749 0.000 0.993 0.704 -0.810 0.000 1.219 -0.802 1.357 0.000 1.014 0.968 0.987 0.831 0.347 1.017 0.965 +1 0.589 -1.288 0.882 0.706 -1.282 0.912 -0.280 1.587 0.000 1.092 -1.156 1.702 2.215 2.027 1.958 -0.071 0.000 1.698 -1.373 0.314 0.000 2.229 1.465 0.985 1.327 1.471 1.195 1.070 +1 0.464 2.117 1.455 1.277 -0.532 0.492 0.348 1.679 0.000 0.620 -2.247 -0.951 0.000 1.811 0.032 0.499 2.548 0.626 1.016 -1.669 3.102 0.577 0.811 1.041 0.637 0.911 1.034 0.860 +0 0.883 0.282 1.525 0.923 -1.277 0.667 0.378 -0.304 0.000 0.604 -0.516 -0.880 1.107 1.025 -0.050 0.353 0.000 1.043 0.576 0.908 1.551 0.892 0.832 0.994 0.772 0.854 0.651 0.709 +0 1.139 1.250 -0.936 0.608 -1.478 2.087 0.351 1.160 1.087 2.580 0.803 -0.582 1.107 1.468 0.184 0.788 0.000 1.053 -0.524 1.014 0.000 0.649 0.844 0.987 0.961 3.510 1.749 1.419 +0 2.183 1.059 -1.225 1.576 -0.637 1.106 -1.504 0.383 2.173 0.680 -0.080 0.720 0.000 0.640 -2.077 0.819 0.000 1.254 -1.310 1.662 3.102 1.355 1.001 1.299 3.215 1.139 2.248 1.881 +1 0.950 -0.127 1.076 1.207 0.094 1.257 0.335 -1.062 2.173 0.612 1.214 0.474 0.000 0.712 -0.390 0.511 0.000 0.597 -0.307 1.683 3.102 0.954 0.739 1.148 1.321 0.657 0.850 0.824 +0 1.061 -0.398 -1.582 1.375 -1.049 0.949 0.906 0.811 0.000 0.788 -1.621 -0.817 0.000 0.569 2.087 0.815 0.000 0.930 -0.289 0.216 3.102 0.984 1.042 0.983 0.723 0.375 0.918 0.986 +1 0.625 -0.581 0.952 1.814 1.733 1.494 -0.925 -0.238 1.087 0.768 -0.362 1.299 2.215 0.441 -0.396 1.636 0.000 0.484 -1.153 -1.126 0.000 0.644 0.873 0.989 0.543 1.613 1.133 0.867 +1 0.282 -0.038 0.753 0.129 1.713 1.088 1.135 -0.984 2.173 1.153 1.030 1.002 2.215 0.656 0.901 -0.295 0.000 0.736 -0.278 0.232 0.000 0.648 0.966 0.608 0.488 1.610 0.912 0.708 +1 0.728 1.193 1.172 1.265 -1.518 1.591 -0.702 0.414 0.000 1.005 2.186 0.308 0.000 1.749 1.475 -1.567 0.000 1.780 0.022 1.448 3.102 0.923 0.960 0.988 0.764 0.287 0.719 0.981 +0 0.521 -1.252 -1.330 1.137 -1.274 1.091 -0.228 -0.701 2.173 1.189 -1.964 0.395 0.000 0.946 -0.794 0.713 0.000 0.618 -0.506 1.521 1.551 1.088 0.866 0.995 1.823 0.806 1.170 1.186 +0 0.425 -0.942 -0.408 1.318 -1.666 0.931 2.046 -0.693 0.000 1.869 0.591 0.778 2.215 1.306 0.330 1.302 0.000 2.187 0.303 -0.676 3.102 0.738 1.018 0.989 1.878 1.777 1.458 1.139 +1 1.152 -0.412 -0.081 0.687 0.449 0.871 -1.778 -1.543 0.000 0.630 -0.600 -1.460 0.000 0.619 -1.361 0.102 0.000 1.057 -1.064 1.658 1.551 0.988 1.231 0.985 0.813 0.780 0.745 0.815 +1 1.073 1.471 -0.864 0.993 0.145 0.655 2.136 0.577 0.000 1.569 0.246 -1.630 2.215 0.466 2.203 1.451 0.000 1.315 0.437 -0.125 1.551 0.715 1.036 1.129 1.403 1.279 1.183 1.034 +0 0.688 0.289 -0.299 0.329 -1.276 0.630 2.799 0.546 0.000 0.519 -1.509 -1.140 2.215 1.299 -1.524 0.950 0.000 0.580 -0.520 -0.535 0.000 0.899 1.022 0.992 0.659 1.087 1.650 1.429 +0 1.109 0.433 -0.468 1.175 0.241 0.701 -1.039 1.730 0.000 0.377 1.571 1.412 2.215 0.591 0.098 0.501 2.548 0.502 -1.305 -0.971 0.000 0.634 0.792 0.985 0.794 0.555 0.787 0.868 +0 0.946 1.536 0.058 0.724 -0.300 1.641 1.067 0.425 2.173 1.580 -0.824 -1.154 0.000 2.171 0.012 -1.604 2.548 1.229 -2.323 1.184 0.000 2.579 2.312 0.994 0.914 2.625 2.842 2.275 +1 0.617 1.733 -0.695 1.110 1.042 1.355 0.182 -1.722 1.087 1.529 -1.026 -0.209 0.000 0.911 -0.820 0.991 2.548 0.591 1.563 -0.394 0.000 2.547 1.688 1.147 1.466 1.186 1.458 1.530 +1 1.852 0.057 -1.327 1.108 1.144 1.124 -0.445 0.673 2.173 0.650 0.023 -0.280 2.215 0.468 0.818 0.158 0.000 1.160 -1.182 -0.682 0.000 0.670 0.666 1.572 1.054 0.999 1.013 0.796 +0 0.869 0.150 -0.228 1.577 0.290 1.197 1.356 -1.683 1.087 0.635 1.798 1.358 0.000 0.743 2.004 0.641 0.000 0.851 -1.142 -1.462 3.102 0.799 1.347 0.987 1.212 1.979 1.515 1.322 +0 1.687 -0.743 0.796 1.179 1.013 2.338 0.190 -0.846 2.173 1.664 -1.004 0.541 0.000 0.810 0.648 -0.799 2.548 1.504 -0.334 -0.332 0.000 1.602 1.480 0.999 2.247 0.457 1.549 1.466 +0 1.525 0.148 -0.799 0.902 -1.485 1.169 -1.336 1.029 1.087 0.726 -1.975 0.299 0.000 1.173 -0.496 -0.983 0.000 1.220 -0.316 1.184 1.551 0.972 0.808 0.984 1.646 0.660 1.081 0.914 +0 0.348 -0.907 0.642 0.978 -0.753 1.609 0.838 -0.160 1.087 2.021 -0.391 1.533 2.215 0.926 0.143 0.604 0.000 1.303 0.069 -1.558 0.000 1.127 1.077 0.989 2.618 3.186 2.117 1.618 +0 0.701 -0.799 -0.975 0.621 -1.159 1.172 -1.742 1.184 0.000 1.609 0.733 -0.314 2.215 0.452 -0.745 0.490 0.000 0.637 -0.571 1.438 3.102 0.998 0.662 0.996 0.982 1.162 1.587 1.303 +1 0.299 1.678 -0.207 1.484 -0.899 0.966 0.987 -0.482 2.173 0.297 2.175 1.023 0.000 0.919 -0.041 1.075 0.000 0.379 1.336 1.696 3.102 0.924 0.783 0.993 0.736 0.624 0.902 0.799 +0 1.447 0.708 -0.072 0.402 0.199 1.086 0.723 -0.923 2.173 1.208 0.486 1.071 2.215 0.656 2.218 1.494 0.000 0.553 0.968 -1.527 0.000 0.527 0.928 0.989 1.044 1.653 1.042 0.912 +0 1.186 1.102 0.204 0.191 -0.201 0.836 -2.042 0.858 0.000 0.837 -0.530 -0.917 2.215 1.103 1.021 -1.524 0.000 1.402 0.403 -1.154 0.000 0.632 0.919 0.983 0.510 0.750 0.705 0.727 +1 1.178 0.413 -0.465 1.224 -1.463 1.037 0.708 0.693 0.000 0.561 -0.348 0.362 0.000 1.109 0.341 -1.441 2.548 0.671 -1.031 -0.785 3.102 1.028 1.123 1.301 0.740 0.695 0.879 0.868 +1 0.598 -1.435 0.398 1.088 -1.164 0.684 -0.904 0.101 2.173 0.911 -1.215 1.288 0.000 1.052 -0.182 1.330 0.000 1.886 -0.185 -0.505 3.102 0.906 0.966 1.102 0.922 0.755 0.729 0.747 +0 0.847 1.179 -1.039 1.368 -1.346 1.598 1.575 0.489 2.173 1.243 -0.272 -1.603 1.107 0.324 0.160 0.237 0.000 0.768 1.562 -0.414 0.000 0.608 0.847 0.983 1.764 2.981 1.777 1.306 +0 0.714 1.317 -1.712 0.959 1.663 1.010 0.010 -1.075 2.173 0.863 1.840 0.742 0.000 1.623 0.889 0.299 2.548 0.858 1.811 0.358 0.000 0.789 0.952 0.988 0.893 1.708 1.250 1.090 +1 0.359 1.363 0.130 0.783 1.588 1.052 -1.057 0.267 0.000 0.948 0.501 -0.992 2.215 0.832 -0.601 1.344 2.548 0.524 -0.414 1.721 0.000 1.143 0.902 0.981 0.747 1.003 0.949 0.830 +1 0.790 -0.299 1.378 1.729 -1.729 1.012 -0.899 0.180 2.173 0.687 -0.128 0.039 0.000 1.620 -0.390 -1.362 2.548 0.739 -1.797 0.015 0.000 0.788 1.008 0.999 0.797 1.616 1.064 1.023 +1 0.552 1.138 0.037 1.701 -0.060 0.905 0.310 1.545 2.173 0.868 1.133 1.550 0.000 1.112 -0.243 -1.115 2.548 1.414 -0.557 -0.012 0.000 2.081 1.395 0.998 1.270 0.922 0.986 0.992 +0 1.524 1.498 -1.736 0.578 -1.106 0.594 -0.362 -0.811 0.000 0.767 0.914 1.116 1.107 1.181 0.357 -0.364 0.000 2.530 0.838 0.506 3.102 0.831 1.183 0.981 1.264 0.658 0.972 1.090 +0 0.918 -1.183 1.706 0.657 -0.908 0.633 -1.169 -0.809 0.000 0.776 -1.040 0.794 1.107 1.051 -1.412 0.211 2.548 0.740 -1.330 1.560 0.000 0.911 0.911 0.989 0.895 0.535 0.689 0.666 +1 0.957 0.098 1.073 1.421 -1.659 1.546 -0.670 -0.233 2.173 1.539 1.046 1.485 2.215 0.667 -0.376 -0.640 0.000 0.911 -0.877 0.274 0.000 0.690 0.640 1.016 0.902 3.200 1.604 1.263 +1 0.511 0.488 0.454 1.708 -0.561 0.567 0.471 0.924 0.000 1.259 -0.153 1.512 2.215 0.547 -0.798 -0.891 2.548 0.372 -1.061 0.558 0.000 0.732 0.759 1.025 0.722 0.799 0.839 0.754 +1 0.468 0.755 -0.715 0.455 -0.858 0.648 1.114 0.868 0.000 1.271 0.497 1.190 2.215 1.252 1.627 -0.608 0.000 1.438 -0.436 -0.099 0.000 1.665 1.045 0.983 1.084 0.587 0.882 0.978 +1 0.947 -1.734 1.738 0.854 -0.164 1.258 1.374 -0.534 0.000 1.244 -0.345 1.712 0.000 1.372 -1.133 1.463 2.548 1.180 -0.476 0.451 3.102 0.887 1.146 1.233 0.879 0.838 0.735 0.851 +1 0.672 -0.078 1.170 1.109 -0.142 0.923 -0.331 1.683 2.173 0.715 -0.892 -0.063 0.000 0.861 -2.060 -0.595 0.000 0.633 -0.388 -0.371 3.102 0.941 0.778 1.106 0.993 0.779 0.871 0.880 +1 1.321 -0.501 -1.721 0.785 0.955 0.419 -0.989 0.146 0.000 1.245 -1.063 -0.729 1.107 0.397 -0.758 -1.328 2.548 0.892 -0.100 0.052 0.000 0.874 1.030 0.990 0.530 0.396 0.673 0.649 +0 0.512 1.691 -1.331 0.552 0.373 1.198 0.838 1.315 2.173 1.404 0.310 -0.260 0.000 0.540 -0.149 -0.932 0.000 0.851 -0.241 1.537 1.551 0.827 0.880 0.980 0.856 0.674 0.990 0.823 +1 0.982 -0.229 -0.513 2.079 -1.097 0.958 1.377 -0.057 0.000 1.838 -0.866 1.253 1.107 0.511 0.332 -1.728 2.548 0.694 -1.417 0.006 0.000 0.704 1.081 0.993 1.624 0.825 1.040 1.113 +1 1.091 -1.010 0.509 0.501 1.338 0.808 0.018 -0.627 2.173 0.425 0.724 1.649 0.000 0.837 2.038 0.871 0.000 0.425 -0.526 1.116 0.000 0.953 0.771 0.992 1.003 0.790 0.878 0.992 +1 0.372 -1.892 0.776 0.680 -0.785 0.806 0.746 -0.040 2.173 0.646 -0.934 -1.720 0.000 1.362 -0.355 1.195 0.000 0.634 -0.010 -0.593 0.000 0.822 0.554 0.989 1.039 0.671 0.862 0.772 +0 1.050 -0.001 -1.376 0.020 0.805 0.742 0.660 0.805 0.000 0.449 -1.209 -0.417 2.215 0.679 -0.535 -0.673 1.274 0.445 -0.135 0.680 0.000 0.388 0.918 0.461 0.428 0.238 0.653 0.585 +0 0.904 1.056 0.005 0.710 0.367 1.104 0.943 1.376 0.000 0.911 1.294 -0.810 2.215 0.370 1.300 -1.609 1.274 0.767 1.655 -0.306 0.000 1.597 0.873 0.990 0.853 0.408 0.734 0.763 +1 0.541 -1.546 -0.236 1.359 1.513 1.211 -1.154 -0.146 2.173 1.203 -1.542 1.533 0.000 1.003 -0.439 -0.550 2.548 0.660 -0.964 0.666 0.000 0.855 1.091 1.187 1.188 0.684 0.964 0.879 +0 1.036 0.784 1.434 1.159 0.645 0.872 0.520 -1.043 2.173 0.852 0.779 -0.635 0.000 1.874 0.418 0.517 1.274 1.150 -0.359 1.514 0.000 0.874 1.007 0.990 0.752 1.572 0.957 0.895 +1 0.914 -0.525 0.572 0.679 -1.285 1.191 0.337 1.619 2.173 0.820 0.434 0.604 0.000 1.828 -0.379 -0.370 0.000 1.657 -0.428 -0.949 1.551 0.399 0.587 1.086 1.021 1.274 0.962 0.836 +1 0.620 0.647 -0.146 1.161 -1.632 0.655 -1.134 0.078 0.000 1.126 -0.900 -1.117 2.215 1.084 -0.192 1.127 0.000 0.637 0.863 -0.773 3.102 1.066 1.102 1.144 1.128 0.908 0.957 0.960 +0 0.756 -0.342 -0.004 0.726 1.286 0.692 0.599 -0.880 2.173 0.485 1.135 -0.486 2.215 0.582 -0.243 0.536 0.000 0.731 1.190 1.430 0.000 0.859 0.887 0.984 0.727 0.384 0.620 0.604 +1 0.810 -0.507 0.384 0.888 1.317 0.867 -1.311 -0.965 0.000 0.920 -1.167 0.570 2.215 1.116 -0.425 -0.699 0.000 1.072 0.097 1.420 3.102 0.887 1.098 0.991 0.790 0.888 0.923 0.865 +0 1.542 1.087 0.611 0.407 0.153 0.475 -2.321 1.049 0.000 1.036 -1.229 -0.647 1.107 1.142 -0.144 -1.375 2.548 0.503 -1.309 1.622 0.000 1.177 1.130 0.982 1.068 0.975 1.145 1.194 +1 0.385 0.857 -1.130 1.611 1.466 1.363 0.351 0.016 2.173 0.393 -0.509 1.735 0.000 0.379 0.639 1.053 2.548 1.131 0.402 -0.900 0.000 0.755 1.138 0.994 0.579 0.737 1.061 0.940 +1 1.010 -0.625 -0.011 0.383 1.620 0.822 -0.547 -1.667 0.000 0.354 -1.294 1.425 0.000 0.917 0.342 0.170 2.548 0.900 0.191 -1.117 3.102 0.755 0.684 0.990 0.632 0.639 0.517 0.506 +0 0.824 -0.613 -0.018 0.446 -1.459 1.084 -0.982 -0.750 1.087 1.622 1.438 0.938 0.000 1.488 2.255 -1.242 0.000 2.901 0.512 1.231 3.102 0.938 0.959 0.990 0.765 2.483 1.806 1.545 +1 1.115 -0.809 -1.350 0.766 -0.213 1.114 -1.178 1.240 0.000 1.833 -0.548 -0.970 2.215 2.052 -0.726 0.318 0.000 1.338 -0.088 1.632 3.102 0.917 0.824 1.094 0.784 1.062 0.839 0.738 +0 2.743 0.288 -1.303 0.672 -0.736 1.816 -1.435 0.680 1.087 0.704 -1.449 0.044 1.107 1.750 -0.910 -1.157 0.000 1.002 -0.533 0.585 0.000 1.484 1.106 0.993 2.713 0.904 1.800 1.471 +0 0.418 0.799 1.673 0.528 -1.454 0.977 -0.306 -0.048 1.087 0.768 -1.166 -1.382 0.000 0.583 -1.048 0.599 0.000 0.697 0.807 1.098 1.551 1.003 0.978 0.987 0.990 0.958 0.845 0.754 +0 1.177 -1.178 -0.344 0.161 1.077 0.549 0.131 -0.908 0.000 0.851 -0.954 0.771 2.215 0.507 -0.232 1.512 0.000 0.725 -0.570 1.208 3.102 0.937 1.033 0.994 0.639 0.292 0.630 0.620 +1 0.594 0.915 0.073 0.854 -1.619 1.197 -0.380 0.974 1.087 0.848 -0.812 -1.081 0.000 0.648 -0.581 -0.152 2.548 0.519 -0.368 1.466 0.000 0.887 1.222 0.987 0.790 0.943 0.866 0.804 +1 1.288 -0.597 -1.190 2.446 -1.338 1.357 1.603 0.658 0.000 0.688 -0.030 -1.473 0.000 1.176 0.181 -0.238 0.000 1.301 -0.251 0.723 1.551 0.830 0.737 0.982 1.092 0.267 0.935 0.925 +1 1.105 1.192 1.173 0.676 0.359 0.945 0.736 1.559 2.173 1.215 2.299 -1.104 0.000 1.028 2.122 -0.095 0.000 0.922 1.277 0.574 3.102 0.791 0.653 0.985 0.930 0.865 0.887 0.782 +0 0.936 1.106 -1.311 0.717 1.476 0.441 0.054 -1.381 0.000 0.635 1.199 -0.002 1.107 1.050 -0.509 0.130 2.548 0.443 1.650 0.312 0.000 1.012 0.993 0.991 0.830 0.893 0.992 0.840 +0 0.724 -0.133 -1.375 1.353 -0.875 1.534 0.876 0.684 2.173 0.517 1.248 0.471 0.000 1.621 -0.399 -0.908 0.000 1.863 0.000 1.507 3.102 0.683 0.977 0.985 1.535 1.458 1.152 0.939 +0 0.711 0.427 1.648 0.862 1.368 0.456 -1.998 -0.954 0.000 1.134 -1.110 0.147 2.215 0.535 -1.132 -0.851 2.548 0.470 -0.463 1.201 0.000 0.867 0.918 0.992 0.700 0.649 0.752 0.701 +0 0.441 1.675 -1.415 0.804 0.367 1.767 0.190 -0.725 2.173 1.638 1.090 0.970 2.215 0.812 1.275 0.296 0.000 1.388 0.750 -1.514 0.000 0.909 1.200 0.986 2.148 2.776 1.741 1.426 +1 1.700 -0.424 -1.294 0.572 -1.057 0.740 -0.733 -0.783 0.000 1.960 -1.319 0.250 2.215 1.264 1.533 1.297 0.000 0.891 -1.819 0.660 0.000 0.868 0.879 0.983 0.542 1.278 1.195 0.996 +0 1.246 -1.193 1.361 0.574 0.328 0.594 -0.467 -1.062 2.173 0.557 -0.922 -0.554 0.000 0.969 -0.370 0.499 2.548 0.443 -1.947 0.746 0.000 0.770 0.738 0.990 0.665 0.933 0.686 0.630 +1 0.538 0.690 -0.179 1.195 -0.928 0.419 1.353 1.032 0.000 0.581 -0.160 -0.501 2.215 1.067 -0.490 0.765 0.000 0.849 0.233 -1.287 1.551 1.357 0.970 0.987 0.874 0.436 0.711 0.831 +0 0.992 -1.496 -0.831 0.806 1.709 1.118 -0.779 -1.099 2.173 1.240 0.182 0.337 2.215 1.330 1.281 0.537 0.000 1.106 0.452 1.061 0.000 0.852 0.902 0.986 0.884 1.884 1.381 1.496 +1 1.963 0.507 0.777 1.179 -0.738 1.353 -0.155 -1.415 2.173 0.549 0.923 1.473 2.215 0.579 -0.164 -0.941 0.000 1.763 -1.087 0.212 0.000 1.004 1.143 2.063 1.677 0.984 1.115 1.090 +0 1.304 1.487 -0.030 1.077 0.961 1.363 1.330 -1.425 0.000 1.301 0.623 0.497 1.107 1.045 0.547 -0.864 2.548 0.643 0.682 1.645 0.000 0.647 0.795 1.280 0.933 1.167 1.022 0.994 +0 0.396 -0.606 0.851 0.564 -0.887 0.757 0.072 -1.294 0.000 1.554 0.412 0.504 2.215 0.460 -0.665 0.179 1.274 0.966 1.096 -1.247 0.000 0.866 0.886 0.983 1.592 0.607 0.983 1.086 +1 1.411 -2.003 -0.452 0.329 0.224 0.687 -1.568 -1.417 2.173 1.022 -0.311 1.391 2.215 0.734 -0.960 -0.794 0.000 0.823 2.281 0.925 0.000 0.613 0.849 0.975 0.868 1.091 0.947 0.777 +0 0.878 -0.751 1.327 3.579 1.408 1.228 -1.540 -0.574 0.000 1.223 1.277 -0.747 0.000 1.139 1.249 0.496 2.548 1.530 1.266 -0.142 0.000 0.942 0.998 0.987 0.852 0.746 1.038 1.340 +0 0.675 -1.009 -0.347 1.295 -0.716 0.946 1.286 1.189 0.000 0.512 2.521 -1.234 0.000 0.459 0.754 -0.861 2.548 0.894 -0.051 1.146 0.000 1.033 0.848 0.986 0.872 0.610 0.603 0.831 +1 1.667 -0.565 0.759 1.695 1.215 1.524 2.534 -0.887 0.000 1.595 -1.159 -0.227 2.215 0.680 -1.005 -1.595 2.548 1.409 -1.178 0.974 0.000 0.729 1.160 0.987 0.815 1.046 1.081 0.876 +1 0.822 0.304 -0.666 1.194 -0.132 1.024 0.116 1.673 2.173 0.587 0.497 0.754 0.000 0.488 -0.871 -0.080 2.548 0.659 0.414 1.192 0.000 0.311 0.692 0.993 0.518 1.011 0.835 0.730 +0 1.133 -0.647 -0.998 0.112 1.470 0.848 -0.343 1.082 0.000 1.305 0.181 -0.200 1.107 1.737 1.726 -1.128 0.000 1.603 0.174 0.595 3.102 0.912 0.922 0.979 0.861 0.858 0.992 0.903 +0 0.593 0.250 -0.005 0.593 -1.536 1.027 -0.787 0.746 2.173 1.521 0.237 -0.282 2.215 2.315 -1.293 -1.360 0.000 1.642 -1.183 0.918 0.000 1.905 1.676 0.987 0.876 1.779 1.613 1.448 +0 1.550 -0.517 -0.243 1.418 -1.149 1.416 -1.681 -1.687 0.000 2.057 -0.067 0.424 2.215 0.548 -0.472 -1.688 2.548 0.938 1.006 0.208 0.000 0.819 0.752 1.495 1.549 1.097 1.546 1.396 +0 0.769 -2.183 0.341 0.621 0.870 0.685 0.452 -0.699 1.087 0.925 -1.167 -1.569 2.215 0.698 -0.263 0.496 0.000 0.554 -0.487 -1.667 0.000 0.646 0.790 0.981 0.888 1.364 1.041 0.818 +0 0.767 -0.494 1.477 1.613 0.797 0.715 -1.306 -1.343 0.000 0.910 -1.513 -0.492 0.000 0.794 -0.889 1.631 1.274 1.404 -0.996 -0.028 3.102 1.029 0.871 0.987 0.721 0.809 0.730 0.775 +1 1.079 -1.245 -1.356 0.789 -1.554 1.017 -0.603 0.136 2.173 0.555 -0.534 0.685 0.000 0.703 -0.828 1.088 2.548 0.448 0.813 -1.102 0.000 0.847 0.841 0.978 0.694 0.813 0.841 0.727 +0 1.117 -1.143 1.593 1.739 -1.417 0.309 -0.462 1.325 0.000 0.831 -1.035 -0.004 2.215 0.940 0.674 0.599 2.548 0.578 -0.890 0.290 0.000 0.989 0.877 0.982 1.666 1.083 1.220 1.185 +1 0.433 -0.543 0.418 0.843 1.568 1.073 0.753 -0.890 0.000 0.701 0.248 -0.434 2.215 0.724 -2.226 0.824 0.000 1.954 0.645 0.614 3.102 1.299 0.973 0.993 1.435 0.900 1.062 1.212 +1 1.255 -0.382 -1.548 0.619 -0.308 1.062 0.288 0.378 2.173 1.067 -1.041 -1.599 0.000 0.765 -0.695 -0.946 1.274 0.835 -0.466 0.414 0.000 1.009 0.666 1.099 1.151 1.214 0.994 0.854 +0 1.279 0.539 -1.468 0.312 0.991 0.667 -1.582 -0.684 0.000 0.945 -0.145 1.385 2.215 1.060 -0.101 -0.180 2.548 0.624 -2.206 0.456 0.000 1.002 1.129 0.984 0.662 1.050 1.015 0.981 +0 2.094 0.416 -1.638 0.462 -0.603 1.339 1.348 -0.356 2.173 1.630 1.087 1.506 0.000 1.023 -0.107 0.183 0.000 1.380 0.423 0.493 3.102 0.944 0.954 1.095 1.484 1.181 1.088 1.044 +0 0.680 -0.768 0.564 1.169 1.527 0.524 -0.053 -0.583 0.000 0.801 0.341 0.220 0.000 1.258 0.538 -1.330 2.548 1.138 -0.083 0.470 1.551 0.952 1.012 0.985 0.733 0.968 0.837 0.832 +1 2.188 0.051 -1.652 0.517 0.457 1.206 0.545 0.158 2.173 0.465 -1.881 0.693 0.000 1.532 0.412 -1.069 2.548 0.531 0.804 0.654 0.000 1.305 1.398 1.394 1.465 1.516 1.174 1.097 +1 1.760 -0.554 0.291 0.506 0.694 1.160 0.212 -1.118 2.173 0.438 0.053 -1.481 0.000 0.443 0.119 0.815 0.000 0.631 0.753 1.224 3.102 0.647 0.689 0.974 0.870 0.840 1.065 0.824 +1 1.685 0.469 -0.039 0.196 0.391 1.476 1.456 1.373 2.173 0.731 0.138 -0.689 0.000 0.820 0.594 -1.669 0.000 0.582 2.296 -0.783 0.000 0.967 0.686 0.993 1.476 1.074 0.994 0.930 +0 1.534 1.079 0.443 0.823 1.317 0.412 2.041 1.690 0.000 0.678 0.288 -0.426 2.215 0.429 0.380 -1.171 0.000 0.649 0.673 -1.384 3.102 0.946 0.949 1.104 0.714 0.481 0.647 0.653 +1 0.590 -0.970 -0.212 1.752 1.189 0.708 -0.681 -1.202 0.000 0.655 -0.142 1.271 1.107 0.765 -1.462 -0.995 0.000 1.625 2.174 -0.213 0.000 0.676 1.006 1.342 0.790 0.409 0.726 0.780 +0 1.707 0.327 -1.150 0.274 0.035 1.279 -1.306 0.536 0.000 0.510 -1.738 -0.003 0.000 0.999 -0.884 -1.137 0.000 1.211 -0.034 1.572 3.102 0.899 1.280 0.990 0.854 0.530 0.750 0.955 +0 0.422 0.755 -0.656 1.486 -0.123 0.597 0.739 1.603 2.173 0.661 -0.740 0.777 0.000 0.728 -1.106 -1.175 2.548 0.514 2.157 1.515 0.000 2.012 1.281 0.989 1.804 1.060 1.274 1.225 +0 0.962 1.619 -1.155 0.727 0.245 0.492 1.882 0.800 0.000 0.593 0.460 -1.629 2.215 0.673 0.232 -0.533 2.548 0.753 0.898 1.112 0.000 0.696 0.756 1.104 0.750 0.565 0.618 0.593 +0 0.412 0.253 -1.542 1.399 -0.477 1.000 0.007 0.499 2.173 0.800 1.572 -1.377 0.000 0.727 0.762 1.587 1.274 0.465 0.210 0.159 0.000 0.976 0.667 0.988 1.143 0.992 0.844 0.792 +1 0.953 0.033 -1.679 1.534 -1.613 0.872 0.529 0.135 1.087 1.122 0.872 0.961 2.215 0.880 -1.567 -0.103 0.000 0.937 0.480 -0.312 0.000 0.705 0.730 0.989 1.304 1.021 0.994 0.789 +0 0.297 1.121 1.505 1.861 -0.493 0.685 -0.992 1.602 0.000 0.759 0.430 0.642 2.215 0.564 -0.762 0.440 0.000 1.102 0.159 -1.512 3.102 0.973 1.009 1.003 0.813 0.777 0.727 0.937 +0 0.860 0.689 -0.496 0.426 0.476 1.227 0.027 0.623 1.087 0.886 -0.615 1.652 2.215 0.383 0.137 0.360 0.000 1.674 -0.877 -1.114 0.000 1.027 0.830 0.988 0.899 1.331 0.941 0.832 +1 0.812 0.543 0.987 0.471 -0.626 0.669 -1.155 0.297 0.000 0.908 0.179 -1.363 2.215 0.975 0.443 0.024 2.548 0.642 -1.721 -1.582 0.000 1.098 1.191 0.987 0.731 0.961 0.975 0.828 +0 0.318 1.916 -1.528 1.226 -0.246 0.461 0.163 1.165 2.173 0.359 1.899 1.459 0.000 0.665 1.098 0.421 2.548 0.536 2.082 -0.759 0.000 0.544 0.830 0.984 0.595 0.570 0.580 0.582 +0 0.901 0.831 -1.387 4.221 -1.484 1.743 -1.194 0.087 0.000 0.867 -0.058 0.357 2.215 0.780 0.780 1.346 2.548 0.887 -0.033 0.785 0.000 1.272 1.104 0.988 0.872 0.797 1.252 1.708 +1 1.020 -0.240 -0.819 1.867 0.123 1.243 2.916 1.640 0.000 1.422 -0.629 -0.174 0.000 1.607 -0.531 1.497 0.000 1.218 0.206 -0.076 3.102 2.315 1.445 1.436 1.076 0.673 1.020 0.949 +0 0.911 -1.205 0.164 0.787 -1.690 1.524 -0.580 -0.014 1.087 1.700 0.042 -1.671 0.000 0.839 0.887 -0.367 0.000 1.374 -1.011 0.986 0.000 0.578 1.540 1.167 1.115 1.804 1.354 1.185 +1 0.792 0.613 1.578 1.155 -1.316 2.242 -0.825 -0.035 0.000 1.424 -0.087 1.543 1.107 1.393 -0.092 0.973 2.548 1.249 0.508 -1.268 0.000 0.827 0.894 0.981 0.697 0.735 0.664 0.626 +1 0.996 -0.088 -1.723 0.728 -0.582 0.423 0.613 -0.434 2.173 0.772 0.651 -1.406 2.215 0.608 -0.967 -0.144 0.000 1.086 0.429 0.340 0.000 0.870 0.995 1.011 0.683 0.646 0.653 0.624 +1 0.819 -1.525 -0.766 0.873 1.129 0.731 -0.920 -0.389 0.000 0.500 -0.073 1.730 0.000 0.974 -0.295 1.031 0.000 0.936 0.894 -1.058 3.102 0.645 0.790 1.161 0.680 1.014 0.868 0.750 +0 0.382 1.907 0.741 0.098 -0.924 0.461 0.789 0.404 0.000 1.287 0.096 -1.020 2.215 0.434 -0.826 0.254 2.548 0.821 0.044 1.141 0.000 0.681 1.071 0.990 0.639 0.836 0.700 0.636 +0 0.625 0.544 -1.069 0.506 0.281 0.560 -0.650 -1.569 0.000 0.936 -0.797 0.513 2.215 0.875 0.185 1.166 2.548 0.863 1.837 -0.844 0.000 0.835 0.856 0.985 0.764 0.742 0.859 0.840 +1 1.636 -0.107 1.626 0.151 0.679 0.668 0.901 -0.472 2.173 0.392 -0.551 1.025 0.000 1.585 0.536 0.402 2.548 1.238 -0.244 -1.031 0.000 0.881 0.965 0.988 1.001 0.932 0.871 0.792 +1 1.095 -0.028 1.404 0.586 0.748 0.653 -0.676 0.787 0.000 1.153 0.041 -1.451 2.215 1.093 0.184 0.472 0.000 1.069 0.617 -0.444 3.102 0.815 0.953 0.978 0.863 0.866 0.885 0.793 +0 1.015 0.154 1.097 0.784 1.205 1.176 0.768 -0.982 2.173 1.263 1.747 1.063 0.000 1.677 1.427 -0.314 0.000 0.875 1.182 0.679 0.000 0.861 1.159 0.991 0.811 0.326 0.991 1.065 +1 1.665 -0.167 0.929 0.991 -0.138 1.314 -1.467 -0.968 2.173 0.623 -2.753 0.752 0.000 0.679 0.065 1.592 0.000 0.685 -1.024 -1.653 3.102 2.128 1.185 1.459 1.800 0.594 1.136 1.198 +0 0.655 -0.854 -1.038 1.626 -1.230 0.813 -0.356 0.861 1.087 0.322 2.792 -0.137 0.000 0.803 -0.822 0.517 2.548 0.524 0.857 -0.547 0.000 0.600 1.371 1.003 1.497 0.414 1.086 1.649 +0 2.814 1.249 -0.195 0.544 1.183 1.116 0.659 0.255 2.173 2.117 -0.086 1.738 1.107 0.688 -2.364 1.356 0.000 0.883 1.439 -1.264 0.000 3.737 2.546 1.623 1.105 2.367 1.996 2.005 +0 0.984 -0.227 -0.807 1.883 -1.121 0.602 -1.129 0.765 0.000 0.679 0.790 0.117 2.215 0.633 0.278 0.605 0.000 0.569 -0.912 1.471 1.551 0.888 0.975 0.993 0.665 0.814 0.869 0.874 +1 0.724 -0.539 0.979 1.230 0.258 0.406 -1.160 -0.796 1.087 0.618 1.000 -1.460 0.000 0.530 -0.930 1.683 1.274 0.561 0.033 0.316 0.000 0.863 0.941 0.996 0.665 0.457 0.628 0.742 +0 1.771 1.430 -1.317 1.005 -1.657 2.017 0.973 0.329 2.173 1.638 0.053 -1.562 2.215 1.384 -0.076 -0.440 0.000 1.237 0.301 0.910 0.000 1.392 1.383 0.996 1.966 2.946 1.685 1.427 +1 0.741 -0.897 -1.012 0.570 -1.041 1.318 -0.287 -0.145 0.000 1.097 -0.642 1.584 2.215 1.767 -1.274 0.871 2.548 1.251 0.010 1.320 0.000 0.830 1.074 0.976 1.007 1.048 0.912 0.868 +0 1.203 -1.280 1.044 0.561 1.738 1.449 -0.130 0.895 2.173 1.366 -0.196 -0.703 0.000 1.280 0.381 -1.051 2.548 0.602 -0.918 -0.229 0.000 0.921 0.856 0.992 0.936 1.733 1.205 1.062 +0 0.943 -1.171 -0.799 0.607 -0.149 0.934 -1.570 -1.099 2.173 1.199 -2.631 0.883 0.000 1.309 -1.370 0.718 2.548 0.655 -0.508 -0.482 0.000 1.809 1.185 0.986 0.914 1.375 1.081 1.058 +0 0.532 -0.051 0.299 0.856 -1.708 1.207 1.661 0.543 0.000 0.793 1.166 -0.169 2.215 0.800 0.905 0.968 0.000 3.044 0.617 -1.456 3.102 0.901 0.941 0.987 0.805 1.326 1.153 0.962 +1 0.283 1.950 -0.709 0.896 1.250 1.061 1.257 -1.517 0.000 1.402 0.759 0.363 2.215 0.983 0.656 -0.450 2.548 0.562 1.300 1.050 0.000 0.886 0.986 0.991 0.858 0.835 0.948 0.803 +1 1.039 -0.094 0.708 1.258 1.289 1.013 -0.687 -0.574 2.173 0.537 -0.562 0.516 0.000 0.551 -1.487 0.632 0.000 0.605 0.553 -1.311 3.102 0.469 0.948 0.997 0.658 0.793 0.935 0.831 +0 1.053 -0.766 -0.682 0.479 0.737 0.855 -0.834 0.172 1.087 0.464 -1.537 1.207 2.215 0.974 -1.017 -1.032 0.000 1.060 -1.535 1.701 0.000 0.913 1.114 0.986 0.709 0.822 0.759 0.670 +1 0.844 0.445 -0.041 0.150 1.562 1.254 1.260 -0.134 2.173 1.849 1.394 -1.716 2.215 0.449 1.315 -1.013 0.000 0.497 0.879 0.495 0.000 0.640 0.783 0.993 1.509 2.225 1.368 1.028 +1 0.919 -0.426 -0.336 0.495 -0.147 0.816 -0.468 1.345 0.000 0.634 -0.630 -1.334 2.215 0.712 1.031 1.021 2.548 0.909 -2.463 -0.486 0.000 2.467 1.512 0.980 1.205 0.945 1.310 1.112 +1 1.391 1.046 -0.479 1.945 -1.100 1.225 0.565 0.811 2.173 0.638 0.566 -1.656 1.107 0.375 -0.242 0.845 0.000 0.538 0.111 -0.022 0.000 0.447 0.552 1.209 0.872 1.034 1.135 0.842 +0 0.347 -1.265 -0.788 0.666 1.196 0.738 -2.518 -1.357 0.000 0.402 -2.857 -0.316 0.000 1.011 -0.839 0.566 2.548 0.629 -0.604 -0.347 0.000 0.884 0.852 0.979 0.558 0.357 0.592 0.672 +0 0.476 0.704 -0.342 1.287 0.069 0.871 -1.401 1.684 2.173 0.574 0.591 -1.326 2.215 0.587 -2.209 0.612 0.000 0.678 -0.578 -0.482 0.000 0.895 0.945 0.989 0.882 1.314 1.003 0.910 +1 0.773 0.965 -0.158 0.632 -1.162 0.660 0.879 -1.466 0.000 0.775 1.477 -0.984 1.107 1.732 0.174 0.534 2.548 0.705 0.592 0.940 0.000 0.891 1.074 0.982 1.142 1.493 0.903 0.844 +0 0.930 0.192 0.893 0.632 -0.962 0.527 -1.255 -0.858 2.173 0.759 0.773 0.234 2.215 0.813 2.611 1.099 0.000 1.362 1.627 -1.434 0.000 0.851 0.929 1.056 0.917 1.378 1.279 1.027 +1 1.297 -0.025 -0.510 0.437 -0.018 1.131 0.322 0.067 0.000 1.249 2.397 1.243 0.000 2.017 0.420 -1.127 2.548 1.207 0.985 0.101 0.000 0.950 1.211 0.984 0.678 0.713 0.739 0.695 +1 1.719 -0.291 -0.375 1.296 0.956 1.201 -0.230 -0.860 2.173 0.996 0.259 1.646 2.215 0.864 0.070 0.257 0.000 0.688 -1.330 -1.538 0.000 0.913 0.795 1.928 1.384 1.308 1.116 0.959 +0 0.843 1.415 0.073 1.151 -0.419 1.254 -1.318 -1.035 0.000 1.018 -0.151 0.908 2.215 0.964 1.235 1.555 0.000 1.038 -1.372 -1.494 0.000 0.909 1.388 0.984 1.266 0.673 1.174 1.077 +1 1.149 0.932 1.718 0.739 -0.164 1.262 0.461 0.631 0.000 1.337 -1.573 -0.825 0.000 1.944 -0.064 1.718 2.548 1.338 0.521 0.132 3.102 0.901 1.275 1.267 1.059 1.296 1.089 1.108 +0 0.805 0.035 -0.385 0.649 0.696 1.300 -0.584 -0.927 2.173 0.792 0.124 0.849 2.215 1.405 -1.619 -1.484 0.000 2.625 -1.612 0.762 0.000 1.912 1.833 0.990 0.981 1.589 1.547 1.237 +1 1.275 2.137 -1.509 1.461 -0.172 0.933 0.510 1.019 2.173 0.455 0.867 -1.676 0.000 0.726 1.134 -0.557 0.000 0.559 0.538 -0.213 1.551 0.762 0.966 1.766 0.979 0.687 1.126 0.906 +1 0.415 1.146 1.261 0.559 -0.892 0.908 -0.319 0.543 0.000 0.716 -0.645 1.135 0.000 1.925 -1.073 -1.116 1.274 0.683 -1.525 -0.713 0.000 0.919 0.854 0.992 0.974 0.966 0.995 0.858 +1 0.574 -2.051 1.238 1.484 -1.151 0.537 -2.667 -0.899 0.000 1.060 0.053 0.433 2.215 0.413 -0.213 0.076 0.000 0.397 -1.119 0.203 0.000 0.782 1.329 1.068 1.681 0.755 1.211 1.120 +1 1.169 0.480 -0.727 0.768 -0.599 1.149 -0.167 1.358 1.087 0.387 -0.989 0.083 2.215 0.436 1.265 -0.976 0.000 0.555 0.956 0.173 0.000 0.471 1.036 1.002 0.676 0.993 0.896 0.786 +0 1.162 1.122 -1.049 0.480 1.708 1.543 0.872 -0.492 2.173 1.163 -1.207 1.004 0.000 1.064 -0.916 1.492 0.000 1.125 0.296 0.678 1.551 0.755 0.969 0.983 1.011 1.268 1.639 1.351 +0 0.488 -2.176 -0.504 3.286 -0.107 1.953 -1.331 1.631 1.087 0.478 -0.872 -1.358 0.000 0.779 -0.706 0.380 2.548 0.373 -1.455 -0.322 0.000 0.504 0.840 0.972 0.746 1.457 1.467 1.080 +1 1.404 -0.574 1.044 1.573 1.541 0.380 0.203 -0.777 0.000 0.585 -0.865 1.486 0.000 1.587 -0.374 -0.224 2.548 1.071 0.836 -0.622 3.102 1.063 0.985 0.989 1.324 0.841 1.102 0.922 +0 0.977 1.238 1.071 0.767 0.128 0.679 0.653 -1.073 2.173 0.725 0.453 0.220 2.215 0.593 0.823 -1.467 0.000 1.519 0.189 1.562 0.000 0.810 0.775 0.989 0.919 0.955 0.697 0.637 +0 0.713 -0.977 -0.273 0.562 1.301 0.801 -0.281 0.032 2.173 0.982 -1.762 -1.240 0.000 0.644 1.129 1.193 2.548 0.810 -1.071 0.628 0.000 0.826 1.421 0.991 1.174 1.078 1.195 0.999 +0 0.473 0.280 0.937 0.552 -1.172 0.824 -0.120 0.750 0.000 0.935 -0.335 1.594 0.000 0.576 -2.485 -0.541 0.000 1.131 0.080 0.314 3.102 1.301 0.929 0.985 0.659 0.572 0.714 0.662 +1 1.880 1.683 1.391 0.678 0.438 0.687 -0.475 -1.475 0.000 1.250 1.326 -0.565 2.215 1.190 0.499 0.131 2.548 1.072 1.984 0.315 0.000 0.705 0.871 1.183 1.257 0.936 1.001 0.821 +1 1.223 0.954 0.544 1.366 1.125 0.967 0.249 -1.510 2.173 1.215 -1.295 -0.534 0.000 0.364 0.059 -0.533 1.274 0.399 -0.818 -1.490 0.000 0.707 1.390 0.992 0.756 0.575 0.876 1.221 +0 2.197 -1.095 -0.518 0.249 1.504 0.968 -0.454 0.142 2.173 0.966 -0.598 -1.230 0.000 1.666 -1.038 1.355 2.548 1.039 -2.367 1.283 0.000 0.992 0.951 0.993 0.950 1.509 1.013 0.906 +0 0.747 -1.363 1.295 0.653 -0.908 0.449 -2.227 0.369 0.000 0.596 0.449 1.153 2.215 0.621 -0.001 -0.581 2.548 0.671 -1.282 -1.073 0.000 0.863 0.903 0.982 1.094 0.664 0.837 0.783 +1 0.563 0.766 -1.404 1.130 1.064 0.829 -0.451 0.429 2.173 0.792 -0.924 -0.754 2.215 0.383 -1.168 1.656 0.000 0.424 -2.226 1.678 0.000 0.334 0.884 0.984 0.982 1.086 0.840 0.773 +0 0.348 -1.274 0.396 0.933 -1.576 0.795 0.939 1.230 0.000 0.977 0.737 -0.302 0.000 1.224 -0.104 -1.431 2.548 1.868 0.413 0.191 1.551 1.847 1.241 0.991 1.252 1.204 1.346 1.581 +1 1.946 1.190 -0.132 1.177 -0.592 0.930 0.963 -1.705 1.087 1.092 1.496 0.644 2.215 0.576 1.659 1.678 0.000 0.366 2.036 -1.492 0.000 0.214 0.650 0.982 1.383 1.337 1.101 0.854 +0 1.871 0.765 -1.514 0.093 1.481 0.586 -1.006 -0.479 2.173 0.856 -1.500 1.166 0.000 0.907 0.698 -0.032 2.548 1.215 -0.918 0.327 0.000 0.958 0.984 0.977 0.892 0.988 0.939 1.056 +1 2.088 0.777 0.525 0.263 -0.637 0.729 -0.605 -1.381 2.173 0.478 0.891 -0.095 2.215 0.670 -0.218 -0.937 0.000 0.803 0.961 -1.712 0.000 0.801 0.759 0.983 0.564 1.080 0.932 0.795 +0 0.608 0.357 1.542 0.345 0.454 0.829 1.626 0.251 2.173 0.742 1.222 -0.653 0.000 1.383 0.645 -1.690 0.000 1.023 1.454 -1.327 0.000 0.828 0.698 0.990 0.795 1.106 0.865 0.736 +0 0.789 0.004 -0.300 0.997 -1.326 0.858 0.317 0.012 0.000 0.968 -0.259 1.356 2.215 0.854 -0.112 -1.243 0.000 0.724 -0.281 0.855 3.102 1.434 0.925 0.987 0.854 0.331 0.758 0.704 +1 1.450 -0.258 0.094 0.563 0.632 0.576 0.869 -0.985 2.173 0.358 -1.118 -0.906 0.000 0.551 0.514 -1.692 2.548 0.580 0.814 1.134 0.000 0.950 0.836 0.996 0.752 0.432 0.688 0.642 +1 2.154 -0.461 -0.590 0.639 -0.435 0.983 0.066 1.562 2.173 0.824 0.221 0.443 2.215 0.575 0.325 -1.633 0.000 0.807 -0.417 0.470 0.000 0.786 0.727 0.988 0.966 1.126 1.031 0.825 +0 1.407 0.002 -1.705 0.547 -1.212 0.702 -0.442 -0.424 2.173 0.615 1.400 -1.461 0.000 0.722 1.071 0.382 2.548 0.732 1.314 0.078 0.000 0.862 1.220 0.995 0.858 0.994 0.833 0.794 +1 2.178 -0.161 -1.600 0.512 -0.812 0.457 0.257 -0.280 0.000 1.012 1.729 0.320 0.000 0.344 -0.889 -0.042 2.548 0.645 0.847 0.944 3.102 1.417 0.865 0.985 0.691 0.505 0.682 0.926 +0 0.805 0.089 -0.785 1.437 -0.133 0.768 -2.669 0.096 0.000 1.747 -0.814 1.323 2.215 1.233 -1.311 -1.076 2.548 1.490 0.217 -1.565 0.000 0.876 1.013 0.987 1.388 1.380 1.375 1.071 +1 0.916 0.395 1.343 1.113 -0.929 0.928 -1.044 1.565 2.173 0.815 -1.244 0.479 0.000 1.458 2.320 1.283 0.000 1.979 -0.948 -0.804 0.000 0.901 0.770 1.244 1.217 1.264 0.901 0.891 +0 0.666 1.839 1.718 0.602 0.661 1.133 0.581 -1.072 1.087 1.360 -0.479 0.289 0.000 0.745 -1.339 1.359 2.548 0.370 0.445 1.067 0.000 0.775 0.888 0.991 1.010 1.679 1.120 1.051 +1 0.848 -1.166 -1.525 0.438 -0.142 0.837 0.250 0.587 2.173 0.657 0.924 1.739 2.215 1.151 -0.229 -0.705 0.000 0.427 0.219 0.227 0.000 0.610 0.885 0.992 0.877 1.017 0.815 0.717 +0 1.611 -1.495 0.112 0.640 0.646 0.756 -0.446 -1.533 0.000 0.513 1.221 -0.239 0.000 0.725 0.808 1.002 2.548 1.709 0.310 -1.260 3.102 1.760 1.099 0.989 1.567 0.791 1.362 1.324 +1 0.748 0.541 1.116 1.108 0.246 0.888 -0.448 -0.682 0.000 0.502 1.019 -1.734 0.000 0.796 -1.341 -1.467 1.274 0.488 1.466 1.009 0.000 0.464 1.175 0.985 0.498 0.826 0.748 0.707 +1 0.371 -0.504 -0.644 1.314 0.718 0.728 0.555 1.234 2.173 0.864 0.594 -0.840 0.000 1.094 1.112 -1.212 0.000 1.594 0.142 0.302 3.102 0.674 1.087 0.987 0.731 0.879 0.870 0.794 +1 0.479 1.532 1.644 1.258 0.374 0.596 -1.460 -1.411 0.000 0.777 0.164 -0.827 2.215 0.426 0.755 1.325 0.000 0.922 -0.491 -0.872 3.102 1.072 0.926 0.986 1.140 0.299 0.881 0.800 +1 0.559 -1.952 -1.249 0.327 0.073 1.554 -0.992 0.946 2.173 1.168 -0.464 -0.416 0.000 0.913 1.058 -0.803 0.000 0.518 -1.177 -1.010 0.000 0.847 0.699 0.986 1.047 0.762 0.885 0.771 +0 0.920 0.594 0.958 0.506 -0.675 0.760 -0.224 0.955 0.000 0.830 -1.259 -0.470 2.215 0.632 -0.610 -1.658 0.000 0.938 0.310 -0.793 3.102 0.924 0.878 0.989 1.214 0.778 0.813 0.803 +0 0.725 -0.151 -1.293 1.845 -0.418 0.614 -0.416 0.599 2.173 0.844 0.909 1.447 0.000 0.721 -0.906 -0.983 0.000 0.850 -1.024 1.437 0.000 0.979 0.979 1.137 0.898 0.638 0.767 0.766 +0 1.142 -0.388 -1.531 0.440 0.548 0.909 -1.397 -1.004 2.173 1.388 -0.665 0.578 2.215 0.483 -2.008 -1.532 0.000 0.498 1.733 0.364 0.000 2.337 1.698 0.984 0.870 1.747 1.382 1.088 +0 0.407 1.116 1.122 0.508 1.593 0.728 -1.158 -1.629 2.173 0.693 0.003 -0.526 0.000 1.245 -0.138 0.018 0.000 0.485 1.244 0.761 3.102 0.680 0.728 0.984 0.796 1.224 0.887 0.773 +0 0.941 1.236 0.309 0.421 -1.729 1.072 -0.268 -1.018 2.173 1.029 -0.012 1.112 2.215 0.714 0.390 -0.114 0.000 0.766 0.124 0.651 0.000 0.531 0.964 0.989 1.058 1.466 1.172 0.919 +1 1.898 1.209 1.677 1.083 1.127 0.789 0.204 0.343 2.173 0.829 0.597 0.044 0.000 0.896 0.837 -1.000 2.548 0.472 1.382 -0.956 0.000 0.778 0.728 0.987 0.854 1.052 0.938 0.823 +0 2.078 0.116 1.589 0.687 -1.296 1.041 0.058 0.074 1.087 0.618 0.450 0.478 1.107 0.591 -0.480 -1.365 0.000 0.771 -0.715 -0.310 0.000 0.619 0.852 0.984 0.900 0.483 0.951 0.810 +0 1.543 0.633 -0.593 0.699 -0.723 1.187 1.320 1.010 2.173 0.527 1.689 0.190 0.000 1.164 -1.048 -1.006 1.274 1.050 -0.152 1.015 0.000 1.265 1.093 0.973 0.950 2.724 1.485 1.248 +0 1.528 -0.490 0.185 1.757 -0.094 0.714 -0.966 -0.363 2.173 1.141 0.650 1.302 0.000 1.764 -1.653 -1.292 0.000 1.726 -0.474 1.288 3.102 0.878 0.930 0.989 0.858 1.195 1.040 1.068 +1 2.925 -0.099 0.405 1.998 0.024 2.810 -0.445 -1.491 0.000 0.753 0.831 -0.707 0.000 0.887 -0.433 1.572 2.548 1.124 -0.502 -0.151 3.102 1.068 0.937 1.126 0.707 0.764 0.828 0.849 +0 0.583 -0.771 -0.660 2.118 -0.751 1.121 0.582 0.760 2.173 0.651 1.387 1.012 0.000 1.812 0.609 1.425 2.548 0.794 0.839 -0.147 0.000 0.834 0.879 0.980 1.516 1.004 1.221 1.018 +1 1.063 -1.308 0.583 1.148 0.628 1.123 -0.293 -1.074 2.173 0.592 -0.667 1.394 0.000 0.411 -1.775 0.286 0.000 1.318 -0.084 -0.206 1.551 0.815 0.838 0.983 1.907 0.917 1.345 1.053 +1 0.688 -0.766 -0.142 2.263 -0.518 0.956 -0.227 1.537 2.173 0.352 1.092 1.367 0.000 0.784 0.362 -0.140 0.000 0.639 -0.198 -1.442 0.000 0.842 1.001 0.977 0.889 0.837 0.988 0.842 +0 0.481 -0.444 -1.645 0.562 1.072 0.723 -1.154 -0.496 2.173 0.868 -0.175 -1.482 0.000 0.819 -0.779 0.873 0.000 0.529 0.107 -1.363 3.102 1.188 1.156 0.985 0.670 0.646 0.694 0.696 +1 0.565 0.263 0.543 1.933 0.794 0.721 0.563 -1.108 2.173 1.281 2.108 -0.517 0.000 0.846 0.139 1.511 0.000 0.569 1.448 -1.175 0.000 0.841 0.714 0.994 0.757 0.722 0.938 0.848 +1 0.441 0.802 -0.334 1.660 0.779 0.985 0.869 -1.440 0.000 0.892 0.055 -0.348 2.215 0.425 2.491 -0.158 0.000 0.574 -0.522 0.822 3.102 1.636 1.272 1.000 0.908 0.605 0.971 0.892 +1 0.534 -0.669 -0.692 0.991 0.309 0.906 0.144 -1.325 0.000 1.372 0.508 0.583 2.215 0.923 0.809 -0.903 0.000 1.382 0.131 1.189 3.102 0.875 0.989 0.987 1.412 0.685 1.009 1.112 +0 0.647 -0.899 0.728 1.828 0.395 1.032 -0.623 -1.280 1.087 0.824 -0.388 1.724 0.000 1.067 -1.951 -0.370 0.000 0.593 0.244 0.752 3.102 1.971 1.278 0.996 1.435 0.892 0.963 1.060 +1 1.044 -0.750 0.623 0.229 0.514 0.900 0.209 -1.175 0.000 0.701 -1.290 1.335 2.215 0.875 0.304 -0.646 0.000 1.105 -0.078 0.451 3.102 0.740 0.948 0.976 0.809 0.771 0.879 0.802 +0 0.451 0.161 -0.277 0.826 1.434 0.494 0.729 0.237 2.173 0.472 1.295 0.603 0.000 0.663 -0.876 -1.177 2.548 0.724 0.652 -1.408 0.000 0.769 0.900 0.985 0.665 0.964 0.683 0.612 +0 0.881 0.500 0.990 2.439 0.462 1.042 2.367 -0.563 0.000 1.659 0.682 1.662 0.000 1.394 -0.484 -0.332 2.548 1.389 0.204 -1.146 0.000 0.834 0.851 0.990 1.040 0.992 1.027 0.965 +0 1.409 -0.207 0.458 1.114 -0.008 1.252 0.411 -1.519 1.087 0.700 1.425 0.797 2.215 0.447 -1.605 -0.844 0.000 0.569 1.222 -0.920 0.000 1.344 1.243 0.995 1.308 1.417 1.319 1.137 +0 0.456 1.473 -1.394 1.272 -0.052 0.935 -2.623 -1.252 0.000 1.038 -0.064 0.183 2.215 0.903 0.756 -1.410 0.000 1.317 0.516 1.177 0.000 0.880 1.103 0.988 0.583 0.460 0.680 0.713 +0 0.484 0.081 -1.641 1.783 1.640 1.009 0.608 -0.083 0.000 1.281 0.111 -0.410 0.000 0.931 0.394 1.129 2.548 0.788 0.493 -1.679 3.102 0.929 1.001 0.991 0.947 0.379 0.813 1.092 +0 1.496 -1.081 0.476 0.509 0.031 0.897 0.243 -1.641 2.173 1.461 -0.152 0.787 1.107 1.100 0.839 -1.206 0.000 1.552 -0.189 -0.703 0.000 1.078 0.978 0.992 1.165 1.414 1.256 1.263 +1 0.533 1.277 0.857 0.958 1.422 0.837 0.584 -0.682 1.087 0.913 -0.999 0.251 0.000 1.016 -0.729 1.535 2.548 0.442 -0.983 -0.898 0.000 0.714 0.810 0.981 1.028 1.357 0.911 0.863 +1 1.070 0.556 -0.033 0.922 -1.274 0.976 0.463 -0.768 2.173 0.916 1.619 0.815 0.000 1.265 1.312 1.316 0.000 1.773 0.869 0.292 0.000 0.920 0.981 1.237 0.795 0.976 0.998 0.885 +1 1.064 -0.151 1.454 0.656 -1.265 0.819 -0.729 -0.449 0.000 1.092 0.621 0.436 2.215 0.789 1.178 -1.253 0.000 1.003 0.747 0.995 0.000 0.903 0.916 0.995 0.740 0.713 0.810 0.766 +1 1.625 0.938 -0.590 0.931 -1.687 0.934 1.613 -0.024 0.000 1.573 2.497 1.499 0.000 1.336 0.678 0.011 0.000 2.557 -0.621 0.823 1.551 0.919 1.162 1.423 1.747 1.029 1.367 1.238 +0 0.396 0.388 0.182 2.253 -1.550 1.976 0.037 -0.864 2.173 2.089 0.743 0.726 0.000 1.490 -0.135 0.498 2.548 1.115 -0.152 0.919 0.000 1.036 0.886 1.309 1.315 2.024 1.605 1.379 +1 1.777 -1.437 0.545 0.324 0.447 0.584 -0.888 0.906 0.000 1.861 -0.663 -1.038 2.215 0.332 0.051 -0.936 0.000 0.402 -1.405 -1.715 3.102 0.881 1.174 0.991 0.612 0.605 0.934 0.802 +1 0.593 -0.792 0.136 1.532 -0.676 0.681 0.531 1.226 1.087 0.536 -0.048 0.391 0.000 0.997 -0.818 1.686 2.548 0.487 1.149 -0.720 0.000 0.762 0.906 0.987 1.116 0.901 0.868 0.752 +1 0.879 -1.438 0.430 0.637 -0.212 0.715 0.352 -1.681 2.173 1.232 1.380 1.275 0.000 1.090 0.707 -0.688 0.000 0.853 0.444 0.489 3.102 1.063 1.113 0.983 0.689 0.769 0.781 0.789 +0 2.928 -0.278 0.129 0.425 1.354 1.341 -0.324 1.666 1.087 0.633 -0.717 -0.765 2.215 0.860 0.126 -1.065 0.000 0.432 0.053 0.757 0.000 0.671 0.831 1.380 0.968 1.137 1.145 0.897 +0 0.496 0.908 0.904 0.830 -0.068 1.198 0.234 0.905 2.173 2.000 0.634 -0.922 2.215 0.973 0.890 1.337 0.000 0.680 1.283 -1.458 0.000 0.995 0.993 0.986 1.319 2.321 1.379 1.088 +1 1.563 -1.398 0.235 0.742 0.283 1.325 -0.746 1.307 2.173 0.506 1.028 -0.961 0.000 1.667 -1.021 -1.202 0.000 1.395 0.609 0.019 0.000 0.907 1.355 0.983 0.786 0.705 0.887 0.880 +1 0.578 1.509 0.206 1.197 1.189 1.189 0.429 -0.579 2.173 1.082 1.004 1.388 2.215 0.472 -0.632 0.596 0.000 0.367 -0.190 -0.902 0.000 0.461 0.802 0.983 1.572 1.712 1.193 0.991 +0 1.841 2.155 0.303 0.209 0.282 1.315 -1.245 -1.571 0.000 0.625 1.725 -1.725 0.000 1.717 -0.084 -0.283 2.548 0.722 0.715 -0.385 1.551 4.184 2.371 0.986 1.601 0.427 1.611 1.885 +0 0.871 -1.296 1.016 0.655 -1.494 0.565 -1.164 -0.190 2.173 0.534 -1.752 0.463 0.000 0.780 -0.413 -1.238 2.548 1.080 -1.257 1.644 0.000 0.877 0.824 0.985 0.820 0.737 0.639 0.617 +0 0.663 -0.509 0.123 1.114 0.846 0.808 -1.095 -1.147 2.173 0.966 -0.181 -0.418 2.215 0.842 -0.571 1.143 0.000 0.610 -1.639 1.217 0.000 0.573 1.013 0.988 1.042 1.008 0.885 0.777 +1 0.756 -0.407 -0.691 0.920 -1.523 0.617 -1.247 -0.132 0.000 0.603 -1.122 1.412 2.215 0.983 -1.259 0.828 0.000 1.068 0.581 0.977 3.102 1.070 0.865 0.983 0.979 0.819 0.809 0.779 +0 0.862 0.015 -1.363 1.406 1.358 0.469 -0.987 0.583 2.173 0.865 0.850 -0.402 0.000 0.834 1.324 0.098 2.548 0.874 0.589 -0.959 0.000 0.878 1.163 0.986 1.017 1.257 0.898 0.838 +1 1.089 0.926 1.253 0.411 -0.863 0.948 0.260 0.973 0.000 1.369 0.240 -0.998 2.215 0.684 0.766 0.266 2.548 0.499 1.237 -1.226 0.000 0.956 0.696 0.987 1.020 0.984 0.831 0.781 +1 1.014 1.240 -0.514 0.689 -0.232 1.706 0.788 1.366 2.173 0.730 0.083 -0.645 0.000 0.657 2.468 -0.237 0.000 0.744 0.800 0.362 3.102 1.870 1.091 0.979 1.756 0.944 1.193 1.122 +0 0.795 -0.492 0.453 0.395 -1.642 0.408 -0.771 1.406 0.000 1.040 -0.318 -0.968 2.215 0.901 -0.754 -0.073 2.548 0.640 -0.111 1.002 0.000 0.379 0.801 0.991 0.632 0.787 0.652 0.600 +0 1.831 -0.047 -1.168 1.818 -1.282 1.438 0.037 0.238 0.000 0.794 -0.241 -0.187 2.215 1.335 -1.116 1.294 0.000 2.173 2.007 0.991 0.000 0.684 1.108 1.001 0.824 1.176 0.884 0.970 +1 1.931 0.996 0.211 0.586 -0.922 1.242 1.125 -1.636 2.173 0.755 1.642 -0.675 0.000 1.369 -0.356 1.010 0.000 0.484 0.782 0.748 0.000 0.814 1.030 1.256 0.887 0.900 0.971 0.826 +1 0.749 -0.553 0.766 0.880 -0.468 0.526 -0.102 -1.634 2.173 1.311 -0.985 1.262 2.215 1.163 -1.190 -0.574 0.000 1.058 -0.735 -0.162 0.000 0.515 0.955 1.008 0.935 0.845 0.870 0.766 +0 1.049 -1.407 1.592 0.688 1.318 0.897 0.353 -0.221 0.000 0.327 -0.336 0.303 2.215 0.296 1.299 -0.030 0.000 0.367 1.160 0.415 0.000 0.672 0.739 0.977 0.847 0.353 0.550 1.020 +0 0.654 -1.450 0.128 0.826 -1.374 0.777 -1.263 -1.186 0.000 1.035 -2.167 -1.257 0.000 1.778 1.053 0.698 1.274 1.582 -0.023 0.122 3.102 0.953 1.697 0.994 1.861 1.026 2.076 1.601 +0 1.066 -0.232 -1.486 0.040 -1.530 0.763 -0.264 0.437 1.087 0.361 1.669 0.550 0.000 1.345 0.276 -1.322 2.548 0.715 1.024 -0.832 0.000 0.648 0.979 0.692 0.435 1.313 0.821 0.705 +1 1.928 0.024 -0.101 0.851 -0.782 0.772 0.495 -1.334 2.173 0.630 0.549 -1.649 2.215 1.472 0.272 1.112 0.000 0.637 0.872 0.033 0.000 0.973 1.032 1.021 0.952 0.289 0.793 0.805 +1 1.909 2.020 0.450 0.509 1.310 0.747 1.331 -1.513 2.173 0.864 0.538 -0.642 2.215 0.805 2.021 1.631 0.000 0.946 1.718 -0.347 0.000 0.944 1.052 0.990 1.114 0.964 0.999 0.856 +1 0.363 -1.188 -0.139 2.492 -1.121 0.929 0.323 1.157 2.173 0.643 -0.660 0.235 0.000 0.942 -0.420 1.228 0.000 1.603 0.069 0.501 0.000 0.906 0.902 1.019 1.677 1.130 1.072 0.997 +0 0.706 0.967 -1.518 1.296 1.045 0.368 1.534 -0.688 0.000 0.690 -1.752 -0.161 0.000 1.451 -1.397 1.330 2.548 1.427 -0.117 -0.691 0.000 0.897 0.872 0.988 1.605 1.279 1.139 1.021 +1 1.347 -0.565 1.673 0.457 1.554 2.792 -0.745 0.474 0.000 2.670 0.405 -0.969 2.215 1.847 0.243 -1.408 2.548 0.595 0.439 1.097 0.000 0.671 0.948 0.986 1.101 0.926 1.283 0.992 +0 0.956 0.597 -1.681 0.213 0.039 0.859 0.074 -0.077 2.173 0.829 -0.366 -1.297 2.215 0.767 0.036 1.026 0.000 0.895 0.790 1.580 0.000 0.951 0.976 0.988 0.895 1.142 0.805 0.720 +1 1.698 -0.884 -0.603 0.720 0.064 0.674 -0.693 1.060 2.173 0.636 -0.198 0.027 0.000 1.237 -0.221 -1.697 1.274 1.113 0.415 1.551 0.000 1.144 0.939 0.988 1.018 0.742 0.861 0.806 +1 1.044 -1.352 -1.229 0.896 -0.263 1.325 -0.786 -1.511 2.173 0.737 -1.496 0.552 0.000 1.154 -0.684 0.172 2.548 1.219 1.058 0.747 0.000 0.661 0.671 1.025 1.018 1.539 1.002 0.870 +1 1.321 -0.470 0.926 0.517 -0.192 1.395 -0.534 -1.425 0.000 1.694 0.479 0.037 2.215 1.220 1.999 1.167 0.000 1.883 0.593 -0.386 3.102 0.770 1.500 0.986 1.010 0.625 1.358 1.135 +1 0.787 0.460 -0.567 1.286 -1.215 1.339 -0.164 -0.732 2.173 2.103 -2.307 0.867 0.000 0.949 -0.867 1.099 0.000 1.086 -0.604 0.172 0.000 0.841 0.785 0.990 0.688 0.850 0.862 0.773 +1 2.035 -0.779 0.207 1.149 0.771 1.028 -0.347 -1.063 1.087 0.701 1.888 -0.165 0.000 0.712 -0.047 -1.640 1.274 2.066 1.162 1.706 0.000 0.993 1.066 1.031 0.979 0.553 1.016 0.852 +0 0.858 -0.158 -0.991 0.605 1.371 0.806 0.158 1.463 0.000 0.744 -0.008 0.007 2.215 1.390 1.293 -0.586 2.548 0.838 0.855 0.922 0.000 0.813 0.987 0.991 1.272 1.003 0.925 0.898 +0 1.084 0.343 1.013 1.366 1.570 0.522 -0.205 0.056 2.173 0.774 -0.523 -0.741 0.000 0.458 -2.346 -0.399 0.000 1.218 0.507 1.621 3.102 1.135 0.970 0.986 0.593 0.905 0.896 0.915 +0 1.823 -0.803 0.155 0.827 0.512 0.969 0.091 -1.276 2.173 0.530 0.507 1.288 0.000 0.437 1.185 -0.948 0.000 0.402 -0.971 1.170 3.102 0.729 0.785 0.978 0.560 0.693 0.991 0.937 +1 0.454 0.409 1.624 0.502 1.660 1.147 -1.001 1.029 2.173 1.228 -0.602 -0.428 2.215 1.590 1.235 -0.293 0.000 0.803 -0.842 -1.686 0.000 0.538 0.919 0.987 1.700 1.723 1.711 1.392 +1 1.254 0.433 -0.735 0.152 1.100 0.928 -0.161 0.625 2.173 0.399 -0.565 1.568 1.107 0.427 -2.404 1.732 0.000 0.501 -1.540 -1.247 0.000 0.306 1.219 0.986 0.652 0.698 0.744 0.782 +0 0.491 1.219 -1.519 1.551 1.009 1.063 0.179 1.736 2.173 1.348 0.804 -0.252 2.215 1.319 -0.218 -0.323 0.000 0.620 -0.769 -1.553 0.000 0.958 1.075 0.989 1.287 1.813 1.222 1.179 +0 0.569 -1.331 -1.239 1.098 1.564 1.255 -1.641 -0.270 0.000 1.206 1.206 1.414 0.000 0.876 0.350 0.811 2.548 0.839 0.617 -0.481 3.102 1.114 0.875 0.982 1.478 0.613 1.215 1.608 +1 1.307 -0.702 -1.619 1.493 -0.015 0.427 0.363 -0.015 2.173 0.552 -0.650 0.640 0.000 0.690 1.035 -0.882 2.548 1.265 -0.056 1.413 0.000 0.920 0.779 1.920 1.110 0.546 0.896 0.770 +1 1.596 -0.384 1.026 1.259 1.449 0.437 -0.998 -0.115 2.173 0.700 -1.327 -1.342 2.215 0.615 1.240 -0.582 0.000 0.390 -0.686 -1.375 0.000 0.798 1.031 0.982 0.943 0.742 0.773 0.837 +1 0.411 0.919 -1.575 1.321 0.230 0.725 1.090 0.992 2.173 0.660 1.415 -0.544 0.000 0.756 0.622 1.572 2.548 0.415 1.639 -0.396 0.000 0.886 0.906 1.020 0.708 0.499 0.602 0.591 +1 0.616 1.620 -0.370 1.340 -0.566 2.037 0.733 0.902 2.173 1.775 -2.561 -1.128 0.000 0.893 0.510 -1.560 0.000 0.748 0.577 0.450 0.000 0.878 1.104 0.990 0.454 0.789 1.026 0.850 +0 1.293 1.119 0.048 0.432 1.649 0.875 1.609 1.048 2.173 1.277 1.158 -1.120 1.107 0.427 0.394 -0.721 0.000 0.366 0.018 0.269 0.000 0.351 0.804 1.027 0.921 1.481 0.886 0.704 +1 2.075 -1.040 -1.303 1.249 -0.448 0.669 -2.452 0.456 0.000 1.050 -1.154 0.270 2.215 1.211 0.337 1.439 0.000 0.711 0.310 -0.863 0.000 0.896 0.707 1.555 1.276 0.655 0.855 0.931 +1 1.613 1.361 1.703 0.769 1.245 0.911 1.745 -0.243 0.000 0.637 0.889 0.579 2.215 0.482 0.751 -1.353 0.000 0.510 0.226 -1.186 1.551 1.158 0.943 0.982 0.709 0.541 0.639 0.750 +0 0.842 0.981 1.457 1.649 0.522 0.521 -0.264 -1.033 0.000 0.773 0.682 -0.528 2.215 0.782 -0.671 -1.576 0.000 0.424 -0.598 1.281 0.000 0.646 0.710 1.218 0.652 0.494 0.646 0.697 +1 1.868 0.681 -1.647 0.801 -1.618 0.470 -0.395 0.517 0.000 1.147 0.901 -0.425 2.215 0.933 1.144 -0.773 0.000 1.020 0.979 0.397 3.102 0.874 0.765 0.982 0.908 0.670 0.868 0.747 +1 0.346 1.307 0.018 0.214 -1.352 0.991 0.202 -0.075 0.000 0.805 0.938 1.566 2.215 0.949 0.075 0.665 0.000 1.309 0.275 -1.189 3.102 0.910 1.094 0.984 0.643 0.641 0.742 0.665 +1 0.679 0.765 0.946 0.806 1.301 0.673 -0.101 -0.330 2.173 0.400 -0.349 0.813 0.000 0.679 1.904 -0.443 0.000 1.145 1.108 -1.406 3.102 1.439 0.989 0.988 0.938 1.053 0.823 0.839 +0 0.538 -1.744 -0.236 1.491 -1.139 0.290 -1.425 1.730 0.000 0.767 -0.734 0.362 2.215 0.522 -2.175 0.131 0.000 0.565 -1.360 0.855 3.102 0.775 0.762 0.989 0.658 0.371 0.622 0.574 +0 1.056 0.704 1.188 1.549 -1.729 1.054 -2.542 -0.295 0.000 0.831 0.013 1.146 2.215 1.535 -0.066 -0.085 0.000 1.038 0.360 -0.620 1.551 1.747 1.031 0.989 0.633 0.856 0.810 0.814 +0 1.974 -0.507 -1.319 0.254 1.283 0.744 1.521 0.952 0.000 0.890 0.823 -0.003 2.215 0.482 1.107 -1.527 0.000 0.513 0.491 -0.386 3.102 0.865 0.957 0.988 0.645 0.221 0.747 0.864 +0 1.419 0.386 0.329 3.072 0.026 1.708 1.157 -1.722 1.087 1.067 0.698 -1.057 0.000 1.743 2.727 1.667 0.000 0.895 -1.022 0.044 3.102 1.043 1.026 0.977 2.329 2.366 1.745 1.381 +1 0.893 0.521 -0.511 0.605 0.911 1.038 1.163 -0.473 2.173 1.056 1.469 -1.092 2.215 1.573 0.378 1.236 0.000 1.675 0.259 0.664 0.000 0.888 1.482 0.987 0.839 0.855 1.184 0.957 +1 0.618 -0.963 -1.669 0.662 1.741 1.219 -0.260 0.422 0.000 0.545 -0.145 -1.129 2.215 0.947 1.119 -1.358 0.000 1.085 0.558 -0.184 3.102 0.474 0.700 0.983 1.463 0.598 1.006 1.294 +0 0.439 -1.789 1.531 1.177 -0.823 0.950 -1.118 1.248 1.087 0.886 0.575 0.250 0.000 1.231 0.870 -1.059 0.000 0.494 -1.366 -0.438 0.000 1.277 0.801 0.991 0.958 1.116 0.917 0.852 +1 0.467 0.548 1.247 0.581 -0.217 2.671 1.131 -1.362 0.000 1.570 -0.929 0.798 0.000 2.021 -0.485 -0.082 2.548 2.711 -0.010 0.284 1.551 0.689 1.126 0.987 0.765 0.746 0.911 0.781 +0 0.326 -1.490 -1.234 0.604 0.494 0.686 -1.240 -0.310 2.173 0.886 -1.046 1.651 0.000 0.479 0.088 -0.726 2.548 0.956 -1.241 0.996 0.000 0.713 1.034 0.997 0.558 0.581 0.704 0.669 +1 0.837 -2.246 0.393 0.690 -0.157 0.650 -1.549 0.027 1.087 1.376 -0.913 -1.272 1.107 1.409 -1.481 -1.662 0.000 0.654 -2.086 1.566 0.000 0.538 1.028 0.974 1.141 1.356 0.864 0.831 +1 1.227 0.764 -1.256 0.257 0.513 0.852 -0.716 0.440 0.000 0.826 0.276 -1.117 2.215 0.530 0.377 0.920 0.000 0.524 -0.111 1.663 3.102 0.884 1.166 0.986 0.517 0.375 0.694 0.679 +1 0.403 1.827 0.337 0.616 -1.165 1.102 1.084 -0.087 0.000 1.028 0.895 -0.420 0.000 1.754 0.517 1.109 0.000 0.901 -0.249 1.556 0.000 0.825 0.755 0.987 0.638 0.757 0.564 0.596 +0 0.781 1.599 0.839 1.846 -0.348 0.996 -0.652 1.677 1.087 0.921 -0.492 -0.703 0.000 1.300 0.219 0.537 1.274 0.587 -0.083 1.263 0.000 0.959 0.973 1.458 2.288 1.381 1.554 1.301 +0 2.006 0.241 -0.748 0.146 -1.374 1.470 -0.488 -1.108 2.173 1.771 -2.059 0.661 0.000 1.613 -0.483 0.148 2.548 3.528 0.089 1.173 0.000 0.906 1.192 0.992 0.839 1.737 1.259 1.025 +1 1.635 -1.594 0.945 0.478 1.327 0.892 -0.844 -0.162 2.173 1.745 -0.917 -1.204 0.000 0.430 -0.671 -0.847 0.000 1.013 0.092 0.555 3.102 0.443 1.057 0.991 1.308 0.787 1.017 1.055 +1 0.811 -0.938 0.851 0.877 -1.434 0.743 -1.007 -0.032 2.173 1.564 -0.496 -1.350 0.000 0.850 0.437 0.425 2.548 1.328 1.149 0.049 0.000 2.721 1.677 1.032 0.879 0.904 1.267 1.094 +0 1.600 -0.949 -0.531 1.396 -0.598 2.468 -1.353 -0.604 0.000 1.386 -0.191 0.995 1.107 2.251 0.605 1.290 1.274 0.976 -0.967 0.802 0.000 2.276 2.531 0.999 1.884 0.970 2.270 1.826 +0 0.482 2.308 1.577 1.585 1.346 0.935 1.048 -1.272 2.173 0.817 -1.365 0.000 0.000 1.947 -0.490 0.495 2.548 1.852 -0.219 -0.640 0.000 1.303 1.236 1.004 0.946 2.245 1.463 1.505 +0 2.797 -0.806 0.021 1.439 0.487 1.557 1.758 1.079 0.000 1.273 -1.175 -1.049 0.000 1.544 -0.657 -1.552 2.548 2.237 -0.361 -1.071 3.102 6.909 4.090 1.134 1.526 0.632 2.554 2.418 +0 0.577 -1.617 0.550 1.626 0.418 0.806 0.805 -1.389 1.087 0.512 1.808 -1.115 0.000 0.608 1.704 0.415 0.000 0.777 -0.875 -1.338 3.102 0.840 0.906 0.981 1.004 0.899 2.068 2.443 +1 1.149 1.214 -1.089 0.707 -0.564 0.576 1.837 0.412 0.000 0.545 0.172 -1.410 2.215 0.674 -0.338 1.301 0.000 1.139 0.437 0.784 3.102 1.720 1.012 0.981 0.858 0.665 0.757 0.851 +1 1.623 0.303 -0.872 0.340 -0.466 2.228 1.041 1.409 0.000 2.718 -0.044 0.143 0.000 0.804 -0.673 -0.818 2.548 1.006 2.146 -1.477 0.000 2.248 1.759 0.993 0.564 0.467 1.448 1.345 +1 0.436 -0.509 -1.555 1.165 -0.011 0.800 0.078 -0.738 0.000 0.890 0.289 1.498 1.107 0.749 1.136 -0.742 0.000 1.384 -0.087 0.693 1.551 0.861 1.090 0.989 0.857 0.695 0.837 0.752 +1 0.662 0.176 -0.210 0.819 1.440 0.913 -0.704 -1.337 2.173 0.548 -1.024 0.729 0.000 1.171 0.214 0.782 0.000 1.452 -0.949 -0.495 1.551 0.850 1.054 1.016 0.906 0.879 0.915 0.808 +0 0.617 -1.438 -1.351 1.439 -0.095 0.629 -0.615 1.157 2.173 0.767 -1.190 -0.608 1.107 0.648 -2.146 1.180 0.000 0.524 -1.539 1.741 0.000 0.353 0.736 1.182 1.026 1.068 0.765 0.682 +0 1.246 -0.426 1.734 0.712 0.462 1.037 -0.780 -0.976 2.173 1.107 2.167 0.348 0.000 0.591 -1.772 0.876 0.000 0.880 0.164 -1.067 0.000 1.290 1.085 1.189 0.677 1.037 0.766 0.730 +1 0.583 0.393 -1.245 1.586 1.218 0.877 -0.834 0.087 1.087 0.929 0.768 -0.582 0.000 0.726 -0.502 -1.354 2.548 1.071 -0.601 1.087 0.000 1.687 1.086 1.062 1.302 0.968 0.922 0.912 +1 0.950 1.222 0.941 0.366 0.017 1.032 2.527 -1.316 0.000 0.722 2.208 -0.447 0.000 1.637 1.281 0.310 2.548 1.297 0.852 1.530 1.551 1.319 1.301 0.989 0.757 1.015 1.116 1.045 +1 0.837 1.281 1.703 1.308 0.426 1.582 1.094 -0.995 0.000 1.412 0.366 0.597 0.000 0.984 1.162 0.785 1.274 1.365 1.303 -0.757 3.102 1.270 0.884 1.324 0.704 0.881 0.878 0.894 +1 1.037 1.955 0.489 0.492 -0.897 1.252 0.563 -1.509 2.173 1.371 -0.159 0.300 0.000 0.998 -1.029 0.804 0.000 1.181 0.540 -0.581 1.551 1.015 0.973 0.987 1.275 0.957 1.048 1.049 +1 1.408 0.505 0.460 1.324 -0.996 0.710 1.561 1.053 2.173 0.825 1.205 0.531 0.000 1.201 1.778 -1.342 0.000 1.353 0.037 -0.293 3.102 0.689 0.963 1.829 0.983 1.307 0.975 0.967 +0 0.627 -0.368 -0.204 1.086 -1.131 0.674 0.176 0.406 2.173 1.090 0.561 -1.586 2.215 0.871 1.295 -0.232 0.000 0.791 -1.121 1.086 0.000 1.911 1.228 0.991 1.117 1.255 0.993 0.990 +0 0.906 0.053 -1.025 1.530 -0.868 0.543 0.146 0.708 0.000 0.869 -0.134 0.025 2.215 1.025 -0.736 1.142 1.274 0.635 0.139 1.171 0.000 0.824 0.809 0.975 1.005 0.914 0.837 0.836 +1 0.848 -0.304 0.395 0.451 1.429 0.383 0.502 0.928 0.000 0.620 1.057 -1.583 1.107 0.982 -0.149 -0.422 0.000 0.805 -0.573 -0.405 1.551 1.101 0.932 0.989 0.667 0.851 0.646 0.609 +1 0.560 -0.423 -1.118 0.705 0.651 0.681 -0.886 -1.730 2.173 1.277 -0.933 -0.917 2.215 0.999 -1.344 0.436 0.000 1.095 -1.613 0.896 0.000 0.822 0.935 0.989 0.830 0.920 0.785 0.686 +0 0.292 -1.473 0.212 2.889 -0.722 0.814 -1.387 0.609 2.173 1.048 -1.752 -1.058 0.000 0.844 -0.834 -0.199 2.548 2.633 -1.540 0.917 0.000 0.922 0.974 0.989 1.293 0.734 0.888 1.047 +0 0.660 -1.565 -0.146 1.221 0.924 0.965 -0.930 0.053 2.173 1.485 -0.516 -1.422 2.215 1.038 -1.196 1.543 0.000 0.792 -1.485 -0.900 0.000 0.841 0.891 1.022 0.850 1.748 1.077 0.910 +0 0.512 -0.689 0.559 1.678 1.386 1.204 -0.541 -1.189 2.173 0.957 0.222 0.093 2.215 0.851 -1.175 -0.108 0.000 0.865 1.165 0.767 0.000 1.820 1.166 0.986 1.175 1.576 1.139 1.027 +1 1.583 0.158 -0.597 0.434 0.915 0.736 -1.038 -1.544 2.173 0.449 -1.431 0.069 2.215 0.316 -1.781 0.437 0.000 0.454 -1.104 0.603 0.000 0.307 0.682 1.124 0.845 0.859 0.814 0.740 +1 0.929 0.634 -1.486 0.290 -0.406 1.568 -1.725 1.243 0.000 1.726 -0.290 -0.298 2.215 1.115 1.417 -0.738 0.000 1.302 0.083 0.645 0.000 0.922 1.200 0.988 1.357 0.920 1.016 0.993 +0 1.532 0.972 1.010 0.872 1.291 0.811 0.208 -1.183 0.000 0.816 1.054 -0.234 2.215 0.846 -0.674 0.228 2.548 0.386 -0.663 -0.375 0.000 0.724 0.909 0.969 1.358 0.990 1.029 0.985 +1 0.468 -0.263 0.809 0.941 -0.254 0.824 0.866 1.305 2.173 1.317 0.874 -1.557 0.000 1.041 0.901 0.192 2.548 1.156 0.116 -0.414 0.000 1.513 1.215 0.994 0.902 0.976 0.907 0.803 +1 3.280 -0.404 1.408 0.578 -0.635 0.672 -0.732 -0.046 2.173 2.028 -0.005 -0.710 0.000 0.519 -0.747 0.972 2.548 0.399 1.031 0.346 0.000 1.230 1.076 1.838 1.409 0.585 0.889 1.044 +1 0.966 -0.702 -1.111 0.749 0.882 0.524 0.466 0.339 2.173 0.294 -1.392 1.628 0.000 0.665 -2.021 0.159 0.000 0.713 0.352 -1.684 3.102 0.713 0.879 1.149 0.900 0.626 0.788 0.714 +0 2.338 -1.420 0.795 0.438 0.075 1.305 -0.920 -0.845 2.173 0.876 -1.080 -0.207 0.000 1.423 -0.980 1.453 2.548 0.642 -1.350 -1.646 0.000 0.959 1.006 0.986 0.865 1.494 1.151 0.966 +0 0.433 0.579 1.001 0.812 -1.199 1.224 1.279 1.078 1.087 1.404 -0.135 -0.652 0.000 0.740 0.493 -1.177 0.000 0.938 -0.359 0.403 3.102 0.895 0.896 0.993 1.355 1.279 1.223 1.027 +0 1.920 -0.563 0.188 0.787 0.596 0.835 -1.803 -1.487 0.000 0.900 -0.889 1.736 1.107 0.349 1.577 -1.561 0.000 0.685 -0.170 -0.885 0.000 0.682 0.899 0.986 0.687 1.195 0.872 0.802 +0 1.451 -0.442 -0.441 0.354 0.333 1.132 -0.626 -0.885 2.173 1.479 -0.780 1.024 0.000 0.770 0.958 1.257 1.274 0.513 -0.180 1.054 0.000 0.339 0.915 0.986 0.811 1.547 1.085 0.976 +1 0.768 -0.836 1.352 1.233 -0.152 0.774 -0.076 1.142 0.000 1.419 -0.195 -0.787 2.215 0.752 -0.260 0.547 0.000 0.857 -0.114 1.683 3.102 0.716 1.340 1.317 0.797 0.790 0.815 0.799 +1 0.973 0.524 1.577 0.499 1.650 1.053 0.959 0.079 0.000 0.518 1.071 1.192 1.107 0.436 -0.327 1.740 2.548 0.577 -2.212 -1.244 0.000 0.928 0.998 0.984 0.710 0.468 0.701 0.807 +1 0.607 -1.734 1.443 1.019 0.645 0.752 0.781 -1.011 2.173 0.520 -0.568 -1.528 0.000 0.629 -0.682 0.615 1.274 0.449 1.810 0.758 0.000 0.845 0.786 0.988 0.515 1.119 0.930 0.769 +1 0.695 1.537 -0.672 1.545 -0.794 0.799 1.111 0.742 2.173 1.166 0.030 1.022 2.215 0.657 1.287 -0.261 0.000 0.681 1.884 1.696 0.000 0.793 0.831 0.989 2.218 0.887 1.554 1.173 +0 2.717 -0.525 0.903 2.327 0.516 1.663 -0.042 -0.959 2.173 1.006 -0.092 -0.360 2.215 0.762 0.082 -1.431 0.000 0.945 -0.021 1.463 0.000 0.480 0.900 1.189 1.402 0.980 1.637 1.265 +1 0.804 0.922 -0.244 0.241 0.738 1.167 0.144 -1.053 2.173 0.715 0.576 0.686 0.000 0.888 0.382 1.720 2.548 0.391 1.332 1.203 0.000 0.476 1.166 0.985 0.728 0.779 0.747 0.693 +1 0.488 1.230 1.552 0.227 1.537 0.510 0.869 -0.654 0.000 0.613 1.645 -0.152 0.000 0.612 -0.717 1.200 2.548 0.481 1.281 1.027 3.102 0.719 1.133 0.989 0.599 0.602 0.698 0.673 +0 0.999 -0.495 -0.580 0.305 0.785 1.170 2.173 -0.569 0.000 1.042 0.916 0.537 2.215 0.889 1.326 1.586 0.000 2.062 -0.591 1.339 3.102 1.842 1.641 0.988 0.879 1.503 1.814 1.715 +1 1.038 -1.460 0.819 0.586 -1.021 1.135 -0.043 -0.539 0.000 0.713 -0.447 1.647 0.000 0.944 -0.206 -1.401 2.548 1.261 0.646 0.346 0.000 1.399 0.983 1.076 0.521 0.418 0.614 0.714 +1 0.485 1.309 -0.784 0.545 -1.079 0.965 -0.525 0.158 0.000 0.934 0.702 -0.993 0.000 1.659 -0.335 1.118 2.548 1.181 0.056 -0.299 0.000 0.923 1.042 0.996 0.981 0.827 0.931 0.807 +1 1.403 1.477 1.714 0.929 -0.956 1.201 1.254 -0.256 2.173 1.164 1.502 1.196 0.000 1.003 0.255 0.500 2.548 0.919 1.642 -1.047 0.000 1.247 1.181 1.062 1.222 1.110 1.039 0.969 +1 0.538 1.221 0.603 0.786 -1.049 1.723 1.436 1.285 0.000 2.041 0.755 -0.481 0.000 1.668 0.233 -1.071 2.548 1.448 0.654 -0.090 0.000 0.772 1.111 0.990 0.844 1.028 0.982 0.925 +0 1.496 1.807 -0.762 0.586 -1.348 0.803 1.347 0.745 0.000 0.433 1.002 -0.730 2.215 0.599 1.272 1.059 0.000 1.012 0.841 1.471 1.551 0.883 0.777 0.991 0.876 0.547 0.618 0.696 +1 0.623 -2.071 -0.523 1.502 0.475 0.701 -0.932 -1.679 2.173 0.380 -1.715 0.910 0.000 1.050 -0.794 -1.222 1.274 0.409 -1.416 -1.203 0.000 0.486 0.569 1.050 1.184 0.430 0.909 0.691 +0 0.869 -1.243 -1.520 1.482 0.181 1.598 -0.898 -1.025 1.087 1.036 1.073 1.064 1.107 0.718 0.844 0.159 0.000 0.680 2.340 0.641 0.000 0.898 0.847 1.571 1.386 2.877 1.926 1.842 +0 0.601 0.889 1.639 0.792 -0.093 0.861 0.359 1.598 2.173 1.388 0.463 1.014 0.000 2.567 -0.430 -0.316 0.000 1.168 1.369 -1.719 0.000 1.459 1.054 0.987 0.662 0.950 0.871 0.766 +0 2.442 -0.542 -0.318 0.938 0.615 0.572 -1.347 0.699 0.000 0.840 -1.013 1.191 0.000 1.383 -1.054 -1.459 2.548 0.802 1.240 1.684 0.000 0.673 0.974 1.563 0.851 0.180 0.843 0.853 +0 0.748 -0.178 -0.751 1.096 -1.097 0.534 1.159 -1.022 0.000 0.552 1.885 -1.740 0.000 1.430 1.143 0.869 2.548 1.566 -0.111 0.134 3.102 0.830 1.010 0.978 0.926 1.111 0.938 0.875 +0 2.328 -0.849 -0.787 0.845 -1.232 1.507 -0.506 0.771 0.000 0.920 -1.056 -1.622 2.215 0.736 -1.174 0.017 2.548 1.017 0.271 0.583 0.000 0.874 0.940 0.978 0.834 0.875 0.920 1.084 +0 0.530 1.968 -0.666 1.719 -1.336 0.811 1.924 1.282 0.000 1.155 0.304 0.217 0.000 1.035 0.932 0.643 1.274 1.272 0.275 -0.747 1.551 2.514 1.423 0.983 1.440 0.889 1.158 1.325 +1 0.714 1.096 -1.608 1.130 -0.665 0.467 0.632 1.157 0.000 0.513 -0.905 0.889 2.215 0.483 -0.154 -1.198 0.000 1.120 0.142 -0.386 3.102 0.922 1.011 0.992 0.772 0.740 0.870 0.763 +1 1.123 1.909 -0.819 0.267 1.591 1.142 2.741 1.347 0.000 1.035 0.886 -1.704 0.000 2.641 0.299 -0.137 2.548 0.800 0.012 -0.542 0.000 0.869 1.075 0.982 0.473 0.762 0.787 0.853 +0 1.014 -1.994 1.303 0.856 0.353 0.458 -2.789 0.058 0.000 0.620 -2.831 -1.428 0.000 0.346 0.246 -0.085 0.000 0.642 -0.897 -1.050 1.551 1.103 0.872 0.988 0.843 0.309 0.800 0.743 +0 1.028 -0.064 0.594 1.347 1.069 0.815 -1.695 -1.162 0.000 1.054 -1.246 -0.660 1.107 0.443 -0.840 0.870 2.548 0.392 2.087 0.055 0.000 3.586 1.938 0.995 1.591 0.725 1.315 1.315 +1 0.590 1.583 0.880 2.002 1.030 1.245 0.454 -0.029 0.000 0.925 -1.633 -1.702 0.000 0.975 0.704 -1.381 0.000 1.409 0.543 -0.534 3.102 0.605 0.666 1.002 2.159 0.625 1.537 1.286 +0 0.573 0.002 -1.054 1.101 -0.091 1.234 0.158 -0.794 2.173 1.285 2.792 0.701 0.000 0.913 1.425 1.564 0.000 1.650 -1.087 1.193 0.000 0.490 0.552 0.985 0.891 0.531 0.849 0.925 +0 0.728 2.079 0.378 1.072 -1.296 0.603 1.038 -0.997 2.173 0.515 -0.350 -0.123 0.000 1.324 0.646 1.181 2.548 0.833 1.004 0.568 0.000 0.891 0.908 1.222 1.095 1.046 0.855 0.855 +0 0.860 1.951 0.577 1.180 -1.724 1.987 0.010 -0.103 0.000 1.173 -2.492 1.607 0.000 0.740 0.815 -1.006 1.274 0.411 2.024 1.505 0.000 0.259 0.600 1.223 0.746 0.437 0.615 0.534 +1 0.788 -0.503 -1.554 0.544 -1.085 0.764 0.483 -0.384 1.087 0.716 -0.335 1.422 2.215 0.622 1.316 -0.784 0.000 0.985 0.401 1.459 0.000 0.892 0.880 0.983 0.834 1.182 0.734 0.681 +1 1.602 -0.482 0.950 1.322 0.472 0.677 1.982 -1.200 0.000 0.700 -1.234 -0.934 2.215 0.898 0.361 -0.506 2.548 0.811 -0.927 1.513 0.000 0.425 0.690 0.985 1.082 0.848 0.917 0.735 +1 1.225 0.116 0.092 0.736 0.873 1.149 -0.047 1.182 0.000 2.263 0.312 -0.762 0.000 0.911 0.663 0.838 2.548 1.177 2.419 -1.470 0.000 0.899 0.789 0.984 0.588 0.397 0.512 0.546 +1 1.898 -0.746 0.746 0.312 -1.590 1.130 -0.815 -0.686 2.173 0.491 0.552 -1.143 2.215 0.779 -0.315 1.072 0.000 0.374 -0.082 0.318 0.000 0.381 0.964 0.988 0.908 0.934 0.934 0.745 +0 1.794 -0.438 0.286 0.532 0.221 0.548 1.236 -1.684 0.000 1.048 0.250 -1.356 2.215 0.857 -0.319 1.316 2.548 1.275 0.023 -0.424 0.000 1.424 1.003 0.988 0.821 0.742 0.853 0.874 +1 1.048 -0.133 -0.045 1.171 -0.480 1.119 -0.481 1.593 0.000 1.116 0.704 1.170 0.000 1.673 -0.078 -0.633 2.548 0.851 0.712 0.208 3.102 0.819 0.671 0.982 0.698 0.769 0.876 0.818 +0 2.582 -1.329 1.319 1.076 1.038 1.456 -1.316 -0.651 0.000 0.828 -0.905 -0.453 0.000 0.724 -0.299 1.223 2.548 0.745 -0.345 0.074 3.102 0.624 1.217 0.993 0.967 0.484 0.760 1.087 +0 0.291 1.048 -0.077 1.333 -0.877 0.546 1.539 0.439 0.000 0.816 -0.192 1.308 2.215 0.828 1.119 1.270 0.000 1.163 0.661 -0.763 3.102 0.846 1.052 0.989 0.843 0.955 1.154 0.989 +1 2.282 -1.030 -1.371 0.678 -0.296 1.450 -1.066 1.378 2.173 1.634 -0.654 -0.119 0.000 0.751 0.007 0.628 2.548 0.701 -1.102 -0.813 0.000 0.938 0.883 1.420 1.338 1.099 1.120 1.077 +1 0.880 1.128 -1.297 0.348 -0.336 0.178 1.480 0.302 0.000 0.498 -0.573 -0.498 2.215 1.024 0.453 0.889 2.548 1.012 2.151 -1.219 0.000 0.850 1.163 0.984 0.673 0.840 1.065 0.912 +1 0.669 -1.065 0.454 1.022 1.188 1.509 0.252 1.534 0.000 2.303 -0.294 -0.544 2.215 0.437 -0.311 0.681 0.000 0.732 0.767 -0.359 3.102 1.108 1.033 0.987 1.376 0.787 1.256 1.075 +1 0.783 0.716 0.830 0.547 -0.838 1.076 0.502 1.729 2.173 0.893 0.347 -0.239 1.107 0.896 1.196 0.098 0.000 0.520 1.227 -1.239 0.000 0.706 1.060 0.985 0.685 1.417 0.837 0.719 +0 1.150 0.215 1.537 0.602 -1.123 0.707 -0.017 0.190 0.000 0.931 -0.567 -0.611 0.000 0.876 0.895 0.484 2.548 0.801 1.320 1.267 0.000 0.910 0.936 0.994 0.568 0.666 0.622 0.627 +1 1.341 -0.542 1.479 0.908 -1.407 0.429 0.149 -0.619 0.000 0.839 -0.121 -0.161 2.215 1.292 0.830 0.684 1.274 0.610 1.166 -0.352 0.000 0.552 0.813 0.986 1.006 0.971 0.907 0.788 +0 0.878 1.195 0.358 2.148 0.590 1.251 1.208 -0.915 2.173 0.741 0.016 -1.312 2.215 0.278 0.172 1.371 0.000 0.716 1.457 1.271 0.000 0.427 0.888 0.992 1.617 1.032 1.394 1.039 +1 0.652 -0.281 0.657 1.086 0.993 0.889 0.225 1.086 1.087 0.756 0.826 -0.581 1.107 1.388 1.540 -0.913 0.000 1.020 2.040 -0.675 0.000 0.581 0.700 0.994 1.056 1.262 1.100 1.572 +0 0.892 -0.959 -1.325 1.056 0.391 0.781 -0.377 0.155 2.173 0.948 0.355 -1.283 0.000 1.174 1.167 0.904 1.274 0.838 0.391 -0.029 0.000 1.052 1.068 1.344 0.920 1.337 1.083 0.982 +0 0.644 -0.473 -0.491 1.978 -0.976 1.653 -0.471 1.029 2.173 1.675 -0.450 -0.770 1.107 0.937 -0.700 0.537 0.000 1.118 -1.380 0.549 0.000 0.864 1.243 0.992 1.837 2.445 1.463 1.229 +1 1.206 -1.561 -1.529 1.907 1.452 1.315 -1.673 -0.019 0.000 0.549 -0.500 0.583 2.215 0.700 -0.725 -1.086 2.548 0.980 -1.242 -0.838 0.000 1.179 0.997 0.994 1.050 0.664 0.782 0.961 +1 2.290 0.260 0.112 1.207 -0.719 1.533 -0.816 1.460 1.087 0.657 -0.482 -0.676 2.215 0.425 0.206 -1.051 0.000 0.488 2.195 1.268 0.000 0.879 1.013 1.568 2.128 1.405 1.400 1.277 +1 3.214 -0.930 -1.477 1.183 1.547 2.422 -0.739 0.156 0.000 0.647 -0.140 -1.317 1.107 0.373 -0.664 0.576 0.000 1.099 0.390 -0.627 0.000 0.805 0.849 1.094 0.749 0.606 0.623 0.847 +0 1.425 -0.176 -1.441 0.432 -0.142 0.677 -0.217 -0.086 2.173 0.835 -1.093 1.179 0.000 0.555 1.103 -0.518 0.000 0.692 0.468 1.065 3.102 1.370 0.849 1.001 0.842 0.688 0.714 0.685 +1 1.636 -1.937 0.185 0.694 0.468 0.998 -0.321 -0.558 2.173 0.954 0.975 -1.685 0.000 0.414 -2.065 0.633 0.000 1.420 -0.648 1.635 3.102 0.672 0.976 1.002 1.047 1.193 1.054 0.840 +0 1.002 -0.503 -1.552 0.504 -0.050 1.645 -2.624 0.577 0.000 1.578 0.388 -1.533 2.215 1.186 1.992 -0.585 0.000 0.956 0.541 0.238 3.102 0.853 0.893 0.989 0.847 1.117 0.979 0.962 +1 0.351 1.357 -0.471 0.487 -0.196 1.034 0.005 1.672 0.000 1.425 0.087 -0.229 2.215 0.977 0.567 -1.479 0.000 1.201 -1.747 0.672 0.000 0.780 0.663 0.979 0.670 0.870 0.935 0.804 +0 0.727 2.288 0.495 0.266 1.052 0.654 0.232 -0.396 2.173 0.746 -1.066 1.568 1.107 0.540 0.318 1.398 0.000 0.827 0.763 -0.684 0.000 0.734 0.908 0.991 0.956 1.247 1.101 0.861 +0 0.401 0.784 0.041 1.706 0.462 1.345 0.185 -0.932 2.173 0.651 0.167 -1.603 0.000 0.938 0.512 1.133 2.548 0.415 -0.183 0.867 0.000 0.554 0.836 0.997 0.970 1.366 1.378 1.101 +0 0.424 0.868 -0.998 1.639 1.255 1.474 0.713 -0.037 2.173 0.339 1.612 -1.394 0.000 0.834 0.376 1.566 2.548 0.570 -0.532 -1.266 0.000 0.816 1.177 1.035 0.567 1.387 0.960 0.818 +0 0.369 0.964 -1.426 0.948 0.991 0.420 -2.202 0.725 0.000 1.112 -0.077 -0.739 1.107 0.621 -0.510 -1.648 0.000 0.863 0.195 0.240 3.102 1.153 1.012 0.988 0.916 0.697 0.894 0.804 +0 0.333 0.151 -0.921 1.240 0.133 1.089 -0.398 -1.031 2.173 0.860 0.725 1.421 0.000 1.010 0.153 0.411 2.548 1.383 1.097 0.808 0.000 0.852 0.847 0.989 1.427 1.314 1.096 1.002 +0 0.861 0.869 1.377 1.051 -0.251 0.697 0.169 -1.222 0.000 0.881 -0.049 0.640 2.215 0.402 -0.966 1.587 0.000 0.776 -0.105 0.063 3.102 0.828 0.995 1.311 0.744 0.372 0.638 0.705 +0 0.954 -0.909 -1.450 0.967 1.149 0.639 0.269 0.909 1.087 1.148 -0.412 -0.625 1.107 0.790 1.038 -0.179 0.000 0.624 1.746 0.893 0.000 0.744 0.849 0.987 0.986 1.316 0.937 0.964 +1 0.540 2.262 0.484 0.673 -0.187 1.044 1.247 -0.608 0.000 1.147 1.438 1.410 2.215 1.899 0.869 1.039 2.548 0.518 0.294 -0.477 0.000 0.543 1.271 0.992 0.899 0.662 1.026 0.873 +1 1.150 0.782 -0.107 0.213 1.400 1.096 0.686 1.405 0.000 0.275 -2.784 -0.645 0.000 0.748 1.283 0.073 2.548 0.882 0.442 -1.498 0.000 0.762 0.973 0.988 0.525 0.683 0.763 0.729 +1 1.225 0.129 -0.695 0.800 -1.683 0.647 -0.224 1.404 0.000 1.348 -1.116 0.879 1.107 0.639 -2.416 -0.604 0.000 0.759 0.269 0.468 0.000 0.869 0.633 1.065 1.352 0.921 0.955 0.885 +1 0.973 -0.601 1.536 1.202 -1.158 1.513 -0.246 -1.580 2.173 2.024 -1.088 0.096 0.000 1.537 -1.033 0.859 0.000 1.341 1.201 -0.222 0.000 1.716 1.158 0.988 0.740 0.979 1.445 1.187 +1 1.394 -0.406 -1.088 0.359 -0.745 1.600 -1.160 0.445 0.000 1.849 0.376 -0.966 2.215 1.368 -0.595 1.068 0.000 0.722 0.627 1.096 0.000 0.836 0.613 0.994 0.686 0.822 0.920 0.806 +0 0.441 -0.672 1.115 1.701 -0.577 0.586 -1.313 1.345 0.000 0.580 0.866 -1.567 1.107 0.771 -0.720 -0.032 2.548 0.744 -1.531 0.496 0.000 0.747 0.745 1.199 1.026 0.967 0.848 0.809 +0 1.050 0.816 0.893 1.093 1.623 1.096 0.049 -1.359 1.087 0.746 0.660 -0.191 2.215 1.012 -1.555 -0.394 0.000 0.969 -2.002 0.292 0.000 0.930 1.320 0.987 1.186 1.233 1.163 1.259 +1 0.687 1.388 -1.210 1.089 0.304 1.684 2.240 1.040 0.000 1.894 -0.240 -0.942 1.107 0.990 -0.276 -0.072 1.274 0.530 -0.935 -1.615 0.000 0.457 0.584 1.173 1.000 1.028 1.111 0.861 +1 0.655 1.750 -1.468 0.571 1.107 1.263 0.466 -0.936 0.000 0.535 2.260 -0.187 0.000 1.054 0.311 0.322 2.548 2.889 1.295 1.024 1.551 1.202 1.110 0.996 0.833 1.166 1.141 0.931 +0 1.265 -0.516 -1.174 0.383 -0.287 1.131 1.131 1.030 0.000 1.633 -0.958 -0.456 2.215 1.490 0.718 1.424 0.000 1.244 0.968 0.275 3.102 0.910 0.961 0.994 0.799 1.840 1.728 1.498 +0 0.362 0.894 0.866 1.503 -0.534 0.805 -0.622 0.985 1.087 1.271 0.207 1.562 2.215 1.174 -0.496 -0.513 0.000 0.869 -0.523 -0.110 0.000 0.399 1.001 0.988 1.174 0.979 1.107 1.015 +1 1.013 0.278 1.406 1.450 1.146 1.421 0.747 -0.911 1.087 0.690 2.763 0.803 0.000 1.361 0.938 -0.404 0.000 0.485 1.149 -0.266 0.000 0.864 1.474 0.988 1.762 1.408 1.309 1.435 +0 0.478 1.732 0.908 0.918 -0.729 0.779 -0.271 0.023 0.000 0.980 0.064 1.456 0.000 1.231 0.744 0.444 2.548 1.220 -0.823 -1.533 0.000 1.000 0.861 0.983 0.741 0.730 0.829 0.810 +1 0.970 1.230 0.418 0.289 0.569 1.060 0.664 -1.239 2.173 0.517 -1.119 0.592 2.215 0.632 0.260 0.822 0.000 0.756 -1.191 -1.335 0.000 1.024 1.204 0.993 0.841 1.572 0.974 0.868 +0 1.638 -0.777 -1.571 1.072 1.424 0.943 0.731 0.182 1.087 0.389 0.215 0.882 0.000 0.478 -1.303 -0.423 2.548 0.729 -0.670 -0.809 0.000 0.786 0.905 0.991 0.804 1.179 1.098 0.858 +1 3.481 0.207 -1.183 1.274 -1.497 1.965 -1.060 0.283 0.000 0.705 0.225 1.663 0.000 0.853 0.888 -0.927 2.548 0.890 2.063 0.237 0.000 1.729 1.119 0.991 1.429 0.914 0.953 1.036 +1 2.031 -0.675 -1.619 0.112 -1.193 0.588 -0.435 -0.641 2.173 0.931 0.709 -0.049 2.215 0.669 -0.902 -0.133 0.000 1.021 2.021 0.980 0.000 0.813 0.859 0.994 0.841 0.872 0.905 0.772 +1 1.005 1.425 -0.123 1.039 -1.255 1.135 1.172 0.147 2.173 1.229 1.453 1.724 0.000 0.635 2.167 -1.737 0.000 0.624 0.912 1.352 3.102 0.619 0.454 1.206 1.018 0.789 0.892 0.824 +1 1.319 -0.586 -0.225 0.620 -0.100 0.767 0.911 1.021 2.173 0.685 1.278 -1.405 2.215 0.720 0.546 1.503 0.000 0.759 1.458 -0.645 0.000 0.908 0.815 0.999 1.602 0.896 1.359 1.152 +0 1.399 -0.020 1.234 1.141 1.494 1.211 -0.726 -0.221 0.000 0.739 0.120 -0.127 0.000 0.640 0.815 1.138 2.548 1.165 -1.098 -1.364 1.551 0.928 1.079 0.980 0.752 1.026 0.755 0.790 +0 0.977 -1.463 1.416 0.427 -1.196 2.019 -1.580 1.110 1.087 1.305 -1.052 -0.989 2.215 1.271 -1.762 -0.881 0.000 1.901 1.908 -0.110 0.000 0.925 0.967 0.995 1.092 2.349 1.354 1.099 +0 0.327 -1.517 0.850 2.173 0.106 0.652 -1.360 -0.293 2.173 1.150 0.641 -1.420 0.000 0.678 0.153 0.744 2.548 0.574 2.042 -1.724 0.000 1.117 0.998 0.987 0.909 0.968 1.273 2.115 +0 0.790 -0.711 0.733 0.422 0.855 0.479 0.009 -1.261 2.173 0.483 0.195 0.324 0.000 1.651 -0.270 -0.407 2.548 1.574 -0.168 1.617 0.000 1.068 1.084 0.988 1.022 0.790 0.911 0.855 +1 0.524 1.129 0.683 1.532 0.579 1.106 0.050 -1.019 2.173 1.058 0.051 1.201 0.000 0.577 -1.155 -1.054 0.000 1.348 -0.265 -0.362 3.102 1.369 1.077 0.994 1.343 0.762 0.928 0.913 +0 0.686 1.798 0.218 1.167 0.756 0.722 0.143 1.338 0.000 0.830 0.445 -0.257 0.000 0.693 1.901 -0.683 0.000 0.569 0.784 0.271 0.000 0.898 0.812 0.976 0.618 0.395 0.564 0.588 +0 2.098 -0.024 -0.138 0.901 -0.242 0.483 -0.642 -0.965 0.000 0.853 0.180 -1.508 0.000 1.094 1.961 1.391 0.000 1.367 -2.052 0.947 0.000 0.867 0.737 0.982 0.805 0.486 0.699 0.733 +0 1.694 0.868 -0.740 0.356 1.339 0.701 -0.015 0.206 2.173 0.571 -0.924 0.726 0.000 0.942 0.176 -1.732 2.548 0.935 -2.316 1.516 0.000 0.909 1.209 1.027 0.963 1.002 1.020 1.161 +0 3.216 0.626 -0.738 0.103 0.336 1.587 -0.781 0.943 1.087 0.888 -1.003 1.308 0.000 1.093 0.045 -0.073 2.548 0.574 -1.396 -1.298 0.000 0.730 0.908 0.986 0.847 1.481 1.572 1.362 +0 0.947 -0.204 -1.015 2.041 -1.426 1.353 0.363 0.454 2.173 0.485 -0.387 -0.335 0.000 0.426 0.817 1.357 0.000 0.459 -1.315 0.760 3.102 0.847 0.920 0.984 0.942 0.974 1.151 0.905 +0 0.830 0.657 -1.403 1.241 -0.608 0.799 -2.113 0.972 0.000 0.928 0.963 -0.805 0.000 0.868 0.457 0.499 2.548 1.060 1.036 1.233 1.551 4.552 2.654 0.984 0.843 0.530 1.757 1.450 +1 1.141 0.353 1.269 0.539 -0.392 1.197 0.839 -0.778 0.000 1.274 0.173 0.266 2.215 1.516 0.271 1.534 2.548 0.728 -0.369 0.690 0.000 1.697 1.427 1.083 0.716 1.347 1.109 0.916 +1 1.359 -0.796 1.287 0.771 -0.849 1.825 -0.994 -1.226 1.087 1.206 -0.107 0.538 0.000 1.763 0.488 0.245 0.000 0.841 -0.676 0.869 0.000 0.938 0.637 1.331 1.164 1.259 1.528 1.258 +1 1.198 1.925 1.155 0.920 0.991 2.569 0.090 -0.804 0.000 1.241 0.633 1.189 2.215 0.945 1.074 0.059 0.000 0.665 0.994 1.000 0.000 0.656 0.812 0.973 0.925 0.760 0.702 0.640 +0 0.495 0.543 0.284 1.707 1.079 0.645 -0.462 -0.194 0.000 0.817 -2.483 0.897 0.000 0.846 0.741 -1.163 2.548 1.112 -0.489 -1.396 0.000 1.146 0.963 0.989 1.019 0.992 0.851 0.799 +0 0.444 -1.225 0.682 0.373 -1.422 0.880 -0.436 1.679 0.000 0.797 -0.863 -0.174 2.215 0.560 -0.516 0.751 0.000 0.854 0.654 -0.240 3.102 0.941 0.976 0.988 0.753 0.700 0.779 0.674 +1 0.564 0.655 1.138 0.603 -0.910 0.906 0.728 -1.291 2.173 0.796 1.149 0.800 0.000 1.414 0.364 0.329 0.000 0.605 -0.859 -0.603 3.102 0.935 1.013 0.983 0.777 0.908 0.953 0.839 +0 0.647 -1.068 -1.451 1.266 -0.865 1.234 -0.729 1.448 1.087 0.781 -1.330 -0.465 0.000 1.134 -0.147 0.717 2.548 1.220 -1.522 -0.014 0.000 0.583 1.078 0.988 1.128 1.001 1.047 0.981 +1 0.966 -0.214 0.730 1.067 -1.154 0.839 0.439 0.846 0.000 0.833 -0.658 -1.447 2.215 0.596 0.216 -0.023 0.000 2.347 -0.015 -0.950 3.102 0.907 1.164 1.396 0.950 0.691 0.921 0.832 +0 0.778 1.505 -1.427 1.772 -1.601 0.478 0.946 0.815 1.087 0.675 0.519 0.119 0.000 0.901 1.209 -0.566 2.548 0.941 -0.433 0.184 0.000 0.576 0.789 0.978 1.127 0.790 0.878 1.084 +0 1.096 1.605 -0.616 0.827 0.033 0.770 0.843 -1.358 2.173 0.930 1.284 0.140 2.215 1.012 0.521 1.229 0.000 0.842 1.650 1.553 0.000 0.831 0.965 0.978 1.122 1.251 0.879 0.857 +0 0.310 0.353 -1.552 0.491 0.828 0.803 0.184 -1.105 2.173 0.789 -0.714 0.713 1.107 0.486 -1.915 0.348 0.000 0.642 -1.649 -0.862 0.000 0.548 1.206 0.984 0.638 1.295 0.881 0.756 +1 1.182 1.084 0.747 1.266 0.237 0.719 0.242 -1.707 2.173 0.542 -2.673 -1.115 0.000 1.166 -0.534 -0.824 0.000 0.590 0.708 1.301 0.000 1.119 0.900 0.985 0.663 0.991 0.894 0.944 +1 0.418 -1.167 0.756 1.154 -0.455 0.574 -0.421 1.280 2.173 0.817 -1.214 -1.259 2.215 0.623 1.179 0.160 0.000 0.535 0.682 1.707 0.000 0.645 0.809 0.989 0.810 0.871 0.822 0.756 +0 1.326 -0.350 -0.204 0.339 0.596 1.056 -0.409 0.869 2.173 1.692 0.569 -1.426 0.000 1.176 0.001 -1.036 2.548 0.627 -1.191 0.728 0.000 2.026 1.245 0.984 0.978 1.403 1.151 1.009 +1 1.242 -0.650 1.320 1.163 0.811 1.024 -0.227 -0.909 2.173 0.493 -1.095 -1.189 0.000 1.006 -0.971 -0.101 2.548 0.684 -0.608 0.243 0.000 0.742 0.772 0.985 0.966 1.003 0.983 0.795 +0 0.623 -1.176 1.542 0.480 0.318 1.275 0.446 0.087 0.000 1.404 -0.055 1.470 0.000 1.996 -0.259 -1.005 2.548 0.636 0.239 -0.768 3.102 2.789 1.542 0.988 1.325 0.309 1.203 1.317 +0 0.636 1.164 0.589 0.595 0.662 1.077 -0.344 -1.399 0.000 1.394 -0.245 0.098 2.215 1.146 -0.175 -0.939 0.000 1.110 -0.835 1.059 3.102 0.879 0.919 1.000 0.791 0.958 0.809 0.736 +1 1.205 -1.088 0.959 1.898 1.151 0.606 -0.653 -0.869 2.173 0.862 0.206 -0.683 0.000 0.786 -0.199 0.203 1.274 0.734 -0.438 -0.342 0.000 0.501 0.560 1.005 1.187 0.733 0.845 0.820 +1 0.333 0.505 0.415 1.146 -0.152 1.701 -0.030 -0.886 0.000 1.798 -1.329 0.652 2.215 1.683 -0.953 1.162 2.548 0.668 -0.054 -1.646 0.000 1.030 1.763 0.991 3.278 0.872 2.361 2.027 +0 0.930 0.179 -0.595 0.271 0.854 0.654 -0.498 1.515 2.173 0.983 0.292 -1.281 2.215 0.667 -0.904 -0.113 0.000 1.184 0.355 0.278 0.000 0.877 0.922 0.985 0.947 0.841 0.727 0.694 +1 1.758 0.195 -1.519 0.551 0.858 1.057 -1.284 -0.245 2.173 0.770 -0.633 -1.630 0.000 1.627 -0.779 0.707 2.548 0.608 0.614 -0.852 0.000 0.883 1.102 1.146 1.586 1.289 1.213 1.020 +1 0.750 -1.544 -0.300 1.308 -1.184 0.505 -0.494 1.389 0.000 0.369 2.001 -1.651 0.000 1.821 0.203 0.657 1.274 1.298 0.184 -0.292 3.102 0.828 0.887 0.987 1.095 0.887 1.188 0.972 +1 0.413 0.727 0.878 1.121 -1.302 1.039 -0.926 0.361 2.173 0.763 -1.196 1.271 2.215 1.300 -0.632 -1.082 0.000 1.142 1.280 0.276 0.000 2.224 1.752 0.988 1.889 0.977 1.439 1.364 +1 0.907 -0.730 -0.315 1.014 0.551 0.922 0.499 -1.373 2.173 0.410 -0.888 1.098 0.000 1.207 0.233 1.535 0.000 0.951 -1.587 -0.162 0.000 0.952 0.993 0.984 0.751 0.238 0.890 0.788 +1 0.336 0.838 -0.813 1.411 1.658 1.419 -0.235 -0.961 2.173 1.061 -0.700 0.977 2.215 1.965 0.650 0.535 0.000 0.403 -2.196 -0.124 0.000 0.854 1.154 0.991 0.847 1.831 0.989 0.862 +1 1.811 -1.649 -1.623 0.461 -0.025 1.072 -0.689 0.366 2.173 0.491 1.920 -0.657 0.000 0.594 -1.202 1.233 0.000 0.416 -0.378 -1.255 3.102 0.762 0.978 1.255 0.656 0.708 0.872 0.729 +0 1.358 0.970 -1.289 1.618 -0.619 0.569 -2.297 -1.411 0.000 1.040 0.206 0.788 0.000 1.280 -0.426 0.552 0.000 1.125 0.924 1.530 3.102 0.718 0.986 1.166 1.453 1.308 1.062 1.098 +1 0.888 0.821 -1.451 0.397 0.583 1.160 -0.424 -0.610 2.173 0.838 1.161 1.378 0.000 0.573 1.619 0.951 0.000 0.713 1.575 0.076 0.000 0.995 0.791 0.982 1.368 0.984 1.076 0.940 +0 1.728 0.897 1.389 1.463 0.518 0.703 1.017 -0.954 2.173 0.653 1.356 -1.345 2.215 0.272 1.139 0.888 0.000 1.260 0.765 0.074 0.000 0.450 0.649 1.556 1.068 0.391 0.902 0.713 +0 1.155 -0.459 1.456 1.320 1.218 1.098 -0.537 -0.472 1.087 0.510 1.885 -0.063 0.000 0.885 0.894 -1.058 0.000 1.186 1.230 0.322 0.000 0.955 0.717 0.989 1.466 0.443 0.960 1.231 +1 0.804 1.268 -1.253 1.299 0.970 0.889 0.453 -0.086 2.173 1.434 0.919 1.437 0.000 0.711 -0.073 -1.488 2.548 1.054 -1.918 -0.587 0.000 0.816 0.784 1.285 1.169 0.982 0.874 0.820 +1 0.570 0.954 -0.795 1.633 -0.072 0.528 -0.750 0.228 0.000 0.716 0.529 1.389 2.215 1.341 -0.920 1.545 1.274 0.750 -0.456 -1.269 0.000 0.943 0.941 0.988 2.060 0.906 1.367 1.189 +1 0.780 0.752 -0.252 0.789 1.520 2.944 1.272 -1.181 0.000 2.788 0.759 0.525 0.000 1.074 0.627 0.020 0.000 1.434 -0.485 1.218 3.102 1.173 0.778 1.086 0.872 0.447 0.876 0.817 +1 1.029 -0.684 -0.892 0.187 -1.685 1.504 -0.338 -0.004 0.000 0.858 0.091 1.026 1.107 2.675 -0.274 -1.719 2.548 1.060 -0.625 0.528 0.000 0.974 1.149 0.977 0.917 1.047 1.272 1.049 +1 1.292 -0.407 -1.136 2.004 -1.701 0.623 2.174 0.223 0.000 1.124 0.286 0.386 2.215 0.498 1.293 -0.336 0.000 0.966 -0.994 1.201 3.102 1.020 0.983 1.084 1.434 0.992 0.995 0.918 +0 1.959 0.647 0.013 1.699 -0.083 1.780 -0.534 1.704 0.000 0.602 -0.068 -0.034 1.107 0.811 0.640 1.584 1.274 0.598 -0.803 -1.209 0.000 0.846 0.917 0.975 0.750 0.794 0.847 1.354 +0 1.792 -0.006 -0.203 0.223 -0.563 1.338 -0.056 1.478 0.000 0.543 -2.690 -0.391 0.000 0.698 1.099 0.132 2.548 0.797 -0.662 1.096 0.000 0.800 0.892 0.998 0.821 0.599 0.802 0.897 +1 0.298 1.472 0.100 1.350 -1.572 0.931 0.243 -0.576 2.173 0.657 -1.300 0.078 0.000 1.254 1.135 0.998 0.000 1.252 0.099 1.390 3.102 0.908 0.813 0.986 1.555 1.121 1.176 0.978 +1 0.476 0.590 -0.322 2.835 0.297 0.669 -0.851 1.474 0.000 0.749 1.646 -1.262 2.215 0.975 -0.904 1.047 0.000 1.849 0.273 -1.327 0.000 1.152 0.808 0.986 0.804 0.803 1.005 0.973 +1 0.555 -0.496 -1.694 0.710 0.315 0.833 -1.168 0.761 0.000 1.086 -1.317 -1.537 1.107 0.986 -1.431 -0.815 2.548 0.788 -1.887 1.049 0.000 0.774 1.018 0.985 1.042 0.677 0.825 0.869 +1 1.117 1.341 -1.362 0.224 -1.573 1.124 0.695 1.193 2.173 0.817 2.147 -0.143 0.000 0.502 0.738 -0.313 0.000 0.444 -0.139 -0.022 0.000 0.759 0.693 0.979 0.942 0.874 0.896 0.809 +1 2.050 -0.109 -1.427 0.683 -1.557 1.620 -1.244 0.798 0.000 1.193 0.134 -1.169 0.000 1.865 -0.135 -0.078 2.548 0.999 -1.282 -0.369 0.000 0.760 0.853 0.977 1.358 0.647 0.932 1.186 +0 1.664 -0.938 0.546 1.304 -1.587 0.527 0.786 1.640 0.000 0.620 0.500 0.916 0.000 1.133 -0.974 -0.054 2.548 1.393 0.013 -0.947 3.102 0.909 0.867 1.917 1.245 0.882 0.925 0.821 +1 0.749 -1.522 -1.680 0.651 0.567 0.670 -1.083 -0.644 0.000 0.841 -0.611 1.414 2.215 1.037 -1.678 -1.167 0.000 1.864 -0.165 0.339 3.102 0.891 1.115 0.989 1.134 0.963 0.967 0.898 +0 0.682 -0.017 0.639 1.210 -1.299 1.350 0.163 1.143 2.173 1.114 -0.852 -0.364 2.215 0.961 0.098 -0.203 0.000 0.691 -1.169 -1.265 0.000 1.047 0.785 1.240 1.050 2.020 1.103 0.948 +0 4.066 0.724 -1.381 0.448 -1.100 2.328 0.087 0.316 1.087 1.025 0.946 -0.799 0.000 0.740 0.737 1.184 0.000 0.593 -0.064 1.110 3.102 0.807 1.282 0.982 0.819 0.822 1.660 1.278 +0 0.374 0.432 0.988 0.244 -1.146 0.946 -1.506 0.236 0.000 0.986 1.123 -1.166 0.000 1.221 -0.511 0.846 0.000 0.599 -1.049 0.750 1.551 0.913 1.051 0.995 0.528 0.762 0.650 0.587 +1 0.920 -0.320 -0.241 0.289 -0.303 0.985 0.635 1.418 0.000 0.599 1.390 -1.545 0.000 0.761 -0.044 -0.787 2.548 0.844 -0.936 0.236 0.000 0.986 1.037 0.999 0.737 0.371 0.686 0.998 +1 0.578 0.643 -1.119 0.853 1.090 1.110 2.041 -0.083 0.000 1.330 0.460 1.081 0.000 0.666 -0.113 1.424 0.000 1.935 -1.051 -1.242 3.102 0.981 0.779 0.987 0.968 0.815 0.971 0.808 +0 0.863 0.927 -1.666 1.122 1.691 1.484 -1.540 0.417 0.000 0.954 0.374 -0.321 2.215 0.785 0.072 1.494 0.000 1.400 0.052 -0.945 3.102 0.874 0.850 1.003 1.291 0.581 0.989 1.083 +0 0.844 -2.102 0.735 1.315 1.312 0.949 -0.769 0.747 1.087 1.383 0.451 -0.375 0.000 0.794 1.383 -1.597 0.000 2.914 0.191 -1.077 1.551 0.879 1.271 0.998 0.835 1.983 1.367 1.568 +1 1.164 0.534 -1.645 0.443 1.258 1.100 0.639 -0.056 2.173 0.711 0.444 -0.872 2.215 0.779 0.113 1.220 0.000 0.495 1.325 0.194 0.000 0.775 0.909 0.987 0.768 0.881 0.859 0.737 +0 0.781 0.153 0.493 0.344 -1.506 0.617 -0.523 -0.380 2.173 0.842 0.453 -0.690 0.000 0.869 -0.828 -1.737 2.548 0.754 0.064 1.116 0.000 0.988 0.948 0.983 0.659 0.875 0.671 0.613 +1 1.065 0.515 0.639 0.692 -1.696 0.721 -0.928 -0.742 0.000 0.752 0.683 1.414 2.215 1.197 -0.483 -0.133 0.000 1.078 -0.028 0.338 3.102 0.935 0.828 1.025 0.605 0.736 0.870 0.825 +1 1.339 -0.342 -1.533 0.894 -0.442 0.787 0.619 1.162 2.173 0.707 0.586 0.207 2.215 0.552 0.512 -0.962 0.000 1.253 -2.279 -0.121 0.000 0.714 0.756 1.262 0.991 0.833 0.882 0.788 +0 1.079 -0.630 -1.332 1.576 -0.588 0.455 -0.411 0.247 2.173 0.531 -0.255 1.211 0.000 0.675 -1.487 1.342 0.000 0.617 0.698 0.617 1.551 0.689 0.732 1.123 0.869 0.417 0.696 0.709 +0 0.779 -1.248 0.918 1.726 0.282 0.945 -0.533 -1.302 2.173 1.093 -0.354 1.267 0.000 0.878 0.686 -1.237 0.000 1.095 0.334 -0.381 3.102 1.460 1.098 0.986 1.490 0.952 1.161 1.184 +0 0.713 1.338 1.365 0.943 -1.454 1.157 1.881 0.033 0.000 1.054 0.877 0.652 1.107 0.790 -0.573 -1.271 2.548 1.528 0.966 -1.278 0.000 2.030 1.531 0.981 1.425 1.259 1.305 1.190 +1 0.650 1.832 1.593 0.369 -1.284 0.644 -0.897 1.493 2.173 0.525 -0.149 -0.162 0.000 0.973 -0.490 0.330 2.548 0.744 0.125 -0.800 0.000 1.008 1.042 0.990 0.974 0.874 0.876 0.925 +0 0.785 -0.020 1.287 0.729 -1.372 0.852 0.733 0.200 1.087 0.647 1.203 -0.543 2.215 0.809 1.110 1.262 0.000 0.375 -1.735 1.283 0.000 0.701 0.864 0.989 1.090 0.732 0.960 0.808 +1 0.411 1.328 -0.967 0.223 0.745 0.560 -0.355 -0.856 2.173 0.969 0.739 -0.450 2.215 1.220 -0.440 0.977 0.000 0.419 -0.791 1.096 0.000 0.203 1.106 0.993 0.656 0.748 0.786 0.677 +1 1.623 0.033 0.952 1.043 0.614 0.782 -1.507 -0.790 2.173 0.587 0.255 0.297 0.000 0.563 0.193 -0.339 2.548 1.057 -0.069 -1.461 0.000 1.041 1.240 0.984 0.776 0.873 0.983 0.867 +1 0.861 -0.887 0.213 0.961 0.893 0.685 1.039 -1.252 2.173 0.598 0.611 1.670 0.000 0.703 -0.287 -0.356 2.548 1.172 0.096 -0.832 0.000 0.892 0.720 0.984 1.220 0.892 0.840 0.745 +0 0.798 0.515 -1.094 1.228 1.126 0.707 1.348 1.252 2.173 1.185 -0.773 -0.810 2.215 1.480 -1.050 -0.117 0.000 0.418 1.077 0.519 0.000 1.427 1.156 1.247 0.838 2.175 1.370 1.169 +1 1.458 -0.595 -0.506 0.854 1.555 0.533 0.683 0.622 2.173 1.355 -0.133 1.223 2.215 0.721 0.704 -1.132 0.000 0.785 0.637 -0.813 0.000 0.236 1.013 1.483 1.133 0.835 0.938 0.836 +1 1.794 -1.064 -0.431 0.537 0.679 1.514 1.337 0.798 0.000 0.644 0.409 -0.759 1.107 1.102 0.477 1.730 2.548 0.563 -1.147 -1.481 0.000 2.865 1.768 1.145 0.953 0.702 1.176 1.396 +1 0.619 -1.821 0.384 1.252 1.212 2.304 -2.713 -0.520 0.000 1.066 -0.611 -1.682 2.215 2.077 -0.602 0.972 0.000 1.120 0.534 1.383 3.102 0.980 2.564 0.993 1.000 0.763 2.300 1.787 +1 0.673 0.434 -0.059 0.832 -1.338 1.018 0.450 0.660 0.000 1.294 0.330 -0.887 2.215 0.601 1.435 0.383 0.000 1.274 0.484 1.620 3.102 0.901 0.923 0.990 0.679 0.906 0.954 0.817 +1 1.110 -0.907 -1.025 0.352 0.389 0.786 0.468 0.767 2.173 0.914 -0.388 -0.803 0.000 1.343 2.121 0.498 0.000 1.326 -0.915 -1.274 0.000 0.781 0.656 0.986 1.252 0.730 0.859 0.798 +1 0.805 -0.718 1.344 0.307 1.602 1.721 -1.175 1.516 2.173 1.713 -0.993 -0.391 0.000 2.114 -0.503 0.466 0.000 1.177 -0.125 0.184 1.551 0.914 0.822 0.984 1.194 1.625 1.306 1.045 +1 1.041 -2.224 1.377 0.756 -0.949 0.675 -0.052 -0.143 2.173 0.609 -1.055 -1.376 2.215 0.537 -0.611 1.262 0.000 0.516 -1.641 -0.054 0.000 0.673 0.794 1.064 0.739 0.987 1.004 0.789 +0 0.280 -1.592 0.254 1.711 0.531 0.563 -1.450 -0.469 0.000 0.915 -1.550 -1.166 0.000 0.760 -1.088 1.050 0.000 0.549 1.284 -0.374 3.102 0.901 0.850 1.003 0.802 0.625 0.903 0.936 +0 0.737 -1.765 -0.399 1.033 0.828 1.196 -2.173 -1.580 0.000 1.495 -0.162 0.657 2.215 2.093 -0.843 -0.938 2.548 1.491 -0.040 0.165 0.000 3.331 2.220 1.081 1.264 2.001 1.804 1.440 +0 2.636 0.430 -0.661 0.213 -0.954 1.642 0.619 1.096 2.173 0.730 -0.451 -1.251 2.215 0.493 0.143 1.195 0.000 0.423 -1.620 0.560 0.000 0.689 1.146 0.989 0.777 1.658 1.293 1.058 +0 0.975 1.711 1.031 1.228 0.575 1.385 -0.090 -0.856 2.173 0.347 -1.088 1.171 0.000 1.281 0.526 1.551 2.548 0.478 0.661 -0.260 0.000 0.779 0.975 0.990 1.296 1.483 1.813 1.446 +0 0.417 -1.488 -1.493 1.198 -0.219 1.453 -0.458 0.890 0.000 1.364 -0.271 -1.145 2.215 0.822 -0.215 -0.073 2.548 0.403 -0.244 1.291 0.000 1.008 0.981 0.986 0.884 0.926 0.899 0.906 +0 1.421 1.507 1.074 0.793 1.700 0.577 2.405 -0.377 0.000 0.317 2.214 -1.606 0.000 0.609 -2.336 -0.280 0.000 0.530 0.335 1.053 3.102 0.815 0.987 0.991 0.520 0.404 0.669 0.711 +1 0.660 -1.796 -0.047 1.053 -0.868 0.926 -1.217 -1.465 2.173 0.937 -1.742 0.874 0.000 1.021 -0.614 -0.768 2.548 1.692 0.104 0.370 0.000 0.897 0.910 0.986 1.122 0.789 0.928 1.167 +1 1.726 -0.551 -0.581 0.320 1.573 0.635 1.003 0.851 0.000 0.386 0.410 -0.683 0.000 0.613 -0.657 0.721 2.548 0.379 0.123 0.271 3.102 1.079 0.859 0.986 0.549 0.220 0.495 0.653 +1 0.339 -1.324 -1.092 1.114 -1.029 0.905 1.047 0.626 0.000 0.852 -0.969 -1.194 2.215 1.030 -0.500 -1.503 2.548 0.447 0.066 -1.549 0.000 1.026 1.212 0.975 0.737 0.355 1.021 0.878 +0 0.889 -0.590 1.653 0.665 -0.396 0.313 -1.403 1.366 0.000 0.763 -0.568 -0.624 2.215 0.782 -1.820 0.231 0.000 0.809 0.973 1.383 1.551 0.804 0.897 1.026 0.798 0.986 0.898 0.761 +1 0.551 0.306 0.773 0.579 1.444 0.506 0.059 -0.257 0.000 0.858 0.202 -1.001 2.215 0.916 -1.411 1.528 0.000 0.374 -0.546 0.180 3.102 1.642 0.883 0.988 0.946 0.502 0.713 0.901 +1 0.688 -0.666 -1.491 0.740 -0.899 0.872 -0.831 -0.302 0.000 1.328 -0.079 1.061 2.215 0.604 0.692 -1.474 0.000 0.809 -0.812 0.272 1.551 1.622 1.001 0.993 1.312 0.748 0.912 0.935 +0 0.966 -0.670 -1.189 0.459 0.749 0.854 0.276 -0.594 2.173 0.825 0.453 0.410 2.215 0.816 -0.206 -1.144 0.000 1.007 -0.215 1.064 0.000 0.913 0.930 0.986 0.959 0.978 0.838 0.739 +1 1.076 0.378 0.100 0.844 -1.085 1.123 0.159 -0.895 2.173 1.050 1.359 0.533 2.215 1.080 -0.560 1.443 0.000 1.038 -0.181 0.682 0.000 0.906 1.455 1.156 0.995 1.860 1.366 1.108 +1 0.468 -1.378 0.424 1.023 -1.355 0.832 -1.234 1.545 2.173 1.172 0.122 -0.276 0.000 0.455 -0.727 -0.560 2.548 0.975 1.466 0.981 0.000 1.789 1.152 0.987 0.744 0.746 1.240 1.201 +0 0.548 1.570 -1.559 1.629 1.487 0.689 -0.913 0.624 2.173 0.789 -1.021 -0.852 2.215 0.881 -0.781 0.018 0.000 0.821 -0.205 -0.719 0.000 0.648 0.735 0.983 1.303 1.056 1.123 0.946 +1 0.636 1.143 0.287 0.579 -0.436 1.003 -0.497 1.733 2.173 0.761 0.424 -0.114 0.000 1.201 -0.382 0.459 0.000 1.110 0.478 -1.323 3.102 0.971 0.992 0.989 1.108 0.757 0.933 0.819 +1 1.441 -1.328 1.689 0.991 1.428 0.950 -0.965 -0.704 0.000 1.093 -1.130 0.268 2.215 0.919 -0.693 1.375 1.274 0.998 0.184 0.304 0.000 0.998 1.179 0.977 1.206 0.920 1.079 1.362 +1 1.977 1.239 -1.385 0.571 -1.705 1.065 -0.115 0.386 2.173 0.565 1.512 0.721 0.000 0.526 0.729 -0.820 2.548 0.880 0.682 -0.375 0.000 0.842 1.060 1.003 0.585 0.937 1.029 0.882 +1 1.201 -1.196 -0.715 1.085 -1.069 0.804 -0.872 0.784 1.087 0.823 -0.217 0.734 2.215 0.571 -0.263 -1.734 0.000 0.584 -0.558 -0.298 0.000 0.625 0.717 0.983 1.069 0.404 0.909 0.716 +0 0.938 -0.932 0.265 1.236 -0.339 0.574 -0.364 1.050 0.000 0.633 -1.026 1.602 1.107 0.699 -2.141 1.324 0.000 0.972 -1.145 -1.345 0.000 0.766 1.004 0.988 0.926 0.777 0.674 0.764 +0 0.577 0.473 -1.180 0.282 -0.146 0.751 1.066 0.510 2.173 0.736 0.866 -0.370 0.000 0.686 0.486 1.613 0.000 0.413 2.440 -1.324 0.000 0.927 0.938 0.984 0.803 0.849 0.703 0.665 +0 0.292 -1.476 0.440 1.559 -1.172 1.234 -1.153 0.216 2.173 1.414 -2.351 0.673 0.000 2.057 -0.359 -1.577 2.548 0.861 -1.230 -0.559 0.000 1.467 1.383 0.988 0.782 2.131 1.539 1.266 +0 1.134 -0.732 1.521 1.380 -1.253 1.184 1.167 0.517 0.000 0.840 -1.872 -0.189 0.000 1.436 -1.119 -1.041 1.274 1.050 -0.562 0.467 0.000 1.064 0.952 1.039 0.758 0.499 0.735 0.763 +1 0.489 -0.899 0.400 0.856 1.635 0.895 -1.208 1.494 0.000 1.475 -1.215 0.028 2.215 0.823 0.278 -0.513 2.548 0.711 -0.900 -1.661 0.000 0.855 1.163 0.993 1.126 1.146 1.006 0.894 +1 1.077 1.583 0.818 1.470 0.143 1.116 1.002 -1.354 2.173 0.332 1.837 -0.894 0.000 0.326 -0.145 -0.366 0.000 0.428 0.842 -0.791 0.000 0.656 0.726 0.995 0.782 0.942 0.982 0.788 +0 0.755 1.796 -0.050 0.452 1.022 0.616 1.000 1.689 2.173 0.591 0.250 1.609 0.000 0.414 1.849 -1.662 0.000 1.932 0.907 -0.041 3.102 0.779 0.986 0.997 0.781 1.155 0.708 0.654 +1 1.098 -0.043 -0.473 0.285 1.440 1.174 0.047 0.125 2.173 1.211 -1.520 -1.220 1.107 0.987 -0.210 1.563 0.000 0.914 -1.317 1.371 0.000 0.982 1.231 0.993 1.295 2.274 1.361 1.108 +1 1.409 0.396 1.067 0.894 0.401 0.634 0.785 -1.389 2.173 0.408 -0.918 -0.078 0.000 0.930 -0.052 -0.581 2.548 0.776 -0.643 -1.739 0.000 0.733 0.930 0.981 0.889 0.763 0.797 0.723 +0 1.415 -1.155 -0.957 0.390 -0.482 1.020 -1.176 0.785 2.173 0.590 -1.812 -1.380 0.000 0.884 -1.319 0.323 0.000 0.696 0.129 -1.676 3.102 1.131 0.928 0.987 1.221 0.960 0.902 0.803 +0 0.507 0.120 -0.589 0.530 1.388 0.509 -0.890 1.412 2.173 0.521 -0.636 -0.572 0.000 0.636 -0.850 0.703 2.548 0.575 -2.096 -0.269 0.000 0.788 0.847 0.990 0.587 0.423 0.563 0.539 +0 1.720 -1.069 0.806 2.594 0.334 0.925 -0.436 -1.617 2.173 1.071 -1.275 -1.139 0.000 1.465 -1.350 -0.025 2.548 2.893 -0.426 -1.079 0.000 0.951 0.972 1.206 0.921 1.640 1.229 1.324 +0 1.059 -0.486 1.117 1.098 1.283 1.081 -0.740 -1.518 0.000 1.692 -0.664 -0.172 2.215 0.615 0.296 -1.077 2.548 0.782 0.153 -0.360 0.000 1.390 0.866 0.978 1.398 0.974 1.019 0.993 +1 0.997 0.217 -1.436 0.470 0.855 0.859 -0.056 0.203 0.000 0.530 -1.229 -0.010 0.000 0.744 1.068 -0.931 2.548 0.824 0.802 0.581 0.000 0.959 1.050 0.991 0.756 0.701 0.856 0.761 +1 1.469 1.811 -0.762 0.532 -0.972 0.833 2.170 1.116 0.000 0.490 2.798 0.060 0.000 1.047 1.418 1.273 2.548 1.315 1.150 -0.456 3.102 1.203 0.894 1.001 0.562 0.900 0.780 0.838 +1 0.388 1.212 -1.048 0.877 1.007 2.610 2.126 -1.301 0.000 3.651 1.052 0.455 2.215 0.375 0.866 0.171 0.000 0.425 1.743 0.560 0.000 1.169 1.381 0.988 1.347 1.396 2.231 1.776 +0 1.322 -0.798 0.213 1.357 1.674 1.610 -0.550 -0.248 2.173 1.051 -0.030 1.508 0.000 1.541 1.063 1.565 2.548 0.616 0.480 -1.280 0.000 0.696 0.769 1.796 1.498 2.734 1.626 1.304 +0 0.449 -1.449 0.341 0.615 -1.252 1.246 -0.923 -1.613 2.173 0.952 0.943 0.171 0.000 1.490 -0.653 0.279 2.548 0.456 -1.908 -1.633 0.000 0.915 0.927 0.982 0.758 1.692 0.895 0.754 +1 0.540 0.208 -0.465 0.992 -1.630 0.468 -0.125 1.540 0.000 0.756 0.784 0.326 2.215 0.920 -0.794 -0.477 0.000 0.932 -1.032 0.917 1.551 0.851 0.878 0.988 0.709 0.995 0.733 0.681 +0 0.915 0.081 0.770 0.242 0.399 0.345 -1.206 -0.313 0.000 1.115 -0.708 -1.016 0.000 1.005 -0.074 1.428 2.548 0.442 0.006 -1.636 3.102 0.845 1.005 0.989 0.570 0.190 0.591 0.753 +1 2.215 -1.195 0.434 1.127 1.137 1.129 -0.780 -0.969 1.087 0.488 -1.027 -0.574 0.000 0.938 -0.854 -1.714 0.000 0.768 -0.914 1.406 1.551 0.890 0.769 1.296 0.737 0.844 1.031 0.862 +1 1.592 0.091 -1.384 0.379 -0.320 0.594 -0.927 0.496 2.173 0.260 0.569 -1.073 0.000 0.862 -1.507 -0.825 2.548 0.912 0.083 0.533 0.000 0.685 0.904 0.988 1.023 0.891 0.894 0.821 +1 1.413 0.724 -1.335 0.307 1.605 1.060 1.686 -0.631 0.000 1.367 0.436 0.721 2.215 0.594 -0.047 -1.629 0.000 2.197 0.305 1.100 3.102 0.940 0.970 0.997 1.111 0.529 0.818 0.767 +1 0.700 -0.136 0.282 1.209 1.320 0.886 0.211 -0.738 2.173 0.463 -1.145 -0.021 0.000 1.031 -0.580 0.899 0.000 0.852 1.200 -1.511 1.551 0.834 1.162 1.026 1.060 0.842 0.917 0.826 +1 0.692 1.943 0.264 0.484 1.602 2.655 -1.037 -0.766 0.000 3.523 1.392 0.882 2.215 1.453 0.600 -1.687 1.274 0.712 0.861 0.108 0.000 2.995 2.608 0.979 1.021 2.010 3.562 2.610 +1 0.775 -1.310 -0.310 0.179 1.211 1.434 -1.137 0.759 0.000 1.369 -0.684 -0.586 0.000 2.098 -0.005 1.118 0.000 4.529 -0.947 -0.989 0.000 0.726 0.991 0.987 0.560 0.727 0.793 0.736 +0 0.503 1.408 0.821 1.373 -1.309 1.176 1.725 -0.823 0.000 1.204 0.656 1.145 2.215 0.766 0.764 0.474 0.000 1.306 -0.227 0.157 3.102 1.761 1.604 1.082 0.955 1.041 1.221 1.038 +0 0.826 0.115 -0.612 1.188 -1.217 0.704 0.552 0.689 1.087 1.247 0.671 -0.916 2.215 1.110 2.340 0.588 0.000 2.232 1.033 1.251 0.000 0.585 1.190 0.990 0.902 1.371 1.130 1.335 +1 1.099 1.414 -0.113 0.379 1.688 0.803 0.439 0.908 2.173 1.053 1.122 -1.247 0.000 1.015 0.318 -0.854 2.548 0.947 1.185 0.368 0.000 0.813 0.747 0.986 0.871 1.126 0.741 0.632 +1 0.664 0.326 -1.298 0.483 1.138 1.000 -0.144 0.156 0.000 1.288 -0.319 -1.405 2.215 0.736 -0.988 -0.162 0.000 1.094 -0.586 1.047 3.102 0.871 0.858 0.990 1.029 0.884 0.924 0.963 +0 0.980 1.726 -0.762 0.966 1.267 0.445 1.965 -0.196 0.000 0.773 0.619 1.039 1.107 0.516 -0.541 -0.403 2.548 0.635 1.066 1.656 0.000 0.859 0.937 1.303 0.950 0.782 0.837 0.742 +1 0.607 -0.025 0.188 0.854 -1.272 1.279 0.472 -1.594 0.000 1.175 0.924 0.160 2.215 1.260 -0.096 0.896 2.548 1.400 2.350 -0.643 0.000 3.360 2.336 0.988 0.770 1.070 1.649 1.376 +0 0.641 1.328 -0.710 0.859 1.091 1.350 1.452 1.484 1.087 1.488 0.655 -1.371 0.000 3.466 0.598 -0.208 2.548 1.295 -0.010 -0.222 0.000 0.960 1.158 1.027 1.194 2.916 1.539 1.195 +0 0.842 -1.910 -0.245 0.222 1.665 1.245 -2.030 -0.630 0.000 2.142 -0.869 1.191 2.215 0.784 -1.486 -1.196 2.548 0.915 -1.342 0.234 0.000 1.080 0.929 0.979 1.140 1.263 0.874 0.799 +1 0.841 0.792 1.305 0.582 -0.907 0.695 0.746 -0.157 0.000 0.682 0.933 -1.063 1.107 1.514 0.407 0.484 1.274 1.723 0.358 -1.693 0.000 1.354 0.987 0.990 0.887 1.100 0.825 0.741 +1 0.658 0.433 -0.926 0.797 -0.157 0.723 1.702 -1.419 0.000 0.662 0.986 -0.297 2.215 1.090 -0.712 0.702 2.548 0.938 1.231 1.198 0.000 0.900 0.939 0.993 0.808 1.164 1.105 1.023 +1 1.935 0.637 -0.310 0.436 -1.062 0.637 0.216 1.727 0.000 0.751 -0.851 1.512 2.215 1.302 0.669 0.821 2.548 0.820 1.394 -0.150 0.000 0.772 1.085 0.991 0.999 1.123 1.025 0.841 +1 1.215 1.178 1.589 0.313 0.430 0.799 -0.674 -1.105 0.000 0.779 1.097 -0.009 0.000 0.634 0.122 0.714 2.548 0.658 -0.857 -1.037 1.551 1.003 0.831 0.986 0.820 0.579 0.813 0.739 +0 0.677 -0.195 0.490 0.996 1.322 1.217 0.296 1.308 0.000 1.638 -0.958 -0.557 0.000 0.813 1.002 1.372 2.548 1.948 0.189 -0.355 1.551 3.624 2.240 0.996 0.597 1.055 1.472 1.200 +1 2.172 0.282 -0.300 0.248 -0.357 0.688 -0.654 1.675 2.173 0.700 2.202 -1.560 0.000 1.343 -2.216 0.808 0.000 0.590 -0.253 -0.016 0.000 0.781 0.868 0.981 0.876 0.934 0.957 0.774 +0 1.415 1.665 -1.552 0.265 0.548 0.800 0.950 0.896 0.000 0.956 0.660 -0.979 2.215 1.308 1.215 -0.288 2.548 0.697 1.370 0.690 0.000 0.451 0.927 0.987 0.787 0.799 0.817 0.763 +0 0.572 0.408 -1.450 1.050 0.570 0.404 1.404 -0.035 0.000 0.482 0.914 -0.976 1.107 1.515 1.520 -0.676 2.548 1.026 1.656 -1.401 0.000 0.969 0.741 1.040 0.689 0.416 0.710 0.666 +0 0.507 -1.546 0.422 0.878 -0.233 1.277 1.155 -0.802 0.000 1.033 -0.615 1.345 2.215 1.053 -1.783 1.190 0.000 1.606 -0.296 -0.567 0.000 0.745 0.715 0.988 0.750 0.287 0.654 0.633 +0 0.694 -1.242 -0.850 0.463 -0.761 0.798 -2.154 -1.497 0.000 0.356 -1.876 1.704 0.000 1.092 -0.923 0.667 2.548 0.452 -0.442 0.546 1.551 0.723 0.618 1.003 0.572 0.143 0.563 0.530 +1 1.300 -1.294 -0.204 0.438 -0.190 1.311 -0.237 1.519 2.173 0.624 0.074 -0.847 1.107 0.471 0.725 0.945 0.000 0.600 0.559 0.207 0.000 0.364 0.871 0.984 0.733 1.145 0.993 0.822 +1 0.384 0.716 -1.380 1.174 0.370 0.712 -1.267 -0.360 0.000 1.068 0.238 1.299 2.215 0.923 -1.585 0.358 0.000 1.144 2.175 1.577 0.000 0.929 0.691 0.990 0.922 0.709 0.778 0.988 +0 0.718 0.087 -0.395 1.370 -1.528 1.276 -0.449 0.185 2.173 1.162 0.024 -1.510 2.215 0.706 -0.399 1.613 0.000 0.886 -0.431 -0.416 0.000 0.845 0.989 1.171 0.715 1.841 1.067 0.856 +1 0.449 -1.027 -0.489 1.103 0.777 1.370 -2.122 0.362 0.000 2.044 1.304 -1.289 0.000 1.942 0.319 -0.654 0.000 1.781 0.203 1.153 3.102 1.623 1.539 0.985 0.725 0.595 1.341 1.080 +1 0.544 0.325 -1.527 0.901 0.787 0.635 0.851 0.318 2.173 0.728 1.678 -0.685 0.000 0.651 1.374 1.716 0.000 0.560 -0.688 -1.339 3.102 0.944 0.816 0.989 0.728 0.867 0.626 0.608 +0 0.956 0.657 -0.187 1.152 0.695 0.985 0.160 -1.007 2.173 0.785 -0.056 -1.605 1.107 0.621 -1.649 -0.923 0.000 0.828 0.405 1.176 0.000 0.848 1.116 1.038 0.971 0.679 0.870 0.795 +1 2.229 0.763 -0.619 0.160 -1.406 0.464 1.431 -1.562 0.000 0.653 1.267 0.301 0.000 1.207 0.755 0.837 2.548 0.943 -0.280 1.728 0.000 1.039 0.945 0.989 0.789 0.224 0.731 0.732 +0 0.584 -1.026 -1.383 1.154 0.977 1.075 -0.564 -0.839 2.173 1.317 1.527 0.455 2.215 1.015 1.157 -1.347 0.000 0.626 0.702 0.586 0.000 0.887 1.001 0.986 1.052 2.770 1.570 1.275 +1 0.580 -1.148 -0.756 1.396 0.364 0.471 -0.133 1.427 2.173 0.423 0.719 -0.365 0.000 0.773 -0.281 -1.282 2.548 1.244 1.260 0.992 0.000 0.964 0.948 1.056 0.906 0.488 0.695 0.902 +1 0.730 -1.363 0.848 1.617 -1.484 0.672 -1.202 -0.443 1.087 0.843 -0.999 -0.761 0.000 1.461 0.037 1.544 2.548 2.116 -0.173 0.233 0.000 1.545 1.048 1.299 1.125 1.461 1.026 1.019 +0 0.893 -1.485 -1.668 0.734 0.542 0.713 -1.776 -0.422 0.000 0.581 -1.913 0.212 0.000 1.169 -1.365 0.955 1.274 1.892 -0.330 -1.303 3.102 0.749 0.943 1.024 0.916 1.211 0.954 0.815 +0 0.448 0.257 -0.056 1.386 1.109 0.750 -1.301 0.226 0.000 1.330 0.450 1.655 2.215 0.781 -2.594 -1.561 0.000 1.077 -2.352 -0.309 0.000 0.915 1.473 0.989 0.873 1.301 1.765 1.449 +1 1.093 1.771 -0.293 2.109 -0.823 1.365 -2.081 1.324 0.000 0.538 0.551 -0.041 0.000 0.676 0.777 0.448 2.548 1.441 0.368 -1.703 3.102 0.895 0.916 0.985 0.957 0.721 0.942 0.796 +1 0.643 -0.142 1.359 0.685 0.623 0.386 0.180 -0.727 2.173 0.505 -2.623 0.609 0.000 0.917 -0.303 1.631 1.274 0.984 -1.565 -0.611 0.000 0.905 1.154 0.991 0.743 0.659 0.907 1.075 +0 0.772 -0.470 -1.466 0.390 -0.344 0.716 -0.303 1.083 2.173 0.785 -0.622 -0.972 0.000 1.154 -1.033 0.117 2.548 0.717 -0.331 1.560 0.000 0.750 0.879 0.984 0.820 0.992 0.774 0.702 +1 0.449 1.490 -0.194 0.734 1.296 0.788 0.953 -1.032 0.000 0.742 1.157 1.083 2.215 0.394 1.927 1.184 0.000 1.080 -0.020 0.432 3.102 1.099 0.946 0.988 1.123 0.687 0.792 0.794 +1 0.765 -0.199 -0.829 0.860 0.921 1.295 0.209 -1.616 0.000 0.735 1.490 -0.805 0.000 1.514 0.697 0.478 1.274 0.812 0.724 1.534 0.000 0.930 1.031 1.124 0.721 0.682 0.720 0.740 +0 0.503 1.577 -1.050 0.824 -0.058 0.665 -0.098 0.936 2.173 0.527 1.422 1.391 0.000 0.641 1.141 0.020 0.000 1.135 0.938 -1.248 3.102 0.847 0.929 0.978 0.644 1.038 0.710 0.659 +0 0.619 -1.216 0.456 1.224 1.275 0.932 -1.042 -0.563 2.173 1.534 0.529 0.956 1.107 1.504 -0.461 -1.186 0.000 0.610 0.788 -1.023 0.000 0.851 1.023 0.987 1.926 2.323 1.560 1.341 +0 0.391 0.134 -1.658 0.595 1.362 0.417 1.242 -1.293 1.087 0.351 0.804 -0.060 0.000 0.759 0.382 0.505 2.548 0.724 -1.061 0.932 0.000 0.971 0.970 0.981 0.876 0.761 0.880 0.763 +0 0.416 0.739 -1.653 0.235 -0.289 0.704 0.339 -0.116 2.173 0.705 -1.881 1.630 0.000 0.476 -0.671 0.696 2.548 0.581 -0.795 -1.050 0.000 0.698 0.615 0.989 0.809 0.631 0.826 0.707 +0 0.384 -0.267 -0.624 1.101 1.228 0.786 1.177 -1.282 2.173 0.987 1.181 0.239 2.215 0.635 1.695 -0.662 0.000 0.602 2.212 0.843 0.000 0.722 0.824 0.985 1.357 1.271 1.199 1.131 +0 1.077 0.114 1.659 0.682 1.302 1.013 -1.251 0.078 0.000 0.900 -0.339 -1.071 1.107 0.735 -1.950 -0.299 0.000 0.543 -2.018 -0.931 0.000 0.912 1.173 0.988 0.596 0.441 0.674 0.737 +0 1.012 -0.965 -1.604 1.170 1.370 0.675 0.819 -0.442 0.000 0.669 0.740 0.003 0.000 0.466 -0.385 -1.542 2.548 0.998 -0.698 0.016 3.102 0.559 0.821 0.995 0.488 0.526 0.615 0.794 +0 1.015 -0.595 1.307 0.910 -1.265 0.863 2.131 -0.320 0.000 1.561 0.250 1.715 2.215 0.577 0.906 -0.700 0.000 1.449 -1.779 0.112 0.000 0.885 0.791 0.988 0.787 0.970 1.146 1.185 +0 0.780 0.545 1.715 1.604 0.482 0.804 0.389 -0.840 2.173 0.697 -0.059 0.589 0.000 0.799 -0.579 -1.157 2.548 0.445 0.174 1.333 0.000 0.734 1.005 1.388 1.045 0.606 0.864 0.774 +0 0.787 -0.604 0.796 0.847 0.961 0.646 -0.146 0.734 2.173 1.011 -0.237 -1.017 0.000 1.408 -0.452 -0.556 2.548 1.068 -0.470 -1.707 0.000 0.818 1.063 0.997 1.072 1.111 0.794 0.809 +1 1.195 -0.031 0.647 0.565 -1.480 0.861 -0.187 -1.335 2.173 0.568 -0.701 0.859 0.000 0.506 -0.865 -1.497 2.548 0.976 -0.499 -0.583 0.000 0.937 0.945 1.071 0.662 0.342 0.613 0.609 +0 0.453 -1.189 0.754 1.448 0.084 0.810 0.213 -0.608 2.173 0.659 0.171 -1.299 0.000 0.656 -0.549 -1.456 0.000 1.084 -1.114 1.152 3.102 0.583 0.786 0.994 0.888 1.305 0.822 0.749 +1 1.064 -1.090 -1.618 1.356 1.291 2.083 2.016 -0.136 0.000 1.653 0.038 1.319 2.215 0.603 -0.956 -0.314 0.000 0.826 -0.801 1.418 3.102 4.514 3.202 0.984 1.334 0.558 2.371 2.682 +1 1.932 1.712 -1.541 0.636 -1.617 0.584 1.076 0.138 2.173 0.507 1.826 -0.118 0.000 0.771 0.866 0.562 2.548 0.887 0.442 -0.346 0.000 0.833 0.608 0.991 0.919 0.317 0.809 0.692 +1 1.985 0.162 0.788 1.081 0.113 0.503 0.446 -0.006 0.000 1.587 0.052 -1.090 2.215 1.268 -0.399 1.594 2.548 0.970 -1.076 -0.470 0.000 1.138 1.137 1.158 1.518 1.065 1.119 1.006 +0 0.334 0.953 -0.717 0.591 1.219 0.594 -1.421 -1.672 0.000 0.803 -1.057 0.279 1.107 1.207 0.950 -0.127 2.548 1.359 2.431 1.483 0.000 5.986 3.388 0.992 0.748 1.390 2.338 1.748 +1 1.679 -1.408 -0.511 0.822 -1.065 1.003 -0.322 1.143 2.173 0.388 -1.111 -0.041 2.215 0.557 -0.366 -0.278 0.000 0.813 -0.366 -1.505 0.000 0.909 0.971 0.996 0.579 0.892 0.930 0.793 +1 1.207 -0.283 1.639 0.398 -0.001 1.356 1.150 -0.442 0.000 2.351 -0.712 1.393 2.215 0.693 -0.532 -0.601 0.000 0.536 0.247 0.602 1.551 0.766 0.686 0.987 0.841 0.858 0.885 0.754 +0 1.031 0.231 -0.085 0.536 -1.741 0.875 0.250 1.214 0.000 0.472 -1.161 0.472 2.215 1.505 1.172 -0.719 2.548 0.439 0.576 -0.928 0.000 0.911 0.906 1.027 0.863 1.623 0.990 0.823 +1 1.304 -0.363 0.540 0.772 -0.769 2.221 -1.045 1.057 0.000 1.280 0.156 -0.854 2.215 1.243 0.961 -0.702 2.548 1.376 -0.513 -1.092 0.000 0.687 0.813 1.284 1.032 0.646 0.858 0.714 +1 2.371 -0.559 -1.223 1.430 -0.543 0.883 -0.637 -0.823 0.000 1.121 1.482 1.128 0.000 2.190 -0.821 -0.307 2.548 4.220 0.594 1.136 0.000 1.184 1.563 1.469 1.191 0.710 1.840 1.790 +1 0.510 1.186 1.656 0.974 0.364 0.924 1.271 -1.012 1.087 0.761 -0.447 1.541 0.000 0.772 -0.257 0.749 0.000 0.772 0.162 0.042 3.102 0.777 0.684 0.986 0.982 0.889 0.922 0.795 +1 0.519 -0.106 0.006 0.694 -1.625 0.390 -1.574 -0.700 0.000 0.842 0.459 -1.033 2.215 0.351 0.323 1.234 0.000 1.326 0.860 0.340 3.102 0.999 0.963 0.984 0.681 0.941 0.843 0.735 +0 1.598 -1.168 1.597 0.933 -1.407 1.194 0.306 0.287 1.087 0.885 -0.698 -0.121 2.215 1.452 0.864 -1.351 0.000 1.601 -0.438 1.029 0.000 0.913 1.300 0.995 1.689 0.977 1.201 1.204 +0 0.858 -0.039 -0.693 1.275 -0.338 0.478 2.232 1.210 0.000 0.734 -2.713 -0.543 0.000 1.031 -0.389 1.373 2.548 2.497 1.262 0.927 0.000 0.998 0.729 0.984 1.107 0.596 1.005 0.978 +1 1.479 -1.297 0.740 0.364 1.719 2.749 -1.327 -0.785 0.000 2.422 -0.789 1.241 2.215 0.863 -1.337 0.348 2.548 1.208 -1.938 0.231 0.000 0.929 1.298 0.987 1.013 1.221 1.686 1.372 +1 0.423 -0.785 -0.613 0.346 -0.819 0.809 0.019 0.523 2.173 0.653 -0.586 -1.320 0.000 1.366 0.112 1.636 0.000 0.499 0.470 0.196 0.000 0.900 0.914 0.989 0.565 0.798 0.679 0.646 +1 1.392 0.046 -0.482 0.210 -0.411 1.475 -0.710 0.876 0.000 0.904 -1.383 -1.196 0.000 0.868 0.751 0.208 2.548 1.133 -0.129 -0.802 0.000 1.004 1.314 1.001 0.524 0.586 0.746 0.680 +1 0.430 1.650 -0.751 0.219 -1.475 0.561 0.691 0.710 0.000 0.755 0.842 -1.543 2.215 1.864 0.578 -0.624 2.548 0.508 2.431 0.024 0.000 1.199 1.044 0.987 0.726 0.940 0.903 0.778 +0 1.652 0.570 0.108 1.134 1.387 1.038 -0.437 0.261 2.173 2.429 -0.059 -1.571 2.215 0.730 1.591 -0.153 0.000 0.860 -1.380 -1.145 0.000 2.417 1.776 1.734 1.725 2.371 1.576 1.404 +1 0.597 0.635 -0.623 0.917 0.484 0.808 0.670 -1.165 1.087 0.853 0.068 0.032 0.000 0.872 -0.869 0.983 2.548 0.851 0.666 1.542 0.000 1.162 0.970 0.985 0.916 1.363 0.873 0.758 +0 0.509 0.729 -1.274 1.057 0.635 1.161 0.682 0.586 1.087 1.301 -0.899 -0.880 2.215 1.542 1.376 -1.479 0.000 1.381 1.204 0.790 0.000 1.131 1.668 1.004 1.306 2.389 1.815 1.376 +1 0.318 2.059 1.091 0.821 0.042 0.636 -2.368 -0.946 0.000 0.883 -0.797 1.707 0.000 0.456 -0.980 0.265 1.274 1.365 1.225 0.310 3.102 0.772 0.707 0.994 0.619 0.994 0.858 0.772 +1 0.865 -1.015 -1.715 0.854 -0.252 0.566 -0.749 1.306 2.173 0.756 -1.099 -1.270 0.000 0.761 -1.120 0.438 0.000 1.983 -0.424 -0.049 3.102 1.033 0.905 1.153 0.780 1.063 0.726 0.665 +1 1.093 0.984 -0.528 1.378 -1.113 1.481 0.419 -1.042 0.000 3.647 -0.986 0.888 0.000 1.097 1.233 -0.045 0.000 1.406 0.896 -1.253 3.102 2.107 1.259 0.981 0.952 1.006 0.993 0.842 +0 2.238 -0.052 0.735 0.379 0.098 0.954 0.616 -0.864 1.087 1.076 0.135 -1.647 2.215 0.840 -1.061 1.175 0.000 1.231 0.775 -0.525 0.000 0.708 0.794 0.987 1.338 1.033 1.048 0.906 +0 0.750 1.574 -0.926 0.796 1.049 0.639 0.736 0.275 1.087 0.753 -0.857 0.257 1.107 0.937 -1.612 -1.532 0.000 0.607 2.471 -0.972 0.000 0.721 0.926 1.047 0.832 0.924 0.974 1.163 +1 1.569 -1.973 -1.496 1.589 -1.347 1.228 0.867 0.654 0.000 0.736 -1.719 -0.613 0.000 1.013 -0.502 -0.151 2.548 0.902 -0.506 1.004 3.102 0.962 0.921 0.975 1.205 0.630 0.883 1.487 +1 1.035 -0.387 0.439 1.890 0.641 0.636 0.712 1.479 0.000 1.179 -0.531 -1.299 2.215 0.373 2.291 0.398 0.000 1.061 0.174 -0.828 3.102 0.725 1.266 0.970 1.104 0.571 1.026 1.103 +1 0.720 -1.027 0.996 0.193 -0.975 0.660 0.884 1.399 0.000 0.845 -0.543 -0.456 2.215 0.559 1.153 -1.398 0.000 1.117 -1.240 0.100 3.102 0.666 1.230 0.994 0.748 0.597 1.034 0.854 +1 1.496 -0.636 -1.432 0.439 -1.604 0.445 -0.627 0.383 2.173 0.289 -2.606 0.633 0.000 0.651 -1.087 0.022 2.548 0.372 -2.326 1.192 0.000 0.207 0.646 0.977 0.860 0.287 0.689 0.730 +0 1.296 -1.759 0.877 0.657 -1.299 1.173 1.038 -0.034 2.173 0.728 -0.049 1.362 2.215 1.886 -1.972 -1.349 0.000 0.995 -1.993 -0.632 0.000 0.926 1.630 1.182 2.610 1.513 2.387 1.932 +0 0.660 -1.418 -1.155 0.338 1.442 1.167 -0.763 -0.356 1.087 1.626 1.326 1.452 0.000 0.983 -1.605 0.338 0.000 0.901 -0.465 0.989 3.102 0.957 0.986 0.995 0.612 1.023 0.718 0.686 +1 1.453 0.019 -0.545 0.610 0.792 2.542 1.641 -1.658 0.000 1.000 1.702 0.264 0.000 1.331 0.860 0.169 0.000 1.267 -1.132 0.054 3.102 1.013 0.793 1.217 0.890 0.913 1.033 0.899 +1 1.205 0.638 -1.497 1.355 -1.554 0.721 -0.710 0.143 0.000 0.482 -1.038 -1.402 2.215 1.095 1.144 -0.040 0.000 0.827 0.331 0.977 3.102 0.920 1.076 0.982 0.726 0.661 0.678 0.745 +0 1.408 -0.338 -0.110 0.783 -1.196 0.563 -1.437 -1.224 0.000 1.242 0.576 0.053 1.107 1.202 -0.528 1.576 2.548 1.204 1.266 1.234 0.000 0.767 0.900 1.206 0.978 1.506 1.310 1.101 +1 0.863 0.702 1.097 1.589 0.376 1.197 1.058 -1.344 2.173 0.344 2.481 -0.142 0.000 0.931 0.640 -0.462 2.548 0.439 1.658 1.488 0.000 0.524 0.927 0.987 0.818 0.970 0.970 0.822 +1 1.247 -0.710 1.130 0.748 0.494 0.907 -0.397 -0.660 2.173 0.479 0.788 0.052 2.215 0.350 -1.445 1.350 0.000 0.496 -2.332 -1.408 0.000 0.739 0.941 0.991 1.015 0.853 0.940 0.829 +0 0.479 2.001 0.272 0.621 0.925 0.627 0.212 -0.307 2.173 0.735 0.189 1.336 0.000 0.941 0.119 -1.119 2.548 0.416 -1.537 0.605 0.000 0.975 1.005 0.985 0.821 0.641 0.703 0.721 +0 0.528 0.582 -1.453 0.983 -0.241 0.529 -2.811 -1.024 0.000 1.419 0.263 0.966 2.215 0.683 0.621 -0.263 0.000 0.482 -0.209 0.971 3.102 3.126 1.722 0.988 0.993 0.198 1.543 1.253 +1 0.374 -0.148 -1.673 1.089 0.726 0.934 1.145 -0.074 0.000 0.881 0.527 1.191 0.000 1.311 0.045 -0.503 2.548 1.239 -0.617 -1.331 0.000 1.398 0.864 0.993 1.041 0.785 0.822 0.829 +0 1.812 1.235 1.393 1.242 -1.598 0.930 1.847 -0.139 0.000 0.702 2.191 0.414 0.000 0.541 2.214 -0.681 0.000 1.003 0.935 -0.829 3.102 0.886 0.955 0.984 0.821 0.517 0.682 0.854 +0 0.369 -0.782 -0.128 0.980 -0.584 0.525 -1.186 1.289 2.173 0.420 0.550 -0.695 2.215 0.957 -0.155 1.486 0.000 0.865 -0.744 0.358 0.000 0.931 0.788 0.975 1.145 0.966 0.774 0.717 +0 0.543 0.952 -1.358 0.507 -0.189 0.541 -1.063 1.246 0.000 0.454 -1.086 0.354 0.000 0.760 -0.090 -1.373 1.274 0.399 -0.834 -0.213 1.551 0.922 0.730 0.987 0.905 0.414 0.730 0.846 +1 0.873 0.914 -1.094 0.738 0.216 0.934 -0.252 1.512 0.000 0.909 0.637 -0.354 2.215 1.099 0.476 0.758 2.548 1.042 -0.728 -1.420 0.000 0.863 1.044 1.029 0.645 0.898 0.944 0.866 +0 0.637 -1.593 1.524 1.188 -1.031 0.600 -1.311 -0.437 2.173 0.647 -0.309 0.690 0.000 1.107 -1.170 0.182 1.274 0.720 -0.421 1.284 0.000 0.997 0.870 0.986 0.931 0.538 0.704 0.713 +0 0.640 0.746 0.003 0.705 -1.690 1.065 0.654 1.034 2.173 0.816 1.513 -1.114 0.000 1.144 -0.427 1.021 0.000 2.143 0.748 -0.768 0.000 0.869 0.924 0.985 0.853 1.600 0.970 0.921 +0 5.331 1.077 0.157 1.011 -0.269 1.522 -0.836 1.559 0.000 1.617 -0.800 -1.230 0.000 1.657 0.058 -1.373 0.000 0.796 -0.060 0.579 3.102 1.196 0.845 1.202 0.976 0.474 0.840 1.604 +1 0.421 0.840 1.604 1.006 1.055 1.271 -2.596 0.445 0.000 1.199 -0.135 0.681 2.215 2.365 0.493 -1.078 0.000 1.256 0.039 -0.296 3.102 7.876 4.278 0.984 1.549 0.860 2.653 3.166 +1 0.694 -0.218 -1.193 1.476 1.305 0.916 -0.660 0.138 1.087 0.629 -1.090 -0.728 2.215 0.443 -1.080 0.169 0.000 1.890 0.219 -1.221 0.000 1.246 0.900 1.090 1.141 0.827 0.859 0.819 +0 0.592 0.660 -1.632 0.779 0.354 0.687 -0.053 -0.199 1.087 1.240 -0.852 0.560 2.215 1.371 -1.811 -1.430 0.000 1.737 0.096 -1.249 0.000 2.158 1.747 0.991 1.302 1.032 1.300 1.310 +0 0.794 1.209 0.682 1.928 0.618 0.998 2.468 -1.284 0.000 1.253 -0.331 -0.450 0.000 0.842 -0.368 1.101 2.548 0.791 1.068 -1.541 3.102 0.841 0.916 0.987 0.871 0.731 0.703 0.808 +1 0.628 1.124 -1.620 0.997 -0.472 0.672 0.167 -1.689 0.000 0.896 -0.652 -1.342 0.000 1.899 -0.643 0.283 2.548 0.840 0.234 -0.489 1.551 0.875 0.806 0.990 1.665 0.793 1.051 1.066 +1 0.877 1.810 1.579 0.904 1.248 1.109 0.490 -0.423 2.173 0.527 0.097 0.300 2.215 0.679 0.200 -1.152 0.000 1.130 0.194 1.108 0.000 0.864 1.043 0.973 0.831 0.718 0.914 0.790 +0 0.659 -0.548 0.212 0.729 -1.662 1.049 0.346 -0.591 2.173 0.709 0.519 1.234 0.000 0.655 0.433 0.604 0.000 0.698 -0.680 -1.290 1.551 0.563 1.136 0.989 0.540 0.772 0.756 0.737 +1 1.594 -0.349 0.503 0.278 0.557 0.962 0.740 -1.020 1.087 0.744 -0.073 1.513 2.215 0.323 0.380 1.090 0.000 0.543 -0.691 -0.051 0.000 0.501 0.820 0.987 0.807 1.079 0.934 0.721 +1 0.956 -0.337 -1.392 1.317 -0.877 0.678 0.692 0.480 2.173 0.589 1.442 0.753 0.000 1.067 1.321 -0.145 2.548 0.924 -1.592 1.390 0.000 0.958 1.011 0.977 1.570 0.698 1.223 0.989 +0 0.489 -0.806 0.339 0.688 -0.603 0.926 1.535 1.062 0.000 1.010 -0.008 -1.016 2.215 0.570 0.287 -0.143 2.548 0.389 1.464 0.368 0.000 0.844 0.881 0.987 1.167 0.586 0.890 1.382 +1 0.590 -0.228 0.443 0.763 -1.724 1.037 0.448 -1.135 2.173 1.572 1.109 1.592 0.000 2.479 0.264 0.186 0.000 0.734 1.285 -0.153 0.000 1.090 1.018 0.986 0.859 0.897 1.007 0.848 +1 2.256 -0.273 -1.452 0.682 0.865 0.437 -0.433 1.308 0.000 1.103 0.006 -0.006 2.215 0.397 -2.701 0.748 0.000 1.308 -1.440 0.065 0.000 0.989 0.975 1.494 0.836 0.234 0.819 0.817 +1 0.607 0.534 -0.418 1.583 0.705 0.547 0.367 0.975 2.173 0.908 0.780 -0.025 2.215 1.168 -0.616 -1.365 0.000 2.258 0.308 -1.236 0.000 0.907 0.945 1.152 0.733 0.844 0.813 0.775 +0 1.026 -1.834 -0.754 1.788 -1.661 0.489 -1.053 -0.534 0.000 0.760 -2.085 1.297 0.000 0.579 -1.253 0.701 2.548 0.788 0.051 -0.045 3.102 1.485 0.899 1.368 1.205 0.516 0.830 0.806 +1 0.524 -1.310 0.683 1.178 -0.708 0.941 0.451 1.734 2.173 0.924 0.300 0.408 2.215 0.750 -1.428 1.098 0.000 0.589 -2.395 -0.731 0.000 0.893 1.469 1.034 1.408 1.281 1.320 1.130 +0 0.777 -0.038 0.681 1.287 0.300 0.994 1.242 -1.187 0.000 0.846 -1.037 0.483 2.215 1.353 0.260 -0.853 2.548 1.301 -0.761 1.678 0.000 0.873 0.866 0.989 1.040 1.347 0.992 0.880 +0 1.083 -0.383 1.261 0.464 -0.084 0.907 -0.437 0.384 1.087 1.264 -0.424 -1.240 2.215 0.894 -0.915 -0.709 0.000 0.744 -0.992 1.112 0.000 0.901 0.934 0.985 0.888 1.566 0.864 0.751 +0 0.823 1.424 -0.732 0.649 0.092 0.353 -0.740 0.472 2.173 0.508 0.912 0.178 2.215 1.437 0.174 -1.612 0.000 0.418 -2.289 0.458 0.000 0.451 0.837 0.989 0.559 0.612 0.645 0.652 +0 0.870 -0.729 0.752 2.530 0.243 1.424 -0.672 -1.506 0.000 0.951 -0.733 -0.339 2.215 1.180 0.035 1.486 2.548 0.830 -0.693 -1.162 0.000 0.524 0.817 0.978 0.870 1.213 0.966 1.092 +0 0.651 -0.395 -0.291 1.377 -0.267 0.988 0.022 -1.353 2.173 0.678 0.243 1.494 0.000 1.028 0.419 0.717 2.548 0.740 -0.905 0.778 0.000 0.865 0.947 0.991 1.262 1.233 1.180 0.996 +1 0.806 -0.368 0.793 1.212 1.500 1.476 -0.012 -0.359 2.173 0.618 0.230 1.155 0.000 0.938 -1.455 -1.604 0.000 0.733 -0.002 0.110 0.000 0.717 1.085 0.981 0.719 0.965 0.957 0.796 +1 0.745 0.643 0.591 0.623 -0.423 0.946 0.974 -0.224 0.000 0.851 1.168 -1.382 1.107 0.840 0.127 -1.714 2.548 0.861 0.992 1.562 0.000 0.832 1.033 0.992 0.831 0.566 0.797 0.712 +0 0.614 0.388 0.588 0.979 1.345 0.756 -0.273 -0.589 0.000 0.626 -1.678 -1.065 0.000 0.438 0.553 1.544 2.548 0.839 -1.592 0.123 0.000 0.833 0.971 0.987 1.081 0.517 0.682 1.036 +0 0.889 0.356 0.261 0.916 0.397 0.856 1.345 -1.114 0.000 1.205 0.903 1.027 1.107 0.790 -0.040 -1.180 2.548 0.606 0.383 -1.528 0.000 0.649 1.141 0.987 0.960 1.084 0.803 0.828 +0 0.704 -0.279 -0.729 0.627 1.103 0.516 0.241 -1.699 0.000 0.471 1.168 0.237 0.000 0.547 2.005 1.304 0.000 0.853 -1.037 -0.009 3.102 0.757 0.989 0.989 0.695 0.205 0.847 0.724 +1 2.526 -0.833 -1.682 0.209 -0.637 0.802 0.034 -0.411 2.173 1.193 -1.641 0.530 0.000 0.580 -0.340 -1.717 2.548 1.087 -1.217 -0.822 0.000 0.908 0.800 0.986 0.480 0.804 0.785 0.744 +1 2.038 -1.164 1.137 1.410 0.222 2.995 0.174 -0.913 0.000 0.851 1.501 0.817 0.000 0.977 0.188 1.027 0.000 0.999 0.088 -0.056 3.102 1.042 0.915 1.724 1.222 0.580 0.906 1.156 +0 1.040 -0.649 1.586 0.540 -1.161 0.626 1.370 1.515 2.173 0.941 2.025 0.161 0.000 0.434 -1.200 -0.653 0.000 1.004 0.933 0.153 3.102 0.792 0.915 0.984 1.213 0.798 1.160 1.334 +0 1.064 -1.689 1.144 0.774 0.844 1.131 -1.562 0.571 2.173 1.159 -1.515 -0.859 0.000 1.715 -1.839 -1.231 0.000 0.455 -0.065 -0.978 3.102 0.872 0.790 0.999 0.803 0.973 1.063 0.988 +1 1.147 -0.364 -1.192 0.174 1.682 0.882 -0.511 1.662 1.087 0.880 0.179 0.271 2.215 1.011 1.140 0.066 0.000 0.461 0.564 -0.417 0.000 0.386 1.304 0.976 1.007 1.316 0.894 0.895 +0 1.253 1.917 -1.135 0.745 -1.330 0.678 -0.123 0.643 0.000 0.924 0.620 0.192 0.000 0.409 -0.843 -1.167 1.274 0.636 0.463 1.276 3.102 0.934 0.896 0.975 0.681 0.446 0.664 0.853 +0 0.830 1.469 -0.503 0.790 0.614 0.966 -0.371 1.505 2.173 0.674 -0.448 -0.643 0.000 0.998 -0.427 -0.145 2.548 0.741 -0.885 0.757 0.000 0.921 1.009 0.989 1.161 1.220 1.222 1.072 +1 0.882 0.743 0.530 0.795 1.574 0.705 0.783 -1.649 0.000 0.422 -0.027 1.480 0.000 1.350 -0.699 -0.073 2.548 1.270 0.792 -0.260 3.102 0.609 0.901 0.990 1.219 0.983 0.911 0.846 +1 0.481 -0.687 -1.417 0.697 -0.018 0.860 0.458 1.046 0.000 1.119 -2.334 -0.377 0.000 0.568 0.788 -0.005 0.000 1.417 1.827 1.735 0.000 1.052 0.694 0.991 0.725 0.210 0.716 0.818 +1 1.004 0.661 -0.862 1.004 1.641 1.170 -0.101 -0.188 2.173 1.154 0.637 -1.689 0.000 1.368 0.143 1.315 2.548 1.259 -1.151 0.404 0.000 2.411 1.491 1.077 1.213 1.553 1.267 1.107 +1 1.074 0.080 0.283 0.860 -0.355 1.057 1.095 1.663 0.000 0.629 0.280 -1.165 2.215 0.734 0.970 -0.486 2.548 0.418 1.224 0.019 0.000 1.028 0.891 0.986 0.785 0.505 0.630 0.710 +1 2.355 -0.491 -0.653 0.748 0.112 0.693 0.664 1.242 1.087 0.835 -0.763 0.666 1.107 0.471 1.108 -1.516 0.000 0.470 1.563 1.150 0.000 0.390 1.024 1.170 1.421 1.049 1.062 0.954 +0 0.821 1.390 0.233 1.465 0.789 1.752 1.034 0.742 2.173 2.724 1.138 -0.872 2.215 1.443 0.503 -1.349 0.000 0.368 1.066 -1.499 0.000 0.318 0.861 0.976 0.682 3.200 1.600 1.286 +0 1.555 1.856 0.835 1.152 0.433 1.091 -0.162 -1.717 0.000 1.276 0.651 -0.331 2.215 0.921 1.470 -0.557 0.000 0.415 0.174 1.275 0.000 0.856 0.763 1.001 0.995 0.824 1.111 0.904 +0 1.705 0.794 0.171 0.232 -0.914 0.926 -0.951 -1.608 0.000 0.501 2.782 0.186 0.000 0.775 0.001 -1.652 0.000 0.498 -1.883 0.961 0.000 1.051 0.954 0.984 0.726 0.529 0.651 0.877 +1 0.918 1.679 1.592 1.745 -1.170 0.529 1.763 -1.473 0.000 1.267 -1.273 -0.291 0.000 1.521 1.078 0.352 2.548 0.876 1.254 1.059 0.000 0.800 0.986 1.066 1.181 0.903 1.021 0.852 +1 0.753 0.058 0.740 0.384 -1.121 0.855 -0.061 -0.586 0.000 1.086 -0.021 1.392 2.215 0.588 -0.959 -0.995 0.000 1.249 -0.863 0.676 3.102 0.803 1.003 0.990 0.763 0.842 0.873 0.807 +1 0.370 -0.660 -0.511 1.211 -0.886 1.205 -0.304 1.491 2.173 1.448 -0.450 0.761 0.000 1.083 0.035 -0.607 1.274 1.105 0.365 -1.257 0.000 0.963 1.042 0.986 1.303 1.373 0.950 0.955 +1 0.392 1.670 0.039 1.678 1.494 1.621 2.615 -0.604 0.000 1.342 0.065 1.014 2.215 0.973 0.800 0.837 0.000 0.932 -0.502 1.531 3.102 1.048 2.494 1.087 1.284 0.570 2.116 1.636 +0 1.950 0.691 0.445 1.452 0.301 2.038 0.179 -1.391 2.173 1.090 0.991 0.058 2.215 0.431 1.294 1.679 0.000 0.455 0.615 1.317 0.000 0.231 0.808 0.991 0.624 2.319 1.669 1.199 +1 0.764 -0.556 0.755 1.054 -0.453 0.385 -0.849 -1.702 1.087 0.598 0.286 -0.822 0.000 0.370 -1.939 0.960 0.000 0.488 -0.495 -0.986 3.102 1.211 0.841 1.101 0.572 0.283 0.496 0.533 +1 0.539 -0.857 -0.632 1.126 0.478 0.911 -0.886 -0.352 1.087 0.720 -0.651 0.485 0.000 2.128 -0.846 -1.536 2.548 0.922 -1.424 -1.471 0.000 1.191 1.046 0.991 1.119 1.520 0.949 0.844 +1 1.475 -0.350 -1.109 1.150 -1.724 1.279 -0.374 0.491 0.000 0.529 -0.017 1.515 0.000 0.689 -0.704 -1.131 0.000 1.108 0.420 -0.669 3.102 0.836 0.996 0.989 0.741 0.463 0.639 0.797 +1 0.465 -0.023 -1.457 0.784 1.389 0.978 -1.036 1.142 0.000 1.218 -1.319 -0.551 0.000 1.211 -0.161 -0.656 1.274 1.100 0.418 0.464 3.102 0.848 0.992 0.986 0.920 0.807 0.851 0.777 +0 0.634 0.703 -0.950 0.989 0.006 0.956 1.559 1.692 2.173 0.611 1.524 -0.640 0.000 1.436 0.935 0.583 2.548 0.714 0.759 1.384 0.000 0.882 0.863 0.986 1.026 1.294 0.865 0.735 +1 2.057 0.872 1.346 0.548 0.381 0.661 0.662 0.675 0.000 1.042 1.068 -0.532 2.215 1.097 -0.064 -1.008 2.548 0.586 -0.113 -0.389 0.000 0.873 0.953 1.123 1.088 0.847 0.938 0.827 +1 0.923 1.122 -0.993 0.633 0.399 0.967 0.482 -1.740 1.087 1.023 1.628 0.609 0.000 0.520 0.787 -0.206 0.000 0.943 -0.374 -0.822 3.102 0.884 1.135 1.007 0.929 0.892 0.966 0.821 +1 1.005 -0.044 1.160 1.181 -1.242 1.010 0.190 -0.615 0.000 0.993 -0.740 1.592 1.107 1.265 -0.512 0.708 1.274 0.945 0.031 0.055 0.000 0.856 1.145 1.252 0.806 0.860 0.967 0.881 +1 1.113 -0.707 -1.194 1.771 -1.438 0.828 -0.182 1.042 0.000 0.778 2.012 0.107 0.000 0.859 -1.119 -0.014 0.000 1.621 -0.477 -0.611 0.000 0.978 0.973 0.975 0.642 0.634 0.785 0.722 +0 2.182 -1.602 -0.583 1.435 -0.473 1.169 -1.218 1.402 0.000 0.590 -0.264 1.383 0.000 0.947 -0.482 0.471 2.548 0.415 -0.702 0.051 0.000 0.915 1.008 0.979 0.790 0.362 0.856 0.968 +0 2.074 0.884 -1.511 0.753 -0.825 1.227 -0.493 0.374 0.000 1.026 -0.828 0.704 0.000 1.526 0.334 -0.805 2.548 0.722 -0.626 1.603 1.551 0.815 0.847 1.004 0.804 0.814 1.020 1.213 +0 0.294 -1.159 0.495 1.037 -1.135 1.006 -0.175 1.154 0.000 0.531 -0.930 0.938 0.000 1.234 1.150 -0.377 1.274 0.803 0.544 0.052 1.551 1.089 0.779 0.989 2.515 0.371 1.643 1.304 +0 0.449 0.050 1.620 1.278 1.619 0.686 -0.266 -0.097 2.173 0.772 1.188 -1.270 0.000 1.218 0.702 -0.608 0.000 0.683 2.243 1.131 0.000 0.902 1.150 1.002 1.422 0.816 1.149 1.075 +1 0.443 0.601 -1.628 0.561 -0.854 0.480 0.671 -0.679 0.000 1.378 -0.830 1.417 2.215 0.980 -0.828 0.102 2.548 0.661 1.792 0.195 0.000 0.922 1.243 0.989 1.989 1.145 1.494 1.272 +0 1.233 -0.165 -0.892 1.341 0.272 0.962 0.106 0.537 2.173 1.849 -0.902 -1.300 0.000 0.923 -0.343 1.190 0.000 0.840 -0.903 0.465 3.102 1.663 1.184 1.545 1.085 0.604 1.107 1.038 +0 1.019 0.081 -0.809 0.778 0.039 0.929 -0.389 0.953 2.173 0.741 0.474 -1.119 0.000 0.399 0.068 -1.648 0.000 0.544 -0.742 1.188 0.000 0.949 1.036 0.988 0.584 0.987 0.761 0.720 +0 0.864 0.543 -0.813 0.968 -0.123 0.537 0.251 1.591 0.000 0.770 -0.331 0.613 0.000 0.705 0.973 -1.677 2.548 0.936 0.091 0.991 1.551 0.896 0.693 0.987 0.819 0.520 0.620 0.649 +1 0.479 -2.138 0.061 0.971 0.883 1.069 -0.727 -0.471 0.000 1.187 -2.466 1.128 0.000 0.899 -0.652 -1.030 2.548 1.115 0.079 -1.290 3.102 0.925 1.269 0.979 0.902 0.365 1.058 0.898 +1 1.385 0.238 0.378 2.601 0.890 1.076 -2.164 -0.774 0.000 0.937 -0.441 -1.099 0.000 1.306 -0.204 1.436 2.548 0.915 -1.354 -1.006 0.000 0.755 0.981 1.172 1.034 1.051 0.879 1.050 +0 0.399 1.075 -1.644 0.639 0.131 0.617 0.670 -1.337 1.087 0.490 1.280 -0.141 2.215 0.684 0.511 0.739 0.000 0.375 -0.881 -0.132 0.000 0.699 0.747 0.986 0.682 0.760 0.611 0.544 +1 1.078 -2.326 1.728 0.597 -1.364 0.514 -1.403 0.600 0.000 0.709 -0.791 -0.278 1.107 0.725 -0.764 -0.736 2.548 1.247 -0.145 0.509 0.000 0.954 0.814 0.985 0.748 0.306 0.671 0.664 +0 0.610 -2.247 -1.013 0.971 1.479 0.642 0.690 -0.186 2.173 0.321 -1.363 -0.113 0.000 0.441 1.121 -1.338 0.000 0.841 -0.344 0.947 3.102 1.125 0.910 0.987 0.723 0.803 1.013 0.886 +0 0.421 -0.788 -1.499 1.773 -1.416 0.983 -0.249 0.713 0.000 0.516 0.241 1.331 0.000 0.671 0.602 -0.132 2.548 0.758 -0.682 -0.304 1.551 0.885 0.856 0.981 0.828 0.449 1.015 1.114 +0 1.796 0.407 0.703 1.094 0.130 0.732 -0.270 -1.028 2.173 0.964 0.219 -0.179 0.000 1.070 0.675 -1.710 2.548 1.646 -0.394 -1.721 0.000 1.717 1.161 0.985 1.007 0.862 0.967 0.957 +0 1.861 -1.475 0.304 1.122 -0.292 1.421 -1.800 -1.176 0.000 1.196 -1.517 0.826 2.215 0.387 -0.858 -1.298 1.274 1.023 -1.107 1.287 0.000 1.535 0.847 1.024 0.926 0.718 0.897 0.961 +1 0.380 -0.735 -0.242 0.247 -0.432 0.676 0.299 0.958 0.000 1.035 0.290 -1.185 1.107 1.483 -0.659 -0.525 2.548 0.637 1.200 0.922 0.000 0.730 1.042 0.986 0.754 1.019 0.805 0.724 +0 0.571 -0.893 1.560 1.094 1.578 0.791 0.563 -0.492 1.087 0.809 0.561 0.503 0.000 0.295 -0.781 0.390 0.000 0.500 -0.982 1.070 1.551 0.980 0.920 0.985 0.609 0.930 0.760 0.699 +1 0.660 -0.125 -0.763 0.941 1.624 0.917 -0.353 0.559 0.000 0.733 -0.488 0.013 0.000 1.139 -1.089 -1.241 2.548 0.748 -1.972 0.974 0.000 0.833 1.099 0.988 0.871 0.826 0.916 0.833 +0 1.394 0.462 -1.523 1.050 1.399 0.548 -0.382 0.343 0.000 0.763 1.041 -0.651 2.215 0.727 -0.911 0.372 2.548 0.916 2.494 -1.211 0.000 1.050 0.918 0.998 0.963 1.162 1.150 1.119 +1 0.610 -0.906 0.438 0.526 1.651 0.793 0.447 0.342 0.000 1.057 0.437 -0.436 1.107 1.805 0.570 1.423 1.274 1.334 0.868 -1.376 0.000 1.640 1.178 0.993 1.575 1.466 1.319 1.258 +1 1.535 0.160 -0.645 1.262 -0.237 0.482 0.607 -1.267 0.000 1.342 -0.792 1.007 2.215 0.630 0.760 1.394 2.548 0.612 -2.304 -1.264 0.000 1.031 1.000 0.988 0.964 0.961 1.034 0.985 +0 0.629 0.488 0.729 0.379 0.944 0.793 -0.728 -0.968 2.173 0.700 -0.127 1.491 0.000 1.086 0.305 -0.199 2.548 0.704 -0.830 0.632 0.000 0.758 0.940 0.998 0.881 0.984 1.091 0.963 +1 0.960 -0.119 0.945 1.133 -1.350 0.731 0.588 -0.320 0.000 0.845 0.730 0.081 0.000 1.872 -0.127 -1.317 1.274 1.493 0.294 1.067 3.102 0.604 0.985 1.271 0.866 1.117 0.982 0.900 +1 3.006 -0.010 -1.063 1.535 -1.244 2.765 0.714 0.673 0.000 1.650 0.647 -0.466 2.215 0.878 -1.643 1.444 0.000 0.753 0.687 1.491 3.102 0.972 1.022 0.972 0.824 0.991 0.912 0.874 +1 1.016 1.157 -1.390 1.023 -0.977 1.063 -0.735 0.448 2.173 0.666 0.426 1.513 0.000 1.370 -2.642 -1.021 0.000 1.352 0.573 0.103 1.551 0.898 1.022 0.991 0.879 1.059 1.094 0.922 +1 1.766 1.984 -1.349 0.532 -0.506 0.491 1.094 -0.631 0.000 0.992 1.698 0.655 0.000 1.038 0.324 1.423 2.548 0.603 0.113 0.171 0.000 0.998 0.975 0.988 0.884 0.447 0.850 0.836 +0 3.221 1.677 -1.400 0.486 -0.899 0.543 0.440 0.221 2.173 0.773 0.678 0.556 2.215 0.957 1.208 0.223 0.000 1.166 1.591 1.321 0.000 1.033 0.899 0.991 1.327 0.308 1.092 0.951 +1 0.464 -0.490 -1.380 1.247 0.801 0.809 2.880 -1.099 0.000 0.681 -0.439 -0.658 0.000 1.196 -0.354 0.182 2.548 1.044 -2.333 0.914 0.000 1.941 1.067 0.986 0.705 0.539 0.840 0.800 +0 0.665 0.741 -0.440 0.778 -1.616 0.575 0.489 1.327 2.173 0.684 -0.924 -0.958 0.000 1.145 0.435 0.028 0.000 0.600 1.046 0.236 0.000 0.623 0.750 0.987 0.556 0.386 0.490 0.548 +0 0.485 -0.845 -0.326 1.232 1.268 0.594 0.879 1.080 2.173 0.772 -0.842 -1.159 2.215 0.992 0.428 -0.332 0.000 0.832 1.164 -0.167 0.000 0.856 0.904 1.062 0.763 1.337 0.892 0.896 +0 1.153 -0.408 -1.306 2.303 -1.040 0.947 -1.214 0.950 0.000 0.772 -1.580 0.505 2.215 1.177 0.212 -0.361 2.548 1.915 -1.973 0.576 0.000 1.415 0.847 0.988 0.883 1.297 1.205 1.545 +0 0.840 -0.388 -1.053 0.413 0.274 0.618 1.072 -1.111 0.000 0.985 -0.816 0.936 2.215 0.475 -0.055 -0.591 0.000 0.630 -0.379 0.707 3.102 0.735 0.759 0.981 0.810 0.201 0.813 0.759 +1 0.412 1.151 0.527 1.116 -0.554 2.422 -1.472 -1.297 2.173 2.455 -0.709 0.734 1.107 1.017 -0.123 0.572 0.000 0.979 0.095 -0.105 0.000 0.645 0.947 0.986 4.949 3.734 3.626 2.589 +0 0.770 1.641 -0.700 0.924 -1.204 0.420 0.855 1.264 2.173 0.710 0.833 -0.086 0.000 1.816 -0.072 0.654 2.548 0.620 0.128 -1.107 0.000 0.755 0.956 0.983 0.761 0.780 0.881 0.751 +1 1.255 -0.090 -0.767 0.691 -0.668 1.566 1.338 1.572 0.000 1.046 0.776 0.395 2.215 1.587 0.292 -0.080 2.548 1.301 -0.214 0.011 0.000 1.091 0.911 0.992 0.964 0.657 0.941 1.061 +0 0.561 1.386 -0.096 0.466 1.011 1.040 0.655 1.690 2.173 1.069 -0.441 0.095 0.000 0.927 -0.214 -0.639 0.000 0.393 -0.949 -1.197 3.102 0.953 0.654 0.990 0.905 0.776 0.929 0.800 +1 1.773 -0.322 -1.192 0.078 0.606 1.200 -0.149 0.664 2.173 0.755 -0.115 -0.416 2.215 0.585 0.949 0.988 0.000 0.753 0.392 -1.216 0.000 0.703 0.960 0.984 0.710 1.158 0.924 0.787 +1 0.873 0.202 -1.363 0.867 0.561 0.839 -2.207 -1.458 0.000 0.873 -1.636 0.479 2.215 0.721 -1.165 0.063 0.000 1.106 -0.907 -0.606 3.102 0.793 1.051 1.189 0.862 0.783 0.860 1.004 +1 1.093 -1.223 -0.568 1.004 1.268 1.552 -0.568 -1.552 0.000 1.946 0.885 -0.241 0.000 1.499 -2.576 1.125 0.000 1.797 -0.739 -0.144 0.000 0.886 1.178 1.446 0.712 0.575 0.727 0.776 +0 0.279 1.305 -0.004 1.147 -1.332 0.827 -1.044 1.238 2.173 0.664 -1.403 -0.669 2.215 0.956 0.073 0.409 0.000 0.973 -1.346 -1.292 0.000 0.871 0.857 0.987 1.038 1.099 0.860 0.755 +1 1.584 -0.159 -1.264 0.965 1.680 0.728 -1.062 0.082 0.000 0.412 -0.225 1.581 0.000 1.459 0.823 -0.361 0.000 0.753 0.042 0.947 3.102 0.983 0.785 0.990 0.820 0.266 0.585 0.675 +1 0.785 -0.150 -1.408 0.870 -0.293 1.235 0.551 -1.405 2.173 1.747 -0.117 0.515 0.000 1.123 -0.034 1.416 2.548 0.910 0.862 -0.964 0.000 1.884 1.315 0.988 0.885 0.937 1.174 0.970 +1 1.688 0.776 -0.115 0.780 0.731 1.259 1.362 -1.541 2.173 0.621 1.077 0.100 2.215 0.349 2.439 1.318 0.000 0.473 1.168 0.586 0.000 0.412 0.772 1.098 0.559 1.308 0.996 0.788 +0 0.374 -1.282 1.521 1.163 -0.217 0.672 0.266 0.861 0.000 1.153 -1.100 -1.525 1.107 0.823 -0.502 0.104 0.000 0.614 -0.999 0.116 3.102 1.015 0.705 0.985 0.960 0.757 0.831 0.864 +1 0.697 0.363 0.412 0.750 -0.788 0.475 0.970 1.516 0.000 0.885 -1.424 0.378 0.000 0.799 0.925 -1.061 2.548 0.767 1.332 -1.461 3.102 2.440 1.713 0.992 0.789 0.275 1.151 0.938 +0 0.927 0.369 0.480 0.409 0.346 1.005 -0.698 -0.954 2.173 0.901 1.030 0.955 0.000 0.803 -0.770 1.366 2.548 1.103 0.065 -0.625 0.000 1.445 1.161 0.990 1.566 0.974 1.131 1.039 +0 0.582 -0.701 0.489 0.402 -1.271 0.773 1.056 1.017 2.173 1.022 0.555 -1.521 2.215 1.177 -2.369 -0.529 0.000 0.555 1.325 0.737 0.000 0.847 0.941 0.989 1.615 1.039 1.238 1.032 +0 1.087 -1.739 -0.368 1.715 0.006 0.743 -0.931 1.510 2.173 0.525 -2.379 -1.570 0.000 0.866 -1.017 -1.039 0.000 1.275 -1.153 0.790 3.102 0.881 0.898 0.986 0.823 0.665 0.875 0.842 +1 0.579 0.926 1.156 1.082 -0.451 0.700 -0.396 1.526 2.173 0.796 -0.513 0.156 2.215 0.951 -0.490 -1.152 0.000 0.746 -0.046 0.672 0.000 0.954 0.801 1.088 1.057 1.040 0.866 0.759 +0 2.940 -0.749 -0.158 1.248 -0.440 3.201 -0.491 1.321 0.000 1.977 -0.697 -0.619 1.107 1.459 -1.106 1.096 2.548 1.200 -0.742 -1.234 0.000 1.026 1.052 0.976 1.413 1.860 1.164 1.071 +1 0.964 0.515 -1.484 0.076 -1.650 0.533 -0.962 0.267 2.173 0.879 -0.584 -0.986 1.107 0.581 0.739 -0.002 0.000 1.172 -0.493 0.950 0.000 0.978 0.986 0.985 0.876 0.930 0.715 0.685 +1 0.348 0.655 -1.708 1.663 1.341 0.973 0.542 -0.788 2.173 0.926 0.423 0.797 2.215 0.746 0.781 0.194 0.000 1.615 1.159 -0.328 0.000 0.813 0.958 1.002 1.383 1.384 1.064 1.001 +1 1.255 -0.446 0.556 1.346 0.581 0.745 -0.451 -1.494 2.173 0.507 -0.705 -0.199 0.000 1.217 1.087 -1.019 2.548 0.772 -0.425 1.303 0.000 0.801 1.093 0.982 1.233 1.199 1.353 1.038 +1 0.892 0.445 1.054 0.665 -1.270 0.831 0.499 -0.968 0.000 0.790 1.227 -1.393 0.000 1.524 1.180 0.340 2.548 0.540 1.179 1.400 0.000 0.927 0.905 0.989 0.874 0.757 0.890 0.774 +0 1.046 1.044 -1.439 0.448 -1.579 0.568 1.320 1.458 0.000 0.698 1.162 0.107 0.000 0.940 0.365 -0.543 2.548 0.814 -0.441 0.540 3.102 1.258 1.018 0.996 0.778 0.641 0.742 0.729 +1 0.501 1.726 0.726 1.090 -0.423 0.820 -0.187 -0.217 2.173 0.764 1.408 1.493 0.000 0.900 -0.310 -1.448 0.000 0.736 0.371 -1.081 3.102 1.428 0.850 0.982 1.675 0.635 1.088 1.093 +0 1.345 0.102 -0.419 0.473 0.006 0.726 0.569 1.415 0.000 1.108 0.958 0.906 2.215 0.892 1.099 -0.807 2.548 0.775 -0.134 -0.953 0.000 1.059 0.972 0.995 0.649 1.062 0.775 0.781 +1 0.799 1.094 0.758 1.387 1.252 1.665 -0.267 -0.671 0.000 1.052 0.414 1.474 0.000 1.724 0.208 0.676 0.000 2.032 0.589 0.008 3.102 1.263 0.997 1.000 0.991 1.261 0.916 0.846 +0 0.770 -2.237 0.267 0.631 0.631 0.531 -1.300 0.633 0.000 1.357 -0.799 -1.406 2.215 0.701 -0.540 -0.179 0.000 1.144 -1.123 -1.333 3.102 0.838 0.836 0.973 1.158 0.308 0.787 0.747 +0 1.141 0.389 0.124 1.332 0.901 1.243 -0.986 -1.471 2.173 0.648 1.108 -0.211 2.215 0.270 1.246 0.881 0.000 0.860 -0.297 -0.516 0.000 0.922 1.008 1.101 0.804 2.075 1.331 1.035 +0 0.437 1.138 -0.669 1.578 -0.367 0.635 1.443 1.194 0.000 0.638 1.520 -1.359 2.215 0.708 2.567 1.076 0.000 0.831 0.449 0.748 3.102 0.896 0.845 0.983 1.007 0.715 0.764 0.798 +1 0.699 1.188 0.119 0.414 -0.856 0.875 0.166 0.611 0.000 0.911 0.231 -0.040 0.000 1.215 0.642 -1.540 2.548 1.327 -1.193 -0.924 0.000 0.914 1.138 0.986 0.800 0.725 0.699 0.739 +1 0.837 -0.774 -1.407 0.914 1.055 0.764 0.296 0.998 2.173 0.984 -1.284 -0.783 0.000 0.713 -0.605 -0.265 2.548 0.452 0.101 0.236 0.000 0.972 1.329 0.989 0.716 0.954 0.824 0.746 +1 0.843 -0.778 -0.682 0.635 -1.702 0.669 0.383 0.653 1.087 0.543 -1.775 0.537 0.000 0.527 -1.515 -0.974 0.000 1.221 0.196 -1.358 3.102 0.806 1.033 0.980 1.162 0.931 0.907 0.828 +0 1.088 1.003 -0.458 0.775 -0.458 0.847 -0.348 1.406 2.173 0.344 1.679 1.118 0.000 0.583 -0.977 -0.762 2.548 0.521 -0.249 0.758 0.000 0.683 0.853 0.989 1.223 0.874 0.859 0.770 +1 1.053 1.552 1.206 0.309 0.197 0.463 -0.530 0.357 2.173 0.558 1.181 -0.222 0.000 0.570 1.460 -1.445 0.000 0.850 0.599 -1.217 3.102 0.790 1.014 0.998 0.656 0.793 0.673 0.648 +1 0.724 0.630 0.129 0.876 1.058 0.667 1.255 1.668 0.000 1.165 -0.627 0.055 2.215 1.023 0.230 -1.368 1.274 1.029 1.898 -0.634 0.000 1.084 0.921 0.985 1.264 1.236 1.066 0.955 +0 1.280 0.201 -1.389 0.264 -0.098 0.601 0.489 0.363 0.000 0.364 0.130 1.580 0.000 0.751 -0.550 -0.704 2.548 0.682 0.936 0.460 3.102 0.902 0.828 0.991 0.660 0.714 0.571 0.583 +0 1.262 -0.761 0.463 0.878 1.448 1.036 1.468 0.609 0.000 0.989 1.118 -0.074 0.000 1.199 1.701 1.437 0.000 3.596 -0.757 -1.070 3.102 1.062 0.976 1.132 1.314 0.349 1.519 1.339 +0 1.720 -0.734 -1.009 1.157 -0.648 0.410 0.131 0.245 0.000 1.365 0.530 1.067 2.215 0.557 0.839 0.798 2.548 0.370 -2.466 1.079 0.000 0.903 0.901 0.980 1.007 0.283 1.065 0.869 +0 0.800 -1.072 1.459 0.526 0.352 0.667 -0.304 1.585 2.173 0.543 -1.339 -0.046 0.000 0.799 -0.045 0.070 2.548 0.612 0.141 -0.417 0.000 0.687 0.921 0.981 0.656 0.898 0.633 0.591 +0 1.049 -0.446 -1.071 0.943 1.152 0.553 0.386 0.958 2.173 0.337 -1.020 -0.559 0.000 0.564 0.139 -0.524 1.274 0.447 -1.521 1.231 0.000 0.543 0.802 1.251 0.732 0.682 0.640 0.586 +0 1.140 -0.340 0.879 0.637 1.601 0.853 0.626 -0.406 0.000 0.948 0.460 -1.298 2.215 1.142 0.056 -1.008 2.548 0.647 1.534 1.170 0.000 0.701 0.891 0.989 0.875 0.366 0.690 0.731 +0 0.660 0.523 0.503 0.993 0.228 0.564 -0.514 1.294 2.173 0.664 -0.307 -1.541 0.000 1.057 -0.006 -0.918 2.548 0.904 -0.560 -0.485 0.000 0.842 0.762 0.988 0.874 0.912 0.724 0.664 +0 0.489 0.172 -0.062 1.035 1.569 0.613 -0.344 -1.131 2.173 0.561 1.017 0.864 0.000 1.079 0.798 0.341 2.548 0.840 0.622 -0.677 0.000 0.890 0.943 0.987 0.784 1.184 0.722 0.664 +1 0.716 -0.646 -1.613 1.112 0.969 1.180 -0.546 0.889 1.087 1.170 -0.415 -1.134 0.000 1.288 1.506 -0.689 0.000 1.386 -0.055 0.098 3.102 2.445 1.636 0.985 0.788 0.946 1.489 1.323 +0 0.600 -0.217 -1.315 2.162 -0.306 1.336 -0.637 1.499 2.173 0.932 -1.264 -1.595 0.000 1.161 -0.864 0.022 2.548 1.782 0.122 0.429 0.000 0.889 0.699 1.246 1.572 1.526 1.144 0.975 +0 0.690 0.674 0.300 1.425 -0.792 0.481 1.638 -0.924 0.000 0.877 -0.038 1.577 0.000 0.988 0.601 -1.472 2.548 1.309 -1.824 1.007 0.000 0.955 0.939 1.143 0.761 0.844 0.720 0.695 +1 0.618 0.679 -0.928 0.364 -0.722 1.717 0.170 -0.590 0.000 1.624 0.512 0.521 0.000 2.327 -0.250 1.522 1.274 2.395 1.940 1.165 0.000 1.068 0.677 0.998 1.006 0.800 1.053 0.863 +1 1.066 0.573 -0.987 1.731 -0.329 0.749 0.082 1.204 1.087 0.577 0.609 0.473 0.000 0.594 -0.194 -0.700 0.000 1.439 0.675 1.614 0.000 0.873 0.856 1.053 0.781 0.721 0.857 0.737 +1 1.524 0.978 -1.113 1.403 -1.173 1.467 0.806 0.657 2.173 0.421 1.719 -0.330 0.000 0.549 0.524 0.340 2.548 0.758 0.490 -1.572 0.000 0.806 1.111 1.000 0.920 0.342 1.185 0.929 +1 0.586 -0.235 1.458 1.141 0.087 1.140 1.377 1.641 0.000 1.219 0.912 -1.474 2.215 2.613 0.311 -0.183 2.548 1.033 -0.596 -1.554 0.000 0.980 1.147 1.069 0.944 1.833 1.083 0.931 +0 0.525 0.274 -1.558 0.587 0.530 0.678 0.697 -1.152 0.000 0.614 0.161 -0.042 0.000 1.485 -1.272 0.788 1.274 0.829 -0.093 -0.573 0.000 0.747 0.853 0.990 1.549 0.603 1.012 0.969 +1 0.535 1.657 0.294 0.931 -0.844 0.815 0.069 0.722 2.173 0.584 0.741 -0.405 0.000 0.672 -0.247 -1.373 0.000 0.990 -0.394 1.661 3.102 0.900 1.009 0.993 1.405 0.757 1.241 1.048 +0 0.327 0.849 -1.103 1.033 0.372 1.390 -2.313 0.642 0.000 1.539 -1.009 -0.842 1.107 0.834 -1.509 -1.551 0.000 0.654 -0.624 -1.732 0.000 0.405 0.765 0.985 0.675 1.078 1.496 1.354 +0 1.009 -0.406 -0.260 0.421 0.948 0.788 0.509 0.732 2.173 0.821 -0.512 -1.448 2.215 0.493 1.307 -0.401 0.000 1.033 0.243 -1.586 0.000 0.838 0.894 0.991 0.815 1.268 0.776 0.709 +1 0.762 -0.592 -0.426 1.483 0.410 0.911 -0.180 1.554 2.173 0.496 -1.206 -1.300 2.215 0.438 0.911 -1.018 0.000 0.607 -0.145 0.019 0.000 0.578 0.814 1.008 0.822 0.765 0.833 0.715 +1 0.348 -1.296 -1.022 1.345 -0.434 2.399 -1.018 0.815 0.000 0.614 -0.151 1.356 0.000 1.882 0.862 -0.971 2.548 0.775 -0.253 0.415 0.000 1.065 1.618 0.985 0.936 0.353 1.802 1.440 +1 1.363 -1.564 1.381 0.684 1.135 0.473 -0.569 1.595 0.000 1.290 -0.308 -0.074 2.215 0.553 -2.670 -0.082 0.000 1.328 -0.626 -0.881 0.000 0.961 1.106 0.982 0.657 0.641 0.836 0.763 +1 0.727 -1.194 1.245 0.328 0.288 0.630 -1.006 -1.277 2.173 0.545 0.091 -0.154 1.107 0.675 -1.954 -0.218 0.000 0.646 0.264 1.542 0.000 1.354 0.980 0.980 0.669 0.888 0.733 0.682 +1 0.530 -1.092 0.801 1.341 -0.818 0.847 -0.485 1.583 0.000 0.638 -0.559 -0.060 0.000 0.987 0.924 0.335 2.548 1.000 0.717 -1.472 3.102 1.556 1.134 1.160 1.274 0.760 0.950 0.925 +0 0.533 -1.281 -0.041 1.600 -1.042 1.059 -0.774 0.147 2.173 0.770 -1.950 -1.631 0.000 1.161 0.138 1.258 2.548 0.593 2.049 1.062 0.000 0.524 1.106 1.003 1.076 1.342 1.025 0.931 +0 0.830 -0.425 0.532 1.999 0.204 1.503 0.159 -1.544 1.087 0.266 -0.121 1.425 0.000 0.347 -2.244 -0.209 0.000 0.639 0.955 -1.269 3.102 0.821 0.909 0.991 2.083 0.590 1.401 1.117 +0 1.344 0.591 -0.856 0.606 0.859 0.246 -1.636 -0.545 0.000 0.537 0.577 1.456 1.107 1.276 -0.889 0.805 1.274 0.404 -0.847 -0.723 0.000 0.173 0.745 1.249 1.210 0.906 0.842 0.745 +1 0.667 0.527 0.146 1.385 -0.883 0.871 -0.105 1.250 2.173 0.742 0.983 -0.355 2.215 0.899 1.597 -1.502 0.000 0.756 0.776 0.861 0.000 0.862 1.116 1.064 0.620 1.366 0.875 0.823 +1 1.470 -0.196 1.517 1.281 -1.078 0.498 1.118 0.817 0.000 0.880 0.466 -0.025 2.215 0.354 0.695 0.613 2.548 0.741 1.589 -0.024 0.000 0.738 0.750 1.369 0.810 0.334 0.772 0.811 +0 0.824 -0.572 0.445 1.562 1.177 0.424 0.986 -0.285 0.000 1.084 -0.533 -1.585 1.107 0.458 1.272 0.093 2.548 1.387 -0.421 -0.625 0.000 0.983 0.867 0.986 0.933 1.132 0.867 0.754 +0 1.107 0.185 0.511 1.841 0.997 1.114 -0.310 0.163 2.173 1.480 -0.102 -1.394 2.215 0.816 1.242 -1.247 0.000 1.930 -0.401 -0.870 0.000 1.537 1.136 0.982 1.146 1.873 1.256 1.215 +1 1.160 0.148 -1.481 1.052 1.393 1.282 0.482 0.127 2.173 1.187 0.880 -0.744 0.000 1.524 -0.554 1.214 2.548 0.577 1.423 -1.029 0.000 0.521 1.197 0.992 0.950 1.760 1.240 1.101 +0 1.125 -0.023 0.571 2.183 0.659 1.180 -2.621 -1.354 0.000 0.493 -0.921 -0.156 0.000 0.536 -1.246 -1.282 2.548 1.003 -0.080 1.445 3.102 0.375 0.671 0.994 1.268 0.520 0.865 0.831 +0 0.572 -1.082 1.237 0.366 0.884 1.160 -0.942 -1.194 2.173 0.931 1.636 -0.235 0.000 0.937 1.232 0.317 0.000 0.795 0.560 1.101 3.102 0.724 0.766 0.983 1.114 1.281 1.517 1.205 +1 0.822 -0.424 0.850 0.847 -0.121 0.648 -0.534 0.507 0.000 0.555 0.537 0.487 0.000 1.544 0.765 -1.156 2.548 1.155 -0.493 -1.511 0.000 0.731 0.859 0.988 1.040 0.877 0.894 0.778 +1 0.814 1.536 0.768 1.029 1.320 0.423 0.643 -0.126 0.000 0.542 0.904 1.058 0.000 0.757 0.133 -1.652 2.548 2.039 -0.514 -0.615 1.551 0.901 1.127 0.993 1.126 0.850 1.502 1.165 +0 0.435 0.596 0.383 1.381 -0.633 1.185 0.587 -0.911 2.173 1.912 0.653 1.163 1.107 0.779 -0.074 0.126 0.000 1.163 0.050 0.725 0.000 0.795 0.931 0.990 0.862 2.118 1.217 0.963 +0 0.282 1.717 -0.049 0.691 1.475 0.545 -1.093 -1.644 2.173 0.375 -0.914 0.318 1.107 0.851 1.328 -1.305 0.000 0.644 0.662 0.129 0.000 0.895 0.889 0.978 0.837 0.654 0.787 0.688 +0 0.967 -1.856 1.209 1.110 0.524 0.561 -1.422 -1.119 2.173 1.007 -1.102 -0.664 1.107 0.479 2.386 0.009 0.000 1.347 -1.531 0.952 0.000 4.126 2.738 0.994 1.213 0.472 1.778 1.882 +1 1.550 0.155 -0.658 1.057 -1.623 1.366 -0.592 0.869 1.087 0.390 0.430 1.415 0.000 1.817 -0.810 -0.788 2.548 0.895 -0.593 0.340 0.000 0.789 1.015 1.354 1.588 1.978 1.294 1.013 +0 0.663 0.443 1.595 1.656 -0.814 0.932 0.949 0.868 0.000 1.306 0.267 -0.527 2.215 0.668 1.585 1.476 0.000 0.520 -0.273 0.949 3.102 0.932 0.713 1.199 0.824 0.757 0.912 0.875 +0 1.217 0.824 -0.703 1.283 -1.079 1.068 0.234 0.445 0.000 0.762 1.072 -1.408 2.215 1.784 -0.205 1.039 2.548 0.703 0.144 -0.338 0.000 0.898 0.833 0.986 0.736 1.334 0.995 0.961 +1 0.364 -0.536 -0.680 1.876 -1.693 1.025 0.700 -0.189 2.173 0.331 1.325 -1.679 0.000 1.138 0.324 0.764 2.548 0.392 0.200 0.503 0.000 0.514 0.733 0.993 0.901 1.046 0.962 0.743 +1 0.464 -1.659 -1.709 0.751 0.889 1.336 -1.347 -0.664 2.173 0.551 -0.034 1.036 0.000 0.652 -0.085 1.577 0.000 0.711 -1.050 0.464 3.102 0.432 0.545 0.980 1.201 0.878 0.882 0.781 +0 0.818 -0.167 0.712 0.375 0.669 0.776 0.948 -0.412 1.087 0.571 1.797 -0.796 0.000 0.582 2.000 1.229 0.000 1.362 0.164 1.638 3.102 0.870 1.002 0.980 1.376 1.129 1.003 1.122 +0 0.685 -0.068 -0.902 0.591 -0.364 1.134 -0.288 -1.151 1.087 0.667 -0.881 0.815 0.000 0.767 0.432 1.133 0.000 1.338 -1.114 -0.134 3.102 0.875 0.984 0.983 0.849 1.257 0.961 0.833 +1 0.759 -2.048 -0.172 0.868 -0.740 0.627 -0.155 -1.540 1.087 0.765 -1.188 0.586 0.000 2.008 -0.541 0.957 2.548 1.348 -0.227 -0.781 0.000 0.674 0.985 0.987 1.005 1.129 0.981 0.805 +1 1.083 -0.260 1.273 0.503 -0.938 0.838 0.314 0.178 0.000 1.490 -0.108 -1.602 2.215 1.038 -0.642 -0.265 0.000 0.515 -0.791 -0.581 0.000 0.929 0.797 0.987 0.714 0.947 0.884 0.753 +1 1.112 0.889 -0.073 1.016 1.533 0.928 1.041 1.197 0.000 0.386 0.631 -1.521 0.000 1.284 0.808 -0.347 1.274 0.615 -0.752 -0.584 1.551 0.850 1.012 1.461 0.920 0.707 0.825 0.781 +1 0.888 0.302 -0.757 0.950 1.723 2.275 -1.051 -0.134 0.000 2.279 0.623 1.436 2.215 1.289 -0.329 1.498 2.548 1.043 0.236 0.040 0.000 1.742 1.911 1.002 1.039 0.958 2.108 1.631 +1 1.774 1.677 1.585 0.296 -0.720 0.859 0.300 0.499 2.173 0.982 -2.216 -0.213 0.000 0.972 -0.283 -1.237 2.548 0.447 -0.110 1.004 0.000 0.713 0.957 0.988 1.104 1.193 1.037 0.987 +0 1.123 -0.804 0.993 1.180 -0.034 0.726 0.198 -1.618 2.173 0.817 0.311 -0.535 2.215 0.309 1.781 0.748 0.000 0.444 -2.015 -0.788 0.000 1.211 0.916 1.274 1.190 0.941 0.938 0.843 +0 0.548 0.383 -1.439 4.161 1.566 1.719 -0.062 -0.525 2.173 0.570 1.069 1.366 2.215 1.896 0.179 0.217 0.000 0.396 -0.309 -0.680 0.000 0.744 0.925 0.995 2.199 1.699 1.489 1.285 +0 0.401 -0.234 -0.005 1.731 1.015 0.416 -0.996 -0.812 2.173 0.466 -2.770 0.696 0.000 0.667 -0.318 -1.051 2.548 0.629 -1.451 1.302 0.000 0.630 0.873 0.984 0.843 0.260 0.634 0.686 +1 3.088 -0.690 0.845 1.949 0.139 3.365 -2.020 -1.150 0.000 1.050 -0.216 -0.058 2.215 1.296 0.027 0.427 2.548 0.948 -0.508 -0.425 0.000 0.825 0.738 2.018 1.291 0.547 0.887 0.823 +0 1.214 -0.221 0.144 0.294 -0.296 0.551 -0.193 -0.874 2.173 0.826 0.409 1.310 2.215 0.813 1.491 0.852 0.000 1.044 -1.803 1.513 0.000 1.050 0.927 0.980 0.767 0.966 0.804 0.767 +1 0.618 -0.874 -0.632 0.707 0.888 0.812 -0.659 0.467 0.000 0.617 -0.591 1.535 0.000 0.586 1.257 -1.571 0.000 1.051 1.229 -0.555 0.000 0.897 0.669 0.984 0.696 0.517 0.489 0.512 +1 1.461 -0.927 0.604 0.819 -0.063 0.969 -0.121 -1.382 2.173 0.406 0.795 -1.081 0.000 0.892 0.500 -0.225 2.548 1.390 -0.188 1.154 0.000 0.941 1.037 0.992 1.043 1.076 1.068 0.888 +0 0.494 -1.134 0.454 0.806 -0.221 1.302 -0.679 -0.981 0.000 1.184 0.148 0.750 2.215 0.719 -1.043 -0.384 2.548 1.168 0.614 1.115 0.000 1.055 0.820 0.992 1.679 1.079 1.096 1.017 +1 1.475 -0.207 -1.642 0.893 -1.355 0.452 -2.122 0.539 0.000 1.489 0.460 0.157 2.215 0.699 0.305 1.118 2.548 0.369 -2.205 -1.591 0.000 0.607 1.058 0.996 1.602 0.829 1.200 1.136 +1 1.268 -0.136 -0.667 0.937 -1.623 0.825 1.929 0.691 0.000 1.436 1.513 -0.343 2.215 1.266 1.040 -1.732 2.548 1.296 0.259 1.166 0.000 1.584 1.217 1.145 1.484 1.393 1.134 1.208 +0 0.293 -0.924 1.135 0.274 0.274 0.999 0.256 1.573 0.000 1.266 1.105 -0.451 2.215 0.565 0.237 -0.070 2.548 0.677 0.956 1.216 0.000 0.701 0.815 0.995 0.851 0.508 0.876 0.743 +0 0.344 -1.962 0.923 0.828 -1.016 1.002 -1.092 1.741 2.173 0.847 0.504 0.631 2.215 1.083 0.537 0.023 0.000 1.290 -0.527 -0.405 0.000 0.979 0.904 0.985 0.770 1.674 1.129 0.941 +1 2.268 0.002 -1.467 0.716 1.054 1.014 -2.028 0.571 0.000 1.280 -0.803 0.197 2.215 0.318 0.778 1.463 0.000 1.311 -0.198 -0.398 0.000 0.818 0.888 1.349 0.697 0.828 0.941 0.795 +0 0.652 -1.514 -0.848 0.754 0.345 0.646 0.049 1.311 2.173 0.846 -0.052 -0.945 0.000 0.978 -1.747 1.403 0.000 1.598 0.232 0.565 3.102 1.027 1.060 0.992 1.345 0.682 1.097 1.177 +0 1.156 -0.306 -0.768 0.686 0.001 0.734 -1.533 1.189 0.000 0.621 -0.917 -1.632 2.215 1.237 -0.003 -1.656 2.548 2.604 -0.559 0.245 0.000 0.977 1.170 0.990 0.758 0.454 0.723 0.761 +1 0.481 1.567 1.452 0.725 0.217 0.786 1.649 0.035 0.000 1.263 1.379 -1.362 0.000 1.261 0.675 -0.105 0.000 1.911 0.935 1.177 3.102 0.886 1.213 0.981 0.740 0.988 0.967 0.802 +1 2.003 -1.935 -0.837 0.898 -1.114 1.113 -1.472 0.662 2.173 0.408 -1.500 -0.126 0.000 0.734 -1.541 1.083 0.000 0.581 -0.202 -1.125 3.102 0.747 0.659 0.990 0.927 1.021 1.128 0.895 +0 1.419 -0.405 1.666 0.851 0.666 1.103 0.506 0.017 2.173 0.974 0.270 -1.143 2.215 0.398 2.606 -1.545 0.000 0.748 -0.638 1.109 0.000 1.844 1.333 1.194 1.333 1.332 1.101 1.090 +0 1.712 0.807 -0.387 0.438 -0.023 0.852 1.157 -1.426 1.087 0.816 0.855 1.522 0.000 0.499 -0.900 0.458 0.000 1.457 0.734 0.614 3.102 1.324 0.960 0.974 1.116 1.149 0.882 0.881 +0 0.997 -0.060 -1.725 0.858 0.967 1.708 -0.072 1.255 1.087 2.161 0.228 -0.285 2.215 1.086 -0.314 -0.575 0.000 1.001 0.343 -1.034 0.000 0.641 0.805 0.987 0.772 2.814 1.408 1.166 +1 0.897 -1.121 -0.198 0.872 -0.064 1.023 0.182 1.481 2.173 0.467 0.571 0.041 0.000 1.482 -1.082 -1.506 2.548 0.438 -0.909 -0.544 0.000 0.629 0.970 0.980 1.120 1.318 1.076 0.875 +0 0.777 -1.080 0.919 0.336 1.664 0.667 -0.843 0.029 1.087 1.013 -2.107 1.360 0.000 1.308 0.078 -0.461 2.548 1.412 -0.102 -1.201 0.000 2.213 1.628 0.995 0.879 0.753 1.230 1.024 +1 1.037 -0.235 1.710 1.065 0.770 1.152 0.552 -0.441 0.000 0.639 0.679 0.018 1.107 1.456 1.574 1.675 0.000 0.697 0.064 0.839 3.102 0.959 0.800 1.091 0.550 0.449 0.572 0.714 +0 0.776 -1.201 1.187 0.940 0.985 1.015 -0.859 1.525 2.173 1.029 0.177 -0.514 0.000 1.678 0.589 -0.118 0.000 1.456 0.357 -1.136 3.102 0.847 0.897 0.984 0.768 1.246 1.214 1.079 +1 0.300 1.489 0.710 1.582 -1.352 0.734 -0.527 -0.915 2.173 1.004 -0.589 0.136 0.000 1.089 0.133 1.458 0.000 0.991 0.513 -0.156 3.102 0.801 0.841 0.990 0.937 0.791 1.233 1.026 +1 1.054 1.127 -0.895 1.464 -1.064 2.065 0.745 -0.767 0.000 1.484 -2.224 0.989 0.000 1.532 0.459 -0.188 1.274 1.428 0.041 1.127 0.000 1.009 1.090 0.987 0.983 1.135 0.857 0.853 +1 0.771 -0.245 -0.555 0.331 0.539 1.323 -0.321 0.821 2.173 1.378 -0.030 -1.411 0.000 1.633 0.826 -0.092 0.000 2.254 0.500 -0.667 3.102 0.791 0.915 0.987 1.085 1.983 1.236 0.999 +0 0.497 -0.892 0.853 2.375 1.693 1.054 -0.912 -1.481 2.173 0.999 1.871 0.139 0.000 1.160 1.508 -0.491 0.000 2.352 0.802 0.198 3.102 0.915 0.898 1.034 0.747 2.460 1.887 1.907 +0 0.416 0.392 -0.759 1.281 0.400 2.364 -0.627 -0.194 1.087 3.397 -0.943 1.511 0.000 0.901 -0.626 -1.349 2.548 0.423 -1.178 -1.569 0.000 0.660 0.805 0.992 1.891 1.570 1.798 1.762 +0 0.708 -0.845 1.199 1.922 0.701 0.879 -0.527 -1.126 0.000 0.753 -0.050 -0.809 0.000 0.689 -0.932 -0.206 2.548 0.438 0.034 -1.389 0.000 0.644 0.779 0.985 0.784 0.582 0.640 0.894 +0 0.660 0.162 1.201 1.598 0.578 1.440 -0.323 -1.692 2.173 0.865 0.521 -0.439 2.215 0.667 2.015 1.666 0.000 2.001 -0.441 -0.057 0.000 0.847 1.015 0.988 1.275 1.655 1.177 1.062 +1 1.633 1.751 0.564 1.253 -0.034 0.486 0.342 -0.395 2.173 1.362 1.139 1.569 2.215 1.600 0.252 -0.935 0.000 0.543 0.501 1.391 0.000 0.905 1.104 1.015 1.037 1.280 1.070 1.034 +1 1.128 -0.570 1.282 1.171 0.793 0.715 -1.000 -0.976 2.173 0.488 0.186 -0.468 0.000 0.902 -2.204 0.123 0.000 1.001 -0.340 -1.231 0.000 0.641 0.617 0.982 0.568 0.654 0.715 0.691 +0 0.668 2.430 0.580 1.473 0.531 0.747 0.524 -1.263 2.173 0.631 1.073 -0.965 0.000 0.630 0.185 0.573 2.548 0.419 0.223 -0.927 0.000 0.290 0.597 0.998 1.304 0.863 0.893 0.748 +0 0.287 1.741 -0.970 1.391 0.141 0.686 -0.264 0.999 2.173 0.831 0.041 -0.889 2.215 0.463 -1.589 -1.335 0.000 0.710 -0.002 1.576 0.000 0.698 0.727 0.988 0.809 1.115 0.792 0.755 +0 1.119 -0.729 1.165 1.498 0.525 1.347 1.253 -1.165 2.173 0.626 1.865 1.032 0.000 1.274 2.024 -0.686 0.000 1.222 0.620 0.374 3.102 1.386 1.127 0.986 2.416 1.382 1.578 1.660 +1 1.094 0.030 1.577 0.553 -0.142 0.943 0.329 0.409 2.173 0.663 -1.068 -1.164 0.000 0.553 1.249 1.526 2.548 0.819 -0.117 -0.550 0.000 0.695 0.961 1.078 0.889 0.904 0.873 0.758 +0 3.461 -0.384 0.379 0.272 0.551 1.042 0.218 -1.297 0.000 0.528 -0.126 -0.579 0.000 0.753 -1.071 -1.543 2.548 0.827 -0.030 1.056 3.102 0.990 0.898 0.994 0.664 0.564 0.762 0.943 +1 0.816 1.238 -0.400 0.827 -0.963 1.249 0.408 1.130 0.000 0.587 0.197 -0.826 0.000 0.477 1.234 -1.710 0.000 0.629 -0.359 0.194 3.102 1.020 0.866 0.983 0.794 0.221 0.712 0.877 +0 2.460 1.484 0.505 0.705 1.606 0.960 0.153 -0.947 2.173 0.578 -0.123 1.218 1.107 1.338 -0.841 -0.801 0.000 0.539 0.403 1.285 0.000 1.144 0.943 1.528 1.173 1.030 1.231 1.233 +0 0.920 0.014 1.698 2.818 -1.393 1.419 -0.921 0.246 1.087 0.723 0.599 0.035 0.000 0.823 0.240 0.364 0.000 1.108 -1.221 -1.034 0.000 0.405 1.118 0.987 0.759 0.845 1.449 1.193 +0 1.293 0.725 1.252 1.644 0.755 0.892 0.657 0.060 0.000 1.631 0.363 -1.043 2.215 0.850 -0.021 1.585 0.000 0.710 0.950 -0.992 3.102 1.630 1.022 0.990 1.574 0.384 1.008 0.987 +0 0.330 0.885 1.074 1.794 0.388 0.730 0.185 -1.184 0.000 0.712 -0.286 -1.466 2.215 1.013 0.494 -0.057 2.548 0.564 0.327 1.417 0.000 0.708 0.840 0.987 1.601 0.945 1.117 1.022 +0 0.656 1.392 1.554 0.670 -0.518 0.514 -1.339 1.739 0.000 1.201 0.789 0.734 2.215 1.125 -0.120 -0.368 2.548 0.560 0.553 -1.163 0.000 1.056 0.980 0.984 0.833 1.199 0.998 0.885 +1 0.423 -2.302 0.267 1.010 -1.158 0.883 -1.273 1.721 0.000 1.078 0.181 -0.416 2.215 0.841 -0.170 0.839 2.548 0.853 -1.063 0.834 0.000 0.952 0.876 0.991 1.177 0.935 0.971 0.896 +1 1.873 1.060 0.675 0.788 0.154 0.610 0.769 1.590 0.000 0.918 1.403 -1.431 2.215 0.815 0.108 -0.711 2.548 0.664 1.562 -0.701 0.000 1.023 0.861 0.990 1.165 0.858 0.891 0.800 +1 1.276 0.166 1.592 0.361 -0.825 1.056 0.197 -0.731 1.087 1.859 0.913 0.689 0.000 1.087 0.805 -1.682 2.548 1.234 0.867 -0.443 0.000 1.684 1.341 0.987 0.953 1.111 1.175 0.985 +1 1.089 1.490 -0.347 0.779 0.861 0.859 0.031 1.633 2.173 0.863 -0.023 -0.767 0.000 1.479 -0.004 0.621 0.000 0.679 -0.575 -0.870 3.102 1.221 1.028 1.131 0.979 0.693 0.938 0.890 +0 0.953 -0.251 0.904 0.959 1.672 0.336 0.816 -1.456 0.000 0.754 0.256 -0.385 1.107 0.403 1.815 1.181 0.000 1.197 -0.583 -0.058 3.102 0.614 1.006 0.989 0.893 0.497 0.705 0.702 +1 0.308 -2.192 0.890 0.521 0.757 1.301 0.095 0.658 2.173 0.951 -0.301 -0.651 0.000 1.563 -0.420 -1.353 0.000 0.750 0.164 -1.030 3.102 1.116 0.643 1.001 0.860 1.045 1.035 0.884 +0 1.462 0.573 -0.047 1.309 -0.472 0.849 -0.441 1.178 0.000 0.702 0.437 0.660 0.000 1.074 -0.263 1.728 2.548 0.977 0.632 -0.350 0.000 0.868 0.945 0.981 0.713 0.659 0.762 0.708 +0 1.580 -1.134 -0.592 0.443 -1.655 0.964 -0.532 -1.665 2.173 1.195 -1.272 0.341 2.215 0.653 0.048 0.520 0.000 0.792 0.922 0.883 0.000 0.770 0.897 0.989 0.951 1.658 0.989 0.922 +1 1.909 0.355 1.698 1.529 0.946 2.153 1.530 0.204 0.000 1.132 0.278 -1.241 0.000 1.810 -1.254 -1.553 0.000 1.594 -0.946 -1.131 3.102 0.997 0.870 1.482 1.005 0.843 1.002 0.889 +1 1.415 0.643 -0.145 1.454 -0.794 0.945 -1.441 0.416 0.000 0.430 -0.744 -0.801 0.000 2.393 -0.038 1.508 1.274 1.212 -0.407 -1.299 3.102 1.296 1.114 1.095 1.590 0.803 1.109 1.267 +1 0.652 0.252 -1.499 1.384 1.618 1.177 0.362 -0.256 2.173 0.479 -1.218 0.398 0.000 1.138 -0.128 1.507 0.000 0.645 -1.022 -0.100 1.551 1.160 0.789 0.984 1.490 0.825 0.987 0.902 +0 1.189 -0.130 -0.622 0.682 1.450 1.221 -0.767 -0.096 1.087 1.489 0.185 1.665 2.215 0.916 0.674 0.708 0.000 0.595 1.868 -0.986 0.000 1.042 1.008 1.193 1.062 2.220 1.291 1.050 +1 0.968 -0.533 1.530 0.743 0.462 0.711 -0.684 -0.377 2.173 0.833 -1.159 0.458 1.107 0.885 0.270 -1.274 0.000 0.626 1.109 1.129 0.000 0.812 1.061 0.987 0.681 0.826 0.891 0.793 +0 0.406 1.366 1.610 0.781 0.135 0.616 1.246 -0.532 0.000 1.021 -0.456 1.117 2.215 0.897 0.084 0.704 0.000 1.844 0.040 -1.681 0.000 1.132 1.023 0.992 1.603 0.813 1.417 1.279 +1 0.447 1.908 0.460 1.002 -0.167 1.029 0.659 -1.673 0.000 0.899 1.239 1.330 2.215 1.184 0.792 -0.067 0.000 0.914 0.673 -0.429 3.102 1.980 1.181 0.993 0.902 0.841 0.859 0.793 +0 1.383 -0.570 -0.270 0.350 1.194 1.593 -1.290 1.526 2.173 1.433 0.163 0.100 0.000 2.176 -0.969 -0.894 1.274 0.883 -0.867 0.942 0.000 0.775 1.077 0.989 1.328 1.918 1.145 0.943 +1 0.544 1.659 1.034 0.499 -1.600 1.212 0.812 -0.784 2.173 1.615 1.098 1.119 2.215 1.523 -2.438 0.353 0.000 0.732 0.806 -0.075 0.000 0.826 0.948 0.987 0.994 2.063 1.040 0.842 +0 0.970 -0.085 0.092 1.275 -0.960 0.467 -0.884 1.163 2.173 0.394 1.020 -1.021 2.215 0.346 -2.272 0.798 0.000 0.734 2.057 0.700 0.000 0.715 0.590 1.251 0.965 0.922 0.850 0.877 +0 0.350 -1.924 0.103 1.524 0.105 0.508 0.522 -0.361 2.173 0.735 -1.044 0.887 0.000 1.191 0.078 -1.191 0.000 0.507 -0.510 -1.129 0.000 0.793 0.951 0.985 0.541 0.432 0.565 0.583 +1 0.871 -0.232 -0.624 0.370 0.847 0.765 -0.658 -1.395 1.087 0.675 -0.359 0.830 0.000 0.990 0.208 -0.784 2.548 0.718 0.587 0.312 0.000 1.158 1.027 0.987 0.754 0.756 0.881 0.792 +0 1.059 0.150 0.915 1.409 -0.156 0.652 -1.456 1.143 0.000 0.845 -0.882 -0.937 2.215 0.757 0.419 -0.969 2.548 0.527 -0.347 -1.527 0.000 0.777 1.006 1.391 1.134 0.628 0.799 0.850 +0 1.475 -0.470 -1.039 1.301 -1.536 1.157 -0.068 0.485 0.000 0.847 0.087 -0.255 2.215 0.747 0.678 0.841 0.000 1.108 -0.294 1.553 3.102 0.875 0.938 0.995 0.682 0.894 0.763 0.966 +0 4.249 0.396 1.708 1.297 -0.379 1.441 0.454 -0.078 0.000 0.455 -0.362 -0.369 0.000 1.290 -0.386 1.415 2.548 1.151 1.283 0.252 1.551 0.861 0.934 3.098 1.675 1.339 1.335 1.356 +1 1.019 0.379 -1.129 0.510 1.353 0.875 0.427 1.390 0.000 0.886 0.573 0.858 0.000 1.797 0.046 -0.869 2.548 1.704 1.894 1.105 0.000 0.878 0.627 0.989 0.732 0.715 0.864 0.752 +1 0.596 0.983 0.343 0.540 -0.977 0.629 -2.631 -1.197 0.000 1.758 -0.941 0.639 1.107 1.330 -0.368 0.057 2.548 0.767 0.184 -0.398 0.000 0.817 1.827 0.988 2.226 0.936 1.507 2.233 +0 1.130 0.638 -0.230 1.695 -0.253 1.325 -1.135 1.463 0.000 0.406 -0.831 0.943 0.000 0.592 0.541 -1.213 1.274 0.484 0.488 1.500 3.102 0.740 1.088 0.995 0.756 0.262 0.681 1.357 +0 0.722 1.565 -0.654 0.779 0.432 1.042 1.780 1.664 0.000 1.244 0.873 0.284 1.107 1.105 0.803 -0.945 2.548 0.627 1.493 -1.101 0.000 0.929 1.124 0.987 0.912 1.115 1.034 0.916 +0 0.618 0.383 -1.335 1.571 -0.298 0.660 0.171 0.944 1.087 0.670 -1.042 0.537 2.215 0.802 0.935 -1.276 0.000 0.910 -0.943 -1.571 0.000 1.252 1.072 1.098 1.042 0.739 0.839 0.843 +0 0.914 0.618 -0.486 0.910 0.350 1.025 0.352 -1.717 0.000 1.079 1.035 1.060 2.215 0.734 2.538 -0.335 0.000 0.958 0.000 -0.795 3.102 2.819 1.724 0.987 0.988 1.047 1.185 1.066 +0 0.503 1.340 0.288 0.767 -0.302 0.733 -1.903 -0.029 0.000 0.782 -0.136 -1.124 0.000 0.776 0.783 1.027 2.548 0.606 -1.304 0.874 3.102 2.110 1.223 0.999 0.716 0.807 1.076 0.977 +1 0.843 0.279 0.455 1.342 1.635 0.753 -0.342 0.191 0.000 1.429 0.595 -0.822 2.215 1.472 0.128 -1.667 2.548 0.500 0.816 0.463 0.000 0.705 1.115 1.288 1.174 1.126 0.946 0.882 +1 0.694 0.222 -1.514 0.771 -0.154 0.805 -0.337 0.935 0.000 1.305 0.524 -0.245 2.215 1.259 0.043 1.434 0.000 0.735 -0.033 -0.698 0.000 0.862 0.984 0.985 0.777 1.021 1.008 0.844 +1 0.392 2.157 -1.651 0.639 0.438 0.568 0.011 -1.332 2.173 0.924 0.867 0.160 0.000 1.248 0.298 1.271 1.274 1.293 0.871 -0.495 0.000 0.799 1.056 0.995 0.728 0.768 0.837 0.732 +0 2.519 -0.075 0.013 0.113 -0.626 2.054 1.511 1.508 0.000 0.960 -0.519 -0.686 2.215 0.596 1.439 0.432 2.548 0.693 0.218 -0.778 0.000 2.025 1.332 0.994 0.810 1.223 1.465 1.489 +0 0.989 -0.344 0.276 0.696 1.670 0.826 -2.074 -0.574 0.000 0.508 0.217 -0.368 2.215 0.810 -0.302 1.527 2.548 1.378 -0.359 0.802 0.000 1.349 0.938 1.093 0.652 0.702 0.647 0.622 +1 0.917 -0.215 0.091 0.536 0.541 0.955 -0.554 0.910 2.173 1.088 -0.155 1.590 1.107 1.552 -2.015 -0.527 0.000 1.027 1.195 -0.815 0.000 0.648 1.548 0.987 1.007 0.912 1.226 1.236 +1 0.709 0.204 0.206 0.982 1.589 1.418 0.142 -0.682 2.173 0.838 0.549 0.972 0.000 0.886 1.217 0.702 0.000 0.833 -0.757 1.378 1.551 0.981 0.870 1.096 1.126 1.273 0.965 0.834 +1 0.820 1.391 0.467 0.880 -1.580 2.628 2.237 -0.579 0.000 3.050 1.546 1.326 0.000 1.393 0.804 0.922 2.548 0.868 0.483 -0.348 1.551 1.041 1.227 1.133 0.776 0.776 1.336 1.126 +1 0.975 0.385 0.742 1.025 1.438 0.602 1.012 -0.824 2.173 0.413 0.299 -0.906 0.000 0.716 1.701 -0.263 0.000 0.655 1.932 1.408 0.000 0.852 0.592 0.990 0.647 0.513 0.711 0.694 +0 1.113 0.476 0.374 0.462 -0.116 1.361 1.590 0.563 0.000 0.984 1.043 -0.980 2.215 1.617 1.001 -1.661 2.548 1.197 0.014 -1.068 0.000 2.592 1.802 0.985 1.047 0.772 1.239 1.040 +1 1.436 -0.799 -0.729 0.619 -0.095 1.255 0.655 -0.688 0.000 1.760 -1.056 1.257 0.000 1.691 0.195 0.897 2.548 1.056 -0.334 -1.738 3.102 4.404 2.358 0.991 1.187 0.778 1.561 1.311 +0 1.126 1.239 -0.945 2.182 -1.471 1.819 0.275 0.252 1.087 1.497 1.521 1.626 0.000 1.109 -0.460 -0.198 0.000 0.928 -0.001 0.887 3.102 3.132 1.788 0.989 2.199 0.768 1.510 1.494 +1 0.597 -1.139 0.845 0.864 -0.867 1.552 0.544 0.719 1.087 1.664 -2.370 -0.557 0.000 1.811 -0.605 -1.699 2.548 0.837 0.348 -1.617 0.000 3.274 2.378 0.995 1.555 2.179 2.579 1.871 +0 0.602 1.322 0.398 0.505 1.426 0.354 1.744 1.191 0.000 0.647 -0.111 -0.249 2.215 0.371 1.241 -1.244 2.548 0.562 1.285 1.714 0.000 0.317 0.898 0.997 0.587 0.585 0.550 0.544 +1 0.845 0.653 -1.409 0.498 0.293 0.548 0.949 -0.228 0.000 1.120 -0.211 1.432 1.107 1.283 -0.044 0.005 0.000 1.150 1.070 -1.657 3.102 0.852 1.072 0.986 0.927 0.916 0.960 0.819 +0 1.273 -0.436 0.729 0.781 -0.075 0.876 1.009 -1.640 0.000 0.582 0.669 -0.311 2.215 1.126 1.430 -1.307 0.000 1.197 -1.355 0.344 1.551 1.004 0.783 0.992 0.780 1.159 1.084 0.956 +0 1.311 -1.875 0.238 0.473 -1.436 1.248 -1.164 0.708 1.087 1.093 0.493 -1.143 0.000 1.019 -0.499 -0.567 2.548 0.532 -1.380 1.368 0.000 0.819 0.782 1.089 0.968 1.358 0.890 0.865 +0 1.038 1.163 -1.259 0.345 0.395 0.401 -0.649 1.058 0.000 0.484 -0.698 -1.510 2.215 0.757 0.942 -0.035 0.000 0.977 0.774 0.673 1.551 0.767 0.892 0.992 0.744 0.809 0.606 0.672 +0 0.729 0.411 0.935 0.267 0.955 0.832 0.260 -0.252 2.173 0.989 0.802 -1.649 2.215 0.490 1.250 -1.364 0.000 0.674 1.059 0.356 0.000 0.634 0.772 0.992 0.980 1.326 0.906 0.770 +1 1.175 0.725 1.159 1.683 0.737 1.061 0.286 -0.972 1.087 0.577 -2.075 -1.139 0.000 0.603 -0.589 -1.068 0.000 1.510 -0.106 0.369 3.102 0.740 1.105 0.998 1.453 1.284 1.031 1.149 +1 0.284 -1.252 -1.640 0.604 -1.592 0.937 -0.452 -0.748 2.173 0.718 0.566 0.849 0.000 1.118 -0.026 1.265 0.000 1.149 0.537 0.026 3.102 0.660 0.775 0.994 0.932 0.949 0.889 0.781 +0 0.734 -1.009 -1.301 0.942 0.395 1.148 -0.681 -1.740 2.173 1.007 -1.120 -0.363 2.215 0.957 -0.751 0.637 0.000 0.448 -1.576 -0.071 0.000 0.591 1.042 1.151 0.778 1.543 0.892 0.760 +1 0.361 -0.003 1.168 1.350 -1.245 0.941 2.218 1.145 0.000 1.639 0.319 -1.209 2.215 0.665 0.600 -0.188 0.000 1.720 -1.481 0.614 0.000 0.924 1.211 0.988 1.375 1.438 1.111 1.110 +1 0.413 1.253 1.293 1.546 -1.119 1.008 -0.320 -0.074 1.087 1.049 -0.609 1.383 0.000 0.345 0.588 1.636 0.000 1.035 0.810 0.186 1.551 0.859 0.935 0.986 1.822 0.790 1.176 1.151 +0 1.018 0.183 -0.998 0.784 1.332 1.249 1.100 0.689 0.000 1.234 -1.629 -1.732 0.000 2.852 -0.151 -0.650 2.548 1.478 1.144 1.091 0.000 0.784 0.970 1.067 1.042 1.451 1.489 1.223 +0 1.709 0.490 -0.954 1.522 -1.049 3.217 0.960 -0.845 2.173 2.449 -0.078 1.016 0.000 1.798 1.779 1.466 0.000 1.647 -0.025 0.762 0.000 0.597 1.381 0.993 0.789 2.929 2.437 1.981 +1 0.289 1.879 1.184 0.791 -0.736 1.091 0.048 0.471 0.000 1.275 -0.105 -1.148 1.107 0.511 0.691 -0.162 0.000 0.398 -0.830 0.888 0.000 0.874 1.012 0.989 0.777 0.915 0.959 0.830 +1 1.161 0.041 1.101 1.481 -1.570 0.802 0.608 -0.833 2.173 0.745 -1.236 0.541 0.000 0.866 -0.528 -0.304 0.000 0.727 0.155 0.405 1.551 0.955 0.672 1.219 1.113 0.747 0.826 0.885 +0 1.326 1.110 -0.130 0.570 -1.387 0.687 2.670 -1.412 0.000 0.392 2.874 1.073 0.000 0.517 0.619 -1.592 2.548 0.612 1.445 0.359 0.000 0.876 0.922 1.090 0.755 0.439 0.941 0.865 +1 1.096 -1.541 0.984 1.706 0.726 1.683 -2.458 -1.181 0.000 1.308 -0.025 0.414 0.000 0.489 -1.034 -1.460 0.000 1.167 -0.117 -0.588 3.102 1.250 1.475 1.000 1.472 0.284 1.189 1.259 +1 1.191 -0.268 -1.276 2.063 -0.507 0.805 0.697 0.834 2.173 0.732 -0.337 1.161 1.107 0.568 0.681 -0.293 0.000 0.580 1.085 0.393 0.000 0.407 0.741 1.389 1.506 0.702 1.081 0.869 +0 0.759 -1.418 1.172 0.650 -0.214 0.647 -0.817 -1.471 0.000 0.405 0.303 0.819 0.000 0.649 -0.937 -0.021 2.548 1.058 -0.971 -0.933 3.102 0.792 0.692 0.987 0.643 0.465 0.470 0.493 +0 1.236 1.322 0.317 0.169 -0.612 0.633 0.634 -0.859 2.173 0.756 -0.008 1.497 2.215 0.352 2.066 0.476 0.000 0.517 1.124 1.722 0.000 0.480 0.731 0.996 0.816 0.929 0.747 0.640 +0 0.450 -2.087 1.150 1.544 -1.348 0.416 0.324 1.526 2.173 0.961 -1.152 0.051 2.215 0.326 -0.962 -0.975 0.000 1.190 -0.302 0.324 0.000 0.678 0.692 0.987 1.011 1.189 0.913 0.758 +0 2.366 -0.595 -1.355 0.526 -0.868 1.111 -0.462 0.165 2.173 0.840 -1.443 -1.740 0.000 1.345 0.239 0.702 2.548 0.678 -1.505 0.156 0.000 0.986 1.309 0.982 1.256 0.908 1.135 1.071 +1 0.359 -1.239 1.738 1.588 -0.422 1.116 -0.081 0.950 0.000 0.617 -0.084 0.318 0.000 0.610 0.431 1.271 0.000 1.700 0.277 -0.596 3.102 0.952 1.089 0.987 0.852 0.743 0.890 0.886 +1 1.344 -1.030 1.373 0.906 -1.352 0.776 -1.175 -0.471 1.087 0.866 -0.480 0.040 2.215 0.557 -2.021 -1.692 0.000 1.154 -0.582 1.000 0.000 0.940 0.988 0.988 1.030 0.685 0.855 0.780 +1 1.208 0.075 -0.296 0.776 -1.691 1.130 -0.875 0.296 2.173 0.530 0.630 -0.526 0.000 0.633 1.059 -1.442 0.000 1.224 -0.023 1.345 0.000 0.991 1.097 1.275 1.195 0.996 1.174 0.991 +0 1.140 0.471 1.050 0.777 0.305 0.398 1.622 0.588 0.000 0.973 0.858 -0.487 2.215 1.606 0.128 -1.619 0.000 1.290 1.291 1.525 0.000 0.819 0.951 0.984 0.996 0.582 0.747 0.767 +1 1.304 1.335 0.020 0.452 -1.309 0.983 -0.200 1.480 2.173 0.808 0.800 -0.493 0.000 0.730 1.265 1.419 0.000 0.979 0.145 0.615 0.000 0.863 0.945 0.990 0.544 0.696 0.857 0.741 +0 1.495 0.446 -1.127 0.129 -0.859 1.627 0.837 0.914 2.173 2.293 -0.455 -0.872 2.215 1.420 1.459 1.302 0.000 1.444 -0.259 -0.286 0.000 0.925 1.302 0.991 1.532 3.474 1.640 1.306 +0 0.404 -1.346 -0.448 1.259 1.599 0.535 -0.632 0.901 2.173 0.533 0.054 -0.814 0.000 1.517 -0.503 -0.122 0.000 1.518 0.685 -1.693 3.102 0.911 0.970 0.987 0.990 1.023 0.878 0.824 +1 0.782 0.424 1.487 0.621 -1.014 1.219 0.611 0.322 2.173 0.764 -1.163 -1.349 0.000 0.555 0.261 -0.943 0.000 0.543 -0.590 1.115 3.102 0.886 0.638 0.982 1.166 0.833 0.966 0.830 +1 0.680 -0.393 0.703 1.655 1.011 1.635 0.717 -0.549 0.000 1.320 0.530 1.333 1.107 0.939 -1.891 -1.114 0.000 0.610 -1.099 1.256 0.000 0.776 0.972 0.982 0.694 0.370 1.056 1.036 +0 0.556 -0.320 0.031 1.343 -0.890 0.766 1.774 1.062 0.000 0.533 1.496 0.289 0.000 0.747 -0.732 1.595 1.274 0.846 1.209 -0.690 3.102 0.889 0.818 0.987 0.728 0.996 0.954 1.171 +0 1.772 0.215 -1.304 0.590 -0.558 1.651 0.335 0.409 0.000 2.030 -0.513 0.383 2.215 2.939 -0.116 -0.854 2.548 4.223 -0.638 1.673 0.000 4.347 2.862 0.989 0.734 2.389 2.189 1.733 +0 1.744 -0.554 -0.571 1.660 -1.072 0.830 -0.535 0.923 0.000 0.662 0.280 0.460 1.107 0.519 -0.810 0.569 2.548 1.089 -0.586 1.722 0.000 0.963 0.862 1.026 0.845 0.393 0.786 0.840 +1 0.711 -1.553 -0.870 0.532 -0.491 0.953 0.089 -0.191 2.173 0.595 -1.818 1.694 0.000 0.722 -0.364 1.029 2.548 1.682 -0.524 1.489 0.000 0.895 0.696 0.976 0.844 0.956 0.977 0.851 +1 1.110 -1.111 1.663 0.573 -0.899 1.023 -1.027 -1.239 2.173 0.760 -2.363 0.948 0.000 1.549 -0.053 0.626 0.000 1.209 -0.479 -0.375 3.102 0.721 0.968 0.986 0.713 0.871 0.896 0.774 +1 1.752 -0.150 0.386 0.593 1.175 1.517 0.492 -1.101 2.173 0.868 0.568 0.421 0.000 0.730 0.403 1.345 2.548 0.472 -1.094 0.523 0.000 0.919 0.736 0.987 1.619 1.057 1.074 0.946 +0 0.473 0.506 -1.549 0.935 -0.117 1.359 0.422 -0.596 2.173 1.270 0.044 1.400 0.000 1.263 0.745 1.371 1.274 0.783 0.443 -0.006 0.000 0.675 1.042 0.988 0.885 1.631 0.906 0.803 +1 0.917 -1.681 0.145 0.334 -1.622 0.742 -1.436 -1.634 0.000 1.001 -1.782 1.281 0.000 0.706 0.402 -0.983 2.548 1.897 -1.075 -0.240 3.102 0.967 1.315 0.985 0.847 1.025 1.062 0.878 +1 0.971 0.163 0.778 1.031 0.206 1.092 0.199 -1.013 2.173 1.799 1.554 1.452 0.000 0.894 0.365 -0.064 1.274 2.427 1.105 -0.756 0.000 0.808 1.267 0.984 1.269 0.938 1.244 1.089 +1 1.590 0.352 -1.421 2.307 1.672 3.202 -1.932 0.194 0.000 0.810 -0.943 -0.542 1.107 0.913 1.072 -1.353 2.548 0.789 0.289 1.626 0.000 0.248 0.750 0.983 0.779 1.330 1.004 0.744 +1 0.568 1.035 0.160 0.449 0.972 1.114 0.668 -1.360 2.173 0.750 -0.121 -1.572 0.000 1.433 1.031 0.459 0.000 1.392 0.290 0.100 0.000 0.799 0.852 0.976 1.050 0.595 0.929 0.815 +1 1.557 -0.936 0.623 1.151 0.265 0.989 -0.450 -0.873 2.173 0.392 1.086 1.712 0.000 0.669 -1.024 -1.689 1.274 0.666 -0.842 1.052 0.000 0.929 1.061 0.993 0.850 0.763 0.989 0.936 +0 1.505 -0.075 0.554 0.589 -0.844 0.882 -0.618 -1.269 0.000 1.403 -0.222 -0.119 0.000 0.942 0.509 1.691 2.548 0.845 -0.539 -0.482 0.000 0.860 0.951 1.241 0.887 0.454 0.693 0.733 +1 0.449 1.020 1.663 1.393 1.238 1.210 -0.425 -0.367 1.087 0.845 -0.547 -1.164 2.215 0.643 -0.714 0.661 0.000 0.439 -2.061 1.302 0.000 0.642 1.108 0.987 0.869 0.984 0.973 0.869 +1 1.106 0.979 0.037 0.767 -0.411 0.363 2.416 0.862 0.000 1.432 -0.508 -1.699 1.107 0.639 1.755 -0.415 0.000 0.762 -0.891 0.671 1.551 0.822 1.453 0.990 1.364 0.838 1.401 1.181 +1 1.638 0.507 0.156 0.644 -0.676 0.941 -0.565 -1.536 1.087 0.415 0.887 0.797 2.215 0.365 0.765 1.182 0.000 0.738 0.602 -0.108 0.000 0.918 0.935 0.988 0.641 1.088 0.910 0.780 +0 0.579 -0.464 0.589 0.457 -0.355 0.781 0.703 0.868 2.173 0.591 1.108 -0.789 0.000 0.910 -0.345 -0.719 0.000 0.968 0.211 1.576 0.000 0.917 0.807 0.983 0.628 0.752 0.578 0.568 +0 0.803 0.002 -0.447 1.649 0.249 0.846 0.976 -1.695 0.000 0.462 1.131 -0.586 0.000 0.854 -0.287 1.186 2.548 0.434 0.458 1.525 3.102 1.123 1.042 0.990 0.637 0.250 0.590 0.738 +0 1.647 1.027 -0.920 0.341 -1.263 1.123 -0.971 0.379 2.173 0.364 -0.521 0.178 2.215 1.387 0.555 -1.598 0.000 0.730 -0.737 -1.588 0.000 0.909 0.833 0.978 1.758 0.271 1.099 1.018 +1 0.765 0.253 -0.110 0.346 -1.634 2.050 0.329 1.508 0.000 1.016 -1.591 -0.400 0.000 1.612 0.129 -0.611 2.548 3.584 -1.926 0.040 0.000 0.903 1.133 0.989 0.770 0.805 0.956 0.984 +1 1.332 0.749 -0.878 1.932 -0.338 0.615 1.444 1.292 0.000 0.715 0.596 1.086 1.107 0.470 1.427 -0.286 0.000 1.154 1.005 0.689 3.102 0.946 0.628 1.041 0.972 0.378 0.830 0.778 +0 0.997 2.070 1.388 0.426 -0.498 0.658 0.033 -0.875 0.000 1.414 1.111 0.772 2.215 0.991 -0.959 -0.068 2.548 0.695 -1.091 -1.192 0.000 0.802 0.828 0.984 0.870 1.888 1.253 1.212 +1 0.746 -1.397 -0.180 0.698 1.158 0.492 0.183 -1.609 2.173 0.636 -0.387 0.555 2.215 0.385 -0.941 1.735 0.000 0.778 2.377 1.592 0.000 0.843 0.734 0.988 0.843 0.802 0.634 0.618 +0 0.471 -0.644 0.501 0.810 -0.872 0.965 0.908 0.704 2.173 0.483 -0.035 -1.179 0.000 1.442 0.988 -1.549 0.000 1.679 0.462 -0.273 0.000 0.924 0.951 0.986 1.683 0.516 1.142 1.014 +0 1.629 0.723 -0.881 0.637 -0.912 1.604 -0.492 0.651 0.000 1.530 0.931 -0.266 2.215 2.833 -0.321 1.578 0.000 1.378 -0.324 -0.833 3.102 2.855 1.969 0.990 0.893 1.147 1.791 1.668 +0 0.979 -1.288 0.852 0.351 0.048 1.152 -0.334 1.566 1.087 0.831 -1.359 -1.050 0.000 0.753 0.301 -0.127 2.548 0.971 1.001 0.454 0.000 0.588 0.559 0.978 0.954 1.225 0.998 0.946 +1 1.017 0.103 1.154 0.107 0.242 0.684 -0.558 0.420 0.000 1.387 0.289 -1.200 2.215 0.432 -2.718 0.149 0.000 1.070 -0.963 -0.039 0.000 0.795 0.677 0.982 1.034 0.890 1.075 0.924 +0 3.398 -0.119 -0.253 3.695 -0.278 3.287 0.509 -1.710 2.173 0.771 0.009 1.272 0.000 1.322 -0.582 -0.063 2.548 1.015 1.020 0.627 0.000 0.976 1.168 0.939 4.146 3.033 2.678 2.062 +0 1.522 0.834 -1.504 0.274 0.381 0.556 0.029 -1.065 0.000 0.841 0.843 -0.335 2.215 1.753 0.351 0.521 1.274 0.592 0.367 1.097 0.000 0.834 1.000 0.990 0.823 0.953 0.826 0.744 +0 1.129 -0.231 -0.809 0.209 1.050 1.385 0.473 -1.171 1.087 2.062 -0.908 0.756 2.215 0.817 -1.883 0.521 0.000 0.596 -0.777 -0.935 0.000 0.875 1.028 0.983 1.049 3.107 1.636 1.264 +1 1.012 -0.457 0.739 0.919 -0.361 0.619 -0.098 -0.584 1.087 0.838 0.381 -1.402 2.215 1.246 -0.656 1.557 0.000 1.255 -1.218 0.478 0.000 1.254 1.215 1.118 0.972 0.759 0.924 0.833 +1 0.983 0.664 0.766 1.162 1.190 0.874 0.747 -1.048 2.173 0.763 0.446 -0.147 2.215 0.830 1.261 -1.443 0.000 1.281 -0.671 0.082 0.000 1.878 1.191 0.996 1.201 0.891 0.926 0.983 +1 1.660 -0.640 -1.128 0.695 0.479 0.549 -1.133 -0.401 2.173 1.081 1.198 0.266 0.000 1.485 -0.380 1.099 0.000 0.660 -2.237 1.422 0.000 0.793 0.987 1.477 0.816 0.550 0.696 0.701 +1 0.493 -0.006 -1.448 1.581 0.234 0.563 1.084 1.118 0.000 0.921 0.462 -0.715 2.215 1.380 -0.010 -1.194 2.548 1.345 0.256 0.587 0.000 0.815 1.094 1.221 0.985 0.581 0.857 0.814 +1 0.656 -0.550 -1.136 0.410 0.751 1.814 -2.044 1.624 0.000 1.062 -0.642 0.696 2.215 1.593 0.826 -0.134 0.000 1.467 0.281 -0.861 0.000 0.705 0.923 0.984 0.805 1.022 0.858 0.835 +1 0.685 -0.058 0.780 1.137 -0.133 0.939 0.587 1.658 0.000 0.997 0.780 0.050 0.000 0.955 0.557 -1.266 2.548 0.372 0.787 1.231 3.102 0.916 0.633 0.985 0.540 0.362 0.534 0.595 +1 0.346 1.534 -1.647 0.851 -1.109 1.364 -0.246 0.346 0.000 0.349 0.260 -1.317 2.215 0.532 -0.227 1.083 0.000 1.098 -1.353 -1.482 3.102 0.944 0.913 0.992 0.887 0.616 0.844 0.812 +1 1.036 1.643 -1.299 0.907 1.242 1.101 0.492 -0.410 1.087 1.996 -2.365 1.484 0.000 1.984 1.264 0.286 2.548 1.184 1.474 -0.664 0.000 0.713 0.850 1.010 1.328 1.373 1.102 0.925 +0 0.841 -1.386 0.869 0.799 0.375 1.197 -0.728 -1.722 2.173 1.046 -1.021 -0.171 2.215 0.796 -1.115 -1.040 0.000 0.750 -2.071 0.869 0.000 1.020 0.940 0.976 1.451 1.644 1.162 0.958 +0 0.719 0.399 0.387 1.283 -0.309 0.973 0.275 1.341 0.000 1.040 1.144 -0.653 0.000 0.999 -1.567 0.800 0.000 1.574 0.882 -1.426 1.551 2.243 1.288 0.991 0.914 0.842 1.127 1.148 +1 1.137 -1.144 -1.640 0.909 -0.835 0.924 -0.785 0.159 2.173 0.532 -1.322 -1.184 0.000 0.427 -0.360 1.243 0.000 0.978 0.408 0.651 3.102 0.838 1.009 0.983 1.075 0.814 0.927 0.791 +0 0.898 -0.293 -0.494 2.126 -1.239 1.519 -0.659 -1.528 0.000 1.561 -0.245 0.707 2.215 2.845 -1.564 0.010 0.000 0.675 -0.414 1.560 1.551 4.212 2.253 1.190 1.518 0.654 1.684 1.497 +1 2.111 -0.336 -1.378 0.443 0.018 1.185 -1.030 -0.932 0.000 1.326 -0.787 0.721 0.000 1.083 -0.896 0.391 2.548 1.033 -0.251 1.206 1.551 0.689 0.597 1.275 1.070 0.610 0.748 0.762 +0 1.455 0.570 0.623 1.174 1.459 0.750 -0.137 -1.127 0.000 1.151 1.288 -1.022 2.215 1.356 -0.461 0.210 2.548 0.423 0.746 0.072 0.000 0.890 0.967 1.239 1.097 1.833 1.158 0.993 +1 0.477 -1.491 -0.775 0.511 -0.515 1.223 -0.347 -1.353 2.173 0.908 -0.784 0.476 2.215 0.389 -2.144 0.479 0.000 0.700 0.001 0.991 0.000 0.885 1.193 0.974 0.810 1.587 0.912 0.799 +0 0.596 -2.202 0.706 1.468 0.635 0.816 -0.868 -1.131 2.173 0.602 -1.357 -1.717 0.000 0.773 -1.700 -0.755 0.000 0.726 0.163 0.291 3.102 0.836 0.906 0.995 1.202 0.905 0.853 0.787 +1 1.320 0.495 -1.630 0.256 -1.696 0.718 -1.938 -0.631 0.000 0.588 0.308 1.388 0.000 1.066 -0.453 -0.353 0.000 2.300 -0.249 0.498 1.551 1.311 0.696 0.984 1.056 0.702 0.850 0.943 +1 0.275 -1.842 -0.284 0.575 0.946 1.446 -0.038 -0.987 2.173 1.322 -0.139 0.715 0.000 1.099 -0.508 -0.369 1.274 1.234 0.427 0.992 0.000 0.690 1.087 0.985 1.061 0.927 1.157 0.934 +1 0.298 0.755 1.272 0.564 0.646 0.814 -0.310 0.199 0.000 1.484 -1.117 -1.114 0.000 0.904 -0.360 -1.353 1.274 0.921 -1.146 1.222 3.102 1.170 0.898 0.999 0.722 0.622 0.741 0.653 +1 0.605 0.477 -1.504 0.683 1.189 0.939 -0.829 -0.731 2.173 0.574 -1.443 -1.519 0.000 0.798 -1.491 1.026 0.000 2.162 -0.248 -0.076 3.102 0.989 1.106 0.987 1.650 0.934 1.242 1.184 +0 0.431 -1.204 1.617 1.120 1.627 0.845 -1.832 0.237 0.000 0.914 -1.215 -0.152 0.000 1.523 -2.588 -1.567 0.000 1.049 -0.254 -0.472 3.102 0.856 0.912 0.979 0.495 0.447 0.629 0.878 +1 1.804 1.536 -0.598 0.427 0.265 1.160 0.782 1.061 2.173 0.621 1.538 1.524 0.000 0.577 2.062 0.119 0.000 0.840 0.130 -0.866 0.000 0.932 1.042 0.981 0.575 0.734 0.864 0.773 +1 0.787 -1.593 0.591 0.926 -0.781 0.706 -0.597 1.714 2.173 0.486 -1.140 -0.609 0.000 0.543 -2.282 0.372 0.000 0.651 0.877 0.908 3.102 0.830 1.043 1.117 1.120 0.817 0.877 0.819 +1 0.832 0.801 -1.079 0.788 -0.169 1.112 -0.437 0.655 2.173 1.165 0.176 -0.912 0.000 1.521 -2.001 1.487 0.000 1.319 0.391 -0.257 0.000 0.930 0.927 0.982 1.520 0.961 1.066 0.988 +1 0.988 0.650 1.654 1.047 1.306 0.940 1.356 -1.574 0.000 1.347 1.423 -0.353 0.000 0.825 1.085 0.772 0.000 1.538 0.445 -0.156 3.102 1.362 1.074 0.984 1.052 0.273 0.814 0.785 +1 0.481 0.002 -0.523 0.985 1.712 0.510 0.873 -0.590 0.000 1.011 -0.675 0.887 2.215 0.762 0.605 1.306 0.000 0.835 1.202 0.298 0.000 0.778 1.042 0.982 0.941 1.432 0.863 0.813 +1 0.852 0.609 1.601 2.043 -1.186 0.863 -0.247 0.730 2.173 0.401 1.394 0.675 0.000 0.532 -0.477 -0.506 2.548 0.792 -0.234 0.008 0.000 0.823 0.796 1.079 0.813 0.766 0.942 0.816 +0 1.614 -0.840 1.436 1.172 0.253 1.512 -0.191 1.730 0.000 1.681 -0.324 0.011 2.215 1.078 -1.057 -0.520 2.548 1.039 -0.047 -0.394 0.000 1.806 1.489 1.667 1.349 0.897 1.249 1.166 +0 0.893 0.463 -0.874 0.901 0.931 0.769 0.862 -0.525 0.000 0.749 -0.899 1.066 2.215 0.519 1.120 -1.688 0.000 0.480 1.006 1.089 0.000 0.935 0.665 1.241 0.961 0.604 0.778 0.720 +1 0.638 -1.060 -1.400 1.158 1.291 1.077 -0.964 -0.273 2.173 0.588 -1.725 -1.459 0.000 0.894 -0.491 0.689 0.000 1.157 -0.323 1.662 0.000 0.879 1.020 0.983 1.247 0.830 0.983 0.890 +0 0.451 2.071 1.727 0.804 -0.440 0.842 1.198 1.204 2.173 0.379 -0.216 -0.619 1.107 0.937 0.496 -0.334 0.000 0.607 -0.227 -1.330 0.000 0.739 1.066 0.993 0.661 1.052 0.709 0.661 +1 0.804 -1.456 -1.009 1.128 0.360 0.885 0.438 -0.260 1.087 1.255 0.373 1.570 0.000 0.368 -0.735 -0.515 0.000 0.515 1.184 1.535 0.000 0.800 0.771 1.245 0.751 0.787 0.937 0.810 +0 0.536 -0.998 1.227 0.992 -1.220 0.729 -1.640 -1.672 0.000 1.518 -0.161 0.118 2.215 1.067 -1.698 -1.057 0.000 1.027 -0.261 1.626 3.102 0.852 0.880 0.994 1.074 1.105 1.180 0.985 +1 1.024 -0.655 -1.617 0.612 -0.070 0.905 -0.158 -0.583 2.173 1.069 -0.957 1.204 0.000 0.793 -0.542 0.557 2.548 0.395 1.142 -0.629 0.000 1.504 0.944 1.080 0.843 0.933 0.876 0.764 +0 0.447 1.887 1.520 0.064 0.653 0.657 -0.650 -0.567 0.000 1.267 -0.628 -0.047 0.000 2.474 0.115 1.581 2.548 0.393 0.594 -0.823 3.102 0.875 0.678 0.978 0.846 0.662 1.067 0.931 +1 1.062 -0.732 1.541 0.972 0.834 0.576 -0.622 0.768 0.000 1.548 0.570 -0.587 1.107 0.443 -0.406 -0.634 0.000 0.607 -0.753 -0.974 0.000 0.912 0.678 0.995 1.739 0.869 1.119 0.927 +0 2.048 -0.606 -1.430 3.840 -1.519 2.456 0.716 -0.074 2.173 2.147 -1.408 0.680 0.000 0.838 2.286 -1.080 0.000 1.132 -0.968 1.674 3.102 0.884 1.692 0.986 0.590 2.609 2.618 2.402 +0 0.877 -0.551 -1.313 0.592 0.528 1.000 -0.935 0.655 2.173 0.518 0.133 1.463 0.000 0.676 0.938 -0.823 2.548 1.164 -0.519 -1.050 0.000 0.873 1.126 0.995 0.767 1.547 0.903 0.759 +1 1.307 0.191 -0.930 0.093 -0.685 0.575 1.046 0.353 0.000 0.552 -1.204 -0.910 1.107 1.085 -1.240 0.407 0.000 1.040 0.637 1.372 0.000 0.953 0.692 0.978 0.620 0.540 0.773 0.738 +1 0.401 1.158 -1.619 1.021 0.640 0.648 0.438 1.196 0.000 1.400 0.652 -1.723 0.000 1.800 -0.086 -0.471 1.274 1.079 1.222 -0.428 3.102 1.015 1.166 0.985 0.953 0.920 1.084 0.909 +0 0.318 2.288 -1.204 0.506 0.671 0.628 0.411 -0.889 0.000 0.928 -0.285 0.690 2.215 0.749 -0.623 -1.519 2.548 0.596 0.342 0.061 0.000 0.706 0.951 0.982 0.817 0.827 0.711 0.672 +1 0.824 2.006 -1.011 2.079 -0.861 0.961 0.697 0.903 2.173 0.791 1.422 0.080 0.000 0.744 1.179 1.119 0.000 0.583 2.085 0.496 0.000 0.952 0.885 0.986 0.604 0.909 0.977 0.877 +0 0.832 -1.220 -0.674 0.304 1.055 0.686 0.345 0.267 2.173 0.880 -0.321 1.389 2.215 0.835 0.067 -1.229 0.000 0.506 -1.727 -1.510 0.000 0.943 1.133 0.981 0.765 1.046 0.805 0.712 +1 0.583 1.537 0.599 1.405 0.275 1.120 0.671 -1.695 2.173 1.117 -0.081 -1.142 2.215 0.930 0.415 0.637 0.000 0.865 0.314 -0.504 0.000 0.848 0.966 0.997 1.906 1.016 1.732 1.373 +0 0.890 0.643 -1.607 0.617 -0.406 0.710 0.123 -0.489 1.087 0.566 -1.063 -0.684 0.000 2.073 -0.458 1.073 2.548 0.521 -0.990 1.147 0.000 0.706 0.936 0.987 0.787 1.567 1.023 0.898 +1 0.389 1.614 1.232 1.334 -0.058 0.688 1.454 -0.734 1.087 0.471 0.264 -1.439 2.215 0.478 2.102 -1.297 0.000 0.701 -0.987 -0.108 0.000 1.864 1.101 0.991 0.822 0.727 0.842 0.947 +0 0.955 -0.204 0.316 1.851 0.537 0.900 -0.066 -1.363 1.087 0.954 0.645 -1.216 0.000 0.924 -0.366 1.433 2.548 1.608 0.113 -0.542 0.000 0.874 0.916 0.988 0.953 0.688 0.994 0.857 +0 0.410 -1.222 -0.060 1.116 0.003 0.628 1.872 0.972 0.000 0.399 2.762 -0.983 0.000 0.525 1.020 -1.120 1.274 0.807 2.433 1.518 0.000 0.778 1.147 1.001 0.734 0.428 0.730 0.932 +0 1.193 0.269 1.529 0.484 1.628 0.751 -0.975 -0.886 0.000 0.661 -0.244 0.273 2.215 0.888 -1.101 0.570 2.548 0.509 -2.078 -0.419 0.000 0.873 0.979 0.992 1.228 0.459 0.886 1.042 +1 1.433 0.048 -0.693 0.881 0.125 1.097 1.032 1.307 2.173 0.687 0.855 -0.201 0.000 0.518 -0.623 1.468 2.548 0.517 1.270 -1.315 0.000 0.703 1.035 1.047 0.772 0.926 0.975 0.832 +1 0.661 0.055 -0.781 1.280 -0.355 1.582 1.092 -0.933 2.173 2.791 2.338 1.036 0.000 0.924 -0.541 0.981 2.548 1.018 0.858 -0.435 0.000 0.681 0.864 0.980 0.886 2.070 1.406 1.082 +0 1.235 -0.272 -0.718 0.739 1.204 1.551 -0.563 0.765 0.000 1.371 -1.067 -0.981 2.215 0.613 -1.731 1.341 0.000 1.023 -0.538 -1.353 3.102 1.527 1.225 1.306 0.988 0.427 1.130 0.997 +1 0.686 -0.001 -0.856 0.462 0.139 0.644 -0.960 0.145 1.087 1.110 -0.650 -1.676 0.000 1.190 0.501 -1.472 2.548 0.472 -1.500 -0.729 0.000 0.907 0.972 0.985 1.033 1.415 0.900 0.888 +1 1.590 1.571 1.733 0.358 -0.930 1.873 -0.241 0.479 0.000 1.309 1.156 -0.760 1.107 0.842 2.248 -1.469 0.000 0.581 -0.238 -1.293 1.551 4.655 2.502 0.987 0.995 0.732 1.753 1.580 +0 1.002 -0.940 -1.429 1.313 1.320 0.526 -1.253 -0.523 0.000 0.701 -0.500 0.199 0.000 0.599 -1.186 1.282 2.548 0.622 0.596 -0.493 3.102 0.925 0.803 0.988 0.853 0.724 0.606 0.688 +1 2.266 0.833 1.241 0.858 1.728 0.520 0.692 1.689 2.173 1.323 0.272 0.130 0.000 1.607 0.423 -0.800 2.548 1.450 -2.137 -0.125 0.000 3.552 2.591 0.984 1.265 0.900 1.786 1.732 +1 0.996 -0.118 -1.449 1.212 1.240 0.667 0.565 -0.007 0.000 1.171 0.338 1.576 1.107 1.012 -0.918 -0.880 0.000 1.731 -0.431 -0.171 0.000 0.943 1.092 1.002 0.622 0.809 0.968 0.861 +0 2.045 0.116 -1.137 0.681 0.203 1.414 0.645 -1.562 2.173 2.243 -0.440 0.339 0.000 0.928 -0.054 0.135 0.000 0.630 -0.059 1.567 3.102 0.596 0.848 1.528 1.218 0.485 1.359 1.202 +1 1.890 -0.302 -0.156 0.895 -0.695 2.438 -1.429 1.669 0.000 1.210 -0.951 0.460 0.000 1.885 0.220 -0.500 2.548 0.660 -0.432 0.866 1.551 1.089 0.965 0.984 0.621 0.870 1.541 1.467 +0 2.911 0.747 -0.821 0.334 0.703 1.003 -0.238 1.580 2.173 0.510 -1.136 0.853 0.000 0.632 0.788 0.555 2.548 0.626 0.382 0.140 0.000 0.800 0.923 1.339 0.923 0.980 1.037 0.940 +1 0.631 0.058 1.386 1.100 0.201 1.302 -0.053 -1.075 1.087 0.355 1.358 0.797 0.000 0.414 -1.318 0.515 0.000 0.772 -0.486 -0.186 3.102 1.158 0.765 1.011 1.143 0.814 0.821 0.756 +1 0.283 -0.180 0.734 1.511 1.625 1.185 1.686 -0.022 0.000 0.954 0.215 1.390 1.107 0.891 2.159 -1.568 0.000 0.977 0.404 -0.050 3.102 0.760 0.890 0.982 0.683 0.847 0.856 0.759 +1 1.394 -1.457 -0.054 0.751 -0.305 1.419 0.278 1.667 0.000 0.693 -1.318 -0.473 0.000 0.943 -0.356 0.253 0.000 0.649 0.229 -1.350 3.102 0.979 0.816 0.993 0.712 0.296 0.589 0.571 +1 0.387 1.246 0.986 1.072 -0.762 1.154 -1.089 1.723 2.173 0.821 0.336 -1.074 2.215 1.110 2.676 0.279 0.000 0.963 -1.019 0.746 0.000 1.047 0.855 0.989 2.568 1.402 1.653 1.331 +1 0.475 -2.102 -1.668 1.675 0.796 1.313 -0.593 -0.919 1.087 0.654 -1.245 -0.316 0.000 0.695 -0.331 0.697 0.000 0.716 -1.043 1.347 1.551 0.954 1.131 0.988 0.559 0.975 1.081 0.927 +0 0.363 1.314 -0.960 0.716 0.587 0.746 -0.276 -1.424 2.173 0.507 -0.964 -1.518 2.215 1.283 -0.498 0.207 0.000 0.570 -2.084 0.972 0.000 0.827 0.853 0.986 0.821 0.338 0.770 0.734 +0 0.477 0.809 -0.827 1.018 1.108 1.103 1.368 0.583 2.173 1.419 1.420 -1.127 0.000 1.057 1.768 -0.656 0.000 0.956 1.208 1.568 3.102 0.898 0.828 0.986 0.945 0.843 1.003 0.913 +0 0.417 -0.707 0.795 1.265 -0.864 0.581 0.325 0.403 2.173 1.372 0.410 -0.541 2.215 1.430 -1.118 1.257 0.000 0.508 1.207 -1.044 0.000 0.825 1.034 1.004 0.944 0.989 0.996 1.090 +0 0.405 -2.066 -0.178 0.885 1.195 0.738 0.608 -1.006 0.000 0.516 -0.228 0.494 0.000 1.112 0.086 1.228 2.548 1.213 0.386 0.378 3.102 0.568 0.533 0.981 0.875 0.637 0.690 0.557 +1 1.189 -0.838 -0.889 1.581 -1.023 1.048 -0.695 0.954 2.173 0.497 -0.343 1.501 0.000 1.260 0.502 0.296 1.274 0.514 -1.281 0.799 0.000 0.568 0.869 0.974 1.480 1.248 1.181 0.930 +1 0.850 1.294 -0.536 0.899 -1.062 1.464 -1.338 0.841 0.000 0.687 -0.422 -0.700 0.000 0.655 -2.677 0.951 0.000 1.439 0.292 -1.329 0.000 0.868 0.833 0.990 1.537 0.777 1.336 1.130 +0 0.849 -0.880 0.092 0.247 -0.102 0.604 -1.365 -0.641 1.087 1.814 -0.707 1.064 2.215 1.308 -0.313 -1.451 0.000 0.556 1.484 -0.558 0.000 1.377 1.382 0.993 1.087 1.622 1.254 1.040 +1 0.619 0.743 0.405 0.681 -0.542 0.815 0.044 1.137 1.087 0.934 -0.883 -0.655 2.215 0.486 -1.332 1.131 0.000 1.143 0.058 -1.706 0.000 0.829 0.913 0.988 0.869 1.432 0.827 0.738 +0 1.048 0.274 0.417 0.837 0.535 0.776 0.467 -1.132 0.000 0.405 -1.236 1.485 2.215 0.338 0.849 0.017 0.000 0.602 -0.767 -1.588 3.102 0.820 0.931 0.979 0.694 0.180 0.585 0.694 +1 2.206 -0.128 1.003 1.607 1.178 1.455 1.667 -1.077 0.000 1.485 -0.243 0.309 0.000 1.816 -1.818 -0.709 0.000 0.821 0.391 -0.542 3.102 3.195 1.926 0.994 0.978 0.432 1.216 1.391 +0 0.667 -1.030 0.426 1.345 -0.453 0.403 -1.029 -0.241 0.000 1.098 -0.577 -1.565 0.000 0.623 -0.763 1.478 1.274 1.010 -0.092 1.160 3.102 1.249 0.912 0.989 0.902 0.283 0.652 0.724 +0 0.802 0.918 1.739 0.459 0.572 0.326 -0.203 -0.012 2.173 0.317 -0.706 -1.374 0.000 0.694 -0.791 0.556 0.000 0.637 0.590 -1.257 3.102 0.825 0.626 0.989 0.842 0.491 0.573 0.607 +0 1.743 0.750 -1.401 0.212 0.763 1.472 -1.209 1.250 0.000 1.875 0.432 -0.122 1.107 1.145 0.278 -0.538 0.000 0.492 -0.588 1.528 3.102 0.641 0.751 0.988 0.674 1.012 0.906 0.730 +0 0.357 1.196 -0.728 1.289 1.367 1.045 -0.264 1.302 2.173 1.384 -0.219 -0.473 0.000 1.156 -0.235 -0.073 0.000 0.955 -0.560 -1.268 3.102 0.685 0.794 0.988 1.586 0.805 1.181 1.280 +1 0.989 -0.715 -1.586 1.540 1.571 1.080 -0.647 -0.398 2.173 0.718 2.572 0.688 0.000 0.823 -0.864 -0.613 2.548 0.833 -0.606 0.611 0.000 0.899 1.871 0.993 1.444 0.286 1.665 1.877 +0 1.168 -0.724 -0.487 0.648 -0.254 0.491 -1.196 1.083 2.173 0.870 0.752 1.486 1.107 0.644 0.060 0.520 0.000 0.562 0.426 -1.731 0.000 1.013 0.859 0.991 0.885 1.167 1.102 1.057 +0 1.621 0.739 -0.830 0.520 1.277 0.677 -0.785 0.929 0.000 0.425 0.624 -1.299 2.215 0.369 0.005 0.273 0.000 0.927 -1.044 0.118 3.102 0.622 0.793 1.204 1.100 0.824 0.736 0.756 +1 0.607 0.563 -1.442 0.838 -0.469 1.257 0.583 0.631 0.000 0.757 2.106 -0.909 0.000 2.248 -0.277 1.554 0.000 2.328 0.203 -0.976 3.102 1.197 0.723 0.995 0.833 0.803 0.867 0.733 +1 1.016 0.561 1.313 0.516 -1.149 0.536 -0.610 -1.517 0.000 0.529 -0.496 1.222 1.107 1.529 0.297 -0.034 0.000 0.680 1.145 -0.472 0.000 0.753 0.953 0.981 0.695 0.480 0.644 0.651 +1 0.451 -1.803 -0.552 1.332 -1.160 1.196 -0.541 0.787 0.000 0.989 -0.134 -1.679 2.215 0.728 -0.873 -0.891 2.548 0.776 -1.863 -0.113 0.000 1.041 0.767 0.988 0.807 0.700 0.753 0.693 +1 0.356 -0.619 -1.621 1.085 0.098 1.120 1.011 1.672 0.000 1.139 0.278 -0.520 2.215 0.503 0.196 0.622 2.548 0.695 0.547 1.021 0.000 0.787 0.725 0.985 1.150 0.689 0.834 1.079 +0 0.566 -1.818 0.342 0.944 -1.202 0.614 -0.915 0.612 2.173 0.699 -1.486 1.494 0.000 0.609 -0.323 -1.296 0.000 1.115 -0.745 -0.377 3.102 0.856 0.871 0.996 0.671 0.682 0.644 0.642 +1 2.422 1.580 0.178 0.782 -0.303 0.471 1.530 1.672 0.000 0.909 1.350 -1.297 1.107 1.255 2.262 -1.403 0.000 1.019 1.332 1.144 0.000 0.839 0.884 0.976 1.181 0.632 0.776 0.859 +1 1.485 0.808 1.386 1.839 0.816 1.137 -0.339 -1.309 0.000 1.154 -0.711 -0.122 2.215 1.263 0.144 -0.429 2.548 0.455 0.664 -1.483 0.000 0.682 0.933 1.123 1.707 0.686 1.239 1.170 +0 2.024 0.862 0.082 0.600 -0.013 2.114 -0.935 1.521 0.000 1.189 -0.352 -0.533 0.000 1.043 -0.307 1.081 2.548 1.259 -1.423 -0.499 0.000 0.902 0.950 0.994 0.792 1.015 0.907 0.922 +0 1.680 -1.235 0.440 0.278 -1.025 0.782 2.034 1.254 0.000 1.182 0.046 -1.022 2.215 0.693 -0.749 -0.806 0.000 0.507 -1.843 1.314 0.000 0.789 0.950 0.990 0.742 0.472 0.812 0.720 +0 1.560 -0.136 -1.101 1.725 -1.354 2.184 1.417 0.287 0.000 0.783 1.619 0.812 0.000 2.469 -0.230 -1.647 2.548 0.870 0.956 -0.143 3.102 1.306 0.869 1.000 0.921 1.386 1.811 1.691 +1 1.057 -0.479 0.365 1.194 -0.796 0.809 -1.656 0.949 0.000 1.078 -0.285 -1.598 2.215 1.568 -0.196 -0.572 1.274 0.745 -0.643 1.230 0.000 0.655 0.819 1.346 1.049 1.104 0.814 0.709 +1 0.949 0.954 0.460 1.049 -0.383 1.384 1.044 1.711 0.000 1.100 1.386 0.781 2.215 0.963 0.253 -0.655 0.000 0.695 -1.191 -0.629 0.000 0.868 0.469 0.987 0.868 0.827 0.895 0.789 +1 0.547 -0.881 0.903 1.451 0.394 1.216 -0.659 0.597 1.087 1.434 -1.754 -1.241 0.000 1.576 -0.348 -1.162 2.548 0.396 -0.074 1.350 0.000 1.185 1.099 0.990 0.759 1.742 1.240 1.176 +1 2.122 0.908 1.423 0.475 -1.112 0.940 -0.540 -0.428 2.173 0.462 1.106 -0.319 0.000 0.720 -0.073 0.698 0.000 0.565 0.378 1.003 3.102 0.907 1.011 1.052 0.559 0.843 0.999 0.845 +1 1.506 0.590 0.447 0.967 -0.680 0.962 0.100 -1.727 2.173 0.496 -0.849 0.971 0.000 1.289 1.641 0.621 0.000 2.345 -0.075 -1.015 3.102 0.932 1.136 1.420 1.278 0.966 1.011 1.043 +1 1.296 0.129 1.413 0.500 1.144 1.311 -0.940 -0.698 2.173 0.218 -1.719 -0.582 0.000 0.487 -2.102 0.662 0.000 0.399 0.953 1.342 3.102 0.470 0.901 0.977 0.581 1.208 0.954 0.841 +1 1.390 0.412 -1.286 0.688 -0.066 0.802 0.956 1.022 2.173 1.336 0.216 -0.688 2.215 1.052 0.008 0.100 0.000 0.978 0.792 -1.543 0.000 1.241 1.020 1.208 1.060 1.629 0.942 0.836 +0 0.904 -0.231 -1.618 0.373 -1.435 1.662 -0.378 0.532 0.000 2.011 -0.354 -1.137 2.215 0.510 0.136 1.030 0.000 1.070 -0.722 0.087 3.102 0.844 0.717 0.994 0.899 1.229 1.239 1.064 +0 0.859 -0.310 0.029 1.435 -1.124 1.643 -0.524 0.892 0.000 1.372 -0.811 0.531 1.107 2.826 -1.992 -1.029 0.000 1.392 0.405 1.671 0.000 0.842 0.771 1.325 1.210 0.946 0.856 0.827 +0 0.800 -0.473 1.620 0.500 -0.271 1.017 -0.846 1.077 0.000 1.118 1.058 -0.440 0.000 1.147 -1.163 -0.098 2.548 1.323 0.666 -1.120 0.000 0.939 1.025 0.990 0.862 1.088 1.099 0.899 +1 0.912 -1.842 -1.458 0.269 0.425 0.914 -1.496 0.530 0.000 0.947 -1.245 -0.999 2.215 0.713 -0.456 0.220 2.548 0.747 0.360 -1.649 0.000 1.844 1.084 0.995 0.664 0.853 0.894 0.778 +1 0.947 0.643 0.575 1.133 -0.306 0.529 2.160 -1.050 0.000 0.479 1.283 0.190 2.215 0.956 1.329 -1.570 0.000 0.754 0.790 1.335 0.000 1.026 0.798 1.023 0.686 0.566 0.549 0.633 +1 0.827 1.568 -0.283 0.336 0.449 0.779 1.352 -1.716 0.000 0.938 -0.427 -0.034 2.215 0.569 0.323 -1.587 2.548 0.456 1.656 0.693 0.000 0.804 0.612 0.998 0.859 0.828 0.894 0.787 +0 1.547 -0.128 -0.252 0.553 1.078 0.878 0.359 0.962 2.173 1.484 -0.198 -0.851 2.215 1.164 1.171 1.385 0.000 0.378 1.479 -1.030 0.000 0.629 0.816 1.194 0.956 1.744 1.058 0.966 +1 1.194 -1.422 -0.170 0.934 -0.883 0.608 -2.400 -1.456 0.000 1.254 0.813 1.472 2.215 1.348 -0.748 0.402 1.274 0.628 -1.356 -0.689 0.000 0.715 1.207 0.986 1.773 1.713 1.612 1.341 +1 0.981 1.375 0.223 0.726 -0.516 0.344 0.021 -1.143 1.087 0.509 -1.405 1.130 2.215 0.596 -0.359 1.314 0.000 0.587 -0.274 0.452 0.000 0.458 0.521 0.983 1.892 0.737 1.243 0.982 +0 1.027 -0.277 -0.714 0.404 0.850 0.801 -0.042 1.142 2.173 0.733 -1.935 1.518 0.000 2.160 0.839 -0.170 1.274 0.614 -0.957 -1.030 0.000 0.756 1.161 0.988 1.158 1.725 1.524 1.184 +1 1.057 0.728 1.152 0.772 0.088 1.602 -0.026 -1.359 2.173 0.777 -0.032 -0.316 0.000 0.870 2.350 -0.213 0.000 0.710 -0.045 1.047 3.102 1.470 0.990 1.024 1.369 0.933 1.088 0.961 +1 2.091 0.008 0.445 0.727 0.091 1.694 -0.339 -1.664 2.173 0.805 1.621 -0.421 0.000 0.682 0.078 0.962 2.548 1.014 -0.173 -0.531 0.000 1.279 1.047 0.991 1.854 0.979 1.255 1.200 +1 1.759 -0.752 -1.552 1.105 -1.515 0.605 -0.845 1.581 2.173 1.533 -0.400 0.146 0.000 1.216 -1.361 -0.341 0.000 1.200 -0.315 1.145 3.102 0.809 0.782 0.985 0.619 0.408 0.587 0.626 +0 0.691 -0.381 0.565 0.834 -1.226 0.863 -0.314 1.648 1.087 0.863 -1.677 -0.010 0.000 0.514 -2.066 0.400 0.000 1.375 -0.613 -0.451 3.102 0.799 1.248 1.051 0.683 1.120 0.845 0.744 +0 0.897 -0.222 1.451 0.656 -0.303 0.712 -0.261 -0.248 0.000 1.160 -0.670 0.910 2.215 0.701 -1.017 1.139 2.548 2.426 -0.595 -1.007 0.000 1.348 1.142 1.062 0.797 0.285 0.950 0.807 +1 1.434 -1.847 1.445 0.681 0.156 0.531 -1.465 0.586 0.000 0.776 -0.117 -1.068 0.000 0.959 -0.150 -0.123 2.548 0.381 -0.546 -1.678 3.102 1.687 0.910 1.256 1.172 0.470 0.741 0.800 +0 0.518 0.202 1.052 1.631 -1.446 0.410 -0.799 0.956 0.000 0.742 -1.244 0.096 0.000 1.063 -0.703 1.626 0.000 1.331 0.560 -0.138 3.102 0.868 1.060 0.991 0.803 0.585 0.691 0.777 +1 0.624 0.939 -0.487 1.463 0.139 1.027 0.883 1.537 2.173 0.639 1.261 0.486 0.000 1.135 1.083 -0.960 2.548 0.383 0.985 -1.582 0.000 0.618 0.783 0.982 0.838 1.064 0.967 0.768 +0 0.655 -0.480 -0.945 1.768 -1.636 0.962 1.347 0.031 2.173 0.503 0.586 -0.343 0.000 0.842 -0.906 1.565 0.000 1.573 -0.490 0.428 3.102 0.752 0.968 0.982 2.180 1.547 1.609 1.424 +0 1.202 0.208 -0.256 1.442 -1.018 0.697 0.066 1.056 0.000 0.896 0.730 1.485 0.000 0.682 2.002 -1.586 0.000 0.581 -0.782 0.607 3.102 0.861 0.801 1.155 0.811 0.391 0.584 0.755 +0 1.012 -0.729 -0.280 1.220 0.383 2.443 -0.434 0.100 2.173 4.423 -0.701 -1.583 0.000 0.956 0.205 1.700 2.548 0.643 0.275 0.596 0.000 2.363 1.451 0.986 0.975 1.994 2.150 1.729 +1 0.694 0.871 -0.528 1.741 -0.581 0.752 -0.304 0.933 2.173 0.591 1.207 0.624 1.107 1.455 -1.240 -1.456 0.000 0.539 0.603 1.101 0.000 1.414 1.180 0.978 0.894 0.881 1.219 1.502 +0 1.353 -1.235 -1.170 1.069 1.729 0.850 0.542 0.364 0.000 0.235 -1.177 0.040 2.215 0.504 -0.188 -0.098 0.000 0.662 0.889 -1.585 3.102 0.659 0.751 0.990 0.645 0.613 0.641 0.816 +1 0.962 -0.703 0.609 1.007 1.614 0.876 -0.962 -1.544 2.173 0.850 -1.950 -0.233 0.000 0.955 -0.060 -0.797 2.548 1.132 0.611 0.609 0.000 2.512 1.591 1.074 0.853 0.889 1.174 1.000 +0 1.840 0.612 -0.438 2.640 -0.071 1.415 1.742 -1.638 0.000 0.770 -0.474 0.104 1.107 1.455 0.913 0.779 0.000 2.160 0.304 -1.720 3.102 0.941 1.410 0.995 0.957 1.269 1.490 1.571 +1 0.479 0.668 -1.534 1.117 0.400 1.106 -0.072 1.684 0.000 1.019 -0.545 -0.010 2.215 1.410 -0.800 -0.955 2.548 1.093 0.042 1.014 0.000 0.962 1.237 0.999 0.899 0.979 1.016 0.942 +1 0.385 1.364 0.641 1.333 -0.802 1.307 1.073 1.415 0.000 1.215 0.802 0.081 2.215 1.485 0.661 -0.794 2.548 0.667 -1.656 0.180 0.000 0.810 1.268 0.988 0.942 1.015 1.025 0.933 +1 1.519 0.448 1.207 0.539 0.561 0.767 -0.684 -0.729 2.173 0.757 0.867 -1.093 0.000 1.512 1.207 1.467 0.000 1.475 0.324 0.137 3.102 0.937 0.938 0.994 0.795 1.016 0.970 0.848 +1 0.571 -1.684 -0.218 1.728 1.013 0.446 -0.528 0.429 0.000 0.600 -0.621 1.299 0.000 1.734 1.256 -0.866 0.000 1.585 0.411 -0.738 3.102 0.848 1.134 1.233 0.868 0.821 1.079 0.890 +0 2.930 -0.533 0.818 1.549 0.762 1.678 0.389 -0.805 0.000 1.130 -0.422 -1.052 1.107 1.830 0.055 0.316 2.548 1.906 0.318 -1.308 0.000 0.952 0.885 0.992 1.127 1.491 1.255 1.201 +0 1.885 -0.389 0.009 0.220 -0.824 1.082 0.350 -0.585 1.087 2.181 0.844 1.524 0.000 1.321 0.612 -1.609 0.000 0.980 0.078 0.804 3.102 0.879 0.990 0.997 0.678 1.045 0.733 0.748 +1 2.172 0.438 -0.966 0.944 -0.172 2.939 0.942 1.304 0.000 1.460 0.414 -0.480 2.215 2.575 0.582 0.041 2.548 0.601 -0.265 1.658 0.000 1.516 2.416 1.303 1.218 0.957 1.836 1.594 +1 0.672 0.598 0.115 0.943 1.079 0.688 -0.581 -0.907 2.173 1.092 -0.843 -0.025 1.107 1.076 -0.923 1.321 0.000 1.178 0.571 0.933 0.000 1.007 0.983 0.990 0.888 0.928 0.807 0.751 +1 2.051 0.531 -0.896 0.185 -0.644 0.808 -0.219 0.918 2.173 0.938 2.256 -0.072 0.000 0.932 0.565 0.568 0.000 1.568 0.523 1.481 3.102 1.328 0.973 0.986 1.224 0.777 0.910 0.834 +1 0.610 -1.119 0.534 0.926 -0.917 0.595 -0.460 -0.039 0.000 0.773 -1.045 1.647 0.000 1.117 -1.363 -0.403 2.548 1.088 1.732 1.647 0.000 0.833 0.837 1.005 0.624 0.784 0.806 0.724 +1 0.581 1.722 0.739 1.170 1.353 0.734 1.635 1.179 0.000 0.910 0.923 -0.329 2.215 1.102 0.753 -0.829 2.548 0.911 0.882 -1.575 0.000 0.972 0.776 0.980 0.903 0.468 0.747 0.662 +0 0.634 0.622 -0.290 1.591 -0.827 0.671 -0.311 -0.235 2.173 0.420 2.155 1.066 0.000 0.837 0.210 0.847 0.000 1.870 -0.846 0.888 0.000 0.919 0.890 0.994 0.650 0.631 0.739 0.773 +1 0.343 -1.060 0.319 1.061 1.425 1.490 0.296 -0.142 0.000 1.381 0.285 1.174 2.215 1.227 0.056 -0.827 2.548 1.213 -2.235 -1.618 0.000 4.769 2.750 0.986 1.814 1.356 1.971 1.810 +1 1.317 0.666 1.713 0.112 -1.100 0.585 0.129 0.369 0.000 0.922 -0.322 -0.521 2.215 0.643 1.231 -1.313 0.000 0.376 -0.843 0.047 3.102 1.315 1.088 0.995 0.636 0.319 0.655 0.678 +1 1.184 0.012 1.149 1.011 -0.052 0.701 -0.466 0.874 0.000 1.035 -0.754 -1.443 2.215 1.154 -1.394 -0.804 2.548 0.602 -0.573 -0.183 0.000 0.817 0.986 1.338 1.230 0.776 0.952 0.838 +0 0.418 -0.272 -1.486 1.111 0.666 0.470 -0.045 -0.039 0.000 0.671 0.506 -1.731 2.215 0.781 0.403 -0.510 2.548 0.577 -1.111 1.622 0.000 0.965 0.876 0.989 0.855 0.686 0.722 0.655 +1 0.825 0.305 -0.935 1.268 -0.176 0.918 -0.708 1.433 1.087 0.401 -1.018 -0.819 0.000 0.580 1.124 1.479 0.000 1.051 0.107 0.607 3.102 0.771 1.134 0.991 0.750 0.838 0.958 0.809 +1 0.424 1.210 0.173 1.267 -1.654 0.956 -0.936 0.158 2.173 1.005 -0.694 -0.903 2.215 0.889 -0.764 1.483 0.000 0.981 0.095 0.940 0.000 0.703 0.968 1.013 1.692 1.190 1.285 1.065 +1 0.812 0.440 0.750 0.874 -1.270 1.054 -1.921 0.081 0.000 1.339 -0.841 -1.560 1.107 0.724 -0.100 -1.350 0.000 0.926 -0.247 0.084 1.551 0.931 0.943 1.131 1.082 1.042 1.059 1.155 +1 2.141 -1.231 -1.289 0.967 0.891 0.556 -1.623 0.161 0.000 0.879 -1.023 0.764 1.107 1.004 -0.364 0.131 2.548 1.275 -1.546 1.616 0.000 0.628 0.892 1.840 1.183 0.633 0.927 0.778 +0 0.822 -0.076 -1.314 1.813 -1.063 0.930 0.771 0.770 0.000 1.106 1.292 0.587 0.000 0.604 0.066 0.378 2.548 0.920 1.916 -0.792 0.000 0.698 0.657 0.984 0.873 0.456 0.590 0.849 +1 1.491 -1.054 -0.510 0.413 -1.371 2.830 1.459 0.211 0.000 2.099 -0.418 0.887 0.000 4.630 -0.136 -1.329 2.548 2.131 -1.218 -1.252 0.000 0.741 2.407 0.994 1.644 1.345 2.791 2.593 +0 1.158 0.766 0.727 4.707 0.447 3.421 -0.590 -1.387 0.000 1.118 0.581 0.126 2.215 0.545 2.616 -0.460 0.000 0.845 -1.238 -1.073 3.102 6.985 3.830 0.975 0.742 1.337 2.501 2.489 +1 0.988 0.732 -1.311 0.840 -0.120 0.802 1.058 -0.919 0.000 1.325 0.984 1.080 2.215 0.618 2.140 -0.518 0.000 0.761 -0.435 0.282 3.102 0.977 1.162 1.109 1.034 0.971 1.013 0.866 +0 0.402 0.619 -1.270 1.127 -0.503 1.307 -0.150 1.047 0.000 1.550 0.124 -1.158 1.107 1.006 0.331 0.800 2.548 2.300 0.050 -0.124 0.000 1.533 1.069 0.988 0.836 1.312 0.958 0.859 +0 0.622 -1.138 0.533 0.103 -0.119 0.747 -0.373 1.595 0.000 0.749 0.916 0.936 2.215 0.877 1.318 -0.602 0.000 1.048 -0.654 -1.112 0.000 0.915 0.959 0.983 0.760 0.705 0.802 0.709 +0 0.448 0.139 -0.803 2.074 -1.706 2.206 0.160 0.179 1.087 1.598 -1.102 1.473 2.215 1.999 2.394 -0.685 0.000 2.166 1.355 -1.004 0.000 0.810 1.162 0.990 1.902 3.183 1.725 1.331 +0 0.774 1.613 -1.342 0.556 1.363 1.102 0.020 -0.461 2.173 2.044 0.286 1.692 2.215 1.644 -0.129 0.112 0.000 0.762 -0.557 0.951 0.000 0.909 1.015 0.977 0.849 2.082 1.255 1.082 +1 1.618 0.054 -1.427 0.413 -0.858 0.621 -1.896 0.151 0.000 0.441 -0.133 1.657 0.000 0.834 0.237 0.975 1.274 0.622 -1.269 0.541 0.000 0.771 0.629 0.990 0.622 0.614 0.585 0.599 +1 0.598 -2.307 1.234 0.302 -0.511 0.632 -0.150 0.204 1.087 0.626 -0.571 1.140 0.000 0.696 0.807 -1.224 2.548 0.547 -1.632 -1.610 0.000 0.735 0.947 0.990 0.894 0.911 0.841 0.742 +1 0.674 1.085 1.262 0.264 1.034 0.778 0.596 -0.377 2.173 1.533 1.063 1.030 0.000 1.280 0.658 1.462 0.000 1.278 1.304 -1.298 0.000 0.909 1.486 0.978 0.864 0.957 1.244 0.995 +0 0.711 0.376 -1.292 0.325 0.082 1.291 0.500 1.690 2.173 0.937 0.358 -0.451 2.215 0.767 1.798 0.283 0.000 1.271 0.416 0.301 0.000 0.879 0.920 0.991 0.934 1.518 1.069 0.877 +1 0.871 0.973 -1.083 0.349 1.286 0.987 0.967 1.418 2.173 0.577 -0.401 -0.167 0.000 0.971 -0.312 -0.497 0.000 1.313 -0.454 0.439 3.102 0.339 0.567 0.995 0.847 1.373 0.970 0.828 +1 0.734 -0.537 0.833 0.257 -0.129 0.625 -0.770 -1.167 0.000 0.501 -1.973 -0.716 0.000 0.730 1.016 0.698 2.548 0.617 -0.944 1.394 3.102 0.922 0.682 0.993 1.109 0.768 0.895 0.813 +1 0.851 -0.040 0.135 0.330 -0.916 0.627 1.824 -1.496 0.000 0.998 0.923 0.387 1.107 0.483 -0.102 -1.165 2.548 0.787 0.658 -1.445 0.000 0.593 1.098 0.985 0.586 0.835 0.738 0.886 +0 1.059 -0.943 -1.225 1.735 1.167 1.697 -0.192 -0.421 2.173 0.707 -1.367 0.410 0.000 1.906 -0.023 1.420 2.548 0.449 -0.205 -1.381 0.000 0.861 1.075 1.565 1.822 2.240 1.448 1.154 +1 0.377 -0.365 -1.472 1.242 -0.681 0.635 1.217 -1.527 1.087 0.859 1.554 1.012 2.215 1.287 -0.173 0.175 0.000 0.664 1.655 0.410 0.000 0.742 1.097 0.984 2.190 0.843 1.649 1.279 +1 0.328 -1.948 -0.733 1.707 -1.225 0.773 2.657 1.732 0.000 0.746 0.632 0.215 2.215 2.115 -0.687 0.631 2.548 0.504 -2.227 0.716 0.000 6.951 3.743 0.992 1.321 1.129 2.677 2.224 +0 0.642 0.760 1.289 0.632 -1.458 0.789 0.723 0.311 1.087 0.704 -0.792 -0.759 2.215 1.045 -0.281 1.483 0.000 0.525 0.400 -0.343 0.000 0.879 0.963 0.989 1.379 1.297 1.078 0.918 +0 1.240 -1.275 0.946 0.340 1.308 0.345 -0.769 -1.089 1.087 0.687 0.217 -0.276 2.215 0.296 2.258 -0.969 0.000 0.482 -1.238 -1.309 0.000 1.492 0.999 0.973 0.724 0.607 0.691 0.790 +1 0.875 -0.360 0.872 0.819 -1.389 0.631 -0.729 0.355 0.000 0.959 -0.188 -0.184 0.000 1.445 0.366 1.554 2.548 0.927 0.938 -0.716 3.102 0.895 1.020 1.048 0.738 0.852 0.933 0.821 +0 0.755 -0.250 -0.273 3.584 -0.737 1.257 0.128 1.271 2.173 0.616 0.664 0.690 0.000 1.725 -0.605 0.992 0.000 0.638 0.596 -1.233 0.000 0.824 0.930 0.988 0.856 0.875 1.231 0.977 +1 0.529 1.162 -1.186 0.281 -0.927 1.010 0.724 1.580 0.000 1.313 0.339 0.340 1.107 0.956 0.019 -0.223 0.000 0.573 -0.528 -0.864 3.102 1.876 1.135 0.991 0.948 0.800 0.925 0.802 +1 0.777 -2.102 -0.247 0.774 0.624 1.268 0.130 -1.248 2.173 0.667 -0.770 1.198 2.215 0.387 -1.490 -0.494 0.000 0.831 0.035 0.362 0.000 0.798 0.975 0.985 0.762 1.268 1.127 0.883 +1 0.594 -0.188 1.651 0.420 0.147 0.965 0.249 0.791 0.000 0.466 -0.116 0.054 0.000 1.532 0.930 -1.227 1.274 0.515 -0.511 -1.221 0.000 0.918 0.872 0.983 1.270 0.532 0.866 0.872 +0 0.910 -0.398 1.689 0.375 -1.181 1.002 -0.126 -0.236 0.000 1.247 -1.085 1.338 1.107 0.709 -0.907 0.049 0.000 0.612 -0.572 -1.590 1.551 0.781 0.775 0.992 1.044 0.417 0.890 0.854 +0 0.946 -0.272 -1.557 0.238 0.026 1.451 0.505 0.756 0.000 1.405 0.488 -1.012 2.215 1.483 1.364 -0.255 2.548 0.538 2.417 1.605 0.000 0.817 0.883 0.982 0.751 1.253 0.854 0.797 +1 0.880 -0.100 -0.730 0.704 -0.077 0.942 1.062 0.730 2.173 0.766 -0.228 -1.390 1.107 0.699 -1.526 -0.854 0.000 0.581 -0.896 1.111 0.000 0.722 0.718 0.997 1.490 1.468 1.116 0.996 +1 0.871 -0.714 -1.039 0.566 0.450 1.114 -0.992 0.611 1.087 1.405 -1.290 -1.352 2.215 1.114 0.193 0.651 0.000 1.405 0.874 -1.366 0.000 0.818 0.934 0.988 0.806 1.829 1.127 0.915 +0 1.237 0.613 -0.408 1.270 0.156 0.896 -1.076 1.540 0.000 1.052 1.226 0.319 2.215 0.863 -1.972 1.611 0.000 1.462 -0.302 -1.517 3.102 0.902 0.906 0.987 0.856 1.506 1.605 1.438 +1 2.128 -0.042 1.686 1.284 -1.273 1.151 0.414 0.191 2.173 0.632 -0.423 0.848 2.215 0.745 0.614 -0.711 0.000 0.473 0.035 0.494 0.000 0.616 0.695 1.049 0.932 0.891 1.120 0.881 +1 0.829 -0.657 -1.270 1.127 0.825 3.386 1.441 1.408 0.000 3.409 -0.309 -0.254 1.107 2.389 0.287 -0.205 0.000 0.930 0.637 -0.364 3.102 2.023 1.193 1.272 1.573 0.927 1.073 1.057 +0 0.975 0.825 1.687 0.343 -0.682 0.375 -0.872 -1.065 2.173 0.549 -0.938 0.117 2.215 0.264 0.759 0.248 0.000 0.819 -1.047 0.817 0.000 0.688 0.651 0.983 1.165 0.585 0.894 0.775 +0 0.604 -0.512 0.195 0.317 -0.767 0.600 -1.102 -1.565 2.173 0.395 -2.850 0.159 0.000 1.309 -0.557 1.172 2.548 0.508 -0.917 -1.095 0.000 0.809 1.050 0.989 0.772 0.744 0.744 0.682 +1 2.344 -1.597 -0.415 0.899 0.074 0.347 -1.683 0.886 0.000 0.593 -0.451 0.596 1.107 1.233 -1.702 -1.705 0.000 1.511 0.101 1.491 3.102 0.896 1.073 0.993 0.947 0.667 1.039 0.944 +1 1.185 0.016 1.614 0.672 -1.226 0.667 -0.123 -1.633 2.173 1.695 -1.275 0.143 0.000 1.352 -0.693 -0.336 0.000 1.409 1.022 1.104 3.102 0.737 0.928 0.995 1.042 0.984 0.935 0.825 +1 0.693 -0.672 0.285 0.213 0.178 0.653 -0.153 0.035 2.173 0.792 -0.328 -1.498 0.000 1.493 0.656 -1.492 0.000 0.881 0.420 -0.276 0.000 0.924 0.726 0.981 0.609 0.578 0.776 0.714 +0 1.017 -0.526 -1.057 5.700 -1.371 3.733 -1.007 0.421 2.173 1.432 0.185 -1.571 2.215 0.800 -1.297 -0.200 0.000 1.142 -1.579 0.438 0.000 0.626 1.153 0.993 1.211 3.968 2.896 2.177 +0 0.833 -1.005 -0.875 0.647 -0.531 0.758 0.232 0.688 0.000 1.031 1.290 0.792 2.215 1.532 -0.027 -1.431 2.548 0.657 -0.354 -0.171 0.000 0.836 0.938 0.976 0.770 1.558 1.035 0.916 +0 1.848 0.713 -0.654 3.006 -0.820 1.255 -0.800 0.878 0.000 1.180 0.097 1.010 0.000 1.408 -0.014 1.297 2.548 1.158 1.274 0.810 0.000 1.077 0.926 0.982 0.724 1.214 1.172 1.531 +1 0.521 0.502 -1.284 0.682 0.471 0.519 -1.008 -0.325 0.000 0.477 1.051 0.728 2.215 1.031 0.384 1.606 1.274 0.933 0.582 -0.274 0.000 0.917 0.720 0.988 0.559 0.586 0.521 0.510 +0 0.836 1.675 -0.549 0.750 -1.631 0.610 1.345 0.805 2.173 0.712 0.273 -1.684 0.000 0.982 0.820 0.318 2.548 0.873 1.056 -0.904 0.000 0.848 0.950 0.984 0.877 0.466 0.698 0.730 +0 1.286 -0.032 1.465 0.269 -0.833 0.643 -0.725 0.464 2.173 0.790 0.984 -1.301 2.215 0.397 0.474 0.583 0.000 0.841 -0.334 -0.268 0.000 0.537 0.764 0.986 0.819 1.474 0.860 0.699 +1 0.969 -0.407 -0.911 0.265 1.281 0.855 -1.414 -0.371 0.000 1.334 -0.159 1.093 0.000 0.710 -1.056 1.368 2.548 0.524 1.001 -0.090 0.000 0.810 0.852 0.993 0.490 0.357 0.544 0.619 +0 0.459 0.263 0.758 0.625 -0.288 0.931 -1.251 0.366 2.173 1.621 0.716 -1.344 0.000 0.497 0.768 -0.970 1.274 0.438 -2.359 1.077 0.000 3.302 1.816 0.982 1.767 1.329 1.538 1.422 +1 0.874 0.654 -1.102 2.128 -0.335 1.107 -0.047 1.233 0.000 0.907 -0.072 -0.226 2.215 0.720 1.473 1.656 0.000 0.962 -0.595 -1.158 0.000 1.420 0.992 1.205 0.790 0.844 0.858 0.965 +1 1.293 -1.676 -0.608 0.248 -1.008 0.998 -0.158 1.461 0.000 0.803 -0.565 -0.729 0.000 0.732 -0.817 0.604 1.274 1.026 -0.130 0.681 3.102 1.151 0.885 0.976 0.854 0.255 0.621 0.745 +0 1.170 1.729 -1.559 0.957 -0.607 0.421 -1.208 1.141 0.000 0.535 0.304 0.533 2.215 0.608 -0.644 -0.457 2.548 0.446 -1.902 -0.618 0.000 0.757 0.851 1.110 1.220 0.572 0.902 1.183 +0 0.895 -1.934 0.879 1.047 -0.273 0.510 -0.575 0.194 0.000 1.195 -0.948 1.556 2.215 1.031 -1.287 -0.867 1.274 0.691 0.370 -0.956 0.000 0.919 0.885 1.155 1.142 1.000 0.858 0.871 +0 0.422 -1.331 1.415 0.978 -1.122 1.086 -2.026 1.159 0.000 0.866 -0.216 0.307 0.000 0.747 -0.374 -0.699 0.000 1.695 0.791 -0.767 3.102 0.892 0.952 0.987 1.489 0.806 1.586 1.248 +1 1.107 -0.050 1.570 0.268 1.357 1.485 -0.803 0.872 1.087 1.285 -2.397 -0.449 0.000 0.831 -1.152 1.461 0.000 1.398 0.428 -0.449 0.000 0.801 0.942 0.987 0.932 2.011 1.193 1.060 +0 0.603 -1.436 1.464 0.826 0.544 0.902 -0.697 0.907 1.087 1.118 0.180 -1.334 2.215 0.530 -1.617 -1.630 0.000 0.919 1.592 -0.501 0.000 0.878 1.068 0.981 1.693 1.494 1.261 1.064 +0 1.167 -0.572 1.323 0.273 -0.272 0.814 -0.018 -0.493 0.000 0.747 -0.451 0.771 2.215 0.461 -1.622 -0.327 0.000 0.894 0.550 -1.432 3.102 1.114 0.970 0.987 0.612 0.804 0.768 0.706 +0 1.361 0.050 -0.308 0.047 0.824 0.726 0.953 0.640 0.000 1.220 0.138 -0.893 2.215 1.015 -0.804 1.672 0.000 1.002 0.661 1.510 1.551 1.176 0.814 0.821 0.696 0.890 0.841 0.770 +1 1.247 1.334 -0.085 1.039 0.219 2.487 1.050 0.934 0.000 1.273 -0.276 -0.865 0.000 2.975 -1.880 -1.351 0.000 1.578 0.363 -0.353 3.102 1.530 0.971 0.991 0.993 0.580 0.679 0.961 +1 1.385 0.466 0.474 0.799 0.724 0.936 0.622 -0.669 2.173 0.568 1.058 -1.732 2.215 0.287 0.636 -1.038 0.000 0.375 1.535 1.573 0.000 0.472 0.742 1.001 0.819 0.913 0.867 0.679 +1 1.595 0.289 -0.478 1.425 -1.099 0.932 0.668 0.465 1.087 0.958 0.711 -1.681 0.000 0.815 1.048 1.413 2.548 1.206 0.754 -0.054 0.000 0.892 0.888 1.107 1.291 0.859 0.971 0.832 +0 0.713 0.664 0.821 0.747 0.014 0.574 -0.469 -0.223 2.173 0.584 -1.687 -1.379 0.000 0.957 -0.808 1.645 0.000 1.137 -0.789 -0.592 3.102 0.827 1.194 0.988 0.752 0.346 0.897 0.780 +1 0.681 0.595 -0.971 0.848 0.441 0.676 -0.335 1.523 0.000 0.792 0.756 0.297 2.215 1.115 -0.083 -0.492 2.548 0.891 -1.141 -1.464 0.000 0.821 1.010 1.006 0.624 0.791 0.886 0.789 +1 1.059 0.074 1.231 0.696 0.047 1.458 0.921 -1.516 0.000 1.866 1.773 -0.528 0.000 1.352 -0.534 -0.066 1.274 1.713 1.402 1.015 0.000 0.806 0.472 1.041 0.825 0.821 0.951 0.819 +0 1.100 1.703 -0.578 0.682 0.101 0.750 1.468 0.153 0.000 0.886 -0.135 -1.739 1.107 0.551 0.776 1.584 2.548 0.740 2.056 0.796 0.000 0.853 0.803 0.979 1.622 0.398 1.041 0.955 +1 0.800 0.355 0.423 0.113 -0.151 0.626 -2.030 -1.070 0.000 0.742 -0.749 1.560 2.215 0.627 -0.347 -0.017 2.548 0.681 1.363 1.468 0.000 0.840 1.158 0.993 0.527 0.731 0.761 0.702 +0 0.833 -0.626 -0.286 1.520 -0.716 0.829 -1.838 1.639 0.000 0.621 -1.434 -0.072 0.000 1.070 -0.524 0.866 2.548 0.624 -0.593 -1.720 3.102 1.551 0.928 0.997 1.036 0.454 0.728 0.965 +1 1.744 0.759 0.988 0.542 0.325 1.105 0.264 -0.896 2.173 0.604 -0.602 0.459 2.215 0.331 0.498 1.736 0.000 0.399 -0.958 1.693 0.000 0.383 0.671 0.993 0.938 1.257 1.066 0.820 +1 1.543 -0.299 -1.358 0.792 -1.229 0.767 0.036 0.436 2.173 0.376 -1.499 0.181 0.000 0.765 -0.647 -1.600 0.000 0.770 -0.820 1.140 1.551 0.894 0.964 0.985 0.701 0.648 0.846 0.727 +1 0.796 1.813 1.170 0.609 -1.121 0.751 0.367 0.223 2.173 0.864 0.060 -0.996 2.215 0.957 1.161 0.446 0.000 0.867 0.036 -1.509 0.000 0.696 1.039 0.984 1.289 1.071 1.111 0.906 +1 1.018 0.391 0.618 1.363 -1.491 0.514 0.738 -1.185 0.000 0.371 -0.433 0.697 2.215 0.709 0.729 -0.125 0.000 0.986 -0.993 0.347 1.551 0.887 1.062 1.544 0.839 0.269 0.718 0.728 +1 0.745 0.432 0.523 1.164 1.223 1.871 1.400 -0.503 2.173 1.198 -1.655 1.100 0.000 1.353 0.944 -1.732 2.548 0.953 1.253 -1.196 0.000 0.639 0.733 0.989 0.986 1.812 1.466 1.101 +0 0.872 -0.695 -0.014 0.701 -0.579 0.858 -0.860 0.840 2.173 0.808 -1.560 -1.532 2.215 0.499 -2.007 0.941 0.000 0.864 -1.355 -0.890 0.000 0.752 0.884 0.984 1.123 1.132 0.954 0.838 +0 0.587 -0.621 1.007 1.651 -1.306 0.647 -0.756 -0.798 2.173 0.847 -0.109 0.867 2.215 0.676 1.191 0.901 0.000 0.537 -1.385 0.195 0.000 1.447 0.940 1.189 0.790 1.145 0.852 0.832 +0 1.119 -0.452 0.458 0.750 0.374 1.002 1.001 -1.561 0.000 0.568 1.142 1.091 0.000 1.056 1.120 -0.224 2.548 1.486 -0.703 -0.972 3.102 1.100 1.139 0.996 0.938 1.324 1.103 1.291 +1 0.677 0.195 -0.300 0.639 -1.232 1.061 -0.791 -1.246 2.173 0.289 2.112 0.299 0.000 0.797 -0.333 0.215 0.000 0.717 0.504 1.475 3.102 1.209 0.808 0.989 1.281 0.915 0.997 0.894 +0 0.760 -1.909 1.008 0.428 -0.981 0.894 -1.311 -0.647 1.087 0.558 -0.924 0.227 0.000 0.391 1.635 1.441 0.000 0.771 -0.935 1.593 3.102 0.782 0.972 0.995 0.534 0.797 0.644 0.603 +0 0.858 -1.415 0.799 2.026 0.184 1.542 1.646 -1.669 0.000 1.251 -0.466 -1.330 0.000 1.713 -0.543 0.263 1.274 1.866 -0.846 -0.411 3.102 0.813 0.938 0.988 0.861 0.832 0.714 0.691 +0 0.778 -0.136 -0.224 1.067 1.272 1.272 -2.125 -0.789 0.000 0.886 -1.116 1.299 2.215 2.398 -0.133 0.515 2.548 2.225 -0.488 -1.363 0.000 1.276 1.015 1.231 0.922 1.292 1.022 0.877 +1 1.520 -0.901 0.514 0.532 0.964 0.621 0.825 -1.570 2.173 0.765 -0.077 -0.729 0.000 0.399 0.605 -0.403 2.548 0.478 0.868 1.294 0.000 0.894 0.771 0.982 0.885 0.541 1.037 0.895 +1 0.404 -1.089 -1.274 2.084 -1.561 0.633 1.273 0.145 2.173 0.236 1.676 -0.411 0.000 0.711 0.169 0.882 0.000 0.425 -0.052 0.093 0.000 0.786 0.663 0.993 0.795 0.881 0.938 0.781 +1 1.612 0.476 0.066 0.285 -0.744 1.120 0.629 -1.673 1.087 0.613 0.540 -0.420 0.000 0.472 -0.093 1.651 0.000 1.740 0.308 1.062 3.102 0.924 1.119 0.989 1.266 0.945 1.025 0.993 +1 0.971 -0.658 0.081 0.805 0.734 0.982 -0.115 -1.250 2.173 0.502 0.160 -0.085 0.000 0.698 0.181 1.703 2.548 0.549 0.264 0.882 0.000 0.526 0.868 0.995 0.906 0.503 0.912 0.761 +0 0.767 1.529 -0.039 1.189 1.076 0.691 0.383 1.544 1.087 0.531 1.572 -0.523 0.000 0.618 -0.237 -1.240 2.548 0.741 -0.035 -0.088 0.000 0.821 0.993 1.116 0.987 0.550 0.791 0.744 +1 0.301 -1.149 -0.120 1.764 -0.871 0.520 -0.664 -0.983 0.000 0.890 0.621 0.765 2.215 0.567 -2.163 1.034 0.000 1.878 -0.658 0.913 3.102 1.331 1.094 0.983 1.074 0.920 1.025 1.008 +1 0.968 1.215 -1.037 0.483 -0.207 0.535 0.467 -0.105 0.000 0.667 1.409 0.371 0.000 1.338 0.146 1.401 2.548 0.785 -0.353 -1.328 3.102 0.834 0.928 0.987 0.902 0.546 0.776 0.718 +1 0.704 0.048 0.349 0.604 1.299 1.511 -0.145 -0.623 2.173 1.211 -0.434 0.779 0.000 0.438 0.899 -1.229 2.548 0.745 0.012 -1.615 0.000 1.071 0.877 0.988 1.198 0.802 0.971 0.859 +0 0.571 1.326 -1.342 0.465 0.854 0.597 0.708 -0.960 2.173 0.638 -0.862 1.306 2.215 0.681 0.389 -0.545 0.000 0.960 1.538 0.368 0.000 0.949 0.807 0.987 0.769 1.143 0.867 0.736 +1 1.068 1.916 0.911 0.905 0.564 1.374 1.058 -0.888 2.173 0.598 1.145 0.753 0.000 0.368 2.600 -1.536 0.000 0.734 0.931 1.625 3.102 0.928 1.267 0.975 0.620 0.817 0.932 0.814 +1 0.662 -1.046 -1.445 1.390 0.908 1.083 -0.347 0.305 2.173 1.506 2.686 -1.067 0.000 0.593 -2.265 1.547 0.000 1.176 -0.733 1.075 3.102 0.838 0.812 1.133 1.063 0.828 0.908 0.814 +1 1.696 -0.640 -0.789 1.464 -0.545 1.428 -0.677 0.603 2.173 0.466 -1.002 0.837 2.215 0.739 -1.617 -0.171 0.000 1.564 0.515 1.614 0.000 0.759 1.119 0.972 0.974 0.327 1.105 0.940 +1 1.091 0.472 -1.066 1.289 0.290 0.958 0.628 -1.556 0.000 1.207 0.427 0.642 1.107 0.721 1.285 0.081 0.000 0.700 1.088 -0.864 3.102 1.602 0.930 1.544 1.043 0.891 0.854 0.834 +1 1.447 -0.413 0.012 1.033 -0.945 0.897 -0.645 1.072 2.173 0.460 -0.931 -1.489 1.107 0.659 0.289 -1.513 0.000 0.720 -1.495 0.195 0.000 1.225 1.012 1.285 0.817 0.714 0.826 0.755 +0 0.750 0.588 0.398 2.234 0.575 0.813 -0.607 -0.803 2.173 0.885 -0.225 -1.421 0.000 1.170 -0.294 1.515 2.548 0.855 -1.325 -0.045 0.000 1.357 0.967 0.981 0.975 1.070 1.003 0.945 +0 0.474 0.540 -1.253 2.915 1.324 0.748 1.480 -0.392 0.000 1.125 -1.506 0.191 0.000 0.655 -1.844 -0.506 0.000 1.413 0.702 0.918 3.102 0.836 1.020 1.190 0.735 1.014 1.138 1.346 +0 0.344 -1.296 0.276 1.257 0.597 0.599 -0.066 1.674 2.173 0.716 1.144 -0.651 0.000 0.923 -0.472 -0.786 2.548 0.545 1.227 1.618 0.000 0.734 0.898 0.989 0.857 0.768 0.721 0.752 +1 1.435 -0.897 -0.695 1.221 -0.819 0.964 -0.248 1.445 2.173 0.825 -0.264 -0.413 0.000 1.354 -1.044 0.783 0.000 1.175 -1.565 0.442 0.000 0.908 0.916 0.998 0.694 0.475 0.839 0.872 +0 0.718 -0.014 -0.392 0.919 1.614 1.231 0.054 0.169 2.173 0.872 -1.130 1.693 2.215 1.031 -1.357 0.860 0.000 1.845 -1.180 -1.227 0.000 1.449 0.956 1.094 1.020 1.791 1.254 1.054 +1 1.278 1.183 0.901 0.237 -1.123 0.718 1.142 -0.407 0.000 0.630 1.348 0.280 0.000 0.839 -0.035 -1.624 2.548 1.093 0.423 -1.288 0.000 0.845 0.930 0.990 0.738 0.516 0.759 0.686 +0 0.738 -0.700 1.165 1.122 0.557 0.754 0.905 -0.558 2.173 0.375 0.285 -0.819 0.000 1.073 0.607 -1.532 2.548 0.512 0.565 1.196 0.000 0.565 0.583 0.991 0.902 0.873 0.863 0.668 +1 1.674 -0.675 0.718 1.564 1.139 0.502 -1.122 -0.583 0.000 1.063 -0.242 -1.110 2.215 1.062 0.870 -0.442 2.548 0.597 0.069 1.691 0.000 0.918 1.021 0.979 1.356 0.965 1.251 1.019 +0 1.360 0.986 1.067 0.376 -0.677 0.888 0.437 -0.861 2.173 1.347 1.525 0.111 2.215 1.326 0.351 1.610 0.000 1.224 0.346 -1.459 0.000 0.840 1.042 0.990 0.918 1.564 1.044 0.882 +1 0.403 -1.843 -0.391 0.840 -0.591 0.956 -1.662 0.906 0.000 1.180 -0.595 0.111 2.215 1.346 -1.809 -1.191 0.000 0.921 -0.232 -1.697 3.102 0.989 0.896 0.984 0.743 0.953 0.862 0.834 +0 0.474 -1.836 1.716 1.584 -1.732 1.074 -0.878 0.533 0.000 1.450 -0.802 -0.841 0.000 1.335 -0.694 -0.047 0.000 1.548 -0.554 1.296 3.102 0.872 0.678 0.977 0.806 0.808 0.618 0.601 +0 0.659 0.090 -0.285 1.295 0.752 0.678 0.685 -0.808 0.000 1.042 -0.114 -1.042 0.000 0.819 0.020 -1.659 1.274 0.996 -2.012 0.909 0.000 0.831 0.745 1.030 0.595 0.904 0.853 0.802 +0 0.793 -0.007 -1.605 1.278 0.044 1.074 1.432 0.130 1.087 1.913 -0.763 -1.285 1.107 0.977 2.610 1.514 0.000 1.603 -0.688 0.575 0.000 0.896 1.519 1.389 1.313 3.511 1.741 1.370 +0 1.829 0.021 1.555 2.846 -1.551 1.839 -0.557 0.085 2.173 0.861 -0.075 -0.480 0.000 0.862 2.436 -1.290 0.000 1.185 0.134 0.247 0.000 0.817 0.991 1.065 2.470 1.503 1.609 1.315 +0 0.779 0.270 -1.020 0.773 1.725 0.300 0.556 -0.073 0.000 0.790 -1.039 0.528 2.215 0.784 -0.249 1.584 2.548 0.557 0.640 -1.228 0.000 0.542 0.850 0.990 0.722 0.762 0.879 0.712 +1 2.224 0.801 0.972 0.917 -1.486 0.880 1.219 -0.242 2.173 1.085 0.151 -0.987 2.215 0.812 1.041 1.365 0.000 0.584 1.689 -0.342 0.000 0.835 1.016 1.584 1.385 1.208 1.140 0.931 +1 1.003 -1.132 1.153 1.325 1.322 0.727 -0.783 -0.284 1.087 0.391 -1.431 -0.199 2.215 0.575 -1.816 -1.077 0.000 0.704 -0.603 0.220 0.000 0.803 0.692 0.989 0.787 0.279 0.854 0.712 +1 1.340 -0.225 0.253 1.498 0.771 1.259 -0.343 -1.201 1.087 0.430 -0.642 -0.471 0.000 0.762 -1.597 1.000 0.000 0.529 -0.042 1.167 3.102 0.990 1.231 0.980 0.535 0.741 0.973 0.903 +0 0.449 1.190 -1.245 0.534 1.027 1.551 0.334 -0.557 2.173 1.287 -0.401 1.067 0.000 1.277 0.451 0.914 0.000 0.612 0.050 -0.974 3.102 0.962 0.877 0.988 1.063 0.405 1.162 0.933 +1 0.504 -1.356 0.964 1.089 0.401 0.551 0.889 1.357 1.087 1.251 0.266 -1.105 2.215 0.373 -1.322 -1.625 0.000 0.598 -1.334 0.420 0.000 0.504 1.002 0.985 2.223 1.047 1.860 1.337 +1 0.371 -0.753 1.642 1.145 0.949 0.849 0.913 -1.129 2.173 0.929 1.244 -0.539 0.000 1.283 0.677 0.577 2.548 1.078 0.498 1.395 0.000 0.857 0.850 0.998 1.007 1.305 0.821 0.753 +1 0.718 1.295 -0.166 0.548 -1.369 0.538 1.438 -1.523 0.000 0.674 -0.940 0.146 2.215 0.599 1.589 0.875 0.000 0.651 0.556 1.096 3.102 0.860 0.587 0.988 0.921 0.708 0.881 0.760 +0 0.731 0.197 1.122 0.999 0.205 0.607 -0.271 -0.595 0.000 1.349 -0.302 1.693 2.215 1.114 -0.478 -0.052 2.548 0.611 0.506 -1.610 0.000 0.850 0.970 0.982 0.654 1.310 0.804 0.742 +1 0.469 -1.268 1.090 1.136 0.012 0.974 -0.539 0.820 2.173 1.046 -0.271 -1.049 0.000 1.117 0.659 -1.378 2.548 0.452 2.279 -0.329 0.000 1.999 1.220 0.987 0.753 1.481 1.238 1.079 +0 0.592 0.410 -1.529 1.128 -0.815 1.011 1.054 -0.189 2.173 1.502 0.514 1.044 2.215 0.904 0.431 0.365 0.000 1.622 -1.923 -1.509 0.000 2.846 2.541 0.981 0.899 1.697 2.076 1.785 +1 0.996 -0.710 0.155 0.892 1.351 1.438 -0.756 1.580 2.173 0.879 -1.360 -0.192 0.000 0.414 -1.924 0.316 0.000 1.450 -0.301 -0.287 3.102 0.530 0.663 1.150 1.045 1.549 1.035 0.886 +1 0.753 0.827 0.749 0.402 -0.597 0.902 0.002 -0.110 0.000 1.199 0.328 -1.603 0.000 1.122 0.099 0.516 2.548 0.551 0.112 -0.527 3.102 2.185 1.166 0.984 0.815 0.486 0.825 0.823 +1 1.018 1.080 0.393 0.716 0.980 0.873 0.149 0.910 0.000 0.767 0.873 -1.196 0.000 1.795 0.995 -0.645 1.274 0.554 -0.194 -1.293 3.102 1.149 0.786 0.984 1.095 0.687 0.833 0.852 +1 2.620 -0.787 -0.453 0.539 -0.867 0.926 -0.026 0.834 2.173 0.729 -2.047 -1.186 0.000 1.674 0.568 1.236 0.000 0.706 -0.617 -1.040 0.000 0.803 0.884 0.978 0.556 0.771 0.902 0.765 +1 2.149 0.844 0.295 0.389 -1.740 0.879 -0.103 1.675 0.000 1.261 0.705 -0.822 1.107 0.500 1.281 -1.586 0.000 0.484 -0.626 -1.514 3.102 1.017 1.169 1.224 0.862 0.703 0.815 0.859 +0 0.731 -0.363 -0.142 1.813 0.676 0.459 1.213 -0.532 0.000 0.513 2.070 0.477 0.000 1.956 -0.543 -1.459 2.548 1.015 0.169 -1.603 3.102 0.945 0.958 1.073 1.308 0.464 1.143 1.135 +0 0.417 -1.590 0.521 1.357 -1.110 0.624 0.461 -0.769 0.000 0.784 -0.448 1.557 2.215 0.966 0.288 0.701 0.000 0.573 -0.059 0.135 3.102 1.358 1.108 1.037 0.737 0.592 0.669 0.857 +0 2.342 1.059 1.722 0.424 1.623 0.906 0.886 -0.445 2.173 1.128 1.518 0.277 0.000 0.500 1.241 0.674 2.548 0.373 1.601 -0.385 0.000 0.830 0.948 0.981 0.700 0.736 0.877 0.824 +1 1.601 0.998 1.435 1.008 -1.328 0.584 1.886 -0.453 0.000 0.524 -0.216 1.558 2.215 0.802 1.009 -0.224 2.548 0.752 1.143 -1.417 0.000 0.819 1.077 1.068 0.902 0.847 0.705 0.735 +0 0.392 1.378 1.567 0.171 1.657 0.864 0.888 1.480 2.173 1.201 0.949 -0.301 2.215 1.277 0.403 0.507 0.000 0.506 -0.554 -0.706 0.000 0.942 0.945 0.984 0.780 1.499 0.917 0.803 +1 1.283 -0.376 1.243 1.343 0.732 0.810 -0.356 -1.151 2.173 0.695 2.033 -0.577 0.000 1.039 -0.166 0.395 0.000 1.247 0.797 -0.559 3.102 0.909 0.994 0.987 1.189 0.927 1.047 0.876 +1 1.168 -0.405 1.234 0.981 1.270 0.961 1.449 -0.624 0.000 0.817 0.830 1.583 2.215 2.374 -0.999 -0.755 2.548 0.489 -0.386 0.520 0.000 1.253 0.985 0.975 1.400 2.112 1.587 1.482 +1 0.805 -1.106 -0.142 0.461 -0.063 0.550 -2.857 -0.241 0.000 0.332 2.482 0.587 0.000 0.979 -0.514 0.890 2.548 2.005 -0.310 -1.515 3.102 0.934 1.098 0.985 0.734 0.891 0.875 0.854 +0 0.668 -0.759 -0.651 1.006 0.633 1.151 -0.233 -0.701 2.173 1.397 0.741 1.411 1.107 0.528 0.042 1.402 0.000 0.648 0.211 -0.363 0.000 1.107 1.103 1.040 1.295 2.012 1.299 1.076 +0 0.569 -1.719 -0.297 0.940 -0.971 1.159 1.841 1.116 0.000 1.190 -0.052 1.528 2.215 1.437 -0.257 0.073 0.000 1.379 -2.480 -1.250 0.000 0.928 0.733 0.995 1.050 1.417 0.936 0.828 +1 0.581 0.390 -1.349 0.917 0.055 0.513 -0.528 1.547 0.000 0.965 -1.286 0.096 2.215 0.772 0.850 1.657 0.000 0.778 -1.116 -1.395 3.102 0.913 0.807 0.986 0.931 0.762 0.883 0.791 +1 1.238 -0.305 1.241 0.539 -0.440 0.839 -2.620 -1.243 0.000 0.510 0.901 -0.070 0.000 1.298 -0.612 -1.525 2.548 2.198 -0.038 0.129 0.000 0.740 0.843 1.129 0.779 0.923 0.874 0.774 +0 2.246 0.114 -0.374 0.575 -0.633 1.481 0.635 -1.590 0.000 1.319 0.993 0.432 2.215 1.371 -0.638 0.147 2.548 2.085 -0.381 1.557 0.000 0.611 1.000 0.994 1.354 1.435 1.087 1.045 +0 0.385 -1.293 1.299 1.246 1.243 0.509 0.022 -0.274 0.000 0.381 1.422 -1.048 0.000 0.585 -0.046 0.206 2.548 0.750 -0.573 0.851 3.102 0.948 0.838 0.972 0.656 0.323 0.530 0.608 +1 0.759 0.450 -0.263 1.068 0.767 0.356 0.536 -0.498 2.173 0.711 -1.272 1.471 2.215 0.937 -1.563 -0.940 0.000 0.833 1.007 1.413 0.000 0.697 0.894 0.998 1.079 1.071 0.793 0.824 +0 3.409 -0.390 0.291 1.448 0.591 1.895 -0.905 -1.437 1.087 0.379 0.392 -0.469 2.215 0.766 1.237 -1.177 0.000 0.658 -0.812 -0.843 0.000 0.757 1.027 0.986 0.884 1.300 1.594 1.191 +1 1.703 -0.370 -1.020 0.819 -1.061 0.637 0.590 0.093 2.173 1.171 0.081 0.789 0.000 0.760 0.355 1.075 2.548 0.829 0.163 -1.733 0.000 0.982 0.929 0.976 0.900 0.677 0.805 0.803 +0 6.067 -0.106 0.526 1.353 -0.793 2.974 -0.253 -0.876 2.173 1.201 -1.684 1.612 0.000 1.581 -0.873 -1.261 0.000 0.753 -0.254 0.927 3.102 1.390 1.065 3.682 3.566 1.582 2.186 2.097 +1 0.643 -1.963 0.790 0.862 -1.660 0.762 0.236 -0.785 2.173 0.378 -1.384 1.607 0.000 1.146 -0.073 0.562 2.548 0.462 -2.013 0.229 0.000 0.587 0.840 0.984 1.962 1.107 1.468 1.101 +1 0.625 0.939 0.327 1.252 -0.750 1.544 -0.923 -1.731 0.000 0.737 -0.239 -0.549 0.000 1.742 -0.626 0.659 2.548 1.876 -0.851 0.109 3.102 0.936 0.986 1.011 1.279 0.697 1.086 0.897 +1 2.660 0.241 -1.365 0.623 0.512 1.043 0.742 1.058 0.000 0.918 -1.016 0.120 1.107 1.930 -1.525 -0.380 0.000 0.949 -0.525 1.488 3.102 0.628 0.722 1.770 1.516 0.814 1.004 0.991 +1 0.686 1.039 1.628 1.186 0.767 0.679 0.481 -0.771 0.000 0.907 -0.063 -0.320 2.215 1.308 -0.233 -1.415 2.548 0.545 0.385 1.043 0.000 0.927 0.818 0.987 0.963 0.972 0.827 0.732 +1 1.086 0.966 -0.067 0.992 1.197 0.911 0.217 -0.697 2.173 0.652 1.786 -1.730 0.000 1.170 0.714 1.601 0.000 1.331 0.414 0.357 3.102 0.763 1.008 1.307 1.124 0.962 0.941 0.864 +0 0.759 1.417 1.312 1.406 -1.574 0.833 1.324 -0.237 2.173 0.407 2.524 -1.313 0.000 0.628 1.438 0.412 2.548 0.730 1.594 0.091 0.000 0.958 0.885 0.991 0.771 0.509 0.818 0.704 +0 1.809 -0.096 1.147 0.382 -1.229 0.685 1.531 0.362 0.000 0.666 0.931 -1.224 2.215 0.767 0.164 -0.362 0.000 0.749 -0.776 -0.852 3.102 1.245 1.039 0.986 0.737 0.727 0.822 0.858 +1 0.965 -1.023 1.664 0.447 1.214 1.124 -0.504 0.021 2.173 0.830 -0.544 1.350 0.000 0.658 -1.076 -0.990 2.548 0.674 -1.400 -0.704 0.000 1.099 0.748 1.000 1.144 0.923 0.817 0.761 +1 0.513 0.943 1.607 1.385 -1.648 0.556 1.589 0.566 2.173 0.570 -0.014 -1.454 0.000 1.605 0.060 -0.138 0.000 0.603 0.449 0.257 1.551 1.362 0.801 0.977 0.908 0.381 0.764 1.002 +0 1.125 0.391 -1.106 0.286 -0.160 0.783 0.796 0.550 2.173 0.691 0.117 0.648 2.215 0.422 1.726 -0.894 0.000 0.781 0.946 1.600 0.000 0.812 0.925 0.980 0.795 0.389 0.728 0.679 +1 0.836 -0.253 -0.008 1.122 -1.322 0.537 -1.391 -0.035 0.000 1.045 0.046 1.432 1.107 0.756 1.400 0.595 0.000 0.586 0.869 -0.921 3.102 0.763 0.841 1.242 0.935 0.705 0.785 0.726 +1 0.305 0.267 -0.111 0.678 0.813 1.114 -1.429 -1.728 0.000 1.401 -0.736 -0.603 0.000 0.515 -1.662 1.131 0.000 1.051 -0.828 0.720 1.551 0.773 0.918 0.997 0.541 0.378 0.636 0.604 +1 0.537 -0.973 -0.549 1.827 1.549 0.965 -1.384 0.461 0.000 1.491 -1.086 -1.328 2.215 1.065 1.502 -0.239 0.000 0.952 -0.937 1.169 3.102 0.484 1.362 1.302 0.683 0.836 0.840 0.799 +0 0.467 0.585 -1.522 2.223 1.341 2.053 0.858 1.129 0.000 3.993 -0.465 -0.346 0.000 1.053 -1.054 -1.441 2.548 1.037 1.484 -1.062 0.000 2.304 1.400 0.997 1.770 0.919 1.417 1.254 +0 0.886 1.185 1.065 1.818 0.495 1.086 -0.379 -0.803 2.173 0.409 0.327 1.451 0.000 0.775 0.556 0.115 1.274 1.163 1.320 -1.626 0.000 1.003 0.765 0.984 2.125 1.029 1.342 1.083 +1 1.226 -0.925 -1.634 1.048 0.239 0.872 -0.574 0.053 2.173 0.849 -0.658 -1.257 0.000 1.116 -0.064 0.806 2.548 0.816 -1.385 1.576 0.000 0.839 0.920 1.559 0.994 0.833 0.823 0.731 +0 0.645 -0.635 0.250 1.541 -0.836 0.607 -1.067 1.393 0.000 0.727 -0.678 0.672 1.107 0.715 -0.139 -0.664 2.548 0.412 0.189 -1.465 0.000 0.942 0.844 1.146 0.882 0.746 0.629 0.682 +1 1.227 0.305 -0.007 0.053 1.055 1.924 -1.222 1.077 0.000 2.580 1.001 -0.383 2.215 1.558 1.097 -1.411 0.000 1.262 0.261 -1.116 3.102 0.558 0.593 0.937 1.211 1.163 0.913 0.918 +0 3.000 0.595 -1.143 0.286 0.726 0.985 -1.055 0.158 0.000 0.528 -0.814 1.258 2.215 0.335 2.681 -0.565 0.000 0.695 0.369 -1.694 0.000 0.966 1.209 1.274 1.492 0.440 1.057 1.042 +1 0.825 0.634 0.124 0.545 -0.789 1.098 -0.240 -1.390 1.087 1.113 0.281 1.102 0.000 1.077 0.234 0.530 1.274 1.329 0.087 -0.431 0.000 1.563 0.994 0.980 0.982 1.380 0.974 0.839 +0 2.047 -0.013 -0.207 0.522 -0.897 0.932 -1.085 1.225 1.087 1.249 -0.972 1.545 0.000 0.986 -0.543 -1.440 2.548 1.034 0.003 0.069 0.000 1.380 1.118 0.983 1.512 0.858 1.056 1.018 +0 2.845 -1.917 -0.008 0.431 -0.350 1.209 -0.040 1.537 2.173 0.425 -0.941 -1.648 2.215 0.609 -0.593 -0.113 0.000 0.740 0.327 -1.468 0.000 0.805 0.940 0.976 0.968 0.580 1.455 1.154 +1 1.950 0.475 0.793 0.076 -1.070 0.575 -2.178 -1.684 0.000 0.962 -0.113 -0.747 1.107 0.336 1.134 -0.055 0.000 0.874 -0.009 0.266 3.102 0.795 0.989 0.987 1.119 0.657 0.843 1.076 +0 1.075 -0.872 1.082 0.660 0.189 1.434 -1.452 -1.460 2.173 1.472 -0.490 0.051 2.215 0.457 -0.861 -0.822 0.000 0.888 -1.148 1.308 0.000 0.678 0.926 0.986 1.198 2.348 1.226 0.930 +1 0.333 1.779 0.070 1.431 -0.077 1.082 -0.065 -0.269 0.000 2.209 0.935 -1.606 0.000 2.272 0.104 0.976 2.548 1.346 0.864 0.484 3.102 1.210 1.360 0.975 1.129 0.859 1.166 1.101 +1 0.455 -1.187 0.661 2.285 0.138 1.165 -0.943 1.411 0.000 2.011 0.426 -0.801 2.215 0.727 0.666 1.662 2.548 0.908 0.857 0.642 0.000 2.082 1.331 0.987 1.551 1.041 1.370 1.281 +0 1.336 0.635 -0.449 0.724 0.378 1.004 -0.661 1.712 0.000 0.408 0.208 -1.299 0.000 0.925 -0.108 0.537 2.548 0.685 -0.817 -1.265 3.102 0.836 0.990 0.987 0.784 0.665 0.616 0.762 +0 0.699 -0.868 0.562 1.028 1.283 0.716 -0.144 -0.818 0.000 0.894 -0.264 1.654 2.215 1.019 0.554 -0.310 1.274 0.612 0.166 0.478 0.000 0.946 0.910 0.986 1.364 1.095 1.016 0.912 +0 0.899 -1.004 -0.112 0.621 0.868 0.861 -1.034 -1.707 1.087 0.942 -0.486 0.195 2.215 0.580 0.883 -0.144 0.000 1.289 -0.169 -1.310 0.000 1.017 0.975 0.989 0.973 1.362 0.922 0.891 +0 1.803 -0.892 0.053 0.736 -0.340 1.116 -1.420 -0.361 0.000 2.177 -0.331 1.409 2.215 1.370 -1.347 -1.695 2.548 0.415 -1.849 -1.699 0.000 1.058 1.147 0.986 1.804 1.264 1.314 1.219 +1 0.518 -0.616 0.407 0.686 -1.274 1.295 0.813 0.575 0.000 1.403 -1.609 1.461 0.000 3.306 -1.150 -0.372 1.274 2.111 -0.349 -1.233 1.551 0.579 1.071 0.986 1.336 1.664 1.263 1.105 +0 0.530 -0.330 -0.209 0.772 -1.498 0.330 -0.054 -1.228 0.000 1.242 0.192 -0.376 2.215 1.449 -0.409 1.246 1.274 0.428 2.266 1.434 0.000 1.145 1.146 0.986 0.862 1.493 1.021 0.836 +0 0.973 -1.433 0.925 0.681 -0.881 0.930 0.844 -1.415 2.173 0.756 0.122 -0.451 0.000 0.936 0.524 0.684 2.548 0.452 -0.253 1.380 0.000 0.927 1.026 1.126 1.097 1.115 1.199 1.077 +0 1.442 0.728 0.963 0.074 -1.734 0.840 0.090 -1.242 2.173 0.534 0.448 -0.247 0.000 1.250 0.016 0.201 2.548 0.647 -2.187 -0.948 0.000 0.853 0.904 0.984 0.760 1.231 0.826 0.708 +1 0.951 2.086 0.781 0.506 -0.813 1.241 0.934 -0.073 2.173 0.898 -2.554 1.363 0.000 0.746 0.347 -0.784 0.000 1.348 1.558 -1.684 0.000 1.207 0.766 0.988 1.017 0.945 0.841 0.782 +0 0.407 -0.196 -0.381 1.596 -1.649 0.908 1.446 0.231 0.000 0.731 -1.539 1.512 0.000 0.495 -0.789 0.203 0.000 0.677 2.424 -0.132 0.000 0.917 0.852 1.015 0.896 0.597 0.907 0.792 +0 1.620 -0.612 -0.866 0.129 1.166 0.573 -0.460 0.499 2.173 0.801 0.472 -1.462 0.000 0.378 0.110 -0.326 0.000 1.540 0.642 1.048 3.102 0.737 0.903 0.992 1.208 0.806 0.892 0.789 +0 1.027 2.419 -0.386 2.278 0.987 0.735 1.310 0.744 1.087 0.569 1.418 1.345 0.000 1.273 0.556 1.563 2.548 0.471 -0.377 0.464 0.000 0.889 0.719 2.004 1.749 0.921 1.221 1.040 +0 0.630 -0.699 0.366 0.684 -0.442 0.802 0.057 0.522 0.000 1.350 -1.436 -1.481 2.215 0.529 -0.602 1.606 2.548 0.548 -0.404 -1.031 0.000 1.036 0.752 0.994 1.357 0.491 0.885 0.848 +0 0.287 -0.084 0.945 1.606 1.096 2.767 -1.438 1.397 2.173 3.320 -0.742 -0.536 0.000 1.811 -0.781 -0.066 2.548 1.377 -1.342 -0.521 0.000 1.218 1.073 0.984 0.939 2.829 2.267 1.790 +0 1.830 -1.026 -1.290 0.601 -1.187 1.151 -0.762 0.224 2.173 1.188 -0.612 1.176 2.215 0.404 -0.274 -0.567 0.000 0.408 -1.537 0.183 0.000 0.474 0.729 0.980 1.413 1.307 1.116 0.841 +1 1.284 -0.304 -1.009 1.383 1.674 1.039 -1.248 -0.509 0.000 0.718 0.078 -0.588 0.000 1.188 -0.231 0.254 2.548 2.875 -0.040 1.029 3.102 0.824 1.069 1.222 1.094 0.920 0.953 0.835 +0 1.873 -0.405 -0.734 5.037 -0.783 2.091 -1.926 0.908 0.000 0.794 1.952 0.919 0.000 1.490 0.587 1.362 2.548 1.089 0.420 -1.185 3.102 0.896 0.935 1.017 0.781 0.732 1.226 1.551 +1 0.941 0.719 0.277 2.166 0.508 1.637 -1.743 -1.687 0.000 1.249 -0.130 -0.279 0.000 0.685 -0.511 1.234 2.548 1.235 0.005 -1.189 3.102 0.991 0.926 0.996 1.069 0.609 0.765 0.767 +0 2.112 0.773 -1.230 0.451 0.127 0.948 -0.661 0.476 1.087 0.323 0.727 1.659 0.000 0.480 -0.260 1.415 2.548 0.562 -1.530 -0.449 0.000 1.072 0.998 1.272 0.795 0.650 1.017 0.884 +1 1.164 1.204 -0.343 1.028 0.109 1.750 1.018 -0.532 0.000 1.141 0.499 1.572 0.000 1.365 0.763 1.015 2.548 0.898 1.016 1.642 0.000 0.482 0.642 0.983 0.975 0.352 0.624 0.717 +0 0.623 0.531 0.124 1.550 1.079 0.898 0.180 1.689 1.087 1.272 -0.578 -0.446 2.215 0.696 -0.136 0.290 0.000 0.711 0.746 -1.622 0.000 0.877 0.958 1.032 0.908 1.602 1.069 0.859 +1 0.322 1.112 -0.512 1.270 0.863 0.491 -1.520 -0.488 0.000 1.224 -0.968 -1.178 0.000 1.152 -0.406 1.026 1.274 1.006 0.803 0.041 3.102 1.058 1.245 0.984 0.696 0.896 1.036 1.532 +0 0.824 0.028 1.210 1.445 0.720 1.552 1.340 -0.965 2.173 2.269 0.800 0.531 1.107 1.594 0.769 -1.359 0.000 0.810 -1.336 -1.075 0.000 0.816 0.873 0.999 0.809 2.792 1.487 1.222 +1 0.612 1.032 -0.800 0.748 1.451 0.852 0.642 0.980 0.000 1.496 0.254 -0.794 2.215 1.131 0.540 0.007 2.548 0.859 1.372 0.467 0.000 0.897 0.874 0.989 0.830 0.942 0.977 0.831 +1 0.727 -0.875 -1.518 0.727 -0.390 0.900 0.075 0.537 2.173 0.674 0.996 -0.795 1.107 0.408 0.889 1.239 0.000 0.462 -0.494 -0.011 0.000 0.600 0.620 0.990 0.945 1.208 0.813 0.645 +1 1.042 -1.986 -1.397 0.952 -0.878 0.899 -1.170 0.376 2.173 0.501 -0.424 0.671 0.000 0.738 2.352 -1.695 0.000 0.811 -1.434 -0.021 0.000 0.854 0.807 0.976 0.792 0.641 0.816 0.698 +0 0.999 1.453 -1.555 0.359 0.825 0.825 1.195 0.438 2.173 1.129 0.549 1.244 2.215 1.372 1.919 -0.614 0.000 1.122 0.726 -0.320 0.000 0.970 1.099 0.991 0.713 1.050 1.035 0.885 +0 3.618 -0.465 0.565 4.835 0.336 4.140 0.036 -1.378 0.000 0.352 0.531 1.739 0.000 1.077 -0.133 -0.872 2.548 0.950 -0.229 -0.157 3.102 1.066 1.048 1.185 0.881 0.467 1.096 2.152 +0 0.332 1.310 -1.286 1.285 -0.030 2.277 0.658 -0.628 2.173 2.259 1.037 1.217 2.215 0.781 1.856 1.392 0.000 0.820 0.134 1.215 0.000 0.929 0.859 0.988 1.746 3.391 1.844 1.422 +0 0.571 1.152 0.449 0.791 1.593 0.531 0.073 0.775 2.173 0.722 1.570 -1.247 0.000 1.517 0.635 -0.640 2.548 0.605 -1.621 0.705 0.000 2.624 1.605 0.989 0.881 1.127 1.140 0.934 +0 0.478 1.680 1.198 2.412 1.349 1.775 -1.052 -0.650 2.173 1.017 0.053 0.900 2.215 1.296 -0.732 -0.072 0.000 0.446 -1.087 -1.197 0.000 0.743 0.988 0.990 6.583 2.268 4.134 3.190 +0 1.328 0.701 0.777 0.836 -0.270 1.105 0.059 -0.799 2.173 0.241 -1.497 0.845 0.000 0.315 -0.604 1.663 0.000 0.841 0.625 1.336 3.102 0.365 0.912 1.181 0.691 1.022 0.844 0.787 +1 0.895 0.999 0.482 0.778 -1.540 1.073 0.445 0.691 0.000 1.331 0.729 -1.598 1.107 0.720 0.881 0.049 0.000 1.744 0.309 -0.704 1.551 0.960 1.157 1.120 0.858 1.026 1.018 0.865 +1 1.148 -0.766 1.489 1.176 0.647 1.364 0.217 -0.663 2.173 0.945 -0.117 1.154 0.000 0.761 0.970 0.377 2.548 0.737 -0.528 -1.308 0.000 0.912 0.914 1.107 1.598 1.163 1.165 0.989 +0 0.664 -0.190 -0.437 1.647 -1.126 0.681 0.062 0.669 0.000 1.138 0.301 -0.483 0.000 2.077 -0.143 1.180 2.548 0.495 0.566 0.213 3.102 1.629 0.875 0.986 1.287 0.680 0.864 0.886 +0 1.102 1.059 0.257 0.428 1.388 0.595 0.527 -0.508 1.087 0.577 0.528 0.054 0.000 1.268 0.743 1.722 2.548 0.635 1.360 -1.128 0.000 0.824 0.829 0.991 0.742 0.991 0.700 0.619 +1 0.517 -0.862 0.411 0.951 0.500 1.804 -0.179 -0.641 0.000 1.324 1.172 1.297 2.215 1.105 0.230 0.732 0.000 2.076 0.800 -1.547 3.102 0.968 1.281 0.990 1.046 0.839 1.003 0.904 +1 0.881 -0.557 0.838 2.421 1.349 3.062 -0.162 -0.253 0.000 1.425 -0.508 1.668 0.000 1.334 -0.248 -1.198 2.548 1.484 0.373 1.282 3.102 4.452 2.613 0.996 0.627 0.935 1.712 1.557 +1 1.176 -0.152 0.820 0.838 -0.125 0.646 1.004 0.932 2.173 0.892 -1.545 -0.046 0.000 1.731 1.129 -0.870 0.000 1.762 0.580 1.607 3.102 1.003 1.005 1.034 0.866 0.669 0.794 0.898 +1 0.917 0.023 -1.116 1.062 1.622 0.773 -0.634 1.183 0.000 1.307 0.055 0.939 0.000 1.712 0.154 -0.453 0.000 1.092 -0.298 -0.752 3.102 0.926 0.980 0.991 0.845 0.492 0.680 0.707 +1 0.285 1.180 -1.395 0.874 -0.100 1.047 -2.411 -1.456 0.000 0.906 -0.403 -1.228 0.000 1.665 -1.129 0.549 1.274 1.266 -0.444 1.465 0.000 0.918 0.992 0.995 0.943 0.597 0.856 0.766 +0 0.615 0.321 -1.440 0.940 0.117 0.659 -1.756 -0.873 0.000 0.574 -1.221 0.205 2.215 0.429 -0.208 1.479 0.000 1.329 1.066 1.215 3.102 1.152 0.881 1.039 0.785 1.434 1.158 0.983 +0 0.849 0.025 0.907 0.311 -0.883 0.814 0.406 0.112 0.000 0.390 -2.292 0.965 0.000 1.352 0.548 -1.257 2.548 0.413 1.892 -0.914 0.000 0.885 0.996 0.992 0.926 1.063 0.845 0.751 +0 0.294 -0.629 0.163 2.151 1.221 1.490 -0.007 -0.838 1.087 0.850 0.225 0.611 1.107 0.867 -0.469 -0.329 0.000 0.511 0.436 -1.707 0.000 0.801 0.809 0.982 1.064 1.610 1.363 1.054 +1 0.620 0.702 0.930 0.622 -0.923 1.023 0.645 -1.191 2.173 0.534 1.050 0.506 0.000 1.407 -0.002 0.496 0.000 1.170 0.452 -0.045 0.000 0.769 0.991 0.985 0.839 0.797 0.928 0.835 +0 0.548 -0.322 0.010 1.802 -1.035 0.598 1.508 0.557 1.087 0.970 0.880 1.381 2.215 0.521 -0.267 -0.805 0.000 0.396 2.116 -1.478 0.000 1.013 0.936 1.113 1.221 0.835 1.103 0.923 +0 2.205 -0.455 -0.427 0.647 -0.381 0.949 -1.460 -1.681 2.173 1.238 -1.046 1.401 0.000 1.317 -1.515 1.001 0.000 1.864 -0.953 -0.049 3.102 0.905 0.914 0.991 0.661 1.420 1.017 1.096 +0 0.721 0.826 -1.392 1.266 1.213 1.075 1.297 0.154 0.000 0.837 -0.933 -1.317 0.000 1.256 -1.335 -0.773 1.274 1.878 -0.874 -0.271 3.102 0.722 2.096 0.990 1.424 0.569 1.793 1.475 +1 0.721 0.562 -0.322 1.328 -1.392 0.858 0.966 0.977 2.173 0.994 1.559 -0.548 0.000 1.513 -0.135 0.304 2.548 0.837 -0.252 1.456 0.000 0.800 0.881 1.113 1.063 1.166 0.946 0.776 +1 0.790 -0.268 -1.410 0.680 -0.373 0.989 -0.245 0.137 0.000 1.361 0.623 -1.661 2.215 0.775 0.082 0.790 2.548 0.575 0.437 -0.362 0.000 0.677 0.640 0.981 1.120 0.927 0.903 0.852 +0 0.618 -1.474 0.951 0.628 -0.559 0.960 -1.308 1.498 2.173 0.710 0.138 1.406 0.000 1.552 0.321 -0.597 2.548 1.267 -0.188 -0.431 0.000 0.879 0.803 0.990 0.913 2.030 1.269 1.085 +1 0.792 0.723 -0.290 0.843 0.910 0.680 1.042 1.454 0.000 1.246 1.393 -0.379 0.000 1.099 0.319 -1.216 1.274 0.720 0.775 0.322 3.102 1.983 1.134 1.000 0.800 0.698 0.839 0.744 +0 1.860 1.421 1.642 1.251 -0.864 0.983 -0.763 0.147 1.087 0.814 -0.613 0.684 0.000 0.306 0.007 1.657 0.000 0.616 -0.032 -1.125 3.102 0.639 0.739 1.635 0.941 0.811 1.467 1.209 +0 2.804 0.565 -1.336 1.919 -0.972 1.694 0.491 0.428 2.173 1.404 0.029 0.801 2.215 0.502 -0.445 -0.925 0.000 0.517 -0.515 -0.082 0.000 0.389 1.024 1.037 1.822 0.918 1.705 1.265 +0 2.910 1.083 1.245 1.379 1.296 2.551 -0.269 -0.662 0.000 0.688 1.028 0.558 2.215 0.565 -0.904 0.096 2.548 0.665 -1.428 0.953 0.000 2.519 1.474 0.982 0.847 0.848 1.272 1.889 +1 0.861 -0.359 0.845 0.846 0.155 0.820 -1.640 -0.177 0.000 1.087 -1.049 -1.540 0.000 1.652 -1.230 1.515 0.000 0.968 0.549 0.551 3.102 0.826 0.651 0.985 0.783 0.586 0.812 0.790 +0 0.956 -0.152 -1.150 0.555 -1.418 1.297 -1.710 -0.251 0.000 1.170 -0.117 0.858 0.000 1.392 0.198 1.643 2.548 0.711 -0.598 -1.183 1.551 1.068 0.921 0.990 0.882 0.563 1.169 0.985 +0 0.596 -1.391 1.520 1.163 -0.743 1.394 0.759 0.781 0.000 0.900 -0.589 -0.682 1.107 1.520 -1.228 0.126 2.548 0.853 0.264 1.446 0.000 1.012 1.611 1.029 0.872 0.953 1.396 1.300 +1 1.526 -0.977 0.157 0.228 0.633 1.495 0.267 -0.743 0.000 1.683 -1.606 1.627 0.000 0.707 -2.103 1.176 0.000 0.945 -1.581 0.180 0.000 0.866 0.844 0.988 1.050 0.639 1.056 0.977 +0 0.593 0.032 0.394 0.441 0.554 0.380 -0.361 1.396 0.000 0.892 0.491 -1.609 0.000 1.050 0.866 0.114 0.000 1.215 0.780 -1.108 3.102 0.958 0.669 0.991 0.555 0.175 0.495 0.555 +0 0.986 -0.946 -1.270 0.806 0.198 0.852 0.076 -0.508 2.173 0.750 0.074 -1.498 0.000 0.867 0.000 0.397 2.548 0.497 -1.956 0.656 0.000 1.426 1.057 1.198 0.956 0.780 0.856 0.786 +1 0.921 0.034 0.288 0.574 1.675 0.503 0.319 -0.303 0.000 0.805 -0.121 1.683 2.215 1.029 0.927 1.167 2.548 0.885 -0.516 -0.630 0.000 0.579 0.994 0.988 0.695 0.727 0.741 0.666 +1 0.611 1.623 -0.025 0.763 1.499 0.712 0.973 0.684 0.000 0.909 1.810 -1.112 0.000 1.402 0.022 -1.252 2.548 0.952 0.029 0.121 3.102 0.857 1.113 0.990 0.700 0.834 0.712 0.682 +0 1.207 -0.395 -1.344 0.876 -0.429 0.853 1.022 0.542 2.173 0.410 -1.478 1.344 0.000 0.531 -0.216 -0.400 2.548 0.454 -0.115 -1.573 0.000 0.503 1.235 1.047 0.558 0.848 0.869 0.784 +1 0.561 0.541 -0.475 0.494 0.499 1.439 0.775 0.797 0.000 1.218 0.362 -1.458 0.000 1.705 -0.470 -0.501 2.548 1.245 -0.982 -1.260 3.102 2.583 2.069 0.982 0.708 0.797 1.515 1.187 +1 1.959 0.669 1.468 0.814 0.414 0.773 0.094 -0.952 2.173 1.370 -0.065 0.060 2.215 0.785 -0.271 1.280 0.000 0.432 2.020 -1.011 0.000 1.300 1.092 1.422 1.336 1.204 1.091 0.966 +0 0.543 0.367 -1.562 0.658 1.292 1.034 0.882 0.056 2.173 1.147 -1.689 -1.376 0.000 0.511 -2.201 -1.110 0.000 0.450 -0.744 0.245 0.000 1.016 0.687 0.999 1.074 0.870 1.320 1.362 +0 1.132 0.770 -1.601 2.048 1.131 0.575 -0.119 -0.270 0.000 0.856 0.511 -0.576 2.215 1.056 -1.033 -0.761 0.000 1.242 0.393 0.474 3.102 0.955 1.007 1.326 1.208 0.755 0.866 1.028 +1 0.992 0.323 1.360 1.309 1.312 0.792 -0.236 -0.180 2.173 0.714 1.669 0.099 0.000 0.366 0.148 0.004 2.548 0.494 -0.639 -1.375 0.000 1.269 1.181 0.991 0.682 0.175 0.761 0.865 +0 1.263 0.591 1.721 0.541 -1.539 0.917 -0.626 0.873 2.173 0.972 -0.950 0.014 0.000 0.949 0.442 -0.122 0.000 1.157 -0.602 -1.547 3.102 0.886 0.838 0.975 1.029 0.894 0.808 0.780 +1 0.613 0.834 1.542 1.257 0.423 1.187 0.826 -1.292 0.000 1.176 0.306 0.291 1.107 0.461 0.964 -0.492 0.000 1.263 -0.080 1.586 3.102 0.891 0.908 1.029 0.735 1.037 0.941 0.848 +0 1.766 -0.540 1.634 0.886 1.251 0.822 0.443 -0.761 0.000 0.932 0.486 0.327 2.215 0.277 0.057 0.010 1.274 0.411 1.708 -0.231 0.000 0.890 0.952 0.994 0.688 0.193 0.840 0.962 +1 0.380 0.822 -0.459 1.136 1.030 0.731 0.463 -1.290 0.000 0.926 -0.227 0.281 2.215 0.993 0.536 1.547 0.000 1.876 -0.024 -0.391 3.102 0.939 0.956 0.984 1.115 0.688 0.908 0.803 +1 1.031 -0.232 1.388 0.919 1.384 2.153 0.966 -0.186 0.000 1.244 1.030 -1.638 1.107 1.028 -0.378 -1.479 2.548 1.241 0.707 1.205 0.000 1.265 1.002 0.991 1.534 0.985 1.020 0.949 +1 0.766 -0.842 0.669 1.097 -0.613 0.959 -0.425 -0.203 2.173 0.470 1.606 1.172 0.000 1.423 0.735 -1.590 0.000 0.806 -1.824 1.458 0.000 0.937 0.637 1.161 0.781 0.691 0.959 0.967 +0 3.148 -1.131 0.928 3.036 0.855 2.728 0.630 -0.797 1.087 0.728 -1.028 1.383 2.215 0.600 1.368 -1.094 0.000 1.221 0.215 -0.431 0.000 0.821 0.873 0.988 0.803 2.750 3.214 2.472 +0 0.652 2.075 0.301 1.056 -0.176 0.761 -2.139 -0.095 0.000 1.745 0.164 -1.541 2.215 0.490 1.889 -1.521 0.000 1.363 1.005 -1.522 1.551 4.618 3.088 0.983 1.495 0.755 2.050 1.846 +0 1.365 -0.329 -1.385 0.978 -0.489 0.604 0.335 0.728 0.000 1.087 0.735 1.613 2.215 1.603 0.065 -0.537 0.000 1.375 1.136 0.658 1.551 1.625 1.251 1.157 1.104 0.903 0.996 0.988 +0 0.414 1.082 -0.073 0.779 -1.015 0.867 0.837 0.944 0.000 0.639 0.331 1.341 2.215 1.114 0.017 -0.787 2.548 0.488 -1.642 0.905 0.000 0.500 0.760 0.982 0.565 0.856 0.632 0.595 +1 1.443 0.081 1.518 0.492 -0.696 0.874 -0.659 0.523 2.173 0.970 1.618 -0.069 0.000 1.579 0.947 -1.335 2.548 0.537 0.431 -0.922 0.000 0.867 1.013 1.064 1.032 2.022 1.258 1.066 +1 0.959 0.631 0.626 1.297 -1.127 1.776 0.034 1.244 2.173 1.614 -1.275 -0.638 0.000 1.121 0.037 -0.239 0.000 1.118 -0.052 0.511 0.000 0.829 0.933 1.546 1.436 0.946 0.996 1.042 +1 1.024 0.823 1.306 0.614 -0.710 1.011 0.085 -0.113 0.000 1.355 0.340 -1.386 2.215 1.332 1.536 0.826 2.548 0.582 -0.634 -0.690 0.000 0.870 0.993 1.066 0.817 1.658 0.907 0.767 +0 0.398 1.393 0.134 0.795 0.231 0.499 0.779 -0.254 0.000 0.733 0.096 1.676 2.215 0.772 1.431 1.270 1.274 0.701 0.409 -1.421 0.000 0.956 1.070 0.999 0.896 0.704 0.728 0.759 +1 1.459 -0.547 0.110 0.413 -1.093 1.113 -0.163 -0.666 2.173 0.903 -0.139 0.892 0.000 0.985 2.192 -1.390 0.000 1.015 -1.096 1.263 0.000 0.892 0.826 0.986 0.859 0.867 0.884 0.806 +1 0.493 -0.955 -1.647 1.599 0.260 0.941 -0.965 1.318 0.000 1.555 0.627 -0.940 0.000 1.586 -0.640 -0.041 1.274 0.783 -1.401 -1.542 0.000 0.845 0.706 1.216 0.773 0.697 0.796 0.744 +1 0.953 -0.214 -1.640 0.630 -0.143 0.994 0.531 -1.572 0.000 1.183 -1.841 -0.164 0.000 2.359 0.271 0.646 2.548 1.014 0.662 -0.970 3.102 4.168 2.462 1.047 1.032 1.211 1.833 1.381 +1 0.690 0.073 -0.358 0.712 -1.421 0.696 -0.193 1.278 0.000 0.911 1.353 0.614 1.107 0.596 0.921 -0.608 2.548 0.379 1.303 -0.896 0.000 0.862 1.016 0.980 0.702 0.714 0.835 0.717 +1 0.837 -0.660 -0.878 0.797 -1.566 1.424 -0.747 -0.627 2.173 0.912 -0.623 1.347 0.000 0.739 0.024 0.358 2.548 1.156 -1.230 1.050 0.000 0.675 0.796 0.988 1.001 1.116 1.018 0.911 +1 0.455 -2.335 -1.625 1.149 0.431 1.018 -0.877 -1.405 2.173 0.617 -1.589 0.333 0.000 0.833 -1.966 -0.548 0.000 0.724 -0.047 1.135 1.551 0.837 0.913 0.989 1.183 0.786 0.856 0.810 +0 0.278 1.453 -1.511 2.099 -0.632 1.225 -0.131 1.121 2.173 0.943 -0.911 -1.204 0.000 0.865 -0.764 0.191 0.000 0.616 0.104 1.663 3.102 1.320 0.859 0.987 1.505 0.447 0.941 0.970 +0 2.265 -1.757 -0.714 0.806 -0.618 1.390 -0.433 1.142 2.173 0.940 -1.120 -0.305 1.107 0.472 2.270 1.601 0.000 0.436 -1.739 0.499 0.000 2.481 2.014 0.985 0.861 1.735 1.580 1.963 +0 1.511 -0.571 -0.841 3.200 -0.505 2.003 -2.480 1.124 0.000 1.232 -0.015 -0.219 0.000 1.349 1.177 1.361 1.274 1.762 0.235 1.220 3.102 1.428 1.279 0.996 2.343 0.639 1.671 1.415 +0 1.460 -0.679 -1.205 0.550 1.183 0.963 -1.503 -0.133 2.173 0.904 -0.649 0.959 2.215 0.449 1.168 1.454 0.000 0.431 -1.594 1.726 0.000 0.718 0.811 1.038 0.846 1.291 0.925 0.739 +0 0.707 -0.365 -0.849 0.528 -0.872 0.202 -1.131 0.550 0.000 0.551 -0.005 0.506 2.215 0.370 -1.459 1.690 2.548 0.408 -1.730 0.927 0.000 0.478 0.529 0.978 0.547 0.598 0.592 0.491 +1 1.283 0.246 -0.730 0.757 -0.500 1.092 -0.297 1.398 2.173 0.971 -1.796 -1.065 0.000 1.445 -0.090 0.664 1.274 0.988 -0.787 0.154 0.000 1.283 1.453 0.989 1.476 0.975 1.157 1.303 +0 1.075 1.250 -0.088 0.981 1.190 0.766 0.624 1.260 1.087 1.473 0.879 -0.849 0.000 0.483 -0.602 0.590 0.000 0.374 0.018 0.398 3.102 1.674 0.950 1.299 0.930 0.435 0.816 0.811 +0 0.574 -1.110 1.061 1.313 -0.371 0.664 0.733 -1.635 2.173 0.639 -2.206 -0.650 0.000 1.122 -0.043 1.164 2.548 0.597 -0.105 0.312 0.000 1.205 1.256 1.155 1.338 0.759 1.132 1.002 +1 0.812 0.883 -1.651 0.479 -0.442 0.824 0.736 -0.537 0.000 1.140 0.584 0.911 0.000 0.649 -1.313 0.870 2.548 1.115 0.573 -1.425 3.102 1.521 1.071 0.989 1.328 1.008 0.916 0.946 +1 1.084 1.831 -1.312 0.792 -0.704 0.550 0.742 0.246 0.000 0.561 0.238 -0.470 1.107 0.552 0.139 1.347 2.548 1.189 -0.563 1.011 0.000 1.056 0.857 0.989 0.777 0.591 0.619 0.706 +1 0.596 -1.860 1.307 0.964 -1.402 0.667 0.614 0.534 1.087 0.431 0.099 0.916 0.000 0.633 1.008 -1.317 2.548 0.925 0.805 -0.609 0.000 0.886 0.711 0.979 1.004 0.830 0.950 0.816 +0 1.242 0.717 -0.164 0.667 -0.984 0.662 0.844 -1.482 2.173 0.750 -0.283 1.444 2.215 1.191 -0.707 0.155 0.000 0.374 -0.665 1.508 0.000 0.691 1.166 0.989 0.936 0.802 0.781 0.773 +0 1.830 0.236 0.039 1.430 -0.142 1.040 -0.831 1.678 0.000 0.868 -0.133 -1.180 2.215 1.221 -0.241 1.342 0.000 0.554 -0.946 0.875 3.102 0.845 0.959 0.980 0.745 0.684 0.772 0.978 +0 1.269 0.041 -1.239 1.690 -0.722 1.347 0.915 -1.022 0.000 1.165 -1.096 0.618 2.215 1.505 -0.247 0.368 0.000 2.723 -0.177 0.979 3.102 0.759 0.822 0.984 1.613 0.919 1.287 1.027 +0 0.802 -0.011 -0.135 1.578 0.973 0.704 -0.933 1.249 2.173 0.754 0.409 -0.745 0.000 0.781 1.651 -0.811 0.000 1.126 1.201 -1.361 3.102 0.890 0.670 1.311 0.960 1.559 1.115 1.038 +1 1.021 -0.482 -0.027 1.195 -1.223 0.555 -1.247 0.522 0.000 0.794 0.484 1.425 2.215 0.641 -1.858 -0.334 0.000 1.284 -0.773 -1.525 3.102 0.863 0.906 1.348 1.049 0.824 0.915 0.860 +1 0.381 -0.514 0.996 1.168 1.562 0.569 -2.456 -0.025 0.000 1.022 -0.725 -0.457 2.215 1.221 0.630 0.502 0.000 2.809 0.878 1.735 0.000 0.792 0.763 0.980 1.255 1.109 0.945 0.862 +1 0.640 -0.490 -0.596 0.723 1.635 1.690 0.027 0.142 0.000 1.304 -0.819 -1.177 0.000 1.765 0.224 1.474 2.548 1.075 -1.213 -0.636 0.000 0.868 0.876 0.984 0.760 0.406 0.905 0.769 +1 0.295 1.251 0.264 2.782 1.122 0.417 -0.247 -0.206 2.173 0.822 1.009 -1.059 2.215 0.469 1.438 -1.375 0.000 1.461 1.324 -0.751 0.000 0.908 0.996 0.990 1.221 0.844 1.160 0.955 +1 1.495 -0.338 -1.049 1.067 -0.902 0.345 1.336 1.473 2.173 1.312 0.626 0.330 2.215 0.510 2.368 0.532 0.000 0.791 1.938 1.293 0.000 0.454 1.081 0.986 0.923 0.921 0.970 1.015 +0 0.659 0.936 -1.531 0.878 -0.677 0.714 -1.147 0.217 2.173 0.325 -1.954 -1.574 0.000 0.423 -0.376 1.603 2.548 0.546 0.206 0.947 0.000 0.868 0.826 0.990 0.830 0.700 1.236 1.091 +1 1.086 -0.797 -0.167 0.570 -1.222 0.990 -1.778 1.459 0.000 0.358 2.543 0.116 0.000 0.716 -0.101 0.458 2.548 0.705 -2.197 -1.679 0.000 0.678 0.963 0.988 0.715 0.576 0.834 0.772 +1 0.935 1.386 0.314 0.735 -1.534 1.892 0.725 0.813 1.087 1.135 -0.049 -1.257 0.000 0.497 0.862 1.026 2.548 2.018 0.889 -0.022 0.000 0.849 1.029 1.144 1.167 0.264 1.433 1.233 +0 1.013 0.642 0.595 0.476 1.088 0.822 2.012 1.408 0.000 1.645 0.881 -0.386 2.215 0.593 1.630 -0.867 0.000 1.310 0.438 -1.521 3.102 1.124 1.010 0.985 1.207 1.159 1.050 1.057 +0 0.492 -1.491 -0.259 1.513 0.715 0.761 0.454 -0.750 0.000 0.830 -1.158 1.700 1.107 1.047 -0.294 0.060 2.548 1.044 0.441 -1.605 0.000 0.948 0.981 0.985 0.853 1.082 0.909 0.925 +1 1.189 1.158 -1.077 0.895 1.303 0.864 0.852 -1.384 0.000 1.442 1.201 0.230 2.215 1.511 0.514 0.997 2.548 1.444 0.809 -0.732 0.000 0.951 1.266 1.200 1.159 1.136 1.084 0.948 +1 0.785 0.175 0.246 1.043 1.381 1.160 -2.034 -0.698 0.000 1.622 -0.842 -1.298 0.000 3.071 -1.208 0.647 2.548 1.542 0.219 -1.643 3.102 0.993 0.764 1.070 1.429 2.072 1.254 1.051 +1 0.520 0.637 1.495 1.219 -1.409 1.301 -0.523 0.278 1.087 0.746 -0.121 0.968 0.000 2.130 0.774 -1.221 0.000 1.967 0.661 0.207 3.102 0.906 1.188 0.978 1.316 1.206 1.313 1.168 +0 0.598 -1.858 -0.120 0.249 -1.365 0.331 -2.332 -1.239 0.000 0.923 0.373 1.524 2.215 0.522 -1.171 0.652 2.548 0.976 -0.581 -0.056 0.000 1.106 0.728 0.986 0.976 0.865 0.863 0.742 +0 0.645 1.182 -1.216 1.166 0.587 0.544 0.695 0.444 2.173 0.338 -2.558 -0.931 0.000 0.582 -0.128 -0.874 0.000 0.401 1.258 1.659 0.000 1.084 0.695 1.200 0.719 0.807 0.850 1.021 +0 0.552 1.397 0.414 0.884 -1.056 0.426 1.775 -0.323 0.000 0.921 -1.982 1.169 0.000 1.629 -0.163 -1.031 2.548 0.846 0.545 0.381 0.000 0.773 0.754 0.986 1.267 1.025 0.899 0.807 +0 1.453 2.074 1.410 0.411 -1.723 0.994 1.816 -0.922 0.000 0.765 1.453 0.732 2.215 0.880 0.306 -0.638 0.000 1.424 -0.200 0.687 1.551 0.849 0.982 0.995 0.726 0.926 0.873 0.851 +1 1.563 -0.440 -0.525 0.291 -1.027 0.937 0.746 1.284 0.000 0.487 -0.326 -0.300 0.000 0.857 -0.483 0.819 2.548 1.091 1.375 1.299 0.000 0.913 0.876 0.984 0.562 0.486 0.573 0.627 +0 0.616 -1.337 0.361 1.222 0.980 1.020 -0.433 -0.552 2.173 0.567 2.504 -1.572 0.000 1.157 0.088 1.524 2.548 0.711 0.658 0.351 0.000 1.173 1.226 0.984 1.613 1.344 1.336 2.007 +1 0.698 1.866 -1.198 0.694 0.469 0.418 0.794 -0.729 0.000 0.937 0.245 1.196 2.215 0.502 1.118 1.245 0.000 0.796 -1.329 -0.627 1.551 0.824 1.063 0.988 0.938 1.136 1.046 0.857 +1 0.486 0.996 -0.332 0.500 0.087 1.116 0.145 0.181 2.173 0.752 -0.105 -1.482 0.000 1.271 0.831 1.606 0.000 0.800 -1.031 1.243 0.000 0.879 1.305 0.987 0.537 0.734 0.791 0.689 +0 0.555 0.308 -1.614 1.483 -0.530 1.186 0.670 1.559 1.087 1.550 0.791 0.397 0.000 0.569 1.139 -0.434 0.000 0.594 -0.199 -0.976 3.102 1.031 0.886 1.043 1.158 0.792 0.945 0.881 +1 0.853 0.725 -0.242 1.191 0.202 1.176 1.081 -0.358 0.000 0.952 0.627 0.952 1.107 0.707 1.639 1.459 0.000 1.298 0.015 -1.662 3.102 1.010 0.977 0.978 1.120 0.779 0.868 0.808 +0 0.713 2.002 1.506 0.589 0.324 0.964 -0.887 -1.201 0.000 0.739 1.144 -0.181 2.215 1.182 0.567 0.992 0.000 1.161 0.799 -1.313 3.102 0.910 0.859 0.989 0.684 0.718 0.634 0.618 +1 0.955 1.692 -0.461 0.558 -0.006 0.977 0.592 -1.369 0.000 1.003 1.527 0.991 2.215 1.252 0.947 0.159 2.548 0.423 -1.318 1.251 0.000 1.086 0.993 0.977 0.951 0.873 0.836 0.775 +0 0.417 1.279 0.590 1.059 -0.408 1.202 -0.306 1.116 1.087 1.499 0.935 -0.514 2.215 1.081 0.351 1.150 0.000 0.524 1.908 1.201 0.000 0.917 1.156 0.989 0.775 2.379 1.265 1.041 +1 0.498 1.290 -1.594 0.869 0.886 1.033 0.239 -0.595 0.000 1.067 0.687 1.450 2.215 0.974 0.933 -0.867 0.000 1.473 0.011 0.478 3.102 0.847 1.111 0.986 0.635 0.957 0.972 0.838 +1 0.995 -0.144 -1.352 0.625 1.117 0.783 0.840 -0.587 0.000 0.647 -0.298 -0.415 2.215 1.401 0.195 1.113 1.274 0.928 -1.547 0.842 0.000 2.662 1.510 0.990 0.701 1.028 1.112 0.937 +0 0.588 0.490 -0.137 1.607 0.439 0.457 -0.449 -1.272 0.000 1.017 -1.317 -0.695 1.107 1.084 -1.229 1.550 0.000 1.156 -0.334 1.638 3.102 0.923 0.967 0.996 1.155 0.974 1.414 1.296 +1 0.361 1.296 0.253 2.219 -0.519 1.460 0.541 1.522 2.173 0.748 0.440 -1.186 2.215 1.310 -0.781 -0.054 0.000 1.239 -0.517 0.422 0.000 1.034 1.124 0.989 2.119 0.992 1.440 1.647 +0 0.522 0.673 1.375 0.810 -1.038 0.827 -1.139 0.225 2.173 0.536 -0.735 -0.198 2.215 1.062 -0.609 1.450 0.000 1.218 0.313 -1.486 0.000 0.908 0.911 0.987 1.033 0.413 0.836 0.752 +0 0.673 -0.093 -0.641 0.895 0.400 0.580 -0.460 -1.628 0.000 0.442 -0.782 -0.766 1.107 1.048 0.022 0.823 2.548 1.249 0.747 -0.067 0.000 0.999 0.876 0.988 0.718 0.780 0.671 0.687 +0 0.323 -0.612 -0.674 1.572 -1.658 1.085 -1.065 0.500 0.000 1.298 -1.658 -0.932 0.000 1.056 -0.243 0.949 2.548 1.301 -1.402 0.851 3.102 0.788 1.140 0.982 1.247 0.694 1.206 1.022 +0 0.795 0.140 -0.749 1.015 -1.068 0.493 -1.159 0.144 2.173 0.667 -0.358 -0.624 0.000 0.699 0.239 1.413 0.000 1.435 -0.861 1.190 3.102 1.065 0.924 0.975 1.341 0.722 1.098 0.908 +1 0.975 2.058 0.686 1.015 0.081 0.831 0.705 -0.999 2.173 0.593 1.054 1.663 0.000 0.716 -2.126 -1.049 0.000 1.030 1.131 0.573 0.000 0.858 0.975 0.992 1.016 0.986 0.945 0.801 +0 0.845 1.026 -0.231 1.208 0.641 1.112 2.071 -1.213 0.000 0.741 -0.818 -0.355 2.215 0.913 -0.531 1.544 0.000 1.169 0.895 1.272 0.000 0.836 0.994 0.991 0.616 0.868 0.785 0.693 +0 0.352 0.838 0.984 2.691 1.306 1.038 1.023 -0.630 2.173 0.880 0.241 -0.729 0.000 1.183 -0.001 0.413 2.548 0.868 0.122 -1.661 0.000 0.849 0.940 0.985 1.826 1.341 1.268 1.051 +0 1.195 0.272 -1.599 0.357 -1.185 0.477 -1.237 1.368 2.173 0.810 0.329 -0.163 0.000 1.148 -0.846 0.348 2.548 0.474 0.662 1.223 0.000 0.788 1.027 0.999 1.264 0.748 1.001 0.861 +0 2.342 -1.590 1.625 0.750 -1.145 1.513 0.440 -0.019 2.173 0.600 -0.055 0.146 0.000 1.147 -0.900 -1.612 2.548 0.371 -0.439 -1.550 0.000 0.632 0.760 1.106 2.743 2.069 1.794 1.322 +0 0.500 -0.872 1.264 0.767 -1.713 1.014 -0.311 0.633 2.173 1.145 -0.142 -1.509 0.000 1.050 0.233 -0.153 1.274 1.684 -1.317 -0.525 0.000 1.022 1.028 0.990 1.393 0.915 1.165 1.148 +0 0.281 0.263 1.035 2.106 -1.301 0.952 -0.352 -0.688 2.173 0.771 -0.351 -0.023 0.000 1.716 -0.995 1.204 2.548 1.554 0.096 0.505 0.000 0.778 0.944 0.986 0.849 1.688 0.992 0.889 +0 1.464 1.744 -0.451 1.253 0.104 1.117 1.434 -1.687 0.000 1.552 0.783 0.142 1.107 1.420 0.664 1.364 2.548 1.304 0.290 -1.564 0.000 1.086 0.876 0.981 1.117 1.409 1.154 1.230 +1 1.150 -1.556 -1.051 0.368 -0.464 0.686 -0.252 -0.469 2.173 0.880 -1.415 0.361 0.000 0.705 -2.230 0.958 0.000 1.166 -0.699 -1.460 3.102 0.891 0.825 1.001 0.751 0.788 0.874 0.780 +1 0.829 0.175 -1.635 1.634 -1.267 0.485 2.156 -0.234 0.000 0.536 -0.679 0.732 0.000 0.626 1.387 0.337 0.000 1.066 -1.199 1.292 3.102 1.243 0.801 0.976 0.819 0.691 0.740 0.814 +0 0.840 -0.872 0.152 0.473 1.329 1.468 -1.238 -0.137 2.173 1.043 0.327 1.603 0.000 1.352 -0.170 -1.531 1.274 0.587 -0.644 1.243 0.000 0.679 0.587 0.990 1.051 1.934 1.262 1.010 +0 0.374 -0.832 0.956 0.923 -0.913 1.463 0.317 0.483 2.173 0.855 -0.982 -1.439 2.215 1.010 0.498 -0.739 0.000 1.367 0.239 -1.502 0.000 0.839 0.962 0.988 1.846 2.006 1.352 1.212 +1 0.875 0.761 -1.431 0.296 -0.013 0.953 0.639 0.586 0.000 1.389 1.333 -0.975 2.215 0.827 1.584 0.685 0.000 0.816 0.992 1.372 3.102 0.929 0.709 0.982 0.928 0.827 0.939 0.852 +1 1.004 2.119 1.087 0.962 -0.192 1.337 1.017 -1.592 2.173 0.932 -0.968 0.263 0.000 0.820 -1.595 0.130 0.000 0.585 1.275 -0.779 1.551 0.949 0.980 1.244 1.435 0.670 1.157 1.290 +0 0.852 0.020 0.730 0.506 -0.891 1.080 0.026 -0.156 2.173 0.883 1.895 -1.649 0.000 1.700 -1.733 -1.540 0.000 1.210 0.614 0.424 0.000 1.553 1.299 0.984 0.816 0.861 1.145 0.916 +0 0.835 -0.911 0.622 0.436 -0.916 1.437 -0.935 -0.856 0.000 1.295 -0.086 1.105 2.215 0.875 0.678 1.310 2.548 0.780 0.642 0.578 0.000 2.205 1.714 0.988 0.787 0.528 1.260 0.999 +1 1.273 0.578 -1.504 0.366 1.536 2.400 0.606 -0.424 0.000 1.108 0.053 0.323 0.000 1.946 0.526 1.486 0.000 2.766 0.983 1.287 3.102 0.937 0.908 0.981 0.792 0.770 0.678 0.641 +1 0.788 -1.615 -0.225 1.188 -0.385 2.395 -1.534 1.187 0.000 2.656 -0.660 -0.647 0.000 1.113 -0.674 0.983 2.548 0.812 0.456 -0.910 3.102 1.158 0.876 0.987 1.323 0.877 1.222 1.265 +1 0.966 0.666 0.055 1.455 0.828 0.355 2.038 0.708 0.000 0.691 -0.369 -1.654 2.215 0.862 0.788 -1.095 2.548 0.713 1.280 -1.219 0.000 0.784 1.113 1.055 0.909 0.674 0.795 0.768 +1 0.404 -1.174 -0.508 1.436 1.090 0.701 0.343 1.637 0.000 0.899 1.893 -1.050 0.000 1.101 -0.514 0.251 2.548 1.519 0.943 -0.096 3.102 0.827 1.134 1.046 1.444 0.991 0.987 0.979 +1 0.706 -0.230 1.538 0.754 1.107 0.891 -0.082 0.774 2.173 1.396 -0.678 -1.064 0.000 1.086 -1.149 -0.332 2.548 1.056 -1.679 -0.950 0.000 0.816 1.237 0.984 0.881 1.283 0.870 0.786 +1 0.996 0.662 0.845 0.471 -0.146 0.621 1.557 -1.517 0.000 1.330 -0.083 0.244 2.215 1.157 -1.036 1.411 0.000 1.063 0.064 -0.977 3.102 0.780 0.767 0.984 0.691 0.961 0.983 0.856 +1 0.290 -0.999 1.421 0.347 0.151 0.555 -0.649 -0.479 2.173 0.756 1.104 1.371 0.000 0.757 0.844 0.486 0.000 1.026 0.756 -1.168 1.551 0.839 0.757 0.983 0.812 0.831 0.805 0.689 +1 0.642 2.091 0.036 1.298 1.111 1.525 2.086 -0.899 0.000 1.078 1.173 1.374 2.215 0.831 0.308 0.465 2.548 1.121 -1.012 1.543 0.000 5.320 2.996 1.042 0.859 0.862 1.872 1.586 +0 0.668 -0.119 0.887 0.428 0.775 0.802 0.245 -1.497 0.000 0.602 0.118 -0.051 2.215 0.885 1.320 -0.178 0.000 1.605 2.452 1.455 0.000 1.255 1.023 0.979 0.516 0.353 0.718 0.828 +1 1.408 1.125 -0.476 0.578 -0.702 2.153 -0.830 0.871 0.000 2.059 0.651 -1.087 2.215 0.966 -0.058 -1.050 2.548 0.990 1.746 0.495 0.000 1.046 1.005 0.985 0.899 0.565 0.861 0.774 +1 0.897 1.228 -0.801 0.264 0.572 0.471 -0.912 -0.001 2.173 0.278 -0.996 1.029 0.000 0.830 0.153 -1.384 2.548 1.308 1.011 -1.550 0.000 1.217 1.157 0.982 0.629 0.863 0.746 0.691 +1 0.887 0.011 1.354 0.823 -1.716 1.108 -0.253 -0.734 0.000 1.137 -0.437 0.405 2.215 0.749 0.785 0.507 0.000 0.449 -1.066 -1.243 3.102 1.761 1.064 0.990 1.172 0.700 0.865 0.918 +0 0.624 0.900 -0.234 2.429 0.436 0.979 1.221 -1.717 2.173 0.638 0.876 -0.794 0.000 0.584 1.727 -1.434 0.000 0.397 -0.132 -1.021 1.551 0.928 0.879 0.989 0.698 0.630 0.922 0.807 +1 0.474 0.890 1.571 0.753 -0.275 0.790 0.082 -1.345 0.000 0.779 0.992 -1.193 0.000 0.667 1.842 0.395 0.000 1.522 0.857 0.746 3.102 0.905 0.863 0.988 0.660 0.309 0.537 0.568 +1 1.151 0.982 1.237 0.765 1.066 1.529 1.164 -0.245 0.000 0.495 -2.483 1.638 0.000 1.267 0.962 -1.540 2.548 0.612 0.551 -0.719 0.000 0.723 1.226 1.000 0.485 0.515 0.734 0.836 +0 0.438 -0.097 -1.222 1.760 0.533 1.386 0.266 -0.953 0.000 1.041 -1.272 1.042 2.215 1.764 0.294 0.465 0.000 0.962 0.695 -0.856 0.000 0.717 1.097 1.217 1.025 1.364 1.001 0.880 +1 1.499 0.005 -0.254 1.205 0.613 0.593 -0.132 1.740 0.000 0.593 -0.122 -0.742 0.000 1.467 0.442 1.566 2.548 0.378 1.176 -0.001 1.551 0.989 0.857 1.311 0.695 0.625 0.777 0.758 +1 0.567 -0.666 -1.415 1.213 0.197 0.799 1.065 -0.010 2.173 0.644 2.845 1.415 0.000 0.950 -0.027 -1.320 0.000 0.392 0.750 -0.840 3.102 0.430 1.212 1.141 0.672 0.406 0.766 1.221 +1 1.073 -1.687 -0.875 1.510 -1.101 1.968 -1.202 0.589 1.087 1.587 -0.716 -0.764 1.107 1.570 -0.107 -1.205 0.000 2.795 0.091 1.105 0.000 2.033 1.855 1.001 2.214 2.521 1.845 1.960 +1 0.638 -1.434 1.738 2.542 1.402 2.976 -0.052 -0.193 0.000 1.138 0.046 -1.440 2.215 1.392 -0.780 1.732 0.000 0.852 -0.084 -0.916 0.000 0.945 0.748 0.993 1.537 1.421 1.164 0.999 +0 0.746 -1.983 0.262 0.211 -0.771 1.027 -2.452 -1.185 0.000 0.413 -1.113 -0.857 0.000 1.133 -1.067 0.053 2.548 3.278 -0.419 1.122 3.102 1.068 1.254 0.977 1.035 1.309 1.406 1.104 +1 1.440 -0.205 -0.386 1.177 -0.009 0.785 2.121 -1.136 0.000 1.739 0.231 1.151 2.215 0.755 -0.148 -1.263 2.548 0.687 -0.996 0.893 0.000 3.069 1.831 0.985 1.595 1.032 1.426 1.485 +1 0.878 0.321 1.706 1.395 0.884 0.773 -0.377 -0.788 1.087 1.270 -0.078 -0.125 2.215 0.928 -0.250 1.059 0.000 0.415 -1.460 -0.935 0.000 0.866 0.987 1.034 1.148 0.850 0.958 0.837 +1 0.778 0.921 -1.002 0.566 0.163 0.825 0.010 1.277 0.000 0.579 -0.708 0.780 0.000 1.415 0.312 -0.736 2.548 0.983 1.105 -1.663 3.102 0.956 0.776 0.989 0.629 0.812 0.778 0.699 +1 0.712 -0.705 0.465 1.298 -1.028 0.642 -0.294 -0.910 0.000 0.756 0.213 1.325 1.107 1.037 -0.471 -0.328 2.548 0.615 2.148 0.963 0.000 2.132 1.404 1.298 0.752 1.003 1.027 0.989 +1 0.964 1.066 0.887 0.490 -0.425 1.223 0.168 -0.097 2.173 0.850 0.358 -1.503 1.107 0.535 -2.569 1.450 0.000 0.893 0.908 1.607 0.000 0.502 0.959 0.987 0.887 1.439 0.962 0.761 +1 0.948 -0.370 0.162 0.995 1.310 0.961 -0.575 -0.146 0.000 1.047 -0.217 1.288 2.215 1.032 0.388 -1.416 2.548 0.930 -0.956 -0.978 0.000 1.068 1.169 1.157 0.752 0.803 0.945 0.839 +1 0.607 0.032 0.377 1.100 0.707 2.024 2.319 -1.078 0.000 1.445 0.319 -0.405 2.215 2.327 1.214 1.023 2.548 1.651 0.514 0.781 0.000 1.157 1.286 0.977 1.941 2.131 1.557 1.249 +0 1.980 -0.593 -1.501 1.676 -1.065 1.343 -0.054 0.357 0.000 0.857 -0.250 -0.102 0.000 1.001 1.020 0.286 2.548 1.124 -0.439 1.096 3.102 0.945 0.970 0.986 0.925 0.921 1.112 1.180 +1 0.494 0.866 0.434 0.534 -1.488 1.251 1.123 0.195 0.000 0.583 0.287 -1.264 2.215 0.865 -0.268 1.394 2.548 0.402 1.372 0.792 0.000 1.182 1.109 0.991 0.602 0.560 0.876 0.768 +1 0.740 -0.267 0.981 1.515 0.088 0.475 1.067 1.192 0.000 0.602 1.909 -1.589 0.000 0.761 0.401 0.283 0.000 1.297 -0.557 -1.317 3.102 0.908 0.771 1.057 0.780 0.872 0.730 0.636 +1 0.418 -1.341 -1.195 0.993 0.620 0.653 -0.192 1.007 2.173 0.878 -0.415 -0.016 2.215 0.466 -2.093 -1.325 0.000 0.398 -0.352 -1.013 0.000 0.531 0.866 0.988 0.977 0.898 0.869 0.738 +0 1.466 -1.441 1.026 1.494 0.805 0.783 1.658 -0.686 0.000 0.631 -0.129 0.439 2.215 1.826 0.418 -0.916 0.000 1.771 -1.033 1.640 3.102 0.433 0.934 1.006 0.943 1.009 0.975 1.426 +1 0.779 -0.316 1.595 0.344 -1.433 1.283 0.948 0.873 0.000 0.787 2.707 -1.023 0.000 0.956 0.137 -0.212 0.000 0.931 -1.955 -0.728 0.000 0.531 0.941 0.986 0.548 0.471 0.600 0.595 +0 1.097 0.508 1.591 1.625 -1.387 0.619 0.752 -0.070 2.173 0.671 0.273 -0.408 2.215 0.771 0.922 1.020 0.000 1.280 1.800 0.904 0.000 0.674 0.939 0.983 0.949 0.365 0.817 0.847 +1 0.941 1.258 -0.703 0.478 0.833 0.905 1.467 0.876 2.173 0.780 2.348 -1.281 0.000 0.454 0.211 -0.214 1.274 0.530 2.477 -0.550 0.000 0.565 0.894 0.988 0.879 0.851 0.831 0.749 +1 0.495 1.051 -0.732 0.939 1.523 0.880 0.234 -0.857 2.173 0.776 2.259 1.686 0.000 0.951 0.020 1.588 2.548 1.479 0.290 0.551 0.000 2.007 1.441 0.986 0.786 0.927 1.161 0.939 +1 0.645 1.438 0.116 1.023 -1.228 1.050 0.312 1.008 0.000 0.713 -0.451 -1.144 2.215 1.051 0.915 -0.063 0.000 1.297 1.072 -1.542 3.102 0.846 0.923 1.052 1.049 0.913 0.927 0.806 +1 1.819 0.386 0.099 0.568 1.696 1.056 -1.300 -0.140 0.000 1.465 0.224 1.503 0.000 0.805 -0.480 0.574 0.000 2.158 0.365 -1.295 3.102 1.197 1.388 1.396 1.111 0.400 1.173 1.078 +1 0.893 0.540 1.113 1.274 0.383 1.452 0.231 -0.396 2.173 1.120 0.231 -1.027 2.215 2.629 -1.920 1.427 0.000 0.613 1.443 1.333 0.000 4.675 3.190 0.991 1.316 1.010 2.430 2.107 +0 1.034 -0.326 -1.629 1.085 0.544 0.566 2.165 1.739 0.000 0.804 0.242 -0.395 2.215 0.371 0.048 0.020 2.548 0.368 -1.210 -0.755 0.000 1.039 0.718 1.358 0.696 0.220 0.614 0.602 +0 1.629 -1.807 0.711 0.336 -1.223 0.668 -0.779 0.984 2.173 1.133 1.442 -1.302 1.107 1.712 2.119 -0.563 0.000 0.421 -1.723 -1.678 0.000 4.241 2.552 1.010 0.777 2.115 2.018 2.377 +1 0.298 1.146 -0.522 1.039 0.127 0.796 0.352 -1.369 2.173 0.565 -0.528 -0.300 0.000 1.141 -0.494 1.251 2.548 0.885 -1.876 -0.097 0.000 0.902 1.036 0.989 0.999 1.000 0.995 0.869 +1 2.461 -0.146 -0.536 0.933 -0.950 0.608 2.724 1.339 0.000 0.550 0.385 0.750 1.107 1.179 -0.735 0.773 1.274 0.508 -0.189 -1.394 0.000 1.863 1.343 0.996 1.282 0.547 1.396 1.438 +0 1.007 -0.208 1.699 0.385 0.259 1.108 0.938 -1.401 2.173 0.929 -1.030 0.158 0.000 0.682 -0.843 0.940 0.000 1.016 0.329 -0.155 3.102 0.795 0.813 0.985 0.892 1.060 1.188 0.955 +0 0.607 -1.016 -0.654 1.997 -1.594 0.729 -0.657 1.229 2.173 0.818 0.157 0.043 2.215 0.852 0.087 0.600 0.000 0.852 0.649 -0.674 0.000 0.917 0.980 1.141 1.229 1.107 0.946 0.887 +0 0.593 0.627 -0.314 1.696 -1.175 0.863 -1.563 1.272 0.000 0.715 0.231 1.258 2.215 0.924 -0.358 -0.121 2.548 0.960 -1.478 0.320 0.000 1.062 1.117 0.987 0.871 0.863 0.906 1.079 +1 0.354 1.548 -1.558 1.560 -0.314 0.760 1.134 0.025 0.000 2.273 -0.074 -1.737 2.215 1.561 0.597 0.667 2.548 1.780 1.083 -0.582 0.000 0.935 1.131 0.986 1.604 1.819 1.481 1.219 +1 0.732 0.451 0.106 1.338 -0.226 1.057 0.191 -0.067 0.000 1.008 -0.448 1.632 2.215 0.932 0.773 0.876 0.000 0.891 0.896 1.554 0.000 0.767 1.106 0.989 1.526 0.428 1.206 1.110 +1 1.328 -0.711 0.349 0.674 1.086 1.126 -1.116 -1.325 2.173 0.625 -1.471 0.091 0.000 0.654 -0.233 1.032 0.000 0.553 -0.737 -0.313 3.102 0.988 1.216 0.983 0.577 0.668 0.831 0.754 +1 1.383 0.264 0.959 0.529 -0.163 0.477 2.741 1.362 0.000 1.543 0.606 -0.769 1.107 0.824 1.165 -0.040 2.548 0.594 -0.253 0.858 0.000 0.783 0.976 1.004 0.743 0.834 0.815 0.713 +1 2.025 0.145 0.064 0.499 -0.305 0.436 1.215 -1.554 0.000 1.252 0.598 1.198 2.215 0.944 0.154 -0.966 0.000 0.886 0.437 0.520 0.000 0.963 0.799 0.995 0.892 0.544 0.896 0.797 +0 0.396 -0.918 0.496 0.672 -1.344 1.037 0.122 0.279 2.173 1.309 -1.081 -0.374 0.000 1.549 -0.890 0.268 0.000 1.874 -1.722 -1.226 0.000 0.928 1.097 0.990 0.729 1.707 1.153 1.016 +0 1.511 -0.077 -0.626 0.982 -0.780 0.587 -1.109 0.956 0.000 0.946 -0.810 0.376 2.215 0.918 -0.374 1.435 0.000 0.949 0.208 -1.616 1.551 0.729 0.797 0.983 0.725 0.965 0.871 0.912 +1 0.676 0.287 -1.097 0.527 1.254 1.203 0.431 0.524 2.173 0.949 0.257 0.025 0.000 1.786 0.264 -1.421 2.548 0.958 -0.922 -1.312 0.000 1.480 1.301 0.982 1.030 1.801 1.152 0.985 +1 0.752 -0.269 -1.204 0.882 0.513 0.607 0.137 0.207 2.173 0.992 0.892 -0.429 2.215 1.258 0.420 1.432 0.000 0.911 1.241 1.647 0.000 0.674 1.020 1.128 0.957 0.770 0.833 0.793 +0 1.300 0.079 -0.980 0.480 1.038 1.511 -1.462 -1.504 0.000 0.987 -0.498 0.515 0.000 0.947 0.055 -0.173 0.000 1.528 0.539 0.954 3.102 0.963 0.950 1.061 0.839 0.615 0.772 0.770 +0 0.566 0.628 -0.331 1.025 -0.687 1.647 -1.046 -0.800 2.173 1.488 -1.590 1.142 0.000 1.365 0.594 0.251 0.000 2.429 0.279 1.140 3.102 0.656 1.496 0.991 0.905 2.622 1.586 1.306 +0 1.744 0.293 -0.501 1.144 -0.967 0.801 1.246 0.922 1.087 1.027 0.370 0.307 0.000 1.556 1.001 1.588 1.274 0.485 -0.372 -1.700 0.000 0.975 0.979 0.993 1.214 0.793 1.067 0.955 +1 1.636 0.355 0.033 0.537 -0.373 2.874 -0.931 1.150 0.000 1.774 -1.398 -0.680 1.107 1.537 -0.201 -0.764 2.548 0.598 -1.045 -1.054 0.000 0.871 0.708 0.992 1.455 1.144 1.033 0.902 +0 1.394 -0.688 1.419 0.428 -1.458 0.627 -2.480 -0.795 0.000 1.055 0.207 0.508 1.107 0.609 1.782 -1.461 0.000 1.314 1.667 0.032 0.000 0.962 0.911 0.989 0.951 0.743 0.841 0.942 +1 2.178 1.001 1.288 0.613 -1.633 1.476 2.505 0.087 0.000 1.890 0.439 -1.052 2.215 0.586 -0.707 0.514 2.548 0.399 -2.246 -0.173 0.000 7.960 4.417 0.983 1.362 1.324 2.875 2.237 +1 1.165 -0.681 0.410 0.326 -0.928 1.231 -0.423 0.099 0.000 1.372 -0.162 -1.586 0.000 1.591 -0.332 -0.959 2.548 1.437 -0.057 1.102 3.102 0.918 1.032 0.994 0.880 1.122 0.914 0.768 +1 0.657 0.125 -0.506 0.259 -0.471 0.698 -0.749 -0.190 2.173 0.999 -1.674 1.528 0.000 0.997 -0.425 0.475 0.000 1.778 -0.347 -1.740 3.102 1.613 1.194 0.987 0.614 1.178 0.961 0.829 +1 0.581 -1.114 0.022 0.488 1.513 1.093 -1.188 0.520 2.173 1.281 -0.653 -1.029 0.000 0.826 -0.825 1.493 2.548 0.552 -1.555 -1.332 0.000 0.755 0.751 0.987 0.887 0.926 0.915 0.789 +0 0.878 0.769 -1.108 0.292 0.224 0.591 -0.751 -0.003 2.173 0.753 -1.743 1.546 0.000 0.810 -0.828 1.041 2.548 0.574 0.627 -0.866 0.000 1.576 0.988 0.988 0.802 0.700 0.764 0.764 +0 1.810 -0.784 -1.512 0.388 1.489 0.456 0.607 -0.996 2.173 0.699 -0.644 0.548 0.000 0.543 -0.419 0.182 1.274 0.779 0.632 0.207 0.000 0.798 0.893 0.980 0.762 0.644 0.646 0.692 +0 1.273 0.617 0.148 0.395 1.155 0.807 0.802 0.645 1.087 0.848 0.857 -1.008 2.215 0.895 1.969 -1.208 0.000 0.462 1.382 -1.728 0.000 0.629 0.854 0.989 0.885 1.213 0.753 0.663 +1 0.713 -1.241 -1.592 1.148 0.674 0.836 -0.711 -1.384 1.087 0.633 -1.450 -0.412 0.000 1.139 -2.346 0.018 0.000 0.718 -1.416 1.015 3.102 0.890 0.744 1.117 0.947 0.800 0.895 0.813 +1 0.934 0.768 1.314 0.395 0.217 0.945 0.540 -1.464 2.173 1.020 -0.419 0.522 2.215 0.416 -0.531 -0.347 0.000 0.845 0.359 -0.124 0.000 0.370 0.863 0.991 1.100 1.588 1.000 0.816 +1 1.049 -0.619 1.501 0.127 -1.021 1.048 -1.099 1.100 0.000 1.186 0.038 -1.022 2.215 1.320 -0.382 0.226 2.548 1.896 -0.103 -0.363 0.000 2.360 1.482 0.982 0.817 1.238 1.186 0.989 +0 1.056 0.171 1.094 0.575 -1.594 1.652 1.936 1.598 0.000 1.352 0.234 0.028 2.215 0.798 -0.013 -0.878 0.000 1.457 -0.311 -0.357 1.551 0.833 0.971 0.979 0.851 0.585 0.788 0.728 +0 1.183 1.136 -1.018 1.394 -0.232 0.707 0.753 1.281 2.173 0.570 0.119 -0.310 0.000 0.452 2.703 -1.515 0.000 0.668 1.460 0.379 0.000 0.965 0.747 1.158 0.765 0.323 0.759 0.679 +0 1.160 -0.248 -0.572 0.633 0.332 0.850 -0.849 -1.077 2.173 1.132 -0.816 0.782 0.000 0.933 -1.308 0.288 0.000 1.249 -0.815 1.742 1.551 0.830 0.912 0.991 0.824 0.620 0.868 0.797 +1 1.114 1.168 1.580 1.282 -0.966 1.040 -0.600 0.056 2.173 0.469 -1.764 1.296 0.000 0.513 -1.080 0.700 0.000 0.802 0.308 -0.793 3.102 0.454 0.921 1.242 0.714 0.829 1.128 1.142 +1 0.632 0.830 -0.929 0.630 -0.487 1.347 -0.019 -0.315 1.087 1.026 -1.208 1.417 0.000 2.047 -0.079 1.015 0.000 1.061 0.000 1.551 3.102 1.570 0.961 0.988 1.520 1.257 1.272 1.495 +0 2.021 0.557 -0.801 0.303 0.156 1.628 1.339 0.979 0.000 2.136 0.843 -1.130 2.215 2.051 -0.069 0.188 0.000 1.661 0.292 1.200 3.102 1.561 1.396 0.990 0.889 1.534 1.519 1.228 +1 0.807 1.393 1.361 0.858 0.326 0.723 1.972 0.026 0.000 0.446 -0.827 -0.041 1.107 1.608 0.265 -1.280 2.548 1.349 -0.389 -1.523 0.000 0.840 0.959 0.986 0.912 0.974 0.841 0.725 +1 0.954 0.667 -1.664 1.061 -0.499 1.025 0.579 0.917 0.000 1.470 0.576 -0.741 1.107 0.502 0.138 0.531 2.548 0.955 1.398 0.888 0.000 0.868 0.597 1.210 0.802 0.856 0.937 0.852 +0 0.283 -0.694 -1.511 1.221 0.586 0.623 -0.662 -0.846 0.000 0.872 -0.529 1.497 2.215 0.486 1.096 -0.440 0.000 0.721 1.291 0.395 3.102 0.805 0.912 0.986 1.527 1.077 1.086 0.982 +0 0.799 -1.774 0.943 1.203 -0.704 0.636 -1.062 -0.047 2.173 0.581 -0.067 1.128 2.215 0.499 -2.052 1.472 0.000 1.106 -0.572 -1.563 0.000 0.886 0.915 1.353 1.093 0.909 0.823 0.798 +1 0.642 1.009 1.411 1.663 -0.376 0.552 0.698 -0.574 2.173 0.764 -0.215 1.252 0.000 1.262 0.268 1.527 0.000 0.708 -0.299 0.254 3.102 0.539 1.063 1.430 0.869 0.582 0.692 0.791 +1 0.480 -1.077 -0.547 0.640 -1.646 0.772 0.024 0.285 0.000 1.193 -0.651 0.543 0.000 2.547 -0.044 -1.158 2.548 1.289 0.800 1.327 3.102 0.860 1.201 0.992 1.602 1.308 1.413 1.370 +1 1.029 -0.317 -1.317 0.819 -0.308 0.905 -0.063 0.898 0.000 0.552 -0.648 0.423 0.000 0.668 0.527 1.537 2.548 2.087 0.160 -0.862 3.102 0.817 1.123 1.005 0.720 0.770 0.721 0.689 +1 1.005 0.449 -1.316 0.636 0.040 0.622 1.018 -0.615 2.173 0.779 -0.606 1.380 0.000 0.909 0.954 0.907 2.548 0.646 -1.102 0.359 0.000 0.805 0.991 1.042 0.680 0.918 0.918 0.794 +1 0.795 1.450 0.435 0.702 -1.369 0.907 0.644 -1.069 0.000 0.755 1.000 -0.687 0.000 1.786 -2.033 1.245 0.000 1.869 0.142 -0.163 3.102 0.682 0.947 1.033 0.995 0.929 0.946 0.876 +1 0.925 0.674 1.630 0.644 -0.917 0.733 0.263 1.162 0.000 1.059 0.492 0.260 0.000 0.822 -0.881 -0.503 2.548 0.528 0.618 -1.197 0.000 0.949 0.942 0.990 0.614 0.149 0.611 0.632 +1 0.818 1.860 0.766 0.462 1.071 0.735 -0.352 -1.232 2.173 0.343 1.304 -0.802 0.000 0.379 1.013 -1.309 0.000 0.775 0.116 -0.268 3.102 0.252 0.663 1.002 0.708 0.642 0.808 0.646 +1 0.309 -0.578 0.052 1.897 0.034 1.197 0.639 -1.522 2.173 0.434 -0.138 -0.028 0.000 0.581 0.680 1.269 0.000 0.413 0.589 -0.007 0.000 0.961 1.039 0.982 1.657 0.812 1.919 1.573 +1 2.452 -0.781 0.204 0.652 0.594 0.444 -0.446 1.300 0.000 0.572 -1.248 -1.144 2.215 0.788 -2.123 -1.248 0.000 1.095 -0.230 -1.413 3.102 1.402 0.979 0.978 1.020 0.413 0.812 0.872 +1 1.815 -1.781 0.031 0.317 1.597 0.720 -0.263 -1.685 2.173 0.661 -2.122 1.219 0.000 1.083 -0.673 -1.189 1.274 0.424 -0.938 0.139 0.000 0.686 1.037 1.038 0.995 0.544 0.948 0.822 +0 1.002 0.257 0.346 0.609 0.424 0.438 -0.647 -1.047 0.000 0.652 -0.127 1.209 2.215 1.243 0.210 -0.523 2.548 0.794 0.742 -1.643 0.000 0.893 0.775 0.990 0.829 0.972 0.750 0.745 +1 0.663 1.282 0.372 0.981 -1.618 0.464 2.050 -1.356 0.000 0.444 0.894 -1.040 2.215 0.450 1.970 -0.214 0.000 0.811 -0.709 1.114 3.102 0.705 1.304 1.090 0.628 0.743 0.788 0.705 +0 1.500 -0.052 -0.710 0.447 -0.970 1.012 0.312 0.632 0.000 1.630 0.414 -1.367 2.215 0.988 1.159 0.693 0.000 0.398 0.482 -1.682 3.102 0.900 0.679 1.002 1.066 0.211 0.951 1.009 +1 0.768 0.027 -0.585 1.095 1.499 0.819 -1.588 0.878 2.173 0.618 -0.358 -1.033 0.000 0.530 -0.470 0.219 2.548 0.588 -1.725 -0.739 0.000 0.780 1.091 1.211 0.714 0.657 0.835 0.774 +0 1.011 1.403 1.410 0.525 0.414 0.695 0.147 0.149 2.173 0.771 -1.548 -0.591 0.000 0.818 0.230 1.474 0.000 1.524 0.723 -0.875 3.102 1.007 0.908 0.985 1.145 0.956 0.892 0.806 +1 0.622 0.798 -0.993 0.353 1.468 0.568 1.235 -0.134 0.000 0.509 0.766 0.466 2.215 1.612 0.824 1.365 2.548 1.804 1.415 -0.647 0.000 0.934 1.088 0.995 0.711 0.700 0.701 0.752 +1 0.617 -0.880 -1.739 1.248 0.669 1.116 0.165 -0.675 1.087 0.815 -0.045 1.301 0.000 0.704 0.018 0.427 0.000 0.458 0.852 -0.708 0.000 0.893 1.091 1.004 0.567 0.425 0.806 0.740 +1 0.976 -0.338 -0.840 0.266 0.011 1.287 0.008 0.746 1.087 0.905 -0.822 -1.277 0.000 0.670 -1.304 -0.510 0.000 0.467 -1.145 1.678 1.551 0.841 0.542 0.986 1.122 0.865 0.931 0.856 +1 1.380 0.468 -0.083 2.257 -0.477 2.122 0.291 -1.013 2.173 1.557 -2.280 0.812 0.000 3.721 -0.845 1.211 0.000 0.916 0.233 0.415 0.000 0.826 0.558 0.992 1.483 1.052 1.097 1.030 +1 0.564 0.065 0.700 0.949 1.742 0.800 0.111 0.145 2.173 0.649 -1.409 -1.368 0.000 0.730 -1.122 -0.364 0.000 0.478 0.843 1.446 3.102 0.839 0.899 0.984 0.930 0.676 0.812 0.842 +0 0.774 -1.904 0.443 1.010 -1.563 0.675 0.459 -0.973 0.000 0.941 0.914 -0.402 2.215 1.028 -0.577 1.687 2.548 0.502 0.056 1.180 0.000 0.847 0.801 1.191 1.985 1.349 1.330 1.131 +1 0.795 0.308 1.304 1.933 0.721 1.018 -0.089 -0.620 2.173 0.628 0.246 -1.405 0.000 0.496 2.611 -1.724 0.000 0.774 0.709 0.007 0.000 0.911 0.859 0.991 0.738 0.759 0.910 0.796 +1 0.908 0.872 -0.430 0.679 0.658 0.418 -2.499 -1.145 0.000 0.371 1.920 -0.982 0.000 0.855 0.376 0.616 2.548 0.755 -0.919 0.975 3.102 0.218 0.939 0.985 0.656 0.548 0.676 0.637 +0 0.293 -1.776 -0.717 1.418 0.858 1.179 -1.578 -0.258 0.000 1.017 -0.472 1.519 2.215 1.014 0.034 -1.400 2.548 0.694 1.898 -0.447 0.000 1.177 1.388 0.992 0.771 0.600 1.056 0.908 +1 0.457 -1.194 -1.139 1.075 -1.722 0.752 -2.528 -1.705 0.000 1.787 0.795 0.412 2.215 1.193 1.079 0.305 2.548 2.026 0.583 -0.628 0.000 0.684 3.049 0.977 1.395 0.323 2.556 1.914 +1 1.825 0.002 -1.336 0.527 1.003 0.620 -0.688 -0.161 2.173 0.475 -1.117 0.068 2.215 1.310 -0.175 0.806 0.000 0.568 -0.791 -1.118 0.000 1.009 0.875 1.167 0.924 0.245 0.757 0.710 +1 1.785 0.701 -1.416 0.053 -1.156 0.897 0.429 -0.283 0.000 1.235 0.491 1.163 2.215 0.568 -0.484 0.163 0.000 0.466 0.998 0.057 0.000 0.817 1.217 1.001 0.939 1.324 1.044 0.965 +1 0.865 -0.648 1.073 2.164 1.625 0.844 -0.216 -0.033 2.173 0.761 -0.293 -1.221 0.000 0.723 0.345 -1.048 2.548 1.089 0.759 0.372 0.000 0.965 0.952 0.988 0.843 0.826 0.928 0.820 +1 0.688 -0.688 -1.275 0.140 -0.418 1.549 -0.583 -0.142 2.173 0.985 -0.287 -1.730 0.000 0.822 1.819 1.080 0.000 1.078 -0.363 -1.080 3.102 0.897 0.737 0.992 1.132 1.028 1.004 0.851 +0 1.738 0.359 1.689 0.741 1.375 0.673 0.258 -1.264 2.173 0.780 -0.461 0.151 0.000 0.649 -0.183 0.539 0.000 0.853 -1.944 -0.817 0.000 1.304 1.313 0.984 1.366 1.290 1.252 1.140 +0 0.576 0.343 1.317 1.394 0.070 1.741 -0.614 -1.353 2.173 1.566 -0.199 0.407 2.215 0.749 -0.813 1.647 0.000 0.688 -0.578 -0.198 0.000 0.793 0.952 1.120 1.601 2.482 1.372 1.061 +1 1.244 -1.721 -0.998 0.505 -0.936 0.874 -1.178 0.721 2.173 0.764 -1.168 0.287 0.000 0.478 -1.487 1.527 0.000 0.500 0.302 -1.336 0.000 0.857 0.698 0.990 1.135 0.698 0.803 0.701 +1 1.525 0.442 1.474 0.419 0.150 0.761 -0.618 -1.002 2.173 0.724 -0.719 0.101 1.107 0.890 0.346 -0.543 0.000 1.087 -0.085 0.530 0.000 0.931 0.953 1.030 0.946 0.918 0.853 0.757 +1 0.313 -0.852 -0.562 0.433 0.533 0.762 0.471 -0.145 2.173 1.144 0.030 -1.449 2.215 0.907 -1.451 1.595 0.000 0.496 -0.354 1.229 0.000 0.517 0.861 0.977 0.663 1.305 0.949 0.847 +0 0.322 -1.405 -0.196 0.868 1.689 0.565 -0.334 0.132 0.000 1.011 -1.308 0.047 1.107 0.828 1.058 -0.940 2.548 0.488 0.055 1.424 0.000 0.912 0.705 0.989 0.954 1.738 1.306 1.053 +0 1.459 -0.899 -1.256 0.285 0.707 0.943 0.720 0.691 2.173 0.500 -1.382 -1.367 0.000 1.204 0.396 -0.543 0.000 0.693 0.098 1.188 0.000 0.865 1.263 0.986 0.687 0.377 0.941 0.817 +0 0.620 0.199 -0.401 0.635 1.359 1.354 0.445 -0.923 2.173 1.631 -1.158 0.437 2.215 1.196 0.065 1.491 0.000 0.762 -2.213 1.149 0.000 1.952 1.622 0.990 0.912 2.871 1.777 1.453 +1 1.961 0.451 1.730 0.609 -1.442 0.798 0.394 -0.097 2.173 0.330 0.117 0.442 0.000 0.596 -0.153 0.753 2.548 0.659 -0.320 -0.722 0.000 0.549 0.519 0.983 0.741 0.644 0.837 0.669 +1 0.691 1.177 0.070 1.096 -0.845 1.005 0.354 1.204 2.173 1.196 -0.020 1.730 0.000 1.209 0.327 -0.670 0.000 1.616 0.431 -0.028 0.000 0.852 0.808 0.986 1.120 0.843 0.871 0.800 +1 0.527 1.443 0.909 0.867 -0.265 0.444 1.925 -0.243 0.000 0.910 0.449 1.309 1.107 0.863 2.241 -1.198 0.000 1.305 -0.240 1.530 3.102 0.893 1.329 0.988 0.832 0.425 1.072 0.904 +1 0.493 -0.642 0.359 0.247 -0.793 0.884 0.643 -1.413 0.000 1.033 0.436 0.617 2.215 1.269 0.929 -0.321 0.000 1.611 -0.050 1.742 1.551 1.626 1.240 0.990 0.693 1.032 0.995 0.825 +1 0.625 -1.036 0.379 0.728 1.580 1.636 0.336 -1.537 2.173 0.719 -0.020 0.561 0.000 1.682 -0.064 -0.115 1.274 0.371 0.151 -0.518 0.000 0.560 1.212 0.990 0.868 2.026 1.098 0.885 +1 0.479 0.731 0.745 2.542 1.423 0.781 0.081 -0.563 2.173 0.319 2.145 -0.077 0.000 0.271 0.549 -0.949 2.548 0.520 1.439 0.292 0.000 0.317 0.757 0.994 0.648 0.247 0.925 0.756 +1 0.787 0.347 -0.441 0.846 0.716 0.596 -0.626 -1.361 2.173 0.680 1.006 0.247 0.000 0.471 2.102 1.325 0.000 1.024 0.648 -1.269 3.102 0.929 0.807 0.988 0.872 0.636 0.874 0.777 +1 0.603 -0.206 1.061 0.813 -0.166 1.174 0.743 1.193 2.173 1.281 0.196 -0.303 0.000 0.905 0.830 -1.384 1.274 0.978 0.123 -0.989 0.000 0.846 0.878 0.987 1.272 0.943 0.996 0.954 +1 0.729 -1.717 -0.464 0.917 -1.510 2.182 -1.620 -0.910 0.000 2.733 -1.271 0.737 2.215 0.994 -0.325 1.068 2.548 0.749 -1.669 1.316 0.000 0.736 0.860 0.987 1.081 1.007 1.122 0.867 +1 0.313 0.134 -1.634 1.235 -0.795 0.955 0.316 1.159 2.173 0.761 -0.944 0.024 2.215 0.344 0.057 -1.025 0.000 0.383 -0.618 -1.676 0.000 0.276 0.602 0.985 0.743 1.378 1.010 0.740 +0 1.242 -0.467 1.335 0.830 -0.557 0.591 0.432 -1.201 2.173 0.769 1.174 0.488 0.000 0.307 0.099 -1.501 1.274 0.586 -2.141 1.225 0.000 2.881 1.527 1.394 0.938 0.166 1.058 0.933 +0 0.867 -0.305 0.857 1.095 -0.599 0.524 0.725 -0.741 0.000 0.773 -0.984 0.094 2.215 0.713 -0.562 -1.381 2.548 0.547 -1.369 1.134 0.000 1.463 0.920 1.305 0.808 0.782 0.738 0.697 +1 1.217 -0.038 0.862 0.793 -1.092 0.745 1.435 1.377 0.000 0.709 -0.380 0.104 0.000 1.246 0.942 -0.445 2.548 1.403 -0.124 -0.834 3.102 0.782 0.820 1.337 0.854 0.722 0.767 0.669 +0 0.696 0.741 1.294 1.161 -0.657 0.798 -0.459 1.189 1.087 0.762 -0.542 -0.597 2.215 0.568 -0.267 0.697 0.000 0.572 1.332 0.245 0.000 0.996 0.914 1.224 0.912 1.148 0.901 0.871 +0 0.749 0.968 0.010 0.992 1.232 0.770 1.412 0.251 1.087 0.554 1.392 -1.208 0.000 1.163 0.183 -1.281 2.548 1.040 -0.613 1.223 0.000 0.864 0.980 1.065 0.748 1.389 1.190 1.030 +1 0.803 1.840 1.150 0.977 1.288 1.547 1.265 0.087 0.000 0.898 0.378 -1.426 0.000 1.745 -0.739 -1.297 2.548 0.799 -0.249 -1.409 3.102 1.204 1.051 0.973 0.791 0.251 1.008 0.877 +1 0.533 -0.703 0.400 1.730 0.180 0.887 0.057 -1.199 2.173 0.793 -1.095 -0.321 0.000 1.593 0.900 1.655 2.548 1.054 -0.279 1.207 0.000 1.266 1.199 0.984 2.469 1.071 1.786 1.409 +1 0.774 -1.057 0.736 0.657 -0.227 0.968 -0.605 -1.141 0.000 1.371 -1.474 1.140 0.000 0.621 -1.151 0.042 2.548 0.744 -0.616 -0.347 1.551 2.450 1.435 0.987 0.524 0.225 0.899 0.810 +0 1.735 -0.522 0.285 0.122 0.516 1.273 0.062 1.657 2.173 0.403 0.051 1.257 0.000 0.736 -2.530 -0.739 0.000 1.688 0.970 1.654 3.102 1.021 0.979 0.993 1.314 0.898 1.064 0.914 +1 0.793 0.503 -0.920 1.837 -0.247 1.180 1.558 0.630 0.000 1.787 1.462 -1.419 2.215 0.914 1.180 1.272 2.548 0.597 0.553 1.730 0.000 1.230 0.816 0.990 1.566 0.903 1.130 1.135 +1 0.793 -0.557 -0.341 0.871 -1.317 1.137 0.152 -1.143 2.173 1.562 0.794 0.960 0.000 2.009 0.559 0.181 2.548 0.410 0.527 -1.601 0.000 0.779 1.065 0.984 0.961 1.805 1.163 1.156 +0 0.826 0.508 -1.254 0.892 -0.563 1.564 -1.330 0.874 0.000 1.390 -0.063 -1.131 2.215 1.442 -0.655 0.304 2.548 1.049 -2.275 -0.879 0.000 0.865 0.976 0.987 0.641 1.532 1.267 1.132 +1 0.827 -1.069 -1.510 1.764 1.710 0.938 0.240 1.347 1.087 0.686 -1.915 -0.125 0.000 1.592 2.013 0.512 0.000 1.298 0.577 -0.182 0.000 0.685 0.767 1.000 0.839 1.273 1.129 1.054 +0 0.401 -0.731 0.449 0.365 0.795 0.764 -0.010 -1.187 2.173 0.844 0.429 1.097 0.000 0.996 0.378 0.070 2.548 0.699 -1.952 -0.297 0.000 2.116 1.445 0.995 0.910 1.012 1.062 0.930 +0 0.336 1.414 -0.213 1.781 1.715 0.706 0.713 -1.307 0.000 0.894 1.808 0.578 0.000 1.047 -0.382 -1.198 0.000 0.982 -0.914 -0.221 0.000 0.942 0.840 1.057 1.479 0.390 0.948 0.941 +1 0.669 -0.352 0.358 0.544 -1.347 0.849 1.402 0.426 0.000 1.410 0.475 -0.315 0.000 2.084 0.167 -1.637 2.548 1.029 0.433 1.173 3.102 0.987 0.794 0.987 1.020 0.668 0.902 0.949 +0 3.480 -0.174 -1.289 0.637 -1.577 1.741 -1.803 0.434 0.000 0.819 -0.354 -0.649 1.107 0.906 -0.814 0.514 2.548 0.911 -1.688 1.211 0.000 1.254 0.910 0.978 0.875 0.830 1.035 1.555 +0 1.373 -2.041 0.824 0.092 0.709 0.858 0.292 -0.222 0.000 0.688 -1.219 1.451 2.215 0.924 -0.274 -1.475 2.548 0.543 -2.269 -1.456 0.000 0.684 0.995 0.989 0.657 0.587 0.950 1.097 +0 0.562 0.972 1.152 0.420 1.356 0.710 -0.650 0.291 2.173 1.035 -0.765 -0.758 1.107 1.535 0.518 -1.509 0.000 0.401 -1.142 0.690 0.000 1.259 1.172 0.992 0.816 1.026 0.946 0.825 +1 0.336 -1.189 -1.503 0.911 -0.072 1.164 0.578 -0.743 2.173 1.233 -0.006 1.452 0.000 1.033 -0.342 0.837 0.000 0.392 -0.351 0.402 3.102 0.966 0.614 0.993 0.876 0.719 0.957 0.813 +1 0.458 0.628 -0.427 0.409 -0.463 0.685 -0.305 -1.486 0.000 1.109 -1.441 0.488 2.215 0.689 -0.845 1.569 0.000 0.609 0.931 1.432 0.000 0.903 0.805 0.986 0.898 0.617 0.907 0.781 +0 0.379 0.037 -0.744 1.226 1.027 0.546 -2.800 0.928 0.000 0.892 0.724 -1.217 2.215 0.702 -0.355 -0.303 1.274 1.372 1.011 -0.723 0.000 0.955 0.899 0.988 0.804 0.796 0.657 0.656 +1 1.727 -0.498 -1.120 0.731 1.664 1.132 -0.573 0.390 2.173 0.658 0.027 -0.251 0.000 0.381 -1.458 0.621 0.000 0.657 0.243 1.679 3.102 0.884 0.781 0.989 0.556 0.932 0.889 0.770 +0 0.628 -1.046 -0.454 0.896 0.196 1.142 0.554 1.387 0.000 2.028 -0.329 -0.851 2.215 0.570 -0.358 1.015 0.000 1.018 0.074 0.501 3.102 0.862 0.768 0.989 1.005 1.249 1.142 0.982 +1 0.574 0.600 0.940 0.846 -0.593 1.459 -0.399 -0.477 2.173 1.322 -0.926 0.987 0.000 1.068 1.908 1.300 0.000 1.023 -0.858 -1.248 3.102 1.190 0.908 0.988 0.922 0.925 1.014 0.882 +0 0.540 1.383 1.538 0.611 -0.367 1.084 0.822 1.311 2.173 1.041 -0.598 -0.529 2.215 0.868 -1.656 0.416 0.000 0.808 -0.847 -1.249 0.000 0.997 0.932 0.986 1.115 1.985 1.388 1.560 +0 1.941 0.336 -0.060 0.369 0.758 1.039 -0.226 -1.656 2.173 0.505 -0.450 1.456 0.000 0.592 -1.327 -0.648 0.000 1.205 -0.858 0.161 3.102 0.906 0.874 0.980 0.870 1.278 1.030 0.897 +1 0.841 -0.136 -1.431 1.450 0.052 0.738 -0.381 -0.146 0.000 1.347 0.766 1.697 2.215 1.095 0.616 0.263 2.548 0.686 -1.326 0.829 0.000 1.089 1.016 1.488 1.312 1.243 1.107 0.984 +1 1.647 -0.522 0.343 0.513 0.082 0.417 0.383 -1.170 1.087 1.214 1.590 -1.348 0.000 0.659 0.371 0.427 2.548 0.533 1.081 0.990 0.000 0.918 0.942 0.984 1.039 0.648 0.733 1.119 +1 0.420 -1.688 -1.293 0.450 -0.670 1.235 -0.211 0.750 0.000 0.857 0.061 -0.893 2.215 1.005 -1.233 -1.687 2.548 0.810 -0.575 0.338 0.000 0.751 0.758 0.981 0.722 0.997 0.648 0.588 +1 0.851 -1.194 -0.806 0.930 0.067 0.610 -0.822 -0.048 0.000 0.422 -0.448 -0.251 0.000 0.619 1.253 -1.673 0.000 0.421 -2.354 -1.614 0.000 0.963 0.976 0.986 0.879 0.372 0.715 0.699 +1 0.424 1.480 -1.248 0.361 -0.496 0.973 -0.054 1.611 0.000 1.458 -0.650 -0.229 2.215 0.596 0.222 0.608 2.548 0.710 -0.578 0.483 0.000 1.156 0.797 0.991 0.933 0.824 0.892 0.785 +0 0.305 -0.054 1.522 0.948 -0.956 0.563 -0.221 -0.141 0.000 0.977 0.415 0.961 2.215 0.714 -2.367 0.901 0.000 1.427 0.344 -1.133 3.102 0.864 1.019 0.988 0.650 1.013 0.738 0.730 +0 0.861 0.407 0.623 2.301 1.114 0.943 1.078 -0.827 0.000 0.569 2.089 -1.018 0.000 1.373 0.271 -1.122 2.548 2.117 -0.388 0.398 3.102 0.904 0.964 0.987 1.201 1.375 1.247 1.246 +1 1.908 -0.359 0.491 1.126 1.018 0.894 -1.042 -0.945 1.087 0.573 -0.377 -0.488 0.000 0.769 -0.626 -1.623 1.274 0.397 0.100 -1.191 0.000 0.400 0.524 0.988 0.868 0.622 0.954 0.765 +0 0.409 -1.936 1.310 1.316 0.147 0.685 0.575 -1.016 2.173 0.737 0.751 0.423 0.000 1.178 -0.024 -1.495 2.548 0.653 -0.977 1.226 0.000 1.187 1.031 0.990 2.364 0.581 1.696 1.495 +1 1.577 0.788 1.222 0.762 0.461 2.274 0.441 -0.055 0.000 1.816 0.993 -1.307 0.000 0.718 0.101 1.606 2.548 0.626 1.497 -0.735 0.000 0.879 0.858 0.986 0.622 0.460 0.539 0.700 +0 1.440 0.466 0.995 0.082 -1.377 0.680 0.374 -0.093 2.173 1.227 -0.834 -1.212 0.000 1.033 -1.169 -0.756 0.000 1.034 -1.883 0.574 0.000 0.783 0.861 0.984 0.861 0.867 0.892 0.881 +1 0.900 -0.534 0.823 0.896 0.038 0.824 1.035 1.216 1.087 1.112 0.683 -1.437 2.215 0.706 -0.547 0.224 0.000 0.872 1.988 -0.706 0.000 0.853 0.922 0.993 1.100 0.989 0.914 0.785 +1 1.722 1.003 0.098 0.698 0.134 0.351 -1.591 0.470 0.000 0.653 0.606 -0.733 1.107 0.872 -0.497 -1.650 2.548 0.753 0.974 -0.734 0.000 0.775 0.740 0.986 1.418 0.771 0.973 0.877 +0 1.564 1.728 1.652 1.153 0.960 0.671 -0.041 -0.412 0.000 0.450 -0.670 1.662 0.000 0.637 1.267 -0.230 2.548 0.475 -0.035 0.331 3.102 1.181 0.973 1.086 0.853 0.386 0.669 0.939 +0 0.519 1.588 -0.921 1.333 1.568 0.617 0.904 0.956 2.173 0.613 -0.101 -0.590 0.000 1.036 1.217 -0.257 2.548 1.038 0.054 0.395 0.000 0.811 0.904 0.986 0.851 0.910 0.701 0.735 +0 0.609 -0.595 1.181 0.710 -0.059 0.585 0.045 -0.519 0.000 0.768 -0.779 -1.297 2.215 0.733 1.140 0.778 2.548 0.925 -0.392 0.380 0.000 0.865 0.896 0.985 0.762 1.234 0.883 0.786 +0 2.584 0.731 -1.123 0.620 0.831 1.953 -0.881 0.541 0.000 1.262 1.422 -1.009 1.107 0.666 -0.483 0.204 0.000 0.564 -0.459 -0.660 0.000 0.717 1.399 1.722 1.075 0.896 1.824 1.658 +1 0.766 -0.440 0.946 0.500 -1.116 1.247 -0.274 1.499 2.173 1.012 -0.504 -0.395 2.215 0.520 0.117 -1.114 0.000 1.899 -0.364 0.178 0.000 1.054 0.771 0.987 0.799 1.650 0.980 0.816 +0 0.440 -0.129 0.770 1.682 -0.567 1.283 0.719 1.450 0.000 0.851 1.030 -0.576 2.215 0.818 0.524 0.645 2.548 1.634 2.092 0.170 0.000 0.832 0.877 1.112 0.848 0.820 0.861 0.910 +1 1.565 1.356 -0.034 0.116 0.649 0.787 -0.072 1.510 2.173 0.913 0.598 -1.229 2.215 0.521 1.789 -0.819 0.000 0.967 1.090 0.592 0.000 0.793 0.851 0.975 1.204 0.891 0.916 0.810 +0 1.516 0.896 -1.496 0.484 0.453 0.345 0.406 1.613 0.000 0.976 0.125 0.125 1.107 0.770 1.186 0.362 2.548 0.465 -1.146 -1.014 0.000 0.755 0.876 1.166 1.034 0.604 0.739 0.710 +0 0.571 1.042 0.164 1.559 0.010 0.547 -0.074 0.884 2.173 0.595 -0.796 -1.468 2.215 0.737 -0.318 1.591 0.000 1.381 0.648 -1.149 0.000 0.958 0.898 0.991 1.916 0.784 1.406 1.202 +1 0.945 2.182 1.374 0.906 1.208 1.373 1.123 -0.480 2.173 0.563 0.904 1.688 0.000 0.452 1.230 0.690 0.000 0.665 0.285 0.460 3.102 0.627 1.088 0.983 0.703 0.862 0.959 0.779 +1 1.131 0.286 -1.370 0.294 1.044 0.474 -0.275 -0.277 2.173 0.825 -0.613 -0.808 2.215 0.941 1.070 0.586 0.000 0.905 0.477 0.192 0.000 0.471 1.103 0.989 0.826 0.454 0.715 0.723 +1 1.120 -1.164 0.433 0.568 1.577 0.887 -1.363 -1.012 0.000 1.696 -0.944 0.711 2.215 1.162 -2.197 -1.052 0.000 0.959 -0.640 1.616 3.102 0.993 0.964 0.987 0.688 0.846 1.165 0.966 +0 0.903 1.364 1.229 1.289 -0.145 0.881 1.112 -1.707 2.173 0.795 1.656 0.149 0.000 0.693 0.571 -0.762 0.000 0.640 1.244 -1.040 0.000 0.822 1.040 1.413 1.212 1.449 1.043 0.906 +0 0.637 1.299 -1.708 1.014 -0.413 1.056 1.213 0.361 2.173 0.826 0.639 -0.599 0.000 1.386 -0.041 -1.595 2.548 1.509 -0.804 1.233 0.000 0.843 0.762 1.024 0.931 1.798 1.118 1.025 +0 0.500 1.707 0.262 0.432 1.336 1.235 0.838 0.571 2.173 1.594 0.653 -0.756 2.215 1.060 0.757 -1.364 0.000 0.931 1.258 1.442 0.000 0.735 1.004 0.989 0.765 1.929 1.098 0.893 +0 1.571 0.271 0.681 0.093 1.619 1.373 0.667 -0.902 0.000 1.394 -0.074 1.265 2.215 0.959 0.047 -0.760 0.000 0.954 0.310 0.177 3.102 0.704 0.887 0.996 0.861 0.896 1.064 0.968 +0 0.420 -1.611 -0.946 0.618 0.873 0.800 -0.321 -0.701 0.000 1.166 -1.162 1.424 2.215 1.302 -0.434 0.078 2.548 0.373 1.367 0.616 0.000 1.232 0.979 0.995 0.769 1.317 1.021 0.835 +1 0.889 -1.466 0.976 0.467 -0.578 1.016 -0.257 -1.222 0.000 1.841 -1.352 -0.295 2.215 0.391 -0.847 1.178 0.000 0.810 -0.121 1.455 0.000 0.925 0.917 0.985 0.946 0.775 1.046 0.877 +0 1.937 -0.778 0.707 0.901 0.859 1.242 1.138 -1.278 0.000 0.526 1.318 -0.886 2.215 0.603 0.573 0.666 0.000 1.016 -0.389 -0.301 3.102 1.368 0.875 0.987 1.696 0.764 1.119 0.975 +0 1.270 -1.031 0.683 0.939 1.614 0.496 1.005 -1.686 0.000 0.524 -1.149 -0.202 1.107 0.995 0.420 -0.313 2.548 1.106 -0.749 -0.976 0.000 0.757 0.906 1.125 0.784 0.708 0.904 1.054 +0 0.588 1.402 -0.394 0.782 1.406 0.435 -0.927 1.446 1.087 1.060 -1.121 -0.440 0.000 0.927 -0.547 0.993 2.548 0.745 -0.145 -0.724 0.000 0.767 0.889 0.987 0.950 0.342 0.723 0.850 +1 0.398 -0.917 -0.986 0.205 0.010 0.844 0.101 -0.141 0.000 1.157 2.254 1.592 0.000 1.188 0.828 -0.235 0.000 1.240 -1.856 -1.654 0.000 0.882 0.879 0.986 0.655 1.001 0.857 0.981 +1 1.103 1.345 -1.159 1.544 0.566 1.054 1.123 -0.203 0.000 0.407 -1.612 0.593 0.000 0.516 1.450 0.877 2.548 1.865 1.016 -1.724 1.551 0.857 0.728 1.808 1.120 0.555 0.766 0.799 +0 2.092 0.338 -0.879 1.476 -1.655 2.391 -0.403 0.713 1.087 1.542 0.150 -1.473 2.215 1.845 0.795 -0.577 0.000 2.320 0.172 0.264 0.000 1.750 1.796 1.566 2.450 2.723 1.862 1.677 +1 0.741 -0.218 -0.446 0.681 0.705 0.381 0.061 -1.139 2.173 0.267 2.094 -0.120 0.000 0.737 -1.243 0.724 2.548 0.550 1.229 -1.637 0.000 0.552 1.081 0.984 0.695 0.833 0.717 0.688 +0 5.039 -0.657 1.147 0.310 1.306 1.227 0.461 -0.717 0.000 1.134 0.935 -0.452 0.000 1.854 -0.180 -0.177 2.548 0.617 -0.408 -1.260 0.000 0.909 0.980 1.007 1.788 1.036 1.180 1.303 +1 0.840 -1.240 -0.221 1.017 0.119 1.294 -1.102 0.029 0.000 0.964 -1.306 -1.578 0.000 1.544 -0.662 1.523 1.274 1.082 -0.845 -0.881 3.102 2.370 1.383 0.989 1.100 0.830 1.063 0.961 +0 1.894 -0.111 1.603 1.188 -1.630 1.198 0.947 -0.274 2.173 0.590 -0.829 1.156 1.107 0.542 0.188 0.516 0.000 0.831 0.038 -0.795 0.000 0.688 0.798 0.989 0.831 1.745 1.255 0.962 +1 0.849 -1.358 -0.567 1.136 -1.581 1.503 -1.430 0.551 2.173 0.539 -0.734 -0.928 0.000 1.240 -2.185 -1.653 0.000 1.250 -1.463 -0.086 0.000 0.923 1.211 1.076 0.662 0.725 0.869 0.790 +1 1.278 -0.699 -1.423 0.984 1.325 0.924 -0.334 0.111 1.087 0.735 -1.070 1.051 0.000 0.361 -0.704 -0.266 0.000 1.041 -0.272 -0.732 3.102 0.857 0.976 0.990 0.726 0.715 0.813 0.726 +1 0.431 1.550 -0.705 1.190 0.815 1.356 0.144 1.419 2.173 0.968 0.455 -0.146 0.000 2.486 0.332 -0.901 0.000 1.294 0.473 0.615 3.102 1.502 1.283 0.987 1.172 0.977 1.255 1.092 +1 0.786 -0.167 0.934 0.946 -1.732 0.915 -0.951 -0.256 1.087 0.644 -2.034 1.467 0.000 1.191 -0.534 -1.102 2.548 1.037 0.363 0.004 0.000 0.641 0.788 0.987 1.293 0.931 0.945 0.778 +0 0.812 -1.023 0.164 1.224 0.842 0.687 -0.900 -1.244 2.173 0.720 0.156 -1.629 0.000 0.528 0.776 0.388 0.000 1.028 0.129 -0.732 3.102 0.976 1.005 0.998 0.821 0.634 0.767 0.743 +0 1.330 -0.807 1.360 1.665 0.244 0.932 0.081 1.474 0.000 1.667 0.867 -0.634 0.000 1.351 0.499 -0.328 1.274 0.651 0.362 -0.986 0.000 0.972 0.928 1.741 1.441 0.391 1.047 0.992 +0 0.681 0.222 -0.517 0.977 0.425 1.144 0.271 -1.348 2.173 1.556 -0.269 0.406 2.215 0.922 0.166 0.866 0.000 0.953 -0.720 -0.514 0.000 1.003 0.946 0.990 1.130 2.038 1.121 0.923 +0 1.059 0.052 0.123 0.730 1.344 0.987 -0.051 1.386 2.173 0.718 -0.729 -1.160 2.215 0.901 0.105 -0.492 0.000 1.027 0.747 0.049 0.000 0.654 0.882 1.086 0.866 1.027 0.863 0.769 +0 1.518 1.227 -1.711 0.913 1.616 1.608 0.493 0.322 0.000 1.558 1.440 -1.213 2.215 0.491 0.027 -0.453 2.548 0.843 0.206 -0.054 0.000 0.896 0.747 0.980 0.976 0.942 1.180 1.099 +0 1.918 -0.086 -0.813 3.775 -0.917 4.190 0.138 0.786 1.087 1.531 -0.861 -1.335 0.000 0.859 1.124 -1.095 2.548 0.781 -1.041 -0.382 0.000 0.928 0.942 0.981 3.987 2.712 2.566 1.882 +1 1.261 -0.336 1.479 0.725 -0.786 0.982 -1.012 -1.582 1.087 1.085 -0.492 0.528 0.000 0.709 2.672 0.411 0.000 1.566 -0.728 -0.758 3.102 0.890 0.928 1.182 0.840 0.894 0.897 0.808 +1 1.194 0.165 0.667 0.634 1.614 0.981 0.797 -0.868 0.000 1.130 -0.396 1.020 2.215 0.731 1.368 -1.340 0.000 0.523 0.473 1.096 0.000 0.831 0.950 0.987 0.718 0.646 1.025 0.889 +1 0.960 0.282 1.241 1.069 -1.214 1.038 0.458 0.729 2.173 1.034 -2.815 -0.873 0.000 0.743 0.805 -0.640 0.000 1.516 0.213 -1.333 0.000 0.780 1.230 1.125 1.140 1.185 1.037 0.925 +1 0.673 -0.414 -1.389 1.812 -0.565 0.515 -0.317 0.450 2.173 0.780 1.019 1.298 2.215 0.584 -1.072 -0.962 0.000 0.811 -1.459 0.079 0.000 0.651 0.724 1.036 1.260 0.944 0.931 0.867 +1 0.942 -0.537 0.408 0.540 -0.258 0.847 -0.308 -1.087 0.000 1.405 -0.552 -0.555 2.215 1.465 -0.960 1.474 0.000 3.348 -0.785 0.539 0.000 1.123 0.727 0.986 0.896 0.809 0.958 0.975 +0 1.249 -1.702 0.479 0.740 0.174 1.648 -0.284 -1.184 0.000 1.383 -0.782 0.301 0.000 1.206 -0.151 1.143 1.274 1.010 -0.967 -0.777 0.000 1.031 0.883 0.993 0.846 0.606 0.732 0.724 +0 1.170 1.016 -0.510 0.573 -1.337 0.673 1.421 0.430 2.173 0.822 -0.102 -1.565 2.215 0.538 -1.736 1.257 0.000 0.384 0.025 -0.250 0.000 0.748 0.764 0.986 0.938 1.413 1.023 0.919 +0 0.534 -1.050 1.221 0.904 -0.219 0.881 -1.299 -0.820 2.173 0.555 2.173 -1.357 0.000 1.954 0.148 0.618 1.274 1.681 0.625 -1.581 0.000 1.054 1.630 0.984 0.840 2.052 1.750 1.376 +0 0.641 0.611 -0.420 0.069 -1.501 1.363 -0.887 1.603 0.000 1.616 1.026 -0.363 1.107 0.911 1.225 -0.021 2.548 0.798 -0.248 -0.187 0.000 0.792 1.837 0.904 0.982 0.434 1.677 1.266 +0 0.876 -0.699 -0.337 1.168 1.427 0.293 1.454 0.424 0.000 0.848 0.980 1.181 1.107 0.293 1.919 -0.382 0.000 0.593 0.986 -1.008 3.102 0.390 0.581 1.401 0.922 0.592 0.844 0.786 +1 0.666 -0.179 0.021 0.839 -1.618 0.727 0.738 -0.316 2.173 0.749 -0.586 0.067 2.215 0.726 -0.343 -1.636 0.000 1.806 0.532 1.458 0.000 0.799 1.092 1.031 0.852 0.870 0.893 0.764 +0 0.467 -1.656 0.367 1.882 1.167 0.903 -1.543 -1.237 0.000 0.882 -1.805 -0.776 0.000 1.434 -0.572 0.461 2.548 0.666 -0.317 -0.578 3.102 0.817 0.746 0.992 0.773 0.609 0.901 0.908 +0 0.543 0.269 -1.743 0.978 0.425 0.918 0.498 -1.502 0.000 1.004 -0.219 -1.563 0.000 1.261 -0.096 -0.053 0.000 0.712 -0.690 -0.079 3.102 0.768 0.981 0.990 0.513 0.442 0.717 0.702 +1 0.331 -0.543 1.403 1.619 -1.716 1.076 -0.451 -0.166 2.173 0.844 0.979 -1.107 0.000 1.408 -0.098 0.595 2.548 0.884 -0.773 1.035 0.000 0.952 0.972 0.988 1.444 1.007 1.297 1.074 +0 0.879 0.034 -1.648 0.513 -0.488 0.604 0.062 -0.019 2.173 0.640 0.600 0.920 2.215 0.383 -0.751 0.782 0.000 0.516 0.356 1.428 0.000 0.424 0.570 0.988 0.700 0.731 0.623 0.522 +0 1.573 0.466 -1.588 0.369 -0.918 0.734 1.324 0.521 0.000 0.724 0.306 0.677 1.107 0.514 1.718 -0.574 0.000 0.662 -0.166 -0.980 1.551 0.971 0.917 0.989 0.876 0.644 0.665 0.767 +1 0.421 -1.576 -0.240 0.774 0.245 0.835 -0.101 1.489 2.173 0.716 -1.025 1.201 0.000 1.243 1.068 -0.274 2.548 1.175 -1.986 -0.188 0.000 0.935 0.759 0.998 0.953 1.536 0.981 0.870 +0 0.881 -1.523 1.146 0.436 -0.732 1.077 -1.301 -0.897 2.173 0.396 -1.149 1.051 0.000 1.610 -0.381 0.709 1.274 0.697 -2.142 -0.841 0.000 0.851 0.898 0.990 0.796 1.797 0.979 0.811 +1 2.210 0.185 -1.499 0.471 -0.443 0.905 -0.123 0.182 0.000 0.759 -0.475 -1.201 0.000 1.015 0.553 0.830 2.548 0.506 0.396 0.064 0.000 0.861 0.932 1.152 0.815 0.320 0.695 0.654 +0 0.830 0.827 0.531 0.880 -1.565 0.923 0.303 -0.603 0.000 0.893 -0.223 -0.888 0.000 1.214 1.214 0.853 0.000 0.827 -0.233 0.952 3.102 0.885 0.725 1.125 0.565 0.510 0.512 0.557 +1 0.936 -0.947 -1.606 0.688 -0.593 0.858 -0.267 -1.444 2.173 0.950 -1.353 0.054 0.000 1.457 -0.542 0.454 0.000 1.124 -0.919 1.097 3.102 0.975 0.831 0.986 0.832 0.900 0.956 0.857 +1 1.994 -0.311 1.146 0.565 -1.536 1.196 -0.666 0.067 2.173 1.313 -0.276 -0.750 2.215 1.147 0.062 1.725 0.000 0.389 -0.705 -1.493 0.000 0.815 0.871 0.989 1.310 1.290 1.123 0.930 +0 1.214 -0.822 0.808 0.553 1.018 1.310 0.553 -1.343 2.173 1.275 0.238 0.106 0.000 0.459 0.319 1.038 0.000 0.813 0.777 -0.575 1.551 0.876 0.699 0.989 1.401 0.727 0.962 0.907 +0 0.793 0.185 0.057 2.109 -0.676 1.061 -0.530 -0.981 2.173 1.052 -0.504 0.852 0.000 1.578 -0.506 1.454 2.548 1.422 0.629 0.821 0.000 0.874 0.942 1.098 0.972 1.308 1.053 0.985 +0 0.866 -0.591 -1.366 0.500 0.339 1.083 -0.511 1.351 2.173 1.474 -1.515 -0.284 2.215 0.725 0.000 1.642 0.000 0.697 0.559 -0.161 0.000 0.827 0.901 0.986 1.081 2.110 1.174 0.945 +0 0.351 1.212 -0.899 1.282 0.761 3.184 0.477 0.074 2.173 3.749 1.111 -1.404 0.000 1.829 -0.155 1.477 0.000 0.575 -0.274 -1.245 0.000 0.860 0.855 0.986 1.832 0.929 1.165 1.103 +0 2.253 0.120 1.482 0.242 -0.943 0.585 1.054 0.307 0.000 0.799 0.592 -1.124 2.215 0.708 -0.341 -0.830 2.548 0.950 1.365 -0.268 0.000 0.654 0.922 0.984 0.801 0.460 0.693 0.793 +1 2.327 -1.339 -0.998 1.421 -1.426 2.703 0.901 0.842 0.000 1.470 -1.536 -0.502 0.000 0.554 -0.781 0.666 2.548 0.708 -0.482 -0.158 1.551 0.834 0.792 0.985 0.835 0.331 0.706 0.673 +0 0.412 -1.225 1.320 0.458 -0.643 0.659 -0.503 1.056 0.000 0.690 -1.304 -0.225 2.215 0.909 -0.862 -1.009 1.274 0.457 -1.314 -1.519 0.000 0.773 0.867 0.980 0.663 0.572 0.631 0.613 +0 1.107 0.512 -0.151 1.110 0.803 0.820 0.639 -1.279 2.173 0.438 0.355 1.629 0.000 0.708 -0.510 -1.686 1.274 0.673 0.996 -0.597 0.000 0.929 0.956 1.165 0.909 0.696 0.820 0.747 +0 0.439 1.428 -0.750 0.801 -1.326 0.997 0.371 0.416 2.173 1.053 0.369 1.544 0.000 0.532 0.863 -1.723 0.000 1.480 -0.695 -0.361 3.102 0.400 1.116 0.999 0.828 1.170 0.962 0.847 +0 0.557 0.046 0.769 1.813 -1.251 0.815 0.145 1.069 0.000 0.838 0.626 -0.573 0.000 1.290 1.198 0.362 1.274 1.071 1.460 1.460 0.000 0.843 0.938 1.350 1.277 1.642 1.055 0.873 +0 1.760 0.062 1.516 1.945 1.172 1.839 -1.119 -0.276 0.000 0.364 -0.882 -1.563 2.215 0.339 -0.975 -0.889 2.548 0.418 -0.357 -0.386 0.000 0.497 0.851 0.984 0.780 0.215 0.550 1.038 +0 0.861 0.523 1.494 1.122 0.162 0.891 -0.998 -1.272 2.173 1.049 0.415 -0.341 2.215 0.763 -1.500 1.383 0.000 0.562 0.391 0.895 0.000 0.966 0.970 1.269 0.896 1.534 1.075 0.979 +1 0.941 0.719 0.837 0.853 -1.300 0.667 -0.281 0.885 2.173 0.851 0.274 0.297 0.000 2.032 0.616 -0.836 2.548 0.766 0.645 1.646 0.000 1.020 1.127 1.164 0.884 1.621 0.938 0.819 +1 0.984 -1.358 -0.851 0.819 1.444 0.574 -0.362 0.316 1.087 0.741 -1.205 0.753 2.215 1.390 -1.706 -1.391 0.000 1.203 -1.337 -0.370 0.000 0.980 0.772 1.092 0.946 0.569 0.702 0.660 +1 0.634 1.476 1.324 0.764 1.313 0.987 1.062 -0.242 1.087 0.723 0.291 -0.622 0.000 1.074 0.638 -1.622 0.000 0.420 -0.286 0.442 0.000 0.842 0.816 1.001 0.636 1.213 0.859 0.731 +0 1.177 -0.160 0.154 3.206 0.497 1.710 -0.932 -1.071 0.000 1.043 1.379 -1.416 0.000 1.712 -0.541 0.648 2.548 1.982 -0.016 -1.328 0.000 0.645 0.912 0.993 0.849 0.984 0.875 1.227 +1 0.991 -0.259 0.322 0.984 1.517 1.253 -0.143 -0.662 0.000 0.518 -0.297 1.470 0.000 0.613 -1.260 1.037 2.548 1.066 0.379 0.500 3.102 0.627 0.912 1.205 0.710 0.712 0.776 0.755 +1 1.908 -1.067 0.395 0.852 0.775 1.158 -0.621 -1.210 1.087 0.427 -0.087 -0.905 0.000 0.808 -0.649 1.078 0.000 0.445 -1.507 -0.495 0.000 0.924 0.918 0.978 0.604 0.758 1.007 0.849 +1 1.091 -1.196 1.560 0.432 -0.523 0.869 -1.486 -0.951 1.087 0.613 -0.964 0.348 0.000 1.456 -1.443 0.892 0.000 0.881 -0.117 -0.312 3.102 0.811 0.887 0.983 0.801 0.846 0.867 0.759 +1 0.947 -1.305 -0.869 1.161 -1.435 0.930 -0.709 1.045 0.000 1.072 -0.187 0.733 1.107 0.894 1.450 -1.010 0.000 0.456 0.498 0.248 3.102 1.015 1.021 0.985 1.006 0.370 1.024 1.044 +0 1.451 -0.692 0.994 0.361 1.178 1.129 0.937 -0.620 0.000 1.459 1.813 1.634 0.000 0.861 -0.077 -1.014 2.548 1.113 0.592 -0.085 0.000 0.820 0.860 0.980 0.848 0.770 0.664 0.860 +1 1.269 -0.268 1.241 1.617 -1.679 0.866 -0.150 -0.472 0.000 0.449 0.468 1.073 2.215 1.565 0.494 0.323 0.000 0.647 0.955 -0.863 3.102 1.562 1.011 0.986 0.656 0.507 0.707 0.895 +0 1.448 0.965 -0.487 2.697 -0.073 1.300 0.295 1.481 0.000 1.441 -0.689 1.743 2.215 0.552 -0.110 0.455 2.548 0.847 -1.679 0.924 0.000 2.006 1.305 1.002 2.334 0.914 1.460 1.460 +0 0.590 -1.376 1.092 1.360 1.324 1.128 -0.535 -0.045 2.173 1.033 0.701 -1.301 2.215 0.623 -0.519 -1.679 0.000 0.716 0.679 -0.268 0.000 0.894 0.992 0.986 1.116 1.792 1.157 0.923 +0 0.865 0.487 1.738 0.740 0.934 1.112 -0.136 -0.952 0.000 1.150 -1.535 0.288 0.000 0.709 -1.080 1.369 1.274 1.040 0.703 0.204 3.102 2.868 1.712 0.995 0.699 0.972 1.217 1.259 +1 0.740 0.949 -0.060 2.050 0.755 0.951 0.166 -1.259 2.173 0.808 -0.488 1.338 2.215 0.936 0.024 -0.862 0.000 0.822 -0.818 -0.307 0.000 0.727 0.790 1.145 1.442 1.026 1.109 0.895 +1 0.493 -0.478 0.990 1.512 -1.057 0.646 1.120 1.258 0.000 0.552 0.940 -1.476 0.000 2.510 -0.589 0.120 2.548 0.429 -0.076 -1.434 3.102 0.801 0.560 1.152 1.203 0.813 1.098 0.992 +1 1.553 -0.881 -1.612 0.653 0.592 0.502 0.056 -0.091 0.000 0.864 0.566 0.356 2.215 1.564 1.418 -1.488 0.000 0.892 0.553 -0.529 0.000 1.160 1.194 1.276 0.759 0.523 0.874 1.070 +1 0.516 -0.842 -0.676 1.048 0.772 0.531 0.651 -1.292 2.173 0.859 -0.383 -0.318 2.215 0.675 -1.213 0.982 0.000 0.908 -0.679 0.347 0.000 1.005 0.964 0.987 0.717 0.940 0.733 0.675 +1 1.209 0.610 0.479 0.644 1.319 0.784 -0.549 -0.882 2.173 0.324 0.652 -0.556 0.000 0.916 1.399 1.624 0.000 0.678 -1.082 0.216 0.000 0.857 1.226 0.987 0.495 0.855 0.751 0.741 +0 0.965 -1.175 1.416 1.102 1.108 1.205 -1.353 -0.624 0.000 0.706 -1.077 0.327 1.107 0.478 -1.661 -1.339 0.000 0.734 -0.150 1.159 3.102 0.879 0.967 0.985 0.773 0.542 0.739 0.836 +1 2.073 0.007 -1.585 0.668 -1.542 1.095 0.093 0.030 2.173 0.439 0.488 0.362 0.000 0.820 0.454 -0.954 2.548 0.896 -1.177 0.654 0.000 0.932 0.923 0.985 1.483 0.946 0.978 0.920 +1 0.550 1.012 0.376 0.513 -0.586 1.114 2.050 -1.254 0.000 1.266 0.195 1.378 2.215 1.336 0.492 -0.656 1.274 2.194 -0.078 0.709 0.000 2.350 1.971 0.989 0.943 1.355 1.517 1.748 +1 2.048 0.231 -1.410 0.208 0.580 0.998 -0.615 -0.081 2.173 0.563 0.426 0.170 0.000 0.661 -1.065 1.034 2.548 0.699 0.911 1.559 0.000 0.959 0.828 0.985 0.851 0.899 0.912 0.785 +0 0.618 -0.641 1.163 0.123 -1.245 0.843 2.839 -1.400 0.000 0.601 -0.942 0.284 0.000 0.640 1.194 0.841 1.274 0.601 0.466 -0.616 3.102 0.847 0.891 0.989 0.624 0.493 0.923 0.826 +1 0.506 -2.134 0.773 2.364 0.911 1.042 -1.430 -1.323 2.173 0.914 -1.238 0.133 2.215 0.527 1.629 -1.055 0.000 0.383 -0.067 -1.022 0.000 1.118 1.375 0.975 1.391 1.393 1.265 1.259 +0 1.516 -0.713 -1.531 0.723 1.410 1.088 -0.722 0.471 2.173 0.825 -0.537 -0.788 2.215 0.669 -1.880 -0.585 0.000 0.399 -0.016 0.110 0.000 0.767 0.927 0.979 0.823 1.270 0.963 0.836 +0 2.129 0.105 0.268 0.595 0.415 1.650 0.844 -1.510 1.087 0.980 0.474 -0.425 2.215 0.465 0.979 0.574 0.000 0.586 -0.754 1.300 0.000 0.754 0.824 0.976 2.028 1.590 1.407 1.086 +1 0.531 -1.204 -0.660 0.899 1.201 0.552 -0.760 -0.924 0.000 1.103 0.804 1.565 1.107 1.418 0.296 0.194 2.548 0.930 1.366 1.671 0.000 0.891 0.975 0.987 1.086 1.299 0.967 0.864 +0 0.366 -1.671 1.642 2.169 -1.083 1.025 -0.783 -0.068 2.173 1.336 -0.860 1.690 0.000 1.192 -0.844 0.602 2.548 0.787 -1.541 0.279 0.000 1.092 0.969 0.985 1.755 0.787 1.325 1.253 +0 1.049 0.231 0.630 1.188 0.483 0.698 -1.771 -0.726 0.000 0.838 0.766 1.274 2.215 1.271 0.633 -0.968 2.548 0.665 1.818 -1.367 0.000 3.762 2.305 0.994 0.998 0.989 1.548 1.360 +0 0.536 1.071 1.154 2.406 -1.595 0.433 0.935 -0.529 0.000 0.952 1.247 0.549 2.215 0.617 -1.526 -0.549 0.000 0.814 -0.265 0.189 0.000 0.990 0.833 0.984 0.867 0.590 0.816 0.755 +1 0.367 -0.946 0.783 0.763 0.280 1.083 -0.028 -1.680 2.173 0.963 -0.678 0.216 0.000 1.192 0.365 -0.900 2.548 0.651 -0.140 1.024 0.000 0.740 1.021 0.985 1.063 0.966 0.910 0.824 +1 1.647 -0.275 0.131 1.107 -0.388 0.715 -0.537 1.055 2.173 0.353 0.537 -0.920 0.000 0.547 0.296 -1.611 2.548 1.160 -0.347 -1.681 0.000 0.915 0.925 0.981 0.811 0.630 0.783 0.739 +1 0.722 -1.195 1.588 1.556 -0.985 1.537 -0.143 0.171 2.173 1.306 -0.145 -1.020 0.000 0.996 -0.411 1.214 2.548 1.526 -2.102 1.173 0.000 3.216 1.935 1.077 1.670 1.267 1.683 1.399 +0 0.696 -0.626 1.022 0.347 -0.622 0.608 -0.146 -0.360 0.000 1.235 -0.288 1.622 2.215 1.095 -0.204 0.373 0.000 0.895 -0.121 -1.149 3.102 0.902 0.772 0.989 0.771 0.575 0.784 0.682 +1 0.734 0.336 -0.446 1.303 0.675 1.052 0.158 -0.917 0.000 0.769 -0.128 1.035 2.215 1.698 -0.299 1.615 0.000 1.385 -0.948 0.299 1.551 1.068 1.220 1.148 0.752 0.754 0.930 0.881 +0 1.506 -0.605 0.967 0.307 -1.503 0.925 0.001 -0.457 0.000 0.523 -1.035 -0.984 2.215 0.569 0.902 1.306 2.548 0.824 -0.361 -1.596 0.000 1.177 0.979 0.987 0.751 0.872 0.691 0.758 +0 1.815 -0.957 -0.074 0.830 0.360 1.510 -1.544 -1.573 0.000 1.295 0.248 1.260 1.107 1.726 -0.469 -0.487 1.274 0.495 1.119 0.585 0.000 2.932 2.116 0.976 1.363 1.708 1.603 1.407 +1 0.891 2.240 -0.237 1.022 -1.255 0.728 -0.032 -0.109 2.173 1.405 -0.485 -1.735 0.000 0.513 2.107 1.130 0.000 0.707 -1.554 -0.763 0.000 0.547 0.661 1.048 1.567 0.934 1.106 1.029 +0 2.514 -0.234 -0.993 0.469 1.493 0.989 -0.682 0.267 2.173 0.476 -0.818 0.792 0.000 0.855 0.129 1.248 2.548 0.589 -0.406 -1.572 0.000 0.598 0.719 1.180 0.922 1.012 0.989 0.784 +0 0.653 -0.978 -0.182 1.204 -1.692 0.750 -0.853 1.134 0.000 1.888 -0.614 -0.567 1.107 1.080 0.349 1.497 2.548 1.061 -0.471 0.413 0.000 0.850 0.905 1.201 1.080 1.661 1.097 0.950 +0 0.348 -0.881 -1.509 2.117 -1.564 0.736 -0.542 0.479 1.087 1.016 0.979 -0.199 1.107 0.541 0.646 0.764 0.000 0.612 0.836 -1.221 0.000 0.627 0.827 0.982 1.214 1.314 1.118 0.861 +1 0.487 0.570 0.401 1.204 -1.029 0.686 1.424 -1.044 2.173 1.223 0.046 0.500 0.000 0.995 0.896 1.587 2.548 0.527 0.571 0.097 0.000 0.503 0.916 1.019 0.720 0.754 0.875 0.766 +1 0.335 1.116 0.228 0.826 1.666 1.327 1.339 -0.764 1.087 1.331 0.246 1.042 0.000 0.339 0.805 -0.619 0.000 0.569 0.098 -0.293 0.000 1.061 0.712 0.987 1.308 0.894 1.025 0.901 +1 1.800 0.443 -0.776 0.216 1.320 0.591 0.624 0.740 2.173 0.703 -0.231 -1.212 0.000 0.528 -0.442 0.675 2.548 0.668 0.132 1.261 0.000 0.729 0.839 0.987 0.781 0.411 0.680 0.633 +0 1.061 -1.299 1.631 2.761 1.591 1.543 1.823 0.423 0.000 2.550 1.417 -0.431 2.215 1.589 0.062 1.685 1.274 1.741 -0.543 -1.182 0.000 1.145 1.615 0.998 1.813 2.583 3.883 3.894 +0 0.722 0.080 -1.689 1.179 -0.974 0.619 0.129 0.516 0.000 0.811 1.050 0.085 0.000 1.223 1.005 -1.232 2.548 1.086 -0.468 1.094 3.102 0.915 1.067 0.986 0.745 1.120 0.799 0.849 +0 1.345 0.738 0.518 0.362 0.762 0.507 0.836 -0.793 2.173 1.029 0.212 -1.697 2.215 0.673 -0.775 -0.985 0.000 0.516 0.972 0.076 0.000 0.938 0.858 0.986 0.890 0.844 0.790 0.712 +0 1.410 -2.134 0.812 0.583 0.177 0.664 -1.013 1.144 1.087 1.198 -0.294 -0.733 0.000 1.360 -0.793 -1.649 2.548 1.650 -0.867 -0.681 0.000 0.752 0.958 0.989 0.773 0.697 0.802 0.910 +0 1.009 0.735 0.973 1.358 0.389 1.978 -0.707 -0.996 2.173 0.747 -1.136 0.431 2.215 0.742 -0.291 1.133 0.000 0.697 -1.073 1.374 0.000 0.434 1.303 0.997 1.460 1.765 1.846 1.406 +1 0.610 0.291 1.128 1.010 -0.127 0.800 1.272 -1.370 1.087 1.209 0.425 0.373 1.107 0.535 2.208 -1.078 0.000 0.907 0.652 1.703 0.000 0.840 1.182 0.987 0.986 1.580 0.903 0.813 +0 0.416 0.559 1.691 0.526 -1.409 0.613 0.907 1.534 2.173 0.917 1.523 -0.365 2.215 0.806 1.822 -1.442 0.000 2.042 0.334 -0.188 0.000 1.794 1.173 0.988 0.967 1.151 1.076 1.076 +0 0.643 -1.666 -0.532 1.340 1.096 1.030 1.136 -0.410 0.000 1.673 -0.891 -0.839 2.215 2.478 -0.293 1.094 0.000 1.215 -0.914 1.362 0.000 0.888 0.837 1.279 1.278 0.948 1.110 1.035 +1 1.121 -0.096 -1.619 1.256 0.935 0.581 -0.862 0.468 2.173 0.941 -1.434 -0.521 0.000 0.742 1.483 -1.237 0.000 1.099 -0.301 0.257 0.000 1.154 0.996 1.224 0.923 0.758 0.702 0.796 +1 1.920 0.327 0.134 0.415 -0.144 0.664 0.531 1.403 2.173 0.549 0.135 1.602 2.215 0.619 1.275 -1.284 0.000 0.461 -0.630 -0.889 0.000 0.794 0.751 0.998 0.910 0.237 0.774 0.697 +1 1.061 -0.613 1.342 0.824 1.192 1.812 1.051 -0.517 0.000 1.040 0.408 0.559 0.000 1.215 0.599 -1.686 2.548 2.248 -0.380 1.690 3.102 0.930 1.063 0.981 0.709 0.752 0.841 0.733 +1 0.722 -0.535 1.040 0.498 -0.360 0.874 0.351 -0.937 0.000 0.753 -0.396 -1.188 0.000 1.532 0.093 0.650 2.548 1.270 0.575 1.561 0.000 0.774 1.024 0.980 0.871 0.633 0.919 0.841 +0 0.910 0.021 1.541 0.421 0.206 0.663 0.597 -0.809 0.000 0.505 1.601 -1.614 0.000 0.903 0.884 0.473 2.548 0.814 -0.249 -0.040 3.102 1.057 0.964 0.990 0.609 0.532 0.683 0.724 +1 1.668 0.993 0.367 0.657 -1.029 2.060 1.715 -1.160 0.000 1.055 0.709 1.387 0.000 1.609 0.484 -0.159 2.548 1.290 -0.098 1.638 0.000 0.746 1.315 1.380 0.657 0.380 0.750 0.770 +1 0.775 -0.824 -1.436 0.873 0.704 0.967 -0.055 1.398 0.000 1.404 -0.757 -0.603 0.000 0.994 -0.696 -0.096 0.000 1.377 0.528 1.060 1.551 0.800 0.621 1.067 0.862 0.700 0.826 0.777 +0 0.907 0.770 0.647 0.126 1.345 0.748 -0.424 -1.040 0.000 0.818 0.804 -0.144 2.215 0.471 -0.124 1.613 0.000 1.021 1.339 1.344 0.000 0.880 1.067 0.979 0.620 0.734 0.695 0.667 +1 1.088 -1.507 0.353 0.544 1.670 1.976 0.784 -1.416 0.000 1.874 -1.169 0.750 0.000 2.659 -0.925 -0.825 2.548 1.683 -0.295 0.487 0.000 0.989 0.883 0.988 1.140 0.738 1.069 0.897 +0 3.344 0.008 -0.364 0.606 -0.566 0.973 0.289 1.363 2.173 0.776 -0.914 1.410 2.215 0.370 1.136 0.338 0.000 0.594 -0.999 -1.692 0.000 0.919 0.873 0.992 1.363 0.840 1.268 1.011 +0 0.842 -0.228 0.625 0.690 -0.464 0.457 -0.745 1.293 0.000 0.648 0.006 -1.536 2.215 1.283 -0.763 -1.443 2.548 1.325 -0.271 -0.150 0.000 1.174 0.977 0.988 0.745 0.429 0.678 0.679 +1 0.933 0.232 0.800 0.365 -0.091 0.770 -0.631 0.940 0.000 0.511 -0.074 0.407 0.000 1.841 -0.267 -1.192 2.548 0.982 1.058 -1.398 0.000 0.724 0.935 0.984 1.001 0.744 0.873 0.761 +1 0.788 -0.858 1.049 0.621 0.426 0.594 0.209 -1.582 0.000 1.148 0.508 0.749 2.215 0.786 -1.002 -1.409 0.000 0.993 0.618 -0.375 0.000 0.886 0.811 0.986 1.410 0.744 1.131 1.022 +1 0.675 0.406 0.824 0.478 1.650 0.772 -0.831 -0.619 2.173 0.711 0.059 0.190 0.000 1.328 -0.398 -1.466 0.000 0.711 -2.262 -1.402 0.000 0.753 1.274 0.983 0.623 0.579 0.900 0.823 +1 0.560 -1.418 -1.552 0.692 -0.663 0.786 0.919 0.431 2.173 0.585 1.478 1.361 2.215 0.640 1.586 -1.054 0.000 0.606 0.037 -0.288 0.000 0.993 1.020 0.989 1.077 0.800 0.939 0.830 +0 0.901 -0.165 -0.759 0.902 1.631 1.112 -1.641 0.223 0.000 1.395 -1.017 -1.421 2.215 1.295 0.872 0.245 0.000 1.088 1.943 0.820 0.000 1.169 1.226 1.042 0.841 0.556 1.583 1.247 +0 0.579 2.041 1.214 0.366 0.264 0.549 1.181 0.083 2.173 0.849 0.136 -1.333 2.215 0.520 0.302 1.574 0.000 0.547 -1.417 0.703 0.000 0.820 1.064 0.977 0.857 1.109 0.791 0.751 +1 1.836 0.054 0.056 1.316 0.152 1.516 -0.374 1.503 0.000 0.906 -0.030 -1.046 0.000 1.403 0.542 -1.280 2.548 1.054 -0.335 -0.485 0.000 0.867 0.750 0.968 0.742 0.847 0.938 0.779 +0 0.925 0.309 -0.652 1.670 -1.509 1.285 -0.494 0.584 2.173 0.714 -0.339 1.248 0.000 1.246 1.204 -1.009 2.548 0.592 1.714 0.044 0.000 1.487 1.238 1.200 1.586 2.266 1.319 1.148 +1 0.359 1.266 1.109 0.561 -0.989 1.047 0.044 -1.335 2.173 1.283 -0.260 1.405 0.000 1.075 0.352 -0.482 2.548 1.809 -0.021 -0.059 0.000 0.903 1.278 0.978 0.733 0.947 1.171 0.950 +0 0.643 0.100 0.608 0.688 -1.504 0.689 -0.457 1.320 2.173 0.712 0.467 -0.720 1.107 1.384 0.208 -0.015 0.000 0.807 -2.393 1.149 0.000 0.874 1.026 0.984 0.760 1.114 1.012 0.837 +0 1.118 1.031 1.539 0.506 0.420 1.159 1.199 -1.089 2.173 0.933 0.534 0.348 1.107 0.915 -0.809 0.541 0.000 0.621 -1.097 -1.167 0.000 0.850 0.988 0.990 0.997 1.560 1.235 1.094 +0 0.356 -0.139 -0.042 1.344 -0.933 0.859 1.091 1.249 2.173 0.441 0.533 -0.476 0.000 0.692 0.703 0.128 0.000 0.537 1.963 -0.039 0.000 0.698 0.941 0.983 0.865 0.807 1.183 1.099 +1 0.963 1.779 -0.894 1.085 -1.076 1.093 0.117 0.958 2.173 0.596 0.785 -0.595 0.000 0.479 -2.333 1.698 0.000 0.687 -0.513 -0.505 3.102 2.254 1.230 0.994 1.487 0.953 1.090 1.233 +0 0.829 0.569 0.657 0.211 -1.478 0.459 -1.329 -1.074 0.000 1.018 0.198 0.290 2.215 0.909 -0.042 -0.700 0.000 1.444 0.064 1.605 3.102 0.893 0.929 0.992 0.816 1.017 0.846 0.891 +1 1.841 0.666 0.930 0.686 -0.355 1.382 0.329 -0.473 2.173 0.465 -0.425 -1.124 0.000 0.599 0.680 -1.615 2.548 0.435 -1.177 -1.203 0.000 0.481 0.811 1.425 0.821 1.000 0.940 0.761 +1 0.683 -2.110 -0.451 0.511 -0.834 0.499 -0.782 1.652 1.087 0.655 0.968 -1.358 2.215 0.515 -1.688 0.574 0.000 2.438 -0.145 0.381 0.000 1.184 1.076 0.980 1.216 0.924 0.983 0.933 +0 0.499 1.128 0.946 0.444 1.220 1.105 0.529 -1.205 2.173 1.158 0.780 0.247 2.215 0.689 0.762 1.407 0.000 0.664 -0.022 -0.276 0.000 0.816 0.851 1.002 0.890 1.623 0.970 0.780 +1 2.721 1.091 0.725 0.523 0.944 0.950 2.432 -0.909 0.000 0.978 1.136 -0.273 1.107 1.025 1.044 1.626 0.000 0.366 0.473 -0.581 0.000 1.017 1.019 0.992 1.188 0.801 0.953 1.056 +0 0.713 -0.752 -0.208 0.853 0.758 0.865 -0.902 -0.886 2.173 0.883 0.348 -1.457 2.215 1.042 -1.191 1.355 0.000 0.651 1.479 -0.264 0.000 0.802 1.060 0.991 0.901 1.077 0.939 0.863 +0 0.794 0.465 0.881 0.667 -0.885 1.590 -0.302 0.394 2.173 0.806 -0.049 1.636 0.000 2.130 0.321 -1.207 2.548 0.603 1.021 0.382 0.000 0.950 0.915 1.008 1.076 2.400 1.179 0.941 +0 0.326 -1.446 0.350 0.375 1.518 1.199 0.057 1.637 2.173 1.089 -0.655 -0.399 2.215 1.077 -0.311 0.562 0.000 0.988 -2.048 -0.599 0.000 0.644 1.152 0.983 0.837 1.741 0.974 0.802 +1 0.916 -1.352 -0.720 0.320 -1.328 0.579 0.012 1.281 2.173 0.452 -1.914 1.714 0.000 0.979 -1.788 1.122 0.000 1.242 -1.082 -0.140 0.000 1.027 1.112 0.984 0.542 0.676 0.700 0.657 +1 0.965 -0.629 1.720 1.446 -0.529 0.981 -1.063 0.751 0.000 1.020 0.041 -0.431 0.000 1.980 -0.631 1.363 1.274 1.695 -0.993 -0.602 3.102 0.844 0.834 1.470 1.190 1.417 0.941 0.860 +1 1.191 0.296 0.117 0.739 -0.683 1.997 1.225 -1.134 0.000 1.033 -0.331 -0.776 0.000 1.862 0.275 1.168 2.548 2.322 0.114 0.630 3.102 0.902 0.877 0.990 0.877 0.753 0.827 0.776 +0 2.178 -0.851 -0.902 0.262 0.985 0.931 -1.386 0.343 2.173 0.861 -1.596 1.596 0.000 1.291 -1.635 -0.001 0.000 0.655 -0.240 0.969 3.102 1.607 1.055 1.037 1.170 0.654 0.803 0.858 +1 0.441 2.150 1.061 1.092 0.345 0.766 1.479 -1.504 0.000 1.028 0.064 -0.042 2.215 0.792 0.416 1.619 0.000 0.818 1.016 -0.900 3.102 0.882 0.652 0.983 0.881 0.768 0.844 0.798 +0 0.305 -0.863 -0.353 2.308 -1.499 1.387 -0.425 0.353 2.173 0.418 -1.168 -1.682 2.215 0.374 0.697 -0.938 0.000 0.516 0.015 -0.457 0.000 0.680 0.985 0.998 0.498 1.169 1.033 0.817 +1 0.821 1.143 -0.065 2.526 0.449 0.959 0.420 -1.343 2.173 1.207 -0.644 1.365 0.000 2.575 1.138 -0.695 0.000 1.398 1.033 1.171 3.102 3.966 2.364 0.990 0.876 1.072 1.516 1.444 +0 0.623 1.018 1.167 1.208 0.277 0.948 -0.727 -1.524 0.000 1.147 0.600 -0.313 2.215 0.829 -0.282 1.579 0.000 0.702 -0.189 0.345 3.102 0.636 0.788 0.993 0.831 0.574 0.921 0.875 +1 0.743 0.543 -0.294 1.523 0.663 0.835 0.027 -0.667 0.000 0.881 -0.160 -1.179 2.215 0.894 0.778 1.239 0.000 1.947 -0.224 1.501 3.102 0.913 0.921 1.119 1.049 0.788 0.878 0.756 +1 2.089 -0.100 -0.859 1.001 0.721 0.707 0.168 1.377 2.173 0.540 -1.098 0.983 2.215 0.403 0.763 0.172 0.000 1.013 -0.923 -0.478 0.000 0.890 0.983 1.982 1.196 0.708 0.930 0.811 +0 0.681 -0.662 1.072 1.462 -1.702 0.786 1.635 0.410 0.000 0.709 1.318 1.059 0.000 1.326 -0.529 -0.724 0.000 1.135 0.593 -1.278 3.102 0.904 1.041 0.992 1.620 0.736 1.140 1.366 +1 0.351 1.397 1.177 2.619 0.702 0.797 -0.949 -1.074 0.000 0.757 -0.930 -0.433 0.000 0.816 0.897 0.538 1.274 1.395 0.589 -1.060 3.102 0.902 1.106 0.987 0.904 0.816 1.042 2.029 +1 1.100 0.399 -0.221 0.474 -1.012 0.803 0.870 -1.327 1.087 0.559 0.625 1.020 2.215 0.613 -1.017 -0.214 0.000 0.974 -0.517 0.665 0.000 0.644 0.753 0.983 0.943 0.851 0.832 0.755 +1 0.759 -0.198 -1.478 0.650 0.587 0.848 2.268 -1.723 0.000 1.543 0.714 -0.523 0.000 2.222 0.819 0.427 2.548 0.956 -1.180 -1.418 0.000 0.753 0.915 0.988 0.965 0.869 0.958 0.798 +0 1.040 -1.543 1.658 0.336 1.525 0.499 -1.047 -0.175 2.173 0.431 -1.398 0.469 0.000 0.344 -2.100 1.392 0.000 0.950 -0.165 -0.496 3.102 0.511 0.689 0.988 0.839 0.383 0.631 0.574 +0 1.038 -1.032 -0.346 0.469 -0.217 1.000 -0.937 0.433 2.173 1.065 -1.583 1.560 0.000 1.364 -0.349 -1.378 2.548 0.841 -0.364 1.624 0.000 0.783 0.864 0.991 0.959 1.510 0.996 0.947 +0 3.587 -0.434 -0.315 2.020 -0.352 0.987 1.635 1.020 0.000 1.659 -0.023 -1.612 0.000 1.312 -0.573 -1.634 0.000 2.954 0.900 1.538 0.000 0.698 0.984 0.957 0.857 0.176 0.912 1.206 +1 0.630 0.889 1.165 1.084 -1.366 1.784 0.840 0.193 0.000 0.418 1.614 1.136 0.000 1.204 -0.695 -1.101 2.548 0.828 1.025 -0.874 3.102 0.903 0.888 0.985 1.314 0.907 0.870 0.840 +0 1.130 -1.702 -0.326 0.521 0.615 0.600 2.505 -0.952 0.000 0.719 -1.466 1.258 2.215 0.856 1.007 1.572 0.000 1.374 0.180 0.778 3.102 0.792 0.829 0.988 0.790 0.951 1.004 1.070 +1 0.621 0.523 -0.117 0.654 -1.100 0.700 0.018 0.522 0.000 1.314 -0.695 -1.074 2.215 0.358 -1.429 0.805 0.000 0.723 -0.271 1.484 3.102 0.965 0.730 0.991 0.746 0.676 0.889 0.763 +0 0.719 -0.560 1.136 2.187 0.795 1.225 0.529 -0.860 0.000 0.845 0.491 1.676 2.215 1.189 -0.646 0.029 2.548 0.463 -0.129 -0.555 0.000 0.511 0.918 0.984 0.939 1.265 1.072 1.241 +0 0.828 -0.387 -0.018 1.212 1.501 0.460 -0.152 -1.027 0.000 1.095 0.395 -1.699 2.215 1.352 1.071 0.319 2.548 0.562 -1.669 0.228 0.000 1.085 1.147 1.359 1.225 1.353 1.083 0.963 +1 0.470 0.733 -0.287 0.872 0.939 0.669 -1.721 -0.688 0.000 0.793 0.828 -0.989 0.000 1.842 0.501 1.062 2.548 1.375 0.313 0.326 3.102 0.805 1.040 0.987 0.881 0.757 0.878 0.790 +1 0.881 1.237 -1.179 1.781 1.428 0.291 1.805 -1.470 0.000 0.543 1.105 -0.166 2.215 1.171 -0.012 0.501 2.548 0.376 -1.829 0.823 0.000 1.904 1.271 1.235 1.220 0.704 0.880 0.966 +0 1.579 -0.834 1.583 0.343 0.590 1.461 -0.754 -1.514 2.173 1.666 -1.289 0.240 1.107 0.765 -1.516 -0.823 0.000 1.180 -2.175 -0.118 0.000 0.807 1.053 0.988 0.881 2.387 1.349 1.125 +1 0.762 0.005 1.172 0.647 -1.651 0.493 1.057 -0.003 2.173 0.489 -1.030 1.174 2.215 0.531 -1.808 -1.126 0.000 0.664 -1.345 0.131 0.000 0.605 1.335 0.994 0.849 1.117 0.869 0.901 +0 0.292 1.288 -0.783 1.748 -0.468 0.478 -2.605 1.314 0.000 0.521 0.449 1.508 2.215 0.990 -1.193 0.496 1.274 0.717 -1.327 -1.437 0.000 0.732 0.824 1.001 0.868 0.978 0.888 1.027 +0 2.183 1.530 0.851 0.237 -0.622 1.283 0.174 -1.023 2.173 1.206 -1.242 1.518 0.000 1.409 -1.634 0.115 0.000 2.140 1.057 -0.884 3.102 0.817 2.119 0.986 1.148 1.030 1.869 1.981 +0 0.894 1.460 -0.680 0.444 0.771 1.434 0.335 -1.416 0.000 1.494 1.168 0.368 0.000 1.830 0.763 1.733 1.274 1.818 1.785 0.338 0.000 0.845 1.026 0.982 1.018 1.377 1.011 0.886 +0 1.700 0.521 -1.286 3.820 -1.622 2.414 -0.217 0.206 0.000 1.502 -0.694 0.387 2.215 1.652 0.225 1.725 2.548 0.926 -0.531 -0.193 0.000 0.939 0.835 1.049 0.674 1.773 1.574 1.810 +0 2.008 -0.041 0.394 0.865 -0.297 0.855 -1.227 1.652 0.000 0.830 0.925 1.460 0.000 0.962 -1.267 -0.797 2.548 1.126 -0.488 -1.338 0.000 0.816 1.078 1.064 1.107 0.549 0.751 0.923 +0 0.787 -0.228 1.161 0.754 0.328 0.848 -1.115 -0.920 2.173 0.614 -0.738 0.420 0.000 0.566 -1.606 1.533 0.000 0.559 -1.535 0.305 0.000 0.897 0.988 0.988 0.844 0.632 0.889 0.822 +0 1.462 -0.502 0.378 0.783 -1.730 1.478 -1.935 -0.907 0.000 2.290 -0.780 0.956 2.215 0.697 -1.074 -0.619 0.000 1.115 0.450 -1.222 1.551 0.863 1.690 1.403 1.074 1.692 1.700 1.404 +0 1.107 1.087 -0.917 0.558 0.209 0.682 -0.115 -0.549 0.000 1.431 -0.392 0.719 2.215 1.156 -0.007 -1.590 2.548 0.707 -0.465 1.107 0.000 1.083 0.892 0.986 1.244 1.223 0.954 0.851 +0 0.351 -1.149 -0.034 0.680 -1.177 1.457 1.061 -1.117 0.000 1.027 -0.449 0.880 0.000 1.882 0.061 0.329 2.548 0.904 -0.157 1.190 3.102 3.347 1.896 0.997 0.887 0.709 1.384 1.089 +1 0.525 0.435 0.544 0.497 -1.068 1.844 -0.943 1.084 2.173 1.646 0.387 -0.039 0.000 1.789 -0.380 -0.927 0.000 1.466 0.124 1.537 3.102 1.005 1.072 0.991 1.097 1.221 1.231 0.991 +0 1.021 0.245 0.585 0.615 -1.644 0.702 0.564 -1.165 2.173 0.805 2.192 -0.097 0.000 0.747 0.813 -0.735 2.548 0.439 0.316 1.619 0.000 1.141 0.817 0.995 0.831 0.371 0.707 0.744 +0 1.270 0.349 1.181 0.911 1.057 0.762 1.532 0.415 0.000 0.717 -0.728 -1.150 2.215 1.564 0.636 -0.620 2.548 0.526 0.430 -0.950 0.000 0.897 1.064 1.000 1.252 1.028 1.057 1.015 +1 1.142 -1.185 0.664 0.773 -0.214 1.147 -0.046 -1.380 0.000 0.707 -0.501 -0.566 0.000 0.788 -0.203 1.156 0.000 1.148 0.119 0.459 1.551 1.157 0.814 0.986 0.518 0.119 0.478 0.532 +0 1.117 -1.139 -0.119 0.637 -1.085 0.830 -0.845 -1.426 2.173 1.237 -0.809 0.900 1.107 0.494 -1.591 -0.385 0.000 0.599 -1.335 0.665 0.000 0.488 0.788 0.990 0.982 1.289 0.865 0.707 +1 0.453 0.662 -0.419 0.878 1.440 0.868 -0.245 -0.142 2.173 1.313 -0.759 -0.749 2.215 1.126 -0.590 1.143 0.000 1.026 0.143 1.231 0.000 0.959 1.091 0.988 0.861 0.921 0.862 0.775 +0 0.442 -0.460 -0.865 1.286 0.697 0.681 2.319 -0.588 0.000 0.679 0.346 -1.534 1.107 0.748 -1.062 1.411 0.000 0.626 0.718 1.625 0.000 0.915 0.959 1.030 0.820 0.880 0.678 0.654 +1 0.353 1.429 0.165 2.087 -1.037 0.816 1.046 0.632 0.000 1.207 -0.203 1.347 2.215 0.942 -0.117 -0.142 2.548 1.164 -2.019 -1.380 0.000 0.729 0.797 1.050 1.557 1.105 1.148 1.016 +0 0.703 -2.264 -1.551 1.113 -0.651 0.537 -0.513 1.143 1.087 0.546 -2.426 0.276 0.000 0.619 0.015 -0.627 2.548 0.511 1.274 0.515 0.000 0.779 0.993 0.984 1.385 0.744 1.107 0.911 +0 0.803 0.056 1.001 0.489 -0.415 0.846 -0.022 -1.109 2.173 1.129 0.870 0.474 0.000 0.697 0.828 1.611 2.548 1.061 1.246 -0.234 0.000 0.953 0.878 0.986 0.856 0.764 0.909 0.843 +1 1.317 -0.640 1.167 0.833 0.914 1.395 0.277 -0.988 2.173 0.596 0.389 0.938 0.000 0.605 0.453 0.086 0.000 0.796 -0.488 -0.235 3.102 0.641 1.191 0.986 0.759 0.860 0.994 0.839 +0 1.259 1.036 -1.153 0.301 0.058 0.975 0.948 1.567 2.173 0.945 2.372 -0.312 0.000 1.113 0.767 -0.163 1.274 1.636 0.832 0.638 0.000 0.902 0.940 0.984 0.706 1.300 0.821 0.775 +0 0.436 1.839 1.296 1.263 -1.498 0.552 1.532 -0.190 0.000 1.187 0.457 -0.452 1.107 1.359 -0.175 1.260 1.274 0.596 -1.420 1.725 0.000 2.310 1.508 0.978 0.872 1.424 1.144 1.014 +0 0.958 -0.691 -0.016 0.249 -1.485 1.483 -0.226 -1.365 0.000 1.265 -1.146 0.613 2.215 2.291 -0.095 0.886 0.000 1.288 -0.894 -0.627 0.000 1.028 0.947 0.988 0.767 1.892 1.030 0.861 +0 0.600 0.233 -1.674 1.774 -0.814 0.521 -0.416 1.142 2.173 0.353 0.192 0.991 2.215 0.988 -1.526 -0.046 0.000 0.482 -2.272 1.258 0.000 0.818 0.964 1.001 0.722 0.216 0.676 0.898 +1 0.979 0.406 -1.731 0.612 0.038 0.718 0.972 -0.509 2.173 1.038 -0.454 1.118 2.215 0.827 -0.247 -0.977 0.000 0.481 1.805 0.365 0.000 1.256 0.910 1.072 0.840 1.613 0.962 0.808 +0 3.062 0.432 -1.004 0.312 0.142 1.045 0.342 1.206 1.087 0.748 0.596 0.298 2.215 0.512 1.902 0.914 0.000 0.554 1.531 0.302 0.000 0.954 0.989 1.164 1.065 0.966 1.062 0.899 +0 0.790 0.390 0.084 0.566 -1.420 1.241 0.719 -0.842 2.173 0.781 0.920 0.801 0.000 1.301 0.046 0.488 0.000 1.556 -0.504 1.173 3.102 0.835 0.917 0.992 0.821 1.773 1.155 0.934 +1 0.690 -0.174 1.030 2.228 1.726 0.599 -0.795 0.145 0.000 0.963 -1.316 -0.713 2.215 0.433 0.221 0.252 2.548 0.456 -1.459 0.045 0.000 0.400 0.683 1.009 0.745 0.803 0.884 0.809 +0 1.475 -0.243 1.490 0.318 1.128 0.297 0.681 1.366 0.000 0.841 0.559 -0.328 2.215 0.936 -0.380 -0.516 1.274 0.923 1.675 -1.310 0.000 0.779 0.891 0.988 0.911 0.513 0.770 0.775 +0 0.912 0.892 0.121 1.597 -0.811 0.599 -1.777 0.810 0.000 0.913 1.569 0.419 2.215 1.022 0.731 1.550 2.548 1.335 0.131 -0.208 0.000 1.895 1.682 1.244 1.002 0.974 1.509 1.358 +1 0.843 -1.378 1.368 0.634 -0.730 1.086 -0.384 -0.976 0.000 0.671 -2.050 0.390 0.000 1.012 -0.304 -0.495 2.548 2.086 -0.307 0.858 3.102 0.922 1.290 0.987 0.836 1.043 0.826 0.836 +0 0.549 -1.856 -1.549 0.495 -0.030 1.290 -0.594 0.680 0.000 1.222 -0.850 -0.150 2.215 2.027 -0.585 1.644 0.000 2.941 0.059 -1.386 1.551 2.220 1.842 0.990 1.009 1.749 1.473 1.155 +0 0.783 0.564 0.989 0.678 -0.482 0.677 -0.900 -0.354 2.173 0.332 -1.533 -0.691 0.000 0.713 -1.920 0.987 0.000 1.574 -0.379 -1.430 3.102 0.772 0.891 0.988 0.994 0.935 0.804 0.861 +0 2.494 -0.012 1.649 0.298 -1.417 1.246 -0.048 1.142 2.173 1.443 0.206 -0.425 0.000 1.818 -0.385 -0.418 0.000 1.145 0.304 0.484 3.102 0.802 0.964 0.983 0.908 0.755 1.182 1.149 +1 0.664 0.861 -0.011 1.508 0.508 0.735 -0.913 -1.446 0.000 0.507 0.294 1.166 2.215 0.550 -1.928 -0.649 0.000 0.617 -1.208 -0.508 3.102 1.045 1.073 0.995 1.476 0.711 0.997 1.385 +0 1.076 0.153 -1.384 0.376 -0.618 1.171 1.424 0.179 0.000 1.658 -1.096 -1.659 2.215 0.747 -0.331 0.165 2.548 0.463 0.189 0.607 0.000 0.839 0.950 0.990 0.901 1.267 1.687 1.338 +1 1.007 -0.996 -1.567 1.078 0.914 1.508 0.107 -1.473 2.173 0.987 -0.357 -0.897 2.215 1.580 2.371 0.434 0.000 1.593 0.136 0.442 0.000 2.708 2.636 1.136 1.302 0.990 2.085 1.964 +1 0.692 1.009 -1.675 0.757 -0.655 1.020 0.637 0.198 1.087 1.341 0.571 -1.128 1.107 0.681 1.474 0.585 0.000 1.023 2.356 1.170 0.000 0.881 0.857 0.991 0.859 1.601 0.984 0.868 +0 0.308 2.046 -1.009 2.050 0.414 0.830 0.855 -1.346 0.000 0.610 2.188 1.617 0.000 0.780 0.458 0.542 2.548 1.268 -0.202 -1.043 3.102 1.327 1.169 1.055 1.545 0.806 1.046 1.055 +0 0.536 1.957 -1.682 1.257 0.581 0.579 -0.306 -1.316 0.000 0.762 0.724 -0.380 1.107 0.751 -0.843 1.219 1.274 0.863 -0.351 -0.514 0.000 0.718 0.798 1.015 1.496 1.097 1.081 1.040 +1 0.875 0.592 0.706 0.127 0.215 1.417 0.946 0.844 0.000 1.160 -0.134 0.632 2.215 2.252 0.951 -1.051 2.548 1.466 -0.301 -0.560 0.000 0.813 0.884 0.982 1.273 2.025 1.140 0.951 +1 2.207 1.249 -0.892 0.772 0.459 0.917 1.187 0.961 2.173 0.791 1.280 -1.638 0.000 0.677 -0.089 0.420 2.548 0.797 0.557 -0.294 0.000 1.027 1.008 1.697 1.138 0.823 0.993 0.857 +0 1.238 -2.110 0.375 0.528 -1.117 0.572 0.007 -0.914 0.000 0.625 -0.907 1.180 2.215 0.338 0.017 0.377 0.000 0.828 -0.439 -1.723 3.102 0.726 0.803 1.091 0.863 0.352 0.661 0.755 +1 0.420 2.222 1.714 1.435 0.203 0.815 0.962 1.369 0.000 0.930 0.252 -1.706 2.215 0.885 1.268 -0.314 0.000 0.998 1.099 -0.694 0.000 1.342 0.997 1.052 1.121 0.964 1.083 0.970 +0 1.214 -0.675 0.853 0.833 0.570 0.840 -0.445 -1.412 0.000 0.623 -0.228 1.444 1.107 1.624 -0.247 -0.338 2.548 0.692 0.615 -0.696 0.000 1.015 1.044 0.987 0.666 1.068 0.775 0.813 +0 1.034 -0.919 -1.362 1.716 -0.783 1.869 -0.767 0.672 0.000 0.670 -2.766 -1.286 0.000 0.798 2.690 1.314 0.000 1.042 0.575 -0.800 3.102 0.973 0.938 0.990 0.798 1.146 0.920 0.875 +1 0.345 -1.529 0.763 0.622 1.602 1.043 -0.486 -0.875 2.173 1.002 0.125 -0.197 2.215 1.368 -0.629 0.616 0.000 0.795 1.562 0.837 0.000 0.984 1.041 0.998 0.954 0.985 0.849 0.762 +0 0.907 -1.692 0.629 2.663 1.169 1.134 -2.243 -0.572 0.000 1.188 -0.054 -1.018 0.000 1.028 -0.108 1.637 2.548 0.713 0.999 0.863 0.000 1.101 1.706 1.007 1.778 0.808 1.374 1.393 +0 0.412 1.642 -1.515 1.506 -0.418 0.762 0.037 0.309 2.173 0.745 0.255 1.047 2.215 1.470 0.159 -1.248 0.000 1.546 -0.779 1.194 0.000 0.587 1.026 0.988 0.959 0.695 0.813 0.780 +1 0.571 0.470 0.782 0.507 -0.725 0.649 1.297 1.192 2.173 0.512 2.902 -1.568 0.000 1.245 0.303 0.419 0.000 0.915 -0.310 -0.875 1.551 2.456 1.517 0.990 0.780 1.097 1.141 0.913 +1 1.616 -0.881 -0.445 0.057 -1.499 1.309 -0.560 1.241 2.173 0.423 -0.318 -0.248 0.000 0.728 -0.168 -1.257 2.548 0.546 0.622 1.402 0.000 0.716 0.949 0.985 0.651 0.973 0.893 0.739 +1 1.125 0.794 0.630 0.104 -0.432 1.160 1.038 1.579 2.173 1.167 1.260 -0.294 0.000 0.971 0.352 -0.872 1.274 1.128 -0.082 1.140 0.000 0.805 1.152 0.988 0.781 1.151 0.842 0.761 +0 0.710 -1.557 -0.409 2.510 -1.025 1.189 -1.080 1.220 0.000 0.612 -0.408 1.041 0.000 0.987 -0.658 0.155 2.548 0.628 -0.427 -0.283 3.102 0.685 0.965 0.984 0.726 0.239 0.725 0.976 +1 0.873 0.267 -1.547 1.460 0.584 0.988 -1.047 0.359 2.173 1.576 -1.102 -1.044 0.000 0.501 -1.715 -0.774 0.000 1.294 -0.172 1.184 3.102 0.617 1.142 1.470 1.301 0.964 1.019 1.093 +1 0.788 -0.083 -1.618 0.631 0.518 1.231 0.305 0.370 2.173 0.799 2.394 -0.855 0.000 1.390 -0.010 -1.081 0.000 0.822 0.616 1.364 3.102 0.842 0.764 0.987 0.892 0.862 0.826 0.717 +0 0.352 -2.175 0.962 2.027 0.308 0.656 0.048 -1.538 0.000 0.953 -1.113 -0.983 2.215 0.698 0.030 1.195 0.000 0.423 -0.710 -0.693 3.102 0.763 1.013 0.980 0.597 0.170 0.677 0.812 +1 0.361 -2.057 0.907 0.563 -0.832 1.042 -0.152 0.251 0.000 1.138 -0.436 -0.871 2.215 1.153 -2.410 1.639 0.000 0.798 -0.718 1.490 3.102 0.893 0.916 0.987 0.729 0.752 0.851 0.756 +0 0.671 -0.885 -1.547 0.682 0.185 0.820 0.378 -1.151 2.173 0.644 -0.351 -0.575 0.000 0.479 0.938 0.801 0.000 1.030 1.235 0.290 3.102 1.030 0.894 0.988 0.932 1.092 0.809 0.717 +1 1.040 -0.061 0.060 0.253 -1.110 1.365 -0.222 -1.568 2.173 1.067 0.058 0.383 2.215 1.128 0.118 -0.291 0.000 0.979 0.394 1.144 0.000 1.133 0.845 0.991 1.146 1.763 1.026 0.887 +1 1.363 0.329 -1.226 0.255 -1.001 0.828 -1.143 0.482 2.173 1.469 0.810 -0.794 0.000 1.282 0.524 1.109 2.548 0.900 0.676 0.346 0.000 0.704 0.871 0.981 0.903 1.430 1.174 0.914 +0 0.536 -0.422 0.445 2.634 1.221 1.317 -1.970 -0.911 0.000 0.872 0.328 0.344 0.000 1.066 0.509 0.888 2.548 1.251 -0.205 -0.345 0.000 0.889 0.924 1.061 0.794 0.969 0.878 0.862 +1 0.879 0.437 -1.656 0.147 -0.207 0.666 0.552 0.083 0.000 0.413 1.393 0.742 0.000 1.587 -0.445 -0.737 2.548 1.124 -0.324 1.409 3.102 0.801 0.937 0.995 0.787 0.955 0.886 0.777 +1 0.401 -0.352 0.840 2.332 -0.370 1.307 0.475 1.511 2.173 0.859 0.597 0.771 0.000 1.035 1.043 -1.400 2.548 1.101 0.482 -0.383 0.000 1.092 0.992 1.188 1.669 0.868 1.219 1.049 +1 0.944 0.599 -0.786 0.972 0.136 1.024 1.151 -1.535 2.173 0.751 1.796 -0.144 0.000 1.477 1.396 1.147 2.548 0.655 1.439 0.429 0.000 0.455 0.832 0.988 1.096 1.054 0.952 0.856 +0 4.126 1.032 -0.120 0.484 -0.605 1.783 0.645 0.120 0.000 5.193 0.059 1.673 2.215 0.613 1.437 1.353 0.000 0.624 0.379 1.723 3.102 1.912 1.239 0.985 3.793 0.330 2.262 2.030 +0 0.346 -1.226 -0.886 1.020 -0.535 0.746 -0.762 -0.785 2.173 0.689 -1.203 1.572 0.000 1.387 -0.077 0.774 2.548 0.844 -2.312 0.104 0.000 0.729 0.979 0.976 0.939 1.326 0.935 0.922 +0 2.247 0.237 -0.648 0.455 0.487 0.642 -0.839 -1.723 1.087 1.400 -0.051 0.978 2.215 0.952 1.521 -0.764 0.000 0.718 -0.629 0.582 0.000 1.639 1.482 1.197 1.177 1.069 1.129 1.063 +0 1.980 -0.614 0.791 0.708 -0.161 1.123 -0.415 0.163 2.173 1.409 -0.920 -1.570 2.215 0.483 0.635 -1.031 0.000 1.238 -0.056 -0.640 0.000 0.697 0.937 1.240 1.292 1.915 1.113 0.911 +1 0.565 0.759 0.197 0.370 -0.298 0.592 1.245 0.483 0.000 1.132 1.303 -0.938 2.215 1.376 1.091 1.738 2.548 1.908 0.507 0.944 0.000 0.845 1.040 0.983 1.153 0.889 0.959 0.954 +1 1.002 0.122 -1.331 0.768 0.872 0.830 1.125 -0.397 1.087 0.570 -1.048 0.303 0.000 0.664 1.816 0.884 0.000 1.915 -0.564 1.482 0.000 1.220 0.876 1.113 1.062 0.870 1.077 0.912 +0 0.700 -1.188 1.138 1.419 1.081 1.411 -0.737 1.570 2.173 2.075 -0.223 -0.626 0.000 1.059 0.025 -0.397 2.548 1.452 2.055 0.014 0.000 1.265 0.766 0.990 1.399 1.608 1.343 1.428 +1 1.591 -0.168 0.294 0.765 1.656 0.294 0.637 0.646 0.000 0.506 0.438 -0.463 0.000 1.026 0.344 -1.213 2.548 0.902 -0.769 -1.652 3.102 0.855 1.047 1.439 1.003 0.586 0.725 0.771 +0 0.973 0.269 -0.492 0.699 -0.831 0.835 -0.331 -1.738 2.173 0.575 0.972 0.474 0.000 1.499 -0.013 0.677 0.000 0.558 1.000 -0.791 3.102 0.798 0.767 0.983 0.957 0.818 0.809 0.825 +1 0.393 -0.576 -1.286 1.527 0.802 2.742 -1.467 -1.003 0.000 0.960 -0.933 -0.088 2.215 1.850 -0.181 0.446 2.548 2.824 -0.567 1.023 0.000 0.741 1.002 1.021 0.844 0.858 0.897 0.759 +0 1.125 -0.104 -0.521 0.849 0.782 0.955 0.055 1.241 2.173 0.555 -0.510 -0.071 0.000 0.884 0.764 -0.582 0.000 1.223 1.071 1.621 3.102 0.923 1.024 1.250 1.020 0.840 0.876 0.819 +1 0.384 -1.197 0.736 0.250 0.678 1.268 0.464 0.316 2.173 0.886 0.162 -1.018 0.000 0.446 1.944 -1.530 0.000 1.349 -0.923 -1.733 0.000 0.947 0.608 0.981 0.798 0.832 0.865 0.731 +1 1.005 -1.101 0.590 0.802 1.058 0.909 0.312 -0.834 2.173 0.857 -0.484 -1.266 2.215 0.647 0.441 0.445 0.000 0.560 -1.336 0.011 0.000 0.865 1.022 0.986 1.119 0.733 1.223 1.004 +0 0.441 0.840 -1.403 0.957 0.783 0.831 -0.990 -1.689 2.173 0.630 -1.125 -0.687 0.000 0.819 -0.179 0.420 0.000 0.399 0.076 -1.377 1.551 1.077 1.088 0.989 0.614 0.388 1.079 1.035 +0 0.520 0.756 -1.121 1.774 -0.091 0.451 0.832 0.664 0.000 0.564 -0.084 1.382 0.000 0.943 0.547 1.566 1.274 1.013 -0.234 -0.859 3.102 0.825 0.827 1.066 0.923 0.699 0.695 0.685 +0 1.791 0.107 -1.054 0.521 -0.554 0.903 0.763 0.597 0.000 0.466 1.282 1.284 0.000 0.818 -0.429 -0.207 2.548 0.669 -0.604 -1.710 3.102 0.886 0.937 0.994 0.772 0.557 0.748 0.810 +1 1.020 -1.592 -0.635 0.144 -0.862 1.925 0.059 1.613 0.000 1.403 -0.704 -0.250 2.215 1.732 0.086 0.164 2.548 1.106 -1.231 0.789 0.000 0.598 1.016 0.981 1.023 0.922 0.867 0.786 +0 0.436 -0.535 0.091 1.171 1.617 0.963 -0.938 -0.493 2.173 0.926 -0.655 0.855 0.000 0.993 -0.652 -1.270 2.548 0.565 -1.680 -0.028 0.000 0.959 0.942 0.987 0.959 0.797 0.794 0.722 +0 1.456 0.204 0.175 1.031 0.797 0.785 -0.964 -1.254 2.173 1.364 -0.643 1.658 0.000 1.673 0.560 -0.359 2.548 0.712 -1.095 0.980 0.000 0.852 0.863 0.984 0.969 1.626 1.145 1.086 +1 0.625 -0.195 0.908 2.331 0.250 1.177 -0.027 -1.330 1.087 0.396 -0.634 -0.655 0.000 0.517 0.523 -1.076 0.000 0.532 0.377 0.402 1.551 0.910 0.955 0.992 0.454 0.861 0.956 0.860 +0 1.384 0.493 0.157 0.734 0.945 0.332 1.101 1.190 0.000 0.320 -0.255 0.019 0.000 0.718 -0.115 -1.037 2.548 1.031 0.343 -1.549 0.000 0.791 0.783 0.988 0.837 0.374 0.727 0.631 +0 0.615 -1.738 -0.412 1.358 0.341 0.531 -1.458 0.885 0.000 0.680 -0.029 -1.570 2.215 0.574 -1.255 -1.507 0.000 1.024 -0.945 -0.855 3.102 0.828 0.876 0.983 0.694 0.629 0.713 0.679 +0 1.047 0.897 0.034 1.458 -0.696 0.790 1.880 1.304 0.000 0.901 0.078 -1.022 2.215 0.980 0.538 0.557 2.548 1.039 0.753 1.351 0.000 0.719 0.886 1.046 0.850 1.021 0.931 0.924 +1 1.147 -0.900 -1.286 1.640 -0.659 1.071 -0.831 0.693 2.173 0.358 0.564 0.098 2.215 0.422 1.479 -1.550 0.000 0.838 0.250 0.740 0.000 0.832 1.242 1.020 0.881 0.844 0.979 0.921 +0 1.336 0.075 1.051 1.103 0.338 1.059 1.423 -1.205 0.000 0.807 1.052 0.427 0.000 0.908 0.589 -1.541 0.000 0.964 -0.219 -0.444 1.551 0.887 1.080 1.007 0.538 0.173 0.679 0.856 +1 0.796 1.024 -1.168 1.188 0.753 1.332 0.351 0.406 2.173 0.602 0.270 -0.743 0.000 0.956 0.219 -1.223 0.000 0.493 -1.134 1.303 0.000 0.943 1.176 1.330 0.675 0.958 0.795 0.800 +0 1.388 1.803 0.595 0.246 1.096 1.173 0.244 -0.971 0.000 1.260 1.009 -0.987 2.215 2.460 -1.408 1.155 0.000 1.332 0.521 0.208 3.102 0.921 0.958 0.987 1.131 1.058 0.829 0.890 +0 0.661 1.740 -0.153 1.012 0.073 0.788 -0.962 -1.415 0.000 1.455 1.098 1.000 2.215 0.464 -2.027 -1.141 0.000 0.859 -0.264 -0.611 3.102 0.776 0.719 0.986 1.041 1.279 1.410 1.287 +1 0.908 -0.466 -0.623 0.763 1.232 0.961 -0.804 1.294 2.173 0.820 -0.018 0.225 0.000 0.784 0.478 -0.573 0.000 0.850 -2.445 -0.897 0.000 0.884 1.153 1.147 0.559 0.520 0.691 0.698 +1 0.914 -0.387 0.049 0.546 1.635 1.172 -0.487 1.024 2.173 1.683 -0.510 -1.071 0.000 1.310 0.901 0.168 0.000 0.917 -0.058 -0.462 0.000 0.923 0.711 0.989 0.845 0.856 0.982 0.828 +0 0.469 1.385 -0.976 0.841 -1.175 0.835 -0.473 1.463 0.000 1.302 1.168 -0.600 2.215 0.949 0.500 1.421 0.000 1.142 0.301 0.422 3.102 0.861 0.903 0.984 0.906 0.997 1.088 0.920 +1 1.350 -2.050 0.497 0.890 -1.703 0.613 0.727 -0.808 1.087 0.521 -0.884 1.096 1.107 0.808 -1.236 0.184 0.000 1.017 -1.047 -1.624 0.000 0.986 1.053 1.392 0.846 1.123 1.326 1.097 +0 0.522 -2.269 -1.317 1.079 1.498 0.950 -0.255 0.015 2.173 0.973 -0.611 0.501 2.215 2.040 -1.305 -1.342 0.000 0.756 0.613 -1.503 0.000 0.699 1.478 0.975 0.966 0.656 1.135 1.264 +0 0.605 -1.998 0.774 0.461 -0.538 0.508 -0.574 1.305 0.000 0.891 -1.615 1.022 0.000 0.933 -0.500 -0.757 2.548 0.878 0.346 -0.822 3.102 0.889 1.072 0.993 0.789 0.349 0.809 0.716 +0 1.956 -1.069 -1.357 0.611 -0.235 1.931 -0.851 -1.715 2.173 1.623 -1.087 0.024 2.215 1.986 -0.269 0.091 0.000 1.362 -0.383 0.680 0.000 0.930 0.956 1.284 1.148 2.627 1.567 1.334 +1 1.442 0.181 1.361 1.048 -1.648 1.291 0.522 -0.096 2.173 0.704 1.255 -1.050 0.000 0.710 1.171 0.541 1.274 0.527 1.563 1.209 0.000 0.747 1.168 0.989 0.980 0.796 1.093 0.974 +1 0.580 -0.264 0.170 0.985 1.340 0.840 0.824 -1.093 0.000 0.790 -0.334 0.773 2.215 1.016 0.622 0.168 2.548 1.332 -0.183 -1.372 0.000 0.975 1.112 0.989 0.616 0.711 0.901 0.781 +0 0.330 0.914 0.964 1.966 -0.627 0.874 0.031 0.586 0.000 0.727 -0.142 -1.194 2.215 0.806 -0.033 1.419 0.000 0.583 -1.757 -1.283 0.000 1.030 1.051 1.104 0.941 0.581 0.724 0.813 +1 0.627 1.152 -1.300 1.295 -1.001 2.262 -1.843 1.337 0.000 1.231 -0.920 0.039 1.107 2.733 0.158 -0.272 2.548 0.478 0.738 1.702 0.000 2.980 2.413 0.991 1.089 1.270 2.305 1.959 +0 0.477 0.153 -0.336 1.240 -1.604 0.561 -1.229 -1.185 1.087 0.599 -0.729 -0.280 0.000 1.280 -1.158 1.430 1.274 1.074 0.865 -0.424 0.000 1.073 1.132 0.988 0.867 0.746 0.960 0.824 +1 1.638 0.269 1.191 0.948 -1.579 0.968 0.375 -0.395 0.000 0.768 2.728 1.241 0.000 0.866 0.143 -0.922 2.548 0.704 0.819 0.880 1.551 1.054 0.885 1.041 0.833 0.648 0.633 0.820 +0 0.392 2.166 -0.353 0.894 1.589 0.650 0.326 0.409 2.173 0.659 1.779 -1.275 0.000 0.744 0.396 1.514 0.000 0.912 -0.267 0.009 3.102 1.029 1.040 0.982 0.871 0.394 0.792 0.721 +0 0.386 0.345 -1.207 1.151 1.078 1.726 -0.581 -0.410 0.000 1.545 -0.244 1.030 1.107 1.522 0.321 1.556 1.274 1.307 -0.656 -0.951 0.000 1.100 1.806 0.988 1.126 0.896 1.438 1.351 +1 1.256 0.932 0.351 0.428 -1.070 1.968 -0.547 -0.124 0.000 1.659 1.008 1.584 2.215 1.031 1.597 1.148 0.000 1.800 0.036 1.550 3.102 0.934 0.965 0.989 1.049 0.810 0.840 0.753 +0 0.950 -0.660 -1.628 0.493 -0.511 0.517 0.048 0.338 1.087 0.968 0.697 1.623 0.000 1.532 -0.395 -0.294 2.548 0.405 0.316 1.326 0.000 0.257 1.158 0.993 0.892 0.657 0.782 0.809 +1 0.721 0.466 -1.301 0.728 0.733 0.745 -0.496 1.463 0.000 1.001 -0.673 -0.237 0.000 1.033 -0.458 0.917 2.548 0.797 -0.882 1.020 1.551 1.841 1.076 0.988 0.683 0.206 0.707 0.683 +0 0.401 0.685 -0.441 1.583 1.556 0.790 -0.568 0.032 1.087 0.686 -1.108 1.127 0.000 1.030 -0.430 -0.572 0.000 1.067 -1.028 -1.469 3.102 1.361 1.032 1.075 1.000 1.004 0.961 0.907 +1 1.067 1.068 -1.688 0.890 1.706 0.607 1.538 0.147 2.173 0.274 -1.542 -1.394 0.000 0.451 0.896 0.153 2.548 0.643 -0.119 -0.020 0.000 0.672 1.194 0.986 0.723 0.188 0.755 0.766 +0 0.661 1.474 -1.575 1.284 -1.045 0.644 0.572 1.524 2.173 1.239 0.658 -0.003 1.107 0.937 -0.822 1.448 0.000 0.577 -0.354 0.757 0.000 0.512 1.175 0.981 0.753 1.291 0.871 0.863 +1 0.489 -0.206 -0.509 1.307 1.678 0.915 0.091 -0.121 0.000 1.197 0.385 1.637 2.215 0.923 -0.777 0.175 0.000 0.438 -1.599 0.362 0.000 0.920 0.968 1.020 0.714 0.397 0.937 0.825 +1 0.676 1.380 -0.709 1.109 1.736 0.584 -0.269 -0.326 2.173 0.657 0.404 0.962 2.215 0.651 -0.178 -1.457 0.000 0.633 -0.206 1.173 0.000 0.494 0.683 0.988 0.869 0.896 0.891 0.755 +1 0.484 0.673 -0.910 0.957 0.980 0.814 -0.069 -0.076 0.000 2.188 -0.315 -1.329 1.107 0.912 -0.076 1.434 0.000 2.825 -1.584 0.242 0.000 0.945 0.973 0.987 0.555 1.543 1.035 0.860 +0 0.776 0.751 -0.628 2.167 -1.088 1.057 -0.216 0.860 2.173 0.861 -0.403 -0.028 2.215 0.352 -0.902 1.493 0.000 0.456 -1.644 1.055 0.000 0.283 0.651 0.985 1.925 1.017 1.429 1.219 +0 1.429 1.336 -1.245 0.457 0.230 1.306 0.983 -0.583 1.087 2.069 1.084 1.062 0.000 0.519 1.677 1.162 0.000 0.573 0.045 0.052 3.102 0.606 0.828 1.088 0.899 0.667 1.123 0.961 +0 1.122 -1.277 1.510 1.002 -1.205 0.994 0.647 0.894 0.000 0.766 -1.204 -0.649 2.215 0.840 0.465 0.323 0.000 1.222 2.054 0.127 0.000 0.819 0.852 0.985 0.784 1.009 1.010 1.178 +0 0.461 0.122 -0.279 1.024 1.236 0.834 0.682 -0.221 1.087 0.619 0.241 0.393 0.000 0.625 0.438 -1.253 0.000 0.956 1.200 1.302 1.551 0.957 0.813 0.988 0.592 0.995 0.667 0.616 +1 0.556 -1.299 -1.630 1.338 -1.145 1.144 0.898 0.861 2.173 0.694 0.935 -0.178 0.000 0.553 0.496 -1.537 2.548 0.662 0.316 -0.582 0.000 0.851 1.159 0.984 1.328 0.842 2.049 1.509 +0 0.317 2.149 0.739 1.641 -1.433 0.423 -1.035 -0.504 0.000 1.163 -0.187 -0.263 0.000 0.635 -0.869 0.827 1.274 0.800 1.954 1.093 0.000 0.729 0.810 0.990 1.492 0.215 1.535 1.716 +1 0.409 -0.276 0.736 0.852 1.559 0.865 0.254 -0.310 2.173 0.894 1.051 1.317 0.000 0.591 -1.981 0.378 0.000 0.626 1.623 -0.540 0.000 0.928 0.904 0.989 1.348 0.295 0.897 1.249 +1 0.599 -0.784 0.997 0.303 -0.814 0.496 -0.771 1.473 0.000 1.182 1.397 -0.043 2.215 0.542 0.096 0.305 0.000 0.592 1.236 -1.121 3.102 0.915 0.858 0.986 1.028 0.623 0.910 0.779 +0 1.848 0.276 0.337 0.516 -0.365 0.843 1.159 1.726 0.000 0.950 0.117 -0.830 1.107 0.972 0.658 1.100 2.548 0.695 0.606 -1.147 0.000 0.665 0.941 0.987 0.824 1.054 0.784 0.844 +1 0.917 0.789 1.371 0.864 1.197 0.416 0.251 1.201 0.000 0.563 -0.322 -0.908 0.000 0.623 1.803 -1.439 0.000 2.020 1.005 -0.082 3.102 1.018 1.137 0.986 0.938 0.682 0.820 0.848 +0 0.555 -0.253 1.152 2.195 0.489 0.741 -0.146 -0.516 2.173 0.810 -0.456 -1.311 0.000 1.743 -1.090 1.665 0.000 1.581 0.652 -0.049 3.102 0.797 0.993 0.992 1.124 0.724 0.949 0.916 +1 0.981 -0.696 1.671 1.532 1.065 0.582 -0.996 -0.808 0.000 0.347 -1.215 0.402 2.215 0.329 -0.338 -0.059 0.000 0.381 -0.418 -0.509 3.102 0.553 0.520 0.986 0.626 0.271 0.493 0.556 +0 0.493 -0.712 -0.901 1.298 1.704 0.786 -0.057 -0.006 2.173 0.200 -0.698 0.361 0.000 0.427 0.615 -1.371 0.000 1.435 -0.596 1.112 3.102 0.562 0.615 0.986 0.764 1.022 0.913 0.737 +0 2.304 0.012 0.524 0.754 0.170 1.234 -0.375 -1.241 1.087 0.316 0.369 -0.577 0.000 0.529 -0.318 -1.633 1.274 0.714 -0.813 1.031 0.000 0.764 0.876 0.993 0.823 0.348 1.035 0.812 +1 2.610 -0.694 -1.074 0.729 -1.250 1.206 1.450 0.524 0.000 1.321 -0.530 0.129 0.000 1.521 -0.669 0.735 2.548 1.287 -0.897 -1.636 3.102 0.525 0.983 0.976 1.383 0.921 0.923 0.924 +0 0.373 2.046 1.675 1.153 0.281 0.867 1.063 -1.290 1.087 1.052 0.743 0.103 2.215 1.291 -2.498 -1.102 0.000 1.069 0.303 0.808 0.000 1.071 0.987 0.985 0.959 1.354 0.905 0.777 +1 1.186 -0.661 -0.822 0.166 -1.686 0.724 0.370 0.867 0.000 0.562 -1.127 -0.458 0.000 0.848 0.204 1.665 2.548 0.831 0.378 0.368 3.102 0.925 0.677 0.987 0.691 0.595 0.560 0.584 +1 0.447 -0.694 0.017 0.862 -1.088 0.761 -1.006 0.972 2.173 0.565 0.572 1.372 2.215 0.490 -1.322 -0.517 0.000 0.554 0.200 -0.332 0.000 0.555 0.815 0.985 1.130 0.925 0.885 0.738 +1 0.652 -1.915 0.285 0.696 -0.015 0.886 -0.240 0.384 2.173 0.710 -1.899 1.647 0.000 1.549 2.103 1.363 0.000 2.459 -0.424 -0.760 1.551 0.683 1.079 1.000 0.756 1.357 1.024 0.881 +0 1.729 -0.186 -0.093 0.462 -1.656 1.188 2.887 -1.378 0.000 0.984 -1.442 0.049 2.215 1.301 -1.180 0.463 0.000 1.523 -0.819 -1.714 3.102 0.882 0.872 1.222 0.985 1.139 0.873 0.792 +1 1.055 -0.247 -0.316 1.754 -1.028 1.198 -0.354 1.109 1.087 0.328 -1.034 -0.719 0.000 0.685 1.354 0.972 0.000 0.862 0.550 0.270 0.000 0.864 1.042 1.127 0.545 0.782 0.922 0.781 +0 0.655 -0.356 0.174 0.918 -1.066 0.308 1.580 1.162 0.000 0.487 -2.458 0.076 0.000 0.723 -0.632 0.475 2.548 1.173 0.742 1.607 3.102 0.814 0.920 0.987 0.875 0.861 0.666 0.689 +1 1.085 0.333 -0.317 0.501 1.572 0.697 0.037 1.500 2.173 0.612 -1.198 0.696 2.215 0.742 1.336 -0.096 0.000 0.441 -0.496 -1.613 0.000 0.989 0.949 1.013 0.887 0.912 0.817 0.732 +0 1.740 0.324 1.688 1.304 1.336 1.865 0.323 -0.065 0.000 0.903 0.135 -1.164 2.215 0.341 0.448 -0.667 0.000 0.884 0.466 0.977 1.551 0.750 0.911 0.975 0.872 0.774 0.836 1.015 +0 1.737 -0.024 1.516 1.275 0.953 0.966 -0.540 -0.262 0.000 0.854 -0.441 0.212 0.000 0.934 -0.068 -0.902 2.548 1.401 0.727 -1.450 3.102 0.805 0.857 1.001 0.902 0.597 0.872 0.974 +1 0.893 -0.581 0.846 0.812 -0.545 0.985 -0.866 1.530 0.000 1.645 -0.624 -0.461 2.215 0.910 0.352 1.385 2.548 0.366 1.994 0.112 0.000 2.424 1.393 1.121 0.891 1.471 1.298 1.056 +0 1.065 -0.997 0.559 0.153 -0.966 1.146 -0.596 -0.978 2.173 1.102 0.460 0.667 2.215 0.783 -1.333 1.472 0.000 0.565 -0.247 -0.151 0.000 0.859 0.966 0.996 0.797 1.892 1.027 0.864 +0 0.636 -1.883 -0.146 1.284 -0.019 2.254 1.181 1.656 2.173 1.822 -0.160 -0.200 0.000 1.209 -0.561 0.301 1.274 1.276 -0.129 1.702 0.000 1.966 1.285 0.987 3.068 2.873 2.101 1.803 +1 0.786 -1.464 1.067 0.565 0.133 0.694 0.004 -1.141 2.173 0.475 0.292 1.153 0.000 0.533 -0.845 0.859 0.000 1.556 0.721 -0.481 3.102 0.543 0.922 0.984 0.952 0.787 0.860 0.742 +0 0.361 -0.339 0.437 0.654 -0.546 0.731 0.101 0.595 2.173 0.696 1.421 -0.553 0.000 0.826 1.427 1.164 2.548 0.452 0.921 1.599 0.000 0.741 0.964 0.991 0.719 0.909 0.699 0.638 +1 0.345 1.528 0.427 1.419 1.252 0.978 -0.692 -1.234 2.173 0.996 0.831 -0.220 1.107 0.882 0.054 0.192 0.000 1.213 0.131 1.292 0.000 0.956 0.946 0.994 1.240 1.696 1.094 0.920 +0 0.372 -0.195 -1.456 1.011 -1.691 0.932 0.506 -0.805 0.000 1.122 1.854 1.043 0.000 1.598 0.509 0.514 2.548 0.981 0.567 -0.210 0.000 1.042 0.787 0.983 1.641 0.197 1.138 1.070 +1 1.489 2.036 -1.683 0.856 1.334 0.712 1.058 0.162 2.173 0.741 0.509 -0.371 2.215 0.622 1.554 -1.110 0.000 0.479 0.999 1.026 0.000 0.585 0.703 0.997 1.148 0.575 0.938 0.730 +1 1.743 -1.008 -0.589 0.828 -0.764 0.862 -0.725 -1.629 2.173 1.274 -0.737 1.033 2.215 1.154 -2.057 0.765 0.000 0.805 0.393 -0.201 0.000 2.111 1.476 0.983 1.056 1.042 1.108 1.116 +1 2.203 0.650 1.706 0.918 -1.202 1.191 0.550 0.100 2.173 0.420 1.270 1.022 1.107 0.619 -0.353 -1.129 0.000 0.735 0.138 0.538 0.000 0.799 1.058 0.987 0.724 0.867 1.020 0.853 +0 0.742 1.048 -1.047 0.467 -0.103 0.433 -0.422 1.422 0.000 0.813 1.227 1.151 2.215 0.735 -0.206 -0.024 2.548 0.892 2.147 -0.084 0.000 1.074 1.053 0.982 0.874 0.980 0.770 0.726 +1 1.150 -1.362 0.377 1.319 1.627 0.643 -0.113 -0.835 2.173 0.522 0.982 0.440 2.215 0.364 1.204 -0.746 0.000 0.562 -0.931 1.595 0.000 0.883 0.743 1.541 1.274 0.928 1.109 0.913 +0 1.379 0.617 -1.381 0.776 1.400 1.317 0.085 0.220 0.000 0.446 -0.008 -0.937 1.107 0.585 0.660 0.629 0.000 0.442 2.083 1.689 0.000 0.765 0.880 0.986 0.507 0.238 0.634 0.736 +1 0.297 0.724 -1.269 1.171 0.679 1.588 -1.342 -1.295 0.000 2.789 -0.320 0.296 0.000 1.924 0.323 -0.904 2.548 1.378 2.169 -1.634 0.000 0.874 0.814 0.986 1.294 1.028 1.123 1.260 +0 1.408 -0.113 1.082 1.619 1.568 0.622 0.167 -0.907 0.000 0.585 -1.109 0.531 1.107 0.755 2.130 -0.370 0.000 1.207 -0.285 -0.126 0.000 0.923 0.946 0.990 0.795 0.365 0.702 0.776 +0 0.670 -0.590 -0.341 1.301 1.427 1.311 -0.117 0.037 2.173 0.945 0.698 1.614 1.107 1.181 -0.133 0.828 0.000 1.650 0.379 -1.400 0.000 1.215 0.994 1.293 1.216 1.767 1.091 0.928 +0 0.745 1.082 0.093 0.862 -0.011 1.527 1.023 0.351 2.173 1.120 1.059 -0.800 0.000 2.215 0.856 -1.621 2.548 0.500 -1.609 1.126 0.000 0.211 2.084 0.983 1.031 2.245 1.946 1.884 +0 0.415 -0.907 -0.839 2.005 -1.355 1.321 0.471 0.721 0.000 0.954 -0.345 0.520 0.000 1.389 0.404 -0.690 2.548 0.683 -0.865 -1.484 3.102 1.107 1.157 0.996 1.834 0.777 1.126 1.529 +1 1.227 0.815 -0.708 0.335 1.163 0.578 0.851 1.294 0.000 0.674 -0.251 -0.160 2.215 1.387 0.129 -1.269 1.274 1.888 1.167 0.671 0.000 0.957 1.198 0.989 0.786 0.889 0.975 0.847 +0 0.795 0.534 1.104 0.866 -0.139 0.806 0.759 -0.317 2.173 0.592 -1.381 1.566 0.000 0.452 1.040 0.326 0.000 1.178 -0.078 1.735 3.102 0.575 0.660 1.034 0.760 1.092 0.965 0.876 +1 0.642 -0.568 -1.703 0.553 -1.021 1.315 0.048 1.187 0.000 1.425 0.561 -0.116 2.215 0.869 1.417 -0.693 2.548 0.983 -0.299 -0.894 0.000 0.836 1.155 0.989 0.776 0.842 0.932 0.829 +0 0.502 -1.035 1.401 0.729 0.143 1.650 -0.443 0.879 2.173 1.418 -0.075 -1.115 2.215 1.266 1.159 -0.756 0.000 1.375 -1.834 -0.846 0.000 0.959 1.425 0.986 1.320 2.232 1.440 1.217 +1 0.709 0.823 0.063 1.784 0.816 0.445 1.439 0.295 0.000 0.652 1.119 -1.560 2.215 0.754 1.515 -1.219 0.000 1.406 0.467 -1.062 3.102 1.025 0.861 0.987 0.886 0.456 0.757 0.710 +1 1.118 -0.684 -0.217 1.177 0.243 0.403 -1.348 -0.891 0.000 0.776 0.898 1.479 2.215 0.717 -1.001 1.353 2.548 0.737 -0.216 1.406 0.000 0.875 1.086 0.979 0.885 0.950 0.880 0.805 +1 0.575 0.350 -0.243 0.704 -1.117 0.716 -0.082 -0.387 0.000 0.881 0.353 0.559 1.107 0.793 -0.565 -1.202 0.000 1.309 0.822 1.352 3.102 0.883 0.860 0.983 0.955 0.705 0.768 0.679 +1 0.600 0.338 -0.789 0.593 0.388 0.952 0.420 1.694 0.000 1.041 0.291 -0.225 2.215 1.086 0.482 1.211 0.000 1.124 2.349 -0.661 0.000 0.780 0.847 0.990 0.712 0.853 0.880 0.801 +1 0.340 1.440 0.456 0.832 -1.118 0.635 0.450 1.291 2.173 1.549 0.366 0.139 1.107 0.827 0.177 -0.527 0.000 1.158 -0.757 -1.636 0.000 1.103 1.007 0.989 0.909 1.259 0.942 0.799 +0 0.862 -2.244 -0.375 1.042 -0.309 0.776 0.521 1.532 2.173 0.557 -1.370 -1.230 0.000 1.166 1.242 0.550 0.000 1.266 0.879 -1.254 3.102 1.132 1.048 1.003 1.736 0.678 1.384 1.111 +1 2.773 0.260 -0.638 1.701 -1.050 1.092 0.722 0.751 2.173 0.543 -0.389 -1.650 0.000 0.883 1.254 1.089 0.000 0.485 1.189 0.500 3.102 0.752 0.832 1.089 1.824 0.325 1.162 1.091 +0 0.816 1.420 -0.547 0.711 -0.983 0.652 0.841 -1.592 1.087 1.545 -1.595 0.497 0.000 0.753 -1.152 -0.153 0.000 0.843 -1.168 -1.641 3.102 0.966 0.931 0.983 1.011 1.082 1.337 2.137 +0 1.834 -0.687 0.012 0.957 0.474 1.776 1.074 -1.559 0.000 1.465 -0.097 -0.123 2.215 1.096 -2.023 1.515 0.000 1.209 -1.077 -0.136 1.551 0.830 0.811 0.988 0.901 0.761 0.966 0.860 +0 0.561 -1.760 -1.367 0.645 0.888 1.158 -0.472 -0.313 1.087 1.187 -0.356 1.701 0.000 1.095 -0.723 1.349 0.000 0.955 -1.614 0.056 0.000 1.034 0.862 0.980 1.017 0.783 0.851 0.766 +0 1.046 1.144 -0.362 1.288 -0.562 0.787 -1.168 1.053 1.087 0.687 -1.225 -1.687 0.000 0.760 0.828 1.108 2.548 1.014 -0.449 -0.392 0.000 1.078 0.981 0.995 0.976 1.202 1.739 1.575 +0 0.847 -0.348 -0.207 1.067 -1.039 0.582 -1.844 1.412 0.000 1.251 -1.160 -0.930 2.215 1.071 0.508 0.587 0.000 2.035 -0.488 0.878 3.102 2.372 1.436 0.984 0.918 1.504 1.231 1.099 +1 0.840 0.073 0.640 1.419 0.600 0.345 -0.144 1.611 2.173 1.259 -0.909 -1.073 2.215 0.474 -1.535 -1.588 0.000 1.147 -1.068 -0.142 0.000 0.801 0.781 0.987 0.757 0.752 0.880 0.762 +1 1.074 -0.135 0.069 0.593 0.888 0.989 -0.873 -0.524 2.173 0.599 1.102 0.970 0.000 0.794 1.369 1.713 0.000 1.083 0.394 -1.603 3.102 0.685 0.600 0.986 0.899 1.208 1.125 1.006 +0 0.953 -1.059 -0.864 0.925 -0.357 1.012 -0.556 0.423 2.173 0.796 0.359 1.715 0.000 0.465 -0.881 -1.696 2.548 0.445 -0.174 1.135 0.000 0.709 1.159 0.985 0.661 0.824 0.840 0.910 +0 1.519 0.204 -1.361 1.493 -1.495 1.843 1.109 0.476 2.173 0.903 0.248 -0.915 2.215 1.581 0.730 0.792 0.000 0.992 1.545 -0.194 0.000 0.914 0.863 0.991 2.015 1.992 1.435 1.141 +1 0.439 0.988 0.008 2.500 -0.421 0.509 1.472 0.973 0.000 0.772 0.751 1.632 2.215 0.779 1.283 1.725 2.548 0.508 0.293 -1.286 0.000 0.835 0.632 0.991 0.982 0.276 0.917 0.804 +1 1.362 1.360 0.778 1.545 1.035 1.002 0.787 -0.860 2.173 0.820 0.859 -0.323 0.000 0.508 -0.438 -0.073 0.000 1.133 0.204 1.614 3.102 0.762 0.855 0.990 1.458 0.945 0.993 0.903 +1 0.639 1.330 -0.725 1.630 1.657 2.171 1.906 0.107 0.000 1.605 0.681 1.631 2.215 0.788 1.743 -1.477 0.000 0.743 -0.782 -1.331 0.000 1.661 1.239 1.185 1.403 1.564 1.090 1.011 +1 1.780 0.330 -0.093 0.331 -1.100 1.341 -0.144 1.292 2.173 1.423 1.336 -0.205 0.000 1.350 -0.181 -1.256 1.274 0.966 1.193 1.495 0.000 1.317 1.663 0.991 1.412 1.253 1.535 1.259 +1 0.393 1.365 1.576 0.792 -1.182 1.192 0.668 -0.137 2.173 1.072 0.375 1.395 0.000 0.586 -0.489 0.568 2.548 0.704 -0.223 1.707 0.000 0.965 0.737 0.998 1.088 0.912 0.823 0.762 +1 0.311 -1.178 0.766 0.660 -1.371 0.764 -0.327 1.188 2.173 1.232 -0.753 -0.274 2.215 0.312 1.222 0.967 0.000 0.798 0.346 -0.818 0.000 0.611 0.925 0.987 0.742 1.420 0.850 0.725 +1 1.192 -1.240 -0.664 1.233 1.285 0.531 -0.545 -0.138 2.173 0.855 -1.865 1.417 0.000 0.971 -1.377 0.035 0.000 0.952 -0.522 -1.542 1.551 1.355 0.991 1.651 1.034 0.718 0.772 0.777 +0 0.435 0.373 -1.099 2.466 -0.458 1.032 0.183 0.983 1.087 0.601 -0.639 1.715 0.000 1.078 1.516 -1.276 0.000 1.663 0.485 0.669 3.102 1.857 1.390 0.989 1.574 0.477 1.100 1.123 +0 0.489 -0.560 -0.287 1.635 -0.867 0.447 0.430 0.168 2.173 1.026 0.881 1.484 0.000 0.699 -0.317 0.185 2.548 0.800 -0.090 1.413 0.000 0.629 0.864 0.987 1.182 0.280 0.809 1.025 +1 0.805 1.554 -0.598 1.345 -1.659 0.490 1.458 -0.087 0.000 0.746 0.212 -1.295 2.215 1.509 0.636 0.379 0.000 1.031 0.314 1.328 1.551 0.879 0.896 1.177 0.900 0.559 0.776 0.827 +0 1.334 -1.125 1.309 0.263 -0.309 0.487 -1.430 0.135 0.000 0.660 0.410 -1.008 2.215 0.430 -0.294 -1.517 0.000 0.384 0.760 0.142 3.102 0.954 0.962 0.984 0.670 0.407 0.625 0.628 +0 1.466 -1.111 1.287 2.576 1.130 1.982 0.371 -0.394 0.000 0.822 -0.059 1.675 2.215 0.875 1.011 -0.483 0.000 0.419 1.416 -1.234 3.102 0.911 0.837 0.979 1.276 0.591 1.261 1.893 +1 0.869 -0.329 1.598 0.669 -0.111 0.816 -1.098 -0.969 0.000 0.745 -0.341 -0.898 0.000 1.503 0.346 0.979 2.548 1.455 0.121 0.019 3.102 1.038 1.001 1.056 0.722 0.871 0.703 0.655 +1 1.333 1.213 1.469 0.725 -1.231 0.800 0.517 0.058 2.173 0.419 -2.324 -0.905 0.000 0.953 0.766 -1.631 0.000 0.778 0.750 -0.763 3.102 2.419 1.525 0.993 1.095 0.585 1.170 1.129 +0 1.927 1.637 0.422 0.302 0.328 1.063 -0.364 -1.678 0.000 1.436 1.680 -0.055 0.000 2.099 0.430 -1.366 1.274 0.597 -0.325 1.477 0.000 0.357 0.944 1.001 1.507 0.930 0.950 1.069 +1 0.650 0.699 -0.098 0.861 -0.605 1.050 -0.275 1.510 0.000 0.973 0.562 1.205 0.000 1.555 0.493 -0.402 2.548 1.143 -0.856 -0.131 3.102 0.982 1.050 0.986 0.802 0.918 0.996 1.174 +1 0.424 -0.694 1.732 1.077 -1.672 0.697 1.163 -0.407 0.000 0.763 0.606 0.867 2.215 0.746 1.843 0.088 0.000 0.834 -0.409 -1.489 3.102 0.798 0.994 0.989 0.720 0.744 1.057 1.630 +0 1.416 -0.247 1.096 0.743 0.092 0.898 0.816 0.945 2.173 1.650 0.240 -1.197 0.000 1.268 -0.115 -0.495 2.548 1.180 -0.074 -1.668 0.000 0.809 0.967 1.116 0.917 1.438 1.058 0.982 +1 0.895 -1.225 -1.193 1.138 -0.531 0.812 -0.412 1.030 2.173 0.771 0.885 0.526 2.215 0.561 -0.913 -1.698 0.000 0.872 -0.199 -0.563 0.000 0.725 0.955 0.993 1.335 0.976 1.333 1.023 +1 0.852 0.515 0.282 0.285 -0.301 0.696 -0.385 -0.909 2.173 0.706 0.717 -0.185 0.000 1.395 -0.472 1.702 2.548 0.906 1.105 0.834 0.000 0.884 1.115 0.990 0.910 0.874 0.927 0.814 +1 0.725 -1.110 -0.840 0.824 1.371 0.477 -0.265 0.721 0.000 1.081 -0.928 -1.331 2.215 0.996 2.482 -0.203 0.000 2.149 0.164 0.240 0.000 0.746 0.612 0.989 0.649 0.761 0.846 0.767 +0 1.464 -0.882 -1.234 1.799 1.631 0.634 -0.446 0.391 0.000 0.789 0.238 1.689 2.215 1.198 -0.880 -0.195 0.000 1.567 2.331 0.760 0.000 0.890 0.919 1.193 0.906 1.225 1.078 1.053 +1 1.383 -0.559 -1.567 2.071 -1.593 1.012 1.868 0.643 0.000 0.542 -0.730 -0.841 0.000 1.768 -0.991 -0.381 2.548 0.854 -0.274 0.621 0.000 0.879 0.797 0.995 1.582 1.411 1.205 0.962 +1 0.685 -2.266 0.131 1.021 -1.211 1.119 -1.195 1.413 2.173 1.132 -1.278 0.217 2.215 0.847 -1.743 -0.462 0.000 1.192 -1.472 -0.940 0.000 0.522 0.995 1.084 0.993 1.463 1.023 0.818 +0 0.690 0.819 0.605 0.214 0.684 0.735 -0.101 -1.670 0.000 1.343 0.341 -0.849 2.215 1.068 -0.258 0.192 2.548 0.756 2.064 0.693 0.000 0.275 1.230 0.982 0.994 1.106 1.073 0.943 +1 1.682 -0.221 1.179 1.701 0.565 2.008 -0.450 -1.437 0.000 1.325 -0.427 0.268 2.215 1.312 0.222 0.744 0.000 2.042 0.521 -1.248 0.000 0.918 1.018 1.231 0.952 0.832 1.140 1.144 +1 0.323 1.083 -1.132 1.769 -0.368 0.976 -0.403 0.707 1.087 1.034 -0.152 1.513 2.215 0.701 -0.297 -0.541 0.000 0.591 -0.383 -1.178 0.000 0.518 0.819 0.993 1.090 0.998 0.960 0.753 +1 0.437 1.255 0.864 1.663 -1.110 1.156 1.154 0.529 2.173 0.984 0.059 1.704 0.000 0.507 0.738 -0.949 0.000 0.650 0.424 -0.420 3.102 0.848 0.660 1.156 1.240 0.757 0.845 0.831 +0 0.418 0.510 1.657 1.129 0.147 0.747 1.242 -0.132 0.000 1.307 1.336 1.542 2.215 1.004 0.559 -0.694 0.000 1.248 -0.149 -1.172 1.551 0.910 0.986 0.987 0.934 1.234 0.998 0.850 +0 0.825 1.625 1.420 0.976 0.629 0.939 0.213 -0.717 2.173 0.309 0.544 -0.961 2.215 0.593 2.447 0.626 0.000 0.702 -0.144 0.584 0.000 0.809 0.852 0.986 0.660 0.220 0.774 0.675 +1 0.616 0.460 -0.636 0.517 1.071 0.792 0.634 0.672 0.000 1.066 0.274 0.166 0.000 0.908 -0.077 -1.740 2.548 1.324 0.862 -0.994 3.102 0.930 1.098 0.988 0.771 0.719 0.858 0.754 +0 1.706 -0.782 1.019 0.541 0.232 0.697 -0.927 1.636 1.087 0.511 0.559 -1.243 0.000 0.867 1.762 -0.874 0.000 1.260 -1.286 -0.111 0.000 0.820 0.838 0.987 0.794 1.072 1.034 1.129 +0 1.156 0.245 0.911 0.656 -0.040 0.733 1.714 -1.412 0.000 0.918 -1.427 -1.586 0.000 1.412 -1.054 -0.314 2.548 2.424 -0.701 0.651 3.102 0.825 0.909 0.982 0.763 1.104 0.854 0.850 +1 1.053 0.575 -0.351 0.275 1.049 0.869 1.137 -1.556 2.173 0.784 -0.192 1.121 0.000 0.737 1.381 0.257 0.000 0.803 -0.695 0.205 0.000 0.830 0.865 0.985 1.014 0.790 0.875 0.771 +0 1.064 -0.335 -0.335 0.990 1.396 2.076 -0.748 -0.152 1.087 1.820 -0.707 1.583 0.000 1.372 -0.310 1.299 0.000 1.124 -1.059 -1.269 3.102 0.773 0.891 1.422 1.354 1.430 1.512 1.234 +1 0.401 0.609 -0.829 0.718 0.151 0.636 2.561 -0.757 0.000 1.013 0.470 1.396 0.000 1.320 -0.779 -0.747 2.548 1.228 -1.153 1.466 3.102 0.810 1.017 0.988 1.630 0.924 1.427 1.199 +0 0.664 -0.231 0.695 0.483 -1.531 0.798 0.855 -0.095 2.173 0.963 -0.941 1.474 2.215 0.904 -2.099 -1.225 0.000 0.445 -1.466 1.006 0.000 0.662 0.788 0.992 0.823 1.858 1.362 1.151 +0 0.729 -0.868 0.536 0.921 -0.171 1.066 0.649 0.139 2.173 2.081 -0.874 -1.616 2.215 1.005 -2.073 -0.629 0.000 1.344 -1.217 1.327 0.000 0.975 1.272 0.985 0.808 2.888 1.687 1.344 +1 1.248 0.981 0.711 1.717 0.664 0.414 1.352 -0.738 2.173 1.052 1.446 1.714 0.000 0.930 -1.834 -0.531 0.000 0.776 1.133 0.110 0.000 1.170 0.941 0.996 0.979 0.628 0.907 0.829 +0 0.844 -0.803 1.380 0.253 -0.546 1.404 -0.079 -1.584 0.000 1.493 -0.961 0.027 1.107 1.636 -0.029 0.043 2.548 0.735 0.553 1.268 0.000 1.025 1.524 0.992 1.024 0.826 1.322 1.043 +1 0.479 -0.725 0.590 2.003 0.007 0.902 2.292 -0.915 0.000 1.512 1.013 1.357 2.215 0.566 0.358 1.408 0.000 0.880 1.114 0.277 3.102 0.642 0.715 0.984 1.601 0.875 1.831 1.428 +1 0.537 -0.241 0.591 0.270 -0.989 1.145 -1.268 0.819 2.173 0.802 -0.345 -1.482 0.000 0.727 -0.691 -0.805 2.548 0.413 -0.911 -1.612 0.000 0.903 0.721 0.992 1.472 1.170 1.035 0.891 +1 0.612 -1.829 1.678 0.552 -0.283 0.943 -0.557 -0.789 2.173 1.378 -0.989 1.252 2.215 0.836 -2.241 0.557 0.000 0.931 -0.463 0.095 0.000 0.916 0.970 0.991 0.804 1.662 1.026 0.835 +0 2.949 0.811 -0.457 0.658 1.456 0.995 0.879 -1.022 2.173 1.296 0.246 1.119 0.000 1.144 0.656 1.451 2.548 2.096 0.860 0.707 0.000 1.158 0.851 1.907 1.159 1.057 1.042 1.134 +0 0.891 -0.762 0.792 0.829 -1.319 0.535 -0.727 -0.906 2.173 0.407 -2.079 -1.269 0.000 0.856 -1.094 0.412 2.548 0.821 -1.518 0.885 0.000 0.873 0.742 1.126 0.719 0.807 0.617 0.578 +0 1.248 -0.447 -0.304 0.772 -1.134 1.085 0.649 1.356 0.000 1.004 0.430 0.639 2.215 1.350 0.924 -0.639 0.000 0.595 -0.743 -1.520 3.102 2.156 1.367 0.988 1.003 0.820 0.976 0.944 +1 0.420 2.058 -0.506 1.262 0.833 0.695 0.002 -0.567 2.173 0.373 0.776 -0.834 0.000 0.953 -1.615 0.929 0.000 0.737 -1.222 -1.297 1.551 1.781 1.078 0.989 1.698 0.764 1.604 1.736 +0 1.374 0.140 -0.684 0.030 -1.145 0.430 -0.240 -1.364 0.000 1.293 -0.216 0.566 0.000 1.593 0.428 1.125 2.548 0.683 1.307 -1.154 0.000 0.871 0.962 0.657 0.466 1.018 0.739 0.679 +0 1.773 0.222 -0.089 1.547 -0.695 0.532 -1.432 1.563 0.000 1.905 0.669 1.082 0.000 0.883 0.030 -1.064 2.548 0.748 -0.810 -0.657 0.000 0.905 0.828 1.191 0.748 0.379 0.576 0.773 +0 0.598 -0.766 -1.703 0.239 -1.381 0.277 -0.584 0.222 1.087 0.526 0.597 -0.065 2.215 0.403 2.198 -1.191 0.000 0.674 0.695 0.756 0.000 0.756 0.831 0.974 0.688 0.387 0.546 0.575 +1 1.806 0.111 -0.461 0.862 0.321 1.045 1.139 1.127 2.173 0.375 1.051 -0.670 0.000 0.593 0.224 1.574 1.274 0.460 2.137 -1.490 0.000 0.571 0.888 1.120 0.832 0.598 0.971 0.844 +1 1.190 -0.245 -1.345 1.290 -1.183 1.205 0.602 0.877 2.173 0.339 2.125 0.830 0.000 1.233 0.268 -0.045 2.548 0.626 -0.883 0.082 0.000 0.953 1.060 0.977 1.025 1.148 1.084 0.890 +0 0.535 1.078 -0.160 1.198 0.640 1.029 0.636 1.291 2.173 0.966 1.269 -0.633 2.215 1.299 -0.293 -0.562 0.000 1.166 0.221 -1.674 0.000 1.072 1.021 0.992 1.192 1.529 1.014 0.878 +0 2.409 -0.060 -1.559 1.080 0.099 0.602 2.163 0.643 0.000 0.974 -0.823 0.122 2.215 1.067 -0.799 -1.157 2.548 0.924 -0.568 1.211 0.000 0.818 0.768 2.228 1.479 0.990 1.047 0.892 +1 0.501 0.046 0.612 0.933 -1.190 0.799 0.680 0.679 0.000 0.637 1.285 -1.433 0.000 0.540 1.341 -0.400 0.000 1.290 0.181 0.276 3.102 1.086 0.876 0.988 0.718 0.597 0.587 0.643 +0 0.670 -1.361 1.200 0.293 -0.196 0.519 1.010 -1.116 0.000 0.770 -0.157 1.563 2.215 0.690 0.707 0.000 0.000 1.348 -1.675 0.352 0.000 0.919 0.959 0.985 0.691 0.722 0.807 0.742 +1 1.531 -0.285 -1.141 0.560 0.651 1.803 0.620 0.358 1.087 1.910 0.260 -1.711 0.000 0.619 0.571 -0.380 2.548 0.576 1.330 -0.730 0.000 1.438 1.061 1.282 1.589 0.813 1.254 1.131 +1 0.755 0.479 0.080 1.012 1.160 0.525 -0.201 -1.567 1.087 0.495 1.542 -1.523 0.000 0.815 -1.232 -0.201 2.548 1.126 1.008 -0.029 0.000 0.966 1.011 1.002 0.997 0.914 0.976 0.852 +1 0.744 0.002 -0.444 0.592 0.883 1.899 -0.348 0.423 0.000 0.909 2.561 -1.191 0.000 2.154 0.573 -1.670 2.548 1.717 -0.449 -0.339 0.000 0.872 0.823 0.982 1.118 0.866 0.843 0.765 +1 0.547 -0.885 -1.384 1.065 0.113 0.602 -0.335 0.744 1.087 1.408 -0.149 1.494 0.000 1.212 0.808 -0.582 1.274 0.883 -0.344 -1.045 0.000 1.111 0.976 1.031 1.061 1.209 0.931 0.868 +1 1.454 0.157 0.903 1.814 -0.021 0.765 1.432 -1.321 1.087 0.954 0.535 -0.005 0.000 1.469 -1.334 -0.952 0.000 0.915 0.615 -1.529 3.102 2.556 1.641 1.663 1.618 0.360 1.439 1.342 +0 1.427 0.261 0.237 0.230 -0.665 1.018 0.458 -1.631 2.173 0.683 2.535 0.201 0.000 0.795 0.912 -1.164 2.548 0.508 0.847 0.217 0.000 0.687 0.924 0.990 0.768 0.551 0.787 0.711 +1 0.628 -1.305 0.374 1.051 1.120 0.369 -0.104 1.621 0.000 0.969 -0.148 -1.204 2.215 1.134 0.777 -0.680 0.000 1.242 0.774 0.454 0.000 1.057 1.013 0.984 0.615 0.709 0.899 1.028 +0 0.447 -0.446 -1.740 0.436 -1.175 1.253 0.355 -0.135 2.173 0.985 -2.165 -1.653 0.000 1.154 -1.600 1.051 0.000 1.134 -1.708 -0.985 0.000 0.790 1.485 0.992 1.733 0.572 1.657 1.343 +1 1.326 1.209 1.193 0.864 0.525 0.442 -0.085 -0.105 0.000 0.838 -0.694 1.523 2.215 0.991 -0.546 -0.533 2.548 1.257 -1.497 -0.950 0.000 0.672 0.954 0.989 1.177 0.931 0.976 0.822 +1 1.126 -0.333 -1.289 0.469 -1.625 1.061 -1.032 0.407 2.173 0.793 -0.931 1.538 2.215 0.416 -0.053 -1.005 0.000 0.628 0.498 0.029 0.000 0.490 0.899 0.981 0.635 1.151 0.860 0.752 +1 1.216 -2.332 1.277 0.179 -1.732 0.591 -1.144 -0.693 0.000 1.010 -0.393 -1.199 2.215 0.496 0.410 1.523 2.548 0.878 -0.578 0.170 0.000 0.904 0.765 0.981 1.140 0.582 0.862 0.776 +1 1.261 -0.304 0.338 1.056 -0.260 2.285 -2.057 0.603 0.000 2.335 -0.612 -0.983 2.215 1.680 -0.733 -1.497 2.548 1.715 -0.059 -1.702 0.000 4.440 2.977 0.993 1.358 0.958 2.343 1.854 +0 0.403 -1.388 0.173 0.641 1.020 0.539 0.510 -1.033 0.000 0.740 -1.074 -1.261 2.215 1.017 0.485 1.201 2.548 0.666 -0.358 1.146 0.000 0.952 0.916 0.996 0.843 1.118 0.905 0.803 +1 0.822 0.391 -1.279 0.727 1.719 0.538 0.858 -0.033 0.000 0.794 -0.250 1.243 1.107 1.194 -0.571 0.242 2.548 0.988 1.036 -0.895 0.000 0.808 1.078 0.990 0.948 0.835 0.950 0.878 +1 1.644 0.072 -0.242 0.865 -0.047 1.376 0.837 -1.131 1.087 0.796 0.724 1.371 2.215 0.977 0.678 0.713 0.000 0.443 0.967 0.446 0.000 0.921 0.875 0.976 1.298 1.195 1.049 0.973 +0 0.846 -0.177 0.966 0.747 -1.374 1.899 -0.695 0.158 0.000 2.111 0.577 -1.351 0.000 0.368 -1.144 -0.027 0.000 1.817 -0.982 1.235 3.102 0.491 0.730 0.989 0.694 0.519 0.800 0.770 +0 0.565 0.888 -1.468 0.909 -0.716 0.725 0.752 1.159 2.173 0.665 -0.011 -1.322 0.000 1.704 0.247 -0.119 2.548 0.686 -0.947 0.826 0.000 0.974 0.991 0.984 0.839 1.311 0.883 0.790 +1 1.101 -0.071 -0.113 0.707 1.039 0.621 -0.828 -0.119 0.000 1.051 0.011 1.564 2.215 0.592 1.143 -1.206 2.548 0.508 -1.249 -1.307 0.000 0.807 1.045 1.054 0.891 0.750 0.830 0.756 +0 0.546 -0.495 0.337 1.251 -1.362 0.963 -0.128 1.330 0.000 1.587 -0.616 -0.327 2.215 1.991 1.908 0.143 0.000 1.093 -1.047 -1.531 3.102 0.766 0.841 1.144 0.990 1.117 1.104 0.914 +0 0.594 0.790 -0.860 1.312 1.255 0.920 -0.057 -0.961 2.173 1.263 0.288 0.690 2.215 0.692 0.693 -0.541 0.000 0.668 1.808 0.875 0.000 0.919 1.015 1.154 1.062 1.606 0.991 0.875 +0 1.799 -1.747 -0.641 0.529 1.738 0.625 -1.521 1.098 0.000 0.530 -0.657 -0.260 2.215 1.192 -0.706 -1.529 1.274 1.121 -2.079 0.597 0.000 0.848 0.966 1.135 0.924 0.770 0.811 0.813 +0 1.904 0.013 0.734 0.936 1.332 0.468 -2.108 -1.264 0.000 1.020 -0.022 -0.804 2.215 0.339 -1.191 0.684 0.000 0.613 -0.739 -0.135 0.000 0.882 1.101 0.986 0.752 0.354 0.799 0.908 +0 0.529 -1.709 1.061 0.860 0.300 0.696 2.226 1.022 0.000 0.944 1.794 -0.681 0.000 0.520 -2.153 -0.440 0.000 0.897 0.001 -1.046 1.551 0.983 0.932 0.996 0.745 0.574 0.633 0.616 +1 1.948 0.490 -1.236 0.703 -0.706 0.718 0.647 0.421 1.087 0.629 1.184 1.540 0.000 0.462 1.617 0.284 0.000 1.269 0.373 1.042 1.551 0.784 0.791 0.999 0.948 0.546 0.857 0.751 +1 0.450 1.186 0.304 1.164 1.652 0.779 -0.545 -1.735 1.087 1.602 0.800 -0.271 2.215 0.757 -0.473 -0.252 0.000 1.073 0.488 1.307 0.000 1.138 0.988 0.985 1.173 2.006 1.272 1.067 +0 2.894 -0.523 1.368 1.396 1.541 0.985 0.182 -0.838 0.000 1.697 0.172 -0.472 0.000 1.682 -0.226 0.784 0.000 1.072 -0.376 -0.161 1.551 0.933 0.688 1.001 0.897 0.217 0.808 0.874 +1 0.736 -0.112 -0.629 0.656 1.114 0.894 0.153 -1.400 0.000 1.739 0.729 0.414 2.215 0.980 0.822 -0.823 0.000 0.886 1.090 -0.520 3.102 0.944 0.885 0.989 0.939 0.893 1.000 0.834 +0 1.233 -1.565 -0.693 0.616 -1.021 0.596 -0.842 1.698 0.000 0.840 -0.009 0.860 2.215 1.340 -0.875 0.463 2.548 0.714 -0.878 -0.786 0.000 0.788 0.934 0.985 1.117 0.683 0.865 0.771 +0 0.342 -1.382 0.717 1.717 -0.962 0.557 2.094 0.791 0.000 0.383 -0.280 1.280 2.215 0.461 0.811 0.101 0.000 0.820 -0.220 -1.581 3.102 0.842 0.665 1.059 0.774 0.270 0.560 0.601 +0 1.008 0.642 0.945 0.575 -0.294 0.886 0.419 -1.434 2.173 0.812 0.100 0.478 2.215 0.567 0.986 -0.167 0.000 0.389 0.025 -1.192 0.000 0.503 0.663 0.985 0.595 1.250 0.750 0.611 +1 1.656 0.172 -0.791 0.337 0.718 0.584 -1.250 1.354 1.087 0.248 -0.550 1.706 0.000 0.756 -0.462 -0.235 0.000 0.470 0.660 1.166 3.102 0.655 0.722 1.012 0.618 0.681 0.734 0.612 +1 0.587 -0.346 -1.266 0.921 0.391 0.774 -0.484 1.570 0.000 0.799 0.712 -1.272 0.000 0.605 2.459 -0.785 0.000 0.704 -0.364 0.292 3.102 0.836 1.027 1.016 0.568 0.265 0.599 0.723 +1 1.068 -0.668 0.501 0.071 -0.769 0.378 -1.749 -1.357 0.000 0.644 -2.378 1.233 0.000 1.374 -0.612 -0.577 2.548 0.435 -1.289 1.625 0.000 0.840 0.769 0.990 0.834 0.606 0.769 0.804 +0 0.523 -1.018 -1.124 0.510 1.477 0.908 -0.347 0.935 1.087 0.799 -0.304 -0.601 0.000 0.956 1.016 -0.948 2.548 0.932 0.004 0.435 0.000 0.921 1.030 0.983 0.751 1.480 0.889 0.766 +0 0.345 1.548 0.043 1.686 -1.351 1.395 -0.953 1.297 0.000 0.882 0.610 0.186 2.215 1.432 0.071 -0.646 0.000 0.883 -0.240 0.844 0.000 0.891 0.845 1.004 1.046 0.771 0.952 1.328 +1 2.088 1.289 0.858 0.958 0.457 0.832 1.065 -0.915 0.000 0.904 0.807 -1.475 2.215 0.612 1.031 -0.298 2.548 0.380 0.065 0.125 0.000 0.824 0.721 1.001 0.765 0.700 0.800 0.785 +0 2.507 -0.757 -0.963 2.588 -0.874 2.588 0.703 0.872 1.087 0.399 0.679 0.585 0.000 0.513 0.764 -1.120 2.548 0.772 -0.089 0.665 0.000 0.304 0.573 0.983 0.830 1.403 2.159 1.557 +1 0.407 -1.400 0.338 1.440 0.913 1.143 -1.127 -1.181 2.173 0.997 0.861 -0.977 0.000 0.678 -0.857 -0.348 0.000 2.496 0.568 0.819 3.102 0.838 1.151 0.982 1.357 2.546 1.371 1.172 +0 0.628 -1.674 -0.693 0.793 1.374 0.653 -0.211 -0.844 0.000 1.081 0.371 0.898 0.000 0.811 -0.322 0.071 2.548 0.682 -0.001 1.643 1.551 0.793 0.744 0.987 0.798 0.571 0.694 0.686 +1 1.464 0.279 -0.404 0.690 -1.184 1.603 0.269 0.842 2.173 1.519 -1.421 -0.299 0.000 1.235 0.757 -0.221 0.000 3.206 -1.055 -1.630 0.000 0.949 1.021 0.990 1.455 0.852 1.320 1.224 +0 0.759 -0.775 1.341 0.344 -1.499 0.698 -0.226 0.215 2.173 0.617 -1.174 1.444 2.215 1.060 1.074 -0.358 0.000 0.711 0.508 -0.504 0.000 0.304 0.804 0.993 0.699 0.995 0.876 0.789 +0 0.829 1.170 -0.587 0.902 -1.469 1.067 0.519 -1.454 1.087 1.056 1.053 1.045 0.000 1.290 1.516 -0.021 0.000 1.033 0.535 0.481 3.102 1.282 0.819 0.986 0.716 1.096 0.801 0.734 +1 1.283 -0.249 0.742 1.618 0.947 0.486 -1.422 -0.534 0.000 1.031 -0.424 -1.070 0.000 0.535 -1.120 0.696 0.000 0.414 -1.397 -1.653 0.000 0.826 0.592 0.993 0.820 0.243 0.618 0.616 +1 0.492 0.549 0.341 1.355 0.763 2.219 -1.413 -0.549 0.000 2.157 0.611 1.441 2.215 0.788 0.099 -1.421 0.000 1.411 0.353 0.957 3.102 2.621 2.345 0.979 1.323 0.686 2.305 2.299 +1 1.218 0.527 0.882 0.262 -0.059 0.481 1.531 -1.516 0.000 0.338 1.167 0.229 0.000 0.588 -1.582 -1.327 2.548 0.570 0.371 0.122 3.102 0.868 0.627 0.990 0.934 0.724 0.859 0.771 +0 0.981 0.952 0.703 0.816 1.731 0.980 -0.480 1.596 2.173 0.511 -0.823 -0.139 0.000 1.812 0.719 -0.131 2.548 0.722 -0.029 -1.287 0.000 0.756 0.959 0.991 1.108 2.009 1.119 0.945 +1 0.995 1.532 -0.700 0.369 -1.635 1.041 1.730 1.485 0.000 1.488 1.033 0.378 1.107 0.679 -0.078 0.572 0.000 1.365 0.476 -1.010 0.000 1.003 0.907 0.983 0.512 0.831 0.687 0.634 +1 1.149 0.211 0.669 0.173 1.675 2.568 -0.941 -0.965 0.000 2.653 -0.095 1.242 1.107 1.790 -1.989 0.370 0.000 0.947 -0.686 0.260 3.102 3.344 2.009 0.989 0.930 1.229 2.008 1.541 +1 0.368 -0.662 -0.424 0.591 -1.015 0.803 0.723 1.073 2.173 1.028 0.283 0.235 2.215 0.846 -1.172 -0.593 0.000 0.887 -0.107 -1.491 0.000 0.906 1.085 0.985 1.970 0.963 1.474 1.180 +0 0.790 -0.202 0.020 1.301 -1.325 0.967 0.183 0.786 2.173 0.371 1.794 0.190 0.000 1.071 0.977 -1.039 2.548 0.460 0.011 -1.575 0.000 0.780 0.872 1.315 0.933 1.392 0.950 0.803 +0 1.416 0.944 -0.630 2.342 -1.052 1.215 1.634 0.733 0.000 0.783 0.834 0.494 2.215 1.201 0.663 -1.574 1.274 1.070 1.429 1.226 0.000 0.749 0.736 0.995 0.835 0.989 0.888 1.110 +1 0.796 1.070 0.404 1.290 0.998 0.995 -0.250 -0.249 0.000 0.904 -2.881 -1.447 0.000 0.993 1.766 -0.909 0.000 0.790 -1.002 -1.703 0.000 1.129 1.397 0.995 0.669 0.554 1.288 1.448 +1 0.676 -1.062 -1.710 1.258 0.175 0.790 -0.031 -0.911 0.000 0.296 0.387 1.288 0.000 0.668 1.123 -1.587 0.000 1.435 -0.713 0.888 3.102 0.967 0.927 1.267 0.755 0.442 0.717 0.717 +1 1.412 -1.356 -0.659 0.778 -1.675 1.062 -1.669 0.612 0.000 0.729 -1.263 1.508 2.215 0.269 -1.139 0.246 2.548 0.468 -0.557 -0.825 0.000 1.107 0.894 1.150 0.606 0.427 0.541 0.652 +1 1.847 0.374 1.454 1.345 0.977 0.724 -0.296 -0.401 2.173 0.630 0.358 -0.516 2.215 0.458 0.211 0.708 0.000 1.136 1.224 -0.794 0.000 0.937 0.961 0.993 1.042 0.353 0.979 0.850 +0 1.673 1.448 0.804 0.860 -1.162 0.598 0.251 -1.130 2.173 0.528 1.805 -1.403 0.000 0.603 1.127 0.326 0.000 1.042 0.129 -0.089 3.102 0.907 0.905 1.629 1.075 0.675 0.895 0.771 +1 1.677 0.192 0.934 1.398 0.205 0.940 0.460 -0.784 2.173 0.517 1.215 -1.356 2.215 1.036 -1.322 -0.710 0.000 0.868 -0.274 1.541 0.000 0.963 1.007 1.294 1.079 0.653 0.988 0.855 +0 0.727 -1.847 0.471 0.702 1.324 0.706 -0.526 -1.372 2.173 0.729 -0.646 0.881 1.107 1.158 -0.217 -0.447 0.000 0.751 0.423 -0.072 0.000 0.889 0.888 0.994 0.897 0.948 0.693 0.710 +0 0.701 0.098 1.256 1.258 0.532 0.731 -0.094 -0.565 1.087 0.702 1.544 0.820 0.000 0.833 0.119 -1.428 2.548 0.943 0.991 -1.127 0.000 1.064 0.910 0.995 1.061 0.691 0.815 0.792 +0 2.237 -0.619 -0.890 0.918 -0.631 0.446 0.364 1.217 0.000 0.690 -0.784 1.407 0.000 1.159 -0.366 0.217 2.548 0.548 -0.579 1.099 3.102 0.759 0.899 0.998 0.772 0.443 0.732 0.810 +0 0.297 2.165 -1.474 0.794 -0.278 1.329 0.977 -1.032 2.173 0.900 -0.297 1.494 2.215 2.527 0.610 0.663 0.000 0.535 -2.451 -0.075 0.000 3.883 2.299 0.977 0.830 1.656 2.025 1.558 +0 1.389 0.383 0.957 1.272 1.351 0.955 1.338 0.420 1.087 1.271 0.848 -1.272 0.000 1.451 1.002 -0.790 0.000 1.699 0.142 -0.515 3.102 0.909 0.930 0.986 1.341 1.313 1.107 1.174 +0 0.725 0.315 -0.538 0.329 0.509 0.548 -0.241 0.701 0.000 0.426 -0.221 0.341 0.000 0.701 -0.439 -0.725 0.000 0.880 -0.445 1.384 3.102 1.078 0.758 0.997 0.644 0.215 0.493 0.513 +1 1.614 -0.040 0.156 0.205 1.047 1.127 -0.891 -1.410 2.173 0.653 -0.648 -0.415 2.215 0.786 -0.380 0.523 0.000 0.901 -0.419 1.629 0.000 0.780 0.978 0.981 0.646 0.998 0.881 0.755 +0 0.451 1.747 -1.208 0.963 0.886 1.021 0.527 -0.594 1.087 0.865 -0.320 1.191 0.000 0.459 -0.396 -0.858 2.548 0.929 -1.356 1.138 0.000 0.815 0.729 0.987 1.005 0.473 0.956 0.933 +1 0.693 1.841 1.348 0.851 -0.082 0.579 0.656 -0.564 2.173 0.563 0.911 1.063 0.000 0.725 -0.691 -1.607 2.548 1.068 0.819 -0.027 0.000 0.841 0.958 1.021 0.857 0.903 0.915 0.778 +1 0.428 -1.772 0.043 0.698 0.888 0.751 -0.714 0.365 0.000 1.367 -0.764 -1.361 2.215 1.041 -0.600 1.279 0.000 1.880 -0.518 -0.592 3.102 0.907 0.951 0.992 0.981 0.932 0.782 0.718 +0 1.904 0.156 0.048 5.442 0.090 3.277 0.743 -1.599 0.000 0.545 0.044 -1.580 0.000 0.514 0.655 0.599 2.548 0.728 0.052 1.050 3.102 1.022 1.054 1.004 0.683 0.242 0.825 1.770 +0 0.411 0.894 -1.290 1.617 0.138 1.047 0.129 -0.611 0.000 1.011 -1.367 -1.736 0.000 1.205 -2.364 1.450 0.000 1.401 -0.166 1.466 3.102 1.160 1.305 1.084 0.651 0.634 1.077 1.461 +0 2.078 1.411 -1.461 0.707 -1.535 1.255 0.319 0.637 0.000 0.956 -1.826 -0.442 0.000 0.560 0.113 -1.089 2.548 0.693 0.844 0.692 3.102 3.656 2.058 0.978 0.817 0.524 1.277 1.835 +1 0.562 -1.089 1.314 0.554 -1.724 2.335 -0.941 0.020 0.000 2.775 -0.744 1.727 0.000 0.928 -1.303 -0.674 2.548 0.689 -2.390 -1.487 0.000 0.846 1.159 0.997 0.510 0.602 0.735 0.714 +1 1.857 0.990 1.110 0.445 0.480 0.342 1.495 -1.319 0.000 0.724 0.324 -0.304 0.000 0.708 0.780 -0.218 2.548 0.564 -0.859 1.675 0.000 1.020 0.722 0.985 1.243 0.728 0.867 0.813 +1 1.212 -0.959 -0.200 0.712 0.808 0.729 -1.404 -1.149 2.173 0.662 -0.656 0.774 0.000 1.520 -1.081 1.560 1.274 0.376 -2.070 -0.697 0.000 0.921 0.924 1.015 0.969 0.857 0.808 0.715 +0 0.842 0.709 -0.515 0.946 1.355 1.221 -0.023 0.457 2.173 0.885 -0.442 -1.133 0.000 1.267 0.069 -1.664 0.000 1.372 -0.787 -0.455 0.000 0.918 0.826 1.229 1.124 0.846 0.855 0.819 +1 1.134 1.405 1.239 0.979 1.709 1.095 0.324 -0.195 2.173 0.794 0.710 0.311 0.000 1.102 -2.046 -1.182 0.000 0.603 -0.111 0.991 3.102 3.328 1.793 0.997 1.364 0.780 1.404 1.425 +1 1.423 -0.755 1.451 1.630 1.387 0.761 -0.631 -0.400 0.000 0.565 -1.532 1.084 2.215 1.918 -1.922 -0.651 0.000 1.575 -0.301 0.206 3.102 1.053 0.992 0.960 1.188 0.817 0.784 0.862 +0 1.298 -0.523 -1.253 0.836 -0.648 0.648 -0.274 0.957 0.000 0.455 0.101 -1.000 2.215 0.478 -1.291 0.568 2.548 0.457 -0.201 0.522 0.000 0.319 0.652 0.986 0.723 0.642 0.545 0.608 +0 0.350 1.113 0.170 1.392 1.472 0.669 0.734 -0.790 2.173 0.448 -0.229 0.413 0.000 1.239 0.037 1.393 2.548 1.440 0.523 -0.327 0.000 0.975 0.793 0.989 1.058 1.122 0.921 0.868 +1 1.092 -0.204 0.106 1.789 0.860 1.372 -1.326 -0.464 0.000 1.666 0.692 1.537 2.215 0.616 0.270 -1.397 0.000 0.543 0.941 -0.303 0.000 0.598 0.788 1.218 0.922 1.059 1.018 0.823 +0 1.875 -0.424 -0.981 0.872 0.531 0.371 1.855 -1.530 0.000 0.484 0.856 0.658 0.000 0.600 1.330 0.596 2.548 1.430 1.671 0.968 0.000 0.951 1.254 1.734 1.244 1.100 0.885 0.947 +1 2.467 -0.070 -0.110 0.273 -0.264 0.556 -0.208 -1.536 2.173 0.673 -1.756 -1.533 0.000 0.512 -1.221 1.526 0.000 0.721 0.368 1.087 3.102 0.399 0.796 1.001 1.071 0.520 0.751 0.892 +1 0.555 -1.600 0.130 1.224 1.262 0.499 0.901 -1.019 2.173 0.386 -0.421 -1.631 0.000 0.805 0.193 0.574 2.548 1.070 -0.761 -0.546 0.000 0.724 0.815 0.988 0.854 0.831 0.958 0.792 +0 1.078 1.109 -0.011 0.395 -1.106 0.665 1.531 1.702 0.000 0.729 0.889 0.662 2.215 0.590 0.727 -1.366 0.000 0.910 -0.410 -0.192 3.102 0.597 1.047 0.986 0.728 0.759 0.756 0.732 +0 1.106 -1.305 -1.238 0.572 -1.465 0.633 0.096 0.255 0.000 0.429 -0.762 1.137 1.107 0.858 0.744 -1.038 2.548 1.213 -0.924 0.274 0.000 0.858 1.106 0.997 0.663 0.827 0.733 0.774 +0 1.533 -0.753 0.693 1.104 1.281 0.767 0.418 -0.236 0.000 1.800 -0.426 -1.471 2.215 1.329 2.273 0.525 0.000 1.653 -0.097 -0.861 3.102 2.517 2.050 0.989 1.286 0.851 1.925 1.759 +0 1.606 0.682 0.108 1.261 0.828 0.785 -1.712 -1.143 0.000 0.487 -1.208 -1.462 2.215 0.640 -0.612 0.723 0.000 0.659 0.842 -1.377 3.102 1.449 0.849 1.192 0.818 0.707 0.880 1.149 +0 0.576 -2.140 -0.756 0.973 1.212 0.815 -0.371 -0.644 2.173 0.665 0.344 0.781 0.000 0.833 0.286 -0.137 0.000 1.346 -0.500 1.236 3.102 0.840 0.975 1.016 0.869 1.107 0.953 1.003 +1 0.787 1.001 -1.558 0.905 -0.247 0.626 1.754 0.470 0.000 0.973 0.758 -0.455 0.000 1.704 0.985 0.197 0.000 2.533 0.523 -1.491 3.102 0.794 0.516 1.082 0.847 0.662 0.920 0.802 +1 1.589 -0.298 0.738 1.615 1.115 1.163 0.425 -0.779 1.087 0.208 1.707 -0.697 0.000 0.698 -0.443 1.705 2.548 1.167 -0.513 0.160 0.000 1.053 1.023 0.997 0.711 1.029 1.175 0.994 +1 1.046 0.225 -0.708 1.166 1.188 0.650 -0.375 -0.458 0.000 1.385 -0.559 -1.235 0.000 1.218 0.316 1.151 2.548 2.399 -0.150 0.499 1.551 1.313 1.405 1.515 1.077 0.804 1.095 0.985 +1 0.572 1.719 1.115 0.765 -0.332 0.739 -0.689 0.020 2.173 0.311 -0.750 -1.035 0.000 0.963 0.438 -1.247 0.000 1.416 -0.766 1.195 3.102 0.709 0.818 0.989 1.856 0.951 1.571 1.180 +0 1.057 -0.048 0.847 1.397 1.628 1.016 0.382 0.486 0.000 0.666 0.310 -1.461 1.107 2.196 0.923 -0.595 2.548 0.424 -0.954 -0.751 0.000 1.234 1.086 1.090 1.535 1.012 1.025 0.994 +1 0.802 -0.561 0.469 0.777 -0.659 0.830 0.209 -1.117 2.173 2.087 0.186 1.386 0.000 1.296 -1.495 -0.256 0.000 0.977 -0.279 0.184 1.551 0.986 0.874 0.983 0.848 0.917 1.050 0.882 +1 2.366 0.640 -0.621 1.002 0.284 1.441 2.203 0.800 0.000 1.140 0.734 -1.732 1.107 0.596 -0.855 1.421 2.548 0.655 2.241 1.069 0.000 0.497 1.640 1.555 1.234 0.874 1.499 1.492 +0 1.957 -0.818 1.536 0.493 0.038 2.023 -1.044 0.185 0.000 1.888 -0.432 -1.439 2.215 0.877 -2.202 -1.220 0.000 0.986 -1.429 -0.050 0.000 0.965 0.829 1.327 1.073 0.877 0.981 0.925 +0 1.038 -0.167 -1.699 0.698 0.531 0.553 -1.483 1.533 0.000 1.106 -2.332 -1.118 0.000 1.947 -1.100 1.083 0.000 2.520 -0.094 -0.398 3.102 0.787 0.852 1.067 0.983 0.944 1.091 0.938 +1 0.636 0.074 0.832 1.040 -0.443 1.107 0.141 -1.310 0.000 1.301 0.378 0.432 2.215 0.863 0.692 1.741 0.000 0.557 1.066 0.814 0.000 0.868 0.746 1.027 0.764 0.871 0.935 0.815 +0 1.142 -0.844 -1.249 1.270 -0.488 2.854 -1.150 -0.707 0.000 3.864 -1.573 0.925 0.000 1.245 -1.393 1.342 2.548 1.097 -0.667 0.732 3.102 7.203 3.792 1.057 1.050 0.574 2.238 1.779 +0 0.908 -1.856 -1.583 1.557 -1.201 1.564 0.789 0.443 2.173 1.094 -1.165 1.520 2.215 0.961 1.258 -0.123 0.000 1.266 2.196 -0.704 0.000 0.840 0.938 0.989 0.793 2.759 1.903 1.593 +1 2.224 0.287 -1.669 1.127 1.662 2.207 -0.706 -0.573 2.173 1.393 0.231 0.956 2.215 1.768 -0.589 -0.096 0.000 1.590 -0.795 1.173 0.000 1.229 1.612 1.002 2.128 2.835 1.801 1.684 +1 0.764 -1.039 -0.447 1.064 1.691 0.759 -1.213 0.142 0.000 0.979 -0.049 1.422 2.215 0.634 0.845 -0.732 2.548 0.617 2.471 1.097 0.000 4.260 2.326 1.171 0.915 0.889 1.505 1.363 +0 1.652 0.814 1.446 0.376 -1.712 1.119 -0.687 -0.198 0.000 0.725 0.253 -0.647 0.000 0.941 0.049 1.366 2.548 0.854 0.682 0.575 3.102 0.827 0.880 0.997 0.732 0.523 0.751 0.981 +0 0.512 -0.464 -0.754 0.358 0.853 0.545 2.665 -1.546 0.000 0.580 1.154 0.409 2.215 0.657 1.455 0.944 0.000 1.080 -0.114 0.250 1.551 1.043 0.933 0.997 0.743 0.519 0.898 1.443 +1 1.035 -0.466 -0.236 1.760 -0.739 0.844 -1.083 0.909 2.173 0.385 -1.398 1.593 0.000 0.987 -0.121 1.734 1.274 0.676 0.692 1.233 0.000 0.951 0.875 0.984 0.941 0.956 1.023 0.869 +1 0.580 -0.270 0.736 0.402 0.011 0.894 -0.949 -0.967 2.173 0.368 -0.077 -1.696 0.000 0.649 0.604 0.337 0.000 0.657 0.422 -0.586 3.102 0.778 1.046 0.993 0.764 0.704 0.704 0.701 +0 0.429 1.159 -0.135 1.571 1.643 0.828 -0.363 1.232 2.173 1.534 0.228 -0.480 2.215 0.380 -1.813 0.674 0.000 0.382 -2.177 0.111 0.000 0.244 0.854 1.137 1.288 1.734 1.168 1.207 +0 1.058 0.270 -0.292 0.886 -0.511 0.976 -0.567 1.035 2.173 0.690 -0.043 0.464 0.000 0.653 0.494 -0.904 0.000 0.371 2.351 -1.053 0.000 0.938 1.073 0.989 1.551 1.562 1.155 0.985 +1 0.345 0.576 -1.477 0.770 -0.569 3.540 1.146 1.298 0.000 2.513 -0.592 -0.053 0.000 2.216 -0.324 -0.367 2.548 1.431 0.292 -0.957 3.102 2.053 1.301 0.984 0.706 0.847 0.981 0.838 +0 3.106 -1.365 0.473 0.357 1.208 1.713 -2.031 -1.033 0.000 0.538 -0.850 0.858 2.215 0.619 -1.070 1.601 2.548 0.479 -2.337 -1.089 0.000 0.539 0.859 0.986 0.566 0.392 0.828 1.046 +0 0.866 0.094 -0.447 1.019 -1.505 0.745 -0.436 0.201 2.173 0.753 -1.474 -1.454 2.215 1.045 -0.884 0.796 0.000 0.647 0.019 -1.724 0.000 0.838 0.842 1.060 0.944 1.263 0.878 0.777 +0 1.415 -0.584 1.725 0.608 -1.018 0.861 0.379 -0.074 0.000 0.450 -1.309 0.826 2.215 0.840 0.113 -0.943 0.000 0.513 -0.451 1.210 0.000 0.965 1.069 0.998 0.583 0.173 0.620 0.709 +0 2.119 -0.329 0.960 1.120 -0.820 1.141 -1.046 -0.641 2.173 1.476 0.332 0.582 0.000 1.236 -0.918 -1.388 2.548 0.842 1.190 -1.699 0.000 1.528 1.679 2.133 1.618 0.922 1.456 1.319 +1 0.425 -0.293 -0.671 0.776 0.900 0.996 0.540 -0.646 0.000 1.127 1.178 0.696 1.107 0.485 0.465 -1.373 0.000 1.259 1.036 -1.615 3.102 0.806 0.777 0.991 1.234 0.938 1.161 1.000 +0 0.427 -1.207 -0.972 2.425 -0.391 0.638 -0.612 1.625 2.173 0.445 1.105 0.400 0.000 0.617 -0.431 -0.315 0.000 2.112 1.032 1.160 0.000 0.934 0.862 0.984 0.745 0.328 0.757 0.691 +0 1.641 -0.177 1.676 1.574 1.217 0.829 -0.908 -0.224 1.087 0.666 -1.074 0.361 0.000 0.785 0.048 -0.981 2.548 0.942 0.495 -0.373 0.000 0.790 0.947 0.990 1.495 0.812 1.018 0.996 +1 1.108 -1.314 0.622 0.196 0.812 1.178 -1.138 -1.417 0.000 0.190 -1.010 -1.031 2.215 0.965 -0.738 0.166 0.000 0.449 -0.287 -0.114 3.102 1.925 0.997 0.981 0.518 0.215 0.617 0.649 +0 1.050 -0.933 -0.031 1.909 -1.300 0.570 -0.076 0.059 0.000 0.736 -0.115 1.700 2.215 0.927 0.731 0.208 2.548 1.057 1.401 1.251 0.000 1.576 0.963 1.785 1.105 0.952 1.027 1.099 +1 2.024 -0.276 -1.364 0.495 -0.713 0.367 -1.145 1.240 0.000 0.691 -0.510 -0.662 0.000 0.968 -0.723 0.538 2.548 0.788 -0.038 0.011 0.000 0.877 0.732 0.988 0.990 0.610 0.793 0.697 +0 1.217 -0.018 0.806 1.435 -0.042 0.849 -1.584 1.479 0.000 0.849 0.785 -0.505 2.215 0.757 -1.712 -1.445 0.000 0.795 -0.430 -0.375 3.102 0.726 0.911 1.266 0.983 0.535 1.176 1.133 +0 1.788 0.664 1.540 1.052 -0.936 0.673 1.589 -0.966 0.000 1.241 1.625 -0.567 0.000 1.086 -0.439 -1.309 2.548 0.962 0.490 0.030 3.102 0.686 0.846 1.501 1.009 0.851 1.008 0.988 +0 1.356 0.434 -1.267 0.388 0.175 0.942 -0.767 1.098 2.173 0.297 -0.394 -1.427 0.000 0.873 -0.598 -0.567 2.548 1.162 -0.255 0.227 0.000 0.764 0.814 0.988 0.703 1.129 0.845 0.700 +0 1.043 -0.709 0.227 0.694 1.734 0.903 -0.891 -0.183 1.087 0.737 -0.726 -0.967 0.000 1.364 -0.545 1.498 0.000 0.785 -1.285 1.152 1.551 0.917 0.854 1.153 0.850 0.881 0.792 0.712 +0 2.891 0.816 0.489 1.355 0.903 1.508 0.814 -1.233 0.000 0.805 0.250 -0.535 2.215 0.526 0.706 1.697 2.548 0.488 1.320 -1.163 0.000 0.519 0.865 1.002 0.820 0.651 0.852 1.040 +0 1.416 0.839 1.323 0.330 0.103 0.690 -0.960 -0.354 0.000 0.650 -0.057 0.930 2.215 0.660 -0.226 -0.481 2.548 0.524 -1.518 -1.228 0.000 0.766 0.953 0.988 0.851 0.669 0.647 0.868 +0 0.764 -1.590 -0.880 0.980 1.331 1.045 -0.945 -0.873 2.173 0.752 -0.543 0.168 1.107 0.808 -1.859 0.593 0.000 1.970 0.712 1.229 0.000 0.899 0.882 1.093 0.962 1.084 0.834 0.769 +1 1.742 0.277 0.223 0.695 -0.414 0.697 0.145 -1.195 2.173 0.438 1.788 1.506 0.000 1.507 0.263 1.447 2.548 0.415 0.286 -0.175 0.000 0.718 0.813 0.984 1.121 0.884 0.908 0.800 +0 1.599 0.469 1.600 1.500 1.024 1.245 -0.991 -0.162 0.000 0.376 0.265 1.077 0.000 0.901 0.865 -1.343 0.000 1.473 1.328 -0.698 3.102 0.794 0.803 1.063 0.856 0.421 0.854 0.710 +1 0.479 0.335 -1.445 0.776 -0.200 1.766 -0.752 1.536 2.173 1.854 -1.284 0.046 0.000 0.827 -1.221 -0.516 0.000 0.426 -0.450 -0.337 3.102 0.921 0.556 0.993 1.177 0.917 1.252 1.016 +0 0.556 0.693 -0.262 0.859 -1.039 0.648 -0.099 0.397 1.087 0.768 0.528 -1.519 2.215 0.681 -0.226 1.238 0.000 0.565 1.612 -0.245 0.000 1.123 0.904 0.982 0.734 1.080 0.717 0.699 +1 1.189 0.199 1.027 1.276 -0.073 1.916 -2.051 -1.527 0.000 1.521 -1.546 -0.047 2.215 1.020 -0.127 0.676 0.000 0.971 -0.388 -0.357 0.000 0.899 1.145 1.427 0.907 0.900 1.097 0.896 +0 1.838 0.222 -1.685 0.875 0.168 1.226 1.229 -1.715 0.000 1.736 0.075 -0.486 1.107 2.821 0.352 0.609 0.000 0.838 -1.375 1.638 0.000 1.458 1.088 1.748 1.380 0.644 1.217 1.144 +0 0.519 -0.573 1.063 0.681 -0.118 1.176 -0.184 0.933 2.173 0.906 0.012 -1.271 0.000 0.772 -1.137 -0.204 2.548 0.674 -1.157 -1.505 0.000 0.860 0.970 0.984 0.774 1.211 0.845 0.730 +0 0.368 1.732 -0.137 0.180 -1.403 1.230 0.608 0.947 2.173 0.764 1.155 -0.401 0.000 0.742 -0.043 0.052 2.548 0.735 -0.190 -1.452 0.000 1.092 0.790 0.987 0.921 0.949 0.842 0.723 +1 0.645 -0.406 1.578 0.704 -0.925 0.334 1.429 1.032 0.000 0.507 0.883 -0.057 1.107 0.357 0.566 -0.846 2.548 0.384 1.708 0.589 0.000 0.272 0.457 0.985 0.659 0.302 0.666 0.746 +1 0.299 1.036 0.844 1.103 1.552 0.670 0.070 0.303 2.173 0.728 -0.431 -0.561 0.000 0.808 0.325 -1.497 2.548 0.776 -1.874 0.511 0.000 0.601 0.825 0.988 1.082 0.925 1.153 1.292 +1 0.893 0.582 0.554 0.810 -0.961 1.367 0.240 0.117 2.173 1.068 0.302 -1.215 0.000 1.075 -0.038 -1.728 0.000 0.589 2.255 -1.507 0.000 1.042 0.724 1.153 0.946 0.984 0.856 0.758 +0 0.886 1.074 -1.738 0.775 0.074 0.748 -0.333 -1.235 0.000 1.091 0.407 0.336 2.215 0.665 -0.549 0.019 0.000 1.192 -0.661 0.646 0.000 1.022 0.825 1.146 0.705 0.944 0.687 0.648 +1 0.731 0.517 0.217 0.579 -0.509 0.623 -0.173 1.437 1.087 0.825 0.953 -0.377 0.000 0.788 0.846 0.797 1.274 0.510 1.669 -1.256 0.000 0.752 1.128 0.986 0.766 0.695 0.743 0.741 +1 0.678 -0.339 -0.972 1.115 0.854 0.782 -1.001 0.187 1.087 0.965 -1.203 1.301 0.000 0.835 -1.442 -0.214 2.548 0.932 -0.377 -0.731 0.000 0.982 0.978 1.201 0.874 0.461 0.685 0.714 +0 1.500 -0.536 -1.566 0.720 -1.487 1.136 -0.465 0.180 2.173 0.950 -0.031 1.507 2.215 1.354 0.483 0.206 0.000 0.592 0.463 -0.838 0.000 0.798 1.005 1.009 1.421 1.462 1.033 0.934 +0 0.902 0.639 1.399 1.332 0.895 0.979 1.377 -0.038 0.000 0.442 1.340 -0.801 0.000 0.964 0.594 -1.364 2.548 0.520 -0.160 -1.325 3.102 0.887 1.005 0.998 0.751 0.236 0.672 0.765 +1 1.124 -0.769 0.169 2.204 0.918 1.460 0.826 -0.652 0.000 0.965 -0.687 1.496 2.215 0.768 0.476 -1.457 2.548 0.484 -1.064 -1.618 0.000 1.896 1.172 1.364 1.005 0.733 1.081 1.271 +0 0.723 -1.497 0.511 0.183 -0.608 0.662 -0.199 1.667 0.000 0.831 0.053 1.010 0.000 0.500 0.625 -0.501 2.548 1.943 -0.812 -0.571 3.102 0.903 0.845 0.995 0.755 0.702 0.842 0.745 +1 1.363 0.637 1.516 1.524 1.080 0.488 0.439 0.413 0.000 1.419 -0.550 -0.315 2.215 0.743 0.091 1.571 0.000 1.103 -0.456 -0.707 0.000 0.944 0.968 0.979 0.839 0.788 1.049 0.870 +1 1.281 0.748 0.223 1.008 -1.311 1.075 2.833 1.072 0.000 1.192 1.035 -0.743 2.215 2.160 1.099 1.640 0.000 1.804 0.184 -0.136 0.000 2.447 1.366 1.547 0.996 0.681 1.002 0.920 +1 0.656 -0.245 -0.850 0.939 0.341 0.849 -0.271 0.141 0.000 0.937 -0.312 -0.239 2.215 2.038 -0.005 -1.600 2.548 1.921 -1.223 1.394 0.000 0.919 0.900 0.987 0.999 1.402 0.877 0.764 +1 1.036 -1.069 0.030 0.269 0.794 1.341 -1.554 -0.982 2.173 0.737 -2.128 -1.204 0.000 0.907 -1.065 0.633 0.000 2.559 1.137 0.805 0.000 1.417 0.970 0.976 1.263 0.542 0.836 0.853 +0 1.263 0.043 1.639 0.826 1.042 1.106 0.908 0.219 0.000 0.888 0.707 -0.806 2.215 0.490 0.549 -1.378 2.548 0.383 0.235 0.685 0.000 0.517 0.940 0.983 0.563 0.348 0.636 0.725 +1 1.628 -0.066 -0.130 0.774 0.215 0.980 -0.840 -1.607 2.173 0.503 -0.532 -1.001 0.000 0.276 0.817 0.819 0.000 1.491 -0.783 0.996 3.102 0.726 0.789 0.992 1.091 0.915 1.120 0.875 +0 1.815 1.433 -0.602 1.080 -0.851 0.710 0.731 1.663 1.087 0.918 1.724 0.764 0.000 1.314 -1.060 -1.620 0.000 1.050 -0.168 0.467 0.000 0.634 1.042 0.979 1.322 0.832 1.087 0.994 +0 0.297 1.365 -1.078 2.092 0.743 1.495 -0.738 -0.300 2.173 1.860 0.856 1.099 2.215 3.441 0.490 -1.054 0.000 1.859 -0.000 1.648 0.000 1.964 2.179 1.089 2.371 3.225 2.074 1.838 +0 1.863 -0.297 -0.984 0.335 -1.412 1.184 -0.459 -1.686 2.173 0.896 0.782 -0.037 0.000 1.828 0.473 0.506 0.000 1.149 -0.392 0.332 3.102 0.965 0.798 0.986 0.903 1.196 1.176 1.122 +0 1.793 -0.871 0.894 0.473 -0.593 1.182 -0.577 -0.596 0.000 1.032 -1.359 -1.488 2.215 0.499 -0.641 0.324 2.548 0.431 -0.849 -1.711 0.000 0.950 1.067 1.242 0.622 0.808 0.711 0.773 +0 0.417 -1.630 0.356 1.734 0.368 0.681 0.726 -1.658 2.173 0.759 -1.555 -1.078 0.000 0.663 -0.676 1.257 2.548 0.795 -0.185 0.700 0.000 0.841 0.833 0.987 0.693 0.791 0.865 0.753 +1 1.282 1.329 1.090 1.073 -1.209 1.106 -1.065 0.462 0.000 2.159 0.690 -0.692 2.215 2.048 0.698 1.518 0.000 0.711 0.234 -0.280 0.000 1.026 0.662 1.426 1.449 0.625 0.932 0.902 +1 0.998 -1.171 -0.297 1.224 -0.069 0.836 0.684 -1.340 0.000 0.689 0.176 1.036 0.000 0.774 0.528 1.529 1.274 1.118 0.854 0.584 3.102 0.907 0.867 0.983 1.018 0.558 0.821 0.763 +0 1.354 1.946 -0.456 0.269 -0.761 0.632 0.107 1.412 0.000 0.671 0.858 0.565 0.000 0.509 1.237 0.808 2.548 0.715 -0.867 -1.224 3.102 1.104 0.969 0.976 0.667 0.826 0.806 0.823 +1 0.477 0.119 0.976 1.381 -0.164 1.233 -2.489 0.912 0.000 1.092 1.651 -1.529 2.215 1.048 0.513 -1.008 0.000 1.407 1.210 -0.676 3.102 0.739 0.788 0.985 0.789 0.791 0.852 0.710 +0 0.694 -0.613 -0.388 0.897 1.338 0.582 -0.205 0.641 1.087 0.338 -0.699 -0.819 0.000 0.535 0.535 1.512 0.000 0.646 0.391 0.241 0.000 0.725 0.705 1.093 0.790 0.923 0.660 0.572 +1 1.927 0.094 0.287 0.250 1.320 0.693 0.044 -1.167 2.173 0.938 -1.206 -0.698 2.215 0.638 -0.363 -1.326 0.000 0.930 0.096 0.989 0.000 0.922 0.937 0.986 1.227 0.951 0.970 0.850 +0 0.430 -1.025 -0.694 1.900 0.124 1.032 0.317 -1.373 2.173 1.586 0.392 1.470 0.000 1.265 -1.032 -0.047 2.548 0.384 0.812 1.246 0.000 1.267 1.044 0.987 0.646 1.740 1.158 1.084 +1 1.008 0.201 0.104 0.483 1.541 1.482 0.194 -0.518 0.000 0.780 0.702 1.196 0.000 1.413 -0.406 0.979 1.274 1.864 -0.867 0.093 0.000 0.972 0.895 0.985 0.549 0.568 0.577 0.565 +0 1.176 -0.273 -0.299 1.586 0.321 1.168 -1.057 1.220 1.087 0.961 -1.470 1.662 0.000 0.850 -0.057 -1.136 2.548 1.381 0.381 -0.669 0.000 0.962 0.785 1.004 1.408 1.240 1.044 0.996 +1 0.451 -0.403 0.147 1.334 1.275 0.754 -0.982 -1.440 2.173 0.745 -1.105 0.255 2.215 1.107 -0.186 0.515 0.000 1.818 -0.696 -0.705 0.000 0.699 0.768 0.988 0.821 1.105 0.715 0.668 +0 1.375 -1.651 0.499 0.766 1.182 1.126 -0.573 -1.363 0.000 1.089 -0.986 -0.093 2.215 0.819 -0.904 1.478 0.000 1.490 -0.436 -0.723 3.102 1.013 0.916 0.988 1.006 0.681 0.902 1.026 +1 1.006 0.844 -0.845 0.823 1.359 0.989 0.628 -0.235 0.000 1.134 1.412 1.026 2.215 1.093 0.212 1.569 2.548 0.602 1.529 -0.120 0.000 0.762 1.159 1.153 0.934 0.948 0.957 0.842 +1 0.992 1.151 0.070 0.079 1.568 0.392 2.324 0.818 0.000 0.686 2.007 -1.509 0.000 0.950 -0.179 -1.567 2.548 1.051 0.722 -0.501 3.102 0.964 0.932 0.987 0.822 0.757 0.900 0.816 +1 1.600 -1.336 1.445 1.207 0.954 0.717 2.694 -0.623 0.000 0.475 0.562 1.299 0.000 0.530 0.776 -0.501 2.548 0.964 -0.590 -1.086 0.000 0.913 0.639 0.995 0.514 0.523 0.724 0.685 +0 0.906 0.008 0.076 1.392 -0.544 0.669 1.253 1.731 0.000 0.709 0.812 0.746 2.215 0.707 2.044 1.239 0.000 0.389 -0.229 -0.808 1.551 0.826 0.842 0.990 0.507 0.543 0.615 0.778 +0 1.168 -0.133 0.060 0.829 -0.944 2.449 -0.266 -0.413 0.000 1.828 -0.653 1.186 2.215 1.751 -1.390 1.241 0.000 1.678 -0.597 1.621 1.551 0.897 1.059 1.071 0.953 0.606 0.925 0.940 +1 1.149 -1.038 0.695 0.522 0.480 0.840 -0.661 -0.713 2.173 0.929 -0.614 1.280 0.000 1.398 -0.217 -1.601 2.548 0.815 -1.760 -0.464 0.000 0.861 1.023 0.986 0.971 1.011 0.880 0.765 +0 3.015 -0.123 0.462 0.390 -0.040 1.515 -0.694 -1.217 2.173 1.193 -1.224 -1.132 0.000 1.543 -0.650 0.788 2.548 0.411 -1.188 1.119 0.000 0.822 1.134 0.974 1.957 1.852 1.395 1.224 +1 1.116 -1.186 0.426 0.812 -0.429 1.155 -0.647 -1.615 2.173 0.750 -1.116 -0.021 0.000 0.320 -1.049 -1.258 0.000 0.766 0.906 0.933 1.551 0.674 0.931 0.984 1.294 1.230 1.084 0.885 +0 0.322 -1.074 -0.104 2.046 0.885 0.545 -1.123 -1.026 0.000 0.815 0.836 -1.045 2.215 0.458 -0.126 0.364 0.000 0.601 -0.001 -0.372 3.102 0.973 1.062 0.989 0.902 0.457 1.261 1.018 +1 1.630 0.466 0.639 0.773 0.003 0.638 -0.176 -1.492 2.173 0.623 -0.594 -0.627 2.215 0.387 -1.345 -0.304 0.000 0.779 -0.148 1.350 0.000 0.736 0.671 0.996 1.004 0.683 0.887 0.760 +0 0.742 -0.919 -0.800 1.034 0.453 0.755 0.464 1.678 0.000 0.798 0.806 0.357 2.215 0.882 0.332 -0.548 2.548 0.843 1.322 1.254 0.000 0.850 0.967 1.097 0.824 0.682 0.771 0.898 +0 0.411 -0.878 1.694 0.878 0.560 1.854 -0.116 1.252 2.173 2.836 -0.119 -0.602 1.107 0.377 0.869 -0.560 0.000 0.373 0.677 0.847 0.000 0.396 0.940 0.987 1.317 3.357 1.551 1.127 +0 0.877 0.477 -0.761 0.667 -0.068 0.764 0.591 0.520 2.173 0.787 0.037 1.695 0.000 1.274 -0.420 1.161 0.000 1.022 1.698 -0.650 0.000 0.887 1.148 0.984 0.635 0.974 0.790 0.727 +1 2.099 -0.197 0.759 0.334 -0.532 1.009 0.375 -1.296 2.173 0.550 -0.003 -0.525 0.000 0.600 0.924 -1.613 0.000 0.549 0.295 -0.117 3.102 0.869 0.730 1.065 0.604 0.688 0.824 0.720 +0 1.083 -0.436 -1.120 0.322 -0.084 1.363 0.158 0.397 2.173 1.197 -2.649 -1.200 0.000 0.881 -0.504 0.811 0.000 0.876 0.094 1.524 3.102 0.897 0.941 0.980 0.694 0.982 0.907 0.764 +0 0.561 -0.838 0.270 1.010 1.245 0.708 -0.547 -0.632 2.173 0.908 0.914 -1.446 2.215 0.559 1.755 -0.058 0.000 0.547 0.326 0.716 0.000 0.648 1.086 0.990 0.950 1.246 0.868 0.808 +0 1.070 0.384 -1.126 0.950 1.650 1.099 -0.269 0.000 0.000 1.761 0.152 1.443 2.215 1.164 0.676 -0.056 0.000 2.042 -0.394 0.279 3.102 1.082 0.847 0.989 0.853 1.581 1.216 1.095 +0 0.277 1.125 -0.627 1.124 -1.666 0.597 -0.437 -0.063 0.000 0.556 -1.270 -0.779 2.215 1.308 -0.297 1.011 2.548 0.401 -1.610 -0.392 0.000 0.649 0.877 0.989 0.734 1.016 0.688 0.669 +1 3.325 0.647 -0.479 0.178 -1.475 0.663 -0.132 -1.711 1.087 1.396 1.928 0.600 0.000 1.205 1.419 1.595 0.000 0.627 0.267 0.366 3.102 1.177 0.850 0.989 1.231 0.670 0.979 1.070 +0 1.464 -0.890 -1.704 0.725 -1.664 0.745 0.239 0.415 0.000 0.646 -0.645 -0.238 1.107 0.952 -0.032 1.680 1.274 1.309 2.134 -0.168 0.000 0.783 0.837 0.984 0.975 0.865 0.774 0.850 +1 1.620 1.667 1.668 0.546 1.172 1.100 1.327 -0.813 2.173 0.737 1.029 -0.226 1.107 0.657 1.124 0.583 0.000 0.874 1.791 1.289 0.000 1.038 1.053 0.995 1.078 0.694 0.967 0.843 +1 0.696 -0.023 0.604 0.521 0.918 0.955 0.286 1.673 2.173 1.260 0.550 -0.292 0.000 1.115 -0.486 1.602 0.000 0.563 0.580 -0.681 1.551 0.861 0.516 0.991 0.939 0.679 0.876 0.799 +0 0.543 0.166 1.541 1.835 1.009 1.304 -0.571 -0.881 1.087 0.721 -1.364 0.960 0.000 0.624 -0.879 0.154 0.000 0.548 0.689 -0.940 0.000 0.850 0.912 0.979 0.764 0.271 1.186 0.954 +0 0.705 -0.531 -0.945 0.842 1.367 0.751 0.679 0.457 1.087 1.457 0.084 -1.244 1.107 0.555 -2.395 0.958 0.000 0.918 -0.987 0.380 0.000 0.733 1.641 0.986 0.899 1.608 1.377 1.098 +1 1.067 -1.176 0.600 0.799 0.368 0.940 -0.190 -1.100 2.173 0.343 -1.237 -1.526 2.215 0.607 -1.568 1.223 0.000 0.908 -0.655 -0.513 0.000 0.911 1.002 1.000 0.721 0.568 1.019 0.827 +1 0.952 0.081 -1.263 0.400 0.930 0.826 -0.254 -0.798 2.173 0.492 0.066 1.025 2.215 0.447 -0.061 0.082 0.000 0.443 -1.901 0.660 0.000 0.896 1.114 0.991 0.622 0.947 0.747 0.667 +0 0.626 0.103 -0.067 1.652 0.180 0.712 0.380 1.588 2.173 1.178 -1.147 -0.610 2.215 1.749 -0.348 1.102 0.000 1.459 -0.092 -1.285 0.000 1.493 1.023 0.984 1.822 1.698 1.389 1.269 +0 0.458 -1.626 -0.020 1.310 -1.232 0.452 -1.032 -0.359 0.000 1.214 -0.914 0.847 0.000 0.524 0.697 -1.458 2.548 0.452 -0.950 1.135 1.551 1.394 1.177 0.987 0.597 0.490 0.760 0.812 +0 2.525 -0.638 -0.603 2.642 -0.361 1.260 0.677 1.301 0.000 1.070 0.096 1.077 2.215 0.582 -1.456 -1.685 0.000 0.807 -1.136 0.884 3.102 1.149 0.957 0.995 1.069 0.686 1.229 1.567 +1 0.738 -0.398 -0.901 0.339 0.768 1.202 0.031 0.123 0.000 1.429 1.983 1.680 0.000 1.323 0.601 1.419 2.548 1.172 -1.709 -0.717 0.000 0.673 0.647 0.989 1.078 1.084 1.082 0.879 +0 0.732 2.051 0.179 1.143 -0.655 1.575 0.685 1.372 2.173 0.926 1.219 -0.547 2.215 0.671 0.654 -0.165 0.000 0.449 2.015 0.884 0.000 0.759 1.170 0.989 0.866 1.826 1.456 1.109 +1 2.046 -0.362 -0.017 0.589 -0.281 0.956 -0.919 1.507 2.173 0.700 -0.297 -1.369 0.000 0.508 -0.849 1.017 2.548 0.461 0.789 -1.025 0.000 0.544 0.874 0.972 0.712 0.372 0.870 0.799 +1 0.406 1.463 0.023 0.905 -1.376 1.043 0.030 -0.602 0.000 1.706 0.222 0.974 2.215 1.119 -0.386 -0.379 0.000 1.171 -1.198 1.581 0.000 0.591 0.886 0.989 1.771 0.241 1.101 1.318 +1 0.680 -0.137 -1.609 1.010 0.605 1.195 0.374 0.782 1.087 2.291 1.407 -1.129 0.000 2.011 0.478 0.368 2.548 1.130 1.897 0.162 0.000 0.906 1.158 1.045 0.810 0.719 0.953 0.905 +1 1.266 -0.645 -0.640 0.819 -0.121 1.370 0.433 1.618 2.173 1.535 -0.321 0.850 0.000 0.942 0.954 -0.201 0.000 1.441 -0.170 -0.967 3.102 1.479 1.242 0.980 1.465 1.182 1.053 1.025 +0 0.363 1.846 1.737 1.889 -0.546 0.396 -0.046 -1.673 0.000 1.208 0.844 1.127 2.215 1.742 0.078 0.722 2.548 0.757 1.632 -1.223 0.000 1.040 0.968 1.014 1.645 0.828 1.236 1.051 +0 0.512 1.422 1.695 0.726 -0.405 0.880 -0.371 1.688 2.173 0.720 0.995 -1.649 2.215 1.423 0.291 0.047 0.000 0.652 1.383 0.387 0.000 0.831 1.004 0.991 1.757 0.902 1.144 1.056 +0 2.565 0.022 -0.718 0.832 1.646 1.067 -1.288 0.988 2.173 0.446 0.136 0.288 0.000 0.329 0.948 -0.436 2.548 0.564 -0.618 0.491 0.000 0.321 0.779 1.715 0.850 1.289 1.228 0.936 +1 0.981 -0.080 0.206 1.335 0.277 1.262 -0.676 1.689 2.173 0.403 1.840 1.396 0.000 1.275 -0.531 0.909 2.548 1.226 0.732 -0.876 0.000 0.947 1.313 0.985 1.447 1.026 1.186 1.199 +1 0.832 0.139 -0.783 0.657 0.048 0.903 -0.363 0.908 2.173 0.986 -0.582 -1.191 0.000 0.794 0.694 1.042 0.000 0.927 -0.027 -0.347 3.102 1.580 1.014 0.995 0.965 0.891 0.845 0.773 +0 0.504 1.073 -1.421 2.067 -1.208 0.758 1.518 0.460 0.000 0.835 2.517 -0.793 0.000 0.740 2.499 0.540 0.000 1.759 1.174 -1.579 3.102 0.914 1.009 0.982 0.723 0.782 0.726 0.883 +0 1.076 0.255 -0.091 1.516 -0.191 0.742 0.085 0.825 0.000 0.873 0.922 1.419 0.000 1.337 0.860 -1.166 2.548 1.228 0.161 -1.259 3.102 1.156 1.018 0.997 1.287 0.386 0.898 1.005 +1 0.640 0.679 -0.351 1.063 -0.729 1.911 0.541 0.735 0.000 1.388 -1.230 1.741 0.000 0.997 -0.206 -0.626 2.548 0.752 -0.199 1.692 0.000 0.716 1.034 0.982 1.277 0.304 0.927 1.267 +0 1.413 -0.483 -0.117 1.367 -0.663 0.881 1.634 -1.587 0.000 0.928 -1.824 0.177 0.000 1.188 0.559 1.394 2.548 0.902 -0.278 1.034 3.102 0.891 0.933 0.987 0.841 0.464 0.842 1.056 +0 0.936 -1.635 -0.006 0.289 -1.504 0.459 0.149 1.122 0.000 1.021 0.237 -1.392 2.215 0.936 0.080 0.074 2.548 0.898 1.384 1.429 0.000 0.856 0.900 0.994 0.744 1.010 0.805 0.871 +0 0.938 0.448 -1.670 1.957 -1.014 0.831 0.532 0.638 0.000 0.971 0.716 -0.084 2.215 0.547 -0.939 1.282 2.548 0.451 1.489 1.132 0.000 0.741 0.875 1.048 1.065 1.067 0.879 0.873 +0 0.868 1.262 1.383 1.460 -1.298 1.157 0.631 -0.349 0.000 2.060 0.358 1.431 2.215 1.436 0.105 -0.086 0.000 1.564 1.619 0.711 0.000 0.819 0.865 1.036 1.139 1.664 1.346 1.265 +0 0.465 2.136 0.846 1.772 -1.572 0.352 -1.001 -0.121 0.000 0.722 -0.167 0.390 0.000 1.193 1.030 -0.003 2.548 1.279 0.563 1.270 3.102 0.964 1.296 1.033 1.130 0.887 1.170 1.652 +1 1.215 1.156 -0.810 1.979 -0.258 2.467 0.741 1.555 0.000 0.920 1.055 -0.222 2.215 1.158 0.215 0.351 2.548 0.765 0.328 -1.473 0.000 0.919 1.526 1.028 0.565 0.723 1.237 1.269 +1 1.640 0.997 -0.587 0.783 -1.455 0.909 -2.309 0.859 0.000 0.809 0.943 -1.021 2.215 0.769 -0.010 0.137 0.000 1.659 -0.102 1.383 1.551 0.669 0.813 1.105 0.588 1.054 0.795 0.733 +1 0.334 -2.183 1.004 1.495 -0.587 1.260 -0.677 -1.713 2.173 0.806 -0.326 -0.196 0.000 0.836 -1.548 0.137 0.000 1.873 -0.811 1.349 3.102 0.855 0.933 0.989 1.363 0.650 0.996 0.910 +1 0.568 1.234 1.207 0.099 1.684 0.716 1.007 -0.302 0.000 0.515 1.950 -0.749 0.000 1.053 -0.004 -1.641 2.548 1.079 0.178 0.631 3.102 0.894 0.942 0.986 0.584 0.729 0.657 0.605 +1 0.900 -0.113 1.540 1.502 -1.635 0.334 0.057 -0.812 2.173 1.922 -0.399 0.050 2.215 0.748 0.481 0.860 0.000 0.828 -0.250 -1.424 0.000 0.851 1.153 0.979 0.772 0.873 1.051 0.887 +0 0.738 -1.292 0.131 0.252 1.197 1.694 -0.723 -1.620 0.000 2.302 -0.354 0.188 2.215 0.690 0.320 -0.807 2.548 0.574 0.118 -1.718 0.000 0.689 0.932 0.991 0.833 1.155 1.352 1.067 +1 0.578 -0.954 1.579 0.608 1.694 0.517 -1.020 1.138 0.000 0.743 -0.663 0.306 1.107 1.414 -0.117 -0.320 2.548 1.086 0.979 -1.046 0.000 1.070 1.590 0.988 0.863 0.658 1.270 1.083 +1 1.418 -0.267 0.490 1.853 1.035 1.269 -2.623 -0.746 0.000 0.918 -0.958 -1.060 2.215 0.869 0.437 -1.738 2.548 0.548 -0.339 1.478 0.000 0.603 0.890 1.059 0.929 0.934 0.962 0.761 +0 0.782 -0.290 1.366 0.585 -1.551 1.083 -0.906 -0.218 2.173 0.634 -2.411 -1.033 0.000 0.562 -0.521 0.808 0.000 0.991 0.621 1.478 3.102 0.698 0.956 0.980 1.108 1.495 0.957 0.815 +0 2.899 -1.209 -0.507 0.512 -1.417 1.270 1.338 0.653 0.000 0.689 -1.206 0.437 2.215 0.950 -0.958 -1.519 2.548 0.881 -1.489 -1.025 0.000 0.859 0.934 1.233 0.916 0.848 0.784 0.792 +0 0.706 1.759 -1.685 2.058 -0.103 0.582 -0.133 0.260 1.087 0.816 1.162 1.586 2.215 0.761 -0.286 -0.915 0.000 0.539 -0.641 0.848 0.000 0.724 0.936 1.652 1.464 1.189 1.111 1.037 +1 0.455 -0.118 1.525 0.992 -0.173 2.415 -1.635 -1.567 0.000 2.366 0.737 -0.047 2.215 0.962 -0.767 0.920 1.274 1.358 -0.092 0.212 0.000 0.773 0.937 0.984 0.676 1.881 1.068 0.848 +1 0.761 2.184 0.117 0.847 1.177 1.023 0.632 -1.151 2.173 0.417 1.107 0.809 0.000 1.086 1.550 -0.396 0.000 0.501 0.605 1.215 0.000 0.908 1.003 0.989 0.733 0.715 0.842 0.733 +1 0.339 -1.498 -1.719 1.190 -1.072 0.676 0.340 1.365 2.173 1.249 -0.550 -0.205 0.000 0.575 -1.102 1.099 2.548 0.483 -0.844 0.702 0.000 0.876 1.134 0.991 0.733 0.691 0.741 0.715 +1 0.710 -0.820 -1.476 0.226 -0.731 0.994 -0.748 1.439 0.000 1.092 -0.596 -0.179 2.215 1.027 0.141 0.251 0.000 1.124 0.642 -0.783 3.102 0.911 1.152 0.975 0.862 0.911 0.990 0.823 +0 2.123 -0.589 1.587 0.710 0.865 0.537 1.648 0.089 0.000 2.134 0.627 -0.589 2.215 1.338 0.483 1.256 2.548 0.642 0.314 -0.456 0.000 0.840 1.110 1.030 1.969 1.791 1.398 1.430 +0 0.410 -1.787 -1.492 1.128 -0.651 1.846 -0.244 -1.352 2.173 1.511 0.448 0.332 0.000 1.448 0.441 0.745 1.274 1.647 0.524 -0.229 0.000 1.010 0.905 0.980 1.018 2.078 1.502 1.274 +0 1.178 -1.318 0.280 1.194 1.625 0.784 -0.940 -1.459 2.173 0.861 0.316 0.275 2.215 0.398 -0.401 0.263 0.000 0.392 0.009 -0.909 0.000 0.684 0.887 1.538 1.057 1.462 1.055 0.831 +1 0.808 -0.482 -0.967 1.203 0.680 0.313 0.680 -0.981 2.173 0.477 -1.173 -1.327 0.000 1.749 1.035 0.405 2.548 0.862 0.109 1.156 0.000 0.922 0.939 1.361 0.856 0.899 0.922 0.807 +0 1.338 -0.377 -0.606 1.093 1.637 0.497 -1.627 0.871 0.000 0.772 -0.804 -0.216 0.000 0.757 0.366 1.327 2.548 1.050 -0.864 1.723 1.551 0.871 1.056 1.508 0.843 0.584 0.707 0.742 +0 0.707 -1.349 -0.309 0.760 1.600 0.746 -0.703 0.242 2.173 0.707 -0.558 -1.277 2.215 0.809 -1.193 -0.812 0.000 1.140 -1.060 1.172 0.000 1.035 0.952 1.004 0.683 1.049 0.718 0.648 +0 0.592 1.128 0.099 0.541 -1.414 1.166 0.440 -0.243 1.087 1.274 0.036 -1.414 2.215 1.351 -0.617 1.111 0.000 0.620 1.373 1.153 0.000 0.937 0.989 0.989 0.793 1.603 0.939 0.776 +1 0.563 -1.613 -0.890 0.557 -0.352 0.977 -0.709 1.416 2.173 0.388 -0.750 -0.805 0.000 1.119 -1.150 0.325 0.000 0.512 2.171 1.742 0.000 0.896 1.085 0.984 0.501 0.670 0.654 0.642 +0 0.392 -0.908 0.968 2.635 0.190 1.103 -1.444 -0.312 1.087 1.186 -0.245 -1.499 0.000 1.812 -0.799 -1.680 2.548 1.245 0.283 1.369 0.000 0.961 0.862 0.983 1.174 1.741 1.249 1.228 +1 0.957 -0.405 -1.194 1.028 1.340 0.887 0.299 -0.900 2.173 1.188 -1.157 0.832 2.215 0.985 0.244 0.722 0.000 0.660 1.493 -0.568 0.000 0.802 1.036 1.039 0.984 1.951 1.078 0.944 +0 0.484 -0.636 1.620 1.631 0.726 0.904 0.636 1.573 2.173 0.746 -2.595 -0.569 0.000 1.148 1.456 -0.078 0.000 1.377 0.438 0.309 1.551 0.964 0.967 0.990 1.455 1.075 1.091 1.414 +0 0.900 -0.421 -1.539 0.720 -0.862 1.054 2.103 0.656 0.000 0.563 2.077 -0.844 0.000 0.357 1.488 1.013 2.548 0.960 1.029 -1.329 3.102 1.598 0.879 0.989 1.056 0.394 0.835 1.449 +1 1.086 0.012 0.124 0.992 -1.318 1.535 0.635 0.706 2.173 0.860 0.774 1.088 1.107 0.642 -1.270 0.642 0.000 0.566 -1.991 0.098 0.000 0.979 1.245 1.385 1.328 0.585 1.136 1.024 +1 1.738 -0.787 -0.346 0.945 -0.002 0.683 -0.268 1.347 2.173 0.597 0.444 -0.026 0.000 0.903 0.478 -1.280 2.548 1.535 -2.092 1.493 0.000 0.839 0.855 0.982 1.229 0.792 1.040 0.932 +0 0.383 0.794 -1.539 1.899 -0.420 1.101 -0.158 1.508 0.000 0.606 -0.393 0.397 2.215 0.741 -0.347 -0.416 1.274 0.646 -1.231 1.563 0.000 0.915 0.999 1.000 0.895 0.477 0.717 0.892 +1 1.079 -0.215 -1.190 0.261 -0.099 1.505 0.381 -1.685 2.173 1.596 -1.609 0.314 0.000 1.323 0.139 0.301 2.548 0.673 0.968 -0.347 0.000 1.188 1.498 0.983 1.155 1.728 1.806 1.386 +1 0.461 2.079 0.765 0.840 0.293 1.307 -0.337 -1.111 2.173 0.311 2.747 -1.356 0.000 0.570 -0.326 0.769 0.000 1.037 0.175 1.046 3.102 0.616 1.113 0.974 0.632 1.197 1.015 0.810 +1 1.757 1.046 0.989 0.222 -1.463 1.163 1.104 -1.233 2.173 2.186 2.139 -0.133 0.000 1.404 0.697 1.556 2.548 1.315 1.352 0.566 0.000 0.783 1.053 0.985 0.715 0.976 0.837 0.738 +0 3.054 -0.487 1.606 0.303 0.819 1.005 0.064 -0.103 1.087 0.588 0.292 0.550 2.215 0.793 -1.514 -0.354 0.000 0.494 1.755 0.055 0.000 1.306 1.151 0.989 1.536 0.641 1.042 1.022 +0 0.733 -1.257 1.157 0.876 -0.994 2.305 -0.557 0.032 2.173 3.278 -1.087 -1.639 2.215 1.252 0.275 1.289 0.000 1.186 -0.221 0.611 0.000 0.863 1.689 1.036 0.999 4.194 2.074 1.573 +1 0.395 -0.893 0.025 1.478 -1.672 1.244 0.377 -0.171 0.000 1.481 0.435 1.009 2.215 0.640 0.378 -0.837 0.000 0.756 0.403 -1.465 3.102 0.908 0.824 1.058 1.236 0.756 0.926 0.984 +1 2.147 -0.242 -1.011 0.602 1.242 0.597 -0.886 0.527 0.000 0.446 0.239 0.757 0.000 0.926 -0.499 1.570 0.000 1.046 -0.453 0.008 1.551 0.772 0.721 1.411 0.745 0.305 0.601 0.619 +0 1.392 -0.645 -1.725 0.301 -1.152 0.547 -1.506 -0.507 0.000 0.816 -0.900 -0.032 1.107 0.846 -0.304 0.616 2.548 0.701 0.145 1.368 0.000 1.300 0.937 0.987 0.926 0.554 0.712 0.701 +0 0.613 -0.687 -1.133 0.798 -1.659 1.495 -0.564 -0.020 0.000 1.370 0.836 1.578 2.215 0.472 0.187 0.081 0.000 1.103 -0.052 1.486 0.000 0.767 0.819 0.994 0.630 1.755 1.323 1.031 +1 0.835 -1.163 0.925 1.230 -0.218 1.763 -1.054 -0.312 1.087 1.918 -2.133 1.640 0.000 1.020 -0.396 1.027 0.000 1.460 -0.882 -1.391 3.102 2.421 1.522 1.203 1.007 1.403 1.643 1.319 +0 0.867 1.098 -1.554 0.456 1.190 2.115 1.008 1.445 2.173 1.933 1.174 -0.506 0.000 1.753 1.589 0.033 0.000 0.995 0.568 -0.035 1.551 0.614 0.587 0.981 0.945 1.515 1.177 1.003 +1 0.555 0.756 -1.112 1.398 0.877 1.097 0.021 1.223 1.087 1.808 -2.346 -0.315 0.000 1.025 -0.373 1.513 0.000 1.541 0.011 -1.006 3.102 0.845 0.804 1.190 0.948 1.246 0.852 0.761 +0 1.122 0.884 0.015 0.375 -0.060 1.155 1.394 0.720 2.173 0.936 0.769 -1.413 0.000 1.650 1.044 -0.764 2.548 1.480 1.635 -1.620 0.000 0.867 0.940 0.985 0.890 1.689 0.952 0.842 +0 1.274 -0.528 -1.570 0.677 -0.341 1.296 0.973 0.824 0.000 1.476 -0.289 -0.897 0.000 0.950 -0.331 -0.129 1.274 1.131 -0.535 0.358 3.102 2.300 1.507 1.152 0.793 0.355 0.979 0.850 +0 0.778 0.133 0.347 1.030 -0.259 1.000 -1.329 -1.680 2.173 0.274 -0.148 1.141 0.000 0.649 -1.433 0.150 2.548 0.381 -1.992 -0.756 0.000 0.711 0.714 0.984 0.614 1.007 0.867 0.694 +1 1.756 0.642 0.282 1.108 0.077 1.110 1.043 1.646 2.173 0.965 0.805 -1.292 0.000 0.404 1.559 0.620 0.000 0.448 0.911 -0.113 1.551 1.043 0.889 0.976 0.516 0.746 0.977 0.884 +0 0.685 -0.181 -0.498 2.083 0.249 0.892 -1.825 1.465 0.000 0.526 0.498 -1.689 2.215 0.484 -0.721 -0.531 2.548 0.559 -1.720 -0.975 0.000 0.880 0.805 1.032 0.958 0.596 0.816 0.973 +1 1.583 -0.427 -1.379 0.255 0.558 0.732 0.715 0.737 2.173 0.654 -0.465 -0.436 2.215 1.041 0.240 0.153 0.000 0.995 2.069 1.568 0.000 1.861 1.263 0.990 0.715 1.099 1.034 1.006 +1 0.921 0.663 -0.987 0.541 1.422 0.859 -0.150 -0.129 0.000 1.011 -0.112 -1.483 1.107 0.626 0.998 1.432 2.548 0.897 -0.160 0.913 0.000 0.927 1.043 0.992 0.567 0.685 0.742 0.731 +1 0.346 0.766 1.130 0.719 -1.001 0.842 -0.252 -0.241 1.087 0.628 -1.295 -0.133 2.215 1.109 1.294 1.690 0.000 1.949 -0.670 1.524 0.000 2.214 1.851 0.985 1.481 0.614 1.366 1.410 +1 0.911 -1.375 0.636 0.662 -0.203 0.741 0.866 1.284 2.173 0.969 -0.435 1.735 2.215 0.450 -1.083 -1.728 0.000 0.560 1.238 1.580 0.000 0.987 0.836 0.990 0.926 1.013 0.951 0.810 +1 0.775 -0.696 -0.158 1.488 0.447 1.236 -0.086 -1.613 2.173 0.752 0.954 0.140 0.000 0.594 -1.402 1.522 0.000 0.743 -0.177 -0.729 3.102 0.766 1.215 0.992 0.656 0.729 0.902 0.808 +1 0.920 -0.744 -1.567 2.355 0.847 1.277 -1.250 -0.488 1.087 0.237 -0.685 0.772 2.215 0.684 -0.866 -1.190 0.000 0.529 0.440 -1.452 0.000 0.559 0.967 1.678 0.751 0.769 1.046 0.867 +0 0.791 -2.041 -0.855 0.859 0.184 0.486 0.226 0.775 0.000 0.472 0.015 1.604 2.215 0.363 -1.713 -0.586 0.000 1.137 -0.801 -1.345 3.102 1.189 0.898 0.985 1.173 0.455 0.831 0.857 +1 0.578 -0.271 1.443 0.983 -1.189 0.888 -0.289 -0.474 2.173 0.930 -0.741 0.209 2.215 0.674 -1.033 -0.823 0.000 0.871 -0.865 1.309 0.000 0.795 0.879 0.996 0.922 0.835 0.801 0.691 +0 1.026 0.621 -0.093 0.134 0.998 1.511 1.427 0.242 2.173 2.045 -0.858 -1.419 0.000 1.119 0.210 -1.697 2.548 1.171 0.816 0.910 0.000 2.774 1.613 0.989 1.209 1.904 2.067 1.557 +0 2.296 0.265 0.258 0.405 0.910 0.913 1.898 -1.350 0.000 1.077 -0.054 0.971 2.215 1.212 -0.770 -0.610 1.274 1.674 -0.488 -1.283 0.000 0.985 1.043 0.981 1.162 1.298 0.934 0.933 +0 2.664 -1.238 0.932 0.893 0.932 1.532 -0.344 -1.079 2.173 1.037 0.571 -0.802 0.000 1.471 -0.587 0.380 2.548 0.509 1.564 0.976 0.000 1.149 1.402 0.970 2.233 1.832 1.562 1.631 +0 0.343 0.937 -0.988 1.529 0.839 1.088 0.155 0.739 0.000 2.426 -0.795 -0.951 2.215 0.580 0.481 0.237 2.548 0.666 -0.807 1.442 0.000 1.089 0.746 1.001 2.044 1.435 1.323 1.208 +1 0.505 -0.756 0.069 1.239 -1.562 1.317 0.915 1.646 1.087 1.061 0.698 0.283 0.000 1.230 1.661 0.344 0.000 1.784 -0.731 -0.708 3.102 1.023 1.712 1.090 0.751 2.166 1.586 1.421 +1 1.071 0.359 -0.694 0.620 1.471 1.074 0.065 0.747 0.000 1.055 0.460 -1.503 1.107 0.718 0.789 0.346 0.000 1.323 -0.336 -0.620 3.102 0.857 1.100 1.048 0.696 0.902 0.938 0.812 +0 0.296 2.274 0.008 1.767 -0.385 1.472 -0.120 1.196 0.000 1.572 0.949 -0.536 0.000 1.391 -0.754 1.251 2.548 0.671 -1.121 -1.356 1.551 3.729 2.220 0.977 1.567 0.560 1.494 1.390 +1 0.791 -0.505 1.396 0.589 -0.218 1.552 0.409 -1.516 0.000 1.655 1.044 0.327 1.107 1.180 0.716 -0.283 2.548 0.698 0.672 1.249 0.000 1.011 1.287 0.987 1.418 0.805 1.229 1.138 +0 0.554 -1.033 1.216 1.045 -0.324 0.514 -2.368 -0.646 0.000 0.567 -1.952 -1.483 0.000 0.939 -1.377 0.752 0.000 0.547 -0.986 -0.716 0.000 0.815 0.648 1.037 1.074 0.539 1.134 0.928 +0 0.780 -0.486 -0.127 1.335 -1.237 0.705 -2.426 1.632 0.000 1.222 -1.634 0.880 0.000 0.982 0.605 -0.136 2.548 1.178 -0.858 -0.572 3.102 0.911 1.014 1.189 0.913 0.845 0.997 0.920 +0 0.804 -1.187 -0.133 0.717 -1.596 0.680 0.997 0.836 0.000 0.721 0.715 -1.127 2.215 0.439 -2.427 -0.354 0.000 0.497 -0.395 1.732 3.102 3.126 1.648 1.018 1.022 0.452 1.153 1.008 +1 0.804 -1.537 -1.094 0.894 -1.336 1.271 -0.130 0.251 2.173 1.560 -0.479 1.151 1.107 1.027 1.710 -0.990 0.000 1.385 -0.239 -1.098 0.000 1.181 1.091 0.982 1.367 1.548 1.156 0.977 +0 0.571 -1.364 0.908 1.134 -1.599 0.978 -0.356 -0.280 0.000 0.797 0.726 1.449 2.215 0.686 0.304 -0.823 0.000 0.791 -0.879 0.676 3.102 0.866 0.848 0.991 0.916 0.869 0.840 0.815 +1 1.095 0.228 1.138 0.693 0.882 0.866 0.787 0.974 0.000 1.320 -0.296 -0.058 2.215 0.522 0.071 1.736 0.000 1.814 0.944 -1.346 0.000 0.885 1.107 0.980 1.035 0.955 0.968 0.904 +0 1.800 -0.128 0.412 1.130 -0.204 1.293 0.082 -1.152 2.173 0.716 -0.169 1.678 1.107 1.309 -1.095 1.447 0.000 1.752 0.911 0.368 0.000 2.741 1.622 1.040 1.481 0.809 1.287 1.210 +1 1.754 -1.471 -0.990 0.450 -0.065 0.648 -1.830 -1.286 0.000 0.846 -1.294 1.440 0.000 1.019 -1.149 0.374 2.548 1.030 0.087 0.553 3.102 0.910 0.796 0.988 0.852 0.591 0.755 0.700 +1 0.495 -0.259 1.553 1.594 -1.062 0.942 1.332 0.782 0.000 0.709 0.618 0.080 2.215 1.019 2.101 -1.110 0.000 0.963 1.915 -0.054 0.000 0.890 1.048 0.985 1.184 0.353 0.918 1.259 +0 0.366 1.094 0.155 1.786 1.074 0.651 -0.847 0.132 0.000 0.751 -0.843 -0.678 0.000 0.884 -0.030 -1.104 2.548 1.092 0.024 -1.625 3.102 0.991 0.985 0.985 1.268 0.340 0.952 1.286 +0 0.576 -0.147 -1.482 1.026 1.318 1.050 -0.121 -0.394 1.087 1.080 1.126 0.996 2.215 0.517 -0.982 -0.423 0.000 0.482 -0.856 -1.663 0.000 0.893 0.963 0.990 1.481 1.836 1.297 1.015 +1 0.850 -0.303 0.739 0.987 1.683 0.685 0.444 -0.743 2.173 0.746 1.209 0.123 0.000 0.840 1.244 1.722 2.548 0.484 0.722 -0.455 0.000 0.417 0.704 0.989 0.940 0.876 0.754 0.708 +0 1.653 -0.613 0.452 1.092 -0.207 1.325 1.167 -1.041 2.173 0.684 1.605 -1.639 0.000 0.881 -0.793 1.091 1.274 0.594 0.626 0.735 0.000 0.799 0.959 1.040 0.790 2.067 1.502 1.285 +0 0.940 -0.689 -0.975 0.522 -0.994 0.425 -0.501 -1.319 0.000 0.561 -0.593 1.120 2.215 1.121 0.381 0.606 0.000 0.860 -0.456 0.441 3.102 1.361 0.884 0.992 0.773 0.361 0.627 0.745 +1 0.810 -0.512 0.267 0.709 -0.885 1.008 -1.029 1.296 0.000 1.121 -0.239 -0.403 2.215 1.049 -1.868 1.028 0.000 1.445 -1.261 -1.079 1.551 1.074 1.149 0.984 0.631 1.019 1.141 0.999 +1 0.581 -0.941 -0.052 0.410 -1.488 1.048 -0.450 0.642 2.173 0.670 -0.428 -0.410 1.107 1.470 -1.003 1.200 0.000 0.762 -1.037 -0.718 0.000 0.890 1.050 0.993 0.607 1.002 0.737 0.652 +1 1.018 0.768 -0.770 0.363 0.563 1.141 -0.452 0.186 2.173 0.904 -1.695 -1.573 0.000 1.053 0.768 -1.386 0.000 1.029 -0.266 1.156 1.551 0.708 0.703 0.984 1.302 0.883 0.935 0.849 +1 0.546 -1.655 1.016 0.470 0.746 0.601 0.151 0.846 0.000 0.544 0.281 -0.122 0.000 1.576 -1.311 -0.884 0.000 0.554 -0.892 -1.176 3.102 0.934 0.778 0.981 0.481 0.216 0.469 0.492 +0 1.141 0.248 0.323 1.058 1.262 0.792 1.243 -0.565 0.000 0.600 1.167 -1.736 1.107 0.765 0.869 -1.354 1.274 0.575 2.324 -1.395 0.000 0.924 0.788 1.140 0.850 0.258 0.648 0.684 +0 0.596 0.780 -0.925 0.754 -0.272 0.785 1.160 -1.031 0.000 1.090 0.215 0.867 0.000 0.589 1.118 0.448 0.000 0.439 1.106 -1.215 3.102 0.920 0.665 0.999 0.508 0.254 0.389 0.501 +1 0.760 0.598 0.270 1.110 1.325 0.659 -0.381 0.015 0.000 0.794 0.216 0.595 0.000 1.157 0.165 -0.944 2.548 0.823 0.907 -1.683 3.102 0.900 0.974 1.036 0.895 0.578 0.756 0.720 +1 1.948 -1.594 -1.045 0.209 -0.670 1.400 -1.326 0.921 2.173 0.588 -1.145 -0.013 0.000 0.607 -0.338 0.156 2.548 0.557 1.673 -1.255 0.000 1.883 1.091 0.976 1.475 0.932 1.237 1.207 +0 0.639 1.236 -1.405 0.314 1.641 1.320 0.143 1.517 0.000 1.144 1.126 -0.645 2.215 0.563 1.903 -0.501 0.000 1.367 0.609 0.203 0.000 0.912 0.788 0.978 0.647 0.796 0.665 0.661 +0 0.652 -0.443 1.706 1.111 -0.951 1.586 -0.937 1.544 0.000 2.081 -0.640 -0.664 2.215 2.181 -0.707 0.771 2.548 1.605 -2.342 0.353 0.000 3.382 2.334 0.989 0.923 2.181 1.996 1.531 +1 0.503 -0.808 -1.388 2.097 1.471 1.049 -0.631 -0.399 2.173 0.423 -1.657 -1.068 0.000 0.647 -0.387 0.729 0.000 1.277 -0.008 0.476 1.551 0.971 0.955 0.994 1.168 0.953 1.140 0.916 +1 0.382 -0.107 1.441 1.569 -0.289 1.317 0.532 -0.903 2.173 1.208 0.643 1.056 1.107 0.597 -0.585 0.987 0.000 0.535 -1.374 0.789 0.000 0.346 0.882 1.072 0.996 1.824 1.113 0.960 +0 0.761 0.033 0.694 0.299 0.556 0.486 -0.329 -1.572 2.173 0.828 0.075 -0.830 2.215 0.398 -0.933 0.933 0.000 0.410 -0.797 -0.761 0.000 0.446 0.578 0.991 0.765 0.610 0.689 0.543 +0 0.472 1.413 -0.726 1.475 0.141 0.734 -0.142 0.862 0.000 0.485 -1.395 -1.246 0.000 0.742 0.522 1.550 0.000 1.166 0.443 -0.319 3.102 0.911 0.967 0.994 0.888 0.713 0.795 0.988 +1 0.480 -1.707 -0.662 0.253 -0.908 1.094 -1.223 0.520 0.000 1.100 -1.347 -1.690 0.000 0.900 -0.942 -1.101 1.274 0.575 -0.089 -0.610 3.102 0.861 0.698 0.980 0.590 0.354 0.470 0.501 +0 2.020 0.547 -0.208 1.077 -0.833 1.704 -1.441 1.408 0.000 0.897 -1.399 -1.637 0.000 2.869 -0.744 -0.063 0.000 0.753 0.950 -1.414 3.102 1.014 0.859 1.088 0.766 0.932 1.098 1.548 +1 1.526 -0.919 0.478 0.697 -0.381 2.496 -0.339 -1.004 0.000 1.768 0.519 0.978 1.107 1.328 -0.282 0.838 0.000 0.694 0.030 -1.634 0.000 0.859 0.892 0.998 1.460 0.877 1.088 0.894 +1 1.923 0.374 0.880 0.499 -1.012 0.407 -0.273 0.817 0.000 0.775 0.347 -1.165 2.215 0.938 -1.128 -0.851 2.548 0.438 -0.607 -0.716 0.000 0.650 0.725 1.345 1.235 0.841 0.910 0.735 +0 2.738 0.149 -1.638 0.834 -1.410 1.423 -0.167 0.188 2.173 0.838 0.861 0.086 0.000 0.340 -0.193 1.156 0.000 1.064 0.779 -0.926 3.102 0.972 0.855 1.008 0.757 1.334 1.284 1.084 +1 0.440 -1.808 -1.046 1.605 1.478 0.984 -0.914 1.094 2.173 0.776 -1.930 -0.799 0.000 1.001 1.219 -1.522 0.000 2.339 -1.915 0.129 0.000 0.729 1.132 0.991 0.821 0.846 0.886 0.793 +0 0.936 -1.643 -1.134 0.276 -1.676 0.621 0.664 1.015 0.000 0.791 -1.204 -0.004 2.215 0.375 -0.989 0.815 0.000 1.198 0.879 -1.221 3.102 0.879 0.935 0.989 0.800 1.470 0.926 0.889 +1 0.429 1.376 -1.513 2.616 -0.793 0.873 0.052 1.170 2.173 1.347 0.789 0.470 2.215 0.604 -0.298 -0.823 0.000 0.689 0.464 1.685 0.000 0.638 0.974 0.989 1.956 1.128 1.505 1.193 +0 0.481 -0.835 0.471 1.247 -0.120 0.699 1.539 -1.634 2.173 0.535 -0.772 -0.794 0.000 0.487 -0.924 0.767 0.000 0.465 -0.523 1.398 0.000 0.995 0.719 0.983 1.249 0.306 0.808 0.764 +1 0.619 1.022 -0.666 1.206 0.995 1.035 0.486 -1.253 0.000 0.563 1.041 1.329 2.215 0.620 1.935 0.430 0.000 1.349 -0.461 0.373 1.551 1.310 0.939 1.194 0.985 0.929 0.784 0.733 +1 0.876 1.565 0.638 0.745 1.647 0.918 1.218 -0.977 2.173 1.029 1.294 1.729 2.215 1.244 0.881 0.133 0.000 1.798 -1.880 0.303 0.000 0.753 0.956 0.984 0.961 0.927 0.758 0.712 +0 0.653 0.272 -1.625 1.052 -0.762 1.289 0.508 1.228 0.000 1.063 0.949 -1.087 0.000 1.129 1.245 -0.511 0.000 1.311 0.705 0.429 3.102 0.897 1.002 0.993 0.691 0.298 0.686 0.638 +1 0.512 -0.945 1.294 1.118 -0.907 1.163 0.711 0.796 2.173 0.973 -0.883 -0.537 0.000 1.072 0.790 -1.027 2.548 0.729 1.972 0.748 0.000 2.898 1.756 0.987 1.684 1.391 1.363 1.361 +1 0.937 0.893 1.654 0.902 -0.462 1.896 0.805 0.606 0.000 1.432 0.199 -0.915 0.000 1.331 1.492 -1.496 0.000 1.237 0.135 -0.171 1.551 1.962 1.123 1.203 0.789 0.700 0.846 0.760 +1 0.611 -0.692 1.699 2.237 -1.210 1.286 -0.624 0.964 2.173 0.687 -1.168 0.055 2.215 0.566 -2.279 -0.151 0.000 0.484 -0.115 0.565 0.000 0.922 1.150 0.986 0.997 1.087 1.105 0.936 +1 0.410 -0.695 -1.683 1.544 -0.202 0.983 0.498 1.264 2.173 1.111 0.104 1.690 0.000 1.258 -0.391 -0.264 2.548 0.616 0.875 -0.242 0.000 1.186 0.945 1.071 0.610 1.516 0.982 0.918 +1 0.620 -1.523 -0.159 1.901 -1.293 1.184 -1.678 0.492 0.000 1.073 -1.200 -0.881 2.215 1.186 -0.535 -1.735 2.548 1.089 -0.673 0.791 0.000 0.918 1.295 1.283 0.749 0.923 1.053 0.994 +1 1.155 -0.319 -0.471 1.473 0.752 0.894 1.531 0.801 0.000 2.013 -1.213 1.539 0.000 1.548 -0.770 1.281 0.000 1.981 -0.559 0.356 0.000 0.830 0.738 1.613 1.549 1.447 1.438 1.311 +0 0.843 0.874 0.215 0.467 1.649 0.684 -0.654 1.738 2.173 0.761 1.156 -0.849 1.107 1.053 -0.378 0.474 0.000 0.688 -1.408 0.706 0.000 1.086 0.978 0.988 0.763 1.364 1.021 0.874 +1 1.200 -0.983 0.244 0.566 0.984 0.784 0.377 -0.659 2.173 0.533 0.142 0.661 0.000 1.093 0.088 -1.712 2.548 0.852 0.776 -0.983 0.000 0.943 0.815 0.986 1.107 0.952 1.060 0.926 +1 1.828 -0.030 -0.244 0.243 -0.428 1.166 1.151 1.424 2.173 0.677 0.591 0.243 1.107 0.570 2.274 -1.322 0.000 0.609 0.887 -0.493 0.000 0.672 0.976 0.980 0.622 1.202 1.016 0.903 +0 0.784 -1.159 0.438 1.004 0.140 1.657 -0.199 0.618 2.173 1.525 -2.254 -1.635 0.000 2.077 -1.026 -1.215 0.000 1.639 -1.153 -0.464 0.000 1.303 1.178 0.995 1.769 1.346 1.430 1.374 +0 1.914 1.142 -1.138 0.769 -0.896 0.697 1.114 0.781 0.000 0.602 -0.770 0.258 1.107 0.748 0.539 -1.622 0.000 0.378 1.380 -0.361 3.102 0.788 0.915 0.973 0.517 0.708 0.976 0.910 +0 0.903 0.281 1.575 0.452 -0.421 1.015 0.538 -0.926 0.000 1.140 0.637 0.490 2.215 0.508 -0.618 0.607 2.548 0.472 -1.363 1.095 0.000 1.731 1.138 0.987 0.887 0.587 0.941 0.797 +0 0.909 1.101 1.568 1.043 -0.444 1.003 0.916 0.086 2.173 0.837 2.528 1.583 0.000 0.600 1.522 1.579 2.548 0.567 0.789 -0.631 0.000 1.165 0.696 1.310 1.000 1.012 0.888 0.816 +1 0.327 0.610 -0.821 1.419 1.498 0.611 -1.214 1.269 0.000 1.421 -0.776 -0.519 2.215 0.725 -1.515 -0.055 0.000 1.252 0.029 1.213 0.000 0.901 0.930 0.991 1.973 0.872 1.219 1.211 +1 0.653 1.786 0.236 1.148 1.143 0.712 -0.170 -0.866 2.173 0.824 1.035 1.653 0.000 0.306 -2.021 1.135 0.000 0.608 0.922 -0.358 3.102 0.865 0.944 0.984 0.610 0.567 0.797 0.702 +0 1.850 0.215 1.006 0.406 1.010 1.252 -1.367 -0.132 2.173 1.604 -0.989 -0.791 0.000 0.939 0.470 -1.205 2.548 1.000 -0.913 0.772 0.000 1.630 1.328 0.971 0.901 1.852 1.467 1.365 +1 1.337 0.078 1.017 1.061 -0.145 1.375 0.171 0.141 0.000 1.637 0.826 -1.177 0.000 0.860 0.603 1.249 2.548 0.398 0.775 -0.457 0.000 0.727 0.962 1.429 0.802 0.563 0.691 0.687 +1 0.503 2.127 1.070 1.729 0.983 0.666 0.392 -0.767 2.173 0.870 -0.305 -0.461 2.215 0.848 0.841 1.689 0.000 0.851 0.188 1.005 0.000 0.839 0.880 0.973 1.179 0.507 1.027 0.832 +1 1.230 0.709 1.481 1.110 0.936 0.662 -1.646 -0.848 0.000 1.615 0.628 -0.314 2.215 1.037 0.230 1.307 0.000 0.684 0.109 0.682 3.102 1.035 0.910 0.997 1.398 0.781 1.041 1.228 +1 1.033 -1.045 1.342 0.171 -0.054 0.530 -0.415 1.048 0.000 0.867 -0.136 -0.744 2.215 1.493 0.575 0.080 2.548 0.414 0.734 1.009 0.000 0.499 0.865 0.983 1.027 0.947 0.813 0.708 +0 1.739 -0.310 -1.341 1.198 0.964 2.075 0.820 0.726 2.173 2.064 1.293 -1.123 2.215 3.014 -1.795 -0.383 0.000 1.223 -0.212 0.983 0.000 0.934 4.758 1.748 2.015 3.129 3.897 2.900 +0 1.671 -0.286 -1.726 1.346 -0.498 1.060 0.900 -0.254 0.000 0.773 0.460 0.910 1.107 1.460 0.796 1.320 2.548 0.775 1.718 0.220 0.000 0.856 1.002 1.859 1.403 0.467 1.001 1.019 +1 1.232 -0.490 1.050 0.888 1.417 1.657 0.424 -0.602 0.000 0.882 -1.280 -0.798 0.000 1.044 -1.135 1.372 2.548 1.552 0.686 0.177 1.551 1.182 0.916 0.996 1.330 1.472 1.141 1.001 +1 1.207 1.403 0.437 0.980 -1.223 1.451 1.847 1.563 0.000 2.569 0.941 -0.480 2.215 0.697 0.705 -1.410 2.548 0.568 0.289 1.074 0.000 0.551 0.639 1.503 1.334 1.067 0.969 0.966 +1 0.555 -0.117 1.592 0.564 0.609 0.908 -0.846 -1.515 0.000 0.947 -0.928 -0.722 2.215 0.750 0.142 1.118 0.000 2.535 -0.153 0.393 3.102 1.071 1.044 0.988 0.826 1.304 0.963 0.887 +0 0.663 0.901 0.754 0.744 -1.421 1.374 0.144 0.352 2.173 1.316 -0.411 -1.546 2.215 0.727 -0.845 -0.250 0.000 0.856 0.181 -1.245 0.000 0.860 0.864 0.990 1.248 2.041 1.252 1.044 +1 0.333 -1.521 0.744 0.817 -1.524 1.543 0.299 0.699 0.000 0.963 -0.626 -0.627 0.000 1.927 -0.405 -1.286 2.548 0.673 0.358 -0.055 3.102 0.765 0.723 0.986 0.628 0.873 0.602 0.566 +1 0.693 2.048 -1.734 0.509 1.310 0.603 0.754 -0.421 0.000 0.583 2.016 -0.186 0.000 1.335 0.726 1.694 1.274 0.733 -0.194 0.550 3.102 0.926 0.912 0.990 0.610 0.766 0.812 0.743 +1 0.873 0.251 -0.607 0.980 -1.533 0.843 0.560 1.673 2.173 0.670 0.569 0.970 0.000 1.475 -0.385 0.065 1.274 0.655 0.992 0.026 0.000 0.977 0.890 0.984 0.927 1.553 0.891 0.755 +1 0.733 0.267 -1.614 0.575 -0.186 1.003 0.439 1.412 2.173 1.286 0.933 0.193 0.000 1.430 0.415 -0.207 0.000 2.257 1.713 -1.445 0.000 0.904 0.718 0.988 0.883 0.702 0.933 0.839 +1 0.555 -0.663 0.483 0.887 -0.568 0.764 -0.328 1.675 2.173 0.541 -1.720 0.209 0.000 0.652 -2.496 1.200 0.000 1.188 -0.538 -0.583 3.102 0.846 0.974 0.992 1.033 0.915 0.927 0.804 +0 1.000 0.540 -0.168 0.518 -0.860 0.530 2.619 0.512 0.000 0.801 0.373 -1.661 1.107 0.816 0.832 -0.526 0.000 1.155 -0.289 1.167 1.551 0.885 1.224 0.982 0.785 0.584 0.992 0.976 +1 1.444 0.528 1.475 1.394 1.063 0.576 0.966 0.903 0.000 1.118 -0.380 -1.168 0.000 0.931 1.839 -0.028 0.000 1.956 1.147 -0.666 3.102 1.018 1.006 0.995 0.590 0.669 0.901 0.820 +0 0.499 -0.777 -1.083 1.272 1.219 0.875 -0.633 0.496 2.173 1.030 0.344 1.698 0.000 0.928 0.046 -0.488 2.548 1.294 1.878 -0.410 0.000 0.849 0.965 0.986 0.872 0.956 0.998 0.957 +0 0.735 -0.014 0.571 1.776 -0.046 1.565 -0.861 -1.686 0.000 0.471 -1.044 -1.014 0.000 0.951 -1.134 0.334 2.548 0.494 -0.128 0.346 3.102 1.057 0.910 0.991 1.026 0.300 0.795 1.057 +0 3.056 -0.780 -0.264 1.062 -0.318 2.004 -0.771 1.545 0.000 1.645 0.006 -0.078 2.215 1.145 -1.312 1.124 1.274 0.994 0.468 -1.498 0.000 0.743 0.944 0.967 0.812 1.727 1.116 1.042 +0 1.868 -1.121 1.249 0.873 1.110 1.074 0.603 -0.732 2.173 0.660 0.436 -0.150 0.000 0.441 -0.412 1.578 0.000 0.563 -0.304 -0.544 3.102 0.913 0.886 0.996 0.848 0.439 1.441 1.156 +1 0.487 -0.160 -1.622 1.242 0.298 0.451 0.542 -0.291 0.000 0.675 -0.472 0.757 0.000 1.275 1.342 -1.641 2.548 0.975 -1.688 -1.038 0.000 1.024 0.591 1.064 1.162 0.771 1.024 0.871 +1 1.641 -0.086 0.907 0.353 -0.349 3.066 -0.254 -0.852 0.000 1.624 -0.273 1.510 0.000 2.305 0.993 0.664 2.548 1.197 -0.676 0.934 3.102 4.020 2.510 0.988 0.960 1.431 2.239 1.708 +0 0.715 0.275 -1.482 1.382 -1.448 0.592 -1.139 1.311 2.173 0.891 -0.564 0.018 2.215 0.944 0.583 0.509 0.000 1.086 0.904 -0.558 0.000 0.949 0.967 0.988 0.790 1.029 0.943 0.950 +0 0.914 -0.161 1.365 1.649 -1.404 0.809 -1.091 0.390 0.000 0.584 -1.398 1.138 0.000 1.096 -1.446 -0.422 2.548 1.099 0.019 -0.489 3.102 0.944 0.954 1.025 0.817 0.753 0.859 0.917 +1 0.780 1.603 -0.435 0.723 0.750 0.716 0.411 0.059 1.087 1.000 1.738 1.513 0.000 0.595 1.262 -1.316 0.000 1.017 0.113 -1.376 3.102 0.702 0.809 0.986 0.969 0.878 0.854 0.819 +1 0.662 0.681 -0.308 0.264 0.653 0.939 0.565 1.081 0.000 0.796 1.529 -1.122 2.215 1.123 0.389 -0.866 1.274 0.625 1.239 0.293 0.000 0.943 1.102 0.979 1.024 0.650 0.845 0.807 +1 0.413 -1.246 1.529 1.073 -0.989 0.349 -1.536 -1.084 0.000 1.003 -1.620 1.414 0.000 0.533 -0.542 0.284 0.000 0.591 -0.997 0.045 3.102 0.979 0.736 0.986 0.805 0.685 0.897 0.803 +0 1.141 0.072 0.334 1.007 1.193 1.943 1.346 -1.120 0.000 1.450 -2.735 -0.014 0.000 1.536 -0.060 -0.047 0.000 2.812 -0.996 -1.681 3.102 0.800 1.866 1.039 1.325 1.220 1.754 1.376 +1 0.884 0.715 1.001 1.227 0.150 0.895 0.641 1.308 2.173 1.270 0.687 -0.538 0.000 1.122 0.291 -1.024 0.000 0.589 0.684 -1.668 0.000 0.961 0.772 1.000 0.873 0.678 0.774 0.735 +1 0.480 1.358 0.070 0.889 1.143 0.503 1.256 -1.103 0.000 0.422 -1.406 0.465 2.215 0.403 0.549 -0.638 2.548 0.369 0.911 1.008 0.000 0.624 1.118 0.991 0.581 0.647 0.665 0.630 +1 0.804 0.664 0.744 1.333 -0.505 0.556 1.999 -1.656 0.000 0.398 -0.419 0.629 2.215 0.630 0.363 -1.083 2.548 0.717 1.270 1.200 0.000 0.571 1.008 1.294 0.734 0.579 0.680 0.716 +0 1.752 0.212 -1.001 0.695 -1.237 0.782 -0.985 0.711 2.173 1.304 0.507 0.622 0.000 1.372 -0.538 -1.068 2.548 1.571 -0.325 0.431 0.000 0.937 1.041 0.997 0.574 1.316 1.054 1.074 +0 1.234 0.157 0.881 0.475 -0.377 1.699 0.551 0.261 0.000 2.461 -1.606 -1.295 0.000 0.490 -0.774 -1.263 2.548 0.992 -1.071 1.303 3.102 7.235 3.684 0.989 0.805 0.410 2.128 1.665 +1 1.129 0.872 -1.256 2.698 0.838 1.651 -2.427 0.613 0.000 2.573 1.181 -0.458 0.000 2.391 0.495 1.101 1.274 0.981 1.468 -1.117 0.000 1.291 0.868 2.297 1.334 0.825 1.275 1.287 +1 1.114 0.343 0.921 0.909 -1.488 1.196 0.589 -1.430 0.000 0.886 1.239 -0.027 1.107 1.530 0.798 0.468 2.548 1.179 -0.338 -0.467 0.000 0.912 1.284 1.151 0.926 0.589 1.026 0.922 +1 0.304 -0.386 -0.965 0.824 -0.295 1.784 0.849 -1.361 0.000 0.896 1.599 0.134 0.000 1.809 0.690 0.100 0.000 1.920 -0.351 1.198 1.551 0.898 0.688 0.983 1.145 0.877 1.065 1.508 +0 2.631 -0.619 0.392 0.673 0.324 1.849 0.761 -1.260 2.173 0.369 0.942 0.259 2.215 0.679 1.090 1.446 0.000 0.370 -0.942 -1.466 0.000 0.845 1.044 0.982 0.939 1.196 1.744 1.334 +1 0.525 1.541 -0.231 0.619 0.919 0.696 -0.459 -1.532 2.173 0.371 -0.009 0.230 0.000 0.528 -1.049 0.694 2.548 1.299 1.305 -0.855 0.000 1.088 1.091 0.988 0.947 0.734 0.868 0.769 +0 0.469 -0.085 0.815 1.358 -1.195 1.340 0.937 1.479 2.173 1.714 -1.179 -0.492 0.000 1.600 -0.664 0.435 1.274 0.462 1.024 -0.578 0.000 1.029 0.864 1.074 1.160 2.262 1.215 1.009 +1 2.277 0.728 -1.009 0.928 -1.618 0.652 -0.415 0.048 0.000 0.934 0.735 0.202 1.107 1.004 0.687 1.012 1.274 0.580 -0.775 0.613 0.000 0.828 0.874 1.048 1.025 0.687 0.905 0.801 +1 0.648 -0.060 -0.206 1.690 0.533 0.697 -1.041 -0.849 2.173 0.638 -0.389 1.506 2.215 0.929 0.051 -1.309 0.000 0.656 -0.993 1.517 0.000 0.859 0.901 0.987 0.887 0.897 0.930 0.768 +0 1.031 0.424 1.175 1.310 -1.679 1.639 1.104 -0.567 0.000 0.901 -0.131 -1.579 2.215 1.547 0.912 0.801 0.000 1.320 0.121 0.082 3.102 0.975 0.958 0.985 0.757 0.991 0.966 0.970 +1 0.835 0.469 -0.615 0.863 0.592 1.419 -0.218 1.116 2.173 1.561 -1.101 -1.532 0.000 1.568 -0.291 -0.306 2.548 1.212 -0.302 -1.051 0.000 1.034 1.343 1.041 1.143 1.784 1.294 1.146 +1 0.292 1.297 0.874 0.780 -1.429 1.078 -0.969 -0.784 1.087 1.403 -0.932 0.616 1.107 0.880 -0.470 -1.558 0.000 0.485 0.569 0.545 0.000 0.823 1.003 0.982 1.026 1.724 1.019 0.851 +1 0.593 0.319 -0.866 0.916 -0.096 0.847 -0.358 -1.579 0.000 0.858 -0.748 -0.238 2.215 1.382 -0.352 1.432 0.000 1.119 0.380 0.684 3.102 0.806 0.962 0.979 0.567 0.871 0.879 0.804 +0 0.964 1.773 -0.733 0.945 -0.956 0.712 0.013 -1.361 0.000 1.264 -0.546 1.027 0.000 1.191 1.221 0.023 2.548 0.570 -0.116 0.478 1.551 1.781 1.029 0.974 0.801 0.568 1.026 1.036 +0 0.279 1.027 -0.611 2.222 -0.826 0.789 0.896 1.144 0.000 0.872 0.068 0.135 2.215 1.031 0.656 0.566 2.548 1.228 1.175 -1.653 0.000 0.954 0.853 0.987 1.853 0.506 1.342 1.212 +1 0.964 0.153 0.614 0.744 -0.684 1.052 0.220 1.496 2.173 0.789 -2.224 -0.400 0.000 0.675 1.339 -0.043 0.000 0.518 0.673 0.475 3.102 3.601 2.010 1.080 0.987 0.664 1.533 1.213 +1 1.270 -0.618 1.024 0.227 -1.674 1.019 0.097 -0.599 1.087 0.972 -1.060 -0.259 2.215 0.659 -0.419 0.708 0.000 1.472 -1.266 1.486 0.000 0.924 1.028 0.992 1.122 1.023 0.998 0.910 +1 1.204 -1.807 -0.729 1.023 -0.604 0.717 -1.520 1.032 2.173 0.647 -2.406 1.121 0.000 1.311 -0.368 0.335 2.548 1.458 -0.717 -1.328 0.000 1.526 1.079 0.998 1.053 1.011 0.952 0.962 +0 0.781 -0.168 -0.989 0.736 0.790 0.896 0.747 0.191 2.173 0.713 0.647 1.528 0.000 0.858 1.118 -1.396 0.000 0.486 -0.867 -0.723 3.102 0.674 1.150 1.050 0.566 0.880 0.797 0.729 +0 0.498 -0.383 -1.115 1.368 1.644 0.654 0.839 -1.635 2.173 1.582 1.342 -0.230 0.000 0.900 1.308 0.812 0.000 1.399 0.554 0.478 1.551 1.478 1.023 0.987 1.295 0.961 1.088 1.430 +0 1.254 0.400 1.364 1.811 1.457 0.608 -1.143 -0.698 0.000 0.687 0.027 -0.532 0.000 1.123 -1.298 0.157 2.548 0.380 2.245 0.271 0.000 0.883 0.932 1.000 0.702 0.612 0.878 0.918 +0 0.928 0.346 -0.669 2.447 -0.930 1.418 -1.319 0.951 2.173 0.770 -1.771 0.412 0.000 0.469 -2.518 1.240 0.000 1.244 -0.746 -0.324 3.102 0.826 0.788 0.990 0.749 1.320 1.375 1.136 +1 0.926 0.177 -0.941 1.615 -0.265 1.308 -2.348 1.301 0.000 2.782 -1.449 -0.746 0.000 2.745 -0.620 0.887 2.548 0.704 0.646 1.410 0.000 3.055 2.192 0.987 1.673 0.981 1.751 1.560 +1 0.407 1.971 1.404 0.714 -1.104 0.984 0.699 0.142 0.000 1.091 0.534 -1.455 2.215 1.041 1.131 1.260 1.274 0.921 1.281 0.056 0.000 0.633 0.970 0.992 0.642 0.827 0.913 0.785 +0 0.561 0.827 -1.057 0.768 1.181 0.883 0.634 0.734 1.087 1.318 0.538 -0.857 0.000 1.227 -1.325 1.376 0.000 2.571 -0.178 -0.312 3.102 1.022 1.025 0.989 0.813 1.469 1.027 0.869 +0 0.770 -0.209 -0.338 1.020 -1.568 0.880 -0.650 1.347 2.173 1.037 -0.905 0.376 0.000 1.801 0.276 0.344 0.000 2.045 1.071 -1.196 1.551 0.772 1.232 1.098 0.912 1.926 1.165 0.985 +0 1.031 -0.663 1.254 0.699 1.738 1.240 0.234 0.841 2.173 1.269 -1.182 -0.366 2.215 1.449 -0.627 -1.065 0.000 1.036 0.190 -0.510 0.000 0.912 0.983 0.977 0.883 2.194 1.271 1.094 +1 0.531 0.612 -0.072 1.121 1.070 0.836 -0.149 -0.649 0.000 1.238 1.139 1.535 0.000 1.295 -1.452 -0.141 2.548 0.927 -1.193 -1.381 3.102 0.887 1.130 0.987 0.870 0.756 0.856 0.811 +0 0.426 -1.745 -0.400 0.549 0.884 0.462 0.004 -1.455 0.000 0.556 -0.054 1.298 0.000 0.706 0.940 -0.531 2.548 1.205 0.125 0.090 3.102 0.660 0.788 0.995 0.830 0.493 0.596 0.611 +0 1.816 -0.146 -1.418 0.712 -0.766 0.910 -0.372 -0.515 2.173 1.237 -0.862 1.255 0.000 1.928 -0.709 0.447 1.274 0.704 -1.346 0.808 0.000 0.658 0.901 0.978 0.871 1.302 1.022 1.000 +1 0.948 0.387 -1.154 0.546 0.478 0.630 0.432 1.368 0.000 0.809 -0.100 -0.184 2.215 0.661 1.166 0.284 2.548 1.635 0.095 -1.530 0.000 0.822 0.935 0.992 0.693 0.663 0.782 0.678 +1 0.934 -1.487 -0.052 0.878 -0.797 1.030 -0.054 1.656 2.173 0.470 -0.525 -0.644 0.000 1.035 -0.144 0.870 2.548 0.626 1.279 -0.240 0.000 0.909 0.878 0.985 1.263 0.841 0.930 0.867 +0 2.965 -0.962 0.278 0.883 -0.931 0.837 -1.377 -1.111 2.173 0.684 -1.555 1.738 0.000 0.935 -0.599 0.854 1.274 1.147 0.725 -1.316 0.000 0.718 0.763 1.986 1.109 1.156 1.053 0.919 +1 1.180 0.275 1.429 0.339 -1.397 0.464 0.709 -1.072 2.173 1.130 1.237 -0.152 2.215 0.549 0.048 -1.299 0.000 0.516 -1.240 -0.655 0.000 0.593 1.162 0.988 0.762 0.841 0.900 0.784 +1 0.603 -0.709 -0.320 0.387 -0.822 0.556 0.112 -0.984 2.173 0.597 0.450 0.856 0.000 0.598 -0.827 0.353 0.000 0.932 1.027 1.374 3.102 0.789 0.899 0.982 0.743 0.789 0.669 0.629 +1 0.445 0.693 -1.156 0.599 -1.446 0.502 -1.833 0.411 0.000 1.565 -0.131 1.660 2.215 0.869 -1.072 -0.380 2.548 1.430 0.660 0.175 0.000 0.853 0.869 0.995 1.628 1.373 1.479 1.217 +0 0.869 -2.209 -0.723 3.064 -0.744 1.429 -0.145 0.688 0.000 1.255 -1.626 -1.343 0.000 1.274 0.603 0.628 2.548 2.117 0.073 1.277 3.102 3.641 2.283 1.013 2.293 0.783 1.787 1.835 +0 0.439 0.442 -0.875 0.514 -0.326 1.398 -1.025 -1.473 0.000 2.047 1.602 1.504 0.000 2.425 0.680 -0.205 1.274 2.631 0.612 0.221 3.102 1.740 1.790 0.989 1.242 0.723 1.455 1.497 +0 0.902 -0.043 -0.626 0.788 1.089 0.479 -0.134 -1.490 2.173 0.857 0.694 -0.201 0.000 0.482 0.674 -1.162 0.000 1.086 -0.254 1.090 3.102 0.751 0.852 1.168 0.711 0.559 0.630 0.607 +1 1.006 -1.638 1.026 1.900 1.666 0.743 -0.869 0.092 0.000 0.358 -0.889 -0.171 2.215 0.812 -0.519 -1.012 0.000 0.604 0.552 -0.765 1.551 1.191 0.841 1.045 0.851 0.425 0.834 0.849 +0 0.833 0.846 -0.541 1.245 -0.609 0.822 0.252 1.571 0.000 0.834 -0.623 1.433 0.000 1.390 0.256 0.748 2.548 0.927 0.136 -0.171 1.551 0.843 0.977 1.003 1.069 0.641 0.742 0.852 +1 0.764 1.178 -1.511 1.760 -0.028 1.777 1.214 1.445 0.000 2.387 1.648 -0.548 0.000 1.495 0.957 0.401 1.274 0.614 1.648 -1.568 0.000 0.925 0.931 1.563 0.783 0.723 0.676 0.739 +1 1.205 0.662 -0.192 2.442 -1.077 2.719 0.009 1.135 1.087 1.857 -1.207 -0.368 0.000 0.843 0.397 -1.188 0.000 1.075 1.006 -0.552 0.000 0.705 0.686 1.698 2.599 1.324 1.656 1.328 +1 0.671 -1.052 -0.795 0.238 1.685 0.871 -0.164 0.099 2.173 1.185 -0.629 -1.447 0.000 1.447 0.003 0.821 2.548 0.369 0.931 -1.444 0.000 0.864 1.105 0.979 0.823 0.855 0.923 0.784 +0 1.532 -0.927 1.398 1.346 -0.250 0.725 0.157 1.133 2.173 0.775 -0.191 -0.424 2.215 0.421 0.275 -1.248 0.000 0.632 -1.302 -0.761 0.000 0.658 0.872 1.982 1.179 1.105 1.003 0.810 +0 0.907 0.905 0.079 1.399 0.604 0.503 -2.003 1.714 0.000 0.546 -0.724 -0.687 2.215 0.710 2.563 -1.572 0.000 1.034 1.227 -0.849 1.551 0.663 0.767 0.999 0.813 0.922 1.067 1.426 +1 0.439 -0.706 -1.292 0.098 -1.362 1.956 0.460 -0.814 0.000 2.094 1.347 0.899 2.215 2.130 1.072 0.304 2.548 1.106 -1.228 -1.148 0.000 0.569 1.141 0.901 0.981 1.170 0.996 0.971 +1 1.095 -1.338 -0.560 1.322 -1.486 1.453 -0.933 0.854 1.087 0.563 -0.946 -0.349 2.215 0.350 -1.087 -0.015 0.000 1.055 -0.877 -1.016 0.000 0.735 1.134 1.234 0.750 1.177 1.023 0.849 +0 1.929 0.669 0.189 0.768 -0.395 0.597 0.752 1.593 0.000 1.467 0.548 -1.049 2.215 0.871 0.421 1.230 2.548 0.698 1.151 0.946 0.000 0.918 0.974 0.984 0.877 1.065 0.917 0.810 +1 1.072 -1.381 0.138 1.003 -0.844 0.646 -0.060 1.366 1.087 1.514 -1.236 1.103 0.000 2.117 -0.990 -0.589 2.548 0.472 -0.997 1.614 0.000 0.491 0.787 1.111 0.753 1.630 1.067 0.970 +1 2.189 0.374 -0.344 0.344 -1.726 0.845 0.621 1.737 2.173 1.011 0.732 0.863 2.215 0.717 1.452 -1.732 0.000 0.704 2.039 -0.133 0.000 0.850 0.988 1.138 1.070 0.969 0.939 0.892 +1 2.755 -0.144 0.809 0.632 0.459 2.459 0.048 -1.186 0.000 1.781 -0.731 0.201 2.215 0.769 -0.675 -0.261 2.548 1.052 -1.146 -1.649 0.000 1.569 1.099 0.982 1.012 0.503 0.973 0.938 +0 0.933 -0.110 -1.470 0.555 -0.132 0.518 1.182 0.892 0.000 1.289 -0.559 1.592 2.215 1.208 0.278 -0.260 2.548 1.481 1.266 0.062 0.000 0.937 0.963 0.986 0.791 1.451 1.183 1.001 +1 1.334 -0.980 1.401 0.743 -1.034 0.616 -1.027 0.881 0.000 0.705 0.804 -0.182 0.000 0.916 -0.979 -0.640 2.548 1.057 -0.859 0.270 0.000 0.906 0.720 1.120 0.617 0.130 0.515 0.534 +0 0.334 -1.396 -1.231 2.396 -0.101 0.779 2.891 1.433 0.000 0.417 0.732 -0.609 2.215 0.524 0.395 1.301 2.548 0.385 2.002 -1.348 0.000 0.542 0.986 1.055 1.135 0.498 0.900 2.277 +0 0.975 -0.839 -1.163 1.959 -0.336 0.868 -0.028 0.518 0.000 0.904 -0.751 1.579 1.107 1.460 0.341 1.713 0.000 1.586 -0.095 -0.222 3.102 1.035 1.093 1.299 0.819 1.139 0.912 1.043 +1 1.153 0.312 -1.227 0.988 1.587 0.546 -0.244 1.473 0.000 1.342 -0.752 0.192 2.215 0.372 -1.064 -0.051 2.548 0.476 1.772 -0.701 0.000 0.924 1.049 0.986 0.845 0.221 0.980 0.825 +1 0.450 0.546 1.041 1.672 -0.685 0.432 2.149 -1.709 0.000 0.399 2.816 0.429 0.000 0.613 0.868 -0.097 2.548 1.282 -0.149 1.283 3.102 0.890 0.825 1.201 0.932 0.759 0.895 0.885 +1 0.802 -2.184 -0.199 2.655 1.638 0.740 1.454 1.414 0.000 1.023 -2.068 -1.644 0.000 0.782 -0.883 -1.631 0.000 0.725 0.431 0.030 3.102 0.840 0.836 2.014 1.830 0.672 1.173 1.008 +1 0.663 -0.287 1.299 0.949 1.428 1.580 -1.219 0.958 0.000 2.823 -0.258 -0.716 2.215 0.518 0.344 1.710 0.000 1.014 -1.717 0.172 0.000 1.465 0.888 0.988 1.786 0.474 1.113 1.029 +1 0.929 0.930 -0.772 0.667 -1.682 1.089 0.468 0.852 0.000 1.042 0.466 -1.122 2.215 0.726 0.840 0.379 0.000 0.996 -0.383 -0.228 3.102 0.756 0.929 0.980 0.581 0.800 0.890 0.797 +0 0.366 -1.676 -0.593 0.283 0.147 0.546 0.535 -0.734 2.173 1.104 -0.147 1.323 0.000 0.831 2.600 -0.014 0.000 0.786 -0.623 1.574 0.000 0.833 0.976 0.999 0.664 0.535 0.693 0.655 +0 0.734 0.101 1.259 1.608 0.570 0.302 -0.692 1.648 0.000 0.478 -1.110 -0.429 0.000 1.224 0.401 -1.434 2.548 1.288 -0.491 -0.836 3.102 0.934 0.754 0.989 0.935 0.714 0.828 0.709 +1 1.853 -0.819 -0.688 1.015 -0.823 2.329 -0.247 -1.041 2.173 3.533 -1.198 0.683 0.000 1.025 0.123 -0.198 0.000 2.463 -0.234 1.064 0.000 0.939 1.588 0.974 1.374 1.867 2.165 1.798 +0 0.654 0.590 0.003 0.733 -1.674 1.044 -0.047 0.645 2.173 1.137 -1.657 -0.831 0.000 0.912 1.157 -1.617 0.000 1.375 0.321 1.046 3.102 0.725 0.815 0.987 0.958 0.526 0.841 0.731 +0 0.927 0.991 1.247 1.380 0.977 0.526 -2.561 0.946 0.000 0.872 2.470 -1.165 0.000 1.120 0.660 0.004 0.000 0.733 -0.401 -0.724 1.551 0.807 0.893 0.996 0.962 0.661 1.333 1.315 +0 0.432 0.696 0.121 0.676 -1.199 0.719 0.391 1.003 2.173 0.709 1.137 -1.338 2.215 0.959 0.176 0.051 0.000 0.434 1.554 -0.775 0.000 0.823 0.869 0.985 0.828 0.993 0.745 0.693 +1 1.403 0.054 0.108 1.591 0.424 1.279 -0.415 1.583 2.173 0.691 1.318 -1.341 0.000 0.914 0.632 -0.393 0.000 0.637 -0.022 -0.541 1.551 1.007 0.667 0.991 1.529 0.918 0.995 1.073 +0 0.796 -0.454 -0.932 0.797 0.833 0.992 -0.317 1.311 2.173 1.287 0.177 -0.032 2.215 0.999 0.816 -0.894 0.000 0.378 -1.445 -1.057 0.000 1.175 1.071 1.104 0.842 1.612 1.023 0.863 +1 0.433 -1.220 -0.991 0.919 0.327 0.611 -0.372 1.070 0.000 0.578 -0.495 -0.490 2.215 1.039 0.343 -1.114 2.548 0.621 0.199 -1.453 0.000 0.833 0.783 0.984 0.580 0.579 0.558 0.541 +0 1.521 -0.271 -1.704 0.830 -1.177 0.945 -2.048 1.033 0.000 0.752 0.249 -0.550 0.000 1.019 -0.361 -0.012 0.000 2.160 0.647 0.021 3.102 0.769 1.021 0.999 1.448 1.104 1.063 0.941 +1 0.592 -0.208 1.647 1.415 -0.546 2.399 -1.259 1.624 0.000 1.023 -0.608 0.060 2.215 1.257 0.254 0.619 0.000 3.246 -0.299 -0.451 3.102 1.037 1.055 1.166 0.814 0.763 0.916 0.838 +0 0.875 0.652 -0.157 0.932 -0.619 0.655 0.855 1.209 0.000 0.748 -0.041 -0.541 2.215 0.823 0.159 -1.644 0.000 0.561 -0.798 1.047 3.102 0.850 1.030 0.987 0.700 0.643 0.676 0.702 +1 0.421 -1.178 0.588 0.950 -1.209 0.833 0.151 -0.251 0.000 0.722 -0.146 0.712 2.215 1.495 -0.290 1.549 2.548 0.968 -0.895 -0.203 0.000 0.898 0.894 0.989 0.720 0.761 0.859 0.749 +0 0.884 1.594 1.703 0.210 0.293 0.920 -1.007 -0.870 0.000 0.370 -2.584 1.203 0.000 1.344 0.946 0.599 2.548 1.087 -0.447 -0.263 3.102 1.136 0.854 0.995 0.755 1.033 1.171 1.103 +0 1.720 -0.230 0.661 2.222 0.416 1.258 -2.050 -1.143 0.000 0.808 -1.139 -1.203 0.000 0.736 -0.804 -0.796 2.548 1.240 0.259 1.226 3.102 0.999 0.798 0.999 0.800 0.845 1.079 1.549 +1 0.667 -0.133 -0.360 1.796 0.465 1.566 0.075 -1.351 2.173 0.824 -0.145 0.223 0.000 1.019 0.231 1.314 2.548 0.451 0.522 -0.226 0.000 0.445 0.725 1.027 1.575 1.069 1.090 0.904 +0 1.100 -0.252 0.581 0.324 0.455 1.065 0.769 -0.247 0.000 1.332 -0.848 1.448 2.215 0.830 0.805 -1.087 0.000 0.669 0.862 1.592 3.102 1.166 0.892 0.983 1.146 0.949 1.198 1.011 +0 0.721 0.481 -0.224 0.743 -0.978 0.507 1.080 0.597 0.000 0.570 1.605 1.085 0.000 0.534 -0.338 -1.023 2.548 0.669 0.951 -1.694 3.102 0.584 0.903 0.995 0.720 0.460 0.568 0.689 +0 0.893 -0.235 1.081 0.987 0.573 1.201 -0.774 -0.696 0.000 0.868 -0.491 -1.391 2.215 0.265 -0.813 -1.559 0.000 0.504 -0.784 0.821 3.102 0.714 0.731 0.981 0.447 0.560 0.594 0.702 +1 0.621 -1.404 0.422 0.993 -0.118 1.447 -0.471 1.679 2.173 0.793 -1.242 -1.336 0.000 0.555 -1.027 -0.043 0.000 0.716 2.058 0.472 0.000 0.938 1.146 0.993 0.548 1.006 0.888 0.816 +1 0.769 0.960 1.263 0.792 -0.409 0.684 0.454 -0.197 2.173 0.909 0.169 -0.985 2.215 0.843 0.358 1.028 0.000 1.452 1.483 1.140 0.000 0.924 1.147 1.079 0.815 0.775 0.913 0.778 +0 0.734 0.746 0.560 1.819 0.747 1.348 0.043 -0.789 1.087 1.107 2.557 1.622 0.000 1.525 0.491 1.297 2.548 2.213 -0.378 -0.484 0.000 1.111 0.875 0.994 1.027 1.759 1.472 1.340 +1 0.663 -0.285 0.814 0.754 -0.480 0.794 -1.620 0.915 0.000 0.999 -0.255 -1.007 2.215 0.863 -0.588 0.146 1.274 0.744 -1.053 -1.295 0.000 1.093 0.912 0.987 0.784 0.871 0.863 0.737 +1 0.777 -1.335 -1.331 1.254 0.999 0.814 -0.179 -0.425 2.173 0.549 1.103 1.109 0.000 0.503 0.611 -1.416 2.548 0.922 -0.019 0.450 0.000 0.768 1.027 1.180 0.943 0.713 0.891 0.887 +0 1.647 0.047 -1.042 1.589 -0.529 1.686 0.250 1.281 2.173 0.689 -1.782 -0.554 0.000 0.911 -0.500 0.925 0.000 1.889 1.021 -0.689 0.000 1.446 0.981 1.002 1.815 1.095 1.228 1.243 +0 0.671 -0.371 1.346 1.062 -1.399 0.676 -1.032 -1.086 2.173 0.540 -1.101 0.434 0.000 0.598 -0.735 -0.153 0.000 1.673 0.050 0.459 3.102 0.824 0.849 0.983 0.897 1.284 0.865 0.748 +1 1.103 -0.972 -1.158 1.158 1.654 0.884 -1.121 0.809 0.000 1.148 -0.992 -1.561 0.000 1.122 -0.598 -0.116 0.000 2.865 -0.647 0.354 3.102 0.899 0.883 0.993 1.274 0.933 0.868 0.805 +0 0.552 0.551 1.364 1.034 0.373 0.850 -1.025 1.594 2.173 1.235 -1.270 -0.189 0.000 0.546 -0.243 0.365 0.000 1.473 -0.005 -1.537 3.102 0.913 1.197 0.986 0.965 0.736 0.955 0.862 +0 0.852 0.225 -0.290 1.351 -1.086 0.792 1.341 0.978 0.000 1.542 1.399 -0.775 1.107 1.620 1.353 0.404 2.548 1.460 2.220 1.109 0.000 0.888 0.938 0.989 0.933 1.468 1.040 1.045 +1 1.136 -0.102 -0.831 2.029 -1.549 0.999 0.659 0.363 2.173 0.858 -0.467 0.601 1.107 0.439 0.679 -1.592 0.000 0.604 1.325 0.262 0.000 0.735 1.000 1.266 1.213 0.874 1.159 0.907 +1 1.449 1.598 1.170 0.818 -0.989 0.835 1.346 0.023 2.173 1.005 0.743 0.672 2.215 0.848 1.362 1.635 0.000 1.398 -0.011 -0.777 0.000 0.859 1.172 1.405 1.041 0.847 0.933 0.868 +1 0.649 -1.287 -0.975 2.096 0.024 1.129 0.862 -1.533 0.000 0.395 -0.162 0.574 0.000 0.493 1.415 0.758 0.000 0.904 0.766 1.235 1.551 0.667 0.685 1.267 1.377 0.528 1.109 0.979 +0 1.160 -0.085 -1.221 0.967 -0.519 1.060 0.459 -1.630 0.000 0.971 -0.657 0.329 2.215 1.609 0.082 0.906 1.274 1.679 -0.009 -0.221 0.000 2.007 1.524 0.989 1.061 0.841 1.116 1.001 +1 1.990 -0.417 -1.137 0.438 -0.833 1.040 -0.868 -0.117 2.173 0.796 -0.836 -1.680 0.000 1.471 -2.412 1.255 0.000 1.035 -1.133 0.930 3.102 0.955 0.703 0.987 1.214 0.929 0.959 1.200 +1 0.682 0.285 -0.112 1.035 1.414 0.429 -0.407 0.847 0.000 1.048 -1.060 -1.194 0.000 1.512 -0.279 -1.737 2.548 1.815 -0.354 0.127 1.551 0.714 0.964 1.142 0.861 1.261 0.795 0.725 +1 2.565 -0.182 -0.534 1.503 -1.626 1.361 0.545 0.620 0.000 1.674 -1.387 1.435 2.215 1.073 -1.764 -0.327 0.000 0.684 -1.015 1.122 3.102 3.825 2.081 2.264 2.077 0.287 1.675 1.642 +0 1.031 0.303 -1.613 1.500 -0.926 0.400 -1.149 0.176 0.000 0.430 -1.492 -1.725 2.215 0.762 -0.258 0.560 2.548 0.816 0.517 0.482 0.000 0.914 0.863 1.001 0.911 0.674 0.764 0.753 +1 1.371 -0.064 1.142 1.761 1.740 1.344 -0.863 -0.114 2.173 0.604 -0.050 -1.071 2.215 0.411 -0.749 -0.999 0.000 0.423 -1.365 1.401 0.000 0.428 0.783 1.106 0.834 1.151 1.195 0.895 +0 1.662 -0.327 0.290 0.880 0.277 1.109 0.738 -0.678 2.173 0.856 -0.187 1.294 0.000 0.849 0.879 0.530 2.548 0.384 1.456 -1.001 0.000 1.069 0.834 0.982 1.657 1.080 1.176 1.045 +0 0.447 -0.303 -0.141 1.109 -1.233 1.248 2.055 0.145 0.000 1.436 -0.035 1.579 2.215 0.619 -1.114 0.733 0.000 0.545 -1.962 1.650 0.000 0.830 1.245 0.985 1.067 0.782 1.039 0.898 +0 1.499 -0.134 -1.461 0.397 -0.008 0.734 1.056 -1.737 2.173 0.758 -0.407 0.283 0.000 0.847 -1.801 0.403 0.000 0.875 -0.095 -1.221 0.000 0.823 0.761 1.033 0.593 0.797 0.641 0.571 +1 0.558 -2.016 0.570 0.533 -0.264 1.156 -1.449 -1.640 1.087 0.892 -1.300 0.435 0.000 0.736 0.200 -0.312 0.000 1.063 -0.049 -1.557 3.102 0.543 0.856 0.987 1.056 0.897 0.846 0.759 +1 0.793 -0.204 -0.140 0.177 0.291 1.078 0.876 0.657 1.087 1.139 -0.345 -1.062 0.000 0.585 0.509 1.285 0.000 0.750 0.664 -1.572 1.551 1.224 0.769 0.985 0.838 0.863 0.927 0.803 +0 0.848 -0.070 -1.657 1.096 -1.122 0.477 -0.497 0.378 1.087 0.709 -1.141 1.337 2.215 0.484 0.983 -0.023 0.000 0.788 -0.897 -0.407 0.000 0.915 0.981 0.977 0.886 0.714 0.683 0.696 +1 0.719 0.747 1.486 1.079 -0.216 0.896 0.633 -0.256 0.000 0.708 0.942 -0.854 0.000 1.439 0.231 1.630 1.274 0.719 0.304 0.930 3.102 0.913 0.840 1.219 0.926 0.461 0.802 0.729 +1 2.564 -0.232 -0.071 0.669 0.122 0.661 -2.363 -1.401 0.000 0.542 0.693 -1.693 2.215 0.734 -0.935 1.303 2.548 0.745 -0.384 1.023 0.000 0.747 0.763 0.985 1.060 0.719 0.940 1.180 +0 0.604 -0.210 0.962 0.977 -0.163 0.969 0.659 -1.230 2.173 0.516 0.604 0.440 0.000 0.943 1.456 -0.080 2.548 1.062 1.125 1.499 0.000 0.865 1.005 0.988 1.168 1.176 1.049 0.912 +1 0.572 -1.787 1.359 0.925 -1.172 2.476 -1.142 -0.138 0.000 2.412 -0.644 -1.660 0.000 1.799 -0.843 1.291 2.548 1.409 -0.976 0.668 0.000 1.894 1.150 0.984 1.221 0.899 1.213 1.276 +0 0.861 0.127 -0.743 0.616 -0.044 0.920 1.146 -0.826 1.087 1.562 -0.794 1.211 2.215 0.507 0.364 1.002 0.000 0.660 -1.300 0.126 0.000 0.860 0.851 0.985 1.181 2.655 1.363 1.085 +1 0.355 1.404 -0.759 1.588 -0.931 0.461 -0.314 0.133 0.000 0.511 0.245 1.229 0.000 0.824 -0.916 0.617 2.548 1.047 -0.911 1.389 3.102 0.910 0.760 1.001 1.000 0.456 0.777 0.710 +0 2.973 2.252 0.375 0.143 -1.633 1.660 0.641 -1.127 1.087 0.762 1.340 0.945 1.107 0.543 0.041 -0.640 0.000 0.947 1.180 1.530 0.000 0.939 0.969 0.987 0.830 1.700 1.599 1.252 +1 1.696 0.572 0.185 1.072 0.975 0.472 -0.443 -0.569 0.000 0.717 -0.221 -1.234 2.215 0.959 1.138 -1.551 2.548 0.772 -0.178 1.488 0.000 0.891 0.941 1.222 1.115 0.754 0.884 0.802 +0 1.320 0.542 0.808 0.443 -1.039 0.491 0.502 -1.231 2.173 0.498 -0.780 0.705 0.000 0.675 -0.993 1.566 2.548 0.553 -0.109 -0.748 0.000 0.701 0.738 1.055 0.835 0.760 0.667 0.602 +0 0.494 1.846 0.751 0.757 -0.958 0.585 0.240 1.503 0.000 0.672 1.049 -0.148 2.215 0.881 0.076 0.969 2.548 0.693 -0.708 -0.812 0.000 1.013 1.019 0.990 0.762 0.811 0.691 0.673 +1 1.681 1.053 0.940 1.210 0.635 1.834 2.026 -0.576 0.000 1.653 0.662 -1.310 2.215 1.584 1.206 1.233 2.548 1.362 0.632 0.605 0.000 2.687 2.117 0.990 1.609 1.409 1.635 1.455 +0 1.250 1.515 1.504 0.613 -0.503 0.713 0.629 0.619 1.087 0.647 -0.348 -1.645 0.000 0.842 -0.241 -0.149 0.000 0.376 -0.202 -0.524 3.102 1.106 1.052 1.179 0.723 0.531 0.669 0.775 +1 0.685 -0.096 0.379 0.306 -0.763 1.091 -0.923 1.362 2.173 1.239 0.855 0.027 0.000 1.448 -0.193 -1.605 0.000 1.481 -0.841 -0.285 3.102 1.353 1.119 0.985 0.919 1.341 0.940 0.816 +1 2.504 0.375 0.868 1.007 -1.213 1.136 1.734 -0.605 0.000 0.425 0.057 -1.067 0.000 1.262 0.453 -0.849 2.548 0.674 0.158 0.399 3.102 1.525 1.061 2.100 1.004 0.645 0.874 1.049 +1 0.656 0.693 0.866 1.562 0.152 0.904 -0.277 -1.690 2.173 0.752 0.675 -1.099 2.215 0.593 -0.353 -0.339 0.000 0.378 -0.608 1.446 0.000 0.530 0.678 0.983 0.960 0.872 0.918 0.721 +0 0.699 1.203 -1.586 0.406 0.865 0.531 -0.773 0.288 2.173 0.813 -0.021 1.574 1.107 0.842 -0.229 -0.317 0.000 0.822 -1.347 -1.158 0.000 0.925 0.951 0.977 0.872 0.962 0.707 0.712 +1 0.998 0.012 -0.781 0.747 1.377 0.528 1.688 0.902 0.000 0.756 -0.218 -0.284 0.000 0.833 -0.054 0.606 0.000 0.671 0.672 -1.256 3.102 0.881 0.896 1.113 0.593 0.248 0.587 0.585 +1 0.829 -0.101 -1.396 0.229 0.335 0.759 0.541 0.615 2.173 0.809 0.449 -1.257 0.000 1.027 0.897 -0.600 0.000 0.911 -0.949 0.671 3.102 0.864 1.155 0.986 0.638 0.834 0.905 0.839 +1 0.804 -0.909 -0.162 0.325 0.733 0.849 0.259 -1.151 0.000 1.447 1.199 0.813 2.215 0.757 0.415 -0.468 0.000 0.679 0.563 -1.676 3.102 0.845 0.607 0.985 2.242 0.744 1.424 1.246 +1 1.244 0.334 -1.315 0.564 0.710 0.692 -0.623 0.903 0.000 1.139 -1.202 0.263 0.000 0.648 1.377 -0.891 0.000 1.341 0.319 1.160 0.000 0.846 1.113 1.123 0.863 0.838 0.881 0.799 +0 1.365 0.707 0.068 0.979 0.372 0.481 0.238 -0.934 0.000 0.421 -0.926 -1.024 1.107 0.281 -1.176 -1.609 0.000 0.624 0.926 -1.456 0.000 0.801 0.584 1.001 0.893 0.275 0.843 0.737 +1 1.279 -1.124 -1.104 0.498 -0.430 1.396 0.150 1.085 1.087 0.885 -1.931 -0.997 0.000 1.215 -0.208 -0.035 0.000 0.859 -0.643 0.374 3.102 0.813 1.131 0.994 0.754 0.889 1.187 0.972 +0 1.473 -0.386 1.581 0.799 -0.974 0.714 1.275 -0.017 2.173 0.782 -0.030 0.628 0.000 0.776 -0.860 -0.355 2.548 0.471 0.425 -1.335 0.000 0.805 0.909 1.119 0.828 1.295 1.037 0.851 +0 0.919 -0.731 -1.431 0.882 0.153 0.612 -1.158 1.667 2.173 1.472 -1.180 -0.539 0.000 1.059 1.787 0.800 0.000 1.403 -0.503 1.206 1.551 0.661 1.072 1.235 0.787 0.486 0.835 0.758 +0 0.509 0.962 1.148 1.353 0.014 0.384 0.139 1.651 0.000 0.602 0.290 -0.658 1.107 0.868 -0.981 0.368 0.000 1.084 0.775 -1.300 3.102 0.999 0.947 0.988 0.735 0.463 0.633 0.713 +1 0.758 -0.346 0.767 1.894 -0.099 0.782 1.033 -1.625 2.173 1.097 1.244 -0.943 2.215 1.066 1.174 1.287 0.000 1.139 1.726 -1.649 0.000 1.057 1.059 1.167 1.490 0.803 1.232 1.039 +1 0.714 0.928 0.306 0.947 0.477 1.116 0.251 -1.695 0.000 1.101 2.261 0.083 0.000 1.112 0.934 -1.084 2.548 0.378 0.773 0.999 1.551 3.737 1.908 0.988 0.999 0.473 1.197 1.087 +0 0.457 1.637 1.439 0.668 -0.571 0.662 2.685 0.637 0.000 0.908 0.852 1.396 2.215 1.213 -0.859 -1.194 0.000 0.801 -1.888 0.543 0.000 0.933 0.574 0.984 0.724 0.772 0.915 0.864 +1 0.934 1.631 0.385 1.771 0.055 0.469 1.214 -1.011 0.000 1.305 0.448 -1.497 2.215 0.394 1.345 0.863 0.000 1.110 0.259 1.048 3.102 0.773 0.806 1.000 1.292 0.819 1.382 1.059 +1 2.389 -0.970 -1.607 0.638 -0.415 0.992 -0.899 0.156 2.173 0.588 -1.401 -1.246 0.000 0.638 -0.987 1.203 2.548 0.813 -1.348 -0.006 0.000 0.814 0.917 1.505 0.836 0.807 0.929 0.784 +0 0.902 0.999 0.163 0.806 1.157 0.931 0.632 -0.968 0.000 1.241 -2.606 0.160 0.000 0.626 1.650 -0.802 0.000 1.245 0.637 1.468 3.102 0.878 0.925 0.985 0.827 0.489 0.739 0.745 +1 0.702 -1.839 -0.259 1.542 0.461 0.472 -2.136 -0.808 0.000 0.534 0.014 -0.911 2.215 1.068 -0.686 -1.461 1.274 0.583 -1.714 1.187 0.000 0.782 0.959 0.989 1.021 0.496 0.810 0.757 +0 0.449 0.090 -0.438 1.032 1.427 0.483 1.126 0.305 0.000 1.138 1.237 -0.726 2.215 0.553 -1.115 0.640 2.548 0.406 1.667 1.464 0.000 0.989 1.104 0.989 0.785 1.566 0.907 0.771 +0 0.688 -2.172 -0.599 0.156 0.688 0.969 2.204 0.975 0.000 1.114 -0.889 -0.422 1.107 1.157 -1.108 -1.355 2.548 0.809 -1.279 0.939 0.000 4.333 3.414 0.995 0.691 0.917 2.367 1.979 +0 1.470 1.012 -1.075 0.594 0.056 1.305 -0.542 -1.145 0.000 1.452 -0.508 0.595 2.215 0.275 2.382 1.550 0.000 0.870 0.281 0.585 0.000 0.862 1.260 1.104 0.657 0.252 0.929 0.827 +1 0.453 -1.567 -0.140 1.007 -0.618 0.787 -0.715 1.199 1.087 0.821 -1.107 -1.706 0.000 0.884 0.727 0.507 0.000 1.336 -0.374 -0.400 3.102 1.895 1.271 0.981 0.517 1.087 0.921 0.843 +1 0.351 -1.095 -0.055 1.109 -1.010 1.555 -0.263 0.602 2.173 0.748 0.241 -1.630 0.000 0.897 0.681 -0.796 2.548 0.688 -0.318 -0.900 0.000 0.736 0.801 0.977 2.011 1.602 1.612 1.311 +1 0.988 0.400 -1.711 0.532 0.251 0.934 0.138 1.147 0.000 0.589 2.666 0.024 0.000 1.594 0.911 -0.794 2.548 0.792 1.180 1.523 0.000 0.997 1.047 0.987 0.838 0.757 0.927 0.771 +1 0.857 -0.393 -0.032 0.526 -1.456 1.741 -0.837 0.410 1.087 0.543 -1.486 1.254 0.000 1.246 -0.401 -1.658 1.274 1.979 -0.570 -1.118 0.000 0.913 0.955 0.991 0.997 1.796 1.162 0.947 +0 0.650 -0.142 -1.074 0.378 0.414 0.769 0.554 0.463 0.000 1.385 0.331 -1.561 1.107 0.898 1.494 0.301 0.000 0.833 0.452 -0.803 3.102 0.876 0.838 0.988 0.805 0.620 0.926 0.772 +0 1.491 0.386 0.097 1.124 0.672 0.691 1.179 -1.463 0.000 0.624 0.457 -0.571 2.215 0.981 -0.361 -1.320 0.000 0.894 -0.506 1.205 3.102 1.317 0.959 0.979 0.829 0.774 0.733 0.850 +1 1.940 -0.109 0.535 0.604 -0.162 1.045 0.084 -1.608 2.173 0.699 0.055 -0.712 2.215 0.670 0.301 -1.183 0.000 0.781 0.906 0.449 0.000 0.852 0.893 0.987 0.868 0.909 0.951 0.807 +1 1.251 -0.453 0.410 0.154 0.433 0.706 -0.089 1.270 1.087 0.606 -0.888 -0.389 0.000 0.339 0.225 -0.502 0.000 1.053 -1.226 -1.270 0.000 0.798 1.075 0.983 0.782 0.693 0.682 0.722 +0 0.557 -0.522 0.956 0.978 -1.175 0.567 2.527 1.658 0.000 0.716 0.086 -0.620 2.215 1.231 -0.566 0.377 2.548 0.956 -1.669 0.242 0.000 0.928 0.934 0.987 0.813 0.860 0.677 0.659 +1 0.606 -0.905 1.691 0.938 0.324 1.095 -0.286 -0.213 2.173 1.076 0.295 1.146 0.000 1.455 0.413 -1.445 2.548 0.720 -0.286 -1.101 0.000 1.097 0.910 0.987 0.902 1.530 0.993 0.887 +1 0.612 -0.987 -1.538 1.312 -0.575 1.071 -0.547 0.651 2.173 0.475 -1.135 -0.687 0.000 0.760 -0.656 1.560 2.548 0.764 0.831 -1.395 0.000 1.127 0.818 0.986 1.220 0.826 0.851 0.832 +0 0.883 0.387 0.738 0.488 1.716 0.892 -0.089 0.149 0.000 0.990 0.457 -1.504 1.107 0.617 -0.058 -0.914 0.000 0.472 -1.009 0.844 0.000 0.825 1.220 0.986 0.542 0.428 0.704 0.702 +1 1.312 0.833 -0.411 1.082 -0.330 0.641 0.643 0.825 2.173 0.373 -0.739 0.726 0.000 0.728 1.587 -1.550 0.000 0.959 0.418 -1.156 1.551 1.300 0.876 1.005 1.094 0.813 0.799 0.801 +1 0.974 0.319 -1.504 0.371 -0.227 0.606 -0.136 -1.114 0.000 0.591 0.664 0.111 0.000 1.618 1.344 0.995 2.548 0.591 1.311 -0.095 3.102 1.255 0.849 0.982 1.178 0.623 0.855 0.806 +1 0.720 -0.736 -1.428 1.399 -0.594 1.457 -0.710 0.496 2.173 0.864 -0.727 1.158 0.000 0.969 0.250 1.627 1.274 1.446 0.842 -1.421 0.000 0.971 0.713 0.989 1.347 1.474 1.086 1.041 +0 0.430 -1.101 1.154 0.999 -0.724 1.260 -1.456 -1.190 2.173 1.762 0.179 0.475 0.000 1.499 0.763 1.107 0.000 1.462 0.071 -1.010 3.102 0.985 1.015 0.989 0.794 1.255 1.346 1.192 +1 0.601 0.201 0.130 0.817 -0.528 1.226 0.610 1.514 0.000 1.379 0.449 -0.060 2.215 0.691 1.392 -1.622 0.000 0.658 0.074 -1.465 3.102 0.935 0.641 0.988 0.734 0.836 0.961 0.870 +1 0.595 0.755 1.308 1.187 -0.270 0.986 -0.738 -0.209 2.173 1.076 0.059 1.528 0.000 0.792 1.087 1.473 0.000 0.556 0.444 0.905 3.102 0.849 0.547 1.151 1.168 0.851 0.979 0.891 +0 1.882 -0.367 0.321 1.090 0.740 0.690 0.338 -1.466 2.173 0.705 -0.723 -0.963 0.000 1.149 -0.613 -1.578 1.274 0.804 -2.347 -0.684 0.000 1.235 1.037 1.000 1.341 0.598 1.006 1.084 +1 1.031 0.338 -0.140 0.135 -1.406 1.030 -0.625 0.869 1.087 1.133 0.262 -1.009 2.215 0.621 -0.015 1.137 0.000 1.362 -0.940 -1.009 0.000 1.119 1.005 0.993 0.952 1.742 0.987 0.846 +1 0.937 -0.606 0.581 1.334 1.018 0.525 0.746 -0.622 2.173 0.695 -1.290 -1.696 0.000 0.645 -1.055 -0.294 2.548 0.665 -1.697 -0.782 0.000 0.724 1.301 0.988 0.745 0.831 0.959 0.880 +0 1.476 -0.564 -1.178 2.767 -0.921 1.256 -0.044 0.578 0.000 1.324 -0.444 1.294 2.215 0.387 0.779 0.568 0.000 0.589 -0.497 -0.175 0.000 0.903 1.102 0.989 1.082 0.561 1.073 1.111 +1 0.414 -1.340 -0.405 3.093 0.084 0.666 -0.456 -1.370 2.173 0.834 1.130 -1.656 0.000 0.960 0.462 1.037 2.548 0.733 -1.302 -1.217 0.000 1.901 1.218 0.975 1.147 0.963 1.027 1.117 +1 0.954 -0.228 1.727 0.344 -0.275 0.983 -0.501 -1.376 0.000 1.246 -0.499 -0.122 1.107 1.142 -0.135 0.357 0.000 1.388 -0.970 1.188 3.102 0.798 1.007 0.989 0.957 1.166 0.821 0.761 +1 1.417 0.391 -0.436 1.364 0.081 1.253 0.451 1.473 1.087 0.458 0.654 1.635 2.215 0.697 0.835 0.401 0.000 0.811 1.455 -1.087 0.000 0.882 1.121 0.994 0.864 0.201 0.995 0.845 +1 1.182 0.284 -1.610 1.247 -1.680 0.781 0.736 0.592 2.173 0.784 0.279 -0.184 0.000 0.384 0.608 -0.542 2.548 0.467 1.036 -0.683 0.000 0.520 0.744 0.985 0.647 0.583 0.748 0.699 +0 1.089 0.390 1.676 0.844 1.092 1.529 -1.237 0.072 1.087 0.505 -1.907 -0.689 0.000 1.010 -1.248 -1.264 2.548 1.938 -0.209 1.657 0.000 0.991 0.986 0.988 1.389 1.449 1.647 1.367 +1 1.010 0.013 0.356 0.253 -0.560 0.617 -0.255 -1.079 0.000 0.995 -1.063 0.321 1.107 0.986 -1.215 1.359 1.274 1.154 0.406 -1.547 0.000 0.719 1.010 0.988 1.018 0.857 0.913 0.899 +0 0.880 1.159 -1.225 0.973 -0.635 0.485 2.474 0.752 0.000 0.695 -0.033 0.053 2.215 0.384 0.578 -1.666 0.000 0.831 0.000 1.493 3.102 1.047 0.968 0.992 1.150 0.661 0.868 0.852 +0 0.867 0.410 -1.278 2.469 1.686 1.847 0.077 0.010 0.000 0.765 1.488 1.656 0.000 0.914 -0.512 0.950 2.548 1.049 -1.060 -0.298 3.102 3.214 2.019 0.993 1.201 0.727 1.361 1.337 +0 1.033 -1.845 1.135 0.887 1.202 0.705 -1.071 -1.063 0.000 1.080 -1.711 -0.419 0.000 1.351 -1.051 -1.575 0.000 1.172 -0.807 0.076 3.102 0.902 0.878 1.004 0.796 0.360 0.593 0.696 +0 0.299 -1.400 -0.350 0.623 1.437 0.764 -0.938 -0.703 2.173 1.121 0.803 1.001 0.000 0.486 0.494 -0.621 2.548 1.131 -0.234 0.816 0.000 0.893 0.869 0.993 0.580 0.622 0.606 0.594 +0 0.689 -0.615 1.012 0.424 -0.617 0.669 -1.359 -0.433 0.000 0.991 -0.389 -0.941 2.215 1.228 -1.252 1.191 1.274 0.706 -1.721 0.410 0.000 0.822 0.896 0.988 0.840 1.251 0.791 0.688 +1 0.573 -0.082 1.354 0.744 0.310 0.685 1.439 0.756 0.000 1.094 0.763 -1.065 1.107 1.073 1.346 -0.402 2.548 1.105 2.323 -0.995 0.000 1.067 0.914 0.989 1.229 0.765 1.066 1.085 +0 0.642 -0.672 -1.224 1.428 0.954 0.585 0.027 -0.732 2.173 0.516 -1.207 1.505 0.000 0.481 -0.494 0.335 1.274 0.588 -1.534 -0.535 0.000 0.726 0.876 1.225 0.654 0.575 0.655 0.617 +0 0.402 -1.052 0.474 2.160 -1.116 0.886 0.839 0.441 0.000 0.482 0.410 1.153 1.107 0.577 1.743 0.843 0.000 1.291 0.802 -0.964 3.102 0.851 1.011 1.278 1.062 0.700 0.952 1.191 +0 0.364 2.045 -0.758 1.858 -1.715 0.727 0.010 -0.526 2.173 1.099 0.244 1.110 0.000 0.811 2.030 -0.024 0.000 0.527 -1.275 -0.946 3.102 2.083 1.678 0.983 1.264 0.607 1.170 1.127 +0 0.338 0.915 -0.926 1.250 1.138 0.725 -0.259 -0.720 2.173 0.274 1.279 1.401 1.107 0.370 1.777 -0.025 0.000 1.091 1.076 0.436 0.000 0.368 1.063 0.984 0.478 0.844 0.878 0.739 +1 0.674 0.985 -1.543 1.054 0.937 0.768 0.877 -0.174 0.000 1.031 0.863 -0.642 1.107 0.991 1.314 1.362 0.000 1.199 -0.371 1.368 1.551 1.603 1.158 0.988 0.665 1.212 0.962 0.839 +1 0.565 -0.595 0.086 1.378 0.012 1.136 0.179 -0.848 0.000 2.397 1.029 1.414 2.215 0.955 -0.294 -0.425 0.000 1.593 0.033 0.533 3.102 0.845 1.160 0.978 1.647 1.570 1.462 1.238 +0 0.696 -0.219 1.653 0.292 -0.733 0.551 0.230 -1.347 2.173 1.445 0.739 0.885 1.107 0.402 -2.129 1.671 0.000 0.519 -0.616 -0.881 0.000 1.214 0.920 0.996 0.878 1.238 0.855 0.733 +1 1.967 -1.011 -0.126 0.612 -0.448 0.896 -0.369 1.201 2.173 0.634 -0.674 -1.200 0.000 0.919 0.277 -0.361 0.000 0.771 1.292 1.264 0.000 1.007 1.170 0.989 1.208 0.844 1.098 0.965 +1 0.688 -0.580 0.343 0.164 -1.239 1.304 -0.540 1.450 2.173 0.754 -0.951 -0.098 0.000 0.465 2.499 1.057 0.000 0.657 -1.353 -0.440 0.000 0.702 1.131 0.995 0.558 0.938 0.782 0.736 +1 0.593 0.082 -0.844 0.444 1.380 0.534 -1.562 0.800 0.000 0.850 -0.984 -0.313 2.215 0.706 -0.262 -1.308 2.548 0.565 -2.159 1.192 0.000 0.531 0.888 0.988 0.560 0.713 0.706 0.647 +0 0.726 0.670 1.161 0.280 -1.589 1.050 -1.423 -1.482 0.000 1.379 0.428 -0.344 2.215 1.930 0.566 0.675 2.548 0.429 -0.808 1.550 0.000 0.474 1.841 0.986 0.877 1.387 1.543 1.195 +1 0.701 1.407 0.913 1.086 -1.579 0.481 0.559 0.433 1.087 1.130 1.164 -1.170 2.215 1.253 -2.459 -0.376 0.000 2.310 0.025 1.062 0.000 1.374 0.855 0.985 0.810 1.131 0.916 0.884 +1 0.507 0.281 1.717 3.300 1.488 1.883 -1.679 0.007 0.000 0.537 0.254 -1.432 2.215 0.607 -0.933 -0.073 1.274 0.543 -1.300 -0.633 0.000 0.849 0.555 0.993 0.776 0.707 0.950 1.276 +1 0.396 -1.013 -1.223 0.146 -0.381 1.367 0.838 -0.174 0.000 1.297 -0.045 -1.130 0.000 3.360 -0.739 1.322 0.000 1.205 0.171 0.447 3.102 0.785 0.892 0.978 0.661 0.447 0.637 0.630 +1 1.184 0.721 -1.023 0.570 -1.394 0.984 1.088 0.008 1.087 0.571 0.007 1.385 0.000 0.723 1.289 1.165 2.548 0.429 1.116 0.502 0.000 0.652 0.941 0.980 0.734 0.922 0.790 0.718 +0 0.500 0.660 0.944 1.270 -1.246 1.477 -0.555 -0.350 2.173 1.942 -0.320 1.460 2.215 0.844 -1.053 -0.032 0.000 0.565 -0.352 0.720 0.000 0.557 1.133 1.017 1.410 2.504 1.384 1.106 +0 1.645 -0.684 -0.131 0.050 0.657 0.542 -0.008 1.500 2.173 0.494 -1.524 0.337 0.000 1.353 -0.644 -1.258 2.548 0.855 -0.366 1.166 0.000 0.764 0.900 0.980 0.919 0.753 0.772 0.713 +1 0.514 -1.314 1.231 1.251 -0.478 1.778 -0.356 -1.630 2.173 1.266 -0.825 0.430 2.215 1.684 -2.074 0.289 0.000 0.687 -0.384 -0.778 0.000 0.737 1.125 1.110 1.424 2.187 1.692 1.321 +1 0.319 -1.741 -1.316 1.523 0.152 0.884 -0.322 1.357 0.000 1.514 -1.436 -0.858 2.215 0.709 -2.338 1.168 0.000 1.601 -0.113 0.687 3.102 1.561 1.070 0.987 0.969 1.716 1.290 1.108 +1 0.954 0.356 0.303 1.442 -0.269 0.917 -0.456 1.568 2.173 0.642 0.689 -1.081 2.215 0.626 -2.117 -0.915 0.000 1.003 -0.574 0.361 0.000 1.128 1.222 0.980 0.800 1.040 1.027 1.156 +0 1.160 -0.281 0.445 1.828 1.004 1.646 -0.959 -1.347 2.173 0.751 -0.412 -0.608 0.000 0.903 0.078 -0.117 2.548 1.202 -0.842 0.857 0.000 1.254 0.872 0.989 1.763 1.598 1.262 1.061 +1 1.017 1.586 -0.767 1.555 -0.346 1.072 0.469 1.295 2.173 0.295 1.613 -1.301 0.000 0.273 0.309 1.596 1.274 0.758 0.343 0.333 0.000 0.738 0.838 0.983 0.654 0.186 0.910 0.728 +1 0.380 1.700 1.080 2.886 1.315 0.594 -2.663 0.007 0.000 1.392 -0.342 -1.446 0.000 1.290 0.106 -0.740 2.548 1.578 0.958 0.026 3.102 0.800 1.677 0.977 1.562 0.911 1.757 4.073 +1 0.296 2.169 -0.415 0.982 0.429 1.498 -0.367 -1.313 0.000 2.287 0.142 0.241 2.215 0.858 1.295 -1.268 0.000 1.294 0.233 0.928 3.102 0.833 0.769 0.982 0.880 0.906 0.917 0.781 +1 0.730 -0.269 0.029 0.738 -1.016 1.058 -1.417 1.323 2.173 0.932 -1.623 -0.274 0.000 0.495 -0.589 -0.824 0.000 0.437 -0.115 1.549 3.102 0.746 1.268 0.986 0.550 0.519 0.880 0.869 +0 0.849 0.602 -0.231 1.561 -0.170 0.920 0.900 0.810 2.173 0.860 -0.411 1.480 2.215 0.872 2.443 -1.406 0.000 1.120 1.922 0.952 0.000 0.949 1.301 0.989 1.118 1.196 1.313 1.426 +1 0.927 -0.944 -1.174 0.627 1.488 0.862 -0.239 -0.207 0.000 1.066 0.930 0.291 0.000 0.744 0.023 1.088 2.548 1.800 0.834 -0.718 3.102 1.014 0.906 0.989 0.658 0.991 0.759 0.833 +0 0.931 1.431 -0.446 1.248 -0.101 0.614 1.441 1.072 2.173 0.642 0.232 1.409 0.000 0.901 0.663 -1.492 2.548 0.378 -0.645 1.140 0.000 0.637 0.703 0.992 0.871 0.765 0.802 0.693 +1 0.693 -0.999 -0.300 0.495 -1.128 1.481 -1.455 -1.563 2.173 1.104 1.887 0.437 0.000 1.386 0.007 0.648 2.548 2.096 -1.931 -0.058 0.000 0.982 0.982 0.987 1.303 2.174 1.194 0.966 +1 1.542 -0.429 -1.444 0.799 -0.573 1.378 -0.586 0.552 2.173 0.542 -1.485 1.689 0.000 0.552 -0.292 -0.783 0.000 0.566 -0.656 -0.349 3.102 0.855 1.212 1.088 0.603 0.685 0.893 0.785 +1 0.643 -0.694 0.966 1.074 -1.351 0.649 -0.073 1.197 2.173 1.322 0.730 -0.278 0.000 0.800 -0.329 -1.170 2.548 0.581 0.877 0.652 0.000 0.866 0.950 1.001 0.721 0.770 0.820 0.813 +0 0.665 -0.350 -0.985 0.374 1.605 0.706 0.703 0.348 2.173 0.817 0.003 0.096 2.215 0.787 0.536 -1.512 0.000 1.123 2.178 -1.649 0.000 1.246 1.323 0.980 0.788 0.476 1.029 0.877 +0 0.730 0.827 0.174 0.491 1.453 1.076 0.390 1.426 2.173 1.159 -0.315 -0.863 2.215 1.264 -0.404 0.478 0.000 1.268 -0.237 -1.432 0.000 0.940 0.965 0.985 1.244 1.568 1.072 0.887 +1 0.703 0.124 -0.096 0.516 -1.083 0.927 -0.668 1.345 0.000 0.728 -0.553 0.800 0.000 1.549 -0.978 -0.385 2.548 1.075 0.219 -1.058 3.102 0.830 1.022 0.994 1.213 0.905 0.945 0.968 +1 1.164 1.075 0.793 1.683 0.199 1.278 0.552 -1.326 2.173 0.703 0.561 -0.328 0.000 0.456 -0.406 1.697 0.000 0.792 0.020 0.591 0.000 0.960 1.004 0.988 0.635 0.865 1.023 0.886 +0 0.448 -1.906 -0.585 0.665 1.106 0.523 -1.055 0.462 0.000 1.108 -0.872 -0.788 1.107 0.590 -0.990 1.320 0.000 1.084 0.406 -1.662 3.102 0.697 0.979 0.981 0.793 1.020 0.786 0.688 +1 1.116 -1.284 1.466 2.313 -0.139 0.741 0.106 -0.089 2.173 1.622 -0.749 -1.682 0.000 1.155 -2.464 -1.415 0.000 1.617 -1.042 -0.298 0.000 0.885 0.876 2.208 1.526 0.777 1.132 0.914 +1 1.035 0.821 -1.549 0.653 -0.921 0.306 1.056 0.419 0.000 0.749 -0.590 0.545 1.107 0.680 0.267 1.219 0.000 1.222 0.152 -0.772 3.102 1.018 1.087 0.979 0.759 0.874 0.906 0.804 +0 0.847 0.235 1.575 0.565 -1.447 0.605 0.986 1.261 0.000 0.731 0.950 0.326 2.215 0.586 0.222 -0.268 2.548 0.994 -1.044 -0.664 0.000 0.842 0.790 0.991 0.724 0.440 0.723 0.692 +1 0.480 -0.357 -1.123 2.404 0.471 1.780 0.346 -1.293 0.000 1.384 -0.800 0.835 0.000 1.584 0.426 -0.394 1.274 0.418 2.051 -1.300 0.000 1.666 1.475 1.475 0.722 0.790 1.112 1.200 +0 2.774 -0.067 -1.332 0.840 -1.302 1.603 -1.229 0.556 0.000 1.461 -0.999 0.149 0.000 1.964 0.397 -1.619 2.548 1.298 -1.285 -0.203 1.551 1.218 0.984 1.008 0.693 1.834 1.646 1.709 +1 0.839 0.427 1.306 0.962 0.475 1.242 0.348 -0.423 2.173 1.311 -0.334 1.054 0.000 1.243 -1.395 -1.117 0.000 0.858 -0.532 -1.691 0.000 0.880 0.680 0.985 1.159 0.916 0.985 0.843 +0 0.846 0.908 -0.961 0.370 1.563 0.670 -0.130 0.286 1.087 0.489 -1.099 0.931 0.000 1.126 -0.031 -1.067 2.548 0.596 -1.820 1.241 0.000 0.434 0.957 0.983 0.853 1.017 0.772 0.755 +0 1.577 0.332 -0.220 0.232 1.369 0.834 -0.246 1.559 0.000 0.767 -0.726 -0.998 1.107 0.539 -1.302 1.187 0.000 1.092 1.161 0.235 3.102 0.854 0.905 0.990 0.636 1.295 0.963 0.901 +0 0.479 -2.150 0.327 0.616 -1.174 0.835 -1.005 1.624 2.173 0.494 -1.227 -0.116 0.000 0.652 -1.666 -1.368 0.000 1.836 -0.765 0.352 3.102 0.825 0.874 0.995 0.742 1.197 0.750 0.660 +0 2.209 -0.247 0.513 0.520 -0.248 0.896 2.815 0.301 0.000 1.802 0.735 -1.709 1.107 1.683 0.385 -1.041 0.000 2.338 0.825 -1.247 0.000 0.746 1.012 0.988 0.800 2.345 1.456 1.369 +0 1.063 1.338 0.762 0.321 -0.428 0.786 0.516 -1.534 0.000 0.569 2.577 1.355 0.000 1.147 -0.876 -0.056 2.548 0.948 0.490 0.059 3.102 0.687 0.810 0.983 1.633 0.687 1.032 0.973 +1 0.731 -1.408 0.973 0.703 0.585 0.903 -0.132 -1.468 0.000 0.751 -1.508 0.469 2.215 0.899 -2.600 -0.515 0.000 1.391 0.444 -0.762 0.000 0.976 1.005 0.974 0.685 0.890 0.858 0.763 +0 1.140 0.158 -0.861 0.843 -0.246 0.354 -1.243 0.775 0.000 0.583 -0.397 0.604 2.215 0.775 -0.109 -1.692 2.548 0.825 -2.088 1.626 0.000 0.794 0.883 0.986 0.776 0.636 0.645 0.755 +1 0.562 -0.792 -1.528 2.153 1.321 1.526 -0.101 0.499 0.000 2.259 0.528 -1.186 2.215 1.110 -0.122 0.087 0.000 2.230 -0.202 -0.577 3.102 0.850 1.303 0.984 2.321 1.335 1.648 1.631 +0 0.560 0.287 0.280 1.995 1.005 0.882 -0.947 -0.246 0.000 0.974 -0.975 -1.178 2.215 1.127 0.944 1.654 2.548 0.386 -2.207 -0.660 0.000 0.909 0.931 0.982 0.792 1.480 1.200 1.293 +0 0.884 1.674 0.582 0.561 1.640 0.765 2.687 -0.469 0.000 0.369 -0.863 0.969 0.000 1.299 0.436 1.628 2.548 1.666 -0.712 -0.628 1.551 0.387 1.043 0.986 1.967 1.290 1.366 1.403 +0 0.582 -2.138 -0.872 0.511 -1.476 0.791 -1.059 0.363 2.173 0.844 0.056 0.799 0.000 0.943 0.536 1.711 0.000 1.528 0.413 -0.747 3.102 1.067 1.034 0.993 0.913 1.404 0.986 0.940 +1 0.677 -0.289 -1.397 2.952 -0.154 1.131 -0.364 0.877 0.000 1.087 1.098 -1.305 2.215 0.333 0.849 1.457 0.000 0.475 0.278 1.104 1.551 0.946 0.522 1.764 1.686 0.598 1.066 1.061 +1 1.006 -0.665 -0.120 1.248 -0.424 1.184 0.872 1.701 2.173 0.698 0.097 -0.769 0.000 0.465 -0.698 1.068 2.548 0.797 0.642 0.134 0.000 0.859 0.961 0.976 0.743 0.987 1.037 0.847 +1 0.779 0.693 -0.840 1.179 -0.104 0.604 0.016 0.989 0.000 0.943 1.065 1.675 1.107 1.201 -0.077 1.375 2.548 0.675 -2.350 -0.025 0.000 0.992 0.923 0.989 0.970 0.769 0.852 0.799 +1 0.906 0.172 0.708 0.768 1.580 0.623 -0.013 1.139 0.000 0.683 -1.353 -0.375 1.107 0.681 -0.385 -1.035 0.000 1.265 -1.462 -1.065 0.000 0.734 0.681 0.987 1.212 0.483 0.792 0.818 +1 1.520 1.104 0.293 0.710 1.053 1.063 2.829 -0.804 0.000 0.841 0.929 1.306 0.000 1.207 0.411 -1.557 2.548 0.423 1.767 -0.106 0.000 0.884 0.807 0.990 0.696 0.679 0.710 0.658 +1 2.354 -1.849 -0.282 0.520 -1.174 0.422 -1.869 0.999 0.000 0.573 -1.395 -1.385 0.000 1.235 -0.871 0.692 2.548 1.387 -0.989 1.625 3.102 0.906 0.839 1.102 1.073 0.754 0.909 0.795 +0 1.186 -1.354 1.684 0.339 -0.553 1.196 -0.736 1.386 2.173 1.582 -1.168 0.124 0.000 0.974 1.423 -1.243 0.000 1.114 -1.212 -0.601 3.102 3.982 2.367 0.989 0.959 1.272 1.728 1.467 +1 0.733 -1.729 0.379 0.932 1.131 2.048 -0.649 1.731 2.173 2.796 -0.797 -0.432 0.000 0.604 -1.872 -0.352 0.000 1.644 -1.475 0.979 0.000 1.036 0.725 0.988 1.290 0.864 1.027 0.891 +1 0.880 1.540 0.212 1.336 -1.667 1.930 0.098 -1.633 0.000 0.741 0.438 -0.271 0.000 1.178 -0.399 0.355 1.274 1.632 0.483 1.370 3.102 2.434 1.520 1.491 1.477 1.011 1.178 1.241 +0 2.679 -0.294 0.333 0.823 -0.135 1.140 -0.234 -0.972 2.173 0.594 -0.345 1.522 2.215 0.672 0.194 1.059 0.000 1.295 0.191 -1.450 0.000 0.794 0.958 0.987 0.988 0.946 1.059 0.925 +1 0.916 0.561 0.980 0.569 0.091 0.462 0.222 -1.301 1.087 0.640 2.170 0.876 0.000 0.345 -0.002 1.319 0.000 1.118 1.159 -0.524 0.000 0.856 0.682 0.990 0.620 0.405 0.572 0.530 +0 0.716 -0.356 1.344 1.289 -1.659 0.801 -0.817 0.244 0.000 0.929 -0.671 -1.021 2.215 0.972 -0.341 0.660 2.548 0.913 0.052 -0.431 0.000 0.957 1.010 0.994 0.864 1.021 0.815 0.857 +0 0.661 -0.586 0.075 1.154 0.984 0.705 -1.089 -1.487 2.173 0.419 -1.041 0.653 0.000 0.734 -1.145 -1.021 2.548 0.493 2.240 -0.439 0.000 0.706 0.814 0.985 0.857 0.373 0.747 0.698 +0 2.172 -0.877 -0.751 0.162 -0.709 0.598 -0.084 0.535 2.173 0.702 0.068 1.571 2.215 0.368 -1.920 0.902 0.000 0.457 -0.363 0.374 0.000 0.472 0.678 0.998 1.039 0.770 0.839 0.706 +0 0.697 -0.264 -0.446 1.803 -1.472 0.694 -0.519 0.490 0.000 1.068 1.063 0.631 2.215 1.377 0.353 -0.888 2.548 0.403 0.116 1.729 0.000 0.775 0.983 1.238 0.811 1.345 1.033 0.921 +1 0.599 0.255 0.425 1.440 -0.646 2.299 1.635 0.800 0.000 1.358 -0.028 -1.177 2.215 0.777 -0.130 1.697 0.000 1.897 -0.929 -0.950 3.102 0.901 0.967 1.057 0.887 0.878 0.805 0.736 +0 1.329 0.083 -1.451 1.941 1.412 0.904 -1.995 -0.068 0.000 0.721 -1.358 -0.384 2.215 1.074 -1.152 0.615 2.548 0.553 1.011 -1.252 0.000 0.285 0.988 1.185 1.276 0.735 1.097 0.935 +1 1.105 0.217 -1.316 0.276 1.097 0.618 0.808 -0.190 0.000 0.651 1.743 0.627 0.000 1.615 -0.529 -0.961 1.274 1.143 -1.187 0.839 3.102 0.858 1.219 0.995 0.829 1.131 0.990 0.847 +0 0.656 1.252 1.684 1.163 -1.618 0.920 1.130 0.127 0.000 0.785 0.639 -1.468 2.215 0.355 -1.993 0.323 0.000 0.898 1.599 0.905 0.000 1.052 1.185 0.988 0.755 0.500 0.697 0.826 +1 0.861 0.603 -0.470 1.125 0.783 0.644 1.325 0.001 0.000 0.886 0.988 1.622 1.107 0.754 -0.791 1.407 2.548 0.647 1.115 -1.046 0.000 0.798 0.940 1.233 0.942 0.960 0.878 0.799 +1 1.299 -0.948 0.998 0.186 1.023 0.657 -0.546 0.272 1.087 1.047 -0.417 -0.330 2.215 1.237 -0.241 -1.571 0.000 0.743 -0.642 -1.406 0.000 0.313 0.964 0.980 0.965 0.636 0.745 0.727 +1 2.234 -0.063 0.720 0.848 0.328 0.387 -0.236 1.738 0.000 0.876 0.241 -0.687 0.000 1.233 0.549 -1.468 0.000 0.880 -0.671 -1.002 3.102 1.055 0.688 0.996 0.609 0.220 0.639 0.701 +0 1.408 -1.646 1.288 0.608 0.023 0.425 -0.921 1.310 1.087 0.654 -1.810 -0.734 0.000 1.160 -0.043 -0.572 2.548 0.422 -0.928 -0.428 0.000 0.322 0.734 1.163 0.681 0.955 0.841 0.722 +1 0.794 0.403 0.028 1.134 -1.051 1.744 -0.228 1.081 2.173 1.247 0.425 -0.623 0.000 1.566 -2.382 -0.975 0.000 1.836 -0.937 0.632 1.551 0.897 0.871 1.086 1.473 1.151 1.155 1.002 +1 0.915 -0.852 1.300 0.450 -1.252 0.677 0.215 -1.174 2.173 2.013 -0.493 -0.826 2.215 3.341 -1.751 0.515 0.000 0.705 0.861 1.456 0.000 0.555 2.349 0.977 1.221 0.829 1.875 1.444 +1 0.527 1.056 1.362 1.859 -0.782 0.870 -0.754 1.149 2.173 0.855 -0.012 0.083 0.000 0.289 -1.301 1.077 0.000 0.480 1.925 -1.419 0.000 0.837 0.919 1.283 1.204 0.810 1.210 1.004 +1 2.151 0.217 -0.811 0.482 -0.913 1.069 -0.260 0.480 2.173 1.096 0.085 1.670 2.215 0.347 -1.043 -0.569 0.000 1.079 0.093 1.116 0.000 0.836 0.828 0.971 1.456 1.426 1.151 0.955 +1 1.205 -0.288 -0.995 1.697 -1.442 1.093 0.139 0.153 2.173 1.429 0.537 0.884 2.215 0.526 0.766 -1.601 0.000 0.558 0.184 -0.666 0.000 0.484 0.842 0.985 1.647 1.188 1.366 1.030 +1 0.425 -2.314 -0.753 1.825 0.557 0.674 -0.267 -1.657 2.173 0.754 -0.511 -0.900 0.000 0.582 -1.296 0.173 0.000 1.164 -0.049 0.821 3.102 0.960 0.926 1.129 1.318 0.745 1.214 1.033 +0 0.366 -1.579 -0.317 0.605 1.616 1.058 -1.021 -0.752 2.173 0.602 1.003 -1.361 0.000 0.590 1.589 1.006 0.000 2.120 0.652 0.665 1.551 0.842 0.885 0.989 0.878 2.236 1.379 1.094 +1 0.495 -1.149 -1.630 1.694 1.001 0.653 0.769 -0.095 1.087 0.397 -1.499 -0.781 0.000 1.106 0.144 -1.239 2.548 0.602 1.033 1.163 0.000 1.355 0.947 0.986 1.219 0.968 0.936 0.853 +0 0.402 0.087 -0.223 0.608 1.650 0.380 0.012 1.321 2.173 0.429 2.868 -1.680 0.000 0.398 -1.297 -0.938 0.000 0.693 1.351 -0.087 0.000 0.868 1.098 0.992 0.551 0.517 0.889 1.051 +0 0.711 -0.785 0.042 1.553 -0.873 1.102 1.521 0.972 2.173 0.707 0.975 0.567 0.000 1.249 -0.765 -0.954 2.548 1.070 0.526 -1.739 0.000 1.012 0.909 1.068 0.616 2.610 1.652 1.329 +1 0.986 -1.067 1.663 0.366 0.481 0.575 0.031 1.132 2.173 0.547 -2.349 -0.415 0.000 0.636 0.856 -1.234 0.000 0.517 0.500 -0.719 0.000 1.501 0.887 0.983 0.899 0.185 0.779 0.727 +0 0.431 1.121 1.177 0.348 0.254 0.592 1.514 -0.160 0.000 0.689 -0.418 -1.441 2.215 0.672 -0.271 0.939 1.274 0.955 -0.632 1.643 0.000 1.984 1.250 0.994 0.701 0.609 0.882 0.755 +0 1.044 1.391 1.531 0.374 0.906 1.852 0.426 1.417 2.173 3.674 -0.917 -0.132 1.107 2.025 0.181 -1.387 0.000 1.060 -1.691 0.014 0.000 1.753 1.698 0.990 2.500 4.733 2.711 2.076 +1 1.003 0.162 -0.705 1.304 0.769 0.678 -2.607 0.930 0.000 1.210 -1.250 -0.751 2.215 1.307 0.608 1.662 2.548 1.154 -0.541 0.242 0.000 0.982 1.261 1.538 1.046 1.880 1.555 1.447 +1 2.018 0.352 -0.465 0.540 0.032 1.174 0.485 1.329 2.173 0.687 0.228 -1.174 0.000 0.700 -0.286 1.170 0.000 0.867 -0.015 0.232 3.102 0.958 0.934 0.988 0.586 0.934 0.955 0.831 +1 0.617 -0.348 1.157 0.869 -0.484 0.661 -1.390 1.644 1.087 0.885 -0.478 1.703 0.000 0.980 -0.801 -0.269 2.548 0.557 -1.711 0.595 0.000 0.896 0.829 1.010 0.870 1.026 0.693 0.636 +1 0.738 -0.389 0.702 0.966 -0.752 1.045 -0.371 1.340 2.173 0.447 1.903 -0.874 0.000 0.646 -0.655 -0.022 2.548 0.707 0.437 -0.323 0.000 0.670 0.874 1.130 0.987 0.981 0.958 0.849 +1 0.851 0.658 0.398 0.365 -0.536 1.012 0.533 1.573 0.000 0.949 1.322 0.146 2.215 1.031 1.601 0.760 0.000 1.313 0.058 -1.476 3.102 1.063 0.854 0.990 0.899 1.220 0.949 0.790 +0 0.463 0.526 -0.130 1.051 1.410 0.524 0.586 0.484 0.000 0.746 -0.062 0.711 2.215 1.823 0.247 -0.916 2.548 0.754 1.245 -0.890 0.000 1.013 1.049 0.986 0.625 1.250 0.816 0.726 +1 0.694 -1.192 -1.266 2.247 -1.438 0.817 -1.375 0.364 0.000 0.776 0.086 0.195 2.215 0.532 -0.751 -0.710 1.274 0.762 0.111 0.728 0.000 0.973 0.876 1.010 0.643 0.593 0.771 0.790 +1 0.363 -0.092 -1.626 0.178 1.557 0.657 -1.220 1.193 0.000 0.946 0.219 0.035 0.000 1.246 -1.096 0.019 2.548 1.688 -0.323 -1.068 0.000 0.921 1.008 0.795 0.384 1.087 0.888 1.027 +0 0.346 0.170 1.101 2.501 -1.555 0.936 -1.165 0.522 1.087 0.557 1.141 -0.030 0.000 0.607 0.173 -0.848 1.274 0.832 -0.654 -0.256 0.000 1.032 0.682 0.983 1.391 1.126 0.969 0.961 +1 0.671 -0.148 -0.808 0.591 -1.700 1.054 -0.658 0.475 0.000 1.076 0.468 -0.491 0.000 1.623 -0.210 -1.671 2.548 1.344 -0.999 1.162 3.102 2.207 1.608 0.996 0.762 0.847 1.199 1.006 +0 1.742 1.881 1.391 0.290 -0.918 0.674 1.506 -0.478 0.000 0.904 1.095 -1.446 1.107 1.120 0.477 -0.159 0.000 1.344 0.896 0.794 3.102 0.924 0.965 0.992 0.817 0.897 0.805 0.869 +1 0.742 -1.044 1.447 0.513 -0.787 0.448 -0.380 -1.293 2.173 0.462 -2.690 -0.649 0.000 0.739 -0.247 0.681 2.548 1.098 -1.704 0.622 0.000 0.912 1.046 0.985 0.722 0.702 0.829 0.720 +0 1.107 1.615 0.786 1.182 0.934 0.884 0.783 0.232 2.173 2.251 -0.058 -1.170 0.000 0.895 1.169 -0.046 0.000 1.082 -0.652 -1.147 1.551 1.043 1.029 0.992 0.832 1.332 0.977 0.826 +1 0.713 -1.450 0.817 0.364 -1.184 0.464 -0.875 1.604 1.087 0.794 2.701 -1.259 0.000 0.664 2.301 0.997 0.000 2.949 -0.517 0.041 3.102 1.007 2.403 0.994 0.867 1.233 2.225 1.830 +1 0.372 -1.721 1.294 0.749 -1.649 0.674 0.133 -1.081 0.000 0.802 0.827 -0.248 0.000 1.470 -1.117 0.521 2.548 1.103 -0.902 -1.106 0.000 0.852 0.936 0.988 0.937 0.475 0.865 0.758 +1 0.832 -1.514 0.993 1.754 0.182 0.629 -1.983 -1.035 0.000 1.338 0.815 -1.665 2.215 0.491 0.437 -1.243 0.000 1.091 -0.591 0.612 0.000 0.951 1.083 1.115 1.018 1.103 1.494 1.152 +1 0.371 -0.122 0.822 1.548 -0.158 0.857 0.628 -1.162 2.173 0.809 -0.156 1.531 0.000 0.508 0.219 -0.683 0.000 1.211 0.537 0.561 3.102 0.918 0.846 0.987 0.924 1.078 1.009 0.862 +1 0.533 0.150 -0.653 0.370 0.791 1.087 -0.747 -1.189 0.000 1.091 -0.434 0.593 2.215 0.727 -0.803 -0.589 0.000 0.700 0.024 1.686 3.102 0.912 0.938 0.991 0.630 0.684 0.713 0.647 +0 1.188 -0.158 -1.590 0.839 -0.584 0.372 -1.353 1.731 0.000 0.750 -0.420 0.950 0.000 1.073 -0.302 -0.355 2.548 1.043 -0.023 0.176 3.102 0.907 0.974 1.090 0.767 0.392 0.652 0.673 +1 1.012 1.947 0.915 0.906 -1.731 0.777 -0.325 -0.094 0.000 0.615 0.406 0.926 0.000 0.872 0.120 -1.384 2.548 0.900 0.632 -1.078 0.000 0.957 0.771 0.985 1.179 0.438 1.108 0.914 +1 0.668 0.057 1.735 2.306 -0.608 1.030 0.344 1.227 0.000 1.222 0.865 0.676 2.215 0.593 -0.201 -0.609 1.274 0.375 0.387 -1.161 0.000 0.795 0.868 1.475 0.696 0.983 0.938 0.893 +1 2.024 -0.778 -1.446 1.060 -0.240 0.606 -1.286 0.151 2.173 0.377 -1.198 1.644 0.000 0.553 -1.836 0.454 0.000 1.384 -0.103 0.675 3.102 0.814 0.738 1.797 1.202 0.746 0.933 0.767 +1 1.278 1.864 -0.153 1.162 -0.151 1.045 0.578 1.419 2.173 0.506 0.075 0.405 0.000 1.129 0.683 -1.420 2.548 0.490 1.424 -0.967 0.000 0.851 0.930 0.990 1.057 0.752 1.070 0.866 +1 0.693 0.234 1.419 0.768 -1.209 0.635 0.287 0.770 2.173 0.920 -2.213 -0.078 0.000 0.432 -1.082 -0.537 0.000 0.814 1.064 -1.317 3.102 0.939 1.717 0.993 0.769 0.823 1.400 1.128 +0 2.060 -0.587 0.380 2.553 1.076 0.877 0.841 -0.321 0.000 1.431 2.537 -1.189 0.000 1.042 0.885 -1.413 1.274 1.006 -1.137 -0.421 3.102 2.912 1.801 1.864 1.267 1.291 1.904 2.378 +1 1.018 1.554 0.979 1.489 -0.657 0.479 0.501 -1.092 1.087 0.625 0.562 0.079 0.000 0.996 -0.748 -0.760 2.548 0.989 0.463 1.327 0.000 0.920 0.851 1.698 1.678 0.665 1.103 0.944 +1 0.779 0.003 -1.265 0.445 0.015 1.259 0.914 -1.537 2.173 1.050 0.093 0.676 0.000 1.477 0.520 0.114 0.000 0.517 -0.117 -0.617 3.102 1.033 0.745 0.986 1.238 0.789 1.037 0.942 +0 0.647 2.022 -1.498 0.823 -0.678 0.791 1.230 0.310 2.173 0.347 0.428 -1.707 0.000 0.484 -1.214 1.385 0.000 0.741 -0.454 0.075 3.102 0.671 1.209 0.991 0.865 0.842 0.765 0.803 +1 0.405 -1.193 -0.681 0.795 1.145 1.365 0.655 0.358 0.000 2.257 -0.772 -1.523 2.215 0.917 0.697 -0.290 0.000 0.542 0.172 -0.281 3.102 1.115 0.672 0.993 1.008 1.038 1.523 1.173 +0 0.595 1.815 1.601 0.456 -1.364 0.636 0.185 0.609 0.000 0.601 -1.790 1.151 0.000 0.675 1.599 0.119 0.000 2.257 0.466 -0.924 3.102 0.892 1.013 0.997 0.544 0.615 0.614 0.598 +1 0.909 -0.257 -0.572 1.501 -1.423 1.137 -1.803 -0.227 0.000 1.445 -0.367 1.241 2.215 0.598 -0.761 -0.088 0.000 1.538 -0.577 1.480 3.102 0.915 1.018 1.121 1.141 0.358 0.792 0.835 +1 0.554 -0.603 0.571 0.430 -1.657 1.108 0.888 -0.505 0.000 1.208 -0.539 1.449 0.000 1.466 0.390 -1.434 2.548 1.055 0.411 -0.084 0.000 0.698 1.100 0.991 0.748 0.880 0.886 0.771 +1 2.221 1.381 0.384 0.319 -1.660 1.808 0.175 -0.991 0.000 1.001 0.216 1.320 2.215 0.692 0.645 0.549 0.000 0.753 -0.198 -0.203 3.102 0.761 0.619 1.123 0.874 0.789 0.839 0.711 +1 0.504 0.625 -0.848 1.359 1.598 2.850 -0.941 1.549 0.000 2.634 1.093 -0.168 1.107 1.602 -2.049 0.065 0.000 1.444 1.120 -0.787 0.000 1.347 1.289 0.987 1.641 0.993 1.108 1.106 +1 1.042 0.122 -1.545 0.824 -1.079 1.171 -0.493 0.188 2.173 0.768 -0.800 0.989 2.215 0.276 -0.549 0.577 0.000 0.805 1.355 -1.544 0.000 0.861 0.971 0.988 1.506 0.950 1.125 0.949 +0 0.664 -0.025 1.616 1.605 -1.083 0.805 -2.581 -0.510 0.000 0.710 -2.405 -1.567 0.000 1.025 -0.747 0.996 0.000 1.687 0.574 0.052 1.551 1.039 1.290 0.982 1.167 0.851 1.121 1.040 +1 0.828 1.095 1.563 0.413 -1.513 0.383 -1.034 0.185 1.087 0.597 0.837 0.697 0.000 0.690 0.521 -1.145 1.274 0.394 2.378 0.398 0.000 0.758 0.776 0.981 0.890 0.829 0.808 0.743 +1 1.125 -0.595 1.383 1.259 1.311 1.069 -0.625 0.548 2.173 1.073 -0.706 -0.068 2.215 2.577 -0.690 -1.170 0.000 0.538 1.090 -1.119 0.000 0.637 0.947 0.996 1.209 0.835 0.968 0.908 +0 0.799 1.486 -0.681 0.653 -0.326 1.030 0.871 0.393 0.000 1.748 0.342 -1.550 2.215 0.693 0.659 1.067 0.000 0.605 -0.702 -0.217 1.551 0.921 1.036 0.991 1.100 1.046 1.148 0.975 +0 1.397 1.504 -1.592 0.707 1.161 0.933 1.702 0.055 0.000 0.639 0.342 -0.887 2.215 0.671 2.130 0.661 0.000 0.859 1.045 -1.197 3.102 0.864 0.886 0.994 0.832 0.362 0.778 0.793 +0 1.208 1.782 0.655 0.994 1.267 0.441 -1.476 0.113 0.000 1.051 -0.151 -1.297 2.215 0.432 1.073 -0.657 2.548 0.692 1.547 -0.797 0.000 2.256 1.320 0.996 1.881 0.645 1.178 1.408 +0 4.114 -1.136 0.450 2.004 -1.726 1.308 -0.879 0.148 2.173 1.928 -0.140 -1.105 0.000 3.007 -0.566 -1.483 2.548 0.593 0.534 0.163 0.000 1.386 1.326 3.680 2.102 2.480 1.967 1.801 +1 1.399 0.606 0.867 1.374 1.353 0.733 1.666 -0.996 0.000 0.746 1.539 -0.368 0.000 0.455 0.557 -1.640 0.000 0.765 0.642 -0.210 1.551 0.931 0.587 0.988 0.594 0.152 0.528 0.632 +1 1.264 1.592 1.627 0.641 -0.991 0.620 -0.262 -0.512 2.173 0.599 1.987 0.752 0.000 1.310 -0.283 -0.005 0.000 0.628 1.085 -1.595 3.102 0.684 0.534 0.991 1.422 0.791 0.891 0.833 +0 1.966 0.808 -0.016 0.658 1.676 0.869 0.184 -0.743 2.173 0.894 1.435 0.678 0.000 1.127 0.708 -1.472 2.548 1.110 -1.667 1.421 0.000 0.401 1.375 1.574 1.115 0.839 1.085 1.171 +1 0.668 -0.818 0.959 0.964 1.486 0.434 -0.861 0.515 1.087 1.381 -0.350 -0.362 2.215 0.754 0.192 1.710 0.000 0.601 1.186 -1.508 0.000 0.510 0.874 0.991 1.403 0.861 0.941 0.984 +0 0.950 -0.329 -1.336 0.631 0.380 0.792 -0.509 0.365 1.087 0.270 -0.188 -0.822 0.000 0.704 -0.269 -1.692 0.000 0.389 1.039 -1.396 3.102 0.473 0.798 1.072 0.614 0.830 0.621 0.542 +0 0.457 -0.153 -1.248 2.186 -0.492 1.606 -0.810 1.150 1.087 1.493 0.732 -0.908 0.000 1.704 -0.727 0.748 1.274 0.514 -0.278 -0.373 0.000 0.841 1.682 0.988 2.009 0.732 1.501 1.380 +1 1.601 0.400 -0.393 0.310 -0.852 0.615 0.275 0.933 1.087 0.558 1.776 -0.774 0.000 0.771 1.275 1.400 2.548 0.891 1.698 1.049 0.000 0.921 1.071 0.988 1.000 0.616 0.803 0.844 +1 0.921 -1.862 0.970 1.384 1.476 0.471 -0.625 1.482 0.000 1.057 0.213 -0.073 2.215 1.496 0.037 -0.841 0.000 0.950 -1.101 0.494 3.102 1.405 1.110 0.994 2.272 0.886 1.417 1.380 +0 0.609 0.134 0.525 0.958 -1.037 0.947 -0.031 -0.127 2.173 0.795 2.651 0.915 0.000 1.442 -0.879 1.616 2.548 0.516 -1.276 -0.280 0.000 3.540 2.550 1.044 0.936 1.618 2.028 1.519 +1 0.467 0.148 1.668 1.245 -0.452 0.916 -1.138 -1.444 1.087 0.834 -1.150 0.448 2.215 0.660 1.114 0.506 0.000 0.493 -1.294 0.267 0.000 1.191 1.029 0.997 1.079 1.275 1.014 0.906 +0 1.084 -0.446 -0.619 0.392 0.302 1.103 -2.219 1.275 0.000 1.380 -0.586 -1.614 2.215 2.583 -1.392 -0.098 0.000 1.768 -2.130 -0.471 0.000 1.486 1.634 0.994 0.993 0.327 1.425 1.124 +1 0.516 -0.346 -0.670 0.711 -1.096 0.662 1.828 0.474 0.000 1.329 1.070 -1.259 2.215 0.849 0.822 1.317 2.548 0.525 -0.438 -0.055 0.000 0.727 0.722 0.978 0.691 0.833 0.840 0.775 +1 0.529 0.466 -0.520 1.165 0.379 0.905 -2.117 -0.369 0.000 0.757 0.476 0.949 0.000 1.933 0.297 1.579 1.274 0.790 1.301 1.562 0.000 1.033 0.941 0.981 0.762 0.710 0.793 0.734 +1 0.820 -1.966 -1.108 0.529 1.227 0.727 0.361 0.863 2.173 0.628 0.478 -0.408 2.215 0.506 -1.657 0.296 0.000 0.450 -0.396 -1.589 0.000 0.647 0.903 0.983 1.047 0.908 0.964 0.783 +0 0.914 0.142 1.209 0.550 1.024 0.897 2.419 0.917 0.000 0.957 1.602 -0.538 2.215 1.718 0.970 -0.906 0.000 1.013 0.157 -0.401 3.102 2.777 1.741 0.990 0.803 0.717 1.181 1.063 +0 1.488 -1.054 1.496 2.561 1.415 1.283 0.340 -0.236 0.000 0.718 -1.456 -0.103 1.107 1.731 -0.431 -1.520 2.548 1.088 -0.685 -0.145 0.000 0.860 0.986 1.011 1.247 1.299 1.105 1.086 +1 0.699 -0.625 -1.286 1.429 -0.411 0.499 -0.014 1.183 0.000 0.806 0.849 1.647 2.215 0.532 -0.354 0.658 2.548 0.539 0.284 -0.063 0.000 0.729 0.690 0.987 0.684 0.716 0.767 0.664 +0 0.841 -1.892 0.086 1.365 0.973 0.711 -0.163 -1.426 2.173 0.830 -0.213 0.104 0.000 1.188 -0.263 -0.644 0.000 0.975 -0.354 1.614 3.102 0.951 1.050 1.064 0.922 0.363 0.965 0.991 +0 0.595 -1.338 -0.997 0.576 -0.474 0.722 -0.540 0.353 0.000 1.074 -1.348 -1.236 2.215 0.893 -0.052 -0.721 0.000 1.102 2.214 0.971 0.000 1.042 1.249 0.979 0.826 0.120 1.590 1.273 +1 0.496 -1.100 1.616 0.091 1.526 0.678 -0.243 -1.641 2.173 0.992 0.663 0.467 0.000 0.743 -0.377 -0.473 2.548 0.548 2.144 -1.410 0.000 0.977 0.960 0.977 0.630 0.772 0.865 0.746 +0 0.513 -0.406 0.299 1.193 -0.665 0.966 1.443 0.629 0.000 1.057 1.082 1.029 2.215 1.962 -0.561 -1.183 2.548 0.562 0.943 -1.051 0.000 1.138 0.834 0.990 0.980 2.046 1.390 1.143 +1 2.090 -0.309 -1.316 1.446 -0.931 0.558 2.211 0.413 0.000 0.923 -1.032 -0.168 2.215 1.064 1.948 1.488 0.000 1.217 -0.207 0.102 0.000 0.735 0.940 0.999 1.205 1.625 1.142 1.039 +0 0.698 2.076 -0.820 0.984 -0.834 0.825 0.417 -1.448 2.173 1.525 0.786 1.103 2.215 1.077 1.786 0.507 0.000 1.264 -0.240 -0.418 0.000 1.029 0.940 1.005 0.876 1.273 0.986 0.918 +1 0.647 -0.037 0.256 1.037 -1.223 0.847 -0.524 0.693 0.000 0.666 0.284 -1.031 2.215 0.502 -1.533 1.225 0.000 0.941 0.132 -0.080 0.000 0.897 1.143 1.103 0.655 0.531 0.726 0.691 +0 0.959 0.989 -0.703 1.886 -1.037 1.833 0.870 -0.484 2.173 1.565 -1.105 1.454 0.000 0.602 -1.598 0.861 0.000 2.223 -0.225 0.953 1.551 0.889 0.998 0.997 1.032 2.430 2.039 1.715 +1 2.171 -0.507 0.843 0.521 -0.981 1.178 -0.703 -0.462 2.173 0.695 -0.649 -1.216 1.107 0.584 0.948 1.070 0.000 0.656 -1.362 -1.407 0.000 1.327 0.927 1.469 1.357 0.837 0.969 0.903 +0 1.174 -0.070 -1.173 1.393 -1.021 0.891 -0.608 0.808 2.173 0.752 0.214 1.371 0.000 1.095 0.919 0.140 2.548 0.370 0.780 0.493 0.000 0.550 0.727 0.982 1.310 1.312 1.186 0.957 +1 1.038 0.306 1.079 0.815 -1.532 0.433 -0.648 1.425 2.173 0.996 -1.295 -0.319 2.215 0.652 0.426 -1.156 0.000 0.412 -0.615 -0.164 0.000 0.577 0.808 0.989 0.570 1.023 0.824 0.686 +0 0.428 -0.635 -0.364 2.359 0.468 0.509 -0.051 0.529 0.000 1.242 0.176 1.464 2.215 1.931 1.810 -1.059 0.000 1.200 -0.454 -1.020 3.102 2.782 1.996 0.990 1.391 0.957 1.418 1.659 +1 0.653 -0.948 -0.506 1.566 -1.326 2.561 1.832 -0.196 0.000 2.335 -0.486 1.281 1.107 1.135 -1.079 1.099 0.000 2.133 -0.511 -1.688 3.102 1.060 3.405 0.989 1.344 0.912 3.093 2.527 +1 0.595 0.316 1.569 1.497 0.466 0.542 -1.361 -0.291 2.173 0.800 0.816 0.997 0.000 0.868 -0.255 -1.253 2.548 0.903 1.006 -0.939 0.000 1.109 0.954 1.096 1.133 0.816 0.975 0.885 +0 0.951 -0.856 0.223 1.308 1.218 2.035 -0.539 -0.913 2.173 2.561 -0.533 0.793 2.215 0.730 -0.796 -0.526 0.000 0.696 0.364 -1.013 0.000 0.890 0.967 1.207 0.911 3.357 1.653 1.268 +0 6.695 -1.608 -0.184 0.835 -1.599 3.701 -1.419 1.443 2.173 1.586 -1.140 1.131 2.215 3.735 0.336 -1.072 0.000 1.133 -1.375 0.203 0.000 3.048 3.870 3.133 4.193 1.096 4.272 4.316 +1 0.824 -0.100 0.108 1.057 0.922 1.720 0.565 0.786 0.000 4.604 -0.181 -0.861 2.215 1.645 0.812 1.254 0.000 2.053 -0.485 0.424 3.102 0.988 1.551 0.982 1.982 2.603 1.947 1.556 +1 0.883 -0.700 -1.093 0.509 1.376 0.785 -1.312 -1.480 0.000 1.399 -1.234 0.394 2.215 0.826 -1.797 0.987 0.000 2.217 -0.949 -0.384 3.102 0.719 1.090 0.988 1.165 1.031 0.953 0.892 +1 0.994 0.404 1.591 0.916 0.679 0.825 1.875 -1.461 0.000 0.560 -0.332 0.850 2.215 1.631 -1.171 -0.153 2.548 1.579 -0.208 -0.388 0.000 2.668 1.813 0.986 1.460 0.941 1.740 1.382 +1 0.787 -0.358 -0.844 0.666 -1.729 1.051 -0.234 -1.547 2.173 1.301 -0.010 0.269 0.000 0.624 -1.774 -0.570 0.000 1.051 -0.292 -0.358 0.000 0.856 1.093 0.987 0.731 1.117 1.038 0.901 +1 0.572 -0.671 0.740 0.721 1.534 0.783 0.553 0.035 2.173 0.648 -0.117 -1.529 1.107 0.439 -1.403 -0.625 0.000 0.484 0.319 -1.401 0.000 0.656 0.894 0.988 0.862 1.096 1.053 0.828 +1 1.323 -0.271 0.767 0.836 -0.650 0.733 0.414 0.672 0.000 0.863 -0.900 -0.784 0.000 0.840 0.087 -1.253 2.548 0.748 -0.709 1.324 1.551 0.700 0.785 1.394 0.758 0.534 0.615 0.599 +1 1.004 0.891 1.219 1.196 -0.309 0.412 1.035 -0.145 0.000 1.127 0.184 1.252 2.215 1.440 0.336 -0.675 0.000 0.686 -0.465 0.313 3.102 0.796 0.735 1.489 1.086 0.667 0.793 0.789 +1 2.658 -1.671 -0.341 0.201 -0.145 1.061 -1.303 1.148 1.087 0.470 -1.076 0.451 0.000 0.859 -1.230 -1.446 2.548 0.618 -1.160 1.513 0.000 0.582 0.581 0.996 1.453 0.855 1.014 0.792 +1 0.525 0.670 -0.169 0.988 0.545 1.234 0.411 -0.375 0.000 0.681 -0.416 0.038 0.000 2.111 -0.879 1.556 2.548 1.295 0.084 -1.164 3.102 0.895 1.032 0.984 0.814 1.070 0.876 0.752 +0 0.850 -0.117 -1.529 1.068 -0.572 0.338 1.628 -1.643 0.000 0.719 1.360 -0.590 2.215 0.906 0.273 1.003 2.548 0.925 -1.042 0.450 0.000 1.107 0.772 1.003 0.882 0.984 0.787 0.758 +1 0.662 2.000 -0.216 1.771 -0.932 1.471 1.444 0.966 1.087 0.669 0.786 -0.664 2.215 0.598 1.548 -0.021 0.000 0.984 1.234 1.653 0.000 0.849 0.928 0.992 0.960 1.530 1.267 0.982 +0 0.453 -1.992 -1.022 1.046 0.951 0.992 -1.053 0.083 2.173 1.115 0.164 -1.565 2.215 0.514 -0.214 -0.554 0.000 0.700 -1.374 1.463 0.000 0.818 0.874 0.986 1.167 1.845 1.400 1.067 +1 1.530 -0.776 1.711 0.090 0.952 1.037 -0.468 -0.173 2.173 0.742 0.865 0.725 2.215 0.929 1.422 -1.279 0.000 0.427 -1.051 1.042 0.000 1.469 1.072 0.980 1.163 1.332 1.050 0.983 +1 1.064 -2.158 -0.937 0.100 1.302 0.553 -2.114 0.481 0.000 1.692 -0.962 -1.236 1.107 1.688 -1.003 0.702 0.000 0.412 -0.374 0.257 3.102 0.998 0.639 0.979 0.830 0.763 1.018 0.878 +1 1.623 0.496 -0.838 0.500 -0.332 0.987 -0.189 0.780 0.000 1.350 0.097 -1.491 2.215 0.791 -0.852 0.303 0.000 0.577 -0.653 -0.383 3.102 0.898 0.710 0.991 1.022 0.763 0.882 0.973 +0 1.831 1.342 1.419 1.523 0.906 1.197 -0.273 -0.334 0.000 0.621 0.689 -0.005 0.000 0.306 0.251 -0.605 0.000 1.026 0.866 -1.159 3.102 0.858 0.758 1.031 1.384 0.645 0.956 0.864 +1 1.025 -0.581 -1.061 0.166 0.973 0.883 -0.590 0.235 2.173 1.278 -0.376 1.411 0.000 1.169 0.009 -0.462 1.274 0.537 -0.896 1.051 0.000 0.512 1.064 0.983 0.805 0.840 0.858 0.797 +0 0.460 -1.792 -0.436 0.542 0.701 1.044 -0.278 -0.669 2.173 0.531 -1.229 -1.169 0.000 0.929 0.849 1.513 0.000 1.518 0.073 0.343 1.551 0.929 1.395 0.979 0.868 1.082 1.128 0.912 +0 0.621 -0.508 -1.679 2.017 0.693 2.477 -0.998 1.368 0.000 2.410 0.297 -0.309 0.000 2.525 1.967 -0.854 0.000 1.123 0.833 -0.365 3.102 1.177 1.903 1.307 1.147 0.444 1.511 1.235 +0 0.712 1.145 -1.266 2.816 1.635 0.984 0.443 -0.169 2.173 0.961 1.071 0.072 2.215 0.467 -0.459 -0.455 0.000 0.955 1.754 1.023 0.000 1.447 1.032 0.987 1.618 0.568 1.205 1.044 +0 0.884 1.111 -0.624 0.325 0.382 0.467 -0.472 1.127 2.173 0.706 -0.099 -0.887 2.215 0.893 -0.957 0.642 0.000 0.639 0.588 1.366 0.000 0.973 0.911 0.986 1.203 0.835 0.925 0.913 +0 1.354 -0.864 0.207 0.362 0.970 0.453 -0.688 -0.610 2.173 1.137 -2.424 -1.248 0.000 0.699 -0.887 1.193 0.000 1.103 0.245 1.496 3.102 1.590 1.264 0.990 0.964 0.807 1.076 0.963 +0 0.448 0.593 -1.605 1.847 -1.423 0.982 0.619 -0.073 2.173 0.428 0.176 0.785 0.000 0.939 0.513 -0.769 2.548 0.494 -1.713 1.194 0.000 0.847 0.961 0.985 1.492 0.704 1.007 0.919 +1 0.506 -0.144 0.562 1.011 -1.057 0.740 -1.048 0.785 0.000 0.569 -0.551 1.457 0.000 1.198 -0.518 -0.044 2.548 1.169 0.767 -1.411 3.102 0.855 0.946 0.987 0.708 1.127 0.899 0.774 +1 0.910 -0.679 0.815 1.748 -0.054 0.680 -0.066 -0.845 0.000 1.251 0.107 1.668 0.000 0.818 -1.345 -1.439 2.548 0.554 0.691 0.183 0.000 0.946 0.939 1.231 0.820 0.224 0.700 0.762 +1 2.991 -0.038 1.261 0.502 0.192 2.866 1.837 -0.359 0.000 1.506 -0.869 -1.554 2.215 0.964 -0.683 1.306 1.274 0.517 -1.369 -1.277 0.000 5.363 3.906 1.392 1.355 0.690 3.164 2.575 +1 0.908 -1.732 -1.690 0.103 0.007 1.106 -0.572 -1.402 2.173 1.804 -1.367 -1.315 2.215 0.874 -0.495 -0.456 0.000 3.539 0.667 0.412 0.000 0.834 1.554 0.988 0.787 0.902 1.206 0.952 +1 1.563 -1.103 0.259 1.207 0.722 1.666 0.205 -1.059 0.000 0.630 0.108 1.219 0.000 1.388 -0.443 -1.381 1.274 1.429 -0.262 0.363 0.000 0.901 1.034 0.985 0.577 0.686 0.786 0.713 +1 0.901 0.728 0.150 0.262 -1.654 0.953 0.263 1.110 0.000 1.582 0.709 -0.907 2.215 0.736 0.930 1.434 1.274 0.846 -0.110 -0.037 0.000 1.211 0.843 0.981 0.954 0.998 0.955 0.845 +1 1.035 -0.281 -1.450 0.468 1.676 0.851 -0.759 -0.963 2.173 0.779 -1.508 -0.406 0.000 0.913 -0.275 1.168 2.548 1.878 1.075 0.320 0.000 1.086 0.999 0.979 0.734 1.063 0.832 0.750 +0 1.211 1.032 -0.717 0.630 1.017 1.913 1.043 -0.157 0.000 1.888 2.323 -1.738 0.000 1.369 -0.538 1.578 1.274 2.015 0.464 0.564 3.102 0.625 1.782 1.210 1.206 1.265 1.753 1.377 +1 0.778 0.192 1.361 0.628 0.559 0.852 -0.649 -0.050 0.000 0.705 1.031 1.642 2.215 1.160 0.101 -0.991 2.548 0.533 -0.464 1.009 0.000 0.841 0.951 0.996 0.908 0.819 0.870 0.792 +1 0.999 1.000 1.506 1.084 -1.032 0.700 0.185 0.554 0.000 0.630 0.848 1.209 1.107 1.588 1.736 -1.388 0.000 2.356 1.041 -0.244 3.102 1.035 1.064 1.089 0.707 1.083 0.786 0.804 +0 0.511 0.703 -0.614 1.148 -0.535 1.136 -0.598 1.424 2.173 0.686 -1.403 1.201 0.000 1.283 -1.543 -1.691 0.000 2.524 0.359 -0.282 0.000 0.755 1.015 0.997 1.249 1.124 0.945 0.938 +1 0.980 -0.305 -1.177 0.647 0.212 0.980 -0.843 -0.499 0.000 1.687 -1.095 0.978 2.215 0.742 -0.591 -1.494 2.548 0.554 0.056 0.219 0.000 0.864 0.812 1.047 1.135 0.984 0.949 0.828 +1 1.148 1.428 0.726 0.795 1.648 0.851 0.334 -1.157 2.173 0.547 0.554 -0.025 0.000 0.722 -0.322 -0.239 2.548 0.917 0.712 1.111 0.000 0.799 0.946 0.988 0.988 0.798 0.883 0.748 +1 0.923 -1.337 -1.720 0.720 -0.078 1.008 0.634 0.693 1.087 0.863 0.411 -0.822 0.000 1.064 -0.003 -1.420 0.000 1.178 2.076 0.353 0.000 0.817 1.375 1.125 0.999 0.980 1.082 0.992 +0 0.683 0.934 -0.071 2.487 0.408 0.807 0.622 -1.480 0.000 1.067 0.155 1.512 1.107 1.751 0.311 -0.749 2.548 0.701 -1.002 0.937 0.000 1.530 1.009 0.988 1.488 1.304 1.308 1.259 +0 1.283 -0.676 -1.231 0.516 -0.752 0.846 0.582 0.433 2.173 0.933 2.126 -1.547 0.000 1.013 -0.693 0.017 2.548 0.520 -1.510 1.008 0.000 3.365 2.190 0.981 0.825 0.940 1.527 1.548 +1 0.617 0.118 1.076 0.933 -1.679 0.976 -0.352 -0.392 2.173 1.066 -0.317 0.225 0.000 1.673 0.419 1.544 0.000 0.704 0.847 1.542 0.000 1.173 0.964 0.986 0.711 0.683 0.757 0.788 +0 0.438 -0.780 -0.328 0.136 1.133 0.935 -0.005 -0.238 1.087 1.769 0.838 1.466 1.107 0.974 0.638 -0.831 0.000 1.110 0.764 0.444 0.000 1.054 0.928 0.977 0.978 2.072 1.101 0.884 +1 0.636 -1.047 -0.807 0.804 0.298 0.901 -0.050 1.145 1.087 1.102 -0.549 -1.613 2.215 1.008 -0.439 -0.172 0.000 0.452 0.129 -0.445 0.000 0.301 0.939 0.986 1.029 0.972 0.980 0.840 +0 1.406 -1.022 1.699 0.183 0.979 0.779 -0.181 0.632 0.000 0.615 -1.101 -0.518 2.215 0.753 -2.681 -1.142 0.000 0.724 0.358 -0.078 3.102 2.915 1.698 0.976 0.767 0.570 1.117 0.981 +1 1.830 0.146 -0.872 0.716 -0.258 0.852 -0.428 0.871 2.173 0.556 0.857 1.557 2.215 0.587 -0.013 -0.004 0.000 0.512 1.550 -0.471 0.000 0.699 1.012 0.986 0.918 0.924 0.928 0.796 +1 1.591 0.464 0.523 1.143 0.779 2.247 0.279 0.974 2.173 4.049 0.231 -1.017 0.000 1.372 0.940 0.122 0.000 1.583 2.290 -0.885 0.000 0.992 1.274 0.991 1.090 0.896 1.949 1.570 +1 1.225 -0.285 0.866 0.852 0.577 1.015 0.207 -1.630 2.173 1.175 -0.251 -0.357 0.000 1.070 -0.493 -1.017 2.548 0.931 0.577 0.519 0.000 1.178 0.984 0.978 1.305 0.845 0.975 0.971 +0 0.842 -0.397 -0.624 0.829 -0.928 0.505 0.427 0.250 0.000 0.728 0.135 1.599 2.215 1.260 -0.809 1.189 1.274 0.770 -0.791 0.626 0.000 0.779 0.856 0.996 1.010 0.658 0.819 0.801 +1 0.898 0.536 -1.164 0.828 -0.418 0.798 0.440 0.307 2.173 0.875 0.059 1.506 0.000 0.449 -0.851 1.557 2.548 0.408 -0.289 0.768 0.000 0.506 0.855 0.993 0.884 0.873 0.790 0.724 +1 1.520 0.393 -0.208 1.042 -0.858 0.906 1.480 -0.707 0.000 1.341 0.592 1.221 2.215 1.192 -0.458 1.274 1.274 1.158 1.988 0.810 0.000 1.694 1.717 0.987 1.184 0.789 1.401 1.249 +1 0.593 0.325 0.843 0.534 1.163 0.794 -0.817 1.728 0.000 1.289 -0.935 -0.523 2.215 0.932 0.607 -0.497 2.548 0.372 -0.540 0.744 0.000 0.648 1.039 0.992 1.018 1.058 0.877 0.808 +1 0.891 -1.507 1.261 0.780 -0.591 0.692 -0.493 -0.114 2.173 1.154 -0.311 0.692 2.215 1.054 -0.440 -1.535 0.000 1.255 -1.370 -1.152 0.000 0.890 1.093 1.149 1.046 0.881 0.929 0.851 +1 0.335 2.040 -0.850 2.260 -0.386 0.506 0.050 -1.673 2.173 1.142 0.496 0.673 2.215 0.779 1.843 1.205 0.000 0.588 1.106 1.737 0.000 0.435 0.851 1.000 1.057 0.991 0.951 0.861 +0 1.889 -0.138 1.150 1.012 1.711 0.836 -0.163 -0.226 1.087 0.719 -0.914 -0.871 0.000 0.571 -1.758 0.111 0.000 0.526 0.487 -0.789 3.102 0.914 0.948 0.990 0.744 0.434 0.838 0.851 +0 0.642 0.476 1.149 0.954 -0.913 1.620 0.137 -1.562 2.173 1.707 -0.328 0.262 0.000 0.582 -0.829 0.117 0.000 0.864 -0.783 -1.143 3.102 0.787 0.976 1.040 0.910 0.847 1.282 1.061 +0 4.682 1.002 -1.371 1.893 -1.244 1.743 0.674 0.110 0.000 2.114 1.093 0.599 0.000 0.760 0.134 -1.208 2.548 1.396 0.367 1.136 3.102 1.963 1.484 0.995 0.826 0.684 1.136 1.698 +1 1.579 -0.703 -1.104 0.579 -1.000 1.557 0.677 1.071 2.173 0.983 1.087 0.002 1.107 0.531 0.215 -0.254 0.000 0.820 -0.410 -0.843 0.000 0.457 0.736 0.989 1.712 1.548 1.351 1.045 +1 3.715 0.702 1.626 0.515 0.860 1.897 2.346 -0.378 0.000 0.675 0.865 0.915 0.000 0.849 -0.254 0.684 2.548 0.798 0.839 -0.647 3.102 0.989 0.872 1.219 1.077 0.729 1.295 1.531 +0 1.767 0.515 -0.613 1.365 -0.317 3.272 -0.745 0.899 0.000 1.620 0.341 -1.032 1.107 1.635 -2.659 -0.919 0.000 0.664 0.044 1.676 3.102 6.848 3.664 0.982 0.910 0.620 2.934 2.543 +1 0.629 -1.419 1.419 0.595 -1.345 1.223 -2.453 0.211 0.000 0.682 -1.972 -1.519 0.000 1.253 -0.517 -1.510 2.548 1.270 -0.324 0.461 3.102 0.803 1.294 0.989 0.585 0.948 1.186 1.066 +1 0.872 1.353 0.808 0.844 0.543 0.334 -1.017 0.308 0.000 1.209 0.229 -0.934 2.215 0.952 -0.150 1.731 2.548 0.454 -1.473 -0.532 0.000 0.467 0.968 0.988 1.422 0.802 1.281 1.308 +0 0.825 0.030 -1.071 0.533 -0.432 0.662 0.209 1.556 0.000 1.127 0.646 0.696 2.215 0.550 0.019 -0.291 0.000 0.914 0.941 -0.698 3.102 1.084 0.981 0.994 0.517 0.898 0.712 0.687 +1 0.510 1.140 -1.013 0.928 -0.318 0.588 -0.153 -1.261 0.000 0.815 -0.626 0.611 2.215 0.581 -0.467 -0.128 0.000 1.024 -1.231 1.473 1.551 0.916 0.917 0.978 2.151 0.677 1.625 1.317 +1 0.628 -0.155 -0.774 1.385 0.836 0.803 -1.028 -0.234 2.173 1.048 0.441 1.389 0.000 1.431 -0.196 -0.002 0.000 1.266 -0.988 -1.650 3.102 0.916 1.109 1.282 1.038 1.024 1.043 0.905 +1 0.709 0.275 1.536 0.093 -0.809 0.706 0.724 -1.149 1.087 0.765 1.682 0.177 0.000 0.848 1.039 0.991 0.000 0.940 -0.107 -0.504 3.102 0.906 0.975 0.935 0.862 0.609 0.796 0.832 +1 0.495 0.914 1.361 1.013 0.052 1.353 -0.039 0.188 2.173 1.157 -0.651 1.515 0.000 1.108 -0.748 -1.236 0.000 1.671 2.292 -1.266 0.000 0.822 1.436 0.990 0.863 0.968 1.126 0.906 +1 3.884 -0.229 1.101 0.210 0.569 1.021 1.231 -0.752 0.000 0.546 -0.552 -0.278 2.215 2.028 0.574 -0.620 0.000 0.760 -0.235 -0.925 0.000 0.862 1.109 0.992 0.484 0.625 0.952 1.319 +0 0.943 1.422 -1.220 0.743 1.134 0.611 0.457 0.878 1.087 0.900 0.757 -1.008 2.215 0.671 2.603 0.379 0.000 0.743 0.842 0.439 0.000 0.828 0.998 0.988 0.744 1.096 0.873 0.768 +0 1.642 -1.163 -0.703 0.295 -1.548 1.217 0.259 1.111 2.173 0.792 -0.088 -0.547 2.215 0.636 -0.102 1.578 0.000 0.845 0.189 0.085 0.000 0.800 0.799 0.986 0.672 1.463 1.086 0.862 +0 1.078 -0.581 1.257 1.322 -1.424 0.632 -0.451 0.600 0.000 1.354 0.112 -0.376 0.000 0.655 0.497 -0.134 2.548 1.195 -0.256 1.728 3.102 1.617 0.936 1.098 0.558 0.735 0.768 0.837 +1 1.392 0.525 -0.445 1.232 -0.989 2.957 -0.287 1.538 0.000 1.594 0.292 0.427 2.215 0.385 -1.839 -0.156 0.000 0.959 1.219 -0.313 3.102 0.297 1.150 0.985 0.729 0.973 0.967 0.956 +0 0.469 0.137 -1.226 0.718 -0.009 0.867 0.936 1.357 1.087 1.321 -0.164 -0.137 2.215 1.149 1.601 -1.497 0.000 0.930 0.879 0.636 0.000 0.967 0.912 0.989 0.759 1.792 1.113 1.088 +1 0.504 -0.674 -0.015 0.663 -0.738 1.001 -0.318 0.660 2.173 1.266 0.614 -1.485 1.107 0.690 1.435 -0.189 0.000 0.483 1.151 -1.502 0.000 0.729 1.067 0.983 1.733 1.752 1.378 1.228 +0 2.450 1.863 0.223 0.080 1.329 1.098 0.937 1.419 0.000 0.736 0.754 -1.513 0.000 0.920 0.829 -0.340 0.000 0.981 -0.897 -1.542 1.551 0.925 1.174 0.994 1.813 0.806 1.244 1.193 +0 0.281 -1.028 1.735 0.685 -1.409 0.500 0.246 -0.617 1.087 0.595 0.856 1.036 0.000 0.645 -0.639 0.187 2.548 0.372 -1.289 1.374 0.000 0.920 0.769 0.993 0.800 0.585 0.604 0.579 +0 0.872 -1.795 1.334 1.056 0.136 0.577 0.578 -0.804 0.000 0.737 1.506 1.057 2.215 0.523 -0.628 -0.618 2.548 0.756 0.851 0.443 0.000 0.936 0.924 1.172 0.766 1.118 1.424 1.248 +1 0.974 -1.169 1.065 0.678 -0.116 1.190 -0.241 0.913 0.000 1.131 -0.156 -1.139 2.215 2.624 -1.122 -0.793 2.548 1.437 -1.642 0.677 0.000 1.995 1.909 0.987 1.096 1.172 1.563 1.227 +1 0.899 1.367 0.973 0.815 0.054 1.133 -1.063 -1.019 0.000 0.734 1.501 -1.093 2.215 1.236 1.101 0.453 1.274 1.649 0.271 -1.709 0.000 1.210 1.030 0.986 0.850 1.011 0.832 0.734 +1 0.865 0.067 -0.502 1.459 0.067 0.946 1.222 1.339 2.173 0.606 0.021 0.617 2.215 0.451 1.096 0.805 0.000 0.483 2.167 -0.651 0.000 0.632 0.769 0.985 1.293 0.986 0.904 0.769 +1 0.781 -0.174 -0.170 0.435 0.657 0.845 0.380 -1.723 0.000 0.605 1.355 0.397 2.215 1.549 1.883 -0.775 0.000 1.388 -0.598 0.959 1.551 0.680 0.929 0.983 0.626 1.121 0.698 0.632 +0 2.163 -0.110 1.211 0.310 -0.041 0.699 1.147 -1.402 0.000 0.941 1.974 -0.467 0.000 1.139 -0.769 0.260 2.548 1.227 -1.056 -1.649 3.102 1.035 1.752 1.025 0.851 0.914 1.413 1.226 +1 0.347 -0.411 1.356 0.739 0.051 1.719 -0.337 -1.466 0.000 0.827 0.456 0.147 2.215 1.159 -1.175 0.577 0.000 1.707 -0.648 -0.007 0.000 0.878 1.029 0.979 1.105 0.792 0.936 0.866 +1 2.388 -0.558 -0.717 0.983 0.208 1.005 -1.236 0.890 0.000 1.158 -0.992 1.733 2.215 0.457 -1.124 -1.513 0.000 0.525 -1.346 0.465 3.102 1.009 0.931 1.573 0.890 0.675 0.904 0.902 +1 0.375 0.611 -1.260 0.401 1.111 1.119 0.185 -0.568 2.173 0.777 0.274 1.654 0.000 0.911 0.171 0.096 2.548 1.306 0.270 0.672 0.000 0.821 0.717 0.984 1.016 0.708 0.799 0.747 +0 2.982 -1.084 -0.989 1.200 -1.739 1.283 -0.458 0.709 0.000 1.061 -0.195 -1.234 2.215 1.526 -0.950 0.533 2.548 1.466 0.193 0.316 0.000 1.038 0.867 1.637 1.053 1.474 1.155 1.313 +0 1.025 -0.811 0.303 1.781 0.763 1.096 -0.753 1.031 2.173 1.218 -1.031 -1.112 1.107 1.785 -0.146 -1.256 0.000 2.085 -0.711 -0.510 0.000 1.524 1.060 0.986 0.833 1.611 1.189 1.199 +0 0.626 -1.363 -0.986 1.620 1.542 0.341 -1.759 -0.783 0.000 0.896 -0.626 0.356 2.215 0.704 0.045 0.364 2.548 0.473 0.139 -1.436 0.000 0.773 0.837 1.061 0.981 0.298 0.802 0.705 +1 0.456 -2.149 0.139 1.536 1.017 0.873 0.276 -0.745 2.173 0.445 -0.660 0.470 0.000 0.873 -0.358 1.733 2.548 0.478 -1.888 -1.596 0.000 0.782 1.148 0.988 0.842 0.935 1.072 0.868 +1 1.219 -1.349 -0.283 1.135 0.238 0.963 0.477 1.562 2.173 0.589 -0.713 -1.057 0.000 0.447 0.106 0.617 1.274 0.487 -2.252 -1.420 0.000 0.835 0.796 0.988 1.617 0.633 1.012 0.957 +0 0.593 -0.770 1.033 1.078 -0.210 1.794 0.159 -1.256 2.173 0.940 0.142 0.571 2.215 1.094 -1.372 1.077 0.000 0.779 1.546 -0.418 0.000 0.819 1.467 0.996 1.421 1.905 1.753 1.355 +1 0.845 -0.685 -1.325 0.722 0.995 0.737 -0.516 -0.030 2.173 0.608 1.254 1.252 0.000 0.455 1.310 -0.306 0.000 0.720 -0.890 1.730 3.102 0.798 0.949 0.987 0.861 0.800 0.844 0.765 +1 0.595 -0.962 -0.632 0.412 -1.535 0.644 -0.072 1.670 2.173 0.584 -0.069 0.512 2.215 0.806 -2.006 1.172 0.000 0.749 -0.895 -0.940 0.000 0.957 0.973 0.989 0.698 0.781 0.790 0.745 +1 0.383 -0.470 0.104 1.169 -0.941 1.033 2.355 1.454 0.000 1.428 1.051 -0.056 2.215 1.389 2.502 1.068 0.000 3.052 0.957 -1.709 3.102 0.991 1.499 0.983 1.031 1.879 1.312 1.188 +0 0.469 1.239 -0.446 0.975 0.558 0.681 -0.235 -1.068 0.000 0.602 -0.766 0.514 0.000 0.974 0.502 -0.429 1.274 1.649 1.403 1.438 3.102 0.741 0.781 0.992 0.975 1.123 0.899 0.809 +0 1.584 0.929 1.170 0.716 0.249 1.641 -0.316 -1.166 0.000 2.496 0.970 0.580 2.215 1.352 -0.544 -0.598 2.548 0.987 -0.984 -1.151 0.000 0.865 0.863 1.088 0.863 2.429 1.928 1.608 +0 0.694 -2.236 -0.003 1.110 0.550 0.708 -1.367 -1.344 0.000 0.848 -1.149 1.132 2.215 0.796 -0.320 -1.683 0.000 1.407 -0.107 -0.305 3.102 0.830 1.032 0.996 0.759 1.094 0.803 0.832 +1 1.580 -0.382 0.353 1.484 -0.194 0.999 -2.563 -1.574 0.000 1.224 0.399 1.371 2.215 1.079 -0.741 -0.192 0.000 0.480 0.352 -1.165 0.000 0.803 1.150 1.002 0.844 0.878 0.964 0.818 +0 1.294 0.637 1.016 1.545 1.425 1.018 0.374 -0.741 0.000 1.230 -0.296 0.546 1.107 0.927 -1.527 -0.641 0.000 1.038 0.956 -0.583 1.551 1.493 0.897 0.991 1.315 1.185 1.023 1.063 +1 0.304 -1.851 -1.119 1.287 -0.544 0.916 -0.426 -0.858 2.173 1.069 -0.867 1.010 2.215 1.159 0.318 1.135 0.000 0.732 -2.125 0.964 0.000 1.083 0.825 0.988 0.612 1.486 0.925 0.848 +0 0.762 -1.328 1.639 1.575 -1.320 1.004 2.830 1.582 0.000 2.089 0.464 0.142 0.000 1.067 0.230 0.697 0.000 1.491 0.105 -0.199 3.102 1.128 0.837 0.983 1.395 0.621 1.201 1.566 +0 0.406 -0.819 0.591 0.865 -0.401 0.855 0.977 1.244 2.173 0.897 -0.615 -1.517 0.000 0.934 -0.845 -0.134 2.548 0.556 -0.432 -1.077 0.000 0.952 0.950 0.987 0.984 1.625 0.901 0.770 +0 1.867 0.678 0.605 0.232 -1.037 0.826 -1.507 -0.800 2.173 0.665 -0.752 0.617 2.215 0.548 -2.062 -1.026 0.000 1.281 -1.854 1.454 0.000 0.728 0.918 0.991 1.700 1.125 1.142 1.190 +0 0.808 1.560 -1.694 0.832 0.318 0.974 0.835 0.083 2.173 1.204 -1.009 -1.391 2.215 0.383 -0.269 0.163 0.000 0.784 -0.470 0.998 0.000 0.421 0.789 1.103 1.881 2.321 1.477 1.113 +1 0.434 -0.154 1.046 1.016 -0.089 1.199 -1.760 0.690 0.000 1.408 -0.136 -1.345 2.215 1.617 -0.315 -0.598 0.000 0.882 -0.465 1.410 3.102 3.012 1.737 0.982 1.099 0.651 1.408 1.313 +0 0.338 -1.998 1.649 1.187 -0.265 0.795 -0.499 0.825 1.087 0.598 2.274 -0.931 0.000 0.601 1.950 1.233 0.000 1.054 0.518 -1.298 3.102 0.859 0.822 0.992 0.919 1.078 1.180 1.354 +0 0.604 0.235 -0.684 0.856 0.393 1.291 -0.864 -1.145 0.000 0.767 -1.966 1.022 0.000 1.269 -1.229 1.426 2.548 2.153 -0.633 0.144 1.551 2.343 1.481 0.985 0.646 1.216 1.180 1.013 +0 1.376 0.523 -0.122 1.959 0.300 0.878 0.803 0.706 2.173 1.125 1.185 1.703 0.000 2.663 0.940 -1.083 2.548 1.109 0.450 -1.530 0.000 0.603 0.933 0.984 0.911 1.915 1.324 1.203 +0 1.517 -0.242 -0.451 0.531 -0.391 0.596 2.512 1.646 0.000 0.864 1.331 1.111 2.215 0.799 -0.557 0.604 2.548 0.513 2.476 -0.517 0.000 0.806 0.897 0.987 0.764 1.097 1.158 1.530 +1 1.235 -0.130 0.821 0.571 -0.243 0.595 0.553 -0.451 2.173 0.758 1.116 -1.449 2.215 0.510 -1.303 0.290 0.000 0.370 -0.554 1.385 0.000 0.554 1.067 0.986 0.788 0.829 0.779 0.727 +1 0.789 -0.956 -0.995 1.743 -1.171 0.403 -2.172 -1.180 0.000 1.297 -0.565 0.171 2.215 0.600 -1.362 -1.679 0.000 0.481 -1.221 0.225 1.551 0.837 0.861 0.991 0.678 0.327 0.936 0.790 +1 0.519 0.668 -1.175 0.505 0.513 0.857 -1.439 -1.561 0.000 0.703 -1.062 -0.863 0.000 1.390 -0.177 0.166 1.274 1.352 -1.351 0.605 3.102 1.016 1.092 0.986 1.071 0.908 1.205 1.351 +1 1.141 -0.488 -0.226 0.652 0.837 0.890 0.157 -1.478 1.087 0.538 0.036 0.081 0.000 0.822 0.988 1.287 2.548 0.514 -0.903 -0.261 0.000 0.455 0.930 0.989 0.967 0.822 0.859 0.728 +0 0.537 -1.408 1.207 0.168 -1.435 0.300 -1.745 0.919 0.000 0.498 -1.628 -1.184 0.000 1.150 -0.370 -0.404 2.548 1.118 0.170 0.252 3.102 0.779 0.961 0.978 0.754 0.553 0.703 0.654 +1 0.706 0.814 1.364 1.370 -1.038 0.777 -0.918 0.549 1.087 0.586 -0.715 -0.792 2.215 0.503 0.403 0.216 0.000 0.371 0.831 -1.642 0.000 0.492 0.764 1.129 0.920 0.934 1.028 0.784 +1 1.005 -0.048 -0.263 0.331 -1.726 0.897 0.251 1.337 2.173 1.272 1.652 -1.610 0.000 0.942 -1.063 -0.038 0.000 1.559 0.081 0.530 3.102 0.398 1.209 0.984 0.726 0.839 1.009 1.033 +0 1.084 1.800 -1.726 0.459 0.283 0.732 0.034 1.222 0.000 0.809 -0.678 -1.030 2.215 0.891 1.611 -0.152 0.000 0.980 0.335 0.135 3.102 0.925 0.935 0.987 0.854 0.838 1.056 0.924 +1 0.692 -1.421 0.463 0.484 0.768 1.721 -0.427 -0.832 0.000 0.583 -0.807 0.690 0.000 0.928 -0.588 -1.481 2.548 1.799 -0.130 0.271 0.000 0.672 0.940 0.973 0.649 0.686 0.647 0.593 +1 0.636 0.312 0.525 0.565 -0.245 0.697 -0.009 0.194 0.000 1.349 0.087 -1.292 2.215 0.643 0.910 0.735 0.000 1.417 -0.561 -1.446 0.000 0.848 1.301 0.989 0.559 0.697 0.784 0.731 +1 0.278 0.267 0.024 1.320 1.467 1.074 1.551 -1.343 2.173 1.138 1.901 0.454 0.000 0.832 0.741 -0.571 2.548 0.912 1.258 -0.312 0.000 0.896 0.873 0.990 1.832 0.881 1.228 1.291 +1 0.467 1.329 -1.705 1.220 -0.992 1.168 0.129 0.759 2.173 0.385 0.333 0.021 0.000 0.481 -0.411 -0.244 2.548 0.656 0.771 -1.012 0.000 0.559 0.872 0.994 0.640 0.784 0.818 0.667 +0 0.497 1.030 -1.296 0.338 0.335 0.979 0.759 -0.699 2.173 2.599 1.084 0.898 2.215 1.386 -0.227 -1.024 0.000 1.240 1.002 0.341 0.000 1.303 1.032 0.987 1.303 2.362 1.409 1.123 +1 0.958 -0.207 0.378 0.449 -1.323 1.083 -1.150 -1.619 2.173 1.135 -0.504 -0.183 0.000 0.662 -1.490 -0.166 0.000 0.935 -1.133 0.914 3.102 0.783 0.808 0.989 0.984 0.813 0.909 0.783 +1 0.765 -0.378 0.170 0.382 1.690 1.818 -2.245 -1.552 0.000 2.478 0.860 -0.246 0.000 0.751 1.718 1.483 0.000 1.124 0.896 0.856 0.000 0.688 0.862 0.986 0.589 0.634 0.592 0.585 +1 0.379 -1.546 0.285 0.872 -0.953 2.658 -0.959 1.091 0.000 1.897 -1.108 -0.805 1.107 0.982 -0.212 -0.635 2.548 1.102 -1.497 -0.778 0.000 0.971 1.614 0.983 0.830 0.716 1.557 1.223 +0 0.807 1.114 0.425 0.833 1.034 1.317 1.457 0.249 0.000 0.672 1.472 -0.647 2.215 1.076 0.758 -1.383 1.274 0.802 -2.008 -1.734 0.000 1.193 1.272 0.986 0.923 0.639 1.069 0.949 +0 0.332 -0.251 0.874 1.217 -1.324 0.594 0.733 0.907 2.173 1.437 -1.019 -0.086 2.215 0.908 2.118 -1.704 0.000 0.476 0.492 -1.355 0.000 0.747 0.888 0.986 1.478 1.745 1.555 1.232 +0 0.721 -0.275 -0.484 0.410 1.493 1.018 -1.039 -0.154 2.173 1.153 0.516 0.952 0.000 0.922 -0.229 1.369 0.000 1.624 0.309 -1.486 3.102 0.861 0.921 0.989 1.074 1.646 1.178 0.963 +1 1.291 0.183 0.608 0.121 -1.695 1.066 -1.234 -0.185 0.000 0.926 0.000 -1.639 2.215 1.431 -0.633 1.733 2.548 0.969 0.047 0.897 0.000 1.137 1.009 0.983 1.062 0.452 0.839 0.796 +0 3.018 -0.100 1.015 0.451 -1.266 1.001 0.624 -0.648 2.173 0.357 0.773 1.470 0.000 0.333 -0.129 -0.763 1.274 1.181 1.560 -0.636 0.000 0.933 0.917 1.431 0.833 0.294 1.007 0.960 +1 0.814 -0.217 0.838 1.581 -1.685 1.207 -0.413 -0.729 2.173 1.628 1.497 0.938 0.000 0.873 -0.464 0.169 0.000 1.190 0.733 -0.666 3.102 0.859 1.062 1.201 1.266 0.884 1.377 1.235 +1 0.866 -0.979 0.565 1.015 -0.546 0.961 -0.560 0.154 2.173 1.339 0.312 -1.714 0.000 0.466 2.047 0.735 0.000 0.704 -0.533 -1.381 3.102 1.670 1.122 1.093 0.748 0.856 1.150 1.089 +1 0.453 -1.405 -0.724 2.920 -0.214 0.803 -2.643 -1.549 0.000 0.921 -2.890 1.168 0.000 1.306 -1.162 1.193 1.274 0.812 -2.151 -1.157 0.000 1.000 0.862 0.989 1.314 0.397 0.815 1.145 +1 0.540 -0.597 -0.998 0.668 0.995 0.583 -0.926 0.781 0.000 1.035 -1.474 -1.372 2.215 1.163 -0.480 -0.672 2.548 0.656 -2.222 0.084 0.000 1.079 1.114 0.987 1.033 0.913 0.867 0.857 +1 0.600 -0.008 0.801 0.348 -1.689 0.617 0.821 1.027 1.087 1.051 0.536 0.268 1.107 2.350 1.051 -1.185 0.000 0.412 -0.024 0.529 0.000 1.280 1.184 0.983 0.758 0.767 0.947 0.789 +0 0.741 -0.091 -0.886 0.306 -1.697 0.722 -2.032 -1.354 0.000 0.718 1.508 1.463 0.000 1.299 0.321 0.506 2.548 1.705 0.511 -0.196 3.102 0.747 0.941 0.988 1.005 0.688 0.824 0.906 +0 1.162 1.105 -1.153 2.117 -0.554 0.802 1.233 1.530 0.000 1.023 1.273 1.027 0.000 1.309 0.494 0.692 2.548 1.635 0.931 0.036 3.102 0.845 0.915 1.120 0.887 0.703 0.900 0.999 +1 1.509 0.778 -1.597 0.496 -1.150 0.951 0.448 0.252 1.087 0.693 1.217 -1.079 0.000 1.042 -0.313 0.218 0.000 1.471 1.357 1.170 3.102 1.664 1.234 0.990 0.956 1.206 0.991 0.956 +1 0.346 -1.470 0.489 0.747 -0.821 0.615 -0.396 1.182 0.000 0.674 0.949 0.736 2.215 1.355 -0.874 -0.917 0.000 0.533 -0.417 1.685 3.102 0.894 0.847 0.996 0.545 0.599 0.576 0.548 +0 0.380 -1.076 -0.065 2.097 -0.635 0.994 1.306 0.439 0.000 0.849 0.927 -1.197 1.107 0.741 1.005 0.864 0.000 2.178 0.669 1.433 3.102 0.877 1.123 0.992 2.424 0.861 2.132 2.366 +0 1.541 0.524 0.392 2.444 0.115 1.003 0.840 -1.635 0.000 1.536 -0.315 -1.629 2.215 0.516 1.046 -0.529 2.548 0.927 -0.927 0.323 0.000 0.669 1.167 0.987 0.794 1.098 1.194 1.175 +1 0.714 1.725 -1.396 0.737 0.739 0.523 0.840 1.612 2.173 0.504 -0.655 -0.298 2.215 0.385 1.562 1.035 0.000 0.643 1.212 -0.217 0.000 0.501 0.784 0.988 0.753 0.978 0.906 0.708 +1 0.606 0.198 1.699 0.964 -0.334 1.242 1.002 0.408 0.000 1.265 1.240 1.669 2.215 1.014 2.128 1.702 0.000 1.116 0.714 -0.714 0.000 1.354 1.042 1.023 0.536 1.064 0.846 0.824 +0 0.647 1.381 0.573 0.873 -0.771 0.756 0.377 1.519 0.000 1.015 0.780 -1.295 2.215 0.646 -1.119 -0.255 0.000 1.323 0.052 -0.385 3.102 0.943 1.036 0.988 0.716 0.862 0.833 0.770 +1 0.640 -1.090 -1.182 1.402 -0.927 0.936 0.051 0.851 0.000 0.619 -0.430 -0.589 2.215 0.748 -1.004 0.498 0.000 1.151 -1.220 1.442 0.000 1.032 1.039 0.986 0.640 0.419 0.670 0.991 +0 1.048 -0.715 1.080 0.741 -1.520 1.127 0.978 -0.173 2.173 0.569 0.058 1.091 2.215 0.817 0.389 -1.381 0.000 0.578 -1.424 1.143 0.000 1.131 0.782 0.988 1.452 1.211 0.990 0.907 +1 0.605 -0.265 -1.416 0.756 -0.384 1.474 0.452 -0.855 0.000 1.622 1.683 1.341 0.000 1.421 1.284 0.652 2.548 0.604 -0.439 1.024 3.102 3.755 2.184 0.987 1.593 0.831 1.465 1.504 +1 1.040 0.702 -0.386 0.369 -1.635 0.572 -0.391 -1.031 2.173 0.839 0.423 0.956 2.215 0.532 0.991 -1.452 0.000 1.298 1.654 0.771 0.000 0.934 0.860 0.987 0.887 1.085 0.901 0.797 +0 1.179 0.565 -0.277 0.678 0.145 0.667 0.225 1.248 2.173 1.217 -0.610 -1.366 2.215 0.287 1.593 -0.392 0.000 0.608 -1.243 0.182 0.000 1.144 0.962 0.991 1.529 1.104 1.134 0.967 +1 0.559 -1.813 0.167 0.511 -0.327 1.033 0.776 -1.449 2.173 0.353 -0.779 1.475 0.000 0.828 -0.380 -0.002 0.000 0.848 0.125 0.853 3.102 0.821 1.198 0.988 0.669 0.924 0.925 0.787 +1 0.965 0.244 0.038 0.745 1.427 0.866 -0.539 0.046 2.173 0.681 1.077 1.484 0.000 1.053 0.008 -1.666 0.000 0.866 -0.201 1.302 3.102 0.934 1.048 1.115 0.635 0.841 0.688 0.666 +1 0.922 -2.005 0.530 0.536 -1.467 0.856 -0.699 0.932 2.173 0.868 -1.003 0.152 2.215 1.152 -1.756 -0.917 0.000 0.878 -1.068 -1.422 0.000 0.845 0.996 0.988 0.870 0.846 0.814 0.786 +0 2.922 -0.164 0.530 1.773 0.496 2.284 0.154 -1.447 2.173 1.570 1.183 -0.076 0.000 0.697 -0.822 1.314 0.000 1.640 -0.821 1.693 0.000 1.251 0.789 0.995 2.636 1.199 1.706 1.352 +0 0.640 -1.085 0.597 0.546 -0.483 0.658 -1.920 -1.038 0.000 0.883 -0.094 0.646 1.107 1.236 -0.588 1.251 0.000 1.211 -0.281 -1.452 3.102 1.817 1.178 0.988 1.044 0.893 0.973 0.906 +0 0.304 2.175 -1.621 0.785 0.753 1.072 0.546 -0.666 2.173 0.649 0.657 1.672 2.215 0.783 1.293 0.769 0.000 0.440 -0.172 0.879 0.000 0.590 1.051 0.992 0.623 1.058 0.755 0.673 +0 0.783 1.646 -0.924 0.535 1.280 0.546 0.562 -1.682 0.000 0.926 1.242 1.074 1.107 1.342 0.039 -0.351 2.548 1.326 0.311 0.025 0.000 1.305 1.004 0.987 0.829 1.378 1.009 0.931 +1 0.354 -1.404 0.210 4.521 0.906 3.115 -1.455 -0.914 0.000 1.436 0.054 0.925 1.107 1.984 1.274 -0.634 0.000 0.963 -1.123 0.941 0.000 2.637 1.675 1.029 1.605 0.263 1.798 1.798 +0 1.035 1.406 -0.724 0.833 -1.274 0.700 1.249 1.325 2.173 0.439 -0.426 0.919 0.000 1.060 0.485 0.754 2.548 0.870 1.637 -0.253 0.000 0.682 0.720 0.988 0.938 0.663 0.776 0.678 +0 0.487 0.597 -0.925 2.710 -1.659 1.634 1.142 0.261 0.000 0.724 0.948 0.641 0.000 1.223 2.258 -0.772 0.000 0.986 -0.853 1.226 3.102 0.800 0.919 0.988 1.129 0.791 1.029 1.131 +1 1.656 -0.268 1.441 0.393 1.029 1.693 -1.917 0.134 0.000 1.505 1.383 -1.297 1.107 0.953 1.967 -0.298 0.000 0.727 0.805 -0.746 0.000 0.995 1.064 0.988 0.570 0.807 0.880 0.927 +1 0.472 1.150 0.226 1.809 -0.403 1.113 -0.507 1.401 2.173 0.860 0.314 -1.396 2.215 0.315 0.028 1.234 0.000 1.015 -0.828 0.083 0.000 0.632 0.836 0.993 1.341 1.040 1.723 1.403 +0 1.371 -0.969 0.236 1.248 -1.078 0.802 0.788 0.950 0.000 0.701 0.783 -1.016 2.215 0.866 -0.549 -1.187 1.274 0.565 -1.522 0.489 0.000 1.819 1.329 1.678 1.327 0.645 0.954 1.037 +1 0.651 0.388 -1.364 0.688 -0.656 0.534 -0.618 -1.625 2.173 0.624 0.697 0.351 0.000 0.819 -0.955 0.415 2.548 0.449 -0.418 1.225 0.000 0.662 0.820 0.991 0.766 0.813 0.620 0.625 +0 1.790 1.406 1.377 0.770 0.712 0.896 1.340 -0.294 2.173 1.149 0.820 -0.677 0.000 0.352 -0.166 0.666 2.548 0.487 -0.161 -1.055 0.000 0.621 0.751 0.986 0.670 0.796 0.825 0.814 +1 1.001 -1.259 -1.235 1.686 -0.553 0.729 -1.044 0.109 2.173 1.165 2.595 1.599 0.000 1.165 0.305 0.744 2.548 0.794 -0.173 -1.331 0.000 2.567 1.951 1.039 0.931 1.063 2.042 2.267 +1 2.324 0.275 -0.179 0.442 -0.875 0.900 0.560 1.596 2.173 0.890 -0.272 0.741 0.000 0.848 -0.720 -1.703 2.548 1.229 0.287 -0.829 0.000 0.846 0.901 0.991 1.095 0.817 1.005 0.821 +1 1.052 -1.171 0.309 1.157 0.046 0.935 -0.301 -1.087 2.173 0.556 -0.089 0.853 0.000 1.234 -0.022 -0.097 0.000 1.645 -0.441 -1.713 0.000 0.923 0.988 0.993 1.617 0.997 1.338 1.100 +1 0.489 -0.121 0.475 1.099 -1.437 1.553 -0.400 0.247 2.173 0.950 -0.347 -1.209 0.000 0.962 -1.602 1.168 0.000 0.867 1.096 -0.980 0.000 1.134 0.790 1.004 1.169 1.025 1.060 0.886 +0 1.175 -0.294 1.320 0.834 -0.271 0.975 0.889 -0.965 2.173 0.735 1.594 1.580 0.000 1.389 0.655 0.194 2.548 0.936 1.160 0.685 0.000 0.871 0.982 1.358 1.256 1.260 0.999 0.905 +1 0.898 0.140 1.504 0.899 -0.141 0.965 0.478 -0.929 0.000 1.299 0.184 1.093 2.215 0.510 -2.382 0.283 0.000 0.972 1.054 -0.532 0.000 0.788 0.738 1.240 0.890 0.832 0.902 0.801 +0 0.930 0.252 -0.816 0.772 -1.401 0.495 1.306 0.386 2.173 0.856 0.083 0.124 2.215 0.697 -0.732 -1.679 0.000 0.697 0.166 1.242 0.000 0.549 0.951 0.984 0.887 0.668 0.835 0.741 +1 1.476 1.568 -0.078 0.447 0.399 1.883 -0.118 -1.072 0.000 2.140 0.793 0.956 2.215 1.098 0.481 0.643 2.548 0.790 0.254 -0.330 0.000 1.222 1.507 0.980 1.219 0.511 1.519 1.331 +0 0.408 -0.006 -0.604 1.358 1.418 1.644 -0.568 -1.521 2.173 1.737 -0.687 0.404 0.000 0.805 -1.159 -0.656 0.000 2.236 -0.445 -0.017 3.102 1.571 1.022 0.999 0.946 1.983 1.376 1.147 +0 0.716 1.172 1.551 1.366 1.627 0.755 -0.034 -1.242 0.000 1.030 -0.473 -0.108 2.215 0.398 2.166 -0.520 0.000 0.624 -0.976 0.794 3.102 0.777 0.832 0.999 1.629 0.583 1.573 1.196 +0 0.529 0.569 0.242 0.801 1.425 1.554 1.491 -1.574 0.000 0.848 0.358 0.701 2.215 0.978 0.377 -0.617 2.548 1.912 -0.483 0.240 0.000 1.038 1.078 0.987 0.648 0.898 0.856 0.756 +0 0.767 -0.513 -0.216 1.728 0.502 0.881 -1.602 1.356 2.173 0.732 -0.981 -1.110 2.215 0.435 -1.506 -1.410 0.000 0.574 1.302 -0.898 0.000 1.346 0.972 0.987 1.329 1.007 1.012 0.985 +0 0.548 1.261 -1.678 2.130 1.332 1.263 -0.548 -0.101 2.173 1.020 -0.249 -1.532 0.000 0.960 -0.284 -0.570 1.274 0.615 1.860 -0.325 0.000 1.144 0.899 0.995 1.758 0.587 1.198 1.063 +1 0.633 0.454 -0.153 2.363 1.139 1.706 1.004 -0.364 0.000 1.312 0.039 0.926 2.215 1.940 1.173 -1.251 0.000 0.636 -1.083 -1.067 0.000 0.747 0.881 1.556 0.900 0.873 0.717 0.719 +0 1.036 0.162 1.057 0.980 1.248 0.653 -0.576 1.666 1.087 0.742 -1.088 -0.453 2.215 1.412 -2.447 -0.101 0.000 1.220 0.209 -1.163 0.000 0.487 0.829 0.989 1.010 1.004 1.052 1.611 +1 1.447 -0.404 0.954 1.265 1.289 0.704 -0.658 -1.309 0.000 0.622 1.512 -1.019 0.000 1.879 1.395 -0.124 0.000 1.268 -0.077 1.525 3.102 1.198 1.430 0.980 1.191 1.148 1.454 1.529 +1 1.593 -0.829 -0.117 0.370 0.548 2.089 1.986 1.554 0.000 1.248 -1.053 0.135 1.107 1.225 -0.265 -0.019 0.000 2.031 -0.380 -1.095 3.102 0.721 0.861 0.980 0.654 1.365 0.806 0.666 +1 0.840 0.098 -0.423 0.974 -0.670 1.541 0.126 -0.165 2.173 1.544 0.450 1.301 0.000 1.057 -0.279 1.657 0.000 0.753 -1.256 -0.230 0.000 0.990 0.769 0.989 0.978 0.884 1.166 1.066 +1 0.629 0.733 1.078 0.766 1.733 1.110 0.394 0.927 0.000 1.041 0.310 -1.192 2.215 1.801 0.423 -0.405 2.548 0.876 0.928 0.522 0.000 0.771 1.295 0.983 1.080 0.954 1.038 0.912 +0 0.840 -2.103 1.356 1.345 -1.621 1.604 -1.130 0.555 2.173 1.297 -0.003 -0.846 2.215 0.759 0.432 -0.466 0.000 0.768 -0.654 -0.439 0.000 0.907 0.966 0.983 1.416 2.390 1.497 1.443 +1 0.875 -0.064 1.001 0.366 0.113 1.112 0.240 -1.741 2.173 1.296 0.167 0.279 0.000 0.679 -2.589 -0.639 0.000 1.834 0.121 -0.918 1.551 0.966 1.089 0.989 0.935 1.025 1.050 0.900 +1 0.731 1.426 0.457 0.537 -1.074 0.797 0.371 1.211 0.000 0.945 1.077 -1.326 2.215 1.534 0.887 -0.468 2.548 0.947 0.623 0.631 0.000 0.709 1.026 0.985 0.829 0.897 0.891 0.836 +1 0.875 0.472 -0.990 0.521 0.757 0.374 0.856 0.491 2.173 1.010 0.443 -0.100 2.215 0.668 -1.545 1.447 0.000 1.309 0.536 -1.476 0.000 1.583 1.226 0.987 0.731 0.496 0.942 0.814 +1 2.396 0.717 -0.491 0.480 -1.022 0.738 0.440 1.367 1.087 0.557 -0.834 1.037 0.000 0.374 0.546 1.049 1.274 1.121 1.494 1.006 0.000 1.802 0.947 0.993 1.216 0.192 0.789 0.884 +0 1.776 0.593 -0.124 0.651 -0.310 0.710 0.948 -1.633 0.000 1.127 0.570 1.129 2.215 1.216 0.489 0.630 2.548 0.716 0.612 -0.765 0.000 0.777 0.966 0.988 1.177 0.542 0.830 0.840 +0 0.900 0.091 -1.078 0.772 1.526 1.529 0.685 0.521 0.000 0.615 1.407 0.028 0.000 1.119 0.919 -1.426 2.548 1.044 0.832 -0.981 3.102 0.823 1.030 0.986 0.582 0.323 0.848 0.781 +0 0.447 -0.436 0.519 1.800 1.402 1.370 0.670 0.562 2.173 1.992 0.601 -0.909 0.000 0.812 0.315 -0.290 0.000 1.003 0.466 -1.325 3.102 1.067 0.731 0.992 1.075 1.232 1.132 1.062 +1 0.461 2.236 0.998 0.623 1.720 0.697 0.434 0.636 0.000 0.948 -0.127 -1.199 0.000 1.096 -0.277 -0.038 0.000 1.330 0.411 0.066 3.102 1.079 0.690 0.980 0.636 1.031 0.861 0.795 +0 2.362 1.842 0.820 1.941 0.692 1.687 2.717 -1.244 0.000 1.032 -0.834 -0.731 2.215 0.796 1.187 0.179 1.274 0.612 1.310 -0.613 0.000 0.929 1.203 0.978 0.853 1.440 2.163 1.605 +0 0.940 0.985 -1.660 1.077 1.299 0.544 0.055 0.593 0.000 0.987 0.528 0.008 0.000 1.261 0.595 -0.866 2.548 0.405 -0.907 -1.450 3.102 0.873 1.038 0.989 0.966 0.604 0.809 0.893 +1 1.198 -1.432 1.002 0.523 0.588 2.532 -0.509 -1.078 0.000 1.107 -0.760 0.656 2.215 2.078 -1.826 0.602 0.000 0.686 1.246 -1.618 0.000 1.035 0.992 1.000 0.591 0.483 0.622 0.621 +0 0.910 -0.301 -1.104 0.698 1.283 1.076 -0.816 -0.953 2.173 1.483 -0.244 0.469 0.000 0.986 1.018 1.053 0.000 0.665 0.030 1.371 0.000 0.958 0.838 0.985 0.897 0.520 0.915 0.807 +1 0.542 0.428 0.866 1.325 -0.417 0.516 0.840 1.287 0.000 1.299 1.411 -0.473 2.215 1.215 0.083 1.630 0.000 0.520 -0.276 1.037 3.102 0.701 0.489 1.074 0.895 1.046 0.901 0.814 +0 0.999 -0.359 -1.350 0.839 0.327 0.612 -0.540 0.782 0.000 1.045 0.005 1.302 1.107 1.060 0.211 0.282 0.000 3.140 0.423 -0.869 3.102 0.855 0.876 1.266 1.097 1.571 1.084 0.934 +1 0.859 1.294 -0.430 0.802 1.612 0.351 -0.218 -0.883 0.000 0.910 2.266 1.057 0.000 0.483 0.569 -1.389 2.548 0.709 0.065 -0.292 1.551 0.923 0.731 1.109 0.697 0.394 0.482 0.672 +0 0.888 -0.256 1.000 1.243 0.203 0.731 -0.179 -1.514 2.173 0.708 0.163 -0.605 0.000 0.426 0.294 -0.950 2.548 0.425 1.382 0.553 0.000 0.849 0.914 0.988 0.692 0.382 0.690 0.677 +0 0.433 0.977 -1.303 1.957 -0.298 0.687 -0.015 1.295 0.000 0.578 0.888 1.109 2.215 1.255 -1.136 -0.329 2.548 1.568 -0.780 1.596 0.000 0.866 0.878 1.004 1.495 1.467 1.120 1.140 +0 1.600 -0.040 -0.314 0.844 0.906 1.197 -2.905 1.189 0.000 1.550 0.644 -1.168 2.215 0.707 1.879 1.653 0.000 1.105 -0.178 0.410 3.102 10.018 5.222 1.435 1.309 1.285 3.675 2.753 +1 0.744 0.660 -0.123 1.376 -1.602 0.654 0.895 0.686 0.000 0.992 1.088 -1.525 2.215 1.150 0.996 -0.631 2.548 1.578 1.634 0.497 0.000 0.898 1.087 1.362 0.823 0.818 0.896 0.856 +1 2.190 0.602 1.577 2.602 -1.692 3.165 -0.914 0.075 0.000 1.212 0.352 -1.655 0.000 0.684 0.466 -0.831 2.548 1.411 0.330 -0.349 3.102 0.771 1.005 0.969 0.920 0.319 0.921 0.833 +1 0.777 -0.015 0.275 1.059 1.347 0.533 0.139 -0.192 1.087 0.712 -0.969 1.401 0.000 1.203 0.048 -1.444 2.548 0.706 1.715 0.019 0.000 0.388 0.935 1.034 0.772 0.903 0.692 0.720 +0 1.222 -0.601 0.475 0.856 -0.182 0.609 -1.256 -1.026 0.000 0.957 -0.447 1.270 2.215 0.727 2.446 -1.397 0.000 0.535 -1.737 -0.164 0.000 0.714 1.036 0.989 0.534 0.173 0.626 0.699 +1 1.239 -0.256 -1.178 0.567 0.319 1.388 1.982 1.389 0.000 1.662 -0.080 -0.243 2.215 1.860 -0.528 0.165 2.548 0.554 0.561 -1.237 0.000 0.622 0.863 1.133 0.930 0.820 0.788 0.683 +1 0.729 -0.603 -1.321 0.620 0.403 2.046 -2.134 0.693 0.000 1.093 -1.442 -1.497 2.215 1.457 -0.777 1.538 0.000 3.858 -0.679 -0.521 3.102 0.906 1.598 0.985 0.910 1.562 1.681 1.293 +0 0.370 -0.694 0.657 0.094 -0.272 1.674 1.120 0.761 2.173 2.037 0.327 -0.879 0.000 0.721 0.617 -1.714 2.548 0.490 0.907 -0.611 0.000 0.590 0.754 0.830 0.864 1.125 1.292 0.990 +1 0.725 -0.266 0.322 2.285 1.034 0.513 0.128 -0.150 0.000 1.324 0.480 -1.444 1.107 0.876 1.170 -0.444 2.548 0.573 0.482 -0.949 0.000 0.578 0.882 1.067 1.261 1.010 1.109 0.906 +1 0.885 -1.423 1.186 0.773 -0.969 0.835 1.075 0.572 2.173 0.691 0.228 -1.461 0.000 0.868 -0.699 -0.474 2.548 0.508 -1.913 -0.597 0.000 1.372 0.914 1.068 1.774 1.419 1.211 1.079 +1 0.688 -1.923 0.359 0.362 0.375 0.990 -0.965 0.936 2.173 0.890 -0.939 -0.935 0.000 0.835 -0.684 -0.088 2.548 0.368 -0.753 -1.377 0.000 0.290 1.052 0.996 0.577 0.912 0.717 0.651 +0 0.601 0.486 -1.037 1.800 -0.090 0.465 -0.109 -0.730 2.173 0.862 -0.329 -1.439 2.215 0.609 -1.453 0.349 0.000 0.929 -0.190 1.008 0.000 0.769 0.882 1.086 0.674 0.565 0.703 0.765 +0 0.465 0.925 1.191 0.736 -0.762 0.998 2.336 -1.640 0.000 1.773 1.662 -0.083 0.000 1.208 0.884 0.362 2.548 1.674 -0.583 -1.427 3.102 0.825 0.852 0.991 0.797 1.488 1.109 1.314 +1 1.742 0.523 1.733 1.093 1.055 0.748 1.424 0.261 2.173 0.237 0.823 0.177 0.000 1.138 1.723 -0.761 0.000 1.021 0.269 -0.831 3.102 0.738 0.773 1.096 0.849 0.942 0.898 0.838 +0 0.924 -1.270 -1.392 1.934 -1.215 3.614 -0.825 0.670 0.000 3.100 -0.509 -1.070 1.107 1.425 -0.503 0.358 2.548 1.784 -1.705 -0.724 0.000 4.459 2.474 1.005 1.872 2.144 2.535 2.255 +0 1.660 -0.592 -0.034 0.537 -0.909 0.877 -2.540 -1.692 0.000 1.236 -0.830 -1.204 2.215 1.495 -1.411 0.639 1.274 1.908 -1.111 1.143 0.000 1.300 0.864 0.986 0.980 1.527 0.979 0.928 +0 2.144 0.100 -0.768 0.690 -1.415 0.693 -0.377 0.097 0.000 0.570 0.804 0.546 0.000 1.416 0.206 1.288 2.548 1.003 0.578 0.200 3.102 0.930 0.961 0.986 0.854 0.786 0.837 0.761 +0 0.339 2.327 -0.609 0.732 0.577 1.173 0.314 0.577 2.173 1.272 0.830 -0.945 2.215 0.913 1.552 1.604 0.000 0.674 -1.568 -1.198 0.000 0.963 0.939 0.986 0.860 1.829 1.005 0.831 +1 0.809 -0.801 1.018 0.449 -0.819 1.119 0.745 -1.236 2.173 0.910 0.285 0.764 0.000 0.470 1.011 -0.496 2.548 0.703 -0.678 0.170 0.000 0.909 1.120 0.993 0.680 0.582 0.722 0.688 +1 1.857 0.222 -1.552 1.416 1.463 1.049 -0.319 -0.430 1.087 0.949 0.113 0.637 1.107 0.333 0.920 -0.178 0.000 0.390 0.688 0.425 0.000 0.309 0.786 0.994 1.088 1.246 1.158 0.889 +0 0.522 -1.696 -0.140 1.285 0.534 0.707 0.039 -1.123 0.000 1.076 1.622 -0.152 0.000 1.190 0.324 1.728 0.000 1.334 -0.454 -1.720 1.551 0.930 0.826 0.991 0.862 0.518 0.560 0.741 +0 0.546 0.346 0.875 0.982 -0.879 0.556 0.747 1.006 2.173 0.759 0.369 -0.888 0.000 0.731 -0.490 0.188 2.548 0.868 -0.431 1.669 0.000 0.926 0.934 1.015 0.686 0.767 0.680 0.621 +1 1.482 -0.171 0.270 0.861 -0.097 0.509 -0.883 0.082 2.173 1.341 -0.781 -1.414 2.215 1.248 0.822 1.456 0.000 0.702 1.376 -1.063 0.000 0.884 1.348 0.980 1.457 1.187 1.147 1.080 +1 0.430 -0.670 1.499 0.456 0.103 0.867 -1.514 -1.709 0.000 0.628 -1.330 -1.029 2.215 1.189 -1.129 -0.092 1.274 1.342 -2.226 0.014 0.000 1.925 1.212 0.989 0.710 0.689 0.893 0.755 +1 0.755 -0.517 -0.381 3.202 -1.002 0.895 0.037 1.459 2.173 1.332 0.240 0.846 0.000 0.571 -0.622 1.489 0.000 0.949 0.987 0.124 0.000 0.946 1.040 1.141 1.438 1.239 1.004 1.014 +0 1.486 0.284 -1.541 0.718 0.122 0.861 -0.668 -0.106 2.173 0.628 -0.493 1.009 0.000 0.751 -0.708 -1.237 0.000 1.193 -0.376 0.510 3.102 0.958 1.000 1.428 0.927 0.577 0.835 0.765 +1 0.609 -0.643 -1.518 0.919 1.312 1.122 0.280 1.631 1.087 1.284 1.244 -0.720 2.215 1.299 0.321 0.235 0.000 1.342 -0.272 -0.394 0.000 0.930 1.255 0.981 0.634 1.764 1.195 1.010 +1 1.099 0.508 1.145 0.660 -1.630 1.227 0.256 -1.515 0.000 2.024 -1.269 0.041 0.000 1.299 1.409 0.696 1.274 1.543 0.174 -0.878 3.102 4.389 2.541 0.992 1.048 1.323 2.074 1.595 +1 0.382 -1.186 1.277 0.523 0.655 0.990 0.113 -1.456 2.173 0.745 0.766 0.246 0.000 0.877 1.452 -0.412 0.000 0.533 -0.830 0.738 3.102 0.862 0.927 1.000 0.899 0.836 0.921 0.799 +0 0.585 1.053 1.593 0.253 -1.195 1.145 0.245 0.349 0.000 1.958 -0.055 -1.371 2.215 2.408 -0.419 -0.087 0.000 1.189 -1.116 -0.191 0.000 0.865 0.952 1.000 0.806 0.478 1.134 0.952 +1 1.340 0.480 -1.731 0.448 0.930 0.988 -0.727 -0.450 1.087 0.532 -1.555 -1.554 2.215 0.803 -0.850 0.276 0.000 0.564 0.417 0.561 0.000 0.609 0.808 0.994 1.218 1.014 0.923 0.792 +0 0.863 -1.714 -1.669 0.227 0.384 0.398 0.100 -0.268 0.000 0.816 0.196 1.501 2.215 1.047 -0.546 0.567 2.548 1.434 -0.891 -0.666 0.000 0.821 1.066 0.993 0.740 0.837 0.765 0.722 +0 1.350 0.840 1.470 0.266 1.728 0.861 -1.170 -0.853 2.173 0.867 -2.180 -0.134 0.000 0.901 0.102 0.903 2.548 0.682 -0.811 0.005 0.000 0.691 0.904 0.987 0.802 1.335 1.319 1.521 +1 0.610 -1.273 -0.606 0.273 0.268 1.218 1.717 -1.523 0.000 1.106 -0.556 -0.491 2.215 0.880 1.153 1.079 0.000 1.566 -0.363 0.335 3.102 0.922 0.945 0.993 0.652 0.809 0.896 0.767 +1 0.441 1.438 -1.216 1.243 0.678 0.449 1.144 -0.723 0.000 0.521 0.958 0.504 0.000 0.679 -0.383 0.876 0.000 0.640 0.481 -0.440 3.102 0.922 0.926 1.015 0.640 0.503 0.687 0.631 +0 1.280 -0.474 -0.203 1.654 -0.722 0.899 -0.086 1.315 2.173 0.492 0.303 0.803 1.107 0.769 0.696 0.210 0.000 0.378 0.629 -0.750 0.000 0.452 0.858 0.989 0.891 0.479 0.924 0.754 +1 0.375 0.035 1.562 1.854 -0.327 0.838 -1.238 0.634 2.173 0.539 -0.855 -1.291 0.000 0.716 -0.797 -1.667 1.274 0.555 -1.061 -0.329 0.000 0.664 0.669 1.145 0.873 0.865 0.896 0.707 +0 0.834 0.192 0.717 0.858 -0.192 0.696 -1.246 1.494 0.000 0.883 -1.604 -0.670 0.000 1.004 0.048 -1.694 2.548 0.489 -0.502 0.480 3.102 1.581 0.958 0.982 0.844 0.527 0.780 0.940 +0 1.750 1.370 0.368 0.582 0.879 0.636 1.132 -1.639 0.000 0.477 2.454 -1.366 0.000 0.477 0.477 1.170 1.274 1.037 0.936 -0.467 0.000 0.920 0.742 0.984 0.650 0.402 0.517 0.651 +1 0.890 -0.737 1.551 0.729 -0.638 0.924 0.251 -0.685 1.087 0.313 -1.167 1.234 0.000 0.678 -2.092 1.004 0.000 1.094 -0.657 0.760 1.551 0.952 0.654 1.028 0.933 1.178 0.927 0.794 +1 1.144 0.466 -1.417 1.713 -1.573 1.543 0.032 -1.425 0.000 3.927 -2.156 0.209 0.000 0.975 0.126 -1.688 0.000 1.501 -0.571 -0.211 3.102 0.530 0.940 0.972 1.103 0.901 0.918 0.810 +1 1.995 -0.914 -0.352 0.483 1.417 0.951 -1.037 1.410 2.173 0.416 -1.616 -0.372 0.000 0.835 -0.820 0.764 0.000 0.382 0.129 -0.827 3.102 0.849 0.909 1.359 0.681 0.704 0.793 0.696 +0 2.339 1.351 -1.071 0.376 -1.258 0.705 -1.583 0.396 0.000 0.672 0.481 1.499 2.215 1.342 -0.371 0.815 1.274 0.983 -0.877 -0.276 0.000 0.867 0.811 0.980 1.832 0.750 1.219 1.088 +1 1.279 0.942 0.293 0.327 1.550 0.744 -0.591 -1.094 2.173 0.438 -0.529 -0.389 0.000 0.777 -0.369 1.052 2.548 0.640 -0.598 1.393 0.000 0.692 0.633 0.987 0.858 0.889 0.951 0.787 +1 0.429 -1.118 1.288 2.003 0.341 1.123 -1.946 -1.480 0.000 0.769 -0.771 -0.248 1.107 0.671 -0.832 -1.489 0.000 0.584 -1.239 -0.130 3.102 0.859 0.792 0.986 0.771 0.223 0.753 0.825 +0 0.684 0.255 1.428 1.156 1.251 1.330 -0.256 0.195 2.173 1.474 0.875 -1.484 2.215 0.732 -0.637 -0.303 0.000 1.169 0.421 -0.765 0.000 0.772 0.976 0.983 0.890 2.413 1.393 1.169 +1 0.680 0.346 0.863 1.610 0.085 0.951 0.897 -1.156 2.173 0.454 0.784 -0.643 2.215 0.465 1.640 1.644 0.000 0.444 1.313 1.039 0.000 0.267 0.655 0.989 0.666 0.435 0.771 0.651 +1 0.672 0.258 -0.037 0.602 1.520 1.438 0.525 -0.297 0.000 1.309 0.542 1.158 0.000 1.865 0.857 -1.636 2.548 0.960 -1.030 1.396 0.000 1.526 1.433 0.986 0.939 1.324 1.207 0.999 +1 0.644 0.375 0.727 1.277 -1.665 1.216 0.507 -0.139 0.000 1.005 0.730 1.468 2.215 0.770 0.063 -1.008 2.548 0.729 -0.143 -0.009 0.000 1.015 0.808 1.049 0.629 0.807 0.821 0.762 +1 1.025 -0.156 0.395 0.555 -0.689 1.617 -0.384 -0.805 1.087 1.186 0.085 1.025 0.000 1.271 -0.836 1.411 0.000 0.817 0.798 0.587 3.102 0.793 0.628 0.990 1.033 1.454 1.044 0.889 +1 1.229 -0.987 0.984 1.625 0.459 1.840 -0.156 -0.814 2.173 0.804 -1.306 1.252 0.000 1.340 -1.950 -1.413 0.000 1.006 -2.377 0.904 0.000 1.036 1.027 0.990 2.115 1.129 1.523 1.357 +1 1.224 0.125 1.468 1.754 0.910 1.006 -0.017 -0.838 0.000 1.224 -0.446 0.042 2.215 0.962 0.128 -1.266 0.000 0.380 -2.194 0.640 0.000 0.678 1.213 0.985 0.731 0.673 0.819 0.921 +0 1.026 -0.837 -1.322 0.871 -0.833 0.778 -0.554 0.961 0.000 0.502 -1.477 0.541 0.000 1.895 -1.229 -0.065 0.000 2.026 -1.270 1.503 3.102 0.825 0.902 0.978 0.569 1.290 0.871 0.872 +1 1.260 0.639 -0.257 1.682 -0.013 1.895 0.822 -0.043 2.173 0.592 2.276 -0.690 0.000 3.260 -0.058 -1.552 2.548 1.826 1.721 1.160 0.000 0.877 1.013 0.992 1.828 3.349 1.716 1.345 +0 0.620 0.314 -0.192 1.328 -0.820 0.422 0.413 1.735 2.173 0.476 0.709 1.095 2.215 0.655 -1.324 0.088 0.000 0.869 -0.361 1.102 0.000 0.795 0.842 0.979 0.788 0.375 0.621 0.796 +1 0.560 -1.264 0.255 0.782 -1.350 1.316 -0.236 0.324 2.173 0.965 -0.090 -1.393 0.000 0.819 2.619 1.632 0.000 1.167 0.052 -0.580 0.000 0.931 0.871 0.985 1.336 0.828 1.067 1.015 +1 0.749 0.204 1.742 0.553 -0.350 1.494 0.653 -1.541 2.173 2.007 0.257 0.570 1.107 2.025 0.668 -0.590 0.000 1.355 -0.515 -0.187 0.000 1.482 1.739 0.990 0.845 2.461 1.593 1.217 +1 0.685 0.424 0.338 0.627 -1.232 0.840 0.925 0.196 0.000 0.901 -0.615 1.411 2.215 0.789 -0.605 -1.231 0.000 0.858 0.527 -1.217 3.102 1.913 1.158 0.988 0.762 0.771 0.938 0.778 +0 1.524 1.163 0.206 0.892 0.930 0.856 2.530 -0.344 0.000 0.875 0.443 -1.453 0.000 0.814 1.232 -1.300 2.548 1.493 0.946 1.337 3.102 0.863 1.083 0.988 0.776 0.591 0.827 0.883 +1 2.726 -0.959 1.515 0.993 0.857 0.689 0.048 -0.499 1.087 0.399 0.377 0.182 0.000 1.044 -0.874 -0.032 0.000 1.665 -0.493 -1.152 3.102 0.864 1.093 1.272 1.508 0.729 1.076 1.039 +1 0.697 -0.862 -1.005 1.485 1.106 1.777 -0.538 1.455 2.173 1.495 -0.146 -1.191 0.000 2.955 -2.213 -0.262 0.000 2.188 0.859 0.280 0.000 0.889 0.654 1.333 1.026 0.824 1.123 1.039 +0 1.584 -0.268 -0.318 2.260 0.082 1.848 0.813 -1.672 2.173 0.909 0.064 1.524 0.000 1.443 0.306 0.379 2.548 0.827 -0.046 -1.237 0.000 0.941 1.015 0.995 2.383 2.019 1.625 1.344 +1 1.610 0.241 0.168 0.129 -0.424 0.883 -1.800 -1.532 0.000 0.933 -1.216 1.120 2.215 0.729 -0.186 -1.606 2.548 0.525 1.093 -0.906 0.000 1.372 0.950 0.980 1.318 0.734 0.920 1.071 +1 0.950 -0.781 -1.158 0.787 -0.639 0.861 0.983 1.330 2.173 0.970 1.000 -0.055 2.215 0.410 1.177 -1.657 0.000 0.481 2.316 0.583 0.000 0.595 0.741 0.994 1.930 1.276 1.557 1.389 +1 1.338 0.583 -1.372 1.228 -0.050 0.819 -0.233 0.465 2.173 0.479 -1.045 -0.913 0.000 0.541 0.606 -1.127 0.000 1.051 0.133 1.188 0.000 0.784 1.007 1.650 0.880 0.575 0.817 0.746 +1 0.753 0.368 -1.717 1.226 0.573 0.918 -0.947 -0.861 2.173 0.525 1.152 -1.702 0.000 0.961 -0.474 0.189 0.000 1.373 0.258 -0.348 3.102 0.834 0.838 1.172 0.829 0.962 0.937 0.795 +0 0.668 -0.091 -0.005 1.274 1.444 0.696 1.454 -0.428 0.000 0.806 0.785 1.439 2.215 0.439 2.187 0.597 0.000 1.025 -1.397 1.628 0.000 0.928 0.968 1.234 0.810 1.103 0.837 0.895 +0 0.898 1.499 -1.176 0.614 -1.661 0.397 0.951 -0.280 2.173 0.591 -0.235 1.231 0.000 0.933 -0.329 0.198 0.000 0.483 0.593 0.886 1.551 0.916 0.859 0.974 0.574 0.406 0.519 0.626 +0 1.626 0.256 1.590 1.225 1.056 0.786 -0.760 -0.263 1.087 1.168 -0.744 -0.741 2.215 1.096 -0.033 -1.398 0.000 1.893 -0.202 0.147 0.000 0.940 1.027 0.992 1.346 0.588 1.103 0.925 +1 0.565 -0.087 1.533 1.256 -0.860 0.984 1.911 0.693 0.000 0.825 0.638 -0.920 0.000 0.367 1.448 1.276 1.274 0.783 0.823 -0.267 3.102 1.876 1.220 0.988 0.682 0.422 0.821 0.720 +0 1.655 0.577 1.033 2.347 1.377 1.478 -0.847 -0.517 0.000 0.503 0.263 -1.367 2.215 0.562 0.668 -0.668 0.000 0.394 -0.173 0.798 1.551 1.460 0.947 0.993 0.815 0.386 0.697 1.105 +1 0.875 -0.071 0.384 0.425 -1.563 1.362 0.424 1.481 1.087 1.129 0.526 -0.258 0.000 1.246 -0.957 -0.650 0.000 0.793 0.611 0.620 0.000 0.884 0.713 0.983 1.067 0.667 0.877 0.797 +1 2.092 -0.951 -1.724 0.926 1.157 0.908 -1.167 -0.445 1.087 0.904 -0.543 0.041 0.000 0.780 -0.269 1.273 0.000 0.429 0.966 0.098 3.102 1.167 1.108 0.999 0.984 1.009 0.998 0.907 +0 0.322 2.013 1.676 2.849 1.332 1.346 -0.884 -0.195 1.087 0.563 0.988 -1.600 0.000 0.609 0.303 0.423 0.000 0.841 -0.451 -0.699 0.000 0.924 0.662 0.985 2.528 1.052 1.598 1.236 +0 0.752 -0.129 -1.204 0.627 -1.436 0.431 0.717 1.551 2.173 0.539 -0.220 -0.295 0.000 1.098 1.507 0.512 2.548 0.630 0.007 0.311 0.000 0.404 0.867 0.984 0.599 0.805 0.690 0.675 +1 0.691 -1.621 -0.259 1.555 0.158 1.274 -0.914 1.587 2.173 0.335 -2.450 -0.486 0.000 0.796 -0.268 -0.984 0.000 0.485 0.649 -1.525 3.102 0.829 0.823 0.993 1.436 0.842 0.989 0.849 +1 1.876 0.909 -1.176 0.559 1.672 0.722 -0.774 0.419 0.000 1.183 1.444 0.731 2.215 0.612 0.041 -0.285 0.000 0.560 0.550 1.452 3.102 0.873 0.757 0.983 1.264 0.543 0.938 0.983 +1 1.331 -0.092 -0.994 1.308 1.712 1.007 0.822 0.281 0.000 0.859 0.135 -1.635 0.000 1.124 -0.660 0.525 1.274 0.603 -1.931 -0.359 0.000 1.697 0.980 1.181 1.099 0.494 0.739 0.776 +1 0.419 2.187 -1.220 1.085 1.131 1.022 0.362 -0.539 0.000 0.755 1.364 1.547 2.215 0.877 0.941 -0.136 0.000 1.038 -0.168 1.066 3.102 0.837 1.057 0.993 0.563 0.788 0.895 0.835 +1 0.691 2.044 -1.157 0.901 0.396 0.849 1.435 1.142 0.000 1.073 0.789 -0.638 2.215 0.798 -0.091 1.287 2.548 1.099 1.484 -0.395 0.000 1.471 1.207 1.077 0.978 1.075 0.964 0.895 +0 1.158 -0.507 -0.255 0.761 1.359 0.912 1.854 -1.017 0.000 0.292 2.200 -0.192 0.000 0.738 0.857 1.122 2.548 0.942 -0.419 1.363 3.102 0.773 0.940 1.292 0.738 0.520 0.911 1.059 +0 1.587 -0.621 -0.508 1.242 -0.873 1.131 -0.936 0.923 0.000 0.952 -0.051 -1.591 2.215 1.112 -0.473 0.509 0.000 0.737 -0.502 1.430 3.102 0.854 1.291 0.984 0.807 0.373 0.769 0.958 +0 1.535 -0.133 1.283 0.386 -1.361 0.879 -0.178 -0.664 2.173 0.844 -0.463 0.324 1.107 0.964 0.076 0.049 0.000 1.198 -0.759 -0.964 0.000 1.031 1.035 0.985 1.069 1.004 0.844 0.802 +0 1.235 1.100 0.371 0.195 1.652 1.328 0.457 -0.068 0.000 1.284 -2.278 -1.038 0.000 1.674 -1.786 -1.418 0.000 1.538 -0.206 0.959 0.000 0.945 0.837 0.984 0.586 0.813 1.082 1.295 +1 0.907 -1.094 1.692 0.265 -0.079 1.223 -0.780 -1.534 0.000 1.545 1.597 0.086 0.000 0.851 -0.122 0.047 2.548 0.932 0.058 -1.118 1.551 1.514 0.987 0.982 0.881 0.594 0.778 0.760 +1 0.734 1.633 0.916 0.758 -1.131 0.888 -0.235 0.128 2.173 0.433 1.692 -1.438 0.000 0.654 0.427 0.970 0.000 0.730 -0.174 -0.965 3.102 0.873 1.187 0.995 0.787 0.710 0.908 0.795 +1 0.726 -0.462 -1.239 3.277 -0.698 2.209 -0.448 1.021 0.000 0.813 0.613 0.920 2.215 1.079 -0.514 -0.117 2.548 0.756 -1.246 -0.849 0.000 2.238 1.577 1.003 0.819 1.019 1.133 1.342 +1 2.003 0.130 0.243 1.402 -0.472 0.413 1.107 -0.456 2.173 0.870 -0.677 -1.525 0.000 1.314 0.099 1.133 0.000 0.897 0.654 -1.409 3.102 0.926 0.697 1.393 1.007 0.499 0.719 0.655 +1 1.214 0.251 -1.419 0.777 -0.443 0.685 1.359 0.071 2.173 0.669 0.347 1.588 0.000 0.444 1.465 0.648 0.000 1.101 1.230 1.150 3.102 0.838 0.916 1.038 0.890 0.760 0.801 0.711 +0 1.751 0.332 -0.335 2.592 -1.139 0.967 0.260 0.921 1.087 1.492 0.565 0.405 0.000 1.208 -0.033 1.388 2.548 0.931 -0.513 -1.369 0.000 1.820 1.278 1.955 1.433 0.585 1.247 1.180 +1 0.939 0.927 -1.455 0.326 -0.624 0.590 1.211 0.906 1.087 1.171 1.130 1.715 2.215 2.160 2.301 -0.013 0.000 0.700 1.796 -0.840 0.000 0.954 1.230 0.990 0.720 0.815 1.108 0.931 +1 0.981 -0.254 0.941 1.470 -1.699 0.966 1.144 -0.794 0.000 1.527 -0.037 0.404 2.215 1.035 0.751 -1.393 0.000 0.708 0.458 -0.085 3.102 0.970 0.762 1.150 1.174 0.488 1.017 1.041 +0 0.523 1.008 1.355 0.833 -0.562 0.564 0.528 -0.076 0.000 0.705 -0.533 1.532 2.215 0.894 0.743 -1.314 2.548 1.540 -0.012 0.720 0.000 1.015 1.006 0.985 0.776 0.773 0.774 0.684 +0 0.639 -0.478 -1.467 1.897 1.313 0.291 -0.117 -0.337 2.173 0.429 1.468 -0.930 0.000 0.886 0.872 -0.204 0.000 0.382 -1.038 0.100 1.551 0.702 0.711 0.987 0.837 0.250 0.562 0.768 +0 0.385 -1.086 0.594 0.279 0.292 1.881 0.494 -1.581 2.173 2.330 -0.184 0.320 2.215 1.086 0.456 -1.139 0.000 0.742 0.610 -0.280 0.000 0.702 1.012 0.982 0.880 3.234 1.517 1.173 +0 0.570 0.216 0.671 0.733 -0.971 0.980 -0.654 -0.909 2.173 0.551 0.333 0.954 0.000 0.562 1.042 -1.561 0.000 2.065 0.946 0.443 3.102 0.931 0.975 0.989 1.006 2.091 1.092 0.889 +0 0.330 -1.826 1.621 1.614 -1.733 1.749 0.072 0.221 0.000 1.558 -0.933 -1.128 2.215 0.696 -0.514 0.733 0.000 0.711 1.300 -0.801 0.000 1.092 0.965 0.992 0.923 0.894 1.239 1.115 +0 1.117 -0.543 0.562 0.337 -1.426 0.551 -0.984 -1.050 2.173 0.858 1.271 1.476 2.215 0.609 1.675 0.018 0.000 0.559 -0.095 -0.603 0.000 0.810 0.863 0.988 0.775 1.631 1.034 0.953 +0 1.211 0.751 -1.263 0.819 1.254 1.252 0.622 0.011 2.173 1.275 -0.417 -1.538 2.215 0.805 0.097 0.416 0.000 0.677 0.647 1.158 0.000 0.576 0.984 1.057 1.243 2.100 1.175 0.932 +0 0.642 -0.181 -1.409 0.687 -1.123 0.893 1.362 0.201 0.000 1.003 -0.090 0.652 0.000 1.150 -0.642 1.589 0.000 0.919 0.347 1.454 0.000 0.888 0.994 0.982 1.559 0.497 1.098 1.000 +1 2.163 0.414 -1.161 1.134 -0.615 0.820 -0.852 0.502 2.173 0.666 -0.323 -1.693 0.000 0.766 0.936 0.893 0.000 0.970 0.736 0.250 3.102 1.131 0.856 1.027 1.601 0.955 1.098 0.980 +0 0.543 0.473 0.242 1.231 -0.853 0.700 0.799 1.131 1.087 0.454 1.647 0.350 0.000 0.566 1.963 1.072 0.000 1.578 0.035 -1.547 3.102 0.930 0.943 0.990 0.924 0.859 0.749 0.693 +0 1.111 -0.047 0.415 0.190 0.984 0.449 1.469 0.037 0.000 0.889 1.165 1.382 1.107 0.935 0.902 -1.419 2.548 1.379 1.439 -0.859 0.000 0.880 1.003 0.985 1.038 0.570 0.899 0.959 +0 1.060 -0.651 -1.154 0.427 -1.155 0.586 0.321 0.817 2.173 0.858 -0.083 0.352 2.215 0.778 1.006 -0.724 0.000 0.801 -0.130 -1.327 0.000 0.738 0.900 0.978 1.193 0.476 0.924 0.878 +0 1.039 -0.349 -1.377 1.023 -0.560 0.831 -1.378 0.530 0.000 1.469 0.009 -1.686 1.107 1.089 -0.490 0.349 0.000 0.898 -0.533 -0.677 3.102 0.803 0.849 0.986 0.879 0.887 1.050 0.945 +0 0.344 -1.812 0.167 0.333 -0.778 1.433 -0.548 -1.281 2.173 1.115 -0.761 0.591 2.215 0.837 1.918 0.265 0.000 0.719 -1.674 1.157 0.000 0.839 1.563 0.990 0.952 1.860 1.569 1.213 +0 3.324 0.471 -0.979 0.980 -0.596 1.497 0.292 0.818 2.173 0.641 -0.249 0.424 0.000 1.615 -0.527 1.039 0.000 1.385 1.067 -1.176 1.551 0.861 0.913 0.997 0.697 1.678 1.398 1.351 +1 0.536 -0.242 1.194 1.174 -0.234 0.598 -0.563 -0.689 0.000 0.871 -1.289 1.003 2.215 1.345 -0.048 0.986 2.548 1.015 -1.673 -0.679 0.000 0.933 1.118 1.055 0.803 0.784 0.945 0.837 +1 1.147 -1.031 -1.239 0.426 1.431 1.639 1.635 1.527 0.000 1.115 -0.408 0.007 2.215 1.803 0.503 0.069 2.548 0.682 -0.751 -0.161 0.000 0.609 0.669 0.984 0.975 0.766 0.928 0.733 +1 0.755 -2.203 -0.889 0.666 0.354 0.754 -0.376 0.014 0.000 0.927 -2.655 -1.572 0.000 0.771 -0.054 -0.311 2.548 1.589 -0.171 1.429 3.102 3.143 1.997 0.987 1.044 0.848 1.378 1.117 +1 0.999 -1.843 -0.413 1.144 -0.594 0.794 -0.472 0.977 2.173 0.446 -2.088 0.772 0.000 0.714 0.197 -1.179 2.548 0.530 -0.491 1.640 0.000 0.705 0.857 1.008 1.230 0.934 0.913 0.791 +0 0.971 0.068 -0.483 0.753 0.269 0.696 0.891 -1.638 0.000 0.539 -0.637 -1.343 0.000 1.221 -0.147 0.808 2.548 0.517 0.452 0.041 1.551 1.168 0.823 0.979 0.845 0.444 0.706 0.715 +0 1.011 -1.399 0.504 0.375 1.333 1.412 -1.194 -0.135 2.173 0.951 -1.113 -1.635 0.000 1.828 -0.520 1.504 0.000 0.429 0.079 -0.420 3.102 0.857 0.790 0.993 1.001 0.611 1.116 0.930 +0 1.798 -0.380 1.201 0.292 -0.363 1.298 0.556 1.519 1.087 2.321 0.781 -0.286 2.215 0.683 0.506 -1.300 0.000 0.561 -1.441 -0.454 0.000 1.072 1.300 0.991 1.694 2.569 1.472 1.226 +1 1.381 1.042 0.792 0.943 -1.682 0.741 2.007 -0.813 0.000 1.353 1.019 -0.145 2.215 0.623 0.896 -1.256 0.000 0.948 1.111 -1.029 1.551 0.818 1.058 1.252 0.812 0.745 0.814 0.801 +1 0.662 -2.291 1.023 0.997 -0.962 0.777 -0.641 -0.630 2.173 0.789 -0.531 0.225 2.215 1.397 -1.186 1.103 0.000 0.782 -1.886 1.742 0.000 0.852 1.025 1.099 1.160 0.804 0.949 0.903 +1 0.745 -0.227 -0.881 0.279 -0.044 0.737 0.507 -1.677 0.000 0.858 -0.464 -0.222 0.000 1.288 -0.537 1.277 2.548 1.624 0.185 0.268 0.000 0.856 1.118 0.988 0.530 0.256 0.664 0.674 +0 0.585 1.509 1.709 1.521 0.616 0.919 -0.517 -1.398 0.000 0.881 0.698 0.136 2.215 0.471 0.250 0.554 2.548 1.016 -0.143 -0.071 0.000 1.397 0.943 1.089 0.844 0.295 0.800 0.969 +1 0.600 -0.148 0.432 0.574 -0.715 1.086 -1.158 0.691 0.000 0.567 -1.347 -1.365 0.000 0.360 -0.871 -0.234 0.000 1.902 -0.289 -1.110 3.102 0.934 0.748 0.986 0.516 0.242 0.537 0.596 +1 1.006 0.090 -0.264 2.000 0.367 1.047 -0.827 -1.487 2.173 0.576 -0.059 1.492 0.000 0.882 0.245 -0.815 0.000 0.572 -0.433 0.832 3.102 0.972 0.961 1.058 0.615 0.722 1.003 0.868 +0 1.356 1.606 -1.658 0.958 0.660 0.526 -2.811 1.407 0.000 0.814 0.816 -0.492 2.215 1.240 0.211 -0.078 2.548 1.254 0.867 0.145 0.000 0.816 0.710 1.372 1.067 0.510 0.920 0.751 +1 0.912 -0.332 1.580 0.317 -1.246 2.927 1.235 -0.135 0.000 2.463 1.021 1.407 0.000 2.055 0.537 -1.557 2.548 1.019 0.660 0.911 0.000 0.932 1.154 0.991 0.860 0.985 1.013 0.865 +0 0.953 -1.152 -0.398 0.699 1.200 0.912 -0.385 1.061 2.173 1.943 -0.983 -0.879 2.215 0.901 -1.101 1.192 0.000 1.052 -1.166 0.464 0.000 0.663 0.717 1.121 0.990 2.025 1.097 0.911 +1 0.830 1.119 1.314 0.918 -1.198 0.641 1.276 -1.065 0.000 0.810 0.948 0.253 0.000 1.145 0.031 0.868 1.274 0.858 1.868 -0.782 0.000 1.140 1.172 0.990 0.606 0.584 0.691 0.689 +1 0.485 1.001 -0.506 1.485 -0.200 1.615 -0.301 -1.710 2.173 1.470 1.471 0.276 0.000 0.860 1.008 1.581 0.000 1.107 0.533 -1.132 3.102 1.631 1.223 0.998 2.858 0.989 1.773 1.573 +1 1.273 -1.399 0.647 1.112 1.260 0.818 -0.549 0.056 0.000 0.611 -0.030 -0.510 2.215 1.634 -0.886 -1.443 2.548 0.983 -0.742 -0.980 0.000 1.125 1.185 0.986 1.186 0.947 0.986 0.941 +1 1.581 0.452 0.538 1.062 -0.067 1.022 -0.940 -1.242 2.173 0.604 -0.481 1.160 2.215 0.484 -1.251 -1.686 0.000 0.686 -0.369 -0.222 0.000 0.687 0.678 0.986 0.823 0.994 1.081 0.854 +1 0.762 0.590 -1.425 0.709 -1.520 0.799 -0.360 -1.533 0.000 1.223 -1.587 0.577 0.000 1.923 0.010 0.079 2.548 1.845 -0.478 -0.506 3.102 2.443 1.691 0.981 1.138 0.841 1.285 1.112 +0 2.481 -0.420 -0.702 0.443 0.261 0.954 -1.506 1.408 0.000 0.771 -1.134 -0.060 2.215 0.826 -0.002 0.978 2.548 0.881 -2.041 0.574 0.000 1.171 1.142 1.108 0.982 0.860 0.881 1.017 +1 0.549 1.171 -1.446 0.446 1.438 0.999 -0.156 0.346 2.173 0.925 -1.075 -0.886 0.000 0.865 -0.039 -1.292 0.000 1.364 0.555 1.112 3.102 0.908 1.215 0.982 0.979 0.945 1.017 0.863 +0 0.839 -0.076 -1.000 1.018 0.212 1.104 -1.153 0.632 0.000 1.291 -1.067 -1.402 2.215 0.647 -1.951 -1.503 0.000 0.539 -1.163 -0.248 1.551 1.623 0.948 1.136 1.109 0.659 0.876 0.907 +1 0.669 -0.427 -1.252 1.465 -0.204 1.268 -1.237 -1.576 0.000 0.862 2.113 0.638 0.000 0.442 -0.467 -1.631 0.000 2.089 0.217 0.292 3.102 0.511 1.583 1.112 0.905 0.650 1.251 1.045 +1 0.348 -1.474 0.773 1.901 1.470 0.905 0.374 -0.524 2.173 0.297 1.349 -0.780 0.000 0.720 0.884 0.592 2.548 0.397 -0.545 0.546 0.000 0.683 0.632 0.983 0.915 0.902 0.973 0.775 +1 1.554 0.932 -0.865 1.540 -1.449 1.958 1.826 0.528 0.000 2.703 -0.172 -1.515 1.107 2.068 0.939 0.287 0.000 0.780 -0.152 -0.372 3.102 1.703 1.640 1.074 1.479 1.124 2.453 1.979 +1 1.881 0.567 -0.339 0.602 0.492 1.177 1.298 -1.643 2.173 0.762 1.127 0.313 0.000 0.436 0.680 -1.477 0.000 1.389 0.582 1.146 3.102 0.902 1.081 1.004 0.909 0.900 1.009 0.839 +1 1.013 0.085 1.562 0.378 -1.614 0.794 0.421 0.840 2.173 1.266 0.298 -0.331 0.000 0.889 -1.103 -1.588 2.548 0.719 -0.501 0.625 0.000 1.071 1.177 0.980 0.804 1.281 1.010 0.953 +1 0.845 0.052 1.722 0.596 0.105 0.774 -0.227 -1.466 0.000 1.048 -0.525 0.707 2.215 1.057 0.223 0.073 2.548 1.064 -0.860 -0.858 0.000 0.922 1.086 0.988 0.778 0.754 0.878 0.755 +0 1.440 -0.114 -0.678 0.885 0.995 1.367 1.746 1.486 0.000 1.816 0.385 0.238 2.215 1.337 0.434 -0.242 0.000 1.819 0.802 -1.450 3.102 2.951 1.793 1.561 1.228 1.705 1.561 1.412 +0 0.749 -0.900 0.788 0.755 1.183 1.346 1.337 -0.415 0.000 1.588 -0.639 -1.277 2.215 1.406 -1.171 1.127 2.548 1.621 -0.831 0.438 0.000 3.653 2.929 0.979 0.603 1.409 2.111 1.927 +1 0.282 -2.020 -0.779 1.574 1.269 0.648 0.499 1.567 0.000 0.582 2.487 -0.403 0.000 0.607 -0.959 0.530 0.000 1.515 0.109 -0.495 0.000 0.795 0.767 0.982 1.892 0.677 1.745 1.319 +0 1.108 1.331 0.422 0.520 -1.624 0.971 0.792 -0.401 2.173 0.829 0.435 1.535 1.107 0.531 -0.780 -1.026 0.000 0.758 -0.841 1.273 0.000 0.616 1.179 1.012 0.796 1.321 0.876 0.859 +0 0.952 -0.554 -1.594 0.613 0.427 0.627 -1.082 1.178 0.000 1.048 -0.184 -1.271 0.000 1.337 0.213 0.303 2.548 0.988 -1.199 -0.643 3.102 0.806 1.062 1.026 0.699 1.057 0.808 0.726 +0 1.206 1.413 0.767 0.807 -0.228 0.678 -1.047 -1.740 2.173 0.654 -2.534 -1.244 0.000 0.407 -1.589 0.291 0.000 0.821 0.108 -0.424 1.551 0.853 0.915 1.067 0.765 0.884 1.138 1.531 +1 0.544 -0.966 -1.593 0.794 0.410 0.717 -0.413 -0.900 0.000 0.616 -1.050 -0.369 2.215 1.436 -0.612 1.264 2.548 0.485 1.061 0.494 0.000 1.210 0.907 0.991 0.695 1.016 0.823 0.709 +0 0.363 -0.747 1.667 2.057 0.504 0.800 1.871 -1.625 0.000 0.662 -2.131 -0.480 0.000 0.675 0.420 -0.526 0.000 1.585 -1.073 -1.154 3.102 0.802 0.894 1.038 1.001 1.209 1.101 1.003 +0 1.639 -0.467 1.004 0.571 1.324 1.114 -1.460 -0.219 0.000 1.803 1.201 -1.306 2.215 0.882 1.389 -0.710 0.000 1.657 0.451 0.892 3.102 0.994 1.044 0.987 0.931 1.534 1.493 1.237 +1 0.613 0.155 1.293 0.884 -1.449 0.899 1.719 0.660 0.000 1.131 0.161 -0.856 2.215 0.884 1.479 -0.989 2.548 0.589 1.568 -0.327 0.000 0.946 0.984 0.981 1.216 0.856 0.909 0.843 +1 0.633 0.586 -0.472 1.327 -1.028 0.627 1.325 1.302 0.000 0.702 0.635 -0.245 2.215 0.470 2.160 -1.545 0.000 0.862 1.523 0.281 0.000 0.936 0.933 0.984 0.721 0.593 0.619 0.799 +1 2.111 0.434 1.391 0.402 0.331 0.708 0.673 0.910 2.173 0.767 1.198 -1.023 0.000 1.143 -1.323 -0.468 2.548 1.346 -0.209 -0.556 0.000 0.752 0.986 1.041 0.644 1.786 1.124 0.964 +1 0.385 -0.358 1.250 1.778 -1.309 1.193 -0.567 -0.392 2.173 0.951 0.110 0.513 2.215 0.461 -1.429 1.370 0.000 0.382 -1.831 -1.391 0.000 0.318 0.961 0.983 1.211 1.266 1.060 0.857 +1 0.666 -1.386 -0.955 1.362 -0.234 1.263 1.129 1.526 0.000 0.845 -0.932 1.108 1.107 2.164 -0.741 -0.076 2.548 0.555 -0.862 -0.630 0.000 0.768 1.003 0.996 0.967 1.263 0.821 0.752 +0 0.725 0.784 1.106 1.408 0.360 0.685 0.274 1.622 2.173 0.689 0.670 -0.063 2.215 0.405 0.649 -1.362 0.000 1.231 -0.537 -1.199 0.000 0.582 0.800 0.987 0.887 1.032 0.713 0.701 +1 2.101 -0.007 -1.083 1.104 -0.630 0.621 -0.279 1.610 0.000 1.390 -0.933 0.582 2.215 0.813 -2.067 -0.753 0.000 1.541 0.436 0.069 3.102 0.943 1.057 0.991 1.030 1.224 1.157 1.093 +0 0.600 -2.395 -1.504 0.886 -0.305 0.662 -0.205 0.920 2.173 0.571 -0.840 -1.041 0.000 0.681 -0.337 1.335 2.548 0.928 -1.002 0.024 0.000 0.793 0.925 0.985 0.850 0.312 0.812 0.712 +0 0.719 -1.899 -0.204 1.242 0.404 0.810 -0.340 -1.164 2.173 0.643 -0.457 -0.700 2.215 0.679 -1.522 1.304 0.000 0.379 1.506 0.617 0.000 1.579 1.097 0.991 1.175 0.436 0.807 0.868 +1 0.628 0.172 0.227 2.754 -0.474 0.823 0.935 1.016 2.173 1.062 -0.269 1.596 1.107 0.914 -0.262 -1.233 0.000 0.469 -0.006 0.939 0.000 0.676 0.937 1.076 1.368 1.126 1.196 0.943 +1 0.483 1.215 -1.188 0.843 1.565 0.622 0.565 -1.394 0.000 1.203 0.517 1.515 0.000 1.271 0.390 -0.194 2.548 2.168 -0.426 0.515 3.102 0.912 1.239 0.980 2.053 0.977 1.430 1.270 +0 1.640 -2.185 1.590 0.717 1.696 0.664 -0.320 -0.715 1.087 0.743 1.023 0.377 2.215 0.725 -2.235 -0.107 0.000 0.625 0.184 0.284 0.000 1.342 1.138 1.005 2.166 1.153 1.522 1.305 +1 1.563 -1.177 -1.545 1.261 -1.092 1.345 -1.148 0.644 2.173 0.596 -0.555 -1.537 0.000 1.068 -0.561 -0.214 2.548 0.608 -1.996 -0.044 0.000 1.121 0.897 0.980 1.582 1.121 1.131 0.950 +1 0.756 0.605 -1.013 1.022 1.101 0.725 0.638 -0.511 0.000 0.687 -0.257 0.249 2.215 0.794 -1.046 1.235 0.000 1.756 -0.175 1.718 1.551 1.951 1.219 1.151 0.784 0.961 0.899 0.826 +0 2.354 0.365 -0.899 0.603 -0.924 1.411 -0.270 1.065 2.173 0.528 0.354 0.823 0.000 0.438 1.329 -0.296 0.000 0.393 1.939 0.762 0.000 0.755 1.066 0.979 0.698 0.641 1.080 0.912 +1 0.844 1.553 0.847 0.809 1.698 0.798 1.020 -1.444 0.000 1.025 0.514 -0.277 2.215 1.220 1.293 0.055 0.000 0.695 0.799 1.479 3.102 0.893 1.039 0.992 0.486 0.780 0.668 0.667 +1 1.098 -1.293 0.346 1.101 -1.129 0.931 0.762 1.685 2.173 1.323 0.046 0.531 0.000 1.739 -1.029 -0.476 0.000 0.961 -0.940 1.610 3.102 0.838 0.604 1.479 1.808 1.102 1.175 0.995 +1 0.406 -0.710 0.939 0.446 -1.314 0.370 -0.512 0.680 0.000 0.565 0.470 1.268 2.215 0.692 -0.124 -0.770 2.548 0.434 -1.402 -0.375 0.000 0.626 0.692 0.981 0.622 0.674 0.526 0.539 +1 0.403 0.775 -0.469 0.559 0.224 0.943 1.609 -0.698 0.000 1.022 -1.756 1.265 0.000 1.376 1.272 0.771 1.274 1.185 1.079 -1.248 0.000 0.813 0.980 0.982 1.189 0.848 0.881 0.982 +1 0.733 1.283 0.610 1.413 0.944 1.168 -0.014 -0.626 2.173 0.543 0.726 -1.698 2.215 0.738 -0.035 -1.306 0.000 0.514 0.947 1.062 0.000 0.713 0.893 0.978 0.929 1.066 1.422 1.107 +0 0.399 0.973 -0.820 1.063 1.682 0.440 1.814 -0.866 0.000 0.933 0.623 0.785 2.215 1.257 0.852 -0.297 2.548 0.509 2.081 1.221 0.000 0.731 0.994 0.979 0.971 0.968 0.869 0.747 +0 0.409 1.925 -0.728 1.518 1.453 0.758 1.030 0.561 2.173 0.681 1.091 -0.676 2.215 0.607 0.024 -0.872 0.000 0.915 1.150 -1.654 0.000 0.946 0.873 1.008 0.865 0.950 0.805 0.736 +1 1.042 -0.999 0.802 0.743 -1.088 0.578 -1.182 -0.591 0.000 1.167 0.105 0.742 1.107 0.695 0.857 -0.882 2.548 0.666 -0.870 -1.154 0.000 0.657 0.948 1.209 1.015 1.037 0.897 0.832 +1 0.389 2.026 -0.986 1.851 -0.766 0.716 -0.741 0.840 2.173 0.457 0.363 -0.794 0.000 0.730 0.275 0.635 0.000 1.330 0.409 1.642 3.102 0.851 0.882 0.980 0.863 0.964 1.054 0.849 +0 0.876 0.135 0.655 1.153 0.480 0.572 -0.981 -1.483 2.173 0.895 -1.483 -0.575 2.215 0.497 0.533 -1.201 0.000 0.533 -0.874 1.483 0.000 0.639 0.869 0.985 0.990 0.821 0.863 0.740 +0 0.400 0.378 1.059 1.725 0.163 0.341 1.893 -1.604 0.000 0.560 0.825 0.586 0.000 1.541 0.615 -1.023 2.548 0.511 0.264 -1.296 0.000 0.998 0.998 0.984 0.587 0.734 0.785 0.828 +1 0.876 0.822 0.658 1.272 0.414 1.271 -0.435 -1.038 2.173 0.473 -1.231 -1.509 0.000 1.247 0.218 0.603 0.000 0.760 -1.202 1.395 3.102 1.478 0.959 0.989 2.221 1.006 1.617 1.383 +1 1.030 -1.509 0.131 0.715 1.329 0.555 -0.837 0.182 2.173 1.358 -1.405 0.838 1.107 2.240 -1.104 -1.520 0.000 1.743 0.347 -0.059 0.000 0.705 1.170 1.048 0.712 0.811 1.007 0.864 +1 1.240 0.483 -1.655 0.731 -0.635 1.010 0.222 -1.017 2.173 1.083 -0.622 0.519 0.000 1.235 1.397 0.360 0.000 0.730 1.749 1.485 0.000 0.938 0.828 1.049 0.709 0.804 0.936 0.821 +0 2.324 0.426 -1.190 0.297 1.053 0.884 0.179 0.373 2.173 0.686 -0.244 0.770 0.000 1.030 1.233 1.507 2.548 1.412 -1.085 0.139 0.000 0.850 0.927 1.036 0.858 1.254 0.986 0.980 +1 0.420 -0.987 0.130 1.341 1.544 1.233 -2.941 1.598 0.000 1.932 -0.011 -0.268 2.215 0.672 0.217 0.066 0.000 1.342 -0.323 0.647 0.000 0.621 0.878 0.994 0.866 0.663 0.948 0.806 +1 0.371 -2.272 -1.504 0.777 -1.741 0.698 -0.976 0.608 0.000 0.990 -0.458 -0.313 1.107 1.638 -0.399 -1.108 2.548 1.031 0.310 0.558 0.000 0.951 0.977 0.995 0.757 0.889 0.905 0.835 +1 0.620 1.262 -1.627 0.367 -0.538 1.096 0.846 -0.300 0.000 1.155 0.925 1.229 2.215 0.691 0.993 0.308 0.000 0.551 -0.777 1.598 3.102 0.837 1.046 0.993 0.827 0.819 0.923 0.794 +1 1.433 -0.277 1.504 0.976 -1.332 1.215 0.453 0.057 0.000 0.660 -0.281 -1.194 2.215 0.596 0.505 0.505 0.000 1.297 0.480 -0.795 0.000 0.893 0.751 0.986 0.706 0.411 0.511 0.601 +0 1.116 0.106 -0.597 3.477 -0.913 1.676 -0.304 0.201 0.000 1.384 1.358 1.636 2.215 1.354 2.386 1.245 0.000 1.700 1.438 1.177 0.000 1.001 1.024 0.991 2.162 1.409 1.567 1.382 +0 0.569 -0.610 0.127 0.645 -0.711 0.636 0.541 -1.697 0.000 1.456 -0.411 0.375 0.000 1.508 -0.865 -1.436 2.548 1.333 -1.163 0.562 0.000 0.889 1.204 0.999 0.528 0.725 0.749 0.663 +0 1.394 -1.095 -0.022 0.527 -1.116 0.690 0.136 1.641 0.000 1.014 -0.914 1.608 0.000 1.970 -0.147 0.980 2.548 1.187 2.280 -0.394 0.000 1.006 1.047 0.989 0.773 1.464 1.018 0.986 +0 0.760 -0.377 -1.609 0.829 -0.808 0.623 -0.158 -0.575 1.087 0.864 0.840 0.947 2.215 0.558 0.767 0.417 0.000 0.372 -0.415 1.119 0.000 0.469 0.648 0.992 1.274 1.205 0.943 0.755 +1 0.998 1.339 -0.989 0.611 -1.345 0.785 0.579 1.094 2.173 1.375 0.751 -0.209 0.000 1.600 0.467 -1.644 1.274 1.250 1.165 0.622 0.000 1.284 1.257 1.003 1.129 0.871 1.028 1.065 +0 0.519 -0.939 1.271 0.238 1.268 0.796 0.634 -0.462 0.000 1.300 0.011 -1.707 2.215 2.143 -0.264 0.047 2.548 1.127 1.106 -1.440 0.000 0.827 0.902 0.989 0.988 1.794 1.030 0.853 +1 0.706 -0.760 1.114 0.423 0.203 0.883 2.281 -0.338 0.000 0.716 0.480 1.445 2.215 0.741 -0.926 -1.068 2.548 0.708 -1.320 -0.026 0.000 0.737 1.231 0.985 0.705 0.876 0.792 0.734 +1 0.937 0.241 1.678 0.614 0.919 0.885 0.729 0.012 0.000 1.196 -0.603 1.566 2.215 1.207 0.044 -0.309 0.000 0.623 -2.277 -1.378 0.000 0.841 0.849 0.987 0.634 0.944 1.037 0.907 +0 0.767 0.028 0.703 1.457 -1.151 0.728 0.738 -1.577 0.000 1.225 -0.532 0.499 2.215 1.016 2.083 0.332 0.000 0.970 1.044 -0.184 0.000 0.769 1.658 1.457 1.166 0.920 1.361 1.191 +0 0.953 -1.325 -0.761 1.863 -1.563 0.403 -0.268 -0.126 2.173 1.137 0.855 0.660 2.215 0.758 -0.751 0.988 0.000 0.639 0.892 -0.336 0.000 0.947 0.865 1.219 2.095 0.887 1.361 1.159 +1 1.419 -2.151 1.506 0.740 0.455 1.292 -0.850 -0.793 2.173 0.523 -1.185 -0.164 1.107 0.827 0.344 1.340 0.000 0.671 -0.141 0.827 0.000 1.195 0.963 1.152 1.604 0.686 1.054 1.061 +1 1.416 0.413 1.361 1.339 -0.090 1.308 0.945 -0.590 0.000 0.452 1.165 1.620 2.215 1.103 2.017 1.284 0.000 0.814 -0.825 0.577 3.102 2.592 1.498 1.842 1.053 0.851 1.240 1.154 +0 1.261 -1.439 -0.828 0.310 0.515 0.664 0.752 0.062 0.000 0.534 -0.255 0.682 0.000 0.908 -0.919 1.051 2.548 1.463 -0.000 -1.079 3.102 0.900 0.876 0.988 0.771 0.949 0.685 0.643 +0 0.609 -0.347 -0.301 0.453 1.602 0.660 -1.038 0.933 0.000 1.056 -0.105 1.166 2.215 0.880 -0.307 -0.613 0.000 1.337 1.392 -0.881 0.000 0.691 0.750 0.986 0.786 0.763 0.579 0.611 +0 1.342 -0.382 0.018 0.121 0.349 1.148 -0.237 -1.104 2.173 1.576 -0.448 0.609 2.215 0.324 -1.028 1.099 0.000 0.977 -0.009 -1.666 0.000 0.524 0.821 0.986 1.091 1.991 1.073 0.853 +0 0.597 1.864 0.287 0.639 0.851 0.625 2.111 1.015 0.000 1.152 -0.277 -0.401 2.215 0.991 -0.655 -1.276 1.274 0.489 -0.562 1.129 0.000 1.616 1.709 0.980 1.066 0.843 1.310 1.076 +0 1.663 -1.109 0.455 1.582 -0.158 1.294 -1.463 -1.708 0.000 1.191 -0.720 -0.447 2.215 1.000 -0.989 1.089 2.548 1.481 -1.864 -1.466 0.000 0.945 0.992 1.177 0.924 1.157 1.092 1.146 +1 1.787 -1.516 -1.071 0.775 0.157 0.627 0.184 0.296 2.173 0.782 -0.841 -1.153 0.000 1.017 -1.065 0.828 0.000 1.138 -0.429 1.496 3.102 1.352 0.872 1.459 1.412 0.851 0.999 0.898 +0 0.516 -0.215 0.373 0.976 -1.309 0.891 0.835 0.635 0.000 0.473 -1.489 -1.224 2.215 0.522 0.093 -0.953 2.548 0.496 1.658 1.498 0.000 0.940 0.866 0.988 0.704 0.498 0.944 0.798 +0 0.375 1.376 -1.710 1.723 0.649 0.538 2.044 -0.581 0.000 0.772 -1.063 0.964 0.000 1.486 0.839 -1.290 2.548 1.611 0.623 -0.559 3.102 0.839 0.876 0.989 0.918 0.732 0.814 0.751 +1 0.331 -0.424 1.182 0.817 0.365 1.313 0.890 -1.076 0.000 0.631 1.284 0.301 2.215 0.480 2.490 0.921 0.000 1.161 1.001 1.532 3.102 2.047 1.267 0.992 0.565 0.694 0.899 0.807 +0 0.401 -0.206 0.531 1.813 1.584 0.597 -0.818 0.016 0.000 0.609 -1.123 0.819 0.000 0.780 0.267 -0.577 2.548 0.441 0.393 -1.465 3.102 0.874 0.898 0.987 0.554 0.324 0.596 0.663 +0 1.962 -0.173 -1.116 0.133 1.145 1.027 -1.829 0.465 0.000 1.834 0.530 -0.927 2.215 1.700 0.814 0.958 0.000 0.818 1.670 0.500 0.000 0.923 0.944 0.990 0.943 1.286 1.124 1.087 +1 1.282 0.212 -0.174 1.081 -0.729 0.741 -1.618 0.297 0.000 1.173 -0.451 -1.462 2.215 1.139 -1.160 1.159 0.000 0.642 1.236 1.113 0.000 1.196 1.018 0.991 1.194 0.461 0.915 1.133 +1 0.942 -1.139 -1.001 0.896 1.549 1.742 -0.839 -1.253 1.087 2.248 2.024 0.509 0.000 1.161 0.015 0.350 2.548 1.983 0.085 -0.391 0.000 1.828 2.187 0.988 0.774 1.926 3.371 2.752 +1 1.201 1.506 -0.147 0.870 -0.726 1.266 0.539 0.853 2.173 0.425 1.782 -1.006 0.000 0.488 1.972 -1.465 0.000 0.510 -0.478 -0.668 1.551 0.304 0.712 0.990 1.317 0.972 0.907 0.834 +1 2.373 -1.468 0.390 0.986 -0.130 0.628 -0.702 -1.114 2.173 0.536 -1.777 -1.105 0.000 0.906 2.009 -1.450 0.000 1.390 -1.222 1.338 1.551 3.963 2.391 0.991 0.971 0.882 1.678 1.837 +0 0.907 0.421 -0.059 0.777 -0.550 0.477 -0.041 -1.590 0.000 1.100 -0.403 1.117 2.215 1.054 0.021 -0.800 2.548 0.572 -1.149 0.721 0.000 0.902 0.793 0.992 1.330 1.158 0.967 0.871 +0 0.340 0.441 0.190 1.619 1.262 0.778 0.773 -0.641 2.173 0.506 1.526 0.255 0.000 0.749 0.399 -1.071 2.548 0.665 -0.271 0.565 0.000 0.854 0.887 0.987 0.807 0.395 0.824 0.766 +0 3.825 -0.254 -1.436 0.872 -1.264 2.110 0.581 0.278 0.000 0.603 -0.123 -0.744 0.000 1.207 0.283 0.716 2.548 0.728 -0.922 1.405 3.102 2.099 1.333 0.999 0.828 0.691 1.015 1.385 +1 1.337 0.712 1.628 1.613 1.731 2.029 0.363 1.741 2.173 1.664 0.890 -0.041 0.000 0.889 1.076 0.366 0.000 1.417 -0.656 0.394 0.000 0.713 0.633 0.986 1.128 1.214 1.365 1.239 +1 0.303 0.242 0.273 0.470 0.527 1.034 -1.528 -0.907 2.173 1.104 -0.889 0.824 2.215 1.218 -0.962 -0.470 0.000 1.225 -0.876 1.638 0.000 1.056 1.072 0.975 2.876 1.650 2.057 1.742 +0 1.123 1.850 -1.063 2.117 -1.537 1.130 0.740 0.143 0.000 1.637 0.496 0.533 2.215 1.498 1.004 -1.674 2.548 0.708 1.327 -0.494 0.000 0.951 0.963 0.982 0.934 1.601 1.484 1.360 +1 1.021 -0.538 -1.726 0.297 -0.440 1.078 -0.232 -0.088 2.173 0.779 -0.924 1.293 0.000 1.341 0.421 0.535 2.548 0.829 -1.372 -1.381 0.000 0.887 1.291 0.991 1.084 0.958 1.032 0.915 +0 1.322 -1.823 0.492 1.347 0.637 0.820 0.157 -1.262 0.000 0.941 -0.833 -0.869 1.107 0.797 -0.740 1.566 2.548 0.395 -1.166 0.341 0.000 1.142 0.880 0.971 1.119 0.747 1.098 1.230 +1 0.561 -1.440 1.539 0.513 0.208 0.565 -0.538 1.297 2.173 0.855 -0.992 -1.166 0.000 0.819 -1.528 -0.535 0.000 1.239 0.402 0.314 3.102 0.814 0.999 0.985 0.720 0.834 0.875 0.740 +0 2.036 0.714 1.101 0.234 1.061 0.592 1.254 0.298 2.173 0.607 0.528 -0.939 0.000 0.998 -2.243 -0.841 0.000 0.863 1.120 -1.196 3.102 0.814 1.058 0.994 0.770 0.738 0.702 0.772 +0 0.726 -0.595 0.928 1.469 0.039 0.593 -1.574 -0.952 2.173 0.451 0.715 1.636 0.000 0.612 -0.967 -1.307 0.000 0.498 1.036 1.198 3.102 0.909 0.996 1.026 0.792 1.237 0.844 0.782 +0 2.145 -0.361 -1.499 0.824 -1.360 1.884 -0.190 0.515 0.000 2.011 0.325 -1.203 1.107 1.666 -0.443 0.138 2.548 1.187 0.219 0.909 0.000 0.951 0.925 0.966 1.135 1.996 1.517 1.453 +1 0.658 -0.108 0.317 0.842 1.307 1.469 0.634 -1.111 0.000 1.151 -0.321 0.772 2.215 1.058 0.169 -0.668 2.548 1.221 2.202 0.707 0.000 3.156 1.966 0.986 0.742 1.171 1.630 1.272 +1 0.638 -1.221 0.768 0.976 -0.904 0.680 0.371 1.017 0.000 1.059 -0.818 0.027 2.215 0.697 0.556 -1.710 0.000 1.631 -0.026 -0.741 3.102 0.794 1.010 1.091 0.798 0.903 0.909 0.876 +0 0.820 0.273 0.965 0.895 -1.306 0.648 0.037 0.413 2.173 1.342 0.009 -0.622 2.215 0.688 1.405 -1.677 0.000 0.601 -0.205 1.344 0.000 0.778 0.902 1.055 0.949 1.103 0.836 0.747 +0 0.765 -0.919 0.449 1.191 1.480 0.614 1.017 -0.021 2.173 1.212 -0.541 -1.182 2.215 0.593 1.935 0.380 0.000 0.580 -0.205 1.698 0.000 1.132 0.850 1.059 0.989 1.565 1.097 1.073 +1 0.579 1.620 0.366 0.592 0.234 2.496 0.569 -1.099 2.173 1.186 1.014 0.792 0.000 1.072 -0.341 1.587 2.548 1.343 0.094 0.691 0.000 0.913 1.210 0.998 1.559 1.678 1.495 1.219 +1 0.910 0.006 -1.343 0.452 0.252 1.069 -0.444 0.140 2.173 0.422 -1.018 0.980 0.000 1.229 0.295 1.592 2.548 0.913 0.277 -0.327 0.000 0.960 0.881 0.985 0.895 1.490 0.838 0.719 +1 1.726 0.627 -1.212 0.734 -0.413 0.714 2.244 1.300 0.000 1.363 0.527 1.688 0.000 2.367 0.220 0.213 2.548 2.095 0.956 -0.105 3.102 0.525 1.312 1.028 1.290 0.932 1.101 1.009 +1 1.212 -0.213 -1.188 1.116 -0.843 1.388 0.195 0.740 2.173 0.801 -0.367 0.256 0.000 0.604 -0.617 -0.871 0.000 1.180 -0.429 1.668 0.000 0.921 1.134 0.989 0.703 0.833 1.000 0.877 +1 0.328 0.149 1.386 1.461 -0.325 1.902 2.817 1.724 0.000 1.248 -0.976 0.133 2.215 1.200 -2.240 1.732 0.000 2.612 0.099 0.740 0.000 1.243 0.991 0.988 1.137 0.559 0.817 0.822 +1 0.651 0.939 1.122 0.484 0.841 1.242 0.783 -0.809 1.087 0.525 0.085 0.948 0.000 0.762 0.231 -1.417 0.000 0.992 -0.422 -0.188 1.551 0.825 1.097 0.973 0.694 1.028 0.880 0.764 +1 0.827 1.549 -0.312 0.925 0.211 1.135 1.107 -1.580 1.087 0.762 1.345 1.067 2.215 0.863 0.700 -0.675 0.000 0.772 1.515 0.146 0.000 0.783 1.058 0.984 0.902 0.956 1.020 0.871 +0 1.104 0.022 0.911 1.388 0.149 0.836 -0.806 -1.384 2.173 0.492 -1.350 1.309 0.000 0.990 -0.977 -0.710 1.274 0.756 -0.409 0.035 0.000 0.813 0.846 1.087 1.030 0.664 0.951 0.795 +1 0.749 -0.727 0.155 1.437 -0.975 1.558 1.341 0.960 2.173 2.399 -0.392 -1.013 2.215 0.659 -0.693 1.186 0.000 1.948 -0.374 0.481 0.000 0.767 1.646 1.222 2.354 3.973 2.083 1.669 +1 1.918 0.720 -1.026 0.225 0.388 0.971 0.652 1.214 2.173 0.503 2.050 0.304 0.000 0.765 0.084 -0.634 0.000 0.797 -0.753 1.272 0.000 0.963 1.076 0.988 0.584 0.767 0.743 0.710 +1 0.402 -0.271 -1.086 1.673 0.093 0.403 -1.100 -0.778 0.000 1.756 -0.977 1.709 2.215 0.634 -1.356 -0.360 0.000 0.979 -1.149 0.373 0.000 0.836 1.124 0.992 0.636 0.889 0.915 0.803 +1 0.873 -0.488 0.996 0.878 -0.448 0.781 0.495 -1.240 2.173 0.646 0.549 0.945 2.215 0.731 -0.471 -1.050 0.000 0.684 0.896 0.315 0.000 1.008 0.832 1.169 0.808 0.964 0.785 0.697 +1 2.351 0.072 -0.404 0.872 -1.037 0.885 -0.053 1.106 2.173 0.380 -0.343 1.510 0.000 0.276 -0.332 0.810 0.000 0.966 0.298 -1.298 3.102 0.907 0.854 1.071 0.711 0.835 0.918 0.785 +1 0.730 -1.946 -0.031 1.203 0.459 1.136 -1.154 -1.233 1.087 0.932 -0.757 1.417 2.215 0.510 -1.463 -0.653 0.000 0.815 0.286 0.465 0.000 1.007 0.904 0.994 1.270 1.076 0.977 0.854 +1 1.047 -0.894 0.676 0.477 -1.095 3.077 -0.057 -0.140 0.000 1.403 -0.097 1.687 2.215 1.809 -0.634 1.307 1.274 3.955 -0.817 -1.612 0.000 5.763 3.590 0.988 0.920 0.765 2.288 1.689 +0 0.539 0.494 0.228 1.143 0.576 0.702 -0.619 1.173 0.000 1.029 -0.491 -0.597 2.215 1.152 -0.230 -1.710 0.000 0.503 0.857 -0.455 3.102 0.888 0.875 0.984 1.533 0.555 0.943 1.074 +0 0.509 0.955 -1.231 1.442 -1.368 0.761 -1.545 -0.515 0.000 1.345 0.987 0.221 2.215 1.009 -0.867 1.111 2.548 1.017 0.758 -1.725 0.000 0.607 0.907 1.001 2.139 1.681 1.600 1.201 +1 1.692 1.393 1.065 1.550 1.099 0.903 1.055 -1.490 0.000 1.847 0.614 -0.425 1.107 0.875 1.224 -0.635 0.000 0.652 0.924 -0.375 1.551 1.063 0.974 1.010 0.875 0.243 1.289 1.049 +1 0.736 1.938 -0.331 2.826 1.072 0.658 0.546 -1.685 2.173 0.661 0.373 0.645 1.107 0.647 1.091 1.271 0.000 0.908 0.772 -1.184 0.000 0.687 0.695 1.906 1.523 0.841 1.130 0.895 +1 0.533 -0.921 -1.009 1.196 -0.277 1.428 0.527 1.627 0.000 1.258 1.469 0.130 2.215 0.663 0.245 -0.275 0.000 0.766 0.855 1.069 3.102 0.565 0.565 0.980 2.824 0.698 1.823 1.621 +1 0.735 1.419 0.312 1.027 1.470 0.743 -0.255 -0.223 2.173 0.290 0.433 0.738 0.000 1.229 -0.652 -1.203 1.274 0.679 1.373 1.711 0.000 0.577 0.967 1.041 1.249 0.961 1.115 0.886 +0 0.533 -0.863 0.926 0.389 0.438 0.798 1.531 0.869 0.000 1.332 0.625 0.243 1.107 1.187 -1.006 -1.719 0.000 0.826 2.049 -1.244 0.000 1.188 0.819 0.981 0.749 1.778 1.232 1.034 +0 0.526 -1.174 0.986 0.561 -0.930 1.164 -0.578 0.580 2.173 0.979 -0.733 -0.885 0.000 1.583 -0.545 1.445 2.548 1.320 1.968 -0.756 0.000 0.847 0.940 0.985 0.880 1.188 0.944 0.789 +1 0.590 0.909 0.724 0.576 1.324 0.940 0.248 -0.967 0.000 0.637 0.377 -0.319 0.000 1.169 -1.108 1.385 1.274 1.295 0.104 0.419 3.102 0.914 0.971 0.990 1.981 0.989 1.312 1.205 +0 0.410 -0.529 1.583 1.783 -0.911 1.045 -1.480 1.028 0.000 0.791 0.540 -0.152 2.215 0.442 -1.377 0.365 0.000 0.798 -0.249 -0.662 3.102 0.902 0.851 0.990 0.512 0.448 0.549 0.595 +1 0.838 -0.410 -0.720 0.318 0.464 0.974 0.206 -1.291 2.173 0.787 0.175 0.959 0.000 0.865 -0.338 -0.014 0.000 2.099 -0.825 -1.323 3.102 0.843 1.060 0.993 0.941 0.963 0.886 0.772 +1 0.791 0.565 1.542 0.707 -0.519 0.699 -0.718 -0.124 2.173 0.420 0.133 1.147 0.000 0.479 1.746 0.940 0.000 0.923 -0.867 -1.495 3.102 0.708 0.946 0.994 0.945 0.814 0.845 0.750 +1 1.049 -0.695 0.610 0.243 0.832 0.818 -0.633 -0.020 0.000 1.509 -0.192 1.494 2.215 1.155 -0.770 -0.576 2.548 0.747 -1.146 -1.263 0.000 1.165 0.775 0.981 0.903 1.420 0.969 0.862 +1 0.763 0.362 -1.422 0.640 -0.624 0.617 1.341 -0.687 0.000 0.790 -0.067 0.312 2.215 0.610 1.799 -1.558 0.000 1.890 0.636 1.053 3.102 0.854 1.090 0.986 0.937 0.830 0.899 0.791 +1 1.008 0.747 -1.499 0.785 -0.734 1.165 0.099 -1.131 0.000 0.895 -0.999 1.026 2.215 2.449 -0.020 0.307 1.274 0.490 0.779 1.551 0.000 0.911 1.354 0.985 1.473 1.259 1.297 1.171 +0 1.662 0.115 -1.137 0.771 0.625 0.649 0.406 -0.201 2.173 0.324 1.861 0.335 0.000 0.286 0.475 -1.685 0.000 0.370 -0.403 -0.184 3.102 0.918 1.172 1.568 0.750 0.240 0.666 0.726 +1 1.694 1.163 -1.231 0.333 0.621 0.753 -0.003 1.250 2.173 0.576 -0.819 0.187 1.107 0.739 0.308 0.230 0.000 1.171 1.063 -0.470 0.000 0.863 1.036 1.035 1.178 0.897 0.947 0.838 +0 0.780 -1.154 1.627 1.226 -1.422 1.497 0.620 -0.024 0.000 2.840 -0.332 -1.419 0.000 1.854 -2.246 0.138 0.000 2.026 0.170 -0.520 3.102 0.659 1.268 0.984 1.654 0.768 1.295 1.093 +1 0.773 0.590 -1.211 1.406 -1.271 0.889 -0.142 0.337 2.173 0.707 0.155 -0.037 0.000 1.335 -0.311 1.074 2.548 0.998 1.980 -0.918 0.000 0.718 1.566 1.004 1.642 0.847 1.304 1.198 +1 1.119 1.135 -1.456 0.793 0.680 1.037 1.132 0.807 2.173 1.384 0.132 -1.246 0.000 1.509 -1.383 -0.417 0.000 1.989 -0.629 0.152 0.000 0.903 0.793 1.224 0.920 0.769 1.513 1.394 +1 0.850 -1.271 -0.412 1.566 1.470 1.066 -0.332 1.195 2.173 1.035 -1.270 0.028 2.215 0.808 0.342 -1.616 0.000 0.771 -0.593 -1.127 0.000 0.620 0.868 1.586 1.108 1.553 1.053 0.933 +0 0.951 2.276 -1.208 0.861 1.387 0.897 0.935 0.668 2.173 0.342 1.734 -0.950 0.000 0.671 1.894 0.453 0.000 1.501 0.553 -0.676 3.102 0.709 0.831 0.989 0.965 1.162 0.935 0.766 +0 0.909 -0.732 -0.083 0.873 -0.649 0.879 -0.344 0.632 1.087 1.072 0.226 1.493 2.215 0.470 0.747 1.069 0.000 0.509 -0.832 -1.348 0.000 0.715 0.745 0.989 1.383 1.084 1.077 0.863 +0 0.479 1.464 1.540 1.131 -0.830 0.959 0.843 1.522 0.000 1.843 0.698 -0.197 2.215 0.576 1.278 0.383 2.548 0.973 -1.281 -1.353 0.000 0.873 0.820 0.984 0.966 0.667 1.039 0.897 +1 0.538 -1.586 1.063 1.116 -1.422 0.903 -0.663 -0.859 0.000 1.074 -0.494 0.364 2.215 0.753 1.052 0.797 2.548 0.703 -0.778 -1.647 0.000 0.809 1.138 0.987 1.928 0.961 1.388 1.180 +0 0.350 -2.067 -1.083 1.232 -0.030 1.314 -1.491 0.887 0.000 1.372 -1.007 -1.054 2.215 0.863 -1.292 1.292 0.000 0.769 0.369 -0.207 0.000 0.977 0.955 0.982 0.601 0.350 0.586 0.617 +1 1.138 -0.202 -0.785 1.315 0.050 1.101 -0.226 1.536 2.173 1.914 -0.582 0.878 0.000 0.887 1.496 -0.246 0.000 1.173 -1.106 -1.511 0.000 0.795 0.636 1.159 1.319 0.846 0.917 0.891 +0 0.324 2.143 -0.796 0.683 -0.168 1.085 -0.081 -1.115 2.173 1.456 2.211 0.624 0.000 0.665 -1.157 -1.230 2.548 0.640 -0.125 0.535 0.000 1.932 2.456 0.990 0.951 0.676 1.853 1.400 +0 0.421 2.067 0.697 0.876 -0.027 1.117 0.587 1.136 1.087 0.387 -0.810 0.818 0.000 1.818 0.968 -0.580 2.548 1.174 -0.739 -1.115 0.000 0.865 1.249 0.989 0.810 1.824 1.192 1.012 +1 0.488 -1.026 -0.184 0.456 -0.058 0.418 -1.253 1.741 0.000 1.109 -0.360 -1.112 0.000 0.805 0.381 -0.117 2.548 1.390 0.827 0.925 3.102 1.026 1.061 0.991 0.786 0.694 0.933 0.809 +1 0.732 -0.578 -1.659 0.856 1.170 0.735 -0.973 1.361 2.173 1.202 -0.551 -1.422 2.215 0.794 -1.781 0.892 0.000 1.762 -0.487 -0.648 0.000 0.992 1.314 0.982 0.586 0.868 1.050 0.983 +0 2.001 -0.593 -1.208 0.748 1.522 1.968 0.630 0.353 2.173 1.474 0.246 -1.538 0.000 0.340 -0.084 -0.350 1.274 0.543 0.960 -0.282 0.000 1.186 0.764 1.068 2.203 0.714 1.334 1.195 +1 0.367 1.523 -0.965 0.962 1.078 0.707 0.125 0.092 1.087 1.132 -0.594 -1.414 2.215 0.482 -0.168 -0.427 0.000 0.427 2.002 0.453 0.000 0.917 0.779 0.989 2.239 1.379 1.631 1.243 +0 1.329 -0.572 -0.110 1.954 -0.926 0.567 -0.343 -1.183 2.173 1.499 -0.579 0.667 0.000 1.119 -0.940 1.228 1.274 0.883 -1.325 -1.349 0.000 1.648 1.043 1.497 0.877 0.888 0.870 0.958 +0 2.287 -0.233 1.342 1.056 1.651 1.079 0.379 -0.110 2.173 0.500 1.028 -1.092 0.000 0.787 -2.528 -0.340 0.000 0.602 1.287 -0.262 0.000 0.517 0.789 0.980 0.810 0.894 1.123 0.982 +1 2.962 -1.207 -1.704 1.348 1.470 2.632 -1.671 -0.163 0.000 1.405 -0.001 1.717 0.000 1.319 -1.450 0.297 2.548 1.226 -0.513 1.087 0.000 0.917 0.999 0.983 0.660 0.863 0.931 0.934 +0 0.551 0.114 -0.638 1.996 -1.352 0.861 -0.652 0.740 0.000 0.963 -0.073 -1.730 2.215 0.990 0.712 0.101 2.548 0.382 -0.676 0.319 0.000 1.146 1.210 0.988 0.949 1.131 0.982 1.058 +1 0.830 0.826 -1.064 0.351 1.371 0.776 -0.061 1.358 0.000 0.912 0.323 -0.484 1.107 0.809 -1.005 -0.056 2.548 0.750 0.923 0.937 0.000 0.842 1.086 0.979 0.866 0.791 0.923 0.903 +0 0.848 0.112 1.029 0.261 1.168 0.500 0.490 -0.244 2.173 0.804 0.823 1.178 2.215 0.828 1.121 -0.997 0.000 1.166 -0.321 -0.678 0.000 1.022 1.058 0.979 0.773 0.909 0.720 0.693 +0 0.787 0.788 -0.347 0.764 -1.488 0.506 0.862 1.464 0.000 1.049 -0.598 -0.272 2.215 1.308 -0.200 1.003 1.274 0.634 -2.240 1.319 0.000 2.495 1.532 0.987 0.874 1.163 1.151 1.023 +1 0.468 0.835 -0.064 1.246 1.375 0.824 -0.719 0.777 0.000 1.299 0.197 -0.731 1.107 0.809 -0.195 1.287 0.000 0.643 -1.581 -0.635 0.000 0.753 0.763 1.019 1.044 0.624 0.882 0.871 +0 0.652 1.362 -0.107 0.962 -1.489 0.664 2.038 0.940 0.000 0.853 -0.236 -0.175 2.215 0.830 1.406 1.672 0.000 0.867 0.642 -0.614 3.102 0.888 0.920 1.038 1.038 0.508 0.992 0.845 +1 0.377 -0.142 -1.187 0.519 1.030 2.284 -0.639 1.501 0.000 2.306 0.477 0.027 0.000 1.283 0.941 -0.335 1.274 1.911 -0.091 -0.951 3.102 1.719 1.096 0.989 0.761 0.959 0.940 0.900 +1 0.631 -0.270 -0.485 0.570 -1.364 0.547 0.843 0.370 0.000 0.778 -0.615 0.054 1.107 1.181 0.839 -1.607 2.548 1.207 -0.184 -1.725 0.000 1.370 1.038 0.979 0.855 1.341 0.879 0.766 +0 2.025 0.586 0.678 1.328 1.288 1.085 0.705 -0.805 0.000 1.239 0.048 -0.691 0.000 1.248 0.826 -1.244 2.548 2.045 -0.155 0.836 3.102 0.869 0.844 1.188 0.802 1.360 1.085 1.171 +0 0.761 -0.477 0.787 1.127 -0.269 0.702 0.149 -1.023 0.000 1.126 -1.665 0.509 0.000 1.085 0.027 1.723 2.548 2.292 -0.536 -0.590 0.000 1.081 1.065 1.044 0.595 0.173 0.650 0.690 +1 1.375 0.268 0.717 0.745 -1.640 0.667 1.209 0.191 0.000 0.935 -1.058 -1.124 2.215 0.804 -1.126 -1.702 0.000 0.389 -0.269 0.023 3.102 2.436 1.278 1.194 1.179 0.514 1.044 0.940 +1 0.806 -0.145 0.914 1.158 0.931 0.851 0.996 -1.104 2.173 0.212 2.477 0.194 0.000 0.915 -0.323 -1.071 2.548 0.784 0.730 0.727 0.000 0.556 0.951 0.974 1.744 0.808 1.166 1.073 +1 0.593 -0.043 0.958 0.716 -1.048 0.737 0.419 0.647 2.173 0.756 0.184 -0.305 2.215 0.500 0.955 1.285 0.000 1.209 -1.325 -0.274 0.000 0.660 0.745 0.985 0.738 0.841 0.697 0.681 +0 0.456 1.554 -0.409 0.711 -0.555 0.519 0.655 1.717 1.087 0.592 1.271 0.135 2.215 0.727 0.269 0.943 0.000 0.399 -0.683 1.385 0.000 0.418 0.670 0.992 0.790 0.851 0.648 0.588 +0 0.435 -1.375 -0.136 0.842 1.268 2.540 -1.040 0.805 1.087 2.839 -0.367 -0.790 2.215 1.292 -1.039 -1.132 0.000 0.665 1.718 -1.496 0.000 2.457 2.009 0.986 1.424 4.143 2.422 2.040 +1 0.905 -0.636 0.545 0.620 -0.516 1.036 -0.412 -1.629 0.000 0.610 -0.509 0.262 0.000 0.779 -0.770 -0.794 2.548 1.059 0.224 1.110 1.551 1.675 1.071 0.989 0.639 0.798 0.750 0.731 +1 0.575 1.621 -1.491 0.456 -1.726 0.940 0.715 0.258 0.000 0.698 -0.218 0.505 0.000 1.312 0.570 -0.775 2.548 0.791 0.840 -1.698 3.102 0.926 0.946 0.997 0.736 0.594 0.795 0.750 +1 0.772 0.068 1.383 0.832 0.170 1.742 0.156 -1.187 0.000 1.928 0.664 0.685 0.000 1.639 -0.491 -0.493 2.548 0.594 -0.112 1.008 3.102 4.001 2.105 0.988 0.914 0.752 1.497 1.168 +0 1.166 -0.732 -1.148 0.936 -0.215 0.631 0.189 1.666 2.173 0.576 0.082 0.532 2.215 0.860 0.801 -0.269 0.000 0.854 -1.640 1.184 0.000 1.177 0.986 1.079 0.845 0.759 0.756 0.741 +1 1.682 -1.750 1.151 0.794 0.808 1.447 -0.818 -1.009 1.087 0.814 -0.727 -0.111 1.107 0.334 -0.977 1.543 0.000 0.456 1.813 0.652 0.000 1.095 1.062 1.003 1.626 1.158 1.173 1.191 +1 0.474 0.161 1.666 1.038 0.233 0.950 1.217 0.908 0.000 2.694 1.395 -1.047 0.000 1.761 0.200 0.619 1.274 1.024 -0.444 -0.237 3.102 0.900 1.079 0.990 0.585 0.818 0.911 0.836 +0 2.158 -1.337 1.011 0.686 0.790 1.696 -0.384 -0.812 2.173 0.637 -0.240 -1.685 0.000 1.118 -0.459 0.732 2.548 0.720 -0.022 -0.340 0.000 0.831 0.943 1.005 0.621 1.691 1.328 1.052 +0 0.585 -1.624 -0.556 0.599 -0.396 0.946 -0.741 -0.208 1.087 1.217 -1.242 1.172 2.215 0.997 -0.551 -0.615 0.000 1.531 -2.461 0.913 0.000 0.954 1.068 0.982 0.645 1.554 0.907 0.791 +0 1.699 1.935 -0.751 1.956 -0.339 0.763 0.419 1.374 0.000 1.075 0.886 0.890 0.000 1.062 1.254 -1.667 2.548 1.073 1.343 0.253 0.000 0.909 0.955 0.987 1.199 0.623 0.940 0.949 +0 0.366 0.045 0.321 2.054 1.539 0.671 0.693 0.113 0.000 0.637 1.601 -0.376 0.000 1.077 -0.889 -1.329 1.274 1.361 -0.223 -0.499 1.551 0.905 0.949 1.070 0.895 0.713 1.003 0.990 +1 0.365 -0.938 -1.087 1.190 1.318 0.910 -0.350 0.593 2.173 1.437 0.212 -1.348 0.000 1.071 0.114 -0.628 2.548 0.636 -0.184 -0.131 0.000 0.950 0.773 0.983 1.238 1.136 1.093 1.105 +1 0.906 0.234 -0.209 0.645 1.010 1.355 0.816 -0.762 2.173 1.274 1.003 1.294 1.107 0.572 0.666 0.526 0.000 0.715 0.302 1.140 0.000 0.393 1.047 0.987 0.994 1.867 1.084 0.844 +1 1.194 -1.871 1.459 0.287 -0.154 1.175 -1.211 0.298 2.173 0.778 -2.156 -1.586 0.000 0.799 -1.151 -0.341 0.000 0.522 -0.378 -1.359 3.102 1.243 0.798 0.990 0.999 0.888 0.841 0.752 +1 0.619 1.214 0.814 0.817 -1.121 0.420 0.447 1.158 0.000 0.598 0.958 0.026 0.000 1.459 -0.506 -1.360 2.548 0.433 -0.846 0.517 3.102 0.952 1.189 0.986 0.803 0.619 0.839 0.771 +1 0.576 -1.656 -0.325 1.879 0.642 1.566 -1.512 -0.882 2.173 0.934 -1.350 0.763 0.000 1.042 -0.994 1.325 0.000 1.214 -0.652 -1.693 3.102 0.768 0.740 1.103 1.500 1.120 1.088 1.007 +1 0.758 1.534 -1.038 1.156 0.013 1.141 0.857 0.961 1.087 0.488 2.527 -0.774 0.000 0.768 1.772 -1.200 0.000 0.520 -0.023 -0.862 3.102 0.527 1.206 1.053 0.685 0.896 0.824 0.767 +1 1.318 1.612 1.403 0.933 0.800 0.561 0.898 -1.728 0.000 0.970 -0.092 -0.851 2.215 0.669 0.698 0.195 2.548 0.574 0.991 -0.731 0.000 0.690 0.823 0.986 0.833 0.791 1.094 0.881 +1 0.943 0.075 -0.223 0.403 1.262 0.729 0.317 0.734 0.000 0.681 0.642 0.098 0.000 1.926 -0.439 -1.487 2.548 0.452 1.138 -1.006 3.102 0.852 0.717 0.987 0.914 0.814 0.911 0.785 +1 2.451 -0.367 -0.039 1.504 0.527 0.624 1.780 -1.690 0.000 0.866 -1.035 1.163 2.215 2.093 0.684 -1.227 2.548 1.115 -1.091 -1.193 0.000 0.848 0.974 1.300 1.195 1.906 1.462 1.429 +0 0.707 -0.145 -0.218 0.523 -0.621 1.199 0.829 1.345 0.000 1.223 -0.205 -1.090 1.107 1.314 -0.607 -1.555 0.000 2.384 -1.007 0.363 3.102 1.603 1.188 0.973 0.795 1.690 1.094 0.919 +0 0.939 1.628 0.479 1.494 -0.246 0.952 -0.312 -1.406 2.173 1.002 1.353 0.995 0.000 0.899 -0.921 -0.762 2.548 0.495 -0.653 1.469 0.000 1.267 1.456 0.996 1.678 0.754 1.461 1.282 +1 2.006 0.036 -1.637 0.703 -0.504 1.058 0.269 -0.111 2.173 0.476 0.936 -1.107 0.000 1.126 -0.424 0.547 2.548 0.854 0.114 1.097 0.000 0.838 0.981 1.403 1.103 0.917 0.994 0.837 +1 0.759 -0.716 -0.232 0.504 0.759 0.794 -0.173 -1.379 0.000 1.121 -0.015 0.147 0.000 1.106 -0.733 -0.516 2.548 1.692 -0.158 1.399 3.102 0.870 0.917 0.985 0.709 1.084 0.753 0.659 +1 1.160 1.255 1.022 1.608 -1.283 0.619 1.654 1.359 0.000 0.658 0.947 -0.508 2.215 1.001 1.255 0.389 0.000 1.455 0.184 -0.404 3.102 1.106 0.993 1.656 1.205 0.351 0.836 0.849 +1 1.283 1.785 1.356 0.418 0.784 1.014 -0.055 -0.329 2.173 0.649 1.246 -0.667 0.000 0.578 2.541 1.257 0.000 0.579 -0.159 -1.464 3.102 0.728 0.982 0.992 0.727 0.694 0.958 0.795 +1 0.916 -1.414 1.696 1.007 0.197 0.904 -0.788 0.351 2.173 0.564 -1.690 0.637 0.000 0.808 -1.874 -0.937 0.000 1.060 -0.814 1.284 3.102 1.037 1.073 1.298 0.732 0.777 0.726 0.690 +1 1.739 1.296 0.266 0.567 0.161 3.012 -1.344 -1.230 0.000 2.255 1.638 -0.824 0.000 1.623 1.476 0.632 0.000 5.594 0.786 0.889 3.102 0.804 2.557 0.983 1.392 0.474 3.157 3.025 +1 1.349 -0.455 1.468 0.761 0.842 0.683 -2.795 -1.054 0.000 0.614 -0.718 -0.148 0.000 0.526 -1.201 -1.415 0.000 0.757 0.129 -0.283 3.102 0.952 1.328 0.985 0.768 0.477 1.202 1.060 +0 1.196 -0.952 -0.900 1.649 -0.844 1.667 -0.044 1.123 2.173 0.958 0.567 -0.652 1.107 0.429 -0.572 0.046 0.000 0.857 -0.580 0.780 0.000 0.907 1.088 0.989 1.629 1.951 1.809 1.405 +0 1.457 0.510 -1.430 0.508 -0.952 0.838 0.737 0.119 2.173 0.714 0.175 0.981 2.215 0.303 -0.237 1.574 0.000 0.552 -1.398 1.731 0.000 0.353 1.058 0.985 0.911 0.861 0.857 0.808 +1 0.389 0.970 1.127 1.173 -0.171 0.664 -1.028 1.328 2.173 0.806 -0.904 -0.986 2.215 0.333 1.214 0.263 0.000 0.626 0.167 -0.667 0.000 1.040 1.491 0.989 1.028 0.938 1.231 1.017 +0 1.105 1.288 0.671 0.631 -0.705 1.206 0.437 0.597 2.173 0.792 0.653 -1.143 0.000 1.407 0.889 -1.560 2.548 0.827 -0.096 -0.202 0.000 0.898 0.837 1.094 0.957 1.573 0.964 0.862 +0 0.951 0.977 -1.008 0.872 0.109 0.695 0.075 -0.039 1.087 1.305 0.705 -1.527 2.215 2.243 -0.145 0.646 0.000 0.792 -0.009 -1.399 0.000 0.950 1.053 1.066 0.939 1.441 0.860 0.764 +1 0.969 0.852 1.435 1.112 0.804 0.343 1.388 -0.525 0.000 0.963 0.016 -0.783 0.000 0.348 0.916 0.244 2.548 0.481 -2.260 0.590 0.000 0.958 0.655 0.985 0.577 0.294 0.419 0.605 +1 0.917 -0.467 -1.628 0.324 -1.624 0.896 -0.199 -0.056 2.173 0.706 1.810 1.411 0.000 0.947 -1.211 -0.480 0.000 0.951 0.752 0.682 0.000 0.842 1.083 0.990 1.157 0.983 1.021 1.124 +0 0.554 -0.453 1.161 0.681 -0.287 1.544 0.567 0.953 0.000 0.819 1.790 1.525 0.000 0.979 0.487 1.357 0.000 2.407 -0.423 -0.935 3.102 0.945 1.178 0.988 0.944 1.166 1.054 0.873 +1 1.024 0.159 0.272 0.791 -1.099 0.804 -1.337 -0.296 2.173 0.685 0.382 0.793 1.107 1.400 -0.120 1.286 0.000 0.791 0.815 1.616 0.000 0.758 0.627 1.177 1.092 1.408 1.024 0.896 +0 1.047 0.598 1.172 0.275 -1.620 0.798 -2.041 -0.320 0.000 0.751 -0.371 -1.317 1.107 0.668 -0.493 0.438 2.548 0.472 -1.560 -1.688 0.000 0.889 0.875 0.983 0.721 0.755 0.779 0.868 +0 0.513 0.861 -0.746 0.371 1.478 0.610 -0.985 -1.114 0.000 0.816 -0.252 0.262 2.215 0.657 -1.392 -0.315 0.000 2.007 0.911 1.446 3.102 0.919 0.887 0.990 0.910 1.316 1.019 0.834 +1 0.865 0.198 0.923 0.718 -0.111 0.913 0.902 -1.199 2.173 0.868 -0.381 0.149 0.000 0.485 0.728 1.623 1.274 0.449 -1.275 1.021 0.000 0.755 0.776 0.984 1.129 0.469 0.863 0.772 +0 2.633 0.596 0.014 0.259 0.372 1.671 0.434 -1.541 0.000 0.701 0.037 1.576 0.000 0.884 -0.789 0.011 1.274 0.608 0.336 0.483 0.000 0.881 0.912 0.982 0.821 0.293 0.918 1.040 +1 0.768 0.980 -0.000 2.006 -0.613 0.932 0.644 1.593 0.000 0.831 1.052 0.690 2.215 0.620 0.252 1.045 0.000 0.707 0.040 -1.128 3.102 0.698 0.821 0.983 0.622 0.786 0.709 0.798 +1 0.513 0.786 1.049 0.349 0.282 1.100 -0.483 -1.309 2.173 0.901 2.337 0.252 0.000 0.594 -1.162 1.165 2.548 0.853 0.429 -0.901 0.000 1.164 1.426 0.989 0.971 0.895 1.220 1.009 +1 0.499 -0.179 1.166 1.121 -0.842 0.787 0.758 0.537 2.173 0.519 -1.465 -1.501 0.000 0.650 -0.925 -0.136 0.000 0.414 0.343 -1.452 1.551 0.868 0.634 1.007 0.991 0.599 0.824 0.729 +1 0.460 1.520 -1.482 0.966 1.572 1.272 2.029 -0.545 0.000 0.996 1.461 0.858 0.000 2.143 1.021 -1.310 1.274 2.003 0.477 0.474 0.000 0.760 0.849 0.987 0.865 0.973 0.884 0.778 +0 1.217 -0.616 1.330 1.447 1.667 0.794 0.199 0.190 0.000 0.731 0.955 -0.593 0.000 1.021 -1.140 -0.783 2.548 0.711 -0.152 0.743 3.102 1.235 0.842 0.990 0.955 0.735 0.850 1.065 +1 0.795 0.690 1.324 1.607 1.471 0.746 1.214 -0.463 2.173 0.282 -0.135 0.773 2.215 0.433 0.061 -0.091 0.000 0.370 1.893 -1.187 0.000 0.711 1.324 0.982 0.540 0.784 0.905 0.880 +1 2.277 -1.311 0.358 1.029 0.447 1.064 -1.545 1.538 2.173 1.140 -1.709 -0.320 0.000 1.179 -0.093 -1.613 2.548 1.040 -1.296 -1.288 0.000 1.095 1.334 0.997 1.341 1.184 1.207 1.145 +0 1.044 -0.167 -1.518 1.361 -1.246 1.274 0.305 1.619 2.173 2.629 -0.072 0.331 0.000 1.079 -2.728 -0.143 0.000 1.119 -0.269 -0.602 3.102 5.378 2.954 0.974 1.187 1.217 2.412 1.915 +1 0.520 -1.957 -0.062 0.933 0.900 0.715 -1.766 0.309 0.000 1.270 -0.954 -1.198 2.215 1.226 -0.921 1.606 0.000 1.672 -2.403 -0.501 0.000 1.477 1.217 0.980 0.999 0.435 1.071 0.930 +0 0.670 -0.366 0.707 0.993 -0.622 0.374 0.463 -0.199 2.173 0.612 -0.141 0.944 0.000 1.379 -0.050 1.635 1.274 0.834 -1.927 -1.050 0.000 1.520 1.206 1.052 0.873 0.923 0.898 0.786 +1 0.545 -0.230 -0.803 0.774 1.253 0.827 0.745 -1.501 2.173 1.343 -0.002 -0.066 0.000 1.210 -0.478 0.395 2.548 0.885 -2.191 1.591 0.000 1.457 1.071 0.986 1.045 1.500 1.031 0.937 +0 1.025 -0.419 1.496 0.105 -0.615 0.720 -1.146 0.752 0.000 0.834 -1.541 -0.083 0.000 1.614 0.997 -1.605 2.548 1.233 0.452 -0.189 3.102 0.819 0.954 0.978 0.821 1.075 1.097 0.898 +1 0.612 -0.351 1.460 0.474 -0.898 1.234 -0.448 0.578 0.000 1.290 -0.287 -1.654 0.000 0.557 2.635 -0.070 0.000 1.510 -0.420 -0.236 3.102 2.431 1.359 0.990 0.773 0.424 0.946 0.836 +1 0.602 -0.257 0.300 1.074 -1.353 0.604 -1.830 0.889 0.000 0.773 0.058 -1.695 1.107 1.467 -0.588 -0.756 2.548 1.540 -1.019 0.293 0.000 0.887 1.276 1.110 0.684 0.941 1.018 0.888 +0 0.451 -2.210 0.224 0.669 -1.028 0.835 -0.653 0.434 2.173 1.377 -2.179 1.391 0.000 0.729 -0.673 -1.175 0.000 0.962 0.411 -1.035 0.000 0.735 0.661 0.989 0.847 0.372 0.778 0.826 +1 0.458 0.705 1.663 0.929 0.001 1.038 1.028 -0.143 0.000 1.959 -0.056 1.589 0.000 1.960 0.240 0.714 0.000 1.602 0.951 1.425 3.102 0.750 0.876 0.990 0.845 1.805 0.940 0.765 +0 0.568 0.112 -1.410 1.840 1.210 1.530 0.533 -0.313 1.087 0.874 -0.638 1.530 2.215 1.171 -0.150 -0.231 0.000 1.271 -0.767 -1.484 0.000 1.072 0.971 0.996 0.662 2.009 1.223 1.033 +1 0.984 -0.777 -0.096 1.824 -0.040 0.867 -0.008 -1.385 2.173 0.791 -0.385 1.169 2.215 0.729 0.009 1.703 0.000 0.428 -1.530 0.779 0.000 0.797 0.784 0.974 1.195 0.937 1.212 0.968 +0 0.777 -1.392 0.349 0.882 -0.612 1.691 -0.720 0.287 1.087 2.260 -0.001 -1.666 0.000 0.379 0.534 -1.195 0.000 1.260 -0.579 -0.919 3.102 0.722 0.889 0.986 0.894 1.368 1.410 1.160 +1 1.291 -0.342 0.966 0.595 0.608 1.952 -2.488 -0.860 0.000 1.395 -0.117 0.303 0.000 1.238 0.634 1.295 2.548 1.597 -0.125 1.624 3.102 0.866 1.068 0.997 1.038 0.564 0.829 0.806 +1 0.579 -0.909 1.027 2.006 1.739 0.913 -0.225 1.276 0.000 1.373 0.748 -0.593 2.215 1.604 -0.176 0.243 0.000 1.419 1.413 -0.375 3.102 1.746 1.818 0.991 2.361 0.653 1.840 1.603 +1 1.151 1.498 -0.214 0.495 1.352 1.223 1.392 -1.022 2.173 1.051 1.236 1.346 0.000 1.114 1.508 0.731 0.000 0.498 0.214 -0.046 3.102 0.930 0.785 1.033 0.932 0.811 0.926 0.801 +1 0.624 0.969 0.058 1.147 1.196 0.934 -2.611 0.089 0.000 0.761 -0.208 -0.630 0.000 0.792 -1.283 -1.242 2.548 1.029 0.042 1.217 1.551 0.824 0.835 1.003 0.630 0.778 0.840 0.769 +0 0.666 1.372 -1.349 1.462 -0.509 0.743 0.632 1.481 0.000 1.000 0.423 0.646 2.215 0.672 -0.314 0.367 2.548 0.624 0.164 -0.739 0.000 0.973 0.916 0.987 1.113 0.411 0.940 0.865 +0 0.407 -1.179 0.094 1.431 1.375 0.702 -1.271 -0.225 0.000 1.104 0.883 -1.520 2.215 0.575 -0.047 -0.378 0.000 1.125 0.929 0.288 0.000 0.765 1.039 0.989 1.700 0.324 1.124 1.082 +1 0.993 -0.991 -1.278 1.566 0.525 1.540 -0.459 0.508 2.173 0.937 -0.230 -1.088 0.000 0.422 -1.139 -1.720 0.000 1.071 -0.384 -0.575 3.102 0.729 0.575 1.725 1.291 1.125 0.942 0.915 +1 0.477 -0.571 -1.629 1.341 -0.294 1.016 -0.225 1.182 0.000 1.144 -0.734 -0.664 0.000 1.381 -0.354 0.588 1.274 0.668 -0.832 -1.295 3.102 2.361 1.310 1.034 0.837 0.763 0.933 0.825 +1 0.850 0.493 1.697 0.336 0.546 0.846 -0.575 -0.913 2.173 0.973 -2.895 -0.636 0.000 1.432 -1.541 -0.202 0.000 2.153 0.408 1.071 0.000 1.189 1.225 0.990 1.215 1.019 0.981 1.201 +1 1.289 -0.220 -0.282 0.256 -0.829 1.085 -1.938 1.533 0.000 0.329 -0.965 -0.294 0.000 0.531 -1.182 1.373 2.548 0.960 -1.028 0.455 3.102 1.134 0.926 0.994 0.717 0.402 0.558 0.736 +1 0.897 0.621 1.579 0.371 1.252 0.969 0.670 -1.679 2.173 0.892 -0.226 -0.756 1.107 1.700 -0.335 0.799 0.000 0.416 -1.486 0.151 0.000 0.878 1.097 0.991 0.784 1.200 1.049 0.870 +0 1.519 -0.989 -1.164 1.261 -0.370 0.748 0.328 0.828 2.173 0.491 0.470 -0.742 1.107 0.666 -0.004 1.596 0.000 0.932 -0.828 0.530 0.000 0.841 0.768 1.258 1.462 0.884 1.001 0.842 +1 0.984 0.832 -1.606 1.409 -0.848 1.217 0.208 0.482 0.000 0.938 0.515 -0.380 0.000 0.632 0.062 -1.385 0.000 1.032 1.119 1.225 3.102 0.970 0.928 1.031 0.817 0.650 0.660 0.656 +0 0.847 0.955 0.702 1.281 -1.608 0.916 0.478 -0.335 2.173 0.479 -0.360 0.948 2.215 0.493 -0.685 -0.489 0.000 0.919 -0.049 -1.340 0.000 0.578 0.750 1.259 0.843 0.990 0.856 0.746 +1 0.688 -1.592 -1.111 0.681 1.649 0.553 0.828 -1.246 0.000 1.195 -0.889 0.093 2.215 0.482 0.123 -0.250 0.000 1.638 -1.668 -1.580 0.000 0.968 0.927 0.987 0.602 0.511 0.654 0.648 +1 1.652 0.143 0.034 0.339 -1.554 0.395 0.219 -0.745 0.000 0.667 -1.206 1.322 1.107 0.441 2.664 -0.129 0.000 1.115 0.031 1.268 0.000 0.950 0.687 1.026 0.733 0.224 0.676 0.615 +0 0.660 -1.887 -0.622 0.606 -1.742 0.472 -0.853 -1.664 2.173 0.797 -2.271 0.456 0.000 0.836 -1.807 -0.328 0.000 0.437 0.835 1.370 1.551 0.843 1.079 0.991 0.761 0.549 0.909 0.775 +0 0.320 0.283 -0.752 0.375 1.354 1.095 1.613 -0.173 2.173 0.989 0.278 -1.058 0.000 1.413 -0.061 1.317 2.548 1.815 0.666 -1.661 0.000 1.009 1.057 0.994 2.126 2.112 1.530 1.313 +1 0.818 -0.534 -1.705 0.947 -0.577 0.525 -1.429 0.869 2.173 0.806 -1.211 1.584 0.000 1.280 -0.009 -0.128 1.274 1.351 -0.868 -0.281 0.000 0.939 0.810 1.037 0.802 1.129 0.770 0.670 +1 1.816 -0.887 -0.305 0.736 -0.626 0.759 -1.346 0.817 1.087 0.921 -1.189 1.248 0.000 1.027 -1.324 1.717 2.548 0.512 -1.477 -0.223 0.000 0.903 0.674 0.982 0.998 0.799 0.882 0.772 +1 1.958 0.542 0.806 0.037 -0.474 0.966 -0.259 -1.092 2.173 0.502 2.869 1.309 0.000 0.884 0.920 -0.598 0.000 1.674 -0.331 -0.303 3.102 1.125 0.954 0.997 1.269 0.884 0.956 0.852 +0 0.499 0.061 1.194 1.030 0.729 1.512 -0.823 1.357 2.173 2.180 0.143 -0.644 2.215 0.659 0.094 -0.014 0.000 0.733 -1.106 -0.588 0.000 0.707 0.880 0.993 1.829 2.933 1.764 1.360 +0 0.911 -1.719 -1.503 0.331 0.737 1.601 -1.058 -0.936 2.173 1.028 -1.387 1.116 0.000 2.172 -2.451 0.492 0.000 0.652 -0.636 0.332 3.102 1.972 1.263 0.986 0.971 0.998 1.553 1.213 +1 1.023 -0.601 -0.153 0.687 1.002 0.765 0.634 1.187 0.000 0.783 0.232 -1.599 2.215 0.887 -1.026 -0.326 1.274 0.720 0.662 -0.729 0.000 1.123 0.826 1.002 0.633 1.038 0.844 0.788 +0 1.293 -1.678 0.809 0.832 1.735 0.947 -1.166 -1.303 0.000 1.314 -1.138 -0.521 2.215 0.962 1.147 0.732 0.000 0.621 -0.310 0.731 3.102 3.123 1.681 1.066 1.180 0.809 1.306 1.251 +1 0.749 -0.440 1.533 1.713 -1.634 0.686 1.548 -0.524 0.000 0.565 -0.400 -1.628 2.215 0.607 -1.273 0.061 0.000 1.854 0.373 0.317 0.000 0.671 0.794 0.973 0.514 0.420 0.746 0.834 +1 0.630 0.298 1.353 1.529 0.815 0.715 -1.308 -1.372 0.000 0.916 -0.996 0.710 0.000 1.068 0.430 -0.541 0.000 2.433 -0.606 -0.653 3.102 1.659 0.928 0.983 1.222 0.860 0.843 0.854 +0 0.602 0.495 0.366 0.763 -1.107 0.574 -0.439 1.693 2.173 0.779 -1.105 -1.122 0.000 0.742 1.985 0.368 0.000 1.220 0.641 0.193 3.102 3.165 1.829 0.988 0.890 1.036 1.236 1.008 +1 0.651 -0.245 0.145 0.658 1.105 1.105 -0.581 -1.325 2.173 0.470 -1.169 -0.506 2.215 0.576 -2.518 0.861 0.000 1.049 -0.815 0.683 0.000 0.883 0.768 0.994 1.047 0.788 0.894 0.784 +0 0.494 -1.311 -1.247 1.854 -1.688 0.978 0.231 0.290 0.000 0.787 -0.477 -0.911 2.215 0.266 1.828 -1.127 0.000 0.891 -0.510 -0.141 3.102 1.274 0.890 0.985 0.730 0.486 0.750 0.858 +0 1.039 -1.339 -1.273 0.361 -0.074 0.800 -1.330 1.257 2.173 0.610 -0.272 -1.522 1.107 0.767 -1.439 0.073 0.000 0.804 0.059 -0.306 0.000 0.850 1.049 0.996 0.620 0.837 0.755 0.683 +1 1.219 0.772 1.575 1.122 -1.321 0.857 0.413 -0.287 2.173 0.788 0.942 0.534 1.107 0.501 -0.341 -0.531 0.000 0.707 0.012 0.762 0.000 0.616 0.648 0.986 1.199 0.883 0.919 0.772 +1 0.630 -0.129 0.406 0.589 -0.626 1.036 0.590 -1.389 2.173 0.862 1.395 1.162 0.000 1.527 0.927 0.582 0.000 0.470 -1.539 -0.066 0.000 0.952 1.041 0.981 0.930 0.933 0.993 0.848 +1 0.746 -0.817 0.878 0.972 0.511 1.092 0.070 -0.781 2.173 1.078 0.207 -1.482 2.215 0.985 -0.368 0.443 0.000 0.463 0.204 1.569 0.000 0.679 1.012 0.981 1.508 0.949 1.341 1.053 +1 0.959 0.278 -0.420 1.214 -1.431 1.327 -0.209 0.963 2.173 0.268 0.596 -1.688 0.000 0.587 0.054 0.039 2.548 0.829 -0.439 -0.823 0.000 0.822 1.241 1.180 0.715 0.827 0.896 0.785 +1 1.346 -0.636 -0.631 1.769 0.142 1.031 -0.440 1.149 2.173 0.646 0.075 0.757 0.000 0.872 0.232 -1.276 2.548 0.499 -0.852 -0.913 0.000 0.854 0.773 1.372 1.067 1.052 1.047 0.843 +0 0.754 0.530 -1.713 1.211 0.712 0.880 0.047 -0.458 2.173 0.727 -0.539 -0.558 2.215 1.023 -1.433 1.403 0.000 0.388 -1.339 -1.570 0.000 0.804 0.935 1.082 1.074 0.375 0.845 0.888 +1 0.454 0.614 -1.567 0.644 -1.634 1.131 -0.289 -1.383 0.000 2.769 0.318 0.537 1.107 1.333 -0.715 -1.089 0.000 0.991 0.692 0.106 0.000 0.799 0.595 0.988 1.567 1.224 1.460 1.180 +0 0.747 0.047 1.658 0.369 -0.946 0.495 -0.047 0.751 2.173 0.480 0.041 -0.684 0.000 1.154 -0.158 -0.163 2.548 0.614 1.495 1.468 0.000 0.964 0.874 0.977 0.768 0.693 0.685 0.643 +1 0.679 -1.702 -0.404 1.032 -0.435 0.782 -1.066 1.055 1.087 0.706 -0.674 -1.605 2.215 0.897 -0.336 -0.450 0.000 0.931 0.173 1.212 0.000 1.047 1.023 0.989 0.834 0.769 0.804 0.750 +0 1.462 -0.329 0.499 1.970 1.076 1.009 -0.502 -0.947 2.173 1.110 0.248 0.093 0.000 1.416 0.510 -1.439 2.548 1.235 -1.313 -1.724 0.000 0.795 1.095 1.168 1.535 1.049 1.219 1.087 +0 1.862 1.526 0.951 0.362 -0.381 0.597 2.298 -0.372 0.000 0.686 1.149 -1.146 1.107 0.407 0.385 -1.542 0.000 0.769 0.532 0.311 3.102 1.242 0.878 1.060 0.892 0.659 0.635 0.683 +0 0.915 -1.307 -0.365 0.800 0.804 1.026 -0.335 -1.180 1.087 0.881 -1.835 0.695 0.000 0.663 -2.595 1.198 0.000 0.690 -1.013 0.994 1.551 0.779 0.552 1.030 1.133 0.918 1.086 0.911 +1 0.725 0.888 1.282 1.432 -1.360 1.067 0.026 0.986 0.000 1.339 0.128 -0.072 0.000 1.095 0.981 -0.582 2.548 0.873 -0.274 -0.884 0.000 0.918 1.251 0.988 0.713 0.697 0.737 0.859 +1 0.585 -0.769 0.927 0.534 -0.942 0.639 1.348 0.139 2.173 0.673 0.368 -0.588 2.215 0.909 -0.688 1.573 0.000 0.719 -0.082 1.448 0.000 0.315 0.798 0.986 1.676 0.768 1.137 0.949 +0 0.304 0.865 0.520 1.296 -1.450 0.472 0.667 0.735 0.000 0.904 0.435 1.727 2.215 0.701 1.170 -0.399 0.000 1.674 -0.206 -0.318 3.102 0.940 0.935 0.985 0.815 1.141 0.785 0.723 +0 0.617 -0.295 1.333 0.701 -1.162 0.885 0.519 -0.278 1.087 0.443 1.405 1.056 2.215 0.942 2.248 1.252 0.000 0.391 1.343 -1.020 0.000 0.664 1.298 0.990 1.108 0.965 1.004 1.177 +0 1.244 -0.669 -1.374 2.193 1.607 2.901 0.308 -0.031 0.000 2.223 -0.619 1.616 2.215 0.937 -0.201 -0.420 0.000 0.609 -1.464 1.283 0.000 1.092 0.663 1.006 0.644 0.906 0.797 0.756 +0 1.276 0.274 0.013 0.593 -0.821 0.771 0.879 0.701 2.173 0.665 1.155 1.203 0.000 0.930 1.221 -1.194 0.000 1.435 1.811 -0.779 0.000 1.006 0.964 0.989 0.981 0.897 0.834 0.809 +0 0.698 -1.181 0.216 0.995 1.541 0.854 -1.149 -1.719 0.000 0.658 -0.826 -0.958 0.000 0.442 -0.169 0.674 0.000 1.723 0.561 0.175 3.102 0.869 0.959 1.074 0.536 0.718 0.763 0.671 +0 1.271 0.454 0.345 0.962 -1.012 0.497 -0.749 -1.400 0.000 0.897 -0.646 0.486 0.000 1.038 0.494 -1.508 2.548 0.955 -0.524 1.110 3.102 1.407 0.853 1.440 0.924 0.714 0.734 0.801 +0 0.874 -0.513 1.471 0.978 -0.445 0.645 -1.407 -0.801 2.173 0.998 0.294 0.340 2.215 0.527 -0.247 1.037 0.000 0.651 -0.155 -0.986 0.000 0.626 0.747 1.266 0.965 1.530 0.899 0.715 +1 0.449 -2.133 -0.985 2.042 -0.405 0.455 -0.866 1.234 0.000 0.820 -0.650 1.638 2.215 1.179 -0.924 0.641 2.548 0.771 0.035 -1.537 0.000 0.691 0.727 0.979 1.081 0.837 0.871 0.782 +0 0.537 2.307 0.512 1.049 -0.668 0.738 1.057 1.379 0.000 0.783 0.145 0.417 1.107 0.717 -0.011 -0.962 2.548 0.637 1.437 -0.588 0.000 1.084 0.923 0.986 1.032 0.757 0.810 0.779 +1 1.378 1.272 -0.687 2.179 -0.041 0.642 0.138 0.476 2.173 0.753 2.599 -1.682 0.000 0.775 0.285 -1.349 0.000 0.636 1.109 0.752 0.000 0.981 1.042 1.321 1.213 0.790 0.984 1.049 +1 1.548 -1.061 -1.359 1.032 -0.777 0.622 -1.039 0.520 2.173 0.486 -1.615 1.299 0.000 1.819 -0.103 0.334 2.548 1.048 -0.977 0.019 0.000 0.880 1.024 0.988 1.077 0.680 1.071 0.885 +0 0.876 0.470 -0.838 1.228 1.543 1.063 -0.382 -0.439 2.173 1.122 1.139 1.545 0.000 0.756 0.875 0.302 0.000 0.806 0.561 0.841 3.102 1.099 1.120 1.206 0.714 1.048 0.856 0.797 +0 2.049 -1.019 -0.809 0.721 0.813 0.530 0.954 0.852 0.000 0.449 -0.439 0.830 2.215 0.563 1.343 -1.181 0.000 0.989 -1.182 0.977 3.102 0.982 0.832 1.675 0.996 0.313 0.790 0.998 +1 0.806 1.488 -0.120 0.184 1.327 0.415 1.390 0.234 1.087 0.968 1.599 -1.696 2.215 0.574 -0.413 1.543 0.000 1.009 -0.402 -0.072 0.000 0.857 1.123 0.988 0.580 0.927 0.780 0.685 +0 1.743 0.849 0.053 2.571 -0.146 1.951 1.238 1.613 0.000 3.401 0.530 -0.116 1.107 1.130 0.925 1.019 1.274 1.357 1.265 -1.396 0.000 1.085 1.038 0.996 0.612 1.846 1.916 1.728 +1 1.117 -0.491 0.267 1.459 -0.330 0.897 2.426 -0.931 0.000 1.030 0.190 0.694 0.000 1.810 1.006 1.324 2.548 0.686 2.406 1.516 0.000 1.000 0.918 0.988 1.847 0.799 1.175 1.729 +0 0.812 0.295 -0.520 0.628 -0.827 0.501 0.899 -1.411 2.173 0.897 0.111 -0.040 1.107 0.986 -0.034 0.880 0.000 1.102 1.045 1.353 0.000 0.926 0.991 0.985 0.898 1.015 0.756 0.799 +1 0.525 0.830 1.108 0.467 -0.779 3.107 1.466 0.438 0.000 3.680 -0.037 -1.337 1.107 1.070 -0.404 -1.525 2.548 0.773 0.283 -1.630 0.000 0.924 1.233 0.980 0.650 0.559 0.755 0.723 +1 0.441 -1.535 0.789 0.313 -1.443 2.026 0.478 -0.636 0.000 1.272 -0.921 -1.408 1.107 3.355 -0.429 0.985 0.000 1.605 -0.721 0.682 3.102 1.520 0.906 0.980 0.811 1.228 1.022 0.845 +1 1.172 0.328 -0.798 0.439 0.756 1.201 0.538 -0.583 0.000 1.877 0.485 0.915 0.000 1.327 -0.211 1.559 2.548 1.135 -1.220 -0.429 0.000 1.115 1.068 0.988 0.840 0.685 0.911 0.765 +0 1.445 -0.437 1.664 0.213 -1.605 0.798 -0.355 0.468 2.173 0.737 -0.252 -0.154 0.000 0.696 -0.923 -0.460 2.548 1.104 -0.161 -0.883 0.000 0.892 0.745 0.975 0.747 0.756 0.749 0.667 +1 0.897 -0.727 -0.032 0.420 -0.592 1.391 -0.142 -1.582 0.000 1.763 -0.110 -0.220 2.215 1.126 -0.872 1.156 1.274 0.611 -1.382 0.738 0.000 0.457 1.074 0.990 0.916 1.559 0.850 0.755 +1 1.029 -0.904 0.352 1.720 0.233 1.501 0.243 -1.406 2.173 0.853 0.435 -1.711 0.000 1.462 0.135 0.374 2.548 0.368 1.136 -0.590 0.000 0.709 0.962 1.002 2.504 1.846 1.766 1.456 +0 0.490 -2.188 -0.962 0.846 -1.112 1.087 -0.530 -0.161 2.173 0.894 -0.541 0.395 2.215 1.554 -1.117 1.492 0.000 0.429 -1.883 1.472 0.000 0.487 0.969 0.985 0.990 0.697 0.947 0.862 +1 1.317 0.051 1.278 0.400 1.496 0.792 0.387 -0.037 2.173 0.795 0.359 -0.892 0.000 0.644 -0.157 -1.405 0.000 0.763 -0.480 0.389 3.102 0.574 0.888 0.982 0.627 0.516 0.733 0.721 +1 0.838 -0.744 0.423 1.073 1.395 0.466 -1.154 -1.500 0.000 0.975 -0.086 0.487 0.000 1.910 0.521 -1.285 2.548 1.347 1.020 0.088 3.102 0.927 0.859 1.010 1.282 1.228 1.079 0.934 +1 0.332 -0.853 -1.678 0.086 0.383 0.486 -0.906 0.740 0.000 1.113 1.058 -1.687 2.215 1.299 -0.444 -0.897 2.548 0.392 2.105 0.212 0.000 1.829 1.444 0.826 0.663 1.393 1.137 0.880 +0 0.757 0.402 0.671 0.423 1.240 0.474 1.175 -1.685 2.173 0.833 -0.434 -0.562 0.000 0.708 1.743 1.431 0.000 0.711 -0.125 0.503 0.000 0.834 1.038 0.990 0.885 0.622 0.678 0.743 +1 0.906 -0.765 1.426 0.162 0.861 1.115 0.881 -0.134 2.173 0.912 -0.189 -1.493 1.107 0.985 1.543 0.723 0.000 0.646 -1.635 -0.777 0.000 0.334 0.821 0.980 1.203 1.632 1.315 1.073 +0 1.000 -0.885 -0.096 0.923 -1.056 0.810 -0.495 0.510 1.087 0.771 -1.950 0.302 0.000 1.975 -0.135 -1.391 2.548 0.861 -1.057 -1.180 0.000 0.754 1.037 1.012 0.986 1.585 0.953 0.847 +0 1.545 -0.060 0.394 0.335 -1.085 0.997 0.434 -0.517 2.173 0.813 0.606 -1.695 0.000 0.872 -0.389 0.886 2.548 1.272 -0.418 -1.556 0.000 0.803 0.833 0.987 0.921 1.223 0.883 0.810 +0 2.294 0.573 -0.676 0.142 1.381 0.941 0.746 1.118 0.000 1.048 0.471 0.690 0.000 0.585 -0.416 -0.011 2.548 2.049 0.139 -1.405 3.102 0.845 0.924 0.987 0.863 0.839 0.883 0.926 +1 0.943 -1.601 -0.014 0.155 -0.967 1.585 -0.723 1.500 2.173 1.705 -0.683 -1.107 0.000 1.145 -0.394 -0.366 1.274 2.083 -0.284 0.379 0.000 0.731 0.983 0.982 0.631 1.686 0.997 0.801 +0 0.507 1.101 1.372 1.546 -1.139 1.091 -0.461 0.193 2.173 1.030 -0.927 1.705 0.000 0.860 0.178 1.555 2.548 0.981 1.764 -0.051 0.000 3.183 1.775 0.989 1.838 1.207 1.395 1.366 +0 1.012 0.183 -1.683 1.439 1.092 0.703 1.708 -0.456 0.000 0.923 -0.565 1.081 0.000 0.920 0.606 -0.876 0.000 0.722 2.018 0.418 0.000 0.963 1.070 1.000 0.740 0.476 0.674 0.844 +1 1.142 -0.288 -0.503 1.143 -0.358 1.044 -0.848 0.732 2.173 0.760 -2.464 -1.340 0.000 1.595 -0.700 1.456 1.274 0.940 -0.982 -0.980 0.000 0.879 1.185 0.987 1.206 0.977 1.065 1.054 +1 0.697 -1.508 0.202 0.506 0.518 0.923 0.122 -1.467 0.000 0.680 0.235 0.218 2.215 1.100 -0.703 -1.577 2.548 1.471 -0.772 -0.236 0.000 1.871 1.193 0.993 0.640 1.039 0.903 0.825 +0 0.480 0.095 0.479 0.917 -1.481 0.700 0.088 0.108 2.173 0.797 0.518 -1.061 1.107 0.559 -0.243 1.297 0.000 0.734 0.851 0.558 0.000 1.040 0.891 0.988 0.834 0.986 0.697 0.654 +0 1.248 0.525 -0.242 1.576 -1.064 0.857 -1.099 1.321 1.087 0.485 0.220 1.475 0.000 0.749 -1.291 0.132 0.000 0.869 -0.357 -0.107 0.000 0.883 0.925 1.309 0.930 0.321 1.092 0.877 +1 0.622 0.279 -0.040 0.658 -1.205 1.306 1.456 0.600 0.000 1.221 1.056 -1.659 0.000 0.655 0.716 0.365 0.000 1.348 1.141 -0.596 3.102 0.938 0.827 0.990 0.615 0.682 0.601 0.570 +1 1.472 0.198 1.370 0.803 -0.211 0.732 1.372 -0.006 0.000 0.597 -0.137 -0.009 2.215 1.264 -0.470 -1.408 2.548 1.182 1.285 1.252 0.000 0.913 0.942 1.490 1.003 0.896 0.890 0.839 +0 0.745 -1.481 -1.571 0.862 0.863 1.320 -1.638 1.571 0.000 1.763 -1.501 -0.041 0.000 2.054 1.150 -0.475 0.000 1.094 -0.760 0.312 3.102 0.832 0.993 0.990 0.658 0.730 0.786 0.677 +0 0.655 -0.480 -0.551 1.810 -0.023 0.660 -0.315 1.133 2.173 0.655 0.128 -1.327 0.000 0.511 2.374 -1.481 0.000 1.242 0.921 -1.146 3.102 0.926 0.770 0.990 1.138 1.127 1.146 1.353 +0 1.485 0.791 0.358 0.864 -0.783 0.895 0.740 1.112 2.173 0.883 0.428 -0.984 0.000 0.501 -0.301 -0.547 0.000 0.631 -0.673 -0.990 3.102 0.562 1.146 1.344 0.895 1.017 0.836 0.776 +1 0.827 -0.119 0.324 0.354 -1.525 1.129 0.229 -1.292 2.173 0.960 1.006 0.413 0.000 1.011 1.030 -0.249 0.000 0.695 2.395 1.308 0.000 0.852 0.717 0.991 0.943 0.831 0.923 0.783 +0 0.620 0.693 -0.546 2.155 0.051 0.927 -0.147 1.577 0.000 0.629 -2.201 -0.981 0.000 0.508 -0.451 0.728 2.548 0.910 -0.464 -1.312 0.000 0.991 0.848 0.982 0.897 0.573 0.968 1.014 +1 1.119 -0.185 1.582 1.461 0.830 1.310 1.121 -0.595 2.173 0.507 -0.599 -0.179 0.000 0.493 -1.139 1.714 0.000 1.127 -0.025 1.121 3.102 0.799 0.692 1.109 1.815 1.509 1.208 1.041 +1 0.769 0.524 -1.224 0.964 0.043 2.024 -0.256 -0.078 0.000 1.791 -0.154 1.137 0.000 1.661 -0.679 -1.547 2.548 1.652 -0.208 1.551 0.000 0.824 1.087 1.085 0.685 0.858 0.920 0.924 +0 1.335 -0.274 0.813 0.599 0.380 0.622 1.964 -0.975 0.000 0.803 2.281 -1.514 0.000 0.527 -1.570 -1.130 0.000 1.596 -0.416 -0.135 3.102 0.753 0.884 0.996 0.770 0.873 1.225 1.447 +0 0.466 0.259 0.837 3.177 1.304 0.893 -0.030 -0.502 0.000 0.486 2.463 -1.333 0.000 0.511 1.394 -0.535 1.274 1.198 -0.604 0.327 3.102 2.517 1.367 0.988 0.898 0.928 1.108 1.356 +0 0.959 0.213 1.514 0.486 0.725 0.920 -0.818 -1.567 2.173 0.796 0.290 -0.520 0.000 0.999 1.287 0.656 0.000 0.442 0.710 0.985 0.000 0.882 0.707 0.984 1.193 1.507 1.266 1.022 +0 0.785 0.899 -0.859 0.984 -0.942 1.400 0.763 0.628 2.173 0.919 0.526 -1.318 2.215 0.643 0.297 0.971 0.000 0.605 1.499 -1.275 0.000 0.830 0.927 0.987 0.886 1.652 1.196 0.940 +1 0.815 -0.281 1.207 0.346 -1.407 0.687 -0.338 -0.432 2.173 0.432 1.885 0.975 0.000 0.627 0.988 -1.372 0.000 1.178 0.192 0.494 1.551 0.768 0.788 0.991 0.898 0.757 0.795 0.880 +1 1.221 -0.739 -0.118 1.345 -0.655 2.465 -1.114 1.121 2.173 2.094 -0.470 -0.703 0.000 1.033 -0.614 0.455 0.000 1.529 0.672 -1.468 0.000 0.650 1.050 0.990 0.632 1.144 1.338 1.057 +1 1.662 -0.322 0.283 0.599 -0.951 1.559 -0.159 1.649 2.173 0.718 -1.067 -1.290 2.215 0.778 1.096 -0.410 0.000 1.527 -0.817 0.863 0.000 0.809 0.964 1.239 1.431 1.054 1.035 0.960 +0 2.307 0.588 -1.143 0.474 0.797 0.557 1.574 0.973 0.000 1.007 0.924 0.230 2.215 0.523 0.233 1.157 0.000 0.640 -0.877 -0.774 3.102 0.714 1.031 1.426 1.181 1.027 0.883 0.848 +0 0.324 -0.991 1.427 3.103 -1.264 2.521 -0.239 0.513 2.173 0.981 -0.534 1.645 2.215 1.246 -2.152 -0.778 0.000 0.814 0.583 -0.045 0.000 1.317 1.289 0.987 2.883 2.004 1.907 1.650 +0 0.393 -1.487 1.508 3.241 -1.659 1.855 0.377 0.158 2.173 2.043 -0.361 0.373 0.000 2.605 -0.003 -1.475 2.548 1.211 0.797 -0.160 0.000 1.289 1.867 0.986 2.997 2.776 3.434 3.564 +1 2.485 -1.087 0.688 0.118 1.500 0.487 -0.116 -1.623 0.000 0.573 -0.026 -0.990 2.215 1.022 -0.956 -0.399 1.274 0.498 -1.858 -1.638 0.000 0.952 0.887 0.997 1.029 0.599 0.795 0.764 +0 0.559 0.649 -0.521 0.262 0.168 0.825 1.025 -1.594 1.087 1.195 1.016 0.252 2.215 0.470 0.454 -0.095 0.000 0.808 -0.632 -1.679 0.000 0.813 1.002 0.986 1.095 1.454 0.997 0.831 +0 3.255 -0.315 0.635 1.214 -0.969 1.268 -0.719 -1.027 2.173 1.506 -0.177 1.071 2.215 0.808 -0.006 -0.648 0.000 0.925 -1.452 -0.462 0.000 0.950 0.866 2.732 1.570 2.009 1.524 1.257 +1 0.384 0.406 0.077 0.588 -0.959 0.673 -0.155 -1.485 0.000 0.810 0.893 1.115 2.215 0.755 0.711 -0.558 0.000 0.862 -0.661 0.296 0.000 0.854 0.842 0.988 0.710 0.289 0.719 0.658 +1 0.900 0.188 -1.630 1.654 -1.084 0.974 0.069 0.405 2.173 0.744 0.597 1.151 2.215 0.851 -0.429 1.137 0.000 0.763 -0.120 -0.994 0.000 0.922 1.093 0.979 1.368 0.852 0.971 0.979 +1 0.924 -0.200 -1.134 0.513 1.320 0.881 -0.275 -0.036 1.087 1.035 1.101 1.358 0.000 0.573 -0.488 -0.568 0.000 0.369 0.797 -0.657 0.000 0.784 1.318 0.992 0.683 0.867 0.787 0.806 +0 1.107 -0.038 1.209 0.764 0.335 0.672 0.089 0.088 0.000 0.821 0.771 -1.191 2.215 0.982 -0.038 -1.238 2.548 0.662 1.268 -0.290 0.000 0.864 0.947 0.985 0.824 0.412 0.733 0.752 +0 0.299 1.073 -0.302 1.292 1.514 1.198 1.125 1.075 2.173 1.673 -0.046 -0.671 1.107 0.799 0.463 -0.460 0.000 0.743 0.164 -1.043 0.000 0.818 0.862 0.992 0.920 2.458 1.465 1.280 +0 0.462 -1.385 -0.656 0.805 0.407 1.613 0.133 -0.394 2.173 1.001 0.172 1.350 2.215 0.954 0.942 -1.562 0.000 1.945 0.546 -0.880 0.000 0.913 1.061 0.989 0.958 1.870 1.127 0.989 +0 2.078 1.567 -1.241 0.731 -0.619 0.768 0.118 1.609 2.173 0.712 -0.315 1.080 0.000 1.414 1.029 0.459 0.000 0.795 -0.752 0.122 3.102 0.873 0.964 0.986 1.380 0.918 1.204 1.093 +0 0.708 -0.021 0.108 0.803 1.360 0.766 -0.291 -1.223 2.173 0.386 -1.065 -0.810 0.000 0.548 -1.694 0.792 0.000 1.326 -1.172 0.229 1.551 0.752 0.882 0.985 0.736 1.205 0.752 0.666 +1 1.709 -0.286 0.393 1.551 -0.574 1.180 -0.530 1.410 0.000 0.950 -0.132 -1.603 2.215 0.988 -2.257 0.806 0.000 0.562 -0.745 0.215 0.000 0.834 0.898 1.725 1.299 0.544 0.875 1.000 +1 0.761 0.834 0.945 0.437 1.329 1.929 -0.148 -1.256 0.000 1.857 0.385 0.399 2.215 1.246 -0.867 -0.291 2.548 1.091 1.532 1.456 0.000 0.787 1.275 0.980 0.968 1.507 1.173 0.977 +0 0.839 0.219 1.120 0.394 -0.580 0.822 1.144 -1.350 2.173 0.893 -0.228 -0.081 0.000 1.163 -0.231 0.636 0.000 1.148 -0.493 -1.219 3.102 0.942 0.960 0.991 0.801 1.024 1.024 0.860 +1 0.650 1.778 -0.991 0.361 -1.194 1.388 0.797 1.133 0.000 1.779 0.314 -0.331 2.215 1.076 0.859 -1.460 0.000 0.629 0.351 0.488 3.102 1.589 0.985 0.976 0.947 0.644 1.159 0.962 +1 0.724 -1.401 -0.903 0.787 1.375 0.933 -0.332 0.091 2.173 0.677 -0.123 -0.923 0.000 1.097 -0.520 0.900 2.548 1.040 -1.796 -1.151 0.000 1.004 0.997 0.986 0.732 0.852 0.752 0.703 +1 0.713 -0.694 0.416 1.053 -1.438 1.403 0.996 -0.328 0.000 1.325 -0.707 0.834 1.107 1.430 0.518 -1.343 0.000 2.091 -0.154 1.408 3.102 2.095 1.945 1.194 0.917 0.849 1.601 1.325 +0 0.935 0.431 0.085 2.172 -0.629 0.808 0.485 0.852 1.087 0.909 -0.322 1.146 0.000 0.823 -0.245 -1.646 0.000 1.616 0.229 -1.190 3.102 0.778 0.855 1.182 1.242 1.173 0.942 0.930 +1 0.781 -1.344 -0.423 1.650 -1.191 1.309 -0.756 1.624 1.087 0.918 -2.102 0.166 0.000 1.258 -0.145 0.051 0.000 0.911 -0.561 0.314 0.000 0.985 1.075 1.002 1.203 0.535 1.044 0.993 +1 0.989 0.064 -0.136 1.499 -0.029 0.966 -0.801 1.731 1.087 0.605 -1.268 -1.198 0.000 0.389 -1.478 1.064 0.000 0.490 -1.050 0.525 3.102 0.675 0.660 0.973 0.852 0.666 1.140 1.020 +1 0.974 -0.986 -0.615 1.136 1.374 1.051 0.801 0.956 2.173 0.733 0.924 -1.443 2.215 1.121 -0.334 -0.641 0.000 0.669 0.307 0.031 0.000 1.101 0.934 1.421 1.621 1.075 1.210 1.277 +1 0.392 -1.473 -1.145 0.698 -0.798 1.313 -0.273 1.091 2.173 0.598 0.150 -0.197 0.000 0.730 1.010 -1.216 0.000 0.602 0.209 -0.639 0.000 0.949 0.861 1.000 1.155 0.556 0.865 0.797 +1 1.601 0.911 -1.635 0.794 -0.078 0.791 -0.005 0.842 2.173 0.684 0.012 -1.079 0.000 1.739 -0.008 0.221 0.000 0.748 -0.396 0.093 3.102 0.988 0.936 1.540 0.976 0.545 0.839 0.818 +0 0.448 0.983 -0.805 1.479 -1.718 0.917 -0.481 0.399 0.000 0.660 -0.286 -0.684 2.215 0.539 -0.306 1.180 0.000 0.625 0.033 0.021 3.102 0.982 0.858 0.982 0.669 0.358 0.548 0.661 +0 0.606 0.358 -1.016 0.891 0.400 1.730 0.749 -0.354 2.173 2.240 0.465 1.166 0.000 1.294 0.420 -1.633 2.548 0.767 0.616 1.466 0.000 0.508 0.805 0.988 0.971 1.726 1.408 1.110 +1 1.228 1.232 0.843 1.033 0.615 2.574 1.336 -1.646 0.000 2.692 0.855 -0.151 2.215 0.427 -0.277 0.147 2.548 0.903 -0.219 -1.176 0.000 0.604 1.212 0.986 0.939 0.776 1.101 0.974 +0 0.933 0.478 -1.239 0.682 0.481 0.510 -0.337 -1.188 0.000 0.928 -0.572 0.983 2.215 0.849 -0.110 -0.354 0.000 0.606 -1.219 0.920 1.551 0.818 1.006 1.105 0.808 0.308 0.663 0.674 +0 0.605 -0.096 -1.398 1.718 0.390 0.991 -2.133 0.306 0.000 1.131 -0.056 1.530 1.107 0.651 -1.299 -0.871 0.000 1.926 0.962 -1.107 1.551 0.804 1.792 1.411 1.285 1.261 1.897 1.531 +0 0.866 -0.913 -0.056 1.060 -0.776 1.016 -1.071 -1.110 2.173 1.500 -0.545 1.297 2.215 0.792 2.214 0.088 0.000 0.690 -0.833 0.218 0.000 2.198 2.230 0.984 0.887 1.575 1.845 1.467 +0 0.621 0.901 0.883 1.117 -0.886 0.367 2.443 0.622 0.000 0.809 -0.787 1.143 2.215 0.694 0.631 -0.339 2.548 0.832 0.748 -1.055 0.000 1.049 0.880 1.153 0.640 1.015 0.805 0.731 +0 0.940 -1.425 1.447 0.668 -0.891 0.534 0.861 -0.145 0.000 1.014 -1.135 -1.138 2.215 0.903 -2.194 -0.050 0.000 1.184 0.126 0.806 1.551 0.441 1.144 0.986 0.851 1.199 1.086 0.904 +1 0.808 0.149 0.766 0.392 -0.784 0.596 -0.199 -0.692 2.173 0.546 2.100 1.285 0.000 0.828 -0.310 1.477 2.548 0.942 0.239 -1.389 0.000 0.855 1.004 0.985 0.717 0.814 0.850 0.784 +1 1.815 0.313 -0.515 1.559 -1.143 1.969 0.672 1.408 0.000 0.615 -0.863 0.401 0.000 0.999 -1.143 0.947 2.548 0.413 -0.244 -0.213 0.000 0.398 0.495 1.248 1.121 0.618 1.057 0.840 +0 0.700 0.899 0.637 1.122 1.652 1.027 -0.521 -1.018 2.173 1.067 1.710 0.020 0.000 0.351 -1.597 0.678 0.000 0.920 1.338 1.223 0.000 1.145 1.136 0.987 1.241 0.654 1.296 1.066 +1 0.979 -0.429 -1.720 1.277 -0.401 0.651 0.940 -0.004 2.173 0.779 0.657 1.455 0.000 0.976 -1.066 1.248 0.000 1.802 0.417 -0.694 0.000 1.440 1.171 1.438 1.171 0.740 0.978 0.983 +1 0.773 -0.046 0.888 0.472 -1.432 2.017 -1.593 -0.435 0.000 1.300 -0.891 1.027 2.215 0.972 -1.974 -1.632 0.000 1.992 -0.145 1.531 3.102 0.828 1.024 0.984 0.669 0.843 0.902 0.771 +1 1.549 0.841 -0.854 0.671 0.763 0.916 2.506 -0.491 0.000 1.376 0.250 0.958 2.215 1.383 0.635 1.520 2.548 1.236 -0.384 1.490 0.000 0.860 0.885 1.404 0.987 0.783 0.889 0.812 +1 0.809 -0.754 -0.774 1.760 -0.247 0.566 -1.765 -1.408 0.000 1.102 -0.157 -1.681 1.107 0.759 -0.521 -0.374 0.000 3.412 -0.600 0.863 3.102 1.211 1.163 1.001 1.446 1.404 1.169 1.088 +1 0.515 0.454 -0.582 0.816 0.400 0.634 0.187 -1.273 2.173 1.158 -0.642 -1.037 0.000 1.444 0.583 0.524 0.000 1.581 0.420 1.672 3.102 0.998 1.172 0.987 0.953 0.523 0.919 0.881 +1 1.937 1.301 -1.301 0.507 0.744 1.204 1.344 -0.303 2.173 0.688 0.741 0.310 2.215 1.433 1.654 1.500 0.000 0.753 -0.418 0.943 0.000 1.737 1.212 1.322 1.211 0.810 1.092 1.005 +1 0.366 1.900 0.901 0.652 0.774 1.237 0.102 -1.481 0.000 1.552 0.659 0.254 1.107 0.706 -0.099 -0.907 0.000 0.500 -0.200 1.354 3.102 0.962 1.130 0.995 0.526 0.765 0.721 0.664 +1 0.451 1.055 -0.298 1.039 -0.141 0.753 -0.727 1.432 0.000 0.678 -0.082 -1.500 0.000 0.631 0.346 -0.521 2.548 1.255 1.004 0.424 0.000 0.881 0.905 0.994 0.664 0.290 0.614 1.182 +0 1.728 1.460 1.198 0.843 -1.619 1.335 -0.940 -0.682 0.000 1.012 0.220 1.562 0.000 0.674 0.864 0.574 2.548 0.587 1.198 -1.404 3.102 2.699 1.738 0.982 0.714 0.485 1.174 1.398 +1 1.138 0.426 -0.047 0.939 -0.774 1.590 0.548 -0.590 2.173 2.121 -0.104 0.804 0.000 1.749 -1.319 1.529 1.274 1.508 0.568 1.423 0.000 1.552 1.919 0.987 0.774 3.134 1.964 1.548 +0 0.943 0.249 1.039 2.680 1.038 1.811 0.576 -0.778 2.173 0.783 0.654 -1.407 2.215 1.240 0.084 0.536 0.000 0.547 0.767 -0.312 0.000 0.738 0.909 0.971 2.398 0.944 1.589 1.253 +0 0.442 0.118 -1.021 1.661 0.789 0.748 1.272 -1.731 1.087 0.640 1.780 -1.121 0.000 1.116 -0.562 0.119 2.548 1.422 1.052 -0.357 0.000 0.867 0.942 1.185 0.824 1.690 1.098 1.017 +0 0.526 -1.433 -0.055 1.779 0.173 1.261 -0.431 1.570 0.000 1.210 0.243 1.299 0.000 2.945 -0.607 -0.394 2.548 0.753 -1.041 -1.463 3.102 1.116 0.926 0.990 0.930 0.993 1.370 1.220 +1 0.844 -1.034 -0.227 0.507 1.204 1.012 0.917 -1.121 0.000 1.206 -1.223 0.858 2.215 0.578 -2.273 -1.053 0.000 0.806 -2.134 0.741 0.000 0.753 0.940 0.988 0.515 0.352 0.591 0.564 +0 0.566 1.264 -0.838 1.090 0.278 0.788 0.854 0.933 0.000 0.864 -0.711 1.682 2.215 1.534 -0.624 -1.077 0.000 0.828 -0.532 -0.172 3.102 1.322 1.038 0.987 1.013 0.761 1.099 1.319 +0 0.723 0.550 0.356 1.616 -0.220 1.122 2.230 -1.471 0.000 1.321 -0.530 0.525 2.215 1.763 1.059 1.229 0.000 1.319 1.308 -1.071 3.102 2.190 1.375 0.982 1.435 1.916 1.901 1.559 +1 1.104 0.483 -1.025 1.548 -1.299 0.860 0.952 0.427 2.173 1.300 -2.416 0.467 0.000 0.549 0.149 -1.422 2.548 0.569 0.143 0.582 0.000 0.504 0.524 0.992 0.483 0.922 0.934 0.759 +0 0.661 -1.443 -0.722 0.529 1.433 0.881 0.465 -0.735 1.087 0.968 0.399 0.961 1.107 0.485 1.478 1.507 0.000 1.256 0.194 0.296 0.000 0.832 0.838 0.990 1.559 1.358 1.428 1.174 +1 0.634 -1.699 1.673 1.496 -0.663 1.558 0.256 0.128 1.087 1.180 -2.581 1.628 0.000 0.888 -0.391 1.360 0.000 0.698 0.585 -1.380 3.102 2.165 1.881 1.162 2.128 1.107 2.082 1.711 +1 1.007 -0.436 -0.640 0.988 0.299 1.680 -0.705 -1.320 0.000 0.836 -0.653 0.287 0.000 1.992 0.207 0.355 2.548 1.046 -0.234 1.488 0.000 1.102 0.986 1.035 0.779 0.688 0.679 0.681 +1 0.943 -0.374 0.316 0.970 -1.180 0.811 0.819 1.608 0.000 1.379 -0.016 0.050 2.215 0.766 1.063 -1.229 0.000 0.560 -0.802 -1.736 3.102 0.817 0.808 1.292 0.942 0.883 0.950 0.882 +1 1.066 -0.699 -1.410 1.098 -0.629 0.700 -0.923 0.866 0.000 1.083 0.247 0.431 2.215 0.737 0.983 -0.712 0.000 1.089 -0.085 0.885 3.102 0.294 0.887 0.986 0.867 0.428 0.842 0.820 +1 0.787 0.894 0.280 1.232 0.433 2.828 -0.055 -1.070 0.000 1.383 -0.503 0.585 2.215 2.322 0.330 1.144 0.000 0.981 -0.440 -0.525 3.102 0.836 1.152 0.984 0.725 0.885 0.947 0.836 +1 1.675 -0.256 -0.504 1.428 0.208 1.302 0.107 1.180 0.000 0.597 0.971 -0.679 1.107 0.443 -0.770 0.030 1.274 0.819 0.629 -1.664 0.000 1.005 0.931 1.284 0.950 0.665 0.795 0.916 +0 2.030 -1.210 -1.250 0.706 1.027 1.388 0.381 0.360 2.173 0.459 -0.087 1.230 0.000 0.653 2.335 1.572 0.000 0.377 0.562 -0.533 3.102 0.773 0.543 1.470 2.077 0.563 1.283 1.425 +0 0.962 0.549 -1.365 1.342 -0.029 1.360 0.340 0.806 2.173 1.613 -0.506 -1.315 2.215 0.899 -0.358 0.602 0.000 1.344 -0.937 -0.540 0.000 1.134 1.244 1.470 1.292 2.272 1.335 1.177 +0 0.673 0.836 -1.359 1.474 -1.417 1.204 -1.465 0.232 2.173 0.615 -0.822 -0.220 0.000 0.986 1.973 -1.602 0.000 1.450 -0.718 1.156 3.102 0.694 0.859 0.999 1.850 1.122 2.452 1.764 +0 0.608 -0.716 1.379 1.963 -1.037 0.921 -0.845 0.721 1.087 0.684 -1.500 -0.469 2.215 0.446 -0.779 -0.408 0.000 0.954 0.635 1.597 0.000 0.953 1.008 1.244 1.260 1.105 0.934 0.854 +1 0.398 1.379 -1.038 1.199 -1.407 0.810 0.054 -0.396 2.173 0.911 1.429 1.013 0.000 0.767 0.619 1.630 0.000 1.417 0.667 0.171 0.000 0.854 0.731 0.977 0.871 0.741 0.844 0.795 +1 0.360 -0.935 -1.049 0.734 1.482 0.813 0.247 1.581 2.173 1.053 -0.524 -0.621 0.000 1.058 -1.117 -0.151 0.000 0.900 0.299 0.311 3.102 0.874 0.858 0.992 1.606 0.826 1.183 1.100 +0 0.594 0.228 1.371 1.115 -1.081 0.946 0.603 -0.196 2.173 0.394 1.126 1.047 0.000 0.432 -2.336 -1.247 0.000 0.452 1.564 -0.074 0.000 0.508 0.727 0.988 0.632 0.832 0.739 0.682 +1 1.605 -0.278 0.033 0.924 0.371 0.959 0.924 -0.880 2.173 0.949 0.559 1.499 2.215 0.698 0.011 1.497 0.000 0.382 0.035 -0.933 0.000 0.464 0.759 0.987 1.315 1.206 1.269 0.951 +1 0.466 1.073 -0.374 0.022 -0.121 0.903 -0.434 0.074 2.173 0.359 2.071 0.647 0.000 1.086 0.905 1.430 1.274 0.506 1.180 -1.405 0.000 0.571 1.289 0.543 0.580 1.506 0.928 0.713 +1 2.477 -1.667 -1.127 0.890 -1.543 1.325 1.062 0.696 0.000 1.611 -0.987 -0.218 2.215 0.731 -1.443 0.961 0.000 0.627 0.026 -1.671 0.000 0.849 1.111 0.986 0.722 1.171 0.989 0.873 +0 2.232 0.127 0.041 3.490 0.385 3.415 -0.387 -1.400 0.000 2.419 0.155 0.468 1.107 2.632 -0.932 -1.261 2.548 1.779 0.313 1.049 0.000 0.898 1.059 1.182 2.644 3.165 1.975 1.544 +0 1.451 -0.835 0.892 1.078 1.329 0.846 -0.055 -0.026 2.173 0.448 -2.645 -0.629 0.000 0.965 -1.019 -1.428 2.548 0.825 0.029 -1.055 0.000 0.671 0.732 0.979 1.118 1.243 0.918 0.941 +1 0.540 -0.885 -0.417 0.279 0.685 0.840 0.785 1.173 0.000 1.233 -0.070 -0.270 2.215 1.109 0.743 -1.710 0.000 0.442 0.175 -1.564 3.102 0.896 0.553 0.999 0.699 0.620 0.866 0.750 +1 0.717 -0.190 0.302 0.557 -0.273 1.164 0.120 0.608 0.000 1.179 2.272 -1.428 0.000 1.671 -0.662 -1.443 2.548 0.711 -0.284 0.022 0.000 0.773 0.689 0.995 1.230 0.894 0.907 0.862 +1 1.233 -0.697 1.293 0.549 1.215 0.907 -0.988 -0.596 0.000 0.931 0.300 1.174 2.215 0.708 -1.311 0.313 0.000 1.110 -1.523 -0.598 0.000 0.982 1.276 0.989 0.597 0.580 0.736 0.721 +0 0.659 -0.998 1.229 0.911 -0.812 1.239 -0.845 -1.251 2.173 1.275 -0.483 0.373 1.107 1.005 -0.942 0.104 0.000 1.532 0.045 1.077 0.000 1.311 0.918 1.035 0.792 1.868 1.127 0.932 +1 1.412 -0.313 1.244 0.694 0.393 1.399 0.640 -0.405 2.173 0.426 2.748 0.748 0.000 0.744 -0.602 -1.601 0.000 0.582 1.486 -1.588 3.102 2.507 1.380 0.987 1.527 1.014 1.198 1.223 +0 0.529 1.170 0.667 1.147 -0.515 1.077 2.521 -0.522 0.000 1.252 -0.133 0.904 2.215 0.825 1.030 1.469 0.000 0.502 -1.983 -1.537 0.000 2.014 1.125 0.991 1.344 0.791 0.946 1.071 +0 1.386 -2.416 0.101 0.602 -1.724 1.413 -0.782 -1.511 2.173 0.879 -0.108 -0.614 0.000 1.813 1.650 0.733 0.000 0.571 -0.294 0.926 1.551 0.974 1.011 1.262 1.772 0.798 1.797 2.525 +0 2.187 0.673 1.662 0.505 0.321 0.735 1.450 -0.108 1.087 0.383 2.292 -0.451 0.000 0.410 1.580 0.814 0.000 0.717 2.177 1.133 0.000 0.681 0.753 1.361 1.248 0.566 0.823 0.795 +0 0.915 0.056 -0.133 1.101 -1.008 0.877 1.444 1.690 2.173 0.636 2.792 0.359 0.000 1.089 1.144 1.110 0.000 0.559 -0.067 -0.480 3.102 1.399 1.191 0.988 1.245 0.930 0.912 1.041 +0 0.476 -2.349 -0.470 0.715 -1.711 1.079 -0.516 -0.157 1.087 0.792 -0.852 1.428 2.215 0.593 -0.036 -1.165 0.000 0.623 0.920 1.280 0.000 0.676 1.047 0.989 0.698 1.368 0.862 0.820 +0 0.372 0.717 0.157 0.535 -1.076 0.701 0.089 1.662 0.000 1.392 -0.564 -0.016 1.107 1.210 0.768 -1.723 0.000 1.465 -0.948 1.550 0.000 0.999 0.921 0.988 0.779 0.502 0.853 0.737 +1 2.200 -0.197 -1.460 1.999 -1.405 1.657 2.025 0.491 0.000 0.485 -0.826 -0.979 0.000 0.892 -0.987 -0.531 2.548 1.535 -0.591 1.242 1.551 1.441 0.921 0.975 1.098 0.910 0.971 1.275 +0 2.318 0.861 1.100 0.884 1.596 0.942 0.426 -0.528 0.000 0.795 -0.322 -0.490 2.215 1.025 0.266 0.412 2.548 1.449 0.047 -1.396 0.000 1.300 0.889 0.985 0.862 0.760 0.921 0.975 +0 1.012 -0.030 0.894 0.879 -0.320 1.125 0.806 1.292 0.000 1.089 1.753 1.613 0.000 2.021 -0.856 -0.884 0.000 0.980 0.452 0.320 3.102 1.384 1.154 1.161 0.629 0.615 0.837 0.897 +1 0.865 -1.037 1.186 0.394 -0.583 0.502 1.204 1.336 2.173 0.684 0.496 0.259 2.215 0.278 0.413 0.987 0.000 0.451 -1.085 0.179 0.000 0.957 1.126 0.990 0.935 0.777 0.985 0.829 +1 1.240 1.307 1.299 1.528 0.079 0.944 0.338 0.051 1.087 1.051 1.890 -1.138 0.000 0.910 2.392 1.443 0.000 0.379 1.399 -1.526 0.000 1.210 1.035 1.698 1.250 0.296 1.158 1.066 +1 0.643 -0.182 -0.340 1.487 0.861 0.603 -0.911 -1.638 2.173 0.604 0.230 -1.586 0.000 0.648 0.136 -0.994 2.548 0.366 0.027 0.884 0.000 0.489 0.552 1.196 0.798 0.611 0.705 0.584 +1 0.829 -0.071 0.543 0.553 1.265 0.747 -0.035 0.101 0.000 1.006 -2.127 0.258 0.000 2.299 -0.580 -1.358 2.548 0.841 -0.419 -0.850 0.000 0.959 1.100 0.994 1.334 0.664 1.004 0.947 +0 1.720 -0.296 -0.169 1.160 -0.078 0.851 -0.393 -1.717 1.087 0.350 -0.436 -0.834 0.000 0.716 0.336 1.027 2.548 0.830 1.407 1.257 0.000 0.779 0.840 0.983 1.382 0.713 0.953 0.885 +0 0.672 0.625 -0.526 0.644 1.011 1.386 -0.151 0.156 2.173 1.178 0.875 1.688 2.215 0.802 0.169 -1.621 0.000 0.615 -1.230 -1.088 0.000 0.805 0.996 0.990 0.867 2.117 1.161 0.923 +0 0.373 0.109 0.285 1.698 -1.451 0.381 -0.396 -0.530 0.000 0.551 0.916 1.497 2.215 0.566 -2.507 0.323 0.000 0.719 -0.878 0.240 3.102 1.085 0.664 1.103 0.698 0.842 0.866 0.866 +1 0.672 -0.009 1.347 1.585 -0.722 0.665 1.433 -0.558 2.173 0.780 -0.339 0.651 0.000 0.818 -0.811 1.463 2.548 0.877 0.943 0.905 0.000 0.907 0.853 1.369 1.109 1.606 0.977 0.916 +0 0.825 0.336 1.379 1.440 -1.182 0.664 -0.103 0.606 2.173 0.384 -1.116 -0.635 0.000 0.480 -2.299 0.784 0.000 0.595 1.104 -0.435 3.102 0.799 1.018 1.119 0.708 0.745 0.852 0.910 +0 0.524 1.551 0.146 1.132 0.718 1.108 -0.010 -0.407 2.173 1.169 0.581 1.459 2.215 0.611 1.057 -0.709 0.000 0.766 -0.025 -1.707 0.000 0.759 0.880 0.990 0.874 1.742 1.009 0.829 +0 1.233 -0.207 1.298 0.877 -1.291 0.460 0.408 -0.213 0.000 0.377 -1.409 0.111 0.000 0.441 -0.590 0.364 2.548 0.963 -0.696 -1.611 0.000 0.981 1.026 1.043 0.661 0.654 0.691 0.709 +0 1.106 -2.254 -1.116 1.357 -1.637 0.854 -0.450 -0.322 1.087 0.558 -0.066 0.288 0.000 1.010 -0.905 1.157 0.000 0.654 1.092 0.727 3.102 0.982 1.032 0.985 1.712 1.019 1.329 1.124 +1 0.748 1.891 -1.163 0.187 0.575 0.598 0.381 1.678 0.000 0.582 -2.101 0.373 0.000 0.606 0.757 -0.258 2.548 1.340 1.884 -0.704 0.000 1.871 1.139 0.996 0.727 0.635 0.816 0.701 +1 0.355 0.772 -0.669 0.158 0.164 1.297 -0.633 -1.256 2.173 1.239 -0.477 1.126 0.000 1.004 -0.484 0.571 0.000 1.413 1.011 -0.150 0.000 0.821 0.744 0.988 0.881 0.990 0.966 0.808 +1 0.675 0.590 -1.736 1.093 0.373 0.393 0.561 -0.798 0.000 0.445 -0.475 -0.058 2.215 0.678 2.048 -0.626 0.000 1.079 -0.640 1.513 1.551 0.888 0.965 1.126 0.838 0.624 0.861 0.774 +0 1.614 0.625 -0.385 0.725 -1.054 1.083 0.623 0.844 0.000 1.065 0.623 -1.284 1.107 0.981 -0.443 0.573 2.548 1.159 0.621 1.397 0.000 0.952 0.946 0.986 0.799 1.259 0.913 0.920 +0 5.215 -0.529 -0.590 0.950 -1.016 2.811 -0.064 1.194 2.173 0.355 -2.657 1.024 0.000 1.084 0.081 0.582 0.000 0.597 -0.591 0.253 3.102 0.970 1.257 1.153 0.838 1.123 2.055 1.554 +0 0.329 -0.022 1.457 1.778 -0.820 1.066 -0.844 0.527 1.087 0.329 0.205 0.964 0.000 0.604 -1.070 -1.117 1.274 0.543 -2.355 1.584 0.000 1.233 1.085 0.989 0.781 1.009 1.012 0.978 +0 0.775 0.068 1.509 0.316 -1.264 1.272 -1.673 -1.235 0.000 0.951 -1.276 0.708 2.215 1.863 -0.539 1.219 2.548 1.413 -0.922 -0.364 0.000 0.904 1.159 0.988 0.817 0.817 0.764 0.722 +0 3.963 -1.376 -1.235 0.880 -0.869 1.393 -0.173 0.660 0.000 1.043 -0.323 0.359 0.000 0.891 -1.368 -1.695 2.548 1.305 0.987 0.400 3.102 0.718 1.051 0.979 0.699 1.668 1.632 1.701 +0 0.992 -0.653 0.341 0.352 -0.721 0.704 -0.348 0.950 0.000 0.655 -2.866 -0.991 0.000 0.928 2.360 1.466 0.000 1.094 1.415 0.681 0.000 0.886 0.850 0.980 1.147 0.223 0.897 1.297 +1 1.554 0.726 -1.122 0.344 -1.194 0.944 1.634 -0.295 0.000 1.908 -0.085 0.964 2.215 0.768 -0.699 -0.675 0.000 1.071 0.390 0.555 0.000 1.111 0.810 0.974 1.665 0.842 1.109 0.981 +0 0.978 -0.864 1.592 0.484 -1.055 0.389 2.279 0.018 0.000 0.494 0.321 -0.083 2.215 0.698 -0.003 1.127 2.548 0.500 0.857 1.398 0.000 0.785 0.868 0.993 0.743 0.563 0.608 0.744 +0 0.598 -0.274 0.944 0.450 1.638 0.505 -1.200 0.496 0.000 0.619 -1.484 1.403 2.215 1.635 -0.882 -0.175 2.548 2.615 1.133 -1.172 0.000 3.416 2.437 0.984 1.260 1.100 1.699 1.363 +1 0.897 -0.100 -0.850 0.931 -1.674 0.684 -0.937 0.423 0.000 0.894 0.137 0.007 0.000 0.823 1.058 -1.600 2.548 0.792 -0.340 -1.740 3.102 1.126 0.945 0.985 0.626 0.542 0.871 0.811 +0 0.891 -0.682 1.531 0.993 1.040 1.086 -0.848 0.122 0.000 1.023 -0.962 -1.575 2.215 0.646 0.195 -0.867 2.548 0.837 -0.897 -0.593 0.000 0.890 0.878 1.000 0.897 0.755 0.838 0.865 +0 0.997 -1.911 -0.309 1.684 0.583 0.604 -0.448 -0.910 1.087 1.524 -0.335 -1.424 0.000 1.157 -0.531 -0.032 0.000 2.897 -0.563 0.560 0.000 0.942 0.885 1.293 1.324 0.980 1.250 1.161 +0 0.377 -2.261 -1.261 1.582 -1.492 0.870 -0.337 -0.204 0.000 0.581 -0.663 0.919 2.215 0.502 -0.212 1.246 2.548 1.082 0.243 0.576 0.000 1.069 0.876 0.990 0.653 0.212 0.577 0.762 +0 1.160 0.945 1.576 1.372 -1.654 1.036 -1.065 -0.700 2.173 1.564 0.551 0.637 0.000 0.744 0.350 0.060 0.000 1.196 1.383 0.149 0.000 0.836 1.341 0.977 2.612 0.719 1.747 1.547 +0 1.610 -0.073 -1.629 0.814 -1.420 2.192 -0.153 -1.307 2.173 3.965 1.303 0.176 1.107 2.119 1.825 1.033 0.000 0.447 1.065 0.413 0.000 0.695 1.726 0.986 0.849 5.516 2.838 2.447 +0 0.959 -0.669 1.719 0.801 0.826 1.088 -0.240 -0.124 0.000 0.584 -0.900 0.473 0.000 1.175 0.170 -1.372 2.548 0.941 -0.666 -1.509 3.102 1.049 1.016 0.987 0.752 0.424 0.842 0.780 +0 0.812 -0.776 0.509 1.106 1.722 0.790 0.735 -1.210 2.173 0.902 0.099 0.543 2.215 0.645 1.288 -0.192 0.000 0.466 0.578 -0.779 0.000 0.739 0.977 1.166 1.217 1.306 0.962 0.963 +1 1.053 2.065 1.531 1.121 0.316 0.493 0.507 -0.865 0.000 0.932 0.351 -0.383 2.215 0.566 0.842 1.712 0.000 0.393 -1.438 1.564 0.000 0.913 0.929 1.337 1.027 0.827 0.987 0.975 +1 1.174 -0.802 -0.337 1.093 0.256 1.047 0.130 -1.520 2.173 0.801 -0.233 1.435 0.000 0.383 -1.093 1.367 0.000 0.601 0.295 0.063 0.000 0.896 0.872 0.988 0.507 0.798 0.837 0.744 +1 1.156 -0.156 -0.606 1.499 0.145 2.677 0.015 -0.843 0.000 1.242 -1.108 1.207 2.215 0.758 -1.393 0.597 2.548 1.185 0.961 1.226 0.000 0.803 0.930 1.142 1.376 0.575 0.953 0.864 +1 0.550 -2.264 1.230 0.503 -0.145 1.215 -0.624 -1.222 2.173 1.072 -0.943 0.632 1.107 0.752 -0.690 -0.021 0.000 1.171 -1.685 0.460 0.000 0.966 1.271 0.988 0.708 1.695 1.178 1.059 +0 0.401 -2.170 0.637 1.628 -1.533 0.400 -0.649 0.903 0.000 0.565 -0.986 -1.469 2.215 0.929 0.369 -0.025 1.274 0.591 -0.580 -0.310 0.000 0.659 0.643 1.038 0.719 0.950 1.074 0.867 +0 1.697 0.760 1.489 0.765 0.524 1.060 0.974 -1.460 2.173 0.774 -0.353 -0.303 2.215 0.637 -0.150 0.456 0.000 2.099 -1.236 0.024 0.000 1.025 0.805 1.205 1.014 1.504 1.383 1.288 +1 1.728 0.082 0.566 0.268 0.951 0.826 -0.327 1.027 0.000 1.018 0.457 -0.752 2.215 1.185 -1.349 -1.349 0.000 1.526 -0.322 0.104 0.000 1.021 1.171 0.979 0.621 0.705 0.748 0.712 +0 0.746 -1.031 -0.754 1.150 1.560 0.592 0.279 0.149 0.000 0.866 0.044 0.974 1.107 0.889 1.420 0.139 0.000 1.161 0.632 -0.784 3.102 0.892 0.974 1.117 1.020 0.963 0.843 0.962 +0 0.891 -0.593 -0.392 0.485 0.351 1.158 0.271 -1.208 2.173 1.146 -0.844 0.889 1.107 0.688 -1.315 0.328 0.000 0.384 -2.013 0.486 0.000 0.729 1.248 0.991 0.969 1.907 1.056 0.903 +0 0.478 -0.632 -0.146 1.589 -1.345 0.691 0.843 0.462 0.000 0.910 0.245 1.391 2.215 1.121 0.984 -0.146 0.000 0.432 -0.723 -1.314 3.102 0.844 0.883 1.064 0.927 0.496 0.754 0.874 +0 0.592 0.791 -1.145 0.650 0.146 1.251 0.221 -1.593 2.173 1.100 0.831 0.272 1.107 0.856 -1.370 0.227 0.000 0.885 1.491 0.811 0.000 0.802 0.879 0.987 1.208 1.803 1.057 0.899 +0 0.656 -1.537 -1.612 0.096 -1.614 0.918 -0.853 0.673 0.000 0.584 -1.514 0.175 1.107 1.423 -0.912 -0.939 0.000 0.900 -0.716 1.589 3.102 1.072 0.793 0.986 0.765 0.668 0.586 0.599 +1 0.446 1.033 -1.526 2.178 1.015 3.186 1.633 -0.909 0.000 1.849 0.000 1.030 2.215 1.969 -0.300 1.519 2.548 1.671 0.216 0.202 0.000 0.710 0.939 1.027 1.228 0.929 0.969 0.930 +0 1.265 1.128 -1.196 0.559 -0.730 1.291 0.474 0.517 2.173 1.436 0.823 -1.658 1.107 1.061 0.350 -0.286 0.000 0.674 -0.161 1.243 0.000 0.955 0.958 0.977 0.808 1.890 1.124 0.941 +0 0.621 -0.943 -1.698 0.950 0.592 0.901 0.176 -1.422 0.000 1.203 -2.449 0.403 0.000 0.736 1.116 0.183 2.548 1.693 0.217 -0.951 3.102 0.530 0.897 0.989 1.093 0.845 0.971 0.861 +1 1.604 -0.042 0.970 0.463 1.134 0.727 -0.174 -0.451 2.173 1.065 0.404 -1.276 2.215 0.559 1.098 0.018 0.000 0.485 0.432 -1.026 0.000 0.633 0.868 0.991 1.036 0.959 0.925 0.783 +1 0.884 -0.477 -0.295 1.082 -1.223 0.539 0.909 -0.914 0.000 0.683 0.727 -1.479 0.000 0.885 -1.670 1.099 0.000 0.888 0.779 0.507 3.102 0.960 0.817 1.004 0.935 1.211 1.038 0.926 +1 0.574 -1.679 -0.752 0.456 -1.193 1.050 1.109 0.936 0.000 0.588 0.079 -1.199 2.215 0.822 0.009 -0.269 2.548 0.604 2.456 0.865 0.000 1.275 1.359 0.999 0.605 0.549 0.995 1.089 +1 0.628 0.652 -1.154 0.795 0.311 0.974 0.533 1.577 0.000 1.365 0.877 0.648 2.215 1.344 0.660 -0.061 2.548 1.977 -1.075 -0.970 0.000 0.971 1.204 0.989 0.837 0.867 0.950 0.801 +0 2.281 -0.391 -0.926 0.356 1.734 0.571 -1.426 1.185 0.000 0.462 -1.721 0.448 0.000 0.726 0.200 0.247 2.548 0.881 0.483 0.602 3.102 0.698 0.969 0.989 0.879 0.222 0.713 0.826 +1 1.951 0.946 1.742 0.614 -1.618 1.437 1.022 -0.045 2.173 0.811 -0.232 0.555 0.000 0.897 0.277 -1.092 0.000 0.645 0.187 1.012 3.102 0.830 1.149 1.000 0.618 0.932 1.063 0.867 +1 0.348 -0.011 1.007 2.427 -0.467 2.984 -2.227 1.355 0.000 2.892 -0.042 -0.274 1.107 0.680 -0.400 1.681 0.000 0.867 0.222 -0.914 3.102 0.852 0.728 1.236 0.867 0.811 0.886 0.798 +1 0.852 1.071 -1.257 0.713 -0.345 0.951 0.552 0.857 2.173 0.836 -1.309 -1.691 1.107 1.256 0.577 -0.185 0.000 1.225 -0.351 -0.315 0.000 0.773 1.184 0.989 1.872 1.748 1.420 1.214 +1 1.495 0.348 -1.077 0.346 0.257 0.268 0.184 -0.569 0.000 0.676 1.381 0.814 0.000 0.783 1.340 1.267 2.548 1.740 1.229 -0.181 3.102 1.043 0.838 0.988 0.858 0.861 0.769 0.704 +1 0.952 -1.752 1.478 1.641 1.348 1.390 -0.572 0.447 0.000 1.845 -0.858 -1.297 0.000 1.668 1.379 -0.023 0.000 1.178 -0.467 1.505 0.000 1.152 0.684 0.974 1.641 0.591 1.185 1.198 +0 0.918 0.801 0.742 0.588 0.864 1.137 -1.025 -1.319 2.173 1.036 -0.269 -0.071 1.107 0.636 -1.627 0.624 0.000 0.705 -0.782 -1.709 0.000 0.714 0.910 0.996 1.379 1.569 1.081 0.907 +1 1.054 1.332 -0.105 1.000 0.479 1.386 -2.825 -0.913 0.000 1.295 1.411 1.422 2.215 1.326 0.304 1.291 2.548 1.076 0.411 -0.576 0.000 0.876 1.087 0.984 0.960 0.836 0.889 0.784 +1 0.472 -1.140 -0.598 2.016 -1.664 0.877 -0.612 0.644 1.087 0.704 0.802 0.349 2.215 0.955 -2.447 -0.773 0.000 0.868 -0.936 -1.373 0.000 1.010 1.495 1.108 1.442 0.959 1.363 1.195 +0 0.599 -0.378 0.609 0.830 -1.452 1.162 -0.054 -1.682 2.173 1.327 -0.377 -0.020 2.215 1.110 -1.464 -0.473 0.000 0.550 -1.066 0.371 0.000 0.908 1.021 0.989 0.750 1.848 1.153 0.952 +1 1.020 0.300 0.840 0.753 -1.363 0.783 -1.019 -1.215 2.173 0.668 -0.246 0.607 0.000 1.556 -0.218 -1.718 2.548 1.998 -0.315 -0.132 0.000 0.935 1.237 1.112 1.069 0.820 0.957 0.873 +0 1.453 -0.314 0.218 1.704 -0.313 1.050 -0.412 1.660 2.173 0.763 -0.628 -0.842 0.000 0.828 0.688 1.099 0.000 1.082 0.971 0.741 0.000 1.053 0.912 1.004 0.915 0.508 1.003 0.863 +1 0.768 0.782 -1.544 0.755 0.748 0.995 0.694 0.800 2.173 1.148 0.494 -1.111 0.000 0.908 -0.255 -0.756 0.000 0.580 0.637 0.329 0.000 0.802 0.681 0.991 0.773 0.693 0.890 0.793 +0 1.902 -0.179 0.551 1.064 0.948 1.233 -0.027 -0.830 0.000 0.539 -1.097 -1.700 0.000 0.487 0.165 -1.225 2.548 0.592 1.229 0.428 3.102 1.581 0.869 0.998 0.887 0.498 0.751 0.934 +0 0.647 0.818 -0.818 2.915 -0.204 1.553 -2.329 1.288 0.000 1.578 0.596 0.900 0.000 0.488 0.147 -0.977 0.000 1.822 0.612 -1.459 3.102 1.370 1.081 1.001 1.164 0.859 0.850 0.955 +1 0.812 -0.887 1.734 1.268 0.883 0.952 0.213 -0.949 0.000 0.920 -0.480 0.935 2.215 1.025 -0.932 -0.109 2.548 1.085 -0.664 0.448 0.000 0.893 0.738 0.987 0.853 0.879 0.642 0.593 +1 0.770 0.035 -0.561 0.875 0.583 1.244 0.125 -1.087 2.173 0.744 -0.151 -1.586 0.000 1.123 -0.287 0.742 0.000 0.873 -0.299 -0.479 1.551 0.896 0.914 0.987 1.012 0.635 1.026 0.852 +1 1.204 1.415 -0.119 1.989 0.617 0.959 0.890 -1.356 2.173 1.079 -0.238 -1.013 0.000 1.148 0.271 0.723 0.000 1.294 -0.599 1.203 3.102 1.770 1.202 1.321 1.504 1.376 1.334 1.275 +1 1.834 0.115 0.306 0.416 -0.469 1.443 -0.025 -1.364 2.173 0.643 0.541 1.070 0.000 0.436 -0.683 -0.209 2.548 0.553 -1.892 0.378 0.000 1.570 0.928 0.987 1.452 0.928 0.973 0.940 +0 1.136 0.164 -1.637 0.889 0.688 0.541 1.327 -0.971 0.000 0.851 0.184 -0.359 2.215 0.484 -1.901 1.110 0.000 0.522 1.891 0.862 0.000 0.899 0.964 1.205 0.804 0.484 0.675 0.734 +0 0.729 1.566 0.191 0.968 0.576 0.915 0.610 -1.256 1.087 0.549 -0.350 1.017 2.215 0.446 -0.861 -1.023 0.000 0.552 0.116 -0.156 0.000 0.496 0.720 0.974 0.741 1.066 0.850 0.712 +1 0.583 -1.451 -0.358 0.547 -1.738 0.853 -0.912 1.626 2.173 0.423 1.488 -0.051 0.000 0.606 -0.139 0.098 2.548 0.551 0.647 -0.401 0.000 0.312 1.353 0.984 0.896 0.948 0.836 1.071 +1 0.753 0.139 -0.183 0.779 0.726 0.536 -0.360 -0.554 1.087 0.940 0.487 1.191 2.215 1.086 0.363 -1.215 0.000 0.545 1.121 0.794 0.000 0.923 0.840 0.986 0.865 1.143 0.734 0.715 +1 0.364 -1.982 -0.273 0.440 -0.697 1.024 -0.066 0.831 2.173 1.204 0.173 -1.315 2.215 0.829 0.270 -0.642 0.000 0.490 0.687 0.438 0.000 0.611 0.905 0.983 0.862 1.540 0.925 0.768 +1 1.360 -1.930 0.538 0.163 -1.234 0.885 -0.353 -0.881 2.173 0.546 -1.685 0.311 0.000 0.528 -1.626 -1.579 0.000 0.825 -0.200 1.333 3.102 0.816 1.091 0.980 0.746 0.826 0.854 0.746 +0 1.384 1.017 -1.205 1.897 -1.704 0.786 0.145 -0.035 0.000 1.220 -0.688 0.655 2.215 0.748 -0.914 0.010 0.000 0.596 -1.108 1.513 0.000 0.839 0.939 0.988 0.578 0.821 1.207 1.183 +0 0.327 2.181 -1.356 2.407 -1.392 0.911 0.478 -0.364 0.000 0.796 1.053 1.677 0.000 1.357 -0.548 0.615 1.274 1.239 -1.373 0.343 0.000 1.830 1.193 1.002 1.594 0.346 1.081 1.058 +1 0.609 -0.033 0.509 0.981 1.509 1.266 0.981 -1.244 0.000 1.427 1.113 0.567 2.215 0.414 -0.418 -0.136 0.000 0.378 0.583 -1.636 3.102 1.502 0.797 0.987 0.848 0.625 0.949 0.822 +1 0.980 -1.254 0.007 0.773 -1.212 1.615 -0.722 1.223 2.173 0.946 -0.850 -0.379 0.000 1.071 -0.966 -1.132 2.548 0.853 -1.535 -0.410 0.000 0.815 0.766 1.073 1.297 1.421 1.003 0.881 +0 1.108 0.045 -0.460 1.435 1.527 0.765 -0.464 1.697 2.173 1.369 0.542 0.243 2.215 0.792 0.027 -0.957 0.000 0.876 -0.879 1.056 0.000 1.034 0.803 1.705 1.287 1.664 1.068 0.906 +1 0.817 0.459 1.471 0.778 -0.129 0.820 0.147 0.339 1.087 0.861 -0.063 -1.314 0.000 1.322 -0.805 -1.587 0.000 2.254 0.391 -0.423 3.102 0.934 1.170 1.095 0.778 0.941 0.939 0.817 +0 1.464 -1.012 -1.334 2.120 1.720 1.255 -0.766 -0.207 0.000 1.186 -1.870 1.135 0.000 1.679 0.315 -0.359 0.000 2.195 -0.984 1.234 3.102 0.752 0.956 0.992 0.923 0.794 0.971 1.058 +0 2.269 -0.404 1.167 0.285 0.009 1.002 0.326 -0.787 0.000 0.726 0.460 -1.479 2.215 0.770 -0.277 0.459 2.548 1.073 -0.167 -0.256 0.000 0.844 0.895 0.986 0.907 0.844 0.699 0.834 +1 2.387 0.556 1.586 0.348 1.673 0.520 0.219 0.398 2.173 0.554 0.663 -0.568 0.000 0.914 -0.421 -0.653 2.548 0.671 1.333 0.270 0.000 0.662 0.757 0.986 1.008 0.760 0.900 0.790 +0 1.259 1.948 -0.756 0.728 -1.416 1.063 0.152 1.585 2.173 1.770 1.575 0.112 2.215 0.527 -0.371 1.015 0.000 0.979 0.224 -1.006 0.000 0.815 0.775 0.989 1.278 2.541 1.628 1.352 +1 0.949 -0.297 -1.008 1.249 -1.150 0.896 -0.684 0.666 1.087 0.545 -0.484 1.298 0.000 0.475 0.171 -0.463 2.548 0.467 -0.004 0.424 0.000 0.494 0.505 0.984 0.554 0.784 0.907 0.733 +1 0.527 1.595 -1.432 1.334 0.709 1.859 -0.529 1.466 2.173 1.124 -1.906 0.076 0.000 1.578 -0.913 -0.157 0.000 1.573 -1.678 -0.627 0.000 0.818 0.941 1.087 2.229 1.115 1.504 1.750 +0 0.327 -1.836 -0.562 1.947 -0.350 0.805 -0.515 0.929 1.087 1.087 0.140 1.266 0.000 0.849 -0.927 1.641 2.548 1.267 0.220 -0.254 0.000 0.759 0.778 0.994 0.944 0.668 0.863 0.841 +1 1.008 0.178 0.694 0.802 -1.168 0.630 1.890 -0.538 0.000 0.481 0.550 -0.438 0.000 0.754 0.344 -1.470 1.274 1.461 -1.362 1.334 0.000 0.852 0.876 1.238 0.733 0.689 0.805 0.767 +1 1.215 0.416 0.546 0.626 -1.677 1.261 -0.650 0.797 2.173 3.439 0.274 -0.576 0.000 3.059 2.110 1.132 0.000 0.943 -0.380 -0.853 0.000 1.060 0.887 1.097 1.037 0.910 1.427 1.209 +0 0.712 -0.662 -0.306 1.765 -0.459 1.566 1.126 1.437 2.173 0.899 0.199 0.296 0.000 0.473 -0.070 -1.130 0.000 0.631 1.122 -1.604 3.102 0.971 0.774 0.984 3.416 0.424 2.154 1.620 +1 0.686 1.003 0.430 0.775 1.716 1.132 -0.735 -0.703 2.173 0.981 -1.004 1.238 2.215 0.941 0.089 -1.350 0.000 1.403 0.751 -0.051 0.000 0.881 0.869 0.988 1.677 1.543 1.388 1.105 +0 0.493 0.427 1.602 1.336 -1.093 0.683 -0.489 1.142 2.173 0.764 -0.886 0.035 0.000 0.417 0.919 -1.252 0.000 1.222 -0.025 0.172 3.102 1.250 1.046 0.987 0.980 0.776 0.951 0.912 +1 0.662 -0.642 0.436 0.751 -1.041 1.345 0.844 1.396 2.173 0.731 0.700 0.088 2.215 0.565 0.846 -0.491 0.000 0.802 0.489 -0.934 0.000 0.315 1.028 0.988 0.744 1.353 0.943 0.776 +0 1.833 2.245 -0.796 1.833 -0.725 1.312 1.418 1.095 0.000 0.529 2.679 0.345 0.000 1.001 0.715 0.821 0.000 0.890 1.190 -1.686 3.102 0.937 0.714 0.995 1.299 0.696 0.901 0.924 +0 1.344 0.701 0.969 1.865 0.841 0.946 1.539 -1.064 0.000 0.774 0.651 -1.092 2.215 2.045 0.696 -0.157 2.548 1.347 0.398 1.636 0.000 1.485 0.929 0.970 1.357 1.000 1.090 1.111 +0 1.478 1.441 1.425 0.896 0.523 1.339 0.407 -0.300 2.173 0.751 0.660 -1.327 2.215 0.615 -0.589 -1.040 0.000 0.402 1.131 -0.743 0.000 0.994 1.328 1.157 1.578 1.195 1.280 1.479 +0 1.430 0.561 -0.506 1.467 -1.097 0.623 0.833 1.461 0.000 1.022 0.693 0.866 2.215 1.105 -0.047 0.476 0.000 0.558 -0.645 -1.198 3.102 1.348 0.938 1.018 1.216 0.857 0.835 0.851 +1 0.472 -1.393 1.435 1.598 -0.689 0.957 0.529 0.236 2.173 1.063 2.096 -1.643 0.000 0.781 -0.592 -0.356 2.548 0.409 -1.248 0.773 0.000 1.009 1.563 1.134 0.688 0.870 1.211 1.611 +0 0.487 -0.589 -1.605 0.258 -0.404 1.175 1.186 -0.936 2.173 0.861 -1.700 0.861 0.000 1.001 -0.858 1.089 0.000 0.825 0.662 0.510 3.102 0.658 1.095 0.987 0.866 1.027 1.674 1.285 +0 1.660 -0.138 -1.048 0.668 -1.678 0.996 -0.922 0.848 2.173 1.123 -0.463 -0.571 2.215 1.422 0.065 0.862 0.000 0.482 0.127 -0.152 0.000 0.724 1.035 0.990 1.270 1.532 1.014 0.909 +1 2.171 0.518 0.144 0.377 -0.252 1.118 0.954 -1.617 1.087 0.558 1.229 0.880 0.000 0.310 0.832 0.473 2.548 0.535 0.831 -0.413 0.000 0.661 0.886 0.989 0.471 0.698 0.886 0.713 +1 0.579 0.473 0.370 0.504 1.061 1.808 2.217 -0.302 0.000 2.174 0.463 1.063 2.215 0.876 -0.458 -1.258 0.000 0.790 -1.975 -1.292 0.000 0.988 0.889 0.989 1.060 0.852 1.241 1.246 +0 1.617 0.139 1.335 0.484 0.102 0.801 1.232 0.288 2.173 0.707 1.641 -0.519 0.000 1.376 -0.378 -1.202 2.548 0.655 0.880 -1.658 0.000 0.810 0.860 1.098 0.948 1.759 1.021 0.938 +0 1.280 -1.517 0.647 0.508 0.572 1.237 -1.686 0.190 2.173 0.891 -1.339 -1.494 0.000 0.719 -0.980 1.674 0.000 0.956 -0.663 -0.261 1.551 0.394 0.735 1.001 0.894 0.706 0.881 0.811 +0 0.711 -1.732 1.238 1.023 0.297 0.590 -0.159 0.782 0.000 0.683 -0.244 -1.544 1.107 0.710 -0.814 -0.827 0.000 0.715 0.843 -0.931 3.102 0.751 0.806 0.988 0.893 0.539 0.795 0.696 +1 0.722 -0.199 -0.403 1.462 0.562 1.406 -1.478 -1.701 0.000 0.823 -0.788 -0.080 0.000 1.486 -0.899 -1.021 2.548 0.817 -0.056 0.313 0.000 1.015 1.081 1.088 0.677 0.920 0.786 0.723 +1 2.102 -0.947 0.254 0.322 0.742 1.126 -0.466 -1.363 2.173 0.530 -0.882 -0.603 0.000 0.400 0.498 1.122 2.548 0.373 0.085 -1.613 0.000 0.550 0.626 0.978 0.825 0.793 1.006 0.784 +1 0.968 -0.534 1.576 0.475 -0.810 0.820 -1.138 -0.713 0.000 1.156 -1.040 -0.164 1.107 1.696 -0.912 1.067 2.548 1.181 0.418 1.221 0.000 0.933 0.953 0.988 1.023 1.333 0.909 0.811 +1 1.336 -1.104 1.706 0.666 -1.135 1.054 -1.363 0.136 2.173 1.201 -1.169 0.823 2.215 1.438 -1.665 -1.458 0.000 0.422 -0.787 0.357 0.000 0.943 1.051 0.982 1.276 0.970 0.987 0.896 +0 0.656 -1.462 -0.762 0.969 -0.093 0.767 -0.732 0.881 2.173 0.795 -0.150 1.637 2.215 1.218 0.347 -1.140 0.000 0.566 0.399 0.740 0.000 0.910 1.108 0.992 1.404 0.800 1.107 1.142 +0 0.854 0.759 1.508 1.592 -1.559 0.612 1.712 0.641 0.000 0.877 0.833 -0.358 1.107 0.734 2.618 -1.542 0.000 1.559 1.405 -0.055 0.000 0.877 0.984 0.980 1.104 0.591 0.787 0.965 +1 1.857 -0.763 -1.541 1.158 1.363 0.782 1.231 0.132 0.000 1.496 -1.237 -0.028 0.000 1.014 0.672 -1.613 1.274 1.191 -0.144 0.243 1.551 1.146 1.054 1.017 0.993 0.924 0.861 1.185 +1 0.789 -1.094 1.277 0.589 0.096 0.397 -1.307 0.891 0.000 0.762 0.701 -0.496 2.215 0.682 0.389 1.018 2.548 0.485 0.279 -0.650 0.000 0.904 0.985 0.982 0.893 0.759 0.933 0.777 +1 1.576 0.585 0.017 0.424 -0.450 1.348 -0.980 -0.961 0.000 1.031 -0.258 -1.484 0.000 1.681 -0.200 0.771 2.548 1.265 0.124 0.354 0.000 0.949 1.012 0.975 0.992 0.631 0.818 0.800 +1 0.718 -1.455 -1.153 0.823 0.267 1.047 -0.159 0.085 1.087 1.401 -0.631 1.709 0.000 0.430 -0.988 -0.989 0.000 0.607 -0.260 -1.338 3.102 0.819 0.490 1.021 1.057 0.812 0.851 0.804 +1 3.508 -0.092 -0.884 0.834 -0.741 1.219 -0.922 0.715 2.173 1.116 -0.738 1.435 0.000 0.937 -0.704 -1.080 0.000 1.580 -1.512 1.115 0.000 0.914 0.932 0.967 0.629 0.787 1.221 1.085 +1 0.788 0.311 -0.603 2.352 -0.597 1.723 -0.020 0.884 0.000 0.878 0.473 -1.242 0.000 0.997 0.100 0.502 1.274 0.550 0.911 -1.220 3.102 0.961 0.823 1.003 0.807 0.636 0.760 0.764 +0 1.171 0.305 -0.155 0.680 -1.067 0.983 -0.072 0.980 2.173 0.633 0.346 1.515 0.000 1.534 0.555 -1.337 2.548 1.337 -1.287 -0.039 0.000 0.891 0.888 0.989 0.815 1.431 0.939 0.779 +1 0.861 0.184 -0.681 0.589 0.888 0.701 -0.011 1.684 2.173 0.866 -0.570 -0.046 0.000 0.538 0.890 0.955 2.548 0.728 -1.008 -0.908 0.000 0.796 0.905 0.986 0.763 0.612 0.751 0.673 +0 1.676 1.033 -0.634 0.244 -1.397 0.741 -2.939 -0.472 0.000 1.048 -0.557 1.181 0.000 1.339 0.798 1.482 2.548 0.769 -0.861 0.387 0.000 0.814 1.131 0.985 0.680 0.189 0.664 0.939 +1 0.771 1.496 1.268 0.877 -0.296 1.658 0.706 -0.314 1.087 0.313 0.915 1.024 0.000 1.290 1.179 -1.738 0.000 0.786 -1.238 1.058 0.000 0.954 1.150 1.124 1.152 1.710 1.195 1.018 +1 0.584 -2.056 1.693 0.361 -1.084 0.956 -1.191 -1.630 1.087 1.182 -1.291 0.581 0.000 1.217 -1.012 -0.147 2.548 0.525 -0.195 -0.862 0.000 1.151 0.854 0.986 0.662 1.309 0.891 0.764 +0 1.059 -0.443 0.136 1.203 -1.700 1.351 2.711 -1.740 0.000 0.589 -2.666 -0.758 0.000 0.714 -1.720 0.111 0.000 1.474 0.300 -0.536 3.102 0.882 1.920 1.559 0.867 0.781 1.899 1.888 +0 1.458 0.275 -1.008 0.537 1.526 0.524 0.415 1.118 2.173 0.743 -0.642 0.638 0.000 0.897 -0.304 0.014 2.548 1.012 -0.436 -0.916 0.000 1.116 0.922 0.985 0.801 0.789 0.681 0.682 +1 0.722 1.079 1.303 1.547 0.656 0.546 -0.263 0.040 0.000 0.779 0.299 1.562 0.000 0.808 0.155 -0.951 0.000 0.799 0.697 -0.807 3.102 0.939 0.926 0.986 0.776 0.763 0.872 0.779 +1 0.970 -0.304 0.373 0.337 0.410 1.155 -0.040 -1.169 2.173 1.284 -0.363 -0.065 1.107 1.400 -1.374 1.418 0.000 1.163 -0.609 1.048 0.000 0.863 1.043 0.986 0.768 1.533 1.020 0.893 +0 1.344 0.397 0.949 1.828 1.165 2.156 0.833 -0.635 2.173 0.823 -1.144 1.035 2.215 0.418 1.067 0.611 0.000 0.466 0.611 -1.226 0.000 0.497 0.902 0.995 2.410 3.044 1.856 1.333 +1 0.961 0.261 0.073 0.578 -1.152 0.688 0.181 -1.238 0.000 0.958 -0.286 -0.728 0.000 1.414 1.262 0.471 0.000 1.727 0.421 0.928 3.102 0.871 0.996 0.989 0.779 0.761 0.869 0.785 +1 1.958 0.099 -1.694 0.308 -0.744 0.895 -0.581 0.025 2.173 0.577 -0.447 0.566 0.000 0.435 -1.329 -1.317 0.000 0.580 0.735 0.218 3.102 0.861 0.790 0.988 0.708 0.626 0.849 0.754 +1 0.679 1.220 -0.669 0.765 0.508 1.249 -0.541 1.192 2.173 0.916 -2.121 -0.813 0.000 0.473 -0.603 0.202 0.000 0.687 -0.045 -1.283 0.000 1.073 0.617 0.987 1.876 0.830 1.259 1.530 +1 0.523 -1.556 0.132 2.268 1.671 0.748 0.270 -0.627 2.173 0.827 -0.026 -1.454 0.000 0.964 0.111 0.365 0.000 0.587 -0.392 1.196 0.000 1.050 1.130 1.484 0.974 0.679 1.119 0.953 +1 0.503 -2.304 -0.071 1.606 -1.099 1.179 2.970 1.552 0.000 1.622 -1.088 -0.274 1.107 1.420 -1.442 0.684 0.000 0.755 -1.917 1.255 0.000 0.701 0.887 0.995 1.212 0.792 0.880 0.901 +1 0.904 -1.791 -0.776 0.673 -1.229 2.019 -0.864 -1.733 0.000 1.186 -0.699 0.496 0.000 2.406 -2.106 -0.373 0.000 3.396 -1.848 -0.020 0.000 0.997 1.079 0.979 0.762 0.875 1.001 0.970 +1 1.168 1.256 -0.248 0.520 1.508 1.513 0.105 1.249 2.173 0.773 -0.462 -0.139 2.215 0.765 0.973 -0.530 0.000 1.107 1.389 -1.063 0.000 0.561 1.057 1.079 1.389 1.582 1.179 1.028 +1 0.923 -1.510 -1.655 0.631 -1.476 0.350 -1.638 -0.137 0.000 0.913 0.215 0.197 2.215 0.776 -0.426 0.949 2.548 0.488 0.028 -1.329 0.000 0.815 0.875 0.989 0.702 0.643 0.760 0.682 +0 1.230 0.746 -1.214 0.153 0.758 0.662 -0.108 0.093 0.000 1.035 1.080 1.332 2.215 0.847 -1.160 -0.077 0.000 0.772 -0.057 -1.307 3.102 0.856 0.813 0.990 0.786 0.752 0.985 0.943 +1 1.172 0.238 0.180 0.846 0.172 1.251 -2.722 -0.447 0.000 2.845 0.841 1.357 0.000 1.133 0.456 -0.590 2.548 1.555 -0.781 1.604 3.102 0.976 0.801 0.999 1.022 1.225 0.874 0.881 +1 0.824 -0.827 1.468 0.701 -0.671 0.784 1.125 -0.762 2.173 0.922 0.397 0.766 0.000 0.444 -0.114 0.400 0.000 0.404 0.973 1.091 3.102 0.413 1.104 0.987 0.684 0.593 0.827 0.767 +1 0.525 -0.000 -0.714 0.202 -1.606 1.157 0.216 1.178 2.173 1.026 -0.387 -1.124 0.000 1.041 -0.814 -1.735 0.000 1.619 0.996 -0.300 0.000 0.920 1.134 0.989 1.061 0.945 0.981 0.958 +1 0.648 -1.535 -1.506 0.945 -0.955 0.482 0.488 0.858 2.173 0.804 -0.442 -0.498 2.215 0.619 -2.584 0.606 0.000 0.610 -0.706 0.973 0.000 0.822 1.084 0.987 0.969 0.971 0.961 0.873 +0 0.780 -1.577 -0.982 0.593 -0.333 0.818 -0.638 1.031 2.173 0.961 -0.454 -1.569 0.000 1.199 -1.030 0.254 2.548 0.669 -0.812 -0.355 0.000 0.966 0.972 0.996 0.767 0.852 0.772 0.732 +1 0.343 1.356 1.125 1.748 0.857 1.831 -1.169 -1.131 0.000 1.796 -0.551 0.169 1.107 1.205 0.683 1.318 2.548 1.001 -0.281 -1.641 0.000 0.950 1.141 0.992 1.304 1.741 2.377 1.794 +0 1.633 -1.022 -0.099 0.629 1.458 1.351 -1.022 -0.635 2.173 1.606 -1.312 1.530 2.215 1.581 -0.873 1.169 0.000 0.452 2.124 -0.090 0.000 2.749 2.403 1.385 1.070 2.042 1.885 1.518 +0 1.001 -0.629 -1.353 0.715 1.007 0.856 0.934 0.469 2.173 1.135 0.136 -0.709 2.215 1.062 0.468 1.469 0.000 1.618 1.888 -1.113 0.000 0.804 1.043 0.995 1.237 1.401 1.087 0.964 +1 1.207 0.579 -0.441 0.771 0.937 1.010 -0.226 -1.077 2.173 1.173 0.882 0.478 2.215 1.241 -1.600 1.033 0.000 0.755 0.594 -1.158 0.000 1.946 1.598 1.265 0.852 1.847 1.447 1.227 +0 1.283 -1.366 0.903 1.769 0.126 1.178 -0.731 -0.676 2.173 1.062 0.016 -0.379 2.215 1.168 -0.791 1.698 0.000 2.336 -0.116 1.543 0.000 0.725 1.374 1.343 1.454 0.770 1.156 1.228 +0 0.543 -0.411 -0.164 1.388 0.699 0.618 -0.992 -0.629 1.087 0.513 0.952 -1.660 0.000 1.554 -0.660 -1.433 2.548 1.214 -0.735 0.610 0.000 1.432 1.196 0.988 0.853 0.826 0.892 0.871 +0 0.758 1.313 -0.571 0.563 -1.134 1.047 -1.160 1.631 2.173 0.667 0.233 -0.026 2.215 0.413 -1.078 0.272 0.000 0.414 -1.838 0.404 0.000 0.712 0.814 0.997 0.671 1.545 1.067 0.859 +1 1.352 0.734 -1.591 0.961 -0.909 1.381 -1.379 -0.253 0.000 1.435 1.137 1.088 2.215 0.672 0.976 -0.302 1.274 1.507 1.850 1.154 0.000 0.831 0.925 0.994 0.701 0.993 0.815 0.714 +1 1.107 -0.919 0.978 1.181 -1.373 0.939 -0.657 -0.145 0.000 1.068 -0.549 0.280 2.215 1.773 -0.369 -1.335 2.548 0.777 -2.495 -1.507 0.000 1.217 0.794 1.353 0.953 1.457 0.949 0.926 +0 1.294 -1.717 0.590 1.636 -1.479 0.698 -1.063 -0.934 0.000 1.874 -0.810 0.589 2.215 1.027 -0.165 -0.841 2.548 1.062 -1.743 -1.358 0.000 0.855 0.864 1.930 1.613 1.498 1.284 1.136 +1 0.607 -1.066 -1.262 1.093 0.558 0.755 0.649 0.855 2.173 0.939 -1.251 -0.870 0.000 0.702 -0.549 -1.637 0.000 0.731 -0.809 -0.125 1.551 0.906 0.676 1.125 1.158 0.937 0.935 0.840 +0 0.521 -1.814 0.525 0.792 -0.408 0.476 -2.736 0.907 0.000 0.455 -2.594 -0.373 0.000 0.576 0.634 -1.376 2.548 0.864 -0.678 -1.677 3.102 0.904 0.962 0.992 0.823 0.470 1.049 0.895 +0 0.619 0.453 -1.257 1.498 1.209 1.178 0.616 -0.136 2.173 0.521 0.038 0.276 0.000 1.165 -0.462 -0.781 0.000 1.862 0.158 1.474 3.102 0.943 0.968 1.060 0.599 1.593 0.971 0.805 +0 1.191 -0.940 0.272 0.635 1.199 0.940 -1.923 -1.028 0.000 0.419 -1.332 -0.424 2.215 0.480 -0.755 1.191 2.548 0.530 -2.339 1.208 0.000 1.086 0.887 0.988 0.623 0.491 0.585 0.652 +0 0.768 0.372 0.409 0.875 1.174 1.264 0.302 -1.530 1.087 1.564 0.744 0.037 2.215 0.675 -0.188 -1.180 0.000 0.483 1.733 0.573 0.000 1.089 1.020 0.984 0.909 2.099 1.143 0.943 +0 2.210 -1.143 -0.318 1.097 0.030 1.688 0.967 1.430 1.087 0.677 -0.533 -1.075 2.215 0.450 -0.819 0.724 0.000 0.444 0.956 -1.083 0.000 0.775 1.133 0.997 0.876 1.794 1.863 1.359 +1 1.238 -0.192 1.263 0.437 -0.043 0.958 -0.181 1.740 2.173 1.545 0.095 -0.125 0.000 0.862 0.043 -1.123 0.000 0.603 -0.672 -0.157 3.102 1.387 0.811 0.990 0.777 0.836 0.893 0.795 +0 1.446 0.988 1.096 0.681 -0.104 0.784 1.815 -1.354 0.000 0.774 0.082 0.056 1.107 1.132 0.136 1.566 2.548 1.593 1.180 -0.330 0.000 1.409 1.414 1.213 0.876 0.973 1.071 0.948 +0 0.925 -0.618 -0.475 0.529 0.150 0.740 0.137 1.246 0.000 0.323 -0.917 1.669 0.000 0.300 0.153 -1.025 2.548 0.566 0.902 -0.302 3.102 0.704 0.760 0.985 0.607 0.243 0.577 0.672 +1 2.620 -0.353 -0.954 1.730 -1.222 2.205 -0.553 0.231 0.000 2.486 -0.455 1.017 0.000 1.374 -1.349 -1.339 2.548 0.373 -0.769 -1.280 3.102 3.243 1.808 0.984 1.165 0.157 1.513 1.638 +0 0.710 0.407 1.069 0.794 -0.264 0.563 0.103 0.270 0.000 0.425 1.023 -0.556 0.000 1.041 0.496 -1.096 2.548 1.262 -0.250 1.509 3.102 0.870 0.911 0.988 0.728 0.732 0.674 0.613 +1 0.969 -0.018 0.378 0.293 -1.147 1.109 -0.112 -0.140 0.000 1.240 0.001 1.319 1.107 1.398 -0.470 -1.398 2.548 0.707 -0.526 -0.839 0.000 0.872 1.130 0.988 0.860 0.967 0.996 0.836 +1 0.844 -0.616 -0.133 0.584 0.271 1.007 -0.744 -1.248 0.000 1.811 -0.237 1.077 0.000 1.127 -0.432 -0.678 2.548 1.600 0.573 -0.069 0.000 0.489 0.840 0.982 0.674 0.645 0.586 0.567 +0 0.550 0.056 0.770 0.801 -1.468 0.799 0.012 0.190 0.000 1.312 -0.891 1.708 2.215 0.700 -0.814 -0.083 2.548 1.297 -0.372 -0.941 0.000 0.957 0.853 0.988 0.662 1.017 0.651 0.593 +1 0.329 1.696 1.392 1.044 -0.886 0.621 -0.438 -1.047 2.173 0.425 1.169 1.056 0.000 0.393 -0.125 1.270 2.548 0.801 2.371 0.292 0.000 0.848 0.828 0.996 0.731 0.543 0.969 0.822 +0 0.716 -0.730 0.546 0.742 -0.046 0.682 -0.676 1.150 1.087 0.348 -2.190 -0.462 0.000 0.597 -1.470 1.131 0.000 1.373 -1.254 -1.249 3.102 0.729 0.803 0.976 0.818 0.954 0.746 0.638 +1 0.624 1.748 -0.489 0.894 0.278 1.285 -0.404 -1.719 2.173 0.530 -1.039 -0.319 1.107 1.140 -0.373 -0.100 0.000 0.825 0.770 -1.727 0.000 1.312 0.930 0.999 1.533 1.227 1.131 0.980 +1 0.447 0.520 0.302 0.362 0.286 0.835 -0.450 0.745 2.173 1.122 -1.459 -1.190 0.000 0.977 -0.792 -1.711 0.000 0.385 -0.879 -0.485 1.551 0.892 0.590 0.992 0.696 0.568 0.796 0.722 +1 0.663 0.344 0.493 0.851 -1.032 0.847 0.393 1.680 0.000 1.061 0.045 -0.083 0.000 1.351 0.301 1.258 0.000 1.417 -0.166 -0.532 3.102 0.882 0.670 1.020 0.648 0.520 0.475 0.515 +1 1.339 1.387 0.467 0.434 -1.261 0.919 -0.473 -1.144 2.173 0.539 0.589 -1.721 0.000 0.543 -0.780 0.719 0.000 0.986 -0.130 0.033 3.102 0.943 0.953 1.056 0.805 0.894 1.004 0.864 +0 1.085 0.552 1.398 0.903 -1.324 0.692 -0.187 -0.456 2.173 0.580 0.578 -0.186 0.000 1.359 0.379 0.818 2.548 0.465 -0.797 -1.156 0.000 0.778 0.835 0.989 0.956 1.163 0.823 0.707 +1 0.566 1.432 0.936 0.418 1.534 0.582 -0.014 -0.035 1.087 0.512 1.329 -0.757 0.000 0.491 2.138 1.590 0.000 0.862 -0.883 1.581 1.551 0.766 1.065 0.975 0.713 0.851 0.912 0.783 +0 0.743 -1.553 -0.241 0.608 -0.784 0.888 -1.358 0.573 2.173 1.151 0.323 1.684 2.215 0.997 0.786 -0.955 0.000 1.177 -1.475 0.917 0.000 0.936 0.975 0.988 0.955 1.897 1.205 1.017 +0 2.597 -0.003 -0.788 0.897 0.664 0.973 0.604 0.046 0.000 1.023 0.465 -1.450 2.215 0.880 -0.670 1.742 0.000 0.379 -0.254 -0.032 0.000 0.651 0.717 2.043 1.106 0.638 0.875 0.740 +1 0.639 0.882 0.924 1.584 -1.193 1.170 -0.152 -0.360 2.173 1.108 0.528 -1.513 0.000 1.066 0.360 1.422 0.000 1.725 1.344 0.541 3.102 0.928 1.212 1.316 1.337 1.861 1.173 1.021 +1 0.646 -0.809 0.126 0.353 0.143 0.562 -1.886 1.300 0.000 0.814 -0.990 -0.644 2.215 1.047 -1.670 -1.246 0.000 1.227 -0.878 1.271 3.102 0.829 1.103 0.988 0.847 0.892 0.913 0.786 +0 0.449 -1.586 -1.570 1.374 -0.919 1.455 -0.916 0.556 0.000 1.308 -2.904 -1.235 0.000 0.636 -0.240 -0.246 2.548 0.693 -0.811 1.036 0.000 0.643 0.891 0.984 0.631 0.551 0.646 0.794 +0 1.050 1.968 -1.704 0.611 -0.058 0.529 1.135 0.079 0.000 1.090 0.889 -1.314 2.215 0.957 0.840 0.993 2.548 0.826 1.636 -0.173 0.000 0.599 1.052 1.105 0.813 0.948 0.745 0.714 +0 0.705 -1.903 0.205 2.272 -0.243 0.557 -0.142 1.618 0.000 1.417 0.631 1.144 2.215 1.092 -1.346 -1.006 2.548 0.725 0.558 -1.125 0.000 0.725 0.992 0.992 3.753 2.091 2.386 1.933 +1 1.410 -0.826 1.118 0.142 -1.159 0.485 0.156 -0.440 0.000 0.607 0.703 -1.306 2.215 0.861 1.294 0.080 2.548 0.485 0.812 -0.157 0.000 0.363 0.551 0.994 1.111 0.778 0.831 0.702 +1 2.047 1.124 -1.365 1.138 -0.537 1.896 -2.868 1.353 0.000 0.998 0.741 0.169 0.000 1.396 0.403 -0.483 2.548 1.289 -0.261 0.591 0.000 0.937 0.751 1.437 1.015 0.864 0.904 0.804 +1 0.559 0.070 -0.519 0.213 -1.411 0.850 -0.104 0.648 2.173 0.475 0.516 -1.052 0.000 0.527 -1.161 -0.360 1.274 0.830 0.752 1.368 0.000 0.946 0.894 0.986 0.929 0.836 0.830 0.757 +0 0.637 -0.505 -1.539 2.538 -0.871 0.744 -0.623 1.149 0.000 0.546 -1.052 0.407 0.000 0.471 0.927 0.317 2.548 0.433 1.857 0.895 0.000 0.896 0.879 0.999 0.626 0.278 0.636 0.773 +1 0.563 0.727 -1.148 0.955 0.405 0.949 1.834 -0.976 0.000 0.770 -0.604 1.025 0.000 1.761 0.625 0.565 2.548 1.021 -0.781 -1.289 0.000 1.022 1.007 1.001 0.714 0.737 0.882 0.778 +0 0.691 1.543 0.106 0.995 1.010 1.251 0.610 0.862 2.173 0.622 -0.408 -0.137 0.000 1.934 1.463 -1.113 0.000 2.082 0.393 -1.055 1.551 1.086 1.011 0.986 0.733 1.691 0.976 0.845 +0 0.461 -0.550 -0.218 0.743 1.066 1.464 -0.130 1.578 2.173 0.892 0.222 0.104 0.000 0.920 1.560 -0.522 0.000 1.061 0.140 -0.597 3.102 1.352 0.881 0.985 0.983 1.235 1.195 0.957 +1 0.766 -1.026 -0.231 1.050 1.394 0.518 0.450 0.106 0.000 1.015 1.068 -1.355 2.215 1.111 0.674 1.099 2.548 0.710 -2.336 -0.474 0.000 0.767 0.865 1.236 1.117 0.927 1.089 0.903 +0 4.233 0.579 0.834 1.008 1.313 1.841 1.613 -0.641 0.000 1.221 0.599 -1.416 1.107 0.514 2.295 -1.312 0.000 0.673 0.490 0.051 3.102 1.281 1.022 1.197 1.544 0.793 1.017 1.485 +1 0.917 -0.616 -0.385 0.629 0.799 0.689 0.191 -1.624 1.087 0.693 -0.923 -1.560 1.107 1.113 -0.758 -0.101 0.000 0.604 -2.088 1.025 0.000 1.144 0.965 0.988 0.973 0.616 0.881 0.774 +0 0.667 -1.880 -0.206 1.170 -0.271 0.844 0.693 1.590 0.000 0.586 -0.084 0.365 2.215 0.366 -0.967 -1.721 1.274 0.432 -0.095 -1.638 0.000 0.441 0.809 0.993 0.646 0.531 0.547 0.783 +1 0.841 -1.475 1.131 0.324 -0.386 1.920 2.867 -0.416 0.000 0.802 -0.430 1.268 0.000 2.063 0.648 1.010 2.548 1.046 -0.147 1.705 1.551 0.934 1.275 0.994 0.640 0.836 0.817 0.755 +1 0.996 0.207 -0.734 1.212 0.745 0.367 1.199 -1.205 0.000 0.882 0.982 -0.348 0.000 1.660 0.121 1.105 2.548 0.827 0.173 -1.350 3.102 0.853 1.270 1.479 0.813 0.718 0.784 0.775 +0 0.343 -1.797 -0.557 2.075 0.126 1.257 0.649 -1.612 2.173 0.664 -0.330 0.806 2.215 0.548 1.136 -0.722 0.000 0.662 -0.157 0.145 0.000 1.067 0.935 0.982 4.330 1.304 2.718 2.415 +0 0.620 1.248 -1.074 1.403 -0.839 0.852 -0.071 0.966 0.000 0.565 0.767 0.362 2.215 0.569 -0.455 -1.594 2.548 0.693 -0.946 0.421 0.000 0.858 0.784 0.993 1.381 0.723 1.010 1.258 +1 0.368 0.145 1.520 0.758 -1.027 1.041 0.646 0.680 2.173 1.622 1.383 -0.999 0.000 1.347 -0.303 0.430 2.548 0.697 -0.305 -0.916 0.000 0.915 0.952 0.996 0.924 0.826 1.018 0.898 +1 1.416 0.242 0.637 0.634 -1.608 0.612 1.377 -0.081 1.087 0.934 1.376 -1.080 2.215 0.351 -1.001 1.181 0.000 0.784 -0.316 -1.407 0.000 0.651 1.157 1.181 1.116 0.871 0.927 0.865 +1 0.709 0.650 0.243 0.671 -0.887 0.797 0.743 0.956 0.000 0.557 1.503 1.495 0.000 0.966 1.334 -0.828 2.548 1.658 -0.170 -0.558 3.102 0.876 1.040 0.988 0.612 0.934 0.942 0.824 +1 2.392 0.939 0.195 1.114 0.514 1.552 1.726 -1.143 0.000 0.911 0.969 0.983 2.215 1.059 0.070 -1.287 2.548 0.401 0.204 1.349 0.000 1.365 1.275 0.998 0.861 1.053 1.039 1.196 +0 0.509 0.785 -0.762 1.793 1.463 1.204 1.012 -0.338 2.173 1.148 1.314 1.240 2.215 0.790 0.088 -0.670 0.000 0.776 1.518 0.780 0.000 1.184 1.012 1.201 0.801 1.735 1.073 0.920 +1 0.343 1.313 -0.568 0.935 1.593 0.937 0.326 1.187 0.000 0.940 0.162 -0.223 2.215 1.305 -0.781 -0.845 2.548 0.574 -0.498 0.536 0.000 0.823 1.073 0.991 0.877 0.888 0.925 0.799 +0 0.461 0.561 1.039 0.132 -0.264 1.203 0.657 -0.542 2.173 0.935 -0.234 1.687 0.000 0.993 -0.658 0.489 2.548 0.840 -2.041 1.445 0.000 0.808 0.790 0.991 1.182 1.495 1.041 0.874 +0 1.072 0.338 0.978 0.782 -1.325 0.763 0.479 -1.471 1.087 0.516 -0.653 -1.014 0.000 1.313 -0.299 0.455 2.548 1.020 1.105 -0.528 0.000 1.174 1.007 1.111 0.746 1.335 1.026 0.924 +0 0.596 -1.673 1.040 0.760 -0.643 0.567 -0.327 1.380 2.173 1.013 -2.015 -1.245 0.000 0.937 -0.135 0.180 0.000 0.626 0.660 -0.019 0.000 0.867 0.909 0.985 1.154 0.464 0.874 0.797 +1 0.885 -1.354 0.225 1.051 1.433 1.114 -0.150 0.312 2.173 0.576 -0.767 1.549 0.000 1.164 -0.953 -1.305 2.548 0.718 0.653 -0.671 0.000 1.055 0.847 1.184 1.193 1.555 0.998 0.895 +0 0.578 0.990 -0.798 0.972 -1.503 0.405 0.521 1.311 2.173 0.516 -1.066 0.700 0.000 0.743 0.580 0.349 2.548 1.494 1.534 -1.302 0.000 0.929 0.869 0.991 0.883 0.522 0.693 0.649 +0 1.223 0.409 1.367 0.740 -1.163 0.937 0.048 -1.049 0.000 1.591 -0.219 0.827 2.215 0.818 0.624 -0.022 0.000 1.137 0.066 -0.503 3.102 1.358 0.803 1.001 1.032 1.146 0.988 0.883 +1 1.635 -0.051 1.565 0.596 -1.497 0.734 1.351 0.287 2.173 0.267 -0.261 1.056 0.000 1.227 -0.004 -0.227 2.548 0.519 -2.114 -0.772 0.000 0.831 0.924 0.977 1.238 1.021 1.107 1.054 +1 0.647 -1.062 1.419 0.091 -0.101 0.771 -0.733 -1.079 2.173 0.977 -2.428 0.863 0.000 0.713 -0.617 0.227 2.548 0.596 1.117 -0.570 0.000 3.454 1.900 0.992 0.804 0.854 1.309 1.078 +1 1.408 1.144 1.329 1.585 0.658 0.755 1.256 -0.208 2.173 0.803 1.334 -0.707 0.000 0.998 0.672 -1.143 1.274 0.660 0.649 -1.733 0.000 0.809 0.751 1.176 1.063 0.858 0.914 0.798 +1 0.684 1.472 -0.848 1.176 -1.545 1.057 0.545 0.603 2.173 0.687 0.872 -0.895 0.000 0.598 0.155 1.290 2.548 0.988 1.332 0.079 0.000 0.910 1.122 0.993 0.989 0.605 1.092 0.929 +1 0.761 0.601 0.391 0.963 -0.493 0.543 0.646 -0.435 2.173 0.733 0.701 1.014 2.215 0.299 1.953 0.659 0.000 0.453 0.683 1.662 0.000 0.431 0.582 0.988 0.785 0.896 0.618 0.518 +0 0.776 0.487 0.887 0.467 -0.878 0.603 1.183 -0.982 1.087 0.548 -1.032 0.813 0.000 0.517 0.966 0.809 0.000 1.501 0.595 -1.592 3.102 1.038 0.992 0.989 0.714 0.580 0.832 0.729 +1 0.416 -0.702 -1.377 1.533 1.337 1.514 0.339 -0.465 2.173 0.808 0.012 1.437 0.000 0.613 -1.023 0.896 0.000 1.111 0.787 0.302 3.102 0.821 0.927 0.986 1.432 0.970 1.034 0.950 +1 0.322 -0.072 -1.528 1.850 -0.617 1.373 -1.529 0.463 2.173 1.881 -1.478 1.501 0.000 1.040 -1.252 -0.798 0.000 0.924 1.076 -1.725 0.000 0.827 0.669 0.988 2.519 0.674 1.524 1.253 +0 0.454 0.236 0.073 0.933 1.034 0.938 0.342 -0.839 2.173 0.804 -0.978 1.380 2.215 0.539 -1.152 0.958 0.000 0.800 -0.716 -1.142 0.000 0.913 0.986 0.986 0.650 1.489 0.918 0.769 +0 0.284 -1.170 0.025 3.605 1.133 1.614 -0.708 -1.181 0.000 1.564 0.698 -0.416 0.000 1.891 -1.126 1.653 2.548 1.311 -1.239 -1.030 0.000 0.931 1.105 1.179 0.949 1.505 1.136 1.232 +1 0.846 -0.236 0.118 0.274 -0.835 1.845 -1.438 -1.235 0.000 1.728 -1.466 0.188 0.000 2.693 0.602 1.011 0.000 1.979 0.894 0.634 0.000 0.954 1.090 0.981 0.677 0.549 0.640 0.724 +0 0.596 -2.088 -0.819 1.921 -1.611 0.733 -0.124 -0.569 1.087 1.343 0.197 0.184 2.215 0.921 -2.326 0.911 0.000 0.708 0.845 0.923 0.000 2.578 1.918 0.988 2.459 0.948 1.768 1.647 +0 0.599 1.160 0.066 1.875 0.687 0.951 1.303 -1.038 0.000 0.813 -0.126 1.682 2.215 0.389 0.432 1.513 2.548 0.796 1.005 -0.406 0.000 0.721 1.161 0.990 0.740 0.204 0.928 0.926 +0 1.044 0.272 -0.841 1.630 0.141 0.483 0.014 0.588 2.173 0.521 1.074 1.633 0.000 0.724 -0.378 1.560 0.000 0.595 1.288 -0.764 0.000 1.040 0.827 1.399 0.862 0.618 0.660 0.652 +0 0.347 -0.450 1.474 1.609 0.762 0.774 -0.878 -0.815 2.173 0.918 -1.542 -1.307 0.000 0.807 -0.629 0.845 0.000 1.474 -0.638 0.028 1.551 1.374 1.082 0.994 0.839 0.781 0.846 0.828 +1 0.563 -1.318 -0.425 1.549 1.291 0.881 0.141 -1.176 2.173 0.647 -0.540 0.119 0.000 0.985 0.392 1.051 2.548 0.373 0.652 0.763 0.000 0.579 0.934 1.294 1.104 1.065 1.040 0.862 +1 0.776 -0.253 -0.699 1.230 1.174 0.950 -1.146 1.506 2.173 1.159 -0.570 -0.261 2.215 0.569 0.711 -0.674 0.000 0.596 -0.750 0.075 0.000 0.727 1.142 1.344 1.023 1.607 0.981 0.833 +0 1.214 0.234 -1.582 1.360 1.385 1.587 -0.548 0.132 2.173 1.936 2.554 -1.687 0.000 1.127 -0.932 -0.194 0.000 1.344 0.038 -0.519 0.000 0.855 0.916 0.989 1.065 1.688 1.288 1.123 +0 0.614 0.466 1.330 1.278 -1.398 0.446 0.012 -0.574 2.173 0.657 -1.562 0.597 0.000 0.649 -0.740 0.049 2.548 0.560 -1.561 -0.653 0.000 0.722 0.885 0.993 1.080 0.456 0.787 0.998 +1 1.679 0.894 0.260 0.741 1.318 1.172 -0.228 -1.124 2.173 0.815 0.348 -1.591 0.000 1.437 -0.118 1.540 2.548 1.798 0.406 -0.156 0.000 0.824 1.218 1.260 1.555 1.094 1.152 1.030 +1 1.033 0.852 0.754 0.183 -0.580 0.505 -0.870 -0.145 2.173 0.746 -0.788 -1.722 2.215 0.438 0.501 -1.196 0.000 0.417 0.322 1.459 0.000 0.323 0.639 0.992 0.871 0.893 0.734 0.589 +0 0.362 -1.062 -0.439 0.478 -0.943 0.516 -0.628 1.028 2.173 0.683 -0.681 -1.551 2.215 0.687 0.329 -0.573 0.000 0.933 0.976 0.496 0.000 0.813 0.966 0.979 0.833 0.637 0.721 0.665 +0 1.429 -0.043 -1.625 0.263 1.354 0.875 1.046 0.462 0.000 0.395 1.737 -0.108 0.000 0.565 2.089 -0.649 0.000 1.739 0.023 -0.628 3.102 0.772 0.893 0.981 0.883 1.238 0.892 0.995 +1 1.074 0.538 -0.339 1.476 -0.567 0.948 0.139 1.456 0.000 0.630 -0.021 1.008 0.000 1.170 0.660 -0.796 2.548 1.047 0.852 1.331 0.000 0.714 0.852 0.977 0.702 0.688 0.764 0.882 +0 1.125 -0.867 -0.472 0.697 0.281 1.685 -1.719 -0.149 0.000 1.626 -0.340 1.511 0.000 2.250 0.425 1.517 2.548 1.504 -0.791 -1.280 3.102 0.601 0.827 0.984 1.721 1.356 1.193 1.020 +1 0.292 1.219 1.480 0.883 -0.114 1.297 0.883 0.545 0.000 1.121 1.074 -1.199 0.000 1.080 0.391 -1.050 2.548 0.511 -0.839 1.192 3.102 2.576 1.634 0.987 0.714 0.677 1.083 0.916 +0 0.332 1.870 0.124 2.092 0.297 0.771 -0.232 -1.301 2.173 0.433 0.443 -0.948 0.000 1.658 0.103 0.816 1.274 2.187 0.957 -1.345 0.000 0.631 0.837 0.981 0.831 1.352 1.013 1.007 +0 1.188 -0.198 0.165 1.091 -0.009 1.241 0.050 -1.661 1.087 0.544 0.931 -0.735 0.000 1.013 1.230 1.052 2.548 0.436 -1.282 -0.473 0.000 1.026 1.099 0.986 1.587 1.323 1.330 1.094 +1 0.787 0.340 -0.263 0.780 1.703 0.629 0.451 0.005 2.173 0.779 1.171 1.645 0.000 1.510 0.938 -1.226 0.000 2.552 0.814 0.749 3.102 0.887 1.198 1.064 0.938 0.905 0.977 0.841 +1 1.071 0.295 -0.971 1.556 -1.172 1.559 0.359 -0.742 2.173 1.477 0.156 0.977 0.000 0.802 -0.663 0.995 1.274 0.932 -1.166 0.112 0.000 0.638 0.579 0.988 0.973 1.606 1.107 0.983 +0 0.936 -1.135 -0.334 0.634 1.043 0.439 0.298 -1.732 0.000 0.705 -0.040 -1.205 0.000 1.022 -0.437 0.459 2.548 0.811 0.627 -0.199 3.102 0.578 0.921 1.010 0.814 0.599 0.634 0.682 +1 0.676 -1.307 -1.546 0.948 0.414 0.533 -1.489 -0.797 0.000 0.563 1.020 -0.622 0.000 1.297 -0.413 0.938 2.548 1.083 0.582 1.663 3.102 0.782 0.972 1.088 1.028 0.779 0.823 0.757 +1 0.627 -0.944 0.512 1.643 -0.266 1.310 0.937 -1.139 2.173 1.351 1.202 1.062 0.000 0.691 2.481 0.880 0.000 0.862 0.232 -1.421 0.000 0.903 1.108 0.982 0.626 0.749 1.416 1.294 +1 1.282 -0.606 -0.657 0.271 1.222 0.567 -0.624 1.068 0.000 0.861 -0.495 0.575 0.000 1.130 -0.693 -1.380 1.274 1.403 0.634 -1.304 3.102 0.646 0.977 0.984 0.941 0.808 0.853 0.794 +0 0.498 -0.861 1.680 1.601 -0.163 0.838 1.235 -1.544 0.000 1.141 -0.826 -0.515 2.215 1.102 0.575 0.582 0.000 0.847 -0.265 0.967 3.102 0.863 1.235 1.232 0.764 0.895 0.772 0.765 +1 0.550 -0.294 -1.344 0.797 0.828 0.715 -0.823 0.459 2.173 0.978 0.643 1.445 0.000 1.569 -0.975 -0.775 2.548 0.498 -1.504 -0.824 0.000 1.643 1.347 0.987 0.847 1.194 1.081 0.892 +1 0.597 -0.032 0.672 0.709 -0.327 0.886 0.123 1.118 0.000 0.599 1.001 1.059 0.000 0.942 0.927 -0.478 1.274 0.905 0.825 -1.309 0.000 0.811 0.895 0.989 0.970 0.807 0.693 0.751 +1 0.615 1.102 -0.556 2.133 -1.262 1.255 0.261 0.274 0.000 1.437 0.574 1.469 2.215 1.212 0.699 0.642 2.548 0.528 -1.296 -1.149 0.000 1.197 1.044 0.986 1.223 0.959 1.020 0.978 +0 0.680 -0.718 -1.579 0.522 0.042 0.772 -1.156 1.340 2.173 0.690 1.144 -0.371 2.215 0.700 -0.358 0.128 0.000 0.809 0.348 -0.386 0.000 0.915 0.957 0.985 0.803 1.900 1.073 0.868 +1 1.207 0.279 -1.732 0.917 1.383 1.070 0.038 -0.186 0.000 0.472 -2.192 -0.128 0.000 0.544 -0.775 0.836 2.548 0.930 0.480 -0.891 1.551 2.139 1.279 0.996 0.775 0.688 0.901 0.942 +1 0.359 0.784 1.375 1.216 -0.794 0.594 -1.031 0.711 2.173 0.741 -0.203 -0.722 0.000 0.507 -0.400 1.392 1.274 0.386 0.632 0.719 0.000 0.757 0.891 0.987 0.616 0.445 0.635 0.587 +1 0.578 1.557 0.509 0.392 -1.574 0.927 0.465 -0.005 1.087 0.630 0.676 -1.110 0.000 1.043 0.068 0.783 2.548 1.913 0.679 1.734 0.000 0.787 0.979 0.978 0.795 0.835 0.889 0.746 +0 2.028 1.164 -1.360 0.311 -1.713 0.772 0.345 0.129 0.000 0.845 0.731 0.720 0.000 1.125 1.276 1.296 2.548 1.462 1.078 -0.455 3.102 0.937 0.967 0.998 0.817 0.982 0.812 0.884 +1 0.321 1.373 1.054 2.149 -1.656 0.563 -0.520 0.211 0.000 0.759 -0.994 -0.051 1.107 0.837 -2.008 1.586 0.000 0.685 0.651 0.376 3.102 0.919 0.744 0.992 0.941 0.713 1.696 1.438 +0 0.559 0.557 1.525 0.670 -0.725 0.618 1.959 -0.005 0.000 0.403 0.275 1.056 1.107 0.751 -1.252 1.254 0.000 0.511 1.137 -0.253 3.102 3.352 1.785 0.987 0.602 0.444 1.050 0.907 +1 0.906 0.488 1.492 0.626 0.678 1.072 -0.840 -0.628 2.173 0.510 -1.201 1.137 1.107 0.314 1.993 -0.202 0.000 0.386 -1.119 1.705 0.000 0.988 1.011 0.985 1.189 1.108 0.929 0.843 +0 0.297 -0.904 -0.088 1.908 1.165 1.124 0.974 -0.974 0.000 1.098 0.005 0.400 2.215 0.637 -0.205 0.703 2.548 0.482 -0.615 -1.255 0.000 1.129 1.083 0.990 1.156 0.261 0.922 1.182 +0 1.472 -0.754 -0.812 1.399 -0.203 0.649 0.482 1.134 0.000 0.636 -1.003 0.754 1.107 0.437 0.043 1.700 0.000 0.755 -0.972 -1.622 1.551 0.926 0.948 1.038 0.754 0.528 0.679 0.824 +0 0.783 -1.773 -0.593 0.982 -0.673 0.578 -0.741 0.671 0.000 0.968 -1.483 1.172 0.000 0.525 0.304 1.684 0.000 0.818 -1.215 -1.044 3.102 0.939 0.879 0.979 0.452 0.162 0.565 0.653 +0 0.537 -0.910 0.559 3.811 0.437 1.901 -1.933 -0.856 0.000 1.274 1.841 1.682 0.000 1.240 2.451 1.593 0.000 0.955 0.077 1.319 3.102 0.804 0.932 0.984 0.848 0.562 0.863 1.641 +0 1.896 0.715 1.332 0.633 0.360 2.523 1.018 -0.985 0.000 1.813 0.479 0.693 0.000 1.481 1.168 0.800 2.548 0.576 0.747 0.242 0.000 0.682 0.795 1.165 0.758 0.947 1.109 1.117 +0 0.707 -1.212 1.634 2.007 -1.174 0.434 -0.823 -0.193 2.173 0.711 0.370 1.108 0.000 0.905 0.259 0.037 0.000 1.889 -0.172 0.609 3.102 0.953 1.008 0.990 1.238 0.703 0.878 0.908 +0 0.736 1.712 1.602 1.198 -1.645 0.951 -0.468 -0.339 2.173 1.081 0.556 0.769 2.215 1.229 0.321 -0.937 0.000 0.483 -0.960 1.723 0.000 0.908 0.928 0.992 0.956 1.496 1.145 0.973 +0 0.714 -0.080 0.761 0.250 0.138 0.625 1.641 -0.085 0.000 1.065 0.270 -1.379 2.215 0.631 2.650 0.404 0.000 0.890 1.097 1.731 3.102 0.896 0.881 0.979 0.903 0.560 0.991 0.860 +1 1.488 0.973 0.943 0.607 0.511 0.649 0.164 -0.965 2.173 0.507 0.360 1.629 2.215 0.807 1.291 -0.856 0.000 0.769 0.601 0.297 0.000 0.811 0.768 0.976 0.766 0.614 0.836 0.727 +0 1.422 0.180 1.035 1.363 1.437 1.143 -0.936 -0.435 2.173 0.570 1.501 0.681 0.000 1.042 -0.215 -0.755 0.000 0.610 0.465 -0.556 3.102 0.805 0.828 0.982 0.761 0.734 1.206 0.969 +1 0.799 -1.018 -0.845 0.239 0.814 0.846 -0.092 -0.968 2.173 1.088 -0.784 1.207 0.000 0.963 0.110 0.931 0.000 1.285 0.214 -0.015 3.102 0.971 0.969 0.988 0.684 0.858 0.853 0.729 +1 0.591 0.026 0.859 1.712 1.497 2.606 1.432 -0.081 0.000 0.978 -0.637 0.368 1.107 1.667 0.250 -1.656 0.000 2.482 1.801 -1.214 0.000 0.766 0.961 0.987 0.920 0.892 0.905 0.799 +1 1.408 -0.310 -0.394 0.470 -0.029 0.969 -1.922 0.094 0.000 1.767 0.089 1.722 2.215 1.363 -0.703 -1.065 0.000 1.779 -0.488 1.034 1.551 0.924 0.924 0.984 1.453 1.082 1.069 0.902 +1 0.559 0.832 0.750 0.904 -0.365 1.723 -0.451 -1.393 2.173 1.426 -1.015 0.591 0.000 0.658 -0.385 0.860 0.000 1.255 -0.897 -0.829 1.551 0.811 0.975 0.990 1.976 0.904 1.413 1.370 +1 1.292 0.504 -0.877 0.684 -0.661 1.155 0.179 0.854 2.173 0.453 1.003 -0.440 0.000 0.777 -0.209 0.403 0.000 1.092 0.659 1.741 3.102 0.878 0.951 0.995 0.794 0.927 0.938 0.807 +0 0.463 1.709 -1.338 0.328 -0.803 0.683 0.665 0.797 2.173 0.439 -1.026 -1.017 0.000 1.128 0.262 1.558 1.274 0.796 0.279 -0.187 0.000 0.780 0.981 0.978 0.681 0.724 0.712 0.659 +1 0.318 1.315 -1.725 1.022 0.699 1.103 0.154 -1.109 0.000 0.723 -0.594 1.005 2.215 0.746 0.664 -0.606 0.000 1.637 0.653 0.314 3.102 0.858 1.180 0.993 0.619 0.938 0.953 0.830 +1 0.699 -0.707 1.685 1.759 -1.572 1.222 -0.358 0.371 0.000 0.895 -0.251 -0.442 2.215 1.021 -0.478 -1.125 0.000 1.109 -0.895 1.062 3.102 1.966 1.257 1.006 1.280 0.956 0.900 1.063 +0 0.524 -2.417 0.264 1.034 1.627 0.654 -0.498 -0.295 2.173 0.497 -1.470 -0.705 0.000 0.451 0.388 -0.927 0.000 0.638 0.879 0.576 0.000 0.832 0.852 0.986 1.129 1.034 0.820 0.764 +1 0.525 -0.807 1.104 0.342 0.305 0.782 0.142 1.024 2.173 0.824 1.698 -0.307 0.000 1.130 0.485 -0.937 2.548 1.027 -1.461 -0.904 0.000 0.894 0.976 0.981 0.642 1.171 0.891 0.780 +1 1.057 0.037 -0.141 1.795 0.513 0.908 -0.855 1.690 2.173 1.248 0.236 -0.953 2.215 0.510 0.565 0.865 0.000 0.706 0.159 1.642 0.000 0.450 0.775 1.062 1.412 1.410 1.181 0.909 +1 0.955 -0.709 0.622 0.880 -0.465 0.644 -1.743 1.532 0.000 0.448 1.841 -0.645 0.000 1.352 0.109 -0.038 1.274 0.751 -0.474 -1.404 0.000 0.838 0.955 1.054 0.740 0.820 0.924 0.817 +1 0.615 0.980 0.296 1.022 -0.971 0.437 -1.195 -0.991 2.173 0.680 -0.345 0.806 2.215 0.343 0.536 -0.072 0.000 0.928 0.674 1.461 0.000 0.615 0.859 0.998 0.911 0.875 0.849 0.697 +1 0.813 -1.079 1.510 0.638 0.281 1.349 -0.442 -1.003 2.173 1.015 -0.220 0.697 0.000 0.997 -0.618 0.068 2.548 0.846 0.660 1.294 0.000 0.891 0.857 0.991 1.235 1.200 1.028 0.963 +0 2.044 1.388 -1.557 0.157 0.224 0.318 -0.465 -0.540 0.000 1.068 -0.058 0.303 0.000 0.627 0.548 1.726 2.548 0.720 0.498 0.109 3.102 0.890 0.864 0.994 0.796 0.510 0.560 0.820 +0 1.150 -1.414 -0.283 0.221 1.447 0.424 1.418 1.143 0.000 0.882 0.060 1.142 1.107 2.048 -0.449 -1.075 2.548 1.166 0.740 0.583 0.000 0.901 0.945 0.984 0.898 1.361 1.082 1.054 +1 0.982 1.267 1.184 1.007 0.246 1.044 0.161 -1.075 2.173 0.797 -0.859 -0.065 1.107 0.351 -1.548 1.447 0.000 0.365 1.398 0.705 0.000 1.061 0.905 1.031 1.337 1.291 1.178 0.990 +1 1.305 -1.001 1.264 0.344 -0.732 0.459 0.699 -1.109 2.173 0.891 -0.655 -0.587 1.107 0.858 -1.908 1.215 0.000 0.786 -0.049 -0.852 0.000 0.988 1.008 0.987 0.850 0.822 0.750 0.697 +0 2.151 0.191 -1.021 0.140 -0.338 0.746 1.324 -1.358 2.173 1.545 -0.480 0.475 0.000 0.765 1.390 0.899 0.000 0.931 -1.010 1.175 0.000 1.085 1.010 0.994 1.019 0.549 1.199 1.077 +1 1.096 0.244 0.715 0.319 -1.298 0.678 0.768 -0.238 0.000 1.321 0.500 1.625 0.000 0.888 -0.207 -1.208 2.548 0.942 -0.304 0.217 3.102 2.015 1.290 0.994 0.643 0.672 0.868 0.762 +0 0.338 2.163 -1.278 1.560 0.649 0.464 2.030 -1.629 0.000 0.735 1.025 -1.332 2.215 0.994 0.476 -0.378 2.548 0.921 1.310 0.549 0.000 0.953 0.991 0.992 0.974 0.730 0.852 0.748 +1 0.513 -0.168 -0.071 1.061 1.014 1.557 -0.558 -0.732 2.173 0.565 1.249 1.548 0.000 0.807 -0.192 1.141 0.000 0.603 -0.895 0.538 1.551 0.927 0.810 0.993 1.431 0.971 1.057 0.943 +0 1.641 0.717 1.329 0.630 0.732 1.082 1.218 -0.978 2.173 0.770 0.531 -0.528 0.000 1.294 -0.616 1.037 2.548 1.334 1.099 -0.094 0.000 0.725 0.886 0.983 1.112 2.166 1.245 1.099 +1 0.894 -0.474 -1.000 1.526 -1.260 1.156 -0.602 1.284 2.173 1.289 0.168 -0.187 0.000 1.041 -0.062 0.572 2.548 1.320 -0.612 0.147 0.000 0.944 0.791 0.997 1.352 0.899 1.021 1.049 +1 0.364 1.454 -0.411 0.631 -1.593 0.837 -0.839 0.571 2.173 0.544 0.446 0.114 2.215 0.333 0.356 -1.051 0.000 0.422 -0.618 -0.410 0.000 0.410 0.570 0.991 0.655 0.802 0.718 0.555 +0 0.516 -0.211 0.119 0.475 1.592 0.726 0.806 -0.200 0.000 0.850 0.869 -1.252 1.107 1.090 0.757 0.389 2.548 0.938 0.408 -1.735 0.000 1.165 1.036 0.993 0.641 1.019 0.744 0.657 +0 2.374 1.171 1.062 2.077 0.665 1.217 0.848 -0.764 0.000 1.411 0.543 -1.084 0.000 2.557 -0.345 -0.420 2.548 3.163 1.282 1.246 3.102 0.894 1.603 1.076 0.871 3.248 1.949 1.848 +0 0.799 0.368 -0.482 0.467 -0.352 0.316 -0.988 -0.934 0.000 0.657 0.243 0.090 0.000 0.786 1.265 1.706 2.548 0.785 -0.017 -1.676 0.000 0.906 0.696 0.985 1.044 0.428 0.898 0.759 +1 1.235 -1.050 -1.651 2.194 -1.243 1.117 -0.622 0.476 2.173 0.756 -2.133 0.000 0.000 0.332 -0.958 0.323 2.548 0.446 -0.124 -0.911 0.000 1.070 1.173 0.989 0.747 0.194 1.009 0.953 +1 1.127 -1.192 -1.016 1.348 -0.880 2.671 -0.242 0.583 0.000 2.840 -0.907 -1.188 2.215 2.267 -0.721 -1.701 2.548 1.467 -0.686 -0.269 0.000 0.822 1.215 0.986 1.177 1.218 1.093 1.085 +0 2.276 0.991 1.723 0.913 1.206 1.618 2.134 -0.080 0.000 1.048 -0.663 -0.238 1.107 2.018 0.037 1.667 2.548 0.491 -1.077 0.579 0.000 3.775 3.206 0.990 1.691 1.632 2.308 1.900 +0 1.483 -0.213 0.091 0.796 -0.037 0.985 -0.077 -1.649 0.000 0.339 -1.571 -1.605 2.215 0.842 0.679 1.578 0.000 0.653 -1.408 -0.002 3.102 0.790 0.876 0.991 0.859 0.422 0.762 0.908 +0 0.615 1.114 1.079 1.099 1.470 0.639 1.289 -0.238 2.173 0.366 2.484 1.666 0.000 0.428 2.048 -1.092 0.000 0.751 -0.073 1.091 0.000 1.077 0.901 0.987 0.700 0.288 0.695 0.682 +0 0.849 0.276 0.851 0.635 0.040 0.730 -0.870 -1.679 2.173 0.758 -1.595 -1.352 0.000 2.077 -0.270 0.237 2.548 0.789 0.614 1.505 0.000 1.596 1.014 0.989 0.973 1.581 1.086 1.100 +0 1.077 -1.263 -0.858 0.720 -0.059 1.068 -0.075 0.169 0.000 0.501 0.684 -1.698 0.000 1.469 -0.108 1.271 2.548 0.729 0.127 -0.127 3.102 1.013 0.888 0.986 1.308 0.760 0.904 1.371 +1 1.266 -0.335 1.009 0.620 0.061 0.845 -0.400 -0.762 2.173 1.073 0.082 -1.410 2.215 0.728 -0.369 0.625 0.000 0.369 0.606 -1.515 0.000 0.992 0.995 0.987 1.010 0.846 0.852 0.766 +1 0.480 0.801 -0.159 0.422 -1.456 1.339 0.731 0.988 2.173 0.886 2.709 -0.635 0.000 0.944 1.516 -0.678 0.000 1.237 0.214 1.667 3.102 0.832 1.460 0.981 1.181 0.849 1.396 1.101 +1 0.890 0.494 0.776 0.709 -0.527 0.685 -0.550 0.238 0.000 1.269 0.562 1.722 2.215 0.700 -0.345 -0.875 2.548 0.848 0.881 -0.506 0.000 0.898 0.788 1.014 0.908 0.875 0.909 0.811 +1 2.806 0.620 0.101 0.453 1.103 0.413 0.025 -0.835 0.000 0.917 0.771 -1.359 1.107 0.965 -0.365 1.289 2.548 0.822 1.291 -1.608 0.000 0.939 0.923 1.228 1.223 0.933 0.981 0.860 +1 0.604 -1.043 -0.827 1.384 1.471 0.893 -0.674 0.736 2.173 0.799 -0.186 -0.531 2.215 0.651 0.513 -0.863 0.000 0.705 -1.569 -1.643 0.000 1.148 0.982 1.112 0.943 1.171 0.851 0.760 +0 0.834 -2.062 1.347 0.374 -0.352 1.033 -0.577 -1.516 1.087 1.606 -0.199 -0.028 0.000 0.574 -0.691 0.479 0.000 0.588 0.687 1.728 3.102 0.770 0.888 0.990 0.950 0.654 0.955 0.913 +0 1.232 1.907 0.835 0.193 0.742 0.449 0.029 1.720 0.000 0.791 -0.628 -0.046 2.215 0.579 -1.515 -1.671 0.000 1.152 0.672 -1.012 3.102 0.876 0.967 0.997 0.841 0.948 0.942 1.004 +1 1.229 2.078 -0.216 0.620 0.677 0.988 0.953 1.605 1.087 0.531 1.286 -0.967 2.215 0.688 0.536 0.273 0.000 0.682 0.577 -1.434 0.000 0.756 0.821 0.984 0.704 0.805 0.834 0.716 +0 2.532 -0.217 0.619 0.653 -0.989 1.040 1.135 -0.973 0.000 1.169 0.088 -0.978 1.107 1.378 0.893 0.753 2.548 0.593 0.691 1.384 0.000 1.038 0.947 1.768 1.209 1.484 1.125 1.121 +0 0.627 -1.430 0.114 1.034 0.642 0.748 -0.286 -0.797 0.000 1.539 0.593 -1.224 1.107 1.883 0.088 0.926 2.548 0.705 -1.818 0.458 0.000 0.815 0.902 0.978 1.888 1.753 2.048 1.564 +1 0.283 1.427 0.075 0.425 -0.866 1.130 0.452 1.346 2.173 0.678 -0.155 -0.161 2.215 1.124 1.134 -0.832 0.000 0.876 0.792 0.804 0.000 1.101 0.947 0.992 1.000 1.322 0.913 0.809 +1 0.778 -1.916 0.691 1.311 0.581 0.925 -0.145 -1.053 2.173 0.665 -0.694 1.558 1.107 0.645 -1.017 0.863 0.000 0.637 0.616 0.132 0.000 0.869 1.032 0.999 0.805 0.883 0.966 0.819 +1 0.551 2.247 -0.698 0.750 1.720 0.506 2.941 1.718 0.000 1.288 0.181 0.193 2.215 0.858 0.369 1.264 1.274 0.831 0.975 -1.014 0.000 0.955 0.979 0.979 0.745 0.927 0.847 0.785 +0 1.441 0.200 -1.023 0.467 1.211 0.846 -0.203 0.900 0.000 0.509 0.768 -1.608 0.000 1.307 -0.939 -0.578 2.548 0.829 -0.832 0.119 3.102 1.295 0.988 1.027 0.920 0.468 0.876 0.811 +1 0.773 -0.170 -1.302 0.821 0.541 0.955 0.922 0.635 0.000 1.074 -0.331 -0.732 2.215 0.619 -1.309 1.157 0.000 1.043 1.059 -1.140 3.102 0.616 0.914 1.099 0.814 0.923 0.922 0.814 +0 0.883 -0.428 -0.326 0.947 0.900 0.470 0.075 1.686 1.087 1.412 -1.894 -0.530 0.000 1.331 -1.205 1.495 1.274 1.249 -1.679 0.841 0.000 0.740 1.089 1.132 0.943 0.766 0.731 0.758 +1 0.756 1.551 1.190 1.277 -1.475 0.439 1.712 -0.152 0.000 0.483 0.122 -1.141 2.215 0.465 0.562 0.844 2.548 0.507 -1.055 0.298 0.000 0.501 0.597 0.992 0.614 0.507 0.523 0.718 +1 0.538 1.339 -0.884 0.305 -0.102 1.148 -0.012 1.392 0.000 1.312 -0.787 -0.553 2.215 0.440 2.354 -0.548 0.000 0.584 -0.218 0.755 3.102 2.505 1.400 0.983 0.892 0.762 1.345 1.057 +0 0.381 -1.726 -0.785 1.179 1.206 0.851 -0.432 -1.358 2.173 0.774 -0.610 -0.515 0.000 1.045 -0.941 0.361 0.000 1.235 0.546 0.674 3.102 1.019 1.045 0.987 0.857 1.219 0.913 0.834 +0 1.247 0.511 0.706 0.241 -1.230 1.034 -0.166 -0.018 2.173 1.387 0.527 -1.684 2.215 0.845 -0.583 -0.844 0.000 0.456 0.593 1.242 0.000 0.821 0.888 0.983 0.853 1.870 0.987 0.821 +1 1.318 0.765 -0.151 1.082 -1.420 1.142 0.543 0.703 0.000 0.818 0.695 -1.469 2.215 1.447 -0.088 -0.668 2.548 0.448 0.169 1.130 0.000 0.452 1.001 1.505 0.990 0.904 0.906 0.875 +1 0.563 1.065 1.005 0.844 -0.793 0.517 0.318 1.367 0.000 0.449 0.978 -0.391 2.215 0.776 1.953 -1.742 0.000 1.355 -0.586 0.105 3.102 0.910 0.760 0.990 0.555 0.744 0.610 0.567 +0 0.833 0.442 -0.225 0.456 0.511 0.813 0.345 0.351 0.000 0.898 0.498 -1.580 2.215 1.242 -1.945 1.324 0.000 1.501 0.022 -0.902 3.102 0.804 1.048 0.995 0.858 0.655 0.747 0.718 +1 1.157 0.183 0.884 1.141 -1.690 2.526 -0.386 -0.286 0.000 1.508 0.198 1.264 2.215 1.547 0.356 -1.201 2.548 1.584 -0.168 1.604 0.000 1.133 1.015 1.166 0.710 1.301 0.894 0.800 +1 2.328 0.425 -1.043 0.560 -1.328 0.831 -1.625 0.213 0.000 1.646 1.078 1.251 2.215 0.407 0.944 0.779 0.000 0.640 0.372 -0.262 3.102 1.926 1.203 0.986 1.552 0.959 1.508 1.381 +0 0.365 -0.841 -1.541 1.557 -0.314 1.042 0.021 1.306 2.173 0.521 -0.311 -0.439 0.000 0.875 -0.316 0.802 2.548 0.525 0.908 0.837 0.000 0.943 0.852 0.989 1.174 0.567 0.822 0.797 +1 0.430 -0.455 -0.570 0.815 0.553 1.132 2.503 -0.304 0.000 1.072 1.114 0.909 0.000 1.280 0.782 1.550 2.548 2.319 -0.075 -1.371 1.551 2.714 2.020 0.994 0.966 0.913 1.773 1.387 +0 0.856 -2.098 1.384 0.484 0.701 1.015 -1.954 -1.572 0.000 1.188 -0.449 -0.108 2.215 0.972 1.463 -0.076 0.000 0.556 0.802 -1.442 3.102 0.895 1.244 0.998 1.075 0.889 1.097 0.952 +0 1.025 0.897 -0.912 0.524 0.696 0.797 -0.436 -0.180 0.000 0.928 1.565 1.715 2.215 1.148 -0.015 0.360 0.000 1.035 -0.719 1.359 3.102 0.884 0.963 1.008 0.789 1.424 1.183 0.983 +0 0.511 -0.434 0.166 0.640 -1.714 0.918 -2.725 0.394 0.000 0.951 0.625 -1.362 2.215 1.180 -0.370 -0.608 0.000 1.095 0.657 1.490 3.102 0.802 0.869 0.989 0.594 0.503 0.638 0.590 +1 0.397 -0.696 -1.173 0.872 0.475 0.849 -2.905 -0.749 0.000 1.295 -1.408 1.239 0.000 1.736 -0.465 0.881 2.548 1.464 -0.166 -0.866 3.102 1.197 1.673 0.987 0.893 1.233 1.492 1.153 +1 0.870 0.361 1.113 0.346 -0.233 0.506 -1.152 1.644 0.000 0.601 1.296 -0.370 2.215 0.421 -0.451 0.859 0.000 0.764 -1.909 -1.335 0.000 0.861 0.690 0.988 0.862 0.746 0.892 0.766 +0 1.682 0.128 0.970 0.416 0.076 0.586 2.046 -0.572 0.000 0.479 0.998 -0.740 2.215 0.723 1.211 1.736 2.548 0.389 -0.334 -1.296 0.000 0.850 0.641 0.987 0.870 0.503 0.699 0.630 +0 0.468 -0.815 -0.828 0.749 1.548 1.598 -0.947 1.533 2.173 1.297 -2.146 -0.058 0.000 1.237 -1.172 0.041 2.548 0.757 -2.101 -0.844 0.000 0.864 0.804 0.989 1.001 1.733 1.341 1.064 +1 2.764 1.050 -0.550 0.151 -0.416 2.144 1.522 0.999 1.087 1.191 1.292 -1.276 2.215 1.279 -0.728 -0.640 0.000 1.179 1.113 0.185 0.000 1.218 1.106 0.975 2.149 2.100 1.562 1.257 +0 2.062 0.014 -0.600 0.400 -0.976 1.204 -0.282 1.243 0.000 0.836 0.717 0.073 2.215 1.183 0.450 -1.570 1.274 0.847 -0.239 0.423 0.000 1.039 1.054 0.980 0.934 1.061 0.911 0.963 +1 1.337 -0.155 1.073 0.332 -0.145 1.713 0.572 0.028 0.000 1.420 0.283 -1.242 1.107 0.844 -0.075 0.641 0.000 2.043 -0.510 -1.462 3.102 0.796 0.968 0.992 0.860 0.780 0.828 0.741 +0 3.188 0.565 -0.604 0.343 1.289 1.171 -0.856 0.975 2.173 1.215 -1.590 1.140 0.000 1.383 0.444 -1.281 2.548 0.776 -1.364 0.321 0.000 0.851 0.825 1.435 0.961 1.829 1.452 1.539 +0 0.394 -0.168 -1.084 1.191 1.338 0.870 -0.620 -0.112 2.173 0.780 1.481 -1.417 0.000 0.766 0.087 0.461 0.000 0.717 0.819 0.753 3.102 1.494 0.887 0.992 1.251 0.951 0.967 0.867 +1 0.658 0.039 -1.411 0.387 -0.695 1.244 1.141 0.649 2.173 0.937 0.882 -1.310 0.000 0.433 0.021 -0.952 0.000 0.507 0.151 -0.117 1.551 0.585 1.250 0.992 0.589 0.683 1.069 0.940 +0 0.436 1.956 -1.347 1.790 0.403 0.511 0.255 0.113 2.173 0.395 1.232 0.113 0.000 0.611 -0.921 -1.693 0.000 1.136 0.056 -0.812 0.000 0.942 0.876 1.224 1.169 0.935 0.943 0.955 +0 0.718 0.965 -1.612 1.311 -0.839 0.350 0.261 0.365 0.000 0.357 1.368 -0.437 2.215 0.916 -0.385 1.403 0.000 1.261 0.737 0.716 3.102 0.894 0.826 0.985 0.877 0.546 0.607 0.652 +1 0.497 -1.434 -0.793 0.964 1.629 0.830 -0.568 1.511 2.173 0.563 0.620 -0.467 0.000 0.918 1.857 0.159 0.000 1.287 0.613 0.005 0.000 0.855 0.726 0.986 1.114 0.748 1.073 1.666 +0 0.658 -0.495 -1.309 0.750 0.946 0.876 0.225 -0.841 2.173 0.790 -1.090 0.250 2.215 1.828 -1.017 1.133 0.000 0.698 -0.498 -0.612 0.000 1.037 0.810 0.984 0.850 1.352 0.887 0.787 +1 0.356 1.631 -0.610 0.621 0.080 0.564 0.349 -1.296 1.087 0.596 -0.956 0.615 0.000 0.586 1.346 0.885 1.274 0.757 -1.327 -1.509 0.000 0.868 1.016 0.988 0.696 0.786 0.854 0.764 +1 0.427 0.188 1.017 0.791 0.332 1.285 0.661 -0.763 0.000 1.649 0.496 1.248 1.107 1.441 1.003 0.656 2.548 0.698 2.192 -0.972 0.000 0.995 1.392 0.996 1.296 0.967 1.165 1.196 +0 2.552 -0.487 1.658 1.232 -1.677 1.837 -1.508 0.922 2.173 2.644 -0.250 -0.485 2.215 1.559 -0.601 -0.852 0.000 1.947 1.637 -0.085 0.000 0.863 0.909 1.001 2.105 3.794 2.214 1.807 +0 0.840 -1.319 -1.679 0.838 -1.306 1.391 -0.813 0.719 0.000 1.611 -0.955 -0.779 2.215 0.570 -0.575 1.128 0.000 0.846 0.488 0.116 3.102 0.596 0.887 0.993 1.166 1.189 1.121 1.182 +0 0.739 1.087 -1.282 1.109 -1.450 1.483 0.446 0.142 0.000 1.470 -0.108 1.493 1.107 0.846 0.132 -0.686 0.000 0.803 0.230 0.994 3.102 0.515 0.681 0.983 0.859 0.467 0.955 0.928 +1 0.898 -0.664 1.556 0.401 -0.545 0.972 0.123 -0.467 2.173 0.638 1.021 0.354 2.215 0.872 -0.025 -1.681 0.000 0.658 -1.955 -1.604 0.000 0.864 0.958 0.988 0.833 0.959 0.758 0.683 +1 0.864 0.614 0.351 1.013 0.405 1.575 -0.541 -1.472 0.000 1.050 -2.306 0.149 0.000 1.868 0.354 1.285 1.274 3.688 0.712 0.162 0.000 1.247 0.907 1.002 1.174 0.921 0.937 0.869 +0 0.778 -1.619 0.257 0.378 0.972 0.490 -0.673 1.066 2.173 0.916 -1.440 -1.250 2.215 0.633 -0.819 -0.366 0.000 0.524 -2.262 -1.065 0.000 0.758 0.832 0.997 0.899 0.948 0.685 0.639 +0 0.832 0.422 0.321 1.179 1.260 0.630 -0.598 0.019 2.173 1.309 -0.206 -1.431 0.000 0.981 0.225 -0.914 0.000 0.879 -0.129 0.607 3.102 0.878 0.931 1.029 0.925 0.436 0.789 0.796 +1 1.141 0.635 -1.644 1.268 0.673 0.584 0.147 -0.083 0.000 0.361 -0.637 -0.006 0.000 0.951 0.217 -0.911 0.000 0.815 -0.486 -1.181 1.551 0.911 0.764 1.448 0.930 0.380 0.617 0.671 +1 1.026 0.159 1.676 1.249 0.824 0.395 -0.639 0.484 0.000 0.863 0.156 -1.167 1.107 0.897 0.452 -0.108 0.000 1.267 -0.699 1.606 0.000 0.922 0.953 1.089 0.891 1.097 0.804 0.718 +0 1.296 -1.030 -0.858 0.654 0.402 1.186 1.973 1.195 0.000 1.049 -0.218 -0.004 2.215 0.474 1.870 0.840 0.000 1.551 -1.948 -0.987 0.000 0.427 0.955 1.156 0.865 1.005 1.261 1.537 +1 1.078 -0.025 -1.273 0.359 -1.625 0.771 0.132 0.473 0.000 1.017 -1.006 -1.164 2.215 0.964 0.680 1.008 0.000 0.576 -0.394 1.385 0.000 0.865 0.846 0.999 0.621 0.321 0.919 0.831 +0 0.884 1.192 1.472 1.034 -1.087 0.461 -0.508 -0.539 2.173 0.336 2.143 1.462 0.000 0.295 -1.268 0.483 2.548 0.687 -2.103 0.732 0.000 0.317 1.005 0.987 0.968 0.421 0.808 0.733 +1 1.067 0.321 0.776 0.499 -0.763 0.971 0.551 -0.717 0.000 0.991 -1.113 0.787 2.215 0.460 -0.328 -1.228 0.000 1.102 -0.248 -0.243 3.102 0.781 0.672 0.994 0.938 0.860 0.931 0.811 +0 1.888 -0.475 -1.610 0.323 0.913 0.684 0.302 0.071 0.000 0.791 0.507 1.203 2.215 0.559 2.589 -0.027 0.000 0.894 -1.106 -0.344 1.551 1.583 0.978 0.986 0.867 1.099 0.881 0.863 +1 0.490 0.415 -0.537 0.749 1.116 0.534 0.910 -0.279 2.173 0.819 -1.406 -0.643 2.215 0.861 -0.765 0.891 0.000 0.753 -1.167 -1.495 0.000 0.785 0.828 0.986 0.814 1.472 0.929 0.782 +0 0.316 -1.311 -1.613 1.057 -1.597 0.803 -0.175 0.214 0.000 1.136 0.740 -0.709 2.215 1.049 -0.626 0.718 0.000 1.245 -0.716 1.490 3.102 0.840 0.925 1.001 0.905 1.381 1.014 0.913 +1 1.509 0.583 -1.271 0.372 0.720 1.105 0.828 -0.514 0.000 1.513 1.241 1.663 2.215 1.115 2.273 0.793 0.000 1.204 -0.977 0.672 0.000 0.985 0.816 1.012 0.852 0.854 0.896 0.787 +0 0.857 -1.512 0.859 1.709 1.270 1.112 -1.249 -0.802 2.173 0.426 -1.308 1.644 0.000 0.774 -0.828 -0.413 2.548 0.825 -0.165 0.220 0.000 0.876 0.997 0.988 1.079 0.446 1.089 0.923 +1 1.914 0.512 1.178 0.482 -0.335 0.856 2.169 -0.792 0.000 0.531 1.089 0.409 2.215 0.366 2.585 -0.632 0.000 0.692 1.270 1.553 0.000 1.083 0.964 1.303 0.687 0.185 0.576 0.774 +1 0.593 0.578 0.587 1.288 -1.495 0.890 -0.151 0.006 0.000 0.868 -1.000 0.969 2.215 0.799 1.066 -1.692 0.000 1.150 -0.580 -1.003 3.102 0.850 0.984 1.156 1.115 0.895 0.935 0.837 +1 2.169 0.924 -0.398 1.800 0.691 0.682 2.837 1.251 0.000 1.286 0.438 -1.730 2.215 0.575 -0.385 0.653 1.274 0.534 -2.218 -1.633 0.000 7.300 3.858 2.276 1.706 0.870 2.333 2.017 +1 0.670 -0.159 0.845 1.888 0.349 0.710 -1.093 -0.980 2.173 0.973 -0.508 -1.646 2.215 0.743 -1.494 -0.178 0.000 0.597 -1.360 -1.658 0.000 0.714 0.850 0.981 1.145 0.778 0.948 0.791 +0 0.643 -0.160 0.658 0.928 -0.854 1.666 0.048 -0.297 2.173 1.737 1.364 1.365 2.215 1.240 0.589 1.091 0.000 0.435 -0.050 -1.033 0.000 0.817 0.905 1.047 0.912 3.095 1.496 1.171 +0 1.329 0.368 -1.029 0.887 0.702 0.549 -1.242 -0.139 0.000 0.628 1.698 1.511 0.000 1.272 0.438 1.487 2.548 0.577 -0.290 -0.968 0.000 0.757 0.809 1.504 0.941 0.888 0.799 0.794 +1 0.646 -0.079 0.632 0.908 0.366 1.965 -1.816 -0.858 0.000 1.585 0.914 1.186 0.000 2.287 -0.214 0.381 1.274 0.670 0.470 -1.525 0.000 0.901 0.781 0.988 0.812 0.844 0.949 0.995 +0 0.701 1.397 1.626 1.127 0.368 0.998 1.010 1.035 0.000 0.678 -2.830 -0.874 0.000 1.099 1.285 -0.450 0.000 0.646 0.820 -0.920 3.102 1.870 1.004 1.115 0.668 0.244 0.639 0.612 +1 0.370 -0.049 -0.070 1.259 -1.072 0.369 -2.238 -1.130 0.000 0.646 -1.036 -0.590 0.000 1.338 -0.597 0.622 2.548 0.704 -0.615 1.167 0.000 0.890 0.808 0.993 1.498 0.555 1.123 0.944 +1 1.285 2.083 -0.454 0.720 -0.119 1.384 0.829 1.555 2.173 0.665 1.172 0.535 2.215 0.387 1.807 0.631 0.000 0.854 0.912 -0.705 0.000 0.660 1.008 1.002 0.762 1.153 1.070 0.832 +0 1.794 -0.777 -0.021 0.726 0.703 0.740 -1.722 -1.728 0.000 0.688 -1.157 -1.512 2.215 0.536 -1.177 -1.076 0.000 0.803 -0.716 0.360 3.102 0.679 0.773 0.988 0.978 0.676 0.638 0.724 +1 0.697 1.341 1.042 1.133 -1.384 0.481 0.391 1.698 2.173 0.549 1.089 -0.615 0.000 0.863 -0.714 0.304 1.274 0.534 -2.428 -0.670 0.000 2.609 1.545 1.006 0.665 0.914 1.078 1.208 +0 1.805 -0.439 -1.416 0.964 1.546 0.661 1.121 0.040 0.000 1.099 -0.119 0.511 0.000 1.699 -0.747 1.469 2.548 2.365 -0.120 -0.179 3.102 0.904 0.975 0.976 0.774 1.616 1.118 1.066 +0 2.005 -0.780 1.343 1.628 -1.403 0.899 -0.035 -0.554 2.173 0.744 -0.269 0.379 0.000 1.063 -0.835 0.114 1.274 0.556 0.215 1.083 0.000 0.574 0.724 1.552 1.248 0.885 1.130 0.911 +1 1.792 0.188 1.387 0.805 0.387 1.272 0.332 -0.892 2.173 0.804 -1.737 -0.031 0.000 0.939 0.473 -1.612 0.000 0.587 0.418 0.229 1.551 0.884 1.108 1.304 0.702 0.779 0.908 0.808 +0 1.584 0.397 -0.001 0.252 1.398 0.840 0.213 -0.808 0.000 1.381 -0.530 1.105 2.215 0.490 0.491 -1.086 2.548 0.860 -0.746 1.456 0.000 1.385 0.793 0.986 1.180 0.946 0.852 0.846 +0 0.309 -1.721 1.566 1.169 0.150 1.557 -0.371 1.438 2.173 0.775 -1.204 -0.409 0.000 1.584 -0.062 -0.199 2.548 0.759 -0.579 -1.349 0.000 0.797 0.857 0.990 1.170 1.971 1.127 0.940 +0 0.887 -0.630 -0.941 0.995 -0.273 0.858 -1.629 1.065 0.000 0.905 -0.649 1.520 2.215 1.480 -0.679 -1.267 1.274 3.871 0.355 -0.349 0.000 1.084 1.419 0.979 0.968 0.727 1.109 1.037 +1 1.758 0.308 1.438 0.345 0.628 1.307 0.344 -0.917 0.000 0.924 0.460 0.473 1.107 0.270 1.208 -0.793 2.548 0.391 2.107 0.136 0.000 1.694 0.887 0.987 0.808 0.536 0.817 0.830 +1 2.097 0.802 -1.582 0.214 -0.718 0.456 0.363 0.713 2.173 0.539 -0.042 -1.229 0.000 0.728 1.051 0.119 0.000 0.941 0.309 0.322 0.000 0.935 0.710 0.992 0.632 0.381 0.590 0.573 +1 0.414 1.530 -0.780 0.994 0.935 0.516 1.260 -0.115 0.000 0.571 -0.540 0.635 2.215 1.095 -0.984 -0.978 2.548 0.869 -1.115 1.713 0.000 2.023 1.271 0.986 2.051 0.864 1.439 1.317 +0 1.402 -1.683 1.204 1.805 0.599 0.769 -0.076 -0.732 2.173 0.595 0.165 0.438 2.215 1.074 -0.628 -1.115 0.000 1.104 -1.570 -0.880 0.000 0.803 0.894 1.142 1.224 0.875 1.254 1.108 +1 0.475 -0.712 -1.352 1.288 -0.334 0.712 1.198 1.027 2.173 0.571 1.816 0.409 0.000 0.654 -2.393 -0.742 0.000 0.867 2.139 1.645 0.000 0.885 0.798 0.989 1.269 0.486 1.287 1.438 +1 0.742 1.852 1.386 0.639 0.232 0.490 0.552 0.033 1.087 0.492 1.611 -1.620 0.000 0.409 -0.026 -1.017 0.000 0.727 -0.617 -1.678 3.102 0.743 0.769 0.989 1.363 0.769 0.995 0.828 +1 1.247 -0.716 0.696 0.583 0.179 0.794 -1.050 -1.493 2.173 0.537 0.040 -0.112 0.000 0.607 -0.864 0.922 0.000 1.242 -0.075 -0.759 3.102 0.848 0.741 0.988 1.049 0.835 0.844 0.732 +1 0.377 0.446 -0.612 2.216 -0.188 1.277 -0.178 1.713 2.173 0.295 -0.052 -0.094 1.107 0.553 -1.251 1.230 0.000 0.612 0.355 0.667 0.000 0.729 0.876 0.995 0.724 0.903 1.343 1.162 +0 1.483 0.433 1.268 0.545 0.255 0.897 0.396 -0.972 2.173 0.570 1.255 0.318 0.000 0.757 0.136 0.540 0.000 0.521 -0.534 -0.327 3.102 0.640 1.089 0.987 0.674 0.560 0.717 0.686 +1 0.528 -1.512 1.715 0.989 0.051 1.072 -0.679 -1.286 0.000 0.485 0.512 0.327 2.215 0.826 -0.771 -0.759 0.000 1.118 -0.743 1.184 3.102 0.787 0.912 0.999 0.909 0.691 0.780 0.770 +0 0.545 -0.423 -0.510 1.760 -0.147 0.555 -1.948 0.966 0.000 0.721 -0.480 0.987 2.215 1.208 -1.157 -1.244 2.548 0.937 0.104 -1.626 0.000 0.868 0.910 0.993 1.042 0.980 1.046 1.148 +0 0.887 0.530 -0.956 0.824 1.335 1.367 1.612 -1.144 2.173 1.163 2.373 -0.251 0.000 1.802 0.177 1.052 2.548 1.108 1.003 -0.451 0.000 1.011 1.326 1.043 0.865 2.354 1.553 1.263 +0 0.527 -0.374 -0.694 1.869 -1.658 1.068 1.084 0.710 2.173 0.981 -0.077 0.079 2.215 0.814 0.055 0.900 0.000 2.625 0.687 -0.914 0.000 1.729 1.333 1.048 1.609 1.240 1.197 1.137 +1 0.860 -0.528 -0.252 1.554 0.298 1.009 -0.986 -1.312 1.087 0.286 -0.434 0.575 0.000 0.450 -0.105 1.179 2.548 0.619 -2.089 1.500 0.000 0.864 1.373 0.984 0.715 0.758 0.861 0.919 +0 0.529 1.705 1.662 0.511 1.182 1.452 1.152 -0.234 0.000 1.243 -1.470 1.059 0.000 2.123 1.197 0.251 0.000 2.588 0.747 -1.293 1.551 1.357 1.865 0.981 0.984 1.395 1.819 1.404 +1 0.392 1.103 0.857 2.116 -0.239 0.851 -0.056 -0.898 2.173 1.349 1.445 0.024 0.000 1.478 2.381 -1.717 0.000 0.926 0.314 1.667 3.102 0.843 1.023 1.053 0.896 0.723 0.858 0.776 +1 0.623 0.360 -1.365 2.851 1.630 2.164 0.858 -0.136 0.000 0.727 0.195 0.221 2.215 0.607 1.407 0.750 0.000 0.941 -0.301 -1.676 3.102 0.833 1.175 0.987 0.762 0.770 0.923 0.933 +0 0.841 0.707 -1.089 0.359 -0.476 0.831 0.144 0.148 0.000 1.344 0.650 1.663 1.107 1.388 0.130 0.688 0.000 0.897 -0.099 -0.782 3.102 0.903 0.881 0.993 0.965 0.894 0.941 0.935 +0 0.553 1.368 1.203 2.258 -1.112 1.361 0.528 0.458 2.173 0.667 0.917 -0.262 0.000 1.262 1.369 -1.497 1.274 1.013 1.434 0.519 0.000 0.813 0.963 1.347 1.733 1.802 1.243 1.021 +0 1.224 0.618 1.165 1.217 -0.165 1.174 -0.306 0.066 0.000 3.201 0.394 1.724 1.107 1.916 0.476 -0.123 2.548 0.635 -0.432 -0.511 0.000 0.669 0.787 1.575 1.661 2.625 1.643 1.370 +1 1.062 -0.368 -0.273 0.586 0.334 0.794 0.652 1.304 2.173 1.295 -1.553 -0.774 0.000 1.079 -0.608 1.732 2.548 0.918 0.682 -1.670 0.000 0.926 0.839 0.990 1.318 0.929 0.960 0.829 +0 1.890 -1.100 0.880 1.248 1.383 1.303 -0.241 -0.302 0.000 0.830 -0.872 -1.581 2.215 0.931 -0.651 -1.013 2.548 0.555 0.784 -0.541 0.000 0.861 0.847 0.995 0.883 0.466 0.803 1.067 +0 0.588 -0.276 -0.088 0.839 -1.011 0.673 0.712 0.626 2.173 0.501 -2.060 1.521 0.000 0.927 -0.517 -0.966 0.000 0.670 -0.090 1.232 3.102 1.214 0.843 0.981 0.840 0.477 0.939 0.880 +1 0.708 0.755 1.368 0.743 -1.644 1.104 1.171 -0.757 2.173 0.606 -0.025 0.501 1.107 0.546 2.076 0.271 0.000 0.550 1.854 1.346 0.000 0.498 0.956 1.000 1.004 1.336 0.958 0.808 +0 3.514 -0.145 -0.297 2.003 -0.307 2.268 -0.268 1.350 0.000 1.383 -0.430 -1.705 0.000 1.337 0.900 0.265 2.548 0.765 -1.000 -0.929 3.102 1.153 0.908 1.022 1.185 1.231 0.987 1.184 +0 1.357 0.731 1.307 0.664 -0.481 0.774 -0.248 -1.477 2.173 0.794 -0.632 -0.685 0.000 1.414 0.133 0.231 2.548 0.522 -1.026 1.021 0.000 0.874 0.915 1.314 1.006 1.329 0.897 0.842 +1 0.934 0.165 -1.524 0.660 1.114 0.892 -0.409 -1.015 2.173 0.833 1.285 0.759 0.000 1.139 1.147 0.103 0.000 1.196 0.920 -0.743 3.102 0.835 0.863 0.988 0.833 0.949 1.070 0.995 +0 1.368 -1.408 -1.029 0.714 -0.942 0.587 0.520 0.622 0.000 0.761 -0.936 0.100 2.215 1.275 -1.902 1.018 0.000 0.687 1.600 -0.570 0.000 0.773 0.928 0.998 0.488 0.508 0.594 0.736 +0 0.717 -0.650 -0.737 0.321 1.169 0.749 -0.160 0.282 0.000 0.720 -0.875 0.795 0.000 1.187 -0.282 -1.540 1.274 0.887 0.443 -1.080 3.102 0.911 0.991 0.989 0.674 0.464 0.788 0.691 +0 0.542 0.941 -1.728 0.745 0.486 0.511 1.309 0.718 0.000 0.611 0.862 -1.237 2.215 0.795 -0.545 -0.930 1.274 1.044 1.852 0.018 0.000 0.839 0.923 0.989 1.128 0.635 0.892 0.800 +0 0.408 1.841 -0.468 1.123 0.406 0.765 0.233 1.708 0.000 0.651 1.080 -0.635 2.215 0.730 -0.728 0.766 2.548 0.844 0.035 -0.914 0.000 0.869 0.875 0.991 0.846 1.071 0.740 0.742 +0 0.856 1.730 -1.730 1.044 0.652 0.647 1.154 -1.230 0.000 0.537 -0.506 -0.172 2.215 0.685 0.735 0.373 0.000 0.572 0.032 0.174 3.102 0.741 0.916 1.098 0.781 0.209 0.809 0.758 +1 1.398 2.073 -1.214 0.434 1.740 0.714 1.332 -0.240 2.173 0.976 0.850 0.639 2.215 1.148 1.357 1.321 0.000 0.639 1.662 -0.918 0.000 0.885 0.957 0.974 1.097 0.922 0.871 0.766 +1 0.559 -0.851 -1.583 0.328 -0.803 0.910 0.304 0.667 2.173 0.629 -0.249 0.859 0.000 0.463 0.462 -0.101 2.548 0.685 2.314 -1.369 0.000 0.597 0.553 0.986 0.575 0.523 0.609 0.525 +0 1.663 1.538 -0.389 0.305 -1.516 1.009 -0.560 -0.434 2.173 1.197 0.481 -1.532 0.000 1.106 0.311 0.359 2.548 1.572 0.760 1.465 0.000 0.972 0.958 0.990 1.782 1.061 1.229 1.226 +0 1.938 -0.591 1.466 0.822 1.004 0.961 -0.454 -0.354 0.000 0.863 0.568 -0.305 2.215 1.906 -0.804 -1.551 2.548 1.266 -0.793 0.191 0.000 0.898 0.927 0.979 0.924 1.645 1.077 1.084 +0 1.167 1.783 0.030 0.351 1.732 0.416 0.938 1.281 1.087 1.013 -0.385 -1.461 0.000 0.876 1.115 -1.232 2.548 0.589 -0.460 0.944 0.000 0.840 0.685 0.984 0.704 0.587 0.572 0.562 +1 0.990 -0.835 -1.465 0.734 -0.932 1.838 -0.029 0.694 2.173 1.594 -0.916 -1.165 0.000 0.564 0.685 -0.190 0.000 0.487 -0.836 1.678 1.551 1.780 1.003 0.984 1.925 0.926 1.285 1.204 +1 0.485 1.319 -1.041 1.556 1.382 0.999 0.650 -0.249 2.173 1.094 -0.010 -1.419 0.000 1.414 -0.660 0.703 0.000 0.489 -0.435 -0.457 1.551 1.928 1.085 0.987 1.207 0.481 1.006 1.050 +0 0.594 -0.004 1.207 0.869 -0.512 0.926 -1.453 1.233 2.173 0.859 -2.684 -0.654 0.000 1.067 -0.297 -0.096 0.000 0.899 -1.031 -1.290 3.102 0.906 0.770 0.996 1.128 0.745 0.788 0.953 +1 1.291 -0.469 1.256 0.469 1.557 1.836 -1.349 0.097 0.000 0.685 -0.022 -1.690 0.000 1.020 0.438 -1.310 2.548 0.693 -0.865 1.609 1.551 0.831 0.978 0.987 0.757 0.622 1.132 1.114 +0 0.868 0.627 0.592 0.610 1.451 0.677 -0.728 -0.224 2.173 0.720 -0.419 -1.569 2.215 0.651 0.646 -1.739 0.000 0.935 0.400 -0.212 0.000 0.850 0.924 0.993 0.739 0.975 0.732 0.678 +1 0.703 -1.245 1.163 1.668 -1.532 0.579 -0.761 0.810 0.000 1.964 -0.696 -0.072 2.215 1.074 -1.236 -0.786 2.548 1.698 -0.651 1.516 0.000 0.898 1.075 0.987 1.513 1.054 1.045 0.980 +0 0.716 -0.978 0.200 1.050 1.346 0.849 0.058 1.121 0.000 0.862 -2.592 -0.072 0.000 1.019 -1.172 -1.331 0.000 1.027 -0.264 -0.950 0.000 0.964 1.180 1.032 0.526 0.497 0.702 0.706 +1 0.917 0.842 -0.130 0.501 -1.405 1.104 -0.256 -1.358 0.000 0.809 -0.799 0.742 2.215 1.258 0.413 0.178 2.548 0.781 1.803 1.164 0.000 1.675 1.356 0.990 0.671 0.899 1.081 0.941 +1 1.326 -0.563 -1.171 0.340 -0.340 1.137 1.143 1.175 0.000 1.178 -0.776 0.130 0.000 1.314 -0.077 0.490 0.000 1.184 0.016 -0.874 3.102 0.924 1.026 0.984 0.797 0.765 0.915 0.832 +0 1.934 0.779 1.371 0.737 1.156 0.972 0.190 -1.561 2.173 0.607 -2.804 0.086 0.000 2.011 0.353 -0.281 2.548 1.132 0.865 -0.506 0.000 3.806 2.781 1.007 1.525 1.602 1.954 2.001 +1 1.225 0.256 -0.608 0.881 1.632 0.898 -0.756 0.013 0.000 0.376 -2.484 0.975 0.000 1.842 0.315 1.530 2.548 0.831 -0.587 -0.875 0.000 0.948 0.983 1.297 0.974 0.919 0.997 0.895 +1 0.539 -1.577 -1.265 0.799 1.298 0.989 -1.023 0.331 0.000 1.523 -0.945 1.589 2.215 1.371 -0.996 -0.564 0.000 0.896 2.078 -0.669 0.000 0.692 0.823 0.991 0.682 0.926 0.928 0.799 +0 2.334 1.293 -0.019 1.183 0.293 1.236 -0.786 -1.599 2.173 0.493 0.472 -0.753 0.000 1.005 -1.500 1.686 0.000 0.919 0.239 1.229 3.102 1.636 1.094 0.974 2.542 0.898 1.593 1.485 +1 0.413 1.511 1.706 2.402 0.955 1.013 -1.805 -0.802 0.000 1.075 0.203 -0.972 1.107 0.838 0.168 0.162 0.000 1.127 0.653 0.909 3.102 2.329 1.836 0.994 0.820 1.028 1.363 2.339 +0 1.202 0.037 -1.413 0.803 0.671 0.474 -0.763 -1.189 0.000 0.624 0.572 0.328 2.215 0.609 -0.685 0.131 2.548 0.420 0.124 0.900 0.000 0.725 0.795 1.297 0.794 0.487 0.620 0.584 +1 0.449 -0.118 -1.668 1.308 -0.073 0.689 -0.098 0.390 0.000 0.966 -1.208 -1.169 2.215 1.167 -0.072 -1.208 2.548 1.161 -0.831 0.846 0.000 0.836 1.108 1.052 0.974 0.696 0.903 0.805 +0 1.249 0.091 1.097 1.708 0.650 0.732 2.362 -0.307 0.000 1.063 -0.443 -0.543 1.107 0.825 -1.444 -1.586 0.000 1.767 0.024 -1.304 1.551 5.156 2.949 0.999 1.226 0.840 1.909 1.704 +0 0.537 0.644 1.467 0.587 -1.407 0.716 1.003 0.837 2.173 0.917 -1.062 -0.452 0.000 0.473 -0.603 0.115 2.548 0.628 1.015 -0.906 0.000 1.467 0.859 0.980 1.023 0.810 0.930 0.831 +1 2.472 0.928 1.701 0.584 -0.326 0.481 -0.275 1.691 0.000 1.092 1.350 0.111 2.215 0.608 -0.277 0.255 0.000 1.066 -0.340 -0.865 1.551 0.936 0.709 1.611 1.333 1.250 1.043 0.948 +1 1.000 1.469 0.261 1.867 1.012 0.708 0.019 -0.894 2.173 0.487 2.141 -1.231 0.000 0.507 0.817 -1.691 2.548 0.468 -1.426 -1.022 0.000 2.163 1.195 1.187 1.520 0.596 0.972 1.051 +1 0.850 -0.237 -0.382 0.637 -1.503 1.128 2.147 0.884 0.000 1.200 0.428 0.080 2.215 2.040 0.007 -1.190 1.274 1.093 -0.507 -1.715 0.000 3.412 2.342 0.986 0.795 1.557 1.813 1.559 +1 0.627 1.697 -0.055 1.096 1.695 0.320 -0.088 1.175 2.173 0.319 0.186 -0.473 2.215 0.292 0.539 1.416 0.000 0.386 -2.239 -1.075 0.000 0.964 0.698 1.148 0.762 0.473 0.642 0.889 +0 1.575 -0.050 1.272 0.972 1.511 1.048 -0.777 0.973 2.173 2.090 0.055 -0.800 2.215 0.934 -1.374 -0.001 0.000 0.488 0.378 -0.060 0.000 0.855 1.015 0.967 1.581 2.372 1.408 1.212 +0 0.782 0.063 -0.724 1.321 0.190 1.207 -0.575 -0.053 0.000 1.095 -1.320 1.469 0.000 1.798 -0.189 1.512 2.548 1.488 -0.252 -1.327 3.102 2.584 1.693 1.034 1.138 0.691 1.219 1.096 +0 0.489 1.233 -0.441 2.737 -0.426 1.564 0.436 1.624 0.000 1.289 -0.223 0.108 2.215 0.849 0.901 1.278 0.000 1.327 -0.532 1.424 3.102 0.861 0.926 0.989 0.917 1.121 1.138 1.232 +0 1.150 -1.923 -1.481 0.482 0.158 1.051 -0.782 1.208 2.173 0.531 -0.362 0.894 0.000 1.475 -0.673 -0.398 2.548 1.018 0.867 -0.867 0.000 1.207 1.119 1.028 1.076 1.540 1.028 1.053 +1 1.157 0.517 0.064 0.803 1.511 0.963 0.605 1.346 0.000 1.280 -0.657 -0.821 0.000 0.389 -1.208 0.563 1.274 0.787 0.492 -0.646 3.102 2.724 1.537 1.289 0.821 0.601 0.973 0.895 +0 1.136 -0.207 -1.172 0.820 1.312 0.642 -0.194 -0.171 2.173 0.405 -1.910 1.253 0.000 0.388 -1.515 -0.831 0.000 1.273 -0.110 1.006 3.102 0.721 0.871 1.050 0.710 0.836 0.690 0.659 +1 0.924 1.972 1.221 1.063 -0.028 1.089 1.253 0.102 2.173 1.126 1.079 1.714 0.000 1.082 0.772 -1.177 0.000 1.216 1.251 -0.689 3.102 0.893 0.838 1.239 0.974 0.810 0.946 0.914 +1 0.899 0.220 0.951 0.376 -0.773 0.926 -0.837 -0.703 0.000 0.798 -0.841 0.663 1.107 0.886 0.089 -0.947 0.000 1.178 -0.544 1.491 0.000 0.872 1.015 0.988 0.879 0.635 0.832 0.806 +1 1.149 0.669 1.100 0.554 -1.500 2.786 1.485 0.108 0.000 2.707 1.144 1.385 0.000 2.373 0.957 -1.160 0.000 1.056 -2.364 -1.308 0.000 2.926 1.712 0.985 0.721 0.265 1.092 0.907 +1 1.249 -0.865 -0.948 0.702 0.526 0.698 -0.765 -1.284 0.000 0.801 -0.938 0.672 2.215 1.142 -1.349 0.997 2.548 0.534 -1.826 -1.401 0.000 1.003 0.872 1.259 0.855 0.396 0.678 0.674 +1 0.916 -1.093 -0.018 1.275 1.686 0.937 -0.548 -1.737 2.173 1.417 -0.327 0.162 0.000 0.722 0.134 -0.574 0.000 0.402 -0.347 0.575 3.102 1.028 0.593 1.496 1.017 0.567 0.825 0.839 +0 0.803 1.453 -1.386 0.608 -0.116 0.477 -0.345 1.064 2.173 0.338 -2.186 -0.366 0.000 0.873 0.715 0.367 2.548 0.656 0.179 0.666 0.000 1.081 1.070 0.988 0.887 0.674 0.712 0.811 +0 0.799 -1.367 1.261 1.395 0.987 0.612 -0.613 -1.039 2.173 0.499 0.597 -0.253 1.107 0.318 0.669 -0.673 0.000 0.637 2.046 0.482 0.000 1.009 0.692 0.995 1.326 0.752 1.250 1.557 +1 0.568 0.436 0.794 0.367 -1.492 0.823 2.219 0.840 0.000 0.822 1.915 0.358 0.000 0.893 0.790 -0.933 2.548 0.749 -1.784 -1.255 0.000 0.774 1.003 0.990 0.829 0.181 0.826 1.052 +0 0.459 -0.592 0.764 1.310 -1.506 0.702 0.644 0.740 0.000 0.803 0.229 -0.354 2.215 0.789 1.334 -0.255 2.548 0.816 2.492 -1.267 0.000 0.694 0.865 0.988 1.004 0.558 0.754 0.726 +1 0.457 -1.456 0.938 0.423 0.662 1.501 -0.469 -0.758 2.173 0.927 0.542 0.736 2.215 0.933 -0.332 1.373 0.000 0.500 0.819 -1.532 0.000 0.664 1.223 0.984 0.686 1.934 1.048 0.877 +0 1.003 -1.368 -1.222 0.939 -0.202 0.711 -0.116 -0.428 1.087 0.821 -0.708 0.018 0.000 0.987 -0.404 1.148 0.000 1.317 -1.126 1.042 3.102 0.858 1.080 1.070 0.836 1.200 0.808 0.754 +0 1.193 -0.149 1.353 0.356 -0.903 2.676 1.070 0.240 1.087 2.697 -0.702 -1.271 0.000 2.205 -0.517 0.917 0.000 1.539 0.373 0.127 0.000 0.789 0.927 0.991 1.038 4.053 2.031 1.595 +0 1.214 -0.297 0.064 0.646 1.040 0.882 -0.044 -0.888 2.173 0.773 0.393 1.242 2.215 1.105 -0.980 1.701 0.000 0.400 1.798 0.414 0.000 0.838 0.932 0.987 0.751 1.175 0.803 0.771 +1 1.118 1.477 1.658 0.585 -0.664 0.915 -1.711 1.079 0.000 1.666 -0.082 0.007 1.107 0.598 0.901 -0.912 0.000 0.631 -0.016 -1.426 3.102 2.744 1.528 0.987 1.435 0.890 1.276 1.379 +1 0.767 -0.951 -0.522 0.550 1.404 0.643 0.956 1.295 0.000 0.876 0.195 0.823 2.215 1.300 0.336 -1.001 0.000 1.706 -0.501 -0.240 3.102 1.526 1.179 0.987 0.672 1.011 0.980 0.848 +1 0.574 -0.288 0.769 0.936 -0.699 0.480 0.033 -0.445 0.000 1.013 -0.232 1.389 0.000 1.458 0.789 -1.521 1.274 1.918 0.224 0.252 1.551 1.489 1.156 0.987 0.969 1.335 0.952 0.826 +1 0.770 -0.528 0.257 0.403 -1.139 0.808 0.294 -1.449 2.173 1.604 -0.745 0.862 0.000 0.906 -0.685 -0.614 2.548 0.413 -0.614 1.734 0.000 0.750 0.991 0.989 1.068 0.935 0.921 0.818 +0 1.052 0.823 -0.622 0.475 -1.122 0.987 0.023 0.307 2.173 0.806 -1.480 -1.353 0.000 0.874 0.903 1.043 2.548 1.311 0.245 1.035 0.000 0.884 1.290 0.995 0.996 0.920 1.060 0.937 +0 1.245 0.017 0.427 2.898 0.460 2.037 -0.983 -1.201 0.000 0.595 -0.714 1.229 2.215 0.515 -0.852 -1.474 2.548 0.571 -0.249 -0.375 0.000 1.259 1.185 0.988 1.181 0.386 0.927 1.428 +0 0.497 -1.109 -0.813 0.622 0.912 0.885 0.961 -1.543 0.000 0.648 1.177 -0.918 1.107 0.881 1.249 0.237 0.000 1.576 0.357 0.626 1.551 1.619 1.024 0.984 1.282 0.970 1.254 1.382 +0 0.627 -0.806 0.570 0.444 -0.870 1.686 -0.202 0.861 1.087 1.282 0.785 -1.672 1.107 2.086 -1.263 -0.213 0.000 1.596 -0.216 -1.264 0.000 0.873 0.938 0.983 0.965 1.996 1.301 1.037 +1 0.732 0.103 -1.694 0.442 1.073 0.589 1.730 -1.121 0.000 0.997 0.087 0.400 2.215 0.429 2.324 0.085 0.000 0.751 0.194 -1.123 3.102 0.884 0.789 0.986 0.895 0.768 0.873 0.772 +1 2.187 -1.674 -1.242 0.508 0.758 0.887 -1.146 0.623 0.000 0.524 -0.306 0.707 1.107 0.467 -0.787 -0.969 0.000 1.136 -1.898 0.348 0.000 0.898 0.854 1.421 0.920 0.748 0.813 0.726 +1 0.976 -0.383 -0.937 0.627 -0.076 0.783 -0.240 -1.295 0.000 0.699 -2.329 0.987 0.000 0.699 -2.615 1.330 0.000 1.039 0.977 -1.310 0.000 0.801 0.905 0.988 0.718 0.451 0.623 0.637 +1 1.147 0.376 -0.871 0.459 0.569 0.915 -0.247 1.244 0.000 0.672 -0.255 -1.632 0.000 1.238 0.810 -0.252 2.548 0.893 -0.728 -0.057 3.102 0.868 0.934 0.987 0.679 0.817 0.917 0.790 +0 0.863 1.191 1.441 1.987 1.107 1.193 1.860 -0.307 0.000 0.392 1.701 0.184 0.000 0.408 0.545 -0.952 2.548 1.166 1.188 -1.222 1.551 0.628 0.851 0.982 0.842 0.258 0.675 0.839 +1 0.475 0.939 0.181 1.085 -0.446 0.526 1.053 -1.211 1.087 0.775 1.599 0.963 2.215 0.987 0.451 0.658 0.000 0.719 -0.300 -1.329 0.000 0.852 0.810 0.983 1.164 0.913 0.882 0.759 +1 1.593 -0.447 -1.070 0.591 -0.913 2.729 -0.351 0.820 0.000 2.010 -0.187 -0.632 2.215 0.957 0.028 -0.865 0.000 1.553 -0.010 -1.379 3.102 0.731 0.748 0.989 0.722 1.004 0.753 0.638 +0 1.430 -0.756 1.228 0.663 0.051 0.648 -0.352 -0.144 2.173 0.654 -1.387 1.558 0.000 0.748 -0.932 -1.244 2.548 0.603 0.221 -0.823 0.000 1.038 0.974 1.177 0.783 0.784 0.697 0.665 +0 0.553 -0.163 0.480 0.628 -1.325 1.236 1.285 0.651 0.000 1.539 0.386 -1.301 2.215 0.686 0.912 0.149 2.548 0.898 -0.391 -0.573 0.000 1.325 0.785 0.989 1.057 1.106 1.055 1.054 +0 0.903 -1.396 -0.128 1.468 -0.683 1.207 -2.117 1.289 0.000 1.540 2.113 1.186 0.000 2.432 -1.202 -0.396 2.548 0.817 0.205 -1.334 0.000 1.003 1.134 0.988 0.741 0.313 1.481 1.582 +0 0.335 1.491 -1.411 1.135 0.797 0.623 0.564 -0.606 2.173 0.640 -0.924 0.112 2.215 0.772 -0.661 1.669 0.000 0.615 -1.820 -1.618 0.000 0.620 0.770 0.988 1.273 0.958 1.450 1.620 +0 0.370 -0.661 1.430 1.327 -0.485 0.799 0.058 -1.737 0.000 1.192 -0.228 0.401 2.215 0.953 0.455 -1.171 0.000 0.770 0.827 0.609 3.102 0.843 0.852 0.987 0.838 0.587 0.850 0.778 +0 1.105 -0.950 -1.484 1.009 1.146 1.312 2.468 0.069 0.000 0.614 -0.452 -0.536 0.000 1.160 -2.041 -0.189 0.000 2.239 0.318 1.606 3.102 0.918 0.981 1.019 0.759 0.513 0.700 0.712 +0 0.472 -1.690 -0.247 1.678 0.694 1.361 -0.736 0.085 2.173 1.194 -0.443 -0.759 0.000 2.373 -1.255 -1.693 1.274 0.929 -0.027 -1.562 0.000 0.955 1.345 0.984 0.899 2.352 1.332 1.162 +0 0.723 -0.930 -0.912 1.104 0.766 0.558 -0.766 1.291 2.173 0.568 -1.413 0.080 0.000 0.675 -1.556 -0.837 0.000 0.462 -1.327 -1.309 3.102 0.709 0.868 1.236 0.644 0.446 0.525 0.553 +0 1.122 -0.987 0.586 0.284 1.648 0.756 -1.002 -0.486 2.173 0.642 -1.047 -1.518 1.107 0.787 -0.244 -0.831 0.000 1.092 0.481 0.884 0.000 0.980 0.904 0.989 0.888 0.822 0.765 0.735 +0 0.730 -0.551 -0.826 0.911 -0.290 0.927 -1.017 0.083 2.173 0.830 -2.559 1.259 0.000 1.437 -0.379 -1.618 0.000 1.241 -0.702 1.314 1.551 2.424 1.406 0.985 1.047 1.022 1.184 1.230 +1 1.748 0.058 1.071 0.155 1.152 0.705 -0.459 -0.565 2.173 0.567 -1.951 0.996 0.000 0.364 -1.289 -0.197 2.548 1.560 0.412 -1.215 0.000 2.262 1.274 0.990 1.148 0.374 0.888 0.956 +1 0.669 -0.844 -0.874 1.200 0.448 0.815 -1.631 1.157 0.000 0.718 -0.701 -0.287 2.215 1.909 -0.347 -1.386 2.548 1.321 -1.111 0.548 0.000 0.866 1.060 1.152 1.084 1.063 1.041 0.899 +1 0.858 0.863 1.331 1.113 -0.570 1.019 0.256 -0.535 0.000 1.735 0.420 0.696 2.215 1.020 -0.505 -1.401 0.000 0.600 0.085 -1.313 3.102 1.500 0.837 1.340 1.172 0.907 1.042 0.948 +1 0.788 0.913 0.877 0.180 1.320 1.248 0.392 -0.841 0.000 0.813 0.165 1.126 2.215 0.763 0.541 1.357 2.548 0.498 -0.713 -1.022 0.000 0.828 0.966 0.981 0.569 0.249 0.795 0.726 +0 0.878 -0.547 0.671 0.471 1.136 0.841 -1.023 -1.613 2.173 0.586 -1.612 0.957 0.000 0.854 1.315 -0.722 0.000 2.465 0.047 -0.193 3.102 0.895 0.963 0.992 0.918 1.701 1.233 1.136 +1 0.783 -0.886 0.272 1.072 1.366 1.213 -0.525 1.055 0.000 1.253 -0.233 -1.244 2.215 2.327 2.022 -0.521 0.000 1.926 -1.583 -0.910 0.000 0.792 1.070 1.059 0.643 1.443 0.967 0.859 +0 0.600 -1.022 0.330 0.999 -1.190 1.656 0.394 1.163 0.000 1.197 0.625 -0.014 2.215 0.680 1.552 0.686 0.000 1.670 -0.607 -0.650 0.000 1.558 1.046 1.051 1.218 0.878 0.990 1.121 +1 0.646 -0.498 1.592 2.034 1.557 0.458 1.382 0.142 2.173 0.565 0.574 0.327 0.000 0.617 0.384 -0.203 0.000 1.093 0.229 -0.722 3.102 0.644 0.563 0.976 0.881 0.687 0.828 0.695 +0 1.070 1.278 -1.053 0.458 0.407 0.556 -0.781 -0.737 2.173 0.571 -0.950 0.531 0.000 0.751 -1.035 1.539 0.000 1.434 0.222 1.147 3.102 0.797 0.836 0.986 0.808 1.073 0.819 0.845 +0 0.978 0.036 0.285 0.594 -0.230 0.565 0.889 -1.471 1.087 0.421 0.733 1.463 2.215 0.805 -2.166 -0.126 0.000 0.436 -1.294 -1.568 0.000 0.688 1.208 0.980 1.114 0.346 1.109 0.958 +0 0.281 -0.030 -1.589 1.904 0.830 0.999 0.441 1.460 2.173 1.741 0.194 -0.651 2.215 1.199 0.210 0.632 0.000 2.255 -0.753 -0.755 0.000 2.035 1.586 0.989 1.194 1.849 1.347 1.255 +0 2.386 -0.512 -0.060 1.210 -0.400 0.455 2.552 1.582 0.000 0.835 -0.298 1.441 0.000 0.509 -0.067 1.123 2.548 1.094 -0.817 -1.099 3.102 0.905 0.766 0.987 0.837 0.583 0.708 0.791 +0 0.282 -1.559 0.832 1.498 0.252 1.201 0.585 -1.358 0.000 0.798 1.429 -1.324 0.000 1.738 0.831 0.471 2.548 1.323 1.070 -0.520 3.102 0.947 0.965 0.999 0.883 0.928 1.047 1.085 +1 1.055 -0.653 -0.688 1.296 -0.809 0.371 0.373 -0.524 0.000 0.787 0.061 0.616 0.000 1.358 0.636 -1.721 2.548 1.464 0.855 0.691 0.000 0.999 1.027 0.981 0.719 0.868 1.053 0.982 +1 0.764 -0.039 -1.021 0.735 -0.583 1.349 0.644 0.974 2.173 0.366 0.499 -1.700 0.000 0.328 0.885 -1.018 2.548 0.498 -0.895 -0.876 0.000 0.618 1.026 0.979 0.660 0.818 1.056 0.825 +1 0.674 1.048 -1.549 1.030 -0.231 1.428 0.094 1.159 1.087 0.581 0.258 -0.894 0.000 0.557 1.282 -0.225 0.000 0.777 -0.364 0.440 3.102 0.722 0.698 1.071 1.327 0.738 0.900 0.818 +1 0.865 0.412 1.511 1.103 -0.849 0.940 0.372 0.295 0.000 1.051 1.211 1.603 2.215 1.065 0.944 -0.097 0.000 0.762 -0.000 -1.141 3.102 0.868 0.886 1.150 0.882 0.735 0.895 0.827 +0 0.577 1.174 -0.049 0.676 1.238 0.518 -0.340 -1.677 0.000 0.588 0.596 -0.265 2.215 0.521 -0.443 0.503 2.548 0.466 1.110 -1.192 0.000 0.773 0.773 0.982 0.575 0.507 0.548 0.536 +1 1.043 0.347 -1.555 1.751 -1.009 0.621 -0.731 0.187 2.173 0.756 0.709 0.739 0.000 0.466 -0.459 1.640 0.000 0.692 0.460 0.434 1.551 0.891 0.916 0.985 0.805 0.503 0.807 0.762 +0 1.167 0.346 1.669 0.806 0.657 1.014 0.861 -0.300 2.173 0.668 -1.183 1.498 0.000 0.836 0.724 0.627 2.548 1.646 1.766 -0.994 0.000 3.654 2.091 1.062 1.144 0.852 1.445 1.187 +0 0.738 1.166 0.550 1.749 1.125 0.518 -2.507 -0.079 0.000 0.570 2.009 -0.984 0.000 1.234 2.294 -1.675 0.000 1.541 0.400 -0.447 3.102 0.805 0.644 0.988 1.211 0.133 0.769 0.838 +0 0.756 0.655 1.156 1.404 -1.559 0.774 -1.715 0.350 0.000 1.094 0.691 -0.786 2.215 1.153 -1.164 1.272 2.548 0.435 -0.812 -0.480 0.000 0.694 0.821 0.990 0.949 1.804 1.233 1.155 +1 0.906 -1.266 -1.600 0.320 0.493 0.449 -0.417 0.235 1.087 0.566 -1.789 -0.858 0.000 1.260 -0.577 -1.597 2.548 1.353 0.593 0.662 0.000 0.808 0.978 0.988 0.704 0.939 0.676 0.642 +1 2.672 0.822 0.926 0.589 0.806 0.830 0.373 -0.601 0.000 0.542 2.024 -0.923 0.000 0.742 0.124 -0.071 0.000 0.947 0.033 -1.593 1.551 1.093 0.762 0.979 0.909 0.684 0.764 0.725 +1 1.448 0.084 1.032 0.540 -0.388 0.478 0.655 0.683 0.000 0.838 0.233 -1.134 2.215 1.386 0.995 -0.519 2.548 0.959 0.283 -1.701 0.000 0.882 0.980 1.173 0.897 0.785 0.812 0.731 +0 1.926 -0.705 1.543 0.467 -0.408 1.291 0.433 -0.489 2.173 2.097 -0.491 0.939 2.215 0.685 0.859 -0.676 0.000 0.781 1.249 -0.895 0.000 0.277 0.572 1.291 1.107 2.609 1.454 1.259 +0 1.087 -0.256 0.022 0.810 1.147 0.819 0.556 -1.158 2.173 0.572 0.927 0.054 0.000 0.831 0.154 1.226 0.000 0.590 0.973 -0.564 3.102 1.015 1.024 1.103 0.730 0.438 0.724 0.688 +0 0.816 -1.556 0.435 1.133 -1.513 0.762 1.570 -0.253 0.000 0.725 0.120 0.753 1.107 0.863 -1.595 -0.495 0.000 1.516 0.658 -1.330 0.000 0.835 1.043 1.310 0.773 0.620 0.763 0.724 +0 0.864 -1.260 0.634 1.216 -0.279 1.181 -0.506 1.451 0.000 0.566 -1.475 -1.592 1.107 1.470 -1.296 -0.598 0.000 0.470 0.106 -0.304 0.000 0.821 0.792 1.041 0.637 0.450 0.571 0.590 +1 0.499 -1.286 0.496 2.036 1.324 2.429 -1.612 -0.519 0.000 2.190 -1.011 1.019 2.215 1.238 -0.684 -0.909 0.000 1.097 -0.444 1.672 3.102 0.836 0.644 0.985 0.827 0.855 0.881 0.859 +1 0.688 -0.530 -0.787 0.254 1.499 0.830 -0.125 -0.096 2.173 1.431 -0.336 1.510 0.000 0.673 -0.701 0.869 1.274 0.463 1.245 -0.738 0.000 1.470 0.981 0.987 0.788 0.773 0.889 0.771 +1 0.841 0.378 -0.212 0.727 -1.375 0.923 0.411 -1.478 0.000 1.128 1.205 0.007 2.215 0.496 -0.741 1.082 0.000 0.677 1.695 -1.014 0.000 0.930 0.905 0.987 0.797 0.737 0.826 0.722 +0 0.584 -1.556 0.625 1.531 0.713 0.577 -1.233 -1.165 1.087 0.474 -1.495 1.552 0.000 1.159 -1.297 -0.514 2.548 0.366 2.416 0.056 0.000 0.270 0.646 0.997 1.071 0.572 0.862 0.716 +0 0.883 -0.139 0.696 1.162 -0.065 0.789 1.363 1.520 2.173 0.872 0.291 -1.461 0.000 1.164 -0.175 -0.550 2.548 0.396 -0.195 0.220 0.000 0.791 0.902 0.988 0.752 1.542 1.111 0.906 +0 1.037 1.273 -1.294 1.058 0.496 1.203 -0.131 0.938 1.087 0.594 0.269 1.259 0.000 0.935 -2.331 -0.562 0.000 2.043 1.311 -0.121 3.102 2.499 2.110 1.450 0.957 2.091 2.122 1.884 +0 1.536 1.372 0.723 0.252 -0.832 1.002 1.644 -0.141 2.173 1.200 1.526 -1.259 2.215 1.571 1.369 -1.722 0.000 2.244 2.348 0.190 0.000 1.121 1.014 0.987 0.899 1.366 1.121 0.978 +1 0.859 -0.860 0.895 1.772 0.283 1.075 -1.054 -1.174 2.173 0.781 -0.546 1.593 0.000 0.535 -0.044 -0.141 2.548 0.371 -1.568 0.328 0.000 0.809 0.891 0.988 0.592 0.908 0.930 0.796 +0 0.600 -1.055 1.710 1.509 -0.510 1.877 -1.129 -1.277 2.173 1.506 -1.297 0.822 0.000 1.310 -1.552 0.258 0.000 1.778 -0.381 0.591 3.102 1.121 0.962 1.199 1.075 2.035 1.486 1.222 +1 1.438 -0.154 -0.687 0.817 -1.689 1.054 -1.212 0.700 0.000 1.104 0.641 -1.571 0.000 2.269 -0.533 0.404 2.548 0.735 0.482 -0.482 3.102 3.217 1.865 1.178 1.282 0.932 1.330 1.169 +1 0.770 1.247 1.158 1.514 1.604 1.379 -1.216 0.478 2.173 1.520 0.389 -0.619 1.107 1.197 0.098 -1.204 0.000 0.567 0.903 -0.365 0.000 0.858 0.894 0.988 3.779 2.627 2.603 1.929 +1 0.798 -0.208 1.463 0.716 -1.277 0.503 -0.974 -0.465 0.000 0.660 -0.680 0.400 0.000 0.846 0.029 -1.442 2.548 0.872 -1.263 0.590 0.000 0.878 1.088 0.985 0.624 0.363 0.638 0.757 +1 1.436 0.216 -1.109 1.003 -0.698 0.902 0.526 1.449 0.000 1.274 -0.118 0.834 2.215 2.421 1.046 0.129 0.000 1.727 0.062 -1.482 0.000 1.013 0.790 0.997 1.286 0.811 0.828 0.863 +1 0.670 -2.150 0.565 0.605 1.695 0.787 -0.437 0.553 0.000 1.003 -0.345 -1.156 2.215 0.737 0.943 -1.494 2.548 0.994 -1.036 0.125 0.000 0.967 1.045 0.990 0.964 0.741 0.910 0.846 +1 1.032 -0.507 -1.220 1.039 1.153 1.510 0.177 0.232 2.173 1.022 2.516 -1.134 0.000 0.940 0.647 1.359 2.548 0.764 -0.578 -0.997 0.000 2.865 1.785 1.209 1.409 1.323 1.690 1.552 +1 0.643 0.086 -1.641 0.362 -0.030 0.770 2.702 1.529 0.000 1.633 -1.075 -0.288 2.215 1.243 -0.219 -0.335 2.548 3.611 0.022 1.205 0.000 0.817 1.278 0.985 0.913 0.679 1.273 1.012 +1 1.483 -1.157 0.931 0.865 -0.763 1.774 0.917 -0.014 0.000 0.958 -0.218 1.465 0.000 1.495 0.265 -1.541 2.548 2.171 -0.638 -1.288 1.551 3.173 2.185 1.567 1.100 0.824 1.622 1.564 +0 0.533 -0.374 -1.210 0.965 1.034 0.494 -2.378 -0.537 0.000 0.739 0.737 -0.186 0.000 0.679 1.471 -0.815 0.000 1.569 0.610 0.685 3.102 0.755 0.861 0.986 0.973 0.724 0.892 0.907 +0 0.429 -1.663 -0.629 0.495 0.883 0.546 -0.122 -1.022 0.000 0.575 2.019 1.451 0.000 0.851 0.291 -0.590 0.000 1.203 -0.232 0.523 3.102 0.761 0.836 0.979 0.646 0.290 0.566 0.565 +1 1.347 -1.055 0.461 0.372 1.661 1.341 -2.340 -1.606 0.000 1.293 -0.799 -0.795 2.215 1.690 -1.193 -0.172 2.548 1.634 -0.826 1.147 0.000 0.848 1.151 0.988 1.005 0.922 0.956 0.823 +0 1.914 -1.517 0.373 0.144 -0.907 1.880 0.584 -0.992 2.173 0.566 0.833 1.743 0.000 1.093 -1.474 0.779 0.000 0.958 -0.684 0.889 3.102 0.939 1.208 0.983 2.438 1.780 1.587 1.580 +1 0.497 -0.833 1.149 1.709 0.131 0.971 0.756 -1.622 0.000 0.396 0.344 -0.548 0.000 0.408 2.066 0.218 0.000 0.901 0.577 1.156 3.102 0.840 1.001 1.014 0.883 0.655 0.872 0.793 +1 0.754 -0.001 1.460 0.978 0.473 0.530 2.321 -1.417 0.000 0.637 1.522 -0.595 0.000 0.814 0.115 -0.694 2.548 1.186 0.358 0.740 1.551 0.968 1.065 0.992 0.599 0.731 0.846 0.966 +1 0.606 0.238 0.945 1.266 -1.351 0.997 0.316 -0.192 1.087 0.615 0.053 1.523 0.000 0.848 -0.327 -0.248 0.000 1.253 -1.008 1.471 0.000 0.775 0.536 1.067 1.051 0.666 0.821 0.747 +0 0.500 1.415 -1.067 0.200 1.704 0.748 -1.263 1.115 0.000 1.002 -0.583 -0.082 1.107 1.301 -0.112 -0.788 2.548 1.054 -1.600 -1.432 0.000 1.108 1.267 0.995 0.679 0.778 0.993 0.924 +1 1.135 -0.308 0.574 0.275 -0.845 0.382 2.606 -1.453 0.000 0.494 -1.318 -1.453 1.107 0.886 -1.125 -0.441 2.548 1.093 -0.005 0.222 0.000 0.845 0.739 0.988 0.685 0.558 0.562 0.545 +1 0.349 1.255 -0.985 0.646 -0.931 0.801 0.927 -0.820 0.000 1.319 -0.162 0.855 0.000 0.517 -1.108 0.861 0.000 0.786 0.203 0.366 3.102 0.696 0.686 0.987 0.648 0.440 0.465 0.582 +1 1.631 0.190 -0.315 1.076 0.096 0.758 0.498 -1.246 2.173 0.645 1.546 1.183 0.000 1.556 0.483 1.021 1.274 0.788 0.971 -1.275 0.000 0.769 0.836 1.002 1.203 1.204 1.033 0.965 +0 2.055 -0.883 0.806 1.140 0.667 1.059 -1.429 -0.809 2.173 0.482 -1.413 0.191 0.000 0.879 -1.058 1.620 2.548 1.301 -0.448 -1.242 0.000 0.856 0.873 0.973 0.871 0.993 1.137 0.957 +0 1.559 -1.783 0.402 0.418 -0.779 0.772 0.259 1.646 2.173 0.789 1.013 -1.347 0.000 0.587 -0.185 0.296 2.548 0.424 2.461 -0.731 0.000 0.925 1.005 0.989 1.602 0.811 1.047 1.477 +1 0.293 2.157 -0.273 0.628 -1.059 1.154 -1.415 0.559 0.000 1.966 0.441 -1.233 2.215 0.835 0.453 0.672 0.000 0.649 -0.824 0.245 0.000 0.728 0.749 0.985 0.815 0.934 0.878 0.751 +0 1.387 0.395 -1.230 1.583 -0.159 1.134 -0.359 1.243 0.000 1.060 1.433 0.314 2.215 1.873 1.004 -1.528 2.548 0.877 1.566 -0.351 0.000 2.578 1.912 1.689 1.324 1.517 1.422 1.299 +0 1.404 -1.323 -1.366 1.188 -0.624 0.397 -2.917 -0.844 0.000 0.743 -1.432 1.255 2.215 1.820 -1.778 0.828 0.000 1.068 -1.122 0.228 3.102 1.719 1.118 1.110 0.935 0.644 0.774 0.879 +0 1.434 0.604 1.658 1.044 -0.194 1.498 2.030 0.028 0.000 1.551 0.294 -1.154 1.107 1.656 0.056 1.292 0.000 0.738 -0.768 -1.730 0.000 0.797 0.864 1.687 1.150 0.988 0.855 0.865 +0 1.175 -0.859 1.664 0.437 -0.777 1.091 -0.473 -0.915 2.173 1.137 -1.338 1.101 2.215 0.533 -2.031 0.746 0.000 1.583 1.255 0.410 0.000 3.219 2.342 0.993 0.784 1.763 1.713 1.384 +1 1.932 0.823 1.054 0.749 0.397 0.542 -0.752 -0.503 0.000 0.448 -0.920 0.286 2.215 0.410 0.928 -0.078 0.000 1.611 0.266 -1.226 0.000 0.913 0.686 0.986 0.914 0.526 0.663 0.719 +0 0.786 0.991 -1.721 0.926 -0.158 0.557 0.261 1.297 2.173 0.786 1.550 -0.360 0.000 0.449 -1.112 1.008 2.548 0.722 0.231 -1.056 0.000 0.903 1.032 1.167 0.934 0.532 0.804 0.738 +0 1.107 0.538 -1.297 0.803 0.959 1.191 0.950 -0.308 2.173 1.461 0.697 1.326 2.215 0.535 -0.241 0.550 0.000 0.648 0.322 -0.673 0.000 0.618 0.872 1.169 1.152 1.946 1.063 0.844 +0 0.662 -0.450 1.677 0.496 0.276 0.896 1.570 1.189 0.000 1.231 -0.736 -0.532 2.215 0.789 1.958 -1.401 0.000 0.930 0.939 0.452 0.000 0.919 0.868 0.987 0.956 0.832 1.268 1.000 +1 0.608 -1.260 0.484 0.594 -1.028 1.014 1.378 -1.740 0.000 1.258 0.629 -0.073 2.215 0.611 1.707 -0.218 0.000 0.779 -0.187 1.037 0.000 1.177 0.939 0.987 1.062 0.686 1.176 1.136 +1 0.321 -1.394 1.010 0.690 -0.991 0.462 1.260 -1.023 0.000 0.537 0.456 -0.572 0.000 1.028 -0.680 0.418 1.274 1.492 0.419 1.103 3.102 0.874 0.951 0.988 0.745 0.835 0.877 0.771 +0 0.904 0.816 0.585 0.300 -0.584 0.869 0.916 -0.872 2.173 0.412 -1.497 1.436 0.000 0.541 2.124 -1.282 0.000 0.588 -0.628 0.910 3.102 2.502 1.437 0.985 0.927 1.039 1.082 0.907 +1 0.451 -0.318 1.440 1.212 -1.287 3.069 -1.133 -0.251 0.000 1.661 1.728 1.537 0.000 1.819 -2.332 1.036 0.000 1.707 -0.030 -1.591 3.102 0.873 0.887 0.998 0.833 0.617 0.845 1.348 +0 0.396 -1.290 -0.945 0.868 1.010 0.486 0.698 -1.297 1.087 0.348 -1.152 0.163 0.000 0.664 -0.224 0.595 0.000 1.279 0.797 -0.363 3.102 0.456 0.861 0.986 1.763 0.631 1.368 1.044 +1 0.925 1.663 1.353 0.569 -1.333 0.441 -1.412 1.673 0.000 1.047 0.328 -0.853 0.000 0.904 -0.381 0.772 0.000 1.952 0.582 0.294 1.551 0.922 1.193 0.992 0.649 0.492 0.714 0.801 +1 1.289 0.147 -1.295 1.515 -1.500 1.028 -0.945 1.042 0.000 1.408 0.797 -0.004 1.107 0.929 -0.670 -0.277 2.548 0.565 0.408 -1.587 0.000 1.223 1.092 0.969 1.650 1.085 1.159 1.147 +1 0.384 -2.007 -1.648 0.629 -0.075 0.800 -0.170 -0.212 1.087 0.811 -1.081 1.690 2.215 0.926 -0.686 -1.119 0.000 1.390 0.568 0.971 0.000 1.541 1.166 0.992 0.764 1.307 0.949 0.810 +1 0.937 -0.781 1.214 0.742 0.051 0.558 0.022 0.114 2.173 0.918 -1.582 1.491 0.000 1.185 -0.304 -1.718 1.274 1.067 0.174 -0.889 0.000 0.854 0.866 1.001 0.775 1.026 0.717 0.693 +1 1.304 -1.634 -1.187 0.228 0.938 1.030 -1.150 0.121 1.087 1.676 -0.531 1.393 0.000 1.383 -1.051 -0.379 0.000 0.526 2.376 1.403 0.000 2.443 1.276 0.987 1.034 0.691 1.041 0.911 +1 1.044 -0.440 0.700 0.313 0.247 0.729 -2.426 -1.354 0.000 0.646 -1.016 0.206 2.215 0.586 0.370 -1.119 0.000 1.043 0.530 1.114 3.102 0.820 1.028 0.984 0.588 0.884 1.092 1.077 +1 2.080 -0.087 -0.289 1.684 -1.713 0.781 0.547 1.462 2.173 0.642 1.252 0.557 2.215 0.472 0.501 -1.018 0.000 1.039 -0.688 0.334 0.000 0.926 0.978 2.486 1.554 0.855 1.163 0.960 +1 0.949 1.439 1.214 1.132 0.723 1.165 1.059 -0.690 2.173 0.901 1.085 1.066 2.215 0.842 0.660 -1.307 0.000 0.646 1.454 -0.198 0.000 0.810 0.833 0.985 1.329 1.508 0.979 0.816 +0 1.062 0.518 -0.468 0.982 0.363 0.849 0.447 -1.435 2.173 0.537 -0.094 -1.231 2.215 0.892 0.303 0.469 0.000 1.242 -0.183 0.973 0.000 0.605 1.062 0.990 0.784 0.331 0.727 0.731 +1 1.709 1.279 1.052 0.163 -0.870 0.889 -0.099 -0.822 1.087 0.641 0.464 -1.623 0.000 0.892 -1.224 0.047 0.000 0.917 -0.003 0.664 0.000 0.915 0.974 0.991 0.596 0.813 0.848 0.740 +1 0.807 0.503 1.346 0.801 -1.045 0.839 -0.392 -1.424 2.173 1.522 -0.022 0.296 0.000 0.756 -0.391 -0.254 0.000 0.725 -0.916 1.003 3.102 0.852 0.789 0.987 0.877 0.735 0.875 0.868 +0 0.491 0.347 -1.264 1.723 -1.184 1.500 0.902 0.215 0.000 1.450 -0.014 -1.689 2.215 1.075 1.346 0.902 2.548 0.852 1.088 -0.276 0.000 0.815 0.940 0.976 0.901 1.447 1.248 1.392 +1 0.599 -0.405 0.601 0.209 -0.102 0.938 0.079 -1.629 2.173 0.783 1.071 0.808 2.215 2.003 0.049 0.556 0.000 2.261 -0.157 -0.626 0.000 1.104 1.098 0.986 0.637 1.223 0.950 0.822 +1 0.894 0.066 -1.633 0.668 0.738 1.339 0.358 -0.323 2.173 0.801 -0.388 1.408 0.000 0.931 -0.349 0.672 0.000 0.580 1.062 -1.351 3.102 0.815 0.822 0.988 1.120 0.866 0.941 0.818 +0 0.494 1.436 -1.058 2.502 -1.318 0.722 0.050 0.362 0.000 0.841 -0.528 0.947 0.000 0.948 -1.570 0.346 0.000 1.543 -0.165 -1.671 3.102 0.971 1.043 0.980 1.459 0.797 1.447 1.668 +1 0.981 -0.917 -0.497 3.311 -0.103 1.579 -0.201 1.577 0.000 0.361 -1.123 -1.244 1.107 0.944 -1.009 1.072 0.000 0.650 0.600 -1.226 3.102 1.412 0.982 0.981 1.228 0.473 0.840 1.222 +1 1.876 0.581 -0.727 0.859 -0.518 0.589 0.890 0.940 2.173 0.846 -0.403 1.613 2.215 0.738 0.874 -0.273 0.000 1.000 -0.337 1.151 0.000 1.146 0.952 0.977 1.109 0.941 1.029 0.893 +1 2.237 -1.294 -1.213 0.199 -1.634 0.849 -0.112 0.654 2.173 0.565 -0.972 0.987 0.000 0.647 -0.244 -0.436 2.548 0.996 -1.192 0.342 0.000 0.577 0.710 0.996 0.745 0.772 0.931 0.809 +1 0.826 0.578 1.616 0.634 0.648 0.932 0.395 0.021 1.087 0.483 0.952 -0.443 0.000 0.703 0.453 -1.442 0.000 1.198 1.320 1.350 3.102 0.734 0.851 0.982 0.558 1.255 0.773 0.691 +0 1.790 -0.021 -0.393 0.500 -1.332 0.656 0.242 0.822 2.173 0.741 1.101 -1.512 2.215 0.620 -0.267 1.138 0.000 0.467 0.895 -0.283 0.000 0.719 0.715 0.987 0.982 1.001 0.849 0.693 +0 0.542 1.145 -1.048 1.179 0.272 0.813 1.206 -1.634 2.173 0.442 0.594 -1.185 0.000 0.272 0.973 1.408 0.000 0.657 0.057 0.691 0.000 0.723 0.744 1.028 1.039 1.529 0.964 0.764 +0 0.466 0.286 1.089 1.319 -0.360 0.609 0.165 0.310 2.173 0.835 0.994 -1.219 2.215 0.700 0.889 0.864 0.000 1.302 2.046 -1.381 0.000 1.275 0.981 1.047 0.664 1.130 0.933 0.867 +0 2.307 0.643 0.837 0.836 1.167 1.330 1.339 -1.080 0.000 0.567 0.532 -0.083 2.215 0.547 1.169 0.702 2.548 0.568 0.806 -0.497 0.000 0.714 0.882 0.981 0.581 0.444 0.656 0.920 +1 0.711 1.297 -1.040 0.813 0.875 1.568 0.960 -1.460 2.173 0.637 -0.367 0.392 0.000 0.930 1.874 -0.736 0.000 2.544 0.971 0.712 3.102 0.917 1.047 1.041 0.984 1.968 1.203 0.972 +0 0.866 -0.016 0.807 0.882 1.110 0.730 0.968 0.121 1.087 0.854 0.119 -1.392 0.000 0.604 -0.523 -0.606 2.548 0.563 -2.466 -0.410 0.000 0.894 1.100 0.987 0.879 0.870 0.760 0.746 +0 1.080 -1.796 0.564 1.837 -0.031 0.926 -0.098 -1.706 2.173 0.653 -0.948 -1.453 0.000 0.919 0.167 -0.389 0.000 0.511 -0.299 -0.164 1.551 0.931 0.989 0.997 0.684 0.722 1.167 0.984 +0 1.484 -0.046 -1.002 0.657 -1.362 2.276 -0.243 1.192 0.000 1.508 1.026 0.040 2.215 2.877 0.616 -0.740 0.000 1.742 0.559 0.881 1.551 1.990 1.785 0.976 1.609 1.044 1.337 1.245 +0 1.019 -0.195 -1.032 0.725 -0.206 0.599 -0.240 0.356 2.173 0.746 0.911 0.391 2.215 0.699 -1.836 -1.460 0.000 1.194 1.359 -0.225 0.000 0.480 0.963 0.979 1.029 0.615 0.754 0.793 +0 0.380 -0.510 -1.706 1.566 -1.321 1.666 0.654 0.846 0.000 1.116 0.332 0.100 2.215 1.736 -0.132 -0.997 2.548 1.095 -1.983 -0.935 0.000 4.946 2.935 1.000 0.846 1.288 1.931 1.627 +0 0.445 -1.332 -0.835 2.036 -1.278 0.691 -0.843 1.692 2.173 0.653 -1.435 -0.272 0.000 1.340 -0.341 0.606 2.548 1.247 -1.643 0.482 0.000 0.795 0.975 0.996 0.779 1.036 0.868 0.947 +1 0.475 -1.115 -1.001 1.167 -1.629 3.671 -0.743 0.358 0.000 3.563 1.157 -1.488 2.215 1.045 2.429 -0.898 0.000 0.661 0.343 1.117 0.000 1.550 1.008 0.987 4.829 0.881 3.016 2.788 +1 0.699 1.625 -1.430 1.074 1.427 1.004 1.033 0.400 0.000 0.448 0.045 -0.779 0.000 1.269 -0.043 1.718 2.548 1.243 0.240 0.159 0.000 0.755 0.972 0.990 1.555 0.904 1.023 1.113 +0 1.079 0.753 -1.345 0.585 -0.316 0.799 0.679 0.481 2.173 0.562 0.900 1.499 0.000 0.518 -0.408 -0.859 2.548 0.547 0.918 0.976 0.000 0.334 0.627 0.984 0.669 0.890 0.715 0.614 +1 0.784 0.785 -0.352 1.044 1.388 1.318 -0.060 -1.513 0.000 1.585 0.267 0.010 2.215 1.744 0.576 0.558 0.000 0.794 1.312 0.856 0.000 0.722 1.006 1.253 0.836 1.011 0.863 0.813 +1 0.588 0.381 -1.398 0.577 -1.697 0.682 -0.130 1.347 0.000 1.718 1.170 -0.363 0.000 0.770 0.004 0.039 1.274 1.099 0.938 0.110 0.000 0.743 0.782 0.997 1.005 1.021 0.889 1.033 +0 1.328 0.723 -1.549 0.442 0.934 0.695 0.745 -0.142 0.000 1.373 0.967 0.677 2.215 2.287 0.449 -1.104 2.548 0.639 -0.068 0.893 0.000 0.927 0.900 0.987 0.899 1.942 1.049 0.917 +1 1.194 0.175 -0.627 0.865 1.711 0.779 -1.010 -1.636 2.173 1.275 0.203 0.571 0.000 0.882 0.980 -0.214 0.000 0.747 -1.030 0.530 3.102 0.634 0.759 1.210 1.025 0.754 0.820 0.777 +1 0.539 -0.219 -1.499 1.333 0.589 0.672 0.839 1.036 2.173 0.509 1.277 -1.368 0.000 1.462 0.211 -1.059 0.000 1.064 -2.342 -0.420 0.000 0.835 1.041 1.118 0.842 0.749 0.826 0.838 +1 0.393 -0.960 -1.464 0.845 0.155 2.371 0.017 -0.575 0.000 3.276 1.408 1.466 0.000 2.470 -0.519 -0.037 2.548 1.681 1.608 1.013 0.000 1.425 1.447 0.987 1.114 0.890 2.211 2.423 +0 0.475 1.613 -0.811 1.170 1.030 1.208 2.203 -1.023 0.000 0.594 -1.646 1.073 0.000 1.231 1.123 0.749 2.548 0.614 1.455 -0.083 0.000 1.036 1.350 1.029 1.151 1.107 1.333 1.079 +0 0.572 0.671 -0.659 0.233 1.209 0.889 0.716 0.138 2.173 0.641 1.764 -1.474 0.000 0.586 1.468 0.780 0.000 0.935 0.042 -1.623 1.551 0.848 1.087 0.990 0.587 1.020 0.788 0.783 +1 0.427 1.496 -1.304 0.448 -0.090 0.978 1.157 0.663 2.173 1.012 0.240 -1.327 0.000 0.825 -0.659 -1.352 0.000 1.020 0.553 1.456 3.102 0.942 0.967 0.987 0.939 0.743 1.086 0.886 +1 0.416 -1.640 0.468 1.435 -0.778 0.955 -1.340 0.849 2.173 1.182 -1.132 0.172 2.215 1.864 -1.418 -1.251 0.000 0.702 -0.214 -1.436 0.000 0.904 1.335 0.987 1.053 0.909 1.049 0.901 +1 0.963 0.668 1.355 0.980 -0.253 0.620 1.028 -1.223 2.173 0.283 1.696 1.417 0.000 0.397 -0.774 -0.524 2.548 0.788 -0.025 0.279 0.000 0.813 0.784 1.336 0.794 0.767 0.672 0.608 +1 0.551 1.117 0.119 0.616 1.554 0.690 0.442 0.628 0.000 1.442 0.712 -0.888 1.107 0.659 -0.841 0.979 0.000 1.094 -0.183 -1.428 0.000 0.972 0.650 0.988 0.897 0.959 0.911 0.765 +1 0.349 -0.501 1.044 0.972 0.565 0.404 0.600 0.866 0.000 1.168 0.257 -0.478 2.215 0.513 0.529 -1.025 0.000 0.473 -0.558 0.491 3.102 0.811 0.836 0.985 0.604 0.608 0.663 0.593 +0 1.797 -0.504 -0.458 0.147 0.716 1.233 -0.853 0.963 0.000 0.500 -1.473 0.575 0.000 1.555 0.495 -0.924 2.548 1.131 -0.157 1.554 3.102 0.798 0.833 0.985 0.834 0.885 1.128 1.013 +0 1.713 0.072 0.879 1.254 -0.532 0.885 -1.776 1.086 0.000 0.907 -2.102 -0.849 0.000 0.811 0.318 -0.465 2.548 1.499 -1.525 1.608 0.000 0.796 0.832 1.940 1.035 0.586 0.964 1.148 +1 0.597 0.711 0.211 0.412 -1.388 0.903 -0.637 0.129 2.173 1.371 -0.349 -1.119 2.215 1.132 -1.200 1.319 0.000 0.600 -1.230 -1.327 0.000 1.191 1.019 0.988 1.325 1.495 1.233 1.026 +0 1.122 0.029 0.755 0.437 -0.552 0.562 -0.061 -0.915 0.000 1.011 -0.815 -0.472 2.215 1.532 1.102 1.212 2.548 1.514 -0.634 1.703 0.000 0.999 0.889 0.987 1.018 2.079 1.066 0.892 +1 0.571 -0.418 -0.193 1.144 0.658 0.823 -0.256 -1.364 2.173 0.721 -0.792 0.970 0.000 0.527 -2.386 0.446 0.000 0.730 -0.595 -0.741 0.000 0.946 0.921 0.983 0.500 0.832 0.706 0.678 +1 1.315 0.375 -0.329 0.574 -0.806 1.210 2.197 -1.210 0.000 0.698 0.430 0.560 0.000 2.124 0.316 1.149 2.548 1.356 -0.322 0.545 3.102 2.774 2.337 0.986 1.290 0.829 1.669 1.368 +0 2.645 0.416 0.161 0.199 1.039 1.712 -0.476 -1.507 0.000 0.884 -0.828 -0.854 2.215 2.169 0.877 0.672 1.274 0.772 -0.458 1.676 0.000 0.476 0.883 0.989 0.845 2.101 1.494 1.400 +0 0.378 -1.194 1.414 1.637 -0.548 1.673 -1.406 0.685 2.173 1.071 -0.259 -0.926 0.000 0.511 1.649 -1.352 0.000 0.884 -1.594 1.519 0.000 1.197 0.821 1.069 1.353 1.151 1.368 1.199 +1 1.245 0.617 1.198 0.967 -1.512 0.961 1.253 -0.247 0.000 0.281 1.391 1.563 2.215 0.946 -0.470 1.706 2.548 0.749 1.505 0.513 0.000 0.894 0.735 0.988 0.751 0.626 0.881 0.822 +1 1.054 -1.099 0.942 0.632 0.379 0.997 2.270 0.751 0.000 1.936 0.131 -0.815 2.215 1.000 0.123 -1.543 2.548 0.698 -0.611 1.174 0.000 0.791 1.041 0.976 0.865 0.902 0.989 0.824 +0 1.932 1.526 0.238 1.138 0.527 1.119 -0.093 -1.162 0.000 0.756 -0.537 -1.702 0.000 0.648 1.308 1.333 2.548 0.646 0.370 -1.588 0.000 1.018 0.950 0.980 0.790 0.961 1.065 1.436 +1 1.857 1.302 -1.007 1.235 1.690 2.371 0.758 0.903 1.087 1.341 0.703 -0.665 0.000 1.350 0.850 -0.131 2.548 0.653 1.559 -0.620 0.000 0.740 0.682 1.369 2.061 1.801 1.499 1.318 +1 1.969 0.641 0.818 0.880 1.480 1.070 0.316 -1.055 2.173 0.851 0.602 -0.611 0.000 0.589 -0.530 0.219 2.548 0.623 -0.936 1.445 0.000 1.307 1.015 1.026 0.821 1.014 0.975 0.898 +1 0.726 -0.378 1.391 1.567 -1.332 1.097 0.761 1.068 0.000 0.676 -0.042 -1.015 0.000 1.704 -0.225 -0.394 2.548 1.802 0.219 0.336 1.551 0.835 0.813 0.987 1.080 0.888 0.947 0.806 +0 1.502 -0.500 -0.017 0.423 1.280 0.679 -0.362 1.679 2.173 0.930 -0.141 1.193 2.215 0.647 0.292 -0.431 0.000 1.290 -0.591 -0.546 0.000 0.553 0.974 1.016 0.920 0.515 0.725 0.702 +0 0.877 1.154 1.697 0.389 0.981 0.629 1.136 0.239 2.173 0.316 0.042 1.384 2.215 0.539 0.291 0.101 0.000 0.761 1.732 -1.044 0.000 0.930 0.736 0.980 0.487 0.679 0.577 0.584 +0 0.919 -0.044 -0.793 1.492 -1.632 0.764 -0.610 1.576 0.000 0.516 -0.826 0.783 0.000 1.518 0.000 -0.203 2.548 0.889 -0.698 -0.429 0.000 0.890 0.862 1.111 1.029 0.859 0.834 0.820 +0 1.040 -1.422 -1.407 1.740 1.243 1.386 1.277 -0.213 0.000 1.367 -1.200 0.628 0.000 1.177 0.487 -0.778 0.000 1.229 0.341 -1.632 3.102 0.959 0.899 1.275 1.186 0.931 0.846 0.852 +1 1.712 0.523 -0.076 1.063 -0.488 1.122 0.778 1.561 2.173 0.486 0.646 0.783 0.000 0.635 1.269 -1.399 0.000 0.481 0.490 -0.562 3.102 0.848 0.764 0.996 0.475 0.736 0.910 0.763 +0 2.331 0.735 0.119 0.647 1.025 1.242 -0.924 -1.485 1.087 0.400 -0.469 0.928 0.000 0.747 0.415 1.235 2.548 1.398 -0.440 -1.120 0.000 0.938 0.860 1.241 0.834 1.181 1.361 1.088 +0 1.218 1.112 -1.160 0.469 1.294 0.741 0.178 -1.347 2.173 0.389 0.685 0.313 1.107 0.582 0.650 0.778 0.000 0.966 -0.867 1.013 0.000 0.841 0.970 0.988 0.698 0.815 0.667 0.758 +0 0.760 -0.145 1.081 0.668 1.098 0.543 0.083 -0.538 0.000 0.639 0.441 -1.313 0.000 1.039 1.232 -0.061 1.274 0.777 -1.967 1.085 0.000 0.837 0.909 0.979 0.848 0.263 0.960 0.862 +1 0.625 -1.161 1.191 0.876 -0.007 1.068 0.048 -0.815 0.000 0.929 0.759 1.559 2.215 0.671 0.689 1.068 0.000 0.675 0.299 -1.053 3.102 0.703 0.742 0.987 0.850 0.529 0.994 0.841 +1 1.574 -0.335 -0.280 0.594 -1.169 0.506 0.511 1.575 0.000 0.759 0.985 -1.586 2.215 0.557 -0.446 0.593 0.000 1.854 1.158 0.654 3.102 0.893 0.966 0.988 1.008 0.986 0.989 0.853 +0 0.935 -1.681 -1.001 0.610 -0.004 0.602 -0.799 1.193 0.000 0.556 -0.639 0.313 0.000 0.763 -0.228 -0.846 2.548 1.222 0.451 1.585 0.000 0.921 1.067 0.992 0.905 0.687 1.046 0.912 +0 3.361 1.523 1.110 0.621 0.654 1.575 2.176 -0.739 0.000 1.231 1.488 -0.202 0.000 1.155 1.682 1.309 0.000 0.782 0.928 0.213 3.102 0.987 0.999 0.981 0.835 0.376 0.617 1.013 +1 2.148 -0.714 -0.349 1.279 0.164 0.401 2.841 1.575 0.000 0.696 -1.656 1.723 0.000 0.839 0.137 -1.601 2.548 0.609 -0.948 1.355 0.000 0.582 0.931 1.024 0.574 0.510 0.710 0.815 +1 1.413 -1.015 -1.027 1.429 -1.424 1.032 -0.242 0.949 2.173 1.044 -0.175 -0.485 0.000 0.665 0.231 0.501 0.000 1.064 0.818 0.737 3.102 1.031 0.904 0.989 1.602 0.753 1.334 1.172 +0 0.912 -0.411 0.378 0.243 0.751 0.885 -1.215 0.439 1.087 0.369 -0.448 1.142 0.000 1.277 -0.358 -1.491 2.548 1.479 1.111 -1.301 0.000 0.893 0.896 0.992 0.929 1.423 0.937 0.788 +0 0.412 0.827 1.322 0.941 -0.562 0.871 0.053 0.184 2.173 0.733 -0.056 -1.342 0.000 0.986 -2.088 1.613 0.000 1.232 0.519 -0.546 3.102 1.081 0.975 0.990 0.772 0.742 0.811 0.700 +0 2.024 -1.353 -0.612 0.156 1.579 0.497 -1.243 1.738 0.000 0.743 0.450 0.938 2.215 0.642 -0.328 1.016 2.548 0.489 -1.433 0.086 0.000 0.884 0.879 0.983 0.905 0.315 0.957 0.802 +1 0.918 -1.014 1.192 1.667 0.842 2.428 -1.842 -0.829 0.000 2.075 -1.258 0.853 1.107 0.642 -0.573 -0.712 1.274 0.399 -2.105 0.309 0.000 1.384 1.059 0.991 0.968 1.280 1.511 1.515 +0 1.313 1.007 -1.461 1.551 -0.896 0.747 -0.422 0.797 2.173 0.739 -0.582 -0.087 0.000 0.774 -2.290 0.484 0.000 1.001 -0.402 1.477 1.551 0.777 0.709 0.989 1.052 0.526 1.095 0.976 +0 1.914 0.188 0.807 0.732 1.394 1.397 0.523 -0.695 2.173 0.711 1.926 -0.811 0.000 1.370 1.118 1.148 1.274 0.478 -0.002 -0.065 0.000 1.003 1.028 0.987 0.908 1.824 1.254 1.115 +1 0.509 -1.066 0.302 0.718 -0.854 1.088 0.863 0.578 0.000 0.709 -2.171 -1.519 0.000 1.432 0.108 -0.518 2.548 1.389 -0.779 -1.515 3.102 0.663 0.803 0.983 0.785 1.032 0.903 0.771 +1 1.134 0.932 -1.637 0.443 1.517 0.969 -0.732 -0.403 2.173 0.923 -0.239 0.522 0.000 0.643 -0.524 1.351 0.000 0.470 -0.474 -1.632 3.102 0.827 1.110 0.987 0.513 0.642 0.799 0.767 +1 1.046 -0.298 -0.199 1.641 0.558 1.040 -0.490 -1.353 0.000 0.692 -1.069 1.723 0.000 0.839 -0.928 -0.764 1.274 1.454 -2.111 0.545 0.000 0.854 0.770 1.145 0.626 0.515 0.606 0.796 +1 0.345 -0.956 -0.146 0.973 -1.306 0.572 1.835 0.584 0.000 1.230 0.774 -1.403 2.215 0.749 1.253 -0.379 1.274 1.040 -0.224 1.296 0.000 0.750 0.773 0.987 0.732 0.867 1.003 1.008 +0 0.807 -0.118 1.385 1.270 -0.937 1.163 0.994 0.627 2.173 0.742 0.754 -1.190 0.000 0.330 0.446 0.302 0.000 1.389 0.796 -0.394 3.102 0.792 0.901 1.216 1.410 1.071 1.014 0.911 +0 1.369 1.925 0.721 0.634 -0.125 0.548 0.580 -1.371 0.000 0.740 1.201 -1.598 2.215 1.125 0.946 -0.253 2.548 0.408 -0.383 1.011 0.000 0.723 0.830 0.981 0.893 0.913 0.728 0.735 +0 2.508 0.626 -0.440 0.163 1.067 0.958 -0.070 1.410 1.087 0.664 0.255 0.317 2.215 0.447 1.721 1.528 0.000 0.479 2.356 -1.492 0.000 0.324 0.882 0.984 1.402 0.998 0.965 0.930 +1 0.493 -0.629 -0.194 0.345 -0.718 1.526 -0.553 -1.251 2.173 1.513 -2.145 0.550 0.000 0.795 -0.206 1.725 0.000 1.247 0.455 -0.386 0.000 1.126 0.927 0.975 1.188 1.223 0.920 0.822 +0 1.860 -0.161 -0.642 0.998 -1.204 0.504 1.528 1.289 0.000 1.045 0.121 0.951 2.215 0.355 1.412 -0.508 0.000 0.731 0.826 0.053 1.551 0.760 0.919 0.984 0.825 0.671 0.872 0.889 +1 0.476 -0.032 0.723 0.508 -0.595 0.513 0.239 -0.606 2.173 0.336 2.667 1.584 0.000 0.365 2.358 -0.938 0.000 0.646 1.194 0.765 3.102 0.652 0.849 0.987 0.925 0.692 0.711 0.830 +1 0.829 -0.029 -0.027 0.810 1.322 0.893 -0.978 1.012 2.173 0.904 0.108 -1.026 0.000 1.265 -0.550 -0.578 0.000 0.626 -1.261 -1.070 3.102 0.922 1.072 1.065 0.726 0.783 0.675 0.680 +1 0.345 2.270 0.764 0.492 -0.533 1.388 1.350 -1.639 0.000 0.889 2.735 0.526 0.000 1.005 0.153 0.430 2.548 1.081 1.006 0.545 0.000 0.780 0.717 0.979 0.686 0.914 0.602 0.539 +1 0.927 -0.744 -0.197 0.262 -1.506 0.511 -1.821 1.546 0.000 0.670 0.317 -1.538 2.215 1.150 0.281 0.244 2.548 0.572 -1.239 0.467 0.000 0.698 1.050 0.986 0.675 0.932 0.907 0.788 +0 0.361 2.091 1.046 1.170 -0.362 0.672 0.024 -1.610 0.000 0.794 -0.035 0.640 2.215 0.510 0.036 1.433 2.548 0.568 0.670 -0.414 0.000 0.914 0.903 0.990 0.738 0.444 0.665 0.672 +0 1.848 0.639 0.656 0.623 -1.653 0.655 0.057 -0.019 2.173 0.675 0.524 1.398 0.000 0.779 -2.109 -1.088 0.000 1.384 -1.005 -1.511 0.000 0.793 0.747 1.298 0.951 0.595 0.858 1.117 +0 0.886 -0.192 1.596 0.957 -1.705 0.486 -0.095 -1.289 0.000 0.662 -1.336 0.546 1.107 0.944 0.549 0.164 2.548 1.018 -0.741 -0.351 0.000 0.916 0.929 0.989 0.899 1.013 0.941 0.851 +0 1.469 -0.493 1.613 0.508 -1.050 0.713 0.625 -0.139 0.000 1.139 0.105 1.221 0.000 0.892 -0.438 -0.380 2.548 1.388 0.936 -0.898 3.102 1.093 0.889 0.985 0.812 0.849 0.750 0.735 +1 0.685 -0.890 0.054 1.076 -1.305 0.889 0.655 0.481 1.087 1.233 1.214 -1.364 2.215 0.357 1.026 0.004 0.000 0.660 1.546 1.281 0.000 0.547 0.727 1.118 1.293 1.601 1.277 1.002 +1 0.675 0.531 1.568 0.658 0.266 1.407 -1.345 1.189 0.000 1.020 -0.438 0.010 2.215 0.728 0.328 -0.415 0.000 1.733 0.613 -1.157 0.000 0.803 1.052 0.990 0.929 0.997 0.913 0.808 +0 0.343 1.638 -1.553 1.313 -0.794 1.419 0.318 0.109 2.173 0.874 1.139 1.040 2.215 0.809 0.496 -1.205 0.000 0.596 1.746 1.425 0.000 0.846 0.777 0.980 1.140 1.415 0.994 0.885 +0 2.759 -0.717 0.845 0.717 1.360 1.243 -1.454 -0.623 0.000 0.720 0.186 1.424 2.215 1.244 -0.865 -0.395 0.000 1.044 -0.304 -1.167 3.102 0.742 0.849 0.985 0.863 0.607 0.972 1.112 +1 1.054 0.201 -0.831 0.557 0.435 0.983 -0.047 1.435 1.087 0.910 -1.366 0.004 0.000 1.351 -0.018 -0.223 2.548 0.981 0.158 -1.720 0.000 0.875 0.871 0.988 0.979 1.432 0.968 0.858 +0 0.508 -0.259 -0.653 0.521 0.648 0.909 -0.359 0.138 2.173 1.000 0.241 1.471 2.215 0.988 0.403 -1.370 0.000 0.411 -2.192 -0.984 0.000 1.577 1.237 0.990 0.864 1.379 1.043 0.916 +0 0.749 -1.210 -1.109 0.680 1.308 0.801 0.935 -0.865 0.000 1.246 -1.171 0.021 2.215 1.411 -2.214 1.336 0.000 1.429 -1.601 0.689 0.000 0.952 0.955 0.990 0.975 0.861 0.906 0.860 +1 1.174 -0.384 -0.297 0.137 -0.145 2.363 1.128 -1.631 0.000 1.885 0.084 0.139 1.107 1.756 -0.930 0.403 0.000 1.685 -1.591 -0.822 0.000 1.905 1.717 1.000 0.785 0.936 1.376 1.135 +1 0.639 0.277 0.556 1.983 -0.042 0.475 1.314 -1.125 0.000 0.980 0.411 -1.320 2.215 0.659 -1.605 1.007 0.000 0.661 0.699 -1.739 0.000 1.318 0.982 0.991 1.196 0.628 0.850 0.861 +0 0.746 -0.928 -0.648 0.947 -1.706 1.091 -0.120 0.023 2.173 0.690 -0.313 1.455 0.000 0.607 -1.377 -1.218 0.000 1.024 0.183 0.856 3.102 0.913 0.795 0.986 1.068 0.786 0.827 0.762 +0 0.529 2.248 -1.281 0.669 -1.276 0.807 -0.629 0.699 0.000 1.448 1.510 -0.547 2.215 1.123 0.734 1.094 2.548 0.782 -0.154 1.611 0.000 0.929 0.916 0.973 0.856 1.446 1.303 1.105 +1 0.568 -2.042 0.675 1.082 -1.296 0.586 -0.526 -0.310 0.000 0.386 -0.933 -1.353 0.000 0.527 1.366 1.174 2.548 0.861 -0.741 0.344 3.102 0.844 1.040 1.063 0.763 0.849 1.100 0.916 +0 0.468 -1.269 1.199 0.828 -1.160 0.870 0.357 -0.229 0.000 0.621 -0.018 0.481 0.000 0.767 -1.829 1.160 0.000 1.424 -0.297 -1.203 3.102 0.979 1.046 0.983 0.629 0.716 0.727 0.684 +1 0.883 -1.111 0.012 1.921 1.456 1.481 0.146 -0.912 0.000 1.301 -0.756 -0.182 0.000 2.130 -0.712 1.507 2.548 1.191 -0.562 0.552 0.000 0.999 0.680 1.739 1.087 0.860 0.895 0.910 +0 1.558 -0.802 1.346 0.662 0.604 1.497 -0.342 0.306 0.000 1.383 0.616 -1.416 0.000 1.125 0.543 -0.580 1.274 0.670 1.490 -1.160 0.000 0.829 0.664 0.980 0.607 0.433 0.805 0.784 +1 1.809 -0.320 -1.566 0.846 1.009 1.637 -0.703 0.419 2.173 1.638 0.986 -1.128 0.000 0.861 -0.690 -0.775 0.000 0.658 0.184 1.101 3.102 0.675 1.040 1.254 0.659 0.824 0.975 0.846 +0 0.929 1.278 -1.016 0.900 1.008 0.962 0.648 1.590 2.173 1.085 0.441 -0.144 0.000 0.585 -0.718 0.903 2.548 0.735 -0.701 -0.642 0.000 0.953 0.852 1.227 0.903 0.913 0.904 0.892 +1 1.185 -2.115 0.376 0.245 -0.246 0.813 -1.042 -1.166 2.173 0.617 1.995 1.418 0.000 0.951 -0.809 0.125 0.000 1.370 -0.489 1.417 1.551 0.734 0.888 1.000 1.036 0.853 0.823 0.729 +1 1.011 0.570 0.528 0.777 1.664 0.862 0.060 0.562 2.173 0.973 1.172 -1.006 0.000 1.025 0.161 -1.216 2.548 0.405 0.189 0.038 0.000 1.022 0.694 1.049 0.759 1.172 0.864 0.780 +1 0.904 0.222 -1.270 0.105 1.392 1.670 0.427 0.698 1.087 0.714 -0.920 -1.700 0.000 1.725 0.219 -0.504 2.548 1.750 -1.701 -1.628 0.000 0.844 1.827 0.992 1.360 1.879 1.847 1.404 +1 0.957 -0.444 -1.638 1.378 -0.931 0.651 -0.112 0.674 2.173 0.477 -0.842 1.521 0.000 1.248 -0.443 -0.083 2.548 1.370 -0.813 0.358 0.000 0.915 0.803 0.986 1.068 0.740 0.831 0.748 +0 0.594 0.279 -1.625 1.771 -0.917 1.524 -0.010 1.530 2.173 0.852 0.126 0.925 0.000 1.608 1.806 0.647 0.000 3.537 -0.813 -0.512 0.000 1.208 0.860 0.984 1.211 1.499 1.013 1.001 +1 0.572 0.728 -1.670 1.083 -0.776 1.771 0.957 -0.247 0.000 1.376 -1.777 1.384 0.000 1.789 1.006 1.232 2.548 1.392 0.896 -0.749 0.000 0.861 0.615 0.989 1.137 1.650 1.491 1.248 +0 1.637 -1.729 -1.164 0.583 -0.943 0.927 -1.060 -0.584 2.173 1.142 -0.902 0.906 0.000 1.464 -0.509 0.600 2.548 1.332 -1.326 1.390 0.000 0.869 0.829 1.001 0.779 1.325 0.978 0.983 +0 0.432 0.215 0.388 0.992 -1.255 1.053 0.114 -0.370 0.000 1.460 -0.002 0.873 2.215 0.598 0.305 -1.737 2.548 1.178 -1.404 -1.404 0.000 1.585 1.092 0.991 0.962 0.725 0.973 0.864 +1 1.223 -2.041 -1.470 0.378 0.467 0.516 0.542 0.929 0.000 0.667 0.626 0.181 1.107 1.132 -0.804 0.166 0.000 1.132 -0.854 -1.009 0.000 1.016 0.900 0.988 1.644 0.805 1.057 1.238 +0 0.413 1.608 1.617 1.680 0.990 0.856 -1.138 -0.474 0.000 0.314 -0.305 0.497 0.000 0.746 0.419 -1.623 2.548 0.866 -0.175 -0.799 3.102 0.969 1.057 0.987 0.850 0.466 0.643 0.855 +1 1.769 0.418 -1.228 2.167 -1.310 1.623 -1.297 -0.016 0.000 0.449 1.276 -1.631 0.000 1.589 -0.567 1.439 2.548 1.215 -0.754 0.979 0.000 1.470 1.082 0.959 1.162 1.089 1.110 1.009 +1 0.381 -0.934 -0.282 0.599 1.164 0.932 0.765 -0.446 0.000 0.457 -0.162 -0.589 0.000 1.129 0.656 1.492 1.274 1.428 0.361 1.075 3.102 0.699 1.066 0.990 0.678 0.382 0.814 0.706 +0 1.013 0.645 -0.404 0.556 1.484 0.955 -0.003 1.294 2.173 1.126 -0.633 -0.576 1.107 0.551 -0.725 0.314 0.000 0.655 0.461 -1.087 0.000 0.794 0.856 1.031 0.940 1.597 0.951 0.767 +1 0.767 1.091 1.651 0.969 0.502 0.673 2.045 -1.729 0.000 1.523 -0.148 0.124 2.215 0.791 -1.558 1.286 0.000 1.634 0.010 -0.577 3.102 4.271 2.535 1.027 1.158 0.850 1.764 1.414 +0 0.385 1.122 0.766 2.140 0.351 0.635 1.000 1.399 2.173 0.931 1.848 -1.119 0.000 1.256 0.922 -1.006 0.000 0.482 0.718 -0.559 3.102 0.780 1.035 0.988 0.740 0.576 0.797 0.905 +1 0.932 -0.963 -0.625 0.936 0.623 1.200 -0.913 0.441 1.087 1.287 -0.418 -1.459 0.000 0.815 -1.228 -1.692 0.000 0.697 0.179 -0.812 1.551 0.816 0.717 1.168 0.855 1.050 0.998 0.873 +0 0.847 -0.443 0.433 0.864 -1.357 0.468 -1.590 1.458 0.000 0.549 1.268 -0.121 2.215 0.633 -1.122 -1.097 0.000 0.630 -0.249 -0.960 3.102 0.751 0.595 1.184 0.957 0.597 0.857 0.754 +0 0.675 -1.974 0.779 2.568 0.053 0.632 -0.248 -1.670 2.173 0.537 1.365 -0.663 0.000 0.662 -1.191 -1.728 2.548 0.913 0.806 -1.489 0.000 0.650 0.884 1.110 0.998 0.449 1.124 1.526 +0 0.677 -1.548 -1.146 0.709 0.861 0.491 -1.646 -0.412 2.173 0.932 -0.740 0.888 2.215 0.496 0.743 -1.152 0.000 0.565 -0.695 1.679 0.000 0.855 0.837 0.987 0.679 1.027 0.696 0.612 +0 1.210 -0.953 0.543 0.969 -0.087 0.997 -0.552 0.820 2.173 1.342 -1.199 -1.598 2.215 1.502 -0.674 -0.994 0.000 1.071 -1.103 -0.304 0.000 0.914 1.032 0.980 0.778 1.514 1.053 0.970 +1 1.176 -0.064 0.093 0.749 -1.443 0.802 -1.073 -1.533 0.000 0.679 -0.115 -1.672 2.215 0.568 1.043 -0.156 2.548 0.394 2.007 -0.025 0.000 1.095 0.828 1.278 0.754 0.785 0.791 0.753 +0 0.873 0.939 -0.974 0.887 -1.246 0.467 0.859 -0.483 2.173 1.190 0.556 0.843 0.000 0.403 0.314 0.024 2.548 1.132 -0.759 1.474 0.000 1.473 0.908 0.981 0.681 0.278 0.751 0.762 +1 1.297 1.167 -0.085 1.130 -0.562 1.288 0.345 1.358 2.173 0.605 1.197 -0.947 0.000 0.900 1.041 0.567 0.000 0.442 0.747 -0.548 3.102 0.847 0.930 0.982 0.485 0.820 1.081 0.872 +0 0.541 -0.261 0.984 0.284 1.303 0.853 0.475 -0.641 2.173 0.677 1.057 1.026 0.000 0.738 1.215 0.765 2.548 0.566 -0.147 -1.361 0.000 0.867 0.992 0.978 1.256 1.040 1.114 0.956 +0 1.096 0.912 -0.939 1.643 -0.367 1.055 -0.577 1.088 0.000 0.657 0.714 -1.591 2.215 0.712 0.835 0.773 0.000 0.792 -0.991 -0.582 3.102 0.956 1.008 0.985 0.922 0.891 0.763 0.928 +0 1.075 -2.015 -0.381 1.527 -0.759 0.657 0.589 1.185 1.087 0.719 -0.626 0.872 0.000 0.317 -0.341 1.554 0.000 0.432 -1.313 0.824 3.102 0.434 0.603 0.991 0.657 0.762 1.136 0.905 +1 2.221 -2.021 1.376 0.428 -0.882 0.648 -0.493 -0.630 2.173 0.818 -2.141 -0.095 0.000 0.764 -1.251 0.628 2.548 0.807 -1.540 -1.461 0.000 1.016 1.087 1.209 0.816 0.888 0.952 0.855 +0 1.203 -1.061 0.774 0.600 1.731 0.379 -0.515 0.369 1.087 0.510 0.993 1.043 0.000 1.041 -0.054 -0.907 2.548 1.134 1.021 -1.052 0.000 0.946 0.940 0.985 0.897 0.737 0.698 0.784 +1 1.489 -1.282 -0.121 0.714 -0.748 0.883 -0.242 0.822 2.173 0.622 -1.083 -1.577 2.215 1.034 -0.843 1.213 0.000 0.723 -0.774 -1.156 0.000 0.806 0.840 0.995 0.834 1.029 0.877 0.756 +1 0.664 -1.265 1.159 0.892 -1.532 1.205 -0.434 -1.470 2.173 2.423 0.616 0.297 0.000 1.348 1.300 1.308 2.548 1.169 0.530 -0.512 0.000 0.725 0.887 0.977 1.259 1.950 1.780 1.546 +0 1.127 -0.113 0.938 0.363 0.945 0.987 -0.891 -0.693 2.173 0.705 -0.734 1.579 0.000 0.373 -1.144 -0.395 0.000 1.306 -0.720 0.493 3.102 0.898 0.921 0.985 0.813 1.054 0.996 0.826 +0 0.876 -0.480 -1.407 0.419 1.075 1.880 1.646 1.672 0.000 2.026 -0.998 -0.076 0.000 0.758 1.140 0.742 2.548 1.493 -0.429 0.331 3.102 1.013 0.833 0.992 0.763 0.867 1.056 0.976 +0 0.428 -0.097 -1.504 0.850 0.201 0.977 1.417 0.578 2.173 0.434 0.007 -0.959 0.000 1.198 -1.237 -0.528 2.548 0.389 -0.396 1.533 0.000 0.438 1.067 0.990 1.160 2.793 1.314 0.991 +1 0.529 -1.463 -0.067 1.024 1.616 0.476 -2.255 0.566 0.000 1.429 -0.271 -0.994 2.215 0.742 -0.243 0.199 2.548 0.942 2.407 1.356 0.000 0.711 0.824 1.018 1.083 0.963 0.995 0.854 +1 1.532 0.109 -0.312 0.712 1.355 0.810 -1.470 1.470 2.173 1.141 -1.268 -0.592 0.000 0.605 0.119 0.272 0.000 0.545 0.284 1.174 3.102 1.040 0.859 1.444 1.431 0.757 0.915 0.879 +1 0.879 -0.125 1.452 1.540 -0.019 0.712 1.124 -1.717 0.000 0.997 -1.006 -0.860 0.000 1.370 0.200 -0.243 2.548 0.968 0.020 0.989 0.000 0.911 0.990 1.563 0.820 0.502 0.646 0.693 +1 0.762 0.161 -0.436 0.600 0.846 0.931 -1.238 -0.524 0.000 1.280 -0.502 1.093 2.215 1.407 -0.050 -1.661 0.000 1.132 -1.167 0.179 0.000 0.936 1.173 0.990 0.825 1.014 1.037 0.856 +1 1.280 0.360 -0.905 1.982 -1.294 1.508 1.474 0.401 0.000 1.123 -0.007 1.260 2.215 0.488 1.204 -1.315 2.548 0.730 0.442 0.414 0.000 0.798 0.918 0.988 1.170 0.804 0.994 1.234 +1 1.933 -0.432 0.170 0.753 -0.480 1.187 -0.420 -1.379 2.173 1.389 -0.477 1.435 1.107 0.724 -1.118 -1.502 0.000 1.249 0.343 0.194 0.000 1.336 1.101 0.984 1.380 1.077 1.149 0.993 +1 1.605 -1.031 0.505 0.629 0.458 0.639 0.150 -1.654 1.087 0.678 -0.218 -1.014 0.000 1.116 -0.682 -0.883 2.548 1.120 0.104 0.939 0.000 1.132 0.880 0.979 1.108 0.833 0.888 0.795 +1 1.527 -2.084 -1.126 0.474 1.595 0.494 -1.911 0.207 0.000 0.757 -0.308 0.442 0.000 1.006 -1.275 1.465 0.000 0.710 -0.449 -0.343 3.102 1.153 0.770 0.987 0.664 0.435 0.556 0.624 +1 1.382 0.293 -1.403 0.937 0.834 0.657 -0.098 0.246 2.173 0.923 0.761 0.719 2.215 0.286 -0.683 -0.843 0.000 0.519 1.213 -0.563 0.000 0.579 0.717 1.422 1.030 0.704 0.789 0.668 +0 1.161 -0.071 -0.808 2.102 -1.214 1.242 -1.930 0.629 0.000 0.933 0.192 1.532 0.000 1.547 0.553 -0.253 2.548 1.168 1.205 0.673 3.102 1.514 1.051 0.996 1.018 0.881 0.944 0.991 +1 1.036 -0.229 1.722 0.800 -0.127 1.059 -0.066 1.238 2.173 1.570 -0.265 -0.567 0.000 0.600 0.606 0.671 0.000 0.596 -0.523 0.457 3.102 1.521 0.926 1.256 0.953 0.595 0.928 0.817 +1 0.902 -0.805 -1.174 0.160 -0.660 0.899 -0.505 -1.556 2.173 0.559 0.863 -0.295 0.000 1.310 0.389 0.203 2.548 1.771 -1.283 -1.520 0.000 0.951 0.918 0.989 0.716 1.503 1.260 1.064 +1 1.028 -0.108 -0.566 0.278 -0.387 1.293 0.840 -0.862 2.173 1.516 0.291 0.369 0.000 2.391 0.338 1.631 1.274 0.714 1.197 1.039 0.000 1.063 1.599 0.989 1.324 1.788 1.536 1.276 +1 0.503 2.280 0.135 2.093 0.688 0.440 1.226 -0.608 1.087 0.302 2.283 -1.362 0.000 0.842 1.077 1.579 0.000 0.947 -0.294 -1.358 3.102 0.933 0.775 1.000 0.892 0.738 0.901 0.787 +1 1.001 -0.717 -1.244 0.461 -0.004 0.838 0.150 0.451 2.173 0.490 -1.210 1.469 0.000 0.849 0.017 -1.364 0.000 0.588 0.198 -1.702 0.000 0.865 1.085 0.988 0.920 0.809 0.859 0.762 +0 0.421 1.109 -1.720 0.612 -0.693 0.525 -1.433 1.046 2.173 0.525 -1.608 -0.742 0.000 0.826 1.061 1.029 1.274 1.137 -0.689 -0.165 0.000 0.658 0.834 0.994 0.799 1.402 0.985 0.848 +1 0.696 -0.207 1.712 0.642 1.307 0.897 -2.334 -0.200 0.000 1.147 0.290 0.983 2.215 1.291 0.130 -0.798 0.000 1.611 0.731 -1.482 3.102 0.821 0.881 0.982 0.719 1.041 0.699 0.640 +0 0.352 0.510 -1.026 1.753 1.285 0.732 0.148 -0.141 2.173 0.746 -0.080 -0.844 2.215 1.124 0.916 1.092 0.000 0.471 1.064 -0.496 0.000 0.802 0.934 0.984 0.974 0.656 0.855 0.751 +1 0.602 -1.540 0.219 0.247 -0.972 1.735 -1.302 0.422 2.173 1.171 -0.820 -1.633 0.000 0.957 -1.241 -1.008 0.000 0.550 -0.070 1.415 3.102 0.966 0.689 0.990 1.008 1.053 1.114 0.913 +1 0.793 1.167 -0.754 0.871 0.495 0.698 -0.540 -1.732 0.000 1.072 1.082 0.178 0.000 1.384 0.930 1.518 2.548 1.080 1.298 -0.241 0.000 0.604 0.993 1.040 0.862 0.761 0.840 0.732 +1 2.264 -0.102 -0.530 1.173 -1.559 0.746 -0.195 0.654 2.173 0.534 1.295 0.905 2.215 0.550 -0.236 1.638 0.000 0.600 1.133 -1.001 0.000 0.720 0.863 1.805 1.338 0.809 1.065 0.857 +1 1.971 -1.403 -0.427 0.870 -0.710 0.744 -1.138 1.454 0.000 0.651 -0.848 0.885 2.215 0.593 -1.311 1.705 2.548 0.449 0.124 1.026 0.000 0.698 0.536 0.995 0.821 0.483 0.735 0.739 +0 0.407 1.071 -0.437 2.735 -0.183 1.358 -0.042 -1.633 1.087 0.667 -0.888 1.308 0.000 0.777 0.553 1.337 0.000 1.396 -0.761 0.252 3.102 0.923 0.942 0.988 2.853 1.587 2.253 1.873 +1 0.792 -0.440 1.480 0.889 0.278 0.665 1.189 -0.391 1.087 0.526 -0.718 -1.473 1.107 0.689 -1.261 0.672 0.000 0.818 1.367 -1.182 0.000 1.969 1.233 1.027 1.119 1.218 1.020 0.901 +1 0.403 -1.974 -0.535 0.365 -0.793 1.021 -1.137 1.394 2.173 0.924 -0.156 -1.062 2.215 1.332 -0.932 -0.065 0.000 0.481 0.395 0.793 0.000 0.952 0.986 0.978 0.980 1.359 0.951 0.802 +1 0.708 -0.678 -0.475 1.088 1.546 0.677 -1.424 1.716 2.173 0.755 -0.036 0.576 2.215 1.093 -0.202 -0.044 0.000 1.324 -0.121 -1.126 0.000 1.099 0.909 1.178 0.787 1.204 0.885 0.779 +1 0.716 0.384 -1.463 0.910 -0.178 0.855 -0.307 1.305 2.173 0.447 0.630 -0.456 0.000 0.746 -0.505 -0.760 0.000 1.146 0.573 0.736 1.551 0.614 1.029 1.025 0.704 0.749 0.725 0.676 +1 0.870 -0.355 -1.737 1.753 -1.128 0.753 2.075 1.196 0.000 1.360 -1.039 0.427 2.215 0.749 0.914 0.060 0.000 1.135 -0.704 -0.220 0.000 0.858 0.872 0.987 0.540 0.845 0.874 0.735 +0 0.515 0.358 1.191 0.697 0.412 0.958 0.213 0.583 1.087 1.166 1.676 -0.754 0.000 0.736 0.564 -0.968 0.000 1.227 1.194 1.731 1.551 0.870 0.840 0.991 0.791 1.234 1.053 1.098 +1 0.395 -1.107 1.010 1.202 -0.101 0.770 -1.068 -0.687 0.000 0.919 0.665 1.366 2.215 1.340 0.167 -1.538 2.548 1.043 -0.229 0.228 0.000 1.153 1.219 0.987 0.967 0.661 1.007 0.877 +1 2.351 0.281 -0.077 0.436 0.494 0.883 0.257 1.178 1.087 0.806 1.256 -0.558 0.000 1.343 0.389 -1.677 0.000 0.703 0.977 0.817 0.000 0.929 0.967 0.984 1.178 1.026 0.950 0.870 +1 0.934 0.895 1.092 0.309 -1.536 0.826 0.822 -0.252 0.000 1.079 0.044 1.121 2.215 0.965 0.424 -1.370 2.548 0.522 1.745 -0.746 0.000 0.803 0.881 0.985 0.963 0.878 0.883 0.824 +1 0.505 -2.290 -1.683 1.270 0.423 0.606 -2.375 -0.850 0.000 0.409 -1.894 -1.080 0.000 1.359 -0.211 1.112 2.548 0.767 -1.975 0.509 0.000 0.980 0.672 1.050 1.341 0.791 0.931 0.870 +1 0.620 1.533 1.443 0.961 -0.090 1.542 -1.041 -1.611 0.000 0.903 1.421 1.006 0.000 2.225 0.541 -0.440 2.548 1.512 0.329 0.366 3.102 0.822 0.877 1.050 1.004 0.941 0.932 0.808 +1 1.229 0.345 -0.481 0.613 -1.691 0.907 0.630 1.118 2.173 1.004 -0.356 -1.131 2.215 1.712 0.813 0.048 0.000 1.573 1.698 1.297 0.000 1.967 1.493 1.067 0.746 1.459 1.346 1.111 +1 1.748 0.020 -0.467 1.432 -1.328 1.175 -0.281 1.065 2.173 0.670 -1.150 1.641 0.000 0.448 -0.275 0.625 0.000 0.508 -0.744 -1.009 3.102 0.768 0.769 1.533 0.745 0.822 0.991 0.846 +0 0.647 0.768 0.969 1.633 0.469 1.752 0.531 0.690 0.000 0.802 1.808 0.910 0.000 2.705 -0.469 -1.247 2.548 3.334 -0.735 -0.848 3.102 1.859 3.034 0.990 1.562 0.914 2.357 1.825 +0 0.404 -0.198 -0.839 1.507 1.275 0.533 0.024 -0.455 2.173 0.778 -0.837 0.368 2.215 0.833 -0.766 1.730 0.000 0.754 -1.146 -0.490 0.000 0.829 0.807 1.021 0.823 0.773 0.708 0.666 +0 3.288 1.124 -1.203 0.257 -0.304 1.738 0.389 0.381 1.087 0.492 -0.131 0.039 0.000 0.823 -0.271 1.211 0.000 1.106 0.224 -1.722 3.102 0.853 0.985 0.988 0.795 1.394 1.392 1.156 +1 0.647 -0.415 -1.729 1.521 -0.576 0.785 -0.185 0.496 2.173 0.807 -1.535 1.667 2.215 0.827 0.286 -0.778 0.000 0.737 -1.555 0.550 0.000 1.391 1.093 1.185 1.014 1.348 0.938 0.861 +1 0.435 0.675 0.884 0.872 -0.188 0.499 0.407 -0.129 0.000 0.475 0.901 1.713 2.215 1.140 0.902 -1.383 2.548 0.739 1.260 1.045 0.000 0.970 0.894 0.990 0.777 0.270 0.686 0.665 +1 0.543 0.339 -0.448 1.041 -1.225 0.406 -0.494 -1.530 0.000 0.379 0.720 0.664 2.215 0.748 -0.776 0.213 0.000 0.917 -1.017 0.686 3.102 1.006 0.776 0.987 0.763 0.610 0.628 0.590 +1 0.645 1.238 1.271 0.650 0.202 0.899 0.922 -0.624 2.173 0.905 0.352 0.974 0.000 1.798 0.353 1.645 0.000 1.388 -0.374 -0.347 3.102 1.114 1.282 0.992 0.911 0.929 1.080 0.894 +0 1.504 1.813 1.651 0.977 -0.922 0.675 0.369 -0.324 0.000 1.203 1.246 0.318 0.000 0.737 0.991 -1.451 0.000 0.820 0.218 0.782 1.551 1.173 0.849 1.232 0.885 0.194 0.718 0.760 +1 0.449 -0.835 -0.988 1.179 -0.174 1.605 -0.745 1.189 0.000 1.212 1.049 -0.438 2.215 1.018 0.565 -0.787 0.000 0.735 -1.206 0.892 0.000 0.872 0.894 0.994 1.626 0.686 1.592 1.229 +1 1.278 -1.050 -1.423 1.866 -1.037 0.608 0.045 0.198 2.173 1.119 -0.433 1.031 2.215 0.432 -0.169 -0.696 0.000 0.811 0.497 0.748 0.000 0.682 0.720 0.996 1.474 0.880 1.213 1.000 +1 1.317 0.291 -0.656 1.144 -0.130 0.850 -0.117 1.182 1.087 0.852 1.104 1.554 2.215 0.381 1.370 -0.210 0.000 0.464 1.691 0.321 0.000 0.459 0.844 0.987 1.079 0.933 1.012 0.789 +1 1.113 1.527 1.484 0.505 -0.585 0.695 -0.094 -0.666 2.173 0.390 -0.226 -0.082 0.000 1.022 -0.104 0.977 2.548 0.822 0.735 0.890 0.000 0.713 0.766 0.995 0.952 1.046 0.901 0.744 +1 0.583 0.917 0.940 1.274 -1.702 1.024 -0.073 0.110 2.173 0.718 -0.368 -0.791 2.215 0.797 0.362 0.858 0.000 1.026 0.265 -1.297 0.000 0.931 1.020 0.990 0.846 0.936 0.867 0.754 +0 0.873 -1.649 0.437 0.731 1.410 1.224 -0.545 -0.187 1.087 0.536 1.192 -1.452 0.000 0.987 0.717 -0.770 0.000 0.484 -1.980 1.100 0.000 0.689 0.855 0.992 1.096 0.801 0.936 0.981 +1 0.783 -0.201 0.450 0.614 0.187 1.944 -0.076 -1.253 2.173 1.710 -0.816 0.652 1.107 1.110 -2.328 -0.103 0.000 1.087 -1.467 1.293 0.000 0.898 1.439 0.982 1.587 2.854 1.840 1.648 +0 1.517 -0.242 -0.299 0.723 -0.159 0.855 -0.660 1.607 2.173 0.875 -0.081 0.852 2.215 0.395 -1.363 -1.150 0.000 0.454 -0.804 -1.513 0.000 0.196 0.674 0.986 1.220 0.885 0.944 0.729 +0 1.311 0.108 -1.666 0.365 -0.507 0.821 -0.182 -0.349 2.173 1.114 -0.454 0.999 2.215 0.759 2.213 -1.587 0.000 1.009 -1.002 0.467 0.000 0.846 1.506 0.986 0.854 1.334 1.317 1.106 +1 0.619 1.689 -1.406 1.007 -0.411 0.710 1.836 0.233 0.000 0.985 -0.012 -1.431 2.215 1.787 0.102 1.299 0.000 1.612 1.160 0.830 0.000 0.919 0.851 0.985 1.467 0.686 0.993 0.986 +1 0.763 0.739 0.345 0.712 1.100 0.965 -0.157 -1.258 2.173 0.581 0.568 -0.852 0.000 0.969 0.235 0.858 0.000 1.094 -1.311 -1.039 3.102 0.887 0.983 0.987 1.040 0.863 0.863 0.775 +0 1.394 -1.038 -1.604 1.092 1.128 0.676 -0.637 0.517 2.173 0.368 -1.611 -1.475 0.000 1.282 0.085 -0.473 2.548 0.642 -1.286 -0.273 0.000 0.560 0.839 1.074 0.953 1.003 0.941 0.777 +0 0.905 0.948 0.036 1.082 -1.163 0.841 0.468 1.122 1.087 0.691 -0.403 -0.040 0.000 0.565 0.362 0.596 0.000 0.700 1.814 1.603 0.000 0.890 0.962 1.209 1.059 1.130 0.833 0.749 +0 0.775 0.464 1.354 0.991 0.188 1.498 0.949 0.419 2.173 2.314 0.365 -1.302 0.000 0.845 0.306 -1.571 2.548 0.818 -0.172 -0.343 0.000 1.463 0.865 1.053 0.858 1.437 1.347 1.089 +0 0.526 -0.687 1.554 2.766 -0.764 0.881 1.487 0.927 0.000 0.987 -0.148 0.712 2.215 0.849 0.786 -1.491 2.548 0.877 -2.044 1.140 0.000 1.811 1.231 1.452 1.360 1.029 1.061 1.363 +1 0.991 0.004 0.705 1.195 -0.739 0.639 -0.749 1.691 2.173 0.743 -2.249 1.451 0.000 1.105 0.962 0.385 0.000 0.877 0.302 -1.044 3.102 3.797 2.172 1.453 1.040 0.680 1.352 1.172 +0 0.363 -1.732 -0.789 2.071 1.102 0.662 -1.157 0.205 0.000 0.644 -1.213 0.958 2.215 0.957 -0.371 -0.962 0.000 0.859 1.400 -0.255 0.000 1.363 1.012 1.191 1.051 0.642 0.720 0.817 +0 0.529 -0.252 -0.961 1.650 -0.564 0.924 -1.192 0.098 2.173 1.720 -0.150 1.353 0.000 0.968 1.435 1.724 0.000 1.087 0.326 -0.767 3.102 0.968 0.895 0.981 1.609 1.198 1.387 1.186 +1 1.227 0.492 0.443 1.480 -1.238 1.484 0.400 -1.114 2.173 0.804 2.285 0.043 0.000 0.369 0.330 0.814 2.548 1.282 -0.980 1.703 0.000 0.624 0.651 1.863 1.317 0.909 1.106 1.059 +1 0.604 -1.295 0.069 0.793 0.783 0.758 -0.485 -1.104 2.173 0.747 -0.168 0.700 2.215 1.424 0.157 -0.467 0.000 0.375 1.645 -1.681 0.000 1.096 1.011 0.986 1.305 1.120 1.051 1.202 +0 1.566 -0.173 0.736 1.850 -1.315 0.733 0.444 -1.518 2.173 1.041 0.453 0.138 0.000 0.877 -1.071 0.416 0.000 1.936 1.121 -1.301 0.000 1.063 0.904 2.268 1.357 0.888 0.998 0.979 +0 0.397 -0.803 0.027 1.017 -0.058 0.490 -0.044 1.621 0.000 0.676 -0.539 -1.050 2.215 1.301 1.268 -0.577 2.548 0.706 -0.213 0.803 0.000 0.887 0.936 0.987 2.764 1.204 1.754 1.438 +1 0.606 -1.310 1.567 0.917 -0.246 1.486 2.334 0.093 0.000 1.601 -0.851 -1.301 2.215 1.417 -0.446 1.714 1.274 1.524 -0.723 0.884 0.000 0.765 0.874 1.031 0.920 0.728 0.821 0.765 +0 1.559 -0.187 -1.727 0.333 1.738 0.647 -0.253 -0.359 1.087 1.119 0.421 0.512 0.000 0.977 -0.301 0.921 2.548 1.915 0.972 -0.799 0.000 1.273 1.059 0.984 0.767 0.906 0.774 0.776 +1 0.624 -0.479 0.230 1.307 -0.850 1.333 -0.620 -0.358 0.000 1.790 0.135 1.596 2.215 1.533 0.116 0.931 2.548 0.649 -0.872 1.013 0.000 0.961 1.431 1.034 1.245 0.992 1.318 1.092 +0 0.915 1.588 -1.124 1.047 -1.461 1.061 1.106 1.244 2.173 0.591 0.995 -0.688 0.000 0.991 1.064 0.223 1.274 1.342 2.022 -0.074 0.000 1.073 0.808 0.975 1.025 1.018 0.909 0.893 +0 0.368 1.543 0.216 1.509 -1.572 0.305 -0.777 1.080 2.173 0.627 -0.336 -1.081 2.215 0.478 0.754 -0.453 0.000 1.119 0.413 0.381 0.000 0.567 0.713 1.031 1.098 0.615 0.873 0.746 +0 1.269 -0.307 -1.573 0.426 0.500 0.770 -0.155 0.422 0.000 0.779 -1.450 -1.536 2.215 0.634 -1.304 0.235 0.000 0.949 -0.294 -0.613 3.102 0.883 0.769 0.987 0.776 0.739 0.773 0.726 +1 1.984 0.777 0.494 0.578 -0.956 0.469 1.264 1.164 2.173 0.553 1.389 -1.713 1.107 0.643 -1.235 -1.324 0.000 1.435 0.705 -1.168 0.000 0.762 0.995 1.431 0.950 0.394 0.721 0.794 +1 0.284 1.928 0.052 1.889 1.004 0.906 -2.170 -0.222 0.000 1.468 0.631 -0.971 2.215 2.292 0.130 0.430 0.000 0.993 -0.086 -1.440 3.102 4.024 2.385 0.996 1.299 0.614 1.978 1.840 +1 1.517 -0.218 -1.492 0.985 1.528 0.904 -0.639 0.343 2.173 0.665 1.220 0.307 0.000 0.414 -1.732 1.467 0.000 0.939 -0.335 -0.995 0.000 0.899 1.009 0.996 0.675 0.647 0.869 0.818 +0 2.636 0.295 -1.045 2.473 -0.724 1.982 -0.327 0.751 2.173 0.626 -0.162 0.125 0.000 0.770 -0.139 1.366 2.548 0.708 -1.922 1.675 0.000 1.405 0.983 1.012 2.683 0.821 1.703 1.481 +1 2.015 -0.460 0.973 1.126 0.680 0.796 -0.749 -1.239 2.173 1.262 -0.171 -0.298 2.215 0.888 1.153 -0.822 0.000 0.961 0.314 1.730 0.000 0.893 1.097 0.989 1.338 1.189 1.135 1.064 +0 0.791 0.797 0.013 0.266 -1.042 0.850 -1.778 0.691 0.000 0.597 2.123 -1.468 0.000 1.134 1.726 -0.248 0.000 1.332 0.934 -1.221 3.102 0.987 0.879 0.986 0.694 0.961 0.726 0.664 +0 0.803 2.166 0.242 0.871 1.314 0.741 1.405 -0.247 2.173 0.563 0.229 1.150 0.000 0.322 0.471 1.535 0.000 0.595 -1.646 -0.634 0.000 0.892 0.604 0.986 0.951 1.079 0.959 1.223 +1 0.585 -1.843 -0.910 1.232 0.606 1.188 -2.283 0.372 0.000 1.033 0.340 -1.432 1.107 0.444 -1.106 0.079 2.548 0.824 -1.012 -1.327 0.000 1.740 0.989 1.152 1.597 0.940 1.418 1.171 +0 1.398 0.743 -0.171 0.585 -0.429 0.568 -0.732 1.420 2.173 0.422 -1.919 -1.366 0.000 0.527 -1.652 0.132 0.000 1.077 0.459 1.389 3.102 0.708 1.082 0.984 1.450 0.572 0.984 1.165 +0 0.534 0.725 0.560 2.122 0.940 2.734 0.648 -0.848 2.173 1.059 0.826 -0.460 0.000 4.288 0.213 1.023 2.548 0.435 0.136 -1.249 0.000 0.655 0.788 0.991 1.708 4.332 2.398 1.795 +0 1.160 0.434 -1.459 1.680 1.470 1.069 1.285 -0.135 1.087 0.818 2.849 1.073 0.000 0.596 0.143 -1.021 0.000 0.562 0.095 0.862 0.000 0.633 0.928 0.989 0.818 0.561 1.052 0.814 +0 0.879 -0.837 -0.121 0.823 -0.525 1.227 -0.833 -1.071 0.000 1.174 1.004 0.787 0.000 1.610 0.220 0.692 2.548 1.789 -0.932 -1.623 0.000 1.124 0.822 0.992 0.953 0.537 1.057 0.960 +1 0.324 -0.596 0.920 1.207 -1.088 1.154 -0.055 1.276 2.173 0.967 1.614 0.190 0.000 0.644 0.821 -0.513 2.548 0.729 -0.005 -0.497 0.000 1.202 0.741 0.989 1.312 1.199 1.059 1.265 +0 1.546 -0.825 -0.568 2.154 -0.029 1.267 -0.241 1.358 0.000 1.672 0.028 -1.608 2.215 0.714 0.768 1.528 2.548 1.949 -0.346 0.243 0.000 1.431 1.132 1.182 1.821 0.605 1.315 1.237 +0 0.481 -1.166 -1.721 1.030 1.628 0.954 1.204 0.594 0.000 0.891 0.615 -1.120 2.215 0.734 1.173 -0.541 2.548 0.476 -0.122 0.081 0.000 0.885 0.819 0.976 2.095 0.515 1.792 1.841 +1 0.433 -0.259 1.216 0.952 0.120 0.886 1.038 -0.364 0.000 0.807 0.860 -0.943 0.000 1.632 1.090 1.035 2.548 1.040 -0.115 -1.604 3.102 0.907 1.008 0.991 0.785 0.993 0.963 0.824 +0 1.067 -1.079 0.736 0.948 1.289 0.942 -0.975 -1.187 0.000 2.344 -0.818 -0.591 0.000 1.712 0.088 0.674 2.548 2.121 -1.799 1.478 0.000 1.634 1.648 0.990 0.760 0.560 1.403 1.224 +1 0.369 0.660 1.732 0.098 -0.648 2.617 1.111 0.657 0.000 2.247 -0.542 -1.032 0.000 1.207 -1.331 1.595 1.274 2.166 -0.944 -0.633 3.102 1.625 1.095 0.838 0.654 1.138 0.939 0.792 +0 0.589 -0.595 0.733 1.041 0.104 0.833 -1.210 1.452 2.173 0.751 -1.220 -0.814 0.000 0.924 -0.510 -0.467 2.548 0.425 -1.480 1.725 0.000 0.588 0.801 0.986 0.745 1.140 0.922 0.825 +0 0.999 -1.420 0.534 1.497 0.946 0.598 -1.028 -0.054 0.000 0.760 -0.382 -1.221 2.215 0.615 -0.411 -0.659 0.000 1.450 0.184 -1.487 1.551 0.652 0.955 0.994 1.350 0.371 1.216 1.028 +0 0.531 -0.441 -0.710 2.335 0.111 2.794 0.285 -1.611 2.173 2.270 0.258 0.288 0.000 0.526 0.987 1.388 1.274 0.747 -0.531 -1.332 0.000 1.863 1.252 1.041 2.405 0.889 1.601 1.500 +1 0.754 -1.222 -0.374 0.476 -1.187 0.820 -0.084 0.025 0.000 1.207 -0.880 1.238 2.215 0.769 -0.809 -1.227 0.000 0.994 0.279 -1.723 3.102 0.899 0.851 0.990 1.125 0.794 0.931 0.816 +0 0.639 -0.527 1.259 0.868 0.950 0.595 -0.285 -1.492 2.173 1.137 1.394 -0.539 2.215 0.780 2.084 -0.248 0.000 0.680 1.990 0.314 0.000 0.392 0.628 0.986 0.826 1.492 1.032 0.990 +1 2.215 0.371 0.053 0.907 -0.404 1.580 0.454 1.648 0.000 0.498 -0.433 0.813 1.107 0.320 0.168 -0.892 0.000 0.579 0.777 -0.967 3.102 0.978 0.952 0.998 0.648 0.604 0.629 0.893 +0 0.645 0.501 -1.263 0.470 -0.245 1.073 1.419 -1.669 0.000 1.529 0.139 0.169 1.107 0.928 -0.721 0.683 0.000 0.947 -0.304 -1.067 3.102 2.875 1.727 0.984 1.111 1.014 1.355 1.131 +1 1.935 0.093 0.397 1.395 0.186 1.188 -0.360 -1.446 2.173 0.370 -1.038 -1.369 0.000 0.617 -2.203 -1.591 0.000 0.463 0.538 -0.421 0.000 0.661 0.669 0.977 0.538 0.739 1.098 0.874 +0 1.827 0.076 1.436 1.031 -0.468 1.050 0.140 0.054 0.000 0.560 -0.822 -0.593 0.000 0.398 -0.633 -0.107 2.548 0.604 -0.070 -1.111 3.102 1.226 0.823 1.881 0.960 0.317 0.625 0.729 +1 0.830 -1.064 -0.818 0.439 1.202 0.905 -1.727 1.367 0.000 1.443 -1.642 -0.299 2.215 0.949 -1.426 -1.509 0.000 1.170 -1.171 0.641 3.102 0.882 0.839 0.986 0.811 0.894 0.942 0.786 +0 0.660 -1.986 -1.308 1.060 0.162 1.050 -0.837 0.965 2.173 1.470 -1.975 -0.168 0.000 2.136 -0.449 1.586 2.548 3.257 -1.096 -0.812 0.000 1.878 2.170 1.124 1.406 1.043 1.692 1.366 +1 1.161 -0.814 1.692 2.167 -1.197 0.816 0.357 0.087 1.087 0.938 -0.816 0.828 2.215 0.894 -1.494 0.382 0.000 0.613 -0.641 -1.181 0.000 0.885 1.154 1.128 1.179 1.143 1.195 0.999 +1 0.577 1.015 -1.222 0.825 -0.058 1.370 1.053 -0.489 2.173 0.998 0.490 1.029 0.000 1.455 -1.684 1.472 0.000 1.267 1.072 1.499 0.000 0.842 0.673 0.984 0.828 1.003 0.987 0.860 +0 0.577 -0.263 -1.424 0.794 0.305 0.809 -1.081 1.041 2.173 1.392 0.387 -0.881 2.215 0.584 -0.307 1.352 0.000 0.878 0.700 0.073 0.000 0.874 0.962 0.984 0.756 1.999 1.031 0.854 +1 2.383 -0.322 -0.920 0.528 -0.697 1.099 1.574 1.009 2.173 0.769 -0.772 -0.922 0.000 1.151 -0.693 0.826 0.000 0.447 0.822 1.344 1.551 1.443 0.962 0.978 2.062 0.316 1.310 1.279 +0 0.739 1.142 1.284 0.945 0.128 0.527 0.726 -0.150 2.173 0.823 2.255 -1.497 0.000 1.057 0.780 0.843 2.548 0.946 -0.033 -0.668 0.000 0.859 0.851 0.999 0.668 0.727 0.591 0.574 +1 0.608 0.729 1.648 1.127 -0.947 0.937 1.175 1.427 2.173 0.506 -0.311 -0.507 1.107 0.420 2.247 -0.371 0.000 0.936 0.722 0.217 0.000 0.708 0.980 0.983 0.861 1.303 0.826 0.756 +1 0.687 0.285 -0.667 0.233 0.478 0.733 0.154 1.666 0.000 1.403 -0.573 1.026 2.215 1.546 -0.861 -0.568 1.274 1.071 -0.625 -1.294 0.000 0.885 1.067 0.987 1.266 1.576 1.197 1.047 +0 1.546 -1.760 -0.550 1.508 -0.105 1.498 -1.324 1.555 0.000 0.225 -1.402 1.027 0.000 0.547 -0.005 0.184 2.548 1.135 -0.254 1.528 0.000 1.062 0.783 0.989 0.490 0.252 0.525 0.638 +0 0.499 -0.452 -1.221 0.313 0.282 1.121 0.623 -0.851 2.173 1.631 -0.668 0.860 2.215 0.607 -0.407 -0.592 0.000 0.517 0.080 1.407 0.000 0.625 0.866 0.990 0.782 2.436 1.198 0.904 +0 1.145 0.971 0.265 0.506 -0.732 0.849 1.028 1.009 2.173 1.129 -0.613 -1.529 2.215 0.776 -0.653 -0.383 0.000 0.796 -1.168 -0.643 0.000 0.364 0.798 0.990 0.882 1.730 1.115 0.991 +0 2.109 0.829 -0.232 0.609 1.696 1.414 0.064 0.022 0.000 1.270 0.706 -1.580 0.000 1.581 -0.256 1.576 2.548 1.337 1.008 1.549 3.102 0.861 1.270 1.549 1.419 0.908 1.047 1.024 +0 1.140 0.551 -0.655 1.880 -1.215 0.953 0.253 -0.513 0.000 1.284 -2.202 1.102 0.000 1.912 -0.345 0.436 2.548 1.244 0.269 1.558 3.102 1.634 1.149 0.988 1.573 1.084 1.084 1.014 +0 0.957 0.902 -1.418 0.476 1.723 0.937 -2.683 0.256 0.000 1.366 -0.873 1.740 2.215 1.197 1.489 -0.350 2.548 0.675 -1.540 0.429 0.000 0.570 1.805 0.987 1.074 2.571 2.666 2.088 +1 0.285 0.871 0.008 1.298 0.277 0.832 -1.822 -1.554 0.000 0.925 -1.033 -1.404 0.000 1.057 -0.677 0.980 2.548 1.038 -0.476 -0.308 3.102 0.784 1.025 0.983 2.140 0.737 1.576 2.339 +1 1.974 -0.009 -0.801 0.329 -1.380 0.860 -0.239 0.808 0.000 0.663 0.801 0.883 0.000 0.887 -1.066 -1.197 0.000 0.813 0.359 -1.300 3.102 0.902 0.884 0.988 0.796 0.587 0.587 0.756 +1 0.643 -0.689 -0.362 2.068 -0.793 0.317 0.398 1.269 0.000 0.937 -1.152 0.801 2.215 0.895 1.312 0.272 0.000 0.907 -0.660 1.212 0.000 0.913 0.874 0.987 1.169 0.754 0.901 1.157 +1 0.990 1.161 0.151 0.918 1.508 1.090 0.764 1.599 2.173 0.771 0.372 -0.533 2.215 0.668 1.667 1.614 0.000 1.432 1.915 -0.112 0.000 1.105 1.011 1.242 0.981 1.293 0.991 0.864 +0 1.774 0.690 -1.043 0.504 -0.332 0.560 -1.958 0.803 0.000 1.170 0.962 -1.685 0.000 0.940 -1.038 -0.531 2.548 2.253 -0.039 0.351 3.102 1.423 1.417 0.989 1.206 1.018 1.131 1.087 +1 0.657 1.143 1.170 0.666 -1.016 0.937 1.058 0.130 0.000 1.357 1.256 -1.206 2.215 0.419 1.559 0.399 0.000 0.739 0.153 1.576 3.102 0.719 0.788 0.990 0.805 0.752 0.824 0.721 +1 0.777 0.438 0.259 0.587 -1.717 0.946 -0.706 -1.299 2.173 1.515 -0.181 0.647 2.215 0.582 -1.415 -0.684 0.000 0.546 -0.314 -0.692 0.000 0.388 1.015 0.983 0.925 1.794 0.943 0.791 +0 1.806 1.848 -0.973 0.721 -1.389 1.046 -0.340 0.813 2.173 0.397 -0.501 0.088 0.000 0.451 1.388 0.438 1.274 0.366 2.068 0.718 0.000 1.089 1.117 0.973 0.766 0.964 1.603 1.271 +0 0.657 -0.367 0.936 0.993 -0.222 0.812 0.583 0.071 1.087 1.121 -1.529 1.740 0.000 1.218 -0.617 -1.519 0.000 0.457 2.474 -1.053 0.000 0.906 0.856 0.989 0.858 0.714 1.161 0.955 +1 1.031 -0.302 -1.196 0.198 0.173 0.794 -0.337 1.579 0.000 1.047 0.890 0.097 2.215 0.582 -0.634 0.666 0.000 0.516 1.035 -0.906 3.102 0.923 0.844 0.987 0.903 0.532 0.837 0.742 +1 0.845 -0.561 1.269 2.064 -1.637 1.052 0.221 0.198 0.000 1.569 -0.774 -0.650 1.107 0.530 -1.475 0.760 0.000 0.949 -0.739 0.418 3.102 0.471 0.988 0.996 0.851 0.906 0.941 0.787 +0 0.802 -0.450 -0.275 0.579 -0.531 1.010 -1.537 1.483 0.000 0.969 -1.947 0.020 0.000 1.048 -0.602 0.493 1.274 1.649 -1.100 -1.629 0.000 0.690 1.132 0.984 0.503 0.679 0.765 0.768 +0 1.493 0.571 0.319 0.585 -0.417 0.634 1.473 -1.575 1.087 0.718 2.164 -1.040 0.000 0.581 1.629 1.425 0.000 1.018 0.546 0.666 3.102 0.818 0.899 0.983 1.031 0.848 0.718 0.742 +0 1.021 -1.082 -1.035 0.562 -1.476 1.414 -0.256 -1.709 0.000 1.786 -1.686 -0.008 0.000 1.735 -0.087 0.085 1.274 2.224 -2.212 -1.541 0.000 0.857 0.774 0.992 1.051 0.878 0.871 0.731 +1 0.578 0.163 -0.534 1.293 0.570 0.881 1.792 -0.549 0.000 1.435 -1.246 -1.540 1.107 0.622 -1.453 -0.113 0.000 1.875 -0.910 1.175 1.551 0.426 0.870 1.005 0.988 0.959 1.036 0.850 +1 0.621 0.102 0.586 0.716 -0.506 0.598 0.753 0.296 0.000 0.913 1.240 -1.672 2.215 0.778 0.028 -0.334 0.000 1.417 0.077 1.672 3.102 0.803 0.965 0.987 1.208 0.657 0.844 0.813 +0 1.440 0.186 -0.506 0.837 1.305 0.470 0.424 -1.630 0.000 1.173 0.102 0.129 2.215 1.036 0.414 1.192 0.000 0.781 2.451 1.501 0.000 0.706 1.097 1.518 0.804 0.736 0.733 0.735 +0 3.021 0.011 -1.714 2.751 -1.559 1.541 -1.833 0.142 0.000 3.094 -0.046 0.351 2.215 1.617 -0.485 -1.203 1.274 0.819 -0.782 1.141 0.000 1.022 0.902 0.998 2.874 2.414 1.952 1.463 +0 1.388 -0.457 1.176 1.108 1.724 0.875 0.918 0.362 2.173 0.629 1.295 -0.486 0.000 0.839 -0.709 -1.047 2.548 0.853 -0.291 0.177 0.000 1.049 0.989 0.984 1.563 1.446 1.127 1.036 +1 0.633 -0.776 -1.364 0.315 0.241 0.829 0.163 1.377 2.173 0.345 -0.389 0.854 0.000 1.438 0.878 -0.480 2.548 0.476 -1.467 -0.117 0.000 0.561 1.072 0.989 0.754 1.464 0.892 0.749 +1 0.903 0.326 0.049 0.403 -1.484 0.611 -0.417 -1.443 1.087 0.917 -1.658 1.005 0.000 0.732 1.469 -0.915 0.000 0.876 0.067 -0.143 0.000 0.936 1.025 0.988 0.573 0.781 0.661 0.620 +1 0.630 1.030 -0.339 0.917 0.750 0.648 0.887 0.590 0.000 0.910 0.250 -1.073 2.215 1.158 -0.581 -1.598 2.548 0.786 -0.532 0.660 0.000 0.924 1.118 0.988 1.358 0.709 0.996 0.925 +1 0.643 1.652 1.314 0.822 0.297 0.949 0.731 0.478 1.087 1.002 0.431 -1.018 2.215 0.723 -0.149 -1.346 0.000 0.466 -0.259 -0.217 0.000 0.547 0.933 0.987 0.930 1.416 0.821 0.722 +0 0.658 -0.020 -1.433 0.813 0.078 1.526 0.241 -0.966 0.000 1.173 0.134 0.854 1.107 0.269 -1.591 0.202 0.000 0.458 1.051 -0.866 3.102 1.678 0.985 0.991 0.800 0.769 0.992 0.815 +1 0.890 1.176 1.335 0.709 -1.082 1.161 0.864 -0.627 2.173 1.487 0.019 1.130 0.000 0.339 -0.132 0.043 0.000 0.688 0.629 0.643 3.102 0.908 0.601 0.987 0.957 0.863 0.945 0.825 +1 0.693 0.131 -0.041 0.912 1.508 0.814 -0.120 -1.213 0.000 0.569 -1.287 -1.297 0.000 1.389 -0.088 0.797 2.548 1.727 -0.497 0.009 3.102 0.930 1.126 1.084 0.721 0.828 0.928 0.814 +1 2.035 -0.933 0.827 0.557 -1.480 0.816 0.160 -1.175 2.173 0.774 -0.006 -0.110 2.215 0.411 0.680 -0.906 0.000 0.910 -1.093 -1.685 0.000 0.932 0.854 1.289 1.327 0.963 1.000 0.837 +1 0.792 -0.079 -1.313 0.521 -1.564 1.441 -0.161 0.316 2.173 0.711 -1.261 0.674 0.000 0.617 -1.004 1.514 2.548 0.850 -1.879 -1.694 0.000 0.994 0.648 0.986 1.424 1.184 1.036 1.123 +1 0.625 0.090 -1.275 0.819 -1.611 0.971 0.055 0.864 2.173 0.539 -0.242 -0.336 2.215 0.780 1.290 0.507 0.000 0.749 0.381 -0.520 0.000 0.794 0.950 0.987 0.743 0.955 0.832 0.850 +1 1.448 0.108 -0.536 0.756 -1.610 1.154 -1.456 -1.528 0.000 1.595 -2.176 1.144 0.000 1.290 1.185 -0.238 0.000 2.161 -0.678 0.066 3.102 0.884 0.621 1.193 1.055 0.887 0.945 0.877 +1 0.587 1.140 0.195 0.717 0.219 0.518 2.926 0.858 0.000 0.768 1.038 -0.686 2.215 0.803 0.395 1.680 1.274 0.702 1.983 -1.429 0.000 0.855 1.134 1.003 0.781 0.756 0.892 1.021 +1 0.408 -1.375 0.076 0.948 -0.243 0.522 -0.554 -0.568 0.000 0.644 0.614 -0.393 0.000 1.518 0.163 1.436 0.000 1.183 -1.180 1.071 3.102 0.801 0.878 0.986 0.910 0.301 0.749 0.693 +0 1.530 -1.100 -0.551 0.348 -0.695 0.569 -0.851 0.123 2.173 0.769 -0.141 1.499 0.000 1.758 0.126 1.029 0.000 0.570 -1.303 -1.424 3.102 0.775 0.862 0.990 0.773 0.631 0.763 0.941 +0 0.777 0.196 -0.328 1.498 -0.489 0.748 -0.445 1.671 0.000 0.709 -1.393 1.526 0.000 0.958 1.166 0.833 0.000 1.489 -0.134 -0.690 3.102 0.947 0.944 0.976 0.738 0.760 0.707 0.755 +1 0.626 -1.904 -1.432 1.225 0.771 0.526 -0.317 -0.356 2.173 0.461 -0.545 -1.666 0.000 0.836 0.300 -0.775 0.000 1.195 0.984 0.487 3.102 0.813 0.928 1.111 1.091 0.900 1.243 1.040 +1 0.440 -0.738 1.663 1.597 -0.026 1.622 -2.425 1.112 0.000 1.062 -0.972 -0.861 2.215 0.982 -2.080 -1.701 0.000 1.901 -0.662 -0.239 3.102 0.592 0.849 1.160 0.675 0.695 0.779 0.787 +0 1.840 -0.674 -0.705 1.012 -1.247 1.074 -0.300 0.621 0.000 0.949 0.099 1.238 2.215 0.843 0.146 -1.444 2.548 0.544 0.373 -0.156 0.000 0.933 0.951 0.988 0.785 0.631 0.857 0.945 +1 0.402 -1.780 -1.184 0.276 0.724 0.907 -1.129 -0.413 0.000 0.872 -0.434 -1.249 2.215 0.903 -0.906 0.230 0.000 1.141 0.020 0.912 0.000 0.898 1.071 0.981 0.642 0.763 0.837 0.716 +0 0.753 -0.153 -0.866 1.029 1.640 0.530 -2.260 -0.153 0.000 0.725 -0.955 -1.033 0.000 0.671 0.343 1.604 2.548 2.069 -0.270 0.504 3.102 1.312 1.345 0.991 0.951 0.820 1.039 0.909 +1 1.696 -1.657 -0.665 0.683 -1.214 0.944 -1.153 0.973 2.173 0.558 -0.517 -1.514 2.215 0.705 -2.316 0.732 0.000 0.918 -1.335 -0.329 0.000 0.909 0.864 0.990 1.366 0.904 0.984 0.847 +0 1.646 -0.031 -0.719 1.286 -0.239 1.056 -0.256 1.362 0.000 0.475 -0.389 -0.197 1.107 0.663 -0.130 -1.624 0.000 1.408 -0.159 0.545 0.000 0.988 0.834 0.978 0.556 0.596 0.572 0.630 +1 1.824 -0.111 -0.650 0.632 -0.048 0.733 -0.050 0.069 0.000 1.313 -0.163 1.231 2.215 0.747 -0.504 1.502 1.274 0.622 0.561 -1.723 0.000 0.902 1.046 0.990 0.902 0.327 0.890 0.783 +0 0.745 -0.859 -1.354 1.732 -0.704 0.606 -0.246 0.180 2.173 0.799 0.825 1.468 0.000 0.980 -0.888 1.257 2.548 0.572 -0.105 -0.999 0.000 0.831 0.960 0.987 0.963 0.869 0.801 0.806 +0 0.478 1.131 1.149 0.361 -0.886 0.730 1.409 -1.430 2.173 0.535 1.238 1.485 0.000 0.873 0.641 0.092 2.548 0.910 -0.269 -0.784 0.000 0.904 0.879 0.994 0.678 1.041 0.726 0.653 +1 1.052 -0.045 -0.633 1.212 0.147 0.932 0.467 1.185 2.173 0.994 -0.533 -0.941 0.000 0.728 -0.616 -1.656 2.548 1.024 -0.097 0.056 0.000 1.050 1.246 1.012 0.837 0.839 0.859 0.814 +0 0.896 -0.111 0.195 0.826 1.419 0.587 -0.522 -1.080 1.087 0.647 1.124 -0.247 0.000 1.332 0.240 -1.554 2.548 0.716 0.378 0.813 0.000 0.790 0.997 1.064 0.830 0.639 0.739 0.709 +1 1.775 -0.030 0.599 0.271 0.043 0.910 -0.155 -1.689 2.173 0.716 -0.084 -0.931 0.000 0.763 -0.336 0.191 0.000 1.058 0.870 -1.184 3.102 0.974 1.020 0.988 0.903 0.805 0.862 0.796 +1 0.626 -0.067 -1.208 0.278 -0.607 1.271 0.654 0.304 2.173 1.486 1.891 -1.193 0.000 0.867 1.916 1.361 0.000 1.507 -0.029 1.044 3.102 1.303 1.738 0.989 1.504 1.043 1.468 1.586 +1 1.210 0.137 0.766 0.401 1.559 1.041 0.338 -0.644 2.173 1.438 -0.696 1.601 0.000 0.619 0.526 -0.252 2.548 0.711 -0.284 -0.086 0.000 1.064 1.066 0.987 1.168 0.366 0.990 0.881 +1 0.497 -0.887 0.723 2.221 1.539 1.334 0.042 -0.065 0.000 0.630 0.141 -1.521 2.215 0.692 0.860 -1.423 0.000 0.772 -0.975 -0.921 3.102 1.814 1.256 0.988 0.744 0.550 0.864 0.970 +1 1.039 -1.923 0.051 0.449 0.432 1.010 0.144 1.585 2.173 0.646 -0.010 0.361 0.000 0.580 0.392 -0.894 0.000 0.721 0.055 -0.618 3.102 0.875 1.017 0.975 0.731 0.827 0.963 0.838 +1 0.694 -2.157 0.736 1.403 -1.709 0.831 -0.920 -0.128 2.173 1.125 -0.348 -0.765 1.107 0.991 -0.364 1.395 0.000 0.546 -0.050 0.880 0.000 0.968 0.993 1.104 1.498 0.875 1.166 0.984 +0 0.840 -0.786 -1.486 0.870 1.032 1.204 0.669 1.065 0.000 1.165 -0.657 -0.938 2.215 1.538 0.023 -0.217 0.000 1.127 -1.033 -0.732 3.102 2.381 1.834 0.986 0.906 0.353 1.325 1.170 +0 0.637 0.411 0.111 0.364 -1.464 1.092 0.249 1.136 1.087 0.909 1.050 -0.209 2.215 1.122 0.755 -0.964 0.000 1.074 1.079 -0.941 0.000 0.985 0.829 0.994 0.922 1.509 0.910 0.859 +0 0.654 -0.750 -0.137 0.381 1.168 0.993 -0.711 -0.524 0.000 1.434 -0.496 1.278 0.000 0.754 -1.207 -1.176 2.548 0.451 0.812 0.568 1.551 1.128 1.066 0.985 0.810 0.775 0.706 0.762 +0 0.720 0.518 0.217 0.693 1.653 0.493 1.074 -0.493 2.173 0.459 1.908 1.400 0.000 0.393 0.110 1.122 0.000 0.416 1.017 1.294 0.000 0.708 0.871 0.990 0.683 0.615 0.637 0.587 +1 0.463 -0.246 1.310 0.474 0.726 0.606 -0.496 -1.322 1.087 0.379 2.417 0.079 0.000 1.048 0.045 -0.158 2.548 0.764 1.027 1.716 0.000 0.841 1.015 0.980 0.977 0.904 0.934 0.812 +1 1.708 -0.077 -0.098 1.133 -0.715 0.741 0.639 1.336 2.173 0.982 0.347 -1.433 2.215 0.522 0.547 0.255 0.000 0.674 -0.597 1.113 0.000 0.650 0.791 1.015 1.273 0.776 0.972 0.792 +0 0.676 0.556 1.494 0.478 -0.453 0.549 -0.256 -0.020 0.000 0.472 1.039 -0.222 0.000 0.494 -0.965 1.466 1.274 1.218 -0.892 -1.401 3.102 0.797 1.055 0.987 0.859 0.314 0.778 0.767 +1 0.795 -0.427 0.789 1.876 1.411 1.426 -0.569 -0.225 2.173 0.353 0.057 -0.790 0.000 0.347 -0.219 -1.627 2.548 1.003 -0.894 -1.684 0.000 0.726 1.051 0.983 0.539 0.849 0.981 0.811 +0 0.416 -1.827 1.318 1.242 -0.210 0.762 -1.094 0.086 2.173 1.006 -0.006 1.374 0.000 1.572 -0.373 -1.505 0.000 0.585 -1.556 -0.515 0.000 0.796 0.843 0.986 0.665 0.533 0.788 0.746 +0 0.449 -0.968 1.600 1.430 -1.015 0.641 -0.304 0.146 2.173 1.125 0.870 0.869 2.215 0.780 0.598 -1.528 0.000 0.701 1.071 -0.312 0.000 0.792 0.979 0.992 0.916 1.100 0.941 0.791 +1 0.563 -1.594 1.430 1.950 0.708 0.698 -1.555 -1.534 2.173 0.861 -0.268 -1.014 2.215 0.610 0.180 0.241 0.000 0.721 -1.510 -0.699 0.000 1.020 1.008 0.987 1.528 0.944 1.102 0.996 +0 0.537 -0.800 -0.244 2.371 -0.687 2.183 -0.319 0.979 2.173 1.136 -0.053 0.591 0.000 2.228 0.134 -0.705 0.000 1.679 -0.227 -1.401 1.551 0.625 1.066 0.996 2.106 1.701 1.450 1.183 +0 2.459 1.308 -1.340 1.364 -1.241 1.315 0.556 0.611 1.087 0.723 1.092 0.198 0.000 1.236 -0.018 -0.012 0.000 0.626 -0.417 -1.019 3.102 0.917 0.940 0.991 0.829 1.095 1.253 1.144 +1 2.337 -1.525 -0.126 0.234 -1.309 1.031 -1.407 0.576 2.173 1.611 -1.296 -1.399 0.000 1.092 -0.786 1.469 2.548 0.704 -2.340 -0.663 0.000 1.408 1.134 0.985 0.949 1.023 1.078 1.019 +1 0.600 1.295 0.591 1.280 -1.266 0.674 0.655 -0.029 2.173 1.058 1.395 1.344 0.000 1.416 0.552 -0.559 2.548 1.347 0.268 1.220 0.000 0.929 1.198 1.208 0.901 0.562 0.946 0.858 +0 1.497 1.063 -0.932 0.237 -0.578 0.334 -0.029 -0.360 0.000 0.786 1.375 1.053 0.000 0.621 -0.407 1.460 2.548 0.996 1.308 0.162 3.102 1.346 0.973 0.998 0.779 0.903 0.697 0.717 +1 0.912 -0.890 -0.370 0.775 1.073 1.174 -0.448 -1.236 2.173 1.015 -1.093 0.341 0.000 0.688 -0.285 0.919 0.000 1.000 -0.613 1.569 1.551 0.832 0.761 1.122 1.039 0.680 0.887 0.780 +1 0.903 -1.971 0.797 0.768 -0.526 1.085 -0.717 -1.125 2.173 0.814 -1.644 -1.681 0.000 1.067 1.878 0.246 0.000 1.590 -1.104 0.526 0.000 0.852 0.952 1.072 0.988 0.869 0.935 0.797 +0 0.744 -1.368 0.370 1.613 0.450 0.965 -0.274 -1.424 2.173 0.518 -0.546 -0.783 0.000 0.699 -0.698 1.512 2.548 0.781 0.410 0.006 0.000 0.709 0.862 0.976 0.776 0.548 0.892 0.771 +0 1.044 -1.073 0.189 0.531 -0.968 0.734 -0.644 -0.936 0.000 0.900 -0.792 0.911 2.215 1.117 -0.855 1.563 1.274 0.790 -1.364 -0.191 0.000 0.939 0.944 0.986 0.761 0.595 0.761 0.693 +0 0.800 0.321 -1.096 1.269 -0.678 1.208 -0.513 1.720 2.173 0.736 0.835 0.268 2.215 0.634 -1.360 0.379 0.000 0.440 1.958 -0.038 0.000 1.962 1.257 0.978 1.096 1.693 1.210 1.078 +1 0.365 -0.761 -0.734 2.465 -1.611 1.754 0.834 -1.695 2.173 1.693 -0.098 0.336 0.000 2.509 1.093 -0.294 0.000 0.954 1.275 0.190 3.102 1.057 0.658 0.985 1.339 1.441 1.214 1.344 +1 0.669 -0.898 -1.284 1.160 -0.208 0.933 -0.155 0.481 0.000 1.371 -0.595 -1.654 2.215 0.794 1.802 -0.557 0.000 0.896 0.057 -0.749 3.102 2.345 1.395 1.007 0.982 0.807 1.278 1.137 +0 0.593 -1.430 0.834 1.488 -0.807 0.912 -0.901 -0.943 2.173 1.585 -0.005 0.583 2.215 1.028 -0.014 1.083 0.000 0.477 0.360 -1.307 0.000 0.668 1.043 1.296 1.526 1.923 1.208 1.012 +1 0.773 0.898 1.701 0.790 -0.497 0.845 1.549 1.128 0.000 0.951 0.468 -0.318 2.215 0.631 0.300 0.851 0.000 0.772 -0.449 -1.283 3.102 0.923 1.084 0.993 0.727 0.721 0.890 0.768 +1 0.953 -0.205 0.999 0.514 -0.066 0.975 1.539 -1.562 0.000 1.088 0.311 0.196 1.107 0.917 -0.624 0.525 0.000 0.562 -0.398 -1.441 3.102 1.216 0.850 0.985 0.814 0.760 0.880 0.750 +1 0.740 -0.851 -1.295 0.760 0.334 1.425 -0.150 -0.200 2.173 1.188 -2.420 1.521 0.000 0.992 -0.366 1.478 0.000 0.628 0.329 0.895 3.102 0.939 1.347 1.034 0.640 0.880 0.819 0.756 +0 0.601 -1.811 1.188 0.845 -1.672 0.501 -1.613 -0.132 0.000 1.170 -0.392 0.773 2.215 0.902 0.059 -1.111 2.548 1.262 -0.720 -0.678 0.000 0.745 0.883 1.001 0.856 1.113 0.859 0.795 +0 0.335 2.180 0.357 1.986 -0.138 0.969 -0.732 1.553 1.087 0.981 -1.400 1.617 0.000 0.665 0.207 -0.711 0.000 1.153 0.159 1.632 3.102 0.935 0.935 0.979 1.871 0.544 1.228 1.284 +1 0.813 0.474 -1.013 1.236 -0.331 1.305 0.664 1.287 0.000 0.892 0.544 0.598 0.000 1.797 0.399 -0.268 2.548 1.384 1.237 1.539 0.000 0.974 0.886 0.990 0.715 0.865 1.051 0.964 +1 0.610 -0.597 1.201 1.076 0.011 1.130 -2.213 1.445 0.000 1.039 -0.982 -0.399 2.215 0.604 -1.938 0.163 0.000 1.937 -0.205 -0.464 3.102 0.932 1.051 0.988 0.757 0.507 0.884 0.788 +1 1.267 -1.487 -1.231 1.257 1.674 0.665 -0.560 -1.738 0.000 1.138 -2.013 -0.075 0.000 1.479 -0.409 -0.142 2.548 1.503 -0.313 0.921 3.102 0.832 0.980 0.989 1.154 0.933 1.082 0.908 +1 0.494 -1.155 0.730 0.291 -0.382 0.419 -2.230 1.185 0.000 0.193 2.663 1.315 0.000 0.527 0.397 -1.079 2.548 0.535 0.184 -0.423 1.551 0.817 1.031 0.988 0.517 0.230 0.678 0.654 +0 0.518 1.730 0.911 0.345 1.156 0.945 0.403 -0.689 2.173 0.793 0.152 -1.149 0.000 0.529 2.288 -0.075 0.000 1.216 0.151 1.459 3.102 1.675 1.199 0.989 0.971 1.068 0.904 0.813 +0 0.940 -1.159 -0.192 1.566 0.495 0.450 0.594 -1.455 0.000 0.505 1.042 1.069 1.107 0.685 -0.128 -0.671 0.000 1.198 -0.516 -1.479 3.102 0.799 1.023 0.989 0.940 0.850 0.910 0.779 +1 1.045 -0.183 1.449 0.646 0.844 0.886 -0.112 -0.739 1.087 1.648 0.383 1.183 2.215 1.819 0.327 -0.112 0.000 2.607 2.496 -1.091 0.000 1.321 1.125 0.981 0.627 1.813 1.145 0.972 +1 0.715 0.040 -0.879 0.991 -1.116 1.281 -0.171 0.983 2.173 0.729 -0.431 0.148 2.215 1.033 -0.706 -0.884 0.000 0.776 -0.744 0.879 0.000 0.989 0.783 0.983 1.378 0.992 0.982 0.840 +0 0.396 -0.917 -1.506 1.370 -0.624 0.631 -0.191 1.740 1.087 0.481 -1.302 0.024 0.000 0.750 -0.852 0.948 0.000 1.072 0.483 0.439 3.102 0.707 0.880 0.993 0.769 0.874 0.686 0.713 +1 0.833 -0.059 1.128 0.517 -1.132 0.651 -0.583 -0.397 0.000 0.844 1.043 1.240 2.215 1.080 -0.339 0.222 0.000 0.963 0.200 -1.022 3.102 0.873 0.676 0.986 0.662 0.806 0.796 0.699 +0 0.591 -0.857 0.600 2.762 1.196 0.587 1.427 -0.377 0.000 0.840 -0.034 -0.806 2.215 0.845 -0.655 -1.392 1.274 1.437 0.413 0.035 0.000 0.964 0.853 0.983 1.238 0.549 0.872 0.820 +0 0.759 1.242 1.069 0.951 0.333 0.814 1.009 -1.573 0.000 0.767 0.396 0.547 2.215 1.221 0.361 -0.975 1.274 1.045 0.828 -0.233 0.000 1.318 0.932 0.982 0.540 1.008 0.792 0.753 +1 0.856 0.447 -0.077 1.302 -1.442 1.142 1.284 1.128 2.173 0.510 1.350 0.311 2.215 0.639 -0.182 0.333 0.000 0.686 0.925 -1.400 0.000 0.893 1.032 1.378 0.894 0.757 0.891 0.766 +0 1.562 0.969 0.909 0.129 0.377 0.548 1.241 -0.864 0.000 0.906 -0.062 -1.711 2.215 1.017 0.961 -0.335 0.000 1.297 0.677 0.127 3.102 0.819 0.862 0.980 0.671 1.071 0.725 0.726 +0 0.387 1.397 -1.144 0.456 0.690 0.968 -0.080 0.298 0.000 0.888 -1.142 -1.340 2.215 0.415 0.561 -1.147 2.548 1.189 -0.827 0.573 0.000 0.871 0.874 0.984 0.906 0.668 0.866 0.762 +1 0.765 -0.213 1.689 2.183 0.964 1.078 -0.524 -0.751 1.087 0.482 0.936 -1.214 0.000 0.605 0.433 -0.035 2.548 0.703 0.241 0.692 0.000 0.795 1.040 1.088 0.844 0.803 0.996 0.844 +0 0.606 0.321 0.148 1.729 -0.963 0.625 -0.140 -1.638 1.087 0.868 1.379 1.512 2.215 0.951 1.900 0.524 0.000 0.389 2.293 -0.395 0.000 0.544 0.780 1.194 0.883 0.989 0.924 0.947 +1 0.369 -1.109 0.247 0.463 -0.160 0.929 0.474 0.509 0.000 0.849 1.317 -1.151 0.000 0.774 1.293 0.427 2.548 1.905 -0.063 -1.102 3.102 0.776 0.871 0.987 0.804 1.187 0.730 0.677 +1 0.982 -1.068 1.308 0.139 1.734 0.721 0.231 -1.268 2.173 0.991 -0.842 0.505 2.215 1.270 -0.962 -0.440 0.000 1.076 0.020 1.594 0.000 0.968 0.867 0.992 0.831 1.436 1.005 0.868 +1 0.305 -0.761 1.686 0.766 0.801 0.893 0.173 -0.688 2.173 0.989 0.037 0.060 2.215 1.398 0.446 1.117 0.000 1.028 1.012 -1.709 0.000 0.889 1.146 0.980 0.969 0.869 0.947 0.810 +1 0.944 0.400 0.208 0.419 -1.015 1.030 0.747 -1.156 2.173 0.839 -2.069 1.034 0.000 1.197 0.729 1.426 0.000 1.140 -2.028 0.325 0.000 0.947 1.200 0.990 0.661 0.899 0.965 0.865 +0 0.919 -0.262 0.255 0.808 0.438 0.912 0.340 -0.974 0.000 1.453 -0.358 0.981 2.215 0.927 -1.190 -1.191 2.548 0.831 -1.155 1.321 0.000 0.852 0.954 0.987 0.917 1.290 0.997 0.950 +1 1.515 0.690 0.523 0.219 1.372 0.751 0.051 0.186 0.000 1.183 -0.857 -0.740 1.107 1.660 -0.850 1.497 0.000 0.654 -0.276 1.576 1.551 2.124 1.167 0.985 1.566 0.725 1.011 1.091 +0 0.681 1.038 0.305 0.330 0.687 1.214 0.966 1.260 2.173 1.431 0.502 -0.382 2.215 0.915 1.866 -1.723 0.000 0.772 1.718 -0.298 0.000 0.890 1.140 0.996 0.871 1.983 1.164 1.025 +1 1.270 -0.071 -0.585 1.650 -1.052 0.216 1.627 1.617 0.000 0.622 0.405 1.209 0.000 0.585 1.136 0.062 0.000 0.878 -0.743 1.177 0.000 0.888 0.582 0.977 1.109 0.369 0.731 0.735 +1 1.295 -0.150 1.639 0.233 -0.943 0.647 -1.047 -0.415 0.000 0.803 -0.841 -1.398 1.107 0.600 -0.616 1.475 0.000 0.673 -0.005 -1.511 0.000 0.837 0.890 0.981 0.535 0.498 0.546 0.639 +1 1.452 -0.083 0.584 1.055 -0.079 0.754 0.685 1.608 2.173 0.513 -0.368 -1.176 2.215 0.604 0.570 0.155 0.000 1.226 0.682 -1.445 0.000 0.946 0.786 0.990 0.837 0.745 0.855 0.763 +1 0.672 -0.178 -0.724 0.901 1.398 0.947 0.848 0.589 2.173 0.905 0.909 1.257 0.000 1.970 0.164 -0.590 2.548 0.706 2.380 -1.176 0.000 0.880 0.786 1.016 0.900 1.599 0.967 0.846 +1 4.570 -0.893 -1.657 0.317 -0.391 2.550 1.647 -0.294 0.000 1.738 -1.288 0.796 0.000 2.580 0.079 0.304 0.000 2.134 0.001 -0.904 3.102 1.274 0.979 1.517 1.377 0.808 1.080 1.169 +0 2.702 -0.626 0.023 0.558 -1.570 0.501 -0.246 1.144 0.000 0.856 0.371 1.643 2.215 0.982 0.977 1.020 0.000 2.027 -0.467 -0.830 3.102 0.907 0.722 1.685 1.108 1.106 1.033 1.032 +1 1.282 2.147 0.804 1.817 0.530 1.523 -0.824 -0.848 0.000 0.756 1.596 1.128 0.000 1.316 0.683 1.547 1.274 0.744 1.036 -0.410 1.551 0.744 0.723 1.006 0.968 0.766 1.138 1.086 +1 1.008 -0.508 1.009 0.899 -0.122 0.577 -0.318 -1.233 0.000 1.023 -0.047 1.631 2.215 1.375 -1.106 0.174 1.274 0.796 -0.572 -0.843 0.000 0.405 0.974 1.123 0.921 1.446 0.838 0.769 +1 0.918 -0.848 0.232 1.577 0.881 0.446 -0.470 0.018 2.173 0.544 0.362 -0.874 0.000 0.536 1.451 -0.861 0.000 1.736 0.047 -1.263 3.102 0.538 0.791 0.987 1.122 0.888 0.788 0.818 +1 0.370 -0.720 0.219 1.117 -0.370 0.606 1.721 -1.626 0.000 0.838 -0.442 -0.637 2.215 0.894 1.114 1.059 0.000 1.342 0.082 0.690 0.000 0.904 0.954 0.984 0.579 0.546 0.681 0.676 +0 0.945 0.508 1.439 0.697 -1.727 0.697 -2.295 -0.014 0.000 0.829 -0.009 -0.757 1.107 1.016 0.612 0.521 0.000 0.940 1.396 1.003 0.000 0.724 1.083 0.994 0.826 0.579 0.742 0.779 +1 0.579 -0.923 0.002 0.773 1.016 0.415 -0.513 -0.038 1.087 0.470 -2.422 1.375 0.000 1.253 -0.501 -1.130 2.548 1.107 -0.496 -0.638 0.000 1.377 0.996 0.996 0.855 0.748 0.749 0.748 +1 1.938 0.364 0.469 0.542 -1.632 0.703 1.773 -1.705 0.000 0.675 -0.380 -1.469 0.000 1.203 1.055 -0.626 2.548 1.034 1.144 0.329 1.551 0.931 0.861 1.347 0.792 0.653 0.743 0.781 +0 0.945 -1.192 1.499 0.697 -0.304 0.608 1.008 0.114 2.173 0.812 0.158 -1.591 2.215 0.430 1.722 1.670 0.000 0.376 0.610 -1.087 0.000 0.384 0.611 1.122 0.916 1.130 0.994 0.854 +1 1.541 0.598 1.354 2.462 0.874 0.829 2.569 -0.668 0.000 1.274 0.290 -0.918 2.215 0.372 1.628 -1.195 0.000 0.449 0.762 0.674 3.102 0.616 0.716 1.129 1.582 0.709 0.970 1.264 +1 0.654 2.053 -1.470 0.708 0.579 0.287 1.354 1.022 0.000 0.705 -0.978 -0.327 2.215 0.703 -0.006 1.699 0.000 0.773 0.819 -1.194 1.551 0.945 1.104 0.988 0.600 0.908 0.930 0.789 +0 0.832 -0.576 -1.577 0.349 -0.105 0.802 0.667 0.800 0.000 1.083 -0.557 -1.072 2.215 1.332 0.035 0.091 0.000 0.802 -1.445 -1.367 0.000 1.262 0.831 0.985 0.711 0.817 0.932 0.880 +1 1.098 -0.550 -0.049 0.474 -0.290 0.762 -0.597 -1.711 2.173 0.614 2.766 -0.925 0.000 1.538 0.135 1.316 2.548 1.209 0.861 -0.032 0.000 0.572 0.911 0.989 1.073 0.749 0.875 0.807 +0 0.741 -0.209 1.287 1.010 -1.238 1.197 -1.366 0.387 1.087 0.626 -1.182 -0.866 2.215 1.126 -0.565 -1.553 0.000 0.382 -1.553 -0.491 0.000 0.762 1.201 0.988 0.832 1.156 1.067 0.883 +1 3.455 0.643 -1.443 0.775 -0.804 1.646 -2.010 0.302 0.000 0.483 0.393 0.138 2.215 0.629 0.343 0.944 0.000 0.567 -1.066 -1.710 1.551 0.709 0.558 1.237 1.062 0.649 0.845 0.732 +0 0.703 -0.687 -0.346 0.709 1.580 0.600 0.049 1.551 0.000 0.607 0.795 -0.395 1.107 0.486 0.624 -0.058 2.548 0.406 -1.973 -0.495 0.000 1.360 0.984 0.989 0.885 0.176 0.751 0.698 +0 0.699 0.928 -1.346 0.880 -0.442 0.256 -2.104 0.838 0.000 1.140 -1.001 0.791 2.215 0.357 0.987 0.013 0.000 0.528 -0.227 -0.800 1.551 1.349 0.999 0.990 0.683 0.745 1.188 1.103 +0 0.513 0.802 1.343 0.426 -0.056 0.470 0.242 1.484 0.000 0.646 1.497 -1.107 1.107 0.395 0.999 0.445 0.000 1.103 -1.015 0.612 0.000 1.051 0.975 0.989 0.911 0.285 0.832 0.731 +0 1.075 1.119 0.848 0.756 0.643 0.405 2.018 -0.119 0.000 0.407 2.783 0.422 0.000 1.460 1.064 -1.398 2.548 0.836 0.642 1.682 3.102 0.545 1.085 0.975 0.733 0.346 0.728 0.726 +1 1.029 0.826 0.278 1.173 0.909 1.250 1.061 -1.067 2.173 0.686 0.469 0.870 0.000 0.788 2.202 -0.415 0.000 1.122 1.123 -1.694 3.102 0.879 0.799 0.985 1.370 0.693 0.923 0.832 +0 1.799 -0.712 0.066 0.367 -0.453 0.498 -0.155 1.693 2.173 0.310 -0.527 1.214 0.000 0.446 -0.088 0.544 0.000 0.430 0.672 1.471 0.000 0.658 0.574 0.981 0.948 0.490 0.759 0.615 +0 1.071 0.549 -0.399 0.766 -0.690 0.942 -0.171 1.598 2.173 0.399 -1.462 1.126 0.000 0.442 -0.217 -0.588 0.000 1.326 0.130 0.496 3.102 0.777 0.832 0.995 0.907 1.010 1.021 0.932 +0 0.319 -1.132 -0.671 0.340 1.426 1.189 0.075 0.401 2.173 1.267 0.303 -1.265 2.215 1.297 -0.696 -0.838 0.000 0.699 0.560 -0.655 0.000 0.877 0.894 0.998 0.911 1.815 0.934 0.777 +1 0.579 -0.063 0.278 0.839 1.691 0.705 -0.242 1.419 0.000 1.817 -0.765 -0.721 0.000 1.051 0.439 1.057 1.274 2.335 0.712 0.200 3.102 0.983 1.454 0.988 0.846 0.865 1.273 1.032 +1 0.704 -2.014 1.178 1.301 -1.727 0.967 -0.841 -0.010 0.000 0.387 -0.821 -0.709 2.215 1.000 -1.425 0.416 0.000 1.009 -1.534 -1.689 0.000 0.926 0.759 0.996 0.709 0.233 0.515 0.689 +1 1.455 0.167 -1.445 0.736 0.315 1.449 -0.086 0.485 1.087 0.656 0.019 -0.622 0.000 1.792 -1.451 -1.291 0.000 1.317 0.898 1.121 3.102 1.764 1.951 1.433 1.300 1.200 1.568 1.300 +1 2.367 -0.639 -0.656 0.359 0.399 0.799 -1.369 0.807 2.173 0.551 -0.029 -1.734 2.215 0.797 -1.370 -1.661 0.000 0.988 1.780 0.928 0.000 0.788 1.047 1.040 0.865 1.023 0.920 0.810 +0 0.633 0.064 -1.334 1.000 0.128 0.956 0.640 1.676 0.000 1.116 0.059 0.347 2.215 0.807 0.506 -1.144 0.000 0.848 0.965 -0.014 3.102 0.893 0.912 1.068 0.735 0.579 0.863 0.765 +1 2.776 -0.612 -1.194 0.130 0.147 0.598 0.271 0.362 1.087 0.998 -0.636 1.221 2.215 0.538 -2.306 0.394 0.000 0.706 -0.257 0.754 0.000 0.948 0.873 0.992 1.229 0.964 0.977 0.923 +0 0.517 1.690 -0.345 1.126 0.991 0.962 1.444 -0.741 1.087 0.702 2.302 -1.503 0.000 1.103 1.332 1.021 2.548 0.504 1.099 0.182 0.000 0.884 0.887 0.988 0.593 1.283 0.786 0.699 +0 0.619 0.140 0.921 0.478 1.671 0.796 -0.763 -1.140 0.000 1.577 -0.569 0.292 0.000 0.708 0.332 -0.609 2.548 0.627 -1.767 -1.658 0.000 0.923 0.927 0.981 0.651 0.732 0.754 0.884 +0 1.358 -0.433 1.557 0.709 -1.410 1.011 0.054 0.316 2.173 0.761 0.178 -1.249 2.215 0.974 0.602 0.946 0.000 0.626 2.399 -0.256 0.000 1.030 0.949 0.994 0.629 1.277 0.890 0.770 +0 0.711 0.307 -1.384 1.191 -0.290 0.678 -1.436 0.519 0.000 0.814 -1.387 -1.071 2.215 0.844 -0.407 1.104 2.548 0.518 -0.393 1.587 0.000 0.873 0.940 1.063 0.845 0.931 0.821 0.813 +0 0.411 0.118 0.407 1.156 -1.227 0.507 -1.526 -0.127 2.173 1.049 -1.288 1.504 2.215 0.745 -2.649 -0.060 0.000 0.437 -0.133 1.116 0.000 1.287 0.850 0.990 0.885 1.074 0.784 0.828 +0 1.904 0.464 -0.985 0.931 -1.030 0.882 -0.853 0.867 1.087 0.555 -0.469 0.405 0.000 0.506 -1.149 0.516 2.548 0.599 1.486 -1.244 0.000 0.836 0.641 0.989 1.226 0.310 1.280 1.142 +1 0.280 1.475 0.951 1.422 -0.105 0.612 1.236 -1.432 2.173 0.546 0.602 -0.068 0.000 0.970 1.411 1.467 0.000 1.295 -0.348 -1.658 3.102 1.068 0.928 0.986 1.974 0.899 1.343 1.104 +1 0.301 -2.034 -1.341 0.852 0.695 1.232 -1.152 -0.576 0.000 1.628 -1.197 1.534 0.000 1.434 0.055 0.590 1.274 1.130 -0.470 -1.314 3.102 0.637 0.728 0.984 0.748 1.011 0.894 0.786 +0 1.048 1.480 -1.298 0.850 0.010 0.609 -1.433 1.158 0.000 0.635 -2.776 -1.014 0.000 0.433 1.830 1.411 0.000 0.631 -0.505 0.354 3.102 0.889 1.111 1.208 0.943 0.435 0.925 0.807 +1 0.604 0.406 0.792 0.334 -0.189 0.632 -1.063 -1.682 2.173 1.026 0.953 -0.612 1.107 0.783 1.419 0.474 0.000 0.868 1.648 -0.054 0.000 0.873 0.993 0.992 0.798 1.741 0.978 0.840 +0 2.120 -1.368 0.042 2.335 0.393 2.222 -0.456 -1.395 0.000 0.515 -0.928 -1.144 0.000 0.372 -1.131 1.194 0.000 1.393 0.000 0.444 3.102 0.764 0.907 0.989 1.013 0.490 0.940 1.396 +1 1.006 0.266 1.642 1.124 -0.368 0.612 0.551 1.027 0.000 1.015 1.014 1.590 0.000 1.105 -0.132 -0.157 2.548 1.332 1.229 -0.109 3.102 0.911 1.097 1.431 0.875 0.837 0.909 0.860 +0 1.420 -0.501 0.440 0.356 1.736 0.545 0.531 1.440 1.087 0.884 -1.106 -0.778 2.215 0.674 -0.342 -0.379 0.000 0.855 -0.477 -1.025 0.000 0.741 0.805 0.989 0.879 1.335 0.845 0.690 +0 0.396 1.911 -1.499 0.799 0.018 0.454 -0.315 1.142 0.000 0.654 -0.866 0.015 2.215 0.672 1.393 -1.712 0.000 0.768 0.841 -0.990 0.000 1.064 0.948 0.984 0.550 0.490 0.613 0.611 +1 1.275 -1.356 0.157 0.891 0.976 1.042 -0.621 1.638 2.173 0.748 -0.825 -0.763 0.000 0.318 0.081 -1.538 0.000 0.486 0.637 1.217 3.102 0.604 0.852 0.993 0.840 0.633 0.838 0.753 +0 0.689 1.186 0.035 1.170 0.691 0.803 -1.749 -1.060 0.000 0.493 -0.998 1.166 2.215 0.564 -1.342 -0.333 0.000 1.103 0.856 -1.457 3.102 0.759 0.839 0.990 0.880 0.936 1.072 1.717 +0 0.716 -1.393 0.142 1.331 1.429 0.685 0.347 0.598 0.000 0.705 -0.617 -1.412 2.215 1.289 -1.925 -0.919 0.000 1.844 -1.020 -0.077 3.102 3.125 1.838 1.240 0.937 1.009 1.209 1.061 +1 0.653 -0.375 -0.971 1.129 0.284 1.125 -0.497 0.906 2.173 1.623 2.058 -1.151 0.000 0.458 -2.183 -1.330 0.000 0.535 -0.698 -0.257 3.102 0.678 0.982 1.076 0.526 0.725 0.634 0.631 +0 1.636 0.395 1.456 0.537 1.192 1.137 0.539 -0.590 2.173 0.514 0.067 -1.061 0.000 1.047 1.153 0.429 0.000 0.666 -0.698 0.778 3.102 1.308 0.956 0.988 1.353 1.109 0.973 0.881 +0 2.065 0.397 -0.001 0.170 1.646 1.295 0.827 -1.700 1.087 1.091 1.893 1.429 0.000 2.255 0.064 -0.203 1.274 0.532 0.457 1.125 0.000 0.932 0.975 0.986 1.432 2.238 1.220 0.968 +1 0.452 1.371 1.491 1.617 -1.174 0.583 0.002 0.871 0.000 1.121 -0.417 -0.020 2.215 0.316 -1.185 -1.076 0.000 1.109 -0.321 -1.678 1.551 0.931 0.881 0.987 1.381 1.004 1.489 1.285 +0 1.787 -1.180 0.895 0.940 -0.456 0.732 -1.569 -0.190 0.000 0.929 -2.198 1.490 0.000 1.139 -1.086 -0.979 2.548 1.283 0.250 1.697 0.000 0.937 0.899 1.684 1.108 0.896 0.814 0.770 +1 0.635 0.503 -0.542 0.739 -1.367 1.340 1.346 0.265 0.000 0.438 -0.236 1.594 0.000 0.742 0.179 -0.777 0.000 1.359 0.698 1.636 3.102 0.911 0.705 0.994 0.811 0.537 0.630 0.607 +0 0.973 0.327 -0.518 0.455 -1.571 0.844 1.084 1.171 0.000 0.710 1.038 -0.334 2.215 0.865 0.373 0.611 2.548 0.778 0.984 -1.439 0.000 0.880 0.978 0.991 0.739 0.687 0.677 0.655 +0 0.765 0.361 -1.549 1.167 0.163 1.345 -0.125 0.889 0.000 0.826 0.644 -0.807 2.215 0.970 -0.505 0.248 0.000 1.731 -0.631 -1.055 3.102 0.753 0.948 1.308 1.020 0.867 0.770 0.729 +1 0.336 1.354 -0.566 0.831 1.434 0.984 -0.012 0.076 2.173 1.216 -0.191 -1.240 0.000 1.359 0.128 0.921 2.548 0.659 -0.593 1.633 0.000 0.735 1.153 0.996 0.921 1.001 0.968 0.822 +0 1.407 -1.371 0.408 0.634 -1.677 1.116 0.733 1.631 2.173 0.720 1.381 -1.536 0.000 1.806 -0.378 -0.133 2.548 0.973 1.753 -0.217 0.000 1.081 1.173 1.248 1.080 2.072 1.444 1.561 +1 1.656 -0.070 -1.498 0.966 -0.970 2.020 -0.544 0.410 0.000 1.693 -0.491 1.579 2.215 0.995 2.592 -0.032 0.000 1.602 -0.325 -1.132 1.551 0.850 0.887 0.979 0.919 0.960 0.921 0.827 +0 1.852 1.435 1.312 0.565 -1.389 0.512 -1.138 0.121 0.000 1.111 -1.763 -0.415 0.000 0.676 1.017 -1.277 2.548 0.767 0.107 -0.069 3.102 0.921 0.885 0.983 0.649 0.564 1.025 1.604 +0 1.047 -0.364 -0.131 0.370 -0.995 1.206 0.459 0.454 2.173 0.873 1.891 1.611 0.000 1.166 -0.216 -1.113 2.548 0.643 0.175 1.688 0.000 0.917 1.559 0.987 0.918 1.552 1.311 1.111 +0 0.614 1.162 1.725 0.771 -0.870 0.734 -0.086 0.077 0.000 0.940 -0.341 1.291 0.000 0.775 -0.163 -0.134 2.548 0.683 -0.895 1.411 1.551 1.584 0.976 0.994 0.700 0.605 0.661 0.691 +1 0.914 -0.659 -0.239 1.033 -1.726 0.672 -0.513 1.045 2.173 0.586 0.765 -0.235 2.215 0.515 -1.035 -0.598 0.000 0.747 -2.340 -0.271 0.000 0.867 1.011 1.310 0.963 1.063 0.989 0.874 +1 0.556 -0.714 -0.800 0.410 -1.511 1.106 0.208 0.436 1.087 1.638 -0.694 -1.050 1.107 0.723 -0.387 1.697 0.000 0.743 -0.569 -0.374 0.000 0.781 1.009 0.984 0.913 2.149 1.104 0.875 +1 0.917 -1.972 0.019 0.745 -0.337 2.405 -0.846 -1.740 0.000 1.706 -1.105 0.185 0.000 1.867 -1.027 -1.049 2.548 1.408 -0.518 0.603 0.000 0.910 0.652 0.991 0.939 0.758 0.918 0.795 +0 0.394 1.102 1.476 1.632 -0.466 0.460 0.137 0.644 2.173 1.118 0.030 -1.537 1.107 1.132 -0.839 -0.125 0.000 0.865 2.110 0.765 0.000 1.274 0.890 1.093 1.103 0.976 0.860 0.899 +0 1.537 0.542 1.310 1.399 1.440 0.666 0.402 -0.244 2.173 0.608 2.321 -0.837 0.000 0.913 2.007 0.036 0.000 0.813 0.150 0.309 1.551 0.979 0.986 0.982 0.766 0.383 0.819 0.942 +0 0.570 0.954 1.581 1.197 1.336 0.429 2.124 -0.084 0.000 0.710 -0.604 -0.194 2.215 1.353 0.092 -1.436 2.548 0.941 -0.260 0.576 0.000 0.901 0.904 0.988 1.742 1.013 1.304 1.086 +1 0.686 -0.222 1.560 0.609 0.543 0.354 -0.544 0.796 0.000 0.553 -0.251 -0.237 0.000 1.159 1.215 -1.451 2.548 1.224 0.839 -0.479 3.102 0.767 0.810 0.991 1.318 0.715 0.992 0.848 +1 0.427 -1.746 1.015 1.573 -1.232 0.582 -0.866 1.379 0.000 0.605 -1.310 -0.024 2.215 0.448 -0.211 -0.913 2.548 0.646 0.699 0.353 0.000 1.174 0.995 1.022 0.702 0.514 0.631 0.751 +1 0.955 -0.511 -0.868 1.260 -0.194 1.996 -2.038 1.143 0.000 1.498 0.295 -1.239 0.000 1.590 0.519 0.016 2.548 0.816 -0.800 0.792 3.102 6.231 3.213 0.993 0.808 0.923 2.444 1.941 +1 1.019 -1.098 1.240 0.763 -0.958 0.968 -1.272 -0.120 0.000 1.342 -0.116 1.466 2.215 0.651 1.678 -0.913 0.000 0.917 -1.254 0.509 0.000 0.789 0.565 1.121 0.945 0.950 0.961 0.839 +1 1.334 -0.657 1.439 0.861 -0.095 1.208 -1.472 0.714 0.000 1.549 -0.173 -1.026 2.215 0.827 0.310 -1.466 0.000 1.124 0.398 -0.443 3.102 0.700 0.751 1.458 0.983 0.717 0.869 0.731 +1 2.062 0.140 0.671 2.363 -0.111 1.191 -0.090 -1.593 0.000 1.276 0.767 -1.277 2.215 0.494 -0.775 0.838 0.000 1.348 0.730 -0.218 3.102 1.046 0.984 1.980 1.109 0.967 1.185 0.997 +0 1.141 -0.595 1.585 1.179 -1.715 0.589 -0.194 0.737 2.173 0.817 -1.029 0.806 2.215 0.484 -0.193 -0.862 0.000 1.420 -0.571 -0.198 0.000 0.909 0.931 0.977 0.862 0.458 0.746 0.757 +1 0.818 -0.283 1.682 0.505 -1.113 1.152 -0.844 0.915 0.000 2.214 -1.481 -0.915 0.000 1.439 -0.762 -0.285 2.548 2.616 -0.543 0.371 3.102 0.839 0.953 0.993 1.077 0.838 0.955 0.958 +1 0.877 -0.834 1.592 1.111 0.421 1.369 -1.683 -1.692 0.000 1.760 -1.538 0.033 0.000 0.703 -1.942 -1.351 0.000 1.272 -1.110 0.645 3.102 1.006 1.039 1.190 0.867 0.935 0.890 0.829 +1 1.092 0.676 0.961 0.240 -0.417 0.560 1.096 0.510 1.087 0.654 -0.073 0.393 0.000 1.298 0.608 -1.332 2.548 2.198 1.471 -0.889 0.000 2.182 1.411 0.988 0.834 1.084 1.002 0.889 +0 0.472 0.464 -0.268 1.190 1.564 0.871 -1.347 0.863 0.000 1.619 -0.331 -1.293 2.215 1.647 -1.247 0.233 0.000 1.538 -1.053 -0.475 1.551 1.160 1.111 1.035 0.956 1.175 1.257 1.200 +1 0.436 -1.117 1.209 1.619 -1.575 0.949 -1.495 -0.406 2.173 0.726 -2.053 0.246 0.000 0.864 -1.175 0.730 1.274 1.095 -1.251 1.484 0.000 1.104 1.075 0.989 0.836 0.971 0.881 0.794 +1 0.661 0.244 0.604 1.937 -0.023 1.170 -0.891 1.661 0.000 1.024 0.746 -1.188 2.215 0.642 1.386 0.570 2.548 0.386 -0.558 -1.290 0.000 0.956 0.833 0.987 0.941 0.924 0.948 0.809 +1 0.645 -0.781 0.115 1.583 0.508 1.415 0.974 -1.264 0.000 0.722 0.725 0.961 1.107 0.585 0.279 -0.907 0.000 1.065 1.136 -0.281 3.102 1.031 0.964 0.980 0.729 0.754 0.784 0.936 +0 0.550 0.603 1.577 1.366 0.567 0.754 0.628 -1.207 2.173 0.619 1.111 -0.478 0.000 1.035 -0.155 1.660 0.000 1.012 -0.222 0.815 0.000 0.781 0.957 0.990 0.994 1.166 0.819 0.743 +1 0.741 -0.434 -0.860 1.170 0.247 1.466 -0.263 -0.052 2.173 2.672 -0.383 -1.687 1.107 0.718 0.700 0.158 0.000 0.394 -0.513 -0.361 0.000 0.836 1.194 1.084 1.447 2.904 1.498 1.210 +1 0.531 1.787 -1.180 0.865 0.931 0.958 0.391 -0.655 1.087 0.542 2.188 -1.568 0.000 0.386 0.839 1.185 2.548 0.719 2.411 0.197 0.000 0.855 0.659 0.986 1.008 0.781 0.900 0.775 +1 0.915 -0.405 -1.526 0.527 0.251 0.589 -0.101 0.511 0.000 1.083 0.120 -1.702 2.215 1.305 -0.118 -0.721 2.548 1.806 -0.979 0.415 0.000 0.898 1.171 0.988 0.694 0.990 1.003 0.825 +0 1.951 -0.613 -0.040 2.305 -0.070 1.823 0.132 -1.706 2.173 1.764 -0.740 1.717 1.107 2.614 -0.401 0.307 0.000 0.801 0.324 -1.161 0.000 1.691 1.863 1.007 2.685 1.229 1.972 1.696 +1 1.031 0.601 1.615 0.392 -0.502 0.945 0.542 0.669 0.000 1.144 -0.286 -0.629 2.215 0.984 0.273 0.080 0.000 1.492 -0.145 -1.631 3.102 0.905 1.163 0.985 1.022 0.928 0.966 0.875 +0 0.568 0.449 0.855 0.613 -1.653 0.509 -0.490 0.436 2.173 0.423 0.099 -1.424 0.000 1.402 0.735 -0.646 2.548 0.797 1.031 -0.262 0.000 0.971 0.865 0.987 1.014 1.140 0.918 0.787 +1 0.706 0.757 -0.685 0.406 1.502 1.178 0.254 -0.906 0.000 0.940 -0.292 0.794 2.215 0.681 0.637 0.260 0.000 0.689 1.632 0.402 0.000 0.818 0.682 0.993 1.137 0.716 0.833 0.726 +0 0.473 -0.610 -1.034 1.456 -0.124 0.363 -2.775 1.336 0.000 0.373 -1.301 0.853 0.000 0.506 0.128 -1.040 1.274 0.724 -0.652 1.520 3.102 0.706 0.996 0.988 0.716 0.408 0.616 0.663 +1 0.727 1.145 -0.634 1.505 -1.102 1.193 -0.666 0.272 2.173 0.803 -1.866 -1.451 0.000 0.796 -0.475 1.400 2.548 0.604 -0.190 0.650 0.000 1.219 0.850 0.976 1.534 1.036 1.079 1.091 +1 0.557 -0.667 -1.469 1.845 -0.794 0.600 0.890 0.493 2.173 0.935 0.584 1.678 0.000 1.268 1.904 0.017 0.000 1.683 0.111 1.231 3.102 0.781 0.899 0.982 1.016 0.777 0.898 0.771 +0 0.578 0.030 0.066 1.544 1.736 0.778 1.144 -1.245 0.000 0.901 0.081 -1.640 2.215 1.426 -0.866 0.549 2.548 2.184 -2.013 -0.258 0.000 0.890 0.915 1.306 1.102 1.286 1.161 0.988 +1 0.764 1.096 -1.676 1.307 -0.256 2.111 0.676 0.319 2.173 1.380 -0.199 -1.136 2.215 1.573 2.025 -1.074 0.000 1.273 -0.442 -0.410 0.000 0.801 0.821 1.326 1.359 2.687 1.435 1.139 +0 0.618 0.543 1.613 1.852 0.789 0.676 -1.577 -0.484 0.000 0.743 -0.340 -1.064 2.215 0.666 -0.099 0.061 2.548 0.375 -1.760 1.301 0.000 0.867 0.869 1.001 0.727 0.641 0.745 0.925 +1 0.871 -0.395 0.418 0.535 -0.287 0.401 -1.893 1.028 0.000 0.639 0.313 -0.640 2.215 0.792 -0.604 1.289 2.548 1.512 -0.046 -1.112 0.000 0.977 0.799 0.986 0.767 0.840 0.618 0.658 +1 0.654 -1.317 1.702 0.344 -0.203 0.741 0.819 -0.661 2.173 0.582 2.131 1.591 0.000 0.572 0.129 0.917 0.000 0.706 -2.481 -0.749 0.000 0.698 0.888 0.992 0.510 0.723 0.665 0.780 +1 0.448 -2.179 0.003 1.617 1.630 0.761 -1.256 -0.196 0.000 0.935 -0.876 0.953 2.215 1.071 -0.941 -0.749 2.548 0.666 -1.101 -1.519 0.000 1.011 1.002 1.173 1.021 1.065 0.891 0.825 +1 1.389 0.236 1.470 0.467 0.354 1.224 -1.919 1.018 0.000 1.465 -0.752 -0.787 1.107 1.546 -0.206 -0.196 2.548 0.808 -1.149 -1.152 0.000 0.774 1.736 0.985 1.284 0.925 1.354 1.299 +1 1.238 -1.766 -1.391 0.273 1.399 0.548 -1.441 0.571 0.000 0.577 -1.126 -1.076 2.215 1.485 0.438 0.453 0.000 0.932 0.851 -1.114 0.000 0.876 1.298 0.984 0.562 0.251 0.743 0.903 +1 0.786 0.317 0.930 0.535 -0.546 0.674 0.687 1.702 0.000 0.793 1.034 -0.345 2.215 0.904 0.802 1.276 2.548 2.090 0.245 -0.772 0.000 0.791 0.942 0.985 0.803 0.898 0.671 0.648 +1 0.781 0.685 -1.089 1.346 -1.670 0.493 2.484 0.344 0.000 0.927 -0.427 -0.536 2.215 0.917 0.194 -1.356 0.000 1.015 0.442 0.954 1.551 1.305 0.867 0.995 1.322 0.961 0.936 0.845 +0 0.623 1.449 -0.108 0.975 -1.454 0.774 -0.200 1.551 2.173 0.493 -0.761 -0.947 0.000 0.816 0.846 0.243 2.548 0.993 0.115 0.306 0.000 0.932 0.922 1.011 0.712 1.089 0.847 0.812 +1 1.115 -0.073 0.703 1.453 -0.261 0.683 -0.467 -1.071 2.173 0.655 0.088 1.554 2.215 0.480 -0.262 1.122 0.000 0.719 -1.283 -0.338 0.000 0.762 0.736 1.345 0.970 0.744 0.818 0.706 +1 0.765 -0.216 -0.502 1.352 0.606 0.781 -0.233 0.011 2.173 0.747 0.591 1.393 0.000 1.318 0.027 -1.467 0.000 1.114 -1.061 -0.812 3.102 0.928 1.107 1.185 0.709 0.855 0.930 0.852 +0 0.366 -1.177 -1.438 0.465 -1.258 0.410 -1.570 0.101 0.000 0.571 -0.688 0.832 0.000 0.683 -0.334 1.660 2.548 0.891 -0.358 -0.590 3.102 0.783 0.744 0.979 0.603 0.535 0.543 0.613 +1 1.604 0.424 0.948 0.730 -1.122 1.063 -0.320 -1.143 2.173 0.700 -0.905 0.809 1.107 0.623 2.553 0.081 0.000 0.587 -0.242 -0.222 0.000 1.020 1.139 1.435 1.252 1.307 1.000 0.928 +1 1.387 -0.621 0.866 0.549 1.364 0.909 -0.722 -0.449 2.173 0.318 -1.708 -0.420 0.000 0.594 -1.075 1.309 0.000 1.006 -0.403 -1.546 3.102 0.694 0.801 0.985 0.691 0.854 0.817 0.706 +1 0.695 0.197 1.125 0.722 -0.202 0.961 1.248 -1.651 0.000 1.814 1.299 0.185 0.000 1.447 0.037 -1.278 2.548 1.003 0.336 -1.701 0.000 0.666 0.862 0.987 0.544 0.880 0.828 0.792 +1 0.754 0.615 -1.346 0.179 1.705 1.028 0.048 0.836 2.173 0.848 -0.654 -0.570 1.107 1.133 0.352 -0.196 0.000 1.692 0.347 1.597 0.000 1.526 1.212 0.979 0.950 1.406 0.996 0.850 +1 0.379 1.340 0.562 1.061 -1.682 1.405 0.579 0.814 2.173 1.318 0.125 0.359 2.215 1.593 1.967 -1.027 0.000 1.293 0.189 -1.267 0.000 0.914 0.878 0.992 1.530 0.923 1.327 1.104 +0 0.464 -2.161 -0.966 1.196 0.693 0.574 -0.551 0.723 0.000 0.862 -0.843 -0.748 2.215 0.722 0.124 -1.322 2.548 0.591 -1.410 1.456 0.000 0.763 0.928 1.029 1.161 0.604 0.885 0.793 +0 0.314 -0.863 1.325 1.115 0.690 0.337 -2.718 -0.632 0.000 0.552 -0.320 0.567 2.215 1.126 0.508 -0.445 2.548 0.640 1.503 1.551 0.000 0.490 0.919 0.985 1.855 0.766 1.222 1.052 +1 0.658 -1.330 0.029 0.982 0.937 1.461 0.109 -1.368 0.000 0.667 -0.512 -0.441 2.215 0.895 -0.414 0.638 2.548 0.971 0.858 0.745 0.000 1.924 1.401 0.987 0.535 0.679 0.984 0.919 +1 1.868 0.019 0.780 0.638 -1.454 2.141 -1.253 -0.081 0.000 1.518 0.639 -0.500 0.000 2.824 -0.343 1.640 2.548 2.813 -1.023 -1.701 3.102 1.024 1.972 1.367 1.156 0.986 1.718 1.499 +1 1.233 -0.275 0.346 1.311 -0.446 0.709 0.816 -1.461 2.173 1.190 1.228 1.629 2.215 0.696 0.084 -0.210 0.000 0.502 0.891 0.102 0.000 0.378 0.953 1.152 1.252 0.558 1.162 0.900 +1 0.419 1.335 0.926 1.191 -1.131 0.799 1.834 -0.351 0.000 1.044 0.512 0.481 2.215 1.179 0.469 1.462 2.548 0.786 0.611 -0.774 0.000 0.843 1.147 0.989 0.705 0.911 0.933 0.812 +1 2.457 0.526 -0.511 0.491 -1.341 1.153 -0.223 0.796 2.173 0.806 1.127 -1.548 0.000 1.040 0.970 -0.579 0.000 1.927 0.977 1.292 3.102 1.078 1.021 1.034 1.538 1.375 1.235 1.108 +0 0.526 0.795 1.480 1.650 1.522 0.437 2.017 -1.312 0.000 0.986 0.451 0.169 2.215 0.593 2.521 -0.360 0.000 0.618 0.699 -0.075 1.551 0.772 0.681 0.993 1.150 0.203 0.784 1.085 +1 0.375 -0.719 0.736 0.819 -0.455 1.523 -1.116 1.504 0.000 0.649 0.145 -0.305 0.000 0.441 -0.464 -0.688 1.274 1.233 -0.577 0.359 0.000 0.829 0.767 0.983 0.611 0.347 0.657 0.668 +0 0.535 0.851 -0.880 1.795 -0.047 0.987 -2.791 1.532 0.000 1.020 -1.514 1.359 0.000 0.432 0.230 0.377 2.548 0.852 -0.508 -0.570 3.102 0.911 0.884 0.983 0.603 0.406 0.641 1.213 +1 1.243 0.351 1.288 1.593 -1.708 0.447 1.109 0.108 2.173 0.481 -0.776 0.344 0.000 1.086 -0.392 -0.366 1.274 0.580 0.496 -0.744 0.000 0.773 0.720 0.991 1.231 0.825 0.936 0.804 +0 0.911 1.078 0.184 1.293 -0.268 1.055 -0.240 -1.250 0.000 0.734 0.336 -1.634 0.000 1.495 0.559 0.869 2.548 1.001 -0.269 0.533 3.102 0.844 1.023 0.988 0.939 0.534 0.908 0.923 +1 0.471 -0.708 0.088 0.343 -1.521 1.074 -0.006 1.648 2.173 1.002 0.437 0.246 1.107 1.044 -0.178 -0.518 0.000 0.808 -1.488 0.775 0.000 0.906 1.032 0.997 0.869 1.497 0.937 0.800 +0 0.551 -1.093 -1.685 0.148 -0.479 0.846 -0.773 -0.540 2.173 0.502 0.255 0.727 0.000 0.687 1.620 1.034 0.000 0.382 2.296 -1.585 0.000 1.037 0.865 0.988 0.840 0.575 0.925 0.777 +1 1.356 -1.317 0.178 2.624 -1.196 0.882 -0.984 -1.432 2.173 0.957 0.664 -1.716 0.000 0.401 -1.476 1.644 0.000 0.389 -1.721 0.173 0.000 1.360 1.168 2.470 1.171 0.518 0.893 1.084 +0 0.406 -0.913 -1.043 1.175 1.127 0.619 0.138 -0.003 2.173 1.197 -0.928 0.478 1.107 1.126 1.021 -1.485 0.000 0.420 -1.370 -1.015 0.000 1.452 1.178 0.989 0.861 0.901 1.054 0.887 +1 1.240 1.038 1.180 2.124 0.604 0.511 0.891 -1.216 2.173 0.808 1.635 -0.872 0.000 0.648 1.067 -0.461 0.000 0.934 0.134 -0.439 3.102 0.785 0.724 1.114 1.103 0.547 0.842 0.772 +1 0.444 0.405 -1.127 1.115 0.611 1.338 -0.545 0.637 2.173 0.745 1.130 -1.608 0.000 0.993 -0.837 -1.184 0.000 0.752 -1.527 -0.527 0.000 0.919 1.372 0.987 0.536 0.844 0.819 0.723 +0 0.706 0.490 -1.417 0.927 -1.560 0.618 -0.236 0.624 2.173 0.463 0.724 0.218 1.107 0.928 0.281 -0.589 0.000 0.398 1.569 -1.264 0.000 0.699 0.924 1.004 0.802 0.491 0.856 0.735 +0 1.022 -1.246 -0.177 0.164 -1.328 0.921 -0.838 -1.616 2.173 1.643 -0.547 0.605 0.000 1.008 -0.447 0.076 2.548 0.771 0.088 -0.898 0.000 1.006 0.883 0.999 0.945 1.218 0.755 0.685 +1 1.171 0.252 0.823 1.653 1.150 1.375 0.971 -1.256 2.173 2.392 2.049 -0.306 0.000 1.578 0.399 1.390 0.000 0.566 1.316 -1.536 0.000 0.805 1.065 0.978 0.573 0.840 1.130 0.937 +1 0.667 -1.332 -0.712 0.877 0.899 0.751 -0.433 0.663 0.000 0.736 -0.534 -0.649 2.215 0.728 0.335 0.203 0.000 3.025 -0.648 -1.501 3.102 0.768 0.930 1.052 0.976 0.948 0.972 0.866 +0 0.526 0.938 -0.817 2.830 -0.253 1.106 -1.670 1.372 0.000 0.847 -0.445 1.122 2.215 1.021 -0.483 -0.648 2.548 0.937 -0.748 1.634 0.000 0.725 0.828 0.988 1.435 0.989 1.369 1.957 +1 1.015 0.316 -0.998 0.958 0.173 1.249 -0.331 1.287 0.000 0.693 1.639 -0.988 0.000 1.835 0.222 0.207 2.548 1.215 0.373 -0.666 3.102 0.454 0.667 1.189 0.851 0.816 0.835 0.768 +0 1.019 0.840 0.317 2.432 1.115 0.448 -0.270 -0.251 2.173 0.510 -1.684 -0.617 0.000 1.206 0.392 -1.192 2.548 0.920 -1.482 -1.455 0.000 0.612 1.167 1.437 1.196 0.762 0.989 1.236 +1 0.749 0.170 -1.286 0.730 1.386 0.492 0.634 1.058 0.000 0.762 0.837 0.172 2.215 1.665 1.125 -0.797 2.548 0.849 -0.365 1.087 0.000 0.560 0.745 0.979 1.272 0.945 0.978 0.863 +1 1.676 -0.182 -0.485 0.958 1.099 0.493 0.861 -0.319 0.000 1.314 0.219 1.088 2.215 0.536 0.232 -1.122 0.000 1.341 0.914 -1.672 3.102 0.999 1.193 1.738 1.256 0.907 0.981 0.943 +0 2.412 0.291 1.412 1.998 1.743 1.097 0.904 0.243 0.000 0.928 0.661 -1.233 0.000 1.426 0.705 -0.549 0.000 1.785 -1.380 0.362 1.551 0.863 0.578 0.972 2.138 0.720 1.388 1.467 +0 1.784 0.008 1.346 0.375 -0.189 0.982 0.676 -0.910 0.000 0.680 -0.188 0.098 0.000 0.674 0.248 0.039 2.548 0.775 0.264 1.694 3.102 0.593 0.593 1.113 0.750 0.551 0.520 0.500 +1 1.376 -0.850 -0.897 0.560 -0.062 0.831 1.151 0.249 0.000 1.130 0.236 -1.596 2.215 0.959 -0.557 1.187 0.000 1.158 0.144 0.758 3.102 2.015 1.145 0.996 0.982 0.881 0.964 0.977 +0 1.139 -0.488 -0.055 1.071 0.467 0.663 -0.732 -1.076 0.000 0.979 -0.905 1.447 2.215 0.761 0.632 -0.897 2.548 0.379 0.523 -1.470 0.000 0.624 0.807 0.994 1.055 1.143 0.919 0.807 +0 1.576 0.206 -0.132 2.022 0.205 1.505 1.250 -1.459 2.173 0.738 0.401 1.219 1.107 0.371 -1.488 0.138 0.000 0.493 0.521 -1.641 0.000 0.806 0.727 0.979 2.331 1.235 1.547 1.223 +1 0.764 1.591 1.305 0.632 -0.078 0.643 -0.061 -1.078 2.173 0.431 -0.960 0.648 2.215 0.674 1.528 0.141 0.000 0.656 0.570 1.585 0.000 0.803 0.940 0.987 1.273 0.859 1.048 0.853 +0 1.511 -0.730 0.934 1.315 -0.907 0.849 0.211 0.984 2.173 2.691 0.527 -0.596 2.215 1.298 -0.996 1.304 0.000 0.785 1.200 -0.167 0.000 1.243 1.047 1.945 2.069 2.231 1.589 1.345 +1 0.657 1.036 0.003 0.551 -1.155 0.365 -0.160 0.364 2.173 0.502 -2.299 0.590 0.000 0.447 -1.503 -1.682 0.000 1.283 -0.867 1.135 0.000 0.893 0.843 0.989 1.276 0.795 0.926 1.324 +0 0.677 -0.636 1.039 0.907 -0.170 0.828 0.114 0.246 2.173 1.146 -0.193 -1.388 2.215 0.726 0.600 -0.607 0.000 0.690 0.760 1.251 0.000 0.783 0.833 0.987 0.677 1.445 0.819 0.727 +1 0.493 0.388 0.925 1.150 0.377 0.914 0.382 -1.323 1.087 0.564 0.633 -0.535 2.215 0.623 -0.108 -1.011 0.000 0.919 1.241 -1.627 0.000 0.903 0.957 0.988 1.249 0.704 0.899 1.151 +0 0.583 -1.298 -0.854 1.265 0.284 0.810 -0.518 -1.452 2.173 0.946 -1.523 -0.244 0.000 1.168 -0.865 1.082 2.548 0.962 -1.733 0.670 0.000 0.967 0.989 1.018 1.057 0.953 0.930 0.816 +1 2.510 0.206 1.077 0.165 -0.323 1.068 -0.954 -0.160 0.000 0.607 0.727 1.643 2.215 1.054 1.011 -0.899 0.000 0.444 0.443 -1.317 3.102 0.849 0.753 0.989 0.661 0.220 0.770 0.857 +1 1.263 0.988 -0.617 0.615 0.309 1.295 1.185 0.630 2.173 0.987 1.045 -0.966 0.000 0.952 -2.625 -1.494 0.000 0.871 1.708 -0.419 0.000 0.837 0.735 0.988 1.041 0.966 0.908 0.795 +0 0.608 -0.054 -0.064 1.794 0.192 0.725 2.000 -0.692 0.000 0.941 1.014 1.082 2.215 1.098 -1.028 -1.288 0.000 0.370 0.642 -0.731 1.551 0.415 0.588 0.983 0.934 0.536 0.909 1.019 +0 1.308 0.667 0.904 0.767 0.140 2.330 0.109 0.682 2.173 3.897 0.335 -1.120 0.000 1.434 0.684 1.240 2.548 2.419 -0.098 -0.519 0.000 1.067 1.762 0.988 0.819 1.324 2.068 1.604 +0 0.594 2.205 -1.599 1.070 1.647 0.866 0.434 1.135 2.173 1.050 0.356 0.382 2.215 1.774 0.455 -0.731 0.000 0.553 0.845 -0.510 0.000 1.028 1.105 0.983 0.883 0.884 0.956 0.967 +0 1.183 0.516 1.575 0.667 -0.826 0.881 -2.022 1.025 0.000 0.681 -1.032 -0.057 2.215 1.134 1.123 -0.700 2.548 0.523 -1.853 -0.074 0.000 0.873 0.919 1.021 0.786 1.418 1.572 1.394 +1 0.760 0.832 1.732 0.826 1.085 0.781 0.988 0.709 2.173 1.464 -1.091 -0.452 0.000 0.608 -0.580 -1.688 0.000 1.582 -0.387 -1.067 3.102 0.771 0.837 0.977 0.848 1.505 1.223 1.062 +1 0.557 0.144 -0.881 1.781 -0.172 0.740 0.959 1.169 2.173 0.500 -0.061 -0.618 0.000 0.975 0.727 -1.536 1.274 0.552 0.388 0.783 0.000 0.823 0.842 0.979 1.105 0.690 0.828 0.780 +0 3.208 -0.165 1.046 2.096 0.836 1.375 0.220 -0.793 0.000 1.625 -0.315 -0.431 2.215 0.685 -0.865 1.631 2.548 1.116 -1.161 -0.912 0.000 0.833 0.982 0.964 1.951 1.132 1.282 1.308 +0 2.028 1.070 1.307 0.746 -1.000 1.553 0.256 -0.548 2.173 1.253 0.108 1.046 2.215 0.848 0.591 -0.945 0.000 0.958 -0.344 0.431 0.000 0.779 1.093 1.489 1.138 2.040 1.378 1.081 +0 1.359 -0.948 1.077 0.145 -0.854 0.548 -1.557 -1.279 2.173 0.614 0.617 0.206 0.000 0.790 0.740 -1.008 2.548 0.520 -0.725 0.511 0.000 0.640 1.126 0.989 0.924 1.243 0.840 0.775 +0 0.481 -0.396 -0.783 0.879 0.976 1.717 -0.718 0.110 2.173 2.501 -1.304 -0.309 0.000 4.993 -0.355 1.696 2.548 2.238 0.214 1.648 0.000 4.079 2.769 0.984 1.224 3.663 2.564 1.899 +0 0.315 -1.370 0.926 1.848 -0.979 0.976 -0.262 0.017 2.173 0.580 -0.321 0.607 0.000 0.658 -0.470 1.608 0.000 0.674 1.002 1.579 3.102 0.749 0.885 1.045 1.195 1.091 1.049 0.875 +1 0.556 -0.208 -1.669 1.334 0.758 0.639 0.616 -0.222 2.173 0.566 -0.387 -0.870 0.000 0.654 0.341 -1.699 0.000 0.878 0.410 1.245 3.102 0.822 0.706 0.988 0.586 0.770 0.682 0.661 +1 0.989 -1.787 -0.247 0.848 -1.586 0.471 0.121 -1.333 2.173 0.854 0.496 0.237 2.215 0.709 -0.977 0.768 0.000 0.879 0.348 1.663 0.000 0.949 0.910 1.185 1.093 0.940 1.097 0.929 +0 1.283 -0.647 -1.623 0.398 1.279 0.633 0.736 -1.126 1.087 0.760 0.217 -0.300 2.215 0.981 -1.344 0.630 0.000 0.722 1.000 1.341 0.000 0.859 0.902 0.989 0.899 0.742 0.722 0.744 +1 0.507 1.448 -0.020 1.083 -1.092 0.538 -0.297 -0.548 0.000 1.142 0.998 1.096 2.215 0.586 -1.057 0.449 0.000 0.923 1.300 -0.994 3.102 0.909 1.105 0.988 1.114 0.917 0.986 1.102 +0 1.517 -0.472 1.501 1.597 1.333 1.022 0.214 -0.357 0.000 1.096 0.070 1.619 2.215 1.008 -0.750 0.141 0.000 1.603 0.972 -0.070 3.102 0.940 1.265 0.981 1.383 1.379 1.124 1.096 +0 1.365 -0.908 1.734 0.941 1.236 3.074 -0.423 1.359 2.173 4.498 1.345 -0.478 2.215 2.485 1.593 -0.147 0.000 0.871 0.939 0.887 0.000 1.188 1.367 0.987 0.848 7.860 3.740 2.959 +1 0.672 -0.656 -1.592 1.342 0.353 1.020 0.468 1.511 0.000 0.860 -0.666 0.064 2.215 1.153 1.387 -1.044 0.000 0.777 -1.820 1.100 0.000 0.745 0.825 1.294 0.844 0.682 0.763 0.723 +0 0.647 0.910 1.638 0.767 -1.437 0.750 0.874 0.088 2.173 0.876 0.887 0.873 2.215 1.008 0.335 -0.962 0.000 1.089 -0.528 -0.516 0.000 0.755 0.998 0.988 0.897 0.776 0.837 0.811 +1 0.916 1.690 -0.634 0.552 1.147 0.748 1.313 -0.865 2.173 1.027 1.136 -1.462 0.000 1.636 1.648 0.380 0.000 1.195 0.611 1.039 3.102 1.150 0.926 0.987 0.729 1.035 0.738 0.661 +0 0.760 -1.133 -0.067 0.769 1.512 1.124 2.093 0.633 0.000 1.058 1.553 -0.522 0.000 1.400 0.468 -1.261 1.274 1.090 1.439 1.381 3.102 0.850 0.887 1.047 1.411 0.891 1.064 1.098 +0 0.812 1.438 0.576 1.265 -0.323 1.119 -0.448 1.418 0.000 0.661 -1.304 -0.785 0.000 0.882 -1.166 1.008 0.000 2.425 0.281 -0.987 3.102 0.999 1.072 1.017 1.156 0.267 1.008 1.221 +1 1.193 0.713 -1.367 1.371 1.463 1.236 -0.612 -0.820 2.173 1.725 0.458 0.154 2.215 1.215 -1.625 0.692 0.000 0.415 -1.908 0.884 0.000 0.884 1.408 0.987 1.443 2.059 1.474 1.468 +1 1.898 0.517 0.949 1.760 0.203 1.243 0.149 -1.353 2.173 1.327 -0.172 1.636 0.000 1.651 0.433 -0.068 0.000 0.933 0.777 0.583 0.000 0.821 0.588 1.575 1.760 0.922 1.161 1.002 +1 0.814 0.462 -0.230 1.436 -0.244 1.475 -0.026 -1.246 0.000 0.757 -1.239 1.238 0.000 1.514 -1.122 0.799 2.548 1.271 -0.319 0.890 0.000 0.709 0.619 0.977 2.115 0.703 1.460 1.292 +1 0.337 -0.054 0.805 1.145 -0.355 0.738 1.140 1.334 2.173 1.058 1.230 -1.579 0.000 1.619 0.039 -0.005 2.548 0.369 0.869 -0.758 0.000 0.556 1.238 0.988 1.567 1.500 1.157 1.112 +1 0.702 0.186 1.242 1.406 -1.603 1.552 -0.553 -0.571 0.000 2.401 0.629 0.935 2.215 1.615 1.044 0.598 2.548 2.641 0.269 -0.751 0.000 0.839 0.886 0.984 1.436 0.819 1.175 1.081 +1 0.821 0.710 1.120 0.679 -1.004 0.749 0.279 -1.359 2.173 0.611 -0.901 0.433 0.000 0.818 0.696 0.263 0.000 0.870 1.151 -1.428 3.102 0.927 0.951 0.989 0.717 0.496 0.728 0.667 +1 1.136 0.719 1.366 0.626 -1.006 0.708 0.728 0.127 1.087 0.815 -0.432 1.490 0.000 1.085 1.123 -0.755 2.548 0.370 1.205 -0.094 0.000 1.055 1.040 0.988 0.757 0.826 0.810 0.737 +1 0.997 1.079 -1.146 0.728 0.204 0.483 -1.041 1.024 2.173 0.472 -0.341 -0.341 2.215 0.810 1.125 1.252 0.000 0.749 2.021 -1.219 0.000 0.871 1.065 1.107 1.217 0.708 1.026 0.901 +1 3.071 -1.038 0.676 0.620 0.675 3.127 -0.650 -1.471 0.000 2.361 -0.480 0.096 1.107 0.442 0.150 -1.294 0.000 0.974 -2.292 -0.005 0.000 0.954 1.249 0.979 1.402 1.137 1.738 1.735 +0 0.923 1.275 -1.046 1.126 -0.379 0.635 1.499 0.862 0.000 0.919 1.058 -0.354 2.215 1.165 0.122 1.188 1.274 0.646 0.884 -1.623 0.000 0.799 0.936 0.984 1.280 1.211 0.924 0.829 +0 0.295 -0.063 -1.391 3.780 -0.320 1.040 -0.865 1.657 0.000 1.172 -0.028 1.183 2.215 0.825 -1.221 1.037 0.000 0.745 1.806 -1.241 0.000 0.969 0.988 1.204 0.755 0.743 0.995 1.128 +0 0.998 -0.746 0.068 0.051 1.718 1.133 -1.050 1.403 2.173 1.674 0.547 -0.348 2.215 0.651 0.711 1.528 0.000 0.701 -1.141 -0.082 0.000 0.808 0.923 0.982 0.883 2.731 1.300 1.036 +0 1.267 -0.708 -1.331 1.436 -0.949 1.664 -0.753 0.541 2.173 0.496 -1.117 -0.732 0.000 0.462 -0.584 0.938 0.000 0.831 0.697 -1.460 3.102 0.756 0.991 0.976 0.680 1.638 1.256 0.976 +1 0.708 -0.807 1.189 0.347 0.755 0.659 -0.668 -0.939 0.000 0.864 0.621 1.064 1.107 1.342 -0.438 -0.339 2.548 1.051 0.523 0.288 0.000 0.954 0.970 0.998 0.626 1.283 0.933 0.820 +1 0.357 -1.029 1.040 1.081 -1.183 0.855 0.323 0.924 0.000 0.287 1.509 0.632 0.000 0.518 -1.317 -1.117 2.548 0.726 0.643 -0.323 3.102 0.739 1.119 0.987 0.633 0.704 0.729 0.687 +1 0.912 -1.211 -0.673 2.702 -0.548 1.063 -0.718 0.673 2.173 1.063 0.719 1.420 0.000 0.774 0.524 -1.649 2.548 0.670 1.081 1.350 0.000 0.662 1.252 0.992 1.833 1.257 1.555 1.557 +1 1.836 -0.141 0.592 0.575 -0.104 0.407 -0.466 -0.892 0.000 0.870 -0.865 -1.670 2.215 0.434 -1.775 0.675 0.000 1.655 0.216 -1.088 3.102 0.965 0.932 0.979 1.044 0.853 0.893 0.792 +0 0.924 -0.301 1.626 0.182 -0.953 1.124 -0.460 -1.121 0.000 0.798 -0.529 0.673 1.107 1.850 0.264 -0.152 2.548 0.848 -1.937 1.224 0.000 1.950 1.476 0.981 0.981 1.036 1.104 0.932 +1 0.485 0.618 -1.639 0.948 -0.102 1.249 0.582 -1.363 0.000 2.288 0.407 0.612 0.000 1.936 -0.389 -0.654 2.548 2.623 -0.173 -1.186 3.102 0.900 0.933 0.989 0.783 0.815 0.938 0.840 +0 0.841 1.305 -0.536 1.533 0.646 0.714 0.483 1.479 1.087 0.477 -2.140 0.091 0.000 0.512 0.803 0.019 0.000 0.714 1.462 -1.182 0.000 0.661 0.799 1.377 1.008 0.411 0.821 0.688 +1 0.570 0.721 0.779 0.601 1.658 0.526 1.125 -0.772 0.000 0.631 1.037 0.235 0.000 1.175 -0.105 1.599 2.548 1.121 -0.223 -0.683 0.000 0.897 1.021 0.985 0.571 0.594 0.717 0.667 +1 0.515 -1.143 -1.551 0.608 -0.138 2.469 -0.342 -1.450 0.000 1.104 0.223 -0.036 0.000 1.793 -1.322 0.449 2.548 1.453 -0.611 1.169 3.102 1.415 1.560 0.987 0.851 0.872 1.518 1.475 +1 0.703 -0.760 1.218 1.352 1.486 0.366 0.445 1.161 0.000 0.758 -0.263 -0.380 2.215 0.550 0.624 0.543 0.000 1.569 1.041 -0.471 3.102 0.438 0.770 0.980 0.995 0.827 0.919 0.745 +1 1.053 -1.488 0.202 1.039 -1.233 0.350 -2.444 -1.188 0.000 0.847 0.096 1.426 2.215 0.879 -0.801 0.445 2.548 0.798 -0.847 -0.592 0.000 0.760 0.830 1.393 1.247 0.849 0.873 0.811 +1 0.506 -1.981 -0.869 0.332 1.418 1.312 -0.826 1.664 2.173 0.990 -1.358 0.088 0.000 0.669 -1.862 0.394 0.000 0.788 1.360 0.090 0.000 0.534 0.496 0.986 0.839 0.900 0.920 0.772 +0 0.576 -1.816 -0.981 0.434 0.782 0.764 -0.078 -0.026 2.173 0.805 -0.749 1.274 0.000 1.067 -0.156 1.739 2.548 0.922 -0.929 -0.219 0.000 0.748 0.963 0.980 0.723 1.126 0.734 0.656 +0 0.779 -0.425 0.558 0.662 -0.622 0.972 -1.031 1.136 2.173 0.784 -0.074 0.181 0.000 1.209 0.626 -1.128 2.548 0.744 -0.532 -1.735 0.000 1.023 0.993 0.988 0.879 1.784 1.003 0.852 +1 0.582 -0.450 -0.398 1.704 0.139 2.506 0.479 -1.057 2.173 3.251 1.373 0.855 0.000 1.588 2.155 -0.417 0.000 1.126 0.038 0.538 0.000 1.007 0.864 0.986 2.596 1.086 1.699 1.364 +1 0.665 0.934 1.095 1.052 -1.646 0.637 1.850 0.035 0.000 0.975 0.258 -0.405 2.215 0.828 -0.153 -1.703 2.548 0.532 1.578 1.279 0.000 0.801 1.078 0.989 0.543 0.904 0.891 0.850 +0 0.829 1.805 -0.112 0.585 1.605 0.319 1.455 0.736 0.000 0.457 -0.395 -0.722 2.215 0.523 0.159 -1.725 0.000 0.448 -0.165 -0.268 0.000 0.776 0.754 0.989 0.735 0.439 0.716 0.626 +1 0.487 0.043 -0.651 0.157 -0.174 0.763 0.428 -1.464 2.173 0.740 0.086 -0.138 2.215 0.892 -1.604 0.528 0.000 1.639 -0.709 1.049 0.000 0.870 1.100 0.851 0.941 1.047 1.058 0.871 +0 1.677 -0.637 -1.491 0.966 -1.148 0.898 -0.832 0.490 2.173 0.640 0.426 0.309 0.000 0.971 0.207 -1.732 0.000 0.930 -0.883 1.157 1.551 0.860 1.098 0.984 1.356 0.557 0.943 1.028 +1 0.729 0.406 -1.293 0.645 1.192 0.751 0.715 1.387 2.173 0.907 0.534 -0.571 2.215 1.363 -0.316 -0.311 0.000 0.861 -0.668 0.766 0.000 1.024 0.937 0.990 0.651 1.197 0.922 0.854 +0 0.741 0.264 0.828 0.930 -1.201 0.476 -0.322 1.091 0.000 1.257 -0.099 0.090 2.215 1.260 -0.016 -1.523 2.548 0.648 -0.498 -0.595 0.000 0.855 0.842 1.112 0.696 1.329 0.800 0.695 +0 0.318 0.096 -0.821 0.529 -1.037 1.175 -1.865 -1.722 0.000 0.836 -0.543 0.460 2.215 1.280 0.961 0.323 2.548 0.803 0.383 -0.583 0.000 2.509 1.785 0.999 1.601 0.996 1.677 1.358 +1 2.360 -0.588 -0.550 0.371 -1.581 1.103 -0.628 1.267 2.173 0.563 -1.421 -0.493 0.000 1.237 0.190 1.466 0.000 1.663 -0.618 0.266 3.102 0.926 1.073 1.038 0.873 1.128 1.006 0.852 +1 1.047 -0.157 0.034 0.452 1.293 0.735 -0.670 -0.557 2.173 0.728 1.142 0.579 0.000 0.791 1.045 1.595 0.000 0.688 0.872 -1.215 3.102 0.923 0.677 0.987 0.831 0.844 0.888 0.765 +0 0.341 1.150 -0.992 0.599 -1.008 0.640 0.458 1.257 2.173 0.894 -0.296 0.045 0.000 0.759 0.571 -1.379 2.548 0.594 0.759 0.500 0.000 0.721 0.894 0.993 0.635 0.606 0.666 0.663 +0 1.458 -0.559 -0.535 0.581 1.639 0.608 -1.230 -1.679 1.087 0.725 -2.228 0.772 0.000 0.755 -0.076 -1.582 2.548 1.352 0.686 0.250 0.000 2.986 1.805 1.180 0.866 0.522 1.192 1.021 +0 0.541 -1.259 1.619 0.384 0.767 0.790 -0.951 0.164 0.000 0.990 -0.278 -0.830 2.215 1.668 0.410 1.697 2.548 1.058 0.112 0.161 0.000 0.802 1.020 0.995 0.775 1.159 1.040 0.865 +0 0.840 2.235 0.046 0.668 -1.252 1.428 0.009 1.485 1.087 1.711 1.268 -0.194 2.215 0.964 -0.503 -1.202 0.000 0.546 1.178 -0.588 0.000 0.992 0.967 0.990 0.882 2.796 1.554 1.301 +0 1.492 -0.796 0.539 0.688 0.323 0.807 -0.794 -1.342 1.087 0.837 -0.473 -0.502 2.215 0.519 -0.229 -0.938 0.000 0.814 -0.902 1.726 0.000 0.573 0.607 0.988 1.202 0.853 0.886 0.722 +0 0.756 1.039 1.335 1.841 -1.470 1.581 0.449 0.232 2.173 1.419 -2.511 -1.624 0.000 1.687 -2.676 -0.559 0.000 1.352 0.069 0.770 0.000 0.646 0.764 0.990 0.441 0.824 1.013 0.819 +0 1.495 0.737 -1.221 0.935 1.017 0.581 0.525 0.065 2.173 0.270 1.377 -1.042 0.000 0.460 0.855 0.345 0.000 0.564 -0.997 0.516 3.102 0.529 0.658 1.477 1.028 0.638 0.796 0.641 +0 1.325 -1.343 0.828 0.525 -0.032 0.440 0.711 -1.146 0.000 0.713 0.467 -0.194 0.000 0.916 -0.675 -1.573 2.548 0.525 1.200 1.394 3.102 0.911 0.960 0.981 0.980 0.740 0.750 0.818 +0 1.082 -0.577 -1.740 0.330 0.107 0.831 -2.592 -0.137 0.000 1.700 -0.147 0.803 2.215 1.931 -0.869 -1.088 2.548 0.553 0.158 0.579 0.000 2.076 1.795 0.990 0.908 2.067 1.687 1.348 +0 0.779 0.473 1.546 0.902 -0.723 0.858 0.541 0.304 0.000 1.111 0.146 -0.244 2.215 1.835 -0.126 -1.553 2.548 0.989 1.099 0.970 0.000 0.970 1.008 1.034 0.785 1.419 1.069 0.897 +1 0.510 -0.502 1.283 1.428 0.220 2.544 0.099 -1.212 0.000 0.744 -0.489 0.404 0.000 0.903 0.149 -0.113 2.548 3.322 -1.220 0.836 1.551 3.037 1.886 0.987 0.880 1.561 1.929 1.510 +1 0.654 -1.137 0.053 0.690 -1.699 1.923 -1.043 1.300 0.000 1.346 -0.194 -0.178 0.000 2.390 -1.264 -0.493 1.274 1.875 -0.952 -1.139 3.102 0.828 1.168 0.986 0.877 0.911 0.953 0.853 +0 1.597 0.091 -0.844 1.228 -1.200 1.056 -0.199 -0.409 2.173 1.127 -0.818 1.677 0.000 1.160 -0.861 0.571 2.548 2.065 -1.092 1.010 0.000 1.220 1.000 0.998 0.881 1.188 1.136 1.114 +1 0.948 -0.751 -0.939 1.035 1.715 0.963 0.521 0.620 2.173 0.440 -0.758 -0.458 0.000 0.683 0.896 -1.288 2.548 0.534 1.898 1.308 0.000 0.745 0.751 0.991 0.748 1.026 0.902 0.817 +0 0.460 0.494 -0.403 1.473 -0.370 1.159 -2.180 0.951 0.000 1.352 -0.261 -1.132 2.215 0.792 0.071 0.243 0.000 0.830 2.429 0.608 0.000 0.590 2.131 0.980 1.321 1.213 2.017 1.621 +1 2.549 -0.524 1.716 2.156 -1.269 1.851 -2.412 0.412 0.000 1.480 0.195 -0.645 0.000 0.568 0.796 -0.816 2.548 0.809 0.908 0.271 3.102 1.109 0.850 1.419 1.105 0.434 0.996 0.946 +0 0.866 0.122 0.781 1.087 1.529 0.661 0.638 -1.143 0.000 0.886 0.425 -0.236 2.215 0.550 -0.187 -0.458 1.274 0.837 0.900 0.715 0.000 0.985 0.896 0.995 0.755 0.284 0.668 0.659 +0 0.557 0.274 1.131 1.700 -1.611 1.087 -1.086 -1.181 1.087 1.343 0.701 0.347 0.000 1.170 0.465 0.802 2.548 1.406 0.734 -0.160 0.000 0.803 0.772 0.993 1.703 1.859 1.433 1.296 +0 1.450 -1.520 0.347 0.942 -0.428 1.532 -0.545 -1.176 2.173 0.449 0.212 -1.331 0.000 1.018 -2.413 1.023 0.000 1.601 0.476 0.932 0.000 1.005 0.649 1.040 1.597 1.100 1.083 1.098 +1 0.634 2.178 -0.858 0.966 -0.583 2.775 1.462 1.187 0.000 1.010 0.737 -0.567 0.000 0.957 0.092 -1.136 2.548 2.613 -0.708 -0.585 0.000 0.916 0.723 0.989 0.691 0.517 0.588 0.589 +1 1.245 2.112 0.378 0.708 1.646 0.332 -2.529 1.463 0.000 0.776 1.141 -0.440 1.107 0.472 -0.204 -1.565 1.274 0.652 -0.035 0.175 0.000 0.603 0.548 1.183 1.061 0.732 0.817 0.692 +0 1.041 -0.311 1.188 0.728 -0.160 0.384 0.206 0.032 0.000 0.488 -0.541 -0.434 2.215 1.271 0.150 1.722 2.548 0.915 -0.397 -1.437 0.000 0.933 0.804 1.131 0.684 0.838 0.640 0.590 +0 1.005 1.683 0.469 0.278 1.217 1.266 0.937 -1.076 0.000 1.105 0.762 0.857 2.215 0.633 0.135 -0.169 2.548 0.668 1.216 1.717 0.000 0.898 0.897 0.988 0.643 0.766 0.886 0.800 +0 0.392 -0.226 0.907 1.461 -1.627 1.673 -1.974 0.185 0.000 0.564 -1.894 1.587 0.000 1.579 -0.865 -1.590 2.548 0.862 -0.944 0.145 0.000 0.955 0.888 0.988 0.597 0.431 0.573 0.549 +1 0.919 0.194 -0.976 0.735 0.382 0.619 -0.242 -0.367 0.000 1.303 -0.022 1.671 2.215 0.823 0.662 0.927 1.274 0.746 1.643 1.380 0.000 1.742 1.123 1.071 0.927 0.804 0.920 0.805 +1 0.539 -0.549 0.493 1.731 -1.450 0.562 0.148 1.189 1.087 0.698 0.483 -0.384 2.215 0.691 -0.212 -0.043 0.000 0.544 0.980 -1.328 0.000 0.807 0.745 1.317 1.005 0.925 0.790 0.688 +0 1.408 1.020 -0.673 0.693 1.157 0.720 -0.442 1.195 0.000 0.777 0.516 1.439 0.000 0.619 -1.233 -0.543 2.548 0.546 -1.350 -0.211 0.000 0.877 0.880 1.364 1.212 0.644 0.796 0.868 +0 0.936 0.174 -0.733 0.654 1.317 1.084 2.003 1.262 0.000 1.201 -0.749 -1.250 0.000 2.018 -0.592 -0.345 1.274 0.969 0.519 0.195 3.102 1.725 1.476 1.043 0.959 0.883 1.100 0.959 +1 1.504 -0.080 1.626 1.061 -0.971 1.701 1.540 0.941 0.000 1.558 -1.209 -0.349 2.215 0.954 0.701 -0.229 2.548 1.038 -0.640 1.254 0.000 0.834 1.145 1.257 1.017 1.544 1.158 0.999 +0 0.463 -0.265 1.143 0.445 -0.907 0.744 -0.470 1.684 2.173 0.614 0.926 0.170 0.000 1.077 1.540 -0.324 2.548 0.794 0.216 1.120 0.000 0.758 1.014 0.985 1.693 1.817 1.256 1.037 +1 1.601 0.205 0.104 0.109 -1.544 1.666 -0.458 -1.544 0.000 1.588 0.041 1.066 2.215 1.751 -0.171 -0.327 0.000 2.025 0.564 -0.577 0.000 1.037 0.986 0.987 1.012 0.488 1.061 0.881 +1 0.436 1.226 1.020 0.521 -0.517 1.356 0.539 -1.599 0.000 0.735 -0.010 0.176 0.000 0.883 0.236 1.151 2.548 1.066 2.313 0.267 0.000 0.802 0.794 0.996 0.746 0.972 0.633 0.594 +0 1.448 -0.116 1.070 0.596 0.270 0.818 -0.288 1.665 2.173 1.961 -0.396 -0.277 1.107 0.594 -0.065 -1.308 0.000 1.084 1.934 -1.711 0.000 1.368 1.404 0.988 1.278 1.836 1.488 1.232 +1 0.876 -0.925 -1.729 1.207 -1.274 0.400 -0.663 1.022 0.000 1.077 -0.110 -0.024 2.215 1.045 0.515 -1.287 0.000 1.649 1.061 0.477 3.102 0.723 0.897 1.001 1.326 1.044 1.047 0.849 +1 1.435 0.393 0.264 1.023 -0.161 1.010 -0.169 1.415 2.173 1.092 -0.949 -1.259 2.215 0.520 -1.350 0.455 0.000 0.936 0.069 -1.405 0.000 1.015 0.925 0.998 1.644 1.213 1.323 1.076 +1 1.611 -1.168 1.419 0.335 -1.111 0.684 -0.301 -0.237 2.173 0.465 -1.417 -0.548 0.000 0.577 -0.161 -0.688 0.000 0.655 -1.573 1.516 0.000 0.901 1.026 0.989 1.041 0.846 0.898 0.781 +1 0.903 0.807 -1.008 0.494 -0.413 0.724 -0.108 0.934 2.173 0.708 0.312 -1.544 1.107 0.614 0.583 0.815 0.000 1.206 -0.479 -0.400 0.000 1.043 0.877 0.988 1.253 0.861 0.896 0.836 +1 0.718 -2.143 0.263 0.519 -1.504 0.640 -1.512 1.171 1.087 0.651 -1.488 -1.527 0.000 1.198 -0.662 -0.494 2.548 0.741 -1.181 0.086 0.000 0.900 0.803 0.989 0.685 1.176 0.707 0.629 +1 0.277 -1.792 -1.324 0.678 0.779 0.678 0.015 1.185 0.000 0.491 0.181 -1.581 0.000 1.481 -0.749 -0.264 2.548 1.146 0.234 -0.895 1.551 0.874 1.035 0.980 0.702 0.788 0.730 0.653 +1 0.534 1.390 0.768 0.365 -1.331 0.538 -0.117 1.730 2.173 0.368 -0.597 -0.171 0.000 0.459 -1.697 -0.146 0.000 0.753 -0.838 0.520 0.000 0.465 0.882 0.993 0.662 0.553 0.661 0.638 +1 0.721 0.578 0.266 0.972 -0.725 1.138 -0.255 0.999 2.173 1.135 0.440 -1.085 0.000 0.515 -0.702 -0.144 2.548 0.803 -0.533 -1.113 0.000 0.715 0.718 0.983 1.305 0.855 0.896 0.883 +1 1.097 0.681 1.297 1.372 0.567 0.933 0.037 -0.984 2.173 0.680 0.846 -0.212 2.215 0.743 -0.531 1.707 0.000 0.869 -0.022 -0.375 0.000 0.882 0.834 1.038 1.299 0.905 0.928 0.813 +1 1.035 1.047 1.431 0.462 0.161 0.849 -0.227 -1.550 2.173 0.704 0.324 -0.087 2.215 0.730 0.715 0.756 0.000 0.820 0.078 -0.655 0.000 0.869 0.943 0.986 0.738 1.148 0.767 0.670 +1 0.692 0.540 -0.531 1.278 -1.293 1.535 0.164 -1.470 0.000 1.471 -0.735 0.074 2.215 2.209 -1.331 0.637 2.548 0.455 0.511 0.620 0.000 1.248 1.866 0.985 1.616 1.158 1.610 1.336 +1 0.485 0.212 -0.200 1.908 0.257 0.802 -0.636 1.648 2.173 0.869 0.125 -1.063 2.215 0.730 -0.801 0.742 0.000 0.563 -1.518 -1.227 0.000 0.775 0.925 0.991 1.695 0.926 1.217 1.117 +1 1.157 0.867 -0.518 0.963 -0.392 0.319 0.850 -1.468 0.000 0.895 1.433 1.368 0.000 0.741 1.901 1.567 0.000 1.201 0.348 0.465 3.102 0.721 0.969 0.980 0.754 0.473 0.683 0.774 +1 0.937 -1.100 -1.091 0.739 1.553 1.115 -0.749 -1.658 2.173 1.389 -0.319 0.297 0.000 1.457 0.794 -0.775 0.000 2.499 -0.112 1.023 0.000 0.960 1.164 0.987 0.659 0.650 1.075 0.908 +1 0.599 -0.499 0.117 1.374 -0.984 1.014 -0.507 1.025 2.173 1.122 -0.790 -0.525 2.215 0.919 -0.917 0.196 0.000 0.819 -1.326 1.691 0.000 0.975 0.948 1.052 0.656 1.564 0.925 0.813 +0 0.564 -1.735 0.062 0.185 -1.231 0.728 0.057 0.910 1.087 1.064 -0.011 -0.908 2.215 1.102 0.774 -1.185 0.000 1.361 1.520 -0.029 0.000 0.968 1.103 0.982 0.803 1.293 0.986 0.952 +1 0.430 1.524 -0.996 0.378 0.658 0.792 -0.227 -0.691 2.173 1.342 -0.792 0.629 0.000 0.457 0.705 -1.642 0.000 0.600 -0.987 1.417 3.102 1.503 0.896 0.994 0.776 0.780 0.832 0.753 +1 0.305 1.173 -0.190 0.257 1.042 1.543 0.999 -1.675 0.000 2.166 0.502 0.171 2.215 1.184 -0.161 -1.684 2.548 0.717 -0.442 0.325 0.000 0.919 0.931 0.987 1.039 1.797 1.231 0.970 +0 1.044 0.869 -0.758 0.706 -1.460 1.206 0.122 0.883 1.087 0.846 -0.110 0.121 0.000 1.604 -1.693 -0.994 0.000 1.269 0.767 0.635 3.102 2.323 2.043 0.987 1.469 0.609 1.539 1.585 +0 0.606 0.522 0.922 1.061 -0.271 0.886 -0.650 1.209 2.173 0.764 0.292 -0.921 2.215 1.407 0.285 0.008 0.000 2.007 -1.438 -1.473 0.000 0.919 0.964 0.988 1.147 1.287 1.034 0.874 +0 0.774 -0.606 -0.130 1.562 0.644 1.005 -1.452 -1.726 2.173 1.106 -1.241 -0.103 2.215 0.780 -0.703 -1.298 0.000 1.100 -0.844 -0.644 0.000 0.909 1.044 0.988 0.832 1.550 1.057 0.947 +0 0.461 -0.009 -1.427 1.320 -0.494 1.618 -0.034 1.169 2.173 0.660 2.458 -0.572 0.000 0.331 -2.591 1.552 0.000 1.307 0.225 -0.360 3.102 0.834 0.995 0.987 1.498 1.529 1.084 1.093 +0 1.651 -0.724 -0.003 0.637 0.604 1.031 0.861 -1.730 2.173 0.465 1.314 -1.063 2.215 0.492 0.012 0.324 0.000 0.464 1.624 0.322 0.000 0.594 0.923 0.989 1.073 0.629 1.106 0.881 +0 1.977 1.600 0.129 0.370 -1.618 0.680 1.261 0.726 0.000 1.168 1.240 -1.281 2.215 0.537 1.682 -1.667 0.000 1.043 0.640 -1.585 3.102 0.959 1.046 1.185 0.922 0.371 0.807 0.755 +0 1.487 1.030 0.198 1.399 0.269 1.832 -1.063 1.564 0.000 1.162 0.535 -0.713 2.215 0.874 1.375 -0.457 0.000 1.005 -0.050 -1.469 3.102 0.510 0.729 0.985 1.268 0.685 1.032 0.790 +0 0.472 1.839 0.474 1.223 1.343 1.371 -0.387 -0.755 0.000 1.335 0.164 0.388 2.215 0.977 1.012 -1.692 0.000 0.435 2.489 -1.348 0.000 0.884 1.307 0.996 0.978 0.675 1.041 1.006 +0 1.267 1.363 -1.494 0.472 -1.248 0.607 0.370 -0.679 0.000 0.969 1.704 1.317 0.000 1.369 -0.885 0.100 2.548 0.843 -0.060 0.258 0.000 0.853 0.925 0.982 0.592 1.038 1.384 1.120 +1 0.408 -1.291 0.096 1.091 -0.296 1.418 0.673 1.507 2.173 0.599 -0.510 0.252 0.000 0.449 0.171 -1.385 0.000 0.400 0.378 -0.516 3.102 0.844 1.203 0.985 0.472 0.777 0.891 0.768 +1 0.813 0.110 1.020 0.796 -0.482 0.547 1.183 -1.261 0.000 0.703 -1.389 1.390 2.215 0.998 1.613 0.525 0.000 1.160 -0.234 0.070 3.102 0.650 0.830 1.088 0.940 0.904 1.047 0.863 +0 1.206 -0.267 0.208 0.594 1.402 0.687 0.551 -1.704 0.000 0.364 0.328 -1.372 2.215 0.783 -1.079 -0.608 2.548 0.405 0.444 0.281 0.000 0.786 0.986 1.032 0.669 0.597 0.594 0.621 +1 0.466 -0.732 -0.382 1.825 0.995 1.036 1.133 -1.170 2.173 0.702 -1.457 0.795 0.000 1.408 -0.127 -0.783 2.548 0.526 0.372 0.947 0.000 0.904 1.126 1.209 1.829 1.154 1.289 1.166 +0 0.308 0.478 -1.365 2.144 -1.700 0.817 -0.306 0.005 0.000 0.654 1.038 0.420 0.000 0.747 0.129 1.410 0.000 0.754 -1.077 -0.157 0.000 0.981 0.656 0.990 0.472 0.240 0.421 0.606 +1 2.065 -0.545 -0.907 0.428 0.719 0.978 -0.843 1.035 2.173 0.897 -1.602 1.690 2.215 0.515 -0.340 0.774 0.000 0.794 0.141 -0.096 0.000 0.994 1.103 1.295 1.084 0.952 0.978 0.887 +1 0.660 -0.712 1.606 0.471 -0.562 1.135 0.341 0.404 0.000 1.730 -1.129 -0.872 2.215 1.478 -1.252 0.958 0.000 1.463 -0.185 -1.369 3.102 2.519 1.792 0.985 1.049 0.936 1.538 1.199 +0 0.662 -1.682 0.254 1.132 -0.909 0.914 -0.743 0.479 0.000 1.280 -0.157 -1.538 0.000 1.070 -0.257 1.129 0.000 1.036 2.429 -1.490 0.000 1.068 0.912 1.039 0.919 0.912 0.983 0.961 +0 0.568 -1.189 -0.872 1.624 1.393 1.026 -0.455 -1.399 0.000 1.313 -1.154 0.613 1.107 0.762 0.205 -0.133 1.274 0.617 1.868 -0.300 0.000 0.258 0.557 1.186 1.000 1.050 1.162 1.213 +1 1.111 1.560 -0.689 0.660 -0.530 2.317 -0.741 0.779 1.087 1.047 -0.114 -0.517 1.107 2.357 0.695 -1.527 0.000 1.710 0.649 -0.207 0.000 0.964 1.491 0.992 2.492 2.233 2.093 1.754 +1 1.802 0.828 -1.468 1.536 1.439 1.159 1.380 0.151 2.173 0.652 0.227 -0.712 2.215 0.779 0.289 -0.025 0.000 0.674 1.729 0.605 0.000 0.906 0.802 1.148 1.646 1.192 1.167 0.965 +1 1.632 1.196 0.552 0.288 0.171 0.768 1.277 -1.299 2.173 1.137 1.306 1.263 0.000 0.458 0.968 -0.376 0.000 1.484 2.035 -0.692 0.000 0.725 0.827 0.981 0.844 1.291 0.962 0.929 +0 0.315 1.183 -0.662 0.470 0.255 0.335 0.951 0.798 2.173 0.619 1.629 1.596 0.000 0.963 1.123 -0.992 2.548 0.398 0.134 0.438 0.000 0.776 0.688 0.994 0.697 0.714 0.664 0.638 +0 2.002 0.597 0.906 1.390 0.357 0.869 0.347 0.251 2.173 2.277 -0.351 -1.134 0.000 0.925 -0.819 -1.700 0.000 0.972 0.262 -1.009 3.102 1.245 0.827 1.096 0.765 0.882 1.087 1.243 +0 0.342 2.116 0.135 1.931 1.302 0.726 1.235 0.395 2.173 0.928 0.558 -0.234 2.215 1.202 1.278 -0.786 0.000 0.865 1.385 -1.581 0.000 0.752 1.001 0.986 1.445 0.770 1.043 0.928 +1 0.969 0.322 0.500 1.210 -0.313 0.645 -1.398 1.215 2.173 0.649 -0.395 -0.355 0.000 1.271 1.244 -1.693 0.000 1.180 1.073 -0.684 0.000 1.067 0.844 1.003 1.311 1.016 1.241 1.088 +0 0.775 -0.724 -1.033 0.673 -0.156 0.870 0.448 0.142 2.173 0.915 -1.909 -0.805 0.000 1.133 -1.390 1.476 2.548 1.443 0.253 1.188 0.000 0.900 0.896 0.994 0.831 1.840 1.462 1.215 +1 0.806 -0.786 1.701 0.751 1.413 1.216 -1.195 -1.073 0.000 1.739 -0.405 0.756 2.215 0.694 -0.475 0.263 0.000 0.934 -0.982 0.216 3.102 0.880 0.956 0.992 0.746 0.701 0.862 0.736 +1 0.470 -0.240 1.010 0.799 -1.036 0.679 0.048 -1.211 2.173 0.845 -0.505 0.266 0.000 0.954 0.319 0.024 0.000 0.450 0.162 0.401 0.000 0.686 1.056 0.993 0.662 0.731 0.832 0.731 +1 2.191 0.725 0.379 0.715 0.114 0.967 0.582 -1.446 1.087 1.482 -0.554 1.133 2.215 1.411 -2.391 -0.642 0.000 0.588 0.530 -1.152 0.000 1.181 1.839 0.986 1.425 1.677 1.819 1.818 +1 1.156 -0.205 0.784 1.604 0.364 1.566 -0.411 -1.711 2.173 1.754 -0.152 -0.684 0.000 0.648 1.293 -0.456 0.000 0.938 -0.906 0.935 3.102 1.465 1.392 0.997 1.588 0.983 1.294 1.265 +1 0.563 -0.399 -1.400 1.293 -0.076 1.038 -1.101 -0.993 0.000 1.166 -0.222 0.356 0.000 1.003 -0.313 -0.364 0.000 2.819 0.076 1.286 1.551 1.006 1.229 1.098 1.124 0.841 0.997 0.897 +1 1.862 -0.351 0.695 0.952 0.257 1.168 0.014 -1.066 2.173 1.166 0.597 1.478 1.107 0.437 -1.017 -0.364 0.000 1.007 0.316 -0.503 0.000 0.607 1.051 0.994 1.574 1.391 1.284 1.019 +0 0.458 0.953 -0.724 1.262 0.721 0.606 0.490 -1.157 2.173 0.958 0.898 -0.005 0.000 1.330 -0.950 1.586 0.000 0.528 1.709 -0.726 0.000 0.785 0.892 1.016 0.983 0.997 0.883 0.798 +0 1.030 0.716 0.225 1.720 0.895 1.390 -0.051 -0.632 0.000 0.430 -2.800 1.186 0.000 1.586 -1.360 -1.354 0.000 1.222 0.058 1.496 3.102 0.808 0.858 1.049 0.822 0.183 0.723 0.871 +1 1.278 2.224 0.845 0.491 -1.106 0.453 1.028 0.098 1.087 0.701 0.462 -1.077 2.215 0.786 0.692 1.222 0.000 0.862 0.876 -0.566 0.000 0.917 0.716 1.079 0.828 0.760 0.809 0.713 +0 0.318 0.709 -1.278 2.134 -0.401 0.990 -1.377 1.107 1.087 0.444 -1.660 1.711 0.000 0.708 -0.428 -0.913 0.000 0.449 0.684 0.884 3.102 0.829 0.965 0.995 0.673 0.960 1.808 1.512 +1 0.639 -0.651 1.540 1.018 1.589 0.403 0.046 -1.731 0.000 0.735 1.250 -0.300 2.215 1.597 0.246 -0.152 0.000 1.102 1.247 -1.640 0.000 0.849 0.971 0.980 1.064 0.916 1.333 1.029 +1 1.749 -1.253 -1.248 0.213 -0.697 1.072 -1.216 -0.634 0.000 1.700 -0.450 1.186 2.215 1.778 0.166 0.270 2.548 0.468 1.604 1.164 0.000 2.727 1.941 0.977 1.235 1.488 1.496 1.301 +1 0.711 1.387 -0.550 0.682 1.169 0.840 0.675 -0.416 0.000 0.670 0.423 1.652 2.215 1.162 -1.547 0.720 2.548 1.221 -1.052 -1.394 0.000 0.820 0.941 0.986 2.025 1.390 1.340 1.174 +1 2.221 -0.485 -0.529 1.659 -1.097 1.174 -0.588 1.232 2.173 0.516 0.873 0.734 2.215 0.480 0.239 0.155 0.000 0.727 -0.675 0.738 0.000 0.491 0.729 1.301 1.295 1.061 1.266 0.968 +1 0.646 1.537 0.597 0.585 -0.993 0.415 -0.661 -0.411 2.173 1.071 1.309 -1.148 0.000 1.105 -0.176 1.601 2.548 0.742 1.322 -0.218 0.000 0.874 1.170 0.984 0.830 0.844 0.915 0.784 +0 1.087 -0.371 -1.135 0.986 1.413 1.557 -0.756 0.358 1.087 0.805 -0.740 -0.141 0.000 1.329 0.961 -1.615 0.000 2.037 -0.953 -0.881 1.551 0.841 1.651 1.073 1.399 1.733 1.539 1.251 +0 1.526 0.015 0.377 1.189 0.370 2.183 -0.313 0.248 1.087 1.187 -1.013 -1.736 0.000 2.235 -0.533 -1.236 2.548 0.977 0.086 1.505 0.000 0.903 0.936 0.989 0.698 2.703 1.564 1.326 +0 0.480 1.213 -1.015 0.266 -1.263 1.325 -0.053 0.257 2.173 0.915 0.854 -1.458 2.215 1.408 2.276 -1.334 0.000 0.644 1.589 0.852 0.000 1.018 1.045 1.000 1.052 1.799 1.584 1.294 +1 0.477 1.135 0.031 0.819 1.296 1.230 0.902 -1.424 2.173 1.009 0.063 0.280 2.215 0.398 1.060 0.973 0.000 0.510 -1.712 -0.307 0.000 0.564 0.803 0.987 1.128 1.787 1.139 0.863 +1 0.847 -0.198 0.589 0.753 -1.645 0.926 -0.963 0.268 0.000 1.480 -0.879 -1.251 0.000 0.834 -0.740 0.969 2.548 0.775 -0.250 -0.407 1.551 2.437 1.362 1.000 0.594 0.602 0.897 0.798 +1 1.502 1.235 -0.315 0.909 0.387 0.979 1.103 -0.781 2.173 1.137 1.371 0.526 2.215 1.361 1.278 1.279 0.000 2.196 -1.677 1.656 0.000 0.478 3.022 0.985 0.742 1.453 2.434 2.159 +1 1.409 -1.383 0.839 1.938 1.094 1.174 -2.623 -1.023 0.000 1.580 -0.498 -0.586 2.215 0.838 -0.857 0.256 0.000 0.943 1.605 1.586 0.000 2.146 1.171 0.992 1.680 0.806 1.074 1.228 +1 1.165 -0.383 -0.555 0.299 1.551 0.830 -0.577 0.269 2.173 0.562 -0.921 1.593 2.215 0.724 1.309 -1.327 0.000 0.623 0.373 1.196 0.000 0.682 0.879 0.990 0.848 0.952 0.858 0.758 +1 1.842 0.028 0.346 0.722 -0.478 0.706 2.140 1.680 0.000 0.740 0.565 -1.321 1.107 1.094 0.686 0.842 0.000 0.618 -0.298 -1.207 0.000 1.021 0.830 1.079 0.693 0.373 0.674 0.654 +1 2.836 -0.194 1.465 0.223 0.455 1.387 -1.969 -0.347 0.000 1.212 0.981 0.516 2.215 0.613 0.204 -1.408 0.000 0.791 0.940 -1.302 3.102 2.610 2.268 0.994 1.309 0.883 2.074 1.746 +1 1.802 1.481 -0.873 0.561 -1.042 1.125 1.176 0.878 2.173 0.758 0.715 -1.725 1.107 0.378 1.608 0.596 0.000 1.163 -0.248 -0.123 0.000 0.999 0.927 0.976 1.490 1.019 1.076 1.006 +1 0.365 0.334 1.060 1.061 -0.253 0.770 1.179 -0.724 2.173 0.593 1.424 0.614 0.000 1.072 1.216 1.351 0.000 1.174 1.919 -0.404 0.000 0.865 0.902 0.985 0.852 1.124 0.894 0.770 +0 0.543 0.053 0.195 1.206 1.437 1.140 -1.940 0.242 0.000 1.352 -1.015 -1.244 2.215 1.086 -0.936 1.184 2.548 0.782 -1.885 -0.817 0.000 0.844 1.038 1.009 1.099 1.050 0.925 0.825 +0 0.460 -1.249 1.561 0.825 0.232 0.783 0.029 0.693 0.000 0.972 -0.197 -0.220 2.215 0.819 -0.243 1.455 0.000 1.181 -1.435 -1.644 0.000 0.937 0.928 0.986 0.709 0.747 0.762 0.679 +1 1.110 0.944 -0.582 1.145 0.092 1.085 0.364 1.453 1.087 0.762 -1.966 -0.135 0.000 0.877 -0.946 -1.470 2.548 0.739 -0.754 -0.896 0.000 0.845 0.801 0.986 1.404 1.099 1.213 1.420 +0 0.928 -0.271 -1.611 0.958 1.294 1.261 0.916 -0.415 2.173 1.222 1.148 -0.091 0.000 2.110 1.517 1.609 0.000 1.554 -0.114 0.474 3.102 0.465 1.552 0.986 1.358 1.346 1.321 1.150 +1 1.328 0.741 1.358 1.446 -0.177 0.695 -0.337 -0.899 2.173 0.632 -0.778 0.155 2.215 0.505 1.032 -0.979 0.000 0.468 1.421 -0.519 0.000 0.266 0.849 1.886 1.319 0.824 1.001 0.810 +0 1.292 -1.242 1.562 0.352 1.736 0.422 0.220 -0.691 2.173 0.764 -0.320 0.758 0.000 1.267 -0.812 0.169 2.548 0.742 -1.429 0.039 0.000 0.944 0.921 0.987 0.959 0.832 0.775 0.718 +0 0.722 0.769 0.402 0.755 -0.706 0.623 0.240 -1.417 2.173 0.492 0.178 1.470 0.000 0.640 -0.381 -0.142 2.548 0.724 1.265 0.620 0.000 0.854 0.786 0.985 0.758 0.765 0.696 0.639 +1 0.615 0.839 -0.767 1.261 0.258 2.247 -0.196 1.439 0.000 3.664 -0.834 -0.517 0.000 1.221 -0.394 0.854 0.000 1.195 0.374 1.203 3.102 0.904 0.634 0.987 0.546 0.108 0.483 0.522 +1 0.414 0.829 -0.924 0.812 -0.354 1.055 0.086 0.211 2.173 1.301 -0.861 1.657 0.000 0.761 -0.024 1.094 2.548 0.581 -1.138 1.554 0.000 0.968 0.819 0.995 1.558 0.800 1.134 1.454 +0 0.556 1.301 0.363 0.855 1.312 0.528 1.230 -0.839 2.173 0.672 2.038 -0.520 0.000 0.500 1.972 0.509 0.000 0.796 0.322 1.557 3.102 0.712 0.844 0.986 0.867 0.646 0.680 0.634 +1 0.951 -2.027 -0.086 0.406 1.271 0.854 -0.939 0.919 0.000 1.204 -1.137 -1.059 0.000 0.441 1.163 1.452 2.548 0.697 -1.798 1.102 0.000 0.982 0.963 0.987 1.150 0.294 0.834 0.784 +1 0.490 0.540 -0.263 0.761 0.846 1.462 -0.835 -1.371 2.173 0.709 -1.214 -0.249 0.000 0.898 -1.101 1.377 1.274 1.007 -1.279 0.585 0.000 0.768 0.783 0.994 2.085 0.919 1.477 1.341 +0 0.749 -0.572 -0.668 1.894 -1.532 1.614 -0.586 0.459 2.173 0.869 -0.400 -1.574 0.000 1.260 0.484 -0.837 2.548 0.665 -1.141 0.698 0.000 1.009 1.073 1.158 1.649 1.944 1.299 1.072 +0 0.821 -1.325 0.287 0.716 -1.569 0.349 -1.035 -1.267 0.000 0.647 -1.459 -0.655 0.000 0.567 -2.189 0.688 0.000 1.204 -0.708 -0.098 0.000 0.866 0.800 1.057 0.594 0.289 0.553 0.552 +1 0.509 1.327 -1.693 0.261 0.069 0.828 -0.338 1.494 0.000 0.961 0.013 0.129 0.000 1.116 -0.056 1.034 1.274 1.278 -1.651 -0.434 0.000 1.818 1.112 0.980 0.651 1.014 0.898 0.755 +0 0.438 1.392 0.296 2.501 0.006 0.946 -0.136 -1.315 2.173 0.829 -0.224 1.425 0.000 0.455 -0.551 -0.014 0.000 1.017 0.473 1.533 3.102 0.924 0.912 0.979 0.950 0.676 1.000 0.878 +0 0.874 0.845 1.136 2.139 -1.655 0.594 -0.141 0.162 2.173 1.010 -1.047 -0.470 2.215 0.570 0.475 -0.735 0.000 1.204 1.149 0.589 0.000 0.940 0.843 1.113 1.803 0.827 1.284 1.084 +0 1.209 -1.003 -1.401 0.251 1.298 1.398 -0.061 0.506 0.000 1.385 -0.492 -1.182 2.215 0.925 0.268 -0.001 0.000 1.727 -0.218 0.953 3.102 0.975 0.864 0.985 0.873 1.321 1.129 1.128 +1 0.964 0.281 0.602 1.107 -1.560 1.464 -0.313 0.539 0.000 0.962 -0.725 -1.175 1.107 1.006 -1.261 -1.132 0.000 1.348 0.285 -0.761 3.102 0.832 1.123 1.331 0.862 0.696 0.758 0.728 +1 0.431 -0.695 0.622 0.647 0.781 0.545 -1.487 -1.200 0.000 0.808 0.113 -0.897 2.215 0.409 -1.359 0.985 0.000 0.929 1.136 1.453 0.000 0.782 0.946 0.992 0.753 0.675 0.691 0.806 +0 1.288 -1.203 -0.728 0.266 -1.432 1.381 -0.259 1.235 2.173 1.336 -2.223 -0.227 0.000 0.576 -0.603 -1.654 0.000 0.698 -1.369 0.126 3.102 1.775 0.987 0.978 1.297 1.169 1.366 1.136 +0 0.533 0.083 -0.154 1.295 0.886 0.924 0.542 -1.511 0.000 0.939 -0.309 0.467 0.000 1.128 -1.054 -0.244 2.548 0.807 0.042 -0.752 0.000 0.898 0.694 0.986 1.080 1.538 0.959 0.891 +1 1.401 0.795 -1.709 1.392 1.551 0.663 0.490 0.272 0.000 0.993 1.206 -0.554 2.215 0.770 0.213 -0.077 2.548 0.840 0.401 1.160 0.000 0.817 0.987 0.997 0.965 0.623 0.927 0.839 +1 0.491 -2.116 0.564 1.785 1.368 0.724 -1.219 -0.406 2.173 0.558 -0.992 -0.975 0.000 0.330 1.140 -0.514 2.548 0.429 -1.989 1.166 0.000 0.761 0.876 0.986 1.403 0.967 1.438 1.093 +1 1.185 -0.447 -1.355 0.318 -0.041 1.166 -0.122 0.094 0.000 0.960 0.011 1.678 2.215 0.491 1.296 1.077 0.000 0.670 0.470 0.721 0.000 0.863 0.983 0.989 0.676 0.631 0.848 0.750 +1 0.915 0.172 -1.613 1.108 -1.053 0.831 0.679 -0.599 0.000 1.694 -0.129 0.975 2.215 1.026 0.419 0.329 1.274 0.447 -1.170 -0.981 0.000 1.202 0.990 0.979 1.230 0.878 1.006 0.958 +0 1.819 -0.873 1.669 0.093 -0.379 1.068 0.666 -0.428 2.173 0.519 -0.546 1.119 0.000 0.295 -0.108 -0.523 2.548 0.458 -1.402 1.003 0.000 0.381 1.307 0.978 0.591 0.291 0.901 0.792 +1 1.832 -0.448 1.628 1.037 -1.564 2.573 2.052 0.737 0.000 1.335 -0.605 -0.853 2.215 2.406 0.527 -0.645 0.000 1.077 0.223 -0.172 3.102 0.834 0.754 0.984 1.139 0.797 0.987 1.052 +0 1.228 0.097 -1.122 0.861 -0.232 0.760 0.013 1.359 2.173 0.747 -0.389 0.161 0.000 0.882 -0.196 1.009 2.548 0.720 -1.327 -0.615 0.000 0.862 1.101 1.024 0.851 0.338 0.726 0.745 +1 1.362 1.350 0.263 1.986 0.918 2.850 -0.546 -0.965 0.000 0.797 0.727 1.172 1.107 0.665 0.529 0.050 2.548 1.399 1.716 0.689 0.000 1.007 0.829 1.268 0.807 0.659 0.655 0.656 +0 0.880 0.372 -1.197 0.660 0.721 0.436 -0.144 -1.471 0.000 1.185 0.119 1.283 2.215 1.173 1.506 0.045 0.000 1.089 -0.241 -0.037 1.551 1.145 0.913 1.042 0.767 0.975 0.884 0.760 +1 0.425 0.146 1.031 2.097 0.343 1.395 0.643 0.035 0.000 1.223 0.811 -1.114 1.107 0.988 -0.154 1.702 2.548 1.034 -0.938 -1.730 0.000 0.529 0.948 0.995 1.090 0.902 0.998 0.879 +1 0.370 -2.328 0.629 2.233 1.262 0.836 -1.265 -0.280 1.087 0.515 -0.831 -1.678 0.000 1.718 -0.877 -0.933 2.548 0.820 -0.253 0.467 0.000 0.827 0.921 0.991 1.289 0.862 1.062 0.879 +0 1.577 -0.364 0.068 1.957 0.034 1.099 0.561 -1.465 0.000 1.063 0.618 1.579 0.000 1.334 -0.084 1.353 1.274 1.117 -0.842 -0.188 3.102 0.892 0.905 0.971 0.504 1.020 0.973 1.305 +1 0.474 1.571 0.085 0.867 -0.462 0.802 1.437 1.598 0.000 0.650 1.786 0.680 0.000 0.874 0.808 -1.272 0.000 1.209 0.411 1.249 3.102 0.900 0.755 0.982 0.738 0.989 0.976 0.868 +1 0.677 -1.667 1.654 1.042 -0.514 1.427 -1.426 -1.463 2.173 1.021 -1.133 0.884 2.215 1.524 -1.698 0.318 0.000 1.573 -2.043 0.102 0.000 0.959 0.972 1.080 0.929 1.537 1.073 0.899 +0 0.854 0.573 -0.283 1.384 0.507 0.892 -0.370 0.267 2.173 1.442 -0.608 -1.724 2.215 1.311 -0.062 -1.429 0.000 0.605 1.295 -1.278 0.000 0.897 1.009 0.987 0.776 1.641 1.105 1.023 +0 0.448 0.449 1.720 0.832 0.398 2.069 -0.561 -1.310 2.173 2.121 -2.766 0.091 0.000 2.703 -0.060 0.915 2.548 1.121 -0.479 -1.665 0.000 0.892 0.841 0.985 0.809 2.773 1.345 1.087 +1 0.766 0.628 1.011 0.421 -1.630 0.810 2.112 -0.804 0.000 1.212 0.335 1.562 0.000 1.088 0.045 0.357 2.548 1.099 -1.062 -0.199 3.102 1.054 0.978 0.995 0.826 0.718 0.795 0.698 +0 0.591 0.922 1.036 1.039 -0.689 0.704 0.259 1.020 2.173 0.559 0.678 -0.453 2.215 1.613 0.172 -1.292 0.000 0.820 0.587 0.616 0.000 1.297 0.893 1.085 0.886 0.919 0.744 0.695 +1 0.960 0.729 -0.016 0.790 -0.715 1.048 -0.770 1.200 2.173 0.411 -1.410 -1.195 0.000 0.619 0.585 1.522 2.548 1.331 -0.435 0.102 0.000 0.971 1.104 0.995 0.738 0.826 0.888 0.797 +1 1.233 1.278 0.160 1.310 -0.216 0.541 0.748 1.499 1.087 0.471 0.838 -1.040 0.000 0.799 0.266 -1.524 0.000 0.961 -1.564 1.309 0.000 0.482 0.538 0.997 1.156 0.769 0.905 0.814 +1 1.687 -0.032 0.254 0.751 0.044 0.888 0.762 -1.276 2.173 1.019 -0.157 1.560 2.215 0.753 -0.267 0.556 0.000 1.117 1.372 -1.381 0.000 0.868 0.991 0.976 1.275 1.027 1.049 0.883 +1 0.753 -0.278 0.418 0.779 -1.083 1.607 0.057 0.934 0.000 1.101 -0.017 -1.124 2.215 2.497 -0.851 -0.356 0.000 1.357 -1.332 -0.471 0.000 0.874 0.955 1.036 0.754 0.850 0.966 0.843 +1 0.641 0.894 1.076 1.262 -1.306 0.849 0.481 0.724 0.000 0.928 0.418 0.204 0.000 1.250 0.869 -1.227 2.548 0.445 -0.611 -0.535 0.000 0.854 0.900 1.046 0.628 0.663 0.846 0.796 +1 0.939 0.243 0.636 0.797 -0.596 0.995 0.316 -0.478 2.173 0.844 0.985 -1.236 2.215 1.759 -0.144 1.364 0.000 1.026 -0.197 0.530 0.000 1.014 1.224 1.073 0.790 0.976 1.040 0.877 +0 0.523 0.402 0.055 1.156 -0.851 0.590 0.575 0.939 2.173 0.735 -0.073 -1.619 2.215 0.776 2.705 -0.308 0.000 0.406 -1.968 1.296 0.000 0.618 0.879 0.987 0.959 0.788 0.896 0.814 +0 0.962 -0.803 -0.057 0.509 0.990 1.105 -1.112 -0.401 1.087 0.932 -0.275 1.454 2.215 1.082 -0.801 1.220 0.000 0.999 0.929 -1.400 0.000 1.567 0.993 0.989 0.910 1.620 1.163 0.957 +1 0.431 0.736 0.022 0.675 -0.068 0.711 1.214 0.405 2.173 1.391 1.022 -1.371 1.107 0.550 2.699 -0.015 0.000 0.931 1.963 1.681 0.000 0.825 0.964 0.996 1.167 1.468 0.962 0.851 +1 1.154 0.646 -0.386 0.780 0.370 0.762 -1.064 1.006 0.000 2.105 -0.086 -1.260 2.215 0.636 1.311 0.866 0.000 0.905 1.365 0.034 0.000 0.695 1.188 0.984 0.673 1.076 1.009 0.834 +1 0.784 -1.019 1.146 1.009 0.261 1.210 0.596 -1.057 2.173 0.712 -1.112 0.682 0.000 0.707 -0.204 1.380 0.000 0.669 1.216 0.743 3.102 0.834 0.840 0.986 1.903 1.040 1.350 1.052 +1 0.586 -1.396 0.349 0.313 1.579 2.581 0.616 -1.511 0.000 3.263 -0.619 0.165 2.215 0.852 -0.910 0.781 0.000 1.613 -0.005 0.294 3.102 0.696 0.759 0.990 1.060 0.707 0.788 0.721 +0 0.530 -1.176 1.471 0.725 0.255 0.395 -1.423 -0.125 0.000 0.696 -0.091 0.191 0.000 1.521 -1.182 -1.532 2.548 0.516 0.697 1.417 3.102 0.864 0.811 0.989 0.916 0.924 0.818 0.714 +0 1.729 1.223 1.279 0.698 0.605 0.715 2.818 -0.420 0.000 0.698 0.166 -1.076 2.215 0.953 -1.072 0.501 1.274 1.008 -0.207 1.404 0.000 1.124 1.006 0.987 1.388 1.063 1.043 1.051 +1 1.504 -1.419 -1.247 0.639 0.984 0.845 -0.824 0.557 2.173 0.449 -1.895 -0.400 0.000 0.401 -0.856 1.434 0.000 0.458 -2.364 -1.005 0.000 0.734 0.800 1.229 1.106 0.792 0.878 0.732 +0 1.809 -0.711 -0.993 1.336 -1.384 0.668 1.511 1.026 0.000 1.034 0.392 0.924 0.000 1.199 -0.140 -0.389 2.548 1.444 0.854 0.224 1.551 1.056 0.897 0.975 0.869 0.820 0.971 1.193 +0 0.527 0.226 1.589 1.443 0.747 1.136 0.720 -0.819 2.173 0.989 0.653 -1.296 2.215 1.059 1.683 0.856 0.000 0.850 0.140 -0.312 0.000 0.837 0.912 0.991 1.247 0.651 0.915 0.829 +0 1.483 -0.707 1.307 0.474 1.044 0.759 -1.456 -0.409 0.000 0.835 -0.601 -0.993 2.215 1.169 0.338 1.008 0.000 1.368 -1.634 -0.959 0.000 0.835 0.843 0.976 0.787 0.697 0.721 0.862 +1 0.374 0.785 -0.924 0.676 -0.936 1.064 -0.433 1.499 0.000 1.564 -0.472 0.292 2.215 1.415 -0.171 -0.729 1.274 1.423 -0.505 0.981 0.000 0.863 1.325 0.994 1.031 1.280 1.106 0.946 +1 0.708 0.893 -0.011 0.756 1.357 1.118 2.309 -0.994 0.000 1.163 -0.176 0.835 2.215 0.606 0.411 -0.326 0.000 0.933 -0.299 1.515 3.102 0.483 0.690 0.987 0.756 0.547 0.691 0.610 +0 0.580 -0.226 -1.304 0.701 -1.535 1.785 -1.710 1.491 0.000 1.356 -1.185 0.061 0.000 1.144 -2.057 -0.438 0.000 1.187 -0.881 -0.521 1.551 1.331 0.867 0.977 0.875 0.674 0.793 1.195 +1 0.604 1.936 0.798 1.423 1.163 3.059 -1.635 -0.905 0.000 1.436 0.200 1.149 0.000 1.633 1.355 0.266 2.548 1.285 1.177 0.693 3.102 2.213 1.298 0.978 0.900 0.418 0.964 0.823 +1 1.030 -0.164 1.538 0.990 0.337 0.661 0.549 -0.920 2.173 1.047 -0.100 -1.464 0.000 1.108 -0.591 0.570 2.548 0.783 0.529 0.118 0.000 1.249 0.898 1.235 0.739 1.242 0.826 0.777 +0 0.443 -0.559 -0.051 0.778 0.730 1.317 0.922 -0.553 1.087 0.862 0.112 1.372 0.000 0.983 0.789 0.929 2.548 1.195 0.605 -1.470 0.000 0.834 0.738 0.998 1.040 1.379 0.981 0.846 +1 1.280 0.126 1.440 0.604 -0.592 1.085 0.657 0.015 0.000 0.798 0.709 -0.382 2.215 1.492 1.010 1.235 0.000 1.468 0.537 -0.948 3.102 0.875 1.062 1.177 0.785 0.478 0.798 0.777 +0 1.185 0.703 0.414 0.733 0.877 0.796 -0.357 1.575 2.173 1.131 0.572 -1.007 0.000 1.365 0.102 -0.495 2.548 0.526 -0.393 0.661 0.000 1.147 1.088 0.989 0.920 1.280 0.882 0.837 +0 1.634 -0.262 1.191 0.357 0.689 1.073 1.440 -0.750 0.000 0.762 -0.357 -1.108 2.215 1.235 0.623 0.596 0.000 2.182 -0.614 0.568 1.551 1.095 1.115 0.997 0.852 1.182 0.966 0.845 +0 0.632 0.571 0.179 1.788 1.099 0.626 0.052 -0.082 0.000 0.926 0.015 -0.945 0.000 0.779 -0.735 -1.454 2.548 0.568 0.820 1.422 3.102 1.135 0.932 1.086 0.555 0.583 0.650 0.768 +0 0.758 0.096 1.217 1.324 -1.089 0.729 -1.443 0.866 0.000 1.081 0.165 -0.561 1.107 0.334 -1.128 -0.639 0.000 0.873 -0.159 0.345 3.102 0.872 0.701 1.214 0.881 0.659 0.811 0.827 +1 0.619 0.775 -0.830 1.351 1.384 0.750 0.793 0.252 1.087 0.709 1.523 0.327 0.000 0.698 -0.904 -0.917 0.000 0.595 0.787 -1.588 3.102 0.925 0.704 1.155 0.555 0.706 0.638 0.600 +0 1.284 1.278 1.390 0.774 -1.665 0.546 0.939 -0.692 2.173 0.545 1.829 -0.243 0.000 0.754 1.805 0.910 0.000 0.696 -1.639 0.883 0.000 0.918 0.823 0.981 0.969 0.648 0.824 0.713 +0 0.949 -0.383 -1.405 1.087 -0.543 0.382 0.446 0.917 1.087 0.652 -0.957 0.696 2.215 0.402 0.779 -0.006 0.000 0.484 -2.257 -0.822 0.000 0.591 1.013 0.987 0.838 0.591 0.694 0.719 +1 0.808 2.013 0.922 0.063 -0.171 1.653 -0.099 -1.181 0.000 1.198 -0.184 0.720 2.215 0.653 0.762 -0.182 0.000 1.656 -0.159 0.073 3.102 0.879 1.185 0.987 1.034 0.700 0.966 1.271 +0 0.412 -1.222 -0.002 0.481 0.737 0.599 -2.505 -1.188 0.000 0.600 -0.024 1.415 0.000 1.222 0.447 -0.086 2.548 1.663 0.179 -1.426 3.102 2.239 1.798 0.983 0.665 1.029 1.433 1.167 +0 1.664 -0.185 1.530 0.582 -1.372 0.535 -0.719 0.248 2.173 0.919 0.686 -0.651 2.215 0.366 2.151 -0.170 0.000 0.471 -0.924 -0.395 0.000 1.262 0.879 0.983 1.013 1.098 0.894 0.847 +1 2.207 -0.601 1.026 1.519 0.635 1.247 -1.247 -1.461 2.173 1.555 -0.706 -0.510 0.000 0.809 0.429 0.199 2.548 0.757 -2.343 -0.943 0.000 1.838 1.602 0.992 1.703 1.747 1.300 1.380 +1 0.640 -1.416 -1.310 0.130 -1.191 0.837 -0.160 0.790 2.173 1.247 -0.978 -0.920 0.000 0.558 -0.867 0.272 2.548 0.714 0.590 -0.115 0.000 1.152 0.834 0.977 0.890 0.516 0.843 0.738 +0 0.999 0.071 0.489 0.356 1.003 1.454 2.324 0.476 0.000 1.706 -0.525 -1.200 2.215 0.909 -0.658 -1.631 0.000 1.170 -1.370 -0.955 0.000 0.848 0.784 0.994 0.825 1.211 0.925 0.799 +0 1.391 -0.717 -0.436 0.578 -1.218 0.375 -0.769 0.347 2.173 0.369 -2.243 -1.240 0.000 1.078 -0.776 -1.637 2.548 0.667 0.510 0.961 0.000 0.847 0.887 0.984 0.693 0.774 0.660 0.754 +1 0.734 0.330 1.378 0.841 0.744 0.516 -1.005 1.306 0.000 0.764 -0.213 -1.606 0.000 1.799 0.100 -0.594 2.548 1.619 -0.703 -0.077 3.102 0.854 1.061 0.992 1.208 0.872 1.043 1.028 +0 1.327 0.496 0.449 1.389 0.374 0.970 1.084 -1.176 0.000 0.943 0.053 -0.115 2.215 1.811 0.894 -1.591 0.000 1.201 0.348 1.353 3.102 0.880 0.909 0.979 0.917 0.948 0.977 1.060 +0 0.496 1.440 -0.982 0.670 -1.459 0.652 0.440 0.833 2.173 0.680 2.504 -0.101 0.000 0.547 -0.859 1.087 0.000 0.965 -0.359 -1.294 0.000 0.827 0.760 0.983 0.457 0.481 0.539 0.542 +1 1.464 -0.523 -0.775 1.184 -1.364 0.938 -0.750 0.403 2.173 0.980 -0.647 1.192 2.215 0.943 -0.250 -1.390 0.000 0.443 2.332 0.519 0.000 0.954 0.901 0.987 1.283 0.922 1.003 0.870 +1 0.784 0.895 0.018 2.781 -0.299 1.149 0.310 1.392 0.000 1.562 1.002 -1.213 2.215 1.376 1.474 0.758 0.000 0.944 1.251 -0.519 0.000 1.150 0.702 0.990 1.481 0.933 1.000 1.010 +0 0.400 1.064 -1.597 0.865 0.295 0.924 0.610 -0.532 2.173 1.465 -0.479 1.143 0.000 0.603 -2.263 -0.459 0.000 1.109 0.343 -1.541 3.102 2.211 1.586 0.986 0.823 0.852 1.413 1.123 +0 1.156 -0.041 -0.575 1.367 -1.061 0.594 -1.300 0.424 0.000 0.706 0.319 0.712 2.215 0.841 0.563 1.313 2.548 0.574 -1.266 -1.659 0.000 0.855 0.939 0.990 0.897 0.439 0.756 0.903 +1 0.857 -0.640 -1.063 0.658 1.466 0.967 -0.748 -0.140 0.000 0.785 0.235 1.507 2.215 0.811 -1.038 0.697 1.274 0.722 -1.493 -0.287 0.000 0.712 0.740 0.991 0.863 0.850 0.844 0.786 +0 0.671 -0.350 -0.605 1.624 1.560 0.962 -1.645 -0.537 0.000 0.719 1.343 1.004 1.107 1.373 0.370 1.280 2.548 1.003 1.205 -0.970 0.000 0.745 0.831 1.343 1.191 0.596 0.836 0.777 +1 1.444 1.002 -0.881 1.068 -0.535 0.547 1.616 1.605 0.000 0.372 0.793 0.537 0.000 1.499 0.635 1.216 1.274 0.911 1.412 0.195 3.102 0.883 0.717 0.983 0.835 0.849 0.874 0.792 +0 2.015 -0.132 0.491 0.673 0.867 0.875 -0.540 -1.119 2.173 0.613 -1.157 -1.674 0.000 0.508 1.780 0.854 0.000 1.507 -0.672 -0.542 3.102 0.842 0.768 0.986 1.079 0.625 1.016 0.853 +0 0.305 2.263 -1.680 2.012 1.192 1.433 -0.890 -0.279 2.173 0.745 -0.158 -1.295 0.000 0.639 -1.364 1.107 0.000 1.069 -0.170 0.991 3.102 1.161 0.818 0.987 2.445 1.279 1.550 1.311 +0 0.552 0.146 -1.597 1.442 0.270 1.141 1.529 0.445 0.000 0.552 1.310 -0.793 0.000 1.880 -0.881 -1.142 2.548 2.129 0.337 1.488 3.102 1.243 1.119 1.228 1.274 1.556 1.055 0.958 +1 0.519 -2.270 -1.371 0.972 1.709 0.854 -1.342 0.229 0.000 0.898 -0.981 -0.543 1.107 1.500 -1.198 1.652 0.000 0.628 -0.597 1.180 0.000 0.919 0.893 0.992 0.596 0.211 0.561 0.635 +0 0.844 1.450 0.161 0.640 -1.186 0.890 0.781 1.731 2.173 0.398 0.200 0.690 0.000 1.047 0.139 0.185 2.548 0.758 1.315 -0.858 0.000 0.882 0.850 0.987 0.755 1.246 0.787 0.672 +0 1.516 0.080 -0.274 0.688 -0.223 1.002 -0.886 -1.578 2.173 1.157 -0.450 0.085 2.215 1.109 -1.480 1.114 0.000 1.555 -1.178 1.591 0.000 0.623 0.842 0.972 0.624 1.618 1.016 1.036 +0 1.426 -1.334 0.582 0.567 -1.037 0.470 -0.226 -0.062 2.173 0.820 -0.749 -1.443 0.000 0.519 0.469 1.277 0.000 0.666 0.186 -1.210 1.551 0.938 0.896 1.238 0.860 0.527 0.663 0.689 +1 0.327 -1.821 1.187 0.640 -1.144 1.036 0.407 -0.127 0.000 1.164 -0.523 -1.639 0.000 0.831 -0.994 0.886 2.548 0.693 -0.289 0.447 1.551 2.555 1.418 0.994 0.686 0.315 0.969 0.818 +1 0.882 0.082 -0.390 1.029 -1.526 1.350 -0.396 -0.136 2.173 1.812 0.335 1.632 0.000 0.728 -2.378 0.491 0.000 0.660 -1.703 0.758 0.000 0.286 0.616 1.126 1.094 0.924 0.936 0.989 +0 0.510 0.903 -0.820 1.152 1.334 1.228 1.279 0.212 2.173 0.848 1.816 -1.210 0.000 0.628 2.430 1.549 0.000 1.455 0.604 1.697 0.000 0.823 0.716 0.990 1.068 0.174 0.845 0.788 +1 1.438 1.638 -0.800 1.245 -0.589 1.279 1.648 1.260 2.173 1.089 1.895 0.760 0.000 0.705 0.090 1.140 0.000 2.331 0.353 -0.272 0.000 1.371 0.991 0.997 1.602 2.166 1.693 1.497 +0 0.721 -0.194 1.034 0.816 -0.549 0.863 0.888 0.795 2.173 1.653 0.100 -1.482 0.000 1.283 0.339 0.200 2.548 1.677 1.323 -0.341 0.000 0.979 1.018 1.052 0.953 0.756 0.880 0.841 +0 1.233 -1.140 -0.060 0.296 -1.683 0.673 -1.851 1.298 0.000 0.885 -1.206 0.584 2.215 1.123 -0.953 -1.129 2.548 0.861 -0.668 1.671 0.000 0.960 0.935 0.982 0.680 1.064 0.712 0.665 +1 0.945 -1.717 -0.925 0.415 0.363 0.551 0.516 -0.277 2.173 0.503 0.548 1.313 2.215 0.419 -1.147 0.789 0.000 0.410 1.085 0.373 0.000 0.777 0.710 0.987 0.941 0.767 0.814 0.693 +1 1.086 0.952 0.313 0.339 -0.992 1.345 0.805 -0.767 0.000 1.690 0.420 0.584 2.215 1.513 0.315 -1.379 0.000 1.390 1.043 1.242 3.102 0.966 1.010 0.980 0.749 0.969 0.704 0.655 +1 1.263 -0.030 1.347 0.977 -1.504 0.987 1.434 -0.683 2.173 0.549 0.702 -0.176 0.000 0.888 -1.697 0.838 0.000 0.443 0.696 0.799 3.102 0.720 0.764 0.986 0.621 0.714 1.040 0.890 +1 1.048 1.027 1.150 0.176 -1.648 0.830 1.132 0.484 0.000 1.429 0.723 1.669 0.000 1.423 0.627 -0.663 2.548 0.873 -0.414 -0.542 0.000 0.893 0.965 0.990 0.927 0.837 0.768 0.723 +0 0.865 0.191 0.409 2.226 0.680 0.858 -0.144 -1.088 0.000 0.532 1.085 -1.274 2.215 0.278 -0.648 -0.915 0.000 0.495 0.964 1.638 0.000 0.937 0.687 1.005 0.605 0.427 0.795 0.845 +1 0.891 -0.318 0.746 1.014 1.541 1.920 -0.895 -1.278 0.000 0.895 0.242 0.592 0.000 0.408 -1.421 0.220 0.000 0.439 -0.519 -1.584 3.102 1.005 1.093 0.992 0.509 0.226 0.644 0.609 +0 0.787 -0.598 -1.676 0.618 -0.302 0.414 -1.767 1.693 0.000 0.995 -0.362 -0.136 2.215 0.679 0.259 1.518 2.548 0.628 -1.355 0.192 0.000 0.763 0.986 0.988 0.604 0.918 0.752 0.677 +1 0.831 -1.128 0.391 1.009 0.398 0.759 -0.347 1.662 1.087 0.818 -1.414 -1.203 0.000 0.275 -1.142 0.028 0.000 1.396 -0.568 -1.004 3.102 0.656 0.843 0.996 1.093 0.755 1.042 0.844 +1 1.690 0.137 -0.577 0.918 0.663 0.622 -0.609 -1.541 2.173 0.954 -1.106 1.168 2.215 0.531 -0.590 -0.688 0.000 0.909 0.353 0.511 0.000 0.802 0.907 1.551 1.130 0.789 0.989 0.812 +1 0.634 0.806 -1.685 1.727 0.746 1.481 -0.587 -0.646 2.173 0.752 -0.666 -1.158 2.215 1.896 -0.514 0.737 0.000 0.834 -0.784 -1.607 0.000 1.218 1.088 1.180 1.889 0.695 1.303 1.205 +0 1.231 -1.796 1.718 0.770 0.158 0.591 -0.226 1.478 2.173 0.657 -1.048 -0.151 0.000 0.548 0.357 -0.227 1.274 0.529 -0.125 -1.183 0.000 0.718 0.834 1.330 1.107 0.743 0.891 0.758 +1 2.556 -0.630 -1.508 0.222 1.467 0.588 -0.500 -0.295 2.173 0.927 -1.198 0.556 2.215 0.651 -0.583 0.545 0.000 0.463 0.114 -0.294 0.000 0.481 0.514 0.978 1.037 0.855 0.926 0.745 +0 0.747 -1.255 0.086 0.519 -0.710 0.918 -0.143 -1.143 2.173 1.092 -0.950 1.247 0.000 1.109 0.130 1.350 2.548 0.587 -0.065 -0.228 0.000 1.122 0.854 0.981 1.369 0.997 1.142 0.988 +0 1.276 -1.162 1.668 1.523 1.615 1.978 -1.024 -1.610 2.173 2.155 0.757 0.309 0.000 1.364 1.332 -0.060 2.548 0.773 1.776 -0.576 0.000 1.221 1.008 1.003 0.847 3.843 1.978 1.651 +0 0.932 -0.139 -1.716 0.365 0.206 1.416 0.108 0.650 2.173 0.882 -1.330 -0.637 0.000 0.992 -0.604 -0.924 0.000 1.006 0.468 -1.280 3.102 0.633 0.898 0.988 0.969 1.278 1.177 0.974 +1 0.982 1.102 -0.291 0.418 0.391 0.690 -0.278 -0.732 1.087 0.634 0.148 0.273 1.107 0.902 -0.237 -1.505 0.000 0.868 0.896 -1.624 0.000 0.709 0.850 0.981 0.789 0.795 0.673 0.659 +0 0.770 -0.170 1.456 0.616 -1.556 0.380 -2.023 0.737 0.000 0.514 -0.697 -0.511 2.215 0.764 2.563 -0.793 0.000 0.656 1.127 -0.290 0.000 0.711 0.862 0.980 0.713 0.541 0.909 1.168 +0 0.994 0.363 0.982 0.555 -0.116 2.053 -0.014 -0.940 2.173 1.648 -0.863 1.175 2.215 0.632 0.696 0.351 0.000 0.763 -1.619 0.341 0.000 1.384 1.270 0.991 1.367 2.832 1.551 1.231 +0 0.559 -0.065 -0.580 0.948 0.871 0.981 -0.053 -1.239 1.087 0.693 1.084 0.256 0.000 0.751 1.922 0.249 0.000 1.140 0.563 1.677 0.000 1.139 1.233 0.988 0.682 0.996 0.790 0.744 +1 0.876 0.437 1.186 0.778 -0.270 0.979 0.375 -1.254 1.087 1.213 -0.456 0.749 2.215 0.790 -0.364 -0.714 0.000 0.958 -0.823 0.136 0.000 0.725 1.052 1.105 0.887 1.710 0.959 0.845 +1 0.587 -1.062 1.328 0.401 -0.768 1.534 -1.172 0.523 0.000 1.188 -1.205 -0.436 0.000 1.274 -0.784 -0.944 0.000 1.103 0.299 1.672 3.102 0.908 0.892 0.991 0.600 0.637 0.804 0.726 +0 2.581 -0.894 1.179 0.958 0.766 2.637 -0.904 -0.347 0.000 1.570 -0.262 1.319 2.215 1.804 -1.188 -0.801 2.548 0.669 1.041 1.348 0.000 1.230 0.954 0.984 1.506 1.944 1.237 1.084 +0 0.404 0.447 -1.001 0.165 -0.160 0.494 -0.952 -1.079 0.000 0.674 0.248 0.332 2.215 0.977 0.879 0.980 2.548 0.421 1.543 0.429 0.000 1.500 1.050 0.983 1.009 0.569 0.779 0.764 +0 2.327 -0.841 0.010 1.353 0.114 0.955 -1.533 0.427 2.173 1.900 -0.234 -1.267 1.107 1.884 0.034 1.462 0.000 1.089 -2.412 -0.870 0.000 0.789 1.160 0.988 1.783 2.419 1.491 1.420 +0 1.898 -1.418 0.112 0.963 -0.251 0.783 0.576 1.584 0.000 0.847 0.518 -1.427 0.000 0.855 0.085 -0.526 2.548 0.859 -0.371 1.180 3.102 0.717 0.921 0.980 0.852 0.679 0.716 1.057 +1 0.866 0.480 0.888 0.755 -1.527 1.104 -0.944 -0.583 0.000 1.023 -0.667 1.277 0.000 0.863 0.059 0.673 0.000 2.828 0.154 -0.773 1.551 0.929 0.675 0.984 1.043 1.137 0.938 0.838 +0 0.831 0.491 1.544 1.572 -1.131 0.965 -0.138 -0.308 1.087 1.114 -0.378 1.292 2.215 0.451 -1.666 -0.625 0.000 0.990 -0.793 0.454 0.000 0.695 0.899 1.059 1.146 1.525 1.031 0.949 +1 0.924 -1.055 0.200 0.279 -0.219 0.807 1.016 -1.446 2.173 0.321 -0.385 1.639 2.215 0.479 0.903 0.019 0.000 0.446 -0.276 0.729 0.000 0.474 0.782 0.993 0.623 0.636 0.797 0.643 +1 1.027 -0.287 -0.757 1.149 0.000 0.855 -0.690 1.268 2.173 0.561 -0.472 -0.177 0.000 0.686 -0.878 -1.608 2.548 0.502 0.619 -1.697 0.000 0.814 0.916 0.987 0.764 0.514 0.774 0.678 +1 0.684 -0.117 1.393 1.871 1.332 1.344 0.339 0.212 2.173 0.509 0.286 -0.876 2.215 0.725 2.343 -1.433 0.000 1.437 0.826 -0.611 0.000 0.980 0.961 0.983 1.803 1.012 1.240 1.483 +0 0.276 -2.108 1.689 1.908 0.589 1.263 1.364 -1.162 0.000 0.570 0.675 -0.769 0.000 1.007 1.524 1.528 0.000 2.206 -0.469 0.276 3.102 0.887 0.706 0.995 0.769 0.596 1.152 1.440 +0 1.653 0.876 1.657 0.545 -0.732 0.875 0.483 0.253 2.173 0.442 -1.485 -0.012 0.000 0.559 -2.070 -0.967 0.000 0.637 -0.634 -0.408 0.000 0.795 1.154 1.099 0.789 0.851 0.820 0.862 +0 1.319 1.010 -1.498 0.022 -0.765 0.331 0.130 -1.672 0.000 0.311 2.369 1.327 0.000 1.603 0.251 -0.775 0.000 0.927 -2.127 0.842 0.000 0.953 0.872 0.888 0.688 0.252 0.600 0.592 +0 0.650 -1.336 -0.668 0.554 1.674 1.244 -0.634 1.459 2.173 1.537 0.093 0.034 0.000 1.509 -0.512 -0.145 2.548 1.196 0.984 -1.334 0.000 0.688 1.151 0.980 1.238 1.695 1.122 1.159 +0 0.445 1.216 -0.759 1.128 1.311 0.678 -0.164 -1.272 0.000 0.719 0.184 1.449 2.215 0.809 1.891 -0.058 0.000 1.506 -0.422 0.086 3.102 0.984 1.050 0.985 1.289 0.945 0.927 0.864 +1 0.662 2.345 -1.062 0.367 -0.008 0.447 -0.114 0.125 2.173 0.452 1.458 1.188 0.000 0.652 1.251 -0.585 0.000 0.751 -0.108 1.419 1.551 0.834 0.846 0.980 0.789 0.563 0.686 0.620 +1 1.226 0.005 0.918 1.842 1.474 1.348 0.175 -0.868 1.087 1.100 0.024 0.131 2.215 0.432 -2.592 0.926 0.000 0.386 2.395 -0.400 0.000 3.945 2.359 1.002 1.564 1.411 1.696 1.493 +1 0.639 -0.021 1.638 1.143 -0.909 0.893 -0.104 -0.161 0.000 0.790 1.891 1.519 0.000 1.819 -0.651 1.266 2.548 0.539 0.432 -1.380 3.102 2.806 1.504 0.989 1.142 0.719 1.344 1.096 +1 0.890 -0.229 -1.301 0.243 0.873 1.570 1.334 -1.696 0.000 1.453 -0.585 0.022 2.215 1.431 -0.776 0.237 0.000 1.853 0.017 0.560 0.000 0.959 0.923 0.990 0.968 0.726 0.669 0.722 +0 0.857 -1.210 1.098 0.632 1.665 1.762 -0.425 0.306 0.000 1.646 -1.407 -1.702 1.107 2.692 -1.539 -0.998 0.000 0.893 -0.378 0.813 0.000 0.846 0.937 0.980 0.801 1.451 1.337 1.217 +0 2.480 -1.516 1.025 0.154 -1.418 0.528 -0.365 -0.686 0.000 0.860 -2.363 -0.748 0.000 1.186 -1.054 -0.166 1.274 0.756 0.205 1.623 1.551 1.096 0.952 0.988 0.871 0.909 0.813 0.803 +1 1.722 1.501 0.186 0.724 1.328 2.403 -1.116 -1.203 2.173 1.845 0.100 0.646 2.215 1.165 0.463 0.272 0.000 0.652 0.615 1.565 0.000 0.890 0.828 1.326 3.880 3.691 2.756 2.029 +0 1.408 -0.358 -0.896 0.411 0.070 0.876 -0.173 -1.564 2.173 0.501 -1.207 0.045 0.000 1.220 0.233 1.024 1.274 0.710 0.347 0.232 0.000 0.732 1.056 0.986 1.011 0.972 0.824 0.759 +0 0.668 0.054 1.590 2.750 -0.462 0.704 -0.261 0.705 2.173 0.940 -0.928 1.627 2.215 0.858 0.327 1.314 0.000 0.584 -1.352 -0.628 0.000 1.190 0.912 1.805 1.459 0.979 1.116 0.948 +0 1.546 -0.990 0.609 2.915 0.994 2.469 0.197 -0.758 2.173 1.335 -2.855 0.137 0.000 1.791 -0.426 -1.180 0.000 1.369 -0.887 1.519 1.551 1.806 1.179 1.002 3.168 2.168 2.052 1.711 +1 1.218 -0.456 -0.737 1.077 -0.032 0.375 -0.507 1.439 0.000 0.647 0.114 0.529 2.215 0.736 1.074 1.685 0.000 0.380 1.164 -0.900 0.000 0.922 0.806 0.988 0.615 0.253 0.541 0.663 +0 1.164 1.141 -0.828 0.412 0.413 0.588 0.430 1.616 0.000 0.702 -0.009 0.145 2.215 0.487 0.778 -1.122 2.548 0.505 -1.660 0.976 0.000 1.356 0.933 0.990 0.739 0.630 0.711 0.724 +1 0.649 -1.279 -0.166 1.104 0.824 0.492 -1.061 -1.429 0.000 0.735 0.368 -0.016 2.215 0.325 0.756 0.108 0.000 0.742 -0.163 -1.123 0.000 1.061 0.936 0.989 1.062 0.689 0.925 0.824 +0 1.933 -0.233 0.941 1.352 0.322 1.189 0.335 -1.385 2.173 0.827 -0.627 -0.966 0.000 0.443 0.175 0.017 0.000 0.649 -1.224 -0.248 3.102 0.823 0.977 1.185 0.863 1.246 1.126 0.947 +0 0.486 -0.556 -1.281 1.151 0.989 0.456 -1.836 -0.807 0.000 0.951 -0.815 -0.669 1.107 1.447 -0.916 0.236 0.000 0.665 -0.647 1.298 1.551 1.341 0.991 0.988 0.486 0.704 0.681 0.679 +1 0.517 1.424 0.029 1.372 1.459 1.410 -0.184 -1.236 0.000 0.920 0.882 0.499 1.107 0.759 0.938 -0.423 2.548 0.736 -1.223 0.378 0.000 1.879 1.428 1.120 0.820 0.658 1.161 1.149 +1 0.704 0.521 1.507 0.456 0.387 0.529 -0.713 -0.524 0.000 0.855 0.411 -0.527 0.000 1.527 0.533 0.925 2.548 0.934 1.142 -1.407 3.102 0.861 0.993 0.988 0.712 0.869 0.915 0.837 +0 0.626 -0.988 0.356 0.241 -0.292 1.501 1.921 1.408 0.000 1.527 -1.003 -0.640 0.000 1.867 -0.399 0.106 2.548 0.677 -1.320 -1.355 0.000 0.878 0.883 0.993 0.696 0.791 0.816 0.747 +0 0.625 -1.759 1.054 1.130 1.647 0.480 0.533 -1.488 0.000 0.742 0.255 -0.584 0.000 1.280 -1.345 -0.005 0.000 1.653 -0.200 1.612 3.102 0.939 0.884 0.981 0.950 1.138 0.846 0.842 +1 0.962 -0.027 -0.561 0.969 0.974 1.095 1.146 -1.609 2.173 1.239 0.000 0.068 1.107 0.596 2.083 0.939 0.000 0.427 1.124 1.407 0.000 0.611 0.979 1.314 0.876 2.006 1.134 1.008 +1 0.571 -0.230 0.124 0.529 1.583 0.937 -0.056 -0.909 0.000 0.896 -2.173 -0.081 0.000 2.361 0.211 0.875 2.548 0.591 -0.687 1.500 3.102 0.921 0.745 0.988 1.071 0.694 0.960 0.874 +0 1.253 0.502 -0.029 0.513 0.928 0.423 2.152 -0.648 0.000 0.774 -0.592 -1.672 2.215 0.286 -0.133 0.194 1.274 0.989 -1.181 -1.352 0.000 0.878 0.595 0.985 0.493 0.511 0.666 0.670 +1 1.961 -1.125 -0.280 1.254 0.309 1.482 -1.159 1.090 2.173 1.201 -0.451 -1.233 2.215 1.497 0.484 -1.559 0.000 0.899 2.377 0.101 0.000 2.241 2.175 1.102 1.535 1.841 2.342 2.171 +0 0.413 0.596 1.637 1.278 -0.669 0.937 1.609 -1.586 0.000 0.979 0.530 0.276 2.215 0.685 0.592 -0.223 2.548 0.588 0.285 1.253 0.000 0.956 0.824 0.987 0.611 0.381 0.593 0.650 +1 0.761 0.544 0.936 1.476 -0.278 1.242 0.661 1.705 0.000 1.137 -0.087 -0.930 2.215 1.414 -1.298 -0.075 2.548 1.734 -0.685 0.776 0.000 0.906 0.837 1.303 1.409 1.348 1.089 0.931 +0 0.605 -0.872 0.083 0.225 1.670 1.212 -0.341 -1.729 2.173 1.483 0.907 -0.407 1.107 0.962 -0.024 0.738 0.000 0.558 -0.478 0.634 0.000 0.236 0.936 0.982 0.927 2.275 1.156 0.925 +0 0.470 -0.217 -1.674 2.262 1.437 1.223 -0.350 -0.173 0.000 0.480 -0.607 -1.011 2.215 0.720 0.418 -0.526 0.000 0.796 -0.456 1.079 0.000 0.938 0.669 1.004 0.797 0.411 0.708 0.669 +0 0.854 1.291 -1.089 1.650 -0.330 0.595 0.391 1.127 2.173 0.643 1.048 1.597 2.215 0.526 1.699 1.057 0.000 0.684 -0.212 1.072 0.000 0.841 0.608 1.041 1.140 0.490 0.826 0.727 +0 0.727 -0.465 -0.480 2.265 0.034 1.057 0.034 -1.528 0.000 1.258 -0.658 -1.416 0.000 1.508 0.763 0.747 0.000 0.952 -0.927 1.120 3.102 0.923 0.985 0.978 0.615 0.274 0.699 0.959 +0 1.689 0.952 -1.425 0.654 0.994 0.482 -1.318 0.112 0.000 0.414 -1.055 1.175 0.000 0.771 1.025 -0.191 1.274 0.590 0.099 0.283 3.102 0.785 1.162 1.194 0.764 0.345 0.676 0.883 +0 1.775 0.392 -1.264 1.961 -0.918 1.126 1.666 -1.157 0.000 2.051 0.556 0.702 0.000 0.990 -2.311 -0.138 0.000 3.277 1.298 0.857 0.000 0.894 0.756 0.985 1.097 0.905 0.858 0.863 +1 0.313 -1.366 1.045 1.315 -1.067 0.671 -0.658 0.028 1.087 1.232 1.039 1.395 2.215 0.955 0.154 -0.498 0.000 0.472 -1.247 0.549 0.000 0.916 0.680 0.985 1.246 1.820 1.082 0.912 +1 0.630 0.869 -1.192 0.963 -0.426 2.050 1.436 1.515 0.000 1.989 0.470 0.113 2.215 0.944 0.254 -1.146 0.000 1.372 -0.145 -0.288 3.102 0.731 1.081 0.991 0.592 0.726 0.700 0.661 +1 0.842 -0.139 0.830 0.643 -0.608 1.098 0.151 1.308 2.173 1.197 0.169 -0.596 0.000 0.491 -0.891 -0.228 0.000 0.486 0.641 -1.110 3.102 0.811 0.573 0.987 0.879 0.678 0.843 0.728 +0 0.604 -0.865 -1.217 0.942 1.496 0.749 0.653 0.071 2.173 0.758 0.309 -0.844 0.000 0.911 -0.781 0.360 1.274 0.789 -0.319 1.676 0.000 0.851 0.970 0.984 0.835 0.898 0.805 0.734 +1 0.638 -1.868 -1.625 1.467 1.470 0.668 -0.854 -0.358 1.087 0.631 -0.875 0.911 0.000 0.726 -1.555 0.346 0.000 0.698 0.003 -0.468 0.000 0.913 0.868 0.983 1.060 0.898 1.001 0.830 +0 1.118 -0.456 -0.446 1.629 -0.956 0.838 -1.229 0.959 1.087 0.704 -0.127 1.198 0.000 0.685 -0.621 0.397 0.000 0.671 0.138 -1.152 3.102 0.781 0.721 0.987 0.509 0.964 0.943 0.796 +1 0.929 0.065 0.522 0.725 -1.283 0.634 -0.035 1.494 0.000 1.298 0.504 -0.376 2.215 0.734 -1.217 1.601 1.274 0.819 -0.042 -0.719 0.000 1.002 1.121 1.135 0.812 1.501 0.889 0.772 +0 1.337 1.197 0.421 1.101 1.441 0.630 1.113 1.409 2.173 1.211 -0.178 -0.684 0.000 0.754 1.508 -0.259 0.000 0.383 -0.359 1.571 3.102 1.640 1.003 1.337 0.796 0.452 0.868 0.895 +0 0.387 -0.890 -0.640 0.293 0.184 0.976 -0.763 -0.365 2.173 0.591 -1.133 -0.890 0.000 2.271 -1.304 1.459 2.548 0.461 -0.763 0.488 0.000 0.649 0.929 0.997 0.949 1.953 1.252 0.959 +1 1.115 0.908 0.313 1.618 -0.319 0.698 -2.746 1.432 0.000 0.794 0.273 -1.427 2.215 1.106 1.309 -0.410 0.000 1.908 1.026 1.591 3.102 0.785 0.930 1.003 1.174 0.713 0.927 0.799 +1 0.694 -0.687 0.664 0.726 -0.744 1.008 -1.205 -0.288 2.173 1.082 -0.327 1.309 2.215 1.154 -0.563 -1.605 0.000 0.517 -1.769 -0.368 0.000 1.040 0.949 0.986 0.834 1.676 0.958 0.811 +0 0.908 -0.352 -0.021 1.681 0.383 1.203 -0.624 -1.644 0.000 0.756 -0.471 -0.837 0.000 0.600 0.514 0.821 2.548 0.432 1.011 -1.166 0.000 0.743 0.735 0.995 0.499 0.380 0.540 0.673 +0 1.288 -1.001 1.127 4.239 0.862 2.040 -0.783 -0.808 0.000 0.506 -0.779 1.713 2.215 1.522 -0.056 -0.522 2.548 0.699 -0.774 -1.399 0.000 0.933 0.955 0.982 0.868 0.913 1.178 1.406 +1 0.382 -0.503 -0.564 1.086 0.363 0.846 -0.035 -1.529 0.000 0.537 -0.218 0.594 2.215 1.192 1.002 0.500 0.000 1.015 0.816 -0.979 3.102 2.054 1.257 0.982 0.735 0.784 0.888 1.097 +1 1.374 0.620 1.203 0.506 -1.541 0.930 -0.018 -0.630 2.173 0.682 -0.175 0.986 1.107 0.555 0.109 0.036 0.000 0.569 -1.026 -0.352 0.000 0.499 0.608 0.986 1.240 1.168 0.920 0.782 +1 1.147 1.810 0.844 0.393 0.190 0.864 1.684 -0.702 2.173 1.126 1.137 1.027 0.000 1.211 1.194 -1.026 0.000 1.444 1.117 1.609 0.000 0.842 1.096 0.978 1.015 0.839 0.959 0.835 +0 1.009 -0.051 1.551 0.463 1.690 0.745 -0.504 1.016 0.000 0.663 -2.576 0.231 0.000 0.551 1.782 0.135 0.000 1.148 0.520 -1.144 3.102 1.020 0.734 0.980 0.791 0.400 0.560 0.598 +1 0.303 -1.054 -1.134 1.234 -0.603 0.749 0.254 -0.137 2.173 0.697 0.333 1.354 0.000 0.618 1.806 -0.757 0.000 0.924 1.016 1.412 0.000 0.942 0.890 0.992 1.823 0.555 1.560 1.450 +0 1.167 1.003 -1.440 0.264 -1.274 0.307 2.156 -1.468 0.000 0.773 0.994 -0.396 0.000 1.698 1.562 0.919 0.000 0.975 0.843 0.546 1.551 1.038 0.812 0.989 0.742 0.661 0.624 0.611 +1 2.120 -0.154 1.328 1.425 1.098 1.473 0.945 -0.736 0.000 0.339 -1.713 -0.042 0.000 0.525 -1.183 1.268 2.548 0.489 2.281 -1.088 0.000 1.377 1.186 0.982 0.566 0.336 1.100 1.339 +0 1.063 1.166 0.270 1.231 0.893 1.272 -0.753 -1.019 2.173 0.823 -0.533 1.637 0.000 0.348 -0.800 -0.055 0.000 1.238 0.122 -0.105 3.102 0.830 0.947 0.993 0.742 1.157 1.227 1.018 +0 0.547 0.599 1.371 3.854 0.864 3.200 0.012 -0.905 0.000 1.316 -1.211 0.966 2.215 1.047 1.099 0.668 2.548 0.384 1.456 -0.008 0.000 0.986 1.992 0.990 2.358 1.973 1.945 2.061 +1 0.570 0.447 0.275 0.103 -0.301 1.132 -0.133 1.729 2.173 0.852 1.034 -0.394 0.000 0.739 -0.721 0.782 2.548 0.672 0.675 -0.760 0.000 0.343 0.975 0.932 0.971 0.941 0.939 0.823 +1 0.888 -1.424 1.050 0.964 -1.704 1.237 0.566 1.637 2.173 2.572 0.081 0.031 0.000 1.173 -0.019 -0.904 0.000 0.702 1.323 -0.505 3.102 1.994 1.350 0.993 1.278 1.061 1.382 1.324 +1 0.369 -0.734 -1.233 0.432 -1.552 0.493 -0.723 -1.700 2.173 0.484 0.298 0.463 2.215 0.558 2.160 0.798 0.000 1.361 0.900 -0.004 0.000 0.921 0.728 0.981 0.783 0.773 0.894 0.777 +0 0.657 0.715 1.037 0.678 -1.669 0.446 -0.653 0.559 0.000 1.109 -0.359 -0.451 2.215 0.691 -1.205 1.632 0.000 0.712 0.003 -0.630 1.551 0.888 1.013 0.984 0.627 0.198 0.630 0.648 +1 0.917 -0.077 -1.717 0.770 0.616 0.901 -1.412 -0.866 2.173 0.757 -1.012 -0.142 2.215 0.666 0.443 0.669 0.000 0.551 -1.437 0.995 0.000 0.920 0.819 1.003 1.170 0.775 0.859 0.789 +1 0.486 -0.517 0.991 1.006 -1.126 0.505 -1.238 0.940 0.000 1.083 -0.382 -0.262 2.215 0.393 -2.238 0.115 0.000 1.050 1.525 1.559 0.000 0.748 1.045 0.991 0.720 0.835 0.828 0.737 +0 1.263 1.327 1.411 0.367 -0.945 0.479 1.259 -1.612 0.000 1.312 1.309 0.291 2.215 0.998 0.465 -0.833 0.000 0.670 1.001 -0.017 3.102 0.938 1.239 0.991 0.638 0.240 0.727 0.698 +0 0.285 1.213 -0.991 1.201 0.723 0.521 1.139 1.520 0.000 0.626 -0.224 0.571 0.000 1.426 0.136 -0.428 2.548 1.779 -0.538 -1.156 3.102 1.289 1.196 0.987 0.945 0.894 0.936 0.815 +1 1.496 0.286 1.390 1.155 0.887 1.071 0.806 -0.515 2.173 0.455 -0.493 -0.205 0.000 0.368 1.077 1.644 2.548 0.410 -0.707 0.572 0.000 0.375 0.847 0.994 0.619 0.742 0.959 0.785 +0 1.984 0.259 -1.545 1.152 1.168 1.226 1.067 0.086 0.000 0.507 0.001 -0.847 2.215 0.563 -0.305 -0.527 2.548 0.809 -1.628 0.939 0.000 0.682 0.742 1.344 0.908 0.188 0.656 0.720 +0 0.485 1.854 -0.409 0.706 -0.910 0.973 1.577 0.778 2.173 0.783 1.220 1.403 0.000 0.553 -0.190 -0.619 0.000 0.709 1.753 -1.323 0.000 1.071 0.951 0.990 1.117 1.495 0.956 0.841 +0 1.265 0.272 1.608 1.288 0.456 1.286 0.404 -0.480 0.000 0.749 -1.348 1.554 2.215 1.215 0.159 -0.037 0.000 1.200 1.555 -1.612 0.000 0.913 1.393 1.523 1.236 0.535 1.196 1.114 +1 0.511 0.288 -1.522 1.224 0.695 0.637 -1.024 1.618 1.087 0.496 -0.170 0.041 0.000 0.276 1.257 0.456 1.274 0.824 0.193 -0.311 0.000 0.733 0.882 0.997 0.524 0.915 0.663 0.659 +0 0.488 -1.409 -1.229 1.234 -0.358 0.607 -1.324 -0.734 2.173 1.511 -0.763 1.245 2.215 0.534 -0.712 0.184 0.000 0.532 -1.874 0.749 0.000 0.552 0.802 0.984 0.683 1.432 1.066 0.835 +1 1.381 -1.134 -1.200 0.979 -0.837 0.747 -0.485 1.315 2.173 0.529 0.023 0.918 0.000 1.336 -0.022 0.142 2.548 1.103 -0.331 -0.826 0.000 0.921 0.950 0.997 1.091 1.123 0.939 0.798 +0 0.424 1.658 1.697 0.588 0.395 0.619 0.496 0.670 0.000 0.419 1.478 -0.247 0.000 0.788 -0.357 -1.247 2.548 0.840 -0.176 1.220 3.102 0.983 1.009 0.994 0.589 0.497 0.658 0.595 +0 1.099 0.267 0.858 0.941 1.270 1.220 -0.452 -0.775 0.000 1.046 -0.130 0.263 2.215 0.710 0.169 1.326 2.548 0.577 -1.975 -1.655 0.000 1.197 1.036 0.979 0.981 0.764 0.845 0.951 +1 0.327 2.427 -0.049 1.459 0.912 0.471 0.442 0.625 0.000 0.716 0.311 -0.532 0.000 1.072 0.830 -1.502 2.548 0.930 0.379 -1.195 0.000 1.008 0.802 0.985 1.149 0.864 0.863 0.750 +1 1.930 -1.199 -0.882 1.893 -0.406 1.430 -1.442 1.263 0.000 0.861 -0.130 0.561 2.215 0.528 -0.954 -0.087 0.000 0.883 0.454 -0.741 0.000 0.786 0.795 1.104 1.380 0.823 1.033 0.865 +1 0.934 -0.643 0.199 0.397 -1.479 0.955 -0.495 -1.464 0.000 1.444 0.010 0.109 2.215 0.823 0.561 -1.249 0.000 1.027 -0.285 1.222 3.102 0.986 0.829 0.988 0.732 0.947 0.975 0.814 +1 2.113 -0.792 -1.488 1.221 1.410 0.811 -0.157 0.139 1.087 0.281 -0.591 -0.918 2.215 0.821 -0.945 0.080 0.000 0.461 -0.070 -0.292 0.000 0.403 0.419 1.125 0.644 0.594 0.892 0.747 +1 0.384 -0.051 0.008 1.563 1.213 0.808 0.040 -0.696 0.000 0.771 1.146 1.543 2.215 1.558 1.504 -0.558 0.000 1.617 0.680 1.138 3.102 0.868 1.250 0.988 0.983 0.405 0.934 0.880 +1 1.076 -1.608 1.373 0.329 -0.024 0.424 -0.160 0.928 1.087 0.265 -2.045 -0.659 0.000 0.455 0.232 -0.458 0.000 1.012 0.068 -0.943 3.102 0.790 0.750 0.987 1.034 0.694 0.803 0.704 +0 1.442 1.224 -1.654 1.550 -1.278 1.174 0.290 0.453 2.173 0.535 0.274 1.642 0.000 0.691 0.874 -0.520 0.000 0.484 2.393 -0.540 0.000 0.925 1.079 0.978 0.760 0.428 1.024 0.841 +0 0.604 0.921 0.437 1.140 -0.590 0.873 1.927 -1.572 0.000 1.100 1.476 -0.091 2.215 1.365 0.837 1.404 2.548 0.543 -1.737 -0.273 0.000 0.893 0.866 0.992 0.794 1.330 0.913 0.907 +0 1.115 1.355 -1.633 1.025 0.596 0.688 -0.342 -0.800 0.000 1.038 -0.653 -0.154 2.215 0.956 0.380 0.951 2.548 0.877 0.174 -1.688 0.000 0.913 0.949 1.341 1.608 1.075 1.081 0.991 +1 1.316 0.365 1.270 0.187 -0.262 0.668 -0.245 -0.196 2.173 0.860 -0.649 -1.264 2.215 0.988 0.816 0.807 0.000 0.709 -0.083 -0.903 0.000 1.040 0.922 0.985 0.824 0.945 0.780 0.724 +1 1.517 -1.215 -0.639 0.286 -1.136 1.179 -0.458 -0.746 0.000 1.235 -0.560 -0.336 0.000 4.053 -0.702 1.419 2.548 1.567 -0.594 0.755 0.000 0.938 0.912 0.989 1.763 1.626 1.534 1.335 +0 0.405 -0.436 -0.566 2.191 -1.508 0.840 1.022 0.058 0.000 1.145 1.618 0.265 0.000 1.291 0.702 1.463 2.548 1.171 -0.050 1.513 0.000 0.775 0.805 0.988 0.937 0.696 0.842 1.102 +1 1.872 0.275 -1.494 0.445 0.573 1.145 0.875 0.186 2.173 0.587 1.603 -1.738 0.000 0.559 -0.470 -0.965 0.000 0.684 -1.878 -1.457 0.000 0.937 1.021 1.211 0.826 0.842 0.920 0.807 +1 1.311 -0.749 1.343 0.229 -0.769 0.726 0.878 -0.452 1.087 0.663 -0.388 -0.156 0.000 0.485 1.243 0.512 0.000 1.027 0.266 -1.557 3.102 0.911 0.934 0.991 0.620 0.811 0.774 0.704 +1 0.707 0.655 0.215 0.397 1.720 1.596 0.289 1.373 1.087 0.919 -1.346 -0.455 0.000 1.518 -0.128 -0.692 0.000 0.681 -0.861 0.343 3.102 1.262 0.868 0.993 0.984 1.183 1.332 1.047 +0 0.812 -1.686 -0.768 0.866 1.600 0.823 0.714 -0.167 2.173 0.755 -0.390 1.303 0.000 0.941 -0.196 0.938 2.548 0.627 -0.354 -0.585 0.000 0.889 1.092 0.987 0.967 1.063 1.220 0.974 +0 1.284 0.596 -1.017 0.169 -0.280 1.466 0.932 -0.437 1.087 1.417 0.933 1.525 0.000 1.853 0.973 0.777 2.548 1.151 0.216 1.055 0.000 0.906 0.958 0.982 0.985 1.829 1.279 1.110 +0 0.617 -0.564 1.060 1.101 0.253 0.372 -0.053 0.305 0.000 0.863 0.067 -0.967 2.215 0.492 0.755 1.572 1.274 0.571 -0.365 -1.322 0.000 0.713 0.665 0.993 0.940 0.587 0.838 0.682 +0 0.433 1.946 1.148 2.090 -0.837 1.783 0.931 0.415 0.000 1.021 0.896 -1.582 1.107 0.930 0.175 -1.039 2.548 0.830 -0.276 1.717 0.000 2.127 1.599 1.287 1.079 0.621 1.167 1.247 +0 1.848 0.807 1.122 1.029 0.123 0.765 -2.911 -0.528 0.000 0.803 -1.244 -1.723 0.000 1.060 -1.364 -0.712 2.548 1.121 -0.376 1.168 3.102 2.130 1.336 1.496 0.975 0.944 1.202 1.925 +0 0.516 -0.463 -1.336 0.118 -0.368 1.246 -0.071 0.458 2.173 1.316 -0.104 -1.646 1.107 0.456 1.011 0.613 0.000 0.383 0.811 -0.393 0.000 0.364 0.821 0.978 1.054 1.785 0.995 0.770 +1 1.405 -1.623 -1.482 0.355 -0.434 0.843 -1.337 0.078 2.173 0.539 -1.129 1.170 0.000 0.635 -0.357 0.798 2.548 0.548 -0.826 -1.019 0.000 0.655 0.795 0.985 0.881 0.713 0.799 0.665 +1 1.126 -0.151 -0.153 1.247 -0.770 0.713 -0.489 0.871 2.173 0.437 -0.035 1.127 0.000 0.883 0.637 1.432 2.548 1.055 -0.718 -1.426 0.000 0.759 0.713 0.981 0.938 0.788 0.859 0.752 +1 1.304 -0.921 -0.486 1.420 -0.886 0.688 0.329 1.112 2.173 0.771 -1.362 1.197 1.107 0.459 -2.619 0.306 0.000 0.589 -0.637 -1.176 0.000 0.915 0.762 0.985 1.594 1.053 1.159 0.986 +1 0.387 -1.681 1.141 0.462 0.893 1.539 0.080 -1.099 0.000 1.155 -0.647 0.984 0.000 1.274 0.044 0.602 1.274 0.790 -0.186 -1.698 0.000 0.902 0.883 0.990 0.646 0.408 0.862 0.781 +0 1.813 0.697 -0.105 0.595 0.050 1.313 0.976 -1.570 1.087 0.547 -1.232 1.157 0.000 0.474 0.376 -0.626 0.000 1.119 0.085 0.589 3.102 1.073 0.771 0.993 1.577 1.329 1.085 1.010 +1 1.577 0.103 0.514 0.304 -1.598 1.355 0.796 -1.201 2.173 0.724 -1.358 -0.582 0.000 0.886 -0.264 0.171 0.000 0.884 0.757 1.394 3.102 0.941 1.070 0.988 1.308 0.834 1.207 1.027 +1 2.559 -0.760 -1.062 0.168 -1.673 1.639 -1.164 0.447 1.087 0.583 -1.710 1.537 0.000 0.383 -0.003 1.613 0.000 0.474 -0.093 -0.527 3.102 0.723 1.214 0.978 0.530 0.887 1.112 0.935 +1 0.512 2.003 -1.193 0.580 1.629 0.790 0.940 0.571 0.000 1.074 0.441 1.239 2.215 1.296 -0.135 -1.076 2.548 0.941 -0.203 -0.570 0.000 0.889 0.855 0.987 0.820 1.156 0.842 0.752 +0 1.237 0.096 -0.398 0.749 -1.372 0.473 1.170 1.165 0.000 1.095 1.036 0.697 2.215 1.150 0.337 -1.724 0.000 0.977 -1.004 -0.893 3.102 0.875 0.916 1.025 0.705 1.599 0.974 0.895 +1 0.421 1.651 -0.894 0.500 -0.813 1.527 0.155 0.372 2.173 0.922 0.060 -1.201 2.215 0.955 -0.768 1.652 0.000 1.020 -1.738 -1.722 0.000 0.729 1.045 0.987 1.138 1.727 1.334 1.117 +1 2.447 0.492 1.136 0.786 0.485 0.733 -0.083 -1.068 0.000 0.892 0.364 -0.445 2.215 1.129 0.671 0.398 0.000 1.745 1.071 -1.176 3.102 1.728 1.111 1.062 1.233 0.869 1.006 0.988 +0 3.276 -0.698 0.157 0.350 1.531 2.247 -1.092 -1.584 1.087 0.595 -0.312 1.463 0.000 0.598 -0.878 -1.355 2.548 1.716 -1.236 0.146 0.000 1.169 0.778 1.402 2.281 0.314 1.409 1.154 +0 0.426 -1.842 1.027 1.309 -1.209 0.869 -0.880 0.284 2.173 0.547 -0.305 0.715 2.215 0.547 -0.956 1.345 0.000 0.941 -1.430 -1.194 0.000 0.762 0.978 0.988 1.099 0.484 0.969 0.801 +0 0.711 -0.606 -1.401 0.559 1.559 1.157 -0.637 -0.798 1.087 1.375 -0.382 0.587 2.215 0.694 0.893 1.407 0.000 1.371 0.495 0.340 0.000 0.907 0.986 0.994 1.051 1.776 1.144 0.981 +1 0.575 1.471 1.241 1.147 -1.157 0.885 -1.095 0.506 2.173 0.485 -1.130 1.292 1.107 0.741 -0.551 -0.698 0.000 0.631 -0.592 -1.269 0.000 0.373 0.884 0.988 1.084 0.627 1.172 0.932 +0 0.417 0.017 -0.260 2.364 -1.152 1.086 1.530 0.761 2.173 0.449 1.709 0.187 0.000 0.584 0.352 -0.593 1.274 0.798 0.463 1.298 0.000 0.824 0.726 0.989 0.557 1.113 1.179 0.957 +1 0.485 0.495 1.011 0.929 -1.153 0.599 -0.231 1.219 0.000 0.855 -0.264 -1.407 0.000 1.049 -1.136 -0.319 2.548 0.825 -1.609 -0.212 0.000 1.062 0.944 0.989 1.340 0.556 0.962 0.906 +0 0.383 1.378 -0.309 3.185 0.576 1.240 1.408 -1.196 0.000 1.147 0.630 -0.794 2.215 1.143 0.831 1.090 2.548 0.708 1.028 1.601 0.000 0.843 0.927 1.097 0.823 1.218 1.040 1.078 +0 1.977 0.276 0.875 1.501 0.437 1.080 -1.059 -0.973 0.000 0.432 -0.351 -0.337 0.000 0.585 0.835 -1.618 1.274 0.688 0.398 -1.077 3.102 0.947 1.098 0.993 0.856 0.252 0.678 1.023 +0 1.488 -0.569 -1.039 0.694 1.479 0.829 0.106 0.304 0.000 0.669 -0.861 0.710 0.000 1.246 0.589 -0.914 2.548 1.266 1.118 0.071 0.000 0.994 0.993 1.078 0.901 1.282 0.969 0.905 +1 0.846 -0.911 0.275 2.294 0.570 0.760 -0.018 -1.738 0.000 0.999 -0.309 -1.184 1.107 1.244 0.447 -1.128 2.548 0.722 1.077 -0.315 0.000 1.346 1.008 1.000 1.915 0.496 1.424 1.343 +1 0.463 -0.458 -1.189 1.113 0.314 1.157 0.018 -1.139 1.087 0.925 -0.549 0.312 1.107 2.565 1.161 1.687 0.000 2.207 0.538 0.119 0.000 1.570 1.086 0.989 0.998 1.537 1.056 0.866 +0 1.575 -0.133 0.690 0.911 1.393 1.700 0.238 -0.678 0.000 1.468 -0.736 1.038 2.215 1.132 0.077 -1.286 1.274 0.494 -0.526 -0.296 0.000 0.788 0.823 0.988 0.695 1.327 1.220 1.108 +1 2.218 -0.819 1.543 0.627 1.024 1.113 -1.041 -0.222 2.173 0.436 -0.638 0.788 0.000 0.910 -1.035 -1.065 2.548 0.449 -0.494 -0.081 0.000 0.407 0.636 0.992 0.874 0.866 1.030 0.786 +0 3.067 -0.053 -1.572 0.692 1.486 1.103 -1.995 0.104 0.000 0.760 0.132 -0.850 2.215 0.695 -1.754 0.704 0.000 1.490 -0.793 0.169 3.102 0.814 0.765 0.979 0.875 0.940 1.040 1.442 +0 2.943 -0.994 1.227 2.299 1.335 3.010 0.280 -0.440 1.087 1.422 -1.647 1.489 0.000 1.146 -0.189 0.038 0.000 0.634 0.990 -0.130 0.000 0.725 1.031 1.011 0.837 0.918 2.221 1.682 +0 2.993 0.024 -0.013 0.531 -0.358 0.810 0.737 1.447 0.000 1.150 1.142 1.680 2.215 1.319 0.129 -0.664 2.548 0.682 -0.802 1.324 0.000 1.099 0.966 0.993 0.794 1.326 1.131 1.095 +1 0.783 -1.438 -1.408 1.690 -1.742 0.815 -1.032 -0.147 2.173 0.892 -0.123 0.578 0.000 0.687 -0.632 0.654 2.548 1.031 -0.209 -0.515 0.000 0.915 0.988 0.975 0.801 0.638 0.850 0.784 +1 0.588 0.577 0.923 1.654 1.678 0.701 1.985 -0.311 0.000 0.642 1.383 0.548 1.107 0.746 0.840 -0.532 1.274 0.796 2.027 -1.418 0.000 0.989 0.876 0.987 0.887 0.636 0.767 0.946 +0 0.555 -1.112 -1.107 0.067 0.634 0.608 0.469 0.617 0.000 0.711 -0.694 0.928 2.215 1.323 0.276 1.555 0.000 1.498 0.452 -0.649 3.102 0.944 0.859 0.998 0.737 1.113 0.694 0.637 +1 1.782 1.678 -0.348 0.701 -0.861 0.411 2.264 -1.468 0.000 0.997 -1.550 1.179 2.215 1.347 0.463 -1.252 1.274 3.225 0.687 0.544 0.000 2.209 1.703 0.983 3.452 1.845 2.222 1.984 +1 1.085 -0.040 -0.512 1.608 -0.953 0.952 -0.662 1.107 2.173 0.568 0.907 1.513 2.215 0.791 -0.338 0.529 0.000 0.578 -2.193 -0.143 0.000 1.120 1.053 0.990 0.890 1.038 1.078 1.112 +1 1.554 -0.281 1.428 0.869 -1.557 0.965 0.685 -0.005 2.173 0.658 0.051 0.640 0.000 0.821 0.624 -1.023 2.548 0.694 -0.719 -0.828 0.000 0.951 0.968 0.989 0.775 0.880 0.933 0.818 +0 0.282 1.135 0.761 0.814 -0.187 0.983 1.931 0.756 0.000 1.244 -0.186 1.685 0.000 1.267 1.297 -0.257 2.548 0.885 -0.349 -0.805 0.000 1.079 0.727 0.978 1.018 0.922 0.919 0.841 +1 2.015 0.074 0.196 0.616 0.850 0.955 0.800 -1.024 2.173 0.329 0.745 0.092 0.000 0.528 -2.331 -1.494 0.000 0.988 0.667 1.568 3.102 0.791 0.918 0.988 0.842 0.741 0.909 0.757 +0 1.381 0.573 0.569 0.751 -0.271 0.683 0.110 1.279 2.173 0.910 -0.546 -1.439 0.000 0.899 0.687 -0.118 2.548 0.822 0.279 -0.967 0.000 0.699 0.923 0.986 0.892 0.983 0.744 0.773 +0 1.675 -0.134 1.429 0.649 -1.330 0.425 -0.200 -0.842 0.000 0.676 0.743 0.559 2.215 1.064 -0.859 -0.133 1.274 0.391 0.491 -0.234 0.000 0.411 0.667 0.989 1.013 1.015 0.858 0.707 +1 0.844 -0.452 1.709 0.991 0.751 1.308 -0.721 -0.545 2.173 0.928 -0.903 1.573 0.000 0.538 0.505 0.257 1.274 0.420 -1.963 0.732 0.000 0.843 0.935 0.989 1.248 0.996 0.933 0.856 +1 1.189 -0.798 -1.716 0.420 0.571 1.276 -0.038 -0.865 2.173 0.731 -0.396 -1.603 2.215 1.377 -1.194 0.208 0.000 0.938 0.130 0.601 0.000 0.853 1.079 0.985 1.201 0.916 1.070 0.934 +1 0.784 0.187 0.216 0.725 -0.398 0.804 1.435 1.632 0.000 0.619 0.380 -1.060 2.215 0.560 1.374 0.779 0.000 0.832 -0.327 0.520 3.102 0.841 1.010 0.986 0.779 0.692 0.739 0.873 +1 1.668 1.308 0.883 0.558 0.885 0.984 0.666 -1.143 2.173 0.800 2.514 -0.599 0.000 0.633 0.751 0.467 2.548 1.028 1.631 0.800 0.000 1.187 0.946 0.978 1.454 0.979 0.987 0.962 +0 1.563 1.440 -1.146 0.238 0.378 0.852 -0.031 0.332 2.173 0.742 0.675 1.345 2.215 0.735 -1.261 0.940 0.000 0.745 -0.744 -0.083 0.000 0.681 0.999 0.993 1.409 1.022 1.006 1.089 +1 1.714 0.914 -0.905 0.505 1.285 1.448 -0.050 1.444 2.173 2.560 0.028 -0.185 2.215 1.294 -1.127 1.120 0.000 2.151 1.269 1.278 0.000 0.864 0.874 1.186 1.390 2.821 1.556 1.251 +0 0.759 0.290 0.245 1.818 -1.090 0.569 -0.706 1.294 2.173 0.719 -1.317 0.788 0.000 0.862 -0.980 -0.112 0.000 1.285 -0.937 -1.461 3.102 0.891 0.882 1.518 1.153 0.585 0.872 0.898 +1 1.395 0.929 0.919 0.583 -1.520 1.351 1.069 -0.869 0.000 1.194 -0.328 -0.335 2.215 0.782 -1.000 0.794 2.548 0.389 0.168 -1.727 0.000 0.922 1.336 1.013 1.032 0.961 1.141 1.064 +1 0.391 2.002 -0.362 1.186 1.525 0.892 0.331 -0.263 2.173 0.682 1.288 -1.468 0.000 0.518 1.891 0.615 0.000 1.185 0.286 0.938 3.102 0.936 0.838 0.988 1.141 0.960 0.838 0.769 +1 1.108 -1.298 1.717 1.306 0.659 0.950 0.075 -0.881 2.173 1.415 -1.919 -0.270 0.000 0.995 2.042 1.537 0.000 1.258 -1.076 1.127 3.102 0.932 1.468 1.358 0.689 1.412 1.330 1.484 +0 1.755 -1.230 1.250 0.664 -0.171 1.111 -2.133 1.553 0.000 0.867 -0.757 -0.383 0.000 1.012 -0.210 -0.919 2.548 1.086 0.309 -0.196 0.000 0.796 0.836 1.432 1.087 0.980 0.831 0.817 +0 2.186 -0.417 0.810 0.954 0.537 2.336 1.234 -0.971 2.173 1.785 -1.049 0.924 1.107 0.522 1.868 -0.974 0.000 0.551 0.181 -0.158 0.000 0.738 0.904 0.981 1.010 5.260 2.620 1.949 +1 0.877 0.086 0.219 1.018 0.250 1.034 1.231 -1.305 0.000 1.131 1.298 0.639 2.215 1.132 0.867 -1.709 0.000 1.358 0.987 -0.072 1.551 0.886 0.909 0.984 1.475 0.674 1.068 1.156 +0 0.303 -1.653 -1.154 0.863 -1.648 0.478 1.243 -0.902 0.000 0.880 -0.822 0.608 0.000 0.754 -0.333 -0.984 2.548 0.683 -1.134 0.835 0.000 0.334 0.776 0.991 0.767 0.621 0.595 0.632 +1 0.724 -0.782 1.216 1.603 -1.699 0.458 2.936 -1.739 0.000 1.464 -0.479 0.215 2.215 1.319 0.586 -0.303 2.548 0.374 -0.214 -0.219 0.000 1.640 1.438 0.987 1.295 1.107 1.582 1.466 +1 1.472 1.506 0.538 1.702 -0.137 1.362 1.593 -1.572 0.000 0.432 0.222 -1.127 1.107 0.607 0.400 -0.047 0.000 0.794 0.480 0.560 1.551 1.897 1.187 1.252 0.740 0.536 0.810 0.949 +0 0.398 -0.991 -0.681 0.656 -0.492 0.607 0.050 1.651 1.087 0.932 0.307 0.721 0.000 0.704 0.780 -1.288 2.548 0.772 -0.446 -0.194 0.000 0.945 0.917 0.993 0.615 0.511 0.664 0.649 +1 2.553 -1.408 0.026 0.592 -0.596 2.056 -1.070 1.049 0.000 1.208 2.483 1.582 0.000 1.226 -1.033 -0.439 0.000 1.880 -1.003 -1.322 1.551 1.056 0.739 0.985 1.148 0.460 0.841 1.054 +1 1.508 -0.041 0.642 0.926 -0.090 1.080 0.458 -1.451 2.173 0.976 1.671 -0.843 0.000 1.088 0.961 1.176 0.000 1.025 0.680 -0.612 1.551 0.588 0.847 1.003 1.341 0.790 0.923 0.934 +1 0.286 1.733 1.709 1.188 0.277 3.223 -0.279 1.616 0.000 3.382 0.276 -0.094 2.215 2.138 1.739 -1.046 0.000 1.926 0.824 0.283 1.551 2.765 2.764 0.982 1.059 1.130 2.564 1.916 +1 1.376 0.906 -0.810 1.142 -0.269 1.449 1.848 1.601 0.000 0.570 0.901 0.504 1.107 0.306 1.439 1.420 0.000 0.462 0.264 0.507 3.102 1.284 0.986 0.987 0.799 0.144 0.733 0.908 +1 0.403 1.239 0.483 1.209 -0.257 1.969 -0.551 1.230 0.000 1.331 0.531 -0.167 2.215 1.386 -0.077 -1.294 0.000 1.525 -1.022 -1.148 3.102 1.052 0.775 0.984 1.296 1.634 1.789 1.554 +0 0.681 -1.348 -1.704 2.095 1.236 2.960 0.096 -0.321 2.173 1.069 1.220 -1.719 0.000 1.014 0.537 0.132 0.000 1.746 1.385 1.121 0.000 1.045 0.722 0.988 3.675 1.966 2.413 2.556 +1 0.536 0.762 0.245 0.990 0.032 1.285 0.794 -1.571 2.173 0.737 0.403 -0.663 0.000 1.276 -0.098 1.114 0.000 0.716 -0.203 -0.374 0.000 1.029 1.278 0.996 0.971 1.105 1.085 1.038 +0 1.405 -0.103 -0.714 0.520 -0.875 0.828 0.161 1.030 1.087 1.526 -0.694 -1.394 2.215 1.207 0.306 0.140 0.000 1.335 -0.700 0.422 0.000 0.941 0.975 0.984 0.863 1.545 1.104 1.005 +1 2.404 -1.208 -1.262 0.375 1.151 0.765 -1.140 0.444 2.173 0.410 -1.886 0.143 0.000 1.061 -1.400 1.158 0.000 0.794 -0.010 -0.369 3.102 0.828 0.873 1.083 1.194 0.741 0.858 0.783 +0 1.051 0.688 1.035 1.562 -1.569 0.645 -0.118 0.542 0.000 1.062 -0.865 -0.669 0.000 0.777 0.926 -1.672 2.548 1.159 -1.904 -0.123 0.000 1.289 1.269 1.269 0.642 0.635 1.142 1.245 +1 0.536 -2.161 -0.523 0.782 0.176 1.007 -0.966 -0.371 1.087 0.774 -0.930 1.006 0.000 1.172 -0.155 1.686 2.548 0.607 0.509 0.937 0.000 0.942 1.091 0.992 0.980 1.415 0.871 0.776 +1 0.755 -1.317 0.774 0.848 -0.680 1.553 -0.864 1.529 0.000 0.833 -0.699 0.070 2.215 0.961 0.540 -0.303 0.000 1.455 -0.317 0.506 3.102 0.912 0.913 1.071 0.706 0.417 0.629 0.691 +0 0.836 1.515 0.305 0.708 -0.432 0.670 0.054 1.031 0.000 0.974 1.228 -1.310 2.215 0.909 -0.941 0.230 0.000 1.018 -1.030 -1.200 0.000 1.025 1.040 0.988 0.986 0.214 1.016 1.277 +1 0.793 -0.546 -1.602 1.018 -1.047 0.707 -0.915 -1.001 2.173 1.143 -0.554 0.737 2.215 0.841 -1.581 0.898 0.000 0.557 -2.003 0.261 0.000 0.484 0.953 0.986 1.122 1.343 0.863 0.792 +0 0.712 1.462 -1.368 0.176 -1.675 1.370 0.889 -1.105 2.173 1.956 1.630 0.438 0.000 0.547 0.076 0.383 0.000 0.958 -0.472 -1.509 3.102 0.623 1.050 0.998 0.654 1.061 0.726 0.649 +1 0.726 0.403 -1.133 1.431 0.362 0.780 -2.228 -1.138 0.000 1.424 0.596 -0.508 2.215 1.182 0.328 1.000 0.000 1.278 1.383 1.202 0.000 0.976 0.718 1.377 0.987 0.532 0.850 0.795 +1 0.478 0.458 1.405 1.527 -0.316 0.742 -0.429 -1.424 0.000 1.032 0.113 1.625 0.000 1.484 -0.256 0.599 2.548 1.374 0.664 -0.187 3.102 0.879 1.235 1.184 0.957 0.941 0.977 0.896 +0 1.003 -0.158 -1.601 1.332 -0.997 0.894 0.334 -1.050 1.087 1.504 -0.424 0.566 0.000 1.177 0.108 0.765 0.000 0.983 -0.978 -0.136 1.551 0.681 0.862 0.991 0.584 1.099 1.043 1.005 +0 0.562 0.100 0.720 0.518 -0.365 0.625 -1.732 0.369 0.000 0.890 0.027 -1.510 1.107 0.867 -1.437 -1.547 2.548 0.667 -0.301 -0.162 0.000 0.869 0.900 0.988 0.857 0.834 0.858 0.740 +0 0.838 -1.188 1.059 0.926 -1.380 0.399 0.190 1.717 0.000 1.029 -0.077 0.161 2.215 0.676 0.698 -1.363 2.548 0.458 1.136 -0.192 0.000 0.762 0.844 0.989 0.913 0.951 0.855 0.757 +1 0.891 -0.684 0.886 0.742 -0.267 0.800 -1.376 1.379 2.173 0.970 -1.909 -0.522 0.000 1.011 -0.042 0.239 1.274 1.366 -1.209 -1.214 0.000 0.958 1.192 0.985 0.591 1.256 1.030 0.893 +1 1.344 1.360 -0.688 0.637 -0.182 2.759 -0.138 1.005 0.000 2.252 0.646 -0.479 2.215 1.341 -1.885 -1.248 0.000 1.029 1.310 -1.575 0.000 1.013 2.467 0.991 0.707 0.850 1.932 1.587 +0 1.293 2.129 -0.515 0.631 1.574 0.695 1.435 0.573 2.173 0.323 0.644 0.630 0.000 1.501 0.622 -1.638 0.000 0.671 0.759 -1.304 3.102 0.951 1.023 1.191 0.713 0.744 0.695 0.759 +1 0.895 1.219 -1.735 0.245 -1.280 0.920 0.198 -0.698 0.000 0.921 1.530 0.830 1.107 0.899 0.245 0.431 2.548 1.430 -1.771 -1.739 0.000 1.402 1.079 0.986 0.909 0.758 0.923 0.833 +1 1.218 0.879 0.954 1.150 1.721 0.609 -0.442 -0.915 0.000 0.633 2.455 0.676 0.000 1.078 0.001 -0.167 1.274 1.212 0.204 -1.244 3.102 0.878 1.124 1.046 1.053 0.729 0.891 0.850 +0 0.278 0.542 -0.238 0.912 0.377 1.701 -1.049 0.808 2.173 0.897 1.528 -0.487 2.215 1.604 -2.647 -1.243 0.000 2.155 2.077 -1.719 0.000 1.065 0.827 0.979 0.910 3.562 1.912 1.533 +1 1.125 0.984 1.054 0.448 0.851 2.358 1.631 0.640 0.000 0.992 0.253 -0.796 1.107 3.351 1.235 -1.003 2.548 0.964 0.560 -1.341 0.000 2.535 2.318 0.984 1.468 1.173 1.884 1.484 +1 1.157 -0.196 -1.152 0.807 0.818 1.395 0.341 1.354 2.173 0.548 0.222 0.567 2.215 0.908 -0.759 -0.178 0.000 2.051 0.204 -0.537 0.000 0.977 0.861 1.311 1.094 0.843 1.066 0.926 +1 0.522 -0.542 1.219 1.440 -0.577 1.053 0.123 1.659 1.087 1.131 -0.649 0.167 0.000 1.092 0.115 0.469 0.000 0.915 -0.565 -0.843 3.102 0.959 0.729 1.200 1.134 0.911 0.834 0.774 +0 0.718 -2.132 1.315 2.040 -1.600 0.732 -1.385 -0.009 2.173 0.705 -1.960 -0.383 0.000 1.103 -0.919 0.486 0.000 0.597 -1.425 0.601 0.000 0.668 0.657 0.990 1.221 0.771 0.802 0.720 +1 0.982 0.610 0.263 0.861 1.364 1.761 1.128 1.404 0.000 1.577 0.660 -0.708 0.000 2.121 -0.678 -0.585 2.548 1.505 -1.030 0.844 0.000 0.974 1.137 1.067 1.368 1.248 1.650 1.302 +1 0.540 0.975 0.523 0.616 -0.129 3.044 0.098 -0.814 2.173 3.624 -0.386 0.425 0.000 2.641 0.796 1.597 0.000 3.132 0.267 1.092 3.102 0.911 1.661 0.984 1.431 3.252 2.331 1.700 +1 1.174 0.696 -1.681 0.560 0.124 1.240 0.971 -1.154 1.087 1.037 0.936 0.729 0.000 1.223 1.191 -0.498 2.548 1.351 -0.995 0.922 0.000 0.639 2.036 1.121 0.906 0.892 1.744 1.370 +1 1.524 0.257 -1.190 0.305 -1.703 0.485 0.862 -1.477 0.000 0.643 1.213 0.704 2.215 0.954 1.234 0.377 0.000 1.315 -0.116 0.160 3.102 1.253 1.040 0.988 0.896 0.736 0.762 0.739 +0 2.042 1.477 0.032 0.224 0.749 0.629 0.153 1.679 2.173 0.690 1.531 -1.692 0.000 0.864 1.288 -1.157 0.000 1.277 0.191 0.026 3.102 0.557 0.995 0.990 1.207 0.946 0.852 0.837 +1 0.852 -0.490 1.494 0.988 0.771 1.084 0.834 -0.587 2.173 0.833 -0.204 0.318 0.000 0.844 -0.733 -1.395 0.000 0.853 0.035 0.793 3.102 1.343 0.837 0.991 1.750 1.051 1.123 0.982 +1 1.554 0.416 1.645 0.750 -0.629 0.996 -0.950 0.696 2.173 0.902 -1.119 -0.325 0.000 0.578 -1.015 -1.500 0.000 0.454 0.737 -0.900 1.551 0.965 1.104 1.329 0.647 1.031 0.951 0.925 +0 0.941 -1.055 -1.462 0.971 0.983 0.975 -0.475 -0.833 1.087 0.986 -1.313 0.475 0.000 0.548 -1.422 -0.477 0.000 1.208 -0.443 0.819 3.102 0.861 1.200 1.067 0.678 1.145 0.835 0.787 +1 0.749 -0.137 1.733 0.557 0.613 0.737 0.926 -1.017 2.173 0.868 -0.348 1.191 0.000 1.137 -0.320 -0.556 2.548 0.765 -1.122 0.469 0.000 0.845 0.975 0.983 1.137 0.910 0.953 0.838 +1 0.764 -0.882 -0.082 1.263 -1.291 0.946 -0.184 0.273 0.000 0.582 -0.344 -1.363 0.000 0.951 -1.591 -0.855 0.000 1.172 0.208 0.853 0.000 0.875 0.846 1.206 0.604 0.150 0.555 0.688 +1 0.803 -0.512 -0.836 0.504 0.497 0.865 -0.159 1.331 0.000 0.615 -0.448 0.463 2.215 1.123 0.683 -0.287 1.274 0.501 -0.316 -0.297 0.000 1.006 1.097 0.988 0.653 0.793 0.730 0.722 +1 0.735 -1.123 1.440 0.679 -0.575 1.272 -0.160 1.407 0.000 1.142 -0.158 0.114 2.215 1.284 -0.031 -0.726 1.274 0.821 2.164 -0.496 0.000 0.820 1.232 0.987 0.999 0.888 0.981 0.914 +1 0.628 -0.892 -0.077 1.314 -0.946 1.313 -0.751 1.277 1.087 0.550 -0.508 -1.522 0.000 0.436 -1.033 -0.525 0.000 0.755 0.621 0.290 0.000 1.013 1.106 0.989 0.690 0.780 0.891 0.843 +1 1.231 0.140 1.388 1.043 -1.195 0.885 0.232 -0.466 2.173 1.700 -2.329 0.530 0.000 1.392 0.744 -1.232 2.548 0.854 0.139 0.751 0.000 2.630 2.794 1.143 0.793 0.971 2.240 1.828 +1 0.546 -0.274 0.313 0.564 1.044 1.113 0.444 1.048 2.173 0.767 1.483 -0.372 0.000 1.101 0.456 -0.518 0.000 1.026 -0.347 -0.799 0.000 0.949 0.668 0.983 0.732 0.528 0.782 0.683 +1 0.927 -2.132 0.691 1.810 0.550 2.071 0.753 -0.957 0.000 1.980 -0.844 -1.682 0.000 2.015 -0.503 0.548 2.548 2.177 0.770 1.635 1.551 0.714 0.976 1.001 2.251 1.854 1.552 1.284 +0 0.556 1.352 -1.036 1.079 0.420 0.701 0.250 -1.082 0.000 0.614 -0.456 0.818 2.215 1.095 -0.191 -1.663 0.000 1.380 -0.087 -0.162 3.102 0.869 0.967 1.037 0.865 0.660 0.751 0.832 +1 0.648 -0.867 0.110 0.256 1.742 1.128 -1.005 -1.677 0.000 1.031 -0.228 -0.122 2.215 1.440 -2.508 0.929 0.000 2.177 -0.201 0.585 3.102 0.925 1.405 0.994 0.678 0.804 1.082 0.890 +0 0.771 -0.232 0.068 0.993 -1.725 0.843 -0.053 -1.217 2.173 1.066 -0.847 0.438 0.000 0.796 -0.153 1.740 2.548 0.798 0.167 0.479 0.000 0.677 0.856 1.211 0.845 0.471 0.820 0.733 +0 1.284 -0.095 -1.055 0.862 0.078 0.485 -1.734 0.653 0.000 0.879 -1.155 1.380 2.215 0.374 -2.442 -0.714 0.000 0.424 0.267 -0.152 3.102 0.811 0.808 1.243 0.592 0.708 0.711 0.790 +1 1.030 1.361 1.228 0.191 -0.570 0.507 0.163 -1.098 2.173 0.770 1.844 1.193 0.000 1.052 1.391 0.484 0.000 1.811 0.911 -0.289 3.102 0.863 1.030 0.984 0.762 0.833 0.887 0.769 +0 0.328 0.676 0.970 2.030 -1.372 0.790 -0.667 0.740 0.000 1.020 -0.832 0.201 0.000 1.263 0.452 -0.616 2.548 1.848 -0.193 -0.293 3.102 0.909 0.989 0.986 0.845 0.549 0.896 1.119 +1 0.370 2.240 -0.983 0.504 -1.527 0.739 1.574 0.135 0.000 0.865 1.110 1.509 2.215 1.186 0.614 0.487 0.000 1.583 -0.132 -1.213 3.102 0.955 1.123 0.998 0.729 0.997 1.015 0.855 +0 1.610 1.244 -0.963 0.917 -0.865 1.014 0.305 0.824 0.000 0.448 0.563 -0.703 2.215 0.774 0.823 1.269 0.000 0.708 0.909 0.514 3.102 0.786 0.904 1.007 0.811 0.471 0.589 0.891 +1 0.670 0.044 1.193 1.258 0.166 1.219 -0.697 -1.654 2.173 1.372 -1.160 -0.085 0.000 0.727 0.430 1.285 2.548 0.384 -2.021 -1.288 0.000 1.041 1.270 1.016 1.256 0.916 1.091 1.003 +0 0.906 -0.455 0.828 0.616 -1.454 0.593 -0.053 0.031 2.173 0.476 0.879 -1.464 0.000 0.613 -0.939 -0.062 0.000 0.664 -0.345 -0.879 3.102 0.703 1.083 0.991 0.591 0.500 0.714 0.690 +1 0.850 -1.142 1.593 0.884 -0.304 0.577 -2.021 0.813 0.000 0.606 -1.016 1.314 2.215 0.622 0.041 -0.442 0.000 0.991 -0.318 -0.946 1.551 0.948 1.107 1.189 0.701 0.669 0.741 0.791 +1 0.548 0.608 0.617 0.143 0.972 1.222 -0.858 -0.988 0.000 1.008 -0.140 1.019 2.215 0.768 -1.176 -0.362 2.548 0.629 -1.059 0.574 0.000 1.349 0.867 0.992 0.658 1.052 0.904 0.783 +1 0.502 0.184 0.905 0.300 0.853 0.818 0.337 -0.563 2.173 1.053 -1.063 0.299 2.215 1.003 -0.543 -1.080 0.000 0.709 -2.013 1.443 0.000 0.843 0.956 0.989 0.693 1.432 0.971 0.871 +1 0.768 -0.710 -1.372 2.113 -0.830 1.058 -0.076 0.956 2.173 0.305 0.492 0.961 0.000 0.441 0.242 0.037 0.000 0.505 -2.495 -0.133 0.000 1.281 1.386 0.978 1.447 0.790 1.052 1.077 +1 0.280 -0.000 0.180 1.718 -1.344 1.089 1.111 1.473 2.173 1.045 1.546 -0.847 0.000 1.048 -0.754 0.319 0.000 1.119 0.644 0.524 3.102 0.873 1.054 0.987 0.964 0.906 1.019 0.853 +1 0.599 0.673 0.220 0.789 -0.813 0.640 -1.287 -1.735 2.173 0.988 -0.398 -1.231 0.000 1.046 -0.279 0.180 0.000 0.855 -1.171 0.414 0.000 0.684 0.911 0.989 0.823 1.647 0.938 0.767 +1 2.046 0.547 1.262 1.213 1.184 1.292 -1.961 -0.739 0.000 1.364 -0.719 -0.171 2.215 0.655 -0.538 1.735 0.000 0.890 -0.133 0.702 3.102 1.805 1.476 0.982 1.646 0.762 1.101 1.468 +1 0.893 -0.013 0.018 1.731 1.153 2.113 0.500 0.430 0.000 4.139 0.983 -1.132 0.000 1.228 -0.033 -0.428 2.548 1.460 2.321 1.464 0.000 1.148 1.614 1.471 1.075 0.882 1.277 1.220 +0 2.224 -1.541 0.973 0.827 0.589 1.046 -1.133 -0.776 0.000 1.026 -0.509 -0.839 0.000 0.722 -0.810 1.586 2.548 0.380 -0.821 0.117 3.102 0.704 0.915 0.987 0.546 0.389 0.568 0.886 +0 0.650 2.150 0.122 0.313 -0.968 1.054 -1.727 -0.603 0.000 1.804 1.197 0.715 2.215 1.438 1.245 -1.565 2.548 1.548 1.204 1.107 0.000 1.421 0.933 0.986 0.914 1.519 1.001 0.857 +0 0.358 -0.760 0.247 1.364 -1.066 0.777 0.145 0.548 0.000 0.843 0.538 0.964 2.215 1.636 0.219 -1.148 2.548 0.561 1.296 1.003 0.000 0.869 1.234 0.987 0.921 1.196 0.830 0.792 +1 1.413 0.070 -1.608 0.641 -1.479 2.098 1.907 0.326 0.000 1.160 1.223 -1.443 2.215 1.792 0.391 -1.100 1.274 1.203 0.219 0.343 0.000 0.777 1.099 0.989 1.214 0.803 0.892 0.954 +0 1.452 -0.446 0.091 0.374 -1.082 0.516 0.567 -0.996 2.173 0.855 -0.657 0.575 1.107 1.058 0.413 1.730 0.000 0.752 -0.837 1.565 0.000 0.797 0.934 0.985 0.794 1.164 0.755 0.726 +1 0.459 -0.580 0.109 0.831 -1.377 0.674 -1.678 -0.483 0.000 0.533 -0.085 1.383 0.000 1.211 1.283 1.254 2.548 0.992 0.625 0.376 3.102 1.701 1.392 0.982 1.616 0.659 1.334 1.168 +0 0.827 -1.997 0.287 1.068 1.401 0.842 0.622 -1.245 1.087 0.560 -0.518 -1.499 0.000 1.361 -0.902 0.173 0.000 1.064 0.313 0.540 3.102 1.371 0.999 1.099 2.045 1.009 1.412 1.179 +0 0.703 -1.039 -1.515 1.416 1.047 1.089 -1.443 0.011 2.173 0.231 -1.010 -0.214 2.215 0.274 -1.638 1.584 0.000 1.070 -0.555 -1.132 0.000 0.522 0.937 1.022 0.613 0.215 0.731 0.643 +1 0.952 -1.600 -0.784 0.575 1.351 0.613 -1.327 -0.195 2.173 0.856 -0.070 1.016 2.215 1.360 -0.679 -0.630 0.000 0.837 -1.608 1.331 0.000 0.828 0.956 0.988 0.708 1.190 0.790 0.698 +0 0.365 -0.408 0.146 1.319 -1.675 0.988 0.314 1.361 2.173 1.168 1.005 0.241 2.215 0.881 2.078 -0.689 0.000 1.266 1.432 -0.216 0.000 0.585 0.908 0.987 0.938 1.459 1.130 1.275 +1 2.138 0.432 0.271 0.399 1.190 1.441 0.969 -0.982 0.000 0.893 1.032 -1.737 0.000 2.921 -0.751 -0.380 0.000 3.389 0.204 1.200 3.102 1.020 0.993 0.987 1.065 0.872 0.913 0.867 +0 1.203 1.930 -0.110 1.060 0.559 0.564 -2.826 1.301 0.000 1.686 0.253 -1.219 2.215 0.773 -0.116 -0.531 0.000 0.826 0.543 0.257 3.102 0.851 0.949 0.982 0.604 1.056 1.097 1.035 +0 0.624 0.911 0.788 2.753 0.122 1.299 1.350 -1.320 0.000 0.612 1.397 1.072 2.215 0.602 1.779 1.512 0.000 0.548 -0.438 -1.395 3.102 1.002 0.987 1.024 0.849 0.730 0.751 0.980 +0 0.329 -2.328 -0.489 2.053 -0.903 1.278 -2.321 1.237 0.000 1.013 -0.592 -0.034 2.215 0.470 -1.863 0.568 0.000 0.545 -0.718 -1.541 3.102 0.819 0.804 0.983 0.938 0.661 1.013 1.033 +0 1.752 -0.927 -1.454 0.722 1.305 0.481 -0.012 1.076 0.000 1.005 -0.690 0.002 2.215 0.813 -0.735 -0.380 2.548 0.476 0.568 -0.972 0.000 0.749 0.881 0.988 0.839 0.328 0.788 0.744 +1 0.593 1.487 -1.644 1.839 1.427 0.851 0.437 -0.405 2.173 0.385 1.149 -0.045 0.000 0.869 -0.150 0.996 2.548 0.648 1.720 -0.058 0.000 0.297 0.807 0.972 1.249 1.073 0.901 0.798 +1 0.491 0.770 0.611 1.057 0.617 0.615 -1.133 0.720 2.173 0.604 -0.720 -0.804 2.215 0.859 1.267 -1.004 0.000 0.411 -2.197 -1.509 0.000 2.433 1.433 0.983 0.646 0.898 1.090 0.947 +0 0.829 -1.553 -0.452 0.317 -0.641 0.701 -1.823 0.641 0.000 1.377 -1.058 -1.441 1.107 0.792 -0.827 0.091 2.548 0.855 1.660 0.928 0.000 1.030 1.119 0.982 0.902 1.094 1.144 0.985 +1 1.856 1.283 0.428 0.544 -1.521 1.035 0.807 -1.411 1.087 0.361 -0.884 -0.885 1.107 0.803 1.054 0.885 0.000 1.296 0.025 0.018 0.000 1.037 0.899 1.368 1.274 0.968 1.013 0.905 +1 1.000 -0.512 -1.416 0.584 -0.015 0.931 -0.925 1.117 0.000 0.448 -1.912 0.711 0.000 0.617 -1.375 -0.552 2.548 1.002 0.750 -0.607 3.102 0.937 0.853 1.009 0.744 0.914 0.810 0.708 +0 0.550 0.637 1.143 1.323 -0.815 0.484 -0.741 -0.217 1.087 0.607 -0.289 0.557 2.215 0.711 -1.555 -1.670 0.000 0.869 -0.008 1.569 0.000 0.850 0.867 1.160 0.867 0.543 0.706 0.761 +0 0.428 -0.488 1.082 1.301 -0.386 0.478 -0.628 -1.453 0.000 0.765 0.232 0.488 2.215 0.575 0.922 -0.325 2.548 0.752 -2.228 1.676 0.000 0.920 0.912 1.002 0.696 0.548 0.659 0.660 +1 1.004 -2.255 0.249 0.635 0.764 1.375 -1.283 -1.009 0.000 0.451 -1.861 1.297 0.000 1.388 0.152 0.532 2.548 0.529 0.278 -1.533 3.102 0.869 0.982 0.983 0.881 0.630 0.866 0.762 +1 0.996 -0.802 0.466 0.788 -0.786 1.626 -0.873 0.186 0.000 2.490 -1.061 -1.495 2.215 0.713 -1.875 1.241 0.000 0.804 -0.559 1.307 3.102 0.765 0.996 1.109 0.670 0.786 0.857 0.738 +0 0.700 -1.020 -0.370 0.707 1.328 1.121 -0.525 -0.200 2.173 1.027 0.151 1.190 0.000 1.493 0.461 -1.556 0.000 0.866 0.694 0.622 3.102 1.221 0.902 0.988 0.853 1.040 1.087 0.930 +1 0.752 0.347 0.920 0.340 -1.173 1.016 0.117 -0.248 1.087 0.964 0.357 0.086 0.000 1.736 0.100 1.379 2.548 2.182 0.382 -1.548 0.000 0.977 0.913 0.983 0.914 1.646 0.889 0.767 +1 0.827 0.848 1.455 1.473 1.543 1.284 0.526 0.076 2.173 0.930 0.902 -0.887 2.215 0.440 0.814 -0.329 0.000 0.427 1.521 1.289 0.000 0.528 0.724 1.000 1.088 1.269 1.150 0.879 +1 0.520 0.381 -1.379 1.028 1.153 0.802 -1.235 -0.748 2.173 0.620 -2.032 0.736 0.000 0.379 0.010 1.467 0.000 0.749 -0.455 -0.531 3.102 0.822 0.719 0.987 0.855 0.334 1.101 0.946 +1 1.468 -0.625 0.233 1.181 -0.277 0.839 2.804 -1.364 0.000 1.400 -0.430 1.185 2.215 0.793 0.726 -1.100 2.548 0.854 0.232 -0.004 0.000 0.585 0.771 0.985 1.166 1.235 1.076 0.827 +1 0.933 1.262 -0.164 0.417 0.814 0.518 -0.250 1.410 2.173 0.466 1.327 -0.893 0.000 0.749 0.670 1.295 0.000 1.146 -0.972 -0.458 3.102 0.883 0.822 0.993 0.967 0.897 0.799 0.754 +1 0.979 -0.755 -0.507 1.468 -1.458 0.870 -1.213 -0.142 0.000 1.129 -0.422 0.123 0.000 1.852 -0.338 1.711 0.000 2.136 -0.587 0.728 1.551 0.989 1.049 1.254 0.875 1.364 1.106 1.031 +0 1.599 0.746 -1.571 1.297 1.247 0.587 -0.515 -0.179 2.173 0.682 0.811 0.105 0.000 0.753 0.773 -0.437 2.548 0.531 1.328 1.563 0.000 0.817 0.931 1.131 0.921 0.641 0.924 0.795 +1 0.829 1.447 1.599 0.221 -1.565 1.473 -2.364 -0.738 0.000 1.360 0.811 1.565 0.000 2.473 0.210 0.222 2.548 1.109 0.859 0.929 0.000 0.881 0.765 0.985 1.161 0.998 0.974 0.834 +1 1.230 -2.115 1.270 0.887 0.717 0.777 -1.067 -0.060 0.000 0.600 -0.492 -0.335 2.215 0.564 -0.915 -1.647 2.548 1.090 -0.098 -1.020 0.000 0.728 0.552 0.977 0.659 0.593 0.690 0.650 +1 0.725 -0.602 -1.143 1.057 0.170 0.956 -0.497 1.723 0.000 0.915 -0.710 -0.704 0.000 1.275 -0.251 0.261 2.548 1.387 -0.646 1.213 3.102 0.790 1.032 1.123 0.797 0.810 0.672 0.677 +0 0.642 0.888 -0.279 0.848 1.307 0.801 1.405 -0.526 2.173 0.476 1.743 0.494 0.000 0.588 2.072 1.671 0.000 1.295 -0.601 1.599 1.551 0.735 0.882 1.012 0.882 1.719 1.078 0.875 +0 0.492 0.150 -1.313 0.819 -0.098 0.709 -0.148 -1.646 0.000 0.905 -0.352 0.720 2.215 0.420 -1.137 -0.997 0.000 0.949 0.652 0.129 3.102 0.785 0.952 0.993 0.561 0.660 0.723 0.746 +0 0.737 -1.336 1.561 0.974 -0.454 0.808 -0.632 0.739 2.173 0.510 0.186 0.444 0.000 1.313 0.094 -1.345 0.000 1.123 0.015 -1.038 3.102 0.762 0.785 1.139 0.961 1.062 0.785 0.685 +0 0.607 -2.180 1.128 0.096 -0.062 1.271 -0.991 1.622 2.173 1.444 -0.295 -0.400 2.215 0.773 0.827 -0.187 0.000 0.469 -1.103 0.834 0.000 0.863 1.045 0.995 0.847 2.056 1.249 1.065 +1 1.130 0.768 0.132 0.347 1.455 1.421 0.130 1.482 2.173 1.036 0.348 -0.405 0.000 0.540 -1.784 -0.248 0.000 0.507 0.986 -0.820 3.102 1.655 1.077 0.988 1.094 0.929 1.155 0.970 +1 0.611 -1.305 -0.121 0.697 0.494 1.343 0.738 -1.289 2.173 0.680 0.729 1.283 2.215 0.894 -0.652 0.095 0.000 1.034 0.233 0.330 0.000 0.845 1.029 0.981 1.413 1.030 1.079 0.968 +0 1.877 1.268 -1.203 0.627 1.454 1.064 0.772 0.527 2.173 1.503 -1.440 -0.624 0.000 1.365 0.250 1.022 0.000 1.076 1.257 -0.014 3.102 0.541 0.816 1.021 1.340 0.678 0.922 0.826 +1 1.338 0.144 -0.177 0.338 -1.198 0.917 0.141 0.877 1.087 0.845 -0.133 -0.548 0.000 1.335 0.741 1.559 2.548 1.256 1.806 -1.644 0.000 0.840 1.283 0.985 0.946 0.923 1.034 0.908 +1 1.694 -0.752 0.165 0.844 1.005 0.706 0.110 1.408 0.000 0.727 0.294 -1.367 0.000 0.726 -0.010 -0.608 2.548 0.777 0.464 -1.012 3.102 0.920 0.836 1.138 0.925 0.263 0.675 0.770 +0 0.445 1.682 -0.952 0.670 -1.073 0.569 1.101 0.296 0.000 0.703 -0.073 -0.871 0.000 0.766 0.033 1.305 1.274 0.608 0.341 1.580 1.551 1.440 1.034 1.000 0.557 0.161 0.630 0.609 +0 0.879 0.731 -0.161 1.550 -0.734 0.858 1.300 0.741 2.173 0.701 1.133 1.707 2.215 0.519 2.360 1.449 0.000 0.776 0.572 0.371 0.000 0.966 0.763 0.985 0.993 0.875 0.977 0.870 +1 0.465 -0.542 0.995 1.102 -1.410 0.774 1.078 1.721 0.000 0.957 0.039 -0.222 2.215 1.327 0.637 0.369 2.548 0.774 -0.209 0.602 0.000 0.889 1.061 0.987 0.877 0.729 0.850 0.779 +1 0.982 -0.028 -0.451 1.189 0.560 1.085 0.683 1.312 2.173 0.718 0.507 0.562 2.215 1.370 0.449 -0.501 0.000 1.408 0.610 -1.117 0.000 0.829 0.972 1.183 1.209 0.820 0.924 0.883 +0 0.772 -0.035 -0.370 0.665 1.344 1.072 0.343 1.011 0.000 1.143 0.193 -0.789 0.000 0.735 0.994 -0.315 2.548 0.770 -0.404 1.600 3.102 2.354 1.346 0.992 0.684 0.758 0.909 0.764 +1 1.030 -0.752 1.278 0.891 1.737 0.500 0.275 -0.379 2.173 0.289 0.792 0.369 0.000 0.326 -0.812 0.873 2.548 0.878 -0.862 -0.457 0.000 0.828 0.598 0.985 0.514 0.551 0.740 0.682 +1 1.174 1.794 0.459 0.792 1.346 1.255 1.092 -0.776 2.173 0.423 0.084 0.031 0.000 0.478 1.347 -1.710 2.548 0.923 -2.391 1.741 0.000 0.918 1.112 0.987 0.608 0.742 0.878 0.787 +0 1.561 -1.619 1.471 0.318 -1.273 0.656 -0.016 0.185 2.173 0.571 -0.232 1.322 1.107 1.175 -1.711 -0.366 0.000 0.439 -0.742 -1.167 0.000 0.664 0.932 0.986 1.175 0.776 0.806 0.800 +1 2.081 0.738 0.887 0.583 1.512 0.602 -0.270 -0.970 1.087 0.735 -0.822 -1.203 0.000 2.013 -0.264 0.024 2.548 1.059 -0.124 1.721 0.000 0.692 1.209 0.984 1.150 1.070 1.053 0.964 +0 1.168 1.045 -1.410 0.729 -1.101 0.705 0.749 0.373 2.173 0.996 0.809 -0.385 0.000 1.465 0.085 -1.502 0.000 2.173 -0.136 1.100 3.102 0.870 0.939 0.978 1.143 1.012 1.130 1.009 +1 0.837 -1.009 1.163 1.118 -1.660 0.555 -0.238 0.132 2.173 0.698 -2.019 -0.931 0.000 1.366 0.955 0.170 0.000 2.147 -0.229 -1.106 0.000 0.883 0.956 0.987 1.096 0.739 0.924 1.056 +0 2.381 -1.203 0.390 0.738 1.013 1.205 0.018 -1.425 0.000 0.848 -1.173 -0.616 0.000 1.165 -0.776 0.945 2.548 1.615 1.759 -0.650 0.000 2.002 1.043 0.986 0.646 0.360 0.878 1.011 +0 0.630 -1.053 -0.137 0.832 1.206 0.949 -0.516 -0.697 2.173 1.199 -1.140 1.142 0.000 0.463 0.312 0.091 2.548 0.637 -0.171 -1.313 0.000 1.077 0.884 0.990 1.004 0.652 0.835 0.765 +1 0.747 -0.331 0.636 1.303 1.250 1.015 0.049 -0.293 0.000 0.643 0.550 0.987 2.215 0.687 -0.399 -0.905 2.548 0.974 0.977 -1.456 0.000 1.593 0.992 0.990 0.855 0.792 0.778 0.921 +0 0.664 -0.213 -1.731 0.881 -1.082 0.563 0.285 -1.016 0.000 1.087 0.665 0.496 1.107 0.541 0.038 0.114 0.000 1.305 -0.369 1.049 3.102 0.854 0.950 0.985 0.779 0.820 0.937 0.830 +0 0.860 1.835 -0.534 0.723 0.849 1.307 0.502 0.801 2.173 1.341 0.842 -0.913 2.215 0.676 0.888 1.580 0.000 0.481 -1.975 -1.115 0.000 1.670 1.587 1.035 1.245 1.979 1.363 1.294 +1 1.913 -0.467 1.698 0.906 -1.395 0.912 -0.042 -0.348 2.173 0.427 1.181 0.668 2.215 0.858 0.548 -1.659 0.000 1.843 -1.988 0.172 0.000 0.485 0.969 0.998 1.189 0.954 1.082 0.892 +1 1.451 0.077 -0.796 0.620 1.665 0.813 -0.071 1.047 2.173 0.622 -1.494 0.611 2.215 0.338 1.395 1.164 0.000 0.640 -2.084 -0.433 0.000 1.962 1.291 1.048 1.012 0.929 0.959 0.940 +0 1.067 0.278 0.954 0.488 -0.887 1.573 -0.315 -1.626 2.173 1.747 0.941 -0.121 0.000 0.377 0.592 0.028 2.548 1.158 -0.059 0.651 0.000 1.558 0.805 0.996 1.037 1.072 1.336 1.049 +1 0.811 0.071 -0.966 0.796 1.316 1.005 0.119 -0.423 1.087 0.743 0.832 -0.512 0.000 1.333 0.707 1.358 0.000 1.652 0.058 -1.691 3.102 1.045 0.941 0.986 0.904 1.241 0.964 0.811 +1 0.969 -0.318 0.620 0.354 1.708 0.935 0.123 -0.883 1.087 0.663 -0.764 -1.063 0.000 0.613 1.893 1.252 0.000 0.921 -0.906 0.383 0.000 0.939 0.807 0.996 0.493 0.752 0.711 0.631 +0 1.487 -0.224 -1.573 1.524 1.538 0.526 0.813 0.970 0.000 1.506 -0.882 -0.251 0.000 0.702 -0.152 0.400 2.548 0.452 -0.893 -0.936 3.102 2.500 1.368 0.993 0.907 0.450 0.825 0.950 +0 1.047 -0.816 -0.010 0.827 0.333 1.003 -0.867 1.124 2.173 0.558 -0.049 1.413 1.107 0.928 0.213 -1.155 0.000 1.893 0.500 -0.671 0.000 0.673 0.840 0.989 1.069 0.546 0.985 1.081 +1 1.226 -0.234 -0.511 0.240 -1.164 0.573 -0.625 1.407 0.000 0.880 0.877 0.085 2.215 1.184 0.031 0.727 2.548 0.938 -0.699 -1.120 0.000 0.859 0.891 0.981 1.053 0.767 0.849 0.822 +0 1.847 0.495 -0.590 0.105 -0.422 1.248 0.153 -1.143 0.000 2.363 0.502 1.526 0.000 1.841 0.540 0.243 2.548 1.670 1.102 0.115 3.102 1.056 1.337 0.988 0.950 0.523 1.022 0.885 +1 1.537 -0.330 0.614 0.831 0.185 0.707 -0.560 -1.445 0.000 0.580 -0.555 1.482 0.000 0.921 0.475 -0.742 2.548 0.386 0.280 -1.199 3.102 0.826 0.879 0.979 0.680 0.187 0.690 0.635 +1 1.338 0.809 -1.659 0.176 -0.641 1.262 0.039 -0.301 2.173 0.896 1.196 0.609 0.000 0.689 1.211 -1.305 0.000 0.605 0.287 -1.686 3.102 1.192 0.774 0.985 1.367 0.888 0.904 0.860 +0 1.150 0.764 0.534 2.573 0.700 1.181 -0.827 -1.126 0.000 1.569 0.205 -1.146 2.215 0.576 1.437 1.146 0.000 0.644 -0.911 0.061 0.000 1.181 0.832 1.001 2.006 0.931 1.331 1.550 +0 1.250 0.715 0.441 0.117 -0.880 2.270 0.313 -1.210 0.000 1.246 0.010 0.574 2.215 0.967 2.219 0.008 0.000 0.672 -0.183 1.174 3.102 3.096 1.735 0.998 0.650 0.436 1.373 1.140 +1 0.646 -1.430 1.648 0.300 0.766 1.251 -0.441 -0.891 0.000 1.297 -1.001 0.558 2.215 1.055 -0.439 -1.525 2.548 1.064 -0.057 -0.027 0.000 0.905 0.878 0.979 0.824 1.235 0.737 0.665 +1 0.570 -0.383 -0.135 0.935 1.683 0.572 0.077 1.611 2.173 0.426 -1.591 -0.121 0.000 0.844 -0.521 -0.647 2.548 0.838 -2.192 0.441 0.000 0.552 0.790 1.009 0.644 0.825 0.852 0.753 +0 1.381 -1.826 0.712 0.589 0.939 0.962 -0.478 -1.145 2.173 0.811 -1.026 -0.236 0.000 0.370 0.392 1.558 0.000 0.496 -0.618 1.739 3.102 1.083 0.987 1.001 0.607 0.389 0.836 0.772 +1 0.904 1.680 -1.541 1.132 -1.041 1.572 -0.151 -0.135 0.000 2.819 1.405 0.387 0.000 2.705 -0.257 -1.685 2.548 2.540 0.652 -1.612 0.000 0.847 1.043 0.998 2.629 0.325 1.651 1.687 +0 1.299 -0.694 1.183 0.765 0.181 0.531 -2.772 -0.286 0.000 0.229 -1.205 -0.751 0.000 0.729 -0.893 -1.603 2.548 0.366 0.171 -1.480 3.102 0.705 0.862 1.084 0.605 0.252 0.629 0.723 +1 0.816 -1.119 0.202 0.792 -1.373 0.831 -0.059 -0.635 0.000 1.534 0.971 1.511 0.000 1.396 0.950 -0.170 0.000 0.661 0.323 0.674 3.102 1.373 0.939 1.101 0.810 0.137 0.671 0.823 +0 2.579 1.044 0.380 0.514 -0.477 0.911 0.070 -1.724 2.173 0.907 -0.753 -1.734 0.000 0.689 1.431 -0.881 0.000 0.375 -0.467 -1.062 0.000 0.724 0.873 1.112 1.072 1.060 1.113 0.913 +1 1.344 -1.256 1.333 0.535 0.291 0.850 2.465 -0.917 0.000 0.931 -0.180 -0.170 2.215 1.136 -0.302 0.998 0.000 0.484 0.877 1.350 3.102 3.826 1.978 0.986 1.061 0.715 1.475 1.788 +0 0.898 -0.140 -1.102 0.982 0.024 1.452 -0.587 0.800 0.000 2.042 0.511 -1.421 2.215 1.195 1.249 -0.310 0.000 1.548 -0.227 -0.015 1.551 0.994 1.070 1.106 1.182 1.666 1.065 0.948 +0 0.415 -1.384 -1.051 0.692 0.473 1.044 -0.323 -1.191 1.087 0.792 1.571 0.322 2.215 1.124 0.476 0.854 0.000 0.394 1.990 -0.651 0.000 1.066 0.797 0.989 0.882 2.002 1.126 0.974 +1 0.947 0.672 0.171 1.010 0.997 1.010 0.416 -1.586 2.173 1.910 0.278 0.290 0.000 1.192 -0.394 1.720 2.548 1.101 1.899 -0.050 0.000 1.247 1.431 0.990 1.073 0.640 1.168 0.983 +0 0.964 0.141 0.787 1.260 -0.184 0.572 -0.173 -1.123 2.173 0.636 0.115 1.470 0.000 0.735 0.856 -0.901 1.274 0.933 -1.133 0.858 0.000 0.967 1.028 1.171 0.932 0.500 0.716 0.748 +0 0.591 1.725 -0.151 1.602 0.229 1.456 -0.315 1.341 0.000 0.618 0.453 -0.874 2.215 0.581 -0.942 1.331 0.000 1.134 -1.688 -0.899 0.000 0.933 1.110 0.985 0.665 0.369 0.954 1.078 +1 1.049 2.093 -1.375 0.922 1.368 0.707 0.737 0.516 1.087 0.728 1.069 -0.613 2.215 0.588 0.616 1.656 0.000 0.853 -0.454 -0.310 0.000 0.918 0.866 0.994 0.857 0.919 0.836 0.814 +0 1.128 -1.356 0.629 0.494 -1.614 0.572 -0.965 -0.153 1.087 0.775 -1.510 -1.382 0.000 0.655 -1.384 -0.698 0.000 0.914 -0.113 1.261 3.102 0.631 0.816 0.985 0.766 0.803 0.653 0.646 +0 0.941 -1.123 -1.040 2.095 0.802 1.836 -0.672 -1.644 1.087 1.276 -0.633 -0.164 0.000 1.282 -0.071 -0.454 0.000 1.198 0.278 0.410 3.102 0.763 0.831 1.938 1.678 1.720 1.321 1.285 +1 0.511 -0.449 1.122 0.284 0.557 1.041 -2.229 0.984 0.000 2.503 -0.361 -0.643 2.215 1.992 -0.106 1.343 0.000 0.756 0.502 0.318 0.000 1.189 0.860 0.989 1.429 0.813 1.123 0.983 +0 1.709 -0.562 -0.247 1.014 0.140 0.631 0.776 -1.329 2.173 0.944 1.234 1.623 0.000 0.323 -1.074 1.320 2.548 0.622 0.791 1.099 0.000 0.482 0.760 0.993 1.465 0.751 0.948 1.059 +0 0.690 2.262 0.632 1.367 -0.377 0.829 1.437 0.563 2.173 0.967 0.913 1.680 0.000 0.956 0.548 -0.780 2.548 0.709 1.449 -0.789 0.000 0.964 1.090 1.061 1.033 1.146 0.873 0.862 +0 0.350 -1.405 0.272 1.234 -1.399 0.990 0.161 -0.262 0.000 0.712 -1.581 1.588 0.000 1.015 -0.704 1.114 0.000 1.683 0.254 0.250 3.102 0.788 1.259 0.991 0.517 0.710 0.765 0.704 +0 1.415 0.037 1.240 0.926 0.690 1.024 0.933 -0.636 0.000 1.092 0.501 -0.982 2.215 0.975 -0.548 0.993 2.548 0.798 0.489 0.035 0.000 0.823 0.742 0.989 0.514 1.252 0.905 0.982 +1 1.085 -0.172 -0.509 0.299 1.402 1.361 -0.408 1.363 0.000 0.784 -1.006 0.008 2.215 1.087 0.292 -0.630 2.548 0.546 -0.095 0.136 0.000 1.194 1.228 0.991 0.668 0.894 0.960 0.817 +1 0.856 -0.263 -0.637 0.927 1.480 0.794 0.272 -1.508 2.173 1.246 -0.871 0.723 0.000 0.830 -0.515 -0.251 2.548 0.621 -0.742 0.191 0.000 0.529 0.672 1.165 0.780 1.016 0.865 0.773 +1 0.773 -0.461 -0.169 0.771 0.663 0.975 -0.060 1.362 0.000 1.377 0.461 -0.877 2.215 0.864 -1.139 0.306 0.000 1.177 -0.569 -1.433 3.102 0.733 0.869 0.987 1.339 0.894 0.931 0.847 +0 0.592 -0.341 -1.697 0.574 0.492 1.049 -0.183 -0.766 2.173 1.029 -0.208 0.636 0.000 1.185 0.145 1.246 2.548 0.760 -0.842 -0.944 0.000 1.234 0.946 0.979 1.019 1.369 0.921 0.821 +1 0.799 -1.181 -0.953 0.058 1.540 1.267 0.052 1.393 0.000 1.605 0.229 -0.192 0.000 1.369 -0.917 -1.305 2.548 1.188 -0.736 -0.103 3.102 0.943 0.763 0.981 0.665 0.863 0.869 0.749 +0 0.486 0.917 -1.428 0.953 0.209 0.477 -0.218 1.431 2.173 0.977 0.292 0.635 0.000 1.078 0.262 -0.502 0.000 0.552 -1.696 -1.136 0.000 1.311 1.029 0.988 0.550 0.556 0.625 0.632 +1 1.416 -0.021 0.339 1.366 0.062 2.933 2.773 1.684 0.000 2.287 0.487 -0.305 1.107 1.477 -0.253 0.013 2.548 1.081 -0.116 -1.267 0.000 5.742 5.296 0.988 0.961 0.956 3.801 3.027 +0 0.879 -0.469 1.725 1.782 1.294 0.822 -0.636 -0.305 0.000 1.469 -0.459 0.256 2.215 0.905 -0.280 -1.149 0.000 1.283 -0.763 -1.042 3.102 1.100 1.134 0.990 0.984 1.174 1.031 0.982 +0 0.558 0.054 -0.966 1.552 0.063 1.944 -1.411 -1.711 2.173 2.035 0.348 -0.189 2.215 1.361 -1.597 1.012 0.000 0.407 0.691 1.267 0.000 1.394 1.504 1.032 0.726 4.128 2.052 1.661 +1 1.555 -1.215 1.137 1.050 1.113 1.293 -2.137 -0.846 0.000 0.974 -0.521 1.120 0.000 1.654 -0.520 -0.562 1.274 1.343 0.070 0.315 3.102 0.839 1.103 0.977 1.260 0.895 1.167 0.989 +0 1.372 -1.143 1.132 0.217 0.252 1.012 -1.329 0.152 2.173 1.175 -1.867 -1.175 0.000 0.574 0.077 0.748 2.548 0.464 1.776 -1.343 0.000 0.814 0.641 0.977 0.952 0.881 0.974 0.860 +0 1.455 -0.422 0.302 0.582 -1.719 1.187 -0.801 -1.720 2.173 1.139 -1.354 -0.258 2.215 0.772 -0.060 0.998 0.000 0.743 -0.531 -1.029 0.000 0.844 1.008 1.235 1.154 1.733 1.061 0.866 +0 0.555 0.969 0.917 1.102 1.643 0.656 -0.543 -1.088 2.173 1.208 0.587 -0.206 2.215 0.590 -0.101 1.229 0.000 0.483 1.454 1.091 0.000 0.892 0.940 0.980 1.259 1.228 1.236 0.973 +1 2.378 0.123 0.742 0.116 1.479 1.044 -0.611 -1.045 2.173 0.560 -0.612 0.118 1.107 0.686 1.684 1.528 0.000 0.623 0.829 0.963 0.000 0.474 0.985 0.978 1.424 0.975 1.032 0.985 +1 1.042 0.051 0.154 0.870 -0.234 1.077 0.370 -1.568 2.173 0.419 -0.289 1.095 1.107 0.346 -0.246 1.630 0.000 0.443 -1.044 0.189 0.000 0.471 0.765 0.987 0.761 0.748 0.866 0.705 +0 0.613 -1.118 1.415 1.209 1.334 1.133 0.753 -0.738 0.000 1.112 -1.066 0.801 1.107 1.404 -0.312 -0.849 2.548 1.102 0.393 -0.156 0.000 0.889 0.919 0.986 0.935 1.419 1.237 1.616 +0 0.386 0.831 -1.302 1.893 0.274 1.105 0.233 0.397 2.173 1.095 -2.515 -1.581 0.000 1.545 0.424 -1.506 2.548 0.990 1.367 -1.249 0.000 5.555 3.481 1.171 0.828 1.622 2.418 2.069 +1 0.845 -1.609 0.810 1.558 1.451 0.335 -1.312 0.191 0.000 0.554 0.584 -0.726 1.107 0.354 0.910 -1.239 2.548 0.427 0.697 1.375 0.000 0.913 0.721 0.994 1.336 0.230 1.216 0.992 +0 0.388 1.704 1.305 0.284 0.853 0.952 1.375 0.538 0.000 1.331 0.463 -1.178 2.215 0.822 1.208 -0.846 2.548 0.676 0.770 1.394 0.000 0.900 0.934 0.997 0.879 0.587 0.913 0.802 +1 1.442 -0.925 1.259 0.212 -0.909 3.287 -1.614 -0.248 0.000 1.999 -0.292 1.527 2.215 0.926 -1.405 -1.179 0.000 0.380 -1.185 0.925 3.102 0.604 1.032 0.984 0.463 0.618 0.630 0.621 +1 1.187 -0.438 1.363 1.574 -1.497 1.670 -0.946 -1.536 2.173 2.983 -0.570 0.003 0.000 1.277 -0.760 0.631 2.548 0.373 0.043 -0.297 0.000 0.577 0.912 1.015 0.832 1.692 1.510 1.331 +1 0.384 -1.929 -1.431 1.918 0.866 0.788 -1.299 -1.085 2.173 0.428 -0.593 -0.693 0.000 0.336 0.424 0.490 1.274 0.665 -1.755 0.121 0.000 0.736 0.704 1.043 0.953 0.906 0.898 0.755 +0 0.485 1.476 -1.048 1.986 -0.266 1.090 -0.293 0.950 0.000 1.013 0.149 -1.665 2.215 1.553 1.353 0.014 0.000 1.163 -1.037 -0.822 3.102 0.820 0.951 0.984 2.193 1.004 1.606 1.529 +1 0.800 -0.470 0.891 1.003 -0.598 0.661 1.229 0.785 0.000 1.181 -0.482 -1.298 2.215 0.840 -0.409 0.633 1.274 0.472 -0.167 0.004 0.000 0.868 0.780 1.209 0.905 1.044 0.913 0.833 +0 0.785 0.352 -0.465 1.626 1.507 1.154 0.304 0.362 0.000 1.221 0.906 -1.094 2.215 1.395 -0.789 -1.121 0.000 0.558 -1.105 0.432 0.000 0.986 0.741 1.532 1.072 1.004 0.902 0.878 +1 2.118 -1.267 0.630 0.610 -1.467 1.291 -0.730 -0.591 0.000 0.990 -1.643 1.628 0.000 0.911 -0.166 -1.588 0.000 1.620 0.229 1.410 3.102 0.915 0.701 1.496 1.264 0.843 0.928 0.964 +1 1.359 -0.088 -0.511 0.603 1.091 1.219 -0.497 -0.967 2.173 1.148 0.278 0.880 2.215 0.777 -0.136 1.147 0.000 1.278 -0.402 1.737 0.000 0.928 0.744 1.244 0.976 1.871 1.033 0.891 +0 1.021 0.166 1.100 0.922 -1.050 0.688 0.604 -0.003 2.173 0.474 0.346 -0.505 0.000 1.134 -0.526 1.316 0.000 0.967 0.963 -0.925 1.551 1.247 0.995 1.255 0.951 0.681 0.765 0.727 +1 0.419 1.625 -1.273 0.463 1.674 0.917 1.419 -0.246 0.000 0.915 0.232 -0.165 1.107 1.746 0.267 1.515 2.548 0.731 -2.251 0.989 0.000 4.994 2.768 0.974 0.685 1.342 1.850 1.409 +1 0.953 0.066 1.573 0.384 -0.934 0.594 -0.620 -0.595 2.173 0.327 -1.509 1.617 2.215 0.387 -1.051 0.022 0.000 0.920 0.640 -0.516 0.000 0.796 0.764 0.994 0.755 0.669 0.553 0.568 +0 2.077 -0.626 -0.641 0.502 -0.547 1.123 -0.659 0.790 1.087 0.956 -0.083 -1.168 0.000 0.681 -1.111 0.373 2.548 0.953 -0.643 -1.639 0.000 1.151 0.970 0.986 1.426 0.504 0.949 0.910 +1 0.282 -1.190 -0.474 1.056 1.229 0.628 0.866 -0.013 2.173 1.082 0.182 -1.174 2.215 1.039 -0.194 0.324 0.000 0.932 -0.784 1.711 0.000 1.107 1.081 0.987 2.156 1.132 1.646 1.291 +0 0.958 -0.018 -0.031 1.109 -0.873 1.060 1.405 1.241 2.173 0.769 -0.539 1.697 0.000 1.335 -0.749 -0.514 2.548 0.630 0.246 0.386 0.000 0.928 0.952 0.989 1.466 2.514 1.316 1.085 +0 0.608 -0.582 0.273 1.292 0.192 0.736 -0.183 1.162 2.173 0.650 -0.253 -1.144 0.000 1.173 0.293 -0.879 0.000 0.880 1.246 1.027 0.000 0.909 0.785 0.977 1.195 1.203 1.256 1.708 +0 0.909 0.076 1.658 2.207 -1.278 1.372 -0.332 0.411 2.173 0.360 -1.976 0.681 0.000 0.702 0.406 -1.497 0.000 1.236 0.116 -0.386 3.102 1.413 1.045 0.988 1.772 0.966 1.182 1.071 +0 0.905 0.422 1.021 0.617 -0.916 0.649 1.023 -0.100 0.000 0.731 0.714 -0.785 0.000 1.585 1.220 1.633 1.274 0.714 1.075 0.914 3.102 0.875 0.759 1.020 0.822 0.491 0.757 0.693 +1 1.363 0.719 -0.809 0.356 -0.158 1.253 1.426 0.815 2.173 0.690 1.173 -0.548 0.000 0.589 0.046 1.433 2.548 0.921 0.195 -1.329 0.000 0.853 0.731 0.989 1.264 0.984 0.911 0.826 +1 0.398 1.952 1.666 0.457 -1.278 1.591 0.129 -0.196 0.000 1.003 0.564 -1.111 1.107 1.124 0.503 1.295 2.548 1.835 -0.513 -1.425 0.000 1.009 1.056 0.989 0.654 0.933 0.748 0.671 +0 0.515 0.347 -1.396 1.738 -1.448 0.720 0.807 -0.248 2.173 0.516 -1.173 0.779 0.000 0.865 0.830 1.198 0.000 1.254 0.325 0.306 3.102 0.946 0.947 0.991 1.079 0.527 0.883 0.929 +1 0.554 -0.461 0.015 0.637 -1.568 0.520 -1.282 1.703 0.000 0.584 -1.070 0.012 0.000 0.971 -0.016 1.527 2.548 0.486 1.170 0.409 3.102 1.175 0.963 0.988 0.578 0.601 0.754 0.687 +0 0.356 1.693 0.726 1.208 -0.809 0.436 0.743 1.252 2.173 0.868 0.099 -1.308 2.215 0.589 -0.071 -0.129 0.000 0.393 0.809 0.167 0.000 0.323 0.635 0.990 0.982 0.733 0.992 0.828 +1 3.036 -0.339 -1.591 0.431 -1.148 1.845 0.268 -0.085 0.000 0.952 -0.010 1.685 2.215 1.540 -0.455 0.300 2.548 1.620 -0.145 1.259 0.000 0.746 0.756 0.994 1.405 1.261 0.973 0.811 +1 1.472 0.834 -0.360 1.221 0.076 1.352 0.558 1.724 2.173 0.605 0.892 0.415 0.000 0.362 -0.271 0.589 2.548 0.514 -0.533 -1.592 0.000 0.943 1.076 0.978 0.607 0.839 1.000 0.830 +0 0.529 -1.785 -0.663 0.412 0.155 0.494 -0.538 -1.395 2.173 0.723 -1.027 1.140 0.000 0.502 -1.215 -1.166 2.548 0.754 -1.703 0.158 0.000 0.894 0.881 0.983 0.567 0.283 0.557 0.560 +0 0.382 -0.738 -1.493 1.946 1.048 0.935 0.890 -0.828 2.173 0.784 1.611 -0.011 0.000 0.651 0.465 -1.480 2.548 0.548 2.411 0.940 0.000 0.856 0.921 0.984 1.399 0.571 0.905 1.044 +0 0.387 1.501 -0.468 2.235 0.531 0.812 0.882 1.185 2.173 0.997 0.874 -0.865 0.000 0.772 1.414 -1.628 0.000 0.580 0.021 -1.035 0.000 0.964 1.112 1.009 0.995 0.898 0.833 0.867 +1 0.376 2.073 0.598 1.021 -1.246 0.911 0.941 -0.626 2.173 0.858 0.243 0.499 0.000 1.401 0.739 -1.440 2.548 1.062 -0.426 1.285 0.000 0.943 1.145 0.983 0.773 0.947 0.976 0.857 +1 0.314 1.057 -0.787 0.841 1.269 0.663 -1.155 1.391 0.000 1.266 -0.931 -0.405 2.215 0.970 0.240 -0.126 2.548 0.598 0.289 1.494 0.000 0.804 1.047 0.988 2.557 0.820 1.636 1.514 +1 0.992 0.438 -0.627 1.443 1.623 0.784 -0.125 1.073 0.000 1.158 0.491 1.364 0.000 1.035 0.872 0.155 2.548 1.034 -0.272 -0.695 3.102 0.833 1.074 1.487 1.034 0.772 0.854 0.847 +0 0.568 0.736 -0.123 1.885 0.807 0.808 2.065 -0.861 0.000 0.487 1.332 1.459 2.215 0.388 2.030 -0.465 0.000 0.858 -0.354 -1.713 3.102 0.359 0.727 1.066 0.866 0.619 0.807 0.872 +1 1.210 -0.124 1.445 0.413 1.641 0.473 -0.929 0.941 0.000 0.550 1.226 -0.848 2.215 0.984 -0.267 -0.197 2.548 0.636 -1.724 -0.962 0.000 0.921 0.886 1.002 0.903 0.799 0.852 0.731 +1 1.016 0.492 -0.322 0.531 1.569 0.730 -0.866 -0.028 0.000 1.075 -0.048 1.436 2.215 2.102 0.459 0.653 2.548 2.269 -0.021 -1.312 0.000 0.489 1.224 1.009 0.895 1.128 1.075 0.936 +0 1.623 -0.061 0.983 0.969 0.476 0.626 0.925 -0.640 0.000 1.009 0.106 -0.512 0.000 1.086 0.428 -1.703 2.548 0.599 0.243 -1.172 0.000 1.039 0.972 0.993 0.880 0.773 0.742 0.722 +0 0.475 -1.581 1.691 1.960 -0.557 1.004 -0.232 1.481 2.173 0.708 -0.396 0.500 0.000 0.566 -1.066 -0.585 2.548 0.381 1.992 1.079 0.000 1.331 1.093 1.201 1.538 1.009 0.986 1.142 +0 0.290 0.429 0.626 0.529 -1.402 0.600 0.672 0.139 2.173 0.519 2.277 1.671 0.000 0.675 0.301 -0.868 2.548 0.997 0.892 1.213 0.000 0.757 0.759 0.989 0.719 0.641 0.731 0.788 +0 0.287 -0.457 0.905 1.937 -1.665 1.984 0.503 -1.193 0.000 1.504 0.153 0.622 2.215 2.698 -0.625 0.520 2.548 1.772 0.294 -0.066 0.000 2.445 2.256 0.992 1.665 0.951 1.857 1.530 +1 0.900 0.531 -0.660 1.408 0.124 1.369 -0.232 -1.486 0.000 0.582 -0.822 -0.197 1.107 1.054 -0.543 0.777 0.000 0.522 0.792 0.535 0.000 0.720 0.712 1.013 0.707 0.676 0.616 0.609 +1 1.370 -0.455 -0.731 0.558 -1.726 1.043 0.124 0.722 2.173 1.292 -0.862 0.068 2.215 0.971 -0.716 1.725 0.000 1.158 0.031 -1.361 0.000 0.960 1.219 0.987 1.157 1.310 1.089 0.947 +1 2.366 -0.719 -1.389 0.746 -0.676 0.680 -0.352 -0.380 0.000 1.615 -0.190 0.703 2.215 0.298 -1.079 1.028 0.000 0.541 -0.525 -0.889 3.102 0.846 1.040 1.105 0.524 0.856 0.981 0.836 +0 1.442 -0.783 -0.616 0.735 -1.374 0.587 -0.044 0.031 0.000 0.860 -1.968 -1.660 0.000 0.882 -0.943 0.775 2.548 0.915 0.305 -1.692 3.102 2.311 1.388 0.985 0.728 0.755 0.962 0.881 +0 0.413 -0.407 0.016 1.470 -0.182 1.656 0.595 -0.703 2.173 2.424 0.866 1.118 1.107 1.107 0.698 1.433 0.000 1.508 1.112 -1.718 0.000 0.819 1.329 0.991 0.965 2.972 1.532 1.289 +0 0.517 1.589 1.444 0.934 -0.764 0.561 -0.916 -1.150 2.173 0.721 0.703 1.601 2.215 0.806 -0.711 0.957 0.000 1.004 -0.652 0.388 0.000 0.869 0.937 0.992 0.897 1.036 1.237 1.632 +1 0.580 -0.841 -1.196 0.523 0.098 0.727 0.047 -0.479 0.000 1.467 0.391 0.919 2.215 0.890 0.828 -0.920 0.000 1.320 0.803 1.631 3.102 0.852 0.980 0.989 1.542 0.832 1.216 1.151 +1 2.135 1.293 0.039 1.032 0.817 0.679 -1.441 -0.703 0.000 0.964 0.788 1.156 0.000 1.121 0.045 1.665 2.548 0.419 0.729 1.554 0.000 0.718 0.731 1.327 1.354 0.755 0.930 0.810 +0 0.561 -0.668 0.907 0.542 -1.451 0.886 -0.495 0.160 2.173 1.210 -0.257 -1.304 2.215 0.703 0.179 1.164 0.000 0.497 -1.239 -0.064 0.000 0.849 0.893 0.984 0.998 1.487 0.958 0.798 +1 0.773 0.715 1.362 1.246 -0.970 0.956 -0.857 -1.139 2.173 2.339 1.960 0.307 0.000 0.806 0.502 1.721 0.000 0.883 0.211 0.050 0.000 0.939 0.851 1.174 1.242 0.781 0.941 0.822 +1 0.595 -0.458 -0.582 1.226 1.728 0.831 -0.954 0.445 0.000 1.064 -1.000 -1.139 2.215 1.169 -0.825 1.103 0.000 0.877 -1.872 0.113 0.000 0.963 0.855 1.032 0.713 0.349 0.730 0.659 +0 1.032 -0.614 0.328 1.380 0.915 0.482 -1.255 -1.401 2.173 0.512 -0.076 -0.661 2.215 0.409 -1.339 -0.122 0.000 1.625 0.052 -1.407 0.000 1.068 0.761 0.988 1.026 0.646 0.763 0.725 +0 1.345 1.439 1.728 0.773 1.176 0.713 -0.585 -0.405 2.173 0.727 -0.278 -1.314 2.215 1.075 -0.658 1.125 0.000 1.208 -0.294 0.286 0.000 0.893 0.965 0.985 0.946 0.790 1.018 0.970 +0 1.379 -0.393 1.219 1.394 0.463 0.641 0.740 -0.442 0.000 0.537 1.435 0.833 2.215 1.123 0.433 -1.391 0.000 1.332 -0.743 -0.780 3.102 1.170 1.028 1.209 1.048 1.363 0.968 0.984 +1 0.607 -0.033 0.814 1.553 -1.663 0.495 0.845 1.535 0.000 1.410 0.916 -0.026 1.107 0.428 0.727 -0.472 0.000 0.495 -0.512 -0.918 3.102 0.804 0.969 1.063 0.603 0.846 0.878 0.747 +0 1.040 -1.192 1.466 1.968 -1.434 1.343 -0.463 0.245 0.000 1.590 -1.632 -0.685 0.000 0.850 -1.037 -0.021 0.000 1.632 -0.115 1.400 3.102 0.801 1.253 1.002 0.644 0.224 0.708 0.948 +1 0.462 -0.495 -1.067 0.718 0.884 0.924 1.623 -1.573 0.000 1.301 -0.248 -0.370 0.000 0.825 -1.039 -0.451 0.000 2.921 -0.440 1.019 3.102 0.737 0.646 0.988 0.869 0.820 0.909 0.828 +1 0.825 -0.127 -1.227 0.832 1.343 1.099 0.047 1.348 0.000 0.995 0.840 -0.884 2.215 0.902 -2.159 -0.483 0.000 1.750 -0.024 0.128 3.102 1.318 1.172 0.988 1.048 1.091 0.969 0.899 +1 1.194 0.168 0.819 0.174 -0.744 0.388 0.895 0.141 0.000 1.140 -0.106 -1.513 2.215 1.206 0.079 -0.357 2.548 0.473 -1.752 1.258 0.000 1.506 1.062 0.980 0.937 1.082 0.877 0.809 +1 0.404 -0.694 -0.389 0.426 -0.307 0.419 -2.583 1.002 0.000 0.377 0.178 -0.842 2.215 0.302 -1.403 1.655 0.000 0.785 -1.116 0.851 3.102 0.645 1.104 0.984 0.891 0.640 0.683 0.996 +0 1.522 1.345 1.522 1.025 1.269 0.733 1.734 1.246 0.000 0.889 0.612 -1.259 2.215 2.506 -0.758 -0.130 0.000 0.852 -0.496 -0.525 0.000 0.772 0.857 0.999 1.088 0.619 0.951 1.607 +1 1.772 0.622 0.317 0.525 -0.582 0.858 1.422 1.522 2.173 0.554 0.250 -0.761 0.000 1.121 0.452 -1.348 2.548 0.798 0.134 1.717 0.000 0.697 1.193 0.986 0.950 0.876 0.918 0.844 +1 0.514 -1.289 0.752 0.382 0.939 0.911 -0.062 -1.072 0.000 0.941 -0.925 -1.305 2.215 2.229 1.928 -0.237 0.000 2.514 0.109 1.289 0.000 1.096 2.290 0.974 0.890 0.485 2.140 1.687 +1 0.880 -0.823 1.590 1.395 -0.832 0.578 -1.066 -0.072 0.000 0.747 0.413 1.349 2.215 0.906 -1.275 0.717 0.000 1.267 0.792 -0.313 3.102 0.874 1.202 1.258 1.168 0.906 0.983 0.951 +0 0.366 -0.851 1.117 1.262 0.701 0.884 0.445 -0.961 0.000 0.873 0.608 -0.354 0.000 0.522 -1.654 0.537 0.000 1.181 1.400 1.621 3.102 0.986 0.866 0.980 2.692 0.174 1.802 1.684 +0 0.514 -1.180 -1.719 4.347 -1.296 2.763 -0.199 0.429 2.173 0.846 -1.426 -1.091 2.215 0.309 0.527 0.176 0.000 0.923 -0.567 0.758 0.000 0.494 0.871 0.991 3.938 2.680 2.511 1.813 +0 0.705 0.344 -0.601 0.751 -0.177 0.846 2.057 1.042 0.000 0.846 1.341 0.405 2.215 1.689 0.487 -1.085 2.548 0.637 2.295 -1.566 0.000 0.886 0.916 1.000 0.874 1.355 1.088 0.944 +0 0.909 1.050 0.943 0.719 -0.721 1.242 1.158 -0.084 0.000 1.280 0.067 1.036 2.215 1.401 1.246 -0.869 0.000 1.012 -1.241 1.728 0.000 0.358 1.168 1.117 1.427 1.606 1.139 1.122 +1 0.623 -0.155 1.012 1.447 -0.742 2.547 -0.840 1.098 0.000 2.741 0.399 -0.515 0.000 1.212 -0.405 -1.276 2.548 0.584 0.988 -1.156 0.000 1.102 0.843 1.316 0.669 0.593 0.562 0.567 +0 0.334 -1.496 -0.004 0.958 -1.511 1.015 0.587 1.475 1.087 1.105 -0.662 -0.048 2.215 0.385 1.071 0.970 0.000 0.393 0.743 -0.101 0.000 0.727 1.103 0.990 1.321 1.861 1.785 1.349 +0 0.654 -0.166 0.400 0.448 1.535 0.886 0.299 0.772 0.000 0.626 2.377 -0.374 0.000 1.220 0.409 -1.186 2.548 0.801 -1.100 -1.331 1.551 2.451 1.737 0.989 0.860 0.766 1.330 1.045 +0 0.516 -0.268 1.597 0.954 0.845 1.086 -1.932 -0.602 0.000 0.999 1.206 1.284 1.107 0.946 -0.990 -0.215 0.000 0.843 -0.081 0.482 0.000 0.846 0.645 0.993 0.649 0.943 0.902 0.806 +0 1.145 -0.463 -0.498 0.567 1.050 0.905 1.445 1.471 0.000 1.397 0.389 -0.695 2.215 1.249 -0.663 0.187 2.548 1.475 -0.432 1.259 0.000 2.072 1.898 1.100 0.707 1.305 1.422 1.175 +0 0.309 1.803 1.452 2.374 0.539 1.059 1.085 -0.903 2.173 0.445 0.644 -1.628 0.000 0.391 -0.906 1.227 2.548 0.448 2.496 1.473 0.000 0.865 0.977 0.982 1.380 1.253 1.060 0.913 +1 0.619 0.904 0.362 0.835 1.260 0.644 -0.173 -1.608 2.173 0.624 0.435 0.543 0.000 0.516 -0.665 -0.340 2.548 0.737 1.487 -0.388 0.000 0.914 1.083 0.989 1.036 0.684 0.915 0.804 +0 1.550 1.100 1.585 0.744 -0.921 0.691 0.894 -1.007 0.000 1.059 0.833 0.829 2.215 1.109 -0.017 0.216 2.548 0.846 1.056 -0.464 0.000 0.590 0.952 1.150 0.965 0.798 0.866 0.819 +1 0.468 0.275 1.620 1.108 -0.690 1.480 -2.234 0.616 0.000 1.908 0.620 -0.762 2.215 1.983 -1.956 1.097 0.000 1.327 -0.359 -0.505 3.102 1.303 1.960 0.992 0.747 0.871 2.870 2.490 +0 0.478 1.998 0.034 0.805 -0.718 0.654 -0.970 0.912 0.000 1.167 0.555 -1.637 2.215 1.224 -0.086 -0.141 2.548 0.683 -1.148 -0.285 0.000 0.923 0.926 0.983 0.917 1.312 0.995 0.970 +1 0.791 0.754 -0.208 0.801 -0.751 0.818 -0.097 0.456 0.000 1.365 0.153 1.460 1.107 0.825 0.703 -1.243 1.274 1.185 0.275 -0.131 0.000 0.826 1.001 0.998 1.116 0.812 0.884 0.819 +0 1.452 0.792 -0.916 1.250 -0.155 0.763 -0.843 1.094 1.087 1.690 0.110 -0.688 2.215 2.089 -1.000 0.561 0.000 1.147 1.788 1.710 0.000 0.973 0.800 1.182 0.882 1.867 1.308 1.417 +0 0.355 0.299 -1.341 1.512 0.258 0.618 -0.660 -1.286 0.000 0.769 -0.071 0.655 2.215 0.772 -1.575 -1.021 0.000 0.848 -0.775 1.272 3.102 0.898 1.138 1.006 0.763 0.502 0.702 0.775 +1 0.810 0.667 -1.653 1.012 -1.178 0.332 0.963 -1.077 0.000 0.460 1.498 0.459 0.000 0.957 -0.686 -0.112 2.548 0.881 0.299 0.983 3.102 0.964 0.855 0.995 0.791 0.716 0.930 0.797 +1 0.653 -0.442 -0.217 1.122 -0.934 1.005 0.420 0.256 2.173 0.858 -0.793 -1.734 0.000 0.646 2.484 -0.725 0.000 1.065 0.346 1.210 3.102 0.897 0.901 0.988 1.381 0.829 1.012 1.242 +1 0.684 0.358 0.030 0.585 -1.570 0.884 0.658 0.731 2.173 1.279 0.223 -0.961 0.000 0.550 -0.845 0.970 1.274 0.654 -0.039 -1.733 0.000 0.781 0.861 0.989 0.790 0.789 0.856 0.752 +1 0.794 -1.088 0.258 1.056 -0.807 1.039 -0.007 1.427 1.087 0.814 -1.152 -0.361 0.000 0.785 -1.058 0.855 1.274 0.516 -2.063 -1.127 0.000 0.794 0.777 1.039 1.262 0.883 0.949 0.852 +1 0.854 1.155 0.099 0.023 -0.410 0.684 0.866 -1.231 0.000 1.025 -0.127 0.837 2.215 0.959 1.278 -0.626 0.000 0.646 0.907 1.539 3.102 0.851 0.651 0.779 0.715 0.644 0.834 0.710 +1 0.430 0.011 1.016 0.891 -1.160 1.518 -1.830 0.377 0.000 1.334 -0.540 -1.095 0.000 0.924 1.142 1.332 2.548 1.056 1.913 -0.783 0.000 0.803 0.911 0.989 0.642 0.763 1.494 1.442 +1 0.390 1.113 -0.115 1.235 -1.016 0.751 0.199 -1.598 0.000 0.816 -0.362 -0.098 2.215 2.008 -0.449 0.891 2.548 0.862 0.124 -1.138 0.000 0.497 0.973 0.986 1.185 1.062 0.908 0.827 +0 0.673 -0.537 1.268 0.263 0.949 1.806 -1.038 1.397 2.173 1.559 -0.646 -0.302 1.107 1.771 -1.650 -0.337 0.000 0.732 -1.104 -0.985 0.000 0.758 0.955 0.976 1.337 2.513 1.455 1.336 +0 0.740 -0.545 0.661 0.955 -0.522 0.586 -0.058 -0.225 0.000 0.932 -0.887 -1.740 2.215 0.784 0.276 -1.180 0.000 1.142 -0.616 1.277 3.102 0.950 0.926 1.019 0.874 0.391 0.732 0.688 +0 0.633 1.154 1.016 2.141 0.848 0.940 0.536 -0.154 2.173 1.112 1.522 -1.295 0.000 0.940 0.004 1.573 0.000 0.729 0.257 -0.724 3.102 0.926 1.150 0.996 0.832 0.443 0.823 0.801 +0 1.811 2.088 -0.245 0.239 -1.275 1.315 2.161 1.648 0.000 0.804 2.225 0.660 0.000 0.819 0.969 -0.975 2.548 0.475 1.376 0.953 3.102 0.765 0.827 0.983 0.606 0.491 0.523 0.543 +0 0.294 1.270 1.325 1.633 -0.714 0.922 1.495 -0.616 2.173 1.486 1.173 1.007 0.000 1.170 2.585 1.226 0.000 1.021 1.221 1.591 0.000 0.975 1.195 0.987 0.637 0.960 1.010 0.876 +0 1.154 1.382 -0.452 0.377 0.483 0.676 1.339 0.692 2.173 1.237 1.262 -1.227 2.215 0.688 2.629 1.617 0.000 0.810 1.704 0.332 0.000 0.835 1.064 0.986 0.807 1.329 0.850 0.769 +1 0.446 1.683 -0.381 1.582 0.402 1.055 -0.768 -1.627 2.173 0.398 0.625 -1.115 0.000 0.677 -0.244 0.139 2.548 0.370 0.868 -0.611 0.000 0.241 0.862 0.980 0.662 1.086 1.116 0.851 +1 0.339 -1.053 -1.249 1.012 -0.330 0.980 0.415 0.655 0.000 0.912 0.959 -1.470 2.215 1.064 -0.396 -0.774 2.548 0.868 1.332 0.568 0.000 0.887 1.203 0.988 1.038 1.016 1.512 1.647 +0 1.419 0.396 1.002 0.443 1.688 0.693 -1.099 0.448 2.173 1.031 -0.404 -0.786 0.000 0.733 0.541 -0.626 0.000 0.566 -0.872 -1.317 0.000 0.771 0.992 0.992 0.480 0.906 0.833 0.763 +1 1.685 0.008 -0.919 0.370 0.504 1.191 -0.664 0.904 1.087 1.072 -1.301 -1.158 1.107 0.593 -1.043 -0.453 0.000 0.785 0.167 0.464 0.000 0.987 0.993 1.049 1.290 1.691 1.106 0.906 +0 1.706 -0.446 -1.112 1.030 -1.699 1.250 -1.446 0.834 1.087 0.717 -0.639 -0.508 2.215 0.650 -1.823 -0.634 0.000 0.683 -0.738 0.988 0.000 0.849 0.925 0.982 0.793 1.424 1.174 0.952 +0 0.887 0.542 0.166 1.002 1.644 1.452 -0.876 0.355 0.000 1.170 -0.545 1.523 1.107 1.629 0.125 -0.843 2.548 1.873 -0.212 -1.522 0.000 1.084 0.943 1.269 0.984 1.347 0.921 0.800 +0 0.692 0.228 -0.092 1.046 -0.932 0.627 0.242 0.412 2.173 1.021 0.254 1.010 0.000 1.121 1.245 -1.117 1.274 0.712 1.731 -1.717 0.000 1.351 1.053 0.990 1.024 1.197 0.878 0.913 +0 0.997 -0.819 0.668 1.295 0.156 0.870 1.141 -1.671 2.173 1.289 2.360 1.557 0.000 1.359 -0.955 -0.501 0.000 0.727 2.118 -0.660 0.000 1.150 0.814 0.987 2.225 0.567 1.392 2.070 +1 0.859 -0.088 0.119 0.486 -0.925 0.485 -0.552 1.280 2.173 1.235 -0.075 -1.038 2.215 1.226 -0.162 0.816 0.000 0.963 0.412 -0.248 0.000 1.064 0.831 0.988 0.833 1.026 0.810 0.736 +0 0.621 -0.297 1.723 0.984 0.918 0.357 -0.457 0.387 2.173 0.448 1.959 -0.750 0.000 0.841 0.707 -0.731 2.548 0.714 0.338 -1.247 0.000 0.733 0.923 0.993 1.089 0.732 0.746 0.865 +0 0.559 -0.091 1.469 1.146 -0.940 0.555 0.034 0.994 2.173 0.863 -0.753 -1.368 0.000 1.044 -0.623 0.524 2.548 0.687 -0.509 0.167 0.000 0.989 0.901 0.988 0.828 0.523 0.666 0.642 +0 1.381 -0.192 0.030 0.137 0.510 0.648 -0.101 1.474 0.000 0.990 -0.256 -1.426 2.215 0.688 -0.328 0.737 0.000 1.181 0.352 -0.608 3.102 0.757 0.893 0.997 1.001 0.740 0.702 0.712 +1 1.108 1.304 -1.228 1.017 0.073 0.640 1.869 -0.415 0.000 1.230 0.442 1.618 2.215 0.814 1.891 0.399 0.000 0.900 1.365 0.941 3.102 0.877 0.727 1.356 1.163 0.807 0.934 0.854 +0 1.145 0.081 1.327 0.797 -1.647 0.946 -0.539 -0.392 2.173 0.457 -2.442 0.534 0.000 0.522 0.194 0.844 0.000 0.766 1.240 -0.702 3.102 1.384 1.300 0.986 0.990 1.109 1.127 1.023 +1 1.500 -0.250 1.285 1.475 0.872 0.984 0.122 -0.981 2.173 1.180 -0.664 -0.203 1.107 0.500 0.735 -1.129 0.000 0.954 -0.147 0.382 0.000 0.923 0.840 0.981 1.380 1.216 1.183 0.990 +1 2.869 -0.817 0.610 1.640 0.326 0.691 -1.833 -1.465 0.000 1.638 -0.569 -0.547 2.215 1.452 1.392 -1.208 0.000 1.027 -0.498 1.445 3.102 0.836 1.051 0.980 1.613 1.141 1.161 1.527 +0 0.638 0.540 0.001 3.379 0.518 1.796 1.780 -0.807 0.000 2.169 1.574 1.612 0.000 0.583 0.766 -0.725 1.274 1.192 1.873 -1.184 0.000 0.900 0.739 0.983 0.708 0.744 1.059 1.459 +0 0.387 1.455 -1.237 0.747 0.654 0.741 0.562 -0.388 2.173 0.935 1.973 -1.633 0.000 0.686 -2.103 -1.635 0.000 1.414 -0.781 0.721 3.102 0.932 0.950 0.990 0.762 1.277 1.232 1.114 +0 1.403 -0.133 0.647 0.965 0.158 0.515 -0.962 1.481 2.173 0.597 1.034 -1.246 0.000 0.788 -1.263 -0.740 2.548 0.457 -0.087 -1.370 0.000 0.428 0.902 0.994 0.868 0.740 0.739 0.796 +0 0.584 -0.643 1.155 0.808 0.437 0.797 -2.921 -0.367 0.000 1.216 0.345 -0.388 2.215 2.678 -1.126 1.647 0.000 0.790 -0.499 0.405 3.102 3.595 2.129 0.991 1.449 0.727 2.035 1.555 +1 1.176 -0.121 -1.273 0.996 1.606 0.657 1.237 0.077 2.173 0.872 0.430 0.880 0.000 0.658 0.659 -0.303 0.000 0.535 0.304 -1.353 1.551 1.030 0.830 0.982 0.516 0.667 0.880 0.809 +1 0.940 0.677 -0.766 2.741 -1.152 1.446 -1.935 0.785 0.000 1.094 0.625 1.118 2.215 0.489 -0.967 1.518 0.000 1.649 1.111 -0.480 0.000 0.715 1.261 0.982 0.820 0.839 0.958 0.870 +1 0.716 0.262 -0.632 1.034 -1.058 0.578 0.171 1.317 2.173 0.553 1.230 -1.104 0.000 1.243 1.072 0.613 2.548 0.799 1.845 0.637 0.000 0.965 1.012 0.987 1.339 0.836 0.973 0.979 +1 0.686 -0.862 1.601 0.258 0.329 0.875 0.405 -1.639 2.173 0.734 -0.527 0.405 0.000 1.237 0.243 -0.112 0.000 1.931 -0.214 -1.028 3.102 0.895 1.074 0.978 0.742 0.860 0.947 0.796 +1 0.359 1.943 -0.187 0.358 -0.808 1.369 0.249 -1.609 2.173 0.865 -0.186 0.190 0.000 0.831 1.888 0.544 0.000 0.433 0.219 -0.694 3.102 0.967 1.318 0.979 0.471 0.599 0.766 0.690 +1 0.828 0.204 -0.552 0.837 0.855 1.019 0.757 -0.824 0.000 1.502 1.059 1.333 0.000 1.023 1.823 -0.517 0.000 1.689 0.214 0.787 1.551 1.312 1.110 1.101 0.708 0.645 0.980 0.861 +1 0.952 1.691 1.533 1.275 -1.169 0.574 -0.337 -0.211 0.000 0.917 0.997 1.107 2.215 1.085 0.298 0.353 2.548 1.069 1.065 -0.449 0.000 1.106 0.830 0.992 0.897 0.766 0.872 0.945 +0 1.037 -1.134 -0.484 0.816 -1.291 0.478 0.453 -1.333 0.000 0.798 -0.328 0.129 1.107 0.808 -0.005 1.184 0.000 0.636 -0.085 0.758 3.102 0.895 0.944 0.990 0.782 0.353 0.682 0.773 +1 0.723 -0.826 1.360 1.080 -0.727 1.647 -1.210 -0.030 2.173 1.139 -1.311 -1.482 0.000 0.978 -2.146 1.661 0.000 0.638 -0.551 0.577 1.551 0.932 0.771 1.166 1.177 0.643 0.962 0.859 +0 1.129 0.615 -0.014 2.421 -0.600 0.901 -1.331 1.108 0.000 1.449 2.416 1.395 0.000 1.495 1.003 0.149 2.548 0.971 1.255 -1.729 3.102 1.596 0.958 1.154 0.911 0.933 1.009 1.243 +1 1.216 0.012 -1.144 1.141 1.605 0.585 1.275 -0.460 2.173 0.630 -2.366 0.463 0.000 1.216 0.732 -1.658 0.000 1.848 0.667 0.462 3.102 0.988 1.028 1.005 1.073 0.856 0.927 0.834 +1 0.525 -0.779 -0.128 1.109 0.427 0.843 -0.123 -0.953 2.173 1.439 0.124 1.424 1.107 1.604 0.167 -0.436 0.000 0.706 -1.601 1.636 0.000 1.200 0.937 0.978 1.665 1.378 1.337 1.165 +0 0.584 -0.425 1.533 0.639 -0.687 0.739 -1.250 -0.559 0.000 0.972 1.363 1.668 2.215 1.033 -0.029 0.567 2.548 0.888 -0.344 0.151 0.000 0.915 0.960 0.990 1.545 1.219 1.242 1.085 +0 0.339 -0.395 -0.970 1.429 1.667 0.560 -1.462 1.586 1.087 0.536 1.957 -0.178 0.000 0.729 0.830 0.247 1.274 0.466 -0.353 0.215 0.000 1.031 0.639 0.989 0.539 1.406 1.117 1.238 +1 0.540 -0.714 1.278 1.993 0.808 1.149 -1.595 -0.912 0.000 0.471 -1.176 -0.793 2.215 0.975 -1.168 -0.161 0.000 1.317 -1.168 1.553 3.102 1.233 1.130 0.997 0.880 0.613 0.669 0.852 +0 0.936 -1.026 -1.696 0.772 0.939 0.640 -2.815 0.080 0.000 0.640 0.008 1.538 2.215 0.568 1.082 -1.334 0.000 1.524 1.045 -0.675 0.000 0.575 0.846 0.985 0.723 0.667 0.633 0.742 +1 0.368 -1.910 -0.446 1.105 1.065 0.854 -1.920 0.020 0.000 1.026 -1.816 1.484 0.000 0.910 -0.096 -1.508 2.548 0.798 -0.944 -1.322 0.000 0.811 0.919 0.989 1.895 0.571 1.364 1.124 +0 0.407 1.742 -1.394 1.689 -0.344 1.100 0.698 -0.819 1.087 1.026 0.421 1.552 0.000 1.708 0.316 0.689 2.548 1.422 -0.382 1.167 0.000 0.901 0.960 0.988 1.261 1.698 1.322 1.367 +1 0.656 -1.870 -1.284 0.374 1.317 1.010 -0.546 0.421 0.000 0.740 -0.816 -0.135 0.000 0.949 0.070 -1.086 1.274 1.694 -0.896 1.739 1.551 0.922 1.139 0.976 0.589 0.802 0.927 0.807 +1 2.363 1.058 0.907 1.071 0.330 1.278 0.820 -0.689 2.173 1.701 0.976 -1.556 0.000 0.351 -0.223 1.208 0.000 1.003 -0.170 0.377 3.102 0.802 0.896 1.093 0.852 1.176 1.130 0.883 +1 0.957 0.582 -0.121 0.996 -1.328 1.093 0.466 -1.608 0.000 2.118 0.394 0.607 2.215 0.551 1.062 0.125 2.548 1.057 0.858 -0.973 0.000 1.004 0.939 1.197 1.267 0.659 1.087 0.947 +1 1.178 -0.144 1.424 1.919 0.931 0.969 -0.214 -0.713 2.173 0.420 -0.360 -1.227 0.000 0.878 0.586 -0.288 0.000 0.873 -0.623 1.648 3.102 0.924 0.834 0.987 0.684 0.867 0.956 0.817 +0 1.270 0.146 0.332 0.048 -0.656 0.761 0.486 -1.297 0.000 0.711 1.044 0.939 2.215 0.653 -1.048 -0.368 2.548 0.870 -0.733 -1.309 0.000 0.921 0.911 0.838 0.763 1.189 0.859 0.780 +1 1.467 0.348 -0.666 0.540 1.315 0.473 0.122 0.813 0.000 0.428 0.754 0.237 0.000 0.677 -0.416 0.152 2.548 1.669 -0.869 1.669 3.102 0.571 1.018 1.205 0.757 0.832 0.774 0.714 +1 1.386 0.234 0.230 0.689 -1.693 0.693 -0.265 1.698 1.087 0.342 1.518 0.582 0.000 0.622 0.571 -1.556 2.548 0.666 -1.366 -0.962 0.000 0.885 0.853 1.336 0.781 0.414 0.665 0.662 +0 0.596 0.024 -0.552 0.841 1.461 0.959 0.848 0.867 2.173 0.751 -0.933 -1.607 0.000 1.535 -0.774 -0.380 2.548 0.672 -1.572 -0.840 0.000 0.738 0.886 0.987 0.996 1.992 1.270 1.000 +1 0.701 -0.496 -0.713 0.201 -1.618 1.329 0.720 0.893 2.173 1.316 0.586 -1.218 0.000 1.351 1.189 -0.913 0.000 1.919 0.304 -0.108 3.102 0.920 1.204 0.990 1.100 1.362 1.259 1.009 +1 1.151 -0.356 1.572 0.904 -1.504 3.212 -0.306 0.421 0.000 1.894 1.602 -1.731 0.000 2.083 -0.624 -1.119 2.548 2.679 -0.582 -0.523 3.102 0.306 0.738 0.989 0.847 0.924 0.865 0.735 +0 1.235 0.102 1.562 1.724 -1.559 0.805 2.612 -0.397 0.000 0.571 -1.552 0.863 0.000 0.714 -0.321 1.048 2.548 0.512 2.325 0.077 0.000 0.661 0.765 0.979 0.681 0.490 0.620 0.655 +0 0.749 -0.033 -0.142 0.891 1.739 0.790 1.264 0.164 0.000 1.345 0.721 -1.132 2.215 1.054 1.550 1.181 0.000 0.449 1.598 0.581 0.000 1.349 0.786 1.123 0.909 0.737 0.884 0.860 +1 0.298 -0.735 -1.386 2.426 -0.254 2.516 0.749 1.581 0.000 1.258 1.281 0.099 0.000 1.231 0.420 0.312 2.548 1.373 0.713 -0.372 3.102 1.718 1.174 1.005 1.011 0.607 0.867 1.309 +1 0.589 -1.402 -1.514 1.061 0.004 0.532 0.654 -0.865 0.000 0.543 -0.830 1.214 2.215 1.017 -1.216 -0.305 2.548 1.152 0.691 1.653 0.000 0.921 0.981 1.073 0.620 0.797 0.903 0.896 +0 0.650 0.044 -0.515 0.686 0.676 0.723 -1.105 0.599 2.173 0.845 -0.455 -1.709 2.215 0.594 -1.295 -1.464 0.000 0.522 -1.545 -0.455 0.000 0.503 0.735 0.987 0.778 1.073 0.690 0.616 +0 0.589 -0.031 -1.150 1.122 0.094 0.463 -0.845 1.099 1.087 0.622 -0.089 1.059 2.215 0.908 -1.505 -0.352 0.000 0.883 -0.364 1.561 0.000 1.167 0.947 1.015 0.788 0.309 0.621 0.656 +1 0.457 -1.114 0.142 0.965 1.086 0.912 -0.164 1.218 2.173 0.958 0.662 -0.897 0.000 0.855 0.063 -1.220 2.548 1.155 0.874 -0.226 0.000 0.855 1.150 0.985 1.183 0.900 1.056 1.114 +1 0.372 -0.862 -0.458 2.275 0.211 1.047 0.045 1.531 2.173 1.138 -0.356 -1.244 0.000 0.425 1.615 -0.462 0.000 0.621 0.871 -0.041 0.000 0.841 0.860 0.989 0.908 0.241 1.238 1.024 +1 0.997 0.520 0.208 1.357 0.153 0.889 0.184 -1.648 2.173 0.502 1.023 -1.149 1.107 0.713 -0.855 -1.088 0.000 0.698 0.508 1.409 0.000 0.899 0.743 0.997 1.448 0.613 0.970 0.903 +1 0.704 0.488 -1.193 1.092 -0.405 0.636 0.024 -0.872 2.173 0.736 0.030 1.026 0.000 0.801 1.306 0.758 0.000 0.802 -0.960 1.603 1.551 0.940 0.991 0.993 0.753 0.760 0.824 0.839 +0 1.702 -0.994 -1.217 1.793 -1.162 0.672 -0.966 0.724 0.000 0.583 1.687 1.444 0.000 0.447 2.194 -0.552 0.000 0.947 -0.848 -0.112 1.551 0.809 1.113 0.976 0.884 0.159 0.948 1.548 +1 0.717 0.047 -1.398 1.491 0.197 0.796 0.465 -0.469 2.173 0.987 0.465 1.340 2.215 1.292 0.850 0.098 0.000 0.571 -1.132 1.686 0.000 1.637 1.170 1.420 1.024 1.302 0.941 0.859 +0 1.272 0.353 0.351 0.752 1.714 0.853 -0.649 0.631 0.000 1.270 -0.698 -1.180 0.000 0.761 -0.043 1.244 2.548 0.441 -1.021 -0.807 1.551 2.208 1.188 1.277 0.709 0.508 0.784 0.809 +0 0.501 -0.358 0.657 0.832 -1.688 0.921 -0.852 1.544 2.173 1.066 -2.468 -0.167 0.000 0.871 -1.475 -0.965 2.548 0.988 -1.944 0.569 0.000 0.835 0.885 0.983 0.667 0.963 1.051 0.887 +1 0.342 -1.776 -1.059 1.905 -0.775 0.949 1.730 0.349 0.000 0.874 0.514 1.279 2.215 1.238 -0.848 1.304 1.274 0.868 -0.479 -0.136 0.000 0.886 1.028 0.979 1.062 0.882 0.962 0.810 +0 0.437 0.079 -1.091 1.127 1.415 2.370 -0.250 -1.716 0.000 2.256 0.743 -0.312 0.000 2.635 -0.835 0.307 0.000 1.244 1.795 1.353 0.000 0.780 0.879 0.985 0.598 1.084 0.865 0.904 +1 0.989 0.263 -0.368 0.472 -0.918 0.461 -1.168 -1.175 2.173 0.956 -1.218 0.640 0.000 1.167 0.640 -1.597 0.000 1.024 -0.172 0.894 0.000 0.764 0.958 0.993 0.635 0.738 0.620 0.664 +0 0.998 0.465 -0.585 0.287 1.674 0.640 0.889 1.272 2.173 0.409 -0.641 -0.562 0.000 1.037 0.433 -0.262 2.548 0.788 1.540 0.970 0.000 0.601 0.775 0.981 0.628 1.020 0.703 0.592 +0 0.903 0.598 -0.689 0.668 0.996 0.963 -0.817 -1.418 0.000 0.882 -0.159 0.696 2.215 0.427 -1.273 0.450 0.000 1.115 -0.359 -0.280 1.551 1.194 0.941 1.075 0.806 0.700 0.783 0.791 +1 1.000 -0.854 -0.659 0.608 -0.095 0.818 -0.640 0.920 0.000 0.616 -0.160 1.526 0.000 0.640 -0.712 1.367 2.548 0.591 2.341 -0.466 0.000 0.862 0.742 0.987 0.766 0.324 0.596 0.699 +0 2.606 1.568 1.167 2.059 0.908 0.504 0.626 -1.029 2.173 1.387 -0.060 -0.874 0.000 1.378 -0.407 -0.169 2.548 0.370 -1.562 -0.735 0.000 0.975 0.876 0.992 1.344 0.935 1.475 1.504 +0 0.477 0.580 0.763 1.469 -1.710 0.549 -0.116 -0.115 2.173 0.308 -0.202 0.390 0.000 0.622 0.538 -1.023 2.548 0.368 -1.732 0.830 0.000 0.502 0.670 0.985 1.035 0.593 0.700 0.707 +0 0.722 0.390 0.861 0.371 -0.438 1.100 -1.069 1.306 0.000 1.844 -0.050 -0.279 2.215 0.813 0.039 -1.238 2.548 0.920 -1.826 -1.439 0.000 1.309 1.203 0.989 1.134 0.991 1.347 1.307 +1 0.938 1.508 1.742 0.833 -0.096 1.016 1.127 0.323 2.173 1.185 0.825 -1.366 0.000 0.950 0.791 0.832 2.548 0.805 1.152 -0.668 0.000 0.824 0.958 1.221 0.966 0.566 0.875 0.793 +0 0.873 1.109 0.317 1.365 0.976 0.850 -0.829 -0.738 2.173 1.824 1.817 -0.914 0.000 1.140 -0.336 1.141 2.548 1.071 -1.269 1.291 0.000 0.904 0.925 0.988 1.217 1.251 1.442 1.372 +0 0.799 -1.630 1.368 1.027 -1.352 0.524 -0.533 1.183 0.000 0.898 0.037 -0.346 2.215 0.729 0.306 0.909 1.274 1.399 -1.357 -0.475 0.000 1.504 1.128 0.981 1.533 0.789 1.184 1.038 +0 1.166 0.661 -1.701 0.679 1.085 0.417 0.316 -0.202 0.000 0.569 1.256 -1.118 0.000 1.353 1.213 0.385 1.274 0.516 -0.145 -1.078 3.102 0.923 0.920 0.984 0.559 0.813 0.713 0.688 +0 1.042 -0.134 -0.324 1.332 0.445 1.496 -0.733 -1.186 1.087 1.112 -1.098 0.539 0.000 1.829 -0.594 1.058 2.548 1.125 0.945 -1.095 0.000 2.490 1.706 1.044 1.482 1.856 1.471 1.259 +1 0.807 1.024 1.355 0.223 -1.074 1.583 -1.823 -1.188 0.000 1.572 -0.524 0.804 2.215 2.125 -0.265 0.172 2.548 1.118 -0.055 -0.920 0.000 2.106 2.376 0.986 1.035 1.075 1.764 1.480 +0 2.082 0.110 -0.753 0.349 1.045 0.984 -0.538 -0.093 2.173 1.222 -0.790 1.517 2.215 0.488 -0.900 1.163 0.000 0.587 0.303 1.024 0.000 0.436 0.816 1.180 1.228 1.617 1.062 0.839 +1 0.385 2.114 -0.222 1.320 -0.943 0.505 -0.032 -0.258 0.000 0.615 0.257 0.907 2.215 1.518 -0.659 1.070 0.000 0.674 -0.031 -1.359 1.551 1.568 0.975 0.993 0.890 0.525 0.654 0.828 +0 0.624 0.610 -0.407 1.033 -1.363 1.284 1.568 -1.628 0.000 1.067 0.709 -0.006 2.215 1.332 1.619 -0.559 0.000 3.348 -0.055 0.925 1.551 0.919 1.133 0.982 0.904 1.448 0.984 0.841 +0 1.204 1.055 -0.376 0.485 0.107 0.671 -1.247 0.635 0.000 1.015 -0.367 -1.366 0.000 0.937 0.413 -1.146 2.548 1.157 -0.440 1.405 0.000 0.855 0.850 0.989 0.867 0.669 0.654 0.904 +0 1.209 1.787 0.916 0.984 0.267 1.026 0.488 0.415 2.173 1.843 0.445 -1.720 0.000 1.648 0.665 -0.462 0.000 1.846 1.211 -0.946 1.551 2.449 1.626 0.987 0.900 1.552 1.342 1.213 +0 0.643 -0.947 0.111 0.792 1.435 1.113 -1.151 -1.739 1.087 1.319 -0.241 -1.269 0.000 2.120 -0.236 0.150 2.548 0.583 -1.289 -1.189 0.000 0.801 0.932 0.989 0.848 2.096 1.192 0.963 +0 0.447 -0.922 1.561 1.690 0.295 0.590 -0.664 -0.955 2.173 0.640 -1.508 1.422 0.000 0.353 -2.031 0.757 0.000 0.764 0.573 -1.177 3.102 0.482 0.963 1.094 0.929 0.539 0.748 0.726 +1 0.823 -0.210 -0.051 0.418 -1.163 0.634 2.049 -0.784 0.000 0.644 -0.740 -0.344 0.000 1.065 -0.267 1.351 1.274 1.341 -0.681 0.841 0.000 1.061 0.886 0.988 0.982 0.701 0.775 0.731 +0 0.604 -1.149 0.898 1.823 1.197 1.452 0.298 -0.447 2.173 1.134 0.205 -1.338 2.215 0.682 1.002 0.175 0.000 0.543 0.166 0.448 0.000 0.353 0.886 0.988 1.674 1.361 1.245 0.983 +1 1.348 -1.798 0.277 0.431 -0.757 0.458 -1.518 0.618 0.000 0.631 -0.797 -0.928 0.000 0.720 -0.165 1.386 0.000 1.153 -0.825 -1.488 3.102 0.924 0.714 0.993 0.676 0.319 0.576 0.555 +0 0.472 -1.655 0.845 1.386 -0.059 1.278 -1.063 -0.363 2.173 1.992 0.607 1.244 2.215 1.023 -0.758 1.714 0.000 0.667 -1.694 -1.624 0.000 0.818 1.110 0.985 3.233 3.233 2.335 1.735 +1 0.752 1.159 1.100 0.849 -1.364 0.852 0.713 0.619 0.000 1.341 0.176 -1.342 2.215 1.688 0.237 -0.072 0.000 0.492 -1.046 1.260 3.102 1.355 1.093 0.989 0.777 0.777 1.044 0.903 +1 0.567 -0.559 1.176 0.601 -1.014 0.788 -0.428 -0.405 0.000 0.659 1.562 1.528 0.000 0.717 -0.530 0.299 2.548 1.212 0.182 1.268 3.102 0.730 1.112 0.989 0.603 0.619 0.741 0.670 +1 0.277 -2.072 0.073 1.109 -1.685 0.900 0.533 -1.107 2.173 0.830 0.032 0.303 0.000 0.866 -0.814 0.915 0.000 0.592 -0.027 -0.145 3.102 0.924 0.602 0.987 1.097 0.630 0.806 0.789 +1 0.524 0.750 -1.028 1.425 -1.514 3.720 -0.536 0.083 0.000 0.949 -0.860 0.530 1.107 2.287 -0.526 1.304 0.000 6.068 -0.601 -1.430 0.000 0.905 0.959 0.989 0.515 0.534 0.694 0.677 +0 0.604 0.470 -1.256 1.095 1.739 1.181 0.549 -0.149 0.000 0.863 -0.161 0.839 2.215 1.312 -0.160 -1.731 2.548 0.587 -0.912 -0.920 0.000 0.806 1.094 0.992 1.004 0.831 0.958 1.037 +1 0.899 0.177 -0.944 0.933 -1.535 0.500 0.840 -0.325 1.087 1.141 -0.699 0.223 2.215 0.777 0.252 1.594 0.000 0.532 -1.406 -1.697 0.000 0.824 1.003 0.985 1.368 1.098 0.963 0.879 +0 2.358 -1.304 -0.542 0.169 -0.327 1.299 -0.718 1.135 2.173 0.418 -0.869 -1.101 0.000 0.612 -0.593 0.554 0.000 0.474 0.162 -1.159 3.102 0.780 0.883 1.002 0.668 0.830 1.013 0.800 +0 1.097 -1.355 1.713 1.449 0.052 0.716 -0.459 1.129 2.173 0.447 -1.160 1.023 0.000 0.587 -1.532 -1.319 0.000 0.872 -0.868 -0.526 0.000 0.804 0.761 1.742 1.064 0.901 0.889 0.726 +1 0.472 -0.794 -1.435 1.257 0.745 0.704 1.635 -0.910 0.000 1.122 -0.053 1.183 0.000 1.429 -0.916 -0.480 2.548 1.840 0.229 -1.051 3.102 0.862 1.157 0.988 0.893 1.045 0.988 0.875 +0 1.287 0.556 0.852 1.862 0.693 1.452 0.483 -1.368 2.173 1.247 1.060 -0.966 0.000 1.322 1.239 0.187 0.000 0.551 0.093 0.101 1.551 1.718 1.028 1.011 1.760 0.935 1.104 1.210 +0 0.340 0.094 1.040 0.889 -0.434 0.844 1.227 -1.538 2.173 1.647 1.146 0.210 2.215 1.343 1.040 1.396 0.000 1.284 0.635 -0.656 0.000 1.420 1.011 0.987 0.789 1.736 1.060 0.868 +0 0.738 -0.560 0.650 0.798 -0.170 0.810 -0.864 -1.189 1.087 0.761 -0.816 -0.057 2.215 0.851 -0.155 1.021 0.000 0.817 -1.296 1.550 0.000 0.868 1.020 0.996 0.683 0.985 0.785 0.737 +0 0.366 -1.129 0.943 0.445 0.087 0.548 0.251 -1.447 0.000 0.893 0.511 1.528 2.215 0.607 -0.054 0.352 2.548 0.506 -0.877 -0.378 0.000 0.869 0.782 0.981 0.520 0.721 0.583 0.568 +0 0.999 0.954 -1.736 0.964 0.973 0.530 -1.735 -0.542 0.000 0.702 -0.494 0.778 2.215 0.818 1.118 -0.495 1.274 0.370 -0.154 -0.956 0.000 0.636 0.851 0.991 0.813 1.079 0.902 1.105 +0 0.640 -1.021 0.114 1.554 1.041 0.618 -0.985 -1.564 0.000 0.531 -2.373 0.469 0.000 0.353 0.182 -1.188 0.000 0.713 1.094 -0.941 3.102 0.601 0.827 1.025 0.959 0.332 0.850 0.773 +0 0.728 -0.686 1.522 1.698 -1.386 0.870 0.082 -0.410 0.000 1.020 0.036 0.395 0.000 0.873 0.070 1.276 2.548 0.664 0.821 -0.588 0.000 0.983 0.829 0.993 1.121 0.305 0.815 0.897 +1 0.906 -0.731 1.016 2.359 1.484 1.056 -1.052 -0.702 2.173 0.856 0.085 -0.046 2.215 0.436 -1.820 1.049 0.000 0.839 -0.602 -0.116 0.000 0.737 0.841 0.975 1.385 1.153 1.234 0.963 +0 0.841 0.048 0.915 0.687 -0.786 0.627 -0.686 -1.516 1.087 0.546 0.190 1.601 0.000 1.253 0.133 -0.135 2.548 0.980 -0.862 -0.036 0.000 0.725 0.879 1.053 0.780 1.150 0.707 0.633 +0 0.871 -1.221 1.366 0.826 -0.322 0.740 -0.558 0.125 1.087 0.492 -0.118 -1.691 2.215 0.799 -0.486 1.159 0.000 0.797 -0.151 -1.075 0.000 0.943 0.899 1.173 0.763 0.908 0.691 0.650 +0 0.710 -0.376 -1.609 1.226 -0.982 0.874 0.646 0.560 1.087 0.464 0.200 0.922 0.000 1.105 0.435 -1.278 2.548 1.163 -0.135 -0.093 0.000 0.779 0.846 0.985 1.144 1.224 0.831 0.740 +1 1.144 0.615 0.168 0.741 -0.292 0.571 1.603 -0.017 0.000 1.367 -0.318 1.642 0.000 1.089 -0.339 -1.521 2.548 0.757 -0.760 1.170 3.102 0.955 0.851 0.981 0.956 0.493 0.693 0.677 +1 1.552 -1.195 -0.462 0.730 -0.275 1.225 -1.226 1.309 1.087 0.665 -1.337 0.470 2.215 0.489 -0.937 -1.439 0.000 0.540 -2.067 -0.469 0.000 0.625 0.896 0.991 0.774 0.917 1.001 0.794 +1 0.289 2.404 -0.247 0.783 -0.856 0.584 0.219 -1.284 0.000 0.588 1.304 -0.253 0.000 0.876 0.693 1.134 2.548 1.444 -0.830 0.915 3.102 0.815 1.102 0.981 0.751 0.873 0.840 0.738 +1 0.945 -0.175 -0.909 1.056 -1.316 0.914 0.127 0.594 2.173 0.825 -0.505 1.575 0.000 0.441 -0.560 0.352 0.000 0.732 -1.076 -0.360 3.102 0.826 0.891 0.989 0.612 0.936 0.885 0.762 +0 0.605 -0.810 1.087 0.445 1.585 0.724 0.044 1.236 1.087 1.240 0.354 -0.262 0.000 1.193 0.775 -1.271 2.548 0.485 1.326 0.201 0.000 0.776 0.944 0.992 1.130 1.014 1.193 1.211 +0 1.070 -2.064 -1.551 1.149 -0.660 0.674 -1.434 0.305 0.000 0.771 -0.824 1.376 2.215 0.686 -0.842 -0.452 2.548 0.907 -0.866 0.961 0.000 0.709 0.767 1.105 0.769 0.771 0.756 0.752 +0 1.404 -0.626 0.868 0.964 -0.130 1.060 -0.165 1.631 2.173 1.078 -2.116 -0.607 0.000 1.289 -1.072 -0.195 2.548 1.164 -1.086 -1.324 0.000 0.964 0.878 1.262 1.195 1.642 1.011 0.915 +1 0.542 0.771 -1.067 1.028 0.667 0.983 -1.319 0.368 0.000 1.029 1.162 0.983 0.000 1.227 -1.078 -0.898 0.000 1.818 0.507 -0.653 3.102 0.793 1.018 1.034 0.750 0.671 0.715 0.655 +1 0.633 -0.076 -0.962 0.978 0.041 1.150 -0.247 1.287 2.173 0.853 1.036 -1.433 0.000 0.557 2.059 -0.307 0.000 0.755 -0.038 -0.103 3.102 0.985 0.749 0.987 1.116 0.942 0.819 0.820 +1 1.941 1.136 -1.738 1.119 -1.053 0.591 0.655 -0.053 2.173 0.462 0.061 -0.398 2.215 0.647 0.904 1.294 0.000 0.868 1.063 0.252 0.000 0.677 0.657 1.183 1.142 0.331 0.822 0.712 +1 1.022 -0.930 -0.240 0.656 0.456 0.552 0.537 -1.578 0.000 0.780 -1.063 -1.484 2.215 0.694 -1.731 0.097 0.000 0.950 -0.814 0.102 3.102 0.823 0.836 0.985 0.522 0.771 0.633 0.691 +0 1.195 -0.791 0.724 1.623 1.446 0.484 -1.318 -0.150 0.000 0.551 -0.934 -1.257 2.215 0.788 -0.109 -0.720 0.000 0.807 1.009 -0.369 1.551 0.899 0.948 1.170 0.843 0.901 0.885 0.842 +1 1.778 0.630 0.709 0.621 1.447 0.953 0.808 -1.552 2.173 0.930 0.680 -0.223 2.215 0.785 -0.309 -0.462 0.000 0.583 0.768 -0.729 0.000 0.533 0.907 0.987 0.940 1.292 0.923 0.805 +0 0.616 -0.057 0.362 0.979 -1.084 0.308 0.681 0.845 0.000 0.668 0.866 -1.300 2.215 0.864 -0.810 0.678 2.548 0.646 -1.162 -0.744 0.000 1.082 0.928 1.038 0.763 1.136 0.721 0.652 +1 0.876 -2.277 -1.225 1.021 -0.260 0.159 0.829 0.144 1.087 0.869 -0.527 1.572 2.215 0.452 -0.895 0.135 0.000 0.642 -1.931 0.637 0.000 0.500 0.793 1.002 1.162 0.666 0.989 0.788 +0 0.960 0.077 -1.037 1.884 -0.413 0.953 -1.034 1.483 2.173 0.486 -0.537 0.867 0.000 0.838 -0.668 -0.044 2.548 0.461 -1.138 1.136 0.000 0.298 0.492 0.995 1.523 1.106 1.038 0.830 +0 0.362 -1.252 -1.699 1.271 1.325 0.647 -0.045 -0.089 0.000 1.158 0.628 -1.167 2.215 0.940 -0.986 0.169 2.548 0.443 0.686 1.271 0.000 0.852 0.980 0.984 1.051 1.497 1.766 1.483 +0 0.988 1.156 -0.804 1.563 -0.898 0.675 1.670 0.875 2.173 0.569 0.234 1.046 2.215 1.132 1.047 0.297 0.000 0.611 1.972 -1.707 0.000 0.845 0.785 0.993 1.364 0.731 0.975 0.839 +0 1.114 0.636 0.369 0.891 1.045 0.615 0.896 -1.478 2.173 0.255 2.861 -1.620 0.000 0.920 0.727 -0.947 1.274 0.438 1.889 1.650 0.000 0.172 0.636 0.993 0.921 0.435 0.717 0.649 +0 0.608 0.001 -1.139 1.407 -0.218 0.751 0.348 0.361 2.173 0.901 2.202 1.446 0.000 1.207 2.709 -0.880 0.000 1.652 0.647 1.352 3.102 0.692 0.777 0.990 0.793 0.952 0.856 0.918 +0 0.319 -2.000 -1.007 1.956 1.396 0.791 0.162 0.194 0.000 0.818 -1.165 -0.106 2.215 1.659 -0.567 -1.506 2.548 0.699 1.101 -0.360 0.000 0.882 1.110 0.987 0.864 1.234 1.047 1.116 +0 1.888 -0.245 -0.586 0.377 1.255 0.460 -2.034 1.632 0.000 0.918 0.508 0.221 2.215 0.732 -0.214 -1.502 0.000 1.030 -0.537 1.203 0.000 0.896 1.058 1.164 0.919 0.392 0.965 0.905 +1 0.898 -1.507 -1.547 0.203 -1.193 0.926 -1.204 0.471 2.173 0.422 -0.929 1.465 0.000 0.940 0.404 -1.419 0.000 1.377 -1.012 -0.338 3.102 0.887 1.003 0.992 1.014 0.796 0.897 0.794 +0 0.372 0.722 1.225 1.699 -0.167 0.536 1.514 1.506 0.000 0.738 1.548 -1.257 0.000 1.387 -0.021 0.481 0.000 0.852 0.478 -0.803 3.102 0.809 0.724 1.047 0.889 0.474 0.599 0.673 +1 2.388 0.090 -0.653 0.571 -0.274 1.073 -0.345 0.862 2.173 0.974 0.617 -1.740 0.000 0.541 0.024 -1.742 2.548 0.522 0.480 -0.067 0.000 0.928 1.147 0.997 0.768 0.700 0.962 0.883 +0 0.606 -1.131 -0.208 0.916 1.695 1.448 -0.138 0.377 2.173 1.134 -0.811 -0.966 1.107 0.983 2.627 -1.353 0.000 1.046 -0.414 1.528 0.000 0.855 0.927 1.022 0.747 1.887 1.064 0.854 +1 0.517 -0.605 1.692 0.838 0.228 1.169 -0.564 0.791 0.000 1.458 -1.222 -1.159 1.107 0.800 -0.885 -0.478 0.000 0.658 -1.044 -0.029 3.102 1.623 0.949 0.986 0.924 0.753 0.986 0.817 +1 0.713 1.926 0.105 1.371 -1.300 1.313 0.357 0.282 2.173 0.828 0.980 -1.317 0.000 0.567 2.040 1.631 0.000 0.471 0.090 1.520 1.551 0.845 0.617 1.307 1.722 0.755 1.104 0.986 +1 1.039 1.036 -1.551 0.804 0.380 0.553 0.051 -0.253 0.000 0.549 -0.923 -0.542 2.215 1.023 -0.451 1.266 1.274 0.527 0.224 1.001 0.000 0.751 0.787 1.248 1.101 0.816 0.864 0.733 +1 0.893 -0.604 -1.067 1.040 0.013 1.538 -1.243 -1.427 0.000 1.124 0.752 0.269 0.000 1.736 -0.488 1.084 2.548 0.995 -1.841 -0.358 0.000 0.962 1.050 1.105 1.039 0.790 0.934 0.896 +0 0.361 -1.235 0.107 0.663 0.752 0.638 -1.422 0.914 0.000 0.790 -0.594 -1.056 2.215 0.277 -0.887 -1.634 0.000 1.193 0.448 -1.077 3.102 0.592 1.067 0.989 0.851 0.533 0.742 0.725 +0 1.852 -0.265 0.916 0.844 -1.361 0.605 1.741 -0.312 0.000 0.415 1.116 -0.888 0.000 0.673 -0.154 0.332 2.548 1.326 -0.120 -1.435 1.551 0.622 1.053 1.536 0.844 0.722 0.767 0.922 +0 0.476 -1.532 -1.726 0.861 -0.413 0.530 0.052 1.279 0.000 0.550 -0.218 -0.551 2.215 0.800 -0.868 0.064 2.548 0.416 -1.405 0.874 0.000 0.751 0.772 0.989 0.606 0.455 0.561 0.550 +0 0.526 0.968 -0.495 1.542 -1.350 0.557 1.246 0.001 0.000 0.362 1.400 -1.635 0.000 0.851 1.254 0.691 2.548 0.369 0.877 0.530 1.551 0.954 0.686 0.990 0.604 0.093 0.601 0.595 +0 0.824 0.771 1.669 0.945 1.050 1.218 0.604 0.084 0.000 0.406 1.079 0.690 0.000 1.020 2.035 -1.532 0.000 1.077 -0.825 -0.615 3.102 0.863 1.198 0.989 0.559 0.444 0.844 0.871 +1 1.464 -0.452 -0.601 0.579 -1.221 0.897 -0.776 1.256 2.173 1.296 -0.921 0.555 2.215 0.693 2.052 -1.517 0.000 0.654 -1.601 0.170 0.000 0.316 0.851 0.992 1.125 0.947 0.962 0.772 +1 0.688 0.310 -1.606 1.170 -0.765 2.223 0.764 0.626 2.173 1.834 2.557 1.091 0.000 3.284 0.738 -0.780 0.000 1.728 0.786 -1.080 1.551 5.418 3.061 0.985 1.817 2.081 2.528 2.135 +1 1.930 0.748 -0.663 0.374 0.176 0.874 -0.463 1.575 2.173 0.623 0.426 1.541 0.000 0.656 -1.210 0.274 2.548 0.940 0.003 0.326 0.000 0.913 0.852 0.988 1.204 0.965 1.121 0.920 +1 1.419 -1.257 -0.894 0.563 1.174 1.506 0.011 1.015 2.173 1.031 -1.231 -0.269 0.000 0.880 -0.225 0.233 2.548 1.040 -0.855 1.671 0.000 1.338 0.978 1.186 1.580 0.948 1.083 1.026 +1 1.704 0.155 1.731 0.736 -1.646 0.938 -1.133 -0.299 2.173 0.740 -0.422 0.405 2.215 0.616 -0.602 1.027 0.000 0.590 -1.297 -0.676 0.000 0.733 0.766 0.992 1.113 0.852 1.238 0.983 +1 1.794 0.282 0.078 0.419 -0.168 0.594 0.764 -1.648 0.000 0.879 -0.543 1.424 2.215 0.712 -1.105 -0.475 0.000 0.380 -0.256 1.144 3.102 1.701 0.932 0.993 1.217 0.146 0.758 0.838 +1 0.583 -0.581 1.146 0.634 -1.108 1.769 1.267 -0.515 0.000 0.967 -0.322 1.742 0.000 1.044 0.801 -1.583 0.000 2.199 0.416 1.061 1.551 0.881 0.860 0.987 0.762 0.940 0.698 0.635 +0 0.736 1.116 -1.708 1.329 1.621 1.207 -1.634 -0.363 0.000 0.695 -1.798 0.380 0.000 0.695 -0.567 -1.398 2.548 1.646 -0.464 0.841 3.102 1.222 1.159 0.977 1.825 0.738 1.346 2.309 +1 1.231 -1.082 -0.787 0.848 -0.418 1.273 1.535 1.076 0.000 0.624 -1.813 -1.176 0.000 0.742 1.125 -0.129 0.000 2.029 0.046 0.303 3.102 0.678 1.314 0.985 0.650 0.919 0.824 0.763 +1 0.398 -0.805 -1.342 0.958 1.518 0.485 -0.971 -0.350 0.000 0.591 -1.101 0.599 0.000 0.681 0.776 1.372 2.548 0.991 -0.159 -0.718 3.102 0.862 1.053 0.977 0.695 0.689 0.692 0.711 +0 0.886 -0.302 1.325 1.746 -1.665 1.058 0.481 -0.350 0.000 0.804 0.014 0.459 2.215 0.824 -1.342 1.540 0.000 0.831 -0.139 -0.580 3.102 0.895 0.897 0.991 0.817 0.598 0.780 0.975 +0 3.399 0.095 1.730 0.744 -1.024 2.037 -0.308 0.310 0.000 0.857 0.147 -1.204 2.215 0.946 -0.487 -0.438 2.548 0.420 -0.004 0.619 0.000 0.444 0.854 1.351 0.793 0.695 0.928 1.188 +1 0.664 -1.225 0.127 0.095 1.597 0.728 -1.023 0.717 0.000 0.898 -0.711 -0.629 2.215 0.818 -1.679 1.281 0.000 1.421 -0.176 1.716 3.102 0.889 0.985 0.984 0.703 0.914 0.856 0.753 +0 1.267 1.307 -1.730 0.879 -1.497 1.341 0.365 0.630 0.000 1.544 0.831 -0.947 2.215 1.718 0.656 0.206 2.548 1.125 1.254 -1.078 0.000 0.915 0.917 0.986 1.159 1.497 1.190 1.309 +0 0.990 -0.020 -0.828 1.176 0.791 0.667 -0.730 1.690 0.000 0.984 -0.325 0.586 1.107 0.734 0.152 -0.016 2.548 0.545 -1.848 -0.110 0.000 1.184 1.026 1.485 0.914 0.519 0.764 0.774 +0 0.875 -0.123 -0.007 0.826 1.533 0.484 -0.943 1.489 2.173 0.250 -0.037 -0.794 0.000 0.342 1.798 0.825 0.000 0.389 -1.262 -0.533 3.102 0.694 0.905 1.158 0.643 0.464 0.606 0.586 +1 0.998 -0.335 1.612 1.479 -1.110 0.665 1.011 0.463 2.173 0.403 0.937 -0.989 0.000 0.969 -0.171 -0.083 2.548 0.786 0.486 1.181 0.000 0.746 0.696 1.070 0.915 0.797 0.943 0.758 +1 1.387 0.206 1.244 0.159 -0.538 1.051 -2.343 -1.162 0.000 1.099 1.305 0.538 0.000 0.902 0.246 0.180 0.000 0.731 -0.470 1.453 3.102 0.888 0.921 0.988 0.529 0.220 0.610 0.620 +1 2.735 0.187 -1.566 0.360 -0.812 2.423 1.770 0.056 0.000 0.664 0.203 -1.164 0.000 1.491 0.473 0.631 2.548 1.331 -0.279 1.600 0.000 1.006 1.129 0.992 0.704 0.891 0.864 0.816 +0 0.336 0.941 0.226 0.291 1.219 1.203 -0.054 1.662 2.173 1.053 0.308 -0.318 2.215 1.213 -0.790 -0.281 0.000 0.434 -0.698 1.241 0.000 0.784 0.823 0.990 0.912 1.648 0.985 0.822 +0 0.900 0.318 1.416 0.455 0.801 0.916 0.734 -0.880 0.000 0.895 -0.189 0.525 2.215 0.878 -0.114 -0.627 0.000 0.738 0.990 1.250 3.102 0.807 0.885 0.982 0.690 0.707 0.822 0.798 +0 0.482 1.737 -1.519 0.486 -0.290 0.775 0.848 -0.525 0.000 0.691 0.563 0.012 2.215 1.810 0.613 1.517 2.548 0.711 2.030 1.298 0.000 1.504 1.033 0.983 0.823 1.162 0.947 0.786 +0 0.915 -0.392 -1.621 1.790 -1.036 0.661 0.162 0.284 0.000 0.788 -0.220 0.889 1.107 0.278 -0.542 1.165 0.000 0.596 1.403 0.584 0.000 0.840 0.766 0.980 0.733 0.819 0.756 0.791 +0 1.519 0.837 0.953 1.421 1.366 0.501 -2.876 1.470 0.000 1.043 0.545 -0.494 0.000 0.629 1.050 -0.097 2.548 0.836 -0.864 -0.492 3.102 4.465 2.349 0.995 0.828 0.761 1.648 1.965 +1 0.752 0.595 -0.077 2.233 0.518 1.511 -1.437 -1.427 0.000 1.145 -0.622 0.201 0.000 0.688 -0.854 1.665 1.274 1.026 0.368 -1.093 3.102 3.012 1.654 0.991 0.928 0.619 1.145 1.319 +0 3.405 0.177 -0.495 3.120 -0.598 3.668 -0.671 1.142 0.000 1.029 -0.760 -0.709 2.215 0.674 0.051 1.569 2.548 0.567 -1.222 1.353 0.000 0.984 0.921 0.962 0.878 0.872 1.253 1.931 +0 0.662 -1.343 1.504 1.003 -0.713 0.840 -2.135 0.066 0.000 1.028 -0.400 -1.564 2.215 1.313 -0.597 0.880 2.548 0.409 -0.852 -0.657 0.000 0.762 1.118 1.028 0.823 1.008 1.025 0.869 +1 1.314 0.194 0.837 0.813 -0.268 0.510 -1.054 -0.093 0.000 0.583 -0.897 -1.019 2.215 1.235 -1.004 1.657 2.548 1.278 1.457 -0.950 0.000 0.912 0.870 1.201 0.943 0.607 0.832 0.767 +1 0.411 0.641 1.160 1.004 -0.152 1.167 -0.001 -1.224 1.087 1.019 0.628 -0.430 0.000 2.239 -1.146 1.285 0.000 0.653 -1.928 0.531 0.000 0.860 1.151 0.987 0.522 1.034 0.801 0.726 +1 1.737 -0.307 -0.520 1.292 -1.310 1.885 1.265 0.975 0.000 1.485 0.795 -0.672 2.215 1.476 0.084 -1.228 2.548 0.816 -1.002 1.226 0.000 0.781 1.904 1.354 1.165 0.955 1.542 1.551 +0 1.297 0.338 1.480 1.147 0.897 1.318 1.038 -0.858 0.000 1.061 0.926 0.358 2.215 0.405 1.064 -1.409 0.000 0.526 -0.282 0.058 3.102 0.632 0.788 0.990 1.024 0.500 0.789 0.924 +0 0.818 0.367 -0.688 1.179 0.522 1.658 0.961 -1.725 2.173 1.074 0.676 0.211 0.000 1.268 0.212 -0.210 1.274 0.425 -0.222 1.503 0.000 0.921 0.717 1.207 1.429 1.896 1.134 0.970 +1 1.381 0.980 -1.538 1.033 1.460 0.503 -1.005 0.352 2.173 0.396 1.179 0.762 0.000 0.386 -0.942 -0.352 0.000 0.717 0.187 -0.606 0.000 0.969 0.837 0.981 0.775 0.742 0.874 0.755 +1 0.835 0.224 0.421 1.738 1.099 0.989 -0.050 -0.423 1.087 0.970 -1.097 -0.980 1.107 0.561 0.833 -1.479 0.000 0.623 -0.847 1.116 0.000 0.870 0.994 0.987 1.308 1.071 1.094 0.903 +1 0.289 -1.889 1.685 1.037 1.414 0.696 -0.652 1.083 2.173 0.651 -0.904 0.175 0.000 1.117 0.899 -0.945 0.000 1.924 -0.359 -0.554 3.102 1.829 1.209 0.992 0.632 1.227 1.006 0.892 +0 1.089 1.102 0.742 0.763 0.279 0.892 1.926 -1.022 0.000 0.605 0.113 0.873 0.000 1.059 0.706 -1.600 2.548 0.856 0.354 0.394 0.000 0.736 0.639 0.997 0.764 0.297 0.691 0.623 +0 0.499 2.010 -1.076 0.983 -1.033 1.110 0.789 -0.668 2.173 2.952 -0.292 0.898 2.215 0.573 -0.475 -0.829 0.000 0.498 -1.998 1.576 0.000 0.803 1.497 0.971 0.699 3.046 1.642 1.419 +1 1.564 -0.959 0.816 1.185 -0.668 0.416 -1.084 -0.424 0.000 1.101 0.202 -1.012 2.215 0.968 2.677 1.552 0.000 1.123 0.002 -0.014 3.102 0.808 0.943 1.834 1.414 0.793 0.964 1.165 +1 1.060 -0.270 1.452 0.472 -0.410 0.773 -0.366 0.482 0.000 0.648 0.454 1.623 2.215 0.464 0.877 -0.859 0.000 1.075 -1.284 -0.335 3.102 0.818 0.955 0.987 0.606 1.157 0.791 0.698 +0 1.876 0.736 1.723 0.744 1.296 0.965 0.891 0.400 2.173 0.675 1.451 -1.019 0.000 1.084 -0.054 -0.970 2.548 1.041 -0.162 0.584 0.000 0.669 0.964 0.983 1.243 1.360 0.997 0.967 +1 1.270 0.291 0.697 1.420 -1.208 0.501 -0.455 -1.257 0.000 0.777 -0.006 1.533 0.000 1.963 0.811 -0.298 2.548 0.655 -2.060 0.956 0.000 0.834 0.754 1.841 1.328 0.822 0.898 0.888 +0 0.630 0.241 -0.805 1.593 1.186 0.944 -0.239 -0.444 2.173 0.864 -0.897 1.203 1.107 0.487 0.423 0.020 0.000 0.749 -0.893 -1.140 0.000 0.806 0.814 1.353 1.198 1.402 0.980 0.798 +0 0.963 2.277 -1.672 0.846 -1.138 0.614 0.839 -0.358 1.087 1.223 0.209 0.622 1.107 0.269 0.097 -1.568 0.000 0.423 1.331 1.392 0.000 0.348 0.623 0.978 0.940 1.067 1.049 0.774 +0 0.800 0.949 0.174 1.257 0.968 0.432 0.320 -0.724 1.087 0.360 -0.926 0.264 0.000 0.788 -0.306 0.973 0.000 0.479 -0.987 1.441 3.102 0.550 0.736 0.984 0.950 0.599 0.755 0.722 +0 1.212 -1.496 1.480 0.614 0.562 1.167 -0.860 -0.715 1.087 1.128 -0.045 1.352 2.215 1.270 -2.696 -0.045 0.000 0.877 -1.125 0.070 0.000 0.793 0.878 0.988 1.191 1.768 1.062 0.843 +0 1.234 -1.096 -0.623 0.897 0.094 0.571 0.532 -1.720 0.000 0.874 0.100 1.168 1.107 0.992 -0.355 -0.933 2.548 0.792 -0.822 0.533 0.000 1.260 0.948 0.992 1.079 0.970 0.789 0.785 +0 1.300 0.825 0.617 1.870 0.057 1.122 -0.787 1.190 0.000 0.991 -1.147 1.536 0.000 2.030 2.350 -0.073 0.000 3.785 -0.034 -0.523 3.102 0.810 0.865 1.043 1.451 1.067 1.306 1.502 +0 2.315 1.083 -1.208 0.454 -0.964 1.399 0.743 -1.519 2.173 1.383 0.380 0.301 0.000 2.614 1.019 0.587 2.548 1.170 0.847 -0.066 0.000 0.753 0.907 0.987 0.908 2.298 1.373 1.305 +1 0.648 -0.362 -1.581 0.181 -0.801 1.511 -2.595 1.587 0.000 1.682 0.491 0.356 0.000 1.618 0.955 -0.790 2.548 1.033 -0.317 0.304 0.000 0.790 0.947 0.989 0.764 0.917 0.953 0.842 +0 0.710 -0.656 -0.163 0.577 1.274 0.603 -1.241 -1.196 0.000 1.109 0.345 1.624 2.215 2.525 -0.258 0.350 2.548 1.468 1.293 -1.192 0.000 0.966 1.012 0.988 0.805 1.722 1.185 0.955 +1 0.839 -1.630 0.660 0.761 -0.788 0.398 1.435 -1.140 2.173 0.283 -2.040 0.277 0.000 0.869 -0.486 1.380 2.548 0.655 0.164 0.561 0.000 0.843 1.005 1.068 1.592 1.020 1.074 1.092 +1 1.118 -0.287 0.737 1.336 0.133 1.924 -0.331 -1.474 2.173 1.520 1.021 0.587 0.000 0.445 1.104 -0.808 0.000 0.963 -0.506 -0.447 3.102 1.204 1.176 0.993 1.770 1.169 1.454 1.262 +1 0.756 0.428 -1.164 0.594 1.552 0.804 -0.922 -1.307 0.000 0.569 -1.601 0.145 0.000 0.756 -0.765 -0.497 2.548 1.691 -0.168 0.399 3.102 0.918 1.032 0.986 0.681 0.684 0.665 0.639 +0 1.973 0.057 -1.278 0.283 -1.287 1.494 0.749 0.736 0.000 2.003 -0.336 -0.829 1.107 1.482 -0.134 0.798 2.548 0.923 1.009 0.268 0.000 0.842 0.918 0.984 0.999 1.830 1.492 1.308 +0 0.487 -0.621 -0.212 1.328 1.615 1.087 -0.444 -0.598 2.173 0.588 0.057 -1.418 0.000 0.916 0.586 1.439 2.548 2.264 -0.145 0.354 0.000 0.776 0.868 1.111 1.032 1.395 0.923 0.844 +1 0.898 1.254 -1.200 0.366 0.031 0.662 -0.372 1.674 1.087 0.341 0.178 -0.334 0.000 0.583 1.195 0.813 0.000 1.579 -0.578 0.602 3.102 0.959 0.914 0.987 1.474 0.907 1.167 1.130 +0 0.356 -0.732 -0.492 1.340 0.052 1.112 0.301 -1.328 2.173 0.852 0.625 1.530 0.000 1.269 0.046 1.055 2.548 1.305 -1.135 -0.082 0.000 0.935 0.924 0.990 1.461 1.255 1.549 1.325 +1 0.513 -0.782 -1.207 1.613 0.194 1.398 1.928 -1.740 0.000 0.837 0.731 -0.250 2.215 1.164 -1.647 0.104 0.000 1.527 -0.782 1.631 0.000 1.587 0.923 1.201 1.043 0.535 1.004 0.869 +1 0.964 0.223 0.913 0.725 0.326 0.564 -0.462 -0.771 0.000 0.527 -1.180 -0.063 0.000 1.085 -0.212 -1.247 2.548 0.838 1.076 0.872 0.000 0.824 0.724 0.985 0.595 0.165 0.579 0.543 +0 0.968 1.482 0.538 1.529 0.021 1.084 -0.646 -1.345 0.000 0.951 1.131 1.323 2.215 0.592 -1.271 -0.631 0.000 0.730 0.022 0.299 3.102 1.025 0.940 0.982 1.090 0.751 1.073 1.546 +0 0.504 -1.328 0.924 0.770 -1.059 0.726 -0.445 1.436 2.173 1.073 0.095 -0.129 2.215 0.620 -0.711 -0.192 0.000 1.110 0.578 -1.634 0.000 1.149 0.947 0.990 1.022 1.332 1.084 0.979 +1 0.964 0.938 -1.532 0.869 -0.428 0.937 0.867 0.516 2.173 1.073 -0.085 1.250 0.000 1.162 0.422 -1.526 2.548 1.533 0.134 -0.636 0.000 0.658 1.034 1.064 0.698 1.282 0.858 0.773 +1 0.556 1.309 1.023 1.582 -1.642 0.849 0.337 -1.456 2.173 1.516 -1.483 0.226 0.000 0.806 0.760 -0.406 0.000 0.730 1.285 1.600 0.000 0.878 0.927 0.991 0.694 0.932 0.729 0.685 +0 1.115 -0.021 0.076 1.497 -0.153 0.791 -1.228 -0.995 2.173 0.596 -0.165 -1.304 0.000 1.064 -2.332 1.206 0.000 1.189 0.348 1.088 3.102 0.855 1.124 0.981 1.002 1.364 1.147 1.128 +0 1.929 0.412 -1.051 1.666 -0.692 0.875 0.392 1.457 2.173 0.904 0.454 0.779 0.000 0.744 -0.235 0.186 0.000 1.482 0.884 0.105 3.102 0.793 0.950 0.989 1.106 1.203 1.064 0.983 +1 1.393 0.238 -1.492 0.735 -0.652 0.789 0.509 0.871 1.087 0.740 0.327 0.177 2.215 0.433 -0.919 -1.370 0.000 0.817 -0.188 0.697 0.000 0.682 0.754 0.987 0.873 0.665 0.785 0.684 +1 1.163 0.604 1.308 0.456 0.120 1.343 -0.380 -1.554 0.000 0.885 0.210 0.200 0.000 0.779 1.709 0.008 0.000 1.091 0.954 0.243 0.000 1.203 0.730 0.985 0.569 0.259 0.459 0.532 +1 1.335 -1.804 1.014 0.617 -1.607 0.898 0.167 -0.415 2.173 0.480 0.125 0.376 0.000 0.858 0.236 1.408 0.000 0.816 -1.251 -0.760 3.102 0.792 0.969 0.991 0.730 0.893 1.180 1.071 +0 0.758 1.354 1.027 1.626 0.874 0.796 1.262 -0.916 2.173 0.815 0.569 -1.704 1.107 1.179 1.838 -0.625 0.000 0.772 1.072 0.636 0.000 1.033 1.081 0.993 1.294 0.877 0.922 0.914 +0 0.789 1.167 -1.095 0.533 -0.822 0.351 1.551 -0.504 0.000 0.819 1.442 0.458 0.000 0.279 -0.931 1.285 1.274 0.738 -0.009 1.214 1.551 0.869 0.927 0.978 0.656 0.184 0.630 0.655 +0 0.744 2.088 1.037 2.002 1.680 0.950 -0.851 -0.129 0.000 0.371 0.013 -0.708 1.107 0.818 0.480 1.540 1.274 0.941 -1.357 0.505 0.000 0.967 0.789 0.988 1.090 0.547 0.982 1.976 +0 0.692 -0.712 1.636 0.879 0.222 1.467 -0.444 0.380 0.000 0.960 -0.006 -1.307 0.000 0.498 1.256 -1.075 1.274 0.821 1.683 1.515 0.000 1.146 0.973 1.033 0.889 0.686 0.821 0.730 +1 1.003 -1.698 -1.084 0.919 -0.793 0.702 0.125 0.850 2.173 0.458 -1.539 1.632 0.000 0.821 0.232 -1.528 2.548 1.212 -1.080 0.578 0.000 0.858 0.947 0.984 0.844 0.797 0.896 0.769 +0 0.367 -1.548 1.143 1.634 -0.738 0.816 0.678 0.997 2.173 0.301 -0.355 1.292 1.107 0.318 1.078 0.578 0.000 0.419 -0.292 -0.617 0.000 0.491 0.584 1.065 0.739 0.444 1.109 0.862 +0 0.867 -0.113 -0.775 0.235 1.385 1.683 0.038 -1.569 2.173 1.237 -0.927 -0.123 0.000 1.755 0.024 0.517 1.274 1.271 -0.542 0.513 0.000 0.920 0.978 0.990 1.073 2.038 1.406 1.117 +1 0.655 1.488 -0.580 0.602 -0.301 0.512 -0.530 0.768 2.173 0.879 -0.992 -1.541 2.215 0.584 -1.491 1.344 0.000 0.460 0.299 -0.125 0.000 0.869 0.725 0.999 0.901 0.894 0.870 0.755 +1 0.353 1.279 -0.718 0.569 -0.442 0.670 -0.161 1.726 0.000 0.753 -0.412 1.017 0.000 1.097 1.175 0.267 2.548 1.128 1.081 -1.245 3.102 0.923 1.072 0.985 0.917 0.832 0.925 0.836 +1 1.937 -0.148 1.216 1.822 1.606 1.331 -1.234 0.913 2.173 1.825 1.178 -0.544 2.215 2.258 -0.339 -0.193 0.000 0.963 -0.907 -1.475 0.000 0.969 1.647 0.980 1.514 4.216 2.185 1.792 +0 0.789 0.965 0.502 2.311 0.839 1.527 -0.456 -0.941 0.000 0.611 0.297 -1.154 0.000 0.306 0.834 -0.488 2.548 0.489 -0.409 0.813 3.102 0.894 0.861 0.986 0.684 0.354 0.561 0.946 +0 0.606 -1.354 -1.573 1.494 -0.555 0.911 0.304 -1.586 2.173 1.039 -1.243 0.979 2.215 1.351 -1.269 -0.133 0.000 0.866 -1.702 -0.109 0.000 0.396 0.952 1.047 1.389 1.644 1.237 1.074 +0 1.967 -0.694 -1.601 0.818 1.603 1.037 -0.752 0.300 1.087 0.717 -0.177 -0.764 0.000 0.821 0.333 1.279 2.548 1.912 -1.018 -0.302 0.000 1.035 1.101 0.983 0.706 1.119 0.991 1.003 +1 0.411 0.204 -0.780 0.887 0.824 1.075 0.886 -0.505 2.173 0.862 -0.098 0.811 0.000 0.533 -0.535 -1.706 0.000 1.011 0.961 -1.544 3.102 0.837 0.857 0.989 1.240 0.900 0.915 0.852 +1 0.657 1.445 1.098 1.277 -0.945 0.938 0.757 -0.061 2.173 0.608 2.351 1.580 0.000 0.611 0.556 1.626 0.000 0.396 1.291 0.609 0.000 0.966 0.952 1.223 1.059 0.743 0.887 0.812 +1 3.063 0.272 0.964 2.048 1.233 3.061 1.445 -0.575 0.000 1.071 -0.001 1.490 2.215 0.438 -0.546 0.133 2.548 0.600 -0.758 -1.168 0.000 3.243 2.116 1.006 0.820 0.719 1.721 1.871 +1 1.723 0.612 0.471 0.785 0.990 0.761 0.785 -0.660 0.000 0.663 -0.481 -0.933 2.215 0.758 -0.394 -1.639 2.548 0.742 0.866 -1.045 0.000 0.795 0.692 0.996 1.015 0.449 0.888 0.744 +1 1.256 -1.245 1.041 1.088 -0.685 0.635 -0.940 -0.793 2.173 1.071 -1.157 -0.058 2.215 1.114 -0.769 1.437 0.000 0.380 2.459 1.510 0.000 0.836 0.966 1.619 0.992 0.761 0.783 0.757 +0 2.093 -0.848 -0.264 1.746 0.123 2.167 -0.456 -1.651 0.000 1.288 -1.061 1.637 0.000 2.372 -0.986 0.267 1.274 1.134 2.072 0.010 0.000 1.302 0.994 0.979 0.745 1.532 1.505 1.552 +1 0.734 0.737 1.555 1.508 -1.273 0.980 1.178 0.289 0.000 0.699 1.475 0.850 0.000 1.622 0.814 -0.869 2.548 0.386 -0.182 1.079 3.102 0.899 0.717 0.986 0.787 0.692 0.827 0.842 +1 1.094 -0.729 -0.742 0.727 0.108 1.036 -0.412 -1.246 2.173 0.511 0.570 1.476 1.107 1.209 -0.728 0.711 0.000 1.164 -0.553 -0.015 0.000 0.801 0.907 0.980 0.964 0.880 0.882 0.827 +1 0.513 -0.061 1.474 0.561 -0.268 0.646 -1.420 -1.207 0.000 0.918 -2.151 -0.662 0.000 1.337 -0.586 0.791 2.548 0.941 -1.164 1.435 3.102 1.012 0.884 0.989 0.714 0.575 0.901 0.769 +1 1.050 -1.102 -0.324 1.257 -0.088 0.881 -0.543 1.327 2.173 0.592 -1.146 0.756 0.000 0.858 -2.508 -0.138 0.000 2.901 -0.447 -1.405 3.102 1.236 1.467 0.996 1.539 1.062 1.275 1.182 +0 0.467 -0.884 0.882 2.474 -1.675 1.283 0.612 0.014 2.173 0.253 0.830 0.429 0.000 1.027 -0.653 -0.994 2.548 0.941 1.163 -1.564 0.000 0.644 0.956 1.107 0.818 1.526 1.384 1.157 +0 1.095 -0.492 0.518 1.169 -0.092 0.933 1.531 -1.238 0.000 0.490 0.407 0.589 2.215 0.646 1.153 1.588 0.000 0.550 1.114 1.053 3.102 0.804 0.979 0.988 0.954 0.293 0.667 1.096 +1 0.978 -0.225 1.629 1.119 -1.151 0.582 -1.567 0.461 2.173 0.842 -0.333 0.058 1.107 0.816 -0.070 -1.497 0.000 0.378 0.497 0.443 0.000 0.764 0.870 0.994 0.967 0.773 0.846 0.710 +1 0.277 -0.553 -1.277 1.442 0.502 0.713 1.311 1.685 2.173 1.561 0.387 -1.406 1.107 1.244 -0.143 0.047 0.000 1.353 0.133 0.970 0.000 0.992 1.192 0.991 1.005 0.919 0.938 0.871 +0 1.204 -0.223 -0.856 0.471 0.852 0.666 -0.365 -0.228 0.000 1.382 -0.142 0.975 2.215 1.153 -0.631 -1.474 2.548 0.891 1.858 -0.748 0.000 0.688 0.976 1.043 0.701 1.141 0.799 0.724 +1 1.416 -0.553 -1.506 1.680 -1.279 1.137 -0.304 0.921 0.000 0.656 2.054 -0.234 0.000 0.368 0.867 0.494 2.548 0.718 -1.423 -0.011 0.000 1.468 0.978 0.999 0.832 0.160 0.677 0.887 +1 0.641 -0.956 1.518 0.402 0.962 1.253 0.420 -0.128 2.173 1.319 -0.350 1.366 0.000 0.617 -0.577 -0.243 0.000 0.997 0.269 -0.940 1.551 0.867 0.904 0.987 1.116 0.793 0.976 0.954 +1 1.186 -0.091 -0.145 0.797 1.523 0.672 1.341 1.351 1.087 0.347 2.089 0.967 0.000 0.755 1.065 -0.553 2.548 1.399 1.169 -1.264 0.000 0.926 0.942 1.344 0.879 0.882 0.845 0.821 +0 1.300 2.183 1.344 1.412 0.793 0.995 1.235 1.051 2.173 1.692 2.285 -1.089 0.000 1.453 0.543 -0.260 0.000 1.873 1.396 -0.768 3.102 1.178 0.946 0.987 0.752 1.475 1.000 1.011 +0 0.641 -0.659 -0.537 0.619 0.808 1.646 -0.454 0.097 2.173 1.024 0.109 1.578 2.215 1.788 -0.616 -1.307 0.000 1.430 -0.852 -1.740 0.000 0.738 0.892 0.981 0.883 1.938 1.326 1.049 +0 0.566 -2.108 -0.472 1.119 -1.237 0.596 -0.318 1.229 0.000 0.725 -0.863 -1.263 2.215 0.579 -1.159 0.158 2.548 0.445 2.261 0.215 0.000 1.819 1.416 0.998 0.584 0.673 1.043 1.067 +1 0.778 -1.146 -0.244 1.537 -1.127 1.069 2.333 0.591 0.000 1.259 0.227 1.491 2.215 0.830 -1.291 0.290 0.000 1.072 -0.629 -1.151 3.102 0.853 0.811 1.081 1.396 0.903 1.051 0.944 +1 2.092 -0.227 -1.335 0.305 -0.052 1.122 0.108 0.402 2.173 0.519 0.408 -0.050 0.000 0.393 -1.188 1.155 0.000 0.752 -0.585 1.491 3.102 0.912 0.803 1.013 0.612 0.904 0.876 0.742 +0 0.409 1.107 1.142 0.696 0.010 1.167 0.217 1.550 0.000 0.683 0.689 -0.871 2.215 0.932 0.222 0.454 0.000 1.070 1.095 -0.316 0.000 0.937 0.796 0.992 0.561 0.200 0.583 0.638 +1 1.582 -0.668 1.466 1.026 -1.531 1.049 -0.172 -0.235 2.173 0.487 -0.007 -1.327 0.000 0.838 0.754 0.083 0.000 0.586 0.174 1.187 3.102 1.026 0.935 0.991 0.643 0.811 0.956 0.891 +0 0.734 -2.036 -0.436 0.074 -1.579 1.048 0.053 1.463 2.173 0.919 0.428 -1.313 2.215 1.462 -0.278 -0.261 0.000 1.998 -0.060 0.304 0.000 0.946 1.266 0.986 1.217 0.906 1.082 1.015 +1 0.627 -0.877 1.299 1.163 0.143 2.519 -0.384 -1.187 0.000 1.407 -0.261 0.525 2.215 1.373 0.590 0.685 1.274 0.853 -0.587 0.905 0.000 0.438 0.511 1.020 0.748 0.733 0.733 0.561 +1 0.903 0.291 -1.699 0.365 -0.039 1.402 0.932 0.683 2.173 0.841 0.950 -1.339 0.000 1.588 -0.216 -0.794 0.000 0.869 0.355 -0.173 3.102 1.014 0.755 0.982 0.990 0.870 1.080 0.901 +1 2.139 -0.406 -0.271 0.629 -0.757 0.906 -0.080 1.461 2.173 0.615 -0.422 0.231 0.000 0.783 -0.600 0.966 0.000 0.459 2.266 -1.577 0.000 0.885 1.061 0.989 0.777 0.464 0.877 0.797 +0 0.560 -2.215 1.475 0.350 -1.638 0.769 -0.346 -1.270 2.173 0.780 0.125 0.690 2.215 0.357 -2.373 0.318 0.000 0.423 0.856 -0.363 0.000 1.307 1.033 0.989 0.802 1.151 0.850 0.775 +1 0.861 -0.598 -0.461 0.844 0.583 0.923 -0.875 -1.358 0.000 0.987 -0.028 1.597 2.215 1.176 0.351 0.279 0.000 1.718 -1.169 -0.179 3.102 0.760 1.101 0.986 0.903 1.464 0.903 0.839 +1 1.157 -0.452 1.219 1.024 -1.381 1.070 -0.037 0.878 0.000 2.073 -0.052 -0.382 1.107 0.612 -1.241 -1.242 0.000 0.961 0.231 -1.103 1.551 1.720 1.187 1.081 1.413 0.798 1.119 1.008 +1 0.953 0.155 0.346 0.571 1.502 1.057 0.408 1.307 2.173 1.333 0.178 -0.325 0.000 1.189 -0.393 -1.554 2.548 0.668 1.165 -0.433 0.000 0.803 1.170 0.987 0.771 0.960 1.048 0.876 +1 0.653 1.237 1.537 1.887 -0.949 1.417 0.379 0.330 1.087 0.677 2.468 1.641 0.000 0.493 -0.718 -0.361 0.000 0.513 0.665 -0.949 0.000 0.939 0.741 1.207 1.635 0.851 1.054 1.010 +0 0.912 1.870 1.192 0.644 1.225 0.848 -0.026 -0.494 2.173 1.045 1.821 1.599 0.000 0.397 0.265 0.404 0.000 0.406 0.480 1.365 1.551 1.228 0.691 0.994 1.284 0.646 0.900 0.823 +0 0.855 -0.682 1.111 1.573 -0.102 0.522 -0.669 -0.405 2.173 0.525 -1.283 -1.651 0.000 1.113 -0.178 1.622 2.548 0.448 -1.790 0.200 0.000 0.764 0.961 1.426 0.814 0.947 0.772 0.789 +1 0.763 0.313 1.712 1.152 -1.238 0.858 0.549 0.342 2.173 0.455 -0.543 0.914 2.215 0.426 2.238 1.678 0.000 1.174 0.357 -0.737 0.000 0.743 0.822 0.981 0.919 0.703 0.863 0.763 +1 0.791 0.128 0.592 0.643 1.393 0.462 -1.042 -0.468 0.000 0.595 0.717 1.040 2.215 1.078 -0.163 -0.992 0.000 1.421 0.301 1.735 3.102 0.888 1.031 0.986 0.757 0.511 0.778 0.694 +1 0.849 1.071 -1.294 0.579 -0.059 0.499 0.358 -0.341 2.173 0.949 1.211 1.444 0.000 1.155 -0.614 0.582 2.548 0.729 0.600 -1.223 0.000 0.785 0.933 0.982 0.962 0.867 0.877 0.762 +1 0.884 -0.029 -0.007 1.037 -1.297 1.143 -0.421 -1.577 2.173 1.327 -0.283 0.391 0.000 0.753 0.585 0.742 2.548 0.943 2.095 -0.489 0.000 2.971 1.641 1.216 0.989 1.194 1.504 1.219 +1 1.098 0.457 -1.569 1.894 -1.483 1.406 0.019 0.125 2.173 0.592 -2.846 0.911 0.000 0.690 0.294 -0.978 2.548 0.745 1.786 0.291 0.000 5.453 2.993 0.976 1.980 1.046 2.047 2.094 +1 1.082 1.205 -0.401 1.359 -0.834 0.605 -0.565 1.500 1.087 0.467 0.751 0.964 2.215 0.622 0.181 0.116 0.000 0.875 -1.949 1.116 0.000 1.480 1.076 0.983 0.921 0.673 1.161 1.478 +1 1.653 0.394 0.442 0.905 0.344 1.101 0.438 -1.681 2.173 0.590 -0.059 -0.524 0.000 0.647 -0.896 -1.589 0.000 0.572 -0.769 -1.032 3.102 0.899 1.045 0.986 0.956 0.774 1.006 0.922 +0 0.774 -0.272 -0.899 2.040 -1.676 2.358 -0.758 0.050 2.173 0.726 -0.134 0.363 0.000 2.806 -0.368 1.624 1.274 1.769 -1.720 1.650 0.000 0.954 0.926 1.121 0.835 3.220 1.757 1.378 +1 1.008 -0.465 0.520 1.383 0.765 2.309 -1.253 1.461 0.000 1.450 -1.379 -0.419 2.215 2.145 -2.273 -0.193 0.000 1.031 -0.415 -0.495 0.000 0.756 0.769 0.988 0.970 0.929 0.934 0.795 +1 0.538 0.152 -0.995 1.099 0.298 0.560 2.908 -0.471 0.000 1.200 -1.545 1.416 0.000 1.377 -1.799 -0.896 0.000 2.433 -0.743 1.611 1.551 0.875 0.989 0.987 1.147 1.116 0.817 0.859 +0 1.545 0.627 1.545 1.826 -1.433 1.797 -0.206 -0.033 1.087 0.695 0.021 1.242 2.215 0.591 1.253 -0.300 0.000 0.585 -0.834 1.111 0.000 1.148 0.842 1.031 2.152 1.512 1.424 1.138 +1 1.100 -0.890 1.619 0.303 -0.686 0.931 -0.064 -0.935 2.173 1.167 -0.164 0.506 2.215 2.051 0.342 0.916 0.000 1.557 -0.761 -0.544 0.000 0.600 1.097 0.984 0.825 1.479 0.901 0.802 +1 0.869 0.410 0.823 0.869 -0.769 0.611 -1.339 1.129 0.000 0.927 -0.956 -1.736 2.215 1.476 0.342 -0.119 2.548 0.596 0.281 -1.138 0.000 1.198 0.840 1.192 0.787 1.535 0.965 0.890 +0 0.436 1.282 0.764 1.658 -0.173 1.575 -0.227 -1.585 0.000 1.109 -0.372 0.295 1.107 0.569 0.280 -0.249 1.274 0.563 0.443 1.425 0.000 0.818 0.952 0.985 1.634 0.496 1.033 1.310 +1 0.299 -1.278 -0.227 1.038 -1.736 1.601 0.163 0.979 2.173 1.294 -0.142 -0.088 0.000 1.035 0.352 -1.208 0.000 0.762 1.247 -0.945 0.000 0.617 0.545 0.990 1.027 0.889 0.893 0.775 +1 2.021 -0.876 0.247 0.713 0.519 0.950 -0.088 -1.467 2.173 0.568 0.272 0.969 2.215 0.922 -0.249 1.496 0.000 1.667 0.915 -0.654 0.000 0.832 0.745 0.998 1.421 0.899 0.969 0.841 +0 0.359 2.293 0.673 0.490 -0.554 0.700 1.846 -1.085 0.000 0.803 1.221 0.429 0.000 0.827 0.493 -1.616 2.548 0.662 0.209 -0.236 3.102 1.568 0.918 0.977 0.670 0.542 0.693 0.646 +1 0.407 -1.107 1.742 1.758 -0.039 1.561 2.138 -1.414 0.000 1.397 -0.644 0.032 2.215 1.487 -1.243 0.367 2.548 2.333 -0.920 1.484 0.000 1.032 1.046 1.172 0.768 0.714 0.949 0.847 +0 0.516 0.963 -1.547 1.652 0.920 0.386 0.933 0.266 0.000 0.815 0.976 -1.038 2.215 0.942 -0.860 0.284 2.548 0.978 -0.748 1.581 0.000 0.971 0.909 1.017 0.920 1.367 0.979 0.858 +1 0.594 0.899 0.975 0.617 0.334 0.466 1.160 -1.420 0.000 0.621 -0.586 -1.004 0.000 0.448 0.496 1.210 2.548 0.505 -2.133 0.645 0.000 1.107 0.910 0.988 0.477 0.125 0.592 0.597 +1 0.321 1.752 -1.487 0.733 -1.266 0.837 1.549 1.120 0.000 0.819 -0.829 0.795 2.215 1.285 2.335 -0.822 0.000 0.729 0.591 -0.286 3.102 2.076 1.330 0.993 0.953 0.834 1.575 1.283 +1 1.395 1.611 -0.320 1.482 0.462 0.592 1.975 -0.947 0.000 0.354 -2.337 1.185 0.000 1.308 1.023 -1.608 2.548 0.863 0.104 0.918 3.102 0.795 0.907 1.291 1.225 0.748 0.907 0.795 +0 0.639 -0.283 0.412 1.448 0.813 0.914 0.513 -1.089 1.087 0.606 0.221 -0.010 2.215 1.402 -1.311 1.596 0.000 1.134 1.274 -0.516 0.000 3.196 1.873 0.978 1.640 0.919 1.303 1.298 +1 1.236 -0.877 -0.832 1.648 0.440 1.171 -0.181 -1.393 2.173 0.389 -1.299 -1.074 0.000 1.279 -1.238 1.040 2.548 0.802 -0.754 0.308 0.000 0.878 0.974 1.802 1.156 1.562 1.182 0.941 +1 0.610 0.505 -1.070 0.959 0.733 1.376 -0.030 1.522 2.173 1.753 1.712 -0.276 0.000 0.640 -0.039 -1.336 2.548 0.973 -0.221 0.504 0.000 0.659 1.171 1.058 0.651 0.625 0.744 0.721 +1 0.658 -0.236 -1.561 1.060 1.141 0.768 0.150 -1.098 0.000 1.025 0.491 0.862 2.215 1.746 0.827 0.181 2.548 1.067 1.292 -0.813 0.000 1.105 1.302 0.987 1.006 0.868 1.067 1.110 +0 1.452 0.447 -0.655 0.779 1.571 0.531 0.251 -0.130 0.000 0.629 1.133 1.639 2.215 0.357 1.307 1.225 2.548 0.559 2.229 0.943 0.000 0.775 0.787 1.336 0.745 0.196 0.575 0.573 +1 0.670 -1.632 -1.241 0.149 0.602 0.741 -0.662 -0.916 1.087 0.907 0.393 1.103 0.000 1.128 -0.605 0.311 2.548 0.844 -1.330 -0.454 0.000 1.750 1.154 0.995 0.668 1.019 0.909 0.776 +1 0.772 0.504 1.460 1.306 -1.183 0.974 0.352 0.400 2.173 0.563 0.944 1.643 0.000 0.987 0.868 -0.298 2.548 0.897 -0.214 0.811 0.000 0.874 0.909 0.987 0.813 0.809 0.849 0.756 +0 2.074 1.036 -1.410 2.308 -0.938 1.880 -1.353 0.566 2.173 0.899 -1.993 1.739 0.000 0.437 -1.148 1.286 0.000 1.839 -1.365 0.154 0.000 0.863 0.836 1.253 0.772 2.725 2.680 2.205 +0 1.075 -1.129 0.553 0.754 -0.035 0.529 0.255 -1.424 0.000 0.976 0.292 -0.665 1.107 1.035 0.240 1.432 2.548 0.568 1.430 1.461 0.000 0.794 0.838 0.990 0.912 1.014 0.833 0.809 +0 0.443 1.680 0.666 3.283 1.198 1.754 0.431 -0.322 0.000 0.805 -0.602 -0.609 2.215 0.821 0.595 -0.815 0.000 2.246 0.867 1.629 3.102 0.957 0.982 0.989 1.296 1.572 1.904 1.927 +0 0.913 -2.064 -0.613 1.183 -0.363 1.050 0.660 0.674 2.173 0.741 -0.711 1.311 0.000 0.656 -2.497 -1.247 0.000 0.960 -1.580 -1.686 0.000 0.824 0.684 0.987 1.994 1.465 1.354 1.175 +1 1.570 -0.554 1.279 0.461 -1.541 0.761 -0.344 -0.007 2.173 0.800 -0.252 -0.827 0.000 0.525 -1.435 0.459 0.000 0.521 0.757 -1.275 3.102 1.151 0.893 0.988 0.638 0.753 0.726 0.724 +0 0.356 0.249 0.242 0.692 -1.534 0.458 1.636 -0.829 0.000 0.457 -0.081 1.358 2.215 1.449 1.335 1.000 1.274 1.439 0.585 -0.418 0.000 0.759 0.923 0.987 1.454 0.787 0.931 0.963 +1 0.771 1.091 -0.086 0.909 0.225 1.721 0.354 -0.228 0.000 1.906 0.733 1.259 0.000 2.556 -0.172 -1.486 0.000 1.311 0.310 0.197 3.102 1.242 1.119 0.985 0.510 0.370 0.906 0.889 +0 0.858 0.592 -1.577 0.595 0.822 0.964 0.425 1.079 1.087 1.775 0.817 -0.710 2.215 0.681 -0.053 1.275 0.000 1.006 -1.649 0.016 0.000 0.840 1.405 0.988 1.124 1.964 1.402 1.112 +0 1.190 -1.429 -1.655 0.443 -0.108 0.503 -0.361 -0.088 0.000 0.424 0.693 -0.873 0.000 0.664 0.227 0.582 2.548 1.219 0.404 1.064 3.102 0.845 0.833 0.990 0.866 0.300 0.745 0.733 +0 0.593 -0.108 1.009 0.315 -1.550 0.714 0.761 1.076 0.000 0.800 0.338 -1.453 2.215 1.395 0.365 -0.441 2.548 0.783 0.373 0.155 0.000 0.863 1.011 0.988 0.684 0.888 0.764 0.679 +1 0.557 -0.533 0.220 0.150 0.996 1.686 0.603 -0.903 2.173 1.502 -0.820 0.966 0.000 1.132 0.037 0.555 0.000 1.125 -0.432 -1.336 3.102 0.815 0.921 0.982 1.104 1.028 1.005 0.853 +0 1.185 0.601 0.929 0.752 0.020 0.838 0.909 -1.534 2.173 0.547 1.628 0.956 0.000 1.582 0.479 -0.727 2.548 1.075 -0.792 -0.239 0.000 1.076 0.939 0.987 0.994 0.996 0.861 0.827 +0 0.906 -0.234 -1.702 0.891 -1.146 1.017 1.583 -0.025 2.173 0.448 0.770 0.702 0.000 1.533 0.720 1.380 2.548 0.898 0.436 -0.827 0.000 0.819 0.899 0.988 0.799 1.618 1.079 0.859 +0 0.660 1.007 1.269 0.596 -0.484 0.966 0.636 1.733 1.087 1.300 0.737 -0.587 2.215 0.911 -0.400 -0.381 0.000 0.852 -0.419 1.295 0.000 0.934 0.936 0.987 0.781 1.433 0.925 0.771 +0 0.790 0.417 1.211 0.383 0.567 1.141 1.773 -1.095 0.000 1.720 0.108 0.569 1.107 0.805 2.065 -1.635 0.000 1.102 0.364 -0.761 3.102 0.906 1.021 0.977 0.802 1.175 1.479 1.326 +1 0.873 -1.555 -1.094 0.611 0.758 0.630 -0.135 -0.970 2.173 0.752 -1.669 1.394 0.000 0.957 -0.997 0.641 0.000 1.425 0.324 0.241 3.102 0.915 1.232 1.007 0.905 0.928 0.967 0.866 +0 0.647 -1.125 0.635 0.244 -0.148 0.821 0.364 -1.277 0.000 0.617 0.804 -0.772 0.000 1.103 0.459 0.886 2.548 0.681 0.463 0.640 3.102 0.752 1.062 0.978 0.537 0.147 0.677 0.669 +1 0.963 -0.376 0.842 1.288 -0.637 0.854 0.341 0.770 0.000 1.064 0.663 -1.226 2.215 0.731 -0.256 -0.510 2.548 0.644 -0.251 -1.236 0.000 1.161 0.907 1.499 1.162 0.733 0.777 0.799 +0 0.465 1.593 1.083 0.111 0.731 0.867 0.147 -1.092 2.173 0.828 -0.849 0.464 2.215 0.633 -0.719 -0.827 0.000 0.881 -0.122 0.774 0.000 0.894 1.006 0.976 0.831 1.397 0.836 0.751 +0 1.770 0.674 1.278 1.333 -1.545 0.956 0.469 0.423 0.000 0.791 0.058 -1.186 2.215 0.757 -0.257 -0.698 2.548 0.409 1.719 -0.875 0.000 1.218 1.016 1.199 0.909 0.378 0.780 0.865 +0 0.813 0.084 0.273 0.847 -0.276 0.781 -1.042 -0.750 2.173 0.693 -0.163 -1.120 0.000 0.870 -0.276 1.498 1.274 1.896 1.576 0.776 0.000 1.488 1.002 0.990 0.804 1.003 0.788 0.788 +0 0.774 0.918 0.674 1.565 0.013 0.850 -0.799 -1.462 2.173 0.643 0.005 1.394 2.215 0.568 0.007 -0.328 0.000 0.390 0.173 1.050 0.000 0.494 0.745 0.990 0.869 0.739 0.987 0.746 +0 1.528 0.589 -0.221 2.069 -0.068 1.517 0.016 -1.721 0.000 1.180 -0.085 1.343 0.000 1.063 0.425 -0.710 2.548 1.068 -0.962 1.472 3.102 0.924 1.027 0.999 1.705 1.051 1.141 1.101 +0 0.479 1.225 -1.623 1.920 -1.209 0.998 -1.466 0.313 2.173 0.477 -1.636 -1.690 0.000 1.197 -1.836 -0.687 0.000 2.006 1.145 0.677 0.000 0.930 1.153 0.980 0.883 0.539 1.209 1.159 +1 2.876 0.394 -1.413 0.380 -1.016 1.438 -0.797 0.169 0.000 1.441 -0.050 0.771 2.215 0.523 -0.281 -1.386 2.548 0.838 0.183 -0.518 0.000 1.323 1.043 0.983 1.472 0.867 0.929 1.135 +1 0.670 1.150 -1.180 0.738 0.148 1.007 -0.495 -1.572 2.173 0.645 0.500 0.214 2.215 0.653 1.461 0.537 0.000 0.816 -0.194 0.598 0.000 0.850 1.351 0.987 0.610 1.341 0.905 0.793 +0 0.704 -0.284 -0.879 0.942 -0.158 0.719 -0.294 1.236 2.173 0.904 -1.440 0.868 2.215 0.659 -0.906 -0.903 0.000 0.473 0.658 -0.849 0.000 0.627 0.979 0.987 1.021 0.840 0.993 0.825 +0 1.065 -1.802 -0.187 0.677 -0.865 1.512 -1.647 -0.575 2.173 1.240 -1.524 0.898 2.215 1.767 -0.792 1.199 0.000 1.361 -1.099 -1.691 0.000 0.957 0.911 0.982 0.833 1.958 1.307 1.163 +0 0.395 2.359 0.175 2.788 -1.684 1.351 0.927 -1.664 0.000 1.107 1.478 0.430 2.215 0.506 2.056 -1.137 0.000 0.842 0.507 -0.402 3.102 1.256 1.032 1.446 1.436 0.723 1.085 1.104 +1 1.650 0.515 -0.188 0.135 -0.695 0.470 -0.066 -1.011 0.000 1.012 0.424 1.230 0.000 0.655 0.577 1.018 2.548 0.803 -0.543 -1.740 1.551 1.024 0.699 0.985 0.731 0.510 0.662 0.655 +1 1.235 -0.734 1.509 0.977 1.384 0.949 -0.137 0.197 1.087 1.345 -0.653 -0.745 2.215 1.094 -1.704 -1.678 0.000 1.412 0.788 -0.302 0.000 1.033 0.946 0.987 1.251 1.327 1.092 0.956 +1 0.986 0.812 0.981 0.411 -1.195 1.009 -0.164 -0.675 2.173 0.422 1.960 0.975 0.000 0.685 0.228 0.236 2.548 0.366 1.489 -1.386 0.000 0.439 1.281 0.986 0.611 0.788 0.810 0.729 +0 0.281 1.588 -0.927 1.059 1.008 0.737 0.098 -0.055 0.000 0.717 0.869 -1.095 0.000 0.419 0.036 0.169 2.548 0.966 -0.287 1.665 3.102 0.920 0.791 0.980 0.545 0.484 0.479 0.508 +0 0.552 0.952 -1.360 1.402 -0.023 1.022 0.339 -0.861 2.173 0.690 -1.573 0.605 0.000 0.508 -0.077 1.489 0.000 0.564 -1.096 1.195 3.102 0.963 0.548 1.138 0.942 1.072 0.886 0.999 +0 0.673 -1.584 -0.298 0.478 1.329 0.864 0.699 -0.968 0.000 1.220 -0.049 0.079 2.215 2.528 -1.088 1.416 1.274 0.569 -1.734 0.125 0.000 2.214 1.576 0.991 0.932 2.076 1.538 1.191 +0 1.074 0.265 -0.145 0.548 -0.039 0.418 -0.650 0.986 0.000 0.721 -0.492 -1.508 1.107 0.507 -1.489 0.791 0.000 1.153 0.411 -1.229 1.551 0.447 0.897 1.001 0.874 0.475 0.681 0.680 +1 0.935 0.086 1.368 1.399 0.417 1.246 -1.042 -0.623 2.173 0.874 -0.874 0.947 2.215 1.468 -0.016 -1.673 0.000 0.454 -0.234 -0.908 0.000 0.808 0.871 1.198 1.547 1.522 1.132 0.975 +1 0.313 2.008 0.367 1.243 0.862 1.182 0.693 -0.693 0.000 0.873 0.666 -1.401 2.215 0.680 -0.922 0.750 1.274 0.700 1.725 1.627 0.000 0.886 1.013 0.999 0.904 1.094 0.778 0.715 +1 0.328 0.448 -1.494 0.815 0.483 1.276 -0.632 0.818 2.173 1.193 -0.508 -1.210 0.000 0.603 2.367 -0.722 0.000 0.688 -0.274 -0.509 3.102 3.016 1.672 0.983 0.763 0.935 1.588 1.266 +0 0.702 -0.046 -0.140 0.905 -1.532 0.504 -1.643 0.557 0.000 1.001 -1.322 -0.544 1.107 1.093 -1.144 1.486 2.548 0.543 -0.407 0.978 0.000 0.576 0.852 1.049 0.861 1.076 0.802 0.735 +1 2.589 -1.034 -0.139 0.604 -0.494 1.009 -0.204 0.984 2.173 0.521 -1.188 0.253 0.000 0.881 -0.653 0.778 2.548 2.100 1.486 -1.025 0.000 0.874 0.747 0.982 1.431 0.374 0.954 0.871 +1 1.594 -1.303 1.472 0.755 0.662 0.826 -0.344 -1.461 0.000 0.971 -0.424 0.194 2.215 1.174 -0.623 -0.392 0.000 1.729 -1.171 -0.646 3.102 0.871 0.918 1.013 1.029 0.996 0.888 0.754 +0 0.908 -1.333 1.595 0.847 -1.164 0.980 -0.655 0.533 2.173 0.735 -0.639 -0.637 0.000 0.767 -0.312 0.013 0.000 0.824 0.494 -1.681 3.102 0.663 0.908 0.989 1.110 1.075 1.047 0.920 +0 1.049 0.704 -1.448 0.787 -0.727 0.787 0.418 0.541 2.173 0.512 1.460 -0.809 0.000 0.622 1.101 1.272 0.000 0.443 0.393 0.116 1.551 0.835 0.942 0.993 0.577 0.234 0.649 0.639 +0 1.014 -1.148 1.527 1.452 -1.078 0.835 1.645 0.331 0.000 1.003 -0.204 -1.659 0.000 1.037 0.602 -0.179 2.548 1.253 0.239 0.258 1.551 2.825 1.659 1.201 1.361 0.373 1.042 1.378 +0 1.026 0.439 -0.752 1.796 1.443 0.702 -1.065 1.028 0.000 0.639 -0.577 -0.124 1.107 0.756 0.200 0.303 2.548 0.470 -1.285 -1.666 0.000 0.610 0.798 1.727 1.184 0.415 0.821 0.853 +1 1.305 -1.012 0.220 2.617 0.400 0.931 -1.340 -1.614 2.173 0.953 1.003 -1.547 0.000 1.133 -1.098 -0.557 2.548 0.654 -2.426 0.831 0.000 1.023 0.898 0.990 1.082 1.047 1.215 1.046 +0 2.769 0.632 -0.193 1.567 -0.817 0.969 -1.016 1.566 2.173 0.970 -0.794 1.177 0.000 0.706 1.402 1.516 0.000 1.569 1.134 -0.491 3.102 0.603 0.875 1.536 0.866 2.321 1.620 1.771 +0 1.914 1.213 0.736 0.769 0.105 1.447 -0.620 -1.227 2.173 0.268 -1.061 1.371 0.000 0.687 -0.243 -0.583 2.548 0.810 -0.350 0.862 0.000 0.339 0.928 0.983 1.067 0.718 1.561 1.196 +0 0.484 0.152 1.701 0.751 0.079 0.700 1.453 -0.666 0.000 0.761 1.414 0.601 0.000 0.851 1.150 -1.704 1.274 0.534 0.717 1.666 3.102 1.410 1.028 0.988 0.633 0.117 0.624 0.816 +0 0.726 1.854 -0.338 0.624 1.622 0.595 -0.003 1.248 2.173 0.484 0.644 0.108 0.000 0.661 -0.185 -1.301 0.000 0.920 -1.669 0.661 0.000 0.914 0.796 0.991 0.992 0.788 0.949 0.831 +0 0.539 -0.677 -0.536 1.206 0.324 1.560 -1.123 0.196 0.000 2.429 -2.388 -1.260 0.000 1.265 -0.495 -1.467 1.274 1.839 -0.022 0.960 3.102 4.963 3.114 0.992 0.777 0.999 2.212 1.884 +0 1.097 -1.459 -0.531 1.007 -0.455 0.753 0.812 -1.528 2.173 0.590 0.296 1.211 0.000 0.910 -0.305 0.209 2.548 0.748 0.529 0.615 0.000 0.627 0.794 0.978 2.478 1.208 1.626 1.516 +0 0.760 -0.844 -0.870 0.961 -0.358 0.733 -1.360 0.978 1.087 0.619 -0.898 0.394 0.000 0.589 -1.722 -0.644 0.000 1.105 -1.186 -1.356 0.000 0.954 0.859 0.989 0.675 0.232 0.772 0.686 +1 0.967 0.792 0.884 0.220 -1.367 0.604 1.037 -1.740 0.000 0.486 2.189 1.434 0.000 1.490 0.913 -0.460 2.548 1.720 0.580 0.129 1.551 0.803 1.131 0.991 0.938 0.647 0.869 0.810 +1 0.930 0.080 -0.163 1.073 -1.266 0.856 -0.346 -1.212 0.000 1.676 0.119 1.001 0.000 0.946 -0.351 -1.740 0.000 1.792 0.586 0.440 3.102 1.003 0.926 1.159 0.566 0.640 0.626 0.621 +1 1.121 0.747 -0.549 1.085 0.468 1.943 -2.429 1.209 0.000 0.863 1.271 -1.148 2.215 2.123 0.109 -0.995 2.548 1.356 0.517 0.351 0.000 1.017 0.964 1.212 1.137 0.927 0.878 0.801 +1 0.676 -1.613 -1.684 0.757 -0.463 0.797 -1.183 1.413 1.087 0.708 -0.875 0.584 2.215 1.363 -1.999 -0.339 0.000 0.843 -1.291 -1.081 0.000 0.829 0.993 0.986 0.800 0.770 0.859 0.745 +0 1.705 0.150 -0.771 1.067 1.240 0.691 0.503 1.208 0.000 1.254 -1.046 -0.439 1.107 1.388 -0.701 1.235 1.274 0.824 0.232 0.414 0.000 0.769 0.858 1.814 1.414 1.416 1.142 1.034 +1 0.514 1.000 1.110 0.961 -1.643 1.453 2.434 0.584 0.000 1.286 1.041 0.889 0.000 1.527 -0.388 -0.962 2.548 1.806 0.624 -1.339 3.102 0.989 1.772 0.987 0.856 0.895 1.873 1.559 +0 1.358 -1.770 -0.624 0.535 0.248 0.533 -0.266 1.692 0.000 0.568 -1.578 1.732 0.000 0.913 -1.349 1.044 1.274 1.162 -0.342 -0.148 1.551 0.855 0.944 0.988 0.819 0.821 0.675 0.726 +0 0.647 -0.509 -0.721 0.974 -1.703 0.790 0.474 -0.070 1.087 0.958 0.152 0.951 2.215 0.306 -1.270 -1.410 0.000 0.645 -1.180 -0.208 0.000 0.433 0.877 0.985 1.015 1.040 0.968 0.781 +1 1.299 -0.067 0.303 0.318 -0.513 0.745 -0.468 -1.709 2.173 0.719 -0.737 -1.089 0.000 1.253 0.836 0.745 0.000 1.017 0.448 -0.611 3.102 0.724 1.030 0.981 0.713 0.911 0.743 0.749 +0 1.014 -0.642 1.378 1.609 1.681 0.969 -0.424 -0.030 0.000 0.731 -0.778 -0.915 1.107 0.777 -1.281 0.029 2.548 0.627 -1.645 0.558 0.000 1.181 0.997 0.987 0.933 0.650 0.757 0.858 +1 0.453 1.202 -1.688 1.452 -0.966 0.658 0.229 0.969 0.000 0.620 -0.510 -1.477 2.215 1.336 0.245 0.367 0.000 0.959 -0.768 -0.407 3.102 0.873 0.963 0.981 0.650 0.589 0.753 0.775 +1 1.648 1.455 -1.610 0.988 -0.817 1.066 1.161 0.639 2.173 0.660 1.066 1.151 0.000 0.862 2.248 0.240 0.000 1.950 1.381 -1.030 0.000 1.206 0.963 1.160 0.843 0.693 0.919 0.856 +0 0.652 -0.137 -0.411 1.548 -1.261 0.654 0.519 0.081 0.000 0.745 -0.429 1.167 1.107 0.762 0.754 -1.451 2.548 0.446 1.113 0.059 0.000 0.345 0.868 0.987 0.721 0.784 0.680 0.729 +1 1.888 -0.987 1.687 3.028 -1.740 2.632 0.945 0.088 0.000 1.077 -0.858 -0.238 2.215 0.957 -0.412 1.288 0.000 1.734 -0.263 -1.317 1.551 3.329 2.522 1.003 1.674 1.079 1.785 2.442 +1 0.951 -0.103 0.628 0.777 -1.299 1.867 -0.150 1.624 2.173 2.080 0.072 -0.072 0.000 0.773 -0.310 -1.208 2.548 1.173 -0.773 -0.294 0.000 0.732 0.654 1.175 1.073 0.841 0.894 0.791 +1 0.662 -0.340 -1.026 0.637 -1.456 0.659 -0.218 -0.015 1.087 1.073 0.427 0.569 0.000 1.594 1.173 -1.608 0.000 0.409 0.853 0.666 3.102 2.052 1.083 0.985 0.976 0.482 0.912 1.114 +1 1.863 -0.563 -0.633 0.606 -0.943 1.305 -0.349 0.076 0.000 2.097 0.816 -1.631 0.000 1.191 -1.102 0.254 0.000 1.298 -0.671 1.483 3.102 0.869 0.891 0.999 1.107 0.531 0.829 0.802 +1 1.841 -1.141 -0.668 0.954 -1.159 0.850 -0.705 0.311 2.173 1.036 -0.197 1.212 2.215 0.582 -0.250 -1.578 0.000 1.211 -0.087 0.580 0.000 0.866 0.812 0.980 1.401 1.063 1.126 0.944 +1 1.801 -0.529 0.511 1.065 1.168 0.571 -0.146 0.143 0.000 2.300 2.695 -1.255 0.000 0.931 0.465 -0.210 2.548 1.257 0.712 1.203 3.102 0.676 0.783 1.072 0.992 0.804 0.808 0.686 +0 1.763 0.366 1.286 0.631 1.567 0.884 0.441 -0.152 1.087 0.960 0.834 -1.174 0.000 1.073 1.314 -0.535 0.000 1.797 0.954 0.858 3.102 0.965 1.059 0.981 0.925 1.156 0.982 1.023 +1 0.498 -0.823 1.189 0.520 -1.534 1.297 0.648 -1.559 0.000 1.436 0.223 0.384 0.000 1.556 0.823 -0.242 2.548 0.484 1.977 -0.366 0.000 1.557 0.861 0.988 0.947 0.576 0.667 0.710 +0 0.628 0.884 1.172 1.073 0.695 1.319 0.866 -1.599 0.000 0.893 -0.178 -0.519 2.215 1.829 -0.442 -0.011 2.548 1.531 -1.202 0.440 0.000 1.284 1.533 0.989 0.955 0.635 1.380 1.167 +0 1.933 0.882 0.964 0.429 -1.481 0.645 0.153 -0.768 2.173 0.567 1.335 -0.521 2.215 0.685 0.861 1.716 0.000 0.679 1.034 0.080 0.000 0.757 0.773 1.019 0.870 0.609 0.801 0.663 +1 0.571 -0.657 0.666 0.666 -1.569 1.017 0.035 0.142 2.173 0.479 -1.108 0.183 0.000 1.449 0.015 -1.371 2.548 0.704 -0.500 -0.474 0.000 0.849 0.925 0.983 0.968 1.480 1.020 0.866 +0 1.361 -1.447 -0.132 0.714 -1.628 0.490 0.052 -0.325 0.000 0.564 0.296 0.590 0.000 1.501 -1.267 -1.241 2.548 1.254 0.906 1.087 3.102 0.832 0.822 1.332 0.909 1.890 1.232 1.076 +0 0.721 0.543 1.158 0.935 -0.943 0.460 -0.761 1.605 0.000 0.498 -2.184 -1.636 0.000 1.078 -1.422 -0.498 2.548 1.569 -0.612 0.453 1.551 0.852 0.987 1.079 1.215 0.862 0.921 0.927 +1 0.370 -1.708 -0.548 1.023 -1.270 0.980 -0.143 0.552 2.173 0.324 0.994 -0.521 0.000 1.077 0.423 1.521 0.000 1.010 0.801 -0.131 3.102 0.910 1.055 0.991 0.853 0.862 0.831 0.773 +1 0.754 -1.504 1.562 0.583 0.535 1.184 2.054 -1.625 0.000 1.237 -0.130 -0.144 2.215 1.943 1.063 -0.812 0.000 2.263 -0.296 0.513 3.102 0.809 1.218 0.990 0.772 0.859 1.088 1.051 +0 0.645 0.838 -1.180 0.197 -0.660 0.942 1.233 -0.463 2.173 1.206 0.431 1.045 0.000 0.812 0.853 0.498 0.000 0.819 0.953 -1.665 3.102 0.812 0.736 0.999 1.032 0.822 0.863 0.826 +0 0.817 0.349 -0.034 0.427 -0.673 0.738 1.252 1.457 0.000 1.543 1.446 -0.199 1.107 1.076 0.460 1.571 0.000 0.724 -0.884 -0.007 0.000 0.802 0.982 0.981 1.368 0.940 0.932 1.016 +1 0.726 0.312 1.062 0.635 -0.028 0.843 -0.342 0.573 0.000 1.411 -0.555 -1.311 2.215 1.017 2.323 0.323 0.000 1.413 -1.110 -0.785 3.102 0.893 0.789 0.992 0.847 0.766 0.773 0.670 +1 1.640 -0.097 -1.259 0.795 -0.366 0.895 0.794 0.413 2.173 0.973 -0.371 0.845 2.215 0.944 0.822 1.444 0.000 0.425 0.082 -0.409 0.000 0.753 0.823 1.141 1.094 1.006 1.012 0.838 +1 0.546 -1.934 1.106 1.127 -1.560 0.915 -0.694 -0.675 0.000 1.544 -2.824 0.222 0.000 1.030 0.342 -1.680 2.548 1.582 -0.779 1.353 3.102 1.189 1.180 0.980 0.898 0.788 0.946 0.836 +0 0.423 1.479 -1.494 0.983 0.163 1.336 -0.295 -0.732 0.000 0.912 0.538 0.430 2.215 1.334 0.730 1.347 2.548 1.901 0.945 -1.617 0.000 0.432 0.960 0.987 0.777 0.875 0.654 0.636 +1 1.118 -0.323 -1.418 0.309 0.934 0.903 -0.377 1.415 0.000 0.815 -2.300 -0.434 0.000 1.237 -1.049 -0.047 2.548 1.005 -1.446 0.535 0.000 0.916 0.876 0.990 0.661 0.726 0.764 0.734 +1 0.997 -0.258 0.109 0.952 1.553 0.623 -0.068 0.737 0.000 1.018 -0.877 -1.039 2.215 0.859 -0.719 -1.646 2.548 0.653 0.936 -0.021 0.000 0.871 0.941 1.301 0.991 0.520 0.825 0.760 +1 1.575 -0.507 -1.086 0.544 -1.424 0.530 -0.810 0.657 1.087 0.763 -0.887 1.195 0.000 1.221 -0.717 -0.214 1.274 0.584 0.059 0.002 0.000 0.885 0.844 0.987 1.017 0.708 0.807 0.732 +0 0.737 -0.746 1.527 0.203 0.362 1.026 -2.822 1.143 0.000 1.038 1.160 -0.857 1.107 1.277 -1.017 -0.388 2.548 0.897 -0.012 1.272 0.000 1.006 0.907 0.984 0.931 1.821 1.014 0.851 +0 1.285 -0.713 0.693 0.961 1.451 0.980 0.358 -1.143 1.087 0.514 1.056 -0.453 0.000 0.542 -0.710 0.353 0.000 0.524 1.184 0.070 3.102 1.023 1.022 0.987 0.867 0.794 0.920 0.824 +0 0.381 0.818 -1.402 1.045 1.204 0.873 -0.583 -0.279 2.173 0.460 -0.397 0.357 0.000 1.017 0.888 -0.934 2.548 0.511 1.948 1.274 0.000 0.670 1.272 0.991 0.932 1.207 0.888 0.832 +0 1.991 0.236 0.088 2.083 -0.011 1.273 -0.230 -0.853 0.000 1.884 -0.706 1.456 2.215 1.184 0.454 1.370 0.000 1.039 0.377 0.596 3.102 0.698 0.747 0.977 2.340 1.184 1.462 1.268 +1 0.943 -1.469 -0.262 2.380 0.365 0.946 -1.298 -1.641 2.173 0.347 -0.529 1.081 2.215 0.449 0.370 -1.585 0.000 0.764 -0.699 1.583 0.000 0.466 0.635 1.112 0.791 0.629 0.959 0.840 +1 0.951 0.704 0.529 0.773 -0.663 0.538 -0.141 1.468 0.000 0.385 0.838 -0.922 0.000 1.002 1.326 1.224 0.000 1.540 0.606 -0.360 0.000 0.935 0.858 1.044 0.875 0.492 0.746 0.699 +0 0.557 -1.212 0.730 0.887 -0.990 0.920 -0.837 0.370 2.173 0.992 -1.676 1.732 1.107 0.645 0.425 0.241 0.000 0.621 -1.231 -1.000 0.000 1.010 0.890 0.988 0.768 1.469 0.916 0.773 +1 1.112 -1.749 1.455 0.377 -0.582 1.581 0.357 -1.000 0.000 1.063 -0.933 1.197 2.215 1.306 -1.758 -0.309 0.000 1.662 1.166 0.654 0.000 0.886 1.026 0.985 0.841 0.497 0.823 0.762 +1 0.325 -1.722 -0.097 2.580 -0.239 0.968 -0.508 -1.319 2.173 0.574 -1.304 -1.113 0.000 1.509 0.717 0.940 0.000 1.145 -1.184 1.011 1.551 0.903 1.055 0.990 1.252 1.095 1.002 1.061 +1 0.739 -0.843 -0.227 0.654 -1.399 0.933 0.762 -0.798 2.173 1.169 -0.365 1.264 0.000 0.402 -0.788 -1.713 0.000 0.992 0.857 0.793 1.551 0.976 0.943 0.987 1.143 1.017 1.099 0.920 +0 0.457 -1.336 0.185 0.880 -1.398 1.205 -0.049 -0.683 2.173 1.137 -1.101 1.055 0.000 1.685 -0.752 1.604 0.000 1.090 -0.568 0.548 3.102 1.061 0.865 0.987 0.886 1.154 1.174 0.937 +1 0.400 -0.015 0.684 2.768 1.514 0.875 -0.098 -0.048 2.173 0.336 2.011 0.382 0.000 0.857 -0.989 -0.933 2.548 0.785 0.298 -0.736 0.000 0.858 0.954 0.992 1.058 0.951 1.032 0.975 +1 0.942 0.718 1.649 1.613 -1.241 0.915 -0.168 -0.183 2.173 1.130 0.875 0.352 0.000 0.998 2.668 1.524 0.000 0.941 1.271 -0.906 0.000 0.886 1.064 0.991 0.719 0.711 0.836 0.746 +1 0.454 1.719 0.069 1.357 -1.684 0.891 1.239 -0.345 0.000 0.405 1.304 0.445 0.000 0.892 -0.215 1.583 2.548 1.047 0.921 1.360 3.102 0.836 0.884 1.088 1.050 0.554 0.799 0.788 +0 1.373 1.370 -1.718 0.494 -0.836 0.452 0.900 0.803 2.173 0.534 0.287 -1.585 0.000 0.685 0.285 0.519 2.548 1.938 0.074 -0.253 0.000 1.243 0.992 0.988 0.893 0.274 0.667 0.785 +0 1.141 0.176 -0.690 1.698 -0.070 1.000 0.238 -1.128 2.173 1.330 -2.274 0.764 0.000 1.125 0.747 1.210 0.000 1.172 -0.355 -1.709 3.102 4.425 2.469 1.024 1.022 0.692 1.888 1.681 +1 0.659 -0.289 1.108 1.131 0.118 1.022 -1.118 -1.720 2.173 0.879 -0.556 0.332 0.000 0.589 1.251 0.149 0.000 0.438 -1.994 -1.285 0.000 1.165 1.087 0.988 1.254 0.904 0.906 0.874 +1 0.930 -1.632 0.827 1.223 -0.824 0.970 -1.600 -0.958 1.087 1.078 -0.770 0.195 2.215 0.722 -1.708 -1.593 0.000 0.599 -1.252 1.184 0.000 0.815 1.020 1.473 1.072 1.445 0.937 0.845 +0 0.533 -1.483 1.324 0.552 -0.596 0.802 0.071 -0.131 2.173 0.483 -0.454 -1.535 0.000 1.310 -0.179 1.155 2.548 0.394 1.147 -0.594 0.000 0.737 0.809 0.989 0.739 1.183 0.744 0.659 +0 1.379 0.643 1.613 0.635 0.818 0.499 0.824 0.459 2.173 0.851 -0.110 -0.988 1.107 1.195 -1.419 -0.511 0.000 0.834 1.659 1.188 0.000 0.852 1.039 0.989 0.751 1.038 0.743 0.720 +0 0.509 1.660 0.101 0.905 1.166 0.827 0.414 0.497 2.173 0.897 -0.476 -0.531 0.000 1.845 0.247 -1.252 2.548 0.508 -0.715 1.664 0.000 0.824 0.897 0.991 1.349 1.543 1.362 1.333 +0 0.652 -0.025 -0.488 0.889 0.642 1.352 1.633 1.191 0.000 1.260 0.404 0.065 2.215 1.243 -1.543 -0.261 0.000 1.768 -0.762 -1.568 1.551 1.196 1.007 0.991 0.798 1.652 1.114 0.910 +0 0.744 -1.339 1.108 1.265 -0.117 1.061 0.033 -1.610 2.173 0.904 0.523 1.071 0.000 1.542 1.263 -0.760 1.274 2.149 0.066 0.350 0.000 0.937 1.030 1.200 2.102 1.604 1.573 1.275 +0 0.780 -0.209 -0.396 1.516 0.604 1.264 -1.444 -1.401 0.000 0.711 -0.710 0.040 0.000 0.966 0.808 0.210 2.548 0.982 0.071 -1.631 3.102 0.922 0.931 1.182 0.840 0.802 0.675 0.653 +0 0.515 -0.616 -0.983 1.371 1.470 0.556 -1.431 -1.266 0.000 0.796 -0.882 -0.335 2.215 1.650 0.404 0.910 1.274 1.796 -1.343 -0.171 0.000 1.280 0.881 0.984 0.879 1.411 1.204 1.042 +0 0.916 -0.774 -1.599 1.237 1.654 0.699 -1.346 -0.683 0.000 1.280 -2.726 0.108 0.000 0.919 0.543 -1.191 2.548 2.122 -1.351 0.795 3.102 2.057 1.613 0.984 0.694 1.779 1.698 1.634 +1 0.373 -1.421 -1.256 1.301 1.070 1.247 -0.256 -0.546 2.173 0.515 -1.144 -0.765 0.000 0.597 0.230 1.645 0.000 0.508 1.607 0.475 0.000 0.895 1.281 0.987 1.205 0.949 1.318 1.087 +0 0.934 0.845 0.735 1.438 1.326 1.287 0.649 -0.063 2.173 0.883 0.176 -1.548 0.000 0.700 1.337 -1.069 0.000 0.466 -0.748 -0.792 0.000 0.945 1.065 0.989 0.689 0.425 0.873 0.813 +1 0.925 -0.516 1.553 1.652 -1.320 1.381 -0.674 0.582 2.173 0.788 0.171 -0.914 0.000 0.747 -0.376 -0.320 2.548 0.874 0.493 -0.489 0.000 0.822 1.096 0.987 0.833 0.934 1.017 0.890 +0 2.976 0.647 1.331 1.349 0.977 1.716 1.199 -0.346 1.087 0.551 0.250 -1.300 2.215 0.603 0.406 -0.864 0.000 0.490 0.517 -0.446 0.000 0.227 0.585 0.987 0.948 1.295 1.467 1.083 +0 1.600 0.098 0.989 1.153 -0.323 0.770 0.652 -1.612 2.173 1.032 -0.993 -0.077 2.215 0.820 -1.548 -1.004 0.000 0.638 -1.462 -0.287 0.000 0.897 0.947 1.741 1.236 1.785 1.142 1.090 +1 0.469 -0.520 -0.197 0.784 0.493 0.706 1.172 -0.188 0.000 0.943 0.652 -1.704 2.215 0.339 0.820 -1.278 0.000 0.909 -0.942 1.443 1.551 0.973 1.006 0.983 0.882 0.895 0.847 0.759 +0 1.012 -1.405 -0.993 0.339 1.459 0.950 -0.743 0.933 2.173 0.684 0.085 -0.010 2.215 0.893 -1.307 0.068 0.000 1.705 -1.928 -1.273 0.000 0.956 0.933 0.989 0.963 1.027 0.798 0.733 +1 1.538 -0.911 -1.649 0.908 -1.338 1.176 -0.450 -0.022 0.000 0.686 0.431 1.465 1.107 1.427 -1.358 -0.508 0.000 1.544 -0.812 -1.018 0.000 0.918 0.881 0.980 1.128 0.338 0.805 0.776 +1 1.822 0.449 -0.602 1.204 1.549 1.469 1.241 0.911 2.173 1.035 -2.882 -0.626 0.000 0.778 0.724 -1.323 1.274 0.445 1.300 1.206 0.000 0.339 0.708 1.914 0.959 1.242 1.161 0.879 +0 1.310 -0.374 0.374 0.728 -0.391 0.820 1.373 1.643 2.173 0.502 0.478 -1.600 2.215 0.588 0.264 -0.007 0.000 0.615 0.397 -1.227 0.000 0.649 1.012 0.991 0.892 0.482 1.087 0.849 +1 0.658 -0.398 1.056 1.011 -1.420 0.639 -2.260 -0.564 0.000 0.838 -0.458 0.564 2.215 0.645 -1.067 -1.598 2.548 0.751 -2.263 -0.833 0.000 0.437 1.011 0.990 0.811 0.778 0.741 0.763 +0 1.411 0.549 0.986 1.593 1.617 1.176 1.928 -0.584 0.000 1.099 0.132 0.543 2.215 0.626 0.672 -0.394 0.000 1.125 0.383 -1.359 3.102 1.025 1.079 1.118 0.986 1.007 1.122 1.146 +0 1.715 -0.244 0.174 1.362 1.027 0.765 -0.958 -0.561 0.000 1.403 -0.976 -1.520 2.215 0.473 -0.121 -1.634 2.548 0.544 0.595 0.529 0.000 1.239 0.827 1.473 1.514 0.400 0.959 0.912 +0 0.402 0.329 0.974 4.086 0.837 1.679 -1.142 -0.863 0.000 1.498 -0.269 -1.014 2.215 2.589 -0.021 1.090 2.548 0.996 -0.507 -0.456 0.000 0.893 0.974 0.988 0.900 2.001 1.525 1.599 +0 0.561 -1.451 -0.368 1.163 -0.726 0.330 1.443 0.731 0.000 0.939 -0.073 1.592 2.215 0.493 0.098 0.709 0.000 0.468 -1.351 -1.458 0.000 0.714 0.641 0.993 0.814 0.450 1.122 0.907 +0 0.701 -1.620 0.066 0.967 -1.049 0.781 -0.442 1.067 2.173 0.496 -0.588 -0.870 0.000 0.508 -1.017 0.551 0.000 1.068 0.262 -1.041 3.102 0.834 0.863 0.987 1.047 0.988 0.956 0.777 +0 0.401 2.426 -0.606 1.490 1.552 1.029 -0.751 -0.573 2.173 0.850 -0.038 -1.131 0.000 1.216 -1.988 1.033 0.000 2.206 1.315 0.532 3.102 0.884 0.940 0.997 1.040 2.675 2.067 1.644 +0 0.831 0.442 -0.560 0.735 0.812 0.867 0.360 -1.449 2.173 0.837 0.873 0.571 2.215 0.767 0.808 -1.130 0.000 0.538 1.355 0.290 0.000 0.730 0.763 1.023 0.676 1.261 0.757 0.654 +1 0.875 1.742 -0.607 1.152 -0.394 1.357 0.734 1.317 2.173 0.925 0.277 -0.263 0.000 1.456 -0.111 0.236 2.548 2.737 0.248 -1.555 0.000 0.911 0.716 0.996 1.476 1.646 1.197 0.995 +1 1.970 -1.563 1.674 0.458 1.198 0.530 -0.770 -1.064 0.000 1.630 -1.088 0.186 2.215 1.104 -0.491 0.859 2.548 0.563 0.079 -0.921 0.000 0.507 1.061 0.997 1.033 0.915 1.077 0.958 +1 0.450 0.935 1.725 1.143 0.293 0.690 -0.337 -1.355 0.000 0.789 -0.795 0.828 2.215 0.809 -1.585 -1.150 0.000 1.681 0.337 0.323 1.551 1.050 1.103 0.989 0.589 0.815 0.972 0.912 +1 0.427 -1.574 -0.395 1.617 0.575 0.723 0.834 -1.380 2.173 0.503 0.861 -0.403 0.000 0.787 0.059 0.618 2.548 0.498 -0.488 -1.535 0.000 0.765 0.705 0.984 1.167 0.988 1.587 1.288 +1 0.828 1.317 -1.179 1.732 0.598 0.919 1.156 0.315 2.173 0.413 1.029 1.732 0.000 0.419 0.559 -1.103 0.000 1.244 -0.241 0.126 3.102 1.028 0.900 1.658 1.006 0.919 1.064 1.143 +0 0.313 1.929 -1.618 0.968 -1.034 1.050 -0.541 1.030 2.173 1.094 -0.941 -0.978 0.000 1.372 0.428 0.457 1.274 0.480 0.493 -1.428 0.000 0.895 1.294 0.992 1.216 1.084 1.036 0.953 +1 0.295 1.743 1.398 0.617 -0.558 1.201 1.148 -1.353 2.173 0.815 0.151 0.607 0.000 1.295 0.920 -0.017 0.000 0.994 0.020 1.345 3.102 1.091 0.916 0.996 0.943 1.023 1.029 0.851 +0 0.656 -1.342 -0.147 0.498 1.556 0.834 -0.052 0.529 0.000 1.124 -0.850 -1.564 0.000 0.457 -0.864 -1.026 2.548 0.964 -0.644 -0.552 0.000 1.074 0.898 0.987 0.534 0.469 0.535 0.522 +1 0.301 -1.398 -0.961 1.635 0.359 0.537 -0.360 0.980 0.000 0.863 -0.250 1.684 2.215 1.350 0.727 -1.105 2.548 0.750 0.585 -0.694 0.000 0.847 0.977 0.989 1.342 0.926 1.515 1.210 +1 0.809 -0.770 -1.438 0.414 -0.017 0.946 -0.015 -0.712 1.087 0.841 -0.102 0.161 2.215 1.645 -0.593 0.972 0.000 0.440 -1.367 1.629 0.000 0.838 0.846 0.983 0.984 0.931 0.893 0.852 +0 0.914 -1.331 0.121 0.891 -0.625 0.363 -0.929 -1.204 2.173 0.690 -1.310 0.843 0.000 0.481 0.398 1.660 2.548 0.373 -1.464 -0.438 0.000 0.681 0.792 0.985 0.727 0.476 0.719 0.664 +0 0.363 1.698 1.031 0.515 -0.515 1.489 0.552 -1.621 1.087 0.965 0.264 -0.365 2.215 1.642 1.183 0.953 0.000 1.235 1.580 0.271 0.000 1.024 1.343 0.992 0.982 1.617 1.300 1.031 +0 0.426 0.962 1.263 1.068 0.715 2.309 0.454 -1.300 0.000 2.321 -0.427 0.388 2.215 0.318 1.250 0.037 2.548 0.543 0.988 -0.961 0.000 0.818 0.900 0.990 0.889 0.987 1.616 1.306 +0 3.174 -0.834 0.584 1.252 0.590 2.229 -0.326 -1.073 0.000 0.854 0.454 1.403 2.215 0.773 -0.557 -0.325 2.548 0.408 0.580 0.001 0.000 0.470 0.481 0.995 1.280 0.992 0.953 0.724 +0 0.744 -1.858 -1.488 0.354 1.529 1.012 -1.097 0.649 1.087 0.902 -1.136 -1.482 2.215 0.897 -0.968 -0.771 0.000 0.704 0.571 -0.403 0.000 0.914 0.910 0.998 0.963 1.321 0.928 0.802 +1 0.427 1.935 -0.740 0.358 1.031 1.412 0.748 1.134 0.000 1.128 2.390 -0.585 0.000 0.619 1.224 1.696 0.000 0.764 -0.889 0.602 3.102 0.958 0.896 0.995 0.791 0.558 0.783 0.704 +0 1.099 0.926 -0.328 2.444 0.807 0.476 0.239 -0.867 0.000 0.679 -0.141 -1.348 2.215 0.875 -0.966 -1.420 2.548 0.731 0.614 0.341 0.000 0.830 0.880 1.938 1.396 0.392 1.207 0.964 +0 1.800 0.980 -0.200 1.139 -0.498 0.794 1.300 0.718 2.173 0.853 -0.486 -1.335 0.000 1.001 1.087 1.336 0.000 1.184 0.389 -1.270 3.102 0.838 1.329 0.975 0.829 1.096 0.896 0.921 +0 1.384 1.429 -1.160 1.575 -0.165 0.769 0.056 1.470 0.000 1.060 0.712 -0.133 2.215 1.068 -0.929 1.203 0.000 0.796 1.044 0.822 3.102 1.001 0.995 1.599 1.037 0.666 0.979 1.200 +0 0.752 0.023 0.513 1.425 -1.452 0.362 -0.547 -0.141 0.000 0.891 1.166 -1.497 0.000 1.164 -0.167 0.882 2.548 1.160 0.836 -0.651 0.000 0.924 1.213 1.406 0.750 0.341 0.730 0.741 +1 0.609 -0.545 -0.152 0.547 -0.941 1.281 0.395 0.938 2.173 0.745 0.074 -1.054 0.000 0.799 -0.626 0.332 2.548 0.858 1.365 -1.130 0.000 0.902 1.071 0.993 1.669 0.973 1.083 1.115 +1 1.732 -0.716 1.307 0.731 -1.693 1.781 1.419 -0.132 0.000 0.805 0.032 -0.962 2.215 0.834 0.187 0.742 2.548 0.593 -1.198 -0.814 0.000 3.242 1.956 0.992 1.065 0.873 1.301 1.588 +1 1.502 -2.000 1.067 0.893 -1.232 0.639 -0.366 0.566 2.173 0.700 -0.957 0.188 0.000 1.100 -0.502 -0.496 2.548 0.370 -1.676 -1.563 0.000 0.763 0.641 1.408 1.263 0.859 1.051 0.845 +0 1.205 0.393 -0.622 1.410 0.215 1.350 -0.152 0.096 2.173 2.229 -0.706 1.528 2.215 0.718 -1.895 -1.602 0.000 1.458 1.348 -1.107 0.000 0.917 0.969 1.238 0.913 2.563 1.530 1.327 +0 0.890 0.409 -1.370 0.294 -0.524 0.777 0.706 1.532 0.000 0.872 0.068 -0.060 2.215 1.042 0.108 0.413 2.548 0.678 0.598 -0.857 0.000 0.925 0.946 0.981 0.882 0.419 0.738 0.711 +0 1.150 1.215 -0.010 0.780 1.369 0.647 1.927 1.408 0.000 1.635 1.296 -0.668 2.215 1.026 -0.217 0.961 2.548 0.597 -1.350 1.513 0.000 0.772 1.152 1.243 0.986 1.819 1.104 0.941 +1 1.501 -1.538 1.725 0.678 -0.128 0.831 -0.384 0.409 2.173 0.634 0.088 -0.893 0.000 0.602 -1.332 -0.146 2.548 0.677 -1.051 1.463 0.000 0.957 1.002 1.391 0.793 0.652 0.827 0.781 +1 0.556 -1.234 -1.384 0.887 0.099 0.782 -2.655 -1.663 0.000 0.959 -1.007 -0.267 2.215 0.548 -0.658 1.458 2.548 0.519 -2.186 0.135 0.000 0.972 0.928 0.987 0.633 0.779 0.886 0.803 +1 1.273 -0.051 1.263 0.909 -0.898 1.198 0.057 0.474 2.173 0.936 -1.019 1.461 0.000 1.977 0.857 -0.510 2.548 0.889 -0.268 -1.109 0.000 0.979 1.402 1.386 1.271 1.725 1.332 1.127 +1 1.031 0.476 -0.487 1.201 -0.238 0.770 -0.599 1.447 2.173 0.449 -1.479 1.050 0.000 0.535 -0.929 -0.913 2.548 0.552 -1.722 -1.273 0.000 0.590 0.677 0.988 0.633 0.699 0.793 0.745 +1 0.652 1.815 -0.194 0.102 0.885 0.669 -0.181 -1.314 2.173 0.688 1.139 0.712 0.000 0.921 1.435 1.492 0.000 0.988 -0.154 -0.005 3.102 0.828 0.961 0.995 0.912 0.796 0.861 0.755 +0 0.619 -1.019 -0.097 1.186 0.560 0.939 1.433 -0.944 2.173 1.077 0.352 -1.623 0.000 1.423 0.230 1.209 2.548 1.101 2.302 -0.156 0.000 0.950 1.025 0.982 1.468 1.623 1.997 1.561 +0 0.682 -0.056 1.375 0.770 -0.795 0.975 -2.354 0.473 0.000 1.348 -0.297 0.206 0.000 1.796 -0.084 -1.317 2.548 1.304 0.643 1.559 0.000 1.110 0.840 0.987 0.499 0.144 0.482 0.508 +0 0.307 0.677 -1.475 0.890 1.507 0.831 1.435 0.616 0.000 0.733 -1.846 -1.350 0.000 1.236 0.466 -0.098 2.548 0.726 0.219 -0.916 0.000 0.618 0.828 0.987 1.078 0.215 0.852 0.775 +0 0.838 0.951 -1.299 1.376 -1.128 0.675 1.143 0.735 0.000 0.655 0.489 -0.250 2.215 1.055 -0.389 0.522 2.548 0.410 0.132 1.657 0.000 0.724 0.817 0.995 0.985 0.709 1.108 0.935 +0 0.363 -0.449 1.045 1.043 1.241 1.332 0.567 0.584 2.173 0.988 2.469 -1.077 0.000 1.982 0.075 -0.862 2.548 1.660 -0.020 1.080 0.000 3.182 2.456 0.988 1.220 2.016 1.828 1.417 +1 0.418 1.992 -0.009 0.434 -1.725 0.520 1.356 0.105 0.000 0.763 0.790 -0.511 0.000 1.242 -0.498 1.233 0.000 1.172 0.363 -1.128 3.102 0.800 0.786 0.983 0.518 0.236 0.520 0.515 +1 0.938 0.586 -0.409 0.308 -0.932 1.032 1.194 0.512 0.000 1.728 1.285 -0.960 2.215 1.771 1.465 0.979 0.000 1.029 0.760 1.330 3.102 1.108 0.830 0.992 0.763 1.090 1.169 0.983 +1 1.024 -0.546 -1.488 0.893 0.742 0.309 -1.944 -0.282 0.000 0.772 1.030 -1.205 2.215 0.605 -0.800 1.106 0.000 0.790 -1.433 0.177 0.000 0.860 0.678 1.199 1.076 0.528 0.838 0.771 +1 0.523 0.925 -0.158 0.718 1.169 0.941 0.426 -1.532 2.173 0.790 -0.219 0.180 0.000 0.708 -0.710 -1.583 0.000 0.990 -2.242 0.625 0.000 0.818 0.769 0.987 0.854 0.900 1.148 0.987 +1 1.260 1.007 0.192 0.505 -1.233 0.367 -1.224 -1.310 2.173 0.626 -0.337 1.075 2.215 0.548 0.249 1.167 0.000 0.751 -0.610 -0.981 0.000 0.758 0.602 1.060 0.886 0.673 0.833 0.692 +1 1.282 0.369 -0.090 0.149 -0.570 0.680 1.053 0.898 1.087 0.685 0.002 1.559 0.000 0.701 -0.793 -1.087 2.548 0.829 1.854 -1.072 0.000 0.944 1.090 0.989 0.709 1.281 0.825 0.759 +0 0.331 -2.005 1.520 0.485 0.069 0.711 -0.243 0.230 0.000 0.824 0.867 -0.716 2.215 1.827 0.362 1.604 2.548 0.387 -0.090 -0.773 0.000 0.631 0.844 0.977 0.973 1.177 0.873 0.786 +1 0.679 -0.432 -0.483 0.444 1.661 0.920 0.431 1.568 2.173 0.841 0.148 -0.191 0.000 1.240 -0.521 0.248 1.274 0.845 -0.812 -1.156 0.000 1.063 0.851 0.982 1.139 1.420 0.927 0.845 +0 0.676 0.091 -1.669 0.964 -0.792 1.991 1.877 1.189 0.000 1.520 -2.296 -0.471 0.000 1.190 -0.065 -0.881 2.548 1.850 1.437 0.521 0.000 0.656 1.024 0.989 0.663 0.348 0.906 0.798 +1 0.280 0.811 0.232 1.078 1.348 0.911 -0.233 -0.860 0.000 1.246 0.155 1.177 2.215 0.842 -0.316 0.228 2.548 1.320 -0.882 -0.546 0.000 0.851 0.870 0.984 0.702 0.868 0.965 0.837 +0 0.763 -0.043 -1.486 0.834 0.666 0.779 0.036 0.883 0.000 0.960 0.481 -0.325 2.215 0.952 -0.682 -0.671 2.548 0.976 -0.578 1.617 0.000 0.957 1.037 1.030 0.852 0.742 0.852 0.739 +0 1.628 -0.421 0.916 0.651 0.386 0.709 0.821 -0.917 0.000 1.225 -0.035 -0.886 1.107 0.658 0.547 1.452 1.274 0.674 -0.036 0.520 0.000 1.122 0.889 0.980 0.667 0.876 0.834 0.800 +1 1.197 1.292 0.632 0.115 -0.768 0.932 -0.352 -1.151 2.173 0.737 0.822 0.914 0.000 1.088 0.232 -0.366 2.548 0.676 -1.164 -1.573 0.000 1.117 1.151 0.992 1.230 0.904 1.176 0.980 +1 0.798 0.803 -0.317 1.451 -0.005 0.545 -0.634 -0.467 0.000 1.418 -0.172 1.628 2.215 0.937 -1.180 1.038 2.548 0.790 -0.053 -1.354 0.000 0.776 1.013 0.976 1.101 0.952 1.042 0.880 +1 0.549 0.720 -1.017 0.378 -0.784 0.672 1.032 1.002 0.000 0.969 0.218 -1.742 0.000 1.688 -0.908 -0.359 0.000 1.551 -0.430 0.175 3.102 0.901 0.881 0.984 0.746 0.584 0.850 0.818 +1 1.388 0.814 -0.929 0.752 1.169 0.966 -0.601 1.475 2.173 0.457 -0.532 0.197 0.000 1.272 -0.627 -0.421 2.548 1.003 0.789 0.530 0.000 0.768 0.867 1.344 1.323 1.369 1.105 0.944 +0 1.201 -0.727 0.120 1.121 0.664 0.489 1.227 0.423 2.173 0.789 0.193 -0.930 0.000 1.027 -1.081 -1.394 1.274 0.629 0.829 1.538 0.000 0.820 0.928 0.980 1.396 1.616 1.136 1.021 +1 2.302 0.967 -0.183 1.723 0.243 1.648 0.265 1.687 2.173 0.565 0.924 -1.578 0.000 0.755 0.772 0.361 2.548 1.112 0.327 -0.693 0.000 0.878 1.029 1.036 0.569 1.356 1.397 1.085 +0 0.772 0.975 -0.343 3.733 0.125 1.062 0.826 -1.451 2.173 0.862 1.219 1.657 2.215 0.917 1.118 0.745 0.000 1.025 0.629 1.344 0.000 0.921 1.197 0.993 1.792 0.556 1.316 1.276 +1 1.322 -0.057 -1.155 1.228 -0.795 0.668 0.883 0.198 2.173 0.497 -2.015 0.196 0.000 0.736 0.090 1.692 0.000 0.842 0.973 0.741 0.000 0.819 0.907 0.990 1.318 0.870 1.055 0.962 +1 1.209 -0.271 1.366 1.608 -1.540 0.841 -0.296 -0.142 2.173 0.672 1.054 0.725 1.107 0.781 -0.259 -0.684 0.000 0.973 1.708 -0.707 0.000 0.912 1.170 0.990 1.142 1.137 1.052 1.023 +1 1.679 0.866 -1.547 0.650 1.446 3.177 -0.994 -0.394 0.000 2.719 0.112 1.184 2.215 0.887 0.390 0.535 0.000 0.676 0.742 1.078 1.551 0.458 0.803 0.985 0.585 0.500 0.924 0.824 +0 0.672 -0.444 -0.749 1.491 0.160 0.503 -0.941 1.711 2.173 0.578 0.193 -1.446 1.107 0.739 0.482 1.240 0.000 0.609 0.990 -0.270 0.000 0.765 0.905 1.014 0.847 0.534 0.711 0.692 +1 0.393 0.298 -0.602 0.761 0.230 0.654 -0.862 -1.403 0.000 1.664 -0.382 -0.068 2.215 1.480 -0.873 1.501 0.000 2.229 0.488 1.183 1.551 0.888 1.376 0.984 0.752 1.802 1.315 1.081 +0 1.965 0.015 -1.078 1.834 -1.513 0.983 -1.160 0.475 0.000 1.370 -0.091 0.653 1.107 1.060 -0.790 -0.699 1.274 0.622 -0.375 0.100 0.000 0.591 0.896 1.002 1.568 1.305 1.136 1.132 +0 1.339 -1.146 1.691 1.009 -0.423 0.553 -1.650 -1.511 0.000 0.754 2.626 0.905 0.000 0.826 -1.249 -0.023 2.548 1.097 -0.354 -0.007 3.102 0.915 1.499 1.522 0.897 0.357 1.496 1.666 +0 0.875 -1.405 1.598 1.502 -1.077 1.243 -0.550 0.708 0.000 0.986 0.597 -0.376 0.000 0.904 -0.325 -0.235 2.548 1.526 -0.561 1.539 3.102 0.795 0.905 1.060 0.957 0.909 0.755 0.890 +0 0.941 -0.130 1.085 1.833 -1.473 0.588 -0.519 -0.682 2.173 0.390 -0.496 0.090 2.215 0.418 1.692 0.810 0.000 0.632 -2.095 -0.876 0.000 2.558 1.411 1.352 1.005 0.452 0.923 0.929 +1 0.410 1.530 -0.047 1.723 1.261 0.834 -0.022 -0.675 0.000 0.941 -0.304 -0.105 0.000 0.886 0.616 -1.442 2.548 1.286 0.535 0.933 3.102 0.965 1.028 1.077 0.689 0.687 0.825 0.985 +0 1.643 1.920 -0.834 0.596 0.819 0.314 0.953 1.240 2.173 0.542 0.136 0.539 0.000 0.657 2.699 -0.002 0.000 0.423 0.761 1.732 1.551 1.819 1.036 1.367 0.871 0.165 0.662 0.724 +1 1.263 -0.324 -1.330 0.879 -1.105 1.129 2.222 0.158 0.000 0.926 0.117 0.672 0.000 0.987 0.075 1.596 2.548 0.752 0.877 -0.111 0.000 0.951 1.655 0.989 0.601 0.866 1.950 1.868 +0 0.458 -0.679 1.010 1.061 0.027 0.459 0.645 -1.276 0.000 0.806 0.345 1.370 2.215 0.600 -0.233 -0.052 0.000 0.995 1.130 -0.823 1.551 0.952 0.829 0.988 1.497 0.854 1.135 0.958 +0 0.929 -1.495 1.261 0.941 0.527 0.755 -0.943 -0.948 2.173 0.302 -2.844 -0.602 0.000 0.651 0.367 -1.636 0.000 0.663 -0.897 0.543 3.102 1.819 1.062 0.991 1.016 0.731 0.770 0.761 +0 0.903 -0.404 0.577 1.613 1.362 0.788 1.083 -0.629 2.173 0.885 2.188 -0.619 0.000 1.406 0.600 1.561 2.548 1.121 1.697 0.121 0.000 0.814 0.865 1.087 0.924 1.240 1.091 1.320 +1 0.430 -0.517 -1.086 1.119 0.367 1.171 -0.970 -1.738 1.087 1.217 -1.147 -0.436 0.000 1.185 -1.075 1.072 2.548 0.724 -2.325 -0.278 0.000 1.109 1.218 0.987 1.173 0.851 1.083 1.027 +1 0.732 -1.361 1.083 1.131 -1.352 0.503 -0.085 -1.569 0.000 0.966 1.076 0.147 0.000 0.826 0.080 -0.802 1.274 0.573 1.918 1.178 0.000 0.991 0.998 1.024 0.679 0.228 0.665 1.072 +0 2.456 -1.429 -1.717 0.197 -1.586 0.648 -1.317 -0.489 0.000 1.775 1.145 0.564 2.215 0.738 -1.293 -0.882 2.548 1.338 0.948 -0.262 0.000 0.815 0.829 0.972 0.712 2.376 1.896 1.550 +1 0.574 -0.903 -0.179 0.218 0.638 0.885 -0.656 -1.050 0.000 1.333 -0.224 0.469 2.215 1.170 -0.151 1.125 0.000 0.396 0.239 -0.684 3.102 0.909 0.635 0.979 0.733 0.590 0.877 0.775 +1 1.100 0.125 1.370 0.797 -1.120 1.204 0.026 0.251 2.173 0.837 0.315 -0.619 0.000 1.156 -0.403 0.940 2.548 0.967 0.745 -1.074 0.000 0.577 1.079 1.016 1.161 0.923 0.901 0.842 +0 0.640 0.554 -0.854 0.517 0.768 0.653 0.651 -1.686 1.087 0.489 -0.892 0.321 0.000 1.065 -0.300 1.280 0.000 1.852 -0.659 -0.284 3.102 0.904 0.939 0.987 0.761 1.441 0.886 0.744 +1 0.898 -0.919 -1.230 1.775 -1.067 3.290 0.745 0.740 2.173 1.155 -1.214 -1.023 0.000 1.936 -0.402 -1.052 2.548 0.686 -1.478 -0.222 0.000 0.826 0.869 0.990 2.817 3.718 2.473 1.997 +0 0.898 -0.579 -0.895 0.481 0.266 1.002 -0.434 1.542 0.000 1.038 -0.381 -0.427 2.215 0.744 -1.161 0.279 2.548 0.731 -1.116 1.159 0.000 0.755 0.893 0.992 0.634 0.700 0.843 0.757 +1 0.976 0.562 -1.314 0.042 -0.888 0.345 -1.612 1.362 0.000 0.469 0.520 0.347 2.215 0.613 -0.417 -1.085 0.000 0.848 0.368 -0.092 0.000 0.840 0.875 0.823 0.626 0.306 0.600 0.583 +1 0.762 -0.230 0.902 1.662 0.148 0.395 -1.424 0.310 1.087 0.751 -1.582 1.687 2.215 0.685 0.651 -1.587 0.000 1.069 -0.819 -0.659 0.000 0.900 1.822 0.988 1.215 0.762 1.540 1.292 +0 0.526 1.027 0.306 0.760 1.101 1.431 0.558 0.021 2.173 1.661 -0.718 -1.312 0.000 1.528 -2.229 0.914 0.000 1.649 -1.052 -0.965 1.551 1.263 0.926 0.989 1.386 2.118 1.710 1.914 +1 0.966 0.863 -0.461 1.140 -1.530 1.987 1.369 -1.721 0.000 3.129 0.610 0.172 2.215 1.659 0.672 -1.583 0.000 1.784 0.367 0.617 3.102 0.796 0.980 1.193 1.560 0.857 1.176 1.078 +0 0.683 -1.500 0.194 0.300 1.120 0.733 -0.964 0.556 2.173 0.439 -1.639 1.018 0.000 1.123 -1.588 -1.356 0.000 1.811 -0.498 -1.045 3.102 0.907 1.008 0.999 0.817 1.231 0.833 0.743 +1 0.707 -0.267 -1.036 0.661 0.297 0.534 -1.130 -0.146 2.173 0.505 -0.033 0.486 0.000 0.866 0.308 1.710 0.000 0.824 -0.957 -1.393 3.102 0.925 0.986 0.982 0.588 0.633 0.667 0.603 +1 0.361 -2.075 -1.158 0.480 -0.690 0.877 -0.763 1.216 0.000 0.516 -0.280 -1.716 0.000 1.029 -1.319 0.420 0.000 2.681 0.307 -0.701 3.102 0.943 1.152 0.997 0.719 0.853 0.769 0.693 +0 0.287 -2.017 -0.482 0.669 1.712 0.784 -0.242 1.412 2.173 0.488 0.324 0.155 0.000 1.219 -0.538 -0.237 2.548 0.723 -0.701 -0.865 0.000 0.781 0.891 0.993 0.753 1.232 0.719 0.641 +0 2.353 -1.596 -1.284 0.329 -0.025 1.118 -0.854 0.258 2.173 0.809 -1.235 1.348 2.215 0.635 -0.373 -0.721 0.000 0.718 -0.569 0.925 0.000 0.749 0.796 1.105 0.885 1.199 1.055 0.854 +0 1.735 -0.152 0.244 0.577 -1.364 1.115 -0.228 -0.997 1.087 0.956 -0.462 0.673 2.215 1.146 -0.902 -1.348 0.000 1.246 -0.704 1.397 0.000 0.822 0.986 1.375 1.165 1.527 0.958 0.903 +0 1.749 0.203 1.214 0.333 -0.163 1.069 -0.108 -0.940 2.173 0.805 -0.412 0.185 2.215 0.483 0.026 -0.166 0.000 0.521 0.638 1.132 0.000 0.677 0.829 1.000 0.823 1.179 0.901 0.721 +0 0.536 -0.150 0.151 1.955 0.167 1.482 -0.858 -1.701 2.173 0.823 -0.418 -0.397 2.215 0.916 -1.344 -1.165 0.000 0.685 -1.110 0.971 0.000 0.822 0.879 0.985 0.790 1.542 1.198 0.963 +1 0.429 0.128 1.159 1.567 -1.045 1.043 1.148 0.606 0.000 0.744 -0.803 1.284 1.107 0.766 0.790 -0.881 0.000 0.944 -0.682 -0.517 3.102 1.583 1.391 1.040 0.891 0.755 1.093 0.961 +1 1.115 0.166 0.395 0.574 -0.059 0.457 -0.565 0.400 0.000 1.514 0.487 -1.270 2.215 0.978 0.689 -0.434 2.548 0.760 -1.484 1.367 0.000 0.899 1.150 0.993 1.318 0.900 1.048 0.941 +0 0.991 -0.327 -0.317 1.242 -1.314 0.777 -0.382 -1.323 2.173 1.058 0.118 0.965 0.000 0.306 -2.208 0.250 0.000 0.821 -0.428 0.725 3.102 1.531 0.844 1.202 0.742 0.815 0.799 0.790 +1 2.115 0.145 1.612 0.171 0.256 1.080 0.163 -0.565 2.173 1.030 0.344 -1.326 2.215 1.993 0.620 -0.872 0.000 2.771 -0.026 0.075 0.000 0.900 1.128 0.989 0.742 0.992 0.973 0.970 +0 0.720 0.215 -1.644 0.663 -1.244 0.996 -0.214 0.097 1.087 0.499 -0.871 0.820 2.215 0.586 -0.894 -0.483 0.000 0.806 -1.133 1.248 0.000 0.772 0.904 0.988 0.702 0.725 0.774 0.668 +0 0.650 1.213 -1.659 1.007 -0.191 0.565 1.235 -0.030 2.173 0.716 1.003 -1.194 0.000 1.297 0.308 0.750 2.548 1.051 0.154 1.728 0.000 0.739 0.968 1.087 0.664 0.852 0.767 0.708 +0 1.389 0.316 0.779 0.515 0.032 0.963 -0.185 -0.320 0.000 0.910 -0.266 -1.534 2.215 0.597 -0.585 -1.080 0.000 1.187 -0.204 1.166 3.102 0.917 0.988 0.985 0.945 0.611 0.749 0.751 +0 1.477 -1.232 -1.403 2.478 0.739 0.722 -2.632 0.550 0.000 1.191 -0.846 0.027 2.215 1.041 -1.295 -1.024 2.548 1.085 -0.912 0.903 0.000 1.241 1.229 2.480 1.551 1.016 1.132 1.103 +0 1.743 0.631 0.983 0.360 -1.295 0.761 -0.091 -1.714 2.173 0.982 0.344 0.303 1.107 1.331 -0.273 -0.559 0.000 0.728 1.061 -0.627 0.000 0.948 0.938 0.987 0.822 1.265 0.869 0.829 +1 0.955 0.720 -0.512 0.748 0.585 0.764 0.066 0.125 0.000 1.089 0.266 1.683 1.107 0.967 0.960 1.435 0.000 0.902 0.011 -1.177 3.102 0.987 0.763 0.988 0.665 0.493 0.649 0.616 +0 0.710 1.010 -0.547 1.962 -0.115 0.517 -1.508 1.463 0.000 0.353 0.763 1.118 1.107 0.569 -0.538 -1.186 0.000 1.116 -2.247 1.369 0.000 0.816 0.920 0.992 0.827 0.260 0.719 1.256 +1 0.791 1.625 -0.842 1.190 0.526 1.009 0.953 -0.496 0.000 0.630 1.433 0.253 0.000 2.080 0.518 -1.537 2.548 0.686 0.206 0.502 0.000 0.983 0.932 1.268 1.309 0.899 0.968 0.888 +0 1.053 1.508 -1.693 0.568 0.846 0.397 -0.074 0.312 0.000 0.471 -1.189 -0.713 0.000 0.663 -0.216 1.325 2.548 1.114 0.919 -0.502 3.102 0.920 0.926 0.982 0.966 0.808 0.751 0.937 +0 0.398 0.361 -1.595 1.257 -0.890 0.488 -0.037 0.201 2.173 0.495 0.920 -1.451 0.000 1.420 -0.910 0.913 2.548 0.773 -1.878 1.382 0.000 1.969 1.339 0.983 1.838 0.809 1.238 1.269 +0 1.436 0.754 1.461 0.679 0.937 1.147 0.907 0.728 2.173 1.259 2.236 -0.826 0.000 0.618 2.559 -0.588 0.000 1.200 0.783 -0.651 0.000 0.839 0.891 0.990 0.894 0.912 1.075 1.055 +1 1.971 0.244 1.418 0.300 0.855 1.172 -0.721 -0.027 2.173 1.213 -0.333 1.476 0.000 1.845 -1.755 -0.914 0.000 1.467 -0.459 0.744 3.102 1.927 1.609 0.987 1.600 0.896 1.170 1.393 +0 0.302 2.018 -1.277 0.597 1.259 1.128 0.103 1.665 2.173 1.425 0.883 0.157 0.000 0.998 1.104 -0.389 2.548 0.521 -0.086 -0.064 0.000 0.944 0.747 0.980 0.757 1.486 1.026 0.844 +0 0.576 -1.449 -0.841 1.060 0.461 0.406 -0.230 -0.552 0.000 0.727 0.336 -1.537 2.215 0.654 -0.839 0.407 0.000 0.528 -0.155 0.309 0.000 0.885 0.779 0.998 0.997 0.491 0.851 0.727 +0 0.689 -0.916 0.548 0.582 -0.777 1.086 -0.155 -0.016 2.173 0.534 -1.636 1.683 0.000 1.166 -0.829 -1.333 2.548 0.914 -0.271 1.172 0.000 0.780 0.686 0.986 0.736 1.408 0.917 0.770 +1 1.186 0.864 -1.263 0.128 -0.595 0.624 -0.351 -0.838 0.000 1.119 -0.320 0.439 1.107 1.216 -1.048 1.183 2.548 0.993 -0.835 -0.519 0.000 0.912 1.011 0.985 1.054 0.932 0.923 0.880 +1 0.971 -0.290 -0.067 0.518 -1.355 1.235 -0.582 1.097 2.173 0.454 0.731 -0.682 0.000 0.612 -2.476 -0.510 0.000 1.040 -1.711 -1.530 0.000 0.985 0.732 0.988 1.027 1.111 0.932 0.804 +0 1.612 -1.261 0.767 0.427 -0.792 1.001 0.579 1.631 2.173 0.632 0.206 -0.626 0.000 0.662 1.057 -0.479 0.000 0.536 -1.199 0.278 0.000 0.907 0.937 1.133 0.672 1.522 1.108 0.945 +0 0.977 -0.370 0.268 0.552 -0.557 0.660 0.655 -1.497 2.173 0.487 0.118 0.080 0.000 0.827 0.266 -1.264 2.548 1.086 -0.301 1.101 0.000 0.788 0.911 0.979 0.724 0.257 0.640 0.625 +0 1.102 0.438 0.216 2.674 -0.117 1.806 0.117 0.347 1.087 1.679 -0.558 1.620 0.000 1.705 -0.046 -1.712 0.000 2.180 -0.314 -1.167 3.102 0.774 0.970 1.008 1.229 2.121 1.557 1.657 +1 0.782 -1.167 0.637 1.254 0.318 0.591 -0.677 1.648 2.173 1.543 -0.682 -1.025 2.215 0.443 2.021 0.329 0.000 0.539 -0.013 -1.063 0.000 0.897 1.132 0.992 1.309 0.938 1.026 1.008 +0 0.410 1.046 -0.996 1.391 0.347 0.950 0.196 1.532 2.173 1.142 -0.547 -0.658 2.215 0.509 -1.036 0.232 0.000 0.367 -0.751 1.202 0.000 0.370 0.789 0.986 1.315 1.530 1.143 0.923 +0 0.479 0.381 -0.501 0.937 0.474 0.583 -0.236 -1.522 0.000 0.595 0.260 0.029 2.215 1.088 -0.392 1.074 2.548 0.984 0.463 -1.118 0.000 0.795 0.810 0.987 0.645 0.756 0.726 0.724 +0 0.560 0.745 -1.406 1.016 -0.085 1.266 1.348 0.797 0.000 2.000 -0.325 -1.488 0.000 1.941 0.705 -0.665 2.548 1.779 -0.105 0.245 0.000 1.021 1.017 0.987 0.704 1.061 1.038 0.952 +1 0.359 0.516 1.009 0.928 -1.661 1.737 -1.449 0.845 0.000 2.331 0.054 -1.149 1.107 0.668 -1.865 -0.914 0.000 0.645 -1.891 1.021 0.000 0.924 0.808 0.991 1.684 0.906 1.283 1.162 +0 0.583 1.670 0.232 1.047 -1.147 0.383 1.250 -0.708 1.087 0.861 0.839 -1.680 2.215 0.922 0.646 0.725 0.000 1.100 -0.605 0.498 0.000 0.897 0.983 1.024 0.790 0.670 0.778 0.800 +1 1.042 0.073 -1.709 0.733 -1.244 0.751 -1.236 0.407 0.000 0.987 -0.109 -1.042 1.107 1.164 0.851 -0.533 2.548 0.412 -0.746 -0.296 0.000 0.525 1.088 0.994 0.820 0.802 0.958 0.957 +0 1.561 0.156 1.161 0.981 0.477 0.919 0.613 -0.100 1.087 0.767 -0.225 -0.788 0.000 1.113 0.213 -1.480 0.000 0.697 0.890 1.516 3.102 0.892 1.152 0.990 0.634 0.863 0.765 0.819 +0 0.380 -1.728 -0.043 1.067 -1.011 0.663 -1.366 1.091 0.000 0.805 -0.549 0.209 2.215 1.101 -1.087 -0.932 2.548 0.528 -0.544 -1.575 0.000 0.695 0.843 0.981 0.755 0.915 0.695 0.658 +1 0.794 -1.267 -1.112 0.105 1.390 2.089 2.397 0.795 0.000 2.684 0.253 -1.161 0.000 1.144 -2.231 0.078 0.000 1.417 -0.103 0.286 3.102 0.684 0.801 0.977 0.765 0.809 0.865 0.769 +1 2.189 -1.398 0.462 0.401 0.213 0.883 -2.216 -1.411 0.000 0.710 -0.133 -1.077 0.000 0.711 -0.443 1.544 2.548 0.556 -0.851 0.821 0.000 0.893 0.687 0.998 0.967 0.720 0.954 0.850 +0 0.378 0.470 -0.426 1.218 1.485 0.638 0.128 1.089 0.000 0.902 -0.260 -0.009 2.215 0.872 1.224 -0.769 2.548 0.467 -0.762 -0.383 0.000 0.928 1.024 0.985 0.849 1.035 0.772 0.712 +0 0.822 -0.857 -1.585 2.376 1.716 1.383 1.061 0.435 2.173 0.729 -0.799 -0.180 0.000 0.823 0.693 -1.267 2.548 0.639 1.594 -0.003 0.000 1.639 1.146 0.987 2.053 1.344 1.349 1.245 +0 1.386 0.269 0.523 0.810 1.034 0.949 2.790 -1.579 0.000 1.040 -0.587 -0.154 2.215 0.908 0.943 -0.851 2.548 0.413 -1.365 0.921 0.000 4.451 2.470 0.990 1.141 1.124 2.102 1.673 +0 0.671 0.041 0.854 0.253 -0.983 0.611 -1.057 1.428 2.173 0.328 1.090 -1.242 0.000 0.560 -0.274 0.226 2.548 1.228 -1.112 0.238 0.000 1.522 0.877 0.988 0.657 0.705 0.721 0.634 +0 0.400 -0.065 0.333 1.495 -1.370 0.814 -0.855 -0.416 2.173 0.980 0.199 0.820 0.000 0.653 -1.121 0.945 0.000 0.456 1.466 1.137 0.000 0.897 1.103 1.071 0.519 0.764 0.697 0.679 +1 1.130 0.874 -0.450 1.158 -1.282 0.844 0.815 1.672 0.000 1.550 0.497 0.023 2.215 0.757 -2.651 -1.588 0.000 0.777 2.167 0.829 0.000 0.612 1.401 1.079 0.644 0.384 0.793 0.791 +1 0.869 1.335 -1.680 1.378 -1.025 1.077 1.231 0.802 1.087 0.902 0.928 -1.072 0.000 1.727 -0.573 0.491 0.000 1.029 0.306 1.705 3.102 0.888 0.710 0.991 1.263 0.953 0.933 0.845 +0 1.130 0.396 1.621 0.779 0.939 0.981 1.259 0.052 0.000 1.422 -0.275 -1.411 2.215 0.928 1.691 0.474 0.000 0.977 0.876 -0.842 3.102 0.815 0.844 0.989 0.883 0.925 1.264 1.130 +0 0.907 0.285 -1.367 0.727 -0.102 0.718 1.421 0.126 2.173 0.738 -1.752 -1.692 0.000 0.487 0.297 1.239 2.548 0.373 2.149 -0.085 0.000 3.096 1.674 1.023 0.926 0.756 1.421 1.116 +1 1.242 -0.631 0.797 0.402 1.067 0.727 1.354 -1.348 0.000 0.768 -1.334 0.655 0.000 1.586 0.488 -0.581 0.000 1.115 -0.354 -0.128 3.102 1.482 1.178 0.998 0.723 0.636 0.866 0.891 +1 0.994 -1.394 1.075 0.724 -0.228 0.775 -1.007 0.141 2.173 1.711 -1.670 -1.147 0.000 0.599 -1.570 0.832 0.000 0.687 -1.874 -1.445 0.000 0.655 0.830 1.084 0.669 0.921 0.623 0.570 +0 1.431 1.705 0.589 0.627 -0.072 1.273 0.069 -1.595 2.173 1.251 1.533 -0.424 0.000 0.685 -0.557 0.764 2.548 1.014 1.388 -0.945 0.000 0.980 1.322 0.980 1.655 1.068 1.196 1.134 +1 1.614 -1.055 -0.165 1.002 1.341 1.906 -0.172 1.364 0.000 1.846 0.055 -0.539 0.000 0.990 0.852 -1.227 2.548 0.645 0.378 -0.236 3.102 3.968 2.132 1.722 1.514 0.499 1.360 1.358 +1 1.113 1.125 1.698 1.183 0.996 0.923 0.040 0.400 2.173 1.709 0.299 -0.882 0.000 0.509 2.066 0.355 0.000 0.902 -0.714 -1.259 3.102 1.919 1.133 0.991 1.276 1.062 1.057 1.186 +1 0.971 1.015 0.913 0.381 0.209 0.907 0.309 -0.639 1.087 0.620 0.796 -1.188 2.215 0.941 0.448 1.723 0.000 0.526 0.093 -0.243 0.000 0.775 0.810 0.994 0.782 0.596 0.724 0.631 +1 1.596 -0.534 -0.244 0.993 0.482 1.555 -0.248 1.567 0.000 1.366 0.231 -0.813 2.215 0.703 -1.605 0.579 0.000 1.647 -0.339 0.589 0.000 0.868 0.655 1.062 1.123 0.725 0.901 0.815 +1 0.499 0.260 -1.622 0.717 1.398 1.161 -0.942 0.522 0.000 1.357 -1.229 -1.139 2.215 0.555 -1.240 -0.296 1.274 0.976 -1.948 -0.797 0.000 0.895 0.737 0.991 2.063 0.636 1.407 1.345 +1 0.338 -2.151 -1.013 1.110 1.079 0.672 0.569 1.450 2.173 0.972 -0.746 -0.986 0.000 1.012 -1.036 -0.251 0.000 0.892 0.044 0.272 3.102 0.977 0.858 0.987 1.081 0.748 0.902 0.850 +0 1.611 -1.225 -0.889 0.743 0.387 0.491 -0.905 -1.456 0.000 0.773 -2.126 -0.293 0.000 1.706 -0.399 1.026 2.548 0.788 -0.341 0.026 0.000 0.956 0.945 1.383 0.717 0.183 0.773 0.689 +1 1.185 0.360 -0.318 0.298 1.062 0.736 -0.466 -0.747 2.173 0.704 0.706 1.573 0.000 1.029 -0.302 1.039 0.000 1.242 1.958 1.368 0.000 0.943 1.178 0.989 0.711 0.877 0.925 0.792 +0 2.497 0.558 1.015 1.826 0.430 2.579 0.443 -0.915 0.000 0.255 0.725 -1.423 0.000 1.055 -0.227 1.333 2.548 0.695 1.208 -0.201 1.551 0.804 0.923 1.486 1.037 0.897 0.971 1.298 +0 0.698 -2.000 0.894 1.397 -0.337 1.332 -2.786 -0.959 0.000 0.481 0.291 1.501 2.215 1.006 -0.852 0.399 2.548 1.037 -1.562 -1.121 0.000 0.910 1.638 1.225 1.286 0.787 1.454 1.203 +1 0.337 -1.037 -0.039 0.893 0.340 1.412 1.135 0.583 0.000 3.551 0.863 -1.677 2.215 1.216 0.206 -0.585 2.548 2.482 0.665 0.137 0.000 0.926 1.034 0.996 1.696 1.984 1.661 1.336 +1 0.413 0.354 0.086 0.927 -1.095 0.817 0.410 0.710 0.000 0.815 0.191 1.278 0.000 1.081 -0.969 -0.486 2.548 0.940 0.627 -1.415 0.000 0.868 1.367 0.986 0.727 0.598 0.858 0.880 +1 0.773 0.328 -1.375 1.655 1.345 1.339 -0.134 -0.354 1.087 0.652 -0.895 0.946 2.215 0.978 0.154 0.256 0.000 1.306 1.670 -1.580 0.000 1.807 1.629 0.999 1.478 1.383 1.340 1.179 +1 0.665 -2.237 -1.046 0.826 -1.293 1.036 -0.730 0.685 2.173 0.334 -0.638 1.087 0.000 1.186 -0.374 -0.047 1.274 1.231 -0.859 -1.214 0.000 1.015 0.949 0.971 1.209 0.877 0.924 0.953 +0 0.286 0.656 0.666 0.731 0.745 0.713 -0.346 -1.022 0.000 0.625 -1.531 -1.727 2.215 0.778 -0.656 0.506 2.548 0.513 -1.869 -0.027 0.000 1.245 0.949 1.001 0.726 0.745 0.696 0.662 +1 0.882 -0.688 0.443 0.627 -1.538 0.817 0.408 1.045 0.000 1.656 0.301 -0.934 0.000 1.463 -0.310 1.069 2.548 1.254 -0.067 -0.445 0.000 0.882 0.649 1.007 0.696 0.766 0.872 0.788 +1 0.715 -0.257 1.054 0.861 -1.434 1.119 -0.832 -1.436 2.173 1.047 0.168 0.686 0.000 1.808 -0.025 0.028 0.000 0.400 -1.329 -0.973 0.000 1.096 0.589 0.987 0.731 0.615 0.819 0.738 +1 0.332 -0.322 0.340 0.585 -0.965 0.435 0.461 0.895 0.000 0.869 -0.835 0.379 2.215 1.393 -0.669 -1.228 2.548 0.757 1.083 1.704 0.000 0.693 1.041 0.983 1.068 1.163 0.962 0.859 +1 1.375 -0.400 -0.117 0.770 -0.626 0.940 -1.284 1.320 0.000 0.490 -1.242 -0.603 2.215 0.674 -1.940 0.813 0.000 0.396 -0.410 -1.394 3.102 0.883 0.934 0.981 0.575 0.306 0.578 0.840 +0 0.717 -0.219 -1.605 0.272 -0.213 0.711 2.248 0.563 0.000 0.788 1.019 -0.194 2.215 1.028 0.474 -1.402 2.548 0.643 1.488 1.594 0.000 0.864 0.951 0.993 0.590 0.885 0.847 0.782 +1 0.688 -0.868 -0.619 1.368 0.303 0.584 -1.894 -1.503 0.000 0.884 0.257 0.713 2.215 0.507 -0.246 1.642 1.274 1.206 -0.120 -0.619 0.000 1.580 0.974 0.994 0.903 0.562 0.913 0.838 +0 0.552 0.712 0.466 1.403 1.145 1.561 0.049 1.200 1.087 2.900 -0.894 -0.665 2.215 0.425 1.004 -0.154 0.000 0.556 -1.275 -0.995 0.000 0.999 1.215 0.985 0.708 3.489 1.732 1.328 +0 0.479 -0.536 -0.643 3.147 0.004 1.617 0.764 0.659 0.000 0.987 1.500 -0.739 1.107 2.318 -0.015 -1.730 0.000 3.221 -0.425 -1.623 3.102 1.129 1.480 0.985 1.733 2.273 1.810 1.721 +1 1.234 0.113 -1.238 1.294 -0.638 1.356 -0.155 -0.773 1.087 2.207 -0.615 -1.120 2.215 6.523 0.503 0.868 0.000 1.161 -0.997 1.248 0.000 0.818 1.234 0.992 0.801 0.992 1.110 0.912 +1 0.406 -1.686 -1.060 1.341 0.139 0.772 -1.264 1.619 2.173 1.449 -0.097 0.432 2.215 0.754 -2.615 -1.071 0.000 0.622 -0.822 -1.471 0.000 0.864 0.872 0.987 1.690 1.675 1.308 1.151 +0 0.601 1.261 -1.003 1.282 0.181 1.482 1.487 1.366 1.087 2.116 1.299 -0.495 1.107 1.137 0.870 1.645 0.000 0.895 1.411 0.516 0.000 1.036 0.892 1.066 0.862 2.600 1.296 1.072 +1 0.307 2.039 0.768 1.287 -0.088 2.655 1.139 1.135 0.000 2.068 0.645 -0.945 1.107 1.182 0.779 -0.269 0.000 2.898 0.008 -0.601 3.102 1.168 0.992 0.981 1.127 0.999 0.908 0.825 +1 1.739 -1.042 -0.513 0.496 1.343 1.236 -0.293 0.841 1.087 0.603 -2.822 -1.483 0.000 0.737 -1.904 -1.591 0.000 0.530 0.049 -0.903 3.102 0.439 0.932 1.280 1.350 0.870 1.239 1.083 +0 0.749 1.581 1.427 1.943 -1.168 0.874 1.595 -0.744 2.173 1.114 1.111 0.826 2.215 2.859 -0.080 0.539 0.000 1.621 1.114 -1.286 0.000 0.828 0.938 1.203 0.872 1.475 0.980 0.793 +1 0.988 -0.305 0.006 0.949 -1.014 0.819 -1.139 -1.625 2.173 0.920 0.339 0.741 0.000 0.528 0.575 -0.311 0.000 1.110 -0.927 -0.428 3.102 0.909 1.138 1.066 1.018 0.890 1.038 0.893 +0 1.306 0.494 -0.051 1.547 -0.598 1.329 0.836 -0.430 2.173 3.610 0.378 1.451 0.000 0.489 -0.807 -0.220 0.000 0.861 -0.777 1.103 3.102 2.464 1.519 0.991 0.596 1.595 1.640 1.453 +1 0.952 -1.626 1.588 1.204 -1.249 1.275 -1.147 0.069 2.173 0.416 -1.203 1.241 0.000 0.470 -2.106 -0.802 0.000 0.788 -0.304 0.657 3.102 0.757 0.985 0.990 0.965 0.688 1.045 0.830 +1 0.763 -0.093 1.647 1.519 0.864 0.889 0.751 -0.381 2.173 0.908 0.308 0.832 0.000 2.052 1.146 -1.000 2.548 0.375 1.043 0.737 0.000 0.374 0.951 0.986 1.619 0.987 1.238 1.011 +1 0.883 -1.128 -1.705 1.412 0.049 0.595 -0.553 1.025 0.000 1.737 -0.961 0.007 0.000 0.757 -0.635 1.495 0.000 1.126 -0.488 -0.748 1.551 0.949 1.185 1.546 0.881 0.473 0.807 0.789 +1 0.755 0.612 -0.190 0.410 -1.450 1.358 -0.040 -0.532 2.173 0.890 0.130 1.673 1.107 0.976 -1.832 0.616 0.000 1.683 0.531 1.223 0.000 0.807 1.105 0.987 0.891 1.486 0.971 0.812 +0 0.682 -0.171 -0.685 1.082 0.961 0.681 -0.546 -0.091 0.000 0.670 -0.725 -1.738 1.107 0.917 0.453 0.951 1.274 0.920 0.468 -1.346 0.000 0.881 0.932 1.185 0.717 0.778 0.710 0.658 +1 0.396 -0.764 -0.193 1.550 1.273 1.358 -0.155 1.647 0.000 1.739 1.096 -0.574 2.215 1.783 0.952 1.068 0.000 2.200 2.485 -0.509 0.000 0.893 0.860 1.052 1.885 1.400 1.270 1.040 +1 0.728 -2.210 0.418 0.481 -0.559 0.955 -1.134 -0.484 0.000 0.658 -1.216 -1.138 0.000 1.848 -1.375 1.376 0.000 1.122 -0.633 0.889 3.102 0.939 1.030 0.993 0.481 0.293 0.624 0.593 +0 0.701 0.605 -1.429 0.987 0.499 0.969 1.748 1.576 0.000 1.983 -0.745 0.034 2.215 1.189 -0.875 -1.154 2.548 0.826 -0.663 0.413 0.000 1.056 0.937 1.136 1.361 1.441 1.111 0.950 +1 0.734 -1.285 -0.627 0.787 -1.654 1.046 -0.965 0.156 2.173 0.882 -0.789 -1.494 0.000 1.117 -0.786 0.705 2.548 0.487 1.551 -1.147 0.000 1.536 1.307 0.982 1.089 0.643 1.097 1.059 +1 1.182 0.013 1.063 0.312 1.360 0.863 0.857 1.514 2.173 1.142 0.126 -0.658 0.000 1.063 0.777 -0.572 0.000 1.323 0.335 0.064 0.000 0.994 0.843 0.990 1.030 0.737 0.872 0.881 +1 0.653 0.439 0.083 0.428 0.052 0.960 -1.125 -1.130 0.000 1.129 -0.052 0.662 2.215 0.658 0.634 -1.529 2.548 0.702 -1.087 1.256 0.000 0.943 0.957 0.979 0.741 0.913 0.911 0.803 +1 2.221 -0.693 1.244 0.570 -1.156 0.899 -0.973 -0.331 2.173 0.356 0.207 0.120 0.000 0.465 0.923 -0.330 2.548 0.579 0.624 -1.335 0.000 0.593 0.855 1.294 1.034 0.944 0.980 0.800 +0 1.442 1.484 0.930 0.637 0.093 0.914 2.178 0.815 0.000 2.322 1.080 -0.996 1.107 0.922 0.957 -0.751 1.274 0.952 -1.445 0.403 0.000 0.846 1.047 0.987 1.490 0.344 1.129 1.017 +0 2.073 0.641 0.075 1.716 -0.228 4.010 -0.595 1.706 2.173 2.140 -0.629 -0.256 0.000 2.537 0.784 0.363 2.548 1.080 -2.189 -1.325 0.000 2.817 3.426 1.003 4.120 4.888 3.219 2.973 +1 0.768 0.407 -0.985 0.890 0.292 0.773 -0.445 -1.107 0.000 0.515 0.314 -0.521 0.000 0.894 1.221 0.926 2.548 1.352 -0.247 0.894 1.551 0.859 1.007 1.046 0.780 0.768 0.873 0.761 +1 1.125 1.229 -0.604 0.330 -1.455 0.987 0.847 0.456 0.000 1.506 1.097 1.573 2.215 0.639 1.191 -1.145 1.274 0.428 0.362 -0.484 0.000 0.773 0.829 0.991 1.065 0.671 0.820 0.803 +0 1.617 -0.783 0.262 0.372 0.937 0.547 -0.974 1.734 1.087 0.377 -1.260 -0.918 2.215 0.594 1.138 1.409 0.000 0.950 0.814 -0.630 0.000 0.807 0.939 0.992 0.899 0.467 0.767 0.872 +0 0.988 0.109 1.692 0.611 0.345 1.186 1.112 -0.674 2.173 1.218 -0.286 0.717 1.107 1.217 0.953 0.663 0.000 1.314 0.890 -1.584 0.000 1.253 1.287 1.008 1.162 2.163 1.243 1.017 +1 0.504 1.216 -0.733 1.923 -0.408 1.108 0.252 0.758 2.173 1.561 -1.496 -1.490 0.000 1.497 0.646 0.129 0.000 1.128 0.940 1.615 3.102 0.741 1.731 0.989 1.300 0.982 1.464 1.389 +1 0.720 1.102 1.415 0.355 0.036 1.241 2.773 1.585 0.000 1.353 -1.029 -0.807 2.215 1.136 -0.392 0.419 2.548 0.974 0.075 0.010 0.000 3.462 3.174 0.990 1.198 1.250 3.072 2.196 +1 1.290 -0.496 1.019 0.618 -0.358 1.181 -0.777 -0.856 0.000 1.017 -1.291 0.793 0.000 0.642 -0.080 0.202 2.548 0.505 -0.388 -1.636 1.551 0.857 0.762 1.170 0.612 0.441 0.506 0.536 +1 0.777 -0.634 1.117 0.893 -0.031 1.135 0.456 1.297 0.000 1.270 0.506 -0.685 2.215 0.991 -0.250 -1.297 2.548 1.181 -1.151 -0.463 0.000 2.574 1.556 0.991 1.083 0.793 1.192 1.022 +1 0.799 -1.564 -1.377 0.395 0.506 1.455 -0.782 0.798 2.173 0.783 -1.049 -1.210 0.000 0.980 -0.211 -0.403 0.000 1.204 1.335 -0.436 0.000 0.894 1.236 0.989 0.994 0.902 1.226 1.029 +1 0.839 0.838 1.258 0.783 -1.188 1.349 1.147 -1.164 0.000 1.859 -1.358 0.615 2.215 0.635 -0.652 0.662 0.000 1.431 -0.624 -0.850 3.102 0.532 0.890 0.988 1.115 1.506 1.590 1.203 +0 0.782 -1.153 -1.374 2.708 -0.599 0.736 0.639 1.362 0.000 1.258 -1.872 -1.618 0.000 2.063 -0.219 0.948 2.548 3.434 -0.880 0.170 0.000 0.767 0.942 1.297 0.669 0.536 1.079 1.095 +0 0.671 -0.423 0.105 0.436 1.464 0.904 0.539 0.102 1.087 1.300 0.066 -1.290 2.215 0.864 -1.040 0.974 0.000 0.419 0.137 0.740 0.000 0.735 1.047 0.992 1.140 1.564 1.053 0.906 +1 0.736 1.192 1.468 0.914 0.260 1.198 -0.367 -0.941 2.173 0.749 0.681 1.400 0.000 0.782 -0.596 0.473 2.548 0.541 0.070 0.746 0.000 0.530 1.208 1.007 0.897 1.167 1.056 0.877 +1 0.419 -2.247 1.488 0.852 0.832 1.935 -1.921 0.078 0.000 2.141 1.598 1.129 0.000 3.370 -0.016 -0.770 2.548 3.197 0.406 -1.536 3.102 13.098 7.392 0.996 1.578 1.721 4.543 3.278 +0 0.984 1.504 0.575 1.586 0.716 1.029 0.355 -0.866 0.000 0.523 -1.136 -0.922 0.000 1.502 -1.753 0.639 0.000 1.150 1.040 -0.702 0.000 1.030 0.862 0.982 1.255 0.506 0.976 1.216 +0 0.507 0.904 1.087 1.248 -0.508 0.881 0.095 1.054 2.173 0.516 0.596 -1.077 0.000 0.686 -0.733 -1.005 0.000 0.794 0.296 0.317 3.102 0.706 1.055 1.092 0.625 0.558 0.694 0.706 +1 0.770 -0.476 0.768 1.330 0.849 0.837 0.359 -1.081 0.000 0.827 0.098 -0.112 0.000 0.806 -2.542 0.034 0.000 1.521 0.757 -1.585 3.102 1.374 1.098 0.992 0.682 0.310 0.982 1.058 +0 0.802 0.631 0.901 1.141 0.431 0.531 1.633 -1.193 0.000 0.542 -0.211 1.625 2.215 0.485 -0.962 -1.619 0.000 0.542 -2.132 -0.100 0.000 0.724 0.729 0.973 0.870 0.576 0.749 1.024 +0 0.852 -1.002 0.740 0.304 -1.283 1.022 0.476 0.704 2.173 1.255 -1.051 -0.599 2.215 0.653 0.443 -1.406 0.000 1.022 -1.233 1.459 0.000 0.793 0.881 0.987 0.863 2.105 1.272 1.005 +1 0.714 0.081 1.160 0.892 0.012 0.915 -0.948 -1.475 0.000 0.804 -0.498 1.029 2.215 1.104 -1.006 -0.903 2.548 1.514 -0.682 0.047 0.000 1.767 1.106 0.987 0.646 1.032 0.857 0.791 +0 1.162 -0.039 0.307 0.826 -0.604 0.847 0.077 -0.270 0.000 0.916 -0.429 1.413 2.215 0.448 -0.511 -1.125 2.548 0.878 0.767 1.228 0.000 1.407 0.896 0.993 0.945 0.515 0.757 0.705 +1 0.609 2.089 1.548 1.898 -0.290 0.882 -0.102 0.620 0.000 0.690 -2.189 1.352 0.000 2.182 0.617 -0.222 2.548 0.731 0.948 -1.461 3.102 2.374 1.819 1.484 1.426 0.896 1.605 2.117 +1 1.925 0.683 -1.310 0.496 -0.611 0.627 -0.706 -0.532 2.173 0.454 -0.522 1.467 0.000 0.879 2.155 0.859 0.000 0.886 0.075 0.253 0.000 1.046 0.866 0.994 0.910 0.477 0.838 0.888 +0 0.667 -0.068 -1.425 0.427 0.836 0.870 -0.162 1.384 2.173 1.669 -0.115 -0.713 2.215 1.017 -0.715 0.575 0.000 0.512 0.129 0.466 0.000 0.778 0.908 0.995 1.032 1.683 0.961 0.858 +0 0.757 -0.232 -0.098 0.644 0.982 0.797 -0.924 -0.969 0.000 1.186 -0.578 0.456 2.215 0.763 -0.010 -1.511 0.000 1.142 -1.106 1.453 3.102 0.938 0.864 0.989 0.777 0.911 0.871 0.832 +1 0.533 1.404 -0.991 0.591 0.235 1.389 2.920 -1.354 0.000 1.257 0.468 -0.032 0.000 1.590 0.180 0.357 0.000 1.184 1.098 0.791 3.102 0.805 0.899 0.995 0.806 0.872 0.911 0.775 +1 0.618 -1.212 -0.702 0.560 0.389 1.122 -0.587 -1.710 1.087 0.747 0.146 -0.208 0.000 1.323 0.098 -1.490 0.000 1.415 0.123 0.460 3.102 1.088 0.802 0.990 0.983 1.335 1.060 0.893 +1 1.001 -0.516 -0.671 0.490 1.045 1.025 0.553 0.711 2.173 0.438 -1.570 -0.905 0.000 0.788 0.023 -0.496 0.000 0.731 -1.744 1.682 0.000 0.886 0.570 0.988 1.015 0.734 0.834 0.722 +0 0.403 0.667 -1.518 1.124 0.285 0.850 -1.029 -1.303 1.087 0.461 -1.096 0.392 0.000 1.110 0.427 0.452 0.000 0.652 -0.195 -1.016 0.000 0.969 1.233 0.987 0.614 0.968 0.799 0.730 +1 0.816 -0.544 -0.577 1.445 0.434 0.716 -0.382 1.544 2.173 0.381 0.094 -1.664 0.000 0.635 -1.875 -0.040 0.000 0.932 0.039 -0.979 3.102 1.155 1.022 1.189 0.791 0.685 0.744 0.721 +0 1.390 0.266 -0.533 1.271 -0.310 1.329 0.124 1.437 2.173 1.560 -0.585 -0.738 2.215 1.239 0.258 0.851 0.000 0.775 1.081 1.073 0.000 0.611 0.837 1.002 0.741 2.111 1.281 1.171 +1 1.262 0.316 -1.008 1.507 -1.171 2.164 1.238 0.913 0.000 1.485 -1.044 -0.227 2.215 0.633 -1.529 -1.602 0.000 1.104 -0.397 -0.919 3.102 4.601 2.788 1.008 2.010 0.760 2.286 1.947 +1 0.442 -1.143 1.198 0.812 -0.094 0.849 0.441 1.554 0.000 1.928 0.213 0.237 2.215 0.736 1.234 -1.428 2.548 1.047 -1.236 -1.596 0.000 1.672 1.310 0.980 1.828 1.473 1.563 1.447 +1 0.446 -1.308 -1.057 1.617 1.411 2.207 -0.844 -0.913 0.000 1.145 -0.271 0.804 2.215 2.078 -1.081 0.583 2.548 1.616 -0.967 0.108 0.000 0.911 0.883 0.989 1.027 0.836 0.812 0.740 +0 0.558 0.238 -1.479 1.509 0.812 0.752 0.121 0.833 2.173 0.563 0.451 -0.024 0.000 1.762 -0.504 -1.085 2.548 0.710 0.869 -0.637 0.000 0.496 0.963 1.119 0.649 1.500 0.923 0.809 +0 0.879 0.088 -0.737 0.743 -0.181 1.159 -0.258 1.321 0.000 1.509 0.371 0.229 2.215 1.999 -0.737 -1.512 2.548 0.760 -0.205 -1.688 0.000 0.938 0.975 0.982 0.880 2.183 1.243 0.998 +0 1.853 0.940 -0.872 0.647 1.591 0.955 0.257 0.644 1.087 0.548 -0.943 -0.240 0.000 0.816 0.476 1.296 2.548 0.456 0.478 -1.507 0.000 0.811 0.950 1.208 0.851 0.626 0.896 0.828 +0 0.749 -0.298 -0.212 0.459 1.728 0.790 0.136 -1.725 0.000 0.871 -1.631 1.473 0.000 1.865 0.798 -0.113 2.548 0.734 -1.265 -1.344 1.551 1.306 1.098 0.982 0.805 1.566 1.033 0.836 +0 0.748 -0.403 -0.542 0.774 -0.261 0.448 -1.752 0.466 0.000 0.665 -1.392 0.995 0.000 1.135 -1.160 -1.534 2.548 0.399 -0.942 -1.138 3.102 0.721 0.774 0.982 0.630 0.183 0.746 0.847 +0 0.333 0.384 0.592 1.948 -0.187 0.687 1.500 1.525 0.000 0.579 0.327 1.577 0.000 0.619 -0.220 -1.632 2.548 0.989 0.448 -0.595 3.102 0.861 0.755 0.986 0.806 0.537 0.615 0.621 +0 0.643 -1.776 -1.451 0.658 -1.041 0.694 -0.151 0.142 2.173 0.730 0.578 -0.964 2.215 0.912 0.373 1.172 0.000 0.581 1.708 0.857 0.000 0.769 0.987 0.995 0.855 0.966 0.796 0.868 +0 0.562 -0.537 -0.541 0.715 0.508 0.792 -0.274 -1.062 1.087 0.690 -1.489 0.374 0.000 0.642 0.188 1.457 2.548 1.459 -1.039 1.217 0.000 0.919 0.891 0.994 0.980 0.713 0.849 0.774 +0 0.799 -0.112 -0.026 0.556 1.105 0.728 1.610 1.536 0.000 0.566 0.829 -1.150 1.107 1.027 0.758 0.413 2.548 0.558 1.209 -0.753 0.000 0.862 0.910 0.987 0.703 0.800 0.640 0.644 +1 0.729 -0.072 0.129 0.760 0.674 0.773 0.324 -0.798 2.173 0.648 0.283 0.688 0.000 1.530 0.100 -1.487 2.548 0.553 1.118 -0.267 0.000 0.950 0.852 0.992 0.941 0.802 0.837 0.728 +0 1.111 0.975 -1.220 0.722 -0.156 1.408 0.437 -1.452 0.000 1.606 0.457 0.413 1.107 1.039 1.056 -0.441 2.548 0.881 -0.080 0.084 0.000 1.735 1.322 1.016 1.104 1.070 1.172 0.977 +1 1.372 -0.040 -0.325 0.336 -1.281 0.787 0.472 1.459 2.173 0.503 1.166 0.316 0.000 0.545 0.810 0.980 2.548 1.111 1.875 -0.726 0.000 0.951 0.733 0.985 0.995 0.382 0.734 0.766 +1 1.260 -0.415 -1.468 0.973 1.404 0.824 -0.539 -0.455 2.173 0.739 1.674 0.491 0.000 0.561 -1.421 0.723 0.000 1.077 0.906 -0.326 1.551 0.864 0.702 0.986 1.100 0.913 0.903 0.933 +1 0.726 -0.993 1.628 1.282 -0.936 1.181 0.242 0.924 1.087 0.798 -0.634 -1.144 0.000 0.988 -0.166 0.348 2.548 0.865 0.524 -1.041 0.000 0.891 0.888 0.988 1.444 0.728 1.006 0.921 +0 2.224 0.516 0.846 0.308 0.297 0.998 0.145 -1.248 2.173 0.898 -0.032 -0.134 0.000 0.940 1.017 -0.900 0.000 0.625 -0.646 -1.340 3.102 1.240 0.897 0.984 1.313 0.405 0.876 0.884 +0 1.402 0.815 -0.603 0.891 1.041 0.487 0.272 1.123 2.173 0.707 1.194 -1.526 1.107 0.492 -1.834 1.326 0.000 1.400 -0.249 -0.377 0.000 0.882 0.855 1.543 0.925 0.729 0.794 0.818 +1 0.881 0.421 0.029 0.684 1.615 1.138 0.455 -1.564 0.000 1.225 -0.489 0.139 2.215 0.658 0.119 -1.004 2.548 0.731 0.386 1.204 0.000 0.839 0.663 1.065 0.895 0.874 0.929 0.797 +1 0.851 -0.896 -0.661 0.450 0.932 0.357 1.549 1.027 0.000 0.931 -0.655 -1.499 2.215 0.561 -0.145 0.445 2.548 0.812 -1.033 -0.158 0.000 1.728 0.987 0.989 0.722 0.781 0.836 0.727 +0 1.292 -1.041 0.545 1.577 0.988 1.476 -0.377 0.391 1.087 0.977 0.325 -0.963 2.215 1.392 -1.271 -1.053 0.000 1.854 -0.357 -1.187 0.000 0.917 1.037 0.990 0.866 1.781 1.315 1.228 +0 1.349 -0.693 -0.284 1.086 -1.730 0.957 1.431 -0.865 0.000 0.836 0.023 1.665 1.107 1.368 -1.341 0.603 0.000 0.794 0.417 0.242 0.000 0.908 1.135 1.617 0.920 0.675 0.750 0.758 +0 0.621 1.075 -1.168 1.425 1.024 0.653 -0.049 1.297 2.173 1.368 0.077 -0.451 0.000 0.718 1.041 0.077 2.548 0.961 -0.170 -1.165 0.000 0.921 0.869 1.199 0.878 0.932 0.823 0.857 +1 0.870 0.786 1.607 0.205 -1.338 0.612 -2.319 -1.573 0.000 1.403 0.597 0.386 2.215 1.397 0.745 -0.492 2.548 0.705 0.320 -0.287 0.000 2.050 2.243 0.981 1.021 1.068 1.740 1.362 +0 1.551 0.529 -1.023 1.002 -1.610 0.806 -0.965 0.617 0.000 1.050 -0.901 0.086 0.000 1.118 -0.242 1.443 2.548 0.820 0.111 -0.401 3.102 0.902 0.849 0.990 0.830 0.744 0.767 0.952 +0 0.745 -1.345 1.399 1.516 0.432 0.833 -0.527 -0.350 2.173 0.890 -2.203 1.617 0.000 0.501 -2.448 1.262 0.000 0.463 0.599 -1.238 0.000 0.388 1.432 1.126 1.098 0.439 1.138 1.006 +1 0.686 0.142 -1.426 0.985 0.120 1.117 -0.531 0.171 0.000 0.981 -0.391 1.636 1.107 0.554 0.857 0.702 2.548 0.701 -1.958 -1.380 0.000 1.934 1.452 1.121 0.878 0.814 1.062 0.923 +1 0.900 -0.445 -0.014 1.414 0.748 0.893 1.252 1.002 0.000 1.715 -1.701 -0.508 0.000 1.062 2.145 -0.723 0.000 3.456 -0.317 -1.399 3.102 1.262 1.254 0.991 1.380 1.162 1.195 1.114 +0 1.143 0.593 0.060 0.792 1.341 0.800 1.178 -1.730 2.173 0.479 0.423 -1.523 0.000 0.962 -0.671 -0.248 1.274 0.518 -0.899 0.520 0.000 0.826 0.934 1.206 0.930 1.621 0.952 0.794 +1 0.406 1.032 -1.285 0.477 0.432 0.894 0.818 -0.251 2.173 1.279 0.633 1.697 0.000 0.689 -0.340 1.413 0.000 0.376 -0.736 0.161 3.102 0.853 0.752 0.981 0.863 0.631 0.866 0.744 +1 1.478 1.156 1.148 1.144 0.412 1.031 0.654 -0.999 2.173 0.614 0.264 1.500 2.215 0.610 1.644 -0.785 0.000 0.747 -0.548 -0.264 0.000 1.216 0.961 1.108 0.813 0.937 0.955 0.880 +1 0.547 -0.326 1.308 0.170 1.534 0.660 1.194 -0.126 0.000 1.052 1.071 1.685 2.215 0.600 0.464 -0.234 0.000 0.850 0.845 -0.551 0.000 0.962 1.064 0.999 0.530 0.531 0.681 0.615 +1 1.062 -0.076 0.594 0.903 -0.298 2.590 -1.528 -1.243 0.000 0.934 -0.878 -0.026 0.000 1.129 -1.416 0.418 0.000 2.018 -0.450 1.205 3.102 0.806 1.121 0.988 0.610 0.854 0.903 0.812 +0 0.860 0.224 -0.608 1.260 -1.455 0.506 0.371 -1.248 0.000 1.251 -0.004 0.534 2.215 1.548 -0.420 0.996 0.000 1.995 -0.679 -0.220 3.102 0.852 0.903 0.996 1.022 1.076 0.943 0.779 +1 1.231 -0.447 -0.724 0.658 -1.395 0.889 -0.337 0.081 2.173 0.314 -1.595 0.647 0.000 0.717 -0.215 -1.517 0.000 1.439 -0.769 1.551 3.102 0.874 0.940 0.990 0.861 1.214 0.847 0.735 +1 1.160 0.229 0.513 0.819 -0.530 1.210 0.110 -0.939 0.000 0.647 -1.320 0.616 2.215 0.615 0.573 0.924 2.548 0.811 0.386 -1.571 0.000 0.857 0.933 1.089 0.934 0.803 0.920 0.838 +1 0.389 -1.290 0.025 0.114 -0.786 0.735 -0.304 0.621 2.173 0.722 1.082 -1.237 0.000 0.596 0.263 1.477 0.000 0.728 -0.275 -0.345 3.102 0.774 1.114 0.987 0.526 0.592 0.716 0.637 +0 1.158 0.807 -0.364 1.093 -0.725 1.006 -0.601 0.594 2.173 0.396 0.157 -1.226 0.000 1.528 0.943 1.711 2.548 0.376 1.235 1.695 0.000 0.433 1.005 0.974 1.155 1.926 1.221 0.923 +0 0.728 -0.882 1.150 0.798 -0.694 0.965 -0.619 0.685 2.173 0.875 1.282 -1.109 0.000 0.909 -0.636 -0.962 2.548 0.723 0.992 0.906 0.000 0.899 0.940 1.051 0.862 1.164 1.047 0.918 +1 1.249 -0.167 -0.846 1.821 -0.240 0.921 -1.055 -1.730 2.173 0.570 -1.218 0.970 0.000 0.771 -0.058 0.913 2.548 0.530 0.713 0.520 0.000 0.946 1.006 1.086 0.940 0.906 1.017 0.885 +0 1.030 1.120 0.018 0.251 -0.492 0.886 1.534 -1.287 0.000 1.162 1.131 1.522 1.107 2.420 1.987 0.432 0.000 0.724 0.334 -0.221 0.000 0.983 0.866 0.990 0.528 0.504 0.661 0.620 +0 0.723 -1.223 0.403 0.461 -1.134 1.047 -0.519 1.136 2.173 0.831 -0.614 -0.524 0.000 0.532 1.003 -1.190 2.548 0.655 0.617 -0.087 0.000 0.802 0.738 0.991 1.092 1.164 1.010 0.929 +1 0.752 1.290 -0.907 0.710 0.893 0.790 0.244 1.034 2.173 1.095 0.933 -0.083 0.000 0.992 0.437 -0.627 0.000 1.266 -0.045 -1.463 3.102 0.847 1.014 1.011 0.881 0.838 0.890 0.796 +1 0.902 -0.291 0.639 1.229 1.317 0.647 -1.570 -0.516 0.000 0.524 0.033 -1.389 0.000 1.051 -0.009 -0.688 2.548 0.832 -0.020 0.844 1.551 0.610 0.614 0.986 0.537 0.702 0.666 0.613 +1 0.381 -0.758 -0.324 1.917 0.675 0.944 0.249 -0.471 0.000 1.031 0.076 -1.703 2.215 0.773 0.641 1.029 0.000 1.766 0.958 -1.263 1.551 0.933 0.854 0.989 1.861 0.831 1.324 1.096 +1 0.369 -1.145 -1.354 0.457 1.547 0.871 -0.258 0.216 2.173 0.870 -0.077 -1.248 0.000 0.874 0.857 -1.674 0.000 0.825 0.066 0.752 3.102 0.871 0.799 0.995 0.935 0.444 0.814 0.710 +0 0.799 1.230 1.526 0.741 0.595 0.418 1.845 -0.544 0.000 0.611 0.539 -1.294 2.215 0.637 0.798 0.204 0.000 0.471 -0.616 1.603 3.102 0.750 0.833 0.990 0.702 0.416 0.594 0.600 +1 0.605 0.386 1.666 0.609 -0.301 1.026 0.395 1.122 0.000 1.024 -0.581 -1.164 0.000 0.708 0.760 0.839 0.000 1.251 0.911 1.406 0.000 0.800 0.683 0.984 0.686 0.181 0.879 0.827 +0 1.482 -0.245 -0.208 0.538 0.254 1.036 -0.317 1.641 0.000 0.191 1.178 -1.043 0.000 0.317 -0.680 0.390 2.548 0.472 1.176 1.168 1.551 1.012 0.763 0.996 0.483 0.430 0.555 0.710 +1 1.334 0.459 0.112 1.735 -0.377 0.998 -0.007 1.465 0.000 0.803 1.274 -1.050 1.107 0.404 -0.387 -0.032 0.000 0.701 0.149 0.985 3.102 1.140 1.240 0.984 0.768 0.769 0.749 0.879 +0 0.973 -1.017 0.711 1.341 1.397 0.389 0.646 -0.905 0.000 0.977 0.066 0.133 1.107 1.509 -1.023 -1.142 0.000 0.463 -0.460 -1.021 0.000 0.420 0.705 0.987 0.731 0.212 0.781 0.747 +1 0.818 -0.461 -0.763 0.888 1.116 0.564 -0.732 1.623 1.087 1.077 -1.430 0.796 2.215 1.007 -0.612 -0.057 0.000 1.123 0.593 -0.544 0.000 0.978 0.927 1.172 0.953 0.890 0.892 0.778 +0 0.351 -1.774 0.847 2.170 0.511 0.725 1.059 -0.921 0.000 0.710 -0.490 1.682 2.215 0.868 1.167 -0.176 0.000 1.193 0.394 -1.417 3.102 0.901 0.788 1.001 0.923 0.509 0.821 1.047 +1 0.751 -1.273 -0.508 0.387 0.768 0.691 0.168 0.033 2.173 0.572 0.802 -1.273 2.215 0.655 0.220 0.876 0.000 1.172 -0.090 -1.498 0.000 0.832 0.879 0.989 0.821 0.908 0.681 0.643 +0 0.693 0.011 1.682 2.099 -1.084 0.926 -0.222 0.378 1.087 0.762 0.779 1.436 2.215 0.594 0.696 0.894 0.000 0.672 1.075 -0.402 0.000 0.667 0.829 1.011 0.903 1.206 1.007 0.838 +1 1.258 -1.820 0.777 0.822 -0.693 1.025 -0.102 -1.187 2.173 0.519 -1.171 0.231 0.000 0.566 -1.657 -1.288 0.000 0.927 -0.532 1.203 3.102 0.855 1.146 1.366 0.865 0.905 1.060 0.869 +1 0.360 -2.304 -1.175 0.378 0.944 0.488 0.859 0.762 2.173 0.436 -0.938 -0.106 2.215 0.600 -1.738 0.871 0.000 0.550 0.788 -1.391 0.000 1.367 0.876 0.977 0.966 0.859 0.768 0.703 +0 0.690 0.677 0.788 2.548 0.235 1.258 0.833 -1.272 1.087 1.138 -2.556 -0.296 0.000 2.626 0.184 1.602 2.548 0.572 -2.416 0.593 0.000 0.769 3.251 0.990 1.716 1.394 2.852 2.423 +0 0.560 -1.821 1.155 1.775 0.682 0.912 0.877 -0.712 2.173 0.666 -0.019 -0.892 0.000 0.680 -0.423 1.098 2.548 0.696 -0.354 -1.623 0.000 0.659 0.706 0.995 0.556 1.212 1.193 0.931 +0 1.835 0.947 -0.665 0.911 -1.381 0.848 -0.350 1.167 0.000 0.738 -1.441 0.961 0.000 0.734 -0.334 0.038 1.274 0.511 -0.821 1.245 3.102 1.049 0.938 1.076 0.955 0.440 0.758 1.053 +1 0.546 1.060 -0.017 1.378 1.673 1.224 0.542 -0.463 2.173 1.515 0.948 1.223 0.000 0.855 0.850 -1.399 2.548 0.798 0.050 0.293 0.000 1.263 0.988 1.201 1.193 0.984 1.040 0.908 +0 0.736 1.285 -0.513 1.678 -0.041 1.041 1.131 0.343 0.000 1.322 0.460 1.739 0.000 0.836 -0.461 -1.731 2.548 0.620 1.416 -1.357 0.000 0.871 0.793 0.979 1.391 0.113 1.231 1.095 +0 1.078 0.087 1.014 1.521 -1.548 0.636 -1.342 0.640 0.000 1.096 0.519 -0.489 2.215 0.464 0.249 -0.071 0.000 0.701 -1.286 -1.361 3.102 1.061 0.829 1.314 1.191 1.128 0.910 0.928 +0 0.812 0.041 -0.323 0.424 0.592 0.655 0.951 1.666 0.000 0.517 1.095 -1.114 2.215 0.879 0.109 -1.456 2.548 0.477 0.782 0.494 0.000 0.744 0.632 0.983 0.655 0.434 0.552 0.547 +1 0.888 -1.152 -1.011 0.659 1.355 0.484 1.141 0.198 2.173 0.441 0.479 -0.644 0.000 0.652 -0.309 -0.371 0.000 1.522 0.453 1.251 3.102 0.802 0.720 0.989 1.478 0.789 1.109 0.987 +0 0.526 1.430 -0.336 1.636 -1.386 0.733 0.339 0.297 0.000 1.049 1.336 1.171 0.000 1.212 0.340 -0.626 2.548 0.383 -0.817 1.328 1.551 1.658 1.090 1.042 0.876 0.637 0.873 0.899 +1 1.166 0.158 -0.711 0.606 -0.288 0.925 -0.444 0.664 2.173 1.069 -0.152 0.063 2.215 1.246 -0.179 -1.651 0.000 0.439 0.188 1.470 0.000 0.316 0.974 0.993 1.057 0.784 0.770 0.769 +1 0.903 0.728 1.547 0.458 -0.191 1.460 -2.134 -0.631 0.000 1.943 0.210 0.972 2.215 1.131 0.332 -0.648 0.000 1.487 -0.445 1.394 3.102 0.805 0.978 0.983 1.004 0.820 0.878 0.803 +1 0.405 0.985 -0.698 0.871 0.163 0.890 0.197 -1.579 0.000 0.436 -1.407 0.121 2.215 0.677 -1.085 0.683 0.000 1.104 1.076 1.707 1.551 0.848 1.029 0.994 0.795 1.309 1.240 1.078 +0 1.658 -1.042 0.132 1.080 0.533 0.930 -0.633 1.677 2.173 0.745 -0.272 -0.843 0.000 0.924 2.127 0.965 0.000 1.167 0.106 -0.481 3.102 0.805 1.192 0.990 1.386 1.113 1.460 1.905 +0 0.813 0.038 -1.396 1.185 -0.174 0.402 -1.269 1.534 0.000 0.679 0.800 0.271 0.000 0.801 0.843 1.737 0.000 0.369 -0.708 -1.556 3.102 0.770 0.539 1.213 0.628 0.148 0.466 0.489 +0 0.764 0.937 -0.993 0.867 -1.466 0.992 -0.363 0.954 0.000 0.681 -0.252 -0.136 1.107 0.571 -0.985 -0.113 0.000 0.771 0.195 -1.254 3.102 0.777 0.771 0.980 0.469 0.577 0.528 0.560 +1 1.421 1.077 0.298 0.253 -1.243 0.921 0.753 -1.492 0.000 0.582 0.817 0.956 2.215 1.224 -0.030 -0.327 2.548 1.091 -0.056 -1.669 0.000 1.121 1.074 0.988 0.611 0.915 0.758 0.715 +1 1.650 0.334 1.108 0.981 -1.428 0.508 -0.754 -0.133 0.000 0.452 0.047 1.361 0.000 0.732 -1.083 0.965 2.548 0.794 -0.610 -1.174 1.551 1.079 0.771 1.333 0.850 0.562 0.710 0.695 +0 0.792 -0.771 0.105 0.596 -1.237 0.569 -0.816 1.058 2.173 0.444 -0.335 -1.118 1.107 0.773 1.178 -0.979 0.000 1.512 0.520 0.901 0.000 0.663 1.066 0.985 0.607 0.706 0.689 0.726 +1 0.920 -1.089 0.682 0.129 -1.596 1.151 -0.931 -0.520 0.000 1.150 1.861 1.519 0.000 1.256 -2.119 1.399 0.000 2.006 -0.881 -0.930 0.000 0.849 1.114 0.987 0.459 0.387 0.705 0.728 +0 0.486 1.519 0.443 0.609 0.943 0.406 -0.679 -1.180 2.173 0.657 -0.385 1.420 2.215 0.595 0.767 -0.578 0.000 0.816 -1.699 -0.372 0.000 1.540 0.962 0.978 0.690 0.555 0.713 0.744 +1 4.314 0.390 0.801 1.470 0.748 2.193 -0.363 -1.123 0.000 1.204 0.238 -0.749 1.107 0.790 0.510 -0.224 0.000 0.906 1.321 -0.295 0.000 0.851 0.878 0.985 0.540 0.808 1.121 0.953 +0 1.768 0.606 1.739 0.554 -1.210 0.955 0.442 0.521 2.173 0.727 0.394 -0.031 0.000 1.274 -0.611 -1.166 2.548 1.205 -0.663 0.132 0.000 0.798 1.036 0.975 1.227 1.593 1.105 1.020 +0 0.695 0.554 0.615 1.008 1.378 1.564 0.975 -0.496 2.173 1.539 0.233 1.386 2.215 0.337 1.265 1.275 0.000 0.721 0.315 -0.542 0.000 0.614 0.799 0.988 1.356 2.426 1.294 0.959 +1 0.709 -1.443 0.093 0.644 -1.704 1.134 -0.541 1.388 2.173 1.116 -0.338 -0.110 0.000 1.148 -0.674 -0.594 0.000 0.948 -0.299 -1.056 3.102 0.810 0.679 0.988 1.077 0.891 0.945 0.895 +0 1.899 0.755 0.127 0.809 -1.464 0.684 -1.040 -1.266 2.173 0.720 0.311 1.343 2.215 0.429 -0.117 -1.605 0.000 0.772 0.787 1.571 0.000 0.397 0.775 1.700 1.065 1.059 1.106 0.845 +1 0.694 0.043 -1.538 1.484 -0.058 1.190 0.925 -0.583 2.173 1.096 -0.119 0.818 0.000 1.690 0.557 1.284 1.274 0.874 -0.465 1.652 0.000 0.914 0.853 1.367 1.105 1.779 1.178 1.035 +1 0.584 2.017 0.156 2.029 -0.060 1.297 -0.483 1.701 0.000 0.398 -1.163 0.748 2.215 0.647 0.392 -0.864 1.274 0.527 0.713 1.119 0.000 1.112 0.943 0.978 1.275 0.725 0.872 1.095 +0 0.829 -0.099 -1.673 1.868 1.312 0.837 -1.625 -0.796 0.000 1.136 -1.133 -0.370 0.000 0.816 0.485 0.435 2.548 0.857 -0.890 0.903 3.102 0.922 0.981 1.000 0.979 0.625 0.955 1.013 +1 2.427 1.329 0.795 0.241 0.633 0.456 1.495 -0.569 2.173 0.987 -0.001 -1.378 0.000 1.055 1.630 0.100 0.000 0.932 0.256 -0.970 3.102 0.726 1.123 0.977 1.048 0.503 0.807 1.021 +0 1.114 -0.469 0.602 1.864 0.224 0.863 0.940 -0.793 0.000 0.736 -0.597 1.589 2.215 1.486 0.721 -1.399 1.274 1.342 -1.072 0.781 0.000 1.134 0.921 0.990 1.023 0.975 1.229 1.491 +0 1.090 0.187 -0.191 1.284 0.515 0.434 0.015 0.575 2.173 0.295 1.378 1.592 0.000 0.927 0.320 -1.299 0.000 1.138 -0.272 -0.777 3.102 0.756 0.852 0.986 0.764 0.710 0.643 0.710 +1 0.583 1.109 -0.748 0.750 -1.632 1.035 0.691 0.472 2.173 0.808 -1.074 1.681 0.000 0.940 -0.294 1.581 0.000 1.407 0.523 -0.309 1.551 0.795 0.895 0.993 1.054 0.828 0.843 0.768 +1 0.489 2.130 0.147 0.337 0.594 0.985 0.543 0.902 1.087 1.478 0.877 -1.247 0.000 0.836 0.981 -0.725 0.000 0.738 0.507 -0.349 3.102 0.788 0.647 1.000 0.789 0.816 0.900 0.786 +1 0.595 -1.501 -0.727 0.688 -1.103 0.686 -0.497 0.814 0.000 2.037 -1.732 0.503 0.000 2.069 0.149 -1.063 2.548 1.034 -1.258 1.399 0.000 0.935 0.929 0.981 0.759 0.939 0.992 0.836 +1 0.996 0.855 1.263 0.860 1.213 1.590 0.046 0.575 2.173 1.027 0.250 -1.522 0.000 1.409 0.617 -0.551 0.000 1.287 1.615 -1.196 0.000 0.794 0.698 0.992 1.642 0.916 1.162 1.040 +1 0.651 -1.376 -1.337 1.135 0.084 1.097 -0.352 1.366 0.000 1.006 -1.606 0.956 0.000 1.028 -2.558 0.322 0.000 1.640 -0.790 -0.298 3.102 1.088 0.952 1.141 0.694 0.452 0.760 0.690 +1 1.295 0.962 -0.633 1.039 -1.417 0.515 0.094 -0.329 2.173 1.100 0.435 0.823 2.215 0.563 -1.429 1.205 0.000 0.715 -0.289 1.548 0.000 0.502 0.872 1.042 0.796 0.975 0.858 0.883 +1 2.256 -1.085 -0.494 0.836 0.817 0.310 -1.738 -1.237 0.000 0.973 -1.386 1.672 2.215 0.768 -0.341 0.264 0.000 2.076 0.320 1.484 3.102 0.849 0.897 1.760 1.276 1.345 1.247 1.025 +1 0.397 0.573 0.095 1.575 -0.081 0.792 0.096 1.580 2.173 0.635 -1.042 -1.284 0.000 0.392 -0.740 -0.007 0.000 0.689 -0.330 1.181 3.102 0.706 0.875 0.980 0.692 0.338 0.785 0.689 +1 0.857 1.579 0.283 0.963 -1.105 0.723 0.182 -1.533 2.173 0.403 -1.070 1.118 2.215 0.978 0.548 0.492 0.000 0.772 0.905 -0.667 0.000 0.861 0.965 1.194 1.270 0.770 0.976 0.834 +0 1.241 0.276 0.156 0.561 1.578 0.746 -0.745 -1.431 1.087 1.131 -0.028 0.857 2.215 1.143 -1.479 -0.830 0.000 0.431 -0.973 -0.427 0.000 0.328 1.246 1.108 1.041 1.292 0.895 0.879 +0 0.482 -0.944 -1.531 1.272 0.510 1.057 -0.565 0.843 2.173 0.390 -1.867 -1.346 0.000 0.789 -0.171 -0.664 2.548 0.750 -0.203 -1.365 0.000 0.867 1.062 1.046 0.768 1.133 0.760 0.689 +1 1.052 1.240 -0.065 0.590 -1.608 0.741 -0.949 0.587 0.000 0.721 -0.516 -1.676 2.215 0.743 0.758 -0.672 2.548 0.539 -0.618 -1.131 0.000 0.971 1.059 1.074 1.058 0.838 0.754 0.871 +1 0.601 1.661 -1.731 1.456 1.002 0.574 0.428 -1.618 0.000 1.079 0.804 -0.350 2.215 1.099 0.464 0.588 0.000 0.707 0.000 -0.279 0.000 0.934 0.918 0.989 1.738 0.959 1.314 1.109 +1 0.535 -1.733 0.749 0.914 0.930 0.968 0.829 -0.754 0.000 0.919 -0.899 1.250 0.000 1.037 -1.620 -0.714 0.000 0.864 -0.882 -1.486 3.102 0.828 0.719 0.993 0.686 0.434 0.515 0.592 +1 2.028 0.788 0.716 0.671 -1.629 0.749 -1.628 -1.024 0.000 0.884 0.458 0.483 0.000 1.351 -0.342 -0.729 1.274 0.704 -0.761 1.550 3.102 2.764 1.526 1.385 1.344 0.690 1.018 1.205 +0 0.655 1.474 1.227 0.755 0.011 1.058 0.477 -1.191 1.087 1.299 -0.137 0.182 2.215 0.499 0.329 0.699 0.000 1.531 0.624 1.732 0.000 0.795 0.883 0.987 1.458 1.719 1.286 1.038 +1 0.499 -0.978 -0.806 1.469 0.596 1.375 0.162 -1.437 0.000 1.343 0.114 -0.085 2.215 0.761 0.885 1.468 0.000 0.697 -0.160 0.875 3.102 1.194 0.926 1.131 1.044 0.679 1.000 1.027 +1 0.703 -0.292 -1.092 0.565 1.199 1.514 -0.152 -0.834 0.000 0.869 -0.740 0.903 0.000 1.210 0.372 0.993 2.548 1.710 -0.563 0.106 3.102 2.549 1.649 0.980 0.892 1.006 1.191 0.990 +1 0.421 -1.418 -0.657 0.738 -0.046 1.169 0.845 -1.250 1.087 1.037 -0.571 -0.523 0.000 1.819 1.881 0.808 0.000 0.873 0.133 0.754 3.102 0.813 0.733 0.997 1.161 1.106 0.914 0.803 +0 0.324 1.762 -1.683 3.700 1.443 1.385 -1.039 -0.163 2.173 0.604 -2.086 -0.456 0.000 0.393 0.910 -0.690 2.548 0.550 -1.662 0.734 0.000 0.662 0.888 0.995 1.124 1.191 4.075 4.044 +1 0.466 -0.675 1.055 0.589 0.649 1.094 -1.485 -1.269 0.000 1.218 -1.302 0.408 2.215 0.585 -1.523 1.478 0.000 0.554 0.032 -0.103 3.102 0.897 0.931 0.979 0.727 0.643 0.892 0.799 +1 1.134 -0.092 1.631 1.231 1.645 0.379 1.442 1.414 0.000 0.807 0.291 -0.507 2.215 1.290 0.711 0.516 2.548 0.923 0.757 -1.226 0.000 0.671 0.834 0.988 1.021 0.903 0.868 0.741 +1 1.243 0.198 1.111 0.266 -1.708 0.921 0.421 -1.010 2.173 0.751 0.037 -0.536 0.000 1.023 -0.241 1.326 0.000 1.324 -0.830 0.711 3.102 0.910 0.967 0.986 1.066 1.476 0.886 0.785 +0 0.728 0.876 1.365 0.600 -0.332 1.248 2.023 0.872 0.000 1.552 0.331 0.228 0.000 1.380 -0.547 -0.303 2.548 1.554 -0.454 -0.692 0.000 0.859 0.765 0.989 0.790 1.823 1.081 0.896 +1 1.128 -0.987 0.759 0.992 1.737 2.239 -0.375 -1.174 0.000 2.114 -0.444 0.340 1.107 1.549 0.324 0.144 0.000 1.091 0.301 1.299 3.102 1.149 0.899 1.131 1.207 1.191 0.911 0.874 +1 1.399 -1.384 -0.039 0.487 1.387 0.716 -1.093 0.494 0.000 1.575 -1.689 -1.513 0.000 0.790 -0.288 -1.513 2.548 1.040 0.287 0.236 0.000 1.083 1.013 1.098 0.590 0.485 0.631 0.653 +1 1.744 0.643 -1.368 0.874 1.555 1.065 -0.202 0.572 1.087 0.767 0.391 -0.994 2.215 0.555 1.587 -0.234 0.000 0.389 -1.086 0.285 0.000 1.144 0.862 0.986 1.381 1.376 1.001 0.894 +1 0.663 -0.902 0.581 0.602 -1.172 0.487 0.544 1.260 0.000 1.230 1.026 -1.195 2.215 1.008 0.839 0.570 0.000 0.396 1.375 -0.492 3.102 0.771 1.138 0.992 0.962 0.419 1.088 1.004 +0 0.878 -0.807 -0.795 1.466 -0.235 0.734 -1.203 1.449 2.173 0.609 -1.638 0.476 0.000 0.902 -0.079 1.294 2.548 0.876 -1.588 -1.315 0.000 0.957 0.982 0.987 1.224 0.619 0.895 0.858 +1 1.250 0.081 -0.122 1.090 -1.068 0.720 0.570 1.039 2.173 0.499 0.391 0.316 0.000 1.019 -0.487 1.674 2.548 0.481 1.577 -1.144 0.000 0.808 0.925 1.216 1.102 0.850 0.854 0.754 +1 0.889 0.439 0.395 1.363 -0.628 0.621 -1.143 0.793 2.173 0.796 -0.394 1.738 0.000 0.929 1.072 -0.647 0.000 0.831 0.230 1.122 0.000 1.069 0.854 1.214 1.242 0.719 0.853 0.828 +0 0.291 2.093 -0.176 1.875 1.128 0.868 -0.420 -0.879 1.087 0.959 -1.114 -1.022 0.000 1.406 0.287 0.530 2.548 0.727 0.767 -1.097 0.000 1.338 0.872 0.987 1.631 1.414 1.896 1.897 +0 0.911 -0.754 -0.992 0.476 0.530 0.781 -1.140 -0.581 0.000 0.933 1.246 1.098 0.000 0.619 0.889 1.727 2.548 0.819 0.157 1.081 3.102 0.889 0.646 0.984 0.899 0.372 0.636 0.780 +0 0.513 1.389 -1.573 1.377 -0.754 0.809 0.763 0.412 0.000 0.681 0.775 1.062 1.107 1.194 1.024 -1.137 2.548 1.043 2.431 0.404 0.000 0.769 0.990 0.982 0.853 0.892 0.673 0.692 +1 1.456 -1.233 0.352 1.908 -0.563 0.797 -0.550 0.645 0.000 1.924 -0.725 -1.349 2.215 0.821 -1.065 1.264 0.000 1.252 -0.837 -0.410 3.102 0.893 0.941 1.694 1.653 1.065 1.072 1.061 +1 0.859 -0.226 1.471 2.092 0.452 0.975 0.551 -0.916 2.173 0.646 0.195 1.593 0.000 0.641 0.349 0.436 2.548 0.493 -0.735 -0.856 0.000 0.725 0.845 1.475 0.749 0.929 0.975 0.813 +1 1.090 1.435 -1.715 1.789 1.430 1.230 2.009 -0.178 0.000 0.420 1.798 -0.999 0.000 0.538 1.217 -1.282 2.548 0.768 -0.670 0.356 3.102 1.039 0.834 1.006 1.094 0.800 1.017 1.085 +0 2.377 -0.255 -0.099 0.231 0.111 0.839 -2.473 -1.522 0.000 0.539 -1.224 0.859 2.215 0.583 -1.536 -0.902 0.000 1.523 -0.519 1.567 3.102 0.848 0.953 0.992 1.084 0.554 0.796 1.059 +1 0.358 -1.786 -0.012 1.796 1.139 0.732 0.191 -0.584 2.173 0.746 -0.212 1.715 2.215 0.483 -0.517 -1.010 0.000 0.442 -0.019 0.008 0.000 0.428 0.515 0.987 1.416 0.980 1.013 0.768 +1 0.688 -0.188 0.301 0.462 -0.683 0.886 -0.743 -0.924 0.000 0.408 -2.185 0.626 0.000 1.336 -0.958 0.897 2.548 1.015 0.169 1.699 3.102 1.639 1.236 0.986 0.750 0.838 0.917 0.785 +1 1.242 0.552 -1.537 0.830 -1.463 0.609 -0.039 0.033 2.173 0.933 -0.404 1.183 2.215 0.493 0.840 -0.752 0.000 0.507 1.557 0.412 0.000 0.926 0.889 0.999 1.023 0.977 0.819 0.753 +0 0.337 -0.619 -1.143 1.744 1.464 0.544 0.352 -0.219 0.000 0.869 0.814 0.827 2.215 0.570 1.887 -0.675 0.000 1.234 -0.436 -0.405 3.102 0.860 0.830 0.988 0.946 1.083 0.788 0.712 +0 0.699 -0.398 1.454 0.200 -1.529 0.949 0.788 0.915 2.173 1.224 -0.560 -0.439 1.107 0.840 -0.105 0.307 0.000 2.005 -0.003 -1.420 0.000 1.433 1.192 0.991 0.779 1.898 1.130 0.937 +1 0.581 -0.191 -1.465 0.269 1.169 0.890 -0.949 0.811 2.173 1.329 -0.473 -0.561 0.000 1.084 -1.007 -1.077 1.274 1.204 -0.025 0.368 0.000 0.797 1.236 0.982 0.636 1.217 0.848 0.742 +1 0.839 -0.255 1.497 0.199 1.436 0.656 0.208 1.725 2.173 0.641 -2.198 -0.113 0.000 0.516 0.045 -0.321 2.548 0.749 -1.311 0.318 0.000 0.466 0.750 0.982 0.808 0.700 0.911 0.794 +0 0.977 0.914 0.763 2.001 1.353 1.272 0.161 -0.900 2.173 1.525 0.182 -0.426 0.000 1.577 -0.112 0.774 2.548 0.458 0.872 0.195 0.000 0.752 0.887 0.988 1.011 1.779 1.294 1.161 +1 1.248 -0.435 1.554 1.281 0.872 0.485 -0.446 -1.234 0.000 0.731 -0.753 -0.713 2.215 1.425 -0.363 0.134 2.548 0.390 0.254 0.129 0.000 0.676 0.777 1.009 0.988 0.777 0.822 0.700 +1 1.029 -1.121 1.143 0.618 -0.826 0.717 -1.336 0.040 0.000 0.527 1.081 -1.696 2.215 0.941 -0.589 -1.010 2.548 0.892 -0.633 0.828 0.000 0.875 0.913 1.082 1.067 0.863 0.916 0.817 +1 0.895 0.360 0.185 1.693 -0.449 2.636 -0.732 1.317 0.000 1.383 0.347 -0.522 1.107 0.570 -1.009 -0.012 0.000 0.483 -1.614 -0.664 0.000 0.637 1.043 0.989 0.749 0.806 0.725 0.690 +0 1.240 0.246 -0.444 0.528 -0.670 0.964 0.909 0.843 1.087 0.497 2.192 0.467 0.000 1.350 1.225 -1.182 0.000 0.906 2.135 -1.213 0.000 0.883 0.869 0.987 1.353 0.588 0.920 1.072 +1 0.790 -1.255 -0.364 1.299 0.861 0.819 0.212 0.229 2.173 1.461 -0.883 -1.232 2.215 1.092 -0.177 -1.721 0.000 0.719 -0.462 0.198 0.000 0.981 0.987 1.254 1.192 1.826 1.147 0.953 +1 0.514 -0.878 -1.250 0.660 0.837 1.102 0.811 -0.895 2.173 0.800 0.710 1.141 0.000 0.597 -0.682 0.972 1.274 1.228 0.655 -0.050 0.000 1.136 0.881 0.983 1.017 1.333 0.910 0.792 +1 0.605 0.348 -0.325 1.493 -1.034 0.530 -0.563 0.267 2.173 1.157 0.207 1.023 2.215 0.695 0.288 -0.922 0.000 0.668 0.713 0.604 0.000 0.765 0.808 0.992 1.135 0.860 0.989 0.786 +0 0.338 0.647 0.201 1.775 1.315 1.107 -2.031 -1.308 0.000 0.823 -1.676 0.888 0.000 0.767 -1.404 0.007 0.000 1.569 -0.283 0.005 1.551 0.876 0.940 0.986 0.965 0.793 0.789 0.858 +0 0.629 0.294 1.035 1.534 0.191 0.883 -0.056 -1.298 0.000 0.713 -0.706 1.281 0.000 0.825 -1.063 -1.097 1.274 1.018 -0.400 0.083 3.102 1.357 0.947 0.987 0.543 0.660 0.720 0.782 +0 0.503 1.243 -0.503 1.248 0.411 0.515 0.988 1.673 0.000 0.594 -0.267 -1.114 2.215 0.320 0.022 0.782 0.000 0.659 -0.736 -1.474 3.102 0.884 0.922 0.979 0.793 0.251 0.656 0.679 +0 1.319 0.844 0.704 0.106 -0.559 0.870 -0.618 -0.898 0.000 0.740 -1.422 0.129 0.000 0.834 0.278 -1.569 2.548 0.651 -1.532 1.508 0.000 0.873 1.076 0.981 0.592 0.292 0.631 0.737 +0 0.761 -1.227 -0.603 3.297 -1.001 1.794 0.074 0.868 1.087 0.991 0.832 0.414 0.000 1.305 0.112 -0.716 2.548 0.523 2.436 0.415 0.000 0.926 1.128 0.988 2.435 1.888 1.640 1.612 +1 1.480 -0.774 0.402 0.765 0.841 0.276 -1.428 0.686 0.000 0.475 0.155 -0.617 2.215 1.989 -0.920 -1.268 2.548 0.677 0.401 1.439 0.000 0.845 1.010 0.986 0.785 0.858 0.937 0.786 +1 0.632 -0.704 -0.259 0.385 0.640 1.176 -1.250 -1.247 0.000 0.598 -1.697 -0.754 0.000 1.445 -1.318 0.803 2.548 0.624 -0.183 -0.347 3.102 0.883 0.805 0.979 0.717 0.784 0.871 0.766 +0 1.710 0.553 1.007 0.943 0.235 1.022 -0.675 -0.241 2.173 1.260 0.662 1.586 2.215 1.554 -0.704 -0.772 0.000 0.432 -1.096 -1.080 0.000 0.549 0.722 1.128 0.989 2.069 1.237 1.110 +0 0.937 -1.652 -0.302 1.216 0.524 0.600 -0.587 -1.675 2.173 0.506 0.758 1.730 2.215 0.799 -0.201 0.136 0.000 0.653 -1.808 1.351 0.000 1.150 0.947 1.002 1.378 0.604 1.015 0.876 +1 0.453 -1.869 1.212 1.513 0.292 0.750 -0.138 -1.448 2.173 0.669 0.474 1.043 2.215 0.643 0.593 -0.310 0.000 0.530 -1.153 -1.242 0.000 0.911 0.831 0.989 1.188 0.879 0.937 0.815 +1 0.892 -0.083 -0.819 0.682 1.029 0.705 -0.895 -0.672 2.173 0.786 0.109 1.161 0.000 1.009 -0.664 0.952 1.274 0.396 0.181 -1.524 0.000 1.034 1.102 1.076 0.743 1.048 0.743 0.689 +1 1.275 1.144 1.160 0.760 0.309 0.643 1.062 -0.762 2.173 0.247 0.942 0.023 0.000 0.452 1.742 0.727 0.000 0.524 -0.579 0.317 0.000 0.938 0.883 0.990 1.010 0.671 0.812 0.716 +1 0.552 -0.755 1.278 0.907 -0.688 0.574 -0.558 0.619 2.173 0.943 -0.374 -0.174 2.215 1.480 -0.800 -1.456 0.000 0.669 1.233 0.641 0.000 0.843 1.000 0.989 0.695 0.717 0.819 0.703 +1 0.427 -0.316 -1.412 2.032 1.559 1.207 1.522 0.239 2.173 0.545 1.243 -1.224 0.000 0.872 0.441 -0.619 2.548 0.416 -1.313 -0.188 0.000 1.199 0.811 0.988 1.572 1.137 1.107 0.982 +1 0.419 -0.195 0.841 1.880 0.141 0.836 0.628 1.421 2.173 0.634 2.623 -1.078 0.000 1.471 1.088 -1.273 2.548 0.901 0.792 0.048 0.000 1.285 1.050 0.991 1.075 0.985 0.966 0.998 +1 0.587 -0.784 1.290 0.148 -1.475 1.800 1.340 0.802 2.173 1.091 1.186 -1.070 2.215 2.017 1.338 -0.701 0.000 0.586 1.992 -0.384 0.000 0.664 0.665 0.979 1.263 2.052 1.291 1.141 +0 1.628 -2.105 0.660 0.399 1.496 0.659 -0.474 0.109 1.087 0.810 -0.595 -1.578 0.000 0.623 -1.486 -1.352 0.000 0.874 -0.073 -0.880 3.102 0.620 1.039 0.985 1.043 0.644 0.859 0.823 +0 1.072 -0.550 -1.394 0.138 -0.867 1.333 -1.024 1.610 2.173 1.381 0.818 -0.125 2.215 1.899 -0.917 0.360 0.000 0.445 -0.721 1.301 0.000 0.838 1.467 0.995 1.097 2.940 1.579 1.266 +1 1.637 0.528 1.738 0.246 0.256 1.435 0.134 -0.198 0.000 1.060 1.143 1.653 0.000 0.864 0.415 1.085 0.000 1.211 -0.597 -0.749 1.551 0.899 1.165 0.984 0.931 0.840 0.942 0.824 +0 2.520 0.563 0.730 0.628 -0.951 0.640 -0.497 1.302 0.000 0.999 -0.872 -0.734 2.215 1.196 0.446 -0.982 0.000 0.539 0.482 -0.556 3.102 1.610 0.946 1.740 1.602 0.545 1.000 0.973 +1 1.133 1.384 -1.226 1.025 -1.735 1.574 1.084 0.280 0.000 0.989 1.995 1.360 0.000 1.749 0.293 -1.220 2.548 0.905 -1.837 0.466 0.000 0.831 0.910 0.975 1.183 0.847 0.901 0.841 +0 1.372 -0.415 -0.373 1.040 -0.660 1.029 -1.197 1.186 2.173 0.402 -2.691 1.410 0.000 0.650 -1.294 0.540 0.000 0.536 0.327 -0.951 3.102 0.794 0.943 0.982 1.625 1.015 1.032 1.041 +0 0.418 1.038 1.293 3.230 -1.493 0.734 -0.423 0.147 2.173 0.663 0.959 -0.282 0.000 1.136 1.356 0.119 2.548 0.476 1.536 0.599 0.000 0.614 0.938 0.989 1.247 1.265 1.398 1.081 +1 0.516 -0.325 -1.148 1.427 0.533 2.421 1.287 -1.574 0.000 1.284 -0.794 0.019 2.215 2.020 -1.579 -0.187 0.000 1.739 -0.372 0.667 1.551 9.420 5.128 1.187 0.847 0.784 3.190 2.329 +1 0.726 0.348 0.200 1.322 0.989 1.293 -2.300 0.420 0.000 1.657 -0.726 0.378 2.215 5.502 -1.315 -1.158 2.548 1.610 0.121 1.334 0.000 0.760 1.174 0.991 3.316 3.354 2.412 1.831 +1 1.207 -0.169 0.687 2.252 1.136 1.195 0.715 -0.373 2.173 0.753 -0.144 -1.152 0.000 0.618 0.945 -1.268 2.548 0.489 -1.026 -1.624 0.000 0.556 1.166 0.997 0.964 0.791 1.136 0.989 +0 0.519 -1.206 1.470 1.417 -1.608 0.962 0.348 -0.085 0.000 0.944 0.401 0.487 2.215 1.218 0.919 1.560 2.548 1.216 0.932 -0.515 0.000 0.901 0.882 0.990 0.854 0.999 0.872 0.951 +0 1.024 -0.217 1.081 0.407 -0.941 1.281 -0.667 -1.727 2.173 0.884 -0.668 -0.488 2.215 0.576 1.281 -0.220 0.000 0.380 0.365 0.468 0.000 0.397 0.791 0.989 0.925 1.408 0.978 0.827 +1 0.288 -1.722 -1.172 1.034 0.304 0.592 1.169 1.720 1.087 0.655 0.689 0.407 2.215 0.655 1.235 -1.110 0.000 0.473 -0.772 -1.264 0.000 0.868 0.816 0.984 1.120 0.876 0.811 0.732 +1 1.556 -0.361 -0.947 0.857 1.688 0.934 0.078 0.418 1.087 0.514 0.107 1.197 0.000 0.419 -1.034 -0.558 1.274 0.469 -0.854 0.058 0.000 0.668 0.654 1.110 0.627 0.783 0.816 0.675 +0 0.379 0.950 -0.639 1.301 1.224 0.623 0.739 0.637 0.000 0.674 -0.074 -0.731 2.215 0.625 -2.111 -0.123 0.000 1.259 0.570 -1.408 3.102 0.865 0.927 0.989 0.688 0.575 0.671 0.653 +0 1.502 -1.716 0.053 0.998 -0.242 0.961 -1.457 -1.503 0.000 0.432 -0.393 -1.553 0.000 0.822 -1.166 1.162 2.548 0.743 -0.612 0.119 3.102 0.766 0.849 0.979 0.959 0.510 0.684 0.865 +1 1.895 -0.610 0.247 0.915 -0.219 1.240 -1.444 -1.644 2.173 0.503 -0.535 0.933 0.000 0.492 -0.796 -0.279 2.548 0.494 -1.647 -1.083 0.000 0.807 0.874 1.003 0.503 0.962 1.015 0.816 +0 2.285 0.628 -0.293 0.146 -1.137 0.682 0.480 1.685 0.000 0.583 0.136 0.635 0.000 0.841 -1.052 1.312 2.548 0.603 -0.308 -1.132 0.000 0.798 0.703 0.987 0.688 0.156 0.777 0.658 +1 1.184 0.663 0.361 1.183 -0.072 0.870 0.748 -1.226 2.173 0.703 -0.052 -0.820 0.000 0.913 0.522 1.246 2.548 0.992 1.913 1.055 0.000 1.926 1.227 0.998 1.203 0.884 0.898 0.924 +0 1.227 0.391 0.979 0.600 1.185 0.953 0.406 -1.619 2.173 1.089 -0.115 -0.110 1.107 0.813 1.145 0.062 0.000 0.805 -2.292 -0.715 0.000 0.845 1.246 1.001 0.965 1.520 1.235 1.310 +0 0.867 -1.036 0.005 0.575 -1.652 0.886 -0.850 -0.707 0.000 0.555 -0.611 -1.423 0.000 0.716 -0.277 0.969 2.548 1.418 -0.610 0.542 3.102 0.911 0.940 0.987 0.643 0.333 0.720 0.635 +1 0.596 0.239 1.590 2.467 0.959 0.912 -0.121 -0.468 0.000 1.432 0.733 -0.535 2.215 0.994 -1.281 1.264 0.000 0.772 1.204 1.424 0.000 0.734 0.830 0.983 0.875 1.028 1.040 0.820 +0 1.172 -0.194 1.462 0.682 -1.700 0.862 -1.113 -0.218 0.000 0.733 -0.965 0.687 2.215 1.442 -0.558 -1.538 2.548 1.584 0.645 -0.318 0.000 0.718 0.949 0.970 0.612 1.013 0.839 0.800 +1 2.157 -0.070 1.114 1.536 0.728 1.108 0.319 -0.610 2.173 0.541 -0.212 -1.125 0.000 0.637 -0.891 0.369 1.274 0.409 -0.925 -1.334 0.000 0.305 0.667 0.984 0.707 1.089 1.120 0.898 +0 0.754 -1.565 0.905 0.721 -0.372 1.041 -0.024 -0.154 2.173 0.517 -1.020 0.453 0.000 2.381 -0.399 -1.629 2.548 1.421 -1.774 1.503 0.000 0.919 1.229 0.991 1.348 1.948 1.312 1.125 +1 1.020 0.691 -0.298 0.197 0.663 1.419 0.295 -1.538 2.173 0.830 1.458 0.411 0.000 0.696 0.321 0.706 1.274 0.545 -0.440 -0.262 0.000 1.163 0.742 0.982 1.143 1.115 0.977 0.854 +0 0.708 0.328 -0.295 0.962 1.122 1.455 -0.163 -1.287 0.000 1.814 0.104 0.730 2.215 0.786 0.211 1.641 0.000 2.118 -0.832 -0.431 1.551 1.001 1.427 1.095 0.838 1.845 1.401 1.135 +0 0.923 0.125 -0.412 0.665 -1.093 0.740 1.071 -1.644 0.000 0.605 -0.104 0.910 1.107 0.865 0.469 0.075 2.548 0.694 1.310 0.711 0.000 0.970 0.929 0.991 0.851 0.579 0.685 0.685 +0 1.023 1.749 1.349 0.435 -1.271 0.828 0.965 0.292 2.173 0.848 -1.200 -1.581 1.107 0.717 0.078 -0.127 0.000 0.433 0.539 -1.556 0.000 0.616 0.869 0.976 0.933 2.060 1.266 0.958 +0 0.754 -0.272 0.435 0.955 1.450 0.702 -0.844 1.304 0.000 1.082 -0.608 -0.158 2.215 1.542 -0.370 -1.140 2.548 0.389 -1.329 -0.430 0.000 0.854 1.001 0.991 0.914 1.073 0.808 0.740 +0 1.133 -0.061 1.707 0.870 -0.792 1.336 0.803 -1.037 2.173 2.750 0.904 0.427 0.000 0.502 0.881 1.630 1.274 0.792 -0.645 1.273 0.000 2.274 1.400 1.070 0.973 0.690 1.395 1.204 +0 0.877 2.411 -0.894 0.696 -0.382 0.737 0.941 0.772 2.173 0.454 -0.624 -0.284 1.107 0.332 0.965 1.525 0.000 0.514 -0.086 1.732 0.000 0.293 0.530 0.997 1.134 1.022 0.948 0.742 +1 1.575 1.095 1.361 0.706 -0.377 0.536 0.462 -0.251 1.087 0.872 0.822 0.882 0.000 0.563 -1.025 -1.006 1.274 0.928 1.119 -0.931 0.000 1.206 0.937 1.461 1.193 0.742 0.874 0.814 +0 0.840 -0.246 0.692 0.613 -1.424 0.861 2.932 -1.688 0.000 0.935 2.431 -0.787 0.000 1.008 0.601 0.738 2.548 1.768 0.186 0.367 1.551 0.846 1.199 0.987 0.701 0.404 1.028 0.960 +0 0.353 -2.228 -0.435 1.961 -1.358 0.649 -0.505 0.852 0.000 0.899 0.346 0.309 1.107 0.575 -0.998 -1.591 0.000 0.997 -0.388 -0.252 3.102 0.945 0.971 0.986 0.858 0.549 1.025 0.899 +1 1.494 -0.034 -0.268 0.933 -0.910 1.080 -0.054 -1.469 1.087 1.102 1.338 0.683 2.215 0.428 2.136 1.068 0.000 0.780 1.228 -0.781 0.000 0.871 1.047 0.990 1.468 1.950 1.264 1.017 +1 0.602 2.366 0.556 0.896 -0.459 0.573 0.450 0.684 1.087 0.884 0.988 -1.200 0.000 1.278 0.443 1.408 0.000 1.413 1.252 -0.390 3.102 1.239 1.073 0.990 0.577 0.943 0.848 0.813 +1 1.368 1.271 -1.230 1.484 1.559 0.891 0.622 0.582 2.173 0.804 0.968 -0.943 0.000 1.171 0.872 0.025 0.000 0.646 0.210 -0.216 3.102 1.140 1.134 1.159 0.861 0.551 0.890 0.855 +1 0.639 0.517 1.404 0.908 0.294 2.246 2.203 -0.966 0.000 2.006 0.710 0.501 0.000 1.355 0.314 0.804 0.000 2.227 0.001 -1.711 3.102 0.840 0.642 0.984 0.847 0.858 0.937 0.788 +1 0.498 -0.293 1.398 1.341 -0.240 0.614 -1.077 0.734 2.173 0.657 0.592 0.813 2.215 0.604 -0.792 -0.735 0.000 0.414 1.812 -0.186 0.000 1.232 0.934 1.127 0.853 0.894 0.840 0.759 +0 0.993 1.122 1.419 1.040 0.934 0.697 0.393 -0.238 0.000 1.041 0.975 -0.779 1.107 1.335 0.008 1.438 0.000 0.780 1.732 -0.631 0.000 0.873 0.642 0.977 1.098 0.696 0.715 0.659 +0 1.246 0.479 0.982 0.769 -1.277 0.789 0.542 1.656 0.000 0.892 0.880 -0.492 2.215 0.712 -0.605 0.024 2.548 0.376 -0.284 -0.564 0.000 0.979 0.993 1.213 0.954 0.828 0.759 0.731 +1 0.950 -1.671 1.292 1.318 -0.641 1.461 -0.358 0.027 0.000 1.365 -1.512 -1.608 0.000 0.907 -2.258 -0.816 0.000 1.423 1.096 1.171 3.102 0.755 0.647 1.528 2.132 0.819 1.447 1.301 +0 2.325 -1.227 -0.936 0.493 1.374 1.102 -0.200 0.302 0.000 0.705 0.908 0.992 2.215 0.487 0.583 -0.256 0.000 1.079 -0.772 1.579 3.102 0.855 0.958 1.294 0.815 0.932 1.070 1.068 +0 0.704 -0.182 -1.558 2.905 -1.583 1.816 -0.965 0.199 2.173 0.953 -0.766 0.880 2.215 1.779 0.446 -1.028 0.000 0.708 -1.124 0.199 0.000 1.712 1.478 0.992 2.155 1.129 1.458 1.426 +1 2.573 -1.145 0.361 0.243 -0.714 0.894 -1.310 -1.093 0.000 0.585 -0.500 -1.421 1.107 0.721 -0.246 1.553 2.548 0.501 -1.783 -1.490 0.000 0.559 0.773 0.988 0.998 0.319 0.765 0.784 +1 0.338 -2.226 0.079 0.246 -0.567 0.789 -0.939 1.291 0.000 0.908 0.085 0.369 2.215 1.028 -0.358 -0.669 2.548 1.407 -0.946 -1.598 0.000 0.837 1.053 0.992 0.755 0.864 0.901 0.768 +1 0.469 0.928 1.710 0.463 -1.315 0.923 -0.718 0.246 0.000 1.047 -0.262 -0.965 2.215 1.676 1.617 -1.651 0.000 1.738 0.047 0.234 0.000 0.775 0.940 0.983 0.685 0.897 0.886 0.799 +1 3.112 -0.993 -0.505 1.488 0.976 1.218 -0.103 -1.348 2.173 1.569 -1.574 1.123 0.000 1.071 -0.204 -0.185 2.548 0.588 -1.095 0.635 0.000 0.567 1.250 2.899 2.070 1.236 1.386 1.359 +1 0.935 -0.311 -0.107 0.151 1.166 0.668 -0.345 -0.978 0.000 0.610 0.893 -1.120 0.000 1.156 0.676 -0.097 1.274 2.065 1.031 0.874 3.102 0.933 0.979 0.988 0.897 0.953 0.964 0.814 +1 1.105 -0.760 -1.537 0.295 -1.075 0.580 -0.919 1.019 0.000 0.443 -1.656 -0.193 0.000 0.978 -1.047 0.273 2.548 0.970 0.296 -0.881 3.102 0.955 0.932 0.996 0.843 0.889 0.734 0.680 +1 1.023 -0.940 0.622 0.471 1.292 1.300 -1.107 -1.070 0.000 1.186 0.057 0.600 1.107 0.625 0.078 -1.119 0.000 0.680 -0.806 -0.596 3.102 1.038 0.638 0.995 0.666 0.836 0.994 0.896 +1 0.716 -1.460 0.782 1.109 1.113 3.671 0.578 -1.698 0.000 2.633 -0.268 -0.142 2.215 3.606 1.040 0.156 0.000 0.606 0.581 0.469 1.551 1.450 1.329 1.003 1.477 0.830 2.172 1.816 +1 0.441 -1.170 0.457 2.301 1.309 1.019 -0.471 -0.205 2.173 0.459 -0.693 1.188 2.215 1.077 -1.339 -1.226 0.000 0.944 -0.569 0.381 0.000 1.192 1.114 0.985 0.471 0.964 0.886 0.825 +0 2.576 0.292 0.224 0.770 1.184 0.632 0.106 -1.176 2.173 1.292 -0.413 -1.737 2.215 0.526 -1.151 -0.523 0.000 0.979 0.997 -0.668 0.000 0.592 0.894 1.486 1.248 0.736 1.107 0.994 +0 1.594 0.209 -1.307 0.786 1.640 0.875 -0.356 0.631 0.000 1.094 0.130 -0.828 2.215 1.130 0.092 -0.127 0.000 1.161 -0.792 1.225 3.102 1.186 0.831 0.984 0.816 1.137 0.922 0.995 +1 0.385 0.138 -0.814 0.454 1.005 0.870 1.156 -1.282 2.173 0.509 1.020 0.558 2.215 0.491 -0.895 -0.576 0.000 0.518 -0.211 0.497 0.000 0.503 1.061 0.989 0.588 0.977 0.748 0.692 +0 1.470 -0.993 -0.651 0.936 1.639 0.930 -0.769 1.612 1.087 0.489 -1.514 -0.100 0.000 1.132 -0.835 0.706 2.548 1.140 -1.302 0.408 0.000 0.942 1.102 1.431 1.028 0.938 0.939 0.941 +1 0.640 1.352 1.370 2.096 0.592 0.917 1.661 -0.389 0.000 1.330 0.952 -1.393 2.215 0.667 1.158 -1.110 2.548 0.752 1.091 0.848 0.000 0.729 0.902 1.035 0.872 0.292 0.881 0.742 +1 0.305 -0.657 -1.369 0.448 0.308 0.949 -0.525 0.593 0.000 2.055 0.334 -0.989 2.215 1.284 0.892 0.638 0.000 1.266 -0.949 0.028 0.000 0.968 0.997 0.997 0.997 0.363 1.182 1.000 +0 0.785 1.286 1.552 1.035 1.643 0.690 1.215 0.065 0.000 0.650 0.534 -1.302 2.215 0.764 0.492 -0.759 0.000 0.869 0.186 0.719 0.000 0.903 0.949 0.977 0.929 0.571 0.770 0.817 +1 0.708 -1.985 -0.401 0.539 1.028 0.905 -0.720 0.048 2.173 1.192 -1.791 -1.481 0.000 0.585 0.206 -1.149 0.000 1.120 -0.532 -1.345 1.551 1.059 0.906 0.983 0.700 1.014 0.682 0.642 +0 0.681 -0.051 0.756 1.667 -0.187 0.564 0.538 1.109 2.173 0.650 1.936 -1.583 0.000 1.393 0.354 -0.661 2.548 0.763 -0.560 1.157 0.000 0.910 0.894 1.108 0.920 1.107 0.788 0.725 +0 0.661 -1.408 -0.363 0.853 -1.078 0.498 0.148 0.208 0.000 0.784 0.411 -0.819 2.215 1.295 -0.120 0.903 1.274 0.644 -1.618 1.477 0.000 1.335 0.960 0.983 1.541 1.112 1.276 1.095 +0 1.428 1.355 1.469 0.740 -0.524 0.360 0.929 0.247 0.000 0.495 0.599 1.685 0.000 0.889 0.261 0.679 2.548 1.863 0.324 -0.677 3.102 0.875 0.830 1.389 0.954 0.926 0.840 0.710 +0 0.703 1.104 0.869 0.851 -1.620 0.868 -0.077 -0.298 1.087 1.073 1.090 1.421 2.215 0.936 1.173 -1.077 0.000 0.754 -0.384 0.864 0.000 1.066 0.920 0.986 1.024 1.683 0.932 0.800 +1 0.691 -0.124 0.394 1.188 0.772 0.545 -1.023 -0.539 0.000 1.120 -1.478 -1.380 0.000 1.208 -0.368 -1.507 2.548 0.649 -0.020 0.010 1.551 1.211 0.985 0.989 0.585 0.675 0.733 1.046 +1 1.696 -0.505 0.798 0.339 -1.273 0.839 0.266 -0.975 2.173 0.590 -1.059 0.302 0.000 1.204 -1.156 -1.420 0.000 1.048 0.935 0.013 3.102 0.788 0.970 1.005 0.922 0.886 0.873 0.757 +1 0.499 -0.672 1.669 0.355 -0.133 1.201 0.166 0.594 2.173 1.938 -1.477 -1.473 0.000 1.369 0.218 -0.873 0.000 1.106 -0.356 -1.216 0.000 0.899 0.776 0.987 0.624 0.544 0.610 0.611 +1 1.029 -1.002 1.587 0.778 -1.162 0.531 -0.225 1.226 0.000 0.446 0.453 -1.391 0.000 1.184 0.124 -0.388 2.548 2.598 0.914 0.305 3.102 0.816 0.918 0.988 2.181 1.031 1.468 1.170 +1 2.857 -0.526 -0.384 0.111 -1.727 1.248 -0.406 1.522 2.173 0.793 0.392 0.319 2.215 0.963 0.187 -1.682 0.000 0.630 -0.585 0.378 0.000 0.918 0.825 0.987 0.907 1.432 1.152 0.937 +1 0.561 0.546 -0.005 0.544 -1.662 1.513 1.131 1.485 0.000 0.778 -0.757 0.529 2.215 1.928 -0.564 -0.511 2.548 0.960 0.472 -0.550 0.000 1.857 1.868 0.986 1.337 1.054 1.570 1.273 +0 0.456 1.699 1.131 0.593 -0.739 1.314 -0.026 -0.303 2.173 1.334 0.550 1.260 1.107 0.576 1.246 1.533 0.000 0.903 0.787 0.195 0.000 0.762 1.158 0.992 0.820 2.010 1.040 0.856 +0 0.493 1.481 0.711 3.382 0.233 1.099 0.050 0.032 2.173 2.022 -0.511 -1.601 2.215 1.078 0.870 -1.116 0.000 1.728 0.503 1.661 0.000 0.937 1.337 0.995 0.974 2.275 1.753 1.486 +1 0.696 1.069 1.236 0.820 -1.160 0.838 0.103 0.113 0.000 0.996 1.021 1.616 2.215 0.662 0.747 -0.845 2.548 0.629 2.011 0.688 0.000 1.008 0.886 0.985 0.625 0.696 0.607 0.579 +1 1.520 0.812 -1.610 1.616 -1.305 1.475 -0.149 0.501 0.000 0.928 0.079 0.071 2.215 1.127 0.827 -0.805 2.548 0.845 -0.102 -1.740 0.000 1.535 1.054 0.990 0.778 0.900 0.972 1.224 +0 1.147 0.341 -1.310 0.590 -0.148 0.650 -1.048 -1.580 2.173 0.469 -0.854 -0.026 0.000 0.923 -0.250 1.034 0.000 1.400 1.195 0.029 3.102 0.881 0.900 0.988 0.838 1.897 1.031 0.882 +1 1.053 0.253 0.526 1.596 -0.270 0.681 0.964 -1.266 2.173 0.848 -0.194 1.574 2.215 0.378 -1.302 -0.595 0.000 0.730 0.110 -1.711 0.000 0.700 0.854 1.180 1.103 0.928 0.941 0.791 +1 0.775 -0.102 0.184 1.164 1.569 1.916 0.282 -0.182 0.000 2.229 -1.929 1.587 0.000 0.489 0.991 -0.374 0.000 0.996 1.090 1.453 3.102 0.778 0.679 1.248 0.838 0.758 0.800 0.839 +0 0.705 0.104 -0.502 0.984 1.131 0.956 0.705 -0.107 2.173 1.185 -0.558 1.328 2.215 0.515 0.190 -1.385 0.000 0.925 -0.978 -1.505 0.000 0.705 0.943 1.148 0.859 1.853 0.978 0.810 +1 0.501 -1.232 -1.501 0.334 -1.063 0.879 1.023 0.309 0.000 0.722 0.082 1.652 2.215 0.537 -0.606 -0.749 0.000 0.698 0.676 1.171 1.551 0.378 0.788 0.996 0.622 0.359 0.593 0.556 +0 0.424 -1.379 -0.987 1.048 -0.640 0.723 0.165 1.731 0.000 1.075 -0.432 0.476 2.215 0.495 -0.569 -0.944 0.000 1.036 -0.967 0.667 3.102 0.840 0.904 1.000 0.957 0.385 0.743 0.740 +1 0.396 -1.560 0.925 1.587 -0.865 0.541 -0.119 1.456 0.000 0.562 1.441 -1.388 0.000 0.431 1.853 -0.087 0.000 1.343 0.044 0.445 0.000 0.868 0.656 1.098 0.696 0.219 0.661 0.661 +0 1.527 -0.559 -1.131 0.688 -0.239 1.129 -1.113 0.969 2.173 0.949 -0.744 1.740 0.000 1.487 -1.355 -0.482 2.548 1.200 1.896 -0.080 0.000 1.001 2.461 1.024 1.299 1.587 1.947 1.548 +0 1.099 0.339 -0.942 0.882 -0.040 0.492 1.737 -1.146 0.000 0.802 0.971 0.419 2.215 0.810 1.527 1.268 0.000 1.218 0.528 1.307 1.551 0.935 0.950 0.992 0.821 0.661 0.675 0.742 +1 1.330 0.713 -0.182 1.194 -0.991 1.348 0.420 1.030 2.173 0.603 -0.545 -1.287 0.000 0.519 -0.991 0.463 2.548 0.838 2.012 -1.362 0.000 1.946 1.484 1.161 1.478 1.005 1.181 1.105 +1 1.005 -0.134 0.804 0.900 -1.617 0.830 0.264 -1.310 0.000 1.414 -0.882 0.186 2.215 0.543 0.043 1.432 2.548 0.767 0.122 -0.656 0.000 0.683 0.599 1.080 1.093 0.958 0.901 0.810 +1 0.587 -1.723 -1.584 1.163 0.500 1.569 -1.331 -0.211 2.173 0.853 -0.989 1.045 0.000 0.491 -0.935 -0.911 0.000 0.473 -0.208 -1.208 3.102 0.975 1.290 1.091 0.701 0.885 0.797 0.761 +1 0.881 -0.756 0.638 0.825 0.596 0.863 0.120 0.465 2.173 1.191 0.818 1.738 0.000 1.096 -0.436 -0.389 2.548 1.870 0.667 -1.356 0.000 0.749 1.100 0.991 1.183 0.921 0.917 1.130 +0 0.574 -0.435 -0.208 0.921 0.753 0.507 -1.484 -1.268 0.000 0.766 -0.558 -0.815 2.215 0.702 -0.057 0.470 0.000 0.522 -1.215 1.562 3.102 1.361 0.936 0.983 0.559 0.546 0.586 0.605 +1 0.874 -0.231 1.279 0.469 -1.241 0.988 0.258 0.595 1.087 0.719 -1.052 -1.670 0.000 0.787 -1.047 -0.678 1.274 1.064 -2.121 -0.425 0.000 1.189 0.741 0.991 0.880 1.308 1.097 1.016 +0 1.773 1.594 -1.689 0.671 -0.863 1.122 -0.250 0.522 1.087 0.486 0.257 0.546 2.215 1.104 -0.209 -0.867 0.000 0.605 0.714 -1.381 0.000 0.725 0.692 1.025 1.940 0.284 1.240 1.003 +0 0.627 -0.171 0.273 1.031 -1.163 1.001 0.629 1.511 1.087 0.750 -0.820 0.742 0.000 0.777 0.718 -0.650 0.000 0.562 1.061 -0.234 0.000 0.942 0.589 1.071 1.010 0.326 0.767 0.720 +1 1.714 0.186 0.528 1.289 1.185 0.419 1.385 -0.250 0.000 0.817 1.379 -1.342 0.000 1.067 0.715 -0.607 2.548 0.632 1.038 -1.016 3.102 1.034 0.747 1.149 0.888 0.269 0.785 0.821 +1 0.880 0.268 -0.328 1.763 -1.128 1.348 0.849 1.488 0.000 1.335 -0.857 0.371 1.107 0.918 -2.090 -0.212 0.000 1.058 -1.066 -0.362 1.551 1.118 1.125 1.139 0.968 0.692 1.015 1.036 +0 1.091 -1.011 -0.472 0.587 -1.164 1.120 1.210 -1.544 2.173 1.256 0.181 1.483 0.000 1.440 0.441 0.437 0.000 1.543 0.854 0.271 0.000 0.937 0.691 0.988 2.388 1.576 1.567 1.310 +0 1.090 -1.603 -0.768 0.212 1.488 0.547 -0.992 -0.248 2.173 0.810 -1.672 1.398 0.000 0.568 -1.030 0.383 0.000 0.853 -0.164 1.645 3.102 0.880 0.889 0.981 0.676 0.776 0.643 0.617 +0 0.536 1.003 -0.547 1.158 1.736 1.231 1.996 0.237 0.000 1.488 1.500 1.442 2.215 1.273 -0.337 -1.143 2.548 0.850 -0.167 -0.292 0.000 0.626 0.768 0.987 0.881 1.939 1.036 0.849 +1 1.541 0.811 1.588 1.078 -1.276 0.845 -0.594 0.683 2.173 0.676 -0.490 -0.093 0.000 1.140 0.310 -0.113 2.548 0.771 -1.945 -0.948 0.000 0.667 0.697 0.984 1.380 1.001 1.048 0.932 +1 0.557 0.582 -1.081 1.370 0.459 1.026 0.333 -1.462 0.000 1.080 1.081 1.019 1.107 1.639 2.198 -0.534 0.000 0.601 -0.953 -0.653 0.000 0.833 1.087 1.190 0.730 0.715 0.732 0.688 +1 0.511 -1.812 -0.744 0.683 -0.679 0.837 -0.542 -1.488 0.000 1.183 0.102 0.801 2.215 0.674 1.938 0.955 0.000 1.212 0.594 -0.059 3.102 0.737 1.006 1.002 1.085 0.827 0.847 0.795 +1 0.773 0.120 1.103 1.161 -1.675 1.079 -2.118 -0.376 0.000 0.936 -1.601 1.130 0.000 1.121 -0.166 -1.252 2.548 1.931 -0.073 -0.086 3.102 0.796 1.149 0.983 1.086 0.978 0.949 1.045 +1 0.406 -1.893 0.722 1.207 1.209 1.436 2.323 -0.439 0.000 0.562 -2.204 -1.249 0.000 0.900 -0.975 1.266 2.548 0.973 -0.562 1.056 3.102 9.576 5.150 0.981 0.527 0.196 3.158 2.476 +0 1.250 -0.255 -0.634 0.954 -1.692 0.752 -1.231 -1.417 1.087 0.986 -0.765 0.945 0.000 1.334 -0.552 0.216 2.548 0.712 -0.114 -1.679 0.000 0.918 0.907 1.233 0.907 1.306 0.914 0.827 +0 0.719 -0.677 -1.742 1.184 0.783 0.459 0.459 -0.907 0.000 0.749 -0.658 -0.314 0.000 0.636 0.187 -1.236 2.548 0.494 -0.618 1.266 3.102 0.982 0.732 0.987 0.758 0.393 0.489 0.616 +1 1.326 -1.194 0.190 0.524 0.404 1.523 1.023 1.721 0.000 0.409 0.330 0.421 0.000 0.627 0.595 -0.091 0.000 0.886 -0.433 -1.110 0.000 0.914 0.763 0.992 0.827 0.386 0.632 0.671 +0 0.884 -1.211 0.814 0.539 0.800 1.025 0.215 -0.300 2.173 0.768 -1.103 -1.617 1.107 0.457 0.433 1.624 0.000 0.379 -2.467 1.372 0.000 1.245 0.829 0.977 1.095 1.540 1.018 0.897 +1 0.793 0.578 -1.437 1.190 1.452 1.517 -2.713 0.064 0.000 0.949 0.011 1.361 0.000 1.605 0.700 1.647 2.548 1.572 -0.838 1.631 3.102 1.060 0.861 0.987 0.606 1.226 0.945 0.936 +1 1.217 1.084 0.024 0.394 0.086 2.125 1.627 -1.580 0.000 2.598 0.250 0.139 2.215 0.899 -0.377 -1.339 0.000 0.779 0.503 1.586 1.551 1.274 0.771 0.995 0.769 1.259 0.935 0.799 +0 0.982 -1.470 1.681 0.499 -1.505 0.552 -0.975 -0.130 0.000 0.942 0.287 -1.059 2.215 0.689 0.648 1.574 0.000 1.465 -0.690 0.479 3.102 0.702 0.955 1.002 0.905 1.217 0.817 0.779 +0 1.270 -0.512 1.322 0.595 0.757 0.453 -2.820 0.081 0.000 1.641 -0.413 -0.654 2.215 0.971 2.571 1.152 0.000 1.293 -0.757 -1.655 3.102 10.180 5.401 0.980 1.295 1.081 3.300 2.413 +0 0.561 -0.002 -1.356 0.975 -0.001 0.691 0.555 0.704 0.000 0.780 2.101 -1.507 0.000 1.668 -0.561 -0.990 2.548 1.177 -0.808 0.438 3.102 1.986 1.896 0.990 0.761 1.046 1.521 1.207 +0 0.898 -0.888 1.496 0.852 0.488 0.786 -1.368 0.343 2.173 1.158 -1.175 -1.254 2.215 0.401 0.119 -1.475 0.000 0.499 -0.862 -0.404 0.000 0.507 0.750 0.988 0.949 1.397 0.856 0.685 +0 0.401 -1.310 1.347 1.524 -0.226 1.071 -0.651 -1.715 2.173 0.712 -2.171 -0.288 0.000 1.153 -0.911 1.069 0.000 1.555 -0.167 0.073 3.102 1.594 1.305 1.070 1.200 1.400 1.123 0.968 +0 0.753 1.034 1.414 0.999 0.420 1.665 0.426 -0.673 0.000 2.501 0.630 0.995 2.215 1.896 -0.072 -0.883 2.548 0.533 0.365 0.584 0.000 1.303 0.973 0.987 0.789 2.453 1.560 1.244 +0 1.460 -1.466 0.411 0.866 -0.507 1.207 -0.818 -1.229 0.000 0.803 -0.383 0.600 2.215 0.281 -0.974 1.283 0.000 1.262 0.329 -1.674 0.000 0.815 0.857 1.146 0.883 0.299 0.741 0.796 +0 1.057 0.392 -1.160 0.794 -0.089 0.859 0.138 0.084 2.173 1.851 -0.622 1.398 0.000 0.793 -1.359 0.919 0.000 0.574 1.664 -0.325 0.000 1.128 1.219 1.044 0.824 0.895 1.108 1.020 +1 0.854 0.541 0.634 1.425 -0.009 1.668 1.234 -1.529 2.173 0.823 2.188 -0.303 0.000 0.539 0.790 -0.559 0.000 1.796 1.467 0.970 0.000 0.800 0.690 0.996 1.605 0.253 0.986 0.923 +1 0.315 1.685 1.697 1.676 0.882 1.172 -0.317 -1.414 2.173 2.171 -0.269 -0.726 0.000 1.801 1.381 0.950 1.274 0.842 -0.339 0.570 0.000 0.839 1.211 0.987 0.781 2.447 1.690 1.416 +0 0.869 0.648 -1.549 0.999 1.491 0.399 0.568 -0.446 0.000 0.500 -0.269 0.387 2.215 0.351 2.252 -0.311 0.000 0.926 1.255 -0.188 3.102 0.743 0.802 0.988 0.783 0.694 0.745 0.675 +0 0.912 1.504 1.203 0.790 0.084 0.846 -1.057 1.581 0.000 1.488 0.111 -0.017 2.215 1.213 -0.412 -0.375 0.000 1.831 1.837 -1.681 0.000 1.875 1.209 0.995 1.179 0.934 1.061 1.188 +1 0.728 -0.063 -0.311 1.206 1.060 0.817 -0.510 0.935 0.000 1.118 -1.131 -0.131 2.215 1.036 0.179 1.304 0.000 1.702 -1.809 -0.738 0.000 0.973 0.938 1.226 1.061 0.930 1.104 0.941 +1 1.040 1.814 0.953 0.785 -0.364 0.974 2.446 -0.476 0.000 0.613 2.292 1.264 0.000 0.945 0.785 1.731 2.548 0.431 1.207 1.453 1.551 1.644 0.982 1.160 0.870 0.189 0.830 0.754 +1 1.215 -0.573 -0.050 0.248 0.289 1.277 -1.133 0.243 0.000 1.883 -1.086 -1.589 0.000 1.283 -0.705 -1.390 2.548 0.489 -1.173 -0.290 0.000 1.000 1.055 0.989 0.932 0.377 0.883 0.775 +0 1.924 -0.015 0.357 0.038 1.009 0.934 0.426 -1.028 1.087 1.252 1.215 -0.350 0.000 2.856 -1.044 1.313 0.000 1.395 1.008 1.049 0.000 0.758 1.239 0.829 1.130 1.309 1.349 1.200 +0 1.267 1.313 0.403 0.729 1.386 0.615 -0.538 0.839 1.087 0.480 0.230 -1.035 2.215 1.548 -0.878 -0.817 0.000 0.922 0.674 -1.456 0.000 1.509 0.876 1.032 1.125 0.856 0.841 1.014 +0 3.593 0.242 0.321 1.261 0.651 1.850 0.154 -1.486 2.173 0.817 2.302 -1.007 0.000 0.866 -0.558 0.698 0.000 1.041 0.931 -1.162 0.000 0.840 0.580 0.975 2.398 0.760 1.517 1.259 +0 0.873 -0.567 -1.218 1.008 1.333 1.187 2.130 -0.527 0.000 1.423 -0.476 1.142 2.215 1.407 0.418 0.258 2.548 1.354 1.086 -0.926 0.000 1.059 1.461 0.987 0.778 1.306 1.757 1.539 +0 0.874 1.474 0.182 1.018 -0.547 1.012 -0.139 1.026 2.173 0.931 0.858 -0.188 2.215 1.054 -0.586 1.578 0.000 0.965 -0.643 -0.908 0.000 0.875 1.002 0.987 0.548 1.481 0.996 0.992 +1 0.359 -0.913 0.872 1.107 1.432 1.509 -0.149 0.046 0.000 0.636 -1.672 -1.575 0.000 1.227 1.568 -1.287 0.000 1.343 0.713 1.602 0.000 0.915 1.014 0.984 0.803 0.840 0.919 0.806 +0 1.900 0.155 0.948 1.185 0.584 0.645 1.440 -1.265 0.000 0.661 -0.942 0.465 2.215 1.128 -0.421 -0.793 2.548 0.857 -0.803 -1.398 0.000 0.501 0.714 0.992 1.261 0.864 0.940 0.884 +0 3.131 -0.361 -0.598 0.745 1.556 1.909 -0.735 1.204 0.000 1.456 -1.008 -0.380 1.107 0.815 -0.377 1.355 1.274 1.005 -1.625 1.242 0.000 1.361 0.818 1.972 1.253 1.214 1.208 1.331 +0 0.673 -0.700 1.510 1.549 -1.554 0.476 -0.059 0.045 0.000 0.775 -0.708 0.666 0.000 0.830 0.599 -0.653 2.548 0.398 -1.126 0.317 0.000 0.813 0.910 0.985 0.818 0.715 0.705 0.764 +0 0.468 -0.783 -0.745 1.750 1.042 1.198 -1.845 -0.575 0.000 2.480 -0.125 1.375 2.215 1.703 0.085 -0.700 0.000 1.753 -0.175 0.264 3.102 0.925 0.872 1.253 1.112 1.587 1.165 1.029 +0 1.199 0.277 -0.242 0.308 0.591 1.072 -0.169 0.965 2.173 1.379 0.361 -0.795 2.215 0.593 -0.045 0.463 0.000 1.072 -0.074 -1.514 0.000 0.860 0.907 0.981 0.979 1.855 1.001 0.818 +1 2.513 -0.748 1.308 0.061 -1.302 0.892 -1.071 -0.890 2.173 0.806 -1.090 0.380 0.000 0.332 0.892 0.068 2.548 0.697 1.539 -0.204 0.000 0.241 1.604 0.982 0.814 0.977 0.940 1.034 +0 0.630 -1.672 -1.182 2.606 -1.578 0.731 -0.752 0.856 0.000 0.766 -0.702 -0.114 0.000 1.425 -0.196 -0.091 2.548 0.427 0.005 0.530 3.102 1.220 0.955 0.981 1.143 0.323 1.386 1.279 +1 1.101 -2.331 -0.601 0.580 0.611 0.571 -0.150 1.585 2.173 0.980 -1.958 0.169 0.000 0.576 -1.793 0.842 0.000 0.760 -0.723 -1.026 3.102 0.658 0.784 0.987 1.371 0.557 0.896 0.834 +1 1.929 0.444 -0.895 0.308 -0.778 1.688 0.644 0.965 2.173 1.353 2.194 -1.070 0.000 1.111 0.309 0.111 1.274 0.500 0.839 1.472 0.000 1.077 1.459 0.986 1.664 1.219 1.436 1.268 +1 0.846 1.102 0.846 0.465 1.693 0.538 0.672 1.645 0.000 2.005 -0.167 -0.178 2.215 1.088 -0.598 -1.039 2.548 0.719 0.229 1.040 0.000 0.531 0.869 0.987 1.835 1.165 1.405 1.128 +0 2.721 -0.080 0.877 0.396 1.161 0.743 -0.583 -0.843 0.000 0.671 0.159 -0.901 0.000 0.925 -0.990 -0.385 2.548 0.916 -0.248 1.211 3.102 0.628 0.721 1.000 1.096 0.753 0.717 0.776 +0 0.335 1.069 -1.529 0.156 0.552 0.669 1.616 0.856 0.000 1.524 -0.610 -1.003 2.215 0.754 0.358 1.021 2.548 0.867 2.003 0.016 0.000 0.923 0.874 0.978 0.826 1.259 1.472 1.191 +0 1.532 -0.071 -0.894 0.077 -0.853 1.843 0.233 -0.458 2.173 3.116 -1.174 1.737 1.107 2.168 1.211 0.455 0.000 2.108 0.421 0.732 0.000 1.158 2.167 0.998 1.946 4.263 2.984 2.207 +0 0.962 -0.519 0.792 1.389 1.306 1.237 2.711 -1.296 0.000 1.350 0.717 0.558 0.000 2.291 0.250 -0.034 1.274 0.435 -1.540 -1.657 0.000 0.848 0.819 0.992 1.262 0.544 1.587 1.654 +0 0.626 0.301 -1.678 0.230 1.094 1.020 -1.143 0.513 2.173 1.183 0.209 -0.928 2.215 0.635 -1.149 1.490 0.000 0.519 -1.270 -0.335 0.000 0.637 0.979 0.992 1.794 1.963 1.369 1.119 +0 0.589 -1.076 -1.285 1.041 0.270 1.329 -1.797 0.219 0.000 1.519 -1.480 1.702 0.000 0.869 -1.034 -1.048 2.548 1.106 -0.409 1.525 0.000 0.950 0.838 1.070 0.575 0.255 0.490 0.602 +1 0.725 1.494 -0.973 1.366 -0.197 1.281 0.573 1.407 2.173 0.505 0.758 -0.718 0.000 0.475 1.593 0.769 0.000 0.395 -1.058 -0.922 0.000 0.823 1.050 0.988 0.915 0.830 1.118 0.885 +0 0.459 0.027 -1.104 1.304 1.564 1.428 0.314 -0.528 2.173 1.706 0.791 1.011 2.215 0.991 0.540 0.130 0.000 0.668 0.114 -1.404 0.000 0.906 1.027 0.982 1.482 2.330 1.483 1.151 +0 0.665 -0.224 1.578 1.155 -0.342 1.073 0.488 -1.440 0.000 0.730 -0.015 0.094 2.215 0.810 -0.795 0.454 0.000 1.132 0.762 0.973 1.551 0.934 0.793 1.199 0.726 0.705 0.650 0.598 +0 0.464 -0.327 0.009 1.798 -1.317 1.031 1.264 0.290 0.000 0.802 0.971 1.131 2.215 0.720 1.188 -0.358 0.000 1.143 -1.065 -1.712 3.102 0.857 1.186 1.176 1.129 1.301 0.885 0.849 +1 0.473 1.141 0.371 1.032 -1.253 2.915 0.341 -1.518 0.000 1.908 0.474 0.491 0.000 1.836 -0.395 -0.234 1.274 2.080 -0.587 0.937 3.102 1.206 1.336 0.989 1.125 1.315 1.109 1.023 +0 0.968 -2.219 -1.049 1.000 -1.408 0.852 0.083 0.364 2.173 0.683 0.656 -1.737 0.000 0.546 0.680 -0.427 0.000 0.607 0.641 0.993 0.000 0.867 0.996 0.990 0.693 0.465 0.995 1.014 +0 0.776 -1.556 0.544 0.437 1.725 0.453 1.130 -1.011 1.087 1.080 0.530 1.614 1.107 0.730 -2.313 0.199 0.000 0.504 0.194 -0.626 0.000 1.334 1.753 0.986 1.042 0.785 1.328 1.082 +1 2.332 0.058 1.446 0.780 -1.460 1.884 -1.155 -0.807 0.000 1.186 -0.377 0.440 0.000 1.115 -0.906 0.453 0.000 0.806 -0.284 0.854 3.102 0.545 0.621 0.987 0.667 0.528 0.727 0.818 +1 0.714 -0.154 1.130 0.623 -0.824 0.558 0.569 -1.271 0.000 0.886 0.513 0.167 2.215 1.313 0.225 -0.860 0.000 0.660 2.012 0.703 0.000 0.790 0.900 0.985 0.683 0.642 0.610 0.584 +1 1.550 -0.157 0.227 1.559 1.558 2.453 1.147 1.137 0.000 2.864 -0.195 -0.223 0.000 2.075 0.607 -0.651 1.274 3.513 0.228 -1.307 0.000 3.564 1.964 2.007 1.600 0.946 1.328 1.332 +1 0.360 0.689 -0.639 2.863 -0.031 1.313 0.146 1.230 0.000 0.766 -0.042 -1.128 0.000 1.123 0.427 -1.631 1.274 0.972 1.530 -0.932 0.000 0.974 0.756 0.987 0.768 0.884 0.867 0.771 +1 0.919 -0.701 -1.249 0.960 0.500 0.800 -1.109 0.748 2.173 0.901 -0.355 0.083 2.215 1.237 -0.191 -1.228 0.000 0.730 1.832 -1.525 0.000 1.615 1.469 1.301 0.893 0.855 1.282 1.086 +1 0.789 0.664 1.558 1.660 1.138 1.195 0.883 -0.415 2.173 0.778 0.528 -1.229 0.000 0.544 -0.253 0.022 2.548 0.905 0.953 0.958 0.000 1.061 0.818 0.989 1.492 0.731 1.045 0.897 +1 0.615 0.017 1.704 0.716 -0.399 1.086 -0.430 -0.758 1.087 0.619 1.515 1.698 0.000 1.682 0.134 0.625 0.000 0.603 -0.568 1.230 3.102 1.761 1.087 0.987 0.738 0.842 1.118 0.927 +0 0.556 -1.548 -1.037 0.856 0.212 0.933 -0.905 -1.292 2.173 0.920 2.570 -0.033 0.000 1.296 -1.176 0.440 0.000 1.451 -0.343 1.508 3.102 0.877 0.872 0.985 1.062 0.779 0.898 0.764 +1 0.522 0.561 -0.392 0.367 1.553 0.621 -0.646 -0.865 0.000 1.155 0.931 1.220 2.215 1.188 0.769 0.298 2.548 0.467 0.950 -1.077 0.000 0.853 1.063 0.995 0.828 0.922 0.931 0.854 +1 1.723 1.079 -0.866 0.191 0.790 1.872 0.813 0.741 2.173 0.968 0.703 -1.259 0.000 0.779 0.582 1.517 0.000 0.502 -0.368 -0.832 3.102 0.978 0.606 0.992 1.554 1.227 1.058 0.999 +1 0.348 -0.112 1.501 1.172 0.418 1.730 1.434 -1.573 2.173 2.008 1.420 -0.417 2.215 1.016 0.881 0.895 0.000 0.473 -0.128 -0.415 0.000 0.929 0.968 0.988 2.752 2.367 2.321 1.720 +1 0.329 0.542 0.901 1.701 -0.135 1.082 0.772 -1.682 2.173 0.532 1.597 0.693 0.000 0.677 0.211 -1.084 2.548 0.470 0.270 -0.217 0.000 0.664 0.972 0.990 0.819 0.623 0.887 0.743 +0 0.591 0.231 0.615 1.102 1.663 0.662 -0.145 1.699 1.087 0.759 0.400 0.948 0.000 1.008 0.362 -0.639 0.000 1.500 -0.176 -0.259 3.102 1.111 1.159 0.987 0.834 1.035 0.763 0.740 +0 1.025 -1.817 -1.011 1.285 -1.580 0.775 -0.523 -0.007 2.173 0.773 -1.053 1.045 0.000 0.858 0.359 -0.669 0.000 0.700 1.317 0.708 0.000 0.971 1.044 0.982 1.333 0.876 1.207 1.392 +1 1.991 -1.558 0.725 0.671 0.446 1.110 -1.014 -1.522 2.173 1.018 -0.341 -0.765 2.215 0.853 -0.362 -0.197 0.000 0.393 0.363 1.272 0.000 0.677 1.025 1.007 1.313 1.124 1.161 0.941 +0 1.172 0.155 -0.552 0.611 -1.569 0.671 -0.833 0.405 0.000 1.070 0.594 -1.652 2.215 0.600 0.481 0.773 0.000 1.276 -0.019 0.191 3.102 0.921 1.279 0.987 0.715 1.104 0.831 0.773 +0 0.410 -0.956 0.737 1.888 0.359 1.378 0.823 -1.663 0.000 0.962 0.625 -0.776 1.107 1.362 -0.845 0.097 2.548 1.040 1.416 -1.534 0.000 0.847 1.096 0.989 0.899 1.363 1.482 2.122 +0 0.317 -0.989 0.432 2.230 -0.761 1.020 0.823 1.739 2.173 0.693 -0.309 1.131 0.000 0.773 -0.682 -0.648 2.548 0.991 -1.804 0.407 0.000 0.883 1.209 1.023 0.504 1.338 1.160 1.289 +1 0.376 1.678 0.710 1.470 -1.277 0.965 1.029 -1.140 2.173 0.986 0.719 0.360 0.000 0.534 -0.036 1.293 2.548 0.705 0.847 -0.260 0.000 0.595 1.128 1.005 0.818 0.885 0.758 0.756 +0 0.506 -0.067 1.275 0.365 -0.463 0.634 -0.824 -0.877 2.173 0.632 -0.171 1.044 0.000 0.409 -1.426 -0.997 0.000 0.910 -0.659 0.873 3.102 0.946 0.952 0.986 0.568 0.804 0.616 0.565 +1 0.894 1.433 -0.254 0.554 1.419 0.746 0.360 -1.308 0.000 1.064 1.248 0.602 2.215 0.490 -1.058 1.443 0.000 1.277 0.536 1.343 3.102 1.131 0.870 0.986 0.730 0.736 0.909 0.857 +0 0.723 -0.942 0.570 2.772 -1.632 0.752 -0.857 -0.366 0.000 1.700 0.281 0.108 2.215 1.312 -0.374 -1.733 1.274 0.731 0.974 0.117 0.000 1.443 1.160 1.797 0.964 1.679 1.391 1.254 +1 1.328 0.802 1.246 1.645 0.636 1.199 0.544 -1.621 0.000 0.422 0.546 -0.296 0.000 1.736 0.602 -0.867 2.548 0.481 0.961 -0.738 3.102 0.937 0.836 1.069 0.728 0.192 0.857 0.757 +1 0.911 -0.888 -0.319 2.009 -1.365 1.312 0.399 0.802 2.173 0.407 0.172 -0.126 0.000 0.300 -0.363 -1.432 0.000 0.552 0.590 -1.104 0.000 0.519 0.829 1.515 0.823 0.498 1.171 0.877 +1 0.470 1.532 -0.364 0.980 1.076 0.668 -0.660 0.864 1.087 0.968 0.331 -0.828 2.215 0.389 2.034 -1.359 0.000 0.455 -0.812 -0.635 0.000 0.977 1.134 0.989 0.874 1.335 0.883 0.786 +1 0.471 1.800 -0.519 0.221 0.342 0.858 1.238 1.570 2.173 0.480 1.323 -0.275 0.000 0.790 1.782 0.863 0.000 1.056 0.245 0.776 3.102 0.857 0.932 0.976 0.627 0.830 0.680 0.656 +0 1.039 -0.887 -0.598 2.530 -0.884 0.738 -1.702 0.992 0.000 1.022 -2.322 0.721 0.000 0.689 -1.284 0.515 2.548 1.090 -1.122 -1.727 3.102 0.774 0.875 0.996 1.074 0.597 0.840 1.191 +0 0.407 1.132 -0.697 1.627 1.534 0.949 -0.443 -1.406 2.173 1.238 0.047 -0.145 0.000 2.054 -0.081 0.526 2.548 0.810 -1.056 -0.412 0.000 0.973 1.073 1.020 1.224 1.742 1.210 1.162 +1 0.574 -0.320 -1.254 0.758 -0.309 1.311 -0.021 -1.688 2.173 1.141 0.145 0.914 0.000 1.425 -0.284 -0.260 0.000 0.893 0.577 -0.567 0.000 0.878 1.340 0.988 0.602 0.678 0.784 0.741 +1 0.755 0.421 -1.067 1.036 1.670 0.690 -0.169 -0.360 1.087 2.072 0.802 0.612 2.215 0.780 0.212 1.553 0.000 0.541 0.173 -1.474 0.000 0.920 1.203 0.989 0.882 1.631 1.147 1.048 +1 1.544 -0.667 1.683 0.460 -0.673 0.901 -0.688 0.382 2.173 0.597 -0.377 -1.029 0.000 0.847 0.262 -0.240 2.548 0.812 0.275 1.323 0.000 0.842 1.027 0.995 0.868 0.803 0.810 0.721 +1 0.703 0.975 -1.061 1.224 1.205 0.560 1.361 -1.363 0.000 1.806 0.808 0.167 2.215 0.868 2.033 1.453 0.000 0.842 0.068 -0.932 3.102 0.903 0.929 1.145 1.182 1.024 1.036 0.901 +1 1.020 -0.172 0.051 0.827 1.679 0.850 0.651 -0.794 0.000 1.380 -0.696 1.451 2.215 1.563 -0.844 0.068 0.000 1.259 0.749 -1.442 0.000 0.891 0.735 1.266 0.985 0.908 0.987 0.886 +0 0.631 0.317 -0.817 0.249 -0.317 0.541 -0.592 1.415 0.000 0.835 -1.181 0.111 2.215 1.423 1.171 -1.178 1.274 1.342 -1.150 0.793 0.000 0.869 0.892 0.976 1.174 2.141 1.345 1.076 +1 0.567 1.648 -0.361 0.958 1.051 0.952 0.926 -0.732 2.173 0.832 0.466 1.667 0.000 1.031 1.290 0.131 2.548 1.429 1.008 1.331 0.000 0.678 1.012 0.988 0.947 0.916 0.891 0.781 +0 1.134 -0.303 -0.806 1.270 -1.358 0.720 0.140 0.842 0.000 0.410 0.097 -0.101 0.000 0.310 1.538 1.343 0.000 0.576 1.156 -1.669 3.102 0.868 0.746 0.984 0.866 0.484 0.709 0.735 +1 0.758 0.682 -0.444 0.938 -1.632 1.179 -0.084 0.624 0.000 0.880 0.535 0.412 0.000 1.320 0.587 1.483 2.548 2.797 0.726 -1.090 1.551 0.934 0.869 1.024 0.698 1.089 0.735 0.650 +1 1.095 -0.733 0.372 0.939 0.191 1.186 0.014 -1.148 2.173 0.896 -0.208 1.243 0.000 0.647 -2.172 -0.622 0.000 0.612 0.423 1.064 3.102 1.923 1.222 1.002 1.687 0.853 1.124 1.103 +1 0.433 0.852 -1.542 1.366 0.726 1.085 -0.534 -1.507 0.000 0.814 -0.632 -1.026 2.215 1.652 -1.256 -0.104 0.000 1.415 0.489 0.883 3.102 0.897 0.972 0.990 0.604 1.151 0.947 1.158 +0 0.416 -1.352 0.839 0.854 1.269 0.978 -0.442 -1.513 2.173 0.898 2.369 0.484 0.000 0.764 0.045 -0.462 2.548 1.698 -0.947 -0.406 0.000 4.819 2.601 0.983 0.871 0.915 1.898 1.486 +0 0.535 1.359 1.126 1.322 -1.572 1.203 1.087 -1.301 1.087 1.973 -0.763 0.410 2.215 1.053 -0.945 -0.083 0.000 0.593 -2.137 -0.134 0.000 0.789 0.844 0.992 0.815 3.348 1.654 1.385 +0 1.719 0.561 1.517 0.784 -1.006 0.942 0.932 0.153 0.000 0.345 0.832 1.035 0.000 0.795 0.857 -1.473 2.548 0.811 0.179 -0.663 3.102 0.866 0.917 1.229 0.758 0.469 0.615 0.697 +1 1.040 -0.803 1.637 0.264 -0.814 1.819 0.055 0.353 0.000 1.541 -0.934 -0.811 2.215 1.701 -0.112 -1.387 0.000 1.762 -0.267 1.163 3.102 1.301 1.135 0.979 0.914 1.534 1.142 1.018 diff --git a/examples/parallel_learning/predict.conf b/examples/parallel_learning/predict.conf new file mode 100644 index 0000000..f42a9be --- /dev/null +++ b/examples/parallel_learning/predict.conf @@ -0,0 +1,5 @@ +task = predict + +data = binary.test + +input_model= LightGBM_model.txt diff --git a/examples/parallel_learning/train.conf b/examples/parallel_learning/train.conf new file mode 100644 index 0000000..64e729b --- /dev/null +++ b/examples/parallel_learning/train.conf @@ -0,0 +1,111 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# alias: application, app +objective = binary + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = binary_logloss,auc + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# column in data to use as label +label_column = 0 + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "binary.train.weight" +# alias: train_data, train +data = binary.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "binary.test.weight" +# alias: valid, test, test_data, +valid_data = binary.test + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 63 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = feature + +# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.8 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = LightGBM_model.txt + +# support continuous train from trained gbdt model +# input_model= trained_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + + +# number of machines in parallel training, alias: num_machine +num_machines = 2 + +# local listening port in parallel training, alias: local_port +local_listen_port = 12400 + +# machines list file for parallel training, alias: mlist +machine_list_file = mlist.txt diff --git a/examples/python-guide/README.md b/examples/python-guide/README.md new file mode 100644 index 0000000..26c867d --- /dev/null +++ b/examples/python-guide/README.md @@ -0,0 +1,66 @@ +Python-package Examples +======================= + +Here is an example for LightGBM to use Python-package. + +You should install LightGBM [Python-package](https://github.com/lightgbm-org/LightGBM/tree/main/python-package) first. + +You also need scikit-learn, pandas, matplotlib (only for plot example), and scipy (only for logistic regression example) to run the examples, but they are not required for the package itself. You can install them with pip: + +``` +pip install scikit-learn pandas matplotlib scipy -U +``` + +Now you can run examples in this folder, for example: + +``` +python simple_example.py +``` + +Examples include: + +- [`dask/`](./dask): examples using Dask for distributed training +- [simple_example.py](https://github.com/lightgbm-org/LightGBM/blob/main/examples/python-guide/simple_example.py) + - Construct Dataset + - Basic train and predict + - Eval during training + - Early stopping + - Save model to file +- [sklearn_example.py](https://github.com/lightgbm-org/LightGBM/blob/main/examples/python-guide/sklearn_example.py) + - Create data for learning with sklearn interface + - Basic train and predict with sklearn interface + - Feature importances with sklearn interface + - Self-defined eval metric with sklearn interface + - Find best parameters for the model with sklearn's GridSearchCV +- [advanced_example.py](https://github.com/lightgbm-org/LightGBM/blob/main/examples/python-guide/advanced_example.py) + - Construct Dataset + - Set feature names + - Directly use categorical features without one-hot encoding + - Save model to file + - Dump model to JSON format + - Get feature names + - Get feature importances + - Load model to predict + - Dump and load model with pickle + - Load model file to continue training + - Change learning rates during training + - Change any parameters during training + - Self-defined objective function + - Self-defined eval metric + - Callback function +- [logistic_regression.py](https://github.com/lightgbm-org/LightGBM/blob/main/examples/python-guide/logistic_regression.py) + - Use objective `xentropy` or `binary` + - Use `xentropy` with binary labels or probability labels + - Use `binary` only with binary labels + - Compare speed of `xentropy` versus `binary` +- [plot_example.py](https://github.com/lightgbm-org/LightGBM/blob/main/examples/python-guide/plot_example.py) + - Construct Dataset + - Train and record eval results for further plotting + - Plot metrics recorded during training + - Plot feature importances + - Plot split value histogram + - Plot one specified tree + - Plot one specified tree with Graphviz +- [dataset_from_multi_hdf5.py](https://github.com/lightgbm-org/LightGBM/blob/main/examples/python-guide/dataset_from_multi_hdf5.py) + - Construct Dataset from multiple HDF5 files + - Avoid loading all data into memory diff --git a/examples/python-guide/advanced_example.py b/examples/python-guide/advanced_example.py new file mode 100644 index 0000000..e7e3381 --- /dev/null +++ b/examples/python-guide/advanced_example.py @@ -0,0 +1,220 @@ +# coding: utf-8 +import copy +import json +import pickle +from pathlib import Path + +import numpy as np +import pandas as pd +from sklearn.metrics import roc_auc_score + +import lightgbm as lgb + +print("Loading data...") +# load or create your dataset +binary_example_dir = Path(__file__).absolute().parents[1] / "binary_classification" +df_train = pd.read_csv(str(binary_example_dir / "binary.train"), header=None, sep="\t") +df_test = pd.read_csv(str(binary_example_dir / "binary.test"), header=None, sep="\t") +W_train = pd.read_csv(str(binary_example_dir / "binary.train.weight"), header=None)[0] +W_test = pd.read_csv(str(binary_example_dir / "binary.test.weight"), header=None)[0] + +y_train = df_train[0] +y_test = df_test[0] +X_train = df_train.drop(0, axis=1) +X_test = df_test.drop(0, axis=1) + +num_train, num_feature = X_train.shape + +# generate feature names +feature_name = [f"feature_{col}" for col in range(num_feature)] + +# create dataset for lightgbm +# if you want to re-use data, remember to set free_raw_data=False +lgb_train = lgb.Dataset( + X_train, y_train, weight=W_train, feature_name=feature_name, categorical_feature=[21], free_raw_data=False +) +lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, weight=W_test, free_raw_data=False) + +# specify your configurations as a dict +params = { + "boosting_type": "gbdt", + "objective": "binary", + "metric": "binary_logloss", + "num_leaves": 31, + "learning_rate": 0.05, + "feature_fraction": 0.9, + "bagging_fraction": 0.8, + "bagging_freq": 5, + "verbose": 0, +} + +print("Starting training...") +# feature_name and categorical_feature +gbm = lgb.train( + params, + lgb_train, + num_boost_round=10, + valid_sets=lgb_train, # eval training data +) + +print("Finished first 10 rounds...") +# check feature name +print(f"7th feature name is: {lgb_train.feature_name[6]}") + +print("Saving model...") +# save model to file +gbm.save_model("model.txt") + +print("Dumping model to JSON...") +# dump model to JSON (and save to file) +model_json = gbm.dump_model() + +with open("model.json", "w+") as f: + json.dump(model_json, f, indent=4) + +# feature names +print(f"Feature names: {gbm.feature_name()}") + +# feature importances +print(f"Feature importances: {list(gbm.feature_importance())}") + +print("Loading model to predict...") +# load model to predict +bst = lgb.Booster(model_file="model.txt") +# can only predict with the best iteration (or the saving iteration) +y_pred = bst.predict(X_test) +# eval with loaded model +auc_loaded_model = roc_auc_score(y_test, y_pred) +print(f"The ROC AUC of loaded model's prediction is: {auc_loaded_model}") + +print("Dumping and loading model with pickle...") +# dump model with pickle +with open("model.pkl", "wb") as fout: + pickle.dump(gbm, fout) +# load model with pickle to predict +with open("model.pkl", "rb") as fin: + pkl_bst = pickle.load(fin) +# can predict with any iteration when loaded in pickle way +y_pred = pkl_bst.predict(X_test, num_iteration=7) +# eval with loaded model +auc_pickled_model = roc_auc_score(y_test, y_pred) +print(f"The ROC AUC of pickled model's prediction is: {auc_pickled_model}") + +# continue training +# init_model accepts: +# 1. model file name +# 2. Booster() +gbm = lgb.train(params, lgb_train, num_boost_round=10, init_model="model.txt", valid_sets=lgb_eval) + +print("Finished 10 - 20 rounds with model file...") + +# decay learning rates +# reset_parameter callback accepts: +# 1. list with length = num_boost_round +# 2. function(curr_iter) +gbm = lgb.train( + params, + lgb_train, + num_boost_round=10, + init_model=gbm, + valid_sets=lgb_eval, + callbacks=[lgb.reset_parameter(learning_rate=lambda iter: 0.05 * (0.99**iter))], +) + +print("Finished 20 - 30 rounds with decay learning rates...") + +# change other parameters during training +gbm = lgb.train( + params, + lgb_train, + num_boost_round=10, + init_model=gbm, + valid_sets=lgb_eval, + callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)], +) + +print("Finished 30 - 40 rounds with changing bagging_fraction...") + + +# self-defined objective function +# f(preds: array, train_data: Dataset) -> grad: array, hess: array +# log likelihood loss +def loglikelihood(preds, train_data): + labels = train_data.get_label() + preds = 1.0 / (1.0 + np.exp(-preds)) + grad = preds - labels + hess = preds * (1.0 - preds) + return grad, hess + + +# self-defined eval metric +# f(preds: array, train_data: Dataset) -> metric_name: str, metric_value: float, maximize: bool +# binary error +# NOTE: when you do customized loss function, the default prediction value is margin +# This may make built-in evaluation metric calculate wrong results +# For example, we are doing log likelihood loss, the prediction is score before logistic transformation +# Keep this in mind when you use the customization +def binary_error(preds, train_data): + labels = train_data.get_label() + preds = 1.0 / (1.0 + np.exp(-preds)) + return "error", np.mean(labels != (preds > 0.5)), False + + +# Pass custom objective function through params +params_custom_obj = copy.deepcopy(params) +params_custom_obj["objective"] = loglikelihood + +gbm = lgb.train( + params_custom_obj, lgb_train, num_boost_round=10, init_model=gbm, feval=binary_error, valid_sets=lgb_eval +) + +print("Finished 40 - 50 rounds with self-defined objective function and eval metric...") + + +# another self-defined eval metric +# f(preds: array, train_data: Dataset) -> metric_name: str, metric_value: float, maximize: bool +# accuracy +# NOTE: when you do customized loss function, the default prediction value is margin +# This may make built-in evaluation metric calculate wrong results +# For example, we are doing log likelihood loss, the prediction is score before logistic transformation +# Keep this in mind when you use the customization +def accuracy(preds, train_data): + labels = train_data.get_label() + preds = 1.0 / (1.0 + np.exp(-preds)) + return "accuracy", np.mean(labels == (preds > 0.5)), True + + +# Pass custom objective function through params +params_custom_obj = copy.deepcopy(params) +params_custom_obj["objective"] = loglikelihood + +gbm = lgb.train( + params_custom_obj, + lgb_train, + num_boost_round=10, + init_model=gbm, + feval=[binary_error, accuracy], + valid_sets=lgb_eval, +) + +print("Finished 50 - 60 rounds with self-defined objective function and multiple self-defined eval metrics...") + +print("Starting a new training job...") + + +# callback +def reset_metrics(): + def callback(env): + lgb_eval_new = lgb.Dataset(X_test, y_test, reference=lgb_train) + if env.iteration - env.begin_iteration == 5: + print("Add a new valid dataset at iteration 5...") + env.model.add_valid(lgb_eval_new, "new_valid") + + callback.before_iteration = True + callback.order = 0 + return callback + + +gbm = lgb.train(params, lgb_train, num_boost_round=10, valid_sets=lgb_train, callbacks=[reset_metrics()]) + +print("Finished first 10 rounds with callback function...") diff --git a/examples/python-guide/dask/README.md b/examples/python-guide/dask/README.md new file mode 100644 index 0000000..dea2c93 --- /dev/null +++ b/examples/python-guide/dask/README.md @@ -0,0 +1,25 @@ +Dask Examples +============= + +This directory contains examples of machine learning workflows with LightGBM and [Dask](https://dask.org/). + +Before running this code, see [the installation instructions for the Dask-package](https://github.com/lightgbm-org/LightGBM/tree/main/python-package#install-dask-package). + +After installing the package and its dependencies, any of the examples here can be run with a command like this: + +```shell +python binary-classification.py +``` + +The examples listed below contain minimal code showing how to train LightGBM models using Dask. + +**Training** + +* [binary-classification.py](./binary-classification.py) +* [multiclass-classification.py](./multiclass-classification.py) +* [ranking.py](./ranking.py) +* [regression.py](./regression.py) + +**Prediction** + +* [prediction.py](./prediction.py) diff --git a/examples/python-guide/dask/binary-classification.py b/examples/python-guide/dask/binary-classification.py new file mode 100644 index 0000000..4de9245 --- /dev/null +++ b/examples/python-guide/dask/binary-classification.py @@ -0,0 +1,30 @@ +import dask.array as da +from distributed import Client, LocalCluster +from sklearn.datasets import make_blobs + +import lightgbm as lgb + +if __name__ == "__main__": + print("loading data") + + X, y = make_blobs(n_samples=1000, n_features=50, centers=2) + + print("initializing a Dask cluster") + + cluster = LocalCluster() + client = Client(cluster) + + print("created a Dask LocalCluster") + + print("distributing training data on the Dask cluster") + + dX = da.from_array(X, chunks=(100, 50)) + dy = da.from_array(y, chunks=(100,)) + + print("beginning training") + + dask_model = lgb.DaskLGBMClassifier(n_estimators=10) + dask_model.fit(dX, dy) + assert dask_model.fitted_ + + print("done training") diff --git a/examples/python-guide/dask/multiclass-classification.py b/examples/python-guide/dask/multiclass-classification.py new file mode 100644 index 0000000..bcda958 --- /dev/null +++ b/examples/python-guide/dask/multiclass-classification.py @@ -0,0 +1,30 @@ +import dask.array as da +from distributed import Client, LocalCluster +from sklearn.datasets import make_blobs + +import lightgbm as lgb + +if __name__ == "__main__": + print("loading data") + + X, y = make_blobs(n_samples=1000, n_features=50, centers=3) + + print("initializing a Dask cluster") + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + print("created a Dask LocalCluster") + + print("distributing training data on the Dask cluster") + + dX = da.from_array(X, chunks=(100, 50)) + dy = da.from_array(y, chunks=(100,)) + + print("beginning training") + + dask_model = lgb.DaskLGBMClassifier(n_estimators=10) + dask_model.fit(dX, dy) + assert dask_model.fitted_ + + print("done training") diff --git a/examples/python-guide/dask/prediction.py b/examples/python-guide/dask/prediction.py new file mode 100644 index 0000000..a4cb5cd --- /dev/null +++ b/examples/python-guide/dask/prediction.py @@ -0,0 +1,48 @@ +import dask.array as da +from distributed import Client, LocalCluster +from sklearn.datasets import make_regression +from sklearn.metrics import mean_squared_error + +import lightgbm as lgb + +if __name__ == "__main__": + print("loading data") + + X, y = make_regression(n_samples=1000, n_features=50) + + print("initializing a Dask cluster") + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + print("created a Dask LocalCluster") + + print("distributing training data on the Dask cluster") + + dX = da.from_array(X, chunks=(100, 50)) + dy = da.from_array(y, chunks=(100,)) + + print("beginning training") + + dask_model = lgb.DaskLGBMRegressor(n_estimators=10) + dask_model.fit(dX, dy) + assert dask_model.fitted_ + + print("done training") + + print("predicting on the training data") + + preds = dask_model.predict(dX) + + # the code below uses sklearn.metrics, but this requires pulling all of the + # predictions and target values back from workers to the client + # + # for larger datasets, consider the metrics from dask-ml instead + # https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics + print("computing MSE") + + preds_local = preds.compute() + actuals_local = dy.compute() + mse = mean_squared_error(actuals_local, preds_local) + + print(f"MSE: {mse}") diff --git a/examples/python-guide/dask/ranking.py b/examples/python-guide/dask/ranking.py new file mode 100644 index 0000000..e812fa3 --- /dev/null +++ b/examples/python-guide/dask/ranking.py @@ -0,0 +1,50 @@ +from pathlib import Path + +import dask.array as da +import numpy as np +from distributed import Client, LocalCluster +from sklearn.datasets import load_svmlight_file + +import lightgbm as lgb + +if __name__ == "__main__": + print("loading data") + + rank_example_dir = Path(__file__).absolute().parents[2] / "lambdarank" + X, y = load_svmlight_file(str(rank_example_dir / "rank.train")) + group = np.loadtxt(str(rank_example_dir / "rank.train.query")) + + print("initializing a Dask cluster") + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + print("created a Dask LocalCluster") + + print("distributing training data on the Dask cluster") + + # split training data into two partitions + rows_in_part1 = int(np.sum(group[:100])) + rows_in_part2 = X.shape[0] - rows_in_part1 + num_features = X.shape[1] + + # make this array dense because we're splitting across + # a sparse boundary to partition the data + X = X.toarray() + + dX = da.from_array(x=X, chunks=[(rows_in_part1, rows_in_part2), (num_features,)]) + dy = da.from_array( + x=y, + chunks=[ + (rows_in_part1, rows_in_part2), + ], + ) + dg = da.from_array(x=group, chunks=[(100, group.size - 100)]) + + print("beginning training") + + dask_model = lgb.DaskLGBMRanker(n_estimators=10) + dask_model.fit(dX, dy, group=dg) + assert dask_model.fitted_ + + print("done training") diff --git a/examples/python-guide/dask/regression.py b/examples/python-guide/dask/regression.py new file mode 100644 index 0000000..4d15547 --- /dev/null +++ b/examples/python-guide/dask/regression.py @@ -0,0 +1,30 @@ +import dask.array as da +from distributed import Client, LocalCluster +from sklearn.datasets import make_regression + +import lightgbm as lgb + +if __name__ == "__main__": + print("loading data") + + X, y = make_regression(n_samples=1000, n_features=50) + + print("initializing a Dask cluster") + + cluster = LocalCluster(n_workers=2) + client = Client(cluster) + + print("created a Dask LocalCluster") + + print("distributing training data on the Dask cluster") + + dX = da.from_array(X, chunks=(100, 50)) + dy = da.from_array(y, chunks=(100,)) + + print("beginning training") + + dask_model = lgb.DaskLGBMRegressor(n_estimators=10) + dask_model.fit(dX, dy) + assert dask_model.fitted_ + + print("done training") diff --git a/examples/python-guide/dataset_from_multi_hdf5.py b/examples/python-guide/dataset_from_multi_hdf5.py new file mode 100644 index 0000000..ae7000f --- /dev/null +++ b/examples/python-guide/dataset_from_multi_hdf5.py @@ -0,0 +1,110 @@ +from pathlib import Path + +import h5py +import numpy as np +import pandas as pd + +import lightgbm as lgb + + +class HDFSequence(lgb.Sequence): + def __init__(self, hdf_dataset, batch_size): + """ + Construct a sequence object from HDF5 with required interface. + + Parameters + ---------- + hdf_dataset : h5py.Dataset + Dataset in HDF5 file. + batch_size : int + Size of a batch. When reading data to construct lightgbm Dataset, each read reads batch_size rows. + """ + # We can also open HDF5 file once and get access to + self.data = hdf_dataset + self.batch_size = batch_size + + def __getitem__(self, idx): + return self.data[idx] + + def __len__(self): + return len(self.data) + + +def create_dataset_from_multiple_hdf(input_flist, batch_size): + data = [] + ylist = [] + for f in input_flist: + f = h5py.File(f, "r") + data.append(HDFSequence(f["X"], batch_size)) + ylist.append(f["Y"][:]) + + params = { + "bin_construct_sample_cnt": 200000, + "max_bin": 255, + } + y = np.concatenate(ylist) + dataset = lgb.Dataset(data, label=y, params=params) + # With binary dataset created, we can use either Python API or cmdline version to train. + # + # Note: in order to create exactly the same dataset with the one created in simple_example.py, we need + # to modify simple_example.py to pass numpy array instead of pandas DataFrame to Dataset constructor. + # The reason is that DataFrame column names will be used in Dataset. For a DataFrame with Int64Index + # as columns, Dataset will use column names like ["0", "1", "2", ...]. While for numpy array, column names + # are using the default one assigned in C++ code (dataset_loader.cpp), like ["Column_0", "Column_1", ...]. + dataset.save_binary("regression.train.from_hdf.bin") + + +def save2hdf(input_data, fname, batch_size): + """Store numpy array to HDF5 file. + + Please note chunk size settings in the implementation for I/O performance optimization. + """ + with h5py.File(fname, "w") as f: + for name, data in input_data.items(): + nrow, ncol = data.shape + if ncol == 1: + # Y has a single column and we read it in single shot. So store it as an 1-d array. + chunk = (nrow,) + data = data.values.flatten() + else: + # We use random access for data sampling when creating LightGBM Dataset from Sequence. + # When accessing any element in a HDF5 chunk, it's read entirely. + # To save I/O for sampling, we should keep number of total chunks much larger than sample count. + # Here we are just creating a chunk size that matches with batch_size. + # + # Also note that the data is stored in row major order to avoid extra copy when passing to + # lightgbm Dataset. + chunk = (batch_size, ncol) + f.create_dataset(name, data=data, chunks=chunk, compression="lzf") + + +def generate_hdf(input_fname, output_basename, batch_size): + # Save to 2 HDF5 files for demonstration. + df = pd.read_csv(input_fname, header=None, sep="\t") + + mid = len(df) // 2 + df1 = df.iloc[:mid] + df2 = df.iloc[mid:] + + # We can store multiple datasets inside a single HDF5 file. + # Separating X and Y for choosing best chunk size for data loading. + fname1 = f"{output_basename}1.h5" + fname2 = f"{output_basename}2.h5" + save2hdf({"Y": df1.iloc[:, :1], "X": df1.iloc[:, 1:]}, fname1, batch_size) + save2hdf({"Y": df2.iloc[:, :1], "X": df2.iloc[:, 1:]}, fname2, batch_size) + + return [fname1, fname2] + + +def main(): + batch_size = 64 + output_basename = "regression" + hdf_files = generate_hdf( + str(Path(__file__).absolute().parents[1] / "regression" / "regression.train"), output_basename, batch_size + ) + + create_dataset_from_multiple_hdf(hdf_files, batch_size=batch_size) + + +if __name__ == "__main__": + main() diff --git a/examples/python-guide/logistic_regression.py b/examples/python-guide/logistic_regression.py new file mode 100644 index 0000000..c73155d --- /dev/null +++ b/examples/python-guide/logistic_regression.py @@ -0,0 +1,101 @@ +# coding: utf-8 +"""Comparison of `binary` and `xentropy` objectives. + +BLUF: The `xentropy` objective does logistic regression and generalizes +to the case where labels are probabilistic (i.e. numbers between 0 and 1). + +Details: Both `binary` and `xentropy` minimize the log loss and use +`boost_from_average = TRUE` by default. Possibly the only difference +between them with default settings is that `binary` may achieve a slight +speed improvement by assuming that the labels are binary instead of +probabilistic. +""" + +import time + +import numpy as np +import pandas as pd +from scipy.special import expit + +import lightgbm as lgb + +################# +# Simulate some binary data with a single categorical and +# single continuous predictor +rng = np.random.default_rng(seed=0) +N = 1000 +X = pd.DataFrame({"continuous": range(N), "categorical": np.repeat([0, 1, 2, 3, 4], N / 5)}) +CATEGORICAL_EFFECTS = [-1, -1, -2, -2, 2] +LINEAR_TERM = np.array( + [-0.5 + 0.01 * X["continuous"][k] + CATEGORICAL_EFFECTS[X["categorical"][k]] for k in range(X.shape[0])] +) + rng.normal(loc=0, scale=1, size=X.shape[0]) +TRUE_PROB = expit(LINEAR_TERM) +Y = rng.binomial(n=1, p=TRUE_PROB, size=N) +DATA = { + "X": X, + "probability_labels": TRUE_PROB, + "binary_labels": Y, + "lgb_with_binary_labels": lgb.Dataset(X, Y), + "lgb_with_probability_labels": lgb.Dataset(X, TRUE_PROB), +} + + +################# +# Set up a couple of utilities for our experiments +def log_loss(preds, labels): + """Logarithmic loss with non-necessarily-binary labels.""" + log_likelihood = np.sum(labels * np.log(preds)) / len(preds) + return -log_likelihood + + +def experiment(objective, label_type, data): + """Measure performance of an objective. + + Parameters + ---------- + objective : {'binary', 'xentropy'} + Objective function. + label_type : {'binary', 'probability'} + Type of the label. + data : dict + Data for training. + + Returns + ------- + result : dict + Experiment summary stats. + """ + nrounds = 5 + lgb_data = data[f"lgb_with_{label_type}_labels"] + params = {"objective": objective, "feature_fraction": 1, "bagging_fraction": 1, "verbose": -1, "seed": 123} + time_zero = time.time() + gbm = lgb.train(params, lgb_data, num_boost_round=nrounds) + y_fitted = gbm.predict(data["X"]) + y_true = data[f"{label_type}_labels"] + duration = time.time() - time_zero + return {"time": duration, "correlation": np.corrcoef(y_fitted, y_true)[0, 1], "logloss": log_loss(y_fitted, y_true)} + + +################# +# Observe the behavior of `binary` and `xentropy` objectives +print("Performance of `binary` objective with binary labels:") +print(experiment("binary", label_type="binary", data=DATA)) + +print("Performance of `xentropy` objective with binary labels:") +print(experiment("xentropy", label_type="binary", data=DATA)) + +print("Performance of `xentropy` objective with probability labels:") +print(experiment("xentropy", label_type="probability", data=DATA)) + +# Trying this throws an error on non-binary values of y: +# experiment('binary', label_type='probability', DATA) + +# The speed of `binary` is not drastically different than +# `xentropy`. `xentropy` runs faster than `binary` in many cases, although +# there are reasons to suspect that `binary` should run faster when the +# label is an integer instead of a float +K = 10 +A = [experiment("binary", label_type="binary", data=DATA)["time"] for k in range(K)] +B = [experiment("xentropy", label_type="binary", data=DATA)["time"] for k in range(K)] +print(f"Best `binary` time: {min(A)}") +print(f"Best `xentropy` time: {min(B)}") diff --git a/examples/python-guide/notebooks/interactive_plot_example.ipynb b/examples/python-guide/notebooks/interactive_plot_example.ipynb new file mode 100644 index 0000000..a8abdf3 --- /dev/null +++ b/examples/python-guide/notebooks/interactive_plot_example.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2018-10-25T22:13:39.152526Z", + "start_time": "2018-10-25T22:13:37.503680Z" + } + }, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import lightgbm as lgb\n", + "\n", + "%matplotlib inline\n", + "\n", + "try:\n", + " # To enable interactive mode you should install ipywidgets\n", + " # https://github.com/jupyter-widgets/ipywidgets\n", + " from ipywidgets import SelectMultiple, interact\n", + "\n", + " INTERACTIVE = True\n", + "except ImportError:\n", + " INTERACTIVE = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2018-10-25T22:13:39.238695Z", + "start_time": "2018-10-25T22:13:39.160165Z" + } + }, + "outputs": [], + "source": [ + "regression_example_dir = Path().absolute().parents[1] / \"regression\"\n", + "df_train = pd.read_csv(str(regression_example_dir / \"regression.train\"), header=None, sep=\"\\t\")\n", + "df_test = pd.read_csv(str(regression_example_dir / \"regression.test\"), header=None, sep=\"\\t\")\n", + "\n", + "y_train = df_train[0]\n", + "y_test = df_test[0]\n", + "X_train = df_train.drop(0, axis=1)\n", + "X_test = df_test.drop(0, axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create Dataset object for LightGBM" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "lgb_train = lgb.Dataset(\n", + " X_train,\n", + " y_train,\n", + " feature_name=[f\"f{i + 1}\" for i in range(X_train.shape[-1])],\n", + " categorical_feature=[21],\n", + ")\n", + "lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuration dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2018-10-25T22:13:39.243104Z", + "start_time": "2018-10-25T22:13:39.240578Z" + } + }, + "outputs": [], + "source": [ + "params = {\"num_leaves\": 5, \"metric\": [\"l1\", \"l2\"], \"verbose\": -1}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2018-10-25T22:13:39.336630Z", + "start_time": "2018-10-25T22:13:39.246006Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10]\ttraining's l1: 0.457448\ttraining's l2: 0.217995\tvalid_1's l1: 0.456464\tvalid_1's l2: 0.21641\n", + "[20]\ttraining's l1: 0.436869\ttraining's l2: 0.205099\tvalid_1's l1: 0.434057\tvalid_1's l2: 0.201616\n", + "[30]\ttraining's l1: 0.421302\ttraining's l2: 0.197421\tvalid_1's l1: 0.417019\tvalid_1's l2: 0.192514\n", + "[40]\ttraining's l1: 0.411107\ttraining's l2: 0.192856\tvalid_1's l1: 0.406303\tvalid_1's l2: 0.187258\n", + "[50]\ttraining's l1: 0.403695\ttraining's l2: 0.189593\tvalid_1's l1: 0.398997\tvalid_1's l2: 0.183688\n", + "[60]\ttraining's l1: 0.398704\ttraining's l2: 0.187043\tvalid_1's l1: 0.393977\tvalid_1's l2: 0.181009\n", + "[70]\ttraining's l1: 0.394876\ttraining's l2: 0.184982\tvalid_1's l1: 0.389805\tvalid_1's l2: 0.178803\n", + "[80]\ttraining's l1: 0.391147\ttraining's l2: 0.1828\tvalid_1's l1: 0.386476\tvalid_1's l2: 0.176799\n", + "[90]\ttraining's l1: 0.388101\ttraining's l2: 0.180817\tvalid_1's l1: 0.384404\tvalid_1's l2: 0.175775\n", + "[100]\ttraining's l1: 0.385174\ttraining's l2: 0.179171\tvalid_1's l1: 0.382929\tvalid_1's l2: 0.175321\n" + ] + } + ], + "source": [ + "evals_result = {} # to record eval results for plotting\n", + "gbm = lgb.train(\n", + " params,\n", + " lgb_train,\n", + " num_boost_round=100,\n", + " valid_sets=[lgb_train, lgb_test],\n", + " callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot metrics recorded during training" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2018-10-25T22:13:39.809203Z", + "start_time": "2018-10-25T22:13:39.338985Z" + } + }, + "outputs": [], + "source": [ + "def render_metric(metric_name):\n", + " lgb.plot_metric(evals_result, metric=metric_name, figsize=(10, 5))\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if INTERACTIVE:\n", + " # create widget to switch between metrics\n", + " interact(render_metric, metric_name=params[\"metric\"])\n", + "else:\n", + " render_metric(params[\"metric\"][0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot feature importances" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2018-10-25T22:13:39.958548Z", + "start_time": "2018-10-25T22:13:39.811530Z" + } + }, + "outputs": [], + "source": [ + "def render_plot_importance(importance_type, max_features=10, ignore_zero=True, precision=3):\n", + " lgb.plot_importance(\n", + " gbm,\n", + " importance_type=importance_type,\n", + " max_num_features=max_features,\n", + " ignore_zero=ignore_zero,\n", + " figsize=(12, 8),\n", + " precision=precision,\n", + " )\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if INTERACTIVE:\n", + " # create widget for interactive feature importance plot\n", + " interact(\n", + " render_plot_importance,\n", + " importance_type=[\"split\", \"gain\"],\n", + " max_features=(1, X_train.shape[-1]),\n", + " precision=(0, 10),\n", + " )\n", + "else:\n", + " render_plot_importance(importance_type=\"split\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot split value histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def render_histogram(feature):\n", + " lgb.plot_split_value_histogram(gbm, feature=feature, bins=\"auto\", figsize=(10, 5))\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if INTERACTIVE:\n", + " # create widget for interactive split value histogram\n", + " interact(render_histogram, feature=gbm.feature_name())\n", + "else:\n", + " render_histogram(feature=\"f26\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2018-10-25T22:13:40.027803Z", + "start_time": "2018-10-25T22:13:39.960713Z" + } + }, + "source": [ + "## Plot trees" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def render_tree(tree_index, show_info, precision=3):\n", + " show_info = None if \"None\" in show_info else show_info\n", + " return lgb.create_tree_digraph(gbm, tree_index=tree_index, show_info=show_info, precision=precision)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if INTERACTIVE:\n", + " # create widget to switch between trees and control info in nodes\n", + " interact(\n", + " render_tree,\n", + " tree_index=(0, gbm.num_trees() - 1),\n", + " show_info=SelectMultiple( # allow multiple values to be selected\n", + " options=[\n", + " \"None\",\n", + " \"split_gain\",\n", + " \"internal_value\",\n", + " \"internal_count\",\n", + " \"internal_weight\",\n", + " \"leaf_count\",\n", + " \"leaf_weight\",\n", + " \"data_percentage\",\n", + " ],\n", + " value=[\"None\"],\n", + " ),\n", + " precision=(0, 10),\n", + " )\n", + " tree = None\n", + "else:\n", + " tree = render_tree(53, [\"None\"])\n", + "tree" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/python-guide/plot_example.py b/examples/python-guide/plot_example.py new file mode 100644 index 0000000..1a80559 --- /dev/null +++ b/examples/python-guide/plot_example.py @@ -0,0 +1,62 @@ +# coding: utf-8 +from pathlib import Path + +import matplotlib.pyplot as plt +import pandas as pd + +import lightgbm as lgb + +print("Loading data...") +# load or create your dataset +regression_example_dir = Path(__file__).absolute().parents[1] / "regression" +df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t") +df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t") + +y_train = df_train[0] +y_test = df_test[0] +X_train = df_train.drop(0, axis=1) +X_test = df_test.drop(0, axis=1) + +# create dataset for lightgbm +lgb_train = lgb.Dataset( + X_train, + y_train, + feature_name=[f"f{i + 1}" for i in range(X_train.shape[-1])], + categorical_feature=[21], +) +lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train) + +# specify your configurations as a dict +params = {"num_leaves": 5, "metric": ("l1", "l2"), "verbose": 0} + +evals_result = {} # to record eval results for plotting + +print("Starting training...") +# train +gbm = lgb.train( + params, + lgb_train, + num_boost_round=100, + valid_sets=[lgb_train, lgb_test], + callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)], +) + +print("Plotting metrics recorded during training...") +ax = lgb.plot_metric(evals_result, metric="l1") +plt.show() + +print("Plotting feature importances...") +ax = lgb.plot_importance(gbm, max_num_features=10) +plt.show() + +print("Plotting split value histogram...") +ax = lgb.plot_split_value_histogram(gbm, feature="f26", bins="auto") +plt.show() + +print("Plotting 54th tree...") # one tree use categorical feature to split +ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=["split_gain"]) +plt.show() + +print("Plotting 54th tree with graphviz...") +graph = lgb.create_tree_digraph(gbm, tree_index=53, name="Tree54") +graph.render(view=True) diff --git a/examples/python-guide/simple_example.py b/examples/python-guide/simple_example.py new file mode 100644 index 0000000..2b4173c --- /dev/null +++ b/examples/python-guide/simple_example.py @@ -0,0 +1,52 @@ +# coding: utf-8 +from pathlib import Path + +import pandas as pd +from sklearn.metrics import mean_squared_error + +import lightgbm as lgb + +print("Loading data...") +# load or create your dataset +regression_example_dir = Path(__file__).absolute().parents[1] / "regression" +df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t") +df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t") + +y_train = df_train[0] +y_test = df_test[0] +X_train = df_train.drop(0, axis=1) +X_test = df_test.drop(0, axis=1) + +# create dataset for lightgbm +lgb_train = lgb.Dataset(X_train, y_train) +lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + +# specify your configurations as a dict +params = { + "boosting_type": "gbdt", + "objective": "regression", + "metric": {"l2", "l1"}, + "num_leaves": 31, + "learning_rate": 0.05, + "feature_fraction": 0.9, + "bagging_fraction": 0.8, + "bagging_freq": 5, + "verbose": 0, +} + +print("Starting training...") +# train +gbm = lgb.train( + params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.early_stopping(stopping_rounds=5)] +) + +print("Saving model...") +# save model to file +gbm.save_model("model.txt") + +print("Starting predicting...") +# predict +y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration) +# eval +rmse_test = mean_squared_error(y_test, y_pred) ** 0.5 +print(f"The RMSE of prediction is: {rmse_test}") diff --git a/examples/python-guide/sklearn_example.py b/examples/python-guide/sklearn_example.py new file mode 100644 index 0000000..2e9cc8a --- /dev/null +++ b/examples/python-guide/sklearn_example.py @@ -0,0 +1,80 @@ +# coding: utf-8 +from pathlib import Path + +import numpy as np +import pandas as pd +from sklearn.metrics import mean_squared_error +from sklearn.model_selection import GridSearchCV + +import lightgbm as lgb + +print("Loading data...") +# load or create your dataset +regression_example_dir = Path(__file__).absolute().parents[1] / "regression" +df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t") +df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t") + +y_train = df_train[0] +y_test = df_test[0] +X_train = df_train.drop(0, axis=1) +X_test = df_test.drop(0, axis=1) + +print("Starting training...") +# train +gbm = lgb.LGBMRegressor(num_leaves=31, learning_rate=0.05, n_estimators=20) +gbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric="l1", callbacks=[lgb.early_stopping(5)]) + +print("Starting predicting...") +# predict +y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_) +# eval +rmse_test = mean_squared_error(y_test, y_pred) ** 0.5 +print(f"The RMSE of prediction is: {rmse_test}") + +# feature importances +print(f"Feature importances: {list(gbm.feature_importances_)}") + + +# self-defined eval metric +# f(y_true: array, y_pred: array) -> metric_name: str, metric_value: float, maximize: bool +# Root Mean Squared Logarithmic Error (RMSLE) +def rmsle(y_true, y_pred): + return "RMSLE", np.sqrt(np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))), False + + +print("Starting training with custom eval function...") +# train +gbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=rmsle, callbacks=[lgb.early_stopping(5)]) + + +# another self-defined eval metric +# f(y_true: array, y_pred: array) -> metric_name: str, metric_value: float, maximize: bool +# Relative Absolute Error (RAE) +def rae(y_true, y_pred): + return "RAE", np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False + + +print("Starting training with multiple custom eval functions...") +# train +gbm.fit( + X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=[rmsle, rae], callbacks=[lgb.early_stopping(5)] +) + +print("Starting predicting...") +# predict +y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_) +# eval +rmsle_test = rmsle(y_test, y_pred)[1] +rae_test = rae(y_test, y_pred)[1] +print(f"The RMSLE of prediction is: {rmsle_test}") +print(f"The RAE of prediction is: {rae_test}") + +# other scikit-learn modules +estimator = lgb.LGBMRegressor(num_leaves=31) + +param_grid = {"learning_rate": [0.01, 0.1, 1], "n_estimators": [20, 40]} + +gbm = GridSearchCV(estimator, param_grid, cv=3) +gbm.fit(X_train, y_train) + +print(f"Best parameters found by grid search are: {gbm.best_params_}") diff --git a/examples/regression/README.md b/examples/regression/README.md new file mode 100644 index 0000000..928c279 --- /dev/null +++ b/examples/regression/README.md @@ -0,0 +1,27 @@ +Regression Example +================== + +Here is an example for LightGBM to run regression task. + +***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) +for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** + +Training +-------- + +Run the following command in this folder: + +```bash +"../../lightgbm" config=train.conf +``` + +Prediction +---------- + +You should finish training first. + +Run the following command in this folder: + +```bash +"../../lightgbm" config=predict.conf +``` diff --git a/examples/regression/forced_bins.json b/examples/regression/forced_bins.json new file mode 100644 index 0000000..86d7f10 --- /dev/null +++ b/examples/regression/forced_bins.json @@ -0,0 +1,18 @@ +[ + { + "feature": 0, + "bin_upper_bound": [ + 0.3, + 0.35, + 0.4 + ] + }, + { + "feature": 1, + "bin_upper_bound": [ + -0.1, + -0.15, + -0.2 + ] + } +] diff --git a/examples/regression/forced_bins2.json b/examples/regression/forced_bins2.json new file mode 100644 index 0000000..43bbe7e --- /dev/null +++ b/examples/regression/forced_bins2.json @@ -0,0 +1,11 @@ +[ + { + "feature": 0, + "bin_upper_bound": [ + 0.19, + 0.39, + 0.59, + 0.79 + ] + } +] diff --git a/examples/regression/predict.conf b/examples/regression/predict.conf new file mode 100644 index 0000000..7ed74ef --- /dev/null +++ b/examples/regression/predict.conf @@ -0,0 +1,5 @@ +task = predict + +data = regression.test + +input_model= LightGBM_model.txt diff --git a/examples/regression/regression.test b/examples/regression/regression.test new file mode 100644 index 0000000..c9674d6 --- /dev/null +++ b/examples/regression/regression.test @@ -0,0 +1,500 @@ +1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796 +0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116 +0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696 +1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 1.231 1.234 +0 0.897 -1.703 -1.306 1.022 -0.729 0.836 0.859 -0.333 2.173 1.336 -0.965 0.972 2.215 0.671 1.021 -1.439 0.000 0.493 -2.019 -0.289 0.000 0.805 0.930 0.984 1.430 2.198 1.934 1.684 +0 0.756 1.126 -0.945 2.355 -0.555 0.889 0.800 1.440 0.000 0.585 0.271 0.631 2.215 0.722 1.744 1.051 0.000 0.618 0.924 0.698 1.551 0.976 0.864 0.988 0.803 0.234 0.822 0.911 +0 1.141 -0.741 0.953 1.478 -0.524 1.197 -0.871 1.689 2.173 0.875 1.321 -0.518 1.107 0.540 0.037 -0.987 0.000 0.879 1.187 0.245 0.000 0.888 0.701 1.747 1.358 2.479 1.491 1.223 +1 0.606 -0.936 -0.384 1.257 -1.162 2.719 -0.600 0.100 2.173 3.303 -0.284 1.561 1.107 0.689 1.786 -0.326 0.000 0.780 -0.532 1.216 0.000 0.936 2.022 0.985 1.574 4.323 2.263 1.742 +1 0.603 0.429 -0.279 1.448 1.301 1.008 2.423 -1.295 0.000 0.452 1.305 0.533 0.000 1.076 1.011 1.256 2.548 2.021 1.260 -0.343 0.000 0.890 0.969 1.281 0.763 0.652 0.827 0.785 +0 1.171 -0.962 0.521 0.841 -0.315 1.196 -0.744 -0.882 2.173 0.726 -1.305 1.377 1.107 0.643 -1.790 -1.264 0.000 1.257 0.222 0.817 0.000 0.862 0.911 0.987 0.846 1.293 0.899 0.756 +1 1.392 -0.358 0.235 1.494 -0.461 0.895 -0.848 1.549 2.173 0.841 -0.384 0.666 1.107 1.199 2.509 -0.891 0.000 1.109 -0.364 -0.945 0.000 0.693 2.135 1.170 1.362 0.959 2.056 1.842 +1 1.024 1.076 -0.886 0.851 1.530 0.673 -0.449 0.187 1.087 0.628 -0.895 1.176 2.215 0.696 -0.232 -0.875 0.000 0.411 1.501 0.048 0.000 0.842 0.919 1.063 1.193 0.777 0.964 0.807 +1 0.890 -0.760 1.182 1.369 0.751 0.696 -0.959 -0.710 1.087 0.775 -0.130 -1.409 2.215 0.701 -0.110 -0.739 0.000 0.508 -0.451 0.390 0.000 0.762 0.738 0.998 1.126 0.788 0.940 0.790 +1 0.460 0.537 0.636 1.442 -0.269 0.585 0.323 -1.731 2.173 0.503 1.034 -0.927 0.000 0.928 -1.024 1.006 2.548 0.513 -0.618 -1.336 0.000 0.802 0.831 0.992 1.019 0.925 1.056 0.833 +1 0.364 1.648 0.560 1.720 0.829 1.110 0.811 -0.588 0.000 0.408 1.045 1.054 2.215 0.319 -1.138 1.545 0.000 0.423 1.025 -1.265 3.102 1.656 0.928 1.003 0.544 0.327 0.670 0.746 +1 0.525 -0.096 1.206 0.948 -1.103 1.519 -0.582 0.606 2.173 1.274 -0.572 -0.934 0.000 0.855 -1.028 -1.222 0.000 0.578 -1.000 -1.725 3.102 0.896 0.878 0.981 0.498 0.909 0.772 0.668 +0 0.536 -0.821 -1.029 0.703 1.113 0.363 -0.711 0.022 1.087 0.325 1.503 1.249 2.215 0.673 1.041 -0.401 0.000 0.480 2.127 1.681 0.000 0.767 1.034 0.990 0.671 0.836 0.669 0.663 +1 1.789 -0.583 1.641 0.897 0.799 0.515 -0.100 -1.483 0.000 1.101 0.031 -0.326 2.215 1.195 0.001 0.126 2.548 0.768 -0.148 0.601 0.000 0.916 0.921 1.207 1.069 0.483 0.934 0.795 +1 1.332 -0.571 0.986 0.580 1.508 0.582 0.634 -0.746 1.087 1.084 -0.964 -0.489 0.000 0.785 0.274 0.343 2.548 0.779 0.721 1.489 0.000 1.733 1.145 0.990 1.270 0.715 0.897 0.915 +0 1.123 0.629 -1.708 0.597 -0.882 0.752 0.195 1.522 2.173 1.671 1.515 -0.003 0.000 0.778 0.514 0.139 1.274 0.801 1.260 1.600 0.000 1.495 0.976 0.988 0.676 0.921 1.010 0.943 +0 1.816 -0.515 0.171 0.980 -0.454 0.870 0.202 -1.399 2.173 1.130 1.066 -1.593 0.000 0.844 0.735 1.275 2.548 1.125 -1.133 0.348 0.000 0.837 0.693 0.988 1.112 0.784 1.009 0.974 +1 0.364 0.694 0.445 1.862 0.159 0.963 -1.356 1.260 1.087 0.887 -0.540 -1.533 2.215 0.658 -2.544 -1.236 0.000 0.516 -0.807 0.039 0.000 0.891 1.004 0.991 1.092 0.976 1.000 0.953 +1 0.790 -1.175 0.475 1.846 0.094 0.999 -1.090 0.257 0.000 1.422 0.854 1.112 2.215 1.302 1.004 -1.702 1.274 2.557 -0.787 -1.048 0.000 0.890 1.429 0.993 2.807 0.840 2.248 1.821 +1 0.765 -0.500 -0.603 1.843 -0.560 1.068 0.007 0.746 2.173 1.154 -0.017 1.329 0.000 1.165 1.791 -1.585 0.000 1.116 0.441 -0.886 0.000 0.774 0.982 0.989 1.102 0.633 1.178 1.021 +1 1.407 1.293 -1.418 0.502 -1.527 2.005 -2.122 0.622 0.000 1.699 1.508 -0.649 2.215 1.665 0.748 -0.755 0.000 2.555 0.811 1.423 1.551 7.531 5.520 0.985 1.115 1.881 4.487 3.379 +1 0.772 -0.186 -1.372 0.823 -0.140 0.781 0.763 0.046 2.173 1.128 0.516 1.380 0.000 0.797 -0.640 -0.134 2.548 2.019 -0.972 -1.670 0.000 2.022 1.466 0.989 0.856 0.808 1.230 0.991 +1 0.546 -0.954 0.715 1.335 -1.689 0.783 -0.443 -1.735 2.173 1.081 0.185 -0.435 0.000 1.433 -0.662 -0.389 0.000 0.969 0.924 1.099 0.000 0.910 0.879 0.988 0.683 0.753 0.878 0.865 +1 0.596 0.276 -1.054 1.358 1.355 1.444 1.813 -0.208 0.000 1.175 -0.949 -1.573 0.000 0.855 -1.228 -0.925 2.548 1.837 -0.400 0.913 0.000 0.637 0.901 1.028 0.553 0.790 0.679 0.677 +0 0.458 2.292 1.530 0.291 1.283 0.749 -0.930 -0.198 0.000 0.300 -1.560 0.990 0.000 0.811 -0.176 0.995 2.548 1.085 -0.178 -1.213 3.102 0.891 0.648 0.999 0.732 0.655 0.619 0.620 +0 0.638 -0.575 -1.048 0.125 0.178 0.846 -0.753 -0.339 1.087 0.799 -0.727 1.182 0.000 0.888 0.283 0.717 0.000 1.051 -1.046 -1.557 3.102 0.889 0.871 0.989 0.884 0.923 0.836 0.779 +1 0.434 -1.119 -0.313 2.427 0.461 0.497 0.261 -1.177 2.173 0.618 -0.737 -0.688 0.000 1.150 -1.237 -1.652 2.548 0.757 -0.054 1.700 0.000 0.809 0.741 0.982 1.450 0.936 1.086 0.910 +1 0.431 -1.144 -1.030 0.778 -0.655 0.490 0.047 -1.546 0.000 1.583 -0.014 0.891 2.215 0.516 0.956 0.567 2.548 0.935 -1.123 -0.082 0.000 0.707 0.995 0.995 0.700 0.602 0.770 0.685 +1 1.894 0.222 1.224 1.578 1.715 0.966 2.890 -0.013 0.000 0.922 -0.703 -0.844 0.000 0.691 2.056 1.039 0.000 0.900 -0.733 -1.240 3.102 1.292 1.992 1.026 0.881 0.684 1.759 1.755 +0 0.985 -0.316 0.141 1.067 -0.946 0.819 -1.177 1.307 2.173 1.080 -0.429 0.557 1.107 1.726 1.435 -1.075 0.000 1.100 1.547 -0.647 0.000 0.873 1.696 1.179 1.146 1.015 1.538 1.270 +0 0.998 -0.187 -0.236 0.882 0.755 0.468 0.950 -0.439 2.173 0.579 -0.550 -0.624 0.000 1.847 1.196 1.384 1.274 0.846 1.273 -1.072 0.000 1.194 0.797 1.013 1.319 1.174 0.963 0.898 +0 0.515 0.246 -0.593 1.082 1.591 0.912 -0.623 -0.957 2.173 0.858 0.418 0.844 0.000 0.948 2.519 1.599 0.000 1.158 1.385 -0.095 3.102 0.973 1.033 0.988 0.998 1.716 1.054 0.901 +0 0.919 -1.001 1.506 1.389 0.653 0.507 -0.616 -0.689 2.173 0.808 0.536 -0.467 2.215 0.496 2.187 -0.859 0.000 0.822 0.807 1.163 0.000 0.876 0.861 1.088 0.947 0.614 0.911 1.087 +0 0.794 0.051 1.477 1.504 -1.695 0.716 0.315 0.264 1.087 0.879 -0.135 -1.094 2.215 1.433 -0.741 0.201 0.000 1.566 0.534 -0.989 0.000 0.627 0.882 0.974 0.807 1.130 0.929 0.925 +1 0.455 -0.946 -1.175 1.453 -0.580 0.763 -0.856 0.840 0.000 0.829 1.223 1.174 2.215 0.714 0.638 -0.466 0.000 1.182 0.223 -1.333 0.000 0.977 0.938 0.986 0.713 0.714 0.796 0.843 +1 0.662 -0.296 -1.287 1.212 -0.707 0.641 1.457 0.222 0.000 0.600 0.525 -1.700 2.215 0.784 -0.835 -0.961 2.548 0.865 1.131 1.162 0.000 0.854 0.877 0.978 0.740 0.734 0.888 0.811 +0 0.390 0.698 -1.629 1.888 0.298 0.990 1.614 -1.572 0.000 1.666 0.170 0.719 2.215 1.590 1.064 -0.886 1.274 0.952 0.305 -1.216 0.000 1.048 0.897 1.173 0.891 1.936 1.273 1.102 +0 1.014 0.117 1.384 0.686 -1.047 0.609 -1.245 -0.850 0.000 1.076 -1.158 0.814 1.107 1.598 -0.389 -0.111 0.000 0.907 1.688 -1.673 0.000 1.333 0.866 0.989 0.975 0.442 0.797 0.788 +0 1.530 -1.408 -0.207 0.440 -1.357 0.902 -0.647 1.325 1.087 1.320 -0.819 0.246 1.107 0.503 1.407 -1.683 0.000 1.189 -0.972 -0.925 0.000 0.386 1.273 0.988 0.829 1.335 1.173 1.149 +1 1.689 -0.590 0.915 2.076 1.202 0.644 -0.478 -0.238 0.000 0.809 -1.660 -1.184 0.000 1.227 -0.224 -0.808 2.548 1.655 1.047 -0.623 0.000 0.621 1.192 0.988 1.309 0.866 0.924 1.012 +0 1.102 0.402 -1.622 1.262 1.022 0.576 0.271 -0.269 0.000 0.591 0.495 -1.278 0.000 1.271 0.209 0.575 2.548 0.941 0.964 -0.685 3.102 0.989 0.963 1.124 0.857 0.858 0.716 0.718 +0 2.491 0.825 0.581 1.593 0.205 0.782 -0.815 1.499 0.000 1.179 -0.999 -1.509 0.000 0.926 0.920 -0.522 2.548 2.068 -1.021 -1.050 3.102 0.874 0.943 0.980 0.945 1.525 1.570 1.652 +0 0.666 0.254 1.601 1.303 -0.250 1.236 -1.929 0.793 0.000 1.074 0.447 -0.871 0.000 0.991 1.059 -0.342 0.000 1.703 -0.393 -1.419 3.102 0.921 0.945 1.285 0.931 0.462 0.770 0.729 +0 0.937 -1.126 1.424 1.395 1.743 0.760 0.428 -0.238 2.173 0.846 0.494 1.320 2.215 0.872 -1.826 -0.507 0.000 0.612 1.860 1.403 0.000 3.402 2.109 0.985 1.298 1.165 1.404 1.240 +1 0.881 -1.086 -0.870 0.513 0.266 2.049 -1.870 1.160 0.000 2.259 -0.428 -0.935 2.215 1.321 -0.655 -0.449 2.548 1.350 -1.766 -0.108 0.000 0.911 1.852 0.987 1.167 0.820 1.903 1.443 +0 0.410 0.835 -0.819 1.257 1.112 0.871 -1.737 -0.401 0.000 0.927 0.158 1.253 0.000 1.183 0.405 -1.570 0.000 0.807 -0.704 -0.438 3.102 0.932 0.962 0.987 0.653 0.315 0.616 0.648 +1 0.634 0.196 -1.679 1.379 -0.967 2.260 -0.273 1.114 0.000 1.458 1.070 -0.278 1.107 1.195 0.110 -0.688 2.548 0.907 0.298 -1.359 0.000 0.949 1.129 0.984 0.675 0.877 0.938 0.824 +1 0.632 -1.254 1.201 0.496 -0.106 0.235 2.731 -0.955 0.000 0.615 -0.805 0.600 0.000 0.633 -0.934 1.641 0.000 1.407 -0.483 -0.962 1.551 0.778 0.797 0.989 0.578 0.722 0.576 0.539 +0 0.714 1.122 1.566 2.399 -1.431 1.665 0.299 0.323 0.000 1.489 1.087 -0.861 2.215 1.174 0.140 1.083 2.548 0.404 -0.968 1.105 0.000 0.867 0.969 0.981 1.039 1.552 1.157 1.173 +1 0.477 -0.321 -0.471 1.966 1.034 2.282 1.359 -0.874 0.000 1.672 -0.258 1.109 0.000 1.537 0.604 0.231 2.548 1.534 -0.640 0.827 0.000 0.746 1.337 1.311 0.653 0.721 0.795 0.742 +1 1.351 0.460 0.031 1.194 -1.185 0.670 -1.157 -1.637 2.173 0.599 -0.823 0.680 0.000 0.478 0.373 1.716 0.000 0.809 -0.919 0.010 1.551 0.859 0.839 1.564 0.994 0.777 0.971 0.826 +1 0.520 -1.442 -0.348 0.840 1.654 1.273 -0.760 1.317 0.000 0.861 2.579 -0.791 0.000 1.779 0.257 -0.703 0.000 2.154 1.928 0.457 0.000 1.629 3.194 0.992 0.730 1.107 2.447 2.747 +0 0.700 -0.308 0.920 0.438 -0.879 0.516 1.409 1.101 0.000 0.960 0.701 -0.049 2.215 1.442 -0.416 -1.439 2.548 0.628 1.009 -0.364 0.000 0.848 0.817 0.987 0.759 1.421 0.937 0.920 +1 0.720 1.061 -0.546 0.798 -1.521 1.066 0.173 0.271 1.087 1.453 0.114 1.336 1.107 0.702 0.616 -0.367 0.000 0.543 -0.386 -1.301 0.000 0.653 0.948 0.989 1.031 1.500 0.965 0.790 +1 0.735 -0.416 0.588 1.308 -0.382 1.042 0.344 1.609 0.000 0.926 0.163 -0.520 1.107 1.050 -0.427 1.159 2.548 0.834 0.613 0.948 0.000 0.848 1.189 1.042 0.844 1.099 0.829 0.843 +1 0.777 -0.396 1.540 1.608 0.638 0.955 0.040 0.918 2.173 1.315 1.116 -0.823 0.000 0.781 -0.762 0.564 2.548 0.945 -0.573 1.379 0.000 0.679 0.706 1.124 0.608 0.593 0.515 0.493 +1 0.934 0.319 -0.257 0.970 -0.980 0.726 0.774 0.731 0.000 0.896 0.038 -1.465 1.107 0.773 -0.055 -0.831 2.548 1.439 -0.229 0.698 0.000 0.964 1.031 0.995 0.845 0.480 0.810 0.762 +0 0.461 0.771 0.019 2.055 -1.288 1.043 0.147 0.261 2.173 0.833 -0.156 1.425 0.000 0.832 0.805 -0.491 2.548 0.589 1.252 1.414 0.000 0.850 0.906 1.245 1.364 0.850 0.908 0.863 +1 0.858 -0.116 -0.937 0.966 1.167 0.825 -0.108 1.111 1.087 0.733 1.163 -0.634 0.000 0.894 0.771 0.020 0.000 0.846 -1.124 -1.195 3.102 0.724 1.194 1.195 0.813 0.969 0.985 0.856 +0 0.720 -0.335 -0.307 1.445 0.540 1.108 -0.034 -1.691 1.087 0.883 -1.356 -0.678 2.215 0.440 1.093 0.253 0.000 0.389 -1.582 -1.097 0.000 1.113 1.034 0.988 1.256 1.572 1.062 0.904 +1 0.750 -0.811 -0.542 0.985 0.408 0.471 0.477 0.355 0.000 1.347 -0.875 -1.556 2.215 0.564 1.082 -0.724 0.000 0.793 -0.958 -0.020 3.102 0.836 0.825 0.986 1.066 0.924 0.927 0.883 +0 0.392 -0.468 -0.216 0.680 1.565 1.086 -0.765 -0.581 1.087 1.264 -1.035 1.189 2.215 0.986 -0.338 0.747 0.000 0.884 -1.328 -0.965 0.000 1.228 0.988 0.982 1.135 1.741 1.108 0.956 +1 0.434 -1.269 0.643 0.713 0.608 0.597 0.832 1.627 0.000 0.708 -0.422 0.079 2.215 1.533 -0.823 -1.127 2.548 0.408 -1.357 -0.828 0.000 1.331 1.087 0.999 1.075 1.015 0.875 0.809 +0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184 +1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822 +1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948 +0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885 +1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871 +1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794 +0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679 +1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903 +1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840 +0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909 +1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892 +1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836 +1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886 +1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661 +1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883 +1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783 +1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838 +0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106 +0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895 +0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352 +1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836 +1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645 +1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866 +0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706 +0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750 +0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720 +0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006 +1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826 +1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908 +0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041 +0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898 +1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823 +0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498 +0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062 +0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863 +1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358 +0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655 +0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089 +1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806 +0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779 +1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832 +1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713 +1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977 +0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749 +1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926 +0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150 +0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107 +1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791 +1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781 +0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692 +1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824 +0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754 +0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969 +0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786 +1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791 +1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798 +1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777 +0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687 +0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806 +1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694 +1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906 +1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770 +1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656 +1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785 +0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827 +0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745 +0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650 +1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489 +1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876 +0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041 +1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242 +0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769 +1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078 +1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623 +1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137 +1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589 +0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816 +1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638 +0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057 +0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774 +1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526 +1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958 +0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902 +0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707 +1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740 +1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949 +0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815 +1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474 +0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183 +1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715 +0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094 +1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777 +0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008 +1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807 +1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879 +1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798 +0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661 +0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870 +1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963 +1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705 +1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866 +0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280 +1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866 +1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878 +0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676 +0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608 +1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726 +0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578 +0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059 +1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939 +0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035 +0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145 +0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276 +0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466 +1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776 +0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278 +0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934 +0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430 +1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841 +1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906 +0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325 +1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786 +0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950 +0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762 +0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861 +0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851 +0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079 +1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724 +1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744 +0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912 +0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012 +0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665 +1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034 +0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095 +0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221 +1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777 +1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917 +1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771 +0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395 +1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865 +1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890 +1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815 +0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131 +0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154 +1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717 +0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775 +1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893 +0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901 +0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769 +1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828 +0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748 +1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599 +0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120 +1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833 +0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051 +1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126 +1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881 +1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135 +1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797 +0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612 +0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556 +0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789 +1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708 +0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878 +0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087 +0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846 +1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028 +0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994 +0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868 +1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913 +0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291 +0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123 +1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878 +0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511 +0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076 +0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112 +1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046 +1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009 +1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891 +1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140 +0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744 +1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238 +0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306 +0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758 +1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172 +1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992 +1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786 +1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922 +0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609 +1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351 +1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836 +0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252 +1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122 +0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042 +0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662 +1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626 +1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920 +1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059 +1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722 +1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941 +0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072 +1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053 +0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310 +0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847 +0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873 +0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183 +1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807 +0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936 +1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013 +1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822 +1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803 +0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176 +0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714 +0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815 +0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918 +0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056 +0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513 +0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773 +0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799 +1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813 +1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978 +1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743 +1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817 +1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875 +0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078 +1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619 +1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726 +0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950 +0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860 +0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034 +0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846 +0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920 +1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668 +0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313 +0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671 +1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224 +1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779 +0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851 +1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860 +0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958 +1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671 +1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127 +1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807 +1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823 +1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872 +1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061 +1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833 +1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780 +0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143 +1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709 +1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858 +1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091 +0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838 +0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661 +0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729 +0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878 +1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838 +1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792 +0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702 +0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068 +1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931 +0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522 +0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807 +1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054 +1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606 +1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961 +0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308 +0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146 +1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709 +0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180 +1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868 +1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779 +0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213 +1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168 +1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238 +0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891 +0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561 +1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911 +0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303 +1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094 +1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825 +1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995 +1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151 +1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280 +1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893 +1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043 +0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226 +1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851 +0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733 +0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764 +0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028 +1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774 +1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661 +1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776 +0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675 +1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931 +1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715 +0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889 +1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886 +0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087 +0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760 +0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006 +1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811 +0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176 +0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096 +0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721 +1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732 +1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874 +1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753 +0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883 +0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018 +1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806 +0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472 +0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827 +1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835 +1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826 +1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640 +1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764 +1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025 +1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223 +1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169 +1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003 +1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908 +1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836 +0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833 +0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294 +1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845 +1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510 +0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445 +1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926 +1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933 +1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853 +0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678 +1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038 +1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197 +1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746 +0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772 +1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983 +1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759 +1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825 +1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776 +0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892 +0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110 +1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762 +0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756 +1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831 +1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748 +0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756 +1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993 +1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881 +1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786 +0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983 +0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983 +1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867 +0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937 +0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293 +1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105 +0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949 +1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895 +0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722 +1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809 +0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007 +1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717 +0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656 +0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856 +1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832 +1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753 +0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954 +1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874 +0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848 +1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518 +1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787 +1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834 +0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722 +1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040 +0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758 +1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854 +1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828 +1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745 +0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701 +1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215 +1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837 +1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867 +0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119 +1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914 +0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373 +1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623 +1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133 +0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079 +0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919 +1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651 +0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207 +0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660 +1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951 +1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910 +0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715 +1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026 +0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239 +1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696 +0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662 +1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910 +0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050 +0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258 +1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884 +0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026 +1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119 +1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926 +0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079 +1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674 +0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733 +0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795 +0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329 +0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161 +0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200 +1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875 +0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071 +1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993 +1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866 +1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978 +1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982 +1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853 +1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122 +1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719 +1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739 +0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690 +1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643 +0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142 +1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758 +1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823 +0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258 +1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836 +0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349 +0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961 diff --git a/examples/regression/regression.test.init b/examples/regression/regression.test.init new file mode 100644 index 0000000..ed8fbce --- /dev/null +++ b/examples/regression/regression.test.init @@ -0,0 +1,500 @@ +0.039 +0.187 +0.831 +0.767 +0.351 +0.377 +0.534 +0.000 +0.241 +0.208 +0.250 +0.806 +0.280 +0.192 +0.504 +0.866 +0.241 +0.079 +0.356 +0.748 +0.551 +0.817 +0.960 +0.793 +0.604 +0.493 +0.040 +0.984 +0.383 +0.152 +0.667 +0.284 +0.586 +0.587 +0.446 +0.836 +0.265 +0.449 +0.538 +0.664 +0.784 +0.395 +0.646 +0.151 +0.933 +0.383 +0.730 +0.020 +0.205 +0.487 +0.878 +0.527 +0.930 +0.484 +0.490 +0.120 +0.803 +0.247 +0.900 +0.911 +0.943 +0.520 +0.677 +0.779 +0.131 +0.601 +0.034 +0.498 +0.155 +0.183 +0.365 +0.432 +0.623 +0.074 +0.504 +0.183 +0.574 +0.637 +0.557 +0.738 +0.336 +0.765 +0.433 +0.484 +0.648 +0.018 +0.654 +0.619 +0.310 +0.086 +0.091 +0.923 +0.689 +0.127 +0.357 +0.592 +0.836 +0.044 +0.237 +0.890 +0.009 +0.201 +0.959 +0.613 +0.262 +0.067 +0.028 +0.245 +0.881 +0.416 +0.720 +0.918 +0.408 +0.191 +0.517 +0.908 +0.804 +0.066 +0.693 +0.572 +0.907 +0.122 +0.534 +0.879 +0.410 +0.482 +0.070 +0.278 +0.325 +0.945 +0.283 +0.461 +0.671 +0.162 +0.486 +0.739 +0.867 +0.626 +0.669 +0.126 +0.946 +0.133 +0.775 +0.265 +0.934 +0.720 +0.754 +0.219 +0.443 +0.618 +0.770 +0.104 +0.962 +0.890 +0.270 +0.823 +0.518 +0.462 +0.314 +0.581 +0.730 +0.411 +0.629 +0.699 +0.711 +0.052 +0.860 +0.458 +0.262 +0.242 +0.483 +0.887 +0.378 +0.750 +0.097 +0.476 +0.992 +0.770 +0.211 +0.501 +0.234 +0.410 +0.780 +0.771 +0.228 +0.922 +0.593 +0.380 +0.502 +0.605 +0.560 +0.486 +0.505 +0.176 +0.813 +0.542 +0.131 +0.766 +0.932 +0.947 +0.369 +0.136 +0.518 +0.113 +0.934 +0.184 +0.253 +0.407 +0.383 +0.795 +0.456 +0.171 +0.267 +0.509 +0.147 +0.612 +0.566 +0.715 +0.938 +0.912 +0.946 +0.245 +0.132 +0.302 +0.895 +0.972 +0.859 +0.110 +0.947 +0.423 +0.009 +0.442 +0.046 +0.544 +0.339 +0.473 +0.613 +0.869 +0.662 +0.434 +0.819 +0.906 +0.120 +0.532 +0.285 +0.047 +0.669 +0.863 +0.163 +0.812 +0.853 +0.914 +0.265 +0.904 +0.321 +0.552 +0.051 +0.044 +0.720 +0.444 +0.256 +0.190 +0.670 +0.000 +0.806 +0.079 +0.191 +0.386 +0.485 +0.355 +0.321 +0.964 +0.642 +0.023 +0.430 +0.875 +0.301 +0.095 +0.758 +0.606 +0.570 +0.054 +0.140 +0.623 +0.208 +0.504 +0.545 +0.284 +0.948 +0.842 +0.722 +0.078 +0.106 +0.493 +0.161 +0.978 +0.159 +0.487 +0.364 +0.639 +0.129 +0.430 +0.275 +0.888 +0.041 +0.914 +0.833 +0.298 +0.789 +0.031 +0.967 +0.527 +0.303 +0.363 +0.066 +0.989 +0.039 +0.655 +0.443 +0.949 +0.246 +0.532 +0.482 +0.703 +0.068 +0.194 +0.215 +0.738 +0.189 +0.573 +0.215 +0.862 +0.942 +0.518 +0.352 +0.234 +0.050 +0.269 +0.654 +0.534 +0.944 +0.396 +0.694 +0.489 +0.513 +0.268 +0.455 +0.471 +0.707 +0.941 +0.329 +0.042 +0.496 +0.544 +0.168 +0.760 +0.985 +0.946 +0.197 +0.875 +0.704 +0.454 +0.541 +0.850 +0.480 +0.373 +0.493 +0.579 +0.189 +0.901 +0.674 +0.633 +0.099 +0.604 +0.121 +0.079 +0.527 +0.403 +0.589 +0.089 +0.431 +0.175 +0.987 +0.561 +0.687 +0.325 +0.095 +0.976 +0.286 +0.424 +0.650 +0.025 +0.810 +0.537 +0.278 +0.062 +0.162 +0.895 +0.686 +0.250 +0.066 +0.691 +0.572 +0.405 +0.364 +0.217 +0.670 +0.971 +0.176 +0.597 +0.424 +0.447 +0.254 +0.825 +0.485 +0.543 +0.305 +0.182 +0.086 +0.714 +0.196 +0.690 +0.390 +0.416 +0.469 +0.368 +0.101 +0.310 +0.664 +0.666 +0.286 +0.460 +0.193 +0.210 +0.023 +0.897 +0.211 +0.228 +0.280 +0.127 +0.639 +0.075 +0.134 +0.645 +0.340 +0.708 +0.557 +0.256 +0.651 +0.116 +0.536 +0.437 +0.268 +0.604 +0.871 +0.999 +0.608 +0.405 +0.225 +0.257 +0.479 +0.367 +0.914 +0.368 +0.373 +0.384 +0.837 +0.651 +0.614 +0.334 +0.818 +0.038 +0.871 +0.513 +0.398 +0.497 +0.667 +0.013 +0.872 +0.447 +0.343 +0.138 +0.439 +0.496 +0.404 +0.679 +0.421 +0.961 +0.599 +0.807 +0.109 +0.397 +0.337 +0.569 +0.861 +0.078 +0.073 +0.850 +0.213 +0.669 diff --git a/examples/regression/regression.train b/examples/regression/regression.train new file mode 100644 index 0000000..6ce09b8 --- /dev/null +++ b/examples/regression/regression.train @@ -0,0 +1,7000 @@ +1 0.869 -0.635 0.226 0.327 -0.690 0.754 -0.249 -1.092 0.000 1.375 -0.654 0.930 1.107 1.139 -1.578 -1.047 0.000 0.658 -0.010 -0.046 3.102 1.354 0.980 0.978 0.920 0.722 0.989 0.877 +1 0.908 0.329 0.359 1.498 -0.313 1.096 -0.558 -1.588 2.173 0.813 -0.214 1.271 2.215 0.500 -1.261 0.732 0.000 0.399 -1.139 -0.001 0.000 0.302 0.833 0.986 0.978 0.780 0.992 0.798 +1 0.799 1.471 -1.636 0.454 0.426 1.105 1.282 1.382 0.000 0.852 1.541 -0.820 2.215 0.993 0.356 -0.209 2.548 1.257 1.129 0.900 0.000 0.910 1.108 0.986 0.951 0.803 0.866 0.780 +0 1.344 -0.877 0.936 1.992 0.882 1.786 -1.647 -0.942 0.000 2.423 -0.676 0.736 2.215 1.299 -1.431 -0.365 0.000 0.745 -0.678 -1.360 0.000 0.947 1.029 0.999 0.728 0.869 1.027 0.958 +1 1.105 0.321 1.522 0.883 -1.205 0.681 -1.070 -0.922 0.000 0.801 1.021 0.971 2.215 0.597 -0.350 0.631 0.000 0.480 -0.374 0.113 0.000 0.756 1.361 0.987 0.838 1.133 0.872 0.808 +0 1.596 -0.608 0.007 1.818 -0.112 0.848 -0.566 1.581 2.173 0.755 0.643 1.426 0.000 0.922 -1.190 -1.616 0.000 0.651 -0.654 -1.274 3.102 0.824 0.938 0.972 0.789 0.431 0.961 0.958 +1 0.409 -1.885 -1.027 1.672 -1.605 1.338 0.055 0.013 2.173 0.510 -1.038 0.708 0.000 0.747 -0.358 -1.647 0.000 0.367 0.069 1.377 3.102 0.869 1.222 1.001 0.545 0.699 0.977 0.829 +1 0.934 0.629 0.528 0.238 -0.967 0.548 -0.059 -1.707 2.173 0.941 -2.654 -0.157 0.000 1.030 -0.176 0.523 2.548 1.374 1.291 -1.467 0.000 0.902 1.084 0.980 0.783 0.849 0.894 0.775 +1 1.405 0.537 0.690 1.180 -0.110 3.202 -1.527 -1.576 0.000 2.932 0.567 -0.130 2.215 1.787 0.899 0.585 2.548 0.402 -0.151 1.163 0.000 1.667 4.039 1.176 1.045 1.543 3.535 2.741 +1 1.177 0.104 1.397 0.480 0.266 1.136 1.535 -0.253 0.000 1.027 0.534 1.180 0.000 2.406 0.088 -0.977 2.548 1.250 0.269 0.530 0.000 0.833 0.774 0.986 1.104 0.849 0.937 0.812 +1 0.946 1.111 1.218 0.908 0.822 1.153 -0.365 -1.566 0.000 0.745 0.721 -0.376 2.215 0.609 0.308 -1.282 0.000 1.598 -0.451 0.064 3.102 0.829 0.981 0.994 0.908 0.776 0.783 0.725 +0 0.739 -0.178 0.830 0.505 -0.130 0.961 -0.356 -1.717 2.173 0.621 -0.482 -1.199 0.000 0.983 0.081 -0.290 0.000 1.065 0.774 0.399 3.102 0.945 1.026 0.982 0.542 1.251 0.830 0.761 +1 1.384 0.117 -1.180 0.763 -0.080 1.020 0.877 1.277 2.173 0.331 1.410 -1.474 0.000 1.283 0.737 -0.225 0.000 1.560 0.847 0.505 3.102 0.959 0.807 1.192 1.221 0.861 0.929 0.838 +1 1.384 0.889 0.619 1.082 0.345 0.956 0.855 -1.129 2.173 0.546 -0.308 -0.623 2.215 0.348 1.024 0.184 0.000 0.781 -1.636 1.144 0.000 0.522 0.738 0.986 1.350 0.813 0.953 0.780 +1 1.344 0.839 -1.061 2.472 -0.573 1.513 1.144 0.856 0.000 0.884 1.475 -1.361 1.107 1.587 2.235 0.078 0.000 1.609 2.396 0.757 0.000 0.934 0.845 1.078 1.400 0.948 1.008 0.901 +0 0.547 -0.350 -0.647 2.040 0.276 0.545 0.839 1.729 0.000 0.653 1.472 1.243 0.000 0.786 -0.044 -1.020 2.548 0.419 -0.629 1.571 3.102 0.689 0.867 1.082 0.664 0.354 0.580 0.817 +1 1.484 1.700 -1.059 2.700 -1.056 2.409 0.457 0.345 0.000 1.415 1.114 -1.449 0.000 1.013 -2.057 1.131 0.000 0.905 2.182 1.043 0.000 1.654 0.994 0.983 0.741 0.163 0.592 0.745 +0 1.058 -0.161 -0.195 2.705 -0.751 1.910 -1.032 0.865 0.000 1.301 0.147 -1.119 1.107 0.967 -0.367 1.108 0.000 0.555 -0.714 1.505 3.102 0.954 0.651 1.125 0.894 0.672 1.182 1.316 +0 0.675 1.121 -0.280 1.540 0.735 0.615 -0.507 0.795 2.173 0.219 -1.894 -0.581 0.000 1.246 -0.348 -0.856 2.548 0.753 -1.146 -1.375 0.000 0.907 0.898 1.120 1.269 1.089 1.015 0.915 +1 0.643 -1.430 1.519 0.941 0.887 1.615 -1.337 -0.267 1.087 1.667 0.656 -1.588 0.000 0.828 1.836 0.408 0.000 1.709 -0.347 -1.183 3.102 0.921 1.373 0.985 1.423 1.547 1.783 1.438 +1 1.102 0.427 1.717 0.934 0.776 1.279 -0.250 -0.926 2.173 1.067 0.434 0.681 0.000 1.054 0.004 0.255 0.000 0.743 1.208 -1.151 0.000 0.709 0.522 1.054 1.273 0.835 0.935 0.865 +1 1.330 0.202 1.173 0.135 -1.083 0.728 1.109 -0.540 1.087 0.462 0.133 -0.561 0.000 0.479 1.187 0.658 0.000 0.670 1.007 0.055 3.102 0.782 0.672 0.990 0.734 0.379 0.765 0.643 +0 1.290 -1.423 -0.687 0.131 -1.136 0.821 0.296 0.168 2.173 0.696 -0.469 -1.151 1.107 0.940 0.273 1.641 0.000 0.720 1.106 0.727 0.000 1.007 0.868 0.999 1.110 1.125 0.883 0.859 +1 1.048 -1.119 -0.957 0.996 -1.550 0.733 0.283 0.919 2.173 1.050 -0.041 0.109 2.215 0.943 0.320 -0.858 0.000 0.628 -0.325 1.217 0.000 0.873 0.873 0.976 1.373 0.888 1.207 0.999 +0 0.488 1.698 0.791 0.894 -0.709 1.563 -0.076 1.739 2.173 0.624 2.395 0.523 0.000 1.661 0.266 -0.218 2.548 0.947 -0.077 0.285 0.000 1.675 1.414 0.988 1.333 2.004 1.551 1.217 +0 1.413 -0.852 0.310 1.128 -1.510 0.820 1.153 -1.670 2.173 1.170 0.100 0.266 0.000 0.852 0.401 -1.334 0.000 1.370 0.960 -0.632 0.000 0.890 0.938 1.745 0.974 0.677 1.136 0.973 +1 0.770 -0.449 -0.986 0.966 -1.301 0.739 -1.033 0.875 1.087 1.369 -1.181 0.167 1.107 1.257 -0.122 -1.588 0.000 0.600 0.611 0.116 0.000 1.048 1.106 0.993 1.132 0.892 0.974 0.951 +0 2.468 0.664 1.024 0.317 1.407 0.996 -0.453 -0.500 0.000 0.348 1.016 -0.161 0.000 0.978 -2.634 -0.285 0.000 1.245 -0.472 1.464 3.102 1.006 0.795 0.996 0.945 0.322 0.735 1.470 +1 1.014 0.013 -0.485 0.695 1.701 0.597 0.076 0.143 2.173 0.917 0.685 1.713 2.215 0.531 -0.987 -1.654 0.000 0.963 1.295 0.264 0.000 1.576 1.067 1.072 0.806 1.130 0.838 0.752 +0 1.251 -0.750 1.090 0.462 -0.381 0.677 0.340 -0.711 0.000 0.601 -0.461 -1.247 0.000 0.822 0.985 -1.653 0.000 0.754 -0.907 0.279 3.102 0.848 0.842 1.021 0.666 0.411 0.607 0.638 +1 1.114 1.782 1.450 0.653 1.513 0.825 1.851 -0.480 0.000 0.846 1.158 0.514 2.215 0.520 2.685 1.542 0.000 1.042 0.549 -0.463 1.551 1.321 1.037 0.997 0.824 0.692 0.804 0.831 +1 0.657 -0.901 -0.855 1.176 1.487 0.745 -1.236 1.649 2.173 0.661 -2.099 0.137 0.000 1.780 -1.036 -0.213 0.000 1.236 -0.185 0.784 3.102 0.861 1.016 1.045 0.759 0.898 0.849 0.765 +0 1.009 -0.660 -1.539 1.316 -1.693 1.146 2.025 0.137 0.000 1.063 -0.539 1.052 2.215 1.124 0.548 -0.887 2.548 1.017 -0.057 0.172 0.000 1.076 0.939 0.974 0.932 1.346 0.854 0.822 +0 2.122 0.792 0.723 2.438 1.064 2.692 0.361 -0.993 2.173 1.725 1.204 0.488 2.215 0.267 -0.767 -1.134 0.000 1.372 0.601 -0.568 0.000 0.727 0.981 0.989 2.837 3.398 2.152 1.568 +1 0.304 -1.425 -1.646 1.166 -1.469 1.458 -0.472 0.510 2.173 0.867 -0.309 -1.605 0.000 1.317 0.136 -0.332 2.548 0.853 0.744 -1.365 0.000 0.760 0.980 0.986 1.376 1.309 1.081 0.957 +1 1.167 0.556 -0.911 0.908 0.051 1.078 0.387 1.253 0.000 1.213 0.155 -0.673 2.215 0.489 -1.384 0.704 0.000 1.348 0.692 -1.502 3.102 0.868 0.829 1.087 0.782 0.878 0.642 0.621 +1 0.880 0.617 -0.649 1.724 1.104 1.213 -0.576 1.216 2.173 0.782 -0.913 -0.102 0.000 1.183 -0.576 -0.783 0.000 0.432 1.286 -0.204 0.000 0.879 0.616 1.706 1.435 0.598 0.911 1.007 +0 0.313 1.256 -0.904 1.002 1.290 1.383 1.295 -1.528 2.173 1.160 -0.765 0.080 1.107 1.060 2.309 -0.340 0.000 0.852 1.129 0.378 0.000 0.911 1.480 0.988 1.000 2.976 1.837 1.444 +0 1.263 0.596 0.460 1.063 1.060 0.709 -0.613 -0.688 0.000 1.464 1.079 1.174 2.215 1.411 0.369 -0.596 1.274 0.611 0.293 -0.894 0.000 1.175 1.244 0.988 0.905 1.623 1.442 1.222 +1 1.121 -0.379 1.363 1.451 0.782 1.088 -0.803 -0.793 1.087 0.515 0.368 -0.665 0.000 0.708 -1.372 1.449 0.000 0.579 0.441 0.238 3.102 1.336 0.869 0.984 1.459 0.905 0.950 0.863 +0 1.205 0.916 -1.209 0.354 -0.706 1.124 1.045 0.787 0.000 0.489 -0.457 -1.033 2.215 0.388 1.276 0.000 0.000 0.443 -0.889 1.403 0.000 0.842 0.653 0.986 0.500 0.532 0.580 0.589 +1 0.420 -0.722 0.732 0.885 -0.724 0.741 1.244 1.619 0.000 1.248 0.281 0.076 2.215 1.085 0.331 1.242 0.000 1.025 0.086 -0.955 1.551 0.919 0.927 0.989 0.744 0.824 0.923 0.798 +0 1.380 1.427 1.105 1.788 0.982 1.955 -0.205 -0.852 1.087 0.901 -0.193 0.854 0.000 1.172 0.352 -0.512 1.274 0.445 -0.158 1.421 0.000 0.403 0.882 1.000 2.450 0.804 1.608 1.272 +1 0.704 0.369 -0.230 1.167 -1.430 0.721 0.012 1.508 2.173 0.683 0.028 0.688 2.215 1.013 -0.764 -0.222 0.000 0.930 0.082 -0.753 0.000 0.865 0.748 1.107 0.835 0.696 0.681 0.604 +1 0.695 0.420 1.203 0.769 -0.911 0.830 1.168 0.076 0.000 0.394 0.392 0.510 2.215 0.747 1.559 0.835 0.000 1.090 -0.422 -1.161 3.102 0.973 0.654 0.987 0.688 0.652 0.784 0.703 +1 0.312 1.722 1.411 1.133 1.163 0.756 1.210 -0.700 2.173 0.755 -0.053 -0.139 2.215 0.812 -0.193 1.153 0.000 0.847 1.298 1.682 0.000 1.010 1.000 0.996 1.118 0.931 0.860 0.794 +0 0.431 0.572 -0.684 2.262 0.155 1.178 0.178 -1.429 2.173 0.463 0.649 0.544 2.215 0.757 0.955 1.552 0.000 0.658 1.073 1.064 0.000 0.344 0.840 0.986 0.580 1.096 0.957 0.821 +0 0.309 -1.951 -1.229 1.592 0.770 0.633 -0.197 -1.568 1.087 0.898 -1.885 -0.257 0.000 0.897 -0.933 0.931 2.548 1.280 -0.431 -0.799 0.000 0.921 0.862 0.990 0.812 0.831 1.026 0.895 +1 0.458 0.129 -0.519 1.195 0.737 0.534 -1.316 -1.729 0.000 0.687 0.351 1.103 2.215 0.911 1.049 -0.219 2.548 0.808 -1.014 -0.367 0.000 0.888 1.371 0.984 0.871 0.852 1.238 1.006 +0 0.637 -0.037 -1.732 1.254 -0.425 0.486 0.090 0.024 2.173 0.675 -1.119 1.644 0.000 0.494 -2.085 0.544 0.000 0.386 -0.239 1.092 0.000 0.913 0.912 1.144 0.698 0.525 0.741 0.726 +1 0.976 0.291 -1.128 0.668 -0.540 0.950 2.026 1.060 0.000 0.678 -0.571 1.307 2.215 1.199 1.293 -0.273 0.000 0.602 1.124 0.825 3.102 1.891 1.026 0.990 0.814 0.693 1.131 1.181 +1 0.535 -1.391 -0.825 1.343 -1.449 1.111 -0.852 -0.484 0.000 1.677 -0.700 1.069 2.215 0.623 0.018 -1.653 0.000 0.925 0.350 0.169 0.000 0.852 1.025 0.986 1.447 0.755 1.273 1.138 +0 2.638 1.289 -0.280 0.991 0.872 1.152 -0.702 1.551 2.173 0.643 -0.767 -1.689 0.000 0.747 -2.603 0.907 0.000 1.259 0.986 -0.759 0.000 0.889 0.937 1.931 2.569 0.709 1.666 1.322 +0 1.541 0.058 1.227 1.217 0.660 0.524 1.040 -0.640 0.000 0.709 -0.226 -0.727 2.215 0.543 1.360 1.720 0.000 0.981 0.326 -0.429 3.102 0.842 0.839 0.988 0.882 0.311 0.754 0.792 +0 2.559 -0.021 -1.615 2.095 -1.335 1.720 -0.641 0.033 2.173 0.737 -0.414 -0.379 0.000 1.158 -0.598 -1.608 2.548 0.847 1.549 0.847 0.000 0.980 0.951 1.004 0.748 1.751 1.606 1.295 +1 1.925 -0.859 1.353 1.769 -1.452 0.756 -0.342 -0.809 2.173 1.734 -0.850 0.151 0.000 0.944 -0.376 0.932 0.000 0.606 0.624 -1.039 0.000 0.964 0.931 1.474 1.062 0.530 0.907 0.819 +1 1.545 0.059 -1.732 1.034 0.807 2.467 -1.237 -0.565 0.000 1.933 2.370 -1.639 0.000 3.921 -0.645 0.727 2.548 1.843 -0.219 -0.527 3.102 2.292 2.692 1.319 1.447 1.914 3.176 2.387 +0 1.200 -1.018 -1.173 0.845 -0.439 0.601 -0.814 1.627 0.000 0.706 -1.103 0.845 0.000 1.111 -0.536 0.424 2.548 1.038 -0.456 -0.630 3.102 0.923 0.890 0.990 0.887 0.667 0.658 0.694 +0 0.609 -0.521 0.287 0.650 0.198 0.511 1.237 -0.670 2.173 0.648 -1.193 -1.686 2.215 0.364 1.444 0.064 0.000 0.451 1.152 0.677 0.000 0.433 0.925 0.983 0.770 1.497 0.925 0.731 +0 0.318 -1.381 -0.250 2.482 0.957 1.383 0.001 -0.222 2.173 1.045 -1.565 1.525 2.215 0.904 2.253 1.645 0.000 1.349 -0.541 -1.383 0.000 0.992 2.146 1.091 0.821 2.375 2.313 2.267 +1 0.947 -0.329 -0.033 0.020 -1.381 1.245 0.865 0.799 2.173 1.130 -0.013 -1.688 0.000 1.371 0.681 -0.931 0.000 0.982 0.958 0.019 0.000 1.001 0.587 0.525 0.860 0.892 0.820 0.697 +0 1.147 0.502 -1.131 1.237 -1.061 0.869 0.812 0.520 0.000 1.011 0.808 1.346 2.215 0.635 1.284 -0.138 0.000 0.538 0.612 0.124 3.102 0.848 0.987 0.993 0.677 0.595 0.704 0.778 +1 1.028 -0.732 1.243 1.198 -0.032 0.756 -1.491 1.404 0.000 1.343 -1.475 -0.263 2.215 0.483 -2.591 1.686 0.000 0.707 -0.687 -1.342 1.551 0.831 0.686 1.402 1.093 0.791 0.829 0.856 +1 0.303 1.225 0.629 1.256 -0.602 0.897 0.529 0.974 2.173 0.913 -0.667 -0.299 2.215 0.991 0.560 1.376 0.000 0.534 -1.176 -0.672 0.000 0.771 1.006 0.988 0.700 1.491 0.876 0.757 +0 0.534 -0.766 -0.533 0.974 -1.501 0.797 -1.574 0.323 2.173 1.137 0.271 -0.998 2.215 2.434 2.003 1.210 0.000 1.956 0.216 -0.272 0.000 3.588 2.573 0.989 1.251 1.990 2.742 2.023 +0 0.459 -1.448 -0.858 0.262 -0.304 0.760 1.090 -0.338 2.173 1.076 -1.079 1.151 2.215 0.357 -0.614 1.522 0.000 0.506 1.609 -1.293 0.000 0.842 0.866 0.988 0.935 2.209 1.120 0.920 +0 1.076 1.912 -0.667 0.618 -0.665 0.496 -1.524 1.127 0.000 0.944 -0.870 0.103 2.215 0.935 1.243 1.271 2.548 1.235 -0.512 -1.578 0.000 0.961 1.036 0.975 0.872 1.634 1.178 1.285 +1 0.442 1.823 -1.466 0.988 -1.565 1.444 -2.428 0.846 0.000 2.252 0.525 -0.141 1.107 2.366 0.328 -1.663 0.000 1.064 -0.091 -0.788 0.000 0.657 0.900 0.991 0.834 1.460 1.053 0.845 +1 0.575 -0.588 1.555 0.501 0.137 0.407 -1.782 1.262 0.000 0.348 -1.980 0.111 0.000 0.942 -0.695 -1.028 2.548 0.607 0.406 -0.667 3.102 0.695 0.884 0.987 0.705 0.428 0.634 0.590 +0 0.999 1.633 1.532 1.019 -0.793 0.613 -0.171 1.109 1.087 0.817 0.619 0.904 0.000 1.225 0.506 -0.244 0.000 1.189 1.033 0.553 0.000 0.992 0.948 1.211 1.278 0.973 1.015 0.924 +1 1.175 -0.643 0.099 1.273 -0.627 0.584 -0.133 -1.130 0.000 0.561 0.226 1.221 0.000 1.565 1.090 1.382 2.548 0.522 0.666 0.624 0.000 0.936 1.043 1.030 0.500 1.077 1.064 0.882 +0 0.733 -0.490 1.685 2.278 1.609 1.372 -1.278 -0.212 0.000 1.102 0.960 1.197 2.215 1.219 -0.308 -0.175 2.548 0.483 -0.242 -0.916 0.000 0.982 0.782 0.988 1.978 1.458 1.476 1.445 +1 1.792 -0.344 0.136 0.841 -0.813 1.685 0.625 1.499 0.000 0.548 0.587 -1.315 0.000 0.806 2.248 -0.160 0.000 1.011 1.329 -0.285 3.102 1.160 0.878 1.283 1.102 0.299 0.793 1.010 +1 0.641 1.633 0.001 1.118 1.010 1.013 0.750 1.516 0.000 1.438 0.526 0.358 2.215 1.649 0.175 -0.915 0.000 1.605 -0.493 -0.864 1.551 0.845 0.645 0.987 0.815 1.472 1.009 0.965 +0 0.442 0.276 0.929 1.638 -1.072 1.752 0.460 -0.802 2.173 1.436 -2.551 0.752 0.000 1.424 0.493 0.587 0.000 1.545 0.634 1.463 3.102 0.521 0.675 1.148 0.917 1.574 1.078 0.926 +1 1.152 0.873 -1.400 0.290 -0.264 0.831 0.373 -0.288 0.000 1.157 0.599 0.723 2.215 1.550 0.878 1.527 1.274 1.283 0.871 -0.714 0.000 0.798 1.181 0.988 0.758 0.975 0.987 0.872 +0 0.546 0.444 -0.292 1.429 -1.480 1.474 0.659 -1.104 2.173 2.622 0.481 0.538 0.000 0.685 -0.777 1.058 2.548 0.564 -1.013 -1.035 0.000 0.413 1.265 1.073 0.854 1.565 0.917 0.799 +1 1.274 -0.150 -0.628 1.824 -0.101 2.833 1.929 -1.628 0.000 1.361 0.040 0.111 2.215 2.690 0.230 0.574 1.274 0.776 0.382 -1.153 0.000 2.074 3.255 0.990 1.344 0.851 2.496 2.299 +1 0.625 -0.506 1.263 0.814 -1.314 1.228 -0.925 -0.091 0.000 1.217 0.430 1.588 2.215 0.976 0.010 -0.291 2.548 0.518 -1.251 0.127 0.000 0.921 0.750 0.986 0.647 1.177 1.064 0.929 +0 0.667 1.941 -0.188 0.446 0.506 1.049 0.577 1.737 1.087 1.508 0.766 -0.323 2.215 0.930 0.075 1.093 0.000 0.677 -0.442 -0.886 0.000 0.930 1.235 0.988 0.754 1.785 1.221 1.047 +1 1.864 0.056 -0.290 0.550 0.224 0.604 0.555 0.877 0.000 1.060 -0.375 1.727 2.215 0.824 -1.420 -0.485 0.000 0.817 0.925 1.318 0.000 0.510 0.916 0.990 0.821 0.441 0.842 0.785 +0 0.732 -0.712 -0.454 0.451 -0.392 1.167 0.448 0.949 2.173 0.920 0.120 1.609 0.000 0.926 1.528 -0.666 2.548 0.615 0.689 -0.687 0.000 0.930 0.983 0.987 1.117 1.539 0.967 0.852 +1 1.065 -0.611 -0.375 1.116 0.990 0.582 -1.434 -0.946 0.000 0.986 -0.550 -1.030 0.000 1.145 1.286 0.130 0.000 1.169 0.648 1.056 3.102 0.936 0.946 1.424 0.845 0.724 0.728 0.717 +1 0.910 -1.631 -0.125 1.964 -0.646 1.310 -0.927 1.357 2.173 0.445 -0.372 0.368 0.000 1.188 -1.481 0.595 0.000 1.407 -0.139 -1.529 3.102 0.984 0.993 0.996 1.619 0.930 1.159 0.979 +0 0.512 0.589 -1.486 0.552 -0.637 0.439 -0.923 -0.210 2.173 1.266 0.445 1.368 2.215 0.366 0.425 -0.052 0.000 0.641 -0.054 0.686 0.000 0.360 0.633 0.983 0.645 1.362 0.814 0.639 +1 1.377 -0.587 -0.869 1.735 -1.399 0.433 -0.277 0.236 2.173 0.921 0.321 1.152 1.107 0.330 -0.051 1.366 0.000 1.935 -2.212 0.028 0.000 0.635 0.758 0.988 0.980 0.740 0.923 0.794 +1 1.825 0.661 -0.885 1.030 0.833 1.565 2.020 -0.009 0.000 1.341 0.817 1.398 1.107 1.286 0.089 -1.706 0.000 1.295 1.032 -1.295 0.000 1.000 0.904 1.900 1.043 0.663 0.883 0.810 +1 1.477 0.870 0.367 0.643 0.024 0.425 0.141 0.632 0.000 1.340 0.221 -1.515 0.000 0.334 0.049 -1.312 2.548 1.172 1.080 -1.022 3.102 1.499 1.109 0.984 0.654 0.340 0.633 0.750 +1 1.074 -0.203 0.943 1.242 -1.727 0.952 -0.813 -0.239 2.173 0.629 -1.616 1.494 0.000 0.759 -0.793 -1.276 2.548 0.668 -0.085 -0.832 0.000 0.921 0.765 1.075 0.735 0.852 0.866 0.765 +1 0.652 0.084 -0.285 0.344 -0.839 1.105 0.260 1.644 2.173 0.700 0.765 -0.311 1.107 0.762 1.143 0.745 0.000 0.977 1.361 0.130 0.000 0.532 1.219 0.991 0.562 1.316 0.871 0.769 +1 1.748 -1.259 -1.568 1.159 -1.308 2.531 -0.895 -0.116 2.173 1.097 -0.529 1.515 1.107 1.602 0.505 1.042 0.000 0.954 -0.732 -1.359 0.000 1.553 1.095 0.985 2.288 2.479 1.717 1.644 +1 0.653 0.816 1.491 1.173 0.353 0.999 0.795 0.099 2.173 1.032 1.716 -0.995 0.000 1.052 0.893 -1.388 0.000 1.044 -0.757 -1.378 0.000 0.849 1.122 1.037 0.773 1.037 1.016 0.879 +1 0.603 -1.305 -0.295 1.986 -0.397 1.038 0.458 1.221 2.173 0.430 0.015 1.719 2.215 0.470 0.031 -0.543 0.000 0.524 -1.371 0.515 0.000 0.682 1.045 0.984 1.363 0.480 1.875 1.364 +0 0.510 -0.400 1.364 1.352 -0.990 0.630 -0.448 0.685 2.173 0.594 -0.795 -0.770 2.215 0.600 0.602 0.801 0.000 0.456 -0.936 1.413 0.000 0.659 0.725 0.988 0.901 0.886 0.668 0.599 +1 0.664 -0.216 0.435 1.156 1.437 1.839 -2.034 0.306 0.000 2.575 0.989 -1.165 2.215 1.506 1.083 -1.623 0.000 0.631 0.661 0.674 3.102 0.839 0.945 0.988 0.541 1.154 0.998 0.837 +1 1.436 1.090 0.733 0.278 -0.823 2.421 1.483 0.320 0.000 2.447 -1.403 -1.503 2.215 2.000 2.287 -1.506 0.000 2.205 1.306 -0.221 0.000 1.660 2.246 0.983 2.974 1.665 3.841 2.825 +1 0.709 0.850 0.672 0.949 -1.138 1.241 0.417 1.582 2.173 0.957 0.470 -0.037 2.215 0.877 0.102 0.661 0.000 1.705 1.461 -0.759 0.000 0.972 0.856 1.134 0.950 1.595 1.049 0.923 +0 1.135 0.285 -1.109 1.089 -0.896 1.103 0.127 0.964 0.000 0.731 -0.489 0.048 2.215 0.754 0.464 0.380 0.000 0.715 -1.183 -0.956 1.551 0.883 0.926 0.987 1.058 0.600 0.887 0.971 +1 1.124 0.354 0.040 1.132 1.620 0.956 1.375 0.416 0.000 1.543 0.437 -0.805 2.215 1.724 1.678 -1.636 0.000 2.128 -0.175 1.562 0.000 0.852 1.251 1.546 0.743 0.139 0.718 0.746 +1 0.341 -1.223 -1.373 0.994 0.692 1.086 0.319 -1.186 0.000 1.213 1.562 0.163 2.215 1.057 0.491 1.657 2.548 0.565 1.305 0.426 0.000 1.430 0.975 0.988 1.257 1.353 1.040 0.963 +0 1.218 -0.308 -1.602 1.532 -1.007 0.556 -0.059 0.820 2.173 0.840 -1.431 0.502 0.000 0.463 -0.801 -0.215 2.548 0.407 -1.488 0.811 0.000 0.627 0.812 0.989 0.704 0.573 0.709 0.765 +1 0.352 -1.440 0.063 0.644 1.519 1.138 0.660 1.460 2.173 1.127 -0.034 -0.520 0.000 0.931 0.095 0.137 0.000 0.496 0.796 -0.591 3.102 0.883 0.568 0.995 0.906 0.773 0.907 0.786 +1 0.637 0.994 1.198 0.755 1.183 0.646 -1.285 0.844 0.000 1.288 0.139 -1.166 2.215 0.826 -1.664 -0.400 0.000 0.702 0.662 -0.712 0.000 0.925 0.712 1.001 0.989 0.705 0.810 0.732 +0 0.802 -0.507 0.519 1.249 -1.030 1.596 0.474 0.732 0.000 1.052 -0.734 -1.455 0.000 1.868 0.130 -0.997 2.548 0.906 -0.195 -0.393 3.102 0.782 0.968 1.366 0.968 0.548 1.045 0.989 +0 1.178 -1.468 -0.931 1.113 0.177 0.710 -1.096 0.586 2.173 0.659 0.755 1.437 0.000 0.792 -1.703 -0.403 0.000 1.131 -0.379 -1.623 3.102 0.736 0.788 1.333 0.960 0.921 0.798 0.689 +1 0.775 -1.014 -0.295 0.804 -1.630 0.743 -0.163 1.621 2.173 0.559 -2.107 -1.004 0.000 0.450 -1.627 0.467 0.000 0.984 0.124 0.343 3.102 0.761 0.978 1.020 0.841 0.839 0.853 0.737 +0 0.928 -0.440 -0.658 1.570 -1.652 1.047 0.218 -0.527 2.173 0.696 -1.161 1.125 0.000 1.395 -0.149 0.858 0.000 0.653 0.972 0.477 1.551 0.890 0.900 1.304 1.137 0.811 0.990 0.961 +0 0.812 -0.292 0.794 0.639 -0.177 1.133 -0.240 -0.137 1.087 1.422 -0.659 1.663 2.215 1.218 0.176 -1.177 0.000 1.184 0.265 1.585 0.000 0.903 0.981 0.995 0.817 1.910 1.048 0.878 +1 1.263 -0.147 -1.176 1.318 -1.711 1.417 0.788 -0.284 2.173 1.114 -2.680 0.734 0.000 0.449 1.895 1.423 0.000 0.646 -0.064 1.098 1.551 6.084 3.149 0.989 1.753 1.062 2.739 2.117 +0 1.277 -0.164 0.024 0.660 -1.226 0.841 -0.443 1.136 2.173 0.788 0.152 -1.739 2.215 0.597 -0.002 -0.640 0.000 0.701 2.486 -1.042 0.000 1.528 1.286 1.148 0.997 0.725 1.191 1.061 +0 1.447 -1.048 1.596 1.251 -1.497 1.650 -0.449 -0.184 0.000 1.313 -0.575 1.523 2.215 0.635 -1.123 0.221 2.548 0.422 0.784 1.142 0.000 1.526 0.981 0.986 0.612 0.949 1.060 1.045 +1 0.923 0.825 -1.263 0.812 -0.403 0.916 0.648 1.435 2.173 0.737 1.715 -0.442 0.000 1.649 1.150 0.529 0.000 1.227 -1.071 -1.294 0.000 0.923 0.931 0.989 0.829 0.607 0.739 0.695 +0 0.863 0.002 -0.218 1.329 -1.344 0.364 1.067 -0.937 0.000 0.431 -0.404 1.713 0.000 0.611 -0.045 0.202 2.548 1.152 -0.293 1.027 0.000 0.897 0.744 1.260 0.757 0.171 0.557 0.563 +0 0.621 0.645 0.474 0.697 0.467 1.350 2.065 -1.640 0.000 1.298 0.237 -0.505 2.215 1.846 0.267 0.252 0.000 2.003 -0.829 1.240 0.000 1.049 1.014 0.994 1.248 0.588 0.902 1.115 +1 0.664 0.845 1.330 0.592 -0.368 1.532 0.473 -1.002 0.000 1.232 1.295 1.302 1.107 1.087 2.506 0.963 0.000 2.042 0.245 0.262 0.000 0.706 1.086 0.987 0.617 1.044 0.747 0.678 +1 1.302 -0.364 0.124 0.147 1.571 0.709 -1.391 0.877 0.000 0.720 0.189 -0.711 0.000 0.985 -0.285 -1.221 1.274 1.175 0.418 1.662 3.102 2.017 1.317 0.990 0.784 0.547 0.909 0.824 +0 1.073 1.932 -1.676 0.803 0.434 1.031 0.949 -1.336 2.173 1.064 0.704 0.255 2.215 0.560 -0.953 -0.640 0.000 1.275 -0.461 0.544 0.000 0.848 0.988 1.216 1.111 1.537 1.088 1.172 +1 0.415 1.473 -0.724 0.829 1.331 0.606 0.368 -0.583 0.000 0.600 -0.576 0.055 1.107 0.394 1.805 0.495 0.000 1.022 -0.054 -0.292 1.551 0.837 0.722 0.991 1.183 0.288 1.121 0.948 +1 1.565 0.915 -1.256 0.485 1.501 0.549 1.244 -0.414 0.000 1.305 1.168 0.520 2.215 0.468 -0.242 -0.108 2.548 0.459 0.355 0.876 0.000 0.920 0.893 0.986 0.723 0.801 0.836 0.726 +0 0.684 0.813 -1.557 0.770 0.439 1.436 0.458 0.763 2.173 0.908 -0.660 -1.353 2.215 1.145 2.230 -1.641 0.000 1.706 -0.599 -0.662 0.000 0.705 0.853 0.988 0.890 1.882 1.347 1.173 +1 0.818 -0.868 0.519 0.705 0.776 1.202 0.951 -1.058 2.173 0.606 0.685 1.517 2.215 0.989 0.391 0.207 0.000 1.054 0.289 0.975 0.000 0.720 0.695 0.993 1.427 0.933 0.971 0.842 +1 0.847 -0.389 0.605 0.414 -0.117 0.884 0.391 1.665 2.173 0.436 -0.325 -0.601 0.000 0.765 -1.419 0.035 0.000 0.418 0.291 1.276 0.000 0.889 0.796 0.978 1.225 0.682 0.863 0.774 +0 0.823 1.163 1.226 1.047 0.010 0.742 1.415 -1.439 2.173 0.798 -1.929 1.047 0.000 0.402 1.814 -1.194 0.000 1.063 -0.775 -0.249 3.102 0.841 0.844 1.144 0.947 1.613 1.691 1.591 +0 0.964 0.029 -0.104 0.350 0.913 0.730 0.093 -1.261 2.173 0.442 1.332 -1.143 2.215 0.661 2.238 0.373 0.000 2.534 1.524 1.156 0.000 1.033 0.915 0.987 0.883 0.578 1.050 0.935 +1 1.240 0.152 0.496 0.770 -0.247 0.771 2.734 -0.009 0.000 0.775 1.245 1.534 0.000 1.190 -0.475 1.513 2.548 1.941 0.445 -1.070 3.102 2.091 1.879 0.990 0.922 1.061 1.663 1.504 +1 0.617 1.431 -1.119 0.798 -0.421 1.166 0.250 -0.175 2.173 1.347 0.837 1.512 0.000 0.416 1.295 1.737 0.000 0.500 0.890 -0.447 0.000 0.801 0.700 0.993 0.812 1.091 1.019 0.885 +1 0.445 -1.120 -1.676 0.837 0.217 1.127 -0.084 -0.095 2.173 0.933 0.224 1.700 0.000 0.526 -0.430 1.275 0.000 0.767 0.662 1.526 0.000 0.557 1.286 0.983 0.931 0.728 0.952 0.939 +1 1.349 -1.565 -0.602 1.033 -1.097 1.380 -0.398 0.928 2.173 0.385 0.596 1.480 0.000 0.600 -0.880 -1.298 0.000 1.038 -0.545 -0.047 0.000 0.982 1.003 0.977 0.661 0.848 1.050 0.904 +1 0.524 -1.114 1.010 0.394 1.008 0.602 -1.107 0.556 2.173 1.212 -0.314 -1.062 2.215 0.666 -0.798 -0.794 0.000 0.464 -1.154 -1.287 0.000 0.307 0.675 0.975 0.940 1.351 0.836 0.680 +1 1.215 0.744 0.537 0.534 -1.342 1.031 0.398 -1.499 2.173 0.964 1.233 0.606 0.000 0.837 1.197 -1.060 2.548 0.521 -2.332 -0.709 0.000 0.617 1.543 1.108 0.994 0.706 1.188 1.068 +0 0.348 -1.520 0.332 0.486 -1.136 1.203 -0.862 -1.639 0.000 0.895 -1.623 -1.204 2.215 1.856 -0.118 0.433 2.548 0.685 -1.014 0.913 0.000 0.935 0.794 0.985 0.818 1.792 1.106 0.912 +1 1.599 -1.367 -1.364 0.376 0.956 0.414 -0.288 0.354 0.000 0.710 0.691 -0.085 2.215 0.572 -0.480 1.130 0.000 0.617 0.713 -1.179 3.102 0.573 0.700 0.988 0.850 0.500 0.873 0.743 +1 1.038 1.267 -0.809 1.920 -1.217 2.402 -1.935 0.666 0.000 1.667 0.815 -1.069 0.000 1.486 2.160 -1.727 0.000 2.174 0.159 -0.459 1.551 0.702 0.889 0.989 1.392 0.525 1.130 1.038 +1 1.040 -0.629 -1.578 0.873 1.250 1.270 -0.737 -0.440 1.087 0.587 -1.312 -1.565 0.000 1.391 -0.061 0.561 2.548 0.701 -1.555 1.379 0.000 0.449 1.047 0.985 1.287 1.419 1.071 0.910 +0 0.488 -1.191 1.315 0.574 -0.503 0.440 0.489 1.144 1.087 0.723 0.911 1.517 0.000 1.571 0.206 -0.050 2.548 0.838 0.142 -0.965 0.000 0.892 1.049 0.988 1.256 0.923 1.148 1.103 +1 0.395 1.580 -0.387 1.730 1.123 0.801 -0.316 -0.692 2.173 0.687 0.675 1.167 1.107 0.557 -0.132 0.231 0.000 0.462 0.820 -1.510 0.000 0.884 0.829 1.120 1.598 1.230 1.081 0.927 +0 0.368 -0.881 0.232 1.259 1.729 0.726 0.981 -0.303 0.000 0.540 2.865 0.353 0.000 1.324 -0.082 1.687 1.274 0.819 1.872 -0.461 0.000 0.807 0.757 0.987 0.882 0.625 0.870 1.198 +1 0.932 0.790 1.624 0.789 -0.636 0.646 -0.122 1.068 1.087 0.691 0.694 -1.189 2.215 0.854 2.067 -0.058 0.000 1.282 0.913 0.298 0.000 0.805 0.995 1.062 0.888 0.975 0.943 0.820 +1 0.876 -0.059 -0.512 0.623 1.056 0.787 0.152 -0.092 0.000 1.525 0.151 -1.709 2.215 0.951 0.673 0.986 1.274 0.770 1.160 -0.496 0.000 0.892 0.926 1.011 0.912 0.918 0.946 0.803 +0 1.861 -0.667 -0.277 1.997 -0.758 0.721 -1.048 1.412 0.000 0.659 -1.964 1.670 0.000 1.140 0.067 0.124 2.548 1.639 -1.440 1.187 0.000 0.807 0.936 1.121 0.968 0.639 0.911 1.058 +0 0.331 1.639 1.172 0.843 1.560 0.537 -0.534 -1.733 0.000 0.811 -0.430 -0.969 0.000 2.548 0.670 0.310 2.548 1.293 0.781 -0.813 3.102 0.895 0.947 0.999 1.144 1.184 1.131 0.959 +1 0.378 -1.116 -0.289 1.450 0.283 0.884 0.966 -1.630 2.173 0.369 1.390 0.267 0.000 0.825 0.734 -0.766 2.548 0.383 0.864 1.276 0.000 0.399 0.669 0.986 1.920 0.753 2.084 1.657 +0 1.399 0.506 1.676 1.639 1.095 1.759 1.332 -1.423 2.173 1.874 0.352 -0.292 2.215 2.006 0.346 0.377 0.000 1.793 0.421 0.028 0.000 0.659 0.947 1.048 1.462 2.654 1.669 1.487 +0 1.763 -1.140 0.063 0.717 -0.207 1.013 -0.222 -1.663 0.000 0.689 -1.374 1.153 1.107 0.649 0.861 -1.090 1.274 0.903 -0.892 -1.149 0.000 0.913 0.969 0.992 1.084 1.228 0.926 0.942 +0 0.598 -1.099 -0.297 0.887 1.681 1.774 -0.119 -0.024 2.173 1.206 -0.239 -1.702 0.000 1.209 0.951 -1.688 1.274 0.810 0.557 1.213 0.000 0.879 0.810 0.987 1.298 2.139 1.309 1.136 +1 0.685 0.643 -1.637 0.794 0.241 0.645 -1.297 -0.422 2.173 0.442 -0.656 1.553 0.000 0.661 -0.569 1.007 2.548 1.208 -0.553 0.371 0.000 0.859 0.709 1.014 0.691 0.831 0.802 0.688 +0 0.381 -0.498 -0.126 0.379 -0.311 0.544 0.665 1.639 2.173 0.913 -0.448 -0.753 2.215 0.277 -0.388 -1.183 0.000 0.723 0.095 0.255 0.000 0.494 0.590 0.984 0.889 1.063 0.753 0.598 +1 0.490 -2.359 -0.439 0.467 -0.508 1.020 -1.376 -1.350 0.000 0.414 0.153 -0.294 0.000 1.583 0.262 0.810 2.548 1.155 -0.548 0.533 1.551 1.628 1.221 0.985 1.198 0.568 1.080 0.939 +0 1.974 -0.520 -0.274 1.046 -0.317 1.240 0.975 1.141 1.087 1.082 0.029 -1.722 0.000 0.638 0.247 0.819 0.000 0.732 0.334 -1.273 3.102 0.971 1.057 1.002 0.898 0.881 1.491 1.227 +1 0.464 -2.190 0.863 1.148 -0.355 0.479 0.768 0.208 2.173 0.599 -1.474 -1.680 0.000 0.808 -0.366 -1.517 2.548 0.430 -0.155 0.625 0.000 0.747 1.047 0.990 0.857 0.913 0.925 0.783 +0 3.524 0.413 1.358 0.502 1.397 1.182 0.445 -0.197 2.173 2.001 0.849 -0.630 0.000 1.133 -0.419 1.143 2.548 0.681 0.639 -0.851 0.000 0.311 0.811 0.987 0.708 1.509 1.232 1.297 +1 1.032 -0.978 0.678 0.604 -1.027 0.743 -1.006 -0.794 1.087 1.060 -0.218 1.430 2.215 1.286 0.382 0.398 0.000 0.456 1.251 -0.015 0.000 0.570 1.009 1.093 0.820 1.298 0.992 0.883 +1 0.796 -0.391 0.418 1.192 -1.625 0.721 0.456 -0.743 0.000 0.782 0.667 0.955 2.215 0.757 1.490 -0.856 0.000 1.124 -0.781 0.166 3.102 0.838 1.091 1.301 0.825 0.944 0.923 0.897 +1 0.583 1.687 -0.278 0.980 1.532 0.654 2.241 0.179 0.000 0.916 0.640 -1.499 2.215 0.730 0.337 -0.457 2.548 0.398 1.153 0.935 0.000 0.604 0.858 1.045 0.820 0.713 0.803 0.722 +0 1.212 0.266 0.342 0.968 0.934 0.798 -0.127 -1.372 2.173 0.661 -0.117 -0.256 0.000 0.689 -0.790 -0.920 1.274 0.748 -0.763 1.364 0.000 0.985 0.905 0.993 0.855 0.506 0.797 0.703 +0 2.110 0.969 0.013 0.566 -1.518 2.075 -1.121 1.561 0.000 1.281 0.153 -0.573 2.215 0.664 -0.617 1.216 0.000 0.524 -2.282 0.079 0.000 0.971 0.762 1.487 1.092 0.546 0.840 1.086 +0 1.006 0.503 -0.135 0.709 0.959 0.661 1.545 -0.028 1.087 0.637 2.118 -1.141 0.000 0.605 1.485 1.235 1.274 1.753 -0.224 -1.704 0.000 2.243 1.345 0.988 0.800 0.716 1.039 0.913 +0 0.628 -0.317 -1.678 0.587 -0.446 1.237 0.947 1.659 2.173 1.313 0.604 0.225 0.000 1.009 -2.638 -1.139 0.000 0.412 1.205 0.642 3.102 5.249 3.002 0.979 1.526 0.629 2.510 1.855 +1 0.464 -1.107 -0.410 0.525 1.402 0.962 -1.204 -0.155 2.173 0.769 -0.136 -1.737 0.000 0.774 0.955 -1.494 0.000 0.918 2.343 0.106 0.000 0.789 0.724 0.984 0.914 1.006 1.024 1.087 +1 1.031 1.057 -0.080 1.233 -0.598 0.590 -0.332 -1.222 2.173 0.506 -0.231 0.536 0.000 1.498 0.105 1.593 0.000 1.397 1.207 0.338 0.000 0.963 0.824 0.990 1.114 0.507 0.998 1.061 +0 0.990 0.753 1.671 0.664 -0.394 0.730 -0.244 -0.925 2.173 0.966 0.689 0.358 2.215 0.345 2.219 1.238 0.000 0.618 0.003 0.968 0.000 0.795 1.088 1.076 0.836 1.287 0.824 0.731 +0 2.066 0.020 0.385 1.332 0.659 1.167 0.934 -1.575 1.087 0.818 0.447 -1.135 1.107 0.788 -2.658 -0.248 0.000 0.462 1.233 -0.762 0.000 2.981 2.199 0.974 1.680 0.658 1.993 1.799 +0 0.872 0.717 1.067 0.425 -1.153 0.856 -1.057 0.204 0.000 1.407 -1.190 -1.741 2.215 1.543 -1.330 -0.421 2.548 0.617 0.194 0.694 0.000 0.907 0.986 0.987 1.742 1.465 1.549 1.312 +0 0.625 0.276 0.272 2.293 0.929 0.991 0.450 1.653 2.173 0.437 -0.252 -1.166 0.000 0.645 -1.489 -0.402 0.000 2.805 0.934 -0.404 3.102 0.901 0.978 0.990 1.051 1.797 1.183 0.965 +1 1.067 -0.284 0.666 0.562 -0.683 0.824 -0.122 -0.604 0.000 1.345 0.099 1.294 2.215 0.612 -1.449 1.524 2.548 1.019 -1.009 -0.562 0.000 0.802 0.958 1.006 0.869 0.939 0.980 0.830 +1 0.471 0.532 1.703 0.764 -1.341 0.834 -0.732 -0.231 0.000 1.166 -0.564 1.506 1.107 1.265 -1.159 0.236 2.548 0.599 -2.162 -1.436 0.000 1.498 1.029 0.996 0.689 1.261 0.980 0.866 +0 0.923 1.977 0.963 0.348 -1.165 0.964 1.353 0.241 2.173 0.877 0.460 -1.350 0.000 0.577 1.670 -0.859 0.000 0.755 -0.619 1.732 3.102 0.928 0.879 0.991 0.829 1.439 0.958 0.842 +1 0.735 1.285 -0.067 0.604 0.201 0.385 -0.484 -1.267 2.173 0.689 0.040 -1.695 0.000 0.531 0.878 -1.477 2.548 0.480 1.001 -0.409 0.000 0.889 0.662 0.985 0.687 0.458 0.590 0.566 +1 0.470 0.019 -0.417 1.425 0.555 1.138 -0.645 -1.303 2.173 0.393 -1.135 0.464 1.107 0.640 -1.579 -0.002 0.000 0.969 -0.878 1.387 0.000 0.878 1.064 0.982 0.778 1.016 1.005 0.918 +1 0.690 -0.866 -1.684 0.493 0.323 0.739 1.529 -0.242 0.000 1.197 0.469 -1.467 2.215 0.924 1.691 0.195 0.000 0.929 -0.031 1.188 3.102 0.818 0.932 0.985 1.281 0.695 0.857 1.106 +1 1.052 0.423 -0.511 1.364 1.328 1.202 -0.309 -1.602 2.173 0.932 0.014 -0.157 0.000 0.499 0.470 1.229 2.548 1.654 -0.738 0.246 0.000 0.947 0.864 1.654 1.248 0.676 0.973 0.956 +1 0.675 -0.414 0.782 0.170 1.003 1.559 1.024 1.680 0.000 2.118 0.665 -0.359 1.107 0.864 0.753 1.272 0.000 0.828 -1.272 -0.183 0.000 0.803 0.914 0.989 1.107 1.024 0.875 0.802 +1 1.645 -0.421 -0.496 0.371 -0.958 1.178 -0.788 1.118 2.173 0.568 -0.388 -1.170 0.000 0.632 -0.688 0.177 1.274 0.485 -0.146 -1.556 0.000 0.245 0.880 0.979 0.657 0.807 0.925 0.743 +1 0.353 1.209 0.993 0.777 0.593 0.734 -0.475 0.589 1.087 0.994 0.301 -1.078 0.000 0.716 1.057 -0.503 0.000 0.625 0.274 -1.448 0.000 0.950 0.804 0.996 0.760 0.903 0.613 0.587 +1 0.424 0.476 1.372 2.397 -1.284 0.818 -0.252 0.083 2.173 1.090 0.366 0.771 2.215 0.430 0.300 -0.351 0.000 0.523 -1.048 -1.537 0.000 0.650 0.811 0.989 1.260 0.922 1.038 0.816 +0 1.330 1.084 0.755 0.416 0.198 0.500 0.181 0.414 0.000 0.819 -0.310 -1.592 2.215 1.121 0.477 -0.994 2.548 1.493 0.929 -0.537 0.000 0.728 0.752 0.990 1.000 0.684 0.822 0.842 +1 1.274 -1.011 -0.817 0.246 -0.339 0.754 -0.031 0.790 2.173 0.961 0.020 -1.485 2.215 0.323 0.139 0.422 0.000 0.435 -2.003 1.055 0.000 0.721 0.887 0.989 1.027 1.111 0.829 0.731 +0 1.045 -1.383 -0.047 0.145 0.425 1.032 -0.769 1.020 0.000 1.052 -1.520 -0.524 2.215 1.971 0.564 -1.104 2.548 1.216 0.284 1.074 0.000 0.969 1.347 0.980 1.303 2.163 1.358 1.110 +1 0.965 0.728 -0.924 0.797 -1.577 0.774 -0.019 0.805 0.000 0.989 -0.081 0.423 2.215 1.061 0.606 -1.266 2.548 0.889 -1.991 0.894 0.000 1.884 1.313 0.997 0.564 1.165 1.148 1.047 +0 1.842 -0.780 -0.583 1.509 -1.214 0.591 -1.562 0.471 0.000 0.575 -0.674 0.889 0.000 0.701 -1.324 0.855 0.000 1.099 -0.173 -1.407 3.102 1.034 0.858 1.243 0.798 0.324 0.578 0.770 +1 0.832 1.305 1.098 1.187 0.502 0.977 2.932 0.745 0.000 1.699 0.550 -1.112 0.000 1.104 0.231 -0.695 0.000 0.945 -0.453 -1.724 3.102 0.839 0.901 0.983 1.166 0.555 1.017 1.085 +1 0.399 0.185 -1.260 1.759 -0.670 0.969 1.055 0.589 0.000 1.884 -0.363 1.360 0.000 1.489 -1.122 -0.162 2.548 1.935 -0.867 0.686 3.102 2.311 1.624 0.994 0.778 0.906 1.241 1.116 +1 0.694 -0.023 0.247 0.844 0.997 0.852 -0.371 1.057 1.087 0.578 -0.083 -0.531 0.000 1.575 -0.947 -0.407 0.000 1.338 -0.710 -1.604 3.102 0.745 0.934 0.982 0.675 0.811 0.897 0.792 +1 0.351 -1.542 -1.212 1.485 1.533 0.972 -0.071 0.116 0.000 1.430 0.320 -1.621 2.215 0.872 -0.511 -0.508 1.274 0.505 1.521 0.384 0.000 0.788 0.976 0.987 1.314 1.140 1.776 1.579 +1 0.878 -0.497 0.312 1.068 0.529 1.362 -0.567 -1.019 2.173 0.496 -1.036 0.853 0.000 0.619 -0.368 1.506 2.548 0.800 0.672 0.349 0.000 0.956 0.676 0.978 1.514 0.876 1.003 0.872 +0 0.701 -0.652 0.516 2.586 0.100 0.739 -0.411 -1.204 2.173 0.664 0.324 -1.287 0.000 0.553 -0.857 1.365 0.000 0.451 1.125 -1.626 0.000 0.897 0.681 0.974 0.923 0.659 0.969 0.906 +0 3.291 0.586 -0.841 0.297 -0.460 1.598 -1.229 1.031 0.000 0.792 -0.490 0.765 2.215 0.757 -0.612 -0.369 2.548 0.775 -0.974 1.610 0.000 0.848 1.126 0.988 1.354 0.705 0.929 1.339 +1 1.037 -0.375 -0.433 1.485 -1.244 0.969 0.344 0.853 0.000 0.798 2.616 1.576 0.000 1.285 0.920 0.123 2.548 1.324 -0.055 -0.813 3.102 0.811 1.015 1.146 1.247 0.933 0.831 0.890 +0 0.465 -0.020 0.313 1.280 -1.013 0.857 0.325 1.216 2.173 1.457 -0.428 -0.829 1.107 1.949 -1.452 0.568 0.000 0.747 -1.525 -0.120 0.000 0.789 1.608 0.994 0.965 1.711 1.409 1.169 +0 1.149 0.542 -0.779 0.412 1.454 0.634 0.510 1.730 2.173 0.806 -0.062 0.175 2.215 0.538 -0.076 1.196 0.000 0.484 -0.450 0.622 0.000 0.307 0.525 0.983 0.769 1.082 0.688 0.567 +1 1.224 -1.015 1.297 0.718 0.394 1.455 -0.730 1.726 0.000 2.353 -0.191 0.087 2.215 0.833 0.346 -0.651 2.548 0.856 -1.437 -1.468 0.000 0.982 1.277 0.987 1.243 1.017 1.457 1.187 +0 0.940 0.183 1.100 1.314 0.247 0.533 -0.899 -0.670 0.000 1.232 0.200 1.522 2.215 1.625 0.124 -0.710 2.548 0.554 -0.782 0.893 0.000 0.819 1.086 1.070 1.061 1.361 0.942 0.861 +1 1.126 -0.754 1.556 1.080 1.743 2.352 -0.618 -0.337 0.000 1.093 -1.694 0.298 0.000 1.396 -1.219 -1.700 0.000 2.313 0.810 1.721 1.551 0.863 0.848 0.985 1.841 0.872 1.406 1.290 +1 0.380 -1.178 0.873 1.237 -1.067 0.855 0.226 0.970 0.000 1.376 -0.159 -0.635 2.215 0.499 1.295 -0.745 0.000 0.804 -0.397 0.357 1.551 1.389 0.915 0.988 0.786 0.756 0.881 0.820 +1 0.793 1.258 -1.374 0.374 0.317 0.564 0.302 1.099 2.173 0.461 2.217 0.043 0.000 0.656 1.539 1.448 0.000 0.836 -0.529 -0.186 3.102 0.950 0.880 0.989 0.700 0.754 0.655 0.600 +1 0.738 -0.628 -1.413 1.892 -0.735 0.694 -0.090 1.403 0.000 1.238 -0.707 0.465 1.107 0.466 0.374 0.372 0.000 0.553 0.863 0.929 3.102 0.860 0.954 0.985 0.983 0.806 0.928 0.860 +1 0.730 -0.971 0.273 1.771 1.435 0.726 -0.923 -0.457 0.000 0.966 0.787 -0.450 0.000 0.788 -0.719 0.811 2.548 0.994 -0.349 -1.088 0.000 0.911 0.946 1.364 0.714 0.529 0.641 0.662 +0 0.669 0.758 -0.462 0.211 0.475 1.541 -0.041 0.031 2.173 2.259 0.322 -1.715 0.000 1.302 -1.141 -1.197 2.548 1.192 2.222 -0.355 0.000 0.899 0.959 0.978 0.815 1.945 1.363 1.107 +1 1.337 0.073 -1.373 0.259 0.715 1.040 0.171 1.256 0.000 1.326 -0.272 0.207 2.215 0.615 -2.401 -0.242 0.000 1.529 0.254 -0.705 3.102 0.934 1.114 0.985 1.032 1.015 0.992 0.864 +0 0.299 -1.604 -0.905 1.220 -0.732 1.313 2.107 1.474 0.000 1.890 -0.639 -0.258 2.215 1.279 1.384 1.245 2.548 1.910 0.672 0.574 0.000 0.975 0.890 0.990 0.905 2.710 1.452 1.214 +0 0.617 -0.042 -1.005 0.378 1.614 0.672 0.984 0.245 1.087 0.383 -0.813 -0.481 0.000 0.847 -1.144 1.130 2.548 0.873 0.749 -1.374 0.000 0.917 0.964 0.989 0.662 1.460 0.958 0.828 +0 0.940 -0.703 1.332 1.113 -0.912 0.504 -0.749 -1.210 2.173 0.601 -0.754 0.402 2.215 0.513 0.242 -0.533 0.000 0.650 -0.944 0.979 0.000 0.789 0.657 1.275 0.839 0.804 0.628 0.563 +0 0.892 1.239 0.822 0.256 -1.493 0.848 0.461 -0.262 1.087 0.330 0.790 0.309 0.000 1.024 1.600 -1.499 0.000 1.017 0.236 1.575 3.102 0.991 1.084 0.978 0.579 0.983 0.758 0.684 +1 0.892 0.780 -1.602 0.373 -0.768 0.603 1.175 -1.077 2.173 0.462 -0.122 1.005 2.215 0.526 -1.449 1.257 0.000 1.674 0.657 0.181 0.000 1.053 0.973 0.985 0.725 0.921 0.780 0.763 +1 0.481 1.083 -1.015 1.993 -0.354 1.192 0.755 1.679 0.000 1.114 -0.428 -0.413 2.215 0.921 0.227 0.266 2.548 2.434 -1.435 0.893 0.000 0.890 1.097 0.983 1.730 0.725 1.186 1.202 +0 1.577 2.017 -1.364 0.560 0.205 0.470 1.358 0.410 0.000 0.607 -0.958 0.699 0.000 1.002 1.173 -0.519 2.548 0.582 0.038 0.567 0.000 0.580 0.687 1.286 0.753 0.661 0.631 0.668 +0 0.517 1.836 0.705 2.437 0.849 0.604 -1.041 -0.422 1.087 0.586 0.199 -0.563 0.000 1.296 -0.246 -1.353 2.548 0.862 1.006 -1.193 0.000 0.694 0.983 0.997 1.394 0.932 1.306 1.065 +1 0.871 0.166 -0.658 1.662 0.113 0.883 -0.065 -1.450 0.000 0.552 0.324 -0.054 2.215 1.552 0.404 0.809 0.000 2.551 -0.779 -1.530 0.000 0.880 1.017 1.068 1.031 0.837 0.759 0.837 +0 1.033 -0.488 -1.260 0.761 0.365 1.177 -1.283 -1.533 2.173 1.050 -0.422 0.232 2.215 0.597 -0.926 1.075 0.000 0.724 -2.056 -0.037 0.000 0.838 0.909 1.222 1.057 1.790 0.996 0.872 +0 2.931 0.181 -0.816 1.290 -1.261 1.699 1.014 1.091 0.000 1.008 1.223 -0.743 1.107 0.968 -0.651 0.752 2.548 1.141 0.309 0.233 0.000 0.856 1.289 1.048 0.972 1.587 1.212 1.367 +1 0.866 -1.386 1.434 0.824 0.737 0.823 0.116 -1.009 2.173 0.318 -1.644 0.333 0.000 0.422 -0.399 0.623 2.548 0.727 -0.996 -0.526 0.000 0.477 0.749 0.978 0.667 0.759 1.033 0.774 +1 0.526 -0.454 -0.254 1.847 0.634 0.835 0.982 -1.610 2.173 0.623 1.186 -0.714 0.000 0.570 0.919 0.517 0.000 0.857 -0.324 -1.116 3.102 0.824 0.886 0.988 0.791 0.772 1.004 0.914 +0 0.697 -1.126 0.719 0.884 -0.586 0.677 0.291 0.103 0.000 1.430 -0.933 1.128 1.107 0.658 0.649 -0.868 0.000 1.880 0.246 -1.217 1.551 0.955 0.936 1.003 0.926 1.610 1.102 0.977 +1 0.877 0.080 0.958 1.354 -0.028 0.608 -0.044 1.594 0.000 0.897 0.333 0.204 0.000 0.878 2.132 -1.621 0.000 2.168 0.305 -1.249 0.000 1.024 0.774 1.171 0.851 0.682 0.813 0.818 +1 0.838 -0.008 -0.861 0.536 1.464 1.230 0.532 -0.581 2.173 1.309 0.320 1.087 1.107 0.717 0.807 1.713 0.000 0.375 2.376 1.015 0.000 0.739 0.920 0.984 0.842 1.873 1.057 0.870 +1 0.693 0.460 1.485 0.579 0.584 1.160 1.208 -0.611 2.173 0.821 2.256 1.369 0.000 1.488 2.077 0.782 0.000 0.801 -0.015 -0.918 0.000 0.855 1.276 0.993 1.405 0.778 1.178 1.257 +0 0.753 -0.642 0.580 0.686 -0.609 0.760 0.133 -1.106 2.173 0.846 0.890 1.018 1.107 0.564 0.343 0.032 0.000 0.795 -0.282 1.402 0.000 0.748 0.774 0.986 1.185 1.207 0.973 0.776 +1 0.493 -1.340 -1.280 1.537 1.615 1.157 -0.847 -0.157 2.173 0.739 0.345 0.179 2.215 1.269 0.123 0.981 0.000 0.876 -2.150 -1.471 0.000 1.185 0.839 0.996 1.672 0.963 1.468 1.594 +0 0.438 -0.827 1.361 0.702 -1.052 0.949 0.046 0.781 2.173 1.777 -0.999 -1.409 0.000 1.263 -0.628 0.189 2.548 1.393 -0.299 0.000 0.000 2.079 1.526 0.990 0.905 0.864 1.213 1.022 +0 0.585 0.462 -0.588 1.346 0.294 1.039 -0.231 -1.064 2.173 1.147 1.579 1.041 2.215 1.686 -0.438 1.187 0.000 1.818 -0.603 -0.517 0.000 0.910 0.941 0.989 1.323 2.294 1.598 1.284 +1 0.307 -1.864 0.730 0.868 -0.308 0.897 -0.021 -0.367 0.000 1.445 0.238 1.102 2.215 0.716 -0.256 -0.948 0.000 1.022 0.253 -1.404 1.551 0.744 0.726 0.981 1.047 0.848 0.894 0.797 +1 0.687 0.401 -1.718 0.229 -0.528 1.048 -0.493 0.765 1.087 0.918 0.993 -1.088 0.000 0.291 -0.495 -0.631 0.000 0.498 0.513 -0.293 3.102 0.756 1.447 0.988 0.579 0.769 0.844 0.743 +0 2.058 0.191 1.474 0.653 -0.064 0.814 1.760 -0.065 0.000 0.554 1.753 -0.800 0.000 0.467 0.595 -1.692 1.274 0.703 1.335 0.524 3.102 0.878 0.839 1.579 0.960 0.453 0.626 0.859 +1 0.647 1.255 -1.699 1.302 0.762 0.717 1.893 0.460 0.000 0.972 -0.472 -0.198 2.215 1.854 -0.757 -1.187 1.274 0.898 0.444 -1.050 0.000 1.507 1.657 1.014 1.754 1.137 1.526 1.340 +0 0.580 -0.676 1.715 1.587 1.682 1.179 1.109 -0.145 2.173 0.661 1.213 0.764 0.000 0.937 0.093 0.099 0.000 1.402 -0.245 -0.345 3.102 0.962 1.289 0.972 3.017 1.073 1.935 1.784 +1 0.288 -2.154 -0.071 1.130 -1.398 0.907 -0.507 -0.392 2.173 1.303 -1.171 0.840 0.000 1.030 -0.936 1.407 0.000 0.973 -0.773 -0.690 1.551 0.883 0.976 0.986 0.873 0.336 0.918 0.806 +1 1.423 1.049 -1.648 1.105 -0.791 0.488 1.094 -0.311 2.173 1.218 0.504 0.584 2.215 0.277 0.555 0.241 0.000 0.693 -0.047 -0.201 0.000 0.903 0.884 1.213 1.267 0.886 0.895 0.821 +0 0.759 0.896 0.919 1.366 -1.734 0.565 0.676 0.154 0.000 0.579 -0.872 -1.190 2.215 0.695 0.123 -0.635 2.548 0.961 -0.907 0.064 0.000 1.136 1.035 0.990 0.784 0.487 0.700 0.781 +1 1.109 -0.751 0.057 0.535 1.697 0.348 -2.114 0.209 0.000 0.646 -0.625 -1.280 1.107 0.476 1.509 1.123 0.000 1.036 -1.012 1.392 0.000 0.914 0.857 1.062 0.854 0.749 0.859 0.747 +1 0.624 -0.150 1.539 0.554 1.434 0.528 0.985 -1.187 0.000 1.411 0.536 -0.142 1.107 1.313 0.494 1.293 0.000 1.824 0.571 -0.790 3.102 1.135 1.180 0.990 1.515 0.804 1.167 1.044 +0 0.569 2.386 1.580 1.233 -0.223 0.426 0.838 0.922 2.173 0.451 -1.694 -1.301 0.000 0.707 -0.124 -0.199 2.548 0.863 -1.071 0.854 0.000 0.780 1.113 1.159 1.264 0.676 0.917 1.449 +0 0.863 -1.453 0.273 1.565 0.306 1.009 -0.750 -0.070 2.173 3.236 0.454 -1.606 2.215 0.696 -2.400 1.145 0.000 0.854 0.322 0.265 0.000 0.915 1.116 0.986 4.261 3.133 2.832 2.157 +1 0.586 -1.273 1.394 0.040 0.717 0.992 -0.297 0.132 2.173 1.030 0.176 -1.342 0.000 0.988 0.370 -0.731 0.000 1.313 0.129 0.964 3.102 0.857 0.844 0.907 0.846 0.868 0.890 0.756 +1 1.674 -0.130 0.319 1.216 0.949 0.840 -0.960 -0.382 2.173 0.754 -0.284 1.270 1.107 1.872 -0.409 -1.069 0.000 0.998 0.776 1.455 0.000 1.609 1.159 1.063 1.175 1.235 1.016 1.016 +0 0.551 1.170 -0.672 1.159 0.359 0.734 0.593 1.340 0.000 0.813 -0.633 0.108 2.215 1.183 -0.654 -1.264 2.548 0.548 -1.177 -0.394 0.000 1.502 1.153 0.984 0.813 0.985 0.874 0.850 +0 0.389 -0.963 1.296 0.386 -0.569 0.804 -1.083 0.729 0.000 0.708 0.570 -0.022 2.215 0.308 2.066 -0.021 0.000 0.629 -0.614 -1.068 0.000 0.905 1.042 0.986 0.581 0.736 0.666 0.634 +1 0.782 -0.170 -0.049 0.863 1.490 1.023 0.352 -1.291 2.173 0.891 0.106 0.827 2.215 1.264 1.808 -0.002 0.000 1.432 1.041 1.642 0.000 1.143 0.971 1.119 0.956 1.337 0.819 0.759 +1 0.632 -1.118 -1.367 0.996 0.795 1.022 -0.367 -1.664 0.000 1.673 -0.159 0.051 2.215 0.512 0.009 1.316 0.000 0.430 -0.703 1.390 3.102 1.112 0.617 1.021 1.129 0.763 0.877 0.796 +1 0.680 1.366 -0.416 1.004 1.071 1.351 -0.737 0.691 0.000 2.494 1.615 -0.978 0.000 1.081 0.734 0.653 2.548 0.867 0.945 -1.476 3.102 0.671 0.595 1.114 0.697 0.706 0.873 0.810 +0 1.369 0.372 1.691 0.342 -0.556 0.510 2.220 0.416 0.000 0.746 1.225 -0.587 2.215 0.764 1.067 -1.405 2.548 1.085 0.682 0.606 0.000 0.899 0.906 0.990 0.559 0.541 0.675 0.705 +0 0.794 1.949 0.703 0.324 -1.319 0.979 -0.166 1.632 1.087 1.493 0.478 -0.283 2.215 0.972 0.729 1.614 0.000 0.518 -0.186 0.244 0.000 0.849 1.047 0.992 1.153 1.856 1.106 0.890 +0 0.534 -0.706 -1.284 1.574 -0.386 0.963 -1.186 0.795 0.000 1.090 -1.384 1.648 0.000 0.758 -0.493 1.047 0.000 1.181 0.782 -1.090 1.551 0.959 1.039 0.991 0.788 1.021 0.976 0.889 +1 1.950 -0.143 -0.222 0.999 -0.740 0.557 -0.133 1.610 0.000 1.007 0.326 0.946 2.215 1.021 0.549 -1.434 2.548 0.412 0.964 0.419 0.000 0.925 0.978 0.996 0.953 0.916 0.914 0.878 +0 0.415 1.655 -1.666 1.441 -0.762 0.630 1.022 0.873 0.000 0.365 -1.530 1.388 0.000 0.450 1.452 0.366 0.000 0.871 -0.061 -0.898 3.102 0.837 0.820 0.996 0.562 0.429 0.529 0.596 +0 0.301 -0.435 1.176 2.128 -1.580 1.009 -0.016 -1.030 2.173 0.990 -0.748 0.609 0.000 0.370 -2.559 1.214 0.000 1.733 -0.414 -0.050 0.000 0.982 0.637 0.981 1.359 0.477 0.878 0.985 +0 0.630 0.685 0.468 0.919 -1.254 0.516 2.425 0.838 0.000 1.141 -0.024 1.388 2.215 1.271 1.927 -0.691 0.000 1.019 1.328 0.085 0.000 0.874 0.501 1.054 0.854 0.701 0.980 0.822 +1 0.454 -1.220 1.122 0.662 1.024 0.894 -0.380 -0.425 0.000 0.683 -0.914 0.068 0.000 1.402 -0.409 1.181 2.548 1.717 -0.250 -1.428 3.102 0.853 1.096 0.987 0.618 0.848 0.914 0.821 +0 1.904 -0.093 -1.471 1.313 -1.123 3.175 -0.233 0.654 0.000 2.244 0.018 -1.012 2.215 1.520 -0.705 -0.819 0.000 1.227 0.523 0.844 3.102 3.983 2.262 0.989 0.780 1.560 2.031 1.754 +1 0.787 -0.891 0.909 0.873 -1.643 2.202 -0.040 -0.601 0.000 1.164 0.923 0.477 0.000 1.368 0.684 1.033 0.000 1.596 0.457 1.509 3.102 0.949 0.918 0.988 0.586 0.731 0.901 1.072 +1 1.474 0.491 0.744 0.392 -0.420 1.040 1.308 0.470 0.000 1.285 -0.710 -1.403 1.107 0.690 -0.517 -0.637 2.548 0.987 0.084 -0.828 0.000 0.626 0.692 0.987 0.819 0.643 0.904 0.738 +0 0.463 1.242 -1.314 2.791 1.720 0.748 1.157 0.372 0.000 0.601 1.056 -0.308 0.000 0.898 0.007 0.125 1.274 0.934 -1.066 -0.693 3.102 0.822 0.723 0.979 1.207 0.674 0.957 1.029 +0 1.079 -0.575 0.495 0.313 0.078 0.524 0.421 -0.528 0.000 0.869 -0.779 -0.583 0.000 2.022 -1.058 1.205 1.274 1.743 -0.434 -1.414 3.102 0.937 0.942 0.985 1.118 1.114 1.064 0.958 +1 0.920 0.674 0.591 0.417 -1.276 0.574 1.485 1.671 0.000 1.258 1.206 -1.256 0.000 0.849 1.905 0.081 0.000 1.387 0.827 -0.189 3.102 0.899 0.980 0.989 0.654 0.569 0.770 0.688 +1 0.697 0.436 1.342 0.199 -1.430 1.067 -1.009 0.838 2.173 0.982 -1.186 -0.471 0.000 0.841 -0.398 -0.803 0.000 0.644 -0.439 -1.181 3.102 0.695 1.354 0.977 0.536 0.878 0.821 0.744 +1 1.002 0.348 -1.265 1.269 1.351 1.317 0.496 -0.267 2.173 0.646 -0.247 -1.579 0.000 0.580 0.400 0.681 0.000 0.854 -0.422 0.340 3.102 0.930 0.915 1.103 1.352 0.830 0.933 0.883 +1 0.401 2.052 -1.082 1.207 0.977 0.463 1.540 0.750 0.000 1.100 1.368 -0.910 1.107 1.013 0.296 -0.526 2.548 0.414 1.733 -0.760 0.000 0.678 0.831 0.988 0.913 0.742 0.773 0.675 +0 0.645 -1.241 0.485 2.162 1.506 1.458 -1.382 -0.385 0.000 0.868 -1.593 -1.584 2.215 1.103 0.572 0.574 2.548 0.775 -0.624 -0.384 0.000 0.533 1.190 1.302 1.419 1.742 1.274 1.213 +1 0.934 -0.787 1.514 0.149 1.322 0.499 0.464 -0.702 0.000 1.009 1.198 -0.343 2.215 0.447 0.787 0.885 2.548 0.900 -1.064 0.468 0.000 1.348 0.904 0.994 1.143 0.652 0.808 0.786 +1 0.801 0.080 -0.663 0.347 -0.820 0.847 -0.042 0.308 0.000 1.145 1.244 -1.143 2.215 1.157 0.661 0.984 0.000 1.263 0.118 -1.674 0.000 0.987 1.133 0.992 0.606 0.600 0.672 0.660 +0 1.440 -0.196 -0.599 1.265 -0.270 0.918 0.980 1.698 0.000 0.608 -0.048 0.489 2.215 0.658 0.829 0.983 0.000 0.470 1.057 -0.584 3.102 0.843 0.956 0.986 0.546 0.523 0.617 0.790 +1 0.928 0.249 -1.091 0.799 1.270 1.109 -0.534 -0.220 0.000 0.883 -0.368 1.682 2.215 0.734 0.227 0.242 0.000 1.005 0.853 1.170 1.551 0.927 1.173 1.012 0.657 0.749 0.936 0.826 +1 0.417 -0.870 -1.429 0.710 -0.594 1.157 -0.427 -1.612 2.173 1.013 0.668 0.813 0.000 1.154 -1.378 -0.811 0.000 1.113 0.527 0.270 0.000 0.820 1.207 0.979 0.831 0.948 0.944 0.823 +1 0.943 -0.893 -0.091 0.943 -1.307 0.968 -0.080 0.616 0.000 0.773 -1.127 -0.775 0.000 1.554 -0.724 -1.512 1.274 0.710 0.497 1.221 1.551 0.892 0.701 1.162 0.834 0.787 0.794 0.780 +0 0.671 -1.476 -1.489 1.633 -0.633 0.479 -1.200 0.092 0.000 0.804 -0.665 1.629 1.107 0.875 -1.185 1.122 1.274 0.666 -1.675 -0.445 0.000 0.536 0.897 1.011 0.882 0.483 0.727 0.671 +1 0.932 0.036 -1.741 1.614 -1.197 0.845 -0.520 0.913 2.173 0.668 0.388 -0.294 2.215 0.953 0.397 0.185 0.000 0.831 0.852 1.054 0.000 0.751 0.900 0.986 0.855 1.113 0.957 0.838 +1 0.810 1.075 -0.641 1.238 1.640 0.436 2.313 -0.797 0.000 0.875 0.733 0.239 2.215 0.535 2.417 0.696 0.000 0.846 0.050 1.516 3.102 0.858 1.056 1.227 0.729 0.761 0.827 0.787 +0 1.256 0.559 0.839 0.545 1.684 0.596 0.949 -0.895 2.173 0.445 0.967 -0.212 0.000 0.640 0.068 0.987 0.000 1.251 -0.582 -1.032 1.551 0.821 0.849 0.982 0.888 0.854 0.827 0.710 +0 1.305 0.256 0.817 0.433 -1.628 0.698 1.286 -0.805 0.000 1.097 -0.728 -0.882 0.000 2.087 -0.792 0.778 2.548 0.716 -1.157 -0.155 0.000 0.798 0.783 0.989 0.840 1.194 0.905 0.829 +1 0.742 0.523 1.054 1.258 -1.253 0.981 -0.809 1.417 2.173 1.091 0.634 -0.318 0.000 0.806 -0.074 0.290 2.548 0.472 2.198 -0.358 0.000 1.090 0.941 1.170 1.160 1.030 1.250 1.058 +0 0.777 2.260 1.450 1.346 -1.116 0.701 1.864 0.312 0.000 0.772 0.291 1.113 0.000 1.467 1.356 -0.605 2.548 0.846 0.553 0.381 0.000 0.765 0.939 1.046 0.941 0.914 0.801 0.841 +0 0.451 -2.143 -0.834 1.143 0.252 1.105 -0.367 0.946 1.087 1.451 -1.128 -0.730 0.000 0.413 1.078 1.694 0.000 1.474 -0.703 1.739 1.551 1.988 1.327 0.990 1.117 0.941 1.146 1.027 +0 0.818 -0.663 1.109 1.043 1.611 0.778 -2.913 -0.636 0.000 1.510 -0.061 0.308 0.000 1.067 -0.136 -0.453 2.548 2.652 0.039 -1.536 3.102 0.792 0.866 0.976 0.760 1.072 0.975 0.878 +1 1.788 0.170 -0.595 0.320 -0.650 0.803 0.713 1.321 2.173 1.119 -0.299 0.652 1.107 0.937 0.698 -0.704 0.000 0.525 1.308 1.318 0.000 0.815 0.864 0.986 1.087 1.090 1.023 0.894 +1 0.558 -0.627 0.091 1.325 -1.043 0.614 -0.332 0.386 0.000 0.691 0.335 -0.040 0.000 0.921 2.498 1.066 0.000 1.627 0.759 1.491 3.102 0.706 0.938 1.015 1.115 0.793 0.811 0.799 +0 0.741 0.706 1.546 0.985 -0.670 0.879 1.520 -0.497 0.000 1.305 1.542 1.493 0.000 0.511 -0.274 0.652 1.274 0.966 1.260 -0.069 3.102 2.219 1.318 1.079 0.708 0.645 0.957 0.840 +1 0.744 -0.659 0.502 0.939 -0.808 1.124 0.855 0.982 0.000 1.157 -0.776 -0.976 0.000 0.662 -0.139 -0.561 2.548 0.894 -0.604 1.446 0.000 1.085 0.966 1.071 0.588 0.305 0.600 0.589 +1 1.780 0.235 -0.147 0.885 0.597 0.338 0.551 1.357 0.000 1.500 1.307 1.716 2.215 0.715 1.376 -0.480 0.000 1.220 0.869 0.342 0.000 0.983 0.940 1.080 0.761 0.960 1.047 0.871 +1 0.721 -1.425 0.859 0.327 -1.571 1.067 -1.671 -0.301 0.000 0.890 -0.866 -1.106 1.107 0.827 -1.035 1.543 2.548 0.669 -1.794 0.511 0.000 0.915 1.037 0.989 0.770 0.634 0.800 0.758 +0 1.954 -0.564 -1.073 0.619 1.349 2.851 0.887 0.839 0.000 1.725 0.325 -0.817 2.215 1.199 1.304 -0.860 0.000 0.647 0.368 -0.191 3.102 1.596 0.900 1.247 1.124 0.512 0.765 0.842 +0 1.815 -1.727 1.495 1.537 -1.659 1.308 0.802 0.074 0.000 1.014 0.513 -0.236 0.000 1.117 -1.150 0.905 2.548 1.721 0.944 -0.701 1.551 0.760 0.933 1.003 0.847 1.912 1.599 1.859 +0 0.439 1.898 0.440 0.923 -1.309 0.589 -0.361 1.084 0.000 0.874 1.102 -0.024 1.107 0.672 0.243 -0.408 2.548 0.398 0.327 0.021 0.000 0.670 0.945 0.984 1.030 0.458 0.807 0.947 +1 0.597 -1.663 -0.154 0.568 -0.946 1.420 -0.949 1.455 0.000 1.441 -0.599 -0.582 0.000 1.014 -1.290 1.064 2.548 1.675 -1.080 0.650 3.102 0.887 0.965 0.985 0.811 0.368 0.724 0.668 +0 1.971 0.855 0.367 0.062 -1.284 0.873 -0.975 -0.945 0.000 0.922 0.088 0.865 2.215 1.025 -1.606 -1.251 0.000 0.987 -0.066 -1.730 3.102 0.822 0.884 0.986 0.719 0.624 0.954 1.118 +0 2.475 -1.448 -0.413 0.593 -1.307 0.890 -0.545 1.193 2.173 0.741 -1.699 0.980 0.000 0.614 -0.074 -1.439 2.548 0.477 -0.576 -0.653 0.000 0.881 0.856 1.210 0.968 0.676 1.024 0.865 +1 0.939 -0.172 1.040 0.513 1.625 1.879 0.740 -1.057 0.000 1.489 -0.058 0.641 2.215 0.848 0.914 0.063 0.000 0.919 0.074 -0.271 0.000 0.744 1.445 0.994 0.913 0.625 1.322 1.195 +1 1.530 0.627 1.239 0.681 -1.076 0.681 -0.039 -0.386 2.173 0.470 0.094 0.035 0.000 0.452 1.011 -0.274 0.000 0.971 -0.738 1.140 3.102 0.607 0.655 1.231 1.063 0.924 0.820 0.664 +1 0.729 0.312 0.462 1.264 1.574 1.043 -0.424 -1.411 0.000 1.822 -0.677 0.286 2.215 0.552 -0.622 -0.767 0.000 0.864 -0.678 1.218 3.102 0.868 0.718 1.121 1.283 0.846 0.929 0.891 +1 0.879 0.320 -0.376 2.413 -0.045 2.062 -0.411 1.571 0.000 1.145 0.065 -0.994 2.215 1.274 -0.835 -0.179 0.000 2.082 -0.950 -1.092 3.102 0.950 1.329 0.994 1.210 0.902 1.353 1.574 +1 0.669 0.353 -1.717 0.385 -1.529 0.786 0.237 0.715 2.173 0.652 2.350 -1.499 0.000 1.169 0.929 -0.239 1.274 1.153 1.550 -0.582 0.000 0.890 0.946 0.984 0.941 1.025 1.024 1.148 +0 1.539 -0.825 -1.022 0.991 -0.049 1.007 0.232 0.363 2.173 0.778 0.959 0.655 0.000 1.578 -0.855 -1.510 1.274 1.366 0.533 1.276 0.000 0.975 0.989 1.316 1.338 1.840 1.142 1.011 +1 2.168 0.073 -0.688 0.725 -0.384 1.384 1.738 0.897 0.000 1.365 0.620 0.905 2.215 0.614 0.677 -0.853 1.274 0.715 -0.493 -1.424 0.000 0.676 0.960 0.998 0.513 0.974 0.922 0.810 +1 1.896 -0.414 -0.963 0.636 0.168 1.093 0.303 0.748 2.173 0.290 -0.549 -1.624 2.215 0.332 0.902 1.217 0.000 0.544 0.131 -0.511 0.000 0.510 0.645 1.297 0.680 0.791 0.895 0.694 +1 0.855 -1.294 0.416 1.312 1.036 0.257 -2.689 -0.308 0.000 0.920 -1.012 -0.838 2.215 0.568 1.189 -0.558 0.000 0.543 -1.191 1.299 0.000 0.688 0.758 0.995 1.534 1.142 1.158 0.954 +1 4.335 -0.902 -0.530 0.835 -1.042 0.836 -1.622 1.248 0.000 1.913 -0.266 0.005 2.215 4.189 1.139 1.302 0.000 1.276 -0.454 -0.891 0.000 0.822 1.007 1.172 0.755 0.774 0.945 0.807 +0 0.656 0.761 -1.076 0.990 -0.311 0.513 -1.057 0.361 0.000 0.794 0.382 1.265 2.215 1.490 -0.942 -0.820 2.548 0.754 -0.484 1.629 0.000 0.894 0.932 0.990 1.005 1.421 1.246 1.129 +0 2.683 -0.605 0.494 0.890 -0.386 1.066 -0.617 -0.904 2.173 0.939 0.447 0.950 2.215 1.416 -0.399 -1.565 0.000 1.330 -1.534 1.240 0.000 1.067 0.832 1.524 1.198 1.687 1.216 1.095 +1 0.565 -0.095 0.713 0.188 -1.350 0.733 -1.734 -0.394 0.000 0.755 -0.288 -1.514 2.215 0.799 -1.464 1.537 2.548 1.071 -0.961 0.253 0.000 0.850 0.913 0.985 0.736 0.661 0.800 0.695 +1 0.621 -1.016 0.577 0.776 -0.343 0.619 0.017 1.555 0.000 0.560 1.141 -0.607 2.215 1.421 -0.690 -1.669 0.000 1.291 -0.424 0.013 3.102 0.776 1.069 0.993 0.798 0.832 0.896 0.798 +0 1.013 -0.552 1.710 0.265 -0.847 0.588 1.633 1.525 1.087 0.975 -0.568 0.404 2.215 1.554 0.756 -0.158 0.000 1.127 0.070 -1.269 0.000 1.350 1.276 0.990 1.616 1.789 1.246 1.156 +0 2.391 -1.293 0.948 1.210 0.949 1.694 -0.775 -0.618 2.173 0.754 -1.064 -1.365 1.107 0.359 -0.875 -1.729 0.000 0.637 0.448 0.233 0.000 0.675 1.017 0.996 1.130 1.068 1.480 1.157 +1 0.948 -0.014 0.759 0.496 -1.070 0.728 0.192 -0.249 1.087 0.995 0.341 -0.718 0.000 1.259 -0.109 1.494 0.000 0.670 0.740 1.408 1.551 1.617 0.975 0.988 0.770 0.782 0.777 0.699 +0 0.950 -1.377 1.522 1.153 0.926 0.463 -0.557 -0.747 0.000 0.866 0.338 -1.272 1.107 0.635 -2.558 -0.132 0.000 1.089 -0.297 0.784 3.102 1.301 1.048 0.984 1.088 0.899 0.988 0.897 +1 0.529 -0.590 -0.818 1.141 1.610 0.849 0.508 0.943 0.000 1.363 -0.856 -0.392 2.215 0.734 -0.058 1.704 0.000 0.747 0.771 -0.168 3.102 0.989 0.835 0.984 1.085 0.957 1.006 0.871 +1 0.865 -0.429 -0.818 0.711 0.995 0.885 -0.933 -0.535 0.000 0.687 -2.766 0.626 0.000 2.097 -1.141 1.208 2.548 1.836 0.759 -0.514 0.000 0.841 0.931 1.084 0.985 0.854 0.914 0.792 +0 2.584 -0.257 -0.364 2.446 -0.585 1.610 0.245 1.426 2.173 0.801 -0.660 1.577 0.000 0.482 -1.194 1.077 0.000 0.539 0.287 0.313 3.102 0.516 0.953 1.000 0.700 0.833 1.426 1.217 +1 0.626 1.159 -0.123 1.036 -1.551 0.809 0.047 0.559 0.000 0.826 -0.442 1.379 2.215 0.758 0.461 -1.064 2.548 0.590 1.088 -1.052 0.000 1.264 0.946 1.071 1.037 0.798 0.738 0.752 +1 0.685 0.490 0.258 2.455 0.996 1.325 1.383 -1.287 0.000 0.740 2.909 1.030 0.000 1.233 0.078 -0.595 2.548 1.876 0.885 -0.234 3.102 2.610 1.936 1.109 1.200 0.698 1.478 1.467 +1 1.567 0.410 -0.444 0.838 0.329 0.804 -2.841 -0.263 0.000 1.123 -0.900 -1.702 2.215 1.271 -0.449 1.271 0.000 1.168 0.468 1.398 1.551 3.238 2.198 1.020 0.882 0.914 1.662 1.689 +0 0.983 1.151 -0.541 1.881 -1.054 0.766 0.843 1.393 2.173 0.876 2.034 0.671 0.000 0.931 1.825 0.212 0.000 1.074 0.867 -1.294 0.000 0.908 1.005 0.981 0.906 0.452 0.823 0.794 +1 0.749 -0.875 -1.195 0.766 -0.553 0.394 1.605 0.657 0.000 0.879 -0.696 1.599 2.215 0.811 -0.739 0.055 2.548 1.469 -1.674 0.157 0.000 0.816 1.087 0.983 0.899 0.884 0.896 1.080 +0 0.697 0.026 -1.442 0.788 0.333 0.893 -0.052 0.780 0.000 1.273 0.831 -1.102 2.215 0.696 -0.198 -0.021 0.000 1.165 -0.198 1.723 0.000 0.945 0.850 1.026 0.909 0.570 0.903 0.766 +0 1.791 -0.412 1.684 0.433 0.714 0.749 -0.996 -0.356 1.087 0.330 1.009 0.025 2.215 0.539 -0.604 1.012 0.000 0.429 -0.283 -1.057 0.000 0.515 0.657 0.989 0.806 0.919 0.837 0.647 +1 0.880 -1.111 -1.408 2.535 -1.146 1.650 2.276 0.851 0.000 1.416 -1.525 -0.564 0.000 1.318 0.221 0.246 2.548 0.906 -0.210 1.615 3.102 1.712 1.206 0.987 1.966 0.816 1.292 1.182 +0 1.210 0.383 -1.246 0.442 -1.513 0.971 -0.784 0.149 1.087 1.459 -0.110 1.579 2.215 0.592 -0.193 -0.015 0.000 0.932 -0.639 -0.294 0.000 0.916 1.085 0.993 1.196 1.788 1.045 0.890 +1 1.166 0.383 -0.953 0.770 -0.463 1.174 1.892 0.787 0.000 1.117 -0.070 -0.848 2.215 1.190 1.062 1.516 0.000 0.919 1.712 -0.028 0.000 1.071 0.958 0.980 0.771 0.548 1.222 1.051 +0 0.662 0.429 1.475 1.126 0.524 1.216 0.360 -1.648 1.087 1.672 0.175 0.058 2.215 0.735 -0.270 -1.222 0.000 0.488 0.375 0.582 0.000 0.706 0.899 0.989 1.065 2.106 1.136 0.894 +1 1.842 -1.461 0.324 0.521 -1.223 0.971 -0.175 1.405 2.173 0.540 -0.776 -1.251 0.000 0.760 0.386 -0.453 2.548 0.868 0.281 -1.269 0.000 0.535 0.790 1.336 1.140 1.113 1.085 0.911 +0 1.781 0.188 1.399 0.115 0.478 0.706 0.339 -0.364 2.173 0.883 0.814 0.372 2.215 0.694 1.483 -1.025 0.000 1.101 1.350 0.928 0.000 0.931 1.001 0.983 1.035 0.772 0.897 0.898 +1 1.542 0.725 -1.055 0.750 0.365 1.171 1.251 1.177 2.173 1.266 -0.753 -0.372 0.000 0.615 0.711 0.725 0.000 1.246 1.012 -1.536 3.102 0.788 0.900 1.428 0.838 0.820 0.878 0.761 +1 1.283 -1.669 0.700 0.582 -0.312 1.563 1.064 -1.179 0.000 2.011 -0.751 0.245 2.215 1.446 -1.420 -1.636 0.000 1.081 -1.181 1.058 0.000 0.908 0.720 0.986 0.894 0.831 0.959 0.830 +1 1.462 -0.491 1.483 0.307 -1.384 0.931 -1.114 -1.440 0.000 1.163 0.044 0.734 2.215 1.423 -1.297 -0.229 0.000 1.308 -0.330 -0.400 3.102 0.961 0.936 0.986 0.997 0.982 0.867 0.826 +1 0.705 -1.570 0.177 1.320 0.677 0.990 -0.449 -1.302 0.000 0.498 0.136 0.772 1.107 0.751 -0.535 -0.324 0.000 0.685 -0.037 -1.726 0.000 0.785 0.637 0.991 1.543 0.443 1.153 0.967 +1 1.210 -1.128 -0.022 0.570 -1.092 0.738 -1.743 1.661 0.000 0.603 -1.304 1.010 0.000 1.044 -0.185 -0.078 2.548 0.883 -0.846 -1.243 3.102 0.841 0.700 0.987 0.728 0.706 0.782 0.729 +1 1.165 -0.590 1.582 0.222 -0.160 0.930 -1.602 -0.033 0.000 1.069 -0.254 0.951 1.107 1.406 -0.435 -1.094 2.548 1.433 -1.433 -1.366 0.000 1.646 1.404 0.990 0.782 1.263 1.175 0.959 +1 1.449 1.070 0.521 0.371 1.673 2.089 -0.175 -0.979 0.000 2.539 0.602 0.127 2.215 2.081 -0.017 -1.639 0.000 1.040 1.750 1.238 0.000 0.935 0.767 0.982 1.070 0.915 1.183 1.015 +1 0.717 -0.863 1.272 1.338 -0.299 0.845 -0.658 0.194 0.000 1.566 0.017 -1.366 2.215 0.439 -1.509 0.879 0.000 0.841 -0.450 1.129 3.102 0.851 0.660 1.340 1.258 0.858 0.927 0.857 +0 0.837 -0.180 1.222 0.715 -1.527 0.643 -0.963 -0.231 2.173 0.678 -2.048 0.504 0.000 1.049 -0.427 -0.928 2.548 0.375 -2.198 1.707 0.000 0.607 1.020 0.990 1.125 0.656 0.814 0.950 +1 1.432 -0.539 -0.390 0.872 0.266 1.837 -0.474 1.632 0.000 1.443 -0.817 0.128 0.000 1.037 0.585 -0.899 2.548 0.887 -0.981 0.743 0.000 0.816 0.914 0.985 0.851 0.646 0.876 0.774 +0 0.371 -1.945 -1.516 1.786 -1.677 0.495 1.134 0.234 1.087 0.884 0.065 0.222 0.000 0.611 0.361 1.535 1.274 0.737 -0.802 0.224 0.000 0.598 0.664 0.975 0.662 0.684 0.899 0.745 +0 0.510 -0.044 0.945 0.957 -0.555 0.859 0.636 0.131 1.087 0.792 -0.678 -1.614 0.000 0.586 -1.731 1.503 0.000 0.462 0.642 -1.662 3.102 0.750 0.694 0.989 0.716 0.668 0.957 0.824 +1 0.731 0.709 -1.671 1.584 0.953 0.627 2.045 -1.655 0.000 1.078 -0.650 -0.547 0.000 0.685 -0.592 0.772 0.000 1.178 0.537 -0.631 3.102 1.223 0.969 1.045 0.649 0.283 0.613 0.791 +0 1.076 0.028 0.125 1.631 -0.604 0.931 0.793 1.415 0.000 1.008 0.624 -1.311 0.000 1.294 1.626 0.248 0.000 1.117 0.707 1.128 1.551 1.311 0.867 1.120 0.911 0.566 0.762 0.871 +1 0.785 -1.019 0.790 0.688 -1.245 0.669 -2.245 -1.196 0.000 0.560 -0.492 -0.182 2.215 1.150 -0.404 1.195 2.548 0.745 -1.913 0.074 0.000 0.982 1.033 0.988 0.653 0.808 0.911 0.772 +1 0.680 0.577 0.657 0.565 -0.732 0.987 1.558 1.467 0.000 0.910 -0.330 -0.488 0.000 0.750 0.910 0.481 2.548 0.982 0.541 -1.308 3.102 0.544 0.791 0.984 0.637 0.666 0.609 0.626 +0 1.612 0.907 -0.118 1.128 0.649 0.525 -1.080 -1.293 0.000 0.386 -1.181 0.488 0.000 2.023 -0.913 1.520 2.548 1.865 0.752 -0.686 3.102 0.957 0.878 1.192 0.937 2.127 1.493 1.302 +0 1.324 0.518 -1.578 0.692 -0.548 0.973 0.664 -0.698 0.000 1.513 -0.837 0.978 0.000 1.712 -0.406 0.401 2.548 1.711 -0.303 -0.530 0.000 0.972 0.829 1.062 1.201 1.117 0.910 0.822 +0 0.952 -1.832 0.426 1.383 1.005 0.655 1.308 -0.956 0.000 0.503 -0.889 -0.591 1.107 0.277 1.172 1.346 0.000 0.759 1.305 -1.325 0.000 0.974 0.676 0.985 1.044 0.617 0.837 0.857 +1 1.674 0.081 -1.698 1.277 -1.290 0.903 0.286 0.439 2.173 0.641 0.513 1.006 0.000 1.194 -0.183 -0.444 2.548 0.431 0.327 -0.186 0.000 0.604 0.753 0.978 1.386 0.978 1.029 0.833 +0 1.436 0.264 -0.970 1.420 -0.439 1.127 -0.676 0.638 2.173 1.465 -0.458 1.314 2.215 0.629 -1.381 -1.115 0.000 0.377 0.698 -0.052 0.000 0.903 1.000 0.988 1.641 1.099 1.355 1.086 +0 1.763 1.187 0.443 0.876 0.808 0.564 -0.542 -1.087 0.000 0.703 0.665 -1.075 2.215 0.799 1.860 1.442 0.000 0.586 -2.287 -0.446 0.000 1.079 0.913 0.991 1.029 0.717 0.739 0.812 +1 0.951 -0.172 0.304 0.352 0.219 1.295 -0.804 0.925 2.173 1.121 -0.104 -0.956 0.000 1.316 -0.407 -0.269 0.000 1.345 -2.407 -1.548 0.000 0.904 0.980 0.989 0.873 0.951 0.867 0.782 +0 1.690 1.438 0.949 0.772 -1.109 0.720 1.100 0.316 1.087 0.717 -1.314 -1.479 2.215 0.553 -1.301 -0.125 0.000 0.915 -0.059 -1.219 0.000 0.881 0.807 1.520 1.002 1.966 1.437 1.233 +0 1.552 0.228 0.776 1.065 -1.439 0.885 0.266 -1.087 2.173 1.055 -0.966 1.285 0.000 0.881 -0.021 -0.509 0.000 1.628 -0.524 0.302 3.102 0.826 1.113 1.623 1.170 1.343 0.984 0.922 +1 0.730 0.076 -0.712 0.749 1.059 0.626 0.958 0.557 1.087 0.663 -0.371 1.699 0.000 0.785 -1.320 -1.239 2.548 1.186 -0.283 0.285 0.000 1.105 1.040 1.024 0.813 1.593 0.902 0.761 +1 0.562 0.301 -1.472 0.944 -0.477 1.072 0.134 -1.675 0.000 1.040 0.045 -0.025 0.000 1.083 -0.337 -0.560 0.000 0.955 1.001 0.855 3.102 0.968 0.935 0.984 0.894 1.024 0.759 0.709 +1 0.667 -0.357 -1.584 1.287 -0.108 1.336 0.390 -0.162 2.173 1.069 0.453 1.255 2.215 1.344 0.321 -1.683 0.000 0.588 1.082 -1.729 0.000 0.484 0.578 1.247 1.018 1.684 1.022 0.942 +1 0.934 0.111 -1.263 2.339 1.684 0.759 1.012 -0.094 2.173 1.016 -0.570 -0.576 0.000 0.911 0.696 0.805 0.000 1.817 2.067 0.498 0.000 0.958 1.122 0.985 0.746 0.420 0.897 0.874 +0 0.463 0.022 -1.153 0.431 -0.343 0.612 -1.019 -0.760 0.000 1.267 -0.497 0.817 2.215 1.331 0.114 -0.745 0.000 2.139 0.837 1.193 0.000 0.990 0.927 0.984 1.293 0.680 0.923 0.939 +0 0.610 -0.721 0.047 1.126 0.749 0.809 0.640 0.567 1.087 0.703 1.973 0.077 0.000 1.261 -0.972 -1.063 0.000 1.639 -1.359 -1.232 0.000 0.787 0.881 0.993 0.981 1.326 0.937 0.881 +1 1.468 0.169 0.736 0.469 1.395 0.628 -0.368 -1.245 2.173 1.108 -0.791 1.408 2.215 1.204 -0.358 -0.170 0.000 0.588 -0.948 -0.524 0.000 1.009 0.908 0.988 1.031 0.881 0.876 0.898 +1 0.380 1.267 1.541 0.874 0.535 0.950 -0.452 1.424 2.173 1.049 -2.570 -1.208 0.000 1.162 -0.563 0.083 0.000 0.815 -0.269 -0.378 0.000 1.057 0.836 0.988 2.190 0.355 1.564 1.363 +1 1.131 -0.147 0.081 2.441 -1.303 1.351 -0.135 -0.277 2.173 0.607 0.242 1.464 0.000 1.307 0.486 0.137 2.548 2.348 -0.362 1.247 0.000 0.988 1.726 2.182 1.525 0.826 1.352 1.312 +0 0.518 2.315 0.329 1.341 -1.002 1.079 0.369 0.688 0.000 0.643 0.470 -1.616 0.000 0.734 1.039 -0.324 2.548 0.863 0.178 -0.994 3.102 0.742 0.831 1.076 0.991 0.452 0.694 0.875 +0 1.107 1.540 -1.021 0.954 -0.132 0.880 0.266 -1.645 0.000 1.130 0.018 -0.303 0.000 0.966 1.813 1.514 0.000 2.144 2.210 0.483 0.000 0.816 0.776 1.023 0.717 0.523 0.722 0.775 +0 2.144 -0.219 -1.342 0.564 -1.422 0.759 0.172 0.152 2.173 1.137 -0.799 -0.749 2.215 1.417 -0.633 0.593 0.000 2.100 0.169 0.967 0.000 1.084 0.984 0.999 1.002 1.221 1.034 1.091 +1 1.197 -0.081 0.919 0.618 -0.264 0.433 -1.525 0.096 2.173 0.618 -0.334 -0.438 0.000 0.632 -0.709 1.717 2.548 1.101 -2.163 -1.515 0.000 0.813 0.821 1.043 0.688 0.696 0.625 0.593 +1 1.061 -0.736 1.441 0.997 0.986 1.499 -0.812 -0.800 2.173 0.569 -0.875 0.247 2.215 0.916 0.182 0.750 0.000 0.448 -0.348 -1.589 0.000 0.646 1.264 0.997 0.748 1.102 1.033 0.900 +1 0.796 2.221 1.734 0.569 0.178 0.432 0.667 0.774 2.173 0.356 0.846 -1.658 0.000 0.576 0.075 1.243 0.000 1.217 -0.336 -0.537 3.102 0.449 0.686 0.991 0.735 0.833 0.823 0.679 +1 0.586 -1.090 -0.937 1.280 0.093 1.070 -0.254 -1.575 0.000 0.740 -1.108 1.684 0.000 0.939 0.358 0.158 1.274 1.243 -0.716 -0.168 0.000 0.944 0.821 0.988 0.960 0.821 0.848 0.895 +0 0.953 -0.957 0.828 1.468 1.565 0.998 -0.700 -0.762 2.173 0.474 -0.139 0.643 0.000 0.426 -0.026 -0.852 0.000 0.810 0.079 0.122 3.102 0.673 0.808 1.011 0.822 0.785 0.877 0.722 +0 1.127 1.349 -0.949 1.410 -0.310 0.641 -0.372 1.146 1.087 0.442 2.154 0.874 0.000 1.092 0.622 1.157 0.000 1.001 0.087 -0.881 3.102 0.939 0.999 0.985 1.408 0.845 0.935 0.925 +1 0.571 -0.457 -1.127 1.556 -0.934 0.586 0.263 1.002 0.000 0.785 0.834 -1.295 0.000 0.699 -0.105 0.709 2.548 1.227 1.004 1.116 3.102 0.860 0.821 0.992 0.887 0.562 0.747 0.742 +1 1.883 -0.760 1.158 1.173 1.232 0.765 -1.050 -0.035 2.173 1.043 -0.192 -0.977 2.215 0.648 -0.252 1.740 0.000 0.871 -0.488 -0.458 0.000 0.771 0.810 0.991 1.391 1.147 1.126 0.902 +1 0.609 -0.069 -1.094 0.776 0.386 1.228 -0.194 0.991 0.000 1.191 -0.244 -0.561 1.107 1.030 1.151 -1.008 0.000 1.297 -0.224 0.443 0.000 0.919 0.697 0.990 0.772 0.577 0.856 0.752 +0 0.805 1.150 1.305 1.911 0.250 0.471 -1.387 1.229 0.000 0.545 -1.035 -0.475 2.215 0.600 -0.439 -1.436 2.548 1.271 1.168 -0.627 0.000 0.907 0.885 1.399 1.137 0.497 1.029 1.465 +0 0.640 0.104 -1.140 1.011 1.345 0.592 0.328 -0.640 2.173 0.910 -1.596 0.378 2.215 0.384 0.133 0.787 0.000 0.865 -0.529 -0.620 0.000 0.658 0.784 0.988 0.845 1.514 0.929 0.731 +1 0.462 0.106 -0.497 0.753 -0.976 0.819 1.280 0.529 0.000 1.223 1.442 -1.138 2.215 0.528 1.678 0.819 0.000 1.265 0.419 1.570 3.102 0.449 0.833 0.985 0.659 0.924 0.855 0.766 +1 1.193 -0.881 -0.713 0.668 1.275 2.249 1.337 0.157 0.000 1.605 -0.887 1.577 1.107 0.660 -0.415 -1.709 2.548 2.670 -1.459 1.743 0.000 0.672 1.197 1.207 0.679 0.319 0.738 0.692 +0 1.116 1.277 0.465 0.612 -1.667 0.891 0.205 1.204 2.173 0.766 1.342 -0.306 2.215 0.514 -0.063 -1.553 0.000 1.328 0.772 -0.698 0.000 0.792 1.010 1.076 0.751 1.409 0.835 0.764 +0 0.566 -0.800 -0.360 1.095 1.021 0.571 0.507 -1.188 2.173 0.518 -1.229 -1.066 0.000 0.839 0.201 0.979 1.274 0.405 -1.411 0.356 0.000 0.586 0.874 1.033 0.677 0.809 0.708 0.661 +0 0.654 -0.280 -1.240 1.077 -0.091 0.792 0.294 0.708 2.173 0.412 -0.304 1.168 0.000 0.541 0.954 1.561 2.548 0.793 1.879 -1.109 0.000 1.407 0.813 1.000 0.903 0.647 0.710 0.766 +0 0.780 -0.675 1.168 0.783 -1.458 1.448 -0.427 0.471 1.087 0.770 0.130 -1.340 0.000 0.678 -1.095 -1.018 0.000 1.022 -1.272 -0.559 0.000 0.858 0.514 0.987 1.255 0.714 0.869 0.837 +1 0.651 -0.058 -0.599 1.620 0.657 1.273 -1.425 -0.619 0.000 1.827 -0.251 0.944 2.215 3.026 2.116 -1.487 0.000 1.516 -0.575 -0.180 0.000 0.886 1.854 1.287 0.963 0.858 1.446 1.203 +1 0.998 0.385 -0.033 1.496 1.038 0.935 1.078 1.641 2.173 0.683 0.925 0.017 2.215 1.047 0.565 -0.977 0.000 0.876 -2.352 -0.508 0.000 0.497 0.773 1.392 0.846 1.172 0.883 0.805 +0 1.818 1.089 1.715 1.050 -0.959 2.660 0.985 0.185 2.173 2.121 0.205 -1.620 1.107 0.620 0.966 0.474 0.000 0.857 1.063 -0.285 0.000 0.516 1.318 1.280 2.142 3.762 1.971 1.439 +1 1.116 0.725 0.711 1.180 1.478 1.636 -2.434 0.743 0.000 1.676 2.760 -1.144 0.000 0.990 0.595 -1.250 0.000 2.721 0.667 -0.725 3.102 0.700 0.636 1.014 0.843 0.451 0.848 0.696 +1 0.488 0.183 -1.314 1.377 -0.839 1.020 0.497 1.353 2.173 1.009 -0.135 -0.349 0.000 0.720 0.280 0.475 2.548 0.483 0.630 -1.636 0.000 0.934 1.218 0.990 0.864 0.767 0.870 0.835 +0 0.963 0.638 1.729 0.933 0.178 1.512 -0.013 -0.216 2.173 2.022 0.660 1.005 2.215 0.805 0.215 -0.809 0.000 1.309 -0.408 1.688 0.000 0.976 1.275 1.293 1.019 2.466 1.348 1.111 +1 1.042 -0.348 -0.758 1.613 -1.110 1.710 -0.231 -0.539 2.173 1.172 -0.332 1.671 1.107 0.596 -2.224 0.319 0.000 1.442 2.169 0.928 0.000 0.930 1.995 0.999 1.087 1.904 2.008 1.918 +0 0.408 -2.071 1.086 1.695 0.551 0.520 -2.288 -1.098 0.000 0.653 -0.978 -1.530 2.215 0.623 -1.358 -0.614 2.548 0.658 -2.475 -0.044 0.000 0.787 0.879 0.979 0.762 0.525 0.660 0.747 +0 1.003 0.687 -1.158 0.648 0.335 1.169 0.570 0.912 1.087 1.743 -0.246 -0.998 2.215 0.345 2.216 0.807 0.000 0.553 -0.369 0.554 0.000 0.965 0.847 1.088 1.015 2.261 1.201 0.983 +0 0.950 -0.796 0.636 0.391 -1.445 0.262 1.049 -1.691 0.000 0.740 -0.349 -0.902 2.215 1.093 1.759 0.988 0.000 1.162 1.066 -0.425 3.102 0.771 0.813 0.988 0.743 0.841 0.857 0.862 +1 1.089 -0.397 -1.419 0.292 0.574 1.074 -0.134 1.248 0.000 0.926 -0.930 -1.088 1.107 2.335 0.291 0.078 0.000 1.343 -1.071 -0.056 0.000 1.760 1.430 0.983 0.746 0.563 1.071 0.910 +1 0.302 1.168 0.252 0.226 0.045 0.722 -0.160 -0.810 2.173 0.751 -2.118 1.693 0.000 0.544 -1.115 0.888 0.000 1.003 0.038 0.725 3.102 0.812 0.986 0.984 0.745 0.889 0.915 0.789 +1 0.652 0.543 -1.645 0.683 0.227 0.712 -0.044 1.111 2.173 0.948 1.066 -0.356 0.000 0.927 1.266 -1.276 0.000 0.785 0.993 0.411 0.000 0.944 1.058 0.986 0.568 0.737 0.724 0.642 +1 1.559 0.828 -1.298 1.116 -0.771 0.923 0.179 0.519 2.173 0.798 -0.285 -0.051 2.215 2.043 0.635 1.316 0.000 0.597 0.107 0.879 0.000 0.713 0.746 0.979 1.015 0.690 0.974 0.801 +0 0.451 1.024 -1.245 0.855 0.187 0.461 0.596 0.217 2.173 0.689 0.160 -1.070 0.000 1.022 0.988 1.031 0.000 0.826 -1.129 -1.409 1.551 1.006 1.009 0.991 0.723 0.989 0.981 0.863 +0 1.579 0.002 -1.705 0.922 1.466 0.838 -0.732 0.980 2.173 1.163 -0.666 -0.354 0.000 1.051 -1.594 -0.423 0.000 1.539 -0.131 0.212 3.102 0.952 0.972 0.987 1.113 0.845 0.928 1.150 +1 0.809 -0.003 0.685 0.716 -1.615 0.763 -0.973 -1.510 0.000 1.279 0.281 0.131 2.215 0.856 -0.457 -0.792 2.548 0.680 -0.836 1.116 0.000 0.770 0.727 0.988 0.915 0.938 0.905 0.777 +0 0.595 1.803 0.639 0.851 -0.324 0.872 -0.593 0.349 2.173 0.891 -0.767 -1.686 0.000 1.360 1.389 -1.475 2.548 0.639 -0.289 -0.446 0.000 0.910 1.059 0.979 0.889 2.196 1.241 1.098 +0 0.775 0.764 -1.566 0.592 1.375 1.671 -0.304 -0.108 2.173 1.266 -0.480 1.463 2.215 0.785 -1.017 -0.154 0.000 0.886 0.808 0.761 0.000 0.873 0.962 0.991 1.371 2.123 1.172 0.959 +1 0.544 -1.350 -0.754 0.636 0.688 1.030 -1.729 0.976 0.000 1.353 -0.723 -0.407 2.215 1.588 0.296 -1.119 0.000 0.981 -0.656 0.865 3.102 0.769 0.606 0.988 1.053 0.948 0.939 0.837 +0 1.037 0.854 0.339 0.543 0.675 1.020 0.228 1.326 2.173 0.529 -0.171 -0.546 0.000 0.470 0.172 0.575 0.000 0.968 -1.496 -1.171 0.000 0.977 1.221 0.989 1.214 1.363 1.034 1.150 +1 0.669 -0.383 -1.047 0.892 0.760 1.403 -1.426 -1.164 0.000 1.645 -1.142 0.322 2.215 0.390 -0.969 -0.689 0.000 0.408 0.074 0.606 0.000 0.912 0.690 1.069 0.972 0.307 0.755 0.685 +0 0.552 1.123 0.951 1.039 -1.464 0.667 0.616 0.033 2.173 0.576 0.449 1.545 0.000 1.125 1.337 0.336 0.000 1.127 -0.101 -1.002 3.102 1.272 0.967 0.990 0.966 0.816 0.836 0.797 +1 0.933 -0.060 -1.544 0.469 -0.324 1.087 -1.162 0.156 2.173 1.023 -1.258 -1.330 0.000 0.733 -0.558 1.640 0.000 1.179 -0.261 0.827 3.102 0.754 0.896 0.985 1.039 0.863 0.916 0.800 +1 0.648 0.439 0.041 1.678 0.075 0.943 0.638 -0.162 0.000 0.774 0.645 1.562 2.215 1.878 -2.326 1.112 0.000 0.857 -0.085 -1.281 0.000 0.588 0.798 1.007 1.556 0.979 1.139 0.996 +0 1.038 -1.641 0.223 0.790 -0.829 1.051 1.667 1.001 0.000 0.937 0.075 -0.693 0.000 0.521 1.437 0.175 0.000 0.743 -0.313 -1.107 3.102 0.906 1.031 1.018 0.715 0.328 0.826 1.448 +1 1.106 0.303 -0.341 1.223 -0.646 0.469 -0.722 1.392 0.000 1.034 -0.496 0.599 1.107 1.321 0.236 1.545 2.548 0.371 -0.723 -0.797 0.000 0.588 0.677 0.996 1.128 1.056 0.942 0.773 +1 0.299 0.479 1.727 1.487 1.049 1.138 -0.706 -0.896 0.000 0.886 -1.420 -1.493 0.000 1.033 0.265 0.616 2.548 1.431 -1.125 0.675 0.000 1.363 1.149 0.981 0.942 0.733 1.075 1.601 +0 0.907 0.334 -0.775 0.580 -1.343 0.339 0.326 0.912 0.000 0.690 2.388 0.191 0.000 0.878 0.636 1.686 2.548 0.453 1.925 -0.183 0.000 0.855 0.695 0.986 0.562 0.219 0.503 0.591 +1 0.558 0.003 1.133 1.205 0.211 0.695 -1.330 0.741 1.087 1.294 -0.261 -1.009 2.215 0.388 -0.428 -0.165 0.000 1.152 0.337 1.688 0.000 0.881 1.172 0.990 1.224 1.602 1.251 1.090 +1 0.696 0.696 0.731 0.731 -1.701 1.863 0.404 -0.606 2.173 2.216 0.396 0.907 0.000 0.865 1.314 1.335 0.000 0.806 1.042 -0.025 0.000 0.871 0.753 0.992 1.298 0.788 0.895 0.843 +1 0.440 -0.663 0.928 1.982 -0.061 0.484 0.416 -1.585 2.173 1.092 1.215 1.643 1.107 0.512 1.192 0.099 0.000 0.416 0.329 1.392 0.000 0.526 0.644 1.006 1.067 0.519 1.164 0.895 +1 1.078 0.892 0.670 1.059 -0.855 0.948 1.322 -1.627 2.173 1.076 1.386 0.246 0.000 0.837 0.249 0.669 0.000 1.508 0.025 -1.268 1.551 0.836 0.914 1.451 1.094 0.974 0.858 0.788 +1 1.242 0.567 -0.166 1.531 1.725 0.626 1.722 -0.065 0.000 0.978 0.245 1.551 2.215 0.365 0.165 0.746 0.000 0.683 -0.294 -0.744 3.102 0.936 0.855 1.893 1.110 0.687 0.782 0.822 +1 0.538 1.149 1.727 0.914 1.430 0.532 -0.276 0.474 0.000 0.610 -0.806 -0.154 0.000 1.149 0.860 -0.788 2.548 0.721 1.107 -0.643 3.102 0.732 0.885 0.984 0.915 0.162 0.736 0.758 +1 1.693 0.392 1.691 1.251 1.241 1.189 0.564 -0.424 2.173 0.647 0.382 0.145 0.000 0.443 -2.715 -1.276 0.000 0.615 0.647 0.578 3.102 2.285 1.470 1.001 1.530 0.717 1.326 1.373 +0 0.917 -0.250 0.669 0.803 1.350 1.759 -2.767 -0.384 0.000 0.872 -0.621 1.370 0.000 1.085 -0.883 -1.464 1.274 1.234 -0.208 -1.544 3.102 1.104 0.949 0.992 0.783 0.331 0.660 0.634 +1 1.305 1.039 1.573 1.512 -1.052 0.812 0.768 0.675 2.173 0.775 1.466 -0.012 2.215 0.637 0.386 -0.719 0.000 0.454 1.074 0.389 0.000 0.564 0.690 1.365 1.095 0.807 0.948 0.746 +1 0.911 0.276 -0.320 1.795 -0.442 0.603 0.832 1.302 0.000 0.613 -0.271 0.813 2.215 0.752 -0.004 1.535 2.548 0.517 1.076 -1.603 0.000 0.783 0.635 0.976 0.954 0.448 0.750 0.757 +1 0.686 -0.500 0.939 1.350 -1.333 0.745 -0.845 -0.527 2.173 0.846 1.423 -1.663 0.000 0.998 -1.130 0.519 2.548 0.946 1.231 0.935 0.000 0.941 1.519 1.185 0.933 0.894 1.173 1.002 +0 1.154 -0.132 -1.172 1.205 -0.379 0.479 -1.096 -0.309 2.173 1.043 -0.651 0.596 1.107 0.975 -0.205 1.443 0.000 0.816 -1.059 1.388 0.000 1.048 0.959 1.072 1.092 0.791 0.781 0.764 +1 0.895 -1.061 0.742 0.840 -1.061 1.095 0.918 -0.389 2.173 0.839 0.630 -1.238 0.000 0.934 0.439 -1.651 0.000 0.673 0.306 0.526 1.551 0.715 1.312 1.200 0.758 0.715 1.035 0.956 +1 1.776 1.181 -1.527 0.775 -1.263 0.720 -0.094 -0.055 2.173 0.604 0.422 0.387 0.000 0.604 1.440 0.657 2.548 0.448 1.799 1.255 0.000 0.815 0.870 0.984 0.858 0.915 0.924 0.795 +1 0.999 -2.310 0.531 0.385 -0.514 0.448 1.585 -1.032 0.000 0.688 -0.803 -1.696 0.000 0.773 -0.943 0.955 0.000 0.621 -2.074 1.410 0.000 0.843 0.813 0.979 0.653 0.223 0.535 0.582 +1 0.879 1.530 -0.715 0.825 0.221 0.556 1.070 1.175 2.173 0.480 1.791 0.360 0.000 1.072 0.322 -1.236 2.548 0.379 -1.707 -1.581 0.000 1.984 1.301 0.986 0.890 0.868 0.910 0.978 +1 2.368 0.796 0.542 1.064 1.237 0.410 1.171 -1.301 0.000 0.462 -0.457 1.680 0.000 1.201 0.601 -0.696 2.548 0.616 -0.965 -1.238 3.102 0.945 0.820 1.291 1.188 0.750 0.978 0.874 +1 1.663 -0.508 0.134 0.601 0.287 0.795 -0.459 1.634 0.000 1.157 0.111 -0.902 2.215 0.776 0.324 0.912 2.548 0.449 0.664 -1.671 0.000 0.631 0.923 1.002 0.701 1.013 0.797 0.802 +0 0.413 0.203 1.624 1.527 1.027 0.677 -0.037 -0.241 2.173 1.438 0.046 -1.649 2.215 1.186 1.332 -0.158 0.000 0.626 1.445 -0.770 0.000 0.923 0.949 0.983 1.040 1.387 0.999 1.078 +1 1.084 0.762 1.222 0.897 -1.221 0.584 0.973 0.305 0.000 0.424 -0.638 -0.397 0.000 0.739 0.724 -0.945 2.548 1.455 0.211 -0.150 0.000 0.886 0.859 1.104 0.672 0.797 0.789 0.882 +1 0.952 -0.757 1.033 0.522 -0.883 0.821 -0.303 1.364 2.173 0.752 -0.788 -0.168 0.000 1.113 0.237 -0.274 0.000 0.976 -1.176 -1.303 3.102 0.799 0.935 0.987 0.686 0.842 0.872 0.742 +1 1.502 1.209 0.702 0.759 1.018 0.895 1.605 -0.734 0.000 0.429 0.898 1.597 0.000 0.429 2.044 -1.154 0.000 0.453 2.215 0.697 0.000 1.074 0.777 0.983 0.613 0.241 0.480 0.681 +0 0.510 -0.845 -0.825 0.923 1.316 0.627 0.712 0.285 2.173 0.923 -1.091 -1.590 2.215 0.531 -2.126 -0.594 0.000 0.983 -0.569 0.180 0.000 0.896 0.926 0.987 0.875 1.622 1.032 0.863 +0 0.937 0.030 -0.446 0.814 1.215 0.240 0.395 1.715 0.000 0.525 -0.233 1.012 2.215 0.735 0.064 0.077 2.548 1.730 -0.389 -0.968 0.000 0.838 0.707 1.207 0.692 0.503 0.514 0.522 +1 1.154 -0.586 -0.910 1.279 -0.295 1.508 -0.209 1.276 2.173 0.619 -0.247 0.484 0.000 0.289 2.100 -0.482 0.000 0.553 -0.039 -0.364 3.102 1.186 0.709 0.984 1.547 0.965 0.977 0.926 +1 1.046 -0.419 -0.646 0.379 -0.816 0.884 -0.646 1.732 0.000 2.073 -1.193 -0.000 0.000 1.723 -0.541 1.309 2.548 0.396 -2.305 -0.722 0.000 1.230 1.440 0.988 1.139 0.682 1.164 1.099 +0 1.434 -0.903 -0.793 0.328 0.160 0.625 -0.750 -1.657 0.000 0.810 -0.641 1.026 2.215 0.901 0.306 0.206 2.548 0.445 -0.961 -0.075 0.000 0.813 0.902 0.988 0.900 0.772 0.714 0.667 +0 1.759 1.471 -1.264 1.191 1.650 0.904 -0.670 0.183 2.173 0.427 1.040 0.708 2.215 0.819 -1.790 -0.173 0.000 0.375 -1.798 1.201 0.000 0.581 0.807 0.988 0.815 0.996 1.383 1.565 +0 1.088 -0.611 1.523 0.421 -0.635 1.282 0.556 -1.466 2.173 1.269 -0.915 -0.515 0.000 2.838 -0.261 0.369 2.548 0.996 -0.471 1.077 0.000 0.872 0.920 0.993 1.208 2.586 1.363 1.051 +0 3.865 -1.225 -0.169 0.486 0.408 2.683 -0.363 1.606 1.087 1.958 -1.265 0.264 2.215 1.240 -2.103 -1.370 0.000 0.837 -0.928 -1.044 0.000 0.784 1.563 0.989 2.964 3.554 2.220 1.832 +0 0.928 0.878 -0.710 2.000 -0.880 0.622 -0.708 1.700 2.173 1.604 -0.162 0.867 0.000 0.733 -0.312 0.269 1.274 0.526 1.809 1.632 0.000 1.113 1.023 0.976 0.885 0.823 0.850 0.960 +0 1.040 0.987 1.700 1.159 -0.835 0.590 -0.071 0.455 0.000 0.777 0.459 0.033 2.215 0.614 1.220 0.784 0.000 1.232 0.106 -1.287 3.102 0.888 0.942 1.151 0.934 0.834 0.690 0.737 +0 2.217 -0.194 0.202 0.677 -0.312 1.576 -0.361 1.544 2.173 0.499 -0.504 -0.912 2.215 0.954 0.392 -0.859 0.000 0.374 0.289 -1.345 0.000 0.281 1.065 0.999 0.800 1.048 1.146 0.916 +0 3.530 -1.640 -0.277 1.379 1.114 3.102 -0.125 1.149 1.087 1.597 -0.746 1.243 1.107 0.826 1.343 -1.556 0.000 1.646 -0.667 -0.759 0.000 1.990 1.895 2.903 3.876 1.104 2.520 2.291 +1 0.656 -0.074 -0.380 0.778 1.296 0.957 -0.518 -0.730 2.173 0.808 0.910 -1.476 0.000 1.184 0.268 1.486 0.000 0.786 1.260 0.584 0.000 0.840 0.850 0.988 0.846 0.838 0.894 0.760 +1 1.246 0.206 -1.344 0.424 -0.901 1.368 0.872 1.430 2.173 1.848 0.918 -0.372 0.000 1.497 0.392 0.981 2.548 1.154 -0.863 0.132 0.000 1.020 1.389 0.984 1.313 0.813 1.255 1.152 +0 0.607 0.109 1.623 0.180 -0.713 0.492 0.652 0.752 0.000 1.722 0.905 -0.711 2.215 0.814 0.403 -1.359 0.000 1.841 1.442 0.404 0.000 0.934 0.907 0.985 1.423 1.404 1.113 1.140 +0 0.686 -1.481 1.477 1.200 -1.048 0.755 -0.235 -1.156 2.173 0.328 0.687 0.285 0.000 1.227 -1.036 0.522 2.548 0.487 -0.995 -0.596 0.000 0.684 0.880 0.988 0.957 1.321 0.916 0.831 +1 0.899 -0.758 0.336 0.892 -1.491 0.954 -0.567 -1.630 0.000 1.337 -0.688 0.071 2.215 1.042 -0.162 -0.929 2.548 0.810 -0.912 1.466 0.000 1.103 0.837 1.237 0.928 1.039 0.878 0.787 +1 1.036 1.431 -0.434 0.735 -1.208 0.853 -0.021 0.667 2.173 0.657 0.803 -1.566 0.000 0.813 0.116 -0.345 0.000 0.803 1.398 1.111 0.000 1.077 1.105 0.986 0.465 0.651 0.733 0.685 +0 1.920 -0.262 -0.501 0.125 -1.455 1.435 1.362 0.847 0.000 0.709 -0.778 -1.399 0.000 0.803 1.271 -0.271 1.274 0.543 1.654 1.534 0.000 0.886 0.980 0.985 0.768 0.448 0.714 1.073 +1 0.935 -0.677 1.227 1.685 0.671 1.040 1.335 -0.993 1.087 0.531 1.327 1.499 0.000 1.149 0.405 -0.811 0.000 0.990 0.792 0.209 3.102 0.915 0.868 0.986 0.828 0.973 1.213 0.983 +1 0.289 -1.962 -0.349 0.403 -0.806 1.102 -0.800 0.764 2.173 0.793 -1.894 -1.180 0.000 0.611 -0.043 -1.295 2.548 0.590 -0.553 0.267 0.000 1.056 0.843 0.978 0.976 1.058 0.859 0.770 +1 0.929 -0.410 -1.322 0.358 0.787 0.907 -0.861 1.236 2.173 1.703 1.927 -0.007 0.000 0.589 -2.041 -0.842 0.000 1.586 -1.496 -1.547 0.000 0.676 1.083 0.991 0.591 0.601 0.731 0.779 +0 0.604 -0.716 -0.886 0.424 0.105 0.489 -0.521 -0.287 2.173 0.617 -0.564 0.672 1.107 1.298 -1.322 1.363 0.000 0.868 -1.460 -1.005 0.000 1.006 1.013 0.982 0.710 0.615 0.708 0.738 +0 1.520 0.177 -0.041 0.385 -1.280 0.763 -0.247 -1.729 0.000 1.466 -0.945 0.210 2.215 0.832 -0.771 -1.434 0.000 1.637 -1.318 1.574 3.102 0.576 0.761 0.986 0.943 1.387 1.006 0.971 +1 1.015 0.441 0.518 0.486 -0.352 0.638 -1.062 1.286 2.173 0.600 0.017 -0.315 0.000 1.295 -0.793 -1.104 1.274 0.554 0.676 0.687 0.000 0.665 0.964 0.987 0.947 0.951 0.808 0.722 +1 0.930 -0.507 -0.821 1.348 1.604 0.279 -0.406 -0.183 0.000 0.620 0.573 -0.425 2.215 0.952 0.460 1.508 1.274 0.958 -1.824 0.475 0.000 0.912 1.111 1.267 0.839 0.805 0.898 0.846 +0 1.520 -0.439 1.458 0.798 1.134 0.687 -0.530 -0.491 0.000 0.494 0.426 0.417 2.215 0.316 1.026 -0.442 2.548 0.606 0.683 0.074 0.000 0.866 0.694 0.986 0.731 0.329 0.565 0.661 +1 0.802 -0.480 0.089 0.713 1.225 2.125 0.779 0.466 0.000 2.200 0.875 -1.341 2.215 1.891 0.187 -1.631 2.548 1.643 1.121 -0.408 0.000 0.889 1.109 0.991 0.931 0.951 0.994 0.924 +0 0.547 -1.312 -0.111 1.370 1.409 1.299 2.403 -0.697 0.000 1.469 0.111 -1.584 2.215 2.116 0.956 0.545 0.000 0.945 0.363 0.680 3.102 3.481 2.120 1.175 1.273 0.964 1.864 2.242 +0 0.485 0.296 1.047 0.483 0.932 0.500 -0.995 -0.115 2.173 0.750 0.038 -1.036 2.215 0.655 1.879 -1.513 0.000 0.578 0.760 -1.625 0.000 0.697 0.729 0.987 0.874 0.827 0.685 0.607 +0 0.806 -0.819 -0.676 0.840 -1.487 1.184 -0.611 -0.043 2.173 1.369 -0.494 1.612 2.215 1.187 -0.002 0.484 0.000 0.483 -0.102 1.476 0.000 0.653 0.913 0.989 1.132 1.870 1.092 0.940 +0 0.819 0.204 0.528 0.681 0.399 0.364 -0.645 -0.344 1.087 1.127 -0.122 -0.919 2.215 0.855 -0.996 1.038 0.000 1.343 -0.409 -1.585 0.000 0.904 1.033 0.990 0.902 0.530 0.859 0.903 +1 0.808 0.326 -1.705 0.460 0.211 0.703 -0.672 0.739 2.173 0.907 0.208 -0.624 0.000 1.145 -0.488 -1.145 0.000 0.995 -0.784 0.255 0.000 0.927 0.718 0.989 0.740 0.531 0.760 0.669 +1 0.794 -1.488 -0.678 0.386 -0.735 0.667 0.554 1.703 2.173 0.698 0.322 0.659 0.000 0.555 -0.807 0.380 0.000 0.616 -0.439 1.695 3.102 0.662 0.928 0.979 0.584 0.387 0.652 0.665 +1 0.299 1.251 -1.641 0.713 -0.331 0.875 -0.458 -1.640 0.000 1.323 -0.835 0.371 0.000 0.711 -1.060 1.310 2.548 1.053 -0.014 -0.401 3.102 2.266 1.302 0.991 0.546 0.773 0.889 0.779 +1 0.770 0.238 -1.724 0.857 -0.297 1.253 -0.041 0.474 1.087 1.185 1.875 1.483 0.000 1.244 -0.300 -0.849 0.000 1.078 0.390 -0.298 1.551 0.632 0.565 1.080 1.016 0.850 0.877 0.765 +1 0.729 -0.410 -0.092 0.613 -1.276 1.212 0.098 1.722 2.173 0.845 0.853 0.273 0.000 0.736 0.321 -0.329 0.000 0.539 -0.419 0.514 3.102 0.703 0.553 0.993 0.951 0.803 0.840 0.733 +1 1.380 1.749 1.237 0.261 -0.646 1.317 0.820 0.146 2.173 1.013 2.789 -1.036 0.000 0.890 1.811 -1.211 0.000 0.919 0.822 1.088 1.551 0.702 1.069 0.992 1.161 0.878 1.293 1.062 +0 1.212 -0.170 -0.364 0.344 -0.602 0.649 -1.609 0.900 0.000 1.054 -0.528 -0.579 2.215 1.369 -0.245 -1.438 0.000 2.053 0.221 0.656 3.102 1.092 1.030 0.986 1.039 1.313 0.977 0.859 +0 2.060 0.441 0.342 0.723 -0.460 1.045 0.925 -1.156 0.000 1.269 -0.149 0.971 2.215 0.617 0.134 -1.540 0.000 0.745 -0.585 -0.977 3.102 0.761 0.753 1.117 1.061 0.897 0.920 0.955 +1 0.897 2.121 0.592 1.129 1.328 1.486 0.322 -1.045 0.000 1.498 1.112 0.683 1.107 0.730 -1.578 -1.656 0.000 0.646 -1.104 -0.347 3.102 1.277 1.218 0.984 0.790 1.568 1.331 1.300 +1 0.911 1.825 1.033 0.276 0.217 0.779 0.936 -0.498 0.000 0.897 0.972 -1.069 0.000 1.146 1.040 1.347 2.548 1.413 0.870 0.427 1.551 0.875 1.004 0.999 0.625 0.719 0.818 0.741 +0 0.568 0.545 -1.368 3.152 -0.728 1.429 0.490 0.741 2.173 0.530 0.157 0.252 0.000 1.888 1.186 1.644 0.000 1.628 1.345 -0.333 3.102 0.962 1.251 1.012 1.860 1.635 1.343 1.246 +0 0.495 -0.522 0.831 0.778 -1.338 0.416 -1.958 -0.059 0.000 0.588 0.936 -1.432 1.107 0.561 0.428 -0.195 1.274 0.465 1.257 0.828 0.000 1.922 1.177 0.986 1.067 0.568 0.925 0.873 +1 2.287 -0.206 -0.855 1.496 0.296 1.100 0.074 0.650 0.000 0.942 0.181 -1.407 0.000 0.991 -0.509 1.578 2.548 0.374 -0.678 -1.486 0.000 1.023 0.933 2.208 1.345 1.049 0.985 0.934 +1 0.487 1.476 -0.178 0.848 -1.075 0.872 0.103 0.873 0.000 1.173 0.796 -0.840 2.215 1.040 0.547 1.395 0.000 1.247 0.393 0.039 3.102 0.886 0.907 0.981 0.624 0.800 0.918 0.798 +1 1.847 -0.658 -0.506 0.289 -1.092 1.252 -0.533 1.050 2.173 0.590 -1.709 1.563 0.000 0.356 -1.737 -1.208 0.000 0.644 -1.067 -1.351 3.102 0.743 0.921 0.989 0.586 0.867 0.917 0.763 +1 0.916 -1.572 1.325 0.251 -0.371 0.624 -1.203 -0.810 0.000 1.074 -0.130 1.187 2.215 1.870 -0.493 0.160 2.548 0.854 -1.943 -1.216 0.000 0.768 1.293 0.992 0.778 1.242 1.104 0.905 +1 0.632 1.011 -1.116 1.253 1.276 0.877 0.580 0.117 2.173 1.001 0.446 -0.349 0.000 1.070 0.747 -1.429 2.548 1.088 -0.787 1.509 0.000 1.703 1.239 1.028 1.034 1.196 0.965 0.906 +1 0.496 -0.645 -0.882 0.955 1.070 0.602 0.041 -1.731 0.000 1.136 -0.089 -0.037 2.215 0.682 1.005 0.572 2.548 0.638 0.757 -1.152 0.000 0.916 1.059 0.986 0.949 0.768 0.880 0.814 +0 1.619 0.557 -1.233 1.899 -0.925 2.482 0.130 0.869 1.087 1.486 0.724 -0.510 2.215 0.758 0.402 -0.067 0.000 0.550 1.708 -1.484 0.000 0.933 0.821 0.976 2.565 2.818 1.887 1.428 +1 0.610 -1.762 -0.635 0.832 0.164 0.988 -0.941 1.129 0.000 0.933 -0.445 -0.569 2.215 0.874 0.405 -1.210 0.000 0.700 2.340 -0.587 0.000 1.158 0.946 0.996 0.661 0.712 0.628 0.639 +0 0.951 1.868 -0.565 1.429 -1.374 1.180 0.888 0.859 2.173 0.590 -0.433 0.306 2.215 0.705 0.993 -1.305 0.000 0.960 1.256 0.216 0.000 0.910 1.003 1.075 1.473 1.065 1.261 0.995 +0 1.031 0.730 0.885 1.183 -0.781 0.457 1.358 -0.144 2.173 0.907 0.032 -1.634 2.215 0.618 0.518 -0.807 0.000 0.673 1.537 1.003 0.000 0.860 0.852 1.527 0.875 1.147 0.810 0.694 +1 0.801 -0.320 -0.450 0.124 -1.296 0.862 0.546 0.511 1.087 1.553 0.375 1.646 0.000 1.094 0.281 0.029 0.000 0.956 1.089 1.405 3.102 0.861 0.879 0.993 0.728 0.785 0.690 0.643 +1 0.748 1.009 -0.466 1.164 1.214 0.684 1.051 1.639 2.173 0.649 0.309 0.300 0.000 0.696 -0.611 -0.612 1.274 0.600 -2.133 0.054 0.000 1.630 1.009 1.290 0.807 1.141 1.121 1.083 +1 1.193 -1.403 -0.085 0.816 -1.139 0.949 -0.738 0.008 2.173 1.086 -1.325 -1.530 0.000 1.686 -2.134 0.954 0.000 0.816 -0.341 -0.807 3.102 1.944 1.422 1.111 0.870 0.643 1.204 1.008 +1 1.839 -1.074 1.122 0.806 1.498 1.029 -1.038 -0.306 2.173 0.822 1.729 -0.471 0.000 1.135 -0.985 -1.399 0.000 1.271 -1.652 0.831 0.000 1.349 0.839 1.001 1.404 0.705 0.919 0.855 +1 1.063 1.222 0.546 1.183 1.151 1.248 -0.357 0.025 0.000 1.352 0.337 -1.118 2.215 0.570 0.225 1.504 2.548 0.389 1.878 0.147 0.000 1.827 1.229 0.981 1.259 0.657 1.032 1.005 +1 1.111 -1.797 0.036 0.529 -0.940 0.751 -1.832 0.580 0.000 0.509 -1.418 1.428 2.215 1.410 -0.030 -1.366 2.548 0.694 -0.463 1.197 0.000 0.961 0.685 0.986 1.125 0.874 0.877 0.822 +1 0.436 1.636 1.156 0.631 -0.584 0.744 0.446 1.137 1.087 0.955 0.376 -1.404 1.107 0.816 0.640 -0.252 0.000 0.622 0.678 0.130 0.000 0.558 0.922 0.981 0.762 0.933 0.806 0.689 +1 1.699 0.749 0.180 0.391 1.545 0.379 -1.137 -0.713 2.173 1.090 0.368 -1.426 2.215 0.672 -0.754 0.404 0.000 0.488 -0.996 1.387 0.000 0.503 0.933 1.064 1.051 0.976 0.902 0.789 +1 0.712 1.437 -0.315 0.527 -1.501 0.490 -1.566 -0.437 1.087 0.450 -0.086 0.472 0.000 0.632 -0.536 1.711 2.548 0.972 0.194 1.352 0.000 0.630 0.962 0.992 1.195 0.740 1.485 1.142 +1 1.067 0.978 0.296 0.821 1.644 1.242 0.979 1.709 2.173 0.908 0.877 -0.734 0.000 0.726 1.093 0.830 2.548 1.254 -0.110 -0.301 0.000 0.919 0.947 1.216 1.010 0.853 0.951 0.848 +1 2.015 -0.442 -0.686 0.868 0.486 0.329 1.213 -1.220 0.000 1.093 0.182 1.307 2.215 0.429 -0.187 0.882 0.000 0.395 0.669 -0.501 0.000 0.828 0.740 1.596 0.855 0.595 0.861 0.756 +1 0.369 -1.946 -1.683 0.662 0.100 0.696 -1.520 -0.623 0.000 1.639 -0.705 1.190 0.000 1.087 -2.389 -0.667 0.000 1.033 -0.630 -1.279 3.102 0.897 0.920 0.980 0.587 0.720 0.815 0.735 +1 1.133 0.679 -1.641 1.875 -1.571 1.967 1.177 0.287 0.000 0.867 2.400 1.151 0.000 1.338 -0.202 -1.070 2.548 1.564 -1.309 -0.843 3.102 1.791 1.956 1.001 1.346 0.836 1.803 1.601 +1 1.279 -0.508 -1.397 0.827 -0.518 0.955 -0.014 -0.854 1.087 0.733 -1.671 0.266 0.000 0.906 -0.311 1.592 2.548 0.952 1.784 0.625 0.000 0.826 0.916 1.015 0.691 0.953 0.943 0.818 +0 0.593 1.520 0.195 0.986 -1.719 0.901 0.099 0.068 2.173 0.869 1.364 -0.191 0.000 1.478 -0.479 1.319 2.548 1.914 -0.832 -1.567 0.000 0.769 1.118 1.047 1.300 1.376 1.167 1.046 +1 0.806 0.343 0.691 1.028 -1.710 0.876 -0.303 0.237 2.173 0.912 1.447 -0.795 0.000 0.597 -0.293 -1.563 1.274 0.397 0.279 -1.466 0.000 0.653 0.749 1.047 0.979 0.900 0.866 0.789 +1 0.645 -1.552 0.848 0.814 -0.964 1.174 -1.072 -1.720 2.173 1.398 -0.661 -1.308 2.215 2.026 -0.015 0.443 0.000 2.718 0.270 -0.077 0.000 0.935 1.633 1.002 0.872 0.784 1.289 1.105 +1 0.862 -0.460 0.781 1.114 -0.301 0.960 -0.170 -1.666 0.000 1.435 -1.251 0.024 2.215 0.969 -1.776 1.203 0.000 0.823 -0.013 -0.717 3.102 1.978 1.305 1.123 0.867 0.902 1.168 0.998 +1 0.799 -0.496 1.631 0.505 1.055 2.129 -0.470 0.093 0.000 1.757 -0.395 -1.518 0.000 1.672 0.337 -1.476 2.548 1.597 0.961 0.797 0.000 0.937 0.959 0.980 0.732 0.636 1.127 1.001 +1 1.883 0.739 -1.420 1.654 0.888 0.328 0.103 -1.184 0.000 0.646 0.844 -0.333 0.000 1.210 -0.167 -0.383 1.274 1.231 -0.634 0.686 3.102 0.779 0.938 2.134 1.481 0.813 1.119 0.928 +0 1.042 -1.796 -1.740 0.744 -0.539 1.752 1.248 0.027 0.000 2.031 -1.125 1.676 2.215 0.516 1.901 -0.225 0.000 0.620 0.533 1.352 3.102 0.824 0.944 1.077 1.012 1.079 2.281 2.187 +1 1.763 1.357 -1.215 0.237 -0.008 0.816 1.278 0.268 2.173 0.394 0.314 0.241 0.000 0.540 2.243 1.471 0.000 0.847 0.376 1.437 1.551 1.109 0.881 0.989 0.683 0.854 0.753 0.699 +0 1.048 0.167 1.301 0.879 -0.834 0.753 0.035 -0.849 0.000 0.995 -0.663 -0.531 0.000 2.804 -1.293 1.050 0.000 1.763 -0.006 0.176 3.102 0.852 0.989 1.248 0.719 1.032 0.749 0.749 +0 0.772 -1.315 0.988 0.614 -0.389 1.371 -1.362 -0.232 2.173 0.884 -2.188 1.679 0.000 1.084 -2.520 -1.257 0.000 1.347 -0.988 1.448 3.102 0.815 0.912 0.983 0.891 1.442 1.169 0.942 +1 0.830 1.685 -0.317 1.009 0.743 0.922 -0.428 1.680 0.000 1.237 0.735 0.026 2.215 1.311 1.807 1.113 0.000 1.210 1.379 -1.370 0.000 0.807 0.943 1.035 0.855 0.754 0.954 1.042 +0 0.438 -0.581 -1.598 1.106 0.362 0.900 -0.164 -1.394 2.173 0.636 0.517 0.367 0.000 0.803 0.589 -1.005 2.548 0.736 0.008 1.362 0.000 0.735 0.980 0.989 0.747 0.572 0.683 0.647 +1 1.208 -1.938 0.301 0.617 1.694 0.651 0.087 0.301 1.087 0.737 -1.155 -1.454 0.000 0.841 -0.535 -1.140 2.548 0.465 -1.413 1.263 0.000 0.525 1.045 1.137 0.941 0.943 0.948 0.797 +0 1.009 0.489 -0.344 0.186 1.110 1.015 0.395 -1.268 1.087 0.755 0.124 0.525 1.107 0.407 -0.249 -0.562 0.000 0.779 -0.857 1.550 0.000 0.635 0.807 0.988 0.750 1.298 0.814 0.732 +0 0.332 -0.778 -0.149 1.561 1.368 0.991 -0.288 0.515 2.173 0.895 -0.173 -0.683 1.107 0.672 -0.894 -0.348 0.000 1.190 -2.350 -1.613 0.000 0.744 0.926 0.988 0.926 1.224 0.836 0.741 +1 0.363 1.356 1.085 1.256 0.275 1.007 0.365 -1.242 0.000 1.293 0.655 0.687 2.215 0.992 -0.142 -0.724 0.000 0.774 1.065 -1.575 3.102 0.941 0.766 0.986 0.672 0.853 0.945 0.859 +0 1.176 0.484 -1.026 1.331 -1.547 0.532 0.422 0.827 2.173 1.039 -1.007 1.358 1.107 1.281 -0.297 -0.239 0.000 1.843 0.669 -0.376 0.000 0.990 0.872 0.996 1.120 1.008 0.909 0.888 +1 1.259 -1.345 1.469 0.534 -0.900 1.459 -2.898 0.759 0.000 2.024 -1.099 -0.817 1.107 0.855 -0.624 1.630 0.000 2.036 -0.473 -0.282 3.102 3.096 2.726 0.990 1.076 0.998 2.072 1.571 +0 1.235 0.497 -0.416 0.199 0.552 0.635 1.995 -1.100 0.000 0.891 0.123 0.941 1.107 1.358 -1.445 0.873 0.000 0.998 0.956 -0.477 0.000 0.846 0.884 0.989 0.847 0.636 0.878 0.827 +1 0.910 1.750 -0.856 1.089 -1.368 0.842 0.083 1.142 2.173 0.472 0.358 0.473 2.215 0.292 1.583 -0.477 0.000 0.414 2.071 -0.488 0.000 0.914 0.811 0.976 1.151 0.543 1.279 1.083 +0 2.233 0.447 1.075 1.085 -0.234 2.101 -0.109 -0.154 0.000 1.976 -0.249 1.414 1.107 1.123 0.113 -0.847 0.000 2.001 0.377 -1.424 3.102 0.843 0.785 1.993 1.519 1.184 1.154 1.033 +1 0.355 -2.258 -0.727 0.916 1.169 1.029 -1.102 -0.624 2.173 0.416 -2.371 1.191 0.000 0.560 -0.656 -0.320 0.000 0.681 1.521 1.707 0.000 1.020 0.995 0.991 0.572 0.749 0.660 0.634 +0 0.312 1.518 -0.017 1.033 1.697 1.179 0.583 0.073 2.173 0.873 -0.603 -1.708 2.215 0.631 -1.421 1.234 0.000 0.601 0.526 -0.828 0.000 1.113 0.799 0.989 1.036 1.773 1.041 0.892 +0 1.134 0.540 0.211 0.764 0.174 1.170 -0.832 -1.057 2.173 1.225 -0.467 1.597 0.000 1.099 0.245 0.907 2.548 0.786 -0.703 0.014 0.000 1.286 0.988 0.983 1.345 1.611 1.048 0.962 +1 1.043 -0.499 1.052 0.746 0.285 0.667 -0.071 -1.052 2.173 0.662 2.284 -1.676 0.000 0.873 -0.518 0.084 1.274 1.043 -2.102 0.883 0.000 0.970 0.943 0.989 0.947 0.847 0.902 0.899 +1 0.409 1.029 -0.046 1.288 0.841 0.940 0.643 -1.218 0.000 1.298 0.371 0.025 2.215 0.804 1.194 -1.713 0.000 0.495 -0.927 1.021 3.102 0.855 0.979 0.987 0.797 0.820 0.941 0.859 +0 1.599 -0.018 0.014 0.778 -1.280 0.849 -0.839 1.739 2.173 1.350 0.311 -0.859 1.107 1.771 -0.740 0.658 0.000 0.551 -0.125 1.633 0.000 0.916 1.015 1.420 0.977 1.491 1.081 0.995 +0 1.974 -0.448 -0.526 1.002 -0.852 0.876 -0.634 1.166 2.173 0.415 -1.481 0.271 2.215 0.901 0.037 1.547 0.000 0.607 -0.573 0.268 0.000 0.804 0.710 0.987 1.401 0.759 0.983 0.820 +0 1.430 0.120 0.450 1.019 -1.307 0.893 -0.345 -1.481 1.087 1.210 0.138 -0.590 2.215 0.716 1.340 1.072 0.000 0.855 -0.465 1.272 0.000 1.047 1.110 1.673 1.103 1.165 0.946 0.904 +0 1.815 0.926 -0.662 0.132 -0.394 1.203 1.021 0.705 0.000 0.851 0.540 1.095 0.000 1.735 0.749 -1.074 2.548 0.630 0.138 1.638 3.102 0.900 0.753 0.979 0.686 0.579 0.938 0.915 +0 0.841 0.636 1.168 1.073 0.417 0.836 0.149 -1.182 0.000 0.829 -0.105 -1.723 2.215 0.891 1.469 0.202 0.000 0.968 0.309 -0.344 3.102 1.924 1.125 0.986 0.824 0.790 0.852 0.823 +1 1.313 -0.893 -1.258 0.920 -0.460 0.413 0.123 1.358 0.000 1.160 0.363 0.554 2.215 0.324 0.818 0.149 2.548 0.417 0.767 -0.727 0.000 0.684 0.812 1.003 0.767 0.289 0.863 0.698 +1 0.986 -0.412 -1.647 0.678 0.520 0.910 -0.711 0.528 0.000 1.273 0.507 -1.145 2.215 0.798 -0.469 -0.136 2.548 0.373 -0.080 -1.123 0.000 0.930 0.668 1.051 0.975 1.025 0.889 0.781 +1 0.382 -1.484 -0.178 0.268 -0.258 0.791 0.137 -1.506 1.087 1.132 -0.921 0.772 2.215 1.268 0.047 -0.749 0.000 1.064 0.386 0.755 0.000 1.279 1.041 0.983 0.876 1.466 0.989 0.828 +0 1.480 -1.407 -1.710 0.685 -0.874 0.381 0.035 -0.047 2.173 0.554 -1.361 0.097 2.215 0.509 0.565 1.204 0.000 0.724 -0.637 0.939 0.000 0.526 0.704 0.986 1.012 0.536 0.742 0.704 +1 1.057 -0.750 -1.238 1.709 1.552 0.723 1.124 0.143 2.173 0.590 1.808 0.371 0.000 0.488 -0.659 1.666 0.000 0.995 -0.540 -0.599 3.102 0.931 1.075 1.094 1.717 1.068 1.143 1.120 +1 0.673 -0.030 -0.051 1.008 -1.395 0.732 -0.725 0.806 0.000 0.882 0.367 -1.089 2.215 1.510 0.104 0.578 0.000 0.791 -0.843 -0.812 3.102 0.910 0.978 1.067 0.661 0.598 0.895 0.789 +0 0.509 2.377 -1.043 0.824 1.685 1.132 0.635 -0.696 2.173 1.184 0.222 0.997 2.215 1.313 -0.632 0.780 0.000 0.709 0.377 -0.125 0.000 1.008 0.850 0.997 1.015 1.738 1.069 1.045 +0 0.674 0.092 0.431 0.362 1.076 1.068 0.104 0.043 2.173 1.022 -1.504 -1.682 0.000 0.626 -0.494 0.535 2.548 1.326 -1.342 -0.858 0.000 1.027 0.969 0.986 0.922 0.551 1.123 0.919 +1 0.557 -0.410 -0.034 0.825 0.782 1.324 0.211 -0.554 2.173 0.481 -0.954 0.556 0.000 1.195 -0.279 1.252 0.000 1.071 -0.192 -1.481 1.551 0.792 0.722 0.987 1.453 0.976 1.008 0.919 +1 0.301 2.128 1.126 1.421 0.364 0.580 -0.401 -0.190 2.173 0.538 0.145 -1.080 0.000 1.145 -1.000 -1.047 2.548 0.936 -2.157 0.859 0.000 0.868 0.839 0.981 1.411 0.795 0.996 0.851 +0 1.192 -0.449 -1.693 1.264 -1.267 1.166 1.036 0.275 0.000 0.867 0.355 -0.236 0.000 1.510 0.639 1.220 1.274 0.895 0.680 -0.886 3.102 0.814 1.119 0.988 0.665 0.843 0.724 0.749 +0 0.750 1.800 -1.700 1.218 0.896 0.631 -0.063 0.765 2.173 0.960 0.526 -1.076 2.215 0.805 -0.068 0.109 0.000 0.411 0.958 -1.465 0.000 0.751 0.737 0.988 1.041 1.194 0.937 0.787 +1 0.705 -0.898 1.526 0.626 0.688 0.850 0.157 0.353 0.000 1.342 0.490 -1.238 2.215 0.843 -1.412 -0.721 0.000 0.681 0.196 1.248 3.102 0.678 0.604 0.995 0.993 0.686 0.826 0.735 +1 0.661 -2.283 -0.198 1.152 -0.090 1.541 -1.150 -1.728 2.173 0.944 -1.168 0.323 0.000 0.884 -0.314 0.595 2.548 0.903 -0.209 -1.041 0.000 1.127 0.778 0.996 1.496 1.399 1.094 0.936 +0 2.199 1.614 -0.241 1.571 -0.502 0.936 -0.010 1.277 2.173 0.913 1.460 1.511 0.000 0.798 1.263 -1.424 2.548 0.822 0.400 0.860 0.000 0.869 0.968 0.993 0.934 1.073 1.298 1.108 +1 1.191 0.989 0.518 1.176 1.150 0.413 -0.268 1.063 0.000 1.446 -0.464 -0.603 2.215 0.375 -0.248 -0.992 2.548 0.940 0.615 -1.255 0.000 0.966 1.125 0.990 0.825 0.281 1.133 0.941 +1 1.050 -1.317 -0.205 0.725 0.486 0.455 -0.260 0.756 0.000 1.160 -0.952 1.435 1.107 0.314 -0.962 0.007 0.000 0.669 1.544 -0.734 0.000 0.813 1.333 0.992 1.220 1.060 1.010 1.092 +0 0.580 -1.170 0.179 3.078 -0.363 1.778 -0.790 1.458 1.087 0.988 -1.800 1.164 0.000 0.672 -0.779 -0.365 1.274 0.875 -1.647 -0.785 0.000 1.193 0.938 0.985 2.092 1.358 1.323 1.211 +1 1.011 -1.861 -0.189 1.571 -0.818 1.393 -0.656 -1.475 2.173 1.303 1.281 0.233 2.215 1.160 -2.356 0.562 0.000 2.296 -0.989 1.227 0.000 0.947 1.736 0.989 2.881 3.037 2.301 1.989 +0 0.720 0.723 -0.954 0.482 -0.483 0.471 0.969 0.947 2.173 0.605 0.106 -1.387 2.215 0.400 -0.362 0.493 0.000 0.714 0.703 0.242 0.000 0.591 0.630 0.992 0.815 0.763 0.696 0.642 +1 0.561 -0.420 1.258 0.147 -0.322 0.740 0.494 -1.164 0.000 1.141 0.799 -0.712 0.000 0.679 0.136 0.328 1.274 0.663 -1.141 1.570 3.102 0.836 0.937 0.991 0.754 0.629 0.806 0.724 +1 1.002 0.494 0.151 1.539 0.876 1.007 1.178 -0.989 1.087 0.755 0.040 1.274 0.000 0.646 2.427 -0.058 0.000 1.261 0.179 -1.550 3.102 2.097 1.403 1.047 1.371 0.836 1.051 1.032 +1 0.883 0.562 1.699 1.298 1.200 1.130 -0.247 -0.472 2.173 0.774 -0.236 0.480 0.000 0.961 0.332 -1.219 1.274 0.435 -0.170 -1.701 0.000 0.698 0.934 0.989 0.838 0.905 1.117 0.937 +0 0.903 1.260 -1.738 0.151 1.045 0.684 -0.091 0.408 0.000 0.776 -0.095 -0.429 2.215 0.458 -1.085 0.819 0.000 0.377 -2.003 -1.166 0.000 0.686 0.829 0.991 0.842 0.623 0.636 0.686 +1 2.631 1.183 0.485 0.401 -0.144 0.981 0.545 1.675 2.173 1.456 -1.294 -1.231 0.000 0.747 0.623 -0.938 0.000 0.892 0.712 0.207 3.102 0.855 1.026 0.994 0.488 0.972 0.887 0.779 +0 0.402 -1.692 0.078 0.632 -1.720 0.785 -1.067 -0.747 0.000 0.898 0.773 0.695 2.215 1.291 0.563 1.532 2.548 0.542 2.256 0.179 0.000 0.893 0.899 0.995 0.940 0.790 0.758 0.807 +1 0.828 -0.961 -0.164 1.830 -1.374 1.110 -0.991 0.660 1.087 1.310 0.073 -1.368 2.215 0.786 -1.699 0.160 0.000 0.438 1.429 0.048 0.000 1.888 1.441 1.511 1.143 1.987 1.280 1.174 +0 1.285 -1.618 -0.050 0.488 -1.005 0.748 -1.306 -0.923 0.000 1.028 1.738 1.213 0.000 1.776 1.136 1.594 2.548 1.381 -0.005 0.220 3.102 4.556 2.569 0.988 2.740 1.386 1.788 2.032 +1 0.982 1.026 0.656 1.963 -1.379 0.553 0.280 0.942 2.173 0.898 2.721 -0.860 0.000 0.676 -0.067 -0.614 0.000 0.800 -0.612 -1.471 3.102 2.389 1.892 1.858 1.169 0.689 1.346 1.199 +1 1.091 1.280 1.624 1.175 -1.139 0.951 0.962 0.185 2.173 0.955 2.604 0.895 0.000 0.707 0.810 -1.679 0.000 1.522 0.267 -0.433 3.102 1.007 1.012 0.989 1.234 0.795 0.944 0.833 +1 0.966 0.273 0.402 1.043 1.419 1.141 1.634 1.460 2.173 1.050 -0.243 -0.726 0.000 1.049 1.112 0.145 0.000 0.630 1.141 -0.288 3.102 1.044 1.177 1.103 0.724 0.906 0.847 0.822 +0 0.448 1.679 1.504 1.756 -0.115 0.888 -0.269 -1.070 0.000 0.901 -0.260 0.855 2.215 0.306 0.844 0.935 2.548 0.443 -1.456 -1.329 0.000 0.804 1.111 1.221 0.656 0.356 0.872 1.086 +0 0.879 -0.948 0.250 0.758 1.048 1.244 0.589 -1.733 1.087 1.253 -0.218 -0.373 0.000 0.380 0.778 -0.974 0.000 0.706 -0.042 0.226 3.102 0.811 0.576 0.981 1.862 1.026 1.188 1.077 +0 1.412 -0.519 0.589 0.874 -0.316 0.785 0.400 -0.827 0.000 1.766 -0.403 1.612 2.215 0.455 0.529 -0.173 1.274 0.594 1.253 0.268 0.000 1.059 0.620 1.120 1.295 1.073 0.940 0.938 +1 1.307 0.127 0.223 0.472 1.399 1.694 -2.066 0.511 0.000 2.344 0.364 -1.028 2.215 2.016 0.062 -1.515 2.548 0.932 -0.255 1.629 0.000 0.659 0.908 0.985 1.061 1.043 1.034 0.810 +1 0.994 0.562 1.612 0.748 0.186 0.504 0.049 -0.551 2.173 1.022 -0.060 -1.419 0.000 0.479 2.088 -0.185 0.000 1.839 0.059 0.436 3.102 0.768 0.965 1.146 0.792 0.791 0.793 0.721 +0 0.398 0.529 -0.313 5.039 -0.763 1.475 0.033 0.790 0.000 1.848 -0.333 1.128 0.000 1.571 1.478 -1.056 2.548 2.096 0.345 1.138 3.102 1.190 0.909 0.982 0.811 1.555 1.276 1.516 +1 1.436 0.204 0.466 1.121 1.171 1.136 0.153 -1.441 1.087 1.030 1.459 0.235 0.000 1.047 -0.051 -0.516 0.000 0.736 -0.404 -1.238 3.102 0.994 1.067 1.043 0.797 0.361 0.845 0.823 +0 0.423 -0.607 -0.638 0.273 1.480 0.721 -0.608 0.209 2.173 0.977 -0.514 -1.727 2.215 0.690 0.439 0.899 0.000 0.966 1.936 1.378 0.000 1.024 1.432 0.988 0.909 1.217 1.192 0.957 +1 1.279 1.695 0.754 0.976 -1.491 0.699 -1.401 1.398 0.000 0.778 -0.389 -0.673 2.215 0.887 0.547 -1.413 0.000 2.165 0.802 0.136 1.551 0.527 0.912 1.393 1.513 1.162 1.125 0.906 +0 1.500 -1.729 -0.283 0.019 -0.582 0.464 -0.285 -1.665 0.000 0.895 0.072 1.251 2.215 0.572 -1.110 0.721 0.000 1.075 0.147 -0.737 3.102 0.895 0.792 0.998 1.217 0.865 0.892 0.774 +0 1.405 -0.187 1.623 1.148 0.628 0.562 1.633 -1.131 0.000 0.764 1.009 0.257 2.215 0.606 0.696 1.296 0.000 1.730 -0.361 -0.255 1.551 0.981 0.928 1.374 1.078 0.954 0.903 0.960 +0 0.960 -0.237 1.295 0.514 0.684 0.414 0.725 1.453 0.000 0.805 -1.280 -0.623 0.000 1.114 -0.283 -0.315 2.548 0.631 -0.389 0.667 1.551 0.952 0.890 0.979 0.502 0.498 0.568 0.597 +1 0.524 0.870 1.305 1.065 -0.394 2.409 -0.012 -1.642 1.087 3.340 1.512 0.178 0.000 0.686 0.325 -0.480 0.000 1.006 -0.100 0.677 0.000 0.822 0.713 1.034 1.519 0.829 0.971 0.888 +1 1.047 -0.444 -0.637 0.346 0.503 0.951 -0.460 0.555 0.000 0.835 -0.422 1.266 0.000 0.888 0.164 -0.336 2.548 2.631 0.261 -1.469 3.102 1.130 1.077 0.988 1.110 0.998 1.022 0.918 +1 0.649 -1.028 0.278 0.624 -1.366 0.968 -0.061 0.972 0.000 0.731 0.831 -0.275 2.215 0.751 -0.307 -1.193 0.000 0.589 -0.952 -1.349 0.000 1.196 0.874 0.990 1.242 0.697 0.790 0.825 +0 1.609 -0.811 -1.614 0.527 -0.440 0.894 -0.232 1.216 2.173 0.788 -0.564 -0.418 0.000 0.465 0.111 0.677 1.274 1.448 -1.666 -0.272 0.000 1.089 0.938 1.112 0.960 0.403 0.947 0.865 +0 0.633 -0.569 -1.082 0.861 -0.066 0.607 1.311 -1.466 2.173 0.436 1.565 -0.016 0.000 1.002 -0.084 1.031 1.274 0.724 -1.265 -0.342 0.000 1.079 0.804 0.991 1.530 1.062 1.078 0.923 +1 2.545 1.411 -1.622 0.611 1.254 2.032 -0.686 0.063 0.000 1.202 -0.022 -1.354 2.215 0.902 1.518 1.417 0.000 1.004 0.650 -0.342 3.102 0.856 1.004 0.984 0.973 0.884 0.985 0.828 +1 2.268 1.403 -0.518 0.437 -0.299 1.423 -1.183 1.099 0.000 0.682 1.323 -0.856 0.000 0.839 -0.139 -0.911 2.548 0.770 -0.330 0.449 3.102 0.926 0.833 0.992 0.877 0.583 0.746 0.689 +1 0.522 -1.186 0.970 1.017 -1.117 1.086 2.217 -0.099 0.000 2.026 -0.854 1.623 1.107 0.789 0.406 0.328 0.000 0.914 -0.982 0.442 0.000 0.853 0.752 0.986 0.936 0.843 0.881 0.803 +0 0.844 0.260 -0.758 0.919 1.612 0.726 2.215 -0.165 0.000 0.581 -0.146 0.499 0.000 0.656 1.367 0.538 0.000 1.443 -0.281 1.558 3.102 0.875 0.943 1.030 0.574 0.665 0.667 0.636 +0 1.722 -0.697 1.243 0.834 1.258 0.765 -0.485 0.585 2.173 1.837 0.156 -0.913 2.215 0.891 0.788 -0.280 0.000 0.482 -1.043 -0.238 0.000 0.917 0.956 0.993 0.898 1.797 1.348 1.145 +0 0.772 0.305 0.990 1.195 -0.510 1.097 -1.251 0.818 2.173 0.745 -0.892 -0.527 0.000 0.881 0.495 1.720 0.000 1.018 0.602 -1.080 0.000 0.693 0.892 1.299 1.456 1.639 1.078 0.953 +0 0.688 0.216 1.378 0.392 1.109 0.543 -0.854 -0.680 0.000 0.930 -0.549 1.728 2.215 0.844 -0.523 -0.175 2.548 0.655 -1.196 0.413 0.000 0.804 0.893 0.989 0.743 0.932 0.627 0.617 +1 1.047 -0.745 -0.368 1.504 -1.445 1.159 -0.826 1.220 2.173 1.018 -0.507 0.464 0.000 0.524 -2.457 -1.289 0.000 0.509 -1.017 -0.665 0.000 0.865 1.024 1.434 0.858 0.593 0.854 0.785 +0 0.849 0.632 0.655 0.427 -0.225 0.812 -0.749 0.041 0.000 1.112 1.177 -1.698 2.215 1.222 -1.500 -0.706 0.000 0.610 -1.398 0.818 3.102 1.132 0.754 0.994 1.087 1.596 1.430 1.162 +0 0.629 0.004 1.257 1.422 0.356 0.708 -0.664 -1.682 2.173 0.909 1.207 -1.668 0.000 1.006 0.713 -0.157 2.548 0.768 1.084 0.757 0.000 0.889 0.918 0.987 0.949 1.326 0.951 0.915 +1 0.473 -1.476 -1.269 1.489 0.857 0.538 -0.103 -1.303 0.000 0.854 0.777 -0.176 2.215 0.549 0.883 1.105 2.548 0.531 -0.308 0.855 0.000 0.767 0.890 1.094 1.096 0.668 1.092 0.891 +1 1.677 0.129 0.400 0.886 1.061 0.808 -0.882 -1.385 2.173 0.703 -0.689 0.385 0.000 0.917 1.251 -0.693 0.000 1.389 -0.369 -0.570 0.000 0.991 1.077 0.986 0.976 0.949 1.002 0.871 +1 0.403 -1.524 -0.423 0.179 0.134 0.722 -0.265 -1.027 1.087 0.855 -1.027 0.768 0.000 1.299 -0.509 1.611 2.548 1.569 -0.866 0.115 0.000 0.837 1.048 0.984 0.708 0.852 0.893 0.771 +0 0.766 1.705 1.333 1.245 0.465 1.304 0.965 -0.952 2.173 0.759 0.528 0.963 0.000 0.747 -0.319 0.129 2.548 0.500 0.209 1.525 0.000 0.406 1.184 0.991 1.213 1.343 1.194 0.983 +0 1.471 -0.052 0.010 0.417 -1.663 0.795 -0.381 0.576 0.000 1.033 0.571 -1.097 0.000 0.881 -0.509 -1.680 1.274 0.745 -0.209 1.351 3.102 0.903 0.816 1.082 0.668 0.265 0.561 0.609 +0 0.924 0.804 0.949 2.844 0.574 1.006 0.319 -1.077 0.000 0.756 -0.525 -1.423 2.215 1.035 0.808 -0.667 0.000 0.542 1.319 1.316 3.102 0.847 0.921 0.991 0.594 0.819 1.064 1.133 +0 2.039 -0.500 1.510 2.119 1.485 2.993 -0.704 1.712 2.173 3.991 -0.281 -0.192 0.000 1.751 -1.763 0.714 0.000 0.574 0.619 -1.358 0.000 2.015 1.163 0.974 1.157 1.955 2.211 1.913 +0 0.559 -1.689 -0.223 1.257 -0.142 0.458 0.998 0.525 0.000 0.379 0.721 0.878 2.215 1.139 -0.842 -1.510 0.000 0.514 1.216 -1.728 0.000 0.685 0.622 0.984 0.823 0.506 0.651 0.691 +1 1.779 -0.989 -0.983 0.410 -0.168 0.828 -0.107 1.078 2.173 0.347 -1.834 0.879 0.000 0.378 0.032 -0.703 2.548 0.626 -1.652 -0.074 0.000 0.460 0.963 0.991 0.512 0.699 0.758 0.711 +1 0.531 1.348 1.628 1.125 0.808 0.575 1.047 -0.075 0.000 1.004 0.596 -0.642 0.000 1.638 0.311 -1.616 2.548 1.627 0.952 1.172 3.102 0.868 1.144 0.985 0.835 0.895 0.936 0.832 +1 2.287 -0.327 1.010 0.560 0.648 0.492 -0.601 -0.038 0.000 0.697 -1.186 -0.752 2.215 0.822 2.214 -1.395 0.000 1.644 -0.489 -1.352 3.102 0.746 0.715 0.986 1.206 0.587 0.933 0.770 +0 0.757 -1.171 1.622 1.045 0.412 0.508 -0.373 -1.306 0.000 0.857 -1.222 0.699 2.215 0.973 0.306 -0.470 1.274 0.946 -1.543 -1.245 0.000 0.857 0.992 1.092 0.631 1.203 0.860 0.789 +0 1.840 0.130 0.400 0.929 0.338 1.292 -0.661 -1.251 2.173 0.771 -0.334 -0.309 0.000 0.740 -1.042 -1.620 0.000 1.245 -0.175 1.460 3.102 1.172 1.039 0.984 0.888 0.916 1.112 0.955 +1 0.529 -0.260 -0.615 0.935 0.768 0.845 -0.527 0.597 0.000 0.934 0.364 -1.099 0.000 0.620 -1.135 0.846 0.000 0.724 1.184 -1.373 3.102 0.556 1.163 0.990 0.499 0.325 0.696 0.625 +1 0.670 -1.182 -1.325 0.915 1.046 0.571 -0.138 1.443 2.173 0.856 -2.268 -0.118 0.000 0.819 -0.315 -0.330 2.548 0.486 -1.855 -0.628 0.000 0.954 1.052 0.986 0.624 0.856 0.972 0.838 +0 0.413 0.291 0.041 1.193 -0.366 0.434 -0.542 0.367 1.087 0.707 -0.193 1.132 0.000 0.737 -0.859 -1.639 2.548 1.140 0.025 -1.675 0.000 0.685 0.731 0.992 0.765 0.699 0.586 0.652 +1 0.801 1.721 0.108 0.865 -1.604 0.636 -0.492 1.591 2.173 0.638 0.819 -0.945 0.000 0.720 -0.154 0.643 0.000 0.770 0.664 0.238 3.102 0.879 0.680 1.153 1.368 0.865 0.905 0.837 +1 0.725 -1.768 0.455 0.344 -0.315 1.074 -1.423 -0.064 2.173 1.658 -0.936 -1.447 2.215 0.963 -2.331 1.379 0.000 0.517 -0.513 0.694 0.000 0.990 1.196 0.982 1.066 1.919 1.141 0.947 +1 0.554 0.461 -0.626 1.569 1.021 0.508 0.505 -0.987 0.000 0.594 -0.149 -0.880 0.000 0.738 -0.986 0.728 2.548 1.126 0.326 1.213 3.102 0.923 0.957 1.287 0.669 0.634 0.666 0.670 +0 1.347 0.691 -0.587 0.128 -1.020 0.494 -0.152 0.568 1.087 1.015 -0.255 -0.891 0.000 1.171 0.323 -1.648 2.548 2.220 0.885 1.045 0.000 1.224 1.012 0.979 0.797 0.896 0.796 0.710 +1 0.619 -0.955 1.499 2.304 0.646 1.729 -1.755 -0.690 0.000 1.276 0.434 1.140 2.215 0.368 0.144 -1.174 2.548 0.711 0.580 -0.391 0.000 0.183 0.823 1.150 0.833 0.643 0.875 0.788 +0 3.324 -0.673 -0.990 0.444 0.717 2.085 -0.158 1.118 0.000 0.854 2.388 0.184 0.000 0.963 -0.667 -0.377 0.000 0.930 0.078 -1.483 0.000 0.982 0.738 1.683 0.935 0.325 0.728 0.655 +1 0.506 -0.169 0.456 1.688 1.549 2.896 -1.339 1.687 0.000 3.969 -0.389 -0.054 1.107 0.818 -1.375 -0.081 0.000 1.053 -0.051 -0.597 1.551 2.775 2.042 1.067 1.908 0.923 2.424 1.903 +0 1.529 0.612 1.162 0.925 -1.310 1.713 -0.112 1.467 2.173 2.798 -1.190 -0.305 0.000 1.462 -0.076 -0.480 2.548 1.831 -1.483 -0.042 0.000 0.873 1.127 1.306 1.092 1.938 1.782 1.668 +1 0.609 0.022 -0.221 1.247 -1.435 0.511 1.060 0.783 1.087 0.284 -0.889 0.960 0.000 0.551 1.712 -0.446 0.000 0.523 -1.789 0.760 0.000 1.290 0.907 1.072 0.607 0.793 0.661 0.667 +0 0.754 -0.922 -0.779 1.369 1.696 1.165 1.732 -0.227 0.000 0.892 -0.422 1.617 2.215 1.055 -1.325 0.714 2.548 0.899 -0.145 0.297 0.000 0.998 1.901 1.113 0.902 0.930 1.754 1.595 +1 0.801 -1.643 -1.022 0.428 -0.115 0.465 -2.446 -0.140 0.000 0.826 -1.072 1.146 2.215 0.969 -1.933 1.421 0.000 0.889 -2.385 -1.282 0.000 0.864 0.947 0.992 0.813 0.298 0.757 0.724 +1 0.558 1.595 -0.901 1.174 -0.242 0.695 0.478 0.127 0.000 1.061 -0.591 1.006 2.215 0.662 0.612 -1.430 1.274 1.482 0.055 0.898 0.000 0.909 1.069 0.982 0.965 0.946 1.571 1.237 +1 1.571 0.860 -1.020 1.754 1.663 1.193 0.637 1.047 2.173 0.600 -0.436 0.128 0.000 0.953 0.845 -0.632 0.000 0.860 0.923 -0.204 3.102 0.805 0.820 1.522 0.964 1.001 0.980 0.833 +1 0.401 2.244 -1.036 0.387 -0.595 1.097 1.629 1.600 2.173 1.537 0.234 0.146 2.215 1.315 0.016 -1.606 0.000 0.947 1.132 -0.032 0.000 0.371 0.944 0.979 0.978 2.350 1.169 0.944 +0 2.150 -0.120 -0.014 0.601 -0.461 0.843 0.469 1.338 0.000 0.786 0.436 -0.754 2.215 1.344 0.833 -1.252 2.548 1.946 1.240 1.607 0.000 1.137 1.043 0.975 0.834 0.539 0.890 1.135 +0 0.921 0.714 0.347 1.167 1.464 0.900 0.951 -1.423 2.173 0.911 1.700 -0.067 0.000 0.506 2.066 1.493 0.000 0.694 0.645 0.102 3.102 1.062 1.204 1.214 0.666 0.824 0.759 0.766 +0 0.816 -0.033 1.174 0.643 1.055 1.187 -1.315 -0.455 0.000 1.308 -1.020 -1.624 2.215 1.318 -0.503 -0.418 1.274 0.671 -1.267 0.975 0.000 0.809 0.927 0.989 1.174 1.284 1.154 0.928 +0 1.738 -0.994 -1.384 2.604 -1.554 1.229 -1.332 0.369 2.173 0.791 -1.036 0.747 0.000 1.634 -1.641 0.059 0.000 1.970 -0.542 -0.926 3.102 1.207 0.827 0.975 1.002 1.623 1.374 1.288 +0 0.720 2.012 -1.469 0.695 -1.509 1.025 0.928 0.025 2.173 0.618 0.574 -0.727 2.215 1.040 0.832 1.399 0.000 1.053 1.524 1.587 0.000 1.011 1.048 0.983 0.685 0.762 0.784 0.743 +1 0.861 -0.006 -1.528 2.018 1.669 1.027 -0.340 0.471 1.087 0.341 -0.258 -1.085 0.000 0.662 0.621 -0.535 2.548 1.117 -1.014 0.134 0.000 0.819 0.845 0.977 0.835 0.978 1.058 0.958 +0 0.882 0.672 -0.704 0.539 1.024 0.396 2.133 1.030 0.000 0.648 -1.635 -0.751 0.000 0.641 0.322 -0.130 2.548 0.936 -1.002 1.455 0.000 0.909 0.751 0.986 0.705 0.443 0.512 0.570 +1 1.666 -0.272 -1.441 0.830 1.219 0.494 -1.346 0.426 2.173 0.650 -1.086 -1.006 0.000 0.799 0.184 0.125 2.548 0.689 -0.563 0.202 0.000 0.796 0.736 1.103 1.043 0.707 0.805 0.712 +0 0.630 -1.064 -0.570 0.612 1.687 0.523 -0.144 1.378 2.173 0.782 0.246 -0.064 0.000 0.782 0.693 0.677 0.000 1.022 0.964 -1.021 3.102 0.804 0.871 0.989 0.762 0.839 0.677 0.665 +0 0.517 -0.170 0.380 0.472 -1.000 1.124 -0.353 1.526 2.173 1.623 0.311 0.243 2.215 1.601 -0.342 -0.053 0.000 1.413 -1.872 -1.250 0.000 1.045 0.879 0.984 0.983 1.945 1.118 0.957 +0 0.385 0.705 1.303 1.534 0.256 1.033 0.751 1.573 2.173 1.228 0.430 -0.227 2.215 1.601 0.740 -1.213 0.000 1.317 -0.229 1.231 0.000 1.584 1.153 0.986 0.941 1.675 1.085 1.044 +1 0.794 0.715 1.444 0.500 -0.958 0.912 0.779 -0.365 1.087 1.066 0.237 0.747 2.215 1.990 -1.218 -1.018 0.000 1.854 1.217 0.921 0.000 0.817 1.023 0.988 0.949 1.286 0.911 0.803 +1 2.172 0.379 0.295 0.618 0.826 1.011 1.058 -1.160 1.087 0.997 0.915 1.040 2.215 1.002 -1.083 -0.797 0.000 1.070 0.184 -1.576 0.000 1.144 1.435 0.985 1.511 1.357 1.164 1.138 +1 1.744 -1.480 0.661 0.889 -0.443 1.301 -2.118 -1.511 0.000 1.756 1.506 0.675 0.000 1.648 -1.363 -1.076 2.548 1.030 -0.538 -0.445 0.000 1.097 0.926 1.447 0.719 0.761 0.789 0.855 +1 0.748 1.087 -1.668 0.021 0.322 0.524 1.945 0.958 0.000 0.776 0.106 -1.249 2.215 0.834 0.624 -0.197 1.274 1.300 1.421 0.122 0.000 0.878 0.840 0.690 0.490 0.738 0.848 0.714 +1 1.617 0.979 -1.741 1.253 -1.586 3.405 -1.444 0.365 2.173 1.975 1.107 -1.166 0.000 1.295 -0.045 -0.804 2.548 1.548 1.527 1.626 0.000 1.558 1.539 1.006 5.880 3.028 3.975 3.496 +0 1.477 -0.002 0.347 0.880 -1.315 1.873 0.939 -1.655 2.173 1.982 0.217 -0.102 2.215 0.516 0.721 0.250 0.000 0.672 0.521 0.894 0.000 0.576 1.008 1.575 1.154 2.985 1.591 1.149 +1 0.982 -2.058 -0.774 0.973 -1.436 0.958 -0.267 1.530 0.000 0.853 -1.245 0.380 0.000 1.048 0.263 -0.489 2.548 0.706 -1.503 -1.146 0.000 1.027 0.880 0.993 1.183 0.931 1.002 0.879 +1 1.477 0.231 1.666 0.759 -1.208 0.697 -0.795 0.604 2.173 0.620 -0.667 -0.180 0.000 0.873 -1.018 -0.486 2.548 0.614 -1.329 1.188 0.000 0.847 0.684 0.984 1.114 0.823 0.989 0.858 +1 0.690 -0.281 -1.113 0.448 -0.466 1.039 1.168 0.555 2.173 0.968 0.857 1.148 2.215 1.002 0.729 -1.465 0.000 0.865 -1.735 -0.748 0.000 0.974 1.028 0.994 0.881 0.783 0.819 0.741 +1 1.136 0.062 -0.641 0.160 0.421 1.112 0.948 1.643 0.000 0.890 -1.225 1.085 1.107 0.821 0.140 0.256 0.000 1.796 0.486 -0.225 0.000 0.633 0.678 0.986 1.235 0.737 0.834 0.783 +1 0.691 0.721 -1.738 0.830 -0.919 2.469 0.751 0.581 0.000 1.873 0.557 -1.002 0.000 1.120 0.660 -1.378 1.274 1.560 1.375 1.727 3.102 1.700 1.740 0.989 0.621 0.592 1.268 1.167 +0 0.849 -1.216 1.586 2.412 -1.307 1.565 -0.936 0.083 0.000 0.956 -0.382 1.445 2.215 0.693 2.635 -0.690 0.000 0.983 -1.383 0.564 0.000 1.055 0.932 1.011 0.947 0.484 0.911 1.086 +0 1.220 -0.315 0.096 2.332 0.648 2.569 0.952 -1.323 2.173 0.862 -0.556 1.176 2.215 0.453 1.568 -0.142 0.000 0.464 -1.440 0.170 0.000 1.384 1.116 1.117 2.930 2.512 1.994 1.579 +1 1.451 -0.627 1.115 1.625 1.591 1.349 -0.182 -0.080 1.087 0.888 0.364 -0.970 2.215 0.417 -0.787 -1.001 0.000 0.517 -0.209 -1.525 0.000 0.282 0.818 0.986 1.293 1.244 1.310 0.953 +0 0.664 1.185 -0.422 1.481 -1.207 1.031 1.249 0.369 2.173 0.544 1.675 1.094 0.000 0.516 1.502 -0.633 0.000 1.680 -0.411 1.696 3.102 0.813 0.825 0.990 1.006 1.896 1.135 0.960 +0 1.043 0.372 -0.267 0.310 0.072 0.619 -0.301 0.649 0.000 0.553 0.387 -0.545 2.215 0.678 2.457 1.286 0.000 1.260 0.880 -1.509 3.102 0.882 0.834 0.996 0.579 0.628 0.596 0.650 +1 0.391 1.039 1.704 0.533 -1.271 1.051 0.932 0.418 0.000 1.285 -0.727 0.382 0.000 1.198 0.517 1.507 2.548 2.418 1.054 -1.001 1.551 0.778 1.044 0.977 1.084 1.106 0.957 0.917 +1 0.927 -1.161 1.473 1.308 -1.306 1.304 -1.396 0.051 2.173 1.694 -0.536 -1.449 0.000 2.041 -0.964 0.478 2.548 0.552 -1.847 0.517 0.000 1.013 1.733 0.990 1.380 0.843 1.414 1.241 +0 2.127 -0.129 0.684 2.429 0.732 3.401 -0.736 -0.819 1.087 4.316 -0.465 0.838 0.000 0.704 -2.230 1.401 0.000 0.506 -0.891 1.672 1.551 3.346 1.795 0.998 3.206 1.108 2.564 2.174 +0 1.605 0.629 -1.432 0.182 -0.363 0.458 1.947 1.285 0.000 1.246 -0.400 -0.050 1.107 0.748 0.302 0.453 0.000 0.686 -0.031 -1.728 3.102 1.197 0.863 0.986 1.305 0.848 0.952 0.896 +0 0.469 -0.238 -1.226 1.624 1.343 0.399 -0.451 0.614 1.087 0.544 0.182 0.110 0.000 1.182 0.726 -0.970 0.000 0.735 -0.546 -0.170 3.102 1.086 0.932 0.987 0.785 0.377 0.597 0.671 +1 0.656 0.643 1.658 0.385 -0.191 1.029 -0.732 -0.989 1.087 1.102 -0.426 0.943 0.000 0.771 -0.553 0.397 0.000 0.592 -1.219 0.749 0.000 0.679 0.604 0.991 1.483 0.653 0.952 0.968 +0 0.383 1.191 1.183 1.646 -1.455 0.734 0.222 0.737 2.173 0.790 -0.841 0.192 2.215 1.241 -0.794 -0.722 0.000 0.551 -1.286 -1.725 0.000 0.814 0.881 0.984 2.127 0.832 1.514 1.317 +1 2.841 0.463 -1.484 0.259 0.488 0.834 1.575 -0.038 2.173 0.608 1.416 0.777 0.000 0.376 0.169 -0.359 0.000 0.703 0.060 0.977 3.102 1.013 0.990 1.164 0.751 0.933 0.990 0.875 +0 1.423 -0.578 1.443 0.463 -0.952 1.136 1.436 0.083 0.000 0.708 -1.394 -1.078 0.000 0.667 0.009 0.782 2.548 0.780 1.126 -1.093 0.000 0.808 0.631 0.986 0.675 0.531 0.522 0.550 +1 0.736 -0.884 -1.632 0.530 0.101 1.294 -0.024 -0.928 2.173 0.860 0.057 0.589 0.000 1.158 -0.599 1.088 1.274 0.486 1.858 0.227 0.000 1.138 1.106 0.989 0.895 1.558 1.134 0.926 +1 0.793 -0.120 0.990 2.447 0.409 0.750 0.916 -0.711 2.173 0.486 1.452 -1.697 0.000 0.620 0.689 1.494 2.548 1.253 2.151 -1.475 0.000 0.869 0.729 0.988 0.881 0.780 1.000 0.880 +1 1.013 -0.437 -1.047 0.905 0.585 1.257 0.083 1.734 2.173 1.329 -0.690 -0.305 0.000 0.878 -0.125 0.820 0.000 0.971 -0.693 0.338 3.102 1.486 0.871 1.320 1.122 1.244 1.064 0.913 +1 1.119 -0.804 0.620 1.092 -0.100 0.924 0.228 -1.687 1.087 0.561 1.888 -1.183 0.000 0.314 -1.223 -0.359 0.000 1.009 0.563 -0.729 0.000 0.896 1.057 0.981 1.400 0.801 0.951 1.195 +1 1.152 -0.448 -0.949 1.240 -1.623 0.702 -0.169 0.634 2.173 0.838 -1.033 -0.071 2.215 0.580 -0.581 -0.467 0.000 0.802 0.094 1.236 0.000 0.953 1.009 0.986 0.965 0.848 0.874 0.788 +0 1.299 -1.578 0.576 0.506 -0.744 0.747 -0.239 -0.193 0.000 0.988 -1.110 -1.409 2.215 1.467 -0.578 0.895 2.548 0.820 0.937 -1.522 0.000 0.870 0.939 1.042 0.933 1.166 0.816 0.757 +0 1.631 0.635 -0.306 0.215 -0.452 0.550 0.697 -1.597 2.173 0.520 0.087 1.156 0.000 0.705 -1.460 1.293 0.000 1.216 -0.483 0.347 3.102 0.891 1.011 1.001 0.976 1.039 0.831 0.928 +0 1.011 -0.372 -0.521 0.203 -1.241 1.022 0.620 -1.302 0.000 1.172 -1.167 -0.005 2.215 1.998 0.005 0.858 2.548 1.364 0.729 1.280 0.000 1.308 1.061 0.985 1.041 1.544 1.072 0.953 +0 1.698 0.254 0.090 1.329 -0.614 0.651 0.267 -1.228 0.000 1.012 -0.919 0.686 2.215 1.434 2.257 1.730 0.000 0.419 1.549 -1.509 0.000 0.730 0.624 1.232 1.267 0.703 0.897 0.890 +0 1.687 -1.122 -0.905 0.405 0.100 1.399 -0.955 -0.228 2.173 1.835 -0.159 1.432 0.000 1.108 0.552 0.820 2.548 1.018 0.322 1.717 0.000 0.671 0.879 0.989 0.883 1.842 1.399 1.270 +1 0.704 -0.200 -0.918 1.037 1.565 0.544 -1.831 0.627 0.000 0.828 -0.151 -0.573 0.000 1.228 -0.568 0.795 2.548 1.541 -0.004 -1.162 3.102 1.095 1.039 0.988 0.598 1.084 0.779 0.714 +1 0.785 -1.415 0.797 0.675 -1.088 0.358 0.167 0.618 2.173 0.638 -1.648 -1.198 0.000 0.819 -0.841 -0.793 0.000 1.364 -0.596 -1.383 3.102 0.857 0.996 1.001 0.783 0.795 0.644 0.663 +0 0.629 0.532 -1.103 0.638 0.875 0.655 -0.656 -0.131 1.087 0.517 -1.705 0.400 0.000 0.967 -0.912 1.497 2.548 0.687 -1.628 -1.119 0.000 0.765 0.780 0.985 1.048 1.001 0.916 0.946 +0 0.580 -0.427 -0.570 1.226 0.234 0.888 0.334 1.126 0.000 0.700 0.935 -1.360 2.215 0.427 -0.247 0.210 0.000 1.429 -0.134 -0.890 3.102 0.883 0.948 0.990 0.847 0.650 0.909 0.897 +1 0.406 -0.898 -0.210 0.259 0.314 1.769 -0.435 0.680 0.000 1.905 -0.717 -1.166 1.107 1.270 0.100 -1.704 2.548 0.649 0.367 -0.290 0.000 1.460 1.442 0.990 1.229 1.056 1.388 1.138 +1 0.554 0.356 -0.511 2.467 0.625 1.100 -0.547 -1.426 1.087 0.500 0.123 1.641 1.107 0.884 -0.084 0.054 0.000 1.244 0.883 -0.908 0.000 1.128 0.865 1.384 1.641 0.552 1.057 0.964 +1 0.906 0.291 -0.761 1.419 -1.296 0.613 -0.392 0.068 0.000 0.579 0.050 0.867 2.215 0.936 -0.955 0.972 2.548 0.452 -0.751 1.326 0.000 0.756 0.657 1.000 0.892 0.458 0.758 0.682 +1 0.295 1.668 -0.353 0.459 0.955 1.234 -0.381 -1.072 2.173 1.093 -0.698 1.074 0.000 1.028 -0.634 0.497 0.000 0.981 0.007 -0.667 0.000 0.808 0.688 0.990 0.995 0.900 0.950 0.814 +0 0.703 -0.281 0.924 1.084 -0.744 1.216 -0.799 -1.426 2.173 1.182 -1.027 0.247 2.215 2.000 0.072 0.222 0.000 0.627 -0.109 1.175 0.000 0.943 0.960 1.206 1.016 1.775 1.196 0.991 +0 0.837 -1.376 -0.253 0.859 -0.871 0.699 1.501 0.691 0.000 0.699 0.106 -1.391 0.000 0.772 -0.047 0.981 2.548 0.777 -1.102 -0.759 0.000 0.910 0.818 0.992 0.680 0.193 0.747 0.753 +0 0.425 1.198 -0.499 1.453 0.767 0.893 0.060 -0.704 2.173 1.395 -0.141 0.785 1.107 1.660 -0.318 -1.193 0.000 0.720 -1.126 -1.608 0.000 0.769 0.893 0.990 1.053 1.609 1.079 1.113 +1 0.591 1.441 -1.402 1.680 0.797 0.913 -1.437 -1.301 0.000 1.172 0.257 -0.222 1.107 0.610 -0.734 0.554 0.000 0.534 0.646 0.663 3.102 0.859 1.021 1.266 1.292 0.543 0.953 1.323 +1 0.703 -0.798 -1.426 0.580 -1.334 0.634 0.633 -1.243 2.173 1.110 1.030 0.336 0.000 0.552 -0.812 -0.805 2.548 0.543 -0.363 0.821 0.000 0.933 0.997 0.988 0.607 0.683 0.799 0.740 +1 0.430 1.295 -1.078 0.335 -0.265 1.109 -0.608 -1.155 2.173 1.182 0.003 0.553 2.215 0.616 1.083 0.512 0.000 0.427 -0.350 1.000 0.000 0.561 1.178 0.999 0.842 1.765 0.946 0.795 +1 0.319 0.162 -0.815 1.435 1.057 0.997 -0.224 -0.995 0.000 1.139 -1.030 -0.785 0.000 2.209 0.224 0.815 0.000 1.389 -0.245 0.181 1.551 1.061 1.152 0.987 1.067 0.905 0.818 0.964 +0 0.645 1.492 1.414 1.287 0.782 0.345 -0.197 -0.207 2.173 0.390 -0.162 0.309 2.215 0.672 1.796 -1.656 0.000 1.027 1.113 -0.733 0.000 0.748 0.837 0.988 0.649 0.242 0.596 0.635 +1 0.976 -0.919 0.680 0.233 -0.734 1.077 -0.738 1.454 2.173 1.119 -0.310 -1.023 2.215 1.600 -0.531 -0.202 0.000 0.580 0.222 1.051 0.000 1.067 1.004 0.984 0.934 1.320 0.977 0.893 +0 0.340 -0.962 0.132 1.928 -0.755 0.965 -0.793 -0.103 2.173 1.168 -0.821 1.158 0.000 1.510 -0.867 1.656 2.548 1.138 -0.579 0.490 0.000 0.858 0.865 0.989 0.878 1.508 0.976 0.962 +1 1.072 0.289 1.245 0.781 -1.440 0.679 -0.312 1.664 2.173 0.852 1.236 0.084 0.000 0.616 -0.362 0.255 0.000 0.784 1.013 -0.987 0.000 0.876 1.088 0.988 0.725 0.849 0.922 0.832 +0 0.471 -0.701 -0.227 1.114 1.618 0.811 -0.490 0.476 0.000 1.142 -0.170 -1.430 2.215 1.490 0.476 -0.090 0.000 0.979 0.443 1.637 0.000 0.965 1.163 0.999 0.646 0.698 0.734 0.673 +0 0.706 1.344 -0.790 0.354 1.581 1.100 0.724 1.582 2.173 0.982 1.139 -0.201 1.107 1.000 2.159 0.824 0.000 1.262 1.888 -0.437 0.000 1.126 1.031 0.993 0.839 1.565 1.147 0.974 +0 0.692 0.556 0.290 1.077 0.173 0.700 0.330 -1.043 2.173 0.490 -0.794 0.859 0.000 0.725 0.636 1.539 0.000 0.752 -0.782 -1.427 3.102 0.924 0.959 0.976 1.218 0.583 0.965 0.896 +1 1.444 -0.011 0.918 0.786 0.033 2.253 -0.561 -0.087 0.000 2.255 -0.565 -1.542 2.215 1.410 0.507 -1.294 2.548 1.322 0.260 1.190 0.000 2.687 2.199 1.056 1.477 1.213 1.804 1.458 +0 0.282 0.276 -0.532 0.852 0.820 0.330 -0.286 -0.258 2.173 0.777 0.804 1.500 0.000 0.662 0.426 -1.512 2.548 0.907 1.056 -0.308 0.000 1.118 0.889 0.989 0.736 0.573 0.659 0.632 +1 0.664 -0.406 -0.535 1.312 -0.146 0.978 0.558 0.113 0.000 1.107 -1.199 1.502 0.000 1.071 0.003 1.405 0.000 0.534 1.065 1.111 0.000 0.953 0.858 0.982 1.001 0.699 0.820 0.751 +0 3.356 -0.497 0.920 2.241 1.150 1.484 -2.553 -0.746 0.000 0.621 -1.832 -0.397 0.000 0.563 -0.851 -1.497 2.548 0.824 -0.435 -1.207 3.102 0.958 1.094 0.981 1.045 0.173 0.851 1.768 +1 0.691 -1.415 1.141 0.553 0.480 1.007 -0.459 -0.737 1.087 0.853 -1.377 0.648 0.000 0.865 -0.344 0.946 0.000 1.649 -0.685 -1.487 3.102 0.797 0.999 0.977 1.031 0.887 0.943 0.814 +1 1.190 -0.865 -1.286 0.551 1.221 1.078 -1.198 0.458 0.000 0.979 -2.270 -0.730 0.000 1.116 -0.884 1.405 2.548 1.137 -0.885 -1.046 3.102 1.008 1.103 0.985 0.572 0.693 0.713 0.656 +1 0.603 -0.301 1.575 0.651 0.827 2.186 -1.834 1.026 0.000 3.067 0.263 -0.854 2.215 1.088 -0.333 -0.127 0.000 0.686 -0.094 -1.218 1.551 0.609 1.094 0.986 0.664 0.492 1.166 0.963 +1 1.447 -1.325 -0.344 0.917 0.129 1.137 -0.756 1.313 1.087 0.330 -1.542 -0.826 0.000 0.715 -1.161 -1.284 2.548 0.594 -1.498 -1.708 0.000 0.416 0.799 0.987 0.792 0.857 1.014 0.774 +0 2.057 1.335 1.524 1.329 0.278 2.270 0.973 -0.001 0.000 1.256 0.942 -1.229 0.000 1.114 -1.910 1.428 0.000 1.918 0.188 -1.473 3.102 1.383 2.253 2.064 1.208 1.569 2.755 2.870 +1 1.539 -0.726 0.540 0.785 0.528 1.167 0.037 -1.166 0.000 0.684 -1.443 1.193 0.000 1.417 0.120 0.092 2.548 0.679 -0.367 -1.647 3.102 2.263 1.198 0.974 1.036 0.781 1.015 1.053 +1 0.841 0.120 -0.965 1.057 1.716 0.914 -0.341 -1.639 1.087 0.830 -0.454 1.237 0.000 1.051 0.813 -0.410 2.548 1.269 -0.804 -0.324 0.000 0.949 0.806 0.992 0.612 1.356 0.914 0.835 +0 0.555 -0.876 -1.496 1.017 1.122 0.737 -1.337 1.015 0.000 0.506 0.767 -0.742 2.215 0.795 -0.222 0.380 0.000 1.143 -0.254 -1.058 3.102 0.872 0.843 0.988 0.694 0.440 0.578 0.598 +0 3.968 -0.884 1.743 1.228 1.674 1.135 1.306 -0.194 0.000 1.021 0.732 0.053 1.107 1.950 -0.837 1.245 2.548 2.057 -1.085 -0.549 0.000 0.824 0.666 0.962 0.915 1.928 1.559 1.909 +1 1.083 -0.136 1.574 0.804 0.047 0.779 0.070 0.346 0.000 1.254 0.487 -1.579 2.215 1.179 0.845 -0.805 1.274 1.028 1.132 0.305 0.000 0.944 1.068 1.268 0.968 0.875 0.957 0.883 +0 0.789 1.014 0.446 0.338 -1.201 0.794 1.247 -1.205 2.173 0.778 -0.132 -1.351 0.000 2.059 0.149 0.616 2.548 0.640 0.209 0.231 0.000 0.927 0.967 0.985 0.758 1.835 1.005 0.824 +0 1.148 0.037 0.824 1.506 0.223 1.823 -0.662 -1.529 0.000 1.535 -0.879 -0.542 0.000 0.947 -0.956 0.725 0.000 1.128 1.048 -0.077 3.102 1.108 1.061 0.988 0.784 0.973 0.745 0.704 +1 0.442 0.931 -1.322 1.243 0.125 1.017 0.422 1.651 0.000 1.439 0.039 0.491 1.107 1.271 -1.064 -0.772 0.000 0.740 2.471 -0.838 0.000 0.900 1.068 0.991 0.899 0.626 0.953 0.911 +0 1.705 -0.324 -1.051 0.503 1.617 0.646 -1.234 0.642 2.173 0.729 1.340 -0.247 0.000 0.545 -2.303 -0.655 0.000 1.065 -1.820 1.396 0.000 0.821 0.872 0.987 0.856 0.760 0.849 0.952 +1 0.365 -0.571 -0.945 0.094 -1.114 0.731 0.746 -0.469 0.000 0.673 0.163 0.140 0.000 1.116 0.286 1.000 2.548 1.431 -0.532 -1.733 3.102 0.895 1.010 0.749 0.680 0.772 0.858 0.704 +1 0.815 0.519 0.070 1.116 -0.367 0.338 -1.486 -1.500 0.000 1.138 -1.056 1.444 2.215 0.707 -1.203 0.435 2.548 1.267 -1.382 -0.943 0.000 0.750 0.804 0.993 1.368 0.760 1.396 1.211 +0 0.950 -0.274 0.300 1.403 -0.356 0.601 -2.283 -0.202 0.000 1.082 -0.716 0.791 2.215 0.636 -0.696 -1.691 0.000 0.739 0.165 -0.373 1.551 1.435 1.133 0.989 1.009 0.802 0.893 0.945 +0 0.919 0.236 -0.612 1.227 0.866 0.665 1.327 0.908 2.173 0.907 1.335 -1.274 2.215 0.726 1.873 -0.761 0.000 0.777 1.537 0.308 0.000 0.686 0.792 1.429 1.128 1.054 0.902 0.822 +0 0.770 0.759 0.736 0.736 -1.027 0.330 -0.940 -0.272 2.173 0.295 0.326 1.407 0.000 0.530 1.248 -0.710 2.548 0.974 1.705 1.241 0.000 0.681 1.113 1.042 0.589 0.775 0.718 0.638 +0 1.045 0.237 -1.715 1.000 0.388 1.114 0.780 -1.023 2.173 1.398 -1.402 0.506 0.000 1.070 2.054 -1.315 0.000 0.955 -1.307 -0.241 0.000 0.943 0.807 1.342 1.162 1.755 1.551 1.295 +1 0.737 0.111 -1.599 1.047 -1.551 0.383 0.519 -1.677 0.000 0.917 -0.411 0.415 0.000 0.505 1.139 0.488 1.274 0.466 -0.195 -1.269 3.102 1.347 0.900 0.991 0.569 0.478 0.567 0.675 +1 0.430 -0.795 0.753 0.833 1.400 0.561 -0.519 1.514 1.087 0.685 1.162 -1.049 0.000 1.267 -1.348 0.172 0.000 1.024 -0.045 -0.336 3.102 0.804 0.872 0.984 0.862 0.820 0.819 1.297 +0 1.267 1.312 -0.193 0.674 1.350 0.788 2.235 1.519 0.000 0.603 1.016 -1.227 2.215 1.217 0.161 0.022 0.000 0.542 0.396 -0.487 0.000 0.956 0.902 1.259 0.752 0.347 0.568 0.654 +0 0.411 -2.314 1.243 0.286 -1.179 1.442 -0.166 0.026 0.000 1.954 -1.225 -1.559 1.107 0.416 0.637 1.702 2.548 0.632 1.717 -0.888 0.000 0.953 0.987 0.997 0.839 1.114 1.345 1.065 +0 0.568 -0.484 -0.507 0.522 -0.660 0.847 -0.634 0.902 2.173 0.953 0.712 1.556 0.000 1.203 -0.088 -0.938 2.548 1.134 -0.078 0.125 0.000 1.423 1.120 0.983 1.069 1.299 0.941 1.019 +1 0.746 0.534 -1.727 0.757 0.116 0.757 -0.347 -0.058 2.173 0.942 -0.169 0.665 1.107 1.354 0.284 -1.322 0.000 0.712 1.602 1.640 0.000 1.088 1.261 1.037 0.841 0.761 1.000 0.830 +1 0.603 1.177 -0.435 1.395 0.143 0.510 0.599 -1.483 2.173 0.647 0.824 -1.093 0.000 0.675 -0.530 0.131 0.000 2.241 -0.716 1.488 3.102 1.200 0.870 0.993 2.475 1.037 1.606 1.301 +0 0.564 -0.290 0.604 0.391 -0.364 1.268 0.363 -1.401 2.173 0.705 -1.356 0.811 0.000 0.652 -2.372 0.147 0.000 1.087 -0.631 -0.013 3.102 0.888 0.773 0.979 1.441 1.391 1.387 1.108 +1 1.091 -0.295 0.673 0.296 0.463 0.853 -0.749 -0.960 1.087 0.495 -0.678 1.098 0.000 0.962 -0.557 0.059 0.000 1.003 -1.329 1.602 0.000 0.853 0.997 0.996 0.625 0.539 0.691 0.640 +0 1.251 -0.903 -1.037 0.290 1.112 0.733 -0.671 0.212 1.087 0.796 -0.140 1.510 0.000 0.835 -0.034 0.612 0.000 0.819 1.161 -1.006 3.102 0.908 0.939 0.981 0.858 1.246 0.818 0.770 +1 0.380 2.223 -1.632 1.200 1.213 0.662 1.012 -1.054 2.173 2.048 0.629 -0.434 2.215 0.956 0.456 0.782 0.000 1.391 1.210 1.111 0.000 0.730 1.054 0.992 1.387 0.965 1.036 0.940 +1 1.337 -0.088 -1.053 0.569 -0.497 1.002 -0.563 1.051 2.173 0.578 -0.901 -1.281 0.000 1.331 -1.320 0.302 2.548 0.389 2.295 1.080 0.000 1.971 1.675 0.987 1.367 1.113 1.350 1.199 +0 2.183 -0.597 0.334 0.133 0.096 1.253 -1.244 -1.085 0.000 0.697 0.675 0.368 0.000 1.001 -1.381 -1.556 0.000 0.939 0.149 1.238 3.102 0.869 0.848 0.987 0.738 0.581 0.777 0.922 +0 1.576 1.712 -1.165 0.511 1.131 1.031 0.142 0.366 0.000 0.437 -0.409 -1.272 2.215 0.393 0.616 -1.734 0.000 0.920 0.091 0.686 0.000 1.128 0.933 1.092 0.682 0.182 0.659 0.818 +0 1.148 -0.158 0.480 0.532 -0.813 0.656 -0.504 -1.600 2.173 0.892 -0.495 -0.264 0.000 0.808 0.685 1.512 2.548 0.452 -0.881 0.842 0.000 0.732 0.928 0.994 0.850 0.680 0.709 0.659 +1 1.229 -1.187 0.889 0.634 1.715 1.019 -0.305 -0.382 0.000 0.850 0.536 1.684 2.215 0.640 0.079 -0.059 2.548 0.883 -1.634 -1.333 0.000 0.717 0.812 0.994 0.957 0.805 0.819 0.747 +0 0.898 -0.837 -0.229 0.972 0.753 2.051 0.304 1.445 1.087 1.007 -1.240 -0.751 0.000 1.833 -0.029 -0.390 1.274 0.563 -1.022 0.596 0.000 0.920 1.031 1.002 1.647 2.442 1.546 1.273 +1 1.808 -1.086 -1.533 0.992 0.029 2.164 -1.137 1.155 2.173 2.107 1.035 -0.170 0.000 1.183 -1.559 -0.868 0.000 1.322 -0.453 -0.846 3.102 4.772 2.620 1.831 1.637 1.826 2.599 2.091 +0 1.280 -0.733 -1.638 1.119 1.336 0.774 -0.951 -0.297 0.000 0.628 -0.707 0.770 2.215 0.986 -0.025 -0.015 0.000 1.391 -0.084 -1.051 3.102 0.855 0.886 0.976 0.737 0.883 0.701 0.809 +0 1.132 1.830 1.010 5.547 1.284 2.728 0.134 -0.603 1.087 1.193 0.327 -0.146 0.000 1.829 2.140 -0.235 0.000 2.644 0.982 1.489 3.102 2.693 2.274 0.980 4.509 3.117 2.898 2.586 +0 0.283 -0.993 1.421 1.151 -1.511 0.728 -0.930 0.526 0.000 0.875 0.909 1.584 2.215 1.163 -0.685 -0.131 0.000 0.888 0.136 -0.668 1.551 0.938 0.861 0.986 0.614 0.780 0.983 0.910 +0 2.298 0.328 -0.186 0.289 0.452 0.705 0.860 0.475 2.173 1.522 0.658 1.728 2.215 0.492 0.228 1.577 0.000 1.233 1.768 -1.530 0.000 0.947 0.910 0.991 1.459 1.385 1.082 0.952 +1 0.824 -1.436 -0.667 0.897 0.891 0.973 -0.852 -1.125 0.000 1.406 0.101 0.925 2.215 0.461 -0.331 -0.634 2.548 0.453 -1.172 -0.297 0.000 0.744 0.490 1.175 1.270 0.868 0.882 0.850 +0 1.644 -1.171 -1.352 0.854 1.194 1.289 -0.954 -0.691 1.087 1.059 -0.819 1.127 2.215 1.345 0.220 0.448 0.000 1.245 -0.709 0.359 0.000 0.827 0.950 1.231 1.197 1.718 1.131 1.077 +0 2.114 -0.490 -1.230 0.143 -0.704 1.362 -0.346 0.591 1.087 0.794 -1.431 0.319 0.000 1.385 -1.097 -0.813 2.548 0.760 -0.571 1.403 0.000 0.933 0.978 0.989 0.812 1.798 1.174 1.010 +1 2.173 0.688 1.385 0.494 0.071 0.449 -0.254 -1.673 0.000 0.778 -0.364 -0.812 2.215 1.611 0.560 0.121 2.548 0.611 -0.493 0.713 0.000 0.683 0.985 1.329 1.166 1.078 0.971 0.825 +0 0.362 -0.623 -0.094 0.652 -0.853 0.726 1.193 1.301 0.000 0.446 0.007 -1.514 1.107 0.989 0.706 -0.233 2.548 0.490 -0.058 0.537 0.000 0.849 0.903 0.989 0.626 0.703 0.631 0.605 +0 0.722 -1.687 1.609 0.718 -0.895 0.614 -0.123 -1.434 2.173 0.972 -0.556 1.142 0.000 0.773 1.853 -0.171 0.000 0.712 0.842 -0.137 3.102 2.581 1.443 0.988 1.239 0.768 1.148 1.561 +0 0.501 2.095 -0.656 3.431 -1.148 0.667 0.755 0.373 2.173 0.720 1.580 1.359 0.000 1.383 1.522 0.393 0.000 0.930 0.064 1.347 3.102 1.165 1.122 0.985 1.450 0.703 1.160 2.031 +1 0.774 -1.179 0.285 0.495 -1.259 0.698 0.033 -1.026 1.087 0.933 -0.441 1.151 2.215 0.910 -1.478 -0.938 0.000 0.706 1.990 -0.314 0.000 3.264 1.910 0.989 0.897 1.136 1.327 1.259 +1 0.974 0.339 -1.483 0.262 0.377 1.363 -1.036 0.863 0.000 2.333 -0.469 -0.472 2.215 1.337 -0.766 -1.492 2.548 1.009 -0.055 0.706 0.000 0.948 1.278 0.982 1.408 1.530 1.396 1.282 +1 0.471 -1.274 -1.286 1.530 -1.608 1.679 0.282 0.426 2.173 0.470 0.560 -0.707 0.000 1.042 1.839 -1.662 0.000 0.683 -0.353 -0.568 1.551 1.173 1.002 0.982 3.275 0.975 2.001 2.262 +1 1.987 0.656 -0.793 0.319 0.549 0.832 1.639 0.155 0.000 1.178 -0.323 -1.722 1.107 1.029 0.371 0.657 2.548 0.947 1.495 -0.278 0.000 0.974 0.893 1.032 1.123 1.079 1.102 0.992 +1 1.098 1.986 0.505 1.088 -0.151 1.104 1.758 -1.154 0.000 1.076 1.207 0.607 0.000 0.975 1.459 -0.352 2.548 1.103 -0.872 -1.398 0.000 0.826 0.885 0.985 0.701 0.779 0.738 0.801 +1 1.879 -0.505 -1.528 0.267 0.397 1.926 -2.132 0.358 0.000 1.979 -0.797 -1.689 2.215 1.056 -0.679 -0.355 0.000 1.159 -0.324 0.376 0.000 0.778 0.788 0.987 0.691 0.862 0.911 0.783 +1 0.675 0.805 1.619 1.846 -1.444 0.391 0.688 -0.920 0.000 1.352 1.136 0.148 2.215 1.016 1.491 -0.337 0.000 1.519 0.426 0.619 3.102 0.794 0.942 0.976 1.538 0.686 1.078 0.964 +0 0.554 0.619 1.040 3.079 1.734 1.070 1.061 0.049 2.173 0.653 -1.762 0.307 0.000 0.731 0.096 -0.934 2.548 0.599 1.712 0.434 0.000 0.970 0.898 1.059 1.652 1.017 1.211 1.324 +0 0.749 -0.649 -0.470 1.787 -0.549 1.492 -0.448 1.201 2.173 0.956 -1.578 -1.133 2.215 0.861 -1.604 0.803 0.000 1.030 -0.922 0.180 0.000 0.905 1.030 0.994 1.383 1.866 1.457 1.195 +1 2.447 -0.507 1.655 0.301 -1.339 0.716 -0.085 -0.443 2.173 0.818 -0.895 0.367 2.215 0.834 -0.837 -0.174 0.000 0.639 -0.099 0.999 0.000 0.776 0.700 0.986 1.098 0.893 0.952 0.789 +1 1.113 -0.430 -1.397 0.714 1.551 0.729 0.332 -0.191 2.173 0.382 -0.755 -0.532 0.000 0.760 -1.037 0.908 0.000 0.726 -0.057 1.149 3.102 0.811 0.929 0.980 0.630 0.737 0.805 0.701 +1 0.336 -1.917 0.471 0.572 -1.013 0.950 -0.600 1.223 0.000 1.313 0.083 -0.504 2.215 0.561 -0.328 0.454 0.000 0.645 0.151 -1.081 3.102 0.856 0.774 0.990 0.812 0.415 0.837 0.722 +1 0.892 0.137 -0.084 0.509 1.530 0.793 1.255 0.144 0.000 0.916 -0.066 -1.342 2.215 1.625 0.534 1.311 2.548 0.379 2.063 -0.849 0.000 0.846 1.186 0.987 0.770 0.984 0.990 0.825 +1 0.395 0.578 0.747 1.939 -0.228 0.831 0.010 -1.220 2.173 0.745 1.928 1.187 0.000 0.387 -1.868 0.355 0.000 0.587 -1.185 0.884 3.102 0.562 0.880 0.991 0.798 0.906 0.790 0.713 +0 0.424 -0.714 0.029 2.095 -0.111 0.609 -0.530 1.204 0.000 0.799 0.599 1.294 0.000 0.592 -0.348 -0.872 2.548 0.813 0.645 -1.435 3.102 0.909 0.867 0.990 0.821 0.414 0.606 0.781 +0 0.582 0.167 1.192 1.301 -0.880 0.395 0.450 -0.209 2.173 0.561 0.658 1.575 0.000 0.830 -0.414 0.728 2.548 0.610 1.342 0.048 0.000 0.834 0.808 1.154 0.695 0.630 0.608 0.607 +0 1.225 -0.893 -1.589 2.172 -1.489 1.795 0.438 0.813 2.173 2.186 -1.190 -1.400 2.215 2.578 1.482 -0.119 0.000 1.288 0.683 0.403 0.000 0.633 0.867 0.990 0.642 3.801 2.230 1.798 +1 2.054 1.082 0.131 0.331 -0.898 0.790 -0.398 0.630 0.000 0.641 0.015 -1.563 0.000 1.588 0.730 -1.139 2.548 1.213 0.538 -1.737 3.102 1.425 1.032 0.986 1.064 0.550 0.886 0.968 +0 1.319 0.241 1.057 1.784 1.604 0.810 -0.905 -0.576 0.000 0.593 -0.062 -0.008 2.215 1.083 -0.516 0.509 0.000 1.332 0.431 -1.042 1.551 1.428 0.897 1.006 0.921 0.686 0.782 0.952 +1 1.471 -1.212 -1.277 1.476 -0.918 0.295 -1.307 0.596 0.000 0.699 0.250 1.208 2.215 1.025 -0.877 0.145 0.000 0.636 0.254 0.223 3.102 0.429 0.843 0.982 1.077 0.467 1.039 0.894 +0 1.424 1.123 -1.552 0.217 -1.560 1.198 1.323 -0.368 0.000 0.558 0.936 -0.906 0.000 1.132 0.134 0.672 2.548 1.592 -0.588 1.259 0.000 0.872 0.785 0.998 0.924 0.255 0.784 0.807 +0 0.440 -0.386 -0.493 1.082 -1.375 0.707 -1.804 -0.421 0.000 1.262 0.290 0.561 2.215 1.206 -0.315 1.091 0.000 1.411 -0.188 -1.576 3.102 2.106 1.459 0.993 1.048 1.173 1.219 1.131 +0 1.237 -0.208 -0.792 0.863 -1.743 1.641 -0.479 0.974 2.173 2.302 0.330 -0.681 2.215 1.387 -0.328 0.530 0.000 0.665 0.754 0.998 0.000 0.837 0.885 1.081 1.017 3.098 1.510 1.241 +0 0.376 -1.987 -1.713 0.334 -1.229 1.472 0.897 -0.083 1.087 1.631 0.038 1.210 1.107 1.729 -0.566 -1.217 0.000 0.508 2.080 0.333 0.000 2.562 1.945 0.979 1.494 2.330 1.661 1.353 +1 0.728 -0.776 1.739 0.607 -0.097 1.218 -1.424 1.078 0.000 1.531 0.614 -1.048 2.215 2.581 -0.009 0.154 1.274 1.906 -0.212 -1.437 0.000 2.305 2.340 0.984 1.302 1.989 1.899 1.457 +1 0.424 -2.146 -0.253 0.266 -1.690 0.755 -0.358 0.744 0.000 0.992 -0.434 -1.321 1.107 0.685 0.315 0.167 1.274 0.404 -0.654 0.898 0.000 1.013 1.008 0.995 0.712 0.924 0.692 0.651 +0 1.152 1.118 0.030 0.354 1.742 0.860 -0.251 1.570 0.000 0.899 0.847 -0.980 2.215 0.921 0.027 0.827 0.000 1.090 0.388 -0.132 3.102 1.020 1.010 0.986 0.775 0.645 0.850 0.842 +0 0.425 0.551 0.925 1.083 -0.025 1.386 0.275 1.186 2.173 1.158 0.717 -0.674 2.215 0.748 -0.445 -1.391 0.000 0.766 0.004 0.735 0.000 0.872 1.113 0.991 1.008 1.903 1.113 0.917 +1 0.892 -0.333 0.294 1.400 0.912 0.369 -0.190 -0.816 0.000 0.570 -0.783 1.161 0.000 1.675 -1.180 -1.046 1.274 0.860 -0.189 -1.234 3.102 0.999 0.948 0.984 0.796 0.537 0.967 0.809 +0 1.548 -1.045 -0.035 0.455 0.117 0.941 -0.445 1.621 0.000 0.460 -0.491 0.883 0.000 0.614 -0.933 -0.659 2.548 1.069 -0.628 -1.110 1.551 0.865 0.853 0.987 0.769 0.259 0.582 0.703 +0 2.321 -0.783 -0.100 1.382 0.565 1.046 -0.550 -0.727 1.087 1.487 -0.711 1.577 0.000 0.640 -0.143 0.451 0.000 0.974 -0.551 -1.675 0.000 0.931 0.941 1.401 1.443 1.536 1.213 0.988 +0 1.642 -0.917 -0.783 0.243 -0.061 1.615 -0.349 1.021 2.173 0.927 -0.055 -0.550 2.215 0.888 0.197 1.673 0.000 0.610 1.555 -0.138 0.000 1.104 0.997 0.987 1.497 1.799 1.134 1.061 +0 0.900 0.096 1.066 1.948 0.350 0.672 0.443 -1.085 2.173 0.918 -2.441 -1.246 0.000 0.405 -1.067 1.671 0.000 0.569 -0.688 0.607 0.000 0.816 1.816 1.103 0.743 0.672 1.390 1.360 +0 0.712 -0.227 0.050 1.660 1.078 1.040 0.764 -1.236 2.173 1.019 -2.099 0.095 0.000 1.103 1.036 -0.736 0.000 1.793 -0.762 1.545 3.102 0.956 0.967 1.205 0.886 1.616 1.102 1.043 +1 1.075 0.719 -0.168 0.938 1.391 1.006 0.266 -0.973 0.000 1.000 0.634 0.852 0.000 1.129 -0.088 1.684 2.548 1.466 0.073 0.307 3.102 2.163 1.391 1.372 0.842 0.935 0.962 0.875 +0 2.243 -1.695 -0.020 1.125 0.059 1.318 -1.387 -1.604 0.000 1.218 -1.428 -1.245 0.000 1.487 -1.289 0.735 1.274 0.626 -0.393 0.823 3.102 0.863 0.977 0.988 1.024 0.366 0.942 1.171 +1 0.531 0.554 1.743 1.299 -0.321 0.922 1.827 -0.462 0.000 0.771 1.311 1.488 0.000 1.145 1.275 1.023 2.548 0.749 0.937 0.134 0.000 0.936 0.783 1.103 0.934 0.810 0.696 0.650 +0 0.399 1.909 -1.123 1.100 1.127 0.595 0.555 1.011 2.173 0.449 2.066 -0.598 0.000 0.805 -0.167 -0.519 2.548 1.426 -0.024 -1.391 0.000 0.857 0.844 0.983 0.876 0.908 0.673 0.646 +1 0.331 0.758 1.528 1.575 -0.447 0.653 0.506 1.115 0.000 0.925 -0.416 0.914 0.000 0.893 0.449 -0.995 2.548 1.245 -0.567 -0.702 3.102 0.858 1.065 0.989 0.899 0.543 0.824 0.850 +0 0.561 -1.671 0.303 1.009 -0.794 1.524 -0.881 0.071 2.173 1.164 -0.005 -1.721 1.107 0.723 1.929 1.327 0.000 0.713 0.784 -1.152 0.000 0.796 1.070 0.986 0.892 2.153 1.618 1.377 +0 0.513 1.738 1.001 1.013 -0.600 0.654 0.783 -1.149 2.173 0.650 -0.163 1.734 0.000 2.189 0.679 0.651 2.548 1.772 0.919 -0.430 0.000 0.820 1.046 0.990 0.795 1.489 0.931 0.827 +1 0.757 0.022 -0.775 1.862 -0.070 1.395 -0.516 1.154 1.087 1.058 1.919 -1.489 0.000 0.757 0.364 -0.395 0.000 1.589 -0.477 -1.284 0.000 0.804 1.225 0.988 0.670 0.467 0.980 0.825 +0 1.177 1.021 0.804 1.538 0.044 1.845 0.051 -1.582 1.087 0.436 0.056 -0.534 1.107 0.564 2.532 0.159 0.000 0.811 -0.892 0.122 0.000 0.523 0.544 1.179 1.986 1.070 1.280 1.171 +0 3.211 -1.053 -1.274 0.813 -1.617 3.254 0.430 0.376 0.000 3.664 -0.912 -1.600 2.215 3.134 0.888 0.385 0.000 1.896 1.036 -1.472 3.102 1.334 1.170 0.988 0.858 3.156 2.371 2.063 +0 1.917 -0.638 1.101 0.687 1.539 0.906 0.047 -0.920 0.000 1.027 0.515 -0.205 2.215 0.544 0.615 -1.685 0.000 1.233 -0.315 0.328 3.102 0.899 0.984 0.991 1.480 0.670 0.985 0.983 +1 0.573 1.144 -0.806 1.199 1.644 0.869 0.572 -0.790 2.173 1.125 0.577 -0.033 2.215 1.109 -0.573 1.332 0.000 0.737 1.324 0.771 0.000 0.774 1.101 0.992 0.976 0.918 0.925 0.979 +0 1.536 -0.761 -0.482 2.613 -0.144 1.034 -1.478 1.349 0.000 0.870 -2.741 1.663 0.000 0.733 0.152 0.849 2.548 0.573 0.084 1.225 0.000 1.047 0.943 0.990 0.936 0.433 0.861 1.078 +0 0.687 1.583 -0.708 1.385 -1.613 0.889 1.434 0.248 0.000 0.404 0.328 -0.463 2.215 0.641 -0.559 -1.331 2.548 1.164 -0.127 0.942 0.000 1.653 1.051 0.986 0.996 0.465 0.842 0.891 +0 1.155 -0.234 -0.225 1.264 -0.759 0.906 -1.571 1.637 2.173 0.784 -0.632 -1.240 0.000 1.311 -0.049 0.522 2.548 1.510 -1.779 0.649 0.000 1.817 1.301 0.983 0.985 1.620 1.135 1.086 +0 0.670 -0.980 -0.184 0.480 1.275 1.140 0.324 -0.626 2.173 0.711 0.551 1.229 0.000 0.758 1.302 0.612 0.000 0.680 -0.862 1.331 0.000 0.811 1.296 0.983 0.583 0.844 0.798 0.706 +1 0.380 -0.978 0.445 0.734 -1.036 1.221 0.040 -0.264 0.000 1.085 -0.433 0.985 0.000 0.999 -0.838 1.426 2.548 1.754 0.886 -1.355 3.102 1.062 0.736 0.982 0.776 1.321 1.042 0.876 +0 0.347 -2.251 0.510 1.561 -1.457 1.496 0.562 1.067 2.173 1.687 -1.045 -0.144 2.215 1.103 0.672 -0.846 0.000 0.902 -0.972 -1.094 0.000 0.678 0.987 1.000 2.922 2.984 2.181 1.584 +1 1.528 0.425 -0.742 0.329 -1.267 1.570 -0.698 1.034 2.173 0.489 -0.703 -0.528 0.000 0.273 2.610 -0.776 0.000 0.453 -0.663 0.060 3.102 1.168 0.741 0.989 1.915 0.688 1.188 1.038 +1 0.766 0.317 -0.285 0.773 -1.326 0.778 -0.231 1.325 2.173 0.446 1.290 -0.571 0.000 0.532 -1.283 -1.319 1.274 1.118 0.325 0.276 0.000 0.778 0.999 0.985 0.874 0.743 0.807 0.721 +0 0.514 -0.069 -0.630 0.477 -1.693 0.998 0.226 0.548 0.000 0.788 1.281 0.044 2.215 1.669 0.616 -1.390 0.000 0.448 0.797 -1.073 3.102 0.759 0.859 0.991 0.473 0.464 0.572 0.598 +0 0.940 -0.694 1.330 0.895 -0.610 0.658 0.214 -0.862 0.000 0.426 -0.015 1.434 0.000 0.898 -0.959 0.407 2.548 0.840 1.838 -0.224 0.000 0.996 1.011 1.251 0.674 0.142 0.655 0.640 +1 0.407 2.138 1.044 0.743 0.819 2.112 0.373 -1.540 0.000 2.009 1.203 0.381 0.000 1.791 0.603 -0.251 1.274 1.107 0.137 -1.202 3.102 0.841 1.017 0.995 0.778 0.857 0.883 0.806 +1 0.787 1.108 -1.695 0.872 1.703 0.854 0.754 -0.130 2.173 0.644 0.076 1.677 2.215 0.548 -0.565 -0.584 0.000 0.744 -1.106 1.149 0.000 0.897 0.779 0.995 1.313 1.154 1.033 0.842 +0 1.365 1.113 1.709 0.438 -0.509 0.784 1.249 1.034 2.173 1.186 1.278 -0.682 2.215 0.377 1.205 -0.290 0.000 1.466 0.056 0.292 0.000 0.687 0.933 0.987 0.789 1.419 0.857 0.781 +1 0.717 -0.525 -0.352 1.629 -0.280 1.262 0.201 1.026 0.000 0.930 -1.141 -0.897 0.000 0.927 -0.576 1.247 2.548 1.330 -0.767 -1.323 1.551 0.909 1.119 0.993 1.078 0.636 0.875 0.922 +1 0.908 0.115 1.149 1.646 1.724 1.225 -0.293 0.400 2.173 1.041 -0.484 -0.481 2.215 0.557 0.108 -1.742 0.000 1.101 -0.088 -1.007 0.000 0.540 1.114 0.993 1.156 1.197 1.073 0.878 +0 0.326 -1.718 -0.762 1.476 -1.081 1.009 -0.526 0.085 2.173 0.807 0.924 -1.573 1.107 0.401 2.102 0.554 0.000 0.737 -2.058 1.475 0.000 0.859 0.825 1.000 1.087 1.706 1.022 0.976 +1 0.410 -0.630 -0.621 0.106 -1.494 0.960 0.757 1.360 0.000 1.260 -0.039 -0.345 2.215 1.099 0.236 0.839 2.548 0.997 1.162 -1.681 0.000 1.058 1.246 0.894 0.728 1.111 1.100 0.872 +1 0.815 0.465 1.062 1.180 -1.456 0.486 -2.050 -0.383 0.000 1.334 0.492 -1.611 1.107 0.702 1.159 -0.504 0.000 0.596 -1.142 -0.013 0.000 1.281 0.743 1.042 0.652 0.855 0.767 0.713 +0 0.677 -1.636 -0.558 0.793 -1.238 1.291 0.166 0.707 0.000 0.964 0.674 1.077 0.000 1.057 -0.317 -0.370 2.548 1.123 -0.462 -1.004 1.551 0.998 1.314 0.981 0.522 0.458 0.976 0.969 +1 0.716 -0.196 -1.450 0.547 0.205 1.360 0.703 1.173 2.173 1.445 1.036 -0.685 2.215 0.664 1.985 -0.341 0.000 0.908 1.186 0.695 0.000 0.979 0.779 0.987 0.947 2.084 1.108 0.918 +1 0.821 -1.303 -0.662 0.255 0.587 0.949 -1.584 0.074 0.000 1.314 -1.036 1.250 1.107 1.682 -0.910 -1.430 2.548 0.405 -2.460 1.057 0.000 1.007 1.273 0.995 0.816 1.050 1.046 0.907 +1 3.017 -0.240 -0.278 0.742 -0.499 1.309 -1.166 1.318 0.000 0.456 -0.627 -1.579 0.000 1.095 -0.002 0.621 2.548 1.182 0.617 -1.491 1.551 0.939 1.133 0.991 1.091 0.886 0.948 1.190 +1 0.525 0.090 -1.129 1.475 1.199 1.179 -0.271 0.808 1.087 1.038 -1.332 -0.208 0.000 1.146 -1.507 -0.625 0.000 1.108 -0.720 -0.937 0.000 0.924 0.923 1.053 0.872 0.798 0.960 0.900 +0 0.541 1.873 0.034 0.982 -1.227 0.820 0.544 -1.361 2.173 0.948 -1.400 0.751 0.000 1.107 1.254 -0.361 0.000 1.776 0.724 0.680 3.102 0.987 0.990 0.988 0.801 1.248 0.832 0.729 +1 0.861 1.019 -0.607 0.939 -1.434 1.869 0.577 0.981 0.000 0.782 0.873 -1.518 0.000 1.714 0.141 -0.476 2.548 1.681 1.314 -1.022 0.000 0.867 0.985 0.988 1.053 0.880 0.885 0.774 +1 0.382 -1.725 1.542 2.375 -0.686 0.412 -0.814 0.416 0.000 0.622 0.718 0.890 1.107 0.843 -1.152 1.337 0.000 0.614 -1.151 1.673 3.102 0.817 0.930 1.197 0.727 0.796 1.106 0.926 +0 1.356 -0.852 0.561 0.841 0.171 1.106 -1.225 -1.355 0.000 0.685 -0.324 -0.375 1.107 0.518 -1.494 0.743 0.000 1.197 -0.464 -1.234 3.102 1.321 0.862 0.977 0.850 0.579 0.741 0.830 +1 1.890 -0.933 0.683 0.671 1.065 0.655 0.224 -1.491 2.173 0.613 -0.680 -1.113 0.000 0.688 -1.341 -0.954 0.000 0.841 -0.399 0.284 0.000 0.896 0.827 1.000 0.760 0.741 0.914 0.781 +1 0.809 -0.053 -0.717 1.257 0.552 0.864 -0.223 1.572 0.000 0.650 0.470 -1.404 0.000 0.960 0.132 -0.166 2.548 1.513 -0.573 0.553 3.102 0.910 1.073 1.272 0.775 0.682 0.831 0.781 +0 0.655 1.421 -0.148 1.282 0.766 0.921 0.374 1.032 2.173 1.782 -0.174 -0.749 0.000 0.993 0.704 -0.810 0.000 1.219 -0.802 1.357 0.000 1.014 0.968 0.987 0.831 0.347 1.017 0.965 +1 0.589 -1.288 0.882 0.706 -1.282 0.912 -0.280 1.587 0.000 1.092 -1.156 1.702 2.215 2.027 1.958 -0.071 0.000 1.698 -1.373 0.314 0.000 2.229 1.465 0.985 1.327 1.471 1.195 1.070 +1 0.464 2.117 1.455 1.277 -0.532 0.492 0.348 1.679 0.000 0.620 -2.247 -0.951 0.000 1.811 0.032 0.499 2.548 0.626 1.016 -1.669 3.102 0.577 0.811 1.041 0.637 0.911 1.034 0.860 +0 0.883 0.282 1.525 0.923 -1.277 0.667 0.378 -0.304 0.000 0.604 -0.516 -0.880 1.107 1.025 -0.050 0.353 0.000 1.043 0.576 0.908 1.551 0.892 0.832 0.994 0.772 0.854 0.651 0.709 +0 1.139 1.250 -0.936 0.608 -1.478 2.087 0.351 1.160 1.087 2.580 0.803 -0.582 1.107 1.468 0.184 0.788 0.000 1.053 -0.524 1.014 0.000 0.649 0.844 0.987 0.961 3.510 1.749 1.419 +0 2.183 1.059 -1.225 1.576 -0.637 1.106 -1.504 0.383 2.173 0.680 -0.080 0.720 0.000 0.640 -2.077 0.819 0.000 1.254 -1.310 1.662 3.102 1.355 1.001 1.299 3.215 1.139 2.248 1.881 +1 0.950 -0.127 1.076 1.207 0.094 1.257 0.335 -1.062 2.173 0.612 1.214 0.474 0.000 0.712 -0.390 0.511 0.000 0.597 -0.307 1.683 3.102 0.954 0.739 1.148 1.321 0.657 0.850 0.824 +0 1.061 -0.398 -1.582 1.375 -1.049 0.949 0.906 0.811 0.000 0.788 -1.621 -0.817 0.000 0.569 2.087 0.815 0.000 0.930 -0.289 0.216 3.102 0.984 1.042 0.983 0.723 0.375 0.918 0.986 +1 0.625 -0.581 0.952 1.814 1.733 1.494 -0.925 -0.238 1.087 0.768 -0.362 1.299 2.215 0.441 -0.396 1.636 0.000 0.484 -1.153 -1.126 0.000 0.644 0.873 0.989 0.543 1.613 1.133 0.867 +1 0.282 -0.038 0.753 0.129 1.713 1.088 1.135 -0.984 2.173 1.153 1.030 1.002 2.215 0.656 0.901 -0.295 0.000 0.736 -0.278 0.232 0.000 0.648 0.966 0.608 0.488 1.610 0.912 0.708 +1 0.728 1.193 1.172 1.265 -1.518 1.591 -0.702 0.414 0.000 1.005 2.186 0.308 0.000 1.749 1.475 -1.567 0.000 1.780 0.022 1.448 3.102 0.923 0.960 0.988 0.764 0.287 0.719 0.981 +0 0.521 -1.252 -1.330 1.137 -1.274 1.091 -0.228 -0.701 2.173 1.189 -1.964 0.395 0.000 0.946 -0.794 0.713 0.000 0.618 -0.506 1.521 1.551 1.088 0.866 0.995 1.823 0.806 1.170 1.186 +0 0.425 -0.942 -0.408 1.318 -1.666 0.931 2.046 -0.693 0.000 1.869 0.591 0.778 2.215 1.306 0.330 1.302 0.000 2.187 0.303 -0.676 3.102 0.738 1.018 0.989 1.878 1.777 1.458 1.139 +1 1.152 -0.412 -0.081 0.687 0.449 0.871 -1.778 -1.543 0.000 0.630 -0.600 -1.460 0.000 0.619 -1.361 0.102 0.000 1.057 -1.064 1.658 1.551 0.988 1.231 0.985 0.813 0.780 0.745 0.815 +1 1.073 1.471 -0.864 0.993 0.145 0.655 2.136 0.577 0.000 1.569 0.246 -1.630 2.215 0.466 2.203 1.451 0.000 1.315 0.437 -0.125 1.551 0.715 1.036 1.129 1.403 1.279 1.183 1.034 +0 0.688 0.289 -0.299 0.329 -1.276 0.630 2.799 0.546 0.000 0.519 -1.509 -1.140 2.215 1.299 -1.524 0.950 0.000 0.580 -0.520 -0.535 0.000 0.899 1.022 0.992 0.659 1.087 1.650 1.429 +0 1.109 0.433 -0.468 1.175 0.241 0.701 -1.039 1.730 0.000 0.377 1.571 1.412 2.215 0.591 0.098 0.501 2.548 0.502 -1.305 -0.971 0.000 0.634 0.792 0.985 0.794 0.555 0.787 0.868 +0 0.946 1.536 0.058 0.724 -0.300 1.641 1.067 0.425 2.173 1.580 -0.824 -1.154 0.000 2.171 0.012 -1.604 2.548 1.229 -2.323 1.184 0.000 2.579 2.312 0.994 0.914 2.625 2.842 2.275 +1 0.617 1.733 -0.695 1.110 1.042 1.355 0.182 -1.722 1.087 1.529 -1.026 -0.209 0.000 0.911 -0.820 0.991 2.548 0.591 1.563 -0.394 0.000 2.547 1.688 1.147 1.466 1.186 1.458 1.530 +1 1.852 0.057 -1.327 1.108 1.144 1.124 -0.445 0.673 2.173 0.650 0.023 -0.280 2.215 0.468 0.818 0.158 0.000 1.160 -1.182 -0.682 0.000 0.670 0.666 1.572 1.054 0.999 1.013 0.796 +0 0.869 0.150 -0.228 1.577 0.290 1.197 1.356 -1.683 1.087 0.635 1.798 1.358 0.000 0.743 2.004 0.641 0.000 0.851 -1.142 -1.462 3.102 0.799 1.347 0.987 1.212 1.979 1.515 1.322 +0 1.687 -0.743 0.796 1.179 1.013 2.338 0.190 -0.846 2.173 1.664 -1.004 0.541 0.000 0.810 0.648 -0.799 2.548 1.504 -0.334 -0.332 0.000 1.602 1.480 0.999 2.247 0.457 1.549 1.466 +0 1.525 0.148 -0.799 0.902 -1.485 1.169 -1.336 1.029 1.087 0.726 -1.975 0.299 0.000 1.173 -0.496 -0.983 0.000 1.220 -0.316 1.184 1.551 0.972 0.808 0.984 1.646 0.660 1.081 0.914 +0 0.348 -0.907 0.642 0.978 -0.753 1.609 0.838 -0.160 1.087 2.021 -0.391 1.533 2.215 0.926 0.143 0.604 0.000 1.303 0.069 -1.558 0.000 1.127 1.077 0.989 2.618 3.186 2.117 1.618 +0 0.701 -0.799 -0.975 0.621 -1.159 1.172 -1.742 1.184 0.000 1.609 0.733 -0.314 2.215 0.452 -0.745 0.490 0.000 0.637 -0.571 1.438 3.102 0.998 0.662 0.996 0.982 1.162 1.587 1.303 +1 0.299 1.678 -0.207 1.484 -0.899 0.966 0.987 -0.482 2.173 0.297 2.175 1.023 0.000 0.919 -0.041 1.075 0.000 0.379 1.336 1.696 3.102 0.924 0.783 0.993 0.736 0.624 0.902 0.799 +0 1.447 0.708 -0.072 0.402 0.199 1.086 0.723 -0.923 2.173 1.208 0.486 1.071 2.215 0.656 2.218 1.494 0.000 0.553 0.968 -1.527 0.000 0.527 0.928 0.989 1.044 1.653 1.042 0.912 +0 1.186 1.102 0.204 0.191 -0.201 0.836 -2.042 0.858 0.000 0.837 -0.530 -0.917 2.215 1.103 1.021 -1.524 0.000 1.402 0.403 -1.154 0.000 0.632 0.919 0.983 0.510 0.750 0.705 0.727 +1 1.178 0.413 -0.465 1.224 -1.463 1.037 0.708 0.693 0.000 0.561 -0.348 0.362 0.000 1.109 0.341 -1.441 2.548 0.671 -1.031 -0.785 3.102 1.028 1.123 1.301 0.740 0.695 0.879 0.868 +1 0.598 -1.435 0.398 1.088 -1.164 0.684 -0.904 0.101 2.173 0.911 -1.215 1.288 0.000 1.052 -0.182 1.330 0.000 1.886 -0.185 -0.505 3.102 0.906 0.966 1.102 0.922 0.755 0.729 0.747 +0 0.847 1.179 -1.039 1.368 -1.346 1.598 1.575 0.489 2.173 1.243 -0.272 -1.603 1.107 0.324 0.160 0.237 0.000 0.768 1.562 -0.414 0.000 0.608 0.847 0.983 1.764 2.981 1.777 1.306 +0 0.714 1.317 -1.712 0.959 1.663 1.010 0.010 -1.075 2.173 0.863 1.840 0.742 0.000 1.623 0.889 0.299 2.548 0.858 1.811 0.358 0.000 0.789 0.952 0.988 0.893 1.708 1.250 1.090 +1 0.359 1.363 0.130 0.783 1.588 1.052 -1.057 0.267 0.000 0.948 0.501 -0.992 2.215 0.832 -0.601 1.344 2.548 0.524 -0.414 1.721 0.000 1.143 0.902 0.981 0.747 1.003 0.949 0.830 +1 0.790 -0.299 1.378 1.729 -1.729 1.012 -0.899 0.180 2.173 0.687 -0.128 0.039 0.000 1.620 -0.390 -1.362 2.548 0.739 -1.797 0.015 0.000 0.788 1.008 0.999 0.797 1.616 1.064 1.023 +1 0.552 1.138 0.037 1.701 -0.060 0.905 0.310 1.545 2.173 0.868 1.133 1.550 0.000 1.112 -0.243 -1.115 2.548 1.414 -0.557 -0.012 0.000 2.081 1.395 0.998 1.270 0.922 0.986 0.992 +0 1.524 1.498 -1.736 0.578 -1.106 0.594 -0.362 -0.811 0.000 0.767 0.914 1.116 1.107 1.181 0.357 -0.364 0.000 2.530 0.838 0.506 3.102 0.831 1.183 0.981 1.264 0.658 0.972 1.090 +0 0.918 -1.183 1.706 0.657 -0.908 0.633 -1.169 -0.809 0.000 0.776 -1.040 0.794 1.107 1.051 -1.412 0.211 2.548 0.740 -1.330 1.560 0.000 0.911 0.911 0.989 0.895 0.535 0.689 0.666 +1 0.957 0.098 1.073 1.421 -1.659 1.546 -0.670 -0.233 2.173 1.539 1.046 1.485 2.215 0.667 -0.376 -0.640 0.000 0.911 -0.877 0.274 0.000 0.690 0.640 1.016 0.902 3.200 1.604 1.263 +1 0.511 0.488 0.454 1.708 -0.561 0.567 0.471 0.924 0.000 1.259 -0.153 1.512 2.215 0.547 -0.798 -0.891 2.548 0.372 -1.061 0.558 0.000 0.732 0.759 1.025 0.722 0.799 0.839 0.754 +1 0.468 0.755 -0.715 0.455 -0.858 0.648 1.114 0.868 0.000 1.271 0.497 1.190 2.215 1.252 1.627 -0.608 0.000 1.438 -0.436 -0.099 0.000 1.665 1.045 0.983 1.084 0.587 0.882 0.978 +1 0.947 -1.734 1.738 0.854 -0.164 1.258 1.374 -0.534 0.000 1.244 -0.345 1.712 0.000 1.372 -1.133 1.463 2.548 1.180 -0.476 0.451 3.102 0.887 1.146 1.233 0.879 0.838 0.735 0.851 +1 0.672 -0.078 1.170 1.109 -0.142 0.923 -0.331 1.683 2.173 0.715 -0.892 -0.063 0.000 0.861 -2.060 -0.595 0.000 0.633 -0.388 -0.371 3.102 0.941 0.778 1.106 0.993 0.779 0.871 0.880 +1 1.321 -0.501 -1.721 0.785 0.955 0.419 -0.989 0.146 0.000 1.245 -1.063 -0.729 1.107 0.397 -0.758 -1.328 2.548 0.892 -0.100 0.052 0.000 0.874 1.030 0.990 0.530 0.396 0.673 0.649 +0 0.512 1.691 -1.331 0.552 0.373 1.198 0.838 1.315 2.173 1.404 0.310 -0.260 0.000 0.540 -0.149 -0.932 0.000 0.851 -0.241 1.537 1.551 0.827 0.880 0.980 0.856 0.674 0.990 0.823 +1 0.982 -0.229 -0.513 2.079 -1.097 0.958 1.377 -0.057 0.000 1.838 -0.866 1.253 1.107 0.511 0.332 -1.728 2.548 0.694 -1.417 0.006 0.000 0.704 1.081 0.993 1.624 0.825 1.040 1.113 +1 1.091 -1.010 0.509 0.501 1.338 0.808 0.018 -0.627 2.173 0.425 0.724 1.649 0.000 0.837 2.038 0.871 0.000 0.425 -0.526 1.116 0.000 0.953 0.771 0.992 1.003 0.790 0.878 0.992 +1 0.372 -1.892 0.776 0.680 -0.785 0.806 0.746 -0.040 2.173 0.646 -0.934 -1.720 0.000 1.362 -0.355 1.195 0.000 0.634 -0.010 -0.593 0.000 0.822 0.554 0.989 1.039 0.671 0.862 0.772 +0 1.050 -0.001 -1.376 0.020 0.805 0.742 0.660 0.805 0.000 0.449 -1.209 -0.417 2.215 0.679 -0.535 -0.673 1.274 0.445 -0.135 0.680 0.000 0.388 0.918 0.461 0.428 0.238 0.653 0.585 +0 0.904 1.056 0.005 0.710 0.367 1.104 0.943 1.376 0.000 0.911 1.294 -0.810 2.215 0.370 1.300 -1.609 1.274 0.767 1.655 -0.306 0.000 1.597 0.873 0.990 0.853 0.408 0.734 0.763 +1 0.541 -1.546 -0.236 1.359 1.513 1.211 -1.154 -0.146 2.173 1.203 -1.542 1.533 0.000 1.003 -0.439 -0.550 2.548 0.660 -0.964 0.666 0.000 0.855 1.091 1.187 1.188 0.684 0.964 0.879 +0 1.036 0.784 1.434 1.159 0.645 0.872 0.520 -1.043 2.173 0.852 0.779 -0.635 0.000 1.874 0.418 0.517 1.274 1.150 -0.359 1.514 0.000 0.874 1.007 0.990 0.752 1.572 0.957 0.895 +1 0.914 -0.525 0.572 0.679 -1.285 1.191 0.337 1.619 2.173 0.820 0.434 0.604 0.000 1.828 -0.379 -0.370 0.000 1.657 -0.428 -0.949 1.551 0.399 0.587 1.086 1.021 1.274 0.962 0.836 +1 0.620 0.647 -0.146 1.161 -1.632 0.655 -1.134 0.078 0.000 1.126 -0.900 -1.117 2.215 1.084 -0.192 1.127 0.000 0.637 0.863 -0.773 3.102 1.066 1.102 1.144 1.128 0.908 0.957 0.960 +0 0.756 -0.342 -0.004 0.726 1.286 0.692 0.599 -0.880 2.173 0.485 1.135 -0.486 2.215 0.582 -0.243 0.536 0.000 0.731 1.190 1.430 0.000 0.859 0.887 0.984 0.727 0.384 0.620 0.604 +1 0.810 -0.507 0.384 0.888 1.317 0.867 -1.311 -0.965 0.000 0.920 -1.167 0.570 2.215 1.116 -0.425 -0.699 0.000 1.072 0.097 1.420 3.102 0.887 1.098 0.991 0.790 0.888 0.923 0.865 +0 1.542 1.087 0.611 0.407 0.153 0.475 -2.321 1.049 0.000 1.036 -1.229 -0.647 1.107 1.142 -0.144 -1.375 2.548 0.503 -1.309 1.622 0.000 1.177 1.130 0.982 1.068 0.975 1.145 1.194 +1 0.385 0.857 -1.130 1.611 1.466 1.363 0.351 0.016 2.173 0.393 -0.509 1.735 0.000 0.379 0.639 1.053 2.548 1.131 0.402 -0.900 0.000 0.755 1.138 0.994 0.579 0.737 1.061 0.940 +1 1.010 -0.625 -0.011 0.383 1.620 0.822 -0.547 -1.667 0.000 0.354 -1.294 1.425 0.000 0.917 0.342 0.170 2.548 0.900 0.191 -1.117 3.102 0.755 0.684 0.990 0.632 0.639 0.517 0.506 +0 0.824 -0.613 -0.018 0.446 -1.459 1.084 -0.982 -0.750 1.087 1.622 1.438 0.938 0.000 1.488 2.255 -1.242 0.000 2.901 0.512 1.231 3.102 0.938 0.959 0.990 0.765 2.483 1.806 1.545 +1 1.115 -0.809 -1.350 0.766 -0.213 1.114 -1.178 1.240 0.000 1.833 -0.548 -0.970 2.215 2.052 -0.726 0.318 0.000 1.338 -0.088 1.632 3.102 0.917 0.824 1.094 0.784 1.062 0.839 0.738 +0 2.743 0.288 -1.303 0.672 -0.736 1.816 -1.435 0.680 1.087 0.704 -1.449 0.044 1.107 1.750 -0.910 -1.157 0.000 1.002 -0.533 0.585 0.000 1.484 1.106 0.993 2.713 0.904 1.800 1.471 +0 0.418 0.799 1.673 0.528 -1.454 0.977 -0.306 -0.048 1.087 0.768 -1.166 -1.382 0.000 0.583 -1.048 0.599 0.000 0.697 0.807 1.098 1.551 1.003 0.978 0.987 0.990 0.958 0.845 0.754 +0 1.177 -1.178 -0.344 0.161 1.077 0.549 0.131 -0.908 0.000 0.851 -0.954 0.771 2.215 0.507 -0.232 1.512 0.000 0.725 -0.570 1.208 3.102 0.937 1.033 0.994 0.639 0.292 0.630 0.620 +1 0.594 0.915 0.073 0.854 -1.619 1.197 -0.380 0.974 1.087 0.848 -0.812 -1.081 0.000 0.648 -0.581 -0.152 2.548 0.519 -0.368 1.466 0.000 0.887 1.222 0.987 0.790 0.943 0.866 0.804 +1 1.288 -0.597 -1.190 2.446 -1.338 1.357 1.603 0.658 0.000 0.688 -0.030 -1.473 0.000 1.176 0.181 -0.238 0.000 1.301 -0.251 0.723 1.551 0.830 0.737 0.982 1.092 0.267 0.935 0.925 +1 1.105 1.192 1.173 0.676 0.359 0.945 0.736 1.559 2.173 1.215 2.299 -1.104 0.000 1.028 2.122 -0.095 0.000 0.922 1.277 0.574 3.102 0.791 0.653 0.985 0.930 0.865 0.887 0.782 +0 0.936 1.106 -1.311 0.717 1.476 0.441 0.054 -1.381 0.000 0.635 1.199 -0.002 1.107 1.050 -0.509 0.130 2.548 0.443 1.650 0.312 0.000 1.012 0.993 0.991 0.830 0.893 0.992 0.840 +0 0.724 -0.133 -1.375 1.353 -0.875 1.534 0.876 0.684 2.173 0.517 1.248 0.471 0.000 1.621 -0.399 -0.908 0.000 1.863 0.000 1.507 3.102 0.683 0.977 0.985 1.535 1.458 1.152 0.939 +0 0.711 0.427 1.648 0.862 1.368 0.456 -1.998 -0.954 0.000 1.134 -1.110 0.147 2.215 0.535 -1.132 -0.851 2.548 0.470 -0.463 1.201 0.000 0.867 0.918 0.992 0.700 0.649 0.752 0.701 +0 0.441 1.675 -1.415 0.804 0.367 1.767 0.190 -0.725 2.173 1.638 1.090 0.970 2.215 0.812 1.275 0.296 0.000 1.388 0.750 -1.514 0.000 0.909 1.200 0.986 2.148 2.776 1.741 1.426 +1 1.700 -0.424 -1.294 0.572 -1.057 0.740 -0.733 -0.783 0.000 1.960 -1.319 0.250 2.215 1.264 1.533 1.297 0.000 0.891 -1.819 0.660 0.000 0.868 0.879 0.983 0.542 1.278 1.195 0.996 +0 1.246 -1.193 1.361 0.574 0.328 0.594 -0.467 -1.062 2.173 0.557 -0.922 -0.554 0.000 0.969 -0.370 0.499 2.548 0.443 -1.947 0.746 0.000 0.770 0.738 0.990 0.665 0.933 0.686 0.630 +1 0.538 0.690 -0.179 1.195 -0.928 0.419 1.353 1.032 0.000 0.581 -0.160 -0.501 2.215 1.067 -0.490 0.765 0.000 0.849 0.233 -1.287 1.551 1.357 0.970 0.987 0.874 0.436 0.711 0.831 +0 0.992 -1.496 -0.831 0.806 1.709 1.118 -0.779 -1.099 2.173 1.240 0.182 0.337 2.215 1.330 1.281 0.537 0.000 1.106 0.452 1.061 0.000 0.852 0.902 0.986 0.884 1.884 1.381 1.496 +1 1.963 0.507 0.777 1.179 -0.738 1.353 -0.155 -1.415 2.173 0.549 0.923 1.473 2.215 0.579 -0.164 -0.941 0.000 1.763 -1.087 0.212 0.000 1.004 1.143 2.063 1.677 0.984 1.115 1.090 +0 1.304 1.487 -0.030 1.077 0.961 1.363 1.330 -1.425 0.000 1.301 0.623 0.497 1.107 1.045 0.547 -0.864 2.548 0.643 0.682 1.645 0.000 0.647 0.795 1.280 0.933 1.167 1.022 0.994 +0 0.396 -0.606 0.851 0.564 -0.887 0.757 0.072 -1.294 0.000 1.554 0.412 0.504 2.215 0.460 -0.665 0.179 1.274 0.966 1.096 -1.247 0.000 0.866 0.886 0.983 1.592 0.607 0.983 1.086 +1 1.411 -2.003 -0.452 0.329 0.224 0.687 -1.568 -1.417 2.173 1.022 -0.311 1.391 2.215 0.734 -0.960 -0.794 0.000 0.823 2.281 0.925 0.000 0.613 0.849 0.975 0.868 1.091 0.947 0.777 +0 0.878 -0.751 1.327 3.579 1.408 1.228 -1.540 -0.574 0.000 1.223 1.277 -0.747 0.000 1.139 1.249 0.496 2.548 1.530 1.266 -0.142 0.000 0.942 0.998 0.987 0.852 0.746 1.038 1.340 +0 0.675 -1.009 -0.347 1.295 -0.716 0.946 1.286 1.189 0.000 0.512 2.521 -1.234 0.000 0.459 0.754 -0.861 2.548 0.894 -0.051 1.146 0.000 1.033 0.848 0.986 0.872 0.610 0.603 0.831 +1 1.667 -0.565 0.759 1.695 1.215 1.524 2.534 -0.887 0.000 1.595 -1.159 -0.227 2.215 0.680 -1.005 -1.595 2.548 1.409 -1.178 0.974 0.000 0.729 1.160 0.987 0.815 1.046 1.081 0.876 +1 0.822 0.304 -0.666 1.194 -0.132 1.024 0.116 1.673 2.173 0.587 0.497 0.754 0.000 0.488 -0.871 -0.080 2.548 0.659 0.414 1.192 0.000 0.311 0.692 0.993 0.518 1.011 0.835 0.730 +0 1.133 -0.647 -0.998 0.112 1.470 0.848 -0.343 1.082 0.000 1.305 0.181 -0.200 1.107 1.737 1.726 -1.128 0.000 1.603 0.174 0.595 3.102 0.912 0.922 0.979 0.861 0.858 0.992 0.903 +0 0.593 0.250 -0.005 0.593 -1.536 1.027 -0.787 0.746 2.173 1.521 0.237 -0.282 2.215 2.315 -1.293 -1.360 0.000 1.642 -1.183 0.918 0.000 1.905 1.676 0.987 0.876 1.779 1.613 1.448 +0 1.550 -0.517 -0.243 1.418 -1.149 1.416 -1.681 -1.687 0.000 2.057 -0.067 0.424 2.215 0.548 -0.472 -1.688 2.548 0.938 1.006 0.208 0.000 0.819 0.752 1.495 1.549 1.097 1.546 1.396 +0 0.769 -2.183 0.341 0.621 0.870 0.685 0.452 -0.699 1.087 0.925 -1.167 -1.569 2.215 0.698 -0.263 0.496 0.000 0.554 -0.487 -1.667 0.000 0.646 0.790 0.981 0.888 1.364 1.041 0.818 +0 0.767 -0.494 1.477 1.613 0.797 0.715 -1.306 -1.343 0.000 0.910 -1.513 -0.492 0.000 0.794 -0.889 1.631 1.274 1.404 -0.996 -0.028 3.102 1.029 0.871 0.987 0.721 0.809 0.730 0.775 +1 1.079 -1.245 -1.356 0.789 -1.554 1.017 -0.603 0.136 2.173 0.555 -0.534 0.685 0.000 0.703 -0.828 1.088 2.548 0.448 0.813 -1.102 0.000 0.847 0.841 0.978 0.694 0.813 0.841 0.727 +0 1.117 -1.143 1.593 1.739 -1.417 0.309 -0.462 1.325 0.000 0.831 -1.035 -0.004 2.215 0.940 0.674 0.599 2.548 0.578 -0.890 0.290 0.000 0.989 0.877 0.982 1.666 1.083 1.220 1.185 +1 0.433 -0.543 0.418 0.843 1.568 1.073 0.753 -0.890 0.000 0.701 0.248 -0.434 2.215 0.724 -2.226 0.824 0.000 1.954 0.645 0.614 3.102 1.299 0.973 0.993 1.435 0.900 1.062 1.212 +1 1.255 -0.382 -1.548 0.619 -0.308 1.062 0.288 0.378 2.173 1.067 -1.041 -1.599 0.000 0.765 -0.695 -0.946 1.274 0.835 -0.466 0.414 0.000 1.009 0.666 1.099 1.151 1.214 0.994 0.854 +0 1.279 0.539 -1.468 0.312 0.991 0.667 -1.582 -0.684 0.000 0.945 -0.145 1.385 2.215 1.060 -0.101 -0.180 2.548 0.624 -2.206 0.456 0.000 1.002 1.129 0.984 0.662 1.050 1.015 0.981 +0 2.094 0.416 -1.638 0.462 -0.603 1.339 1.348 -0.356 2.173 1.630 1.087 1.506 0.000 1.023 -0.107 0.183 0.000 1.380 0.423 0.493 3.102 0.944 0.954 1.095 1.484 1.181 1.088 1.044 +0 0.680 -0.768 0.564 1.169 1.527 0.524 -0.053 -0.583 0.000 0.801 0.341 0.220 0.000 1.258 0.538 -1.330 2.548 1.138 -0.083 0.470 1.551 0.952 1.012 0.985 0.733 0.968 0.837 0.832 +1 2.188 0.051 -1.652 0.517 0.457 1.206 0.545 0.158 2.173 0.465 -1.881 0.693 0.000 1.532 0.412 -1.069 2.548 0.531 0.804 0.654 0.000 1.305 1.398 1.394 1.465 1.516 1.174 1.097 +1 1.760 -0.554 0.291 0.506 0.694 1.160 0.212 -1.118 2.173 0.438 0.053 -1.481 0.000 0.443 0.119 0.815 0.000 0.631 0.753 1.224 3.102 0.647 0.689 0.974 0.870 0.840 1.065 0.824 +1 1.685 0.469 -0.039 0.196 0.391 1.476 1.456 1.373 2.173 0.731 0.138 -0.689 0.000 0.820 0.594 -1.669 0.000 0.582 2.296 -0.783 0.000 0.967 0.686 0.993 1.476 1.074 0.994 0.930 +0 1.534 1.079 0.443 0.823 1.317 0.412 2.041 1.690 0.000 0.678 0.288 -0.426 2.215 0.429 0.380 -1.171 0.000 0.649 0.673 -1.384 3.102 0.946 0.949 1.104 0.714 0.481 0.647 0.653 +1 0.590 -0.970 -0.212 1.752 1.189 0.708 -0.681 -1.202 0.000 0.655 -0.142 1.271 1.107 0.765 -1.462 -0.995 0.000 1.625 2.174 -0.213 0.000 0.676 1.006 1.342 0.790 0.409 0.726 0.780 +0 1.707 0.327 -1.150 0.274 0.035 1.279 -1.306 0.536 0.000 0.510 -1.738 -0.003 0.000 0.999 -0.884 -1.137 0.000 1.211 -0.034 1.572 3.102 0.899 1.280 0.990 0.854 0.530 0.750 0.955 +0 0.422 0.755 -0.656 1.486 -0.123 0.597 0.739 1.603 2.173 0.661 -0.740 0.777 0.000 0.728 -1.106 -1.175 2.548 0.514 2.157 1.515 0.000 2.012 1.281 0.989 1.804 1.060 1.274 1.225 +0 0.962 1.619 -1.155 0.727 0.245 0.492 1.882 0.800 0.000 0.593 0.460 -1.629 2.215 0.673 0.232 -0.533 2.548 0.753 0.898 1.112 0.000 0.696 0.756 1.104 0.750 0.565 0.618 0.593 +0 0.412 0.253 -1.542 1.399 -0.477 1.000 0.007 0.499 2.173 0.800 1.572 -1.377 0.000 0.727 0.762 1.587 1.274 0.465 0.210 0.159 0.000 0.976 0.667 0.988 1.143 0.992 0.844 0.792 +1 0.953 0.033 -1.679 1.534 -1.613 0.872 0.529 0.135 1.087 1.122 0.872 0.961 2.215 0.880 -1.567 -0.103 0.000 0.937 0.480 -0.312 0.000 0.705 0.730 0.989 1.304 1.021 0.994 0.789 +0 0.297 1.121 1.505 1.861 -0.493 0.685 -0.992 1.602 0.000 0.759 0.430 0.642 2.215 0.564 -0.762 0.440 0.000 1.102 0.159 -1.512 3.102 0.973 1.009 1.003 0.813 0.777 0.727 0.937 +0 0.860 0.689 -0.496 0.426 0.476 1.227 0.027 0.623 1.087 0.886 -0.615 1.652 2.215 0.383 0.137 0.360 0.000 1.674 -0.877 -1.114 0.000 1.027 0.830 0.988 0.899 1.331 0.941 0.832 +1 0.812 0.543 0.987 0.471 -0.626 0.669 -1.155 0.297 0.000 0.908 0.179 -1.363 2.215 0.975 0.443 0.024 2.548 0.642 -1.721 -1.582 0.000 1.098 1.191 0.987 0.731 0.961 0.975 0.828 +0 0.318 1.916 -1.528 1.226 -0.246 0.461 0.163 1.165 2.173 0.359 1.899 1.459 0.000 0.665 1.098 0.421 2.548 0.536 2.082 -0.759 0.000 0.544 0.830 0.984 0.595 0.570 0.580 0.582 +0 0.901 0.831 -1.387 4.221 -1.484 1.743 -1.194 0.087 0.000 0.867 -0.058 0.357 2.215 0.780 0.780 1.346 2.548 0.887 -0.033 0.785 0.000 1.272 1.104 0.988 0.872 0.797 1.252 1.708 +1 1.020 -0.240 -0.819 1.867 0.123 1.243 2.916 1.640 0.000 1.422 -0.629 -0.174 0.000 1.607 -0.531 1.497 0.000 1.218 0.206 -0.076 3.102 2.315 1.445 1.436 1.076 0.673 1.020 0.949 +0 0.911 -1.205 0.164 0.787 -1.690 1.524 -0.580 -0.014 1.087 1.700 0.042 -1.671 0.000 0.839 0.887 -0.367 0.000 1.374 -1.011 0.986 0.000 0.578 1.540 1.167 1.115 1.804 1.354 1.185 +1 0.792 0.613 1.578 1.155 -1.316 2.242 -0.825 -0.035 0.000 1.424 -0.087 1.543 1.107 1.393 -0.092 0.973 2.548 1.249 0.508 -1.268 0.000 0.827 0.894 0.981 0.697 0.735 0.664 0.626 +1 0.996 -0.088 -1.723 0.728 -0.582 0.423 0.613 -0.434 2.173 0.772 0.651 -1.406 2.215 0.608 -0.967 -0.144 0.000 1.086 0.429 0.340 0.000 0.870 0.995 1.011 0.683 0.646 0.653 0.624 +1 0.819 -1.525 -0.766 0.873 1.129 0.731 -0.920 -0.389 0.000 0.500 -0.073 1.730 0.000 0.974 -0.295 1.031 0.000 0.936 0.894 -1.058 3.102 0.645 0.790 1.161 0.680 1.014 0.868 0.750 +0 0.382 1.907 0.741 0.098 -0.924 0.461 0.789 0.404 0.000 1.287 0.096 -1.020 2.215 0.434 -0.826 0.254 2.548 0.821 0.044 1.141 0.000 0.681 1.071 0.990 0.639 0.836 0.700 0.636 +0 0.625 0.544 -1.069 0.506 0.281 0.560 -0.650 -1.569 0.000 0.936 -0.797 0.513 2.215 0.875 0.185 1.166 2.548 0.863 1.837 -0.844 0.000 0.835 0.856 0.985 0.764 0.742 0.859 0.840 +1 1.636 -0.107 1.626 0.151 0.679 0.668 0.901 -0.472 2.173 0.392 -0.551 1.025 0.000 1.585 0.536 0.402 2.548 1.238 -0.244 -1.031 0.000 0.881 0.965 0.988 1.001 0.932 0.871 0.792 +1 1.095 -0.028 1.404 0.586 0.748 0.653 -0.676 0.787 0.000 1.153 0.041 -1.451 2.215 1.093 0.184 0.472 0.000 1.069 0.617 -0.444 3.102 0.815 0.953 0.978 0.863 0.866 0.885 0.793 +0 1.015 0.154 1.097 0.784 1.205 1.176 0.768 -0.982 2.173 1.263 1.747 1.063 0.000 1.677 1.427 -0.314 0.000 0.875 1.182 0.679 0.000 0.861 1.159 0.991 0.811 0.326 0.991 1.065 +1 1.665 -0.167 0.929 0.991 -0.138 1.314 -1.467 -0.968 2.173 0.623 -2.753 0.752 0.000 0.679 0.065 1.592 0.000 0.685 -1.024 -1.653 3.102 2.128 1.185 1.459 1.800 0.594 1.136 1.198 +0 0.655 -0.854 -1.038 1.626 -1.230 0.813 -0.356 0.861 1.087 0.322 2.792 -0.137 0.000 0.803 -0.822 0.517 2.548 0.524 0.857 -0.547 0.000 0.600 1.371 1.003 1.497 0.414 1.086 1.649 +0 2.814 1.249 -0.195 0.544 1.183 1.116 0.659 0.255 2.173 2.117 -0.086 1.738 1.107 0.688 -2.364 1.356 0.000 0.883 1.439 -1.264 0.000 3.737 2.546 1.623 1.105 2.367 1.996 2.005 +0 0.984 -0.227 -0.807 1.883 -1.121 0.602 -1.129 0.765 0.000 0.679 0.790 0.117 2.215 0.633 0.278 0.605 0.000 0.569 -0.912 1.471 1.551 0.888 0.975 0.993 0.665 0.814 0.869 0.874 +1 0.724 -0.539 0.979 1.230 0.258 0.406 -1.160 -0.796 1.087 0.618 1.000 -1.460 0.000 0.530 -0.930 1.683 1.274 0.561 0.033 0.316 0.000 0.863 0.941 0.996 0.665 0.457 0.628 0.742 +0 1.771 1.430 -1.317 1.005 -1.657 2.017 0.973 0.329 2.173 1.638 0.053 -1.562 2.215 1.384 -0.076 -0.440 0.000 1.237 0.301 0.910 0.000 1.392 1.383 0.996 1.966 2.946 1.685 1.427 +1 0.741 -0.897 -1.012 0.570 -1.041 1.318 -0.287 -0.145 0.000 1.097 -0.642 1.584 2.215 1.767 -1.274 0.871 2.548 1.251 0.010 1.320 0.000 0.830 1.074 0.976 1.007 1.048 0.912 0.868 +0 1.203 -1.280 1.044 0.561 1.738 1.449 -0.130 0.895 2.173 1.366 -0.196 -0.703 0.000 1.280 0.381 -1.051 2.548 0.602 -0.918 -0.229 0.000 0.921 0.856 0.992 0.936 1.733 1.205 1.062 +0 0.943 -1.171 -0.799 0.607 -0.149 0.934 -1.570 -1.099 2.173 1.199 -2.631 0.883 0.000 1.309 -1.370 0.718 2.548 0.655 -0.508 -0.482 0.000 1.809 1.185 0.986 0.914 1.375 1.081 1.058 +0 0.532 -0.051 0.299 0.856 -1.708 1.207 1.661 0.543 0.000 0.793 1.166 -0.169 2.215 0.800 0.905 0.968 0.000 3.044 0.617 -1.456 3.102 0.901 0.941 0.987 0.805 1.326 1.153 0.962 +1 0.283 1.950 -0.709 0.896 1.250 1.061 1.257 -1.517 0.000 1.402 0.759 0.363 2.215 0.983 0.656 -0.450 2.548 0.562 1.300 1.050 0.000 0.886 0.986 0.991 0.858 0.835 0.948 0.803 +1 1.039 -0.094 0.708 1.258 1.289 1.013 -0.687 -0.574 2.173 0.537 -0.562 0.516 0.000 0.551 -1.487 0.632 0.000 0.605 0.553 -1.311 3.102 0.469 0.948 0.997 0.658 0.793 0.935 0.831 +0 1.053 -0.766 -0.682 0.479 0.737 0.855 -0.834 0.172 1.087 0.464 -1.537 1.207 2.215 0.974 -1.017 -1.032 0.000 1.060 -1.535 1.701 0.000 0.913 1.114 0.986 0.709 0.822 0.759 0.670 +1 0.844 0.445 -0.041 0.150 1.562 1.254 1.260 -0.134 2.173 1.849 1.394 -1.716 2.215 0.449 1.315 -1.013 0.000 0.497 0.879 0.495 0.000 0.640 0.783 0.993 1.509 2.225 1.368 1.028 +1 0.919 -0.426 -0.336 0.495 -0.147 0.816 -0.468 1.345 0.000 0.634 -0.630 -1.334 2.215 0.712 1.031 1.021 2.548 0.909 -2.463 -0.486 0.000 2.467 1.512 0.980 1.205 0.945 1.310 1.112 +1 1.391 1.046 -0.479 1.945 -1.100 1.225 0.565 0.811 2.173 0.638 0.566 -1.656 1.107 0.375 -0.242 0.845 0.000 0.538 0.111 -0.022 0.000 0.447 0.552 1.209 0.872 1.034 1.135 0.842 +0 0.347 -1.265 -0.788 0.666 1.196 0.738 -2.518 -1.357 0.000 0.402 -2.857 -0.316 0.000 1.011 -0.839 0.566 2.548 0.629 -0.604 -0.347 0.000 0.884 0.852 0.979 0.558 0.357 0.592 0.672 +0 0.476 0.704 -0.342 1.287 0.069 0.871 -1.401 1.684 2.173 0.574 0.591 -1.326 2.215 0.587 -2.209 0.612 0.000 0.678 -0.578 -0.482 0.000 0.895 0.945 0.989 0.882 1.314 1.003 0.910 +1 0.773 0.965 -0.158 0.632 -1.162 0.660 0.879 -1.466 0.000 0.775 1.477 -0.984 1.107 1.732 0.174 0.534 2.548 0.705 0.592 0.940 0.000 0.891 1.074 0.982 1.142 1.493 0.903 0.844 +0 0.930 0.192 0.893 0.632 -0.962 0.527 -1.255 -0.858 2.173 0.759 0.773 0.234 2.215 0.813 2.611 1.099 0.000 1.362 1.627 -1.434 0.000 0.851 0.929 1.056 0.917 1.378 1.279 1.027 +1 1.297 -0.025 -0.510 0.437 -0.018 1.131 0.322 0.067 0.000 1.249 2.397 1.243 0.000 2.017 0.420 -1.127 2.548 1.207 0.985 0.101 0.000 0.950 1.211 0.984 0.678 0.713 0.739 0.695 +1 1.719 -0.291 -0.375 1.296 0.956 1.201 -0.230 -0.860 2.173 0.996 0.259 1.646 2.215 0.864 0.070 0.257 0.000 0.688 -1.330 -1.538 0.000 0.913 0.795 1.928 1.384 1.308 1.116 0.959 +0 0.843 1.415 0.073 1.151 -0.419 1.254 -1.318 -1.035 0.000 1.018 -0.151 0.908 2.215 0.964 1.235 1.555 0.000 1.038 -1.372 -1.494 0.000 0.909 1.388 0.984 1.266 0.673 1.174 1.077 +1 1.149 0.932 1.718 0.739 -0.164 1.262 0.461 0.631 0.000 1.337 -1.573 -0.825 0.000 1.944 -0.064 1.718 2.548 1.338 0.521 0.132 3.102 0.901 1.275 1.267 1.059 1.296 1.089 1.108 +0 0.805 0.035 -0.385 0.649 0.696 1.300 -0.584 -0.927 2.173 0.792 0.124 0.849 2.215 1.405 -1.619 -1.484 0.000 2.625 -1.612 0.762 0.000 1.912 1.833 0.990 0.981 1.589 1.547 1.237 +1 1.275 2.137 -1.509 1.461 -0.172 0.933 0.510 1.019 2.173 0.455 0.867 -1.676 0.000 0.726 1.134 -0.557 0.000 0.559 0.538 -0.213 1.551 0.762 0.966 1.766 0.979 0.687 1.126 0.906 +1 0.415 1.146 1.261 0.559 -0.892 0.908 -0.319 0.543 0.000 0.716 -0.645 1.135 0.000 1.925 -1.073 -1.116 1.274 0.683 -1.525 -0.713 0.000 0.919 0.854 0.992 0.974 0.966 0.995 0.858 +1 0.574 -2.051 1.238 1.484 -1.151 0.537 -2.667 -0.899 0.000 1.060 0.053 0.433 2.215 0.413 -0.213 0.076 0.000 0.397 -1.119 0.203 0.000 0.782 1.329 1.068 1.681 0.755 1.211 1.120 +1 1.169 0.480 -0.727 0.768 -0.599 1.149 -0.167 1.358 1.087 0.387 -0.989 0.083 2.215 0.436 1.265 -0.976 0.000 0.555 0.956 0.173 0.000 0.471 1.036 1.002 0.676 0.993 0.896 0.786 +0 1.162 1.122 -1.049 0.480 1.708 1.543 0.872 -0.492 2.173 1.163 -1.207 1.004 0.000 1.064 -0.916 1.492 0.000 1.125 0.296 0.678 1.551 0.755 0.969 0.983 1.011 1.268 1.639 1.351 +0 0.488 -2.176 -0.504 3.286 -0.107 1.953 -1.331 1.631 1.087 0.478 -0.872 -1.358 0.000 0.779 -0.706 0.380 2.548 0.373 -1.455 -0.322 0.000 0.504 0.840 0.972 0.746 1.457 1.467 1.080 +1 1.404 -0.574 1.044 1.573 1.541 0.380 0.203 -0.777 0.000 0.585 -0.865 1.486 0.000 1.587 -0.374 -0.224 2.548 1.071 0.836 -0.622 3.102 1.063 0.985 0.989 1.324 0.841 1.102 0.922 +0 0.977 1.238 1.071 0.767 0.128 0.679 0.653 -1.073 2.173 0.725 0.453 0.220 2.215 0.593 0.823 -1.467 0.000 1.519 0.189 1.562 0.000 0.810 0.775 0.989 0.919 0.955 0.697 0.637 +0 0.713 -0.977 -0.273 0.562 1.301 0.801 -0.281 0.032 2.173 0.982 -1.762 -1.240 0.000 0.644 1.129 1.193 2.548 0.810 -1.071 0.628 0.000 0.826 1.421 0.991 1.174 1.078 1.195 0.999 +0 0.473 0.280 0.937 0.552 -1.172 0.824 -0.120 0.750 0.000 0.935 -0.335 1.594 0.000 0.576 -2.485 -0.541 0.000 1.131 0.080 0.314 3.102 1.301 0.929 0.985 0.659 0.572 0.714 0.662 +1 1.880 1.683 1.391 0.678 0.438 0.687 -0.475 -1.475 0.000 1.250 1.326 -0.565 2.215 1.190 0.499 0.131 2.548 1.072 1.984 0.315 0.000 0.705 0.871 1.183 1.257 0.936 1.001 0.821 +1 1.223 0.954 0.544 1.366 1.125 0.967 0.249 -1.510 2.173 1.215 -1.295 -0.534 0.000 0.364 0.059 -0.533 1.274 0.399 -0.818 -1.490 0.000 0.707 1.390 0.992 0.756 0.575 0.876 1.221 +0 2.197 -1.095 -0.518 0.249 1.504 0.968 -0.454 0.142 2.173 0.966 -0.598 -1.230 0.000 1.666 -1.038 1.355 2.548 1.039 -2.367 1.283 0.000 0.992 0.951 0.993 0.950 1.509 1.013 0.906 +0 0.747 -1.363 1.295 0.653 -0.908 0.449 -2.227 0.369 0.000 0.596 0.449 1.153 2.215 0.621 -0.001 -0.581 2.548 0.671 -1.282 -1.073 0.000 0.863 0.903 0.982 1.094 0.664 0.837 0.783 +1 0.563 0.766 -1.404 1.130 1.064 0.829 -0.451 0.429 2.173 0.792 -0.924 -0.754 2.215 0.383 -1.168 1.656 0.000 0.424 -2.226 1.678 0.000 0.334 0.884 0.984 0.982 1.086 0.840 0.773 +0 0.348 -1.274 0.396 0.933 -1.576 0.795 0.939 1.230 0.000 0.977 0.737 -0.302 0.000 1.224 -0.104 -1.431 2.548 1.868 0.413 0.191 1.551 1.847 1.241 0.991 1.252 1.204 1.346 1.581 +1 1.946 1.190 -0.132 1.177 -0.592 0.930 0.963 -1.705 1.087 1.092 1.496 0.644 2.215 0.576 1.659 1.678 0.000 0.366 2.036 -1.492 0.000 0.214 0.650 0.982 1.383 1.337 1.101 0.854 +0 1.871 0.765 -1.514 0.093 1.481 0.586 -1.006 -0.479 2.173 0.856 -1.500 1.166 0.000 0.907 0.698 -0.032 2.548 1.215 -0.918 0.327 0.000 0.958 0.984 0.977 0.892 0.988 0.939 1.056 +1 2.088 0.777 0.525 0.263 -0.637 0.729 -0.605 -1.381 2.173 0.478 0.891 -0.095 2.215 0.670 -0.218 -0.937 0.000 0.803 0.961 -1.712 0.000 0.801 0.759 0.983 0.564 1.080 0.932 0.795 +0 0.608 0.357 1.542 0.345 0.454 0.829 1.626 0.251 2.173 0.742 1.222 -0.653 0.000 1.383 0.645 -1.690 0.000 1.023 1.454 -1.327 0.000 0.828 0.698 0.990 0.795 1.106 0.865 0.736 +0 0.789 0.004 -0.300 0.997 -1.326 0.858 0.317 0.012 0.000 0.968 -0.259 1.356 2.215 0.854 -0.112 -1.243 0.000 0.724 -0.281 0.855 3.102 1.434 0.925 0.987 0.854 0.331 0.758 0.704 +1 1.450 -0.258 0.094 0.563 0.632 0.576 0.869 -0.985 2.173 0.358 -1.118 -0.906 0.000 0.551 0.514 -1.692 2.548 0.580 0.814 1.134 0.000 0.950 0.836 0.996 0.752 0.432 0.688 0.642 +1 2.154 -0.461 -0.590 0.639 -0.435 0.983 0.066 1.562 2.173 0.824 0.221 0.443 2.215 0.575 0.325 -1.633 0.000 0.807 -0.417 0.470 0.000 0.786 0.727 0.988 0.966 1.126 1.031 0.825 +0 1.407 0.002 -1.705 0.547 -1.212 0.702 -0.442 -0.424 2.173 0.615 1.400 -1.461 0.000 0.722 1.071 0.382 2.548 0.732 1.314 0.078 0.000 0.862 1.220 0.995 0.858 0.994 0.833 0.794 +1 2.178 -0.161 -1.600 0.512 -0.812 0.457 0.257 -0.280 0.000 1.012 1.729 0.320 0.000 0.344 -0.889 -0.042 2.548 0.645 0.847 0.944 3.102 1.417 0.865 0.985 0.691 0.505 0.682 0.926 +0 0.805 0.089 -0.785 1.437 -0.133 0.768 -2.669 0.096 0.000 1.747 -0.814 1.323 2.215 1.233 -1.311 -1.076 2.548 1.490 0.217 -1.565 0.000 0.876 1.013 0.987 1.388 1.380 1.375 1.071 +1 0.916 0.395 1.343 1.113 -0.929 0.928 -1.044 1.565 2.173 0.815 -1.244 0.479 0.000 1.458 2.320 1.283 0.000 1.979 -0.948 -0.804 0.000 0.901 0.770 1.244 1.217 1.264 0.901 0.891 +0 0.666 1.839 1.718 0.602 0.661 1.133 0.581 -1.072 1.087 1.360 -0.479 0.289 0.000 0.745 -1.339 1.359 2.548 0.370 0.445 1.067 0.000 0.775 0.888 0.991 1.010 1.679 1.120 1.051 +1 0.848 -1.166 -1.525 0.438 -0.142 0.837 0.250 0.587 2.173 0.657 0.924 1.739 2.215 1.151 -0.229 -0.705 0.000 0.427 0.219 0.227 0.000 0.610 0.885 0.992 0.877 1.017 0.815 0.717 +0 1.611 -1.495 0.112 0.640 0.646 0.756 -0.446 -1.533 0.000 0.513 1.221 -0.239 0.000 0.725 0.808 1.002 2.548 1.709 0.310 -1.260 3.102 1.760 1.099 0.989 1.567 0.791 1.362 1.324 +1 0.748 0.541 1.116 1.108 0.246 0.888 -0.448 -0.682 0.000 0.502 1.019 -1.734 0.000 0.796 -1.341 -1.467 1.274 0.488 1.466 1.009 0.000 0.464 1.175 0.985 0.498 0.826 0.748 0.707 +1 0.371 -0.504 -0.644 1.314 0.718 0.728 0.555 1.234 2.173 0.864 0.594 -0.840 0.000 1.094 1.112 -1.212 0.000 1.594 0.142 0.302 3.102 0.674 1.087 0.987 0.731 0.879 0.870 0.794 +1 0.479 1.532 1.644 1.258 0.374 0.596 -1.460 -1.411 0.000 0.777 0.164 -0.827 2.215 0.426 0.755 1.325 0.000 0.922 -0.491 -0.872 3.102 1.072 0.926 0.986 1.140 0.299 0.881 0.800 +1 0.559 -1.952 -1.249 0.327 0.073 1.554 -0.992 0.946 2.173 1.168 -0.464 -0.416 0.000 0.913 1.058 -0.803 0.000 0.518 -1.177 -1.010 0.000 0.847 0.699 0.986 1.047 0.762 0.885 0.771 +0 0.920 0.594 0.958 0.506 -0.675 0.760 -0.224 0.955 0.000 0.830 -1.259 -0.470 2.215 0.632 -0.610 -1.658 0.000 0.938 0.310 -0.793 3.102 0.924 0.878 0.989 1.214 0.778 0.813 0.803 +0 0.725 -0.151 -1.293 1.845 -0.418 0.614 -0.416 0.599 2.173 0.844 0.909 1.447 0.000 0.721 -0.906 -0.983 0.000 0.850 -1.024 1.437 0.000 0.979 0.979 1.137 0.898 0.638 0.767 0.766 +0 1.142 -0.388 -1.531 0.440 0.548 0.909 -1.397 -1.004 2.173 1.388 -0.665 0.578 2.215 0.483 -2.008 -1.532 0.000 0.498 1.733 0.364 0.000 2.337 1.698 0.984 0.870 1.747 1.382 1.088 +0 0.407 1.116 1.122 0.508 1.593 0.728 -1.158 -1.629 2.173 0.693 0.003 -0.526 0.000 1.245 -0.138 0.018 0.000 0.485 1.244 0.761 3.102 0.680 0.728 0.984 0.796 1.224 0.887 0.773 +0 0.941 1.236 0.309 0.421 -1.729 1.072 -0.268 -1.018 2.173 1.029 -0.012 1.112 2.215 0.714 0.390 -0.114 0.000 0.766 0.124 0.651 0.000 0.531 0.964 0.989 1.058 1.466 1.172 0.919 +1 1.898 1.209 1.677 1.083 1.127 0.789 0.204 0.343 2.173 0.829 0.597 0.044 0.000 0.896 0.837 -1.000 2.548 0.472 1.382 -0.956 0.000 0.778 0.728 0.987 0.854 1.052 0.938 0.823 +0 2.078 0.116 1.589 0.687 -1.296 1.041 0.058 0.074 1.087 0.618 0.450 0.478 1.107 0.591 -0.480 -1.365 0.000 0.771 -0.715 -0.310 0.000 0.619 0.852 0.984 0.900 0.483 0.951 0.810 +0 1.543 0.633 -0.593 0.699 -0.723 1.187 1.320 1.010 2.173 0.527 1.689 0.190 0.000 1.164 -1.048 -1.006 1.274 1.050 -0.152 1.015 0.000 1.265 1.093 0.973 0.950 2.724 1.485 1.248 +0 1.528 -0.490 0.185 1.757 -0.094 0.714 -0.966 -0.363 2.173 1.141 0.650 1.302 0.000 1.764 -1.653 -1.292 0.000 1.726 -0.474 1.288 3.102 0.878 0.930 0.989 0.858 1.195 1.040 1.068 +1 2.925 -0.099 0.405 1.998 0.024 2.810 -0.445 -1.491 0.000 0.753 0.831 -0.707 0.000 0.887 -0.433 1.572 2.548 1.124 -0.502 -0.151 3.102 1.068 0.937 1.126 0.707 0.764 0.828 0.849 +0 0.583 -0.771 -0.660 2.118 -0.751 1.121 0.582 0.760 2.173 0.651 1.387 1.012 0.000 1.812 0.609 1.425 2.548 0.794 0.839 -0.147 0.000 0.834 0.879 0.980 1.516 1.004 1.221 1.018 +1 1.063 -1.308 0.583 1.148 0.628 1.123 -0.293 -1.074 2.173 0.592 -0.667 1.394 0.000 0.411 -1.775 0.286 0.000 1.318 -0.084 -0.206 1.551 0.815 0.838 0.983 1.907 0.917 1.345 1.053 +1 0.688 -0.766 -0.142 2.263 -0.518 0.956 -0.227 1.537 2.173 0.352 1.092 1.367 0.000 0.784 0.362 -0.140 0.000 0.639 -0.198 -1.442 0.000 0.842 1.001 0.977 0.889 0.837 0.988 0.842 +0 0.481 -0.444 -1.645 0.562 1.072 0.723 -1.154 -0.496 2.173 0.868 -0.175 -1.482 0.000 0.819 -0.779 0.873 0.000 0.529 0.107 -1.363 3.102 1.188 1.156 0.985 0.670 0.646 0.694 0.696 +1 0.565 0.263 0.543 1.933 0.794 0.721 0.563 -1.108 2.173 1.281 2.108 -0.517 0.000 0.846 0.139 1.511 0.000 0.569 1.448 -1.175 0.000 0.841 0.714 0.994 0.757 0.722 0.938 0.848 +1 0.441 0.802 -0.334 1.660 0.779 0.985 0.869 -1.440 0.000 0.892 0.055 -0.348 2.215 0.425 2.491 -0.158 0.000 0.574 -0.522 0.822 3.102 1.636 1.272 1.000 0.908 0.605 0.971 0.892 +1 0.534 -0.669 -0.692 0.991 0.309 0.906 0.144 -1.325 0.000 1.372 0.508 0.583 2.215 0.923 0.809 -0.903 0.000 1.382 0.131 1.189 3.102 0.875 0.989 0.987 1.412 0.685 1.009 1.112 +0 0.647 -0.899 0.728 1.828 0.395 1.032 -0.623 -1.280 1.087 0.824 -0.388 1.724 0.000 1.067 -1.951 -0.370 0.000 0.593 0.244 0.752 3.102 1.971 1.278 0.996 1.435 0.892 0.963 1.060 +1 1.044 -0.750 0.623 0.229 0.514 0.900 0.209 -1.175 0.000 0.701 -1.290 1.335 2.215 0.875 0.304 -0.646 0.000 1.105 -0.078 0.451 3.102 0.740 0.948 0.976 0.809 0.771 0.879 0.802 +0 0.451 0.161 -0.277 0.826 1.434 0.494 0.729 0.237 2.173 0.472 1.295 0.603 0.000 0.663 -0.876 -1.177 2.548 0.724 0.652 -1.408 0.000 0.769 0.900 0.985 0.665 0.964 0.683 0.612 +0 0.881 0.500 0.990 2.439 0.462 1.042 2.367 -0.563 0.000 1.659 0.682 1.662 0.000 1.394 -0.484 -0.332 2.548 1.389 0.204 -1.146 0.000 0.834 0.851 0.990 1.040 0.992 1.027 0.965 +0 1.409 -0.207 0.458 1.114 -0.008 1.252 0.411 -1.519 1.087 0.700 1.425 0.797 2.215 0.447 -1.605 -0.844 0.000 0.569 1.222 -0.920 0.000 1.344 1.243 0.995 1.308 1.417 1.319 1.137 +0 0.456 1.473 -1.394 1.272 -0.052 0.935 -2.623 -1.252 0.000 1.038 -0.064 0.183 2.215 0.903 0.756 -1.410 0.000 1.317 0.516 1.177 0.000 0.880 1.103 0.988 0.583 0.460 0.680 0.713 +0 0.484 0.081 -1.641 1.783 1.640 1.009 0.608 -0.083 0.000 1.281 0.111 -0.410 0.000 0.931 0.394 1.129 2.548 0.788 0.493 -1.679 3.102 0.929 1.001 0.991 0.947 0.379 0.813 1.092 +0 1.496 -1.081 0.476 0.509 0.031 0.897 0.243 -1.641 2.173 1.461 -0.152 0.787 1.107 1.100 0.839 -1.206 0.000 1.552 -0.189 -0.703 0.000 1.078 0.978 0.992 1.165 1.414 1.256 1.263 +1 0.533 1.277 0.857 0.958 1.422 0.837 0.584 -0.682 1.087 0.913 -0.999 0.251 0.000 1.016 -0.729 1.535 2.548 0.442 -0.983 -0.898 0.000 0.714 0.810 0.981 1.028 1.357 0.911 0.863 +1 1.070 0.556 -0.033 0.922 -1.274 0.976 0.463 -0.768 2.173 0.916 1.619 0.815 0.000 1.265 1.312 1.316 0.000 1.773 0.869 0.292 0.000 0.920 0.981 1.237 0.795 0.976 0.998 0.885 +1 1.064 -0.151 1.454 0.656 -1.265 0.819 -0.729 -0.449 0.000 1.092 0.621 0.436 2.215 0.789 1.178 -1.253 0.000 1.003 0.747 0.995 0.000 0.903 0.916 0.995 0.740 0.713 0.810 0.766 +1 1.625 0.938 -0.590 0.931 -1.687 0.934 1.613 -0.024 0.000 1.573 2.497 1.499 0.000 1.336 0.678 0.011 0.000 2.557 -0.621 0.823 1.551 0.919 1.162 1.423 1.747 1.029 1.367 1.238 +0 0.396 0.388 0.182 2.253 -1.550 1.976 0.037 -0.864 2.173 2.089 0.743 0.726 0.000 1.490 -0.135 0.498 2.548 1.115 -0.152 0.919 0.000 1.036 0.886 1.309 1.315 2.024 1.605 1.379 +1 1.777 -1.437 0.545 0.324 0.447 0.584 -0.888 0.906 0.000 1.861 -0.663 -1.038 2.215 0.332 0.051 -0.936 0.000 0.402 -1.405 -1.715 3.102 0.881 1.174 0.991 0.612 0.605 0.934 0.802 +1 0.593 -0.792 0.136 1.532 -0.676 0.681 0.531 1.226 1.087 0.536 -0.048 0.391 0.000 0.997 -0.818 1.686 2.548 0.487 1.149 -0.720 0.000 0.762 0.906 0.987 1.116 0.901 0.868 0.752 +1 0.879 -1.438 0.430 0.637 -0.212 0.715 0.352 -1.681 2.173 1.232 1.380 1.275 0.000 1.090 0.707 -0.688 0.000 0.853 0.444 0.489 3.102 1.063 1.113 0.983 0.689 0.769 0.781 0.789 +0 2.928 -0.278 0.129 0.425 1.354 1.341 -0.324 1.666 1.087 0.633 -0.717 -0.765 2.215 0.860 0.126 -1.065 0.000 0.432 0.053 0.757 0.000 0.671 0.831 1.380 0.968 1.137 1.145 0.897 +0 0.496 0.908 0.904 0.830 -0.068 1.198 0.234 0.905 2.173 2.000 0.634 -0.922 2.215 0.973 0.890 1.337 0.000 0.680 1.283 -1.458 0.000 0.995 0.993 0.986 1.319 2.321 1.379 1.088 +1 1.563 -1.398 0.235 0.742 0.283 1.325 -0.746 1.307 2.173 0.506 1.028 -0.961 0.000 1.667 -1.021 -1.202 0.000 1.395 0.609 0.019 0.000 0.907 1.355 0.983 0.786 0.705 0.887 0.880 +1 0.578 1.509 0.206 1.197 1.189 1.189 0.429 -0.579 2.173 1.082 1.004 1.388 2.215 0.472 -0.632 0.596 0.000 0.367 -0.190 -0.902 0.000 0.461 0.802 0.983 1.572 1.712 1.193 0.991 +0 1.841 2.155 0.303 0.209 0.282 1.315 -1.245 -1.571 0.000 0.625 1.725 -1.725 0.000 1.717 -0.084 -0.283 2.548 0.722 0.715 -0.385 1.551 4.184 2.371 0.986 1.601 0.427 1.611 1.885 +0 0.871 -1.296 1.016 0.655 -1.494 0.565 -1.164 -0.190 2.173 0.534 -1.752 0.463 0.000 0.780 -0.413 -1.238 2.548 1.080 -1.257 1.644 0.000 0.877 0.824 0.985 0.820 0.737 0.639 0.617 +0 0.663 -0.509 0.123 1.114 0.846 0.808 -1.095 -1.147 2.173 0.966 -0.181 -0.418 2.215 0.842 -0.571 1.143 0.000 0.610 -1.639 1.217 0.000 0.573 1.013 0.988 1.042 1.008 0.885 0.777 +1 0.756 -0.407 -0.691 0.920 -1.523 0.617 -1.247 -0.132 0.000 0.603 -1.122 1.412 2.215 0.983 -1.259 0.828 0.000 1.068 0.581 0.977 3.102 1.070 0.865 0.983 0.979 0.819 0.809 0.779 +0 0.862 0.015 -1.363 1.406 1.358 0.469 -0.987 0.583 2.173 0.865 0.850 -0.402 0.000 0.834 1.324 0.098 2.548 0.874 0.589 -0.959 0.000 0.878 1.163 0.986 1.017 1.257 0.898 0.838 +1 1.089 0.926 1.253 0.411 -0.863 0.948 0.260 0.973 0.000 1.369 0.240 -0.998 2.215 0.684 0.766 0.266 2.548 0.499 1.237 -1.226 0.000 0.956 0.696 0.987 1.020 0.984 0.831 0.781 +1 1.014 1.240 -0.514 0.689 -0.232 1.706 0.788 1.366 2.173 0.730 0.083 -0.645 0.000 0.657 2.468 -0.237 0.000 0.744 0.800 0.362 3.102 1.870 1.091 0.979 1.756 0.944 1.193 1.122 +0 0.795 -0.492 0.453 0.395 -1.642 0.408 -0.771 1.406 0.000 1.040 -0.318 -0.968 2.215 0.901 -0.754 -0.073 2.548 0.640 -0.111 1.002 0.000 0.379 0.801 0.991 0.632 0.787 0.652 0.600 +0 1.831 -0.047 -1.168 1.818 -1.282 1.438 0.037 0.238 0.000 0.794 -0.241 -0.187 2.215 1.335 -1.116 1.294 0.000 2.173 2.007 0.991 0.000 0.684 1.108 1.001 0.824 1.176 0.884 0.970 +1 1.931 0.996 0.211 0.586 -0.922 1.242 1.125 -1.636 2.173 0.755 1.642 -0.675 0.000 1.369 -0.356 1.010 0.000 0.484 0.782 0.748 0.000 0.814 1.030 1.256 0.887 0.900 0.971 0.826 +1 0.749 -0.553 0.766 0.880 -0.468 0.526 -0.102 -1.634 2.173 1.311 -0.985 1.262 2.215 1.163 -1.190 -0.574 0.000 1.058 -0.735 -0.162 0.000 0.515 0.955 1.008 0.935 0.845 0.870 0.766 +0 1.049 -1.407 1.592 0.688 1.318 0.897 0.353 -0.221 0.000 0.327 -0.336 0.303 2.215 0.296 1.299 -0.030 0.000 0.367 1.160 0.415 0.000 0.672 0.739 0.977 0.847 0.353 0.550 1.020 +0 0.654 -1.450 0.128 0.826 -1.374 0.777 -1.263 -1.186 0.000 1.035 -2.167 -1.257 0.000 1.778 1.053 0.698 1.274 1.582 -0.023 0.122 3.102 0.953 1.697 0.994 1.861 1.026 2.076 1.601 +0 1.066 -0.232 -1.486 0.040 -1.530 0.763 -0.264 0.437 1.087 0.361 1.669 0.550 0.000 1.345 0.276 -1.322 2.548 0.715 1.024 -0.832 0.000 0.648 0.979 0.692 0.435 1.313 0.821 0.705 +1 1.928 0.024 -0.101 0.851 -0.782 0.772 0.495 -1.334 2.173 0.630 0.549 -1.649 2.215 1.472 0.272 1.112 0.000 0.637 0.872 0.033 0.000 0.973 1.032 1.021 0.952 0.289 0.793 0.805 +1 1.909 2.020 0.450 0.509 1.310 0.747 1.331 -1.513 2.173 0.864 0.538 -0.642 2.215 0.805 2.021 1.631 0.000 0.946 1.718 -0.347 0.000 0.944 1.052 0.990 1.114 0.964 0.999 0.856 +1 0.363 -1.188 -0.139 2.492 -1.121 0.929 0.323 1.157 2.173 0.643 -0.660 0.235 0.000 0.942 -0.420 1.228 0.000 1.603 0.069 0.501 0.000 0.906 0.902 1.019 1.677 1.130 1.072 0.997 +0 0.706 0.967 -1.518 1.296 1.045 0.368 1.534 -0.688 0.000 0.690 -1.752 -0.161 0.000 1.451 -1.397 1.330 2.548 1.427 -0.117 -0.691 0.000 0.897 0.872 0.988 1.605 1.279 1.139 1.021 +1 1.347 -0.565 1.673 0.457 1.554 2.792 -0.745 0.474 0.000 2.670 0.405 -0.969 2.215 1.847 0.243 -1.408 2.548 0.595 0.439 1.097 0.000 0.671 0.948 0.986 1.101 0.926 1.283 0.992 +0 0.956 0.597 -1.681 0.213 0.039 0.859 0.074 -0.077 2.173 0.829 -0.366 -1.297 2.215 0.767 0.036 1.026 0.000 0.895 0.790 1.580 0.000 0.951 0.976 0.988 0.895 1.142 0.805 0.720 +1 1.698 -0.884 -0.603 0.720 0.064 0.674 -0.693 1.060 2.173 0.636 -0.198 0.027 0.000 1.237 -0.221 -1.697 1.274 1.113 0.415 1.551 0.000 1.144 0.939 0.988 1.018 0.742 0.861 0.806 +1 1.044 -1.352 -1.229 0.896 -0.263 1.325 -0.786 -1.511 2.173 0.737 -1.496 0.552 0.000 1.154 -0.684 0.172 2.548 1.219 1.058 0.747 0.000 0.661 0.671 1.025 1.018 1.539 1.002 0.870 +1 1.321 -0.470 0.926 0.517 -0.192 1.395 -0.534 -1.425 0.000 1.694 0.479 0.037 2.215 1.220 1.999 1.167 0.000 1.883 0.593 -0.386 3.102 0.770 1.500 0.986 1.010 0.625 1.358 1.135 +1 0.787 0.460 -0.567 1.286 -1.215 1.339 -0.164 -0.732 2.173 2.103 -2.307 0.867 0.000 0.949 -0.867 1.099 0.000 1.086 -0.604 0.172 0.000 0.841 0.785 0.990 0.688 0.850 0.862 0.773 +1 2.035 -0.779 0.207 1.149 0.771 1.028 -0.347 -1.063 1.087 0.701 1.888 -0.165 0.000 0.712 -0.047 -1.640 1.274 2.066 1.162 1.706 0.000 0.993 1.066 1.031 0.979 0.553 1.016 0.852 +0 0.858 -0.158 -0.991 0.605 1.371 0.806 0.158 1.463 0.000 0.744 -0.008 0.007 2.215 1.390 1.293 -0.586 2.548 0.838 0.855 0.922 0.000 0.813 0.987 0.991 1.272 1.003 0.925 0.898 +0 1.084 0.343 1.013 1.366 1.570 0.522 -0.205 0.056 2.173 0.774 -0.523 -0.741 0.000 0.458 -2.346 -0.399 0.000 1.218 0.507 1.621 3.102 1.135 0.970 0.986 0.593 0.905 0.896 0.915 +0 1.823 -0.803 0.155 0.827 0.512 0.969 0.091 -1.276 2.173 0.530 0.507 1.288 0.000 0.437 1.185 -0.948 0.000 0.402 -0.971 1.170 3.102 0.729 0.785 0.978 0.560 0.693 0.991 0.937 +1 0.454 0.409 1.624 0.502 1.660 1.147 -1.001 1.029 2.173 1.228 -0.602 -0.428 2.215 1.590 1.235 -0.293 0.000 0.803 -0.842 -1.686 0.000 0.538 0.919 0.987 1.700 1.723 1.711 1.392 +1 1.254 0.433 -0.735 0.152 1.100 0.928 -0.161 0.625 2.173 0.399 -0.565 1.568 1.107 0.427 -2.404 1.732 0.000 0.501 -1.540 -1.247 0.000 0.306 1.219 0.986 0.652 0.698 0.744 0.782 +0 0.491 1.219 -1.519 1.551 1.009 1.063 0.179 1.736 2.173 1.348 0.804 -0.252 2.215 1.319 -0.218 -0.323 0.000 0.620 -0.769 -1.553 0.000 0.958 1.075 0.989 1.287 1.813 1.222 1.179 +0 0.569 -1.331 -1.239 1.098 1.564 1.255 -1.641 -0.270 0.000 1.206 1.206 1.414 0.000 0.876 0.350 0.811 2.548 0.839 0.617 -0.481 3.102 1.114 0.875 0.982 1.478 0.613 1.215 1.608 +1 1.307 -0.702 -1.619 1.493 -0.015 0.427 0.363 -0.015 2.173 0.552 -0.650 0.640 0.000 0.690 1.035 -0.882 2.548 1.265 -0.056 1.413 0.000 0.920 0.779 1.920 1.110 0.546 0.896 0.770 +1 1.596 -0.384 1.026 1.259 1.449 0.437 -0.998 -0.115 2.173 0.700 -1.327 -1.342 2.215 0.615 1.240 -0.582 0.000 0.390 -0.686 -1.375 0.000 0.798 1.031 0.982 0.943 0.742 0.773 0.837 +1 0.411 0.919 -1.575 1.321 0.230 0.725 1.090 0.992 2.173 0.660 1.415 -0.544 0.000 0.756 0.622 1.572 2.548 0.415 1.639 -0.396 0.000 0.886 0.906 1.020 0.708 0.499 0.602 0.591 +1 0.616 1.620 -0.370 1.340 -0.566 2.037 0.733 0.902 2.173 1.775 -2.561 -1.128 0.000 0.893 0.510 -1.560 0.000 0.748 0.577 0.450 0.000 0.878 1.104 0.990 0.454 0.789 1.026 0.850 +0 1.293 1.119 0.048 0.432 1.649 0.875 1.609 1.048 2.173 1.277 1.158 -1.120 1.107 0.427 0.394 -0.721 0.000 0.366 0.018 0.269 0.000 0.351 0.804 1.027 0.921 1.481 0.886 0.704 +1 2.075 -1.040 -1.303 1.249 -0.448 0.669 -2.452 0.456 0.000 1.050 -1.154 0.270 2.215 1.211 0.337 1.439 0.000 0.711 0.310 -0.863 0.000 0.896 0.707 1.555 1.276 0.655 0.855 0.931 +1 1.613 1.361 1.703 0.769 1.245 0.911 1.745 -0.243 0.000 0.637 0.889 0.579 2.215 0.482 0.751 -1.353 0.000 0.510 0.226 -1.186 1.551 1.158 0.943 0.982 0.709 0.541 0.639 0.750 +0 0.842 0.981 1.457 1.649 0.522 0.521 -0.264 -1.033 0.000 0.773 0.682 -0.528 2.215 0.782 -0.671 -1.576 0.000 0.424 -0.598 1.281 0.000 0.646 0.710 1.218 0.652 0.494 0.646 0.697 +1 1.868 0.681 -1.647 0.801 -1.618 0.470 -0.395 0.517 0.000 1.147 0.901 -0.425 2.215 0.933 1.144 -0.773 0.000 1.020 0.979 0.397 3.102 0.874 0.765 0.982 0.908 0.670 0.868 0.747 +1 0.346 1.307 0.018 0.214 -1.352 0.991 0.202 -0.075 0.000 0.805 0.938 1.566 2.215 0.949 0.075 0.665 0.000 1.309 0.275 -1.189 3.102 0.910 1.094 0.984 0.643 0.641 0.742 0.665 +1 0.679 0.765 0.946 0.806 1.301 0.673 -0.101 -0.330 2.173 0.400 -0.349 0.813 0.000 0.679 1.904 -0.443 0.000 1.145 1.108 -1.406 3.102 1.439 0.989 0.988 0.938 1.053 0.823 0.839 +0 0.538 -1.744 -0.236 1.491 -1.139 0.290 -1.425 1.730 0.000 0.767 -0.734 0.362 2.215 0.522 -2.175 0.131 0.000 0.565 -1.360 0.855 3.102 0.775 0.762 0.989 0.658 0.371 0.622 0.574 +0 1.056 0.704 1.188 1.549 -1.729 1.054 -2.542 -0.295 0.000 0.831 0.013 1.146 2.215 1.535 -0.066 -0.085 0.000 1.038 0.360 -0.620 1.551 1.747 1.031 0.989 0.633 0.856 0.810 0.814 +0 1.974 -0.507 -1.319 0.254 1.283 0.744 1.521 0.952 0.000 0.890 0.823 -0.003 2.215 0.482 1.107 -1.527 0.000 0.513 0.491 -0.386 3.102 0.865 0.957 0.988 0.645 0.221 0.747 0.864 +0 1.419 0.386 0.329 3.072 0.026 1.708 1.157 -1.722 1.087 1.067 0.698 -1.057 0.000 1.743 2.727 1.667 0.000 0.895 -1.022 0.044 3.102 1.043 1.026 0.977 2.329 2.366 1.745 1.381 +1 0.893 0.521 -0.511 0.605 0.911 1.038 1.163 -0.473 2.173 1.056 1.469 -1.092 2.215 1.573 0.378 1.236 0.000 1.675 0.259 0.664 0.000 0.888 1.482 0.987 0.839 0.855 1.184 0.957 +1 0.618 -0.963 -1.669 0.662 1.741 1.219 -0.260 0.422 0.000 0.545 -0.145 -1.129 2.215 0.947 1.119 -1.358 0.000 1.085 0.558 -0.184 3.102 0.474 0.700 0.983 1.463 0.598 1.006 1.294 +0 0.439 -1.789 1.531 1.177 -0.823 0.950 -1.118 1.248 1.087 0.886 0.575 0.250 0.000 1.231 0.870 -1.059 0.000 0.494 -1.366 -0.438 0.000 1.277 0.801 0.991 0.958 1.116 0.917 0.852 +1 0.467 0.548 1.247 0.581 -0.217 2.671 1.131 -1.362 0.000 1.570 -0.929 0.798 0.000 2.021 -0.485 -0.082 2.548 2.711 -0.010 0.284 1.551 0.689 1.126 0.987 0.765 0.746 0.911 0.781 +0 0.326 -1.490 -1.234 0.604 0.494 0.686 -1.240 -0.310 2.173 0.886 -1.046 1.651 0.000 0.479 0.088 -0.726 2.548 0.956 -1.241 0.996 0.000 0.713 1.034 0.997 0.558 0.581 0.704 0.669 +1 0.837 -2.246 0.393 0.690 -0.157 0.650 -1.549 0.027 1.087 1.376 -0.913 -1.272 1.107 1.409 -1.481 -1.662 0.000 0.654 -2.086 1.566 0.000 0.538 1.028 0.974 1.141 1.356 0.864 0.831 +1 1.227 0.764 -1.256 0.257 0.513 0.852 -0.716 0.440 0.000 0.826 0.276 -1.117 2.215 0.530 0.377 0.920 0.000 0.524 -0.111 1.663 3.102 0.884 1.166 0.986 0.517 0.375 0.694 0.679 +1 0.403 1.827 0.337 0.616 -1.165 1.102 1.084 -0.087 0.000 1.028 0.895 -0.420 0.000 1.754 0.517 1.109 0.000 0.901 -0.249 1.556 0.000 0.825 0.755 0.987 0.638 0.757 0.564 0.596 +0 0.781 1.599 0.839 1.846 -0.348 0.996 -0.652 1.677 1.087 0.921 -0.492 -0.703 0.000 1.300 0.219 0.537 1.274 0.587 -0.083 1.263 0.000 0.959 0.973 1.458 2.288 1.381 1.554 1.301 +0 2.006 0.241 -0.748 0.146 -1.374 1.470 -0.488 -1.108 2.173 1.771 -2.059 0.661 0.000 1.613 -0.483 0.148 2.548 3.528 0.089 1.173 0.000 0.906 1.192 0.992 0.839 1.737 1.259 1.025 +1 1.635 -1.594 0.945 0.478 1.327 0.892 -0.844 -0.162 2.173 1.745 -0.917 -1.204 0.000 0.430 -0.671 -0.847 0.000 1.013 0.092 0.555 3.102 0.443 1.057 0.991 1.308 0.787 1.017 1.055 +1 0.811 -0.938 0.851 0.877 -1.434 0.743 -1.007 -0.032 2.173 1.564 -0.496 -1.350 0.000 0.850 0.437 0.425 2.548 1.328 1.149 0.049 0.000 2.721 1.677 1.032 0.879 0.904 1.267 1.094 +0 1.600 -0.949 -0.531 1.396 -0.598 2.468 -1.353 -0.604 0.000 1.386 -0.191 0.995 1.107 2.251 0.605 1.290 1.274 0.976 -0.967 0.802 0.000 2.276 2.531 0.999 1.884 0.970 2.270 1.826 +0 0.482 2.308 1.577 1.585 1.346 0.935 1.048 -1.272 2.173 0.817 -1.365 0.000 0.000 1.947 -0.490 0.495 2.548 1.852 -0.219 -0.640 0.000 1.303 1.236 1.004 0.946 2.245 1.463 1.505 +0 2.797 -0.806 0.021 1.439 0.487 1.557 1.758 1.079 0.000 1.273 -1.175 -1.049 0.000 1.544 -0.657 -1.552 2.548 2.237 -0.361 -1.071 3.102 6.909 4.090 1.134 1.526 0.632 2.554 2.418 +0 0.577 -1.617 0.550 1.626 0.418 0.806 0.805 -1.389 1.087 0.512 1.808 -1.115 0.000 0.608 1.704 0.415 0.000 0.777 -0.875 -1.338 3.102 0.840 0.906 0.981 1.004 0.899 2.068 2.443 +1 1.149 1.214 -1.089 0.707 -0.564 0.576 1.837 0.412 0.000 0.545 0.172 -1.410 2.215 0.674 -0.338 1.301 0.000 1.139 0.437 0.784 3.102 1.720 1.012 0.981 0.858 0.665 0.757 0.851 +1 1.623 0.303 -0.872 0.340 -0.466 2.228 1.041 1.409 0.000 2.718 -0.044 0.143 0.000 0.804 -0.673 -0.818 2.548 1.006 2.146 -1.477 0.000 2.248 1.759 0.993 0.564 0.467 1.448 1.345 +1 0.436 -0.509 -1.555 1.165 -0.011 0.800 0.078 -0.738 0.000 0.890 0.289 1.498 1.107 0.749 1.136 -0.742 0.000 1.384 -0.087 0.693 1.551 0.861 1.090 0.989 0.857 0.695 0.837 0.752 +1 0.662 0.176 -0.210 0.819 1.440 0.913 -0.704 -1.337 2.173 0.548 -1.024 0.729 0.000 1.171 0.214 0.782 0.000 1.452 -0.949 -0.495 1.551 0.850 1.054 1.016 0.906 0.879 0.915 0.808 +0 0.617 -1.438 -1.351 1.439 -0.095 0.629 -0.615 1.157 2.173 0.767 -1.190 -0.608 1.107 0.648 -2.146 1.180 0.000 0.524 -1.539 1.741 0.000 0.353 0.736 1.182 1.026 1.068 0.765 0.682 +0 1.246 -0.426 1.734 0.712 0.462 1.037 -0.780 -0.976 2.173 1.107 2.167 0.348 0.000 0.591 -1.772 0.876 0.000 0.880 0.164 -1.067 0.000 1.290 1.085 1.189 0.677 1.037 0.766 0.730 +1 0.583 0.393 -1.245 1.586 1.218 0.877 -0.834 0.087 1.087 0.929 0.768 -0.582 0.000 0.726 -0.502 -1.354 2.548 1.071 -0.601 1.087 0.000 1.687 1.086 1.062 1.302 0.968 0.922 0.912 +1 0.950 1.222 0.941 0.366 0.017 1.032 2.527 -1.316 0.000 0.722 2.208 -0.447 0.000 1.637 1.281 0.310 2.548 1.297 0.852 1.530 1.551 1.319 1.301 0.989 0.757 1.015 1.116 1.045 +1 0.837 1.281 1.703 1.308 0.426 1.582 1.094 -0.995 0.000 1.412 0.366 0.597 0.000 0.984 1.162 0.785 1.274 1.365 1.303 -0.757 3.102 1.270 0.884 1.324 0.704 0.881 0.878 0.894 +1 1.037 1.955 0.489 0.492 -0.897 1.252 0.563 -1.509 2.173 1.371 -0.159 0.300 0.000 0.998 -1.029 0.804 0.000 1.181 0.540 -0.581 1.551 1.015 0.973 0.987 1.275 0.957 1.048 1.049 +1 1.408 0.505 0.460 1.324 -0.996 0.710 1.561 1.053 2.173 0.825 1.205 0.531 0.000 1.201 1.778 -1.342 0.000 1.353 0.037 -0.293 3.102 0.689 0.963 1.829 0.983 1.307 0.975 0.967 +0 0.627 -0.368 -0.204 1.086 -1.131 0.674 0.176 0.406 2.173 1.090 0.561 -1.586 2.215 0.871 1.295 -0.232 0.000 0.791 -1.121 1.086 0.000 1.911 1.228 0.991 1.117 1.255 0.993 0.990 +0 0.906 0.053 -1.025 1.530 -0.868 0.543 0.146 0.708 0.000 0.869 -0.134 0.025 2.215 1.025 -0.736 1.142 1.274 0.635 0.139 1.171 0.000 0.824 0.809 0.975 1.005 0.914 0.837 0.836 +1 0.848 -0.304 0.395 0.451 1.429 0.383 0.502 0.928 0.000 0.620 1.057 -1.583 1.107 0.982 -0.149 -0.422 0.000 0.805 -0.573 -0.405 1.551 1.101 0.932 0.989 0.667 0.851 0.646 0.609 +1 0.560 -0.423 -1.118 0.705 0.651 0.681 -0.886 -1.730 2.173 1.277 -0.933 -0.917 2.215 0.999 -1.344 0.436 0.000 1.095 -1.613 0.896 0.000 0.822 0.935 0.989 0.830 0.920 0.785 0.686 +0 0.292 -1.473 0.212 2.889 -0.722 0.814 -1.387 0.609 2.173 1.048 -1.752 -1.058 0.000 0.844 -0.834 -0.199 2.548 2.633 -1.540 0.917 0.000 0.922 0.974 0.989 1.293 0.734 0.888 1.047 +0 0.660 -1.565 -0.146 1.221 0.924 0.965 -0.930 0.053 2.173 1.485 -0.516 -1.422 2.215 1.038 -1.196 1.543 0.000 0.792 -1.485 -0.900 0.000 0.841 0.891 1.022 0.850 1.748 1.077 0.910 +0 0.512 -0.689 0.559 1.678 1.386 1.204 -0.541 -1.189 2.173 0.957 0.222 0.093 2.215 0.851 -1.175 -0.108 0.000 0.865 1.165 0.767 0.000 1.820 1.166 0.986 1.175 1.576 1.139 1.027 +1 1.583 0.158 -0.597 0.434 0.915 0.736 -1.038 -1.544 2.173 0.449 -1.431 0.069 2.215 0.316 -1.781 0.437 0.000 0.454 -1.104 0.603 0.000 0.307 0.682 1.124 0.845 0.859 0.814 0.740 +1 0.929 0.634 -1.486 0.290 -0.406 1.568 -1.725 1.243 0.000 1.726 -0.290 -0.298 2.215 1.115 1.417 -0.738 0.000 1.302 0.083 0.645 0.000 0.922 1.200 0.988 1.357 0.920 1.016 0.993 +0 1.532 0.972 1.010 0.872 1.291 0.811 0.208 -1.183 0.000 0.816 1.054 -0.234 2.215 0.846 -0.674 0.228 2.548 0.386 -0.663 -0.375 0.000 0.724 0.909 0.969 1.358 0.990 1.029 0.985 +1 0.468 -0.263 0.809 0.941 -0.254 0.824 0.866 1.305 2.173 1.317 0.874 -1.557 0.000 1.041 0.901 0.192 2.548 1.156 0.116 -0.414 0.000 1.513 1.215 0.994 0.902 0.976 0.907 0.803 +1 3.280 -0.404 1.408 0.578 -0.635 0.672 -0.732 -0.046 2.173 2.028 -0.005 -0.710 0.000 0.519 -0.747 0.972 2.548 0.399 1.031 0.346 0.000 1.230 1.076 1.838 1.409 0.585 0.889 1.044 +1 0.966 -0.702 -1.111 0.749 0.882 0.524 0.466 0.339 2.173 0.294 -1.392 1.628 0.000 0.665 -2.021 0.159 0.000 0.713 0.352 -1.684 3.102 0.713 0.879 1.149 0.900 0.626 0.788 0.714 +0 2.338 -1.420 0.795 0.438 0.075 1.305 -0.920 -0.845 2.173 0.876 -1.080 -0.207 0.000 1.423 -0.980 1.453 2.548 0.642 -1.350 -1.646 0.000 0.959 1.006 0.986 0.865 1.494 1.151 0.966 +0 0.433 0.579 1.001 0.812 -1.199 1.224 1.279 1.078 1.087 1.404 -0.135 -0.652 0.000 0.740 0.493 -1.177 0.000 0.938 -0.359 0.403 3.102 0.895 0.896 0.993 1.355 1.279 1.223 1.027 +0 1.920 -0.563 0.188 0.787 0.596 0.835 -1.803 -1.487 0.000 0.900 -0.889 1.736 1.107 0.349 1.577 -1.561 0.000 0.685 -0.170 -0.885 0.000 0.682 0.899 0.986 0.687 1.195 0.872 0.802 +0 1.451 -0.442 -0.441 0.354 0.333 1.132 -0.626 -0.885 2.173 1.479 -0.780 1.024 0.000 0.770 0.958 1.257 1.274 0.513 -0.180 1.054 0.000 0.339 0.915 0.986 0.811 1.547 1.085 0.976 +1 0.768 -0.836 1.352 1.233 -0.152 0.774 -0.076 1.142 0.000 1.419 -0.195 -0.787 2.215 0.752 -0.260 0.547 0.000 0.857 -0.114 1.683 3.102 0.716 1.340 1.317 0.797 0.790 0.815 0.799 +1 0.973 0.524 1.577 0.499 1.650 1.053 0.959 0.079 0.000 0.518 1.071 1.192 1.107 0.436 -0.327 1.740 2.548 0.577 -2.212 -1.244 0.000 0.928 0.998 0.984 0.710 0.468 0.701 0.807 +1 0.607 -1.734 1.443 1.019 0.645 0.752 0.781 -1.011 2.173 0.520 -0.568 -1.528 0.000 0.629 -0.682 0.615 1.274 0.449 1.810 0.758 0.000 0.845 0.786 0.988 0.515 1.119 0.930 0.769 +1 0.695 1.537 -0.672 1.545 -0.794 0.799 1.111 0.742 2.173 1.166 0.030 1.022 2.215 0.657 1.287 -0.261 0.000 0.681 1.884 1.696 0.000 0.793 0.831 0.989 2.218 0.887 1.554 1.173 +0 2.717 -0.525 0.903 2.327 0.516 1.663 -0.042 -0.959 2.173 1.006 -0.092 -0.360 2.215 0.762 0.082 -1.431 0.000 0.945 -0.021 1.463 0.000 0.480 0.900 1.189 1.402 0.980 1.637 1.265 +1 0.804 0.922 -0.244 0.241 0.738 1.167 0.144 -1.053 2.173 0.715 0.576 0.686 0.000 0.888 0.382 1.720 2.548 0.391 1.332 1.203 0.000 0.476 1.166 0.985 0.728 0.779 0.747 0.693 +1 0.488 1.230 1.552 0.227 1.537 0.510 0.869 -0.654 0.000 0.613 1.645 -0.152 0.000 0.612 -0.717 1.200 2.548 0.481 1.281 1.027 3.102 0.719 1.133 0.989 0.599 0.602 0.698 0.673 +0 0.999 -0.495 -0.580 0.305 0.785 1.170 2.173 -0.569 0.000 1.042 0.916 0.537 2.215 0.889 1.326 1.586 0.000 2.062 -0.591 1.339 3.102 1.842 1.641 0.988 0.879 1.503 1.814 1.715 +1 1.038 -1.460 0.819 0.586 -1.021 1.135 -0.043 -0.539 0.000 0.713 -0.447 1.647 0.000 0.944 -0.206 -1.401 2.548 1.261 0.646 0.346 0.000 1.399 0.983 1.076 0.521 0.418 0.614 0.714 +1 0.485 1.309 -0.784 0.545 -1.079 0.965 -0.525 0.158 0.000 0.934 0.702 -0.993 0.000 1.659 -0.335 1.118 2.548 1.181 0.056 -0.299 0.000 0.923 1.042 0.996 0.981 0.827 0.931 0.807 +1 1.403 1.477 1.714 0.929 -0.956 1.201 1.254 -0.256 2.173 1.164 1.502 1.196 0.000 1.003 0.255 0.500 2.548 0.919 1.642 -1.047 0.000 1.247 1.181 1.062 1.222 1.110 1.039 0.969 +1 0.538 1.221 0.603 0.786 -1.049 1.723 1.436 1.285 0.000 2.041 0.755 -0.481 0.000 1.668 0.233 -1.071 2.548 1.448 0.654 -0.090 0.000 0.772 1.111 0.990 0.844 1.028 0.982 0.925 +0 1.496 1.807 -0.762 0.586 -1.348 0.803 1.347 0.745 0.000 0.433 1.002 -0.730 2.215 0.599 1.272 1.059 0.000 1.012 0.841 1.471 1.551 0.883 0.777 0.991 0.876 0.547 0.618 0.696 +1 0.623 -2.071 -0.523 1.502 0.475 0.701 -0.932 -1.679 2.173 0.380 -1.715 0.910 0.000 1.050 -0.794 -1.222 1.274 0.409 -1.416 -1.203 0.000 0.486 0.569 1.050 1.184 0.430 0.909 0.691 +0 0.869 -1.243 -1.520 1.482 0.181 1.598 -0.898 -1.025 1.087 1.036 1.073 1.064 1.107 0.718 0.844 0.159 0.000 0.680 2.340 0.641 0.000 0.898 0.847 1.571 1.386 2.877 1.926 1.842 +0 0.601 0.889 1.639 0.792 -0.093 0.861 0.359 1.598 2.173 1.388 0.463 1.014 0.000 2.567 -0.430 -0.316 0.000 1.168 1.369 -1.719 0.000 1.459 1.054 0.987 0.662 0.950 0.871 0.766 +0 2.442 -0.542 -0.318 0.938 0.615 0.572 -1.347 0.699 0.000 0.840 -1.013 1.191 0.000 1.383 -1.054 -1.459 2.548 0.802 1.240 1.684 0.000 0.673 0.974 1.563 0.851 0.180 0.843 0.853 +0 0.748 -0.178 -0.751 1.096 -1.097 0.534 1.159 -1.022 0.000 0.552 1.885 -1.740 0.000 1.430 1.143 0.869 2.548 1.566 -0.111 0.134 3.102 0.830 1.010 0.978 0.926 1.111 0.938 0.875 +0 2.328 -0.849 -0.787 0.845 -1.232 1.507 -0.506 0.771 0.000 0.920 -1.056 -1.622 2.215 0.736 -1.174 0.017 2.548 1.017 0.271 0.583 0.000 0.874 0.940 0.978 0.834 0.875 0.920 1.084 +0 0.530 1.968 -0.666 1.719 -1.336 0.811 1.924 1.282 0.000 1.155 0.304 0.217 0.000 1.035 0.932 0.643 1.274 1.272 0.275 -0.747 1.551 2.514 1.423 0.983 1.440 0.889 1.158 1.325 +1 0.714 1.096 -1.608 1.130 -0.665 0.467 0.632 1.157 0.000 0.513 -0.905 0.889 2.215 0.483 -0.154 -1.198 0.000 1.120 0.142 -0.386 3.102 0.922 1.011 0.992 0.772 0.740 0.870 0.763 +1 1.123 1.909 -0.819 0.267 1.591 1.142 2.741 1.347 0.000 1.035 0.886 -1.704 0.000 2.641 0.299 -0.137 2.548 0.800 0.012 -0.542 0.000 0.869 1.075 0.982 0.473 0.762 0.787 0.853 +0 1.014 -1.994 1.303 0.856 0.353 0.458 -2.789 0.058 0.000 0.620 -2.831 -1.428 0.000 0.346 0.246 -0.085 0.000 0.642 -0.897 -1.050 1.551 1.103 0.872 0.988 0.843 0.309 0.800 0.743 +0 1.028 -0.064 0.594 1.347 1.069 0.815 -1.695 -1.162 0.000 1.054 -1.246 -0.660 1.107 0.443 -0.840 0.870 2.548 0.392 2.087 0.055 0.000 3.586 1.938 0.995 1.591 0.725 1.315 1.315 +1 0.590 1.583 0.880 2.002 1.030 1.245 0.454 -0.029 0.000 0.925 -1.633 -1.702 0.000 0.975 0.704 -1.381 0.000 1.409 0.543 -0.534 3.102 0.605 0.666 1.002 2.159 0.625 1.537 1.286 +0 0.573 0.002 -1.054 1.101 -0.091 1.234 0.158 -0.794 2.173 1.285 2.792 0.701 0.000 0.913 1.425 1.564 0.000 1.650 -1.087 1.193 0.000 0.490 0.552 0.985 0.891 0.531 0.849 0.925 +0 0.728 2.079 0.378 1.072 -1.296 0.603 1.038 -0.997 2.173 0.515 -0.350 -0.123 0.000 1.324 0.646 1.181 2.548 0.833 1.004 0.568 0.000 0.891 0.908 1.222 1.095 1.046 0.855 0.855 +0 0.860 1.951 0.577 1.180 -1.724 1.987 0.010 -0.103 0.000 1.173 -2.492 1.607 0.000 0.740 0.815 -1.006 1.274 0.411 2.024 1.505 0.000 0.259 0.600 1.223 0.746 0.437 0.615 0.534 +1 0.788 -0.503 -1.554 0.544 -1.085 0.764 0.483 -0.384 1.087 0.716 -0.335 1.422 2.215 0.622 1.316 -0.784 0.000 0.985 0.401 1.459 0.000 0.892 0.880 0.983 0.834 1.182 0.734 0.681 +1 1.602 -0.482 0.950 1.322 0.472 0.677 1.982 -1.200 0.000 0.700 -1.234 -0.934 2.215 0.898 0.361 -0.506 2.548 0.811 -0.927 1.513 0.000 0.425 0.690 0.985 1.082 0.848 0.917 0.735 +1 1.225 0.116 0.092 0.736 0.873 1.149 -0.047 1.182 0.000 2.263 0.312 -0.762 0.000 0.911 0.663 0.838 2.548 1.177 2.419 -1.470 0.000 0.899 0.789 0.984 0.588 0.397 0.512 0.546 +1 1.898 -0.746 0.746 0.312 -1.590 1.130 -0.815 -0.686 2.173 0.491 0.552 -1.143 2.215 0.779 -0.315 1.072 0.000 0.374 -0.082 0.318 0.000 0.381 0.964 0.988 0.908 0.934 0.934 0.745 +0 1.794 -0.438 0.286 0.532 0.221 0.548 1.236 -1.684 0.000 1.048 0.250 -1.356 2.215 0.857 -0.319 1.316 2.548 1.275 0.023 -0.424 0.000 1.424 1.003 0.988 0.821 0.742 0.853 0.874 +1 1.048 -0.133 -0.045 1.171 -0.480 1.119 -0.481 1.593 0.000 1.116 0.704 1.170 0.000 1.673 -0.078 -0.633 2.548 0.851 0.712 0.208 3.102 0.819 0.671 0.982 0.698 0.769 0.876 0.818 +0 2.582 -1.329 1.319 1.076 1.038 1.456 -1.316 -0.651 0.000 0.828 -0.905 -0.453 0.000 0.724 -0.299 1.223 2.548 0.745 -0.345 0.074 3.102 0.624 1.217 0.993 0.967 0.484 0.760 1.087 +0 0.291 1.048 -0.077 1.333 -0.877 0.546 1.539 0.439 0.000 0.816 -0.192 1.308 2.215 0.828 1.119 1.270 0.000 1.163 0.661 -0.763 3.102 0.846 1.052 0.989 0.843 0.955 1.154 0.989 +1 2.282 -1.030 -1.371 0.678 -0.296 1.450 -1.066 1.378 2.173 1.634 -0.654 -0.119 0.000 0.751 0.007 0.628 2.548 0.701 -1.102 -0.813 0.000 0.938 0.883 1.420 1.338 1.099 1.120 1.077 +1 0.880 1.128 -1.297 0.348 -0.336 0.178 1.480 0.302 0.000 0.498 -0.573 -0.498 2.215 1.024 0.453 0.889 2.548 1.012 2.151 -1.219 0.000 0.850 1.163 0.984 0.673 0.840 1.065 0.912 +1 0.669 -1.065 0.454 1.022 1.188 1.509 0.252 1.534 0.000 2.303 -0.294 -0.544 2.215 0.437 -0.311 0.681 0.000 0.732 0.767 -0.359 3.102 1.108 1.033 0.987 1.376 0.787 1.256 1.075 +1 0.783 0.716 0.830 0.547 -0.838 1.076 0.502 1.729 2.173 0.893 0.347 -0.239 1.107 0.896 1.196 0.098 0.000 0.520 1.227 -1.239 0.000 0.706 1.060 0.985 0.685 1.417 0.837 0.719 +0 1.150 0.215 1.537 0.602 -1.123 0.707 -0.017 0.190 0.000 0.931 -0.567 -0.611 0.000 0.876 0.895 0.484 2.548 0.801 1.320 1.267 0.000 0.910 0.936 0.994 0.568 0.666 0.622 0.627 +1 1.341 -0.542 1.479 0.908 -1.407 0.429 0.149 -0.619 0.000 0.839 -0.121 -0.161 2.215 1.292 0.830 0.684 1.274 0.610 1.166 -0.352 0.000 0.552 0.813 0.986 1.006 0.971 0.907 0.788 +0 0.878 1.195 0.358 2.148 0.590 1.251 1.208 -0.915 2.173 0.741 0.016 -1.312 2.215 0.278 0.172 1.371 0.000 0.716 1.457 1.271 0.000 0.427 0.888 0.992 1.617 1.032 1.394 1.039 +1 0.652 -0.281 0.657 1.086 0.993 0.889 0.225 1.086 1.087 0.756 0.826 -0.581 1.107 1.388 1.540 -0.913 0.000 1.020 2.040 -0.675 0.000 0.581 0.700 0.994 1.056 1.262 1.100 1.572 +0 0.892 -0.959 -1.325 1.056 0.391 0.781 -0.377 0.155 2.173 0.948 0.355 -1.283 0.000 1.174 1.167 0.904 1.274 0.838 0.391 -0.029 0.000 1.052 1.068 1.344 0.920 1.337 1.083 0.982 +0 0.644 -0.473 -0.491 1.978 -0.976 1.653 -0.471 1.029 2.173 1.675 -0.450 -0.770 1.107 0.937 -0.700 0.537 0.000 1.118 -1.380 0.549 0.000 0.864 1.243 0.992 1.837 2.445 1.463 1.229 +1 1.206 -1.561 -1.529 1.907 1.452 1.315 -1.673 -0.019 0.000 0.549 -0.500 0.583 2.215 0.700 -0.725 -1.086 2.548 0.980 -1.242 -0.838 0.000 1.179 0.997 0.994 1.050 0.664 0.782 0.961 +1 2.290 0.260 0.112 1.207 -0.719 1.533 -0.816 1.460 1.087 0.657 -0.482 -0.676 2.215 0.425 0.206 -1.051 0.000 0.488 2.195 1.268 0.000 0.879 1.013 1.568 2.128 1.405 1.400 1.277 +1 3.214 -0.930 -1.477 1.183 1.547 2.422 -0.739 0.156 0.000 0.647 -0.140 -1.317 1.107 0.373 -0.664 0.576 0.000 1.099 0.390 -0.627 0.000 0.805 0.849 1.094 0.749 0.606 0.623 0.847 +0 1.425 -0.176 -1.441 0.432 -0.142 0.677 -0.217 -0.086 2.173 0.835 -1.093 1.179 0.000 0.555 1.103 -0.518 0.000 0.692 0.468 1.065 3.102 1.370 0.849 1.001 0.842 0.688 0.714 0.685 +1 1.636 -1.937 0.185 0.694 0.468 0.998 -0.321 -0.558 2.173 0.954 0.975 -1.685 0.000 0.414 -2.065 0.633 0.000 1.420 -0.648 1.635 3.102 0.672 0.976 1.002 1.047 1.193 1.054 0.840 +0 1.002 -0.503 -1.552 0.504 -0.050 1.645 -2.624 0.577 0.000 1.578 0.388 -1.533 2.215 1.186 1.992 -0.585 0.000 0.956 0.541 0.238 3.102 0.853 0.893 0.989 0.847 1.117 0.979 0.962 +1 0.351 1.357 -0.471 0.487 -0.196 1.034 0.005 1.672 0.000 1.425 0.087 -0.229 2.215 0.977 0.567 -1.479 0.000 1.201 -1.747 0.672 0.000 0.780 0.663 0.979 0.670 0.870 0.935 0.804 +0 0.727 2.288 0.495 0.266 1.052 0.654 0.232 -0.396 2.173 0.746 -1.066 1.568 1.107 0.540 0.318 1.398 0.000 0.827 0.763 -0.684 0.000 0.734 0.908 0.991 0.956 1.247 1.101 0.861 +0 0.401 0.784 0.041 1.706 0.462 1.345 0.185 -0.932 2.173 0.651 0.167 -1.603 0.000 0.938 0.512 1.133 2.548 0.415 -0.183 0.867 0.000 0.554 0.836 0.997 0.970 1.366 1.378 1.101 +0 0.424 0.868 -0.998 1.639 1.255 1.474 0.713 -0.037 2.173 0.339 1.612 -1.394 0.000 0.834 0.376 1.566 2.548 0.570 -0.532 -1.266 0.000 0.816 1.177 1.035 0.567 1.387 0.960 0.818 +0 0.369 0.964 -1.426 0.948 0.991 0.420 -2.202 0.725 0.000 1.112 -0.077 -0.739 1.107 0.621 -0.510 -1.648 0.000 0.863 0.195 0.240 3.102 1.153 1.012 0.988 0.916 0.697 0.894 0.804 +0 0.333 0.151 -0.921 1.240 0.133 1.089 -0.398 -1.031 2.173 0.860 0.725 1.421 0.000 1.010 0.153 0.411 2.548 1.383 1.097 0.808 0.000 0.852 0.847 0.989 1.427 1.314 1.096 1.002 +0 0.861 0.869 1.377 1.051 -0.251 0.697 0.169 -1.222 0.000 0.881 -0.049 0.640 2.215 0.402 -0.966 1.587 0.000 0.776 -0.105 0.063 3.102 0.828 0.995 1.311 0.744 0.372 0.638 0.705 +0 0.954 -0.909 -1.450 0.967 1.149 0.639 0.269 0.909 1.087 1.148 -0.412 -0.625 1.107 0.790 1.038 -0.179 0.000 0.624 1.746 0.893 0.000 0.744 0.849 0.987 0.986 1.316 0.937 0.964 +1 0.540 2.262 0.484 0.673 -0.187 1.044 1.247 -0.608 0.000 1.147 1.438 1.410 2.215 1.899 0.869 1.039 2.548 0.518 0.294 -0.477 0.000 0.543 1.271 0.992 0.899 0.662 1.026 0.873 +1 1.150 0.782 -0.107 0.213 1.400 1.096 0.686 1.405 0.000 0.275 -2.784 -0.645 0.000 0.748 1.283 0.073 2.548 0.882 0.442 -1.498 0.000 0.762 0.973 0.988 0.525 0.683 0.763 0.729 +1 1.225 0.129 -0.695 0.800 -1.683 0.647 -0.224 1.404 0.000 1.348 -1.116 0.879 1.107 0.639 -2.416 -0.604 0.000 0.759 0.269 0.468 0.000 0.869 0.633 1.065 1.352 0.921 0.955 0.885 +1 0.973 -0.601 1.536 1.202 -1.158 1.513 -0.246 -1.580 2.173 2.024 -1.088 0.096 0.000 1.537 -1.033 0.859 0.000 1.341 1.201 -0.222 0.000 1.716 1.158 0.988 0.740 0.979 1.445 1.187 +1 1.394 -0.406 -1.088 0.359 -0.745 1.600 -1.160 0.445 0.000 1.849 0.376 -0.966 2.215 1.368 -0.595 1.068 0.000 0.722 0.627 1.096 0.000 0.836 0.613 0.994 0.686 0.822 0.920 0.806 +0 0.441 -0.672 1.115 1.701 -0.577 0.586 -1.313 1.345 0.000 0.580 0.866 -1.567 1.107 0.771 -0.720 -0.032 2.548 0.744 -1.531 0.496 0.000 0.747 0.745 1.199 1.026 0.967 0.848 0.809 +0 1.050 0.816 0.893 1.093 1.623 1.096 0.049 -1.359 1.087 0.746 0.660 -0.191 2.215 1.012 -1.555 -0.394 0.000 0.969 -2.002 0.292 0.000 0.930 1.320 0.987 1.186 1.233 1.163 1.259 +1 0.687 1.388 -1.210 1.089 0.304 1.684 2.240 1.040 0.000 1.894 -0.240 -0.942 1.107 0.990 -0.276 -0.072 1.274 0.530 -0.935 -1.615 0.000 0.457 0.584 1.173 1.000 1.028 1.111 0.861 +1 0.655 1.750 -1.468 0.571 1.107 1.263 0.466 -0.936 0.000 0.535 2.260 -0.187 0.000 1.054 0.311 0.322 2.548 2.889 1.295 1.024 1.551 1.202 1.110 0.996 0.833 1.166 1.141 0.931 +0 1.265 -0.516 -1.174 0.383 -0.287 1.131 1.131 1.030 0.000 1.633 -0.958 -0.456 2.215 1.490 0.718 1.424 0.000 1.244 0.968 0.275 3.102 0.910 0.961 0.994 0.799 1.840 1.728 1.498 +0 0.362 0.894 0.866 1.503 -0.534 0.805 -0.622 0.985 1.087 1.271 0.207 1.562 2.215 1.174 -0.496 -0.513 0.000 0.869 -0.523 -0.110 0.000 0.399 1.001 0.988 1.174 0.979 1.107 1.015 +1 1.013 0.278 1.406 1.450 1.146 1.421 0.747 -0.911 1.087 0.690 2.763 0.803 0.000 1.361 0.938 -0.404 0.000 0.485 1.149 -0.266 0.000 0.864 1.474 0.988 1.762 1.408 1.309 1.435 +0 0.478 1.732 0.908 0.918 -0.729 0.779 -0.271 0.023 0.000 0.980 0.064 1.456 0.000 1.231 0.744 0.444 2.548 1.220 -0.823 -1.533 0.000 1.000 0.861 0.983 0.741 0.730 0.829 0.810 +1 0.970 1.230 0.418 0.289 0.569 1.060 0.664 -1.239 2.173 0.517 -1.119 0.592 2.215 0.632 0.260 0.822 0.000 0.756 -1.191 -1.335 0.000 1.024 1.204 0.993 0.841 1.572 0.974 0.868 +0 1.638 -0.777 -1.571 1.072 1.424 0.943 0.731 0.182 1.087 0.389 0.215 0.882 0.000 0.478 -1.303 -0.423 2.548 0.729 -0.670 -0.809 0.000 0.786 0.905 0.991 0.804 1.179 1.098 0.858 +1 3.481 0.207 -1.183 1.274 -1.497 1.965 -1.060 0.283 0.000 0.705 0.225 1.663 0.000 0.853 0.888 -0.927 2.548 0.890 2.063 0.237 0.000 1.729 1.119 0.991 1.429 0.914 0.953 1.036 +1 2.031 -0.675 -1.619 0.112 -1.193 0.588 -0.435 -0.641 2.173 0.931 0.709 -0.049 2.215 0.669 -0.902 -0.133 0.000 1.021 2.021 0.980 0.000 0.813 0.859 0.994 0.841 0.872 0.905 0.772 +1 1.005 1.425 -0.123 1.039 -1.255 1.135 1.172 0.147 2.173 1.229 1.453 1.724 0.000 0.635 2.167 -1.737 0.000 0.624 0.912 1.352 3.102 0.619 0.454 1.206 1.018 0.789 0.892 0.824 +1 1.319 -0.586 -0.225 0.620 -0.100 0.767 0.911 1.021 2.173 0.685 1.278 -1.405 2.215 0.720 0.546 1.503 0.000 0.759 1.458 -0.645 0.000 0.908 0.815 0.999 1.602 0.896 1.359 1.152 +0 1.399 -0.020 1.234 1.141 1.494 1.211 -0.726 -0.221 0.000 0.739 0.120 -0.127 0.000 0.640 0.815 1.138 2.548 1.165 -1.098 -1.364 1.551 0.928 1.079 0.980 0.752 1.026 0.755 0.790 +0 0.977 -1.463 1.416 0.427 -1.196 2.019 -1.580 1.110 1.087 1.305 -1.052 -0.989 2.215 1.271 -1.762 -0.881 0.000 1.901 1.908 -0.110 0.000 0.925 0.967 0.995 1.092 2.349 1.354 1.099 +0 0.327 -1.517 0.850 2.173 0.106 0.652 -1.360 -0.293 2.173 1.150 0.641 -1.420 0.000 0.678 0.153 0.744 2.548 0.574 2.042 -1.724 0.000 1.117 0.998 0.987 0.909 0.968 1.273 2.115 +0 0.790 -0.711 0.733 0.422 0.855 0.479 0.009 -1.261 2.173 0.483 0.195 0.324 0.000 1.651 -0.270 -0.407 2.548 1.574 -0.168 1.617 0.000 1.068 1.084 0.988 1.022 0.790 0.911 0.855 +1 0.524 1.129 0.683 1.532 0.579 1.106 0.050 -1.019 2.173 1.058 0.051 1.201 0.000 0.577 -1.155 -1.054 0.000 1.348 -0.265 -0.362 3.102 1.369 1.077 0.994 1.343 0.762 0.928 0.913 +0 0.686 1.798 0.218 1.167 0.756 0.722 0.143 1.338 0.000 0.830 0.445 -0.257 0.000 0.693 1.901 -0.683 0.000 0.569 0.784 0.271 0.000 0.898 0.812 0.976 0.618 0.395 0.564 0.588 +0 2.098 -0.024 -0.138 0.901 -0.242 0.483 -0.642 -0.965 0.000 0.853 0.180 -1.508 0.000 1.094 1.961 1.391 0.000 1.367 -2.052 0.947 0.000 0.867 0.737 0.982 0.805 0.486 0.699 0.733 +0 1.694 0.868 -0.740 0.356 1.339 0.701 -0.015 0.206 2.173 0.571 -0.924 0.726 0.000 0.942 0.176 -1.732 2.548 0.935 -2.316 1.516 0.000 0.909 1.209 1.027 0.963 1.002 1.020 1.161 +0 3.216 0.626 -0.738 0.103 0.336 1.587 -0.781 0.943 1.087 0.888 -1.003 1.308 0.000 1.093 0.045 -0.073 2.548 0.574 -1.396 -1.298 0.000 0.730 0.908 0.986 0.847 1.481 1.572 1.362 +0 0.947 -0.204 -1.015 2.041 -1.426 1.353 0.363 0.454 2.173 0.485 -0.387 -0.335 0.000 0.426 0.817 1.357 0.000 0.459 -1.315 0.760 3.102 0.847 0.920 0.984 0.942 0.974 1.151 0.905 +0 0.830 0.657 -1.403 1.241 -0.608 0.799 -2.113 0.972 0.000 0.928 0.963 -0.805 0.000 0.868 0.457 0.499 2.548 1.060 1.036 1.233 1.551 4.552 2.654 0.984 0.843 0.530 1.757 1.450 +1 1.141 0.353 1.269 0.539 -0.392 1.197 0.839 -0.778 0.000 1.274 0.173 0.266 2.215 1.516 0.271 1.534 2.548 0.728 -0.369 0.690 0.000 1.697 1.427 1.083 0.716 1.347 1.109 0.916 +1 1.359 -0.796 1.287 0.771 -0.849 1.825 -0.994 -1.226 1.087 1.206 -0.107 0.538 0.000 1.763 0.488 0.245 0.000 0.841 -0.676 0.869 0.000 0.938 0.637 1.331 1.164 1.259 1.528 1.258 +1 1.198 1.925 1.155 0.920 0.991 2.569 0.090 -0.804 0.000 1.241 0.633 1.189 2.215 0.945 1.074 0.059 0.000 0.665 0.994 1.000 0.000 0.656 0.812 0.973 0.925 0.760 0.702 0.640 +0 0.495 0.543 0.284 1.707 1.079 0.645 -0.462 -0.194 0.000 0.817 -2.483 0.897 0.000 0.846 0.741 -1.163 2.548 1.112 -0.489 -1.396 0.000 1.146 0.963 0.989 1.019 0.992 0.851 0.799 +0 0.444 -1.225 0.682 0.373 -1.422 0.880 -0.436 1.679 0.000 0.797 -0.863 -0.174 2.215 0.560 -0.516 0.751 0.000 0.854 0.654 -0.240 3.102 0.941 0.976 0.988 0.753 0.700 0.779 0.674 +1 0.564 0.655 1.138 0.603 -0.910 0.906 0.728 -1.291 2.173 0.796 1.149 0.800 0.000 1.414 0.364 0.329 0.000 0.605 -0.859 -0.603 3.102 0.935 1.013 0.983 0.777 0.908 0.953 0.839 +0 0.647 -1.068 -1.451 1.266 -0.865 1.234 -0.729 1.448 1.087 0.781 -1.330 -0.465 0.000 1.134 -0.147 0.717 2.548 1.220 -1.522 -0.014 0.000 0.583 1.078 0.988 1.128 1.001 1.047 0.981 +1 0.966 -0.214 0.730 1.067 -1.154 0.839 0.439 0.846 0.000 0.833 -0.658 -1.447 2.215 0.596 0.216 -0.023 0.000 2.347 -0.015 -0.950 3.102 0.907 1.164 1.396 0.950 0.691 0.921 0.832 +0 0.778 1.505 -1.427 1.772 -1.601 0.478 0.946 0.815 1.087 0.675 0.519 0.119 0.000 0.901 1.209 -0.566 2.548 0.941 -0.433 0.184 0.000 0.576 0.789 0.978 1.127 0.790 0.878 1.084 +0 1.096 1.605 -0.616 0.827 0.033 0.770 0.843 -1.358 2.173 0.930 1.284 0.140 2.215 1.012 0.521 1.229 0.000 0.842 1.650 1.553 0.000 0.831 0.965 0.978 1.122 1.251 0.879 0.857 +0 0.310 0.353 -1.552 0.491 0.828 0.803 0.184 -1.105 2.173 0.789 -0.714 0.713 1.107 0.486 -1.915 0.348 0.000 0.642 -1.649 -0.862 0.000 0.548 1.206 0.984 0.638 1.295 0.881 0.756 +1 1.182 1.084 0.747 1.266 0.237 0.719 0.242 -1.707 2.173 0.542 -2.673 -1.115 0.000 1.166 -0.534 -0.824 0.000 0.590 0.708 1.301 0.000 1.119 0.900 0.985 0.663 0.991 0.894 0.944 +1 0.418 -1.167 0.756 1.154 -0.455 0.574 -0.421 1.280 2.173 0.817 -1.214 -1.259 2.215 0.623 1.179 0.160 0.000 0.535 0.682 1.707 0.000 0.645 0.809 0.989 0.810 0.871 0.822 0.756 +0 1.326 -0.350 -0.204 0.339 0.596 1.056 -0.409 0.869 2.173 1.692 0.569 -1.426 0.000 1.176 0.001 -1.036 2.548 0.627 -1.191 0.728 0.000 2.026 1.245 0.984 0.978 1.403 1.151 1.009 +1 1.242 -0.650 1.320 1.163 0.811 1.024 -0.227 -0.909 2.173 0.493 -1.095 -1.189 0.000 1.006 -0.971 -0.101 2.548 0.684 -0.608 0.243 0.000 0.742 0.772 0.985 0.966 1.003 0.983 0.795 +0 0.623 -1.176 1.542 0.480 0.318 1.275 0.446 0.087 0.000 1.404 -0.055 1.470 0.000 1.996 -0.259 -1.005 2.548 0.636 0.239 -0.768 3.102 2.789 1.542 0.988 1.325 0.309 1.203 1.317 +0 0.636 1.164 0.589 0.595 0.662 1.077 -0.344 -1.399 0.000 1.394 -0.245 0.098 2.215 1.146 -0.175 -0.939 0.000 1.110 -0.835 1.059 3.102 0.879 0.919 1.000 0.791 0.958 0.809 0.736 +1 1.205 -1.088 0.959 1.898 1.151 0.606 -0.653 -0.869 2.173 0.862 0.206 -0.683 0.000 0.786 -0.199 0.203 1.274 0.734 -0.438 -0.342 0.000 0.501 0.560 1.005 1.187 0.733 0.845 0.820 +1 0.333 0.505 0.415 1.146 -0.152 1.701 -0.030 -0.886 0.000 1.798 -1.329 0.652 2.215 1.683 -0.953 1.162 2.548 0.668 -0.054 -1.646 0.000 1.030 1.763 0.991 3.278 0.872 2.361 2.027 +0 0.930 0.179 -0.595 0.271 0.854 0.654 -0.498 1.515 2.173 0.983 0.292 -1.281 2.215 0.667 -0.904 -0.113 0.000 1.184 0.355 0.278 0.000 0.877 0.922 0.985 0.947 0.841 0.727 0.694 +1 1.758 0.195 -1.519 0.551 0.858 1.057 -1.284 -0.245 2.173 0.770 -0.633 -1.630 0.000 1.627 -0.779 0.707 2.548 0.608 0.614 -0.852 0.000 0.883 1.102 1.146 1.586 1.289 1.213 1.020 +1 0.750 -1.544 -0.300 1.308 -1.184 0.505 -0.494 1.389 0.000 0.369 2.001 -1.651 0.000 1.821 0.203 0.657 1.274 1.298 0.184 -0.292 3.102 0.828 0.887 0.987 1.095 0.887 1.188 0.972 +1 0.413 0.727 0.878 1.121 -1.302 1.039 -0.926 0.361 2.173 0.763 -1.196 1.271 2.215 1.300 -0.632 -1.082 0.000 1.142 1.280 0.276 0.000 2.224 1.752 0.988 1.889 0.977 1.439 1.364 +1 0.907 -0.730 -0.315 1.014 0.551 0.922 0.499 -1.373 2.173 0.410 -0.888 1.098 0.000 1.207 0.233 1.535 0.000 0.951 -1.587 -0.162 0.000 0.952 0.993 0.984 0.751 0.238 0.890 0.788 +1 0.336 0.838 -0.813 1.411 1.658 1.419 -0.235 -0.961 2.173 1.061 -0.700 0.977 2.215 1.965 0.650 0.535 0.000 0.403 -2.196 -0.124 0.000 0.854 1.154 0.991 0.847 1.831 0.989 0.862 +1 1.811 -1.649 -1.623 0.461 -0.025 1.072 -0.689 0.366 2.173 0.491 1.920 -0.657 0.000 0.594 -1.202 1.233 0.000 0.416 -0.378 -1.255 3.102 0.762 0.978 1.255 0.656 0.708 0.872 0.729 +0 1.358 0.970 -1.289 1.618 -0.619 0.569 -2.297 -1.411 0.000 1.040 0.206 0.788 0.000 1.280 -0.426 0.552 0.000 1.125 0.924 1.530 3.102 0.718 0.986 1.166 1.453 1.308 1.062 1.098 +1 0.888 0.821 -1.451 0.397 0.583 1.160 -0.424 -0.610 2.173 0.838 1.161 1.378 0.000 0.573 1.619 0.951 0.000 0.713 1.575 0.076 0.000 0.995 0.791 0.982 1.368 0.984 1.076 0.940 +0 1.728 0.897 1.389 1.463 0.518 0.703 1.017 -0.954 2.173 0.653 1.356 -1.345 2.215 0.272 1.139 0.888 0.000 1.260 0.765 0.074 0.000 0.450 0.649 1.556 1.068 0.391 0.902 0.713 +0 1.155 -0.459 1.456 1.320 1.218 1.098 -0.537 -0.472 1.087 0.510 1.885 -0.063 0.000 0.885 0.894 -1.058 0.000 1.186 1.230 0.322 0.000 0.955 0.717 0.989 1.466 0.443 0.960 1.231 +1 0.804 1.268 -1.253 1.299 0.970 0.889 0.453 -0.086 2.173 1.434 0.919 1.437 0.000 0.711 -0.073 -1.488 2.548 1.054 -1.918 -0.587 0.000 0.816 0.784 1.285 1.169 0.982 0.874 0.820 +1 0.570 0.954 -0.795 1.633 -0.072 0.528 -0.750 0.228 0.000 0.716 0.529 1.389 2.215 1.341 -0.920 1.545 1.274 0.750 -0.456 -1.269 0.000 0.943 0.941 0.988 2.060 0.906 1.367 1.189 +1 0.780 0.752 -0.252 0.789 1.520 2.944 1.272 -1.181 0.000 2.788 0.759 0.525 0.000 1.074 0.627 0.020 0.000 1.434 -0.485 1.218 3.102 1.173 0.778 1.086 0.872 0.447 0.876 0.817 +1 1.029 -0.684 -0.892 0.187 -1.685 1.504 -0.338 -0.004 0.000 0.858 0.091 1.026 1.107 2.675 -0.274 -1.719 2.548 1.060 -0.625 0.528 0.000 0.974 1.149 0.977 0.917 1.047 1.272 1.049 +1 1.292 -0.407 -1.136 2.004 -1.701 0.623 2.174 0.223 0.000 1.124 0.286 0.386 2.215 0.498 1.293 -0.336 0.000 0.966 -0.994 1.201 3.102 1.020 0.983 1.084 1.434 0.992 0.995 0.918 +0 1.959 0.647 0.013 1.699 -0.083 1.780 -0.534 1.704 0.000 0.602 -0.068 -0.034 1.107 0.811 0.640 1.584 1.274 0.598 -0.803 -1.209 0.000 0.846 0.917 0.975 0.750 0.794 0.847 1.354 +0 1.792 -0.006 -0.203 0.223 -0.563 1.338 -0.056 1.478 0.000 0.543 -2.690 -0.391 0.000 0.698 1.099 0.132 2.548 0.797 -0.662 1.096 0.000 0.800 0.892 0.998 0.821 0.599 0.802 0.897 +1 0.298 1.472 0.100 1.350 -1.572 0.931 0.243 -0.576 2.173 0.657 -1.300 0.078 0.000 1.254 1.135 0.998 0.000 1.252 0.099 1.390 3.102 0.908 0.813 0.986 1.555 1.121 1.176 0.978 +1 0.476 0.590 -0.322 2.835 0.297 0.669 -0.851 1.474 0.000 0.749 1.646 -1.262 2.215 0.975 -0.904 1.047 0.000 1.849 0.273 -1.327 0.000 1.152 0.808 0.986 0.804 0.803 1.005 0.973 +1 0.555 -0.496 -1.694 0.710 0.315 0.833 -1.168 0.761 0.000 1.086 -1.317 -1.537 1.107 0.986 -1.431 -0.815 2.548 0.788 -1.887 1.049 0.000 0.774 1.018 0.985 1.042 0.677 0.825 0.869 +1 1.117 1.341 -1.362 0.224 -1.573 1.124 0.695 1.193 2.173 0.817 2.147 -0.143 0.000 0.502 0.738 -0.313 0.000 0.444 -0.139 -0.022 0.000 0.759 0.693 0.979 0.942 0.874 0.896 0.809 +1 2.050 -0.109 -1.427 0.683 -1.557 1.620 -1.244 0.798 0.000 1.193 0.134 -1.169 0.000 1.865 -0.135 -0.078 2.548 0.999 -1.282 -0.369 0.000 0.760 0.853 0.977 1.358 0.647 0.932 1.186 +0 1.664 -0.938 0.546 1.304 -1.587 0.527 0.786 1.640 0.000 0.620 0.500 0.916 0.000 1.133 -0.974 -0.054 2.548 1.393 0.013 -0.947 3.102 0.909 0.867 1.917 1.245 0.882 0.925 0.821 +1 0.749 -1.522 -1.680 0.651 0.567 0.670 -1.083 -0.644 0.000 0.841 -0.611 1.414 2.215 1.037 -1.678 -1.167 0.000 1.864 -0.165 0.339 3.102 0.891 1.115 0.989 1.134 0.963 0.967 0.898 +0 0.682 -0.017 0.639 1.210 -1.299 1.350 0.163 1.143 2.173 1.114 -0.852 -0.364 2.215 0.961 0.098 -0.203 0.000 0.691 -1.169 -1.265 0.000 1.047 0.785 1.240 1.050 2.020 1.103 0.948 +0 4.066 0.724 -1.381 0.448 -1.100 2.328 0.087 0.316 1.087 1.025 0.946 -0.799 0.000 0.740 0.737 1.184 0.000 0.593 -0.064 1.110 3.102 0.807 1.282 0.982 0.819 0.822 1.660 1.278 +0 0.374 0.432 0.988 0.244 -1.146 0.946 -1.506 0.236 0.000 0.986 1.123 -1.166 0.000 1.221 -0.511 0.846 0.000 0.599 -1.049 0.750 1.551 0.913 1.051 0.995 0.528 0.762 0.650 0.587 +1 0.920 -0.320 -0.241 0.289 -0.303 0.985 0.635 1.418 0.000 0.599 1.390 -1.545 0.000 0.761 -0.044 -0.787 2.548 0.844 -0.936 0.236 0.000 0.986 1.037 0.999 0.737 0.371 0.686 0.998 +1 0.578 0.643 -1.119 0.853 1.090 1.110 2.041 -0.083 0.000 1.330 0.460 1.081 0.000 0.666 -0.113 1.424 0.000 1.935 -1.051 -1.242 3.102 0.981 0.779 0.987 0.968 0.815 0.971 0.808 +0 0.863 0.927 -1.666 1.122 1.691 1.484 -1.540 0.417 0.000 0.954 0.374 -0.321 2.215 0.785 0.072 1.494 0.000 1.400 0.052 -0.945 3.102 0.874 0.850 1.003 1.291 0.581 0.989 1.083 +0 0.844 -2.102 0.735 1.315 1.312 0.949 -0.769 0.747 1.087 1.383 0.451 -0.375 0.000 0.794 1.383 -1.597 0.000 2.914 0.191 -1.077 1.551 0.879 1.271 0.998 0.835 1.983 1.367 1.568 +1 1.164 0.534 -1.645 0.443 1.258 1.100 0.639 -0.056 2.173 0.711 0.444 -0.872 2.215 0.779 0.113 1.220 0.000 0.495 1.325 0.194 0.000 0.775 0.909 0.987 0.768 0.881 0.859 0.737 +0 0.781 0.153 0.493 0.344 -1.506 0.617 -0.523 -0.380 2.173 0.842 0.453 -0.690 0.000 0.869 -0.828 -1.737 2.548 0.754 0.064 1.116 0.000 0.988 0.948 0.983 0.659 0.875 0.671 0.613 +1 1.065 0.515 0.639 0.692 -1.696 0.721 -0.928 -0.742 0.000 0.752 0.683 1.414 2.215 1.197 -0.483 -0.133 0.000 1.078 -0.028 0.338 3.102 0.935 0.828 1.025 0.605 0.736 0.870 0.825 +1 1.339 -0.342 -1.533 0.894 -0.442 0.787 0.619 1.162 2.173 0.707 0.586 0.207 2.215 0.552 0.512 -0.962 0.000 1.253 -2.279 -0.121 0.000 0.714 0.756 1.262 0.991 0.833 0.882 0.788 +0 1.079 -0.630 -1.332 1.576 -0.588 0.455 -0.411 0.247 2.173 0.531 -0.255 1.211 0.000 0.675 -1.487 1.342 0.000 0.617 0.698 0.617 1.551 0.689 0.732 1.123 0.869 0.417 0.696 0.709 +0 0.779 -1.248 0.918 1.726 0.282 0.945 -0.533 -1.302 2.173 1.093 -0.354 1.267 0.000 0.878 0.686 -1.237 0.000 1.095 0.334 -0.381 3.102 1.460 1.098 0.986 1.490 0.952 1.161 1.184 +0 0.713 1.338 1.365 0.943 -1.454 1.157 1.881 0.033 0.000 1.054 0.877 0.652 1.107 0.790 -0.573 -1.271 2.548 1.528 0.966 -1.278 0.000 2.030 1.531 0.981 1.425 1.259 1.305 1.190 +1 0.650 1.832 1.593 0.369 -1.284 0.644 -0.897 1.493 2.173 0.525 -0.149 -0.162 0.000 0.973 -0.490 0.330 2.548 0.744 0.125 -0.800 0.000 1.008 1.042 0.990 0.974 0.874 0.876 0.925 +0 0.785 -0.020 1.287 0.729 -1.372 0.852 0.733 0.200 1.087 0.647 1.203 -0.543 2.215 0.809 1.110 1.262 0.000 0.375 -1.735 1.283 0.000 0.701 0.864 0.989 1.090 0.732 0.960 0.808 +1 0.411 1.328 -0.967 0.223 0.745 0.560 -0.355 -0.856 2.173 0.969 0.739 -0.450 2.215 1.220 -0.440 0.977 0.000 0.419 -0.791 1.096 0.000 0.203 1.106 0.993 0.656 0.748 0.786 0.677 +1 1.623 0.033 0.952 1.043 0.614 0.782 -1.507 -0.790 2.173 0.587 0.255 0.297 0.000 0.563 0.193 -0.339 2.548 1.057 -0.069 -1.461 0.000 1.041 1.240 0.984 0.776 0.873 0.983 0.867 +1 0.861 -0.887 0.213 0.961 0.893 0.685 1.039 -1.252 2.173 0.598 0.611 1.670 0.000 0.703 -0.287 -0.356 2.548 1.172 0.096 -0.832 0.000 0.892 0.720 0.984 1.220 0.892 0.840 0.745 +0 0.798 0.515 -1.094 1.228 1.126 0.707 1.348 1.252 2.173 1.185 -0.773 -0.810 2.215 1.480 -1.050 -0.117 0.000 0.418 1.077 0.519 0.000 1.427 1.156 1.247 0.838 2.175 1.370 1.169 +1 1.458 -0.595 -0.506 0.854 1.555 0.533 0.683 0.622 2.173 1.355 -0.133 1.223 2.215 0.721 0.704 -1.132 0.000 0.785 0.637 -0.813 0.000 0.236 1.013 1.483 1.133 0.835 0.938 0.836 +1 1.794 -1.064 -0.431 0.537 0.679 1.514 1.337 0.798 0.000 0.644 0.409 -0.759 1.107 1.102 0.477 1.730 2.548 0.563 -1.147 -1.481 0.000 2.865 1.768 1.145 0.953 0.702 1.176 1.396 +1 0.619 -1.821 0.384 1.252 1.212 2.304 -2.713 -0.520 0.000 1.066 -0.611 -1.682 2.215 2.077 -0.602 0.972 0.000 1.120 0.534 1.383 3.102 0.980 2.564 0.993 1.000 0.763 2.300 1.787 +1 0.673 0.434 -0.059 0.832 -1.338 1.018 0.450 0.660 0.000 1.294 0.330 -0.887 2.215 0.601 1.435 0.383 0.000 1.274 0.484 1.620 3.102 0.901 0.923 0.990 0.679 0.906 0.954 0.817 +1 1.110 -0.907 -1.025 0.352 0.389 0.786 0.468 0.767 2.173 0.914 -0.388 -0.803 0.000 1.343 2.121 0.498 0.000 1.326 -0.915 -1.274 0.000 0.781 0.656 0.986 1.252 0.730 0.859 0.798 +1 0.805 -0.718 1.344 0.307 1.602 1.721 -1.175 1.516 2.173 1.713 -0.993 -0.391 0.000 2.114 -0.503 0.466 0.000 1.177 -0.125 0.184 1.551 0.914 0.822 0.984 1.194 1.625 1.306 1.045 +1 1.041 -2.224 1.377 0.756 -0.949 0.675 -0.052 -0.143 2.173 0.609 -1.055 -1.376 2.215 0.537 -0.611 1.262 0.000 0.516 -1.641 -0.054 0.000 0.673 0.794 1.064 0.739 0.987 1.004 0.789 +0 0.280 -1.592 0.254 1.711 0.531 0.563 -1.450 -0.469 0.000 0.915 -1.550 -1.166 0.000 0.760 -1.088 1.050 0.000 0.549 1.284 -0.374 3.102 0.901 0.850 1.003 0.802 0.625 0.903 0.936 +0 0.737 -1.765 -0.399 1.033 0.828 1.196 -2.173 -1.580 0.000 1.495 -0.162 0.657 2.215 2.093 -0.843 -0.938 2.548 1.491 -0.040 0.165 0.000 3.331 2.220 1.081 1.264 2.001 1.804 1.440 +0 2.636 0.430 -0.661 0.213 -0.954 1.642 0.619 1.096 2.173 0.730 -0.451 -1.251 2.215 0.493 0.143 1.195 0.000 0.423 -1.620 0.560 0.000 0.689 1.146 0.989 0.777 1.658 1.293 1.058 +0 0.975 1.711 1.031 1.228 0.575 1.385 -0.090 -0.856 2.173 0.347 -1.088 1.171 0.000 1.281 0.526 1.551 2.548 0.478 0.661 -0.260 0.000 0.779 0.975 0.990 1.296 1.483 1.813 1.446 +0 0.417 -1.488 -1.493 1.198 -0.219 1.453 -0.458 0.890 0.000 1.364 -0.271 -1.145 2.215 0.822 -0.215 -0.073 2.548 0.403 -0.244 1.291 0.000 1.008 0.981 0.986 0.884 0.926 0.899 0.906 +0 1.421 1.507 1.074 0.793 1.700 0.577 2.405 -0.377 0.000 0.317 2.214 -1.606 0.000 0.609 -2.336 -0.280 0.000 0.530 0.335 1.053 3.102 0.815 0.987 0.991 0.520 0.404 0.669 0.711 +1 0.660 -1.796 -0.047 1.053 -0.868 0.926 -1.217 -1.465 2.173 0.937 -1.742 0.874 0.000 1.021 -0.614 -0.768 2.548 1.692 0.104 0.370 0.000 0.897 0.910 0.986 1.122 0.789 0.928 1.167 +1 1.726 -0.551 -0.581 0.320 1.573 0.635 1.003 0.851 0.000 0.386 0.410 -0.683 0.000 0.613 -0.657 0.721 2.548 0.379 0.123 0.271 3.102 1.079 0.859 0.986 0.549 0.220 0.495 0.653 +1 0.339 -1.324 -1.092 1.114 -1.029 0.905 1.047 0.626 0.000 0.852 -0.969 -1.194 2.215 1.030 -0.500 -1.503 2.548 0.447 0.066 -1.549 0.000 1.026 1.212 0.975 0.737 0.355 1.021 0.878 +0 0.889 -0.590 1.653 0.665 -0.396 0.313 -1.403 1.366 0.000 0.763 -0.568 -0.624 2.215 0.782 -1.820 0.231 0.000 0.809 0.973 1.383 1.551 0.804 0.897 1.026 0.798 0.986 0.898 0.761 +1 0.551 0.306 0.773 0.579 1.444 0.506 0.059 -0.257 0.000 0.858 0.202 -1.001 2.215 0.916 -1.411 1.528 0.000 0.374 -0.546 0.180 3.102 1.642 0.883 0.988 0.946 0.502 0.713 0.901 +1 0.688 -0.666 -1.491 0.740 -0.899 0.872 -0.831 -0.302 0.000 1.328 -0.079 1.061 2.215 0.604 0.692 -1.474 0.000 0.809 -0.812 0.272 1.551 1.622 1.001 0.993 1.312 0.748 0.912 0.935 +0 0.966 -0.670 -1.189 0.459 0.749 0.854 0.276 -0.594 2.173 0.825 0.453 0.410 2.215 0.816 -0.206 -1.144 0.000 1.007 -0.215 1.064 0.000 0.913 0.930 0.986 0.959 0.978 0.838 0.739 +1 1.076 0.378 0.100 0.844 -1.085 1.123 0.159 -0.895 2.173 1.050 1.359 0.533 2.215 1.080 -0.560 1.443 0.000 1.038 -0.181 0.682 0.000 0.906 1.455 1.156 0.995 1.860 1.366 1.108 +1 0.468 -1.378 0.424 1.023 -1.355 0.832 -1.234 1.545 2.173 1.172 0.122 -0.276 0.000 0.455 -0.727 -0.560 2.548 0.975 1.466 0.981 0.000 1.789 1.152 0.987 0.744 0.746 1.240 1.201 +0 0.548 1.570 -1.559 1.629 1.487 0.689 -0.913 0.624 2.173 0.789 -1.021 -0.852 2.215 0.881 -0.781 0.018 0.000 0.821 -0.205 -0.719 0.000 0.648 0.735 0.983 1.303 1.056 1.123 0.946 +1 0.636 1.143 0.287 0.579 -0.436 1.003 -0.497 1.733 2.173 0.761 0.424 -0.114 0.000 1.201 -0.382 0.459 0.000 1.110 0.478 -1.323 3.102 0.971 0.992 0.989 1.108 0.757 0.933 0.819 +1 1.441 -1.328 1.689 0.991 1.428 0.950 -0.965 -0.704 0.000 1.093 -1.130 0.268 2.215 0.919 -0.693 1.375 1.274 0.998 0.184 0.304 0.000 0.998 1.179 0.977 1.206 0.920 1.079 1.362 +1 1.977 1.239 -1.385 0.571 -1.705 1.065 -0.115 0.386 2.173 0.565 1.512 0.721 0.000 0.526 0.729 -0.820 2.548 0.880 0.682 -0.375 0.000 0.842 1.060 1.003 0.585 0.937 1.029 0.882 +1 1.201 -1.196 -0.715 1.085 -1.069 0.804 -0.872 0.784 1.087 0.823 -0.217 0.734 2.215 0.571 -0.263 -1.734 0.000 0.584 -0.558 -0.298 0.000 0.625 0.717 0.983 1.069 0.404 0.909 0.716 +0 0.938 -0.932 0.265 1.236 -0.339 0.574 -0.364 1.050 0.000 0.633 -1.026 1.602 1.107 0.699 -2.141 1.324 0.000 0.972 -1.145 -1.345 0.000 0.766 1.004 0.988 0.926 0.777 0.674 0.764 +0 0.577 0.473 -1.180 0.282 -0.146 0.751 1.066 0.510 2.173 0.736 0.866 -0.370 0.000 0.686 0.486 1.613 0.000 0.413 2.440 -1.324 0.000 0.927 0.938 0.984 0.803 0.849 0.703 0.665 +0 0.292 -1.476 0.440 1.559 -1.172 1.234 -1.153 0.216 2.173 1.414 -2.351 0.673 0.000 2.057 -0.359 -1.577 2.548 0.861 -1.230 -0.559 0.000 1.467 1.383 0.988 0.782 2.131 1.539 1.266 +0 1.134 -0.732 1.521 1.380 -1.253 1.184 1.167 0.517 0.000 0.840 -1.872 -0.189 0.000 1.436 -1.119 -1.041 1.274 1.050 -0.562 0.467 0.000 1.064 0.952 1.039 0.758 0.499 0.735 0.763 +1 0.489 -0.899 0.400 0.856 1.635 0.895 -1.208 1.494 0.000 1.475 -1.215 0.028 2.215 0.823 0.278 -0.513 2.548 0.711 -0.900 -1.661 0.000 0.855 1.163 0.993 1.126 1.146 1.006 0.894 +1 1.077 1.583 0.818 1.470 0.143 1.116 1.002 -1.354 2.173 0.332 1.837 -0.894 0.000 0.326 -0.145 -0.366 0.000 0.428 0.842 -0.791 0.000 0.656 0.726 0.995 0.782 0.942 0.982 0.788 +0 0.755 1.796 -0.050 0.452 1.022 0.616 1.000 1.689 2.173 0.591 0.250 1.609 0.000 0.414 1.849 -1.662 0.000 1.932 0.907 -0.041 3.102 0.779 0.986 0.997 0.781 1.155 0.708 0.654 +1 1.098 -0.043 -0.473 0.285 1.440 1.174 0.047 0.125 2.173 1.211 -1.520 -1.220 1.107 0.987 -0.210 1.563 0.000 0.914 -1.317 1.371 0.000 0.982 1.231 0.993 1.295 2.274 1.361 1.108 +1 1.409 0.396 1.067 0.894 0.401 0.634 0.785 -1.389 2.173 0.408 -0.918 -0.078 0.000 0.930 -0.052 -0.581 2.548 0.776 -0.643 -1.739 0.000 0.733 0.930 0.981 0.889 0.763 0.797 0.723 +0 1.415 -1.155 -0.957 0.390 -0.482 1.020 -1.176 0.785 2.173 0.590 -1.812 -1.380 0.000 0.884 -1.319 0.323 0.000 0.696 0.129 -1.676 3.102 1.131 0.928 0.987 1.221 0.960 0.902 0.803 +0 0.507 0.120 -0.589 0.530 1.388 0.509 -0.890 1.412 2.173 0.521 -0.636 -0.572 0.000 0.636 -0.850 0.703 2.548 0.575 -2.096 -0.269 0.000 0.788 0.847 0.990 0.587 0.423 0.563 0.539 +0 1.720 -1.069 0.806 2.594 0.334 0.925 -0.436 -1.617 2.173 1.071 -1.275 -1.139 0.000 1.465 -1.350 -0.025 2.548 2.893 -0.426 -1.079 0.000 0.951 0.972 1.206 0.921 1.640 1.229 1.324 +0 1.059 -0.486 1.117 1.098 1.283 1.081 -0.740 -1.518 0.000 1.692 -0.664 -0.172 2.215 0.615 0.296 -1.077 2.548 0.782 0.153 -0.360 0.000 1.390 0.866 0.978 1.398 0.974 1.019 0.993 +1 0.997 0.217 -1.436 0.470 0.855 0.859 -0.056 0.203 0.000 0.530 -1.229 -0.010 0.000 0.744 1.068 -0.931 2.548 0.824 0.802 0.581 0.000 0.959 1.050 0.991 0.756 0.701 0.856 0.761 +1 1.469 1.811 -0.762 0.532 -0.972 0.833 2.170 1.116 0.000 0.490 2.798 0.060 0.000 1.047 1.418 1.273 2.548 1.315 1.150 -0.456 3.102 1.203 0.894 1.001 0.562 0.900 0.780 0.838 +1 0.388 1.212 -1.048 0.877 1.007 2.610 2.126 -1.301 0.000 3.651 1.052 0.455 2.215 0.375 0.866 0.171 0.000 0.425 1.743 0.560 0.000 1.169 1.381 0.988 1.347 1.396 2.231 1.776 +0 1.322 -0.798 0.213 1.357 1.674 1.610 -0.550 -0.248 2.173 1.051 -0.030 1.508 0.000 1.541 1.063 1.565 2.548 0.616 0.480 -1.280 0.000 0.696 0.769 1.796 1.498 2.734 1.626 1.304 +0 0.449 -1.449 0.341 0.615 -1.252 1.246 -0.923 -1.613 2.173 0.952 0.943 0.171 0.000 1.490 -0.653 0.279 2.548 0.456 -1.908 -1.633 0.000 0.915 0.927 0.982 0.758 1.692 0.895 0.754 +1 0.540 0.208 -0.465 0.992 -1.630 0.468 -0.125 1.540 0.000 0.756 0.784 0.326 2.215 0.920 -0.794 -0.477 0.000 0.932 -1.032 0.917 1.551 0.851 0.878 0.988 0.709 0.995 0.733 0.681 +0 0.915 0.081 0.770 0.242 0.399 0.345 -1.206 -0.313 0.000 1.115 -0.708 -1.016 0.000 1.005 -0.074 1.428 2.548 0.442 0.006 -1.636 3.102 0.845 1.005 0.989 0.570 0.190 0.591 0.753 +1 2.215 -1.195 0.434 1.127 1.137 1.129 -0.780 -0.969 1.087 0.488 -1.027 -0.574 0.000 0.938 -0.854 -1.714 0.000 0.768 -0.914 1.406 1.551 0.890 0.769 1.296 0.737 0.844 1.031 0.862 +1 1.592 0.091 -1.384 0.379 -0.320 0.594 -0.927 0.496 2.173 0.260 0.569 -1.073 0.000 0.862 -1.507 -0.825 2.548 0.912 0.083 0.533 0.000 0.685 0.904 0.988 1.023 0.891 0.894 0.821 +1 1.413 0.724 -1.335 0.307 1.605 1.060 1.686 -0.631 0.000 1.367 0.436 0.721 2.215 0.594 -0.047 -1.629 0.000 2.197 0.305 1.100 3.102 0.940 0.970 0.997 1.111 0.529 0.818 0.767 +1 0.700 -0.136 0.282 1.209 1.320 0.886 0.211 -0.738 2.173 0.463 -1.145 -0.021 0.000 1.031 -0.580 0.899 0.000 0.852 1.200 -1.511 1.551 0.834 1.162 1.026 1.060 0.842 0.917 0.826 +1 0.692 1.943 0.264 0.484 1.602 2.655 -1.037 -0.766 0.000 3.523 1.392 0.882 2.215 1.453 0.600 -1.687 1.274 0.712 0.861 0.108 0.000 2.995 2.608 0.979 1.021 2.010 3.562 2.610 +1 0.775 -1.310 -0.310 0.179 1.211 1.434 -1.137 0.759 0.000 1.369 -0.684 -0.586 0.000 2.098 -0.005 1.118 0.000 4.529 -0.947 -0.989 0.000 0.726 0.991 0.987 0.560 0.727 0.793 0.736 +0 0.503 1.408 0.821 1.373 -1.309 1.176 1.725 -0.823 0.000 1.204 0.656 1.145 2.215 0.766 0.764 0.474 0.000 1.306 -0.227 0.157 3.102 1.761 1.604 1.082 0.955 1.041 1.221 1.038 +0 0.826 0.115 -0.612 1.188 -1.217 0.704 0.552 0.689 1.087 1.247 0.671 -0.916 2.215 1.110 2.340 0.588 0.000 2.232 1.033 1.251 0.000 0.585 1.190 0.990 0.902 1.371 1.130 1.335 +1 1.099 1.414 -0.113 0.379 1.688 0.803 0.439 0.908 2.173 1.053 1.122 -1.247 0.000 1.015 0.318 -0.854 2.548 0.947 1.185 0.368 0.000 0.813 0.747 0.986 0.871 1.126 0.741 0.632 +1 0.664 0.326 -1.298 0.483 1.138 1.000 -0.144 0.156 0.000 1.288 -0.319 -1.405 2.215 0.736 -0.988 -0.162 0.000 1.094 -0.586 1.047 3.102 0.871 0.858 0.990 1.029 0.884 0.924 0.963 +0 0.980 1.726 -0.762 0.966 1.267 0.445 1.965 -0.196 0.000 0.773 0.619 1.039 1.107 0.516 -0.541 -0.403 2.548 0.635 1.066 1.656 0.000 0.859 0.937 1.303 0.950 0.782 0.837 0.742 +1 0.607 -0.025 0.188 0.854 -1.272 1.279 0.472 -1.594 0.000 1.175 0.924 0.160 2.215 1.260 -0.096 0.896 2.548 1.400 2.350 -0.643 0.000 3.360 2.336 0.988 0.770 1.070 1.649 1.376 +0 0.641 1.328 -0.710 0.859 1.091 1.350 1.452 1.484 1.087 1.488 0.655 -1.371 0.000 3.466 0.598 -0.208 2.548 1.295 -0.010 -0.222 0.000 0.960 1.158 1.027 1.194 2.916 1.539 1.195 +0 0.842 -1.910 -0.245 0.222 1.665 1.245 -2.030 -0.630 0.000 2.142 -0.869 1.191 2.215 0.784 -1.486 -1.196 2.548 0.915 -1.342 0.234 0.000 1.080 0.929 0.979 1.140 1.263 0.874 0.799 +1 0.841 0.792 1.305 0.582 -0.907 0.695 0.746 -0.157 0.000 0.682 0.933 -1.063 1.107 1.514 0.407 0.484 1.274 1.723 0.358 -1.693 0.000 1.354 0.987 0.990 0.887 1.100 0.825 0.741 +1 0.658 0.433 -0.926 0.797 -0.157 0.723 1.702 -1.419 0.000 0.662 0.986 -0.297 2.215 1.090 -0.712 0.702 2.548 0.938 1.231 1.198 0.000 0.900 0.939 0.993 0.808 1.164 1.105 1.023 +1 1.935 0.637 -0.310 0.436 -1.062 0.637 0.216 1.727 0.000 0.751 -0.851 1.512 2.215 1.302 0.669 0.821 2.548 0.820 1.394 -0.150 0.000 0.772 1.085 0.991 0.999 1.123 1.025 0.841 +1 1.215 1.178 1.589 0.313 0.430 0.799 -0.674 -1.105 0.000 0.779 1.097 -0.009 0.000 0.634 0.122 0.714 2.548 0.658 -0.857 -1.037 1.551 1.003 0.831 0.986 0.820 0.579 0.813 0.739 +0 0.677 -0.195 0.490 0.996 1.322 1.217 0.296 1.308 0.000 1.638 -0.958 -0.557 0.000 0.813 1.002 1.372 2.548 1.948 0.189 -0.355 1.551 3.624 2.240 0.996 0.597 1.055 1.472 1.200 +1 2.172 0.282 -0.300 0.248 -0.357 0.688 -0.654 1.675 2.173 0.700 2.202 -1.560 0.000 1.343 -2.216 0.808 0.000 0.590 -0.253 -0.016 0.000 0.781 0.868 0.981 0.876 0.934 0.957 0.774 +0 1.415 1.665 -1.552 0.265 0.548 0.800 0.950 0.896 0.000 0.956 0.660 -0.979 2.215 1.308 1.215 -0.288 2.548 0.697 1.370 0.690 0.000 0.451 0.927 0.987 0.787 0.799 0.817 0.763 +0 0.572 0.408 -1.450 1.050 0.570 0.404 1.404 -0.035 0.000 0.482 0.914 -0.976 1.107 1.515 1.520 -0.676 2.548 1.026 1.656 -1.401 0.000 0.969 0.741 1.040 0.689 0.416 0.710 0.666 +0 0.507 -1.546 0.422 0.878 -0.233 1.277 1.155 -0.802 0.000 1.033 -0.615 1.345 2.215 1.053 -1.783 1.190 0.000 1.606 -0.296 -0.567 0.000 0.745 0.715 0.988 0.750 0.287 0.654 0.633 +0 0.694 -1.242 -0.850 0.463 -0.761 0.798 -2.154 -1.497 0.000 0.356 -1.876 1.704 0.000 1.092 -0.923 0.667 2.548 0.452 -0.442 0.546 1.551 0.723 0.618 1.003 0.572 0.143 0.563 0.530 +1 1.300 -1.294 -0.204 0.438 -0.190 1.311 -0.237 1.519 2.173 0.624 0.074 -0.847 1.107 0.471 0.725 0.945 0.000 0.600 0.559 0.207 0.000 0.364 0.871 0.984 0.733 1.145 0.993 0.822 +1 0.384 0.716 -1.380 1.174 0.370 0.712 -1.267 -0.360 0.000 1.068 0.238 1.299 2.215 0.923 -1.585 0.358 0.000 1.144 2.175 1.577 0.000 0.929 0.691 0.990 0.922 0.709 0.778 0.988 +0 0.718 0.087 -0.395 1.370 -1.528 1.276 -0.449 0.185 2.173 1.162 0.024 -1.510 2.215 0.706 -0.399 1.613 0.000 0.886 -0.431 -0.416 0.000 0.845 0.989 1.171 0.715 1.841 1.067 0.856 +1 0.449 -1.027 -0.489 1.103 0.777 1.370 -2.122 0.362 0.000 2.044 1.304 -1.289 0.000 1.942 0.319 -0.654 0.000 1.781 0.203 1.153 3.102 1.623 1.539 0.985 0.725 0.595 1.341 1.080 +1 0.544 0.325 -1.527 0.901 0.787 0.635 0.851 0.318 2.173 0.728 1.678 -0.685 0.000 0.651 1.374 1.716 0.000 0.560 -0.688 -1.339 3.102 0.944 0.816 0.989 0.728 0.867 0.626 0.608 +0 0.956 0.657 -0.187 1.152 0.695 0.985 0.160 -1.007 2.173 0.785 -0.056 -1.605 1.107 0.621 -1.649 -0.923 0.000 0.828 0.405 1.176 0.000 0.848 1.116 1.038 0.971 0.679 0.870 0.795 +1 2.229 0.763 -0.619 0.160 -1.406 0.464 1.431 -1.562 0.000 0.653 1.267 0.301 0.000 1.207 0.755 0.837 2.548 0.943 -0.280 1.728 0.000 1.039 0.945 0.989 0.789 0.224 0.731 0.732 +0 0.584 -1.026 -1.383 1.154 0.977 1.075 -0.564 -0.839 2.173 1.317 1.527 0.455 2.215 1.015 1.157 -1.347 0.000 0.626 0.702 0.586 0.000 0.887 1.001 0.986 1.052 2.770 1.570 1.275 +1 0.580 -1.148 -0.756 1.396 0.364 0.471 -0.133 1.427 2.173 0.423 0.719 -0.365 0.000 0.773 -0.281 -1.282 2.548 1.244 1.260 0.992 0.000 0.964 0.948 1.056 0.906 0.488 0.695 0.902 +1 0.730 -1.363 0.848 1.617 -1.484 0.672 -1.202 -0.443 1.087 0.843 -0.999 -0.761 0.000 1.461 0.037 1.544 2.548 2.116 -0.173 0.233 0.000 1.545 1.048 1.299 1.125 1.461 1.026 1.019 +0 0.893 -1.485 -1.668 0.734 0.542 0.713 -1.776 -0.422 0.000 0.581 -1.913 0.212 0.000 1.169 -1.365 0.955 1.274 1.892 -0.330 -1.303 3.102 0.749 0.943 1.024 0.916 1.211 0.954 0.815 +0 0.448 0.257 -0.056 1.386 1.109 0.750 -1.301 0.226 0.000 1.330 0.450 1.655 2.215 0.781 -2.594 -1.561 0.000 1.077 -2.352 -0.309 0.000 0.915 1.473 0.989 0.873 1.301 1.765 1.449 +1 1.093 1.771 -0.293 2.109 -0.823 1.365 -2.081 1.324 0.000 0.538 0.551 -0.041 0.000 0.676 0.777 0.448 2.548 1.441 0.368 -1.703 3.102 0.895 0.916 0.985 0.957 0.721 0.942 0.796 +1 0.643 -0.142 1.359 0.685 0.623 0.386 0.180 -0.727 2.173 0.505 -2.623 0.609 0.000 0.917 -0.303 1.631 1.274 0.984 -1.565 -0.611 0.000 0.905 1.154 0.991 0.743 0.659 0.907 1.075 +0 0.772 -0.470 -1.466 0.390 -0.344 0.716 -0.303 1.083 2.173 0.785 -0.622 -0.972 0.000 1.154 -1.033 0.117 2.548 0.717 -0.331 1.560 0.000 0.750 0.879 0.984 0.820 0.992 0.774 0.702 +1 0.449 1.490 -0.194 0.734 1.296 0.788 0.953 -1.032 0.000 0.742 1.157 1.083 2.215 0.394 1.927 1.184 0.000 1.080 -0.020 0.432 3.102 1.099 0.946 0.988 1.123 0.687 0.792 0.794 +1 0.765 -0.199 -0.829 0.860 0.921 1.295 0.209 -1.616 0.000 0.735 1.490 -0.805 0.000 1.514 0.697 0.478 1.274 0.812 0.724 1.534 0.000 0.930 1.031 1.124 0.721 0.682 0.720 0.740 +0 0.503 1.577 -1.050 0.824 -0.058 0.665 -0.098 0.936 2.173 0.527 1.422 1.391 0.000 0.641 1.141 0.020 0.000 1.135 0.938 -1.248 3.102 0.847 0.929 0.978 0.644 1.038 0.710 0.659 +0 0.619 -1.216 0.456 1.224 1.275 0.932 -1.042 -0.563 2.173 1.534 0.529 0.956 1.107 1.504 -0.461 -1.186 0.000 0.610 0.788 -1.023 0.000 0.851 1.023 0.987 1.926 2.323 1.560 1.341 +0 0.391 0.134 -1.658 0.595 1.362 0.417 1.242 -1.293 1.087 0.351 0.804 -0.060 0.000 0.759 0.382 0.505 2.548 0.724 -1.061 0.932 0.000 0.971 0.970 0.981 0.876 0.761 0.880 0.763 +0 0.416 0.739 -1.653 0.235 -0.289 0.704 0.339 -0.116 2.173 0.705 -1.881 1.630 0.000 0.476 -0.671 0.696 2.548 0.581 -0.795 -1.050 0.000 0.698 0.615 0.989 0.809 0.631 0.826 0.707 +0 0.384 -0.267 -0.624 1.101 1.228 0.786 1.177 -1.282 2.173 0.987 1.181 0.239 2.215 0.635 1.695 -0.662 0.000 0.602 2.212 0.843 0.000 0.722 0.824 0.985 1.357 1.271 1.199 1.131 +0 1.077 0.114 1.659 0.682 1.302 1.013 -1.251 0.078 0.000 0.900 -0.339 -1.071 1.107 0.735 -1.950 -0.299 0.000 0.543 -2.018 -0.931 0.000 0.912 1.173 0.988 0.596 0.441 0.674 0.737 +0 1.012 -0.965 -1.604 1.170 1.370 0.675 0.819 -0.442 0.000 0.669 0.740 0.003 0.000 0.466 -0.385 -1.542 2.548 0.998 -0.698 0.016 3.102 0.559 0.821 0.995 0.488 0.526 0.615 0.794 +0 1.015 -0.595 1.307 0.910 -1.265 0.863 2.131 -0.320 0.000 1.561 0.250 1.715 2.215 0.577 0.906 -0.700 0.000 1.449 -1.779 0.112 0.000 0.885 0.791 0.988 0.787 0.970 1.146 1.185 +0 0.780 0.545 1.715 1.604 0.482 0.804 0.389 -0.840 2.173 0.697 -0.059 0.589 0.000 0.799 -0.579 -1.157 2.548 0.445 0.174 1.333 0.000 0.734 1.005 1.388 1.045 0.606 0.864 0.774 +0 0.787 -0.604 0.796 0.847 0.961 0.646 -0.146 0.734 2.173 1.011 -0.237 -1.017 0.000 1.408 -0.452 -0.556 2.548 1.068 -0.470 -1.707 0.000 0.818 1.063 0.997 1.072 1.111 0.794 0.809 +1 1.195 -0.031 0.647 0.565 -1.480 0.861 -0.187 -1.335 2.173 0.568 -0.701 0.859 0.000 0.506 -0.865 -1.497 2.548 0.976 -0.499 -0.583 0.000 0.937 0.945 1.071 0.662 0.342 0.613 0.609 +0 0.453 -1.189 0.754 1.448 0.084 0.810 0.213 -0.608 2.173 0.659 0.171 -1.299 0.000 0.656 -0.549 -1.456 0.000 1.084 -1.114 1.152 3.102 0.583 0.786 0.994 0.888 1.305 0.822 0.749 +1 1.064 -1.090 -1.618 1.356 1.291 2.083 2.016 -0.136 0.000 1.653 0.038 1.319 2.215 0.603 -0.956 -0.314 0.000 0.826 -0.801 1.418 3.102 4.514 3.202 0.984 1.334 0.558 2.371 2.682 +1 1.932 1.712 -1.541 0.636 -1.617 0.584 1.076 0.138 2.173 0.507 1.826 -0.118 0.000 0.771 0.866 0.562 2.548 0.887 0.442 -0.346 0.000 0.833 0.608 0.991 0.919 0.317 0.809 0.692 +1 1.985 0.162 0.788 1.081 0.113 0.503 0.446 -0.006 0.000 1.587 0.052 -1.090 2.215 1.268 -0.399 1.594 2.548 0.970 -1.076 -0.470 0.000 1.138 1.137 1.158 1.518 1.065 1.119 1.006 +0 0.334 0.953 -0.717 0.591 1.219 0.594 -1.421 -1.672 0.000 0.803 -1.057 0.279 1.107 1.207 0.950 -0.127 2.548 1.359 2.431 1.483 0.000 5.986 3.388 0.992 0.748 1.390 2.338 1.748 +1 1.679 -1.408 -0.511 0.822 -1.065 1.003 -0.322 1.143 2.173 0.388 -1.111 -0.041 2.215 0.557 -0.366 -0.278 0.000 0.813 -0.366 -1.505 0.000 0.909 0.971 0.996 0.579 0.892 0.930 0.793 +1 1.207 -0.283 1.639 0.398 -0.001 1.356 1.150 -0.442 0.000 2.351 -0.712 1.393 2.215 0.693 -0.532 -0.601 0.000 0.536 0.247 0.602 1.551 0.766 0.686 0.987 0.841 0.858 0.885 0.754 +0 1.031 0.231 -0.085 0.536 -1.741 0.875 0.250 1.214 0.000 0.472 -1.161 0.472 2.215 1.505 1.172 -0.719 2.548 0.439 0.576 -0.928 0.000 0.911 0.906 1.027 0.863 1.623 0.990 0.823 +1 1.304 -0.363 0.540 0.772 -0.769 2.221 -1.045 1.057 0.000 1.280 0.156 -0.854 2.215 1.243 0.961 -0.702 2.548 1.376 -0.513 -1.092 0.000 0.687 0.813 1.284 1.032 0.646 0.858 0.714 +1 2.371 -0.559 -1.223 1.430 -0.543 0.883 -0.637 -0.823 0.000 1.121 1.482 1.128 0.000 2.190 -0.821 -0.307 2.548 4.220 0.594 1.136 0.000 1.184 1.563 1.469 1.191 0.710 1.840 1.790 +1 0.510 1.186 1.656 0.974 0.364 0.924 1.271 -1.012 1.087 0.761 -0.447 1.541 0.000 0.772 -0.257 0.749 0.000 0.772 0.162 0.042 3.102 0.777 0.684 0.986 0.982 0.889 0.922 0.795 +1 0.519 -0.106 0.006 0.694 -1.625 0.390 -1.574 -0.700 0.000 0.842 0.459 -1.033 2.215 0.351 0.323 1.234 0.000 1.326 0.860 0.340 3.102 0.999 0.963 0.984 0.681 0.941 0.843 0.735 +0 1.598 -1.168 1.597 0.933 -1.407 1.194 0.306 0.287 1.087 0.885 -0.698 -0.121 2.215 1.452 0.864 -1.351 0.000 1.601 -0.438 1.029 0.000 0.913 1.300 0.995 1.689 0.977 1.201 1.204 +0 0.858 -0.039 -0.693 1.275 -0.338 0.478 2.232 1.210 0.000 0.734 -2.713 -0.543 0.000 1.031 -0.389 1.373 2.548 2.497 1.262 0.927 0.000 0.998 0.729 0.984 1.107 0.596 1.005 0.978 +1 1.479 -1.297 0.740 0.364 1.719 2.749 -1.327 -0.785 0.000 2.422 -0.789 1.241 2.215 0.863 -1.337 0.348 2.548 1.208 -1.938 0.231 0.000 0.929 1.298 0.987 1.013 1.221 1.686 1.372 +1 0.423 -0.785 -0.613 0.346 -0.819 0.809 0.019 0.523 2.173 0.653 -0.586 -1.320 0.000 1.366 0.112 1.636 0.000 0.499 0.470 0.196 0.000 0.900 0.914 0.989 0.565 0.798 0.679 0.646 +1 1.392 0.046 -0.482 0.210 -0.411 1.475 -0.710 0.876 0.000 0.904 -1.383 -1.196 0.000 0.868 0.751 0.208 2.548 1.133 -0.129 -0.802 0.000 1.004 1.314 1.001 0.524 0.586 0.746 0.680 +1 0.430 1.650 -0.751 0.219 -1.475 0.561 0.691 0.710 0.000 0.755 0.842 -1.543 2.215 1.864 0.578 -0.624 2.548 0.508 2.431 0.024 0.000 1.199 1.044 0.987 0.726 0.940 0.903 0.778 +0 1.652 0.570 0.108 1.134 1.387 1.038 -0.437 0.261 2.173 2.429 -0.059 -1.571 2.215 0.730 1.591 -0.153 0.000 0.860 -1.380 -1.145 0.000 2.417 1.776 1.734 1.725 2.371 1.576 1.404 +1 0.597 0.635 -0.623 0.917 0.484 0.808 0.670 -1.165 1.087 0.853 0.068 0.032 0.000 0.872 -0.869 0.983 2.548 0.851 0.666 1.542 0.000 1.162 0.970 0.985 0.916 1.363 0.873 0.758 +0 0.509 0.729 -1.274 1.057 0.635 1.161 0.682 0.586 1.087 1.301 -0.899 -0.880 2.215 1.542 1.376 -1.479 0.000 1.381 1.204 0.790 0.000 1.131 1.668 1.004 1.306 2.389 1.815 1.376 +1 0.318 2.059 1.091 0.821 0.042 0.636 -2.368 -0.946 0.000 0.883 -0.797 1.707 0.000 0.456 -0.980 0.265 1.274 1.365 1.225 0.310 3.102 0.772 0.707 0.994 0.619 0.994 0.858 0.772 +1 0.865 -1.015 -1.715 0.854 -0.252 0.566 -0.749 1.306 2.173 0.756 -1.099 -1.270 0.000 0.761 -1.120 0.438 0.000 1.983 -0.424 -0.049 3.102 1.033 0.905 1.153 0.780 1.063 0.726 0.665 +1 1.093 0.984 -0.528 1.378 -1.113 1.481 0.419 -1.042 0.000 3.647 -0.986 0.888 0.000 1.097 1.233 -0.045 0.000 1.406 0.896 -1.253 3.102 2.107 1.259 0.981 0.952 1.006 0.993 0.842 +0 2.238 -0.052 0.735 0.379 0.098 0.954 0.616 -0.864 1.087 1.076 0.135 -1.647 2.215 0.840 -1.061 1.175 0.000 1.231 0.775 -0.525 0.000 0.708 0.794 0.987 1.338 1.033 1.048 0.906 +0 0.750 1.574 -0.926 0.796 1.049 0.639 0.736 0.275 1.087 0.753 -0.857 0.257 1.107 0.937 -1.612 -1.532 0.000 0.607 2.471 -0.972 0.000 0.721 0.926 1.047 0.832 0.924 0.974 1.163 +1 1.569 -1.973 -1.496 1.589 -1.347 1.228 0.867 0.654 0.000 0.736 -1.719 -0.613 0.000 1.013 -0.502 -0.151 2.548 0.902 -0.506 1.004 3.102 0.962 0.921 0.975 1.205 0.630 0.883 1.487 +1 1.035 -0.387 0.439 1.890 0.641 0.636 0.712 1.479 0.000 1.179 -0.531 -1.299 2.215 0.373 2.291 0.398 0.000 1.061 0.174 -0.828 3.102 0.725 1.266 0.970 1.104 0.571 1.026 1.103 +1 0.720 -1.027 0.996 0.193 -0.975 0.660 0.884 1.399 0.000 0.845 -0.543 -0.456 2.215 0.559 1.153 -1.398 0.000 1.117 -1.240 0.100 3.102 0.666 1.230 0.994 0.748 0.597 1.034 0.854 +1 1.496 -0.636 -1.432 0.439 -1.604 0.445 -0.627 0.383 2.173 0.289 -2.606 0.633 0.000 0.651 -1.087 0.022 2.548 0.372 -2.326 1.192 0.000 0.207 0.646 0.977 0.860 0.287 0.689 0.730 +0 1.296 -1.759 0.877 0.657 -1.299 1.173 1.038 -0.034 2.173 0.728 -0.049 1.362 2.215 1.886 -1.972 -1.349 0.000 0.995 -1.993 -0.632 0.000 0.926 1.630 1.182 2.610 1.513 2.387 1.932 +0 0.660 -1.418 -1.155 0.338 1.442 1.167 -0.763 -0.356 1.087 1.626 1.326 1.452 0.000 0.983 -1.605 0.338 0.000 0.901 -0.465 0.989 3.102 0.957 0.986 0.995 0.612 1.023 0.718 0.686 +1 1.453 0.019 -0.545 0.610 0.792 2.542 1.641 -1.658 0.000 1.000 1.702 0.264 0.000 1.331 0.860 0.169 0.000 1.267 -1.132 0.054 3.102 1.013 0.793 1.217 0.890 0.913 1.033 0.899 +1 1.205 0.638 -1.497 1.355 -1.554 0.721 -0.710 0.143 0.000 0.482 -1.038 -1.402 2.215 1.095 1.144 -0.040 0.000 0.827 0.331 0.977 3.102 0.920 1.076 0.982 0.726 0.661 0.678 0.745 +0 1.408 -0.338 -0.110 0.783 -1.196 0.563 -1.437 -1.224 0.000 1.242 0.576 0.053 1.107 1.202 -0.528 1.576 2.548 1.204 1.266 1.234 0.000 0.767 0.900 1.206 0.978 1.506 1.310 1.101 +1 0.863 0.702 1.097 1.589 0.376 1.197 1.058 -1.344 2.173 0.344 2.481 -0.142 0.000 0.931 0.640 -0.462 2.548 0.439 1.658 1.488 0.000 0.524 0.927 0.987 0.818 0.970 0.970 0.822 +1 1.247 -0.710 1.130 0.748 0.494 0.907 -0.397 -0.660 2.173 0.479 0.788 0.052 2.215 0.350 -1.445 1.350 0.000 0.496 -2.332 -1.408 0.000 0.739 0.941 0.991 1.015 0.853 0.940 0.829 +0 0.479 2.001 0.272 0.621 0.925 0.627 0.212 -0.307 2.173 0.735 0.189 1.336 0.000 0.941 0.119 -1.119 2.548 0.416 -1.537 0.605 0.000 0.975 1.005 0.985 0.821 0.641 0.703 0.721 +0 0.528 0.582 -1.453 0.983 -0.241 0.529 -2.811 -1.024 0.000 1.419 0.263 0.966 2.215 0.683 0.621 -0.263 0.000 0.482 -0.209 0.971 3.102 3.126 1.722 0.988 0.993 0.198 1.543 1.253 +1 0.374 -0.148 -1.673 1.089 0.726 0.934 1.145 -0.074 0.000 0.881 0.527 1.191 0.000 1.311 0.045 -0.503 2.548 1.239 -0.617 -1.331 0.000 1.398 0.864 0.993 1.041 0.785 0.822 0.829 +0 1.812 1.235 1.393 1.242 -1.598 0.930 1.847 -0.139 0.000 0.702 2.191 0.414 0.000 0.541 2.214 -0.681 0.000 1.003 0.935 -0.829 3.102 0.886 0.955 0.984 0.821 0.517 0.682 0.854 +0 0.369 -0.782 -0.128 0.980 -0.584 0.525 -1.186 1.289 2.173 0.420 0.550 -0.695 2.215 0.957 -0.155 1.486 0.000 0.865 -0.744 0.358 0.000 0.931 0.788 0.975 1.145 0.966 0.774 0.717 +0 0.543 0.952 -1.358 0.507 -0.189 0.541 -1.063 1.246 0.000 0.454 -1.086 0.354 0.000 0.760 -0.090 -1.373 1.274 0.399 -0.834 -0.213 1.551 0.922 0.730 0.987 0.905 0.414 0.730 0.846 +1 0.873 0.914 -1.094 0.738 0.216 0.934 -0.252 1.512 0.000 0.909 0.637 -0.354 2.215 1.099 0.476 0.758 2.548 1.042 -0.728 -1.420 0.000 0.863 1.044 1.029 0.645 0.898 0.944 0.866 +0 0.637 -1.593 1.524 1.188 -1.031 0.600 -1.311 -0.437 2.173 0.647 -0.309 0.690 0.000 1.107 -1.170 0.182 1.274 0.720 -0.421 1.284 0.000 0.997 0.870 0.986 0.931 0.538 0.704 0.713 +0 0.640 0.746 0.003 0.705 -1.690 1.065 0.654 1.034 2.173 0.816 1.513 -1.114 0.000 1.144 -0.427 1.021 0.000 2.143 0.748 -0.768 0.000 0.869 0.924 0.985 0.853 1.600 0.970 0.921 +0 5.331 1.077 0.157 1.011 -0.269 1.522 -0.836 1.559 0.000 1.617 -0.800 -1.230 0.000 1.657 0.058 -1.373 0.000 0.796 -0.060 0.579 3.102 1.196 0.845 1.202 0.976 0.474 0.840 1.604 +1 0.421 0.840 1.604 1.006 1.055 1.271 -2.596 0.445 0.000 1.199 -0.135 0.681 2.215 2.365 0.493 -1.078 0.000 1.256 0.039 -0.296 3.102 7.876 4.278 0.984 1.549 0.860 2.653 3.166 +1 0.694 -0.218 -1.193 1.476 1.305 0.916 -0.660 0.138 1.087 0.629 -1.090 -0.728 2.215 0.443 -1.080 0.169 0.000 1.890 0.219 -1.221 0.000 1.246 0.900 1.090 1.141 0.827 0.859 0.819 +0 0.592 0.660 -1.632 0.779 0.354 0.687 -0.053 -0.199 1.087 1.240 -0.852 0.560 2.215 1.371 -1.811 -1.430 0.000 1.737 0.096 -1.249 0.000 2.158 1.747 0.991 1.302 1.032 1.300 1.310 +0 0.794 1.209 0.682 1.928 0.618 0.998 2.468 -1.284 0.000 1.253 -0.331 -0.450 0.000 0.842 -0.368 1.101 2.548 0.791 1.068 -1.541 3.102 0.841 0.916 0.987 0.871 0.731 0.703 0.808 +1 0.628 1.124 -1.620 0.997 -0.472 0.672 0.167 -1.689 0.000 0.896 -0.652 -1.342 0.000 1.899 -0.643 0.283 2.548 0.840 0.234 -0.489 1.551 0.875 0.806 0.990 1.665 0.793 1.051 1.066 +1 0.877 1.810 1.579 0.904 1.248 1.109 0.490 -0.423 2.173 0.527 0.097 0.300 2.215 0.679 0.200 -1.152 0.000 1.130 0.194 1.108 0.000 0.864 1.043 0.973 0.831 0.718 0.914 0.790 +0 0.659 -0.548 0.212 0.729 -1.662 1.049 0.346 -0.591 2.173 0.709 0.519 1.234 0.000 0.655 0.433 0.604 0.000 0.698 -0.680 -1.290 1.551 0.563 1.136 0.989 0.540 0.772 0.756 0.737 +1 1.594 -0.349 0.503 0.278 0.557 0.962 0.740 -1.020 1.087 0.744 -0.073 1.513 2.215 0.323 0.380 1.090 0.000 0.543 -0.691 -0.051 0.000 0.501 0.820 0.987 0.807 1.079 0.934 0.721 +1 0.956 -0.337 -1.392 1.317 -0.877 0.678 0.692 0.480 2.173 0.589 1.442 0.753 0.000 1.067 1.321 -0.145 2.548 0.924 -1.592 1.390 0.000 0.958 1.011 0.977 1.570 0.698 1.223 0.989 +0 0.489 -0.806 0.339 0.688 -0.603 0.926 1.535 1.062 0.000 1.010 -0.008 -1.016 2.215 0.570 0.287 -0.143 2.548 0.389 1.464 0.368 0.000 0.844 0.881 0.987 1.167 0.586 0.890 1.382 +1 0.590 -0.228 0.443 0.763 -1.724 1.037 0.448 -1.135 2.173 1.572 1.109 1.592 0.000 2.479 0.264 0.186 0.000 0.734 1.285 -0.153 0.000 1.090 1.018 0.986 0.859 0.897 1.007 0.848 +1 2.256 -0.273 -1.452 0.682 0.865 0.437 -0.433 1.308 0.000 1.103 0.006 -0.006 2.215 0.397 -2.701 0.748 0.000 1.308 -1.440 0.065 0.000 0.989 0.975 1.494 0.836 0.234 0.819 0.817 +1 0.607 0.534 -0.418 1.583 0.705 0.547 0.367 0.975 2.173 0.908 0.780 -0.025 2.215 1.168 -0.616 -1.365 0.000 2.258 0.308 -1.236 0.000 0.907 0.945 1.152 0.733 0.844 0.813 0.775 +0 1.026 -1.834 -0.754 1.788 -1.661 0.489 -1.053 -0.534 0.000 0.760 -2.085 1.297 0.000 0.579 -1.253 0.701 2.548 0.788 0.051 -0.045 3.102 1.485 0.899 1.368 1.205 0.516 0.830 0.806 +1 0.524 -1.310 0.683 1.178 -0.708 0.941 0.451 1.734 2.173 0.924 0.300 0.408 2.215 0.750 -1.428 1.098 0.000 0.589 -2.395 -0.731 0.000 0.893 1.469 1.034 1.408 1.281 1.320 1.130 +0 0.777 -0.038 0.681 1.287 0.300 0.994 1.242 -1.187 0.000 0.846 -1.037 0.483 2.215 1.353 0.260 -0.853 2.548 1.301 -0.761 1.678 0.000 0.873 0.866 0.989 1.040 1.347 0.992 0.880 +0 1.083 -0.383 1.261 0.464 -0.084 0.907 -0.437 0.384 1.087 1.264 -0.424 -1.240 2.215 0.894 -0.915 -0.709 0.000 0.744 -0.992 1.112 0.000 0.901 0.934 0.985 0.888 1.566 0.864 0.751 +0 0.823 1.424 -0.732 0.649 0.092 0.353 -0.740 0.472 2.173 0.508 0.912 0.178 2.215 1.437 0.174 -1.612 0.000 0.418 -2.289 0.458 0.000 0.451 0.837 0.989 0.559 0.612 0.645 0.652 +0 0.870 -0.729 0.752 2.530 0.243 1.424 -0.672 -1.506 0.000 0.951 -0.733 -0.339 2.215 1.180 0.035 1.486 2.548 0.830 -0.693 -1.162 0.000 0.524 0.817 0.978 0.870 1.213 0.966 1.092 +0 0.651 -0.395 -0.291 1.377 -0.267 0.988 0.022 -1.353 2.173 0.678 0.243 1.494 0.000 1.028 0.419 0.717 2.548 0.740 -0.905 0.778 0.000 0.865 0.947 0.991 1.262 1.233 1.180 0.996 +1 0.806 -0.368 0.793 1.212 1.500 1.476 -0.012 -0.359 2.173 0.618 0.230 1.155 0.000 0.938 -1.455 -1.604 0.000 0.733 -0.002 0.110 0.000 0.717 1.085 0.981 0.719 0.965 0.957 0.796 +1 0.745 0.643 0.591 0.623 -0.423 0.946 0.974 -0.224 0.000 0.851 1.168 -1.382 1.107 0.840 0.127 -1.714 2.548 0.861 0.992 1.562 0.000 0.832 1.033 0.992 0.831 0.566 0.797 0.712 +0 0.614 0.388 0.588 0.979 1.345 0.756 -0.273 -0.589 0.000 0.626 -1.678 -1.065 0.000 0.438 0.553 1.544 2.548 0.839 -1.592 0.123 0.000 0.833 0.971 0.987 1.081 0.517 0.682 1.036 +0 0.889 0.356 0.261 0.916 0.397 0.856 1.345 -1.114 0.000 1.205 0.903 1.027 1.107 0.790 -0.040 -1.180 2.548 0.606 0.383 -1.528 0.000 0.649 1.141 0.987 0.960 1.084 0.803 0.828 +0 0.704 -0.279 -0.729 0.627 1.103 0.516 0.241 -1.699 0.000 0.471 1.168 0.237 0.000 0.547 2.005 1.304 0.000 0.853 -1.037 -0.009 3.102 0.757 0.989 0.989 0.695 0.205 0.847 0.724 +1 2.526 -0.833 -1.682 0.209 -0.637 0.802 0.034 -0.411 2.173 1.193 -1.641 0.530 0.000 0.580 -0.340 -1.717 2.548 1.087 -1.217 -0.822 0.000 0.908 0.800 0.986 0.480 0.804 0.785 0.744 +1 2.038 -1.164 1.137 1.410 0.222 2.995 0.174 -0.913 0.000 0.851 1.501 0.817 0.000 0.977 0.188 1.027 0.000 0.999 0.088 -0.056 3.102 1.042 0.915 1.724 1.222 0.580 0.906 1.156 +0 1.040 -0.649 1.586 0.540 -1.161 0.626 1.370 1.515 2.173 0.941 2.025 0.161 0.000 0.434 -1.200 -0.653 0.000 1.004 0.933 0.153 3.102 0.792 0.915 0.984 1.213 0.798 1.160 1.334 +0 1.064 -1.689 1.144 0.774 0.844 1.131 -1.562 0.571 2.173 1.159 -1.515 -0.859 0.000 1.715 -1.839 -1.231 0.000 0.455 -0.065 -0.978 3.102 0.872 0.790 0.999 0.803 0.973 1.063 0.988 +1 1.147 -0.364 -1.192 0.174 1.682 0.882 -0.511 1.662 1.087 0.880 0.179 0.271 2.215 1.011 1.140 0.066 0.000 0.461 0.564 -0.417 0.000 0.386 1.304 0.976 1.007 1.316 0.894 0.895 +0 1.253 1.917 -1.135 0.745 -1.330 0.678 -0.123 0.643 0.000 0.924 0.620 0.192 0.000 0.409 -0.843 -1.167 1.274 0.636 0.463 1.276 3.102 0.934 0.896 0.975 0.681 0.446 0.664 0.853 +0 0.830 1.469 -0.503 0.790 0.614 0.966 -0.371 1.505 2.173 0.674 -0.448 -0.643 0.000 0.998 -0.427 -0.145 2.548 0.741 -0.885 0.757 0.000 0.921 1.009 0.989 1.161 1.220 1.222 1.072 +1 0.882 0.743 0.530 0.795 1.574 0.705 0.783 -1.649 0.000 0.422 -0.027 1.480 0.000 1.350 -0.699 -0.073 2.548 1.270 0.792 -0.260 3.102 0.609 0.901 0.990 1.219 0.983 0.911 0.846 +1 0.481 -0.687 -1.417 0.697 -0.018 0.860 0.458 1.046 0.000 1.119 -2.334 -0.377 0.000 0.568 0.788 -0.005 0.000 1.417 1.827 1.735 0.000 1.052 0.694 0.991 0.725 0.210 0.716 0.818 +1 1.004 0.661 -0.862 1.004 1.641 1.170 -0.101 -0.188 2.173 1.154 0.637 -1.689 0.000 1.368 0.143 1.315 2.548 1.259 -1.151 0.404 0.000 2.411 1.491 1.077 1.213 1.553 1.267 1.107 +1 1.074 0.080 0.283 0.860 -0.355 1.057 1.095 1.663 0.000 0.629 0.280 -1.165 2.215 0.734 0.970 -0.486 2.548 0.418 1.224 0.019 0.000 1.028 0.891 0.986 0.785 0.505 0.630 0.710 +1 2.355 -0.491 -0.653 0.748 0.112 0.693 0.664 1.242 1.087 0.835 -0.763 0.666 1.107 0.471 1.108 -1.516 0.000 0.470 1.563 1.150 0.000 0.390 1.024 1.170 1.421 1.049 1.062 0.954 +0 0.821 1.390 0.233 1.465 0.789 1.752 1.034 0.742 2.173 2.724 1.138 -0.872 2.215 1.443 0.503 -1.349 0.000 0.368 1.066 -1.499 0.000 0.318 0.861 0.976 0.682 3.200 1.600 1.286 +0 1.555 1.856 0.835 1.152 0.433 1.091 -0.162 -1.717 0.000 1.276 0.651 -0.331 2.215 0.921 1.470 -0.557 0.000 0.415 0.174 1.275 0.000 0.856 0.763 1.001 0.995 0.824 1.111 0.904 +0 1.705 0.794 0.171 0.232 -0.914 0.926 -0.951 -1.608 0.000 0.501 2.782 0.186 0.000 0.775 0.001 -1.652 0.000 0.498 -1.883 0.961 0.000 1.051 0.954 0.984 0.726 0.529 0.651 0.877 +1 0.918 1.679 1.592 1.745 -1.170 0.529 1.763 -1.473 0.000 1.267 -1.273 -0.291 0.000 1.521 1.078 0.352 2.548 0.876 1.254 1.059 0.000 0.800 0.986 1.066 1.181 0.903 1.021 0.852 +1 0.753 0.058 0.740 0.384 -1.121 0.855 -0.061 -0.586 0.000 1.086 -0.021 1.392 2.215 0.588 -0.959 -0.995 0.000 1.249 -0.863 0.676 3.102 0.803 1.003 0.990 0.763 0.842 0.873 0.807 +1 0.370 -0.660 -0.511 1.211 -0.886 1.205 -0.304 1.491 2.173 1.448 -0.450 0.761 0.000 1.083 0.035 -0.607 1.274 1.105 0.365 -1.257 0.000 0.963 1.042 0.986 1.303 1.373 0.950 0.955 +1 0.392 1.670 0.039 1.678 1.494 1.621 2.615 -0.604 0.000 1.342 0.065 1.014 2.215 0.973 0.800 0.837 0.000 0.932 -0.502 1.531 3.102 1.048 2.494 1.087 1.284 0.570 2.116 1.636 +0 1.950 0.691 0.445 1.452 0.301 2.038 0.179 -1.391 2.173 1.090 0.991 0.058 2.215 0.431 1.294 1.679 0.000 0.455 0.615 1.317 0.000 0.231 0.808 0.991 0.624 2.319 1.669 1.199 +1 0.764 -0.556 0.755 1.054 -0.453 0.385 -0.849 -1.702 1.087 0.598 0.286 -0.822 0.000 0.370 -1.939 0.960 0.000 0.488 -0.495 -0.986 3.102 1.211 0.841 1.101 0.572 0.283 0.496 0.533 +1 0.539 -0.857 -0.632 1.126 0.478 0.911 -0.886 -0.352 1.087 0.720 -0.651 0.485 0.000 2.128 -0.846 -1.536 2.548 0.922 -1.424 -1.471 0.000 1.191 1.046 0.991 1.119 1.520 0.949 0.844 +1 1.475 -0.350 -1.109 1.150 -1.724 1.279 -0.374 0.491 0.000 0.529 -0.017 1.515 0.000 0.689 -0.704 -1.131 0.000 1.108 0.420 -0.669 3.102 0.836 0.996 0.989 0.741 0.463 0.639 0.797 +1 0.465 -0.023 -1.457 0.784 1.389 0.978 -1.036 1.142 0.000 1.218 -1.319 -0.551 0.000 1.211 -0.161 -0.656 1.274 1.100 0.418 0.464 3.102 0.848 0.992 0.986 0.920 0.807 0.851 0.777 +0 0.634 0.703 -0.950 0.989 0.006 0.956 1.559 1.692 2.173 0.611 1.524 -0.640 0.000 1.436 0.935 0.583 2.548 0.714 0.759 1.384 0.000 0.882 0.863 0.986 1.026 1.294 0.865 0.735 +1 2.057 0.872 1.346 0.548 0.381 0.661 0.662 0.675 0.000 1.042 1.068 -0.532 2.215 1.097 -0.064 -1.008 2.548 0.586 -0.113 -0.389 0.000 0.873 0.953 1.123 1.088 0.847 0.938 0.827 +1 0.923 1.122 -0.993 0.633 0.399 0.967 0.482 -1.740 1.087 1.023 1.628 0.609 0.000 0.520 0.787 -0.206 0.000 0.943 -0.374 -0.822 3.102 0.884 1.135 1.007 0.929 0.892 0.966 0.821 +1 1.005 -0.044 1.160 1.181 -1.242 1.010 0.190 -0.615 0.000 0.993 -0.740 1.592 1.107 1.265 -0.512 0.708 1.274 0.945 0.031 0.055 0.000 0.856 1.145 1.252 0.806 0.860 0.967 0.881 +1 1.113 -0.707 -1.194 1.771 -1.438 0.828 -0.182 1.042 0.000 0.778 2.012 0.107 0.000 0.859 -1.119 -0.014 0.000 1.621 -0.477 -0.611 0.000 0.978 0.973 0.975 0.642 0.634 0.785 0.722 +0 2.182 -1.602 -0.583 1.435 -0.473 1.169 -1.218 1.402 0.000 0.590 -0.264 1.383 0.000 0.947 -0.482 0.471 2.548 0.415 -0.702 0.051 0.000 0.915 1.008 0.979 0.790 0.362 0.856 0.968 +0 2.074 0.884 -1.511 0.753 -0.825 1.227 -0.493 0.374 0.000 1.026 -0.828 0.704 0.000 1.526 0.334 -0.805 2.548 0.722 -0.626 1.603 1.551 0.815 0.847 1.004 0.804 0.814 1.020 1.213 +0 0.294 -1.159 0.495 1.037 -1.135 1.006 -0.175 1.154 0.000 0.531 -0.930 0.938 0.000 1.234 1.150 -0.377 1.274 0.803 0.544 0.052 1.551 1.089 0.779 0.989 2.515 0.371 1.643 1.304 +0 0.449 0.050 1.620 1.278 1.619 0.686 -0.266 -0.097 2.173 0.772 1.188 -1.270 0.000 1.218 0.702 -0.608 0.000 0.683 2.243 1.131 0.000 0.902 1.150 1.002 1.422 0.816 1.149 1.075 +1 0.443 0.601 -1.628 0.561 -0.854 0.480 0.671 -0.679 0.000 1.378 -0.830 1.417 2.215 0.980 -0.828 0.102 2.548 0.661 1.792 0.195 0.000 0.922 1.243 0.989 1.989 1.145 1.494 1.272 +0 1.233 -0.165 -0.892 1.341 0.272 0.962 0.106 0.537 2.173 1.849 -0.902 -1.300 0.000 0.923 -0.343 1.190 0.000 0.840 -0.903 0.465 3.102 1.663 1.184 1.545 1.085 0.604 1.107 1.038 +0 1.019 0.081 -0.809 0.778 0.039 0.929 -0.389 0.953 2.173 0.741 0.474 -1.119 0.000 0.399 0.068 -1.648 0.000 0.544 -0.742 1.188 0.000 0.949 1.036 0.988 0.584 0.987 0.761 0.720 +0 0.864 0.543 -0.813 0.968 -0.123 0.537 0.251 1.591 0.000 0.770 -0.331 0.613 0.000 0.705 0.973 -1.677 2.548 0.936 0.091 0.991 1.551 0.896 0.693 0.987 0.819 0.520 0.620 0.649 +1 0.479 -2.138 0.061 0.971 0.883 1.069 -0.727 -0.471 0.000 1.187 -2.466 1.128 0.000 0.899 -0.652 -1.030 2.548 1.115 0.079 -1.290 3.102 0.925 1.269 0.979 0.902 0.365 1.058 0.898 +1 1.385 0.238 0.378 2.601 0.890 1.076 -2.164 -0.774 0.000 0.937 -0.441 -1.099 0.000 1.306 -0.204 1.436 2.548 0.915 -1.354 -1.006 0.000 0.755 0.981 1.172 1.034 1.051 0.879 1.050 +0 0.399 1.075 -1.644 0.639 0.131 0.617 0.670 -1.337 1.087 0.490 1.280 -0.141 2.215 0.684 0.511 0.739 0.000 0.375 -0.881 -0.132 0.000 0.699 0.747 0.986 0.682 0.760 0.611 0.544 +1 1.078 -2.326 1.728 0.597 -1.364 0.514 -1.403 0.600 0.000 0.709 -0.791 -0.278 1.107 0.725 -0.764 -0.736 2.548 1.247 -0.145 0.509 0.000 0.954 0.814 0.985 0.748 0.306 0.671 0.664 +0 0.610 -2.247 -1.013 0.971 1.479 0.642 0.690 -0.186 2.173 0.321 -1.363 -0.113 0.000 0.441 1.121 -1.338 0.000 0.841 -0.344 0.947 3.102 1.125 0.910 0.987 0.723 0.803 1.013 0.886 +0 0.421 -0.788 -1.499 1.773 -1.416 0.983 -0.249 0.713 0.000 0.516 0.241 1.331 0.000 0.671 0.602 -0.132 2.548 0.758 -0.682 -0.304 1.551 0.885 0.856 0.981 0.828 0.449 1.015 1.114 +0 1.796 0.407 0.703 1.094 0.130 0.732 -0.270 -1.028 2.173 0.964 0.219 -0.179 0.000 1.070 0.675 -1.710 2.548 1.646 -0.394 -1.721 0.000 1.717 1.161 0.985 1.007 0.862 0.967 0.957 +0 1.861 -1.475 0.304 1.122 -0.292 1.421 -1.800 -1.176 0.000 1.196 -1.517 0.826 2.215 0.387 -0.858 -1.298 1.274 1.023 -1.107 1.287 0.000 1.535 0.847 1.024 0.926 0.718 0.897 0.961 +1 0.380 -0.735 -0.242 0.247 -0.432 0.676 0.299 0.958 0.000 1.035 0.290 -1.185 1.107 1.483 -0.659 -0.525 2.548 0.637 1.200 0.922 0.000 0.730 1.042 0.986 0.754 1.019 0.805 0.724 +0 0.571 -0.893 1.560 1.094 1.578 0.791 0.563 -0.492 1.087 0.809 0.561 0.503 0.000 0.295 -0.781 0.390 0.000 0.500 -0.982 1.070 1.551 0.980 0.920 0.985 0.609 0.930 0.760 0.699 +1 0.660 -0.125 -0.763 0.941 1.624 0.917 -0.353 0.559 0.000 0.733 -0.488 0.013 0.000 1.139 -1.089 -1.241 2.548 0.748 -1.972 0.974 0.000 0.833 1.099 0.988 0.871 0.826 0.916 0.833 +0 1.394 0.462 -1.523 1.050 1.399 0.548 -0.382 0.343 0.000 0.763 1.041 -0.651 2.215 0.727 -0.911 0.372 2.548 0.916 2.494 -1.211 0.000 1.050 0.918 0.998 0.963 1.162 1.150 1.119 +1 0.610 -0.906 0.438 0.526 1.651 0.793 0.447 0.342 0.000 1.057 0.437 -0.436 1.107 1.805 0.570 1.423 1.274 1.334 0.868 -1.376 0.000 1.640 1.178 0.993 1.575 1.466 1.319 1.258 +1 1.535 0.160 -0.645 1.262 -0.237 0.482 0.607 -1.267 0.000 1.342 -0.792 1.007 2.215 0.630 0.760 1.394 2.548 0.612 -2.304 -1.264 0.000 1.031 1.000 0.988 0.964 0.961 1.034 0.985 +0 0.629 0.488 0.729 0.379 0.944 0.793 -0.728 -0.968 2.173 0.700 -0.127 1.491 0.000 1.086 0.305 -0.199 2.548 0.704 -0.830 0.632 0.000 0.758 0.940 0.998 0.881 0.984 1.091 0.963 +1 0.960 -0.119 0.945 1.133 -1.350 0.731 0.588 -0.320 0.000 0.845 0.730 0.081 0.000 1.872 -0.127 -1.317 1.274 1.493 0.294 1.067 3.102 0.604 0.985 1.271 0.866 1.117 0.982 0.900 +1 3.006 -0.010 -1.063 1.535 -1.244 2.765 0.714 0.673 0.000 1.650 0.647 -0.466 2.215 0.878 -1.643 1.444 0.000 0.753 0.687 1.491 3.102 0.972 1.022 0.972 0.824 0.991 0.912 0.874 +1 1.016 1.157 -1.390 1.023 -0.977 1.063 -0.735 0.448 2.173 0.666 0.426 1.513 0.000 1.370 -2.642 -1.021 0.000 1.352 0.573 0.103 1.551 0.898 1.022 0.991 0.879 1.059 1.094 0.922 +1 1.766 1.984 -1.349 0.532 -0.506 0.491 1.094 -0.631 0.000 0.992 1.698 0.655 0.000 1.038 0.324 1.423 2.548 0.603 0.113 0.171 0.000 0.998 0.975 0.988 0.884 0.447 0.850 0.836 +0 3.221 1.677 -1.400 0.486 -0.899 0.543 0.440 0.221 2.173 0.773 0.678 0.556 2.215 0.957 1.208 0.223 0.000 1.166 1.591 1.321 0.000 1.033 0.899 0.991 1.327 0.308 1.092 0.951 +1 0.464 -0.490 -1.380 1.247 0.801 0.809 2.880 -1.099 0.000 0.681 -0.439 -0.658 0.000 1.196 -0.354 0.182 2.548 1.044 -2.333 0.914 0.000 1.941 1.067 0.986 0.705 0.539 0.840 0.800 +0 0.665 0.741 -0.440 0.778 -1.616 0.575 0.489 1.327 2.173 0.684 -0.924 -0.958 0.000 1.145 0.435 0.028 0.000 0.600 1.046 0.236 0.000 0.623 0.750 0.987 0.556 0.386 0.490 0.548 +0 0.485 -0.845 -0.326 1.232 1.268 0.594 0.879 1.080 2.173 0.772 -0.842 -1.159 2.215 0.992 0.428 -0.332 0.000 0.832 1.164 -0.167 0.000 0.856 0.904 1.062 0.763 1.337 0.892 0.896 +0 1.153 -0.408 -1.306 2.303 -1.040 0.947 -1.214 0.950 0.000 0.772 -1.580 0.505 2.215 1.177 0.212 -0.361 2.548 1.915 -1.973 0.576 0.000 1.415 0.847 0.988 0.883 1.297 1.205 1.545 +0 0.840 -0.388 -1.053 0.413 0.274 0.618 1.072 -1.111 0.000 0.985 -0.816 0.936 2.215 0.475 -0.055 -0.591 0.000 0.630 -0.379 0.707 3.102 0.735 0.759 0.981 0.810 0.201 0.813 0.759 +1 0.412 1.151 0.527 1.116 -0.554 2.422 -1.472 -1.297 2.173 2.455 -0.709 0.734 1.107 1.017 -0.123 0.572 0.000 0.979 0.095 -0.105 0.000 0.645 0.947 0.986 4.949 3.734 3.626 2.589 +0 0.770 1.641 -0.700 0.924 -1.204 0.420 0.855 1.264 2.173 0.710 0.833 -0.086 0.000 1.816 -0.072 0.654 2.548 0.620 0.128 -1.107 0.000 0.755 0.956 0.983 0.761 0.780 0.881 0.751 +1 1.255 -0.090 -0.767 0.691 -0.668 1.566 1.338 1.572 0.000 1.046 0.776 0.395 2.215 1.587 0.292 -0.080 2.548 1.301 -0.214 0.011 0.000 1.091 0.911 0.992 0.964 0.657 0.941 1.061 +0 0.561 1.386 -0.096 0.466 1.011 1.040 0.655 1.690 2.173 1.069 -0.441 0.095 0.000 0.927 -0.214 -0.639 0.000 0.393 -0.949 -1.197 3.102 0.953 0.654 0.990 0.905 0.776 0.929 0.800 +1 1.773 -0.322 -1.192 0.078 0.606 1.200 -0.149 0.664 2.173 0.755 -0.115 -0.416 2.215 0.585 0.949 0.988 0.000 0.753 0.392 -1.216 0.000 0.703 0.960 0.984 0.710 1.158 0.924 0.787 +1 0.873 0.202 -1.363 0.867 0.561 0.839 -2.207 -1.458 0.000 0.873 -1.636 0.479 2.215 0.721 -1.165 0.063 0.000 1.106 -0.907 -0.606 3.102 0.793 1.051 1.189 0.862 0.783 0.860 1.004 +1 1.093 -1.223 -0.568 1.004 1.268 1.552 -0.568 -1.552 0.000 1.946 0.885 -0.241 0.000 1.499 -2.576 1.125 0.000 1.797 -0.739 -0.144 0.000 0.886 1.178 1.446 0.712 0.575 0.727 0.776 +0 0.279 1.305 -0.004 1.147 -1.332 0.827 -1.044 1.238 2.173 0.664 -1.403 -0.669 2.215 0.956 0.073 0.409 0.000 0.973 -1.346 -1.292 0.000 0.871 0.857 0.987 1.038 1.099 0.860 0.755 +1 1.584 -0.159 -1.264 0.965 1.680 0.728 -1.062 0.082 0.000 0.412 -0.225 1.581 0.000 1.459 0.823 -0.361 0.000 0.753 0.042 0.947 3.102 0.983 0.785 0.990 0.820 0.266 0.585 0.675 +1 0.785 -0.150 -1.408 0.870 -0.293 1.235 0.551 -1.405 2.173 1.747 -0.117 0.515 0.000 1.123 -0.034 1.416 2.548 0.910 0.862 -0.964 0.000 1.884 1.315 0.988 0.885 0.937 1.174 0.970 +1 1.688 0.776 -0.115 0.780 0.731 1.259 1.362 -1.541 2.173 0.621 1.077 0.100 2.215 0.349 2.439 1.318 0.000 0.473 1.168 0.586 0.000 0.412 0.772 1.098 0.559 1.308 0.996 0.788 +0 0.374 -1.282 1.521 1.163 -0.217 0.672 0.266 0.861 0.000 1.153 -1.100 -1.525 1.107 0.823 -0.502 0.104 0.000 0.614 -0.999 0.116 3.102 1.015 0.705 0.985 0.960 0.757 0.831 0.864 +1 0.697 0.363 0.412 0.750 -0.788 0.475 0.970 1.516 0.000 0.885 -1.424 0.378 0.000 0.799 0.925 -1.061 2.548 0.767 1.332 -1.461 3.102 2.440 1.713 0.992 0.789 0.275 1.151 0.938 +0 0.927 0.369 0.480 0.409 0.346 1.005 -0.698 -0.954 2.173 0.901 1.030 0.955 0.000 0.803 -0.770 1.366 2.548 1.103 0.065 -0.625 0.000 1.445 1.161 0.990 1.566 0.974 1.131 1.039 +0 0.582 -0.701 0.489 0.402 -1.271 0.773 1.056 1.017 2.173 1.022 0.555 -1.521 2.215 1.177 -2.369 -0.529 0.000 0.555 1.325 0.737 0.000 0.847 0.941 0.989 1.615 1.039 1.238 1.032 +0 1.087 -1.739 -0.368 1.715 0.006 0.743 -0.931 1.510 2.173 0.525 -2.379 -1.570 0.000 0.866 -1.017 -1.039 0.000 1.275 -1.153 0.790 3.102 0.881 0.898 0.986 0.823 0.665 0.875 0.842 +1 0.579 0.926 1.156 1.082 -0.451 0.700 -0.396 1.526 2.173 0.796 -0.513 0.156 2.215 0.951 -0.490 -1.152 0.000 0.746 -0.046 0.672 0.000 0.954 0.801 1.088 1.057 1.040 0.866 0.759 +0 2.940 -0.749 -0.158 1.248 -0.440 3.201 -0.491 1.321 0.000 1.977 -0.697 -0.619 1.107 1.459 -1.106 1.096 2.548 1.200 -0.742 -1.234 0.000 1.026 1.052 0.976 1.413 1.860 1.164 1.071 +1 0.964 0.515 -1.484 0.076 -1.650 0.533 -0.962 0.267 2.173 0.879 -0.584 -0.986 1.107 0.581 0.739 -0.002 0.000 1.172 -0.493 0.950 0.000 0.978 0.986 0.985 0.876 0.930 0.715 0.685 +1 0.348 0.655 -1.708 1.663 1.341 0.973 0.542 -0.788 2.173 0.926 0.423 0.797 2.215 0.746 0.781 0.194 0.000 1.615 1.159 -0.328 0.000 0.813 0.958 1.002 1.383 1.384 1.064 1.001 +1 1.255 -0.446 0.556 1.346 0.581 0.745 -0.451 -1.494 2.173 0.507 -0.705 -0.199 0.000 1.217 1.087 -1.019 2.548 0.772 -0.425 1.303 0.000 0.801 1.093 0.982 1.233 1.199 1.353 1.038 +1 0.892 0.445 1.054 0.665 -1.270 0.831 0.499 -0.968 0.000 0.790 1.227 -1.393 0.000 1.524 1.180 0.340 2.548 0.540 1.179 1.400 0.000 0.927 0.905 0.989 0.874 0.757 0.890 0.774 +0 1.046 1.044 -1.439 0.448 -1.579 0.568 1.320 1.458 0.000 0.698 1.162 0.107 0.000 0.940 0.365 -0.543 2.548 0.814 -0.441 0.540 3.102 1.258 1.018 0.996 0.778 0.641 0.742 0.729 +1 0.501 1.726 0.726 1.090 -0.423 0.820 -0.187 -0.217 2.173 0.764 1.408 1.493 0.000 0.900 -0.310 -1.448 0.000 0.736 0.371 -1.081 3.102 1.428 0.850 0.982 1.675 0.635 1.088 1.093 +0 1.345 0.102 -0.419 0.473 0.006 0.726 0.569 1.415 0.000 1.108 0.958 0.906 2.215 0.892 1.099 -0.807 2.548 0.775 -0.134 -0.953 0.000 1.059 0.972 0.995 0.649 1.062 0.775 0.781 +1 0.799 1.094 0.758 1.387 1.252 1.665 -0.267 -0.671 0.000 1.052 0.414 1.474 0.000 1.724 0.208 0.676 0.000 2.032 0.589 0.008 3.102 1.263 0.997 1.000 0.991 1.261 0.916 0.846 +0 0.770 -2.237 0.267 0.631 0.631 0.531 -1.300 0.633 0.000 1.357 -0.799 -1.406 2.215 0.701 -0.540 -0.179 0.000 1.144 -1.123 -1.333 3.102 0.838 0.836 0.973 1.158 0.308 0.787 0.747 +0 1.141 0.389 0.124 1.332 0.901 1.243 -0.986 -1.471 2.173 0.648 1.108 -0.211 2.215 0.270 1.246 0.881 0.000 0.860 -0.297 -0.516 0.000 0.922 1.008 1.101 0.804 2.075 1.331 1.035 +0 0.437 1.138 -0.669 1.578 -0.367 0.635 1.443 1.194 0.000 0.638 1.520 -1.359 2.215 0.708 2.567 1.076 0.000 0.831 0.449 0.748 3.102 0.896 0.845 0.983 1.007 0.715 0.764 0.798 +1 0.699 1.188 0.119 0.414 -0.856 0.875 0.166 0.611 0.000 0.911 0.231 -0.040 0.000 1.215 0.642 -1.540 2.548 1.327 -1.193 -0.924 0.000 0.914 1.138 0.986 0.800 0.725 0.699 0.739 +1 0.837 -0.774 -1.407 0.914 1.055 0.764 0.296 0.998 2.173 0.984 -1.284 -0.783 0.000 0.713 -0.605 -0.265 2.548 0.452 0.101 0.236 0.000 0.972 1.329 0.989 0.716 0.954 0.824 0.746 +1 0.843 -0.778 -0.682 0.635 -1.702 0.669 0.383 0.653 1.087 0.543 -1.775 0.537 0.000 0.527 -1.515 -0.974 0.000 1.221 0.196 -1.358 3.102 0.806 1.033 0.980 1.162 0.931 0.907 0.828 +0 1.088 1.003 -0.458 0.775 -0.458 0.847 -0.348 1.406 2.173 0.344 1.679 1.118 0.000 0.583 -0.977 -0.762 2.548 0.521 -0.249 0.758 0.000 0.683 0.853 0.989 1.223 0.874 0.859 0.770 +1 1.053 1.552 1.206 0.309 0.197 0.463 -0.530 0.357 2.173 0.558 1.181 -0.222 0.000 0.570 1.460 -1.445 0.000 0.850 0.599 -1.217 3.102 0.790 1.014 0.998 0.656 0.793 0.673 0.648 +1 0.724 0.630 0.129 0.876 1.058 0.667 1.255 1.668 0.000 1.165 -0.627 0.055 2.215 1.023 0.230 -1.368 1.274 1.029 1.898 -0.634 0.000 1.084 0.921 0.985 1.264 1.236 1.066 0.955 +0 1.280 0.201 -1.389 0.264 -0.098 0.601 0.489 0.363 0.000 0.364 0.130 1.580 0.000 0.751 -0.550 -0.704 2.548 0.682 0.936 0.460 3.102 0.902 0.828 0.991 0.660 0.714 0.571 0.583 +0 1.262 -0.761 0.463 0.878 1.448 1.036 1.468 0.609 0.000 0.989 1.118 -0.074 0.000 1.199 1.701 1.437 0.000 3.596 -0.757 -1.070 3.102 1.062 0.976 1.132 1.314 0.349 1.519 1.339 +0 1.720 -0.734 -1.009 1.157 -0.648 0.410 0.131 0.245 0.000 1.365 0.530 1.067 2.215 0.557 0.839 0.798 2.548 0.370 -2.466 1.079 0.000 0.903 0.901 0.980 1.007 0.283 1.065 0.869 +0 0.800 -1.072 1.459 0.526 0.352 0.667 -0.304 1.585 2.173 0.543 -1.339 -0.046 0.000 0.799 -0.045 0.070 2.548 0.612 0.141 -0.417 0.000 0.687 0.921 0.981 0.656 0.898 0.633 0.591 +0 1.049 -0.446 -1.071 0.943 1.152 0.553 0.386 0.958 2.173 0.337 -1.020 -0.559 0.000 0.564 0.139 -0.524 1.274 0.447 -1.521 1.231 0.000 0.543 0.802 1.251 0.732 0.682 0.640 0.586 +0 1.140 -0.340 0.879 0.637 1.601 0.853 0.626 -0.406 0.000 0.948 0.460 -1.298 2.215 1.142 0.056 -1.008 2.548 0.647 1.534 1.170 0.000 0.701 0.891 0.989 0.875 0.366 0.690 0.731 +0 0.660 0.523 0.503 0.993 0.228 0.564 -0.514 1.294 2.173 0.664 -0.307 -1.541 0.000 1.057 -0.006 -0.918 2.548 0.904 -0.560 -0.485 0.000 0.842 0.762 0.988 0.874 0.912 0.724 0.664 +0 0.489 0.172 -0.062 1.035 1.569 0.613 -0.344 -1.131 2.173 0.561 1.017 0.864 0.000 1.079 0.798 0.341 2.548 0.840 0.622 -0.677 0.000 0.890 0.943 0.987 0.784 1.184 0.722 0.664 +1 0.716 -0.646 -1.613 1.112 0.969 1.180 -0.546 0.889 1.087 1.170 -0.415 -1.134 0.000 1.288 1.506 -0.689 0.000 1.386 -0.055 0.098 3.102 2.445 1.636 0.985 0.788 0.946 1.489 1.323 +0 0.600 -0.217 -1.315 2.162 -0.306 1.336 -0.637 1.499 2.173 0.932 -1.264 -1.595 0.000 1.161 -0.864 0.022 2.548 1.782 0.122 0.429 0.000 0.889 0.699 1.246 1.572 1.526 1.144 0.975 +0 0.690 0.674 0.300 1.425 -0.792 0.481 1.638 -0.924 0.000 0.877 -0.038 1.577 0.000 0.988 0.601 -1.472 2.548 1.309 -1.824 1.007 0.000 0.955 0.939 1.143 0.761 0.844 0.720 0.695 +1 0.618 0.679 -0.928 0.364 -0.722 1.717 0.170 -0.590 0.000 1.624 0.512 0.521 0.000 2.327 -0.250 1.522 1.274 2.395 1.940 1.165 0.000 1.068 0.677 0.998 1.006 0.800 1.053 0.863 +1 1.066 0.573 -0.987 1.731 -0.329 0.749 0.082 1.204 1.087 0.577 0.609 0.473 0.000 0.594 -0.194 -0.700 0.000 1.439 0.675 1.614 0.000 0.873 0.856 1.053 0.781 0.721 0.857 0.737 +1 1.524 0.978 -1.113 1.403 -1.173 1.467 0.806 0.657 2.173 0.421 1.719 -0.330 0.000 0.549 0.524 0.340 2.548 0.758 0.490 -1.572 0.000 0.806 1.111 1.000 0.920 0.342 1.185 0.929 +1 0.586 -0.235 1.458 1.141 0.087 1.140 1.377 1.641 0.000 1.219 0.912 -1.474 2.215 2.613 0.311 -0.183 2.548 1.033 -0.596 -1.554 0.000 0.980 1.147 1.069 0.944 1.833 1.083 0.931 +0 0.525 0.274 -1.558 0.587 0.530 0.678 0.697 -1.152 0.000 0.614 0.161 -0.042 0.000 1.485 -1.272 0.788 1.274 0.829 -0.093 -0.573 0.000 0.747 0.853 0.990 1.549 0.603 1.012 0.969 +1 0.535 1.657 0.294 0.931 -0.844 0.815 0.069 0.722 2.173 0.584 0.741 -0.405 0.000 0.672 -0.247 -1.373 0.000 0.990 -0.394 1.661 3.102 0.900 1.009 0.993 1.405 0.757 1.241 1.048 +0 0.327 0.849 -1.103 1.033 0.372 1.390 -2.313 0.642 0.000 1.539 -1.009 -0.842 1.107 0.834 -1.509 -1.551 0.000 0.654 -0.624 -1.732 0.000 0.405 0.765 0.985 0.675 1.078 1.496 1.354 +0 1.009 -0.406 -0.260 0.421 0.948 0.788 0.509 0.732 2.173 0.821 -0.512 -1.448 2.215 0.493 1.307 -0.401 0.000 1.033 0.243 -1.586 0.000 0.838 0.894 0.991 0.815 1.268 0.776 0.709 +1 0.762 -0.592 -0.426 1.483 0.410 0.911 -0.180 1.554 2.173 0.496 -1.206 -1.300 2.215 0.438 0.911 -1.018 0.000 0.607 -0.145 0.019 0.000 0.578 0.814 1.008 0.822 0.765 0.833 0.715 +1 0.348 -1.296 -1.022 1.345 -0.434 2.399 -1.018 0.815 0.000 0.614 -0.151 1.356 0.000 1.882 0.862 -0.971 2.548 0.775 -0.253 0.415 0.000 1.065 1.618 0.985 0.936 0.353 1.802 1.440 +1 1.363 -1.564 1.381 0.684 1.135 0.473 -0.569 1.595 0.000 1.290 -0.308 -0.074 2.215 0.553 -2.670 -0.082 0.000 1.328 -0.626 -0.881 0.000 0.961 1.106 0.982 0.657 0.641 0.836 0.763 +1 0.727 -1.194 1.245 0.328 0.288 0.630 -1.006 -1.277 2.173 0.545 0.091 -0.154 1.107 0.675 -1.954 -0.218 0.000 0.646 0.264 1.542 0.000 1.354 0.980 0.980 0.669 0.888 0.733 0.682 +1 0.530 -1.092 0.801 1.341 -0.818 0.847 -0.485 1.583 0.000 0.638 -0.559 -0.060 0.000 0.987 0.924 0.335 2.548 1.000 0.717 -1.472 3.102 1.556 1.134 1.160 1.274 0.760 0.950 0.925 +0 0.533 -1.281 -0.041 1.600 -1.042 1.059 -0.774 0.147 2.173 0.770 -1.950 -1.631 0.000 1.161 0.138 1.258 2.548 0.593 2.049 1.062 0.000 0.524 1.106 1.003 1.076 1.342 1.025 0.931 +0 0.830 -0.425 0.532 1.999 0.204 1.503 0.159 -1.544 1.087 0.266 -0.121 1.425 0.000 0.347 -2.244 -0.209 0.000 0.639 0.955 -1.269 3.102 0.821 0.909 0.991 2.083 0.590 1.401 1.117 +0 1.344 0.591 -0.856 0.606 0.859 0.246 -1.636 -0.545 0.000 0.537 0.577 1.456 1.107 1.276 -0.889 0.805 1.274 0.404 -0.847 -0.723 0.000 0.173 0.745 1.249 1.210 0.906 0.842 0.745 +1 0.667 0.527 0.146 1.385 -0.883 0.871 -0.105 1.250 2.173 0.742 0.983 -0.355 2.215 0.899 1.597 -1.502 0.000 0.756 0.776 0.861 0.000 0.862 1.116 1.064 0.620 1.366 0.875 0.823 +1 1.470 -0.196 1.517 1.281 -1.078 0.498 1.118 0.817 0.000 0.880 0.466 -0.025 2.215 0.354 0.695 0.613 2.548 0.741 1.589 -0.024 0.000 0.738 0.750 1.369 0.810 0.334 0.772 0.811 +0 0.824 -0.572 0.445 1.562 1.177 0.424 0.986 -0.285 0.000 1.084 -0.533 -1.585 1.107 0.458 1.272 0.093 2.548 1.387 -0.421 -0.625 0.000 0.983 0.867 0.986 0.933 1.132 0.867 0.754 +0 1.107 0.185 0.511 1.841 0.997 1.114 -0.310 0.163 2.173 1.480 -0.102 -1.394 2.215 0.816 1.242 -1.247 0.000 1.930 -0.401 -0.870 0.000 1.537 1.136 0.982 1.146 1.873 1.256 1.215 +1 1.160 0.148 -1.481 1.052 1.393 1.282 0.482 0.127 2.173 1.187 0.880 -0.744 0.000 1.524 -0.554 1.214 2.548 0.577 1.423 -1.029 0.000 0.521 1.197 0.992 0.950 1.760 1.240 1.101 +0 1.125 -0.023 0.571 2.183 0.659 1.180 -2.621 -1.354 0.000 0.493 -0.921 -0.156 0.000 0.536 -1.246 -1.282 2.548 1.003 -0.080 1.445 3.102 0.375 0.671 0.994 1.268 0.520 0.865 0.831 +0 0.572 -1.082 1.237 0.366 0.884 1.160 -0.942 -1.194 2.173 0.931 1.636 -0.235 0.000 0.937 1.232 0.317 0.000 0.795 0.560 1.101 3.102 0.724 0.766 0.983 1.114 1.281 1.517 1.205 +1 0.822 -0.424 0.850 0.847 -0.121 0.648 -0.534 0.507 0.000 0.555 0.537 0.487 0.000 1.544 0.765 -1.156 2.548 1.155 -0.493 -1.511 0.000 0.731 0.859 0.988 1.040 0.877 0.894 0.778 +1 0.814 1.536 0.768 1.029 1.320 0.423 0.643 -0.126 0.000 0.542 0.904 1.058 0.000 0.757 0.133 -1.652 2.548 2.039 -0.514 -0.615 1.551 0.901 1.127 0.993 1.126 0.850 1.502 1.165 +0 0.435 0.596 0.383 1.381 -0.633 1.185 0.587 -0.911 2.173 1.912 0.653 1.163 1.107 0.779 -0.074 0.126 0.000 1.163 0.050 0.725 0.000 0.795 0.931 0.990 0.862 2.118 1.217 0.963 +0 0.282 1.717 -0.049 0.691 1.475 0.545 -1.093 -1.644 2.173 0.375 -0.914 0.318 1.107 0.851 1.328 -1.305 0.000 0.644 0.662 0.129 0.000 0.895 0.889 0.978 0.837 0.654 0.787 0.688 +0 0.967 -1.856 1.209 1.110 0.524 0.561 -1.422 -1.119 2.173 1.007 -1.102 -0.664 1.107 0.479 2.386 0.009 0.000 1.347 -1.531 0.952 0.000 4.126 2.738 0.994 1.213 0.472 1.778 1.882 +1 1.550 0.155 -0.658 1.057 -1.623 1.366 -0.592 0.869 1.087 0.390 0.430 1.415 0.000 1.817 -0.810 -0.788 2.548 0.895 -0.593 0.340 0.000 0.789 1.015 1.354 1.588 1.978 1.294 1.013 +0 0.663 0.443 1.595 1.656 -0.814 0.932 0.949 0.868 0.000 1.306 0.267 -0.527 2.215 0.668 1.585 1.476 0.000 0.520 -0.273 0.949 3.102 0.932 0.713 1.199 0.824 0.757 0.912 0.875 +0 1.217 0.824 -0.703 1.283 -1.079 1.068 0.234 0.445 0.000 0.762 1.072 -1.408 2.215 1.784 -0.205 1.039 2.548 0.703 0.144 -0.338 0.000 0.898 0.833 0.986 0.736 1.334 0.995 0.961 +1 0.364 -0.536 -0.680 1.876 -1.693 1.025 0.700 -0.189 2.173 0.331 1.325 -1.679 0.000 1.138 0.324 0.764 2.548 0.392 0.200 0.503 0.000 0.514 0.733 0.993 0.901 1.046 0.962 0.743 +1 0.464 -1.659 -1.709 0.751 0.889 1.336 -1.347 -0.664 2.173 0.551 -0.034 1.036 0.000 0.652 -0.085 1.577 0.000 0.711 -1.050 0.464 3.102 0.432 0.545 0.980 1.201 0.878 0.882 0.781 +0 0.818 -0.167 0.712 0.375 0.669 0.776 0.948 -0.412 1.087 0.571 1.797 -0.796 0.000 0.582 2.000 1.229 0.000 1.362 0.164 1.638 3.102 0.870 1.002 0.980 1.376 1.129 1.003 1.122 +0 0.685 -0.068 -0.902 0.591 -0.364 1.134 -0.288 -1.151 1.087 0.667 -0.881 0.815 0.000 0.767 0.432 1.133 0.000 1.338 -1.114 -0.134 3.102 0.875 0.984 0.983 0.849 1.257 0.961 0.833 +1 0.759 -2.048 -0.172 0.868 -0.740 0.627 -0.155 -1.540 1.087 0.765 -1.188 0.586 0.000 2.008 -0.541 0.957 2.548 1.348 -0.227 -0.781 0.000 0.674 0.985 0.987 1.005 1.129 0.981 0.805 +1 1.083 -0.260 1.273 0.503 -0.938 0.838 0.314 0.178 0.000 1.490 -0.108 -1.602 2.215 1.038 -0.642 -0.265 0.000 0.515 -0.791 -0.581 0.000 0.929 0.797 0.987 0.714 0.947 0.884 0.753 +1 1.112 0.889 -0.073 1.016 1.533 0.928 1.041 1.197 0.000 0.386 0.631 -1.521 0.000 1.284 0.808 -0.347 1.274 0.615 -0.752 -0.584 1.551 0.850 1.012 1.461 0.920 0.707 0.825 0.781 +1 0.888 0.302 -0.757 0.950 1.723 2.275 -1.051 -0.134 0.000 2.279 0.623 1.436 2.215 1.289 -0.329 1.498 2.548 1.043 0.236 0.040 0.000 1.742 1.911 1.002 1.039 0.958 2.108 1.631 +1 1.774 1.677 1.585 0.296 -0.720 0.859 0.300 0.499 2.173 0.982 -2.216 -0.213 0.000 0.972 -0.283 -1.237 2.548 0.447 -0.110 1.004 0.000 0.713 0.957 0.988 1.104 1.193 1.037 0.987 +0 1.123 -0.804 0.993 1.180 -0.034 0.726 0.198 -1.618 2.173 0.817 0.311 -0.535 2.215 0.309 1.781 0.748 0.000 0.444 -2.015 -0.788 0.000 1.211 0.916 1.274 1.190 0.941 0.938 0.843 +0 0.548 0.383 -1.439 4.161 1.566 1.719 -0.062 -0.525 2.173 0.570 1.069 1.366 2.215 1.896 0.179 0.217 0.000 0.396 -0.309 -0.680 0.000 0.744 0.925 0.995 2.199 1.699 1.489 1.285 +0 0.401 -0.234 -0.005 1.731 1.015 0.416 -0.996 -0.812 2.173 0.466 -2.770 0.696 0.000 0.667 -0.318 -1.051 2.548 0.629 -1.451 1.302 0.000 0.630 0.873 0.984 0.843 0.260 0.634 0.686 +1 3.088 -0.690 0.845 1.949 0.139 3.365 -2.020 -1.150 0.000 1.050 -0.216 -0.058 2.215 1.296 0.027 0.427 2.548 0.948 -0.508 -0.425 0.000 0.825 0.738 2.018 1.291 0.547 0.887 0.823 +0 1.214 -0.221 0.144 0.294 -0.296 0.551 -0.193 -0.874 2.173 0.826 0.409 1.310 2.215 0.813 1.491 0.852 0.000 1.044 -1.803 1.513 0.000 1.050 0.927 0.980 0.767 0.966 0.804 0.767 +1 0.618 -0.874 -0.632 0.707 0.888 0.812 -0.659 0.467 0.000 0.617 -0.591 1.535 0.000 0.586 1.257 -1.571 0.000 1.051 1.229 -0.555 0.000 0.897 0.669 0.984 0.696 0.517 0.489 0.512 +1 1.461 -0.927 0.604 0.819 -0.063 0.969 -0.121 -1.382 2.173 0.406 0.795 -1.081 0.000 0.892 0.500 -0.225 2.548 1.390 -0.188 1.154 0.000 0.941 1.037 0.992 1.043 1.076 1.068 0.888 +0 0.494 -1.134 0.454 0.806 -0.221 1.302 -0.679 -0.981 0.000 1.184 0.148 0.750 2.215 0.719 -1.043 -0.384 2.548 1.168 0.614 1.115 0.000 1.055 0.820 0.992 1.679 1.079 1.096 1.017 +1 1.475 -0.207 -1.642 0.893 -1.355 0.452 -2.122 0.539 0.000 1.489 0.460 0.157 2.215 0.699 0.305 1.118 2.548 0.369 -2.205 -1.591 0.000 0.607 1.058 0.996 1.602 0.829 1.200 1.136 +1 1.268 -0.136 -0.667 0.937 -1.623 0.825 1.929 0.691 0.000 1.436 1.513 -0.343 2.215 1.266 1.040 -1.732 2.548 1.296 0.259 1.166 0.000 1.584 1.217 1.145 1.484 1.393 1.134 1.208 +0 0.293 -0.924 1.135 0.274 0.274 0.999 0.256 1.573 0.000 1.266 1.105 -0.451 2.215 0.565 0.237 -0.070 2.548 0.677 0.956 1.216 0.000 0.701 0.815 0.995 0.851 0.508 0.876 0.743 +0 0.344 -1.962 0.923 0.828 -1.016 1.002 -1.092 1.741 2.173 0.847 0.504 0.631 2.215 1.083 0.537 0.023 0.000 1.290 -0.527 -0.405 0.000 0.979 0.904 0.985 0.770 1.674 1.129 0.941 +1 2.268 0.002 -1.467 0.716 1.054 1.014 -2.028 0.571 0.000 1.280 -0.803 0.197 2.215 0.318 0.778 1.463 0.000 1.311 -0.198 -0.398 0.000 0.818 0.888 1.349 0.697 0.828 0.941 0.795 +0 0.652 -1.514 -0.848 0.754 0.345 0.646 0.049 1.311 2.173 0.846 -0.052 -0.945 0.000 0.978 -1.747 1.403 0.000 1.598 0.232 0.565 3.102 1.027 1.060 0.992 1.345 0.682 1.097 1.177 +0 1.156 -0.306 -0.768 0.686 0.001 0.734 -1.533 1.189 0.000 0.621 -0.917 -1.632 2.215 1.237 -0.003 -1.656 2.548 2.604 -0.559 0.245 0.000 0.977 1.170 0.990 0.758 0.454 0.723 0.761 +1 0.481 1.567 1.452 0.725 0.217 0.786 1.649 0.035 0.000 1.263 1.379 -1.362 0.000 1.261 0.675 -0.105 0.000 1.911 0.935 1.177 3.102 0.886 1.213 0.981 0.740 0.988 0.967 0.802 +1 2.003 -1.935 -0.837 0.898 -1.114 1.113 -1.472 0.662 2.173 0.408 -1.500 -0.126 0.000 0.734 -1.541 1.083 0.000 0.581 -0.202 -1.125 3.102 0.747 0.659 0.990 0.927 1.021 1.128 0.895 +0 1.419 -0.405 1.666 0.851 0.666 1.103 0.506 0.017 2.173 0.974 0.270 -1.143 2.215 0.398 2.606 -1.545 0.000 0.748 -0.638 1.109 0.000 1.844 1.333 1.194 1.333 1.332 1.101 1.090 +0 1.712 0.807 -0.387 0.438 -0.023 0.852 1.157 -1.426 1.087 0.816 0.855 1.522 0.000 0.499 -0.900 0.458 0.000 1.457 0.734 0.614 3.102 1.324 0.960 0.974 1.116 1.149 0.882 0.881 +0 0.997 -0.060 -1.725 0.858 0.967 1.708 -0.072 1.255 1.087 2.161 0.228 -0.285 2.215 1.086 -0.314 -0.575 0.000 1.001 0.343 -1.034 0.000 0.641 0.805 0.987 0.772 2.814 1.408 1.166 +1 0.897 -1.121 -0.198 0.872 -0.064 1.023 0.182 1.481 2.173 0.467 0.571 0.041 0.000 1.482 -1.082 -1.506 2.548 0.438 -0.909 -0.544 0.000 0.629 0.970 0.980 1.120 1.318 1.076 0.875 +0 0.777 -1.080 0.919 0.336 1.664 0.667 -0.843 0.029 1.087 1.013 -2.107 1.360 0.000 1.308 0.078 -0.461 2.548 1.412 -0.102 -1.201 0.000 2.213 1.628 0.995 0.879 0.753 1.230 1.024 +1 1.037 -0.235 1.710 1.065 0.770 1.152 0.552 -0.441 0.000 0.639 0.679 0.018 1.107 1.456 1.574 1.675 0.000 0.697 0.064 0.839 3.102 0.959 0.800 1.091 0.550 0.449 0.572 0.714 +0 0.776 -1.201 1.187 0.940 0.985 1.015 -0.859 1.525 2.173 1.029 0.177 -0.514 0.000 1.678 0.589 -0.118 0.000 1.456 0.357 -1.136 3.102 0.847 0.897 0.984 0.768 1.246 1.214 1.079 +1 0.300 1.489 0.710 1.582 -1.352 0.734 -0.527 -0.915 2.173 1.004 -0.589 0.136 0.000 1.089 0.133 1.458 0.000 0.991 0.513 -0.156 3.102 0.801 0.841 0.990 0.937 0.791 1.233 1.026 +1 1.054 1.127 -0.895 1.464 -1.064 2.065 0.745 -0.767 0.000 1.484 -2.224 0.989 0.000 1.532 0.459 -0.188 1.274 1.428 0.041 1.127 0.000 1.009 1.090 0.987 0.983 1.135 0.857 0.853 +1 0.771 -0.245 -0.555 0.331 0.539 1.323 -0.321 0.821 2.173 1.378 -0.030 -1.411 0.000 1.633 0.826 -0.092 0.000 2.254 0.500 -0.667 3.102 0.791 0.915 0.987 1.085 1.983 1.236 0.999 +0 0.497 -0.892 0.853 2.375 1.693 1.054 -0.912 -1.481 2.173 0.999 1.871 0.139 0.000 1.160 1.508 -0.491 0.000 2.352 0.802 0.198 3.102 0.915 0.898 1.034 0.747 2.460 1.887 1.907 +0 0.416 0.392 -0.759 1.281 0.400 2.364 -0.627 -0.194 1.087 3.397 -0.943 1.511 0.000 0.901 -0.626 -1.349 2.548 0.423 -1.178 -1.569 0.000 0.660 0.805 0.992 1.891 1.570 1.798 1.762 +0 0.708 -0.845 1.199 1.922 0.701 0.879 -0.527 -1.126 0.000 0.753 -0.050 -0.809 0.000 0.689 -0.932 -0.206 2.548 0.438 0.034 -1.389 0.000 0.644 0.779 0.985 0.784 0.582 0.640 0.894 +0 0.660 0.162 1.201 1.598 0.578 1.440 -0.323 -1.692 2.173 0.865 0.521 -0.439 2.215 0.667 2.015 1.666 0.000 2.001 -0.441 -0.057 0.000 0.847 1.015 0.988 1.275 1.655 1.177 1.062 +1 1.633 1.751 0.564 1.253 -0.034 0.486 0.342 -0.395 2.173 1.362 1.139 1.569 2.215 1.600 0.252 -0.935 0.000 0.543 0.501 1.391 0.000 0.905 1.104 1.015 1.037 1.280 1.070 1.034 +1 1.128 -0.570 1.282 1.171 0.793 0.715 -1.000 -0.976 2.173 0.488 0.186 -0.468 0.000 0.902 -2.204 0.123 0.000 1.001 -0.340 -1.231 0.000 0.641 0.617 0.982 0.568 0.654 0.715 0.691 +0 0.668 2.430 0.580 1.473 0.531 0.747 0.524 -1.263 2.173 0.631 1.073 -0.965 0.000 0.630 0.185 0.573 2.548 0.419 0.223 -0.927 0.000 0.290 0.597 0.998 1.304 0.863 0.893 0.748 +0 0.287 1.741 -0.970 1.391 0.141 0.686 -0.264 0.999 2.173 0.831 0.041 -0.889 2.215 0.463 -1.589 -1.335 0.000 0.710 -0.002 1.576 0.000 0.698 0.727 0.988 0.809 1.115 0.792 0.755 +0 1.119 -0.729 1.165 1.498 0.525 1.347 1.253 -1.165 2.173 0.626 1.865 1.032 0.000 1.274 2.024 -0.686 0.000 1.222 0.620 0.374 3.102 1.386 1.127 0.986 2.416 1.382 1.578 1.660 +1 1.094 0.030 1.577 0.553 -0.142 0.943 0.329 0.409 2.173 0.663 -1.068 -1.164 0.000 0.553 1.249 1.526 2.548 0.819 -0.117 -0.550 0.000 0.695 0.961 1.078 0.889 0.904 0.873 0.758 +0 3.461 -0.384 0.379 0.272 0.551 1.042 0.218 -1.297 0.000 0.528 -0.126 -0.579 0.000 0.753 -1.071 -1.543 2.548 0.827 -0.030 1.056 3.102 0.990 0.898 0.994 0.664 0.564 0.762 0.943 +1 0.816 1.238 -0.400 0.827 -0.963 1.249 0.408 1.130 0.000 0.587 0.197 -0.826 0.000 0.477 1.234 -1.710 0.000 0.629 -0.359 0.194 3.102 1.020 0.866 0.983 0.794 0.221 0.712 0.877 +0 2.460 1.484 0.505 0.705 1.606 0.960 0.153 -0.947 2.173 0.578 -0.123 1.218 1.107 1.338 -0.841 -0.801 0.000 0.539 0.403 1.285 0.000 1.144 0.943 1.528 1.173 1.030 1.231 1.233 +0 0.920 0.014 1.698 2.818 -1.393 1.419 -0.921 0.246 1.087 0.723 0.599 0.035 0.000 0.823 0.240 0.364 0.000 1.108 -1.221 -1.034 0.000 0.405 1.118 0.987 0.759 0.845 1.449 1.193 +0 1.293 0.725 1.252 1.644 0.755 0.892 0.657 0.060 0.000 1.631 0.363 -1.043 2.215 0.850 -0.021 1.585 0.000 0.710 0.950 -0.992 3.102 1.630 1.022 0.990 1.574 0.384 1.008 0.987 +0 0.330 0.885 1.074 1.794 0.388 0.730 0.185 -1.184 0.000 0.712 -0.286 -1.466 2.215 1.013 0.494 -0.057 2.548 0.564 0.327 1.417 0.000 0.708 0.840 0.987 1.601 0.945 1.117 1.022 +0 0.656 1.392 1.554 0.670 -0.518 0.514 -1.339 1.739 0.000 1.201 0.789 0.734 2.215 1.125 -0.120 -0.368 2.548 0.560 0.553 -1.163 0.000 1.056 0.980 0.984 0.833 1.199 0.998 0.885 +1 0.423 -2.302 0.267 1.010 -1.158 0.883 -1.273 1.721 0.000 1.078 0.181 -0.416 2.215 0.841 -0.170 0.839 2.548 0.853 -1.063 0.834 0.000 0.952 0.876 0.991 1.177 0.935 0.971 0.896 +1 1.873 1.060 0.675 0.788 0.154 0.610 0.769 1.590 0.000 0.918 1.403 -1.431 2.215 0.815 0.108 -0.711 2.548 0.664 1.562 -0.701 0.000 1.023 0.861 0.990 1.165 0.858 0.891 0.800 +1 1.276 0.166 1.592 0.361 -0.825 1.056 0.197 -0.731 1.087 1.859 0.913 0.689 0.000 1.087 0.805 -1.682 2.548 1.234 0.867 -0.443 0.000 1.684 1.341 0.987 0.953 1.111 1.175 0.985 +1 1.089 1.490 -0.347 0.779 0.861 0.859 0.031 1.633 2.173 0.863 -0.023 -0.767 0.000 1.479 -0.004 0.621 0.000 0.679 -0.575 -0.870 3.102 1.221 1.028 1.131 0.979 0.693 0.938 0.890 +0 0.953 -0.251 0.904 0.959 1.672 0.336 0.816 -1.456 0.000 0.754 0.256 -0.385 1.107 0.403 1.815 1.181 0.000 1.197 -0.583 -0.058 3.102 0.614 1.006 0.989 0.893 0.497 0.705 0.702 +1 0.308 -2.192 0.890 0.521 0.757 1.301 0.095 0.658 2.173 0.951 -0.301 -0.651 0.000 1.563 -0.420 -1.353 0.000 0.750 0.164 -1.030 3.102 1.116 0.643 1.001 0.860 1.045 1.035 0.884 +0 1.462 0.573 -0.047 1.309 -0.472 0.849 -0.441 1.178 0.000 0.702 0.437 0.660 0.000 1.074 -0.263 1.728 2.548 0.977 0.632 -0.350 0.000 0.868 0.945 0.981 0.713 0.659 0.762 0.708 +0 1.580 -1.134 -0.592 0.443 -1.655 0.964 -0.532 -1.665 2.173 1.195 -1.272 0.341 2.215 0.653 0.048 0.520 0.000 0.792 0.922 0.883 0.000 0.770 0.897 0.989 0.951 1.658 0.989 0.922 +1 1.909 0.355 1.698 1.529 0.946 2.153 1.530 0.204 0.000 1.132 0.278 -1.241 0.000 1.810 -1.254 -1.553 0.000 1.594 -0.946 -1.131 3.102 0.997 0.870 1.482 1.005 0.843 1.002 0.889 +1 1.415 0.643 -0.145 1.454 -0.794 0.945 -1.441 0.416 0.000 0.430 -0.744 -0.801 0.000 2.393 -0.038 1.508 1.274 1.212 -0.407 -1.299 3.102 1.296 1.114 1.095 1.590 0.803 1.109 1.267 +1 0.652 0.252 -1.499 1.384 1.618 1.177 0.362 -0.256 2.173 0.479 -1.218 0.398 0.000 1.138 -0.128 1.507 0.000 0.645 -1.022 -0.100 1.551 1.160 0.789 0.984 1.490 0.825 0.987 0.902 +0 1.189 -0.130 -0.622 0.682 1.450 1.221 -0.767 -0.096 1.087 1.489 0.185 1.665 2.215 0.916 0.674 0.708 0.000 0.595 1.868 -0.986 0.000 1.042 1.008 1.193 1.062 2.220 1.291 1.050 +1 0.968 -0.533 1.530 0.743 0.462 0.711 -0.684 -0.377 2.173 0.833 -1.159 0.458 1.107 0.885 0.270 -1.274 0.000 0.626 1.109 1.129 0.000 0.812 1.061 0.987 0.681 0.826 0.891 0.793 +0 0.406 1.366 1.610 0.781 0.135 0.616 1.246 -0.532 0.000 1.021 -0.456 1.117 2.215 0.897 0.084 0.704 0.000 1.844 0.040 -1.681 0.000 1.132 1.023 0.992 1.603 0.813 1.417 1.279 +1 0.447 1.908 0.460 1.002 -0.167 1.029 0.659 -1.673 0.000 0.899 1.239 1.330 2.215 1.184 0.792 -0.067 0.000 0.914 0.673 -0.429 3.102 1.980 1.181 0.993 0.902 0.841 0.859 0.793 +0 1.383 -0.570 -0.270 0.350 1.194 1.593 -1.290 1.526 2.173 1.433 0.163 0.100 0.000 2.176 -0.969 -0.894 1.274 0.883 -0.867 0.942 0.000 0.775 1.077 0.989 1.328 1.918 1.145 0.943 +1 0.544 1.659 1.034 0.499 -1.600 1.212 0.812 -0.784 2.173 1.615 1.098 1.119 2.215 1.523 -2.438 0.353 0.000 0.732 0.806 -0.075 0.000 0.826 0.948 0.987 0.994 2.063 1.040 0.842 +0 0.970 -0.085 0.092 1.275 -0.960 0.467 -0.884 1.163 2.173 0.394 1.020 -1.021 2.215 0.346 -2.272 0.798 0.000 0.734 2.057 0.700 0.000 0.715 0.590 1.251 0.965 0.922 0.850 0.877 +0 0.350 -1.924 0.103 1.524 0.105 0.508 0.522 -0.361 2.173 0.735 -1.044 0.887 0.000 1.191 0.078 -1.191 0.000 0.507 -0.510 -1.129 0.000 0.793 0.951 0.985 0.541 0.432 0.565 0.583 +1 0.871 -0.232 -0.624 0.370 0.847 0.765 -0.658 -1.395 1.087 0.675 -0.359 0.830 0.000 0.990 0.208 -0.784 2.548 0.718 0.587 0.312 0.000 1.158 1.027 0.987 0.754 0.756 0.881 0.792 +0 1.059 0.150 0.915 1.409 -0.156 0.652 -1.456 1.143 0.000 0.845 -0.882 -0.937 2.215 0.757 0.419 -0.969 2.548 0.527 -0.347 -1.527 0.000 0.777 1.006 1.391 1.134 0.628 0.799 0.850 +0 1.475 -0.470 -1.039 1.301 -1.536 1.157 -0.068 0.485 0.000 0.847 0.087 -0.255 2.215 0.747 0.678 0.841 0.000 1.108 -0.294 1.553 3.102 0.875 0.938 0.995 0.682 0.894 0.763 0.966 +0 4.249 0.396 1.708 1.297 -0.379 1.441 0.454 -0.078 0.000 0.455 -0.362 -0.369 0.000 1.290 -0.386 1.415 2.548 1.151 1.283 0.252 1.551 0.861 0.934 3.098 1.675 1.339 1.335 1.356 +1 1.019 0.379 -1.129 0.510 1.353 0.875 0.427 1.390 0.000 0.886 0.573 0.858 0.000 1.797 0.046 -0.869 2.548 1.704 1.894 1.105 0.000 0.878 0.627 0.989 0.732 0.715 0.864 0.752 +1 0.596 0.983 0.343 0.540 -0.977 0.629 -2.631 -1.197 0.000 1.758 -0.941 0.639 1.107 1.330 -0.368 0.057 2.548 0.767 0.184 -0.398 0.000 0.817 1.827 0.988 2.226 0.936 1.507 2.233 +0 1.130 0.638 -0.230 1.695 -0.253 1.325 -1.135 1.463 0.000 0.406 -0.831 0.943 0.000 0.592 0.541 -1.213 1.274 0.484 0.488 1.500 3.102 0.740 1.088 0.995 0.756 0.262 0.681 1.357 +0 0.722 1.565 -0.654 0.779 0.432 1.042 1.780 1.664 0.000 1.244 0.873 0.284 1.107 1.105 0.803 -0.945 2.548 0.627 1.493 -1.101 0.000 0.929 1.124 0.987 0.912 1.115 1.034 0.916 +0 0.618 0.383 -1.335 1.571 -0.298 0.660 0.171 0.944 1.087 0.670 -1.042 0.537 2.215 0.802 0.935 -1.276 0.000 0.910 -0.943 -1.571 0.000 1.252 1.072 1.098 1.042 0.739 0.839 0.843 +0 0.914 0.618 -0.486 0.910 0.350 1.025 0.352 -1.717 0.000 1.079 1.035 1.060 2.215 0.734 2.538 -0.335 0.000 0.958 0.000 -0.795 3.102 2.819 1.724 0.987 0.988 1.047 1.185 1.066 +0 0.503 1.340 0.288 0.767 -0.302 0.733 -1.903 -0.029 0.000 0.782 -0.136 -1.124 0.000 0.776 0.783 1.027 2.548 0.606 -1.304 0.874 3.102 2.110 1.223 0.999 0.716 0.807 1.076 0.977 +1 0.843 0.279 0.455 1.342 1.635 0.753 -0.342 0.191 0.000 1.429 0.595 -0.822 2.215 1.472 0.128 -1.667 2.548 0.500 0.816 0.463 0.000 0.705 1.115 1.288 1.174 1.126 0.946 0.882 +1 0.694 0.222 -1.514 0.771 -0.154 0.805 -0.337 0.935 0.000 1.305 0.524 -0.245 2.215 1.259 0.043 1.434 0.000 0.735 -0.033 -0.698 0.000 0.862 0.984 0.985 0.777 1.021 1.008 0.844 +1 0.392 2.157 -1.651 0.639 0.438 0.568 0.011 -1.332 2.173 0.924 0.867 0.160 0.000 1.248 0.298 1.271 1.274 1.293 0.871 -0.495 0.000 0.799 1.056 0.995 0.728 0.768 0.837 0.732 +0 2.519 -0.075 0.013 0.113 -0.626 2.054 1.511 1.508 0.000 0.960 -0.519 -0.686 2.215 0.596 1.439 0.432 2.548 0.693 0.218 -0.778 0.000 2.025 1.332 0.994 0.810 1.223 1.465 1.489 +0 0.989 -0.344 0.276 0.696 1.670 0.826 -2.074 -0.574 0.000 0.508 0.217 -0.368 2.215 0.810 -0.302 1.527 2.548 1.378 -0.359 0.802 0.000 1.349 0.938 1.093 0.652 0.702 0.647 0.622 +1 0.917 -0.215 0.091 0.536 0.541 0.955 -0.554 0.910 2.173 1.088 -0.155 1.590 1.107 1.552 -2.015 -0.527 0.000 1.027 1.195 -0.815 0.000 0.648 1.548 0.987 1.007 0.912 1.226 1.236 +1 0.709 0.204 0.206 0.982 1.589 1.418 0.142 -0.682 2.173 0.838 0.549 0.972 0.000 0.886 1.217 0.702 0.000 0.833 -0.757 1.378 1.551 0.981 0.870 1.096 1.126 1.273 0.965 0.834 +1 0.820 1.391 0.467 0.880 -1.580 2.628 2.237 -0.579 0.000 3.050 1.546 1.326 0.000 1.393 0.804 0.922 2.548 0.868 0.483 -0.348 1.551 1.041 1.227 1.133 0.776 0.776 1.336 1.126 +1 0.975 0.385 0.742 1.025 1.438 0.602 1.012 -0.824 2.173 0.413 0.299 -0.906 0.000 0.716 1.701 -0.263 0.000 0.655 1.932 1.408 0.000 0.852 0.592 0.990 0.647 0.513 0.711 0.694 +0 1.113 0.476 0.374 0.462 -0.116 1.361 1.590 0.563 0.000 0.984 1.043 -0.980 2.215 1.617 1.001 -1.661 2.548 1.197 0.014 -1.068 0.000 2.592 1.802 0.985 1.047 0.772 1.239 1.040 +1 1.436 -0.799 -0.729 0.619 -0.095 1.255 0.655 -0.688 0.000 1.760 -1.056 1.257 0.000 1.691 0.195 0.897 2.548 1.056 -0.334 -1.738 3.102 4.404 2.358 0.991 1.187 0.778 1.561 1.311 +0 1.126 1.239 -0.945 2.182 -1.471 1.819 0.275 0.252 1.087 1.497 1.521 1.626 0.000 1.109 -0.460 -0.198 0.000 0.928 -0.001 0.887 3.102 3.132 1.788 0.989 2.199 0.768 1.510 1.494 +1 0.597 -1.139 0.845 0.864 -0.867 1.552 0.544 0.719 1.087 1.664 -2.370 -0.557 0.000 1.811 -0.605 -1.699 2.548 0.837 0.348 -1.617 0.000 3.274 2.378 0.995 1.555 2.179 2.579 1.871 +0 0.602 1.322 0.398 0.505 1.426 0.354 1.744 1.191 0.000 0.647 -0.111 -0.249 2.215 0.371 1.241 -1.244 2.548 0.562 1.285 1.714 0.000 0.317 0.898 0.997 0.587 0.585 0.550 0.544 +1 0.845 0.653 -1.409 0.498 0.293 0.548 0.949 -0.228 0.000 1.120 -0.211 1.432 1.107 1.283 -0.044 0.005 0.000 1.150 1.070 -1.657 3.102 0.852 1.072 0.986 0.927 0.916 0.960 0.819 +0 1.273 -0.436 0.729 0.781 -0.075 0.876 1.009 -1.640 0.000 0.582 0.669 -0.311 2.215 1.126 1.430 -1.307 0.000 1.197 -1.355 0.344 1.551 1.004 0.783 0.992 0.780 1.159 1.084 0.956 +0 1.311 -1.875 0.238 0.473 -1.436 1.248 -1.164 0.708 1.087 1.093 0.493 -1.143 0.000 1.019 -0.499 -0.567 2.548 0.532 -1.380 1.368 0.000 0.819 0.782 1.089 0.968 1.358 0.890 0.865 +0 1.038 1.163 -1.259 0.345 0.395 0.401 -0.649 1.058 0.000 0.484 -0.698 -1.510 2.215 0.757 0.942 -0.035 0.000 0.977 0.774 0.673 1.551 0.767 0.892 0.992 0.744 0.809 0.606 0.672 +0 0.729 0.411 0.935 0.267 0.955 0.832 0.260 -0.252 2.173 0.989 0.802 -1.649 2.215 0.490 1.250 -1.364 0.000 0.674 1.059 0.356 0.000 0.634 0.772 0.992 0.980 1.326 0.906 0.770 +1 1.175 0.725 1.159 1.683 0.737 1.061 0.286 -0.972 1.087 0.577 -2.075 -1.139 0.000 0.603 -0.589 -1.068 0.000 1.510 -0.106 0.369 3.102 0.740 1.105 0.998 1.453 1.284 1.031 1.149 +1 0.284 -1.252 -1.640 0.604 -1.592 0.937 -0.452 -0.748 2.173 0.718 0.566 0.849 0.000 1.118 -0.026 1.265 0.000 1.149 0.537 0.026 3.102 0.660 0.775 0.994 0.932 0.949 0.889 0.781 +0 0.734 -1.009 -1.301 0.942 0.395 1.148 -0.681 -1.740 2.173 1.007 -1.120 -0.363 2.215 0.957 -0.751 0.637 0.000 0.448 -1.576 -0.071 0.000 0.591 1.042 1.151 0.778 1.543 0.892 0.760 +1 0.361 -0.003 1.168 1.350 -1.245 0.941 2.218 1.145 0.000 1.639 0.319 -1.209 2.215 0.665 0.600 -0.188 0.000 1.720 -1.481 0.614 0.000 0.924 1.211 0.988 1.375 1.438 1.111 1.110 +1 0.413 1.253 1.293 1.546 -1.119 1.008 -0.320 -0.074 1.087 1.049 -0.609 1.383 0.000 0.345 0.588 1.636 0.000 1.035 0.810 0.186 1.551 0.859 0.935 0.986 1.822 0.790 1.176 1.151 +0 1.018 0.183 -0.998 0.784 1.332 1.249 1.100 0.689 0.000 1.234 -1.629 -1.732 0.000 2.852 -0.151 -0.650 2.548 1.478 1.144 1.091 0.000 0.784 0.970 1.067 1.042 1.451 1.489 1.223 +0 1.709 0.490 -0.954 1.522 -1.049 3.217 0.960 -0.845 2.173 2.449 -0.078 1.016 0.000 1.798 1.779 1.466 0.000 1.647 -0.025 0.762 0.000 0.597 1.381 0.993 0.789 2.929 2.437 1.981 +1 0.289 1.879 1.184 0.791 -0.736 1.091 0.048 0.471 0.000 1.275 -0.105 -1.148 1.107 0.511 0.691 -0.162 0.000 0.398 -0.830 0.888 0.000 0.874 1.012 0.989 0.777 0.915 0.959 0.830 +1 1.161 0.041 1.101 1.481 -1.570 0.802 0.608 -0.833 2.173 0.745 -1.236 0.541 0.000 0.866 -0.528 -0.304 0.000 0.727 0.155 0.405 1.551 0.955 0.672 1.219 1.113 0.747 0.826 0.885 +0 1.326 1.110 -0.130 0.570 -1.387 0.687 2.670 -1.412 0.000 0.392 2.874 1.073 0.000 0.517 0.619 -1.592 2.548 0.612 1.445 0.359 0.000 0.876 0.922 1.090 0.755 0.439 0.941 0.865 +1 1.096 -1.541 0.984 1.706 0.726 1.683 -2.458 -1.181 0.000 1.308 -0.025 0.414 0.000 0.489 -1.034 -1.460 0.000 1.167 -0.117 -0.588 3.102 1.250 1.475 1.000 1.472 0.284 1.189 1.259 +1 1.191 -0.268 -1.276 2.063 -0.507 0.805 0.697 0.834 2.173 0.732 -0.337 1.161 1.107 0.568 0.681 -0.293 0.000 0.580 1.085 0.393 0.000 0.407 0.741 1.389 1.506 0.702 1.081 0.869 +0 0.759 -1.418 1.172 0.650 -0.214 0.647 -0.817 -1.471 0.000 0.405 0.303 0.819 0.000 0.649 -0.937 -0.021 2.548 1.058 -0.971 -0.933 3.102 0.792 0.692 0.987 0.643 0.465 0.470 0.493 +0 1.236 1.322 0.317 0.169 -0.612 0.633 0.634 -0.859 2.173 0.756 -0.008 1.497 2.215 0.352 2.066 0.476 0.000 0.517 1.124 1.722 0.000 0.480 0.731 0.996 0.816 0.929 0.747 0.640 +0 0.450 -2.087 1.150 1.544 -1.348 0.416 0.324 1.526 2.173 0.961 -1.152 0.051 2.215 0.326 -0.962 -0.975 0.000 1.190 -0.302 0.324 0.000 0.678 0.692 0.987 1.011 1.189 0.913 0.758 +0 2.366 -0.595 -1.355 0.526 -0.868 1.111 -0.462 0.165 2.173 0.840 -1.443 -1.740 0.000 1.345 0.239 0.702 2.548 0.678 -1.505 0.156 0.000 0.986 1.309 0.982 1.256 0.908 1.135 1.071 +1 0.359 -1.239 1.738 1.588 -0.422 1.116 -0.081 0.950 0.000 0.617 -0.084 0.318 0.000 0.610 0.431 1.271 0.000 1.700 0.277 -0.596 3.102 0.952 1.089 0.987 0.852 0.743 0.890 0.886 +1 1.344 -1.030 1.373 0.906 -1.352 0.776 -1.175 -0.471 1.087 0.866 -0.480 0.040 2.215 0.557 -2.021 -1.692 0.000 1.154 -0.582 1.000 0.000 0.940 0.988 0.988 1.030 0.685 0.855 0.780 +1 1.208 0.075 -0.296 0.776 -1.691 1.130 -0.875 0.296 2.173 0.530 0.630 -0.526 0.000 0.633 1.059 -1.442 0.000 1.224 -0.023 1.345 0.000 0.991 1.097 1.275 1.195 0.996 1.174 0.991 +0 1.140 0.471 1.050 0.777 0.305 0.398 1.622 0.588 0.000 0.973 0.858 -0.487 2.215 1.606 0.128 -1.619 0.000 1.290 1.291 1.525 0.000 0.819 0.951 0.984 0.996 0.582 0.747 0.767 +1 1.304 1.335 0.020 0.452 -1.309 0.983 -0.200 1.480 2.173 0.808 0.800 -0.493 0.000 0.730 1.265 1.419 0.000 0.979 0.145 0.615 0.000 0.863 0.945 0.990 0.544 0.696 0.857 0.741 +0 1.495 0.446 -1.127 0.129 -0.859 1.627 0.837 0.914 2.173 2.293 -0.455 -0.872 2.215 1.420 1.459 1.302 0.000 1.444 -0.259 -0.286 0.000 0.925 1.302 0.991 1.532 3.474 1.640 1.306 +0 0.404 -1.346 -0.448 1.259 1.599 0.535 -0.632 0.901 2.173 0.533 0.054 -0.814 0.000 1.517 -0.503 -0.122 0.000 1.518 0.685 -1.693 3.102 0.911 0.970 0.987 0.990 1.023 0.878 0.824 +1 0.782 0.424 1.487 0.621 -1.014 1.219 0.611 0.322 2.173 0.764 -1.163 -1.349 0.000 0.555 0.261 -0.943 0.000 0.543 -0.590 1.115 3.102 0.886 0.638 0.982 1.166 0.833 0.966 0.830 +1 0.680 -0.393 0.703 1.655 1.011 1.635 0.717 -0.549 0.000 1.320 0.530 1.333 1.107 0.939 -1.891 -1.114 0.000 0.610 -1.099 1.256 0.000 0.776 0.972 0.982 0.694 0.370 1.056 1.036 +0 0.556 -0.320 0.031 1.343 -0.890 0.766 1.774 1.062 0.000 0.533 1.496 0.289 0.000 0.747 -0.732 1.595 1.274 0.846 1.209 -0.690 3.102 0.889 0.818 0.987 0.728 0.996 0.954 1.171 +0 1.772 0.215 -1.304 0.590 -0.558 1.651 0.335 0.409 0.000 2.030 -0.513 0.383 2.215 2.939 -0.116 -0.854 2.548 4.223 -0.638 1.673 0.000 4.347 2.862 0.989 0.734 2.389 2.189 1.733 +0 1.744 -0.554 -0.571 1.660 -1.072 0.830 -0.535 0.923 0.000 0.662 0.280 0.460 1.107 0.519 -0.810 0.569 2.548 1.089 -0.586 1.722 0.000 0.963 0.862 1.026 0.845 0.393 0.786 0.840 +1 0.711 -1.553 -0.870 0.532 -0.491 0.953 0.089 -0.191 2.173 0.595 -1.818 1.694 0.000 0.722 -0.364 1.029 2.548 1.682 -0.524 1.489 0.000 0.895 0.696 0.976 0.844 0.956 0.977 0.851 +1 1.110 -1.111 1.663 0.573 -0.899 1.023 -1.027 -1.239 2.173 0.760 -2.363 0.948 0.000 1.549 -0.053 0.626 0.000 1.209 -0.479 -0.375 3.102 0.721 0.968 0.986 0.713 0.871 0.896 0.774 +1 1.752 -0.150 0.386 0.593 1.175 1.517 0.492 -1.101 2.173 0.868 0.568 0.421 0.000 0.730 0.403 1.345 2.548 0.472 -1.094 0.523 0.000 0.919 0.736 0.987 1.619 1.057 1.074 0.946 +0 0.473 0.506 -1.549 0.935 -0.117 1.359 0.422 -0.596 2.173 1.270 0.044 1.400 0.000 1.263 0.745 1.371 1.274 0.783 0.443 -0.006 0.000 0.675 1.042 0.988 0.885 1.631 0.906 0.803 +1 0.917 -1.681 0.145 0.334 -1.622 0.742 -1.436 -1.634 0.000 1.001 -1.782 1.281 0.000 0.706 0.402 -0.983 2.548 1.897 -1.075 -0.240 3.102 0.967 1.315 0.985 0.847 1.025 1.062 0.878 +1 0.971 0.163 0.778 1.031 0.206 1.092 0.199 -1.013 2.173 1.799 1.554 1.452 0.000 0.894 0.365 -0.064 1.274 2.427 1.105 -0.756 0.000 0.808 1.267 0.984 1.269 0.938 1.244 1.089 +1 1.590 0.352 -1.421 2.307 1.672 3.202 -1.932 0.194 0.000 0.810 -0.943 -0.542 1.107 0.913 1.072 -1.353 2.548 0.789 0.289 1.626 0.000 0.248 0.750 0.983 0.779 1.330 1.004 0.744 +1 0.568 1.035 0.160 0.449 0.972 1.114 0.668 -1.360 2.173 0.750 -0.121 -1.572 0.000 1.433 1.031 0.459 0.000 1.392 0.290 0.100 0.000 0.799 0.852 0.976 1.050 0.595 0.929 0.815 +1 1.557 -0.936 0.623 1.151 0.265 0.989 -0.450 -0.873 2.173 0.392 1.086 1.712 0.000 0.669 -1.024 -1.689 1.274 0.666 -0.842 1.052 0.000 0.929 1.061 0.993 0.850 0.763 0.989 0.936 +0 1.505 -0.075 0.554 0.589 -0.844 0.882 -0.618 -1.269 0.000 1.403 -0.222 -0.119 0.000 0.942 0.509 1.691 2.548 0.845 -0.539 -0.482 0.000 0.860 0.951 1.241 0.887 0.454 0.693 0.733 +1 0.449 1.020 1.663 1.393 1.238 1.210 -0.425 -0.367 1.087 0.845 -0.547 -1.164 2.215 0.643 -0.714 0.661 0.000 0.439 -2.061 1.302 0.000 0.642 1.108 0.987 0.869 0.984 0.973 0.869 +1 1.106 0.979 0.037 0.767 -0.411 0.363 2.416 0.862 0.000 1.432 -0.508 -1.699 1.107 0.639 1.755 -0.415 0.000 0.762 -0.891 0.671 1.551 0.822 1.453 0.990 1.364 0.838 1.401 1.181 +1 1.638 0.507 0.156 0.644 -0.676 0.941 -0.565 -1.536 1.087 0.415 0.887 0.797 2.215 0.365 0.765 1.182 0.000 0.738 0.602 -0.108 0.000 0.918 0.935 0.988 0.641 1.088 0.910 0.780 +0 0.579 -0.464 0.589 0.457 -0.355 0.781 0.703 0.868 2.173 0.591 1.108 -0.789 0.000 0.910 -0.345 -0.719 0.000 0.968 0.211 1.576 0.000 0.917 0.807 0.983 0.628 0.752 0.578 0.568 +0 0.803 0.002 -0.447 1.649 0.249 0.846 0.976 -1.695 0.000 0.462 1.131 -0.586 0.000 0.854 -0.287 1.186 2.548 0.434 0.458 1.525 3.102 1.123 1.042 0.990 0.637 0.250 0.590 0.738 +0 1.647 1.027 -0.920 0.341 -1.263 1.123 -0.971 0.379 2.173 0.364 -0.521 0.178 2.215 1.387 0.555 -1.598 0.000 0.730 -0.737 -1.588 0.000 0.909 0.833 0.978 1.758 0.271 1.099 1.018 +1 0.765 0.253 -0.110 0.346 -1.634 2.050 0.329 1.508 0.000 1.016 -1.591 -0.400 0.000 1.612 0.129 -0.611 2.548 3.584 -1.926 0.040 0.000 0.903 1.133 0.989 0.770 0.805 0.956 0.984 +1 1.332 0.749 -0.878 1.932 -0.338 0.615 1.444 1.292 0.000 0.715 0.596 1.086 1.107 0.470 1.427 -0.286 0.000 1.154 1.005 0.689 3.102 0.946 0.628 1.041 0.972 0.378 0.830 0.778 +0 0.997 2.070 1.388 0.426 -0.498 0.658 0.033 -0.875 0.000 1.414 1.111 0.772 2.215 0.991 -0.959 -0.068 2.548 0.695 -1.091 -1.192 0.000 0.802 0.828 0.984 0.870 1.888 1.253 1.212 +1 0.746 -1.397 -0.180 0.698 1.158 0.492 0.183 -1.609 2.173 0.636 -0.387 0.555 2.215 0.385 -0.941 1.735 0.000 0.778 2.377 1.592 0.000 0.843 0.734 0.988 0.843 0.802 0.634 0.618 +0 0.471 -0.644 0.501 0.810 -0.872 0.965 0.908 0.704 2.173 0.483 -0.035 -1.179 0.000 1.442 0.988 -1.549 0.000 1.679 0.462 -0.273 0.000 0.924 0.951 0.986 1.683 0.516 1.142 1.014 +0 1.629 0.723 -0.881 0.637 -0.912 1.604 -0.492 0.651 0.000 1.530 0.931 -0.266 2.215 2.833 -0.321 1.578 0.000 1.378 -0.324 -0.833 3.102 2.855 1.969 0.990 0.893 1.147 1.791 1.668 +0 0.979 -1.288 0.852 0.351 0.048 1.152 -0.334 1.566 1.087 0.831 -1.359 -1.050 0.000 0.753 0.301 -0.127 2.548 0.971 1.001 0.454 0.000 0.588 0.559 0.978 0.954 1.225 0.998 0.946 +1 1.017 0.103 1.154 0.107 0.242 0.684 -0.558 0.420 0.000 1.387 0.289 -1.200 2.215 0.432 -2.718 0.149 0.000 1.070 -0.963 -0.039 0.000 0.795 0.677 0.982 1.034 0.890 1.075 0.924 +0 3.398 -0.119 -0.253 3.695 -0.278 3.287 0.509 -1.710 2.173 0.771 0.009 1.272 0.000 1.322 -0.582 -0.063 2.548 1.015 1.020 0.627 0.000 0.976 1.168 0.939 4.146 3.033 2.678 2.062 +0 1.522 0.834 -1.504 0.274 0.381 0.556 0.029 -1.065 0.000 0.841 0.843 -0.335 2.215 1.753 0.351 0.521 1.274 0.592 0.367 1.097 0.000 0.834 1.000 0.990 0.823 0.953 0.826 0.744 +0 1.129 -0.231 -0.809 0.209 1.050 1.385 0.473 -1.171 1.087 2.062 -0.908 0.756 2.215 0.817 -1.883 0.521 0.000 0.596 -0.777 -0.935 0.000 0.875 1.028 0.983 1.049 3.107 1.636 1.264 +1 1.012 -0.457 0.739 0.919 -0.361 0.619 -0.098 -0.584 1.087 0.838 0.381 -1.402 2.215 1.246 -0.656 1.557 0.000 1.255 -1.218 0.478 0.000 1.254 1.215 1.118 0.972 0.759 0.924 0.833 +1 0.983 0.664 0.766 1.162 1.190 0.874 0.747 -1.048 2.173 0.763 0.446 -0.147 2.215 0.830 1.261 -1.443 0.000 1.281 -0.671 0.082 0.000 1.878 1.191 0.996 1.201 0.891 0.926 0.983 +1 1.660 -0.640 -1.128 0.695 0.479 0.549 -1.133 -0.401 2.173 1.081 1.198 0.266 0.000 1.485 -0.380 1.099 0.000 0.660 -2.237 1.422 0.000 0.793 0.987 1.477 0.816 0.550 0.696 0.701 +1 0.493 -0.006 -1.448 1.581 0.234 0.563 1.084 1.118 0.000 0.921 0.462 -0.715 2.215 1.380 -0.010 -1.194 2.548 1.345 0.256 0.587 0.000 0.815 1.094 1.221 0.985 0.581 0.857 0.814 +1 0.656 -0.550 -1.136 0.410 0.751 1.814 -2.044 1.624 0.000 1.062 -0.642 0.696 2.215 1.593 0.826 -0.134 0.000 1.467 0.281 -0.861 0.000 0.705 0.923 0.984 0.805 1.022 0.858 0.835 +1 0.685 -0.058 0.780 1.137 -0.133 0.939 0.587 1.658 0.000 0.997 0.780 0.050 0.000 0.955 0.557 -1.266 2.548 0.372 0.787 1.231 3.102 0.916 0.633 0.985 0.540 0.362 0.534 0.595 +1 0.346 1.534 -1.647 0.851 -1.109 1.364 -0.246 0.346 0.000 0.349 0.260 -1.317 2.215 0.532 -0.227 1.083 0.000 1.098 -1.353 -1.482 3.102 0.944 0.913 0.992 0.887 0.616 0.844 0.812 +1 1.036 1.643 -1.299 0.907 1.242 1.101 0.492 -0.410 1.087 1.996 -2.365 1.484 0.000 1.984 1.264 0.286 2.548 1.184 1.474 -0.664 0.000 0.713 0.850 1.010 1.328 1.373 1.102 0.925 +0 0.841 -1.386 0.869 0.799 0.375 1.197 -0.728 -1.722 2.173 1.046 -1.021 -0.171 2.215 0.796 -1.115 -1.040 0.000 0.750 -2.071 0.869 0.000 1.020 0.940 0.976 1.451 1.644 1.162 0.958 +0 0.719 0.399 0.387 1.283 -0.309 0.973 0.275 1.341 0.000 1.040 1.144 -0.653 0.000 0.999 -1.567 0.800 0.000 1.574 0.882 -1.426 1.551 2.243 1.288 0.991 0.914 0.842 1.127 1.148 +1 1.137 -1.144 -1.640 0.909 -0.835 0.924 -0.785 0.159 2.173 0.532 -1.322 -1.184 0.000 0.427 -0.360 1.243 0.000 0.978 0.408 0.651 3.102 0.838 1.009 0.983 1.075 0.814 0.927 0.791 +0 0.898 -0.293 -0.494 2.126 -1.239 1.519 -0.659 -1.528 0.000 1.561 -0.245 0.707 2.215 2.845 -1.564 0.010 0.000 0.675 -0.414 1.560 1.551 4.212 2.253 1.190 1.518 0.654 1.684 1.497 +1 2.111 -0.336 -1.378 0.443 0.018 1.185 -1.030 -0.932 0.000 1.326 -0.787 0.721 0.000 1.083 -0.896 0.391 2.548 1.033 -0.251 1.206 1.551 0.689 0.597 1.275 1.070 0.610 0.748 0.762 +0 1.455 0.570 0.623 1.174 1.459 0.750 -0.137 -1.127 0.000 1.151 1.288 -1.022 2.215 1.356 -0.461 0.210 2.548 0.423 0.746 0.072 0.000 0.890 0.967 1.239 1.097 1.833 1.158 0.993 +1 0.477 -1.491 -0.775 0.511 -0.515 1.223 -0.347 -1.353 2.173 0.908 -0.784 0.476 2.215 0.389 -2.144 0.479 0.000 0.700 0.001 0.991 0.000 0.885 1.193 0.974 0.810 1.587 0.912 0.799 +0 0.596 -2.202 0.706 1.468 0.635 0.816 -0.868 -1.131 2.173 0.602 -1.357 -1.717 0.000 0.773 -1.700 -0.755 0.000 0.726 0.163 0.291 3.102 0.836 0.906 0.995 1.202 0.905 0.853 0.787 +1 1.320 0.495 -1.630 0.256 -1.696 0.718 -1.938 -0.631 0.000 0.588 0.308 1.388 0.000 1.066 -0.453 -0.353 0.000 2.300 -0.249 0.498 1.551 1.311 0.696 0.984 1.056 0.702 0.850 0.943 +1 0.275 -1.842 -0.284 0.575 0.946 1.446 -0.038 -0.987 2.173 1.322 -0.139 0.715 0.000 1.099 -0.508 -0.369 1.274 1.234 0.427 0.992 0.000 0.690 1.087 0.985 1.061 0.927 1.157 0.934 +1 0.298 0.755 1.272 0.564 0.646 0.814 -0.310 0.199 0.000 1.484 -1.117 -1.114 0.000 0.904 -0.360 -1.353 1.274 0.921 -1.146 1.222 3.102 1.170 0.898 0.999 0.722 0.622 0.741 0.653 +1 0.605 0.477 -1.504 0.683 1.189 0.939 -0.829 -0.731 2.173 0.574 -1.443 -1.519 0.000 0.798 -1.491 1.026 0.000 2.162 -0.248 -0.076 3.102 0.989 1.106 0.987 1.650 0.934 1.242 1.184 +0 0.431 -1.204 1.617 1.120 1.627 0.845 -1.832 0.237 0.000 0.914 -1.215 -0.152 0.000 1.523 -2.588 -1.567 0.000 1.049 -0.254 -0.472 3.102 0.856 0.912 0.979 0.495 0.447 0.629 0.878 +1 1.804 1.536 -0.598 0.427 0.265 1.160 0.782 1.061 2.173 0.621 1.538 1.524 0.000 0.577 2.062 0.119 0.000 0.840 0.130 -0.866 0.000 0.932 1.042 0.981 0.575 0.734 0.864 0.773 +1 0.787 -1.593 0.591 0.926 -0.781 0.706 -0.597 1.714 2.173 0.486 -1.140 -0.609 0.000 0.543 -2.282 0.372 0.000 0.651 0.877 0.908 3.102 0.830 1.043 1.117 1.120 0.817 0.877 0.819 +1 0.832 0.801 -1.079 0.788 -0.169 1.112 -0.437 0.655 2.173 1.165 0.176 -0.912 0.000 1.521 -2.001 1.487 0.000 1.319 0.391 -0.257 0.000 0.930 0.927 0.982 1.520 0.961 1.066 0.988 +1 0.988 0.650 1.654 1.047 1.306 0.940 1.356 -1.574 0.000 1.347 1.423 -0.353 0.000 0.825 1.085 0.772 0.000 1.538 0.445 -0.156 3.102 1.362 1.074 0.984 1.052 0.273 0.814 0.785 +1 0.481 0.002 -0.523 0.985 1.712 0.510 0.873 -0.590 0.000 1.011 -0.675 0.887 2.215 0.762 0.605 1.306 0.000 0.835 1.202 0.298 0.000 0.778 1.042 0.982 0.941 1.432 0.863 0.813 +1 0.852 0.609 1.601 2.043 -1.186 0.863 -0.247 0.730 2.173 0.401 1.394 0.675 0.000 0.532 -0.477 -0.506 2.548 0.792 -0.234 0.008 0.000 0.823 0.796 1.079 0.813 0.766 0.942 0.816 +0 1.614 -0.840 1.436 1.172 0.253 1.512 -0.191 1.730 0.000 1.681 -0.324 0.011 2.215 1.078 -1.057 -0.520 2.548 1.039 -0.047 -0.394 0.000 1.806 1.489 1.667 1.349 0.897 1.249 1.166 +0 0.893 0.463 -0.874 0.901 0.931 0.769 0.862 -0.525 0.000 0.749 -0.899 1.066 2.215 0.519 1.120 -1.688 0.000 0.480 1.006 1.089 0.000 0.935 0.665 1.241 0.961 0.604 0.778 0.720 +1 0.638 -1.060 -1.400 1.158 1.291 1.077 -0.964 -0.273 2.173 0.588 -1.725 -1.459 0.000 0.894 -0.491 0.689 0.000 1.157 -0.323 1.662 0.000 0.879 1.020 0.983 1.247 0.830 0.983 0.890 +0 0.451 2.071 1.727 0.804 -0.440 0.842 1.198 1.204 2.173 0.379 -0.216 -0.619 1.107 0.937 0.496 -0.334 0.000 0.607 -0.227 -1.330 0.000 0.739 1.066 0.993 0.661 1.052 0.709 0.661 +1 0.804 -1.456 -1.009 1.128 0.360 0.885 0.438 -0.260 1.087 1.255 0.373 1.570 0.000 0.368 -0.735 -0.515 0.000 0.515 1.184 1.535 0.000 0.800 0.771 1.245 0.751 0.787 0.937 0.810 +0 0.536 -0.998 1.227 0.992 -1.220 0.729 -1.640 -1.672 0.000 1.518 -0.161 0.118 2.215 1.067 -1.698 -1.057 0.000 1.027 -0.261 1.626 3.102 0.852 0.880 0.994 1.074 1.105 1.180 0.985 +1 1.024 -0.655 -1.617 0.612 -0.070 0.905 -0.158 -0.583 2.173 1.069 -0.957 1.204 0.000 0.793 -0.542 0.557 2.548 0.395 1.142 -0.629 0.000 1.504 0.944 1.080 0.843 0.933 0.876 0.764 +0 0.447 1.887 1.520 0.064 0.653 0.657 -0.650 -0.567 0.000 1.267 -0.628 -0.047 0.000 2.474 0.115 1.581 2.548 0.393 0.594 -0.823 3.102 0.875 0.678 0.978 0.846 0.662 1.067 0.931 +1 1.062 -0.732 1.541 0.972 0.834 0.576 -0.622 0.768 0.000 1.548 0.570 -0.587 1.107 0.443 -0.406 -0.634 0.000 0.607 -0.753 -0.974 0.000 0.912 0.678 0.995 1.739 0.869 1.119 0.927 +0 2.048 -0.606 -1.430 3.840 -1.519 2.456 0.716 -0.074 2.173 2.147 -1.408 0.680 0.000 0.838 2.286 -1.080 0.000 1.132 -0.968 1.674 3.102 0.884 1.692 0.986 0.590 2.609 2.618 2.402 +0 0.877 -0.551 -1.313 0.592 0.528 1.000 -0.935 0.655 2.173 0.518 0.133 1.463 0.000 0.676 0.938 -0.823 2.548 1.164 -0.519 -1.050 0.000 0.873 1.126 0.995 0.767 1.547 0.903 0.759 +1 1.307 0.191 -0.930 0.093 -0.685 0.575 1.046 0.353 0.000 0.552 -1.204 -0.910 1.107 1.085 -1.240 0.407 0.000 1.040 0.637 1.372 0.000 0.953 0.692 0.978 0.620 0.540 0.773 0.738 +1 0.401 1.158 -1.619 1.021 0.640 0.648 0.438 1.196 0.000 1.400 0.652 -1.723 0.000 1.800 -0.086 -0.471 1.274 1.079 1.222 -0.428 3.102 1.015 1.166 0.985 0.953 0.920 1.084 0.909 +0 0.318 2.288 -1.204 0.506 0.671 0.628 0.411 -0.889 0.000 0.928 -0.285 0.690 2.215 0.749 -0.623 -1.519 2.548 0.596 0.342 0.061 0.000 0.706 0.951 0.982 0.817 0.827 0.711 0.672 +1 0.824 2.006 -1.011 2.079 -0.861 0.961 0.697 0.903 2.173 0.791 1.422 0.080 0.000 0.744 1.179 1.119 0.000 0.583 2.085 0.496 0.000 0.952 0.885 0.986 0.604 0.909 0.977 0.877 +0 0.832 -1.220 -0.674 0.304 1.055 0.686 0.345 0.267 2.173 0.880 -0.321 1.389 2.215 0.835 0.067 -1.229 0.000 0.506 -1.727 -1.510 0.000 0.943 1.133 0.981 0.765 1.046 0.805 0.712 +1 0.583 1.537 0.599 1.405 0.275 1.120 0.671 -1.695 2.173 1.117 -0.081 -1.142 2.215 0.930 0.415 0.637 0.000 0.865 0.314 -0.504 0.000 0.848 0.966 0.997 1.906 1.016 1.732 1.373 +0 0.890 0.643 -1.607 0.617 -0.406 0.710 0.123 -0.489 1.087 0.566 -1.063 -0.684 0.000 2.073 -0.458 1.073 2.548 0.521 -0.990 1.147 0.000 0.706 0.936 0.987 0.787 1.567 1.023 0.898 +1 0.389 1.614 1.232 1.334 -0.058 0.688 1.454 -0.734 1.087 0.471 0.264 -1.439 2.215 0.478 2.102 -1.297 0.000 0.701 -0.987 -0.108 0.000 1.864 1.101 0.991 0.822 0.727 0.842 0.947 +0 0.955 -0.204 0.316 1.851 0.537 0.900 -0.066 -1.363 1.087 0.954 0.645 -1.216 0.000 0.924 -0.366 1.433 2.548 1.608 0.113 -0.542 0.000 0.874 0.916 0.988 0.953 0.688 0.994 0.857 +0 0.410 -1.222 -0.060 1.116 0.003 0.628 1.872 0.972 0.000 0.399 2.762 -0.983 0.000 0.525 1.020 -1.120 1.274 0.807 2.433 1.518 0.000 0.778 1.147 1.001 0.734 0.428 0.730 0.932 +0 1.193 0.269 1.529 0.484 1.628 0.751 -0.975 -0.886 0.000 0.661 -0.244 0.273 2.215 0.888 -1.101 0.570 2.548 0.509 -2.078 -0.419 0.000 0.873 0.979 0.992 1.228 0.459 0.886 1.042 +1 1.433 0.048 -0.693 0.881 0.125 1.097 1.032 1.307 2.173 0.687 0.855 -0.201 0.000 0.518 -0.623 1.468 2.548 0.517 1.270 -1.315 0.000 0.703 1.035 1.047 0.772 0.926 0.975 0.832 +1 0.661 0.055 -0.781 1.280 -0.355 1.582 1.092 -0.933 2.173 2.791 2.338 1.036 0.000 0.924 -0.541 0.981 2.548 1.018 0.858 -0.435 0.000 0.681 0.864 0.980 0.886 2.070 1.406 1.082 +0 1.235 -0.272 -0.718 0.739 1.204 1.551 -0.563 0.765 0.000 1.371 -1.067 -0.981 2.215 0.613 -1.731 1.341 0.000 1.023 -0.538 -1.353 3.102 1.527 1.225 1.306 0.988 0.427 1.130 0.997 +1 0.686 -0.001 -0.856 0.462 0.139 0.644 -0.960 0.145 1.087 1.110 -0.650 -1.676 0.000 1.190 0.501 -1.472 2.548 0.472 -1.500 -0.729 0.000 0.907 0.972 0.985 1.033 1.415 0.900 0.888 +1 1.590 1.571 1.733 0.358 -0.930 1.873 -0.241 0.479 0.000 1.309 1.156 -0.760 1.107 0.842 2.248 -1.469 0.000 0.581 -0.238 -1.293 1.551 4.655 2.502 0.987 0.995 0.732 1.753 1.580 +0 1.002 -0.940 -1.429 1.313 1.320 0.526 -1.253 -0.523 0.000 0.701 -0.500 0.199 0.000 0.599 -1.186 1.282 2.548 0.622 0.596 -0.493 3.102 0.925 0.803 0.988 0.853 0.724 0.606 0.688 +1 2.266 0.833 1.241 0.858 1.728 0.520 0.692 1.689 2.173 1.323 0.272 0.130 0.000 1.607 0.423 -0.800 2.548 1.450 -2.137 -0.125 0.000 3.552 2.591 0.984 1.265 0.900 1.786 1.732 +1 0.996 -0.118 -1.449 1.212 1.240 0.667 0.565 -0.007 0.000 1.171 0.338 1.576 1.107 1.012 -0.918 -0.880 0.000 1.731 -0.431 -0.171 0.000 0.943 1.092 1.002 0.622 0.809 0.968 0.861 +0 2.045 0.116 -1.137 0.681 0.203 1.414 0.645 -1.562 2.173 2.243 -0.440 0.339 0.000 0.928 -0.054 0.135 0.000 0.630 -0.059 1.567 3.102 0.596 0.848 1.528 1.218 0.485 1.359 1.202 +1 1.890 -0.302 -0.156 0.895 -0.695 2.438 -1.429 1.669 0.000 1.210 -0.951 0.460 0.000 1.885 0.220 -0.500 2.548 0.660 -0.432 0.866 1.551 1.089 0.965 0.984 0.621 0.870 1.541 1.467 +0 2.911 0.747 -0.821 0.334 0.703 1.003 -0.238 1.580 2.173 0.510 -1.136 0.853 0.000 0.632 0.788 0.555 2.548 0.626 0.382 0.140 0.000 0.800 0.923 1.339 0.923 0.980 1.037 0.940 +1 0.631 0.058 1.386 1.100 0.201 1.302 -0.053 -1.075 1.087 0.355 1.358 0.797 0.000 0.414 -1.318 0.515 0.000 0.772 -0.486 -0.186 3.102 1.158 0.765 1.011 1.143 0.814 0.821 0.756 +1 0.283 -0.180 0.734 1.511 1.625 1.185 1.686 -0.022 0.000 0.954 0.215 1.390 1.107 0.891 2.159 -1.568 0.000 0.977 0.404 -0.050 3.102 0.760 0.890 0.982 0.683 0.847 0.856 0.759 +1 1.394 -1.457 -0.054 0.751 -0.305 1.419 0.278 1.667 0.000 0.693 -1.318 -0.473 0.000 0.943 -0.356 0.253 0.000 0.649 0.229 -1.350 3.102 0.979 0.816 0.993 0.712 0.296 0.589 0.571 +1 0.387 1.246 0.986 1.072 -0.762 1.154 -1.089 1.723 2.173 0.821 0.336 -1.074 2.215 1.110 2.676 0.279 0.000 0.963 -1.019 0.746 0.000 1.047 0.855 0.989 2.568 1.402 1.653 1.331 +1 0.475 -2.102 -1.668 1.675 0.796 1.313 -0.593 -0.919 1.087 0.654 -1.245 -0.316 0.000 0.695 -0.331 0.697 0.000 0.716 -1.043 1.347 1.551 0.954 1.131 0.988 0.559 0.975 1.081 0.927 +0 0.363 1.314 -0.960 0.716 0.587 0.746 -0.276 -1.424 2.173 0.507 -0.964 -1.518 2.215 1.283 -0.498 0.207 0.000 0.570 -2.084 0.972 0.000 0.827 0.853 0.986 0.821 0.338 0.770 0.734 +0 0.477 0.809 -0.827 1.018 1.108 1.103 1.368 0.583 2.173 1.419 1.420 -1.127 0.000 1.057 1.768 -0.656 0.000 0.956 1.208 1.568 3.102 0.898 0.828 0.986 0.945 0.843 1.003 0.913 +0 0.417 -0.707 0.795 1.265 -0.864 0.581 0.325 0.403 2.173 1.372 0.410 -0.541 2.215 1.430 -1.118 1.257 0.000 0.508 1.207 -1.044 0.000 0.825 1.034 1.004 0.944 0.989 0.996 1.090 +0 0.405 -2.066 -0.178 0.885 1.195 0.738 0.608 -1.006 0.000 0.516 -0.228 0.494 0.000 1.112 0.086 1.228 2.548 1.213 0.386 0.378 3.102 0.568 0.533 0.981 0.875 0.637 0.690 0.557 +1 1.189 -0.838 -0.889 1.581 -1.023 1.048 -0.695 0.954 2.173 0.497 -0.343 1.501 0.000 1.260 0.502 0.296 1.274 0.514 -1.281 0.799 0.000 0.568 0.869 0.974 1.480 1.248 1.181 0.930 +1 0.850 1.294 -0.536 0.899 -1.062 1.464 -1.338 0.841 0.000 0.687 -0.422 -0.700 0.000 0.655 -2.677 0.951 0.000 1.439 0.292 -1.329 0.000 0.868 0.833 0.990 1.537 0.777 1.336 1.130 +0 0.849 -0.880 0.092 0.247 -0.102 0.604 -1.365 -0.641 1.087 1.814 -0.707 1.064 2.215 1.308 -0.313 -1.451 0.000 0.556 1.484 -0.558 0.000 1.377 1.382 0.993 1.087 1.622 1.254 1.040 +1 0.619 0.743 0.405 0.681 -0.542 0.815 0.044 1.137 1.087 0.934 -0.883 -0.655 2.215 0.486 -1.332 1.131 0.000 1.143 0.058 -1.706 0.000 0.829 0.913 0.988 0.869 1.432 0.827 0.738 +0 1.048 0.274 0.417 0.837 0.535 0.776 0.467 -1.132 0.000 0.405 -1.236 1.485 2.215 0.338 0.849 0.017 0.000 0.602 -0.767 -1.588 3.102 0.820 0.931 0.979 0.694 0.180 0.585 0.694 +1 2.206 -0.128 1.003 1.607 1.178 1.455 1.667 -1.077 0.000 1.485 -0.243 0.309 0.000 1.816 -1.818 -0.709 0.000 0.821 0.391 -0.542 3.102 3.195 1.926 0.994 0.978 0.432 1.216 1.391 +0 0.667 -1.030 0.426 1.345 -0.453 0.403 -1.029 -0.241 0.000 1.098 -0.577 -1.565 0.000 0.623 -0.763 1.478 1.274 1.010 -0.092 1.160 3.102 1.249 0.912 0.989 0.902 0.283 0.652 0.724 +0 0.802 0.918 1.739 0.459 0.572 0.326 -0.203 -0.012 2.173 0.317 -0.706 -1.374 0.000 0.694 -0.791 0.556 0.000 0.637 0.590 -1.257 3.102 0.825 0.626 0.989 0.842 0.491 0.573 0.607 +0 1.743 0.750 -1.401 0.212 0.763 1.472 -1.209 1.250 0.000 1.875 0.432 -0.122 1.107 1.145 0.278 -0.538 0.000 0.492 -0.588 1.528 3.102 0.641 0.751 0.988 0.674 1.012 0.906 0.730 +0 0.357 1.196 -0.728 1.289 1.367 1.045 -0.264 1.302 2.173 1.384 -0.219 -0.473 0.000 1.156 -0.235 -0.073 0.000 0.955 -0.560 -1.268 3.102 0.685 0.794 0.988 1.586 0.805 1.181 1.280 +1 0.989 -0.715 -1.586 1.540 1.571 1.080 -0.647 -0.398 2.173 0.718 2.572 0.688 0.000 0.823 -0.864 -0.613 2.548 0.833 -0.606 0.611 0.000 0.899 1.871 0.993 1.444 0.286 1.665 1.877 +0 1.168 -0.724 -0.487 0.648 -0.254 0.491 -1.196 1.083 2.173 0.870 0.752 1.486 1.107 0.644 0.060 0.520 0.000 0.562 0.426 -1.731 0.000 1.013 0.859 0.991 0.885 1.167 1.102 1.057 +0 1.621 0.739 -0.830 0.520 1.277 0.677 -0.785 0.929 0.000 0.425 0.624 -1.299 2.215 0.369 0.005 0.273 0.000 0.927 -1.044 0.118 3.102 0.622 0.793 1.204 1.100 0.824 0.736 0.756 +1 0.607 0.563 -1.442 0.838 -0.469 1.257 0.583 0.631 0.000 0.757 2.106 -0.909 0.000 2.248 -0.277 1.554 0.000 2.328 0.203 -0.976 3.102 1.197 0.723 0.995 0.833 0.803 0.867 0.733 +1 1.016 0.561 1.313 0.516 -1.149 0.536 -0.610 -1.517 0.000 0.529 -0.496 1.222 1.107 1.529 0.297 -0.034 0.000 0.680 1.145 -0.472 0.000 0.753 0.953 0.981 0.695 0.480 0.644 0.651 +1 0.451 -1.803 -0.552 1.332 -1.160 1.196 -0.541 0.787 0.000 0.989 -0.134 -1.679 2.215 0.728 -0.873 -0.891 2.548 0.776 -1.863 -0.113 0.000 1.041 0.767 0.988 0.807 0.700 0.753 0.693 +1 0.356 -0.619 -1.621 1.085 0.098 1.120 1.011 1.672 0.000 1.139 0.278 -0.520 2.215 0.503 0.196 0.622 2.548 0.695 0.547 1.021 0.000 0.787 0.725 0.985 1.150 0.689 0.834 1.079 +0 0.566 -1.818 0.342 0.944 -1.202 0.614 -0.915 0.612 2.173 0.699 -1.486 1.494 0.000 0.609 -0.323 -1.296 0.000 1.115 -0.745 -0.377 3.102 0.856 0.871 0.996 0.671 0.682 0.644 0.642 +1 2.422 1.580 0.178 0.782 -0.303 0.471 1.530 1.672 0.000 0.909 1.350 -1.297 1.107 1.255 2.262 -1.403 0.000 1.019 1.332 1.144 0.000 0.839 0.884 0.976 1.181 0.632 0.776 0.859 +1 1.485 0.808 1.386 1.839 0.816 1.137 -0.339 -1.309 0.000 1.154 -0.711 -0.122 2.215 1.263 0.144 -0.429 2.548 0.455 0.664 -1.483 0.000 0.682 0.933 1.123 1.707 0.686 1.239 1.170 +0 2.024 0.862 0.082 0.600 -0.013 2.114 -0.935 1.521 0.000 1.189 -0.352 -0.533 0.000 1.043 -0.307 1.081 2.548 1.259 -1.423 -0.499 0.000 0.902 0.950 0.994 0.792 1.015 0.907 0.922 +0 1.680 -1.235 0.440 0.278 -1.025 0.782 2.034 1.254 0.000 1.182 0.046 -1.022 2.215 0.693 -0.749 -0.806 0.000 0.507 -1.843 1.314 0.000 0.789 0.950 0.990 0.742 0.472 0.812 0.720 +0 1.560 -0.136 -1.101 1.725 -1.354 2.184 1.417 0.287 0.000 0.783 1.619 0.812 0.000 2.469 -0.230 -1.647 2.548 0.870 0.956 -0.143 3.102 1.306 0.869 1.000 0.921 1.386 1.811 1.691 +1 1.057 -0.479 0.365 1.194 -0.796 0.809 -1.656 0.949 0.000 1.078 -0.285 -1.598 2.215 1.568 -0.196 -0.572 1.274 0.745 -0.643 1.230 0.000 0.655 0.819 1.346 1.049 1.104 0.814 0.709 +1 0.949 0.954 0.460 1.049 -0.383 1.384 1.044 1.711 0.000 1.100 1.386 0.781 2.215 0.963 0.253 -0.655 0.000 0.695 -1.191 -0.629 0.000 0.868 0.469 0.987 0.868 0.827 0.895 0.789 +1 0.547 -0.881 0.903 1.451 0.394 1.216 -0.659 0.597 1.087 1.434 -1.754 -1.241 0.000 1.576 -0.348 -1.162 2.548 0.396 -0.074 1.350 0.000 1.185 1.099 0.990 0.759 1.742 1.240 1.176 +1 2.122 0.908 1.423 0.475 -1.112 0.940 -0.540 -0.428 2.173 0.462 1.106 -0.319 0.000 0.720 -0.073 0.698 0.000 0.565 0.378 1.003 3.102 0.907 1.011 1.052 0.559 0.843 0.999 0.845 +1 1.506 0.590 0.447 0.967 -0.680 0.962 0.100 -1.727 2.173 0.496 -0.849 0.971 0.000 1.289 1.641 0.621 0.000 2.345 -0.075 -1.015 3.102 0.932 1.136 1.420 1.278 0.966 1.011 1.043 +1 1.296 0.129 1.413 0.500 1.144 1.311 -0.940 -0.698 2.173 0.218 -1.719 -0.582 0.000 0.487 -2.102 0.662 0.000 0.399 0.953 1.342 3.102 0.470 0.901 0.977 0.581 1.208 0.954 0.841 +1 1.390 0.412 -1.286 0.688 -0.066 0.802 0.956 1.022 2.173 1.336 0.216 -0.688 2.215 1.052 0.008 0.100 0.000 0.978 0.792 -1.543 0.000 1.241 1.020 1.208 1.060 1.629 0.942 0.836 +0 0.904 -0.231 -1.618 0.373 -1.435 1.662 -0.378 0.532 0.000 2.011 -0.354 -1.137 2.215 0.510 0.136 1.030 0.000 1.070 -0.722 0.087 3.102 0.844 0.717 0.994 0.899 1.229 1.239 1.064 +0 0.859 -0.310 0.029 1.435 -1.124 1.643 -0.524 0.892 0.000 1.372 -0.811 0.531 1.107 2.826 -1.992 -1.029 0.000 1.392 0.405 1.671 0.000 0.842 0.771 1.325 1.210 0.946 0.856 0.827 +0 0.800 -0.473 1.620 0.500 -0.271 1.017 -0.846 1.077 0.000 1.118 1.058 -0.440 0.000 1.147 -1.163 -0.098 2.548 1.323 0.666 -1.120 0.000 0.939 1.025 0.990 0.862 1.088 1.099 0.899 +1 0.912 -1.842 -1.458 0.269 0.425 0.914 -1.496 0.530 0.000 0.947 -1.245 -0.999 2.215 0.713 -0.456 0.220 2.548 0.747 0.360 -1.649 0.000 1.844 1.084 0.995 0.664 0.853 0.894 0.778 +1 0.947 0.643 0.575 1.133 -0.306 0.529 2.160 -1.050 0.000 0.479 1.283 0.190 2.215 0.956 1.329 -1.570 0.000 0.754 0.790 1.335 0.000 1.026 0.798 1.023 0.686 0.566 0.549 0.633 +1 0.827 1.568 -0.283 0.336 0.449 0.779 1.352 -1.716 0.000 0.938 -0.427 -0.034 2.215 0.569 0.323 -1.587 2.548 0.456 1.656 0.693 0.000 0.804 0.612 0.998 0.859 0.828 0.894 0.787 +0 1.547 -0.128 -0.252 0.553 1.078 0.878 0.359 0.962 2.173 1.484 -0.198 -0.851 2.215 1.164 1.171 1.385 0.000 0.378 1.479 -1.030 0.000 0.629 0.816 1.194 0.956 1.744 1.058 0.966 +1 1.194 -1.422 -0.170 0.934 -0.883 0.608 -2.400 -1.456 0.000 1.254 0.813 1.472 2.215 1.348 -0.748 0.402 1.274 0.628 -1.356 -0.689 0.000 0.715 1.207 0.986 1.773 1.713 1.612 1.341 +1 0.981 1.375 0.223 0.726 -0.516 0.344 0.021 -1.143 1.087 0.509 -1.405 1.130 2.215 0.596 -0.359 1.314 0.000 0.587 -0.274 0.452 0.000 0.458 0.521 0.983 1.892 0.737 1.243 0.982 +0 1.027 -0.277 -0.714 0.404 0.850 0.801 -0.042 1.142 2.173 0.733 -1.935 1.518 0.000 2.160 0.839 -0.170 1.274 0.614 -0.957 -1.030 0.000 0.756 1.161 0.988 1.158 1.725 1.524 1.184 +1 1.057 0.728 1.152 0.772 0.088 1.602 -0.026 -1.359 2.173 0.777 -0.032 -0.316 0.000 0.870 2.350 -0.213 0.000 0.710 -0.045 1.047 3.102 1.470 0.990 1.024 1.369 0.933 1.088 0.961 +1 2.091 0.008 0.445 0.727 0.091 1.694 -0.339 -1.664 2.173 0.805 1.621 -0.421 0.000 0.682 0.078 0.962 2.548 1.014 -0.173 -0.531 0.000 1.279 1.047 0.991 1.854 0.979 1.255 1.200 +1 1.759 -0.752 -1.552 1.105 -1.515 0.605 -0.845 1.581 2.173 1.533 -0.400 0.146 0.000 1.216 -1.361 -0.341 0.000 1.200 -0.315 1.145 3.102 0.809 0.782 0.985 0.619 0.408 0.587 0.626 +0 0.691 -0.381 0.565 0.834 -1.226 0.863 -0.314 1.648 1.087 0.863 -1.677 -0.010 0.000 0.514 -2.066 0.400 0.000 1.375 -0.613 -0.451 3.102 0.799 1.248 1.051 0.683 1.120 0.845 0.744 +0 0.897 -0.222 1.451 0.656 -0.303 0.712 -0.261 -0.248 0.000 1.160 -0.670 0.910 2.215 0.701 -1.017 1.139 2.548 2.426 -0.595 -1.007 0.000 1.348 1.142 1.062 0.797 0.285 0.950 0.807 +1 1.434 -1.847 1.445 0.681 0.156 0.531 -1.465 0.586 0.000 0.776 -0.117 -1.068 0.000 0.959 -0.150 -0.123 2.548 0.381 -0.546 -1.678 3.102 1.687 0.910 1.256 1.172 0.470 0.741 0.800 +0 0.518 0.202 1.052 1.631 -1.446 0.410 -0.799 0.956 0.000 0.742 -1.244 0.096 0.000 1.063 -0.703 1.626 0.000 1.331 0.560 -0.138 3.102 0.868 1.060 0.991 0.803 0.585 0.691 0.777 +1 0.624 0.939 -0.487 1.463 0.139 1.027 0.883 1.537 2.173 0.639 1.261 0.486 0.000 1.135 1.083 -0.960 2.548 0.383 0.985 -1.582 0.000 0.618 0.783 0.982 0.838 1.064 0.967 0.768 +0 0.655 -0.480 -0.945 1.768 -1.636 0.962 1.347 0.031 2.173 0.503 0.586 -0.343 0.000 0.842 -0.906 1.565 0.000 1.573 -0.490 0.428 3.102 0.752 0.968 0.982 2.180 1.547 1.609 1.424 +0 1.202 0.208 -0.256 1.442 -1.018 0.697 0.066 1.056 0.000 0.896 0.730 1.485 0.000 0.682 2.002 -1.586 0.000 0.581 -0.782 0.607 3.102 0.861 0.801 1.155 0.811 0.391 0.584 0.755 +0 1.012 -0.729 -0.280 1.220 0.383 2.443 -0.434 0.100 2.173 4.423 -0.701 -1.583 0.000 0.956 0.205 1.700 2.548 0.643 0.275 0.596 0.000 2.363 1.451 0.986 0.975 1.994 2.150 1.729 +1 0.694 0.871 -0.528 1.741 -0.581 0.752 -0.304 0.933 2.173 0.591 1.207 0.624 1.107 1.455 -1.240 -1.456 0.000 0.539 0.603 1.101 0.000 1.414 1.180 0.978 0.894 0.881 1.219 1.502 +0 1.353 -1.235 -1.170 1.069 1.729 0.850 0.542 0.364 0.000 0.235 -1.177 0.040 2.215 0.504 -0.188 -0.098 0.000 0.662 0.889 -1.585 3.102 0.659 0.751 0.990 0.645 0.613 0.641 0.816 +1 0.962 -0.703 0.609 1.007 1.614 0.876 -0.962 -1.544 2.173 0.850 -1.950 -0.233 0.000 0.955 -0.060 -0.797 2.548 1.132 0.611 0.609 0.000 2.512 1.591 1.074 0.853 0.889 1.174 1.000 +0 1.840 0.612 -0.438 2.640 -0.071 1.415 1.742 -1.638 0.000 0.770 -0.474 0.104 1.107 1.455 0.913 0.779 0.000 2.160 0.304 -1.720 3.102 0.941 1.410 0.995 0.957 1.269 1.490 1.571 +1 0.479 0.668 -1.534 1.117 0.400 1.106 -0.072 1.684 0.000 1.019 -0.545 -0.010 2.215 1.410 -0.800 -0.955 2.548 1.093 0.042 1.014 0.000 0.962 1.237 0.999 0.899 0.979 1.016 0.942 +1 0.385 1.364 0.641 1.333 -0.802 1.307 1.073 1.415 0.000 1.215 0.802 0.081 2.215 1.485 0.661 -0.794 2.548 0.667 -1.656 0.180 0.000 0.810 1.268 0.988 0.942 1.015 1.025 0.933 +1 1.519 0.448 1.207 0.539 0.561 0.767 -0.684 -0.729 2.173 0.757 0.867 -1.093 0.000 1.512 1.207 1.467 0.000 1.475 0.324 0.137 3.102 0.937 0.938 0.994 0.795 1.016 0.970 0.848 +1 0.571 -1.684 -0.218 1.728 1.013 0.446 -0.528 0.429 0.000 0.600 -0.621 1.299 0.000 1.734 1.256 -0.866 0.000 1.585 0.411 -0.738 3.102 0.848 1.134 1.233 0.868 0.821 1.079 0.890 +0 2.930 -0.533 0.818 1.549 0.762 1.678 0.389 -0.805 0.000 1.130 -0.422 -1.052 1.107 1.830 0.055 0.316 2.548 1.906 0.318 -1.308 0.000 0.952 0.885 0.992 1.127 1.491 1.255 1.201 +0 1.885 -0.389 0.009 0.220 -0.824 1.082 0.350 -0.585 1.087 2.181 0.844 1.524 0.000 1.321 0.612 -1.609 0.000 0.980 0.078 0.804 3.102 0.879 0.990 0.997 0.678 1.045 0.733 0.748 +1 2.172 0.438 -0.966 0.944 -0.172 2.939 0.942 1.304 0.000 1.460 0.414 -0.480 2.215 2.575 0.582 0.041 2.548 0.601 -0.265 1.658 0.000 1.516 2.416 1.303 1.218 0.957 1.836 1.594 +1 0.672 0.598 0.115 0.943 1.079 0.688 -0.581 -0.907 2.173 1.092 -0.843 -0.025 1.107 1.076 -0.923 1.321 0.000 1.178 0.571 0.933 0.000 1.007 0.983 0.990 0.888 0.928 0.807 0.751 +1 2.051 0.531 -0.896 0.185 -0.644 0.808 -0.219 0.918 2.173 0.938 2.256 -0.072 0.000 0.932 0.565 0.568 0.000 1.568 0.523 1.481 3.102 1.328 0.973 0.986 1.224 0.777 0.910 0.834 +1 0.610 -1.119 0.534 0.926 -0.917 0.595 -0.460 -0.039 0.000 0.773 -1.045 1.647 0.000 1.117 -1.363 -0.403 2.548 1.088 1.732 1.647 0.000 0.833 0.837 1.005 0.624 0.784 0.806 0.724 +1 0.581 1.722 0.739 1.170 1.353 0.734 1.635 1.179 0.000 0.910 0.923 -0.329 2.215 1.102 0.753 -0.829 2.548 0.911 0.882 -1.575 0.000 0.972 0.776 0.980 0.903 0.468 0.747 0.662 +0 0.634 0.622 -0.290 1.591 -0.827 0.671 -0.311 -0.235 2.173 0.420 2.155 1.066 0.000 0.837 0.210 0.847 0.000 1.870 -0.846 0.888 0.000 0.919 0.890 0.994 0.650 0.631 0.739 0.773 +1 0.343 -1.060 0.319 1.061 1.425 1.490 0.296 -0.142 0.000 1.381 0.285 1.174 2.215 1.227 0.056 -0.827 2.548 1.213 -2.235 -1.618 0.000 4.769 2.750 0.986 1.814 1.356 1.971 1.810 +1 1.317 0.666 1.713 0.112 -1.100 0.585 0.129 0.369 0.000 0.922 -0.322 -0.521 2.215 0.643 1.231 -1.313 0.000 0.376 -0.843 0.047 3.102 1.315 1.088 0.995 0.636 0.319 0.655 0.678 +1 1.184 0.012 1.149 1.011 -0.052 0.701 -0.466 0.874 0.000 1.035 -0.754 -1.443 2.215 1.154 -1.394 -0.804 2.548 0.602 -0.573 -0.183 0.000 0.817 0.986 1.338 1.230 0.776 0.952 0.838 +0 0.418 -0.272 -1.486 1.111 0.666 0.470 -0.045 -0.039 0.000 0.671 0.506 -1.731 2.215 0.781 0.403 -0.510 2.548 0.577 -1.111 1.622 0.000 0.965 0.876 0.989 0.855 0.686 0.722 0.655 +1 0.825 0.305 -0.935 1.268 -0.176 0.918 -0.708 1.433 1.087 0.401 -1.018 -0.819 0.000 0.580 1.124 1.479 0.000 1.051 0.107 0.607 3.102 0.771 1.134 0.991 0.750 0.838 0.958 0.809 +1 0.424 1.210 0.173 1.267 -1.654 0.956 -0.936 0.158 2.173 1.005 -0.694 -0.903 2.215 0.889 -0.764 1.483 0.000 0.981 0.095 0.940 0.000 0.703 0.968 1.013 1.692 1.190 1.285 1.065 +1 0.812 0.440 0.750 0.874 -1.270 1.054 -1.921 0.081 0.000 1.339 -0.841 -1.560 1.107 0.724 -0.100 -1.350 0.000 0.926 -0.247 0.084 1.551 0.931 0.943 1.131 1.082 1.042 1.059 1.155 +1 2.141 -1.231 -1.289 0.967 0.891 0.556 -1.623 0.161 0.000 0.879 -1.023 0.764 1.107 1.004 -0.364 0.131 2.548 1.275 -1.546 1.616 0.000 0.628 0.892 1.840 1.183 0.633 0.927 0.778 +0 0.822 -0.076 -1.314 1.813 -1.063 0.930 0.771 0.770 0.000 1.106 1.292 0.587 0.000 0.604 0.066 0.378 2.548 0.920 1.916 -0.792 0.000 0.698 0.657 0.984 0.873 0.456 0.590 0.849 +1 1.491 -1.054 -0.510 0.413 -1.371 2.830 1.459 0.211 0.000 2.099 -0.418 0.887 0.000 4.630 -0.136 -1.329 2.548 2.131 -1.218 -1.252 0.000 0.741 2.407 0.994 1.644 1.345 2.791 2.593 +0 1.158 0.766 0.727 4.707 0.447 3.421 -0.590 -1.387 0.000 1.118 0.581 0.126 2.215 0.545 2.616 -0.460 0.000 0.845 -1.238 -1.073 3.102 6.985 3.830 0.975 0.742 1.337 2.501 2.489 +1 0.988 0.732 -1.311 0.840 -0.120 0.802 1.058 -0.919 0.000 1.325 0.984 1.080 2.215 0.618 2.140 -0.518 0.000 0.761 -0.435 0.282 3.102 0.977 1.162 1.109 1.034 0.971 1.013 0.866 +0 0.402 0.619 -1.270 1.127 -0.503 1.307 -0.150 1.047 0.000 1.550 0.124 -1.158 1.107 1.006 0.331 0.800 2.548 2.300 0.050 -0.124 0.000 1.533 1.069 0.988 0.836 1.312 0.958 0.859 +0 0.622 -1.138 0.533 0.103 -0.119 0.747 -0.373 1.595 0.000 0.749 0.916 0.936 2.215 0.877 1.318 -0.602 0.000 1.048 -0.654 -1.112 0.000 0.915 0.959 0.983 0.760 0.705 0.802 0.709 +0 0.448 0.139 -0.803 2.074 -1.706 2.206 0.160 0.179 1.087 1.598 -1.102 1.473 2.215 1.999 2.394 -0.685 0.000 2.166 1.355 -1.004 0.000 0.810 1.162 0.990 1.902 3.183 1.725 1.331 +0 0.774 1.613 -1.342 0.556 1.363 1.102 0.020 -0.461 2.173 2.044 0.286 1.692 2.215 1.644 -0.129 0.112 0.000 0.762 -0.557 0.951 0.000 0.909 1.015 0.977 0.849 2.082 1.255 1.082 +1 1.618 0.054 -1.427 0.413 -0.858 0.621 -1.896 0.151 0.000 0.441 -0.133 1.657 0.000 0.834 0.237 0.975 1.274 0.622 -1.269 0.541 0.000 0.771 0.629 0.990 0.622 0.614 0.585 0.599 +1 0.598 -2.307 1.234 0.302 -0.511 0.632 -0.150 0.204 1.087 0.626 -0.571 1.140 0.000 0.696 0.807 -1.224 2.548 0.547 -1.632 -1.610 0.000 0.735 0.947 0.990 0.894 0.911 0.841 0.742 +1 0.674 1.085 1.262 0.264 1.034 0.778 0.596 -0.377 2.173 1.533 1.063 1.030 0.000 1.280 0.658 1.462 0.000 1.278 1.304 -1.298 0.000 0.909 1.486 0.978 0.864 0.957 1.244 0.995 +0 0.711 0.376 -1.292 0.325 0.082 1.291 0.500 1.690 2.173 0.937 0.358 -0.451 2.215 0.767 1.798 0.283 0.000 1.271 0.416 0.301 0.000 0.879 0.920 0.991 0.934 1.518 1.069 0.877 +1 0.871 0.973 -1.083 0.349 1.286 0.987 0.967 1.418 2.173 0.577 -0.401 -0.167 0.000 0.971 -0.312 -0.497 0.000 1.313 -0.454 0.439 3.102 0.339 0.567 0.995 0.847 1.373 0.970 0.828 +1 0.734 -0.537 0.833 0.257 -0.129 0.625 -0.770 -1.167 0.000 0.501 -1.973 -0.716 0.000 0.730 1.016 0.698 2.548 0.617 -0.944 1.394 3.102 0.922 0.682 0.993 1.109 0.768 0.895 0.813 +1 0.851 -0.040 0.135 0.330 -0.916 0.627 1.824 -1.496 0.000 0.998 0.923 0.387 1.107 0.483 -0.102 -1.165 2.548 0.787 0.658 -1.445 0.000 0.593 1.098 0.985 0.586 0.835 0.738 0.886 +0 1.059 -0.943 -1.225 1.735 1.167 1.697 -0.192 -0.421 2.173 0.707 -1.367 0.410 0.000 1.906 -0.023 1.420 2.548 0.449 -0.205 -1.381 0.000 0.861 1.075 1.565 1.822 2.240 1.448 1.154 +1 0.377 -0.365 -1.472 1.242 -0.681 0.635 1.217 -1.527 1.087 0.859 1.554 1.012 2.215 1.287 -0.173 0.175 0.000 0.664 1.655 0.410 0.000 0.742 1.097 0.984 2.190 0.843 1.649 1.279 +1 0.328 -1.948 -0.733 1.707 -1.225 0.773 2.657 1.732 0.000 0.746 0.632 0.215 2.215 2.115 -0.687 0.631 2.548 0.504 -2.227 0.716 0.000 6.951 3.743 0.992 1.321 1.129 2.677 2.224 +0 0.642 0.760 1.289 0.632 -1.458 0.789 0.723 0.311 1.087 0.704 -0.792 -0.759 2.215 1.045 -0.281 1.483 0.000 0.525 0.400 -0.343 0.000 0.879 0.963 0.989 1.379 1.297 1.078 0.918 +0 1.240 -1.275 0.946 0.340 1.308 0.345 -0.769 -1.089 1.087 0.687 0.217 -0.276 2.215 0.296 2.258 -0.969 0.000 0.482 -1.238 -1.309 0.000 1.492 0.999 0.973 0.724 0.607 0.691 0.790 +1 0.875 -0.360 0.872 0.819 -1.389 0.631 -0.729 0.355 0.000 0.959 -0.188 -0.184 0.000 1.445 0.366 1.554 2.548 0.927 0.938 -0.716 3.102 0.895 1.020 1.048 0.738 0.852 0.933 0.821 +0 0.755 -0.250 -0.273 3.584 -0.737 1.257 0.128 1.271 2.173 0.616 0.664 0.690 0.000 1.725 -0.605 0.992 0.000 0.638 0.596 -1.233 0.000 0.824 0.930 0.988 0.856 0.875 1.231 0.977 +1 0.529 1.162 -1.186 0.281 -0.927 1.010 0.724 1.580 0.000 1.313 0.339 0.340 1.107 0.956 0.019 -0.223 0.000 0.573 -0.528 -0.864 3.102 1.876 1.135 0.991 0.948 0.800 0.925 0.802 +1 0.777 -2.102 -0.247 0.774 0.624 1.268 0.130 -1.248 2.173 0.667 -0.770 1.198 2.215 0.387 -1.490 -0.494 0.000 0.831 0.035 0.362 0.000 0.798 0.975 0.985 0.762 1.268 1.127 0.883 +1 0.594 -0.188 1.651 0.420 0.147 0.965 0.249 0.791 0.000 0.466 -0.116 0.054 0.000 1.532 0.930 -1.227 1.274 0.515 -0.511 -1.221 0.000 0.918 0.872 0.983 1.270 0.532 0.866 0.872 +0 0.910 -0.398 1.689 0.375 -1.181 1.002 -0.126 -0.236 0.000 1.247 -1.085 1.338 1.107 0.709 -0.907 0.049 0.000 0.612 -0.572 -1.590 1.551 0.781 0.775 0.992 1.044 0.417 0.890 0.854 +0 0.946 -0.272 -1.557 0.238 0.026 1.451 0.505 0.756 0.000 1.405 0.488 -1.012 2.215 1.483 1.364 -0.255 2.548 0.538 2.417 1.605 0.000 0.817 0.883 0.982 0.751 1.253 0.854 0.797 +1 0.880 -0.100 -0.730 0.704 -0.077 0.942 1.062 0.730 2.173 0.766 -0.228 -1.390 1.107 0.699 -1.526 -0.854 0.000 0.581 -0.896 1.111 0.000 0.722 0.718 0.997 1.490 1.468 1.116 0.996 +1 0.871 -0.714 -1.039 0.566 0.450 1.114 -0.992 0.611 1.087 1.405 -1.290 -1.352 2.215 1.114 0.193 0.651 0.000 1.405 0.874 -1.366 0.000 0.818 0.934 0.988 0.806 1.829 1.127 0.915 +0 1.237 0.613 -0.408 1.270 0.156 0.896 -1.076 1.540 0.000 1.052 1.226 0.319 2.215 0.863 -1.972 1.611 0.000 1.462 -0.302 -1.517 3.102 0.902 0.906 0.987 0.856 1.506 1.605 1.438 +1 2.128 -0.042 1.686 1.284 -1.273 1.151 0.414 0.191 2.173 0.632 -0.423 0.848 2.215 0.745 0.614 -0.711 0.000 0.473 0.035 0.494 0.000 0.616 0.695 1.049 0.932 0.891 1.120 0.881 +1 0.829 -0.657 -1.270 1.127 0.825 3.386 1.441 1.408 0.000 3.409 -0.309 -0.254 1.107 2.389 0.287 -0.205 0.000 0.930 0.637 -0.364 3.102 2.023 1.193 1.272 1.573 0.927 1.073 1.057 +0 0.975 0.825 1.687 0.343 -0.682 0.375 -0.872 -1.065 2.173 0.549 -0.938 0.117 2.215 0.264 0.759 0.248 0.000 0.819 -1.047 0.817 0.000 0.688 0.651 0.983 1.165 0.585 0.894 0.775 +0 0.604 -0.512 0.195 0.317 -0.767 0.600 -1.102 -1.565 2.173 0.395 -2.850 0.159 0.000 1.309 -0.557 1.172 2.548 0.508 -0.917 -1.095 0.000 0.809 1.050 0.989 0.772 0.744 0.744 0.682 +1 2.344 -1.597 -0.415 0.899 0.074 0.347 -1.683 0.886 0.000 0.593 -0.451 0.596 1.107 1.233 -1.702 -1.705 0.000 1.511 0.101 1.491 3.102 0.896 1.073 0.993 0.947 0.667 1.039 0.944 +1 1.185 0.016 1.614 0.672 -1.226 0.667 -0.123 -1.633 2.173 1.695 -1.275 0.143 0.000 1.352 -0.693 -0.336 0.000 1.409 1.022 1.104 3.102 0.737 0.928 0.995 1.042 0.984 0.935 0.825 +1 0.693 -0.672 0.285 0.213 0.178 0.653 -0.153 0.035 2.173 0.792 -0.328 -1.498 0.000 1.493 0.656 -1.492 0.000 0.881 0.420 -0.276 0.000 0.924 0.726 0.981 0.609 0.578 0.776 0.714 +0 1.017 -0.526 -1.057 5.700 -1.371 3.733 -1.007 0.421 2.173 1.432 0.185 -1.571 2.215 0.800 -1.297 -0.200 0.000 1.142 -1.579 0.438 0.000 0.626 1.153 0.993 1.211 3.968 2.896 2.177 +0 0.833 -1.005 -0.875 0.647 -0.531 0.758 0.232 0.688 0.000 1.031 1.290 0.792 2.215 1.532 -0.027 -1.431 2.548 0.657 -0.354 -0.171 0.000 0.836 0.938 0.976 0.770 1.558 1.035 0.916 +0 1.848 0.713 -0.654 3.006 -0.820 1.255 -0.800 0.878 0.000 1.180 0.097 1.010 0.000 1.408 -0.014 1.297 2.548 1.158 1.274 0.810 0.000 1.077 0.926 0.982 0.724 1.214 1.172 1.531 +1 0.521 0.502 -1.284 0.682 0.471 0.519 -1.008 -0.325 0.000 0.477 1.051 0.728 2.215 1.031 0.384 1.606 1.274 0.933 0.582 -0.274 0.000 0.917 0.720 0.988 0.559 0.586 0.521 0.510 +0 0.836 1.675 -0.549 0.750 -1.631 0.610 1.345 0.805 2.173 0.712 0.273 -1.684 0.000 0.982 0.820 0.318 2.548 0.873 1.056 -0.904 0.000 0.848 0.950 0.984 0.877 0.466 0.698 0.730 +0 1.286 -0.032 1.465 0.269 -0.833 0.643 -0.725 0.464 2.173 0.790 0.984 -1.301 2.215 0.397 0.474 0.583 0.000 0.841 -0.334 -0.268 0.000 0.537 0.764 0.986 0.819 1.474 0.860 0.699 +1 0.969 -0.407 -0.911 0.265 1.281 0.855 -1.414 -0.371 0.000 1.334 -0.159 1.093 0.000 0.710 -1.056 1.368 2.548 0.524 1.001 -0.090 0.000 0.810 0.852 0.993 0.490 0.357 0.544 0.619 +0 0.459 0.263 0.758 0.625 -0.288 0.931 -1.251 0.366 2.173 1.621 0.716 -1.344 0.000 0.497 0.768 -0.970 1.274 0.438 -2.359 1.077 0.000 3.302 1.816 0.982 1.767 1.329 1.538 1.422 +1 0.874 0.654 -1.102 2.128 -0.335 1.107 -0.047 1.233 0.000 0.907 -0.072 -0.226 2.215 0.720 1.473 1.656 0.000 0.962 -0.595 -1.158 0.000 1.420 0.992 1.205 0.790 0.844 0.858 0.965 +1 1.293 -1.676 -0.608 0.248 -1.008 0.998 -0.158 1.461 0.000 0.803 -0.565 -0.729 0.000 0.732 -0.817 0.604 1.274 1.026 -0.130 0.681 3.102 1.151 0.885 0.976 0.854 0.255 0.621 0.745 +0 1.170 1.729 -1.559 0.957 -0.607 0.421 -1.208 1.141 0.000 0.535 0.304 0.533 2.215 0.608 -0.644 -0.457 2.548 0.446 -1.902 -0.618 0.000 0.757 0.851 1.110 1.220 0.572 0.902 1.183 +0 0.895 -1.934 0.879 1.047 -0.273 0.510 -0.575 0.194 0.000 1.195 -0.948 1.556 2.215 1.031 -1.287 -0.867 1.274 0.691 0.370 -0.956 0.000 0.919 0.885 1.155 1.142 1.000 0.858 0.871 +0 0.422 -1.331 1.415 0.978 -1.122 1.086 -2.026 1.159 0.000 0.866 -0.216 0.307 0.000 0.747 -0.374 -0.699 0.000 1.695 0.791 -0.767 3.102 0.892 0.952 0.987 1.489 0.806 1.586 1.248 +1 1.107 -0.050 1.570 0.268 1.357 1.485 -0.803 0.872 1.087 1.285 -2.397 -0.449 0.000 0.831 -1.152 1.461 0.000 1.398 0.428 -0.449 0.000 0.801 0.942 0.987 0.932 2.011 1.193 1.060 +0 0.603 -1.436 1.464 0.826 0.544 0.902 -0.697 0.907 1.087 1.118 0.180 -1.334 2.215 0.530 -1.617 -1.630 0.000 0.919 1.592 -0.501 0.000 0.878 1.068 0.981 1.693 1.494 1.261 1.064 +0 1.167 -0.572 1.323 0.273 -0.272 0.814 -0.018 -0.493 0.000 0.747 -0.451 0.771 2.215 0.461 -1.622 -0.327 0.000 0.894 0.550 -1.432 3.102 1.114 0.970 0.987 0.612 0.804 0.768 0.706 +0 1.361 0.050 -0.308 0.047 0.824 0.726 0.953 0.640 0.000 1.220 0.138 -0.893 2.215 1.015 -0.804 1.672 0.000 1.002 0.661 1.510 1.551 1.176 0.814 0.821 0.696 0.890 0.841 0.770 +1 1.247 1.334 -0.085 1.039 0.219 2.487 1.050 0.934 0.000 1.273 -0.276 -0.865 0.000 2.975 -1.880 -1.351 0.000 1.578 0.363 -0.353 3.102 1.530 0.971 0.991 0.993 0.580 0.679 0.961 +1 1.385 0.466 0.474 0.799 0.724 0.936 0.622 -0.669 2.173 0.568 1.058 -1.732 2.215 0.287 0.636 -1.038 0.000 0.375 1.535 1.573 0.000 0.472 0.742 1.001 0.819 0.913 0.867 0.679 +1 1.595 0.289 -0.478 1.425 -1.099 0.932 0.668 0.465 1.087 0.958 0.711 -1.681 0.000 0.815 1.048 1.413 2.548 1.206 0.754 -0.054 0.000 0.892 0.888 1.107 1.291 0.859 0.971 0.832 +0 0.713 0.664 0.821 0.747 0.014 0.574 -0.469 -0.223 2.173 0.584 -1.687 -1.379 0.000 0.957 -0.808 1.645 0.000 1.137 -0.789 -0.592 3.102 0.827 1.194 0.988 0.752 0.346 0.897 0.780 +1 0.681 0.595 -0.971 0.848 0.441 0.676 -0.335 1.523 0.000 0.792 0.756 0.297 2.215 1.115 -0.083 -0.492 2.548 0.891 -1.141 -1.464 0.000 0.821 1.010 1.006 0.624 0.791 0.886 0.789 +1 1.059 0.074 1.231 0.696 0.047 1.458 0.921 -1.516 0.000 1.866 1.773 -0.528 0.000 1.352 -0.534 -0.066 1.274 1.713 1.402 1.015 0.000 0.806 0.472 1.041 0.825 0.821 0.951 0.819 +0 1.100 1.703 -0.578 0.682 0.101 0.750 1.468 0.153 0.000 0.886 -0.135 -1.739 1.107 0.551 0.776 1.584 2.548 0.740 2.056 0.796 0.000 0.853 0.803 0.979 1.622 0.398 1.041 0.955 +1 0.800 0.355 0.423 0.113 -0.151 0.626 -2.030 -1.070 0.000 0.742 -0.749 1.560 2.215 0.627 -0.347 -0.017 2.548 0.681 1.363 1.468 0.000 0.840 1.158 0.993 0.527 0.731 0.761 0.702 +0 0.833 -0.626 -0.286 1.520 -0.716 0.829 -1.838 1.639 0.000 0.621 -1.434 -0.072 0.000 1.070 -0.524 0.866 2.548 0.624 -0.593 -1.720 3.102 1.551 0.928 0.997 1.036 0.454 0.728 0.965 +1 1.744 0.759 0.988 0.542 0.325 1.105 0.264 -0.896 2.173 0.604 -0.602 0.459 2.215 0.331 0.498 1.736 0.000 0.399 -0.958 1.693 0.000 0.383 0.671 0.993 0.938 1.257 1.066 0.820 +1 1.543 -0.299 -1.358 0.792 -1.229 0.767 0.036 0.436 2.173 0.376 -1.499 0.181 0.000 0.765 -0.647 -1.600 0.000 0.770 -0.820 1.140 1.551 0.894 0.964 0.985 0.701 0.648 0.846 0.727 +1 0.796 1.813 1.170 0.609 -1.121 0.751 0.367 0.223 2.173 0.864 0.060 -0.996 2.215 0.957 1.161 0.446 0.000 0.867 0.036 -1.509 0.000 0.696 1.039 0.984 1.289 1.071 1.111 0.906 +1 1.018 0.391 0.618 1.363 -1.491 0.514 0.738 -1.185 0.000 0.371 -0.433 0.697 2.215 0.709 0.729 -0.125 0.000 0.986 -0.993 0.347 1.551 0.887 1.062 1.544 0.839 0.269 0.718 0.728 +1 0.745 0.432 0.523 1.164 1.223 1.871 1.400 -0.503 2.173 1.198 -1.655 1.100 0.000 1.353 0.944 -1.732 2.548 0.953 1.253 -1.196 0.000 0.639 0.733 0.989 0.986 1.812 1.466 1.101 +0 0.872 -0.695 -0.014 0.701 -0.579 0.858 -0.860 0.840 2.173 0.808 -1.560 -1.532 2.215 0.499 -2.007 0.941 0.000 0.864 -1.355 -0.890 0.000 0.752 0.884 0.984 1.123 1.132 0.954 0.838 +0 0.587 -0.621 1.007 1.651 -1.306 0.647 -0.756 -0.798 2.173 0.847 -0.109 0.867 2.215 0.676 1.191 0.901 0.000 0.537 -1.385 0.195 0.000 1.447 0.940 1.189 0.790 1.145 0.852 0.832 +0 1.119 -0.452 0.458 0.750 0.374 1.002 1.001 -1.561 0.000 0.568 1.142 1.091 0.000 1.056 1.120 -0.224 2.548 1.486 -0.703 -0.972 3.102 1.100 1.139 0.996 0.938 1.324 1.103 1.291 +1 0.677 0.195 -0.300 0.639 -1.232 1.061 -0.791 -1.246 2.173 0.289 2.112 0.299 0.000 0.797 -0.333 0.215 0.000 0.717 0.504 1.475 3.102 1.209 0.808 0.989 1.281 0.915 0.997 0.894 +0 0.760 -1.909 1.008 0.428 -0.981 0.894 -1.311 -0.647 1.087 0.558 -0.924 0.227 0.000 0.391 1.635 1.441 0.000 0.771 -0.935 1.593 3.102 0.782 0.972 0.995 0.534 0.797 0.644 0.603 +0 0.858 -1.415 0.799 2.026 0.184 1.542 1.646 -1.669 0.000 1.251 -0.466 -1.330 0.000 1.713 -0.543 0.263 1.274 1.866 -0.846 -0.411 3.102 0.813 0.938 0.988 0.861 0.832 0.714 0.691 +0 0.778 -0.136 -0.224 1.067 1.272 1.272 -2.125 -0.789 0.000 0.886 -1.116 1.299 2.215 2.398 -0.133 0.515 2.548 2.225 -0.488 -1.363 0.000 1.276 1.015 1.231 0.922 1.292 1.022 0.877 +1 1.520 -0.901 0.514 0.532 0.964 0.621 0.825 -1.570 2.173 0.765 -0.077 -0.729 0.000 0.399 0.605 -0.403 2.548 0.478 0.868 1.294 0.000 0.894 0.771 0.982 0.885 0.541 1.037 0.895 +1 0.404 -1.089 -1.274 2.084 -1.561 0.633 1.273 0.145 2.173 0.236 1.676 -0.411 0.000 0.711 0.169 0.882 0.000 0.425 -0.052 0.093 0.000 0.786 0.663 0.993 0.795 0.881 0.938 0.781 +1 1.612 0.476 0.066 0.285 -0.744 1.120 0.629 -1.673 1.087 0.613 0.540 -0.420 0.000 0.472 -0.093 1.651 0.000 1.740 0.308 1.062 3.102 0.924 1.119 0.989 1.266 0.945 1.025 0.993 +1 0.971 -0.658 0.081 0.805 0.734 0.982 -0.115 -1.250 2.173 0.502 0.160 -0.085 0.000 0.698 0.181 1.703 2.548 0.549 0.264 0.882 0.000 0.526 0.868 0.995 0.906 0.503 0.912 0.761 +0 0.767 1.529 -0.039 1.189 1.076 0.691 0.383 1.544 1.087 0.531 1.572 -0.523 0.000 0.618 -0.237 -1.240 2.548 0.741 -0.035 -0.088 0.000 0.821 0.993 1.116 0.987 0.550 0.791 0.744 +1 0.301 -1.149 -0.120 1.764 -0.871 0.520 -0.664 -0.983 0.000 0.890 0.621 0.765 2.215 0.567 -2.163 1.034 0.000 1.878 -0.658 0.913 3.102 1.331 1.094 0.983 1.074 0.920 1.025 1.008 +1 0.968 1.215 -1.037 0.483 -0.207 0.535 0.467 -0.105 0.000 0.667 1.409 0.371 0.000 1.338 0.146 1.401 2.548 0.785 -0.353 -1.328 3.102 0.834 0.928 0.987 0.902 0.546 0.776 0.718 +1 0.704 0.048 0.349 0.604 1.299 1.511 -0.145 -0.623 2.173 1.211 -0.434 0.779 0.000 0.438 0.899 -1.229 2.548 0.745 0.012 -1.615 0.000 1.071 0.877 0.988 1.198 0.802 0.971 0.859 +0 0.571 1.326 -1.342 0.465 0.854 0.597 0.708 -0.960 2.173 0.638 -0.862 1.306 2.215 0.681 0.389 -0.545 0.000 0.960 1.538 0.368 0.000 0.949 0.807 0.987 0.769 1.143 0.867 0.736 +1 1.068 1.916 0.911 0.905 0.564 1.374 1.058 -0.888 2.173 0.598 1.145 0.753 0.000 0.368 2.600 -1.536 0.000 0.734 0.931 1.625 3.102 0.928 1.267 0.975 0.620 0.817 0.932 0.814 +1 0.662 -1.046 -1.445 1.390 0.908 1.083 -0.347 0.305 2.173 1.506 2.686 -1.067 0.000 0.593 -2.265 1.547 0.000 1.176 -0.733 1.075 3.102 0.838 0.812 1.133 1.063 0.828 0.908 0.814 +1 1.696 -0.640 -0.789 1.464 -0.545 1.428 -0.677 0.603 2.173 0.466 -1.002 0.837 2.215 0.739 -1.617 -0.171 0.000 1.564 0.515 1.614 0.000 0.759 1.119 0.972 0.974 0.327 1.105 0.940 +1 1.091 0.472 -1.066 1.289 0.290 0.958 0.628 -1.556 0.000 1.207 0.427 0.642 1.107 0.721 1.285 0.081 0.000 0.700 1.088 -0.864 3.102 1.602 0.930 1.544 1.043 0.891 0.854 0.834 +1 1.447 -0.413 0.012 1.033 -0.945 0.897 -0.645 1.072 2.173 0.460 -0.931 -1.489 1.107 0.659 0.289 -1.513 0.000 0.720 -1.495 0.195 0.000 1.225 1.012 1.285 0.817 0.714 0.826 0.755 +0 0.750 0.588 0.398 2.234 0.575 0.813 -0.607 -0.803 2.173 0.885 -0.225 -1.421 0.000 1.170 -0.294 1.515 2.548 0.855 -1.325 -0.045 0.000 1.357 0.967 0.981 0.975 1.070 1.003 0.945 +0 0.474 0.540 -1.253 2.915 1.324 0.748 1.480 -0.392 0.000 1.125 -1.506 0.191 0.000 0.655 -1.844 -0.506 0.000 1.413 0.702 0.918 3.102 0.836 1.020 1.190 0.735 1.014 1.138 1.346 +0 0.344 -1.296 0.276 1.257 0.597 0.599 -0.066 1.674 2.173 0.716 1.144 -0.651 0.000 0.923 -0.472 -0.786 2.548 0.545 1.227 1.618 0.000 0.734 0.898 0.989 0.857 0.768 0.721 0.752 +1 1.435 -0.897 -0.695 1.221 -0.819 0.964 -0.248 1.445 2.173 0.825 -0.264 -0.413 0.000 1.354 -1.044 0.783 0.000 1.175 -1.565 0.442 0.000 0.908 0.916 0.998 0.694 0.475 0.839 0.872 +0 0.718 -0.014 -0.392 0.919 1.614 1.231 0.054 0.169 2.173 0.872 -1.130 1.693 2.215 1.031 -1.357 0.860 0.000 1.845 -1.180 -1.227 0.000 1.449 0.956 1.094 1.020 1.791 1.254 1.054 +1 1.278 1.183 0.901 0.237 -1.123 0.718 1.142 -0.407 0.000 0.630 1.348 0.280 0.000 0.839 -0.035 -1.624 2.548 1.093 0.423 -1.288 0.000 0.845 0.930 0.990 0.738 0.516 0.759 0.686 +0 0.738 -0.700 1.165 1.122 0.557 0.754 0.905 -0.558 2.173 0.375 0.285 -0.819 0.000 1.073 0.607 -1.532 2.548 0.512 0.565 1.196 0.000 0.565 0.583 0.991 0.902 0.873 0.863 0.668 +1 1.674 -0.675 0.718 1.564 1.139 0.502 -1.122 -0.583 0.000 1.063 -0.242 -1.110 2.215 1.062 0.870 -0.442 2.548 0.597 0.069 1.691 0.000 0.918 1.021 0.979 1.356 0.965 1.251 1.019 +0 1.360 0.986 1.067 0.376 -0.677 0.888 0.437 -0.861 2.173 1.347 1.525 0.111 2.215 1.326 0.351 1.610 0.000 1.224 0.346 -1.459 0.000 0.840 1.042 0.990 0.918 1.564 1.044 0.882 +1 0.403 -1.843 -0.391 0.840 -0.591 0.956 -1.662 0.906 0.000 1.180 -0.595 0.111 2.215 1.346 -1.809 -1.191 0.000 0.921 -0.232 -1.697 3.102 0.989 0.896 0.984 0.743 0.953 0.862 0.834 +0 0.474 -1.836 1.716 1.584 -1.732 1.074 -0.878 0.533 0.000 1.450 -0.802 -0.841 0.000 1.335 -0.694 -0.047 0.000 1.548 -0.554 1.296 3.102 0.872 0.678 0.977 0.806 0.808 0.618 0.601 +0 0.659 0.090 -0.285 1.295 0.752 0.678 0.685 -0.808 0.000 1.042 -0.114 -1.042 0.000 0.819 0.020 -1.659 1.274 0.996 -2.012 0.909 0.000 0.831 0.745 1.030 0.595 0.904 0.853 0.802 +0 0.793 -0.007 -1.605 1.278 0.044 1.074 1.432 0.130 1.087 1.913 -0.763 -1.285 1.107 0.977 2.610 1.514 0.000 1.603 -0.688 0.575 0.000 0.896 1.519 1.389 1.313 3.511 1.741 1.370 +0 1.829 0.021 1.555 2.846 -1.551 1.839 -0.557 0.085 2.173 0.861 -0.075 -0.480 0.000 0.862 2.436 -1.290 0.000 1.185 0.134 0.247 0.000 0.817 0.991 1.065 2.470 1.503 1.609 1.315 +0 0.779 0.270 -1.020 0.773 1.725 0.300 0.556 -0.073 0.000 0.790 -1.039 0.528 2.215 0.784 -0.249 1.584 2.548 0.557 0.640 -1.228 0.000 0.542 0.850 0.990 0.722 0.762 0.879 0.712 +1 2.224 0.801 0.972 0.917 -1.486 0.880 1.219 -0.242 2.173 1.085 0.151 -0.987 2.215 0.812 1.041 1.365 0.000 0.584 1.689 -0.342 0.000 0.835 1.016 1.584 1.385 1.208 1.140 0.931 +1 1.003 -1.132 1.153 1.325 1.322 0.727 -0.783 -0.284 1.087 0.391 -1.431 -0.199 2.215 0.575 -1.816 -1.077 0.000 0.704 -0.603 0.220 0.000 0.803 0.692 0.989 0.787 0.279 0.854 0.712 +1 1.340 -0.225 0.253 1.498 0.771 1.259 -0.343 -1.201 1.087 0.430 -0.642 -0.471 0.000 0.762 -1.597 1.000 0.000 0.529 -0.042 1.167 3.102 0.990 1.231 0.980 0.535 0.741 0.973 0.903 +0 0.449 1.190 -1.245 0.534 1.027 1.551 0.334 -0.557 2.173 1.287 -0.401 1.067 0.000 1.277 0.451 0.914 0.000 0.612 0.050 -0.974 3.102 0.962 0.877 0.988 1.063 0.405 1.162 0.933 +1 0.504 -1.356 0.964 1.089 0.401 0.551 0.889 1.357 1.087 1.251 0.266 -1.105 2.215 0.373 -1.322 -1.625 0.000 0.598 -1.334 0.420 0.000 0.504 1.002 0.985 2.223 1.047 1.860 1.337 +1 0.371 -0.753 1.642 1.145 0.949 0.849 0.913 -1.129 2.173 0.929 1.244 -0.539 0.000 1.283 0.677 0.577 2.548 1.078 0.498 1.395 0.000 0.857 0.850 0.998 1.007 1.305 0.821 0.753 +1 0.718 1.295 -0.166 0.548 -1.369 0.538 1.438 -1.523 0.000 0.674 -0.940 0.146 2.215 0.599 1.589 0.875 0.000 0.651 0.556 1.096 3.102 0.860 0.587 0.988 0.921 0.708 0.881 0.760 +0 0.731 0.197 1.122 0.999 0.205 0.607 -0.271 -0.595 0.000 1.349 -0.302 1.693 2.215 1.114 -0.478 -0.052 2.548 0.611 0.506 -1.610 0.000 0.850 0.970 0.982 0.654 1.310 0.804 0.742 +1 0.469 -1.268 1.090 1.136 0.012 0.974 -0.539 0.820 2.173 1.046 -0.271 -1.049 0.000 1.117 0.659 -1.378 2.548 0.452 2.279 -0.329 0.000 1.999 1.220 0.987 0.753 1.481 1.238 1.079 +0 0.592 0.410 -1.529 1.128 -0.815 1.011 1.054 -0.189 2.173 1.502 0.514 1.044 2.215 0.904 0.431 0.365 0.000 1.622 -1.923 -1.509 0.000 2.846 2.541 0.981 0.899 1.697 2.076 1.785 +1 0.996 -0.710 0.155 0.892 1.351 1.438 -0.756 1.580 2.173 0.879 -1.360 -0.192 0.000 0.414 -1.924 0.316 0.000 1.450 -0.301 -0.287 3.102 0.530 0.663 1.150 1.045 1.549 1.035 0.886 +1 0.753 0.827 0.749 0.402 -0.597 0.902 0.002 -0.110 0.000 1.199 0.328 -1.603 0.000 1.122 0.099 0.516 2.548 0.551 0.112 -0.527 3.102 2.185 1.166 0.984 0.815 0.486 0.825 0.823 +1 1.018 1.080 0.393 0.716 0.980 0.873 0.149 0.910 0.000 0.767 0.873 -1.196 0.000 1.795 0.995 -0.645 1.274 0.554 -0.194 -1.293 3.102 1.149 0.786 0.984 1.095 0.687 0.833 0.852 +1 2.620 -0.787 -0.453 0.539 -0.867 0.926 -0.026 0.834 2.173 0.729 -2.047 -1.186 0.000 1.674 0.568 1.236 0.000 0.706 -0.617 -1.040 0.000 0.803 0.884 0.978 0.556 0.771 0.902 0.765 +1 2.149 0.844 0.295 0.389 -1.740 0.879 -0.103 1.675 0.000 1.261 0.705 -0.822 1.107 0.500 1.281 -1.586 0.000 0.484 -0.626 -1.514 3.102 1.017 1.169 1.224 0.862 0.703 0.815 0.859 +0 0.731 -0.363 -0.142 1.813 0.676 0.459 1.213 -0.532 0.000 0.513 2.070 0.477 0.000 1.956 -0.543 -1.459 2.548 1.015 0.169 -1.603 3.102 0.945 0.958 1.073 1.308 0.464 1.143 1.135 +0 0.417 -1.590 0.521 1.357 -1.110 0.624 0.461 -0.769 0.000 0.784 -0.448 1.557 2.215 0.966 0.288 0.701 0.000 0.573 -0.059 0.135 3.102 1.358 1.108 1.037 0.737 0.592 0.669 0.857 +0 2.342 1.059 1.722 0.424 1.623 0.906 0.886 -0.445 2.173 1.128 1.518 0.277 0.000 0.500 1.241 0.674 2.548 0.373 1.601 -0.385 0.000 0.830 0.948 0.981 0.700 0.736 0.877 0.824 +1 1.601 0.998 1.435 1.008 -1.328 0.584 1.886 -0.453 0.000 0.524 -0.216 1.558 2.215 0.802 1.009 -0.224 2.548 0.752 1.143 -1.417 0.000 0.819 1.077 1.068 0.902 0.847 0.705 0.735 +0 0.392 1.378 1.567 0.171 1.657 0.864 0.888 1.480 2.173 1.201 0.949 -0.301 2.215 1.277 0.403 0.507 0.000 0.506 -0.554 -0.706 0.000 0.942 0.945 0.984 0.780 1.499 0.917 0.803 +1 1.283 -0.376 1.243 1.343 0.732 0.810 -0.356 -1.151 2.173 0.695 2.033 -0.577 0.000 1.039 -0.166 0.395 0.000 1.247 0.797 -0.559 3.102 0.909 0.994 0.987 1.189 0.927 1.047 0.876 +1 1.168 -0.405 1.234 0.981 1.270 0.961 1.449 -0.624 0.000 0.817 0.830 1.583 2.215 2.374 -0.999 -0.755 2.548 0.489 -0.386 0.520 0.000 1.253 0.985 0.975 1.400 2.112 1.587 1.482 +1 0.805 -1.106 -0.142 0.461 -0.063 0.550 -2.857 -0.241 0.000 0.332 2.482 0.587 0.000 0.979 -0.514 0.890 2.548 2.005 -0.310 -1.515 3.102 0.934 1.098 0.985 0.734 0.891 0.875 0.854 +0 0.668 -0.759 -0.651 1.006 0.633 1.151 -0.233 -0.701 2.173 1.397 0.741 1.411 1.107 0.528 0.042 1.402 0.000 0.648 0.211 -0.363 0.000 1.107 1.103 1.040 1.295 2.012 1.299 1.076 +0 0.569 -1.719 -0.297 0.940 -0.971 1.159 1.841 1.116 0.000 1.190 -0.052 1.528 2.215 1.437 -0.257 0.073 0.000 1.379 -2.480 -1.250 0.000 0.928 0.733 0.995 1.050 1.417 0.936 0.828 +1 0.581 0.390 -1.349 0.917 0.055 0.513 -0.528 1.547 0.000 0.965 -1.286 0.096 2.215 0.772 0.850 1.657 0.000 0.778 -1.116 -1.395 3.102 0.913 0.807 0.986 0.931 0.762 0.883 0.791 +1 1.238 -0.305 1.241 0.539 -0.440 0.839 -2.620 -1.243 0.000 0.510 0.901 -0.070 0.000 1.298 -0.612 -1.525 2.548 2.198 -0.038 0.129 0.000 0.740 0.843 1.129 0.779 0.923 0.874 0.774 +0 2.246 0.114 -0.374 0.575 -0.633 1.481 0.635 -1.590 0.000 1.319 0.993 0.432 2.215 1.371 -0.638 0.147 2.548 2.085 -0.381 1.557 0.000 0.611 1.000 0.994 1.354 1.435 1.087 1.045 +0 0.385 -1.293 1.299 1.246 1.243 0.509 0.022 -0.274 0.000 0.381 1.422 -1.048 0.000 0.585 -0.046 0.206 2.548 0.750 -0.573 0.851 3.102 0.948 0.838 0.972 0.656 0.323 0.530 0.608 +1 0.759 0.450 -0.263 1.068 0.767 0.356 0.536 -0.498 2.173 0.711 -1.272 1.471 2.215 0.937 -1.563 -0.940 0.000 0.833 1.007 1.413 0.000 0.697 0.894 0.998 1.079 1.071 0.793 0.824 +0 3.409 -0.390 0.291 1.448 0.591 1.895 -0.905 -1.437 1.087 0.379 0.392 -0.469 2.215 0.766 1.237 -1.177 0.000 0.658 -0.812 -0.843 0.000 0.757 1.027 0.986 0.884 1.300 1.594 1.191 +1 1.703 -0.370 -1.020 0.819 -1.061 0.637 0.590 0.093 2.173 1.171 0.081 0.789 0.000 0.760 0.355 1.075 2.548 0.829 0.163 -1.733 0.000 0.982 0.929 0.976 0.900 0.677 0.805 0.803 +0 6.067 -0.106 0.526 1.353 -0.793 2.974 -0.253 -0.876 2.173 1.201 -1.684 1.612 0.000 1.581 -0.873 -1.261 0.000 0.753 -0.254 0.927 3.102 1.390 1.065 3.682 3.566 1.582 2.186 2.097 +1 0.643 -1.963 0.790 0.862 -1.660 0.762 0.236 -0.785 2.173 0.378 -1.384 1.607 0.000 1.146 -0.073 0.562 2.548 0.462 -2.013 0.229 0.000 0.587 0.840 0.984 1.962 1.107 1.468 1.101 +1 0.625 0.939 0.327 1.252 -0.750 1.544 -0.923 -1.731 0.000 0.737 -0.239 -0.549 0.000 1.742 -0.626 0.659 2.548 1.876 -0.851 0.109 3.102 0.936 0.986 1.011 1.279 0.697 1.086 0.897 +1 2.660 0.241 -1.365 0.623 0.512 1.043 0.742 1.058 0.000 0.918 -1.016 0.120 1.107 1.930 -1.525 -0.380 0.000 0.949 -0.525 1.488 3.102 0.628 0.722 1.770 1.516 0.814 1.004 0.991 +1 0.686 1.039 1.628 1.186 0.767 0.679 0.481 -0.771 0.000 0.907 -0.063 -0.320 2.215 1.308 -0.233 -1.415 2.548 0.545 0.385 1.043 0.000 0.927 0.818 0.987 0.963 0.972 0.827 0.732 +1 1.086 0.966 -0.067 0.992 1.197 0.911 0.217 -0.697 2.173 0.652 1.786 -1.730 0.000 1.170 0.714 1.601 0.000 1.331 0.414 0.357 3.102 0.763 1.008 1.307 1.124 0.962 0.941 0.864 +0 0.759 1.417 1.312 1.406 -1.574 0.833 1.324 -0.237 2.173 0.407 2.524 -1.313 0.000 0.628 1.438 0.412 2.548 0.730 1.594 0.091 0.000 0.958 0.885 0.991 0.771 0.509 0.818 0.704 +0 1.809 -0.096 1.147 0.382 -1.229 0.685 1.531 0.362 0.000 0.666 0.931 -1.224 2.215 0.767 0.164 -0.362 0.000 0.749 -0.776 -0.852 3.102 1.245 1.039 0.986 0.737 0.727 0.822 0.858 +1 0.965 -1.023 1.664 0.447 1.214 1.124 -0.504 0.021 2.173 0.830 -0.544 1.350 0.000 0.658 -1.076 -0.990 2.548 0.674 -1.400 -0.704 0.000 1.099 0.748 1.000 1.144 0.923 0.817 0.761 +1 0.513 0.943 1.607 1.385 -1.648 0.556 1.589 0.566 2.173 0.570 -0.014 -1.454 0.000 1.605 0.060 -0.138 0.000 0.603 0.449 0.257 1.551 1.362 0.801 0.977 0.908 0.381 0.764 1.002 +0 1.125 0.391 -1.106 0.286 -0.160 0.783 0.796 0.550 2.173 0.691 0.117 0.648 2.215 0.422 1.726 -0.894 0.000 0.781 0.946 1.600 0.000 0.812 0.925 0.980 0.795 0.389 0.728 0.679 +1 0.836 -0.253 -0.008 1.122 -1.322 0.537 -1.391 -0.035 0.000 1.045 0.046 1.432 1.107 0.756 1.400 0.595 0.000 0.586 0.869 -0.921 3.102 0.763 0.841 1.242 0.935 0.705 0.785 0.726 +1 0.305 0.267 -0.111 0.678 0.813 1.114 -1.429 -1.728 0.000 1.401 -0.736 -0.603 0.000 0.515 -1.662 1.131 0.000 1.051 -0.828 0.720 1.551 0.773 0.918 0.997 0.541 0.378 0.636 0.604 +1 0.537 -0.973 -0.549 1.827 1.549 0.965 -1.384 0.461 0.000 1.491 -1.086 -1.328 2.215 1.065 1.502 -0.239 0.000 0.952 -0.937 1.169 3.102 0.484 1.362 1.302 0.683 0.836 0.840 0.799 +0 0.467 0.585 -1.522 2.223 1.341 2.053 0.858 1.129 0.000 3.993 -0.465 -0.346 0.000 1.053 -1.054 -1.441 2.548 1.037 1.484 -1.062 0.000 2.304 1.400 0.997 1.770 0.919 1.417 1.254 +0 0.886 1.185 1.065 1.818 0.495 1.086 -0.379 -0.803 2.173 0.409 0.327 1.451 0.000 0.775 0.556 0.115 1.274 1.163 1.320 -1.626 0.000 1.003 0.765 0.984 2.125 1.029 1.342 1.083 +1 1.226 -0.925 -1.634 1.048 0.239 0.872 -0.574 0.053 2.173 0.849 -0.658 -1.257 0.000 1.116 -0.064 0.806 2.548 0.816 -1.385 1.576 0.000 0.839 0.920 1.559 0.994 0.833 0.823 0.731 +0 0.645 -0.635 0.250 1.541 -0.836 0.607 -1.067 1.393 0.000 0.727 -0.678 0.672 1.107 0.715 -0.139 -0.664 2.548 0.412 0.189 -1.465 0.000 0.942 0.844 1.146 0.882 0.746 0.629 0.682 +1 1.227 0.305 -0.007 0.053 1.055 1.924 -1.222 1.077 0.000 2.580 1.001 -0.383 2.215 1.558 1.097 -1.411 0.000 1.262 0.261 -1.116 3.102 0.558 0.593 0.937 1.211 1.163 0.913 0.918 +0 3.000 0.595 -1.143 0.286 0.726 0.985 -1.055 0.158 0.000 0.528 -0.814 1.258 2.215 0.335 2.681 -0.565 0.000 0.695 0.369 -1.694 0.000 0.966 1.209 1.274 1.492 0.440 1.057 1.042 +1 0.825 0.634 0.124 0.545 -0.789 1.098 -0.240 -1.390 1.087 1.113 0.281 1.102 0.000 1.077 0.234 0.530 1.274 1.329 0.087 -0.431 0.000 1.563 0.994 0.980 0.982 1.380 0.974 0.839 +0 2.047 -0.013 -0.207 0.522 -0.897 0.932 -1.085 1.225 1.087 1.249 -0.972 1.545 0.000 0.986 -0.543 -1.440 2.548 1.034 0.003 0.069 0.000 1.380 1.118 0.983 1.512 0.858 1.056 1.018 +0 2.845 -1.917 -0.008 0.431 -0.350 1.209 -0.040 1.537 2.173 0.425 -0.941 -1.648 2.215 0.609 -0.593 -0.113 0.000 0.740 0.327 -1.468 0.000 0.805 0.940 0.976 0.968 0.580 1.455 1.154 +1 1.950 0.475 0.793 0.076 -1.070 0.575 -2.178 -1.684 0.000 0.962 -0.113 -0.747 1.107 0.336 1.134 -0.055 0.000 0.874 -0.009 0.266 3.102 0.795 0.989 0.987 1.119 0.657 0.843 1.076 +0 1.075 -0.872 1.082 0.660 0.189 1.434 -1.452 -1.460 2.173 1.472 -0.490 0.051 2.215 0.457 -0.861 -0.822 0.000 0.888 -1.148 1.308 0.000 0.678 0.926 0.986 1.198 2.348 1.226 0.930 +1 0.333 1.779 0.070 1.431 -0.077 1.082 -0.065 -0.269 0.000 2.209 0.935 -1.606 0.000 2.272 0.104 0.976 2.548 1.346 0.864 0.484 3.102 1.210 1.360 0.975 1.129 0.859 1.166 1.101 +1 0.455 -1.187 0.661 2.285 0.138 1.165 -0.943 1.411 0.000 2.011 0.426 -0.801 2.215 0.727 0.666 1.662 2.548 0.908 0.857 0.642 0.000 2.082 1.331 0.987 1.551 1.041 1.370 1.281 +0 1.336 0.635 -0.449 0.724 0.378 1.004 -0.661 1.712 0.000 0.408 0.208 -1.299 0.000 0.925 -0.108 0.537 2.548 0.685 -0.817 -1.265 3.102 0.836 0.990 0.987 0.784 0.665 0.616 0.762 +0 0.699 -0.868 0.562 1.028 1.283 0.716 -0.144 -0.818 0.000 0.894 -0.264 1.654 2.215 1.019 0.554 -0.310 1.274 0.612 0.166 0.478 0.000 0.946 0.910 0.986 1.364 1.095 1.016 0.912 +0 0.899 -1.004 -0.112 0.621 0.868 0.861 -1.034 -1.707 1.087 0.942 -0.486 0.195 2.215 0.580 0.883 -0.144 0.000 1.289 -0.169 -1.310 0.000 1.017 0.975 0.989 0.973 1.362 0.922 0.891 +0 1.803 -0.892 0.053 0.736 -0.340 1.116 -1.420 -0.361 0.000 2.177 -0.331 1.409 2.215 1.370 -1.347 -1.695 2.548 0.415 -1.849 -1.699 0.000 1.058 1.147 0.986 1.804 1.264 1.314 1.219 +1 0.518 -0.616 0.407 0.686 -1.274 1.295 0.813 0.575 0.000 1.403 -1.609 1.461 0.000 3.306 -1.150 -0.372 1.274 2.111 -0.349 -1.233 1.551 0.579 1.071 0.986 1.336 1.664 1.263 1.105 +0 0.530 -0.330 -0.209 0.772 -1.498 0.330 -0.054 -1.228 0.000 1.242 0.192 -0.376 2.215 1.449 -0.409 1.246 1.274 0.428 2.266 1.434 0.000 1.145 1.146 0.986 0.862 1.493 1.021 0.836 +0 0.973 -1.433 0.925 0.681 -0.881 0.930 0.844 -1.415 2.173 0.756 0.122 -0.451 0.000 0.936 0.524 0.684 2.548 0.452 -0.253 1.380 0.000 0.927 1.026 1.126 1.097 1.115 1.199 1.077 +0 1.442 0.728 0.963 0.074 -1.734 0.840 0.090 -1.242 2.173 0.534 0.448 -0.247 0.000 1.250 0.016 0.201 2.548 0.647 -2.187 -0.948 0.000 0.853 0.904 0.984 0.760 1.231 0.826 0.708 +1 0.951 2.086 0.781 0.506 -0.813 1.241 0.934 -0.073 2.173 0.898 -2.554 1.363 0.000 0.746 0.347 -0.784 0.000 1.348 1.558 -1.684 0.000 1.207 0.766 0.988 1.017 0.945 0.841 0.782 +0 0.407 -0.196 -0.381 1.596 -1.649 0.908 1.446 0.231 0.000 0.731 -1.539 1.512 0.000 0.495 -0.789 0.203 0.000 0.677 2.424 -0.132 0.000 0.917 0.852 1.015 0.896 0.597 0.907 0.792 +0 1.620 -0.612 -0.866 0.129 1.166 0.573 -0.460 0.499 2.173 0.801 0.472 -1.462 0.000 0.378 0.110 -0.326 0.000 1.540 0.642 1.048 3.102 0.737 0.903 0.992 1.208 0.806 0.892 0.789 +0 1.027 2.419 -0.386 2.278 0.987 0.735 1.310 0.744 1.087 0.569 1.418 1.345 0.000 1.273 0.556 1.563 2.548 0.471 -0.377 0.464 0.000 0.889 0.719 2.004 1.749 0.921 1.221 1.040 +0 0.630 -0.699 0.366 0.684 -0.442 0.802 0.057 0.522 0.000 1.350 -1.436 -1.481 2.215 0.529 -0.602 1.606 2.548 0.548 -0.404 -1.031 0.000 1.036 0.752 0.994 1.357 0.491 0.885 0.848 +0 0.287 -0.084 0.945 1.606 1.096 2.767 -1.438 1.397 2.173 3.320 -0.742 -0.536 0.000 1.811 -0.781 -0.066 2.548 1.377 -1.342 -0.521 0.000 1.218 1.073 0.984 0.939 2.829 2.267 1.790 +0 1.830 -1.026 -1.290 0.601 -1.187 1.151 -0.762 0.224 2.173 1.188 -0.612 1.176 2.215 0.404 -0.274 -0.567 0.000 0.408 -1.537 0.183 0.000 0.474 0.729 0.980 1.413 1.307 1.116 0.841 +1 1.284 -0.304 -1.009 1.383 1.674 1.039 -1.248 -0.509 0.000 0.718 0.078 -0.588 0.000 1.188 -0.231 0.254 2.548 2.875 -0.040 1.029 3.102 0.824 1.069 1.222 1.094 0.920 0.953 0.835 +0 1.873 -0.405 -0.734 5.037 -0.783 2.091 -1.926 0.908 0.000 0.794 1.952 0.919 0.000 1.490 0.587 1.362 2.548 1.089 0.420 -1.185 3.102 0.896 0.935 1.017 0.781 0.732 1.226 1.551 +1 0.941 0.719 0.277 2.166 0.508 1.637 -1.743 -1.687 0.000 1.249 -0.130 -0.279 0.000 0.685 -0.511 1.234 2.548 1.235 0.005 -1.189 3.102 0.991 0.926 0.996 1.069 0.609 0.765 0.767 +0 2.112 0.773 -1.230 0.451 0.127 0.948 -0.661 0.476 1.087 0.323 0.727 1.659 0.000 0.480 -0.260 1.415 2.548 0.562 -1.530 -0.449 0.000 1.072 0.998 1.272 0.795 0.650 1.017 0.884 +1 1.164 1.204 -0.343 1.028 0.109 1.750 1.018 -0.532 0.000 1.141 0.499 1.572 0.000 1.365 0.763 1.015 2.548 0.898 1.016 1.642 0.000 0.482 0.642 0.983 0.975 0.352 0.624 0.717 +0 0.623 0.531 0.124 1.550 1.079 0.898 0.180 1.689 1.087 1.272 -0.578 -0.446 2.215 0.696 -0.136 0.290 0.000 0.711 0.746 -1.622 0.000 0.877 0.958 1.032 0.908 1.602 1.069 0.859 +1 0.322 1.112 -0.512 1.270 0.863 0.491 -1.520 -0.488 0.000 1.224 -0.968 -1.178 0.000 1.152 -0.406 1.026 1.274 1.006 0.803 0.041 3.102 1.058 1.245 0.984 0.696 0.896 1.036 1.532 +0 0.824 0.028 1.210 1.445 0.720 1.552 1.340 -0.965 2.173 2.269 0.800 0.531 1.107 1.594 0.769 -1.359 0.000 0.810 -1.336 -1.075 0.000 0.816 0.873 0.999 0.809 2.792 1.487 1.222 +1 0.612 1.032 -0.800 0.748 1.451 0.852 0.642 0.980 0.000 1.496 0.254 -0.794 2.215 1.131 0.540 0.007 2.548 0.859 1.372 0.467 0.000 0.897 0.874 0.989 0.830 0.942 0.977 0.831 +1 0.727 -0.875 -1.518 0.727 -0.390 0.900 0.075 0.537 2.173 0.674 0.996 -0.795 1.107 0.408 0.889 1.239 0.000 0.462 -0.494 -0.011 0.000 0.600 0.620 0.990 0.945 1.208 0.813 0.645 +1 1.042 -1.986 -1.397 0.952 -0.878 0.899 -1.170 0.376 2.173 0.501 -0.424 0.671 0.000 0.738 2.352 -1.695 0.000 0.811 -1.434 -0.021 0.000 0.854 0.807 0.976 0.792 0.641 0.816 0.698 +0 0.999 1.453 -1.555 0.359 0.825 0.825 1.195 0.438 2.173 1.129 0.549 1.244 2.215 1.372 1.919 -0.614 0.000 1.122 0.726 -0.320 0.000 0.970 1.099 0.991 0.713 1.050 1.035 0.885 +0 3.618 -0.465 0.565 4.835 0.336 4.140 0.036 -1.378 0.000 0.352 0.531 1.739 0.000 1.077 -0.133 -0.872 2.548 0.950 -0.229 -0.157 3.102 1.066 1.048 1.185 0.881 0.467 1.096 2.152 +0 0.332 1.310 -1.286 1.285 -0.030 2.277 0.658 -0.628 2.173 2.259 1.037 1.217 2.215 0.781 1.856 1.392 0.000 0.820 0.134 1.215 0.000 0.929 0.859 0.988 1.746 3.391 1.844 1.422 +0 0.571 1.152 0.449 0.791 1.593 0.531 0.073 0.775 2.173 0.722 1.570 -1.247 0.000 1.517 0.635 -0.640 2.548 0.605 -1.621 0.705 0.000 2.624 1.605 0.989 0.881 1.127 1.140 0.934 +0 0.478 1.680 1.198 2.412 1.349 1.775 -1.052 -0.650 2.173 1.017 0.053 0.900 2.215 1.296 -0.732 -0.072 0.000 0.446 -1.087 -1.197 0.000 0.743 0.988 0.990 6.583 2.268 4.134 3.190 +0 1.328 0.701 0.777 0.836 -0.270 1.105 0.059 -0.799 2.173 0.241 -1.497 0.845 0.000 0.315 -0.604 1.663 0.000 0.841 0.625 1.336 3.102 0.365 0.912 1.181 0.691 1.022 0.844 0.787 +1 0.895 0.999 0.482 0.778 -1.540 1.073 0.445 0.691 0.000 1.331 0.729 -1.598 1.107 0.720 0.881 0.049 0.000 1.744 0.309 -0.704 1.551 0.960 1.157 1.120 0.858 1.026 1.018 0.865 +1 1.148 -0.766 1.489 1.176 0.647 1.364 0.217 -0.663 2.173 0.945 -0.117 1.154 0.000 0.761 0.970 0.377 2.548 0.737 -0.528 -1.308 0.000 0.912 0.914 1.107 1.598 1.163 1.165 0.989 +0 0.664 -0.190 -0.437 1.647 -1.126 0.681 0.062 0.669 0.000 1.138 0.301 -0.483 0.000 2.077 -0.143 1.180 2.548 0.495 0.566 0.213 3.102 1.629 0.875 0.986 1.287 0.680 0.864 0.886 +0 1.102 1.059 0.257 0.428 1.388 0.595 0.527 -0.508 1.087 0.577 0.528 0.054 0.000 1.268 0.743 1.722 2.548 0.635 1.360 -1.128 0.000 0.824 0.829 0.991 0.742 0.991 0.700 0.619 +1 0.517 -0.862 0.411 0.951 0.500 1.804 -0.179 -0.641 0.000 1.324 1.172 1.297 2.215 1.105 0.230 0.732 0.000 2.076 0.800 -1.547 3.102 0.968 1.281 0.990 1.046 0.839 1.003 0.904 +1 0.881 -0.557 0.838 2.421 1.349 3.062 -0.162 -0.253 0.000 1.425 -0.508 1.668 0.000 1.334 -0.248 -1.198 2.548 1.484 0.373 1.282 3.102 4.452 2.613 0.996 0.627 0.935 1.712 1.557 +1 1.176 -0.152 0.820 0.838 -0.125 0.646 1.004 0.932 2.173 0.892 -1.545 -0.046 0.000 1.731 1.129 -0.870 0.000 1.762 0.580 1.607 3.102 1.003 1.005 1.034 0.866 0.669 0.794 0.898 +1 0.917 0.023 -1.116 1.062 1.622 0.773 -0.634 1.183 0.000 1.307 0.055 0.939 0.000 1.712 0.154 -0.453 0.000 1.092 -0.298 -0.752 3.102 0.926 0.980 0.991 0.845 0.492 0.680 0.707 +1 0.285 1.180 -1.395 0.874 -0.100 1.047 -2.411 -1.456 0.000 0.906 -0.403 -1.228 0.000 1.665 -1.129 0.549 1.274 1.266 -0.444 1.465 0.000 0.918 0.992 0.995 0.943 0.597 0.856 0.766 +0 0.615 0.321 -1.440 0.940 0.117 0.659 -1.756 -0.873 0.000 0.574 -1.221 0.205 2.215 0.429 -0.208 1.479 0.000 1.329 1.066 1.215 3.102 1.152 0.881 1.039 0.785 1.434 1.158 0.983 +0 0.849 0.025 0.907 0.311 -0.883 0.814 0.406 0.112 0.000 0.390 -2.292 0.965 0.000 1.352 0.548 -1.257 2.548 0.413 1.892 -0.914 0.000 0.885 0.996 0.992 0.926 1.063 0.845 0.751 +0 0.294 -0.629 0.163 2.151 1.221 1.490 -0.007 -0.838 1.087 0.850 0.225 0.611 1.107 0.867 -0.469 -0.329 0.000 0.511 0.436 -1.707 0.000 0.801 0.809 0.982 1.064 1.610 1.363 1.054 +1 0.620 0.702 0.930 0.622 -0.923 1.023 0.645 -1.191 2.173 0.534 1.050 0.506 0.000 1.407 -0.002 0.496 0.000 1.170 0.452 -0.045 0.000 0.769 0.991 0.985 0.839 0.797 0.928 0.835 +0 0.548 -0.322 0.010 1.802 -1.035 0.598 1.508 0.557 1.087 0.970 0.880 1.381 2.215 0.521 -0.267 -0.805 0.000 0.396 2.116 -1.478 0.000 1.013 0.936 1.113 1.221 0.835 1.103 0.923 +0 2.205 -0.455 -0.427 0.647 -0.381 0.949 -1.460 -1.681 2.173 1.238 -1.046 1.401 0.000 1.317 -1.515 1.001 0.000 1.864 -0.953 -0.049 3.102 0.905 0.914 0.991 0.661 1.420 1.017 1.096 +0 0.721 0.826 -1.392 1.266 1.213 1.075 1.297 0.154 0.000 0.837 -0.933 -1.317 0.000 1.256 -1.335 -0.773 1.274 1.878 -0.874 -0.271 3.102 0.722 2.096 0.990 1.424 0.569 1.793 1.475 +1 0.721 0.562 -0.322 1.328 -1.392 0.858 0.966 0.977 2.173 0.994 1.559 -0.548 0.000 1.513 -0.135 0.304 2.548 0.837 -0.252 1.456 0.000 0.800 0.881 1.113 1.063 1.166 0.946 0.776 +1 0.790 -0.268 -1.410 0.680 -0.373 0.989 -0.245 0.137 0.000 1.361 0.623 -1.661 2.215 0.775 0.082 0.790 2.548 0.575 0.437 -0.362 0.000 0.677 0.640 0.981 1.120 0.927 0.903 0.852 +0 0.618 -1.474 0.951 0.628 -0.559 0.960 -1.308 1.498 2.173 0.710 0.138 1.406 0.000 1.552 0.321 -0.597 2.548 1.267 -0.188 -0.431 0.000 0.879 0.803 0.990 0.913 2.030 1.269 1.085 +1 0.792 0.723 -0.290 0.843 0.910 0.680 1.042 1.454 0.000 1.246 1.393 -0.379 0.000 1.099 0.319 -1.216 1.274 0.720 0.775 0.322 3.102 1.983 1.134 1.000 0.800 0.698 0.839 0.744 +0 1.860 1.421 1.642 1.251 -0.864 0.983 -0.763 0.147 1.087 0.814 -0.613 0.684 0.000 0.306 0.007 1.657 0.000 0.616 -0.032 -1.125 3.102 0.639 0.739 1.635 0.941 0.811 1.467 1.209 +0 2.804 0.565 -1.336 1.919 -0.972 1.694 0.491 0.428 2.173 1.404 0.029 0.801 2.215 0.502 -0.445 -0.925 0.000 0.517 -0.515 -0.082 0.000 0.389 1.024 1.037 1.822 0.918 1.705 1.265 +0 2.910 1.083 1.245 1.379 1.296 2.551 -0.269 -0.662 0.000 0.688 1.028 0.558 2.215 0.565 -0.904 0.096 2.548 0.665 -1.428 0.953 0.000 2.519 1.474 0.982 0.847 0.848 1.272 1.889 +1 0.861 -0.359 0.845 0.846 0.155 0.820 -1.640 -0.177 0.000 1.087 -1.049 -1.540 0.000 1.652 -1.230 1.515 0.000 0.968 0.549 0.551 3.102 0.826 0.651 0.985 0.783 0.586 0.812 0.790 +0 0.956 -0.152 -1.150 0.555 -1.418 1.297 -1.710 -0.251 0.000 1.170 -0.117 0.858 0.000 1.392 0.198 1.643 2.548 0.711 -0.598 -1.183 1.551 1.068 0.921 0.990 0.882 0.563 1.169 0.985 +0 0.596 -1.391 1.520 1.163 -0.743 1.394 0.759 0.781 0.000 0.900 -0.589 -0.682 1.107 1.520 -1.228 0.126 2.548 0.853 0.264 1.446 0.000 1.012 1.611 1.029 0.872 0.953 1.396 1.300 +1 1.526 -0.977 0.157 0.228 0.633 1.495 0.267 -0.743 0.000 1.683 -1.606 1.627 0.000 0.707 -2.103 1.176 0.000 0.945 -1.581 0.180 0.000 0.866 0.844 0.988 1.050 0.639 1.056 0.977 +0 0.593 0.032 0.394 0.441 0.554 0.380 -0.361 1.396 0.000 0.892 0.491 -1.609 0.000 1.050 0.866 0.114 0.000 1.215 0.780 -1.108 3.102 0.958 0.669 0.991 0.555 0.175 0.495 0.555 +0 0.986 -0.946 -1.270 0.806 0.198 0.852 0.076 -0.508 2.173 0.750 0.074 -1.498 0.000 0.867 0.000 0.397 2.548 0.497 -1.956 0.656 0.000 1.426 1.057 1.198 0.956 0.780 0.856 0.786 +1 0.921 0.034 0.288 0.574 1.675 0.503 0.319 -0.303 0.000 0.805 -0.121 1.683 2.215 1.029 0.927 1.167 2.548 0.885 -0.516 -0.630 0.000 0.579 0.994 0.988 0.695 0.727 0.741 0.666 +1 0.611 1.623 -0.025 0.763 1.499 0.712 0.973 0.684 0.000 0.909 1.810 -1.112 0.000 1.402 0.022 -1.252 2.548 0.952 0.029 0.121 3.102 0.857 1.113 0.990 0.700 0.834 0.712 0.682 +0 1.207 -0.395 -1.344 0.876 -0.429 0.853 1.022 0.542 2.173 0.410 -1.478 1.344 0.000 0.531 -0.216 -0.400 2.548 0.454 -0.115 -1.573 0.000 0.503 1.235 1.047 0.558 0.848 0.869 0.784 +1 0.561 0.541 -0.475 0.494 0.499 1.439 0.775 0.797 0.000 1.218 0.362 -1.458 0.000 1.705 -0.470 -0.501 2.548 1.245 -0.982 -1.260 3.102 2.583 2.069 0.982 0.708 0.797 1.515 1.187 +1 1.959 0.669 1.468 0.814 0.414 0.773 0.094 -0.952 2.173 1.370 -0.065 0.060 2.215 0.785 -0.271 1.280 0.000 0.432 2.020 -1.011 0.000 1.300 1.092 1.422 1.336 1.204 1.091 0.966 +0 0.543 0.367 -1.562 0.658 1.292 1.034 0.882 0.056 2.173 1.147 -1.689 -1.376 0.000 0.511 -2.201 -1.110 0.000 0.450 -0.744 0.245 0.000 1.016 0.687 0.999 1.074 0.870 1.320 1.362 +0 1.132 0.770 -1.601 2.048 1.131 0.575 -0.119 -0.270 0.000 0.856 0.511 -0.576 2.215 1.056 -1.033 -0.761 0.000 1.242 0.393 0.474 3.102 0.955 1.007 1.326 1.208 0.755 0.866 1.028 +1 0.992 0.323 1.360 1.309 1.312 0.792 -0.236 -0.180 2.173 0.714 1.669 0.099 0.000 0.366 0.148 0.004 2.548 0.494 -0.639 -1.375 0.000 1.269 1.181 0.991 0.682 0.175 0.761 0.865 +0 1.263 0.591 1.721 0.541 -1.539 0.917 -0.626 0.873 2.173 0.972 -0.950 0.014 0.000 0.949 0.442 -0.122 0.000 1.157 -0.602 -1.547 3.102 0.886 0.838 0.975 1.029 0.894 0.808 0.780 +1 0.613 0.834 1.542 1.257 0.423 1.187 0.826 -1.292 0.000 1.176 0.306 0.291 1.107 0.461 0.964 -0.492 0.000 1.263 -0.080 1.586 3.102 0.891 0.908 1.029 0.735 1.037 0.941 0.848 +0 1.766 -0.540 1.634 0.886 1.251 0.822 0.443 -0.761 0.000 0.932 0.486 0.327 2.215 0.277 0.057 0.010 1.274 0.411 1.708 -0.231 0.000 0.890 0.952 0.994 0.688 0.193 0.840 0.962 +1 0.380 0.822 -0.459 1.136 1.030 0.731 0.463 -1.290 0.000 0.926 -0.227 0.281 2.215 0.993 0.536 1.547 0.000 1.876 -0.024 -0.391 3.102 0.939 0.956 0.984 1.115 0.688 0.908 0.803 +1 1.031 -0.232 1.388 0.919 1.384 2.153 0.966 -0.186 0.000 1.244 1.030 -1.638 1.107 1.028 -0.378 -1.479 2.548 1.241 0.707 1.205 0.000 1.265 1.002 0.991 1.534 0.985 1.020 0.949 +1 0.766 -0.842 0.669 1.097 -0.613 0.959 -0.425 -0.203 2.173 0.470 1.606 1.172 0.000 1.423 0.735 -1.590 0.000 0.806 -1.824 1.458 0.000 0.937 0.637 1.161 0.781 0.691 0.959 0.967 +0 3.148 -1.131 0.928 3.036 0.855 2.728 0.630 -0.797 1.087 0.728 -1.028 1.383 2.215 0.600 1.368 -1.094 0.000 1.221 0.215 -0.431 0.000 0.821 0.873 0.988 0.803 2.750 3.214 2.472 +0 0.652 2.075 0.301 1.056 -0.176 0.761 -2.139 -0.095 0.000 1.745 0.164 -1.541 2.215 0.490 1.889 -1.521 0.000 1.363 1.005 -1.522 1.551 4.618 3.088 0.983 1.495 0.755 2.050 1.846 +0 1.365 -0.329 -1.385 0.978 -0.489 0.604 0.335 0.728 0.000 1.087 0.735 1.613 2.215 1.603 0.065 -0.537 0.000 1.375 1.136 0.658 1.551 1.625 1.251 1.157 1.104 0.903 0.996 0.988 +0 0.414 1.082 -0.073 0.779 -1.015 0.867 0.837 0.944 0.000 0.639 0.331 1.341 2.215 1.114 0.017 -0.787 2.548 0.488 -1.642 0.905 0.000 0.500 0.760 0.982 0.565 0.856 0.632 0.595 +1 1.443 0.081 1.518 0.492 -0.696 0.874 -0.659 0.523 2.173 0.970 1.618 -0.069 0.000 1.579 0.947 -1.335 2.548 0.537 0.431 -0.922 0.000 0.867 1.013 1.064 1.032 2.022 1.258 1.066 +1 0.959 0.631 0.626 1.297 -1.127 1.776 0.034 1.244 2.173 1.614 -1.275 -0.638 0.000 1.121 0.037 -0.239 0.000 1.118 -0.052 0.511 0.000 0.829 0.933 1.546 1.436 0.946 0.996 1.042 +1 1.024 0.823 1.306 0.614 -0.710 1.011 0.085 -0.113 0.000 1.355 0.340 -1.386 2.215 1.332 1.536 0.826 2.548 0.582 -0.634 -0.690 0.000 0.870 0.993 1.066 0.817 1.658 0.907 0.767 +0 0.398 1.393 0.134 0.795 0.231 0.499 0.779 -0.254 0.000 0.733 0.096 1.676 2.215 0.772 1.431 1.270 1.274 0.701 0.409 -1.421 0.000 0.956 1.070 0.999 0.896 0.704 0.728 0.759 +1 1.459 -0.547 0.110 0.413 -1.093 1.113 -0.163 -0.666 2.173 0.903 -0.139 0.892 0.000 0.985 2.192 -1.390 0.000 1.015 -1.096 1.263 0.000 0.892 0.826 0.986 0.859 0.867 0.884 0.806 +1 0.493 -0.955 -1.647 1.599 0.260 0.941 -0.965 1.318 0.000 1.555 0.627 -0.940 0.000 1.586 -0.640 -0.041 1.274 0.783 -1.401 -1.542 0.000 0.845 0.706 1.216 0.773 0.697 0.796 0.744 +1 0.953 -0.214 -1.640 0.630 -0.143 0.994 0.531 -1.572 0.000 1.183 -1.841 -0.164 0.000 2.359 0.271 0.646 2.548 1.014 0.662 -0.970 3.102 4.168 2.462 1.047 1.032 1.211 1.833 1.381 +1 0.690 0.073 -0.358 0.712 -1.421 0.696 -0.193 1.278 0.000 0.911 1.353 0.614 1.107 0.596 0.921 -0.608 2.548 0.379 1.303 -0.896 0.000 0.862 1.016 0.980 0.702 0.714 0.835 0.717 +1 0.837 -0.660 -0.878 0.797 -1.566 1.424 -0.747 -0.627 2.173 0.912 -0.623 1.347 0.000 0.739 0.024 0.358 2.548 1.156 -1.230 1.050 0.000 0.675 0.796 0.988 1.001 1.116 1.018 0.911 +1 0.455 -2.335 -1.625 1.149 0.431 1.018 -0.877 -1.405 2.173 0.617 -1.589 0.333 0.000 0.833 -1.966 -0.548 0.000 0.724 -0.047 1.135 1.551 0.837 0.913 0.989 1.183 0.786 0.856 0.810 +0 0.278 1.453 -1.511 2.099 -0.632 1.225 -0.131 1.121 2.173 0.943 -0.911 -1.204 0.000 0.865 -0.764 0.191 0.000 0.616 0.104 1.663 3.102 1.320 0.859 0.987 1.505 0.447 0.941 0.970 +0 2.265 -1.757 -0.714 0.806 -0.618 1.390 -0.433 1.142 2.173 0.940 -1.120 -0.305 1.107 0.472 2.270 1.601 0.000 0.436 -1.739 0.499 0.000 2.481 2.014 0.985 0.861 1.735 1.580 1.963 +0 1.511 -0.571 -0.841 3.200 -0.505 2.003 -2.480 1.124 0.000 1.232 -0.015 -0.219 0.000 1.349 1.177 1.361 1.274 1.762 0.235 1.220 3.102 1.428 1.279 0.996 2.343 0.639 1.671 1.415 +0 1.460 -0.679 -1.205 0.550 1.183 0.963 -1.503 -0.133 2.173 0.904 -0.649 0.959 2.215 0.449 1.168 1.454 0.000 0.431 -1.594 1.726 0.000 0.718 0.811 1.038 0.846 1.291 0.925 0.739 +0 0.707 -0.365 -0.849 0.528 -0.872 0.202 -1.131 0.550 0.000 0.551 -0.005 0.506 2.215 0.370 -1.459 1.690 2.548 0.408 -1.730 0.927 0.000 0.478 0.529 0.978 0.547 0.598 0.592 0.491 +1 1.283 0.246 -0.730 0.757 -0.500 1.092 -0.297 1.398 2.173 0.971 -1.796 -1.065 0.000 1.445 -0.090 0.664 1.274 0.988 -0.787 0.154 0.000 1.283 1.453 0.989 1.476 0.975 1.157 1.303 +0 1.075 1.250 -0.088 0.981 1.190 0.766 0.624 1.260 1.087 1.473 0.879 -0.849 0.000 0.483 -0.602 0.590 0.000 0.374 0.018 0.398 3.102 1.674 0.950 1.299 0.930 0.435 0.816 0.811 +0 0.574 -1.110 1.061 1.313 -0.371 0.664 0.733 -1.635 2.173 0.639 -2.206 -0.650 0.000 1.122 -0.043 1.164 2.548 0.597 -0.105 0.312 0.000 1.205 1.256 1.155 1.338 0.759 1.132 1.002 +1 0.812 0.883 -1.651 0.479 -0.442 0.824 0.736 -0.537 0.000 1.140 0.584 0.911 0.000 0.649 -1.313 0.870 2.548 1.115 0.573 -1.425 3.102 1.521 1.071 0.989 1.328 1.008 0.916 0.946 +1 1.084 1.831 -1.312 0.792 -0.704 0.550 0.742 0.246 0.000 0.561 0.238 -0.470 1.107 0.552 0.139 1.347 2.548 1.189 -0.563 1.011 0.000 1.056 0.857 0.989 0.777 0.591 0.619 0.706 +1 0.596 -1.860 1.307 0.964 -1.402 0.667 0.614 0.534 1.087 0.431 0.099 0.916 0.000 0.633 1.008 -1.317 2.548 0.925 0.805 -0.609 0.000 0.886 0.711 0.979 1.004 0.830 0.950 0.816 +0 1.242 0.717 -0.164 0.667 -0.984 0.662 0.844 -1.482 2.173 0.750 -0.283 1.444 2.215 1.191 -0.707 0.155 0.000 0.374 -0.665 1.508 0.000 0.691 1.166 0.989 0.936 0.802 0.781 0.773 +0 1.830 0.236 0.039 1.430 -0.142 1.040 -0.831 1.678 0.000 0.868 -0.133 -1.180 2.215 1.221 -0.241 1.342 0.000 0.554 -0.946 0.875 3.102 0.845 0.959 0.980 0.745 0.684 0.772 0.978 +0 1.269 0.041 -1.239 1.690 -0.722 1.347 0.915 -1.022 0.000 1.165 -1.096 0.618 2.215 1.505 -0.247 0.368 0.000 2.723 -0.177 0.979 3.102 0.759 0.822 0.984 1.613 0.919 1.287 1.027 +0 0.802 -0.011 -0.135 1.578 0.973 0.704 -0.933 1.249 2.173 0.754 0.409 -0.745 0.000 0.781 1.651 -0.811 0.000 1.126 1.201 -1.361 3.102 0.890 0.670 1.311 0.960 1.559 1.115 1.038 +1 1.021 -0.482 -0.027 1.195 -1.223 0.555 -1.247 0.522 0.000 0.794 0.484 1.425 2.215 0.641 -1.858 -0.334 0.000 1.284 -0.773 -1.525 3.102 0.863 0.906 1.348 1.049 0.824 0.915 0.860 +1 0.381 -0.514 0.996 1.168 1.562 0.569 -2.456 -0.025 0.000 1.022 -0.725 -0.457 2.215 1.221 0.630 0.502 0.000 2.809 0.878 1.735 0.000 0.792 0.763 0.980 1.255 1.109 0.945 0.862 +1 0.640 -0.490 -0.596 0.723 1.635 1.690 0.027 0.142 0.000 1.304 -0.819 -1.177 0.000 1.765 0.224 1.474 2.548 1.075 -1.213 -0.636 0.000 0.868 0.876 0.984 0.760 0.406 0.905 0.769 +1 0.295 1.251 0.264 2.782 1.122 0.417 -0.247 -0.206 2.173 0.822 1.009 -1.059 2.215 0.469 1.438 -1.375 0.000 1.461 1.324 -0.751 0.000 0.908 0.996 0.990 1.221 0.844 1.160 0.955 +1 1.495 -0.338 -1.049 1.067 -0.902 0.345 1.336 1.473 2.173 1.312 0.626 0.330 2.215 0.510 2.368 0.532 0.000 0.791 1.938 1.293 0.000 0.454 1.081 0.986 0.923 0.921 0.970 1.015 +0 0.659 0.936 -1.531 0.878 -0.677 0.714 -1.147 0.217 2.173 0.325 -1.954 -1.574 0.000 0.423 -0.376 1.603 2.548 0.546 0.206 0.947 0.000 0.868 0.826 0.990 0.830 0.700 1.236 1.091 +1 1.086 -0.797 -0.167 0.570 -1.222 0.990 -1.778 1.459 0.000 0.358 2.543 0.116 0.000 0.716 -0.101 0.458 2.548 0.705 -2.197 -1.679 0.000 0.678 0.963 0.988 0.715 0.576 0.834 0.772 +1 0.935 1.386 0.314 0.735 -1.534 1.892 0.725 0.813 1.087 1.135 -0.049 -1.257 0.000 0.497 0.862 1.026 2.548 2.018 0.889 -0.022 0.000 0.849 1.029 1.144 1.167 0.264 1.433 1.233 +0 1.013 0.642 0.595 0.476 1.088 0.822 2.012 1.408 0.000 1.645 0.881 -0.386 2.215 0.593 1.630 -0.867 0.000 1.310 0.438 -1.521 3.102 1.124 1.010 0.985 1.207 1.159 1.050 1.057 +0 0.492 -1.491 -0.259 1.513 0.715 0.761 0.454 -0.750 0.000 0.830 -1.158 1.700 1.107 1.047 -0.294 0.060 2.548 1.044 0.441 -1.605 0.000 0.948 0.981 0.985 0.853 1.082 0.909 0.925 +1 1.189 1.158 -1.077 0.895 1.303 0.864 0.852 -1.384 0.000 1.442 1.201 0.230 2.215 1.511 0.514 0.997 2.548 1.444 0.809 -0.732 0.000 0.951 1.266 1.200 1.159 1.136 1.084 0.948 +1 0.785 0.175 0.246 1.043 1.381 1.160 -2.034 -0.698 0.000 1.622 -0.842 -1.298 0.000 3.071 -1.208 0.647 2.548 1.542 0.219 -1.643 3.102 0.993 0.764 1.070 1.429 2.072 1.254 1.051 +1 0.520 0.637 1.495 1.219 -1.409 1.301 -0.523 0.278 1.087 0.746 -0.121 0.968 0.000 2.130 0.774 -1.221 0.000 1.967 0.661 0.207 3.102 0.906 1.188 0.978 1.316 1.206 1.313 1.168 +0 0.598 -1.858 -0.120 0.249 -1.365 0.331 -2.332 -1.239 0.000 0.923 0.373 1.524 2.215 0.522 -1.171 0.652 2.548 0.976 -0.581 -0.056 0.000 1.106 0.728 0.986 0.976 0.865 0.863 0.742 +0 0.645 1.182 -1.216 1.166 0.587 0.544 0.695 0.444 2.173 0.338 -2.558 -0.931 0.000 0.582 -0.128 -0.874 0.000 0.401 1.258 1.659 0.000 1.084 0.695 1.200 0.719 0.807 0.850 1.021 +0 0.552 1.397 0.414 0.884 -1.056 0.426 1.775 -0.323 0.000 0.921 -1.982 1.169 0.000 1.629 -0.163 -1.031 2.548 0.846 0.545 0.381 0.000 0.773 0.754 0.986 1.267 1.025 0.899 0.807 +0 1.453 2.074 1.410 0.411 -1.723 0.994 1.816 -0.922 0.000 0.765 1.453 0.732 2.215 0.880 0.306 -0.638 0.000 1.424 -0.200 0.687 1.551 0.849 0.982 0.995 0.726 0.926 0.873 0.851 +1 1.563 -0.440 -0.525 0.291 -1.027 0.937 0.746 1.284 0.000 0.487 -0.326 -0.300 0.000 0.857 -0.483 0.819 2.548 1.091 1.375 1.299 0.000 0.913 0.876 0.984 0.562 0.486 0.573 0.627 +0 0.616 -1.337 0.361 1.222 0.980 1.020 -0.433 -0.552 2.173 0.567 2.504 -1.572 0.000 1.157 0.088 1.524 2.548 0.711 0.658 0.351 0.000 1.173 1.226 0.984 1.613 1.344 1.336 2.007 +1 0.698 1.866 -1.198 0.694 0.469 0.418 0.794 -0.729 0.000 0.937 0.245 1.196 2.215 0.502 1.118 1.245 0.000 0.796 -1.329 -0.627 1.551 0.824 1.063 0.988 0.938 1.136 1.046 0.857 +1 0.486 0.996 -0.332 0.500 0.087 1.116 0.145 0.181 2.173 0.752 -0.105 -1.482 0.000 1.271 0.831 1.606 0.000 0.800 -1.031 1.243 0.000 0.879 1.305 0.987 0.537 0.734 0.791 0.689 +0 0.555 0.308 -1.614 1.483 -0.530 1.186 0.670 1.559 1.087 1.550 0.791 0.397 0.000 0.569 1.139 -0.434 0.000 0.594 -0.199 -0.976 3.102 1.031 0.886 1.043 1.158 0.792 0.945 0.881 +1 0.853 0.725 -0.242 1.191 0.202 1.176 1.081 -0.358 0.000 0.952 0.627 0.952 1.107 0.707 1.639 1.459 0.000 1.298 0.015 -1.662 3.102 1.010 0.977 0.978 1.120 0.779 0.868 0.808 +0 0.713 2.002 1.506 0.589 0.324 0.964 -0.887 -1.201 0.000 0.739 1.144 -0.181 2.215 1.182 0.567 0.992 0.000 1.161 0.799 -1.313 3.102 0.910 0.859 0.989 0.684 0.718 0.634 0.618 +1 0.955 1.692 -0.461 0.558 -0.006 0.977 0.592 -1.369 0.000 1.003 1.527 0.991 2.215 1.252 0.947 0.159 2.548 0.423 -1.318 1.251 0.000 1.086 0.993 0.977 0.951 0.873 0.836 0.775 +0 0.417 1.279 0.590 1.059 -0.408 1.202 -0.306 1.116 1.087 1.499 0.935 -0.514 2.215 1.081 0.351 1.150 0.000 0.524 1.908 1.201 0.000 0.917 1.156 0.989 0.775 2.379 1.265 1.041 +1 0.498 1.290 -1.594 0.869 0.886 1.033 0.239 -0.595 0.000 1.067 0.687 1.450 2.215 0.974 0.933 -0.867 0.000 1.473 0.011 0.478 3.102 0.847 1.111 0.986 0.635 0.957 0.972 0.838 +1 0.995 -0.144 -1.352 0.625 1.117 0.783 0.840 -0.587 0.000 0.647 -0.298 -0.415 2.215 1.401 0.195 1.113 1.274 0.928 -1.547 0.842 0.000 2.662 1.510 0.990 0.701 1.028 1.112 0.937 +0 0.588 0.490 -0.137 1.607 0.439 0.457 -0.449 -1.272 0.000 1.017 -1.317 -0.695 1.107 1.084 -1.229 1.550 0.000 1.156 -0.334 1.638 3.102 0.923 0.967 0.996 1.155 0.974 1.414 1.296 +1 0.361 1.296 0.253 2.219 -0.519 1.460 0.541 1.522 2.173 0.748 0.440 -1.186 2.215 1.310 -0.781 -0.054 0.000 1.239 -0.517 0.422 0.000 1.034 1.124 0.989 2.119 0.992 1.440 1.647 +0 0.522 0.673 1.375 0.810 -1.038 0.827 -1.139 0.225 2.173 0.536 -0.735 -0.198 2.215 1.062 -0.609 1.450 0.000 1.218 0.313 -1.486 0.000 0.908 0.911 0.987 1.033 0.413 0.836 0.752 +0 0.673 -0.093 -0.641 0.895 0.400 0.580 -0.460 -1.628 0.000 0.442 -0.782 -0.766 1.107 1.048 0.022 0.823 2.548 1.249 0.747 -0.067 0.000 0.999 0.876 0.988 0.718 0.780 0.671 0.687 +0 0.323 -0.612 -0.674 1.572 -1.658 1.085 -1.065 0.500 0.000 1.298 -1.658 -0.932 0.000 1.056 -0.243 0.949 2.548 1.301 -1.402 0.851 3.102 0.788 1.140 0.982 1.247 0.694 1.206 1.022 +0 0.795 0.140 -0.749 1.015 -1.068 0.493 -1.159 0.144 2.173 0.667 -0.358 -0.624 0.000 0.699 0.239 1.413 0.000 1.435 -0.861 1.190 3.102 1.065 0.924 0.975 1.341 0.722 1.098 0.908 +1 0.975 2.058 0.686 1.015 0.081 0.831 0.705 -0.999 2.173 0.593 1.054 1.663 0.000 0.716 -2.126 -1.049 0.000 1.030 1.131 0.573 0.000 0.858 0.975 0.992 1.016 0.986 0.945 0.801 +0 0.845 1.026 -0.231 1.208 0.641 1.112 2.071 -1.213 0.000 0.741 -0.818 -0.355 2.215 0.913 -0.531 1.544 0.000 1.169 0.895 1.272 0.000 0.836 0.994 0.991 0.616 0.868 0.785 0.693 +0 0.352 0.838 0.984 2.691 1.306 1.038 1.023 -0.630 2.173 0.880 0.241 -0.729 0.000 1.183 -0.001 0.413 2.548 0.868 0.122 -1.661 0.000 0.849 0.940 0.985 1.826 1.341 1.268 1.051 +0 1.195 0.272 -1.599 0.357 -1.185 0.477 -1.237 1.368 2.173 0.810 0.329 -0.163 0.000 1.148 -0.846 0.348 2.548 0.474 0.662 1.223 0.000 0.788 1.027 0.999 1.264 0.748 1.001 0.861 +0 2.342 -1.590 1.625 0.750 -1.145 1.513 0.440 -0.019 2.173 0.600 -0.055 0.146 0.000 1.147 -0.900 -1.612 2.548 0.371 -0.439 -1.550 0.000 0.632 0.760 1.106 2.743 2.069 1.794 1.322 +0 0.500 -0.872 1.264 0.767 -1.713 1.014 -0.311 0.633 2.173 1.145 -0.142 -1.509 0.000 1.050 0.233 -0.153 1.274 1.684 -1.317 -0.525 0.000 1.022 1.028 0.990 1.393 0.915 1.165 1.148 +0 0.281 0.263 1.035 2.106 -1.301 0.952 -0.352 -0.688 2.173 0.771 -0.351 -0.023 0.000 1.716 -0.995 1.204 2.548 1.554 0.096 0.505 0.000 0.778 0.944 0.986 0.849 1.688 0.992 0.889 +0 1.464 1.744 -0.451 1.253 0.104 1.117 1.434 -1.687 0.000 1.552 0.783 0.142 1.107 1.420 0.664 1.364 2.548 1.304 0.290 -1.564 0.000 1.086 0.876 0.981 1.117 1.409 1.154 1.230 +1 1.150 -1.556 -1.051 0.368 -0.464 0.686 -0.252 -0.469 2.173 0.880 -1.415 0.361 0.000 0.705 -2.230 0.958 0.000 1.166 -0.699 -1.460 3.102 0.891 0.825 1.001 0.751 0.788 0.874 0.780 +1 0.829 0.175 -1.635 1.634 -1.267 0.485 2.156 -0.234 0.000 0.536 -0.679 0.732 0.000 0.626 1.387 0.337 0.000 1.066 -1.199 1.292 3.102 1.243 0.801 0.976 0.819 0.691 0.740 0.814 +0 0.840 -0.872 0.152 0.473 1.329 1.468 -1.238 -0.137 2.173 1.043 0.327 1.603 0.000 1.352 -0.170 -1.531 1.274 0.587 -0.644 1.243 0.000 0.679 0.587 0.990 1.051 1.934 1.262 1.010 +0 0.374 -0.832 0.956 0.923 -0.913 1.463 0.317 0.483 2.173 0.855 -0.982 -1.439 2.215 1.010 0.498 -0.739 0.000 1.367 0.239 -1.502 0.000 0.839 0.962 0.988 1.846 2.006 1.352 1.212 +1 0.875 0.761 -1.431 0.296 -0.013 0.953 0.639 0.586 0.000 1.389 1.333 -0.975 2.215 0.827 1.584 0.685 0.000 0.816 0.992 1.372 3.102 0.929 0.709 0.982 0.928 0.827 0.939 0.852 +1 1.004 2.119 1.087 0.962 -0.192 1.337 1.017 -1.592 2.173 0.932 -0.968 0.263 0.000 0.820 -1.595 0.130 0.000 0.585 1.275 -0.779 1.551 0.949 0.980 1.244 1.435 0.670 1.157 1.290 +0 0.852 0.020 0.730 0.506 -0.891 1.080 0.026 -0.156 2.173 0.883 1.895 -1.649 0.000 1.700 -1.733 -1.540 0.000 1.210 0.614 0.424 0.000 1.553 1.299 0.984 0.816 0.861 1.145 0.916 +0 0.835 -0.911 0.622 0.436 -0.916 1.437 -0.935 -0.856 0.000 1.295 -0.086 1.105 2.215 0.875 0.678 1.310 2.548 0.780 0.642 0.578 0.000 2.205 1.714 0.988 0.787 0.528 1.260 0.999 +1 1.273 0.578 -1.504 0.366 1.536 2.400 0.606 -0.424 0.000 1.108 0.053 0.323 0.000 1.946 0.526 1.486 0.000 2.766 0.983 1.287 3.102 0.937 0.908 0.981 0.792 0.770 0.678 0.641 +1 0.788 -1.615 -0.225 1.188 -0.385 2.395 -1.534 1.187 0.000 2.656 -0.660 -0.647 0.000 1.113 -0.674 0.983 2.548 0.812 0.456 -0.910 3.102 1.158 0.876 0.987 1.323 0.877 1.222 1.265 +1 0.966 0.666 0.055 1.455 0.828 0.355 2.038 0.708 0.000 0.691 -0.369 -1.654 2.215 0.862 0.788 -1.095 2.548 0.713 1.280 -1.219 0.000 0.784 1.113 1.055 0.909 0.674 0.795 0.768 +1 0.404 -1.174 -0.508 1.436 1.090 0.701 0.343 1.637 0.000 0.899 1.893 -1.050 0.000 1.101 -0.514 0.251 2.548 1.519 0.943 -0.096 3.102 0.827 1.134 1.046 1.444 0.991 0.987 0.979 +1 0.706 -0.230 1.538 0.754 1.107 0.891 -0.082 0.774 2.173 1.396 -0.678 -1.064 0.000 1.086 -1.149 -0.332 2.548 1.056 -1.679 -0.950 0.000 0.816 1.237 0.984 0.881 1.283 0.870 0.786 +1 0.996 0.662 0.845 0.471 -0.146 0.621 1.557 -1.517 0.000 1.330 -0.083 0.244 2.215 1.157 -1.036 1.411 0.000 1.063 0.064 -0.977 3.102 0.780 0.767 0.984 0.691 0.961 0.983 0.856 +1 0.290 -0.999 1.421 0.347 0.151 0.555 -0.649 -0.479 2.173 0.756 1.104 1.371 0.000 0.757 0.844 0.486 0.000 1.026 0.756 -1.168 1.551 0.839 0.757 0.983 0.812 0.831 0.805 0.689 +1 0.642 2.091 0.036 1.298 1.111 1.525 2.086 -0.899 0.000 1.078 1.173 1.374 2.215 0.831 0.308 0.465 2.548 1.121 -1.012 1.543 0.000 5.320 2.996 1.042 0.859 0.862 1.872 1.586 +0 0.668 -0.119 0.887 0.428 0.775 0.802 0.245 -1.497 0.000 0.602 0.118 -0.051 2.215 0.885 1.320 -0.178 0.000 1.605 2.452 1.455 0.000 1.255 1.023 0.979 0.516 0.353 0.718 0.828 +1 1.408 1.125 -0.476 0.578 -0.702 2.153 -0.830 0.871 0.000 2.059 0.651 -1.087 2.215 0.966 -0.058 -1.050 2.548 0.990 1.746 0.495 0.000 1.046 1.005 0.985 0.899 0.565 0.861 0.774 +1 0.897 1.228 -0.801 0.264 0.572 0.471 -0.912 -0.001 2.173 0.278 -0.996 1.029 0.000 0.830 0.153 -1.384 2.548 1.308 1.011 -1.550 0.000 1.217 1.157 0.982 0.629 0.863 0.746 0.691 +1 0.887 0.011 1.354 0.823 -1.716 1.108 -0.253 -0.734 0.000 1.137 -0.437 0.405 2.215 0.749 0.785 0.507 0.000 0.449 -1.066 -1.243 3.102 1.761 1.064 0.990 1.172 0.700 0.865 0.918 +0 0.624 0.900 -0.234 2.429 0.436 0.979 1.221 -1.717 2.173 0.638 0.876 -0.794 0.000 0.584 1.727 -1.434 0.000 0.397 -0.132 -1.021 1.551 0.928 0.879 0.989 0.698 0.630 0.922 0.807 +1 0.474 0.890 1.571 0.753 -0.275 0.790 0.082 -1.345 0.000 0.779 0.992 -1.193 0.000 0.667 1.842 0.395 0.000 1.522 0.857 0.746 3.102 0.905 0.863 0.988 0.660 0.309 0.537 0.568 +1 1.151 0.982 1.237 0.765 1.066 1.529 1.164 -0.245 0.000 0.495 -2.483 1.638 0.000 1.267 0.962 -1.540 2.548 0.612 0.551 -0.719 0.000 0.723 1.226 1.000 0.485 0.515 0.734 0.836 +0 0.438 -0.097 -1.222 1.760 0.533 1.386 0.266 -0.953 0.000 1.041 -1.272 1.042 2.215 1.764 0.294 0.465 0.000 0.962 0.695 -0.856 0.000 0.717 1.097 1.217 1.025 1.364 1.001 0.880 +1 1.499 0.005 -0.254 1.205 0.613 0.593 -0.132 1.740 0.000 0.593 -0.122 -0.742 0.000 1.467 0.442 1.566 2.548 0.378 1.176 -0.001 1.551 0.989 0.857 1.311 0.695 0.625 0.777 0.758 +1 0.567 -0.666 -1.415 1.213 0.197 0.799 1.065 -0.010 2.173 0.644 2.845 1.415 0.000 0.950 -0.027 -1.320 0.000 0.392 0.750 -0.840 3.102 0.430 1.212 1.141 0.672 0.406 0.766 1.221 +1 1.073 -1.687 -0.875 1.510 -1.101 1.968 -1.202 0.589 1.087 1.587 -0.716 -0.764 1.107 1.570 -0.107 -1.205 0.000 2.795 0.091 1.105 0.000 2.033 1.855 1.001 2.214 2.521 1.845 1.960 +1 0.638 -1.434 1.738 2.542 1.402 2.976 -0.052 -0.193 0.000 1.138 0.046 -1.440 2.215 1.392 -0.780 1.732 0.000 0.852 -0.084 -0.916 0.000 0.945 0.748 0.993 1.537 1.421 1.164 0.999 +0 0.746 -1.983 0.262 0.211 -0.771 1.027 -2.452 -1.185 0.000 0.413 -1.113 -0.857 0.000 1.133 -1.067 0.053 2.548 3.278 -0.419 1.122 3.102 1.068 1.254 0.977 1.035 1.309 1.406 1.104 +1 1.440 -0.205 -0.386 1.177 -0.009 0.785 2.121 -1.136 0.000 1.739 0.231 1.151 2.215 0.755 -0.148 -1.263 2.548 0.687 -0.996 0.893 0.000 3.069 1.831 0.985 1.595 1.032 1.426 1.485 +1 0.878 0.321 1.706 1.395 0.884 0.773 -0.377 -0.788 1.087 1.270 -0.078 -0.125 2.215 0.928 -0.250 1.059 0.000 0.415 -1.460 -0.935 0.000 0.866 0.987 1.034 1.148 0.850 0.958 0.837 +1 0.778 0.921 -1.002 0.566 0.163 0.825 0.010 1.277 0.000 0.579 -0.708 0.780 0.000 1.415 0.312 -0.736 2.548 0.983 1.105 -1.663 3.102 0.956 0.776 0.989 0.629 0.812 0.778 0.699 +1 0.712 -0.705 0.465 1.298 -1.028 0.642 -0.294 -0.910 0.000 0.756 0.213 1.325 1.107 1.037 -0.471 -0.328 2.548 0.615 2.148 0.963 0.000 2.132 1.404 1.298 0.752 1.003 1.027 0.989 +1 0.964 1.066 0.887 0.490 -0.425 1.223 0.168 -0.097 2.173 0.850 0.358 -1.503 1.107 0.535 -2.569 1.450 0.000 0.893 0.908 1.607 0.000 0.502 0.959 0.987 0.887 1.439 0.962 0.761 +1 0.948 -0.370 0.162 0.995 1.310 0.961 -0.575 -0.146 0.000 1.047 -0.217 1.288 2.215 1.032 0.388 -1.416 2.548 0.930 -0.956 -0.978 0.000 1.068 1.169 1.157 0.752 0.803 0.945 0.839 +1 0.607 0.032 0.377 1.100 0.707 2.024 2.319 -1.078 0.000 1.445 0.319 -0.405 2.215 2.327 1.214 1.023 2.548 1.651 0.514 0.781 0.000 1.157 1.286 0.977 1.941 2.131 1.557 1.249 +0 1.980 -0.593 -1.501 1.676 -1.065 1.343 -0.054 0.357 0.000 0.857 -0.250 -0.102 0.000 1.001 1.020 0.286 2.548 1.124 -0.439 1.096 3.102 0.945 0.970 0.986 0.925 0.921 1.112 1.180 +1 0.494 0.866 0.434 0.534 -1.488 1.251 1.123 0.195 0.000 0.583 0.287 -1.264 2.215 0.865 -0.268 1.394 2.548 0.402 1.372 0.792 0.000 1.182 1.109 0.991 0.602 0.560 0.876 0.768 +1 0.740 -0.267 0.981 1.515 0.088 0.475 1.067 1.192 0.000 0.602 1.909 -1.589 0.000 0.761 0.401 0.283 0.000 1.297 -0.557 -1.317 3.102 0.908 0.771 1.057 0.780 0.872 0.730 0.636 +1 0.418 -1.341 -1.195 0.993 0.620 0.653 -0.192 1.007 2.173 0.878 -0.415 -0.016 2.215 0.466 -2.093 -1.325 0.000 0.398 -0.352 -1.013 0.000 0.531 0.866 0.988 0.977 0.898 0.869 0.738 +0 1.466 -1.441 1.026 1.494 0.805 0.783 1.658 -0.686 0.000 0.631 -0.129 0.439 2.215 1.826 0.418 -0.916 0.000 1.771 -1.033 1.640 3.102 0.433 0.934 1.006 0.943 1.009 0.975 1.426 +1 0.779 -0.316 1.595 0.344 -1.433 1.283 0.948 0.873 0.000 0.787 2.707 -1.023 0.000 0.956 0.137 -0.212 0.000 0.931 -1.955 -0.728 0.000 0.531 0.941 0.986 0.548 0.471 0.600 0.595 +0 1.097 0.508 1.591 1.625 -1.387 0.619 0.752 -0.070 2.173 0.671 0.273 -0.408 2.215 0.771 0.922 1.020 0.000 1.280 1.800 0.904 0.000 0.674 0.939 0.983 0.949 0.365 0.817 0.847 +1 0.941 1.258 -0.703 0.478 0.833 0.905 1.467 0.876 2.173 0.780 2.348 -1.281 0.000 0.454 0.211 -0.214 1.274 0.530 2.477 -0.550 0.000 0.565 0.894 0.988 0.879 0.851 0.831 0.749 +1 0.495 1.051 -0.732 0.939 1.523 0.880 0.234 -0.857 2.173 0.776 2.259 1.686 0.000 0.951 0.020 1.588 2.548 1.479 0.290 0.551 0.000 2.007 1.441 0.986 0.786 0.927 1.161 0.939 +1 0.645 1.438 0.116 1.023 -1.228 1.050 0.312 1.008 0.000 0.713 -0.451 -1.144 2.215 1.051 0.915 -0.063 0.000 1.297 1.072 -1.542 3.102 0.846 0.923 1.052 1.049 0.913 0.927 0.806 +1 1.819 0.386 0.099 0.568 1.696 1.056 -1.300 -0.140 0.000 1.465 0.224 1.503 0.000 0.805 -0.480 0.574 0.000 2.158 0.365 -1.295 3.102 1.197 1.388 1.396 1.111 0.400 1.173 1.078 +1 0.893 0.540 1.113 1.274 0.383 1.452 0.231 -0.396 2.173 1.120 0.231 -1.027 2.215 2.629 -1.920 1.427 0.000 0.613 1.443 1.333 0.000 4.675 3.190 0.991 1.316 1.010 2.430 2.107 +0 1.034 -0.326 -1.629 1.085 0.544 0.566 2.165 1.739 0.000 0.804 0.242 -0.395 2.215 0.371 0.048 0.020 2.548 0.368 -1.210 -0.755 0.000 1.039 0.718 1.358 0.696 0.220 0.614 0.602 +0 1.629 -1.807 0.711 0.336 -1.223 0.668 -0.779 0.984 2.173 1.133 1.442 -1.302 1.107 1.712 2.119 -0.563 0.000 0.421 -1.723 -1.678 0.000 4.241 2.552 1.010 0.777 2.115 2.018 2.377 +1 0.298 1.146 -0.522 1.039 0.127 0.796 0.352 -1.369 2.173 0.565 -0.528 -0.300 0.000 1.141 -0.494 1.251 2.548 0.885 -1.876 -0.097 0.000 0.902 1.036 0.989 0.999 1.000 0.995 0.869 +1 2.461 -0.146 -0.536 0.933 -0.950 0.608 2.724 1.339 0.000 0.550 0.385 0.750 1.107 1.179 -0.735 0.773 1.274 0.508 -0.189 -1.394 0.000 1.863 1.343 0.996 1.282 0.547 1.396 1.438 +0 1.007 -0.208 1.699 0.385 0.259 1.108 0.938 -1.401 2.173 0.929 -1.030 0.158 0.000 0.682 -0.843 0.940 0.000 1.016 0.329 -0.155 3.102 0.795 0.813 0.985 0.892 1.060 1.188 0.955 +0 0.607 -1.016 -0.654 1.997 -1.594 0.729 -0.657 1.229 2.173 0.818 0.157 0.043 2.215 0.852 0.087 0.600 0.000 0.852 0.649 -0.674 0.000 0.917 0.980 1.141 1.229 1.107 0.946 0.887 +0 0.593 0.627 -0.314 1.696 -1.175 0.863 -1.563 1.272 0.000 0.715 0.231 1.258 2.215 0.924 -0.358 -0.121 2.548 0.960 -1.478 0.320 0.000 1.062 1.117 0.987 0.871 0.863 0.906 1.079 +1 0.354 1.548 -1.558 1.560 -0.314 0.760 1.134 0.025 0.000 2.273 -0.074 -1.737 2.215 1.561 0.597 0.667 2.548 1.780 1.083 -0.582 0.000 0.935 1.131 0.986 1.604 1.819 1.481 1.219 +1 0.732 0.451 0.106 1.338 -0.226 1.057 0.191 -0.067 0.000 1.008 -0.448 1.632 2.215 0.932 0.773 0.876 0.000 0.891 0.896 1.554 0.000 0.767 1.106 0.989 1.526 0.428 1.206 1.110 +1 1.328 -0.711 0.349 0.674 1.086 1.126 -1.116 -1.325 2.173 0.625 -1.471 0.091 0.000 0.654 -0.233 1.032 0.000 0.553 -0.737 -0.313 3.102 0.988 1.216 0.983 0.577 0.668 0.831 0.754 +1 1.383 0.264 0.959 0.529 -0.163 0.477 2.741 1.362 0.000 1.543 0.606 -0.769 1.107 0.824 1.165 -0.040 2.548 0.594 -0.253 0.858 0.000 0.783 0.976 1.004 0.743 0.834 0.815 0.713 +1 2.025 0.145 0.064 0.499 -0.305 0.436 1.215 -1.554 0.000 1.252 0.598 1.198 2.215 0.944 0.154 -0.966 0.000 0.886 0.437 0.520 0.000 0.963 0.799 0.995 0.892 0.544 0.896 0.797 +0 0.396 -0.918 0.496 0.672 -1.344 1.037 0.122 0.279 2.173 1.309 -1.081 -0.374 0.000 1.549 -0.890 0.268 0.000 1.874 -1.722 -1.226 0.000 0.928 1.097 0.990 0.729 1.707 1.153 1.016 +0 1.511 -0.077 -0.626 0.982 -0.780 0.587 -1.109 0.956 0.000 0.946 -0.810 0.376 2.215 0.918 -0.374 1.435 0.000 0.949 0.208 -1.616 1.551 0.729 0.797 0.983 0.725 0.965 0.871 0.912 +1 0.676 0.287 -1.097 0.527 1.254 1.203 0.431 0.524 2.173 0.949 0.257 0.025 0.000 1.786 0.264 -1.421 2.548 0.958 -0.922 -1.312 0.000 1.480 1.301 0.982 1.030 1.801 1.152 0.985 +1 0.752 -0.269 -1.204 0.882 0.513 0.607 0.137 0.207 2.173 0.992 0.892 -0.429 2.215 1.258 0.420 1.432 0.000 0.911 1.241 1.647 0.000 0.674 1.020 1.128 0.957 0.770 0.833 0.793 +0 1.300 0.079 -0.980 0.480 1.038 1.511 -1.462 -1.504 0.000 0.987 -0.498 0.515 0.000 0.947 0.055 -0.173 0.000 1.528 0.539 0.954 3.102 0.963 0.950 1.061 0.839 0.615 0.772 0.770 +0 0.566 0.628 -0.331 1.025 -0.687 1.647 -1.046 -0.800 2.173 1.488 -1.590 1.142 0.000 1.365 0.594 0.251 0.000 2.429 0.279 1.140 3.102 0.656 1.496 0.991 0.905 2.622 1.586 1.306 +0 1.744 0.293 -0.501 1.144 -0.967 0.801 1.246 0.922 1.087 1.027 0.370 0.307 0.000 1.556 1.001 1.588 1.274 0.485 -0.372 -1.700 0.000 0.975 0.979 0.993 1.214 0.793 1.067 0.955 +1 1.636 0.355 0.033 0.537 -0.373 2.874 -0.931 1.150 0.000 1.774 -1.398 -0.680 1.107 1.537 -0.201 -0.764 2.548 0.598 -1.045 -1.054 0.000 0.871 0.708 0.992 1.455 1.144 1.033 0.902 +0 1.394 -0.688 1.419 0.428 -1.458 0.627 -2.480 -0.795 0.000 1.055 0.207 0.508 1.107 0.609 1.782 -1.461 0.000 1.314 1.667 0.032 0.000 0.962 0.911 0.989 0.951 0.743 0.841 0.942 +1 2.178 1.001 1.288 0.613 -1.633 1.476 2.505 0.087 0.000 1.890 0.439 -1.052 2.215 0.586 -0.707 0.514 2.548 0.399 -2.246 -0.173 0.000 7.960 4.417 0.983 1.362 1.324 2.875 2.237 +1 1.165 -0.681 0.410 0.326 -0.928 1.231 -0.423 0.099 0.000 1.372 -0.162 -1.586 0.000 1.591 -0.332 -0.959 2.548 1.437 -0.057 1.102 3.102 0.918 1.032 0.994 0.880 1.122 0.914 0.768 +1 0.657 0.125 -0.506 0.259 -0.471 0.698 -0.749 -0.190 2.173 0.999 -1.674 1.528 0.000 0.997 -0.425 0.475 0.000 1.778 -0.347 -1.740 3.102 1.613 1.194 0.987 0.614 1.178 0.961 0.829 +1 0.581 -1.114 0.022 0.488 1.513 1.093 -1.188 0.520 2.173 1.281 -0.653 -1.029 0.000 0.826 -0.825 1.493 2.548 0.552 -1.555 -1.332 0.000 0.755 0.751 0.987 0.887 0.926 0.915 0.789 +0 0.878 0.769 -1.108 0.292 0.224 0.591 -0.751 -0.003 2.173 0.753 -1.743 1.546 0.000 0.810 -0.828 1.041 2.548 0.574 0.627 -0.866 0.000 1.576 0.988 0.988 0.802 0.700 0.764 0.764 +0 1.810 -0.784 -1.512 0.388 1.489 0.456 0.607 -0.996 2.173 0.699 -0.644 0.548 0.000 0.543 -0.419 0.182 1.274 0.779 0.632 0.207 0.000 0.798 0.893 0.980 0.762 0.644 0.646 0.692 +0 1.273 0.617 0.148 0.395 1.155 0.807 0.802 0.645 1.087 0.848 0.857 -1.008 2.215 0.895 1.969 -1.208 0.000 0.462 1.382 -1.728 0.000 0.629 0.854 0.989 0.885 1.213 0.753 0.663 +1 0.713 -1.241 -1.592 1.148 0.674 0.836 -0.711 -1.384 1.087 0.633 -1.450 -0.412 0.000 1.139 -2.346 0.018 0.000 0.718 -1.416 1.015 3.102 0.890 0.744 1.117 0.947 0.800 0.895 0.813 +1 0.934 0.768 1.314 0.395 0.217 0.945 0.540 -1.464 2.173 1.020 -0.419 0.522 2.215 0.416 -0.531 -0.347 0.000 0.845 0.359 -0.124 0.000 0.370 0.863 0.991 1.100 1.588 1.000 0.816 +1 1.049 -0.619 1.501 0.127 -1.021 1.048 -1.099 1.100 0.000 1.186 0.038 -1.022 2.215 1.320 -0.382 0.226 2.548 1.896 -0.103 -0.363 0.000 2.360 1.482 0.982 0.817 1.238 1.186 0.989 +0 1.056 0.171 1.094 0.575 -1.594 1.652 1.936 1.598 0.000 1.352 0.234 0.028 2.215 0.798 -0.013 -0.878 0.000 1.457 -0.311 -0.357 1.551 0.833 0.971 0.979 0.851 0.585 0.788 0.728 +0 1.183 1.136 -1.018 1.394 -0.232 0.707 0.753 1.281 2.173 0.570 0.119 -0.310 0.000 0.452 2.703 -1.515 0.000 0.668 1.460 0.379 0.000 0.965 0.747 1.158 0.765 0.323 0.759 0.679 +0 1.160 -0.248 -0.572 0.633 0.332 0.850 -0.849 -1.077 2.173 1.132 -0.816 0.782 0.000 0.933 -1.308 0.288 0.000 1.249 -0.815 1.742 1.551 0.830 0.912 0.991 0.824 0.620 0.868 0.797 +1 1.114 1.168 1.580 1.282 -0.966 1.040 -0.600 0.056 2.173 0.469 -1.764 1.296 0.000 0.513 -1.080 0.700 0.000 0.802 0.308 -0.793 3.102 0.454 0.921 1.242 0.714 0.829 1.128 1.142 +1 0.632 0.830 -0.929 0.630 -0.487 1.347 -0.019 -0.315 1.087 1.026 -1.208 1.417 0.000 2.047 -0.079 1.015 0.000 1.061 0.000 1.551 3.102 1.570 0.961 0.988 1.520 1.257 1.272 1.495 +0 2.021 0.557 -0.801 0.303 0.156 1.628 1.339 0.979 0.000 2.136 0.843 -1.130 2.215 2.051 -0.069 0.188 0.000 1.661 0.292 1.200 3.102 1.561 1.396 0.990 0.889 1.534 1.519 1.228 +1 0.807 1.393 1.361 0.858 0.326 0.723 1.972 0.026 0.000 0.446 -0.827 -0.041 1.107 1.608 0.265 -1.280 2.548 1.349 -0.389 -1.523 0.000 0.840 0.959 0.986 0.912 0.974 0.841 0.725 +1 0.954 0.667 -1.664 1.061 -0.499 1.025 0.579 0.917 0.000 1.470 0.576 -0.741 1.107 0.502 0.138 0.531 2.548 0.955 1.398 0.888 0.000 0.868 0.597 1.210 0.802 0.856 0.937 0.852 +0 0.283 -0.694 -1.511 1.221 0.586 0.623 -0.662 -0.846 0.000 0.872 -0.529 1.497 2.215 0.486 1.096 -0.440 0.000 0.721 1.291 0.395 3.102 0.805 0.912 0.986 1.527 1.077 1.086 0.982 +0 0.799 -1.774 0.943 1.203 -0.704 0.636 -1.062 -0.047 2.173 0.581 -0.067 1.128 2.215 0.499 -2.052 1.472 0.000 1.106 -0.572 -1.563 0.000 0.886 0.915 1.353 1.093 0.909 0.823 0.798 +1 0.642 1.009 1.411 1.663 -0.376 0.552 0.698 -0.574 2.173 0.764 -0.215 1.252 0.000 1.262 0.268 1.527 0.000 0.708 -0.299 0.254 3.102 0.539 1.063 1.430 0.869 0.582 0.692 0.791 +1 0.480 -1.077 -0.547 0.640 -1.646 0.772 0.024 0.285 0.000 1.193 -0.651 0.543 0.000 2.547 -0.044 -1.158 2.548 1.289 0.800 1.327 3.102 0.860 1.201 0.992 1.602 1.308 1.413 1.370 +1 1.029 -0.317 -1.317 0.819 -0.308 0.905 -0.063 0.898 0.000 0.552 -0.648 0.423 0.000 0.668 0.527 1.537 2.548 2.087 0.160 -0.862 3.102 0.817 1.123 1.005 0.720 0.770 0.721 0.689 +1 1.005 0.449 -1.316 0.636 0.040 0.622 1.018 -0.615 2.173 0.779 -0.606 1.380 0.000 0.909 0.954 0.907 2.548 0.646 -1.102 0.359 0.000 0.805 0.991 1.042 0.680 0.918 0.918 0.794 +1 0.795 1.450 0.435 0.702 -1.369 0.907 0.644 -1.069 0.000 0.755 1.000 -0.687 0.000 1.786 -2.033 1.245 0.000 1.869 0.142 -0.163 3.102 0.682 0.947 1.033 0.995 0.929 0.946 0.876 +1 0.925 0.674 1.630 0.644 -0.917 0.733 0.263 1.162 0.000 1.059 0.492 0.260 0.000 0.822 -0.881 -0.503 2.548 0.528 0.618 -1.197 0.000 0.949 0.942 0.990 0.614 0.149 0.611 0.632 +1 0.818 1.860 0.766 0.462 1.071 0.735 -0.352 -1.232 2.173 0.343 1.304 -0.802 0.000 0.379 1.013 -1.309 0.000 0.775 0.116 -0.268 3.102 0.252 0.663 1.002 0.708 0.642 0.808 0.646 +1 0.309 -0.578 0.052 1.897 0.034 1.197 0.639 -1.522 2.173 0.434 -0.138 -0.028 0.000 0.581 0.680 1.269 0.000 0.413 0.589 -0.007 0.000 0.961 1.039 0.982 1.657 0.812 1.919 1.573 +1 2.452 -0.781 0.204 0.652 0.594 0.444 -0.446 1.300 0.000 0.572 -1.248 -1.144 2.215 0.788 -2.123 -1.248 0.000 1.095 -0.230 -1.413 3.102 1.402 0.979 0.978 1.020 0.413 0.812 0.872 +1 1.815 -1.781 0.031 0.317 1.597 0.720 -0.263 -1.685 2.173 0.661 -2.122 1.219 0.000 1.083 -0.673 -1.189 1.274 0.424 -0.938 0.139 0.000 0.686 1.037 1.038 0.995 0.544 0.948 0.822 +0 1.002 0.257 0.346 0.609 0.424 0.438 -0.647 -1.047 0.000 0.652 -0.127 1.209 2.215 1.243 0.210 -0.523 2.548 0.794 0.742 -1.643 0.000 0.893 0.775 0.990 0.829 0.972 0.750 0.745 +1 0.663 1.282 0.372 0.981 -1.618 0.464 2.050 -1.356 0.000 0.444 0.894 -1.040 2.215 0.450 1.970 -0.214 0.000 0.811 -0.709 1.114 3.102 0.705 1.304 1.090 0.628 0.743 0.788 0.705 +0 1.500 -0.052 -0.710 0.447 -0.970 1.012 0.312 0.632 0.000 1.630 0.414 -1.367 2.215 0.988 1.159 0.693 0.000 0.398 0.482 -1.682 3.102 0.900 0.679 1.002 1.066 0.211 0.951 1.009 +1 0.768 0.027 -0.585 1.095 1.499 0.819 -1.588 0.878 2.173 0.618 -0.358 -1.033 0.000 0.530 -0.470 0.219 2.548 0.588 -1.725 -0.739 0.000 0.780 1.091 1.211 0.714 0.657 0.835 0.774 +0 1.011 1.403 1.410 0.525 0.414 0.695 0.147 0.149 2.173 0.771 -1.548 -0.591 0.000 0.818 0.230 1.474 0.000 1.524 0.723 -0.875 3.102 1.007 0.908 0.985 1.145 0.956 0.892 0.806 +1 0.622 0.798 -0.993 0.353 1.468 0.568 1.235 -0.134 0.000 0.509 0.766 0.466 2.215 1.612 0.824 1.365 2.548 1.804 1.415 -0.647 0.000 0.934 1.088 0.995 0.711 0.700 0.701 0.752 +1 0.617 -0.880 -1.739 1.248 0.669 1.116 0.165 -0.675 1.087 0.815 -0.045 1.301 0.000 0.704 0.018 0.427 0.000 0.458 0.852 -0.708 0.000 0.893 1.091 1.004 0.567 0.425 0.806 0.740 +1 0.976 -0.338 -0.840 0.266 0.011 1.287 0.008 0.746 1.087 0.905 -0.822 -1.277 0.000 0.670 -1.304 -0.510 0.000 0.467 -1.145 1.678 1.551 0.841 0.542 0.986 1.122 0.865 0.931 0.856 +1 1.380 0.468 -0.083 2.257 -0.477 2.122 0.291 -1.013 2.173 1.557 -2.280 0.812 0.000 3.721 -0.845 1.211 0.000 0.916 0.233 0.415 0.000 0.826 0.558 0.992 1.483 1.052 1.097 1.030 +1 0.564 0.065 0.700 0.949 1.742 0.800 0.111 0.145 2.173 0.649 -1.409 -1.368 0.000 0.730 -1.122 -0.364 0.000 0.478 0.843 1.446 3.102 0.839 0.899 0.984 0.930 0.676 0.812 0.842 +0 0.774 -1.904 0.443 1.010 -1.563 0.675 0.459 -0.973 0.000 0.941 0.914 -0.402 2.215 1.028 -0.577 1.687 2.548 0.502 0.056 1.180 0.000 0.847 0.801 1.191 1.985 1.349 1.330 1.131 +1 0.795 0.308 1.304 1.933 0.721 1.018 -0.089 -0.620 2.173 0.628 0.246 -1.405 0.000 0.496 2.611 -1.724 0.000 0.774 0.709 0.007 0.000 0.911 0.859 0.991 0.738 0.759 0.910 0.796 +1 0.908 0.872 -0.430 0.679 0.658 0.418 -2.499 -1.145 0.000 0.371 1.920 -0.982 0.000 0.855 0.376 0.616 2.548 0.755 -0.919 0.975 3.102 0.218 0.939 0.985 0.656 0.548 0.676 0.637 +0 0.293 -1.776 -0.717 1.418 0.858 1.179 -1.578 -0.258 0.000 1.017 -0.472 1.519 2.215 1.014 0.034 -1.400 2.548 0.694 1.898 -0.447 0.000 1.177 1.388 0.992 0.771 0.600 1.056 0.908 +1 0.457 -1.194 -1.139 1.075 -1.722 0.752 -2.528 -1.705 0.000 1.787 0.795 0.412 2.215 1.193 1.079 0.305 2.548 2.026 0.583 -0.628 0.000 0.684 3.049 0.977 1.395 0.323 2.556 1.914 +1 1.825 0.002 -1.336 0.527 1.003 0.620 -0.688 -0.161 2.173 0.475 -1.117 0.068 2.215 1.310 -0.175 0.806 0.000 0.568 -0.791 -1.118 0.000 1.009 0.875 1.167 0.924 0.245 0.757 0.710 +1 1.785 0.701 -1.416 0.053 -1.156 0.897 0.429 -0.283 0.000 1.235 0.491 1.163 2.215 0.568 -0.484 0.163 0.000 0.466 0.998 0.057 0.000 0.817 1.217 1.001 0.939 1.324 1.044 0.965 +1 0.865 -0.648 1.073 2.164 1.625 0.844 -0.216 -0.033 2.173 0.761 -0.293 -1.221 0.000 0.723 0.345 -1.048 2.548 1.089 0.759 0.372 0.000 0.965 0.952 0.988 0.843 0.826 0.928 0.820 +1 0.688 -0.688 -1.275 0.140 -0.418 1.549 -0.583 -0.142 2.173 0.985 -0.287 -1.730 0.000 0.822 1.819 1.080 0.000 1.078 -0.363 -1.080 3.102 0.897 0.737 0.992 1.132 1.028 1.004 0.851 +0 1.738 0.359 1.689 0.741 1.375 0.673 0.258 -1.264 2.173 0.780 -0.461 0.151 0.000 0.649 -0.183 0.539 0.000 0.853 -1.944 -0.817 0.000 1.304 1.313 0.984 1.366 1.290 1.252 1.140 +0 0.576 0.343 1.317 1.394 0.070 1.741 -0.614 -1.353 2.173 1.566 -0.199 0.407 2.215 0.749 -0.813 1.647 0.000 0.688 -0.578 -0.198 0.000 0.793 0.952 1.120 1.601 2.482 1.372 1.061 +1 1.244 -1.721 -0.998 0.505 -0.936 0.874 -1.178 0.721 2.173 0.764 -1.168 0.287 0.000 0.478 -1.487 1.527 0.000 0.500 0.302 -1.336 0.000 0.857 0.698 0.990 1.135 0.698 0.803 0.701 +1 1.525 0.442 1.474 0.419 0.150 0.761 -0.618 -1.002 2.173 0.724 -0.719 0.101 1.107 0.890 0.346 -0.543 0.000 1.087 -0.085 0.530 0.000 0.931 0.953 1.030 0.946 0.918 0.853 0.757 +1 0.313 -0.852 -0.562 0.433 0.533 0.762 0.471 -0.145 2.173 1.144 0.030 -1.449 2.215 0.907 -1.451 1.595 0.000 0.496 -0.354 1.229 0.000 0.517 0.861 0.977 0.663 1.305 0.949 0.847 +0 0.322 -1.405 -0.196 0.868 1.689 0.565 -0.334 0.132 0.000 1.011 -1.308 0.047 1.107 0.828 1.058 -0.940 2.548 0.488 0.055 1.424 0.000 0.912 0.705 0.989 0.954 1.738 1.306 1.053 +0 1.459 -0.899 -1.256 0.285 0.707 0.943 0.720 0.691 2.173 0.500 -1.382 -1.367 0.000 1.204 0.396 -0.543 0.000 0.693 0.098 1.188 0.000 0.865 1.263 0.986 0.687 0.377 0.941 0.817 +0 0.620 0.199 -0.401 0.635 1.359 1.354 0.445 -0.923 2.173 1.631 -1.158 0.437 2.215 1.196 0.065 1.491 0.000 0.762 -2.213 1.149 0.000 1.952 1.622 0.990 0.912 2.871 1.777 1.453 +1 1.961 0.451 1.730 0.609 -1.442 0.798 0.394 -0.097 2.173 0.330 0.117 0.442 0.000 0.596 -0.153 0.753 2.548 0.659 -0.320 -0.722 0.000 0.549 0.519 0.983 0.741 0.644 0.837 0.669 +1 0.691 1.177 0.070 1.096 -0.845 1.005 0.354 1.204 2.173 1.196 -0.020 1.730 0.000 1.209 0.327 -0.670 0.000 1.616 0.431 -0.028 0.000 0.852 0.808 0.986 1.120 0.843 0.871 0.800 +1 0.527 1.443 0.909 0.867 -0.265 0.444 1.925 -0.243 0.000 0.910 0.449 1.309 1.107 0.863 2.241 -1.198 0.000 1.305 -0.240 1.530 3.102 0.893 1.329 0.988 0.832 0.425 1.072 0.904 +1 0.493 -0.642 0.359 0.247 -0.793 0.884 0.643 -1.413 0.000 1.033 0.436 0.617 2.215 1.269 0.929 -0.321 0.000 1.611 -0.050 1.742 1.551 1.626 1.240 0.990 0.693 1.032 0.995 0.825 +1 0.625 -1.036 0.379 0.728 1.580 1.636 0.336 -1.537 2.173 0.719 -0.020 0.561 0.000 1.682 -0.064 -0.115 1.274 0.371 0.151 -0.518 0.000 0.560 1.212 0.990 0.868 2.026 1.098 0.885 +1 0.479 0.731 0.745 2.542 1.423 0.781 0.081 -0.563 2.173 0.319 2.145 -0.077 0.000 0.271 0.549 -0.949 2.548 0.520 1.439 0.292 0.000 0.317 0.757 0.994 0.648 0.247 0.925 0.756 +1 0.787 0.347 -0.441 0.846 0.716 0.596 -0.626 -1.361 2.173 0.680 1.006 0.247 0.000 0.471 2.102 1.325 0.000 1.024 0.648 -1.269 3.102 0.929 0.807 0.988 0.872 0.636 0.874 0.777 +1 0.603 -0.206 1.061 0.813 -0.166 1.174 0.743 1.193 2.173 1.281 0.196 -0.303 0.000 0.905 0.830 -1.384 1.274 0.978 0.123 -0.989 0.000 0.846 0.878 0.987 1.272 0.943 0.996 0.954 +1 0.729 -1.717 -0.464 0.917 -1.510 2.182 -1.620 -0.910 0.000 2.733 -1.271 0.737 2.215 0.994 -0.325 1.068 2.548 0.749 -1.669 1.316 0.000 0.736 0.860 0.987 1.081 1.007 1.122 0.867 +1 0.313 0.134 -1.634 1.235 -0.795 0.955 0.316 1.159 2.173 0.761 -0.944 0.024 2.215 0.344 0.057 -1.025 0.000 0.383 -0.618 -1.676 0.000 0.276 0.602 0.985 0.743 1.378 1.010 0.740 +0 1.242 -0.467 1.335 0.830 -0.557 0.591 0.432 -1.201 2.173 0.769 1.174 0.488 0.000 0.307 0.099 -1.501 1.274 0.586 -2.141 1.225 0.000 2.881 1.527 1.394 0.938 0.166 1.058 0.933 +0 0.867 -0.305 0.857 1.095 -0.599 0.524 0.725 -0.741 0.000 0.773 -0.984 0.094 2.215 0.713 -0.562 -1.381 2.548 0.547 -1.369 1.134 0.000 1.463 0.920 1.305 0.808 0.782 0.738 0.697 +1 1.217 -0.038 0.862 0.793 -1.092 0.745 1.435 1.377 0.000 0.709 -0.380 0.104 0.000 1.246 0.942 -0.445 2.548 1.403 -0.124 -0.834 3.102 0.782 0.820 1.337 0.854 0.722 0.767 0.669 +0 0.696 0.741 1.294 1.161 -0.657 0.798 -0.459 1.189 1.087 0.762 -0.542 -0.597 2.215 0.568 -0.267 0.697 0.000 0.572 1.332 0.245 0.000 0.996 0.914 1.224 0.912 1.148 0.901 0.871 +0 0.749 0.968 0.010 0.992 1.232 0.770 1.412 0.251 1.087 0.554 1.392 -1.208 0.000 1.163 0.183 -1.281 2.548 1.040 -0.613 1.223 0.000 0.864 0.980 1.065 0.748 1.389 1.190 1.030 +1 0.803 1.840 1.150 0.977 1.288 1.547 1.265 0.087 0.000 0.898 0.378 -1.426 0.000 1.745 -0.739 -1.297 2.548 0.799 -0.249 -1.409 3.102 1.204 1.051 0.973 0.791 0.251 1.008 0.877 +1 0.533 -0.703 0.400 1.730 0.180 0.887 0.057 -1.199 2.173 0.793 -1.095 -0.321 0.000 1.593 0.900 1.655 2.548 1.054 -0.279 1.207 0.000 1.266 1.199 0.984 2.469 1.071 1.786 1.409 +1 0.774 -1.057 0.736 0.657 -0.227 0.968 -0.605 -1.141 0.000 1.371 -1.474 1.140 0.000 0.621 -1.151 0.042 2.548 0.744 -0.616 -0.347 1.551 2.450 1.435 0.987 0.524 0.225 0.899 0.810 +0 1.735 -0.522 0.285 0.122 0.516 1.273 0.062 1.657 2.173 0.403 0.051 1.257 0.000 0.736 -2.530 -0.739 0.000 1.688 0.970 1.654 3.102 1.021 0.979 0.993 1.314 0.898 1.064 0.914 +1 0.793 0.503 -0.920 1.837 -0.247 1.180 1.558 0.630 0.000 1.787 1.462 -1.419 2.215 0.914 1.180 1.272 2.548 0.597 0.553 1.730 0.000 1.230 0.816 0.990 1.566 0.903 1.130 1.135 +1 0.793 -0.557 -0.341 0.871 -1.317 1.137 0.152 -1.143 2.173 1.562 0.794 0.960 0.000 2.009 0.559 0.181 2.548 0.410 0.527 -1.601 0.000 0.779 1.065 0.984 0.961 1.805 1.163 1.156 +0 0.826 0.508 -1.254 0.892 -0.563 1.564 -1.330 0.874 0.000 1.390 -0.063 -1.131 2.215 1.442 -0.655 0.304 2.548 1.049 -2.275 -0.879 0.000 0.865 0.976 0.987 0.641 1.532 1.267 1.132 +1 0.827 -1.069 -1.510 1.764 1.710 0.938 0.240 1.347 1.087 0.686 -1.915 -0.125 0.000 1.592 2.013 0.512 0.000 1.298 0.577 -0.182 0.000 0.685 0.767 1.000 0.839 1.273 1.129 1.054 +0 0.401 -0.731 0.449 0.365 0.795 0.764 -0.010 -1.187 2.173 0.844 0.429 1.097 0.000 0.996 0.378 0.070 2.548 0.699 -1.952 -0.297 0.000 2.116 1.445 0.995 0.910 1.012 1.062 0.930 +0 0.336 1.414 -0.213 1.781 1.715 0.706 0.713 -1.307 0.000 0.894 1.808 0.578 0.000 1.047 -0.382 -1.198 0.000 0.982 -0.914 -0.221 0.000 0.942 0.840 1.057 1.479 0.390 0.948 0.941 +1 0.669 -0.352 0.358 0.544 -1.347 0.849 1.402 0.426 0.000 1.410 0.475 -0.315 0.000 2.084 0.167 -1.637 2.548 1.029 0.433 1.173 3.102 0.987 0.794 0.987 1.020 0.668 0.902 0.949 +0 3.480 -0.174 -1.289 0.637 -1.577 1.741 -1.803 0.434 0.000 0.819 -0.354 -0.649 1.107 0.906 -0.814 0.514 2.548 0.911 -1.688 1.211 0.000 1.254 0.910 0.978 0.875 0.830 1.035 1.555 +0 1.373 -2.041 0.824 0.092 0.709 0.858 0.292 -0.222 0.000 0.688 -1.219 1.451 2.215 0.924 -0.274 -1.475 2.548 0.543 -2.269 -1.456 0.000 0.684 0.995 0.989 0.657 0.587 0.950 1.097 +0 0.562 0.972 1.152 0.420 1.356 0.710 -0.650 0.291 2.173 1.035 -0.765 -0.758 1.107 1.535 0.518 -1.509 0.000 0.401 -1.142 0.690 0.000 1.259 1.172 0.992 0.816 1.026 0.946 0.825 +1 0.336 -1.189 -1.503 0.911 -0.072 1.164 0.578 -0.743 2.173 1.233 -0.006 1.452 0.000 1.033 -0.342 0.837 0.000 0.392 -0.351 0.402 3.102 0.966 0.614 0.993 0.876 0.719 0.957 0.813 +1 0.458 0.628 -0.427 0.409 -0.463 0.685 -0.305 -1.486 0.000 1.109 -1.441 0.488 2.215 0.689 -0.845 1.569 0.000 0.609 0.931 1.432 0.000 0.903 0.805 0.986 0.898 0.617 0.907 0.781 +0 0.379 0.037 -0.744 1.226 1.027 0.546 -2.800 0.928 0.000 0.892 0.724 -1.217 2.215 0.702 -0.355 -0.303 1.274 1.372 1.011 -0.723 0.000 0.955 0.899 0.988 0.804 0.796 0.657 0.656 +1 1.727 -0.498 -1.120 0.731 1.664 1.132 -0.573 0.390 2.173 0.658 0.027 -0.251 0.000 0.381 -1.458 0.621 0.000 0.657 0.243 1.679 3.102 0.884 0.781 0.989 0.556 0.932 0.889 0.770 +0 0.628 -1.046 -0.454 0.896 0.196 1.142 0.554 1.387 0.000 2.028 -0.329 -0.851 2.215 0.570 -0.358 1.015 0.000 1.018 0.074 0.501 3.102 0.862 0.768 0.989 1.005 1.249 1.142 0.982 +1 0.574 0.600 0.940 0.846 -0.593 1.459 -0.399 -0.477 2.173 1.322 -0.926 0.987 0.000 1.068 1.908 1.300 0.000 1.023 -0.858 -1.248 3.102 1.190 0.908 0.988 0.922 0.925 1.014 0.882 +0 0.540 1.383 1.538 0.611 -0.367 1.084 0.822 1.311 2.173 1.041 -0.598 -0.529 2.215 0.868 -1.656 0.416 0.000 0.808 -0.847 -1.249 0.000 0.997 0.932 0.986 1.115 1.985 1.388 1.560 +0 1.941 0.336 -0.060 0.369 0.758 1.039 -0.226 -1.656 2.173 0.505 -0.450 1.456 0.000 0.592 -1.327 -0.648 0.000 1.205 -0.858 0.161 3.102 0.906 0.874 0.980 0.870 1.278 1.030 0.897 +1 0.841 -0.136 -1.431 1.450 0.052 0.738 -0.381 -0.146 0.000 1.347 0.766 1.697 2.215 1.095 0.616 0.263 2.548 0.686 -1.326 0.829 0.000 1.089 1.016 1.488 1.312 1.243 1.107 0.984 +1 1.647 -0.522 0.343 0.513 0.082 0.417 0.383 -1.170 1.087 1.214 1.590 -1.348 0.000 0.659 0.371 0.427 2.548 0.533 1.081 0.990 0.000 0.918 0.942 0.984 1.039 0.648 0.733 1.119 +1 0.420 -1.688 -1.293 0.450 -0.670 1.235 -0.211 0.750 0.000 0.857 0.061 -0.893 2.215 1.005 -1.233 -1.687 2.548 0.810 -0.575 0.338 0.000 0.751 0.758 0.981 0.722 0.997 0.648 0.588 +1 0.851 -1.194 -0.806 0.930 0.067 0.610 -0.822 -0.048 0.000 0.422 -0.448 -0.251 0.000 0.619 1.253 -1.673 0.000 0.421 -2.354 -1.614 0.000 0.963 0.976 0.986 0.879 0.372 0.715 0.699 +1 0.424 1.480 -1.248 0.361 -0.496 0.973 -0.054 1.611 0.000 1.458 -0.650 -0.229 2.215 0.596 0.222 0.608 2.548 0.710 -0.578 0.483 0.000 1.156 0.797 0.991 0.933 0.824 0.892 0.785 +0 0.305 -0.054 1.522 0.948 -0.956 0.563 -0.221 -0.141 0.000 0.977 0.415 0.961 2.215 0.714 -2.367 0.901 0.000 1.427 0.344 -1.133 3.102 0.864 1.019 0.988 0.650 1.013 0.738 0.730 +0 0.861 0.407 0.623 2.301 1.114 0.943 1.078 -0.827 0.000 0.569 2.089 -1.018 0.000 1.373 0.271 -1.122 2.548 2.117 -0.388 0.398 3.102 0.904 0.964 0.987 1.201 1.375 1.247 1.246 +1 1.908 -0.359 0.491 1.126 1.018 0.894 -1.042 -0.945 1.087 0.573 -0.377 -0.488 0.000 0.769 -0.626 -1.623 1.274 0.397 0.100 -1.191 0.000 0.400 0.524 0.988 0.868 0.622 0.954 0.765 +0 0.409 -1.936 1.310 1.316 0.147 0.685 0.575 -1.016 2.173 0.737 0.751 0.423 0.000 1.178 -0.024 -1.495 2.548 0.653 -0.977 1.226 0.000 1.187 1.031 0.990 2.364 0.581 1.696 1.495 +1 1.577 0.788 1.222 0.762 0.461 2.274 0.441 -0.055 0.000 1.816 0.993 -1.307 0.000 0.718 0.101 1.606 2.548 0.626 1.497 -0.735 0.000 0.879 0.858 0.986 0.622 0.460 0.539 0.700 +0 1.440 0.466 0.995 0.082 -1.377 0.680 0.374 -0.093 2.173 1.227 -0.834 -1.212 0.000 1.033 -1.169 -0.756 0.000 1.034 -1.883 0.574 0.000 0.783 0.861 0.984 0.861 0.867 0.892 0.881 +1 0.900 -0.534 0.823 0.896 0.038 0.824 1.035 1.216 1.087 1.112 0.683 -1.437 2.215 0.706 -0.547 0.224 0.000 0.872 1.988 -0.706 0.000 0.853 0.922 0.993 1.100 0.989 0.914 0.785 +1 1.722 1.003 0.098 0.698 0.134 0.351 -1.591 0.470 0.000 0.653 0.606 -0.733 1.107 0.872 -0.497 -1.650 2.548 0.753 0.974 -0.734 0.000 0.775 0.740 0.986 1.418 0.771 0.973 0.877 +0 1.564 1.728 1.652 1.153 0.960 0.671 -0.041 -0.412 0.000 0.450 -0.670 1.662 0.000 0.637 1.267 -0.230 2.548 0.475 -0.035 0.331 3.102 1.181 0.973 1.086 0.853 0.386 0.669 0.939 +0 0.519 1.588 -0.921 1.333 1.568 0.617 0.904 0.956 2.173 0.613 -0.101 -0.590 0.000 1.036 1.217 -0.257 2.548 1.038 0.054 0.395 0.000 0.811 0.904 0.986 0.851 0.910 0.701 0.735 +0 0.609 -0.595 1.181 0.710 -0.059 0.585 0.045 -0.519 0.000 0.768 -0.779 -1.297 2.215 0.733 1.140 0.778 2.548 0.925 -0.392 0.380 0.000 0.865 0.896 0.985 0.762 1.234 0.883 0.786 +0 2.584 0.731 -1.123 0.620 0.831 1.953 -0.881 0.541 0.000 1.262 1.422 -1.009 1.107 0.666 -0.483 0.204 0.000 0.564 -0.459 -0.660 0.000 0.717 1.399 1.722 1.075 0.896 1.824 1.658 +1 0.766 -0.440 0.946 0.500 -1.116 1.247 -0.274 1.499 2.173 1.012 -0.504 -0.395 2.215 0.520 0.117 -1.114 0.000 1.899 -0.364 0.178 0.000 1.054 0.771 0.987 0.799 1.650 0.980 0.816 +0 0.440 -0.129 0.770 1.682 -0.567 1.283 0.719 1.450 0.000 0.851 1.030 -0.576 2.215 0.818 0.524 0.645 2.548 1.634 2.092 0.170 0.000 0.832 0.877 1.112 0.848 0.820 0.861 0.910 +1 1.565 1.356 -0.034 0.116 0.649 0.787 -0.072 1.510 2.173 0.913 0.598 -1.229 2.215 0.521 1.789 -0.819 0.000 0.967 1.090 0.592 0.000 0.793 0.851 0.975 1.204 0.891 0.916 0.810 +0 1.516 0.896 -1.496 0.484 0.453 0.345 0.406 1.613 0.000 0.976 0.125 0.125 1.107 0.770 1.186 0.362 2.548 0.465 -1.146 -1.014 0.000 0.755 0.876 1.166 1.034 0.604 0.739 0.710 +0 0.571 1.042 0.164 1.559 0.010 0.547 -0.074 0.884 2.173 0.595 -0.796 -1.468 2.215 0.737 -0.318 1.591 0.000 1.381 0.648 -1.149 0.000 0.958 0.898 0.991 1.916 0.784 1.406 1.202 +1 0.945 2.182 1.374 0.906 1.208 1.373 1.123 -0.480 2.173 0.563 0.904 1.688 0.000 0.452 1.230 0.690 0.000 0.665 0.285 0.460 3.102 0.627 1.088 0.983 0.703 0.862 0.959 0.779 +1 1.131 0.286 -1.370 0.294 1.044 0.474 -0.275 -0.277 2.173 0.825 -0.613 -0.808 2.215 0.941 1.070 0.586 0.000 0.905 0.477 0.192 0.000 0.471 1.103 0.989 0.826 0.454 0.715 0.723 +1 1.120 -1.164 0.433 0.568 1.577 0.887 -1.363 -1.012 0.000 1.696 -0.944 0.711 2.215 1.162 -2.197 -1.052 0.000 0.959 -0.640 1.616 3.102 0.993 0.964 0.987 0.688 0.846 1.165 0.966 +0 0.903 1.364 1.229 1.289 -0.145 0.881 1.112 -1.707 2.173 0.795 1.656 0.149 0.000 0.693 0.571 -0.762 0.000 0.640 1.244 -1.040 0.000 0.822 1.040 1.413 1.212 1.449 1.043 0.906 +0 0.637 1.299 -1.708 1.014 -0.413 1.056 1.213 0.361 2.173 0.826 0.639 -0.599 0.000 1.386 -0.041 -1.595 2.548 1.509 -0.804 1.233 0.000 0.843 0.762 1.024 0.931 1.798 1.118 1.025 +0 0.500 1.707 0.262 0.432 1.336 1.235 0.838 0.571 2.173 1.594 0.653 -0.756 2.215 1.060 0.757 -1.364 0.000 0.931 1.258 1.442 0.000 0.735 1.004 0.989 0.765 1.929 1.098 0.893 +0 1.571 0.271 0.681 0.093 1.619 1.373 0.667 -0.902 0.000 1.394 -0.074 1.265 2.215 0.959 0.047 -0.760 0.000 0.954 0.310 0.177 3.102 0.704 0.887 0.996 0.861 0.896 1.064 0.968 +0 0.420 -1.611 -0.946 0.618 0.873 0.800 -0.321 -0.701 0.000 1.166 -1.162 1.424 2.215 1.302 -0.434 0.078 2.548 0.373 1.367 0.616 0.000 1.232 0.979 0.995 0.769 1.317 1.021 0.835 +1 0.889 -1.466 0.976 0.467 -0.578 1.016 -0.257 -1.222 0.000 1.841 -1.352 -0.295 2.215 0.391 -0.847 1.178 0.000 0.810 -0.121 1.455 0.000 0.925 0.917 0.985 0.946 0.775 1.046 0.877 +0 1.937 -0.778 0.707 0.901 0.859 1.242 1.138 -1.278 0.000 0.526 1.318 -0.886 2.215 0.603 0.573 0.666 0.000 1.016 -0.389 -0.301 3.102 1.368 0.875 0.987 1.696 0.764 1.119 0.975 +0 1.270 -1.031 0.683 0.939 1.614 0.496 1.005 -1.686 0.000 0.524 -1.149 -0.202 1.107 0.995 0.420 -0.313 2.548 1.106 -0.749 -0.976 0.000 0.757 0.906 1.125 0.784 0.708 0.904 1.054 +0 0.588 1.402 -0.394 0.782 1.406 0.435 -0.927 1.446 1.087 1.060 -1.121 -0.440 0.000 0.927 -0.547 0.993 2.548 0.745 -0.145 -0.724 0.000 0.767 0.889 0.987 0.950 0.342 0.723 0.850 +1 0.398 -0.917 -0.986 0.205 0.010 0.844 0.101 -0.141 0.000 1.157 2.254 1.592 0.000 1.188 0.828 -0.235 0.000 1.240 -1.856 -1.654 0.000 0.882 0.879 0.986 0.655 1.001 0.857 0.981 +1 1.103 1.345 -1.159 1.544 0.566 1.054 1.123 -0.203 0.000 0.407 -1.612 0.593 0.000 0.516 1.450 0.877 2.548 1.865 1.016 -1.724 1.551 0.857 0.728 1.808 1.120 0.555 0.766 0.799 +0 2.092 0.338 -0.879 1.476 -1.655 2.391 -0.403 0.713 1.087 1.542 0.150 -1.473 2.215 1.845 0.795 -0.577 0.000 2.320 0.172 0.264 0.000 1.750 1.796 1.566 2.450 2.723 1.862 1.677 +1 0.741 -0.218 -0.446 0.681 0.705 0.381 0.061 -1.139 2.173 0.267 2.094 -0.120 0.000 0.737 -1.243 0.724 2.548 0.550 1.229 -1.637 0.000 0.552 1.081 0.984 0.695 0.833 0.717 0.688 +0 5.039 -0.657 1.147 0.310 1.306 1.227 0.461 -0.717 0.000 1.134 0.935 -0.452 0.000 1.854 -0.180 -0.177 2.548 0.617 -0.408 -1.260 0.000 0.909 0.980 1.007 1.788 1.036 1.180 1.303 +1 0.840 -1.240 -0.221 1.017 0.119 1.294 -1.102 0.029 0.000 0.964 -1.306 -1.578 0.000 1.544 -0.662 1.523 1.274 1.082 -0.845 -0.881 3.102 2.370 1.383 0.989 1.100 0.830 1.063 0.961 +0 1.894 -0.111 1.603 1.188 -1.630 1.198 0.947 -0.274 2.173 0.590 -0.829 1.156 1.107 0.542 0.188 0.516 0.000 0.831 0.038 -0.795 0.000 0.688 0.798 0.989 0.831 1.745 1.255 0.962 +1 0.849 -1.358 -0.567 1.136 -1.581 1.503 -1.430 0.551 2.173 0.539 -0.734 -0.928 0.000 1.240 -2.185 -1.653 0.000 1.250 -1.463 -0.086 0.000 0.923 1.211 1.076 0.662 0.725 0.869 0.790 +1 1.278 -0.699 -1.423 0.984 1.325 0.924 -0.334 0.111 1.087 0.735 -1.070 1.051 0.000 0.361 -0.704 -0.266 0.000 1.041 -0.272 -0.732 3.102 0.857 0.976 0.990 0.726 0.715 0.813 0.726 +1 0.431 1.550 -0.705 1.190 0.815 1.356 0.144 1.419 2.173 0.968 0.455 -0.146 0.000 2.486 0.332 -0.901 0.000 1.294 0.473 0.615 3.102 1.502 1.283 0.987 1.172 0.977 1.255 1.092 +1 0.786 -0.167 0.934 0.946 -1.732 0.915 -0.951 -0.256 1.087 0.644 -2.034 1.467 0.000 1.191 -0.534 -1.102 2.548 1.037 0.363 0.004 0.000 0.641 0.788 0.987 1.293 0.931 0.945 0.778 +0 0.812 -1.023 0.164 1.224 0.842 0.687 -0.900 -1.244 2.173 0.720 0.156 -1.629 0.000 0.528 0.776 0.388 0.000 1.028 0.129 -0.732 3.102 0.976 1.005 0.998 0.821 0.634 0.767 0.743 +0 1.330 -0.807 1.360 1.665 0.244 0.932 0.081 1.474 0.000 1.667 0.867 -0.634 0.000 1.351 0.499 -0.328 1.274 0.651 0.362 -0.986 0.000 0.972 0.928 1.741 1.441 0.391 1.047 0.992 +0 0.681 0.222 -0.517 0.977 0.425 1.144 0.271 -1.348 2.173 1.556 -0.269 0.406 2.215 0.922 0.166 0.866 0.000 0.953 -0.720 -0.514 0.000 1.003 0.946 0.990 1.130 2.038 1.121 0.923 +0 1.059 0.052 0.123 0.730 1.344 0.987 -0.051 1.386 2.173 0.718 -0.729 -1.160 2.215 0.901 0.105 -0.492 0.000 1.027 0.747 0.049 0.000 0.654 0.882 1.086 0.866 1.027 0.863 0.769 +0 1.518 1.227 -1.711 0.913 1.616 1.608 0.493 0.322 0.000 1.558 1.440 -1.213 2.215 0.491 0.027 -0.453 2.548 0.843 0.206 -0.054 0.000 0.896 0.747 0.980 0.976 0.942 1.180 1.099 +0 1.918 -0.086 -0.813 3.775 -0.917 4.190 0.138 0.786 1.087 1.531 -0.861 -1.335 0.000 0.859 1.124 -1.095 2.548 0.781 -1.041 -0.382 0.000 0.928 0.942 0.981 3.987 2.712 2.566 1.882 +1 1.261 -0.336 1.479 0.725 -0.786 0.982 -1.012 -1.582 1.087 1.085 -0.492 0.528 0.000 0.709 2.672 0.411 0.000 1.566 -0.728 -0.758 3.102 0.890 0.928 1.182 0.840 0.894 0.897 0.808 +1 1.194 0.165 0.667 0.634 1.614 0.981 0.797 -0.868 0.000 1.130 -0.396 1.020 2.215 0.731 1.368 -1.340 0.000 0.523 0.473 1.096 0.000 0.831 0.950 0.987 0.718 0.646 1.025 0.889 +1 0.960 0.282 1.241 1.069 -1.214 1.038 0.458 0.729 2.173 1.034 -2.815 -0.873 0.000 0.743 0.805 -0.640 0.000 1.516 0.213 -1.333 0.000 0.780 1.230 1.125 1.140 1.185 1.037 0.925 +1 0.673 -0.414 -1.389 1.812 -0.565 0.515 -0.317 0.450 2.173 0.780 1.019 1.298 2.215 0.584 -1.072 -0.962 0.000 0.811 -1.459 0.079 0.000 0.651 0.724 1.036 1.260 0.944 0.931 0.867 +1 0.942 -0.537 0.408 0.540 -0.258 0.847 -0.308 -1.087 0.000 1.405 -0.552 -0.555 2.215 1.465 -0.960 1.474 0.000 3.348 -0.785 0.539 0.000 1.123 0.727 0.986 0.896 0.809 0.958 0.975 +0 1.249 -1.702 0.479 0.740 0.174 1.648 -0.284 -1.184 0.000 1.383 -0.782 0.301 0.000 1.206 -0.151 1.143 1.274 1.010 -0.967 -0.777 0.000 1.031 0.883 0.993 0.846 0.606 0.732 0.724 +0 1.170 1.016 -0.510 0.573 -1.337 0.673 1.421 0.430 2.173 0.822 -0.102 -1.565 2.215 0.538 -1.736 1.257 0.000 0.384 0.025 -0.250 0.000 0.748 0.764 0.986 0.938 1.413 1.023 0.919 +0 0.534 -1.050 1.221 0.904 -0.219 0.881 -1.299 -0.820 2.173 0.555 2.173 -1.357 0.000 1.954 0.148 0.618 1.274 1.681 0.625 -1.581 0.000 1.054 1.630 0.984 0.840 2.052 1.750 1.376 +0 0.641 0.611 -0.420 0.069 -1.501 1.363 -0.887 1.603 0.000 1.616 1.026 -0.363 1.107 0.911 1.225 -0.021 2.548 0.798 -0.248 -0.187 0.000 0.792 1.837 0.904 0.982 0.434 1.677 1.266 +0 0.876 -0.699 -0.337 1.168 1.427 0.293 1.454 0.424 0.000 0.848 0.980 1.181 1.107 0.293 1.919 -0.382 0.000 0.593 0.986 -1.008 3.102 0.390 0.581 1.401 0.922 0.592 0.844 0.786 +1 0.666 -0.179 0.021 0.839 -1.618 0.727 0.738 -0.316 2.173 0.749 -0.586 0.067 2.215 0.726 -0.343 -1.636 0.000 1.806 0.532 1.458 0.000 0.799 1.092 1.031 0.852 0.870 0.893 0.764 +0 0.467 -1.656 0.367 1.882 1.167 0.903 -1.543 -1.237 0.000 0.882 -1.805 -0.776 0.000 1.434 -0.572 0.461 2.548 0.666 -0.317 -0.578 3.102 0.817 0.746 0.992 0.773 0.609 0.901 0.908 +0 0.543 0.269 -1.743 0.978 0.425 0.918 0.498 -1.502 0.000 1.004 -0.219 -1.563 0.000 1.261 -0.096 -0.053 0.000 0.712 -0.690 -0.079 3.102 0.768 0.981 0.990 0.513 0.442 0.717 0.702 +1 0.331 -0.543 1.403 1.619 -1.716 1.076 -0.451 -0.166 2.173 0.844 0.979 -1.107 0.000 1.408 -0.098 0.595 2.548 0.884 -0.773 1.035 0.000 0.952 0.972 0.988 1.444 1.007 1.297 1.074 +0 0.879 0.034 -1.648 0.513 -0.488 0.604 0.062 -0.019 2.173 0.640 0.600 0.920 2.215 0.383 -0.751 0.782 0.000 0.516 0.356 1.428 0.000 0.424 0.570 0.988 0.700 0.731 0.623 0.522 +0 1.573 0.466 -1.588 0.369 -0.918 0.734 1.324 0.521 0.000 0.724 0.306 0.677 1.107 0.514 1.718 -0.574 0.000 0.662 -0.166 -0.980 1.551 0.971 0.917 0.989 0.876 0.644 0.665 0.767 +1 0.421 -1.576 -0.240 0.774 0.245 0.835 -0.101 1.489 2.173 0.716 -1.025 1.201 0.000 1.243 1.068 -0.274 2.548 1.175 -1.986 -0.188 0.000 0.935 0.759 0.998 0.953 1.536 0.981 0.870 +0 0.881 -1.523 1.146 0.436 -0.732 1.077 -1.301 -0.897 2.173 0.396 -1.149 1.051 0.000 1.610 -0.381 0.709 1.274 0.697 -2.142 -0.841 0.000 0.851 0.898 0.990 0.796 1.797 0.979 0.811 +1 2.210 0.185 -1.499 0.471 -0.443 0.905 -0.123 0.182 0.000 0.759 -0.475 -1.201 0.000 1.015 0.553 0.830 2.548 0.506 0.396 0.064 0.000 0.861 0.932 1.152 0.815 0.320 0.695 0.654 +0 0.830 0.827 0.531 0.880 -1.565 0.923 0.303 -0.603 0.000 0.893 -0.223 -0.888 0.000 1.214 1.214 0.853 0.000 0.827 -0.233 0.952 3.102 0.885 0.725 1.125 0.565 0.510 0.512 0.557 +1 0.936 -0.947 -1.606 0.688 -0.593 0.858 -0.267 -1.444 2.173 0.950 -1.353 0.054 0.000 1.457 -0.542 0.454 0.000 1.124 -0.919 1.097 3.102 0.975 0.831 0.986 0.832 0.900 0.956 0.857 +1 1.994 -0.311 1.146 0.565 -1.536 1.196 -0.666 0.067 2.173 1.313 -0.276 -0.750 2.215 1.147 0.062 1.725 0.000 0.389 -0.705 -1.493 0.000 0.815 0.871 0.989 1.310 1.290 1.123 0.930 +0 1.214 -0.822 0.808 0.553 1.018 1.310 0.553 -1.343 2.173 1.275 0.238 0.106 0.000 0.459 0.319 1.038 0.000 0.813 0.777 -0.575 1.551 0.876 0.699 0.989 1.401 0.727 0.962 0.907 +0 0.793 0.185 0.057 2.109 -0.676 1.061 -0.530 -0.981 2.173 1.052 -0.504 0.852 0.000 1.578 -0.506 1.454 2.548 1.422 0.629 0.821 0.000 0.874 0.942 1.098 0.972 1.308 1.053 0.985 +0 0.866 -0.591 -1.366 0.500 0.339 1.083 -0.511 1.351 2.173 1.474 -1.515 -0.284 2.215 0.725 0.000 1.642 0.000 0.697 0.559 -0.161 0.000 0.827 0.901 0.986 1.081 2.110 1.174 0.945 +0 0.351 1.212 -0.899 1.282 0.761 3.184 0.477 0.074 2.173 3.749 1.111 -1.404 0.000 1.829 -0.155 1.477 0.000 0.575 -0.274 -1.245 0.000 0.860 0.855 0.986 1.832 0.929 1.165 1.103 +0 2.253 0.120 1.482 0.242 -0.943 0.585 1.054 0.307 0.000 0.799 0.592 -1.124 2.215 0.708 -0.341 -0.830 2.548 0.950 1.365 -0.268 0.000 0.654 0.922 0.984 0.801 0.460 0.693 0.793 +1 2.327 -1.339 -0.998 1.421 -1.426 2.703 0.901 0.842 0.000 1.470 -1.536 -0.502 0.000 0.554 -0.781 0.666 2.548 0.708 -0.482 -0.158 1.551 0.834 0.792 0.985 0.835 0.331 0.706 0.673 +0 0.412 -1.225 1.320 0.458 -0.643 0.659 -0.503 1.056 0.000 0.690 -1.304 -0.225 2.215 0.909 -0.862 -1.009 1.274 0.457 -1.314 -1.519 0.000 0.773 0.867 0.980 0.663 0.572 0.631 0.613 +0 1.107 0.512 -0.151 1.110 0.803 0.820 0.639 -1.279 2.173 0.438 0.355 1.629 0.000 0.708 -0.510 -1.686 1.274 0.673 0.996 -0.597 0.000 0.929 0.956 1.165 0.909 0.696 0.820 0.747 +0 0.439 1.428 -0.750 0.801 -1.326 0.997 0.371 0.416 2.173 1.053 0.369 1.544 0.000 0.532 0.863 -1.723 0.000 1.480 -0.695 -0.361 3.102 0.400 1.116 0.999 0.828 1.170 0.962 0.847 +0 0.557 0.046 0.769 1.813 -1.251 0.815 0.145 1.069 0.000 0.838 0.626 -0.573 0.000 1.290 1.198 0.362 1.274 1.071 1.460 1.460 0.000 0.843 0.938 1.350 1.277 1.642 1.055 0.873 +0 1.760 0.062 1.516 1.945 1.172 1.839 -1.119 -0.276 0.000 0.364 -0.882 -1.563 2.215 0.339 -0.975 -0.889 2.548 0.418 -0.357 -0.386 0.000 0.497 0.851 0.984 0.780 0.215 0.550 1.038 +0 0.861 0.523 1.494 1.122 0.162 0.891 -0.998 -1.272 2.173 1.049 0.415 -0.341 2.215 0.763 -1.500 1.383 0.000 0.562 0.391 0.895 0.000 0.966 0.970 1.269 0.896 1.534 1.075 0.979 +1 0.941 0.719 0.837 0.853 -1.300 0.667 -0.281 0.885 2.173 0.851 0.274 0.297 0.000 2.032 0.616 -0.836 2.548 0.766 0.645 1.646 0.000 1.020 1.127 1.164 0.884 1.621 0.938 0.819 +1 0.984 -1.358 -0.851 0.819 1.444 0.574 -0.362 0.316 1.087 0.741 -1.205 0.753 2.215 1.390 -1.706 -1.391 0.000 1.203 -1.337 -0.370 0.000 0.980 0.772 1.092 0.946 0.569 0.702 0.660 +1 0.634 1.476 1.324 0.764 1.313 0.987 1.062 -0.242 1.087 0.723 0.291 -0.622 0.000 1.074 0.638 -1.622 0.000 0.420 -0.286 0.442 0.000 0.842 0.816 1.001 0.636 1.213 0.859 0.731 +0 1.177 -0.160 0.154 3.206 0.497 1.710 -0.932 -1.071 0.000 1.043 1.379 -1.416 0.000 1.712 -0.541 0.648 2.548 1.982 -0.016 -1.328 0.000 0.645 0.912 0.993 0.849 0.984 0.875 1.227 +1 0.991 -0.259 0.322 0.984 1.517 1.253 -0.143 -0.662 0.000 0.518 -0.297 1.470 0.000 0.613 -1.260 1.037 2.548 1.066 0.379 0.500 3.102 0.627 0.912 1.205 0.710 0.712 0.776 0.755 +1 1.908 -1.067 0.395 0.852 0.775 1.158 -0.621 -1.210 1.087 0.427 -0.087 -0.905 0.000 0.808 -0.649 1.078 0.000 0.445 -1.507 -0.495 0.000 0.924 0.918 0.978 0.604 0.758 1.007 0.849 +1 1.091 -1.196 1.560 0.432 -0.523 0.869 -1.486 -0.951 1.087 0.613 -0.964 0.348 0.000 1.456 -1.443 0.892 0.000 0.881 -0.117 -0.312 3.102 0.811 0.887 0.983 0.801 0.846 0.867 0.759 +1 0.947 -1.305 -0.869 1.161 -1.435 0.930 -0.709 1.045 0.000 1.072 -0.187 0.733 1.107 0.894 1.450 -1.010 0.000 0.456 0.498 0.248 3.102 1.015 1.021 0.985 1.006 0.370 1.024 1.044 +0 1.451 -0.692 0.994 0.361 1.178 1.129 0.937 -0.620 0.000 1.459 1.813 1.634 0.000 0.861 -0.077 -1.014 2.548 1.113 0.592 -0.085 0.000 0.820 0.860 0.980 0.848 0.770 0.664 0.860 +1 1.269 -0.268 1.241 1.617 -1.679 0.866 -0.150 -0.472 0.000 0.449 0.468 1.073 2.215 1.565 0.494 0.323 0.000 0.647 0.955 -0.863 3.102 1.562 1.011 0.986 0.656 0.507 0.707 0.895 +0 1.448 0.965 -0.487 2.697 -0.073 1.300 0.295 1.481 0.000 1.441 -0.689 1.743 2.215 0.552 -0.110 0.455 2.548 0.847 -1.679 0.924 0.000 2.006 1.305 1.002 2.334 0.914 1.460 1.460 +0 0.590 -1.376 1.092 1.360 1.324 1.128 -0.535 -0.045 2.173 1.033 0.701 -1.301 2.215 0.623 -0.519 -1.679 0.000 0.716 0.679 -0.268 0.000 0.894 0.992 0.986 1.116 1.792 1.157 0.923 +0 0.865 0.487 1.738 0.740 0.934 1.112 -0.136 -0.952 0.000 1.150 -1.535 0.288 0.000 0.709 -1.080 1.369 1.274 1.040 0.703 0.204 3.102 2.868 1.712 0.995 0.699 0.972 1.217 1.259 +1 0.740 0.949 -0.060 2.050 0.755 0.951 0.166 -1.259 2.173 0.808 -0.488 1.338 2.215 0.936 0.024 -0.862 0.000 0.822 -0.818 -0.307 0.000 0.727 0.790 1.145 1.442 1.026 1.109 0.895 +1 0.493 -0.478 0.990 1.512 -1.057 0.646 1.120 1.258 0.000 0.552 0.940 -1.476 0.000 2.510 -0.589 0.120 2.548 0.429 -0.076 -1.434 3.102 0.801 0.560 1.152 1.203 0.813 1.098 0.992 +1 1.553 -0.881 -1.612 0.653 0.592 0.502 0.056 -0.091 0.000 0.864 0.566 0.356 2.215 1.564 1.418 -1.488 0.000 0.892 0.553 -0.529 0.000 1.160 1.194 1.276 0.759 0.523 0.874 1.070 +1 0.516 -0.842 -0.676 1.048 0.772 0.531 0.651 -1.292 2.173 0.859 -0.383 -0.318 2.215 0.675 -1.213 0.982 0.000 0.908 -0.679 0.347 0.000 1.005 0.964 0.987 0.717 0.940 0.733 0.675 +1 1.209 0.610 0.479 0.644 1.319 0.784 -0.549 -0.882 2.173 0.324 0.652 -0.556 0.000 0.916 1.399 1.624 0.000 0.678 -1.082 0.216 0.000 0.857 1.226 0.987 0.495 0.855 0.751 0.741 +0 0.965 -1.175 1.416 1.102 1.108 1.205 -1.353 -0.624 0.000 0.706 -1.077 0.327 1.107 0.478 -1.661 -1.339 0.000 0.734 -0.150 1.159 3.102 0.879 0.967 0.985 0.773 0.542 0.739 0.836 +1 2.073 0.007 -1.585 0.668 -1.542 1.095 0.093 0.030 2.173 0.439 0.488 0.362 0.000 0.820 0.454 -0.954 2.548 0.896 -1.177 0.654 0.000 0.932 0.923 0.985 1.483 0.946 0.978 0.920 +1 0.550 1.012 0.376 0.513 -0.586 1.114 2.050 -1.254 0.000 1.266 0.195 1.378 2.215 1.336 0.492 -0.656 1.274 2.194 -0.078 0.709 0.000 2.350 1.971 0.989 0.943 1.355 1.517 1.748 +1 2.048 0.231 -1.410 0.208 0.580 0.998 -0.615 -0.081 2.173 0.563 0.426 0.170 0.000 0.661 -1.065 1.034 2.548 0.699 0.911 1.559 0.000 0.959 0.828 0.985 0.851 0.899 0.912 0.785 +0 0.618 -0.641 1.163 0.123 -1.245 0.843 2.839 -1.400 0.000 0.601 -0.942 0.284 0.000 0.640 1.194 0.841 1.274 0.601 0.466 -0.616 3.102 0.847 0.891 0.989 0.624 0.493 0.923 0.826 +1 0.506 -2.134 0.773 2.364 0.911 1.042 -1.430 -1.323 2.173 0.914 -1.238 0.133 2.215 0.527 1.629 -1.055 0.000 0.383 -0.067 -1.022 0.000 1.118 1.375 0.975 1.391 1.393 1.265 1.259 +0 1.516 -0.713 -1.531 0.723 1.410 1.088 -0.722 0.471 2.173 0.825 -0.537 -0.788 2.215 0.669 -1.880 -0.585 0.000 0.399 -0.016 0.110 0.000 0.767 0.927 0.979 0.823 1.270 0.963 0.836 +0 2.129 0.105 0.268 0.595 0.415 1.650 0.844 -1.510 1.087 0.980 0.474 -0.425 2.215 0.465 0.979 0.574 0.000 0.586 -0.754 1.300 0.000 0.754 0.824 0.976 2.028 1.590 1.407 1.086 +1 0.531 -1.204 -0.660 0.899 1.201 0.552 -0.760 -0.924 0.000 1.103 0.804 1.565 1.107 1.418 0.296 0.194 2.548 0.930 1.366 1.671 0.000 0.891 0.975 0.987 1.086 1.299 0.967 0.864 +0 0.366 -1.671 1.642 2.169 -1.083 1.025 -0.783 -0.068 2.173 1.336 -0.860 1.690 0.000 1.192 -0.844 0.602 2.548 0.787 -1.541 0.279 0.000 1.092 0.969 0.985 1.755 0.787 1.325 1.253 +0 1.049 0.231 0.630 1.188 0.483 0.698 -1.771 -0.726 0.000 0.838 0.766 1.274 2.215 1.271 0.633 -0.968 2.548 0.665 1.818 -1.367 0.000 3.762 2.305 0.994 0.998 0.989 1.548 1.360 +0 0.536 1.071 1.154 2.406 -1.595 0.433 0.935 -0.529 0.000 0.952 1.247 0.549 2.215 0.617 -1.526 -0.549 0.000 0.814 -0.265 0.189 0.000 0.990 0.833 0.984 0.867 0.590 0.816 0.755 +1 0.367 -0.946 0.783 0.763 0.280 1.083 -0.028 -1.680 2.173 0.963 -0.678 0.216 0.000 1.192 0.365 -0.900 2.548 0.651 -0.140 1.024 0.000 0.740 1.021 0.985 1.063 0.966 0.910 0.824 +1 1.647 -0.275 0.131 1.107 -0.388 0.715 -0.537 1.055 2.173 0.353 0.537 -0.920 0.000 0.547 0.296 -1.611 2.548 1.160 -0.347 -1.681 0.000 0.915 0.925 0.981 0.811 0.630 0.783 0.739 +1 0.722 -1.195 1.588 1.556 -0.985 1.537 -0.143 0.171 2.173 1.306 -0.145 -1.020 0.000 0.996 -0.411 1.214 2.548 1.526 -2.102 1.173 0.000 3.216 1.935 1.077 1.670 1.267 1.683 1.399 +0 0.696 -0.626 1.022 0.347 -0.622 0.608 -0.146 -0.360 0.000 1.235 -0.288 1.622 2.215 1.095 -0.204 0.373 0.000 0.895 -0.121 -1.149 3.102 0.902 0.772 0.989 0.771 0.575 0.784 0.682 +1 0.734 0.336 -0.446 1.303 0.675 1.052 0.158 -0.917 0.000 0.769 -0.128 1.035 2.215 1.698 -0.299 1.615 0.000 1.385 -0.948 0.299 1.551 1.068 1.220 1.148 0.752 0.754 0.930 0.881 +0 1.506 -0.605 0.967 0.307 -1.503 0.925 0.001 -0.457 0.000 0.523 -1.035 -0.984 2.215 0.569 0.902 1.306 2.548 0.824 -0.361 -1.596 0.000 1.177 0.979 0.987 0.751 0.872 0.691 0.758 +0 1.815 -0.957 -0.074 0.830 0.360 1.510 -1.544 -1.573 0.000 1.295 0.248 1.260 1.107 1.726 -0.469 -0.487 1.274 0.495 1.119 0.585 0.000 2.932 2.116 0.976 1.363 1.708 1.603 1.407 +1 0.891 2.240 -0.237 1.022 -1.255 0.728 -0.032 -0.109 2.173 1.405 -0.485 -1.735 0.000 0.513 2.107 1.130 0.000 0.707 -1.554 -0.763 0.000 0.547 0.661 1.048 1.567 0.934 1.106 1.029 +0 2.514 -0.234 -0.993 0.469 1.493 0.989 -0.682 0.267 2.173 0.476 -0.818 0.792 0.000 0.855 0.129 1.248 2.548 0.589 -0.406 -1.572 0.000 0.598 0.719 1.180 0.922 1.012 0.989 0.784 +0 0.653 -0.978 -0.182 1.204 -1.692 0.750 -0.853 1.134 0.000 1.888 -0.614 -0.567 1.107 1.080 0.349 1.497 2.548 1.061 -0.471 0.413 0.000 0.850 0.905 1.201 1.080 1.661 1.097 0.950 +0 0.348 -0.881 -1.509 2.117 -1.564 0.736 -0.542 0.479 1.087 1.016 0.979 -0.199 1.107 0.541 0.646 0.764 0.000 0.612 0.836 -1.221 0.000 0.627 0.827 0.982 1.214 1.314 1.118 0.861 +1 0.487 0.570 0.401 1.204 -1.029 0.686 1.424 -1.044 2.173 1.223 0.046 0.500 0.000 0.995 0.896 1.587 2.548 0.527 0.571 0.097 0.000 0.503 0.916 1.019 0.720 0.754 0.875 0.766 +1 0.335 1.116 0.228 0.826 1.666 1.327 1.339 -0.764 1.087 1.331 0.246 1.042 0.000 0.339 0.805 -0.619 0.000 0.569 0.098 -0.293 0.000 1.061 0.712 0.987 1.308 0.894 1.025 0.901 +1 1.800 0.443 -0.776 0.216 1.320 0.591 0.624 0.740 2.173 0.703 -0.231 -1.212 0.000 0.528 -0.442 0.675 2.548 0.668 0.132 1.261 0.000 0.729 0.839 0.987 0.781 0.411 0.680 0.633 +0 1.061 -1.299 1.631 2.761 1.591 1.543 1.823 0.423 0.000 2.550 1.417 -0.431 2.215 1.589 0.062 1.685 1.274 1.741 -0.543 -1.182 0.000 1.145 1.615 0.998 1.813 2.583 3.883 3.894 +0 0.722 0.080 -1.689 1.179 -0.974 0.619 0.129 0.516 0.000 0.811 1.050 0.085 0.000 1.223 1.005 -1.232 2.548 1.086 -0.468 1.094 3.102 0.915 1.067 0.986 0.745 1.120 0.799 0.849 +0 1.345 0.738 0.518 0.362 0.762 0.507 0.836 -0.793 2.173 1.029 0.212 -1.697 2.215 0.673 -0.775 -0.985 0.000 0.516 0.972 0.076 0.000 0.938 0.858 0.986 0.890 0.844 0.790 0.712 +0 1.410 -2.134 0.812 0.583 0.177 0.664 -1.013 1.144 1.087 1.198 -0.294 -0.733 0.000 1.360 -0.793 -1.649 2.548 1.650 -0.867 -0.681 0.000 0.752 0.958 0.989 0.773 0.697 0.802 0.910 +0 1.009 0.735 0.973 1.358 0.389 1.978 -0.707 -0.996 2.173 0.747 -1.136 0.431 2.215 0.742 -0.291 1.133 0.000 0.697 -1.073 1.374 0.000 0.434 1.303 0.997 1.460 1.765 1.846 1.406 +1 0.610 0.291 1.128 1.010 -0.127 0.800 1.272 -1.370 1.087 1.209 0.425 0.373 1.107 0.535 2.208 -1.078 0.000 0.907 0.652 1.703 0.000 0.840 1.182 0.987 0.986 1.580 0.903 0.813 +0 0.416 0.559 1.691 0.526 -1.409 0.613 0.907 1.534 2.173 0.917 1.523 -0.365 2.215 0.806 1.822 -1.442 0.000 2.042 0.334 -0.188 0.000 1.794 1.173 0.988 0.967 1.151 1.076 1.076 +0 0.643 -1.666 -0.532 1.340 1.096 1.030 1.136 -0.410 0.000 1.673 -0.891 -0.839 2.215 2.478 -0.293 1.094 0.000 1.215 -0.914 1.362 0.000 0.888 0.837 1.279 1.278 0.948 1.110 1.035 +1 1.121 -0.096 -1.619 1.256 0.935 0.581 -0.862 0.468 2.173 0.941 -1.434 -0.521 0.000 0.742 1.483 -1.237 0.000 1.099 -0.301 0.257 0.000 1.154 0.996 1.224 0.923 0.758 0.702 0.796 +1 1.920 0.327 0.134 0.415 -0.144 0.664 0.531 1.403 2.173 0.549 0.135 1.602 2.215 0.619 1.275 -1.284 0.000 0.461 -0.630 -0.889 0.000 0.794 0.751 0.998 0.910 0.237 0.774 0.697 +1 1.061 -0.613 1.342 0.824 1.192 1.812 1.051 -0.517 0.000 1.040 0.408 0.559 0.000 1.215 0.599 -1.686 2.548 2.248 -0.380 1.690 3.102 0.930 1.063 0.981 0.709 0.752 0.841 0.733 +1 0.722 -0.535 1.040 0.498 -0.360 0.874 0.351 -0.937 0.000 0.753 -0.396 -1.188 0.000 1.532 0.093 0.650 2.548 1.270 0.575 1.561 0.000 0.774 1.024 0.980 0.871 0.633 0.919 0.841 +0 0.910 0.021 1.541 0.421 0.206 0.663 0.597 -0.809 0.000 0.505 1.601 -1.614 0.000 0.903 0.884 0.473 2.548 0.814 -0.249 -0.040 3.102 1.057 0.964 0.990 0.609 0.532 0.683 0.724 +1 1.668 0.993 0.367 0.657 -1.029 2.060 1.715 -1.160 0.000 1.055 0.709 1.387 0.000 1.609 0.484 -0.159 2.548 1.290 -0.098 1.638 0.000 0.746 1.315 1.380 0.657 0.380 0.750 0.770 +1 0.775 -0.824 -1.436 0.873 0.704 0.967 -0.055 1.398 0.000 1.404 -0.757 -0.603 0.000 0.994 -0.696 -0.096 0.000 1.377 0.528 1.060 1.551 0.800 0.621 1.067 0.862 0.700 0.826 0.777 +0 0.907 0.770 0.647 0.126 1.345 0.748 -0.424 -1.040 0.000 0.818 0.804 -0.144 2.215 0.471 -0.124 1.613 0.000 1.021 1.339 1.344 0.000 0.880 1.067 0.979 0.620 0.734 0.695 0.667 +1 1.088 -1.507 0.353 0.544 1.670 1.976 0.784 -1.416 0.000 1.874 -1.169 0.750 0.000 2.659 -0.925 -0.825 2.548 1.683 -0.295 0.487 0.000 0.989 0.883 0.988 1.140 0.738 1.069 0.897 +0 3.344 0.008 -0.364 0.606 -0.566 0.973 0.289 1.363 2.173 0.776 -0.914 1.410 2.215 0.370 1.136 0.338 0.000 0.594 -0.999 -1.692 0.000 0.919 0.873 0.992 1.363 0.840 1.268 1.011 +0 0.842 -0.228 0.625 0.690 -0.464 0.457 -0.745 1.293 0.000 0.648 0.006 -1.536 2.215 1.283 -0.763 -1.443 2.548 1.325 -0.271 -0.150 0.000 1.174 0.977 0.988 0.745 0.429 0.678 0.679 +1 0.933 0.232 0.800 0.365 -0.091 0.770 -0.631 0.940 0.000 0.511 -0.074 0.407 0.000 1.841 -0.267 -1.192 2.548 0.982 1.058 -1.398 0.000 0.724 0.935 0.984 1.001 0.744 0.873 0.761 +1 0.788 -0.858 1.049 0.621 0.426 0.594 0.209 -1.582 0.000 1.148 0.508 0.749 2.215 0.786 -1.002 -1.409 0.000 0.993 0.618 -0.375 0.000 0.886 0.811 0.986 1.410 0.744 1.131 1.022 +1 0.675 0.406 0.824 0.478 1.650 0.772 -0.831 -0.619 2.173 0.711 0.059 0.190 0.000 1.328 -0.398 -1.466 0.000 0.711 -2.262 -1.402 0.000 0.753 1.274 0.983 0.623 0.579 0.900 0.823 +1 0.560 -1.418 -1.552 0.692 -0.663 0.786 0.919 0.431 2.173 0.585 1.478 1.361 2.215 0.640 1.586 -1.054 0.000 0.606 0.037 -0.288 0.000 0.993 1.020 0.989 1.077 0.800 0.939 0.830 +0 0.901 -0.165 -0.759 0.902 1.631 1.112 -1.641 0.223 0.000 1.395 -1.017 -1.421 2.215 1.295 0.872 0.245 0.000 1.088 1.943 0.820 0.000 1.169 1.226 1.042 0.841 0.556 1.583 1.247 +0 0.579 2.041 1.214 0.366 0.264 0.549 1.181 0.083 2.173 0.849 0.136 -1.333 2.215 0.520 0.302 1.574 0.000 0.547 -1.417 0.703 0.000 0.820 1.064 0.977 0.857 1.109 0.791 0.751 +1 1.836 0.054 0.056 1.316 0.152 1.516 -0.374 1.503 0.000 0.906 -0.030 -1.046 0.000 1.403 0.542 -1.280 2.548 1.054 -0.335 -0.485 0.000 0.867 0.750 0.968 0.742 0.847 0.938 0.779 +0 0.925 0.309 -0.652 1.670 -1.509 1.285 -0.494 0.584 2.173 0.714 -0.339 1.248 0.000 1.246 1.204 -1.009 2.548 0.592 1.714 0.044 0.000 1.487 1.238 1.200 1.586 2.266 1.319 1.148 +1 0.359 1.266 1.109 0.561 -0.989 1.047 0.044 -1.335 2.173 1.283 -0.260 1.405 0.000 1.075 0.352 -0.482 2.548 1.809 -0.021 -0.059 0.000 0.903 1.278 0.978 0.733 0.947 1.171 0.950 +0 0.643 0.100 0.608 0.688 -1.504 0.689 -0.457 1.320 2.173 0.712 0.467 -0.720 1.107 1.384 0.208 -0.015 0.000 0.807 -2.393 1.149 0.000 0.874 1.026 0.984 0.760 1.114 1.012 0.837 +0 1.118 1.031 1.539 0.506 0.420 1.159 1.199 -1.089 2.173 0.933 0.534 0.348 1.107 0.915 -0.809 0.541 0.000 0.621 -1.097 -1.167 0.000 0.850 0.988 0.990 0.997 1.560 1.235 1.094 +0 0.356 -0.139 -0.042 1.344 -0.933 0.859 1.091 1.249 2.173 0.441 0.533 -0.476 0.000 0.692 0.703 0.128 0.000 0.537 1.963 -0.039 0.000 0.698 0.941 0.983 0.865 0.807 1.183 1.099 +1 0.963 1.779 -0.894 1.085 -1.076 1.093 0.117 0.958 2.173 0.596 0.785 -0.595 0.000 0.479 -2.333 1.698 0.000 0.687 -0.513 -0.505 3.102 2.254 1.230 0.994 1.487 0.953 1.090 1.233 +0 0.829 0.569 0.657 0.211 -1.478 0.459 -1.329 -1.074 0.000 1.018 0.198 0.290 2.215 0.909 -0.042 -0.700 0.000 1.444 0.064 1.605 3.102 0.893 0.929 0.992 0.816 1.017 0.846 0.891 +1 1.841 0.666 0.930 0.686 -0.355 1.382 0.329 -0.473 2.173 0.465 -0.425 -1.124 0.000 0.599 0.680 -1.615 2.548 0.435 -1.177 -1.203 0.000 0.481 0.811 1.425 0.821 1.000 0.940 0.761 +1 0.683 -2.110 -0.451 0.511 -0.834 0.499 -0.782 1.652 1.087 0.655 0.968 -1.358 2.215 0.515 -1.688 0.574 0.000 2.438 -0.145 0.381 0.000 1.184 1.076 0.980 1.216 0.924 0.983 0.933 +0 0.499 1.128 0.946 0.444 1.220 1.105 0.529 -1.205 2.173 1.158 0.780 0.247 2.215 0.689 0.762 1.407 0.000 0.664 -0.022 -0.276 0.000 0.816 0.851 1.002 0.890 1.623 0.970 0.780 +1 2.721 1.091 0.725 0.523 0.944 0.950 2.432 -0.909 0.000 0.978 1.136 -0.273 1.107 1.025 1.044 1.626 0.000 0.366 0.473 -0.581 0.000 1.017 1.019 0.992 1.188 0.801 0.953 1.056 +0 0.713 -0.752 -0.208 0.853 0.758 0.865 -0.902 -0.886 2.173 0.883 0.348 -1.457 2.215 1.042 -1.191 1.355 0.000 0.651 1.479 -0.264 0.000 0.802 1.060 0.991 0.901 1.077 0.939 0.863 +0 0.794 0.465 0.881 0.667 -0.885 1.590 -0.302 0.394 2.173 0.806 -0.049 1.636 0.000 2.130 0.321 -1.207 2.548 0.603 1.021 0.382 0.000 0.950 0.915 1.008 1.076 2.400 1.179 0.941 +0 0.326 -1.446 0.350 0.375 1.518 1.199 0.057 1.637 2.173 1.089 -0.655 -0.399 2.215 1.077 -0.311 0.562 0.000 0.988 -2.048 -0.599 0.000 0.644 1.152 0.983 0.837 1.741 0.974 0.802 +1 0.916 -1.352 -0.720 0.320 -1.328 0.579 0.012 1.281 2.173 0.452 -1.914 1.714 0.000 0.979 -1.788 1.122 0.000 1.242 -1.082 -0.140 0.000 1.027 1.112 0.984 0.542 0.676 0.700 0.657 +1 0.965 -0.629 1.720 1.446 -0.529 0.981 -1.063 0.751 0.000 1.020 0.041 -0.431 0.000 1.980 -0.631 1.363 1.274 1.695 -0.993 -0.602 3.102 0.844 0.834 1.470 1.190 1.417 0.941 0.860 +1 1.191 0.296 0.117 0.739 -0.683 1.997 1.225 -1.134 0.000 1.033 -0.331 -0.776 0.000 1.862 0.275 1.168 2.548 2.322 0.114 0.630 3.102 0.902 0.877 0.990 0.877 0.753 0.827 0.776 +0 2.178 -0.851 -0.902 0.262 0.985 0.931 -1.386 0.343 2.173 0.861 -1.596 1.596 0.000 1.291 -1.635 -0.001 0.000 0.655 -0.240 0.969 3.102 1.607 1.055 1.037 1.170 0.654 0.803 0.858 +1 0.441 2.150 1.061 1.092 0.345 0.766 1.479 -1.504 0.000 1.028 0.064 -0.042 2.215 0.792 0.416 1.619 0.000 0.818 1.016 -0.900 3.102 0.882 0.652 0.983 0.881 0.768 0.844 0.798 +0 0.305 -0.863 -0.353 2.308 -1.499 1.387 -0.425 0.353 2.173 0.418 -1.168 -1.682 2.215 0.374 0.697 -0.938 0.000 0.516 0.015 -0.457 0.000 0.680 0.985 0.998 0.498 1.169 1.033 0.817 +1 0.821 1.143 -0.065 2.526 0.449 0.959 0.420 -1.343 2.173 1.207 -0.644 1.365 0.000 2.575 1.138 -0.695 0.000 1.398 1.033 1.171 3.102 3.966 2.364 0.990 0.876 1.072 1.516 1.444 +0 0.623 1.018 1.167 1.208 0.277 0.948 -0.727 -1.524 0.000 1.147 0.600 -0.313 2.215 0.829 -0.282 1.579 0.000 0.702 -0.189 0.345 3.102 0.636 0.788 0.993 0.831 0.574 0.921 0.875 +1 0.743 0.543 -0.294 1.523 0.663 0.835 0.027 -0.667 0.000 0.881 -0.160 -1.179 2.215 0.894 0.778 1.239 0.000 1.947 -0.224 1.501 3.102 0.913 0.921 1.119 1.049 0.788 0.878 0.756 +1 2.089 -0.100 -0.859 1.001 0.721 0.707 0.168 1.377 2.173 0.540 -1.098 0.983 2.215 0.403 0.763 0.172 0.000 1.013 -0.923 -0.478 0.000 0.890 0.983 1.982 1.196 0.708 0.930 0.811 +0 0.681 -0.662 1.072 1.462 -1.702 0.786 1.635 0.410 0.000 0.709 1.318 1.059 0.000 1.326 -0.529 -0.724 0.000 1.135 0.593 -1.278 3.102 0.904 1.041 0.992 1.620 0.736 1.140 1.366 +1 0.351 1.397 1.177 2.619 0.702 0.797 -0.949 -1.074 0.000 0.757 -0.930 -0.433 0.000 0.816 0.897 0.538 1.274 1.395 0.589 -1.060 3.102 0.902 1.106 0.987 0.904 0.816 1.042 2.029 +1 1.100 0.399 -0.221 0.474 -1.012 0.803 0.870 -1.327 1.087 0.559 0.625 1.020 2.215 0.613 -1.017 -0.214 0.000 0.974 -0.517 0.665 0.000 0.644 0.753 0.983 0.943 0.851 0.832 0.755 +1 0.759 -0.198 -1.478 0.650 0.587 0.848 2.268 -1.723 0.000 1.543 0.714 -0.523 0.000 2.222 0.819 0.427 2.548 0.956 -1.180 -1.418 0.000 0.753 0.915 0.988 0.965 0.869 0.958 0.798 +0 1.040 -1.543 1.658 0.336 1.525 0.499 -1.047 -0.175 2.173 0.431 -1.398 0.469 0.000 0.344 -2.100 1.392 0.000 0.950 -0.165 -0.496 3.102 0.511 0.689 0.988 0.839 0.383 0.631 0.574 +0 1.038 -1.032 -0.346 0.469 -0.217 1.000 -0.937 0.433 2.173 1.065 -1.583 1.560 0.000 1.364 -0.349 -1.378 2.548 0.841 -0.364 1.624 0.000 0.783 0.864 0.991 0.959 1.510 0.996 0.947 +0 3.587 -0.434 -0.315 2.020 -0.352 0.987 1.635 1.020 0.000 1.659 -0.023 -1.612 0.000 1.312 -0.573 -1.634 0.000 2.954 0.900 1.538 0.000 0.698 0.984 0.957 0.857 0.176 0.912 1.206 +1 0.630 0.889 1.165 1.084 -1.366 1.784 0.840 0.193 0.000 0.418 1.614 1.136 0.000 1.204 -0.695 -1.101 2.548 0.828 1.025 -0.874 3.102 0.903 0.888 0.985 1.314 0.907 0.870 0.840 +0 1.130 -1.702 -0.326 0.521 0.615 0.600 2.505 -0.952 0.000 0.719 -1.466 1.258 2.215 0.856 1.007 1.572 0.000 1.374 0.180 0.778 3.102 0.792 0.829 0.988 0.790 0.951 1.004 1.070 +1 0.621 0.523 -0.117 0.654 -1.100 0.700 0.018 0.522 0.000 1.314 -0.695 -1.074 2.215 0.358 -1.429 0.805 0.000 0.723 -0.271 1.484 3.102 0.965 0.730 0.991 0.746 0.676 0.889 0.763 +0 0.719 -0.560 1.136 2.187 0.795 1.225 0.529 -0.860 0.000 0.845 0.491 1.676 2.215 1.189 -0.646 0.029 2.548 0.463 -0.129 -0.555 0.000 0.511 0.918 0.984 0.939 1.265 1.072 1.241 +0 0.828 -0.387 -0.018 1.212 1.501 0.460 -0.152 -1.027 0.000 1.095 0.395 -1.699 2.215 1.352 1.071 0.319 2.548 0.562 -1.669 0.228 0.000 1.085 1.147 1.359 1.225 1.353 1.083 0.963 +1 0.470 0.733 -0.287 0.872 0.939 0.669 -1.721 -0.688 0.000 0.793 0.828 -0.989 0.000 1.842 0.501 1.062 2.548 1.375 0.313 0.326 3.102 0.805 1.040 0.987 0.881 0.757 0.878 0.790 +1 0.881 1.237 -1.179 1.781 1.428 0.291 1.805 -1.470 0.000 0.543 1.105 -0.166 2.215 1.171 -0.012 0.501 2.548 0.376 -1.829 0.823 0.000 1.904 1.271 1.235 1.220 0.704 0.880 0.966 +0 1.579 -0.834 1.583 0.343 0.590 1.461 -0.754 -1.514 2.173 1.666 -1.289 0.240 1.107 0.765 -1.516 -0.823 0.000 1.180 -2.175 -0.118 0.000 0.807 1.053 0.988 0.881 2.387 1.349 1.125 +1 0.762 0.005 1.172 0.647 -1.651 0.493 1.057 -0.003 2.173 0.489 -1.030 1.174 2.215 0.531 -1.808 -1.126 0.000 0.664 -1.345 0.131 0.000 0.605 1.335 0.994 0.849 1.117 0.869 0.901 +0 0.292 1.288 -0.783 1.748 -0.468 0.478 -2.605 1.314 0.000 0.521 0.449 1.508 2.215 0.990 -1.193 0.496 1.274 0.717 -1.327 -1.437 0.000 0.732 0.824 1.001 0.868 0.978 0.888 1.027 +0 2.183 1.530 0.851 0.237 -0.622 1.283 0.174 -1.023 2.173 1.206 -1.242 1.518 0.000 1.409 -1.634 0.115 0.000 2.140 1.057 -0.884 3.102 0.817 2.119 0.986 1.148 1.030 1.869 1.981 +0 0.894 1.460 -0.680 0.444 0.771 1.434 0.335 -1.416 0.000 1.494 1.168 0.368 0.000 1.830 0.763 1.733 1.274 1.818 1.785 0.338 0.000 0.845 1.026 0.982 1.018 1.377 1.011 0.886 +0 1.700 0.521 -1.286 3.820 -1.622 2.414 -0.217 0.206 0.000 1.502 -0.694 0.387 2.215 1.652 0.225 1.725 2.548 0.926 -0.531 -0.193 0.000 0.939 0.835 1.049 0.674 1.773 1.574 1.810 +0 2.008 -0.041 0.394 0.865 -0.297 0.855 -1.227 1.652 0.000 0.830 0.925 1.460 0.000 0.962 -1.267 -0.797 2.548 1.126 -0.488 -1.338 0.000 0.816 1.078 1.064 1.107 0.549 0.751 0.923 +0 0.787 -0.228 1.161 0.754 0.328 0.848 -1.115 -0.920 2.173 0.614 -0.738 0.420 0.000 0.566 -1.606 1.533 0.000 0.559 -1.535 0.305 0.000 0.897 0.988 0.988 0.844 0.632 0.889 0.822 +0 1.462 -0.502 0.378 0.783 -1.730 1.478 -1.935 -0.907 0.000 2.290 -0.780 0.956 2.215 0.697 -1.074 -0.619 0.000 1.115 0.450 -1.222 1.551 0.863 1.690 1.403 1.074 1.692 1.700 1.404 +0 1.107 1.087 -0.917 0.558 0.209 0.682 -0.115 -0.549 0.000 1.431 -0.392 0.719 2.215 1.156 -0.007 -1.590 2.548 0.707 -0.465 1.107 0.000 1.083 0.892 0.986 1.244 1.223 0.954 0.851 +0 0.351 -1.149 -0.034 0.680 -1.177 1.457 1.061 -1.117 0.000 1.027 -0.449 0.880 0.000 1.882 0.061 0.329 2.548 0.904 -0.157 1.190 3.102 3.347 1.896 0.997 0.887 0.709 1.384 1.089 +1 0.525 0.435 0.544 0.497 -1.068 1.844 -0.943 1.084 2.173 1.646 0.387 -0.039 0.000 1.789 -0.380 -0.927 0.000 1.466 0.124 1.537 3.102 1.005 1.072 0.991 1.097 1.221 1.231 0.991 +0 1.021 0.245 0.585 0.615 -1.644 0.702 0.564 -1.165 2.173 0.805 2.192 -0.097 0.000 0.747 0.813 -0.735 2.548 0.439 0.316 1.619 0.000 1.141 0.817 0.995 0.831 0.371 0.707 0.744 +0 1.270 0.349 1.181 0.911 1.057 0.762 1.532 0.415 0.000 0.717 -0.728 -1.150 2.215 1.564 0.636 -0.620 2.548 0.526 0.430 -0.950 0.000 0.897 1.064 1.000 1.252 1.028 1.057 1.015 +1 1.142 -1.185 0.664 0.773 -0.214 1.147 -0.046 -1.380 0.000 0.707 -0.501 -0.566 0.000 0.788 -0.203 1.156 0.000 1.148 0.119 0.459 1.551 1.157 0.814 0.986 0.518 0.119 0.478 0.532 +0 1.117 -1.139 -0.119 0.637 -1.085 0.830 -0.845 -1.426 2.173 1.237 -0.809 0.900 1.107 0.494 -1.591 -0.385 0.000 0.599 -1.335 0.665 0.000 0.488 0.788 0.990 0.982 1.289 0.865 0.707 +1 0.453 0.662 -0.419 0.878 1.440 0.868 -0.245 -0.142 2.173 1.313 -0.759 -0.749 2.215 1.126 -0.590 1.143 0.000 1.026 0.143 1.231 0.000 0.959 1.091 0.988 0.861 0.921 0.862 0.775 +0 0.442 -0.460 -0.865 1.286 0.697 0.681 2.319 -0.588 0.000 0.679 0.346 -1.534 1.107 0.748 -1.062 1.411 0.000 0.626 0.718 1.625 0.000 0.915 0.959 1.030 0.820 0.880 0.678 0.654 +1 0.353 1.429 0.165 2.087 -1.037 0.816 1.046 0.632 0.000 1.207 -0.203 1.347 2.215 0.942 -0.117 -0.142 2.548 1.164 -2.019 -1.380 0.000 0.729 0.797 1.050 1.557 1.105 1.148 1.016 +0 0.703 -2.264 -1.551 1.113 -0.651 0.537 -0.513 1.143 1.087 0.546 -2.426 0.276 0.000 0.619 0.015 -0.627 2.548 0.511 1.274 0.515 0.000 0.779 0.993 0.984 1.385 0.744 1.107 0.911 +0 0.803 0.056 1.001 0.489 -0.415 0.846 -0.022 -1.109 2.173 1.129 0.870 0.474 0.000 0.697 0.828 1.611 2.548 1.061 1.246 -0.234 0.000 0.953 0.878 0.986 0.856 0.764 0.909 0.843 +1 1.317 -0.640 1.167 0.833 0.914 1.395 0.277 -0.988 2.173 0.596 0.389 0.938 0.000 0.605 0.453 0.086 0.000 0.796 -0.488 -0.235 3.102 0.641 1.191 0.986 0.759 0.860 0.994 0.839 +0 1.259 1.036 -1.153 0.301 0.058 0.975 0.948 1.567 2.173 0.945 2.372 -0.312 0.000 1.113 0.767 -0.163 1.274 1.636 0.832 0.638 0.000 0.902 0.940 0.984 0.706 1.300 0.821 0.775 +0 0.436 1.839 1.296 1.263 -1.498 0.552 1.532 -0.190 0.000 1.187 0.457 -0.452 1.107 1.359 -0.175 1.260 1.274 0.596 -1.420 1.725 0.000 2.310 1.508 0.978 0.872 1.424 1.144 1.014 +0 0.958 -0.691 -0.016 0.249 -1.485 1.483 -0.226 -1.365 0.000 1.265 -1.146 0.613 2.215 2.291 -0.095 0.886 0.000 1.288 -0.894 -0.627 0.000 1.028 0.947 0.988 0.767 1.892 1.030 0.861 +0 0.600 0.233 -1.674 1.774 -0.814 0.521 -0.416 1.142 2.173 0.353 0.192 0.991 2.215 0.988 -1.526 -0.046 0.000 0.482 -2.272 1.258 0.000 0.818 0.964 1.001 0.722 0.216 0.676 0.898 +1 0.979 0.406 -1.731 0.612 0.038 0.718 0.972 -0.509 2.173 1.038 -0.454 1.118 2.215 0.827 -0.247 -0.977 0.000 0.481 1.805 0.365 0.000 1.256 0.910 1.072 0.840 1.613 0.962 0.808 +0 3.062 0.432 -1.004 0.312 0.142 1.045 0.342 1.206 1.087 0.748 0.596 0.298 2.215 0.512 1.902 0.914 0.000 0.554 1.531 0.302 0.000 0.954 0.989 1.164 1.065 0.966 1.062 0.899 +0 0.790 0.390 0.084 0.566 -1.420 1.241 0.719 -0.842 2.173 0.781 0.920 0.801 0.000 1.301 0.046 0.488 0.000 1.556 -0.504 1.173 3.102 0.835 0.917 0.992 0.821 1.773 1.155 0.934 +1 0.690 -0.174 1.030 2.228 1.726 0.599 -0.795 0.145 0.000 0.963 -1.316 -0.713 2.215 0.433 0.221 0.252 2.548 0.456 -1.459 0.045 0.000 0.400 0.683 1.009 0.745 0.803 0.884 0.809 +0 1.475 -0.243 1.490 0.318 1.128 0.297 0.681 1.366 0.000 0.841 0.559 -0.328 2.215 0.936 -0.380 -0.516 1.274 0.923 1.675 -1.310 0.000 0.779 0.891 0.988 0.911 0.513 0.770 0.775 +0 0.912 0.892 0.121 1.597 -0.811 0.599 -1.777 0.810 0.000 0.913 1.569 0.419 2.215 1.022 0.731 1.550 2.548 1.335 0.131 -0.208 0.000 1.895 1.682 1.244 1.002 0.974 1.509 1.358 +1 0.843 -1.378 1.368 0.634 -0.730 1.086 -0.384 -0.976 0.000 0.671 -2.050 0.390 0.000 1.012 -0.304 -0.495 2.548 2.086 -0.307 0.858 3.102 0.922 1.290 0.987 0.836 1.043 0.826 0.836 +0 0.549 -1.856 -1.549 0.495 -0.030 1.290 -0.594 0.680 0.000 1.222 -0.850 -0.150 2.215 2.027 -0.585 1.644 0.000 2.941 0.059 -1.386 1.551 2.220 1.842 0.990 1.009 1.749 1.473 1.155 +0 0.783 0.564 0.989 0.678 -0.482 0.677 -0.900 -0.354 2.173 0.332 -1.533 -0.691 0.000 0.713 -1.920 0.987 0.000 1.574 -0.379 -1.430 3.102 0.772 0.891 0.988 0.994 0.935 0.804 0.861 +0 2.494 -0.012 1.649 0.298 -1.417 1.246 -0.048 1.142 2.173 1.443 0.206 -0.425 0.000 1.818 -0.385 -0.418 0.000 1.145 0.304 0.484 3.102 0.802 0.964 0.983 0.908 0.755 1.182 1.149 +1 0.664 0.861 -0.011 1.508 0.508 0.735 -0.913 -1.446 0.000 0.507 0.294 1.166 2.215 0.550 -1.928 -0.649 0.000 0.617 -1.208 -0.508 3.102 1.045 1.073 0.995 1.476 0.711 0.997 1.385 +0 1.076 0.153 -1.384 0.376 -0.618 1.171 1.424 0.179 0.000 1.658 -1.096 -1.659 2.215 0.747 -0.331 0.165 2.548 0.463 0.189 0.607 0.000 0.839 0.950 0.990 0.901 1.267 1.687 1.338 +1 1.007 -0.996 -1.567 1.078 0.914 1.508 0.107 -1.473 2.173 0.987 -0.357 -0.897 2.215 1.580 2.371 0.434 0.000 1.593 0.136 0.442 0.000 2.708 2.636 1.136 1.302 0.990 2.085 1.964 +1 0.692 1.009 -1.675 0.757 -0.655 1.020 0.637 0.198 1.087 1.341 0.571 -1.128 1.107 0.681 1.474 0.585 0.000 1.023 2.356 1.170 0.000 0.881 0.857 0.991 0.859 1.601 0.984 0.868 +0 0.308 2.046 -1.009 2.050 0.414 0.830 0.855 -1.346 0.000 0.610 2.188 1.617 0.000 0.780 0.458 0.542 2.548 1.268 -0.202 -1.043 3.102 1.327 1.169 1.055 1.545 0.806 1.046 1.055 +0 0.536 1.957 -1.682 1.257 0.581 0.579 -0.306 -1.316 0.000 0.762 0.724 -0.380 1.107 0.751 -0.843 1.219 1.274 0.863 -0.351 -0.514 0.000 0.718 0.798 1.015 1.496 1.097 1.081 1.040 +1 0.875 0.592 0.706 0.127 0.215 1.417 0.946 0.844 0.000 1.160 -0.134 0.632 2.215 2.252 0.951 -1.051 2.548 1.466 -0.301 -0.560 0.000 0.813 0.884 0.982 1.273 2.025 1.140 0.951 +1 2.207 1.249 -0.892 0.772 0.459 0.917 1.187 0.961 2.173 0.791 1.280 -1.638 0.000 0.677 -0.089 0.420 2.548 0.797 0.557 -0.294 0.000 1.027 1.008 1.697 1.138 0.823 0.993 0.857 +0 1.238 -2.110 0.375 0.528 -1.117 0.572 0.007 -0.914 0.000 0.625 -0.907 1.180 2.215 0.338 0.017 0.377 0.000 0.828 -0.439 -1.723 3.102 0.726 0.803 1.091 0.863 0.352 0.661 0.755 +1 0.420 2.222 1.714 1.435 0.203 0.815 0.962 1.369 0.000 0.930 0.252 -1.706 2.215 0.885 1.268 -0.314 0.000 0.998 1.099 -0.694 0.000 1.342 0.997 1.052 1.121 0.964 1.083 0.970 +0 1.214 -0.675 0.853 0.833 0.570 0.840 -0.445 -1.412 0.000 0.623 -0.228 1.444 1.107 1.624 -0.247 -0.338 2.548 0.692 0.615 -0.696 0.000 1.015 1.044 0.987 0.666 1.068 0.775 0.813 +0 1.034 -0.919 -1.362 1.716 -0.783 1.869 -0.767 0.672 0.000 0.670 -2.766 -1.286 0.000 0.798 2.690 1.314 0.000 1.042 0.575 -0.800 3.102 0.973 0.938 0.990 0.798 1.146 0.920 0.875 +1 0.345 -1.529 0.763 0.622 1.602 1.043 -0.486 -0.875 2.173 1.002 0.125 -0.197 2.215 1.368 -0.629 0.616 0.000 0.795 1.562 0.837 0.000 0.984 1.041 0.998 0.954 0.985 0.849 0.762 +0 0.907 -1.692 0.629 2.663 1.169 1.134 -2.243 -0.572 0.000 1.188 -0.054 -1.018 0.000 1.028 -0.108 1.637 2.548 0.713 0.999 0.863 0.000 1.101 1.706 1.007 1.778 0.808 1.374 1.393 +0 0.412 1.642 -1.515 1.506 -0.418 0.762 0.037 0.309 2.173 0.745 0.255 1.047 2.215 1.470 0.159 -1.248 0.000 1.546 -0.779 1.194 0.000 0.587 1.026 0.988 0.959 0.695 0.813 0.780 +1 0.571 0.470 0.782 0.507 -0.725 0.649 1.297 1.192 2.173 0.512 2.902 -1.568 0.000 1.245 0.303 0.419 0.000 0.915 -0.310 -0.875 1.551 2.456 1.517 0.990 0.780 1.097 1.141 0.913 +1 1.616 -0.881 -0.445 0.057 -1.499 1.309 -0.560 1.241 2.173 0.423 -0.318 -0.248 0.000 0.728 -0.168 -1.257 2.548 0.546 0.622 1.402 0.000 0.716 0.949 0.985 0.651 0.973 0.893 0.739 +1 1.125 0.794 0.630 0.104 -0.432 1.160 1.038 1.579 2.173 1.167 1.260 -0.294 0.000 0.971 0.352 -0.872 1.274 1.128 -0.082 1.140 0.000 0.805 1.152 0.988 0.781 1.151 0.842 0.761 +0 0.710 -1.557 -0.409 2.510 -1.025 1.189 -1.080 1.220 0.000 0.612 -0.408 1.041 0.000 0.987 -0.658 0.155 2.548 0.628 -0.427 -0.283 3.102 0.685 0.965 0.984 0.726 0.239 0.725 0.976 +1 0.873 0.267 -1.547 1.460 0.584 0.988 -1.047 0.359 2.173 1.576 -1.102 -1.044 0.000 0.501 -1.715 -0.774 0.000 1.294 -0.172 1.184 3.102 0.617 1.142 1.470 1.301 0.964 1.019 1.093 +1 0.788 -0.083 -1.618 0.631 0.518 1.231 0.305 0.370 2.173 0.799 2.394 -0.855 0.000 1.390 -0.010 -1.081 0.000 0.822 0.616 1.364 3.102 0.842 0.764 0.987 0.892 0.862 0.826 0.717 +0 0.352 -2.175 0.962 2.027 0.308 0.656 0.048 -1.538 0.000 0.953 -1.113 -0.983 2.215 0.698 0.030 1.195 0.000 0.423 -0.710 -0.693 3.102 0.763 1.013 0.980 0.597 0.170 0.677 0.812 +1 0.361 -2.057 0.907 0.563 -0.832 1.042 -0.152 0.251 0.000 1.138 -0.436 -0.871 2.215 1.153 -2.410 1.639 0.000 0.798 -0.718 1.490 3.102 0.893 0.916 0.987 0.729 0.752 0.851 0.756 +0 0.671 -0.885 -1.547 0.682 0.185 0.820 0.378 -1.151 2.173 0.644 -0.351 -0.575 0.000 0.479 0.938 0.801 0.000 1.030 1.235 0.290 3.102 1.030 0.894 0.988 0.932 1.092 0.809 0.717 +1 1.040 -0.061 0.060 0.253 -1.110 1.365 -0.222 -1.568 2.173 1.067 0.058 0.383 2.215 1.128 0.118 -0.291 0.000 0.979 0.394 1.144 0.000 1.133 0.845 0.991 1.146 1.763 1.026 0.887 +1 1.363 0.329 -1.226 0.255 -1.001 0.828 -1.143 0.482 2.173 1.469 0.810 -0.794 0.000 1.282 0.524 1.109 2.548 0.900 0.676 0.346 0.000 0.704 0.871 0.981 0.903 1.430 1.174 0.914 +0 0.536 -0.422 0.445 2.634 1.221 1.317 -1.970 -0.911 0.000 0.872 0.328 0.344 0.000 1.066 0.509 0.888 2.548 1.251 -0.205 -0.345 0.000 0.889 0.924 1.061 0.794 0.969 0.878 0.862 +1 0.879 0.437 -1.656 0.147 -0.207 0.666 0.552 0.083 0.000 0.413 1.393 0.742 0.000 1.587 -0.445 -0.737 2.548 1.124 -0.324 1.409 3.102 0.801 0.937 0.995 0.787 0.955 0.886 0.777 +1 0.401 -0.352 0.840 2.332 -0.370 1.307 0.475 1.511 2.173 0.859 0.597 0.771 0.000 1.035 1.043 -1.400 2.548 1.101 0.482 -0.383 0.000 1.092 0.992 1.188 1.669 0.868 1.219 1.049 +1 0.944 0.599 -0.786 0.972 0.136 1.024 1.151 -1.535 2.173 0.751 1.796 -0.144 0.000 1.477 1.396 1.147 2.548 0.655 1.439 0.429 0.000 0.455 0.832 0.988 1.096 1.054 0.952 0.856 +0 4.126 1.032 -0.120 0.484 -0.605 1.783 0.645 0.120 0.000 5.193 0.059 1.673 2.215 0.613 1.437 1.353 0.000 0.624 0.379 1.723 3.102 1.912 1.239 0.985 3.793 0.330 2.262 2.030 +0 0.346 -1.226 -0.886 1.020 -0.535 0.746 -0.762 -0.785 2.173 0.689 -1.203 1.572 0.000 1.387 -0.077 0.774 2.548 0.844 -2.312 0.104 0.000 0.729 0.979 0.976 0.939 1.326 0.935 0.922 +0 2.247 0.237 -0.648 0.455 0.487 0.642 -0.839 -1.723 1.087 1.400 -0.051 0.978 2.215 0.952 1.521 -0.764 0.000 0.718 -0.629 0.582 0.000 1.639 1.482 1.197 1.177 1.069 1.129 1.063 +0 1.980 -0.614 0.791 0.708 -0.161 1.123 -0.415 0.163 2.173 1.409 -0.920 -1.570 2.215 0.483 0.635 -1.031 0.000 1.238 -0.056 -0.640 0.000 0.697 0.937 1.240 1.292 1.915 1.113 0.911 +1 0.565 0.759 0.197 0.370 -0.298 0.592 1.245 0.483 0.000 1.132 1.303 -0.938 2.215 1.376 1.091 1.738 2.548 1.908 0.507 0.944 0.000 0.845 1.040 0.983 1.153 0.889 0.959 0.954 +1 1.002 0.122 -1.331 0.768 0.872 0.830 1.125 -0.397 1.087 0.570 -1.048 0.303 0.000 0.664 1.816 0.884 0.000 1.915 -0.564 1.482 0.000 1.220 0.876 1.113 1.062 0.870 1.077 0.912 +0 0.700 -1.188 1.138 1.419 1.081 1.411 -0.737 1.570 2.173 2.075 -0.223 -0.626 0.000 1.059 0.025 -0.397 2.548 1.452 2.055 0.014 0.000 1.265 0.766 0.990 1.399 1.608 1.343 1.428 +1 1.591 -0.168 0.294 0.765 1.656 0.294 0.637 0.646 0.000 0.506 0.438 -0.463 0.000 1.026 0.344 -1.213 2.548 0.902 -0.769 -1.652 3.102 0.855 1.047 1.439 1.003 0.586 0.725 0.771 +0 0.973 0.269 -0.492 0.699 -0.831 0.835 -0.331 -1.738 2.173 0.575 0.972 0.474 0.000 1.499 -0.013 0.677 0.000 0.558 1.000 -0.791 3.102 0.798 0.767 0.983 0.957 0.818 0.809 0.825 +1 0.393 -0.576 -1.286 1.527 0.802 2.742 -1.467 -1.003 0.000 0.960 -0.933 -0.088 2.215 1.850 -0.181 0.446 2.548 2.824 -0.567 1.023 0.000 0.741 1.002 1.021 0.844 0.858 0.897 0.759 +0 1.125 -0.104 -0.521 0.849 0.782 0.955 0.055 1.241 2.173 0.555 -0.510 -0.071 0.000 0.884 0.764 -0.582 0.000 1.223 1.071 1.621 3.102 0.923 1.024 1.250 1.020 0.840 0.876 0.819 +1 0.384 -1.197 0.736 0.250 0.678 1.268 0.464 0.316 2.173 0.886 0.162 -1.018 0.000 0.446 1.944 -1.530 0.000 1.349 -0.923 -1.733 0.000 0.947 0.608 0.981 0.798 0.832 0.865 0.731 +1 1.005 -1.101 0.590 0.802 1.058 0.909 0.312 -0.834 2.173 0.857 -0.484 -1.266 2.215 0.647 0.441 0.445 0.000 0.560 -1.336 0.011 0.000 0.865 1.022 0.986 1.119 0.733 1.223 1.004 +0 0.441 0.840 -1.403 0.957 0.783 0.831 -0.990 -1.689 2.173 0.630 -1.125 -0.687 0.000 0.819 -0.179 0.420 0.000 0.399 0.076 -1.377 1.551 1.077 1.088 0.989 0.614 0.388 1.079 1.035 +0 0.520 0.756 -1.121 1.774 -0.091 0.451 0.832 0.664 0.000 0.564 -0.084 1.382 0.000 0.943 0.547 1.566 1.274 1.013 -0.234 -0.859 3.102 0.825 0.827 1.066 0.923 0.699 0.695 0.685 +0 1.791 0.107 -1.054 0.521 -0.554 0.903 0.763 0.597 0.000 0.466 1.282 1.284 0.000 0.818 -0.429 -0.207 2.548 0.669 -0.604 -1.710 3.102 0.886 0.937 0.994 0.772 0.557 0.748 0.810 +1 1.020 -1.592 -0.635 0.144 -0.862 1.925 0.059 1.613 0.000 1.403 -0.704 -0.250 2.215 1.732 0.086 0.164 2.548 1.106 -1.231 0.789 0.000 0.598 1.016 0.981 1.023 0.922 0.867 0.786 +0 0.436 -0.535 0.091 1.171 1.617 0.963 -0.938 -0.493 2.173 0.926 -0.655 0.855 0.000 0.993 -0.652 -1.270 2.548 0.565 -1.680 -0.028 0.000 0.959 0.942 0.987 0.959 0.797 0.794 0.722 +0 1.456 0.204 0.175 1.031 0.797 0.785 -0.964 -1.254 2.173 1.364 -0.643 1.658 0.000 1.673 0.560 -0.359 2.548 0.712 -1.095 0.980 0.000 0.852 0.863 0.984 0.969 1.626 1.145 1.086 +1 0.625 -0.195 0.908 2.331 0.250 1.177 -0.027 -1.330 1.087 0.396 -0.634 -0.655 0.000 0.517 0.523 -1.076 0.000 0.532 0.377 0.402 1.551 0.910 0.955 0.992 0.454 0.861 0.956 0.860 +0 1.384 0.493 0.157 0.734 0.945 0.332 1.101 1.190 0.000 0.320 -0.255 0.019 0.000 0.718 -0.115 -1.037 2.548 1.031 0.343 -1.549 0.000 0.791 0.783 0.988 0.837 0.374 0.727 0.631 +0 0.615 -1.738 -0.412 1.358 0.341 0.531 -1.458 0.885 0.000 0.680 -0.029 -1.570 2.215 0.574 -1.255 -1.507 0.000 1.024 -0.945 -0.855 3.102 0.828 0.876 0.983 0.694 0.629 0.713 0.679 +0 1.047 0.897 0.034 1.458 -0.696 0.790 1.880 1.304 0.000 0.901 0.078 -1.022 2.215 0.980 0.538 0.557 2.548 1.039 0.753 1.351 0.000 0.719 0.886 1.046 0.850 1.021 0.931 0.924 +1 1.147 -0.900 -1.286 1.640 -0.659 1.071 -0.831 0.693 2.173 0.358 0.564 0.098 2.215 0.422 1.479 -1.550 0.000 0.838 0.250 0.740 0.000 0.832 1.242 1.020 0.881 0.844 0.979 0.921 +0 1.336 0.075 1.051 1.103 0.338 1.059 1.423 -1.205 0.000 0.807 1.052 0.427 0.000 0.908 0.589 -1.541 0.000 0.964 -0.219 -0.444 1.551 0.887 1.080 1.007 0.538 0.173 0.679 0.856 +1 0.796 1.024 -1.168 1.188 0.753 1.332 0.351 0.406 2.173 0.602 0.270 -0.743 0.000 0.956 0.219 -1.223 0.000 0.493 -1.134 1.303 0.000 0.943 1.176 1.330 0.675 0.958 0.795 0.800 +0 1.388 1.803 0.595 0.246 1.096 1.173 0.244 -0.971 0.000 1.260 1.009 -0.987 2.215 2.460 -1.408 1.155 0.000 1.332 0.521 0.208 3.102 0.921 0.958 0.987 1.131 1.058 0.829 0.890 +0 0.661 1.740 -0.153 1.012 0.073 0.788 -0.962 -1.415 0.000 1.455 1.098 1.000 2.215 0.464 -2.027 -1.141 0.000 0.859 -0.264 -0.611 3.102 0.776 0.719 0.986 1.041 1.279 1.410 1.287 +1 0.908 -0.466 -0.623 0.763 1.232 0.961 -0.804 1.294 2.173 0.820 -0.018 0.225 0.000 0.784 0.478 -0.573 0.000 0.850 -2.445 -0.897 0.000 0.884 1.153 1.147 0.559 0.520 0.691 0.698 +1 0.914 -0.387 0.049 0.546 1.635 1.172 -0.487 1.024 2.173 1.683 -0.510 -1.071 0.000 1.310 0.901 0.168 0.000 0.917 -0.058 -0.462 0.000 0.923 0.711 0.989 0.845 0.856 0.982 0.828 +0 0.469 1.385 -0.976 0.841 -1.175 0.835 -0.473 1.463 0.000 1.302 1.168 -0.600 2.215 0.949 0.500 1.421 0.000 1.142 0.301 0.422 3.102 0.861 0.903 0.984 0.906 0.997 1.088 0.920 +1 1.350 -2.050 0.497 0.890 -1.703 0.613 0.727 -0.808 1.087 0.521 -0.884 1.096 1.107 0.808 -1.236 0.184 0.000 1.017 -1.047 -1.624 0.000 0.986 1.053 1.392 0.846 1.123 1.326 1.097 +0 0.522 -2.269 -1.317 1.079 1.498 0.950 -0.255 0.015 2.173 0.973 -0.611 0.501 2.215 2.040 -1.305 -1.342 0.000 0.756 0.613 -1.503 0.000 0.699 1.478 0.975 0.966 0.656 1.135 1.264 +0 0.605 -1.998 0.774 0.461 -0.538 0.508 -0.574 1.305 0.000 0.891 -1.615 1.022 0.000 0.933 -0.500 -0.757 2.548 0.878 0.346 -0.822 3.102 0.889 1.072 0.993 0.789 0.349 0.809 0.716 +0 1.956 -1.069 -1.357 0.611 -0.235 1.931 -0.851 -1.715 2.173 1.623 -1.087 0.024 2.215 1.986 -0.269 0.091 0.000 1.362 -0.383 0.680 0.000 0.930 0.956 1.284 1.148 2.627 1.567 1.334 +1 1.442 0.181 1.361 1.048 -1.648 1.291 0.522 -0.096 2.173 0.704 1.255 -1.050 0.000 0.710 1.171 0.541 1.274 0.527 1.563 1.209 0.000 0.747 1.168 0.989 0.980 0.796 1.093 0.974 +1 0.580 -0.264 0.170 0.985 1.340 0.840 0.824 -1.093 0.000 0.790 -0.334 0.773 2.215 1.016 0.622 0.168 2.548 1.332 -0.183 -1.372 0.000 0.975 1.112 0.989 0.616 0.711 0.901 0.781 +0 0.330 0.914 0.964 1.966 -0.627 0.874 0.031 0.586 0.000 0.727 -0.142 -1.194 2.215 0.806 -0.033 1.419 0.000 0.583 -1.757 -1.283 0.000 1.030 1.051 1.104 0.941 0.581 0.724 0.813 +1 0.627 1.152 -1.300 1.295 -1.001 2.262 -1.843 1.337 0.000 1.231 -0.920 0.039 1.107 2.733 0.158 -0.272 2.548 0.478 0.738 1.702 0.000 2.980 2.413 0.991 1.089 1.270 2.305 1.959 +0 0.477 0.153 -0.336 1.240 -1.604 0.561 -1.229 -1.185 1.087 0.599 -0.729 -0.280 0.000 1.280 -1.158 1.430 1.274 1.074 0.865 -0.424 0.000 1.073 1.132 0.988 0.867 0.746 0.960 0.824 +1 1.638 0.269 1.191 0.948 -1.579 0.968 0.375 -0.395 0.000 0.768 2.728 1.241 0.000 0.866 0.143 -0.922 2.548 0.704 0.819 0.880 1.551 1.054 0.885 1.041 0.833 0.648 0.633 0.820 +0 0.392 2.166 -0.353 0.894 1.589 0.650 0.326 0.409 2.173 0.659 1.779 -1.275 0.000 0.744 0.396 1.514 0.000 0.912 -0.267 0.009 3.102 1.029 1.040 0.982 0.871 0.394 0.792 0.721 +0 0.386 0.345 -1.207 1.151 1.078 1.726 -0.581 -0.410 0.000 1.545 -0.244 1.030 1.107 1.522 0.321 1.556 1.274 1.307 -0.656 -0.951 0.000 1.100 1.806 0.988 1.126 0.896 1.438 1.351 +1 1.256 0.932 0.351 0.428 -1.070 1.968 -0.547 -0.124 0.000 1.659 1.008 1.584 2.215 1.031 1.597 1.148 0.000 1.800 0.036 1.550 3.102 0.934 0.965 0.989 1.049 0.810 0.840 0.753 +0 0.950 -0.660 -1.628 0.493 -0.511 0.517 0.048 0.338 1.087 0.968 0.697 1.623 0.000 1.532 -0.395 -0.294 2.548 0.405 0.316 1.326 0.000 0.257 1.158 0.993 0.892 0.657 0.782 0.809 +1 0.721 0.466 -1.301 0.728 0.733 0.745 -0.496 1.463 0.000 1.001 -0.673 -0.237 0.000 1.033 -0.458 0.917 2.548 0.797 -0.882 1.020 1.551 1.841 1.076 0.988 0.683 0.206 0.707 0.683 +0 0.401 0.685 -0.441 1.583 1.556 0.790 -0.568 0.032 1.087 0.686 -1.108 1.127 0.000 1.030 -0.430 -0.572 0.000 1.067 -1.028 -1.469 3.102 1.361 1.032 1.075 1.000 1.004 0.961 0.907 +1 1.067 1.068 -1.688 0.890 1.706 0.607 1.538 0.147 2.173 0.274 -1.542 -1.394 0.000 0.451 0.896 0.153 2.548 0.643 -0.119 -0.020 0.000 0.672 1.194 0.986 0.723 0.188 0.755 0.766 +0 0.661 1.474 -1.575 1.284 -1.045 0.644 0.572 1.524 2.173 1.239 0.658 -0.003 1.107 0.937 -0.822 1.448 0.000 0.577 -0.354 0.757 0.000 0.512 1.175 0.981 0.753 1.291 0.871 0.863 +1 0.489 -0.206 -0.509 1.307 1.678 0.915 0.091 -0.121 0.000 1.197 0.385 1.637 2.215 0.923 -0.777 0.175 0.000 0.438 -1.599 0.362 0.000 0.920 0.968 1.020 0.714 0.397 0.937 0.825 +1 0.676 1.380 -0.709 1.109 1.736 0.584 -0.269 -0.326 2.173 0.657 0.404 0.962 2.215 0.651 -0.178 -1.457 0.000 0.633 -0.206 1.173 0.000 0.494 0.683 0.988 0.869 0.896 0.891 0.755 +1 0.484 0.673 -0.910 0.957 0.980 0.814 -0.069 -0.076 0.000 2.188 -0.315 -1.329 1.107 0.912 -0.076 1.434 0.000 2.825 -1.584 0.242 0.000 0.945 0.973 0.987 0.555 1.543 1.035 0.860 +0 0.776 0.751 -0.628 2.167 -1.088 1.057 -0.216 0.860 2.173 0.861 -0.403 -0.028 2.215 0.352 -0.902 1.493 0.000 0.456 -1.644 1.055 0.000 0.283 0.651 0.985 1.925 1.017 1.429 1.219 +0 1.429 1.336 -1.245 0.457 0.230 1.306 0.983 -0.583 1.087 2.069 1.084 1.062 0.000 0.519 1.677 1.162 0.000 0.573 0.045 0.052 3.102 0.606 0.828 1.088 0.899 0.667 1.123 0.961 +0 1.122 -1.277 1.510 1.002 -1.205 0.994 0.647 0.894 0.000 0.766 -1.204 -0.649 2.215 0.840 0.465 0.323 0.000 1.222 2.054 0.127 0.000 0.819 0.852 0.985 0.784 1.009 1.010 1.178 +0 0.461 0.122 -0.279 1.024 1.236 0.834 0.682 -0.221 1.087 0.619 0.241 0.393 0.000 0.625 0.438 -1.253 0.000 0.956 1.200 1.302 1.551 0.957 0.813 0.988 0.592 0.995 0.667 0.616 +1 0.556 -1.299 -1.630 1.338 -1.145 1.144 0.898 0.861 2.173 0.694 0.935 -0.178 0.000 0.553 0.496 -1.537 2.548 0.662 0.316 -0.582 0.000 0.851 1.159 0.984 1.328 0.842 2.049 1.509 +0 0.317 2.149 0.739 1.641 -1.433 0.423 -1.035 -0.504 0.000 1.163 -0.187 -0.263 0.000 0.635 -0.869 0.827 1.274 0.800 1.954 1.093 0.000 0.729 0.810 0.990 1.492 0.215 1.535 1.716 +1 0.409 -0.276 0.736 0.852 1.559 0.865 0.254 -0.310 2.173 0.894 1.051 1.317 0.000 0.591 -1.981 0.378 0.000 0.626 1.623 -0.540 0.000 0.928 0.904 0.989 1.348 0.295 0.897 1.249 +1 0.599 -0.784 0.997 0.303 -0.814 0.496 -0.771 1.473 0.000 1.182 1.397 -0.043 2.215 0.542 0.096 0.305 0.000 0.592 1.236 -1.121 3.102 0.915 0.858 0.986 1.028 0.623 0.910 0.779 +0 1.848 0.276 0.337 0.516 -0.365 0.843 1.159 1.726 0.000 0.950 0.117 -0.830 1.107 0.972 0.658 1.100 2.548 0.695 0.606 -1.147 0.000 0.665 0.941 0.987 0.824 1.054 0.784 0.844 +1 0.917 0.789 1.371 0.864 1.197 0.416 0.251 1.201 0.000 0.563 -0.322 -0.908 0.000 0.623 1.803 -1.439 0.000 2.020 1.005 -0.082 3.102 1.018 1.137 0.986 0.938 0.682 0.820 0.848 +0 0.555 -0.253 1.152 2.195 0.489 0.741 -0.146 -0.516 2.173 0.810 -0.456 -1.311 0.000 1.743 -1.090 1.665 0.000 1.581 0.652 -0.049 3.102 0.797 0.993 0.992 1.124 0.724 0.949 0.916 +1 0.981 -0.696 1.671 1.532 1.065 0.582 -0.996 -0.808 0.000 0.347 -1.215 0.402 2.215 0.329 -0.338 -0.059 0.000 0.381 -0.418 -0.509 3.102 0.553 0.520 0.986 0.626 0.271 0.493 0.556 +0 0.493 -0.712 -0.901 1.298 1.704 0.786 -0.057 -0.006 2.173 0.200 -0.698 0.361 0.000 0.427 0.615 -1.371 0.000 1.435 -0.596 1.112 3.102 0.562 0.615 0.986 0.764 1.022 0.913 0.737 +0 2.304 0.012 0.524 0.754 0.170 1.234 -0.375 -1.241 1.087 0.316 0.369 -0.577 0.000 0.529 -0.318 -1.633 1.274 0.714 -0.813 1.031 0.000 0.764 0.876 0.993 0.823 0.348 1.035 0.812 +1 2.610 -0.694 -1.074 0.729 -1.250 1.206 1.450 0.524 0.000 1.321 -0.530 0.129 0.000 1.521 -0.669 0.735 2.548 1.287 -0.897 -1.636 3.102 0.525 0.983 0.976 1.383 0.921 0.923 0.924 +0 0.373 2.046 1.675 1.153 0.281 0.867 1.063 -1.290 1.087 1.052 0.743 0.103 2.215 1.291 -2.498 -1.102 0.000 1.069 0.303 0.808 0.000 1.071 0.987 0.985 0.959 1.354 0.905 0.777 +1 1.186 -0.661 -0.822 0.166 -1.686 0.724 0.370 0.867 0.000 0.562 -1.127 -0.458 0.000 0.848 0.204 1.665 2.548 0.831 0.378 0.368 3.102 0.925 0.677 0.987 0.691 0.595 0.560 0.584 +1 0.447 -0.694 0.017 0.862 -1.088 0.761 -1.006 0.972 2.173 0.565 0.572 1.372 2.215 0.490 -1.322 -0.517 0.000 0.554 0.200 -0.332 0.000 0.555 0.815 0.985 1.130 0.925 0.885 0.738 +1 0.652 -1.915 0.285 0.696 -0.015 0.886 -0.240 0.384 2.173 0.710 -1.899 1.647 0.000 1.549 2.103 1.363 0.000 2.459 -0.424 -0.760 1.551 0.683 1.079 1.000 0.756 1.357 1.024 0.881 +0 1.729 -0.186 -0.093 0.462 -1.656 1.188 2.887 -1.378 0.000 0.984 -1.442 0.049 2.215 1.301 -1.180 0.463 0.000 1.523 -0.819 -1.714 3.102 0.882 0.872 1.222 0.985 1.139 0.873 0.792 +1 1.055 -0.247 -0.316 1.754 -1.028 1.198 -0.354 1.109 1.087 0.328 -1.034 -0.719 0.000 0.685 1.354 0.972 0.000 0.862 0.550 0.270 0.000 0.864 1.042 1.127 0.545 0.782 0.922 0.781 +0 0.655 -0.356 0.174 0.918 -1.066 0.308 1.580 1.162 0.000 0.487 -2.458 0.076 0.000 0.723 -0.632 0.475 2.548 1.173 0.742 1.607 3.102 0.814 0.920 0.987 0.875 0.861 0.666 0.689 +1 1.085 0.333 -0.317 0.501 1.572 0.697 0.037 1.500 2.173 0.612 -1.198 0.696 2.215 0.742 1.336 -0.096 0.000 0.441 -0.496 -1.613 0.000 0.989 0.949 1.013 0.887 0.912 0.817 0.732 +0 1.740 0.324 1.688 1.304 1.336 1.865 0.323 -0.065 0.000 0.903 0.135 -1.164 2.215 0.341 0.448 -0.667 0.000 0.884 0.466 0.977 1.551 0.750 0.911 0.975 0.872 0.774 0.836 1.015 +0 1.737 -0.024 1.516 1.275 0.953 0.966 -0.540 -0.262 0.000 0.854 -0.441 0.212 0.000 0.934 -0.068 -0.902 2.548 1.401 0.727 -1.450 3.102 0.805 0.857 1.001 0.902 0.597 0.872 0.974 +1 0.893 -0.581 0.846 0.812 -0.545 0.985 -0.866 1.530 0.000 1.645 -0.624 -0.461 2.215 0.910 0.352 1.385 2.548 0.366 1.994 0.112 0.000 2.424 1.393 1.121 0.891 1.471 1.298 1.056 +0 1.065 -0.997 0.559 0.153 -0.966 1.146 -0.596 -0.978 2.173 1.102 0.460 0.667 2.215 0.783 -1.333 1.472 0.000 0.565 -0.247 -0.151 0.000 0.859 0.966 0.996 0.797 1.892 1.027 0.864 +0 0.636 -1.883 -0.146 1.284 -0.019 2.254 1.181 1.656 2.173 1.822 -0.160 -0.200 0.000 1.209 -0.561 0.301 1.274 1.276 -0.129 1.702 0.000 1.966 1.285 0.987 3.068 2.873 2.101 1.803 +1 0.786 -1.464 1.067 0.565 0.133 0.694 0.004 -1.141 2.173 0.475 0.292 1.153 0.000 0.533 -0.845 0.859 0.000 1.556 0.721 -0.481 3.102 0.543 0.922 0.984 0.952 0.787 0.860 0.742 +0 0.361 -0.339 0.437 0.654 -0.546 0.731 0.101 0.595 2.173 0.696 1.421 -0.553 0.000 0.826 1.427 1.164 2.548 0.452 0.921 1.599 0.000 0.741 0.964 0.991 0.719 0.909 0.699 0.638 +1 0.345 1.528 0.427 1.419 1.252 0.978 -0.692 -1.234 2.173 0.996 0.831 -0.220 1.107 0.882 0.054 0.192 0.000 1.213 0.131 1.292 0.000 0.956 0.946 0.994 1.240 1.696 1.094 0.920 +0 0.372 -0.195 -1.456 1.011 -1.691 0.932 0.506 -0.805 0.000 1.122 1.854 1.043 0.000 1.598 0.509 0.514 2.548 0.981 0.567 -0.210 0.000 1.042 0.787 0.983 1.641 0.197 1.138 1.070 +1 1.489 2.036 -1.683 0.856 1.334 0.712 1.058 0.162 2.173 0.741 0.509 -0.371 2.215 0.622 1.554 -1.110 0.000 0.479 0.999 1.026 0.000 0.585 0.703 0.997 1.148 0.575 0.938 0.730 +1 1.743 -1.008 -0.589 0.828 -0.764 0.862 -0.725 -1.629 2.173 1.274 -0.737 1.033 2.215 1.154 -2.057 0.765 0.000 0.805 0.393 -0.201 0.000 2.111 1.476 0.983 1.056 1.042 1.108 1.116 +1 2.203 0.650 1.706 0.918 -1.202 1.191 0.550 0.100 2.173 0.420 1.270 1.022 1.107 0.619 -0.353 -1.129 0.000 0.735 0.138 0.538 0.000 0.799 1.058 0.987 0.724 0.867 1.020 0.853 +0 0.742 1.048 -1.047 0.467 -0.103 0.433 -0.422 1.422 0.000 0.813 1.227 1.151 2.215 0.735 -0.206 -0.024 2.548 0.892 2.147 -0.084 0.000 1.074 1.053 0.982 0.874 0.980 0.770 0.726 +1 1.150 -1.362 0.377 1.319 1.627 0.643 -0.113 -0.835 2.173 0.522 0.982 0.440 2.215 0.364 1.204 -0.746 0.000 0.562 -0.931 1.595 0.000 0.883 0.743 1.541 1.274 0.928 1.109 0.913 +0 1.379 0.617 -1.381 0.776 1.400 1.317 0.085 0.220 0.000 0.446 -0.008 -0.937 1.107 0.585 0.660 0.629 0.000 0.442 2.083 1.689 0.000 0.765 0.880 0.986 0.507 0.238 0.634 0.736 +1 0.297 0.724 -1.269 1.171 0.679 1.588 -1.342 -1.295 0.000 2.789 -0.320 0.296 0.000 1.924 0.323 -0.904 2.548 1.378 2.169 -1.634 0.000 0.874 0.814 0.986 1.294 1.028 1.123 1.260 +0 1.408 -0.113 1.082 1.619 1.568 0.622 0.167 -0.907 0.000 0.585 -1.109 0.531 1.107 0.755 2.130 -0.370 0.000 1.207 -0.285 -0.126 0.000 0.923 0.946 0.990 0.795 0.365 0.702 0.776 +0 0.670 -0.590 -0.341 1.301 1.427 1.311 -0.117 0.037 2.173 0.945 0.698 1.614 1.107 1.181 -0.133 0.828 0.000 1.650 0.379 -1.400 0.000 1.215 0.994 1.293 1.216 1.767 1.091 0.928 +0 0.745 1.082 0.093 0.862 -0.011 1.527 1.023 0.351 2.173 1.120 1.059 -0.800 0.000 2.215 0.856 -1.621 2.548 0.500 -1.609 1.126 0.000 0.211 2.084 0.983 1.031 2.245 1.946 1.884 +0 0.415 -0.907 -0.839 2.005 -1.355 1.321 0.471 0.721 0.000 0.954 -0.345 0.520 0.000 1.389 0.404 -0.690 2.548 0.683 -0.865 -1.484 3.102 1.107 1.157 0.996 1.834 0.777 1.126 1.529 +1 1.227 0.815 -0.708 0.335 1.163 0.578 0.851 1.294 0.000 0.674 -0.251 -0.160 2.215 1.387 0.129 -1.269 1.274 1.888 1.167 0.671 0.000 0.957 1.198 0.989 0.786 0.889 0.975 0.847 +0 0.795 0.534 1.104 0.866 -0.139 0.806 0.759 -0.317 2.173 0.592 -1.381 1.566 0.000 0.452 1.040 0.326 0.000 1.178 -0.078 1.735 3.102 0.575 0.660 1.034 0.760 1.092 0.965 0.876 +1 0.642 -0.568 -1.703 0.553 -1.021 1.315 0.048 1.187 0.000 1.425 0.561 -0.116 2.215 0.869 1.417 -0.693 2.548 0.983 -0.299 -0.894 0.000 0.836 1.155 0.989 0.776 0.842 0.932 0.829 +0 0.502 -1.035 1.401 0.729 0.143 1.650 -0.443 0.879 2.173 1.418 -0.075 -1.115 2.215 1.266 1.159 -0.756 0.000 1.375 -1.834 -0.846 0.000 0.959 1.425 0.986 1.320 2.232 1.440 1.217 +1 0.709 0.823 0.063 1.784 0.816 0.445 1.439 0.295 0.000 0.652 1.119 -1.560 2.215 0.754 1.515 -1.219 0.000 1.406 0.467 -1.062 3.102 1.025 0.861 0.987 0.886 0.456 0.757 0.710 +1 1.118 -0.684 -0.217 1.177 0.243 0.403 -1.348 -0.891 0.000 0.776 0.898 1.479 2.215 0.717 -1.001 1.353 2.548 0.737 -0.216 1.406 0.000 0.875 1.086 0.979 0.885 0.950 0.880 0.805 +1 0.575 0.350 -0.243 0.704 -1.117 0.716 -0.082 -0.387 0.000 0.881 0.353 0.559 1.107 0.793 -0.565 -1.202 0.000 1.309 0.822 1.352 3.102 0.883 0.860 0.983 0.955 0.705 0.768 0.679 +1 0.600 0.338 -0.789 0.593 0.388 0.952 0.420 1.694 0.000 1.041 0.291 -0.225 2.215 1.086 0.482 1.211 0.000 1.124 2.349 -0.661 0.000 0.780 0.847 0.990 0.712 0.853 0.880 0.801 +1 0.340 1.440 0.456 0.832 -1.118 0.635 0.450 1.291 2.173 1.549 0.366 0.139 1.107 0.827 0.177 -0.527 0.000 1.158 -0.757 -1.636 0.000 1.103 1.007 0.989 0.909 1.259 0.942 0.799 +0 0.862 -2.244 -0.375 1.042 -0.309 0.776 0.521 1.532 2.173 0.557 -1.370 -1.230 0.000 1.166 1.242 0.550 0.000 1.266 0.879 -1.254 3.102 1.132 1.048 1.003 1.736 0.678 1.384 1.111 +1 2.773 0.260 -0.638 1.701 -1.050 1.092 0.722 0.751 2.173 0.543 -0.389 -1.650 0.000 0.883 1.254 1.089 0.000 0.485 1.189 0.500 3.102 0.752 0.832 1.089 1.824 0.325 1.162 1.091 +0 0.816 1.420 -0.547 0.711 -0.983 0.652 0.841 -1.592 1.087 1.545 -1.595 0.497 0.000 0.753 -1.152 -0.153 0.000 0.843 -1.168 -1.641 3.102 0.966 0.931 0.983 1.011 1.082 1.337 2.137 +0 1.834 -0.687 0.012 0.957 0.474 1.776 1.074 -1.559 0.000 1.465 -0.097 -0.123 2.215 1.096 -2.023 1.515 0.000 1.209 -1.077 -0.136 1.551 0.830 0.811 0.988 0.901 0.761 0.966 0.860 +0 0.561 -1.760 -1.367 0.645 0.888 1.158 -0.472 -0.313 1.087 1.187 -0.356 1.701 0.000 1.095 -0.723 1.349 0.000 0.955 -1.614 0.056 0.000 1.034 0.862 0.980 1.017 0.783 0.851 0.766 +0 1.046 1.144 -0.362 1.288 -0.562 0.787 -1.168 1.053 1.087 0.687 -1.225 -1.687 0.000 0.760 0.828 1.108 2.548 1.014 -0.449 -0.392 0.000 1.078 0.981 0.995 0.976 1.202 1.739 1.575 +0 0.847 -0.348 -0.207 1.067 -1.039 0.582 -1.844 1.412 0.000 1.251 -1.160 -0.930 2.215 1.071 0.508 0.587 0.000 2.035 -0.488 0.878 3.102 2.372 1.436 0.984 0.918 1.504 1.231 1.099 +1 0.840 0.073 0.640 1.419 0.600 0.345 -0.144 1.611 2.173 1.259 -0.909 -1.073 2.215 0.474 -1.535 -1.588 0.000 1.147 -1.068 -0.142 0.000 0.801 0.781 0.987 0.757 0.752 0.880 0.762 +1 1.074 -0.135 0.069 0.593 0.888 0.989 -0.873 -0.524 2.173 0.599 1.102 0.970 0.000 0.794 1.369 1.713 0.000 1.083 0.394 -1.603 3.102 0.685 0.600 0.986 0.899 1.208 1.125 1.006 +0 0.953 -1.059 -0.864 0.925 -0.357 1.012 -0.556 0.423 2.173 0.796 0.359 1.715 0.000 0.465 -0.881 -1.696 2.548 0.445 -0.174 1.135 0.000 0.709 1.159 0.985 0.661 0.824 0.840 0.910 +0 1.519 0.204 -1.361 1.493 -1.495 1.843 1.109 0.476 2.173 0.903 0.248 -0.915 2.215 1.581 0.730 0.792 0.000 0.992 1.545 -0.194 0.000 0.914 0.863 0.991 2.015 1.992 1.435 1.141 +1 0.439 0.988 0.008 2.500 -0.421 0.509 1.472 0.973 0.000 0.772 0.751 1.632 2.215 0.779 1.283 1.725 2.548 0.508 0.293 -1.286 0.000 0.835 0.632 0.991 0.982 0.276 0.917 0.804 +1 1.362 1.360 0.778 1.545 1.035 1.002 0.787 -0.860 2.173 0.820 0.859 -0.323 0.000 0.508 -0.438 -0.073 0.000 1.133 0.204 1.614 3.102 0.762 0.855 0.990 1.458 0.945 0.993 0.903 +1 0.639 1.330 -0.725 1.630 1.657 2.171 1.906 0.107 0.000 1.605 0.681 1.631 2.215 0.788 1.743 -1.477 0.000 0.743 -0.782 -1.331 0.000 1.661 1.239 1.185 1.403 1.564 1.090 1.011 +1 1.780 0.330 -0.093 0.331 -1.100 1.341 -0.144 1.292 2.173 1.423 1.336 -0.205 0.000 1.350 -0.181 -1.256 1.274 0.966 1.193 1.495 0.000 1.317 1.663 0.991 1.412 1.253 1.535 1.259 +1 0.393 1.365 1.576 0.792 -1.182 1.192 0.668 -0.137 2.173 1.072 0.375 1.395 0.000 0.586 -0.489 0.568 2.548 0.704 -0.223 1.707 0.000 0.965 0.737 0.998 1.088 0.912 0.823 0.762 +1 0.311 -1.178 0.766 0.660 -1.371 0.764 -0.327 1.188 2.173 1.232 -0.753 -0.274 2.215 0.312 1.222 0.967 0.000 0.798 0.346 -0.818 0.000 0.611 0.925 0.987 0.742 1.420 0.850 0.725 +1 1.192 -1.240 -0.664 1.233 1.285 0.531 -0.545 -0.138 2.173 0.855 -1.865 1.417 0.000 0.971 -1.377 0.035 0.000 0.952 -0.522 -1.542 1.551 1.355 0.991 1.651 1.034 0.718 0.772 0.777 +0 0.435 0.373 -1.099 2.466 -0.458 1.032 0.183 0.983 1.087 0.601 -0.639 1.715 0.000 1.078 1.516 -1.276 0.000 1.663 0.485 0.669 3.102 1.857 1.390 0.989 1.574 0.477 1.100 1.123 +0 0.489 -0.560 -0.287 1.635 -0.867 0.447 0.430 0.168 2.173 1.026 0.881 1.484 0.000 0.699 -0.317 0.185 2.548 0.800 -0.090 1.413 0.000 0.629 0.864 0.987 1.182 0.280 0.809 1.025 +1 0.805 1.554 -0.598 1.345 -1.659 0.490 1.458 -0.087 0.000 0.746 0.212 -1.295 2.215 1.509 0.636 0.379 0.000 1.031 0.314 1.328 1.551 0.879 0.896 1.177 0.900 0.559 0.776 0.827 +0 1.334 -1.125 1.309 0.263 -0.309 0.487 -1.430 0.135 0.000 0.660 0.410 -1.008 2.215 0.430 -0.294 -1.517 0.000 0.384 0.760 0.142 3.102 0.954 0.962 0.984 0.670 0.407 0.625 0.628 +0 1.466 -1.111 1.287 2.576 1.130 1.982 0.371 -0.394 0.000 0.822 -0.059 1.675 2.215 0.875 1.011 -0.483 0.000 0.419 1.416 -1.234 3.102 0.911 0.837 0.979 1.276 0.591 1.261 1.893 +1 0.869 -0.329 1.598 0.669 -0.111 0.816 -1.098 -0.969 0.000 0.745 -0.341 -0.898 0.000 1.503 0.346 0.979 2.548 1.455 0.121 0.019 3.102 1.038 1.001 1.056 0.722 0.871 0.703 0.655 +1 1.333 1.213 1.469 0.725 -1.231 0.800 0.517 0.058 2.173 0.419 -2.324 -0.905 0.000 0.953 0.766 -1.631 0.000 0.778 0.750 -0.763 3.102 2.419 1.525 0.993 1.095 0.585 1.170 1.129 +0 1.927 1.637 0.422 0.302 0.328 1.063 -0.364 -1.678 0.000 1.436 1.680 -0.055 0.000 2.099 0.430 -1.366 1.274 0.597 -0.325 1.477 0.000 0.357 0.944 1.001 1.507 0.930 0.950 1.069 +1 0.650 0.699 -0.098 0.861 -0.605 1.050 -0.275 1.510 0.000 0.973 0.562 1.205 0.000 1.555 0.493 -0.402 2.548 1.143 -0.856 -0.131 3.102 0.982 1.050 0.986 0.802 0.918 0.996 1.174 +1 0.424 -0.694 1.732 1.077 -1.672 0.697 1.163 -0.407 0.000 0.763 0.606 0.867 2.215 0.746 1.843 0.088 0.000 0.834 -0.409 -1.489 3.102 0.798 0.994 0.989 0.720 0.744 1.057 1.630 +0 1.416 -0.247 1.096 0.743 0.092 0.898 0.816 0.945 2.173 1.650 0.240 -1.197 0.000 1.268 -0.115 -0.495 2.548 1.180 -0.074 -1.668 0.000 0.809 0.967 1.116 0.917 1.438 1.058 0.982 +1 0.895 -1.225 -1.193 1.138 -0.531 0.812 -0.412 1.030 2.173 0.771 0.885 0.526 2.215 0.561 -0.913 -1.698 0.000 0.872 -0.199 -0.563 0.000 0.725 0.955 0.993 1.335 0.976 1.333 1.023 +1 0.852 0.515 0.282 0.285 -0.301 0.696 -0.385 -0.909 2.173 0.706 0.717 -0.185 0.000 1.395 -0.472 1.702 2.548 0.906 1.105 0.834 0.000 0.884 1.115 0.990 0.910 0.874 0.927 0.814 +1 0.725 -1.110 -0.840 0.824 1.371 0.477 -0.265 0.721 0.000 1.081 -0.928 -1.331 2.215 0.996 2.482 -0.203 0.000 2.149 0.164 0.240 0.000 0.746 0.612 0.989 0.649 0.761 0.846 0.767 +0 1.464 -0.882 -1.234 1.799 1.631 0.634 -0.446 0.391 0.000 0.789 0.238 1.689 2.215 1.198 -0.880 -0.195 0.000 1.567 2.331 0.760 0.000 0.890 0.919 1.193 0.906 1.225 1.078 1.053 +1 1.383 -0.559 -1.567 2.071 -1.593 1.012 1.868 0.643 0.000 0.542 -0.730 -0.841 0.000 1.768 -0.991 -0.381 2.548 0.854 -0.274 0.621 0.000 0.879 0.797 0.995 1.582 1.411 1.205 0.962 +1 0.685 -2.266 0.131 1.021 -1.211 1.119 -1.195 1.413 2.173 1.132 -1.278 0.217 2.215 0.847 -1.743 -0.462 0.000 1.192 -1.472 -0.940 0.000 0.522 0.995 1.084 0.993 1.463 1.023 0.818 +0 0.690 0.819 0.605 0.214 0.684 0.735 -0.101 -1.670 0.000 1.343 0.341 -0.849 2.215 1.068 -0.258 0.192 2.548 0.756 2.064 0.693 0.000 0.275 1.230 0.982 0.994 1.106 1.073 0.943 +1 1.682 -0.221 1.179 1.701 0.565 2.008 -0.450 -1.437 0.000 1.325 -0.427 0.268 2.215 1.312 0.222 0.744 0.000 2.042 0.521 -1.248 0.000 0.918 1.018 1.231 0.952 0.832 1.140 1.144 +1 0.323 1.083 -1.132 1.769 -0.368 0.976 -0.403 0.707 1.087 1.034 -0.152 1.513 2.215 0.701 -0.297 -0.541 0.000 0.591 -0.383 -1.178 0.000 0.518 0.819 0.993 1.090 0.998 0.960 0.753 +1 0.437 1.255 0.864 1.663 -1.110 1.156 1.154 0.529 2.173 0.984 0.059 1.704 0.000 0.507 0.738 -0.949 0.000 0.650 0.424 -0.420 3.102 0.848 0.660 1.156 1.240 0.757 0.845 0.831 +0 0.418 0.510 1.657 1.129 0.147 0.747 1.242 -0.132 0.000 1.307 1.336 1.542 2.215 1.004 0.559 -0.694 0.000 1.248 -0.149 -1.172 1.551 0.910 0.986 0.987 0.934 1.234 0.998 0.850 +0 0.825 1.625 1.420 0.976 0.629 0.939 0.213 -0.717 2.173 0.309 0.544 -0.961 2.215 0.593 2.447 0.626 0.000 0.702 -0.144 0.584 0.000 0.809 0.852 0.986 0.660 0.220 0.774 0.675 +1 0.616 0.460 -0.636 0.517 1.071 0.792 0.634 0.672 0.000 1.066 0.274 0.166 0.000 0.908 -0.077 -1.740 2.548 1.324 0.862 -0.994 3.102 0.930 1.098 0.988 0.771 0.719 0.858 0.754 +0 1.706 -0.782 1.019 0.541 0.232 0.697 -0.927 1.636 1.087 0.511 0.559 -1.243 0.000 0.867 1.762 -0.874 0.000 1.260 -1.286 -0.111 0.000 0.820 0.838 0.987 0.794 1.072 1.034 1.129 +0 1.156 0.245 0.911 0.656 -0.040 0.733 1.714 -1.412 0.000 0.918 -1.427 -1.586 0.000 1.412 -1.054 -0.314 2.548 2.424 -0.701 0.651 3.102 0.825 0.909 0.982 0.763 1.104 0.854 0.850 +1 1.053 0.575 -0.351 0.275 1.049 0.869 1.137 -1.556 2.173 0.784 -0.192 1.121 0.000 0.737 1.381 0.257 0.000 0.803 -0.695 0.205 0.000 0.830 0.865 0.985 1.014 0.790 0.875 0.771 +0 1.064 -0.335 -0.335 0.990 1.396 2.076 -0.748 -0.152 1.087 1.820 -0.707 1.583 0.000 1.372 -0.310 1.299 0.000 1.124 -1.059 -1.269 3.102 0.773 0.891 1.422 1.354 1.430 1.512 1.234 +1 0.401 0.609 -0.829 0.718 0.151 0.636 2.561 -0.757 0.000 1.013 0.470 1.396 0.000 1.320 -0.779 -0.747 2.548 1.228 -1.153 1.466 3.102 0.810 1.017 0.988 1.630 0.924 1.427 1.199 +0 0.664 -0.231 0.695 0.483 -1.531 0.798 0.855 -0.095 2.173 0.963 -0.941 1.474 2.215 0.904 -2.099 -1.225 0.000 0.445 -1.466 1.006 0.000 0.662 0.788 0.992 0.823 1.858 1.362 1.151 +0 0.729 -0.868 0.536 0.921 -0.171 1.066 0.649 0.139 2.173 2.081 -0.874 -1.616 2.215 1.005 -2.073 -0.629 0.000 1.344 -1.217 1.327 0.000 0.975 1.272 0.985 0.808 2.888 1.687 1.344 +1 1.248 0.981 0.711 1.717 0.664 0.414 1.352 -0.738 2.173 1.052 1.446 1.714 0.000 0.930 -1.834 -0.531 0.000 0.776 1.133 0.110 0.000 1.170 0.941 0.996 0.979 0.628 0.907 0.829 +0 0.844 -0.803 1.380 0.253 -0.546 1.404 -0.079 -1.584 0.000 1.493 -0.961 0.027 1.107 1.636 -0.029 0.043 2.548 0.735 0.553 1.268 0.000 1.025 1.524 0.992 1.024 0.826 1.322 1.043 +1 0.479 -0.725 0.590 2.003 0.007 0.902 2.292 -0.915 0.000 1.512 1.013 1.357 2.215 0.566 0.358 1.408 0.000 0.880 1.114 0.277 3.102 0.642 0.715 0.984 1.601 0.875 1.831 1.428 +1 0.537 -0.241 0.591 0.270 -0.989 1.145 -1.268 0.819 2.173 0.802 -0.345 -1.482 0.000 0.727 -0.691 -0.805 2.548 0.413 -0.911 -1.612 0.000 0.903 0.721 0.992 1.472 1.170 1.035 0.891 +1 0.612 -1.829 1.678 0.552 -0.283 0.943 -0.557 -0.789 2.173 1.378 -0.989 1.252 2.215 0.836 -2.241 0.557 0.000 0.931 -0.463 0.095 0.000 0.916 0.970 0.991 0.804 1.662 1.026 0.835 +0 2.949 0.811 -0.457 0.658 1.456 0.995 0.879 -1.022 2.173 1.296 0.246 1.119 0.000 1.144 0.656 1.451 2.548 2.096 0.860 0.707 0.000 1.158 0.851 1.907 1.159 1.057 1.042 1.134 +0 0.891 -0.762 0.792 0.829 -1.319 0.535 -0.727 -0.906 2.173 0.407 -2.079 -1.269 0.000 0.856 -1.094 0.412 2.548 0.821 -1.518 0.885 0.000 0.873 0.742 1.126 0.719 0.807 0.617 0.578 +0 1.248 -0.447 -0.304 0.772 -1.134 1.085 0.649 1.356 0.000 1.004 0.430 0.639 2.215 1.350 0.924 -0.639 0.000 0.595 -0.743 -1.520 3.102 2.156 1.367 0.988 1.003 0.820 0.976 0.944 +1 0.420 2.058 -0.506 1.262 0.833 0.695 0.002 -0.567 2.173 0.373 0.776 -0.834 0.000 0.953 -1.615 0.929 0.000 0.737 -1.222 -1.297 1.551 1.781 1.078 0.989 1.698 0.764 1.604 1.736 +0 1.374 0.140 -0.684 0.030 -1.145 0.430 -0.240 -1.364 0.000 1.293 -0.216 0.566 0.000 1.593 0.428 1.125 2.548 0.683 1.307 -1.154 0.000 0.871 0.962 0.657 0.466 1.018 0.739 0.679 +0 1.773 0.222 -0.089 1.547 -0.695 0.532 -1.432 1.563 0.000 1.905 0.669 1.082 0.000 0.883 0.030 -1.064 2.548 0.748 -0.810 -0.657 0.000 0.905 0.828 1.191 0.748 0.379 0.576 0.773 +0 0.598 -0.766 -1.703 0.239 -1.381 0.277 -0.584 0.222 1.087 0.526 0.597 -0.065 2.215 0.403 2.198 -1.191 0.000 0.674 0.695 0.756 0.000 0.756 0.831 0.974 0.688 0.387 0.546 0.575 +1 1.806 0.111 -0.461 0.862 0.321 1.045 1.139 1.127 2.173 0.375 1.051 -0.670 0.000 0.593 0.224 1.574 1.274 0.460 2.137 -1.490 0.000 0.571 0.888 1.120 0.832 0.598 0.971 0.844 +1 1.190 -0.245 -1.345 1.290 -1.183 1.205 0.602 0.877 2.173 0.339 2.125 0.830 0.000 1.233 0.268 -0.045 2.548 0.626 -0.883 0.082 0.000 0.953 1.060 0.977 1.025 1.148 1.084 0.890 +0 0.535 1.078 -0.160 1.198 0.640 1.029 0.636 1.291 2.173 0.966 1.269 -0.633 2.215 1.299 -0.293 -0.562 0.000 1.166 0.221 -1.674 0.000 1.072 1.021 0.992 1.192 1.529 1.014 0.878 +0 2.409 -0.060 -1.559 1.080 0.099 0.602 2.163 0.643 0.000 0.974 -0.823 0.122 2.215 1.067 -0.799 -1.157 2.548 0.924 -0.568 1.211 0.000 0.818 0.768 2.228 1.479 0.990 1.047 0.892 +1 0.501 0.046 0.612 0.933 -1.190 0.799 0.680 0.679 0.000 0.637 1.285 -1.433 0.000 0.540 1.341 -0.400 0.000 1.290 0.181 0.276 3.102 1.086 0.876 0.988 0.718 0.597 0.587 0.643 +0 0.670 -1.361 1.200 0.293 -0.196 0.519 1.010 -1.116 0.000 0.770 -0.157 1.563 2.215 0.690 0.707 0.000 0.000 1.348 -1.675 0.352 0.000 0.919 0.959 0.985 0.691 0.722 0.807 0.742 +1 1.531 -0.285 -1.141 0.560 0.651 1.803 0.620 0.358 1.087 1.910 0.260 -1.711 0.000 0.619 0.571 -0.380 2.548 0.576 1.330 -0.730 0.000 1.438 1.061 1.282 1.589 0.813 1.254 1.131 +1 0.755 0.479 0.080 1.012 1.160 0.525 -0.201 -1.567 1.087 0.495 1.542 -1.523 0.000 0.815 -1.232 -0.201 2.548 1.126 1.008 -0.029 0.000 0.966 1.011 1.002 0.997 0.914 0.976 0.852 +1 0.744 0.002 -0.444 0.592 0.883 1.899 -0.348 0.423 0.000 0.909 2.561 -1.191 0.000 2.154 0.573 -1.670 2.548 1.717 -0.449 -0.339 0.000 0.872 0.823 0.982 1.118 0.866 0.843 0.765 +1 0.547 -0.885 -1.384 1.065 0.113 0.602 -0.335 0.744 1.087 1.408 -0.149 1.494 0.000 1.212 0.808 -0.582 1.274 0.883 -0.344 -1.045 0.000 1.111 0.976 1.031 1.061 1.209 0.931 0.868 +1 1.454 0.157 0.903 1.814 -0.021 0.765 1.432 -1.321 1.087 0.954 0.535 -0.005 0.000 1.469 -1.334 -0.952 0.000 0.915 0.615 -1.529 3.102 2.556 1.641 1.663 1.618 0.360 1.439 1.342 +0 1.427 0.261 0.237 0.230 -0.665 1.018 0.458 -1.631 2.173 0.683 2.535 0.201 0.000 0.795 0.912 -1.164 2.548 0.508 0.847 0.217 0.000 0.687 0.924 0.990 0.768 0.551 0.787 0.711 +1 0.628 -1.305 0.374 1.051 1.120 0.369 -0.104 1.621 0.000 0.969 -0.148 -1.204 2.215 1.134 0.777 -0.680 0.000 1.242 0.774 0.454 0.000 1.057 1.013 0.984 0.615 0.709 0.899 1.028 +0 0.447 -0.446 -1.740 0.436 -1.175 1.253 0.355 -0.135 2.173 0.985 -2.165 -1.653 0.000 1.154 -1.600 1.051 0.000 1.134 -1.708 -0.985 0.000 0.790 1.485 0.992 1.733 0.572 1.657 1.343 +1 1.326 1.209 1.193 0.864 0.525 0.442 -0.085 -0.105 0.000 0.838 -0.694 1.523 2.215 0.991 -0.546 -0.533 2.548 1.257 -1.497 -0.950 0.000 0.672 0.954 0.989 1.177 0.931 0.976 0.822 +1 1.126 -0.333 -1.289 0.469 -1.625 1.061 -1.032 0.407 2.173 0.793 -0.931 1.538 2.215 0.416 -0.053 -1.005 0.000 0.628 0.498 0.029 0.000 0.490 0.899 0.981 0.635 1.151 0.860 0.752 +1 1.216 -2.332 1.277 0.179 -1.732 0.591 -1.144 -0.693 0.000 1.010 -0.393 -1.199 2.215 0.496 0.410 1.523 2.548 0.878 -0.578 0.170 0.000 0.904 0.765 0.981 1.140 0.582 0.862 0.776 +1 1.261 -0.304 0.338 1.056 -0.260 2.285 -2.057 0.603 0.000 2.335 -0.612 -0.983 2.215 1.680 -0.733 -1.497 2.548 1.715 -0.059 -1.702 0.000 4.440 2.977 0.993 1.358 0.958 2.343 1.854 +0 0.403 -1.388 0.173 0.641 1.020 0.539 0.510 -1.033 0.000 0.740 -1.074 -1.261 2.215 1.017 0.485 1.201 2.548 0.666 -0.358 1.146 0.000 0.952 0.916 0.996 0.843 1.118 0.905 0.803 +1 0.822 0.391 -1.279 0.727 1.719 0.538 0.858 -0.033 0.000 0.794 -0.250 1.243 1.107 1.194 -0.571 0.242 2.548 0.988 1.036 -0.895 0.000 0.808 1.078 0.990 0.948 0.835 0.950 0.878 +1 1.644 0.072 -0.242 0.865 -0.047 1.376 0.837 -1.131 1.087 0.796 0.724 1.371 2.215 0.977 0.678 0.713 0.000 0.443 0.967 0.446 0.000 0.921 0.875 0.976 1.298 1.195 1.049 0.973 +0 0.846 -0.177 0.966 0.747 -1.374 1.899 -0.695 0.158 0.000 2.111 0.577 -1.351 0.000 0.368 -1.144 -0.027 0.000 1.817 -0.982 1.235 3.102 0.491 0.730 0.989 0.694 0.519 0.800 0.770 +0 0.565 0.888 -1.468 0.909 -0.716 0.725 0.752 1.159 2.173 0.665 -0.011 -1.322 0.000 1.704 0.247 -0.119 2.548 0.686 -0.947 0.826 0.000 0.974 0.991 0.984 0.839 1.311 0.883 0.790 +1 1.101 -0.071 -0.113 0.707 1.039 0.621 -0.828 -0.119 0.000 1.051 0.011 1.564 2.215 0.592 1.143 -1.206 2.548 0.508 -1.249 -1.307 0.000 0.807 1.045 1.054 0.891 0.750 0.830 0.756 +0 0.546 -0.495 0.337 1.251 -1.362 0.963 -0.128 1.330 0.000 1.587 -0.616 -0.327 2.215 1.991 1.908 0.143 0.000 1.093 -1.047 -1.531 3.102 0.766 0.841 1.144 0.990 1.117 1.104 0.914 +0 0.594 0.790 -0.860 1.312 1.255 0.920 -0.057 -0.961 2.173 1.263 0.288 0.690 2.215 0.692 0.693 -0.541 0.000 0.668 1.808 0.875 0.000 0.919 1.015 1.154 1.062 1.606 0.991 0.875 +0 1.799 -1.747 -0.641 0.529 1.738 0.625 -1.521 1.098 0.000 0.530 -0.657 -0.260 2.215 1.192 -0.706 -1.529 1.274 1.121 -2.079 0.597 0.000 0.848 0.966 1.135 0.924 0.770 0.811 0.813 +0 1.904 0.013 0.734 0.936 1.332 0.468 -2.108 -1.264 0.000 1.020 -0.022 -0.804 2.215 0.339 -1.191 0.684 0.000 0.613 -0.739 -0.135 0.000 0.882 1.101 0.986 0.752 0.354 0.799 0.908 +0 0.529 -1.709 1.061 0.860 0.300 0.696 2.226 1.022 0.000 0.944 1.794 -0.681 0.000 0.520 -2.153 -0.440 0.000 0.897 0.001 -1.046 1.551 0.983 0.932 0.996 0.745 0.574 0.633 0.616 +1 1.948 0.490 -1.236 0.703 -0.706 0.718 0.647 0.421 1.087 0.629 1.184 1.540 0.000 0.462 1.617 0.284 0.000 1.269 0.373 1.042 1.551 0.784 0.791 0.999 0.948 0.546 0.857 0.751 +1 0.450 1.186 0.304 1.164 1.652 0.779 -0.545 -1.735 1.087 1.602 0.800 -0.271 2.215 0.757 -0.473 -0.252 0.000 1.073 0.488 1.307 0.000 1.138 0.988 0.985 1.173 2.006 1.272 1.067 +0 2.894 -0.523 1.368 1.396 1.541 0.985 0.182 -0.838 0.000 1.697 0.172 -0.472 0.000 1.682 -0.226 0.784 0.000 1.072 -0.376 -0.161 1.551 0.933 0.688 1.001 0.897 0.217 0.808 0.874 +1 0.736 -0.112 -0.629 0.656 1.114 0.894 0.153 -1.400 0.000 1.739 0.729 0.414 2.215 0.980 0.822 -0.823 0.000 0.886 1.090 -0.520 3.102 0.944 0.885 0.989 0.939 0.893 1.000 0.834 +0 1.233 -1.565 -0.693 0.616 -1.021 0.596 -0.842 1.698 0.000 0.840 -0.009 0.860 2.215 1.340 -0.875 0.463 2.548 0.714 -0.878 -0.786 0.000 0.788 0.934 0.985 1.117 0.683 0.865 0.771 +0 0.342 -1.382 0.717 1.717 -0.962 0.557 2.094 0.791 0.000 0.383 -0.280 1.280 2.215 0.461 0.811 0.101 0.000 0.820 -0.220 -1.581 3.102 0.842 0.665 1.059 0.774 0.270 0.560 0.601 +0 1.008 0.642 0.945 0.575 -0.294 0.886 0.419 -1.434 2.173 0.812 0.100 0.478 2.215 0.567 0.986 -0.167 0.000 0.389 0.025 -1.192 0.000 0.503 0.663 0.985 0.595 1.250 0.750 0.611 +1 1.656 0.172 -0.791 0.337 0.718 0.584 -1.250 1.354 1.087 0.248 -0.550 1.706 0.000 0.756 -0.462 -0.235 0.000 0.470 0.660 1.166 3.102 0.655 0.722 1.012 0.618 0.681 0.734 0.612 +1 0.587 -0.346 -1.266 0.921 0.391 0.774 -0.484 1.570 0.000 0.799 0.712 -1.272 0.000 0.605 2.459 -0.785 0.000 0.704 -0.364 0.292 3.102 0.836 1.027 1.016 0.568 0.265 0.599 0.723 +1 1.068 -0.668 0.501 0.071 -0.769 0.378 -1.749 -1.357 0.000 0.644 -2.378 1.233 0.000 1.374 -0.612 -0.577 2.548 0.435 -1.289 1.625 0.000 0.840 0.769 0.990 0.834 0.606 0.769 0.804 +0 0.523 -1.018 -1.124 0.510 1.477 0.908 -0.347 0.935 1.087 0.799 -0.304 -0.601 0.000 0.956 1.016 -0.948 2.548 0.932 0.004 0.435 0.000 0.921 1.030 0.983 0.751 1.480 0.889 0.766 +0 0.345 1.548 0.043 1.686 -1.351 1.395 -0.953 1.297 0.000 0.882 0.610 0.186 2.215 1.432 0.071 -0.646 0.000 0.883 -0.240 0.844 0.000 0.891 0.845 1.004 1.046 0.771 0.952 1.328 +1 2.088 1.289 0.858 0.958 0.457 0.832 1.065 -0.915 0.000 0.904 0.807 -1.475 2.215 0.612 1.031 -0.298 2.548 0.380 0.065 0.125 0.000 0.824 0.721 1.001 0.765 0.700 0.800 0.785 +0 2.507 -0.757 -0.963 2.588 -0.874 2.588 0.703 0.872 1.087 0.399 0.679 0.585 0.000 0.513 0.764 -1.120 2.548 0.772 -0.089 0.665 0.000 0.304 0.573 0.983 0.830 1.403 2.159 1.557 +1 0.407 -1.400 0.338 1.440 0.913 1.143 -1.127 -1.181 2.173 0.997 0.861 -0.977 0.000 0.678 -0.857 -0.348 0.000 2.496 0.568 0.819 3.102 0.838 1.151 0.982 1.357 2.546 1.371 1.172 +0 0.628 -1.674 -0.693 0.793 1.374 0.653 -0.211 -0.844 0.000 1.081 0.371 0.898 0.000 0.811 -0.322 0.071 2.548 0.682 -0.001 1.643 1.551 0.793 0.744 0.987 0.798 0.571 0.694 0.686 +1 1.464 0.279 -0.404 0.690 -1.184 1.603 0.269 0.842 2.173 1.519 -1.421 -0.299 0.000 1.235 0.757 -0.221 0.000 3.206 -1.055 -1.630 0.000 0.949 1.021 0.990 1.455 0.852 1.320 1.224 +0 0.759 -0.775 1.341 0.344 -1.499 0.698 -0.226 0.215 2.173 0.617 -1.174 1.444 2.215 1.060 1.074 -0.358 0.000 0.711 0.508 -0.504 0.000 0.304 0.804 0.993 0.699 0.995 0.876 0.789 +0 0.829 1.170 -0.587 0.902 -1.469 1.067 0.519 -1.454 1.087 1.056 1.053 1.045 0.000 1.290 1.516 -0.021 0.000 1.033 0.535 0.481 3.102 1.282 0.819 0.986 0.716 1.096 0.801 0.734 +1 1.283 -0.249 0.742 1.618 0.947 0.486 -1.422 -0.534 0.000 1.031 -0.424 -1.070 0.000 0.535 -1.120 0.696 0.000 0.414 -1.397 -1.653 0.000 0.826 0.592 0.993 0.820 0.243 0.618 0.616 +1 0.492 0.549 0.341 1.355 0.763 2.219 -1.413 -0.549 0.000 2.157 0.611 1.441 2.215 0.788 0.099 -1.421 0.000 1.411 0.353 0.957 3.102 2.621 2.345 0.979 1.323 0.686 2.305 2.299 +1 1.218 0.527 0.882 0.262 -0.059 0.481 1.531 -1.516 0.000 0.338 1.167 0.229 0.000 0.588 -1.582 -1.327 2.548 0.570 0.371 0.122 3.102 0.868 0.627 0.990 0.934 0.724 0.859 0.771 +0 0.981 0.952 0.703 0.816 1.731 0.980 -0.480 1.596 2.173 0.511 -0.823 -0.139 0.000 1.812 0.719 -0.131 2.548 0.722 -0.029 -1.287 0.000 0.756 0.959 0.991 1.108 2.009 1.119 0.945 +1 0.995 1.532 -0.700 0.369 -1.635 1.041 1.730 1.485 0.000 1.488 1.033 0.378 1.107 0.679 -0.078 0.572 0.000 1.365 0.476 -1.010 0.000 1.003 0.907 0.983 0.512 0.831 0.687 0.634 +1 1.149 0.211 0.669 0.173 1.675 2.568 -0.941 -0.965 0.000 2.653 -0.095 1.242 1.107 1.790 -1.989 0.370 0.000 0.947 -0.686 0.260 3.102 3.344 2.009 0.989 0.930 1.229 2.008 1.541 +1 0.368 -0.662 -0.424 0.591 -1.015 0.803 0.723 1.073 2.173 1.028 0.283 0.235 2.215 0.846 -1.172 -0.593 0.000 0.887 -0.107 -1.491 0.000 0.906 1.085 0.985 1.970 0.963 1.474 1.180 +0 0.790 -0.202 0.020 1.301 -1.325 0.967 0.183 0.786 2.173 0.371 1.794 0.190 0.000 1.071 0.977 -1.039 2.548 0.460 0.011 -1.575 0.000 0.780 0.872 1.315 0.933 1.392 0.950 0.803 +0 1.416 0.944 -0.630 2.342 -1.052 1.215 1.634 0.733 0.000 0.783 0.834 0.494 2.215 1.201 0.663 -1.574 1.274 1.070 1.429 1.226 0.000 0.749 0.736 0.995 0.835 0.989 0.888 1.110 +1 0.796 1.070 0.404 1.290 0.998 0.995 -0.250 -0.249 0.000 0.904 -2.881 -1.447 0.000 0.993 1.766 -0.909 0.000 0.790 -1.002 -1.703 0.000 1.129 1.397 0.995 0.669 0.554 1.288 1.448 +1 0.676 -1.062 -1.710 1.258 0.175 0.790 -0.031 -0.911 0.000 0.296 0.387 1.288 0.000 0.668 1.123 -1.587 0.000 1.435 -0.713 0.888 3.102 0.967 0.927 1.267 0.755 0.442 0.717 0.717 +1 1.412 -1.356 -0.659 0.778 -1.675 1.062 -1.669 0.612 0.000 0.729 -1.263 1.508 2.215 0.269 -1.139 0.246 2.548 0.468 -0.557 -0.825 0.000 1.107 0.894 1.150 0.606 0.427 0.541 0.652 +1 1.847 0.374 1.454 1.345 0.977 0.724 -0.296 -0.401 2.173 0.630 0.358 -0.516 2.215 0.458 0.211 0.708 0.000 1.136 1.224 -0.794 0.000 0.937 0.961 0.993 1.042 0.353 0.979 0.850 +0 1.673 1.448 0.804 0.860 -1.162 0.598 0.251 -1.130 2.173 0.528 1.805 -1.403 0.000 0.603 1.127 0.326 0.000 1.042 0.129 -0.089 3.102 0.907 0.905 1.629 1.075 0.675 0.895 0.771 +1 1.677 0.192 0.934 1.398 0.205 0.940 0.460 -0.784 2.173 0.517 1.215 -1.356 2.215 1.036 -1.322 -0.710 0.000 0.868 -0.274 1.541 0.000 0.963 1.007 1.294 1.079 0.653 0.988 0.855 +0 0.727 -1.847 0.471 0.702 1.324 0.706 -0.526 -1.372 2.173 0.729 -0.646 0.881 1.107 1.158 -0.217 -0.447 0.000 0.751 0.423 -0.072 0.000 0.889 0.888 0.994 0.897 0.948 0.693 0.710 +0 0.701 0.098 1.256 1.258 0.532 0.731 -0.094 -0.565 1.087 0.702 1.544 0.820 0.000 0.833 0.119 -1.428 2.548 0.943 0.991 -1.127 0.000 1.064 0.910 0.995 1.061 0.691 0.815 0.792 +0 2.237 -0.619 -0.890 0.918 -0.631 0.446 0.364 1.217 0.000 0.690 -0.784 1.407 0.000 1.159 -0.366 0.217 2.548 0.548 -0.579 1.099 3.102 0.759 0.899 0.998 0.772 0.443 0.732 0.810 +0 0.297 2.165 -1.474 0.794 -0.278 1.329 0.977 -1.032 2.173 0.900 -0.297 1.494 2.215 2.527 0.610 0.663 0.000 0.535 -2.451 -0.075 0.000 3.883 2.299 0.977 0.830 1.656 2.025 1.558 +0 1.389 0.383 0.957 1.272 1.351 0.955 1.338 0.420 1.087 1.271 0.848 -1.272 0.000 1.451 1.002 -0.790 0.000 1.699 0.142 -0.515 3.102 0.909 0.930 0.986 1.341 1.313 1.107 1.174 +0 0.725 0.315 -0.538 0.329 0.509 0.548 -0.241 0.701 0.000 0.426 -0.221 0.341 0.000 0.701 -0.439 -0.725 0.000 0.880 -0.445 1.384 3.102 1.078 0.758 0.997 0.644 0.215 0.493 0.513 +1 1.614 -0.040 0.156 0.205 1.047 1.127 -0.891 -1.410 2.173 0.653 -0.648 -0.415 2.215 0.786 -0.380 0.523 0.000 0.901 -0.419 1.629 0.000 0.780 0.978 0.981 0.646 0.998 0.881 0.755 +0 0.451 1.747 -1.208 0.963 0.886 1.021 0.527 -0.594 1.087 0.865 -0.320 1.191 0.000 0.459 -0.396 -0.858 2.548 0.929 -1.356 1.138 0.000 0.815 0.729 0.987 1.005 0.473 0.956 0.933 +1 0.693 1.841 1.348 0.851 -0.082 0.579 0.656 -0.564 2.173 0.563 0.911 1.063 0.000 0.725 -0.691 -1.607 2.548 1.068 0.819 -0.027 0.000 0.841 0.958 1.021 0.857 0.903 0.915 0.778 +1 0.428 -1.772 0.043 0.698 0.888 0.751 -0.714 0.365 0.000 1.367 -0.764 -1.361 2.215 1.041 -0.600 1.279 0.000 1.880 -0.518 -0.592 3.102 0.907 0.951 0.992 0.981 0.932 0.782 0.718 +0 1.904 0.156 0.048 5.442 0.090 3.277 0.743 -1.599 0.000 0.545 0.044 -1.580 0.000 0.514 0.655 0.599 2.548 0.728 0.052 1.050 3.102 1.022 1.054 1.004 0.683 0.242 0.825 1.770 +0 0.411 0.894 -1.290 1.617 0.138 1.047 0.129 -0.611 0.000 1.011 -1.367 -1.736 0.000 1.205 -2.364 1.450 0.000 1.401 -0.166 1.466 3.102 1.160 1.305 1.084 0.651 0.634 1.077 1.461 +0 2.078 1.411 -1.461 0.707 -1.535 1.255 0.319 0.637 0.000 0.956 -1.826 -0.442 0.000 0.560 0.113 -1.089 2.548 0.693 0.844 0.692 3.102 3.656 2.058 0.978 0.817 0.524 1.277 1.835 +1 0.562 -1.089 1.314 0.554 -1.724 2.335 -0.941 0.020 0.000 2.775 -0.744 1.727 0.000 0.928 -1.303 -0.674 2.548 0.689 -2.390 -1.487 0.000 0.846 1.159 0.997 0.510 0.602 0.735 0.714 +1 1.857 0.990 1.110 0.445 0.480 0.342 1.495 -1.319 0.000 0.724 0.324 -0.304 0.000 0.708 0.780 -0.218 2.548 0.564 -0.859 1.675 0.000 1.020 0.722 0.985 1.243 0.728 0.867 0.813 +1 1.212 -0.959 -0.200 0.712 0.808 0.729 -1.404 -1.149 2.173 0.662 -0.656 0.774 0.000 1.520 -1.081 1.560 1.274 0.376 -2.070 -0.697 0.000 0.921 0.924 1.015 0.969 0.857 0.808 0.715 +0 0.842 0.709 -0.515 0.946 1.355 1.221 -0.023 0.457 2.173 0.885 -0.442 -1.133 0.000 1.267 0.069 -1.664 0.000 1.372 -0.787 -0.455 0.000 0.918 0.826 1.229 1.124 0.846 0.855 0.819 +1 1.134 1.405 1.239 0.979 1.709 1.095 0.324 -0.195 2.173 0.794 0.710 0.311 0.000 1.102 -2.046 -1.182 0.000 0.603 -0.111 0.991 3.102 3.328 1.793 0.997 1.364 0.780 1.404 1.425 +1 1.423 -0.755 1.451 1.630 1.387 0.761 -0.631 -0.400 0.000 0.565 -1.532 1.084 2.215 1.918 -1.922 -0.651 0.000 1.575 -0.301 0.206 3.102 1.053 0.992 0.960 1.188 0.817 0.784 0.862 +0 1.298 -0.523 -1.253 0.836 -0.648 0.648 -0.274 0.957 0.000 0.455 0.101 -1.000 2.215 0.478 -1.291 0.568 2.548 0.457 -0.201 0.522 0.000 0.319 0.652 0.986 0.723 0.642 0.545 0.608 +0 0.350 1.113 0.170 1.392 1.472 0.669 0.734 -0.790 2.173 0.448 -0.229 0.413 0.000 1.239 0.037 1.393 2.548 1.440 0.523 -0.327 0.000 0.975 0.793 0.989 1.058 1.122 0.921 0.868 +1 1.092 -0.204 0.106 1.789 0.860 1.372 -1.326 -0.464 0.000 1.666 0.692 1.537 2.215 0.616 0.270 -1.397 0.000 0.543 0.941 -0.303 0.000 0.598 0.788 1.218 0.922 1.059 1.018 0.823 +0 1.875 -0.424 -0.981 0.872 0.531 0.371 1.855 -1.530 0.000 0.484 0.856 0.658 0.000 0.600 1.330 0.596 2.548 1.430 1.671 0.968 0.000 0.951 1.254 1.734 1.244 1.100 0.885 0.947 +1 2.467 -0.070 -0.110 0.273 -0.264 0.556 -0.208 -1.536 2.173 0.673 -1.756 -1.533 0.000 0.512 -1.221 1.526 0.000 0.721 0.368 1.087 3.102 0.399 0.796 1.001 1.071 0.520 0.751 0.892 +1 0.555 -1.600 0.130 1.224 1.262 0.499 0.901 -1.019 2.173 0.386 -0.421 -1.631 0.000 0.805 0.193 0.574 2.548 1.070 -0.761 -0.546 0.000 0.724 0.815 0.988 0.854 0.831 0.958 0.792 +0 1.078 1.109 -0.011 0.395 -1.106 0.665 1.531 1.702 0.000 0.729 0.889 0.662 2.215 0.590 0.727 -1.366 0.000 0.910 -0.410 -0.192 3.102 0.597 1.047 0.986 0.728 0.759 0.756 0.732 +0 1.106 -1.305 -1.238 0.572 -1.465 0.633 0.096 0.255 0.000 0.429 -0.762 1.137 1.107 0.858 0.744 -1.038 2.548 1.213 -0.924 0.274 0.000 0.858 1.106 0.997 0.663 0.827 0.733 0.774 +0 1.533 -0.753 0.693 1.104 1.281 0.767 0.418 -0.236 0.000 1.800 -0.426 -1.471 2.215 1.329 2.273 0.525 0.000 1.653 -0.097 -0.861 3.102 2.517 2.050 0.989 1.286 0.851 1.925 1.759 +0 1.606 0.682 0.108 1.261 0.828 0.785 -1.712 -1.143 0.000 0.487 -1.208 -1.462 2.215 0.640 -0.612 0.723 0.000 0.659 0.842 -1.377 3.102 1.449 0.849 1.192 0.818 0.707 0.880 1.149 +0 0.576 -2.140 -0.756 0.973 1.212 0.815 -0.371 -0.644 2.173 0.665 0.344 0.781 0.000 0.833 0.286 -0.137 0.000 1.346 -0.500 1.236 3.102 0.840 0.975 1.016 0.869 1.107 0.953 1.003 +1 0.787 1.001 -1.558 0.905 -0.247 0.626 1.754 0.470 0.000 0.973 0.758 -0.455 0.000 1.704 0.985 0.197 0.000 2.533 0.523 -1.491 3.102 0.794 0.516 1.082 0.847 0.662 0.920 0.802 +1 1.589 -0.298 0.738 1.615 1.115 1.163 0.425 -0.779 1.087 0.208 1.707 -0.697 0.000 0.698 -0.443 1.705 2.548 1.167 -0.513 0.160 0.000 1.053 1.023 0.997 0.711 1.029 1.175 0.994 +1 1.046 0.225 -0.708 1.166 1.188 0.650 -0.375 -0.458 0.000 1.385 -0.559 -1.235 0.000 1.218 0.316 1.151 2.548 2.399 -0.150 0.499 1.551 1.313 1.405 1.515 1.077 0.804 1.095 0.985 +1 0.572 1.719 1.115 0.765 -0.332 0.739 -0.689 0.020 2.173 0.311 -0.750 -1.035 0.000 0.963 0.438 -1.247 0.000 1.416 -0.766 1.195 3.102 0.709 0.818 0.989 1.856 0.951 1.571 1.180 +0 1.057 -0.048 0.847 1.397 1.628 1.016 0.382 0.486 0.000 0.666 0.310 -1.461 1.107 2.196 0.923 -0.595 2.548 0.424 -0.954 -0.751 0.000 1.234 1.086 1.090 1.535 1.012 1.025 0.994 +1 0.802 -0.561 0.469 0.777 -0.659 0.830 0.209 -1.117 2.173 2.087 0.186 1.386 0.000 1.296 -1.495 -0.256 0.000 0.977 -0.279 0.184 1.551 0.986 0.874 0.983 0.848 0.917 1.050 0.882 +1 2.366 0.640 -0.621 1.002 0.284 1.441 2.203 0.800 0.000 1.140 0.734 -1.732 1.107 0.596 -0.855 1.421 2.548 0.655 2.241 1.069 0.000 0.497 1.640 1.555 1.234 0.874 1.499 1.492 +0 1.957 -0.818 1.536 0.493 0.038 2.023 -1.044 0.185 0.000 1.888 -0.432 -1.439 2.215 0.877 -2.202 -1.220 0.000 0.986 -1.429 -0.050 0.000 0.965 0.829 1.327 1.073 0.877 0.981 0.925 +0 1.038 -0.167 -1.699 0.698 0.531 0.553 -1.483 1.533 0.000 1.106 -2.332 -1.118 0.000 1.947 -1.100 1.083 0.000 2.520 -0.094 -0.398 3.102 0.787 0.852 1.067 0.983 0.944 1.091 0.938 +1 0.636 0.074 0.832 1.040 -0.443 1.107 0.141 -1.310 0.000 1.301 0.378 0.432 2.215 0.863 0.692 1.741 0.000 0.557 1.066 0.814 0.000 0.868 0.746 1.027 0.764 0.871 0.935 0.815 +0 1.142 -0.844 -1.249 1.270 -0.488 2.854 -1.150 -0.707 0.000 3.864 -1.573 0.925 0.000 1.245 -1.393 1.342 2.548 1.097 -0.667 0.732 3.102 7.203 3.792 1.057 1.050 0.574 2.238 1.779 +0 0.908 -1.856 -1.583 1.557 -1.201 1.564 0.789 0.443 2.173 1.094 -1.165 1.520 2.215 0.961 1.258 -0.123 0.000 1.266 2.196 -0.704 0.000 0.840 0.938 0.989 0.793 2.759 1.903 1.593 +1 2.224 0.287 -1.669 1.127 1.662 2.207 -0.706 -0.573 2.173 1.393 0.231 0.956 2.215 1.768 -0.589 -0.096 0.000 1.590 -0.795 1.173 0.000 1.229 1.612 1.002 2.128 2.835 1.801 1.684 +1 0.764 -1.039 -0.447 1.064 1.691 0.759 -1.213 0.142 0.000 0.979 -0.049 1.422 2.215 0.634 0.845 -0.732 2.548 0.617 2.471 1.097 0.000 4.260 2.326 1.171 0.915 0.889 1.505 1.363 +0 1.652 0.814 1.446 0.376 -1.712 1.119 -0.687 -0.198 0.000 0.725 0.253 -0.647 0.000 0.941 0.049 1.366 2.548 0.854 0.682 0.575 3.102 0.827 0.880 0.997 0.732 0.523 0.751 0.981 +0 0.512 -0.464 -0.754 0.358 0.853 0.545 2.665 -1.546 0.000 0.580 1.154 0.409 2.215 0.657 1.455 0.944 0.000 1.080 -0.114 0.250 1.551 1.043 0.933 0.997 0.743 0.519 0.898 1.443 +1 1.035 -0.466 -0.236 1.760 -0.739 0.844 -1.083 0.909 2.173 0.385 -1.398 1.593 0.000 0.987 -0.121 1.734 1.274 0.676 0.692 1.233 0.000 0.951 0.875 0.984 0.941 0.956 1.023 0.869 +1 0.580 -0.270 0.736 0.402 0.011 0.894 -0.949 -0.967 2.173 0.368 -0.077 -1.696 0.000 0.649 0.604 0.337 0.000 0.657 0.422 -0.586 3.102 0.778 1.046 0.993 0.764 0.704 0.704 0.701 +0 0.429 1.159 -0.135 1.571 1.643 0.828 -0.363 1.232 2.173 1.534 0.228 -0.480 2.215 0.380 -1.813 0.674 0.000 0.382 -2.177 0.111 0.000 0.244 0.854 1.137 1.288 1.734 1.168 1.207 +0 1.058 0.270 -0.292 0.886 -0.511 0.976 -0.567 1.035 2.173 0.690 -0.043 0.464 0.000 0.653 0.494 -0.904 0.000 0.371 2.351 -1.053 0.000 0.938 1.073 0.989 1.551 1.562 1.155 0.985 +1 0.345 0.576 -1.477 0.770 -0.569 3.540 1.146 1.298 0.000 2.513 -0.592 -0.053 0.000 2.216 -0.324 -0.367 2.548 1.431 0.292 -0.957 3.102 2.053 1.301 0.984 0.706 0.847 0.981 0.838 +0 3.106 -1.365 0.473 0.357 1.208 1.713 -2.031 -1.033 0.000 0.538 -0.850 0.858 2.215 0.619 -1.070 1.601 2.548 0.479 -2.337 -1.089 0.000 0.539 0.859 0.986 0.566 0.392 0.828 1.046 +0 0.866 0.094 -0.447 1.019 -1.505 0.745 -0.436 0.201 2.173 0.753 -1.474 -1.454 2.215 1.045 -0.884 0.796 0.000 0.647 0.019 -1.724 0.000 0.838 0.842 1.060 0.944 1.263 0.878 0.777 +0 1.415 -0.584 1.725 0.608 -1.018 0.861 0.379 -0.074 0.000 0.450 -1.309 0.826 2.215 0.840 0.113 -0.943 0.000 0.513 -0.451 1.210 0.000 0.965 1.069 0.998 0.583 0.173 0.620 0.709 +0 2.119 -0.329 0.960 1.120 -0.820 1.141 -1.046 -0.641 2.173 1.476 0.332 0.582 0.000 1.236 -0.918 -1.388 2.548 0.842 1.190 -1.699 0.000 1.528 1.679 2.133 1.618 0.922 1.456 1.319 +1 0.425 -0.293 -0.671 0.776 0.900 0.996 0.540 -0.646 0.000 1.127 1.178 0.696 1.107 0.485 0.465 -1.373 0.000 1.259 1.036 -1.615 3.102 0.806 0.777 0.991 1.234 0.938 1.161 1.000 +0 0.427 -1.207 -0.972 2.425 -0.391 0.638 -0.612 1.625 2.173 0.445 1.105 0.400 0.000 0.617 -0.431 -0.315 0.000 2.112 1.032 1.160 0.000 0.934 0.862 0.984 0.745 0.328 0.757 0.691 +0 1.641 -0.177 1.676 1.574 1.217 0.829 -0.908 -0.224 1.087 0.666 -1.074 0.361 0.000 0.785 0.048 -0.981 2.548 0.942 0.495 -0.373 0.000 0.790 0.947 0.990 1.495 0.812 1.018 0.996 +1 1.108 -1.314 0.622 0.196 0.812 1.178 -1.138 -1.417 0.000 0.190 -1.010 -1.031 2.215 0.965 -0.738 0.166 0.000 0.449 -0.287 -0.114 3.102 1.925 0.997 0.981 0.518 0.215 0.617 0.649 +0 1.050 -0.933 -0.031 1.909 -1.300 0.570 -0.076 0.059 0.000 0.736 -0.115 1.700 2.215 0.927 0.731 0.208 2.548 1.057 1.401 1.251 0.000 1.576 0.963 1.785 1.105 0.952 1.027 1.099 +1 2.024 -0.276 -1.364 0.495 -0.713 0.367 -1.145 1.240 0.000 0.691 -0.510 -0.662 0.000 0.968 -0.723 0.538 2.548 0.788 -0.038 0.011 0.000 0.877 0.732 0.988 0.990 0.610 0.793 0.697 +0 1.217 -0.018 0.806 1.435 -0.042 0.849 -1.584 1.479 0.000 0.849 0.785 -0.505 2.215 0.757 -1.712 -1.445 0.000 0.795 -0.430 -0.375 3.102 0.726 0.911 1.266 0.983 0.535 1.176 1.133 +0 1.788 0.664 1.540 1.052 -0.936 0.673 1.589 -0.966 0.000 1.241 1.625 -0.567 0.000 1.086 -0.439 -1.309 2.548 0.962 0.490 0.030 3.102 0.686 0.846 1.501 1.009 0.851 1.008 0.988 +0 1.356 0.434 -1.267 0.388 0.175 0.942 -0.767 1.098 2.173 0.297 -0.394 -1.427 0.000 0.873 -0.598 -0.567 2.548 1.162 -0.255 0.227 0.000 0.764 0.814 0.988 0.703 1.129 0.845 0.700 +0 1.043 -0.709 0.227 0.694 1.734 0.903 -0.891 -0.183 1.087 0.737 -0.726 -0.967 0.000 1.364 -0.545 1.498 0.000 0.785 -1.285 1.152 1.551 0.917 0.854 1.153 0.850 0.881 0.792 0.712 +0 2.891 0.816 0.489 1.355 0.903 1.508 0.814 -1.233 0.000 0.805 0.250 -0.535 2.215 0.526 0.706 1.697 2.548 0.488 1.320 -1.163 0.000 0.519 0.865 1.002 0.820 0.651 0.852 1.040 +0 1.416 0.839 1.323 0.330 0.103 0.690 -0.960 -0.354 0.000 0.650 -0.057 0.930 2.215 0.660 -0.226 -0.481 2.548 0.524 -1.518 -1.228 0.000 0.766 0.953 0.988 0.851 0.669 0.647 0.868 +0 0.764 -1.590 -0.880 0.980 1.331 1.045 -0.945 -0.873 2.173 0.752 -0.543 0.168 1.107 0.808 -1.859 0.593 0.000 1.970 0.712 1.229 0.000 0.899 0.882 1.093 0.962 1.084 0.834 0.769 +1 1.742 0.277 0.223 0.695 -0.414 0.697 0.145 -1.195 2.173 0.438 1.788 1.506 0.000 1.507 0.263 1.447 2.548 0.415 0.286 -0.175 0.000 0.718 0.813 0.984 1.121 0.884 0.908 0.800 +0 1.599 0.469 1.600 1.500 1.024 1.245 -0.991 -0.162 0.000 0.376 0.265 1.077 0.000 0.901 0.865 -1.343 0.000 1.473 1.328 -0.698 3.102 0.794 0.803 1.063 0.856 0.421 0.854 0.710 +1 0.479 0.335 -1.445 0.776 -0.200 1.766 -0.752 1.536 2.173 1.854 -1.284 0.046 0.000 0.827 -1.221 -0.516 0.000 0.426 -0.450 -0.337 3.102 0.921 0.556 0.993 1.177 0.917 1.252 1.016 +0 0.556 0.693 -0.262 0.859 -1.039 0.648 -0.099 0.397 1.087 0.768 0.528 -1.519 2.215 0.681 -0.226 1.238 0.000 0.565 1.612 -0.245 0.000 1.123 0.904 0.982 0.734 1.080 0.717 0.699 +1 1.189 0.199 1.027 1.276 -0.073 1.916 -2.051 -1.527 0.000 1.521 -1.546 -0.047 2.215 1.020 -0.127 0.676 0.000 0.971 -0.388 -0.357 0.000 0.899 1.145 1.427 0.907 0.900 1.097 0.896 +0 1.838 0.222 -1.685 0.875 0.168 1.226 1.229 -1.715 0.000 1.736 0.075 -0.486 1.107 2.821 0.352 0.609 0.000 0.838 -1.375 1.638 0.000 1.458 1.088 1.748 1.380 0.644 1.217 1.144 +0 0.519 -0.573 1.063 0.681 -0.118 1.176 -0.184 0.933 2.173 0.906 0.012 -1.271 0.000 0.772 -1.137 -0.204 2.548 0.674 -1.157 -1.505 0.000 0.860 0.970 0.984 0.774 1.211 0.845 0.730 +0 0.368 1.732 -0.137 0.180 -1.403 1.230 0.608 0.947 2.173 0.764 1.155 -0.401 0.000 0.742 -0.043 0.052 2.548 0.735 -0.190 -1.452 0.000 1.092 0.790 0.987 0.921 0.949 0.842 0.723 +1 0.645 -0.406 1.578 0.704 -0.925 0.334 1.429 1.032 0.000 0.507 0.883 -0.057 1.107 0.357 0.566 -0.846 2.548 0.384 1.708 0.589 0.000 0.272 0.457 0.985 0.659 0.302 0.666 0.746 +1 0.299 1.036 0.844 1.103 1.552 0.670 0.070 0.303 2.173 0.728 -0.431 -0.561 0.000 0.808 0.325 -1.497 2.548 0.776 -1.874 0.511 0.000 0.601 0.825 0.988 1.082 0.925 1.153 1.292 +1 0.893 0.582 0.554 0.810 -0.961 1.367 0.240 0.117 2.173 1.068 0.302 -1.215 0.000 1.075 -0.038 -1.728 0.000 0.589 2.255 -1.507 0.000 1.042 0.724 1.153 0.946 0.984 0.856 0.758 +0 0.886 1.074 -1.738 0.775 0.074 0.748 -0.333 -1.235 0.000 1.091 0.407 0.336 2.215 0.665 -0.549 0.019 0.000 1.192 -0.661 0.646 0.000 1.022 0.825 1.146 0.705 0.944 0.687 0.648 +1 0.731 0.517 0.217 0.579 -0.509 0.623 -0.173 1.437 1.087 0.825 0.953 -0.377 0.000 0.788 0.846 0.797 1.274 0.510 1.669 -1.256 0.000 0.752 1.128 0.986 0.766 0.695 0.743 0.741 +1 0.678 -0.339 -0.972 1.115 0.854 0.782 -1.001 0.187 1.087 0.965 -1.203 1.301 0.000 0.835 -1.442 -0.214 2.548 0.932 -0.377 -0.731 0.000 0.982 0.978 1.201 0.874 0.461 0.685 0.714 +0 1.500 -0.536 -1.566 0.720 -1.487 1.136 -0.465 0.180 2.173 0.950 -0.031 1.507 2.215 1.354 0.483 0.206 0.000 0.592 0.463 -0.838 0.000 0.798 1.005 1.009 1.421 1.462 1.033 0.934 +0 0.902 0.639 1.399 1.332 0.895 0.979 1.377 -0.038 0.000 0.442 1.340 -0.801 0.000 0.964 0.594 -1.364 2.548 0.520 -0.160 -1.325 3.102 0.887 1.005 0.998 0.751 0.236 0.672 0.765 +1 1.124 -0.769 0.169 2.204 0.918 1.460 0.826 -0.652 0.000 0.965 -0.687 1.496 2.215 0.768 0.476 -1.457 2.548 0.484 -1.064 -1.618 0.000 1.896 1.172 1.364 1.005 0.733 1.081 1.271 +0 0.723 -1.497 0.511 0.183 -0.608 0.662 -0.199 1.667 0.000 0.831 0.053 1.010 0.000 0.500 0.625 -0.501 2.548 1.943 -0.812 -0.571 3.102 0.903 0.845 0.995 0.755 0.702 0.842 0.745 +1 1.363 0.637 1.516 1.524 1.080 0.488 0.439 0.413 0.000 1.419 -0.550 -0.315 2.215 0.743 0.091 1.571 0.000 1.103 -0.456 -0.707 0.000 0.944 0.968 0.979 0.839 0.788 1.049 0.870 +1 1.281 0.748 0.223 1.008 -1.311 1.075 2.833 1.072 0.000 1.192 1.035 -0.743 2.215 2.160 1.099 1.640 0.000 1.804 0.184 -0.136 0.000 2.447 1.366 1.547 0.996 0.681 1.002 0.920 +1 0.656 -0.245 -0.850 0.939 0.341 0.849 -0.271 0.141 0.000 0.937 -0.312 -0.239 2.215 2.038 -0.005 -1.600 2.548 1.921 -1.223 1.394 0.000 0.919 0.900 0.987 0.999 1.402 0.877 0.764 +1 1.036 -1.069 0.030 0.269 0.794 1.341 -1.554 -0.982 2.173 0.737 -2.128 -1.204 0.000 0.907 -1.065 0.633 0.000 2.559 1.137 0.805 0.000 1.417 0.970 0.976 1.263 0.542 0.836 0.853 +0 1.263 0.043 1.639 0.826 1.042 1.106 0.908 0.219 0.000 0.888 0.707 -0.806 2.215 0.490 0.549 -1.378 2.548 0.383 0.235 0.685 0.000 0.517 0.940 0.983 0.563 0.348 0.636 0.725 +1 1.628 -0.066 -0.130 0.774 0.215 0.980 -0.840 -1.607 2.173 0.503 -0.532 -1.001 0.000 0.276 0.817 0.819 0.000 1.491 -0.783 0.996 3.102 0.726 0.789 0.992 1.091 0.915 1.120 0.875 +0 1.815 1.433 -0.602 1.080 -0.851 0.710 0.731 1.663 1.087 0.918 1.724 0.764 0.000 1.314 -1.060 -1.620 0.000 1.050 -0.168 0.467 0.000 0.634 1.042 0.979 1.322 0.832 1.087 0.994 +0 0.297 1.365 -1.078 2.092 0.743 1.495 -0.738 -0.300 2.173 1.860 0.856 1.099 2.215 3.441 0.490 -1.054 0.000 1.859 -0.000 1.648 0.000 1.964 2.179 1.089 2.371 3.225 2.074 1.838 +0 1.863 -0.297 -0.984 0.335 -1.412 1.184 -0.459 -1.686 2.173 0.896 0.782 -0.037 0.000 1.828 0.473 0.506 0.000 1.149 -0.392 0.332 3.102 0.965 0.798 0.986 0.903 1.196 1.176 1.122 +0 1.793 -0.871 0.894 0.473 -0.593 1.182 -0.577 -0.596 0.000 1.032 -1.359 -1.488 2.215 0.499 -0.641 0.324 2.548 0.431 -0.849 -1.711 0.000 0.950 1.067 1.242 0.622 0.808 0.711 0.773 +0 0.417 -1.630 0.356 1.734 0.368 0.681 0.726 -1.658 2.173 0.759 -1.555 -1.078 0.000 0.663 -0.676 1.257 2.548 0.795 -0.185 0.700 0.000 0.841 0.833 0.987 0.693 0.791 0.865 0.753 +1 1.282 1.329 1.090 1.073 -1.209 1.106 -1.065 0.462 0.000 2.159 0.690 -0.692 2.215 2.048 0.698 1.518 0.000 0.711 0.234 -0.280 0.000 1.026 0.662 1.426 1.449 0.625 0.932 0.902 +1 0.998 -1.171 -0.297 1.224 -0.069 0.836 0.684 -1.340 0.000 0.689 0.176 1.036 0.000 0.774 0.528 1.529 1.274 1.118 0.854 0.584 3.102 0.907 0.867 0.983 1.018 0.558 0.821 0.763 +0 1.354 1.946 -0.456 0.269 -0.761 0.632 0.107 1.412 0.000 0.671 0.858 0.565 0.000 0.509 1.237 0.808 2.548 0.715 -0.867 -1.224 3.102 1.104 0.969 0.976 0.667 0.826 0.806 0.823 +1 0.477 0.119 0.976 1.381 -0.164 1.233 -2.489 0.912 0.000 1.092 1.651 -1.529 2.215 1.048 0.513 -1.008 0.000 1.407 1.210 -0.676 3.102 0.739 0.788 0.985 0.789 0.791 0.852 0.710 +0 0.694 -0.613 -0.388 0.897 1.338 0.582 -0.205 0.641 1.087 0.338 -0.699 -0.819 0.000 0.535 0.535 1.512 0.000 0.646 0.391 0.241 0.000 0.725 0.705 1.093 0.790 0.923 0.660 0.572 +1 1.927 0.094 0.287 0.250 1.320 0.693 0.044 -1.167 2.173 0.938 -1.206 -0.698 2.215 0.638 -0.363 -1.326 0.000 0.930 0.096 0.989 0.000 0.922 0.937 0.986 1.227 0.951 0.970 0.850 +0 0.430 -1.025 -0.694 1.900 0.124 1.032 0.317 -1.373 2.173 1.586 0.392 1.470 0.000 1.265 -1.032 -0.047 2.548 0.384 0.812 1.246 0.000 1.267 1.044 0.987 0.646 1.740 1.158 1.084 +1 1.008 0.201 0.104 0.483 1.541 1.482 0.194 -0.518 0.000 0.780 0.702 1.196 0.000 1.413 -0.406 0.979 1.274 1.864 -0.867 0.093 0.000 0.972 0.895 0.985 0.549 0.568 0.577 0.565 +0 1.176 -0.273 -0.299 1.586 0.321 1.168 -1.057 1.220 1.087 0.961 -1.470 1.662 0.000 0.850 -0.057 -1.136 2.548 1.381 0.381 -0.669 0.000 0.962 0.785 1.004 1.408 1.240 1.044 0.996 +1 0.451 -0.403 0.147 1.334 1.275 0.754 -0.982 -1.440 2.173 0.745 -1.105 0.255 2.215 1.107 -0.186 0.515 0.000 1.818 -0.696 -0.705 0.000 0.699 0.768 0.988 0.821 1.105 0.715 0.668 +0 1.375 -1.651 0.499 0.766 1.182 1.126 -0.573 -1.363 0.000 1.089 -0.986 -0.093 2.215 0.819 -0.904 1.478 0.000 1.490 -0.436 -0.723 3.102 1.013 0.916 0.988 1.006 0.681 0.902 1.026 +1 1.006 0.844 -0.845 0.823 1.359 0.989 0.628 -0.235 0.000 1.134 1.412 1.026 2.215 1.093 0.212 1.569 2.548 0.602 1.529 -0.120 0.000 0.762 1.159 1.153 0.934 0.948 0.957 0.842 +1 0.992 1.151 0.070 0.079 1.568 0.392 2.324 0.818 0.000 0.686 2.007 -1.509 0.000 0.950 -0.179 -1.567 2.548 1.051 0.722 -0.501 3.102 0.964 0.932 0.987 0.822 0.757 0.900 0.816 +1 1.600 -1.336 1.445 1.207 0.954 0.717 2.694 -0.623 0.000 0.475 0.562 1.299 0.000 0.530 0.776 -0.501 2.548 0.964 -0.590 -1.086 0.000 0.913 0.639 0.995 0.514 0.523 0.724 0.685 +0 0.906 0.008 0.076 1.392 -0.544 0.669 1.253 1.731 0.000 0.709 0.812 0.746 2.215 0.707 2.044 1.239 0.000 0.389 -0.229 -0.808 1.551 0.826 0.842 0.990 0.507 0.543 0.615 0.778 +0 1.168 -0.133 0.060 0.829 -0.944 2.449 -0.266 -0.413 0.000 1.828 -0.653 1.186 2.215 1.751 -1.390 1.241 0.000 1.678 -0.597 1.621 1.551 0.897 1.059 1.071 0.953 0.606 0.925 0.940 +1 1.149 -1.038 0.695 0.522 0.480 0.840 -0.661 -0.713 2.173 0.929 -0.614 1.280 0.000 1.398 -0.217 -1.601 2.548 0.815 -1.760 -0.464 0.000 0.861 1.023 0.986 0.971 1.011 0.880 0.765 +0 3.015 -0.123 0.462 0.390 -0.040 1.515 -0.694 -1.217 2.173 1.193 -1.224 -1.132 0.000 1.543 -0.650 0.788 2.548 0.411 -1.188 1.119 0.000 0.822 1.134 0.974 1.957 1.852 1.395 1.224 +1 1.116 -1.186 0.426 0.812 -0.429 1.155 -0.647 -1.615 2.173 0.750 -1.116 -0.021 0.000 0.320 -1.049 -1.258 0.000 0.766 0.906 0.933 1.551 0.674 0.931 0.984 1.294 1.230 1.084 0.885 +0 0.322 -1.074 -0.104 2.046 0.885 0.545 -1.123 -1.026 0.000 0.815 0.836 -1.045 2.215 0.458 -0.126 0.364 0.000 0.601 -0.001 -0.372 3.102 0.973 1.062 0.989 0.902 0.457 1.261 1.018 +1 1.630 0.466 0.639 0.773 0.003 0.638 -0.176 -1.492 2.173 0.623 -0.594 -0.627 2.215 0.387 -1.345 -0.304 0.000 0.779 -0.148 1.350 0.000 0.736 0.671 0.996 1.004 0.683 0.887 0.760 +0 0.742 -0.919 -0.800 1.034 0.453 0.755 0.464 1.678 0.000 0.798 0.806 0.357 2.215 0.882 0.332 -0.548 2.548 0.843 1.322 1.254 0.000 0.850 0.967 1.097 0.824 0.682 0.771 0.898 +0 0.411 -0.878 1.694 0.878 0.560 1.854 -0.116 1.252 2.173 2.836 -0.119 -0.602 1.107 0.377 0.869 -0.560 0.000 0.373 0.677 0.847 0.000 0.396 0.940 0.987 1.317 3.357 1.551 1.127 +0 0.877 0.477 -0.761 0.667 -0.068 0.764 0.591 0.520 2.173 0.787 0.037 1.695 0.000 1.274 -0.420 1.161 0.000 1.022 1.698 -0.650 0.000 0.887 1.148 0.984 0.635 0.974 0.790 0.727 +1 2.099 -0.197 0.759 0.334 -0.532 1.009 0.375 -1.296 2.173 0.550 -0.003 -0.525 0.000 0.600 0.924 -1.613 0.000 0.549 0.295 -0.117 3.102 0.869 0.730 1.065 0.604 0.688 0.824 0.720 +0 1.083 -0.436 -1.120 0.322 -0.084 1.363 0.158 0.397 2.173 1.197 -2.649 -1.200 0.000 0.881 -0.504 0.811 0.000 0.876 0.094 1.524 3.102 0.897 0.941 0.980 0.694 0.982 0.907 0.764 +0 0.561 -0.838 0.270 1.010 1.245 0.708 -0.547 -0.632 2.173 0.908 0.914 -1.446 2.215 0.559 1.755 -0.058 0.000 0.547 0.326 0.716 0.000 0.648 1.086 0.990 0.950 1.246 0.868 0.808 +0 1.070 0.384 -1.126 0.950 1.650 1.099 -0.269 0.000 0.000 1.761 0.152 1.443 2.215 1.164 0.676 -0.056 0.000 2.042 -0.394 0.279 3.102 1.082 0.847 0.989 0.853 1.581 1.216 1.095 +0 0.277 1.125 -0.627 1.124 -1.666 0.597 -0.437 -0.063 0.000 0.556 -1.270 -0.779 2.215 1.308 -0.297 1.011 2.548 0.401 -1.610 -0.392 0.000 0.649 0.877 0.989 0.734 1.016 0.688 0.669 +1 3.325 0.647 -0.479 0.178 -1.475 0.663 -0.132 -1.711 1.087 1.396 1.928 0.600 0.000 1.205 1.419 1.595 0.000 0.627 0.267 0.366 3.102 1.177 0.850 0.989 1.231 0.670 0.979 1.070 +0 1.464 -0.890 -1.704 0.725 -1.664 0.745 0.239 0.415 0.000 0.646 -0.645 -0.238 1.107 0.952 -0.032 1.680 1.274 1.309 2.134 -0.168 0.000 0.783 0.837 0.984 0.975 0.865 0.774 0.850 +1 1.620 1.667 1.668 0.546 1.172 1.100 1.327 -0.813 2.173 0.737 1.029 -0.226 1.107 0.657 1.124 0.583 0.000 0.874 1.791 1.289 0.000 1.038 1.053 0.995 1.078 0.694 0.967 0.843 +1 0.696 -0.023 0.604 0.521 0.918 0.955 0.286 1.673 2.173 1.260 0.550 -0.292 0.000 1.115 -0.486 1.602 0.000 0.563 0.580 -0.681 1.551 0.861 0.516 0.991 0.939 0.679 0.876 0.799 +0 0.543 0.166 1.541 1.835 1.009 1.304 -0.571 -0.881 1.087 0.721 -1.364 0.960 0.000 0.624 -0.879 0.154 0.000 0.548 0.689 -0.940 0.000 0.850 0.912 0.979 0.764 0.271 1.186 0.954 +0 0.705 -0.531 -0.945 0.842 1.367 0.751 0.679 0.457 1.087 1.457 0.084 -1.244 1.107 0.555 -2.395 0.958 0.000 0.918 -0.987 0.380 0.000 0.733 1.641 0.986 0.899 1.608 1.377 1.098 +1 1.067 -1.176 0.600 0.799 0.368 0.940 -0.190 -1.100 2.173 0.343 -1.237 -1.526 2.215 0.607 -1.568 1.223 0.000 0.908 -0.655 -0.513 0.000 0.911 1.002 1.000 0.721 0.568 1.019 0.827 +1 0.952 0.081 -1.263 0.400 0.930 0.826 -0.254 -0.798 2.173 0.492 0.066 1.025 2.215 0.447 -0.061 0.082 0.000 0.443 -1.901 0.660 0.000 0.896 1.114 0.991 0.622 0.947 0.747 0.667 +0 0.626 0.103 -0.067 1.652 0.180 0.712 0.380 1.588 2.173 1.178 -1.147 -0.610 2.215 1.749 -0.348 1.102 0.000 1.459 -0.092 -1.285 0.000 1.493 1.023 0.984 1.822 1.698 1.389 1.269 +0 0.458 -1.626 -0.020 1.310 -1.232 0.452 -1.032 -0.359 0.000 1.214 -0.914 0.847 0.000 0.524 0.697 -1.458 2.548 0.452 -0.950 1.135 1.551 1.394 1.177 0.987 0.597 0.490 0.760 0.812 +0 2.525 -0.638 -0.603 2.642 -0.361 1.260 0.677 1.301 0.000 1.070 0.096 1.077 2.215 0.582 -1.456 -1.685 0.000 0.807 -1.136 0.884 3.102 1.149 0.957 0.995 1.069 0.686 1.229 1.567 +1 0.738 -0.398 -0.901 0.339 0.768 1.202 0.031 0.123 0.000 1.429 1.983 1.680 0.000 1.323 0.601 1.419 2.548 1.172 -1.709 -0.717 0.000 0.673 0.647 0.989 1.078 1.084 1.082 0.879 +0 0.732 2.051 0.179 1.143 -0.655 1.575 0.685 1.372 2.173 0.926 1.219 -0.547 2.215 0.671 0.654 -0.165 0.000 0.449 2.015 0.884 0.000 0.759 1.170 0.989 0.866 1.826 1.456 1.109 +1 2.046 -0.362 -0.017 0.589 -0.281 0.956 -0.919 1.507 2.173 0.700 -0.297 -1.369 0.000 0.508 -0.849 1.017 2.548 0.461 0.789 -1.025 0.000 0.544 0.874 0.972 0.712 0.372 0.870 0.799 +1 0.406 1.463 0.023 0.905 -1.376 1.043 0.030 -0.602 0.000 1.706 0.222 0.974 2.215 1.119 -0.386 -0.379 0.000 1.171 -1.198 1.581 0.000 0.591 0.886 0.989 1.771 0.241 1.101 1.318 +1 0.680 -0.137 -1.609 1.010 0.605 1.195 0.374 0.782 1.087 2.291 1.407 -1.129 0.000 2.011 0.478 0.368 2.548 1.130 1.897 0.162 0.000 0.906 1.158 1.045 0.810 0.719 0.953 0.905 +1 1.266 -0.645 -0.640 0.819 -0.121 1.370 0.433 1.618 2.173 1.535 -0.321 0.850 0.000 0.942 0.954 -0.201 0.000 1.441 -0.170 -0.967 3.102 1.479 1.242 0.980 1.465 1.182 1.053 1.025 +0 0.363 1.846 1.737 1.889 -0.546 0.396 -0.046 -1.673 0.000 1.208 0.844 1.127 2.215 1.742 0.078 0.722 2.548 0.757 1.632 -1.223 0.000 1.040 0.968 1.014 1.645 0.828 1.236 1.051 +0 0.512 1.422 1.695 0.726 -0.405 0.880 -0.371 1.688 2.173 0.720 0.995 -1.649 2.215 1.423 0.291 0.047 0.000 0.652 1.383 0.387 0.000 0.831 1.004 0.991 1.757 0.902 1.144 1.056 +0 2.565 0.022 -0.718 0.832 1.646 1.067 -1.288 0.988 2.173 0.446 0.136 0.288 0.000 0.329 0.948 -0.436 2.548 0.564 -0.618 0.491 0.000 0.321 0.779 1.715 0.850 1.289 1.228 0.936 +1 0.981 -0.080 0.206 1.335 0.277 1.262 -0.676 1.689 2.173 0.403 1.840 1.396 0.000 1.275 -0.531 0.909 2.548 1.226 0.732 -0.876 0.000 0.947 1.313 0.985 1.447 1.026 1.186 1.199 +1 0.832 0.139 -0.783 0.657 0.048 0.903 -0.363 0.908 2.173 0.986 -0.582 -1.191 0.000 0.794 0.694 1.042 0.000 0.927 -0.027 -0.347 3.102 1.580 1.014 0.995 0.965 0.891 0.845 0.773 +0 0.504 1.073 -1.421 2.067 -1.208 0.758 1.518 0.460 0.000 0.835 2.517 -0.793 0.000 0.740 2.499 0.540 0.000 1.759 1.174 -1.579 3.102 0.914 1.009 0.982 0.723 0.782 0.726 0.883 +0 1.076 0.255 -0.091 1.516 -0.191 0.742 0.085 0.825 0.000 0.873 0.922 1.419 0.000 1.337 0.860 -1.166 2.548 1.228 0.161 -1.259 3.102 1.156 1.018 0.997 1.287 0.386 0.898 1.005 +1 0.640 0.679 -0.351 1.063 -0.729 1.911 0.541 0.735 0.000 1.388 -1.230 1.741 0.000 0.997 -0.206 -0.626 2.548 0.752 -0.199 1.692 0.000 0.716 1.034 0.982 1.277 0.304 0.927 1.267 +0 1.413 -0.483 -0.117 1.367 -0.663 0.881 1.634 -1.587 0.000 0.928 -1.824 0.177 0.000 1.188 0.559 1.394 2.548 0.902 -0.278 1.034 3.102 0.891 0.933 0.987 0.841 0.464 0.842 1.056 +0 0.936 -1.635 -0.006 0.289 -1.504 0.459 0.149 1.122 0.000 1.021 0.237 -1.392 2.215 0.936 0.080 0.074 2.548 0.898 1.384 1.429 0.000 0.856 0.900 0.994 0.744 1.010 0.805 0.871 +0 0.938 0.448 -1.670 1.957 -1.014 0.831 0.532 0.638 0.000 0.971 0.716 -0.084 2.215 0.547 -0.939 1.282 2.548 0.451 1.489 1.132 0.000 0.741 0.875 1.048 1.065 1.067 0.879 0.873 +0 0.868 1.262 1.383 1.460 -1.298 1.157 0.631 -0.349 0.000 2.060 0.358 1.431 2.215 1.436 0.105 -0.086 0.000 1.564 1.619 0.711 0.000 0.819 0.865 1.036 1.139 1.664 1.346 1.265 +0 0.465 2.136 0.846 1.772 -1.572 0.352 -1.001 -0.121 0.000 0.722 -0.167 0.390 0.000 1.193 1.030 -0.003 2.548 1.279 0.563 1.270 3.102 0.964 1.296 1.033 1.130 0.887 1.170 1.652 +1 1.215 1.156 -0.810 1.979 -0.258 2.467 0.741 1.555 0.000 0.920 1.055 -0.222 2.215 1.158 0.215 0.351 2.548 0.765 0.328 -1.473 0.000 0.919 1.526 1.028 0.565 0.723 1.237 1.269 +1 1.640 0.997 -0.587 0.783 -1.455 0.909 -2.309 0.859 0.000 0.809 0.943 -1.021 2.215 0.769 -0.010 0.137 0.000 1.659 -0.102 1.383 1.551 0.669 0.813 1.105 0.588 1.054 0.795 0.733 +1 0.334 -2.183 1.004 1.495 -0.587 1.260 -0.677 -1.713 2.173 0.806 -0.326 -0.196 0.000 0.836 -1.548 0.137 0.000 1.873 -0.811 1.349 3.102 0.855 0.933 0.989 1.363 0.650 0.996 0.910 +1 0.568 1.234 1.207 0.099 1.684 0.716 1.007 -0.302 0.000 0.515 1.950 -0.749 0.000 1.053 -0.004 -1.641 2.548 1.079 0.178 0.631 3.102 0.894 0.942 0.986 0.584 0.729 0.657 0.605 +1 0.900 -0.113 1.540 1.502 -1.635 0.334 0.057 -0.812 2.173 1.922 -0.399 0.050 2.215 0.748 0.481 0.860 0.000 0.828 -0.250 -1.424 0.000 0.851 1.153 0.979 0.772 0.873 1.051 0.887 +0 0.738 -1.292 0.131 0.252 1.197 1.694 -0.723 -1.620 0.000 2.302 -0.354 0.188 2.215 0.690 0.320 -0.807 2.548 0.574 0.118 -1.718 0.000 0.689 0.932 0.991 0.833 1.155 1.352 1.067 +1 0.578 -0.954 1.579 0.608 1.694 0.517 -1.020 1.138 0.000 0.743 -0.663 0.306 1.107 1.414 -0.117 -0.320 2.548 1.086 0.979 -1.046 0.000 1.070 1.590 0.988 0.863 0.658 1.270 1.083 +1 1.418 -0.267 0.490 1.853 1.035 1.269 -2.623 -0.746 0.000 0.918 -0.958 -1.060 2.215 0.869 0.437 -1.738 2.548 0.548 -0.339 1.478 0.000 0.603 0.890 1.059 0.929 0.934 0.962 0.761 +0 0.782 -0.290 1.366 0.585 -1.551 1.083 -0.906 -0.218 2.173 0.634 -2.411 -1.033 0.000 0.562 -0.521 0.808 0.000 0.991 0.621 1.478 3.102 0.698 0.956 0.980 1.108 1.495 0.957 0.815 +0 2.899 -1.209 -0.507 0.512 -1.417 1.270 1.338 0.653 0.000 0.689 -1.206 0.437 2.215 0.950 -0.958 -1.519 2.548 0.881 -1.489 -1.025 0.000 0.859 0.934 1.233 0.916 0.848 0.784 0.792 +0 0.706 1.759 -1.685 2.058 -0.103 0.582 -0.133 0.260 1.087 0.816 1.162 1.586 2.215 0.761 -0.286 -0.915 0.000 0.539 -0.641 0.848 0.000 0.724 0.936 1.652 1.464 1.189 1.111 1.037 +1 0.455 -0.118 1.525 0.992 -0.173 2.415 -1.635 -1.567 0.000 2.366 0.737 -0.047 2.215 0.962 -0.767 0.920 1.274 1.358 -0.092 0.212 0.000 0.773 0.937 0.984 0.676 1.881 1.068 0.848 +1 0.761 2.184 0.117 0.847 1.177 1.023 0.632 -1.151 2.173 0.417 1.107 0.809 0.000 1.086 1.550 -0.396 0.000 0.501 0.605 1.215 0.000 0.908 1.003 0.989 0.733 0.715 0.842 0.733 +1 0.339 -1.498 -1.719 1.190 -1.072 0.676 0.340 1.365 2.173 1.249 -0.550 -0.205 0.000 0.575 -1.102 1.099 2.548 0.483 -0.844 0.702 0.000 0.876 1.134 0.991 0.733 0.691 0.741 0.715 +1 0.710 -0.820 -1.476 0.226 -0.731 0.994 -0.748 1.439 0.000 1.092 -0.596 -0.179 2.215 1.027 0.141 0.251 0.000 1.124 0.642 -0.783 3.102 0.911 1.152 0.975 0.862 0.911 0.990 0.823 +0 2.123 -0.589 1.587 0.710 0.865 0.537 1.648 0.089 0.000 2.134 0.627 -0.589 2.215 1.338 0.483 1.256 2.548 0.642 0.314 -0.456 0.000 0.840 1.110 1.030 1.969 1.791 1.398 1.430 +0 0.410 -1.787 -1.492 1.128 -0.651 1.846 -0.244 -1.352 2.173 1.511 0.448 0.332 0.000 1.448 0.441 0.745 1.274 1.647 0.524 -0.229 0.000 1.010 0.905 0.980 1.018 2.078 1.502 1.274 +0 1.178 -1.318 0.280 1.194 1.625 0.784 -0.940 -1.459 2.173 0.861 0.316 0.275 2.215 0.398 -0.401 0.263 0.000 0.392 0.009 -0.909 0.000 0.684 0.887 1.538 1.057 1.462 1.055 0.831 +1 0.808 -0.482 -0.967 1.203 0.680 0.313 0.680 -0.981 2.173 0.477 -1.173 -1.327 0.000 1.749 1.035 0.405 2.548 0.862 0.109 1.156 0.000 0.922 0.939 1.361 0.856 0.899 0.922 0.807 +0 1.338 -0.377 -0.606 1.093 1.637 0.497 -1.627 0.871 0.000 0.772 -0.804 -0.216 0.000 0.757 0.366 1.327 2.548 1.050 -0.864 1.723 1.551 0.871 1.056 1.508 0.843 0.584 0.707 0.742 +0 0.707 -1.349 -0.309 0.760 1.600 0.746 -0.703 0.242 2.173 0.707 -0.558 -1.277 2.215 0.809 -1.193 -0.812 0.000 1.140 -1.060 1.172 0.000 1.035 0.952 1.004 0.683 1.049 0.718 0.648 +0 0.592 1.128 0.099 0.541 -1.414 1.166 0.440 -0.243 1.087 1.274 0.036 -1.414 2.215 1.351 -0.617 1.111 0.000 0.620 1.373 1.153 0.000 0.937 0.989 0.989 0.793 1.603 0.939 0.776 +1 0.563 -1.613 -0.890 0.557 -0.352 0.977 -0.709 1.416 2.173 0.388 -0.750 -0.805 0.000 1.119 -1.150 0.325 0.000 0.512 2.171 1.742 0.000 0.896 1.085 0.984 0.501 0.670 0.654 0.642 +0 0.392 -0.908 0.968 2.635 0.190 1.103 -1.444 -0.312 1.087 1.186 -0.245 -1.499 0.000 1.812 -0.799 -1.680 2.548 1.245 0.283 1.369 0.000 0.961 0.862 0.983 1.174 1.741 1.249 1.228 +1 0.957 -0.405 -1.194 1.028 1.340 0.887 0.299 -0.900 2.173 1.188 -1.157 0.832 2.215 0.985 0.244 0.722 0.000 0.660 1.493 -0.568 0.000 0.802 1.036 1.039 0.984 1.951 1.078 0.944 +0 0.484 -0.636 1.620 1.631 0.726 0.904 0.636 1.573 2.173 0.746 -2.595 -0.569 0.000 1.148 1.456 -0.078 0.000 1.377 0.438 0.309 1.551 0.964 0.967 0.990 1.455 1.075 1.091 1.414 +0 0.900 -0.421 -1.539 0.720 -0.862 1.054 2.103 0.656 0.000 0.563 2.077 -0.844 0.000 0.357 1.488 1.013 2.548 0.960 1.029 -1.329 3.102 1.598 0.879 0.989 1.056 0.394 0.835 1.449 +1 1.086 0.012 0.124 0.992 -1.318 1.535 0.635 0.706 2.173 0.860 0.774 1.088 1.107 0.642 -1.270 0.642 0.000 0.566 -1.991 0.098 0.000 0.979 1.245 1.385 1.328 0.585 1.136 1.024 +1 1.738 -0.787 -0.346 0.945 -0.002 0.683 -0.268 1.347 2.173 0.597 0.444 -0.026 0.000 0.903 0.478 -1.280 2.548 1.535 -2.092 1.493 0.000 0.839 0.855 0.982 1.229 0.792 1.040 0.932 +0 0.383 0.794 -1.539 1.899 -0.420 1.101 -0.158 1.508 0.000 0.606 -0.393 0.397 2.215 0.741 -0.347 -0.416 1.274 0.646 -1.231 1.563 0.000 0.915 0.999 1.000 0.895 0.477 0.717 0.892 +1 1.079 -0.215 -1.190 0.261 -0.099 1.505 0.381 -1.685 2.173 1.596 -1.609 0.314 0.000 1.323 0.139 0.301 2.548 0.673 0.968 -0.347 0.000 1.188 1.498 0.983 1.155 1.728 1.806 1.386 +1 0.461 2.079 0.765 0.840 0.293 1.307 -0.337 -1.111 2.173 0.311 2.747 -1.356 0.000 0.570 -0.326 0.769 0.000 1.037 0.175 1.046 3.102 0.616 1.113 0.974 0.632 1.197 1.015 0.810 +1 1.757 1.046 0.989 0.222 -1.463 1.163 1.104 -1.233 2.173 2.186 2.139 -0.133 0.000 1.404 0.697 1.556 2.548 1.315 1.352 0.566 0.000 0.783 1.053 0.985 0.715 0.976 0.837 0.738 +0 3.054 -0.487 1.606 0.303 0.819 1.005 0.064 -0.103 1.087 0.588 0.292 0.550 2.215 0.793 -1.514 -0.354 0.000 0.494 1.755 0.055 0.000 1.306 1.151 0.989 1.536 0.641 1.042 1.022 +0 0.733 -1.257 1.157 0.876 -0.994 2.305 -0.557 0.032 2.173 3.278 -1.087 -1.639 2.215 1.252 0.275 1.289 0.000 1.186 -0.221 0.611 0.000 0.863 1.689 1.036 0.999 4.194 2.074 1.573 +1 0.395 -0.893 0.025 1.478 -1.672 1.244 0.377 -0.171 0.000 1.481 0.435 1.009 2.215 0.640 0.378 -0.837 0.000 0.756 0.403 -1.465 3.102 0.908 0.824 1.058 1.236 0.756 0.926 0.984 +1 2.147 -0.242 -1.011 0.602 1.242 0.597 -0.886 0.527 0.000 0.446 0.239 0.757 0.000 0.926 -0.499 1.570 0.000 1.046 -0.453 0.008 1.551 0.772 0.721 1.411 0.745 0.305 0.601 0.619 +0 1.392 -0.645 -1.725 0.301 -1.152 0.547 -1.506 -0.507 0.000 0.816 -0.900 -0.032 1.107 0.846 -0.304 0.616 2.548 0.701 0.145 1.368 0.000 1.300 0.937 0.987 0.926 0.554 0.712 0.701 +0 0.613 -0.687 -1.133 0.798 -1.659 1.495 -0.564 -0.020 0.000 1.370 0.836 1.578 2.215 0.472 0.187 0.081 0.000 1.103 -0.052 1.486 0.000 0.767 0.819 0.994 0.630 1.755 1.323 1.031 +1 0.835 -1.163 0.925 1.230 -0.218 1.763 -1.054 -0.312 1.087 1.918 -2.133 1.640 0.000 1.020 -0.396 1.027 0.000 1.460 -0.882 -1.391 3.102 2.421 1.522 1.203 1.007 1.403 1.643 1.319 +0 0.867 1.098 -1.554 0.456 1.190 2.115 1.008 1.445 2.173 1.933 1.174 -0.506 0.000 1.753 1.589 0.033 0.000 0.995 0.568 -0.035 1.551 0.614 0.587 0.981 0.945 1.515 1.177 1.003 +1 0.555 0.756 -1.112 1.398 0.877 1.097 0.021 1.223 1.087 1.808 -2.346 -0.315 0.000 1.025 -0.373 1.513 0.000 1.541 0.011 -1.006 3.102 0.845 0.804 1.190 0.948 1.246 0.852 0.761 +0 1.122 0.884 0.015 0.375 -0.060 1.155 1.394 0.720 2.173 0.936 0.769 -1.413 0.000 1.650 1.044 -0.764 2.548 1.480 1.635 -1.620 0.000 0.867 0.940 0.985 0.890 1.689 0.952 0.842 +0 1.274 -0.528 -1.570 0.677 -0.341 1.296 0.973 0.824 0.000 1.476 -0.289 -0.897 0.000 0.950 -0.331 -0.129 1.274 1.131 -0.535 0.358 3.102 2.300 1.507 1.152 0.793 0.355 0.979 0.850 +0 0.778 0.133 0.347 1.030 -0.259 1.000 -1.329 -1.680 2.173 0.274 -0.148 1.141 0.000 0.649 -1.433 0.150 2.548 0.381 -1.992 -0.756 0.000 0.711 0.714 0.984 0.614 1.007 0.867 0.694 +1 1.756 0.642 0.282 1.108 0.077 1.110 1.043 1.646 2.173 0.965 0.805 -1.292 0.000 0.404 1.559 0.620 0.000 0.448 0.911 -0.113 1.551 1.043 0.889 0.976 0.516 0.746 0.977 0.884 +0 0.685 -0.181 -0.498 2.083 0.249 0.892 -1.825 1.465 0.000 0.526 0.498 -1.689 2.215 0.484 -0.721 -0.531 2.548 0.559 -1.720 -0.975 0.000 0.880 0.805 1.032 0.958 0.596 0.816 0.973 +1 1.583 -0.427 -1.379 0.255 0.558 0.732 0.715 0.737 2.173 0.654 -0.465 -0.436 2.215 1.041 0.240 0.153 0.000 0.995 2.069 1.568 0.000 1.861 1.263 0.990 0.715 1.099 1.034 1.006 +1 0.921 0.663 -0.987 0.541 1.422 0.859 -0.150 -0.129 0.000 1.011 -0.112 -1.483 1.107 0.626 0.998 1.432 2.548 0.897 -0.160 0.913 0.000 0.927 1.043 0.992 0.567 0.685 0.742 0.731 +1 0.346 0.766 1.130 0.719 -1.001 0.842 -0.252 -0.241 1.087 0.628 -1.295 -0.133 2.215 1.109 1.294 1.690 0.000 1.949 -0.670 1.524 0.000 2.214 1.851 0.985 1.481 0.614 1.366 1.410 +1 0.911 -1.375 0.636 0.662 -0.203 0.741 0.866 1.284 2.173 0.969 -0.435 1.735 2.215 0.450 -1.083 -1.728 0.000 0.560 1.238 1.580 0.000 0.987 0.836 0.990 0.926 1.013 0.951 0.810 +1 0.775 -0.696 -0.158 1.488 0.447 1.236 -0.086 -1.613 2.173 0.752 0.954 0.140 0.000 0.594 -1.402 1.522 0.000 0.743 -0.177 -0.729 3.102 0.766 1.215 0.992 0.656 0.729 0.902 0.808 +1 0.920 -0.744 -1.567 2.355 0.847 1.277 -1.250 -0.488 1.087 0.237 -0.685 0.772 2.215 0.684 -0.866 -1.190 0.000 0.529 0.440 -1.452 0.000 0.559 0.967 1.678 0.751 0.769 1.046 0.867 +0 0.791 -2.041 -0.855 0.859 0.184 0.486 0.226 0.775 0.000 0.472 0.015 1.604 2.215 0.363 -1.713 -0.586 0.000 1.137 -0.801 -1.345 3.102 1.189 0.898 0.985 1.173 0.455 0.831 0.857 +1 0.578 -0.271 1.443 0.983 -1.189 0.888 -0.289 -0.474 2.173 0.930 -0.741 0.209 2.215 0.674 -1.033 -0.823 0.000 0.871 -0.865 1.309 0.000 0.795 0.879 0.996 0.922 0.835 0.801 0.691 +0 1.026 0.621 -0.093 0.134 0.998 1.511 1.427 0.242 2.173 2.045 -0.858 -1.419 0.000 1.119 0.210 -1.697 2.548 1.171 0.816 0.910 0.000 2.774 1.613 0.989 1.209 1.904 2.067 1.557 +0 2.296 0.265 0.258 0.405 0.910 0.913 1.898 -1.350 0.000 1.077 -0.054 0.971 2.215 1.212 -0.770 -0.610 1.274 1.674 -0.488 -1.283 0.000 0.985 1.043 0.981 1.162 1.298 0.934 0.933 +0 2.664 -1.238 0.932 0.893 0.932 1.532 -0.344 -1.079 2.173 1.037 0.571 -0.802 0.000 1.471 -0.587 0.380 2.548 0.509 1.564 0.976 0.000 1.149 1.402 0.970 2.233 1.832 1.562 1.631 +0 0.343 0.937 -0.988 1.529 0.839 1.088 0.155 0.739 0.000 2.426 -0.795 -0.951 2.215 0.580 0.481 0.237 2.548 0.666 -0.807 1.442 0.000 1.089 0.746 1.001 2.044 1.435 1.323 1.208 +1 0.505 -0.756 0.069 1.239 -1.562 1.317 0.915 1.646 1.087 1.061 0.698 0.283 0.000 1.230 1.661 0.344 0.000 1.784 -0.731 -0.708 3.102 1.023 1.712 1.090 0.751 2.166 1.586 1.421 +1 1.071 0.359 -0.694 0.620 1.471 1.074 0.065 0.747 0.000 1.055 0.460 -1.503 1.107 0.718 0.789 0.346 0.000 1.323 -0.336 -0.620 3.102 0.857 1.100 1.048 0.696 0.902 0.938 0.812 +0 0.296 2.274 0.008 1.767 -0.385 1.472 -0.120 1.196 0.000 1.572 0.949 -0.536 0.000 1.391 -0.754 1.251 2.548 0.671 -1.121 -1.356 1.551 3.729 2.220 0.977 1.567 0.560 1.494 1.390 +1 0.791 -0.505 1.396 0.589 -0.218 1.552 0.409 -1.516 0.000 1.655 1.044 0.327 1.107 1.180 0.716 -0.283 2.548 0.698 0.672 1.249 0.000 1.011 1.287 0.987 1.418 0.805 1.229 1.138 +0 0.554 -1.033 1.216 1.045 -0.324 0.514 -2.368 -0.646 0.000 0.567 -1.952 -1.483 0.000 0.939 -1.377 0.752 0.000 0.547 -0.986 -0.716 0.000 0.815 0.648 1.037 1.074 0.539 1.134 0.928 +0 0.780 -0.486 -0.127 1.335 -1.237 0.705 -2.426 1.632 0.000 1.222 -1.634 0.880 0.000 0.982 0.605 -0.136 2.548 1.178 -0.858 -0.572 3.102 0.911 1.014 1.189 0.913 0.845 0.997 0.920 +0 0.804 -1.187 -0.133 0.717 -1.596 0.680 0.997 0.836 0.000 0.721 0.715 -1.127 2.215 0.439 -2.427 -0.354 0.000 0.497 -0.395 1.732 3.102 3.126 1.648 1.018 1.022 0.452 1.153 1.008 +1 0.804 -1.537 -1.094 0.894 -1.336 1.271 -0.130 0.251 2.173 1.560 -0.479 1.151 1.107 1.027 1.710 -0.990 0.000 1.385 -0.239 -1.098 0.000 1.181 1.091 0.982 1.367 1.548 1.156 0.977 +0 0.571 -1.364 0.908 1.134 -1.599 0.978 -0.356 -0.280 0.000 0.797 0.726 1.449 2.215 0.686 0.304 -0.823 0.000 0.791 -0.879 0.676 3.102 0.866 0.848 0.991 0.916 0.869 0.840 0.815 +1 1.095 0.228 1.138 0.693 0.882 0.866 0.787 0.974 0.000 1.320 -0.296 -0.058 2.215 0.522 0.071 1.736 0.000 1.814 0.944 -1.346 0.000 0.885 1.107 0.980 1.035 0.955 0.968 0.904 +0 1.800 -0.128 0.412 1.130 -0.204 1.293 0.082 -1.152 2.173 0.716 -0.169 1.678 1.107 1.309 -1.095 1.447 0.000 1.752 0.911 0.368 0.000 2.741 1.622 1.040 1.481 0.809 1.287 1.210 +1 1.754 -1.471 -0.990 0.450 -0.065 0.648 -1.830 -1.286 0.000 0.846 -1.294 1.440 0.000 1.019 -1.149 0.374 2.548 1.030 0.087 0.553 3.102 0.910 0.796 0.988 0.852 0.591 0.755 0.700 +1 0.495 -0.259 1.553 1.594 -1.062 0.942 1.332 0.782 0.000 0.709 0.618 0.080 2.215 1.019 2.101 -1.110 0.000 0.963 1.915 -0.054 0.000 0.890 1.048 0.985 1.184 0.353 0.918 1.259 +0 0.366 1.094 0.155 1.786 1.074 0.651 -0.847 0.132 0.000 0.751 -0.843 -0.678 0.000 0.884 -0.030 -1.104 2.548 1.092 0.024 -1.625 3.102 0.991 0.985 0.985 1.268 0.340 0.952 1.286 +0 0.576 -0.147 -1.482 1.026 1.318 1.050 -0.121 -0.394 1.087 1.080 1.126 0.996 2.215 0.517 -0.982 -0.423 0.000 0.482 -0.856 -1.663 0.000 0.893 0.963 0.990 1.481 1.836 1.297 1.015 +1 0.850 -0.303 0.739 0.987 1.683 0.685 0.444 -0.743 2.173 0.746 1.209 0.123 0.000 0.840 1.244 1.722 2.548 0.484 0.722 -0.455 0.000 0.417 0.704 0.989 0.940 0.876 0.754 0.708 +0 1.653 -0.613 0.452 1.092 -0.207 1.325 1.167 -1.041 2.173 0.684 1.605 -1.639 0.000 0.881 -0.793 1.091 1.274 0.594 0.626 0.735 0.000 0.799 0.959 1.040 0.790 2.067 1.502 1.285 +0 0.940 -0.689 -0.975 0.522 -0.994 0.425 -0.501 -1.319 0.000 0.561 -0.593 1.120 2.215 1.121 0.381 0.606 0.000 0.860 -0.456 0.441 3.102 1.361 0.884 0.992 0.773 0.361 0.627 0.745 +1 0.810 -0.512 0.267 0.709 -0.885 1.008 -1.029 1.296 0.000 1.121 -0.239 -0.403 2.215 1.049 -1.868 1.028 0.000 1.445 -1.261 -1.079 1.551 1.074 1.149 0.984 0.631 1.019 1.141 0.999 +1 0.581 -0.941 -0.052 0.410 -1.488 1.048 -0.450 0.642 2.173 0.670 -0.428 -0.410 1.107 1.470 -1.003 1.200 0.000 0.762 -1.037 -0.718 0.000 0.890 1.050 0.993 0.607 1.002 0.737 0.652 +1 1.018 0.768 -0.770 0.363 0.563 1.141 -0.452 0.186 2.173 0.904 -1.695 -1.573 0.000 1.053 0.768 -1.386 0.000 1.029 -0.266 1.156 1.551 0.708 0.703 0.984 1.302 0.883 0.935 0.849 +1 0.546 -1.655 1.016 0.470 0.746 0.601 0.151 0.846 0.000 0.544 0.281 -0.122 0.000 1.576 -1.311 -0.884 0.000 0.554 -0.892 -1.176 3.102 0.934 0.778 0.981 0.481 0.216 0.469 0.492 +0 1.141 0.248 0.323 1.058 1.262 0.792 1.243 -0.565 0.000 0.600 1.167 -1.736 1.107 0.765 0.869 -1.354 1.274 0.575 2.324 -1.395 0.000 0.924 0.788 1.140 0.850 0.258 0.648 0.684 +0 0.596 0.780 -0.925 0.754 -0.272 0.785 1.160 -1.031 0.000 1.090 0.215 0.867 0.000 0.589 1.118 0.448 0.000 0.439 1.106 -1.215 3.102 0.920 0.665 0.999 0.508 0.254 0.389 0.501 +1 0.760 0.598 0.270 1.110 1.325 0.659 -0.381 0.015 0.000 0.794 0.216 0.595 0.000 1.157 0.165 -0.944 2.548 0.823 0.907 -1.683 3.102 0.900 0.974 1.036 0.895 0.578 0.756 0.720 +1 1.948 -1.594 -1.045 0.209 -0.670 1.400 -1.326 0.921 2.173 0.588 -1.145 -0.013 0.000 0.607 -0.338 0.156 2.548 0.557 1.673 -1.255 0.000 1.883 1.091 0.976 1.475 0.932 1.237 1.207 +0 0.639 1.236 -1.405 0.314 1.641 1.320 0.143 1.517 0.000 1.144 1.126 -0.645 2.215 0.563 1.903 -0.501 0.000 1.367 0.609 0.203 0.000 0.912 0.788 0.978 0.647 0.796 0.665 0.661 +0 0.652 -0.443 1.706 1.111 -0.951 1.586 -0.937 1.544 0.000 2.081 -0.640 -0.664 2.215 2.181 -0.707 0.771 2.548 1.605 -2.342 0.353 0.000 3.382 2.334 0.989 0.923 2.181 1.996 1.531 +1 0.503 -0.808 -1.388 2.097 1.471 1.049 -0.631 -0.399 2.173 0.423 -1.657 -1.068 0.000 0.647 -0.387 0.729 0.000 1.277 -0.008 0.476 1.551 0.971 0.955 0.994 1.168 0.953 1.140 0.916 +1 0.382 -0.107 1.441 1.569 -0.289 1.317 0.532 -0.903 2.173 1.208 0.643 1.056 1.107 0.597 -0.585 0.987 0.000 0.535 -1.374 0.789 0.000 0.346 0.882 1.072 0.996 1.824 1.113 0.960 +0 0.761 0.033 0.694 0.299 0.556 0.486 -0.329 -1.572 2.173 0.828 0.075 -0.830 2.215 0.398 -0.933 0.933 0.000 0.410 -0.797 -0.761 0.000 0.446 0.578 0.991 0.765 0.610 0.689 0.543 +0 0.472 1.413 -0.726 1.475 0.141 0.734 -0.142 0.862 0.000 0.485 -1.395 -1.246 0.000 0.742 0.522 1.550 0.000 1.166 0.443 -0.319 3.102 0.911 0.967 0.994 0.888 0.713 0.795 0.988 +1 0.480 -1.707 -0.662 0.253 -0.908 1.094 -1.223 0.520 0.000 1.100 -1.347 -1.690 0.000 0.900 -0.942 -1.101 1.274 0.575 -0.089 -0.610 3.102 0.861 0.698 0.980 0.590 0.354 0.470 0.501 +0 2.020 0.547 -0.208 1.077 -0.833 1.704 -1.441 1.408 0.000 0.897 -1.399 -1.637 0.000 2.869 -0.744 -0.063 0.000 0.753 0.950 -1.414 3.102 1.014 0.859 1.088 0.766 0.932 1.098 1.548 +1 1.526 -0.919 0.478 0.697 -0.381 2.496 -0.339 -1.004 0.000 1.768 0.519 0.978 1.107 1.328 -0.282 0.838 0.000 0.694 0.030 -1.634 0.000 0.859 0.892 0.998 1.460 0.877 1.088 0.894 +1 1.923 0.374 0.880 0.499 -1.012 0.407 -0.273 0.817 0.000 0.775 0.347 -1.165 2.215 0.938 -1.128 -0.851 2.548 0.438 -0.607 -0.716 0.000 0.650 0.725 1.345 1.235 0.841 0.910 0.735 +0 2.738 0.149 -1.638 0.834 -1.410 1.423 -0.167 0.188 2.173 0.838 0.861 0.086 0.000 0.340 -0.193 1.156 0.000 1.064 0.779 -0.926 3.102 0.972 0.855 1.008 0.757 1.334 1.284 1.084 +1 0.440 -1.808 -1.046 1.605 1.478 0.984 -0.914 1.094 2.173 0.776 -1.930 -0.799 0.000 1.001 1.219 -1.522 0.000 2.339 -1.915 0.129 0.000 0.729 1.132 0.991 0.821 0.846 0.886 0.793 +0 0.936 -1.643 -1.134 0.276 -1.676 0.621 0.664 1.015 0.000 0.791 -1.204 -0.004 2.215 0.375 -0.989 0.815 0.000 1.198 0.879 -1.221 3.102 0.879 0.935 0.989 0.800 1.470 0.926 0.889 +1 0.429 1.376 -1.513 2.616 -0.793 0.873 0.052 1.170 2.173 1.347 0.789 0.470 2.215 0.604 -0.298 -0.823 0.000 0.689 0.464 1.685 0.000 0.638 0.974 0.989 1.956 1.128 1.505 1.193 +0 0.481 -0.835 0.471 1.247 -0.120 0.699 1.539 -1.634 2.173 0.535 -0.772 -0.794 0.000 0.487 -0.924 0.767 0.000 0.465 -0.523 1.398 0.000 0.995 0.719 0.983 1.249 0.306 0.808 0.764 +1 0.619 1.022 -0.666 1.206 0.995 1.035 0.486 -1.253 0.000 0.563 1.041 1.329 2.215 0.620 1.935 0.430 0.000 1.349 -0.461 0.373 1.551 1.310 0.939 1.194 0.985 0.929 0.784 0.733 +1 0.876 1.565 0.638 0.745 1.647 0.918 1.218 -0.977 2.173 1.029 1.294 1.729 2.215 1.244 0.881 0.133 0.000 1.798 -1.880 0.303 0.000 0.753 0.956 0.984 0.961 0.927 0.758 0.712 +0 0.653 0.272 -1.625 1.052 -0.762 1.289 0.508 1.228 0.000 1.063 0.949 -1.087 0.000 1.129 1.245 -0.511 0.000 1.311 0.705 0.429 3.102 0.897 1.002 0.993 0.691 0.298 0.686 0.638 +1 0.512 -0.945 1.294 1.118 -0.907 1.163 0.711 0.796 2.173 0.973 -0.883 -0.537 0.000 1.072 0.790 -1.027 2.548 0.729 1.972 0.748 0.000 2.898 1.756 0.987 1.684 1.391 1.363 1.361 +1 0.937 0.893 1.654 0.902 -0.462 1.896 0.805 0.606 0.000 1.432 0.199 -0.915 0.000 1.331 1.492 -1.496 0.000 1.237 0.135 -0.171 1.551 1.962 1.123 1.203 0.789 0.700 0.846 0.760 +1 0.611 -0.692 1.699 2.237 -1.210 1.286 -0.624 0.964 2.173 0.687 -1.168 0.055 2.215 0.566 -2.279 -0.151 0.000 0.484 -0.115 0.565 0.000 0.922 1.150 0.986 0.997 1.087 1.105 0.936 +1 0.410 -0.695 -1.683 1.544 -0.202 0.983 0.498 1.264 2.173 1.111 0.104 1.690 0.000 1.258 -0.391 -0.264 2.548 0.616 0.875 -0.242 0.000 1.186 0.945 1.071 0.610 1.516 0.982 0.918 +1 0.620 -1.523 -0.159 1.901 -1.293 1.184 -1.678 0.492 0.000 1.073 -1.200 -0.881 2.215 1.186 -0.535 -1.735 2.548 1.089 -0.673 0.791 0.000 0.918 1.295 1.283 0.749 0.923 1.053 0.994 +1 1.155 -0.319 -0.471 1.473 0.752 0.894 1.531 0.801 0.000 2.013 -1.213 1.539 0.000 1.548 -0.770 1.281 0.000 1.981 -0.559 0.356 0.000 0.830 0.738 1.613 1.549 1.447 1.438 1.311 +0 0.843 0.874 0.215 0.467 1.649 0.684 -0.654 1.738 2.173 0.761 1.156 -0.849 1.107 1.053 -0.378 0.474 0.000 0.688 -1.408 0.706 0.000 1.086 0.978 0.988 0.763 1.364 1.021 0.874 +1 1.200 -0.983 0.244 0.566 0.984 0.784 0.377 -0.659 2.173 0.533 0.142 0.661 0.000 1.093 0.088 -1.712 2.548 0.852 0.776 -0.983 0.000 0.943 0.815 0.986 1.107 0.952 1.060 0.926 +1 1.828 -0.030 -0.244 0.243 -0.428 1.166 1.151 1.424 2.173 0.677 0.591 0.243 1.107 0.570 2.274 -1.322 0.000 0.609 0.887 -0.493 0.000 0.672 0.976 0.980 0.622 1.202 1.016 0.903 +0 0.784 -1.159 0.438 1.004 0.140 1.657 -0.199 0.618 2.173 1.525 -2.254 -1.635 0.000 2.077 -1.026 -1.215 0.000 1.639 -1.153 -0.464 0.000 1.303 1.178 0.995 1.769 1.346 1.430 1.374 +0 1.914 1.142 -1.138 0.769 -0.896 0.697 1.114 0.781 0.000 0.602 -0.770 0.258 1.107 0.748 0.539 -1.622 0.000 0.378 1.380 -0.361 3.102 0.788 0.915 0.973 0.517 0.708 0.976 0.910 +0 0.903 0.281 1.575 0.452 -0.421 1.015 0.538 -0.926 0.000 1.140 0.637 0.490 2.215 0.508 -0.618 0.607 2.548 0.472 -1.363 1.095 0.000 1.731 1.138 0.987 0.887 0.587 0.941 0.797 +0 0.909 1.101 1.568 1.043 -0.444 1.003 0.916 0.086 2.173 0.837 2.528 1.583 0.000 0.600 1.522 1.579 2.548 0.567 0.789 -0.631 0.000 1.165 0.696 1.310 1.000 1.012 0.888 0.816 +1 0.327 0.610 -0.821 1.419 1.498 0.611 -1.214 1.269 0.000 1.421 -0.776 -0.519 2.215 0.725 -1.515 -0.055 0.000 1.252 0.029 1.213 0.000 0.901 0.930 0.991 1.973 0.872 1.219 1.211 +1 0.653 1.786 0.236 1.148 1.143 0.712 -0.170 -0.866 2.173 0.824 1.035 1.653 0.000 0.306 -2.021 1.135 0.000 0.608 0.922 -0.358 3.102 0.865 0.944 0.984 0.610 0.567 0.797 0.702 +0 1.850 0.215 1.006 0.406 1.010 1.252 -1.367 -0.132 2.173 1.604 -0.989 -0.791 0.000 0.939 0.470 -1.205 2.548 1.000 -0.913 0.772 0.000 1.630 1.328 0.971 0.901 1.852 1.467 1.365 +1 1.337 0.078 1.017 1.061 -0.145 1.375 0.171 0.141 0.000 1.637 0.826 -1.177 0.000 0.860 0.603 1.249 2.548 0.398 0.775 -0.457 0.000 0.727 0.962 1.429 0.802 0.563 0.691 0.687 +1 0.503 2.127 1.070 1.729 0.983 0.666 0.392 -0.767 2.173 0.870 -0.305 -0.461 2.215 0.848 0.841 1.689 0.000 0.851 0.188 1.005 0.000 0.839 0.880 0.973 1.179 0.507 1.027 0.832 +1 1.230 0.709 1.481 1.110 0.936 0.662 -1.646 -0.848 0.000 1.615 0.628 -0.314 2.215 1.037 0.230 1.307 0.000 0.684 0.109 0.682 3.102 1.035 0.910 0.997 1.398 0.781 1.041 1.228 +1 1.033 -1.045 1.342 0.171 -0.054 0.530 -0.415 1.048 0.000 0.867 -0.136 -0.744 2.215 1.493 0.575 0.080 2.548 0.414 0.734 1.009 0.000 0.499 0.865 0.983 1.027 0.947 0.813 0.708 +0 1.739 -0.310 -1.341 1.198 0.964 2.075 0.820 0.726 2.173 2.064 1.293 -1.123 2.215 3.014 -1.795 -0.383 0.000 1.223 -0.212 0.983 0.000 0.934 4.758 1.748 2.015 3.129 3.897 2.900 +0 1.671 -0.286 -1.726 1.346 -0.498 1.060 0.900 -0.254 0.000 0.773 0.460 0.910 1.107 1.460 0.796 1.320 2.548 0.775 1.718 0.220 0.000 0.856 1.002 1.859 1.403 0.467 1.001 1.019 +1 1.232 -0.490 1.050 0.888 1.417 1.657 0.424 -0.602 0.000 0.882 -1.280 -0.798 0.000 1.044 -1.135 1.372 2.548 1.552 0.686 0.177 1.551 1.182 0.916 0.996 1.330 1.472 1.141 1.001 +1 1.207 1.403 0.437 0.980 -1.223 1.451 1.847 1.563 0.000 2.569 0.941 -0.480 2.215 0.697 0.705 -1.410 2.548 0.568 0.289 1.074 0.000 0.551 0.639 1.503 1.334 1.067 0.969 0.966 +1 0.555 -0.117 1.592 0.564 0.609 0.908 -0.846 -1.515 0.000 0.947 -0.928 -0.722 2.215 0.750 0.142 1.118 0.000 2.535 -0.153 0.393 3.102 1.071 1.044 0.988 0.826 1.304 0.963 0.887 +0 0.663 0.901 0.754 0.744 -1.421 1.374 0.144 0.352 2.173 1.316 -0.411 -1.546 2.215 0.727 -0.845 -0.250 0.000 0.856 0.181 -1.245 0.000 0.860 0.864 0.990 1.248 2.041 1.252 1.044 +1 0.333 -1.521 0.744 0.817 -1.524 1.543 0.299 0.699 0.000 0.963 -0.626 -0.627 0.000 1.927 -0.405 -1.286 2.548 0.673 0.358 -0.055 3.102 0.765 0.723 0.986 0.628 0.873 0.602 0.566 +1 0.693 2.048 -1.734 0.509 1.310 0.603 0.754 -0.421 0.000 0.583 2.016 -0.186 0.000 1.335 0.726 1.694 1.274 0.733 -0.194 0.550 3.102 0.926 0.912 0.990 0.610 0.766 0.812 0.743 +1 0.873 0.251 -0.607 0.980 -1.533 0.843 0.560 1.673 2.173 0.670 0.569 0.970 0.000 1.475 -0.385 0.065 1.274 0.655 0.992 0.026 0.000 0.977 0.890 0.984 0.927 1.553 0.891 0.755 +1 0.733 0.267 -1.614 0.575 -0.186 1.003 0.439 1.412 2.173 1.286 0.933 0.193 0.000 1.430 0.415 -0.207 0.000 2.257 1.713 -1.445 0.000 0.904 0.718 0.988 0.883 0.702 0.933 0.839 +1 0.555 -0.663 0.483 0.887 -0.568 0.764 -0.328 1.675 2.173 0.541 -1.720 0.209 0.000 0.652 -2.496 1.200 0.000 1.188 -0.538 -0.583 3.102 0.846 0.974 0.992 1.033 0.915 0.927 0.804 +0 1.000 0.540 -0.168 0.518 -0.860 0.530 2.619 0.512 0.000 0.801 0.373 -1.661 1.107 0.816 0.832 -0.526 0.000 1.155 -0.289 1.167 1.551 0.885 1.224 0.982 0.785 0.584 0.992 0.976 +1 1.444 0.528 1.475 1.394 1.063 0.576 0.966 0.903 0.000 1.118 -0.380 -1.168 0.000 0.931 1.839 -0.028 0.000 1.956 1.147 -0.666 3.102 1.018 1.006 0.995 0.590 0.669 0.901 0.820 +0 0.499 -0.777 -1.083 1.272 1.219 0.875 -0.633 0.496 2.173 1.030 0.344 1.698 0.000 0.928 0.046 -0.488 2.548 1.294 1.878 -0.410 0.000 0.849 0.965 0.986 0.872 0.956 0.998 0.957 +0 0.735 -0.014 0.571 1.776 -0.046 1.565 -0.861 -1.686 0.000 0.471 -1.044 -1.014 0.000 0.951 -1.134 0.334 2.548 0.494 -0.128 0.346 3.102 1.057 0.910 0.991 1.026 0.300 0.795 1.057 +0 3.056 -0.780 -0.264 1.062 -0.318 2.004 -0.771 1.545 0.000 1.645 0.006 -0.078 2.215 1.145 -1.312 1.124 1.274 0.994 0.468 -1.498 0.000 0.743 0.944 0.967 0.812 1.727 1.116 1.042 +0 1.868 -1.121 1.249 0.873 1.110 1.074 0.603 -0.732 2.173 0.660 0.436 -0.150 0.000 0.441 -0.412 1.578 0.000 0.563 -0.304 -0.544 3.102 0.913 0.886 0.996 0.848 0.439 1.441 1.156 +1 0.487 -0.160 -1.622 1.242 0.298 0.451 0.542 -0.291 0.000 0.675 -0.472 0.757 0.000 1.275 1.342 -1.641 2.548 0.975 -1.688 -1.038 0.000 1.024 0.591 1.064 1.162 0.771 1.024 0.871 +1 1.641 -0.086 0.907 0.353 -0.349 3.066 -0.254 -0.852 0.000 1.624 -0.273 1.510 0.000 2.305 0.993 0.664 2.548 1.197 -0.676 0.934 3.102 4.020 2.510 0.988 0.960 1.431 2.239 1.708 +0 0.715 0.275 -1.482 1.382 -1.448 0.592 -1.139 1.311 2.173 0.891 -0.564 0.018 2.215 0.944 0.583 0.509 0.000 1.086 0.904 -0.558 0.000 0.949 0.967 0.988 0.790 1.029 0.943 0.950 +0 0.914 -0.161 1.365 1.649 -1.404 0.809 -1.091 0.390 0.000 0.584 -1.398 1.138 0.000 1.096 -1.446 -0.422 2.548 1.099 0.019 -0.489 3.102 0.944 0.954 1.025 0.817 0.753 0.859 0.917 +1 0.780 1.603 -0.435 0.723 0.750 0.716 0.411 0.059 1.087 1.000 1.738 1.513 0.000 0.595 1.262 -1.316 0.000 1.017 0.113 -1.376 3.102 0.702 0.809 0.986 0.969 0.878 0.854 0.819 +1 0.662 0.681 -0.308 0.264 0.653 0.939 0.565 1.081 0.000 0.796 1.529 -1.122 2.215 1.123 0.389 -0.866 1.274 0.625 1.239 0.293 0.000 0.943 1.102 0.979 1.024 0.650 0.845 0.807 +1 0.413 -1.246 1.529 1.073 -0.989 0.349 -1.536 -1.084 0.000 1.003 -1.620 1.414 0.000 0.533 -0.542 0.284 0.000 0.591 -0.997 0.045 3.102 0.979 0.736 0.986 0.805 0.685 0.897 0.803 +0 1.141 0.072 0.334 1.007 1.193 1.943 1.346 -1.120 0.000 1.450 -2.735 -0.014 0.000 1.536 -0.060 -0.047 0.000 2.812 -0.996 -1.681 3.102 0.800 1.866 1.039 1.325 1.220 1.754 1.376 +1 0.884 0.715 1.001 1.227 0.150 0.895 0.641 1.308 2.173 1.270 0.687 -0.538 0.000 1.122 0.291 -1.024 0.000 0.589 0.684 -1.668 0.000 0.961 0.772 1.000 0.873 0.678 0.774 0.735 +1 0.480 1.358 0.070 0.889 1.143 0.503 1.256 -1.103 0.000 0.422 -1.406 0.465 2.215 0.403 0.549 -0.638 2.548 0.369 0.911 1.008 0.000 0.624 1.118 0.991 0.581 0.647 0.665 0.630 +1 0.804 0.664 0.744 1.333 -0.505 0.556 1.999 -1.656 0.000 0.398 -0.419 0.629 2.215 0.630 0.363 -1.083 2.548 0.717 1.270 1.200 0.000 0.571 1.008 1.294 0.734 0.579 0.680 0.716 +0 1.752 0.212 -1.001 0.695 -1.237 0.782 -0.985 0.711 2.173 1.304 0.507 0.622 0.000 1.372 -0.538 -1.068 2.548 1.571 -0.325 0.431 0.000 0.937 1.041 0.997 0.574 1.316 1.054 1.074 +0 1.234 0.157 0.881 0.475 -0.377 1.699 0.551 0.261 0.000 2.461 -1.606 -1.295 0.000 0.490 -0.774 -1.263 2.548 0.992 -1.071 1.303 3.102 7.235 3.684 0.989 0.805 0.410 2.128 1.665 +1 1.129 0.872 -1.256 2.698 0.838 1.651 -2.427 0.613 0.000 2.573 1.181 -0.458 0.000 2.391 0.495 1.101 1.274 0.981 1.468 -1.117 0.000 1.291 0.868 2.297 1.334 0.825 1.275 1.287 +1 1.114 0.343 0.921 0.909 -1.488 1.196 0.589 -1.430 0.000 0.886 1.239 -0.027 1.107 1.530 0.798 0.468 2.548 1.179 -0.338 -0.467 0.000 0.912 1.284 1.151 0.926 0.589 1.026 0.922 +1 0.304 -0.386 -0.965 0.824 -0.295 1.784 0.849 -1.361 0.000 0.896 1.599 0.134 0.000 1.809 0.690 0.100 0.000 1.920 -0.351 1.198 1.551 0.898 0.688 0.983 1.145 0.877 1.065 1.508 +0 2.631 -0.619 0.392 0.673 0.324 1.849 0.761 -1.260 2.173 0.369 0.942 0.259 2.215 0.679 1.090 1.446 0.000 0.370 -0.942 -1.466 0.000 0.845 1.044 0.982 0.939 1.196 1.744 1.334 +1 0.525 1.541 -0.231 0.619 0.919 0.696 -0.459 -1.532 2.173 0.371 -0.009 0.230 0.000 0.528 -1.049 0.694 2.548 1.299 1.305 -0.855 0.000 1.088 1.091 0.988 0.947 0.734 0.868 0.769 +0 0.469 -0.085 0.815 1.358 -1.195 1.340 0.937 1.479 2.173 1.714 -1.179 -0.492 0.000 1.600 -0.664 0.435 1.274 0.462 1.024 -0.578 0.000 1.029 0.864 1.074 1.160 2.262 1.215 1.009 +1 2.277 0.728 -1.009 0.928 -1.618 0.652 -0.415 0.048 0.000 0.934 0.735 0.202 1.107 1.004 0.687 1.012 1.274 0.580 -0.775 0.613 0.000 0.828 0.874 1.048 1.025 0.687 0.905 0.801 +1 0.648 -0.060 -0.206 1.690 0.533 0.697 -1.041 -0.849 2.173 0.638 -0.389 1.506 2.215 0.929 0.051 -1.309 0.000 0.656 -0.993 1.517 0.000 0.859 0.901 0.987 0.887 0.897 0.930 0.768 +0 1.031 0.424 1.175 1.310 -1.679 1.639 1.104 -0.567 0.000 0.901 -0.131 -1.579 2.215 1.547 0.912 0.801 0.000 1.320 0.121 0.082 3.102 0.975 0.958 0.985 0.757 0.991 0.966 0.970 +1 0.835 0.469 -0.615 0.863 0.592 1.419 -0.218 1.116 2.173 1.561 -1.101 -1.532 0.000 1.568 -0.291 -0.306 2.548 1.212 -0.302 -1.051 0.000 1.034 1.343 1.041 1.143 1.784 1.294 1.146 +1 0.292 1.297 0.874 0.780 -1.429 1.078 -0.969 -0.784 1.087 1.403 -0.932 0.616 1.107 0.880 -0.470 -1.558 0.000 0.485 0.569 0.545 0.000 0.823 1.003 0.982 1.026 1.724 1.019 0.851 +1 0.593 0.319 -0.866 0.916 -0.096 0.847 -0.358 -1.579 0.000 0.858 -0.748 -0.238 2.215 1.382 -0.352 1.432 0.000 1.119 0.380 0.684 3.102 0.806 0.962 0.979 0.567 0.871 0.879 0.804 +0 0.964 1.773 -0.733 0.945 -0.956 0.712 0.013 -1.361 0.000 1.264 -0.546 1.027 0.000 1.191 1.221 0.023 2.548 0.570 -0.116 0.478 1.551 1.781 1.029 0.974 0.801 0.568 1.026 1.036 +0 0.279 1.027 -0.611 2.222 -0.826 0.789 0.896 1.144 0.000 0.872 0.068 0.135 2.215 1.031 0.656 0.566 2.548 1.228 1.175 -1.653 0.000 0.954 0.853 0.987 1.853 0.506 1.342 1.212 +1 0.964 0.153 0.614 0.744 -0.684 1.052 0.220 1.496 2.173 0.789 -2.224 -0.400 0.000 0.675 1.339 -0.043 0.000 0.518 0.673 0.475 3.102 3.601 2.010 1.080 0.987 0.664 1.533 1.213 +1 1.270 -0.618 1.024 0.227 -1.674 1.019 0.097 -0.599 1.087 0.972 -1.060 -0.259 2.215 0.659 -0.419 0.708 0.000 1.472 -1.266 1.486 0.000 0.924 1.028 0.992 1.122 1.023 0.998 0.910 +1 1.204 -1.807 -0.729 1.023 -0.604 0.717 -1.520 1.032 2.173 0.647 -2.406 1.121 0.000 1.311 -0.368 0.335 2.548 1.458 -0.717 -1.328 0.000 1.526 1.079 0.998 1.053 1.011 0.952 0.962 +0 0.781 -0.168 -0.989 0.736 0.790 0.896 0.747 0.191 2.173 0.713 0.647 1.528 0.000 0.858 1.118 -1.396 0.000 0.486 -0.867 -0.723 3.102 0.674 1.150 1.050 0.566 0.880 0.797 0.729 +0 0.498 -0.383 -1.115 1.368 1.644 0.654 0.839 -1.635 2.173 1.582 1.342 -0.230 0.000 0.900 1.308 0.812 0.000 1.399 0.554 0.478 1.551 1.478 1.023 0.987 1.295 0.961 1.088 1.430 +0 1.254 0.400 1.364 1.811 1.457 0.608 -1.143 -0.698 0.000 0.687 0.027 -0.532 0.000 1.123 -1.298 0.157 2.548 0.380 2.245 0.271 0.000 0.883 0.932 1.000 0.702 0.612 0.878 0.918 +0 0.928 0.346 -0.669 2.447 -0.930 1.418 -1.319 0.951 2.173 0.770 -1.771 0.412 0.000 0.469 -2.518 1.240 0.000 1.244 -0.746 -0.324 3.102 0.826 0.788 0.990 0.749 1.320 1.375 1.136 +1 0.926 0.177 -0.941 1.615 -0.265 1.308 -2.348 1.301 0.000 2.782 -1.449 -0.746 0.000 2.745 -0.620 0.887 2.548 0.704 0.646 1.410 0.000 3.055 2.192 0.987 1.673 0.981 1.751 1.560 +1 0.407 1.971 1.404 0.714 -1.104 0.984 0.699 0.142 0.000 1.091 0.534 -1.455 2.215 1.041 1.131 1.260 1.274 0.921 1.281 0.056 0.000 0.633 0.970 0.992 0.642 0.827 0.913 0.785 +0 0.561 0.827 -1.057 0.768 1.181 0.883 0.634 0.734 1.087 1.318 0.538 -0.857 0.000 1.227 -1.325 1.376 0.000 2.571 -0.178 -0.312 3.102 1.022 1.025 0.989 0.813 1.469 1.027 0.869 +0 0.770 -0.209 -0.338 1.020 -1.568 0.880 -0.650 1.347 2.173 1.037 -0.905 0.376 0.000 1.801 0.276 0.344 0.000 2.045 1.071 -1.196 1.551 0.772 1.232 1.098 0.912 1.926 1.165 0.985 +0 1.031 -0.663 1.254 0.699 1.738 1.240 0.234 0.841 2.173 1.269 -1.182 -0.366 2.215 1.449 -0.627 -1.065 0.000 1.036 0.190 -0.510 0.000 0.912 0.983 0.977 0.883 2.194 1.271 1.094 +1 0.531 0.612 -0.072 1.121 1.070 0.836 -0.149 -0.649 0.000 1.238 1.139 1.535 0.000 1.295 -1.452 -0.141 2.548 0.927 -1.193 -1.381 3.102 0.887 1.130 0.987 0.870 0.756 0.856 0.811 +0 0.426 -1.745 -0.400 0.549 0.884 0.462 0.004 -1.455 0.000 0.556 -0.054 1.298 0.000 0.706 0.940 -0.531 2.548 1.205 0.125 0.090 3.102 0.660 0.788 0.995 0.830 0.493 0.596 0.611 +0 1.816 -0.146 -1.418 0.712 -0.766 0.910 -0.372 -0.515 2.173 1.237 -0.862 1.255 0.000 1.928 -0.709 0.447 1.274 0.704 -1.346 0.808 0.000 0.658 0.901 0.978 0.871 1.302 1.022 1.000 +1 0.948 0.387 -1.154 0.546 0.478 0.630 0.432 1.368 0.000 0.809 -0.100 -0.184 2.215 0.661 1.166 0.284 2.548 1.635 0.095 -1.530 0.000 0.822 0.935 0.992 0.693 0.663 0.782 0.678 +1 0.934 -1.487 -0.052 0.878 -0.797 1.030 -0.054 1.656 2.173 0.470 -0.525 -0.644 0.000 1.035 -0.144 0.870 2.548 0.626 1.279 -0.240 0.000 0.909 0.878 0.985 1.263 0.841 0.930 0.867 +0 2.965 -0.962 0.278 0.883 -0.931 0.837 -1.377 -1.111 2.173 0.684 -1.555 1.738 0.000 0.935 -0.599 0.854 1.274 1.147 0.725 -1.316 0.000 0.718 0.763 1.986 1.109 1.156 1.053 0.919 +1 1.180 0.275 1.429 0.339 -1.397 0.464 0.709 -1.072 2.173 1.130 1.237 -0.152 2.215 0.549 0.048 -1.299 0.000 0.516 -1.240 -0.655 0.000 0.593 1.162 0.988 0.762 0.841 0.900 0.784 +1 0.603 -0.709 -0.320 0.387 -0.822 0.556 0.112 -0.984 2.173 0.597 0.450 0.856 0.000 0.598 -0.827 0.353 0.000 0.932 1.027 1.374 3.102 0.789 0.899 0.982 0.743 0.789 0.669 0.629 +1 0.445 0.693 -1.156 0.599 -1.446 0.502 -1.833 0.411 0.000 1.565 -0.131 1.660 2.215 0.869 -1.072 -0.380 2.548 1.430 0.660 0.175 0.000 0.853 0.869 0.995 1.628 1.373 1.479 1.217 +0 0.869 -2.209 -0.723 3.064 -0.744 1.429 -0.145 0.688 0.000 1.255 -1.626 -1.343 0.000 1.274 0.603 0.628 2.548 2.117 0.073 1.277 3.102 3.641 2.283 1.013 2.293 0.783 1.787 1.835 +0 0.439 0.442 -0.875 0.514 -0.326 1.398 -1.025 -1.473 0.000 2.047 1.602 1.504 0.000 2.425 0.680 -0.205 1.274 2.631 0.612 0.221 3.102 1.740 1.790 0.989 1.242 0.723 1.455 1.497 +0 0.902 -0.043 -0.626 0.788 1.089 0.479 -0.134 -1.490 2.173 0.857 0.694 -0.201 0.000 0.482 0.674 -1.162 0.000 1.086 -0.254 1.090 3.102 0.751 0.852 1.168 0.711 0.559 0.630 0.607 +1 1.006 -1.638 1.026 1.900 1.666 0.743 -0.869 0.092 0.000 0.358 -0.889 -0.171 2.215 0.812 -0.519 -1.012 0.000 0.604 0.552 -0.765 1.551 1.191 0.841 1.045 0.851 0.425 0.834 0.849 +0 0.833 0.846 -0.541 1.245 -0.609 0.822 0.252 1.571 0.000 0.834 -0.623 1.433 0.000 1.390 0.256 0.748 2.548 0.927 0.136 -0.171 1.551 0.843 0.977 1.003 1.069 0.641 0.742 0.852 +1 0.764 1.178 -1.511 1.760 -0.028 1.777 1.214 1.445 0.000 2.387 1.648 -0.548 0.000 1.495 0.957 0.401 1.274 0.614 1.648 -1.568 0.000 0.925 0.931 1.563 0.783 0.723 0.676 0.739 +1 1.205 0.662 -0.192 2.442 -1.077 2.719 0.009 1.135 1.087 1.857 -1.207 -0.368 0.000 0.843 0.397 -1.188 0.000 1.075 1.006 -0.552 0.000 0.705 0.686 1.698 2.599 1.324 1.656 1.328 +1 0.671 -1.052 -0.795 0.238 1.685 0.871 -0.164 0.099 2.173 1.185 -0.629 -1.447 0.000 1.447 0.003 0.821 2.548 0.369 0.931 -1.444 0.000 0.864 1.105 0.979 0.823 0.855 0.923 0.784 +0 1.532 -0.927 1.398 1.346 -0.250 0.725 0.157 1.133 2.173 0.775 -0.191 -0.424 2.215 0.421 0.275 -1.248 0.000 0.632 -1.302 -0.761 0.000 0.658 0.872 1.982 1.179 1.105 1.003 0.810 +0 0.907 0.905 0.079 1.399 0.604 0.503 -2.003 1.714 0.000 0.546 -0.724 -0.687 2.215 0.710 2.563 -1.572 0.000 1.034 1.227 -0.849 1.551 0.663 0.767 0.999 0.813 0.922 1.067 1.426 +1 0.439 -0.706 -1.292 0.098 -1.362 1.956 0.460 -0.814 0.000 2.094 1.347 0.899 2.215 2.130 1.072 0.304 2.548 1.106 -1.228 -1.148 0.000 0.569 1.141 0.901 0.981 1.170 0.996 0.971 +1 1.095 -1.338 -0.560 1.322 -1.486 1.453 -0.933 0.854 1.087 0.563 -0.946 -0.349 2.215 0.350 -1.087 -0.015 0.000 1.055 -0.877 -1.016 0.000 0.735 1.134 1.234 0.750 1.177 1.023 0.849 +0 1.929 0.669 0.189 0.768 -0.395 0.597 0.752 1.593 0.000 1.467 0.548 -1.049 2.215 0.871 0.421 1.230 2.548 0.698 1.151 0.946 0.000 0.918 0.974 0.984 0.877 1.065 0.917 0.810 +1 1.072 -1.381 0.138 1.003 -0.844 0.646 -0.060 1.366 1.087 1.514 -1.236 1.103 0.000 2.117 -0.990 -0.589 2.548 0.472 -0.997 1.614 0.000 0.491 0.787 1.111 0.753 1.630 1.067 0.970 +1 2.189 0.374 -0.344 0.344 -1.726 0.845 0.621 1.737 2.173 1.011 0.732 0.863 2.215 0.717 1.452 -1.732 0.000 0.704 2.039 -0.133 0.000 0.850 0.988 1.138 1.070 0.969 0.939 0.892 +1 2.755 -0.144 0.809 0.632 0.459 2.459 0.048 -1.186 0.000 1.781 -0.731 0.201 2.215 0.769 -0.675 -0.261 2.548 1.052 -1.146 -1.649 0.000 1.569 1.099 0.982 1.012 0.503 0.973 0.938 +0 0.933 -0.110 -1.470 0.555 -0.132 0.518 1.182 0.892 0.000 1.289 -0.559 1.592 2.215 1.208 0.278 -0.260 2.548 1.481 1.266 0.062 0.000 0.937 0.963 0.986 0.791 1.451 1.183 1.001 +1 1.334 -0.980 1.401 0.743 -1.034 0.616 -1.027 0.881 0.000 0.705 0.804 -0.182 0.000 0.916 -0.979 -0.640 2.548 1.057 -0.859 0.270 0.000 0.906 0.720 1.120 0.617 0.130 0.515 0.534 +0 0.334 -1.396 -1.231 2.396 -0.101 0.779 2.891 1.433 0.000 0.417 0.732 -0.609 2.215 0.524 0.395 1.301 2.548 0.385 2.002 -1.348 0.000 0.542 0.986 1.055 1.135 0.498 0.900 2.277 +0 0.975 -0.839 -1.163 1.959 -0.336 0.868 -0.028 0.518 0.000 0.904 -0.751 1.579 1.107 1.460 0.341 1.713 0.000 1.586 -0.095 -0.222 3.102 1.035 1.093 1.299 0.819 1.139 0.912 1.043 +1 1.153 0.312 -1.227 0.988 1.587 0.546 -0.244 1.473 0.000 1.342 -0.752 0.192 2.215 0.372 -1.064 -0.051 2.548 0.476 1.772 -0.701 0.000 0.924 1.049 0.986 0.845 0.221 0.980 0.825 +1 0.450 0.546 1.041 1.672 -0.685 0.432 2.149 -1.709 0.000 0.399 2.816 0.429 0.000 0.613 0.868 -0.097 2.548 1.282 -0.149 1.283 3.102 0.890 0.825 1.201 0.932 0.759 0.895 0.885 +1 0.802 -2.184 -0.199 2.655 1.638 0.740 1.454 1.414 0.000 1.023 -2.068 -1.644 0.000 0.782 -0.883 -1.631 0.000 0.725 0.431 0.030 3.102 0.840 0.836 2.014 1.830 0.672 1.173 1.008 +1 0.663 -0.287 1.299 0.949 1.428 1.580 -1.219 0.958 0.000 2.823 -0.258 -0.716 2.215 0.518 0.344 1.710 0.000 1.014 -1.717 0.172 0.000 1.465 0.888 0.988 1.786 0.474 1.113 1.029 +1 0.929 0.930 -0.772 0.667 -1.682 1.089 0.468 0.852 0.000 1.042 0.466 -1.122 2.215 0.726 0.840 0.379 0.000 0.996 -0.383 -0.228 3.102 0.756 0.929 0.980 0.581 0.800 0.890 0.797 +0 0.366 -1.676 -0.593 0.283 0.147 0.546 0.535 -0.734 2.173 1.104 -0.147 1.323 0.000 0.831 2.600 -0.014 0.000 0.786 -0.623 1.574 0.000 0.833 0.976 0.999 0.664 0.535 0.693 0.655 +0 0.734 0.101 1.259 1.608 0.570 0.302 -0.692 1.648 0.000 0.478 -1.110 -0.429 0.000 1.224 0.401 -1.434 2.548 1.288 -0.491 -0.836 3.102 0.934 0.754 0.989 0.935 0.714 0.828 0.709 +1 1.853 -0.819 -0.688 1.015 -0.823 2.329 -0.247 -1.041 2.173 3.533 -1.198 0.683 0.000 1.025 0.123 -0.198 0.000 2.463 -0.234 1.064 0.000 0.939 1.588 0.974 1.374 1.867 2.165 1.798 +0 0.654 0.590 0.003 0.733 -1.674 1.044 -0.047 0.645 2.173 1.137 -1.657 -0.831 0.000 0.912 1.157 -1.617 0.000 1.375 0.321 1.046 3.102 0.725 0.815 0.987 0.958 0.526 0.841 0.731 +0 0.927 0.991 1.247 1.380 0.977 0.526 -2.561 0.946 0.000 0.872 2.470 -1.165 0.000 1.120 0.660 0.004 0.000 0.733 -0.401 -0.724 1.551 0.807 0.893 0.996 0.962 0.661 1.333 1.315 +0 0.432 0.696 0.121 0.676 -1.199 0.719 0.391 1.003 2.173 0.709 1.137 -1.338 2.215 0.959 0.176 0.051 0.000 0.434 1.554 -0.775 0.000 0.823 0.869 0.985 0.828 0.993 0.745 0.693 +1 1.403 0.054 0.108 1.591 0.424 1.279 -0.415 1.583 2.173 0.691 1.318 -1.341 0.000 0.914 0.632 -0.393 0.000 0.637 -0.022 -0.541 1.551 1.007 0.667 0.991 1.529 0.918 0.995 1.073 +0 0.796 -0.454 -0.932 0.797 0.833 0.992 -0.317 1.311 2.173 1.287 0.177 -0.032 2.215 0.999 0.816 -0.894 0.000 0.378 -1.445 -1.057 0.000 1.175 1.071 1.104 0.842 1.612 1.023 0.863 +1 0.433 -1.220 -0.991 0.919 0.327 0.611 -0.372 1.070 0.000 0.578 -0.495 -0.490 2.215 1.039 0.343 -1.114 2.548 0.621 0.199 -1.453 0.000 0.833 0.783 0.984 0.580 0.579 0.558 0.541 +0 1.521 -0.271 -1.704 0.830 -1.177 0.945 -2.048 1.033 0.000 0.752 0.249 -0.550 0.000 1.019 -0.361 -0.012 0.000 2.160 0.647 0.021 3.102 0.769 1.021 0.999 1.448 1.104 1.063 0.941 +1 0.592 -0.208 1.647 1.415 -0.546 2.399 -1.259 1.624 0.000 1.023 -0.608 0.060 2.215 1.257 0.254 0.619 0.000 3.246 -0.299 -0.451 3.102 1.037 1.055 1.166 0.814 0.763 0.916 0.838 +0 0.875 0.652 -0.157 0.932 -0.619 0.655 0.855 1.209 0.000 0.748 -0.041 -0.541 2.215 0.823 0.159 -1.644 0.000 0.561 -0.798 1.047 3.102 0.850 1.030 0.987 0.700 0.643 0.676 0.702 +1 0.421 -1.178 0.588 0.950 -1.209 0.833 0.151 -0.251 0.000 0.722 -0.146 0.712 2.215 1.495 -0.290 1.549 2.548 0.968 -0.895 -0.203 0.000 0.898 0.894 0.989 0.720 0.761 0.859 0.749 +0 0.884 1.594 1.703 0.210 0.293 0.920 -1.007 -0.870 0.000 0.370 -2.584 1.203 0.000 1.344 0.946 0.599 2.548 1.087 -0.447 -0.263 3.102 1.136 0.854 0.995 0.755 1.033 1.171 1.103 +0 1.720 -0.230 0.661 2.222 0.416 1.258 -2.050 -1.143 0.000 0.808 -1.139 -1.203 0.000 0.736 -0.804 -0.796 2.548 1.240 0.259 1.226 3.102 0.999 0.798 0.999 0.800 0.845 1.079 1.549 +1 0.667 -0.133 -0.360 1.796 0.465 1.566 0.075 -1.351 2.173 0.824 -0.145 0.223 0.000 1.019 0.231 1.314 2.548 0.451 0.522 -0.226 0.000 0.445 0.725 1.027 1.575 1.069 1.090 0.904 +0 1.100 -0.252 0.581 0.324 0.455 1.065 0.769 -0.247 0.000 1.332 -0.848 1.448 2.215 0.830 0.805 -1.087 0.000 0.669 0.862 1.592 3.102 1.166 0.892 0.983 1.146 0.949 1.198 1.011 +0 0.721 0.481 -0.224 0.743 -0.978 0.507 1.080 0.597 0.000 0.570 1.605 1.085 0.000 0.534 -0.338 -1.023 2.548 0.669 0.951 -1.694 3.102 0.584 0.903 0.995 0.720 0.460 0.568 0.689 +0 0.893 -0.235 1.081 0.987 0.573 1.201 -0.774 -0.696 0.000 0.868 -0.491 -1.391 2.215 0.265 -0.813 -1.559 0.000 0.504 -0.784 0.821 3.102 0.714 0.731 0.981 0.447 0.560 0.594 0.702 +1 0.621 -1.404 0.422 0.993 -0.118 1.447 -0.471 1.679 2.173 0.793 -1.242 -1.336 0.000 0.555 -1.027 -0.043 0.000 0.716 2.058 0.472 0.000 0.938 1.146 0.993 0.548 1.006 0.888 0.816 +1 0.769 0.960 1.263 0.792 -0.409 0.684 0.454 -0.197 2.173 0.909 0.169 -0.985 2.215 0.843 0.358 1.028 0.000 1.452 1.483 1.140 0.000 0.924 1.147 1.079 0.815 0.775 0.913 0.778 +0 0.734 0.746 0.560 1.819 0.747 1.348 0.043 -0.789 1.087 1.107 2.557 1.622 0.000 1.525 0.491 1.297 2.548 2.213 -0.378 -0.484 0.000 1.111 0.875 0.994 1.027 1.759 1.472 1.340 +1 0.663 -0.285 0.814 0.754 -0.480 0.794 -1.620 0.915 0.000 0.999 -0.255 -1.007 2.215 0.863 -0.588 0.146 1.274 0.744 -1.053 -1.295 0.000 1.093 0.912 0.987 0.784 0.871 0.863 0.737 +1 0.777 -1.335 -1.331 1.254 0.999 0.814 -0.179 -0.425 2.173 0.549 1.103 1.109 0.000 0.503 0.611 -1.416 2.548 0.922 -0.019 0.450 0.000 0.768 1.027 1.180 0.943 0.713 0.891 0.887 +0 1.647 0.047 -1.042 1.589 -0.529 1.686 0.250 1.281 2.173 0.689 -1.782 -0.554 0.000 0.911 -0.500 0.925 0.000 1.889 1.021 -0.689 0.000 1.446 0.981 1.002 1.815 1.095 1.228 1.243 +0 0.671 -0.371 1.346 1.062 -1.399 0.676 -1.032 -1.086 2.173 0.540 -1.101 0.434 0.000 0.598 -0.735 -0.153 0.000 1.673 0.050 0.459 3.102 0.824 0.849 0.983 0.897 1.284 0.865 0.748 +1 1.103 -0.972 -1.158 1.158 1.654 0.884 -1.121 0.809 0.000 1.148 -0.992 -1.561 0.000 1.122 -0.598 -0.116 0.000 2.865 -0.647 0.354 3.102 0.899 0.883 0.993 1.274 0.933 0.868 0.805 +0 0.552 0.551 1.364 1.034 0.373 0.850 -1.025 1.594 2.173 1.235 -1.270 -0.189 0.000 0.546 -0.243 0.365 0.000 1.473 -0.005 -1.537 3.102 0.913 1.197 0.986 0.965 0.736 0.955 0.862 +0 0.852 0.225 -0.290 1.351 -1.086 0.792 1.341 0.978 0.000 1.542 1.399 -0.775 1.107 1.620 1.353 0.404 2.548 1.460 2.220 1.109 0.000 0.888 0.938 0.989 0.933 1.468 1.040 1.045 +1 1.136 -0.102 -0.831 2.029 -1.549 0.999 0.659 0.363 2.173 0.858 -0.467 0.601 1.107 0.439 0.679 -1.592 0.000 0.604 1.325 0.262 0.000 0.735 1.000 1.266 1.213 0.874 1.159 0.907 +1 1.449 1.598 1.170 0.818 -0.989 0.835 1.346 0.023 2.173 1.005 0.743 0.672 2.215 0.848 1.362 1.635 0.000 1.398 -0.011 -0.777 0.000 0.859 1.172 1.405 1.041 0.847 0.933 0.868 +1 0.649 -1.287 -0.975 2.096 0.024 1.129 0.862 -1.533 0.000 0.395 -0.162 0.574 0.000 0.493 1.415 0.758 0.000 0.904 0.766 1.235 1.551 0.667 0.685 1.267 1.377 0.528 1.109 0.979 +0 1.160 -0.085 -1.221 0.967 -0.519 1.060 0.459 -1.630 0.000 0.971 -0.657 0.329 2.215 1.609 0.082 0.906 1.274 1.679 -0.009 -0.221 0.000 2.007 1.524 0.989 1.061 0.841 1.116 1.001 +1 1.990 -0.417 -1.137 0.438 -0.833 1.040 -0.868 -0.117 2.173 0.796 -0.836 -1.680 0.000 1.471 -2.412 1.255 0.000 1.035 -1.133 0.930 3.102 0.955 0.703 0.987 1.214 0.929 0.959 1.200 +1 0.682 0.285 -0.112 1.035 1.414 0.429 -0.407 0.847 0.000 1.048 -1.060 -1.194 0.000 1.512 -0.279 -1.737 2.548 1.815 -0.354 0.127 1.551 0.714 0.964 1.142 0.861 1.261 0.795 0.725 +1 2.565 -0.182 -0.534 1.503 -1.626 1.361 0.545 0.620 0.000 1.674 -1.387 1.435 2.215 1.073 -1.764 -0.327 0.000 0.684 -1.015 1.122 3.102 3.825 2.081 2.264 2.077 0.287 1.675 1.642 +0 1.031 0.303 -1.613 1.500 -0.926 0.400 -1.149 0.176 0.000 0.430 -1.492 -1.725 2.215 0.762 -0.258 0.560 2.548 0.816 0.517 0.482 0.000 0.914 0.863 1.001 0.911 0.674 0.764 0.753 +1 1.371 -0.064 1.142 1.761 1.740 1.344 -0.863 -0.114 2.173 0.604 -0.050 -1.071 2.215 0.411 -0.749 -0.999 0.000 0.423 -1.365 1.401 0.000 0.428 0.783 1.106 0.834 1.151 1.195 0.895 +0 1.662 -0.327 0.290 0.880 0.277 1.109 0.738 -0.678 2.173 0.856 -0.187 1.294 0.000 0.849 0.879 0.530 2.548 0.384 1.456 -1.001 0.000 1.069 0.834 0.982 1.657 1.080 1.176 1.045 +0 0.447 -0.303 -0.141 1.109 -1.233 1.248 2.055 0.145 0.000 1.436 -0.035 1.579 2.215 0.619 -1.114 0.733 0.000 0.545 -1.962 1.650 0.000 0.830 1.245 0.985 1.067 0.782 1.039 0.898 +0 1.499 -0.134 -1.461 0.397 -0.008 0.734 1.056 -1.737 2.173 0.758 -0.407 0.283 0.000 0.847 -1.801 0.403 0.000 0.875 -0.095 -1.221 0.000 0.823 0.761 1.033 0.593 0.797 0.641 0.571 +1 0.558 -2.016 0.570 0.533 -0.264 1.156 -1.449 -1.640 1.087 0.892 -1.300 0.435 0.000 0.736 0.200 -0.312 0.000 1.063 -0.049 -1.557 3.102 0.543 0.856 0.987 1.056 0.897 0.846 0.759 +1 0.793 -0.204 -0.140 0.177 0.291 1.078 0.876 0.657 1.087 1.139 -0.345 -1.062 0.000 0.585 0.509 1.285 0.000 0.750 0.664 -1.572 1.551 1.224 0.769 0.985 0.838 0.863 0.927 0.803 +0 0.848 -0.070 -1.657 1.096 -1.122 0.477 -0.497 0.378 1.087 0.709 -1.141 1.337 2.215 0.484 0.983 -0.023 0.000 0.788 -0.897 -0.407 0.000 0.915 0.981 0.977 0.886 0.714 0.683 0.696 +1 0.719 0.747 1.486 1.079 -0.216 0.896 0.633 -0.256 0.000 0.708 0.942 -0.854 0.000 1.439 0.231 1.630 1.274 0.719 0.304 0.930 3.102 0.913 0.840 1.219 0.926 0.461 0.802 0.729 +1 2.564 -0.232 -0.071 0.669 0.122 0.661 -2.363 -1.401 0.000 0.542 0.693 -1.693 2.215 0.734 -0.935 1.303 2.548 0.745 -0.384 1.023 0.000 0.747 0.763 0.985 1.060 0.719 0.940 1.180 +0 0.604 -0.210 0.962 0.977 -0.163 0.969 0.659 -1.230 2.173 0.516 0.604 0.440 0.000 0.943 1.456 -0.080 2.548 1.062 1.125 1.499 0.000 0.865 1.005 0.988 1.168 1.176 1.049 0.912 +1 0.572 -1.787 1.359 0.925 -1.172 2.476 -1.142 -0.138 0.000 2.412 -0.644 -1.660 0.000 1.799 -0.843 1.291 2.548 1.409 -0.976 0.668 0.000 1.894 1.150 0.984 1.221 0.899 1.213 1.276 +0 0.861 0.127 -0.743 0.616 -0.044 0.920 1.146 -0.826 1.087 1.562 -0.794 1.211 2.215 0.507 0.364 1.002 0.000 0.660 -1.300 0.126 0.000 0.860 0.851 0.985 1.181 2.655 1.363 1.085 +1 0.355 1.404 -0.759 1.588 -0.931 0.461 -0.314 0.133 0.000 0.511 0.245 1.229 0.000 0.824 -0.916 0.617 2.548 1.047 -0.911 1.389 3.102 0.910 0.760 1.001 1.000 0.456 0.777 0.710 +0 2.973 2.252 0.375 0.143 -1.633 1.660 0.641 -1.127 1.087 0.762 1.340 0.945 1.107 0.543 0.041 -0.640 0.000 0.947 1.180 1.530 0.000 0.939 0.969 0.987 0.830 1.700 1.599 1.252 +1 1.696 0.572 0.185 1.072 0.975 0.472 -0.443 -0.569 0.000 0.717 -0.221 -1.234 2.215 0.959 1.138 -1.551 2.548 0.772 -0.178 1.488 0.000 0.891 0.941 1.222 1.115 0.754 0.884 0.802 +0 1.320 0.542 0.808 0.443 -1.039 0.491 0.502 -1.231 2.173 0.498 -0.780 0.705 0.000 0.675 -0.993 1.566 2.548 0.553 -0.109 -0.748 0.000 0.701 0.738 1.055 0.835 0.760 0.667 0.602 +0 0.494 1.846 0.751 0.757 -0.958 0.585 0.240 1.503 0.000 0.672 1.049 -0.148 2.215 0.881 0.076 0.969 2.548 0.693 -0.708 -0.812 0.000 1.013 1.019 0.990 0.762 0.811 0.691 0.673 +1 1.681 1.053 0.940 1.210 0.635 1.834 2.026 -0.576 0.000 1.653 0.662 -1.310 2.215 1.584 1.206 1.233 2.548 1.362 0.632 0.605 0.000 2.687 2.117 0.990 1.609 1.409 1.635 1.455 +0 1.250 1.515 1.504 0.613 -0.503 0.713 0.629 0.619 1.087 0.647 -0.348 -1.645 0.000 0.842 -0.241 -0.149 0.000 0.376 -0.202 -0.524 3.102 1.106 1.052 1.179 0.723 0.531 0.669 0.775 +1 0.685 -0.096 0.379 0.306 -0.763 1.091 -0.923 1.362 2.173 1.239 0.855 0.027 0.000 1.448 -0.193 -1.605 0.000 1.481 -0.841 -0.285 3.102 1.353 1.119 0.985 0.919 1.341 0.940 0.816 +1 2.504 0.375 0.868 1.007 -1.213 1.136 1.734 -0.605 0.000 0.425 0.057 -1.067 0.000 1.262 0.453 -0.849 2.548 0.674 0.158 0.399 3.102 1.525 1.061 2.100 1.004 0.645 0.874 1.049 +1 0.656 0.693 0.866 1.562 0.152 0.904 -0.277 -1.690 2.173 0.752 0.675 -1.099 2.215 0.593 -0.353 -0.339 0.000 0.378 -0.608 1.446 0.000 0.530 0.678 0.983 0.960 0.872 0.918 0.721 +0 0.699 1.203 -1.586 0.406 0.865 0.531 -0.773 0.288 2.173 0.813 -0.021 1.574 1.107 0.842 -0.229 -0.317 0.000 0.822 -1.347 -1.158 0.000 0.925 0.951 0.977 0.872 0.962 0.707 0.712 +1 0.998 0.012 -0.781 0.747 1.377 0.528 1.688 0.902 0.000 0.756 -0.218 -0.284 0.000 0.833 -0.054 0.606 0.000 0.671 0.672 -1.256 3.102 0.881 0.896 1.113 0.593 0.248 0.587 0.585 +1 0.829 -0.101 -1.396 0.229 0.335 0.759 0.541 0.615 2.173 0.809 0.449 -1.257 0.000 1.027 0.897 -0.600 0.000 0.911 -0.949 0.671 3.102 0.864 1.155 0.986 0.638 0.834 0.905 0.839 +1 0.804 -0.909 -0.162 0.325 0.733 0.849 0.259 -1.151 0.000 1.447 1.199 0.813 2.215 0.757 0.415 -0.468 0.000 0.679 0.563 -1.676 3.102 0.845 0.607 0.985 2.242 0.744 1.424 1.246 +1 1.244 0.334 -1.315 0.564 0.710 0.692 -0.623 0.903 0.000 1.139 -1.202 0.263 0.000 0.648 1.377 -0.891 0.000 1.341 0.319 1.160 0.000 0.846 1.113 1.123 0.863 0.838 0.881 0.799 +0 1.365 0.707 0.068 0.979 0.372 0.481 0.238 -0.934 0.000 0.421 -0.926 -1.024 1.107 0.281 -1.176 -1.609 0.000 0.624 0.926 -1.456 0.000 0.801 0.584 1.001 0.893 0.275 0.843 0.737 +1 1.279 -1.124 -1.104 0.498 -0.430 1.396 0.150 1.085 1.087 0.885 -1.931 -0.997 0.000 1.215 -0.208 -0.035 0.000 0.859 -0.643 0.374 3.102 0.813 1.131 0.994 0.754 0.889 1.187 0.972 +0 1.473 -0.386 1.581 0.799 -0.974 0.714 1.275 -0.017 2.173 0.782 -0.030 0.628 0.000 0.776 -0.860 -0.355 2.548 0.471 0.425 -1.335 0.000 0.805 0.909 1.119 0.828 1.295 1.037 0.851 +0 0.919 -0.731 -1.431 0.882 0.153 0.612 -1.158 1.667 2.173 1.472 -1.180 -0.539 0.000 1.059 1.787 0.800 0.000 1.403 -0.503 1.206 1.551 0.661 1.072 1.235 0.787 0.486 0.835 0.758 +0 0.509 0.962 1.148 1.353 0.014 0.384 0.139 1.651 0.000 0.602 0.290 -0.658 1.107 0.868 -0.981 0.368 0.000 1.084 0.775 -1.300 3.102 0.999 0.947 0.988 0.735 0.463 0.633 0.713 +1 0.758 -0.346 0.767 1.894 -0.099 0.782 1.033 -1.625 2.173 1.097 1.244 -0.943 2.215 1.066 1.174 1.287 0.000 1.139 1.726 -1.649 0.000 1.057 1.059 1.167 1.490 0.803 1.232 1.039 +1 0.714 0.928 0.306 0.947 0.477 1.116 0.251 -1.695 0.000 1.101 2.261 0.083 0.000 1.112 0.934 -1.084 2.548 0.378 0.773 0.999 1.551 3.737 1.908 0.988 0.999 0.473 1.197 1.087 +0 0.457 1.637 1.439 0.668 -0.571 0.662 2.685 0.637 0.000 0.908 0.852 1.396 2.215 1.213 -0.859 -1.194 0.000 0.801 -1.888 0.543 0.000 0.933 0.574 0.984 0.724 0.772 0.915 0.864 +1 0.934 1.631 0.385 1.771 0.055 0.469 1.214 -1.011 0.000 1.305 0.448 -1.497 2.215 0.394 1.345 0.863 0.000 1.110 0.259 1.048 3.102 0.773 0.806 1.000 1.292 0.819 1.382 1.059 +1 2.389 -0.970 -1.607 0.638 -0.415 0.992 -0.899 0.156 2.173 0.588 -1.401 -1.246 0.000 0.638 -0.987 1.203 2.548 0.813 -1.348 -0.006 0.000 0.814 0.917 1.505 0.836 0.807 0.929 0.784 +0 0.902 0.999 0.163 0.806 1.157 0.931 0.632 -0.968 0.000 1.241 -2.606 0.160 0.000 0.626 1.650 -0.802 0.000 1.245 0.637 1.468 3.102 0.878 0.925 0.985 0.827 0.489 0.739 0.745 +1 0.702 -1.839 -0.259 1.542 0.461 0.472 -2.136 -0.808 0.000 0.534 0.014 -0.911 2.215 1.068 -0.686 -1.461 1.274 0.583 -1.714 1.187 0.000 0.782 0.959 0.989 1.021 0.496 0.810 0.757 +0 0.449 0.090 -0.438 1.032 1.427 0.483 1.126 0.305 0.000 1.138 1.237 -0.726 2.215 0.553 -1.115 0.640 2.548 0.406 1.667 1.464 0.000 0.989 1.104 0.989 0.785 1.566 0.907 0.771 +0 0.688 -2.172 -0.599 0.156 0.688 0.969 2.204 0.975 0.000 1.114 -0.889 -0.422 1.107 1.157 -1.108 -1.355 2.548 0.809 -1.279 0.939 0.000 4.333 3.414 0.995 0.691 0.917 2.367 1.979 +0 1.470 1.012 -1.075 0.594 0.056 1.305 -0.542 -1.145 0.000 1.452 -0.508 0.595 2.215 0.275 2.382 1.550 0.000 0.870 0.281 0.585 0.000 0.862 1.260 1.104 0.657 0.252 0.929 0.827 +1 0.453 -1.567 -0.140 1.007 -0.618 0.787 -0.715 1.199 1.087 0.821 -1.107 -1.706 0.000 0.884 0.727 0.507 0.000 1.336 -0.374 -0.400 3.102 1.895 1.271 0.981 0.517 1.087 0.921 0.843 +1 0.351 -1.095 -0.055 1.109 -1.010 1.555 -0.263 0.602 2.173 0.748 0.241 -1.630 0.000 0.897 0.681 -0.796 2.548 0.688 -0.318 -0.900 0.000 0.736 0.801 0.977 2.011 1.602 1.612 1.311 +1 0.988 0.400 -1.711 0.532 0.251 0.934 0.138 1.147 0.000 0.589 2.666 0.024 0.000 1.594 0.911 -0.794 2.548 0.792 1.180 1.523 0.000 0.997 1.047 0.987 0.838 0.757 0.927 0.771 +1 0.857 -0.393 -0.032 0.526 -1.456 1.741 -0.837 0.410 1.087 0.543 -1.486 1.254 0.000 1.246 -0.401 -1.658 1.274 1.979 -0.570 -1.118 0.000 0.913 0.955 0.991 0.997 1.796 1.162 0.947 +0 0.650 -0.142 -1.074 0.378 0.414 0.769 0.554 0.463 0.000 1.385 0.331 -1.561 1.107 0.898 1.494 0.301 0.000 0.833 0.452 -0.803 3.102 0.876 0.838 0.988 0.805 0.620 0.926 0.772 +0 1.491 0.386 0.097 1.124 0.672 0.691 1.179 -1.463 0.000 0.624 0.457 -0.571 2.215 0.981 -0.361 -1.320 0.000 0.894 -0.506 1.205 3.102 1.317 0.959 0.979 0.829 0.774 0.733 0.850 +1 1.940 -0.109 0.535 0.604 -0.162 1.045 0.084 -1.608 2.173 0.699 0.055 -0.712 2.215 0.670 0.301 -1.183 0.000 0.781 0.906 0.449 0.000 0.852 0.893 0.987 0.868 0.909 0.951 0.807 +1 1.251 -0.453 0.410 0.154 0.433 0.706 -0.089 1.270 1.087 0.606 -0.888 -0.389 0.000 0.339 0.225 -0.502 0.000 1.053 -1.226 -1.270 0.000 0.798 1.075 0.983 0.782 0.693 0.682 0.722 +0 0.557 -0.522 0.956 0.978 -1.175 0.567 2.527 1.658 0.000 0.716 0.086 -0.620 2.215 1.231 -0.566 0.377 2.548 0.956 -1.669 0.242 0.000 0.928 0.934 0.987 0.813 0.860 0.677 0.659 +1 0.606 -0.905 1.691 0.938 0.324 1.095 -0.286 -0.213 2.173 1.076 0.295 1.146 0.000 1.455 0.413 -1.445 2.548 0.720 -0.286 -1.101 0.000 1.097 0.910 0.987 0.902 1.530 0.993 0.887 +1 0.612 -0.987 -1.538 1.312 -0.575 1.071 -0.547 0.651 2.173 0.475 -1.135 -0.687 0.000 0.760 -0.656 1.560 2.548 0.764 0.831 -1.395 0.000 1.127 0.818 0.986 1.220 0.826 0.851 0.832 +0 0.883 0.387 0.738 0.488 1.716 0.892 -0.089 0.149 0.000 0.990 0.457 -1.504 1.107 0.617 -0.058 -0.914 0.000 0.472 -1.009 0.844 0.000 0.825 1.220 0.986 0.542 0.428 0.704 0.702 +1 1.312 0.833 -0.411 1.082 -0.330 0.641 0.643 0.825 2.173 0.373 -0.739 0.726 0.000 0.728 1.587 -1.550 0.000 0.959 0.418 -1.156 1.551 1.300 0.876 1.005 1.094 0.813 0.799 0.801 +1 0.974 0.319 -1.504 0.371 -0.227 0.606 -0.136 -1.114 0.000 0.591 0.664 0.111 0.000 1.618 1.344 0.995 2.548 0.591 1.311 -0.095 3.102 1.255 0.849 0.982 1.178 0.623 0.855 0.806 +1 0.720 -0.736 -1.428 1.399 -0.594 1.457 -0.710 0.496 2.173 0.864 -0.727 1.158 0.000 0.969 0.250 1.627 1.274 1.446 0.842 -1.421 0.000 0.971 0.713 0.989 1.347 1.474 1.086 1.041 +0 0.430 -1.101 1.154 0.999 -0.724 1.260 -1.456 -1.190 2.173 1.762 0.179 0.475 0.000 1.499 0.763 1.107 0.000 1.462 0.071 -1.010 3.102 0.985 1.015 0.989 0.794 1.255 1.346 1.192 +1 0.601 0.201 0.130 0.817 -0.528 1.226 0.610 1.514 0.000 1.379 0.449 -0.060 2.215 0.691 1.392 -1.622 0.000 0.658 0.074 -1.465 3.102 0.935 0.641 0.988 0.734 0.836 0.961 0.870 +1 0.595 0.755 1.308 1.187 -0.270 0.986 -0.738 -0.209 2.173 1.076 0.059 1.528 0.000 0.792 1.087 1.473 0.000 0.556 0.444 0.905 3.102 0.849 0.547 1.151 1.168 0.851 0.979 0.891 +0 1.882 -0.367 0.321 1.090 0.740 0.690 0.338 -1.466 2.173 0.705 -0.723 -0.963 0.000 1.149 -0.613 -1.578 1.274 0.804 -2.347 -0.684 0.000 1.235 1.037 1.000 1.341 0.598 1.006 1.084 +1 1.031 0.338 -0.140 0.135 -1.406 1.030 -0.625 0.869 1.087 1.133 0.262 -1.009 2.215 0.621 -0.015 1.137 0.000 1.362 -0.940 -1.009 0.000 1.119 1.005 0.993 0.952 1.742 0.987 0.846 +1 0.937 -0.606 0.581 1.334 1.018 0.525 0.746 -0.622 2.173 0.695 -1.290 -1.696 0.000 0.645 -1.055 -0.294 2.548 0.665 -1.697 -0.782 0.000 0.724 1.301 0.988 0.745 0.831 0.959 0.880 +0 1.476 -0.564 -1.178 2.767 -0.921 1.256 -0.044 0.578 0.000 1.324 -0.444 1.294 2.215 0.387 0.779 0.568 0.000 0.589 -0.497 -0.175 0.000 0.903 1.102 0.989 1.082 0.561 1.073 1.111 +1 0.414 -1.340 -0.405 3.093 0.084 0.666 -0.456 -1.370 2.173 0.834 1.130 -1.656 0.000 0.960 0.462 1.037 2.548 0.733 -1.302 -1.217 0.000 1.901 1.218 0.975 1.147 0.963 1.027 1.117 +1 0.954 -0.228 1.727 0.344 -0.275 0.983 -0.501 -1.376 0.000 1.246 -0.499 -0.122 1.107 1.142 -0.135 0.357 0.000 1.388 -0.970 1.188 3.102 0.798 1.007 0.989 0.957 1.166 0.821 0.761 +1 1.417 0.391 -0.436 1.364 0.081 1.253 0.451 1.473 1.087 0.458 0.654 1.635 2.215 0.697 0.835 0.401 0.000 0.811 1.455 -1.087 0.000 0.882 1.121 0.994 0.864 0.201 0.995 0.845 +1 1.182 0.284 -1.610 1.247 -1.680 0.781 0.736 0.592 2.173 0.784 0.279 -0.184 0.000 0.384 0.608 -0.542 2.548 0.467 1.036 -0.683 0.000 0.520 0.744 0.985 0.647 0.583 0.748 0.699 +0 1.089 0.390 1.676 0.844 1.092 1.529 -1.237 0.072 1.087 0.505 -1.907 -0.689 0.000 1.010 -1.248 -1.264 2.548 1.938 -0.209 1.657 0.000 0.991 0.986 0.988 1.389 1.449 1.647 1.367 +1 1.010 0.013 0.356 0.253 -0.560 0.617 -0.255 -1.079 0.000 0.995 -1.063 0.321 1.107 0.986 -1.215 1.359 1.274 1.154 0.406 -1.547 0.000 0.719 1.010 0.988 1.018 0.857 0.913 0.899 +0 0.880 1.159 -1.225 0.973 -0.635 0.485 2.474 0.752 0.000 0.695 -0.033 0.053 2.215 0.384 0.578 -1.666 0.000 0.831 0.000 1.493 3.102 1.047 0.968 0.992 1.150 0.661 0.868 0.852 +0 0.867 0.410 -1.278 2.469 1.686 1.847 0.077 0.010 0.000 0.765 1.488 1.656 0.000 0.914 -0.512 0.950 2.548 1.049 -1.060 -0.298 3.102 3.214 2.019 0.993 1.201 0.727 1.361 1.337 +0 1.033 -1.845 1.135 0.887 1.202 0.705 -1.071 -1.063 0.000 1.080 -1.711 -0.419 0.000 1.351 -1.051 -1.575 0.000 1.172 -0.807 0.076 3.102 0.902 0.878 1.004 0.796 0.360 0.593 0.696 +0 0.299 -1.400 -0.350 0.623 1.437 0.764 -0.938 -0.703 2.173 1.121 0.803 1.001 0.000 0.486 0.494 -0.621 2.548 1.131 -0.234 0.816 0.000 0.893 0.869 0.993 0.580 0.622 0.606 0.594 +0 0.689 -0.615 1.012 0.424 -0.617 0.669 -1.359 -0.433 0.000 0.991 -0.389 -0.941 2.215 1.228 -1.252 1.191 1.274 0.706 -1.721 0.410 0.000 0.822 0.896 0.988 0.840 1.251 0.791 0.688 +1 0.573 -0.082 1.354 0.744 0.310 0.685 1.439 0.756 0.000 1.094 0.763 -1.065 1.107 1.073 1.346 -0.402 2.548 1.105 2.323 -0.995 0.000 1.067 0.914 0.989 1.229 0.765 1.066 1.085 +0 0.642 -0.672 -1.224 1.428 0.954 0.585 0.027 -0.732 2.173 0.516 -1.207 1.505 0.000 0.481 -0.494 0.335 1.274 0.588 -1.534 -0.535 0.000 0.726 0.876 1.225 0.654 0.575 0.655 0.617 +0 0.402 -1.052 0.474 2.160 -1.116 0.886 0.839 0.441 0.000 0.482 0.410 1.153 1.107 0.577 1.743 0.843 0.000 1.291 0.802 -0.964 3.102 0.851 1.011 1.278 1.062 0.700 0.952 1.191 +0 0.364 2.045 -0.758 1.858 -1.715 0.727 0.010 -0.526 2.173 1.099 0.244 1.110 0.000 0.811 2.030 -0.024 0.000 0.527 -1.275 -0.946 3.102 2.083 1.678 0.983 1.264 0.607 1.170 1.127 +0 0.338 0.915 -0.926 1.250 1.138 0.725 -0.259 -0.720 2.173 0.274 1.279 1.401 1.107 0.370 1.777 -0.025 0.000 1.091 1.076 0.436 0.000 0.368 1.063 0.984 0.478 0.844 0.878 0.739 +1 0.674 0.985 -1.543 1.054 0.937 0.768 0.877 -0.174 0.000 1.031 0.863 -0.642 1.107 0.991 1.314 1.362 0.000 1.199 -0.371 1.368 1.551 1.603 1.158 0.988 0.665 1.212 0.962 0.839 +1 0.565 -0.595 0.086 1.378 0.012 1.136 0.179 -0.848 0.000 2.397 1.029 1.414 2.215 0.955 -0.294 -0.425 0.000 1.593 0.033 0.533 3.102 0.845 1.160 0.978 1.647 1.570 1.462 1.238 +0 0.696 -0.219 1.653 0.292 -0.733 0.551 0.230 -1.347 2.173 1.445 0.739 0.885 1.107 0.402 -2.129 1.671 0.000 0.519 -0.616 -0.881 0.000 1.214 0.920 0.996 0.878 1.238 0.855 0.733 +1 1.967 -1.011 -0.126 0.612 -0.448 0.896 -0.369 1.201 2.173 0.634 -0.674 -1.200 0.000 0.919 0.277 -0.361 0.000 0.771 1.292 1.264 0.000 1.007 1.170 0.989 1.208 0.844 1.098 0.965 +1 0.688 -0.580 0.343 0.164 -1.239 1.304 -0.540 1.450 2.173 0.754 -0.951 -0.098 0.000 0.465 2.499 1.057 0.000 0.657 -1.353 -0.440 0.000 0.702 1.131 0.995 0.558 0.938 0.782 0.736 +1 0.593 0.082 -0.844 0.444 1.380 0.534 -1.562 0.800 0.000 0.850 -0.984 -0.313 2.215 0.706 -0.262 -1.308 2.548 0.565 -2.159 1.192 0.000 0.531 0.888 0.988 0.560 0.713 0.706 0.647 +0 0.726 0.670 1.161 0.280 -1.589 1.050 -1.423 -1.482 0.000 1.379 0.428 -0.344 2.215 1.930 0.566 0.675 2.548 0.429 -0.808 1.550 0.000 0.474 1.841 0.986 0.877 1.387 1.543 1.195 +1 0.701 1.407 0.913 1.086 -1.579 0.481 0.559 0.433 1.087 1.130 1.164 -1.170 2.215 1.253 -2.459 -0.376 0.000 2.310 0.025 1.062 0.000 1.374 0.855 0.985 0.810 1.131 0.916 0.884 +1 0.507 0.281 1.717 3.300 1.488 1.883 -1.679 0.007 0.000 0.537 0.254 -1.432 2.215 0.607 -0.933 -0.073 1.274 0.543 -1.300 -0.633 0.000 0.849 0.555 0.993 0.776 0.707 0.950 1.276 +1 0.396 -1.013 -1.223 0.146 -0.381 1.367 0.838 -0.174 0.000 1.297 -0.045 -1.130 0.000 3.360 -0.739 1.322 0.000 1.205 0.171 0.447 3.102 0.785 0.892 0.978 0.661 0.447 0.637 0.630 +1 1.184 0.721 -1.023 0.570 -1.394 0.984 1.088 0.008 1.087 0.571 0.007 1.385 0.000 0.723 1.289 1.165 2.548 0.429 1.116 0.502 0.000 0.652 0.941 0.980 0.734 0.922 0.790 0.718 +0 0.500 0.660 0.944 1.270 -1.246 1.477 -0.555 -0.350 2.173 1.942 -0.320 1.460 2.215 0.844 -1.053 -0.032 0.000 0.565 -0.352 0.720 0.000 0.557 1.133 1.017 1.410 2.504 1.384 1.106 +0 1.645 -0.684 -0.131 0.050 0.657 0.542 -0.008 1.500 2.173 0.494 -1.524 0.337 0.000 1.353 -0.644 -1.258 2.548 0.855 -0.366 1.166 0.000 0.764 0.900 0.980 0.919 0.753 0.772 0.713 +1 0.514 -1.314 1.231 1.251 -0.478 1.778 -0.356 -1.630 2.173 1.266 -0.825 0.430 2.215 1.684 -2.074 0.289 0.000 0.687 -0.384 -0.778 0.000 0.737 1.125 1.110 1.424 2.187 1.692 1.321 +1 0.319 -1.741 -1.316 1.523 0.152 0.884 -0.322 1.357 0.000 1.514 -1.436 -0.858 2.215 0.709 -2.338 1.168 0.000 1.601 -0.113 0.687 3.102 1.561 1.070 0.987 0.969 1.716 1.290 1.108 +1 0.954 0.356 0.303 1.442 -0.269 0.917 -0.456 1.568 2.173 0.642 0.689 -1.081 2.215 0.626 -2.117 -0.915 0.000 1.003 -0.574 0.361 0.000 1.128 1.222 0.980 0.800 1.040 1.027 1.156 +0 1.160 -0.281 0.445 1.828 1.004 1.646 -0.959 -1.347 2.173 0.751 -0.412 -0.608 0.000 0.903 0.078 -0.117 2.548 1.202 -0.842 0.857 0.000 1.254 0.872 0.989 1.763 1.598 1.262 1.061 +1 1.017 1.586 -0.767 1.555 -0.346 1.072 0.469 1.295 2.173 0.295 1.613 -1.301 0.000 0.273 0.309 1.596 1.274 0.758 0.343 0.333 0.000 0.738 0.838 0.983 0.654 0.186 0.910 0.728 +1 0.380 1.700 1.080 2.886 1.315 0.594 -2.663 0.007 0.000 1.392 -0.342 -1.446 0.000 1.290 0.106 -0.740 2.548 1.578 0.958 0.026 3.102 0.800 1.677 0.977 1.562 0.911 1.757 4.073 +1 0.296 2.169 -0.415 0.982 0.429 1.498 -0.367 -1.313 0.000 2.287 0.142 0.241 2.215 0.858 1.295 -1.268 0.000 1.294 0.233 0.928 3.102 0.833 0.769 0.982 0.880 0.906 0.917 0.781 +1 0.730 -0.269 0.029 0.738 -1.016 1.058 -1.417 1.323 2.173 0.932 -1.623 -0.274 0.000 0.495 -0.589 -0.824 0.000 0.437 -0.115 1.549 3.102 0.746 1.268 0.986 0.550 0.519 0.880 0.869 +0 0.849 0.602 -0.231 1.561 -0.170 0.920 0.900 0.810 2.173 0.860 -0.411 1.480 2.215 0.872 2.443 -1.406 0.000 1.120 1.922 0.952 0.000 0.949 1.301 0.989 1.118 1.196 1.313 1.426 +1 0.927 -0.944 -1.174 0.627 1.488 0.862 -0.239 -0.207 0.000 1.066 0.930 0.291 0.000 0.744 0.023 1.088 2.548 1.800 0.834 -0.718 3.102 1.014 0.906 0.989 0.658 0.991 0.759 0.833 +0 0.931 1.431 -0.446 1.248 -0.101 0.614 1.441 1.072 2.173 0.642 0.232 1.409 0.000 0.901 0.663 -1.492 2.548 0.378 -0.645 1.140 0.000 0.637 0.703 0.992 0.871 0.765 0.802 0.693 +1 0.693 -0.999 -0.300 0.495 -1.128 1.481 -1.455 -1.563 2.173 1.104 1.887 0.437 0.000 1.386 0.007 0.648 2.548 2.096 -1.931 -0.058 0.000 0.982 0.982 0.987 1.303 2.174 1.194 0.966 +1 1.542 -0.429 -1.444 0.799 -0.573 1.378 -0.586 0.552 2.173 0.542 -1.485 1.689 0.000 0.552 -0.292 -0.783 0.000 0.566 -0.656 -0.349 3.102 0.855 1.212 1.088 0.603 0.685 0.893 0.785 +1 0.643 -0.694 0.966 1.074 -1.351 0.649 -0.073 1.197 2.173 1.322 0.730 -0.278 0.000 0.800 -0.329 -1.170 2.548 0.581 0.877 0.652 0.000 0.866 0.950 1.001 0.721 0.770 0.820 0.813 +0 0.665 -0.350 -0.985 0.374 1.605 0.706 0.703 0.348 2.173 0.817 0.003 0.096 2.215 0.787 0.536 -1.512 0.000 1.123 2.178 -1.649 0.000 1.246 1.323 0.980 0.788 0.476 1.029 0.877 +0 0.730 0.827 0.174 0.491 1.453 1.076 0.390 1.426 2.173 1.159 -0.315 -0.863 2.215 1.264 -0.404 0.478 0.000 1.268 -0.237 -1.432 0.000 0.940 0.965 0.985 1.244 1.568 1.072 0.887 +1 0.703 0.124 -0.096 0.516 -1.083 0.927 -0.668 1.345 0.000 0.728 -0.553 0.800 0.000 1.549 -0.978 -0.385 2.548 1.075 0.219 -1.058 3.102 0.830 1.022 0.994 1.213 0.905 0.945 0.968 +1 1.164 1.075 0.793 1.683 0.199 1.278 0.552 -1.326 2.173 0.703 0.561 -0.328 0.000 0.456 -0.406 1.697 0.000 0.792 0.020 0.591 0.000 0.960 1.004 0.988 0.635 0.865 1.023 0.886 +0 0.448 -1.906 -0.585 0.665 1.106 0.523 -1.055 0.462 0.000 1.108 -0.872 -0.788 1.107 0.590 -0.990 1.320 0.000 1.084 0.406 -1.662 3.102 0.697 0.979 0.981 0.793 1.020 0.786 0.688 +1 1.116 -1.284 1.466 2.313 -0.139 0.741 0.106 -0.089 2.173 1.622 -0.749 -1.682 0.000 1.155 -2.464 -1.415 0.000 1.617 -1.042 -0.298 0.000 0.885 0.876 2.208 1.526 0.777 1.132 0.914 +1 1.035 0.821 -1.549 0.653 -0.921 0.306 1.056 0.419 0.000 0.749 -0.590 0.545 1.107 0.680 0.267 1.219 0.000 1.222 0.152 -0.772 3.102 1.018 1.087 0.979 0.759 0.874 0.906 0.804 +0 0.847 0.235 1.575 0.565 -1.447 0.605 0.986 1.261 0.000 0.731 0.950 0.326 2.215 0.586 0.222 -0.268 2.548 0.994 -1.044 -0.664 0.000 0.842 0.790 0.991 0.724 0.440 0.723 0.692 +1 0.480 -0.357 -1.123 2.404 0.471 1.780 0.346 -1.293 0.000 1.384 -0.800 0.835 0.000 1.584 0.426 -0.394 1.274 0.418 2.051 -1.300 0.000 1.666 1.475 1.475 0.722 0.790 1.112 1.200 +0 2.774 -0.067 -1.332 0.840 -1.302 1.603 -1.229 0.556 0.000 1.461 -0.999 0.149 0.000 1.964 0.397 -1.619 2.548 1.298 -1.285 -0.203 1.551 1.218 0.984 1.008 0.693 1.834 1.646 1.709 +1 0.839 0.427 1.306 0.962 0.475 1.242 0.348 -0.423 2.173 1.311 -0.334 1.054 0.000 1.243 -1.395 -1.117 0.000 0.858 -0.532 -1.691 0.000 0.880 0.680 0.985 1.159 0.916 0.985 0.843 +0 0.846 0.908 -0.961 0.370 1.563 0.670 -0.130 0.286 1.087 0.489 -1.099 0.931 0.000 1.126 -0.031 -1.067 2.548 0.596 -1.820 1.241 0.000 0.434 0.957 0.983 0.853 1.017 0.772 0.755 +0 1.577 0.332 -0.220 0.232 1.369 0.834 -0.246 1.559 0.000 0.767 -0.726 -0.998 1.107 0.539 -1.302 1.187 0.000 1.092 1.161 0.235 3.102 0.854 0.905 0.990 0.636 1.295 0.963 0.901 +0 0.479 -2.150 0.327 0.616 -1.174 0.835 -1.005 1.624 2.173 0.494 -1.227 -0.116 0.000 0.652 -1.666 -1.368 0.000 1.836 -0.765 0.352 3.102 0.825 0.874 0.995 0.742 1.197 0.750 0.660 +0 2.209 -0.247 0.513 0.520 -0.248 0.896 2.815 0.301 0.000 1.802 0.735 -1.709 1.107 1.683 0.385 -1.041 0.000 2.338 0.825 -1.247 0.000 0.746 1.012 0.988 0.800 2.345 1.456 1.369 +0 1.063 1.338 0.762 0.321 -0.428 0.786 0.516 -1.534 0.000 0.569 2.577 1.355 0.000 1.147 -0.876 -0.056 2.548 0.948 0.490 0.059 3.102 0.687 0.810 0.983 1.633 0.687 1.032 0.973 +1 0.731 -1.408 0.973 0.703 0.585 0.903 -0.132 -1.468 0.000 0.751 -1.508 0.469 2.215 0.899 -2.600 -0.515 0.000 1.391 0.444 -0.762 0.000 0.976 1.005 0.974 0.685 0.890 0.858 0.763 +0 1.140 0.158 -0.861 0.843 -0.246 0.354 -1.243 0.775 0.000 0.583 -0.397 0.604 2.215 0.775 -0.109 -1.692 2.548 0.825 -2.088 1.626 0.000 0.794 0.883 0.986 0.776 0.636 0.645 0.755 +1 0.562 -0.792 -1.528 2.153 1.321 1.526 -0.101 0.499 0.000 2.259 0.528 -1.186 2.215 1.110 -0.122 0.087 0.000 2.230 -0.202 -0.577 3.102 0.850 1.303 0.984 2.321 1.335 1.648 1.631 +0 0.560 0.287 0.280 1.995 1.005 0.882 -0.947 -0.246 0.000 0.974 -0.975 -1.178 2.215 1.127 0.944 1.654 2.548 0.386 -2.207 -0.660 0.000 0.909 0.931 0.982 0.792 1.480 1.200 1.293 +0 0.884 1.674 0.582 0.561 1.640 0.765 2.687 -0.469 0.000 0.369 -0.863 0.969 0.000 1.299 0.436 1.628 2.548 1.666 -0.712 -0.628 1.551 0.387 1.043 0.986 1.967 1.290 1.366 1.403 +0 0.582 -2.138 -0.872 0.511 -1.476 0.791 -1.059 0.363 2.173 0.844 0.056 0.799 0.000 0.943 0.536 1.711 0.000 1.528 0.413 -0.747 3.102 1.067 1.034 0.993 0.913 1.404 0.986 0.940 +1 0.677 -0.289 -1.397 2.952 -0.154 1.131 -0.364 0.877 0.000 1.087 1.098 -1.305 2.215 0.333 0.849 1.457 0.000 0.475 0.278 1.104 1.551 0.946 0.522 1.764 1.686 0.598 1.066 1.061 +1 1.006 -0.665 -0.120 1.248 -0.424 1.184 0.872 1.701 2.173 0.698 0.097 -0.769 0.000 0.465 -0.698 1.068 2.548 0.797 0.642 0.134 0.000 0.859 0.961 0.976 0.743 0.987 1.037 0.847 +1 0.779 0.693 -0.840 1.179 -0.104 0.604 0.016 0.989 0.000 0.943 1.065 1.675 1.107 1.201 -0.077 1.375 2.548 0.675 -2.350 -0.025 0.000 0.992 0.923 0.989 0.970 0.769 0.852 0.799 +1 0.906 0.172 0.708 0.768 1.580 0.623 -0.013 1.139 0.000 0.683 -1.353 -0.375 1.107 0.681 -0.385 -1.035 0.000 1.265 -1.462 -1.065 0.000 0.734 0.681 0.987 1.212 0.483 0.792 0.818 +1 1.520 1.104 0.293 0.710 1.053 1.063 2.829 -0.804 0.000 0.841 0.929 1.306 0.000 1.207 0.411 -1.557 2.548 0.423 1.767 -0.106 0.000 0.884 0.807 0.990 0.696 0.679 0.710 0.658 +1 2.354 -1.849 -0.282 0.520 -1.174 0.422 -1.869 0.999 0.000 0.573 -1.395 -1.385 0.000 1.235 -0.871 0.692 2.548 1.387 -0.989 1.625 3.102 0.906 0.839 1.102 1.073 0.754 0.909 0.795 +0 1.186 -1.354 1.684 0.339 -0.553 1.196 -0.736 1.386 2.173 1.582 -1.168 0.124 0.000 0.974 1.423 -1.243 0.000 1.114 -1.212 -0.601 3.102 3.982 2.367 0.989 0.959 1.272 1.728 1.467 +1 0.733 -1.729 0.379 0.932 1.131 2.048 -0.649 1.731 2.173 2.796 -0.797 -0.432 0.000 0.604 -1.872 -0.352 0.000 1.644 -1.475 0.979 0.000 1.036 0.725 0.988 1.290 0.864 1.027 0.891 +1 0.880 1.540 0.212 1.336 -1.667 1.930 0.098 -1.633 0.000 0.741 0.438 -0.271 0.000 1.178 -0.399 0.355 1.274 1.632 0.483 1.370 3.102 2.434 1.520 1.491 1.477 1.011 1.178 1.241 +0 2.679 -0.294 0.333 0.823 -0.135 1.140 -0.234 -0.972 2.173 0.594 -0.345 1.522 2.215 0.672 0.194 1.059 0.000 1.295 0.191 -1.450 0.000 0.794 0.958 0.987 0.988 0.946 1.059 0.925 +1 0.916 0.561 0.980 0.569 0.091 0.462 0.222 -1.301 1.087 0.640 2.170 0.876 0.000 0.345 -0.002 1.319 0.000 1.118 1.159 -0.524 0.000 0.856 0.682 0.990 0.620 0.405 0.572 0.530 +0 0.716 -0.356 1.344 1.289 -1.659 0.801 -0.817 0.244 0.000 0.929 -0.671 -1.021 2.215 0.972 -0.341 0.660 2.548 0.913 0.052 -0.431 0.000 0.957 1.010 0.994 0.864 1.021 0.815 0.857 +0 0.661 -0.586 0.075 1.154 0.984 0.705 -1.089 -1.487 2.173 0.419 -1.041 0.653 0.000 0.734 -1.145 -1.021 2.548 0.493 2.240 -0.439 0.000 0.706 0.814 0.985 0.857 0.373 0.747 0.698 +0 2.172 -0.877 -0.751 0.162 -0.709 0.598 -0.084 0.535 2.173 0.702 0.068 1.571 2.215 0.368 -1.920 0.902 0.000 0.457 -0.363 0.374 0.000 0.472 0.678 0.998 1.039 0.770 0.839 0.706 +0 0.697 -0.264 -0.446 1.803 -1.472 0.694 -0.519 0.490 0.000 1.068 1.063 0.631 2.215 1.377 0.353 -0.888 2.548 0.403 0.116 1.729 0.000 0.775 0.983 1.238 0.811 1.345 1.033 0.921 +1 0.599 0.255 0.425 1.440 -0.646 2.299 1.635 0.800 0.000 1.358 -0.028 -1.177 2.215 0.777 -0.130 1.697 0.000 1.897 -0.929 -0.950 3.102 0.901 0.967 1.057 0.887 0.878 0.805 0.736 +0 1.329 0.083 -1.451 1.941 1.412 0.904 -1.995 -0.068 0.000 0.721 -1.358 -0.384 2.215 1.074 -1.152 0.615 2.548 0.553 1.011 -1.252 0.000 0.285 0.988 1.185 1.276 0.735 1.097 0.935 +1 1.105 0.217 -1.316 0.276 1.097 0.618 0.808 -0.190 0.000 0.651 1.743 0.627 0.000 1.615 -0.529 -0.961 1.274 1.143 -1.187 0.839 3.102 0.858 1.219 0.995 0.829 1.131 0.990 0.847 +0 0.656 1.252 1.684 1.163 -1.618 0.920 1.130 0.127 0.000 0.785 0.639 -1.468 2.215 0.355 -1.993 0.323 0.000 0.898 1.599 0.905 0.000 1.052 1.185 0.988 0.755 0.500 0.697 0.826 +1 0.861 0.603 -0.470 1.125 0.783 0.644 1.325 0.001 0.000 0.886 0.988 1.622 1.107 0.754 -0.791 1.407 2.548 0.647 1.115 -1.046 0.000 0.798 0.940 1.233 0.942 0.960 0.878 0.799 +1 1.299 -0.948 0.998 0.186 1.023 0.657 -0.546 0.272 1.087 1.047 -0.417 -0.330 2.215 1.237 -0.241 -1.571 0.000 0.743 -0.642 -1.406 0.000 0.313 0.964 0.980 0.965 0.636 0.745 0.727 +1 2.234 -0.063 0.720 0.848 0.328 0.387 -0.236 1.738 0.000 0.876 0.241 -0.687 0.000 1.233 0.549 -1.468 0.000 0.880 -0.671 -1.002 3.102 1.055 0.688 0.996 0.609 0.220 0.639 0.701 +0 1.408 -1.646 1.288 0.608 0.023 0.425 -0.921 1.310 1.087 0.654 -1.810 -0.734 0.000 1.160 -0.043 -0.572 2.548 0.422 -0.928 -0.428 0.000 0.322 0.734 1.163 0.681 0.955 0.841 0.722 +1 0.794 0.403 0.028 1.134 -1.051 1.744 -0.228 1.081 2.173 1.247 0.425 -0.623 0.000 1.566 -2.382 -0.975 0.000 1.836 -0.937 0.632 1.551 0.897 0.871 1.086 1.473 1.151 1.155 1.002 +1 0.915 -0.852 1.300 0.450 -1.252 0.677 0.215 -1.174 2.173 2.013 -0.493 -0.826 2.215 3.341 -1.751 0.515 0.000 0.705 0.861 1.456 0.000 0.555 2.349 0.977 1.221 0.829 1.875 1.444 +1 0.527 1.056 1.362 1.859 -0.782 0.870 -0.754 1.149 2.173 0.855 -0.012 0.083 0.000 0.289 -1.301 1.077 0.000 0.480 1.925 -1.419 0.000 0.837 0.919 1.283 1.204 0.810 1.210 1.004 +1 2.151 0.217 -0.811 0.482 -0.913 1.069 -0.260 0.480 2.173 1.096 0.085 1.670 2.215 0.347 -1.043 -0.569 0.000 1.079 0.093 1.116 0.000 0.836 0.828 0.971 1.456 1.426 1.151 0.955 +1 1.205 -0.288 -0.995 1.697 -1.442 1.093 0.139 0.153 2.173 1.429 0.537 0.884 2.215 0.526 0.766 -1.601 0.000 0.558 0.184 -0.666 0.000 0.484 0.842 0.985 1.647 1.188 1.366 1.030 +1 0.425 -2.314 -0.753 1.825 0.557 0.674 -0.267 -1.657 2.173 0.754 -0.511 -0.900 0.000 0.582 -1.296 0.173 0.000 1.164 -0.049 0.821 3.102 0.960 0.926 1.129 1.318 0.745 1.214 1.033 +0 0.366 -1.579 -0.317 0.605 1.616 1.058 -1.021 -0.752 2.173 0.602 1.003 -1.361 0.000 0.590 1.589 1.006 0.000 2.120 0.652 0.665 1.551 0.842 0.885 0.989 0.878 2.236 1.379 1.094 +1 0.495 -1.149 -1.630 1.694 1.001 0.653 0.769 -0.095 1.087 0.397 -1.499 -0.781 0.000 1.106 0.144 -1.239 2.548 0.602 1.033 1.163 0.000 1.355 0.947 0.986 1.219 0.968 0.936 0.853 +0 0.402 0.087 -0.223 0.608 1.650 0.380 0.012 1.321 2.173 0.429 2.868 -1.680 0.000 0.398 -1.297 -0.938 0.000 0.693 1.351 -0.087 0.000 0.868 1.098 0.992 0.551 0.517 0.889 1.051 +0 0.711 -0.785 0.042 1.553 -0.873 1.102 1.521 0.972 2.173 0.707 0.975 0.567 0.000 1.249 -0.765 -0.954 2.548 1.070 0.526 -1.739 0.000 1.012 0.909 1.068 0.616 2.610 1.652 1.329 +1 0.986 -1.067 1.663 0.366 0.481 0.575 0.031 1.132 2.173 0.547 -2.349 -0.415 0.000 0.636 0.856 -1.234 0.000 0.517 0.500 -0.719 0.000 1.501 0.887 0.983 0.899 0.185 0.779 0.727 +0 0.431 1.121 1.177 0.348 0.254 0.592 1.514 -0.160 0.000 0.689 -0.418 -1.441 2.215 0.672 -0.271 0.939 1.274 0.955 -0.632 1.643 0.000 1.984 1.250 0.994 0.701 0.609 0.882 0.755 +0 1.044 1.391 1.531 0.374 0.906 1.852 0.426 1.417 2.173 3.674 -0.917 -0.132 1.107 2.025 0.181 -1.387 0.000 1.060 -1.691 0.014 0.000 1.753 1.698 0.990 2.500 4.733 2.711 2.076 +1 1.003 0.162 -0.705 1.304 0.769 0.678 -2.607 0.930 0.000 1.210 -1.250 -0.751 2.215 1.307 0.608 1.662 2.548 1.154 -0.541 0.242 0.000 0.982 1.261 1.538 1.046 1.880 1.555 1.447 +1 2.018 0.352 -0.465 0.540 0.032 1.174 0.485 1.329 2.173 0.687 0.228 -1.174 0.000 0.700 -0.286 1.170 0.000 0.867 -0.015 0.232 3.102 0.958 0.934 0.988 0.586 0.934 0.955 0.831 +1 0.617 -0.348 1.157 0.869 -0.484 0.661 -1.390 1.644 1.087 0.885 -0.478 1.703 0.000 0.980 -0.801 -0.269 2.548 0.557 -1.711 0.595 0.000 0.896 0.829 1.010 0.870 1.026 0.693 0.636 +1 0.738 -0.389 0.702 0.966 -0.752 1.045 -0.371 1.340 2.173 0.447 1.903 -0.874 0.000 0.646 -0.655 -0.022 2.548 0.707 0.437 -0.323 0.000 0.670 0.874 1.130 0.987 0.981 0.958 0.849 +1 0.851 0.658 0.398 0.365 -0.536 1.012 0.533 1.573 0.000 0.949 1.322 0.146 2.215 1.031 1.601 0.760 0.000 1.313 0.058 -1.476 3.102 1.063 0.854 0.990 0.899 1.220 0.949 0.790 +0 0.463 0.526 -0.130 1.051 1.410 0.524 0.586 0.484 0.000 0.746 -0.062 0.711 2.215 1.823 0.247 -0.916 2.548 0.754 1.245 -0.890 0.000 1.013 1.049 0.986 0.625 1.250 0.816 0.726 +1 0.694 -1.192 -1.266 2.247 -1.438 0.817 -1.375 0.364 0.000 0.776 0.086 0.195 2.215 0.532 -0.751 -0.710 1.274 0.762 0.111 0.728 0.000 0.973 0.876 1.010 0.643 0.593 0.771 0.790 +1 0.363 -0.092 -1.626 0.178 1.557 0.657 -1.220 1.193 0.000 0.946 0.219 0.035 0.000 1.246 -1.096 0.019 2.548 1.688 -0.323 -1.068 0.000 0.921 1.008 0.795 0.384 1.087 0.888 1.027 +0 0.346 0.170 1.101 2.501 -1.555 0.936 -1.165 0.522 1.087 0.557 1.141 -0.030 0.000 0.607 0.173 -0.848 1.274 0.832 -0.654 -0.256 0.000 1.032 0.682 0.983 1.391 1.126 0.969 0.961 +1 0.671 -0.148 -0.808 0.591 -1.700 1.054 -0.658 0.475 0.000 1.076 0.468 -0.491 0.000 1.623 -0.210 -1.671 2.548 1.344 -0.999 1.162 3.102 2.207 1.608 0.996 0.762 0.847 1.199 1.006 +0 1.742 1.881 1.391 0.290 -0.918 0.674 1.506 -0.478 0.000 0.904 1.095 -1.446 1.107 1.120 0.477 -0.159 0.000 1.344 0.896 0.794 3.102 0.924 0.965 0.992 0.817 0.897 0.805 0.869 +1 0.742 -1.044 1.447 0.513 -0.787 0.448 -0.380 -1.293 2.173 0.462 -2.690 -0.649 0.000 0.739 -0.247 0.681 2.548 1.098 -1.704 0.622 0.000 0.912 1.046 0.985 0.722 0.702 0.829 0.720 +0 1.107 1.615 0.786 1.182 0.934 0.884 0.783 0.232 2.173 2.251 -0.058 -1.170 0.000 0.895 1.169 -0.046 0.000 1.082 -0.652 -1.147 1.551 1.043 1.029 0.992 0.832 1.332 0.977 0.826 +1 0.713 -1.450 0.817 0.364 -1.184 0.464 -0.875 1.604 1.087 0.794 2.701 -1.259 0.000 0.664 2.301 0.997 0.000 2.949 -0.517 0.041 3.102 1.007 2.403 0.994 0.867 1.233 2.225 1.830 +1 0.372 -1.721 1.294 0.749 -1.649 0.674 0.133 -1.081 0.000 0.802 0.827 -0.248 0.000 1.470 -1.117 0.521 2.548 1.103 -0.902 -1.106 0.000 0.852 0.936 0.988 0.937 0.475 0.865 0.758 +1 0.832 -1.514 0.993 1.754 0.182 0.629 -1.983 -1.035 0.000 1.338 0.815 -1.665 2.215 0.491 0.437 -1.243 0.000 1.091 -0.591 0.612 0.000 0.951 1.083 1.115 1.018 1.103 1.494 1.152 +1 0.371 -0.122 0.822 1.548 -0.158 0.857 0.628 -1.162 2.173 0.809 -0.156 1.531 0.000 0.508 0.219 -0.683 0.000 1.211 0.537 0.561 3.102 0.918 0.846 0.987 0.924 1.078 1.009 0.862 +1 0.533 0.150 -0.653 0.370 0.791 1.087 -0.747 -1.189 0.000 1.091 -0.434 0.593 2.215 0.727 -0.803 -0.589 0.000 0.700 0.024 1.686 3.102 0.912 0.938 0.991 0.630 0.684 0.713 0.647 +0 1.188 -0.158 -1.590 0.839 -0.584 0.372 -1.353 1.731 0.000 0.750 -0.420 0.950 0.000 1.073 -0.302 -0.355 2.548 1.043 -0.023 0.176 3.102 0.907 0.974 1.090 0.767 0.392 0.652 0.673 +1 1.012 1.947 0.915 0.906 -1.731 0.777 -0.325 -0.094 0.000 0.615 0.406 0.926 0.000 0.872 0.120 -1.384 2.548 0.900 0.632 -1.078 0.000 0.957 0.771 0.985 1.179 0.438 1.108 0.914 +1 0.668 0.057 1.735 2.306 -0.608 1.030 0.344 1.227 0.000 1.222 0.865 0.676 2.215 0.593 -0.201 -0.609 1.274 0.375 0.387 -1.161 0.000 0.795 0.868 1.475 0.696 0.983 0.938 0.893 +1 2.024 -0.778 -1.446 1.060 -0.240 0.606 -1.286 0.151 2.173 0.377 -1.198 1.644 0.000 0.553 -1.836 0.454 0.000 1.384 -0.103 0.675 3.102 0.814 0.738 1.797 1.202 0.746 0.933 0.767 +1 1.278 1.864 -0.153 1.162 -0.151 1.045 0.578 1.419 2.173 0.506 0.075 0.405 0.000 1.129 0.683 -1.420 2.548 0.490 1.424 -0.967 0.000 0.851 0.930 0.990 1.057 0.752 1.070 0.866 +1 0.693 0.234 1.419 0.768 -1.209 0.635 0.287 0.770 2.173 0.920 -2.213 -0.078 0.000 0.432 -1.082 -0.537 0.000 0.814 1.064 -1.317 3.102 0.939 1.717 0.993 0.769 0.823 1.400 1.128 +0 2.060 -0.587 0.380 2.553 1.076 0.877 0.841 -0.321 0.000 1.431 2.537 -1.189 0.000 1.042 0.885 -1.413 1.274 1.006 -1.137 -0.421 3.102 2.912 1.801 1.864 1.267 1.291 1.904 2.378 +1 1.018 1.554 0.979 1.489 -0.657 0.479 0.501 -1.092 1.087 0.625 0.562 0.079 0.000 0.996 -0.748 -0.760 2.548 0.989 0.463 1.327 0.000 0.920 0.851 1.698 1.678 0.665 1.103 0.944 +1 0.779 0.003 -1.265 0.445 0.015 1.259 0.914 -1.537 2.173 1.050 0.093 0.676 0.000 1.477 0.520 0.114 0.000 0.517 -0.117 -0.617 3.102 1.033 0.745 0.986 1.238 0.789 1.037 0.942 +0 0.647 2.022 -1.498 0.823 -0.678 0.791 1.230 0.310 2.173 0.347 0.428 -1.707 0.000 0.484 -1.214 1.385 0.000 0.741 -0.454 0.075 3.102 0.671 1.209 0.991 0.865 0.842 0.765 0.803 +1 0.405 -1.193 -0.681 0.795 1.145 1.365 0.655 0.358 0.000 2.257 -0.772 -1.523 2.215 0.917 0.697 -0.290 0.000 0.542 0.172 -0.281 3.102 1.115 0.672 0.993 1.008 1.038 1.523 1.173 +0 0.595 1.815 1.601 0.456 -1.364 0.636 0.185 0.609 0.000 0.601 -1.790 1.151 0.000 0.675 1.599 0.119 0.000 2.257 0.466 -0.924 3.102 0.892 1.013 0.997 0.544 0.615 0.614 0.598 +1 0.909 -0.257 -0.572 1.501 -1.423 1.137 -1.803 -0.227 0.000 1.445 -0.367 1.241 2.215 0.598 -0.761 -0.088 0.000 1.538 -0.577 1.480 3.102 0.915 1.018 1.121 1.141 0.358 0.792 0.835 +1 0.554 -0.603 0.571 0.430 -1.657 1.108 0.888 -0.505 0.000 1.208 -0.539 1.449 0.000 1.466 0.390 -1.434 2.548 1.055 0.411 -0.084 0.000 0.698 1.100 0.991 0.748 0.880 0.886 0.771 +1 2.221 1.381 0.384 0.319 -1.660 1.808 0.175 -0.991 0.000 1.001 0.216 1.320 2.215 0.692 0.645 0.549 0.000 0.753 -0.198 -0.203 3.102 0.761 0.619 1.123 0.874 0.789 0.839 0.711 +1 0.504 0.625 -0.848 1.359 1.598 2.850 -0.941 1.549 0.000 2.634 1.093 -0.168 1.107 1.602 -2.049 0.065 0.000 1.444 1.120 -0.787 0.000 1.347 1.289 0.987 1.641 0.993 1.108 1.106 +1 1.042 0.122 -1.545 0.824 -1.079 1.171 -0.493 0.188 2.173 0.768 -0.800 0.989 2.215 0.276 -0.549 0.577 0.000 0.805 1.355 -1.544 0.000 0.861 0.971 0.988 1.506 0.950 1.125 0.949 +0 0.664 -0.025 1.616 1.605 -1.083 0.805 -2.581 -0.510 0.000 0.710 -2.405 -1.567 0.000 1.025 -0.747 0.996 0.000 1.687 0.574 0.052 1.551 1.039 1.290 0.982 1.167 0.851 1.121 1.040 +1 0.828 1.095 1.563 0.413 -1.513 0.383 -1.034 0.185 1.087 0.597 0.837 0.697 0.000 0.690 0.521 -1.145 1.274 0.394 2.378 0.398 0.000 0.758 0.776 0.981 0.890 0.829 0.808 0.743 +1 1.125 -0.595 1.383 1.259 1.311 1.069 -0.625 0.548 2.173 1.073 -0.706 -0.068 2.215 2.577 -0.690 -1.170 0.000 0.538 1.090 -1.119 0.000 0.637 0.947 0.996 1.209 0.835 0.968 0.908 +0 0.799 1.486 -0.681 0.653 -0.326 1.030 0.871 0.393 0.000 1.748 0.342 -1.550 2.215 0.693 0.659 1.067 0.000 0.605 -0.702 -0.217 1.551 0.921 1.036 0.991 1.100 1.046 1.148 0.975 +0 1.397 1.504 -1.592 0.707 1.161 0.933 1.702 0.055 0.000 0.639 0.342 -0.887 2.215 0.671 2.130 0.661 0.000 0.859 1.045 -1.197 3.102 0.864 0.886 0.994 0.832 0.362 0.778 0.793 +0 1.208 1.782 0.655 0.994 1.267 0.441 -1.476 0.113 0.000 1.051 -0.151 -1.297 2.215 0.432 1.073 -0.657 2.548 0.692 1.547 -0.797 0.000 2.256 1.320 0.996 1.881 0.645 1.178 1.408 +0 4.114 -1.136 0.450 2.004 -1.726 1.308 -0.879 0.148 2.173 1.928 -0.140 -1.105 0.000 3.007 -0.566 -1.483 2.548 0.593 0.534 0.163 0.000 1.386 1.326 3.680 2.102 2.480 1.967 1.801 +1 1.399 0.606 0.867 1.374 1.353 0.733 1.666 -0.996 0.000 0.746 1.539 -0.368 0.000 0.455 0.557 -1.640 0.000 0.765 0.642 -0.210 1.551 0.931 0.587 0.988 0.594 0.152 0.528 0.632 +1 1.264 1.592 1.627 0.641 -0.991 0.620 -0.262 -0.512 2.173 0.599 1.987 0.752 0.000 1.310 -0.283 -0.005 0.000 0.628 1.085 -1.595 3.102 0.684 0.534 0.991 1.422 0.791 0.891 0.833 +0 1.966 0.808 -0.016 0.658 1.676 0.869 0.184 -0.743 2.173 0.894 1.435 0.678 0.000 1.127 0.708 -1.472 2.548 1.110 -1.667 1.421 0.000 0.401 1.375 1.574 1.115 0.839 1.085 1.171 +1 0.668 -0.818 0.959 0.964 1.486 0.434 -0.861 0.515 1.087 1.381 -0.350 -0.362 2.215 0.754 0.192 1.710 0.000 0.601 1.186 -1.508 0.000 0.510 0.874 0.991 1.403 0.861 0.941 0.984 +0 0.950 -0.329 -1.336 0.631 0.380 0.792 -0.509 0.365 1.087 0.270 -0.188 -0.822 0.000 0.704 -0.269 -1.692 0.000 0.389 1.039 -1.396 3.102 0.473 0.798 1.072 0.614 0.830 0.621 0.542 +0 0.457 -0.153 -1.248 2.186 -0.492 1.606 -0.810 1.150 1.087 1.493 0.732 -0.908 0.000 1.704 -0.727 0.748 1.274 0.514 -0.278 -0.373 0.000 0.841 1.682 0.988 2.009 0.732 1.501 1.380 +1 1.601 0.400 -0.393 0.310 -0.852 0.615 0.275 0.933 1.087 0.558 1.776 -0.774 0.000 0.771 1.275 1.400 2.548 0.891 1.698 1.049 0.000 0.921 1.071 0.988 1.000 0.616 0.803 0.844 +1 0.921 -1.862 0.970 1.384 1.476 0.471 -0.625 1.482 0.000 1.057 0.213 -0.073 2.215 1.496 0.037 -0.841 0.000 0.950 -1.101 0.494 3.102 1.405 1.110 0.994 2.272 0.886 1.417 1.380 +0 0.609 0.134 0.525 0.958 -1.037 0.947 -0.031 -0.127 2.173 0.795 2.651 0.915 0.000 1.442 -0.879 1.616 2.548 0.516 -1.276 -0.280 0.000 3.540 2.550 1.044 0.936 1.618 2.028 1.519 +1 0.467 0.148 1.668 1.245 -0.452 0.916 -1.138 -1.444 1.087 0.834 -1.150 0.448 2.215 0.660 1.114 0.506 0.000 0.493 -1.294 0.267 0.000 1.191 1.029 0.997 1.079 1.275 1.014 0.906 +0 1.084 -0.446 -0.619 0.392 0.302 1.103 -2.219 1.275 0.000 1.380 -0.586 -1.614 2.215 2.583 -1.392 -0.098 0.000 1.768 -2.130 -0.471 0.000 1.486 1.634 0.994 0.993 0.327 1.425 1.124 +1 0.516 -0.346 -0.670 0.711 -1.096 0.662 1.828 0.474 0.000 1.329 1.070 -1.259 2.215 0.849 0.822 1.317 2.548 0.525 -0.438 -0.055 0.000 0.727 0.722 0.978 0.691 0.833 0.840 0.775 +1 0.529 0.466 -0.520 1.165 0.379 0.905 -2.117 -0.369 0.000 0.757 0.476 0.949 0.000 1.933 0.297 1.579 1.274 0.790 1.301 1.562 0.000 1.033 0.941 0.981 0.762 0.710 0.793 0.734 +1 0.820 -1.966 -1.108 0.529 1.227 0.727 0.361 0.863 2.173 0.628 0.478 -0.408 2.215 0.506 -1.657 0.296 0.000 0.450 -0.396 -1.589 0.000 0.647 0.903 0.983 1.047 0.908 0.964 0.783 +0 0.914 0.142 1.209 0.550 1.024 0.897 2.419 0.917 0.000 0.957 1.602 -0.538 2.215 1.718 0.970 -0.906 0.000 1.013 0.157 -0.401 3.102 2.777 1.741 0.990 0.803 0.717 1.181 1.063 +0 1.488 -1.054 1.496 2.561 1.415 1.283 0.340 -0.236 0.000 0.718 -1.456 -0.103 1.107 1.731 -0.431 -1.520 2.548 1.088 -0.685 -0.145 0.000 0.860 0.986 1.011 1.247 1.299 1.105 1.086 +1 0.699 -0.625 -1.286 1.429 -0.411 0.499 -0.014 1.183 0.000 0.806 0.849 1.647 2.215 0.532 -0.354 0.658 2.548 0.539 0.284 -0.063 0.000 0.729 0.690 0.987 0.684 0.716 0.767 0.664 +0 0.841 -1.892 0.086 1.365 0.973 0.711 -0.163 -1.426 2.173 0.830 -0.213 0.104 0.000 1.188 -0.263 -0.644 0.000 0.975 -0.354 1.614 3.102 0.951 1.050 1.064 0.922 0.363 0.965 0.991 +0 0.595 -1.338 -0.997 0.576 -0.474 0.722 -0.540 0.353 0.000 1.074 -1.348 -1.236 2.215 0.893 -0.052 -0.721 0.000 1.102 2.214 0.971 0.000 1.042 1.249 0.979 0.826 0.120 1.590 1.273 +1 0.496 -1.100 1.616 0.091 1.526 0.678 -0.243 -1.641 2.173 0.992 0.663 0.467 0.000 0.743 -0.377 -0.473 2.548 0.548 2.144 -1.410 0.000 0.977 0.960 0.977 0.630 0.772 0.865 0.746 +0 0.513 -0.406 0.299 1.193 -0.665 0.966 1.443 0.629 0.000 1.057 1.082 1.029 2.215 1.962 -0.561 -1.183 2.548 0.562 0.943 -1.051 0.000 1.138 0.834 0.990 0.980 2.046 1.390 1.143 +1 2.090 -0.309 -1.316 1.446 -0.931 0.558 2.211 0.413 0.000 0.923 -1.032 -0.168 2.215 1.064 1.948 1.488 0.000 1.217 -0.207 0.102 0.000 0.735 0.940 0.999 1.205 1.625 1.142 1.039 +0 0.698 2.076 -0.820 0.984 -0.834 0.825 0.417 -1.448 2.173 1.525 0.786 1.103 2.215 1.077 1.786 0.507 0.000 1.264 -0.240 -0.418 0.000 1.029 0.940 1.005 0.876 1.273 0.986 0.918 +1 0.647 -0.037 0.256 1.037 -1.223 0.847 -0.524 0.693 0.000 0.666 0.284 -1.031 2.215 0.502 -1.533 1.225 0.000 0.941 0.132 -0.080 0.000 0.897 1.143 1.103 0.655 0.531 0.726 0.691 +0 0.959 0.989 -0.703 1.886 -1.037 1.833 0.870 -0.484 2.173 1.565 -1.105 1.454 0.000 0.602 -1.598 0.861 0.000 2.223 -0.225 0.953 1.551 0.889 0.998 0.997 1.032 2.430 2.039 1.715 +1 2.171 -0.507 0.843 0.521 -0.981 1.178 -0.703 -0.462 2.173 0.695 -0.649 -1.216 1.107 0.584 0.948 1.070 0.000 0.656 -1.362 -1.407 0.000 1.327 0.927 1.469 1.357 0.837 0.969 0.903 +0 1.174 -0.070 -1.173 1.393 -1.021 0.891 -0.608 0.808 2.173 0.752 0.214 1.371 0.000 1.095 0.919 0.140 2.548 0.370 0.780 0.493 0.000 0.550 0.727 0.982 1.310 1.312 1.186 0.957 +1 1.038 0.306 1.079 0.815 -1.532 0.433 -0.648 1.425 2.173 0.996 -1.295 -0.319 2.215 0.652 0.426 -1.156 0.000 0.412 -0.615 -0.164 0.000 0.577 0.808 0.989 0.570 1.023 0.824 0.686 +0 0.428 -0.635 -0.364 2.359 0.468 0.509 -0.051 0.529 0.000 1.242 0.176 1.464 2.215 1.931 1.810 -1.059 0.000 1.200 -0.454 -1.020 3.102 2.782 1.996 0.990 1.391 0.957 1.418 1.659 +1 0.653 -0.948 -0.506 1.566 -1.326 2.561 1.832 -0.196 0.000 2.335 -0.486 1.281 1.107 1.135 -1.079 1.099 0.000 2.133 -0.511 -1.688 3.102 1.060 3.405 0.989 1.344 0.912 3.093 2.527 +1 0.595 0.316 1.569 1.497 0.466 0.542 -1.361 -0.291 2.173 0.800 0.816 0.997 0.000 0.868 -0.255 -1.253 2.548 0.903 1.006 -0.939 0.000 1.109 0.954 1.096 1.133 0.816 0.975 0.885 +0 0.951 -0.856 0.223 1.308 1.218 2.035 -0.539 -0.913 2.173 2.561 -0.533 0.793 2.215 0.730 -0.796 -0.526 0.000 0.696 0.364 -1.013 0.000 0.890 0.967 1.207 0.911 3.357 1.653 1.268 +0 6.695 -1.608 -0.184 0.835 -1.599 3.701 -1.419 1.443 2.173 1.586 -1.140 1.131 2.215 3.735 0.336 -1.072 0.000 1.133 -1.375 0.203 0.000 3.048 3.870 3.133 4.193 1.096 4.272 4.316 +1 0.824 -0.100 0.108 1.057 0.922 1.720 0.565 0.786 0.000 4.604 -0.181 -0.861 2.215 1.645 0.812 1.254 0.000 2.053 -0.485 0.424 3.102 0.988 1.551 0.982 1.982 2.603 1.947 1.556 +1 0.883 -0.700 -1.093 0.509 1.376 0.785 -1.312 -1.480 0.000 1.399 -1.234 0.394 2.215 0.826 -1.797 0.987 0.000 2.217 -0.949 -0.384 3.102 0.719 1.090 0.988 1.165 1.031 0.953 0.892 +1 0.994 0.404 1.591 0.916 0.679 0.825 1.875 -1.461 0.000 0.560 -0.332 0.850 2.215 1.631 -1.171 -0.153 2.548 1.579 -0.208 -0.388 0.000 2.668 1.813 0.986 1.460 0.941 1.740 1.382 +1 0.787 -0.358 -0.844 0.666 -1.729 1.051 -0.234 -1.547 2.173 1.301 -0.010 0.269 0.000 0.624 -1.774 -0.570 0.000 1.051 -0.292 -0.358 0.000 0.856 1.093 0.987 0.731 1.117 1.038 0.901 +1 0.572 -0.671 0.740 0.721 1.534 0.783 0.553 0.035 2.173 0.648 -0.117 -1.529 1.107 0.439 -1.403 -0.625 0.000 0.484 0.319 -1.401 0.000 0.656 0.894 0.988 0.862 1.096 1.053 0.828 +1 1.323 -0.271 0.767 0.836 -0.650 0.733 0.414 0.672 0.000 0.863 -0.900 -0.784 0.000 0.840 0.087 -1.253 2.548 0.748 -0.709 1.324 1.551 0.700 0.785 1.394 0.758 0.534 0.615 0.599 +1 1.004 0.891 1.219 1.196 -0.309 0.412 1.035 -0.145 0.000 1.127 0.184 1.252 2.215 1.440 0.336 -0.675 0.000 0.686 -0.465 0.313 3.102 0.796 0.735 1.489 1.086 0.667 0.793 0.789 +1 2.658 -1.671 -0.341 0.201 -0.145 1.061 -1.303 1.148 1.087 0.470 -1.076 0.451 0.000 0.859 -1.230 -1.446 2.548 0.618 -1.160 1.513 0.000 0.582 0.581 0.996 1.453 0.855 1.014 0.792 +1 0.525 0.670 -0.169 0.988 0.545 1.234 0.411 -0.375 0.000 0.681 -0.416 0.038 0.000 2.111 -0.879 1.556 2.548 1.295 0.084 -1.164 3.102 0.895 1.032 0.984 0.814 1.070 0.876 0.752 +0 0.850 -0.117 -1.529 1.068 -0.572 0.338 1.628 -1.643 0.000 0.719 1.360 -0.590 2.215 0.906 0.273 1.003 2.548 0.925 -1.042 0.450 0.000 1.107 0.772 1.003 0.882 0.984 0.787 0.758 +1 0.662 2.000 -0.216 1.771 -0.932 1.471 1.444 0.966 1.087 0.669 0.786 -0.664 2.215 0.598 1.548 -0.021 0.000 0.984 1.234 1.653 0.000 0.849 0.928 0.992 0.960 1.530 1.267 0.982 +0 0.453 -1.992 -1.022 1.046 0.951 0.992 -1.053 0.083 2.173 1.115 0.164 -1.565 2.215 0.514 -0.214 -0.554 0.000 0.700 -1.374 1.463 0.000 0.818 0.874 0.986 1.167 1.845 1.400 1.067 +1 1.530 -0.776 1.711 0.090 0.952 1.037 -0.468 -0.173 2.173 0.742 0.865 0.725 2.215 0.929 1.422 -1.279 0.000 0.427 -1.051 1.042 0.000 1.469 1.072 0.980 1.163 1.332 1.050 0.983 +1 1.064 -2.158 -0.937 0.100 1.302 0.553 -2.114 0.481 0.000 1.692 -0.962 -1.236 1.107 1.688 -1.003 0.702 0.000 0.412 -0.374 0.257 3.102 0.998 0.639 0.979 0.830 0.763 1.018 0.878 +1 1.623 0.496 -0.838 0.500 -0.332 0.987 -0.189 0.780 0.000 1.350 0.097 -1.491 2.215 0.791 -0.852 0.303 0.000 0.577 -0.653 -0.383 3.102 0.898 0.710 0.991 1.022 0.763 0.882 0.973 +0 1.831 1.342 1.419 1.523 0.906 1.197 -0.273 -0.334 0.000 0.621 0.689 -0.005 0.000 0.306 0.251 -0.605 0.000 1.026 0.866 -1.159 3.102 0.858 0.758 1.031 1.384 0.645 0.956 0.864 +1 1.025 -0.581 -1.061 0.166 0.973 0.883 -0.590 0.235 2.173 1.278 -0.376 1.411 0.000 1.169 0.009 -0.462 1.274 0.537 -0.896 1.051 0.000 0.512 1.064 0.983 0.805 0.840 0.858 0.797 +0 0.460 -1.792 -0.436 0.542 0.701 1.044 -0.278 -0.669 2.173 0.531 -1.229 -1.169 0.000 0.929 0.849 1.513 0.000 1.518 0.073 0.343 1.551 0.929 1.395 0.979 0.868 1.082 1.128 0.912 +0 0.621 -0.508 -1.679 2.017 0.693 2.477 -0.998 1.368 0.000 2.410 0.297 -0.309 0.000 2.525 1.967 -0.854 0.000 1.123 0.833 -0.365 3.102 1.177 1.903 1.307 1.147 0.444 1.511 1.235 +0 0.712 1.145 -1.266 2.816 1.635 0.984 0.443 -0.169 2.173 0.961 1.071 0.072 2.215 0.467 -0.459 -0.455 0.000 0.955 1.754 1.023 0.000 1.447 1.032 0.987 1.618 0.568 1.205 1.044 +0 0.884 1.111 -0.624 0.325 0.382 0.467 -0.472 1.127 2.173 0.706 -0.099 -0.887 2.215 0.893 -0.957 0.642 0.000 0.639 0.588 1.366 0.000 0.973 0.911 0.986 1.203 0.835 0.925 0.913 +0 1.354 -0.864 0.207 0.362 0.970 0.453 -0.688 -0.610 2.173 1.137 -2.424 -1.248 0.000 0.699 -0.887 1.193 0.000 1.103 0.245 1.496 3.102 1.590 1.264 0.990 0.964 0.807 1.076 0.963 +0 0.448 0.593 -1.605 1.847 -1.423 0.982 0.619 -0.073 2.173 0.428 0.176 0.785 0.000 0.939 0.513 -0.769 2.548 0.494 -1.713 1.194 0.000 0.847 0.961 0.985 1.492 0.704 1.007 0.919 +1 0.506 -0.144 0.562 1.011 -1.057 0.740 -1.048 0.785 0.000 0.569 -0.551 1.457 0.000 1.198 -0.518 -0.044 2.548 1.169 0.767 -1.411 3.102 0.855 0.946 0.987 0.708 1.127 0.899 0.774 +1 0.910 -0.679 0.815 1.748 -0.054 0.680 -0.066 -0.845 0.000 1.251 0.107 1.668 0.000 0.818 -1.345 -1.439 2.548 0.554 0.691 0.183 0.000 0.946 0.939 1.231 0.820 0.224 0.700 0.762 +1 2.991 -0.038 1.261 0.502 0.192 2.866 1.837 -0.359 0.000 1.506 -0.869 -1.554 2.215 0.964 -0.683 1.306 1.274 0.517 -1.369 -1.277 0.000 5.363 3.906 1.392 1.355 0.690 3.164 2.575 +1 0.908 -1.732 -1.690 0.103 0.007 1.106 -0.572 -1.402 2.173 1.804 -1.367 -1.315 2.215 0.874 -0.495 -0.456 0.000 3.539 0.667 0.412 0.000 0.834 1.554 0.988 0.787 0.902 1.206 0.952 +1 1.563 -1.103 0.259 1.207 0.722 1.666 0.205 -1.059 0.000 0.630 0.108 1.219 0.000 1.388 -0.443 -1.381 1.274 1.429 -0.262 0.363 0.000 0.901 1.034 0.985 0.577 0.686 0.786 0.713 +1 0.901 0.728 0.150 0.262 -1.654 0.953 0.263 1.110 0.000 1.582 0.709 -0.907 2.215 0.736 0.930 1.434 1.274 0.846 -0.110 -0.037 0.000 1.211 0.843 0.981 0.954 0.998 0.955 0.845 +1 1.035 -0.281 -1.450 0.468 1.676 0.851 -0.759 -0.963 2.173 0.779 -1.508 -0.406 0.000 0.913 -0.275 1.168 2.548 1.878 1.075 0.320 0.000 1.086 0.999 0.979 0.734 1.063 0.832 0.750 +0 1.211 1.032 -0.717 0.630 1.017 1.913 1.043 -0.157 0.000 1.888 2.323 -1.738 0.000 1.369 -0.538 1.578 1.274 2.015 0.464 0.564 3.102 0.625 1.782 1.210 1.206 1.265 1.753 1.377 +1 0.778 0.192 1.361 0.628 0.559 0.852 -0.649 -0.050 0.000 0.705 1.031 1.642 2.215 1.160 0.101 -0.991 2.548 0.533 -0.464 1.009 0.000 0.841 0.951 0.996 0.908 0.819 0.870 0.792 +1 0.999 1.000 1.506 1.084 -1.032 0.700 0.185 0.554 0.000 0.630 0.848 1.209 1.107 1.588 1.736 -1.388 0.000 2.356 1.041 -0.244 3.102 1.035 1.064 1.089 0.707 1.083 0.786 0.804 +0 0.511 0.703 -0.614 1.148 -0.535 1.136 -0.598 1.424 2.173 0.686 -1.403 1.201 0.000 1.283 -1.543 -1.691 0.000 2.524 0.359 -0.282 0.000 0.755 1.015 0.997 1.249 1.124 0.945 0.938 +1 0.980 -0.305 -1.177 0.647 0.212 0.980 -0.843 -0.499 0.000 1.687 -1.095 0.978 2.215 0.742 -0.591 -1.494 2.548 0.554 0.056 0.219 0.000 0.864 0.812 1.047 1.135 0.984 0.949 0.828 +1 1.148 1.428 0.726 0.795 1.648 0.851 0.334 -1.157 2.173 0.547 0.554 -0.025 0.000 0.722 -0.322 -0.239 2.548 0.917 0.712 1.111 0.000 0.799 0.946 0.988 0.988 0.798 0.883 0.748 +1 0.923 -1.337 -1.720 0.720 -0.078 1.008 0.634 0.693 1.087 0.863 0.411 -0.822 0.000 1.064 -0.003 -1.420 0.000 1.178 2.076 0.353 0.000 0.817 1.375 1.125 0.999 0.980 1.082 0.992 +0 0.683 0.934 -0.071 2.487 0.408 0.807 0.622 -1.480 0.000 1.067 0.155 1.512 1.107 1.751 0.311 -0.749 2.548 0.701 -1.002 0.937 0.000 1.530 1.009 0.988 1.488 1.304 1.308 1.259 +0 1.283 -0.676 -1.231 0.516 -0.752 0.846 0.582 0.433 2.173 0.933 2.126 -1.547 0.000 1.013 -0.693 0.017 2.548 0.520 -1.510 1.008 0.000 3.365 2.190 0.981 0.825 0.940 1.527 1.548 +1 0.617 0.118 1.076 0.933 -1.679 0.976 -0.352 -0.392 2.173 1.066 -0.317 0.225 0.000 1.673 0.419 1.544 0.000 0.704 0.847 1.542 0.000 1.173 0.964 0.986 0.711 0.683 0.757 0.788 +0 0.438 -0.780 -0.328 0.136 1.133 0.935 -0.005 -0.238 1.087 1.769 0.838 1.466 1.107 0.974 0.638 -0.831 0.000 1.110 0.764 0.444 0.000 1.054 0.928 0.977 0.978 2.072 1.101 0.884 +1 0.636 -1.047 -0.807 0.804 0.298 0.901 -0.050 1.145 1.087 1.102 -0.549 -1.613 2.215 1.008 -0.439 -0.172 0.000 0.452 0.129 -0.445 0.000 0.301 0.939 0.986 1.029 0.972 0.980 0.840 +0 1.406 -1.022 1.699 0.183 0.979 0.779 -0.181 0.632 0.000 0.615 -1.101 -0.518 2.215 0.753 -2.681 -1.142 0.000 0.724 0.358 -0.078 3.102 2.915 1.698 0.976 0.767 0.570 1.117 0.981 +1 1.830 0.146 -0.872 0.716 -0.258 0.852 -0.428 0.871 2.173 0.556 0.857 1.557 2.215 0.587 -0.013 -0.004 0.000 0.512 1.550 -0.471 0.000 0.699 1.012 0.986 0.918 0.924 0.928 0.796 +1 1.591 0.464 0.523 1.143 0.779 2.247 0.279 0.974 2.173 4.049 0.231 -1.017 0.000 1.372 0.940 0.122 0.000 1.583 2.290 -0.885 0.000 0.992 1.274 0.991 1.090 0.896 1.949 1.570 +1 1.225 -0.285 0.866 0.852 0.577 1.015 0.207 -1.630 2.173 1.175 -0.251 -0.357 0.000 1.070 -0.493 -1.017 2.548 0.931 0.577 0.519 0.000 1.178 0.984 0.978 1.305 0.845 0.975 0.971 +0 0.842 -0.397 -0.624 0.829 -0.928 0.505 0.427 0.250 0.000 0.728 0.135 1.599 2.215 1.260 -0.809 1.189 1.274 0.770 -0.791 0.626 0.000 0.779 0.856 0.996 1.010 0.658 0.819 0.801 +1 0.898 0.536 -1.164 0.828 -0.418 0.798 0.440 0.307 2.173 0.875 0.059 1.506 0.000 0.449 -0.851 1.557 2.548 0.408 -0.289 0.768 0.000 0.506 0.855 0.993 0.884 0.873 0.790 0.724 +1 1.520 0.393 -0.208 1.042 -0.858 0.906 1.480 -0.707 0.000 1.341 0.592 1.221 2.215 1.192 -0.458 1.274 1.274 1.158 1.988 0.810 0.000 1.694 1.717 0.987 1.184 0.789 1.401 1.249 +1 0.593 0.325 0.843 0.534 1.163 0.794 -0.817 1.728 0.000 1.289 -0.935 -0.523 2.215 0.932 0.607 -0.497 2.548 0.372 -0.540 0.744 0.000 0.648 1.039 0.992 1.018 1.058 0.877 0.808 +1 0.891 -1.507 1.261 0.780 -0.591 0.692 -0.493 -0.114 2.173 1.154 -0.311 0.692 2.215 1.054 -0.440 -1.535 0.000 1.255 -1.370 -1.152 0.000 0.890 1.093 1.149 1.046 0.881 0.929 0.851 +1 0.335 2.040 -0.850 2.260 -0.386 0.506 0.050 -1.673 2.173 1.142 0.496 0.673 2.215 0.779 1.843 1.205 0.000 0.588 1.106 1.737 0.000 0.435 0.851 1.000 1.057 0.991 0.951 0.861 +0 1.889 -0.138 1.150 1.012 1.711 0.836 -0.163 -0.226 1.087 0.719 -0.914 -0.871 0.000 0.571 -1.758 0.111 0.000 0.526 0.487 -0.789 3.102 0.914 0.948 0.990 0.744 0.434 0.838 0.851 +0 0.642 0.476 1.149 0.954 -0.913 1.620 0.137 -1.562 2.173 1.707 -0.328 0.262 0.000 0.582 -0.829 0.117 0.000 0.864 -0.783 -1.143 3.102 0.787 0.976 1.040 0.910 0.847 1.282 1.061 +0 4.682 1.002 -1.371 1.893 -1.244 1.743 0.674 0.110 0.000 2.114 1.093 0.599 0.000 0.760 0.134 -1.208 2.548 1.396 0.367 1.136 3.102 1.963 1.484 0.995 0.826 0.684 1.136 1.698 +1 1.579 -0.703 -1.104 0.579 -1.000 1.557 0.677 1.071 2.173 0.983 1.087 0.002 1.107 0.531 0.215 -0.254 0.000 0.820 -0.410 -0.843 0.000 0.457 0.736 0.989 1.712 1.548 1.351 1.045 +1 3.715 0.702 1.626 0.515 0.860 1.897 2.346 -0.378 0.000 0.675 0.865 0.915 0.000 0.849 -0.254 0.684 2.548 0.798 0.839 -0.647 3.102 0.989 0.872 1.219 1.077 0.729 1.295 1.531 +0 1.767 0.515 -0.613 1.365 -0.317 3.272 -0.745 0.899 0.000 1.620 0.341 -1.032 1.107 1.635 -2.659 -0.919 0.000 0.664 0.044 1.676 3.102 6.848 3.664 0.982 0.910 0.620 2.934 2.543 +1 0.629 -1.419 1.419 0.595 -1.345 1.223 -2.453 0.211 0.000 0.682 -1.972 -1.519 0.000 1.253 -0.517 -1.510 2.548 1.270 -0.324 0.461 3.102 0.803 1.294 0.989 0.585 0.948 1.186 1.066 +1 0.872 1.353 0.808 0.844 0.543 0.334 -1.017 0.308 0.000 1.209 0.229 -0.934 2.215 0.952 -0.150 1.731 2.548 0.454 -1.473 -0.532 0.000 0.467 0.968 0.988 1.422 0.802 1.281 1.308 +0 0.825 0.030 -1.071 0.533 -0.432 0.662 0.209 1.556 0.000 1.127 0.646 0.696 2.215 0.550 0.019 -0.291 0.000 0.914 0.941 -0.698 3.102 1.084 0.981 0.994 0.517 0.898 0.712 0.687 +1 0.510 1.140 -1.013 0.928 -0.318 0.588 -0.153 -1.261 0.000 0.815 -0.626 0.611 2.215 0.581 -0.467 -0.128 0.000 1.024 -1.231 1.473 1.551 0.916 0.917 0.978 2.151 0.677 1.625 1.317 +1 0.628 -0.155 -0.774 1.385 0.836 0.803 -1.028 -0.234 2.173 1.048 0.441 1.389 0.000 1.431 -0.196 -0.002 0.000 1.266 -0.988 -1.650 3.102 0.916 1.109 1.282 1.038 1.024 1.043 0.905 +1 0.709 0.275 1.536 0.093 -0.809 0.706 0.724 -1.149 1.087 0.765 1.682 0.177 0.000 0.848 1.039 0.991 0.000 0.940 -0.107 -0.504 3.102 0.906 0.975 0.935 0.862 0.609 0.796 0.832 +1 0.495 0.914 1.361 1.013 0.052 1.353 -0.039 0.188 2.173 1.157 -0.651 1.515 0.000 1.108 -0.748 -1.236 0.000 1.671 2.292 -1.266 0.000 0.822 1.436 0.990 0.863 0.968 1.126 0.906 +1 3.884 -0.229 1.101 0.210 0.569 1.021 1.231 -0.752 0.000 0.546 -0.552 -0.278 2.215 2.028 0.574 -0.620 0.000 0.760 -0.235 -0.925 0.000 0.862 1.109 0.992 0.484 0.625 0.952 1.319 +0 0.943 1.422 -1.220 0.743 1.134 0.611 0.457 0.878 1.087 0.900 0.757 -1.008 2.215 0.671 2.603 0.379 0.000 0.743 0.842 0.439 0.000 0.828 0.998 0.988 0.744 1.096 0.873 0.768 +0 1.642 -1.163 -0.703 0.295 -1.548 1.217 0.259 1.111 2.173 0.792 -0.088 -0.547 2.215 0.636 -0.102 1.578 0.000 0.845 0.189 0.085 0.000 0.800 0.799 0.986 0.672 1.463 1.086 0.862 +0 1.078 -0.581 1.257 1.322 -1.424 0.632 -0.451 0.600 0.000 1.354 0.112 -0.376 0.000 0.655 0.497 -0.134 2.548 1.195 -0.256 1.728 3.102 1.617 0.936 1.098 0.558 0.735 0.768 0.837 +1 1.392 0.525 -0.445 1.232 -0.989 2.957 -0.287 1.538 0.000 1.594 0.292 0.427 2.215 0.385 -1.839 -0.156 0.000 0.959 1.219 -0.313 3.102 0.297 1.150 0.985 0.729 0.973 0.967 0.956 +0 0.469 0.137 -1.226 0.718 -0.009 0.867 0.936 1.357 1.087 1.321 -0.164 -0.137 2.215 1.149 1.601 -1.497 0.000 0.930 0.879 0.636 0.000 0.967 0.912 0.989 0.759 1.792 1.113 1.088 +1 0.504 -0.674 -0.015 0.663 -0.738 1.001 -0.318 0.660 2.173 1.266 0.614 -1.485 1.107 0.690 1.435 -0.189 0.000 0.483 1.151 -1.502 0.000 0.729 1.067 0.983 1.733 1.752 1.378 1.228 +0 2.450 1.863 0.223 0.080 1.329 1.098 0.937 1.419 0.000 0.736 0.754 -1.513 0.000 0.920 0.829 -0.340 0.000 0.981 -0.897 -1.542 1.551 0.925 1.174 0.994 1.813 0.806 1.244 1.193 +0 0.281 -1.028 1.735 0.685 -1.409 0.500 0.246 -0.617 1.087 0.595 0.856 1.036 0.000 0.645 -0.639 0.187 2.548 0.372 -1.289 1.374 0.000 0.920 0.769 0.993 0.800 0.585 0.604 0.579 +0 0.872 -1.795 1.334 1.056 0.136 0.577 0.578 -0.804 0.000 0.737 1.506 1.057 2.215 0.523 -0.628 -0.618 2.548 0.756 0.851 0.443 0.000 0.936 0.924 1.172 0.766 1.118 1.424 1.248 +1 0.974 -1.169 1.065 0.678 -0.116 1.190 -0.241 0.913 0.000 1.131 -0.156 -1.139 2.215 2.624 -1.122 -0.793 2.548 1.437 -1.642 0.677 0.000 1.995 1.909 0.987 1.096 1.172 1.563 1.227 +1 0.899 1.367 0.973 0.815 0.054 1.133 -1.063 -1.019 0.000 0.734 1.501 -1.093 2.215 1.236 1.101 0.453 1.274 1.649 0.271 -1.709 0.000 1.210 1.030 0.986 0.850 1.011 0.832 0.734 +1 0.865 0.067 -0.502 1.459 0.067 0.946 1.222 1.339 2.173 0.606 0.021 0.617 2.215 0.451 1.096 0.805 0.000 0.483 2.167 -0.651 0.000 0.632 0.769 0.985 1.293 0.986 0.904 0.769 +1 0.781 -0.174 -0.170 0.435 0.657 0.845 0.380 -1.723 0.000 0.605 1.355 0.397 2.215 1.549 1.883 -0.775 0.000 1.388 -0.598 0.959 1.551 0.680 0.929 0.983 0.626 1.121 0.698 0.632 +0 2.163 -0.110 1.211 0.310 -0.041 0.699 1.147 -1.402 0.000 0.941 1.974 -0.467 0.000 1.139 -0.769 0.260 2.548 1.227 -1.056 -1.649 3.102 1.035 1.752 1.025 0.851 0.914 1.413 1.226 +1 0.347 -0.411 1.356 0.739 0.051 1.719 -0.337 -1.466 0.000 0.827 0.456 0.147 2.215 1.159 -1.175 0.577 0.000 1.707 -0.648 -0.007 0.000 0.878 1.029 0.979 1.105 0.792 0.936 0.866 +1 2.388 -0.558 -0.717 0.983 0.208 1.005 -1.236 0.890 0.000 1.158 -0.992 1.733 2.215 0.457 -1.124 -1.513 0.000 0.525 -1.346 0.465 3.102 1.009 0.931 1.573 0.890 0.675 0.904 0.902 +1 0.375 0.611 -1.260 0.401 1.111 1.119 0.185 -0.568 2.173 0.777 0.274 1.654 0.000 0.911 0.171 0.096 2.548 1.306 0.270 0.672 0.000 0.821 0.717 0.984 1.016 0.708 0.799 0.747 +0 2.982 -1.084 -0.989 1.200 -1.739 1.283 -0.458 0.709 0.000 1.061 -0.195 -1.234 2.215 1.526 -0.950 0.533 2.548 1.466 0.193 0.316 0.000 1.038 0.867 1.637 1.053 1.474 1.155 1.313 +0 1.025 -0.811 0.303 1.781 0.763 1.096 -0.753 1.031 2.173 1.218 -1.031 -1.112 1.107 1.785 -0.146 -1.256 0.000 2.085 -0.711 -0.510 0.000 1.524 1.060 0.986 0.833 1.611 1.189 1.199 +0 0.626 -1.363 -0.986 1.620 1.542 0.341 -1.759 -0.783 0.000 0.896 -0.626 0.356 2.215 0.704 0.045 0.364 2.548 0.473 0.139 -1.436 0.000 0.773 0.837 1.061 0.981 0.298 0.802 0.705 +1 0.456 -2.149 0.139 1.536 1.017 0.873 0.276 -0.745 2.173 0.445 -0.660 0.470 0.000 0.873 -0.358 1.733 2.548 0.478 -1.888 -1.596 0.000 0.782 1.148 0.988 0.842 0.935 1.072 0.868 +1 1.219 -1.349 -0.283 1.135 0.238 0.963 0.477 1.562 2.173 0.589 -0.713 -1.057 0.000 0.447 0.106 0.617 1.274 0.487 -2.252 -1.420 0.000 0.835 0.796 0.988 1.617 0.633 1.012 0.957 +0 0.593 -0.770 1.033 1.078 -0.210 1.794 0.159 -1.256 2.173 0.940 0.142 0.571 2.215 1.094 -1.372 1.077 0.000 0.779 1.546 -0.418 0.000 0.819 1.467 0.996 1.421 1.905 1.753 1.355 +1 0.845 -0.685 -1.325 0.722 0.995 0.737 -0.516 -0.030 2.173 0.608 1.254 1.252 0.000 0.455 1.310 -0.306 0.000 0.720 -0.890 1.730 3.102 0.798 0.949 0.987 0.861 0.800 0.844 0.765 +1 0.595 -0.962 -0.632 0.412 -1.535 0.644 -0.072 1.670 2.173 0.584 -0.069 0.512 2.215 0.806 -2.006 1.172 0.000 0.749 -0.895 -0.940 0.000 0.957 0.973 0.989 0.698 0.781 0.790 0.745 +1 0.383 -0.470 0.104 1.169 -0.941 1.033 2.355 1.454 0.000 1.428 1.051 -0.056 2.215 1.389 2.502 1.068 0.000 3.052 0.957 -1.709 3.102 0.991 1.499 0.983 1.031 1.879 1.312 1.188 +0 0.469 1.239 -0.446 0.975 0.558 0.681 -0.235 -1.068 0.000 0.602 -0.766 0.514 0.000 0.974 0.502 -0.429 1.274 1.649 1.403 1.438 3.102 0.741 0.781 0.992 0.975 1.123 0.899 0.809 +0 1.584 0.929 1.170 0.716 0.249 1.641 -0.316 -1.166 0.000 2.496 0.970 0.580 2.215 1.352 -0.544 -0.598 2.548 0.987 -0.984 -1.151 0.000 0.865 0.863 1.088 0.863 2.429 1.928 1.608 +0 0.694 -2.236 -0.003 1.110 0.550 0.708 -1.367 -1.344 0.000 0.848 -1.149 1.132 2.215 0.796 -0.320 -1.683 0.000 1.407 -0.107 -0.305 3.102 0.830 1.032 0.996 0.759 1.094 0.803 0.832 +1 1.580 -0.382 0.353 1.484 -0.194 0.999 -2.563 -1.574 0.000 1.224 0.399 1.371 2.215 1.079 -0.741 -0.192 0.000 0.480 0.352 -1.165 0.000 0.803 1.150 1.002 0.844 0.878 0.964 0.818 +0 1.294 0.637 1.016 1.545 1.425 1.018 0.374 -0.741 0.000 1.230 -0.296 0.546 1.107 0.927 -1.527 -0.641 0.000 1.038 0.956 -0.583 1.551 1.493 0.897 0.991 1.315 1.185 1.023 1.063 +1 0.304 -1.851 -1.119 1.287 -0.544 0.916 -0.426 -0.858 2.173 1.069 -0.867 1.010 2.215 1.159 0.318 1.135 0.000 0.732 -2.125 0.964 0.000 1.083 0.825 0.988 0.612 1.486 0.925 0.848 +0 0.762 -1.328 1.639 1.575 -1.320 1.004 2.830 1.582 0.000 2.089 0.464 0.142 0.000 1.067 0.230 0.697 0.000 1.491 0.105 -0.199 3.102 1.128 0.837 0.983 1.395 0.621 1.201 1.566 +0 0.406 -0.819 0.591 0.865 -0.401 0.855 0.977 1.244 2.173 0.897 -0.615 -1.517 0.000 0.934 -0.845 -0.134 2.548 0.556 -0.432 -1.077 0.000 0.952 0.950 0.987 0.984 1.625 0.901 0.770 +0 1.867 0.678 0.605 0.232 -1.037 0.826 -1.507 -0.800 2.173 0.665 -0.752 0.617 2.215 0.548 -2.062 -1.026 0.000 1.281 -1.854 1.454 0.000 0.728 0.918 0.991 1.700 1.125 1.142 1.190 +0 0.808 1.560 -1.694 0.832 0.318 0.974 0.835 0.083 2.173 1.204 -1.009 -1.391 2.215 0.383 -0.269 0.163 0.000 0.784 -0.470 0.998 0.000 0.421 0.789 1.103 1.881 2.321 1.477 1.113 +1 0.434 -0.154 1.046 1.016 -0.089 1.199 -1.760 0.690 0.000 1.408 -0.136 -1.345 2.215 1.617 -0.315 -0.598 0.000 0.882 -0.465 1.410 3.102 3.012 1.737 0.982 1.099 0.651 1.408 1.313 +0 0.338 -1.998 1.649 1.187 -0.265 0.795 -0.499 0.825 1.087 0.598 2.274 -0.931 0.000 0.601 1.950 1.233 0.000 1.054 0.518 -1.298 3.102 0.859 0.822 0.992 0.919 1.078 1.180 1.354 +0 0.604 0.235 -0.684 0.856 0.393 1.291 -0.864 -1.145 0.000 0.767 -1.966 1.022 0.000 1.269 -1.229 1.426 2.548 2.153 -0.633 0.144 1.551 2.343 1.481 0.985 0.646 1.216 1.180 1.013 +0 1.376 0.523 -0.122 1.959 0.300 0.878 0.803 0.706 2.173 1.125 1.185 1.703 0.000 2.663 0.940 -1.083 2.548 1.109 0.450 -1.530 0.000 0.603 0.933 0.984 0.911 1.915 1.324 1.203 +0 1.517 -0.242 -0.451 0.531 -0.391 0.596 2.512 1.646 0.000 0.864 1.331 1.111 2.215 0.799 -0.557 0.604 2.548 0.513 2.476 -0.517 0.000 0.806 0.897 0.987 0.764 1.097 1.158 1.530 +1 1.235 -0.130 0.821 0.571 -0.243 0.595 0.553 -0.451 2.173 0.758 1.116 -1.449 2.215 0.510 -1.303 0.290 0.000 0.370 -0.554 1.385 0.000 0.554 1.067 0.986 0.788 0.829 0.779 0.727 +1 0.789 -0.956 -0.995 1.743 -1.171 0.403 -2.172 -1.180 0.000 1.297 -0.565 0.171 2.215 0.600 -1.362 -1.679 0.000 0.481 -1.221 0.225 1.551 0.837 0.861 0.991 0.678 0.327 0.936 0.790 +1 0.519 0.668 -1.175 0.505 0.513 0.857 -1.439 -1.561 0.000 0.703 -1.062 -0.863 0.000 1.390 -0.177 0.166 1.274 1.352 -1.351 0.605 3.102 1.016 1.092 0.986 1.071 0.908 1.205 1.351 +1 1.141 -0.488 -0.226 0.652 0.837 0.890 0.157 -1.478 1.087 0.538 0.036 0.081 0.000 0.822 0.988 1.287 2.548 0.514 -0.903 -0.261 0.000 0.455 0.930 0.989 0.967 0.822 0.859 0.728 +0 0.537 -1.408 1.207 0.168 -1.435 0.300 -1.745 0.919 0.000 0.498 -1.628 -1.184 0.000 1.150 -0.370 -0.404 2.548 1.118 0.170 0.252 3.102 0.779 0.961 0.978 0.754 0.553 0.703 0.654 +1 0.706 0.814 1.364 1.370 -1.038 0.777 -0.918 0.549 1.087 0.586 -0.715 -0.792 2.215 0.503 0.403 0.216 0.000 0.371 0.831 -1.642 0.000 0.492 0.764 1.129 0.920 0.934 1.028 0.784 +1 1.005 -0.048 -0.263 0.331 -1.726 0.897 0.251 1.337 2.173 1.272 1.652 -1.610 0.000 0.942 -1.063 -0.038 0.000 1.559 0.081 0.530 3.102 0.398 1.209 0.984 0.726 0.839 1.009 1.033 +0 1.084 1.800 -1.726 0.459 0.283 0.732 0.034 1.222 0.000 0.809 -0.678 -1.030 2.215 0.891 1.611 -0.152 0.000 0.980 0.335 0.135 3.102 0.925 0.935 0.987 0.854 0.838 1.056 0.924 +1 0.692 -1.421 0.463 0.484 0.768 1.721 -0.427 -0.832 0.000 0.583 -0.807 0.690 0.000 0.928 -0.588 -1.481 2.548 1.799 -0.130 0.271 0.000 0.672 0.940 0.973 0.649 0.686 0.647 0.593 +1 0.636 0.312 0.525 0.565 -0.245 0.697 -0.009 0.194 0.000 1.349 0.087 -1.292 2.215 0.643 0.910 0.735 0.000 1.417 -0.561 -1.446 0.000 0.848 1.301 0.989 0.559 0.697 0.784 0.731 +1 0.278 0.267 0.024 1.320 1.467 1.074 1.551 -1.343 2.173 1.138 1.901 0.454 0.000 0.832 0.741 -0.571 2.548 0.912 1.258 -0.312 0.000 0.896 0.873 0.990 1.832 0.881 1.228 1.291 +1 0.467 1.329 -1.705 1.220 -0.992 1.168 0.129 0.759 2.173 0.385 0.333 0.021 0.000 0.481 -0.411 -0.244 2.548 0.656 0.771 -1.012 0.000 0.559 0.872 0.994 0.640 0.784 0.818 0.667 +0 0.497 1.030 -1.296 0.338 0.335 0.979 0.759 -0.699 2.173 2.599 1.084 0.898 2.215 1.386 -0.227 -1.024 0.000 1.240 1.002 0.341 0.000 1.303 1.032 0.987 1.303 2.362 1.409 1.123 +1 0.958 -0.207 0.378 0.449 -1.323 1.083 -1.150 -1.619 2.173 1.135 -0.504 -0.183 0.000 0.662 -1.490 -0.166 0.000 0.935 -1.133 0.914 3.102 0.783 0.808 0.989 0.984 0.813 0.909 0.783 +1 0.765 -0.378 0.170 0.382 1.690 1.818 -2.245 -1.552 0.000 2.478 0.860 -0.246 0.000 0.751 1.718 1.483 0.000 1.124 0.896 0.856 0.000 0.688 0.862 0.986 0.589 0.634 0.592 0.585 +1 0.379 -1.546 0.285 0.872 -0.953 2.658 -0.959 1.091 0.000 1.897 -1.108 -0.805 1.107 0.982 -0.212 -0.635 2.548 1.102 -1.497 -0.778 0.000 0.971 1.614 0.983 0.830 0.716 1.557 1.223 +0 0.807 1.114 0.425 0.833 1.034 1.317 1.457 0.249 0.000 0.672 1.472 -0.647 2.215 1.076 0.758 -1.383 1.274 0.802 -2.008 -1.734 0.000 1.193 1.272 0.986 0.923 0.639 1.069 0.949 +0 0.332 -0.251 0.874 1.217 -1.324 0.594 0.733 0.907 2.173 1.437 -1.019 -0.086 2.215 0.908 2.118 -1.704 0.000 0.476 0.492 -1.355 0.000 0.747 0.888 0.986 1.478 1.745 1.555 1.232 +0 0.721 -0.275 -0.484 0.410 1.493 1.018 -1.039 -0.154 2.173 1.153 0.516 0.952 0.000 0.922 -0.229 1.369 0.000 1.624 0.309 -1.486 3.102 0.861 0.921 0.989 1.074 1.646 1.178 0.963 +1 1.291 0.183 0.608 0.121 -1.695 1.066 -1.234 -0.185 0.000 0.926 0.000 -1.639 2.215 1.431 -0.633 1.733 2.548 0.969 0.047 0.897 0.000 1.137 1.009 0.983 1.062 0.452 0.839 0.796 +0 3.018 -0.100 1.015 0.451 -1.266 1.001 0.624 -0.648 2.173 0.357 0.773 1.470 0.000 0.333 -0.129 -0.763 1.274 1.181 1.560 -0.636 0.000 0.933 0.917 1.431 0.833 0.294 1.007 0.960 +1 0.814 -0.217 0.838 1.581 -1.685 1.207 -0.413 -0.729 2.173 1.628 1.497 0.938 0.000 0.873 -0.464 0.169 0.000 1.190 0.733 -0.666 3.102 0.859 1.062 1.201 1.266 0.884 1.377 1.235 +1 0.866 -0.979 0.565 1.015 -0.546 0.961 -0.560 0.154 2.173 1.339 0.312 -1.714 0.000 0.466 2.047 0.735 0.000 0.704 -0.533 -1.381 3.102 1.670 1.122 1.093 0.748 0.856 1.150 1.089 +1 0.453 -1.405 -0.724 2.920 -0.214 0.803 -2.643 -1.549 0.000 0.921 -2.890 1.168 0.000 1.306 -1.162 1.193 1.274 0.812 -2.151 -1.157 0.000 1.000 0.862 0.989 1.314 0.397 0.815 1.145 +1 0.540 -0.597 -0.998 0.668 0.995 0.583 -0.926 0.781 0.000 1.035 -1.474 -1.372 2.215 1.163 -0.480 -0.672 2.548 0.656 -2.222 0.084 0.000 1.079 1.114 0.987 1.033 0.913 0.867 0.857 +1 0.600 -0.008 0.801 0.348 -1.689 0.617 0.821 1.027 1.087 1.051 0.536 0.268 1.107 2.350 1.051 -1.185 0.000 0.412 -0.024 0.529 0.000 1.280 1.184 0.983 0.758 0.767 0.947 0.789 +0 0.741 -0.091 -0.886 0.306 -1.697 0.722 -2.032 -1.354 0.000 0.718 1.508 1.463 0.000 1.299 0.321 0.506 2.548 1.705 0.511 -0.196 3.102 0.747 0.941 0.988 1.005 0.688 0.824 0.906 +0 1.162 1.105 -1.153 2.117 -0.554 0.802 1.233 1.530 0.000 1.023 1.273 1.027 0.000 1.309 0.494 0.692 2.548 1.635 0.931 0.036 3.102 0.845 0.915 1.120 0.887 0.703 0.900 0.999 +1 1.509 0.778 -1.597 0.496 -1.150 0.951 0.448 0.252 1.087 0.693 1.217 -1.079 0.000 1.042 -0.313 0.218 0.000 1.471 1.357 1.170 3.102 1.664 1.234 0.990 0.956 1.206 0.991 0.956 +1 0.346 -1.470 0.489 0.747 -0.821 0.615 -0.396 1.182 0.000 0.674 0.949 0.736 2.215 1.355 -0.874 -0.917 0.000 0.533 -0.417 1.685 3.102 0.894 0.847 0.996 0.545 0.599 0.576 0.548 +0 0.380 -1.076 -0.065 2.097 -0.635 0.994 1.306 0.439 0.000 0.849 0.927 -1.197 1.107 0.741 1.005 0.864 0.000 2.178 0.669 1.433 3.102 0.877 1.123 0.992 2.424 0.861 2.132 2.366 +0 1.541 0.524 0.392 2.444 0.115 1.003 0.840 -1.635 0.000 1.536 -0.315 -1.629 2.215 0.516 1.046 -0.529 2.548 0.927 -0.927 0.323 0.000 0.669 1.167 0.987 0.794 1.098 1.194 1.175 +1 0.714 1.725 -1.396 0.737 0.739 0.523 0.840 1.612 2.173 0.504 -0.655 -0.298 2.215 0.385 1.562 1.035 0.000 0.643 1.212 -0.217 0.000 0.501 0.784 0.988 0.753 0.978 0.906 0.708 +1 0.606 0.198 1.699 0.964 -0.334 1.242 1.002 0.408 0.000 1.265 1.240 1.669 2.215 1.014 2.128 1.702 0.000 1.116 0.714 -0.714 0.000 1.354 1.042 1.023 0.536 1.064 0.846 0.824 +0 0.647 1.381 0.573 0.873 -0.771 0.756 0.377 1.519 0.000 1.015 0.780 -1.295 2.215 0.646 -1.119 -0.255 0.000 1.323 0.052 -0.385 3.102 0.943 1.036 0.988 0.716 0.862 0.833 0.770 +1 0.640 -1.090 -1.182 1.402 -0.927 0.936 0.051 0.851 0.000 0.619 -0.430 -0.589 2.215 0.748 -1.004 0.498 0.000 1.151 -1.220 1.442 0.000 1.032 1.039 0.986 0.640 0.419 0.670 0.991 +0 1.048 -0.715 1.080 0.741 -1.520 1.127 0.978 -0.173 2.173 0.569 0.058 1.091 2.215 0.817 0.389 -1.381 0.000 0.578 -1.424 1.143 0.000 1.131 0.782 0.988 1.452 1.211 0.990 0.907 +1 0.605 -0.265 -1.416 0.756 -0.384 1.474 0.452 -0.855 0.000 1.622 1.683 1.341 0.000 1.421 1.284 0.652 2.548 0.604 -0.439 1.024 3.102 3.755 2.184 0.987 1.593 0.831 1.465 1.504 +1 1.040 0.702 -0.386 0.369 -1.635 0.572 -0.391 -1.031 2.173 0.839 0.423 0.956 2.215 0.532 0.991 -1.452 0.000 1.298 1.654 0.771 0.000 0.934 0.860 0.987 0.887 1.085 0.901 0.797 +0 1.179 0.565 -0.277 0.678 0.145 0.667 0.225 1.248 2.173 1.217 -0.610 -1.366 2.215 0.287 1.593 -0.392 0.000 0.608 -1.243 0.182 0.000 1.144 0.962 0.991 1.529 1.104 1.134 0.967 +1 0.559 -1.813 0.167 0.511 -0.327 1.033 0.776 -1.449 2.173 0.353 -0.779 1.475 0.000 0.828 -0.380 -0.002 0.000 0.848 0.125 0.853 3.102 0.821 1.198 0.988 0.669 0.924 0.925 0.787 +1 0.965 0.244 0.038 0.745 1.427 0.866 -0.539 0.046 2.173 0.681 1.077 1.484 0.000 1.053 0.008 -1.666 0.000 0.866 -0.201 1.302 3.102 0.934 1.048 1.115 0.635 0.841 0.688 0.666 +1 0.922 -2.005 0.530 0.536 -1.467 0.856 -0.699 0.932 2.173 0.868 -1.003 0.152 2.215 1.152 -1.756 -0.917 0.000 0.878 -1.068 -1.422 0.000 0.845 0.996 0.988 0.870 0.846 0.814 0.786 +0 2.922 -0.164 0.530 1.773 0.496 2.284 0.154 -1.447 2.173 1.570 1.183 -0.076 0.000 0.697 -0.822 1.314 0.000 1.640 -0.821 1.693 0.000 1.251 0.789 0.995 2.636 1.199 1.706 1.352 +0 0.640 -1.085 0.597 0.546 -0.483 0.658 -1.920 -1.038 0.000 0.883 -0.094 0.646 1.107 1.236 -0.588 1.251 0.000 1.211 -0.281 -1.452 3.102 1.817 1.178 0.988 1.044 0.893 0.973 0.906 +0 0.304 2.175 -1.621 0.785 0.753 1.072 0.546 -0.666 2.173 0.649 0.657 1.672 2.215 0.783 1.293 0.769 0.000 0.440 -0.172 0.879 0.000 0.590 1.051 0.992 0.623 1.058 0.755 0.673 +0 0.783 1.646 -0.924 0.535 1.280 0.546 0.562 -1.682 0.000 0.926 1.242 1.074 1.107 1.342 0.039 -0.351 2.548 1.326 0.311 0.025 0.000 1.305 1.004 0.987 0.829 1.378 1.009 0.931 +1 0.354 -1.404 0.210 4.521 0.906 3.115 -1.455 -0.914 0.000 1.436 0.054 0.925 1.107 1.984 1.274 -0.634 0.000 0.963 -1.123 0.941 0.000 2.637 1.675 1.029 1.605 0.263 1.798 1.798 +0 1.035 1.406 -0.724 0.833 -1.274 0.700 1.249 1.325 2.173 0.439 -0.426 0.919 0.000 1.060 0.485 0.754 2.548 0.870 1.637 -0.253 0.000 0.682 0.720 0.988 0.938 0.663 0.776 0.678 +0 0.487 0.597 -0.925 2.710 -1.659 1.634 1.142 0.261 0.000 0.724 0.948 0.641 0.000 1.223 2.258 -0.772 0.000 0.986 -0.853 1.226 3.102 0.800 0.919 0.988 1.129 0.791 1.029 1.131 +1 1.656 -0.268 1.441 0.393 1.029 1.693 -1.917 0.134 0.000 1.505 1.383 -1.297 1.107 0.953 1.967 -0.298 0.000 0.727 0.805 -0.746 0.000 0.995 1.064 0.988 0.570 0.807 0.880 0.927 +1 0.472 1.150 0.226 1.809 -0.403 1.113 -0.507 1.401 2.173 0.860 0.314 -1.396 2.215 0.315 0.028 1.234 0.000 1.015 -0.828 0.083 0.000 0.632 0.836 0.993 1.341 1.040 1.723 1.403 +0 1.371 -0.969 0.236 1.248 -1.078 0.802 0.788 0.950 0.000 0.701 0.783 -1.016 2.215 0.866 -0.549 -1.187 1.274 0.565 -1.522 0.489 0.000 1.819 1.329 1.678 1.327 0.645 0.954 1.037 +1 0.651 0.388 -1.364 0.688 -0.656 0.534 -0.618 -1.625 2.173 0.624 0.697 0.351 0.000 0.819 -0.955 0.415 2.548 0.449 -0.418 1.225 0.000 0.662 0.820 0.991 0.766 0.813 0.620 0.625 +0 1.790 1.406 1.377 0.770 0.712 0.896 1.340 -0.294 2.173 1.149 0.820 -0.677 0.000 0.352 -0.166 0.666 2.548 0.487 -0.161 -1.055 0.000 0.621 0.751 0.986 0.670 0.796 0.825 0.814 +1 1.001 -1.259 -1.235 1.686 -0.553 0.729 -1.044 0.109 2.173 1.165 2.595 1.599 0.000 1.165 0.305 0.744 2.548 0.794 -0.173 -1.331 0.000 2.567 1.951 1.039 0.931 1.063 2.042 2.267 +1 2.324 0.275 -0.179 0.442 -0.875 0.900 0.560 1.596 2.173 0.890 -0.272 0.741 0.000 0.848 -0.720 -1.703 2.548 1.229 0.287 -0.829 0.000 0.846 0.901 0.991 1.095 0.817 1.005 0.821 +1 1.052 -1.171 0.309 1.157 0.046 0.935 -0.301 -1.087 2.173 0.556 -0.089 0.853 0.000 1.234 -0.022 -0.097 0.000 1.645 -0.441 -1.713 0.000 0.923 0.988 0.993 1.617 0.997 1.338 1.100 +1 0.489 -0.121 0.475 1.099 -1.437 1.553 -0.400 0.247 2.173 0.950 -0.347 -1.209 0.000 0.962 -1.602 1.168 0.000 0.867 1.096 -0.980 0.000 1.134 0.790 1.004 1.169 1.025 1.060 0.886 +0 1.175 -0.294 1.320 0.834 -0.271 0.975 0.889 -0.965 2.173 0.735 1.594 1.580 0.000 1.389 0.655 0.194 2.548 0.936 1.160 0.685 0.000 0.871 0.982 1.358 1.256 1.260 0.999 0.905 +1 0.898 0.140 1.504 0.899 -0.141 0.965 0.478 -0.929 0.000 1.299 0.184 1.093 2.215 0.510 -2.382 0.283 0.000 0.972 1.054 -0.532 0.000 0.788 0.738 1.240 0.890 0.832 0.902 0.801 +0 0.930 0.252 -0.816 0.772 -1.401 0.495 1.306 0.386 2.173 0.856 0.083 0.124 2.215 0.697 -0.732 -1.679 0.000 0.697 0.166 1.242 0.000 0.549 0.951 0.984 0.887 0.668 0.835 0.741 +1 1.476 1.568 -0.078 0.447 0.399 1.883 -0.118 -1.072 0.000 2.140 0.793 0.956 2.215 1.098 0.481 0.643 2.548 0.790 0.254 -0.330 0.000 1.222 1.507 0.980 1.219 0.511 1.519 1.331 +0 0.408 -0.006 -0.604 1.358 1.418 1.644 -0.568 -1.521 2.173 1.737 -0.687 0.404 0.000 0.805 -1.159 -0.656 0.000 2.236 -0.445 -0.017 3.102 1.571 1.022 0.999 0.946 1.983 1.376 1.147 +0 0.716 1.172 1.551 1.366 1.627 0.755 -0.034 -1.242 0.000 1.030 -0.473 -0.108 2.215 0.398 2.166 -0.520 0.000 0.624 -0.976 0.794 3.102 0.777 0.832 0.999 1.629 0.583 1.573 1.196 +0 0.529 0.569 0.242 0.801 1.425 1.554 1.491 -1.574 0.000 0.848 0.358 0.701 2.215 0.978 0.377 -0.617 2.548 1.912 -0.483 0.240 0.000 1.038 1.078 0.987 0.648 0.898 0.856 0.756 +0 0.767 -0.513 -0.216 1.728 0.502 0.881 -1.602 1.356 2.173 0.732 -0.981 -1.110 2.215 0.435 -1.506 -1.410 0.000 0.574 1.302 -0.898 0.000 1.346 0.972 0.987 1.329 1.007 1.012 0.985 +0 0.548 1.261 -1.678 2.130 1.332 1.263 -0.548 -0.101 2.173 1.020 -0.249 -1.532 0.000 0.960 -0.284 -0.570 1.274 0.615 1.860 -0.325 0.000 1.144 0.899 0.995 1.758 0.587 1.198 1.063 +1 0.633 0.454 -0.153 2.363 1.139 1.706 1.004 -0.364 0.000 1.312 0.039 0.926 2.215 1.940 1.173 -1.251 0.000 0.636 -1.083 -1.067 0.000 0.747 0.881 1.556 0.900 0.873 0.717 0.719 +0 1.036 0.162 1.057 0.980 1.248 0.653 -0.576 1.666 1.087 0.742 -1.088 -0.453 2.215 1.412 -2.447 -0.101 0.000 1.220 0.209 -1.163 0.000 0.487 0.829 0.989 1.010 1.004 1.052 1.611 +1 1.447 -0.404 0.954 1.265 1.289 0.704 -0.658 -1.309 0.000 0.622 1.512 -1.019 0.000 1.879 1.395 -0.124 0.000 1.268 -0.077 1.525 3.102 1.198 1.430 0.980 1.191 1.148 1.454 1.529 +1 1.593 -0.829 -0.117 0.370 0.548 2.089 1.986 1.554 0.000 1.248 -1.053 0.135 1.107 1.225 -0.265 -0.019 0.000 2.031 -0.380 -1.095 3.102 0.721 0.861 0.980 0.654 1.365 0.806 0.666 +1 0.840 0.098 -0.423 0.974 -0.670 1.541 0.126 -0.165 2.173 1.544 0.450 1.301 0.000 1.057 -0.279 1.657 0.000 0.753 -1.256 -0.230 0.000 0.990 0.769 0.989 0.978 0.884 1.166 1.066 +1 0.629 0.733 1.078 0.766 1.733 1.110 0.394 0.927 0.000 1.041 0.310 -1.192 2.215 1.801 0.423 -0.405 2.548 0.876 0.928 0.522 0.000 0.771 1.295 0.983 1.080 0.954 1.038 0.912 +0 0.840 -2.103 1.356 1.345 -1.621 1.604 -1.130 0.555 2.173 1.297 -0.003 -0.846 2.215 0.759 0.432 -0.466 0.000 0.768 -0.654 -0.439 0.000 0.907 0.966 0.983 1.416 2.390 1.497 1.443 +1 0.875 -0.064 1.001 0.366 0.113 1.112 0.240 -1.741 2.173 1.296 0.167 0.279 0.000 0.679 -2.589 -0.639 0.000 1.834 0.121 -0.918 1.551 0.966 1.089 0.989 0.935 1.025 1.050 0.900 +1 0.731 1.426 0.457 0.537 -1.074 0.797 0.371 1.211 0.000 0.945 1.077 -1.326 2.215 1.534 0.887 -0.468 2.548 0.947 0.623 0.631 0.000 0.709 1.026 0.985 0.829 0.897 0.891 0.836 +1 0.875 0.472 -0.990 0.521 0.757 0.374 0.856 0.491 2.173 1.010 0.443 -0.100 2.215 0.668 -1.545 1.447 0.000 1.309 0.536 -1.476 0.000 1.583 1.226 0.987 0.731 0.496 0.942 0.814 +1 2.396 0.717 -0.491 0.480 -1.022 0.738 0.440 1.367 1.087 0.557 -0.834 1.037 0.000 0.374 0.546 1.049 1.274 1.121 1.494 1.006 0.000 1.802 0.947 0.993 1.216 0.192 0.789 0.884 +0 1.776 0.593 -0.124 0.651 -0.310 0.710 0.948 -1.633 0.000 1.127 0.570 1.129 2.215 1.216 0.489 0.630 2.548 0.716 0.612 -0.765 0.000 0.777 0.966 0.988 1.177 0.542 0.830 0.840 +0 0.900 0.091 -1.078 0.772 1.526 1.529 0.685 0.521 0.000 0.615 1.407 0.028 0.000 1.119 0.919 -1.426 2.548 1.044 0.832 -0.981 3.102 0.823 1.030 0.986 0.582 0.323 0.848 0.781 +0 0.447 -0.436 0.519 1.800 1.402 1.370 0.670 0.562 2.173 1.992 0.601 -0.909 0.000 0.812 0.315 -0.290 0.000 1.003 0.466 -1.325 3.102 1.067 0.731 0.992 1.075 1.232 1.132 1.062 +1 0.461 2.236 0.998 0.623 1.720 0.697 0.434 0.636 0.000 0.948 -0.127 -1.199 0.000 1.096 -0.277 -0.038 0.000 1.330 0.411 0.066 3.102 1.079 0.690 0.980 0.636 1.031 0.861 0.795 +0 2.362 1.842 0.820 1.941 0.692 1.687 2.717 -1.244 0.000 1.032 -0.834 -0.731 2.215 0.796 1.187 0.179 1.274 0.612 1.310 -0.613 0.000 0.929 1.203 0.978 0.853 1.440 2.163 1.605 +0 0.940 0.985 -1.660 1.077 1.299 0.544 0.055 0.593 0.000 0.987 0.528 0.008 0.000 1.261 0.595 -0.866 2.548 0.405 -0.907 -1.450 3.102 0.873 1.038 0.989 0.966 0.604 0.809 0.893 +1 1.198 -1.432 1.002 0.523 0.588 2.532 -0.509 -1.078 0.000 1.107 -0.760 0.656 2.215 2.078 -1.826 0.602 0.000 0.686 1.246 -1.618 0.000 1.035 0.992 1.000 0.591 0.483 0.622 0.621 +0 0.910 -0.301 -1.104 0.698 1.283 1.076 -0.816 -0.953 2.173 1.483 -0.244 0.469 0.000 0.986 1.018 1.053 0.000 0.665 0.030 1.371 0.000 0.958 0.838 0.985 0.897 0.520 0.915 0.807 +1 0.542 0.428 0.866 1.325 -0.417 0.516 0.840 1.287 0.000 1.299 1.411 -0.473 2.215 1.215 0.083 1.630 0.000 0.520 -0.276 1.037 3.102 0.701 0.489 1.074 0.895 1.046 0.901 0.814 +0 0.999 -0.359 -1.350 0.839 0.327 0.612 -0.540 0.782 0.000 1.045 0.005 1.302 1.107 1.060 0.211 0.282 0.000 3.140 0.423 -0.869 3.102 0.855 0.876 1.266 1.097 1.571 1.084 0.934 +1 0.859 1.294 -0.430 0.802 1.612 0.351 -0.218 -0.883 0.000 0.910 2.266 1.057 0.000 0.483 0.569 -1.389 2.548 0.709 0.065 -0.292 1.551 0.923 0.731 1.109 0.697 0.394 0.482 0.672 +0 0.888 -0.256 1.000 1.243 0.203 0.731 -0.179 -1.514 2.173 0.708 0.163 -0.605 0.000 0.426 0.294 -0.950 2.548 0.425 1.382 0.553 0.000 0.849 0.914 0.988 0.692 0.382 0.690 0.677 +0 0.433 0.977 -1.303 1.957 -0.298 0.687 -0.015 1.295 0.000 0.578 0.888 1.109 2.215 1.255 -1.136 -0.329 2.548 1.568 -0.780 1.596 0.000 0.866 0.878 1.004 1.495 1.467 1.120 1.140 +0 1.600 -0.040 -0.314 0.844 0.906 1.197 -2.905 1.189 0.000 1.550 0.644 -1.168 2.215 0.707 1.879 1.653 0.000 1.105 -0.178 0.410 3.102 10.018 5.222 1.435 1.309 1.285 3.675 2.753 +1 0.744 0.660 -0.123 1.376 -1.602 0.654 0.895 0.686 0.000 0.992 1.088 -1.525 2.215 1.150 0.996 -0.631 2.548 1.578 1.634 0.497 0.000 0.898 1.087 1.362 0.823 0.818 0.896 0.856 +1 2.190 0.602 1.577 2.602 -1.692 3.165 -0.914 0.075 0.000 1.212 0.352 -1.655 0.000 0.684 0.466 -0.831 2.548 1.411 0.330 -0.349 3.102 0.771 1.005 0.969 0.920 0.319 0.921 0.833 +1 0.777 -0.015 0.275 1.059 1.347 0.533 0.139 -0.192 1.087 0.712 -0.969 1.401 0.000 1.203 0.048 -1.444 2.548 0.706 1.715 0.019 0.000 0.388 0.935 1.034 0.772 0.903 0.692 0.720 +0 1.222 -0.601 0.475 0.856 -0.182 0.609 -1.256 -1.026 0.000 0.957 -0.447 1.270 2.215 0.727 2.446 -1.397 0.000 0.535 -1.737 -0.164 0.000 0.714 1.036 0.989 0.534 0.173 0.626 0.699 +1 1.239 -0.256 -1.178 0.567 0.319 1.388 1.982 1.389 0.000 1.662 -0.080 -0.243 2.215 1.860 -0.528 0.165 2.548 0.554 0.561 -1.237 0.000 0.622 0.863 1.133 0.930 0.820 0.788 0.683 +1 0.729 -0.603 -1.321 0.620 0.403 2.046 -2.134 0.693 0.000 1.093 -1.442 -1.497 2.215 1.457 -0.777 1.538 0.000 3.858 -0.679 -0.521 3.102 0.906 1.598 0.985 0.910 1.562 1.681 1.293 +0 0.370 -0.694 0.657 0.094 -0.272 1.674 1.120 0.761 2.173 2.037 0.327 -0.879 0.000 0.721 0.617 -1.714 2.548 0.490 0.907 -0.611 0.000 0.590 0.754 0.830 0.864 1.125 1.292 0.990 +1 0.725 -0.266 0.322 2.285 1.034 0.513 0.128 -0.150 0.000 1.324 0.480 -1.444 1.107 0.876 1.170 -0.444 2.548 0.573 0.482 -0.949 0.000 0.578 0.882 1.067 1.261 1.010 1.109 0.906 +1 0.885 -1.423 1.186 0.773 -0.969 0.835 1.075 0.572 2.173 0.691 0.228 -1.461 0.000 0.868 -0.699 -0.474 2.548 0.508 -1.913 -0.597 0.000 1.372 0.914 1.068 1.774 1.419 1.211 1.079 +1 0.688 -1.923 0.359 0.362 0.375 0.990 -0.965 0.936 2.173 0.890 -0.939 -0.935 0.000 0.835 -0.684 -0.088 2.548 0.368 -0.753 -1.377 0.000 0.290 1.052 0.996 0.577 0.912 0.717 0.651 +0 0.601 0.486 -1.037 1.800 -0.090 0.465 -0.109 -0.730 2.173 0.862 -0.329 -1.439 2.215 0.609 -1.453 0.349 0.000 0.929 -0.190 1.008 0.000 0.769 0.882 1.086 0.674 0.565 0.703 0.765 +0 0.465 0.925 1.191 0.736 -0.762 0.998 2.336 -1.640 0.000 1.773 1.662 -0.083 0.000 1.208 0.884 0.362 2.548 1.674 -0.583 -1.427 3.102 0.825 0.852 0.991 0.797 1.488 1.109 1.314 +1 1.742 0.523 1.733 1.093 1.055 0.748 1.424 0.261 2.173 0.237 0.823 0.177 0.000 1.138 1.723 -0.761 0.000 1.021 0.269 -0.831 3.102 0.738 0.773 1.096 0.849 0.942 0.898 0.838 +0 0.924 -1.270 -1.392 1.934 -1.215 3.614 -0.825 0.670 0.000 3.100 -0.509 -1.070 1.107 1.425 -0.503 0.358 2.548 1.784 -1.705 -0.724 0.000 4.459 2.474 1.005 1.872 2.144 2.535 2.255 +0 1.660 -0.592 -0.034 0.537 -0.909 0.877 -2.540 -1.692 0.000 1.236 -0.830 -1.204 2.215 1.495 -1.411 0.639 1.274 1.908 -1.111 1.143 0.000 1.300 0.864 0.986 0.980 1.527 0.979 0.928 +0 2.144 0.100 -0.768 0.690 -1.415 0.693 -0.377 0.097 0.000 0.570 0.804 0.546 0.000 1.416 0.206 1.288 2.548 1.003 0.578 0.200 3.102 0.930 0.961 0.986 0.854 0.786 0.837 0.761 +0 0.339 2.327 -0.609 0.732 0.577 1.173 0.314 0.577 2.173 1.272 0.830 -0.945 2.215 0.913 1.552 1.604 0.000 0.674 -1.568 -1.198 0.000 0.963 0.939 0.986 0.860 1.829 1.005 0.831 +1 0.809 -0.801 1.018 0.449 -0.819 1.119 0.745 -1.236 2.173 0.910 0.285 0.764 0.000 0.470 1.011 -0.496 2.548 0.703 -0.678 0.170 0.000 0.909 1.120 0.993 0.680 0.582 0.722 0.688 +1 1.857 0.222 -1.552 1.416 1.463 1.049 -0.319 -0.430 1.087 0.949 0.113 0.637 1.107 0.333 0.920 -0.178 0.000 0.390 0.688 0.425 0.000 0.309 0.786 0.994 1.088 1.246 1.158 0.889 +0 0.522 -1.696 -0.140 1.285 0.534 0.707 0.039 -1.123 0.000 1.076 1.622 -0.152 0.000 1.190 0.324 1.728 0.000 1.334 -0.454 -1.720 1.551 0.930 0.826 0.991 0.862 0.518 0.560 0.741 +0 0.546 0.346 0.875 0.982 -0.879 0.556 0.747 1.006 2.173 0.759 0.369 -0.888 0.000 0.731 -0.490 0.188 2.548 0.868 -0.431 1.669 0.000 0.926 0.934 1.015 0.686 0.767 0.680 0.621 +1 1.482 -0.171 0.270 0.861 -0.097 0.509 -0.883 0.082 2.173 1.341 -0.781 -1.414 2.215 1.248 0.822 1.456 0.000 0.702 1.376 -1.063 0.000 0.884 1.348 0.980 1.457 1.187 1.147 1.080 +1 0.430 -0.670 1.499 0.456 0.103 0.867 -1.514 -1.709 0.000 0.628 -1.330 -1.029 2.215 1.189 -1.129 -0.092 1.274 1.342 -2.226 0.014 0.000 1.925 1.212 0.989 0.710 0.689 0.893 0.755 +1 0.755 -0.517 -0.381 3.202 -1.002 0.895 0.037 1.459 2.173 1.332 0.240 0.846 0.000 0.571 -0.622 1.489 0.000 0.949 0.987 0.124 0.000 0.946 1.040 1.141 1.438 1.239 1.004 1.014 +0 1.486 0.284 -1.541 0.718 0.122 0.861 -0.668 -0.106 2.173 0.628 -0.493 1.009 0.000 0.751 -0.708 -1.237 0.000 1.193 -0.376 0.510 3.102 0.958 1.000 1.428 0.927 0.577 0.835 0.765 +1 0.609 -0.643 -1.518 0.919 1.312 1.122 0.280 1.631 1.087 1.284 1.244 -0.720 2.215 1.299 0.321 0.235 0.000 1.342 -0.272 -0.394 0.000 0.930 1.255 0.981 0.634 1.764 1.195 1.010 +1 1.099 0.508 1.145 0.660 -1.630 1.227 0.256 -1.515 0.000 2.024 -1.269 0.041 0.000 1.299 1.409 0.696 1.274 1.543 0.174 -0.878 3.102 4.389 2.541 0.992 1.048 1.323 2.074 1.595 +1 0.382 -1.186 1.277 0.523 0.655 0.990 0.113 -1.456 2.173 0.745 0.766 0.246 0.000 0.877 1.452 -0.412 0.000 0.533 -0.830 0.738 3.102 0.862 0.927 1.000 0.899 0.836 0.921 0.799 +0 0.585 1.053 1.593 0.253 -1.195 1.145 0.245 0.349 0.000 1.958 -0.055 -1.371 2.215 2.408 -0.419 -0.087 0.000 1.189 -1.116 -0.191 0.000 0.865 0.952 1.000 0.806 0.478 1.134 0.952 +1 1.340 0.480 -1.731 0.448 0.930 0.988 -0.727 -0.450 1.087 0.532 -1.555 -1.554 2.215 0.803 -0.850 0.276 0.000 0.564 0.417 0.561 0.000 0.609 0.808 0.994 1.218 1.014 0.923 0.792 +0 0.863 -1.714 -1.669 0.227 0.384 0.398 0.100 -0.268 0.000 0.816 0.196 1.501 2.215 1.047 -0.546 0.567 2.548 1.434 -0.891 -0.666 0.000 0.821 1.066 0.993 0.740 0.837 0.765 0.722 +0 1.350 0.840 1.470 0.266 1.728 0.861 -1.170 -0.853 2.173 0.867 -2.180 -0.134 0.000 0.901 0.102 0.903 2.548 0.682 -0.811 0.005 0.000 0.691 0.904 0.987 0.802 1.335 1.319 1.521 +1 0.610 -1.273 -0.606 0.273 0.268 1.218 1.717 -1.523 0.000 1.106 -0.556 -0.491 2.215 0.880 1.153 1.079 0.000 1.566 -0.363 0.335 3.102 0.922 0.945 0.993 0.652 0.809 0.896 0.767 +1 0.441 1.438 -1.216 1.243 0.678 0.449 1.144 -0.723 0.000 0.521 0.958 0.504 0.000 0.679 -0.383 0.876 0.000 0.640 0.481 -0.440 3.102 0.922 0.926 1.015 0.640 0.503 0.687 0.631 +0 1.280 -0.474 -0.203 1.654 -0.722 0.899 -0.086 1.315 2.173 0.492 0.303 0.803 1.107 0.769 0.696 0.210 0.000 0.378 0.629 -0.750 0.000 0.452 0.858 0.989 0.891 0.479 0.924 0.754 +1 0.375 0.035 1.562 1.854 -0.327 0.838 -1.238 0.634 2.173 0.539 -0.855 -1.291 0.000 0.716 -0.797 -1.667 1.274 0.555 -1.061 -0.329 0.000 0.664 0.669 1.145 0.873 0.865 0.896 0.707 +0 0.834 0.192 0.717 0.858 -0.192 0.696 -1.246 1.494 0.000 0.883 -1.604 -0.670 0.000 1.004 0.048 -1.694 2.548 0.489 -0.502 0.480 3.102 1.581 0.958 0.982 0.844 0.527 0.780 0.940 +0 1.750 1.370 0.368 0.582 0.879 0.636 1.132 -1.639 0.000 0.477 2.454 -1.366 0.000 0.477 0.477 1.170 1.274 1.037 0.936 -0.467 0.000 0.920 0.742 0.984 0.650 0.402 0.517 0.651 +1 0.890 -0.737 1.551 0.729 -0.638 0.924 0.251 -0.685 1.087 0.313 -1.167 1.234 0.000 0.678 -2.092 1.004 0.000 1.094 -0.657 0.760 1.551 0.952 0.654 1.028 0.933 1.178 0.927 0.794 +1 1.144 0.466 -1.417 1.713 -1.573 1.543 0.032 -1.425 0.000 3.927 -2.156 0.209 0.000 0.975 0.126 -1.688 0.000 1.501 -0.571 -0.211 3.102 0.530 0.940 0.972 1.103 0.901 0.918 0.810 +1 1.995 -0.914 -0.352 0.483 1.417 0.951 -1.037 1.410 2.173 0.416 -1.616 -0.372 0.000 0.835 -0.820 0.764 0.000 0.382 0.129 -0.827 3.102 0.849 0.909 1.359 0.681 0.704 0.793 0.696 +0 2.339 1.351 -1.071 0.376 -1.258 0.705 -1.583 0.396 0.000 0.672 0.481 1.499 2.215 1.342 -0.371 0.815 1.274 0.983 -0.877 -0.276 0.000 0.867 0.811 0.980 1.832 0.750 1.219 1.088 +1 1.279 0.942 0.293 0.327 1.550 0.744 -0.591 -1.094 2.173 0.438 -0.529 -0.389 0.000 0.777 -0.369 1.052 2.548 0.640 -0.598 1.393 0.000 0.692 0.633 0.987 0.858 0.889 0.951 0.787 +1 0.429 -1.118 1.288 2.003 0.341 1.123 -1.946 -1.480 0.000 0.769 -0.771 -0.248 1.107 0.671 -0.832 -1.489 0.000 0.584 -1.239 -0.130 3.102 0.859 0.792 0.986 0.771 0.223 0.753 0.825 +0 0.684 0.255 1.428 1.156 1.251 1.330 -0.256 0.195 2.173 1.474 0.875 -1.484 2.215 0.732 -0.637 -0.303 0.000 1.169 0.421 -0.765 0.000 0.772 0.976 0.983 0.890 2.413 1.393 1.169 +1 0.680 0.346 0.863 1.610 0.085 0.951 0.897 -1.156 2.173 0.454 0.784 -0.643 2.215 0.465 1.640 1.644 0.000 0.444 1.313 1.039 0.000 0.267 0.655 0.989 0.666 0.435 0.771 0.651 +1 0.672 0.258 -0.037 0.602 1.520 1.438 0.525 -0.297 0.000 1.309 0.542 1.158 0.000 1.865 0.857 -1.636 2.548 0.960 -1.030 1.396 0.000 1.526 1.433 0.986 0.939 1.324 1.207 0.999 +1 0.644 0.375 0.727 1.277 -1.665 1.216 0.507 -0.139 0.000 1.005 0.730 1.468 2.215 0.770 0.063 -1.008 2.548 0.729 -0.143 -0.009 0.000 1.015 0.808 1.049 0.629 0.807 0.821 0.762 +1 1.025 -0.156 0.395 0.555 -0.689 1.617 -0.384 -0.805 1.087 1.186 0.085 1.025 0.000 1.271 -0.836 1.411 0.000 0.817 0.798 0.587 3.102 0.793 0.628 0.990 1.033 1.454 1.044 0.889 +1 1.229 -0.987 0.984 1.625 0.459 1.840 -0.156 -0.814 2.173 0.804 -1.306 1.252 0.000 1.340 -1.950 -1.413 0.000 1.006 -2.377 0.904 0.000 1.036 1.027 0.990 2.115 1.129 1.523 1.357 +1 1.224 0.125 1.468 1.754 0.910 1.006 -0.017 -0.838 0.000 1.224 -0.446 0.042 2.215 0.962 0.128 -1.266 0.000 0.380 -2.194 0.640 0.000 0.678 1.213 0.985 0.731 0.673 0.819 0.921 +0 1.026 -0.837 -1.322 0.871 -0.833 0.778 -0.554 0.961 0.000 0.502 -1.477 0.541 0.000 1.895 -1.229 -0.065 0.000 2.026 -1.270 1.503 3.102 0.825 0.902 0.978 0.569 1.290 0.871 0.872 +1 1.260 0.639 -0.257 1.682 -0.013 1.895 0.822 -0.043 2.173 0.592 2.276 -0.690 0.000 3.260 -0.058 -1.552 2.548 1.826 1.721 1.160 0.000 0.877 1.013 0.992 1.828 3.349 1.716 1.345 +0 0.620 0.314 -0.192 1.328 -0.820 0.422 0.413 1.735 2.173 0.476 0.709 1.095 2.215 0.655 -1.324 0.088 0.000 0.869 -0.361 1.102 0.000 0.795 0.842 0.979 0.788 0.375 0.621 0.796 +1 0.560 -1.264 0.255 0.782 -1.350 1.316 -0.236 0.324 2.173 0.965 -0.090 -1.393 0.000 0.819 2.619 1.632 0.000 1.167 0.052 -0.580 0.000 0.931 0.871 0.985 1.336 0.828 1.067 1.015 +1 0.749 0.204 1.742 0.553 -0.350 1.494 0.653 -1.541 2.173 2.007 0.257 0.570 1.107 2.025 0.668 -0.590 0.000 1.355 -0.515 -0.187 0.000 1.482 1.739 0.990 0.845 2.461 1.593 1.217 +1 0.685 0.424 0.338 0.627 -1.232 0.840 0.925 0.196 0.000 0.901 -0.615 1.411 2.215 0.789 -0.605 -1.231 0.000 0.858 0.527 -1.217 3.102 1.913 1.158 0.988 0.762 0.771 0.938 0.778 +0 1.524 1.163 0.206 0.892 0.930 0.856 2.530 -0.344 0.000 0.875 0.443 -1.453 0.000 0.814 1.232 -1.300 2.548 1.493 0.946 1.337 3.102 0.863 1.083 0.988 0.776 0.591 0.827 0.883 +1 2.726 -0.959 1.515 0.993 0.857 0.689 0.048 -0.499 1.087 0.399 0.377 0.182 0.000 1.044 -0.874 -0.032 0.000 1.665 -0.493 -1.152 3.102 0.864 1.093 1.272 1.508 0.729 1.076 1.039 +1 0.697 -0.862 -1.005 1.485 1.106 1.777 -0.538 1.455 2.173 1.495 -0.146 -1.191 0.000 2.955 -2.213 -0.262 0.000 2.188 0.859 0.280 0.000 0.889 0.654 1.333 1.026 0.824 1.123 1.039 +0 1.584 -0.268 -0.318 2.260 0.082 1.848 0.813 -1.672 2.173 0.909 0.064 1.524 0.000 1.443 0.306 0.379 2.548 0.827 -0.046 -1.237 0.000 0.941 1.015 0.995 2.383 2.019 1.625 1.344 +1 1.610 0.241 0.168 0.129 -0.424 0.883 -1.800 -1.532 0.000 0.933 -1.216 1.120 2.215 0.729 -0.186 -1.606 2.548 0.525 1.093 -0.906 0.000 1.372 0.950 0.980 1.318 0.734 0.920 1.071 +1 0.950 -0.781 -1.158 0.787 -0.639 0.861 0.983 1.330 2.173 0.970 1.000 -0.055 2.215 0.410 1.177 -1.657 0.000 0.481 2.316 0.583 0.000 0.595 0.741 0.994 1.930 1.276 1.557 1.389 +1 1.338 0.583 -1.372 1.228 -0.050 0.819 -0.233 0.465 2.173 0.479 -1.045 -0.913 0.000 0.541 0.606 -1.127 0.000 1.051 0.133 1.188 0.000 0.784 1.007 1.650 0.880 0.575 0.817 0.746 +1 0.753 0.368 -1.717 1.226 0.573 0.918 -0.947 -0.861 2.173 0.525 1.152 -1.702 0.000 0.961 -0.474 0.189 0.000 1.373 0.258 -0.348 3.102 0.834 0.838 1.172 0.829 0.962 0.937 0.795 +0 0.668 -0.091 -0.005 1.274 1.444 0.696 1.454 -0.428 0.000 0.806 0.785 1.439 2.215 0.439 2.187 0.597 0.000 1.025 -1.397 1.628 0.000 0.928 0.968 1.234 0.810 1.103 0.837 0.895 +0 0.898 1.499 -1.176 0.614 -1.661 0.397 0.951 -0.280 2.173 0.591 -0.235 1.231 0.000 0.933 -0.329 0.198 0.000 0.483 0.593 0.886 1.551 0.916 0.859 0.974 0.574 0.406 0.519 0.626 +0 1.626 0.256 1.590 1.225 1.056 0.786 -0.760 -0.263 1.087 1.168 -0.744 -0.741 2.215 1.096 -0.033 -1.398 0.000 1.893 -0.202 0.147 0.000 0.940 1.027 0.992 1.346 0.588 1.103 0.925 +1 0.565 -0.087 1.533 1.256 -0.860 0.984 1.911 0.693 0.000 0.825 0.638 -0.920 0.000 0.367 1.448 1.276 1.274 0.783 0.823 -0.267 3.102 1.876 1.220 0.988 0.682 0.422 0.821 0.720 +0 1.655 0.577 1.033 2.347 1.377 1.478 -0.847 -0.517 0.000 0.503 0.263 -1.367 2.215 0.562 0.668 -0.668 0.000 0.394 -0.173 0.798 1.551 1.460 0.947 0.993 0.815 0.386 0.697 1.105 +1 0.875 -0.071 0.384 0.425 -1.563 1.362 0.424 1.481 1.087 1.129 0.526 -0.258 0.000 1.246 -0.957 -0.650 0.000 0.793 0.611 0.620 0.000 0.884 0.713 0.983 1.067 0.667 0.877 0.797 +1 2.092 -0.951 -1.724 0.926 1.157 0.908 -1.167 -0.445 1.087 0.904 -0.543 0.041 0.000 0.780 -0.269 1.273 0.000 0.429 0.966 0.098 3.102 1.167 1.108 0.999 0.984 1.009 0.998 0.907 +0 0.322 2.013 1.676 2.849 1.332 1.346 -0.884 -0.195 1.087 0.563 0.988 -1.600 0.000 0.609 0.303 0.423 0.000 0.841 -0.451 -0.699 0.000 0.924 0.662 0.985 2.528 1.052 1.598 1.236 +0 0.752 -0.129 -1.204 0.627 -1.436 0.431 0.717 1.551 2.173 0.539 -0.220 -0.295 0.000 1.098 1.507 0.512 2.548 0.630 0.007 0.311 0.000 0.404 0.867 0.984 0.599 0.805 0.690 0.675 +1 0.691 -1.621 -0.259 1.555 0.158 1.274 -0.914 1.587 2.173 0.335 -2.450 -0.486 0.000 0.796 -0.268 -0.984 0.000 0.485 0.649 -1.525 3.102 0.829 0.823 0.993 1.436 0.842 0.989 0.849 +1 1.876 0.909 -1.176 0.559 1.672 0.722 -0.774 0.419 0.000 1.183 1.444 0.731 2.215 0.612 0.041 -0.285 0.000 0.560 0.550 1.452 3.102 0.873 0.757 0.983 1.264 0.543 0.938 0.983 +1 1.331 -0.092 -0.994 1.308 1.712 1.007 0.822 0.281 0.000 0.859 0.135 -1.635 0.000 1.124 -0.660 0.525 1.274 0.603 -1.931 -0.359 0.000 1.697 0.980 1.181 1.099 0.494 0.739 0.776 +1 0.419 2.187 -1.220 1.085 1.131 1.022 0.362 -0.539 0.000 0.755 1.364 1.547 2.215 0.877 0.941 -0.136 0.000 1.038 -0.168 1.066 3.102 0.837 1.057 0.993 0.563 0.788 0.895 0.835 +1 0.691 2.044 -1.157 0.901 0.396 0.849 1.435 1.142 0.000 1.073 0.789 -0.638 2.215 0.798 -0.091 1.287 2.548 1.099 1.484 -0.395 0.000 1.471 1.207 1.077 0.978 1.075 0.964 0.895 +0 1.158 -0.507 -0.255 0.761 1.359 0.912 1.854 -1.017 0.000 0.292 2.200 -0.192 0.000 0.738 0.857 1.122 2.548 0.942 -0.419 1.363 3.102 0.773 0.940 1.292 0.738 0.520 0.911 1.059 +0 1.587 -0.621 -0.508 1.242 -0.873 1.131 -0.936 0.923 0.000 0.952 -0.051 -1.591 2.215 1.112 -0.473 0.509 0.000 0.737 -0.502 1.430 3.102 0.854 1.291 0.984 0.807 0.373 0.769 0.958 +0 1.535 -0.133 1.283 0.386 -1.361 0.879 -0.178 -0.664 2.173 0.844 -0.463 0.324 1.107 0.964 0.076 0.049 0.000 1.198 -0.759 -0.964 0.000 1.031 1.035 0.985 1.069 1.004 0.844 0.802 +0 1.235 1.100 0.371 0.195 1.652 1.328 0.457 -0.068 0.000 1.284 -2.278 -1.038 0.000 1.674 -1.786 -1.418 0.000 1.538 -0.206 0.959 0.000 0.945 0.837 0.984 0.586 0.813 1.082 1.295 +1 0.907 -1.094 1.692 0.265 -0.079 1.223 -0.780 -1.534 0.000 1.545 1.597 0.086 0.000 0.851 -0.122 0.047 2.548 0.932 0.058 -1.118 1.551 1.514 0.987 0.982 0.881 0.594 0.778 0.760 +1 0.734 1.633 0.916 0.758 -1.131 0.888 -0.235 0.128 2.173 0.433 1.692 -1.438 0.000 0.654 0.427 0.970 0.000 0.730 -0.174 -0.965 3.102 0.873 1.187 0.995 0.787 0.710 0.908 0.795 +1 0.726 -0.462 -1.239 3.277 -0.698 2.209 -0.448 1.021 0.000 0.813 0.613 0.920 2.215 1.079 -0.514 -0.117 2.548 0.756 -1.246 -0.849 0.000 2.238 1.577 1.003 0.819 1.019 1.133 1.342 +1 2.003 0.130 0.243 1.402 -0.472 0.413 1.107 -0.456 2.173 0.870 -0.677 -1.525 0.000 1.314 0.099 1.133 0.000 0.897 0.654 -1.409 3.102 0.926 0.697 1.393 1.007 0.499 0.719 0.655 +1 1.214 0.251 -1.419 0.777 -0.443 0.685 1.359 0.071 2.173 0.669 0.347 1.588 0.000 0.444 1.465 0.648 0.000 1.101 1.230 1.150 3.102 0.838 0.916 1.038 0.890 0.760 0.801 0.711 +0 1.751 0.332 -0.335 2.592 -1.139 0.967 0.260 0.921 1.087 1.492 0.565 0.405 0.000 1.208 -0.033 1.388 2.548 0.931 -0.513 -1.369 0.000 1.820 1.278 1.955 1.433 0.585 1.247 1.180 +1 0.939 0.927 -1.455 0.326 -0.624 0.590 1.211 0.906 1.087 1.171 1.130 1.715 2.215 2.160 2.301 -0.013 0.000 0.700 1.796 -0.840 0.000 0.954 1.230 0.990 0.720 0.815 1.108 0.931 +1 0.981 -0.254 0.941 1.470 -1.699 0.966 1.144 -0.794 0.000 1.527 -0.037 0.404 2.215 1.035 0.751 -1.393 0.000 0.708 0.458 -0.085 3.102 0.970 0.762 1.150 1.174 0.488 1.017 1.041 +0 0.523 1.008 1.355 0.833 -0.562 0.564 0.528 -0.076 0.000 0.705 -0.533 1.532 2.215 0.894 0.743 -1.314 2.548 1.540 -0.012 0.720 0.000 1.015 1.006 0.985 0.776 0.773 0.774 0.684 +0 0.639 -0.478 -1.467 1.897 1.313 0.291 -0.117 -0.337 2.173 0.429 1.468 -0.930 0.000 0.886 0.872 -0.204 0.000 0.382 -1.038 0.100 1.551 0.702 0.711 0.987 0.837 0.250 0.562 0.768 +0 0.385 -1.086 0.594 0.279 0.292 1.881 0.494 -1.581 2.173 2.330 -0.184 0.320 2.215 1.086 0.456 -1.139 0.000 0.742 0.610 -0.280 0.000 0.702 1.012 0.982 0.880 3.234 1.517 1.173 +0 0.570 0.216 0.671 0.733 -0.971 0.980 -0.654 -0.909 2.173 0.551 0.333 0.954 0.000 0.562 1.042 -1.561 0.000 2.065 0.946 0.443 3.102 0.931 0.975 0.989 1.006 2.091 1.092 0.889 +0 0.330 -1.826 1.621 1.614 -1.733 1.749 0.072 0.221 0.000 1.558 -0.933 -1.128 2.215 0.696 -0.514 0.733 0.000 0.711 1.300 -0.801 0.000 1.092 0.965 0.992 0.923 0.894 1.239 1.115 +0 1.117 -0.543 0.562 0.337 -1.426 0.551 -0.984 -1.050 2.173 0.858 1.271 1.476 2.215 0.609 1.675 0.018 0.000 0.559 -0.095 -0.603 0.000 0.810 0.863 0.988 0.775 1.631 1.034 0.953 +0 1.211 0.751 -1.263 0.819 1.254 1.252 0.622 0.011 2.173 1.275 -0.417 -1.538 2.215 0.805 0.097 0.416 0.000 0.677 0.647 1.158 0.000 0.576 0.984 1.057 1.243 2.100 1.175 0.932 +0 0.642 -0.181 -1.409 0.687 -1.123 0.893 1.362 0.201 0.000 1.003 -0.090 0.652 0.000 1.150 -0.642 1.589 0.000 0.919 0.347 1.454 0.000 0.888 0.994 0.982 1.559 0.497 1.098 1.000 +1 2.163 0.414 -1.161 1.134 -0.615 0.820 -0.852 0.502 2.173 0.666 -0.323 -1.693 0.000 0.766 0.936 0.893 0.000 0.970 0.736 0.250 3.102 1.131 0.856 1.027 1.601 0.955 1.098 0.980 +0 0.543 0.473 0.242 1.231 -0.853 0.700 0.799 1.131 1.087 0.454 1.647 0.350 0.000 0.566 1.963 1.072 0.000 1.578 0.035 -1.547 3.102 0.930 0.943 0.990 0.924 0.859 0.749 0.693 +0 1.111 -0.047 0.415 0.190 0.984 0.449 1.469 0.037 0.000 0.889 1.165 1.382 1.107 0.935 0.902 -1.419 2.548 1.379 1.439 -0.859 0.000 0.880 1.003 0.985 1.038 0.570 0.899 0.959 +0 1.060 -0.651 -1.154 0.427 -1.155 0.586 0.321 0.817 2.173 0.858 -0.083 0.352 2.215 0.778 1.006 -0.724 0.000 0.801 -0.130 -1.327 0.000 0.738 0.900 0.978 1.193 0.476 0.924 0.878 +0 1.039 -0.349 -1.377 1.023 -0.560 0.831 -1.378 0.530 0.000 1.469 0.009 -1.686 1.107 1.089 -0.490 0.349 0.000 0.898 -0.533 -0.677 3.102 0.803 0.849 0.986 0.879 0.887 1.050 0.945 +0 0.344 -1.812 0.167 0.333 -0.778 1.433 -0.548 -1.281 2.173 1.115 -0.761 0.591 2.215 0.837 1.918 0.265 0.000 0.719 -1.674 1.157 0.000 0.839 1.563 0.990 0.952 1.860 1.569 1.213 +0 3.324 0.471 -0.979 0.980 -0.596 1.497 0.292 0.818 2.173 0.641 -0.249 0.424 0.000 1.615 -0.527 1.039 0.000 1.385 1.067 -1.176 1.551 0.861 0.913 0.997 0.697 1.678 1.398 1.351 +1 0.536 -0.242 1.194 1.174 -0.234 0.598 -0.563 -0.689 0.000 0.871 -1.289 1.003 2.215 1.345 -0.048 0.986 2.548 1.015 -1.673 -0.679 0.000 0.933 1.118 1.055 0.803 0.784 0.945 0.837 +1 1.147 -1.031 -1.239 0.426 1.431 1.639 1.635 1.527 0.000 1.115 -0.408 0.007 2.215 1.803 0.503 0.069 2.548 0.682 -0.751 -0.161 0.000 0.609 0.669 0.984 0.975 0.766 0.928 0.733 +1 0.755 -2.203 -0.889 0.666 0.354 0.754 -0.376 0.014 0.000 0.927 -2.655 -1.572 0.000 0.771 -0.054 -0.311 2.548 1.589 -0.171 1.429 3.102 3.143 1.997 0.987 1.044 0.848 1.378 1.117 +1 0.999 -1.843 -0.413 1.144 -0.594 0.794 -0.472 0.977 2.173 0.446 -2.088 0.772 0.000 0.714 0.197 -1.179 2.548 0.530 -0.491 1.640 0.000 0.705 0.857 1.008 1.230 0.934 0.913 0.791 +0 0.971 0.068 -0.483 0.753 0.269 0.696 0.891 -1.638 0.000 0.539 -0.637 -1.343 0.000 1.221 -0.147 0.808 2.548 0.517 0.452 0.041 1.551 1.168 0.823 0.979 0.845 0.444 0.706 0.715 +0 1.011 -1.399 0.504 0.375 1.333 1.412 -1.194 -0.135 2.173 0.951 -1.113 -1.635 0.000 1.828 -0.520 1.504 0.000 0.429 0.079 -0.420 3.102 0.857 0.790 0.993 1.001 0.611 1.116 0.930 +0 1.798 -0.380 1.201 0.292 -0.363 1.298 0.556 1.519 1.087 2.321 0.781 -0.286 2.215 0.683 0.506 -1.300 0.000 0.561 -1.441 -0.454 0.000 1.072 1.300 0.991 1.694 2.569 1.472 1.226 +1 1.381 1.042 0.792 0.943 -1.682 0.741 2.007 -0.813 0.000 1.353 1.019 -0.145 2.215 0.623 0.896 -1.256 0.000 0.948 1.111 -1.029 1.551 0.818 1.058 1.252 0.812 0.745 0.814 0.801 +1 0.662 -2.291 1.023 0.997 -0.962 0.777 -0.641 -0.630 2.173 0.789 -0.531 0.225 2.215 1.397 -1.186 1.103 0.000 0.782 -1.886 1.742 0.000 0.852 1.025 1.099 1.160 0.804 0.949 0.903 +1 0.745 -0.227 -0.881 0.279 -0.044 0.737 0.507 -1.677 0.000 0.858 -0.464 -0.222 0.000 1.288 -0.537 1.277 2.548 1.624 0.185 0.268 0.000 0.856 1.118 0.988 0.530 0.256 0.664 0.674 +0 0.585 1.509 1.709 1.521 0.616 0.919 -0.517 -1.398 0.000 0.881 0.698 0.136 2.215 0.471 0.250 0.554 2.548 1.016 -0.143 -0.071 0.000 1.397 0.943 1.089 0.844 0.295 0.800 0.969 +1 0.600 -0.148 0.432 0.574 -0.715 1.086 -1.158 0.691 0.000 0.567 -1.347 -1.365 0.000 0.360 -0.871 -0.234 0.000 1.902 -0.289 -1.110 3.102 0.934 0.748 0.986 0.516 0.242 0.537 0.596 +1 1.006 0.090 -0.264 2.000 0.367 1.047 -0.827 -1.487 2.173 0.576 -0.059 1.492 0.000 0.882 0.245 -0.815 0.000 0.572 -0.433 0.832 3.102 0.972 0.961 1.058 0.615 0.722 1.003 0.868 +0 1.356 1.606 -1.658 0.958 0.660 0.526 -2.811 1.407 0.000 0.814 0.816 -0.492 2.215 1.240 0.211 -0.078 2.548 1.254 0.867 0.145 0.000 0.816 0.710 1.372 1.067 0.510 0.920 0.751 +1 0.912 -0.332 1.580 0.317 -1.246 2.927 1.235 -0.135 0.000 2.463 1.021 1.407 0.000 2.055 0.537 -1.557 2.548 1.019 0.660 0.911 0.000 0.932 1.154 0.991 0.860 0.985 1.013 0.865 +0 0.953 -1.152 -0.398 0.699 1.200 0.912 -0.385 1.061 2.173 1.943 -0.983 -0.879 2.215 0.901 -1.101 1.192 0.000 1.052 -1.166 0.464 0.000 0.663 0.717 1.121 0.990 2.025 1.097 0.911 +1 0.830 1.119 1.314 0.918 -1.198 0.641 1.276 -1.065 0.000 0.810 0.948 0.253 0.000 1.145 0.031 0.868 1.274 0.858 1.868 -0.782 0.000 1.140 1.172 0.990 0.606 0.584 0.691 0.689 +1 0.485 1.001 -0.506 1.485 -0.200 1.615 -0.301 -1.710 2.173 1.470 1.471 0.276 0.000 0.860 1.008 1.581 0.000 1.107 0.533 -1.132 3.102 1.631 1.223 0.998 2.858 0.989 1.773 1.573 +1 1.273 -1.399 0.647 1.112 1.260 0.818 -0.549 0.056 0.000 0.611 -0.030 -0.510 2.215 1.634 -0.886 -1.443 2.548 0.983 -0.742 -0.980 0.000 1.125 1.185 0.986 1.186 0.947 0.986 0.941 +1 1.581 0.452 0.538 1.062 -0.067 1.022 -0.940 -1.242 2.173 0.604 -0.481 1.160 2.215 0.484 -1.251 -1.686 0.000 0.686 -0.369 -0.222 0.000 0.687 0.678 0.986 0.823 0.994 1.081 0.854 +1 0.762 0.590 -1.425 0.709 -1.520 0.799 -0.360 -1.533 0.000 1.223 -1.587 0.577 0.000 1.923 0.010 0.079 2.548 1.845 -0.478 -0.506 3.102 2.443 1.691 0.981 1.138 0.841 1.285 1.112 +0 2.481 -0.420 -0.702 0.443 0.261 0.954 -1.506 1.408 0.000 0.771 -1.134 -0.060 2.215 0.826 -0.002 0.978 2.548 0.881 -2.041 0.574 0.000 1.171 1.142 1.108 0.982 0.860 0.881 1.017 +1 0.549 1.171 -1.446 0.446 1.438 0.999 -0.156 0.346 2.173 0.925 -1.075 -0.886 0.000 0.865 -0.039 -1.292 0.000 1.364 0.555 1.112 3.102 0.908 1.215 0.982 0.979 0.945 1.017 0.863 +0 0.839 -0.076 -1.000 1.018 0.212 1.104 -1.153 0.632 0.000 1.291 -1.067 -1.402 2.215 0.647 -1.951 -1.503 0.000 0.539 -1.163 -0.248 1.551 1.623 0.948 1.136 1.109 0.659 0.876 0.907 +1 0.669 -0.427 -1.252 1.465 -0.204 1.268 -1.237 -1.576 0.000 0.862 2.113 0.638 0.000 0.442 -0.467 -1.631 0.000 2.089 0.217 0.292 3.102 0.511 1.583 1.112 0.905 0.650 1.251 1.045 +1 0.348 -1.474 0.773 1.901 1.470 0.905 0.374 -0.524 2.173 0.297 1.349 -0.780 0.000 0.720 0.884 0.592 2.548 0.397 -0.545 0.546 0.000 0.683 0.632 0.983 0.915 0.902 0.973 0.775 +1 1.554 0.932 -0.865 1.540 -1.449 1.958 1.826 0.528 0.000 2.703 -0.172 -1.515 1.107 2.068 0.939 0.287 0.000 0.780 -0.152 -0.372 3.102 1.703 1.640 1.074 1.479 1.124 2.453 1.979 +1 1.881 0.567 -0.339 0.602 0.492 1.177 1.298 -1.643 2.173 0.762 1.127 0.313 0.000 0.436 0.680 -1.477 0.000 1.389 0.582 1.146 3.102 0.902 1.081 1.004 0.909 0.900 1.009 0.839 +1 1.013 0.085 1.562 0.378 -1.614 0.794 0.421 0.840 2.173 1.266 0.298 -0.331 0.000 0.889 -1.103 -1.588 2.548 0.719 -0.501 0.625 0.000 1.071 1.177 0.980 0.804 1.281 1.010 0.953 +1 0.845 0.052 1.722 0.596 0.105 0.774 -0.227 -1.466 0.000 1.048 -0.525 0.707 2.215 1.057 0.223 0.073 2.548 1.064 -0.860 -0.858 0.000 0.922 1.086 0.988 0.778 0.754 0.878 0.755 +0 1.440 -0.114 -0.678 0.885 0.995 1.367 1.746 1.486 0.000 1.816 0.385 0.238 2.215 1.337 0.434 -0.242 0.000 1.819 0.802 -1.450 3.102 2.951 1.793 1.561 1.228 1.705 1.561 1.412 +0 0.749 -0.900 0.788 0.755 1.183 1.346 1.337 -0.415 0.000 1.588 -0.639 -1.277 2.215 1.406 -1.171 1.127 2.548 1.621 -0.831 0.438 0.000 3.653 2.929 0.979 0.603 1.409 2.111 1.927 +1 0.282 -2.020 -0.779 1.574 1.269 0.648 0.499 1.567 0.000 0.582 2.487 -0.403 0.000 0.607 -0.959 0.530 0.000 1.515 0.109 -0.495 0.000 0.795 0.767 0.982 1.892 0.677 1.745 1.319 +0 1.108 1.331 0.422 0.520 -1.624 0.971 0.792 -0.401 2.173 0.829 0.435 1.535 1.107 0.531 -0.780 -1.026 0.000 0.758 -0.841 1.273 0.000 0.616 1.179 1.012 0.796 1.321 0.876 0.859 +0 0.952 -0.554 -1.594 0.613 0.427 0.627 -1.082 1.178 0.000 1.048 -0.184 -1.271 0.000 1.337 0.213 0.303 2.548 0.988 -1.199 -0.643 3.102 0.806 1.062 1.026 0.699 1.057 0.808 0.726 +0 1.206 1.413 0.767 0.807 -0.228 0.678 -1.047 -1.740 2.173 0.654 -2.534 -1.244 0.000 0.407 -1.589 0.291 0.000 0.821 0.108 -0.424 1.551 0.853 0.915 1.067 0.765 0.884 1.138 1.531 +1 0.544 -0.966 -1.593 0.794 0.410 0.717 -0.413 -0.900 0.000 0.616 -1.050 -0.369 2.215 1.436 -0.612 1.264 2.548 0.485 1.061 0.494 0.000 1.210 0.907 0.991 0.695 1.016 0.823 0.709 +0 0.363 -0.747 1.667 2.057 0.504 0.800 1.871 -1.625 0.000 0.662 -2.131 -0.480 0.000 0.675 0.420 -0.526 0.000 1.585 -1.073 -1.154 3.102 0.802 0.894 1.038 1.001 1.209 1.101 1.003 +0 1.639 -0.467 1.004 0.571 1.324 1.114 -1.460 -0.219 0.000 1.803 1.201 -1.306 2.215 0.882 1.389 -0.710 0.000 1.657 0.451 0.892 3.102 0.994 1.044 0.987 0.931 1.534 1.493 1.237 +1 0.613 0.155 1.293 0.884 -1.449 0.899 1.719 0.660 0.000 1.131 0.161 -0.856 2.215 0.884 1.479 -0.989 2.548 0.589 1.568 -0.327 0.000 0.946 0.984 0.981 1.216 0.856 0.909 0.843 +1 0.633 0.586 -0.472 1.327 -1.028 0.627 1.325 1.302 0.000 0.702 0.635 -0.245 2.215 0.470 2.160 -1.545 0.000 0.862 1.523 0.281 0.000 0.936 0.933 0.984 0.721 0.593 0.619 0.799 +1 2.111 0.434 1.391 0.402 0.331 0.708 0.673 0.910 2.173 0.767 1.198 -1.023 0.000 1.143 -1.323 -0.468 2.548 1.346 -0.209 -0.556 0.000 0.752 0.986 1.041 0.644 1.786 1.124 0.964 +1 0.385 -0.358 1.250 1.778 -1.309 1.193 -0.567 -0.392 2.173 0.951 0.110 0.513 2.215 0.461 -1.429 1.370 0.000 0.382 -1.831 -1.391 0.000 0.318 0.961 0.983 1.211 1.266 1.060 0.857 +1 0.666 -1.386 -0.955 1.362 -0.234 1.263 1.129 1.526 0.000 0.845 -0.932 1.108 1.107 2.164 -0.741 -0.076 2.548 0.555 -0.862 -0.630 0.000 0.768 1.003 0.996 0.967 1.263 0.821 0.752 +0 0.725 0.784 1.106 1.408 0.360 0.685 0.274 1.622 2.173 0.689 0.670 -0.063 2.215 0.405 0.649 -1.362 0.000 1.231 -0.537 -1.199 0.000 0.582 0.800 0.987 0.887 1.032 0.713 0.701 +1 2.101 -0.007 -1.083 1.104 -0.630 0.621 -0.279 1.610 0.000 1.390 -0.933 0.582 2.215 0.813 -2.067 -0.753 0.000 1.541 0.436 0.069 3.102 0.943 1.057 0.991 1.030 1.224 1.157 1.093 +0 0.600 -2.395 -1.504 0.886 -0.305 0.662 -0.205 0.920 2.173 0.571 -0.840 -1.041 0.000 0.681 -0.337 1.335 2.548 0.928 -1.002 0.024 0.000 0.793 0.925 0.985 0.850 0.312 0.812 0.712 +0 0.719 -1.899 -0.204 1.242 0.404 0.810 -0.340 -1.164 2.173 0.643 -0.457 -0.700 2.215 0.679 -1.522 1.304 0.000 0.379 1.506 0.617 0.000 1.579 1.097 0.991 1.175 0.436 0.807 0.868 +1 0.628 0.172 0.227 2.754 -0.474 0.823 0.935 1.016 2.173 1.062 -0.269 1.596 1.107 0.914 -0.262 -1.233 0.000 0.469 -0.006 0.939 0.000 0.676 0.937 1.076 1.368 1.126 1.196 0.943 +1 0.483 1.215 -1.188 0.843 1.565 0.622 0.565 -1.394 0.000 1.203 0.517 1.515 0.000 1.271 0.390 -0.194 2.548 2.168 -0.426 0.515 3.102 0.912 1.239 0.980 2.053 0.977 1.430 1.270 +0 1.640 -2.185 1.590 0.717 1.696 0.664 -0.320 -0.715 1.087 0.743 1.023 0.377 2.215 0.725 -2.235 -0.107 0.000 0.625 0.184 0.284 0.000 1.342 1.138 1.005 2.166 1.153 1.522 1.305 +1 1.563 -1.177 -1.545 1.261 -1.092 1.345 -1.148 0.644 2.173 0.596 -0.555 -1.537 0.000 1.068 -0.561 -0.214 2.548 0.608 -1.996 -0.044 0.000 1.121 0.897 0.980 1.582 1.121 1.131 0.950 +1 0.756 0.605 -1.013 1.022 1.101 0.725 0.638 -0.511 0.000 0.687 -0.257 0.249 2.215 0.794 -1.046 1.235 0.000 1.756 -0.175 1.718 1.551 1.951 1.219 1.151 0.784 0.961 0.899 0.826 +0 2.354 0.365 -0.899 0.603 -0.924 1.411 -0.270 1.065 2.173 0.528 0.354 0.823 0.000 0.438 1.329 -0.296 0.000 0.393 1.939 0.762 0.000 0.755 1.066 0.979 0.698 0.641 1.080 0.912 +1 0.844 1.553 0.847 0.809 1.698 0.798 1.020 -1.444 0.000 1.025 0.514 -0.277 2.215 1.220 1.293 0.055 0.000 0.695 0.799 1.479 3.102 0.893 1.039 0.992 0.486 0.780 0.668 0.667 +1 1.098 -1.293 0.346 1.101 -1.129 0.931 0.762 1.685 2.173 1.323 0.046 0.531 0.000 1.739 -1.029 -0.476 0.000 0.961 -0.940 1.610 3.102 0.838 0.604 1.479 1.808 1.102 1.175 0.995 +1 0.406 -0.710 0.939 0.446 -1.314 0.370 -0.512 0.680 0.000 0.565 0.470 1.268 2.215 0.692 -0.124 -0.770 2.548 0.434 -1.402 -0.375 0.000 0.626 0.692 0.981 0.622 0.674 0.526 0.539 +1 0.403 0.775 -0.469 0.559 0.224 0.943 1.609 -0.698 0.000 1.022 -1.756 1.265 0.000 1.376 1.272 0.771 1.274 1.185 1.079 -1.248 0.000 0.813 0.980 0.982 1.189 0.848 0.881 0.982 +1 0.733 1.283 0.610 1.413 0.944 1.168 -0.014 -0.626 2.173 0.543 0.726 -1.698 2.215 0.738 -0.035 -1.306 0.000 0.514 0.947 1.062 0.000 0.713 0.893 0.978 0.929 1.066 1.422 1.107 +0 0.399 0.973 -0.820 1.063 1.682 0.440 1.814 -0.866 0.000 0.933 0.623 0.785 2.215 1.257 0.852 -0.297 2.548 0.509 2.081 1.221 0.000 0.731 0.994 0.979 0.971 0.968 0.869 0.747 +0 0.409 1.925 -0.728 1.518 1.453 0.758 1.030 0.561 2.173 0.681 1.091 -0.676 2.215 0.607 0.024 -0.872 0.000 0.915 1.150 -1.654 0.000 0.946 0.873 1.008 0.865 0.950 0.805 0.736 +1 1.042 -0.999 0.802 0.743 -1.088 0.578 -1.182 -0.591 0.000 1.167 0.105 0.742 1.107 0.695 0.857 -0.882 2.548 0.666 -0.870 -1.154 0.000 0.657 0.948 1.209 1.015 1.037 0.897 0.832 +1 0.389 2.026 -0.986 1.851 -0.766 0.716 -0.741 0.840 2.173 0.457 0.363 -0.794 0.000 0.730 0.275 0.635 0.000 1.330 0.409 1.642 3.102 0.851 0.882 0.980 0.863 0.964 1.054 0.849 +0 0.876 0.135 0.655 1.153 0.480 0.572 -0.981 -1.483 2.173 0.895 -1.483 -0.575 2.215 0.497 0.533 -1.201 0.000 0.533 -0.874 1.483 0.000 0.639 0.869 0.985 0.990 0.821 0.863 0.740 +0 0.400 0.378 1.059 1.725 0.163 0.341 1.893 -1.604 0.000 0.560 0.825 0.586 0.000 1.541 0.615 -1.023 2.548 0.511 0.264 -1.296 0.000 0.998 0.998 0.984 0.587 0.734 0.785 0.828 +1 0.876 0.822 0.658 1.272 0.414 1.271 -0.435 -1.038 2.173 0.473 -1.231 -1.509 0.000 1.247 0.218 0.603 0.000 0.760 -1.202 1.395 3.102 1.478 0.959 0.989 2.221 1.006 1.617 1.383 +1 1.030 -1.509 0.131 0.715 1.329 0.555 -0.837 0.182 2.173 1.358 -1.405 0.838 1.107 2.240 -1.104 -1.520 0.000 1.743 0.347 -0.059 0.000 0.705 1.170 1.048 0.712 0.811 1.007 0.864 +1 1.240 0.483 -1.655 0.731 -0.635 1.010 0.222 -1.017 2.173 1.083 -0.622 0.519 0.000 1.235 1.397 0.360 0.000 0.730 1.749 1.485 0.000 0.938 0.828 1.049 0.709 0.804 0.936 0.821 +0 2.324 0.426 -1.190 0.297 1.053 0.884 0.179 0.373 2.173 0.686 -0.244 0.770 0.000 1.030 1.233 1.507 2.548 1.412 -1.085 0.139 0.000 0.850 0.927 1.036 0.858 1.254 0.986 0.980 +1 0.420 -0.987 0.130 1.341 1.544 1.233 -2.941 1.598 0.000 1.932 -0.011 -0.268 2.215 0.672 0.217 0.066 0.000 1.342 -0.323 0.647 0.000 0.621 0.878 0.994 0.866 0.663 0.948 0.806 +1 0.371 -2.272 -1.504 0.777 -1.741 0.698 -0.976 0.608 0.000 0.990 -0.458 -0.313 1.107 1.638 -0.399 -1.108 2.548 1.031 0.310 0.558 0.000 0.951 0.977 0.995 0.757 0.889 0.905 0.835 +1 0.620 1.262 -1.627 0.367 -0.538 1.096 0.846 -0.300 0.000 1.155 0.925 1.229 2.215 0.691 0.993 0.308 0.000 0.551 -0.777 1.598 3.102 0.837 1.046 0.993 0.827 0.819 0.923 0.794 +1 1.433 -0.277 1.504 0.976 -1.332 1.215 0.453 0.057 0.000 0.660 -0.281 -1.194 2.215 0.596 0.505 0.505 0.000 1.297 0.480 -0.795 0.000 0.893 0.751 0.986 0.706 0.411 0.511 0.601 +0 1.116 0.106 -0.597 3.477 -0.913 1.676 -0.304 0.201 0.000 1.384 1.358 1.636 2.215 1.354 2.386 1.245 0.000 1.700 1.438 1.177 0.000 1.001 1.024 0.991 2.162 1.409 1.567 1.382 +0 0.569 -0.610 0.127 0.645 -0.711 0.636 0.541 -1.697 0.000 1.456 -0.411 0.375 0.000 1.508 -0.865 -1.436 2.548 1.333 -1.163 0.562 0.000 0.889 1.204 0.999 0.528 0.725 0.749 0.663 +0 1.394 -1.095 -0.022 0.527 -1.116 0.690 0.136 1.641 0.000 1.014 -0.914 1.608 0.000 1.970 -0.147 0.980 2.548 1.187 2.280 -0.394 0.000 1.006 1.047 0.989 0.773 1.464 1.018 0.986 +0 0.760 -0.377 -1.609 0.829 -0.808 0.623 -0.158 -0.575 1.087 0.864 0.840 0.947 2.215 0.558 0.767 0.417 0.000 0.372 -0.415 1.119 0.000 0.469 0.648 0.992 1.274 1.205 0.943 0.755 +1 0.998 1.339 -0.989 0.611 -1.345 0.785 0.579 1.094 2.173 1.375 0.751 -0.209 0.000 1.600 0.467 -1.644 1.274 1.250 1.165 0.622 0.000 1.284 1.257 1.003 1.129 0.871 1.028 1.065 +0 0.519 -0.939 1.271 0.238 1.268 0.796 0.634 -0.462 0.000 1.300 0.011 -1.707 2.215 2.143 -0.264 0.047 2.548 1.127 1.106 -1.440 0.000 0.827 0.902 0.989 0.988 1.794 1.030 0.853 +1 0.706 -0.760 1.114 0.423 0.203 0.883 2.281 -0.338 0.000 0.716 0.480 1.445 2.215 0.741 -0.926 -1.068 2.548 0.708 -1.320 -0.026 0.000 0.737 1.231 0.985 0.705 0.876 0.792 0.734 +1 0.937 0.241 1.678 0.614 0.919 0.885 0.729 0.012 0.000 1.196 -0.603 1.566 2.215 1.207 0.044 -0.309 0.000 0.623 -2.277 -1.378 0.000 0.841 0.849 0.987 0.634 0.944 1.037 0.907 +0 0.767 0.028 0.703 1.457 -1.151 0.728 0.738 -1.577 0.000 1.225 -0.532 0.499 2.215 1.016 2.083 0.332 0.000 0.970 1.044 -0.184 0.000 0.769 1.658 1.457 1.166 0.920 1.361 1.191 +0 0.953 -1.325 -0.761 1.863 -1.563 0.403 -0.268 -0.126 2.173 1.137 0.855 0.660 2.215 0.758 -0.751 0.988 0.000 0.639 0.892 -0.336 0.000 0.947 0.865 1.219 2.095 0.887 1.361 1.159 +1 1.419 -2.151 1.506 0.740 0.455 1.292 -0.850 -0.793 2.173 0.523 -1.185 -0.164 1.107 0.827 0.344 1.340 0.000 0.671 -0.141 0.827 0.000 1.195 0.963 1.152 1.604 0.686 1.054 1.061 +1 1.416 0.413 1.361 1.339 -0.090 1.308 0.945 -0.590 0.000 0.452 1.165 1.620 2.215 1.103 2.017 1.284 0.000 0.814 -0.825 0.577 3.102 2.592 1.498 1.842 1.053 0.851 1.240 1.154 +0 1.261 -1.439 -0.828 0.310 0.515 0.664 0.752 0.062 0.000 0.534 -0.255 0.682 0.000 0.908 -0.919 1.051 2.548 1.463 -0.000 -1.079 3.102 0.900 0.876 0.988 0.771 0.949 0.685 0.643 +0 0.609 -0.347 -0.301 0.453 1.602 0.660 -1.038 0.933 0.000 1.056 -0.105 1.166 2.215 0.880 -0.307 -0.613 0.000 1.337 1.392 -0.881 0.000 0.691 0.750 0.986 0.786 0.763 0.579 0.611 +0 1.342 -0.382 0.018 0.121 0.349 1.148 -0.237 -1.104 2.173 1.576 -0.448 0.609 2.215 0.324 -1.028 1.099 0.000 0.977 -0.009 -1.666 0.000 0.524 0.821 0.986 1.091 1.991 1.073 0.853 +0 0.597 1.864 0.287 0.639 0.851 0.625 2.111 1.015 0.000 1.152 -0.277 -0.401 2.215 0.991 -0.655 -1.276 1.274 0.489 -0.562 1.129 0.000 1.616 1.709 0.980 1.066 0.843 1.310 1.076 +0 1.663 -1.109 0.455 1.582 -0.158 1.294 -1.463 -1.708 0.000 1.191 -0.720 -0.447 2.215 1.000 -0.989 1.089 2.548 1.481 -1.864 -1.466 0.000 0.945 0.992 1.177 0.924 1.157 1.092 1.146 +1 1.787 -1.516 -1.071 0.775 0.157 0.627 0.184 0.296 2.173 0.782 -0.841 -1.153 0.000 1.017 -1.065 0.828 0.000 1.138 -0.429 1.496 3.102 1.352 0.872 1.459 1.412 0.851 0.999 0.898 +0 0.516 -0.215 0.373 0.976 -1.309 0.891 0.835 0.635 0.000 0.473 -1.489 -1.224 2.215 0.522 0.093 -0.953 2.548 0.496 1.658 1.498 0.000 0.940 0.866 0.988 0.704 0.498 0.944 0.798 +0 0.375 1.376 -1.710 1.723 0.649 0.538 2.044 -0.581 0.000 0.772 -1.063 0.964 0.000 1.486 0.839 -1.290 2.548 1.611 0.623 -0.559 3.102 0.839 0.876 0.989 0.918 0.732 0.814 0.751 +1 0.331 -0.424 1.182 0.817 0.365 1.313 0.890 -1.076 0.000 0.631 1.284 0.301 2.215 0.480 2.490 0.921 0.000 1.161 1.001 1.532 3.102 2.047 1.267 0.992 0.565 0.694 0.899 0.807 +0 0.401 -0.206 0.531 1.813 1.584 0.597 -0.818 0.016 0.000 0.609 -1.123 0.819 0.000 0.780 0.267 -0.577 2.548 0.441 0.393 -1.465 3.102 0.874 0.898 0.987 0.554 0.324 0.596 0.663 +0 1.962 -0.173 -1.116 0.133 1.145 1.027 -1.829 0.465 0.000 1.834 0.530 -0.927 2.215 1.700 0.814 0.958 0.000 0.818 1.670 0.500 0.000 0.923 0.944 0.990 0.943 1.286 1.124 1.087 +1 1.282 0.212 -0.174 1.081 -0.729 0.741 -1.618 0.297 0.000 1.173 -0.451 -1.462 2.215 1.139 -1.160 1.159 0.000 0.642 1.236 1.113 0.000 1.196 1.018 0.991 1.194 0.461 0.915 1.133 +1 0.942 -1.139 -1.001 0.896 1.549 1.742 -0.839 -1.253 1.087 2.248 2.024 0.509 0.000 1.161 0.015 0.350 2.548 1.983 0.085 -0.391 0.000 1.828 2.187 0.988 0.774 1.926 3.371 2.752 +1 1.201 1.506 -0.147 0.870 -0.726 1.266 0.539 0.853 2.173 0.425 1.782 -1.006 0.000 0.488 1.972 -1.465 0.000 0.510 -0.478 -0.668 1.551 0.304 0.712 0.990 1.317 0.972 0.907 0.834 +1 2.373 -1.468 0.390 0.986 -0.130 0.628 -0.702 -1.114 2.173 0.536 -1.777 -1.105 0.000 0.906 2.009 -1.450 0.000 1.390 -1.222 1.338 1.551 3.963 2.391 0.991 0.971 0.882 1.678 1.837 +0 0.907 0.421 -0.059 0.777 -0.550 0.477 -0.041 -1.590 0.000 1.100 -0.403 1.117 2.215 1.054 0.021 -0.800 2.548 0.572 -1.149 0.721 0.000 0.902 0.793 0.992 1.330 1.158 0.967 0.871 +0 0.340 0.441 0.190 1.619 1.262 0.778 0.773 -0.641 2.173 0.506 1.526 0.255 0.000 0.749 0.399 -1.071 2.548 0.665 -0.271 0.565 0.000 0.854 0.887 0.987 0.807 0.395 0.824 0.766 +0 3.825 -0.254 -1.436 0.872 -1.264 2.110 0.581 0.278 0.000 0.603 -0.123 -0.744 0.000 1.207 0.283 0.716 2.548 0.728 -0.922 1.405 3.102 2.099 1.333 0.999 0.828 0.691 1.015 1.385 +1 1.337 0.712 1.628 1.613 1.731 2.029 0.363 1.741 2.173 1.664 0.890 -0.041 0.000 0.889 1.076 0.366 0.000 1.417 -0.656 0.394 0.000 0.713 0.633 0.986 1.128 1.214 1.365 1.239 +1 0.303 0.242 0.273 0.470 0.527 1.034 -1.528 -0.907 2.173 1.104 -0.889 0.824 2.215 1.218 -0.962 -0.470 0.000 1.225 -0.876 1.638 0.000 1.056 1.072 0.975 2.876 1.650 2.057 1.742 +0 1.123 1.850 -1.063 2.117 -1.537 1.130 0.740 0.143 0.000 1.637 0.496 0.533 2.215 1.498 1.004 -1.674 2.548 0.708 1.327 -0.494 0.000 0.951 0.963 0.982 0.934 1.601 1.484 1.360 +1 1.021 -0.538 -1.726 0.297 -0.440 1.078 -0.232 -0.088 2.173 0.779 -0.924 1.293 0.000 1.341 0.421 0.535 2.548 0.829 -1.372 -1.381 0.000 0.887 1.291 0.991 1.084 0.958 1.032 0.915 +0 1.322 -1.823 0.492 1.347 0.637 0.820 0.157 -1.262 0.000 0.941 -0.833 -0.869 1.107 0.797 -0.740 1.566 2.548 0.395 -1.166 0.341 0.000 1.142 0.880 0.971 1.119 0.747 1.098 1.230 +1 0.561 -1.440 1.539 0.513 0.208 0.565 -0.538 1.297 2.173 0.855 -0.992 -1.166 0.000 0.819 -1.528 -0.535 0.000 1.239 0.402 0.314 3.102 0.814 0.999 0.985 0.720 0.834 0.875 0.740 +0 2.036 0.714 1.101 0.234 1.061 0.592 1.254 0.298 2.173 0.607 0.528 -0.939 0.000 0.998 -2.243 -0.841 0.000 0.863 1.120 -1.196 3.102 0.814 1.058 0.994 0.770 0.738 0.702 0.772 +0 0.726 -0.595 0.928 1.469 0.039 0.593 -1.574 -0.952 2.173 0.451 0.715 1.636 0.000 0.612 -0.967 -1.307 0.000 0.498 1.036 1.198 3.102 0.909 0.996 1.026 0.792 1.237 0.844 0.782 +0 2.145 -0.361 -1.499 0.824 -1.360 1.884 -0.190 0.515 0.000 2.011 0.325 -1.203 1.107 1.666 -0.443 0.138 2.548 1.187 0.219 0.909 0.000 0.951 0.925 0.966 1.135 1.996 1.517 1.453 +1 0.658 -0.108 0.317 0.842 1.307 1.469 0.634 -1.111 0.000 1.151 -0.321 0.772 2.215 1.058 0.169 -0.668 2.548 1.221 2.202 0.707 0.000 3.156 1.966 0.986 0.742 1.171 1.630 1.272 +1 0.638 -1.221 0.768 0.976 -0.904 0.680 0.371 1.017 0.000 1.059 -0.818 0.027 2.215 0.697 0.556 -1.710 0.000 1.631 -0.026 -0.741 3.102 0.794 1.010 1.091 0.798 0.903 0.909 0.876 +0 0.820 0.273 0.965 0.895 -1.306 0.648 0.037 0.413 2.173 1.342 0.009 -0.622 2.215 0.688 1.405 -1.677 0.000 0.601 -0.205 1.344 0.000 0.778 0.902 1.055 0.949 1.103 0.836 0.747 +0 0.765 -0.919 0.449 1.191 1.480 0.614 1.017 -0.021 2.173 1.212 -0.541 -1.182 2.215 0.593 1.935 0.380 0.000 0.580 -0.205 1.698 0.000 1.132 0.850 1.059 0.989 1.565 1.097 1.073 +1 0.579 1.620 0.366 0.592 0.234 2.496 0.569 -1.099 2.173 1.186 1.014 0.792 0.000 1.072 -0.341 1.587 2.548 1.343 0.094 0.691 0.000 0.913 1.210 0.998 1.559 1.678 1.495 1.219 +1 0.910 0.006 -1.343 0.452 0.252 1.069 -0.444 0.140 2.173 0.422 -1.018 0.980 0.000 1.229 0.295 1.592 2.548 0.913 0.277 -0.327 0.000 0.960 0.881 0.985 0.895 1.490 0.838 0.719 +1 1.726 0.627 -1.212 0.734 -0.413 0.714 2.244 1.300 0.000 1.363 0.527 1.688 0.000 2.367 0.220 0.213 2.548 2.095 0.956 -0.105 3.102 0.525 1.312 1.028 1.290 0.932 1.101 1.009 +1 1.212 -0.213 -1.188 1.116 -0.843 1.388 0.195 0.740 2.173 0.801 -0.367 0.256 0.000 0.604 -0.617 -0.871 0.000 1.180 -0.429 1.668 0.000 0.921 1.134 0.989 0.703 0.833 1.000 0.877 +1 0.328 0.149 1.386 1.461 -0.325 1.902 2.817 1.724 0.000 1.248 -0.976 0.133 2.215 1.200 -2.240 1.732 0.000 2.612 0.099 0.740 0.000 1.243 0.991 0.988 1.137 0.559 0.817 0.822 +1 0.651 0.939 1.122 0.484 0.841 1.242 0.783 -0.809 1.087 0.525 0.085 0.948 0.000 0.762 0.231 -1.417 0.000 0.992 -0.422 -0.188 1.551 0.825 1.097 0.973 0.694 1.028 0.880 0.764 +1 0.827 1.549 -0.312 0.925 0.211 1.135 1.107 -1.580 1.087 0.762 1.345 1.067 2.215 0.863 0.700 -0.675 0.000 0.772 1.515 0.146 0.000 0.783 1.058 0.984 0.902 0.956 1.020 0.871 +0 1.104 0.022 0.911 1.388 0.149 0.836 -0.806 -1.384 2.173 0.492 -1.350 1.309 0.000 0.990 -0.977 -0.710 1.274 0.756 -0.409 0.035 0.000 0.813 0.846 1.087 1.030 0.664 0.951 0.795 +1 0.749 -0.727 0.155 1.437 -0.975 1.558 1.341 0.960 2.173 2.399 -0.392 -1.013 2.215 0.659 -0.693 1.186 0.000 1.948 -0.374 0.481 0.000 0.767 1.646 1.222 2.354 3.973 2.083 1.669 +1 1.918 0.720 -1.026 0.225 0.388 0.971 0.652 1.214 2.173 0.503 2.050 0.304 0.000 0.765 0.084 -0.634 0.000 0.797 -0.753 1.272 0.000 0.963 1.076 0.988 0.584 0.767 0.743 0.710 +1 0.402 -0.271 -1.086 1.673 0.093 0.403 -1.100 -0.778 0.000 1.756 -0.977 1.709 2.215 0.634 -1.356 -0.360 0.000 0.979 -1.149 0.373 0.000 0.836 1.124 0.992 0.636 0.889 0.915 0.803 +1 0.873 -0.488 0.996 0.878 -0.448 0.781 0.495 -1.240 2.173 0.646 0.549 0.945 2.215 0.731 -0.471 -1.050 0.000 0.684 0.896 0.315 0.000 1.008 0.832 1.169 0.808 0.964 0.785 0.697 +1 2.351 0.072 -0.404 0.872 -1.037 0.885 -0.053 1.106 2.173 0.380 -0.343 1.510 0.000 0.276 -0.332 0.810 0.000 0.966 0.298 -1.298 3.102 0.907 0.854 1.071 0.711 0.835 0.918 0.785 +1 0.730 -1.946 -0.031 1.203 0.459 1.136 -1.154 -1.233 1.087 0.932 -0.757 1.417 2.215 0.510 -1.463 -0.653 0.000 0.815 0.286 0.465 0.000 1.007 0.904 0.994 1.270 1.076 0.977 0.854 +1 1.047 -0.894 0.676 0.477 -1.095 3.077 -0.057 -0.140 0.000 1.403 -0.097 1.687 2.215 1.809 -0.634 1.307 1.274 3.955 -0.817 -1.612 0.000 5.763 3.590 0.988 0.920 0.765 2.288 1.689 +0 0.539 0.494 0.228 1.143 0.576 0.702 -0.619 1.173 0.000 1.029 -0.491 -0.597 2.215 1.152 -0.230 -1.710 0.000 0.503 0.857 -0.455 3.102 0.888 0.875 0.984 1.533 0.555 0.943 1.074 +0 0.509 0.955 -1.231 1.442 -1.368 0.761 -1.545 -0.515 0.000 1.345 0.987 0.221 2.215 1.009 -0.867 1.111 2.548 1.017 0.758 -1.725 0.000 0.607 0.907 1.001 2.139 1.681 1.600 1.201 +1 1.692 1.393 1.065 1.550 1.099 0.903 1.055 -1.490 0.000 1.847 0.614 -0.425 1.107 0.875 1.224 -0.635 0.000 0.652 0.924 -0.375 1.551 1.063 0.974 1.010 0.875 0.243 1.289 1.049 +1 0.736 1.938 -0.331 2.826 1.072 0.658 0.546 -1.685 2.173 0.661 0.373 0.645 1.107 0.647 1.091 1.271 0.000 0.908 0.772 -1.184 0.000 0.687 0.695 1.906 1.523 0.841 1.130 0.895 +1 0.533 -0.921 -1.009 1.196 -0.277 1.428 0.527 1.627 0.000 1.258 1.469 0.130 2.215 0.663 0.245 -0.275 0.000 0.766 0.855 1.069 3.102 0.565 0.565 0.980 2.824 0.698 1.823 1.621 +1 0.735 1.419 0.312 1.027 1.470 0.743 -0.255 -0.223 2.173 0.290 0.433 0.738 0.000 1.229 -0.652 -1.203 1.274 0.679 1.373 1.711 0.000 0.577 0.967 1.041 1.249 0.961 1.115 0.886 +0 0.533 -0.863 0.926 0.389 0.438 0.798 1.531 0.869 0.000 1.332 0.625 0.243 1.107 1.187 -1.006 -1.719 0.000 0.826 2.049 -1.244 0.000 1.188 0.819 0.981 0.749 1.778 1.232 1.034 +0 0.526 -1.174 0.986 0.561 -0.930 1.164 -0.578 0.580 2.173 0.979 -0.733 -0.885 0.000 1.583 -0.545 1.445 2.548 1.320 1.968 -0.756 0.000 0.847 0.940 0.985 0.880 1.188 0.944 0.789 +1 0.590 0.909 0.724 0.576 1.324 0.940 0.248 -0.967 0.000 0.637 0.377 -0.319 0.000 1.169 -1.108 1.385 1.274 1.295 0.104 0.419 3.102 0.914 0.971 0.990 1.981 0.989 1.312 1.205 +0 0.410 -0.529 1.583 1.783 -0.911 1.045 -1.480 1.028 0.000 0.791 0.540 -0.152 2.215 0.442 -1.377 0.365 0.000 0.798 -0.249 -0.662 3.102 0.902 0.851 0.990 0.512 0.448 0.549 0.595 +1 0.838 -0.410 -0.720 0.318 0.464 0.974 0.206 -1.291 2.173 0.787 0.175 0.959 0.000 0.865 -0.338 -0.014 0.000 2.099 -0.825 -1.323 3.102 0.843 1.060 0.993 0.941 0.963 0.886 0.772 +1 0.791 0.565 1.542 0.707 -0.519 0.699 -0.718 -0.124 2.173 0.420 0.133 1.147 0.000 0.479 1.746 0.940 0.000 0.923 -0.867 -1.495 3.102 0.708 0.946 0.994 0.945 0.814 0.845 0.750 +1 1.049 -0.695 0.610 0.243 0.832 0.818 -0.633 -0.020 0.000 1.509 -0.192 1.494 2.215 1.155 -0.770 -0.576 2.548 0.747 -1.146 -1.263 0.000 1.165 0.775 0.981 0.903 1.420 0.969 0.862 +1 0.763 0.362 -1.422 0.640 -0.624 0.617 1.341 -0.687 0.000 0.790 -0.067 0.312 2.215 0.610 1.799 -1.558 0.000 1.890 0.636 1.053 3.102 0.854 1.090 0.986 0.937 0.830 0.899 0.791 +1 1.008 0.747 -1.499 0.785 -0.734 1.165 0.099 -1.131 0.000 0.895 -0.999 1.026 2.215 2.449 -0.020 0.307 1.274 0.490 0.779 1.551 0.000 0.911 1.354 0.985 1.473 1.259 1.297 1.171 +0 1.662 0.115 -1.137 0.771 0.625 0.649 0.406 -0.201 2.173 0.324 1.861 0.335 0.000 0.286 0.475 -1.685 0.000 0.370 -0.403 -0.184 3.102 0.918 1.172 1.568 0.750 0.240 0.666 0.726 +1 1.694 1.163 -1.231 0.333 0.621 0.753 -0.003 1.250 2.173 0.576 -0.819 0.187 1.107 0.739 0.308 0.230 0.000 1.171 1.063 -0.470 0.000 0.863 1.036 1.035 1.178 0.897 0.947 0.838 +0 0.780 -1.154 1.627 1.226 -1.422 1.497 0.620 -0.024 0.000 2.840 -0.332 -1.419 0.000 1.854 -2.246 0.138 0.000 2.026 0.170 -0.520 3.102 0.659 1.268 0.984 1.654 0.768 1.295 1.093 +1 0.773 0.590 -1.211 1.406 -1.271 0.889 -0.142 0.337 2.173 0.707 0.155 -0.037 0.000 1.335 -0.311 1.074 2.548 0.998 1.980 -0.918 0.000 0.718 1.566 1.004 1.642 0.847 1.304 1.198 +1 1.119 1.135 -1.456 0.793 0.680 1.037 1.132 0.807 2.173 1.384 0.132 -1.246 0.000 1.509 -1.383 -0.417 0.000 1.989 -0.629 0.152 0.000 0.903 0.793 1.224 0.920 0.769 1.513 1.394 +1 0.850 -1.271 -0.412 1.566 1.470 1.066 -0.332 1.195 2.173 1.035 -1.270 0.028 2.215 0.808 0.342 -1.616 0.000 0.771 -0.593 -1.127 0.000 0.620 0.868 1.586 1.108 1.553 1.053 0.933 +0 0.951 2.276 -1.208 0.861 1.387 0.897 0.935 0.668 2.173 0.342 1.734 -0.950 0.000 0.671 1.894 0.453 0.000 1.501 0.553 -0.676 3.102 0.709 0.831 0.989 0.965 1.162 0.935 0.766 +0 0.909 -0.732 -0.083 0.873 -0.649 0.879 -0.344 0.632 1.087 1.072 0.226 1.493 2.215 0.470 0.747 1.069 0.000 0.509 -0.832 -1.348 0.000 0.715 0.745 0.989 1.383 1.084 1.077 0.863 +0 0.479 1.464 1.540 1.131 -0.830 0.959 0.843 1.522 0.000 1.843 0.698 -0.197 2.215 0.576 1.278 0.383 2.548 0.973 -1.281 -1.353 0.000 0.873 0.820 0.984 0.966 0.667 1.039 0.897 +1 0.538 -1.586 1.063 1.116 -1.422 0.903 -0.663 -0.859 0.000 1.074 -0.494 0.364 2.215 0.753 1.052 0.797 2.548 0.703 -0.778 -1.647 0.000 0.809 1.138 0.987 1.928 0.961 1.388 1.180 +0 0.350 -2.067 -1.083 1.232 -0.030 1.314 -1.491 0.887 0.000 1.372 -1.007 -1.054 2.215 0.863 -1.292 1.292 0.000 0.769 0.369 -0.207 0.000 0.977 0.955 0.982 0.601 0.350 0.586 0.617 +1 1.138 -0.202 -0.785 1.315 0.050 1.101 -0.226 1.536 2.173 1.914 -0.582 0.878 0.000 0.887 1.496 -0.246 0.000 1.173 -1.106 -1.511 0.000 0.795 0.636 1.159 1.319 0.846 0.917 0.891 +0 0.324 2.143 -0.796 0.683 -0.168 1.085 -0.081 -1.115 2.173 1.456 2.211 0.624 0.000 0.665 -1.157 -1.230 2.548 0.640 -0.125 0.535 0.000 1.932 2.456 0.990 0.951 0.676 1.853 1.400 +0 0.421 2.067 0.697 0.876 -0.027 1.117 0.587 1.136 1.087 0.387 -0.810 0.818 0.000 1.818 0.968 -0.580 2.548 1.174 -0.739 -1.115 0.000 0.865 1.249 0.989 0.810 1.824 1.192 1.012 +1 0.488 -1.026 -0.184 0.456 -0.058 0.418 -1.253 1.741 0.000 1.109 -0.360 -1.112 0.000 0.805 0.381 -0.117 2.548 1.390 0.827 0.925 3.102 1.026 1.061 0.991 0.786 0.694 0.933 0.809 +1 0.732 -0.578 -1.659 0.856 1.170 0.735 -0.973 1.361 2.173 1.202 -0.551 -1.422 2.215 0.794 -1.781 0.892 0.000 1.762 -0.487 -0.648 0.000 0.992 1.314 0.982 0.586 0.868 1.050 0.983 +0 2.001 -0.593 -1.208 0.748 1.522 1.968 0.630 0.353 2.173 1.474 0.246 -1.538 0.000 0.340 -0.084 -0.350 1.274 0.543 0.960 -0.282 0.000 1.186 0.764 1.068 2.203 0.714 1.334 1.195 +1 0.367 1.523 -0.965 0.962 1.078 0.707 0.125 0.092 1.087 1.132 -0.594 -1.414 2.215 0.482 -0.168 -0.427 0.000 0.427 2.002 0.453 0.000 0.917 0.779 0.989 2.239 1.379 1.631 1.243 +0 1.329 -0.572 -0.110 1.954 -0.926 0.567 -0.343 -1.183 2.173 1.499 -0.579 0.667 0.000 1.119 -0.940 1.228 1.274 0.883 -1.325 -1.349 0.000 1.648 1.043 1.497 0.877 0.888 0.870 0.958 +0 2.287 -0.233 1.342 1.056 1.651 1.079 0.379 -0.110 2.173 0.500 1.028 -1.092 0.000 0.787 -2.528 -0.340 0.000 0.602 1.287 -0.262 0.000 0.517 0.789 0.980 0.810 0.894 1.123 0.982 +1 2.962 -1.207 -1.704 1.348 1.470 2.632 -1.671 -0.163 0.000 1.405 -0.001 1.717 0.000 1.319 -1.450 0.297 2.548 1.226 -0.513 1.087 0.000 0.917 0.999 0.983 0.660 0.863 0.931 0.934 +0 0.551 0.114 -0.638 1.996 -1.352 0.861 -0.652 0.740 0.000 0.963 -0.073 -1.730 2.215 0.990 0.712 0.101 2.548 0.382 -0.676 0.319 0.000 1.146 1.210 0.988 0.949 1.131 0.982 1.058 +1 0.830 0.826 -1.064 0.351 1.371 0.776 -0.061 1.358 0.000 0.912 0.323 -0.484 1.107 0.809 -1.005 -0.056 2.548 0.750 0.923 0.937 0.000 0.842 1.086 0.979 0.866 0.791 0.923 0.903 +0 0.848 0.112 1.029 0.261 1.168 0.500 0.490 -0.244 2.173 0.804 0.823 1.178 2.215 0.828 1.121 -0.997 0.000 1.166 -0.321 -0.678 0.000 1.022 1.058 0.979 0.773 0.909 0.720 0.693 +0 0.787 0.788 -0.347 0.764 -1.488 0.506 0.862 1.464 0.000 1.049 -0.598 -0.272 2.215 1.308 -0.200 1.003 1.274 0.634 -2.240 1.319 0.000 2.495 1.532 0.987 0.874 1.163 1.151 1.023 +1 0.468 0.835 -0.064 1.246 1.375 0.824 -0.719 0.777 0.000 1.299 0.197 -0.731 1.107 0.809 -0.195 1.287 0.000 0.643 -1.581 -0.635 0.000 0.753 0.763 1.019 1.044 0.624 0.882 0.871 +0 0.652 1.362 -0.107 0.962 -1.489 0.664 2.038 0.940 0.000 0.853 -0.236 -0.175 2.215 0.830 1.406 1.672 0.000 0.867 0.642 -0.614 3.102 0.888 0.920 1.038 1.038 0.508 0.992 0.845 +1 0.377 -0.142 -1.187 0.519 1.030 2.284 -0.639 1.501 0.000 2.306 0.477 0.027 0.000 1.283 0.941 -0.335 1.274 1.911 -0.091 -0.951 3.102 1.719 1.096 0.989 0.761 0.959 0.940 0.900 +1 0.631 -0.270 -0.485 0.570 -1.364 0.547 0.843 0.370 0.000 0.778 -0.615 0.054 1.107 1.181 0.839 -1.607 2.548 1.207 -0.184 -1.725 0.000 1.370 1.038 0.979 0.855 1.341 0.879 0.766 +0 2.025 0.586 0.678 1.328 1.288 1.085 0.705 -0.805 0.000 1.239 0.048 -0.691 0.000 1.248 0.826 -1.244 2.548 2.045 -0.155 0.836 3.102 0.869 0.844 1.188 0.802 1.360 1.085 1.171 +0 0.761 -0.477 0.787 1.127 -0.269 0.702 0.149 -1.023 0.000 1.126 -1.665 0.509 0.000 1.085 0.027 1.723 2.548 2.292 -0.536 -0.590 0.000 1.081 1.065 1.044 0.595 0.173 0.650 0.690 +1 1.375 0.268 0.717 0.745 -1.640 0.667 1.209 0.191 0.000 0.935 -1.058 -1.124 2.215 0.804 -1.126 -1.702 0.000 0.389 -0.269 0.023 3.102 2.436 1.278 1.194 1.179 0.514 1.044 0.940 +1 0.806 -0.145 0.914 1.158 0.931 0.851 0.996 -1.104 2.173 0.212 2.477 0.194 0.000 0.915 -0.323 -1.071 2.548 0.784 0.730 0.727 0.000 0.556 0.951 0.974 1.744 0.808 1.166 1.073 +1 0.593 -0.043 0.958 0.716 -1.048 0.737 0.419 0.647 2.173 0.756 0.184 -0.305 2.215 0.500 0.955 1.285 0.000 1.209 -1.325 -0.274 0.000 0.660 0.745 0.985 0.738 0.841 0.697 0.681 +0 0.456 1.554 -0.409 0.711 -0.555 0.519 0.655 1.717 1.087 0.592 1.271 0.135 2.215 0.727 0.269 0.943 0.000 0.399 -0.683 1.385 0.000 0.418 0.670 0.992 0.790 0.851 0.648 0.588 +0 0.435 -1.375 -0.136 0.842 1.268 2.540 -1.040 0.805 1.087 2.839 -0.367 -0.790 2.215 1.292 -1.039 -1.132 0.000 0.665 1.718 -1.496 0.000 2.457 2.009 0.986 1.424 4.143 2.422 2.040 +1 0.905 -0.636 0.545 0.620 -0.516 1.036 -0.412 -1.629 0.000 0.610 -0.509 0.262 0.000 0.779 -0.770 -0.794 2.548 1.059 0.224 1.110 1.551 1.675 1.071 0.989 0.639 0.798 0.750 0.731 +1 0.575 1.621 -1.491 0.456 -1.726 0.940 0.715 0.258 0.000 0.698 -0.218 0.505 0.000 1.312 0.570 -0.775 2.548 0.791 0.840 -1.698 3.102 0.926 0.946 0.997 0.736 0.594 0.795 0.750 +1 0.772 0.068 1.383 0.832 0.170 1.742 0.156 -1.187 0.000 1.928 0.664 0.685 0.000 1.639 -0.491 -0.493 2.548 0.594 -0.112 1.008 3.102 4.001 2.105 0.988 0.914 0.752 1.497 1.168 +0 1.166 -0.732 -1.148 0.936 -0.215 0.631 0.189 1.666 2.173 0.576 0.082 0.532 2.215 0.860 0.801 -0.269 0.000 0.854 -1.640 1.184 0.000 1.177 0.986 1.079 0.845 0.759 0.756 0.741 +1 1.682 -1.750 1.151 0.794 0.808 1.447 -0.818 -1.009 1.087 0.814 -0.727 -0.111 1.107 0.334 -0.977 1.543 0.000 0.456 1.813 0.652 0.000 1.095 1.062 1.003 1.626 1.158 1.173 1.191 +1 0.474 0.161 1.666 1.038 0.233 0.950 1.217 0.908 0.000 2.694 1.395 -1.047 0.000 1.761 0.200 0.619 1.274 1.024 -0.444 -0.237 3.102 0.900 1.079 0.990 0.585 0.818 0.911 0.836 +0 2.158 -1.337 1.011 0.686 0.790 1.696 -0.384 -0.812 2.173 0.637 -0.240 -1.685 0.000 1.118 -0.459 0.732 2.548 0.720 -0.022 -0.340 0.000 0.831 0.943 1.005 0.621 1.691 1.328 1.052 +0 0.585 -1.624 -0.556 0.599 -0.396 0.946 -0.741 -0.208 1.087 1.217 -1.242 1.172 2.215 0.997 -0.551 -0.615 0.000 1.531 -2.461 0.913 0.000 0.954 1.068 0.982 0.645 1.554 0.907 0.791 +0 1.699 1.935 -0.751 1.956 -0.339 0.763 0.419 1.374 0.000 1.075 0.886 0.890 0.000 1.062 1.254 -1.667 2.548 1.073 1.343 0.253 0.000 0.909 0.955 0.987 1.199 0.623 0.940 0.949 +0 0.366 0.045 0.321 2.054 1.539 0.671 0.693 0.113 0.000 0.637 1.601 -0.376 0.000 1.077 -0.889 -1.329 1.274 1.361 -0.223 -0.499 1.551 0.905 0.949 1.070 0.895 0.713 1.003 0.990 +1 0.365 -0.938 -1.087 1.190 1.318 0.910 -0.350 0.593 2.173 1.437 0.212 -1.348 0.000 1.071 0.114 -0.628 2.548 0.636 -0.184 -0.131 0.000 0.950 0.773 0.983 1.238 1.136 1.093 1.105 +1 0.906 0.234 -0.209 0.645 1.010 1.355 0.816 -0.762 2.173 1.274 1.003 1.294 1.107 0.572 0.666 0.526 0.000 0.715 0.302 1.140 0.000 0.393 1.047 0.987 0.994 1.867 1.084 0.844 +1 1.194 -1.871 1.459 0.287 -0.154 1.175 -1.211 0.298 2.173 0.778 -2.156 -1.586 0.000 0.799 -1.151 -0.341 0.000 0.522 -0.378 -1.359 3.102 1.243 0.798 0.990 0.999 0.888 0.841 0.752 +1 0.619 1.214 0.814 0.817 -1.121 0.420 0.447 1.158 0.000 0.598 0.958 0.026 0.000 1.459 -0.506 -1.360 2.548 0.433 -0.846 0.517 3.102 0.952 1.189 0.986 0.803 0.619 0.839 0.771 +1 0.576 -1.656 -0.325 1.879 0.642 1.566 -1.512 -0.882 2.173 0.934 -1.350 0.763 0.000 1.042 -0.994 1.325 0.000 1.214 -0.652 -1.693 3.102 0.768 0.740 1.103 1.500 1.120 1.088 1.007 +1 0.758 1.534 -1.038 1.156 0.013 1.141 0.857 0.961 1.087 0.488 2.527 -0.774 0.000 0.768 1.772 -1.200 0.000 0.520 -0.023 -0.862 3.102 0.527 1.206 1.053 0.685 0.896 0.824 0.767 +1 1.318 1.612 1.403 0.933 0.800 0.561 0.898 -1.728 0.000 0.970 -0.092 -0.851 2.215 0.669 0.698 0.195 2.548 0.574 0.991 -0.731 0.000 0.690 0.823 0.986 0.833 0.791 1.094 0.881 +1 0.943 0.075 -0.223 0.403 1.262 0.729 0.317 0.734 0.000 0.681 0.642 0.098 0.000 1.926 -0.439 -1.487 2.548 0.452 1.138 -1.006 3.102 0.852 0.717 0.987 0.914 0.814 0.911 0.785 +1 2.451 -0.367 -0.039 1.504 0.527 0.624 1.780 -1.690 0.000 0.866 -1.035 1.163 2.215 2.093 0.684 -1.227 2.548 1.115 -1.091 -1.193 0.000 0.848 0.974 1.300 1.195 1.906 1.462 1.429 +0 0.707 -0.145 -0.218 0.523 -0.621 1.199 0.829 1.345 0.000 1.223 -0.205 -1.090 1.107 1.314 -0.607 -1.555 0.000 2.384 -1.007 0.363 3.102 1.603 1.188 0.973 0.795 1.690 1.094 0.919 +0 0.939 1.628 0.479 1.494 -0.246 0.952 -0.312 -1.406 2.173 1.002 1.353 0.995 0.000 0.899 -0.921 -0.762 2.548 0.495 -0.653 1.469 0.000 1.267 1.456 0.996 1.678 0.754 1.461 1.282 +1 2.006 0.036 -1.637 0.703 -0.504 1.058 0.269 -0.111 2.173 0.476 0.936 -1.107 0.000 1.126 -0.424 0.547 2.548 0.854 0.114 1.097 0.000 0.838 0.981 1.403 1.103 0.917 0.994 0.837 +1 0.759 -0.716 -0.232 0.504 0.759 0.794 -0.173 -1.379 0.000 1.121 -0.015 0.147 0.000 1.106 -0.733 -0.516 2.548 1.692 -0.158 1.399 3.102 0.870 0.917 0.985 0.709 1.084 0.753 0.659 +1 1.160 1.255 1.022 1.608 -1.283 0.619 1.654 1.359 0.000 0.658 0.947 -0.508 2.215 1.001 1.255 0.389 0.000 1.455 0.184 -0.404 3.102 1.106 0.993 1.656 1.205 0.351 0.836 0.849 +1 1.283 1.785 1.356 0.418 0.784 1.014 -0.055 -0.329 2.173 0.649 1.246 -0.667 0.000 0.578 2.541 1.257 0.000 0.579 -0.159 -1.464 3.102 0.728 0.982 0.992 0.727 0.694 0.958 0.795 +1 0.916 -1.414 1.696 1.007 0.197 0.904 -0.788 0.351 2.173 0.564 -1.690 0.637 0.000 0.808 -1.874 -0.937 0.000 1.060 -0.814 1.284 3.102 1.037 1.073 1.298 0.732 0.777 0.726 0.690 +1 1.739 1.296 0.266 0.567 0.161 3.012 -1.344 -1.230 0.000 2.255 1.638 -0.824 0.000 1.623 1.476 0.632 0.000 5.594 0.786 0.889 3.102 0.804 2.557 0.983 1.392 0.474 3.157 3.025 +1 1.349 -0.455 1.468 0.761 0.842 0.683 -2.795 -1.054 0.000 0.614 -0.718 -0.148 0.000 0.526 -1.201 -1.415 0.000 0.757 0.129 -0.283 3.102 0.952 1.328 0.985 0.768 0.477 1.202 1.060 +0 1.196 -0.952 -0.900 1.649 -0.844 1.667 -0.044 1.123 2.173 0.958 0.567 -0.652 1.107 0.429 -0.572 0.046 0.000 0.857 -0.580 0.780 0.000 0.907 1.088 0.989 1.629 1.951 1.809 1.405 +0 1.457 0.510 -1.430 0.508 -0.952 0.838 0.737 0.119 2.173 0.714 0.175 0.981 2.215 0.303 -0.237 1.574 0.000 0.552 -1.398 1.731 0.000 0.353 1.058 0.985 0.911 0.861 0.857 0.808 +1 0.389 0.970 1.127 1.173 -0.171 0.664 -1.028 1.328 2.173 0.806 -0.904 -0.986 2.215 0.333 1.214 0.263 0.000 0.626 0.167 -0.667 0.000 1.040 1.491 0.989 1.028 0.938 1.231 1.017 +0 1.105 1.288 0.671 0.631 -0.705 1.206 0.437 0.597 2.173 0.792 0.653 -1.143 0.000 1.407 0.889 -1.560 2.548 0.827 -0.096 -0.202 0.000 0.898 0.837 1.094 0.957 1.573 0.964 0.862 +0 0.951 0.977 -1.008 0.872 0.109 0.695 0.075 -0.039 1.087 1.305 0.705 -1.527 2.215 2.243 -0.145 0.646 0.000 0.792 -0.009 -1.399 0.000 0.950 1.053 1.066 0.939 1.441 0.860 0.764 +1 0.969 0.852 1.435 1.112 0.804 0.343 1.388 -0.525 0.000 0.963 0.016 -0.783 0.000 0.348 0.916 0.244 2.548 0.481 -2.260 0.590 0.000 0.958 0.655 0.985 0.577 0.294 0.419 0.605 +1 0.917 -0.467 -1.628 0.324 -1.624 0.896 -0.199 -0.056 2.173 0.706 1.810 1.411 0.000 0.947 -1.211 -0.480 0.000 0.951 0.752 0.682 0.000 0.842 1.083 0.990 1.157 0.983 1.021 1.124 +0 0.554 -0.453 1.161 0.681 -0.287 1.544 0.567 0.953 0.000 0.819 1.790 1.525 0.000 0.979 0.487 1.357 0.000 2.407 -0.423 -0.935 3.102 0.945 1.178 0.988 0.944 1.166 1.054 0.873 +1 1.024 0.159 0.272 0.791 -1.099 0.804 -1.337 -0.296 2.173 0.685 0.382 0.793 1.107 1.400 -0.120 1.286 0.000 0.791 0.815 1.616 0.000 0.758 0.627 1.177 1.092 1.408 1.024 0.896 +0 1.047 0.598 1.172 0.275 -1.620 0.798 -2.041 -0.320 0.000 0.751 -0.371 -1.317 1.107 0.668 -0.493 0.438 2.548 0.472 -1.560 -1.688 0.000 0.889 0.875 0.983 0.721 0.755 0.779 0.868 +0 0.513 0.861 -0.746 0.371 1.478 0.610 -0.985 -1.114 0.000 0.816 -0.252 0.262 2.215 0.657 -1.392 -0.315 0.000 2.007 0.911 1.446 3.102 0.919 0.887 0.990 0.910 1.316 1.019 0.834 +1 0.865 0.198 0.923 0.718 -0.111 0.913 0.902 -1.199 2.173 0.868 -0.381 0.149 0.000 0.485 0.728 1.623 1.274 0.449 -1.275 1.021 0.000 0.755 0.776 0.984 1.129 0.469 0.863 0.772 +0 2.633 0.596 0.014 0.259 0.372 1.671 0.434 -1.541 0.000 0.701 0.037 1.576 0.000 0.884 -0.789 0.011 1.274 0.608 0.336 0.483 0.000 0.881 0.912 0.982 0.821 0.293 0.918 1.040 +1 0.768 0.980 -0.000 2.006 -0.613 0.932 0.644 1.593 0.000 0.831 1.052 0.690 2.215 0.620 0.252 1.045 0.000 0.707 0.040 -1.128 3.102 0.698 0.821 0.983 0.622 0.786 0.709 0.798 +1 0.513 0.786 1.049 0.349 0.282 1.100 -0.483 -1.309 2.173 0.901 2.337 0.252 0.000 0.594 -1.162 1.165 2.548 0.853 0.429 -0.901 0.000 1.164 1.426 0.989 0.971 0.895 1.220 1.009 +1 0.499 -0.179 1.166 1.121 -0.842 0.787 0.758 0.537 2.173 0.519 -1.465 -1.501 0.000 0.650 -0.925 -0.136 0.000 0.414 0.343 -1.452 1.551 0.868 0.634 1.007 0.991 0.599 0.824 0.729 +1 0.460 1.520 -1.482 0.966 1.572 1.272 2.029 -0.545 0.000 0.996 1.461 0.858 0.000 2.143 1.021 -1.310 1.274 2.003 0.477 0.474 0.000 0.760 0.849 0.987 0.865 0.973 0.884 0.778 +0 1.217 -0.616 1.330 1.447 1.667 0.794 0.199 0.190 0.000 0.731 0.955 -0.593 0.000 1.021 -1.140 -0.783 2.548 0.711 -0.152 0.743 3.102 1.235 0.842 0.990 0.955 0.735 0.850 1.065 +1 0.795 0.690 1.324 1.607 1.471 0.746 1.214 -0.463 2.173 0.282 -0.135 0.773 2.215 0.433 0.061 -0.091 0.000 0.370 1.893 -1.187 0.000 0.711 1.324 0.982 0.540 0.784 0.905 0.880 +1 2.277 -1.311 0.358 1.029 0.447 1.064 -1.545 1.538 2.173 1.140 -1.709 -0.320 0.000 1.179 -0.093 -1.613 2.548 1.040 -1.296 -1.288 0.000 1.095 1.334 0.997 1.341 1.184 1.207 1.145 +0 1.044 -0.167 -1.518 1.361 -1.246 1.274 0.305 1.619 2.173 2.629 -0.072 0.331 0.000 1.079 -2.728 -0.143 0.000 1.119 -0.269 -0.602 3.102 5.378 2.954 0.974 1.187 1.217 2.412 1.915 +1 0.520 -1.957 -0.062 0.933 0.900 0.715 -1.766 0.309 0.000 1.270 -0.954 -1.198 2.215 1.226 -0.921 1.606 0.000 1.672 -2.403 -0.501 0.000 1.477 1.217 0.980 0.999 0.435 1.071 0.930 +0 0.670 -0.366 0.707 0.993 -0.622 0.374 0.463 -0.199 2.173 0.612 -0.141 0.944 0.000 1.379 -0.050 1.635 1.274 0.834 -1.927 -1.050 0.000 1.520 1.206 1.052 0.873 0.923 0.898 0.786 +1 0.545 -0.230 -0.803 0.774 1.253 0.827 0.745 -1.501 2.173 1.343 -0.002 -0.066 0.000 1.210 -0.478 0.395 2.548 0.885 -2.191 1.591 0.000 1.457 1.071 0.986 1.045 1.500 1.031 0.937 +0 1.025 -0.419 1.496 0.105 -0.615 0.720 -1.146 0.752 0.000 0.834 -1.541 -0.083 0.000 1.614 0.997 -1.605 2.548 1.233 0.452 -0.189 3.102 0.819 0.954 0.978 0.821 1.075 1.097 0.898 +1 0.612 -0.351 1.460 0.474 -0.898 1.234 -0.448 0.578 0.000 1.290 -0.287 -1.654 0.000 0.557 2.635 -0.070 0.000 1.510 -0.420 -0.236 3.102 2.431 1.359 0.990 0.773 0.424 0.946 0.836 +1 0.602 -0.257 0.300 1.074 -1.353 0.604 -1.830 0.889 0.000 0.773 0.058 -1.695 1.107 1.467 -0.588 -0.756 2.548 1.540 -1.019 0.293 0.000 0.887 1.276 1.110 0.684 0.941 1.018 0.888 +0 0.451 -2.210 0.224 0.669 -1.028 0.835 -0.653 0.434 2.173 1.377 -2.179 1.391 0.000 0.729 -0.673 -1.175 0.000 0.962 0.411 -1.035 0.000 0.735 0.661 0.989 0.847 0.372 0.778 0.826 +1 0.458 0.705 1.663 0.929 0.001 1.038 1.028 -0.143 0.000 1.959 -0.056 1.589 0.000 1.960 0.240 0.714 0.000 1.602 0.951 1.425 3.102 0.750 0.876 0.990 0.845 1.805 0.940 0.765 +0 0.568 0.112 -1.410 1.840 1.210 1.530 0.533 -0.313 1.087 0.874 -0.638 1.530 2.215 1.171 -0.150 -0.231 0.000 1.271 -0.767 -1.484 0.000 1.072 0.971 0.996 0.662 2.009 1.223 1.033 +1 0.984 -0.777 -0.096 1.824 -0.040 0.867 -0.008 -1.385 2.173 0.791 -0.385 1.169 2.215 0.729 0.009 1.703 0.000 0.428 -1.530 0.779 0.000 0.797 0.784 0.974 1.195 0.937 1.212 0.968 +0 0.777 -1.392 0.349 0.882 -0.612 1.691 -0.720 0.287 1.087 2.260 -0.001 -1.666 0.000 0.379 0.534 -1.195 0.000 1.260 -0.579 -0.919 3.102 0.722 0.889 0.986 0.894 1.368 1.410 1.160 +1 1.291 -0.342 0.966 0.595 0.608 1.952 -2.488 -0.860 0.000 1.395 -0.117 0.303 0.000 1.238 0.634 1.295 2.548 1.597 -0.125 1.624 3.102 0.866 1.068 0.997 1.038 0.564 0.829 0.806 +1 0.579 -0.909 1.027 2.006 1.739 0.913 -0.225 1.276 0.000 1.373 0.748 -0.593 2.215 1.604 -0.176 0.243 0.000 1.419 1.413 -0.375 3.102 1.746 1.818 0.991 2.361 0.653 1.840 1.603 +1 1.151 1.498 -0.214 0.495 1.352 1.223 1.392 -1.022 2.173 1.051 1.236 1.346 0.000 1.114 1.508 0.731 0.000 0.498 0.214 -0.046 3.102 0.930 0.785 1.033 0.932 0.811 0.926 0.801 +1 0.624 0.969 0.058 1.147 1.196 0.934 -2.611 0.089 0.000 0.761 -0.208 -0.630 0.000 0.792 -1.283 -1.242 2.548 1.029 0.042 1.217 1.551 0.824 0.835 1.003 0.630 0.778 0.840 0.769 +0 0.666 1.372 -1.349 1.462 -0.509 0.743 0.632 1.481 0.000 1.000 0.423 0.646 2.215 0.672 -0.314 0.367 2.548 0.624 0.164 -0.739 0.000 0.973 0.916 0.987 1.113 0.411 0.940 0.865 +0 0.407 -1.179 0.094 1.431 1.375 0.702 -1.271 -0.225 0.000 1.104 0.883 -1.520 2.215 0.575 -0.047 -0.378 0.000 1.125 0.929 0.288 0.000 0.765 1.039 0.989 1.700 0.324 1.124 1.082 +1 0.993 -0.991 -1.278 1.566 0.525 1.540 -0.459 0.508 2.173 0.937 -0.230 -1.088 0.000 0.422 -1.139 -1.720 0.000 1.071 -0.384 -0.575 3.102 0.729 0.575 1.725 1.291 1.125 0.942 0.915 +1 0.477 -0.571 -1.629 1.341 -0.294 1.016 -0.225 1.182 0.000 1.144 -0.734 -0.664 0.000 1.381 -0.354 0.588 1.274 0.668 -0.832 -1.295 3.102 2.361 1.310 1.034 0.837 0.763 0.933 0.825 +1 0.850 0.493 1.697 0.336 0.546 0.846 -0.575 -0.913 2.173 0.973 -2.895 -0.636 0.000 1.432 -1.541 -0.202 0.000 2.153 0.408 1.071 0.000 1.189 1.225 0.990 1.215 1.019 0.981 1.201 +1 1.289 -0.220 -0.282 0.256 -0.829 1.085 -1.938 1.533 0.000 0.329 -0.965 -0.294 0.000 0.531 -1.182 1.373 2.548 0.960 -1.028 0.455 3.102 1.134 0.926 0.994 0.717 0.402 0.558 0.736 +1 0.897 0.621 1.579 0.371 1.252 0.969 0.670 -1.679 2.173 0.892 -0.226 -0.756 1.107 1.700 -0.335 0.799 0.000 0.416 -1.486 0.151 0.000 0.878 1.097 0.991 0.784 1.200 1.049 0.870 +0 1.519 -0.989 -1.164 1.261 -0.370 0.748 0.328 0.828 2.173 0.491 0.470 -0.742 1.107 0.666 -0.004 1.596 0.000 0.932 -0.828 0.530 0.000 0.841 0.768 1.258 1.462 0.884 1.001 0.842 +1 0.984 0.832 -1.606 1.409 -0.848 1.217 0.208 0.482 0.000 0.938 0.515 -0.380 0.000 0.632 0.062 -1.385 0.000 1.032 1.119 1.225 3.102 0.970 0.928 1.031 0.817 0.650 0.660 0.656 +0 0.847 0.955 0.702 1.281 -1.608 0.916 0.478 -0.335 2.173 0.479 -0.360 0.948 2.215 0.493 -0.685 -0.489 0.000 0.919 -0.049 -1.340 0.000 0.578 0.750 1.259 0.843 0.990 0.856 0.746 +1 0.688 -1.592 -1.111 0.681 1.649 0.553 0.828 -1.246 0.000 1.195 -0.889 0.093 2.215 0.482 0.123 -0.250 0.000 1.638 -1.668 -1.580 0.000 0.968 0.927 0.987 0.602 0.511 0.654 0.648 +1 1.652 0.143 0.034 0.339 -1.554 0.395 0.219 -0.745 0.000 0.667 -1.206 1.322 1.107 0.441 2.664 -0.129 0.000 1.115 0.031 1.268 0.000 0.950 0.687 1.026 0.733 0.224 0.676 0.615 +0 0.660 -1.887 -0.622 0.606 -1.742 0.472 -0.853 -1.664 2.173 0.797 -2.271 0.456 0.000 0.836 -1.807 -0.328 0.000 0.437 0.835 1.370 1.551 0.843 1.079 0.991 0.761 0.549 0.909 0.775 +0 0.320 0.283 -0.752 0.375 1.354 1.095 1.613 -0.173 2.173 0.989 0.278 -1.058 0.000 1.413 -0.061 1.317 2.548 1.815 0.666 -1.661 0.000 1.009 1.057 0.994 2.126 2.112 1.530 1.313 +1 0.818 -0.534 -1.705 0.947 -0.577 0.525 -1.429 0.869 2.173 0.806 -1.211 1.584 0.000 1.280 -0.009 -0.128 1.274 1.351 -0.868 -0.281 0.000 0.939 0.810 1.037 0.802 1.129 0.770 0.670 +1 1.816 -0.887 -0.305 0.736 -0.626 0.759 -1.346 0.817 1.087 0.921 -1.189 1.248 0.000 1.027 -1.324 1.717 2.548 0.512 -1.477 -0.223 0.000 0.903 0.674 0.982 0.998 0.799 0.882 0.772 +1 1.958 0.542 0.806 0.037 -0.474 0.966 -0.259 -1.092 2.173 0.502 2.869 1.309 0.000 0.884 0.920 -0.598 0.000 1.674 -0.331 -0.303 3.102 1.125 0.954 0.997 1.269 0.884 0.956 0.852 +0 0.499 0.061 1.194 1.030 0.729 1.512 -0.823 1.357 2.173 2.180 0.143 -0.644 2.215 0.659 0.094 -0.014 0.000 0.733 -1.106 -0.588 0.000 0.707 0.880 0.993 1.829 2.933 1.764 1.360 +0 0.911 -1.719 -1.503 0.331 0.737 1.601 -1.058 -0.936 2.173 1.028 -1.387 1.116 0.000 2.172 -2.451 0.492 0.000 0.652 -0.636 0.332 3.102 1.972 1.263 0.986 0.971 0.998 1.553 1.213 +1 1.023 -0.601 -0.153 0.687 1.002 0.765 0.634 1.187 0.000 0.783 0.232 -1.599 2.215 0.887 -1.026 -0.326 1.274 0.720 0.662 -0.729 0.000 1.123 0.826 1.002 0.633 1.038 0.844 0.788 +0 1.293 -1.678 0.809 0.832 1.735 0.947 -1.166 -1.303 0.000 1.314 -1.138 -0.521 2.215 0.962 1.147 0.732 0.000 0.621 -0.310 0.731 3.102 3.123 1.681 1.066 1.180 0.809 1.306 1.251 +1 0.749 -0.440 1.533 1.713 -1.634 0.686 1.548 -0.524 0.000 0.565 -0.400 -1.628 2.215 0.607 -1.273 0.061 0.000 1.854 0.373 0.317 0.000 0.671 0.794 0.973 0.514 0.420 0.746 0.834 +1 0.630 0.298 1.353 1.529 0.815 0.715 -1.308 -1.372 0.000 0.916 -0.996 0.710 0.000 1.068 0.430 -0.541 0.000 2.433 -0.606 -0.653 3.102 1.659 0.928 0.983 1.222 0.860 0.843 0.854 +0 0.602 0.495 0.366 0.763 -1.107 0.574 -0.439 1.693 2.173 0.779 -1.105 -1.122 0.000 0.742 1.985 0.368 0.000 1.220 0.641 0.193 3.102 3.165 1.829 0.988 0.890 1.036 1.236 1.008 +1 0.651 -0.245 0.145 0.658 1.105 1.105 -0.581 -1.325 2.173 0.470 -1.169 -0.506 2.215 0.576 -2.518 0.861 0.000 1.049 -0.815 0.683 0.000 0.883 0.768 0.994 1.047 0.788 0.894 0.784 +0 0.494 -1.311 -1.247 1.854 -1.688 0.978 0.231 0.290 0.000 0.787 -0.477 -0.911 2.215 0.266 1.828 -1.127 0.000 0.891 -0.510 -0.141 3.102 1.274 0.890 0.985 0.730 0.486 0.750 0.858 +0 1.039 -1.339 -1.273 0.361 -0.074 0.800 -1.330 1.257 2.173 0.610 -0.272 -1.522 1.107 0.767 -1.439 0.073 0.000 0.804 0.059 -0.306 0.000 0.850 1.049 0.996 0.620 0.837 0.755 0.683 +1 1.219 0.772 1.575 1.122 -1.321 0.857 0.413 -0.287 2.173 0.788 0.942 0.534 1.107 0.501 -0.341 -0.531 0.000 0.707 0.012 0.762 0.000 0.616 0.648 0.986 1.199 0.883 0.919 0.772 +1 0.630 -0.129 0.406 0.589 -0.626 1.036 0.590 -1.389 2.173 0.862 1.395 1.162 0.000 1.527 0.927 0.582 0.000 0.470 -1.539 -0.066 0.000 0.952 1.041 0.981 0.930 0.933 0.993 0.848 +1 0.746 -0.817 0.878 0.972 0.511 1.092 0.070 -0.781 2.173 1.078 0.207 -1.482 2.215 0.985 -0.368 0.443 0.000 0.463 0.204 1.569 0.000 0.679 1.012 0.981 1.508 0.949 1.341 1.053 +1 0.959 0.278 -0.420 1.214 -1.431 1.327 -0.209 0.963 2.173 0.268 0.596 -1.688 0.000 0.587 0.054 0.039 2.548 0.829 -0.439 -0.823 0.000 0.822 1.241 1.180 0.715 0.827 0.896 0.785 +1 1.346 -0.636 -0.631 1.769 0.142 1.031 -0.440 1.149 2.173 0.646 0.075 0.757 0.000 0.872 0.232 -1.276 2.548 0.499 -0.852 -0.913 0.000 0.854 0.773 1.372 1.067 1.052 1.047 0.843 +0 0.754 0.530 -1.713 1.211 0.712 0.880 0.047 -0.458 2.173 0.727 -0.539 -0.558 2.215 1.023 -1.433 1.403 0.000 0.388 -1.339 -1.570 0.000 0.804 0.935 1.082 1.074 0.375 0.845 0.888 +1 0.454 0.614 -1.567 0.644 -1.634 1.131 -0.289 -1.383 0.000 2.769 0.318 0.537 1.107 1.333 -0.715 -1.089 0.000 0.991 0.692 0.106 0.000 0.799 0.595 0.988 1.567 1.224 1.460 1.180 +0 0.747 0.047 1.658 0.369 -0.946 0.495 -0.047 0.751 2.173 0.480 0.041 -0.684 0.000 1.154 -0.158 -0.163 2.548 0.614 1.495 1.468 0.000 0.964 0.874 0.977 0.768 0.693 0.685 0.643 +1 0.679 -1.702 -0.404 1.032 -0.435 0.782 -1.066 1.055 1.087 0.706 -0.674 -1.605 2.215 0.897 -0.336 -0.450 0.000 0.931 0.173 1.212 0.000 1.047 1.023 0.989 0.834 0.769 0.804 0.750 +0 1.462 -0.329 0.499 1.970 1.076 1.009 -0.502 -0.947 2.173 1.110 0.248 0.093 0.000 1.416 0.510 -1.439 2.548 1.235 -1.313 -1.724 0.000 0.795 1.095 1.168 1.535 1.049 1.219 1.087 +0 1.862 1.526 0.951 0.362 -0.381 0.597 2.298 -0.372 0.000 0.686 1.149 -1.146 1.107 0.407 0.385 -1.542 0.000 0.769 0.532 0.311 3.102 1.242 0.878 1.060 0.892 0.659 0.635 0.683 +0 0.915 -1.307 -0.365 0.800 0.804 1.026 -0.335 -1.180 1.087 0.881 -1.835 0.695 0.000 0.663 -2.595 1.198 0.000 0.690 -1.013 0.994 1.551 0.779 0.552 1.030 1.133 0.918 1.086 0.911 +1 0.725 0.888 1.282 1.432 -1.360 1.067 0.026 0.986 0.000 1.339 0.128 -0.072 0.000 1.095 0.981 -0.582 2.548 0.873 -0.274 -0.884 0.000 0.918 1.251 0.988 0.713 0.697 0.737 0.859 +1 0.585 -0.769 0.927 0.534 -0.942 0.639 1.348 0.139 2.173 0.673 0.368 -0.588 2.215 0.909 -0.688 1.573 0.000 0.719 -0.082 1.448 0.000 0.315 0.798 0.986 1.676 0.768 1.137 0.949 +0 0.304 0.865 0.520 1.296 -1.450 0.472 0.667 0.735 0.000 0.904 0.435 1.727 2.215 0.701 1.170 -0.399 0.000 1.674 -0.206 -0.318 3.102 0.940 0.935 0.985 0.815 1.141 0.785 0.723 +0 0.617 -0.295 1.333 0.701 -1.162 0.885 0.519 -0.278 1.087 0.443 1.405 1.056 2.215 0.942 2.248 1.252 0.000 0.391 1.343 -1.020 0.000 0.664 1.298 0.990 1.108 0.965 1.004 1.177 +0 1.244 -0.669 -1.374 2.193 1.607 2.901 0.308 -0.031 0.000 2.223 -0.619 1.616 2.215 0.937 -0.201 -0.420 0.000 0.609 -1.464 1.283 0.000 1.092 0.663 1.006 0.644 0.906 0.797 0.756 +0 1.276 0.274 0.013 0.593 -0.821 0.771 0.879 0.701 2.173 0.665 1.155 1.203 0.000 0.930 1.221 -1.194 0.000 1.435 1.811 -0.779 0.000 1.006 0.964 0.989 0.981 0.897 0.834 0.809 +0 0.698 -1.181 0.216 0.995 1.541 0.854 -1.149 -1.719 0.000 0.658 -0.826 -0.958 0.000 0.442 -0.169 0.674 0.000 1.723 0.561 0.175 3.102 0.869 0.959 1.074 0.536 0.718 0.763 0.671 +0 1.271 0.454 0.345 0.962 -1.012 0.497 -0.749 -1.400 0.000 0.897 -0.646 0.486 0.000 1.038 0.494 -1.508 2.548 0.955 -0.524 1.110 3.102 1.407 0.853 1.440 0.924 0.714 0.734 0.801 +0 0.874 -0.513 1.471 0.978 -0.445 0.645 -1.407 -0.801 2.173 0.998 0.294 0.340 2.215 0.527 -0.247 1.037 0.000 0.651 -0.155 -0.986 0.000 0.626 0.747 1.266 0.965 1.530 0.899 0.715 +1 0.449 -2.133 -0.985 2.042 -0.405 0.455 -0.866 1.234 0.000 0.820 -0.650 1.638 2.215 1.179 -0.924 0.641 2.548 0.771 0.035 -1.537 0.000 0.691 0.727 0.979 1.081 0.837 0.871 0.782 +0 0.537 2.307 0.512 1.049 -0.668 0.738 1.057 1.379 0.000 0.783 0.145 0.417 1.107 0.717 -0.011 -0.962 2.548 0.637 1.437 -0.588 0.000 1.084 0.923 0.986 1.032 0.757 0.810 0.779 +1 1.378 1.272 -0.687 2.179 -0.041 0.642 0.138 0.476 2.173 0.753 2.599 -1.682 0.000 0.775 0.285 -1.349 0.000 0.636 1.109 0.752 0.000 0.981 1.042 1.321 1.213 0.790 0.984 1.049 +1 1.548 -1.061 -1.359 1.032 -0.777 0.622 -1.039 0.520 2.173 0.486 -1.615 1.299 0.000 1.819 -0.103 0.334 2.548 1.048 -0.977 0.019 0.000 0.880 1.024 0.988 1.077 0.680 1.071 0.885 +0 0.876 0.470 -0.838 1.228 1.543 1.063 -0.382 -0.439 2.173 1.122 1.139 1.545 0.000 0.756 0.875 0.302 0.000 0.806 0.561 0.841 3.102 1.099 1.120 1.206 0.714 1.048 0.856 0.797 +0 2.049 -1.019 -0.809 0.721 0.813 0.530 0.954 0.852 0.000 0.449 -0.439 0.830 2.215 0.563 1.343 -1.181 0.000 0.989 -1.182 0.977 3.102 0.982 0.832 1.675 0.996 0.313 0.790 0.998 +1 0.806 1.488 -0.120 0.184 1.327 0.415 1.390 0.234 1.087 0.968 1.599 -1.696 2.215 0.574 -0.413 1.543 0.000 1.009 -0.402 -0.072 0.000 0.857 1.123 0.988 0.580 0.927 0.780 0.685 +0 1.743 0.849 0.053 2.571 -0.146 1.951 1.238 1.613 0.000 3.401 0.530 -0.116 1.107 1.130 0.925 1.019 1.274 1.357 1.265 -1.396 0.000 1.085 1.038 0.996 0.612 1.846 1.916 1.728 +1 1.117 -0.491 0.267 1.459 -0.330 0.897 2.426 -0.931 0.000 1.030 0.190 0.694 0.000 1.810 1.006 1.324 2.548 0.686 2.406 1.516 0.000 1.000 0.918 0.988 1.847 0.799 1.175 1.729 +0 0.812 0.295 -0.520 0.628 -0.827 0.501 0.899 -1.411 2.173 0.897 0.111 -0.040 1.107 0.986 -0.034 0.880 0.000 1.102 1.045 1.353 0.000 0.926 0.991 0.985 0.898 1.015 0.756 0.799 +1 0.525 0.830 1.108 0.467 -0.779 3.107 1.466 0.438 0.000 3.680 -0.037 -1.337 1.107 1.070 -0.404 -1.525 2.548 0.773 0.283 -1.630 0.000 0.924 1.233 0.980 0.650 0.559 0.755 0.723 +1 0.441 -1.535 0.789 0.313 -1.443 2.026 0.478 -0.636 0.000 1.272 -0.921 -1.408 1.107 3.355 -0.429 0.985 0.000 1.605 -0.721 0.682 3.102 1.520 0.906 0.980 0.811 1.228 1.022 0.845 +1 1.172 0.328 -0.798 0.439 0.756 1.201 0.538 -0.583 0.000 1.877 0.485 0.915 0.000 1.327 -0.211 1.559 2.548 1.135 -1.220 -0.429 0.000 1.115 1.068 0.988 0.840 0.685 0.911 0.765 +0 1.445 -0.437 1.664 0.213 -1.605 0.798 -0.355 0.468 2.173 0.737 -0.252 -0.154 0.000 0.696 -0.923 -0.460 2.548 1.104 -0.161 -0.883 0.000 0.892 0.745 0.975 0.747 0.756 0.749 0.667 +1 0.897 -0.727 -0.032 0.420 -0.592 1.391 -0.142 -1.582 0.000 1.763 -0.110 -0.220 2.215 1.126 -0.872 1.156 1.274 0.611 -1.382 0.738 0.000 0.457 1.074 0.990 0.916 1.559 0.850 0.755 +1 1.029 -0.904 0.352 1.720 0.233 1.501 0.243 -1.406 2.173 0.853 0.435 -1.711 0.000 1.462 0.135 0.374 2.548 0.368 1.136 -0.590 0.000 0.709 0.962 1.002 2.504 1.846 1.766 1.456 +0 0.490 -2.188 -0.962 0.846 -1.112 1.087 -0.530 -0.161 2.173 0.894 -0.541 0.395 2.215 1.554 -1.117 1.492 0.000 0.429 -1.883 1.472 0.000 0.487 0.969 0.985 0.990 0.697 0.947 0.862 +1 1.317 0.051 1.278 0.400 1.496 0.792 0.387 -0.037 2.173 0.795 0.359 -0.892 0.000 0.644 -0.157 -1.405 0.000 0.763 -0.480 0.389 3.102 0.574 0.888 0.982 0.627 0.516 0.733 0.721 +1 0.838 -0.744 0.423 1.073 1.395 0.466 -1.154 -1.500 0.000 0.975 -0.086 0.487 0.000 1.910 0.521 -1.285 2.548 1.347 1.020 0.088 3.102 0.927 0.859 1.010 1.282 1.228 1.079 0.934 +1 0.332 -0.853 -1.678 0.086 0.383 0.486 -0.906 0.740 0.000 1.113 1.058 -1.687 2.215 1.299 -0.444 -0.897 2.548 0.392 2.105 0.212 0.000 1.829 1.444 0.826 0.663 1.393 1.137 0.880 +0 0.757 0.402 0.671 0.423 1.240 0.474 1.175 -1.685 2.173 0.833 -0.434 -0.562 0.000 0.708 1.743 1.431 0.000 0.711 -0.125 0.503 0.000 0.834 1.038 0.990 0.885 0.622 0.678 0.743 +1 0.906 -0.765 1.426 0.162 0.861 1.115 0.881 -0.134 2.173 0.912 -0.189 -1.493 1.107 0.985 1.543 0.723 0.000 0.646 -1.635 -0.777 0.000 0.334 0.821 0.980 1.203 1.632 1.315 1.073 +0 1.000 -0.885 -0.096 0.923 -1.056 0.810 -0.495 0.510 1.087 0.771 -1.950 0.302 0.000 1.975 -0.135 -1.391 2.548 0.861 -1.057 -1.180 0.000 0.754 1.037 1.012 0.986 1.585 0.953 0.847 +0 1.545 -0.060 0.394 0.335 -1.085 0.997 0.434 -0.517 2.173 0.813 0.606 -1.695 0.000 0.872 -0.389 0.886 2.548 1.272 -0.418 -1.556 0.000 0.803 0.833 0.987 0.921 1.223 0.883 0.810 +0 2.294 0.573 -0.676 0.142 1.381 0.941 0.746 1.118 0.000 1.048 0.471 0.690 0.000 0.585 -0.416 -0.011 2.548 2.049 0.139 -1.405 3.102 0.845 0.924 0.987 0.863 0.839 0.883 0.926 +1 0.943 -1.601 -0.014 0.155 -0.967 1.585 -0.723 1.500 2.173 1.705 -0.683 -1.107 0.000 1.145 -0.394 -0.366 1.274 2.083 -0.284 0.379 0.000 0.731 0.983 0.982 0.631 1.686 0.997 0.801 +0 0.507 1.101 1.372 1.546 -1.139 1.091 -0.461 0.193 2.173 1.030 -0.927 1.705 0.000 0.860 0.178 1.555 2.548 0.981 1.764 -0.051 0.000 3.183 1.775 0.989 1.838 1.207 1.395 1.366 +0 1.012 0.183 -1.683 1.439 1.092 0.703 1.708 -0.456 0.000 0.923 -0.565 1.081 0.000 0.920 0.606 -0.876 0.000 0.722 2.018 0.418 0.000 0.963 1.070 1.000 0.740 0.476 0.674 0.844 +1 1.142 -0.288 -0.503 1.143 -0.358 1.044 -0.848 0.732 2.173 0.760 -2.464 -1.340 0.000 1.595 -0.700 1.456 1.274 0.940 -0.982 -0.980 0.000 0.879 1.185 0.987 1.206 0.977 1.065 1.054 +1 0.697 -1.508 0.202 0.506 0.518 0.923 0.122 -1.467 0.000 0.680 0.235 0.218 2.215 1.100 -0.703 -1.577 2.548 1.471 -0.772 -0.236 0.000 1.871 1.193 0.993 0.640 1.039 0.903 0.825 +0 0.480 0.095 0.479 0.917 -1.481 0.700 0.088 0.108 2.173 0.797 0.518 -1.061 1.107 0.559 -0.243 1.297 0.000 0.734 0.851 0.558 0.000 1.040 0.891 0.988 0.834 0.986 0.697 0.654 +0 1.248 0.525 -0.242 1.576 -1.064 0.857 -1.099 1.321 1.087 0.485 0.220 1.475 0.000 0.749 -1.291 0.132 0.000 0.869 -0.357 -0.107 0.000 0.883 0.925 1.309 0.930 0.321 1.092 0.877 +1 0.622 0.279 -0.040 0.658 -1.205 1.306 1.456 0.600 0.000 1.221 1.056 -1.659 0.000 0.655 0.716 0.365 0.000 1.348 1.141 -0.596 3.102 0.938 0.827 0.990 0.615 0.682 0.601 0.570 +1 1.472 0.198 1.370 0.803 -0.211 0.732 1.372 -0.006 0.000 0.597 -0.137 -0.009 2.215 1.264 -0.470 -1.408 2.548 1.182 1.285 1.252 0.000 0.913 0.942 1.490 1.003 0.896 0.890 0.839 +0 0.745 -1.481 -1.571 0.862 0.863 1.320 -1.638 1.571 0.000 1.763 -1.501 -0.041 0.000 2.054 1.150 -0.475 0.000 1.094 -0.760 0.312 3.102 0.832 0.993 0.990 0.658 0.730 0.786 0.677 +0 0.655 -0.480 -0.551 1.810 -0.023 0.660 -0.315 1.133 2.173 0.655 0.128 -1.327 0.000 0.511 2.374 -1.481 0.000 1.242 0.921 -1.146 3.102 0.926 0.770 0.990 1.138 1.127 1.146 1.353 +0 1.485 0.791 0.358 0.864 -0.783 0.895 0.740 1.112 2.173 0.883 0.428 -0.984 0.000 0.501 -0.301 -0.547 0.000 0.631 -0.673 -0.990 3.102 0.562 1.146 1.344 0.895 1.017 0.836 0.776 +1 0.827 -0.119 0.324 0.354 -1.525 1.129 0.229 -1.292 2.173 0.960 1.006 0.413 0.000 1.011 1.030 -0.249 0.000 0.695 2.395 1.308 0.000 0.852 0.717 0.991 0.943 0.831 0.923 0.783 +0 0.620 0.693 -0.546 2.155 0.051 0.927 -0.147 1.577 0.000 0.629 -2.201 -0.981 0.000 0.508 -0.451 0.728 2.548 0.910 -0.464 -1.312 0.000 0.991 0.848 0.982 0.897 0.573 0.968 1.014 +1 1.119 -0.185 1.582 1.461 0.830 1.310 1.121 -0.595 2.173 0.507 -0.599 -0.179 0.000 0.493 -1.139 1.714 0.000 1.127 -0.025 1.121 3.102 0.799 0.692 1.109 1.815 1.509 1.208 1.041 +1 0.769 0.524 -1.224 0.964 0.043 2.024 -0.256 -0.078 0.000 1.791 -0.154 1.137 0.000 1.661 -0.679 -1.547 2.548 1.652 -0.208 1.551 0.000 0.824 1.087 1.085 0.685 0.858 0.920 0.924 +0 1.335 -0.274 0.813 0.599 0.380 0.622 1.964 -0.975 0.000 0.803 2.281 -1.514 0.000 0.527 -1.570 -1.130 0.000 1.596 -0.416 -0.135 3.102 0.753 0.884 0.996 0.770 0.873 1.225 1.447 +0 0.466 0.259 0.837 3.177 1.304 0.893 -0.030 -0.502 0.000 0.486 2.463 -1.333 0.000 0.511 1.394 -0.535 1.274 1.198 -0.604 0.327 3.102 2.517 1.367 0.988 0.898 0.928 1.108 1.356 +0 0.959 0.213 1.514 0.486 0.725 0.920 -0.818 -1.567 2.173 0.796 0.290 -0.520 0.000 0.999 1.287 0.656 0.000 0.442 0.710 0.985 0.000 0.882 0.707 0.984 1.193 1.507 1.266 1.022 +0 0.785 0.899 -0.859 0.984 -0.942 1.400 0.763 0.628 2.173 0.919 0.526 -1.318 2.215 0.643 0.297 0.971 0.000 0.605 1.499 -1.275 0.000 0.830 0.927 0.987 0.886 1.652 1.196 0.940 +1 0.815 -0.281 1.207 0.346 -1.407 0.687 -0.338 -0.432 2.173 0.432 1.885 0.975 0.000 0.627 0.988 -1.372 0.000 1.178 0.192 0.494 1.551 0.768 0.788 0.991 0.898 0.757 0.795 0.880 +1 1.221 -0.739 -0.118 1.345 -0.655 2.465 -1.114 1.121 2.173 2.094 -0.470 -0.703 0.000 1.033 -0.614 0.455 0.000 1.529 0.672 -1.468 0.000 0.650 1.050 0.990 0.632 1.144 1.338 1.057 +1 1.662 -0.322 0.283 0.599 -0.951 1.559 -0.159 1.649 2.173 0.718 -1.067 -1.290 2.215 0.778 1.096 -0.410 0.000 1.527 -0.817 0.863 0.000 0.809 0.964 1.239 1.431 1.054 1.035 0.960 +0 2.307 0.588 -1.143 0.474 0.797 0.557 1.574 0.973 0.000 1.007 0.924 0.230 2.215 0.523 0.233 1.157 0.000 0.640 -0.877 -0.774 3.102 0.714 1.031 1.426 1.181 1.027 0.883 0.848 +0 0.324 -0.991 1.427 3.103 -1.264 2.521 -0.239 0.513 2.173 0.981 -0.534 1.645 2.215 1.246 -2.152 -0.778 0.000 0.814 0.583 -0.045 0.000 1.317 1.289 0.987 2.883 2.004 1.907 1.650 +0 0.393 -1.487 1.508 3.241 -1.659 1.855 0.377 0.158 2.173 2.043 -0.361 0.373 0.000 2.605 -0.003 -1.475 2.548 1.211 0.797 -0.160 0.000 1.289 1.867 0.986 2.997 2.776 3.434 3.564 +1 2.485 -1.087 0.688 0.118 1.500 0.487 -0.116 -1.623 0.000 0.573 -0.026 -0.990 2.215 1.022 -0.956 -0.399 1.274 0.498 -1.858 -1.638 0.000 0.952 0.887 0.997 1.029 0.599 0.795 0.764 +0 0.559 0.649 -0.521 0.262 0.168 0.825 1.025 -1.594 1.087 1.195 1.016 0.252 2.215 0.470 0.454 -0.095 0.000 0.808 -0.632 -1.679 0.000 0.813 1.002 0.986 1.095 1.454 0.997 0.831 +0 3.255 -0.315 0.635 1.214 -0.969 1.268 -0.719 -1.027 2.173 1.506 -0.177 1.071 2.215 0.808 -0.006 -0.648 0.000 0.925 -1.452 -0.462 0.000 0.950 0.866 2.732 1.570 2.009 1.524 1.257 +1 0.384 0.406 0.077 0.588 -0.959 0.673 -0.155 -1.485 0.000 0.810 0.893 1.115 2.215 0.755 0.711 -0.558 0.000 0.862 -0.661 0.296 0.000 0.854 0.842 0.988 0.710 0.289 0.719 0.658 +1 0.900 0.188 -1.630 1.654 -1.084 0.974 0.069 0.405 2.173 0.744 0.597 1.151 2.215 0.851 -0.429 1.137 0.000 0.763 -0.120 -0.994 0.000 0.922 1.093 0.979 1.368 0.852 0.971 0.979 +1 0.924 -0.200 -1.134 0.513 1.320 0.881 -0.275 -0.036 1.087 1.035 1.101 1.358 0.000 0.573 -0.488 -0.568 0.000 0.369 0.797 -0.657 0.000 0.784 1.318 0.992 0.683 0.867 0.787 0.806 +0 1.107 -0.038 1.209 0.764 0.335 0.672 0.089 0.088 0.000 0.821 0.771 -1.191 2.215 0.982 -0.038 -1.238 2.548 0.662 1.268 -0.290 0.000 0.864 0.947 0.985 0.824 0.412 0.733 0.752 +0 0.299 1.073 -0.302 1.292 1.514 1.198 1.125 1.075 2.173 1.673 -0.046 -0.671 1.107 0.799 0.463 -0.460 0.000 0.743 0.164 -1.043 0.000 0.818 0.862 0.992 0.920 2.458 1.465 1.280 +0 0.462 -1.385 -0.656 0.805 0.407 1.613 0.133 -0.394 2.173 1.001 0.172 1.350 2.215 0.954 0.942 -1.562 0.000 1.945 0.546 -0.880 0.000 0.913 1.061 0.989 0.958 1.870 1.127 0.989 +0 2.078 1.567 -1.241 0.731 -0.619 0.768 0.118 1.609 2.173 0.712 -0.315 1.080 0.000 1.414 1.029 0.459 0.000 0.795 -0.752 0.122 3.102 0.873 0.964 0.986 1.380 0.918 1.204 1.093 +0 0.708 -0.021 0.108 0.803 1.360 0.766 -0.291 -1.223 2.173 0.386 -1.065 -0.810 0.000 0.548 -1.694 0.792 0.000 1.326 -1.172 0.229 1.551 0.752 0.882 0.985 0.736 1.205 0.752 0.666 +1 1.709 -0.286 0.393 1.551 -0.574 1.180 -0.530 1.410 0.000 0.950 -0.132 -1.603 2.215 0.988 -2.257 0.806 0.000 0.562 -0.745 0.215 0.000 0.834 0.898 1.725 1.299 0.544 0.875 1.000 +1 0.761 0.834 0.945 0.437 1.329 1.929 -0.148 -1.256 0.000 1.857 0.385 0.399 2.215 1.246 -0.867 -0.291 2.548 1.091 1.532 1.456 0.000 0.787 1.275 0.980 0.968 1.507 1.173 0.977 +0 0.839 0.219 1.120 0.394 -0.580 0.822 1.144 -1.350 2.173 0.893 -0.228 -0.081 0.000 1.163 -0.231 0.636 0.000 1.148 -0.493 -1.219 3.102 0.942 0.960 0.991 0.801 1.024 1.024 0.860 +1 0.650 1.778 -0.991 0.361 -1.194 1.388 0.797 1.133 0.000 1.779 0.314 -0.331 2.215 1.076 0.859 -1.460 0.000 0.629 0.351 0.488 3.102 1.589 0.985 0.976 0.947 0.644 1.159 0.962 +1 0.724 -1.401 -0.903 0.787 1.375 0.933 -0.332 0.091 2.173 0.677 -0.123 -0.923 0.000 1.097 -0.520 0.900 2.548 1.040 -1.796 -1.151 0.000 1.004 0.997 0.986 0.732 0.852 0.752 0.703 +1 0.713 -0.694 0.416 1.053 -1.438 1.403 0.996 -0.328 0.000 1.325 -0.707 0.834 1.107 1.430 0.518 -1.343 0.000 2.091 -0.154 1.408 3.102 2.095 1.945 1.194 0.917 0.849 1.601 1.325 +0 0.935 0.431 0.085 2.172 -0.629 0.808 0.485 0.852 1.087 0.909 -0.322 1.146 0.000 0.823 -0.245 -1.646 0.000 1.616 0.229 -1.190 3.102 0.778 0.855 1.182 1.242 1.173 0.942 0.930 +1 0.781 -1.344 -0.423 1.650 -1.191 1.309 -0.756 1.624 1.087 0.918 -2.102 0.166 0.000 1.258 -0.145 0.051 0.000 0.911 -0.561 0.314 0.000 0.985 1.075 1.002 1.203 0.535 1.044 0.993 +1 0.989 0.064 -0.136 1.499 -0.029 0.966 -0.801 1.731 1.087 0.605 -1.268 -1.198 0.000 0.389 -1.478 1.064 0.000 0.490 -1.050 0.525 3.102 0.675 0.660 0.973 0.852 0.666 1.140 1.020 +1 0.974 -0.986 -0.615 1.136 1.374 1.051 0.801 0.956 2.173 0.733 0.924 -1.443 2.215 1.121 -0.334 -0.641 0.000 0.669 0.307 0.031 0.000 1.101 0.934 1.421 1.621 1.075 1.210 1.277 +1 0.392 -1.473 -1.145 0.698 -0.798 1.313 -0.273 1.091 2.173 0.598 0.150 -0.197 0.000 0.730 1.010 -1.216 0.000 0.602 0.209 -0.639 0.000 0.949 0.861 1.000 1.155 0.556 0.865 0.797 +1 1.601 0.911 -1.635 0.794 -0.078 0.791 -0.005 0.842 2.173 0.684 0.012 -1.079 0.000 1.739 -0.008 0.221 0.000 0.748 -0.396 0.093 3.102 0.988 0.936 1.540 0.976 0.545 0.839 0.818 +0 0.448 0.983 -0.805 1.479 -1.718 0.917 -0.481 0.399 0.000 0.660 -0.286 -0.684 2.215 0.539 -0.306 1.180 0.000 0.625 0.033 0.021 3.102 0.982 0.858 0.982 0.669 0.358 0.548 0.661 +0 0.606 0.358 -1.016 0.891 0.400 1.730 0.749 -0.354 2.173 2.240 0.465 1.166 0.000 1.294 0.420 -1.633 2.548 0.767 0.616 1.466 0.000 0.508 0.805 0.988 0.971 1.726 1.408 1.110 +1 1.228 1.232 0.843 1.033 0.615 2.574 1.336 -1.646 0.000 2.692 0.855 -0.151 2.215 0.427 -0.277 0.147 2.548 0.903 -0.219 -1.176 0.000 0.604 1.212 0.986 0.939 0.776 1.101 0.974 +0 0.933 0.478 -1.239 0.682 0.481 0.510 -0.337 -1.188 0.000 0.928 -0.572 0.983 2.215 0.849 -0.110 -0.354 0.000 0.606 -1.219 0.920 1.551 0.818 1.006 1.105 0.808 0.308 0.663 0.674 +0 0.605 -0.096 -1.398 1.718 0.390 0.991 -2.133 0.306 0.000 1.131 -0.056 1.530 1.107 0.651 -1.299 -0.871 0.000 1.926 0.962 -1.107 1.551 0.804 1.792 1.411 1.285 1.261 1.897 1.531 +0 0.866 -0.913 -0.056 1.060 -0.776 1.016 -1.071 -1.110 2.173 1.500 -0.545 1.297 2.215 0.792 2.214 0.088 0.000 0.690 -0.833 0.218 0.000 2.198 2.230 0.984 0.887 1.575 1.845 1.467 +0 0.621 0.901 0.883 1.117 -0.886 0.367 2.443 0.622 0.000 0.809 -0.787 1.143 2.215 0.694 0.631 -0.339 2.548 0.832 0.748 -1.055 0.000 1.049 0.880 1.153 0.640 1.015 0.805 0.731 +0 0.940 -1.425 1.447 0.668 -0.891 0.534 0.861 -0.145 0.000 1.014 -1.135 -1.138 2.215 0.903 -2.194 -0.050 0.000 1.184 0.126 0.806 1.551 0.441 1.144 0.986 0.851 1.199 1.086 0.904 +1 0.808 0.149 0.766 0.392 -0.784 0.596 -0.199 -0.692 2.173 0.546 2.100 1.285 0.000 0.828 -0.310 1.477 2.548 0.942 0.239 -1.389 0.000 0.855 1.004 0.985 0.717 0.814 0.850 0.784 +1 1.815 0.313 -0.515 1.559 -1.143 1.969 0.672 1.408 0.000 0.615 -0.863 0.401 0.000 0.999 -1.143 0.947 2.548 0.413 -0.244 -0.213 0.000 0.398 0.495 1.248 1.121 0.618 1.057 0.840 +0 0.700 0.899 0.637 1.122 1.652 1.027 -0.521 -1.018 2.173 1.067 1.710 0.020 0.000 0.351 -1.597 0.678 0.000 0.920 1.338 1.223 0.000 1.145 1.136 0.987 1.241 0.654 1.296 1.066 +1 0.979 -0.429 -1.720 1.277 -0.401 0.651 0.940 -0.004 2.173 0.779 0.657 1.455 0.000 0.976 -1.066 1.248 0.000 1.802 0.417 -0.694 0.000 1.440 1.171 1.438 1.171 0.740 0.978 0.983 +1 0.773 -0.046 0.888 0.472 -1.432 2.017 -1.593 -0.435 0.000 1.300 -0.891 1.027 2.215 0.972 -1.974 -1.632 0.000 1.992 -0.145 1.531 3.102 0.828 1.024 0.984 0.669 0.843 0.902 0.771 +1 1.549 0.841 -0.854 0.671 0.763 0.916 2.506 -0.491 0.000 1.376 0.250 0.958 2.215 1.383 0.635 1.520 2.548 1.236 -0.384 1.490 0.000 0.860 0.885 1.404 0.987 0.783 0.889 0.812 +1 0.809 -0.754 -0.774 1.760 -0.247 0.566 -1.765 -1.408 0.000 1.102 -0.157 -1.681 1.107 0.759 -0.521 -0.374 0.000 3.412 -0.600 0.863 3.102 1.211 1.163 1.001 1.446 1.404 1.169 1.088 +1 0.515 0.454 -0.582 0.816 0.400 0.634 0.187 -1.273 2.173 1.158 -0.642 -1.037 0.000 1.444 0.583 0.524 0.000 1.581 0.420 1.672 3.102 0.998 1.172 0.987 0.953 0.523 0.919 0.881 +1 1.937 1.301 -1.301 0.507 0.744 1.204 1.344 -0.303 2.173 0.688 0.741 0.310 2.215 1.433 1.654 1.500 0.000 0.753 -0.418 0.943 0.000 1.737 1.212 1.322 1.211 0.810 1.092 1.005 +1 0.366 1.900 0.901 0.652 0.774 1.237 0.102 -1.481 0.000 1.552 0.659 0.254 1.107 0.706 -0.099 -0.907 0.000 0.500 -0.200 1.354 3.102 0.962 1.130 0.995 0.526 0.765 0.721 0.664 +1 0.451 1.055 -0.298 1.039 -0.141 0.753 -0.727 1.432 0.000 0.678 -0.082 -1.500 0.000 0.631 0.346 -0.521 2.548 1.255 1.004 0.424 0.000 0.881 0.905 0.994 0.664 0.290 0.614 1.182 +0 1.728 1.460 1.198 0.843 -1.619 1.335 -0.940 -0.682 0.000 1.012 0.220 1.562 0.000 0.674 0.864 0.574 2.548 0.587 1.198 -1.404 3.102 2.699 1.738 0.982 0.714 0.485 1.174 1.398 +1 1.138 0.426 -0.047 0.939 -0.774 1.590 0.548 -0.590 2.173 2.121 -0.104 0.804 0.000 1.749 -1.319 1.529 1.274 1.508 0.568 1.423 0.000 1.552 1.919 0.987 0.774 3.134 1.964 1.548 +0 0.943 0.249 1.039 2.680 1.038 1.811 0.576 -0.778 2.173 0.783 0.654 -1.407 2.215 1.240 0.084 0.536 0.000 0.547 0.767 -0.312 0.000 0.738 0.909 0.971 2.398 0.944 1.589 1.253 +0 0.442 0.118 -1.021 1.661 0.789 0.748 1.272 -1.731 1.087 0.640 1.780 -1.121 0.000 1.116 -0.562 0.119 2.548 1.422 1.052 -0.357 0.000 0.867 0.942 1.185 0.824 1.690 1.098 1.017 +0 0.526 -1.433 -0.055 1.779 0.173 1.261 -0.431 1.570 0.000 1.210 0.243 1.299 0.000 2.945 -0.607 -0.394 2.548 0.753 -1.041 -1.463 3.102 1.116 0.926 0.990 0.930 0.993 1.370 1.220 +1 0.844 -1.034 -0.227 0.507 1.204 1.012 0.917 -1.121 0.000 1.206 -1.223 0.858 2.215 0.578 -2.273 -1.053 0.000 0.806 -2.134 0.741 0.000 0.753 0.940 0.988 0.515 0.352 0.591 0.564 +0 0.566 1.264 -0.838 1.090 0.278 0.788 0.854 0.933 0.000 0.864 -0.711 1.682 2.215 1.534 -0.624 -1.077 0.000 0.828 -0.532 -0.172 3.102 1.322 1.038 0.987 1.013 0.761 1.099 1.319 +0 0.723 0.550 0.356 1.616 -0.220 1.122 2.230 -1.471 0.000 1.321 -0.530 0.525 2.215 1.763 1.059 1.229 0.000 1.319 1.308 -1.071 3.102 2.190 1.375 0.982 1.435 1.916 1.901 1.559 +1 1.104 0.483 -1.025 1.548 -1.299 0.860 0.952 0.427 2.173 1.300 -2.416 0.467 0.000 0.549 0.149 -1.422 2.548 0.569 0.143 0.582 0.000 0.504 0.524 0.992 0.483 0.922 0.934 0.759 +0 0.661 -1.443 -0.722 0.529 1.433 0.881 0.465 -0.735 1.087 0.968 0.399 0.961 1.107 0.485 1.478 1.507 0.000 1.256 0.194 0.296 0.000 0.832 0.838 0.990 1.559 1.358 1.428 1.174 +1 0.634 -1.699 1.673 1.496 -0.663 1.558 0.256 0.128 1.087 1.180 -2.581 1.628 0.000 0.888 -0.391 1.360 0.000 0.698 0.585 -1.380 3.102 2.165 1.881 1.162 2.128 1.107 2.082 1.711 +1 1.007 -0.436 -0.640 0.988 0.299 1.680 -0.705 -1.320 0.000 0.836 -0.653 0.287 0.000 1.992 0.207 0.355 2.548 1.046 -0.234 1.488 0.000 1.102 0.986 1.035 0.779 0.688 0.679 0.681 +1 0.943 -0.374 0.316 0.970 -1.180 0.811 0.819 1.608 0.000 1.379 -0.016 0.050 2.215 0.766 1.063 -1.229 0.000 0.560 -0.802 -1.736 3.102 0.817 0.808 1.292 0.942 0.883 0.950 0.882 +1 1.066 -0.699 -1.410 1.098 -0.629 0.700 -0.923 0.866 0.000 1.083 0.247 0.431 2.215 0.737 0.983 -0.712 0.000 1.089 -0.085 0.885 3.102 0.294 0.887 0.986 0.867 0.428 0.842 0.820 +1 0.787 0.894 0.280 1.232 0.433 2.828 -0.055 -1.070 0.000 1.383 -0.503 0.585 2.215 2.322 0.330 1.144 0.000 0.981 -0.440 -0.525 3.102 0.836 1.152 0.984 0.725 0.885 0.947 0.836 +1 1.675 -0.256 -0.504 1.428 0.208 1.302 0.107 1.180 0.000 0.597 0.971 -0.679 1.107 0.443 -0.770 0.030 1.274 0.819 0.629 -1.664 0.000 1.005 0.931 1.284 0.950 0.665 0.795 0.916 +0 2.030 -1.210 -1.250 0.706 1.027 1.388 0.381 0.360 2.173 0.459 -0.087 1.230 0.000 0.653 2.335 1.572 0.000 0.377 0.562 -0.533 3.102 0.773 0.543 1.470 2.077 0.563 1.283 1.425 +0 0.962 0.549 -1.365 1.342 -0.029 1.360 0.340 0.806 2.173 1.613 -0.506 -1.315 2.215 0.899 -0.358 0.602 0.000 1.344 -0.937 -0.540 0.000 1.134 1.244 1.470 1.292 2.272 1.335 1.177 +0 0.673 0.836 -1.359 1.474 -1.417 1.204 -1.465 0.232 2.173 0.615 -0.822 -0.220 0.000 0.986 1.973 -1.602 0.000 1.450 -0.718 1.156 3.102 0.694 0.859 0.999 1.850 1.122 2.452 1.764 +0 0.608 -0.716 1.379 1.963 -1.037 0.921 -0.845 0.721 1.087 0.684 -1.500 -0.469 2.215 0.446 -0.779 -0.408 0.000 0.954 0.635 1.597 0.000 0.953 1.008 1.244 1.260 1.105 0.934 0.854 +1 0.398 1.379 -1.038 1.199 -1.407 0.810 0.054 -0.396 2.173 0.911 1.429 1.013 0.000 0.767 0.619 1.630 0.000 1.417 0.667 0.171 0.000 0.854 0.731 0.977 0.871 0.741 0.844 0.795 +1 0.360 -0.935 -1.049 0.734 1.482 0.813 0.247 1.581 2.173 1.053 -0.524 -0.621 0.000 1.058 -1.117 -0.151 0.000 0.900 0.299 0.311 3.102 0.874 0.858 0.992 1.606 0.826 1.183 1.100 +0 0.594 0.228 1.371 1.115 -1.081 0.946 0.603 -0.196 2.173 0.394 1.126 1.047 0.000 0.432 -2.336 -1.247 0.000 0.452 1.564 -0.074 0.000 0.508 0.727 0.988 0.632 0.832 0.739 0.682 +1 1.605 -0.278 0.033 0.924 0.371 0.959 0.924 -0.880 2.173 0.949 0.559 1.499 2.215 0.698 0.011 1.497 0.000 0.382 0.035 -0.933 0.000 0.464 0.759 0.987 1.315 1.206 1.269 0.951 +1 0.466 1.073 -0.374 0.022 -0.121 0.903 -0.434 0.074 2.173 0.359 2.071 0.647 0.000 1.086 0.905 1.430 1.274 0.506 1.180 -1.405 0.000 0.571 1.289 0.543 0.580 1.506 0.928 0.713 +1 2.477 -1.667 -1.127 0.890 -1.543 1.325 1.062 0.696 0.000 1.611 -0.987 -0.218 2.215 0.731 -1.443 0.961 0.000 0.627 0.026 -1.671 0.000 0.849 1.111 0.986 0.722 1.171 0.989 0.873 +0 2.232 0.127 0.041 3.490 0.385 3.415 -0.387 -1.400 0.000 2.419 0.155 0.468 1.107 2.632 -0.932 -1.261 2.548 1.779 0.313 1.049 0.000 0.898 1.059 1.182 2.644 3.165 1.975 1.544 +0 1.451 -0.835 0.892 1.078 1.329 0.846 -0.055 -0.026 2.173 0.448 -2.645 -0.629 0.000 0.965 -1.019 -1.428 2.548 0.825 0.029 -1.055 0.000 0.671 0.732 0.979 1.118 1.243 0.918 0.941 +1 0.540 -0.885 -0.417 0.279 0.685 0.840 0.785 1.173 0.000 1.233 -0.070 -0.270 2.215 1.109 0.743 -1.710 0.000 0.442 0.175 -1.564 3.102 0.896 0.553 0.999 0.699 0.620 0.866 0.750 +1 0.717 -0.190 0.302 0.557 -0.273 1.164 0.120 0.608 0.000 1.179 2.272 -1.428 0.000 1.671 -0.662 -1.443 2.548 0.711 -0.284 0.022 0.000 0.773 0.689 0.995 1.230 0.894 0.907 0.862 +1 1.233 -0.697 1.293 0.549 1.215 0.907 -0.988 -0.596 0.000 0.931 0.300 1.174 2.215 0.708 -1.311 0.313 0.000 1.110 -1.523 -0.598 0.000 0.982 1.276 0.989 0.597 0.580 0.736 0.721 +0 0.659 -0.998 1.229 0.911 -0.812 1.239 -0.845 -1.251 2.173 1.275 -0.483 0.373 1.107 1.005 -0.942 0.104 0.000 1.532 0.045 1.077 0.000 1.311 0.918 1.035 0.792 1.868 1.127 0.932 +1 1.412 -0.313 1.244 0.694 0.393 1.399 0.640 -0.405 2.173 0.426 2.748 0.748 0.000 0.744 -0.602 -1.601 0.000 0.582 1.486 -1.588 3.102 2.507 1.380 0.987 1.527 1.014 1.198 1.223 +0 0.529 1.170 0.667 1.147 -0.515 1.077 2.521 -0.522 0.000 1.252 -0.133 0.904 2.215 0.825 1.030 1.469 0.000 0.502 -1.983 -1.537 0.000 2.014 1.125 0.991 1.344 0.791 0.946 1.071 +0 1.386 -2.416 0.101 0.602 -1.724 1.413 -0.782 -1.511 2.173 0.879 -0.108 -0.614 0.000 1.813 1.650 0.733 0.000 0.571 -0.294 0.926 1.551 0.974 1.011 1.262 1.772 0.798 1.797 2.525 +0 2.187 0.673 1.662 0.505 0.321 0.735 1.450 -0.108 1.087 0.383 2.292 -0.451 0.000 0.410 1.580 0.814 0.000 0.717 2.177 1.133 0.000 0.681 0.753 1.361 1.248 0.566 0.823 0.795 +0 0.915 0.056 -0.133 1.101 -1.008 0.877 1.444 1.690 2.173 0.636 2.792 0.359 0.000 1.089 1.144 1.110 0.000 0.559 -0.067 -0.480 3.102 1.399 1.191 0.988 1.245 0.930 0.912 1.041 +0 0.476 -2.349 -0.470 0.715 -1.711 1.079 -0.516 -0.157 1.087 0.792 -0.852 1.428 2.215 0.593 -0.036 -1.165 0.000 0.623 0.920 1.280 0.000 0.676 1.047 0.989 0.698 1.368 0.862 0.820 +0 0.372 0.717 0.157 0.535 -1.076 0.701 0.089 1.662 0.000 1.392 -0.564 -0.016 1.107 1.210 0.768 -1.723 0.000 1.465 -0.948 1.550 0.000 0.999 0.921 0.988 0.779 0.502 0.853 0.737 +1 2.200 -0.197 -1.460 1.999 -1.405 1.657 2.025 0.491 0.000 0.485 -0.826 -0.979 0.000 0.892 -0.987 -0.531 2.548 1.535 -0.591 1.242 1.551 1.441 0.921 0.975 1.098 0.910 0.971 1.275 +0 2.318 0.861 1.100 0.884 1.596 0.942 0.426 -0.528 0.000 0.795 -0.322 -0.490 2.215 1.025 0.266 0.412 2.548 1.449 0.047 -1.396 0.000 1.300 0.889 0.985 0.862 0.760 0.921 0.975 +0 1.012 -0.030 0.894 0.879 -0.320 1.125 0.806 1.292 0.000 1.089 1.753 1.613 0.000 2.021 -0.856 -0.884 0.000 0.980 0.452 0.320 3.102 1.384 1.154 1.161 0.629 0.615 0.837 0.897 +1 0.865 -1.037 1.186 0.394 -0.583 0.502 1.204 1.336 2.173 0.684 0.496 0.259 2.215 0.278 0.413 0.987 0.000 0.451 -1.085 0.179 0.000 0.957 1.126 0.990 0.935 0.777 0.985 0.829 +1 1.240 1.307 1.299 1.528 0.079 0.944 0.338 0.051 1.087 1.051 1.890 -1.138 0.000 0.910 2.392 1.443 0.000 0.379 1.399 -1.526 0.000 1.210 1.035 1.698 1.250 0.296 1.158 1.066 +1 0.643 -0.182 -0.340 1.487 0.861 0.603 -0.911 -1.638 2.173 0.604 0.230 -1.586 0.000 0.648 0.136 -0.994 2.548 0.366 0.027 0.884 0.000 0.489 0.552 1.196 0.798 0.611 0.705 0.584 +1 0.829 -0.071 0.543 0.553 1.265 0.747 -0.035 0.101 0.000 1.006 -2.127 0.258 0.000 2.299 -0.580 -1.358 2.548 0.841 -0.419 -0.850 0.000 0.959 1.100 0.994 1.334 0.664 1.004 0.947 +0 1.720 -0.296 -0.169 1.160 -0.078 0.851 -0.393 -1.717 1.087 0.350 -0.436 -0.834 0.000 0.716 0.336 1.027 2.548 0.830 1.407 1.257 0.000 0.779 0.840 0.983 1.382 0.713 0.953 0.885 +0 0.672 0.625 -0.526 0.644 1.011 1.386 -0.151 0.156 2.173 1.178 0.875 1.688 2.215 0.802 0.169 -1.621 0.000 0.615 -1.230 -1.088 0.000 0.805 0.996 0.990 0.867 2.117 1.161 0.923 +0 0.373 0.109 0.285 1.698 -1.451 0.381 -0.396 -0.530 0.000 0.551 0.916 1.497 2.215 0.566 -2.507 0.323 0.000 0.719 -0.878 0.240 3.102 1.085 0.664 1.103 0.698 0.842 0.866 0.866 +1 0.672 -0.009 1.347 1.585 -0.722 0.665 1.433 -0.558 2.173 0.780 -0.339 0.651 0.000 0.818 -0.811 1.463 2.548 0.877 0.943 0.905 0.000 0.907 0.853 1.369 1.109 1.606 0.977 0.916 +0 0.825 0.336 1.379 1.440 -1.182 0.664 -0.103 0.606 2.173 0.384 -1.116 -0.635 0.000 0.480 -2.299 0.784 0.000 0.595 1.104 -0.435 3.102 0.799 1.018 1.119 0.708 0.745 0.852 0.910 +0 0.524 1.551 0.146 1.132 0.718 1.108 -0.010 -0.407 2.173 1.169 0.581 1.459 2.215 0.611 1.057 -0.709 0.000 0.766 -0.025 -1.707 0.000 0.759 0.880 0.990 0.874 1.742 1.009 0.829 +0 1.233 -0.207 1.298 0.877 -1.291 0.460 0.408 -0.213 0.000 0.377 -1.409 0.111 0.000 0.441 -0.590 0.364 2.548 0.963 -0.696 -1.611 0.000 0.981 1.026 1.043 0.661 0.654 0.691 0.709 +0 1.106 -2.254 -1.116 1.357 -1.637 0.854 -0.450 -0.322 1.087 0.558 -0.066 0.288 0.000 1.010 -0.905 1.157 0.000 0.654 1.092 0.727 3.102 0.982 1.032 0.985 1.712 1.019 1.329 1.124 +1 0.748 1.891 -1.163 0.187 0.575 0.598 0.381 1.678 0.000 0.582 -2.101 0.373 0.000 0.606 0.757 -0.258 2.548 1.340 1.884 -0.704 0.000 1.871 1.139 0.996 0.727 0.635 0.816 0.701 +1 0.355 0.772 -0.669 0.158 0.164 1.297 -0.633 -1.256 2.173 1.239 -0.477 1.126 0.000 1.004 -0.484 0.571 0.000 1.413 1.011 -0.150 0.000 0.821 0.744 0.988 0.881 0.990 0.966 0.808 +1 0.675 0.590 -1.736 1.093 0.373 0.393 0.561 -0.798 0.000 0.445 -0.475 -0.058 2.215 0.678 2.048 -0.626 0.000 1.079 -0.640 1.513 1.551 0.888 0.965 1.126 0.838 0.624 0.861 0.774 +0 1.614 0.625 -0.385 0.725 -1.054 1.083 0.623 0.844 0.000 1.065 0.623 -1.284 1.107 0.981 -0.443 0.573 2.548 1.159 0.621 1.397 0.000 0.952 0.946 0.986 0.799 1.259 0.913 0.920 +0 5.215 -0.529 -0.590 0.950 -1.016 2.811 -0.064 1.194 2.173 0.355 -2.657 1.024 0.000 1.084 0.081 0.582 0.000 0.597 -0.591 0.253 3.102 0.970 1.257 1.153 0.838 1.123 2.055 1.554 +0 0.329 -0.022 1.457 1.778 -0.820 1.066 -0.844 0.527 1.087 0.329 0.205 0.964 0.000 0.604 -1.070 -1.117 1.274 0.543 -2.355 1.584 0.000 1.233 1.085 0.989 0.781 1.009 1.012 0.978 +0 0.775 0.068 1.509 0.316 -1.264 1.272 -1.673 -1.235 0.000 0.951 -1.276 0.708 2.215 1.863 -0.539 1.219 2.548 1.413 -0.922 -0.364 0.000 0.904 1.159 0.988 0.817 0.817 0.764 0.722 +0 3.963 -1.376 -1.235 0.880 -0.869 1.393 -0.173 0.660 0.000 1.043 -0.323 0.359 0.000 0.891 -1.368 -1.695 2.548 1.305 0.987 0.400 3.102 0.718 1.051 0.979 0.699 1.668 1.632 1.701 +0 0.992 -0.653 0.341 0.352 -0.721 0.704 -0.348 0.950 0.000 0.655 -2.866 -0.991 0.000 0.928 2.360 1.466 0.000 1.094 1.415 0.681 0.000 0.886 0.850 0.980 1.147 0.223 0.897 1.297 +1 1.554 0.726 -1.122 0.344 -1.194 0.944 1.634 -0.295 0.000 1.908 -0.085 0.964 2.215 0.768 -0.699 -0.675 0.000 1.071 0.390 0.555 0.000 1.111 0.810 0.974 1.665 0.842 1.109 0.981 +0 0.978 -0.864 1.592 0.484 -1.055 0.389 2.279 0.018 0.000 0.494 0.321 -0.083 2.215 0.698 -0.003 1.127 2.548 0.500 0.857 1.398 0.000 0.785 0.868 0.993 0.743 0.563 0.608 0.744 +0 0.598 -0.274 0.944 0.450 1.638 0.505 -1.200 0.496 0.000 0.619 -1.484 1.403 2.215 1.635 -0.882 -0.175 2.548 2.615 1.133 -1.172 0.000 3.416 2.437 0.984 1.260 1.100 1.699 1.363 +1 0.897 -0.100 -0.850 0.931 -1.674 0.684 -0.937 0.423 0.000 0.894 0.137 0.007 0.000 0.823 1.058 -1.600 2.548 0.792 -0.340 -1.740 3.102 1.126 0.945 0.985 0.626 0.542 0.871 0.811 +0 0.891 -0.682 1.531 0.993 1.040 1.086 -0.848 0.122 0.000 1.023 -0.962 -1.575 2.215 0.646 0.195 -0.867 2.548 0.837 -0.897 -0.593 0.000 0.890 0.878 1.000 0.897 0.755 0.838 0.865 +0 0.997 -1.911 -0.309 1.684 0.583 0.604 -0.448 -0.910 1.087 1.524 -0.335 -1.424 0.000 1.157 -0.531 -0.032 0.000 2.897 -0.563 0.560 0.000 0.942 0.885 1.293 1.324 0.980 1.250 1.161 +0 0.377 -2.261 -1.261 1.582 -1.492 0.870 -0.337 -0.204 0.000 0.581 -0.663 0.919 2.215 0.502 -0.212 1.246 2.548 1.082 0.243 0.576 0.000 1.069 0.876 0.990 0.653 0.212 0.577 0.762 +0 1.160 0.945 1.576 1.372 -1.654 1.036 -1.065 -0.700 2.173 1.564 0.551 0.637 0.000 0.744 0.350 0.060 0.000 1.196 1.383 0.149 0.000 0.836 1.341 0.977 2.612 0.719 1.747 1.547 +0 1.610 -0.073 -1.629 0.814 -1.420 2.192 -0.153 -1.307 2.173 3.965 1.303 0.176 1.107 2.119 1.825 1.033 0.000 0.447 1.065 0.413 0.000 0.695 1.726 0.986 0.849 5.516 2.838 2.447 +0 0.959 -0.669 1.719 0.801 0.826 1.088 -0.240 -0.124 0.000 0.584 -0.900 0.473 0.000 1.175 0.170 -1.372 2.548 0.941 -0.666 -1.509 3.102 1.049 1.016 0.987 0.752 0.424 0.842 0.780 +0 0.812 -0.776 0.509 1.106 1.722 0.790 0.735 -1.210 2.173 0.902 0.099 0.543 2.215 0.645 1.288 -0.192 0.000 0.466 0.578 -0.779 0.000 0.739 0.977 1.166 1.217 1.306 0.962 0.963 +1 1.053 2.065 1.531 1.121 0.316 0.493 0.507 -0.865 0.000 0.932 0.351 -0.383 2.215 0.566 0.842 1.712 0.000 0.393 -1.438 1.564 0.000 0.913 0.929 1.337 1.027 0.827 0.987 0.975 +1 1.174 -0.802 -0.337 1.093 0.256 1.047 0.130 -1.520 2.173 0.801 -0.233 1.435 0.000 0.383 -1.093 1.367 0.000 0.601 0.295 0.063 0.000 0.896 0.872 0.988 0.507 0.798 0.837 0.744 +1 1.156 -0.156 -0.606 1.499 0.145 2.677 0.015 -0.843 0.000 1.242 -1.108 1.207 2.215 0.758 -1.393 0.597 2.548 1.185 0.961 1.226 0.000 0.803 0.930 1.142 1.376 0.575 0.953 0.864 +1 0.550 -2.264 1.230 0.503 -0.145 1.215 -0.624 -1.222 2.173 1.072 -0.943 0.632 1.107 0.752 -0.690 -0.021 0.000 1.171 -1.685 0.460 0.000 0.966 1.271 0.988 0.708 1.695 1.178 1.059 +0 0.401 -2.170 0.637 1.628 -1.533 0.400 -0.649 0.903 0.000 0.565 -0.986 -1.469 2.215 0.929 0.369 -0.025 1.274 0.591 -0.580 -0.310 0.000 0.659 0.643 1.038 0.719 0.950 1.074 0.867 +0 1.697 0.760 1.489 0.765 0.524 1.060 0.974 -1.460 2.173 0.774 -0.353 -0.303 2.215 0.637 -0.150 0.456 0.000 2.099 -1.236 0.024 0.000 1.025 0.805 1.205 1.014 1.504 1.383 1.288 +1 1.728 0.082 0.566 0.268 0.951 0.826 -0.327 1.027 0.000 1.018 0.457 -0.752 2.215 1.185 -1.349 -1.349 0.000 1.526 -0.322 0.104 0.000 1.021 1.171 0.979 0.621 0.705 0.748 0.712 +0 0.746 -1.031 -0.754 1.150 1.560 0.592 0.279 0.149 0.000 0.866 0.044 0.974 1.107 0.889 1.420 0.139 0.000 1.161 0.632 -0.784 3.102 0.892 0.974 1.117 1.020 0.963 0.843 0.962 +0 0.891 -0.593 -0.392 0.485 0.351 1.158 0.271 -1.208 2.173 1.146 -0.844 0.889 1.107 0.688 -1.315 0.328 0.000 0.384 -2.013 0.486 0.000 0.729 1.248 0.991 0.969 1.907 1.056 0.903 +0 0.478 -0.632 -0.146 1.589 -1.345 0.691 0.843 0.462 0.000 0.910 0.245 1.391 2.215 1.121 0.984 -0.146 0.000 0.432 -0.723 -1.314 3.102 0.844 0.883 1.064 0.927 0.496 0.754 0.874 +0 0.592 0.791 -1.145 0.650 0.146 1.251 0.221 -1.593 2.173 1.100 0.831 0.272 1.107 0.856 -1.370 0.227 0.000 0.885 1.491 0.811 0.000 0.802 0.879 0.987 1.208 1.803 1.057 0.899 +0 0.656 -1.537 -1.612 0.096 -1.614 0.918 -0.853 0.673 0.000 0.584 -1.514 0.175 1.107 1.423 -0.912 -0.939 0.000 0.900 -0.716 1.589 3.102 1.072 0.793 0.986 0.765 0.668 0.586 0.599 +1 0.446 1.033 -1.526 2.178 1.015 3.186 1.633 -0.909 0.000 1.849 0.000 1.030 2.215 1.969 -0.300 1.519 2.548 1.671 0.216 0.202 0.000 0.710 0.939 1.027 1.228 0.929 0.969 0.930 +0 1.265 1.128 -1.196 0.559 -0.730 1.291 0.474 0.517 2.173 1.436 0.823 -1.658 1.107 1.061 0.350 -0.286 0.000 0.674 -0.161 1.243 0.000 0.955 0.958 0.977 0.808 1.890 1.124 0.941 +0 0.621 -0.943 -1.698 0.950 0.592 0.901 0.176 -1.422 0.000 1.203 -2.449 0.403 0.000 0.736 1.116 0.183 2.548 1.693 0.217 -0.951 3.102 0.530 0.897 0.989 1.093 0.845 0.971 0.861 +1 1.604 -0.042 0.970 0.463 1.134 0.727 -0.174 -0.451 2.173 1.065 0.404 -1.276 2.215 0.559 1.098 0.018 0.000 0.485 0.432 -1.026 0.000 0.633 0.868 0.991 1.036 0.959 0.925 0.783 +1 0.884 -0.477 -0.295 1.082 -1.223 0.539 0.909 -0.914 0.000 0.683 0.727 -1.479 0.000 0.885 -1.670 1.099 0.000 0.888 0.779 0.507 3.102 0.960 0.817 1.004 0.935 1.211 1.038 0.926 +1 0.574 -1.679 -0.752 0.456 -1.193 1.050 1.109 0.936 0.000 0.588 0.079 -1.199 2.215 0.822 0.009 -0.269 2.548 0.604 2.456 0.865 0.000 1.275 1.359 0.999 0.605 0.549 0.995 1.089 +1 0.628 0.652 -1.154 0.795 0.311 0.974 0.533 1.577 0.000 1.365 0.877 0.648 2.215 1.344 0.660 -0.061 2.548 1.977 -1.075 -0.970 0.000 0.971 1.204 0.989 0.837 0.867 0.950 0.801 +0 2.281 -0.391 -0.926 0.356 1.734 0.571 -1.426 1.185 0.000 0.462 -1.721 0.448 0.000 0.726 0.200 0.247 2.548 0.881 0.483 0.602 3.102 0.698 0.969 0.989 0.879 0.222 0.713 0.826 +1 1.951 0.946 1.742 0.614 -1.618 1.437 1.022 -0.045 2.173 0.811 -0.232 0.555 0.000 0.897 0.277 -1.092 0.000 0.645 0.187 1.012 3.102 0.830 1.149 1.000 0.618 0.932 1.063 0.867 +1 0.348 -0.011 1.007 2.427 -0.467 2.984 -2.227 1.355 0.000 2.892 -0.042 -0.274 1.107 0.680 -0.400 1.681 0.000 0.867 0.222 -0.914 3.102 0.852 0.728 1.236 0.867 0.811 0.886 0.798 +1 0.852 1.071 -1.257 0.713 -0.345 0.951 0.552 0.857 2.173 0.836 -1.309 -1.691 1.107 1.256 0.577 -0.185 0.000 1.225 -0.351 -0.315 0.000 0.773 1.184 0.989 1.872 1.748 1.420 1.214 +1 1.495 0.348 -1.077 0.346 0.257 0.268 0.184 -0.569 0.000 0.676 1.381 0.814 0.000 0.783 1.340 1.267 2.548 1.740 1.229 -0.181 3.102 1.043 0.838 0.988 0.858 0.861 0.769 0.704 +1 0.952 -1.752 1.478 1.641 1.348 1.390 -0.572 0.447 0.000 1.845 -0.858 -1.297 0.000 1.668 1.379 -0.023 0.000 1.178 -0.467 1.505 0.000 1.152 0.684 0.974 1.641 0.591 1.185 1.198 +0 0.918 0.801 0.742 0.588 0.864 1.137 -1.025 -1.319 2.173 1.036 -0.269 -0.071 1.107 0.636 -1.627 0.624 0.000 0.705 -0.782 -1.709 0.000 0.714 0.910 0.996 1.379 1.569 1.081 0.907 +1 1.054 1.332 -0.105 1.000 0.479 1.386 -2.825 -0.913 0.000 1.295 1.411 1.422 2.215 1.326 0.304 1.291 2.548 1.076 0.411 -0.576 0.000 0.876 1.087 0.984 0.960 0.836 0.889 0.784 +1 0.472 -1.140 -0.598 2.016 -1.664 0.877 -0.612 0.644 1.087 0.704 0.802 0.349 2.215 0.955 -2.447 -0.773 0.000 0.868 -0.936 -1.373 0.000 1.010 1.495 1.108 1.442 0.959 1.363 1.195 +0 0.599 -0.378 0.609 0.830 -1.452 1.162 -0.054 -1.682 2.173 1.327 -0.377 -0.020 2.215 1.110 -1.464 -0.473 0.000 0.550 -1.066 0.371 0.000 0.908 1.021 0.989 0.750 1.848 1.153 0.952 +1 1.020 0.300 0.840 0.753 -1.363 0.783 -1.019 -1.215 2.173 0.668 -0.246 0.607 0.000 1.556 -0.218 -1.718 2.548 1.998 -0.315 -0.132 0.000 0.935 1.237 1.112 1.069 0.820 0.957 0.873 +0 1.453 -0.314 0.218 1.704 -0.313 1.050 -0.412 1.660 2.173 0.763 -0.628 -0.842 0.000 0.828 0.688 1.099 0.000 1.082 0.971 0.741 0.000 1.053 0.912 1.004 0.915 0.508 1.003 0.863 +1 0.768 0.782 -1.544 0.755 0.748 0.995 0.694 0.800 2.173 1.148 0.494 -1.111 0.000 0.908 -0.255 -0.756 0.000 0.580 0.637 0.329 0.000 0.802 0.681 0.991 0.773 0.693 0.890 0.793 +0 1.902 -0.179 0.551 1.064 0.948 1.233 -0.027 -0.830 0.000 0.539 -1.097 -1.700 0.000 0.487 0.165 -1.225 2.548 0.592 1.229 0.428 3.102 1.581 0.869 0.998 0.887 0.498 0.751 0.934 +0 0.647 0.818 -0.818 2.915 -0.204 1.553 -2.329 1.288 0.000 1.578 0.596 0.900 0.000 0.488 0.147 -0.977 0.000 1.822 0.612 -1.459 3.102 1.370 1.081 1.001 1.164 0.859 0.850 0.955 +1 0.812 -0.887 1.734 1.268 0.883 0.952 0.213 -0.949 0.000 0.920 -0.480 0.935 2.215 1.025 -0.932 -0.109 2.548 1.085 -0.664 0.448 0.000 0.893 0.738 0.987 0.853 0.879 0.642 0.593 +1 0.770 0.035 -0.561 0.875 0.583 1.244 0.125 -1.087 2.173 0.744 -0.151 -1.586 0.000 1.123 -0.287 0.742 0.000 0.873 -0.299 -0.479 1.551 0.896 0.914 0.987 1.012 0.635 1.026 0.852 +1 1.204 1.415 -0.119 1.989 0.617 0.959 0.890 -1.356 2.173 1.079 -0.238 -1.013 0.000 1.148 0.271 0.723 0.000 1.294 -0.599 1.203 3.102 1.770 1.202 1.321 1.504 1.376 1.334 1.275 +1 1.834 0.115 0.306 0.416 -0.469 1.443 -0.025 -1.364 2.173 0.643 0.541 1.070 0.000 0.436 -0.683 -0.209 2.548 0.553 -1.892 0.378 0.000 1.570 0.928 0.987 1.452 0.928 0.973 0.940 +0 1.136 0.164 -1.637 0.889 0.688 0.541 1.327 -0.971 0.000 0.851 0.184 -0.359 2.215 0.484 -1.901 1.110 0.000 0.522 1.891 0.862 0.000 0.899 0.964 1.205 0.804 0.484 0.675 0.734 +0 0.729 1.566 0.191 0.968 0.576 0.915 0.610 -1.256 1.087 0.549 -0.350 1.017 2.215 0.446 -0.861 -1.023 0.000 0.552 0.116 -0.156 0.000 0.496 0.720 0.974 0.741 1.066 0.850 0.712 +1 0.583 -1.451 -0.358 0.547 -1.738 0.853 -0.912 1.626 2.173 0.423 1.488 -0.051 0.000 0.606 -0.139 0.098 2.548 0.551 0.647 -0.401 0.000 0.312 1.353 0.984 0.896 0.948 0.836 1.071 +1 0.753 0.139 -0.183 0.779 0.726 0.536 -0.360 -0.554 1.087 0.940 0.487 1.191 2.215 1.086 0.363 -1.215 0.000 0.545 1.121 0.794 0.000 0.923 0.840 0.986 0.865 1.143 0.734 0.715 +1 0.364 -1.982 -0.273 0.440 -0.697 1.024 -0.066 0.831 2.173 1.204 0.173 -1.315 2.215 0.829 0.270 -0.642 0.000 0.490 0.687 0.438 0.000 0.611 0.905 0.983 0.862 1.540 0.925 0.768 +1 1.360 -1.930 0.538 0.163 -1.234 0.885 -0.353 -0.881 2.173 0.546 -1.685 0.311 0.000 0.528 -1.626 -1.579 0.000 0.825 -0.200 1.333 3.102 0.816 1.091 0.980 0.746 0.826 0.854 0.746 +0 1.384 1.017 -1.205 1.897 -1.704 0.786 0.145 -0.035 0.000 1.220 -0.688 0.655 2.215 0.748 -0.914 0.010 0.000 0.596 -1.108 1.513 0.000 0.839 0.939 0.988 0.578 0.821 1.207 1.183 +0 0.327 2.181 -1.356 2.407 -1.392 0.911 0.478 -0.364 0.000 0.796 1.053 1.677 0.000 1.357 -0.548 0.615 1.274 1.239 -1.373 0.343 0.000 1.830 1.193 1.002 1.594 0.346 1.081 1.058 +1 0.609 -0.033 0.509 0.981 1.509 1.266 0.981 -1.244 0.000 1.427 1.113 0.567 2.215 0.414 -0.418 -0.136 0.000 0.378 0.583 -1.636 3.102 1.502 0.797 0.987 0.848 0.625 0.949 0.822 +1 0.980 -1.254 0.007 0.773 -1.212 1.615 -0.722 1.223 2.173 0.946 -0.850 -0.379 0.000 1.071 -0.966 -1.132 2.548 0.853 -1.535 -0.410 0.000 0.815 0.766 1.073 1.297 1.421 1.003 0.881 +0 1.108 0.045 -0.460 1.435 1.527 0.765 -0.464 1.697 2.173 1.369 0.542 0.243 2.215 0.792 0.027 -0.957 0.000 0.876 -0.879 1.056 0.000 1.034 0.803 1.705 1.287 1.664 1.068 0.906 +1 0.817 0.459 1.471 0.778 -0.129 0.820 0.147 0.339 1.087 0.861 -0.063 -1.314 0.000 1.322 -0.805 -1.587 0.000 2.254 0.391 -0.423 3.102 0.934 1.170 1.095 0.778 0.941 0.939 0.817 +0 1.464 -1.012 -1.334 2.120 1.720 1.255 -0.766 -0.207 0.000 1.186 -1.870 1.135 0.000 1.679 0.315 -0.359 0.000 2.195 -0.984 1.234 3.102 0.752 0.956 0.992 0.923 0.794 0.971 1.058 +0 2.269 -0.404 1.167 0.285 0.009 1.002 0.326 -0.787 0.000 0.726 0.460 -1.479 2.215 0.770 -0.277 0.459 2.548 1.073 -0.167 -0.256 0.000 0.844 0.895 0.986 0.907 0.844 0.699 0.834 +1 2.387 0.556 1.586 0.348 1.673 0.520 0.219 0.398 2.173 0.554 0.663 -0.568 0.000 0.914 -0.421 -0.653 2.548 0.671 1.333 0.270 0.000 0.662 0.757 0.986 1.008 0.760 0.900 0.790 +0 1.259 1.948 -0.756 0.728 -1.416 1.063 0.152 1.585 2.173 1.770 1.575 0.112 2.215 0.527 -0.371 1.015 0.000 0.979 0.224 -1.006 0.000 0.815 0.775 0.989 1.278 2.541 1.628 1.352 +1 0.949 -0.297 -1.008 1.249 -1.150 0.896 -0.684 0.666 1.087 0.545 -0.484 1.298 0.000 0.475 0.171 -0.463 2.548 0.467 -0.004 0.424 0.000 0.494 0.505 0.984 0.554 0.784 0.907 0.733 +1 0.527 1.595 -1.432 1.334 0.709 1.859 -0.529 1.466 2.173 1.124 -1.906 0.076 0.000 1.578 -0.913 -0.157 0.000 1.573 -1.678 -0.627 0.000 0.818 0.941 1.087 2.229 1.115 1.504 1.750 +0 0.327 -1.836 -0.562 1.947 -0.350 0.805 -0.515 0.929 1.087 1.087 0.140 1.266 0.000 0.849 -0.927 1.641 2.548 1.267 0.220 -0.254 0.000 0.759 0.778 0.994 0.944 0.668 0.863 0.841 +1 1.008 0.178 0.694 0.802 -1.168 0.630 1.890 -0.538 0.000 0.481 0.550 -0.438 0.000 0.754 0.344 -1.470 1.274 1.461 -1.362 1.334 0.000 0.852 0.876 1.238 0.733 0.689 0.805 0.767 +1 1.215 0.416 0.546 0.626 -1.677 1.261 -0.650 0.797 2.173 3.439 0.274 -0.576 0.000 3.059 2.110 1.132 0.000 0.943 -0.380 -0.853 0.000 1.060 0.887 1.097 1.037 0.910 1.427 1.209 +0 0.712 -0.662 -0.306 1.765 -0.459 1.566 1.126 1.437 2.173 0.899 0.199 0.296 0.000 0.473 -0.070 -1.130 0.000 0.631 1.122 -1.604 3.102 0.971 0.774 0.984 3.416 0.424 2.154 1.620 +1 0.686 1.003 0.430 0.775 1.716 1.132 -0.735 -0.703 2.173 0.981 -1.004 1.238 2.215 0.941 0.089 -1.350 0.000 1.403 0.751 -0.051 0.000 0.881 0.869 0.988 1.677 1.543 1.388 1.105 +0 0.493 0.427 1.602 1.336 -1.093 0.683 -0.489 1.142 2.173 0.764 -0.886 0.035 0.000 0.417 0.919 -1.252 0.000 1.222 -0.025 0.172 3.102 1.250 1.046 0.987 0.980 0.776 0.951 0.912 +1 0.662 -0.642 0.436 0.751 -1.041 1.345 0.844 1.396 2.173 0.731 0.700 0.088 2.215 0.565 0.846 -0.491 0.000 0.802 0.489 -0.934 0.000 0.315 1.028 0.988 0.744 1.353 0.943 0.776 +0 1.833 2.245 -0.796 1.833 -0.725 1.312 1.418 1.095 0.000 0.529 2.679 0.345 0.000 1.001 0.715 0.821 0.000 0.890 1.190 -1.686 3.102 0.937 0.714 0.995 1.299 0.696 0.901 0.924 +0 1.344 0.701 0.969 1.865 0.841 0.946 1.539 -1.064 0.000 0.774 0.651 -1.092 2.215 2.045 0.696 -0.157 2.548 1.347 0.398 1.636 0.000 1.485 0.929 0.970 1.357 1.000 1.090 1.111 +0 1.478 1.441 1.425 0.896 0.523 1.339 0.407 -0.300 2.173 0.751 0.660 -1.327 2.215 0.615 -0.589 -1.040 0.000 0.402 1.131 -0.743 0.000 0.994 1.328 1.157 1.578 1.195 1.280 1.479 +0 1.430 0.561 -0.506 1.467 -1.097 0.623 0.833 1.461 0.000 1.022 0.693 0.866 2.215 1.105 -0.047 0.476 0.000 0.558 -0.645 -1.198 3.102 1.348 0.938 1.018 1.216 0.857 0.835 0.851 +1 0.472 -1.393 1.435 1.598 -0.689 0.957 0.529 0.236 2.173 1.063 2.096 -1.643 0.000 0.781 -0.592 -0.356 2.548 0.409 -1.248 0.773 0.000 1.009 1.563 1.134 0.688 0.870 1.211 1.611 +0 0.487 -0.589 -1.605 0.258 -0.404 1.175 1.186 -0.936 2.173 0.861 -1.700 0.861 0.000 1.001 -0.858 1.089 0.000 0.825 0.662 0.510 3.102 0.658 1.095 0.987 0.866 1.027 1.674 1.285 +0 1.660 -0.138 -1.048 0.668 -1.678 0.996 -0.922 0.848 2.173 1.123 -0.463 -0.571 2.215 1.422 0.065 0.862 0.000 0.482 0.127 -0.152 0.000 0.724 1.035 0.990 1.270 1.532 1.014 0.909 +1 2.171 0.518 0.144 0.377 -0.252 1.118 0.954 -1.617 1.087 0.558 1.229 0.880 0.000 0.310 0.832 0.473 2.548 0.535 0.831 -0.413 0.000 0.661 0.886 0.989 0.471 0.698 0.886 0.713 +1 0.579 0.473 0.370 0.504 1.061 1.808 2.217 -0.302 0.000 2.174 0.463 1.063 2.215 0.876 -0.458 -1.258 0.000 0.790 -1.975 -1.292 0.000 0.988 0.889 0.989 1.060 0.852 1.241 1.246 +0 1.617 0.139 1.335 0.484 0.102 0.801 1.232 0.288 2.173 0.707 1.641 -0.519 0.000 1.376 -0.378 -1.202 2.548 0.655 0.880 -1.658 0.000 0.810 0.860 1.098 0.948 1.759 1.021 0.938 +0 1.280 -1.517 0.647 0.508 0.572 1.237 -1.686 0.190 2.173 0.891 -1.339 -1.494 0.000 0.719 -0.980 1.674 0.000 0.956 -0.663 -0.261 1.551 0.394 0.735 1.001 0.894 0.706 0.881 0.811 +0 0.711 -1.732 1.238 1.023 0.297 0.590 -0.159 0.782 0.000 0.683 -0.244 -1.544 1.107 0.710 -0.814 -0.827 0.000 0.715 0.843 -0.931 3.102 0.751 0.806 0.988 0.893 0.539 0.795 0.696 +1 0.722 -0.199 -0.403 1.462 0.562 1.406 -1.478 -1.701 0.000 0.823 -0.788 -0.080 0.000 1.486 -0.899 -1.021 2.548 0.817 -0.056 0.313 0.000 1.015 1.081 1.088 0.677 0.920 0.786 0.723 +1 2.102 -0.947 0.254 0.322 0.742 1.126 -0.466 -1.363 2.173 0.530 -0.882 -0.603 0.000 0.400 0.498 1.122 2.548 0.373 0.085 -1.613 0.000 0.550 0.626 0.978 0.825 0.793 1.006 0.784 +1 0.968 -0.534 1.576 0.475 -0.810 0.820 -1.138 -0.713 0.000 1.156 -1.040 -0.164 1.107 1.696 -0.912 1.067 2.548 1.181 0.418 1.221 0.000 0.933 0.953 0.988 1.023 1.333 0.909 0.811 +1 1.336 -1.104 1.706 0.666 -1.135 1.054 -1.363 0.136 2.173 1.201 -1.169 0.823 2.215 1.438 -1.665 -1.458 0.000 0.422 -0.787 0.357 0.000 0.943 1.051 0.982 1.276 0.970 0.987 0.896 +0 0.656 -1.462 -0.762 0.969 -0.093 0.767 -0.732 0.881 2.173 0.795 -0.150 1.637 2.215 1.218 0.347 -1.140 0.000 0.566 0.399 0.740 0.000 0.910 1.108 0.992 1.404 0.800 1.107 1.142 +0 0.854 0.759 1.508 1.592 -1.559 0.612 1.712 0.641 0.000 0.877 0.833 -0.358 1.107 0.734 2.618 -1.542 0.000 1.559 1.405 -0.055 0.000 0.877 0.984 0.980 1.104 0.591 0.787 0.965 +1 1.857 -0.763 -1.541 1.158 1.363 0.782 1.231 0.132 0.000 1.496 -1.237 -0.028 0.000 1.014 0.672 -1.613 1.274 1.191 -0.144 0.243 1.551 1.146 1.054 1.017 0.993 0.924 0.861 1.185 +1 0.789 -1.094 1.277 0.589 0.096 0.397 -1.307 0.891 0.000 0.762 0.701 -0.496 2.215 0.682 0.389 1.018 2.548 0.485 0.279 -0.650 0.000 0.904 0.985 0.982 0.893 0.759 0.933 0.777 +1 1.576 0.585 0.017 0.424 -0.450 1.348 -0.980 -0.961 0.000 1.031 -0.258 -1.484 0.000 1.681 -0.200 0.771 2.548 1.265 0.124 0.354 0.000 0.949 1.012 0.975 0.992 0.631 0.818 0.800 +1 0.718 -1.455 -1.153 0.823 0.267 1.047 -0.159 0.085 1.087 1.401 -0.631 1.709 0.000 0.430 -0.988 -0.989 0.000 0.607 -0.260 -1.338 3.102 0.819 0.490 1.021 1.057 0.812 0.851 0.804 +1 3.508 -0.092 -0.884 0.834 -0.741 1.219 -0.922 0.715 2.173 1.116 -0.738 1.435 0.000 0.937 -0.704 -1.080 0.000 1.580 -1.512 1.115 0.000 0.914 0.932 0.967 0.629 0.787 1.221 1.085 +1 0.788 0.311 -0.603 2.352 -0.597 1.723 -0.020 0.884 0.000 0.878 0.473 -1.242 0.000 0.997 0.100 0.502 1.274 0.550 0.911 -1.220 3.102 0.961 0.823 1.003 0.807 0.636 0.760 0.764 +0 1.171 0.305 -0.155 0.680 -1.067 0.983 -0.072 0.980 2.173 0.633 0.346 1.515 0.000 1.534 0.555 -1.337 2.548 1.337 -1.287 -0.039 0.000 0.891 0.888 0.989 0.815 1.431 0.939 0.779 +1 0.861 0.184 -0.681 0.589 0.888 0.701 -0.011 1.684 2.173 0.866 -0.570 -0.046 0.000 0.538 0.890 0.955 2.548 0.728 -1.008 -0.908 0.000 0.796 0.905 0.986 0.763 0.612 0.751 0.673 +0 1.676 1.033 -0.634 0.244 -1.397 0.741 -2.939 -0.472 0.000 1.048 -0.557 1.181 0.000 1.339 0.798 1.482 2.548 0.769 -0.861 0.387 0.000 0.814 1.131 0.985 0.680 0.189 0.664 0.939 +1 0.771 1.496 1.268 0.877 -0.296 1.658 0.706 -0.314 1.087 0.313 0.915 1.024 0.000 1.290 1.179 -1.738 0.000 0.786 -1.238 1.058 0.000 0.954 1.150 1.124 1.152 1.710 1.195 1.018 +1 0.584 -2.056 1.693 0.361 -1.084 0.956 -1.191 -1.630 1.087 1.182 -1.291 0.581 0.000 1.217 -1.012 -0.147 2.548 0.525 -0.195 -0.862 0.000 1.151 0.854 0.986 0.662 1.309 0.891 0.764 +0 1.059 -0.443 0.136 1.203 -1.700 1.351 2.711 -1.740 0.000 0.589 -2.666 -0.758 0.000 0.714 -1.720 0.111 0.000 1.474 0.300 -0.536 3.102 0.882 1.920 1.559 0.867 0.781 1.899 1.888 +0 1.458 0.275 -1.008 0.537 1.526 0.524 0.415 1.118 2.173 0.743 -0.642 0.638 0.000 0.897 -0.304 0.014 2.548 1.012 -0.436 -0.916 0.000 1.116 0.922 0.985 0.801 0.789 0.681 0.682 +1 0.722 1.079 1.303 1.547 0.656 0.546 -0.263 0.040 0.000 0.779 0.299 1.562 0.000 0.808 0.155 -0.951 0.000 0.799 0.697 -0.807 3.102 0.939 0.926 0.986 0.776 0.763 0.872 0.779 +1 0.970 -0.304 0.373 0.337 0.410 1.155 -0.040 -1.169 2.173 1.284 -0.363 -0.065 1.107 1.400 -1.374 1.418 0.000 1.163 -0.609 1.048 0.000 0.863 1.043 0.986 0.768 1.533 1.020 0.893 +0 1.344 0.397 0.949 1.828 1.165 2.156 0.833 -0.635 2.173 0.823 -1.144 1.035 2.215 0.418 1.067 0.611 0.000 0.466 0.611 -1.226 0.000 0.497 0.902 0.995 2.410 3.044 1.856 1.333 +1 0.961 0.261 0.073 0.578 -1.152 0.688 0.181 -1.238 0.000 0.958 -0.286 -0.728 0.000 1.414 1.262 0.471 0.000 1.727 0.421 0.928 3.102 0.871 0.996 0.989 0.779 0.761 0.869 0.785 +1 1.958 0.099 -1.694 0.308 -0.744 0.895 -0.581 0.025 2.173 0.577 -0.447 0.566 0.000 0.435 -1.329 -1.317 0.000 0.580 0.735 0.218 3.102 0.861 0.790 0.988 0.708 0.626 0.849 0.754 +1 0.679 1.220 -0.669 0.765 0.508 1.249 -0.541 1.192 2.173 0.916 -2.121 -0.813 0.000 0.473 -0.603 0.202 0.000 0.687 -0.045 -1.283 0.000 1.073 0.617 0.987 1.876 0.830 1.259 1.530 +1 0.523 -1.556 0.132 2.268 1.671 0.748 0.270 -0.627 2.173 0.827 -0.026 -1.454 0.000 0.964 0.111 0.365 0.000 0.587 -0.392 1.196 0.000 1.050 1.130 1.484 0.974 0.679 1.119 0.953 +1 0.503 -2.304 -0.071 1.606 -1.099 1.179 2.970 1.552 0.000 1.622 -1.088 -0.274 1.107 1.420 -1.442 0.684 0.000 0.755 -1.917 1.255 0.000 0.701 0.887 0.995 1.212 0.792 0.880 0.901 +1 0.904 -1.791 -0.776 0.673 -1.229 2.019 -0.864 -1.733 0.000 1.186 -0.699 0.496 0.000 2.406 -2.106 -0.373 0.000 3.396 -1.848 -0.020 0.000 0.997 1.079 0.979 0.762 0.875 1.001 0.970 +1 1.168 1.256 -0.248 0.520 1.508 1.513 0.105 1.249 2.173 0.773 -0.462 -0.139 2.215 0.765 0.973 -0.530 0.000 1.107 1.389 -1.063 0.000 0.561 1.057 1.079 1.389 1.582 1.179 1.028 +1 0.923 -1.510 -1.655 0.631 -1.476 0.350 -1.638 -0.137 0.000 0.913 0.215 0.197 2.215 0.776 -0.426 0.949 2.548 0.488 0.028 -1.329 0.000 0.815 0.875 0.989 0.702 0.643 0.760 0.682 +0 1.230 0.746 -1.214 0.153 0.758 0.662 -0.108 0.093 0.000 1.035 1.080 1.332 2.215 0.847 -1.160 -0.077 0.000 0.772 -0.057 -1.307 3.102 0.856 0.813 0.990 0.786 0.752 0.985 0.943 +1 1.172 0.238 0.180 0.846 0.172 1.251 -2.722 -0.447 0.000 2.845 0.841 1.357 0.000 1.133 0.456 -0.590 2.548 1.555 -0.781 1.604 3.102 0.976 0.801 0.999 1.022 1.225 0.874 0.881 +1 0.824 -0.827 1.468 0.701 -0.671 0.784 1.125 -0.762 2.173 0.922 0.397 0.766 0.000 0.444 -0.114 0.400 0.000 0.404 0.973 1.091 3.102 0.413 1.104 0.987 0.684 0.593 0.827 0.767 +1 0.525 -0.000 -0.714 0.202 -1.606 1.157 0.216 1.178 2.173 1.026 -0.387 -1.124 0.000 1.041 -0.814 -1.735 0.000 1.619 0.996 -0.300 0.000 0.920 1.134 0.989 1.061 0.945 0.981 0.958 +1 0.648 -1.535 -1.506 0.945 -0.955 0.482 0.488 0.858 2.173 0.804 -0.442 -0.498 2.215 0.619 -2.584 0.606 0.000 0.610 -0.706 0.973 0.000 0.822 1.084 0.987 0.969 0.971 0.961 0.873 +0 0.780 -1.577 -0.982 0.593 -0.333 0.818 -0.638 1.031 2.173 0.961 -0.454 -1.569 0.000 1.199 -1.030 0.254 2.548 0.669 -0.812 -0.355 0.000 0.966 0.972 0.996 0.767 0.852 0.772 0.732 +1 0.343 1.356 1.125 1.748 0.857 1.831 -1.169 -1.131 0.000 1.796 -0.551 0.169 1.107 1.205 0.683 1.318 2.548 1.001 -0.281 -1.641 0.000 0.950 1.141 0.992 1.304 1.741 2.377 1.794 +0 1.633 -1.022 -0.099 0.629 1.458 1.351 -1.022 -0.635 2.173 1.606 -1.312 1.530 2.215 1.581 -0.873 1.169 0.000 0.452 2.124 -0.090 0.000 2.749 2.403 1.385 1.070 2.042 1.885 1.518 +0 1.001 -0.629 -1.353 0.715 1.007 0.856 0.934 0.469 2.173 1.135 0.136 -0.709 2.215 1.062 0.468 1.469 0.000 1.618 1.888 -1.113 0.000 0.804 1.043 0.995 1.237 1.401 1.087 0.964 +1 1.207 0.579 -0.441 0.771 0.937 1.010 -0.226 -1.077 2.173 1.173 0.882 0.478 2.215 1.241 -1.600 1.033 0.000 0.755 0.594 -1.158 0.000 1.946 1.598 1.265 0.852 1.847 1.447 1.227 +0 1.283 -1.366 0.903 1.769 0.126 1.178 -0.731 -0.676 2.173 1.062 0.016 -0.379 2.215 1.168 -0.791 1.698 0.000 2.336 -0.116 1.543 0.000 0.725 1.374 1.343 1.454 0.770 1.156 1.228 +0 0.543 -0.411 -0.164 1.388 0.699 0.618 -0.992 -0.629 1.087 0.513 0.952 -1.660 0.000 1.554 -0.660 -1.433 2.548 1.214 -0.735 0.610 0.000 1.432 1.196 0.988 0.853 0.826 0.892 0.871 +0 0.758 1.313 -0.571 0.563 -1.134 1.047 -1.160 1.631 2.173 0.667 0.233 -0.026 2.215 0.413 -1.078 0.272 0.000 0.414 -1.838 0.404 0.000 0.712 0.814 0.997 0.671 1.545 1.067 0.859 +1 1.352 0.734 -1.591 0.961 -0.909 1.381 -1.379 -0.253 0.000 1.435 1.137 1.088 2.215 0.672 0.976 -0.302 1.274 1.507 1.850 1.154 0.000 0.831 0.925 0.994 0.701 0.993 0.815 0.714 +1 1.107 -0.919 0.978 1.181 -1.373 0.939 -0.657 -0.145 0.000 1.068 -0.549 0.280 2.215 1.773 -0.369 -1.335 2.548 0.777 -2.495 -1.507 0.000 1.217 0.794 1.353 0.953 1.457 0.949 0.926 +0 1.294 -1.717 0.590 1.636 -1.479 0.698 -1.063 -0.934 0.000 1.874 -0.810 0.589 2.215 1.027 -0.165 -0.841 2.548 1.062 -1.743 -1.358 0.000 0.855 0.864 1.930 1.613 1.498 1.284 1.136 +1 0.607 -1.066 -1.262 1.093 0.558 0.755 0.649 0.855 2.173 0.939 -1.251 -0.870 0.000 0.702 -0.549 -1.637 0.000 0.731 -0.809 -0.125 1.551 0.906 0.676 1.125 1.158 0.937 0.935 0.840 +0 0.521 -1.814 0.525 0.792 -0.408 0.476 -2.736 0.907 0.000 0.455 -2.594 -0.373 0.000 0.576 0.634 -1.376 2.548 0.864 -0.678 -1.677 3.102 0.904 0.962 0.992 0.823 0.470 1.049 0.895 +0 0.619 0.453 -1.257 1.498 1.209 1.178 0.616 -0.136 2.173 0.521 0.038 0.276 0.000 1.165 -0.462 -0.781 0.000 1.862 0.158 1.474 3.102 0.943 0.968 1.060 0.599 1.593 0.971 0.805 +0 1.191 -0.940 0.272 0.635 1.199 0.940 -1.923 -1.028 0.000 0.419 -1.332 -0.424 2.215 0.480 -0.755 1.191 2.548 0.530 -2.339 1.208 0.000 1.086 0.887 0.988 0.623 0.491 0.585 0.652 +0 0.768 0.372 0.409 0.875 1.174 1.264 0.302 -1.530 1.087 1.564 0.744 0.037 2.215 0.675 -0.188 -1.180 0.000 0.483 1.733 0.573 0.000 1.089 1.020 0.984 0.909 2.099 1.143 0.943 +0 2.210 -1.143 -0.318 1.097 0.030 1.688 0.967 1.430 1.087 0.677 -0.533 -1.075 2.215 0.450 -0.819 0.724 0.000 0.444 0.956 -1.083 0.000 0.775 1.133 0.997 0.876 1.794 1.863 1.359 +1 1.238 -0.192 1.263 0.437 -0.043 0.958 -0.181 1.740 2.173 1.545 0.095 -0.125 0.000 0.862 0.043 -1.123 0.000 0.603 -0.672 -0.157 3.102 1.387 0.811 0.990 0.777 0.836 0.893 0.795 +0 1.446 0.988 1.096 0.681 -0.104 0.784 1.815 -1.354 0.000 0.774 0.082 0.056 1.107 1.132 0.136 1.566 2.548 1.593 1.180 -0.330 0.000 1.409 1.414 1.213 0.876 0.973 1.071 0.948 +0 0.925 -0.618 -0.475 0.529 0.150 0.740 0.137 1.246 0.000 0.323 -0.917 1.669 0.000 0.300 0.153 -1.025 2.548 0.566 0.902 -0.302 3.102 0.704 0.760 0.985 0.607 0.243 0.577 0.672 +1 2.620 -0.353 -0.954 1.730 -1.222 2.205 -0.553 0.231 0.000 2.486 -0.455 1.017 0.000 1.374 -1.349 -1.339 2.548 0.373 -0.769 -1.280 3.102 3.243 1.808 0.984 1.165 0.157 1.513 1.638 +0 0.710 0.407 1.069 0.794 -0.264 0.563 0.103 0.270 0.000 0.425 1.023 -0.556 0.000 1.041 0.496 -1.096 2.548 1.262 -0.250 1.509 3.102 0.870 0.911 0.988 0.728 0.732 0.674 0.613 +1 0.969 -0.018 0.378 0.293 -1.147 1.109 -0.112 -0.140 0.000 1.240 0.001 1.319 1.107 1.398 -0.470 -1.398 2.548 0.707 -0.526 -0.839 0.000 0.872 1.130 0.988 0.860 0.967 0.996 0.836 +1 0.844 -0.616 -0.133 0.584 0.271 1.007 -0.744 -1.248 0.000 1.811 -0.237 1.077 0.000 1.127 -0.432 -0.678 2.548 1.600 0.573 -0.069 0.000 0.489 0.840 0.982 0.674 0.645 0.586 0.567 +0 0.550 0.056 0.770 0.801 -1.468 0.799 0.012 0.190 0.000 1.312 -0.891 1.708 2.215 0.700 -0.814 -0.083 2.548 1.297 -0.372 -0.941 0.000 0.957 0.853 0.988 0.662 1.017 0.651 0.593 +1 0.329 1.696 1.392 1.044 -0.886 0.621 -0.438 -1.047 2.173 0.425 1.169 1.056 0.000 0.393 -0.125 1.270 2.548 0.801 2.371 0.292 0.000 0.848 0.828 0.996 0.731 0.543 0.969 0.822 +0 0.716 -0.730 0.546 0.742 -0.046 0.682 -0.676 1.150 1.087 0.348 -2.190 -0.462 0.000 0.597 -1.470 1.131 0.000 1.373 -1.254 -1.249 3.102 0.729 0.803 0.976 0.818 0.954 0.746 0.638 +1 0.624 1.748 -0.489 0.894 0.278 1.285 -0.404 -1.719 2.173 0.530 -1.039 -0.319 1.107 1.140 -0.373 -0.100 0.000 0.825 0.770 -1.727 0.000 1.312 0.930 0.999 1.533 1.227 1.131 0.980 +1 0.447 0.520 0.302 0.362 0.286 0.835 -0.450 0.745 2.173 1.122 -1.459 -1.190 0.000 0.977 -0.792 -1.711 0.000 0.385 -0.879 -0.485 1.551 0.892 0.590 0.992 0.696 0.568 0.796 0.722 +1 0.663 0.344 0.493 0.851 -1.032 0.847 0.393 1.680 0.000 1.061 0.045 -0.083 0.000 1.351 0.301 1.258 0.000 1.417 -0.166 -0.532 3.102 0.882 0.670 1.020 0.648 0.520 0.475 0.515 +1 1.339 1.387 0.467 0.434 -1.261 0.919 -0.473 -1.144 2.173 0.539 0.589 -1.721 0.000 0.543 -0.780 0.719 0.000 0.986 -0.130 0.033 3.102 0.943 0.953 1.056 0.805 0.894 1.004 0.864 +0 1.085 0.552 1.398 0.903 -1.324 0.692 -0.187 -0.456 2.173 0.580 0.578 -0.186 0.000 1.359 0.379 0.818 2.548 0.465 -0.797 -1.156 0.000 0.778 0.835 0.989 0.956 1.163 0.823 0.707 +1 0.566 1.432 0.936 0.418 1.534 0.582 -0.014 -0.035 1.087 0.512 1.329 -0.757 0.000 0.491 2.138 1.590 0.000 0.862 -0.883 1.581 1.551 0.766 1.065 0.975 0.713 0.851 0.912 0.783 +0 0.743 -1.553 -0.241 0.608 -0.784 0.888 -1.358 0.573 2.173 1.151 0.323 1.684 2.215 0.997 0.786 -0.955 0.000 1.177 -1.475 0.917 0.000 0.936 0.975 0.988 0.955 1.897 1.205 1.017 +0 2.597 -0.003 -0.788 0.897 0.664 0.973 0.604 0.046 0.000 1.023 0.465 -1.450 2.215 0.880 -0.670 1.742 0.000 0.379 -0.254 -0.032 0.000 0.651 0.717 2.043 1.106 0.638 0.875 0.740 +1 0.639 0.882 0.924 1.584 -1.193 1.170 -0.152 -0.360 2.173 1.108 0.528 -1.513 0.000 1.066 0.360 1.422 0.000 1.725 1.344 0.541 3.102 0.928 1.212 1.316 1.337 1.861 1.173 1.021 +1 0.646 -0.809 0.126 0.353 0.143 0.562 -1.886 1.300 0.000 0.814 -0.990 -0.644 2.215 1.047 -1.670 -1.246 0.000 1.227 -0.878 1.271 3.102 0.829 1.103 0.988 0.847 0.892 0.913 0.786 +0 0.449 -1.586 -1.570 1.374 -0.919 1.455 -0.916 0.556 0.000 1.308 -2.904 -1.235 0.000 0.636 -0.240 -0.246 2.548 0.693 -0.811 1.036 0.000 0.643 0.891 0.984 0.631 0.551 0.646 0.794 +0 1.050 1.968 -1.704 0.611 -0.058 0.529 1.135 0.079 0.000 1.090 0.889 -1.314 2.215 0.957 0.840 0.993 2.548 0.826 1.636 -0.173 0.000 0.599 1.052 1.105 0.813 0.948 0.745 0.714 +0 0.705 -1.903 0.205 2.272 -0.243 0.557 -0.142 1.618 0.000 1.417 0.631 1.144 2.215 1.092 -1.346 -1.006 2.548 0.725 0.558 -1.125 0.000 0.725 0.992 0.992 3.753 2.091 2.386 1.933 +1 1.410 -0.826 1.118 0.142 -1.159 0.485 0.156 -0.440 0.000 0.607 0.703 -1.306 2.215 0.861 1.294 0.080 2.548 0.485 0.812 -0.157 0.000 0.363 0.551 0.994 1.111 0.778 0.831 0.702 +1 2.047 1.124 -1.365 1.138 -0.537 1.896 -2.868 1.353 0.000 0.998 0.741 0.169 0.000 1.396 0.403 -0.483 2.548 1.289 -0.261 0.591 0.000 0.937 0.751 1.437 1.015 0.864 0.904 0.804 +1 0.559 0.070 -0.519 0.213 -1.411 0.850 -0.104 0.648 2.173 0.475 0.516 -1.052 0.000 0.527 -1.161 -0.360 1.274 0.830 0.752 1.368 0.000 0.946 0.894 0.986 0.929 0.836 0.830 0.757 +0 0.637 -0.505 -1.539 2.538 -0.871 0.744 -0.623 1.149 0.000 0.546 -1.052 0.407 0.000 0.471 0.927 0.317 2.548 0.433 1.857 0.895 0.000 0.896 0.879 0.999 0.626 0.278 0.636 0.773 +1 0.563 0.727 -1.148 0.955 0.405 0.949 1.834 -0.976 0.000 0.770 -0.604 1.025 0.000 1.761 0.625 0.565 2.548 1.021 -0.781 -1.289 0.000 1.022 1.007 1.001 0.714 0.737 0.882 0.778 +0 0.691 1.543 0.106 0.995 1.010 1.251 0.610 0.862 2.173 0.622 -0.408 -0.137 0.000 1.934 1.463 -1.113 0.000 2.082 0.393 -1.055 1.551 1.086 1.011 0.986 0.733 1.691 0.976 0.845 +0 0.461 -0.550 -0.218 0.743 1.066 1.464 -0.130 1.578 2.173 0.892 0.222 0.104 0.000 0.920 1.560 -0.522 0.000 1.061 0.140 -0.597 3.102 1.352 0.881 0.985 0.983 1.235 1.195 0.957 +1 0.766 -1.026 -0.231 1.050 1.394 0.518 0.450 0.106 0.000 1.015 1.068 -1.355 2.215 1.111 0.674 1.099 2.548 0.710 -2.336 -0.474 0.000 0.767 0.865 1.236 1.117 0.927 1.089 0.903 +0 4.233 0.579 0.834 1.008 1.313 1.841 1.613 -0.641 0.000 1.221 0.599 -1.416 1.107 0.514 2.295 -1.312 0.000 0.673 0.490 0.051 3.102 1.281 1.022 1.197 1.544 0.793 1.017 1.485 +1 0.917 -0.616 -0.385 0.629 0.799 0.689 0.191 -1.624 1.087 0.693 -0.923 -1.560 1.107 1.113 -0.758 -0.101 0.000 0.604 -2.088 1.025 0.000 1.144 0.965 0.988 0.973 0.616 0.881 0.774 +0 0.667 -1.880 -0.206 1.170 -0.271 0.844 0.693 1.590 0.000 0.586 -0.084 0.365 2.215 0.366 -0.967 -1.721 1.274 0.432 -0.095 -1.638 0.000 0.441 0.809 0.993 0.646 0.531 0.547 0.783 +1 0.841 -1.475 1.131 0.324 -0.386 1.920 2.867 -0.416 0.000 0.802 -0.430 1.268 0.000 2.063 0.648 1.010 2.548 1.046 -0.147 1.705 1.551 0.934 1.275 0.994 0.640 0.836 0.817 0.755 +1 0.996 0.207 -0.734 1.212 0.745 0.367 1.199 -1.205 0.000 0.882 0.982 -0.348 0.000 1.660 0.121 1.105 2.548 0.827 0.173 -1.350 3.102 0.853 1.270 1.479 0.813 0.718 0.784 0.775 +0 0.343 -1.797 -0.557 2.075 0.126 1.257 0.649 -1.612 2.173 0.664 -0.330 0.806 2.215 0.548 1.136 -0.722 0.000 0.662 -0.157 0.145 0.000 1.067 0.935 0.982 4.330 1.304 2.718 2.415 +0 0.620 1.248 -1.074 1.403 -0.839 0.852 -0.071 0.966 0.000 0.565 0.767 0.362 2.215 0.569 -0.455 -1.594 2.548 0.693 -0.946 0.421 0.000 0.858 0.784 0.993 1.381 0.723 1.010 1.258 +1 0.368 0.145 1.520 0.758 -1.027 1.041 0.646 0.680 2.173 1.622 1.383 -0.999 0.000 1.347 -0.303 0.430 2.548 0.697 -0.305 -0.916 0.000 0.915 0.952 0.996 0.924 0.826 1.018 0.898 +1 1.416 0.242 0.637 0.634 -1.608 0.612 1.377 -0.081 1.087 0.934 1.376 -1.080 2.215 0.351 -1.001 1.181 0.000 0.784 -0.316 -1.407 0.000 0.651 1.157 1.181 1.116 0.871 0.927 0.865 +1 0.709 0.650 0.243 0.671 -0.887 0.797 0.743 0.956 0.000 0.557 1.503 1.495 0.000 0.966 1.334 -0.828 2.548 1.658 -0.170 -0.558 3.102 0.876 1.040 0.988 0.612 0.934 0.942 0.824 +1 2.392 0.939 0.195 1.114 0.514 1.552 1.726 -1.143 0.000 0.911 0.969 0.983 2.215 1.059 0.070 -1.287 2.548 0.401 0.204 1.349 0.000 1.365 1.275 0.998 0.861 1.053 1.039 1.196 +0 0.509 0.785 -0.762 1.793 1.463 1.204 1.012 -0.338 2.173 1.148 1.314 1.240 2.215 0.790 0.088 -0.670 0.000 0.776 1.518 0.780 0.000 1.184 1.012 1.201 0.801 1.735 1.073 0.920 +1 0.343 1.313 -0.568 0.935 1.593 0.937 0.326 1.187 0.000 0.940 0.162 -0.223 2.215 1.305 -0.781 -0.845 2.548 0.574 -0.498 0.536 0.000 0.823 1.073 0.991 0.877 0.888 0.925 0.799 +0 0.461 0.561 1.039 0.132 -0.264 1.203 0.657 -0.542 2.173 0.935 -0.234 1.687 0.000 0.993 -0.658 0.489 2.548 0.840 -2.041 1.445 0.000 0.808 0.790 0.991 1.182 1.495 1.041 0.874 +0 1.072 0.338 0.978 0.782 -1.325 0.763 0.479 -1.471 1.087 0.516 -0.653 -1.014 0.000 1.313 -0.299 0.455 2.548 1.020 1.105 -0.528 0.000 1.174 1.007 1.111 0.746 1.335 1.026 0.924 +0 0.596 -1.673 1.040 0.760 -0.643 0.567 -0.327 1.380 2.173 1.013 -2.015 -1.245 0.000 0.937 -0.135 0.180 0.000 0.626 0.660 -0.019 0.000 0.867 0.909 0.985 1.154 0.464 0.874 0.797 +1 0.885 -1.354 0.225 1.051 1.433 1.114 -0.150 0.312 2.173 0.576 -0.767 1.549 0.000 1.164 -0.953 -1.305 2.548 0.718 0.653 -0.671 0.000 1.055 0.847 1.184 1.193 1.555 0.998 0.895 +0 0.578 0.990 -0.798 0.972 -1.503 0.405 0.521 1.311 2.173 0.516 -1.066 0.700 0.000 0.743 0.580 0.349 2.548 1.494 1.534 -1.302 0.000 0.929 0.869 0.991 0.883 0.522 0.693 0.649 +0 1.223 0.409 1.367 0.740 -1.163 0.937 0.048 -1.049 0.000 1.591 -0.219 0.827 2.215 0.818 0.624 -0.022 0.000 1.137 0.066 -0.503 3.102 1.358 0.803 1.001 1.032 1.146 0.988 0.883 +1 1.635 -0.051 1.565 0.596 -1.497 0.734 1.351 0.287 2.173 0.267 -0.261 1.056 0.000 1.227 -0.004 -0.227 2.548 0.519 -2.114 -0.772 0.000 0.831 0.924 0.977 1.238 1.021 1.107 1.054 +1 0.647 -1.062 1.419 0.091 -0.101 0.771 -0.733 -1.079 2.173 0.977 -2.428 0.863 0.000 0.713 -0.617 0.227 2.548 0.596 1.117 -0.570 0.000 3.454 1.900 0.992 0.804 0.854 1.309 1.078 +1 1.408 1.144 1.329 1.585 0.658 0.755 1.256 -0.208 2.173 0.803 1.334 -0.707 0.000 0.998 0.672 -1.143 1.274 0.660 0.649 -1.733 0.000 0.809 0.751 1.176 1.063 0.858 0.914 0.798 +1 0.684 1.472 -0.848 1.176 -1.545 1.057 0.545 0.603 2.173 0.687 0.872 -0.895 0.000 0.598 0.155 1.290 2.548 0.988 1.332 0.079 0.000 0.910 1.122 0.993 0.989 0.605 1.092 0.929 +1 0.761 0.601 0.391 0.963 -0.493 0.543 0.646 -0.435 2.173 0.733 0.701 1.014 2.215 0.299 1.953 0.659 0.000 0.453 0.683 1.662 0.000 0.431 0.582 0.988 0.785 0.896 0.618 0.518 +0 0.776 0.487 0.887 0.467 -0.878 0.603 1.183 -0.982 1.087 0.548 -1.032 0.813 0.000 0.517 0.966 0.809 0.000 1.501 0.595 -1.592 3.102 1.038 0.992 0.989 0.714 0.580 0.832 0.729 +1 0.416 -0.702 -1.377 1.533 1.337 1.514 0.339 -0.465 2.173 0.808 0.012 1.437 0.000 0.613 -1.023 0.896 0.000 1.111 0.787 0.302 3.102 0.821 0.927 0.986 1.432 0.970 1.034 0.950 +1 0.322 -0.072 -1.528 1.850 -0.617 1.373 -1.529 0.463 2.173 1.881 -1.478 1.501 0.000 1.040 -1.252 -0.798 0.000 0.924 1.076 -1.725 0.000 0.827 0.669 0.988 2.519 0.674 1.524 1.253 +0 0.454 0.236 0.073 0.933 1.034 0.938 0.342 -0.839 2.173 0.804 -0.978 1.380 2.215 0.539 -1.152 0.958 0.000 0.800 -0.716 -1.142 0.000 0.913 0.986 0.986 0.650 1.489 0.918 0.769 +0 0.284 -1.170 0.025 3.605 1.133 1.614 -0.708 -1.181 0.000 1.564 0.698 -0.416 0.000 1.891 -1.126 1.653 2.548 1.311 -1.239 -1.030 0.000 0.931 1.105 1.179 0.949 1.505 1.136 1.232 +1 0.846 -0.236 0.118 0.274 -0.835 1.845 -1.438 -1.235 0.000 1.728 -1.466 0.188 0.000 2.693 0.602 1.011 0.000 1.979 0.894 0.634 0.000 0.954 1.090 0.981 0.677 0.549 0.640 0.724 +0 0.596 -2.088 -0.819 1.921 -1.611 0.733 -0.124 -0.569 1.087 1.343 0.197 0.184 2.215 0.921 -2.326 0.911 0.000 0.708 0.845 0.923 0.000 2.578 1.918 0.988 2.459 0.948 1.768 1.647 +0 0.599 1.160 0.066 1.875 0.687 0.951 1.303 -1.038 0.000 0.813 -0.126 1.682 2.215 0.389 0.432 1.513 2.548 0.796 1.005 -0.406 0.000 0.721 1.161 0.990 0.740 0.204 0.928 0.926 +0 1.044 0.272 -0.841 1.630 0.141 0.483 0.014 0.588 2.173 0.521 1.074 1.633 0.000 0.724 -0.378 1.560 0.000 0.595 1.288 -0.764 0.000 1.040 0.827 1.399 0.862 0.618 0.660 0.652 +0 0.347 -0.450 1.474 1.609 0.762 0.774 -0.878 -0.815 2.173 0.918 -1.542 -1.307 0.000 0.807 -0.629 0.845 0.000 1.474 -0.638 0.028 1.551 1.374 1.082 0.994 0.839 0.781 0.846 0.828 +1 0.563 -1.318 -0.425 1.549 1.291 0.881 0.141 -1.176 2.173 0.647 -0.540 0.119 0.000 0.985 0.392 1.051 2.548 0.373 0.652 0.763 0.000 0.579 0.934 1.294 1.104 1.065 1.040 0.862 +1 0.776 -0.253 -0.699 1.230 1.174 0.950 -1.146 1.506 2.173 1.159 -0.570 -0.261 2.215 0.569 0.711 -0.674 0.000 0.596 -0.750 0.075 0.000 0.727 1.142 1.344 1.023 1.607 0.981 0.833 +0 1.214 0.234 -1.582 1.360 1.385 1.587 -0.548 0.132 2.173 1.936 2.554 -1.687 0.000 1.127 -0.932 -0.194 0.000 1.344 0.038 -0.519 0.000 0.855 0.916 0.989 1.065 1.688 1.288 1.123 +0 0.614 0.466 1.330 1.278 -1.398 0.446 0.012 -0.574 2.173 0.657 -1.562 0.597 0.000 0.649 -0.740 0.049 2.548 0.560 -1.561 -0.653 0.000 0.722 0.885 0.993 1.080 0.456 0.787 0.998 +1 1.679 0.894 0.260 0.741 1.318 1.172 -0.228 -1.124 2.173 0.815 0.348 -1.591 0.000 1.437 -0.118 1.540 2.548 1.798 0.406 -0.156 0.000 0.824 1.218 1.260 1.555 1.094 1.152 1.030 +1 1.033 0.852 0.754 0.183 -0.580 0.505 -0.870 -0.145 2.173 0.746 -0.788 -1.722 2.215 0.438 0.501 -1.196 0.000 0.417 0.322 1.459 0.000 0.323 0.639 0.992 0.871 0.893 0.734 0.589 +0 0.362 -1.062 -0.439 0.478 -0.943 0.516 -0.628 1.028 2.173 0.683 -0.681 -1.551 2.215 0.687 0.329 -0.573 0.000 0.933 0.976 0.496 0.000 0.813 0.966 0.979 0.833 0.637 0.721 0.665 +0 1.429 -0.043 -1.625 0.263 1.354 0.875 1.046 0.462 0.000 0.395 1.737 -0.108 0.000 0.565 2.089 -0.649 0.000 1.739 0.023 -0.628 3.102 0.772 0.893 0.981 0.883 1.238 0.892 0.995 +1 1.074 0.538 -0.339 1.476 -0.567 0.948 0.139 1.456 0.000 0.630 -0.021 1.008 0.000 1.170 0.660 -0.796 2.548 1.047 0.852 1.331 0.000 0.714 0.852 0.977 0.702 0.688 0.764 0.882 +0 1.125 -0.867 -0.472 0.697 0.281 1.685 -1.719 -0.149 0.000 1.626 -0.340 1.511 0.000 2.250 0.425 1.517 2.548 1.504 -0.791 -1.280 3.102 0.601 0.827 0.984 1.721 1.356 1.193 1.020 +1 0.292 1.219 1.480 0.883 -0.114 1.297 0.883 0.545 0.000 1.121 1.074 -1.199 0.000 1.080 0.391 -1.050 2.548 0.511 -0.839 1.192 3.102 2.576 1.634 0.987 0.714 0.677 1.083 0.916 +0 0.332 1.870 0.124 2.092 0.297 0.771 -0.232 -1.301 2.173 0.433 0.443 -0.948 0.000 1.658 0.103 0.816 1.274 2.187 0.957 -1.345 0.000 0.631 0.837 0.981 0.831 1.352 1.013 1.007 +0 1.188 -0.198 0.165 1.091 -0.009 1.241 0.050 -1.661 1.087 0.544 0.931 -0.735 0.000 1.013 1.230 1.052 2.548 0.436 -1.282 -0.473 0.000 1.026 1.099 0.986 1.587 1.323 1.330 1.094 +1 0.787 0.340 -0.263 0.780 1.703 0.629 0.451 0.005 2.173 0.779 1.171 1.645 0.000 1.510 0.938 -1.226 0.000 2.552 0.814 0.749 3.102 0.887 1.198 1.064 0.938 0.905 0.977 0.841 +1 1.071 0.295 -0.971 1.556 -1.172 1.559 0.359 -0.742 2.173 1.477 0.156 0.977 0.000 0.802 -0.663 0.995 1.274 0.932 -1.166 0.112 0.000 0.638 0.579 0.988 0.973 1.606 1.107 0.983 +0 0.936 -1.135 -0.334 0.634 1.043 0.439 0.298 -1.732 0.000 0.705 -0.040 -1.205 0.000 1.022 -0.437 0.459 2.548 0.811 0.627 -0.199 3.102 0.578 0.921 1.010 0.814 0.599 0.634 0.682 +1 0.676 -1.307 -1.546 0.948 0.414 0.533 -1.489 -0.797 0.000 0.563 1.020 -0.622 0.000 1.297 -0.413 0.938 2.548 1.083 0.582 1.663 3.102 0.782 0.972 1.088 1.028 0.779 0.823 0.757 +1 0.627 -0.944 0.512 1.643 -0.266 1.310 0.937 -1.139 2.173 1.351 1.202 1.062 0.000 0.691 2.481 0.880 0.000 0.862 0.232 -1.421 0.000 0.903 1.108 0.982 0.626 0.749 1.416 1.294 +1 1.282 -0.606 -0.657 0.271 1.222 0.567 -0.624 1.068 0.000 0.861 -0.495 0.575 0.000 1.130 -0.693 -1.380 1.274 1.403 0.634 -1.304 3.102 0.646 0.977 0.984 0.941 0.808 0.853 0.794 +0 0.498 -0.861 1.680 1.601 -0.163 0.838 1.235 -1.544 0.000 1.141 -0.826 -0.515 2.215 1.102 0.575 0.582 0.000 0.847 -0.265 0.967 3.102 0.863 1.235 1.232 0.764 0.895 0.772 0.765 +1 0.550 -0.294 -1.344 0.797 0.828 0.715 -0.823 0.459 2.173 0.978 0.643 1.445 0.000 1.569 -0.975 -0.775 2.548 0.498 -1.504 -0.824 0.000 1.643 1.347 0.987 0.847 1.194 1.081 0.892 +1 0.597 -0.032 0.672 0.709 -0.327 0.886 0.123 1.118 0.000 0.599 1.001 1.059 0.000 0.942 0.927 -0.478 1.274 0.905 0.825 -1.309 0.000 0.811 0.895 0.989 0.970 0.807 0.693 0.751 +1 0.615 1.102 -0.556 2.133 -1.262 1.255 0.261 0.274 0.000 1.437 0.574 1.469 2.215 1.212 0.699 0.642 2.548 0.528 -1.296 -1.149 0.000 1.197 1.044 0.986 1.223 0.959 1.020 0.978 +0 0.680 -0.718 -1.579 0.522 0.042 0.772 -1.156 1.340 2.173 0.690 1.144 -0.371 2.215 0.700 -0.358 0.128 0.000 0.809 0.348 -0.386 0.000 0.915 0.957 0.985 0.803 1.900 1.073 0.868 +1 1.207 0.279 -1.732 0.917 1.383 1.070 0.038 -0.186 0.000 0.472 -2.192 -0.128 0.000 0.544 -0.775 0.836 2.548 0.930 0.480 -0.891 1.551 2.139 1.279 0.996 0.775 0.688 0.901 0.942 +1 0.359 0.784 1.375 1.216 -0.794 0.594 -1.031 0.711 2.173 0.741 -0.203 -0.722 0.000 0.507 -0.400 1.392 1.274 0.386 0.632 0.719 0.000 0.757 0.891 0.987 0.616 0.445 0.635 0.587 +1 0.578 1.557 0.509 0.392 -1.574 0.927 0.465 -0.005 1.087 0.630 0.676 -1.110 0.000 1.043 0.068 0.783 2.548 1.913 0.679 1.734 0.000 0.787 0.979 0.978 0.795 0.835 0.889 0.746 +0 2.028 1.164 -1.360 0.311 -1.713 0.772 0.345 0.129 0.000 0.845 0.731 0.720 0.000 1.125 1.276 1.296 2.548 1.462 1.078 -0.455 3.102 0.937 0.967 0.998 0.817 0.982 0.812 0.884 +1 0.321 1.373 1.054 2.149 -1.656 0.563 -0.520 0.211 0.000 0.759 -0.994 -0.051 1.107 0.837 -2.008 1.586 0.000 0.685 0.651 0.376 3.102 0.919 0.744 0.992 0.941 0.713 1.696 1.438 +0 0.559 0.557 1.525 0.670 -0.725 0.618 1.959 -0.005 0.000 0.403 0.275 1.056 1.107 0.751 -1.252 1.254 0.000 0.511 1.137 -0.253 3.102 3.352 1.785 0.987 0.602 0.444 1.050 0.907 +1 0.906 0.488 1.492 0.626 0.678 1.072 -0.840 -0.628 2.173 0.510 -1.201 1.137 1.107 0.314 1.993 -0.202 0.000 0.386 -1.119 1.705 0.000 0.988 1.011 0.985 1.189 1.108 0.929 0.843 +0 0.297 -0.904 -0.088 1.908 1.165 1.124 0.974 -0.974 0.000 1.098 0.005 0.400 2.215 0.637 -0.205 0.703 2.548 0.482 -0.615 -1.255 0.000 1.129 1.083 0.990 1.156 0.261 0.922 1.182 +0 1.472 -0.754 -0.812 1.399 -0.203 0.649 0.482 1.134 0.000 0.636 -1.003 0.754 1.107 0.437 0.043 1.700 0.000 0.755 -0.972 -1.622 1.551 0.926 0.948 1.038 0.754 0.528 0.679 0.824 +0 0.783 -1.773 -0.593 0.982 -0.673 0.578 -0.741 0.671 0.000 0.968 -1.483 1.172 0.000 0.525 0.304 1.684 0.000 0.818 -1.215 -1.044 3.102 0.939 0.879 0.979 0.452 0.162 0.565 0.653 +0 0.537 -0.910 0.559 3.811 0.437 1.901 -1.933 -0.856 0.000 1.274 1.841 1.682 0.000 1.240 2.451 1.593 0.000 0.955 0.077 1.319 3.102 0.804 0.932 0.984 0.848 0.562 0.863 1.641 +0 1.896 0.715 1.332 0.633 0.360 2.523 1.018 -0.985 0.000 1.813 0.479 0.693 0.000 1.481 1.168 0.800 2.548 0.576 0.747 0.242 0.000 0.682 0.795 1.165 0.758 0.947 1.109 1.117 +0 0.707 -1.212 1.634 2.007 -1.174 0.434 -0.823 -0.193 2.173 0.711 0.370 1.108 0.000 0.905 0.259 0.037 0.000 1.889 -0.172 0.609 3.102 0.953 1.008 0.990 1.238 0.703 0.878 0.908 +0 0.736 1.712 1.602 1.198 -1.645 0.951 -0.468 -0.339 2.173 1.081 0.556 0.769 2.215 1.229 0.321 -0.937 0.000 0.483 -0.960 1.723 0.000 0.908 0.928 0.992 0.956 1.496 1.145 0.973 +0 0.714 -0.080 0.761 0.250 0.138 0.625 1.641 -0.085 0.000 1.065 0.270 -1.379 2.215 0.631 2.650 0.404 0.000 0.890 1.097 1.731 3.102 0.896 0.881 0.979 0.903 0.560 0.991 0.860 +1 1.488 0.973 0.943 0.607 0.511 0.649 0.164 -0.965 2.173 0.507 0.360 1.629 2.215 0.807 1.291 -0.856 0.000 0.769 0.601 0.297 0.000 0.811 0.768 0.976 0.766 0.614 0.836 0.727 +0 1.422 0.180 1.035 1.363 1.437 1.143 -0.936 -0.435 2.173 0.570 1.501 0.681 0.000 1.042 -0.215 -0.755 0.000 0.610 0.465 -0.556 3.102 0.805 0.828 0.982 0.761 0.734 1.206 0.969 +1 0.799 -1.018 -0.845 0.239 0.814 0.846 -0.092 -0.968 2.173 1.088 -0.784 1.207 0.000 0.963 0.110 0.931 0.000 1.285 0.214 -0.015 3.102 0.971 0.969 0.988 0.684 0.858 0.853 0.729 +1 0.591 0.026 0.859 1.712 1.497 2.606 1.432 -0.081 0.000 0.978 -0.637 0.368 1.107 1.667 0.250 -1.656 0.000 2.482 1.801 -1.214 0.000 0.766 0.961 0.987 0.920 0.892 0.905 0.799 +1 1.408 -0.310 -0.394 0.470 -0.029 0.969 -1.922 0.094 0.000 1.767 0.089 1.722 2.215 1.363 -0.703 -1.065 0.000 1.779 -0.488 1.034 1.551 0.924 0.924 0.984 1.453 1.082 1.069 0.902 +1 0.559 0.832 0.750 0.904 -0.365 1.723 -0.451 -1.393 2.173 1.426 -1.015 0.591 0.000 0.658 -0.385 0.860 0.000 1.255 -0.897 -0.829 1.551 0.811 0.975 0.990 1.976 0.904 1.413 1.370 +1 1.292 0.504 -0.877 0.684 -0.661 1.155 0.179 0.854 2.173 0.453 1.003 -0.440 0.000 0.777 -0.209 0.403 0.000 1.092 0.659 1.741 3.102 0.878 0.951 0.995 0.794 0.927 0.938 0.807 +0 0.463 1.709 -1.338 0.328 -0.803 0.683 0.665 0.797 2.173 0.439 -1.026 -1.017 0.000 1.128 0.262 1.558 1.274 0.796 0.279 -0.187 0.000 0.780 0.981 0.978 0.681 0.724 0.712 0.659 +1 0.318 1.315 -1.725 1.022 0.699 1.103 0.154 -1.109 0.000 0.723 -0.594 1.005 2.215 0.746 0.664 -0.606 0.000 1.637 0.653 0.314 3.102 0.858 1.180 0.993 0.619 0.938 0.953 0.830 +1 0.699 -0.707 1.685 1.759 -1.572 1.222 -0.358 0.371 0.000 0.895 -0.251 -0.442 2.215 1.021 -0.478 -1.125 0.000 1.109 -0.895 1.062 3.102 1.966 1.257 1.006 1.280 0.956 0.900 1.063 +0 0.524 -2.417 0.264 1.034 1.627 0.654 -0.498 -0.295 2.173 0.497 -1.470 -0.705 0.000 0.451 0.388 -0.927 0.000 0.638 0.879 0.576 0.000 0.832 0.852 0.986 1.129 1.034 0.820 0.764 +1 0.525 -0.807 1.104 0.342 0.305 0.782 0.142 1.024 2.173 0.824 1.698 -0.307 0.000 1.130 0.485 -0.937 2.548 1.027 -1.461 -0.904 0.000 0.894 0.976 0.981 0.642 1.171 0.891 0.780 +1 1.057 0.037 -0.141 1.795 0.513 0.908 -0.855 1.690 2.173 1.248 0.236 -0.953 2.215 0.510 0.565 0.865 0.000 0.706 0.159 1.642 0.000 0.450 0.775 1.062 1.412 1.410 1.181 0.909 +1 0.955 -0.709 0.622 0.880 -0.465 0.644 -1.743 1.532 0.000 0.448 1.841 -0.645 0.000 1.352 0.109 -0.038 1.274 0.751 -0.474 -1.404 0.000 0.838 0.955 1.054 0.740 0.820 0.924 0.817 +1 0.615 0.980 0.296 1.022 -0.971 0.437 -1.195 -0.991 2.173 0.680 -0.345 0.806 2.215 0.343 0.536 -0.072 0.000 0.928 0.674 1.461 0.000 0.615 0.859 0.998 0.911 0.875 0.849 0.697 +1 0.813 -1.079 1.510 0.638 0.281 1.349 -0.442 -1.003 2.173 1.015 -0.220 0.697 0.000 0.997 -0.618 0.068 2.548 0.846 0.660 1.294 0.000 0.891 0.857 0.991 1.235 1.200 1.028 0.963 +0 2.044 1.388 -1.557 0.157 0.224 0.318 -0.465 -0.540 0.000 1.068 -0.058 0.303 0.000 0.627 0.548 1.726 2.548 0.720 0.498 0.109 3.102 0.890 0.864 0.994 0.796 0.510 0.560 0.820 +0 1.150 -1.414 -0.283 0.221 1.447 0.424 1.418 1.143 0.000 0.882 0.060 1.142 1.107 2.048 -0.449 -1.075 2.548 1.166 0.740 0.583 0.000 0.901 0.945 0.984 0.898 1.361 1.082 1.054 +1 0.982 1.267 1.184 1.007 0.246 1.044 0.161 -1.075 2.173 0.797 -0.859 -0.065 1.107 0.351 -1.548 1.447 0.000 0.365 1.398 0.705 0.000 1.061 0.905 1.031 1.337 1.291 1.178 0.990 +1 1.305 -1.001 1.264 0.344 -0.732 0.459 0.699 -1.109 2.173 0.891 -0.655 -0.587 1.107 0.858 -1.908 1.215 0.000 0.786 -0.049 -0.852 0.000 0.988 1.008 0.987 0.850 0.822 0.750 0.697 +0 2.151 0.191 -1.021 0.140 -0.338 0.746 1.324 -1.358 2.173 1.545 -0.480 0.475 0.000 0.765 1.390 0.899 0.000 0.931 -1.010 1.175 0.000 1.085 1.010 0.994 1.019 0.549 1.199 1.077 +1 1.096 0.244 0.715 0.319 -1.298 0.678 0.768 -0.238 0.000 1.321 0.500 1.625 0.000 0.888 -0.207 -1.208 2.548 0.942 -0.304 0.217 3.102 2.015 1.290 0.994 0.643 0.672 0.868 0.762 +0 0.338 2.163 -1.278 1.560 0.649 0.464 2.030 -1.629 0.000 0.735 1.025 -1.332 2.215 0.994 0.476 -0.378 2.548 0.921 1.310 0.549 0.000 0.953 0.991 0.992 0.974 0.730 0.852 0.748 +1 0.513 -0.168 -0.071 1.061 1.014 1.557 -0.558 -0.732 2.173 0.565 1.249 1.548 0.000 0.807 -0.192 1.141 0.000 0.603 -0.895 0.538 1.551 0.927 0.810 0.993 1.431 0.971 1.057 0.943 +0 1.641 0.717 1.329 0.630 0.732 1.082 1.218 -0.978 2.173 0.770 0.531 -0.528 0.000 1.294 -0.616 1.037 2.548 1.334 1.099 -0.094 0.000 0.725 0.886 0.983 1.112 2.166 1.245 1.099 +1 0.894 -0.474 -1.000 1.526 -1.260 1.156 -0.602 1.284 2.173 1.289 0.168 -0.187 0.000 1.041 -0.062 0.572 2.548 1.320 -0.612 0.147 0.000 0.944 0.791 0.997 1.352 0.899 1.021 1.049 +1 0.364 1.454 -0.411 0.631 -1.593 0.837 -0.839 0.571 2.173 0.544 0.446 0.114 2.215 0.333 0.356 -1.051 0.000 0.422 -0.618 -0.410 0.000 0.410 0.570 0.991 0.655 0.802 0.718 0.555 +0 0.516 -0.211 0.119 0.475 1.592 0.726 0.806 -0.200 0.000 0.850 0.869 -1.252 1.107 1.090 0.757 0.389 2.548 0.938 0.408 -1.735 0.000 1.165 1.036 0.993 0.641 1.019 0.744 0.657 +0 2.374 1.171 1.062 2.077 0.665 1.217 0.848 -0.764 0.000 1.411 0.543 -1.084 0.000 2.557 -0.345 -0.420 2.548 3.163 1.282 1.246 3.102 0.894 1.603 1.076 0.871 3.248 1.949 1.848 +0 0.799 0.368 -0.482 0.467 -0.352 0.316 -0.988 -0.934 0.000 0.657 0.243 0.090 0.000 0.786 1.265 1.706 2.548 0.785 -0.017 -1.676 0.000 0.906 0.696 0.985 1.044 0.428 0.898 0.759 +1 1.235 -1.050 -1.651 2.194 -1.243 1.117 -0.622 0.476 2.173 0.756 -2.133 0.000 0.000 0.332 -0.958 0.323 2.548 0.446 -0.124 -0.911 0.000 1.070 1.173 0.989 0.747 0.194 1.009 0.953 +1 1.127 -1.192 -1.016 1.348 -0.880 2.671 -0.242 0.583 0.000 2.840 -0.907 -1.188 2.215 2.267 -0.721 -1.701 2.548 1.467 -0.686 -0.269 0.000 0.822 1.215 0.986 1.177 1.218 1.093 1.085 +0 2.276 0.991 1.723 0.913 1.206 1.618 2.134 -0.080 0.000 1.048 -0.663 -0.238 1.107 2.018 0.037 1.667 2.548 0.491 -1.077 0.579 0.000 3.775 3.206 0.990 1.691 1.632 2.308 1.900 +0 1.483 -0.213 0.091 0.796 -0.037 0.985 -0.077 -1.649 0.000 0.339 -1.571 -1.605 2.215 0.842 0.679 1.578 0.000 0.653 -1.408 -0.002 3.102 0.790 0.876 0.991 0.859 0.422 0.762 0.908 +0 0.615 1.114 1.079 1.099 1.470 0.639 1.289 -0.238 2.173 0.366 2.484 1.666 0.000 0.428 2.048 -1.092 0.000 0.751 -0.073 1.091 0.000 1.077 0.901 0.987 0.700 0.288 0.695 0.682 +0 0.849 0.276 0.851 0.635 0.040 0.730 -0.870 -1.679 2.173 0.758 -1.595 -1.352 0.000 2.077 -0.270 0.237 2.548 0.789 0.614 1.505 0.000 1.596 1.014 0.989 0.973 1.581 1.086 1.100 +0 1.077 -1.263 -0.858 0.720 -0.059 1.068 -0.075 0.169 0.000 0.501 0.684 -1.698 0.000 1.469 -0.108 1.271 2.548 0.729 0.127 -0.127 3.102 1.013 0.888 0.986 1.308 0.760 0.904 1.371 +1 1.266 -0.335 1.009 0.620 0.061 0.845 -0.400 -0.762 2.173 1.073 0.082 -1.410 2.215 0.728 -0.369 0.625 0.000 0.369 0.606 -1.515 0.000 0.992 0.995 0.987 1.010 0.846 0.852 0.766 +1 0.480 0.801 -0.159 0.422 -1.456 1.339 0.731 0.988 2.173 0.886 2.709 -0.635 0.000 0.944 1.516 -0.678 0.000 1.237 0.214 1.667 3.102 0.832 1.460 0.981 1.181 0.849 1.396 1.101 +1 0.890 0.494 0.776 0.709 -0.527 0.685 -0.550 0.238 0.000 1.269 0.562 1.722 2.215 0.700 -0.345 -0.875 2.548 0.848 0.881 -0.506 0.000 0.898 0.788 1.014 0.908 0.875 0.909 0.811 +1 2.806 0.620 0.101 0.453 1.103 0.413 0.025 -0.835 0.000 0.917 0.771 -1.359 1.107 0.965 -0.365 1.289 2.548 0.822 1.291 -1.608 0.000 0.939 0.923 1.228 1.223 0.933 0.981 0.860 +1 0.604 -1.043 -0.827 1.384 1.471 0.893 -0.674 0.736 2.173 0.799 -0.186 -0.531 2.215 0.651 0.513 -0.863 0.000 0.705 -1.569 -1.643 0.000 1.148 0.982 1.112 0.943 1.171 0.851 0.760 +0 0.834 -2.062 1.347 0.374 -0.352 1.033 -0.577 -1.516 1.087 1.606 -0.199 -0.028 0.000 0.574 -0.691 0.479 0.000 0.588 0.687 1.728 3.102 0.770 0.888 0.990 0.950 0.654 0.955 0.913 +0 1.232 1.907 0.835 0.193 0.742 0.449 0.029 1.720 0.000 0.791 -0.628 -0.046 2.215 0.579 -1.515 -1.671 0.000 1.152 0.672 -1.012 3.102 0.876 0.967 0.997 0.841 0.948 0.942 1.004 +1 1.229 2.078 -0.216 0.620 0.677 0.988 0.953 1.605 1.087 0.531 1.286 -0.967 2.215 0.688 0.536 0.273 0.000 0.682 0.577 -1.434 0.000 0.756 0.821 0.984 0.704 0.805 0.834 0.716 +0 2.532 -0.217 0.619 0.653 -0.989 1.040 1.135 -0.973 0.000 1.169 0.088 -0.978 1.107 1.378 0.893 0.753 2.548 0.593 0.691 1.384 0.000 1.038 0.947 1.768 1.209 1.484 1.125 1.121 +0 0.627 -1.430 0.114 1.034 0.642 0.748 -0.286 -0.797 0.000 1.539 0.593 -1.224 1.107 1.883 0.088 0.926 2.548 0.705 -1.818 0.458 0.000 0.815 0.902 0.978 1.888 1.753 2.048 1.564 +1 0.283 1.427 0.075 0.425 -0.866 1.130 0.452 1.346 2.173 0.678 -0.155 -0.161 2.215 1.124 1.134 -0.832 0.000 0.876 0.792 0.804 0.000 1.101 0.947 0.992 1.000 1.322 0.913 0.809 +1 0.778 -1.916 0.691 1.311 0.581 0.925 -0.145 -1.053 2.173 0.665 -0.694 1.558 1.107 0.645 -1.017 0.863 0.000 0.637 0.616 0.132 0.000 0.869 1.032 0.999 0.805 0.883 0.966 0.819 +1 0.551 2.247 -0.698 0.750 1.720 0.506 2.941 1.718 0.000 1.288 0.181 0.193 2.215 0.858 0.369 1.264 1.274 0.831 0.975 -1.014 0.000 0.955 0.979 0.979 0.745 0.927 0.847 0.785 +0 1.441 0.200 -1.023 0.467 1.211 0.846 -0.203 0.900 0.000 0.509 0.768 -1.608 0.000 1.307 -0.939 -0.578 2.548 0.829 -0.832 0.119 3.102 1.295 0.988 1.027 0.920 0.468 0.876 0.811 +1 0.773 -0.170 -1.302 0.821 0.541 0.955 0.922 0.635 0.000 1.074 -0.331 -0.732 2.215 0.619 -1.309 1.157 0.000 1.043 1.059 -1.140 3.102 0.616 0.914 1.099 0.814 0.923 0.922 0.814 +0 0.883 -0.428 -0.326 0.947 0.900 0.470 0.075 1.686 1.087 1.412 -1.894 -0.530 0.000 1.331 -1.205 1.495 1.274 1.249 -1.679 0.841 0.000 0.740 1.089 1.132 0.943 0.766 0.731 0.758 +1 0.756 1.551 1.190 1.277 -1.475 0.439 1.712 -0.152 0.000 0.483 0.122 -1.141 2.215 0.465 0.562 0.844 2.548 0.507 -1.055 0.298 0.000 0.501 0.597 0.992 0.614 0.507 0.523 0.718 +1 0.538 1.339 -0.884 0.305 -0.102 1.148 -0.012 1.392 0.000 1.312 -0.787 -0.553 2.215 0.440 2.354 -0.548 0.000 0.584 -0.218 0.755 3.102 2.505 1.400 0.983 0.892 0.762 1.345 1.057 +0 0.381 -1.726 -0.785 1.179 1.206 0.851 -0.432 -1.358 2.173 0.774 -0.610 -0.515 0.000 1.045 -0.941 0.361 0.000 1.235 0.546 0.674 3.102 1.019 1.045 0.987 0.857 1.219 0.913 0.834 +0 1.247 0.511 0.706 0.241 -1.230 1.034 -0.166 -0.018 2.173 1.387 0.527 -1.684 2.215 0.845 -0.583 -0.844 0.000 0.456 0.593 1.242 0.000 0.821 0.888 0.983 0.853 1.870 0.987 0.821 +1 1.318 0.765 -0.151 1.082 -1.420 1.142 0.543 0.703 0.000 0.818 0.695 -1.469 2.215 1.447 -0.088 -0.668 2.548 0.448 0.169 1.130 0.000 0.452 1.001 1.505 0.990 0.904 0.906 0.875 +1 0.563 1.065 1.005 0.844 -0.793 0.517 0.318 1.367 0.000 0.449 0.978 -0.391 2.215 0.776 1.953 -1.742 0.000 1.355 -0.586 0.105 3.102 0.910 0.760 0.990 0.555 0.744 0.610 0.567 +0 0.833 0.442 -0.225 0.456 0.511 0.813 0.345 0.351 0.000 0.898 0.498 -1.580 2.215 1.242 -1.945 1.324 0.000 1.501 0.022 -0.902 3.102 0.804 1.048 0.995 0.858 0.655 0.747 0.718 +1 1.157 0.183 0.884 1.141 -1.690 2.526 -0.386 -0.286 0.000 1.508 0.198 1.264 2.215 1.547 0.356 -1.201 2.548 1.584 -0.168 1.604 0.000 1.133 1.015 1.166 0.710 1.301 0.894 0.800 +1 2.328 0.425 -1.043 0.560 -1.328 0.831 -1.625 0.213 0.000 1.646 1.078 1.251 2.215 0.407 0.944 0.779 0.000 0.640 0.372 -0.262 3.102 1.926 1.203 0.986 1.552 0.959 1.508 1.381 +0 0.365 -0.841 -1.541 1.557 -0.314 1.042 0.021 1.306 2.173 0.521 -0.311 -0.439 0.000 0.875 -0.316 0.802 2.548 0.525 0.908 0.837 0.000 0.943 0.852 0.989 1.174 0.567 0.822 0.797 +1 0.430 -0.455 -0.570 0.815 0.553 1.132 2.503 -0.304 0.000 1.072 1.114 0.909 0.000 1.280 0.782 1.550 2.548 2.319 -0.075 -1.371 1.551 2.714 2.020 0.994 0.966 0.913 1.773 1.387 +0 0.856 -2.098 1.384 0.484 0.701 1.015 -1.954 -1.572 0.000 1.188 -0.449 -0.108 2.215 0.972 1.463 -0.076 0.000 0.556 0.802 -1.442 3.102 0.895 1.244 0.998 1.075 0.889 1.097 0.952 +0 1.025 0.897 -0.912 0.524 0.696 0.797 -0.436 -0.180 0.000 0.928 1.565 1.715 2.215 1.148 -0.015 0.360 0.000 1.035 -0.719 1.359 3.102 0.884 0.963 1.008 0.789 1.424 1.183 0.983 +0 0.511 -0.434 0.166 0.640 -1.714 0.918 -2.725 0.394 0.000 0.951 0.625 -1.362 2.215 1.180 -0.370 -0.608 0.000 1.095 0.657 1.490 3.102 0.802 0.869 0.989 0.594 0.503 0.638 0.590 +1 0.397 -0.696 -1.173 0.872 0.475 0.849 -2.905 -0.749 0.000 1.295 -1.408 1.239 0.000 1.736 -0.465 0.881 2.548 1.464 -0.166 -0.866 3.102 1.197 1.673 0.987 0.893 1.233 1.492 1.153 +1 0.870 0.361 1.113 0.346 -0.233 0.506 -1.152 1.644 0.000 0.601 1.296 -0.370 2.215 0.421 -0.451 0.859 0.000 0.764 -1.909 -1.335 0.000 0.861 0.690 0.988 0.862 0.746 0.892 0.766 +0 1.682 0.128 0.970 0.416 0.076 0.586 2.046 -0.572 0.000 0.479 0.998 -0.740 2.215 0.723 1.211 1.736 2.548 0.389 -0.334 -1.296 0.000 0.850 0.641 0.987 0.870 0.503 0.699 0.630 +0 0.468 -0.815 -0.828 0.749 1.548 1.598 -0.947 1.533 2.173 1.297 -2.146 -0.058 0.000 1.237 -1.172 0.041 2.548 0.757 -2.101 -0.844 0.000 0.864 0.804 0.989 1.001 1.733 1.341 1.064 +1 2.764 1.050 -0.550 0.151 -0.416 2.144 1.522 0.999 1.087 1.191 1.292 -1.276 2.215 1.279 -0.728 -0.640 0.000 1.179 1.113 0.185 0.000 1.218 1.106 0.975 2.149 2.100 1.562 1.257 +0 2.062 0.014 -0.600 0.400 -0.976 1.204 -0.282 1.243 0.000 0.836 0.717 0.073 2.215 1.183 0.450 -1.570 1.274 0.847 -0.239 0.423 0.000 1.039 1.054 0.980 0.934 1.061 0.911 0.963 +1 1.337 -0.155 1.073 0.332 -0.145 1.713 0.572 0.028 0.000 1.420 0.283 -1.242 1.107 0.844 -0.075 0.641 0.000 2.043 -0.510 -1.462 3.102 0.796 0.968 0.992 0.860 0.780 0.828 0.741 +0 3.188 0.565 -0.604 0.343 1.289 1.171 -0.856 0.975 2.173 1.215 -1.590 1.140 0.000 1.383 0.444 -1.281 2.548 0.776 -1.364 0.321 0.000 0.851 0.825 1.435 0.961 1.829 1.452 1.539 +0 0.394 -0.168 -1.084 1.191 1.338 0.870 -0.620 -0.112 2.173 0.780 1.481 -1.417 0.000 0.766 0.087 0.461 0.000 0.717 0.819 0.753 3.102 1.494 0.887 0.992 1.251 0.951 0.967 0.867 +1 0.658 0.039 -1.411 0.387 -0.695 1.244 1.141 0.649 2.173 0.937 0.882 -1.310 0.000 0.433 0.021 -0.952 0.000 0.507 0.151 -0.117 1.551 0.585 1.250 0.992 0.589 0.683 1.069 0.940 +0 0.436 1.956 -1.347 1.790 0.403 0.511 0.255 0.113 2.173 0.395 1.232 0.113 0.000 0.611 -0.921 -1.693 0.000 1.136 0.056 -0.812 0.000 0.942 0.876 1.224 1.169 0.935 0.943 0.955 +0 0.718 0.965 -1.612 1.311 -0.839 0.350 0.261 0.365 0.000 0.357 1.368 -0.437 2.215 0.916 -0.385 1.403 0.000 1.261 0.737 0.716 3.102 0.894 0.826 0.985 0.877 0.546 0.607 0.652 +1 0.497 -1.434 -0.793 0.964 1.629 0.830 -0.568 1.511 2.173 0.563 0.620 -0.467 0.000 0.918 1.857 0.159 0.000 1.287 0.613 0.005 0.000 0.855 0.726 0.986 1.114 0.748 1.073 1.666 +0 0.658 -0.495 -1.309 0.750 0.946 0.876 0.225 -0.841 2.173 0.790 -1.090 0.250 2.215 1.828 -1.017 1.133 0.000 0.698 -0.498 -0.612 0.000 1.037 0.810 0.984 0.850 1.352 0.887 0.787 +1 0.356 1.631 -0.610 0.621 0.080 0.564 0.349 -1.296 1.087 0.596 -0.956 0.615 0.000 0.586 1.346 0.885 1.274 0.757 -1.327 -1.509 0.000 0.868 1.016 0.988 0.696 0.786 0.854 0.764 +1 0.427 0.188 1.017 0.791 0.332 1.285 0.661 -0.763 0.000 1.649 0.496 1.248 1.107 1.441 1.003 0.656 2.548 0.698 2.192 -0.972 0.000 0.995 1.392 0.996 1.296 0.967 1.165 1.196 +0 2.552 -0.487 1.658 1.232 -1.677 1.837 -1.508 0.922 2.173 2.644 -0.250 -0.485 2.215 1.559 -0.601 -0.852 0.000 1.947 1.637 -0.085 0.000 0.863 0.909 1.001 2.105 3.794 2.214 1.807 +0 0.840 -1.319 -1.679 0.838 -1.306 1.391 -0.813 0.719 0.000 1.611 -0.955 -0.779 2.215 0.570 -0.575 1.128 0.000 0.846 0.488 0.116 3.102 0.596 0.887 0.993 1.166 1.189 1.121 1.182 +0 0.739 1.087 -1.282 1.109 -1.450 1.483 0.446 0.142 0.000 1.470 -0.108 1.493 1.107 0.846 0.132 -0.686 0.000 0.803 0.230 0.994 3.102 0.515 0.681 0.983 0.859 0.467 0.955 0.928 +1 0.898 -0.664 1.556 0.401 -0.545 0.972 0.123 -0.467 2.173 0.638 1.021 0.354 2.215 0.872 -0.025 -1.681 0.000 0.658 -1.955 -1.604 0.000 0.864 0.958 0.988 0.833 0.959 0.758 0.683 +1 0.864 0.614 0.351 1.013 0.405 1.575 -0.541 -1.472 0.000 1.050 -2.306 0.149 0.000 1.868 0.354 1.285 1.274 3.688 0.712 0.162 0.000 1.247 0.907 1.002 1.174 0.921 0.937 0.869 +0 0.778 -1.619 0.257 0.378 0.972 0.490 -0.673 1.066 2.173 0.916 -1.440 -1.250 2.215 0.633 -0.819 -0.366 0.000 0.524 -2.262 -1.065 0.000 0.758 0.832 0.997 0.899 0.948 0.685 0.639 +0 0.832 0.422 0.321 1.179 1.260 0.630 -0.598 0.019 2.173 1.309 -0.206 -1.431 0.000 0.981 0.225 -0.914 0.000 0.879 -0.129 0.607 3.102 0.878 0.931 1.029 0.925 0.436 0.789 0.796 +1 1.141 0.635 -1.644 1.268 0.673 0.584 0.147 -0.083 0.000 0.361 -0.637 -0.006 0.000 0.951 0.217 -0.911 0.000 0.815 -0.486 -1.181 1.551 0.911 0.764 1.448 0.930 0.380 0.617 0.671 +1 1.026 0.159 1.676 1.249 0.824 0.395 -0.639 0.484 0.000 0.863 0.156 -1.167 1.107 0.897 0.452 -0.108 0.000 1.267 -0.699 1.606 0.000 0.922 0.953 1.089 0.891 1.097 0.804 0.718 +0 1.296 -1.030 -0.858 0.654 0.402 1.186 1.973 1.195 0.000 1.049 -0.218 -0.004 2.215 0.474 1.870 0.840 0.000 1.551 -1.948 -0.987 0.000 0.427 0.955 1.156 0.865 1.005 1.261 1.537 +1 1.078 -0.025 -1.273 0.359 -1.625 0.771 0.132 0.473 0.000 1.017 -1.006 -1.164 2.215 0.964 0.680 1.008 0.000 0.576 -0.394 1.385 0.000 0.865 0.846 0.999 0.621 0.321 0.919 0.831 +0 0.884 1.192 1.472 1.034 -1.087 0.461 -0.508 -0.539 2.173 0.336 2.143 1.462 0.000 0.295 -1.268 0.483 2.548 0.687 -2.103 0.732 0.000 0.317 1.005 0.987 0.968 0.421 0.808 0.733 +1 1.067 0.321 0.776 0.499 -0.763 0.971 0.551 -0.717 0.000 0.991 -1.113 0.787 2.215 0.460 -0.328 -1.228 0.000 1.102 -0.248 -0.243 3.102 0.781 0.672 0.994 0.938 0.860 0.931 0.811 +0 1.888 -0.475 -1.610 0.323 0.913 0.684 0.302 0.071 0.000 0.791 0.507 1.203 2.215 0.559 2.589 -0.027 0.000 0.894 -1.106 -0.344 1.551 1.583 0.978 0.986 0.867 1.099 0.881 0.863 +1 0.490 0.415 -0.537 0.749 1.116 0.534 0.910 -0.279 2.173 0.819 -1.406 -0.643 2.215 0.861 -0.765 0.891 0.000 0.753 -1.167 -1.495 0.000 0.785 0.828 0.986 0.814 1.472 0.929 0.782 +0 0.316 -1.311 -1.613 1.057 -1.597 0.803 -0.175 0.214 0.000 1.136 0.740 -0.709 2.215 1.049 -0.626 0.718 0.000 1.245 -0.716 1.490 3.102 0.840 0.925 1.001 0.905 1.381 1.014 0.913 +1 1.509 0.583 -1.271 0.372 0.720 1.105 0.828 -0.514 0.000 1.513 1.241 1.663 2.215 1.115 2.273 0.793 0.000 1.204 -0.977 0.672 0.000 0.985 0.816 1.012 0.852 0.854 0.896 0.787 +0 0.857 -1.512 0.859 1.709 1.270 1.112 -1.249 -0.802 2.173 0.426 -1.308 1.644 0.000 0.774 -0.828 -0.413 2.548 0.825 -0.165 0.220 0.000 0.876 0.997 0.988 1.079 0.446 1.089 0.923 +1 1.914 0.512 1.178 0.482 -0.335 0.856 2.169 -0.792 0.000 0.531 1.089 0.409 2.215 0.366 2.585 -0.632 0.000 0.692 1.270 1.553 0.000 1.083 0.964 1.303 0.687 0.185 0.576 0.774 +1 0.593 0.578 0.587 1.288 -1.495 0.890 -0.151 0.006 0.000 0.868 -1.000 0.969 2.215 0.799 1.066 -1.692 0.000 1.150 -0.580 -1.003 3.102 0.850 0.984 1.156 1.115 0.895 0.935 0.837 +1 2.169 0.924 -0.398 1.800 0.691 0.682 2.837 1.251 0.000 1.286 0.438 -1.730 2.215 0.575 -0.385 0.653 1.274 0.534 -2.218 -1.633 0.000 7.300 3.858 2.276 1.706 0.870 2.333 2.017 +1 0.670 -0.159 0.845 1.888 0.349 0.710 -1.093 -0.980 2.173 0.973 -0.508 -1.646 2.215 0.743 -1.494 -0.178 0.000 0.597 -1.360 -1.658 0.000 0.714 0.850 0.981 1.145 0.778 0.948 0.791 +0 0.643 -0.160 0.658 0.928 -0.854 1.666 0.048 -0.297 2.173 1.737 1.364 1.365 2.215 1.240 0.589 1.091 0.000 0.435 -0.050 -1.033 0.000 0.817 0.905 1.047 0.912 3.095 1.496 1.171 +0 1.329 0.368 -1.029 0.887 0.702 0.549 -1.242 -0.139 0.000 0.628 1.698 1.511 0.000 1.272 0.438 1.487 2.548 0.577 -0.290 -0.968 0.000 0.757 0.809 1.504 0.941 0.888 0.799 0.794 +1 0.646 -0.079 0.632 0.908 0.366 1.965 -1.816 -0.858 0.000 1.585 0.914 1.186 0.000 2.287 -0.214 0.381 1.274 0.670 0.470 -1.525 0.000 0.901 0.781 0.988 0.812 0.844 0.949 0.995 +0 0.701 1.397 1.626 1.127 0.368 0.998 1.010 1.035 0.000 0.678 -2.830 -0.874 0.000 1.099 1.285 -0.450 0.000 0.646 0.820 -0.920 3.102 1.870 1.004 1.115 0.668 0.244 0.639 0.612 +1 0.370 -0.049 -0.070 1.259 -1.072 0.369 -2.238 -1.130 0.000 0.646 -1.036 -0.590 0.000 1.338 -0.597 0.622 2.548 0.704 -0.615 1.167 0.000 0.890 0.808 0.993 1.498 0.555 1.123 0.944 +1 1.285 2.083 -0.454 0.720 -0.119 1.384 0.829 1.555 2.173 0.665 1.172 0.535 2.215 0.387 1.807 0.631 0.000 0.854 0.912 -0.705 0.000 0.660 1.008 1.002 0.762 1.153 1.070 0.832 +0 1.794 -0.777 -0.021 0.726 0.703 0.740 -1.722 -1.728 0.000 0.688 -1.157 -1.512 2.215 0.536 -1.177 -1.076 0.000 0.803 -0.716 0.360 3.102 0.679 0.773 0.988 0.978 0.676 0.638 0.724 +1 0.697 1.341 1.042 1.133 -1.384 0.481 0.391 1.698 2.173 0.549 1.089 -0.615 0.000 0.863 -0.714 0.304 1.274 0.534 -2.428 -0.670 0.000 2.609 1.545 1.006 0.665 0.914 1.078 1.208 +0 1.805 -0.439 -1.416 0.964 1.546 0.661 1.121 0.040 0.000 1.099 -0.119 0.511 0.000 1.699 -0.747 1.469 2.548 2.365 -0.120 -0.179 3.102 0.904 0.975 0.976 0.774 1.616 1.118 1.066 +0 2.005 -0.780 1.343 1.628 -1.403 0.899 -0.035 -0.554 2.173 0.744 -0.269 0.379 0.000 1.063 -0.835 0.114 1.274 0.556 0.215 1.083 0.000 0.574 0.724 1.552 1.248 0.885 1.130 0.911 +1 1.792 0.188 1.387 0.805 0.387 1.272 0.332 -0.892 2.173 0.804 -1.737 -0.031 0.000 0.939 0.473 -1.612 0.000 0.587 0.418 0.229 1.551 0.884 1.108 1.304 0.702 0.779 0.908 0.808 +0 1.584 0.397 -0.001 0.252 1.398 0.840 0.213 -0.808 0.000 1.381 -0.530 1.105 2.215 0.490 0.491 -1.086 2.548 0.860 -0.746 1.456 0.000 1.385 0.793 0.986 1.180 0.946 0.852 0.846 +0 0.309 -1.721 1.566 1.169 0.150 1.557 -0.371 1.438 2.173 0.775 -1.204 -0.409 0.000 1.584 -0.062 -0.199 2.548 0.759 -0.579 -1.349 0.000 0.797 0.857 0.990 1.170 1.971 1.127 0.940 +0 0.887 -0.630 -0.941 0.995 -0.273 0.858 -1.629 1.065 0.000 0.905 -0.649 1.520 2.215 1.480 -0.679 -1.267 1.274 3.871 0.355 -0.349 0.000 1.084 1.419 0.979 0.968 0.727 1.109 1.037 +1 1.758 0.308 1.438 0.345 0.628 1.307 0.344 -0.917 0.000 0.924 0.460 0.473 1.107 0.270 1.208 -0.793 2.548 0.391 2.107 0.136 0.000 1.694 0.887 0.987 0.808 0.536 0.817 0.830 +1 2.097 0.802 -1.582 0.214 -0.718 0.456 0.363 0.713 2.173 0.539 -0.042 -1.229 0.000 0.728 1.051 0.119 0.000 0.941 0.309 0.322 0.000 0.935 0.710 0.992 0.632 0.381 0.590 0.573 +1 0.414 1.530 -0.780 0.994 0.935 0.516 1.260 -0.115 0.000 0.571 -0.540 0.635 2.215 1.095 -0.984 -0.978 2.548 0.869 -1.115 1.713 0.000 2.023 1.271 0.986 2.051 0.864 1.439 1.317 +0 1.402 -1.683 1.204 1.805 0.599 0.769 -0.076 -0.732 2.173 0.595 0.165 0.438 2.215 1.074 -0.628 -1.115 0.000 1.104 -1.570 -0.880 0.000 0.803 0.894 1.142 1.224 0.875 1.254 1.108 +1 0.475 -0.712 -1.352 1.288 -0.334 0.712 1.198 1.027 2.173 0.571 1.816 0.409 0.000 0.654 -2.393 -0.742 0.000 0.867 2.139 1.645 0.000 0.885 0.798 0.989 1.269 0.486 1.287 1.438 +1 0.742 1.852 1.386 0.639 0.232 0.490 0.552 0.033 1.087 0.492 1.611 -1.620 0.000 0.409 -0.026 -1.017 0.000 0.727 -0.617 -1.678 3.102 0.743 0.769 0.989 1.363 0.769 0.995 0.828 +1 1.247 -0.716 0.696 0.583 0.179 0.794 -1.050 -1.493 2.173 0.537 0.040 -0.112 0.000 0.607 -0.864 0.922 0.000 1.242 -0.075 -0.759 3.102 0.848 0.741 0.988 1.049 0.835 0.844 0.732 +1 0.377 0.446 -0.612 2.216 -0.188 1.277 -0.178 1.713 2.173 0.295 -0.052 -0.094 1.107 0.553 -1.251 1.230 0.000 0.612 0.355 0.667 0.000 0.729 0.876 0.995 0.724 0.903 1.343 1.162 +0 1.483 0.433 1.268 0.545 0.255 0.897 0.396 -0.972 2.173 0.570 1.255 0.318 0.000 0.757 0.136 0.540 0.000 0.521 -0.534 -0.327 3.102 0.640 1.089 0.987 0.674 0.560 0.717 0.686 +1 0.528 -1.512 1.715 0.989 0.051 1.072 -0.679 -1.286 0.000 0.485 0.512 0.327 2.215 0.826 -0.771 -0.759 0.000 1.118 -0.743 1.184 3.102 0.787 0.912 0.999 0.909 0.691 0.780 0.770 +0 0.545 -0.423 -0.510 1.760 -0.147 0.555 -1.948 0.966 0.000 0.721 -0.480 0.987 2.215 1.208 -1.157 -1.244 2.548 0.937 0.104 -1.626 0.000 0.868 0.910 0.993 1.042 0.980 1.046 1.148 +0 0.887 0.530 -0.956 0.824 1.335 1.367 1.612 -1.144 2.173 1.163 2.373 -0.251 0.000 1.802 0.177 1.052 2.548 1.108 1.003 -0.451 0.000 1.011 1.326 1.043 0.865 2.354 1.553 1.263 +0 0.527 -0.374 -0.694 1.869 -1.658 1.068 1.084 0.710 2.173 0.981 -0.077 0.079 2.215 0.814 0.055 0.900 0.000 2.625 0.687 -0.914 0.000 1.729 1.333 1.048 1.609 1.240 1.197 1.137 +1 0.860 -0.528 -0.252 1.554 0.298 1.009 -0.986 -1.312 1.087 0.286 -0.434 0.575 0.000 0.450 -0.105 1.179 2.548 0.619 -2.089 1.500 0.000 0.864 1.373 0.984 0.715 0.758 0.861 0.919 +0 0.529 1.705 1.662 0.511 1.182 1.452 1.152 -0.234 0.000 1.243 -1.470 1.059 0.000 2.123 1.197 0.251 0.000 2.588 0.747 -1.293 1.551 1.357 1.865 0.981 0.984 1.395 1.819 1.404 +1 0.392 1.103 0.857 2.116 -0.239 0.851 -0.056 -0.898 2.173 1.349 1.445 0.024 0.000 1.478 2.381 -1.717 0.000 0.926 0.314 1.667 3.102 0.843 1.023 1.053 0.896 0.723 0.858 0.776 +1 0.623 0.360 -1.365 2.851 1.630 2.164 0.858 -0.136 0.000 0.727 0.195 0.221 2.215 0.607 1.407 0.750 0.000 0.941 -0.301 -1.676 3.102 0.833 1.175 0.987 0.762 0.770 0.923 0.933 +0 0.841 0.707 -1.089 0.359 -0.476 0.831 0.144 0.148 0.000 1.344 0.650 1.663 1.107 1.388 0.130 0.688 0.000 0.897 -0.099 -0.782 3.102 0.903 0.881 0.993 0.965 0.894 0.941 0.935 +0 0.553 1.368 1.203 2.258 -1.112 1.361 0.528 0.458 2.173 0.667 0.917 -0.262 0.000 1.262 1.369 -1.497 1.274 1.013 1.434 0.519 0.000 0.813 0.963 1.347 1.733 1.802 1.243 1.021 +0 1.224 0.618 1.165 1.217 -0.165 1.174 -0.306 0.066 0.000 3.201 0.394 1.724 1.107 1.916 0.476 -0.123 2.548 0.635 -0.432 -0.511 0.000 0.669 0.787 1.575 1.661 2.625 1.643 1.370 +1 1.062 -0.368 -0.273 0.586 0.334 0.794 0.652 1.304 2.173 1.295 -1.553 -0.774 0.000 1.079 -0.608 1.732 2.548 0.918 0.682 -1.670 0.000 0.926 0.839 0.990 1.318 0.929 0.960 0.829 +0 1.890 -1.100 0.880 1.248 1.383 1.303 -0.241 -0.302 0.000 0.830 -0.872 -1.581 2.215 0.931 -0.651 -1.013 2.548 0.555 0.784 -0.541 0.000 0.861 0.847 0.995 0.883 0.466 0.803 1.067 +0 0.588 -0.276 -0.088 0.839 -1.011 0.673 0.712 0.626 2.173 0.501 -2.060 1.521 0.000 0.927 -0.517 -0.966 0.000 0.670 -0.090 1.232 3.102 1.214 0.843 0.981 0.840 0.477 0.939 0.880 +1 0.708 0.755 1.368 0.743 -1.644 1.104 1.171 -0.757 2.173 0.606 -0.025 0.501 1.107 0.546 2.076 0.271 0.000 0.550 1.854 1.346 0.000 0.498 0.956 1.000 1.004 1.336 0.958 0.808 +0 3.514 -0.145 -0.297 2.003 -0.307 2.268 -0.268 1.350 0.000 1.383 -0.430 -1.705 0.000 1.337 0.900 0.265 2.548 0.765 -1.000 -0.929 3.102 1.153 0.908 1.022 1.185 1.231 0.987 1.184 +0 1.357 0.731 1.307 0.664 -0.481 0.774 -0.248 -1.477 2.173 0.794 -0.632 -0.685 0.000 1.414 0.133 0.231 2.548 0.522 -1.026 1.021 0.000 0.874 0.915 1.314 1.006 1.329 0.897 0.842 +1 0.934 0.165 -1.524 0.660 1.114 0.892 -0.409 -1.015 2.173 0.833 1.285 0.759 0.000 1.139 1.147 0.103 0.000 1.196 0.920 -0.743 3.102 0.835 0.863 0.988 0.833 0.949 1.070 0.995 +0 1.368 -1.408 -1.029 0.714 -0.942 0.587 0.520 0.622 0.000 0.761 -0.936 0.100 2.215 1.275 -1.902 1.018 0.000 0.687 1.600 -0.570 0.000 0.773 0.928 0.998 0.488 0.508 0.594 0.736 +0 0.717 -0.650 -0.737 0.321 1.169 0.749 -0.160 0.282 0.000 0.720 -0.875 0.795 0.000 1.187 -0.282 -1.540 1.274 0.887 0.443 -1.080 3.102 0.911 0.991 0.989 0.674 0.464 0.788 0.691 +0 0.542 0.941 -1.728 0.745 0.486 0.511 1.309 0.718 0.000 0.611 0.862 -1.237 2.215 0.795 -0.545 -0.930 1.274 1.044 1.852 0.018 0.000 0.839 0.923 0.989 1.128 0.635 0.892 0.800 +0 0.408 1.841 -0.468 1.123 0.406 0.765 0.233 1.708 0.000 0.651 1.080 -0.635 2.215 0.730 -0.728 0.766 2.548 0.844 0.035 -0.914 0.000 0.869 0.875 0.991 0.846 1.071 0.740 0.742 +0 0.856 1.730 -1.730 1.044 0.652 0.647 1.154 -1.230 0.000 0.537 -0.506 -0.172 2.215 0.685 0.735 0.373 0.000 0.572 0.032 0.174 3.102 0.741 0.916 1.098 0.781 0.209 0.809 0.758 +1 1.398 2.073 -1.214 0.434 1.740 0.714 1.332 -0.240 2.173 0.976 0.850 0.639 2.215 1.148 1.357 1.321 0.000 0.639 1.662 -0.918 0.000 0.885 0.957 0.974 1.097 0.922 0.871 0.766 +1 0.559 -0.851 -1.583 0.328 -0.803 0.910 0.304 0.667 2.173 0.629 -0.249 0.859 0.000 0.463 0.462 -0.101 2.548 0.685 2.314 -1.369 0.000 0.597 0.553 0.986 0.575 0.523 0.609 0.525 +0 1.663 1.538 -0.389 0.305 -1.516 1.009 -0.560 -0.434 2.173 1.197 0.481 -1.532 0.000 1.106 0.311 0.359 2.548 1.572 0.760 1.465 0.000 0.972 0.958 0.990 1.782 1.061 1.229 1.226 +0 1.938 -0.591 1.466 0.822 1.004 0.961 -0.454 -0.354 0.000 0.863 0.568 -0.305 2.215 1.906 -0.804 -1.551 2.548 1.266 -0.793 0.191 0.000 0.898 0.927 0.979 0.924 1.645 1.077 1.084 +0 1.167 1.783 0.030 0.351 1.732 0.416 0.938 1.281 1.087 1.013 -0.385 -1.461 0.000 0.876 1.115 -1.232 2.548 0.589 -0.460 0.944 0.000 0.840 0.685 0.984 0.704 0.587 0.572 0.562 +1 0.990 -0.835 -1.465 0.734 -0.932 1.838 -0.029 0.694 2.173 1.594 -0.916 -1.165 0.000 0.564 0.685 -0.190 0.000 0.487 -0.836 1.678 1.551 1.780 1.003 0.984 1.925 0.926 1.285 1.204 +1 0.485 1.319 -1.041 1.556 1.382 0.999 0.650 -0.249 2.173 1.094 -0.010 -1.419 0.000 1.414 -0.660 0.703 0.000 0.489 -0.435 -0.457 1.551 1.928 1.085 0.987 1.207 0.481 1.006 1.050 +0 0.594 -0.004 1.207 0.869 -0.512 0.926 -1.453 1.233 2.173 0.859 -2.684 -0.654 0.000 1.067 -0.297 -0.096 0.000 0.899 -1.031 -1.290 3.102 0.906 0.770 0.996 1.128 0.745 0.788 0.953 +1 1.291 -0.469 1.256 0.469 1.557 1.836 -1.349 0.097 0.000 0.685 -0.022 -1.690 0.000 1.020 0.438 -1.310 2.548 0.693 -0.865 1.609 1.551 0.831 0.978 0.987 0.757 0.622 1.132 1.114 +0 0.868 0.627 0.592 0.610 1.451 0.677 -0.728 -0.224 2.173 0.720 -0.419 -1.569 2.215 0.651 0.646 -1.739 0.000 0.935 0.400 -0.212 0.000 0.850 0.924 0.993 0.739 0.975 0.732 0.678 +1 0.703 -1.245 1.163 1.668 -1.532 0.579 -0.761 0.810 0.000 1.964 -0.696 -0.072 2.215 1.074 -1.236 -0.786 2.548 1.698 -0.651 1.516 0.000 0.898 1.075 0.987 1.513 1.054 1.045 0.980 +0 0.716 -0.978 0.200 1.050 1.346 0.849 0.058 1.121 0.000 0.862 -2.592 -0.072 0.000 1.019 -1.172 -1.331 0.000 1.027 -0.264 -0.950 0.000 0.964 1.180 1.032 0.526 0.497 0.702 0.706 +1 0.917 0.842 -0.130 0.501 -1.405 1.104 -0.256 -1.358 0.000 0.809 -0.799 0.742 2.215 1.258 0.413 0.178 2.548 0.781 1.803 1.164 0.000 1.675 1.356 0.990 0.671 0.899 1.081 0.941 +1 1.326 -0.563 -1.171 0.340 -0.340 1.137 1.143 1.175 0.000 1.178 -0.776 0.130 0.000 1.314 -0.077 0.490 0.000 1.184 0.016 -0.874 3.102 0.924 1.026 0.984 0.797 0.765 0.915 0.832 +0 1.934 0.779 1.371 0.737 1.156 0.972 0.190 -1.561 2.173 0.607 -2.804 0.086 0.000 2.011 0.353 -0.281 2.548 1.132 0.865 -0.506 0.000 3.806 2.781 1.007 1.525 1.602 1.954 2.001 +1 1.225 0.256 -0.608 0.881 1.632 0.898 -0.756 0.013 0.000 0.376 -2.484 0.975 0.000 1.842 0.315 1.530 2.548 0.831 -0.587 -0.875 0.000 0.948 0.983 1.297 0.974 0.919 0.997 0.895 +1 0.539 -1.577 -1.265 0.799 1.298 0.989 -1.023 0.331 0.000 1.523 -0.945 1.589 2.215 1.371 -0.996 -0.564 0.000 0.896 2.078 -0.669 0.000 0.692 0.823 0.991 0.682 0.926 0.928 0.799 +0 2.334 1.293 -0.019 1.183 0.293 1.236 -0.786 -1.599 2.173 0.493 0.472 -0.753 0.000 1.005 -1.500 1.686 0.000 0.919 0.239 1.229 3.102 1.636 1.094 0.974 2.542 0.898 1.593 1.485 +1 0.413 1.511 1.706 2.402 0.955 1.013 -1.805 -0.802 0.000 1.075 0.203 -0.972 1.107 0.838 0.168 0.162 0.000 1.127 0.653 0.909 3.102 2.329 1.836 0.994 0.820 1.028 1.363 2.339 +0 1.202 0.037 -1.413 0.803 0.671 0.474 -0.763 -1.189 0.000 0.624 0.572 0.328 2.215 0.609 -0.685 0.131 2.548 0.420 0.124 0.900 0.000 0.725 0.795 1.297 0.794 0.487 0.620 0.584 +1 0.449 -0.118 -1.668 1.308 -0.073 0.689 -0.098 0.390 0.000 0.966 -1.208 -1.169 2.215 1.167 -0.072 -1.208 2.548 1.161 -0.831 0.846 0.000 0.836 1.108 1.052 0.974 0.696 0.903 0.805 +0 1.249 0.091 1.097 1.708 0.650 0.732 2.362 -0.307 0.000 1.063 -0.443 -0.543 1.107 0.825 -1.444 -1.586 0.000 1.767 0.024 -1.304 1.551 5.156 2.949 0.999 1.226 0.840 1.909 1.704 +0 0.537 0.644 1.467 0.587 -1.407 0.716 1.003 0.837 2.173 0.917 -1.062 -0.452 0.000 0.473 -0.603 0.115 2.548 0.628 1.015 -0.906 0.000 1.467 0.859 0.980 1.023 0.810 0.930 0.831 +1 2.472 0.928 1.701 0.584 -0.326 0.481 -0.275 1.691 0.000 1.092 1.350 0.111 2.215 0.608 -0.277 0.255 0.000 1.066 -0.340 -0.865 1.551 0.936 0.709 1.611 1.333 1.250 1.043 0.948 +1 1.000 1.469 0.261 1.867 1.012 0.708 0.019 -0.894 2.173 0.487 2.141 -1.231 0.000 0.507 0.817 -1.691 2.548 0.468 -1.426 -1.022 0.000 2.163 1.195 1.187 1.520 0.596 0.972 1.051 +1 0.850 -0.237 -0.382 0.637 -1.503 1.128 2.147 0.884 0.000 1.200 0.428 0.080 2.215 2.040 0.007 -1.190 1.274 1.093 -0.507 -1.715 0.000 3.412 2.342 0.986 0.795 1.557 1.813 1.559 +1 0.627 1.697 -0.055 1.096 1.695 0.320 -0.088 1.175 2.173 0.319 0.186 -0.473 2.215 0.292 0.539 1.416 0.000 0.386 -2.239 -1.075 0.000 0.964 0.698 1.148 0.762 0.473 0.642 0.889 +0 1.575 -0.050 1.272 0.972 1.511 1.048 -0.777 0.973 2.173 2.090 0.055 -0.800 2.215 0.934 -1.374 -0.001 0.000 0.488 0.378 -0.060 0.000 0.855 1.015 0.967 1.581 2.372 1.408 1.212 +0 0.782 0.063 -0.724 1.321 0.190 1.207 -0.575 -0.053 0.000 1.095 -1.320 1.469 0.000 1.798 -0.189 1.512 2.548 1.488 -0.252 -1.327 3.102 2.584 1.693 1.034 1.138 0.691 1.219 1.096 +0 0.489 1.233 -0.441 2.737 -0.426 1.564 0.436 1.624 0.000 1.289 -0.223 0.108 2.215 0.849 0.901 1.278 0.000 1.327 -0.532 1.424 3.102 0.861 0.926 0.989 0.917 1.121 1.138 1.232 +0 1.150 -1.923 -1.481 0.482 0.158 1.051 -0.782 1.208 2.173 0.531 -0.362 0.894 0.000 1.475 -0.673 -0.398 2.548 1.018 0.867 -0.867 0.000 1.207 1.119 1.028 1.076 1.540 1.028 1.053 +1 1.157 0.517 0.064 0.803 1.511 0.963 0.605 1.346 0.000 1.280 -0.657 -0.821 0.000 0.389 -1.208 0.563 1.274 0.787 0.492 -0.646 3.102 2.724 1.537 1.289 0.821 0.601 0.973 0.895 +0 1.136 -0.207 -1.172 0.820 1.312 0.642 -0.194 -0.171 2.173 0.405 -1.910 1.253 0.000 0.388 -1.515 -0.831 0.000 1.273 -0.110 1.006 3.102 0.721 0.871 1.050 0.710 0.836 0.690 0.659 +1 0.924 1.972 1.221 1.063 -0.028 1.089 1.253 0.102 2.173 1.126 1.079 1.714 0.000 1.082 0.772 -1.177 0.000 1.216 1.251 -0.689 3.102 0.893 0.838 1.239 0.974 0.810 0.946 0.914 +1 0.899 0.220 0.951 0.376 -0.773 0.926 -0.837 -0.703 0.000 0.798 -0.841 0.663 1.107 0.886 0.089 -0.947 0.000 1.178 -0.544 1.491 0.000 0.872 1.015 0.988 0.879 0.635 0.832 0.806 +1 1.149 0.669 1.100 0.554 -1.500 2.786 1.485 0.108 0.000 2.707 1.144 1.385 0.000 2.373 0.957 -1.160 0.000 1.056 -2.364 -1.308 0.000 2.926 1.712 0.985 0.721 0.265 1.092 0.907 +1 1.249 -0.865 -0.948 0.702 0.526 0.698 -0.765 -1.284 0.000 0.801 -0.938 0.672 2.215 1.142 -1.349 0.997 2.548 0.534 -1.826 -1.401 0.000 1.003 0.872 1.259 0.855 0.396 0.678 0.674 +1 0.916 -1.093 -0.018 1.275 1.686 0.937 -0.548 -1.737 2.173 1.417 -0.327 0.162 0.000 0.722 0.134 -0.574 0.000 0.402 -0.347 0.575 3.102 1.028 0.593 1.496 1.017 0.567 0.825 0.839 +0 0.803 1.453 -1.386 0.608 -0.116 0.477 -0.345 1.064 2.173 0.338 -2.186 -0.366 0.000 0.873 0.715 0.367 2.548 0.656 0.179 0.666 0.000 1.081 1.070 0.988 0.887 0.674 0.712 0.811 +0 0.799 -1.367 1.261 1.395 0.987 0.612 -0.613 -1.039 2.173 0.499 0.597 -0.253 1.107 0.318 0.669 -0.673 0.000 0.637 2.046 0.482 0.000 1.009 0.692 0.995 1.326 0.752 1.250 1.557 +1 0.568 0.436 0.794 0.367 -1.492 0.823 2.219 0.840 0.000 0.822 1.915 0.358 0.000 0.893 0.790 -0.933 2.548 0.749 -1.784 -1.255 0.000 0.774 1.003 0.990 0.829 0.181 0.826 1.052 +0 0.459 -0.592 0.764 1.310 -1.506 0.702 0.644 0.740 0.000 0.803 0.229 -0.354 2.215 0.789 1.334 -0.255 2.548 0.816 2.492 -1.267 0.000 0.694 0.865 0.988 1.004 0.558 0.754 0.726 +1 0.457 -1.456 0.938 0.423 0.662 1.501 -0.469 -0.758 2.173 0.927 0.542 0.736 2.215 0.933 -0.332 1.373 0.000 0.500 0.819 -1.532 0.000 0.664 1.223 0.984 0.686 1.934 1.048 0.877 +0 1.003 -1.368 -1.222 0.939 -0.202 0.711 -0.116 -0.428 1.087 0.821 -0.708 0.018 0.000 0.987 -0.404 1.148 0.000 1.317 -1.126 1.042 3.102 0.858 1.080 1.070 0.836 1.200 0.808 0.754 +0 1.193 -0.149 1.353 0.356 -0.903 2.676 1.070 0.240 1.087 2.697 -0.702 -1.271 0.000 2.205 -0.517 0.917 0.000 1.539 0.373 0.127 0.000 0.789 0.927 0.991 1.038 4.053 2.031 1.595 +0 1.214 -0.297 0.064 0.646 1.040 0.882 -0.044 -0.888 2.173 0.773 0.393 1.242 2.215 1.105 -0.980 1.701 0.000 0.400 1.798 0.414 0.000 0.838 0.932 0.987 0.751 1.175 0.803 0.771 +1 1.118 1.477 1.658 0.585 -0.664 0.915 -1.711 1.079 0.000 1.666 -0.082 0.007 1.107 0.598 0.901 -0.912 0.000 0.631 -0.016 -1.426 3.102 2.744 1.528 0.987 1.435 0.890 1.276 1.379 +1 0.767 -0.951 -0.522 0.550 1.404 0.643 0.956 1.295 0.000 0.876 0.195 0.823 2.215 1.300 0.336 -1.001 0.000 1.706 -0.501 -0.240 3.102 1.526 1.179 0.987 0.672 1.011 0.980 0.848 +1 0.574 -0.288 0.769 0.936 -0.699 0.480 0.033 -0.445 0.000 1.013 -0.232 1.389 0.000 1.458 0.789 -1.521 1.274 1.918 0.224 0.252 1.551 1.489 1.156 0.987 0.969 1.335 0.952 0.826 +1 0.770 -0.528 0.257 0.403 -1.139 0.808 0.294 -1.449 2.173 1.604 -0.745 0.862 0.000 0.906 -0.685 -0.614 2.548 0.413 -0.614 1.734 0.000 0.750 0.991 0.989 1.068 0.935 0.921 0.818 +0 1.052 0.823 -0.622 0.475 -1.122 0.987 0.023 0.307 2.173 0.806 -1.480 -1.353 0.000 0.874 0.903 1.043 2.548 1.311 0.245 1.035 0.000 0.884 1.290 0.995 0.996 0.920 1.060 0.937 +0 1.245 0.017 0.427 2.898 0.460 2.037 -0.983 -1.201 0.000 0.595 -0.714 1.229 2.215 0.515 -0.852 -1.474 2.548 0.571 -0.249 -0.375 0.000 1.259 1.185 0.988 1.181 0.386 0.927 1.428 +0 0.497 -1.109 -0.813 0.622 0.912 0.885 0.961 -1.543 0.000 0.648 1.177 -0.918 1.107 0.881 1.249 0.237 0.000 1.576 0.357 0.626 1.551 1.619 1.024 0.984 1.282 0.970 1.254 1.382 +0 0.627 -0.806 0.570 0.444 -0.870 1.686 -0.202 0.861 1.087 1.282 0.785 -1.672 1.107 2.086 -1.263 -0.213 0.000 1.596 -0.216 -1.264 0.000 0.873 0.938 0.983 0.965 1.996 1.301 1.037 +1 0.732 0.103 -1.694 0.442 1.073 0.589 1.730 -1.121 0.000 0.997 0.087 0.400 2.215 0.429 2.324 0.085 0.000 0.751 0.194 -1.123 3.102 0.884 0.789 0.986 0.895 0.768 0.873 0.772 +1 2.187 -1.674 -1.242 0.508 0.758 0.887 -1.146 0.623 0.000 0.524 -0.306 0.707 1.107 0.467 -0.787 -0.969 0.000 1.136 -1.898 0.348 0.000 0.898 0.854 1.421 0.920 0.748 0.813 0.726 +1 0.976 -0.383 -0.937 0.627 -0.076 0.783 -0.240 -1.295 0.000 0.699 -2.329 0.987 0.000 0.699 -2.615 1.330 0.000 1.039 0.977 -1.310 0.000 0.801 0.905 0.988 0.718 0.451 0.623 0.637 +1 1.147 0.376 -0.871 0.459 0.569 0.915 -0.247 1.244 0.000 0.672 -0.255 -1.632 0.000 1.238 0.810 -0.252 2.548 0.893 -0.728 -0.057 3.102 0.868 0.934 0.987 0.679 0.817 0.917 0.790 +0 0.863 1.191 1.441 1.987 1.107 1.193 1.860 -0.307 0.000 0.392 1.701 0.184 0.000 0.408 0.545 -0.952 2.548 1.166 1.188 -1.222 1.551 0.628 0.851 0.982 0.842 0.258 0.675 0.839 +1 0.475 0.939 0.181 1.085 -0.446 0.526 1.053 -1.211 1.087 0.775 1.599 0.963 2.215 0.987 0.451 0.658 0.000 0.719 -0.300 -1.329 0.000 0.852 0.810 0.983 1.164 0.913 0.882 0.759 +1 1.593 -0.447 -1.070 0.591 -0.913 2.729 -0.351 0.820 0.000 2.010 -0.187 -0.632 2.215 0.957 0.028 -0.865 0.000 1.553 -0.010 -1.379 3.102 0.731 0.748 0.989 0.722 1.004 0.753 0.638 +0 1.430 -0.756 1.228 0.663 0.051 0.648 -0.352 -0.144 2.173 0.654 -1.387 1.558 0.000 0.748 -0.932 -1.244 2.548 0.603 0.221 -0.823 0.000 1.038 0.974 1.177 0.783 0.784 0.697 0.665 +0 0.553 -0.163 0.480 0.628 -1.325 1.236 1.285 0.651 0.000 1.539 0.386 -1.301 2.215 0.686 0.912 0.149 2.548 0.898 -0.391 -0.573 0.000 1.325 0.785 0.989 1.057 1.106 1.055 1.054 +0 0.903 -1.396 -0.128 1.468 -0.683 1.207 -2.117 1.289 0.000 1.540 2.113 1.186 0.000 2.432 -1.202 -0.396 2.548 0.817 0.205 -1.334 0.000 1.003 1.134 0.988 0.741 0.313 1.481 1.582 +0 0.335 1.491 -1.411 1.135 0.797 0.623 0.564 -0.606 2.173 0.640 -0.924 0.112 2.215 0.772 -0.661 1.669 0.000 0.615 -1.820 -1.618 0.000 0.620 0.770 0.988 1.273 0.958 1.450 1.620 +0 0.370 -0.661 1.430 1.327 -0.485 0.799 0.058 -1.737 0.000 1.192 -0.228 0.401 2.215 0.953 0.455 -1.171 0.000 0.770 0.827 0.609 3.102 0.843 0.852 0.987 0.838 0.587 0.850 0.778 +0 1.105 -0.950 -1.484 1.009 1.146 1.312 2.468 0.069 0.000 0.614 -0.452 -0.536 0.000 1.160 -2.041 -0.189 0.000 2.239 0.318 1.606 3.102 0.918 0.981 1.019 0.759 0.513 0.700 0.712 +0 0.472 -1.690 -0.247 1.678 0.694 1.361 -0.736 0.085 2.173 1.194 -0.443 -0.759 0.000 2.373 -1.255 -1.693 1.274 0.929 -0.027 -1.562 0.000 0.955 1.345 0.984 0.899 2.352 1.332 1.162 +0 0.723 -0.930 -0.912 1.104 0.766 0.558 -0.766 1.291 2.173 0.568 -1.413 0.080 0.000 0.675 -1.556 -0.837 0.000 0.462 -1.327 -1.309 3.102 0.709 0.868 1.236 0.644 0.446 0.525 0.553 +0 1.122 -0.987 0.586 0.284 1.648 0.756 -1.002 -0.486 2.173 0.642 -1.047 -1.518 1.107 0.787 -0.244 -0.831 0.000 1.092 0.481 0.884 0.000 0.980 0.904 0.989 0.888 0.822 0.765 0.735 +0 0.730 -0.551 -0.826 0.911 -0.290 0.927 -1.017 0.083 2.173 0.830 -2.559 1.259 0.000 1.437 -0.379 -1.618 0.000 1.241 -0.702 1.314 1.551 2.424 1.406 0.985 1.047 1.022 1.184 1.230 +1 1.748 0.058 1.071 0.155 1.152 0.705 -0.459 -0.565 2.173 0.567 -1.951 0.996 0.000 0.364 -1.289 -0.197 2.548 1.560 0.412 -1.215 0.000 2.262 1.274 0.990 1.148 0.374 0.888 0.956 +1 0.669 -0.844 -0.874 1.200 0.448 0.815 -1.631 1.157 0.000 0.718 -0.701 -0.287 2.215 1.909 -0.347 -1.386 2.548 1.321 -1.111 0.548 0.000 0.866 1.060 1.152 1.084 1.063 1.041 0.899 +1 0.858 0.863 1.331 1.113 -0.570 1.019 0.256 -0.535 0.000 1.735 0.420 0.696 2.215 1.020 -0.505 -1.401 0.000 0.600 0.085 -1.313 3.102 1.500 0.837 1.340 1.172 0.907 1.042 0.948 +1 0.788 0.913 0.877 0.180 1.320 1.248 0.392 -0.841 0.000 0.813 0.165 1.126 2.215 0.763 0.541 1.357 2.548 0.498 -0.713 -1.022 0.000 0.828 0.966 0.981 0.569 0.249 0.795 0.726 +0 0.878 -0.547 0.671 0.471 1.136 0.841 -1.023 -1.613 2.173 0.586 -1.612 0.957 0.000 0.854 1.315 -0.722 0.000 2.465 0.047 -0.193 3.102 0.895 0.963 0.992 0.918 1.701 1.233 1.136 +1 0.783 -0.886 0.272 1.072 1.366 1.213 -0.525 1.055 0.000 1.253 -0.233 -1.244 2.215 2.327 2.022 -0.521 0.000 1.926 -1.583 -0.910 0.000 0.792 1.070 1.059 0.643 1.443 0.967 0.859 +0 0.600 -1.022 0.330 0.999 -1.190 1.656 0.394 1.163 0.000 1.197 0.625 -0.014 2.215 0.680 1.552 0.686 0.000 1.670 -0.607 -0.650 0.000 1.558 1.046 1.051 1.218 0.878 0.990 1.121 +1 0.646 -0.498 1.592 2.034 1.557 0.458 1.382 0.142 2.173 0.565 0.574 0.327 0.000 0.617 0.384 -0.203 0.000 1.093 0.229 -0.722 3.102 0.644 0.563 0.976 0.881 0.687 0.828 0.695 +0 1.070 1.278 -1.053 0.458 0.407 0.556 -0.781 -0.737 2.173 0.571 -0.950 0.531 0.000 0.751 -1.035 1.539 0.000 1.434 0.222 1.147 3.102 0.797 0.836 0.986 0.808 1.073 0.819 0.845 +0 0.978 0.036 0.285 0.594 -0.230 0.565 0.889 -1.471 1.087 0.421 0.733 1.463 2.215 0.805 -2.166 -0.126 0.000 0.436 -1.294 -1.568 0.000 0.688 1.208 0.980 1.114 0.346 1.109 0.958 +0 0.281 -0.030 -1.589 1.904 0.830 0.999 0.441 1.460 2.173 1.741 0.194 -0.651 2.215 1.199 0.210 0.632 0.000 2.255 -0.753 -0.755 0.000 2.035 1.586 0.989 1.194 1.849 1.347 1.255 +0 2.386 -0.512 -0.060 1.210 -0.400 0.455 2.552 1.582 0.000 0.835 -0.298 1.441 0.000 0.509 -0.067 1.123 2.548 1.094 -0.817 -1.099 3.102 0.905 0.766 0.987 0.837 0.583 0.708 0.791 +0 0.282 -1.559 0.832 1.498 0.252 1.201 0.585 -1.358 0.000 0.798 1.429 -1.324 0.000 1.738 0.831 0.471 2.548 1.323 1.070 -0.520 3.102 0.947 0.965 0.999 0.883 0.928 1.047 1.085 +1 1.055 -0.653 -0.688 1.296 -0.809 0.371 0.373 -0.524 0.000 0.787 0.061 0.616 0.000 1.358 0.636 -1.721 2.548 1.464 0.855 0.691 0.000 0.999 1.027 0.981 0.719 0.868 1.053 0.982 +1 0.764 -0.039 -1.021 0.735 -0.583 1.349 0.644 0.974 2.173 0.366 0.499 -1.700 0.000 0.328 0.885 -1.018 2.548 0.498 -0.895 -0.876 0.000 0.618 1.026 0.979 0.660 0.818 1.056 0.825 +1 0.674 1.048 -1.549 1.030 -0.231 1.428 0.094 1.159 1.087 0.581 0.258 -0.894 0.000 0.557 1.282 -0.225 0.000 0.777 -0.364 0.440 3.102 0.722 0.698 1.071 1.327 0.738 0.900 0.818 +1 0.865 0.412 1.511 1.103 -0.849 0.940 0.372 0.295 0.000 1.051 1.211 1.603 2.215 1.065 0.944 -0.097 0.000 0.762 -0.000 -1.141 3.102 0.868 0.886 1.150 0.882 0.735 0.895 0.827 +0 0.577 1.174 -0.049 0.676 1.238 0.518 -0.340 -1.677 0.000 0.588 0.596 -0.265 2.215 0.521 -0.443 0.503 2.548 0.466 1.110 -1.192 0.000 0.773 0.773 0.982 0.575 0.507 0.548 0.536 +1 1.043 0.347 -1.555 1.751 -1.009 0.621 -0.731 0.187 2.173 0.756 0.709 0.739 0.000 0.466 -0.459 1.640 0.000 0.692 0.460 0.434 1.551 0.891 0.916 0.985 0.805 0.503 0.807 0.762 +0 1.167 0.346 1.669 0.806 0.657 1.014 0.861 -0.300 2.173 0.668 -1.183 1.498 0.000 0.836 0.724 0.627 2.548 1.646 1.766 -0.994 0.000 3.654 2.091 1.062 1.144 0.852 1.445 1.187 +0 0.738 1.166 0.550 1.749 1.125 0.518 -2.507 -0.079 0.000 0.570 2.009 -0.984 0.000 1.234 2.294 -1.675 0.000 1.541 0.400 -0.447 3.102 0.805 0.644 0.988 1.211 0.133 0.769 0.838 +0 0.756 0.655 1.156 1.404 -1.559 0.774 -1.715 0.350 0.000 1.094 0.691 -0.786 2.215 1.153 -1.164 1.272 2.548 0.435 -0.812 -0.480 0.000 0.694 0.821 0.990 0.949 1.804 1.233 1.155 +1 0.906 -1.266 -1.600 0.320 0.493 0.449 -0.417 0.235 1.087 0.566 -1.789 -0.858 0.000 1.260 -0.577 -1.597 2.548 1.353 0.593 0.662 0.000 0.808 0.978 0.988 0.704 0.939 0.676 0.642 +1 2.672 0.822 0.926 0.589 0.806 0.830 0.373 -0.601 0.000 0.542 2.024 -0.923 0.000 0.742 0.124 -0.071 0.000 0.947 0.033 -1.593 1.551 1.093 0.762 0.979 0.909 0.684 0.764 0.725 +1 1.448 0.084 1.032 0.540 -0.388 0.478 0.655 0.683 0.000 0.838 0.233 -1.134 2.215 1.386 0.995 -0.519 2.548 0.959 0.283 -1.701 0.000 0.882 0.980 1.173 0.897 0.785 0.812 0.731 +0 1.926 -0.705 1.543 0.467 -0.408 1.291 0.433 -0.489 2.173 2.097 -0.491 0.939 2.215 0.685 0.859 -0.676 0.000 0.781 1.249 -0.895 0.000 0.277 0.572 1.291 1.107 2.609 1.454 1.259 +0 1.087 -0.256 0.022 0.810 1.147 0.819 0.556 -1.158 2.173 0.572 0.927 0.054 0.000 0.831 0.154 1.226 0.000 0.590 0.973 -0.564 3.102 1.015 1.024 1.103 0.730 0.438 0.724 0.688 +0 0.816 -1.556 0.435 1.133 -1.513 0.762 1.570 -0.253 0.000 0.725 0.120 0.753 1.107 0.863 -1.595 -0.495 0.000 1.516 0.658 -1.330 0.000 0.835 1.043 1.310 0.773 0.620 0.763 0.724 +0 0.864 -1.260 0.634 1.216 -0.279 1.181 -0.506 1.451 0.000 0.566 -1.475 -1.592 1.107 1.470 -1.296 -0.598 0.000 0.470 0.106 -0.304 0.000 0.821 0.792 1.041 0.637 0.450 0.571 0.590 +1 0.499 -1.286 0.496 2.036 1.324 2.429 -1.612 -0.519 0.000 2.190 -1.011 1.019 2.215 1.238 -0.684 -0.909 0.000 1.097 -0.444 1.672 3.102 0.836 0.644 0.985 0.827 0.855 0.881 0.859 +1 0.688 -0.530 -0.787 0.254 1.499 0.830 -0.125 -0.096 2.173 1.431 -0.336 1.510 0.000 0.673 -0.701 0.869 1.274 0.463 1.245 -0.738 0.000 1.470 0.981 0.987 0.788 0.773 0.889 0.771 +1 0.841 0.378 -0.212 0.727 -1.375 0.923 0.411 -1.478 0.000 1.128 1.205 0.007 2.215 0.496 -0.741 1.082 0.000 0.677 1.695 -1.014 0.000 0.930 0.905 0.987 0.797 0.737 0.826 0.722 +0 0.584 -1.556 0.625 1.531 0.713 0.577 -1.233 -1.165 1.087 0.474 -1.495 1.552 0.000 1.159 -1.297 -0.514 2.548 0.366 2.416 0.056 0.000 0.270 0.646 0.997 1.071 0.572 0.862 0.716 +0 0.883 -0.139 0.696 1.162 -0.065 0.789 1.363 1.520 2.173 0.872 0.291 -1.461 0.000 1.164 -0.175 -0.550 2.548 0.396 -0.195 0.220 0.000 0.791 0.902 0.988 0.752 1.542 1.111 0.906 +0 1.037 1.273 -1.294 1.058 0.496 1.203 -0.131 0.938 1.087 0.594 0.269 1.259 0.000 0.935 -2.331 -0.562 0.000 2.043 1.311 -0.121 3.102 2.499 2.110 1.450 0.957 2.091 2.122 1.884 +0 1.536 1.372 0.723 0.252 -0.832 1.002 1.644 -0.141 2.173 1.200 1.526 -1.259 2.215 1.571 1.369 -1.722 0.000 2.244 2.348 0.190 0.000 1.121 1.014 0.987 0.899 1.366 1.121 0.978 +1 0.859 -0.860 0.895 1.772 0.283 1.075 -1.054 -1.174 2.173 0.781 -0.546 1.593 0.000 0.535 -0.044 -0.141 2.548 0.371 -1.568 0.328 0.000 0.809 0.891 0.988 0.592 0.908 0.930 0.796 +0 0.600 -1.055 1.710 1.509 -0.510 1.877 -1.129 -1.277 2.173 1.506 -1.297 0.822 0.000 1.310 -1.552 0.258 0.000 1.778 -0.381 0.591 3.102 1.121 0.962 1.199 1.075 2.035 1.486 1.222 +1 1.438 -0.154 -0.687 0.817 -1.689 1.054 -1.212 0.700 0.000 1.104 0.641 -1.571 0.000 2.269 -0.533 0.404 2.548 0.735 0.482 -0.482 3.102 3.217 1.865 1.178 1.282 0.932 1.330 1.169 +1 0.770 1.247 1.158 1.514 1.604 1.379 -1.216 0.478 2.173 1.520 0.389 -0.619 1.107 1.197 0.098 -1.204 0.000 0.567 0.903 -0.365 0.000 0.858 0.894 0.988 3.779 2.627 2.603 1.929 +1 0.798 -0.208 1.463 0.716 -1.277 0.503 -0.974 -0.465 0.000 0.660 -0.680 0.400 0.000 0.846 0.029 -1.442 2.548 0.872 -1.263 0.590 0.000 0.878 1.088 0.985 0.624 0.363 0.638 0.757 +1 1.436 0.216 -1.109 1.003 -0.698 0.902 0.526 1.449 0.000 1.274 -0.118 0.834 2.215 2.421 1.046 0.129 0.000 1.727 0.062 -1.482 0.000 1.013 0.790 0.997 1.286 0.811 0.828 0.863 +1 0.670 -2.150 0.565 0.605 1.695 0.787 -0.437 0.553 0.000 1.003 -0.345 -1.156 2.215 0.737 0.943 -1.494 2.548 0.994 -1.036 0.125 0.000 0.967 1.045 0.990 0.964 0.741 0.910 0.846 +1 1.032 -0.507 -1.220 1.039 1.153 1.510 0.177 0.232 2.173 1.022 2.516 -1.134 0.000 0.940 0.647 1.359 2.548 0.764 -0.578 -0.997 0.000 2.865 1.785 1.209 1.409 1.323 1.690 1.552 +1 0.643 0.086 -1.641 0.362 -0.030 0.770 2.702 1.529 0.000 1.633 -1.075 -0.288 2.215 1.243 -0.219 -0.335 2.548 3.611 0.022 1.205 0.000 0.817 1.278 0.985 0.913 0.679 1.273 1.012 +1 1.483 -1.157 0.931 0.865 -0.763 1.774 0.917 -0.014 0.000 0.958 -0.218 1.465 0.000 1.495 0.265 -1.541 2.548 2.171 -0.638 -1.288 1.551 3.173 2.185 1.567 1.100 0.824 1.622 1.564 +0 0.533 -0.374 -1.210 0.965 1.034 0.494 -2.378 -0.537 0.000 0.739 0.737 -0.186 0.000 0.679 1.471 -0.815 0.000 1.569 0.610 0.685 3.102 0.755 0.861 0.986 0.973 0.724 0.892 0.907 +0 0.429 -1.663 -0.629 0.495 0.883 0.546 -0.122 -1.022 0.000 0.575 2.019 1.451 0.000 0.851 0.291 -0.590 0.000 1.203 -0.232 0.523 3.102 0.761 0.836 0.979 0.646 0.290 0.566 0.565 +1 1.347 -1.055 0.461 0.372 1.661 1.341 -2.340 -1.606 0.000 1.293 -0.799 -0.795 2.215 1.690 -1.193 -0.172 2.548 1.634 -0.826 1.147 0.000 0.848 1.151 0.988 1.005 0.922 0.956 0.823 +0 1.914 -1.517 0.373 0.144 -0.907 1.880 0.584 -0.992 2.173 0.566 0.833 1.743 0.000 1.093 -1.474 0.779 0.000 0.958 -0.684 0.889 3.102 0.939 1.208 0.983 2.438 1.780 1.587 1.580 +1 0.497 -0.833 1.149 1.709 0.131 0.971 0.756 -1.622 0.000 0.396 0.344 -0.548 0.000 0.408 2.066 0.218 0.000 0.901 0.577 1.156 3.102 0.840 1.001 1.014 0.883 0.655 0.872 0.793 +1 0.754 -0.001 1.460 0.978 0.473 0.530 2.321 -1.417 0.000 0.637 1.522 -0.595 0.000 0.814 0.115 -0.694 2.548 1.186 0.358 0.740 1.551 0.968 1.065 0.992 0.599 0.731 0.846 0.966 +1 0.606 0.238 0.945 1.266 -1.351 0.997 0.316 -0.192 1.087 0.615 0.053 1.523 0.000 0.848 -0.327 -0.248 0.000 1.253 -1.008 1.471 0.000 0.775 0.536 1.067 1.051 0.666 0.821 0.747 +0 0.500 1.415 -1.067 0.200 1.704 0.748 -1.263 1.115 0.000 1.002 -0.583 -0.082 1.107 1.301 -0.112 -0.788 2.548 1.054 -1.600 -1.432 0.000 1.108 1.267 0.995 0.679 0.778 0.993 0.924 +1 1.135 -0.308 0.574 0.275 -0.845 0.382 2.606 -1.453 0.000 0.494 -1.318 -1.453 1.107 0.886 -1.125 -0.441 2.548 1.093 -0.005 0.222 0.000 0.845 0.739 0.988 0.685 0.558 0.562 0.545 +1 0.349 1.255 -0.985 0.646 -0.931 0.801 0.927 -0.820 0.000 1.319 -0.162 0.855 0.000 0.517 -1.108 0.861 0.000 0.786 0.203 0.366 3.102 0.696 0.686 0.987 0.648 0.440 0.465 0.582 +1 1.631 0.190 -0.315 1.076 0.096 0.758 0.498 -1.246 2.173 0.645 1.546 1.183 0.000 1.556 0.483 1.021 1.274 0.788 0.971 -1.275 0.000 0.769 0.836 1.002 1.203 1.204 1.033 0.965 +0 2.055 -0.883 0.806 1.140 0.667 1.059 -1.429 -0.809 2.173 0.482 -1.413 0.191 0.000 0.879 -1.058 1.620 2.548 1.301 -0.448 -1.242 0.000 0.856 0.873 0.973 0.871 0.993 1.137 0.957 +0 1.559 -1.783 0.402 0.418 -0.779 0.772 0.259 1.646 2.173 0.789 1.013 -1.347 0.000 0.587 -0.185 0.296 2.548 0.424 2.461 -0.731 0.000 0.925 1.005 0.989 1.602 0.811 1.047 1.477 +1 0.293 2.157 -0.273 0.628 -1.059 1.154 -1.415 0.559 0.000 1.966 0.441 -1.233 2.215 0.835 0.453 0.672 0.000 0.649 -0.824 0.245 0.000 0.728 0.749 0.985 0.815 0.934 0.878 0.751 +0 1.387 0.395 -1.230 1.583 -0.159 1.134 -0.359 1.243 0.000 1.060 1.433 0.314 2.215 1.873 1.004 -1.528 2.548 0.877 1.566 -0.351 0.000 2.578 1.912 1.689 1.324 1.517 1.422 1.299 +0 1.404 -1.323 -1.366 1.188 -0.624 0.397 -2.917 -0.844 0.000 0.743 -1.432 1.255 2.215 1.820 -1.778 0.828 0.000 1.068 -1.122 0.228 3.102 1.719 1.118 1.110 0.935 0.644 0.774 0.879 +0 1.434 0.604 1.658 1.044 -0.194 1.498 2.030 0.028 0.000 1.551 0.294 -1.154 1.107 1.656 0.056 1.292 0.000 0.738 -0.768 -1.730 0.000 0.797 0.864 1.687 1.150 0.988 0.855 0.865 +0 1.175 -0.859 1.664 0.437 -0.777 1.091 -0.473 -0.915 2.173 1.137 -1.338 1.101 2.215 0.533 -2.031 0.746 0.000 1.583 1.255 0.410 0.000 3.219 2.342 0.993 0.784 1.763 1.713 1.384 +1 1.932 0.823 1.054 0.749 0.397 0.542 -0.752 -0.503 0.000 0.448 -0.920 0.286 2.215 0.410 0.928 -0.078 0.000 1.611 0.266 -1.226 0.000 0.913 0.686 0.986 0.914 0.526 0.663 0.719 +0 0.786 0.991 -1.721 0.926 -0.158 0.557 0.261 1.297 2.173 0.786 1.550 -0.360 0.000 0.449 -1.112 1.008 2.548 0.722 0.231 -1.056 0.000 0.903 1.032 1.167 0.934 0.532 0.804 0.738 +0 1.107 0.538 -1.297 0.803 0.959 1.191 0.950 -0.308 2.173 1.461 0.697 1.326 2.215 0.535 -0.241 0.550 0.000 0.648 0.322 -0.673 0.000 0.618 0.872 1.169 1.152 1.946 1.063 0.844 +0 0.662 -0.450 1.677 0.496 0.276 0.896 1.570 1.189 0.000 1.231 -0.736 -0.532 2.215 0.789 1.958 -1.401 0.000 0.930 0.939 0.452 0.000 0.919 0.868 0.987 0.956 0.832 1.268 1.000 +1 0.608 -1.260 0.484 0.594 -1.028 1.014 1.378 -1.740 0.000 1.258 0.629 -0.073 2.215 0.611 1.707 -0.218 0.000 0.779 -0.187 1.037 0.000 1.177 0.939 0.987 1.062 0.686 1.176 1.136 +1 0.321 -1.394 1.010 0.690 -0.991 0.462 1.260 -1.023 0.000 0.537 0.456 -0.572 0.000 1.028 -0.680 0.418 1.274 1.492 0.419 1.103 3.102 0.874 0.951 0.988 0.745 0.835 0.877 0.771 +0 0.904 0.816 0.585 0.300 -0.584 0.869 0.916 -0.872 2.173 0.412 -1.497 1.436 0.000 0.541 2.124 -1.282 0.000 0.588 -0.628 0.910 3.102 2.502 1.437 0.985 0.927 1.039 1.082 0.907 +1 0.451 -0.318 1.440 1.212 -1.287 3.069 -1.133 -0.251 0.000 1.661 1.728 1.537 0.000 1.819 -2.332 1.036 0.000 1.707 -0.030 -1.591 3.102 0.873 0.887 0.998 0.833 0.617 0.845 1.348 +0 0.396 -1.290 -0.945 0.868 1.010 0.486 0.698 -1.297 1.087 0.348 -1.152 0.163 0.000 0.664 -0.224 0.595 0.000 1.279 0.797 -0.363 3.102 0.456 0.861 0.986 1.763 0.631 1.368 1.044 +1 0.925 1.663 1.353 0.569 -1.333 0.441 -1.412 1.673 0.000 1.047 0.328 -0.853 0.000 0.904 -0.381 0.772 0.000 1.952 0.582 0.294 1.551 0.922 1.193 0.992 0.649 0.492 0.714 0.801 +1 1.289 0.147 -1.295 1.515 -1.500 1.028 -0.945 1.042 0.000 1.408 0.797 -0.004 1.107 0.929 -0.670 -0.277 2.548 0.565 0.408 -1.587 0.000 1.223 1.092 0.969 1.650 1.085 1.159 1.147 +1 0.384 -2.007 -1.648 0.629 -0.075 0.800 -0.170 -0.212 1.087 0.811 -1.081 1.690 2.215 0.926 -0.686 -1.119 0.000 1.390 0.568 0.971 0.000 1.541 1.166 0.992 0.764 1.307 0.949 0.810 +1 0.937 -0.781 1.214 0.742 0.051 0.558 0.022 0.114 2.173 0.918 -1.582 1.491 0.000 1.185 -0.304 -1.718 1.274 1.067 0.174 -0.889 0.000 0.854 0.866 1.001 0.775 1.026 0.717 0.693 +1 1.304 -1.634 -1.187 0.228 0.938 1.030 -1.150 0.121 1.087 1.676 -0.531 1.393 0.000 1.383 -1.051 -0.379 0.000 0.526 2.376 1.403 0.000 2.443 1.276 0.987 1.034 0.691 1.041 0.911 +1 1.044 -0.440 0.700 0.313 0.247 0.729 -2.426 -1.354 0.000 0.646 -1.016 0.206 2.215 0.586 0.370 -1.119 0.000 1.043 0.530 1.114 3.102 0.820 1.028 0.984 0.588 0.884 1.092 1.077 +1 2.080 -0.087 -0.289 1.684 -1.713 0.781 0.547 1.462 2.173 0.642 1.252 0.557 2.215 0.472 0.501 -1.018 0.000 1.039 -0.688 0.334 0.000 0.926 0.978 2.486 1.554 0.855 1.163 0.960 +1 0.949 1.439 1.214 1.132 0.723 1.165 1.059 -0.690 2.173 0.901 1.085 1.066 2.215 0.842 0.660 -1.307 0.000 0.646 1.454 -0.198 0.000 0.810 0.833 0.985 1.329 1.508 0.979 0.816 +0 1.062 0.518 -0.468 0.982 0.363 0.849 0.447 -1.435 2.173 0.537 -0.094 -1.231 2.215 0.892 0.303 0.469 0.000 1.242 -0.183 0.973 0.000 0.605 1.062 0.990 0.784 0.331 0.727 0.731 +1 1.709 1.279 1.052 0.163 -0.870 0.889 -0.099 -0.822 1.087 0.641 0.464 -1.623 0.000 0.892 -1.224 0.047 0.000 0.917 -0.003 0.664 0.000 0.915 0.974 0.991 0.596 0.813 0.848 0.740 +1 0.807 0.503 1.346 0.801 -1.045 0.839 -0.392 -1.424 2.173 1.522 -0.022 0.296 0.000 0.756 -0.391 -0.254 0.000 0.725 -0.916 1.003 3.102 0.852 0.789 0.987 0.877 0.735 0.875 0.868 +0 0.491 0.347 -1.264 1.723 -1.184 1.500 0.902 0.215 0.000 1.450 -0.014 -1.689 2.215 1.075 1.346 0.902 2.548 0.852 1.088 -0.276 0.000 0.815 0.940 0.976 0.901 1.447 1.248 1.392 +1 0.599 -0.405 0.601 0.209 -0.102 0.938 0.079 -1.629 2.173 0.783 1.071 0.808 2.215 2.003 0.049 0.556 0.000 2.261 -0.157 -0.626 0.000 1.104 1.098 0.986 0.637 1.223 0.950 0.822 +1 0.894 0.066 -1.633 0.668 0.738 1.339 0.358 -0.323 2.173 0.801 -0.388 1.408 0.000 0.931 -0.349 0.672 0.000 0.580 1.062 -1.351 3.102 0.815 0.822 0.988 1.120 0.866 0.941 0.818 +0 0.494 1.436 -1.058 2.502 -1.318 0.722 0.050 0.362 0.000 0.841 -0.528 0.947 0.000 0.948 -1.570 0.346 0.000 1.543 -0.165 -1.671 3.102 0.971 1.043 0.980 1.459 0.797 1.447 1.668 +1 0.981 -0.917 -0.497 3.311 -0.103 1.579 -0.201 1.577 0.000 0.361 -1.123 -1.244 1.107 0.944 -1.009 1.072 0.000 0.650 0.600 -1.226 3.102 1.412 0.982 0.981 1.228 0.473 0.840 1.222 +1 1.876 0.581 -0.727 0.859 -0.518 0.589 0.890 0.940 2.173 0.846 -0.403 1.613 2.215 0.738 0.874 -0.273 0.000 1.000 -0.337 1.151 0.000 1.146 0.952 0.977 1.109 0.941 1.029 0.893 +1 2.237 -1.294 -1.213 0.199 -1.634 0.849 -0.112 0.654 2.173 0.565 -0.972 0.987 0.000 0.647 -0.244 -0.436 2.548 0.996 -1.192 0.342 0.000 0.577 0.710 0.996 0.745 0.772 0.931 0.809 +1 0.826 0.578 1.616 0.634 0.648 0.932 0.395 0.021 1.087 0.483 0.952 -0.443 0.000 0.703 0.453 -1.442 0.000 1.198 1.320 1.350 3.102 0.734 0.851 0.982 0.558 1.255 0.773 0.691 +0 1.790 -0.021 -0.393 0.500 -1.332 0.656 0.242 0.822 2.173 0.741 1.101 -1.512 2.215 0.620 -0.267 1.138 0.000 0.467 0.895 -0.283 0.000 0.719 0.715 0.987 0.982 1.001 0.849 0.693 +0 0.542 1.145 -1.048 1.179 0.272 0.813 1.206 -1.634 2.173 0.442 0.594 -1.185 0.000 0.272 0.973 1.408 0.000 0.657 0.057 0.691 0.000 0.723 0.744 1.028 1.039 1.529 0.964 0.764 +0 0.466 0.286 1.089 1.319 -0.360 0.609 0.165 0.310 2.173 0.835 0.994 -1.219 2.215 0.700 0.889 0.864 0.000 1.302 2.046 -1.381 0.000 1.275 0.981 1.047 0.664 1.130 0.933 0.867 +0 2.307 0.643 0.837 0.836 1.167 1.330 1.339 -1.080 0.000 0.567 0.532 -0.083 2.215 0.547 1.169 0.702 2.548 0.568 0.806 -0.497 0.000 0.714 0.882 0.981 0.581 0.444 0.656 0.920 +1 0.711 1.297 -1.040 0.813 0.875 1.568 0.960 -1.460 2.173 0.637 -0.367 0.392 0.000 0.930 1.874 -0.736 0.000 2.544 0.971 0.712 3.102 0.917 1.047 1.041 0.984 1.968 1.203 0.972 +0 0.866 -0.016 0.807 0.882 1.110 0.730 0.968 0.121 1.087 0.854 0.119 -1.392 0.000 0.604 -0.523 -0.606 2.548 0.563 -2.466 -0.410 0.000 0.894 1.100 0.987 0.879 0.870 0.760 0.746 +0 1.080 -1.796 0.564 1.837 -0.031 0.926 -0.098 -1.706 2.173 0.653 -0.948 -1.453 0.000 0.919 0.167 -0.389 0.000 0.511 -0.299 -0.164 1.551 0.931 0.989 0.997 0.684 0.722 1.167 0.984 +0 1.484 -0.046 -1.002 0.657 -1.362 2.276 -0.243 1.192 0.000 1.508 1.026 0.040 2.215 2.877 0.616 -0.740 0.000 1.742 0.559 0.881 1.551 1.990 1.785 0.976 1.609 1.044 1.337 1.245 +0 1.019 -0.195 -1.032 0.725 -0.206 0.599 -0.240 0.356 2.173 0.746 0.911 0.391 2.215 0.699 -1.836 -1.460 0.000 1.194 1.359 -0.225 0.000 0.480 0.963 0.979 1.029 0.615 0.754 0.793 +0 0.380 -0.510 -1.706 1.566 -1.321 1.666 0.654 0.846 0.000 1.116 0.332 0.100 2.215 1.736 -0.132 -0.997 2.548 1.095 -1.983 -0.935 0.000 4.946 2.935 1.000 0.846 1.288 1.931 1.627 +0 0.445 -1.332 -0.835 2.036 -1.278 0.691 -0.843 1.692 2.173 0.653 -1.435 -0.272 0.000 1.340 -0.341 0.606 2.548 1.247 -1.643 0.482 0.000 0.795 0.975 0.996 0.779 1.036 0.868 0.947 +1 0.475 -1.115 -1.001 1.167 -1.629 3.671 -0.743 0.358 0.000 3.563 1.157 -1.488 2.215 1.045 2.429 -0.898 0.000 0.661 0.343 1.117 0.000 1.550 1.008 0.987 4.829 0.881 3.016 2.788 +1 0.699 1.625 -1.430 1.074 1.427 1.004 1.033 0.400 0.000 0.448 0.045 -0.779 0.000 1.269 -0.043 1.718 2.548 1.243 0.240 0.159 0.000 0.755 0.972 0.990 1.555 0.904 1.023 1.113 +0 1.079 0.753 -1.345 0.585 -0.316 0.799 0.679 0.481 2.173 0.562 0.900 1.499 0.000 0.518 -0.408 -0.859 2.548 0.547 0.918 0.976 0.000 0.334 0.627 0.984 0.669 0.890 0.715 0.614 +1 0.784 0.785 -0.352 1.044 1.388 1.318 -0.060 -1.513 0.000 1.585 0.267 0.010 2.215 1.744 0.576 0.558 0.000 0.794 1.312 0.856 0.000 0.722 1.006 1.253 0.836 1.011 0.863 0.813 +1 0.588 0.381 -1.398 0.577 -1.697 0.682 -0.130 1.347 0.000 1.718 1.170 -0.363 0.000 0.770 0.004 0.039 1.274 1.099 0.938 0.110 0.000 0.743 0.782 0.997 1.005 1.021 0.889 1.033 +0 1.328 0.723 -1.549 0.442 0.934 0.695 0.745 -0.142 0.000 1.373 0.967 0.677 2.215 2.287 0.449 -1.104 2.548 0.639 -0.068 0.893 0.000 0.927 0.900 0.987 0.899 1.942 1.049 0.917 +1 1.194 0.175 -0.627 0.865 1.711 0.779 -1.010 -1.636 2.173 1.275 0.203 0.571 0.000 0.882 0.980 -0.214 0.000 0.747 -1.030 0.530 3.102 0.634 0.759 1.210 1.025 0.754 0.820 0.777 +1 0.539 -0.219 -1.499 1.333 0.589 0.672 0.839 1.036 2.173 0.509 1.277 -1.368 0.000 1.462 0.211 -1.059 0.000 1.064 -2.342 -0.420 0.000 0.835 1.041 1.118 0.842 0.749 0.826 0.838 +1 0.393 -0.960 -1.464 0.845 0.155 2.371 0.017 -0.575 0.000 3.276 1.408 1.466 0.000 2.470 -0.519 -0.037 2.548 1.681 1.608 1.013 0.000 1.425 1.447 0.987 1.114 0.890 2.211 2.423 +0 0.475 1.613 -0.811 1.170 1.030 1.208 2.203 -1.023 0.000 0.594 -1.646 1.073 0.000 1.231 1.123 0.749 2.548 0.614 1.455 -0.083 0.000 1.036 1.350 1.029 1.151 1.107 1.333 1.079 +0 0.572 0.671 -0.659 0.233 1.209 0.889 0.716 0.138 2.173 0.641 1.764 -1.474 0.000 0.586 1.468 0.780 0.000 0.935 0.042 -1.623 1.551 0.848 1.087 0.990 0.587 1.020 0.788 0.783 +1 0.427 1.496 -1.304 0.448 -0.090 0.978 1.157 0.663 2.173 1.012 0.240 -1.327 0.000 0.825 -0.659 -1.352 0.000 1.020 0.553 1.456 3.102 0.942 0.967 0.987 0.939 0.743 1.086 0.886 +1 0.416 -1.640 0.468 1.435 -0.778 0.955 -1.340 0.849 2.173 1.182 -1.132 0.172 2.215 1.864 -1.418 -1.251 0.000 0.702 -0.214 -1.436 0.000 0.904 1.335 0.987 1.053 0.909 1.049 0.901 +1 0.963 0.668 1.355 0.980 -0.253 0.620 1.028 -1.223 2.173 0.283 1.696 1.417 0.000 0.397 -0.774 -0.524 2.548 0.788 -0.025 0.279 0.000 0.813 0.784 1.336 0.794 0.767 0.672 0.608 +1 0.551 1.117 0.119 0.616 1.554 0.690 0.442 0.628 0.000 1.442 0.712 -0.888 1.107 0.659 -0.841 0.979 0.000 1.094 -0.183 -1.428 0.000 0.972 0.650 0.988 0.897 0.959 0.911 0.765 +1 0.349 -0.501 1.044 0.972 0.565 0.404 0.600 0.866 0.000 1.168 0.257 -0.478 2.215 0.513 0.529 -1.025 0.000 0.473 -0.558 0.491 3.102 0.811 0.836 0.985 0.604 0.608 0.663 0.593 +0 1.797 -0.504 -0.458 0.147 0.716 1.233 -0.853 0.963 0.000 0.500 -1.473 0.575 0.000 1.555 0.495 -0.924 2.548 1.131 -0.157 1.554 3.102 0.798 0.833 0.985 0.834 0.885 1.128 1.013 +0 1.713 0.072 0.879 1.254 -0.532 0.885 -1.776 1.086 0.000 0.907 -2.102 -0.849 0.000 0.811 0.318 -0.465 2.548 1.499 -1.525 1.608 0.000 0.796 0.832 1.940 1.035 0.586 0.964 1.148 +1 0.597 0.711 0.211 0.412 -1.388 0.903 -0.637 0.129 2.173 1.371 -0.349 -1.119 2.215 1.132 -1.200 1.319 0.000 0.600 -1.230 -1.327 0.000 1.191 1.019 0.988 1.325 1.495 1.233 1.026 +0 1.122 0.029 0.755 0.437 -0.552 0.562 -0.061 -0.915 0.000 1.011 -0.815 -0.472 2.215 1.532 1.102 1.212 2.548 1.514 -0.634 1.703 0.000 0.999 0.889 0.987 1.018 2.079 1.066 0.892 +1 0.571 -0.418 -0.193 1.144 0.658 0.823 -0.256 -1.364 2.173 0.721 -0.792 0.970 0.000 0.527 -2.386 0.446 0.000 0.730 -0.595 -0.741 0.000 0.946 0.921 0.983 0.500 0.832 0.706 0.678 +1 1.315 0.375 -0.329 0.574 -0.806 1.210 2.197 -1.210 0.000 0.698 0.430 0.560 0.000 2.124 0.316 1.149 2.548 1.356 -0.322 0.545 3.102 2.774 2.337 0.986 1.290 0.829 1.669 1.368 +0 2.645 0.416 0.161 0.199 1.039 1.712 -0.476 -1.507 0.000 0.884 -0.828 -0.854 2.215 2.169 0.877 0.672 1.274 0.772 -0.458 1.676 0.000 0.476 0.883 0.989 0.845 2.101 1.494 1.400 +0 0.378 -1.194 1.414 1.637 -0.548 1.673 -1.406 0.685 2.173 1.071 -0.259 -0.926 0.000 0.511 1.649 -1.352 0.000 0.884 -1.594 1.519 0.000 1.197 0.821 1.069 1.353 1.151 1.368 1.199 +1 1.245 0.617 1.198 0.967 -1.512 0.961 1.253 -0.247 0.000 0.281 1.391 1.563 2.215 0.946 -0.470 1.706 2.548 0.749 1.505 0.513 0.000 0.894 0.735 0.988 0.751 0.626 0.881 0.822 +1 1.054 -1.099 0.942 0.632 0.379 0.997 2.270 0.751 0.000 1.936 0.131 -0.815 2.215 1.000 0.123 -1.543 2.548 0.698 -0.611 1.174 0.000 0.791 1.041 0.976 0.865 0.902 0.989 0.824 +0 1.932 1.526 0.238 1.138 0.527 1.119 -0.093 -1.162 0.000 0.756 -0.537 -1.702 0.000 0.648 1.308 1.333 2.548 0.646 0.370 -1.588 0.000 1.018 0.950 0.980 0.790 0.961 1.065 1.436 +1 1.857 1.302 -1.007 1.235 1.690 2.371 0.758 0.903 1.087 1.341 0.703 -0.665 0.000 1.350 0.850 -0.131 2.548 0.653 1.559 -0.620 0.000 0.740 0.682 1.369 2.061 1.801 1.499 1.318 +1 1.969 0.641 0.818 0.880 1.480 1.070 0.316 -1.055 2.173 0.851 0.602 -0.611 0.000 0.589 -0.530 0.219 2.548 0.623 -0.936 1.445 0.000 1.307 1.015 1.026 0.821 1.014 0.975 0.898 +1 0.726 -0.378 1.391 1.567 -1.332 1.097 0.761 1.068 0.000 0.676 -0.042 -1.015 0.000 1.704 -0.225 -0.394 2.548 1.802 0.219 0.336 1.551 0.835 0.813 0.987 1.080 0.888 0.947 0.806 +0 1.502 -0.500 -0.017 0.423 1.280 0.679 -0.362 1.679 2.173 0.930 -0.141 1.193 2.215 0.647 0.292 -0.431 0.000 1.290 -0.591 -0.546 0.000 0.553 0.974 1.016 0.920 0.515 0.725 0.702 +0 0.877 1.154 1.697 0.389 0.981 0.629 1.136 0.239 2.173 0.316 0.042 1.384 2.215 0.539 0.291 0.101 0.000 0.761 1.732 -1.044 0.000 0.930 0.736 0.980 0.487 0.679 0.577 0.584 +0 0.919 -0.044 -0.793 1.492 -1.632 0.764 -0.610 1.576 0.000 0.516 -0.826 0.783 0.000 1.518 0.000 -0.203 2.548 0.889 -0.698 -0.429 0.000 0.890 0.862 1.111 1.029 0.859 0.834 0.820 +0 1.040 -1.422 -1.407 1.740 1.243 1.386 1.277 -0.213 0.000 1.367 -1.200 0.628 0.000 1.177 0.487 -0.778 0.000 1.229 0.341 -1.632 3.102 0.959 0.899 1.275 1.186 0.931 0.846 0.852 +1 1.712 0.523 -0.076 1.063 -0.488 1.122 0.778 1.561 2.173 0.486 0.646 0.783 0.000 0.635 1.269 -1.399 0.000 0.481 0.490 -0.562 3.102 0.848 0.764 0.996 0.475 0.736 0.910 0.763 +0 2.331 0.735 0.119 0.647 1.025 1.242 -0.924 -1.485 1.087 0.400 -0.469 0.928 0.000 0.747 0.415 1.235 2.548 1.398 -0.440 -1.120 0.000 0.938 0.860 1.241 0.834 1.181 1.361 1.088 +0 1.218 1.112 -1.160 0.469 1.294 0.741 0.178 -1.347 2.173 0.389 0.685 0.313 1.107 0.582 0.650 0.778 0.000 0.966 -0.867 1.013 0.000 0.841 0.970 0.988 0.698 0.815 0.667 0.758 +0 0.760 -0.145 1.081 0.668 1.098 0.543 0.083 -0.538 0.000 0.639 0.441 -1.313 0.000 1.039 1.232 -0.061 1.274 0.777 -1.967 1.085 0.000 0.837 0.909 0.979 0.848 0.263 0.960 0.862 +1 0.625 -1.161 1.191 0.876 -0.007 1.068 0.048 -0.815 0.000 0.929 0.759 1.559 2.215 0.671 0.689 1.068 0.000 0.675 0.299 -1.053 3.102 0.703 0.742 0.987 0.850 0.529 0.994 0.841 +1 1.574 -0.335 -0.280 0.594 -1.169 0.506 0.511 1.575 0.000 0.759 0.985 -1.586 2.215 0.557 -0.446 0.593 0.000 1.854 1.158 0.654 3.102 0.893 0.966 0.988 1.008 0.986 0.989 0.853 +0 0.935 -1.681 -1.001 0.610 -0.004 0.602 -0.799 1.193 0.000 0.556 -0.639 0.313 0.000 0.763 -0.228 -0.846 2.548 1.222 0.451 1.585 0.000 0.921 1.067 0.992 0.905 0.687 1.046 0.912 +0 3.361 1.523 1.110 0.621 0.654 1.575 2.176 -0.739 0.000 1.231 1.488 -0.202 0.000 1.155 1.682 1.309 0.000 0.782 0.928 0.213 3.102 0.987 0.999 0.981 0.835 0.376 0.617 1.013 +1 2.148 -0.714 -0.349 1.279 0.164 0.401 2.841 1.575 0.000 0.696 -1.656 1.723 0.000 0.839 0.137 -1.601 2.548 0.609 -0.948 1.355 0.000 0.582 0.931 1.024 0.574 0.510 0.710 0.815 +1 1.413 -1.015 -1.027 1.429 -1.424 1.032 -0.242 0.949 2.173 1.044 -0.175 -0.485 0.000 0.665 0.231 0.501 0.000 1.064 0.818 0.737 3.102 1.031 0.904 0.989 1.602 0.753 1.334 1.172 +0 0.912 -0.411 0.378 0.243 0.751 0.885 -1.215 0.439 1.087 0.369 -0.448 1.142 0.000 1.277 -0.358 -1.491 2.548 1.479 1.111 -1.301 0.000 0.893 0.896 0.992 0.929 1.423 0.937 0.788 +0 0.412 0.827 1.322 0.941 -0.562 0.871 0.053 0.184 2.173 0.733 -0.056 -1.342 0.000 0.986 -2.088 1.613 0.000 1.232 0.519 -0.546 3.102 1.081 0.975 0.990 0.772 0.742 0.811 0.700 +0 2.024 -1.353 -0.612 0.156 1.579 0.497 -1.243 1.738 0.000 0.743 0.450 0.938 2.215 0.642 -0.328 1.016 2.548 0.489 -1.433 0.086 0.000 0.884 0.879 0.983 0.905 0.315 0.957 0.802 +1 0.918 -1.014 1.192 1.667 0.842 2.428 -1.842 -0.829 0.000 2.075 -1.258 0.853 1.107 0.642 -0.573 -0.712 1.274 0.399 -2.105 0.309 0.000 1.384 1.059 0.991 0.968 1.280 1.511 1.515 +0 1.313 1.007 -1.461 1.551 -0.896 0.747 -0.422 0.797 2.173 0.739 -0.582 -0.087 0.000 0.774 -2.290 0.484 0.000 1.001 -0.402 1.477 1.551 0.777 0.709 0.989 1.052 0.526 1.095 0.976 +0 1.914 0.188 0.807 0.732 1.394 1.397 0.523 -0.695 2.173 0.711 1.926 -0.811 0.000 1.370 1.118 1.148 1.274 0.478 -0.002 -0.065 0.000 1.003 1.028 0.987 0.908 1.824 1.254 1.115 +1 0.509 -1.066 0.302 0.718 -0.854 1.088 0.863 0.578 0.000 0.709 -2.171 -1.519 0.000 1.432 0.108 -0.518 2.548 1.389 -0.779 -1.515 3.102 0.663 0.803 0.983 0.785 1.032 0.903 0.771 +1 1.134 0.932 -1.637 0.443 1.517 0.969 -0.732 -0.403 2.173 0.923 -0.239 0.522 0.000 0.643 -0.524 1.351 0.000 0.470 -0.474 -1.632 3.102 0.827 1.110 0.987 0.513 0.642 0.799 0.767 +1 1.046 -0.298 -0.199 1.641 0.558 1.040 -0.490 -1.353 0.000 0.692 -1.069 1.723 0.000 0.839 -0.928 -0.764 1.274 1.454 -2.111 0.545 0.000 0.854 0.770 1.145 0.626 0.515 0.606 0.796 +1 0.345 -0.956 -0.146 0.973 -1.306 0.572 1.835 0.584 0.000 1.230 0.774 -1.403 2.215 0.749 1.253 -0.379 1.274 1.040 -0.224 1.296 0.000 0.750 0.773 0.987 0.732 0.867 1.003 1.008 +0 0.807 -0.118 1.385 1.270 -0.937 1.163 0.994 0.627 2.173 0.742 0.754 -1.190 0.000 0.330 0.446 0.302 0.000 1.389 0.796 -0.394 3.102 0.792 0.901 1.216 1.410 1.071 1.014 0.911 +0 1.369 1.925 0.721 0.634 -0.125 0.548 0.580 -1.371 0.000 0.740 1.201 -1.598 2.215 1.125 0.946 -0.253 2.548 0.408 -0.383 1.011 0.000 0.723 0.830 0.981 0.893 0.913 0.728 0.735 +0 2.508 0.626 -0.440 0.163 1.067 0.958 -0.070 1.410 1.087 0.664 0.255 0.317 2.215 0.447 1.721 1.528 0.000 0.479 2.356 -1.492 0.000 0.324 0.882 0.984 1.402 0.998 0.965 0.930 +1 0.493 -0.629 -0.194 0.345 -0.718 1.526 -0.553 -1.251 2.173 1.513 -2.145 0.550 0.000 0.795 -0.206 1.725 0.000 1.247 0.455 -0.386 0.000 1.126 0.927 0.975 1.188 1.223 0.920 0.822 +0 1.860 -0.161 -0.642 0.998 -1.204 0.504 1.528 1.289 0.000 1.045 0.121 0.951 2.215 0.355 1.412 -0.508 0.000 0.731 0.826 0.053 1.551 0.760 0.919 0.984 0.825 0.671 0.872 0.889 +1 0.476 -0.032 0.723 0.508 -0.595 0.513 0.239 -0.606 2.173 0.336 2.667 1.584 0.000 0.365 2.358 -0.938 0.000 0.646 1.194 0.765 3.102 0.652 0.849 0.987 0.925 0.692 0.711 0.830 +1 0.829 -0.029 -0.027 0.810 1.322 0.893 -0.978 1.012 2.173 0.904 0.108 -1.026 0.000 1.265 -0.550 -0.578 0.000 0.626 -1.261 -1.070 3.102 0.922 1.072 1.065 0.726 0.783 0.675 0.680 +1 0.345 2.270 0.764 0.492 -0.533 1.388 1.350 -1.639 0.000 0.889 2.735 0.526 0.000 1.005 0.153 0.430 2.548 1.081 1.006 0.545 0.000 0.780 0.717 0.979 0.686 0.914 0.602 0.539 +1 0.927 -0.744 -0.197 0.262 -1.506 0.511 -1.821 1.546 0.000 0.670 0.317 -1.538 2.215 1.150 0.281 0.244 2.548 0.572 -1.239 0.467 0.000 0.698 1.050 0.986 0.675 0.932 0.907 0.788 +0 0.361 2.091 1.046 1.170 -0.362 0.672 0.024 -1.610 0.000 0.794 -0.035 0.640 2.215 0.510 0.036 1.433 2.548 0.568 0.670 -0.414 0.000 0.914 0.903 0.990 0.738 0.444 0.665 0.672 +0 1.848 0.639 0.656 0.623 -1.653 0.655 0.057 -0.019 2.173 0.675 0.524 1.398 0.000 0.779 -2.109 -1.088 0.000 1.384 -1.005 -1.511 0.000 0.793 0.747 1.298 0.951 0.595 0.858 1.117 +0 0.886 -0.192 1.596 0.957 -1.705 0.486 -0.095 -1.289 0.000 0.662 -1.336 0.546 1.107 0.944 0.549 0.164 2.548 1.018 -0.741 -0.351 0.000 0.916 0.929 0.989 0.899 1.013 0.941 0.851 +0 1.469 -0.493 1.613 0.508 -1.050 0.713 0.625 -0.139 0.000 1.139 0.105 1.221 0.000 0.892 -0.438 -0.380 2.548 1.388 0.936 -0.898 3.102 1.093 0.889 0.985 0.812 0.849 0.750 0.735 +1 0.685 -0.890 0.054 1.076 -1.305 0.889 0.655 0.481 1.087 1.233 1.214 -1.364 2.215 0.357 1.026 0.004 0.000 0.660 1.546 1.281 0.000 0.547 0.727 1.118 1.293 1.601 1.277 1.002 +1 0.675 0.531 1.568 0.658 0.266 1.407 -1.345 1.189 0.000 1.020 -0.438 0.010 2.215 0.728 0.328 -0.415 0.000 1.733 0.613 -1.157 0.000 0.803 1.052 0.990 0.929 0.997 0.913 0.808 +0 0.343 1.638 -1.553 1.313 -0.794 1.419 0.318 0.109 2.173 0.874 1.139 1.040 2.215 0.809 0.496 -1.205 0.000 0.596 1.746 1.425 0.000 0.846 0.777 0.980 1.140 1.415 0.994 0.885 +0 2.759 -0.717 0.845 0.717 1.360 1.243 -1.454 -0.623 0.000 0.720 0.186 1.424 2.215 1.244 -0.865 -0.395 0.000 1.044 -0.304 -1.167 3.102 0.742 0.849 0.985 0.863 0.607 0.972 1.112 +1 1.054 0.201 -0.831 0.557 0.435 0.983 -0.047 1.435 1.087 0.910 -1.366 0.004 0.000 1.351 -0.018 -0.223 2.548 0.981 0.158 -1.720 0.000 0.875 0.871 0.988 0.979 1.432 0.968 0.858 +0 0.508 -0.259 -0.653 0.521 0.648 0.909 -0.359 0.138 2.173 1.000 0.241 1.471 2.215 0.988 0.403 -1.370 0.000 0.411 -2.192 -0.984 0.000 1.577 1.237 0.990 0.864 1.379 1.043 0.916 +0 0.749 -1.210 -1.109 0.680 1.308 0.801 0.935 -0.865 0.000 1.246 -1.171 0.021 2.215 1.411 -2.214 1.336 0.000 1.429 -1.601 0.689 0.000 0.952 0.955 0.990 0.975 0.861 0.906 0.860 +1 1.174 -0.384 -0.297 0.137 -0.145 2.363 1.128 -1.631 0.000 1.885 0.084 0.139 1.107 1.756 -0.930 0.403 0.000 1.685 -1.591 -0.822 0.000 1.905 1.717 1.000 0.785 0.936 1.376 1.135 +1 0.639 0.277 0.556 1.983 -0.042 0.475 1.314 -1.125 0.000 0.980 0.411 -1.320 2.215 0.659 -1.605 1.007 0.000 0.661 0.699 -1.739 0.000 1.318 0.982 0.991 1.196 0.628 0.850 0.861 +0 0.746 -0.928 -0.648 0.947 -1.706 1.091 -0.120 0.023 2.173 0.690 -0.313 1.455 0.000 0.607 -1.377 -1.218 0.000 1.024 0.183 0.856 3.102 0.913 0.795 0.986 1.068 0.786 0.827 0.762 +0 0.529 2.248 -1.281 0.669 -1.276 0.807 -0.629 0.699 0.000 1.448 1.510 -0.547 2.215 1.123 0.734 1.094 2.548 0.782 -0.154 1.611 0.000 0.929 0.916 0.973 0.856 1.446 1.303 1.105 +1 0.568 -2.042 0.675 1.082 -1.296 0.586 -0.526 -0.310 0.000 0.386 -0.933 -1.353 0.000 0.527 1.366 1.174 2.548 0.861 -0.741 0.344 3.102 0.844 1.040 1.063 0.763 0.849 1.100 0.916 +0 0.468 -1.269 1.199 0.828 -1.160 0.870 0.357 -0.229 0.000 0.621 -0.018 0.481 0.000 0.767 -1.829 1.160 0.000 1.424 -0.297 -1.203 3.102 0.979 1.046 0.983 0.629 0.716 0.727 0.684 +1 0.883 -1.111 0.012 1.921 1.456 1.481 0.146 -0.912 0.000 1.301 -0.756 -0.182 0.000 2.130 -0.712 1.507 2.548 1.191 -0.562 0.552 0.000 0.999 0.680 1.739 1.087 0.860 0.895 0.910 +0 1.558 -0.802 1.346 0.662 0.604 1.497 -0.342 0.306 0.000 1.383 0.616 -1.416 0.000 1.125 0.543 -0.580 1.274 0.670 1.490 -1.160 0.000 0.829 0.664 0.980 0.607 0.433 0.805 0.784 +1 1.809 -0.320 -1.566 0.846 1.009 1.637 -0.703 0.419 2.173 1.638 0.986 -1.128 0.000 0.861 -0.690 -0.775 0.000 0.658 0.184 1.101 3.102 0.675 1.040 1.254 0.659 0.824 0.975 0.846 +0 0.929 1.278 -1.016 0.900 1.008 0.962 0.648 1.590 2.173 1.085 0.441 -0.144 0.000 0.585 -0.718 0.903 2.548 0.735 -0.701 -0.642 0.000 0.953 0.852 1.227 0.903 0.913 0.904 0.892 +1 1.185 -2.115 0.376 0.245 -0.246 0.813 -1.042 -1.166 2.173 0.617 1.995 1.418 0.000 0.951 -0.809 0.125 0.000 1.370 -0.489 1.417 1.551 0.734 0.888 1.000 1.036 0.853 0.823 0.729 +1 1.011 0.570 0.528 0.777 1.664 0.862 0.060 0.562 2.173 0.973 1.172 -1.006 0.000 1.025 0.161 -1.216 2.548 0.405 0.189 0.038 0.000 1.022 0.694 1.049 0.759 1.172 0.864 0.780 +1 0.904 0.222 -1.270 0.105 1.392 1.670 0.427 0.698 1.087 0.714 -0.920 -1.700 0.000 1.725 0.219 -0.504 2.548 1.750 -1.701 -1.628 0.000 0.844 1.827 0.992 1.360 1.879 1.847 1.404 +1 0.957 -0.444 -1.638 1.378 -0.931 0.651 -0.112 0.674 2.173 0.477 -0.842 1.521 0.000 1.248 -0.443 -0.083 2.548 1.370 -0.813 0.358 0.000 0.915 0.803 0.986 1.068 0.740 0.831 0.748 +0 0.594 0.279 -1.625 1.771 -0.917 1.524 -0.010 1.530 2.173 0.852 0.126 0.925 0.000 1.608 1.806 0.647 0.000 3.537 -0.813 -0.512 0.000 1.208 0.860 0.984 1.211 1.499 1.013 1.001 +1 0.572 0.728 -1.670 1.083 -0.776 1.771 0.957 -0.247 0.000 1.376 -1.777 1.384 0.000 1.789 1.006 1.232 2.548 1.392 0.896 -0.749 0.000 0.861 0.615 0.989 1.137 1.650 1.491 1.248 +0 1.637 -1.729 -1.164 0.583 -0.943 0.927 -1.060 -0.584 2.173 1.142 -0.902 0.906 0.000 1.464 -0.509 0.600 2.548 1.332 -1.326 1.390 0.000 0.869 0.829 1.001 0.779 1.325 0.978 0.983 +0 0.432 0.215 0.388 0.992 -1.255 1.053 0.114 -0.370 0.000 1.460 -0.002 0.873 2.215 0.598 0.305 -1.737 2.548 1.178 -1.404 -1.404 0.000 1.585 1.092 0.991 0.962 0.725 0.973 0.864 +1 1.223 -2.041 -1.470 0.378 0.467 0.516 0.542 0.929 0.000 0.667 0.626 0.181 1.107 1.132 -0.804 0.166 0.000 1.132 -0.854 -1.009 0.000 1.016 0.900 0.988 1.644 0.805 1.057 1.238 +0 0.413 1.608 1.617 1.680 0.990 0.856 -1.138 -0.474 0.000 0.314 -0.305 0.497 0.000 0.746 0.419 -1.623 2.548 0.866 -0.175 -0.799 3.102 0.969 1.057 0.987 0.850 0.466 0.643 0.855 +1 1.769 0.418 -1.228 2.167 -1.310 1.623 -1.297 -0.016 0.000 0.449 1.276 -1.631 0.000 1.589 -0.567 1.439 2.548 1.215 -0.754 0.979 0.000 1.470 1.082 0.959 1.162 1.089 1.110 1.009 +1 0.381 -0.934 -0.282 0.599 1.164 0.932 0.765 -0.446 0.000 0.457 -0.162 -0.589 0.000 1.129 0.656 1.492 1.274 1.428 0.361 1.075 3.102 0.699 1.066 0.990 0.678 0.382 0.814 0.706 +0 1.013 0.645 -0.404 0.556 1.484 0.955 -0.003 1.294 2.173 1.126 -0.633 -0.576 1.107 0.551 -0.725 0.314 0.000 0.655 0.461 -1.087 0.000 0.794 0.856 1.031 0.940 1.597 0.951 0.767 +1 0.767 1.091 1.651 0.969 0.502 0.673 2.045 -1.729 0.000 1.523 -0.148 0.124 2.215 0.791 -1.558 1.286 0.000 1.634 0.010 -0.577 3.102 4.271 2.535 1.027 1.158 0.850 1.764 1.414 +0 0.385 1.122 0.766 2.140 0.351 0.635 1.000 1.399 2.173 0.931 1.848 -1.119 0.000 1.256 0.922 -1.006 0.000 0.482 0.718 -0.559 3.102 0.780 1.035 0.988 0.740 0.576 0.797 0.905 +1 0.932 -0.963 -0.625 0.936 0.623 1.200 -0.913 0.441 1.087 1.287 -0.418 -1.459 0.000 0.815 -1.228 -1.692 0.000 0.697 0.179 -0.812 1.551 0.816 0.717 1.168 0.855 1.050 0.998 0.873 +0 0.847 -0.443 0.433 0.864 -1.357 0.468 -1.590 1.458 0.000 0.549 1.268 -0.121 2.215 0.633 -1.122 -1.097 0.000 0.630 -0.249 -0.960 3.102 0.751 0.595 1.184 0.957 0.597 0.857 0.754 +0 0.675 -1.974 0.779 2.568 0.053 0.632 -0.248 -1.670 2.173 0.537 1.365 -0.663 0.000 0.662 -1.191 -1.728 2.548 0.913 0.806 -1.489 0.000 0.650 0.884 1.110 0.998 0.449 1.124 1.526 +0 0.677 -1.548 -1.146 0.709 0.861 0.491 -1.646 -0.412 2.173 0.932 -0.740 0.888 2.215 0.496 0.743 -1.152 0.000 0.565 -0.695 1.679 0.000 0.855 0.837 0.987 0.679 1.027 0.696 0.612 +0 1.210 -0.953 0.543 0.969 -0.087 0.997 -0.552 0.820 2.173 1.342 -1.199 -1.598 2.215 1.502 -0.674 -0.994 0.000 1.071 -1.103 -0.304 0.000 0.914 1.032 0.980 0.778 1.514 1.053 0.970 +1 1.176 -0.064 0.093 0.749 -1.443 0.802 -1.073 -1.533 0.000 0.679 -0.115 -1.672 2.215 0.568 1.043 -0.156 2.548 0.394 2.007 -0.025 0.000 1.095 0.828 1.278 0.754 0.785 0.791 0.753 +0 0.873 0.939 -0.974 0.887 -1.246 0.467 0.859 -0.483 2.173 1.190 0.556 0.843 0.000 0.403 0.314 0.024 2.548 1.132 -0.759 1.474 0.000 1.473 0.908 0.981 0.681 0.278 0.751 0.762 +1 1.297 1.167 -0.085 1.130 -0.562 1.288 0.345 1.358 2.173 0.605 1.197 -0.947 0.000 0.900 1.041 0.567 0.000 0.442 0.747 -0.548 3.102 0.847 0.930 0.982 0.485 0.820 1.081 0.872 +0 0.541 -0.261 0.984 0.284 1.303 0.853 0.475 -0.641 2.173 0.677 1.057 1.026 0.000 0.738 1.215 0.765 2.548 0.566 -0.147 -1.361 0.000 0.867 0.992 0.978 1.256 1.040 1.114 0.956 +0 1.096 0.912 -0.939 1.643 -0.367 1.055 -0.577 1.088 0.000 0.657 0.714 -1.591 2.215 0.712 0.835 0.773 0.000 0.792 -0.991 -0.582 3.102 0.956 1.008 0.985 0.922 0.891 0.763 0.928 +0 1.075 -2.015 -0.381 1.527 -0.759 0.657 0.589 1.185 1.087 0.719 -0.626 0.872 0.000 0.317 -0.341 1.554 0.000 0.432 -1.313 0.824 3.102 0.434 0.603 0.991 0.657 0.762 1.136 0.905 +1 2.221 -2.021 1.376 0.428 -0.882 0.648 -0.493 -0.630 2.173 0.818 -2.141 -0.095 0.000 0.764 -1.251 0.628 2.548 0.807 -1.540 -1.461 0.000 1.016 1.087 1.209 0.816 0.888 0.952 0.855 +0 1.203 -1.061 0.774 0.600 1.731 0.379 -0.515 0.369 1.087 0.510 0.993 1.043 0.000 1.041 -0.054 -0.907 2.548 1.134 1.021 -1.052 0.000 0.946 0.940 0.985 0.897 0.737 0.698 0.784 +1 1.489 -1.282 -0.121 0.714 -0.748 0.883 -0.242 0.822 2.173 0.622 -1.083 -1.577 2.215 1.034 -0.843 1.213 0.000 0.723 -0.774 -1.156 0.000 0.806 0.840 0.995 0.834 1.029 0.877 0.756 +1 0.664 -1.265 1.159 0.892 -1.532 1.205 -0.434 -1.470 2.173 2.423 0.616 0.297 0.000 1.348 1.300 1.308 2.548 1.169 0.530 -0.512 0.000 0.725 0.887 0.977 1.259 1.950 1.780 1.546 +0 1.127 -0.113 0.938 0.363 0.945 0.987 -0.891 -0.693 2.173 0.705 -0.734 1.579 0.000 0.373 -1.144 -0.395 0.000 1.306 -0.720 0.493 3.102 0.898 0.921 0.985 0.813 1.054 0.996 0.826 +0 0.876 -0.480 -1.407 0.419 1.075 1.880 1.646 1.672 0.000 2.026 -0.998 -0.076 0.000 0.758 1.140 0.742 2.548 1.493 -0.429 0.331 3.102 1.013 0.833 0.992 0.763 0.867 1.056 0.976 +0 0.428 -0.097 -1.504 0.850 0.201 0.977 1.417 0.578 2.173 0.434 0.007 -0.959 0.000 1.198 -1.237 -0.528 2.548 0.389 -0.396 1.533 0.000 0.438 1.067 0.990 1.160 2.793 1.314 0.991 +1 0.529 -1.463 -0.067 1.024 1.616 0.476 -2.255 0.566 0.000 1.429 -0.271 -0.994 2.215 0.742 -0.243 0.199 2.548 0.942 2.407 1.356 0.000 0.711 0.824 1.018 1.083 0.963 0.995 0.854 +1 1.532 0.109 -0.312 0.712 1.355 0.810 -1.470 1.470 2.173 1.141 -1.268 -0.592 0.000 0.605 0.119 0.272 0.000 0.545 0.284 1.174 3.102 1.040 0.859 1.444 1.431 0.757 0.915 0.879 +1 0.879 -0.125 1.452 1.540 -0.019 0.712 1.124 -1.717 0.000 0.997 -1.006 -0.860 0.000 1.370 0.200 -0.243 2.548 0.968 0.020 0.989 0.000 0.911 0.990 1.563 0.820 0.502 0.646 0.693 +1 0.762 0.161 -0.436 0.600 0.846 0.931 -1.238 -0.524 0.000 1.280 -0.502 1.093 2.215 1.407 -0.050 -1.661 0.000 1.132 -1.167 0.179 0.000 0.936 1.173 0.990 0.825 1.014 1.037 0.856 +1 1.280 0.360 -0.905 1.982 -1.294 1.508 1.474 0.401 0.000 1.123 -0.007 1.260 2.215 0.488 1.204 -1.315 2.548 0.730 0.442 0.414 0.000 0.798 0.918 0.988 1.170 0.804 0.994 1.234 +1 1.933 -0.432 0.170 0.753 -0.480 1.187 -0.420 -1.379 2.173 1.389 -0.477 1.435 1.107 0.724 -1.118 -1.502 0.000 1.249 0.343 0.194 0.000 1.336 1.101 0.984 1.380 1.077 1.149 0.993 +1 1.605 -1.031 0.505 0.629 0.458 0.639 0.150 -1.654 1.087 0.678 -0.218 -1.014 0.000 1.116 -0.682 -0.883 2.548 1.120 0.104 0.939 0.000 1.132 0.880 0.979 1.108 0.833 0.888 0.795 +1 1.527 -2.084 -1.126 0.474 1.595 0.494 -1.911 0.207 0.000 0.757 -0.308 0.442 0.000 1.006 -1.275 1.465 0.000 0.710 -0.449 -0.343 3.102 1.153 0.770 0.987 0.664 0.435 0.556 0.624 +1 1.382 0.293 -1.403 0.937 0.834 0.657 -0.098 0.246 2.173 0.923 0.761 0.719 2.215 0.286 -0.683 -0.843 0.000 0.519 1.213 -0.563 0.000 0.579 0.717 1.422 1.030 0.704 0.789 0.668 +0 1.161 -0.071 -0.808 2.102 -1.214 1.242 -1.930 0.629 0.000 0.933 0.192 1.532 0.000 1.547 0.553 -0.253 2.548 1.168 1.205 0.673 3.102 1.514 1.051 0.996 1.018 0.881 0.944 0.991 +1 1.036 -0.229 1.722 0.800 -0.127 1.059 -0.066 1.238 2.173 1.570 -0.265 -0.567 0.000 0.600 0.606 0.671 0.000 0.596 -0.523 0.457 3.102 1.521 0.926 1.256 0.953 0.595 0.928 0.817 +1 0.902 -0.805 -1.174 0.160 -0.660 0.899 -0.505 -1.556 2.173 0.559 0.863 -0.295 0.000 1.310 0.389 0.203 2.548 1.771 -1.283 -1.520 0.000 0.951 0.918 0.989 0.716 1.503 1.260 1.064 +1 1.028 -0.108 -0.566 0.278 -0.387 1.293 0.840 -0.862 2.173 1.516 0.291 0.369 0.000 2.391 0.338 1.631 1.274 0.714 1.197 1.039 0.000 1.063 1.599 0.989 1.324 1.788 1.536 1.276 +1 0.503 2.280 0.135 2.093 0.688 0.440 1.226 -0.608 1.087 0.302 2.283 -1.362 0.000 0.842 1.077 1.579 0.000 0.947 -0.294 -1.358 3.102 0.933 0.775 1.000 0.892 0.738 0.901 0.787 +1 1.001 -0.717 -1.244 0.461 -0.004 0.838 0.150 0.451 2.173 0.490 -1.210 1.469 0.000 0.849 0.017 -1.364 0.000 0.588 0.198 -1.702 0.000 0.865 1.085 0.988 0.920 0.809 0.859 0.762 +0 0.421 1.109 -1.720 0.612 -0.693 0.525 -1.433 1.046 2.173 0.525 -1.608 -0.742 0.000 0.826 1.061 1.029 1.274 1.137 -0.689 -0.165 0.000 0.658 0.834 0.994 0.799 1.402 0.985 0.848 +1 0.696 -0.207 1.712 0.642 1.307 0.897 -2.334 -0.200 0.000 1.147 0.290 0.983 2.215 1.291 0.130 -0.798 0.000 1.611 0.731 -1.482 3.102 0.821 0.881 0.982 0.719 1.041 0.699 0.640 +0 0.352 0.510 -1.026 1.753 1.285 0.732 0.148 -0.141 2.173 0.746 -0.080 -0.844 2.215 1.124 0.916 1.092 0.000 0.471 1.064 -0.496 0.000 0.802 0.934 0.984 0.974 0.656 0.855 0.751 +1 0.602 -1.540 0.219 0.247 -0.972 1.735 -1.302 0.422 2.173 1.171 -0.820 -1.633 0.000 0.957 -1.241 -1.008 0.000 0.550 -0.070 1.415 3.102 0.966 0.689 0.990 1.008 1.053 1.114 0.913 +1 0.793 1.167 -0.754 0.871 0.495 0.698 -0.540 -1.732 0.000 1.072 1.082 0.178 0.000 1.384 0.930 1.518 2.548 1.080 1.298 -0.241 0.000 0.604 0.993 1.040 0.862 0.761 0.840 0.732 +1 2.264 -0.102 -0.530 1.173 -1.559 0.746 -0.195 0.654 2.173 0.534 1.295 0.905 2.215 0.550 -0.236 1.638 0.000 0.600 1.133 -1.001 0.000 0.720 0.863 1.805 1.338 0.809 1.065 0.857 +1 1.971 -1.403 -0.427 0.870 -0.710 0.744 -1.138 1.454 0.000 0.651 -0.848 0.885 2.215 0.593 -1.311 1.705 2.548 0.449 0.124 1.026 0.000 0.698 0.536 0.995 0.821 0.483 0.735 0.739 +0 0.407 1.071 -0.437 2.735 -0.183 1.358 -0.042 -1.633 1.087 0.667 -0.888 1.308 0.000 0.777 0.553 1.337 0.000 1.396 -0.761 0.252 3.102 0.923 0.942 0.988 2.853 1.587 2.253 1.873 +1 0.792 -0.440 1.480 0.889 0.278 0.665 1.189 -0.391 1.087 0.526 -0.718 -1.473 1.107 0.689 -1.261 0.672 0.000 0.818 1.367 -1.182 0.000 1.969 1.233 1.027 1.119 1.218 1.020 0.901 +1 0.403 -1.974 -0.535 0.365 -0.793 1.021 -1.137 1.394 2.173 0.924 -0.156 -1.062 2.215 1.332 -0.932 -0.065 0.000 0.481 0.395 0.793 0.000 0.952 0.986 0.978 0.980 1.359 0.951 0.802 +1 0.708 -0.678 -0.475 1.088 1.546 0.677 -1.424 1.716 2.173 0.755 -0.036 0.576 2.215 1.093 -0.202 -0.044 0.000 1.324 -0.121 -1.126 0.000 1.099 0.909 1.178 0.787 1.204 0.885 0.779 +1 0.716 0.384 -1.463 0.910 -0.178 0.855 -0.307 1.305 2.173 0.447 0.630 -0.456 0.000 0.746 -0.505 -0.760 0.000 1.146 0.573 0.736 1.551 0.614 1.029 1.025 0.704 0.749 0.725 0.676 +1 0.870 -0.355 -1.737 1.753 -1.128 0.753 2.075 1.196 0.000 1.360 -1.039 0.427 2.215 0.749 0.914 0.060 0.000 1.135 -0.704 -0.220 0.000 0.858 0.872 0.987 0.540 0.845 0.874 0.735 +0 0.515 0.358 1.191 0.697 0.412 0.958 0.213 0.583 1.087 1.166 1.676 -0.754 0.000 0.736 0.564 -0.968 0.000 1.227 1.194 1.731 1.551 0.870 0.840 0.991 0.791 1.234 1.053 1.098 +1 0.395 -1.107 1.010 1.202 -0.101 0.770 -1.068 -0.687 0.000 0.919 0.665 1.366 2.215 1.340 0.167 -1.538 2.548 1.043 -0.229 0.228 0.000 1.153 1.219 0.987 0.967 0.661 1.007 0.877 +1 2.351 0.281 -0.077 0.436 0.494 0.883 0.257 1.178 1.087 0.806 1.256 -0.558 0.000 1.343 0.389 -1.677 0.000 0.703 0.977 0.817 0.000 0.929 0.967 0.984 1.178 1.026 0.950 0.870 +1 0.934 0.895 1.092 0.309 -1.536 0.826 0.822 -0.252 0.000 1.079 0.044 1.121 2.215 0.965 0.424 -1.370 2.548 0.522 1.745 -0.746 0.000 0.803 0.881 0.985 0.963 0.878 0.883 0.824 +1 0.505 -2.290 -1.683 1.270 0.423 0.606 -2.375 -0.850 0.000 0.409 -1.894 -1.080 0.000 1.359 -0.211 1.112 2.548 0.767 -1.975 0.509 0.000 0.980 0.672 1.050 1.341 0.791 0.931 0.870 +1 0.620 1.533 1.443 0.961 -0.090 1.542 -1.041 -1.611 0.000 0.903 1.421 1.006 0.000 2.225 0.541 -0.440 2.548 1.512 0.329 0.366 3.102 0.822 0.877 1.050 1.004 0.941 0.932 0.808 +1 1.229 0.345 -0.481 0.613 -1.691 0.907 0.630 1.118 2.173 1.004 -0.356 -1.131 2.215 1.712 0.813 0.048 0.000 1.573 1.698 1.297 0.000 1.967 1.493 1.067 0.746 1.459 1.346 1.111 +1 1.748 0.020 -0.467 1.432 -1.328 1.175 -0.281 1.065 2.173 0.670 -1.150 1.641 0.000 0.448 -0.275 0.625 0.000 0.508 -0.744 -1.009 3.102 0.768 0.769 1.533 0.745 0.822 0.991 0.846 +0 0.647 0.768 0.969 1.633 0.469 1.752 0.531 0.690 0.000 0.802 1.808 0.910 0.000 2.705 -0.469 -1.247 2.548 3.334 -0.735 -0.848 3.102 1.859 3.034 0.990 1.562 0.914 2.357 1.825 +0 0.404 -0.198 -0.839 1.507 1.275 0.533 0.024 -0.455 2.173 0.778 -0.837 0.368 2.215 0.833 -0.766 1.730 0.000 0.754 -1.146 -0.490 0.000 0.829 0.807 1.021 0.823 0.773 0.708 0.666 +0 3.288 1.124 -1.203 0.257 -0.304 1.738 0.389 0.381 1.087 0.492 -0.131 0.039 0.000 0.823 -0.271 1.211 0.000 1.106 0.224 -1.722 3.102 0.853 0.985 0.988 0.795 1.394 1.392 1.156 +1 0.647 -0.415 -1.729 1.521 -0.576 0.785 -0.185 0.496 2.173 0.807 -1.535 1.667 2.215 0.827 0.286 -0.778 0.000 0.737 -1.555 0.550 0.000 1.391 1.093 1.185 1.014 1.348 0.938 0.861 +1 0.435 0.675 0.884 0.872 -0.188 0.499 0.407 -0.129 0.000 0.475 0.901 1.713 2.215 1.140 0.902 -1.383 2.548 0.739 1.260 1.045 0.000 0.970 0.894 0.990 0.777 0.270 0.686 0.665 +1 0.543 0.339 -0.448 1.041 -1.225 0.406 -0.494 -1.530 0.000 0.379 0.720 0.664 2.215 0.748 -0.776 0.213 0.000 0.917 -1.017 0.686 3.102 1.006 0.776 0.987 0.763 0.610 0.628 0.590 +1 0.645 1.238 1.271 0.650 0.202 0.899 0.922 -0.624 2.173 0.905 0.352 0.974 0.000 1.798 0.353 1.645 0.000 1.388 -0.374 -0.347 3.102 1.114 1.282 0.992 0.911 0.929 1.080 0.894 +0 1.504 1.813 1.651 0.977 -0.922 0.675 0.369 -0.324 0.000 1.203 1.246 0.318 0.000 0.737 0.991 -1.451 0.000 0.820 0.218 0.782 1.551 1.173 0.849 1.232 0.885 0.194 0.718 0.760 +1 0.449 -0.835 -0.988 1.179 -0.174 1.605 -0.745 1.189 0.000 1.212 1.049 -0.438 2.215 1.018 0.565 -0.787 0.000 0.735 -1.206 0.892 0.000 0.872 0.894 0.994 1.626 0.686 1.592 1.229 +1 1.278 -1.050 -1.423 1.866 -1.037 0.608 0.045 0.198 2.173 1.119 -0.433 1.031 2.215 0.432 -0.169 -0.696 0.000 0.811 0.497 0.748 0.000 0.682 0.720 0.996 1.474 0.880 1.213 1.000 +1 1.317 0.291 -0.656 1.144 -0.130 0.850 -0.117 1.182 1.087 0.852 1.104 1.554 2.215 0.381 1.370 -0.210 0.000 0.464 1.691 0.321 0.000 0.459 0.844 0.987 1.079 0.933 1.012 0.789 +1 1.113 1.527 1.484 0.505 -0.585 0.695 -0.094 -0.666 2.173 0.390 -0.226 -0.082 0.000 1.022 -0.104 0.977 2.548 0.822 0.735 0.890 0.000 0.713 0.766 0.995 0.952 1.046 0.901 0.744 +1 0.583 0.917 0.940 1.274 -1.702 1.024 -0.073 0.110 2.173 0.718 -0.368 -0.791 2.215 0.797 0.362 0.858 0.000 1.026 0.265 -1.297 0.000 0.931 1.020 0.990 0.846 0.936 0.867 0.754 +0 0.873 -1.649 0.437 0.731 1.410 1.224 -0.545 -0.187 1.087 0.536 1.192 -1.452 0.000 0.987 0.717 -0.770 0.000 0.484 -1.980 1.100 0.000 0.689 0.855 0.992 1.096 0.801 0.936 0.981 +1 0.783 -0.201 0.450 0.614 0.187 1.944 -0.076 -1.253 2.173 1.710 -0.816 0.652 1.107 1.110 -2.328 -0.103 0.000 1.087 -1.467 1.293 0.000 0.898 1.439 0.982 1.587 2.854 1.840 1.648 +0 1.517 -0.242 -0.299 0.723 -0.159 0.855 -0.660 1.607 2.173 0.875 -0.081 0.852 2.215 0.395 -1.363 -1.150 0.000 0.454 -0.804 -1.513 0.000 0.196 0.674 0.986 1.220 0.885 0.944 0.729 +0 1.311 0.108 -1.666 0.365 -0.507 0.821 -0.182 -0.349 2.173 1.114 -0.454 0.999 2.215 0.759 2.213 -1.587 0.000 1.009 -1.002 0.467 0.000 0.846 1.506 0.986 0.854 1.334 1.317 1.106 +1 0.619 1.689 -1.406 1.007 -0.411 0.710 1.836 0.233 0.000 0.985 -0.012 -1.431 2.215 1.787 0.102 1.299 0.000 1.612 1.160 0.830 0.000 0.919 0.851 0.985 1.467 0.686 0.993 0.986 +1 0.763 0.739 0.345 0.712 1.100 0.965 -0.157 -1.258 2.173 0.581 0.568 -0.852 0.000 0.969 0.235 0.858 0.000 1.094 -1.311 -1.039 3.102 0.887 0.983 0.987 1.040 0.863 0.863 0.775 +0 1.394 -1.038 -1.604 1.092 1.128 0.676 -0.637 0.517 2.173 0.368 -1.611 -1.475 0.000 1.282 0.085 -0.473 2.548 0.642 -1.286 -0.273 0.000 0.560 0.839 1.074 0.953 1.003 0.941 0.777 +0 0.905 0.948 0.036 1.082 -1.163 0.841 0.468 1.122 1.087 0.691 -0.403 -0.040 0.000 0.565 0.362 0.596 0.000 0.700 1.814 1.603 0.000 0.890 0.962 1.209 1.059 1.130 0.833 0.749 +0 0.775 0.464 1.354 0.991 0.188 1.498 0.949 0.419 2.173 2.314 0.365 -1.302 0.000 0.845 0.306 -1.571 2.548 0.818 -0.172 -0.343 0.000 1.463 0.865 1.053 0.858 1.437 1.347 1.089 +0 0.526 -0.687 1.554 2.766 -0.764 0.881 1.487 0.927 0.000 0.987 -0.148 0.712 2.215 0.849 0.786 -1.491 2.548 0.877 -2.044 1.140 0.000 1.811 1.231 1.452 1.360 1.029 1.061 1.363 +1 0.991 0.004 0.705 1.195 -0.739 0.639 -0.749 1.691 2.173 0.743 -2.249 1.451 0.000 1.105 0.962 0.385 0.000 0.877 0.302 -1.044 3.102 3.797 2.172 1.453 1.040 0.680 1.352 1.172 +0 0.363 -1.732 -0.789 2.071 1.102 0.662 -1.157 0.205 0.000 0.644 -1.213 0.958 2.215 0.957 -0.371 -0.962 0.000 0.859 1.400 -0.255 0.000 1.363 1.012 1.191 1.051 0.642 0.720 0.817 +0 0.529 -0.252 -0.961 1.650 -0.564 0.924 -1.192 0.098 2.173 1.720 -0.150 1.353 0.000 0.968 1.435 1.724 0.000 1.087 0.326 -0.767 3.102 0.968 0.895 0.981 1.609 1.198 1.387 1.186 +1 1.227 0.492 0.443 1.480 -1.238 1.484 0.400 -1.114 2.173 0.804 2.285 0.043 0.000 0.369 0.330 0.814 2.548 1.282 -0.980 1.703 0.000 0.624 0.651 1.863 1.317 0.909 1.106 1.059 +1 0.604 -1.295 0.069 0.793 0.783 0.758 -0.485 -1.104 2.173 0.747 -0.168 0.700 2.215 1.424 0.157 -0.467 0.000 0.375 1.645 -1.681 0.000 1.096 1.011 0.986 1.305 1.120 1.051 1.202 +0 1.566 -0.173 0.736 1.850 -1.315 0.733 0.444 -1.518 2.173 1.041 0.453 0.138 0.000 0.877 -1.071 0.416 0.000 1.936 1.121 -1.301 0.000 1.063 0.904 2.268 1.357 0.888 0.998 0.979 +0 0.397 -0.803 0.027 1.017 -0.058 0.490 -0.044 1.621 0.000 0.676 -0.539 -1.050 2.215 1.301 1.268 -0.577 2.548 0.706 -0.213 0.803 0.000 0.887 0.936 0.987 2.764 1.204 1.754 1.438 +1 0.606 -1.310 1.567 0.917 -0.246 1.486 2.334 0.093 0.000 1.601 -0.851 -1.301 2.215 1.417 -0.446 1.714 1.274 1.524 -0.723 0.884 0.000 0.765 0.874 1.031 0.920 0.728 0.821 0.765 +0 1.559 -0.187 -1.727 0.333 1.738 0.647 -0.253 -0.359 1.087 1.119 0.421 0.512 0.000 0.977 -0.301 0.921 2.548 1.915 0.972 -0.799 0.000 1.273 1.059 0.984 0.767 0.906 0.774 0.776 +1 0.624 -0.479 0.230 1.307 -0.850 1.333 -0.620 -0.358 0.000 1.790 0.135 1.596 2.215 1.533 0.116 0.931 2.548 0.649 -0.872 1.013 0.000 0.961 1.431 1.034 1.245 0.992 1.318 1.092 +0 0.915 1.588 -1.124 1.047 -1.461 1.061 1.106 1.244 2.173 0.591 0.995 -0.688 0.000 0.991 1.064 0.223 1.274 1.342 2.022 -0.074 0.000 1.073 0.808 0.975 1.025 1.018 0.909 0.893 +0 0.368 1.543 0.216 1.509 -1.572 0.305 -0.777 1.080 2.173 0.627 -0.336 -1.081 2.215 0.478 0.754 -0.453 0.000 1.119 0.413 0.381 0.000 0.567 0.713 1.031 1.098 0.615 0.873 0.746 +0 1.269 -0.307 -1.573 0.426 0.500 0.770 -0.155 0.422 0.000 0.779 -1.450 -1.536 2.215 0.634 -1.304 0.235 0.000 0.949 -0.294 -0.613 3.102 0.883 0.769 0.987 0.776 0.739 0.773 0.726 +1 1.984 0.777 0.494 0.578 -0.956 0.469 1.264 1.164 2.173 0.553 1.389 -1.713 1.107 0.643 -1.235 -1.324 0.000 1.435 0.705 -1.168 0.000 0.762 0.995 1.431 0.950 0.394 0.721 0.794 +1 0.284 1.928 0.052 1.889 1.004 0.906 -2.170 -0.222 0.000 1.468 0.631 -0.971 2.215 2.292 0.130 0.430 0.000 0.993 -0.086 -1.440 3.102 4.024 2.385 0.996 1.299 0.614 1.978 1.840 +1 1.517 -0.218 -1.492 0.985 1.528 0.904 -0.639 0.343 2.173 0.665 1.220 0.307 0.000 0.414 -1.732 1.467 0.000 0.939 -0.335 -0.995 0.000 0.899 1.009 0.996 0.675 0.647 0.869 0.818 +0 2.636 0.295 -1.045 2.473 -0.724 1.982 -0.327 0.751 2.173 0.626 -0.162 0.125 0.000 0.770 -0.139 1.366 2.548 0.708 -1.922 1.675 0.000 1.405 0.983 1.012 2.683 0.821 1.703 1.481 +1 2.015 -0.460 0.973 1.126 0.680 0.796 -0.749 -1.239 2.173 1.262 -0.171 -0.298 2.215 0.888 1.153 -0.822 0.000 0.961 0.314 1.730 0.000 0.893 1.097 0.989 1.338 1.189 1.135 1.064 +0 0.791 0.797 0.013 0.266 -1.042 0.850 -1.778 0.691 0.000 0.597 2.123 -1.468 0.000 1.134 1.726 -0.248 0.000 1.332 0.934 -1.221 3.102 0.987 0.879 0.986 0.694 0.961 0.726 0.664 +0 0.803 2.166 0.242 0.871 1.314 0.741 1.405 -0.247 2.173 0.563 0.229 1.150 0.000 0.322 0.471 1.535 0.000 0.595 -1.646 -0.634 0.000 0.892 0.604 0.986 0.951 1.079 0.959 1.223 +1 0.585 -1.843 -0.910 1.232 0.606 1.188 -2.283 0.372 0.000 1.033 0.340 -1.432 1.107 0.444 -1.106 0.079 2.548 0.824 -1.012 -1.327 0.000 1.740 0.989 1.152 1.597 0.940 1.418 1.171 +0 1.398 0.743 -0.171 0.585 -0.429 0.568 -0.732 1.420 2.173 0.422 -1.919 -1.366 0.000 0.527 -1.652 0.132 0.000 1.077 0.459 1.389 3.102 0.708 1.082 0.984 1.450 0.572 0.984 1.165 +0 0.534 0.725 0.560 2.122 0.940 2.734 0.648 -0.848 2.173 1.059 0.826 -0.460 0.000 4.288 0.213 1.023 2.548 0.435 0.136 -1.249 0.000 0.655 0.788 0.991 1.708 4.332 2.398 1.795 +0 1.160 0.434 -1.459 1.680 1.470 1.069 1.285 -0.135 1.087 0.818 2.849 1.073 0.000 0.596 0.143 -1.021 0.000 0.562 0.095 0.862 0.000 0.633 0.928 0.989 0.818 0.561 1.052 0.814 +0 0.879 -0.837 -0.121 0.823 -0.525 1.227 -0.833 -1.071 0.000 1.174 1.004 0.787 0.000 1.610 0.220 0.692 2.548 1.789 -0.932 -1.623 0.000 1.124 0.822 0.992 0.953 0.537 1.057 0.960 +1 0.324 -0.596 0.920 1.207 -1.088 1.154 -0.055 1.276 2.173 0.967 1.614 0.190 0.000 0.644 0.821 -0.513 2.548 0.729 -0.005 -0.497 0.000 1.202 0.741 0.989 1.312 1.199 1.059 1.265 +0 1.546 -0.825 -0.568 2.154 -0.029 1.267 -0.241 1.358 0.000 1.672 0.028 -1.608 2.215 0.714 0.768 1.528 2.548 1.949 -0.346 0.243 0.000 1.431 1.132 1.182 1.821 0.605 1.315 1.237 +0 0.481 -1.166 -1.721 1.030 1.628 0.954 1.204 0.594 0.000 0.891 0.615 -1.120 2.215 0.734 1.173 -0.541 2.548 0.476 -0.122 0.081 0.000 0.885 0.819 0.976 2.095 0.515 1.792 1.841 +1 0.433 -0.259 1.216 0.952 0.120 0.886 1.038 -0.364 0.000 0.807 0.860 -0.943 0.000 1.632 1.090 1.035 2.548 1.040 -0.115 -1.604 3.102 0.907 1.008 0.991 0.785 0.993 0.963 0.824 +0 1.067 -1.079 0.736 0.948 1.289 0.942 -0.975 -1.187 0.000 2.344 -0.818 -0.591 0.000 1.712 0.088 0.674 2.548 2.121 -1.799 1.478 0.000 1.634 1.648 0.990 0.760 0.560 1.403 1.224 +1 0.369 0.660 1.732 0.098 -0.648 2.617 1.111 0.657 0.000 2.247 -0.542 -1.032 0.000 1.207 -1.331 1.595 1.274 2.166 -0.944 -0.633 3.102 1.625 1.095 0.838 0.654 1.138 0.939 0.792 +0 0.589 -0.595 0.733 1.041 0.104 0.833 -1.210 1.452 2.173 0.751 -1.220 -0.814 0.000 0.924 -0.510 -0.467 2.548 0.425 -1.480 1.725 0.000 0.588 0.801 0.986 0.745 1.140 0.922 0.825 +0 0.999 -1.420 0.534 1.497 0.946 0.598 -1.028 -0.054 0.000 0.760 -0.382 -1.221 2.215 0.615 -0.411 -0.659 0.000 1.450 0.184 -1.487 1.551 0.652 0.955 0.994 1.350 0.371 1.216 1.028 +0 0.531 -0.441 -0.710 2.335 0.111 2.794 0.285 -1.611 2.173 2.270 0.258 0.288 0.000 0.526 0.987 1.388 1.274 0.747 -0.531 -1.332 0.000 1.863 1.252 1.041 2.405 0.889 1.601 1.500 +1 0.754 -1.222 -0.374 0.476 -1.187 0.820 -0.084 0.025 0.000 1.207 -0.880 1.238 2.215 0.769 -0.809 -1.227 0.000 0.994 0.279 -1.723 3.102 0.899 0.851 0.990 1.125 0.794 0.931 0.816 +0 0.639 -0.527 1.259 0.868 0.950 0.595 -0.285 -1.492 2.173 1.137 1.394 -0.539 2.215 0.780 2.084 -0.248 0.000 0.680 1.990 0.314 0.000 0.392 0.628 0.986 0.826 1.492 1.032 0.990 +1 2.215 0.371 0.053 0.907 -0.404 1.580 0.454 1.648 0.000 0.498 -0.433 0.813 1.107 0.320 0.168 -0.892 0.000 0.579 0.777 -0.967 3.102 0.978 0.952 0.998 0.648 0.604 0.629 0.893 +0 0.645 0.501 -1.263 0.470 -0.245 1.073 1.419 -1.669 0.000 1.529 0.139 0.169 1.107 0.928 -0.721 0.683 0.000 0.947 -0.304 -1.067 3.102 2.875 1.727 0.984 1.111 1.014 1.355 1.131 +1 1.935 0.093 0.397 1.395 0.186 1.188 -0.360 -1.446 2.173 0.370 -1.038 -1.369 0.000 0.617 -2.203 -1.591 0.000 0.463 0.538 -0.421 0.000 0.661 0.669 0.977 0.538 0.739 1.098 0.874 +0 1.827 0.076 1.436 1.031 -0.468 1.050 0.140 0.054 0.000 0.560 -0.822 -0.593 0.000 0.398 -0.633 -0.107 2.548 0.604 -0.070 -1.111 3.102 1.226 0.823 1.881 0.960 0.317 0.625 0.729 +1 0.830 -1.064 -0.818 0.439 1.202 0.905 -1.727 1.367 0.000 1.443 -1.642 -0.299 2.215 0.949 -1.426 -1.509 0.000 1.170 -1.171 0.641 3.102 0.882 0.839 0.986 0.811 0.894 0.942 0.786 +0 0.660 -1.986 -1.308 1.060 0.162 1.050 -0.837 0.965 2.173 1.470 -1.975 -0.168 0.000 2.136 -0.449 1.586 2.548 3.257 -1.096 -0.812 0.000 1.878 2.170 1.124 1.406 1.043 1.692 1.366 +1 1.161 -0.814 1.692 2.167 -1.197 0.816 0.357 0.087 1.087 0.938 -0.816 0.828 2.215 0.894 -1.494 0.382 0.000 0.613 -0.641 -1.181 0.000 0.885 1.154 1.128 1.179 1.143 1.195 0.999 +1 0.577 1.015 -1.222 0.825 -0.058 1.370 1.053 -0.489 2.173 0.998 0.490 1.029 0.000 1.455 -1.684 1.472 0.000 1.267 1.072 1.499 0.000 0.842 0.673 0.984 0.828 1.003 0.987 0.860 +0 0.577 -0.263 -1.424 0.794 0.305 0.809 -1.081 1.041 2.173 1.392 0.387 -0.881 2.215 0.584 -0.307 1.352 0.000 0.878 0.700 0.073 0.000 0.874 0.962 0.984 0.756 1.999 1.031 0.854 +1 2.383 -0.322 -0.920 0.528 -0.697 1.099 1.574 1.009 2.173 0.769 -0.772 -0.922 0.000 1.151 -0.693 0.826 0.000 0.447 0.822 1.344 1.551 1.443 0.962 0.978 2.062 0.316 1.310 1.279 +0 0.739 1.142 1.284 0.945 0.128 0.527 0.726 -0.150 2.173 0.823 2.255 -1.497 0.000 1.057 0.780 0.843 2.548 0.946 -0.033 -0.668 0.000 0.859 0.851 0.999 0.668 0.727 0.591 0.574 +1 0.608 0.729 1.648 1.127 -0.947 0.937 1.175 1.427 2.173 0.506 -0.311 -0.507 1.107 0.420 2.247 -0.371 0.000 0.936 0.722 0.217 0.000 0.708 0.980 0.983 0.861 1.303 0.826 0.756 +1 0.687 0.285 -0.667 0.233 0.478 0.733 0.154 1.666 0.000 1.403 -0.573 1.026 2.215 1.546 -0.861 -0.568 1.274 1.071 -0.625 -1.294 0.000 0.885 1.067 0.987 1.266 1.576 1.197 1.047 +0 1.546 -1.760 -0.550 1.508 -0.105 1.498 -1.324 1.555 0.000 0.225 -1.402 1.027 0.000 0.547 -0.005 0.184 2.548 1.135 -0.254 1.528 0.000 1.062 0.783 0.989 0.490 0.252 0.525 0.638 +0 0.499 -0.452 -1.221 0.313 0.282 1.121 0.623 -0.851 2.173 1.631 -0.668 0.860 2.215 0.607 -0.407 -0.592 0.000 0.517 0.080 1.407 0.000 0.625 0.866 0.990 0.782 2.436 1.198 0.904 +0 1.145 0.971 0.265 0.506 -0.732 0.849 1.028 1.009 2.173 1.129 -0.613 -1.529 2.215 0.776 -0.653 -0.383 0.000 0.796 -1.168 -0.643 0.000 0.364 0.798 0.990 0.882 1.730 1.115 0.991 +0 2.109 0.829 -0.232 0.609 1.696 1.414 0.064 0.022 0.000 1.270 0.706 -1.580 0.000 1.581 -0.256 1.576 2.548 1.337 1.008 1.549 3.102 0.861 1.270 1.549 1.419 0.908 1.047 1.024 +0 1.140 0.551 -0.655 1.880 -1.215 0.953 0.253 -0.513 0.000 1.284 -2.202 1.102 0.000 1.912 -0.345 0.436 2.548 1.244 0.269 1.558 3.102 1.634 1.149 0.988 1.573 1.084 1.084 1.014 +0 0.957 0.902 -1.418 0.476 1.723 0.937 -2.683 0.256 0.000 1.366 -0.873 1.740 2.215 1.197 1.489 -0.350 2.548 0.675 -1.540 0.429 0.000 0.570 1.805 0.987 1.074 2.571 2.666 2.088 +1 0.285 0.871 0.008 1.298 0.277 0.832 -1.822 -1.554 0.000 0.925 -1.033 -1.404 0.000 1.057 -0.677 0.980 2.548 1.038 -0.476 -0.308 3.102 0.784 1.025 0.983 2.140 0.737 1.576 2.339 +1 1.974 -0.009 -0.801 0.329 -1.380 0.860 -0.239 0.808 0.000 0.663 0.801 0.883 0.000 0.887 -1.066 -1.197 0.000 0.813 0.359 -1.300 3.102 0.902 0.884 0.988 0.796 0.587 0.587 0.756 +1 0.643 -0.689 -0.362 2.068 -0.793 0.317 0.398 1.269 0.000 0.937 -1.152 0.801 2.215 0.895 1.312 0.272 0.000 0.907 -0.660 1.212 0.000 0.913 0.874 0.987 1.169 0.754 0.901 1.157 +1 0.990 1.161 0.151 0.918 1.508 1.090 0.764 1.599 2.173 0.771 0.372 -0.533 2.215 0.668 1.667 1.614 0.000 1.432 1.915 -0.112 0.000 1.105 1.011 1.242 0.981 1.293 0.991 0.864 +0 1.774 0.690 -1.043 0.504 -0.332 0.560 -1.958 0.803 0.000 1.170 0.962 -1.685 0.000 0.940 -1.038 -0.531 2.548 2.253 -0.039 0.351 3.102 1.423 1.417 0.989 1.206 1.018 1.131 1.087 +1 0.657 1.143 1.170 0.666 -1.016 0.937 1.058 0.130 0.000 1.357 1.256 -1.206 2.215 0.419 1.559 0.399 0.000 0.739 0.153 1.576 3.102 0.719 0.788 0.990 0.805 0.752 0.824 0.721 +1 0.777 0.438 0.259 0.587 -1.717 0.946 -0.706 -1.299 2.173 1.515 -0.181 0.647 2.215 0.582 -1.415 -0.684 0.000 0.546 -0.314 -0.692 0.000 0.388 1.015 0.983 0.925 1.794 0.943 0.791 +0 1.806 1.848 -0.973 0.721 -1.389 1.046 -0.340 0.813 2.173 0.397 -0.501 0.088 0.000 0.451 1.388 0.438 1.274 0.366 2.068 0.718 0.000 1.089 1.117 0.973 0.766 0.964 1.603 1.271 +0 0.657 -0.367 0.936 0.993 -0.222 0.812 0.583 0.071 1.087 1.121 -1.529 1.740 0.000 1.218 -0.617 -1.519 0.000 0.457 2.474 -1.053 0.000 0.906 0.856 0.989 0.858 0.714 1.161 0.955 +1 1.031 -0.302 -1.196 0.198 0.173 0.794 -0.337 1.579 0.000 1.047 0.890 0.097 2.215 0.582 -0.634 0.666 0.000 0.516 1.035 -0.906 3.102 0.923 0.844 0.987 0.903 0.532 0.837 0.742 +1 0.845 -0.561 1.269 2.064 -1.637 1.052 0.221 0.198 0.000 1.569 -0.774 -0.650 1.107 0.530 -1.475 0.760 0.000 0.949 -0.739 0.418 3.102 0.471 0.988 0.996 0.851 0.906 0.941 0.787 +0 0.802 -0.450 -0.275 0.579 -0.531 1.010 -1.537 1.483 0.000 0.969 -1.947 0.020 0.000 1.048 -0.602 0.493 1.274 1.649 -1.100 -1.629 0.000 0.690 1.132 0.984 0.503 0.679 0.765 0.768 +0 1.493 0.571 0.319 0.585 -0.417 0.634 1.473 -1.575 1.087 0.718 2.164 -1.040 0.000 0.581 1.629 1.425 0.000 1.018 0.546 0.666 3.102 0.818 0.899 0.983 1.031 0.848 0.718 0.742 +0 1.021 -1.082 -1.035 0.562 -1.476 1.414 -0.256 -1.709 0.000 1.786 -1.686 -0.008 0.000 1.735 -0.087 0.085 1.274 2.224 -2.212 -1.541 0.000 0.857 0.774 0.992 1.051 0.878 0.871 0.731 +1 0.578 0.163 -0.534 1.293 0.570 0.881 1.792 -0.549 0.000 1.435 -1.246 -1.540 1.107 0.622 -1.453 -0.113 0.000 1.875 -0.910 1.175 1.551 0.426 0.870 1.005 0.988 0.959 1.036 0.850 +1 0.621 0.102 0.586 0.716 -0.506 0.598 0.753 0.296 0.000 0.913 1.240 -1.672 2.215 0.778 0.028 -0.334 0.000 1.417 0.077 1.672 3.102 0.803 0.965 0.987 1.208 0.657 0.844 0.813 +0 1.440 0.186 -0.506 0.837 1.305 0.470 0.424 -1.630 0.000 1.173 0.102 0.129 2.215 1.036 0.414 1.192 0.000 0.781 2.451 1.501 0.000 0.706 1.097 1.518 0.804 0.736 0.733 0.735 +0 3.021 0.011 -1.714 2.751 -1.559 1.541 -1.833 0.142 0.000 3.094 -0.046 0.351 2.215 1.617 -0.485 -1.203 1.274 0.819 -0.782 1.141 0.000 1.022 0.902 0.998 2.874 2.414 1.952 1.463 +0 1.388 -0.457 1.176 1.108 1.724 0.875 0.918 0.362 2.173 0.629 1.295 -0.486 0.000 0.839 -0.709 -1.047 2.548 0.853 -0.291 0.177 0.000 1.049 0.989 0.984 1.563 1.446 1.127 1.036 +1 0.633 -0.776 -1.364 0.315 0.241 0.829 0.163 1.377 2.173 0.345 -0.389 0.854 0.000 1.438 0.878 -0.480 2.548 0.476 -1.467 -0.117 0.000 0.561 1.072 0.989 0.754 1.464 0.892 0.749 +1 0.903 0.326 0.049 0.403 -1.484 0.611 -0.417 -1.443 1.087 0.917 -1.658 1.005 0.000 0.732 1.469 -0.915 0.000 0.876 0.067 -0.143 0.000 0.936 1.025 0.988 0.573 0.781 0.661 0.620 +1 0.630 1.030 -0.339 0.917 0.750 0.648 0.887 0.590 0.000 0.910 0.250 -1.073 2.215 1.158 -0.581 -1.598 2.548 0.786 -0.532 0.660 0.000 0.924 1.118 0.988 1.358 0.709 0.996 0.925 +1 0.643 1.652 1.314 0.822 0.297 0.949 0.731 0.478 1.087 1.002 0.431 -1.018 2.215 0.723 -0.149 -1.346 0.000 0.466 -0.259 -0.217 0.000 0.547 0.933 0.987 0.930 1.416 0.821 0.722 +0 0.658 -0.020 -1.433 0.813 0.078 1.526 0.241 -0.966 0.000 1.173 0.134 0.854 1.107 0.269 -1.591 0.202 0.000 0.458 1.051 -0.866 3.102 1.678 0.985 0.991 0.800 0.769 0.992 0.815 +1 0.890 1.176 1.335 0.709 -1.082 1.161 0.864 -0.627 2.173 1.487 0.019 1.130 0.000 0.339 -0.132 0.043 0.000 0.688 0.629 0.643 3.102 0.908 0.601 0.987 0.957 0.863 0.945 0.825 +1 0.693 0.131 -0.041 0.912 1.508 0.814 -0.120 -1.213 0.000 0.569 -1.287 -1.297 0.000 1.389 -0.088 0.797 2.548 1.727 -0.497 0.009 3.102 0.930 1.126 1.084 0.721 0.828 0.928 0.814 +1 2.035 -0.933 0.827 0.557 -1.480 0.816 0.160 -1.175 2.173 0.774 -0.006 -0.110 2.215 0.411 0.680 -0.906 0.000 0.910 -1.093 -1.685 0.000 0.932 0.854 1.289 1.327 0.963 1.000 0.837 +1 0.792 -0.079 -1.313 0.521 -1.564 1.441 -0.161 0.316 2.173 0.711 -1.261 0.674 0.000 0.617 -1.004 1.514 2.548 0.850 -1.879 -1.694 0.000 0.994 0.648 0.986 1.424 1.184 1.036 1.123 +1 0.625 0.090 -1.275 0.819 -1.611 0.971 0.055 0.864 2.173 0.539 -0.242 -0.336 2.215 0.780 1.290 0.507 0.000 0.749 0.381 -0.520 0.000 0.794 0.950 0.987 0.743 0.955 0.832 0.850 +1 1.448 0.108 -0.536 0.756 -1.610 1.154 -1.456 -1.528 0.000 1.595 -2.176 1.144 0.000 1.290 1.185 -0.238 0.000 2.161 -0.678 0.066 3.102 0.884 0.621 1.193 1.055 0.887 0.945 0.877 +1 0.587 1.140 0.195 0.717 0.219 0.518 2.926 0.858 0.000 0.768 1.038 -0.686 2.215 0.803 0.395 1.680 1.274 0.702 1.983 -1.429 0.000 0.855 1.134 1.003 0.781 0.756 0.892 1.021 +1 0.408 -1.375 0.076 0.948 -0.243 0.522 -0.554 -0.568 0.000 0.644 0.614 -0.393 0.000 1.518 0.163 1.436 0.000 1.183 -1.180 1.071 3.102 0.801 0.878 0.986 0.910 0.301 0.749 0.693 +0 1.530 -1.100 -0.551 0.348 -0.695 0.569 -0.851 0.123 2.173 0.769 -0.141 1.499 0.000 1.758 0.126 1.029 0.000 0.570 -1.303 -1.424 3.102 0.775 0.862 0.990 0.773 0.631 0.763 0.941 +0 0.777 0.196 -0.328 1.498 -0.489 0.748 -0.445 1.671 0.000 0.709 -1.393 1.526 0.000 0.958 1.166 0.833 0.000 1.489 -0.134 -0.690 3.102 0.947 0.944 0.976 0.738 0.760 0.707 0.755 +1 0.626 -1.904 -1.432 1.225 0.771 0.526 -0.317 -0.356 2.173 0.461 -0.545 -1.666 0.000 0.836 0.300 -0.775 0.000 1.195 0.984 0.487 3.102 0.813 0.928 1.111 1.091 0.900 1.243 1.040 +1 0.440 -0.738 1.663 1.597 -0.026 1.622 -2.425 1.112 0.000 1.062 -0.972 -0.861 2.215 0.982 -2.080 -1.701 0.000 1.901 -0.662 -0.239 3.102 0.592 0.849 1.160 0.675 0.695 0.779 0.787 +0 1.840 -0.674 -0.705 1.012 -1.247 1.074 -0.300 0.621 0.000 0.949 0.099 1.238 2.215 0.843 0.146 -1.444 2.548 0.544 0.373 -0.156 0.000 0.933 0.951 0.988 0.785 0.631 0.857 0.945 +1 0.402 -1.780 -1.184 0.276 0.724 0.907 -1.129 -0.413 0.000 0.872 -0.434 -1.249 2.215 0.903 -0.906 0.230 0.000 1.141 0.020 0.912 0.000 0.898 1.071 0.981 0.642 0.763 0.837 0.716 +0 0.753 -0.153 -0.866 1.029 1.640 0.530 -2.260 -0.153 0.000 0.725 -0.955 -1.033 0.000 0.671 0.343 1.604 2.548 2.069 -0.270 0.504 3.102 1.312 1.345 0.991 0.951 0.820 1.039 0.909 +1 1.696 -1.657 -0.665 0.683 -1.214 0.944 -1.153 0.973 2.173 0.558 -0.517 -1.514 2.215 0.705 -2.316 0.732 0.000 0.918 -1.335 -0.329 0.000 0.909 0.864 0.990 1.366 0.904 0.984 0.847 +0 1.646 -0.031 -0.719 1.286 -0.239 1.056 -0.256 1.362 0.000 0.475 -0.389 -0.197 1.107 0.663 -0.130 -1.624 0.000 1.408 -0.159 0.545 0.000 0.988 0.834 0.978 0.556 0.596 0.572 0.630 +1 1.824 -0.111 -0.650 0.632 -0.048 0.733 -0.050 0.069 0.000 1.313 -0.163 1.231 2.215 0.747 -0.504 1.502 1.274 0.622 0.561 -1.723 0.000 0.902 1.046 0.990 0.902 0.327 0.890 0.783 +0 0.745 -0.859 -1.354 1.732 -0.704 0.606 -0.246 0.180 2.173 0.799 0.825 1.468 0.000 0.980 -0.888 1.257 2.548 0.572 -0.105 -0.999 0.000 0.831 0.960 0.987 0.963 0.869 0.801 0.806 +0 0.478 1.131 1.149 0.361 -0.886 0.730 1.409 -1.430 2.173 0.535 1.238 1.485 0.000 0.873 0.641 0.092 2.548 0.910 -0.269 -0.784 0.000 0.904 0.879 0.994 0.678 1.041 0.726 0.653 +1 1.052 -0.045 -0.633 1.212 0.147 0.932 0.467 1.185 2.173 0.994 -0.533 -0.941 0.000 0.728 -0.616 -1.656 2.548 1.024 -0.097 0.056 0.000 1.050 1.246 1.012 0.837 0.839 0.859 0.814 +0 0.896 -0.111 0.195 0.826 1.419 0.587 -0.522 -1.080 1.087 0.647 1.124 -0.247 0.000 1.332 0.240 -1.554 2.548 0.716 0.378 0.813 0.000 0.790 0.997 1.064 0.830 0.639 0.739 0.709 +1 1.775 -0.030 0.599 0.271 0.043 0.910 -0.155 -1.689 2.173 0.716 -0.084 -0.931 0.000 0.763 -0.336 0.191 0.000 1.058 0.870 -1.184 3.102 0.974 1.020 0.988 0.903 0.805 0.862 0.796 +1 0.626 -0.067 -1.208 0.278 -0.607 1.271 0.654 0.304 2.173 1.486 1.891 -1.193 0.000 0.867 1.916 1.361 0.000 1.507 -0.029 1.044 3.102 1.303 1.738 0.989 1.504 1.043 1.468 1.586 +1 1.210 0.137 0.766 0.401 1.559 1.041 0.338 -0.644 2.173 1.438 -0.696 1.601 0.000 0.619 0.526 -0.252 2.548 0.711 -0.284 -0.086 0.000 1.064 1.066 0.987 1.168 0.366 0.990 0.881 +1 0.497 -0.887 0.723 2.221 1.539 1.334 0.042 -0.065 0.000 0.630 0.141 -1.521 2.215 0.692 0.860 -1.423 0.000 0.772 -0.975 -0.921 3.102 1.814 1.256 0.988 0.744 0.550 0.864 0.970 +1 1.039 -1.923 0.051 0.449 0.432 1.010 0.144 1.585 2.173 0.646 -0.010 0.361 0.000 0.580 0.392 -0.894 0.000 0.721 0.055 -0.618 3.102 0.875 1.017 0.975 0.731 0.827 0.963 0.838 +1 0.694 -2.157 0.736 1.403 -1.709 0.831 -0.920 -0.128 2.173 1.125 -0.348 -0.765 1.107 0.991 -0.364 1.395 0.000 0.546 -0.050 0.880 0.000 0.968 0.993 1.104 1.498 0.875 1.166 0.984 +0 0.840 -0.786 -1.486 0.870 1.032 1.204 0.669 1.065 0.000 1.165 -0.657 -0.938 2.215 1.538 0.023 -0.217 0.000 1.127 -1.033 -0.732 3.102 2.381 1.834 0.986 0.906 0.353 1.325 1.170 +0 0.637 0.411 0.111 0.364 -1.464 1.092 0.249 1.136 1.087 0.909 1.050 -0.209 2.215 1.122 0.755 -0.964 0.000 1.074 1.079 -0.941 0.000 0.985 0.829 0.994 0.922 1.509 0.910 0.859 +0 0.654 -0.750 -0.137 0.381 1.168 0.993 -0.711 -0.524 0.000 1.434 -0.496 1.278 0.000 0.754 -1.207 -1.176 2.548 0.451 0.812 0.568 1.551 1.128 1.066 0.985 0.810 0.775 0.706 0.762 +0 0.720 0.518 0.217 0.693 1.653 0.493 1.074 -0.493 2.173 0.459 1.908 1.400 0.000 0.393 0.110 1.122 0.000 0.416 1.017 1.294 0.000 0.708 0.871 0.990 0.683 0.615 0.637 0.587 +1 0.463 -0.246 1.310 0.474 0.726 0.606 -0.496 -1.322 1.087 0.379 2.417 0.079 0.000 1.048 0.045 -0.158 2.548 0.764 1.027 1.716 0.000 0.841 1.015 0.980 0.977 0.904 0.934 0.812 +1 1.708 -0.077 -0.098 1.133 -0.715 0.741 0.639 1.336 2.173 0.982 0.347 -1.433 2.215 0.522 0.547 0.255 0.000 0.674 -0.597 1.113 0.000 0.650 0.791 1.015 1.273 0.776 0.972 0.792 +0 0.676 0.556 1.494 0.478 -0.453 0.549 -0.256 -0.020 0.000 0.472 1.039 -0.222 0.000 0.494 -0.965 1.466 1.274 1.218 -0.892 -1.401 3.102 0.797 1.055 0.987 0.859 0.314 0.778 0.767 +1 0.795 -0.427 0.789 1.876 1.411 1.426 -0.569 -0.225 2.173 0.353 0.057 -0.790 0.000 0.347 -0.219 -1.627 2.548 1.003 -0.894 -1.684 0.000 0.726 1.051 0.983 0.539 0.849 0.981 0.811 +0 0.416 -1.827 1.318 1.242 -0.210 0.762 -1.094 0.086 2.173 1.006 -0.006 1.374 0.000 1.572 -0.373 -1.505 0.000 0.585 -1.556 -0.515 0.000 0.796 0.843 0.986 0.665 0.533 0.788 0.746 +0 0.449 -0.968 1.600 1.430 -1.015 0.641 -0.304 0.146 2.173 1.125 0.870 0.869 2.215 0.780 0.598 -1.528 0.000 0.701 1.071 -0.312 0.000 0.792 0.979 0.992 0.916 1.100 0.941 0.791 +1 0.563 -1.594 1.430 1.950 0.708 0.698 -1.555 -1.534 2.173 0.861 -0.268 -1.014 2.215 0.610 0.180 0.241 0.000 0.721 -1.510 -0.699 0.000 1.020 1.008 0.987 1.528 0.944 1.102 0.996 +0 0.537 -0.800 -0.244 2.371 -0.687 2.183 -0.319 0.979 2.173 1.136 -0.053 0.591 0.000 2.228 0.134 -0.705 0.000 1.679 -0.227 -1.401 1.551 0.625 1.066 0.996 2.106 1.701 1.450 1.183 +0 2.459 1.308 -1.340 1.364 -1.241 1.315 0.556 0.611 1.087 0.723 1.092 0.198 0.000 1.236 -0.018 -0.012 0.000 0.626 -0.417 -1.019 3.102 0.917 0.940 0.991 0.829 1.095 1.253 1.144 +1 2.337 -1.525 -0.126 0.234 -1.309 1.031 -1.407 0.576 2.173 1.611 -1.296 -1.399 0.000 1.092 -0.786 1.469 2.548 0.704 -2.340 -0.663 0.000 1.408 1.134 0.985 0.949 1.023 1.078 1.019 +1 0.600 1.295 0.591 1.280 -1.266 0.674 0.655 -0.029 2.173 1.058 1.395 1.344 0.000 1.416 0.552 -0.559 2.548 1.347 0.268 1.220 0.000 0.929 1.198 1.208 0.901 0.562 0.946 0.858 +0 1.497 1.063 -0.932 0.237 -0.578 0.334 -0.029 -0.360 0.000 0.786 1.375 1.053 0.000 0.621 -0.407 1.460 2.548 0.996 1.308 0.162 3.102 1.346 0.973 0.998 0.779 0.903 0.697 0.717 +1 0.912 -0.890 -0.370 0.775 1.073 1.174 -0.448 -1.236 2.173 1.015 -1.093 0.341 0.000 0.688 -0.285 0.919 0.000 1.000 -0.613 1.569 1.551 0.832 0.761 1.122 1.039 0.680 0.887 0.780 +1 0.903 -1.971 0.797 0.768 -0.526 1.085 -0.717 -1.125 2.173 0.814 -1.644 -1.681 0.000 1.067 1.878 0.246 0.000 1.590 -1.104 0.526 0.000 0.852 0.952 1.072 0.988 0.869 0.935 0.797 +0 0.744 -1.368 0.370 1.613 0.450 0.965 -0.274 -1.424 2.173 0.518 -0.546 -0.783 0.000 0.699 -0.698 1.512 2.548 0.781 0.410 0.006 0.000 0.709 0.862 0.976 0.776 0.548 0.892 0.771 +0 1.044 -1.073 0.189 0.531 -0.968 0.734 -0.644 -0.936 0.000 0.900 -0.792 0.911 2.215 1.117 -0.855 1.563 1.274 0.790 -1.364 -0.191 0.000 0.939 0.944 0.986 0.761 0.595 0.761 0.693 +0 0.800 0.321 -1.096 1.269 -0.678 1.208 -0.513 1.720 2.173 0.736 0.835 0.268 2.215 0.634 -1.360 0.379 0.000 0.440 1.958 -0.038 0.000 1.962 1.257 0.978 1.096 1.693 1.210 1.078 +1 0.365 -0.761 -0.734 2.465 -1.611 1.754 0.834 -1.695 2.173 1.693 -0.098 0.336 0.000 2.509 1.093 -0.294 0.000 0.954 1.275 0.190 3.102 1.057 0.658 0.985 1.339 1.441 1.214 1.344 +1 0.669 -0.898 -1.284 1.160 -0.208 0.933 -0.155 0.481 0.000 1.371 -0.595 -1.654 2.215 0.794 1.802 -0.557 0.000 0.896 0.057 -0.749 3.102 2.345 1.395 1.007 0.982 0.807 1.278 1.137 +0 0.593 -1.430 0.834 1.488 -0.807 0.912 -0.901 -0.943 2.173 1.585 -0.005 0.583 2.215 1.028 -0.014 1.083 0.000 0.477 0.360 -1.307 0.000 0.668 1.043 1.296 1.526 1.923 1.208 1.012 +1 0.773 0.898 1.701 0.790 -0.497 0.845 1.549 1.128 0.000 0.951 0.468 -0.318 2.215 0.631 0.300 0.851 0.000 0.772 -0.449 -1.283 3.102 0.923 1.084 0.993 0.727 0.721 0.890 0.768 +1 0.953 -0.205 0.999 0.514 -0.066 0.975 1.539 -1.562 0.000 1.088 0.311 0.196 1.107 0.917 -0.624 0.525 0.000 0.562 -0.398 -1.441 3.102 1.216 0.850 0.985 0.814 0.760 0.880 0.750 +1 0.740 -0.851 -1.295 0.760 0.334 1.425 -0.150 -0.200 2.173 1.188 -2.420 1.521 0.000 0.992 -0.366 1.478 0.000 0.628 0.329 0.895 3.102 0.939 1.347 1.034 0.640 0.880 0.819 0.756 +0 0.601 -1.811 1.188 0.845 -1.672 0.501 -1.613 -0.132 0.000 1.170 -0.392 0.773 2.215 0.902 0.059 -1.111 2.548 1.262 -0.720 -0.678 0.000 0.745 0.883 1.001 0.856 1.113 0.859 0.795 +0 0.335 2.180 0.357 1.986 -0.138 0.969 -0.732 1.553 1.087 0.981 -1.400 1.617 0.000 0.665 0.207 -0.711 0.000 1.153 0.159 1.632 3.102 0.935 0.935 0.979 1.871 0.544 1.228 1.284 +1 0.813 0.474 -1.013 1.236 -0.331 1.305 0.664 1.287 0.000 0.892 0.544 0.598 0.000 1.797 0.399 -0.268 2.548 1.384 1.237 1.539 0.000 0.974 0.886 0.990 0.715 0.865 1.051 0.964 +1 0.610 -0.597 1.201 1.076 0.011 1.130 -2.213 1.445 0.000 1.039 -0.982 -0.399 2.215 0.604 -1.938 0.163 0.000 1.937 -0.205 -0.464 3.102 0.932 1.051 0.988 0.757 0.507 0.884 0.788 +1 1.267 -1.487 -1.231 1.257 1.674 0.665 -0.560 -1.738 0.000 1.138 -2.013 -0.075 0.000 1.479 -0.409 -0.142 2.548 1.503 -0.313 0.921 3.102 0.832 0.980 0.989 1.154 0.933 1.082 0.908 +1 0.494 -1.155 0.730 0.291 -0.382 0.419 -2.230 1.185 0.000 0.193 2.663 1.315 0.000 0.527 0.397 -1.079 2.548 0.535 0.184 -0.423 1.551 0.817 1.031 0.988 0.517 0.230 0.678 0.654 +0 0.518 1.730 0.911 0.345 1.156 0.945 0.403 -0.689 2.173 0.793 0.152 -1.149 0.000 0.529 2.288 -0.075 0.000 1.216 0.151 1.459 3.102 1.675 1.199 0.989 0.971 1.068 0.904 0.813 +0 0.940 -1.159 -0.192 1.566 0.495 0.450 0.594 -1.455 0.000 0.505 1.042 1.069 1.107 0.685 -0.128 -0.671 0.000 1.198 -0.516 -1.479 3.102 0.799 1.023 0.989 0.940 0.850 0.910 0.779 +1 1.045 -0.183 1.449 0.646 0.844 0.886 -0.112 -0.739 1.087 1.648 0.383 1.183 2.215 1.819 0.327 -0.112 0.000 2.607 2.496 -1.091 0.000 1.321 1.125 0.981 0.627 1.813 1.145 0.972 +1 0.715 0.040 -0.879 0.991 -1.116 1.281 -0.171 0.983 2.173 0.729 -0.431 0.148 2.215 1.033 -0.706 -0.884 0.000 0.776 -0.744 0.879 0.000 0.989 0.783 0.983 1.378 0.992 0.982 0.840 +0 0.396 -0.917 -1.506 1.370 -0.624 0.631 -0.191 1.740 1.087 0.481 -1.302 0.024 0.000 0.750 -0.852 0.948 0.000 1.072 0.483 0.439 3.102 0.707 0.880 0.993 0.769 0.874 0.686 0.713 +1 0.833 -0.059 1.128 0.517 -1.132 0.651 -0.583 -0.397 0.000 0.844 1.043 1.240 2.215 1.080 -0.339 0.222 0.000 0.963 0.200 -1.022 3.102 0.873 0.676 0.986 0.662 0.806 0.796 0.699 +0 0.591 -0.857 0.600 2.762 1.196 0.587 1.427 -0.377 0.000 0.840 -0.034 -0.806 2.215 0.845 -0.655 -1.392 1.274 1.437 0.413 0.035 0.000 0.964 0.853 0.983 1.238 0.549 0.872 0.820 +0 0.759 1.242 1.069 0.951 0.333 0.814 1.009 -1.573 0.000 0.767 0.396 0.547 2.215 1.221 0.361 -0.975 1.274 1.045 0.828 -0.233 0.000 1.318 0.932 0.982 0.540 1.008 0.792 0.753 +1 0.856 0.447 -0.077 1.302 -1.442 1.142 1.284 1.128 2.173 0.510 1.350 0.311 2.215 0.639 -0.182 0.333 0.000 0.686 0.925 -1.400 0.000 0.893 1.032 1.378 0.894 0.757 0.891 0.766 +0 1.562 0.969 0.909 0.129 0.377 0.548 1.241 -0.864 0.000 0.906 -0.062 -1.711 2.215 1.017 0.961 -0.335 0.000 1.297 0.677 0.127 3.102 0.819 0.862 0.980 0.671 1.071 0.725 0.726 +0 0.387 1.397 -1.144 0.456 0.690 0.968 -0.080 0.298 0.000 0.888 -1.142 -1.340 2.215 0.415 0.561 -1.147 2.548 1.189 -0.827 0.573 0.000 0.871 0.874 0.984 0.906 0.668 0.866 0.762 +1 0.765 -0.213 1.689 2.183 0.964 1.078 -0.524 -0.751 1.087 0.482 0.936 -1.214 0.000 0.605 0.433 -0.035 2.548 0.703 0.241 0.692 0.000 0.795 1.040 1.088 0.844 0.803 0.996 0.844 +0 0.606 0.321 0.148 1.729 -0.963 0.625 -0.140 -1.638 1.087 0.868 1.379 1.512 2.215 0.951 1.900 0.524 0.000 0.389 2.293 -0.395 0.000 0.544 0.780 1.194 0.883 0.989 0.924 0.947 +1 0.369 -1.109 0.247 0.463 -0.160 0.929 0.474 0.509 0.000 0.849 1.317 -1.151 0.000 0.774 1.293 0.427 2.548 1.905 -0.063 -1.102 3.102 0.776 0.871 0.987 0.804 1.187 0.730 0.677 +1 0.982 -1.068 1.308 0.139 1.734 0.721 0.231 -1.268 2.173 0.991 -0.842 0.505 2.215 1.270 -0.962 -0.440 0.000 1.076 0.020 1.594 0.000 0.968 0.867 0.992 0.831 1.436 1.005 0.868 +1 0.305 -0.761 1.686 0.766 0.801 0.893 0.173 -0.688 2.173 0.989 0.037 0.060 2.215 1.398 0.446 1.117 0.000 1.028 1.012 -1.709 0.000 0.889 1.146 0.980 0.969 0.869 0.947 0.810 +1 0.944 0.400 0.208 0.419 -1.015 1.030 0.747 -1.156 2.173 0.839 -2.069 1.034 0.000 1.197 0.729 1.426 0.000 1.140 -2.028 0.325 0.000 0.947 1.200 0.990 0.661 0.899 0.965 0.865 +0 0.919 -0.262 0.255 0.808 0.438 0.912 0.340 -0.974 0.000 1.453 -0.358 0.981 2.215 0.927 -1.190 -1.191 2.548 0.831 -1.155 1.321 0.000 0.852 0.954 0.987 0.917 1.290 0.997 0.950 +1 1.515 0.690 0.523 0.219 1.372 0.751 0.051 0.186 0.000 1.183 -0.857 -0.740 1.107 1.660 -0.850 1.497 0.000 0.654 -0.276 1.576 1.551 2.124 1.167 0.985 1.566 0.725 1.011 1.091 +0 0.681 1.038 0.305 0.330 0.687 1.214 0.966 1.260 2.173 1.431 0.502 -0.382 2.215 0.915 1.866 -1.723 0.000 0.772 1.718 -0.298 0.000 0.890 1.140 0.996 0.871 1.983 1.164 1.025 +1 1.270 -0.071 -0.585 1.650 -1.052 0.216 1.627 1.617 0.000 0.622 0.405 1.209 0.000 0.585 1.136 0.062 0.000 0.878 -0.743 1.177 0.000 0.888 0.582 0.977 1.109 0.369 0.731 0.735 +1 1.295 -0.150 1.639 0.233 -0.943 0.647 -1.047 -0.415 0.000 0.803 -0.841 -1.398 1.107 0.600 -0.616 1.475 0.000 0.673 -0.005 -1.511 0.000 0.837 0.890 0.981 0.535 0.498 0.546 0.639 +1 1.452 -0.083 0.584 1.055 -0.079 0.754 0.685 1.608 2.173 0.513 -0.368 -1.176 2.215 0.604 0.570 0.155 0.000 1.226 0.682 -1.445 0.000 0.946 0.786 0.990 0.837 0.745 0.855 0.763 +1 0.672 -0.178 -0.724 0.901 1.398 0.947 0.848 0.589 2.173 0.905 0.909 1.257 0.000 1.970 0.164 -0.590 2.548 0.706 2.380 -1.176 0.000 0.880 0.786 1.016 0.900 1.599 0.967 0.846 +1 4.570 -0.893 -1.657 0.317 -0.391 2.550 1.647 -0.294 0.000 1.738 -1.288 0.796 0.000 2.580 0.079 0.304 0.000 2.134 0.001 -0.904 3.102 1.274 0.979 1.517 1.377 0.808 1.080 1.169 +0 2.702 -0.626 0.023 0.558 -1.570 0.501 -0.246 1.144 0.000 0.856 0.371 1.643 2.215 0.982 0.977 1.020 0.000 2.027 -0.467 -0.830 3.102 0.907 0.722 1.685 1.108 1.106 1.033 1.032 +1 1.282 2.147 0.804 1.817 0.530 1.523 -0.824 -0.848 0.000 0.756 1.596 1.128 0.000 1.316 0.683 1.547 1.274 0.744 1.036 -0.410 1.551 0.744 0.723 1.006 0.968 0.766 1.138 1.086 +1 1.008 -0.508 1.009 0.899 -0.122 0.577 -0.318 -1.233 0.000 1.023 -0.047 1.631 2.215 1.375 -1.106 0.174 1.274 0.796 -0.572 -0.843 0.000 0.405 0.974 1.123 0.921 1.446 0.838 0.769 +1 0.918 -0.848 0.232 1.577 0.881 0.446 -0.470 0.018 2.173 0.544 0.362 -0.874 0.000 0.536 1.451 -0.861 0.000 1.736 0.047 -1.263 3.102 0.538 0.791 0.987 1.122 0.888 0.788 0.818 +1 0.370 -0.720 0.219 1.117 -0.370 0.606 1.721 -1.626 0.000 0.838 -0.442 -0.637 2.215 0.894 1.114 1.059 0.000 1.342 0.082 0.690 0.000 0.904 0.954 0.984 0.579 0.546 0.681 0.676 +0 0.945 0.508 1.439 0.697 -1.727 0.697 -2.295 -0.014 0.000 0.829 -0.009 -0.757 1.107 1.016 0.612 0.521 0.000 0.940 1.396 1.003 0.000 0.724 1.083 0.994 0.826 0.579 0.742 0.779 +1 0.579 -0.923 0.002 0.773 1.016 0.415 -0.513 -0.038 1.087 0.470 -2.422 1.375 0.000 1.253 -0.501 -1.130 2.548 1.107 -0.496 -0.638 0.000 1.377 0.996 0.996 0.855 0.748 0.749 0.748 +1 1.938 0.364 0.469 0.542 -1.632 0.703 1.773 -1.705 0.000 0.675 -0.380 -1.469 0.000 1.203 1.055 -0.626 2.548 1.034 1.144 0.329 1.551 0.931 0.861 1.347 0.792 0.653 0.743 0.781 +0 0.945 -1.192 1.499 0.697 -0.304 0.608 1.008 0.114 2.173 0.812 0.158 -1.591 2.215 0.430 1.722 1.670 0.000 0.376 0.610 -1.087 0.000 0.384 0.611 1.122 0.916 1.130 0.994 0.854 +1 1.541 0.598 1.354 2.462 0.874 0.829 2.569 -0.668 0.000 1.274 0.290 -0.918 2.215 0.372 1.628 -1.195 0.000 0.449 0.762 0.674 3.102 0.616 0.716 1.129 1.582 0.709 0.970 1.264 +1 0.654 2.053 -1.470 0.708 0.579 0.287 1.354 1.022 0.000 0.705 -0.978 -0.327 2.215 0.703 -0.006 1.699 0.000 0.773 0.819 -1.194 1.551 0.945 1.104 0.988 0.600 0.908 0.930 0.789 +0 0.832 -0.576 -1.577 0.349 -0.105 0.802 0.667 0.800 0.000 1.083 -0.557 -1.072 2.215 1.332 0.035 0.091 0.000 0.802 -1.445 -1.367 0.000 1.262 0.831 0.985 0.711 0.817 0.932 0.880 +1 1.098 -0.550 -0.049 0.474 -0.290 0.762 -0.597 -1.711 2.173 0.614 2.766 -0.925 0.000 1.538 0.135 1.316 2.548 1.209 0.861 -0.032 0.000 0.572 0.911 0.989 1.073 0.749 0.875 0.807 +0 0.741 -0.209 1.287 1.010 -1.238 1.197 -1.366 0.387 1.087 0.626 -1.182 -0.866 2.215 1.126 -0.565 -1.553 0.000 0.382 -1.553 -0.491 0.000 0.762 1.201 0.988 0.832 1.156 1.067 0.883 +1 3.455 0.643 -1.443 0.775 -0.804 1.646 -2.010 0.302 0.000 0.483 0.393 0.138 2.215 0.629 0.343 0.944 0.000 0.567 -1.066 -1.710 1.551 0.709 0.558 1.237 1.062 0.649 0.845 0.732 +0 0.703 -0.687 -0.346 0.709 1.580 0.600 0.049 1.551 0.000 0.607 0.795 -0.395 1.107 0.486 0.624 -0.058 2.548 0.406 -1.973 -0.495 0.000 1.360 0.984 0.989 0.885 0.176 0.751 0.698 +0 0.699 0.928 -1.346 0.880 -0.442 0.256 -2.104 0.838 0.000 1.140 -1.001 0.791 2.215 0.357 0.987 0.013 0.000 0.528 -0.227 -0.800 1.551 1.349 0.999 0.990 0.683 0.745 1.188 1.103 +0 0.513 0.802 1.343 0.426 -0.056 0.470 0.242 1.484 0.000 0.646 1.497 -1.107 1.107 0.395 0.999 0.445 0.000 1.103 -1.015 0.612 0.000 1.051 0.975 0.989 0.911 0.285 0.832 0.731 +0 1.075 1.119 0.848 0.756 0.643 0.405 2.018 -0.119 0.000 0.407 2.783 0.422 0.000 1.460 1.064 -1.398 2.548 0.836 0.642 1.682 3.102 0.545 1.085 0.975 0.733 0.346 0.728 0.726 +1 1.029 0.826 0.278 1.173 0.909 1.250 1.061 -1.067 2.173 0.686 0.469 0.870 0.000 0.788 2.202 -0.415 0.000 1.122 1.123 -1.694 3.102 0.879 0.799 0.985 1.370 0.693 0.923 0.832 +0 1.799 -0.712 0.066 0.367 -0.453 0.498 -0.155 1.693 2.173 0.310 -0.527 1.214 0.000 0.446 -0.088 0.544 0.000 0.430 0.672 1.471 0.000 0.658 0.574 0.981 0.948 0.490 0.759 0.615 +0 1.071 0.549 -0.399 0.766 -0.690 0.942 -0.171 1.598 2.173 0.399 -1.462 1.126 0.000 0.442 -0.217 -0.588 0.000 1.326 0.130 0.496 3.102 0.777 0.832 0.995 0.907 1.010 1.021 0.932 +0 0.319 -1.132 -0.671 0.340 1.426 1.189 0.075 0.401 2.173 1.267 0.303 -1.265 2.215 1.297 -0.696 -0.838 0.000 0.699 0.560 -0.655 0.000 0.877 0.894 0.998 0.911 1.815 0.934 0.777 +1 0.579 -0.063 0.278 0.839 1.691 0.705 -0.242 1.419 0.000 1.817 -0.765 -0.721 0.000 1.051 0.439 1.057 1.274 2.335 0.712 0.200 3.102 0.983 1.454 0.988 0.846 0.865 1.273 1.032 +1 0.704 -2.014 1.178 1.301 -1.727 0.967 -0.841 -0.010 0.000 0.387 -0.821 -0.709 2.215 1.000 -1.425 0.416 0.000 1.009 -1.534 -1.689 0.000 0.926 0.759 0.996 0.709 0.233 0.515 0.689 +1 1.455 0.167 -1.445 0.736 0.315 1.449 -0.086 0.485 1.087 0.656 0.019 -0.622 0.000 1.792 -1.451 -1.291 0.000 1.317 0.898 1.121 3.102 1.764 1.951 1.433 1.300 1.200 1.568 1.300 +1 2.367 -0.639 -0.656 0.359 0.399 0.799 -1.369 0.807 2.173 0.551 -0.029 -1.734 2.215 0.797 -1.370 -1.661 0.000 0.988 1.780 0.928 0.000 0.788 1.047 1.040 0.865 1.023 0.920 0.810 +0 0.633 0.064 -1.334 1.000 0.128 0.956 0.640 1.676 0.000 1.116 0.059 0.347 2.215 0.807 0.506 -1.144 0.000 0.848 0.965 -0.014 3.102 0.893 0.912 1.068 0.735 0.579 0.863 0.765 +1 2.776 -0.612 -1.194 0.130 0.147 0.598 0.271 0.362 1.087 0.998 -0.636 1.221 2.215 0.538 -2.306 0.394 0.000 0.706 -0.257 0.754 0.000 0.948 0.873 0.992 1.229 0.964 0.977 0.923 +0 0.517 1.690 -0.345 1.126 0.991 0.962 1.444 -0.741 1.087 0.702 2.302 -1.503 0.000 1.103 1.332 1.021 2.548 0.504 1.099 0.182 0.000 0.884 0.887 0.988 0.593 1.283 0.786 0.699 +0 0.619 0.140 0.921 0.478 1.671 0.796 -0.763 -1.140 0.000 1.577 -0.569 0.292 0.000 0.708 0.332 -0.609 2.548 0.627 -1.767 -1.658 0.000 0.923 0.927 0.981 0.651 0.732 0.754 0.884 +0 1.358 -0.433 1.557 0.709 -1.410 1.011 0.054 0.316 2.173 0.761 0.178 -1.249 2.215 0.974 0.602 0.946 0.000 0.626 2.399 -0.256 0.000 1.030 0.949 0.994 0.629 1.277 0.890 0.770 +0 0.711 0.307 -1.384 1.191 -0.290 0.678 -1.436 0.519 0.000 0.814 -1.387 -1.071 2.215 0.844 -0.407 1.104 2.548 0.518 -0.393 1.587 0.000 0.873 0.940 1.063 0.845 0.931 0.821 0.813 +0 0.411 0.118 0.407 1.156 -1.227 0.507 -1.526 -0.127 2.173 1.049 -1.288 1.504 2.215 0.745 -2.649 -0.060 0.000 0.437 -0.133 1.116 0.000 1.287 0.850 0.990 0.885 1.074 0.784 0.828 +0 1.904 0.464 -0.985 0.931 -1.030 0.882 -0.853 0.867 1.087 0.555 -0.469 0.405 0.000 0.506 -1.149 0.516 2.548 0.599 1.486 -1.244 0.000 0.836 0.641 0.989 1.226 0.310 1.280 1.142 +1 0.280 1.475 0.951 1.422 -0.105 0.612 1.236 -1.432 2.173 0.546 0.602 -0.068 0.000 0.970 1.411 1.467 0.000 1.295 -0.348 -1.658 3.102 1.068 0.928 0.986 1.974 0.899 1.343 1.104 +1 0.301 -2.034 -1.341 0.852 0.695 1.232 -1.152 -0.576 0.000 1.628 -1.197 1.534 0.000 1.434 0.055 0.590 1.274 1.130 -0.470 -1.314 3.102 0.637 0.728 0.984 0.748 1.011 0.894 0.786 +0 1.048 1.480 -1.298 0.850 0.010 0.609 -1.433 1.158 0.000 0.635 -2.776 -1.014 0.000 0.433 1.830 1.411 0.000 0.631 -0.505 0.354 3.102 0.889 1.111 1.208 0.943 0.435 0.925 0.807 +1 0.604 0.406 0.792 0.334 -0.189 0.632 -1.063 -1.682 2.173 1.026 0.953 -0.612 1.107 0.783 1.419 0.474 0.000 0.868 1.648 -0.054 0.000 0.873 0.993 0.992 0.798 1.741 0.978 0.840 +0 2.120 -1.368 0.042 2.335 0.393 2.222 -0.456 -1.395 0.000 0.515 -0.928 -1.144 0.000 0.372 -1.131 1.194 0.000 1.393 0.000 0.444 3.102 0.764 0.907 0.989 1.013 0.490 0.940 1.396 +1 1.006 0.266 1.642 1.124 -0.368 0.612 0.551 1.027 0.000 1.015 1.014 1.590 0.000 1.105 -0.132 -0.157 2.548 1.332 1.229 -0.109 3.102 0.911 1.097 1.431 0.875 0.837 0.909 0.860 +0 1.420 -0.501 0.440 0.356 1.736 0.545 0.531 1.440 1.087 0.884 -1.106 -0.778 2.215 0.674 -0.342 -0.379 0.000 0.855 -0.477 -1.025 0.000 0.741 0.805 0.989 0.879 1.335 0.845 0.690 +0 0.396 1.911 -1.499 0.799 0.018 0.454 -0.315 1.142 0.000 0.654 -0.866 0.015 2.215 0.672 1.393 -1.712 0.000 0.768 0.841 -0.990 0.000 1.064 0.948 0.984 0.550 0.490 0.613 0.611 +1 1.275 -1.356 0.157 0.891 0.976 1.042 -0.621 1.638 2.173 0.748 -0.825 -0.763 0.000 0.318 0.081 -1.538 0.000 0.486 0.637 1.217 3.102 0.604 0.852 0.993 0.840 0.633 0.838 0.753 +0 0.689 1.186 0.035 1.170 0.691 0.803 -1.749 -1.060 0.000 0.493 -0.998 1.166 2.215 0.564 -1.342 -0.333 0.000 1.103 0.856 -1.457 3.102 0.759 0.839 0.990 0.880 0.936 1.072 1.717 +0 0.716 -1.393 0.142 1.331 1.429 0.685 0.347 0.598 0.000 0.705 -0.617 -1.412 2.215 1.289 -1.925 -0.919 0.000 1.844 -1.020 -0.077 3.102 3.125 1.838 1.240 0.937 1.009 1.209 1.061 +1 0.653 -0.375 -0.971 1.129 0.284 1.125 -0.497 0.906 2.173 1.623 2.058 -1.151 0.000 0.458 -2.183 -1.330 0.000 0.535 -0.698 -0.257 3.102 0.678 0.982 1.076 0.526 0.725 0.634 0.631 +0 1.636 0.395 1.456 0.537 1.192 1.137 0.539 -0.590 2.173 0.514 0.067 -1.061 0.000 1.047 1.153 0.429 0.000 0.666 -0.698 0.778 3.102 1.308 0.956 0.988 1.353 1.109 0.973 0.881 +0 2.065 0.397 -0.001 0.170 1.646 1.295 0.827 -1.700 1.087 1.091 1.893 1.429 0.000 2.255 0.064 -0.203 1.274 0.532 0.457 1.125 0.000 0.932 0.975 0.986 1.432 2.238 1.220 0.968 +1 0.452 1.371 1.491 1.617 -1.174 0.583 0.002 0.871 0.000 1.121 -0.417 -0.020 2.215 0.316 -1.185 -1.076 0.000 1.109 -0.321 -1.678 1.551 0.931 0.881 0.987 1.381 1.004 1.489 1.285 +0 1.787 -1.180 0.895 0.940 -0.456 0.732 -1.569 -0.190 0.000 0.929 -2.198 1.490 0.000 1.139 -1.086 -0.979 2.548 1.283 0.250 1.697 0.000 0.937 0.899 1.684 1.108 0.896 0.814 0.770 +1 0.635 0.503 -0.542 0.739 -1.367 1.340 1.346 0.265 0.000 0.438 -0.236 1.594 0.000 0.742 0.179 -0.777 0.000 1.359 0.698 1.636 3.102 0.911 0.705 0.994 0.811 0.537 0.630 0.607 +0 0.973 0.327 -0.518 0.455 -1.571 0.844 1.084 1.171 0.000 0.710 1.038 -0.334 2.215 0.865 0.373 0.611 2.548 0.778 0.984 -1.439 0.000 0.880 0.978 0.991 0.739 0.687 0.677 0.655 +0 0.765 0.361 -1.549 1.167 0.163 1.345 -0.125 0.889 0.000 0.826 0.644 -0.807 2.215 0.970 -0.505 0.248 0.000 1.731 -0.631 -1.055 3.102 0.753 0.948 1.308 1.020 0.867 0.770 0.729 +1 0.336 1.354 -0.566 0.831 1.434 0.984 -0.012 0.076 2.173 1.216 -0.191 -1.240 0.000 1.359 0.128 0.921 2.548 0.659 -0.593 1.633 0.000 0.735 1.153 0.996 0.921 1.001 0.968 0.822 +0 1.407 -1.371 0.408 0.634 -1.677 1.116 0.733 1.631 2.173 0.720 1.381 -1.536 0.000 1.806 -0.378 -0.133 2.548 0.973 1.753 -0.217 0.000 1.081 1.173 1.248 1.080 2.072 1.444 1.561 +1 1.656 -0.070 -1.498 0.966 -0.970 2.020 -0.544 0.410 0.000 1.693 -0.491 1.579 2.215 0.995 2.592 -0.032 0.000 1.602 -0.325 -1.132 1.551 0.850 0.887 0.979 0.919 0.960 0.921 0.827 +0 1.852 1.435 1.312 0.565 -1.389 0.512 -1.138 0.121 0.000 1.111 -1.763 -0.415 0.000 0.676 1.017 -1.277 2.548 0.767 0.107 -0.069 3.102 0.921 0.885 0.983 0.649 0.564 1.025 1.604 +0 1.047 -0.364 -0.131 0.370 -0.995 1.206 0.459 0.454 2.173 0.873 1.891 1.611 0.000 1.166 -0.216 -1.113 2.548 0.643 0.175 1.688 0.000 0.917 1.559 0.987 0.918 1.552 1.311 1.111 +0 0.614 1.162 1.725 0.771 -0.870 0.734 -0.086 0.077 0.000 0.940 -0.341 1.291 0.000 0.775 -0.163 -0.134 2.548 0.683 -0.895 1.411 1.551 1.584 0.976 0.994 0.700 0.605 0.661 0.691 +1 0.914 -0.659 -0.239 1.033 -1.726 0.672 -0.513 1.045 2.173 0.586 0.765 -0.235 2.215 0.515 -1.035 -0.598 0.000 0.747 -2.340 -0.271 0.000 0.867 1.011 1.310 0.963 1.063 0.989 0.874 +1 0.556 -0.714 -0.800 0.410 -1.511 1.106 0.208 0.436 1.087 1.638 -0.694 -1.050 1.107 0.723 -0.387 1.697 0.000 0.743 -0.569 -0.374 0.000 0.781 1.009 0.984 0.913 2.149 1.104 0.875 +1 0.917 -1.972 0.019 0.745 -0.337 2.405 -0.846 -1.740 0.000 1.706 -1.105 0.185 0.000 1.867 -1.027 -1.049 2.548 1.408 -0.518 0.603 0.000 0.910 0.652 0.991 0.939 0.758 0.918 0.795 +0 0.394 1.102 1.476 1.632 -0.466 0.460 0.137 0.644 2.173 1.118 0.030 -1.537 1.107 1.132 -0.839 -0.125 0.000 0.865 2.110 0.765 0.000 1.274 0.890 1.093 1.103 0.976 0.860 0.899 +0 1.537 0.542 1.310 1.399 1.440 0.666 0.402 -0.244 2.173 0.608 2.321 -0.837 0.000 0.913 2.007 0.036 0.000 0.813 0.150 0.309 1.551 0.979 0.986 0.982 0.766 0.383 0.819 0.942 +0 0.570 0.954 1.581 1.197 1.336 0.429 2.124 -0.084 0.000 0.710 -0.604 -0.194 2.215 1.353 0.092 -1.436 2.548 0.941 -0.260 0.576 0.000 0.901 0.904 0.988 1.742 1.013 1.304 1.086 +1 0.686 -0.222 1.560 0.609 0.543 0.354 -0.544 0.796 0.000 0.553 -0.251 -0.237 0.000 1.159 1.215 -1.451 2.548 1.224 0.839 -0.479 3.102 0.767 0.810 0.991 1.318 0.715 0.992 0.848 +1 0.427 -1.746 1.015 1.573 -1.232 0.582 -0.866 1.379 0.000 0.605 -1.310 -0.024 2.215 0.448 -0.211 -0.913 2.548 0.646 0.699 0.353 0.000 1.174 0.995 1.022 0.702 0.514 0.631 0.751 +1 0.955 -0.511 -0.868 1.260 -0.194 1.996 -2.038 1.143 0.000 1.498 0.295 -1.239 0.000 1.590 0.519 0.016 2.548 0.816 -0.800 0.792 3.102 6.231 3.213 0.993 0.808 0.923 2.444 1.941 +1 1.019 -1.098 1.240 0.763 -0.958 0.968 -1.272 -0.120 0.000 1.342 -0.116 1.466 2.215 0.651 1.678 -0.913 0.000 0.917 -1.254 0.509 0.000 0.789 0.565 1.121 0.945 0.950 0.961 0.839 +1 1.334 -0.657 1.439 0.861 -0.095 1.208 -1.472 0.714 0.000 1.549 -0.173 -1.026 2.215 0.827 0.310 -1.466 0.000 1.124 0.398 -0.443 3.102 0.700 0.751 1.458 0.983 0.717 0.869 0.731 +1 2.062 0.140 0.671 2.363 -0.111 1.191 -0.090 -1.593 0.000 1.276 0.767 -1.277 2.215 0.494 -0.775 0.838 0.000 1.348 0.730 -0.218 3.102 1.046 0.984 1.980 1.109 0.967 1.185 0.997 +0 1.141 -0.595 1.585 1.179 -1.715 0.589 -0.194 0.737 2.173 0.817 -1.029 0.806 2.215 0.484 -0.193 -0.862 0.000 1.420 -0.571 -0.198 0.000 0.909 0.931 0.977 0.862 0.458 0.746 0.757 +1 0.818 -0.283 1.682 0.505 -1.113 1.152 -0.844 0.915 0.000 2.214 -1.481 -0.915 0.000 1.439 -0.762 -0.285 2.548 2.616 -0.543 0.371 3.102 0.839 0.953 0.993 1.077 0.838 0.955 0.958 +1 0.877 -0.834 1.592 1.111 0.421 1.369 -1.683 -1.692 0.000 1.760 -1.538 0.033 0.000 0.703 -1.942 -1.351 0.000 1.272 -1.110 0.645 3.102 1.006 1.039 1.190 0.867 0.935 0.890 0.829 +1 1.092 0.676 0.961 0.240 -0.417 0.560 1.096 0.510 1.087 0.654 -0.073 0.393 0.000 1.298 0.608 -1.332 2.548 2.198 1.471 -0.889 0.000 2.182 1.411 0.988 0.834 1.084 1.002 0.889 +0 0.472 0.464 -0.268 1.190 1.564 0.871 -1.347 0.863 0.000 1.619 -0.331 -1.293 2.215 1.647 -1.247 0.233 0.000 1.538 -1.053 -0.475 1.551 1.160 1.111 1.035 0.956 1.175 1.257 1.200 +1 0.436 -1.117 1.209 1.619 -1.575 0.949 -1.495 -0.406 2.173 0.726 -2.053 0.246 0.000 0.864 -1.175 0.730 1.274 1.095 -1.251 1.484 0.000 1.104 1.075 0.989 0.836 0.971 0.881 0.794 +1 0.661 0.244 0.604 1.937 -0.023 1.170 -0.891 1.661 0.000 1.024 0.746 -1.188 2.215 0.642 1.386 0.570 2.548 0.386 -0.558 -1.290 0.000 0.956 0.833 0.987 0.941 0.924 0.948 0.809 +1 0.645 -0.781 0.115 1.583 0.508 1.415 0.974 -1.264 0.000 0.722 0.725 0.961 1.107 0.585 0.279 -0.907 0.000 1.065 1.136 -0.281 3.102 1.031 0.964 0.980 0.729 0.754 0.784 0.936 +0 0.550 0.603 1.577 1.366 0.567 0.754 0.628 -1.207 2.173 0.619 1.111 -0.478 0.000 1.035 -0.155 1.660 0.000 1.012 -0.222 0.815 0.000 0.781 0.957 0.990 0.994 1.166 0.819 0.743 +1 0.741 -0.434 -0.860 1.170 0.247 1.466 -0.263 -0.052 2.173 2.672 -0.383 -1.687 1.107 0.718 0.700 0.158 0.000 0.394 -0.513 -0.361 0.000 0.836 1.194 1.084 1.447 2.904 1.498 1.210 +1 0.531 1.787 -1.180 0.865 0.931 0.958 0.391 -0.655 1.087 0.542 2.188 -1.568 0.000 0.386 0.839 1.185 2.548 0.719 2.411 0.197 0.000 0.855 0.659 0.986 1.008 0.781 0.900 0.775 +1 0.915 -0.405 -1.526 0.527 0.251 0.589 -0.101 0.511 0.000 1.083 0.120 -1.702 2.215 1.305 -0.118 -0.721 2.548 1.806 -0.979 0.415 0.000 0.898 1.171 0.988 0.694 0.990 1.003 0.825 +0 1.951 -0.613 -0.040 2.305 -0.070 1.823 0.132 -1.706 2.173 1.764 -0.740 1.717 1.107 2.614 -0.401 0.307 0.000 0.801 0.324 -1.161 0.000 1.691 1.863 1.007 2.685 1.229 1.972 1.696 +1 1.031 0.601 1.615 0.392 -0.502 0.945 0.542 0.669 0.000 1.144 -0.286 -0.629 2.215 0.984 0.273 0.080 0.000 1.492 -0.145 -1.631 3.102 0.905 1.163 0.985 1.022 0.928 0.966 0.875 +0 0.568 0.449 0.855 0.613 -1.653 0.509 -0.490 0.436 2.173 0.423 0.099 -1.424 0.000 1.402 0.735 -0.646 2.548 0.797 1.031 -0.262 0.000 0.971 0.865 0.987 1.014 1.140 0.918 0.787 +1 0.706 0.757 -0.685 0.406 1.502 1.178 0.254 -0.906 0.000 0.940 -0.292 0.794 2.215 0.681 0.637 0.260 0.000 0.689 1.632 0.402 0.000 0.818 0.682 0.993 1.137 0.716 0.833 0.726 +0 0.473 -0.610 -1.034 1.456 -0.124 0.363 -2.775 1.336 0.000 0.373 -1.301 0.853 0.000 0.506 0.128 -1.040 1.274 0.724 -0.652 1.520 3.102 0.706 0.996 0.988 0.716 0.408 0.616 0.663 +1 0.727 1.145 -0.634 1.505 -1.102 1.193 -0.666 0.272 2.173 0.803 -1.866 -1.451 0.000 0.796 -0.475 1.400 2.548 0.604 -0.190 0.650 0.000 1.219 0.850 0.976 1.534 1.036 1.079 1.091 +1 0.557 -0.667 -1.469 1.845 -0.794 0.600 0.890 0.493 2.173 0.935 0.584 1.678 0.000 1.268 1.904 0.017 0.000 1.683 0.111 1.231 3.102 0.781 0.899 0.982 1.016 0.777 0.898 0.771 +0 0.578 0.030 0.066 1.544 1.736 0.778 1.144 -1.245 0.000 0.901 0.081 -1.640 2.215 1.426 -0.866 0.549 2.548 2.184 -2.013 -0.258 0.000 0.890 0.915 1.306 1.102 1.286 1.161 0.988 +1 0.764 1.096 -1.676 1.307 -0.256 2.111 0.676 0.319 2.173 1.380 -0.199 -1.136 2.215 1.573 2.025 -1.074 0.000 1.273 -0.442 -0.410 0.000 0.801 0.821 1.326 1.359 2.687 1.435 1.139 +0 0.618 0.543 1.613 1.852 0.789 0.676 -1.577 -0.484 0.000 0.743 -0.340 -1.064 2.215 0.666 -0.099 0.061 2.548 0.375 -1.760 1.301 0.000 0.867 0.869 1.001 0.727 0.641 0.745 0.925 +1 0.871 -0.395 0.418 0.535 -0.287 0.401 -1.893 1.028 0.000 0.639 0.313 -0.640 2.215 0.792 -0.604 1.289 2.548 1.512 -0.046 -1.112 0.000 0.977 0.799 0.986 0.767 0.840 0.618 0.658 +1 0.654 -1.317 1.702 0.344 -0.203 0.741 0.819 -0.661 2.173 0.582 2.131 1.591 0.000 0.572 0.129 0.917 0.000 0.706 -2.481 -0.749 0.000 0.698 0.888 0.992 0.510 0.723 0.665 0.780 +1 0.448 -2.179 0.003 1.617 1.630 0.761 -1.256 -0.196 0.000 0.935 -0.876 0.953 2.215 1.071 -0.941 -0.749 2.548 0.666 -1.101 -1.519 0.000 1.011 1.002 1.173 1.021 1.065 0.891 0.825 +1 1.389 0.236 1.470 0.467 0.354 1.224 -1.919 1.018 0.000 1.465 -0.752 -0.787 1.107 1.546 -0.206 -0.196 2.548 0.808 -1.149 -1.152 0.000 0.774 1.736 0.985 1.284 0.925 1.354 1.299 +1 1.238 -1.766 -1.391 0.273 1.399 0.548 -1.441 0.571 0.000 0.577 -1.126 -1.076 2.215 1.485 0.438 0.453 0.000 0.932 0.851 -1.114 0.000 0.876 1.298 0.984 0.562 0.251 0.743 0.903 +1 0.786 0.317 0.930 0.535 -0.546 0.674 0.687 1.702 0.000 0.793 1.034 -0.345 2.215 0.904 0.802 1.276 2.548 2.090 0.245 -0.772 0.000 0.791 0.942 0.985 0.803 0.898 0.671 0.648 +1 0.781 0.685 -1.089 1.346 -1.670 0.493 2.484 0.344 0.000 0.927 -0.427 -0.536 2.215 0.917 0.194 -1.356 0.000 1.015 0.442 0.954 1.551 1.305 0.867 0.995 1.322 0.961 0.936 0.845 +0 0.623 1.449 -0.108 0.975 -1.454 0.774 -0.200 1.551 2.173 0.493 -0.761 -0.947 0.000 0.816 0.846 0.243 2.548 0.993 0.115 0.306 0.000 0.932 0.922 1.011 0.712 1.089 0.847 0.812 +1 1.115 -0.073 0.703 1.453 -0.261 0.683 -0.467 -1.071 2.173 0.655 0.088 1.554 2.215 0.480 -0.262 1.122 0.000 0.719 -1.283 -0.338 0.000 0.762 0.736 1.345 0.970 0.744 0.818 0.706 +1 0.765 -0.216 -0.502 1.352 0.606 0.781 -0.233 0.011 2.173 0.747 0.591 1.393 0.000 1.318 0.027 -1.467 0.000 1.114 -1.061 -0.812 3.102 0.928 1.107 1.185 0.709 0.855 0.930 0.852 +0 0.366 -1.177 -1.438 0.465 -1.258 0.410 -1.570 0.101 0.000 0.571 -0.688 0.832 0.000 0.683 -0.334 1.660 2.548 0.891 -0.358 -0.590 3.102 0.783 0.744 0.979 0.603 0.535 0.543 0.613 +1 1.604 0.424 0.948 0.730 -1.122 1.063 -0.320 -1.143 2.173 0.700 -0.905 0.809 1.107 0.623 2.553 0.081 0.000 0.587 -0.242 -0.222 0.000 1.020 1.139 1.435 1.252 1.307 1.000 0.928 +1 1.387 -0.621 0.866 0.549 1.364 0.909 -0.722 -0.449 2.173 0.318 -1.708 -0.420 0.000 0.594 -1.075 1.309 0.000 1.006 -0.403 -1.546 3.102 0.694 0.801 0.985 0.691 0.854 0.817 0.706 +1 0.695 0.197 1.125 0.722 -0.202 0.961 1.248 -1.651 0.000 1.814 1.299 0.185 0.000 1.447 0.037 -1.278 2.548 1.003 0.336 -1.701 0.000 0.666 0.862 0.987 0.544 0.880 0.828 0.792 +1 0.754 0.615 -1.346 0.179 1.705 1.028 0.048 0.836 2.173 0.848 -0.654 -0.570 1.107 1.133 0.352 -0.196 0.000 1.692 0.347 1.597 0.000 1.526 1.212 0.979 0.950 1.406 0.996 0.850 +1 0.379 1.340 0.562 1.061 -1.682 1.405 0.579 0.814 2.173 1.318 0.125 0.359 2.215 1.593 1.967 -1.027 0.000 1.293 0.189 -1.267 0.000 0.914 0.878 0.992 1.530 0.923 1.327 1.104 +0 0.464 -2.161 -0.966 1.196 0.693 0.574 -0.551 0.723 0.000 0.862 -0.843 -0.748 2.215 0.722 0.124 -1.322 2.548 0.591 -1.410 1.456 0.000 0.763 0.928 1.029 1.161 0.604 0.885 0.793 +0 0.314 -0.863 1.325 1.115 0.690 0.337 -2.718 -0.632 0.000 0.552 -0.320 0.567 2.215 1.126 0.508 -0.445 2.548 0.640 1.503 1.551 0.000 0.490 0.919 0.985 1.855 0.766 1.222 1.052 +1 0.658 -1.330 0.029 0.982 0.937 1.461 0.109 -1.368 0.000 0.667 -0.512 -0.441 2.215 0.895 -0.414 0.638 2.548 0.971 0.858 0.745 0.000 1.924 1.401 0.987 0.535 0.679 0.984 0.919 +1 1.868 0.019 0.780 0.638 -1.454 2.141 -1.253 -0.081 0.000 1.518 0.639 -0.500 0.000 2.824 -0.343 1.640 2.548 2.813 -1.023 -1.701 3.102 1.024 1.972 1.367 1.156 0.986 1.718 1.499 +1 1.233 -0.275 0.346 1.311 -0.446 0.709 0.816 -1.461 2.173 1.190 1.228 1.629 2.215 0.696 0.084 -0.210 0.000 0.502 0.891 0.102 0.000 0.378 0.953 1.152 1.252 0.558 1.162 0.900 +1 0.419 1.335 0.926 1.191 -1.131 0.799 1.834 -0.351 0.000 1.044 0.512 0.481 2.215 1.179 0.469 1.462 2.548 0.786 0.611 -0.774 0.000 0.843 1.147 0.989 0.705 0.911 0.933 0.812 +1 2.457 0.526 -0.511 0.491 -1.341 1.153 -0.223 0.796 2.173 0.806 1.127 -1.548 0.000 1.040 0.970 -0.579 0.000 1.927 0.977 1.292 3.102 1.078 1.021 1.034 1.538 1.375 1.235 1.108 +0 0.526 0.795 1.480 1.650 1.522 0.437 2.017 -1.312 0.000 0.986 0.451 0.169 2.215 0.593 2.521 -0.360 0.000 0.618 0.699 -0.075 1.551 0.772 0.681 0.993 1.150 0.203 0.784 1.085 +1 0.375 -0.719 0.736 0.819 -0.455 1.523 -1.116 1.504 0.000 0.649 0.145 -0.305 0.000 0.441 -0.464 -0.688 1.274 1.233 -0.577 0.359 0.000 0.829 0.767 0.983 0.611 0.347 0.657 0.668 +0 0.535 0.851 -0.880 1.795 -0.047 0.987 -2.791 1.532 0.000 1.020 -1.514 1.359 0.000 0.432 0.230 0.377 2.548 0.852 -0.508 -0.570 3.102 0.911 0.884 0.983 0.603 0.406 0.641 1.213 +1 1.243 0.351 1.288 1.593 -1.708 0.447 1.109 0.108 2.173 0.481 -0.776 0.344 0.000 1.086 -0.392 -0.366 1.274 0.580 0.496 -0.744 0.000 0.773 0.720 0.991 1.231 0.825 0.936 0.804 +0 0.911 1.078 0.184 1.293 -0.268 1.055 -0.240 -1.250 0.000 0.734 0.336 -1.634 0.000 1.495 0.559 0.869 2.548 1.001 -0.269 0.533 3.102 0.844 1.023 0.988 0.939 0.534 0.908 0.923 +1 0.471 -0.708 0.088 0.343 -1.521 1.074 -0.006 1.648 2.173 1.002 0.437 0.246 1.107 1.044 -0.178 -0.518 0.000 0.808 -1.488 0.775 0.000 0.906 1.032 0.997 0.869 1.497 0.937 0.800 +0 0.551 -1.093 -1.685 0.148 -0.479 0.846 -0.773 -0.540 2.173 0.502 0.255 0.727 0.000 0.687 1.620 1.034 0.000 0.382 2.296 -1.585 0.000 1.037 0.865 0.988 0.840 0.575 0.925 0.777 +1 1.356 -1.317 0.178 2.624 -1.196 0.882 -0.984 -1.432 2.173 0.957 0.664 -1.716 0.000 0.401 -1.476 1.644 0.000 0.389 -1.721 0.173 0.000 1.360 1.168 2.470 1.171 0.518 0.893 1.084 +0 0.406 -0.913 -1.043 1.175 1.127 0.619 0.138 -0.003 2.173 1.197 -0.928 0.478 1.107 1.126 1.021 -1.485 0.000 0.420 -1.370 -1.015 0.000 1.452 1.178 0.989 0.861 0.901 1.054 0.887 +1 1.240 1.038 1.180 2.124 0.604 0.511 0.891 -1.216 2.173 0.808 1.635 -0.872 0.000 0.648 1.067 -0.461 0.000 0.934 0.134 -0.439 3.102 0.785 0.724 1.114 1.103 0.547 0.842 0.772 +1 0.444 0.405 -1.127 1.115 0.611 1.338 -0.545 0.637 2.173 0.745 1.130 -1.608 0.000 0.993 -0.837 -1.184 0.000 0.752 -1.527 -0.527 0.000 0.919 1.372 0.987 0.536 0.844 0.819 0.723 +0 0.706 0.490 -1.417 0.927 -1.560 0.618 -0.236 0.624 2.173 0.463 0.724 0.218 1.107 0.928 0.281 -0.589 0.000 0.398 1.569 -1.264 0.000 0.699 0.924 1.004 0.802 0.491 0.856 0.735 +0 1.022 -1.246 -0.177 0.164 -1.328 0.921 -0.838 -1.616 2.173 1.643 -0.547 0.605 0.000 1.008 -0.447 0.076 2.548 0.771 0.088 -0.898 0.000 1.006 0.883 0.999 0.945 1.218 0.755 0.685 +1 1.171 0.252 0.823 1.653 1.150 1.375 0.971 -1.256 2.173 2.392 2.049 -0.306 0.000 1.578 0.399 1.390 0.000 0.566 1.316 -1.536 0.000 0.805 1.065 0.978 0.573 0.840 1.130 0.937 +1 0.667 -1.332 -0.712 0.877 0.899 0.751 -0.433 0.663 0.000 0.736 -0.534 -0.649 2.215 0.728 0.335 0.203 0.000 3.025 -0.648 -1.501 3.102 0.768 0.930 1.052 0.976 0.948 0.972 0.866 +0 0.526 0.938 -0.817 2.830 -0.253 1.106 -1.670 1.372 0.000 0.847 -0.445 1.122 2.215 1.021 -0.483 -0.648 2.548 0.937 -0.748 1.634 0.000 0.725 0.828 0.988 1.435 0.989 1.369 1.957 +1 1.015 0.316 -0.998 0.958 0.173 1.249 -0.331 1.287 0.000 0.693 1.639 -0.988 0.000 1.835 0.222 0.207 2.548 1.215 0.373 -0.666 3.102 0.454 0.667 1.189 0.851 0.816 0.835 0.768 +0 1.019 0.840 0.317 2.432 1.115 0.448 -0.270 -0.251 2.173 0.510 -1.684 -0.617 0.000 1.206 0.392 -1.192 2.548 0.920 -1.482 -1.455 0.000 0.612 1.167 1.437 1.196 0.762 0.989 1.236 +1 0.749 0.170 -1.286 0.730 1.386 0.492 0.634 1.058 0.000 0.762 0.837 0.172 2.215 1.665 1.125 -0.797 2.548 0.849 -0.365 1.087 0.000 0.560 0.745 0.979 1.272 0.945 0.978 0.863 +1 1.676 -0.182 -0.485 0.958 1.099 0.493 0.861 -0.319 0.000 1.314 0.219 1.088 2.215 0.536 0.232 -1.122 0.000 1.341 0.914 -1.672 3.102 0.999 1.193 1.738 1.256 0.907 0.981 0.943 +0 2.412 0.291 1.412 1.998 1.743 1.097 0.904 0.243 0.000 0.928 0.661 -1.233 0.000 1.426 0.705 -0.549 0.000 1.785 -1.380 0.362 1.551 0.863 0.578 0.972 2.138 0.720 1.388 1.467 +0 1.784 0.008 1.346 0.375 -0.189 0.982 0.676 -0.910 0.000 0.680 -0.188 0.098 0.000 0.674 0.248 0.039 2.548 0.775 0.264 1.694 3.102 0.593 0.593 1.113 0.750 0.551 0.520 0.500 +1 1.376 -0.850 -0.897 0.560 -0.062 0.831 1.151 0.249 0.000 1.130 0.236 -1.596 2.215 0.959 -0.557 1.187 0.000 1.158 0.144 0.758 3.102 2.015 1.145 0.996 0.982 0.881 0.964 0.977 +0 1.139 -0.488 -0.055 1.071 0.467 0.663 -0.732 -1.076 0.000 0.979 -0.905 1.447 2.215 0.761 0.632 -0.897 2.548 0.379 0.523 -1.470 0.000 0.624 0.807 0.994 1.055 1.143 0.919 0.807 +0 1.576 0.206 -0.132 2.022 0.205 1.505 1.250 -1.459 2.173 0.738 0.401 1.219 1.107 0.371 -1.488 0.138 0.000 0.493 0.521 -1.641 0.000 0.806 0.727 0.979 2.331 1.235 1.547 1.223 +1 0.764 1.591 1.305 0.632 -0.078 0.643 -0.061 -1.078 2.173 0.431 -0.960 0.648 2.215 0.674 1.528 0.141 0.000 0.656 0.570 1.585 0.000 0.803 0.940 0.987 1.273 0.859 1.048 0.853 +0 1.511 -0.730 0.934 1.315 -0.907 0.849 0.211 0.984 2.173 2.691 0.527 -0.596 2.215 1.298 -0.996 1.304 0.000 0.785 1.200 -0.167 0.000 1.243 1.047 1.945 2.069 2.231 1.589 1.345 +1 0.657 1.036 0.003 0.551 -1.155 0.365 -0.160 0.364 2.173 0.502 -2.299 0.590 0.000 0.447 -1.503 -1.682 0.000 1.283 -0.867 1.135 0.000 0.893 0.843 0.989 1.276 0.795 0.926 1.324 +0 0.677 -0.636 1.039 0.907 -0.170 0.828 0.114 0.246 2.173 1.146 -0.193 -1.388 2.215 0.726 0.600 -0.607 0.000 0.690 0.760 1.251 0.000 0.783 0.833 0.987 0.677 1.445 0.819 0.727 +1 0.493 0.388 0.925 1.150 0.377 0.914 0.382 -1.323 1.087 0.564 0.633 -0.535 2.215 0.623 -0.108 -1.011 0.000 0.919 1.241 -1.627 0.000 0.903 0.957 0.988 1.249 0.704 0.899 1.151 +0 0.583 -1.298 -0.854 1.265 0.284 0.810 -0.518 -1.452 2.173 0.946 -1.523 -0.244 0.000 1.168 -0.865 1.082 2.548 0.962 -1.733 0.670 0.000 0.967 0.989 1.018 1.057 0.953 0.930 0.816 +1 2.510 0.206 1.077 0.165 -0.323 1.068 -0.954 -0.160 0.000 0.607 0.727 1.643 2.215 1.054 1.011 -0.899 0.000 0.444 0.443 -1.317 3.102 0.849 0.753 0.989 0.661 0.220 0.770 0.857 +1 1.263 0.988 -0.617 0.615 0.309 1.295 1.185 0.630 2.173 0.987 1.045 -0.966 0.000 0.952 -2.625 -1.494 0.000 0.871 1.708 -0.419 0.000 0.837 0.735 0.988 1.041 0.966 0.908 0.795 +0 0.608 -0.054 -0.064 1.794 0.192 0.725 2.000 -0.692 0.000 0.941 1.014 1.082 2.215 1.098 -1.028 -1.288 0.000 0.370 0.642 -0.731 1.551 0.415 0.588 0.983 0.934 0.536 0.909 1.019 +0 1.308 0.667 0.904 0.767 0.140 2.330 0.109 0.682 2.173 3.897 0.335 -1.120 0.000 1.434 0.684 1.240 2.548 2.419 -0.098 -0.519 0.000 1.067 1.762 0.988 0.819 1.324 2.068 1.604 +0 0.594 2.205 -1.599 1.070 1.647 0.866 0.434 1.135 2.173 1.050 0.356 0.382 2.215 1.774 0.455 -0.731 0.000 0.553 0.845 -0.510 0.000 1.028 1.105 0.983 0.883 0.884 0.956 0.967 +0 1.183 0.516 1.575 0.667 -0.826 0.881 -2.022 1.025 0.000 0.681 -1.032 -0.057 2.215 1.134 1.123 -0.700 2.548 0.523 -1.853 -0.074 0.000 0.873 0.919 1.021 0.786 1.418 1.572 1.394 +1 0.760 0.832 1.732 0.826 1.085 0.781 0.988 0.709 2.173 1.464 -1.091 -0.452 0.000 0.608 -0.580 -1.688 0.000 1.582 -0.387 -1.067 3.102 0.771 0.837 0.977 0.848 1.505 1.223 1.062 +1 0.557 0.144 -0.881 1.781 -0.172 0.740 0.959 1.169 2.173 0.500 -0.061 -0.618 0.000 0.975 0.727 -1.536 1.274 0.552 0.388 0.783 0.000 0.823 0.842 0.979 1.105 0.690 0.828 0.780 +0 3.208 -0.165 1.046 2.096 0.836 1.375 0.220 -0.793 0.000 1.625 -0.315 -0.431 2.215 0.685 -0.865 1.631 2.548 1.116 -1.161 -0.912 0.000 0.833 0.982 0.964 1.951 1.132 1.282 1.308 +0 2.028 1.070 1.307 0.746 -1.000 1.553 0.256 -0.548 2.173 1.253 0.108 1.046 2.215 0.848 0.591 -0.945 0.000 0.958 -0.344 0.431 0.000 0.779 1.093 1.489 1.138 2.040 1.378 1.081 +0 1.359 -0.948 1.077 0.145 -0.854 0.548 -1.557 -1.279 2.173 0.614 0.617 0.206 0.000 0.790 0.740 -1.008 2.548 0.520 -0.725 0.511 0.000 0.640 1.126 0.989 0.924 1.243 0.840 0.775 +0 0.481 -0.396 -0.783 0.879 0.976 1.717 -0.718 0.110 2.173 2.501 -1.304 -0.309 0.000 4.993 -0.355 1.696 2.548 2.238 0.214 1.648 0.000 4.079 2.769 0.984 1.224 3.663 2.564 1.899 +0 0.315 -1.370 0.926 1.848 -0.979 0.976 -0.262 0.017 2.173 0.580 -0.321 0.607 0.000 0.658 -0.470 1.608 0.000 0.674 1.002 1.579 3.102 0.749 0.885 1.045 1.195 1.091 1.049 0.875 +1 0.556 -0.208 -1.669 1.334 0.758 0.639 0.616 -0.222 2.173 0.566 -0.387 -0.870 0.000 0.654 0.341 -1.699 0.000 0.878 0.410 1.245 3.102 0.822 0.706 0.988 0.586 0.770 0.682 0.661 +1 0.989 -1.787 -0.247 0.848 -1.586 0.471 0.121 -1.333 2.173 0.854 0.496 0.237 2.215 0.709 -0.977 0.768 0.000 0.879 0.348 1.663 0.000 0.949 0.910 1.185 1.093 0.940 1.097 0.929 +0 1.283 -0.647 -1.623 0.398 1.279 0.633 0.736 -1.126 1.087 0.760 0.217 -0.300 2.215 0.981 -1.344 0.630 0.000 0.722 1.000 1.341 0.000 0.859 0.902 0.989 0.899 0.742 0.722 0.744 +1 0.507 1.448 -0.020 1.083 -1.092 0.538 -0.297 -0.548 0.000 1.142 0.998 1.096 2.215 0.586 -1.057 0.449 0.000 0.923 1.300 -0.994 3.102 0.909 1.105 0.988 1.114 0.917 0.986 1.102 +0 1.517 -0.472 1.501 1.597 1.333 1.022 0.214 -0.357 0.000 1.096 0.070 1.619 2.215 1.008 -0.750 0.141 0.000 1.603 0.972 -0.070 3.102 0.940 1.265 0.981 1.383 1.379 1.124 1.096 +0 1.365 -0.908 1.734 0.941 1.236 3.074 -0.423 1.359 2.173 4.498 1.345 -0.478 2.215 2.485 1.593 -0.147 0.000 0.871 0.939 0.887 0.000 1.188 1.367 0.987 0.848 7.860 3.740 2.959 +1 0.672 -0.656 -1.592 1.342 0.353 1.020 0.468 1.511 0.000 0.860 -0.666 0.064 2.215 1.153 1.387 -1.044 0.000 0.777 -1.820 1.100 0.000 0.745 0.825 1.294 0.844 0.682 0.763 0.723 +0 0.647 0.910 1.638 0.767 -1.437 0.750 0.874 0.088 2.173 0.876 0.887 0.873 2.215 1.008 0.335 -0.962 0.000 1.089 -0.528 -0.516 0.000 0.755 0.998 0.988 0.897 0.776 0.837 0.811 +1 0.916 1.690 -0.634 0.552 1.147 0.748 1.313 -0.865 2.173 1.027 1.136 -1.462 0.000 1.636 1.648 0.380 0.000 1.195 0.611 1.039 3.102 1.150 0.926 0.987 0.729 1.035 0.738 0.661 +0 0.760 -1.133 -0.067 0.769 1.512 1.124 2.093 0.633 0.000 1.058 1.553 -0.522 0.000 1.400 0.468 -1.261 1.274 1.090 1.439 1.381 3.102 0.850 0.887 1.047 1.411 0.891 1.064 1.098 +0 0.812 1.438 0.576 1.265 -0.323 1.119 -0.448 1.418 0.000 0.661 -1.304 -0.785 0.000 0.882 -1.166 1.008 0.000 2.425 0.281 -0.987 3.102 0.999 1.072 1.017 1.156 0.267 1.008 1.221 +1 1.193 0.713 -1.367 1.371 1.463 1.236 -0.612 -0.820 2.173 1.725 0.458 0.154 2.215 1.215 -1.625 0.692 0.000 0.415 -1.908 0.884 0.000 0.884 1.408 0.987 1.443 2.059 1.474 1.468 +1 1.898 0.517 0.949 1.760 0.203 1.243 0.149 -1.353 2.173 1.327 -0.172 1.636 0.000 1.651 0.433 -0.068 0.000 0.933 0.777 0.583 0.000 0.821 0.588 1.575 1.760 0.922 1.161 1.002 +1 0.814 0.462 -0.230 1.436 -0.244 1.475 -0.026 -1.246 0.000 0.757 -1.239 1.238 0.000 1.514 -1.122 0.799 2.548 1.271 -0.319 0.890 0.000 0.709 0.619 0.977 2.115 0.703 1.460 1.292 +1 0.337 -0.054 0.805 1.145 -0.355 0.738 1.140 1.334 2.173 1.058 1.230 -1.579 0.000 1.619 0.039 -0.005 2.548 0.369 0.869 -0.758 0.000 0.556 1.238 0.988 1.567 1.500 1.157 1.112 +1 0.702 0.186 1.242 1.406 -1.603 1.552 -0.553 -0.571 0.000 2.401 0.629 0.935 2.215 1.615 1.044 0.598 2.548 2.641 0.269 -0.751 0.000 0.839 0.886 0.984 1.436 0.819 1.175 1.081 +1 0.821 0.710 1.120 0.679 -1.004 0.749 0.279 -1.359 2.173 0.611 -0.901 0.433 0.000 0.818 0.696 0.263 0.000 0.870 1.151 -1.428 3.102 0.927 0.951 0.989 0.717 0.496 0.728 0.667 +1 1.136 0.719 1.366 0.626 -1.006 0.708 0.728 0.127 1.087 0.815 -0.432 1.490 0.000 1.085 1.123 -0.755 2.548 0.370 1.205 -0.094 0.000 1.055 1.040 0.988 0.757 0.826 0.810 0.737 +1 0.997 1.079 -1.146 0.728 0.204 0.483 -1.041 1.024 2.173 0.472 -0.341 -0.341 2.215 0.810 1.125 1.252 0.000 0.749 2.021 -1.219 0.000 0.871 1.065 1.107 1.217 0.708 1.026 0.901 +1 3.071 -1.038 0.676 0.620 0.675 3.127 -0.650 -1.471 0.000 2.361 -0.480 0.096 1.107 0.442 0.150 -1.294 0.000 0.974 -2.292 -0.005 0.000 0.954 1.249 0.979 1.402 1.137 1.738 1.735 +0 0.923 1.275 -1.046 1.126 -0.379 0.635 1.499 0.862 0.000 0.919 1.058 -0.354 2.215 1.165 0.122 1.188 1.274 0.646 0.884 -1.623 0.000 0.799 0.936 0.984 1.280 1.211 0.924 0.829 +0 0.295 -0.063 -1.391 3.780 -0.320 1.040 -0.865 1.657 0.000 1.172 -0.028 1.183 2.215 0.825 -1.221 1.037 0.000 0.745 1.806 -1.241 0.000 0.969 0.988 1.204 0.755 0.743 0.995 1.128 +0 0.998 -0.746 0.068 0.051 1.718 1.133 -1.050 1.403 2.173 1.674 0.547 -0.348 2.215 0.651 0.711 1.528 0.000 0.701 -1.141 -0.082 0.000 0.808 0.923 0.982 0.883 2.731 1.300 1.036 +0 1.267 -0.708 -1.331 1.436 -0.949 1.664 -0.753 0.541 2.173 0.496 -1.117 -0.732 0.000 0.462 -0.584 0.938 0.000 0.831 0.697 -1.460 3.102 0.756 0.991 0.976 0.680 1.638 1.256 0.976 +1 0.708 -0.807 1.189 0.347 0.755 0.659 -0.668 -0.939 0.000 0.864 0.621 1.064 1.107 1.342 -0.438 -0.339 2.548 1.051 0.523 0.288 0.000 0.954 0.970 0.998 0.626 1.283 0.933 0.820 +1 0.357 -1.029 1.040 1.081 -1.183 0.855 0.323 0.924 0.000 0.287 1.509 0.632 0.000 0.518 -1.317 -1.117 2.548 0.726 0.643 -0.323 3.102 0.739 1.119 0.987 0.633 0.704 0.729 0.687 +1 0.912 -1.211 -0.673 2.702 -0.548 1.063 -0.718 0.673 2.173 1.063 0.719 1.420 0.000 0.774 0.524 -1.649 2.548 0.670 1.081 1.350 0.000 0.662 1.252 0.992 1.833 1.257 1.555 1.557 +1 1.836 -0.141 0.592 0.575 -0.104 0.407 -0.466 -0.892 0.000 0.870 -0.865 -1.670 2.215 0.434 -1.775 0.675 0.000 1.655 0.216 -1.088 3.102 0.965 0.932 0.979 1.044 0.853 0.893 0.792 +0 0.924 -0.301 1.626 0.182 -0.953 1.124 -0.460 -1.121 0.000 0.798 -0.529 0.673 1.107 1.850 0.264 -0.152 2.548 0.848 -1.937 1.224 0.000 1.950 1.476 0.981 0.981 1.036 1.104 0.932 +1 0.485 0.618 -1.639 0.948 -0.102 1.249 0.582 -1.363 0.000 2.288 0.407 0.612 0.000 1.936 -0.389 -0.654 2.548 2.623 -0.173 -1.186 3.102 0.900 0.933 0.989 0.783 0.815 0.938 0.840 +0 0.841 1.305 -0.536 1.533 0.646 0.714 0.483 1.479 1.087 0.477 -2.140 0.091 0.000 0.512 0.803 0.019 0.000 0.714 1.462 -1.182 0.000 0.661 0.799 1.377 1.008 0.411 0.821 0.688 +1 0.570 0.721 0.779 0.601 1.658 0.526 1.125 -0.772 0.000 0.631 1.037 0.235 0.000 1.175 -0.105 1.599 2.548 1.121 -0.223 -0.683 0.000 0.897 1.021 0.985 0.571 0.594 0.717 0.667 +1 0.515 -1.143 -1.551 0.608 -0.138 2.469 -0.342 -1.450 0.000 1.104 0.223 -0.036 0.000 1.793 -1.322 0.449 2.548 1.453 -0.611 1.169 3.102 1.415 1.560 0.987 0.851 0.872 1.518 1.475 +1 0.703 -0.760 1.218 1.352 1.486 0.366 0.445 1.161 0.000 0.758 -0.263 -0.380 2.215 0.550 0.624 0.543 0.000 1.569 1.041 -0.471 3.102 0.438 0.770 0.980 0.995 0.827 0.919 0.745 +1 1.053 -1.488 0.202 1.039 -1.233 0.350 -2.444 -1.188 0.000 0.847 0.096 1.426 2.215 0.879 -0.801 0.445 2.548 0.798 -0.847 -0.592 0.000 0.760 0.830 1.393 1.247 0.849 0.873 0.811 +1 0.506 -1.981 -0.869 0.332 1.418 1.312 -0.826 1.664 2.173 0.990 -1.358 0.088 0.000 0.669 -1.862 0.394 0.000 0.788 1.360 0.090 0.000 0.534 0.496 0.986 0.839 0.900 0.920 0.772 +0 0.576 -1.816 -0.981 0.434 0.782 0.764 -0.078 -0.026 2.173 0.805 -0.749 1.274 0.000 1.067 -0.156 1.739 2.548 0.922 -0.929 -0.219 0.000 0.748 0.963 0.980 0.723 1.126 0.734 0.656 +0 0.779 -0.425 0.558 0.662 -0.622 0.972 -1.031 1.136 2.173 0.784 -0.074 0.181 0.000 1.209 0.626 -1.128 2.548 0.744 -0.532 -1.735 0.000 1.023 0.993 0.988 0.879 1.784 1.003 0.852 +1 0.582 -0.450 -0.398 1.704 0.139 2.506 0.479 -1.057 2.173 3.251 1.373 0.855 0.000 1.588 2.155 -0.417 0.000 1.126 0.038 0.538 0.000 1.007 0.864 0.986 2.596 1.086 1.699 1.364 +1 0.665 0.934 1.095 1.052 -1.646 0.637 1.850 0.035 0.000 0.975 0.258 -0.405 2.215 0.828 -0.153 -1.703 2.548 0.532 1.578 1.279 0.000 0.801 1.078 0.989 0.543 0.904 0.891 0.850 +0 0.829 1.805 -0.112 0.585 1.605 0.319 1.455 0.736 0.000 0.457 -0.395 -0.722 2.215 0.523 0.159 -1.725 0.000 0.448 -0.165 -0.268 0.000 0.776 0.754 0.989 0.735 0.439 0.716 0.626 +1 0.487 0.043 -0.651 0.157 -0.174 0.763 0.428 -1.464 2.173 0.740 0.086 -0.138 2.215 0.892 -1.604 0.528 0.000 1.639 -0.709 1.049 0.000 0.870 1.100 0.851 0.941 1.047 1.058 0.871 +0 1.677 -0.637 -1.491 0.966 -1.148 0.898 -0.832 0.490 2.173 0.640 0.426 0.309 0.000 0.971 0.207 -1.732 0.000 0.930 -0.883 1.157 1.551 0.860 1.098 0.984 1.356 0.557 0.943 1.028 +1 0.729 0.406 -1.293 0.645 1.192 0.751 0.715 1.387 2.173 0.907 0.534 -0.571 2.215 1.363 -0.316 -0.311 0.000 0.861 -0.668 0.766 0.000 1.024 0.937 0.990 0.651 1.197 0.922 0.854 +0 0.741 0.264 0.828 0.930 -1.201 0.476 -0.322 1.091 0.000 1.257 -0.099 0.090 2.215 1.260 -0.016 -1.523 2.548 0.648 -0.498 -0.595 0.000 0.855 0.842 1.112 0.696 1.329 0.800 0.695 +0 0.318 0.096 -0.821 0.529 -1.037 1.175 -1.865 -1.722 0.000 0.836 -0.543 0.460 2.215 1.280 0.961 0.323 2.548 0.803 0.383 -0.583 0.000 2.509 1.785 0.999 1.601 0.996 1.677 1.358 +1 2.360 -0.588 -0.550 0.371 -1.581 1.103 -0.628 1.267 2.173 0.563 -1.421 -0.493 0.000 1.237 0.190 1.466 0.000 1.663 -0.618 0.266 3.102 0.926 1.073 1.038 0.873 1.128 1.006 0.852 +1 1.047 -0.157 0.034 0.452 1.293 0.735 -0.670 -0.557 2.173 0.728 1.142 0.579 0.000 0.791 1.045 1.595 0.000 0.688 0.872 -1.215 3.102 0.923 0.677 0.987 0.831 0.844 0.888 0.765 +0 0.341 1.150 -0.992 0.599 -1.008 0.640 0.458 1.257 2.173 0.894 -0.296 0.045 0.000 0.759 0.571 -1.379 2.548 0.594 0.759 0.500 0.000 0.721 0.894 0.993 0.635 0.606 0.666 0.663 +0 1.458 -0.559 -0.535 0.581 1.639 0.608 -1.230 -1.679 1.087 0.725 -2.228 0.772 0.000 0.755 -0.076 -1.582 2.548 1.352 0.686 0.250 0.000 2.986 1.805 1.180 0.866 0.522 1.192 1.021 +0 0.541 -1.259 1.619 0.384 0.767 0.790 -0.951 0.164 0.000 0.990 -0.278 -0.830 2.215 1.668 0.410 1.697 2.548 1.058 0.112 0.161 0.000 0.802 1.020 0.995 0.775 1.159 1.040 0.865 +0 0.840 2.235 0.046 0.668 -1.252 1.428 0.009 1.485 1.087 1.711 1.268 -0.194 2.215 0.964 -0.503 -1.202 0.000 0.546 1.178 -0.588 0.000 0.992 0.967 0.990 0.882 2.796 1.554 1.301 +0 1.492 -0.796 0.539 0.688 0.323 0.807 -0.794 -1.342 1.087 0.837 -0.473 -0.502 2.215 0.519 -0.229 -0.938 0.000 0.814 -0.902 1.726 0.000 0.573 0.607 0.988 1.202 0.853 0.886 0.722 +0 0.756 1.039 1.335 1.841 -1.470 1.581 0.449 0.232 2.173 1.419 -2.511 -1.624 0.000 1.687 -2.676 -0.559 0.000 1.352 0.069 0.770 0.000 0.646 0.764 0.990 0.441 0.824 1.013 0.819 +0 1.495 0.737 -1.221 0.935 1.017 0.581 0.525 0.065 2.173 0.270 1.377 -1.042 0.000 0.460 0.855 0.345 0.000 0.564 -0.997 0.516 3.102 0.529 0.658 1.477 1.028 0.638 0.796 0.641 +0 1.325 -1.343 0.828 0.525 -0.032 0.440 0.711 -1.146 0.000 0.713 0.467 -0.194 0.000 0.916 -0.675 -1.573 2.548 0.525 1.200 1.394 3.102 0.911 0.960 0.981 0.980 0.740 0.750 0.818 +0 1.082 -0.577 -1.740 0.330 0.107 0.831 -2.592 -0.137 0.000 1.700 -0.147 0.803 2.215 1.931 -0.869 -1.088 2.548 0.553 0.158 0.579 0.000 2.076 1.795 0.990 0.908 2.067 1.687 1.348 +0 0.779 0.473 1.546 0.902 -0.723 0.858 0.541 0.304 0.000 1.111 0.146 -0.244 2.215 1.835 -0.126 -1.553 2.548 0.989 1.099 0.970 0.000 0.970 1.008 1.034 0.785 1.419 1.069 0.897 +1 0.510 -0.502 1.283 1.428 0.220 2.544 0.099 -1.212 0.000 0.744 -0.489 0.404 0.000 0.903 0.149 -0.113 2.548 3.322 -1.220 0.836 1.551 3.037 1.886 0.987 0.880 1.561 1.929 1.510 +1 0.654 -1.137 0.053 0.690 -1.699 1.923 -1.043 1.300 0.000 1.346 -0.194 -0.178 0.000 2.390 -1.264 -0.493 1.274 1.875 -0.952 -1.139 3.102 0.828 1.168 0.986 0.877 0.911 0.953 0.853 +0 1.597 0.091 -0.844 1.228 -1.200 1.056 -0.199 -0.409 2.173 1.127 -0.818 1.677 0.000 1.160 -0.861 0.571 2.548 2.065 -1.092 1.010 0.000 1.220 1.000 0.998 0.881 1.188 1.136 1.114 +1 0.948 -0.751 -0.939 1.035 1.715 0.963 0.521 0.620 2.173 0.440 -0.758 -0.458 0.000 0.683 0.896 -1.288 2.548 0.534 1.898 1.308 0.000 0.745 0.751 0.991 0.748 1.026 0.902 0.817 +0 0.460 0.494 -0.403 1.473 -0.370 1.159 -2.180 0.951 0.000 1.352 -0.261 -1.132 2.215 0.792 0.071 0.243 0.000 0.830 2.429 0.608 0.000 0.590 2.131 0.980 1.321 1.213 2.017 1.621 +1 2.549 -0.524 1.716 2.156 -1.269 1.851 -2.412 0.412 0.000 1.480 0.195 -0.645 0.000 0.568 0.796 -0.816 2.548 0.809 0.908 0.271 3.102 1.109 0.850 1.419 1.105 0.434 0.996 0.946 +0 0.866 0.122 0.781 1.087 1.529 0.661 0.638 -1.143 0.000 0.886 0.425 -0.236 2.215 0.550 -0.187 -0.458 1.274 0.837 0.900 0.715 0.000 0.985 0.896 0.995 0.755 0.284 0.668 0.659 +0 0.557 0.274 1.131 1.700 -1.611 1.087 -1.086 -1.181 1.087 1.343 0.701 0.347 0.000 1.170 0.465 0.802 2.548 1.406 0.734 -0.160 0.000 0.803 0.772 0.993 1.703 1.859 1.433 1.296 +0 1.450 -1.520 0.347 0.942 -0.428 1.532 -0.545 -1.176 2.173 0.449 0.212 -1.331 0.000 1.018 -2.413 1.023 0.000 1.601 0.476 0.932 0.000 1.005 0.649 1.040 1.597 1.100 1.083 1.098 +1 0.634 2.178 -0.858 0.966 -0.583 2.775 1.462 1.187 0.000 1.010 0.737 -0.567 0.000 0.957 0.092 -1.136 2.548 2.613 -0.708 -0.585 0.000 0.916 0.723 0.989 0.691 0.517 0.588 0.589 +1 1.245 2.112 0.378 0.708 1.646 0.332 -2.529 1.463 0.000 0.776 1.141 -0.440 1.107 0.472 -0.204 -1.565 1.274 0.652 -0.035 0.175 0.000 0.603 0.548 1.183 1.061 0.732 0.817 0.692 +0 1.041 -0.311 1.188 0.728 -0.160 0.384 0.206 0.032 0.000 0.488 -0.541 -0.434 2.215 1.271 0.150 1.722 2.548 0.915 -0.397 -1.437 0.000 0.933 0.804 1.131 0.684 0.838 0.640 0.590 +0 1.005 1.683 0.469 0.278 1.217 1.266 0.937 -1.076 0.000 1.105 0.762 0.857 2.215 0.633 0.135 -0.169 2.548 0.668 1.216 1.717 0.000 0.898 0.897 0.988 0.643 0.766 0.886 0.800 +0 0.392 -0.226 0.907 1.461 -1.627 1.673 -1.974 0.185 0.000 0.564 -1.894 1.587 0.000 1.579 -0.865 -1.590 2.548 0.862 -0.944 0.145 0.000 0.955 0.888 0.988 0.597 0.431 0.573 0.549 +1 0.919 0.194 -0.976 0.735 0.382 0.619 -0.242 -0.367 0.000 1.303 -0.022 1.671 2.215 0.823 0.662 0.927 1.274 0.746 1.643 1.380 0.000 1.742 1.123 1.071 0.927 0.804 0.920 0.805 +1 0.539 -0.549 0.493 1.731 -1.450 0.562 0.148 1.189 1.087 0.698 0.483 -0.384 2.215 0.691 -0.212 -0.043 0.000 0.544 0.980 -1.328 0.000 0.807 0.745 1.317 1.005 0.925 0.790 0.688 +0 1.408 1.020 -0.673 0.693 1.157 0.720 -0.442 1.195 0.000 0.777 0.516 1.439 0.000 0.619 -1.233 -0.543 2.548 0.546 -1.350 -0.211 0.000 0.877 0.880 1.364 1.212 0.644 0.796 0.868 +0 0.936 0.174 -0.733 0.654 1.317 1.084 2.003 1.262 0.000 1.201 -0.749 -1.250 0.000 2.018 -0.592 -0.345 1.274 0.969 0.519 0.195 3.102 1.725 1.476 1.043 0.959 0.883 1.100 0.959 +1 1.504 -0.080 1.626 1.061 -0.971 1.701 1.540 0.941 0.000 1.558 -1.209 -0.349 2.215 0.954 0.701 -0.229 2.548 1.038 -0.640 1.254 0.000 0.834 1.145 1.257 1.017 1.544 1.158 0.999 +0 0.463 -0.265 1.143 0.445 -0.907 0.744 -0.470 1.684 2.173 0.614 0.926 0.170 0.000 1.077 1.540 -0.324 2.548 0.794 0.216 1.120 0.000 0.758 1.014 0.985 1.693 1.817 1.256 1.037 +1 1.601 0.205 0.104 0.109 -1.544 1.666 -0.458 -1.544 0.000 1.588 0.041 1.066 2.215 1.751 -0.171 -0.327 0.000 2.025 0.564 -0.577 0.000 1.037 0.986 0.987 1.012 0.488 1.061 0.881 +1 0.436 1.226 1.020 0.521 -0.517 1.356 0.539 -1.599 0.000 0.735 -0.010 0.176 0.000 0.883 0.236 1.151 2.548 1.066 2.313 0.267 0.000 0.802 0.794 0.996 0.746 0.972 0.633 0.594 +0 1.448 -0.116 1.070 0.596 0.270 0.818 -0.288 1.665 2.173 1.961 -0.396 -0.277 1.107 0.594 -0.065 -1.308 0.000 1.084 1.934 -1.711 0.000 1.368 1.404 0.988 1.278 1.836 1.488 1.232 +1 0.876 -0.925 -1.729 1.207 -1.274 0.400 -0.663 1.022 0.000 1.077 -0.110 -0.024 2.215 1.045 0.515 -1.287 0.000 1.649 1.061 0.477 3.102 0.723 0.897 1.001 1.326 1.044 1.047 0.849 +1 1.435 0.393 0.264 1.023 -0.161 1.010 -0.169 1.415 2.173 1.092 -0.949 -1.259 2.215 0.520 -1.350 0.455 0.000 0.936 0.069 -1.405 0.000 1.015 0.925 0.998 1.644 1.213 1.323 1.076 +1 1.611 -1.168 1.419 0.335 -1.111 0.684 -0.301 -0.237 2.173 0.465 -1.417 -0.548 0.000 0.577 -0.161 -0.688 0.000 0.655 -1.573 1.516 0.000 0.901 1.026 0.989 1.041 0.846 0.898 0.781 +1 0.903 0.807 -1.008 0.494 -0.413 0.724 -0.108 0.934 2.173 0.708 0.312 -1.544 1.107 0.614 0.583 0.815 0.000 1.206 -0.479 -0.400 0.000 1.043 0.877 0.988 1.253 0.861 0.896 0.836 +1 0.718 -2.143 0.263 0.519 -1.504 0.640 -1.512 1.171 1.087 0.651 -1.488 -1.527 0.000 1.198 -0.662 -0.494 2.548 0.741 -1.181 0.086 0.000 0.900 0.803 0.989 0.685 1.176 0.707 0.629 +1 0.277 -1.792 -1.324 0.678 0.779 0.678 0.015 1.185 0.000 0.491 0.181 -1.581 0.000 1.481 -0.749 -0.264 2.548 1.146 0.234 -0.895 1.551 0.874 1.035 0.980 0.702 0.788 0.730 0.653 +1 0.534 1.390 0.768 0.365 -1.331 0.538 -0.117 1.730 2.173 0.368 -0.597 -0.171 0.000 0.459 -1.697 -0.146 0.000 0.753 -0.838 0.520 0.000 0.465 0.882 0.993 0.662 0.553 0.661 0.638 +1 0.721 0.578 0.266 0.972 -0.725 1.138 -0.255 0.999 2.173 1.135 0.440 -1.085 0.000 0.515 -0.702 -0.144 2.548 0.803 -0.533 -1.113 0.000 0.715 0.718 0.983 1.305 0.855 0.896 0.883 +1 1.097 0.681 1.297 1.372 0.567 0.933 0.037 -0.984 2.173 0.680 0.846 -0.212 2.215 0.743 -0.531 1.707 0.000 0.869 -0.022 -0.375 0.000 0.882 0.834 1.038 1.299 0.905 0.928 0.813 +1 1.035 1.047 1.431 0.462 0.161 0.849 -0.227 -1.550 2.173 0.704 0.324 -0.087 2.215 0.730 0.715 0.756 0.000 0.820 0.078 -0.655 0.000 0.869 0.943 0.986 0.738 1.148 0.767 0.670 +1 0.692 0.540 -0.531 1.278 -1.293 1.535 0.164 -1.470 0.000 1.471 -0.735 0.074 2.215 2.209 -1.331 0.637 2.548 0.455 0.511 0.620 0.000 1.248 1.866 0.985 1.616 1.158 1.610 1.336 +1 0.485 0.212 -0.200 1.908 0.257 0.802 -0.636 1.648 2.173 0.869 0.125 -1.063 2.215 0.730 -0.801 0.742 0.000 0.563 -1.518 -1.227 0.000 0.775 0.925 0.991 1.695 0.926 1.217 1.117 +1 1.157 0.867 -0.518 0.963 -0.392 0.319 0.850 -1.468 0.000 0.895 1.433 1.368 0.000 0.741 1.901 1.567 0.000 1.201 0.348 0.465 3.102 0.721 0.969 0.980 0.754 0.473 0.683 0.774 +1 0.937 -1.100 -1.091 0.739 1.553 1.115 -0.749 -1.658 2.173 1.389 -0.319 0.297 0.000 1.457 0.794 -0.775 0.000 2.499 -0.112 1.023 0.000 0.960 1.164 0.987 0.659 0.650 1.075 0.908 +1 0.599 -0.499 0.117 1.374 -0.984 1.014 -0.507 1.025 2.173 1.122 -0.790 -0.525 2.215 0.919 -0.917 0.196 0.000 0.819 -1.326 1.691 0.000 0.975 0.948 1.052 0.656 1.564 0.925 0.813 +0 0.564 -1.735 0.062 0.185 -1.231 0.728 0.057 0.910 1.087 1.064 -0.011 -0.908 2.215 1.102 0.774 -1.185 0.000 1.361 1.520 -0.029 0.000 0.968 1.103 0.982 0.803 1.293 0.986 0.952 +1 0.430 1.524 -0.996 0.378 0.658 0.792 -0.227 -0.691 2.173 1.342 -0.792 0.629 0.000 0.457 0.705 -1.642 0.000 0.600 -0.987 1.417 3.102 1.503 0.896 0.994 0.776 0.780 0.832 0.753 +1 0.305 1.173 -0.190 0.257 1.042 1.543 0.999 -1.675 0.000 2.166 0.502 0.171 2.215 1.184 -0.161 -1.684 2.548 0.717 -0.442 0.325 0.000 0.919 0.931 0.987 1.039 1.797 1.231 0.970 +0 1.044 0.869 -0.758 0.706 -1.460 1.206 0.122 0.883 1.087 0.846 -0.110 0.121 0.000 1.604 -1.693 -0.994 0.000 1.269 0.767 0.635 3.102 2.323 2.043 0.987 1.469 0.609 1.539 1.585 +0 0.606 0.522 0.922 1.061 -0.271 0.886 -0.650 1.209 2.173 0.764 0.292 -0.921 2.215 1.407 0.285 0.008 0.000 2.007 -1.438 -1.473 0.000 0.919 0.964 0.988 1.147 1.287 1.034 0.874 +0 0.774 -0.606 -0.130 1.562 0.644 1.005 -1.452 -1.726 2.173 1.106 -1.241 -0.103 2.215 0.780 -0.703 -1.298 0.000 1.100 -0.844 -0.644 0.000 0.909 1.044 0.988 0.832 1.550 1.057 0.947 +0 0.461 -0.009 -1.427 1.320 -0.494 1.618 -0.034 1.169 2.173 0.660 2.458 -0.572 0.000 0.331 -2.591 1.552 0.000 1.307 0.225 -0.360 3.102 0.834 0.995 0.987 1.498 1.529 1.084 1.093 +0 1.651 -0.724 -0.003 0.637 0.604 1.031 0.861 -1.730 2.173 0.465 1.314 -1.063 2.215 0.492 0.012 0.324 0.000 0.464 1.624 0.322 0.000 0.594 0.923 0.989 1.073 0.629 1.106 0.881 +0 1.977 1.600 0.129 0.370 -1.618 0.680 1.261 0.726 0.000 1.168 1.240 -1.281 2.215 0.537 1.682 -1.667 0.000 1.043 0.640 -1.585 3.102 0.959 1.046 1.185 0.922 0.371 0.807 0.755 +0 1.487 1.030 0.198 1.399 0.269 1.832 -1.063 1.564 0.000 1.162 0.535 -0.713 2.215 0.874 1.375 -0.457 0.000 1.005 -0.050 -1.469 3.102 0.510 0.729 0.985 1.268 0.685 1.032 0.790 +0 0.472 1.839 0.474 1.223 1.343 1.371 -0.387 -0.755 0.000 1.335 0.164 0.388 2.215 0.977 1.012 -1.692 0.000 0.435 2.489 -1.348 0.000 0.884 1.307 0.996 0.978 0.675 1.041 1.006 +0 1.267 1.363 -1.494 0.472 -1.248 0.607 0.370 -0.679 0.000 0.969 1.704 1.317 0.000 1.369 -0.885 0.100 2.548 0.843 -0.060 0.258 0.000 0.853 0.925 0.982 0.592 1.038 1.384 1.120 +1 0.408 -1.291 0.096 1.091 -0.296 1.418 0.673 1.507 2.173 0.599 -0.510 0.252 0.000 0.449 0.171 -1.385 0.000 0.400 0.378 -0.516 3.102 0.844 1.203 0.985 0.472 0.777 0.891 0.768 +1 0.813 0.110 1.020 0.796 -0.482 0.547 1.183 -1.261 0.000 0.703 -1.389 1.390 2.215 0.998 1.613 0.525 0.000 1.160 -0.234 0.070 3.102 0.650 0.830 1.088 0.940 0.904 1.047 0.863 +0 1.206 -0.267 0.208 0.594 1.402 0.687 0.551 -1.704 0.000 0.364 0.328 -1.372 2.215 0.783 -1.079 -0.608 2.548 0.405 0.444 0.281 0.000 0.786 0.986 1.032 0.669 0.597 0.594 0.621 +1 0.466 -0.732 -0.382 1.825 0.995 1.036 1.133 -1.170 2.173 0.702 -1.457 0.795 0.000 1.408 -0.127 -0.783 2.548 0.526 0.372 0.947 0.000 0.904 1.126 1.209 1.829 1.154 1.289 1.166 +0 0.308 0.478 -1.365 2.144 -1.700 0.817 -0.306 0.005 0.000 0.654 1.038 0.420 0.000 0.747 0.129 1.410 0.000 0.754 -1.077 -0.157 0.000 0.981 0.656 0.990 0.472 0.240 0.421 0.606 +1 2.065 -0.545 -0.907 0.428 0.719 0.978 -0.843 1.035 2.173 0.897 -1.602 1.690 2.215 0.515 -0.340 0.774 0.000 0.794 0.141 -0.096 0.000 0.994 1.103 1.295 1.084 0.952 0.978 0.887 +1 0.660 -0.712 1.606 0.471 -0.562 1.135 0.341 0.404 0.000 1.730 -1.129 -0.872 2.215 1.478 -1.252 0.958 0.000 1.463 -0.185 -1.369 3.102 2.519 1.792 0.985 1.049 0.936 1.538 1.199 +0 0.662 -1.682 0.254 1.132 -0.909 0.914 -0.743 0.479 0.000 1.280 -0.157 -1.538 0.000 1.070 -0.257 1.129 0.000 1.036 2.429 -1.490 0.000 1.068 0.912 1.039 0.919 0.912 0.983 0.961 +0 0.568 -1.189 -0.872 1.624 1.393 1.026 -0.455 -1.399 0.000 1.313 -1.154 0.613 1.107 0.762 0.205 -0.133 1.274 0.617 1.868 -0.300 0.000 0.258 0.557 1.186 1.000 1.050 1.162 1.213 +1 1.111 1.560 -0.689 0.660 -0.530 2.317 -0.741 0.779 1.087 1.047 -0.114 -0.517 1.107 2.357 0.695 -1.527 0.000 1.710 0.649 -0.207 0.000 0.964 1.491 0.992 2.492 2.233 2.093 1.754 +1 1.802 0.828 -1.468 1.536 1.439 1.159 1.380 0.151 2.173 0.652 0.227 -0.712 2.215 0.779 0.289 -0.025 0.000 0.674 1.729 0.605 0.000 0.906 0.802 1.148 1.646 1.192 1.167 0.965 +1 1.632 1.196 0.552 0.288 0.171 0.768 1.277 -1.299 2.173 1.137 1.306 1.263 0.000 0.458 0.968 -0.376 0.000 1.484 2.035 -0.692 0.000 0.725 0.827 0.981 0.844 1.291 0.962 0.929 +0 0.315 1.183 -0.662 0.470 0.255 0.335 0.951 0.798 2.173 0.619 1.629 1.596 0.000 0.963 1.123 -0.992 2.548 0.398 0.134 0.438 0.000 0.776 0.688 0.994 0.697 0.714 0.664 0.638 +0 2.002 0.597 0.906 1.390 0.357 0.869 0.347 0.251 2.173 2.277 -0.351 -1.134 0.000 0.925 -0.819 -1.700 0.000 0.972 0.262 -1.009 3.102 1.245 0.827 1.096 0.765 0.882 1.087 1.243 +0 0.342 2.116 0.135 1.931 1.302 0.726 1.235 0.395 2.173 0.928 0.558 -0.234 2.215 1.202 1.278 -0.786 0.000 0.865 1.385 -1.581 0.000 0.752 1.001 0.986 1.445 0.770 1.043 0.928 +1 0.969 0.322 0.500 1.210 -0.313 0.645 -1.398 1.215 2.173 0.649 -0.395 -0.355 0.000 1.271 1.244 -1.693 0.000 1.180 1.073 -0.684 0.000 1.067 0.844 1.003 1.311 1.016 1.241 1.088 +0 0.775 -0.724 -1.033 0.673 -0.156 0.870 0.448 0.142 2.173 0.915 -1.909 -0.805 0.000 1.133 -1.390 1.476 2.548 1.443 0.253 1.188 0.000 0.900 0.896 0.994 0.831 1.840 1.462 1.215 +1 0.806 -0.786 1.701 0.751 1.413 1.216 -1.195 -1.073 0.000 1.739 -0.405 0.756 2.215 0.694 -0.475 0.263 0.000 0.934 -0.982 0.216 3.102 0.880 0.956 0.992 0.746 0.701 0.862 0.736 +1 0.470 -0.240 1.010 0.799 -1.036 0.679 0.048 -1.211 2.173 0.845 -0.505 0.266 0.000 0.954 0.319 0.024 0.000 0.450 0.162 0.401 0.000 0.686 1.056 0.993 0.662 0.731 0.832 0.731 +1 2.191 0.725 0.379 0.715 0.114 0.967 0.582 -1.446 1.087 1.482 -0.554 1.133 2.215 1.411 -2.391 -0.642 0.000 0.588 0.530 -1.152 0.000 1.181 1.839 0.986 1.425 1.677 1.819 1.818 +1 1.156 -0.205 0.784 1.604 0.364 1.566 -0.411 -1.711 2.173 1.754 -0.152 -0.684 0.000 0.648 1.293 -0.456 0.000 0.938 -0.906 0.935 3.102 1.465 1.392 0.997 1.588 0.983 1.294 1.265 +1 0.563 -0.399 -1.400 1.293 -0.076 1.038 -1.101 -0.993 0.000 1.166 -0.222 0.356 0.000 1.003 -0.313 -0.364 0.000 2.819 0.076 1.286 1.551 1.006 1.229 1.098 1.124 0.841 0.997 0.897 +1 1.862 -0.351 0.695 0.952 0.257 1.168 0.014 -1.066 2.173 1.166 0.597 1.478 1.107 0.437 -1.017 -0.364 0.000 1.007 0.316 -0.503 0.000 0.607 1.051 0.994 1.574 1.391 1.284 1.019 +0 0.458 0.953 -0.724 1.262 0.721 0.606 0.490 -1.157 2.173 0.958 0.898 -0.005 0.000 1.330 -0.950 1.586 0.000 0.528 1.709 -0.726 0.000 0.785 0.892 1.016 0.983 0.997 0.883 0.798 +0 1.030 0.716 0.225 1.720 0.895 1.390 -0.051 -0.632 0.000 0.430 -2.800 1.186 0.000 1.586 -1.360 -1.354 0.000 1.222 0.058 1.496 3.102 0.808 0.858 1.049 0.822 0.183 0.723 0.871 +1 1.278 2.224 0.845 0.491 -1.106 0.453 1.028 0.098 1.087 0.701 0.462 -1.077 2.215 0.786 0.692 1.222 0.000 0.862 0.876 -0.566 0.000 0.917 0.716 1.079 0.828 0.760 0.809 0.713 +0 0.318 0.709 -1.278 2.134 -0.401 0.990 -1.377 1.107 1.087 0.444 -1.660 1.711 0.000 0.708 -0.428 -0.913 0.000 0.449 0.684 0.884 3.102 0.829 0.965 0.995 0.673 0.960 1.808 1.512 +1 0.639 -0.651 1.540 1.018 1.589 0.403 0.046 -1.731 0.000 0.735 1.250 -0.300 2.215 1.597 0.246 -0.152 0.000 1.102 1.247 -1.640 0.000 0.849 0.971 0.980 1.064 0.916 1.333 1.029 +1 1.749 -1.253 -1.248 0.213 -0.697 1.072 -1.216 -0.634 0.000 1.700 -0.450 1.186 2.215 1.778 0.166 0.270 2.548 0.468 1.604 1.164 0.000 2.727 1.941 0.977 1.235 1.488 1.496 1.301 +1 0.711 1.387 -0.550 0.682 1.169 0.840 0.675 -0.416 0.000 0.670 0.423 1.652 2.215 1.162 -1.547 0.720 2.548 1.221 -1.052 -1.394 0.000 0.820 0.941 0.986 2.025 1.390 1.340 1.174 +1 2.221 -0.485 -0.529 1.659 -1.097 1.174 -0.588 1.232 2.173 0.516 0.873 0.734 2.215 0.480 0.239 0.155 0.000 0.727 -0.675 0.738 0.000 0.491 0.729 1.301 1.295 1.061 1.266 0.968 +1 0.646 1.537 0.597 0.585 -0.993 0.415 -0.661 -0.411 2.173 1.071 1.309 -1.148 0.000 1.105 -0.176 1.601 2.548 0.742 1.322 -0.218 0.000 0.874 1.170 0.984 0.830 0.844 0.915 0.784 +0 1.087 -0.371 -1.135 0.986 1.413 1.557 -0.756 0.358 1.087 0.805 -0.740 -0.141 0.000 1.329 0.961 -1.615 0.000 2.037 -0.953 -0.881 1.551 0.841 1.651 1.073 1.399 1.733 1.539 1.251 +0 1.526 0.015 0.377 1.189 0.370 2.183 -0.313 0.248 1.087 1.187 -1.013 -1.736 0.000 2.235 -0.533 -1.236 2.548 0.977 0.086 1.505 0.000 0.903 0.936 0.989 0.698 2.703 1.564 1.326 +0 0.480 1.213 -1.015 0.266 -1.263 1.325 -0.053 0.257 2.173 0.915 0.854 -1.458 2.215 1.408 2.276 -1.334 0.000 0.644 1.589 0.852 0.000 1.018 1.045 1.000 1.052 1.799 1.584 1.294 +1 0.477 1.135 0.031 0.819 1.296 1.230 0.902 -1.424 2.173 1.009 0.063 0.280 2.215 0.398 1.060 0.973 0.000 0.510 -1.712 -0.307 0.000 0.564 0.803 0.987 1.128 1.787 1.139 0.863 +1 0.847 -0.198 0.589 0.753 -1.645 0.926 -0.963 0.268 0.000 1.480 -0.879 -1.251 0.000 0.834 -0.740 0.969 2.548 0.775 -0.250 -0.407 1.551 2.437 1.362 1.000 0.594 0.602 0.897 0.798 +1 1.502 1.235 -0.315 0.909 0.387 0.979 1.103 -0.781 2.173 1.137 1.371 0.526 2.215 1.361 1.278 1.279 0.000 2.196 -1.677 1.656 0.000 0.478 3.022 0.985 0.742 1.453 2.434 2.159 +1 1.409 -1.383 0.839 1.938 1.094 1.174 -2.623 -1.023 0.000 1.580 -0.498 -0.586 2.215 0.838 -0.857 0.256 0.000 0.943 1.605 1.586 0.000 2.146 1.171 0.992 1.680 0.806 1.074 1.228 +1 1.165 -0.383 -0.555 0.299 1.551 0.830 -0.577 0.269 2.173 0.562 -0.921 1.593 2.215 0.724 1.309 -1.327 0.000 0.623 0.373 1.196 0.000 0.682 0.879 0.990 0.848 0.952 0.858 0.758 +1 1.842 0.028 0.346 0.722 -0.478 0.706 2.140 1.680 0.000 0.740 0.565 -1.321 1.107 1.094 0.686 0.842 0.000 0.618 -0.298 -1.207 0.000 1.021 0.830 1.079 0.693 0.373 0.674 0.654 +1 2.836 -0.194 1.465 0.223 0.455 1.387 -1.969 -0.347 0.000 1.212 0.981 0.516 2.215 0.613 0.204 -1.408 0.000 0.791 0.940 -1.302 3.102 2.610 2.268 0.994 1.309 0.883 2.074 1.746 +1 1.802 1.481 -0.873 0.561 -1.042 1.125 1.176 0.878 2.173 0.758 0.715 -1.725 1.107 0.378 1.608 0.596 0.000 1.163 -0.248 -0.123 0.000 0.999 0.927 0.976 1.490 1.019 1.076 1.006 +1 0.365 0.334 1.060 1.061 -0.253 0.770 1.179 -0.724 2.173 0.593 1.424 0.614 0.000 1.072 1.216 1.351 0.000 1.174 1.919 -0.404 0.000 0.865 0.902 0.985 0.852 1.124 0.894 0.770 +0 0.543 0.053 0.195 1.206 1.437 1.140 -1.940 0.242 0.000 1.352 -1.015 -1.244 2.215 1.086 -0.936 1.184 2.548 0.782 -1.885 -0.817 0.000 0.844 1.038 1.009 1.099 1.050 0.925 0.825 +0 0.460 -1.249 1.561 0.825 0.232 0.783 0.029 0.693 0.000 0.972 -0.197 -0.220 2.215 0.819 -0.243 1.455 0.000 1.181 -1.435 -1.644 0.000 0.937 0.928 0.986 0.709 0.747 0.762 0.679 +1 1.110 0.944 -0.582 1.145 0.092 1.085 0.364 1.453 1.087 0.762 -1.966 -0.135 0.000 0.877 -0.946 -1.470 2.548 0.739 -0.754 -0.896 0.000 0.845 0.801 0.986 1.404 1.099 1.213 1.420 +0 0.928 -0.271 -1.611 0.958 1.294 1.261 0.916 -0.415 2.173 1.222 1.148 -0.091 0.000 2.110 1.517 1.609 0.000 1.554 -0.114 0.474 3.102 0.465 1.552 0.986 1.358 1.346 1.321 1.150 +1 1.328 0.741 1.358 1.446 -0.177 0.695 -0.337 -0.899 2.173 0.632 -0.778 0.155 2.215 0.505 1.032 -0.979 0.000 0.468 1.421 -0.519 0.000 0.266 0.849 1.886 1.319 0.824 1.001 0.810 +0 1.292 -1.242 1.562 0.352 1.736 0.422 0.220 -0.691 2.173 0.764 -0.320 0.758 0.000 1.267 -0.812 0.169 2.548 0.742 -1.429 0.039 0.000 0.944 0.921 0.987 0.959 0.832 0.775 0.718 +0 0.722 0.769 0.402 0.755 -0.706 0.623 0.240 -1.417 2.173 0.492 0.178 1.470 0.000 0.640 -0.381 -0.142 2.548 0.724 1.265 0.620 0.000 0.854 0.786 0.985 0.758 0.765 0.696 0.639 +1 0.615 0.839 -0.767 1.261 0.258 2.247 -0.196 1.439 0.000 3.664 -0.834 -0.517 0.000 1.221 -0.394 0.854 0.000 1.195 0.374 1.203 3.102 0.904 0.634 0.987 0.546 0.108 0.483 0.522 +1 0.414 0.829 -0.924 0.812 -0.354 1.055 0.086 0.211 2.173 1.301 -0.861 1.657 0.000 0.761 -0.024 1.094 2.548 0.581 -1.138 1.554 0.000 0.968 0.819 0.995 1.558 0.800 1.134 1.454 +0 0.556 1.301 0.363 0.855 1.312 0.528 1.230 -0.839 2.173 0.672 2.038 -0.520 0.000 0.500 1.972 0.509 0.000 0.796 0.322 1.557 3.102 0.712 0.844 0.986 0.867 0.646 0.680 0.634 +1 0.951 -2.027 -0.086 0.406 1.271 0.854 -0.939 0.919 0.000 1.204 -1.137 -1.059 0.000 0.441 1.163 1.452 2.548 0.697 -1.798 1.102 0.000 0.982 0.963 0.987 1.150 0.294 0.834 0.784 +1 0.490 0.540 -0.263 0.761 0.846 1.462 -0.835 -1.371 2.173 0.709 -1.214 -0.249 0.000 0.898 -1.101 1.377 1.274 1.007 -1.279 0.585 0.000 0.768 0.783 0.994 2.085 0.919 1.477 1.341 +0 0.749 -0.572 -0.668 1.894 -1.532 1.614 -0.586 0.459 2.173 0.869 -0.400 -1.574 0.000 1.260 0.484 -0.837 2.548 0.665 -1.141 0.698 0.000 1.009 1.073 1.158 1.649 1.944 1.299 1.072 +0 0.821 -1.325 0.287 0.716 -1.569 0.349 -1.035 -1.267 0.000 0.647 -1.459 -0.655 0.000 0.567 -2.189 0.688 0.000 1.204 -0.708 -0.098 0.000 0.866 0.800 1.057 0.594 0.289 0.553 0.552 +1 0.509 1.327 -1.693 0.261 0.069 0.828 -0.338 1.494 0.000 0.961 0.013 0.129 0.000 1.116 -0.056 1.034 1.274 1.278 -1.651 -0.434 0.000 1.818 1.112 0.980 0.651 1.014 0.898 0.755 +0 0.438 1.392 0.296 2.501 0.006 0.946 -0.136 -1.315 2.173 0.829 -0.224 1.425 0.000 0.455 -0.551 -0.014 0.000 1.017 0.473 1.533 3.102 0.924 0.912 0.979 0.950 0.676 1.000 0.878 +0 0.874 0.845 1.136 2.139 -1.655 0.594 -0.141 0.162 2.173 1.010 -1.047 -0.470 2.215 0.570 0.475 -0.735 0.000 1.204 1.149 0.589 0.000 0.940 0.843 1.113 1.803 0.827 1.284 1.084 +0 1.209 -1.003 -1.401 0.251 1.298 1.398 -0.061 0.506 0.000 1.385 -0.492 -1.182 2.215 0.925 0.268 -0.001 0.000 1.727 -0.218 0.953 3.102 0.975 0.864 0.985 0.873 1.321 1.129 1.128 +1 0.964 0.281 0.602 1.107 -1.560 1.464 -0.313 0.539 0.000 0.962 -0.725 -1.175 1.107 1.006 -1.261 -1.132 0.000 1.348 0.285 -0.761 3.102 0.832 1.123 1.331 0.862 0.696 0.758 0.728 +1 0.431 -0.695 0.622 0.647 0.781 0.545 -1.487 -1.200 0.000 0.808 0.113 -0.897 2.215 0.409 -1.359 0.985 0.000 0.929 1.136 1.453 0.000 0.782 0.946 0.992 0.753 0.675 0.691 0.806 +0 1.288 -1.203 -0.728 0.266 -1.432 1.381 -0.259 1.235 2.173 1.336 -2.223 -0.227 0.000 0.576 -0.603 -1.654 0.000 0.698 -1.369 0.126 3.102 1.775 0.987 0.978 1.297 1.169 1.366 1.136 +0 0.533 0.083 -0.154 1.295 0.886 0.924 0.542 -1.511 0.000 0.939 -0.309 0.467 0.000 1.128 -1.054 -0.244 2.548 0.807 0.042 -0.752 0.000 0.898 0.694 0.986 1.080 1.538 0.959 0.891 +1 1.401 0.795 -1.709 1.392 1.551 0.663 0.490 0.272 0.000 0.993 1.206 -0.554 2.215 0.770 0.213 -0.077 2.548 0.840 0.401 1.160 0.000 0.817 0.987 0.997 0.965 0.623 0.927 0.839 +1 0.491 -2.116 0.564 1.785 1.368 0.724 -1.219 -0.406 2.173 0.558 -0.992 -0.975 0.000 0.330 1.140 -0.514 2.548 0.429 -1.989 1.166 0.000 0.761 0.876 0.986 1.403 0.967 1.438 1.093 +1 1.185 -0.447 -1.355 0.318 -0.041 1.166 -0.122 0.094 0.000 0.960 0.011 1.678 2.215 0.491 1.296 1.077 0.000 0.670 0.470 0.721 0.000 0.863 0.983 0.989 0.676 0.631 0.848 0.750 +1 0.915 0.172 -1.613 1.108 -1.053 0.831 0.679 -0.599 0.000 1.694 -0.129 0.975 2.215 1.026 0.419 0.329 1.274 0.447 -1.170 -0.981 0.000 1.202 0.990 0.979 1.230 0.878 1.006 0.958 +0 1.819 -0.873 1.669 0.093 -0.379 1.068 0.666 -0.428 2.173 0.519 -0.546 1.119 0.000 0.295 -0.108 -0.523 2.548 0.458 -1.402 1.003 0.000 0.381 1.307 0.978 0.591 0.291 0.901 0.792 +1 1.832 -0.448 1.628 1.037 -1.564 2.573 2.052 0.737 0.000 1.335 -0.605 -0.853 2.215 2.406 0.527 -0.645 0.000 1.077 0.223 -0.172 3.102 0.834 0.754 0.984 1.139 0.797 0.987 1.052 +0 1.228 0.097 -1.122 0.861 -0.232 0.760 0.013 1.359 2.173 0.747 -0.389 0.161 0.000 0.882 -0.196 1.009 2.548 0.720 -1.327 -0.615 0.000 0.862 1.101 1.024 0.851 0.338 0.726 0.745 +1 1.362 1.350 0.263 1.986 0.918 2.850 -0.546 -0.965 0.000 0.797 0.727 1.172 1.107 0.665 0.529 0.050 2.548 1.399 1.716 0.689 0.000 1.007 0.829 1.268 0.807 0.659 0.655 0.656 +0 0.880 0.372 -1.197 0.660 0.721 0.436 -0.144 -1.471 0.000 1.185 0.119 1.283 2.215 1.173 1.506 0.045 0.000 1.089 -0.241 -0.037 1.551 1.145 0.913 1.042 0.767 0.975 0.884 0.760 +1 0.425 0.146 1.031 2.097 0.343 1.395 0.643 0.035 0.000 1.223 0.811 -1.114 1.107 0.988 -0.154 1.702 2.548 1.034 -0.938 -1.730 0.000 0.529 0.948 0.995 1.090 0.902 0.998 0.879 +1 0.370 -2.328 0.629 2.233 1.262 0.836 -1.265 -0.280 1.087 0.515 -0.831 -1.678 0.000 1.718 -0.877 -0.933 2.548 0.820 -0.253 0.467 0.000 0.827 0.921 0.991 1.289 0.862 1.062 0.879 +0 1.577 -0.364 0.068 1.957 0.034 1.099 0.561 -1.465 0.000 1.063 0.618 1.579 0.000 1.334 -0.084 1.353 1.274 1.117 -0.842 -0.188 3.102 0.892 0.905 0.971 0.504 1.020 0.973 1.305 +1 0.474 1.571 0.085 0.867 -0.462 0.802 1.437 1.598 0.000 0.650 1.786 0.680 0.000 0.874 0.808 -1.272 0.000 1.209 0.411 1.249 3.102 0.900 0.755 0.982 0.738 0.989 0.976 0.868 +1 0.677 -1.667 1.654 1.042 -0.514 1.427 -1.426 -1.463 2.173 1.021 -1.133 0.884 2.215 1.524 -1.698 0.318 0.000 1.573 -2.043 0.102 0.000 0.959 0.972 1.080 0.929 1.537 1.073 0.899 +0 0.854 0.573 -0.283 1.384 0.507 0.892 -0.370 0.267 2.173 1.442 -0.608 -1.724 2.215 1.311 -0.062 -1.429 0.000 0.605 1.295 -1.278 0.000 0.897 1.009 0.987 0.776 1.641 1.105 1.023 +0 0.448 0.449 1.720 0.832 0.398 2.069 -0.561 -1.310 2.173 2.121 -2.766 0.091 0.000 2.703 -0.060 0.915 2.548 1.121 -0.479 -1.665 0.000 0.892 0.841 0.985 0.809 2.773 1.345 1.087 +1 0.766 0.628 1.011 0.421 -1.630 0.810 2.112 -0.804 0.000 1.212 0.335 1.562 0.000 1.088 0.045 0.357 2.548 1.099 -1.062 -0.199 3.102 1.054 0.978 0.995 0.826 0.718 0.795 0.698 +0 0.591 0.922 1.036 1.039 -0.689 0.704 0.259 1.020 2.173 0.559 0.678 -0.453 2.215 1.613 0.172 -1.292 0.000 0.820 0.587 0.616 0.000 1.297 0.893 1.085 0.886 0.919 0.744 0.695 +1 0.960 0.729 -0.016 0.790 -0.715 1.048 -0.770 1.200 2.173 0.411 -1.410 -1.195 0.000 0.619 0.585 1.522 2.548 1.331 -0.435 0.102 0.000 0.971 1.104 0.995 0.738 0.826 0.888 0.797 +1 1.233 1.278 0.160 1.310 -0.216 0.541 0.748 1.499 1.087 0.471 0.838 -1.040 0.000 0.799 0.266 -1.524 0.000 0.961 -1.564 1.309 0.000 0.482 0.538 0.997 1.156 0.769 0.905 0.814 +1 1.687 -0.032 0.254 0.751 0.044 0.888 0.762 -1.276 2.173 1.019 -0.157 1.560 2.215 0.753 -0.267 0.556 0.000 1.117 1.372 -1.381 0.000 0.868 0.991 0.976 1.275 1.027 1.049 0.883 +1 0.753 -0.278 0.418 0.779 -1.083 1.607 0.057 0.934 0.000 1.101 -0.017 -1.124 2.215 2.497 -0.851 -0.356 0.000 1.357 -1.332 -0.471 0.000 0.874 0.955 1.036 0.754 0.850 0.966 0.843 +1 0.641 0.894 1.076 1.262 -1.306 0.849 0.481 0.724 0.000 0.928 0.418 0.204 0.000 1.250 0.869 -1.227 2.548 0.445 -0.611 -0.535 0.000 0.854 0.900 1.046 0.628 0.663 0.846 0.796 +1 0.939 0.243 0.636 0.797 -0.596 0.995 0.316 -0.478 2.173 0.844 0.985 -1.236 2.215 1.759 -0.144 1.364 0.000 1.026 -0.197 0.530 0.000 1.014 1.224 1.073 0.790 0.976 1.040 0.877 +0 0.523 0.402 0.055 1.156 -0.851 0.590 0.575 0.939 2.173 0.735 -0.073 -1.619 2.215 0.776 2.705 -0.308 0.000 0.406 -1.968 1.296 0.000 0.618 0.879 0.987 0.959 0.788 0.896 0.814 +0 0.962 -0.803 -0.057 0.509 0.990 1.105 -1.112 -0.401 1.087 0.932 -0.275 1.454 2.215 1.082 -0.801 1.220 0.000 0.999 0.929 -1.400 0.000 1.567 0.993 0.989 0.910 1.620 1.163 0.957 +1 0.431 0.736 0.022 0.675 -0.068 0.711 1.214 0.405 2.173 1.391 1.022 -1.371 1.107 0.550 2.699 -0.015 0.000 0.931 1.963 1.681 0.000 0.825 0.964 0.996 1.167 1.468 0.962 0.851 +1 1.154 0.646 -0.386 0.780 0.370 0.762 -1.064 1.006 0.000 2.105 -0.086 -1.260 2.215 0.636 1.311 0.866 0.000 0.905 1.365 0.034 0.000 0.695 1.188 0.984 0.673 1.076 1.009 0.834 +1 0.784 -1.019 1.146 1.009 0.261 1.210 0.596 -1.057 2.173 0.712 -1.112 0.682 0.000 0.707 -0.204 1.380 0.000 0.669 1.216 0.743 3.102 0.834 0.840 0.986 1.903 1.040 1.350 1.052 +1 0.586 -1.396 0.349 0.313 1.579 2.581 0.616 -1.511 0.000 3.263 -0.619 0.165 2.215 0.852 -0.910 0.781 0.000 1.613 -0.005 0.294 3.102 0.696 0.759 0.990 1.060 0.707 0.788 0.721 +0 0.530 -1.176 1.471 0.725 0.255 0.395 -1.423 -0.125 0.000 0.696 -0.091 0.191 0.000 1.521 -1.182 -1.532 2.548 0.516 0.697 1.417 3.102 0.864 0.811 0.989 0.916 0.924 0.818 0.714 +0 1.729 1.223 1.279 0.698 0.605 0.715 2.818 -0.420 0.000 0.698 0.166 -1.076 2.215 0.953 -1.072 0.501 1.274 1.008 -0.207 1.404 0.000 1.124 1.006 0.987 1.388 1.063 1.043 1.051 +1 1.504 -1.419 -1.247 0.639 0.984 0.845 -0.824 0.557 2.173 0.449 -1.895 -0.400 0.000 0.401 -0.856 1.434 0.000 0.458 -2.364 -1.005 0.000 0.734 0.800 1.229 1.106 0.792 0.878 0.732 +0 1.809 -0.711 -0.993 1.336 -1.384 0.668 1.511 1.026 0.000 1.034 0.392 0.924 0.000 1.199 -0.140 -0.389 2.548 1.444 0.854 0.224 1.551 1.056 0.897 0.975 0.869 0.820 0.971 1.193 +0 0.527 0.226 1.589 1.443 0.747 1.136 0.720 -0.819 2.173 0.989 0.653 -1.296 2.215 1.059 1.683 0.856 0.000 0.850 0.140 -0.312 0.000 0.837 0.912 0.991 1.247 0.651 0.915 0.829 +0 1.483 -0.707 1.307 0.474 1.044 0.759 -1.456 -0.409 0.000 0.835 -0.601 -0.993 2.215 1.169 0.338 1.008 0.000 1.368 -1.634 -0.959 0.000 0.835 0.843 0.976 0.787 0.697 0.721 0.862 +1 0.374 0.785 -0.924 0.676 -0.936 1.064 -0.433 1.499 0.000 1.564 -0.472 0.292 2.215 1.415 -0.171 -0.729 1.274 1.423 -0.505 0.981 0.000 0.863 1.325 0.994 1.031 1.280 1.106 0.946 +1 0.708 0.893 -0.011 0.756 1.357 1.118 2.309 -0.994 0.000 1.163 -0.176 0.835 2.215 0.606 0.411 -0.326 0.000 0.933 -0.299 1.515 3.102 0.483 0.690 0.987 0.756 0.547 0.691 0.610 +0 0.580 -0.226 -1.304 0.701 -1.535 1.785 -1.710 1.491 0.000 1.356 -1.185 0.061 0.000 1.144 -2.057 -0.438 0.000 1.187 -0.881 -0.521 1.551 1.331 0.867 0.977 0.875 0.674 0.793 1.195 +1 0.604 1.936 0.798 1.423 1.163 3.059 -1.635 -0.905 0.000 1.436 0.200 1.149 0.000 1.633 1.355 0.266 2.548 1.285 1.177 0.693 3.102 2.213 1.298 0.978 0.900 0.418 0.964 0.823 +1 1.030 -0.164 1.538 0.990 0.337 0.661 0.549 -0.920 2.173 1.047 -0.100 -1.464 0.000 1.108 -0.591 0.570 2.548 0.783 0.529 0.118 0.000 1.249 0.898 1.235 0.739 1.242 0.826 0.777 +0 0.443 -0.559 -0.051 0.778 0.730 1.317 0.922 -0.553 1.087 0.862 0.112 1.372 0.000 0.983 0.789 0.929 2.548 1.195 0.605 -1.470 0.000 0.834 0.738 0.998 1.040 1.379 0.981 0.846 +1 1.280 0.126 1.440 0.604 -0.592 1.085 0.657 0.015 0.000 0.798 0.709 -0.382 2.215 1.492 1.010 1.235 0.000 1.468 0.537 -0.948 3.102 0.875 1.062 1.177 0.785 0.478 0.798 0.777 +0 1.185 0.703 0.414 0.733 0.877 0.796 -0.357 1.575 2.173 1.131 0.572 -1.007 0.000 1.365 0.102 -0.495 2.548 0.526 -0.393 0.661 0.000 1.147 1.088 0.989 0.920 1.280 0.882 0.837 +0 1.634 -0.262 1.191 0.357 0.689 1.073 1.440 -0.750 0.000 0.762 -0.357 -1.108 2.215 1.235 0.623 0.596 0.000 2.182 -0.614 0.568 1.551 1.095 1.115 0.997 0.852 1.182 0.966 0.845 +0 0.632 0.571 0.179 1.788 1.099 0.626 0.052 -0.082 0.000 0.926 0.015 -0.945 0.000 0.779 -0.735 -1.454 2.548 0.568 0.820 1.422 3.102 1.135 0.932 1.086 0.555 0.583 0.650 0.768 +0 0.758 0.096 1.217 1.324 -1.089 0.729 -1.443 0.866 0.000 1.081 0.165 -0.561 1.107 0.334 -1.128 -0.639 0.000 0.873 -0.159 0.345 3.102 0.872 0.701 1.214 0.881 0.659 0.811 0.827 +1 0.619 0.775 -0.830 1.351 1.384 0.750 0.793 0.252 1.087 0.709 1.523 0.327 0.000 0.698 -0.904 -0.917 0.000 0.595 0.787 -1.588 3.102 0.925 0.704 1.155 0.555 0.706 0.638 0.600 +0 1.284 1.278 1.390 0.774 -1.665 0.546 0.939 -0.692 2.173 0.545 1.829 -0.243 0.000 0.754 1.805 0.910 0.000 0.696 -1.639 0.883 0.000 0.918 0.823 0.981 0.969 0.648 0.824 0.713 +0 0.949 -0.383 -1.405 1.087 -0.543 0.382 0.446 0.917 1.087 0.652 -0.957 0.696 2.215 0.402 0.779 -0.006 0.000 0.484 -2.257 -0.822 0.000 0.591 1.013 0.987 0.838 0.591 0.694 0.719 +1 0.808 2.013 0.922 0.063 -0.171 1.653 -0.099 -1.181 0.000 1.198 -0.184 0.720 2.215 0.653 0.762 -0.182 0.000 1.656 -0.159 0.073 3.102 0.879 1.185 0.987 1.034 0.700 0.966 1.271 +0 0.412 -1.222 -0.002 0.481 0.737 0.599 -2.505 -1.188 0.000 0.600 -0.024 1.415 0.000 1.222 0.447 -0.086 2.548 1.663 0.179 -1.426 3.102 2.239 1.798 0.983 0.665 1.029 1.433 1.167 +0 1.664 -0.185 1.530 0.582 -1.372 0.535 -0.719 0.248 2.173 0.919 0.686 -0.651 2.215 0.366 2.151 -0.170 0.000 0.471 -0.924 -0.395 0.000 1.262 0.879 0.983 1.013 1.098 0.894 0.847 +1 2.207 -0.601 1.026 1.519 0.635 1.247 -1.247 -1.461 2.173 1.555 -0.706 -0.510 0.000 0.809 0.429 0.199 2.548 0.757 -2.343 -0.943 0.000 1.838 1.602 0.992 1.703 1.747 1.300 1.380 +1 0.640 -1.416 -1.310 0.130 -1.191 0.837 -0.160 0.790 2.173 1.247 -0.978 -0.920 0.000 0.558 -0.867 0.272 2.548 0.714 0.590 -0.115 0.000 1.152 0.834 0.977 0.890 0.516 0.843 0.738 +0 0.999 0.071 0.489 0.356 1.003 1.454 2.324 0.476 0.000 1.706 -0.525 -1.200 2.215 0.909 -0.658 -1.631 0.000 1.170 -1.370 -0.955 0.000 0.848 0.784 0.994 0.825 1.211 0.925 0.799 +0 1.391 -0.717 -0.436 0.578 -1.218 0.375 -0.769 0.347 2.173 0.369 -2.243 -1.240 0.000 1.078 -0.776 -1.637 2.548 0.667 0.510 0.961 0.000 0.847 0.887 0.984 0.693 0.774 0.660 0.754 +1 0.734 0.330 1.378 0.841 0.744 0.516 -1.005 1.306 0.000 0.764 -0.213 -1.606 0.000 1.799 0.100 -0.594 2.548 1.619 -0.703 -0.077 3.102 0.854 1.061 0.992 1.208 0.872 1.043 1.028 +0 1.327 0.496 0.449 1.389 0.374 0.970 1.084 -1.176 0.000 0.943 0.053 -0.115 2.215 1.811 0.894 -1.591 0.000 1.201 0.348 1.353 3.102 0.880 0.909 0.979 0.917 0.948 0.977 1.060 +0 0.496 1.440 -0.982 0.670 -1.459 0.652 0.440 0.833 2.173 0.680 2.504 -0.101 0.000 0.547 -0.859 1.087 0.000 0.965 -0.359 -1.294 0.000 0.827 0.760 0.983 0.457 0.481 0.539 0.542 +1 1.464 -0.523 -0.775 1.184 -1.364 0.938 -0.750 0.403 2.173 0.980 -0.647 1.192 2.215 0.943 -0.250 -1.390 0.000 0.443 2.332 0.519 0.000 0.954 0.901 0.987 1.283 0.922 1.003 0.870 +1 0.784 0.895 0.018 2.781 -0.299 1.149 0.310 1.392 0.000 1.562 1.002 -1.213 2.215 1.376 1.474 0.758 0.000 0.944 1.251 -0.519 0.000 1.150 0.702 0.990 1.481 0.933 1.000 1.010 +0 0.400 1.064 -1.597 0.865 0.295 0.924 0.610 -0.532 2.173 1.465 -0.479 1.143 0.000 0.603 -2.263 -0.459 0.000 1.109 0.343 -1.541 3.102 2.211 1.586 0.986 0.823 0.852 1.413 1.123 +0 1.156 -0.041 -0.575 1.367 -1.061 0.594 -1.300 0.424 0.000 0.706 0.319 0.712 2.215 0.841 0.563 1.313 2.548 0.574 -1.266 -1.659 0.000 0.855 0.939 0.990 0.897 0.439 0.756 0.903 +1 0.857 -0.640 -1.063 0.658 1.466 0.967 -0.748 -0.140 0.000 0.785 0.235 1.507 2.215 0.811 -1.038 0.697 1.274 0.722 -1.493 -0.287 0.000 0.712 0.740 0.991 0.863 0.850 0.844 0.786 +0 0.671 -0.350 -0.605 1.624 1.560 0.962 -1.645 -0.537 0.000 0.719 1.343 1.004 1.107 1.373 0.370 1.280 2.548 1.003 1.205 -0.970 0.000 0.745 0.831 1.343 1.191 0.596 0.836 0.777 +1 1.444 1.002 -0.881 1.068 -0.535 0.547 1.616 1.605 0.000 0.372 0.793 0.537 0.000 1.499 0.635 1.216 1.274 0.911 1.412 0.195 3.102 0.883 0.717 0.983 0.835 0.849 0.874 0.792 +0 2.015 -0.132 0.491 0.673 0.867 0.875 -0.540 -1.119 2.173 0.613 -1.157 -1.674 0.000 0.508 1.780 0.854 0.000 1.507 -0.672 -0.542 3.102 0.842 0.768 0.986 1.079 0.625 1.016 0.853 +0 0.305 2.263 -1.680 2.012 1.192 1.433 -0.890 -0.279 2.173 0.745 -0.158 -1.295 0.000 0.639 -1.364 1.107 0.000 1.069 -0.170 0.991 3.102 1.161 0.818 0.987 2.445 1.279 1.550 1.311 +0 0.552 0.146 -1.597 1.442 0.270 1.141 1.529 0.445 0.000 0.552 1.310 -0.793 0.000 1.880 -0.881 -1.142 2.548 2.129 0.337 1.488 3.102 1.243 1.119 1.228 1.274 1.556 1.055 0.958 +1 0.519 -2.270 -1.371 0.972 1.709 0.854 -1.342 0.229 0.000 0.898 -0.981 -0.543 1.107 1.500 -1.198 1.652 0.000 0.628 -0.597 1.180 0.000 0.919 0.893 0.992 0.596 0.211 0.561 0.635 +0 0.844 1.450 0.161 0.640 -1.186 0.890 0.781 1.731 2.173 0.398 0.200 0.690 0.000 1.047 0.139 0.185 2.548 0.758 1.315 -0.858 0.000 0.882 0.850 0.987 0.755 1.246 0.787 0.672 +0 1.516 0.080 -0.274 0.688 -0.223 1.002 -0.886 -1.578 2.173 1.157 -0.450 0.085 2.215 1.109 -1.480 1.114 0.000 1.555 -1.178 1.591 0.000 0.623 0.842 0.972 0.624 1.618 1.016 1.036 +0 1.426 -1.334 0.582 0.567 -1.037 0.470 -0.226 -0.062 2.173 0.820 -0.749 -1.443 0.000 0.519 0.469 1.277 0.000 0.666 0.186 -1.210 1.551 0.938 0.896 1.238 0.860 0.527 0.663 0.689 +1 0.327 -1.821 1.187 0.640 -1.144 1.036 0.407 -0.127 0.000 1.164 -0.523 -1.639 0.000 0.831 -0.994 0.886 2.548 0.693 -0.289 0.447 1.551 2.555 1.418 0.994 0.686 0.315 0.969 0.818 +1 0.882 0.082 -0.390 1.029 -1.526 1.350 -0.396 -0.136 2.173 1.812 0.335 1.632 0.000 0.728 -2.378 0.491 0.000 0.660 -1.703 0.758 0.000 0.286 0.616 1.126 1.094 0.924 0.936 0.989 +0 0.510 0.903 -0.820 1.152 1.334 1.228 1.279 0.212 2.173 0.848 1.816 -1.210 0.000 0.628 2.430 1.549 0.000 1.455 0.604 1.697 0.000 0.823 0.716 0.990 1.068 0.174 0.845 0.788 +1 1.438 1.638 -0.800 1.245 -0.589 1.279 1.648 1.260 2.173 1.089 1.895 0.760 0.000 0.705 0.090 1.140 0.000 2.331 0.353 -0.272 0.000 1.371 0.991 0.997 1.602 2.166 1.693 1.497 +0 0.721 -0.194 1.034 0.816 -0.549 0.863 0.888 0.795 2.173 1.653 0.100 -1.482 0.000 1.283 0.339 0.200 2.548 1.677 1.323 -0.341 0.000 0.979 1.018 1.052 0.953 0.756 0.880 0.841 +0 1.233 -1.140 -0.060 0.296 -1.683 0.673 -1.851 1.298 0.000 0.885 -1.206 0.584 2.215 1.123 -0.953 -1.129 2.548 0.861 -0.668 1.671 0.000 0.960 0.935 0.982 0.680 1.064 0.712 0.665 +1 0.945 -1.717 -0.925 0.415 0.363 0.551 0.516 -0.277 2.173 0.503 0.548 1.313 2.215 0.419 -1.147 0.789 0.000 0.410 1.085 0.373 0.000 0.777 0.710 0.987 0.941 0.767 0.814 0.693 +1 1.086 0.952 0.313 0.339 -0.992 1.345 0.805 -0.767 0.000 1.690 0.420 0.584 2.215 1.513 0.315 -1.379 0.000 1.390 1.043 1.242 3.102 0.966 1.010 0.980 0.749 0.969 0.704 0.655 +1 1.263 -0.030 1.347 0.977 -1.504 0.987 1.434 -0.683 2.173 0.549 0.702 -0.176 0.000 0.888 -1.697 0.838 0.000 0.443 0.696 0.799 3.102 0.720 0.764 0.986 0.621 0.714 1.040 0.890 +1 1.048 1.027 1.150 0.176 -1.648 0.830 1.132 0.484 0.000 1.429 0.723 1.669 0.000 1.423 0.627 -0.663 2.548 0.873 -0.414 -0.542 0.000 0.893 0.965 0.990 0.927 0.837 0.768 0.723 +0 0.865 0.191 0.409 2.226 0.680 0.858 -0.144 -1.088 0.000 0.532 1.085 -1.274 2.215 0.278 -0.648 -0.915 0.000 0.495 0.964 1.638 0.000 0.937 0.687 1.005 0.605 0.427 0.795 0.845 +1 0.891 -0.318 0.746 1.014 1.541 1.920 -0.895 -1.278 0.000 0.895 0.242 0.592 0.000 0.408 -1.421 0.220 0.000 0.439 -0.519 -1.584 3.102 1.005 1.093 0.992 0.509 0.226 0.644 0.609 +0 0.787 -0.598 -1.676 0.618 -0.302 0.414 -1.767 1.693 0.000 0.995 -0.362 -0.136 2.215 0.679 0.259 1.518 2.548 0.628 -1.355 0.192 0.000 0.763 0.986 0.988 0.604 0.918 0.752 0.677 +1 0.831 -1.128 0.391 1.009 0.398 0.759 -0.347 1.662 1.087 0.818 -1.414 -1.203 0.000 0.275 -1.142 0.028 0.000 1.396 -0.568 -1.004 3.102 0.656 0.843 0.996 1.093 0.755 1.042 0.844 +1 1.690 0.137 -0.577 0.918 0.663 0.622 -0.609 -1.541 2.173 0.954 -1.106 1.168 2.215 0.531 -0.590 -0.688 0.000 0.909 0.353 0.511 0.000 0.802 0.907 1.551 1.130 0.789 0.989 0.812 +1 0.634 0.806 -1.685 1.727 0.746 1.481 -0.587 -0.646 2.173 0.752 -0.666 -1.158 2.215 1.896 -0.514 0.737 0.000 0.834 -0.784 -1.607 0.000 1.218 1.088 1.180 1.889 0.695 1.303 1.205 +0 1.231 -1.796 1.718 0.770 0.158 0.591 -0.226 1.478 2.173 0.657 -1.048 -0.151 0.000 0.548 0.357 -0.227 1.274 0.529 -0.125 -1.183 0.000 0.718 0.834 1.330 1.107 0.743 0.891 0.758 +1 2.556 -0.630 -1.508 0.222 1.467 0.588 -0.500 -0.295 2.173 0.927 -1.198 0.556 2.215 0.651 -0.583 0.545 0.000 0.463 0.114 -0.294 0.000 0.481 0.514 0.978 1.037 0.855 0.926 0.745 +0 0.747 -1.255 0.086 0.519 -0.710 0.918 -0.143 -1.143 2.173 1.092 -0.950 1.247 0.000 1.109 0.130 1.350 2.548 0.587 -0.065 -0.228 0.000 1.122 0.854 0.981 1.369 0.997 1.142 0.988 +0 1.276 -1.162 1.668 1.523 1.615 1.978 -1.024 -1.610 2.173 2.155 0.757 0.309 0.000 1.364 1.332 -0.060 2.548 0.773 1.776 -0.576 0.000 1.221 1.008 1.003 0.847 3.843 1.978 1.651 +0 0.932 -0.139 -1.716 0.365 0.206 1.416 0.108 0.650 2.173 0.882 -1.330 -0.637 0.000 0.992 -0.604 -0.924 0.000 1.006 0.468 -1.280 3.102 0.633 0.898 0.988 0.969 1.278 1.177 0.974 +1 0.982 1.102 -0.291 0.418 0.391 0.690 -0.278 -0.732 1.087 0.634 0.148 0.273 1.107 0.902 -0.237 -1.505 0.000 0.868 0.896 -1.624 0.000 0.709 0.850 0.981 0.789 0.795 0.673 0.659 +0 0.770 -0.170 1.456 0.616 -1.556 0.380 -2.023 0.737 0.000 0.514 -0.697 -0.511 2.215 0.764 2.563 -0.793 0.000 0.656 1.127 -0.290 0.000 0.711 0.862 0.980 0.713 0.541 0.909 1.168 +0 0.994 0.363 0.982 0.555 -0.116 2.053 -0.014 -0.940 2.173 1.648 -0.863 1.175 2.215 0.632 0.696 0.351 0.000 0.763 -1.619 0.341 0.000 1.384 1.270 0.991 1.367 2.832 1.551 1.231 +0 0.559 -0.065 -0.580 0.948 0.871 0.981 -0.053 -1.239 1.087 0.693 1.084 0.256 0.000 0.751 1.922 0.249 0.000 1.140 0.563 1.677 0.000 1.139 1.233 0.988 0.682 0.996 0.790 0.744 +1 0.876 0.437 1.186 0.778 -0.270 0.979 0.375 -1.254 1.087 1.213 -0.456 0.749 2.215 0.790 -0.364 -0.714 0.000 0.958 -0.823 0.136 0.000 0.725 1.052 1.105 0.887 1.710 0.959 0.845 +1 0.587 -1.062 1.328 0.401 -0.768 1.534 -1.172 0.523 0.000 1.188 -1.205 -0.436 0.000 1.274 -0.784 -0.944 0.000 1.103 0.299 1.672 3.102 0.908 0.892 0.991 0.600 0.637 0.804 0.726 +0 2.581 -0.894 1.179 0.958 0.766 2.637 -0.904 -0.347 0.000 1.570 -0.262 1.319 2.215 1.804 -1.188 -0.801 2.548 0.669 1.041 1.348 0.000 1.230 0.954 0.984 1.506 1.944 1.237 1.084 +0 0.404 0.447 -1.001 0.165 -0.160 0.494 -0.952 -1.079 0.000 0.674 0.248 0.332 2.215 0.977 0.879 0.980 2.548 0.421 1.543 0.429 0.000 1.500 1.050 0.983 1.009 0.569 0.779 0.764 +0 2.327 -0.841 0.010 1.353 0.114 0.955 -1.533 0.427 2.173 1.900 -0.234 -1.267 1.107 1.884 0.034 1.462 0.000 1.089 -2.412 -0.870 0.000 0.789 1.160 0.988 1.783 2.419 1.491 1.420 +0 1.898 -1.418 0.112 0.963 -0.251 0.783 0.576 1.584 0.000 0.847 0.518 -1.427 0.000 0.855 0.085 -0.526 2.548 0.859 -0.371 1.180 3.102 0.717 0.921 0.980 0.852 0.679 0.716 1.057 +1 0.866 0.480 0.888 0.755 -1.527 1.104 -0.944 -0.583 0.000 1.023 -0.667 1.277 0.000 0.863 0.059 0.673 0.000 2.828 0.154 -0.773 1.551 0.929 0.675 0.984 1.043 1.137 0.938 0.838 +0 0.831 0.491 1.544 1.572 -1.131 0.965 -0.138 -0.308 1.087 1.114 -0.378 1.292 2.215 0.451 -1.666 -0.625 0.000 0.990 -0.793 0.454 0.000 0.695 0.899 1.059 1.146 1.525 1.031 0.949 +1 0.924 -1.055 0.200 0.279 -0.219 0.807 1.016 -1.446 2.173 0.321 -0.385 1.639 2.215 0.479 0.903 0.019 0.000 0.446 -0.276 0.729 0.000 0.474 0.782 0.993 0.623 0.636 0.797 0.643 +1 1.027 -0.287 -0.757 1.149 0.000 0.855 -0.690 1.268 2.173 0.561 -0.472 -0.177 0.000 0.686 -0.878 -1.608 2.548 0.502 0.619 -1.697 0.000 0.814 0.916 0.987 0.764 0.514 0.774 0.678 +1 0.684 -0.117 1.393 1.871 1.332 1.344 0.339 0.212 2.173 0.509 0.286 -0.876 2.215 0.725 2.343 -1.433 0.000 1.437 0.826 -0.611 0.000 0.980 0.961 0.983 1.803 1.012 1.240 1.483 +0 0.276 -2.108 1.689 1.908 0.589 1.263 1.364 -1.162 0.000 0.570 0.675 -0.769 0.000 1.007 1.524 1.528 0.000 2.206 -0.469 0.276 3.102 0.887 0.706 0.995 0.769 0.596 1.152 1.440 +0 1.653 0.876 1.657 0.545 -0.732 0.875 0.483 0.253 2.173 0.442 -1.485 -0.012 0.000 0.559 -2.070 -0.967 0.000 0.637 -0.634 -0.408 0.000 0.795 1.154 1.099 0.789 0.851 0.820 0.862 +0 1.319 1.010 -1.498 0.022 -0.765 0.331 0.130 -1.672 0.000 0.311 2.369 1.327 0.000 1.603 0.251 -0.775 0.000 0.927 -2.127 0.842 0.000 0.953 0.872 0.888 0.688 0.252 0.600 0.592 +0 0.650 -1.336 -0.668 0.554 1.674 1.244 -0.634 1.459 2.173 1.537 0.093 0.034 0.000 1.509 -0.512 -0.145 2.548 1.196 0.984 -1.334 0.000 0.688 1.151 0.980 1.238 1.695 1.122 1.159 +0 0.445 1.216 -0.759 1.128 1.311 0.678 -0.164 -1.272 0.000 0.719 0.184 1.449 2.215 0.809 1.891 -0.058 0.000 1.506 -0.422 0.086 3.102 0.984 1.050 0.985 1.289 0.945 0.927 0.864 +1 0.662 2.345 -1.062 0.367 -0.008 0.447 -0.114 0.125 2.173 0.452 1.458 1.188 0.000 0.652 1.251 -0.585 0.000 0.751 -0.108 1.419 1.551 0.834 0.846 0.980 0.789 0.563 0.686 0.620 +1 1.226 0.005 0.918 1.842 1.474 1.348 0.175 -0.868 1.087 1.100 0.024 0.131 2.215 0.432 -2.592 0.926 0.000 0.386 2.395 -0.400 0.000 3.945 2.359 1.002 1.564 1.411 1.696 1.493 +1 0.639 -0.021 1.638 1.143 -0.909 0.893 -0.104 -0.161 0.000 0.790 1.891 1.519 0.000 1.819 -0.651 1.266 2.548 0.539 0.432 -1.380 3.102 2.806 1.504 0.989 1.142 0.719 1.344 1.096 +1 0.890 -0.229 -1.301 0.243 0.873 1.570 1.334 -1.696 0.000 1.453 -0.585 0.022 2.215 1.431 -0.776 0.237 0.000 1.853 0.017 0.560 0.000 0.959 0.923 0.990 0.968 0.726 0.669 0.722 +0 0.857 -1.210 1.098 0.632 1.665 1.762 -0.425 0.306 0.000 1.646 -1.407 -1.702 1.107 2.692 -1.539 -0.998 0.000 0.893 -0.378 0.813 0.000 0.846 0.937 0.980 0.801 1.451 1.337 1.217 +0 2.480 -1.516 1.025 0.154 -1.418 0.528 -0.365 -0.686 0.000 0.860 -2.363 -0.748 0.000 1.186 -1.054 -0.166 1.274 0.756 0.205 1.623 1.551 1.096 0.952 0.988 0.871 0.909 0.813 0.803 +1 1.722 1.501 0.186 0.724 1.328 2.403 -1.116 -1.203 2.173 1.845 0.100 0.646 2.215 1.165 0.463 0.272 0.000 0.652 0.615 1.565 0.000 0.890 0.828 1.326 3.880 3.691 2.756 2.029 +0 1.408 -0.358 -0.896 0.411 0.070 0.876 -0.173 -1.564 2.173 0.501 -1.207 0.045 0.000 1.220 0.233 1.024 1.274 0.710 0.347 0.232 0.000 0.732 1.056 0.986 1.011 0.972 0.824 0.759 +0 0.668 0.054 1.590 2.750 -0.462 0.704 -0.261 0.705 2.173 0.940 -0.928 1.627 2.215 0.858 0.327 1.314 0.000 0.584 -1.352 -0.628 0.000 1.190 0.912 1.805 1.459 0.979 1.116 0.948 +0 1.546 -0.990 0.609 2.915 0.994 2.469 0.197 -0.758 2.173 1.335 -2.855 0.137 0.000 1.791 -0.426 -1.180 0.000 1.369 -0.887 1.519 1.551 1.806 1.179 1.002 3.168 2.168 2.052 1.711 +1 1.218 -0.456 -0.737 1.077 -0.032 0.375 -0.507 1.439 0.000 0.647 0.114 0.529 2.215 0.736 1.074 1.685 0.000 0.380 1.164 -0.900 0.000 0.922 0.806 0.988 0.615 0.253 0.541 0.663 +0 1.164 1.141 -0.828 0.412 0.413 0.588 0.430 1.616 0.000 0.702 -0.009 0.145 2.215 0.487 0.778 -1.122 2.548 0.505 -1.660 0.976 0.000 1.356 0.933 0.990 0.739 0.630 0.711 0.724 +1 0.649 -1.279 -0.166 1.104 0.824 0.492 -1.061 -1.429 0.000 0.735 0.368 -0.016 2.215 0.325 0.756 0.108 0.000 0.742 -0.163 -1.123 0.000 1.061 0.936 0.989 1.062 0.689 0.925 0.824 +0 1.933 -0.233 0.941 1.352 0.322 1.189 0.335 -1.385 2.173 0.827 -0.627 -0.966 0.000 0.443 0.175 0.017 0.000 0.649 -1.224 -0.248 3.102 0.823 0.977 1.185 0.863 1.246 1.126 0.947 +0 0.486 -0.556 -1.281 1.151 0.989 0.456 -1.836 -0.807 0.000 0.951 -0.815 -0.669 1.107 1.447 -0.916 0.236 0.000 0.665 -0.647 1.298 1.551 1.341 0.991 0.988 0.486 0.704 0.681 0.679 +1 0.517 1.424 0.029 1.372 1.459 1.410 -0.184 -1.236 0.000 0.920 0.882 0.499 1.107 0.759 0.938 -0.423 2.548 0.736 -1.223 0.378 0.000 1.879 1.428 1.120 0.820 0.658 1.161 1.149 +1 0.704 0.521 1.507 0.456 0.387 0.529 -0.713 -0.524 0.000 0.855 0.411 -0.527 0.000 1.527 0.533 0.925 2.548 0.934 1.142 -1.407 3.102 0.861 0.993 0.988 0.712 0.869 0.915 0.837 +0 0.626 -0.988 0.356 0.241 -0.292 1.501 1.921 1.408 0.000 1.527 -1.003 -0.640 0.000 1.867 -0.399 0.106 2.548 0.677 -1.320 -1.355 0.000 0.878 0.883 0.993 0.696 0.791 0.816 0.747 +0 0.625 -1.759 1.054 1.130 1.647 0.480 0.533 -1.488 0.000 0.742 0.255 -0.584 0.000 1.280 -1.345 -0.005 0.000 1.653 -0.200 1.612 3.102 0.939 0.884 0.981 0.950 1.138 0.846 0.842 +1 0.962 -0.027 -0.561 0.969 0.974 1.095 1.146 -1.609 2.173 1.239 0.000 0.068 1.107 0.596 2.083 0.939 0.000 0.427 1.124 1.407 0.000 0.611 0.979 1.314 0.876 2.006 1.134 1.008 +1 0.571 -0.230 0.124 0.529 1.583 0.937 -0.056 -0.909 0.000 0.896 -2.173 -0.081 0.000 2.361 0.211 0.875 2.548 0.591 -0.687 1.500 3.102 0.921 0.745 0.988 1.071 0.694 0.960 0.874 +0 1.253 0.502 -0.029 0.513 0.928 0.423 2.152 -0.648 0.000 0.774 -0.592 -1.672 2.215 0.286 -0.133 0.194 1.274 0.989 -1.181 -1.352 0.000 0.878 0.595 0.985 0.493 0.511 0.666 0.670 +1 1.961 -1.125 -0.280 1.254 0.309 1.482 -1.159 1.090 2.173 1.201 -0.451 -1.233 2.215 1.497 0.484 -1.559 0.000 0.899 2.377 0.101 0.000 2.241 2.175 1.102 1.535 1.841 2.342 2.171 +0 0.413 0.596 1.637 1.278 -0.669 0.937 1.609 -1.586 0.000 0.979 0.530 0.276 2.215 0.685 0.592 -0.223 2.548 0.588 0.285 1.253 0.000 0.956 0.824 0.987 0.611 0.381 0.593 0.650 +1 0.761 0.544 0.936 1.476 -0.278 1.242 0.661 1.705 0.000 1.137 -0.087 -0.930 2.215 1.414 -1.298 -0.075 2.548 1.734 -0.685 0.776 0.000 0.906 0.837 1.303 1.409 1.348 1.089 0.931 +0 0.605 -0.872 0.083 0.225 1.670 1.212 -0.341 -1.729 2.173 1.483 0.907 -0.407 1.107 0.962 -0.024 0.738 0.000 0.558 -0.478 0.634 0.000 0.236 0.936 0.982 0.927 2.275 1.156 0.925 +0 0.470 -0.217 -1.674 2.262 1.437 1.223 -0.350 -0.173 0.000 0.480 -0.607 -1.011 2.215 0.720 0.418 -0.526 0.000 0.796 -0.456 1.079 0.000 0.938 0.669 1.004 0.797 0.411 0.708 0.669 +0 0.854 1.291 -1.089 1.650 -0.330 0.595 0.391 1.127 2.173 0.643 1.048 1.597 2.215 0.526 1.699 1.057 0.000 0.684 -0.212 1.072 0.000 0.841 0.608 1.041 1.140 0.490 0.826 0.727 +0 0.727 -0.465 -0.480 2.265 0.034 1.057 0.034 -1.528 0.000 1.258 -0.658 -1.416 0.000 1.508 0.763 0.747 0.000 0.952 -0.927 1.120 3.102 0.923 0.985 0.978 0.615 0.274 0.699 0.959 +0 1.689 0.952 -1.425 0.654 0.994 0.482 -1.318 0.112 0.000 0.414 -1.055 1.175 0.000 0.771 1.025 -0.191 1.274 0.590 0.099 0.283 3.102 0.785 1.162 1.194 0.764 0.345 0.676 0.883 +0 1.775 0.392 -1.264 1.961 -0.918 1.126 1.666 -1.157 0.000 2.051 0.556 0.702 0.000 0.990 -2.311 -0.138 0.000 3.277 1.298 0.857 0.000 0.894 0.756 0.985 1.097 0.905 0.858 0.863 +1 0.313 -1.366 1.045 1.315 -1.067 0.671 -0.658 0.028 1.087 1.232 1.039 1.395 2.215 0.955 0.154 -0.498 0.000 0.472 -1.247 0.549 0.000 0.916 0.680 0.985 1.246 1.820 1.082 0.912 +1 0.630 0.869 -1.192 0.963 -0.426 2.050 1.436 1.515 0.000 1.989 0.470 0.113 2.215 0.944 0.254 -1.146 0.000 1.372 -0.145 -0.288 3.102 0.731 1.081 0.991 0.592 0.726 0.700 0.661 +1 0.842 -0.139 0.830 0.643 -0.608 1.098 0.151 1.308 2.173 1.197 0.169 -0.596 0.000 0.491 -0.891 -0.228 0.000 0.486 0.641 -1.110 3.102 0.811 0.573 0.987 0.879 0.678 0.843 0.728 +0 0.604 -0.865 -1.217 0.942 1.496 0.749 0.653 0.071 2.173 0.758 0.309 -0.844 0.000 0.911 -0.781 0.360 1.274 0.789 -0.319 1.676 0.000 0.851 0.970 0.984 0.835 0.898 0.805 0.734 +1 0.638 -1.868 -1.625 1.467 1.470 0.668 -0.854 -0.358 1.087 0.631 -0.875 0.911 0.000 0.726 -1.555 0.346 0.000 0.698 0.003 -0.468 0.000 0.913 0.868 0.983 1.060 0.898 1.001 0.830 +0 1.118 -0.456 -0.446 1.629 -0.956 0.838 -1.229 0.959 1.087 0.704 -0.127 1.198 0.000 0.685 -0.621 0.397 0.000 0.671 0.138 -1.152 3.102 0.781 0.721 0.987 0.509 0.964 0.943 0.796 +1 0.929 0.065 0.522 0.725 -1.283 0.634 -0.035 1.494 0.000 1.298 0.504 -0.376 2.215 0.734 -1.217 1.601 1.274 0.819 -0.042 -0.719 0.000 1.002 1.121 1.135 0.812 1.501 0.889 0.772 +0 1.337 1.197 0.421 1.101 1.441 0.630 1.113 1.409 2.173 1.211 -0.178 -0.684 0.000 0.754 1.508 -0.259 0.000 0.383 -0.359 1.571 3.102 1.640 1.003 1.337 0.796 0.452 0.868 0.895 +0 0.387 -0.890 -0.640 0.293 0.184 0.976 -0.763 -0.365 2.173 0.591 -1.133 -0.890 0.000 2.271 -1.304 1.459 2.548 0.461 -0.763 0.488 0.000 0.649 0.929 0.997 0.949 1.953 1.252 0.959 +1 1.115 0.908 0.313 1.618 -0.319 0.698 -2.746 1.432 0.000 0.794 0.273 -1.427 2.215 1.106 1.309 -0.410 0.000 1.908 1.026 1.591 3.102 0.785 0.930 1.003 1.174 0.713 0.927 0.799 +1 0.694 -0.687 0.664 0.726 -0.744 1.008 -1.205 -0.288 2.173 1.082 -0.327 1.309 2.215 1.154 -0.563 -1.605 0.000 0.517 -1.769 -0.368 0.000 1.040 0.949 0.986 0.834 1.676 0.958 0.811 +0 0.908 -0.352 -0.021 1.681 0.383 1.203 -0.624 -1.644 0.000 0.756 -0.471 -0.837 0.000 0.600 0.514 0.821 2.548 0.432 1.011 -1.166 0.000 0.743 0.735 0.995 0.499 0.380 0.540 0.673 +0 1.288 -1.001 1.127 4.239 0.862 2.040 -0.783 -0.808 0.000 0.506 -0.779 1.713 2.215 1.522 -0.056 -0.522 2.548 0.699 -0.774 -1.399 0.000 0.933 0.955 0.982 0.868 0.913 1.178 1.406 +1 0.382 -0.503 -0.564 1.086 0.363 0.846 -0.035 -1.529 0.000 0.537 -0.218 0.594 2.215 1.192 1.002 0.500 0.000 1.015 0.816 -0.979 3.102 2.054 1.257 0.982 0.735 0.784 0.888 1.097 +1 1.374 0.620 1.203 0.506 -1.541 0.930 -0.018 -0.630 2.173 0.682 -0.175 0.986 1.107 0.555 0.109 0.036 0.000 0.569 -1.026 -0.352 0.000 0.499 0.608 0.986 1.240 1.168 0.920 0.782 +1 1.147 1.810 0.844 0.393 0.190 0.864 1.684 -0.702 2.173 1.126 1.137 1.027 0.000 1.211 1.194 -1.026 0.000 1.444 1.117 1.609 0.000 0.842 1.096 0.978 1.015 0.839 0.959 0.835 +0 1.009 -0.051 1.551 0.463 1.690 0.745 -0.504 1.016 0.000 0.663 -2.576 0.231 0.000 0.551 1.782 0.135 0.000 1.148 0.520 -1.144 3.102 1.020 0.734 0.980 0.791 0.400 0.560 0.598 +1 0.303 -1.054 -1.134 1.234 -0.603 0.749 0.254 -0.137 2.173 0.697 0.333 1.354 0.000 0.618 1.806 -0.757 0.000 0.924 1.016 1.412 0.000 0.942 0.890 0.992 1.823 0.555 1.560 1.450 +0 1.167 1.003 -1.440 0.264 -1.274 0.307 2.156 -1.468 0.000 0.773 0.994 -0.396 0.000 1.698 1.562 0.919 0.000 0.975 0.843 0.546 1.551 1.038 0.812 0.989 0.742 0.661 0.624 0.611 +1 2.120 -0.154 1.328 1.425 1.098 1.473 0.945 -0.736 0.000 0.339 -1.713 -0.042 0.000 0.525 -1.183 1.268 2.548 0.489 2.281 -1.088 0.000 1.377 1.186 0.982 0.566 0.336 1.100 1.339 +0 1.063 1.166 0.270 1.231 0.893 1.272 -0.753 -1.019 2.173 0.823 -0.533 1.637 0.000 0.348 -0.800 -0.055 0.000 1.238 0.122 -0.105 3.102 0.830 0.947 0.993 0.742 1.157 1.227 1.018 +0 0.547 0.599 1.371 3.854 0.864 3.200 0.012 -0.905 0.000 1.316 -1.211 0.966 2.215 1.047 1.099 0.668 2.548 0.384 1.456 -0.008 0.000 0.986 1.992 0.990 2.358 1.973 1.945 2.061 +1 0.570 0.447 0.275 0.103 -0.301 1.132 -0.133 1.729 2.173 0.852 1.034 -0.394 0.000 0.739 -0.721 0.782 2.548 0.672 0.675 -0.760 0.000 0.343 0.975 0.932 0.971 0.941 0.939 0.823 +1 0.888 -1.424 1.050 0.964 -1.704 1.237 0.566 1.637 2.173 2.572 0.081 0.031 0.000 1.173 -0.019 -0.904 0.000 0.702 1.323 -0.505 3.102 1.994 1.350 0.993 1.278 1.061 1.382 1.324 +1 0.369 -0.734 -1.233 0.432 -1.552 0.493 -0.723 -1.700 2.173 0.484 0.298 0.463 2.215 0.558 2.160 0.798 0.000 1.361 0.900 -0.004 0.000 0.921 0.728 0.981 0.783 0.773 0.894 0.777 +0 0.657 0.715 1.037 0.678 -1.669 0.446 -0.653 0.559 0.000 1.109 -0.359 -0.451 2.215 0.691 -1.205 1.632 0.000 0.712 0.003 -0.630 1.551 0.888 1.013 0.984 0.627 0.198 0.630 0.648 +1 0.917 -0.077 -1.717 0.770 0.616 0.901 -1.412 -0.866 2.173 0.757 -1.012 -0.142 2.215 0.666 0.443 0.669 0.000 0.551 -1.437 0.995 0.000 0.920 0.819 1.003 1.170 0.775 0.859 0.789 +1 0.486 -0.517 0.991 1.006 -1.126 0.505 -1.238 0.940 0.000 1.083 -0.382 -0.262 2.215 0.393 -2.238 0.115 0.000 1.050 1.525 1.559 0.000 0.748 1.045 0.991 0.720 0.835 0.828 0.737 +0 1.263 1.327 1.411 0.367 -0.945 0.479 1.259 -1.612 0.000 1.312 1.309 0.291 2.215 0.998 0.465 -0.833 0.000 0.670 1.001 -0.017 3.102 0.938 1.239 0.991 0.638 0.240 0.727 0.698 +0 0.285 1.213 -0.991 1.201 0.723 0.521 1.139 1.520 0.000 0.626 -0.224 0.571 0.000 1.426 0.136 -0.428 2.548 1.779 -0.538 -1.156 3.102 1.289 1.196 0.987 0.945 0.894 0.936 0.815 +1 1.496 0.286 1.390 1.155 0.887 1.071 0.806 -0.515 2.173 0.455 -0.493 -0.205 0.000 0.368 1.077 1.644 2.548 0.410 -0.707 0.572 0.000 0.375 0.847 0.994 0.619 0.742 0.959 0.785 +0 1.984 0.259 -1.545 1.152 1.168 1.226 1.067 0.086 0.000 0.507 0.001 -0.847 2.215 0.563 -0.305 -0.527 2.548 0.809 -1.628 0.939 0.000 0.682 0.742 1.344 0.908 0.188 0.656 0.720 +0 0.485 1.854 -0.409 0.706 -0.910 0.973 1.577 0.778 2.173 0.783 1.220 1.403 0.000 0.553 -0.190 -0.619 0.000 0.709 1.753 -1.323 0.000 1.071 0.951 0.990 1.117 1.495 0.956 0.841 +0 1.265 0.272 1.608 1.288 0.456 1.286 0.404 -0.480 0.000 0.749 -1.348 1.554 2.215 1.215 0.159 -0.037 0.000 1.200 1.555 -1.612 0.000 0.913 1.393 1.523 1.236 0.535 1.196 1.114 +1 0.511 0.288 -1.522 1.224 0.695 0.637 -1.024 1.618 1.087 0.496 -0.170 0.041 0.000 0.276 1.257 0.456 1.274 0.824 0.193 -0.311 0.000 0.733 0.882 0.997 0.524 0.915 0.663 0.659 +0 0.488 -1.409 -1.229 1.234 -0.358 0.607 -1.324 -0.734 2.173 1.511 -0.763 1.245 2.215 0.534 -0.712 0.184 0.000 0.532 -1.874 0.749 0.000 0.552 0.802 0.984 0.683 1.432 1.066 0.835 +1 1.381 -1.134 -1.200 0.979 -0.837 0.747 -0.485 1.315 2.173 0.529 0.023 0.918 0.000 1.336 -0.022 0.142 2.548 1.103 -0.331 -0.826 0.000 0.921 0.950 0.997 1.091 1.123 0.939 0.798 +0 0.424 1.658 1.697 0.588 0.395 0.619 0.496 0.670 0.000 0.419 1.478 -0.247 0.000 0.788 -0.357 -1.247 2.548 0.840 -0.176 1.220 3.102 0.983 1.009 0.994 0.589 0.497 0.658 0.595 +0 1.099 0.267 0.858 0.941 1.270 1.220 -0.452 -0.775 0.000 1.046 -0.130 0.263 2.215 0.710 0.169 1.326 2.548 0.577 -1.975 -1.655 0.000 1.197 1.036 0.979 0.981 0.764 0.845 0.951 +1 0.327 2.427 -0.049 1.459 0.912 0.471 0.442 0.625 0.000 0.716 0.311 -0.532 0.000 1.072 0.830 -1.502 2.548 0.930 0.379 -1.195 0.000 1.008 0.802 0.985 1.149 0.864 0.863 0.750 +1 1.930 -1.199 -0.882 1.893 -0.406 1.430 -1.442 1.263 0.000 0.861 -0.130 0.561 2.215 0.528 -0.954 -0.087 0.000 0.883 0.454 -0.741 0.000 0.786 0.795 1.104 1.380 0.823 1.033 0.865 +1 0.934 -0.643 0.199 0.397 -1.479 0.955 -0.495 -1.464 0.000 1.444 0.010 0.109 2.215 0.823 0.561 -1.249 0.000 1.027 -0.285 1.222 3.102 0.986 0.829 0.988 0.732 0.947 0.975 0.814 +1 2.113 -0.792 -1.488 1.221 1.410 0.811 -0.157 0.139 1.087 0.281 -0.591 -0.918 2.215 0.821 -0.945 0.080 0.000 0.461 -0.070 -0.292 0.000 0.403 0.419 1.125 0.644 0.594 0.892 0.747 +1 0.384 -0.051 0.008 1.563 1.213 0.808 0.040 -0.696 0.000 0.771 1.146 1.543 2.215 1.558 1.504 -0.558 0.000 1.617 0.680 1.138 3.102 0.868 1.250 0.988 0.983 0.405 0.934 0.880 +1 1.076 -1.608 1.373 0.329 -0.024 0.424 -0.160 0.928 1.087 0.265 -2.045 -0.659 0.000 0.455 0.232 -0.458 0.000 1.012 0.068 -0.943 3.102 0.790 0.750 0.987 1.034 0.694 0.803 0.704 +0 1.442 1.224 -1.654 1.550 -1.278 1.174 0.290 0.453 2.173 0.535 0.274 1.642 0.000 0.691 0.874 -0.520 0.000 0.484 2.393 -0.540 0.000 0.925 1.079 0.978 0.760 0.428 1.024 0.841 +0 0.604 0.921 0.437 1.140 -0.590 0.873 1.927 -1.572 0.000 1.100 1.476 -0.091 2.215 1.365 0.837 1.404 2.548 0.543 -1.737 -0.273 0.000 0.893 0.866 0.992 0.794 1.330 0.913 0.907 +0 1.115 1.355 -1.633 1.025 0.596 0.688 -0.342 -0.800 0.000 1.038 -0.653 -0.154 2.215 0.956 0.380 0.951 2.548 0.877 0.174 -1.688 0.000 0.913 0.949 1.341 1.608 1.075 1.081 0.991 +1 1.316 0.365 1.270 0.187 -0.262 0.668 -0.245 -0.196 2.173 0.860 -0.649 -1.264 2.215 0.988 0.816 0.807 0.000 0.709 -0.083 -0.903 0.000 1.040 0.922 0.985 0.824 0.945 0.780 0.724 +1 1.517 -1.215 -0.639 0.286 -1.136 1.179 -0.458 -0.746 0.000 1.235 -0.560 -0.336 0.000 4.053 -0.702 1.419 2.548 1.567 -0.594 0.755 0.000 0.938 0.912 0.989 1.763 1.626 1.534 1.335 +0 0.405 -0.436 -0.566 2.191 -1.508 0.840 1.022 0.058 0.000 1.145 1.618 0.265 0.000 1.291 0.702 1.463 2.548 1.171 -0.050 1.513 0.000 0.775 0.805 0.988 0.937 0.696 0.842 1.102 +1 1.872 0.275 -1.494 0.445 0.573 1.145 0.875 0.186 2.173 0.587 1.603 -1.738 0.000 0.559 -0.470 -0.965 0.000 0.684 -1.878 -1.457 0.000 0.937 1.021 1.211 0.826 0.842 0.920 0.807 +1 1.311 -0.749 1.343 0.229 -0.769 0.726 0.878 -0.452 1.087 0.663 -0.388 -0.156 0.000 0.485 1.243 0.512 0.000 1.027 0.266 -1.557 3.102 0.911 0.934 0.991 0.620 0.811 0.774 0.704 +1 0.707 0.655 0.215 0.397 1.720 1.596 0.289 1.373 1.087 0.919 -1.346 -0.455 0.000 1.518 -0.128 -0.692 0.000 0.681 -0.861 0.343 3.102 1.262 0.868 0.993 0.984 1.183 1.332 1.047 +0 0.812 -1.686 -0.768 0.866 1.600 0.823 0.714 -0.167 2.173 0.755 -0.390 1.303 0.000 0.941 -0.196 0.938 2.548 0.627 -0.354 -0.585 0.000 0.889 1.092 0.987 0.967 1.063 1.220 0.974 +0 1.284 0.596 -1.017 0.169 -0.280 1.466 0.932 -0.437 1.087 1.417 0.933 1.525 0.000 1.853 0.973 0.777 2.548 1.151 0.216 1.055 0.000 0.906 0.958 0.982 0.985 1.829 1.279 1.110 +0 0.617 -0.564 1.060 1.101 0.253 0.372 -0.053 0.305 0.000 0.863 0.067 -0.967 2.215 0.492 0.755 1.572 1.274 0.571 -0.365 -1.322 0.000 0.713 0.665 0.993 0.940 0.587 0.838 0.682 +0 0.433 1.946 1.148 2.090 -0.837 1.783 0.931 0.415 0.000 1.021 0.896 -1.582 1.107 0.930 0.175 -1.039 2.548 0.830 -0.276 1.717 0.000 2.127 1.599 1.287 1.079 0.621 1.167 1.247 +0 1.848 0.807 1.122 1.029 0.123 0.765 -2.911 -0.528 0.000 0.803 -1.244 -1.723 0.000 1.060 -1.364 -0.712 2.548 1.121 -0.376 1.168 3.102 2.130 1.336 1.496 0.975 0.944 1.202 1.925 +0 0.516 -0.463 -1.336 0.118 -0.368 1.246 -0.071 0.458 2.173 1.316 -0.104 -1.646 1.107 0.456 1.011 0.613 0.000 0.383 0.811 -0.393 0.000 0.364 0.821 0.978 1.054 1.785 0.995 0.770 +1 1.405 -1.623 -1.482 0.355 -0.434 0.843 -1.337 0.078 2.173 0.539 -1.129 1.170 0.000 0.635 -0.357 0.798 2.548 0.548 -0.826 -1.019 0.000 0.655 0.795 0.985 0.881 0.713 0.799 0.665 +1 1.126 -0.151 -0.153 1.247 -0.770 0.713 -0.489 0.871 2.173 0.437 -0.035 1.127 0.000 0.883 0.637 1.432 2.548 1.055 -0.718 -1.426 0.000 0.759 0.713 0.981 0.938 0.788 0.859 0.752 +1 1.304 -0.921 -0.486 1.420 -0.886 0.688 0.329 1.112 2.173 0.771 -1.362 1.197 1.107 0.459 -2.619 0.306 0.000 0.589 -0.637 -1.176 0.000 0.915 0.762 0.985 1.594 1.053 1.159 0.986 +1 0.387 -1.681 1.141 0.462 0.893 1.539 0.080 -1.099 0.000 1.155 -0.647 0.984 0.000 1.274 0.044 0.602 1.274 0.790 -0.186 -1.698 0.000 0.902 0.883 0.990 0.646 0.408 0.862 0.781 +0 1.813 0.697 -0.105 0.595 0.050 1.313 0.976 -1.570 1.087 0.547 -1.232 1.157 0.000 0.474 0.376 -0.626 0.000 1.119 0.085 0.589 3.102 1.073 0.771 0.993 1.577 1.329 1.085 1.010 +1 1.577 0.103 0.514 0.304 -1.598 1.355 0.796 -1.201 2.173 0.724 -1.358 -0.582 0.000 0.886 -0.264 0.171 0.000 0.884 0.757 1.394 3.102 0.941 1.070 0.988 1.308 0.834 1.207 1.027 +1 2.559 -0.760 -1.062 0.168 -1.673 1.639 -1.164 0.447 1.087 0.583 -1.710 1.537 0.000 0.383 -0.003 1.613 0.000 0.474 -0.093 -0.527 3.102 0.723 1.214 0.978 0.530 0.887 1.112 0.935 +1 0.512 2.003 -1.193 0.580 1.629 0.790 0.940 0.571 0.000 1.074 0.441 1.239 2.215 1.296 -0.135 -1.076 2.548 0.941 -0.203 -0.570 0.000 0.889 0.855 0.987 0.820 1.156 0.842 0.752 +0 1.237 0.096 -0.398 0.749 -1.372 0.473 1.170 1.165 0.000 1.095 1.036 0.697 2.215 1.150 0.337 -1.724 0.000 0.977 -1.004 -0.893 3.102 0.875 0.916 1.025 0.705 1.599 0.974 0.895 +1 0.421 1.651 -0.894 0.500 -0.813 1.527 0.155 0.372 2.173 0.922 0.060 -1.201 2.215 0.955 -0.768 1.652 0.000 1.020 -1.738 -1.722 0.000 0.729 1.045 0.987 1.138 1.727 1.334 1.117 +1 2.447 0.492 1.136 0.786 0.485 0.733 -0.083 -1.068 0.000 0.892 0.364 -0.445 2.215 1.129 0.671 0.398 0.000 1.745 1.071 -1.176 3.102 1.728 1.111 1.062 1.233 0.869 1.006 0.988 +0 3.276 -0.698 0.157 0.350 1.531 2.247 -1.092 -1.584 1.087 0.595 -0.312 1.463 0.000 0.598 -0.878 -1.355 2.548 1.716 -1.236 0.146 0.000 1.169 0.778 1.402 2.281 0.314 1.409 1.154 +0 0.426 -1.842 1.027 1.309 -1.209 0.869 -0.880 0.284 2.173 0.547 -0.305 0.715 2.215 0.547 -0.956 1.345 0.000 0.941 -1.430 -1.194 0.000 0.762 0.978 0.988 1.099 0.484 0.969 0.801 +0 0.711 -0.606 -1.401 0.559 1.559 1.157 -0.637 -0.798 1.087 1.375 -0.382 0.587 2.215 0.694 0.893 1.407 0.000 1.371 0.495 0.340 0.000 0.907 0.986 0.994 1.051 1.776 1.144 0.981 +1 0.575 1.471 1.241 1.147 -1.157 0.885 -1.095 0.506 2.173 0.485 -1.130 1.292 1.107 0.741 -0.551 -0.698 0.000 0.631 -0.592 -1.269 0.000 0.373 0.884 0.988 1.084 0.627 1.172 0.932 +0 0.417 0.017 -0.260 2.364 -1.152 1.086 1.530 0.761 2.173 0.449 1.709 0.187 0.000 0.584 0.352 -0.593 1.274 0.798 0.463 1.298 0.000 0.824 0.726 0.989 0.557 1.113 1.179 0.957 +1 0.485 0.495 1.011 0.929 -1.153 0.599 -0.231 1.219 0.000 0.855 -0.264 -1.407 0.000 1.049 -1.136 -0.319 2.548 0.825 -1.609 -0.212 0.000 1.062 0.944 0.989 1.340 0.556 0.962 0.906 +0 0.383 1.378 -0.309 3.185 0.576 1.240 1.408 -1.196 0.000 1.147 0.630 -0.794 2.215 1.143 0.831 1.090 2.548 0.708 1.028 1.601 0.000 0.843 0.927 1.097 0.823 1.218 1.040 1.078 +0 1.977 0.276 0.875 1.501 0.437 1.080 -1.059 -0.973 0.000 0.432 -0.351 -0.337 0.000 0.585 0.835 -1.618 1.274 0.688 0.398 -1.077 3.102 0.947 1.098 0.993 0.856 0.252 0.678 1.023 +0 1.488 -0.569 -1.039 0.694 1.479 0.829 0.106 0.304 0.000 0.669 -0.861 0.710 0.000 1.246 0.589 -0.914 2.548 1.266 1.118 0.071 0.000 0.994 0.993 1.078 0.901 1.282 0.969 0.905 +1 0.846 -0.911 0.275 2.294 0.570 0.760 -0.018 -1.738 0.000 0.999 -0.309 -1.184 1.107 1.244 0.447 -1.128 2.548 0.722 1.077 -0.315 0.000 1.346 1.008 1.000 1.915 0.496 1.424 1.343 +1 0.463 -0.458 -1.189 1.113 0.314 1.157 0.018 -1.139 1.087 0.925 -0.549 0.312 1.107 2.565 1.161 1.687 0.000 2.207 0.538 0.119 0.000 1.570 1.086 0.989 0.998 1.537 1.056 0.866 +0 1.575 -0.133 0.690 0.911 1.393 1.700 0.238 -0.678 0.000 1.468 -0.736 1.038 2.215 1.132 0.077 -1.286 1.274 0.494 -0.526 -0.296 0.000 0.788 0.823 0.988 0.695 1.327 1.220 1.108 +1 2.218 -0.819 1.543 0.627 1.024 1.113 -1.041 -0.222 2.173 0.436 -0.638 0.788 0.000 0.910 -1.035 -1.065 2.548 0.449 -0.494 -0.081 0.000 0.407 0.636 0.992 0.874 0.866 1.030 0.786 +0 3.067 -0.053 -1.572 0.692 1.486 1.103 -1.995 0.104 0.000 0.760 0.132 -0.850 2.215 0.695 -1.754 0.704 0.000 1.490 -0.793 0.169 3.102 0.814 0.765 0.979 0.875 0.940 1.040 1.442 +0 2.943 -0.994 1.227 2.299 1.335 3.010 0.280 -0.440 1.087 1.422 -1.647 1.489 0.000 1.146 -0.189 0.038 0.000 0.634 0.990 -0.130 0.000 0.725 1.031 1.011 0.837 0.918 2.221 1.682 +0 2.993 0.024 -0.013 0.531 -0.358 0.810 0.737 1.447 0.000 1.150 1.142 1.680 2.215 1.319 0.129 -0.664 2.548 0.682 -0.802 1.324 0.000 1.099 0.966 0.993 0.794 1.326 1.131 1.095 +1 0.783 -1.438 -1.408 1.690 -1.742 0.815 -1.032 -0.147 2.173 0.892 -0.123 0.578 0.000 0.687 -0.632 0.654 2.548 1.031 -0.209 -0.515 0.000 0.915 0.988 0.975 0.801 0.638 0.850 0.784 +1 0.588 0.577 0.923 1.654 1.678 0.701 1.985 -0.311 0.000 0.642 1.383 0.548 1.107 0.746 0.840 -0.532 1.274 0.796 2.027 -1.418 0.000 0.989 0.876 0.987 0.887 0.636 0.767 0.946 +0 0.555 -1.112 -1.107 0.067 0.634 0.608 0.469 0.617 0.000 0.711 -0.694 0.928 2.215 1.323 0.276 1.555 0.000 1.498 0.452 -0.649 3.102 0.944 0.859 0.998 0.737 1.113 0.694 0.637 +1 1.782 1.678 -0.348 0.701 -0.861 0.411 2.264 -1.468 0.000 0.997 -1.550 1.179 2.215 1.347 0.463 -1.252 1.274 3.225 0.687 0.544 0.000 2.209 1.703 0.983 3.452 1.845 2.222 1.984 +1 1.085 -0.040 -0.512 1.608 -0.953 0.952 -0.662 1.107 2.173 0.568 0.907 1.513 2.215 0.791 -0.338 0.529 0.000 0.578 -2.193 -0.143 0.000 1.120 1.053 0.990 0.890 1.038 1.078 1.112 +1 1.554 -0.281 1.428 0.869 -1.557 0.965 0.685 -0.005 2.173 0.658 0.051 0.640 0.000 0.821 0.624 -1.023 2.548 0.694 -0.719 -0.828 0.000 0.951 0.968 0.989 0.775 0.880 0.933 0.818 +0 0.282 1.135 0.761 0.814 -0.187 0.983 1.931 0.756 0.000 1.244 -0.186 1.685 0.000 1.267 1.297 -0.257 2.548 0.885 -0.349 -0.805 0.000 1.079 0.727 0.978 1.018 0.922 0.919 0.841 +1 2.015 0.074 0.196 0.616 0.850 0.955 0.800 -1.024 2.173 0.329 0.745 0.092 0.000 0.528 -2.331 -1.494 0.000 0.988 0.667 1.568 3.102 0.791 0.918 0.988 0.842 0.741 0.909 0.757 +0 1.381 0.573 0.569 0.751 -0.271 0.683 0.110 1.279 2.173 0.910 -0.546 -1.439 0.000 0.899 0.687 -0.118 2.548 0.822 0.279 -0.967 0.000 0.699 0.923 0.986 0.892 0.983 0.744 0.773 +0 1.675 -0.134 1.429 0.649 -1.330 0.425 -0.200 -0.842 0.000 0.676 0.743 0.559 2.215 1.064 -0.859 -0.133 1.274 0.391 0.491 -0.234 0.000 0.411 0.667 0.989 1.013 1.015 0.858 0.707 +1 0.844 -0.452 1.709 0.991 0.751 1.308 -0.721 -0.545 2.173 0.928 -0.903 1.573 0.000 0.538 0.505 0.257 1.274 0.420 -1.963 0.732 0.000 0.843 0.935 0.989 1.248 0.996 0.933 0.856 +1 1.189 -0.798 -1.716 0.420 0.571 1.276 -0.038 -0.865 2.173 0.731 -0.396 -1.603 2.215 1.377 -1.194 0.208 0.000 0.938 0.130 0.601 0.000 0.853 1.079 0.985 1.201 0.916 1.070 0.934 +1 0.784 0.187 0.216 0.725 -0.398 0.804 1.435 1.632 0.000 0.619 0.380 -1.060 2.215 0.560 1.374 0.779 0.000 0.832 -0.327 0.520 3.102 0.841 1.010 0.986 0.779 0.692 0.739 0.873 +1 1.668 1.308 0.883 0.558 0.885 0.984 0.666 -1.143 2.173 0.800 2.514 -0.599 0.000 0.633 0.751 0.467 2.548 1.028 1.631 0.800 0.000 1.187 0.946 0.978 1.454 0.979 0.987 0.962 +0 1.563 1.440 -1.146 0.238 0.378 0.852 -0.031 0.332 2.173 0.742 0.675 1.345 2.215 0.735 -1.261 0.940 0.000 0.745 -0.744 -0.083 0.000 0.681 0.999 0.993 1.409 1.022 1.006 1.089 +1 1.714 0.914 -0.905 0.505 1.285 1.448 -0.050 1.444 2.173 2.560 0.028 -0.185 2.215 1.294 -1.127 1.120 0.000 2.151 1.269 1.278 0.000 0.864 0.874 1.186 1.390 2.821 1.556 1.251 +0 0.759 0.290 0.245 1.818 -1.090 0.569 -0.706 1.294 2.173 0.719 -1.317 0.788 0.000 0.862 -0.980 -0.112 0.000 1.285 -0.937 -1.461 3.102 0.891 0.882 1.518 1.153 0.585 0.872 0.898 +1 1.395 0.929 0.919 0.583 -1.520 1.351 1.069 -0.869 0.000 1.194 -0.328 -0.335 2.215 0.782 -1.000 0.794 2.548 0.389 0.168 -1.727 0.000 0.922 1.336 1.013 1.032 0.961 1.141 1.064 +1 0.391 2.002 -0.362 1.186 1.525 0.892 0.331 -0.263 2.173 0.682 1.288 -1.468 0.000 0.518 1.891 0.615 0.000 1.185 0.286 0.938 3.102 0.936 0.838 0.988 1.141 0.960 0.838 0.769 +1 1.108 -1.298 1.717 1.306 0.659 0.950 0.075 -0.881 2.173 1.415 -1.919 -0.270 0.000 0.995 2.042 1.537 0.000 1.258 -1.076 1.127 3.102 0.932 1.468 1.358 0.689 1.412 1.330 1.484 +0 1.755 -1.230 1.250 0.664 -0.171 1.111 -2.133 1.553 0.000 0.867 -0.757 -0.383 0.000 1.012 -0.210 -0.919 2.548 1.086 0.309 -0.196 0.000 0.796 0.836 1.432 1.087 0.980 0.831 0.817 +0 2.186 -0.417 0.810 0.954 0.537 2.336 1.234 -0.971 2.173 1.785 -1.049 0.924 1.107 0.522 1.868 -0.974 0.000 0.551 0.181 -0.158 0.000 0.738 0.904 0.981 1.010 5.260 2.620 1.949 +1 0.877 0.086 0.219 1.018 0.250 1.034 1.231 -1.305 0.000 1.131 1.298 0.639 2.215 1.132 0.867 -1.709 0.000 1.358 0.987 -0.072 1.551 0.886 0.909 0.984 1.475 0.674 1.068 1.156 +0 0.303 -1.653 -1.154 0.863 -1.648 0.478 1.243 -0.902 0.000 0.880 -0.822 0.608 0.000 0.754 -0.333 -0.984 2.548 0.683 -1.134 0.835 0.000 0.334 0.776 0.991 0.767 0.621 0.595 0.632 +1 0.724 -0.782 1.216 1.603 -1.699 0.458 2.936 -1.739 0.000 1.464 -0.479 0.215 2.215 1.319 0.586 -0.303 2.548 0.374 -0.214 -0.219 0.000 1.640 1.438 0.987 1.295 1.107 1.582 1.466 +1 1.472 1.506 0.538 1.702 -0.137 1.362 1.593 -1.572 0.000 0.432 0.222 -1.127 1.107 0.607 0.400 -0.047 0.000 0.794 0.480 0.560 1.551 1.897 1.187 1.252 0.740 0.536 0.810 0.949 +0 0.398 -0.991 -0.681 0.656 -0.492 0.607 0.050 1.651 1.087 0.932 0.307 0.721 0.000 0.704 0.780 -1.288 2.548 0.772 -0.446 -0.194 0.000 0.945 0.917 0.993 0.615 0.511 0.664 0.649 +1 2.553 -1.408 0.026 0.592 -0.596 2.056 -1.070 1.049 0.000 1.208 2.483 1.582 0.000 1.226 -1.033 -0.439 0.000 1.880 -1.003 -1.322 1.551 1.056 0.739 0.985 1.148 0.460 0.841 1.054 +1 1.508 -0.041 0.642 0.926 -0.090 1.080 0.458 -1.451 2.173 0.976 1.671 -0.843 0.000 1.088 0.961 1.176 0.000 1.025 0.680 -0.612 1.551 0.588 0.847 1.003 1.341 0.790 0.923 0.934 +1 0.286 1.733 1.709 1.188 0.277 3.223 -0.279 1.616 0.000 3.382 0.276 -0.094 2.215 2.138 1.739 -1.046 0.000 1.926 0.824 0.283 1.551 2.765 2.764 0.982 1.059 1.130 2.564 1.916 +1 1.376 0.906 -0.810 1.142 -0.269 1.449 1.848 1.601 0.000 0.570 0.901 0.504 1.107 0.306 1.439 1.420 0.000 0.462 0.264 0.507 3.102 1.284 0.986 0.987 0.799 0.144 0.733 0.908 +1 0.403 1.239 0.483 1.209 -0.257 1.969 -0.551 1.230 0.000 1.331 0.531 -0.167 2.215 1.386 -0.077 -1.294 0.000 1.525 -1.022 -1.148 3.102 1.052 0.775 0.984 1.296 1.634 1.789 1.554 +0 0.681 -1.348 -1.704 2.095 1.236 2.960 0.096 -0.321 2.173 1.069 1.220 -1.719 0.000 1.014 0.537 0.132 0.000 1.746 1.385 1.121 0.000 1.045 0.722 0.988 3.675 1.966 2.413 2.556 +1 0.536 0.762 0.245 0.990 0.032 1.285 0.794 -1.571 2.173 0.737 0.403 -0.663 0.000 1.276 -0.098 1.114 0.000 0.716 -0.203 -0.374 0.000 1.029 1.278 0.996 0.971 1.105 1.085 1.038 +0 1.405 -0.103 -0.714 0.520 -0.875 0.828 0.161 1.030 1.087 1.526 -0.694 -1.394 2.215 1.207 0.306 0.140 0.000 1.335 -0.700 0.422 0.000 0.941 0.975 0.984 0.863 1.545 1.104 1.005 +1 2.404 -1.208 -1.262 0.375 1.151 0.765 -1.140 0.444 2.173 0.410 -1.886 0.143 0.000 1.061 -1.400 1.158 0.000 0.794 -0.010 -0.369 3.102 0.828 0.873 1.083 1.194 0.741 0.858 0.783 +0 1.051 0.688 1.035 1.562 -1.569 0.645 -0.118 0.542 0.000 1.062 -0.865 -0.669 0.000 0.777 0.926 -1.672 2.548 1.159 -1.904 -0.123 0.000 1.289 1.269 1.269 0.642 0.635 1.142 1.245 +1 0.536 -2.161 -0.523 0.782 0.176 1.007 -0.966 -0.371 1.087 0.774 -0.930 1.006 0.000 1.172 -0.155 1.686 2.548 0.607 0.509 0.937 0.000 0.942 1.091 0.992 0.980 1.415 0.871 0.776 +1 0.755 -1.317 0.774 0.848 -0.680 1.553 -0.864 1.529 0.000 0.833 -0.699 0.070 2.215 0.961 0.540 -0.303 0.000 1.455 -0.317 0.506 3.102 0.912 0.913 1.071 0.706 0.417 0.629 0.691 +0 0.836 1.515 0.305 0.708 -0.432 0.670 0.054 1.031 0.000 0.974 1.228 -1.310 2.215 0.909 -0.941 0.230 0.000 1.018 -1.030 -1.200 0.000 1.025 1.040 0.988 0.986 0.214 1.016 1.277 +1 0.793 -0.546 -1.602 1.018 -1.047 0.707 -0.915 -1.001 2.173 1.143 -0.554 0.737 2.215 0.841 -1.581 0.898 0.000 0.557 -2.003 0.261 0.000 0.484 0.953 0.986 1.122 1.343 0.863 0.792 +0 0.712 1.462 -1.368 0.176 -1.675 1.370 0.889 -1.105 2.173 1.956 1.630 0.438 0.000 0.547 0.076 0.383 0.000 0.958 -0.472 -1.509 3.102 0.623 1.050 0.998 0.654 1.061 0.726 0.649 +1 0.726 0.403 -1.133 1.431 0.362 0.780 -2.228 -1.138 0.000 1.424 0.596 -0.508 2.215 1.182 0.328 1.000 0.000 1.278 1.383 1.202 0.000 0.976 0.718 1.377 0.987 0.532 0.850 0.795 +1 0.478 0.458 1.405 1.527 -0.316 0.742 -0.429 -1.424 0.000 1.032 0.113 1.625 0.000 1.484 -0.256 0.599 2.548 1.374 0.664 -0.187 3.102 0.879 1.235 1.184 0.957 0.941 0.977 0.896 +0 1.003 -0.158 -1.601 1.332 -0.997 0.894 0.334 -1.050 1.087 1.504 -0.424 0.566 0.000 1.177 0.108 0.765 0.000 0.983 -0.978 -0.136 1.551 0.681 0.862 0.991 0.584 1.099 1.043 1.005 +0 0.562 0.100 0.720 0.518 -0.365 0.625 -1.732 0.369 0.000 0.890 0.027 -1.510 1.107 0.867 -1.437 -1.547 2.548 0.667 -0.301 -0.162 0.000 0.869 0.900 0.988 0.857 0.834 0.858 0.740 +0 0.838 -1.188 1.059 0.926 -1.380 0.399 0.190 1.717 0.000 1.029 -0.077 0.161 2.215 0.676 0.698 -1.363 2.548 0.458 1.136 -0.192 0.000 0.762 0.844 0.989 0.913 0.951 0.855 0.757 +1 0.891 -0.684 0.886 0.742 -0.267 0.800 -1.376 1.379 2.173 0.970 -1.909 -0.522 0.000 1.011 -0.042 0.239 1.274 1.366 -1.209 -1.214 0.000 0.958 1.192 0.985 0.591 1.256 1.030 0.893 +1 1.344 1.360 -0.688 0.637 -0.182 2.759 -0.138 1.005 0.000 2.252 0.646 -0.479 2.215 1.341 -1.885 -1.248 0.000 1.029 1.310 -1.575 0.000 1.013 2.467 0.991 0.707 0.850 1.932 1.587 +0 1.293 2.129 -0.515 0.631 1.574 0.695 1.435 0.573 2.173 0.323 0.644 0.630 0.000 1.501 0.622 -1.638 0.000 0.671 0.759 -1.304 3.102 0.951 1.023 1.191 0.713 0.744 0.695 0.759 +1 0.895 1.219 -1.735 0.245 -1.280 0.920 0.198 -0.698 0.000 0.921 1.530 0.830 1.107 0.899 0.245 0.431 2.548 1.430 -1.771 -1.739 0.000 1.402 1.079 0.986 0.909 0.758 0.923 0.833 +1 1.218 0.879 0.954 1.150 1.721 0.609 -0.442 -0.915 0.000 0.633 2.455 0.676 0.000 1.078 0.001 -0.167 1.274 1.212 0.204 -1.244 3.102 0.878 1.124 1.046 1.053 0.729 0.891 0.850 +0 0.278 0.542 -0.238 0.912 0.377 1.701 -1.049 0.808 2.173 0.897 1.528 -0.487 2.215 1.604 -2.647 -1.243 0.000 2.155 2.077 -1.719 0.000 1.065 0.827 0.979 0.910 3.562 1.912 1.533 +1 1.125 0.984 1.054 0.448 0.851 2.358 1.631 0.640 0.000 0.992 0.253 -0.796 1.107 3.351 1.235 -1.003 2.548 0.964 0.560 -1.341 0.000 2.535 2.318 0.984 1.468 1.173 1.884 1.484 +1 1.157 -0.196 -1.152 0.807 0.818 1.395 0.341 1.354 2.173 0.548 0.222 0.567 2.215 0.908 -0.759 -0.178 0.000 2.051 0.204 -0.537 0.000 0.977 0.861 1.311 1.094 0.843 1.066 0.926 +1 0.522 -0.542 1.219 1.440 -0.577 1.053 0.123 1.659 1.087 1.131 -0.649 0.167 0.000 1.092 0.115 0.469 0.000 0.915 -0.565 -0.843 3.102 0.959 0.729 1.200 1.134 0.911 0.834 0.774 +0 0.718 -2.132 1.315 2.040 -1.600 0.732 -1.385 -0.009 2.173 0.705 -1.960 -0.383 0.000 1.103 -0.919 0.486 0.000 0.597 -1.425 0.601 0.000 0.668 0.657 0.990 1.221 0.771 0.802 0.720 +1 0.982 0.610 0.263 0.861 1.364 1.761 1.128 1.404 0.000 1.577 0.660 -0.708 0.000 2.121 -0.678 -0.585 2.548 1.505 -1.030 0.844 0.000 0.974 1.137 1.067 1.368 1.248 1.650 1.302 +1 0.540 0.975 0.523 0.616 -0.129 3.044 0.098 -0.814 2.173 3.624 -0.386 0.425 0.000 2.641 0.796 1.597 0.000 3.132 0.267 1.092 3.102 0.911 1.661 0.984 1.431 3.252 2.331 1.700 +1 1.174 0.696 -1.681 0.560 0.124 1.240 0.971 -1.154 1.087 1.037 0.936 0.729 0.000 1.223 1.191 -0.498 2.548 1.351 -0.995 0.922 0.000 0.639 2.036 1.121 0.906 0.892 1.744 1.370 +1 1.524 0.257 -1.190 0.305 -1.703 0.485 0.862 -1.477 0.000 0.643 1.213 0.704 2.215 0.954 1.234 0.377 0.000 1.315 -0.116 0.160 3.102 1.253 1.040 0.988 0.896 0.736 0.762 0.739 +0 2.042 1.477 0.032 0.224 0.749 0.629 0.153 1.679 2.173 0.690 1.531 -1.692 0.000 0.864 1.288 -1.157 0.000 1.277 0.191 0.026 3.102 0.557 0.995 0.990 1.207 0.946 0.852 0.837 +1 0.852 -0.490 1.494 0.988 0.771 1.084 0.834 -0.587 2.173 0.833 -0.204 0.318 0.000 0.844 -0.733 -1.395 0.000 0.853 0.035 0.793 3.102 1.343 0.837 0.991 1.750 1.051 1.123 0.982 +1 1.554 0.416 1.645 0.750 -0.629 0.996 -0.950 0.696 2.173 0.902 -1.119 -0.325 0.000 0.578 -1.015 -1.500 0.000 0.454 0.737 -0.900 1.551 0.965 1.104 1.329 0.647 1.031 0.951 0.925 +0 0.941 -1.055 -1.462 0.971 0.983 0.975 -0.475 -0.833 1.087 0.986 -1.313 0.475 0.000 0.548 -1.422 -0.477 0.000 1.208 -0.443 0.819 3.102 0.861 1.200 1.067 0.678 1.145 0.835 0.787 +1 0.749 -0.137 1.733 0.557 0.613 0.737 0.926 -1.017 2.173 0.868 -0.348 1.191 0.000 1.137 -0.320 -0.556 2.548 0.765 -1.122 0.469 0.000 0.845 0.975 0.983 1.137 0.910 0.953 0.838 +1 0.764 -0.882 -0.082 1.263 -1.291 0.946 -0.184 0.273 0.000 0.582 -0.344 -1.363 0.000 0.951 -1.591 -0.855 0.000 1.172 0.208 0.853 0.000 0.875 0.846 1.206 0.604 0.150 0.555 0.688 +1 0.803 -0.512 -0.836 0.504 0.497 0.865 -0.159 1.331 0.000 0.615 -0.448 0.463 2.215 1.123 0.683 -0.287 1.274 0.501 -0.316 -0.297 0.000 1.006 1.097 0.988 0.653 0.793 0.730 0.722 +1 0.735 -1.123 1.440 0.679 -0.575 1.272 -0.160 1.407 0.000 1.142 -0.158 0.114 2.215 1.284 -0.031 -0.726 1.274 0.821 2.164 -0.496 0.000 0.820 1.232 0.987 0.999 0.888 0.981 0.914 +1 0.628 -0.892 -0.077 1.314 -0.946 1.313 -0.751 1.277 1.087 0.550 -0.508 -1.522 0.000 0.436 -1.033 -0.525 0.000 0.755 0.621 0.290 0.000 1.013 1.106 0.989 0.690 0.780 0.891 0.843 +1 1.231 0.140 1.388 1.043 -1.195 0.885 0.232 -0.466 2.173 1.700 -2.329 0.530 0.000 1.392 0.744 -1.232 2.548 0.854 0.139 0.751 0.000 2.630 2.794 1.143 0.793 0.971 2.240 1.828 +1 0.546 -0.274 0.313 0.564 1.044 1.113 0.444 1.048 2.173 0.767 1.483 -0.372 0.000 1.101 0.456 -0.518 0.000 1.026 -0.347 -0.799 0.000 0.949 0.668 0.983 0.732 0.528 0.782 0.683 +1 0.927 -2.132 0.691 1.810 0.550 2.071 0.753 -0.957 0.000 1.980 -0.844 -1.682 0.000 2.015 -0.503 0.548 2.548 2.177 0.770 1.635 1.551 0.714 0.976 1.001 2.251 1.854 1.552 1.284 +0 0.556 1.352 -1.036 1.079 0.420 0.701 0.250 -1.082 0.000 0.614 -0.456 0.818 2.215 1.095 -0.191 -1.663 0.000 1.380 -0.087 -0.162 3.102 0.869 0.967 1.037 0.865 0.660 0.751 0.832 +1 0.648 -0.867 0.110 0.256 1.742 1.128 -1.005 -1.677 0.000 1.031 -0.228 -0.122 2.215 1.440 -2.508 0.929 0.000 2.177 -0.201 0.585 3.102 0.925 1.405 0.994 0.678 0.804 1.082 0.890 +0 0.771 -0.232 0.068 0.993 -1.725 0.843 -0.053 -1.217 2.173 1.066 -0.847 0.438 0.000 0.796 -0.153 1.740 2.548 0.798 0.167 0.479 0.000 0.677 0.856 1.211 0.845 0.471 0.820 0.733 +0 1.284 -0.095 -1.055 0.862 0.078 0.485 -1.734 0.653 0.000 0.879 -1.155 1.380 2.215 0.374 -2.442 -0.714 0.000 0.424 0.267 -0.152 3.102 0.811 0.808 1.243 0.592 0.708 0.711 0.790 +1 1.030 1.361 1.228 0.191 -0.570 0.507 0.163 -1.098 2.173 0.770 1.844 1.193 0.000 1.052 1.391 0.484 0.000 1.811 0.911 -0.289 3.102 0.863 1.030 0.984 0.762 0.833 0.887 0.769 +0 0.328 0.676 0.970 2.030 -1.372 0.790 -0.667 0.740 0.000 1.020 -0.832 0.201 0.000 1.263 0.452 -0.616 2.548 1.848 -0.193 -0.293 3.102 0.909 0.989 0.986 0.845 0.549 0.896 1.119 +1 0.370 2.240 -0.983 0.504 -1.527 0.739 1.574 0.135 0.000 0.865 1.110 1.509 2.215 1.186 0.614 0.487 0.000 1.583 -0.132 -1.213 3.102 0.955 1.123 0.998 0.729 0.997 1.015 0.855 +0 1.610 1.244 -0.963 0.917 -0.865 1.014 0.305 0.824 0.000 0.448 0.563 -0.703 2.215 0.774 0.823 1.269 0.000 0.708 0.909 0.514 3.102 0.786 0.904 1.007 0.811 0.471 0.589 0.891 +1 0.670 0.044 1.193 1.258 0.166 1.219 -0.697 -1.654 2.173 1.372 -1.160 -0.085 0.000 0.727 0.430 1.285 2.548 0.384 -2.021 -1.288 0.000 1.041 1.270 1.016 1.256 0.916 1.091 1.003 +0 0.906 -0.455 0.828 0.616 -1.454 0.593 -0.053 0.031 2.173 0.476 0.879 -1.464 0.000 0.613 -0.939 -0.062 0.000 0.664 -0.345 -0.879 3.102 0.703 1.083 0.991 0.591 0.500 0.714 0.690 +1 0.850 -1.142 1.593 0.884 -0.304 0.577 -2.021 0.813 0.000 0.606 -1.016 1.314 2.215 0.622 0.041 -0.442 0.000 0.991 -0.318 -0.946 1.551 0.948 1.107 1.189 0.701 0.669 0.741 0.791 +1 0.548 0.608 0.617 0.143 0.972 1.222 -0.858 -0.988 0.000 1.008 -0.140 1.019 2.215 0.768 -1.176 -0.362 2.548 0.629 -1.059 0.574 0.000 1.349 0.867 0.992 0.658 1.052 0.904 0.783 +1 0.502 0.184 0.905 0.300 0.853 0.818 0.337 -0.563 2.173 1.053 -1.063 0.299 2.215 1.003 -0.543 -1.080 0.000 0.709 -2.013 1.443 0.000 0.843 0.956 0.989 0.693 1.432 0.971 0.871 +1 0.768 -0.710 -1.372 2.113 -0.830 1.058 -0.076 0.956 2.173 0.305 0.492 0.961 0.000 0.441 0.242 0.037 0.000 0.505 -2.495 -0.133 0.000 1.281 1.386 0.978 1.447 0.790 1.052 1.077 +1 0.280 -0.000 0.180 1.718 -1.344 1.089 1.111 1.473 2.173 1.045 1.546 -0.847 0.000 1.048 -0.754 0.319 0.000 1.119 0.644 0.524 3.102 0.873 1.054 0.987 0.964 0.906 1.019 0.853 +1 0.599 0.673 0.220 0.789 -0.813 0.640 -1.287 -1.735 2.173 0.988 -0.398 -1.231 0.000 1.046 -0.279 0.180 0.000 0.855 -1.171 0.414 0.000 0.684 0.911 0.989 0.823 1.647 0.938 0.767 +1 2.046 0.547 1.262 1.213 1.184 1.292 -1.961 -0.739 0.000 1.364 -0.719 -0.171 2.215 0.655 -0.538 1.735 0.000 0.890 -0.133 0.702 3.102 1.805 1.476 0.982 1.646 0.762 1.101 1.468 +1 0.893 -0.013 0.018 1.731 1.153 2.113 0.500 0.430 0.000 4.139 0.983 -1.132 0.000 1.228 -0.033 -0.428 2.548 1.460 2.321 1.464 0.000 1.148 1.614 1.471 1.075 0.882 1.277 1.220 +0 2.224 -1.541 0.973 0.827 0.589 1.046 -1.133 -0.776 0.000 1.026 -0.509 -0.839 0.000 0.722 -0.810 1.586 2.548 0.380 -0.821 0.117 3.102 0.704 0.915 0.987 0.546 0.389 0.568 0.886 +0 0.650 2.150 0.122 0.313 -0.968 1.054 -1.727 -0.603 0.000 1.804 1.197 0.715 2.215 1.438 1.245 -1.565 2.548 1.548 1.204 1.107 0.000 1.421 0.933 0.986 0.914 1.519 1.001 0.857 +0 0.358 -0.760 0.247 1.364 -1.066 0.777 0.145 0.548 0.000 0.843 0.538 0.964 2.215 1.636 0.219 -1.148 2.548 0.561 1.296 1.003 0.000 0.869 1.234 0.987 0.921 1.196 0.830 0.792 +1 1.413 0.070 -1.608 0.641 -1.479 2.098 1.907 0.326 0.000 1.160 1.223 -1.443 2.215 1.792 0.391 -1.100 1.274 1.203 0.219 0.343 0.000 0.777 1.099 0.989 1.214 0.803 0.892 0.954 +0 1.452 -0.446 0.091 0.374 -1.082 0.516 0.567 -0.996 2.173 0.855 -0.657 0.575 1.107 1.058 0.413 1.730 0.000 0.752 -0.837 1.565 0.000 0.797 0.934 0.985 0.794 1.164 0.755 0.726 +1 0.459 -0.580 0.109 0.831 -1.377 0.674 -1.678 -0.483 0.000 0.533 -0.085 1.383 0.000 1.211 1.283 1.254 2.548 0.992 0.625 0.376 3.102 1.701 1.392 0.982 1.616 0.659 1.334 1.168 +0 0.827 -1.997 0.287 1.068 1.401 0.842 0.622 -1.245 1.087 0.560 -0.518 -1.499 0.000 1.361 -0.902 0.173 0.000 1.064 0.313 0.540 3.102 1.371 0.999 1.099 2.045 1.009 1.412 1.179 +0 0.703 -1.039 -1.515 1.416 1.047 1.089 -1.443 0.011 2.173 0.231 -1.010 -0.214 2.215 0.274 -1.638 1.584 0.000 1.070 -0.555 -1.132 0.000 0.522 0.937 1.022 0.613 0.215 0.731 0.643 +1 0.952 -1.600 -0.784 0.575 1.351 0.613 -1.327 -0.195 2.173 0.856 -0.070 1.016 2.215 1.360 -0.679 -0.630 0.000 0.837 -1.608 1.331 0.000 0.828 0.956 0.988 0.708 1.190 0.790 0.698 +0 0.365 -0.408 0.146 1.319 -1.675 0.988 0.314 1.361 2.173 1.168 1.005 0.241 2.215 0.881 2.078 -0.689 0.000 1.266 1.432 -0.216 0.000 0.585 0.908 0.987 0.938 1.459 1.130 1.275 +1 2.138 0.432 0.271 0.399 1.190 1.441 0.969 -0.982 0.000 0.893 1.032 -1.737 0.000 2.921 -0.751 -0.380 0.000 3.389 0.204 1.200 3.102 1.020 0.993 0.987 1.065 0.872 0.913 0.867 +0 1.203 1.930 -0.110 1.060 0.559 0.564 -2.826 1.301 0.000 1.686 0.253 -1.219 2.215 0.773 -0.116 -0.531 0.000 0.826 0.543 0.257 3.102 0.851 0.949 0.982 0.604 1.056 1.097 1.035 +0 0.624 0.911 0.788 2.753 0.122 1.299 1.350 -1.320 0.000 0.612 1.397 1.072 2.215 0.602 1.779 1.512 0.000 0.548 -0.438 -1.395 3.102 1.002 0.987 1.024 0.849 0.730 0.751 0.980 +0 0.329 -2.328 -0.489 2.053 -0.903 1.278 -2.321 1.237 0.000 1.013 -0.592 -0.034 2.215 0.470 -1.863 0.568 0.000 0.545 -0.718 -1.541 3.102 0.819 0.804 0.983 0.938 0.661 1.013 1.033 +0 1.752 -0.927 -1.454 0.722 1.305 0.481 -0.012 1.076 0.000 1.005 -0.690 0.002 2.215 0.813 -0.735 -0.380 2.548 0.476 0.568 -0.972 0.000 0.749 0.881 0.988 0.839 0.328 0.788 0.744 +1 0.593 1.487 -1.644 1.839 1.427 0.851 0.437 -0.405 2.173 0.385 1.149 -0.045 0.000 0.869 -0.150 0.996 2.548 0.648 1.720 -0.058 0.000 0.297 0.807 0.972 1.249 1.073 0.901 0.798 +1 0.491 0.770 0.611 1.057 0.617 0.615 -1.133 0.720 2.173 0.604 -0.720 -0.804 2.215 0.859 1.267 -1.004 0.000 0.411 -2.197 -1.509 0.000 2.433 1.433 0.983 0.646 0.898 1.090 0.947 +0 0.829 -1.553 -0.452 0.317 -0.641 0.701 -1.823 0.641 0.000 1.377 -1.058 -1.441 1.107 0.792 -0.827 0.091 2.548 0.855 1.660 0.928 0.000 1.030 1.119 0.982 0.902 1.094 1.144 0.985 +1 1.856 1.283 0.428 0.544 -1.521 1.035 0.807 -1.411 1.087 0.361 -0.884 -0.885 1.107 0.803 1.054 0.885 0.000 1.296 0.025 0.018 0.000 1.037 0.899 1.368 1.274 0.968 1.013 0.905 +1 1.000 -0.512 -1.416 0.584 -0.015 0.931 -0.925 1.117 0.000 0.448 -1.912 0.711 0.000 0.617 -1.375 -0.552 2.548 1.002 0.750 -0.607 3.102 0.937 0.853 1.009 0.744 0.914 0.810 0.708 +0 0.550 0.637 1.143 1.323 -0.815 0.484 -0.741 -0.217 1.087 0.607 -0.289 0.557 2.215 0.711 -1.555 -1.670 0.000 0.869 -0.008 1.569 0.000 0.850 0.867 1.160 0.867 0.543 0.706 0.761 +0 0.428 -0.488 1.082 1.301 -0.386 0.478 -0.628 -1.453 0.000 0.765 0.232 0.488 2.215 0.575 0.922 -0.325 2.548 0.752 -2.228 1.676 0.000 0.920 0.912 1.002 0.696 0.548 0.659 0.660 +1 1.004 -2.255 0.249 0.635 0.764 1.375 -1.283 -1.009 0.000 0.451 -1.861 1.297 0.000 1.388 0.152 0.532 2.548 0.529 0.278 -1.533 3.102 0.869 0.982 0.983 0.881 0.630 0.866 0.762 +1 0.996 -0.802 0.466 0.788 -0.786 1.626 -0.873 0.186 0.000 2.490 -1.061 -1.495 2.215 0.713 -1.875 1.241 0.000 0.804 -0.559 1.307 3.102 0.765 0.996 1.109 0.670 0.786 0.857 0.738 +0 0.700 -1.020 -0.370 0.707 1.328 1.121 -0.525 -0.200 2.173 1.027 0.151 1.190 0.000 1.493 0.461 -1.556 0.000 0.866 0.694 0.622 3.102 1.221 0.902 0.988 0.853 1.040 1.087 0.930 +1 0.752 0.347 0.920 0.340 -1.173 1.016 0.117 -0.248 1.087 0.964 0.357 0.086 0.000 1.736 0.100 1.379 2.548 2.182 0.382 -1.548 0.000 0.977 0.913 0.983 0.914 1.646 0.889 0.767 +1 0.827 0.848 1.455 1.473 1.543 1.284 0.526 0.076 2.173 0.930 0.902 -0.887 2.215 0.440 0.814 -0.329 0.000 0.427 1.521 1.289 0.000 0.528 0.724 1.000 1.088 1.269 1.150 0.879 +1 0.520 0.381 -1.379 1.028 1.153 0.802 -1.235 -0.748 2.173 0.620 -2.032 0.736 0.000 0.379 0.010 1.467 0.000 0.749 -0.455 -0.531 3.102 0.822 0.719 0.987 0.855 0.334 1.101 0.946 +1 1.468 -0.625 0.233 1.181 -0.277 0.839 2.804 -1.364 0.000 1.400 -0.430 1.185 2.215 0.793 0.726 -1.100 2.548 0.854 0.232 -0.004 0.000 0.585 0.771 0.985 1.166 1.235 1.076 0.827 +1 0.933 1.262 -0.164 0.417 0.814 0.518 -0.250 1.410 2.173 0.466 1.327 -0.893 0.000 0.749 0.670 1.295 0.000 1.146 -0.972 -0.458 3.102 0.883 0.822 0.993 0.967 0.897 0.799 0.754 +1 0.979 -0.755 -0.507 1.468 -1.458 0.870 -1.213 -0.142 0.000 1.129 -0.422 0.123 0.000 1.852 -0.338 1.711 0.000 2.136 -0.587 0.728 1.551 0.989 1.049 1.254 0.875 1.364 1.106 1.031 +0 1.599 0.746 -1.571 1.297 1.247 0.587 -0.515 -0.179 2.173 0.682 0.811 0.105 0.000 0.753 0.773 -0.437 2.548 0.531 1.328 1.563 0.000 0.817 0.931 1.131 0.921 0.641 0.924 0.795 +1 0.829 1.447 1.599 0.221 -1.565 1.473 -2.364 -0.738 0.000 1.360 0.811 1.565 0.000 2.473 0.210 0.222 2.548 1.109 0.859 0.929 0.000 0.881 0.765 0.985 1.161 0.998 0.974 0.834 +1 1.230 -2.115 1.270 0.887 0.717 0.777 -1.067 -0.060 0.000 0.600 -0.492 -0.335 2.215 0.564 -0.915 -1.647 2.548 1.090 -0.098 -1.020 0.000 0.728 0.552 0.977 0.659 0.593 0.690 0.650 +1 0.725 -0.602 -1.143 1.057 0.170 0.956 -0.497 1.723 0.000 0.915 -0.710 -0.704 0.000 1.275 -0.251 0.261 2.548 1.387 -0.646 1.213 3.102 0.790 1.032 1.123 0.797 0.810 0.672 0.677 +0 0.642 0.888 -0.279 0.848 1.307 0.801 1.405 -0.526 2.173 0.476 1.743 0.494 0.000 0.588 2.072 1.671 0.000 1.295 -0.601 1.599 1.551 0.735 0.882 1.012 0.882 1.719 1.078 0.875 +0 0.492 0.150 -1.313 0.819 -0.098 0.709 -0.148 -1.646 0.000 0.905 -0.352 0.720 2.215 0.420 -1.137 -0.997 0.000 0.949 0.652 0.129 3.102 0.785 0.952 0.993 0.561 0.660 0.723 0.746 +0 0.737 -1.336 1.561 0.974 -0.454 0.808 -0.632 0.739 2.173 0.510 0.186 0.444 0.000 1.313 0.094 -1.345 0.000 1.123 0.015 -1.038 3.102 0.762 0.785 1.139 0.961 1.062 0.785 0.685 +0 0.607 -2.180 1.128 0.096 -0.062 1.271 -0.991 1.622 2.173 1.444 -0.295 -0.400 2.215 0.773 0.827 -0.187 0.000 0.469 -1.103 0.834 0.000 0.863 1.045 0.995 0.847 2.056 1.249 1.065 +1 1.130 0.768 0.132 0.347 1.455 1.421 0.130 1.482 2.173 1.036 0.348 -0.405 0.000 0.540 -1.784 -0.248 0.000 0.507 0.986 -0.820 3.102 1.655 1.077 0.988 1.094 0.929 1.155 0.970 +1 0.611 -1.305 -0.121 0.697 0.494 1.343 0.738 -1.289 2.173 0.680 0.729 1.283 2.215 0.894 -0.652 0.095 0.000 1.034 0.233 0.330 0.000 0.845 1.029 0.981 1.413 1.030 1.079 0.968 +0 1.877 1.268 -1.203 0.627 1.454 1.064 0.772 0.527 2.173 1.503 -1.440 -0.624 0.000 1.365 0.250 1.022 0.000 1.076 1.257 -0.014 3.102 0.541 0.816 1.021 1.340 0.678 0.922 0.826 +1 1.338 0.144 -0.177 0.338 -1.198 0.917 0.141 0.877 1.087 0.845 -0.133 -0.548 0.000 1.335 0.741 1.559 2.548 1.256 1.806 -1.644 0.000 0.840 1.283 0.985 0.946 0.923 1.034 0.908 +1 1.694 -0.752 0.165 0.844 1.005 0.706 0.110 1.408 0.000 0.727 0.294 -1.367 0.000 0.726 -0.010 -0.608 2.548 0.777 0.464 -1.012 3.102 0.920 0.836 1.138 0.925 0.263 0.675 0.770 +0 0.445 1.682 -0.952 0.670 -1.073 0.569 1.101 0.296 0.000 0.703 -0.073 -0.871 0.000 0.766 0.033 1.305 1.274 0.608 0.341 1.580 1.551 1.440 1.034 1.000 0.557 0.161 0.630 0.609 +0 0.879 0.731 -0.161 1.550 -0.734 0.858 1.300 0.741 2.173 0.701 1.133 1.707 2.215 0.519 2.360 1.449 0.000 0.776 0.572 0.371 0.000 0.966 0.763 0.985 0.993 0.875 0.977 0.870 +1 0.465 -0.542 0.995 1.102 -1.410 0.774 1.078 1.721 0.000 0.957 0.039 -0.222 2.215 1.327 0.637 0.369 2.548 0.774 -0.209 0.602 0.000 0.889 1.061 0.987 0.877 0.729 0.850 0.779 +1 0.982 -0.028 -0.451 1.189 0.560 1.085 0.683 1.312 2.173 0.718 0.507 0.562 2.215 1.370 0.449 -0.501 0.000 1.408 0.610 -1.117 0.000 0.829 0.972 1.183 1.209 0.820 0.924 0.883 +0 0.772 -0.035 -0.370 0.665 1.344 1.072 0.343 1.011 0.000 1.143 0.193 -0.789 0.000 0.735 0.994 -0.315 2.548 0.770 -0.404 1.600 3.102 2.354 1.346 0.992 0.684 0.758 0.909 0.764 +1 1.030 -0.752 1.278 0.891 1.737 0.500 0.275 -0.379 2.173 0.289 0.792 0.369 0.000 0.326 -0.812 0.873 2.548 0.878 -0.862 -0.457 0.000 0.828 0.598 0.985 0.514 0.551 0.740 0.682 +1 1.174 1.794 0.459 0.792 1.346 1.255 1.092 -0.776 2.173 0.423 0.084 0.031 0.000 0.478 1.347 -1.710 2.548 0.923 -2.391 1.741 0.000 0.918 1.112 0.987 0.608 0.742 0.878 0.787 +0 1.561 -1.619 1.471 0.318 -1.273 0.656 -0.016 0.185 2.173 0.571 -0.232 1.322 1.107 1.175 -1.711 -0.366 0.000 0.439 -0.742 -1.167 0.000 0.664 0.932 0.986 1.175 0.776 0.806 0.800 +1 2.081 0.738 0.887 0.583 1.512 0.602 -0.270 -0.970 1.087 0.735 -0.822 -1.203 0.000 2.013 -0.264 0.024 2.548 1.059 -0.124 1.721 0.000 0.692 1.209 0.984 1.150 1.070 1.053 0.964 +0 1.168 1.045 -1.410 0.729 -1.101 0.705 0.749 0.373 2.173 0.996 0.809 -0.385 0.000 1.465 0.085 -1.502 0.000 2.173 -0.136 1.100 3.102 0.870 0.939 0.978 1.143 1.012 1.130 1.009 +1 0.837 -1.009 1.163 1.118 -1.660 0.555 -0.238 0.132 2.173 0.698 -2.019 -0.931 0.000 1.366 0.955 0.170 0.000 2.147 -0.229 -1.106 0.000 0.883 0.956 0.987 1.096 0.739 0.924 1.056 +0 2.381 -1.203 0.390 0.738 1.013 1.205 0.018 -1.425 0.000 0.848 -1.173 -0.616 0.000 1.165 -0.776 0.945 2.548 1.615 1.759 -0.650 0.000 2.002 1.043 0.986 0.646 0.360 0.878 1.011 +0 0.630 -1.053 -0.137 0.832 1.206 0.949 -0.516 -0.697 2.173 1.199 -1.140 1.142 0.000 0.463 0.312 0.091 2.548 0.637 -0.171 -1.313 0.000 1.077 0.884 0.990 1.004 0.652 0.835 0.765 +1 0.747 -0.331 0.636 1.303 1.250 1.015 0.049 -0.293 0.000 0.643 0.550 0.987 2.215 0.687 -0.399 -0.905 2.548 0.974 0.977 -1.456 0.000 1.593 0.992 0.990 0.855 0.792 0.778 0.921 +0 0.664 -0.213 -1.731 0.881 -1.082 0.563 0.285 -1.016 0.000 1.087 0.665 0.496 1.107 0.541 0.038 0.114 0.000 1.305 -0.369 1.049 3.102 0.854 0.950 0.985 0.779 0.820 0.937 0.830 +0 0.860 1.835 -0.534 0.723 0.849 1.307 0.502 0.801 2.173 1.341 0.842 -0.913 2.215 0.676 0.888 1.580 0.000 0.481 -1.975 -1.115 0.000 1.670 1.587 1.035 1.245 1.979 1.363 1.294 +1 1.913 -0.467 1.698 0.906 -1.395 0.912 -0.042 -0.348 2.173 0.427 1.181 0.668 2.215 0.858 0.548 -1.659 0.000 1.843 -1.988 0.172 0.000 0.485 0.969 0.998 1.189 0.954 1.082 0.892 +1 1.451 0.077 -0.796 0.620 1.665 0.813 -0.071 1.047 2.173 0.622 -1.494 0.611 2.215 0.338 1.395 1.164 0.000 0.640 -2.084 -0.433 0.000 1.962 1.291 1.048 1.012 0.929 0.959 0.940 +0 1.067 0.278 0.954 0.488 -0.887 1.573 -0.315 -1.626 2.173 1.747 0.941 -0.121 0.000 0.377 0.592 0.028 2.548 1.158 -0.059 0.651 0.000 1.558 0.805 0.996 1.037 1.072 1.336 1.049 +1 0.811 0.071 -0.966 0.796 1.316 1.005 0.119 -0.423 1.087 0.743 0.832 -0.512 0.000 1.333 0.707 1.358 0.000 1.652 0.058 -1.691 3.102 1.045 0.941 0.986 0.904 1.241 0.964 0.811 +1 0.969 -0.318 0.620 0.354 1.708 0.935 0.123 -0.883 1.087 0.663 -0.764 -1.063 0.000 0.613 1.893 1.252 0.000 0.921 -0.906 0.383 0.000 0.939 0.807 0.996 0.493 0.752 0.711 0.631 +0 1.487 -0.224 -1.573 1.524 1.538 0.526 0.813 0.970 0.000 1.506 -0.882 -0.251 0.000 0.702 -0.152 0.400 2.548 0.452 -0.893 -0.936 3.102 2.500 1.368 0.993 0.907 0.450 0.825 0.950 +0 1.047 -0.816 -0.010 0.827 0.333 1.003 -0.867 1.124 2.173 0.558 -0.049 1.413 1.107 0.928 0.213 -1.155 0.000 1.893 0.500 -0.671 0.000 0.673 0.840 0.989 1.069 0.546 0.985 1.081 +1 1.226 -0.234 -0.511 0.240 -1.164 0.573 -0.625 1.407 0.000 0.880 0.877 0.085 2.215 1.184 0.031 0.727 2.548 0.938 -0.699 -1.120 0.000 0.859 0.891 0.981 1.053 0.767 0.849 0.822 +0 1.847 0.495 -0.590 0.105 -0.422 1.248 0.153 -1.143 0.000 2.363 0.502 1.526 0.000 1.841 0.540 0.243 2.548 1.670 1.102 0.115 3.102 1.056 1.337 0.988 0.950 0.523 1.022 0.885 +1 1.537 -0.330 0.614 0.831 0.185 0.707 -0.560 -1.445 0.000 0.580 -0.555 1.482 0.000 0.921 0.475 -0.742 2.548 0.386 0.280 -1.199 3.102 0.826 0.879 0.979 0.680 0.187 0.690 0.635 +1 1.338 0.809 -1.659 0.176 -0.641 1.262 0.039 -0.301 2.173 0.896 1.196 0.609 0.000 0.689 1.211 -1.305 0.000 0.605 0.287 -1.686 3.102 1.192 0.774 0.985 1.367 0.888 0.904 0.860 +0 1.150 0.764 0.534 2.573 0.700 1.181 -0.827 -1.126 0.000 1.569 0.205 -1.146 2.215 0.576 1.437 1.146 0.000 0.644 -0.911 0.061 0.000 1.181 0.832 1.001 2.006 0.931 1.331 1.550 +0 1.250 0.715 0.441 0.117 -0.880 2.270 0.313 -1.210 0.000 1.246 0.010 0.574 2.215 0.967 2.219 0.008 0.000 0.672 -0.183 1.174 3.102 3.096 1.735 0.998 0.650 0.436 1.373 1.140 +1 0.646 -1.430 1.648 0.300 0.766 1.251 -0.441 -0.891 0.000 1.297 -1.001 0.558 2.215 1.055 -0.439 -1.525 2.548 1.064 -0.057 -0.027 0.000 0.905 0.878 0.979 0.824 1.235 0.737 0.665 +1 0.570 -0.383 -0.135 0.935 1.683 0.572 0.077 1.611 2.173 0.426 -1.591 -0.121 0.000 0.844 -0.521 -0.647 2.548 0.838 -2.192 0.441 0.000 0.552 0.790 1.009 0.644 0.825 0.852 0.753 +0 1.381 -1.826 0.712 0.589 0.939 0.962 -0.478 -1.145 2.173 0.811 -1.026 -0.236 0.000 0.370 0.392 1.558 0.000 0.496 -0.618 1.739 3.102 1.083 0.987 1.001 0.607 0.389 0.836 0.772 +1 0.904 1.680 -1.541 1.132 -1.041 1.572 -0.151 -0.135 0.000 2.819 1.405 0.387 0.000 2.705 -0.257 -1.685 2.548 2.540 0.652 -1.612 0.000 0.847 1.043 0.998 2.629 0.325 1.651 1.687 +0 1.299 -0.694 1.183 0.765 0.181 0.531 -2.772 -0.286 0.000 0.229 -1.205 -0.751 0.000 0.729 -0.893 -1.603 2.548 0.366 0.171 -1.480 3.102 0.705 0.862 1.084 0.605 0.252 0.629 0.723 +1 0.816 -1.119 0.202 0.792 -1.373 0.831 -0.059 -0.635 0.000 1.534 0.971 1.511 0.000 1.396 0.950 -0.170 0.000 0.661 0.323 0.674 3.102 1.373 0.939 1.101 0.810 0.137 0.671 0.823 +0 2.579 1.044 0.380 0.514 -0.477 0.911 0.070 -1.724 2.173 0.907 -0.753 -1.734 0.000 0.689 1.431 -0.881 0.000 0.375 -0.467 -1.062 0.000 0.724 0.873 1.112 1.072 1.060 1.113 0.913 +1 1.344 -1.256 1.333 0.535 0.291 0.850 2.465 -0.917 0.000 0.931 -0.180 -0.170 2.215 1.136 -0.302 0.998 0.000 0.484 0.877 1.350 3.102 3.826 1.978 0.986 1.061 0.715 1.475 1.788 +0 0.898 -0.140 -1.102 0.982 0.024 1.452 -0.587 0.800 0.000 2.042 0.511 -1.421 2.215 1.195 1.249 -0.310 0.000 1.548 -0.227 -0.015 1.551 0.994 1.070 1.106 1.182 1.666 1.065 0.948 +0 0.415 -1.384 -1.051 0.692 0.473 1.044 -0.323 -1.191 1.087 0.792 1.571 0.322 2.215 1.124 0.476 0.854 0.000 0.394 1.990 -0.651 0.000 1.066 0.797 0.989 0.882 2.002 1.126 0.974 +1 0.947 0.672 0.171 1.010 0.997 1.010 0.416 -1.586 2.173 1.910 0.278 0.290 0.000 1.192 -0.394 1.720 2.548 1.101 1.899 -0.050 0.000 1.247 1.431 0.990 1.073 0.640 1.168 0.983 +0 0.964 0.141 0.787 1.260 -0.184 0.572 -0.173 -1.123 2.173 0.636 0.115 1.470 0.000 0.735 0.856 -0.901 1.274 0.933 -1.133 0.858 0.000 0.967 1.028 1.171 0.932 0.500 0.716 0.748 +0 0.591 1.725 -0.151 1.602 0.229 1.456 -0.315 1.341 0.000 0.618 0.453 -0.874 2.215 0.581 -0.942 1.331 0.000 1.134 -1.688 -0.899 0.000 0.933 1.110 0.985 0.665 0.369 0.954 1.078 +1 1.049 2.093 -1.375 0.922 1.368 0.707 0.737 0.516 1.087 0.728 1.069 -0.613 2.215 0.588 0.616 1.656 0.000 0.853 -0.454 -0.310 0.000 0.918 0.866 0.994 0.857 0.919 0.836 0.814 +0 1.128 -1.356 0.629 0.494 -1.614 0.572 -0.965 -0.153 1.087 0.775 -1.510 -1.382 0.000 0.655 -1.384 -0.698 0.000 0.914 -0.113 1.261 3.102 0.631 0.816 0.985 0.766 0.803 0.653 0.646 +0 0.941 -1.123 -1.040 2.095 0.802 1.836 -0.672 -1.644 1.087 1.276 -0.633 -0.164 0.000 1.282 -0.071 -0.454 0.000 1.198 0.278 0.410 3.102 0.763 0.831 1.938 1.678 1.720 1.321 1.285 +1 0.511 -0.449 1.122 0.284 0.557 1.041 -2.229 0.984 0.000 2.503 -0.361 -0.643 2.215 1.992 -0.106 1.343 0.000 0.756 0.502 0.318 0.000 1.189 0.860 0.989 1.429 0.813 1.123 0.983 +0 1.709 -0.562 -0.247 1.014 0.140 0.631 0.776 -1.329 2.173 0.944 1.234 1.623 0.000 0.323 -1.074 1.320 2.548 0.622 0.791 1.099 0.000 0.482 0.760 0.993 1.465 0.751 0.948 1.059 +0 0.690 2.262 0.632 1.367 -0.377 0.829 1.437 0.563 2.173 0.967 0.913 1.680 0.000 0.956 0.548 -0.780 2.548 0.709 1.449 -0.789 0.000 0.964 1.090 1.061 1.033 1.146 0.873 0.862 +0 0.350 -1.405 0.272 1.234 -1.399 0.990 0.161 -0.262 0.000 0.712 -1.581 1.588 0.000 1.015 -0.704 1.114 0.000 1.683 0.254 0.250 3.102 0.788 1.259 0.991 0.517 0.710 0.765 0.704 +0 1.415 0.037 1.240 0.926 0.690 1.024 0.933 -0.636 0.000 1.092 0.501 -0.982 2.215 0.975 -0.548 0.993 2.548 0.798 0.489 0.035 0.000 0.823 0.742 0.989 0.514 1.252 0.905 0.982 +1 1.085 -0.172 -0.509 0.299 1.402 1.361 -0.408 1.363 0.000 0.784 -1.006 0.008 2.215 1.087 0.292 -0.630 2.548 0.546 -0.095 0.136 0.000 1.194 1.228 0.991 0.668 0.894 0.960 0.817 +1 0.856 -0.263 -0.637 0.927 1.480 0.794 0.272 -1.508 2.173 1.246 -0.871 0.723 0.000 0.830 -0.515 -0.251 2.548 0.621 -0.742 0.191 0.000 0.529 0.672 1.165 0.780 1.016 0.865 0.773 +1 0.773 -0.461 -0.169 0.771 0.663 0.975 -0.060 1.362 0.000 1.377 0.461 -0.877 2.215 0.864 -1.139 0.306 0.000 1.177 -0.569 -1.433 3.102 0.733 0.869 0.987 1.339 0.894 0.931 0.847 +0 0.592 -0.341 -1.697 0.574 0.492 1.049 -0.183 -0.766 2.173 1.029 -0.208 0.636 0.000 1.185 0.145 1.246 2.548 0.760 -0.842 -0.944 0.000 1.234 0.946 0.979 1.019 1.369 0.921 0.821 +1 0.799 -1.181 -0.953 0.058 1.540 1.267 0.052 1.393 0.000 1.605 0.229 -0.192 0.000 1.369 -0.917 -1.305 2.548 1.188 -0.736 -0.103 3.102 0.943 0.763 0.981 0.665 0.863 0.869 0.749 +0 0.486 0.917 -1.428 0.953 0.209 0.477 -0.218 1.431 2.173 0.977 0.292 0.635 0.000 1.078 0.262 -0.502 0.000 0.552 -1.696 -1.136 0.000 1.311 1.029 0.988 0.550 0.556 0.625 0.632 +1 1.416 -0.021 0.339 1.366 0.062 2.933 2.773 1.684 0.000 2.287 0.487 -0.305 1.107 1.477 -0.253 0.013 2.548 1.081 -0.116 -1.267 0.000 5.742 5.296 0.988 0.961 0.956 3.801 3.027 +0 0.879 -0.469 1.725 1.782 1.294 0.822 -0.636 -0.305 0.000 1.469 -0.459 0.256 2.215 0.905 -0.280 -1.149 0.000 1.283 -0.763 -1.042 3.102 1.100 1.134 0.990 0.984 1.174 1.031 0.982 +0 0.558 0.054 -0.966 1.552 0.063 1.944 -1.411 -1.711 2.173 2.035 0.348 -0.189 2.215 1.361 -1.597 1.012 0.000 0.407 0.691 1.267 0.000 1.394 1.504 1.032 0.726 4.128 2.052 1.661 +1 1.555 -1.215 1.137 1.050 1.113 1.293 -2.137 -0.846 0.000 0.974 -0.521 1.120 0.000 1.654 -0.520 -0.562 1.274 1.343 0.070 0.315 3.102 0.839 1.103 0.977 1.260 0.895 1.167 0.989 +0 1.372 -1.143 1.132 0.217 0.252 1.012 -1.329 0.152 2.173 1.175 -1.867 -1.175 0.000 0.574 0.077 0.748 2.548 0.464 1.776 -1.343 0.000 0.814 0.641 0.977 0.952 0.881 0.974 0.860 +0 1.455 -0.422 0.302 0.582 -1.719 1.187 -0.801 -1.720 2.173 1.139 -1.354 -0.258 2.215 0.772 -0.060 0.998 0.000 0.743 -0.531 -1.029 0.000 0.844 1.008 1.235 1.154 1.733 1.061 0.866 +0 0.555 0.969 0.917 1.102 1.643 0.656 -0.543 -1.088 2.173 1.208 0.587 -0.206 2.215 0.590 -0.101 1.229 0.000 0.483 1.454 1.091 0.000 0.892 0.940 0.980 1.259 1.228 1.236 0.973 +1 2.378 0.123 0.742 0.116 1.479 1.044 -0.611 -1.045 2.173 0.560 -0.612 0.118 1.107 0.686 1.684 1.528 0.000 0.623 0.829 0.963 0.000 0.474 0.985 0.978 1.424 0.975 1.032 0.985 +1 1.042 0.051 0.154 0.870 -0.234 1.077 0.370 -1.568 2.173 0.419 -0.289 1.095 1.107 0.346 -0.246 1.630 0.000 0.443 -1.044 0.189 0.000 0.471 0.765 0.987 0.761 0.748 0.866 0.705 +0 0.613 -1.118 1.415 1.209 1.334 1.133 0.753 -0.738 0.000 1.112 -1.066 0.801 1.107 1.404 -0.312 -0.849 2.548 1.102 0.393 -0.156 0.000 0.889 0.919 0.986 0.935 1.419 1.237 1.616 +0 0.386 0.831 -1.302 1.893 0.274 1.105 0.233 0.397 2.173 1.095 -2.515 -1.581 0.000 1.545 0.424 -1.506 2.548 0.990 1.367 -1.249 0.000 5.555 3.481 1.171 0.828 1.622 2.418 2.069 +1 0.845 -1.609 0.810 1.558 1.451 0.335 -1.312 0.191 0.000 0.554 0.584 -0.726 1.107 0.354 0.910 -1.239 2.548 0.427 0.697 1.375 0.000 0.913 0.721 0.994 1.336 0.230 1.216 0.992 +0 0.388 1.704 1.305 0.284 0.853 0.952 1.375 0.538 0.000 1.331 0.463 -1.178 2.215 0.822 1.208 -0.846 2.548 0.676 0.770 1.394 0.000 0.900 0.934 0.997 0.879 0.587 0.913 0.802 +1 1.442 -0.925 1.259 0.212 -0.909 3.287 -1.614 -0.248 0.000 1.999 -0.292 1.527 2.215 0.926 -1.405 -1.179 0.000 0.380 -1.185 0.925 3.102 0.604 1.032 0.984 0.463 0.618 0.630 0.621 +1 1.187 -0.438 1.363 1.574 -1.497 1.670 -0.946 -1.536 2.173 2.983 -0.570 0.003 0.000 1.277 -0.760 0.631 2.548 0.373 0.043 -0.297 0.000 0.577 0.912 1.015 0.832 1.692 1.510 1.331 +1 0.384 -1.929 -1.431 1.918 0.866 0.788 -1.299 -1.085 2.173 0.428 -0.593 -0.693 0.000 0.336 0.424 0.490 1.274 0.665 -1.755 0.121 0.000 0.736 0.704 1.043 0.953 0.906 0.898 0.755 +0 0.485 1.476 -1.048 1.986 -0.266 1.090 -0.293 0.950 0.000 1.013 0.149 -1.665 2.215 1.553 1.353 0.014 0.000 1.163 -1.037 -0.822 3.102 0.820 0.951 0.984 2.193 1.004 1.606 1.529 +1 0.800 -0.470 0.891 1.003 -0.598 0.661 1.229 0.785 0.000 1.181 -0.482 -1.298 2.215 0.840 -0.409 0.633 1.274 0.472 -0.167 0.004 0.000 0.868 0.780 1.209 0.905 1.044 0.913 0.833 +0 0.785 0.352 -0.465 1.626 1.507 1.154 0.304 0.362 0.000 1.221 0.906 -1.094 2.215 1.395 -0.789 -1.121 0.000 0.558 -1.105 0.432 0.000 0.986 0.741 1.532 1.072 1.004 0.902 0.878 +1 2.118 -1.267 0.630 0.610 -1.467 1.291 -0.730 -0.591 0.000 0.990 -1.643 1.628 0.000 0.911 -0.166 -1.588 0.000 1.620 0.229 1.410 3.102 0.915 0.701 1.496 1.264 0.843 0.928 0.964 +1 1.359 -0.088 -0.511 0.603 1.091 1.219 -0.497 -0.967 2.173 1.148 0.278 0.880 2.215 0.777 -0.136 1.147 0.000 1.278 -0.402 1.737 0.000 0.928 0.744 1.244 0.976 1.871 1.033 0.891 +0 1.021 0.166 1.100 0.922 -1.050 0.688 0.604 -0.003 2.173 0.474 0.346 -0.505 0.000 1.134 -0.526 1.316 0.000 0.967 0.963 -0.925 1.551 1.247 0.995 1.255 0.951 0.681 0.765 0.727 +1 0.419 1.625 -1.273 0.463 1.674 0.917 1.419 -0.246 0.000 0.915 0.232 -0.165 1.107 1.746 0.267 1.515 2.548 0.731 -2.251 0.989 0.000 4.994 2.768 0.974 0.685 1.342 1.850 1.409 +1 0.953 0.066 1.573 0.384 -0.934 0.594 -0.620 -0.595 2.173 0.327 -1.509 1.617 2.215 0.387 -1.051 0.022 0.000 0.920 0.640 -0.516 0.000 0.796 0.764 0.994 0.755 0.669 0.553 0.568 +0 2.077 -0.626 -0.641 0.502 -0.547 1.123 -0.659 0.790 1.087 0.956 -0.083 -1.168 0.000 0.681 -1.111 0.373 2.548 0.953 -0.643 -1.639 0.000 1.151 0.970 0.986 1.426 0.504 0.949 0.910 +1 0.282 -1.190 -0.474 1.056 1.229 0.628 0.866 -0.013 2.173 1.082 0.182 -1.174 2.215 1.039 -0.194 0.324 0.000 0.932 -0.784 1.711 0.000 1.107 1.081 0.987 2.156 1.132 1.646 1.291 +0 0.958 -0.018 -0.031 1.109 -0.873 1.060 1.405 1.241 2.173 0.769 -0.539 1.697 0.000 1.335 -0.749 -0.514 2.548 0.630 0.246 0.386 0.000 0.928 0.952 0.989 1.466 2.514 1.316 1.085 +0 0.608 -0.582 0.273 1.292 0.192 0.736 -0.183 1.162 2.173 0.650 -0.253 -1.144 0.000 1.173 0.293 -0.879 0.000 0.880 1.246 1.027 0.000 0.909 0.785 0.977 1.195 1.203 1.256 1.708 +0 0.909 0.076 1.658 2.207 -1.278 1.372 -0.332 0.411 2.173 0.360 -1.976 0.681 0.000 0.702 0.406 -1.497 0.000 1.236 0.116 -0.386 3.102 1.413 1.045 0.988 1.772 0.966 1.182 1.071 +0 0.905 0.422 1.021 0.617 -0.916 0.649 1.023 -0.100 0.000 0.731 0.714 -0.785 0.000 1.585 1.220 1.633 1.274 0.714 1.075 0.914 3.102 0.875 0.759 1.020 0.822 0.491 0.757 0.693 +1 1.363 0.719 -0.809 0.356 -0.158 1.253 1.426 0.815 2.173 0.690 1.173 -0.548 0.000 0.589 0.046 1.433 2.548 0.921 0.195 -1.329 0.000 0.853 0.731 0.989 1.264 0.984 0.911 0.826 +1 0.398 1.952 1.666 0.457 -1.278 1.591 0.129 -0.196 0.000 1.003 0.564 -1.111 1.107 1.124 0.503 1.295 2.548 1.835 -0.513 -1.425 0.000 1.009 1.056 0.989 0.654 0.933 0.748 0.671 +0 0.515 0.347 -1.396 1.738 -1.448 0.720 0.807 -0.248 2.173 0.516 -1.173 0.779 0.000 0.865 0.830 1.198 0.000 1.254 0.325 0.306 3.102 0.946 0.947 0.991 1.079 0.527 0.883 0.929 +1 0.554 -0.461 0.015 0.637 -1.568 0.520 -1.282 1.703 0.000 0.584 -1.070 0.012 0.000 0.971 -0.016 1.527 2.548 0.486 1.170 0.409 3.102 1.175 0.963 0.988 0.578 0.601 0.754 0.687 +0 0.356 1.693 0.726 1.208 -0.809 0.436 0.743 1.252 2.173 0.868 0.099 -1.308 2.215 0.589 -0.071 -0.129 0.000 0.393 0.809 0.167 0.000 0.323 0.635 0.990 0.982 0.733 0.992 0.828 +1 3.036 -0.339 -1.591 0.431 -1.148 1.845 0.268 -0.085 0.000 0.952 -0.010 1.685 2.215 1.540 -0.455 0.300 2.548 1.620 -0.145 1.259 0.000 0.746 0.756 0.994 1.405 1.261 0.973 0.811 +1 1.472 0.834 -0.360 1.221 0.076 1.352 0.558 1.724 2.173 0.605 0.892 0.415 0.000 0.362 -0.271 0.589 2.548 0.514 -0.533 -1.592 0.000 0.943 1.076 0.978 0.607 0.839 1.000 0.830 +0 0.529 -1.785 -0.663 0.412 0.155 0.494 -0.538 -1.395 2.173 0.723 -1.027 1.140 0.000 0.502 -1.215 -1.166 2.548 0.754 -1.703 0.158 0.000 0.894 0.881 0.983 0.567 0.283 0.557 0.560 +0 0.382 -0.738 -1.493 1.946 1.048 0.935 0.890 -0.828 2.173 0.784 1.611 -0.011 0.000 0.651 0.465 -1.480 2.548 0.548 2.411 0.940 0.000 0.856 0.921 0.984 1.399 0.571 0.905 1.044 +0 0.387 1.501 -0.468 2.235 0.531 0.812 0.882 1.185 2.173 0.997 0.874 -0.865 0.000 0.772 1.414 -1.628 0.000 0.580 0.021 -1.035 0.000 0.964 1.112 1.009 0.995 0.898 0.833 0.867 +1 0.376 2.073 0.598 1.021 -1.246 0.911 0.941 -0.626 2.173 0.858 0.243 0.499 0.000 1.401 0.739 -1.440 2.548 1.062 -0.426 1.285 0.000 0.943 1.145 0.983 0.773 0.947 0.976 0.857 +1 0.314 1.057 -0.787 0.841 1.269 0.663 -1.155 1.391 0.000 1.266 -0.931 -0.405 2.215 0.970 0.240 -0.126 2.548 0.598 0.289 1.494 0.000 0.804 1.047 0.988 2.557 0.820 1.636 1.514 +1 0.992 0.438 -0.627 1.443 1.623 0.784 -0.125 1.073 0.000 1.158 0.491 1.364 0.000 1.035 0.872 0.155 2.548 1.034 -0.272 -0.695 3.102 0.833 1.074 1.487 1.034 0.772 0.854 0.847 +0 0.568 0.736 -0.123 1.885 0.807 0.808 2.065 -0.861 0.000 0.487 1.332 1.459 2.215 0.388 2.030 -0.465 0.000 0.858 -0.354 -1.713 3.102 0.359 0.727 1.066 0.866 0.619 0.807 0.872 +1 1.210 -0.124 1.445 0.413 1.641 0.473 -0.929 0.941 0.000 0.550 1.226 -0.848 2.215 0.984 -0.267 -0.197 2.548 0.636 -1.724 -0.962 0.000 0.921 0.886 1.002 0.903 0.799 0.852 0.731 +1 1.016 0.492 -0.322 0.531 1.569 0.730 -0.866 -0.028 0.000 1.075 -0.048 1.436 2.215 2.102 0.459 0.653 2.548 2.269 -0.021 -1.312 0.000 0.489 1.224 1.009 0.895 1.128 1.075 0.936 +0 1.623 -0.061 0.983 0.969 0.476 0.626 0.925 -0.640 0.000 1.009 0.106 -0.512 0.000 1.086 0.428 -1.703 2.548 0.599 0.243 -1.172 0.000 1.039 0.972 0.993 0.880 0.773 0.742 0.722 +0 0.475 -1.581 1.691 1.960 -0.557 1.004 -0.232 1.481 2.173 0.708 -0.396 0.500 0.000 0.566 -1.066 -0.585 2.548 0.381 1.992 1.079 0.000 1.331 1.093 1.201 1.538 1.009 0.986 1.142 +0 0.290 0.429 0.626 0.529 -1.402 0.600 0.672 0.139 2.173 0.519 2.277 1.671 0.000 0.675 0.301 -0.868 2.548 0.997 0.892 1.213 0.000 0.757 0.759 0.989 0.719 0.641 0.731 0.788 +0 0.287 -0.457 0.905 1.937 -1.665 1.984 0.503 -1.193 0.000 1.504 0.153 0.622 2.215 2.698 -0.625 0.520 2.548 1.772 0.294 -0.066 0.000 2.445 2.256 0.992 1.665 0.951 1.857 1.530 +1 0.900 0.531 -0.660 1.408 0.124 1.369 -0.232 -1.486 0.000 0.582 -0.822 -0.197 1.107 1.054 -0.543 0.777 0.000 0.522 0.792 0.535 0.000 0.720 0.712 1.013 0.707 0.676 0.616 0.609 +1 1.370 -0.455 -0.731 0.558 -1.726 1.043 0.124 0.722 2.173 1.292 -0.862 0.068 2.215 0.971 -0.716 1.725 0.000 1.158 0.031 -1.361 0.000 0.960 1.219 0.987 1.157 1.310 1.089 0.947 +1 2.366 -0.719 -1.389 0.746 -0.676 0.680 -0.352 -0.380 0.000 1.615 -0.190 0.703 2.215 0.298 -1.079 1.028 0.000 0.541 -0.525 -0.889 3.102 0.846 1.040 1.105 0.524 0.856 0.981 0.836 +0 1.442 -0.783 -0.616 0.735 -1.374 0.587 -0.044 0.031 0.000 0.860 -1.968 -1.660 0.000 0.882 -0.943 0.775 2.548 0.915 0.305 -1.692 3.102 2.311 1.388 0.985 0.728 0.755 0.962 0.881 +0 0.413 -0.407 0.016 1.470 -0.182 1.656 0.595 -0.703 2.173 2.424 0.866 1.118 1.107 1.107 0.698 1.433 0.000 1.508 1.112 -1.718 0.000 0.819 1.329 0.991 0.965 2.972 1.532 1.289 +0 0.517 1.589 1.444 0.934 -0.764 0.561 -0.916 -1.150 2.173 0.721 0.703 1.601 2.215 0.806 -0.711 0.957 0.000 1.004 -0.652 0.388 0.000 0.869 0.937 0.992 0.897 1.036 1.237 1.632 +1 0.580 -0.841 -1.196 0.523 0.098 0.727 0.047 -0.479 0.000 1.467 0.391 0.919 2.215 0.890 0.828 -0.920 0.000 1.320 0.803 1.631 3.102 0.852 0.980 0.989 1.542 0.832 1.216 1.151 +1 2.135 1.293 0.039 1.032 0.817 0.679 -1.441 -0.703 0.000 0.964 0.788 1.156 0.000 1.121 0.045 1.665 2.548 0.419 0.729 1.554 0.000 0.718 0.731 1.327 1.354 0.755 0.930 0.810 +0 0.561 -0.668 0.907 0.542 -1.451 0.886 -0.495 0.160 2.173 1.210 -0.257 -1.304 2.215 0.703 0.179 1.164 0.000 0.497 -1.239 -0.064 0.000 0.849 0.893 0.984 0.998 1.487 0.958 0.798 +1 0.773 0.715 1.362 1.246 -0.970 0.956 -0.857 -1.139 2.173 2.339 1.960 0.307 0.000 0.806 0.502 1.721 0.000 0.883 0.211 0.050 0.000 0.939 0.851 1.174 1.242 0.781 0.941 0.822 +1 0.595 -0.458 -0.582 1.226 1.728 0.831 -0.954 0.445 0.000 1.064 -1.000 -1.139 2.215 1.169 -0.825 1.103 0.000 0.877 -1.872 0.113 0.000 0.963 0.855 1.032 0.713 0.349 0.730 0.659 +0 1.032 -0.614 0.328 1.380 0.915 0.482 -1.255 -1.401 2.173 0.512 -0.076 -0.661 2.215 0.409 -1.339 -0.122 0.000 1.625 0.052 -1.407 0.000 1.068 0.761 0.988 1.026 0.646 0.763 0.725 +0 1.345 1.439 1.728 0.773 1.176 0.713 -0.585 -0.405 2.173 0.727 -0.278 -1.314 2.215 1.075 -0.658 1.125 0.000 1.208 -0.294 0.286 0.000 0.893 0.965 0.985 0.946 0.790 1.018 0.970 +0 1.379 -0.393 1.219 1.394 0.463 0.641 0.740 -0.442 0.000 0.537 1.435 0.833 2.215 1.123 0.433 -1.391 0.000 1.332 -0.743 -0.780 3.102 1.170 1.028 1.209 1.048 1.363 0.968 0.984 +1 0.607 -0.033 0.814 1.553 -1.663 0.495 0.845 1.535 0.000 1.410 0.916 -0.026 1.107 0.428 0.727 -0.472 0.000 0.495 -0.512 -0.918 3.102 0.804 0.969 1.063 0.603 0.846 0.878 0.747 +0 1.040 -1.192 1.466 1.968 -1.434 1.343 -0.463 0.245 0.000 1.590 -1.632 -0.685 0.000 0.850 -1.037 -0.021 0.000 1.632 -0.115 1.400 3.102 0.801 1.253 1.002 0.644 0.224 0.708 0.948 +1 0.462 -0.495 -1.067 0.718 0.884 0.924 1.623 -1.573 0.000 1.301 -0.248 -0.370 0.000 0.825 -1.039 -0.451 0.000 2.921 -0.440 1.019 3.102 0.737 0.646 0.988 0.869 0.820 0.909 0.828 +1 0.825 -0.127 -1.227 0.832 1.343 1.099 0.047 1.348 0.000 0.995 0.840 -0.884 2.215 0.902 -2.159 -0.483 0.000 1.750 -0.024 0.128 3.102 1.318 1.172 0.988 1.048 1.091 0.969 0.899 +1 1.194 0.168 0.819 0.174 -0.744 0.388 0.895 0.141 0.000 1.140 -0.106 -1.513 2.215 1.206 0.079 -0.357 2.548 0.473 -1.752 1.258 0.000 1.506 1.062 0.980 0.937 1.082 0.877 0.809 +1 0.404 -0.694 -0.389 0.426 -0.307 0.419 -2.583 1.002 0.000 0.377 0.178 -0.842 2.215 0.302 -1.403 1.655 0.000 0.785 -1.116 0.851 3.102 0.645 1.104 0.984 0.891 0.640 0.683 0.996 +0 1.522 1.345 1.522 1.025 1.269 0.733 1.734 1.246 0.000 0.889 0.612 -1.259 2.215 2.506 -0.758 -0.130 0.000 0.852 -0.496 -0.525 0.000 0.772 0.857 0.999 1.088 0.619 0.951 1.607 +1 1.772 0.622 0.317 0.525 -0.582 0.858 1.422 1.522 2.173 0.554 0.250 -0.761 0.000 1.121 0.452 -1.348 2.548 0.798 0.134 1.717 0.000 0.697 1.193 0.986 0.950 0.876 0.918 0.844 +1 0.514 -1.289 0.752 0.382 0.939 0.911 -0.062 -1.072 0.000 0.941 -0.925 -1.305 2.215 2.229 1.928 -0.237 0.000 2.514 0.109 1.289 0.000 1.096 2.290 0.974 0.890 0.485 2.140 1.687 +1 0.880 -0.823 1.590 1.395 -0.832 0.578 -1.066 -0.072 0.000 0.747 0.413 1.349 2.215 0.906 -1.275 0.717 0.000 1.267 0.792 -0.313 3.102 0.874 1.202 1.258 1.168 0.906 0.983 0.951 +0 0.366 -0.851 1.117 1.262 0.701 0.884 0.445 -0.961 0.000 0.873 0.608 -0.354 0.000 0.522 -1.654 0.537 0.000 1.181 1.400 1.621 3.102 0.986 0.866 0.980 2.692 0.174 1.802 1.684 +0 0.514 -1.180 -1.719 4.347 -1.296 2.763 -0.199 0.429 2.173 0.846 -1.426 -1.091 2.215 0.309 0.527 0.176 0.000 0.923 -0.567 0.758 0.000 0.494 0.871 0.991 3.938 2.680 2.511 1.813 +0 0.705 0.344 -0.601 0.751 -0.177 0.846 2.057 1.042 0.000 0.846 1.341 0.405 2.215 1.689 0.487 -1.085 2.548 0.637 2.295 -1.566 0.000 0.886 0.916 1.000 0.874 1.355 1.088 0.944 +0 0.909 1.050 0.943 0.719 -0.721 1.242 1.158 -0.084 0.000 1.280 0.067 1.036 2.215 1.401 1.246 -0.869 0.000 1.012 -1.241 1.728 0.000 0.358 1.168 1.117 1.427 1.606 1.139 1.122 +1 0.623 -0.155 1.012 1.447 -0.742 2.547 -0.840 1.098 0.000 2.741 0.399 -0.515 0.000 1.212 -0.405 -1.276 2.548 0.584 0.988 -1.156 0.000 1.102 0.843 1.316 0.669 0.593 0.562 0.567 +0 0.334 -1.496 -0.004 0.958 -1.511 1.015 0.587 1.475 1.087 1.105 -0.662 -0.048 2.215 0.385 1.071 0.970 0.000 0.393 0.743 -0.101 0.000 0.727 1.103 0.990 1.321 1.861 1.785 1.349 +0 0.654 -0.166 0.400 0.448 1.535 0.886 0.299 0.772 0.000 0.626 2.377 -0.374 0.000 1.220 0.409 -1.186 2.548 0.801 -1.100 -1.331 1.551 2.451 1.737 0.989 0.860 0.766 1.330 1.045 +0 0.516 -0.268 1.597 0.954 0.845 1.086 -1.932 -0.602 0.000 0.999 1.206 1.284 1.107 0.946 -0.990 -0.215 0.000 0.843 -0.081 0.482 0.000 0.846 0.645 0.993 0.649 0.943 0.902 0.806 +0 1.145 -0.463 -0.498 0.567 1.050 0.905 1.445 1.471 0.000 1.397 0.389 -0.695 2.215 1.249 -0.663 0.187 2.548 1.475 -0.432 1.259 0.000 2.072 1.898 1.100 0.707 1.305 1.422 1.175 +0 0.309 1.803 1.452 2.374 0.539 1.059 1.085 -0.903 2.173 0.445 0.644 -1.628 0.000 0.391 -0.906 1.227 2.548 0.448 2.496 1.473 0.000 0.865 0.977 0.982 1.380 1.253 1.060 0.913 +1 0.619 0.904 0.362 0.835 1.260 0.644 -0.173 -1.608 2.173 0.624 0.435 0.543 0.000 0.516 -0.665 -0.340 2.548 0.737 1.487 -0.388 0.000 0.914 1.083 0.989 1.036 0.684 0.915 0.804 +0 1.550 1.100 1.585 0.744 -0.921 0.691 0.894 -1.007 0.000 1.059 0.833 0.829 2.215 1.109 -0.017 0.216 2.548 0.846 1.056 -0.464 0.000 0.590 0.952 1.150 0.965 0.798 0.866 0.819 +1 0.468 0.275 1.620 1.108 -0.690 1.480 -2.234 0.616 0.000 1.908 0.620 -0.762 2.215 1.983 -1.956 1.097 0.000 1.327 -0.359 -0.505 3.102 1.303 1.960 0.992 0.747 0.871 2.870 2.490 +0 0.478 1.998 0.034 0.805 -0.718 0.654 -0.970 0.912 0.000 1.167 0.555 -1.637 2.215 1.224 -0.086 -0.141 2.548 0.683 -1.148 -0.285 0.000 0.923 0.926 0.983 0.917 1.312 0.995 0.970 +1 0.791 0.754 -0.208 0.801 -0.751 0.818 -0.097 0.456 0.000 1.365 0.153 1.460 1.107 0.825 0.703 -1.243 1.274 1.185 0.275 -0.131 0.000 0.826 1.001 0.998 1.116 0.812 0.884 0.819 +0 1.452 0.792 -0.916 1.250 -0.155 0.763 -0.843 1.094 1.087 1.690 0.110 -0.688 2.215 2.089 -1.000 0.561 0.000 1.147 1.788 1.710 0.000 0.973 0.800 1.182 0.882 1.867 1.308 1.417 +0 0.355 0.299 -1.341 1.512 0.258 0.618 -0.660 -1.286 0.000 0.769 -0.071 0.655 2.215 0.772 -1.575 -1.021 0.000 0.848 -0.775 1.272 3.102 0.898 1.138 1.006 0.763 0.502 0.702 0.775 +1 0.810 0.667 -1.653 1.012 -1.178 0.332 0.963 -1.077 0.000 0.460 1.498 0.459 0.000 0.957 -0.686 -0.112 2.548 0.881 0.299 0.983 3.102 0.964 0.855 0.995 0.791 0.716 0.930 0.797 +1 0.653 -0.442 -0.217 1.122 -0.934 1.005 0.420 0.256 2.173 0.858 -0.793 -1.734 0.000 0.646 2.484 -0.725 0.000 1.065 0.346 1.210 3.102 0.897 0.901 0.988 1.381 0.829 1.012 1.242 +1 0.684 0.358 0.030 0.585 -1.570 0.884 0.658 0.731 2.173 1.279 0.223 -0.961 0.000 0.550 -0.845 0.970 1.274 0.654 -0.039 -1.733 0.000 0.781 0.861 0.989 0.790 0.789 0.856 0.752 +1 0.794 -1.088 0.258 1.056 -0.807 1.039 -0.007 1.427 1.087 0.814 -1.152 -0.361 0.000 0.785 -1.058 0.855 1.274 0.516 -2.063 -1.127 0.000 0.794 0.777 1.039 1.262 0.883 0.949 0.852 +1 0.854 1.155 0.099 0.023 -0.410 0.684 0.866 -1.231 0.000 1.025 -0.127 0.837 2.215 0.959 1.278 -0.626 0.000 0.646 0.907 1.539 3.102 0.851 0.651 0.779 0.715 0.644 0.834 0.710 +1 0.430 0.011 1.016 0.891 -1.160 1.518 -1.830 0.377 0.000 1.334 -0.540 -1.095 0.000 0.924 1.142 1.332 2.548 1.056 1.913 -0.783 0.000 0.803 0.911 0.989 0.642 0.763 1.494 1.442 +1 0.390 1.113 -0.115 1.235 -1.016 0.751 0.199 -1.598 0.000 0.816 -0.362 -0.098 2.215 2.008 -0.449 0.891 2.548 0.862 0.124 -1.138 0.000 0.497 0.973 0.986 1.185 1.062 0.908 0.827 +0 0.673 -0.537 1.268 0.263 0.949 1.806 -1.038 1.397 2.173 1.559 -0.646 -0.302 1.107 1.771 -1.650 -0.337 0.000 0.732 -1.104 -0.985 0.000 0.758 0.955 0.976 1.337 2.513 1.455 1.336 +0 0.740 -0.545 0.661 0.955 -0.522 0.586 -0.058 -0.225 0.000 0.932 -0.887 -1.740 2.215 0.784 0.276 -1.180 0.000 1.142 -0.616 1.277 3.102 0.950 0.926 1.019 0.874 0.391 0.732 0.688 +0 0.633 1.154 1.016 2.141 0.848 0.940 0.536 -0.154 2.173 1.112 1.522 -1.295 0.000 0.940 0.004 1.573 0.000 0.729 0.257 -0.724 3.102 0.926 1.150 0.996 0.832 0.443 0.823 0.801 +0 1.811 2.088 -0.245 0.239 -1.275 1.315 2.161 1.648 0.000 0.804 2.225 0.660 0.000 0.819 0.969 -0.975 2.548 0.475 1.376 0.953 3.102 0.765 0.827 0.983 0.606 0.491 0.523 0.543 +0 0.294 1.270 1.325 1.633 -0.714 0.922 1.495 -0.616 2.173 1.486 1.173 1.007 0.000 1.170 2.585 1.226 0.000 1.021 1.221 1.591 0.000 0.975 1.195 0.987 0.637 0.960 1.010 0.876 +0 1.154 1.382 -0.452 0.377 0.483 0.676 1.339 0.692 2.173 1.237 1.262 -1.227 2.215 0.688 2.629 1.617 0.000 0.810 1.704 0.332 0.000 0.835 1.064 0.986 0.807 1.329 0.850 0.769 +1 0.446 1.683 -0.381 1.582 0.402 1.055 -0.768 -1.627 2.173 0.398 0.625 -1.115 0.000 0.677 -0.244 0.139 2.548 0.370 0.868 -0.611 0.000 0.241 0.862 0.980 0.662 1.086 1.116 0.851 +1 0.339 -1.053 -1.249 1.012 -0.330 0.980 0.415 0.655 0.000 0.912 0.959 -1.470 2.215 1.064 -0.396 -0.774 2.548 0.868 1.332 0.568 0.000 0.887 1.203 0.988 1.038 1.016 1.512 1.647 +0 1.419 0.396 1.002 0.443 1.688 0.693 -1.099 0.448 2.173 1.031 -0.404 -0.786 0.000 0.733 0.541 -0.626 0.000 0.566 -0.872 -1.317 0.000 0.771 0.992 0.992 0.480 0.906 0.833 0.763 +1 1.685 0.008 -0.919 0.370 0.504 1.191 -0.664 0.904 1.087 1.072 -1.301 -1.158 1.107 0.593 -1.043 -0.453 0.000 0.785 0.167 0.464 0.000 0.987 0.993 1.049 1.290 1.691 1.106 0.906 +0 1.706 -0.446 -1.112 1.030 -1.699 1.250 -1.446 0.834 1.087 0.717 -0.639 -0.508 2.215 0.650 -1.823 -0.634 0.000 0.683 -0.738 0.988 0.000 0.849 0.925 0.982 0.793 1.424 1.174 0.952 +0 0.887 0.542 0.166 1.002 1.644 1.452 -0.876 0.355 0.000 1.170 -0.545 1.523 1.107 1.629 0.125 -0.843 2.548 1.873 -0.212 -1.522 0.000 1.084 0.943 1.269 0.984 1.347 0.921 0.800 +0 0.692 0.228 -0.092 1.046 -0.932 0.627 0.242 0.412 2.173 1.021 0.254 1.010 0.000 1.121 1.245 -1.117 1.274 0.712 1.731 -1.717 0.000 1.351 1.053 0.990 1.024 1.197 0.878 0.913 +0 0.997 -0.819 0.668 1.295 0.156 0.870 1.141 -1.671 2.173 1.289 2.360 1.557 0.000 1.359 -0.955 -0.501 0.000 0.727 2.118 -0.660 0.000 1.150 0.814 0.987 2.225 0.567 1.392 2.070 +1 0.859 -0.088 0.119 0.486 -0.925 0.485 -0.552 1.280 2.173 1.235 -0.075 -1.038 2.215 1.226 -0.162 0.816 0.000 0.963 0.412 -0.248 0.000 1.064 0.831 0.988 0.833 1.026 0.810 0.736 +0 0.621 -0.297 1.723 0.984 0.918 0.357 -0.457 0.387 2.173 0.448 1.959 -0.750 0.000 0.841 0.707 -0.731 2.548 0.714 0.338 -1.247 0.000 0.733 0.923 0.993 1.089 0.732 0.746 0.865 +0 0.559 -0.091 1.469 1.146 -0.940 0.555 0.034 0.994 2.173 0.863 -0.753 -1.368 0.000 1.044 -0.623 0.524 2.548 0.687 -0.509 0.167 0.000 0.989 0.901 0.988 0.828 0.523 0.666 0.642 +0 1.381 -0.192 0.030 0.137 0.510 0.648 -0.101 1.474 0.000 0.990 -0.256 -1.426 2.215 0.688 -0.328 0.737 0.000 1.181 0.352 -0.608 3.102 0.757 0.893 0.997 1.001 0.740 0.702 0.712 +1 1.108 1.304 -1.228 1.017 0.073 0.640 1.869 -0.415 0.000 1.230 0.442 1.618 2.215 0.814 1.891 0.399 0.000 0.900 1.365 0.941 3.102 0.877 0.727 1.356 1.163 0.807 0.934 0.854 +0 1.145 0.081 1.327 0.797 -1.647 0.946 -0.539 -0.392 2.173 0.457 -2.442 0.534 0.000 0.522 0.194 0.844 0.000 0.766 1.240 -0.702 3.102 1.384 1.300 0.986 0.990 1.109 1.127 1.023 +1 1.500 -0.250 1.285 1.475 0.872 0.984 0.122 -0.981 2.173 1.180 -0.664 -0.203 1.107 0.500 0.735 -1.129 0.000 0.954 -0.147 0.382 0.000 0.923 0.840 0.981 1.380 1.216 1.183 0.990 +1 2.869 -0.817 0.610 1.640 0.326 0.691 -1.833 -1.465 0.000 1.638 -0.569 -0.547 2.215 1.452 1.392 -1.208 0.000 1.027 -0.498 1.445 3.102 0.836 1.051 0.980 1.613 1.141 1.161 1.527 +0 0.638 0.540 0.001 3.379 0.518 1.796 1.780 -0.807 0.000 2.169 1.574 1.612 0.000 0.583 0.766 -0.725 1.274 1.192 1.873 -1.184 0.000 0.900 0.739 0.983 0.708 0.744 1.059 1.459 +0 0.387 1.455 -1.237 0.747 0.654 0.741 0.562 -0.388 2.173 0.935 1.973 -1.633 0.000 0.686 -2.103 -1.635 0.000 1.414 -0.781 0.721 3.102 0.932 0.950 0.990 0.762 1.277 1.232 1.114 +0 1.403 -0.133 0.647 0.965 0.158 0.515 -0.962 1.481 2.173 0.597 1.034 -1.246 0.000 0.788 -1.263 -0.740 2.548 0.457 -0.087 -1.370 0.000 0.428 0.902 0.994 0.868 0.740 0.739 0.796 +0 0.584 -0.643 1.155 0.808 0.437 0.797 -2.921 -0.367 0.000 1.216 0.345 -0.388 2.215 2.678 -1.126 1.647 0.000 0.790 -0.499 0.405 3.102 3.595 2.129 0.991 1.449 0.727 2.035 1.555 +1 1.176 -0.121 -1.273 0.996 1.606 0.657 1.237 0.077 2.173 0.872 0.430 0.880 0.000 0.658 0.659 -0.303 0.000 0.535 0.304 -1.353 1.551 1.030 0.830 0.982 0.516 0.667 0.880 0.809 +1 0.940 0.677 -0.766 2.741 -1.152 1.446 -1.935 0.785 0.000 1.094 0.625 1.118 2.215 0.489 -0.967 1.518 0.000 1.649 1.111 -0.480 0.000 0.715 1.261 0.982 0.820 0.839 0.958 0.870 +1 0.716 0.262 -0.632 1.034 -1.058 0.578 0.171 1.317 2.173 0.553 1.230 -1.104 0.000 1.243 1.072 0.613 2.548 0.799 1.845 0.637 0.000 0.965 1.012 0.987 1.339 0.836 0.973 0.979 +1 0.686 -0.862 1.601 0.258 0.329 0.875 0.405 -1.639 2.173 0.734 -0.527 0.405 0.000 1.237 0.243 -0.112 0.000 1.931 -0.214 -1.028 3.102 0.895 1.074 0.978 0.742 0.860 0.947 0.796 +1 0.359 1.943 -0.187 0.358 -0.808 1.369 0.249 -1.609 2.173 0.865 -0.186 0.190 0.000 0.831 1.888 0.544 0.000 0.433 0.219 -0.694 3.102 0.967 1.318 0.979 0.471 0.599 0.766 0.690 +1 0.828 0.204 -0.552 0.837 0.855 1.019 0.757 -0.824 0.000 1.502 1.059 1.333 0.000 1.023 1.823 -0.517 0.000 1.689 0.214 0.787 1.551 1.312 1.110 1.101 0.708 0.645 0.980 0.861 +1 0.952 1.691 1.533 1.275 -1.169 0.574 -0.337 -0.211 0.000 0.917 0.997 1.107 2.215 1.085 0.298 0.353 2.548 1.069 1.065 -0.449 0.000 1.106 0.830 0.992 0.897 0.766 0.872 0.945 +0 1.037 -1.134 -0.484 0.816 -1.291 0.478 0.453 -1.333 0.000 0.798 -0.328 0.129 1.107 0.808 -0.005 1.184 0.000 0.636 -0.085 0.758 3.102 0.895 0.944 0.990 0.782 0.353 0.682 0.773 +1 0.723 -0.826 1.360 1.080 -0.727 1.647 -1.210 -0.030 2.173 1.139 -1.311 -1.482 0.000 0.978 -2.146 1.661 0.000 0.638 -0.551 0.577 1.551 0.932 0.771 1.166 1.177 0.643 0.962 0.859 +0 1.129 0.615 -0.014 2.421 -0.600 0.901 -1.331 1.108 0.000 1.449 2.416 1.395 0.000 1.495 1.003 0.149 2.548 0.971 1.255 -1.729 3.102 1.596 0.958 1.154 0.911 0.933 1.009 1.243 +1 1.216 0.012 -1.144 1.141 1.605 0.585 1.275 -0.460 2.173 0.630 -2.366 0.463 0.000 1.216 0.732 -1.658 0.000 1.848 0.667 0.462 3.102 0.988 1.028 1.005 1.073 0.856 0.927 0.834 +1 0.525 -0.779 -0.128 1.109 0.427 0.843 -0.123 -0.953 2.173 1.439 0.124 1.424 1.107 1.604 0.167 -0.436 0.000 0.706 -1.601 1.636 0.000 1.200 0.937 0.978 1.665 1.378 1.337 1.165 +0 0.584 -0.425 1.533 0.639 -0.687 0.739 -1.250 -0.559 0.000 0.972 1.363 1.668 2.215 1.033 -0.029 0.567 2.548 0.888 -0.344 0.151 0.000 0.915 0.960 0.990 1.545 1.219 1.242 1.085 +0 0.339 -0.395 -0.970 1.429 1.667 0.560 -1.462 1.586 1.087 0.536 1.957 -0.178 0.000 0.729 0.830 0.247 1.274 0.466 -0.353 0.215 0.000 1.031 0.639 0.989 0.539 1.406 1.117 1.238 +1 0.540 -0.714 1.278 1.993 0.808 1.149 -1.595 -0.912 0.000 0.471 -1.176 -0.793 2.215 0.975 -1.168 -0.161 0.000 1.317 -1.168 1.553 3.102 1.233 1.130 0.997 0.880 0.613 0.669 0.852 +0 0.936 -1.026 -1.696 0.772 0.939 0.640 -2.815 0.080 0.000 0.640 0.008 1.538 2.215 0.568 1.082 -1.334 0.000 1.524 1.045 -0.675 0.000 0.575 0.846 0.985 0.723 0.667 0.633 0.742 +1 0.368 -1.910 -0.446 1.105 1.065 0.854 -1.920 0.020 0.000 1.026 -1.816 1.484 0.000 0.910 -0.096 -1.508 2.548 0.798 -0.944 -1.322 0.000 0.811 0.919 0.989 1.895 0.571 1.364 1.124 +0 0.407 1.742 -1.394 1.689 -0.344 1.100 0.698 -0.819 1.087 1.026 0.421 1.552 0.000 1.708 0.316 0.689 2.548 1.422 -0.382 1.167 0.000 0.901 0.960 0.988 1.261 1.698 1.322 1.367 +1 0.656 -1.870 -1.284 0.374 1.317 1.010 -0.546 0.421 0.000 0.740 -0.816 -0.135 0.000 0.949 0.070 -1.086 1.274 1.694 -0.896 1.739 1.551 0.922 1.139 0.976 0.589 0.802 0.927 0.807 +1 2.363 1.058 0.907 1.071 0.330 1.278 0.820 -0.689 2.173 1.701 0.976 -1.556 0.000 0.351 -0.223 1.208 0.000 1.003 -0.170 0.377 3.102 0.802 0.896 1.093 0.852 1.176 1.130 0.883 +1 0.957 0.582 -0.121 0.996 -1.328 1.093 0.466 -1.608 0.000 2.118 0.394 0.607 2.215 0.551 1.062 0.125 2.548 1.057 0.858 -0.973 0.000 1.004 0.939 1.197 1.267 0.659 1.087 0.947 +1 1.178 -0.144 1.424 1.919 0.931 0.969 -0.214 -0.713 2.173 0.420 -0.360 -1.227 0.000 0.878 0.586 -0.288 0.000 0.873 -0.623 1.648 3.102 0.924 0.834 0.987 0.684 0.867 0.956 0.817 +0 1.270 0.146 0.332 0.048 -0.656 0.761 0.486 -1.297 0.000 0.711 1.044 0.939 2.215 0.653 -1.048 -0.368 2.548 0.870 -0.733 -1.309 0.000 0.921 0.911 0.838 0.763 1.189 0.859 0.780 +1 1.467 0.348 -0.666 0.540 1.315 0.473 0.122 0.813 0.000 0.428 0.754 0.237 0.000 0.677 -0.416 0.152 2.548 1.669 -0.869 1.669 3.102 0.571 1.018 1.205 0.757 0.832 0.774 0.714 +1 1.386 0.234 0.230 0.689 -1.693 0.693 -0.265 1.698 1.087 0.342 1.518 0.582 0.000 0.622 0.571 -1.556 2.548 0.666 -1.366 -0.962 0.000 0.885 0.853 1.336 0.781 0.414 0.665 0.662 +0 0.596 0.024 -0.552 0.841 1.461 0.959 0.848 0.867 2.173 0.751 -0.933 -1.607 0.000 1.535 -0.774 -0.380 2.548 0.672 -1.572 -0.840 0.000 0.738 0.886 0.987 0.996 1.992 1.270 1.000 +1 0.701 -0.496 -0.713 0.201 -1.618 1.329 0.720 0.893 2.173 1.316 0.586 -1.218 0.000 1.351 1.189 -0.913 0.000 1.919 0.304 -0.108 3.102 0.920 1.204 0.990 1.100 1.362 1.259 1.009 +1 1.151 -0.356 1.572 0.904 -1.504 3.212 -0.306 0.421 0.000 1.894 1.602 -1.731 0.000 2.083 -0.624 -1.119 2.548 2.679 -0.582 -0.523 3.102 0.306 0.738 0.989 0.847 0.924 0.865 0.735 +0 1.235 0.102 1.562 1.724 -1.559 0.805 2.612 -0.397 0.000 0.571 -1.552 0.863 0.000 0.714 -0.321 1.048 2.548 0.512 2.325 0.077 0.000 0.661 0.765 0.979 0.681 0.490 0.620 0.655 +0 0.749 -0.033 -0.142 0.891 1.739 0.790 1.264 0.164 0.000 1.345 0.721 -1.132 2.215 1.054 1.550 1.181 0.000 0.449 1.598 0.581 0.000 1.349 0.786 1.123 0.909 0.737 0.884 0.860 +1 0.298 -0.735 -1.386 2.426 -0.254 2.516 0.749 1.581 0.000 1.258 1.281 0.099 0.000 1.231 0.420 0.312 2.548 1.373 0.713 -0.372 3.102 1.718 1.174 1.005 1.011 0.607 0.867 1.309 +1 0.589 -1.402 -1.514 1.061 0.004 0.532 0.654 -0.865 0.000 0.543 -0.830 1.214 2.215 1.017 -1.216 -0.305 2.548 1.152 0.691 1.653 0.000 0.921 0.981 1.073 0.620 0.797 0.903 0.896 +0 0.650 0.044 -0.515 0.686 0.676 0.723 -1.105 0.599 2.173 0.845 -0.455 -1.709 2.215 0.594 -1.295 -1.464 0.000 0.522 -1.545 -0.455 0.000 0.503 0.735 0.987 0.778 1.073 0.690 0.616 +0 0.589 -0.031 -1.150 1.122 0.094 0.463 -0.845 1.099 1.087 0.622 -0.089 1.059 2.215 0.908 -1.505 -0.352 0.000 0.883 -0.364 1.561 0.000 1.167 0.947 1.015 0.788 0.309 0.621 0.656 +1 0.457 -1.114 0.142 0.965 1.086 0.912 -0.164 1.218 2.173 0.958 0.662 -0.897 0.000 0.855 0.063 -1.220 2.548 1.155 0.874 -0.226 0.000 0.855 1.150 0.985 1.183 0.900 1.056 1.114 +1 0.372 -0.862 -0.458 2.275 0.211 1.047 0.045 1.531 2.173 1.138 -0.356 -1.244 0.000 0.425 1.615 -0.462 0.000 0.621 0.871 -0.041 0.000 0.841 0.860 0.989 0.908 0.241 1.238 1.024 +1 0.997 0.520 0.208 1.357 0.153 0.889 0.184 -1.648 2.173 0.502 1.023 -1.149 1.107 0.713 -0.855 -1.088 0.000 0.698 0.508 1.409 0.000 0.899 0.743 0.997 1.448 0.613 0.970 0.903 +1 0.704 0.488 -1.193 1.092 -0.405 0.636 0.024 -0.872 2.173 0.736 0.030 1.026 0.000 0.801 1.306 0.758 0.000 0.802 -0.960 1.603 1.551 0.940 0.991 0.993 0.753 0.760 0.824 0.839 +0 1.702 -0.994 -1.217 1.793 -1.162 0.672 -0.966 0.724 0.000 0.583 1.687 1.444 0.000 0.447 2.194 -0.552 0.000 0.947 -0.848 -0.112 1.551 0.809 1.113 0.976 0.884 0.159 0.948 1.548 +1 0.717 0.047 -1.398 1.491 0.197 0.796 0.465 -0.469 2.173 0.987 0.465 1.340 2.215 1.292 0.850 0.098 0.000 0.571 -1.132 1.686 0.000 1.637 1.170 1.420 1.024 1.302 0.941 0.859 +0 1.272 0.353 0.351 0.752 1.714 0.853 -0.649 0.631 0.000 1.270 -0.698 -1.180 0.000 0.761 -0.043 1.244 2.548 0.441 -1.021 -0.807 1.551 2.208 1.188 1.277 0.709 0.508 0.784 0.809 +0 0.501 -0.358 0.657 0.832 -1.688 0.921 -0.852 1.544 2.173 1.066 -2.468 -0.167 0.000 0.871 -1.475 -0.965 2.548 0.988 -1.944 0.569 0.000 0.835 0.885 0.983 0.667 0.963 1.051 0.887 +1 0.342 -1.776 -1.059 1.905 -0.775 0.949 1.730 0.349 0.000 0.874 0.514 1.279 2.215 1.238 -0.848 1.304 1.274 0.868 -0.479 -0.136 0.000 0.886 1.028 0.979 1.062 0.882 0.962 0.810 +0 0.437 0.079 -1.091 1.127 1.415 2.370 -0.250 -1.716 0.000 2.256 0.743 -0.312 0.000 2.635 -0.835 0.307 0.000 1.244 1.795 1.353 0.000 0.780 0.879 0.985 0.598 1.084 0.865 0.904 +1 0.989 0.263 -0.368 0.472 -0.918 0.461 -1.168 -1.175 2.173 0.956 -1.218 0.640 0.000 1.167 0.640 -1.597 0.000 1.024 -0.172 0.894 0.000 0.764 0.958 0.993 0.635 0.738 0.620 0.664 +0 0.998 0.465 -0.585 0.287 1.674 0.640 0.889 1.272 2.173 0.409 -0.641 -0.562 0.000 1.037 0.433 -0.262 2.548 0.788 1.540 0.970 0.000 0.601 0.775 0.981 0.628 1.020 0.703 0.592 +0 0.903 0.598 -0.689 0.668 0.996 0.963 -0.817 -1.418 0.000 0.882 -0.159 0.696 2.215 0.427 -1.273 0.450 0.000 1.115 -0.359 -0.280 1.551 1.194 0.941 1.075 0.806 0.700 0.783 0.791 +1 1.000 -0.854 -0.659 0.608 -0.095 0.818 -0.640 0.920 0.000 0.616 -0.160 1.526 0.000 0.640 -0.712 1.367 2.548 0.591 2.341 -0.466 0.000 0.862 0.742 0.987 0.766 0.324 0.596 0.699 +0 2.606 1.568 1.167 2.059 0.908 0.504 0.626 -1.029 2.173 1.387 -0.060 -0.874 0.000 1.378 -0.407 -0.169 2.548 0.370 -1.562 -0.735 0.000 0.975 0.876 0.992 1.344 0.935 1.475 1.504 +0 0.477 0.580 0.763 1.469 -1.710 0.549 -0.116 -0.115 2.173 0.308 -0.202 0.390 0.000 0.622 0.538 -1.023 2.548 0.368 -1.732 0.830 0.000 0.502 0.670 0.985 1.035 0.593 0.700 0.707 +0 0.722 0.390 0.861 0.371 -0.438 1.100 -1.069 1.306 0.000 1.844 -0.050 -0.279 2.215 0.813 0.039 -1.238 2.548 0.920 -1.826 -1.439 0.000 1.309 1.203 0.989 1.134 0.991 1.347 1.307 +1 0.938 1.508 1.742 0.833 -0.096 1.016 1.127 0.323 2.173 1.185 0.825 -1.366 0.000 0.950 0.791 0.832 2.548 0.805 1.152 -0.668 0.000 0.824 0.958 1.221 0.966 0.566 0.875 0.793 +0 0.873 1.109 0.317 1.365 0.976 0.850 -0.829 -0.738 2.173 1.824 1.817 -0.914 0.000 1.140 -0.336 1.141 2.548 1.071 -1.269 1.291 0.000 0.904 0.925 0.988 1.217 1.251 1.442 1.372 +0 0.799 -1.630 1.368 1.027 -1.352 0.524 -0.533 1.183 0.000 0.898 0.037 -0.346 2.215 0.729 0.306 0.909 1.274 1.399 -1.357 -0.475 0.000 1.504 1.128 0.981 1.533 0.789 1.184 1.038 +0 1.166 0.661 -1.701 0.679 1.085 0.417 0.316 -0.202 0.000 0.569 1.256 -1.118 0.000 1.353 1.213 0.385 1.274 0.516 -0.145 -1.078 3.102 0.923 0.920 0.984 0.559 0.813 0.713 0.688 +0 1.042 -0.134 -0.324 1.332 0.445 1.496 -0.733 -1.186 1.087 1.112 -1.098 0.539 0.000 1.829 -0.594 1.058 2.548 1.125 0.945 -1.095 0.000 2.490 1.706 1.044 1.482 1.856 1.471 1.259 +1 0.807 1.024 1.355 0.223 -1.074 1.583 -1.823 -1.188 0.000 1.572 -0.524 0.804 2.215 2.125 -0.265 0.172 2.548 1.118 -0.055 -0.920 0.000 2.106 2.376 0.986 1.035 1.075 1.764 1.480 +0 2.082 0.110 -0.753 0.349 1.045 0.984 -0.538 -0.093 2.173 1.222 -0.790 1.517 2.215 0.488 -0.900 1.163 0.000 0.587 0.303 1.024 0.000 0.436 0.816 1.180 1.228 1.617 1.062 0.839 +1 0.385 2.114 -0.222 1.320 -0.943 0.505 -0.032 -0.258 0.000 0.615 0.257 0.907 2.215 1.518 -0.659 1.070 0.000 0.674 -0.031 -1.359 1.551 1.568 0.975 0.993 0.890 0.525 0.654 0.828 +0 0.624 0.610 -0.407 1.033 -1.363 1.284 1.568 -1.628 0.000 1.067 0.709 -0.006 2.215 1.332 1.619 -0.559 0.000 3.348 -0.055 0.925 1.551 0.919 1.133 0.982 0.904 1.448 0.984 0.841 +0 1.204 1.055 -0.376 0.485 0.107 0.671 -1.247 0.635 0.000 1.015 -0.367 -1.366 0.000 0.937 0.413 -1.146 2.548 1.157 -0.440 1.405 0.000 0.855 0.850 0.989 0.867 0.669 0.654 0.904 +0 1.209 1.787 0.916 0.984 0.267 1.026 0.488 0.415 2.173 1.843 0.445 -1.720 0.000 1.648 0.665 -0.462 0.000 1.846 1.211 -0.946 1.551 2.449 1.626 0.987 0.900 1.552 1.342 1.213 +0 0.643 -0.947 0.111 0.792 1.435 1.113 -1.151 -1.739 1.087 1.319 -0.241 -1.269 0.000 2.120 -0.236 0.150 2.548 0.583 -1.289 -1.189 0.000 0.801 0.932 0.989 0.848 2.096 1.192 0.963 +0 0.447 -0.922 1.561 1.690 0.295 0.590 -0.664 -0.955 2.173 0.640 -1.508 1.422 0.000 0.353 -2.031 0.757 0.000 0.764 0.573 -1.177 3.102 0.482 0.963 1.094 0.929 0.539 0.748 0.726 +1 0.823 -0.210 -0.051 0.418 -1.163 0.634 2.049 -0.784 0.000 0.644 -0.740 -0.344 0.000 1.065 -0.267 1.351 1.274 1.341 -0.681 0.841 0.000 1.061 0.886 0.988 0.982 0.701 0.775 0.731 +0 0.604 -1.149 0.898 1.823 1.197 1.452 0.298 -0.447 2.173 1.134 0.205 -1.338 2.215 0.682 1.002 0.175 0.000 0.543 0.166 0.448 0.000 0.353 0.886 0.988 1.674 1.361 1.245 0.983 +1 1.348 -1.798 0.277 0.431 -0.757 0.458 -1.518 0.618 0.000 0.631 -0.797 -0.928 0.000 0.720 -0.165 1.386 0.000 1.153 -0.825 -1.488 3.102 0.924 0.714 0.993 0.676 0.319 0.576 0.555 +0 0.472 -1.655 0.845 1.386 -0.059 1.278 -1.063 -0.363 2.173 1.992 0.607 1.244 2.215 1.023 -0.758 1.714 0.000 0.667 -1.694 -1.624 0.000 0.818 1.110 0.985 3.233 3.233 2.335 1.735 +1 0.752 1.159 1.100 0.849 -1.364 0.852 0.713 0.619 0.000 1.341 0.176 -1.342 2.215 1.688 0.237 -0.072 0.000 0.492 -1.046 1.260 3.102 1.355 1.093 0.989 0.777 0.777 1.044 0.903 +1 0.567 -0.559 1.176 0.601 -1.014 0.788 -0.428 -0.405 0.000 0.659 1.562 1.528 0.000 0.717 -0.530 0.299 2.548 1.212 0.182 1.268 3.102 0.730 1.112 0.989 0.603 0.619 0.741 0.670 +1 0.277 -2.072 0.073 1.109 -1.685 0.900 0.533 -1.107 2.173 0.830 0.032 0.303 0.000 0.866 -0.814 0.915 0.000 0.592 -0.027 -0.145 3.102 0.924 0.602 0.987 1.097 0.630 0.806 0.789 +1 0.524 0.750 -1.028 1.425 -1.514 3.720 -0.536 0.083 0.000 0.949 -0.860 0.530 1.107 2.287 -0.526 1.304 0.000 6.068 -0.601 -1.430 0.000 0.905 0.959 0.989 0.515 0.534 0.694 0.677 +0 0.604 0.470 -1.256 1.095 1.739 1.181 0.549 -0.149 0.000 0.863 -0.161 0.839 2.215 1.312 -0.160 -1.731 2.548 0.587 -0.912 -0.920 0.000 0.806 1.094 0.992 1.004 0.831 0.958 1.037 +1 0.899 0.177 -0.944 0.933 -1.535 0.500 0.840 -0.325 1.087 1.141 -0.699 0.223 2.215 0.777 0.252 1.594 0.000 0.532 -1.406 -1.697 0.000 0.824 1.003 0.985 1.368 1.098 0.963 0.879 +0 2.358 -1.304 -0.542 0.169 -0.327 1.299 -0.718 1.135 2.173 0.418 -0.869 -1.101 0.000 0.612 -0.593 0.554 0.000 0.474 0.162 -1.159 3.102 0.780 0.883 1.002 0.668 0.830 1.013 0.800 +0 1.097 -1.355 1.713 1.449 0.052 0.716 -0.459 1.129 2.173 0.447 -1.160 1.023 0.000 0.587 -1.532 -1.319 0.000 0.872 -0.868 -0.526 0.000 0.804 0.761 1.742 1.064 0.901 0.889 0.726 +1 0.472 -0.794 -1.435 1.257 0.745 0.704 1.635 -0.910 0.000 1.122 -0.053 1.183 0.000 1.429 -0.916 -0.480 2.548 1.840 0.229 -1.051 3.102 0.862 1.157 0.988 0.893 1.045 0.988 0.875 +0 1.287 0.556 0.852 1.862 0.693 1.452 0.483 -1.368 2.173 1.247 1.060 -0.966 0.000 1.322 1.239 0.187 0.000 0.551 0.093 0.101 1.551 1.718 1.028 1.011 1.760 0.935 1.104 1.210 +0 0.340 0.094 1.040 0.889 -0.434 0.844 1.227 -1.538 2.173 1.647 1.146 0.210 2.215 1.343 1.040 1.396 0.000 1.284 0.635 -0.656 0.000 1.420 1.011 0.987 0.789 1.736 1.060 0.868 +0 0.738 -0.560 0.650 0.798 -0.170 0.810 -0.864 -1.189 1.087 0.761 -0.816 -0.057 2.215 0.851 -0.155 1.021 0.000 0.817 -1.296 1.550 0.000 0.868 1.020 0.996 0.683 0.985 0.785 0.737 +0 0.366 -1.129 0.943 0.445 0.087 0.548 0.251 -1.447 0.000 0.893 0.511 1.528 2.215 0.607 -0.054 0.352 2.548 0.506 -0.877 -0.378 0.000 0.869 0.782 0.981 0.520 0.721 0.583 0.568 +0 0.999 0.954 -1.736 0.964 0.973 0.530 -1.735 -0.542 0.000 0.702 -0.494 0.778 2.215 0.818 1.118 -0.495 1.274 0.370 -0.154 -0.956 0.000 0.636 0.851 0.991 0.813 1.079 0.902 1.105 +0 0.640 -1.021 0.114 1.554 1.041 0.618 -0.985 -1.564 0.000 0.531 -2.373 0.469 0.000 0.353 0.182 -1.188 0.000 0.713 1.094 -0.941 3.102 0.601 0.827 1.025 0.959 0.332 0.850 0.773 +0 0.728 -0.686 1.522 1.698 -1.386 0.870 0.082 -0.410 0.000 1.020 0.036 0.395 0.000 0.873 0.070 1.276 2.548 0.664 0.821 -0.588 0.000 0.983 0.829 0.993 1.121 0.305 0.815 0.897 +1 0.906 -0.731 1.016 2.359 1.484 1.056 -1.052 -0.702 2.173 0.856 0.085 -0.046 2.215 0.436 -1.820 1.049 0.000 0.839 -0.602 -0.116 0.000 0.737 0.841 0.975 1.385 1.153 1.234 0.963 +0 0.841 0.048 0.915 0.687 -0.786 0.627 -0.686 -1.516 1.087 0.546 0.190 1.601 0.000 1.253 0.133 -0.135 2.548 0.980 -0.862 -0.036 0.000 0.725 0.879 1.053 0.780 1.150 0.707 0.633 +0 0.871 -1.221 1.366 0.826 -0.322 0.740 -0.558 0.125 1.087 0.492 -0.118 -1.691 2.215 0.799 -0.486 1.159 0.000 0.797 -0.151 -1.075 0.000 0.943 0.899 1.173 0.763 0.908 0.691 0.650 +0 0.710 -0.376 -1.609 1.226 -0.982 0.874 0.646 0.560 1.087 0.464 0.200 0.922 0.000 1.105 0.435 -1.278 2.548 1.163 -0.135 -0.093 0.000 0.779 0.846 0.985 1.144 1.224 0.831 0.740 +1 1.144 0.615 0.168 0.741 -0.292 0.571 1.603 -0.017 0.000 1.367 -0.318 1.642 0.000 1.089 -0.339 -1.521 2.548 0.757 -0.760 1.170 3.102 0.955 0.851 0.981 0.956 0.493 0.693 0.677 +1 1.552 -1.195 -0.462 0.730 -0.275 1.225 -1.226 1.309 1.087 0.665 -1.337 0.470 2.215 0.489 -0.937 -1.439 0.000 0.540 -2.067 -0.469 0.000 0.625 0.896 0.991 0.774 0.917 1.001 0.794 +1 0.289 2.404 -0.247 0.783 -0.856 0.584 0.219 -1.284 0.000 0.588 1.304 -0.253 0.000 0.876 0.693 1.134 2.548 1.444 -0.830 0.915 3.102 0.815 1.102 0.981 0.751 0.873 0.840 0.738 +1 0.945 -0.175 -0.909 1.056 -1.316 0.914 0.127 0.594 2.173 0.825 -0.505 1.575 0.000 0.441 -0.560 0.352 0.000 0.732 -1.076 -0.360 3.102 0.826 0.891 0.989 0.612 0.936 0.885 0.762 +0 0.605 -0.810 1.087 0.445 1.585 0.724 0.044 1.236 1.087 1.240 0.354 -0.262 0.000 1.193 0.775 -1.271 2.548 0.485 1.326 0.201 0.000 0.776 0.944 0.992 1.130 1.014 1.193 1.211 +0 1.070 -2.064 -1.551 1.149 -0.660 0.674 -1.434 0.305 0.000 0.771 -0.824 1.376 2.215 0.686 -0.842 -0.452 2.548 0.907 -0.866 0.961 0.000 0.709 0.767 1.105 0.769 0.771 0.756 0.752 +0 1.404 -0.626 0.868 0.964 -0.130 1.060 -0.165 1.631 2.173 1.078 -2.116 -0.607 0.000 1.289 -1.072 -0.195 2.548 1.164 -1.086 -1.324 0.000 0.964 0.878 1.262 1.195 1.642 1.011 0.915 +1 0.542 0.771 -1.067 1.028 0.667 0.983 -1.319 0.368 0.000 1.029 1.162 0.983 0.000 1.227 -1.078 -0.898 0.000 1.818 0.507 -0.653 3.102 0.793 1.018 1.034 0.750 0.671 0.715 0.655 +1 0.633 -0.076 -0.962 0.978 0.041 1.150 -0.247 1.287 2.173 0.853 1.036 -1.433 0.000 0.557 2.059 -0.307 0.000 0.755 -0.038 -0.103 3.102 0.985 0.749 0.987 1.116 0.942 0.819 0.820 +1 1.941 1.136 -1.738 1.119 -1.053 0.591 0.655 -0.053 2.173 0.462 0.061 -0.398 2.215 0.647 0.904 1.294 0.000 0.868 1.063 0.252 0.000 0.677 0.657 1.183 1.142 0.331 0.822 0.712 +1 1.022 -0.930 -0.240 0.656 0.456 0.552 0.537 -1.578 0.000 0.780 -1.063 -1.484 2.215 0.694 -1.731 0.097 0.000 0.950 -0.814 0.102 3.102 0.823 0.836 0.985 0.522 0.771 0.633 0.691 +0 1.195 -0.791 0.724 1.623 1.446 0.484 -1.318 -0.150 0.000 0.551 -0.934 -1.257 2.215 0.788 -0.109 -0.720 0.000 0.807 1.009 -0.369 1.551 0.899 0.948 1.170 0.843 0.901 0.885 0.842 +1 1.778 0.630 0.709 0.621 1.447 0.953 0.808 -1.552 2.173 0.930 0.680 -0.223 2.215 0.785 -0.309 -0.462 0.000 0.583 0.768 -0.729 0.000 0.533 0.907 0.987 0.940 1.292 0.923 0.805 +0 0.616 -0.057 0.362 0.979 -1.084 0.308 0.681 0.845 0.000 0.668 0.866 -1.300 2.215 0.864 -0.810 0.678 2.548 0.646 -1.162 -0.744 0.000 1.082 0.928 1.038 0.763 1.136 0.721 0.652 +1 0.876 -2.277 -1.225 1.021 -0.260 0.159 0.829 0.144 1.087 0.869 -0.527 1.572 2.215 0.452 -0.895 0.135 0.000 0.642 -1.931 0.637 0.000 0.500 0.793 1.002 1.162 0.666 0.989 0.788 +0 0.960 0.077 -1.037 1.884 -0.413 0.953 -1.034 1.483 2.173 0.486 -0.537 0.867 0.000 0.838 -0.668 -0.044 2.548 0.461 -1.138 1.136 0.000 0.298 0.492 0.995 1.523 1.106 1.038 0.830 +0 0.362 -1.252 -1.699 1.271 1.325 0.647 -0.045 -0.089 0.000 1.158 0.628 -1.167 2.215 0.940 -0.986 0.169 2.548 0.443 0.686 1.271 0.000 0.852 0.980 0.984 1.051 1.497 1.766 1.483 +0 0.988 1.156 -0.804 1.563 -0.898 0.675 1.670 0.875 2.173 0.569 0.234 1.046 2.215 1.132 1.047 0.297 0.000 0.611 1.972 -1.707 0.000 0.845 0.785 0.993 1.364 0.731 0.975 0.839 +0 1.114 0.636 0.369 0.891 1.045 0.615 0.896 -1.478 2.173 0.255 2.861 -1.620 0.000 0.920 0.727 -0.947 1.274 0.438 1.889 1.650 0.000 0.172 0.636 0.993 0.921 0.435 0.717 0.649 +0 0.608 0.001 -1.139 1.407 -0.218 0.751 0.348 0.361 2.173 0.901 2.202 1.446 0.000 1.207 2.709 -0.880 0.000 1.652 0.647 1.352 3.102 0.692 0.777 0.990 0.793 0.952 0.856 0.918 +0 0.319 -2.000 -1.007 1.956 1.396 0.791 0.162 0.194 0.000 0.818 -1.165 -0.106 2.215 1.659 -0.567 -1.506 2.548 0.699 1.101 -0.360 0.000 0.882 1.110 0.987 0.864 1.234 1.047 1.116 +0 1.888 -0.245 -0.586 0.377 1.255 0.460 -2.034 1.632 0.000 0.918 0.508 0.221 2.215 0.732 -0.214 -1.502 0.000 1.030 -0.537 1.203 0.000 0.896 1.058 1.164 0.919 0.392 0.965 0.905 +1 0.898 -1.507 -1.547 0.203 -1.193 0.926 -1.204 0.471 2.173 0.422 -0.929 1.465 0.000 0.940 0.404 -1.419 0.000 1.377 -1.012 -0.338 3.102 0.887 1.003 0.992 1.014 0.796 0.897 0.794 +0 0.372 0.722 1.225 1.699 -0.167 0.536 1.514 1.506 0.000 0.738 1.548 -1.257 0.000 1.387 -0.021 0.481 0.000 0.852 0.478 -0.803 3.102 0.809 0.724 1.047 0.889 0.474 0.599 0.673 +1 2.388 0.090 -0.653 0.571 -0.274 1.073 -0.345 0.862 2.173 0.974 0.617 -1.740 0.000 0.541 0.024 -1.742 2.548 0.522 0.480 -0.067 0.000 0.928 1.147 0.997 0.768 0.700 0.962 0.883 +0 0.606 -1.131 -0.208 0.916 1.695 1.448 -0.138 0.377 2.173 1.134 -0.811 -0.966 1.107 0.983 2.627 -1.353 0.000 1.046 -0.414 1.528 0.000 0.855 0.927 1.022 0.747 1.887 1.064 0.854 +1 0.517 -0.605 1.692 0.838 0.228 1.169 -0.564 0.791 0.000 1.458 -1.222 -1.159 1.107 0.800 -0.885 -0.478 0.000 0.658 -1.044 -0.029 3.102 1.623 0.949 0.986 0.924 0.753 0.986 0.817 +1 0.713 1.926 0.105 1.371 -1.300 1.313 0.357 0.282 2.173 0.828 0.980 -1.317 0.000 0.567 2.040 1.631 0.000 0.471 0.090 1.520 1.551 0.845 0.617 1.307 1.722 0.755 1.104 0.986 +1 1.039 1.036 -1.551 0.804 0.380 0.553 0.051 -0.253 0.000 0.549 -0.923 -0.542 2.215 1.023 -0.451 1.266 1.274 0.527 0.224 1.001 0.000 0.751 0.787 1.248 1.101 0.816 0.864 0.733 +1 0.893 -0.604 -1.067 1.040 0.013 1.538 -1.243 -1.427 0.000 1.124 0.752 0.269 0.000 1.736 -0.488 1.084 2.548 0.995 -1.841 -0.358 0.000 0.962 1.050 1.105 1.039 0.790 0.934 0.896 +0 0.361 -1.235 0.107 0.663 0.752 0.638 -1.422 0.914 0.000 0.790 -0.594 -1.056 2.215 0.277 -0.887 -1.634 0.000 1.193 0.448 -1.077 3.102 0.592 1.067 0.989 0.851 0.533 0.742 0.725 +0 1.852 -0.265 0.916 0.844 -1.361 0.605 1.741 -0.312 0.000 0.415 1.116 -0.888 0.000 0.673 -0.154 0.332 2.548 1.326 -0.120 -1.435 1.551 0.622 1.053 1.536 0.844 0.722 0.767 0.922 +0 0.476 -1.532 -1.726 0.861 -0.413 0.530 0.052 1.279 0.000 0.550 -0.218 -0.551 2.215 0.800 -0.868 0.064 2.548 0.416 -1.405 0.874 0.000 0.751 0.772 0.989 0.606 0.455 0.561 0.550 +0 0.526 0.968 -0.495 1.542 -1.350 0.557 1.246 0.001 0.000 0.362 1.400 -1.635 0.000 0.851 1.254 0.691 2.548 0.369 0.877 0.530 1.551 0.954 0.686 0.990 0.604 0.093 0.601 0.595 +0 0.824 0.771 1.669 0.945 1.050 1.218 0.604 0.084 0.000 0.406 1.079 0.690 0.000 1.020 2.035 -1.532 0.000 1.077 -0.825 -0.615 3.102 0.863 1.198 0.989 0.559 0.444 0.844 0.871 +1 1.464 -0.452 -0.601 0.579 -1.221 0.897 -0.776 1.256 2.173 1.296 -0.921 0.555 2.215 0.693 2.052 -1.517 0.000 0.654 -1.601 0.170 0.000 0.316 0.851 0.992 1.125 0.947 0.962 0.772 +1 0.688 0.310 -1.606 1.170 -0.765 2.223 0.764 0.626 2.173 1.834 2.557 1.091 0.000 3.284 0.738 -0.780 0.000 1.728 0.786 -1.080 1.551 5.418 3.061 0.985 1.817 2.081 2.528 2.135 +1 1.930 0.748 -0.663 0.374 0.176 0.874 -0.463 1.575 2.173 0.623 0.426 1.541 0.000 0.656 -1.210 0.274 2.548 0.940 0.003 0.326 0.000 0.913 0.852 0.988 1.204 0.965 1.121 0.920 +1 1.419 -1.257 -0.894 0.563 1.174 1.506 0.011 1.015 2.173 1.031 -1.231 -0.269 0.000 0.880 -0.225 0.233 2.548 1.040 -0.855 1.671 0.000 1.338 0.978 1.186 1.580 0.948 1.083 1.026 +1 1.704 0.155 1.731 0.736 -1.646 0.938 -1.133 -0.299 2.173 0.740 -0.422 0.405 2.215 0.616 -0.602 1.027 0.000 0.590 -1.297 -0.676 0.000 0.733 0.766 0.992 1.113 0.852 1.238 0.983 +1 1.794 0.282 0.078 0.419 -0.168 0.594 0.764 -1.648 0.000 0.879 -0.543 1.424 2.215 0.712 -1.105 -0.475 0.000 0.380 -0.256 1.144 3.102 1.701 0.932 0.993 1.217 0.146 0.758 0.838 +1 0.583 -0.581 1.146 0.634 -1.108 1.769 1.267 -0.515 0.000 0.967 -0.322 1.742 0.000 1.044 0.801 -1.583 0.000 2.199 0.416 1.061 1.551 0.881 0.860 0.987 0.762 0.940 0.698 0.635 +0 0.736 1.116 -1.708 1.329 1.621 1.207 -1.634 -0.363 0.000 0.695 -1.798 0.380 0.000 0.695 -0.567 -1.398 2.548 1.646 -0.464 0.841 3.102 1.222 1.159 0.977 1.825 0.738 1.346 2.309 +1 1.231 -1.082 -0.787 0.848 -0.418 1.273 1.535 1.076 0.000 0.624 -1.813 -1.176 0.000 0.742 1.125 -0.129 0.000 2.029 0.046 0.303 3.102 0.678 1.314 0.985 0.650 0.919 0.824 0.763 +1 0.398 -0.805 -1.342 0.958 1.518 0.485 -0.971 -0.350 0.000 0.591 -1.101 0.599 0.000 0.681 0.776 1.372 2.548 0.991 -0.159 -0.718 3.102 0.862 1.053 0.977 0.695 0.689 0.692 0.711 +0 0.886 -0.302 1.325 1.746 -1.665 1.058 0.481 -0.350 0.000 0.804 0.014 0.459 2.215 0.824 -1.342 1.540 0.000 0.831 -0.139 -0.580 3.102 0.895 0.897 0.991 0.817 0.598 0.780 0.975 +0 3.399 0.095 1.730 0.744 -1.024 2.037 -0.308 0.310 0.000 0.857 0.147 -1.204 2.215 0.946 -0.487 -0.438 2.548 0.420 -0.004 0.619 0.000 0.444 0.854 1.351 0.793 0.695 0.928 1.188 +1 0.664 -1.225 0.127 0.095 1.597 0.728 -1.023 0.717 0.000 0.898 -0.711 -0.629 2.215 0.818 -1.679 1.281 0.000 1.421 -0.176 1.716 3.102 0.889 0.985 0.984 0.703 0.914 0.856 0.753 +0 1.267 1.307 -1.730 0.879 -1.497 1.341 0.365 0.630 0.000 1.544 0.831 -0.947 2.215 1.718 0.656 0.206 2.548 1.125 1.254 -1.078 0.000 0.915 0.917 0.986 1.159 1.497 1.190 1.309 +0 0.990 -0.020 -0.828 1.176 0.791 0.667 -0.730 1.690 0.000 0.984 -0.325 0.586 1.107 0.734 0.152 -0.016 2.548 0.545 -1.848 -0.110 0.000 1.184 1.026 1.485 0.914 0.519 0.764 0.774 +0 0.875 -0.123 -0.007 0.826 1.533 0.484 -0.943 1.489 2.173 0.250 -0.037 -0.794 0.000 0.342 1.798 0.825 0.000 0.389 -1.262 -0.533 3.102 0.694 0.905 1.158 0.643 0.464 0.606 0.586 +1 0.998 -0.335 1.612 1.479 -1.110 0.665 1.011 0.463 2.173 0.403 0.937 -0.989 0.000 0.969 -0.171 -0.083 2.548 0.786 0.486 1.181 0.000 0.746 0.696 1.070 0.915 0.797 0.943 0.758 +1 1.387 0.206 1.244 0.159 -0.538 1.051 -2.343 -1.162 0.000 1.099 1.305 0.538 0.000 0.902 0.246 0.180 0.000 0.731 -0.470 1.453 3.102 0.888 0.921 0.988 0.529 0.220 0.610 0.620 +1 2.735 0.187 -1.566 0.360 -0.812 2.423 1.770 0.056 0.000 0.664 0.203 -1.164 0.000 1.491 0.473 0.631 2.548 1.331 -0.279 1.600 0.000 1.006 1.129 0.992 0.704 0.891 0.864 0.816 +0 0.336 0.941 0.226 0.291 1.219 1.203 -0.054 1.662 2.173 1.053 0.308 -0.318 2.215 1.213 -0.790 -0.281 0.000 0.434 -0.698 1.241 0.000 0.784 0.823 0.990 0.912 1.648 0.985 0.822 +0 0.900 0.318 1.416 0.455 0.801 0.916 0.734 -0.880 0.000 0.895 -0.189 0.525 2.215 0.878 -0.114 -0.627 0.000 0.738 0.990 1.250 3.102 0.807 0.885 0.982 0.690 0.707 0.822 0.798 +0 0.482 1.737 -1.519 0.486 -0.290 0.775 0.848 -0.525 0.000 0.691 0.563 0.012 2.215 1.810 0.613 1.517 2.548 0.711 2.030 1.298 0.000 1.504 1.033 0.983 0.823 1.162 0.947 0.786 +0 0.915 -0.392 -1.621 1.790 -1.036 0.661 0.162 0.284 0.000 0.788 -0.220 0.889 1.107 0.278 -0.542 1.165 0.000 0.596 1.403 0.584 0.000 0.840 0.766 0.980 0.733 0.819 0.756 0.791 +0 1.519 0.837 0.953 1.421 1.366 0.501 -2.876 1.470 0.000 1.043 0.545 -0.494 0.000 0.629 1.050 -0.097 2.548 0.836 -0.864 -0.492 3.102 4.465 2.349 0.995 0.828 0.761 1.648 1.965 +1 0.752 0.595 -0.077 2.233 0.518 1.511 -1.437 -1.427 0.000 1.145 -0.622 0.201 0.000 0.688 -0.854 1.665 1.274 1.026 0.368 -1.093 3.102 3.012 1.654 0.991 0.928 0.619 1.145 1.319 +0 3.405 0.177 -0.495 3.120 -0.598 3.668 -0.671 1.142 0.000 1.029 -0.760 -0.709 2.215 0.674 0.051 1.569 2.548 0.567 -1.222 1.353 0.000 0.984 0.921 0.962 0.878 0.872 1.253 1.931 +0 0.662 -1.343 1.504 1.003 -0.713 0.840 -2.135 0.066 0.000 1.028 -0.400 -1.564 2.215 1.313 -0.597 0.880 2.548 0.409 -0.852 -0.657 0.000 0.762 1.118 1.028 0.823 1.008 1.025 0.869 +1 1.314 0.194 0.837 0.813 -0.268 0.510 -1.054 -0.093 0.000 0.583 -0.897 -1.019 2.215 1.235 -1.004 1.657 2.548 1.278 1.457 -0.950 0.000 0.912 0.870 1.201 0.943 0.607 0.832 0.767 +1 0.411 0.641 1.160 1.004 -0.152 1.167 -0.001 -1.224 1.087 1.019 0.628 -0.430 0.000 2.239 -1.146 1.285 0.000 0.653 -1.928 0.531 0.000 0.860 1.151 0.987 0.522 1.034 0.801 0.726 +1 1.737 -0.307 -0.520 1.292 -1.310 1.885 1.265 0.975 0.000 1.485 0.795 -0.672 2.215 1.476 0.084 -1.228 2.548 0.816 -1.002 1.226 0.000 0.781 1.904 1.354 1.165 0.955 1.542 1.551 +0 1.297 0.338 1.480 1.147 0.897 1.318 1.038 -0.858 0.000 1.061 0.926 0.358 2.215 0.405 1.064 -1.409 0.000 0.526 -0.282 0.058 3.102 0.632 0.788 0.990 1.024 0.500 0.789 0.924 +0 0.818 0.367 -0.688 1.179 0.522 1.658 0.961 -1.725 2.173 1.074 0.676 0.211 0.000 1.268 0.212 -0.210 1.274 0.425 -0.222 1.503 0.000 0.921 0.717 1.207 1.429 1.896 1.134 0.970 +1 1.381 0.980 -1.538 1.033 1.460 0.503 -1.005 0.352 2.173 0.396 1.179 0.762 0.000 0.386 -0.942 -0.352 0.000 0.717 0.187 -0.606 0.000 0.969 0.837 0.981 0.775 0.742 0.874 0.755 +1 0.835 0.224 0.421 1.738 1.099 0.989 -0.050 -0.423 1.087 0.970 -1.097 -0.980 1.107 0.561 0.833 -1.479 0.000 0.623 -0.847 1.116 0.000 0.870 0.994 0.987 1.308 1.071 1.094 0.903 +1 0.289 -1.889 1.685 1.037 1.414 0.696 -0.652 1.083 2.173 0.651 -0.904 0.175 0.000 1.117 0.899 -0.945 0.000 1.924 -0.359 -0.554 3.102 1.829 1.209 0.992 0.632 1.227 1.006 0.892 +0 1.089 1.102 0.742 0.763 0.279 0.892 1.926 -1.022 0.000 0.605 0.113 0.873 0.000 1.059 0.706 -1.600 2.548 0.856 0.354 0.394 0.000 0.736 0.639 0.997 0.764 0.297 0.691 0.623 +0 0.499 2.010 -1.076 0.983 -1.033 1.110 0.789 -0.668 2.173 2.952 -0.292 0.898 2.215 0.573 -0.475 -0.829 0.000 0.498 -1.998 1.576 0.000 0.803 1.497 0.971 0.699 3.046 1.642 1.419 +1 1.564 -0.959 0.816 1.185 -0.668 0.416 -1.084 -0.424 0.000 1.101 0.202 -1.012 2.215 0.968 2.677 1.552 0.000 1.123 0.002 -0.014 3.102 0.808 0.943 1.834 1.414 0.793 0.964 1.165 +1 1.060 -0.270 1.452 0.472 -0.410 0.773 -0.366 0.482 0.000 0.648 0.454 1.623 2.215 0.464 0.877 -0.859 0.000 1.075 -1.284 -0.335 3.102 0.818 0.955 0.987 0.606 1.157 0.791 0.698 +0 1.876 0.736 1.723 0.744 1.296 0.965 0.891 0.400 2.173 0.675 1.451 -1.019 0.000 1.084 -0.054 -0.970 2.548 1.041 -0.162 0.584 0.000 0.669 0.964 0.983 1.243 1.360 0.997 0.967 +1 1.270 0.291 0.697 1.420 -1.208 0.501 -0.455 -1.257 0.000 0.777 -0.006 1.533 0.000 1.963 0.811 -0.298 2.548 0.655 -2.060 0.956 0.000 0.834 0.754 1.841 1.328 0.822 0.898 0.888 +0 0.630 0.241 -0.805 1.593 1.186 0.944 -0.239 -0.444 2.173 0.864 -0.897 1.203 1.107 0.487 0.423 0.020 0.000 0.749 -0.893 -1.140 0.000 0.806 0.814 1.353 1.198 1.402 0.980 0.798 +0 0.963 2.277 -1.672 0.846 -1.138 0.614 0.839 -0.358 1.087 1.223 0.209 0.622 1.107 0.269 0.097 -1.568 0.000 0.423 1.331 1.392 0.000 0.348 0.623 0.978 0.940 1.067 1.049 0.774 +0 0.800 0.949 0.174 1.257 0.968 0.432 0.320 -0.724 1.087 0.360 -0.926 0.264 0.000 0.788 -0.306 0.973 0.000 0.479 -0.987 1.441 3.102 0.550 0.736 0.984 0.950 0.599 0.755 0.722 +0 1.212 -1.496 1.480 0.614 0.562 1.167 -0.860 -0.715 1.087 1.128 -0.045 1.352 2.215 1.270 -2.696 -0.045 0.000 0.877 -1.125 0.070 0.000 0.793 0.878 0.988 1.191 1.768 1.062 0.843 +0 1.234 -1.096 -0.623 0.897 0.094 0.571 0.532 -1.720 0.000 0.874 0.100 1.168 1.107 0.992 -0.355 -0.933 2.548 0.792 -0.822 0.533 0.000 1.260 0.948 0.992 1.079 0.970 0.789 0.785 +0 1.300 0.825 0.617 1.870 0.057 1.122 -0.787 1.190 0.000 0.991 -1.147 1.536 0.000 2.030 2.350 -0.073 0.000 3.785 -0.034 -0.523 3.102 0.810 0.865 1.043 1.451 1.067 1.306 1.502 +0 2.315 1.083 -1.208 0.454 -0.964 1.399 0.743 -1.519 2.173 1.383 0.380 0.301 0.000 2.614 1.019 0.587 2.548 1.170 0.847 -0.066 0.000 0.753 0.907 0.987 0.908 2.298 1.373 1.305 +1 0.648 -0.362 -1.581 0.181 -0.801 1.511 -2.595 1.587 0.000 1.682 0.491 0.356 0.000 1.618 0.955 -0.790 2.548 1.033 -0.317 0.304 0.000 0.790 0.947 0.989 0.764 0.917 0.953 0.842 +0 0.710 -0.656 -0.163 0.577 1.274 0.603 -1.241 -1.196 0.000 1.109 0.345 1.624 2.215 2.525 -0.258 0.350 2.548 1.468 1.293 -1.192 0.000 0.966 1.012 0.988 0.805 1.722 1.185 0.955 +1 0.839 -1.630 0.660 0.761 -0.788 0.398 1.435 -1.140 2.173 0.283 -2.040 0.277 0.000 0.869 -0.486 1.380 2.548 0.655 0.164 0.561 0.000 0.843 1.005 1.068 1.592 1.020 1.074 1.092 +1 1.118 -0.287 0.737 1.336 0.133 1.924 -0.331 -1.474 2.173 1.520 1.021 0.587 0.000 0.445 1.104 -0.808 0.000 0.963 -0.506 -0.447 3.102 1.204 1.176 0.993 1.770 1.169 1.454 1.262 +1 0.756 0.428 -1.164 0.594 1.552 0.804 -0.922 -1.307 0.000 0.569 -1.601 0.145 0.000 0.756 -0.765 -0.497 2.548 1.691 -0.168 0.399 3.102 0.918 1.032 0.986 0.681 0.684 0.665 0.639 +0 1.973 0.057 -1.278 0.283 -1.287 1.494 0.749 0.736 0.000 2.003 -0.336 -0.829 1.107 1.482 -0.134 0.798 2.548 0.923 1.009 0.268 0.000 0.842 0.918 0.984 0.999 1.830 1.492 1.308 +0 0.487 -0.621 -0.212 1.328 1.615 1.087 -0.444 -0.598 2.173 0.588 0.057 -1.418 0.000 0.916 0.586 1.439 2.548 2.264 -0.145 0.354 0.000 0.776 0.868 1.111 1.032 1.395 0.923 0.844 +1 0.898 1.254 -1.200 0.366 0.031 0.662 -0.372 1.674 1.087 0.341 0.178 -0.334 0.000 0.583 1.195 0.813 0.000 1.579 -0.578 0.602 3.102 0.959 0.914 0.987 1.474 0.907 1.167 1.130 +0 0.356 -0.732 -0.492 1.340 0.052 1.112 0.301 -1.328 2.173 0.852 0.625 1.530 0.000 1.269 0.046 1.055 2.548 1.305 -1.135 -0.082 0.000 0.935 0.924 0.990 1.461 1.255 1.549 1.325 +1 0.513 -0.782 -1.207 1.613 0.194 1.398 1.928 -1.740 0.000 0.837 0.731 -0.250 2.215 1.164 -1.647 0.104 0.000 1.527 -0.782 1.631 0.000 1.587 0.923 1.201 1.043 0.535 1.004 0.869 +1 0.964 0.223 0.913 0.725 0.326 0.564 -0.462 -0.771 0.000 0.527 -1.180 -0.063 0.000 1.085 -0.212 -1.247 2.548 0.838 1.076 0.872 0.000 0.824 0.724 0.985 0.595 0.165 0.579 0.543 +0 0.968 1.482 0.538 1.529 0.021 1.084 -0.646 -1.345 0.000 0.951 1.131 1.323 2.215 0.592 -1.271 -0.631 0.000 0.730 0.022 0.299 3.102 1.025 0.940 0.982 1.090 0.751 1.073 1.546 +0 0.504 -1.328 0.924 0.770 -1.059 0.726 -0.445 1.436 2.173 1.073 0.095 -0.129 2.215 0.620 -0.711 -0.192 0.000 1.110 0.578 -1.634 0.000 1.149 0.947 0.990 1.022 1.332 1.084 0.979 +1 0.964 0.938 -1.532 0.869 -0.428 0.937 0.867 0.516 2.173 1.073 -0.085 1.250 0.000 1.162 0.422 -1.526 2.548 1.533 0.134 -0.636 0.000 0.658 1.034 1.064 0.698 1.282 0.858 0.773 +1 0.556 1.309 1.023 1.582 -1.642 0.849 0.337 -1.456 2.173 1.516 -1.483 0.226 0.000 0.806 0.760 -0.406 0.000 0.730 1.285 1.600 0.000 0.878 0.927 0.991 0.694 0.932 0.729 0.685 +0 1.115 -0.021 0.076 1.497 -0.153 0.791 -1.228 -0.995 2.173 0.596 -0.165 -1.304 0.000 1.064 -2.332 1.206 0.000 1.189 0.348 1.088 3.102 0.855 1.124 0.981 1.002 1.364 1.147 1.128 +0 1.929 0.412 -1.051 1.666 -0.692 0.875 0.392 1.457 2.173 0.904 0.454 0.779 0.000 0.744 -0.235 0.186 0.000 1.482 0.884 0.105 3.102 0.793 0.950 0.989 1.106 1.203 1.064 0.983 +1 1.393 0.238 -1.492 0.735 -0.652 0.789 0.509 0.871 1.087 0.740 0.327 0.177 2.215 0.433 -0.919 -1.370 0.000 0.817 -0.188 0.697 0.000 0.682 0.754 0.987 0.873 0.665 0.785 0.684 +1 1.163 0.604 1.308 0.456 0.120 1.343 -0.380 -1.554 0.000 0.885 0.210 0.200 0.000 0.779 1.709 0.008 0.000 1.091 0.954 0.243 0.000 1.203 0.730 0.985 0.569 0.259 0.459 0.532 +1 1.335 -1.804 1.014 0.617 -1.607 0.898 0.167 -0.415 2.173 0.480 0.125 0.376 0.000 0.858 0.236 1.408 0.000 0.816 -1.251 -0.760 3.102 0.792 0.969 0.991 0.730 0.893 1.180 1.071 +0 0.758 1.354 1.027 1.626 0.874 0.796 1.262 -0.916 2.173 0.815 0.569 -1.704 1.107 1.179 1.838 -0.625 0.000 0.772 1.072 0.636 0.000 1.033 1.081 0.993 1.294 0.877 0.922 0.914 +0 0.789 1.167 -1.095 0.533 -0.822 0.351 1.551 -0.504 0.000 0.819 1.442 0.458 0.000 0.279 -0.931 1.285 1.274 0.738 -0.009 1.214 1.551 0.869 0.927 0.978 0.656 0.184 0.630 0.655 +0 0.744 2.088 1.037 2.002 1.680 0.950 -0.851 -0.129 0.000 0.371 0.013 -0.708 1.107 0.818 0.480 1.540 1.274 0.941 -1.357 0.505 0.000 0.967 0.789 0.988 1.090 0.547 0.982 1.976 +0 0.692 -0.712 1.636 0.879 0.222 1.467 -0.444 0.380 0.000 0.960 -0.006 -1.307 0.000 0.498 1.256 -1.075 1.274 0.821 1.683 1.515 0.000 1.146 0.973 1.033 0.889 0.686 0.821 0.730 +1 1.003 -1.698 -1.084 0.919 -0.793 0.702 0.125 0.850 2.173 0.458 -1.539 1.632 0.000 0.821 0.232 -1.528 2.548 1.212 -1.080 0.578 0.000 0.858 0.947 0.984 0.844 0.797 0.896 0.769 +0 0.367 -1.548 1.143 1.634 -0.738 0.816 0.678 0.997 2.173 0.301 -0.355 1.292 1.107 0.318 1.078 0.578 0.000 0.419 -0.292 -0.617 0.000 0.491 0.584 1.065 0.739 0.444 1.109 0.862 +0 0.867 -0.113 -0.775 0.235 1.385 1.683 0.038 -1.569 2.173 1.237 -0.927 -0.123 0.000 1.755 0.024 0.517 1.274 1.271 -0.542 0.513 0.000 0.920 0.978 0.990 1.073 2.038 1.406 1.117 +1 0.655 1.488 -0.580 0.602 -0.301 0.512 -0.530 0.768 2.173 0.879 -0.992 -1.541 2.215 0.584 -1.491 1.344 0.000 0.460 0.299 -0.125 0.000 0.869 0.725 0.999 0.901 0.894 0.870 0.755 +1 0.353 1.279 -0.718 0.569 -0.442 0.670 -0.161 1.726 0.000 0.753 -0.412 1.017 0.000 1.097 1.175 0.267 2.548 1.128 1.081 -1.245 3.102 0.923 1.072 0.985 0.917 0.832 0.925 0.836 +1 1.937 -0.148 1.216 1.822 1.606 1.331 -1.234 0.913 2.173 1.825 1.178 -0.544 2.215 2.258 -0.339 -0.193 0.000 0.963 -0.907 -1.475 0.000 0.969 1.647 0.980 1.514 4.216 2.185 1.792 +0 0.789 0.965 0.502 2.311 0.839 1.527 -0.456 -0.941 0.000 0.611 0.297 -1.154 0.000 0.306 0.834 -0.488 2.548 0.489 -0.409 0.813 3.102 0.894 0.861 0.986 0.684 0.354 0.561 0.946 +0 0.606 -1.354 -1.573 1.494 -0.555 0.911 0.304 -1.586 2.173 1.039 -1.243 0.979 2.215 1.351 -1.269 -0.133 0.000 0.866 -1.702 -0.109 0.000 0.396 0.952 1.047 1.389 1.644 1.237 1.074 +0 1.967 -0.694 -1.601 0.818 1.603 1.037 -0.752 0.300 1.087 0.717 -0.177 -0.764 0.000 0.821 0.333 1.279 2.548 1.912 -1.018 -0.302 0.000 1.035 1.101 0.983 0.706 1.119 0.991 1.003 +1 0.411 0.204 -0.780 0.887 0.824 1.075 0.886 -0.505 2.173 0.862 -0.098 0.811 0.000 0.533 -0.535 -1.706 0.000 1.011 0.961 -1.544 3.102 0.837 0.857 0.989 1.240 0.900 0.915 0.852 +1 0.657 1.445 1.098 1.277 -0.945 0.938 0.757 -0.061 2.173 0.608 2.351 1.580 0.000 0.611 0.556 1.626 0.000 0.396 1.291 0.609 0.000 0.966 0.952 1.223 1.059 0.743 0.887 0.812 +1 3.063 0.272 0.964 2.048 1.233 3.061 1.445 -0.575 0.000 1.071 -0.001 1.490 2.215 0.438 -0.546 0.133 2.548 0.600 -0.758 -1.168 0.000 3.243 2.116 1.006 0.820 0.719 1.721 1.871 +1 1.723 0.612 0.471 0.785 0.990 0.761 0.785 -0.660 0.000 0.663 -0.481 -0.933 2.215 0.758 -0.394 -1.639 2.548 0.742 0.866 -1.045 0.000 0.795 0.692 0.996 1.015 0.449 0.888 0.744 +1 1.256 -1.245 1.041 1.088 -0.685 0.635 -0.940 -0.793 2.173 1.071 -1.157 -0.058 2.215 1.114 -0.769 1.437 0.000 0.380 2.459 1.510 0.000 0.836 0.966 1.619 0.992 0.761 0.783 0.757 +0 2.093 -0.848 -0.264 1.746 0.123 2.167 -0.456 -1.651 0.000 1.288 -1.061 1.637 0.000 2.372 -0.986 0.267 1.274 1.134 2.072 0.010 0.000 1.302 0.994 0.979 0.745 1.532 1.505 1.552 +1 0.734 0.737 1.555 1.508 -1.273 0.980 1.178 0.289 0.000 0.699 1.475 0.850 0.000 1.622 0.814 -0.869 2.548 0.386 -0.182 1.079 3.102 0.899 0.717 0.986 0.787 0.692 0.827 0.842 +1 1.094 -0.729 -0.742 0.727 0.108 1.036 -0.412 -1.246 2.173 0.511 0.570 1.476 1.107 1.209 -0.728 0.711 0.000 1.164 -0.553 -0.015 0.000 0.801 0.907 0.980 0.964 0.880 0.882 0.827 +1 0.513 -0.061 1.474 0.561 -0.268 0.646 -1.420 -1.207 0.000 0.918 -2.151 -0.662 0.000 1.337 -0.586 0.791 2.548 0.941 -1.164 1.435 3.102 1.012 0.884 0.989 0.714 0.575 0.901 0.769 +1 1.050 -1.102 -0.324 1.257 -0.088 0.881 -0.543 1.327 2.173 0.592 -1.146 0.756 0.000 0.858 -2.508 -0.138 0.000 2.901 -0.447 -1.405 3.102 1.236 1.467 0.996 1.539 1.062 1.275 1.182 +0 0.467 -0.884 0.882 2.474 -1.675 1.283 0.612 0.014 2.173 0.253 0.830 0.429 0.000 1.027 -0.653 -0.994 2.548 0.941 1.163 -1.564 0.000 0.644 0.956 1.107 0.818 1.526 1.384 1.157 +0 1.095 -0.492 0.518 1.169 -0.092 0.933 1.531 -1.238 0.000 0.490 0.407 0.589 2.215 0.646 1.153 1.588 0.000 0.550 1.114 1.053 3.102 0.804 0.979 0.988 0.954 0.293 0.667 1.096 +1 0.978 -0.225 1.629 1.119 -1.151 0.582 -1.567 0.461 2.173 0.842 -0.333 0.058 1.107 0.816 -0.070 -1.497 0.000 0.378 0.497 0.443 0.000 0.764 0.870 0.994 0.967 0.773 0.846 0.710 +1 0.277 -0.553 -1.277 1.442 0.502 0.713 1.311 1.685 2.173 1.561 0.387 -1.406 1.107 1.244 -0.143 0.047 0.000 1.353 0.133 0.970 0.000 0.992 1.192 0.991 1.005 0.919 0.938 0.871 +0 1.204 -0.223 -0.856 0.471 0.852 0.666 -0.365 -0.228 0.000 1.382 -0.142 0.975 2.215 1.153 -0.631 -1.474 2.548 0.891 1.858 -0.748 0.000 0.688 0.976 1.043 0.701 1.141 0.799 0.724 +1 1.416 -0.553 -1.506 1.680 -1.279 1.137 -0.304 0.921 0.000 0.656 2.054 -0.234 0.000 0.368 0.867 0.494 2.548 0.718 -1.423 -0.011 0.000 1.468 0.978 0.999 0.832 0.160 0.677 0.887 +1 0.641 -0.956 1.518 0.402 0.962 1.253 0.420 -0.128 2.173 1.319 -0.350 1.366 0.000 0.617 -0.577 -0.243 0.000 0.997 0.269 -0.940 1.551 0.867 0.904 0.987 1.116 0.793 0.976 0.954 +1 1.186 -0.091 -0.145 0.797 1.523 0.672 1.341 1.351 1.087 0.347 2.089 0.967 0.000 0.755 1.065 -0.553 2.548 1.399 1.169 -1.264 0.000 0.926 0.942 1.344 0.879 0.882 0.845 0.821 +0 1.300 2.183 1.344 1.412 0.793 0.995 1.235 1.051 2.173 1.692 2.285 -1.089 0.000 1.453 0.543 -0.260 0.000 1.873 1.396 -0.768 3.102 1.178 0.946 0.987 0.752 1.475 1.000 1.011 +0 0.641 -0.659 -0.537 0.619 0.808 1.646 -0.454 0.097 2.173 1.024 0.109 1.578 2.215 1.788 -0.616 -1.307 0.000 1.430 -0.852 -1.740 0.000 0.738 0.892 0.981 0.883 1.938 1.326 1.049 +0 0.566 -2.108 -0.472 1.119 -1.237 0.596 -0.318 1.229 0.000 0.725 -0.863 -1.263 2.215 0.579 -1.159 0.158 2.548 0.445 2.261 0.215 0.000 1.819 1.416 0.998 0.584 0.673 1.043 1.067 +1 0.778 -1.146 -0.244 1.537 -1.127 1.069 2.333 0.591 0.000 1.259 0.227 1.491 2.215 0.830 -1.291 0.290 0.000 1.072 -0.629 -1.151 3.102 0.853 0.811 1.081 1.396 0.903 1.051 0.944 +1 2.092 -0.227 -1.335 0.305 -0.052 1.122 0.108 0.402 2.173 0.519 0.408 -0.050 0.000 0.393 -1.188 1.155 0.000 0.752 -0.585 1.491 3.102 0.912 0.803 1.013 0.612 0.904 0.876 0.742 +0 0.409 1.107 1.142 0.696 0.010 1.167 0.217 1.550 0.000 0.683 0.689 -0.871 2.215 0.932 0.222 0.454 0.000 1.070 1.095 -0.316 0.000 0.937 0.796 0.992 0.561 0.200 0.583 0.638 +1 1.582 -0.668 1.466 1.026 -1.531 1.049 -0.172 -0.235 2.173 0.487 -0.007 -1.327 0.000 0.838 0.754 0.083 0.000 0.586 0.174 1.187 3.102 1.026 0.935 0.991 0.643 0.811 0.956 0.891 +0 0.734 -2.036 -0.436 0.074 -1.579 1.048 0.053 1.463 2.173 0.919 0.428 -1.313 2.215 1.462 -0.278 -0.261 0.000 1.998 -0.060 0.304 0.000 0.946 1.266 0.986 1.217 0.906 1.082 1.015 +1 0.627 -0.877 1.299 1.163 0.143 2.519 -0.384 -1.187 0.000 1.407 -0.261 0.525 2.215 1.373 0.590 0.685 1.274 0.853 -0.587 0.905 0.000 0.438 0.511 1.020 0.748 0.733 0.733 0.561 +1 0.903 0.291 -1.699 0.365 -0.039 1.402 0.932 0.683 2.173 0.841 0.950 -1.339 0.000 1.588 -0.216 -0.794 0.000 0.869 0.355 -0.173 3.102 1.014 0.755 0.982 0.990 0.870 1.080 0.901 +1 2.139 -0.406 -0.271 0.629 -0.757 0.906 -0.080 1.461 2.173 0.615 -0.422 0.231 0.000 0.783 -0.600 0.966 0.000 0.459 2.266 -1.577 0.000 0.885 1.061 0.989 0.777 0.464 0.877 0.797 +0 0.560 -2.215 1.475 0.350 -1.638 0.769 -0.346 -1.270 2.173 0.780 0.125 0.690 2.215 0.357 -2.373 0.318 0.000 0.423 0.856 -0.363 0.000 1.307 1.033 0.989 0.802 1.151 0.850 0.775 +1 0.861 -0.598 -0.461 0.844 0.583 0.923 -0.875 -1.358 0.000 0.987 -0.028 1.597 2.215 1.176 0.351 0.279 0.000 1.718 -1.169 -0.179 3.102 0.760 1.101 0.986 0.903 1.464 0.903 0.839 +1 1.157 -0.452 1.219 1.024 -1.381 1.070 -0.037 0.878 0.000 2.073 -0.052 -0.382 1.107 0.612 -1.241 -1.242 0.000 0.961 0.231 -1.103 1.551 1.720 1.187 1.081 1.413 0.798 1.119 1.008 +1 0.953 0.155 0.346 0.571 1.502 1.057 0.408 1.307 2.173 1.333 0.178 -0.325 0.000 1.189 -0.393 -1.554 2.548 0.668 1.165 -0.433 0.000 0.803 1.170 0.987 0.771 0.960 1.048 0.876 +1 0.653 1.237 1.537 1.887 -0.949 1.417 0.379 0.330 1.087 0.677 2.468 1.641 0.000 0.493 -0.718 -0.361 0.000 0.513 0.665 -0.949 0.000 0.939 0.741 1.207 1.635 0.851 1.054 1.010 +0 0.912 1.870 1.192 0.644 1.225 0.848 -0.026 -0.494 2.173 1.045 1.821 1.599 0.000 0.397 0.265 0.404 0.000 0.406 0.480 1.365 1.551 1.228 0.691 0.994 1.284 0.646 0.900 0.823 +0 0.855 -0.682 1.111 1.573 -0.102 0.522 -0.669 -0.405 2.173 0.525 -1.283 -1.651 0.000 1.113 -0.178 1.622 2.548 0.448 -1.790 0.200 0.000 0.764 0.961 1.426 0.814 0.947 0.772 0.789 +1 0.763 0.313 1.712 1.152 -1.238 0.858 0.549 0.342 2.173 0.455 -0.543 0.914 2.215 0.426 2.238 1.678 0.000 1.174 0.357 -0.737 0.000 0.743 0.822 0.981 0.919 0.703 0.863 0.763 +1 0.791 0.128 0.592 0.643 1.393 0.462 -1.042 -0.468 0.000 0.595 0.717 1.040 2.215 1.078 -0.163 -0.992 0.000 1.421 0.301 1.735 3.102 0.888 1.031 0.986 0.757 0.511 0.778 0.694 +1 0.849 1.071 -1.294 0.579 -0.059 0.499 0.358 -0.341 2.173 0.949 1.211 1.444 0.000 1.155 -0.614 0.582 2.548 0.729 0.600 -1.223 0.000 0.785 0.933 0.982 0.962 0.867 0.877 0.762 +1 0.884 -0.029 -0.007 1.037 -1.297 1.143 -0.421 -1.577 2.173 1.327 -0.283 0.391 0.000 0.753 0.585 0.742 2.548 0.943 2.095 -0.489 0.000 2.971 1.641 1.216 0.989 1.194 1.504 1.219 +1 1.098 0.457 -1.569 1.894 -1.483 1.406 0.019 0.125 2.173 0.592 -2.846 0.911 0.000 0.690 0.294 -0.978 2.548 0.745 1.786 0.291 0.000 5.453 2.993 0.976 1.980 1.046 2.047 2.094 +1 1.082 1.205 -0.401 1.359 -0.834 0.605 -0.565 1.500 1.087 0.467 0.751 0.964 2.215 0.622 0.181 0.116 0.000 0.875 -1.949 1.116 0.000 1.480 1.076 0.983 0.921 0.673 1.161 1.478 +1 1.653 0.394 0.442 0.905 0.344 1.101 0.438 -1.681 2.173 0.590 -0.059 -0.524 0.000 0.647 -0.896 -1.589 0.000 0.572 -0.769 -1.032 3.102 0.899 1.045 0.986 0.956 0.774 1.006 0.922 +0 0.774 -0.272 -0.899 2.040 -1.676 2.358 -0.758 0.050 2.173 0.726 -0.134 0.363 0.000 2.806 -0.368 1.624 1.274 1.769 -1.720 1.650 0.000 0.954 0.926 1.121 0.835 3.220 1.757 1.378 +1 1.008 -0.465 0.520 1.383 0.765 2.309 -1.253 1.461 0.000 1.450 -1.379 -0.419 2.215 2.145 -2.273 -0.193 0.000 1.031 -0.415 -0.495 0.000 0.756 0.769 0.988 0.970 0.929 0.934 0.795 +1 0.538 0.152 -0.995 1.099 0.298 0.560 2.908 -0.471 0.000 1.200 -1.545 1.416 0.000 1.377 -1.799 -0.896 0.000 2.433 -0.743 1.611 1.551 0.875 0.989 0.987 1.147 1.116 0.817 0.859 +0 1.545 0.627 1.545 1.826 -1.433 1.797 -0.206 -0.033 1.087 0.695 0.021 1.242 2.215 0.591 1.253 -0.300 0.000 0.585 -0.834 1.111 0.000 1.148 0.842 1.031 2.152 1.512 1.424 1.138 +1 1.100 -0.890 1.619 0.303 -0.686 0.931 -0.064 -0.935 2.173 1.167 -0.164 0.506 2.215 2.051 0.342 0.916 0.000 1.557 -0.761 -0.544 0.000 0.600 1.097 0.984 0.825 1.479 0.901 0.802 +1 0.869 0.410 0.823 0.869 -0.769 0.611 -1.339 1.129 0.000 0.927 -0.956 -1.736 2.215 1.476 0.342 -0.119 2.548 0.596 0.281 -1.138 0.000 1.198 0.840 1.192 0.787 1.535 0.965 0.890 +0 0.436 1.282 0.764 1.658 -0.173 1.575 -0.227 -1.585 0.000 1.109 -0.372 0.295 1.107 0.569 0.280 -0.249 1.274 0.563 0.443 1.425 0.000 0.818 0.952 0.985 1.634 0.496 1.033 1.310 +1 0.299 -1.278 -0.227 1.038 -1.736 1.601 0.163 0.979 2.173 1.294 -0.142 -0.088 0.000 1.035 0.352 -1.208 0.000 0.762 1.247 -0.945 0.000 0.617 0.545 0.990 1.027 0.889 0.893 0.775 +1 2.021 -0.876 0.247 0.713 0.519 0.950 -0.088 -1.467 2.173 0.568 0.272 0.969 2.215 0.922 -0.249 1.496 0.000 1.667 0.915 -0.654 0.000 0.832 0.745 0.998 1.421 0.899 0.969 0.841 +0 0.359 2.293 0.673 0.490 -0.554 0.700 1.846 -1.085 0.000 0.803 1.221 0.429 0.000 0.827 0.493 -1.616 2.548 0.662 0.209 -0.236 3.102 1.568 0.918 0.977 0.670 0.542 0.693 0.646 +1 0.407 -1.107 1.742 1.758 -0.039 1.561 2.138 -1.414 0.000 1.397 -0.644 0.032 2.215 1.487 -1.243 0.367 2.548 2.333 -0.920 1.484 0.000 1.032 1.046 1.172 0.768 0.714 0.949 0.847 +0 0.516 0.963 -1.547 1.652 0.920 0.386 0.933 0.266 0.000 0.815 0.976 -1.038 2.215 0.942 -0.860 0.284 2.548 0.978 -0.748 1.581 0.000 0.971 0.909 1.017 0.920 1.367 0.979 0.858 +1 0.594 0.899 0.975 0.617 0.334 0.466 1.160 -1.420 0.000 0.621 -0.586 -1.004 0.000 0.448 0.496 1.210 2.548 0.505 -2.133 0.645 0.000 1.107 0.910 0.988 0.477 0.125 0.592 0.597 +1 0.321 1.752 -1.487 0.733 -1.266 0.837 1.549 1.120 0.000 0.819 -0.829 0.795 2.215 1.285 2.335 -0.822 0.000 0.729 0.591 -0.286 3.102 2.076 1.330 0.993 0.953 0.834 1.575 1.283 +1 1.395 1.611 -0.320 1.482 0.462 0.592 1.975 -0.947 0.000 0.354 -2.337 1.185 0.000 1.308 1.023 -1.608 2.548 0.863 0.104 0.918 3.102 0.795 0.907 1.291 1.225 0.748 0.907 0.795 +0 0.639 -0.283 0.412 1.448 0.813 0.914 0.513 -1.089 1.087 0.606 0.221 -0.010 2.215 1.402 -1.311 1.596 0.000 1.134 1.274 -0.516 0.000 3.196 1.873 0.978 1.640 0.919 1.303 1.298 +1 1.236 -0.877 -0.832 1.648 0.440 1.171 -0.181 -1.393 2.173 0.389 -1.299 -1.074 0.000 1.279 -1.238 1.040 2.548 0.802 -0.754 0.308 0.000 0.878 0.974 1.802 1.156 1.562 1.182 0.941 +1 0.610 0.505 -1.070 0.959 0.733 1.376 -0.030 1.522 2.173 1.753 1.712 -0.276 0.000 0.640 -0.039 -1.336 2.548 0.973 -0.221 0.504 0.000 0.659 1.171 1.058 0.651 0.625 0.744 0.721 +1 0.658 -0.236 -1.561 1.060 1.141 0.768 0.150 -1.098 0.000 1.025 0.491 0.862 2.215 1.746 0.827 0.181 2.548 1.067 1.292 -0.813 0.000 1.105 1.302 0.987 1.006 0.868 1.067 1.110 +0 1.452 0.447 -0.655 0.779 1.571 0.531 0.251 -0.130 0.000 0.629 1.133 1.639 2.215 0.357 1.307 1.225 2.548 0.559 2.229 0.943 0.000 0.775 0.787 1.336 0.745 0.196 0.575 0.573 +1 0.670 -1.632 -1.241 0.149 0.602 0.741 -0.662 -0.916 1.087 0.907 0.393 1.103 0.000 1.128 -0.605 0.311 2.548 0.844 -1.330 -0.454 0.000 1.750 1.154 0.995 0.668 1.019 0.909 0.776 +1 0.772 0.504 1.460 1.306 -1.183 0.974 0.352 0.400 2.173 0.563 0.944 1.643 0.000 0.987 0.868 -0.298 2.548 0.897 -0.214 0.811 0.000 0.874 0.909 0.987 0.813 0.809 0.849 0.756 +0 2.074 1.036 -1.410 2.308 -0.938 1.880 -1.353 0.566 2.173 0.899 -1.993 1.739 0.000 0.437 -1.148 1.286 0.000 1.839 -1.365 0.154 0.000 0.863 0.836 1.253 0.772 2.725 2.680 2.205 +0 1.075 -1.129 0.553 0.754 -0.035 0.529 0.255 -1.424 0.000 0.976 0.292 -0.665 1.107 1.035 0.240 1.432 2.548 0.568 1.430 1.461 0.000 0.794 0.838 0.990 0.912 1.014 0.833 0.809 +0 0.443 1.680 0.666 3.283 1.198 1.754 0.431 -0.322 0.000 0.805 -0.602 -0.609 2.215 0.821 0.595 -0.815 0.000 2.246 0.867 1.629 3.102 0.957 0.982 0.989 1.296 1.572 1.904 1.927 +0 0.913 -2.064 -0.613 1.183 -0.363 1.050 0.660 0.674 2.173 0.741 -0.711 1.311 0.000 0.656 -2.497 -1.247 0.000 0.960 -1.580 -1.686 0.000 0.824 0.684 0.987 1.994 1.465 1.354 1.175 +1 1.570 -0.554 1.279 0.461 -1.541 0.761 -0.344 -0.007 2.173 0.800 -0.252 -0.827 0.000 0.525 -1.435 0.459 0.000 0.521 0.757 -1.275 3.102 1.151 0.893 0.988 0.638 0.753 0.726 0.724 +0 0.356 0.249 0.242 0.692 -1.534 0.458 1.636 -0.829 0.000 0.457 -0.081 1.358 2.215 1.449 1.335 1.000 1.274 1.439 0.585 -0.418 0.000 0.759 0.923 0.987 1.454 0.787 0.931 0.963 +1 0.771 1.091 -0.086 0.909 0.225 1.721 0.354 -0.228 0.000 1.906 0.733 1.259 0.000 2.556 -0.172 -1.486 0.000 1.311 0.310 0.197 3.102 1.242 1.119 0.985 0.510 0.370 0.906 0.889 +0 0.858 0.592 -1.577 0.595 0.822 0.964 0.425 1.079 1.087 1.775 0.817 -0.710 2.215 0.681 -0.053 1.275 0.000 1.006 -1.649 0.016 0.000 0.840 1.405 0.988 1.124 1.964 1.402 1.112 +0 1.190 -1.429 -1.655 0.443 -0.108 0.503 -0.361 -0.088 0.000 0.424 0.693 -0.873 0.000 0.664 0.227 0.582 2.548 1.219 0.404 1.064 3.102 0.845 0.833 0.990 0.866 0.300 0.745 0.733 +0 0.593 -0.108 1.009 0.315 -1.550 0.714 0.761 1.076 0.000 0.800 0.338 -1.453 2.215 1.395 0.365 -0.441 2.548 0.783 0.373 0.155 0.000 0.863 1.011 0.988 0.684 0.888 0.764 0.679 +1 0.557 -0.533 0.220 0.150 0.996 1.686 0.603 -0.903 2.173 1.502 -0.820 0.966 0.000 1.132 0.037 0.555 0.000 1.125 -0.432 -1.336 3.102 0.815 0.921 0.982 1.104 1.028 1.005 0.853 +0 1.185 0.601 0.929 0.752 0.020 0.838 0.909 -1.534 2.173 0.547 1.628 0.956 0.000 1.582 0.479 -0.727 2.548 1.075 -0.792 -0.239 0.000 1.076 0.939 0.987 0.994 0.996 0.861 0.827 +0 0.906 -0.234 -1.702 0.891 -1.146 1.017 1.583 -0.025 2.173 0.448 0.770 0.702 0.000 1.533 0.720 1.380 2.548 0.898 0.436 -0.827 0.000 0.819 0.899 0.988 0.799 1.618 1.079 0.859 +0 0.660 1.007 1.269 0.596 -0.484 0.966 0.636 1.733 1.087 1.300 0.737 -0.587 2.215 0.911 -0.400 -0.381 0.000 0.852 -0.419 1.295 0.000 0.934 0.936 0.987 0.781 1.433 0.925 0.771 +0 0.790 0.417 1.211 0.383 0.567 1.141 1.773 -1.095 0.000 1.720 0.108 0.569 1.107 0.805 2.065 -1.635 0.000 1.102 0.364 -0.761 3.102 0.906 1.021 0.977 0.802 1.175 1.479 1.326 +1 0.873 -1.555 -1.094 0.611 0.758 0.630 -0.135 -0.970 2.173 0.752 -1.669 1.394 0.000 0.957 -0.997 0.641 0.000 1.425 0.324 0.241 3.102 0.915 1.232 1.007 0.905 0.928 0.967 0.866 +0 0.647 -1.125 0.635 0.244 -0.148 0.821 0.364 -1.277 0.000 0.617 0.804 -0.772 0.000 1.103 0.459 0.886 2.548 0.681 0.463 0.640 3.102 0.752 1.062 0.978 0.537 0.147 0.677 0.669 +1 0.963 -0.376 0.842 1.288 -0.637 0.854 0.341 0.770 0.000 1.064 0.663 -1.226 2.215 0.731 -0.256 -0.510 2.548 0.644 -0.251 -1.236 0.000 1.161 0.907 1.499 1.162 0.733 0.777 0.799 +0 0.465 1.593 1.083 0.111 0.731 0.867 0.147 -1.092 2.173 0.828 -0.849 0.464 2.215 0.633 -0.719 -0.827 0.000 0.881 -0.122 0.774 0.000 0.894 1.006 0.976 0.831 1.397 0.836 0.751 +0 1.770 0.674 1.278 1.333 -1.545 0.956 0.469 0.423 0.000 0.791 0.058 -1.186 2.215 0.757 -0.257 -0.698 2.548 0.409 1.719 -0.875 0.000 1.218 1.016 1.199 0.909 0.378 0.780 0.865 +0 0.813 0.084 0.273 0.847 -0.276 0.781 -1.042 -0.750 2.173 0.693 -0.163 -1.120 0.000 0.870 -0.276 1.498 1.274 1.896 1.576 0.776 0.000 1.488 1.002 0.990 0.804 1.003 0.788 0.788 +0 0.774 0.918 0.674 1.565 0.013 0.850 -0.799 -1.462 2.173 0.643 0.005 1.394 2.215 0.568 0.007 -0.328 0.000 0.390 0.173 1.050 0.000 0.494 0.745 0.990 0.869 0.739 0.987 0.746 +0 1.528 0.589 -0.221 2.069 -0.068 1.517 0.016 -1.721 0.000 1.180 -0.085 1.343 0.000 1.063 0.425 -0.710 2.548 1.068 -0.962 1.472 3.102 0.924 1.027 0.999 1.705 1.051 1.141 1.101 +0 0.479 1.225 -1.623 1.920 -1.209 0.998 -1.466 0.313 2.173 0.477 -1.636 -1.690 0.000 1.197 -1.836 -0.687 0.000 2.006 1.145 0.677 0.000 0.930 1.153 0.980 0.883 0.539 1.209 1.159 +1 2.876 0.394 -1.413 0.380 -1.016 1.438 -0.797 0.169 0.000 1.441 -0.050 0.771 2.215 0.523 -0.281 -1.386 2.548 0.838 0.183 -0.518 0.000 1.323 1.043 0.983 1.472 0.867 0.929 1.135 +1 0.670 1.150 -1.180 0.738 0.148 1.007 -0.495 -1.572 2.173 0.645 0.500 0.214 2.215 0.653 1.461 0.537 0.000 0.816 -0.194 0.598 0.000 0.850 1.351 0.987 0.610 1.341 0.905 0.793 +0 0.704 -0.284 -0.879 0.942 -0.158 0.719 -0.294 1.236 2.173 0.904 -1.440 0.868 2.215 0.659 -0.906 -0.903 0.000 0.473 0.658 -0.849 0.000 0.627 0.979 0.987 1.021 0.840 0.993 0.825 +0 1.065 -1.802 -0.187 0.677 -0.865 1.512 -1.647 -0.575 2.173 1.240 -1.524 0.898 2.215 1.767 -0.792 1.199 0.000 1.361 -1.099 -1.691 0.000 0.957 0.911 0.982 0.833 1.958 1.307 1.163 +0 0.395 2.359 0.175 2.788 -1.684 1.351 0.927 -1.664 0.000 1.107 1.478 0.430 2.215 0.506 2.056 -1.137 0.000 0.842 0.507 -0.402 3.102 1.256 1.032 1.446 1.436 0.723 1.085 1.104 +1 1.650 0.515 -0.188 0.135 -0.695 0.470 -0.066 -1.011 0.000 1.012 0.424 1.230 0.000 0.655 0.577 1.018 2.548 0.803 -0.543 -1.740 1.551 1.024 0.699 0.985 0.731 0.510 0.662 0.655 +1 1.235 -0.734 1.509 0.977 1.384 0.949 -0.137 0.197 1.087 1.345 -0.653 -0.745 2.215 1.094 -1.704 -1.678 0.000 1.412 0.788 -0.302 0.000 1.033 0.946 0.987 1.251 1.327 1.092 0.956 +1 0.986 0.812 0.981 0.411 -1.195 1.009 -0.164 -0.675 2.173 0.422 1.960 0.975 0.000 0.685 0.228 0.236 2.548 0.366 1.489 -1.386 0.000 0.439 1.281 0.986 0.611 0.788 0.810 0.729 +0 0.281 1.588 -0.927 1.059 1.008 0.737 0.098 -0.055 0.000 0.717 0.869 -1.095 0.000 0.419 0.036 0.169 2.548 0.966 -0.287 1.665 3.102 0.920 0.791 0.980 0.545 0.484 0.479 0.508 +0 0.552 0.952 -1.360 1.402 -0.023 1.022 0.339 -0.861 2.173 0.690 -1.573 0.605 0.000 0.508 -0.077 1.489 0.000 0.564 -1.096 1.195 3.102 0.963 0.548 1.138 0.942 1.072 0.886 0.999 +0 0.673 -1.584 -0.298 0.478 1.329 0.864 0.699 -0.968 0.000 1.220 -0.049 0.079 2.215 2.528 -1.088 1.416 1.274 0.569 -1.734 0.125 0.000 2.214 1.576 0.991 0.932 2.076 1.538 1.191 +0 1.074 0.265 -0.145 0.548 -0.039 0.418 -0.650 0.986 0.000 0.721 -0.492 -1.508 1.107 0.507 -1.489 0.791 0.000 1.153 0.411 -1.229 1.551 0.447 0.897 1.001 0.874 0.475 0.681 0.680 +1 0.935 0.086 1.368 1.399 0.417 1.246 -1.042 -0.623 2.173 0.874 -0.874 0.947 2.215 1.468 -0.016 -1.673 0.000 0.454 -0.234 -0.908 0.000 0.808 0.871 1.198 1.547 1.522 1.132 0.975 +1 0.313 2.008 0.367 1.243 0.862 1.182 0.693 -0.693 0.000 0.873 0.666 -1.401 2.215 0.680 -0.922 0.750 1.274 0.700 1.725 1.627 0.000 0.886 1.013 0.999 0.904 1.094 0.778 0.715 +1 0.328 0.448 -1.494 0.815 0.483 1.276 -0.632 0.818 2.173 1.193 -0.508 -1.210 0.000 0.603 2.367 -0.722 0.000 0.688 -0.274 -0.509 3.102 3.016 1.672 0.983 0.763 0.935 1.588 1.266 +0 0.702 -0.046 -0.140 0.905 -1.532 0.504 -1.643 0.557 0.000 1.001 -1.322 -0.544 1.107 1.093 -1.144 1.486 2.548 0.543 -0.407 0.978 0.000 0.576 0.852 1.049 0.861 1.076 0.802 0.735 +1 2.589 -1.034 -0.139 0.604 -0.494 1.009 -0.204 0.984 2.173 0.521 -1.188 0.253 0.000 0.881 -0.653 0.778 2.548 2.100 1.486 -1.025 0.000 0.874 0.747 0.982 1.431 0.374 0.954 0.871 +1 1.594 -1.303 1.472 0.755 0.662 0.826 -0.344 -1.461 0.000 0.971 -0.424 0.194 2.215 1.174 -0.623 -0.392 0.000 1.729 -1.171 -0.646 3.102 0.871 0.918 1.013 1.029 0.996 0.888 0.754 +0 0.908 -1.333 1.595 0.847 -1.164 0.980 -0.655 0.533 2.173 0.735 -0.639 -0.637 0.000 0.767 -0.312 0.013 0.000 0.824 0.494 -1.681 3.102 0.663 0.908 0.989 1.110 1.075 1.047 0.920 +0 1.049 0.704 -1.448 0.787 -0.727 0.787 0.418 0.541 2.173 0.512 1.460 -0.809 0.000 0.622 1.101 1.272 0.000 0.443 0.393 0.116 1.551 0.835 0.942 0.993 0.577 0.234 0.649 0.639 +0 1.014 -1.148 1.527 1.452 -1.078 0.835 1.645 0.331 0.000 1.003 -0.204 -1.659 0.000 1.037 0.602 -0.179 2.548 1.253 0.239 0.258 1.551 2.825 1.659 1.201 1.361 0.373 1.042 1.378 +0 1.026 0.439 -0.752 1.796 1.443 0.702 -1.065 1.028 0.000 0.639 -0.577 -0.124 1.107 0.756 0.200 0.303 2.548 0.470 -1.285 -1.666 0.000 0.610 0.798 1.727 1.184 0.415 0.821 0.853 +1 1.305 -1.012 0.220 2.617 0.400 0.931 -1.340 -1.614 2.173 0.953 1.003 -1.547 0.000 1.133 -1.098 -0.557 2.548 0.654 -2.426 0.831 0.000 1.023 0.898 0.990 1.082 1.047 1.215 1.046 +0 2.769 0.632 -0.193 1.567 -0.817 0.969 -1.016 1.566 2.173 0.970 -0.794 1.177 0.000 0.706 1.402 1.516 0.000 1.569 1.134 -0.491 3.102 0.603 0.875 1.536 0.866 2.321 1.620 1.771 +0 1.914 1.213 0.736 0.769 0.105 1.447 -0.620 -1.227 2.173 0.268 -1.061 1.371 0.000 0.687 -0.243 -0.583 2.548 0.810 -0.350 0.862 0.000 0.339 0.928 0.983 1.067 0.718 1.561 1.196 +0 0.484 0.152 1.701 0.751 0.079 0.700 1.453 -0.666 0.000 0.761 1.414 0.601 0.000 0.851 1.150 -1.704 1.274 0.534 0.717 1.666 3.102 1.410 1.028 0.988 0.633 0.117 0.624 0.816 +0 0.726 1.854 -0.338 0.624 1.622 0.595 -0.003 1.248 2.173 0.484 0.644 0.108 0.000 0.661 -0.185 -1.301 0.000 0.920 -1.669 0.661 0.000 0.914 0.796 0.991 0.992 0.788 0.949 0.831 +0 0.539 -0.677 -0.536 1.206 0.324 1.560 -1.123 0.196 0.000 2.429 -2.388 -1.260 0.000 1.265 -0.495 -1.467 1.274 1.839 -0.022 0.960 3.102 4.963 3.114 0.992 0.777 0.999 2.212 1.884 +0 1.097 -1.459 -0.531 1.007 -0.455 0.753 0.812 -1.528 2.173 0.590 0.296 1.211 0.000 0.910 -0.305 0.209 2.548 0.748 0.529 0.615 0.000 0.627 0.794 0.978 2.478 1.208 1.626 1.516 +0 0.760 -0.844 -0.870 0.961 -0.358 0.733 -1.360 0.978 1.087 0.619 -0.898 0.394 0.000 0.589 -1.722 -0.644 0.000 1.105 -1.186 -1.356 0.000 0.954 0.859 0.989 0.675 0.232 0.772 0.686 +1 0.967 0.792 0.884 0.220 -1.367 0.604 1.037 -1.740 0.000 0.486 2.189 1.434 0.000 1.490 0.913 -0.460 2.548 1.720 0.580 0.129 1.551 0.803 1.131 0.991 0.938 0.647 0.869 0.810 +1 0.930 0.080 -0.163 1.073 -1.266 0.856 -0.346 -1.212 0.000 1.676 0.119 1.001 0.000 0.946 -0.351 -1.740 0.000 1.792 0.586 0.440 3.102 1.003 0.926 1.159 0.566 0.640 0.626 0.621 +1 1.121 0.747 -0.549 1.085 0.468 1.943 -2.429 1.209 0.000 0.863 1.271 -1.148 2.215 2.123 0.109 -0.995 2.548 1.356 0.517 0.351 0.000 1.017 0.964 1.212 1.137 0.927 0.878 0.801 +1 0.676 -1.613 -1.684 0.757 -0.463 0.797 -1.183 1.413 1.087 0.708 -0.875 0.584 2.215 1.363 -1.999 -0.339 0.000 0.843 -1.291 -1.081 0.000 0.829 0.993 0.986 0.800 0.770 0.859 0.745 +0 1.705 0.150 -0.771 1.067 1.240 0.691 0.503 1.208 0.000 1.254 -1.046 -0.439 1.107 1.388 -0.701 1.235 1.274 0.824 0.232 0.414 0.000 0.769 0.858 1.814 1.414 1.416 1.142 1.034 +1 0.514 1.000 1.110 0.961 -1.643 1.453 2.434 0.584 0.000 1.286 1.041 0.889 0.000 1.527 -0.388 -0.962 2.548 1.806 0.624 -1.339 3.102 0.989 1.772 0.987 0.856 0.895 1.873 1.559 +0 1.358 -1.770 -0.624 0.535 0.248 0.533 -0.266 1.692 0.000 0.568 -1.578 1.732 0.000 0.913 -1.349 1.044 1.274 1.162 -0.342 -0.148 1.551 0.855 0.944 0.988 0.819 0.821 0.675 0.726 +0 0.647 -0.509 -0.721 0.974 -1.703 0.790 0.474 -0.070 1.087 0.958 0.152 0.951 2.215 0.306 -1.270 -1.410 0.000 0.645 -1.180 -0.208 0.000 0.433 0.877 0.985 1.015 1.040 0.968 0.781 +1 1.299 -0.067 0.303 0.318 -0.513 0.745 -0.468 -1.709 2.173 0.719 -0.737 -1.089 0.000 1.253 0.836 0.745 0.000 1.017 0.448 -0.611 3.102 0.724 1.030 0.981 0.713 0.911 0.743 0.749 +0 1.014 -0.642 1.378 1.609 1.681 0.969 -0.424 -0.030 0.000 0.731 -0.778 -0.915 1.107 0.777 -1.281 0.029 2.548 0.627 -1.645 0.558 0.000 1.181 0.997 0.987 0.933 0.650 0.757 0.858 +1 0.453 1.202 -1.688 1.452 -0.966 0.658 0.229 0.969 0.000 0.620 -0.510 -1.477 2.215 1.336 0.245 0.367 0.000 0.959 -0.768 -0.407 3.102 0.873 0.963 0.981 0.650 0.589 0.753 0.775 +1 1.648 1.455 -1.610 0.988 -0.817 1.066 1.161 0.639 2.173 0.660 1.066 1.151 0.000 0.862 2.248 0.240 0.000 1.950 1.381 -1.030 0.000 1.206 0.963 1.160 0.843 0.693 0.919 0.856 +0 0.652 -0.137 -0.411 1.548 -1.261 0.654 0.519 0.081 0.000 0.745 -0.429 1.167 1.107 0.762 0.754 -1.451 2.548 0.446 1.113 0.059 0.000 0.345 0.868 0.987 0.721 0.784 0.680 0.729 +1 1.888 -0.987 1.687 3.028 -1.740 2.632 0.945 0.088 0.000 1.077 -0.858 -0.238 2.215 0.957 -0.412 1.288 0.000 1.734 -0.263 -1.317 1.551 3.329 2.522 1.003 1.674 1.079 1.785 2.442 +1 0.951 -0.103 0.628 0.777 -1.299 1.867 -0.150 1.624 2.173 2.080 0.072 -0.072 0.000 0.773 -0.310 -1.208 2.548 1.173 -0.773 -0.294 0.000 0.732 0.654 1.175 1.073 0.841 0.894 0.791 +1 0.662 -0.340 -1.026 0.637 -1.456 0.659 -0.218 -0.015 1.087 1.073 0.427 0.569 0.000 1.594 1.173 -1.608 0.000 0.409 0.853 0.666 3.102 2.052 1.083 0.985 0.976 0.482 0.912 1.114 +1 1.863 -0.563 -0.633 0.606 -0.943 1.305 -0.349 0.076 0.000 2.097 0.816 -1.631 0.000 1.191 -1.102 0.254 0.000 1.298 -0.671 1.483 3.102 0.869 0.891 0.999 1.107 0.531 0.829 0.802 +1 1.841 -1.141 -0.668 0.954 -1.159 0.850 -0.705 0.311 2.173 1.036 -0.197 1.212 2.215 0.582 -0.250 -1.578 0.000 1.211 -0.087 0.580 0.000 0.866 0.812 0.980 1.401 1.063 1.126 0.944 +1 1.801 -0.529 0.511 1.065 1.168 0.571 -0.146 0.143 0.000 2.300 2.695 -1.255 0.000 0.931 0.465 -0.210 2.548 1.257 0.712 1.203 3.102 0.676 0.783 1.072 0.992 0.804 0.808 0.686 +0 1.763 0.366 1.286 0.631 1.567 0.884 0.441 -0.152 1.087 0.960 0.834 -1.174 0.000 1.073 1.314 -0.535 0.000 1.797 0.954 0.858 3.102 0.965 1.059 0.981 0.925 1.156 0.982 1.023 +1 0.498 -0.823 1.189 0.520 -1.534 1.297 0.648 -1.559 0.000 1.436 0.223 0.384 0.000 1.556 0.823 -0.242 2.548 0.484 1.977 -0.366 0.000 1.557 0.861 0.988 0.947 0.576 0.667 0.710 +0 0.628 0.884 1.172 1.073 0.695 1.319 0.866 -1.599 0.000 0.893 -0.178 -0.519 2.215 1.829 -0.442 -0.011 2.548 1.531 -1.202 0.440 0.000 1.284 1.533 0.989 0.955 0.635 1.380 1.167 +0 1.933 0.882 0.964 0.429 -1.481 0.645 0.153 -0.768 2.173 0.567 1.335 -0.521 2.215 0.685 0.861 1.716 0.000 0.679 1.034 0.080 0.000 0.757 0.773 1.019 0.870 0.609 0.801 0.663 +1 0.571 -0.657 0.666 0.666 -1.569 1.017 0.035 0.142 2.173 0.479 -1.108 0.183 0.000 1.449 0.015 -1.371 2.548 0.704 -0.500 -0.474 0.000 0.849 0.925 0.983 0.968 1.480 1.020 0.866 +0 1.361 -1.447 -0.132 0.714 -1.628 0.490 0.052 -0.325 0.000 0.564 0.296 0.590 0.000 1.501 -1.267 -1.241 2.548 1.254 0.906 1.087 3.102 0.832 0.822 1.332 0.909 1.890 1.232 1.076 +0 0.721 0.543 1.158 0.935 -0.943 0.460 -0.761 1.605 0.000 0.498 -2.184 -1.636 0.000 1.078 -1.422 -0.498 2.548 1.569 -0.612 0.453 1.551 0.852 0.987 1.079 1.215 0.862 0.921 0.927 +1 0.370 -1.708 -0.548 1.023 -1.270 0.980 -0.143 0.552 2.173 0.324 0.994 -0.521 0.000 1.077 0.423 1.521 0.000 1.010 0.801 -0.131 3.102 0.910 1.055 0.991 0.853 0.862 0.831 0.773 +1 0.754 -1.504 1.562 0.583 0.535 1.184 2.054 -1.625 0.000 1.237 -0.130 -0.144 2.215 1.943 1.063 -0.812 0.000 2.263 -0.296 0.513 3.102 0.809 1.218 0.990 0.772 0.859 1.088 1.051 +0 0.645 0.838 -1.180 0.197 -0.660 0.942 1.233 -0.463 2.173 1.206 0.431 1.045 0.000 0.812 0.853 0.498 0.000 0.819 0.953 -1.665 3.102 0.812 0.736 0.999 1.032 0.822 0.863 0.826 +0 0.817 0.349 -0.034 0.427 -0.673 0.738 1.252 1.457 0.000 1.543 1.446 -0.199 1.107 1.076 0.460 1.571 0.000 0.724 -0.884 -0.007 0.000 0.802 0.982 0.981 1.368 0.940 0.932 1.016 +1 0.726 0.312 1.062 0.635 -0.028 0.843 -0.342 0.573 0.000 1.411 -0.555 -1.311 2.215 1.017 2.323 0.323 0.000 1.413 -1.110 -0.785 3.102 0.893 0.789 0.992 0.847 0.766 0.773 0.670 +1 1.640 -0.097 -1.259 0.795 -0.366 0.895 0.794 0.413 2.173 0.973 -0.371 0.845 2.215 0.944 0.822 1.444 0.000 0.425 0.082 -0.409 0.000 0.753 0.823 1.141 1.094 1.006 1.012 0.838 +1 0.546 -1.934 1.106 1.127 -1.560 0.915 -0.694 -0.675 0.000 1.544 -2.824 0.222 0.000 1.030 0.342 -1.680 2.548 1.582 -0.779 1.353 3.102 1.189 1.180 0.980 0.898 0.788 0.946 0.836 +0 0.423 1.479 -1.494 0.983 0.163 1.336 -0.295 -0.732 0.000 0.912 0.538 0.430 2.215 1.334 0.730 1.347 2.548 1.901 0.945 -1.617 0.000 0.432 0.960 0.987 0.777 0.875 0.654 0.636 +1 1.118 -0.323 -1.418 0.309 0.934 0.903 -0.377 1.415 0.000 0.815 -2.300 -0.434 0.000 1.237 -1.049 -0.047 2.548 1.005 -1.446 0.535 0.000 0.916 0.876 0.990 0.661 0.726 0.764 0.734 +1 0.997 -0.258 0.109 0.952 1.553 0.623 -0.068 0.737 0.000 1.018 -0.877 -1.039 2.215 0.859 -0.719 -1.646 2.548 0.653 0.936 -0.021 0.000 0.871 0.941 1.301 0.991 0.520 0.825 0.760 +1 1.575 -0.507 -1.086 0.544 -1.424 0.530 -0.810 0.657 1.087 0.763 -0.887 1.195 0.000 1.221 -0.717 -0.214 1.274 0.584 0.059 0.002 0.000 0.885 0.844 0.987 1.017 0.708 0.807 0.732 +0 0.737 -0.746 1.527 0.203 0.362 1.026 -2.822 1.143 0.000 1.038 1.160 -0.857 1.107 1.277 -1.017 -0.388 2.548 0.897 -0.012 1.272 0.000 1.006 0.907 0.984 0.931 1.821 1.014 0.851 +0 1.285 -0.713 0.693 0.961 1.451 0.980 0.358 -1.143 1.087 0.514 1.056 -0.453 0.000 0.542 -0.710 0.353 0.000 0.524 1.184 0.070 3.102 1.023 1.022 0.987 0.867 0.794 0.920 0.824 +0 0.381 0.818 -1.402 1.045 1.204 0.873 -0.583 -0.279 2.173 0.460 -0.397 0.357 0.000 1.017 0.888 -0.934 2.548 0.511 1.948 1.274 0.000 0.670 1.272 0.991 0.932 1.207 0.888 0.832 +0 1.991 0.236 0.088 2.083 -0.011 1.273 -0.230 -0.853 0.000 1.884 -0.706 1.456 2.215 1.184 0.454 1.370 0.000 1.039 0.377 0.596 3.102 0.698 0.747 0.977 2.340 1.184 1.462 1.268 +1 0.943 -1.469 -0.262 2.380 0.365 0.946 -1.298 -1.641 2.173 0.347 -0.529 1.081 2.215 0.449 0.370 -1.585 0.000 0.764 -0.699 1.583 0.000 0.466 0.635 1.112 0.791 0.629 0.959 0.840 +1 0.951 0.704 0.529 0.773 -0.663 0.538 -0.141 1.468 0.000 0.385 0.838 -0.922 0.000 1.002 1.326 1.224 0.000 1.540 0.606 -0.360 0.000 0.935 0.858 1.044 0.875 0.492 0.746 0.699 +0 0.557 -1.212 0.730 0.887 -0.990 0.920 -0.837 0.370 2.173 0.992 -1.676 1.732 1.107 0.645 0.425 0.241 0.000 0.621 -1.231 -1.000 0.000 1.010 0.890 0.988 0.768 1.469 0.916 0.773 +1 1.112 -1.749 1.455 0.377 -0.582 1.581 0.357 -1.000 0.000 1.063 -0.933 1.197 2.215 1.306 -1.758 -0.309 0.000 1.662 1.166 0.654 0.000 0.886 1.026 0.985 0.841 0.497 0.823 0.762 +1 0.325 -1.722 -0.097 2.580 -0.239 0.968 -0.508 -1.319 2.173 0.574 -1.304 -1.113 0.000 1.509 0.717 0.940 0.000 1.145 -1.184 1.011 1.551 0.903 1.055 0.990 1.252 1.095 1.002 1.061 +1 0.739 -0.843 -0.227 0.654 -1.399 0.933 0.762 -0.798 2.173 1.169 -0.365 1.264 0.000 0.402 -0.788 -1.713 0.000 0.992 0.857 0.793 1.551 0.976 0.943 0.987 1.143 1.017 1.099 0.920 +0 0.457 -1.336 0.185 0.880 -1.398 1.205 -0.049 -0.683 2.173 1.137 -1.101 1.055 0.000 1.685 -0.752 1.604 0.000 1.090 -0.568 0.548 3.102 1.061 0.865 0.987 0.886 1.154 1.174 0.937 +1 0.400 -0.015 0.684 2.768 1.514 0.875 -0.098 -0.048 2.173 0.336 2.011 0.382 0.000 0.857 -0.989 -0.933 2.548 0.785 0.298 -0.736 0.000 0.858 0.954 0.992 1.058 0.951 1.032 0.975 +1 0.942 0.718 1.649 1.613 -1.241 0.915 -0.168 -0.183 2.173 1.130 0.875 0.352 0.000 0.998 2.668 1.524 0.000 0.941 1.271 -0.906 0.000 0.886 1.064 0.991 0.719 0.711 0.836 0.746 +1 0.454 1.719 0.069 1.357 -1.684 0.891 1.239 -0.345 0.000 0.405 1.304 0.445 0.000 0.892 -0.215 1.583 2.548 1.047 0.921 1.360 3.102 0.836 0.884 1.088 1.050 0.554 0.799 0.788 +0 1.373 1.370 -1.718 0.494 -0.836 0.452 0.900 0.803 2.173 0.534 0.287 -1.585 0.000 0.685 0.285 0.519 2.548 1.938 0.074 -0.253 0.000 1.243 0.992 0.988 0.893 0.274 0.667 0.785 +0 1.141 0.176 -0.690 1.698 -0.070 1.000 0.238 -1.128 2.173 1.330 -2.274 0.764 0.000 1.125 0.747 1.210 0.000 1.172 -0.355 -1.709 3.102 4.425 2.469 1.024 1.022 0.692 1.888 1.681 +1 0.659 -0.289 1.108 1.131 0.118 1.022 -1.118 -1.720 2.173 0.879 -0.556 0.332 0.000 0.589 1.251 0.149 0.000 0.438 -1.994 -1.285 0.000 1.165 1.087 0.988 1.254 0.904 0.906 0.874 +1 0.930 -1.632 0.827 1.223 -0.824 0.970 -1.600 -0.958 1.087 1.078 -0.770 0.195 2.215 0.722 -1.708 -1.593 0.000 0.599 -1.252 1.184 0.000 0.815 1.020 1.473 1.072 1.445 0.937 0.845 +0 0.533 -1.483 1.324 0.552 -0.596 0.802 0.071 -0.131 2.173 0.483 -0.454 -1.535 0.000 1.310 -0.179 1.155 2.548 0.394 1.147 -0.594 0.000 0.737 0.809 0.989 0.739 1.183 0.744 0.659 +0 1.379 0.643 1.613 0.635 0.818 0.499 0.824 0.459 2.173 0.851 -0.110 -0.988 1.107 1.195 -1.419 -0.511 0.000 0.834 1.659 1.188 0.000 0.852 1.039 0.989 0.751 1.038 0.743 0.720 +0 0.509 1.660 0.101 0.905 1.166 0.827 0.414 0.497 2.173 0.897 -0.476 -0.531 0.000 1.845 0.247 -1.252 2.548 0.508 -0.715 1.664 0.000 0.824 0.897 0.991 1.349 1.543 1.362 1.333 +0 0.652 -0.025 -0.488 0.889 0.642 1.352 1.633 1.191 0.000 1.260 0.404 0.065 2.215 1.243 -1.543 -0.261 0.000 1.768 -0.762 -1.568 1.551 1.196 1.007 0.991 0.798 1.652 1.114 0.910 +0 0.744 -1.339 1.108 1.265 -0.117 1.061 0.033 -1.610 2.173 0.904 0.523 1.071 0.000 1.542 1.263 -0.760 1.274 2.149 0.066 0.350 0.000 0.937 1.030 1.200 2.102 1.604 1.573 1.275 +0 0.780 -0.209 -0.396 1.516 0.604 1.264 -1.444 -1.401 0.000 0.711 -0.710 0.040 0.000 0.966 0.808 0.210 2.548 0.982 0.071 -1.631 3.102 0.922 0.931 1.182 0.840 0.802 0.675 0.653 +0 0.515 -0.616 -0.983 1.371 1.470 0.556 -1.431 -1.266 0.000 0.796 -0.882 -0.335 2.215 1.650 0.404 0.910 1.274 1.796 -1.343 -0.171 0.000 1.280 0.881 0.984 0.879 1.411 1.204 1.042 +0 0.916 -0.774 -1.599 1.237 1.654 0.699 -1.346 -0.683 0.000 1.280 -2.726 0.108 0.000 0.919 0.543 -1.191 2.548 2.122 -1.351 0.795 3.102 2.057 1.613 0.984 0.694 1.779 1.698 1.634 +1 0.373 -1.421 -1.256 1.301 1.070 1.247 -0.256 -0.546 2.173 0.515 -1.144 -0.765 0.000 0.597 0.230 1.645 0.000 0.508 1.607 0.475 0.000 0.895 1.281 0.987 1.205 0.949 1.318 1.087 +0 0.934 0.845 0.735 1.438 1.326 1.287 0.649 -0.063 2.173 0.883 0.176 -1.548 0.000 0.700 1.337 -1.069 0.000 0.466 -0.748 -0.792 0.000 0.945 1.065 0.989 0.689 0.425 0.873 0.813 +1 0.925 -0.516 1.553 1.652 -1.320 1.381 -0.674 0.582 2.173 0.788 0.171 -0.914 0.000 0.747 -0.376 -0.320 2.548 0.874 0.493 -0.489 0.000 0.822 1.096 0.987 0.833 0.934 1.017 0.890 +0 2.976 0.647 1.331 1.349 0.977 1.716 1.199 -0.346 1.087 0.551 0.250 -1.300 2.215 0.603 0.406 -0.864 0.000 0.490 0.517 -0.446 0.000 0.227 0.585 0.987 0.948 1.295 1.467 1.083 +0 1.600 0.098 0.989 1.153 -0.323 0.770 0.652 -1.612 2.173 1.032 -0.993 -0.077 2.215 0.820 -1.548 -1.004 0.000 0.638 -1.462 -0.287 0.000 0.897 0.947 1.741 1.236 1.785 1.142 1.090 +1 0.469 -0.520 -0.197 0.784 0.493 0.706 1.172 -0.188 0.000 0.943 0.652 -1.704 2.215 0.339 0.820 -1.278 0.000 0.909 -0.942 1.443 1.551 0.973 1.006 0.983 0.882 0.895 0.847 0.759 +0 1.012 -1.405 -0.993 0.339 1.459 0.950 -0.743 0.933 2.173 0.684 0.085 -0.010 2.215 0.893 -1.307 0.068 0.000 1.705 -1.928 -1.273 0.000 0.956 0.933 0.989 0.963 1.027 0.798 0.733 +1 1.538 -0.911 -1.649 0.908 -1.338 1.176 -0.450 -0.022 0.000 0.686 0.431 1.465 1.107 1.427 -1.358 -0.508 0.000 1.544 -0.812 -1.018 0.000 0.918 0.881 0.980 1.128 0.338 0.805 0.776 +1 1.822 0.449 -0.602 1.204 1.549 1.469 1.241 0.911 2.173 1.035 -2.882 -0.626 0.000 0.778 0.724 -1.323 1.274 0.445 1.300 1.206 0.000 0.339 0.708 1.914 0.959 1.242 1.161 0.879 +0 1.310 -0.374 0.374 0.728 -0.391 0.820 1.373 1.643 2.173 0.502 0.478 -1.600 2.215 0.588 0.264 -0.007 0.000 0.615 0.397 -1.227 0.000 0.649 1.012 0.991 0.892 0.482 1.087 0.849 +1 0.658 -0.398 1.056 1.011 -1.420 0.639 -2.260 -0.564 0.000 0.838 -0.458 0.564 2.215 0.645 -1.067 -1.598 2.548 0.751 -2.263 -0.833 0.000 0.437 1.011 0.990 0.811 0.778 0.741 0.763 +0 1.411 0.549 0.986 1.593 1.617 1.176 1.928 -0.584 0.000 1.099 0.132 0.543 2.215 0.626 0.672 -0.394 0.000 1.125 0.383 -1.359 3.102 1.025 1.079 1.118 0.986 1.007 1.122 1.146 +0 1.715 -0.244 0.174 1.362 1.027 0.765 -0.958 -0.561 0.000 1.403 -0.976 -1.520 2.215 0.473 -0.121 -1.634 2.548 0.544 0.595 0.529 0.000 1.239 0.827 1.473 1.514 0.400 0.959 0.912 +0 0.402 0.329 0.974 4.086 0.837 1.679 -1.142 -0.863 0.000 1.498 -0.269 -1.014 2.215 2.589 -0.021 1.090 2.548 0.996 -0.507 -0.456 0.000 0.893 0.974 0.988 0.900 2.001 1.525 1.599 +0 0.561 -1.451 -0.368 1.163 -0.726 0.330 1.443 0.731 0.000 0.939 -0.073 1.592 2.215 0.493 0.098 0.709 0.000 0.468 -1.351 -1.458 0.000 0.714 0.641 0.993 0.814 0.450 1.122 0.907 +0 0.701 -1.620 0.066 0.967 -1.049 0.781 -0.442 1.067 2.173 0.496 -0.588 -0.870 0.000 0.508 -1.017 0.551 0.000 1.068 0.262 -1.041 3.102 0.834 0.863 0.987 1.047 0.988 0.956 0.777 +0 0.401 2.426 -0.606 1.490 1.552 1.029 -0.751 -0.573 2.173 0.850 -0.038 -1.131 0.000 1.216 -1.988 1.033 0.000 2.206 1.315 0.532 3.102 0.884 0.940 0.997 1.040 2.675 2.067 1.644 +0 0.831 0.442 -0.560 0.735 0.812 0.867 0.360 -1.449 2.173 0.837 0.873 0.571 2.215 0.767 0.808 -1.130 0.000 0.538 1.355 0.290 0.000 0.730 0.763 1.023 0.676 1.261 0.757 0.654 +1 0.875 1.742 -0.607 1.152 -0.394 1.357 0.734 1.317 2.173 0.925 0.277 -0.263 0.000 1.456 -0.111 0.236 2.548 2.737 0.248 -1.555 0.000 0.911 0.716 0.996 1.476 1.646 1.197 0.995 +1 1.970 -1.563 1.674 0.458 1.198 0.530 -0.770 -1.064 0.000 1.630 -1.088 0.186 2.215 1.104 -0.491 0.859 2.548 0.563 0.079 -0.921 0.000 0.507 1.061 0.997 1.033 0.915 1.077 0.958 +1 0.450 0.935 1.725 1.143 0.293 0.690 -0.337 -1.355 0.000 0.789 -0.795 0.828 2.215 0.809 -1.585 -1.150 0.000 1.681 0.337 0.323 1.551 1.050 1.103 0.989 0.589 0.815 0.972 0.912 +1 0.427 -1.574 -0.395 1.617 0.575 0.723 0.834 -1.380 2.173 0.503 0.861 -0.403 0.000 0.787 0.059 0.618 2.548 0.498 -0.488 -1.535 0.000 0.765 0.705 0.984 1.167 0.988 1.587 1.288 +1 0.828 1.317 -1.179 1.732 0.598 0.919 1.156 0.315 2.173 0.413 1.029 1.732 0.000 0.419 0.559 -1.103 0.000 1.244 -0.241 0.126 3.102 1.028 0.900 1.658 1.006 0.919 1.064 1.143 +0 0.313 1.929 -1.618 0.968 -1.034 1.050 -0.541 1.030 2.173 1.094 -0.941 -0.978 0.000 1.372 0.428 0.457 1.274 0.480 0.493 -1.428 0.000 0.895 1.294 0.992 1.216 1.084 1.036 0.953 +1 0.295 1.743 1.398 0.617 -0.558 1.201 1.148 -1.353 2.173 0.815 0.151 0.607 0.000 1.295 0.920 -0.017 0.000 0.994 0.020 1.345 3.102 1.091 0.916 0.996 0.943 1.023 1.029 0.851 +0 0.656 -1.342 -0.147 0.498 1.556 0.834 -0.052 0.529 0.000 1.124 -0.850 -1.564 0.000 0.457 -0.864 -1.026 2.548 0.964 -0.644 -0.552 0.000 1.074 0.898 0.987 0.534 0.469 0.535 0.522 +1 0.301 -1.398 -0.961 1.635 0.359 0.537 -0.360 0.980 0.000 0.863 -0.250 1.684 2.215 1.350 0.727 -1.105 2.548 0.750 0.585 -0.694 0.000 0.847 0.977 0.989 1.342 0.926 1.515 1.210 +1 0.809 -0.770 -1.438 0.414 -0.017 0.946 -0.015 -0.712 1.087 0.841 -0.102 0.161 2.215 1.645 -0.593 0.972 0.000 0.440 -1.367 1.629 0.000 0.838 0.846 0.983 0.984 0.931 0.893 0.852 +0 0.914 -1.331 0.121 0.891 -0.625 0.363 -0.929 -1.204 2.173 0.690 -1.310 0.843 0.000 0.481 0.398 1.660 2.548 0.373 -1.464 -0.438 0.000 0.681 0.792 0.985 0.727 0.476 0.719 0.664 +0 0.363 1.698 1.031 0.515 -0.515 1.489 0.552 -1.621 1.087 0.965 0.264 -0.365 2.215 1.642 1.183 0.953 0.000 1.235 1.580 0.271 0.000 1.024 1.343 0.992 0.982 1.617 1.300 1.031 +0 0.426 0.962 1.263 1.068 0.715 2.309 0.454 -1.300 0.000 2.321 -0.427 0.388 2.215 0.318 1.250 0.037 2.548 0.543 0.988 -0.961 0.000 0.818 0.900 0.990 0.889 0.987 1.616 1.306 +0 3.174 -0.834 0.584 1.252 0.590 2.229 -0.326 -1.073 0.000 0.854 0.454 1.403 2.215 0.773 -0.557 -0.325 2.548 0.408 0.580 0.001 0.000 0.470 0.481 0.995 1.280 0.992 0.953 0.724 +0 0.744 -1.858 -1.488 0.354 1.529 1.012 -1.097 0.649 1.087 0.902 -1.136 -1.482 2.215 0.897 -0.968 -0.771 0.000 0.704 0.571 -0.403 0.000 0.914 0.910 0.998 0.963 1.321 0.928 0.802 +1 0.427 1.935 -0.740 0.358 1.031 1.412 0.748 1.134 0.000 1.128 2.390 -0.585 0.000 0.619 1.224 1.696 0.000 0.764 -0.889 0.602 3.102 0.958 0.896 0.995 0.791 0.558 0.783 0.704 +0 1.099 0.926 -0.328 2.444 0.807 0.476 0.239 -0.867 0.000 0.679 -0.141 -1.348 2.215 0.875 -0.966 -1.420 2.548 0.731 0.614 0.341 0.000 0.830 0.880 1.938 1.396 0.392 1.207 0.964 +0 1.800 0.980 -0.200 1.139 -0.498 0.794 1.300 0.718 2.173 0.853 -0.486 -1.335 0.000 1.001 1.087 1.336 0.000 1.184 0.389 -1.270 3.102 0.838 1.329 0.975 0.829 1.096 0.896 0.921 +0 1.384 1.429 -1.160 1.575 -0.165 0.769 0.056 1.470 0.000 1.060 0.712 -0.133 2.215 1.068 -0.929 1.203 0.000 0.796 1.044 0.822 3.102 1.001 0.995 1.599 1.037 0.666 0.979 1.200 +0 0.752 0.023 0.513 1.425 -1.452 0.362 -0.547 -0.141 0.000 0.891 1.166 -1.497 0.000 1.164 -0.167 0.882 2.548 1.160 0.836 -0.651 0.000 0.924 1.213 1.406 0.750 0.341 0.730 0.741 +1 0.609 -0.545 -0.152 0.547 -0.941 1.281 0.395 0.938 2.173 0.745 0.074 -1.054 0.000 0.799 -0.626 0.332 2.548 0.858 1.365 -1.130 0.000 0.902 1.071 0.993 1.669 0.973 1.083 1.115 +1 1.732 -0.716 1.307 0.731 -1.693 1.781 1.419 -0.132 0.000 0.805 0.032 -0.962 2.215 0.834 0.187 0.742 2.548 0.593 -1.198 -0.814 0.000 3.242 1.956 0.992 1.065 0.873 1.301 1.588 +1 1.502 -2.000 1.067 0.893 -1.232 0.639 -0.366 0.566 2.173 0.700 -0.957 0.188 0.000 1.100 -0.502 -0.496 2.548 0.370 -1.676 -1.563 0.000 0.763 0.641 1.408 1.263 0.859 1.051 0.845 +0 1.205 0.393 -0.622 1.410 0.215 1.350 -0.152 0.096 2.173 2.229 -0.706 1.528 2.215 0.718 -1.895 -1.602 0.000 1.458 1.348 -1.107 0.000 0.917 0.969 1.238 0.913 2.563 1.530 1.327 +0 0.890 0.409 -1.370 0.294 -0.524 0.777 0.706 1.532 0.000 0.872 0.068 -0.060 2.215 1.042 0.108 0.413 2.548 0.678 0.598 -0.857 0.000 0.925 0.946 0.981 0.882 0.419 0.738 0.711 +0 1.150 1.215 -0.010 0.780 1.369 0.647 1.927 1.408 0.000 1.635 1.296 -0.668 2.215 1.026 -0.217 0.961 2.548 0.597 -1.350 1.513 0.000 0.772 1.152 1.243 0.986 1.819 1.104 0.941 +1 1.501 -1.538 1.725 0.678 -0.128 0.831 -0.384 0.409 2.173 0.634 0.088 -0.893 0.000 0.602 -1.332 -0.146 2.548 0.677 -1.051 1.463 0.000 0.957 1.002 1.391 0.793 0.652 0.827 0.781 +1 0.556 -1.234 -1.384 0.887 0.099 0.782 -2.655 -1.663 0.000 0.959 -1.007 -0.267 2.215 0.548 -0.658 1.458 2.548 0.519 -2.186 0.135 0.000 0.972 0.928 0.987 0.633 0.779 0.886 0.803 +1 1.273 -0.051 1.263 0.909 -0.898 1.198 0.057 0.474 2.173 0.936 -1.019 1.461 0.000 1.977 0.857 -0.510 2.548 0.889 -0.268 -1.109 0.000 0.979 1.402 1.386 1.271 1.725 1.332 1.127 +1 1.031 0.476 -0.487 1.201 -0.238 0.770 -0.599 1.447 2.173 0.449 -1.479 1.050 0.000 0.535 -0.929 -0.913 2.548 0.552 -1.722 -1.273 0.000 0.590 0.677 0.988 0.633 0.699 0.793 0.745 +1 0.652 1.815 -0.194 0.102 0.885 0.669 -0.181 -1.314 2.173 0.688 1.139 0.712 0.000 0.921 1.435 1.492 0.000 0.988 -0.154 -0.005 3.102 0.828 0.961 0.995 0.912 0.796 0.861 0.755 +0 0.619 -1.019 -0.097 1.186 0.560 0.939 1.433 -0.944 2.173 1.077 0.352 -1.623 0.000 1.423 0.230 1.209 2.548 1.101 2.302 -0.156 0.000 0.950 1.025 0.982 1.468 1.623 1.997 1.561 +0 0.682 -0.056 1.375 0.770 -0.795 0.975 -2.354 0.473 0.000 1.348 -0.297 0.206 0.000 1.796 -0.084 -1.317 2.548 1.304 0.643 1.559 0.000 1.110 0.840 0.987 0.499 0.144 0.482 0.508 +0 0.307 0.677 -1.475 0.890 1.507 0.831 1.435 0.616 0.000 0.733 -1.846 -1.350 0.000 1.236 0.466 -0.098 2.548 0.726 0.219 -0.916 0.000 0.618 0.828 0.987 1.078 0.215 0.852 0.775 +0 0.838 0.951 -1.299 1.376 -1.128 0.675 1.143 0.735 0.000 0.655 0.489 -0.250 2.215 1.055 -0.389 0.522 2.548 0.410 0.132 1.657 0.000 0.724 0.817 0.995 0.985 0.709 1.108 0.935 +0 0.363 -0.449 1.045 1.043 1.241 1.332 0.567 0.584 2.173 0.988 2.469 -1.077 0.000 1.982 0.075 -0.862 2.548 1.660 -0.020 1.080 0.000 3.182 2.456 0.988 1.220 2.016 1.828 1.417 +1 0.418 1.992 -0.009 0.434 -1.725 0.520 1.356 0.105 0.000 0.763 0.790 -0.511 0.000 1.242 -0.498 1.233 0.000 1.172 0.363 -1.128 3.102 0.800 0.786 0.983 0.518 0.236 0.520 0.515 +1 0.938 0.586 -0.409 0.308 -0.932 1.032 1.194 0.512 0.000 1.728 1.285 -0.960 2.215 1.771 1.465 0.979 0.000 1.029 0.760 1.330 3.102 1.108 0.830 0.992 0.763 1.090 1.169 0.983 +1 1.024 -0.546 -1.488 0.893 0.742 0.309 -1.944 -0.282 0.000 0.772 1.030 -1.205 2.215 0.605 -0.800 1.106 0.000 0.790 -1.433 0.177 0.000 0.860 0.678 1.199 1.076 0.528 0.838 0.771 +1 0.523 0.925 -0.158 0.718 1.169 0.941 0.426 -1.532 2.173 0.790 -0.219 0.180 0.000 0.708 -0.710 -1.583 0.000 0.990 -2.242 0.625 0.000 0.818 0.769 0.987 0.854 0.900 1.148 0.987 +1 1.260 1.007 0.192 0.505 -1.233 0.367 -1.224 -1.310 2.173 0.626 -0.337 1.075 2.215 0.548 0.249 1.167 0.000 0.751 -0.610 -0.981 0.000 0.758 0.602 1.060 0.886 0.673 0.833 0.692 +1 1.282 0.369 -0.090 0.149 -0.570 0.680 1.053 0.898 1.087 0.685 0.002 1.559 0.000 0.701 -0.793 -1.087 2.548 0.829 1.854 -1.072 0.000 0.944 1.090 0.989 0.709 1.281 0.825 0.759 +0 0.331 -2.005 1.520 0.485 0.069 0.711 -0.243 0.230 0.000 0.824 0.867 -0.716 2.215 1.827 0.362 1.604 2.548 0.387 -0.090 -0.773 0.000 0.631 0.844 0.977 0.973 1.177 0.873 0.786 +1 0.679 -0.432 -0.483 0.444 1.661 0.920 0.431 1.568 2.173 0.841 0.148 -0.191 0.000 1.240 -0.521 0.248 1.274 0.845 -0.812 -1.156 0.000 1.063 0.851 0.982 1.139 1.420 0.927 0.845 +0 0.676 0.091 -1.669 0.964 -0.792 1.991 1.877 1.189 0.000 1.520 -2.296 -0.471 0.000 1.190 -0.065 -0.881 2.548 1.850 1.437 0.521 0.000 0.656 1.024 0.989 0.663 0.348 0.906 0.798 +1 0.280 0.811 0.232 1.078 1.348 0.911 -0.233 -0.860 0.000 1.246 0.155 1.177 2.215 0.842 -0.316 0.228 2.548 1.320 -0.882 -0.546 0.000 0.851 0.870 0.984 0.702 0.868 0.965 0.837 +0 0.763 -0.043 -1.486 0.834 0.666 0.779 0.036 0.883 0.000 0.960 0.481 -0.325 2.215 0.952 -0.682 -0.671 2.548 0.976 -0.578 1.617 0.000 0.957 1.037 1.030 0.852 0.742 0.852 0.739 +0 1.628 -0.421 0.916 0.651 0.386 0.709 0.821 -0.917 0.000 1.225 -0.035 -0.886 1.107 0.658 0.547 1.452 1.274 0.674 -0.036 0.520 0.000 1.122 0.889 0.980 0.667 0.876 0.834 0.800 +1 1.197 1.292 0.632 0.115 -0.768 0.932 -0.352 -1.151 2.173 0.737 0.822 0.914 0.000 1.088 0.232 -0.366 2.548 0.676 -1.164 -1.573 0.000 1.117 1.151 0.992 1.230 0.904 1.176 0.980 +1 0.798 0.803 -0.317 1.451 -0.005 0.545 -0.634 -0.467 0.000 1.418 -0.172 1.628 2.215 0.937 -1.180 1.038 2.548 0.790 -0.053 -1.354 0.000 0.776 1.013 0.976 1.101 0.952 1.042 0.880 +1 0.549 0.720 -1.017 0.378 -0.784 0.672 1.032 1.002 0.000 0.969 0.218 -1.742 0.000 1.688 -0.908 -0.359 0.000 1.551 -0.430 0.175 3.102 0.901 0.881 0.984 0.746 0.584 0.850 0.818 +1 1.388 0.814 -0.929 0.752 1.169 0.966 -0.601 1.475 2.173 0.457 -0.532 0.197 0.000 1.272 -0.627 -0.421 2.548 1.003 0.789 0.530 0.000 0.768 0.867 1.344 1.323 1.369 1.105 0.944 +0 1.201 -0.727 0.120 1.121 0.664 0.489 1.227 0.423 2.173 0.789 0.193 -0.930 0.000 1.027 -1.081 -1.394 1.274 0.629 0.829 1.538 0.000 0.820 0.928 0.980 1.396 1.616 1.136 1.021 +1 2.302 0.967 -0.183 1.723 0.243 1.648 0.265 1.687 2.173 0.565 0.924 -1.578 0.000 0.755 0.772 0.361 2.548 1.112 0.327 -0.693 0.000 0.878 1.029 1.036 0.569 1.356 1.397 1.085 +0 0.772 0.975 -0.343 3.733 0.125 1.062 0.826 -1.451 2.173 0.862 1.219 1.657 2.215 0.917 1.118 0.745 0.000 1.025 0.629 1.344 0.000 0.921 1.197 0.993 1.792 0.556 1.316 1.276 +1 1.322 -0.057 -1.155 1.228 -0.795 0.668 0.883 0.198 2.173 0.497 -2.015 0.196 0.000 0.736 0.090 1.692 0.000 0.842 0.973 0.741 0.000 0.819 0.907 0.990 1.318 0.870 1.055 0.962 +1 1.209 -0.271 1.366 1.608 -1.540 0.841 -0.296 -0.142 2.173 0.672 1.054 0.725 1.107 0.781 -0.259 -0.684 0.000 0.973 1.708 -0.707 0.000 0.912 1.170 0.990 1.142 1.137 1.052 1.023 +1 1.679 0.866 -1.547 0.650 1.446 3.177 -0.994 -0.394 0.000 2.719 0.112 1.184 2.215 0.887 0.390 0.535 0.000 0.676 0.742 1.078 1.551 0.458 0.803 0.985 0.585 0.500 0.924 0.824 +0 0.672 -0.444 -0.749 1.491 0.160 0.503 -0.941 1.711 2.173 0.578 0.193 -1.446 1.107 0.739 0.482 1.240 0.000 0.609 0.990 -0.270 0.000 0.765 0.905 1.014 0.847 0.534 0.711 0.692 +1 0.393 0.298 -0.602 0.761 0.230 0.654 -0.862 -1.403 0.000 1.664 -0.382 -0.068 2.215 1.480 -0.873 1.501 0.000 2.229 0.488 1.183 1.551 0.888 1.376 0.984 0.752 1.802 1.315 1.081 +0 1.965 0.015 -1.078 1.834 -1.513 0.983 -1.160 0.475 0.000 1.370 -0.091 0.653 1.107 1.060 -0.790 -0.699 1.274 0.622 -0.375 0.100 0.000 0.591 0.896 1.002 1.568 1.305 1.136 1.132 +0 1.339 -1.146 1.691 1.009 -0.423 0.553 -1.650 -1.511 0.000 0.754 2.626 0.905 0.000 0.826 -1.249 -0.023 2.548 1.097 -0.354 -0.007 3.102 0.915 1.499 1.522 0.897 0.357 1.496 1.666 +0 0.875 -1.405 1.598 1.502 -1.077 1.243 -0.550 0.708 0.000 0.986 0.597 -0.376 0.000 0.904 -0.325 -0.235 2.548 1.526 -0.561 1.539 3.102 0.795 0.905 1.060 0.957 0.909 0.755 0.890 +0 0.941 -0.130 1.085 1.833 -1.473 0.588 -0.519 -0.682 2.173 0.390 -0.496 0.090 2.215 0.418 1.692 0.810 0.000 0.632 -2.095 -0.876 0.000 2.558 1.411 1.352 1.005 0.452 0.923 0.929 +1 0.410 1.530 -0.047 1.723 1.261 0.834 -0.022 -0.675 0.000 0.941 -0.304 -0.105 0.000 0.886 0.616 -1.442 2.548 1.286 0.535 0.933 3.102 0.965 1.028 1.077 0.689 0.687 0.825 0.985 +0 1.643 1.920 -0.834 0.596 0.819 0.314 0.953 1.240 2.173 0.542 0.136 0.539 0.000 0.657 2.699 -0.002 0.000 0.423 0.761 1.732 1.551 1.819 1.036 1.367 0.871 0.165 0.662 0.724 +1 1.263 -0.324 -1.330 0.879 -1.105 1.129 2.222 0.158 0.000 0.926 0.117 0.672 0.000 0.987 0.075 1.596 2.548 0.752 0.877 -0.111 0.000 0.951 1.655 0.989 0.601 0.866 1.950 1.868 +0 0.458 -0.679 1.010 1.061 0.027 0.459 0.645 -1.276 0.000 0.806 0.345 1.370 2.215 0.600 -0.233 -0.052 0.000 0.995 1.130 -0.823 1.551 0.952 0.829 0.988 1.497 0.854 1.135 0.958 +0 0.929 -1.495 1.261 0.941 0.527 0.755 -0.943 -0.948 2.173 0.302 -2.844 -0.602 0.000 0.651 0.367 -1.636 0.000 0.663 -0.897 0.543 3.102 1.819 1.062 0.991 1.016 0.731 0.770 0.761 +0 0.903 -0.404 0.577 1.613 1.362 0.788 1.083 -0.629 2.173 0.885 2.188 -0.619 0.000 1.406 0.600 1.561 2.548 1.121 1.697 0.121 0.000 0.814 0.865 1.087 0.924 1.240 1.091 1.320 +1 0.430 -0.517 -1.086 1.119 0.367 1.171 -0.970 -1.738 1.087 1.217 -1.147 -0.436 0.000 1.185 -1.075 1.072 2.548 0.724 -2.325 -0.278 0.000 1.109 1.218 0.987 1.173 0.851 1.083 1.027 +1 0.732 -1.361 1.083 1.131 -1.352 0.503 -0.085 -1.569 0.000 0.966 1.076 0.147 0.000 0.826 0.080 -0.802 1.274 0.573 1.918 1.178 0.000 0.991 0.998 1.024 0.679 0.228 0.665 1.072 +0 2.456 -1.429 -1.717 0.197 -1.586 0.648 -1.317 -0.489 0.000 1.775 1.145 0.564 2.215 0.738 -1.293 -0.882 2.548 1.338 0.948 -0.262 0.000 0.815 0.829 0.972 0.712 2.376 1.896 1.550 +1 0.574 -0.903 -0.179 0.218 0.638 0.885 -0.656 -1.050 0.000 1.333 -0.224 0.469 2.215 1.170 -0.151 1.125 0.000 0.396 0.239 -0.684 3.102 0.909 0.635 0.979 0.733 0.590 0.877 0.775 +1 1.100 0.125 1.370 0.797 -1.120 1.204 0.026 0.251 2.173 0.837 0.315 -0.619 0.000 1.156 -0.403 0.940 2.548 0.967 0.745 -1.074 0.000 0.577 1.079 1.016 1.161 0.923 0.901 0.842 +0 0.640 0.554 -0.854 0.517 0.768 0.653 0.651 -1.686 1.087 0.489 -0.892 0.321 0.000 1.065 -0.300 1.280 0.000 1.852 -0.659 -0.284 3.102 0.904 0.939 0.987 0.761 1.441 0.886 0.744 +1 0.898 -0.919 -1.230 1.775 -1.067 3.290 0.745 0.740 2.173 1.155 -1.214 -1.023 0.000 1.936 -0.402 -1.052 2.548 0.686 -1.478 -0.222 0.000 0.826 0.869 0.990 2.817 3.718 2.473 1.997 +0 0.898 -0.579 -0.895 0.481 0.266 1.002 -0.434 1.542 0.000 1.038 -0.381 -0.427 2.215 0.744 -1.161 0.279 2.548 0.731 -1.116 1.159 0.000 0.755 0.893 0.992 0.634 0.700 0.843 0.757 +1 0.976 0.562 -1.314 0.042 -0.888 0.345 -1.612 1.362 0.000 0.469 0.520 0.347 2.215 0.613 -0.417 -1.085 0.000 0.848 0.368 -0.092 0.000 0.840 0.875 0.823 0.626 0.306 0.600 0.583 +1 0.762 -0.230 0.902 1.662 0.148 0.395 -1.424 0.310 1.087 0.751 -1.582 1.687 2.215 0.685 0.651 -1.587 0.000 1.069 -0.819 -0.659 0.000 0.900 1.822 0.988 1.215 0.762 1.540 1.292 +0 0.526 1.027 0.306 0.760 1.101 1.431 0.558 0.021 2.173 1.661 -0.718 -1.312 0.000 1.528 -2.229 0.914 0.000 1.649 -1.052 -0.965 1.551 1.263 0.926 0.989 1.386 2.118 1.710 1.914 +1 0.966 0.863 -0.461 1.140 -1.530 1.987 1.369 -1.721 0.000 3.129 0.610 0.172 2.215 1.659 0.672 -1.583 0.000 1.784 0.367 0.617 3.102 0.796 0.980 1.193 1.560 0.857 1.176 1.078 +0 0.683 -1.500 0.194 0.300 1.120 0.733 -0.964 0.556 2.173 0.439 -1.639 1.018 0.000 1.123 -1.588 -1.356 0.000 1.811 -0.498 -1.045 3.102 0.907 1.008 0.999 0.817 1.231 0.833 0.743 +1 0.707 -0.267 -1.036 0.661 0.297 0.534 -1.130 -0.146 2.173 0.505 -0.033 0.486 0.000 0.866 0.308 1.710 0.000 0.824 -0.957 -1.393 3.102 0.925 0.986 0.982 0.588 0.633 0.667 0.603 +1 0.361 -2.075 -1.158 0.480 -0.690 0.877 -0.763 1.216 0.000 0.516 -0.280 -1.716 0.000 1.029 -1.319 0.420 0.000 2.681 0.307 -0.701 3.102 0.943 1.152 0.997 0.719 0.853 0.769 0.693 +0 0.287 -2.017 -0.482 0.669 1.712 0.784 -0.242 1.412 2.173 0.488 0.324 0.155 0.000 1.219 -0.538 -0.237 2.548 0.723 -0.701 -0.865 0.000 0.781 0.891 0.993 0.753 1.232 0.719 0.641 +0 2.353 -1.596 -1.284 0.329 -0.025 1.118 -0.854 0.258 2.173 0.809 -1.235 1.348 2.215 0.635 -0.373 -0.721 0.000 0.718 -0.569 0.925 0.000 0.749 0.796 1.105 0.885 1.199 1.055 0.854 +0 1.735 -0.152 0.244 0.577 -1.364 1.115 -0.228 -0.997 1.087 0.956 -0.462 0.673 2.215 1.146 -0.902 -1.348 0.000 1.246 -0.704 1.397 0.000 0.822 0.986 1.375 1.165 1.527 0.958 0.903 +0 1.749 0.203 1.214 0.333 -0.163 1.069 -0.108 -0.940 2.173 0.805 -0.412 0.185 2.215 0.483 0.026 -0.166 0.000 0.521 0.638 1.132 0.000 0.677 0.829 1.000 0.823 1.179 0.901 0.721 +0 0.536 -0.150 0.151 1.955 0.167 1.482 -0.858 -1.701 2.173 0.823 -0.418 -0.397 2.215 0.916 -1.344 -1.165 0.000 0.685 -1.110 0.971 0.000 0.822 0.879 0.985 0.790 1.542 1.198 0.963 +1 0.429 0.128 1.159 1.567 -1.045 1.043 1.148 0.606 0.000 0.744 -0.803 1.284 1.107 0.766 0.790 -0.881 0.000 0.944 -0.682 -0.517 3.102 1.583 1.391 1.040 0.891 0.755 1.093 0.961 +1 1.115 0.166 0.395 0.574 -0.059 0.457 -0.565 0.400 0.000 1.514 0.487 -1.270 2.215 0.978 0.689 -0.434 2.548 0.760 -1.484 1.367 0.000 0.899 1.150 0.993 1.318 0.900 1.048 0.941 +0 0.991 -0.327 -0.317 1.242 -1.314 0.777 -0.382 -1.323 2.173 1.058 0.118 0.965 0.000 0.306 -2.208 0.250 0.000 0.821 -0.428 0.725 3.102 1.531 0.844 1.202 0.742 0.815 0.799 0.790 +1 2.115 0.145 1.612 0.171 0.256 1.080 0.163 -0.565 2.173 1.030 0.344 -1.326 2.215 1.993 0.620 -0.872 0.000 2.771 -0.026 0.075 0.000 0.900 1.128 0.989 0.742 0.992 0.973 0.970 +0 0.720 0.215 -1.644 0.663 -1.244 0.996 -0.214 0.097 1.087 0.499 -0.871 0.820 2.215 0.586 -0.894 -0.483 0.000 0.806 -1.133 1.248 0.000 0.772 0.904 0.988 0.702 0.725 0.774 0.668 +0 0.650 1.213 -1.659 1.007 -0.191 0.565 1.235 -0.030 2.173 0.716 1.003 -1.194 0.000 1.297 0.308 0.750 2.548 1.051 0.154 1.728 0.000 0.739 0.968 1.087 0.664 0.852 0.767 0.708 +0 1.389 0.316 0.779 0.515 0.032 0.963 -0.185 -0.320 0.000 0.910 -0.266 -1.534 2.215 0.597 -0.585 -1.080 0.000 1.187 -0.204 1.166 3.102 0.917 0.988 0.985 0.945 0.611 0.749 0.751 +0 1.477 -1.232 -1.403 2.478 0.739 0.722 -2.632 0.550 0.000 1.191 -0.846 0.027 2.215 1.041 -1.295 -1.024 2.548 1.085 -0.912 0.903 0.000 1.241 1.229 2.480 1.551 1.016 1.132 1.103 +0 1.743 0.631 0.983 0.360 -1.295 0.761 -0.091 -1.714 2.173 0.982 0.344 0.303 1.107 1.331 -0.273 -0.559 0.000 0.728 1.061 -0.627 0.000 0.948 0.938 0.987 0.822 1.265 0.869 0.829 +1 0.955 0.720 -0.512 0.748 0.585 0.764 0.066 0.125 0.000 1.089 0.266 1.683 1.107 0.967 0.960 1.435 0.000 0.902 0.011 -1.177 3.102 0.987 0.763 0.988 0.665 0.493 0.649 0.616 +0 0.710 1.010 -0.547 1.962 -0.115 0.517 -1.508 1.463 0.000 0.353 0.763 1.118 1.107 0.569 -0.538 -1.186 0.000 1.116 -2.247 1.369 0.000 0.816 0.920 0.992 0.827 0.260 0.719 1.256 +1 0.791 1.625 -0.842 1.190 0.526 1.009 0.953 -0.496 0.000 0.630 1.433 0.253 0.000 2.080 0.518 -1.537 2.548 0.686 0.206 0.502 0.000 0.983 0.932 1.268 1.309 0.899 0.968 0.888 +0 1.053 1.508 -1.693 0.568 0.846 0.397 -0.074 0.312 0.000 0.471 -1.189 -0.713 0.000 0.663 -0.216 1.325 2.548 1.114 0.919 -0.502 3.102 0.920 0.926 0.982 0.966 0.808 0.751 0.937 +0 0.398 0.361 -1.595 1.257 -0.890 0.488 -0.037 0.201 2.173 0.495 0.920 -1.451 0.000 1.420 -0.910 0.913 2.548 0.773 -1.878 1.382 0.000 1.969 1.339 0.983 1.838 0.809 1.238 1.269 +0 1.436 0.754 1.461 0.679 0.937 1.147 0.907 0.728 2.173 1.259 2.236 -0.826 0.000 0.618 2.559 -0.588 0.000 1.200 0.783 -0.651 0.000 0.839 0.891 0.990 0.894 0.912 1.075 1.055 +1 1.971 0.244 1.418 0.300 0.855 1.172 -0.721 -0.027 2.173 1.213 -0.333 1.476 0.000 1.845 -1.755 -0.914 0.000 1.467 -0.459 0.744 3.102 1.927 1.609 0.987 1.600 0.896 1.170 1.393 +0 0.302 2.018 -1.277 0.597 1.259 1.128 0.103 1.665 2.173 1.425 0.883 0.157 0.000 0.998 1.104 -0.389 2.548 0.521 -0.086 -0.064 0.000 0.944 0.747 0.980 0.757 1.486 1.026 0.844 +0 0.576 -1.449 -0.841 1.060 0.461 0.406 -0.230 -0.552 0.000 0.727 0.336 -1.537 2.215 0.654 -0.839 0.407 0.000 0.528 -0.155 0.309 0.000 0.885 0.779 0.998 0.997 0.491 0.851 0.727 +0 0.689 -0.916 0.548 0.582 -0.777 1.086 -0.155 -0.016 2.173 0.534 -1.636 1.683 0.000 1.166 -0.829 -1.333 2.548 0.914 -0.271 1.172 0.000 0.780 0.686 0.986 0.736 1.408 0.917 0.770 +1 1.186 0.864 -1.263 0.128 -0.595 0.624 -0.351 -0.838 0.000 1.119 -0.320 0.439 1.107 1.216 -1.048 1.183 2.548 0.993 -0.835 -0.519 0.000 0.912 1.011 0.985 1.054 0.932 0.923 0.880 +1 0.971 -0.290 -0.067 0.518 -1.355 1.235 -0.582 1.097 2.173 0.454 0.731 -0.682 0.000 0.612 -2.476 -0.510 0.000 1.040 -1.711 -1.530 0.000 0.985 0.732 0.988 1.027 1.111 0.932 0.804 +0 1.612 -1.261 0.767 0.427 -0.792 1.001 0.579 1.631 2.173 0.632 0.206 -0.626 0.000 0.662 1.057 -0.479 0.000 0.536 -1.199 0.278 0.000 0.907 0.937 1.133 0.672 1.522 1.108 0.945 +0 0.977 -0.370 0.268 0.552 -0.557 0.660 0.655 -1.497 2.173 0.487 0.118 0.080 0.000 0.827 0.266 -1.264 2.548 1.086 -0.301 1.101 0.000 0.788 0.911 0.979 0.724 0.257 0.640 0.625 +0 1.102 0.438 0.216 2.674 -0.117 1.806 0.117 0.347 1.087 1.679 -0.558 1.620 0.000 1.705 -0.046 -1.712 0.000 2.180 -0.314 -1.167 3.102 0.774 0.970 1.008 1.229 2.121 1.557 1.657 +1 0.782 -1.167 0.637 1.254 0.318 0.591 -0.677 1.648 2.173 1.543 -0.682 -1.025 2.215 0.443 2.021 0.329 0.000 0.539 -0.013 -1.063 0.000 0.897 1.132 0.992 1.309 0.938 1.026 1.008 +0 0.410 1.046 -0.996 1.391 0.347 0.950 0.196 1.532 2.173 1.142 -0.547 -0.658 2.215 0.509 -1.036 0.232 0.000 0.367 -0.751 1.202 0.000 0.370 0.789 0.986 1.315 1.530 1.143 0.923 +0 0.479 0.381 -0.501 0.937 0.474 0.583 -0.236 -1.522 0.000 0.595 0.260 0.029 2.215 1.088 -0.392 1.074 2.548 0.984 0.463 -1.118 0.000 0.795 0.810 0.987 0.645 0.756 0.726 0.724 +0 0.560 0.745 -1.406 1.016 -0.085 1.266 1.348 0.797 0.000 2.000 -0.325 -1.488 0.000 1.941 0.705 -0.665 2.548 1.779 -0.105 0.245 0.000 1.021 1.017 0.987 0.704 1.061 1.038 0.952 +1 0.359 0.516 1.009 0.928 -1.661 1.737 -1.449 0.845 0.000 2.331 0.054 -1.149 1.107 0.668 -1.865 -0.914 0.000 0.645 -1.891 1.021 0.000 0.924 0.808 0.991 1.684 0.906 1.283 1.162 +0 0.583 1.670 0.232 1.047 -1.147 0.383 1.250 -0.708 1.087 0.861 0.839 -1.680 2.215 0.922 0.646 0.725 0.000 1.100 -0.605 0.498 0.000 0.897 0.983 1.024 0.790 0.670 0.778 0.800 +1 1.042 0.073 -1.709 0.733 -1.244 0.751 -1.236 0.407 0.000 0.987 -0.109 -1.042 1.107 1.164 0.851 -0.533 2.548 0.412 -0.746 -0.296 0.000 0.525 1.088 0.994 0.820 0.802 0.958 0.957 +0 1.561 0.156 1.161 0.981 0.477 0.919 0.613 -0.100 1.087 0.767 -0.225 -0.788 0.000 1.113 0.213 -1.480 0.000 0.697 0.890 1.516 3.102 0.892 1.152 0.990 0.634 0.863 0.765 0.819 +0 0.380 -1.728 -0.043 1.067 -1.011 0.663 -1.366 1.091 0.000 0.805 -0.549 0.209 2.215 1.101 -1.087 -0.932 2.548 0.528 -0.544 -1.575 0.000 0.695 0.843 0.981 0.755 0.915 0.695 0.658 +1 0.794 -1.267 -1.112 0.105 1.390 2.089 2.397 0.795 0.000 2.684 0.253 -1.161 0.000 1.144 -2.231 0.078 0.000 1.417 -0.103 0.286 3.102 0.684 0.801 0.977 0.765 0.809 0.865 0.769 +1 2.189 -1.398 0.462 0.401 0.213 0.883 -2.216 -1.411 0.000 0.710 -0.133 -1.077 0.000 0.711 -0.443 1.544 2.548 0.556 -0.851 0.821 0.000 0.893 0.687 0.998 0.967 0.720 0.954 0.850 +0 0.378 0.470 -0.426 1.218 1.485 0.638 0.128 1.089 0.000 0.902 -0.260 -0.009 2.215 0.872 1.224 -0.769 2.548 0.467 -0.762 -0.383 0.000 0.928 1.024 0.985 0.849 1.035 0.772 0.712 +0 0.822 -0.857 -1.585 2.376 1.716 1.383 1.061 0.435 2.173 0.729 -0.799 -0.180 0.000 0.823 0.693 -1.267 2.548 0.639 1.594 -0.003 0.000 1.639 1.146 0.987 2.053 1.344 1.349 1.245 +0 1.386 0.269 0.523 0.810 1.034 0.949 2.790 -1.579 0.000 1.040 -0.587 -0.154 2.215 0.908 0.943 -0.851 2.548 0.413 -1.365 0.921 0.000 4.451 2.470 0.990 1.141 1.124 2.102 1.673 +0 0.671 0.041 0.854 0.253 -0.983 0.611 -1.057 1.428 2.173 0.328 1.090 -1.242 0.000 0.560 -0.274 0.226 2.548 1.228 -1.112 0.238 0.000 1.522 0.877 0.988 0.657 0.705 0.721 0.634 +0 0.400 -0.065 0.333 1.495 -1.370 0.814 -0.855 -0.416 2.173 0.980 0.199 0.820 0.000 0.653 -1.121 0.945 0.000 0.456 1.466 1.137 0.000 0.897 1.103 1.071 0.519 0.764 0.697 0.679 +1 1.130 0.874 -0.450 1.158 -1.282 0.844 0.815 1.672 0.000 1.550 0.497 0.023 2.215 0.757 -2.651 -1.588 0.000 0.777 2.167 0.829 0.000 0.612 1.401 1.079 0.644 0.384 0.793 0.791 +1 0.869 1.335 -1.680 1.378 -1.025 1.077 1.231 0.802 1.087 0.902 0.928 -1.072 0.000 1.727 -0.573 0.491 0.000 1.029 0.306 1.705 3.102 0.888 0.710 0.991 1.263 0.953 0.933 0.845 +0 1.130 0.396 1.621 0.779 0.939 0.981 1.259 0.052 0.000 1.422 -0.275 -1.411 2.215 0.928 1.691 0.474 0.000 0.977 0.876 -0.842 3.102 0.815 0.844 0.989 0.883 0.925 1.264 1.130 +0 0.907 0.285 -1.367 0.727 -0.102 0.718 1.421 0.126 2.173 0.738 -1.752 -1.692 0.000 0.487 0.297 1.239 2.548 0.373 2.149 -0.085 0.000 3.096 1.674 1.023 0.926 0.756 1.421 1.116 +1 1.242 -0.631 0.797 0.402 1.067 0.727 1.354 -1.348 0.000 0.768 -1.334 0.655 0.000 1.586 0.488 -0.581 0.000 1.115 -0.354 -0.128 3.102 1.482 1.178 0.998 0.723 0.636 0.866 0.891 +1 0.994 -1.394 1.075 0.724 -0.228 0.775 -1.007 0.141 2.173 1.711 -1.670 -1.147 0.000 0.599 -1.570 0.832 0.000 0.687 -1.874 -1.445 0.000 0.655 0.830 1.084 0.669 0.921 0.623 0.570 +0 1.431 1.705 0.589 0.627 -0.072 1.273 0.069 -1.595 2.173 1.251 1.533 -0.424 0.000 0.685 -0.557 0.764 2.548 1.014 1.388 -0.945 0.000 0.980 1.322 0.980 1.655 1.068 1.196 1.134 +1 1.614 -1.055 -0.165 1.002 1.341 1.906 -0.172 1.364 0.000 1.846 0.055 -0.539 0.000 0.990 0.852 -1.227 2.548 0.645 0.378 -0.236 3.102 3.968 2.132 1.722 1.514 0.499 1.360 1.358 +1 1.113 1.125 1.698 1.183 0.996 0.923 0.040 0.400 2.173 1.709 0.299 -0.882 0.000 0.509 2.066 0.355 0.000 0.902 -0.714 -1.259 3.102 1.919 1.133 0.991 1.276 1.062 1.057 1.186 +1 0.971 1.015 0.913 0.381 0.209 0.907 0.309 -0.639 1.087 0.620 0.796 -1.188 2.215 0.941 0.448 1.723 0.000 0.526 0.093 -0.243 0.000 0.775 0.810 0.994 0.782 0.596 0.724 0.631 +1 1.596 -0.534 -0.244 0.993 0.482 1.555 -0.248 1.567 0.000 1.366 0.231 -0.813 2.215 0.703 -1.605 0.579 0.000 1.647 -0.339 0.589 0.000 0.868 0.655 1.062 1.123 0.725 0.901 0.815 +1 0.499 0.260 -1.622 0.717 1.398 1.161 -0.942 0.522 0.000 1.357 -1.229 -1.139 2.215 0.555 -1.240 -0.296 1.274 0.976 -1.948 -0.797 0.000 0.895 0.737 0.991 2.063 0.636 1.407 1.345 +1 0.338 -2.151 -1.013 1.110 1.079 0.672 0.569 1.450 2.173 0.972 -0.746 -0.986 0.000 1.012 -1.036 -0.251 0.000 0.892 0.044 0.272 3.102 0.977 0.858 0.987 1.081 0.748 0.902 0.850 +0 1.611 -1.225 -0.889 0.743 0.387 0.491 -0.905 -1.456 0.000 0.773 -2.126 -0.293 0.000 1.706 -0.399 1.026 2.548 0.788 -0.341 0.026 0.000 0.956 0.945 1.383 0.717 0.183 0.773 0.689 +1 1.185 0.360 -0.318 0.298 1.062 0.736 -0.466 -0.747 2.173 0.704 0.706 1.573 0.000 1.029 -0.302 1.039 0.000 1.242 1.958 1.368 0.000 0.943 1.178 0.989 0.711 0.877 0.925 0.792 +0 2.497 0.558 1.015 1.826 0.430 2.579 0.443 -0.915 0.000 0.255 0.725 -1.423 0.000 1.055 -0.227 1.333 2.548 0.695 1.208 -0.201 1.551 0.804 0.923 1.486 1.037 0.897 0.971 1.298 +0 0.698 -2.000 0.894 1.397 -0.337 1.332 -2.786 -0.959 0.000 0.481 0.291 1.501 2.215 1.006 -0.852 0.399 2.548 1.037 -1.562 -1.121 0.000 0.910 1.638 1.225 1.286 0.787 1.454 1.203 +1 0.337 -1.037 -0.039 0.893 0.340 1.412 1.135 0.583 0.000 3.551 0.863 -1.677 2.215 1.216 0.206 -0.585 2.548 2.482 0.665 0.137 0.000 0.926 1.034 0.996 1.696 1.984 1.661 1.336 +1 0.413 0.354 0.086 0.927 -1.095 0.817 0.410 0.710 0.000 0.815 0.191 1.278 0.000 1.081 -0.969 -0.486 2.548 0.940 0.627 -1.415 0.000 0.868 1.367 0.986 0.727 0.598 0.858 0.880 +1 0.773 0.328 -1.375 1.655 1.345 1.339 -0.134 -0.354 1.087 0.652 -0.895 0.946 2.215 0.978 0.154 0.256 0.000 1.306 1.670 -1.580 0.000 1.807 1.629 0.999 1.478 1.383 1.340 1.179 +1 0.665 -2.237 -1.046 0.826 -1.293 1.036 -0.730 0.685 2.173 0.334 -0.638 1.087 0.000 1.186 -0.374 -0.047 1.274 1.231 -0.859 -1.214 0.000 1.015 0.949 0.971 1.209 0.877 0.924 0.953 +0 0.286 0.656 0.666 0.731 0.745 0.713 -0.346 -1.022 0.000 0.625 -1.531 -1.727 2.215 0.778 -0.656 0.506 2.548 0.513 -1.869 -0.027 0.000 1.245 0.949 1.001 0.726 0.745 0.696 0.662 +1 0.882 -0.688 0.443 0.627 -1.538 0.817 0.408 1.045 0.000 1.656 0.301 -0.934 0.000 1.463 -0.310 1.069 2.548 1.254 -0.067 -0.445 0.000 0.882 0.649 1.007 0.696 0.766 0.872 0.788 +1 0.715 -0.257 1.054 0.861 -1.434 1.119 -0.832 -1.436 2.173 1.047 0.168 0.686 0.000 1.808 -0.025 0.028 0.000 0.400 -1.329 -0.973 0.000 1.096 0.589 0.987 0.731 0.615 0.819 0.738 +1 0.332 -0.322 0.340 0.585 -0.965 0.435 0.461 0.895 0.000 0.869 -0.835 0.379 2.215 1.393 -0.669 -1.228 2.548 0.757 1.083 1.704 0.000 0.693 1.041 0.983 1.068 1.163 0.962 0.859 +1 1.375 -0.400 -0.117 0.770 -0.626 0.940 -1.284 1.320 0.000 0.490 -1.242 -0.603 2.215 0.674 -1.940 0.813 0.000 0.396 -0.410 -1.394 3.102 0.883 0.934 0.981 0.575 0.306 0.578 0.840 +0 0.717 -0.219 -1.605 0.272 -0.213 0.711 2.248 0.563 0.000 0.788 1.019 -0.194 2.215 1.028 0.474 -1.402 2.548 0.643 1.488 1.594 0.000 0.864 0.951 0.993 0.590 0.885 0.847 0.782 +1 0.688 -0.868 -0.619 1.368 0.303 0.584 -1.894 -1.503 0.000 0.884 0.257 0.713 2.215 0.507 -0.246 1.642 1.274 1.206 -0.120 -0.619 0.000 1.580 0.974 0.994 0.903 0.562 0.913 0.838 +0 0.552 0.712 0.466 1.403 1.145 1.561 0.049 1.200 1.087 2.900 -0.894 -0.665 2.215 0.425 1.004 -0.154 0.000 0.556 -1.275 -0.995 0.000 0.999 1.215 0.985 0.708 3.489 1.732 1.328 +0 0.479 -0.536 -0.643 3.147 0.004 1.617 0.764 0.659 0.000 0.987 1.500 -0.739 1.107 2.318 -0.015 -1.730 0.000 3.221 -0.425 -1.623 3.102 1.129 1.480 0.985 1.733 2.273 1.810 1.721 +1 1.234 0.113 -1.238 1.294 -0.638 1.356 -0.155 -0.773 1.087 2.207 -0.615 -1.120 2.215 6.523 0.503 0.868 0.000 1.161 -0.997 1.248 0.000 0.818 1.234 0.992 0.801 0.992 1.110 0.912 +1 0.406 -1.686 -1.060 1.341 0.139 0.772 -1.264 1.619 2.173 1.449 -0.097 0.432 2.215 0.754 -2.615 -1.071 0.000 0.622 -0.822 -1.471 0.000 0.864 0.872 0.987 1.690 1.675 1.308 1.151 +0 0.601 1.261 -1.003 1.282 0.181 1.482 1.487 1.366 1.087 2.116 1.299 -0.495 1.107 1.137 0.870 1.645 0.000 0.895 1.411 0.516 0.000 1.036 0.892 1.066 0.862 2.600 1.296 1.072 +1 0.307 2.039 0.768 1.287 -0.088 2.655 1.139 1.135 0.000 2.068 0.645 -0.945 1.107 1.182 0.779 -0.269 0.000 2.898 0.008 -0.601 3.102 1.168 0.992 0.981 1.127 0.999 0.908 0.825 +1 1.739 -1.042 -0.513 0.496 1.343 1.236 -0.293 0.841 1.087 0.603 -2.822 -1.483 0.000 0.737 -1.904 -1.591 0.000 0.530 0.049 -0.903 3.102 0.439 0.932 1.280 1.350 0.870 1.239 1.083 +0 0.749 1.581 1.427 1.943 -1.168 0.874 1.595 -0.744 2.173 1.114 1.111 0.826 2.215 2.859 -0.080 0.539 0.000 1.621 1.114 -1.286 0.000 0.828 0.938 1.203 0.872 1.475 0.980 0.793 +1 0.988 -0.305 0.006 0.949 -1.014 0.819 -1.139 -1.625 2.173 0.920 0.339 0.741 0.000 0.528 0.575 -0.311 0.000 1.110 -0.927 -0.428 3.102 0.909 1.138 1.066 1.018 0.890 1.038 0.893 +0 1.306 0.494 -0.051 1.547 -0.598 1.329 0.836 -0.430 2.173 3.610 0.378 1.451 0.000 0.489 -0.807 -0.220 0.000 0.861 -0.777 1.103 3.102 2.464 1.519 0.991 0.596 1.595 1.640 1.453 +1 0.952 -1.626 1.588 1.204 -1.249 1.275 -1.147 0.069 2.173 0.416 -1.203 1.241 0.000 0.470 -2.106 -0.802 0.000 0.788 -0.304 0.657 3.102 0.757 0.985 0.990 0.965 0.688 1.045 0.830 +1 0.763 -0.093 1.647 1.519 0.864 0.889 0.751 -0.381 2.173 0.908 0.308 0.832 0.000 2.052 1.146 -1.000 2.548 0.375 1.043 0.737 0.000 0.374 0.951 0.986 1.619 0.987 1.238 1.011 +1 0.883 -1.128 -1.705 1.412 0.049 0.595 -0.553 1.025 0.000 1.737 -0.961 0.007 0.000 0.757 -0.635 1.495 0.000 1.126 -0.488 -0.748 1.551 0.949 1.185 1.546 0.881 0.473 0.807 0.789 +1 0.755 0.612 -0.190 0.410 -1.450 1.358 -0.040 -0.532 2.173 0.890 0.130 1.673 1.107 0.976 -1.832 0.616 0.000 1.683 0.531 1.223 0.000 0.807 1.105 0.987 0.891 1.486 0.971 0.812 +0 0.682 -0.171 -0.685 1.082 0.961 0.681 -0.546 -0.091 0.000 0.670 -0.725 -1.738 1.107 0.917 0.453 0.951 1.274 0.920 0.468 -1.346 0.000 0.881 0.932 1.185 0.717 0.778 0.710 0.658 +1 0.396 -0.764 -0.193 1.550 1.273 1.358 -0.155 1.647 0.000 1.739 1.096 -0.574 2.215 1.783 0.952 1.068 0.000 2.200 2.485 -0.509 0.000 0.893 0.860 1.052 1.885 1.400 1.270 1.040 +1 0.728 -2.210 0.418 0.481 -0.559 0.955 -1.134 -0.484 0.000 0.658 -1.216 -1.138 0.000 1.848 -1.375 1.376 0.000 1.122 -0.633 0.889 3.102 0.939 1.030 0.993 0.481 0.293 0.624 0.593 +0 0.701 0.605 -1.429 0.987 0.499 0.969 1.748 1.576 0.000 1.983 -0.745 0.034 2.215 1.189 -0.875 -1.154 2.548 0.826 -0.663 0.413 0.000 1.056 0.937 1.136 1.361 1.441 1.111 0.950 +1 0.734 -1.285 -0.627 0.787 -1.654 1.046 -0.965 0.156 2.173 0.882 -0.789 -1.494 0.000 1.117 -0.786 0.705 2.548 0.487 1.551 -1.147 0.000 1.536 1.307 0.982 1.089 0.643 1.097 1.059 +1 1.182 0.013 1.063 0.312 1.360 0.863 0.857 1.514 2.173 1.142 0.126 -0.658 0.000 1.063 0.777 -0.572 0.000 1.323 0.335 0.064 0.000 0.994 0.843 0.990 1.030 0.737 0.872 0.881 +1 0.653 0.439 0.083 0.428 0.052 0.960 -1.125 -1.130 0.000 1.129 -0.052 0.662 2.215 0.658 0.634 -1.529 2.548 0.702 -1.087 1.256 0.000 0.943 0.957 0.979 0.741 0.913 0.911 0.803 +1 2.221 -0.693 1.244 0.570 -1.156 0.899 -0.973 -0.331 2.173 0.356 0.207 0.120 0.000 0.465 0.923 -0.330 2.548 0.579 0.624 -1.335 0.000 0.593 0.855 1.294 1.034 0.944 0.980 0.800 +0 1.442 1.484 0.930 0.637 0.093 0.914 2.178 0.815 0.000 2.322 1.080 -0.996 1.107 0.922 0.957 -0.751 1.274 0.952 -1.445 0.403 0.000 0.846 1.047 0.987 1.490 0.344 1.129 1.017 +0 2.073 0.641 0.075 1.716 -0.228 4.010 -0.595 1.706 2.173 2.140 -0.629 -0.256 0.000 2.537 0.784 0.363 2.548 1.080 -2.189 -1.325 0.000 2.817 3.426 1.003 4.120 4.888 3.219 2.973 +1 0.768 0.407 -0.985 0.890 0.292 0.773 -0.445 -1.107 0.000 0.515 0.314 -0.521 0.000 0.894 1.221 0.926 2.548 1.352 -0.247 0.894 1.551 0.859 1.007 1.046 0.780 0.768 0.873 0.761 +1 1.125 1.229 -0.604 0.330 -1.455 0.987 0.847 0.456 0.000 1.506 1.097 1.573 2.215 0.639 1.191 -1.145 1.274 0.428 0.362 -0.484 0.000 0.773 0.829 0.991 1.065 0.671 0.820 0.803 +0 1.617 -0.783 0.262 0.372 0.937 0.547 -0.974 1.734 1.087 0.377 -1.260 -0.918 2.215 0.594 1.138 1.409 0.000 0.950 0.814 -0.630 0.000 0.807 0.939 0.992 0.899 0.467 0.767 0.872 +0 0.988 0.109 1.692 0.611 0.345 1.186 1.112 -0.674 2.173 1.218 -0.286 0.717 1.107 1.217 0.953 0.663 0.000 1.314 0.890 -1.584 0.000 1.253 1.287 1.008 1.162 2.163 1.243 1.017 +1 0.504 1.216 -0.733 1.923 -0.408 1.108 0.252 0.758 2.173 1.561 -1.496 -1.490 0.000 1.497 0.646 0.129 0.000 1.128 0.940 1.615 3.102 0.741 1.731 0.989 1.300 0.982 1.464 1.389 +1 0.720 1.102 1.415 0.355 0.036 1.241 2.773 1.585 0.000 1.353 -1.029 -0.807 2.215 1.136 -0.392 0.419 2.548 0.974 0.075 0.010 0.000 3.462 3.174 0.990 1.198 1.250 3.072 2.196 +1 1.290 -0.496 1.019 0.618 -0.358 1.181 -0.777 -0.856 0.000 1.017 -1.291 0.793 0.000 0.642 -0.080 0.202 2.548 0.505 -0.388 -1.636 1.551 0.857 0.762 1.170 0.612 0.441 0.506 0.536 +1 0.777 -0.634 1.117 0.893 -0.031 1.135 0.456 1.297 0.000 1.270 0.506 -0.685 2.215 0.991 -0.250 -1.297 2.548 1.181 -1.151 -0.463 0.000 2.574 1.556 0.991 1.083 0.793 1.192 1.022 +1 0.799 -1.564 -1.377 0.395 0.506 1.455 -0.782 0.798 2.173 0.783 -1.049 -1.210 0.000 0.980 -0.211 -0.403 0.000 1.204 1.335 -0.436 0.000 0.894 1.236 0.989 0.994 0.902 1.226 1.029 +1 0.839 0.838 1.258 0.783 -1.188 1.349 1.147 -1.164 0.000 1.859 -1.358 0.615 2.215 0.635 -0.652 0.662 0.000 1.431 -0.624 -0.850 3.102 0.532 0.890 0.988 1.115 1.506 1.590 1.203 +0 0.782 -1.153 -1.374 2.708 -0.599 0.736 0.639 1.362 0.000 1.258 -1.872 -1.618 0.000 2.063 -0.219 0.948 2.548 3.434 -0.880 0.170 0.000 0.767 0.942 1.297 0.669 0.536 1.079 1.095 +0 0.671 -0.423 0.105 0.436 1.464 0.904 0.539 0.102 1.087 1.300 0.066 -1.290 2.215 0.864 -1.040 0.974 0.000 0.419 0.137 0.740 0.000 0.735 1.047 0.992 1.140 1.564 1.053 0.906 +1 0.736 1.192 1.468 0.914 0.260 1.198 -0.367 -0.941 2.173 0.749 0.681 1.400 0.000 0.782 -0.596 0.473 2.548 0.541 0.070 0.746 0.000 0.530 1.208 1.007 0.897 1.167 1.056 0.877 +1 0.419 -2.247 1.488 0.852 0.832 1.935 -1.921 0.078 0.000 2.141 1.598 1.129 0.000 3.370 -0.016 -0.770 2.548 3.197 0.406 -1.536 3.102 13.098 7.392 0.996 1.578 1.721 4.543 3.278 +0 0.984 1.504 0.575 1.586 0.716 1.029 0.355 -0.866 0.000 0.523 -1.136 -0.922 0.000 1.502 -1.753 0.639 0.000 1.150 1.040 -0.702 0.000 1.030 0.862 0.982 1.255 0.506 0.976 1.216 +0 0.507 0.904 1.087 1.248 -0.508 0.881 0.095 1.054 2.173 0.516 0.596 -1.077 0.000 0.686 -0.733 -1.005 0.000 0.794 0.296 0.317 3.102 0.706 1.055 1.092 0.625 0.558 0.694 0.706 +1 0.770 -0.476 0.768 1.330 0.849 0.837 0.359 -1.081 0.000 0.827 0.098 -0.112 0.000 0.806 -2.542 0.034 0.000 1.521 0.757 -1.585 3.102 1.374 1.098 0.992 0.682 0.310 0.982 1.058 +0 0.802 0.631 0.901 1.141 0.431 0.531 1.633 -1.193 0.000 0.542 -0.211 1.625 2.215 0.485 -0.962 -1.619 0.000 0.542 -2.132 -0.100 0.000 0.724 0.729 0.973 0.870 0.576 0.749 1.024 +0 0.852 -1.002 0.740 0.304 -1.283 1.022 0.476 0.704 2.173 1.255 -1.051 -0.599 2.215 0.653 0.443 -1.406 0.000 1.022 -1.233 1.459 0.000 0.793 0.881 0.987 0.863 2.105 1.272 1.005 +1 0.714 0.081 1.160 0.892 0.012 0.915 -0.948 -1.475 0.000 0.804 -0.498 1.029 2.215 1.104 -1.006 -0.903 2.548 1.514 -0.682 0.047 0.000 1.767 1.106 0.987 0.646 1.032 0.857 0.791 +0 1.162 -0.039 0.307 0.826 -0.604 0.847 0.077 -0.270 0.000 0.916 -0.429 1.413 2.215 0.448 -0.511 -1.125 2.548 0.878 0.767 1.228 0.000 1.407 0.896 0.993 0.945 0.515 0.757 0.705 +1 0.609 2.089 1.548 1.898 -0.290 0.882 -0.102 0.620 0.000 0.690 -2.189 1.352 0.000 2.182 0.617 -0.222 2.548 0.731 0.948 -1.461 3.102 2.374 1.819 1.484 1.426 0.896 1.605 2.117 +1 1.925 0.683 -1.310 0.496 -0.611 0.627 -0.706 -0.532 2.173 0.454 -0.522 1.467 0.000 0.879 2.155 0.859 0.000 0.886 0.075 0.253 0.000 1.046 0.866 0.994 0.910 0.477 0.838 0.888 +0 0.667 -0.068 -1.425 0.427 0.836 0.870 -0.162 1.384 2.173 1.669 -0.115 -0.713 2.215 1.017 -0.715 0.575 0.000 0.512 0.129 0.466 0.000 0.778 0.908 0.995 1.032 1.683 0.961 0.858 +0 0.757 -0.232 -0.098 0.644 0.982 0.797 -0.924 -0.969 0.000 1.186 -0.578 0.456 2.215 0.763 -0.010 -1.511 0.000 1.142 -1.106 1.453 3.102 0.938 0.864 0.989 0.777 0.911 0.871 0.832 +1 0.533 1.404 -0.991 0.591 0.235 1.389 2.920 -1.354 0.000 1.257 0.468 -0.032 0.000 1.590 0.180 0.357 0.000 1.184 1.098 0.791 3.102 0.805 0.899 0.995 0.806 0.872 0.911 0.775 +1 0.618 -1.212 -0.702 0.560 0.389 1.122 -0.587 -1.710 1.087 0.747 0.146 -0.208 0.000 1.323 0.098 -1.490 0.000 1.415 0.123 0.460 3.102 1.088 0.802 0.990 0.983 1.335 1.060 0.893 +1 1.001 -0.516 -0.671 0.490 1.045 1.025 0.553 0.711 2.173 0.438 -1.570 -0.905 0.000 0.788 0.023 -0.496 0.000 0.731 -1.744 1.682 0.000 0.886 0.570 0.988 1.015 0.734 0.834 0.722 +0 0.403 0.667 -1.518 1.124 0.285 0.850 -1.029 -1.303 1.087 0.461 -1.096 0.392 0.000 1.110 0.427 0.452 0.000 0.652 -0.195 -1.016 0.000 0.969 1.233 0.987 0.614 0.968 0.799 0.730 +1 0.816 -0.544 -0.577 1.445 0.434 0.716 -0.382 1.544 2.173 0.381 0.094 -1.664 0.000 0.635 -1.875 -0.040 0.000 0.932 0.039 -0.979 3.102 1.155 1.022 1.189 0.791 0.685 0.744 0.721 +0 1.390 0.266 -0.533 1.271 -0.310 1.329 0.124 1.437 2.173 1.560 -0.585 -0.738 2.215 1.239 0.258 0.851 0.000 0.775 1.081 1.073 0.000 0.611 0.837 1.002 0.741 2.111 1.281 1.171 +1 1.262 0.316 -1.008 1.507 -1.171 2.164 1.238 0.913 0.000 1.485 -1.044 -0.227 2.215 0.633 -1.529 -1.602 0.000 1.104 -0.397 -0.919 3.102 4.601 2.788 1.008 2.010 0.760 2.286 1.947 +1 0.442 -1.143 1.198 0.812 -0.094 0.849 0.441 1.554 0.000 1.928 0.213 0.237 2.215 0.736 1.234 -1.428 2.548 1.047 -1.236 -1.596 0.000 1.672 1.310 0.980 1.828 1.473 1.563 1.447 +1 0.446 -1.308 -1.057 1.617 1.411 2.207 -0.844 -0.913 0.000 1.145 -0.271 0.804 2.215 2.078 -1.081 0.583 2.548 1.616 -0.967 0.108 0.000 0.911 0.883 0.989 1.027 0.836 0.812 0.740 +0 0.558 0.238 -1.479 1.509 0.812 0.752 0.121 0.833 2.173 0.563 0.451 -0.024 0.000 1.762 -0.504 -1.085 2.548 0.710 0.869 -0.637 0.000 0.496 0.963 1.119 0.649 1.500 0.923 0.809 +0 0.879 0.088 -0.737 0.743 -0.181 1.159 -0.258 1.321 0.000 1.509 0.371 0.229 2.215 1.999 -0.737 -1.512 2.548 0.760 -0.205 -1.688 0.000 0.938 0.975 0.982 0.880 2.183 1.243 0.998 +0 1.853 0.940 -0.872 0.647 1.591 0.955 0.257 0.644 1.087 0.548 -0.943 -0.240 0.000 0.816 0.476 1.296 2.548 0.456 0.478 -1.507 0.000 0.811 0.950 1.208 0.851 0.626 0.896 0.828 +0 0.749 -0.298 -0.212 0.459 1.728 0.790 0.136 -1.725 0.000 0.871 -1.631 1.473 0.000 1.865 0.798 -0.113 2.548 0.734 -1.265 -1.344 1.551 1.306 1.098 0.982 0.805 1.566 1.033 0.836 +0 0.748 -0.403 -0.542 0.774 -0.261 0.448 -1.752 0.466 0.000 0.665 -1.392 0.995 0.000 1.135 -1.160 -1.534 2.548 0.399 -0.942 -1.138 3.102 0.721 0.774 0.982 0.630 0.183 0.746 0.847 +0 0.333 0.384 0.592 1.948 -0.187 0.687 1.500 1.525 0.000 0.579 0.327 1.577 0.000 0.619 -0.220 -1.632 2.548 0.989 0.448 -0.595 3.102 0.861 0.755 0.986 0.806 0.537 0.615 0.621 +0 0.643 -1.776 -1.451 0.658 -1.041 0.694 -0.151 0.142 2.173 0.730 0.578 -0.964 2.215 0.912 0.373 1.172 0.000 0.581 1.708 0.857 0.000 0.769 0.987 0.995 0.855 0.966 0.796 0.868 +0 0.562 -0.537 -0.541 0.715 0.508 0.792 -0.274 -1.062 1.087 0.690 -1.489 0.374 0.000 0.642 0.188 1.457 2.548 1.459 -1.039 1.217 0.000 0.919 0.891 0.994 0.980 0.713 0.849 0.774 +0 0.799 -0.112 -0.026 0.556 1.105 0.728 1.610 1.536 0.000 0.566 0.829 -1.150 1.107 1.027 0.758 0.413 2.548 0.558 1.209 -0.753 0.000 0.862 0.910 0.987 0.703 0.800 0.640 0.644 +1 0.729 -0.072 0.129 0.760 0.674 0.773 0.324 -0.798 2.173 0.648 0.283 0.688 0.000 1.530 0.100 -1.487 2.548 0.553 1.118 -0.267 0.000 0.950 0.852 0.992 0.941 0.802 0.837 0.728 +0 1.111 0.975 -1.220 0.722 -0.156 1.408 0.437 -1.452 0.000 1.606 0.457 0.413 1.107 1.039 1.056 -0.441 2.548 0.881 -0.080 0.084 0.000 1.735 1.322 1.016 1.104 1.070 1.172 0.977 +1 1.372 -0.040 -0.325 0.336 -1.281 0.787 0.472 1.459 2.173 0.503 1.166 0.316 0.000 0.545 0.810 0.980 2.548 1.111 1.875 -0.726 0.000 0.951 0.733 0.985 0.995 0.382 0.734 0.766 +1 1.260 -0.415 -1.468 0.973 1.404 0.824 -0.539 -0.455 2.173 0.739 1.674 0.491 0.000 0.561 -1.421 0.723 0.000 1.077 0.906 -0.326 1.551 0.864 0.702 0.986 1.100 0.913 0.903 0.933 +1 0.726 -0.993 1.628 1.282 -0.936 1.181 0.242 0.924 1.087 0.798 -0.634 -1.144 0.000 0.988 -0.166 0.348 2.548 0.865 0.524 -1.041 0.000 0.891 0.888 0.988 1.444 0.728 1.006 0.921 +0 2.224 0.516 0.846 0.308 0.297 0.998 0.145 -1.248 2.173 0.898 -0.032 -0.134 0.000 0.940 1.017 -0.900 0.000 0.625 -0.646 -1.340 3.102 1.240 0.897 0.984 1.313 0.405 0.876 0.884 +0 1.402 0.815 -0.603 0.891 1.041 0.487 0.272 1.123 2.173 0.707 1.194 -1.526 1.107 0.492 -1.834 1.326 0.000 1.400 -0.249 -0.377 0.000 0.882 0.855 1.543 0.925 0.729 0.794 0.818 +1 0.881 0.421 0.029 0.684 1.615 1.138 0.455 -1.564 0.000 1.225 -0.489 0.139 2.215 0.658 0.119 -1.004 2.548 0.731 0.386 1.204 0.000 0.839 0.663 1.065 0.895 0.874 0.929 0.797 +1 0.851 -0.896 -0.661 0.450 0.932 0.357 1.549 1.027 0.000 0.931 -0.655 -1.499 2.215 0.561 -0.145 0.445 2.548 0.812 -1.033 -0.158 0.000 1.728 0.987 0.989 0.722 0.781 0.836 0.727 +0 1.292 -1.041 0.545 1.577 0.988 1.476 -0.377 0.391 1.087 0.977 0.325 -0.963 2.215 1.392 -1.271 -1.053 0.000 1.854 -0.357 -1.187 0.000 0.917 1.037 0.990 0.866 1.781 1.315 1.228 +0 1.349 -0.693 -0.284 1.086 -1.730 0.957 1.431 -0.865 0.000 0.836 0.023 1.665 1.107 1.368 -1.341 0.603 0.000 0.794 0.417 0.242 0.000 0.908 1.135 1.617 0.920 0.675 0.750 0.758 +0 0.621 1.075 -1.168 1.425 1.024 0.653 -0.049 1.297 2.173 1.368 0.077 -0.451 0.000 0.718 1.041 0.077 2.548 0.961 -0.170 -1.165 0.000 0.921 0.869 1.199 0.878 0.932 0.823 0.857 +1 0.870 0.786 1.607 0.205 -1.338 0.612 -2.319 -1.573 0.000 1.403 0.597 0.386 2.215 1.397 0.745 -0.492 2.548 0.705 0.320 -0.287 0.000 2.050 2.243 0.981 1.021 1.068 1.740 1.362 +0 1.551 0.529 -1.023 1.002 -1.610 0.806 -0.965 0.617 0.000 1.050 -0.901 0.086 0.000 1.118 -0.242 1.443 2.548 0.820 0.111 -0.401 3.102 0.902 0.849 0.990 0.830 0.744 0.767 0.952 +0 0.745 -1.345 1.399 1.516 0.432 0.833 -0.527 -0.350 2.173 0.890 -2.203 1.617 0.000 0.501 -2.448 1.262 0.000 0.463 0.599 -1.238 0.000 0.388 1.432 1.126 1.098 0.439 1.138 1.006 +1 0.686 0.142 -1.426 0.985 0.120 1.117 -0.531 0.171 0.000 0.981 -0.391 1.636 1.107 0.554 0.857 0.702 2.548 0.701 -1.958 -1.380 0.000 1.934 1.452 1.121 0.878 0.814 1.062 0.923 +1 0.900 -0.445 -0.014 1.414 0.748 0.893 1.252 1.002 0.000 1.715 -1.701 -0.508 0.000 1.062 2.145 -0.723 0.000 3.456 -0.317 -1.399 3.102 1.262 1.254 0.991 1.380 1.162 1.195 1.114 +0 1.143 0.593 0.060 0.792 1.341 0.800 1.178 -1.730 2.173 0.479 0.423 -1.523 0.000 0.962 -0.671 -0.248 1.274 0.518 -0.899 0.520 0.000 0.826 0.934 1.206 0.930 1.621 0.952 0.794 +1 0.406 1.032 -1.285 0.477 0.432 0.894 0.818 -0.251 2.173 1.279 0.633 1.697 0.000 0.689 -0.340 1.413 0.000 0.376 -0.736 0.161 3.102 0.853 0.752 0.981 0.863 0.631 0.866 0.744 +1 1.478 1.156 1.148 1.144 0.412 1.031 0.654 -0.999 2.173 0.614 0.264 1.500 2.215 0.610 1.644 -0.785 0.000 0.747 -0.548 -0.264 0.000 1.216 0.961 1.108 0.813 0.937 0.955 0.880 +1 0.547 -0.326 1.308 0.170 1.534 0.660 1.194 -0.126 0.000 1.052 1.071 1.685 2.215 0.600 0.464 -0.234 0.000 0.850 0.845 -0.551 0.000 0.962 1.064 0.999 0.530 0.531 0.681 0.615 +1 1.062 -0.076 0.594 0.903 -0.298 2.590 -1.528 -1.243 0.000 0.934 -0.878 -0.026 0.000 1.129 -1.416 0.418 0.000 2.018 -0.450 1.205 3.102 0.806 1.121 0.988 0.610 0.854 0.903 0.812 +0 0.860 0.224 -0.608 1.260 -1.455 0.506 0.371 -1.248 0.000 1.251 -0.004 0.534 2.215 1.548 -0.420 0.996 0.000 1.995 -0.679 -0.220 3.102 0.852 0.903 0.996 1.022 1.076 0.943 0.779 +1 1.231 -0.447 -0.724 0.658 -1.395 0.889 -0.337 0.081 2.173 0.314 -1.595 0.647 0.000 0.717 -0.215 -1.517 0.000 1.439 -0.769 1.551 3.102 0.874 0.940 0.990 0.861 1.214 0.847 0.735 +1 1.160 0.229 0.513 0.819 -0.530 1.210 0.110 -0.939 0.000 0.647 -1.320 0.616 2.215 0.615 0.573 0.924 2.548 0.811 0.386 -1.571 0.000 0.857 0.933 1.089 0.934 0.803 0.920 0.838 +1 0.389 -1.290 0.025 0.114 -0.786 0.735 -0.304 0.621 2.173 0.722 1.082 -1.237 0.000 0.596 0.263 1.477 0.000 0.728 -0.275 -0.345 3.102 0.774 1.114 0.987 0.526 0.592 0.716 0.637 +0 1.158 0.807 -0.364 1.093 -0.725 1.006 -0.601 0.594 2.173 0.396 0.157 -1.226 0.000 1.528 0.943 1.711 2.548 0.376 1.235 1.695 0.000 0.433 1.005 0.974 1.155 1.926 1.221 0.923 +0 0.728 -0.882 1.150 0.798 -0.694 0.965 -0.619 0.685 2.173 0.875 1.282 -1.109 0.000 0.909 -0.636 -0.962 2.548 0.723 0.992 0.906 0.000 0.899 0.940 1.051 0.862 1.164 1.047 0.918 +1 1.249 -0.167 -0.846 1.821 -0.240 0.921 -1.055 -1.730 2.173 0.570 -1.218 0.970 0.000 0.771 -0.058 0.913 2.548 0.530 0.713 0.520 0.000 0.946 1.006 1.086 0.940 0.906 1.017 0.885 +0 1.030 1.120 0.018 0.251 -0.492 0.886 1.534 -1.287 0.000 1.162 1.131 1.522 1.107 2.420 1.987 0.432 0.000 0.724 0.334 -0.221 0.000 0.983 0.866 0.990 0.528 0.504 0.661 0.620 +0 0.723 -1.223 0.403 0.461 -1.134 1.047 -0.519 1.136 2.173 0.831 -0.614 -0.524 0.000 0.532 1.003 -1.190 2.548 0.655 0.617 -0.087 0.000 0.802 0.738 0.991 1.092 1.164 1.010 0.929 +1 0.752 1.290 -0.907 0.710 0.893 0.790 0.244 1.034 2.173 1.095 0.933 -0.083 0.000 0.992 0.437 -0.627 0.000 1.266 -0.045 -1.463 3.102 0.847 1.014 1.011 0.881 0.838 0.890 0.796 +1 0.902 -0.291 0.639 1.229 1.317 0.647 -1.570 -0.516 0.000 0.524 0.033 -1.389 0.000 1.051 -0.009 -0.688 2.548 0.832 -0.020 0.844 1.551 0.610 0.614 0.986 0.537 0.702 0.666 0.613 +1 0.381 -0.758 -0.324 1.917 0.675 0.944 0.249 -0.471 0.000 1.031 0.076 -1.703 2.215 0.773 0.641 1.029 0.000 1.766 0.958 -1.263 1.551 0.933 0.854 0.989 1.861 0.831 1.324 1.096 +1 0.369 -1.145 -1.354 0.457 1.547 0.871 -0.258 0.216 2.173 0.870 -0.077 -1.248 0.000 0.874 0.857 -1.674 0.000 0.825 0.066 0.752 3.102 0.871 0.799 0.995 0.935 0.444 0.814 0.710 +0 0.799 1.230 1.526 0.741 0.595 0.418 1.845 -0.544 0.000 0.611 0.539 -1.294 2.215 0.637 0.798 0.204 0.000 0.471 -0.616 1.603 3.102 0.750 0.833 0.990 0.702 0.416 0.594 0.600 +1 0.605 0.386 1.666 0.609 -0.301 1.026 0.395 1.122 0.000 1.024 -0.581 -1.164 0.000 0.708 0.760 0.839 0.000 1.251 0.911 1.406 0.000 0.800 0.683 0.984 0.686 0.181 0.879 0.827 +0 1.482 -0.245 -0.208 0.538 0.254 1.036 -0.317 1.641 0.000 0.191 1.178 -1.043 0.000 0.317 -0.680 0.390 2.548 0.472 1.176 1.168 1.551 1.012 0.763 0.996 0.483 0.430 0.555 0.710 +1 1.334 0.459 0.112 1.735 -0.377 0.998 -0.007 1.465 0.000 0.803 1.274 -1.050 1.107 0.404 -0.387 -0.032 0.000 0.701 0.149 0.985 3.102 1.140 1.240 0.984 0.768 0.769 0.749 0.879 +0 0.973 -1.017 0.711 1.341 1.397 0.389 0.646 -0.905 0.000 0.977 0.066 0.133 1.107 1.509 -1.023 -1.142 0.000 0.463 -0.460 -1.021 0.000 0.420 0.705 0.987 0.731 0.212 0.781 0.747 +1 0.818 -0.461 -0.763 0.888 1.116 0.564 -0.732 1.623 1.087 1.077 -1.430 0.796 2.215 1.007 -0.612 -0.057 0.000 1.123 0.593 -0.544 0.000 0.978 0.927 1.172 0.953 0.890 0.892 0.778 +0 0.351 -1.774 0.847 2.170 0.511 0.725 1.059 -0.921 0.000 0.710 -0.490 1.682 2.215 0.868 1.167 -0.176 0.000 1.193 0.394 -1.417 3.102 0.901 0.788 1.001 0.923 0.509 0.821 1.047 +1 0.751 -1.273 -0.508 0.387 0.768 0.691 0.168 0.033 2.173 0.572 0.802 -1.273 2.215 0.655 0.220 0.876 0.000 1.172 -0.090 -1.498 0.000 0.832 0.879 0.989 0.821 0.908 0.681 0.643 +0 0.693 0.011 1.682 2.099 -1.084 0.926 -0.222 0.378 1.087 0.762 0.779 1.436 2.215 0.594 0.696 0.894 0.000 0.672 1.075 -0.402 0.000 0.667 0.829 1.011 0.903 1.206 1.007 0.838 +1 1.258 -1.820 0.777 0.822 -0.693 1.025 -0.102 -1.187 2.173 0.519 -1.171 0.231 0.000 0.566 -1.657 -1.288 0.000 0.927 -0.532 1.203 3.102 0.855 1.146 1.366 0.865 0.905 1.060 0.869 +1 0.360 -2.304 -1.175 0.378 0.944 0.488 0.859 0.762 2.173 0.436 -0.938 -0.106 2.215 0.600 -1.738 0.871 0.000 0.550 0.788 -1.391 0.000 1.367 0.876 0.977 0.966 0.859 0.768 0.703 +0 0.690 0.677 0.788 2.548 0.235 1.258 0.833 -1.272 1.087 1.138 -2.556 -0.296 0.000 2.626 0.184 1.602 2.548 0.572 -2.416 0.593 0.000 0.769 3.251 0.990 1.716 1.394 2.852 2.423 +0 0.560 -1.821 1.155 1.775 0.682 0.912 0.877 -0.712 2.173 0.666 -0.019 -0.892 0.000 0.680 -0.423 1.098 2.548 0.696 -0.354 -1.623 0.000 0.659 0.706 0.995 0.556 1.212 1.193 0.931 +0 1.835 0.947 -0.665 0.911 -1.381 0.848 -0.350 1.167 0.000 0.738 -1.441 0.961 0.000 0.734 -0.334 0.038 1.274 0.511 -0.821 1.245 3.102 1.049 0.938 1.076 0.955 0.440 0.758 1.053 +1 0.546 1.060 -0.017 1.378 1.673 1.224 0.542 -0.463 2.173 1.515 0.948 1.223 0.000 0.855 0.850 -1.399 2.548 0.798 0.050 0.293 0.000 1.263 0.988 1.201 1.193 0.984 1.040 0.908 +0 0.736 1.285 -0.513 1.678 -0.041 1.041 1.131 0.343 0.000 1.322 0.460 1.739 0.000 0.836 -0.461 -1.731 2.548 0.620 1.416 -1.357 0.000 0.871 0.793 0.979 1.391 0.113 1.231 1.095 +0 1.078 0.087 1.014 1.521 -1.548 0.636 -1.342 0.640 0.000 1.096 0.519 -0.489 2.215 0.464 0.249 -0.071 0.000 0.701 -1.286 -1.361 3.102 1.061 0.829 1.314 1.191 1.128 0.910 0.928 +0 0.812 0.041 -0.323 0.424 0.592 0.655 0.951 1.666 0.000 0.517 1.095 -1.114 2.215 0.879 0.109 -1.456 2.548 0.477 0.782 0.494 0.000 0.744 0.632 0.983 0.655 0.434 0.552 0.547 +1 0.888 -1.152 -1.011 0.659 1.355 0.484 1.141 0.198 2.173 0.441 0.479 -0.644 0.000 0.652 -0.309 -0.371 0.000 1.522 0.453 1.251 3.102 0.802 0.720 0.989 1.478 0.789 1.109 0.987 +0 0.526 1.430 -0.336 1.636 -1.386 0.733 0.339 0.297 0.000 1.049 1.336 1.171 0.000 1.212 0.340 -0.626 2.548 0.383 -0.817 1.328 1.551 1.658 1.090 1.042 0.876 0.637 0.873 0.899 +1 1.166 0.158 -0.711 0.606 -0.288 0.925 -0.444 0.664 2.173 1.069 -0.152 0.063 2.215 1.246 -0.179 -1.651 0.000 0.439 0.188 1.470 0.000 0.316 0.974 0.993 1.057 0.784 0.770 0.769 +1 0.903 0.728 1.547 0.458 -0.191 1.460 -2.134 -0.631 0.000 1.943 0.210 0.972 2.215 1.131 0.332 -0.648 0.000 1.487 -0.445 1.394 3.102 0.805 0.978 0.983 1.004 0.820 0.878 0.803 +1 0.405 0.985 -0.698 0.871 0.163 0.890 0.197 -1.579 0.000 0.436 -1.407 0.121 2.215 0.677 -1.085 0.683 0.000 1.104 1.076 1.707 1.551 0.848 1.029 0.994 0.795 1.309 1.240 1.078 +0 1.658 -1.042 0.132 1.080 0.533 0.930 -0.633 1.677 2.173 0.745 -0.272 -0.843 0.000 0.924 2.127 0.965 0.000 1.167 0.106 -0.481 3.102 0.805 1.192 0.990 1.386 1.113 1.460 1.905 +0 0.813 0.038 -1.396 1.185 -0.174 0.402 -1.269 1.534 0.000 0.679 0.800 0.271 0.000 0.801 0.843 1.737 0.000 0.369 -0.708 -1.556 3.102 0.770 0.539 1.213 0.628 0.148 0.466 0.489 +0 0.764 0.937 -0.993 0.867 -1.466 0.992 -0.363 0.954 0.000 0.681 -0.252 -0.136 1.107 0.571 -0.985 -0.113 0.000 0.771 0.195 -1.254 3.102 0.777 0.771 0.980 0.469 0.577 0.528 0.560 +1 1.421 1.077 0.298 0.253 -1.243 0.921 0.753 -1.492 0.000 0.582 0.817 0.956 2.215 1.224 -0.030 -0.327 2.548 1.091 -0.056 -1.669 0.000 1.121 1.074 0.988 0.611 0.915 0.758 0.715 +1 1.650 0.334 1.108 0.981 -1.428 0.508 -0.754 -0.133 0.000 0.452 0.047 1.361 0.000 0.732 -1.083 0.965 2.548 0.794 -0.610 -1.174 1.551 1.079 0.771 1.333 0.850 0.562 0.710 0.695 +0 0.792 -0.771 0.105 0.596 -1.237 0.569 -0.816 1.058 2.173 0.444 -0.335 -1.118 1.107 0.773 1.178 -0.979 0.000 1.512 0.520 0.901 0.000 0.663 1.066 0.985 0.607 0.706 0.689 0.726 +1 0.920 -1.089 0.682 0.129 -1.596 1.151 -0.931 -0.520 0.000 1.150 1.861 1.519 0.000 1.256 -2.119 1.399 0.000 2.006 -0.881 -0.930 0.000 0.849 1.114 0.987 0.459 0.387 0.705 0.728 +0 0.486 1.519 0.443 0.609 0.943 0.406 -0.679 -1.180 2.173 0.657 -0.385 1.420 2.215 0.595 0.767 -0.578 0.000 0.816 -1.699 -0.372 0.000 1.540 0.962 0.978 0.690 0.555 0.713 0.744 +1 4.314 0.390 0.801 1.470 0.748 2.193 -0.363 -1.123 0.000 1.204 0.238 -0.749 1.107 0.790 0.510 -0.224 0.000 0.906 1.321 -0.295 0.000 0.851 0.878 0.985 0.540 0.808 1.121 0.953 +0 1.768 0.606 1.739 0.554 -1.210 0.955 0.442 0.521 2.173 0.727 0.394 -0.031 0.000 1.274 -0.611 -1.166 2.548 1.205 -0.663 0.132 0.000 0.798 1.036 0.975 1.227 1.593 1.105 1.020 +0 0.695 0.554 0.615 1.008 1.378 1.564 0.975 -0.496 2.173 1.539 0.233 1.386 2.215 0.337 1.265 1.275 0.000 0.721 0.315 -0.542 0.000 0.614 0.799 0.988 1.356 2.426 1.294 0.959 +1 0.709 -1.443 0.093 0.644 -1.704 1.134 -0.541 1.388 2.173 1.116 -0.338 -0.110 0.000 1.148 -0.674 -0.594 0.000 0.948 -0.299 -1.056 3.102 0.810 0.679 0.988 1.077 0.891 0.945 0.895 +0 1.899 0.755 0.127 0.809 -1.464 0.684 -1.040 -1.266 2.173 0.720 0.311 1.343 2.215 0.429 -0.117 -1.605 0.000 0.772 0.787 1.571 0.000 0.397 0.775 1.700 1.065 1.059 1.106 0.845 +1 0.694 0.043 -1.538 1.484 -0.058 1.190 0.925 -0.583 2.173 1.096 -0.119 0.818 0.000 1.690 0.557 1.284 1.274 0.874 -0.465 1.652 0.000 0.914 0.853 1.367 1.105 1.779 1.178 1.035 +1 0.584 2.017 0.156 2.029 -0.060 1.297 -0.483 1.701 0.000 0.398 -1.163 0.748 2.215 0.647 0.392 -0.864 1.274 0.527 0.713 1.119 0.000 1.112 0.943 0.978 1.275 0.725 0.872 1.095 +0 0.829 -0.099 -1.673 1.868 1.312 0.837 -1.625 -0.796 0.000 1.136 -1.133 -0.370 0.000 0.816 0.485 0.435 2.548 0.857 -0.890 0.903 3.102 0.922 0.981 1.000 0.979 0.625 0.955 1.013 +1 2.427 1.329 0.795 0.241 0.633 0.456 1.495 -0.569 2.173 0.987 -0.001 -1.378 0.000 1.055 1.630 0.100 0.000 0.932 0.256 -0.970 3.102 0.726 1.123 0.977 1.048 0.503 0.807 1.021 +0 1.114 -0.469 0.602 1.864 0.224 0.863 0.940 -0.793 0.000 0.736 -0.597 1.589 2.215 1.486 0.721 -1.399 1.274 1.342 -1.072 0.781 0.000 1.134 0.921 0.990 1.023 0.975 1.229 1.491 +0 1.090 0.187 -0.191 1.284 0.515 0.434 0.015 0.575 2.173 0.295 1.378 1.592 0.000 0.927 0.320 -1.299 0.000 1.138 -0.272 -0.777 3.102 0.756 0.852 0.986 0.764 0.710 0.643 0.710 +1 0.583 1.109 -0.748 0.750 -1.632 1.035 0.691 0.472 2.173 0.808 -1.074 1.681 0.000 0.940 -0.294 1.581 0.000 1.407 0.523 -0.309 1.551 0.795 0.895 0.993 1.054 0.828 0.843 0.768 +1 0.489 2.130 0.147 0.337 0.594 0.985 0.543 0.902 1.087 1.478 0.877 -1.247 0.000 0.836 0.981 -0.725 0.000 0.738 0.507 -0.349 3.102 0.788 0.647 1.000 0.789 0.816 0.900 0.786 +1 0.595 -1.501 -0.727 0.688 -1.103 0.686 -0.497 0.814 0.000 2.037 -1.732 0.503 0.000 2.069 0.149 -1.063 2.548 1.034 -1.258 1.399 0.000 0.935 0.929 0.981 0.759 0.939 0.992 0.836 +1 0.996 0.855 1.263 0.860 1.213 1.590 0.046 0.575 2.173 1.027 0.250 -1.522 0.000 1.409 0.617 -0.551 0.000 1.287 1.615 -1.196 0.000 0.794 0.698 0.992 1.642 0.916 1.162 1.040 +1 0.651 -1.376 -1.337 1.135 0.084 1.097 -0.352 1.366 0.000 1.006 -1.606 0.956 0.000 1.028 -2.558 0.322 0.000 1.640 -0.790 -0.298 3.102 1.088 0.952 1.141 0.694 0.452 0.760 0.690 +1 1.295 0.962 -0.633 1.039 -1.417 0.515 0.094 -0.329 2.173 1.100 0.435 0.823 2.215 0.563 -1.429 1.205 0.000 0.715 -0.289 1.548 0.000 0.502 0.872 1.042 0.796 0.975 0.858 0.883 +1 2.256 -1.085 -0.494 0.836 0.817 0.310 -1.738 -1.237 0.000 0.973 -1.386 1.672 2.215 0.768 -0.341 0.264 0.000 2.076 0.320 1.484 3.102 0.849 0.897 1.760 1.276 1.345 1.247 1.025 +1 0.397 0.573 0.095 1.575 -0.081 0.792 0.096 1.580 2.173 0.635 -1.042 -1.284 0.000 0.392 -0.740 -0.007 0.000 0.689 -0.330 1.181 3.102 0.706 0.875 0.980 0.692 0.338 0.785 0.689 +1 0.857 1.579 0.283 0.963 -1.105 0.723 0.182 -1.533 2.173 0.403 -1.070 1.118 2.215 0.978 0.548 0.492 0.000 0.772 0.905 -0.667 0.000 0.861 0.965 1.194 1.270 0.770 0.976 0.834 +0 1.241 0.276 0.156 0.561 1.578 0.746 -0.745 -1.431 1.087 1.131 -0.028 0.857 2.215 1.143 -1.479 -0.830 0.000 0.431 -0.973 -0.427 0.000 0.328 1.246 1.108 1.041 1.292 0.895 0.879 +0 0.482 -0.944 -1.531 1.272 0.510 1.057 -0.565 0.843 2.173 0.390 -1.867 -1.346 0.000 0.789 -0.171 -0.664 2.548 0.750 -0.203 -1.365 0.000 0.867 1.062 1.046 0.768 1.133 0.760 0.689 +1 1.052 1.240 -0.065 0.590 -1.608 0.741 -0.949 0.587 0.000 0.721 -0.516 -1.676 2.215 0.743 0.758 -0.672 2.548 0.539 -0.618 -1.131 0.000 0.971 1.059 1.074 1.058 0.838 0.754 0.871 +1 0.601 1.661 -1.731 1.456 1.002 0.574 0.428 -1.618 0.000 1.079 0.804 -0.350 2.215 1.099 0.464 0.588 0.000 0.707 0.000 -0.279 0.000 0.934 0.918 0.989 1.738 0.959 1.314 1.109 +1 0.535 -1.733 0.749 0.914 0.930 0.968 0.829 -0.754 0.000 0.919 -0.899 1.250 0.000 1.037 -1.620 -0.714 0.000 0.864 -0.882 -1.486 3.102 0.828 0.719 0.993 0.686 0.434 0.515 0.592 +1 2.028 0.788 0.716 0.671 -1.629 0.749 -1.628 -1.024 0.000 0.884 0.458 0.483 0.000 1.351 -0.342 -0.729 1.274 0.704 -0.761 1.550 3.102 2.764 1.526 1.385 1.344 0.690 1.018 1.205 +0 0.655 1.474 1.227 0.755 0.011 1.058 0.477 -1.191 1.087 1.299 -0.137 0.182 2.215 0.499 0.329 0.699 0.000 1.531 0.624 1.732 0.000 0.795 0.883 0.987 1.458 1.719 1.286 1.038 +1 0.499 -0.978 -0.806 1.469 0.596 1.375 0.162 -1.437 0.000 1.343 0.114 -0.085 2.215 0.761 0.885 1.468 0.000 0.697 -0.160 0.875 3.102 1.194 0.926 1.131 1.044 0.679 1.000 1.027 +1 0.703 -0.292 -1.092 0.565 1.199 1.514 -0.152 -0.834 0.000 0.869 -0.740 0.903 0.000 1.210 0.372 0.993 2.548 1.710 -0.563 0.106 3.102 2.549 1.649 0.980 0.892 1.006 1.191 0.990 +1 0.421 -1.418 -0.657 0.738 -0.046 1.169 0.845 -1.250 1.087 1.037 -0.571 -0.523 0.000 1.819 1.881 0.808 0.000 0.873 0.133 0.754 3.102 0.813 0.733 0.997 1.161 1.106 0.914 0.803 +0 0.324 1.762 -1.683 3.700 1.443 1.385 -1.039 -0.163 2.173 0.604 -2.086 -0.456 0.000 0.393 0.910 -0.690 2.548 0.550 -1.662 0.734 0.000 0.662 0.888 0.995 1.124 1.191 4.075 4.044 +1 0.466 -0.675 1.055 0.589 0.649 1.094 -1.485 -1.269 0.000 1.218 -1.302 0.408 2.215 0.585 -1.523 1.478 0.000 0.554 0.032 -0.103 3.102 0.897 0.931 0.979 0.727 0.643 0.892 0.799 +1 1.134 -0.092 1.631 1.231 1.645 0.379 1.442 1.414 0.000 0.807 0.291 -0.507 2.215 1.290 0.711 0.516 2.548 0.923 0.757 -1.226 0.000 0.671 0.834 0.988 1.021 0.903 0.868 0.741 +1 1.243 0.198 1.111 0.266 -1.708 0.921 0.421 -1.010 2.173 0.751 0.037 -0.536 0.000 1.023 -0.241 1.326 0.000 1.324 -0.830 0.711 3.102 0.910 0.967 0.986 1.066 1.476 0.886 0.785 +0 0.728 0.876 1.365 0.600 -0.332 1.248 2.023 0.872 0.000 1.552 0.331 0.228 0.000 1.380 -0.547 -0.303 2.548 1.554 -0.454 -0.692 0.000 0.859 0.765 0.989 0.790 1.823 1.081 0.896 +1 1.128 -0.987 0.759 0.992 1.737 2.239 -0.375 -1.174 0.000 2.114 -0.444 0.340 1.107 1.549 0.324 0.144 0.000 1.091 0.301 1.299 3.102 1.149 0.899 1.131 1.207 1.191 0.911 0.874 +1 1.399 -1.384 -0.039 0.487 1.387 0.716 -1.093 0.494 0.000 1.575 -1.689 -1.513 0.000 0.790 -0.288 -1.513 2.548 1.040 0.287 0.236 0.000 1.083 1.013 1.098 0.590 0.485 0.631 0.653 +1 1.744 0.643 -1.368 0.874 1.555 1.065 -0.202 0.572 1.087 0.767 0.391 -0.994 2.215 0.555 1.587 -0.234 0.000 0.389 -1.086 0.285 0.000 1.144 0.862 0.986 1.381 1.376 1.001 0.894 +1 0.663 -0.902 0.581 0.602 -1.172 0.487 0.544 1.260 0.000 1.230 1.026 -1.195 2.215 1.008 0.839 0.570 0.000 0.396 1.375 -0.492 3.102 0.771 1.138 0.992 0.962 0.419 1.088 1.004 +0 0.878 -0.807 -0.795 1.466 -0.235 0.734 -1.203 1.449 2.173 0.609 -1.638 0.476 0.000 0.902 -0.079 1.294 2.548 0.876 -1.588 -1.315 0.000 0.957 0.982 0.987 1.224 0.619 0.895 0.858 +1 1.250 0.081 -0.122 1.090 -1.068 0.720 0.570 1.039 2.173 0.499 0.391 0.316 0.000 1.019 -0.487 1.674 2.548 0.481 1.577 -1.144 0.000 0.808 0.925 1.216 1.102 0.850 0.854 0.754 +1 0.889 0.439 0.395 1.363 -0.628 0.621 -1.143 0.793 2.173 0.796 -0.394 1.738 0.000 0.929 1.072 -0.647 0.000 0.831 0.230 1.122 0.000 1.069 0.854 1.214 1.242 0.719 0.853 0.828 +0 0.291 2.093 -0.176 1.875 1.128 0.868 -0.420 -0.879 1.087 0.959 -1.114 -1.022 0.000 1.406 0.287 0.530 2.548 0.727 0.767 -1.097 0.000 1.338 0.872 0.987 1.631 1.414 1.896 1.897 +0 0.911 -0.754 -0.992 0.476 0.530 0.781 -1.140 -0.581 0.000 0.933 1.246 1.098 0.000 0.619 0.889 1.727 2.548 0.819 0.157 1.081 3.102 0.889 0.646 0.984 0.899 0.372 0.636 0.780 +0 0.513 1.389 -1.573 1.377 -0.754 0.809 0.763 0.412 0.000 0.681 0.775 1.062 1.107 1.194 1.024 -1.137 2.548 1.043 2.431 0.404 0.000 0.769 0.990 0.982 0.853 0.892 0.673 0.692 +1 1.456 -1.233 0.352 1.908 -0.563 0.797 -0.550 0.645 0.000 1.924 -0.725 -1.349 2.215 0.821 -1.065 1.264 0.000 1.252 -0.837 -0.410 3.102 0.893 0.941 1.694 1.653 1.065 1.072 1.061 +1 0.859 -0.226 1.471 2.092 0.452 0.975 0.551 -0.916 2.173 0.646 0.195 1.593 0.000 0.641 0.349 0.436 2.548 0.493 -0.735 -0.856 0.000 0.725 0.845 1.475 0.749 0.929 0.975 0.813 +1 1.090 1.435 -1.715 1.789 1.430 1.230 2.009 -0.178 0.000 0.420 1.798 -0.999 0.000 0.538 1.217 -1.282 2.548 0.768 -0.670 0.356 3.102 1.039 0.834 1.006 1.094 0.800 1.017 1.085 +0 2.377 -0.255 -0.099 0.231 0.111 0.839 -2.473 -1.522 0.000 0.539 -1.224 0.859 2.215 0.583 -1.536 -0.902 0.000 1.523 -0.519 1.567 3.102 0.848 0.953 0.992 1.084 0.554 0.796 1.059 +1 0.358 -1.786 -0.012 1.796 1.139 0.732 0.191 -0.584 2.173 0.746 -0.212 1.715 2.215 0.483 -0.517 -1.010 0.000 0.442 -0.019 0.008 0.000 0.428 0.515 0.987 1.416 0.980 1.013 0.768 +1 0.688 -0.188 0.301 0.462 -0.683 0.886 -0.743 -0.924 0.000 0.408 -2.185 0.626 0.000 1.336 -0.958 0.897 2.548 1.015 0.169 1.699 3.102 1.639 1.236 0.986 0.750 0.838 0.917 0.785 +1 1.242 0.552 -1.537 0.830 -1.463 0.609 -0.039 0.033 2.173 0.933 -0.404 1.183 2.215 0.493 0.840 -0.752 0.000 0.507 1.557 0.412 0.000 0.926 0.889 0.999 1.023 0.977 0.819 0.753 +0 0.337 -0.619 -1.143 1.744 1.464 0.544 0.352 -0.219 0.000 0.869 0.814 0.827 2.215 0.570 1.887 -0.675 0.000 1.234 -0.436 -0.405 3.102 0.860 0.830 0.988 0.946 1.083 0.788 0.712 +0 0.699 -0.398 1.454 0.200 -1.529 0.949 0.788 0.915 2.173 1.224 -0.560 -0.439 1.107 0.840 -0.105 0.307 0.000 2.005 -0.003 -1.420 0.000 1.433 1.192 0.991 0.779 1.898 1.130 0.937 +1 0.581 -0.191 -1.465 0.269 1.169 0.890 -0.949 0.811 2.173 1.329 -0.473 -0.561 0.000 1.084 -1.007 -1.077 1.274 1.204 -0.025 0.368 0.000 0.797 1.236 0.982 0.636 1.217 0.848 0.742 +1 0.839 -0.255 1.497 0.199 1.436 0.656 0.208 1.725 2.173 0.641 -2.198 -0.113 0.000 0.516 0.045 -0.321 2.548 0.749 -1.311 0.318 0.000 0.466 0.750 0.982 0.808 0.700 0.911 0.794 +0 0.977 0.914 0.763 2.001 1.353 1.272 0.161 -0.900 2.173 1.525 0.182 -0.426 0.000 1.577 -0.112 0.774 2.548 0.458 0.872 0.195 0.000 0.752 0.887 0.988 1.011 1.779 1.294 1.161 +1 1.248 -0.435 1.554 1.281 0.872 0.485 -0.446 -1.234 0.000 0.731 -0.753 -0.713 2.215 1.425 -0.363 0.134 2.548 0.390 0.254 0.129 0.000 0.676 0.777 1.009 0.988 0.777 0.822 0.700 +1 1.029 -1.121 1.143 0.618 -0.826 0.717 -1.336 0.040 0.000 0.527 1.081 -1.696 2.215 0.941 -0.589 -1.010 2.548 0.892 -0.633 0.828 0.000 0.875 0.913 1.082 1.067 0.863 0.916 0.817 +1 0.895 0.360 0.185 1.693 -0.449 2.636 -0.732 1.317 0.000 1.383 0.347 -0.522 1.107 0.570 -1.009 -0.012 0.000 0.483 -1.614 -0.664 0.000 0.637 1.043 0.989 0.749 0.806 0.725 0.690 +0 1.240 0.246 -0.444 0.528 -0.670 0.964 0.909 0.843 1.087 0.497 2.192 0.467 0.000 1.350 1.225 -1.182 0.000 0.906 2.135 -1.213 0.000 0.883 0.869 0.987 1.353 0.588 0.920 1.072 +1 0.790 -1.255 -0.364 1.299 0.861 0.819 0.212 0.229 2.173 1.461 -0.883 -1.232 2.215 1.092 -0.177 -1.721 0.000 0.719 -0.462 0.198 0.000 0.981 0.987 1.254 1.192 1.826 1.147 0.953 +1 0.514 -0.878 -1.250 0.660 0.837 1.102 0.811 -0.895 2.173 0.800 0.710 1.141 0.000 0.597 -0.682 0.972 1.274 1.228 0.655 -0.050 0.000 1.136 0.881 0.983 1.017 1.333 0.910 0.792 +1 0.605 0.348 -0.325 1.493 -1.034 0.530 -0.563 0.267 2.173 1.157 0.207 1.023 2.215 0.695 0.288 -0.922 0.000 0.668 0.713 0.604 0.000 0.765 0.808 0.992 1.135 0.860 0.989 0.786 +0 0.338 0.647 0.201 1.775 1.315 1.107 -2.031 -1.308 0.000 0.823 -1.676 0.888 0.000 0.767 -1.404 0.007 0.000 1.569 -0.283 0.005 1.551 0.876 0.940 0.986 0.965 0.793 0.789 0.858 +0 0.629 0.294 1.035 1.534 0.191 0.883 -0.056 -1.298 0.000 0.713 -0.706 1.281 0.000 0.825 -1.063 -1.097 1.274 1.018 -0.400 0.083 3.102 1.357 0.947 0.987 0.543 0.660 0.720 0.782 +0 0.503 1.243 -0.503 1.248 0.411 0.515 0.988 1.673 0.000 0.594 -0.267 -1.114 2.215 0.320 0.022 0.782 0.000 0.659 -0.736 -1.474 3.102 0.884 0.922 0.979 0.793 0.251 0.656 0.679 +0 1.319 0.844 0.704 0.106 -0.559 0.870 -0.618 -0.898 0.000 0.740 -1.422 0.129 0.000 0.834 0.278 -1.569 2.548 0.651 -1.532 1.508 0.000 0.873 1.076 0.981 0.592 0.292 0.631 0.737 +0 0.761 -1.227 -0.603 3.297 -1.001 1.794 0.074 0.868 1.087 0.991 0.832 0.414 0.000 1.305 0.112 -0.716 2.548 0.523 2.436 0.415 0.000 0.926 1.128 0.988 2.435 1.888 1.640 1.612 +1 1.480 -0.774 0.402 0.765 0.841 0.276 -1.428 0.686 0.000 0.475 0.155 -0.617 2.215 1.989 -0.920 -1.268 2.548 0.677 0.401 1.439 0.000 0.845 1.010 0.986 0.785 0.858 0.937 0.786 +1 0.632 -0.704 -0.259 0.385 0.640 1.176 -1.250 -1.247 0.000 0.598 -1.697 -0.754 0.000 1.445 -1.318 0.803 2.548 0.624 -0.183 -0.347 3.102 0.883 0.805 0.979 0.717 0.784 0.871 0.766 +0 1.710 0.553 1.007 0.943 0.235 1.022 -0.675 -0.241 2.173 1.260 0.662 1.586 2.215 1.554 -0.704 -0.772 0.000 0.432 -1.096 -1.080 0.000 0.549 0.722 1.128 0.989 2.069 1.237 1.110 +0 0.937 -1.652 -0.302 1.216 0.524 0.600 -0.587 -1.675 2.173 0.506 0.758 1.730 2.215 0.799 -0.201 0.136 0.000 0.653 -1.808 1.351 0.000 1.150 0.947 1.002 1.378 0.604 1.015 0.876 +1 0.453 -1.869 1.212 1.513 0.292 0.750 -0.138 -1.448 2.173 0.669 0.474 1.043 2.215 0.643 0.593 -0.310 0.000 0.530 -1.153 -1.242 0.000 0.911 0.831 0.989 1.188 0.879 0.937 0.815 +1 0.892 -0.083 -0.819 0.682 1.029 0.705 -0.895 -0.672 2.173 0.786 0.109 1.161 0.000 1.009 -0.664 0.952 1.274 0.396 0.181 -1.524 0.000 1.034 1.102 1.076 0.743 1.048 0.743 0.689 +1 1.275 1.144 1.160 0.760 0.309 0.643 1.062 -0.762 2.173 0.247 0.942 0.023 0.000 0.452 1.742 0.727 0.000 0.524 -0.579 0.317 0.000 0.938 0.883 0.990 1.010 0.671 0.812 0.716 +1 0.552 -0.755 1.278 0.907 -0.688 0.574 -0.558 0.619 2.173 0.943 -0.374 -0.174 2.215 1.480 -0.800 -1.456 0.000 0.669 1.233 0.641 0.000 0.843 1.000 0.989 0.695 0.717 0.819 0.703 +1 0.427 -0.316 -1.412 2.032 1.559 1.207 1.522 0.239 2.173 0.545 1.243 -1.224 0.000 0.872 0.441 -0.619 2.548 0.416 -1.313 -0.188 0.000 1.199 0.811 0.988 1.572 1.137 1.107 0.982 +1 0.419 -0.195 0.841 1.880 0.141 0.836 0.628 1.421 2.173 0.634 2.623 -1.078 0.000 1.471 1.088 -1.273 2.548 0.901 0.792 0.048 0.000 1.285 1.050 0.991 1.075 0.985 0.966 0.998 +1 0.587 -0.784 1.290 0.148 -1.475 1.800 1.340 0.802 2.173 1.091 1.186 -1.070 2.215 2.017 1.338 -0.701 0.000 0.586 1.992 -0.384 0.000 0.664 0.665 0.979 1.263 2.052 1.291 1.141 +0 1.628 -2.105 0.660 0.399 1.496 0.659 -0.474 0.109 1.087 0.810 -0.595 -1.578 0.000 0.623 -1.486 -1.352 0.000 0.874 -0.073 -0.880 3.102 0.620 1.039 0.985 1.043 0.644 0.859 0.823 +0 1.072 -0.550 -1.394 0.138 -0.867 1.333 -1.024 1.610 2.173 1.381 0.818 -0.125 2.215 1.899 -0.917 0.360 0.000 0.445 -0.721 1.301 0.000 0.838 1.467 0.995 1.097 2.940 1.579 1.266 +1 1.637 0.528 1.738 0.246 0.256 1.435 0.134 -0.198 0.000 1.060 1.143 1.653 0.000 0.864 0.415 1.085 0.000 1.211 -0.597 -0.749 1.551 0.899 1.165 0.984 0.931 0.840 0.942 0.824 +0 2.520 0.563 0.730 0.628 -0.951 0.640 -0.497 1.302 0.000 0.999 -0.872 -0.734 2.215 1.196 0.446 -0.982 0.000 0.539 0.482 -0.556 3.102 1.610 0.946 1.740 1.602 0.545 1.000 0.973 +1 1.133 1.384 -1.226 1.025 -1.735 1.574 1.084 0.280 0.000 0.989 1.995 1.360 0.000 1.749 0.293 -1.220 2.548 0.905 -1.837 0.466 0.000 0.831 0.910 0.975 1.183 0.847 0.901 0.841 +0 1.372 -0.415 -0.373 1.040 -0.660 1.029 -1.197 1.186 2.173 0.402 -2.691 1.410 0.000 0.650 -1.294 0.540 0.000 0.536 0.327 -0.951 3.102 0.794 0.943 0.982 1.625 1.015 1.032 1.041 +0 0.418 1.038 1.293 3.230 -1.493 0.734 -0.423 0.147 2.173 0.663 0.959 -0.282 0.000 1.136 1.356 0.119 2.548 0.476 1.536 0.599 0.000 0.614 0.938 0.989 1.247 1.265 1.398 1.081 +1 0.516 -0.325 -1.148 1.427 0.533 2.421 1.287 -1.574 0.000 1.284 -0.794 0.019 2.215 2.020 -1.579 -0.187 0.000 1.739 -0.372 0.667 1.551 9.420 5.128 1.187 0.847 0.784 3.190 2.329 +1 0.726 0.348 0.200 1.322 0.989 1.293 -2.300 0.420 0.000 1.657 -0.726 0.378 2.215 5.502 -1.315 -1.158 2.548 1.610 0.121 1.334 0.000 0.760 1.174 0.991 3.316 3.354 2.412 1.831 +1 1.207 -0.169 0.687 2.252 1.136 1.195 0.715 -0.373 2.173 0.753 -0.144 -1.152 0.000 0.618 0.945 -1.268 2.548 0.489 -1.026 -1.624 0.000 0.556 1.166 0.997 0.964 0.791 1.136 0.989 +0 0.519 -1.206 1.470 1.417 -1.608 0.962 0.348 -0.085 0.000 0.944 0.401 0.487 2.215 1.218 0.919 1.560 2.548 1.216 0.932 -0.515 0.000 0.901 0.882 0.990 0.854 0.999 0.872 0.951 +0 1.024 -0.217 1.081 0.407 -0.941 1.281 -0.667 -1.727 2.173 0.884 -0.668 -0.488 2.215 0.576 1.281 -0.220 0.000 0.380 0.365 0.468 0.000 0.397 0.791 0.989 0.925 1.408 0.978 0.827 +1 0.288 -1.722 -1.172 1.034 0.304 0.592 1.169 1.720 1.087 0.655 0.689 0.407 2.215 0.655 1.235 -1.110 0.000 0.473 -0.772 -1.264 0.000 0.868 0.816 0.984 1.120 0.876 0.811 0.732 +1 1.556 -0.361 -0.947 0.857 1.688 0.934 0.078 0.418 1.087 0.514 0.107 1.197 0.000 0.419 -1.034 -0.558 1.274 0.469 -0.854 0.058 0.000 0.668 0.654 1.110 0.627 0.783 0.816 0.675 +0 0.379 0.950 -0.639 1.301 1.224 0.623 0.739 0.637 0.000 0.674 -0.074 -0.731 2.215 0.625 -2.111 -0.123 0.000 1.259 0.570 -1.408 3.102 0.865 0.927 0.989 0.688 0.575 0.671 0.653 +0 1.502 -1.716 0.053 0.998 -0.242 0.961 -1.457 -1.503 0.000 0.432 -0.393 -1.553 0.000 0.822 -1.166 1.162 2.548 0.743 -0.612 0.119 3.102 0.766 0.849 0.979 0.959 0.510 0.684 0.865 +1 1.895 -0.610 0.247 0.915 -0.219 1.240 -1.444 -1.644 2.173 0.503 -0.535 0.933 0.000 0.492 -0.796 -0.279 2.548 0.494 -1.647 -1.083 0.000 0.807 0.874 1.003 0.503 0.962 1.015 0.816 +0 2.285 0.628 -0.293 0.146 -1.137 0.682 0.480 1.685 0.000 0.583 0.136 0.635 0.000 0.841 -1.052 1.312 2.548 0.603 -0.308 -1.132 0.000 0.798 0.703 0.987 0.688 0.156 0.777 0.658 +1 1.184 0.663 0.361 1.183 -0.072 0.870 0.748 -1.226 2.173 0.703 -0.052 -0.820 0.000 0.913 0.522 1.246 2.548 0.992 1.913 1.055 0.000 1.926 1.227 0.998 1.203 0.884 0.898 0.924 +0 1.227 0.391 0.979 0.600 1.185 0.953 0.406 -1.619 2.173 1.089 -0.115 -0.110 1.107 0.813 1.145 0.062 0.000 0.805 -2.292 -0.715 0.000 0.845 1.246 1.001 0.965 1.520 1.235 1.310 +0 0.867 -1.036 0.005 0.575 -1.652 0.886 -0.850 -0.707 0.000 0.555 -0.611 -1.423 0.000 0.716 -0.277 0.969 2.548 1.418 -0.610 0.542 3.102 0.911 0.940 0.987 0.643 0.333 0.720 0.635 +1 0.596 0.239 1.590 2.467 0.959 0.912 -0.121 -0.468 0.000 1.432 0.733 -0.535 2.215 0.994 -1.281 1.264 0.000 0.772 1.204 1.424 0.000 0.734 0.830 0.983 0.875 1.028 1.040 0.820 +0 1.172 -0.194 1.462 0.682 -1.700 0.862 -1.113 -0.218 0.000 0.733 -0.965 0.687 2.215 1.442 -0.558 -1.538 2.548 1.584 0.645 -0.318 0.000 0.718 0.949 0.970 0.612 1.013 0.839 0.800 +1 2.157 -0.070 1.114 1.536 0.728 1.108 0.319 -0.610 2.173 0.541 -0.212 -1.125 0.000 0.637 -0.891 0.369 1.274 0.409 -0.925 -1.334 0.000 0.305 0.667 0.984 0.707 1.089 1.120 0.898 +0 0.754 -1.565 0.905 0.721 -0.372 1.041 -0.024 -0.154 2.173 0.517 -1.020 0.453 0.000 2.381 -0.399 -1.629 2.548 1.421 -1.774 1.503 0.000 0.919 1.229 0.991 1.348 1.948 1.312 1.125 +1 1.020 0.691 -0.298 0.197 0.663 1.419 0.295 -1.538 2.173 0.830 1.458 0.411 0.000 0.696 0.321 0.706 1.274 0.545 -0.440 -0.262 0.000 1.163 0.742 0.982 1.143 1.115 0.977 0.854 +0 0.708 0.328 -0.295 0.962 1.122 1.455 -0.163 -1.287 0.000 1.814 0.104 0.730 2.215 0.786 0.211 1.641 0.000 2.118 -0.832 -0.431 1.551 1.001 1.427 1.095 0.838 1.845 1.401 1.135 +0 0.923 0.125 -0.412 0.665 -1.093 0.740 1.071 -1.644 0.000 0.605 -0.104 0.910 1.107 0.865 0.469 0.075 2.548 0.694 1.310 0.711 0.000 0.970 0.929 0.991 0.851 0.579 0.685 0.685 +0 1.023 1.749 1.349 0.435 -1.271 0.828 0.965 0.292 2.173 0.848 -1.200 -1.581 1.107 0.717 0.078 -0.127 0.000 0.433 0.539 -1.556 0.000 0.616 0.869 0.976 0.933 2.060 1.266 0.958 +0 0.754 -0.272 0.435 0.955 1.450 0.702 -0.844 1.304 0.000 1.082 -0.608 -0.158 2.215 1.542 -0.370 -1.140 2.548 0.389 -1.329 -0.430 0.000 0.854 1.001 0.991 0.914 1.073 0.808 0.740 +0 1.133 -0.061 1.707 0.870 -0.792 1.336 0.803 -1.037 2.173 2.750 0.904 0.427 0.000 0.502 0.881 1.630 1.274 0.792 -0.645 1.273 0.000 2.274 1.400 1.070 0.973 0.690 1.395 1.204 +0 0.877 2.411 -0.894 0.696 -0.382 0.737 0.941 0.772 2.173 0.454 -0.624 -0.284 1.107 0.332 0.965 1.525 0.000 0.514 -0.086 1.732 0.000 0.293 0.530 0.997 1.134 1.022 0.948 0.742 +1 1.575 1.095 1.361 0.706 -0.377 0.536 0.462 -0.251 1.087 0.872 0.822 0.882 0.000 0.563 -1.025 -1.006 1.274 0.928 1.119 -0.931 0.000 1.206 0.937 1.461 1.193 0.742 0.874 0.814 +0 0.840 -0.246 0.692 0.613 -1.424 0.861 2.932 -1.688 0.000 0.935 2.431 -0.787 0.000 1.008 0.601 0.738 2.548 1.768 0.186 0.367 1.551 0.846 1.199 0.987 0.701 0.404 1.028 0.960 +0 0.353 -2.228 -0.435 1.961 -1.358 0.649 -0.505 0.852 0.000 0.899 0.346 0.309 1.107 0.575 -0.998 -1.591 0.000 0.997 -0.388 -0.252 3.102 0.945 0.971 0.986 0.858 0.549 1.025 0.899 +1 1.494 -0.034 -0.268 0.933 -0.910 1.080 -0.054 -1.469 1.087 1.102 1.338 0.683 2.215 0.428 2.136 1.068 0.000 0.780 1.228 -0.781 0.000 0.871 1.047 0.990 1.468 1.950 1.264 1.017 +1 0.602 2.366 0.556 0.896 -0.459 0.573 0.450 0.684 1.087 0.884 0.988 -1.200 0.000 1.278 0.443 1.408 0.000 1.413 1.252 -0.390 3.102 1.239 1.073 0.990 0.577 0.943 0.848 0.813 +1 1.368 1.271 -1.230 1.484 1.559 0.891 0.622 0.582 2.173 0.804 0.968 -0.943 0.000 1.171 0.872 0.025 0.000 0.646 0.210 -0.216 3.102 1.140 1.134 1.159 0.861 0.551 0.890 0.855 +1 0.639 0.517 1.404 0.908 0.294 2.246 2.203 -0.966 0.000 2.006 0.710 0.501 0.000 1.355 0.314 0.804 0.000 2.227 0.001 -1.711 3.102 0.840 0.642 0.984 0.847 0.858 0.937 0.788 +1 0.498 -0.293 1.398 1.341 -0.240 0.614 -1.077 0.734 2.173 0.657 0.592 0.813 2.215 0.604 -0.792 -0.735 0.000 0.414 1.812 -0.186 0.000 1.232 0.934 1.127 0.853 0.894 0.840 0.759 +0 0.993 1.122 1.419 1.040 0.934 0.697 0.393 -0.238 0.000 1.041 0.975 -0.779 1.107 1.335 0.008 1.438 0.000 0.780 1.732 -0.631 0.000 0.873 0.642 0.977 1.098 0.696 0.715 0.659 +0 1.246 0.479 0.982 0.769 -1.277 0.789 0.542 1.656 0.000 0.892 0.880 -0.492 2.215 0.712 -0.605 0.024 2.548 0.376 -0.284 -0.564 0.000 0.979 0.993 1.213 0.954 0.828 0.759 0.731 +1 0.950 -1.671 1.292 1.318 -0.641 1.461 -0.358 0.027 0.000 1.365 -1.512 -1.608 0.000 0.907 -2.258 -0.816 0.000 1.423 1.096 1.171 3.102 0.755 0.647 1.528 2.132 0.819 1.447 1.301 +0 2.325 -1.227 -0.936 0.493 1.374 1.102 -0.200 0.302 0.000 0.705 0.908 0.992 2.215 0.487 0.583 -0.256 0.000 1.079 -0.772 1.579 3.102 0.855 0.958 1.294 0.815 0.932 1.070 1.068 +0 0.704 -0.182 -1.558 2.905 -1.583 1.816 -0.965 0.199 2.173 0.953 -0.766 0.880 2.215 1.779 0.446 -1.028 0.000 0.708 -1.124 0.199 0.000 1.712 1.478 0.992 2.155 1.129 1.458 1.426 +1 2.573 -1.145 0.361 0.243 -0.714 0.894 -1.310 -1.093 0.000 0.585 -0.500 -1.421 1.107 0.721 -0.246 1.553 2.548 0.501 -1.783 -1.490 0.000 0.559 0.773 0.988 0.998 0.319 0.765 0.784 +1 0.338 -2.226 0.079 0.246 -0.567 0.789 -0.939 1.291 0.000 0.908 0.085 0.369 2.215 1.028 -0.358 -0.669 2.548 1.407 -0.946 -1.598 0.000 0.837 1.053 0.992 0.755 0.864 0.901 0.768 +1 0.469 0.928 1.710 0.463 -1.315 0.923 -0.718 0.246 0.000 1.047 -0.262 -0.965 2.215 1.676 1.617 -1.651 0.000 1.738 0.047 0.234 0.000 0.775 0.940 0.983 0.685 0.897 0.886 0.799 +1 3.112 -0.993 -0.505 1.488 0.976 1.218 -0.103 -1.348 2.173 1.569 -1.574 1.123 0.000 1.071 -0.204 -0.185 2.548 0.588 -1.095 0.635 0.000 0.567 1.250 2.899 2.070 1.236 1.386 1.359 +1 0.935 -0.311 -0.107 0.151 1.166 0.668 -0.345 -0.978 0.000 0.610 0.893 -1.120 0.000 1.156 0.676 -0.097 1.274 2.065 1.031 0.874 3.102 0.933 0.979 0.988 0.897 0.953 0.964 0.814 +1 1.105 -0.760 -1.537 0.295 -1.075 0.580 -0.919 1.019 0.000 0.443 -1.656 -0.193 0.000 0.978 -1.047 0.273 2.548 0.970 0.296 -0.881 3.102 0.955 0.932 0.996 0.843 0.889 0.734 0.680 +1 1.023 -0.940 0.622 0.471 1.292 1.300 -1.107 -1.070 0.000 1.186 0.057 0.600 1.107 0.625 0.078 -1.119 0.000 0.680 -0.806 -0.596 3.102 1.038 0.638 0.995 0.666 0.836 0.994 0.896 +1 0.716 -1.460 0.782 1.109 1.113 3.671 0.578 -1.698 0.000 2.633 -0.268 -0.142 2.215 3.606 1.040 0.156 0.000 0.606 0.581 0.469 1.551 1.450 1.329 1.003 1.477 0.830 2.172 1.816 +1 0.441 -1.170 0.457 2.301 1.309 1.019 -0.471 -0.205 2.173 0.459 -0.693 1.188 2.215 1.077 -1.339 -1.226 0.000 0.944 -0.569 0.381 0.000 1.192 1.114 0.985 0.471 0.964 0.886 0.825 +0 2.576 0.292 0.224 0.770 1.184 0.632 0.106 -1.176 2.173 1.292 -0.413 -1.737 2.215 0.526 -1.151 -0.523 0.000 0.979 0.997 -0.668 0.000 0.592 0.894 1.486 1.248 0.736 1.107 0.994 +0 1.594 0.209 -1.307 0.786 1.640 0.875 -0.356 0.631 0.000 1.094 0.130 -0.828 2.215 1.130 0.092 -0.127 0.000 1.161 -0.792 1.225 3.102 1.186 0.831 0.984 0.816 1.137 0.922 0.995 +1 0.385 0.138 -0.814 0.454 1.005 0.870 1.156 -1.282 2.173 0.509 1.020 0.558 2.215 0.491 -0.895 -0.576 0.000 0.518 -0.211 0.497 0.000 0.503 1.061 0.989 0.588 0.977 0.748 0.692 +0 1.470 -0.993 -0.651 0.936 1.639 0.930 -0.769 1.612 1.087 0.489 -1.514 -0.100 0.000 1.132 -0.835 0.706 2.548 1.140 -1.302 0.408 0.000 0.942 1.102 1.431 1.028 0.938 0.939 0.941 +1 0.640 1.352 1.370 2.096 0.592 0.917 1.661 -0.389 0.000 1.330 0.952 -1.393 2.215 0.667 1.158 -1.110 2.548 0.752 1.091 0.848 0.000 0.729 0.902 1.035 0.872 0.292 0.881 0.742 +1 0.305 -0.657 -1.369 0.448 0.308 0.949 -0.525 0.593 0.000 2.055 0.334 -0.989 2.215 1.284 0.892 0.638 0.000 1.266 -0.949 0.028 0.000 0.968 0.997 0.997 0.997 0.363 1.182 1.000 +0 0.785 1.286 1.552 1.035 1.643 0.690 1.215 0.065 0.000 0.650 0.534 -1.302 2.215 0.764 0.492 -0.759 0.000 0.869 0.186 0.719 0.000 0.903 0.949 0.977 0.929 0.571 0.770 0.817 +1 0.708 -1.985 -0.401 0.539 1.028 0.905 -0.720 0.048 2.173 1.192 -1.791 -1.481 0.000 0.585 0.206 -1.149 0.000 1.120 -0.532 -1.345 1.551 1.059 0.906 0.983 0.700 1.014 0.682 0.642 +0 0.681 -0.051 0.756 1.667 -0.187 0.564 0.538 1.109 2.173 0.650 1.936 -1.583 0.000 1.393 0.354 -0.661 2.548 0.763 -0.560 1.157 0.000 0.910 0.894 1.108 0.920 1.107 0.788 0.725 +0 0.661 -1.408 -0.363 0.853 -1.078 0.498 0.148 0.208 0.000 0.784 0.411 -0.819 2.215 1.295 -0.120 0.903 1.274 0.644 -1.618 1.477 0.000 1.335 0.960 0.983 1.541 1.112 1.276 1.095 +0 1.428 1.355 1.469 0.740 -0.524 0.360 0.929 0.247 0.000 0.495 0.599 1.685 0.000 0.889 0.261 0.679 2.548 1.863 0.324 -0.677 3.102 0.875 0.830 1.389 0.954 0.926 0.840 0.710 +0 0.703 1.104 0.869 0.851 -1.620 0.868 -0.077 -0.298 1.087 1.073 1.090 1.421 2.215 0.936 1.173 -1.077 0.000 0.754 -0.384 0.864 0.000 1.066 0.920 0.986 1.024 1.683 0.932 0.800 +1 0.691 -0.124 0.394 1.188 0.772 0.545 -1.023 -0.539 0.000 1.120 -1.478 -1.380 0.000 1.208 -0.368 -1.507 2.548 0.649 -0.020 0.010 1.551 1.211 0.985 0.989 0.585 0.675 0.733 1.046 +1 1.696 -0.505 0.798 0.339 -1.273 0.839 0.266 -0.975 2.173 0.590 -1.059 0.302 0.000 1.204 -1.156 -1.420 0.000 1.048 0.935 0.013 3.102 0.788 0.970 1.005 0.922 0.886 0.873 0.757 +1 0.499 -0.672 1.669 0.355 -0.133 1.201 0.166 0.594 2.173 1.938 -1.477 -1.473 0.000 1.369 0.218 -0.873 0.000 1.106 -0.356 -1.216 0.000 0.899 0.776 0.987 0.624 0.544 0.610 0.611 +1 1.029 -1.002 1.587 0.778 -1.162 0.531 -0.225 1.226 0.000 0.446 0.453 -1.391 0.000 1.184 0.124 -0.388 2.548 2.598 0.914 0.305 3.102 0.816 0.918 0.988 2.181 1.031 1.468 1.170 +1 2.857 -0.526 -0.384 0.111 -1.727 1.248 -0.406 1.522 2.173 0.793 0.392 0.319 2.215 0.963 0.187 -1.682 0.000 0.630 -0.585 0.378 0.000 0.918 0.825 0.987 0.907 1.432 1.152 0.937 +1 0.561 0.546 -0.005 0.544 -1.662 1.513 1.131 1.485 0.000 0.778 -0.757 0.529 2.215 1.928 -0.564 -0.511 2.548 0.960 0.472 -0.550 0.000 1.857 1.868 0.986 1.337 1.054 1.570 1.273 +0 0.456 1.699 1.131 0.593 -0.739 1.314 -0.026 -0.303 2.173 1.334 0.550 1.260 1.107 0.576 1.246 1.533 0.000 0.903 0.787 0.195 0.000 0.762 1.158 0.992 0.820 2.010 1.040 0.856 +0 0.493 1.481 0.711 3.382 0.233 1.099 0.050 0.032 2.173 2.022 -0.511 -1.601 2.215 1.078 0.870 -1.116 0.000 1.728 0.503 1.661 0.000 0.937 1.337 0.995 0.974 2.275 1.753 1.486 +1 0.696 1.069 1.236 0.820 -1.160 0.838 0.103 0.113 0.000 0.996 1.021 1.616 2.215 0.662 0.747 -0.845 2.548 0.629 2.011 0.688 0.000 1.008 0.886 0.985 0.625 0.696 0.607 0.579 +1 1.520 0.812 -1.610 1.616 -1.305 1.475 -0.149 0.501 0.000 0.928 0.079 0.071 2.215 1.127 0.827 -0.805 2.548 0.845 -0.102 -1.740 0.000 1.535 1.054 0.990 0.778 0.900 0.972 1.224 +0 1.147 0.341 -1.310 0.590 -0.148 0.650 -1.048 -1.580 2.173 0.469 -0.854 -0.026 0.000 0.923 -0.250 1.034 0.000 1.400 1.195 0.029 3.102 0.881 0.900 0.988 0.838 1.897 1.031 0.882 +1 1.053 0.253 0.526 1.596 -0.270 0.681 0.964 -1.266 2.173 0.848 -0.194 1.574 2.215 0.378 -1.302 -0.595 0.000 0.730 0.110 -1.711 0.000 0.700 0.854 1.180 1.103 0.928 0.941 0.791 +1 0.775 -0.102 0.184 1.164 1.569 1.916 0.282 -0.182 0.000 2.229 -1.929 1.587 0.000 0.489 0.991 -0.374 0.000 0.996 1.090 1.453 3.102 0.778 0.679 1.248 0.838 0.758 0.800 0.839 +0 0.705 0.104 -0.502 0.984 1.131 0.956 0.705 -0.107 2.173 1.185 -0.558 1.328 2.215 0.515 0.190 -1.385 0.000 0.925 -0.978 -1.505 0.000 0.705 0.943 1.148 0.859 1.853 0.978 0.810 +1 0.501 -1.232 -1.501 0.334 -1.063 0.879 1.023 0.309 0.000 0.722 0.082 1.652 2.215 0.537 -0.606 -0.749 0.000 0.698 0.676 1.171 1.551 0.378 0.788 0.996 0.622 0.359 0.593 0.556 +0 0.424 -1.379 -0.987 1.048 -0.640 0.723 0.165 1.731 0.000 1.075 -0.432 0.476 2.215 0.495 -0.569 -0.944 0.000 1.036 -0.967 0.667 3.102 0.840 0.904 1.000 0.957 0.385 0.743 0.740 +1 0.396 -1.560 0.925 1.587 -0.865 0.541 -0.119 1.456 0.000 0.562 1.441 -1.388 0.000 0.431 1.853 -0.087 0.000 1.343 0.044 0.445 0.000 0.868 0.656 1.098 0.696 0.219 0.661 0.661 +0 1.527 -0.559 -1.131 0.688 -0.239 1.129 -1.113 0.969 2.173 0.949 -0.744 1.740 0.000 1.487 -1.355 -0.482 2.548 1.200 1.896 -0.080 0.000 1.001 2.461 1.024 1.299 1.587 1.947 1.548 +0 1.099 0.339 -0.942 0.882 -0.040 0.492 1.737 -1.146 0.000 0.802 0.971 0.419 2.215 0.810 1.527 1.268 0.000 1.218 0.528 1.307 1.551 0.935 0.950 0.992 0.821 0.661 0.675 0.742 +1 1.330 0.713 -0.182 1.194 -0.991 1.348 0.420 1.030 2.173 0.603 -0.545 -1.287 0.000 0.519 -0.991 0.463 2.548 0.838 2.012 -1.362 0.000 1.946 1.484 1.161 1.478 1.005 1.181 1.105 +1 1.005 -0.134 0.804 0.900 -1.617 0.830 0.264 -1.310 0.000 1.414 -0.882 0.186 2.215 0.543 0.043 1.432 2.548 0.767 0.122 -0.656 0.000 0.683 0.599 1.080 1.093 0.958 0.901 0.810 +1 0.587 -1.723 -1.584 1.163 0.500 1.569 -1.331 -0.211 2.173 0.853 -0.989 1.045 0.000 0.491 -0.935 -0.911 0.000 0.473 -0.208 -1.208 3.102 0.975 1.290 1.091 0.701 0.885 0.797 0.761 +1 0.881 -0.756 0.638 0.825 0.596 0.863 0.120 0.465 2.173 1.191 0.818 1.738 0.000 1.096 -0.436 -0.389 2.548 1.870 0.667 -1.356 0.000 0.749 1.100 0.991 1.183 0.921 0.917 1.130 +0 0.574 -0.435 -0.208 0.921 0.753 0.507 -1.484 -1.268 0.000 0.766 -0.558 -0.815 2.215 0.702 -0.057 0.470 0.000 0.522 -1.215 1.562 3.102 1.361 0.936 0.983 0.559 0.546 0.586 0.605 +1 0.874 -0.231 1.279 0.469 -1.241 0.988 0.258 0.595 1.087 0.719 -1.052 -1.670 0.000 0.787 -1.047 -0.678 1.274 1.064 -2.121 -0.425 0.000 1.189 0.741 0.991 0.880 1.308 1.097 1.016 +0 1.773 1.594 -1.689 0.671 -0.863 1.122 -0.250 0.522 1.087 0.486 0.257 0.546 2.215 1.104 -0.209 -0.867 0.000 0.605 0.714 -1.381 0.000 0.725 0.692 1.025 1.940 0.284 1.240 1.003 +0 0.627 -0.171 0.273 1.031 -1.163 1.001 0.629 1.511 1.087 0.750 -0.820 0.742 0.000 0.777 0.718 -0.650 0.000 0.562 1.061 -0.234 0.000 0.942 0.589 1.071 1.010 0.326 0.767 0.720 +1 1.714 0.186 0.528 1.289 1.185 0.419 1.385 -0.250 0.000 0.817 1.379 -1.342 0.000 1.067 0.715 -0.607 2.548 0.632 1.038 -1.016 3.102 1.034 0.747 1.149 0.888 0.269 0.785 0.821 +1 0.880 0.268 -0.328 1.763 -1.128 1.348 0.849 1.488 0.000 1.335 -0.857 0.371 1.107 0.918 -2.090 -0.212 0.000 1.058 -1.066 -0.362 1.551 1.118 1.125 1.139 0.968 0.692 1.015 1.036 +0 1.091 -1.011 -0.472 0.587 -1.164 1.120 1.210 -1.544 2.173 1.256 0.181 1.483 0.000 1.440 0.441 0.437 0.000 1.543 0.854 0.271 0.000 0.937 0.691 0.988 2.388 1.576 1.567 1.310 +0 1.090 -1.603 -0.768 0.212 1.488 0.547 -0.992 -0.248 2.173 0.810 -1.672 1.398 0.000 0.568 -1.030 0.383 0.000 0.853 -0.164 1.645 3.102 0.880 0.889 0.981 0.676 0.776 0.643 0.617 +0 0.536 1.003 -0.547 1.158 1.736 1.231 1.996 0.237 0.000 1.488 1.500 1.442 2.215 1.273 -0.337 -1.143 2.548 0.850 -0.167 -0.292 0.000 0.626 0.768 0.987 0.881 1.939 1.036 0.849 +1 1.541 0.811 1.588 1.078 -1.276 0.845 -0.594 0.683 2.173 0.676 -0.490 -0.093 0.000 1.140 0.310 -0.113 2.548 0.771 -1.945 -0.948 0.000 0.667 0.697 0.984 1.380 1.001 1.048 0.932 +1 0.557 0.582 -1.081 1.370 0.459 1.026 0.333 -1.462 0.000 1.080 1.081 1.019 1.107 1.639 2.198 -0.534 0.000 0.601 -0.953 -0.653 0.000 0.833 1.087 1.190 0.730 0.715 0.732 0.688 +1 0.511 -1.812 -0.744 0.683 -0.679 0.837 -0.542 -1.488 0.000 1.183 0.102 0.801 2.215 0.674 1.938 0.955 0.000 1.212 0.594 -0.059 3.102 0.737 1.006 1.002 1.085 0.827 0.847 0.795 +1 0.773 0.120 1.103 1.161 -1.675 1.079 -2.118 -0.376 0.000 0.936 -1.601 1.130 0.000 1.121 -0.166 -1.252 2.548 1.931 -0.073 -0.086 3.102 0.796 1.149 0.983 1.086 0.978 0.949 1.045 +1 0.406 -1.893 0.722 1.207 1.209 1.436 2.323 -0.439 0.000 0.562 -2.204 -1.249 0.000 0.900 -0.975 1.266 2.548 0.973 -0.562 1.056 3.102 9.576 5.150 0.981 0.527 0.196 3.158 2.476 +0 1.250 -0.255 -0.634 0.954 -1.692 0.752 -1.231 -1.417 1.087 0.986 -0.765 0.945 0.000 1.334 -0.552 0.216 2.548 0.712 -0.114 -1.679 0.000 0.918 0.907 1.233 0.907 1.306 0.914 0.827 +0 0.719 -0.677 -1.742 1.184 0.783 0.459 0.459 -0.907 0.000 0.749 -0.658 -0.314 0.000 0.636 0.187 -1.236 2.548 0.494 -0.618 1.266 3.102 0.982 0.732 0.987 0.758 0.393 0.489 0.616 +1 1.326 -1.194 0.190 0.524 0.404 1.523 1.023 1.721 0.000 0.409 0.330 0.421 0.000 0.627 0.595 -0.091 0.000 0.886 -0.433 -1.110 0.000 0.914 0.763 0.992 0.827 0.386 0.632 0.671 +0 0.884 -1.211 0.814 0.539 0.800 1.025 0.215 -0.300 2.173 0.768 -1.103 -1.617 1.107 0.457 0.433 1.624 0.000 0.379 -2.467 1.372 0.000 1.245 0.829 0.977 1.095 1.540 1.018 0.897 +1 0.793 0.578 -1.437 1.190 1.452 1.517 -2.713 0.064 0.000 0.949 0.011 1.361 0.000 1.605 0.700 1.647 2.548 1.572 -0.838 1.631 3.102 1.060 0.861 0.987 0.606 1.226 0.945 0.936 +1 1.217 1.084 0.024 0.394 0.086 2.125 1.627 -1.580 0.000 2.598 0.250 0.139 2.215 0.899 -0.377 -1.339 0.000 0.779 0.503 1.586 1.551 1.274 0.771 0.995 0.769 1.259 0.935 0.799 +0 0.982 -1.470 1.681 0.499 -1.505 0.552 -0.975 -0.130 0.000 0.942 0.287 -1.059 2.215 0.689 0.648 1.574 0.000 1.465 -0.690 0.479 3.102 0.702 0.955 1.002 0.905 1.217 0.817 0.779 +0 1.270 -0.512 1.322 0.595 0.757 0.453 -2.820 0.081 0.000 1.641 -0.413 -0.654 2.215 0.971 2.571 1.152 0.000 1.293 -0.757 -1.655 3.102 10.180 5.401 0.980 1.295 1.081 3.300 2.413 +0 0.561 -0.002 -1.356 0.975 -0.001 0.691 0.555 0.704 0.000 0.780 2.101 -1.507 0.000 1.668 -0.561 -0.990 2.548 1.177 -0.808 0.438 3.102 1.986 1.896 0.990 0.761 1.046 1.521 1.207 +0 0.898 -0.888 1.496 0.852 0.488 0.786 -1.368 0.343 2.173 1.158 -1.175 -1.254 2.215 0.401 0.119 -1.475 0.000 0.499 -0.862 -0.404 0.000 0.507 0.750 0.988 0.949 1.397 0.856 0.685 +0 0.401 -1.310 1.347 1.524 -0.226 1.071 -0.651 -1.715 2.173 0.712 -2.171 -0.288 0.000 1.153 -0.911 1.069 0.000 1.555 -0.167 0.073 3.102 1.594 1.305 1.070 1.200 1.400 1.123 0.968 +0 0.753 1.034 1.414 0.999 0.420 1.665 0.426 -0.673 0.000 2.501 0.630 0.995 2.215 1.896 -0.072 -0.883 2.548 0.533 0.365 0.584 0.000 1.303 0.973 0.987 0.789 2.453 1.560 1.244 +0 1.460 -1.466 0.411 0.866 -0.507 1.207 -0.818 -1.229 0.000 0.803 -0.383 0.600 2.215 0.281 -0.974 1.283 0.000 1.262 0.329 -1.674 0.000 0.815 0.857 1.146 0.883 0.299 0.741 0.796 +0 1.057 0.392 -1.160 0.794 -0.089 0.859 0.138 0.084 2.173 1.851 -0.622 1.398 0.000 0.793 -1.359 0.919 0.000 0.574 1.664 -0.325 0.000 1.128 1.219 1.044 0.824 0.895 1.108 1.020 +1 0.854 0.541 0.634 1.425 -0.009 1.668 1.234 -1.529 2.173 0.823 2.188 -0.303 0.000 0.539 0.790 -0.559 0.000 1.796 1.467 0.970 0.000 0.800 0.690 0.996 1.605 0.253 0.986 0.923 +1 0.315 1.685 1.697 1.676 0.882 1.172 -0.317 -1.414 2.173 2.171 -0.269 -0.726 0.000 1.801 1.381 0.950 1.274 0.842 -0.339 0.570 0.000 0.839 1.211 0.987 0.781 2.447 1.690 1.416 +0 0.869 0.648 -1.549 0.999 1.491 0.399 0.568 -0.446 0.000 0.500 -0.269 0.387 2.215 0.351 2.252 -0.311 0.000 0.926 1.255 -0.188 3.102 0.743 0.802 0.988 0.783 0.694 0.745 0.675 +0 0.912 1.504 1.203 0.790 0.084 0.846 -1.057 1.581 0.000 1.488 0.111 -0.017 2.215 1.213 -0.412 -0.375 0.000 1.831 1.837 -1.681 0.000 1.875 1.209 0.995 1.179 0.934 1.061 1.188 +1 0.728 -0.063 -0.311 1.206 1.060 0.817 -0.510 0.935 0.000 1.118 -1.131 -0.131 2.215 1.036 0.179 1.304 0.000 1.702 -1.809 -0.738 0.000 0.973 0.938 1.226 1.061 0.930 1.104 0.941 +1 1.040 1.814 0.953 0.785 -0.364 0.974 2.446 -0.476 0.000 0.613 2.292 1.264 0.000 0.945 0.785 1.731 2.548 0.431 1.207 1.453 1.551 1.644 0.982 1.160 0.870 0.189 0.830 0.754 +1 1.215 -0.573 -0.050 0.248 0.289 1.277 -1.133 0.243 0.000 1.883 -1.086 -1.589 0.000 1.283 -0.705 -1.390 2.548 0.489 -1.173 -0.290 0.000 1.000 1.055 0.989 0.932 0.377 0.883 0.775 +0 1.924 -0.015 0.357 0.038 1.009 0.934 0.426 -1.028 1.087 1.252 1.215 -0.350 0.000 2.856 -1.044 1.313 0.000 1.395 1.008 1.049 0.000 0.758 1.239 0.829 1.130 1.309 1.349 1.200 +0 1.267 1.313 0.403 0.729 1.386 0.615 -0.538 0.839 1.087 0.480 0.230 -1.035 2.215 1.548 -0.878 -0.817 0.000 0.922 0.674 -1.456 0.000 1.509 0.876 1.032 1.125 0.856 0.841 1.014 +0 3.593 0.242 0.321 1.261 0.651 1.850 0.154 -1.486 2.173 0.817 2.302 -1.007 0.000 0.866 -0.558 0.698 0.000 1.041 0.931 -1.162 0.000 0.840 0.580 0.975 2.398 0.760 1.517 1.259 +0 0.873 -0.567 -1.218 1.008 1.333 1.187 2.130 -0.527 0.000 1.423 -0.476 1.142 2.215 1.407 0.418 0.258 2.548 1.354 1.086 -0.926 0.000 1.059 1.461 0.987 0.778 1.306 1.757 1.539 +0 0.874 1.474 0.182 1.018 -0.547 1.012 -0.139 1.026 2.173 0.931 0.858 -0.188 2.215 1.054 -0.586 1.578 0.000 0.965 -0.643 -0.908 0.000 0.875 1.002 0.987 0.548 1.481 0.996 0.992 +1 0.359 -0.913 0.872 1.107 1.432 1.509 -0.149 0.046 0.000 0.636 -1.672 -1.575 0.000 1.227 1.568 -1.287 0.000 1.343 0.713 1.602 0.000 0.915 1.014 0.984 0.803 0.840 0.919 0.806 +0 1.900 0.155 0.948 1.185 0.584 0.645 1.440 -1.265 0.000 0.661 -0.942 0.465 2.215 1.128 -0.421 -0.793 2.548 0.857 -0.803 -1.398 0.000 0.501 0.714 0.992 1.261 0.864 0.940 0.884 +0 3.131 -0.361 -0.598 0.745 1.556 1.909 -0.735 1.204 0.000 1.456 -1.008 -0.380 1.107 0.815 -0.377 1.355 1.274 1.005 -1.625 1.242 0.000 1.361 0.818 1.972 1.253 1.214 1.208 1.331 +0 0.673 -0.700 1.510 1.549 -1.554 0.476 -0.059 0.045 0.000 0.775 -0.708 0.666 0.000 0.830 0.599 -0.653 2.548 0.398 -1.126 0.317 0.000 0.813 0.910 0.985 0.818 0.715 0.705 0.764 +0 0.468 -0.783 -0.745 1.750 1.042 1.198 -1.845 -0.575 0.000 2.480 -0.125 1.375 2.215 1.703 0.085 -0.700 0.000 1.753 -0.175 0.264 3.102 0.925 0.872 1.253 1.112 1.587 1.165 1.029 +0 1.199 0.277 -0.242 0.308 0.591 1.072 -0.169 0.965 2.173 1.379 0.361 -0.795 2.215 0.593 -0.045 0.463 0.000 1.072 -0.074 -1.514 0.000 0.860 0.907 0.981 0.979 1.855 1.001 0.818 +1 2.513 -0.748 1.308 0.061 -1.302 0.892 -1.071 -0.890 2.173 0.806 -1.090 0.380 0.000 0.332 0.892 0.068 2.548 0.697 1.539 -0.204 0.000 0.241 1.604 0.982 0.814 0.977 0.940 1.034 +0 0.630 -1.672 -1.182 2.606 -1.578 0.731 -0.752 0.856 0.000 0.766 -0.702 -0.114 0.000 1.425 -0.196 -0.091 2.548 0.427 0.005 0.530 3.102 1.220 0.955 0.981 1.143 0.323 1.386 1.279 +1 1.101 -2.331 -0.601 0.580 0.611 0.571 -0.150 1.585 2.173 0.980 -1.958 0.169 0.000 0.576 -1.793 0.842 0.000 0.760 -0.723 -1.026 3.102 0.658 0.784 0.987 1.371 0.557 0.896 0.834 +1 1.929 0.444 -0.895 0.308 -0.778 1.688 0.644 0.965 2.173 1.353 2.194 -1.070 0.000 1.111 0.309 0.111 1.274 0.500 0.839 1.472 0.000 1.077 1.459 0.986 1.664 1.219 1.436 1.268 +1 0.846 1.102 0.846 0.465 1.693 0.538 0.672 1.645 0.000 2.005 -0.167 -0.178 2.215 1.088 -0.598 -1.039 2.548 0.719 0.229 1.040 0.000 0.531 0.869 0.987 1.835 1.165 1.405 1.128 +0 2.721 -0.080 0.877 0.396 1.161 0.743 -0.583 -0.843 0.000 0.671 0.159 -0.901 0.000 0.925 -0.990 -0.385 2.548 0.916 -0.248 1.211 3.102 0.628 0.721 1.000 1.096 0.753 0.717 0.776 +0 0.335 1.069 -1.529 0.156 0.552 0.669 1.616 0.856 0.000 1.524 -0.610 -1.003 2.215 0.754 0.358 1.021 2.548 0.867 2.003 0.016 0.000 0.923 0.874 0.978 0.826 1.259 1.472 1.191 +0 1.532 -0.071 -0.894 0.077 -0.853 1.843 0.233 -0.458 2.173 3.116 -1.174 1.737 1.107 2.168 1.211 0.455 0.000 2.108 0.421 0.732 0.000 1.158 2.167 0.998 1.946 4.263 2.984 2.207 +0 0.962 -0.519 0.792 1.389 1.306 1.237 2.711 -1.296 0.000 1.350 0.717 0.558 0.000 2.291 0.250 -0.034 1.274 0.435 -1.540 -1.657 0.000 0.848 0.819 0.992 1.262 0.544 1.587 1.654 +0 0.626 0.301 -1.678 0.230 1.094 1.020 -1.143 0.513 2.173 1.183 0.209 -0.928 2.215 0.635 -1.149 1.490 0.000 0.519 -1.270 -0.335 0.000 0.637 0.979 0.992 1.794 1.963 1.369 1.119 +0 0.589 -1.076 -1.285 1.041 0.270 1.329 -1.797 0.219 0.000 1.519 -1.480 1.702 0.000 0.869 -1.034 -1.048 2.548 1.106 -0.409 1.525 0.000 0.950 0.838 1.070 0.575 0.255 0.490 0.602 +1 0.725 1.494 -0.973 1.366 -0.197 1.281 0.573 1.407 2.173 0.505 0.758 -0.718 0.000 0.475 1.593 0.769 0.000 0.395 -1.058 -0.922 0.000 0.823 1.050 0.988 0.915 0.830 1.118 0.885 +0 0.459 0.027 -1.104 1.304 1.564 1.428 0.314 -0.528 2.173 1.706 0.791 1.011 2.215 0.991 0.540 0.130 0.000 0.668 0.114 -1.404 0.000 0.906 1.027 0.982 1.482 2.330 1.483 1.151 +0 0.665 -0.224 1.578 1.155 -0.342 1.073 0.488 -1.440 0.000 0.730 -0.015 0.094 2.215 0.810 -0.795 0.454 0.000 1.132 0.762 0.973 1.551 0.934 0.793 1.199 0.726 0.705 0.650 0.598 +0 0.464 -0.327 0.009 1.798 -1.317 1.031 1.264 0.290 0.000 0.802 0.971 1.131 2.215 0.720 1.188 -0.358 0.000 1.143 -1.065 -1.712 3.102 0.857 1.186 1.176 1.129 1.301 0.885 0.849 +1 0.473 1.141 0.371 1.032 -1.253 2.915 0.341 -1.518 0.000 1.908 0.474 0.491 0.000 1.836 -0.395 -0.234 1.274 2.080 -0.587 0.937 3.102 1.206 1.336 0.989 1.125 1.315 1.109 1.023 +0 0.968 -2.219 -1.049 1.000 -1.408 0.852 0.083 0.364 2.173 0.683 0.656 -1.737 0.000 0.546 0.680 -0.427 0.000 0.607 0.641 0.993 0.000 0.867 0.996 0.990 0.693 0.465 0.995 1.014 +0 0.776 -1.556 0.544 0.437 1.725 0.453 1.130 -1.011 1.087 1.080 0.530 1.614 1.107 0.730 -2.313 0.199 0.000 0.504 0.194 -0.626 0.000 1.334 1.753 0.986 1.042 0.785 1.328 1.082 +1 2.332 0.058 1.446 0.780 -1.460 1.884 -1.155 -0.807 0.000 1.186 -0.377 0.440 0.000 1.115 -0.906 0.453 0.000 0.806 -0.284 0.854 3.102 0.545 0.621 0.987 0.667 0.528 0.727 0.818 +1 0.714 -0.154 1.130 0.623 -0.824 0.558 0.569 -1.271 0.000 0.886 0.513 0.167 2.215 1.313 0.225 -0.860 0.000 0.660 2.012 0.703 0.000 0.790 0.900 0.985 0.683 0.642 0.610 0.584 +1 1.550 -0.157 0.227 1.559 1.558 2.453 1.147 1.137 0.000 2.864 -0.195 -0.223 0.000 2.075 0.607 -0.651 1.274 3.513 0.228 -1.307 0.000 3.564 1.964 2.007 1.600 0.946 1.328 1.332 +1 0.360 0.689 -0.639 2.863 -0.031 1.313 0.146 1.230 0.000 0.766 -0.042 -1.128 0.000 1.123 0.427 -1.631 1.274 0.972 1.530 -0.932 0.000 0.974 0.756 0.987 0.768 0.884 0.867 0.771 +1 0.919 -0.701 -1.249 0.960 0.500 0.800 -1.109 0.748 2.173 0.901 -0.355 0.083 2.215 1.237 -0.191 -1.228 0.000 0.730 1.832 -1.525 0.000 1.615 1.469 1.301 0.893 0.855 1.282 1.086 +1 0.789 0.664 1.558 1.660 1.138 1.195 0.883 -0.415 2.173 0.778 0.528 -1.229 0.000 0.544 -0.253 0.022 2.548 0.905 0.953 0.958 0.000 1.061 0.818 0.989 1.492 0.731 1.045 0.897 +1 0.615 0.017 1.704 0.716 -0.399 1.086 -0.430 -0.758 1.087 0.619 1.515 1.698 0.000 1.682 0.134 0.625 0.000 0.603 -0.568 1.230 3.102 1.761 1.087 0.987 0.738 0.842 1.118 0.927 +0 0.556 -1.548 -1.037 0.856 0.212 0.933 -0.905 -1.292 2.173 0.920 2.570 -0.033 0.000 1.296 -1.176 0.440 0.000 1.451 -0.343 1.508 3.102 0.877 0.872 0.985 1.062 0.779 0.898 0.764 +1 0.522 0.561 -0.392 0.367 1.553 0.621 -0.646 -0.865 0.000 1.155 0.931 1.220 2.215 1.188 0.769 0.298 2.548 0.467 0.950 -1.077 0.000 0.853 1.063 0.995 0.828 0.922 0.931 0.854 +1 1.723 1.079 -0.866 0.191 0.790 1.872 0.813 0.741 2.173 0.968 0.703 -1.259 0.000 0.779 0.582 1.517 0.000 0.502 -0.368 -0.832 3.102 0.978 0.606 0.992 1.554 1.227 1.058 0.999 +1 0.348 -0.112 1.501 1.172 0.418 1.730 1.434 -1.573 2.173 2.008 1.420 -0.417 2.215 1.016 0.881 0.895 0.000 0.473 -0.128 -0.415 0.000 0.929 0.968 0.988 2.752 2.367 2.321 1.720 +1 0.329 0.542 0.901 1.701 -0.135 1.082 0.772 -1.682 2.173 0.532 1.597 0.693 0.000 0.677 0.211 -1.084 2.548 0.470 0.270 -0.217 0.000 0.664 0.972 0.990 0.819 0.623 0.887 0.743 +0 0.591 0.231 0.615 1.102 1.663 0.662 -0.145 1.699 1.087 0.759 0.400 0.948 0.000 1.008 0.362 -0.639 0.000 1.500 -0.176 -0.259 3.102 1.111 1.159 0.987 0.834 1.035 0.763 0.740 +0 1.025 -1.817 -1.011 1.285 -1.580 0.775 -0.523 -0.007 2.173 0.773 -1.053 1.045 0.000 0.858 0.359 -0.669 0.000 0.700 1.317 0.708 0.000 0.971 1.044 0.982 1.333 0.876 1.207 1.392 +1 1.991 -1.558 0.725 0.671 0.446 1.110 -1.014 -1.522 2.173 1.018 -0.341 -0.765 2.215 0.853 -0.362 -0.197 0.000 0.393 0.363 1.272 0.000 0.677 1.025 1.007 1.313 1.124 1.161 0.941 +0 1.172 0.155 -0.552 0.611 -1.569 0.671 -0.833 0.405 0.000 1.070 0.594 -1.652 2.215 0.600 0.481 0.773 0.000 1.276 -0.019 0.191 3.102 0.921 1.279 0.987 0.715 1.104 0.831 0.773 +0 0.410 -0.956 0.737 1.888 0.359 1.378 0.823 -1.663 0.000 0.962 0.625 -0.776 1.107 1.362 -0.845 0.097 2.548 1.040 1.416 -1.534 0.000 0.847 1.096 0.989 0.899 1.363 1.482 2.122 +0 0.317 -0.989 0.432 2.230 -0.761 1.020 0.823 1.739 2.173 0.693 -0.309 1.131 0.000 0.773 -0.682 -0.648 2.548 0.991 -1.804 0.407 0.000 0.883 1.209 1.023 0.504 1.338 1.160 1.289 +1 0.376 1.678 0.710 1.470 -1.277 0.965 1.029 -1.140 2.173 0.986 0.719 0.360 0.000 0.534 -0.036 1.293 2.548 0.705 0.847 -0.260 0.000 0.595 1.128 1.005 0.818 0.885 0.758 0.756 +0 0.506 -0.067 1.275 0.365 -0.463 0.634 -0.824 -0.877 2.173 0.632 -0.171 1.044 0.000 0.409 -1.426 -0.997 0.000 0.910 -0.659 0.873 3.102 0.946 0.952 0.986 0.568 0.804 0.616 0.565 +1 0.894 1.433 -0.254 0.554 1.419 0.746 0.360 -1.308 0.000 1.064 1.248 0.602 2.215 0.490 -1.058 1.443 0.000 1.277 0.536 1.343 3.102 1.131 0.870 0.986 0.730 0.736 0.909 0.857 +0 0.723 -0.942 0.570 2.772 -1.632 0.752 -0.857 -0.366 0.000 1.700 0.281 0.108 2.215 1.312 -0.374 -1.733 1.274 0.731 0.974 0.117 0.000 1.443 1.160 1.797 0.964 1.679 1.391 1.254 +1 1.328 0.802 1.246 1.645 0.636 1.199 0.544 -1.621 0.000 0.422 0.546 -0.296 0.000 1.736 0.602 -0.867 2.548 0.481 0.961 -0.738 3.102 0.937 0.836 1.069 0.728 0.192 0.857 0.757 +1 0.911 -0.888 -0.319 2.009 -1.365 1.312 0.399 0.802 2.173 0.407 0.172 -0.126 0.000 0.300 -0.363 -1.432 0.000 0.552 0.590 -1.104 0.000 0.519 0.829 1.515 0.823 0.498 1.171 0.877 +1 0.470 1.532 -0.364 0.980 1.076 0.668 -0.660 0.864 1.087 0.968 0.331 -0.828 2.215 0.389 2.034 -1.359 0.000 0.455 -0.812 -0.635 0.000 0.977 1.134 0.989 0.874 1.335 0.883 0.786 +1 0.471 1.800 -0.519 0.221 0.342 0.858 1.238 1.570 2.173 0.480 1.323 -0.275 0.000 0.790 1.782 0.863 0.000 1.056 0.245 0.776 3.102 0.857 0.932 0.976 0.627 0.830 0.680 0.656 +0 1.039 -0.887 -0.598 2.530 -0.884 0.738 -1.702 0.992 0.000 1.022 -2.322 0.721 0.000 0.689 -1.284 0.515 2.548 1.090 -1.122 -1.727 3.102 0.774 0.875 0.996 1.074 0.597 0.840 1.191 +0 0.407 1.132 -0.697 1.627 1.534 0.949 -0.443 -1.406 2.173 1.238 0.047 -0.145 0.000 2.054 -0.081 0.526 2.548 0.810 -1.056 -0.412 0.000 0.973 1.073 1.020 1.224 1.742 1.210 1.162 +1 0.574 -0.320 -1.254 0.758 -0.309 1.311 -0.021 -1.688 2.173 1.141 0.145 0.914 0.000 1.425 -0.284 -0.260 0.000 0.893 0.577 -0.567 0.000 0.878 1.340 0.988 0.602 0.678 0.784 0.741 +1 0.755 0.421 -1.067 1.036 1.670 0.690 -0.169 -0.360 1.087 2.072 0.802 0.612 2.215 0.780 0.212 1.553 0.000 0.541 0.173 -1.474 0.000 0.920 1.203 0.989 0.882 1.631 1.147 1.048 +1 1.544 -0.667 1.683 0.460 -0.673 0.901 -0.688 0.382 2.173 0.597 -0.377 -1.029 0.000 0.847 0.262 -0.240 2.548 0.812 0.275 1.323 0.000 0.842 1.027 0.995 0.868 0.803 0.810 0.721 +1 0.703 0.975 -1.061 1.224 1.205 0.560 1.361 -1.363 0.000 1.806 0.808 0.167 2.215 0.868 2.033 1.453 0.000 0.842 0.068 -0.932 3.102 0.903 0.929 1.145 1.182 1.024 1.036 0.901 +1 1.020 -0.172 0.051 0.827 1.679 0.850 0.651 -0.794 0.000 1.380 -0.696 1.451 2.215 1.563 -0.844 0.068 0.000 1.259 0.749 -1.442 0.000 0.891 0.735 1.266 0.985 0.908 0.987 0.886 +0 0.631 0.317 -0.817 0.249 -0.317 0.541 -0.592 1.415 0.000 0.835 -1.181 0.111 2.215 1.423 1.171 -1.178 1.274 1.342 -1.150 0.793 0.000 0.869 0.892 0.976 1.174 2.141 1.345 1.076 +1 0.567 1.648 -0.361 0.958 1.051 0.952 0.926 -0.732 2.173 0.832 0.466 1.667 0.000 1.031 1.290 0.131 2.548 1.429 1.008 1.331 0.000 0.678 1.012 0.988 0.947 0.916 0.891 0.781 +0 1.134 -0.303 -0.806 1.270 -1.358 0.720 0.140 0.842 0.000 0.410 0.097 -0.101 0.000 0.310 1.538 1.343 0.000 0.576 1.156 -1.669 3.102 0.868 0.746 0.984 0.866 0.484 0.709 0.735 +1 0.758 0.682 -0.444 0.938 -1.632 1.179 -0.084 0.624 0.000 0.880 0.535 0.412 0.000 1.320 0.587 1.483 2.548 2.797 0.726 -1.090 1.551 0.934 0.869 1.024 0.698 1.089 0.735 0.650 +1 1.095 -0.733 0.372 0.939 0.191 1.186 0.014 -1.148 2.173 0.896 -0.208 1.243 0.000 0.647 -2.172 -0.622 0.000 0.612 0.423 1.064 3.102 1.923 1.222 1.002 1.687 0.853 1.124 1.103 +1 0.433 0.852 -1.542 1.366 0.726 1.085 -0.534 -1.507 0.000 0.814 -0.632 -1.026 2.215 1.652 -1.256 -0.104 0.000 1.415 0.489 0.883 3.102 0.897 0.972 0.990 0.604 1.151 0.947 1.158 +0 0.416 -1.352 0.839 0.854 1.269 0.978 -0.442 -1.513 2.173 0.898 2.369 0.484 0.000 0.764 0.045 -0.462 2.548 1.698 -0.947 -0.406 0.000 4.819 2.601 0.983 0.871 0.915 1.898 1.486 +0 0.535 1.359 1.126 1.322 -1.572 1.203 1.087 -1.301 1.087 1.973 -0.763 0.410 2.215 1.053 -0.945 -0.083 0.000 0.593 -2.137 -0.134 0.000 0.789 0.844 0.992 0.815 3.348 1.654 1.385 +0 1.719 0.561 1.517 0.784 -1.006 0.942 0.932 0.153 0.000 0.345 0.832 1.035 0.000 0.795 0.857 -1.473 2.548 0.811 0.179 -0.663 3.102 0.866 0.917 1.229 0.758 0.469 0.615 0.697 +1 1.040 -0.803 1.637 0.264 -0.814 1.819 0.055 0.353 0.000 1.541 -0.934 -0.811 2.215 1.701 -0.112 -1.387 0.000 1.762 -0.267 1.163 3.102 1.301 1.135 0.979 0.914 1.534 1.142 1.018 diff --git a/examples/regression/regression.train.init b/examples/regression/regression.train.init new file mode 100644 index 0000000..fdb0fb1 --- /dev/null +++ b/examples/regression/regression.train.init @@ -0,0 +1,7000 @@ +0.375 +0.951 +0.732 +0.599 +0.156 +0.156 +0.058 +0.866 +0.601 +0.708 +0.021 +0.970 +0.832 +0.212 +0.182 +0.183 +0.304 +0.525 +0.432 +0.291 +0.612 +0.139 +0.292 +0.366 +0.456 +0.785 +0.200 +0.514 +0.592 +0.046 +0.608 +0.171 +0.065 +0.949 +0.966 +0.808 +0.305 +0.098 +0.684 +0.440 +0.122 +0.495 +0.034 +0.909 +0.259 +0.663 +0.312 +0.520 +0.547 +0.185 +0.970 +0.775 +0.939 +0.895 +0.598 +0.922 +0.088 +0.196 +0.045 +0.325 +0.389 +0.271 +0.829 +0.357 +0.281 +0.543 +0.141 +0.802 +0.075 +0.987 +0.772 +0.199 +0.006 +0.815 +0.707 +0.729 +0.771 +0.074 +0.358 +0.116 +0.863 +0.623 +0.331 +0.064 +0.311 +0.325 +0.730 +0.638 +0.887 +0.472 +0.120 +0.713 +0.761 +0.561 +0.771 +0.494 +0.523 +0.428 +0.025 +0.108 +0.031 +0.636 +0.314 +0.509 +0.908 +0.249 +0.410 +0.756 +0.229 +0.077 +0.290 +0.161 +0.930 +0.808 +0.633 +0.871 +0.804 +0.187 +0.893 +0.539 +0.807 +0.896 +0.318 +0.110 +0.228 +0.427 +0.818 +0.861 +0.007 +0.511 +0.417 +0.222 +0.120 +0.338 +0.943 +0.323 +0.519 +0.703 +0.364 +0.972 +0.962 +0.252 +0.497 +0.301 +0.285 +0.037 +0.610 +0.503 +0.051 +0.279 +0.908 +0.240 +0.145 +0.489 +0.986 +0.242 +0.672 +0.762 +0.238 +0.728 +0.368 +0.632 +0.634 +0.536 +0.090 +0.835 +0.321 +0.187 +0.041 +0.591 +0.678 +0.017 +0.512 +0.226 +0.645 +0.174 +0.691 +0.387 +0.937 +0.138 +0.341 +0.113 +0.925 +0.877 +0.258 +0.660 +0.817 +0.555 +0.530 +0.242 +0.093 +0.897 +0.900 +0.633 +0.339 +0.349 +0.726 +0.897 +0.887 +0.780 +0.642 +0.084 +0.162 +0.899 +0.606 +0.009 +0.101 +0.664 +0.005 +0.161 +0.549 +0.692 +0.652 +0.224 +0.712 +0.237 +0.325 +0.746 +0.650 +0.849 +0.658 +0.568 +0.094 +0.368 +0.265 +0.244 +0.973 +0.393 +0.892 +0.631 +0.795 +0.503 +0.577 +0.493 +0.195 +0.722 +0.281 +0.024 +0.645 +0.177 +0.940 +0.954 +0.915 +0.370 +0.015 +0.928 +0.428 +0.967 +0.964 +0.853 +0.294 +0.385 +0.851 +0.317 +0.169 +0.557 +0.936 +0.696 +0.570 +0.097 +0.615 +0.990 +0.140 +0.518 +0.877 +0.741 +0.697 +0.702 +0.359 +0.294 +0.809 +0.810 +0.867 +0.913 +0.511 +0.502 +0.798 +0.650 +0.702 +0.796 +0.890 +0.338 +0.376 +0.094 +0.578 +0.036 +0.466 +0.543 +0.287 +0.591 +0.031 +0.037 +0.823 +0.360 +0.127 +0.522 +0.770 +0.216 +0.623 +0.085 +0.052 +0.531 +0.541 +0.637 +0.726 +0.976 +0.516 +0.323 +0.795 +0.271 +0.439 +0.078 +0.025 +0.963 +0.836 +0.696 +0.409 +0.173 +0.156 +0.250 +0.549 +0.715 +0.660 +0.280 +0.955 +0.738 +0.554 +0.612 +0.420 +0.248 +0.356 +0.758 +0.014 +0.116 +0.046 +0.041 +0.855 +0.704 +0.474 +0.098 +0.492 +0.473 +0.173 +0.434 +0.399 +0.616 +0.635 +0.045 +0.375 +0.626 +0.503 +0.856 +0.659 +0.163 +0.071 +0.642 +0.027 +0.586 +0.940 +0.575 +0.388 +0.643 +0.458 +0.546 +0.941 +0.386 +0.961 +0.905 +0.196 +0.069 +0.101 +0.018 +0.094 +0.683 +0.071 +0.319 +0.845 +0.023 +0.814 +0.282 +0.118 +0.697 +0.629 +0.877 +0.735 +0.803 +0.282 +0.177 +0.751 +0.807 +0.991 +0.413 +0.372 +0.776 +0.341 +0.931 +0.858 +0.429 +0.751 +0.755 +0.103 +0.903 +0.505 +0.826 +0.320 +0.896 +0.389 +0.011 +0.905 +0.091 +0.319 +0.950 +0.951 +0.573 +0.632 +0.448 +0.293 +0.329 +0.673 +0.752 +0.792 +0.790 +0.091 +0.494 +0.058 +0.550 +0.442 +0.888 +0.351 +0.117 +0.143 +0.762 +0.618 +0.101 +0.084 +0.701 +0.073 +0.822 +0.706 +0.081 +0.085 +0.987 +0.374 +0.371 +0.813 +0.947 +0.986 +0.753 +0.376 +0.084 +0.777 +0.558 +0.424 +0.906 +0.111 +0.493 +0.011 +0.469 +0.056 +0.119 +0.118 +0.649 +0.746 +0.583 +0.962 +0.375 +0.286 +0.869 +0.224 +0.963 +0.012 +0.970 +0.043 +0.891 +0.528 +0.993 +0.074 +0.554 +0.969 +0.523 +0.629 +0.696 +0.455 +0.628 +0.584 +0.901 +0.045 +0.281 +0.950 +0.890 +0.456 +0.620 +0.277 +0.188 +0.464 +0.353 +0.584 +0.078 +0.974 +0.986 +0.698 +0.536 +0.310 +0.814 +0.685 +0.163 +0.911 +0.823 +0.950 +0.726 +0.613 +0.418 +0.933 +0.866 +0.045 +0.026 +0.376 +0.811 +0.987 +0.150 +0.594 +0.381 +0.970 +0.842 +0.838 +0.469 +0.415 +0.273 +0.056 +0.865 +0.813 +1.000 +0.997 +0.555 +0.769 +0.945 +0.850 +0.247 +0.451 +0.129 +0.954 +0.606 +0.229 +0.672 +0.618 +0.358 +0.114 +0.672 +0.520 +0.772 +0.520 +0.852 +0.552 +0.561 +0.877 +0.403 +0.134 +0.029 +0.755 +0.620 +0.704 +0.213 +0.136 +0.015 +0.351 +0.590 +0.392 +0.437 +0.904 +0.348 +0.514 +0.784 +0.397 +0.622 +0.862 +0.950 +0.147 +0.927 +0.492 +0.258 +0.459 +0.980 +0.493 +0.329 +0.633 +0.240 +0.076 +0.129 +0.128 +0.152 +0.139 +0.641 +0.182 +0.346 +0.897 +0.474 +0.668 +0.172 +0.192 +0.041 +0.169 +0.279 +0.177 +0.089 +0.121 +0.461 +0.206 +0.364 +0.503 +0.690 +0.039 +0.799 +0.628 +0.082 +0.874 +0.921 +0.061 +0.277 +0.806 +0.748 +0.185 +0.209 +0.370 +0.485 +0.618 +0.369 +0.463 +0.747 +0.037 +0.252 +0.713 +0.895 +0.512 +0.532 +0.107 +0.447 +0.533 +0.242 +0.269 +0.377 +0.020 +0.322 +0.211 +0.327 +0.120 +0.891 +0.594 +0.679 +0.789 +0.498 +0.087 +0.537 +0.587 +0.745 +0.432 +0.128 +0.284 +0.363 +0.646 +0.571 +0.356 +0.987 +0.606 +0.237 +0.102 +0.153 +0.246 +0.161 +0.187 +0.285 +0.173 +0.897 +0.080 +0.525 +0.410 +0.982 +0.112 +0.398 +0.969 +0.866 +0.817 +0.258 +0.171 +0.669 +0.929 +0.557 +0.572 +0.280 +0.769 +0.187 +0.324 +0.425 +0.508 +0.242 +0.115 +0.611 +0.289 +0.581 +0.154 +0.481 +0.533 +0.052 +0.337 +0.134 +0.063 +0.990 +0.322 +0.810 +0.255 +0.682 +0.760 +0.596 +0.472 +0.412 +0.349 +0.930 +0.831 +0.965 +0.124 +0.731 +0.938 +0.181 +0.066 +0.741 +0.574 +0.842 +0.140 +0.795 +0.202 +0.164 +0.164 +0.815 +0.665 +0.523 +0.359 +0.877 +0.392 +0.817 +0.439 +0.377 +0.463 +0.301 +0.748 +0.503 +0.232 +0.900 +0.384 +0.544 +0.906 +0.624 +0.117 +0.940 +0.628 +0.335 +0.139 +0.794 +0.620 +0.533 +0.894 +0.789 +0.152 +0.312 +0.248 +0.744 +0.034 +0.570 +0.762 +0.877 +0.342 +0.821 +0.111 +0.846 +0.127 +0.397 +0.797 +0.150 +0.229 +0.722 +0.720 +0.641 +0.694 +0.543 +0.252 +0.346 +0.182 +0.908 +0.583 +0.401 +0.462 +0.947 +0.153 +0.586 +0.506 +0.611 +0.018 +0.872 +0.932 +0.565 +0.697 +0.922 +0.707 +0.153 +0.576 +0.607 +0.424 +0.736 +0.934 +0.926 +0.451 +0.113 +0.985 +0.839 +0.125 +0.921 +0.870 +0.519 +0.591 +0.399 +0.055 +0.335 +0.803 +0.005 +0.333 +0.398 +0.537 +0.920 +0.346 +0.347 +0.738 +0.452 +0.225 +0.452 +0.141 +0.176 +0.498 +0.419 +0.915 +0.362 +0.581 +0.632 +0.013 +0.664 +0.178 +0.961 +0.149 +0.415 +0.085 +0.997 +0.502 +0.595 +0.067 +0.750 +0.210 +0.898 +0.205 +0.191 +0.037 +0.472 +0.565 +0.066 +0.776 +0.453 +0.524 +0.441 +0.401 +0.560 +0.155 +0.182 +0.862 +0.946 +0.373 +0.271 +0.644 +0.409 +0.025 +0.156 +0.716 +0.659 +0.027 +0.222 +0.231 +0.672 +0.020 +0.104 +0.800 +0.179 +0.653 +0.238 +0.099 +0.243 +0.722 +0.856 +0.830 +0.397 +0.668 +0.205 +0.293 +0.896 +0.013 +0.086 +0.208 +0.027 +0.181 +0.583 +0.421 +0.893 +0.817 +0.342 +0.259 +0.380 +0.590 +0.268 +0.624 +0.409 +0.552 +0.436 +0.294 +0.948 +0.764 +0.140 +0.868 +0.487 +0.895 +0.800 +0.425 +0.022 +0.269 +0.542 +0.633 +0.258 +0.139 +0.835 +0.984 +0.526 +0.172 +0.272 +0.018 +0.914 +0.118 +0.577 +0.274 +0.554 +0.651 +0.830 +0.206 +0.011 +0.137 +0.900 +0.874 +0.597 +0.601 +0.665 +0.175 +0.914 +0.419 +0.383 +0.519 +0.047 +0.166 +0.738 +0.083 +0.603 +0.245 +0.389 +0.289 +0.356 +0.719 +0.297 +0.566 +0.476 +0.664 +0.937 +0.733 +0.215 +0.031 +0.262 +0.595 +0.051 +0.496 +0.597 +0.334 +0.771 +0.107 +0.075 +0.728 +0.495 +0.688 +0.435 +0.246 +0.819 +0.799 +0.695 +0.272 +0.590 +0.361 +0.092 +0.917 +0.137 +0.950 +0.446 +0.185 +0.542 +0.873 +0.732 +0.807 +0.659 +0.692 +0.849 +0.250 +0.489 +0.221 +0.988 +0.944 +0.039 +0.706 +0.925 +0.181 +0.568 +0.915 +0.034 +0.697 +0.297 +0.924 +0.971 +0.944 +0.474 +0.862 +0.845 +0.319 +0.829 +0.037 +0.596 +0.230 +0.121 +0.077 +0.696 +0.340 +0.725 +0.065 +0.315 +0.539 +0.791 +0.319 +0.626 +0.886 +0.616 +0.233 +0.024 +0.870 +0.021 +0.875 +0.529 +0.939 +0.799 +0.998 +0.351 +0.767 +0.402 +0.480 +0.628 +0.874 +0.984 +0.768 +0.418 +0.421 +0.738 +0.239 +0.110 +0.355 +0.287 +0.296 +0.234 +0.042 +0.018 +0.988 +0.428 +0.384 +0.680 +0.218 +0.950 +0.786 +0.089 +0.418 +0.879 +0.945 +0.467 +0.613 +0.167 +0.991 +0.232 +0.943 +0.650 +0.608 +0.513 +0.231 +0.177 +0.220 +0.186 +0.780 +0.350 +0.058 +0.969 +0.884 +0.928 +0.995 +0.174 +0.396 +0.758 +0.696 +0.154 +0.816 +0.224 +0.224 +0.537 +0.593 +0.580 +0.091 +0.877 +0.266 +0.130 +0.889 +0.956 +0.862 +0.810 +0.655 +0.551 +0.087 +0.408 +0.373 +0.260 +0.723 +0.496 +0.081 +0.220 +0.683 +0.076 +0.851 +0.495 +0.481 +0.592 +0.825 +0.348 +0.678 +0.566 +0.267 +0.879 +0.797 +0.658 +0.851 +0.867 +0.708 +0.837 +0.697 +0.680 +0.619 +0.753 +0.159 +0.881 +0.872 +0.029 +0.826 +0.129 +0.335 +0.744 +0.161 +0.818 +0.832 +0.507 +0.006 +0.287 +0.617 +0.981 +0.632 +0.260 +0.634 +0.540 +0.780 +0.107 +0.761 +0.541 +0.963 +0.342 +0.633 +0.932 +0.103 +0.937 +0.688 +0.068 +0.301 +0.708 +0.067 +0.582 +0.346 +0.621 +0.046 +0.872 +0.973 +0.969 +0.750 +0.130 +0.758 +0.025 +0.022 +0.324 +0.489 +0.770 +0.683 +0.446 +0.274 +0.997 +0.426 +0.451 +0.164 +0.795 +0.694 +0.221 +0.082 +0.680 +0.655 +0.273 +0.951 +0.151 +0.432 +0.944 +0.420 +0.639 +0.398 +0.274 +0.984 +0.409 +0.894 +0.230 +0.213 +0.031 +0.652 +0.369 +0.864 +0.473 +0.968 +0.186 +0.869 +0.777 +0.771 +0.845 +0.761 +0.626 +0.131 +0.033 +0.921 +0.617 +0.797 +0.482 +0.117 +0.125 +0.686 +0.430 +0.201 +0.492 +0.064 +0.582 +0.269 +0.798 +0.310 +0.455 +0.012 +0.072 +0.392 +0.480 +0.600 +0.292 +0.695 +0.860 +0.780 +0.040 +0.481 +0.105 +0.242 +0.987 +0.142 +0.499 +0.618 +0.702 +0.560 +0.010 +0.326 +0.518 +0.088 +0.351 +0.033 +0.079 +0.397 +0.133 +0.568 +0.689 +0.801 +0.200 +0.167 +0.105 +0.636 +0.706 +0.032 +0.936 +0.052 +0.541 +0.709 +0.871 +0.714 +0.802 +0.339 +0.815 +0.080 +0.895 +0.548 +0.817 +0.452 +0.644 +0.526 +0.732 +0.082 +0.060 +0.247 +0.160 +0.872 +0.219 +0.976 +0.337 +0.182 +0.790 +0.659 +0.498 +0.555 +0.719 +0.228 +0.996 +0.975 +0.650 +0.200 +0.680 +0.072 +0.031 +0.258 +0.463 +0.868 +0.727 +0.743 +0.425 +0.346 +0.371 +0.988 +0.040 +0.867 +0.579 +0.439 +0.725 +0.487 +0.873 +0.901 +0.422 +0.277 +0.592 +0.912 +0.211 +0.623 +0.632 +0.733 +0.132 +0.716 +0.909 +0.180 +0.238 +0.971 +0.181 +0.854 +0.492 +0.247 +0.871 +0.445 +0.515 +0.359 +0.593 +0.164 +0.391 +0.969 +0.258 +0.657 +0.325 +0.773 +0.131 +0.970 +0.454 +0.236 +0.073 +0.170 +0.520 +0.337 +0.829 +0.431 +0.249 +0.617 +0.707 +0.167 +0.168 +0.037 +0.736 +0.664 +0.475 +0.844 +0.806 +0.585 +0.868 +0.206 +0.112 +0.270 +0.057 +0.531 +0.937 +0.039 +0.122 +0.452 +0.934 +0.316 +0.507 +0.042 +0.148 +0.987 +0.965 +0.005 +0.952 +0.639 +0.868 +0.455 +0.516 +0.489 +0.667 +0.140 +0.030 +0.308 +0.705 +0.202 +0.673 +0.970 +0.094 +0.673 +0.444 +0.868 +0.177 +0.693 +0.838 +0.945 +0.683 +0.497 +0.618 +0.869 +0.571 +0.030 +0.931 +0.690 +0.677 +0.216 +0.659 +0.394 +0.651 +0.107 +0.658 +0.999 +0.048 +0.977 +0.407 +0.871 +0.782 +0.567 +0.738 +0.879 +0.404 +0.327 +0.668 +0.808 +0.762 +0.798 +0.436 +0.818 +0.120 +0.544 +0.006 +0.325 +0.366 +0.396 +0.695 +0.389 +0.449 +0.238 +0.373 +0.227 +0.073 +0.603 +0.668 +0.619 +0.463 +0.380 +0.863 +0.519 +0.479 +0.026 +0.341 +0.380 +0.399 +0.580 +0.534 +0.608 +0.765 +0.813 +0.718 +0.956 +0.018 +0.196 +0.008 +0.647 +0.898 +0.243 +0.927 +0.060 +0.934 +0.352 +0.101 +0.486 +0.257 +0.285 +0.307 +0.803 +0.539 +0.311 +0.610 +0.716 +0.273 +0.414 +0.122 +0.181 +0.681 +0.181 +0.525 +0.709 +0.107 +0.567 +0.257 +0.963 +0.484 +0.806 +0.550 +0.043 +0.633 +0.951 +0.602 +0.819 +0.884 +0.228 +0.212 +0.611 +0.411 +0.840 +0.900 +0.353 +0.237 +0.781 +0.275 +0.823 +0.424 +0.668 +0.096 +0.624 +0.452 +0.587 +0.168 +0.737 +0.863 +0.217 +0.096 +0.024 +0.642 +0.607 +0.547 +0.232 +0.391 +0.594 +0.497 +0.988 +0.136 +0.695 +0.404 +0.428 +0.718 +0.692 +0.991 +0.128 +0.104 +0.724 +0.578 +0.274 +0.079 +0.086 +0.894 +0.192 +0.323 +0.227 +0.355 +0.069 +0.519 +0.068 +0.800 +0.234 +0.540 +0.880 +0.651 +0.533 +0.324 +0.333 +0.669 +0.994 +0.662 +0.558 +0.731 +0.465 +0.060 +0.562 +0.958 +0.175 +0.690 +0.201 +0.536 +0.097 +0.450 +0.756 +0.348 +0.665 +0.795 +0.927 +0.235 +0.399 +0.152 +0.992 +0.927 +0.540 +0.842 +0.521 +0.624 +0.089 +0.755 +0.128 +0.826 +0.782 +0.709 +0.036 +0.303 +0.263 +0.360 +0.088 +0.937 +0.554 +0.306 +0.397 +0.447 +0.601 +0.516 +0.919 +0.497 +0.992 +0.851 +0.209 +0.931 +0.116 +0.817 +0.381 +0.878 +0.868 +0.806 +0.790 +0.305 +0.081 +0.403 +0.174 +0.695 +0.346 +0.976 +0.641 +0.822 +0.133 +0.862 +0.923 +0.487 +0.606 +0.765 +0.175 +0.503 +0.399 +0.146 +0.368 +0.068 +0.026 +0.135 +0.963 +0.550 +0.966 +0.432 +0.312 +0.506 +0.440 +0.106 +0.641 +0.216 +0.620 +0.650 +0.152 +0.061 +0.781 +0.460 +0.058 +0.995 +0.058 +0.695 +0.984 +0.239 +0.142 +0.121 +0.303 +0.101 +0.692 +0.062 +0.509 +0.997 +0.814 +0.615 +0.306 +0.624 +0.527 +0.426 +0.131 +0.887 +0.450 +0.195 +0.368 +0.414 +0.828 +0.734 +0.769 +0.011 +0.416 +0.481 +0.019 +0.260 +0.760 +0.137 +0.535 +0.215 +0.012 +0.241 +0.976 +0.802 +0.960 +0.488 +0.110 +0.548 +0.454 +0.844 +0.098 +0.488 +0.150 +0.325 +0.737 +0.476 +0.376 +0.394 +0.459 +0.785 +0.892 +0.955 +0.787 +0.315 +0.688 +0.438 +0.255 +0.841 +0.038 +0.902 +0.461 +0.637 +0.659 +0.895 +0.637 +0.614 +0.067 +0.518 +0.150 +0.737 +0.512 +0.680 +0.042 +0.085 +0.716 +0.072 +0.071 +0.012 +0.957 +0.738 +0.353 +0.297 +0.350 +0.775 +0.661 +0.185 +0.174 +0.098 +0.660 +0.764 +0.265 +0.021 +0.082 +0.968 +0.295 +0.769 +0.625 +0.382 +0.206 +0.121 +0.615 +0.775 +0.644 +0.530 +0.042 +0.968 +0.799 +0.293 +0.980 +0.602 +0.582 +0.748 +0.812 +0.656 +0.128 +0.338 +0.928 +0.225 +0.372 +0.432 +0.439 +0.613 +0.943 +0.241 +0.122 +0.197 +0.887 +0.646 +0.286 +0.816 +0.861 +0.847 +0.919 +0.252 +0.755 +0.461 +0.842 +0.728 +0.776 +0.656 +0.177 +0.545 +0.985 +0.937 +0.043 +0.165 +0.132 +0.726 +0.818 +0.214 +0.506 +0.841 +0.733 +0.542 +0.590 +0.508 +0.298 +0.565 +0.689 +0.873 +0.636 +0.761 +0.160 +0.462 +0.009 +0.247 +0.726 +0.992 +0.099 +0.401 +0.800 +0.204 +0.555 +0.733 +0.616 +0.188 +0.355 +0.784 +0.554 +0.005 +0.761 +0.035 +0.746 +0.202 +0.958 +0.368 +0.327 +0.149 +0.306 +0.877 +0.996 +0.368 +0.449 +0.722 +0.886 +0.593 +0.392 +0.413 +0.696 +0.003 +0.620 +0.355 +0.794 +0.093 +0.588 +0.481 +0.642 +0.065 +0.580 +0.561 +0.561 +0.603 +0.676 +0.805 +0.270 +0.825 +0.498 +0.077 +0.059 +0.334 +0.785 +0.708 +0.789 +0.517 +0.440 +0.147 +0.328 +0.434 +0.089 +0.221 +0.598 +0.736 +0.998 +0.933 +0.643 +0.421 +0.636 +0.786 +0.118 +0.410 +0.840 +0.384 +0.572 +0.588 +0.184 +0.362 +0.335 +0.026 +0.024 +0.832 +0.273 +0.518 +0.299 +0.941 +0.259 +0.430 +0.873 +0.842 +0.186 +0.803 +0.458 +0.483 +0.133 +0.081 +0.728 +0.496 +0.437 +0.730 +0.766 +0.159 +0.610 +0.135 +0.751 +0.657 +0.957 +0.069 +0.057 +0.282 +0.262 +0.247 +0.906 +0.250 +0.272 +0.759 +0.450 +0.777 +0.065 +0.488 +0.034 +0.063 +0.906 +0.139 +0.532 +0.411 +0.347 +0.900 +0.022 +0.664 +0.963 +0.560 +0.937 +0.052 +0.419 +0.260 +0.731 +0.981 +0.257 +0.654 +0.198 +0.565 +0.464 +0.972 +0.609 +0.350 +0.114 +0.151 +0.225 +0.251 +0.851 +0.561 +0.523 +0.115 +0.860 +0.723 +0.068 +0.708 +0.544 +0.082 +0.458 +0.485 +0.166 +0.946 +0.850 +0.669 +0.462 +0.412 +0.651 +0.545 +0.062 +0.513 +0.806 +0.459 +0.052 +0.786 +0.201 +0.259 +0.165 +0.330 +0.757 +0.519 +0.205 +0.878 +0.880 +0.871 +0.239 +0.451 +0.985 +0.772 +0.027 +0.065 +0.464 +0.909 +0.539 +0.498 +0.105 +0.657 +0.822 +0.380 +0.776 +0.964 +0.204 +0.523 +0.287 +0.793 +0.578 +0.635 +0.798 +0.396 +0.915 +0.533 +0.158 +0.696 +0.793 +0.317 +0.857 +0.906 +0.277 +0.984 +0.141 +0.202 +0.184 +0.894 +0.654 +0.152 +0.440 +0.615 +0.083 +0.882 +0.804 +0.505 +0.967 +0.418 +0.984 +0.668 +0.635 +0.166 +0.882 +0.427 +0.162 +0.013 +0.560 +0.527 +0.719 +0.890 +0.079 +0.731 +0.187 +0.858 +0.819 +0.541 +0.710 +0.314 +0.471 +0.822 +0.459 +0.358 +0.494 +0.828 +0.335 +0.174 +0.712 +0.826 +0.101 +0.240 +0.142 +0.348 +0.450 +0.749 +0.651 +0.621 +0.352 +0.841 +0.471 +0.979 +0.634 +0.126 +0.676 +0.325 +0.686 +0.070 +0.175 +0.856 +0.227 +0.837 +0.279 +0.643 +0.694 +0.513 +0.305 +0.213 +0.033 +0.304 +0.653 +0.938 +0.871 +0.766 +0.788 +0.665 +0.260 +0.907 +0.671 +0.560 +0.111 +0.447 +0.460 +0.865 +0.547 +0.380 +0.977 +0.111 +0.423 +0.042 +0.740 +0.918 +0.280 +0.858 +0.292 +0.911 +0.754 +0.805 +0.018 +0.963 +0.727 +0.305 +0.829 +0.282 +0.873 +0.113 +0.704 +0.541 +0.097 +0.242 +0.012 +0.469 +0.301 +0.598 +0.297 +0.300 +0.743 +0.048 +0.903 +0.852 +0.668 +0.593 +0.892 +0.185 +0.079 +0.240 +0.795 +0.035 +0.583 +0.995 +0.856 +0.521 +0.064 +0.831 +0.599 +0.115 +0.094 +0.910 +0.669 +0.829 +0.879 +0.572 +0.517 +0.430 +0.317 +0.435 +0.774 +0.602 +0.893 +0.443 +0.607 +0.631 +0.592 +0.703 +0.237 +0.512 +0.104 +0.385 +0.488 +0.652 +0.951 +0.601 +0.744 +0.506 +0.634 +0.071 +0.254 +0.362 +0.472 +0.046 +0.140 +0.277 +0.972 +0.331 +0.482 +0.196 +0.611 +0.281 +0.207 +0.517 +0.006 +0.008 +0.219 +0.037 +0.108 +0.339 +0.803 +0.572 +0.513 +0.293 +0.932 +0.397 +0.087 +0.617 +0.114 +0.345 +0.507 +0.874 +0.494 +0.702 +0.993 +0.131 +0.275 +0.395 +0.422 +0.411 +0.908 +0.714 +0.608 +0.309 +0.824 +0.955 +0.821 +0.002 +0.636 +0.051 +0.258 +0.060 +0.604 +0.687 +0.114 +0.384 +0.456 +0.369 +0.121 +0.419 +0.751 +0.071 +0.080 +0.355 +0.942 +0.669 +0.679 +0.362 +0.594 +0.010 +0.636 +0.913 +0.613 +0.874 +0.724 +0.121 +0.902 +0.066 +0.534 +0.142 +0.012 +0.422 +0.295 +0.486 +0.577 +0.044 +0.123 +0.559 +0.343 +0.729 +0.652 +0.846 +0.692 +0.430 +0.673 +0.275 +0.306 +0.789 +0.446 +0.798 +0.822 +0.858 +0.917 +0.431 +0.319 +0.582 +0.371 +0.601 +0.706 +0.688 +0.375 +0.167 +0.431 +0.143 +0.890 +0.346 +0.154 +0.025 +0.646 +0.637 +0.341 +0.072 +0.410 +0.311 +0.677 +0.606 +0.365 +0.218 +0.988 +0.454 +0.688 +0.141 +0.486 +0.028 +0.505 +0.964 +0.384 +0.039 +0.031 +0.388 +0.160 +0.023 +0.756 +0.459 +0.289 +0.900 +0.116 +0.956 +0.314 +0.888 +0.603 +0.827 +0.984 +0.288 +0.961 +0.389 +0.386 +0.340 +0.541 +0.154 +0.554 +0.542 +0.762 +0.834 +0.440 +0.302 +0.259 +0.195 +0.058 +0.342 +0.270 +0.966 +0.558 +0.347 +0.580 +0.139 +0.444 +0.626 +0.489 +0.402 +0.994 +0.880 +0.623 +0.569 +0.621 +0.201 +0.395 +0.039 +0.476 +0.543 +0.228 +0.964 +0.909 +0.722 +0.533 +0.870 +0.131 +0.791 +0.125 +0.794 +0.276 +0.877 +0.944 +0.149 +0.463 +0.981 +0.483 +0.864 +0.589 +0.375 +0.286 +0.203 +0.762 +0.387 +0.511 +0.492 +0.577 +0.866 +0.981 +0.408 +0.828 +0.765 +0.574 +0.956 +0.200 +0.109 +0.854 +0.439 +0.847 +0.893 +0.062 +0.883 +0.448 +0.510 +0.627 +0.926 +0.019 +0.477 +0.688 +0.723 +0.693 +0.134 +0.299 +0.359 +0.804 +0.279 +0.211 +0.957 +0.009 +0.998 +0.677 +0.828 +0.295 +0.014 +0.738 +0.834 +0.740 +0.143 +0.753 +0.769 +0.659 +0.766 +0.846 +0.614 +0.089 +0.488 +0.078 +0.408 +0.407 +0.066 +0.349 +0.111 +0.808 +0.948 +0.072 +0.955 +0.523 +0.300 +0.077 +0.501 +0.795 +0.707 +0.050 +0.073 +0.403 +0.295 +0.232 +0.281 +0.803 +0.929 +0.405 +0.906 +0.321 +0.476 +0.226 +0.640 +0.979 +0.603 +0.358 +0.648 +0.123 +0.889 +0.503 +0.449 +0.586 +0.625 +0.072 +0.683 +0.242 +0.714 +0.823 +0.804 +0.553 +0.520 +0.143 +0.775 +0.271 +0.497 +0.284 +0.134 +0.630 +0.054 +0.749 +0.318 +0.000 +0.511 +0.047 +0.276 +0.707 +0.063 +0.839 +0.004 +0.247 +0.741 +0.316 +0.102 +0.360 +0.270 +0.843 +0.313 +0.789 +0.892 +0.434 +0.910 +0.377 +0.964 +0.089 +0.687 +0.494 +0.388 +0.633 +0.704 +0.004 +0.167 +0.713 +0.666 +0.966 +0.761 +0.951 +0.703 +0.298 +0.105 +0.782 +0.644 +0.048 +0.360 +0.957 +0.500 +0.433 +0.458 +0.209 +0.369 +0.370 +0.052 +0.768 +0.417 +0.822 +0.850 +0.212 +0.657 +0.472 +0.880 +0.216 +0.678 +0.608 +0.295 +0.137 +0.652 +0.739 +0.316 +0.645 +0.395 +0.713 +0.199 +0.890 +0.287 +0.368 +0.058 +0.112 +0.516 +0.268 +0.835 +0.015 +0.379 +0.337 +0.019 +0.124 +0.414 +0.493 +0.404 +0.531 +0.595 +0.010 +0.464 +0.963 +0.519 +0.678 +0.312 +0.774 +0.773 +0.521 +0.976 +0.126 +0.017 +0.770 +0.807 +0.120 +0.266 +0.018 +0.293 +0.773 +0.518 +0.348 +0.372 +0.001 +0.300 +0.646 +0.974 +0.847 +0.024 +0.899 +0.783 +0.780 +0.458 +0.398 +0.303 +0.066 +0.228 +0.247 +0.484 +0.747 +0.474 +0.058 +0.958 +0.943 +0.785 +0.991 +0.544 +0.963 +0.076 +0.366 +0.225 +0.196 +0.141 +0.622 +0.781 +0.578 +0.147 +0.811 +0.636 +0.388 +0.674 +0.260 +0.345 +0.917 +0.291 +0.470 +0.890 +0.708 +0.062 +0.147 +0.008 +0.631 +0.448 +0.134 +0.958 +0.530 +0.242 +0.501 +0.680 +0.076 +0.275 +0.807 +0.460 +0.547 +0.433 +0.044 +0.166 +0.446 +0.209 +0.050 +0.844 +0.981 +0.793 +0.854 +0.242 +0.961 +0.197 +0.951 +0.995 +0.712 +0.981 +0.570 +0.260 +0.437 +0.594 +0.073 +0.622 +0.981 +0.190 +0.793 +0.908 +0.944 +0.960 +0.521 +0.977 +0.757 +0.162 +0.477 +0.718 +0.247 +0.641 +0.667 +0.163 +0.565 +0.772 +0.499 +0.012 +0.009 +0.357 +0.926 +0.229 +0.634 +0.222 +0.322 +0.848 +0.729 +0.095 +0.429 +0.029 +0.481 +0.662 +0.119 +0.289 +0.398 +0.920 +0.993 +0.045 +0.761 +0.372 +0.392 +0.754 +0.918 +0.951 +0.577 +0.357 +0.788 +0.251 +0.564 +0.359 +0.657 +0.240 +0.192 +0.918 +0.102 +0.506 +0.221 +0.039 +0.036 +0.175 +0.867 +0.282 +0.950 +0.582 +0.437 +0.580 +0.517 +0.759 +0.282 +0.353 +0.894 +0.946 +0.893 +0.419 +0.780 +0.476 +0.498 +0.205 +0.591 +0.186 +0.332 +0.855 +0.207 +0.071 +0.069 +0.941 +0.507 +0.409 +0.811 +0.836 +0.332 +0.694 +0.771 +0.655 +0.152 +0.876 +0.539 +0.282 +0.425 +0.038 +0.128 +0.766 +0.000 +0.417 +0.523 +0.055 +0.973 +0.226 +0.304 +0.304 +0.230 +0.001 +0.729 +0.967 +0.224 +0.663 +0.742 +0.848 +0.423 +0.303 +0.325 +0.713 +0.817 +0.182 +0.371 +0.902 +0.807 +0.985 +0.754 +0.393 +0.591 +0.661 +0.078 +0.544 +0.709 +0.167 +0.781 +0.584 +0.952 +0.042 +0.265 +0.602 +0.297 +0.714 +0.759 +0.103 +0.514 +0.509 +0.369 +0.933 +0.828 +0.697 +0.714 +0.462 +0.921 +0.695 +0.729 +0.862 +0.274 +0.807 +0.195 +0.345 +0.336 +0.979 +0.857 +0.701 +0.727 +0.562 +0.947 +0.496 +0.381 +0.163 +0.786 +0.734 +0.384 +0.025 +0.839 +0.011 +0.704 +0.970 +0.438 +0.235 +0.705 +0.817 +0.546 +0.967 +0.052 +0.505 +0.718 +0.863 +0.179 +0.800 +0.553 +0.397 +0.132 +0.865 +0.157 +0.310 +0.290 +0.871 +0.673 +0.797 +0.250 +0.625 +0.572 +0.833 +0.906 +0.012 +0.674 +0.052 +0.549 +0.288 +0.307 +0.353 +0.621 +0.334 +0.733 +0.405 +0.068 +0.784 +0.286 +0.433 +0.685 +0.332 +0.057 +0.374 +0.944 +0.642 +0.671 +0.632 +0.199 +0.418 +0.751 +0.101 +0.278 +0.276 +0.432 +0.980 +0.068 +0.519 +0.179 +0.971 +0.113 +0.404 +0.738 +0.705 +0.423 +0.347 +0.398 +0.264 +0.205 +0.483 +0.269 +0.287 +0.657 +0.969 +0.604 +0.077 +0.076 +0.951 +0.297 +0.092 +0.599 +0.624 +0.649 +0.267 +0.015 +0.965 +0.251 +0.676 +0.707 +0.610 +0.313 +0.271 +0.598 +0.866 +0.947 +0.106 +0.155 +0.945 +0.737 +0.883 +0.203 +0.588 +0.701 +0.680 +0.408 +0.015 +0.583 +0.253 +0.450 +0.958 +0.399 +0.840 +0.189 +0.672 +0.977 +0.102 +0.008 +0.434 +0.093 +0.748 +0.915 +0.434 +0.259 +0.434 +0.723 +0.009 +0.589 +0.613 +0.638 +0.242 +0.714 +0.091 +0.199 +0.877 +0.739 +0.014 +0.248 +0.214 +0.271 +0.248 +0.063 +0.459 +0.733 +0.607 +0.673 +0.081 +0.951 +0.838 +0.805 +0.823 +0.933 +0.544 +0.200 +0.617 +0.743 +0.738 +0.521 +0.068 +0.371 +0.921 +0.584 +0.538 +0.269 +0.368 +0.895 +0.666 +0.787 +0.454 +0.630 +0.248 +0.705 +0.428 +0.443 +0.649 +0.936 +0.064 +0.825 +0.292 +0.444 +0.022 +0.301 +0.503 +0.056 +0.491 +0.927 +0.105 +0.764 +0.410 +0.655 +0.260 +0.159 +0.160 +0.070 +0.186 +0.664 +0.882 +0.814 +0.685 +0.110 +0.289 +0.310 +0.250 +0.515 +0.536 +0.357 +0.354 +0.829 +0.789 +0.308 +0.914 +0.953 +0.327 +0.354 +0.506 +0.941 +0.876 +0.103 +0.393 +0.553 +0.503 +0.194 +0.859 +0.677 +0.838 +0.859 +0.748 +0.439 +0.611 +0.160 +0.674 +0.179 +0.694 +0.230 +0.118 +0.165 +0.002 +0.719 +0.732 +0.515 +0.161 +0.084 +0.019 +0.166 +0.891 +0.242 +0.354 +0.105 +0.222 +0.519 +0.608 +0.245 +0.058 +0.391 +0.234 +0.220 +0.960 +0.616 +0.557 +0.416 +0.429 +0.541 +0.696 +0.702 +0.172 +0.500 +0.412 +0.871 +0.631 +0.533 +0.115 +0.606 +0.117 +0.337 +0.143 +0.692 +0.206 +0.392 +0.896 +0.204 +0.508 +0.419 +0.018 +0.793 +0.069 +0.474 +0.561 +0.628 +0.689 +0.253 +0.010 +0.723 +0.536 +0.836 +0.821 +0.843 +0.485 +0.334 +0.792 +0.451 +0.183 +0.855 +0.883 +0.466 +0.076 +0.388 +0.804 +0.902 +0.203 +0.067 +0.877 +0.389 +0.542 +0.968 +0.067 +0.648 +0.074 +0.375 +0.804 +0.433 +0.997 +0.559 +0.321 +0.220 +0.351 +0.373 +0.069 +0.370 +0.464 +0.723 +0.657 +0.709 +0.008 +0.218 +0.662 +0.484 +0.005 +0.804 +0.773 +0.548 +0.066 +0.767 +0.584 +0.782 +0.752 +0.803 +0.518 +0.140 +0.671 +0.620 +0.742 +0.170 +0.195 +0.890 +0.750 +0.908 +0.759 +0.597 +0.654 +0.889 +0.579 +0.632 +0.156 +0.474 +0.716 +0.271 +0.202 +0.314 +0.242 +0.215 +0.425 +0.908 +0.507 +0.188 +0.077 +0.696 +0.383 +0.822 +0.660 +0.796 +0.272 +0.692 +0.264 +0.939 +0.636 +0.325 +0.270 +0.191 +0.695 +0.219 +0.595 +0.265 +0.662 +0.815 +0.778 +0.761 +0.188 +0.088 +0.699 +0.368 +0.432 +0.031 +0.260 +0.034 +0.879 +0.243 +0.557 +0.039 +0.667 +0.323 +0.898 +0.888 +0.325 +0.901 +0.996 +0.825 +0.845 +0.249 +0.577 +0.067 +0.095 +0.999 +0.327 +0.748 +0.807 +0.858 +0.998 +0.241 +0.040 +0.411 +0.130 +0.022 +0.360 +0.784 +0.566 +0.313 +0.654 +0.232 +0.014 +0.764 +0.624 +0.762 +0.039 +0.837 +0.620 +0.563 +0.625 +0.864 +0.587 +0.581 +0.991 +0.757 +0.442 +0.707 +0.389 +0.229 +0.597 +0.928 +0.929 +0.342 +0.528 +0.212 +0.996 +0.981 +0.650 +0.804 +0.715 +0.593 +0.053 +0.455 +0.675 +0.678 +0.373 +0.942 +0.167 +0.500 +0.691 +0.697 +0.649 +0.275 +0.156 +0.636 +0.599 +0.179 +0.705 +0.455 +0.668 +0.837 +0.170 +0.019 +0.779 +0.610 +0.700 +0.838 +0.803 +0.961 +0.536 +0.488 +0.402 +0.154 +0.573 +0.277 +0.921 +0.583 +0.593 +0.355 +0.052 +0.032 +0.423 +0.085 +0.607 +0.881 +0.883 +0.659 +0.211 +0.863 +0.886 +0.197 +0.737 +0.287 +0.803 +0.997 +0.030 +0.897 +0.623 +0.973 +0.465 +0.847 +0.062 +0.335 +0.067 +0.975 +0.817 +0.853 +0.938 +0.085 +0.386 +0.071 +0.211 +0.229 +0.469 +0.269 +0.101 +0.167 +0.147 +0.973 +0.759 +0.968 +0.440 +0.278 +0.798 +0.326 +0.299 +0.233 +0.130 +0.256 +0.355 +0.674 +0.063 +0.211 +0.809 +0.147 +0.343 +0.865 +0.155 +0.083 +0.485 +0.302 +0.563 +0.804 +0.137 +0.581 +0.506 +0.144 +0.624 +0.274 +0.488 +0.082 +0.460 +0.306 +0.822 +0.057 +0.419 +0.458 +0.725 +0.574 +0.667 +0.777 +0.862 +0.314 +0.538 +0.840 +0.989 +0.890 +0.371 +0.195 +0.489 +0.742 +0.493 +0.483 +0.838 +0.361 +0.860 +0.407 +0.328 +0.454 +0.762 +0.126 +0.196 +0.951 +0.175 +0.568 +0.579 +0.490 +0.645 +0.230 +0.553 +0.372 +0.662 +0.141 +0.571 +0.185 +0.279 +0.219 +0.183 +0.826 +0.286 +0.927 +0.970 +0.571 +0.143 +0.375 +0.798 +0.367 +0.087 +0.557 +0.845 +0.796 +0.175 +0.673 +0.221 +0.218 +0.874 +0.250 +0.263 +0.001 +0.871 +0.793 +0.627 +0.750 +0.152 +0.458 +0.351 +0.094 +0.486 +0.921 +0.040 +0.291 +0.208 +0.238 +0.908 +0.468 +0.466 +0.761 +0.154 +0.487 +0.430 +0.597 +1.000 +0.769 +0.398 +0.828 +0.171 +0.030 +0.204 +0.340 +0.511 +0.615 +0.911 +0.510 +0.501 +0.050 +0.035 +0.551 +0.438 +0.839 +0.161 +0.025 +0.449 +0.237 +0.050 +0.725 +0.112 +0.608 +0.281 +0.173 +0.380 +0.801 +0.392 +0.751 +0.125 +0.773 +0.237 +0.677 +0.566 +0.929 +0.387 +0.066 +0.019 +0.827 +0.525 +0.775 +0.234 +0.345 +0.030 +0.961 +0.668 +0.933 +0.265 +0.611 +0.678 +0.318 +0.848 +0.947 +0.885 +0.739 +0.277 +0.282 +0.963 +0.010 +0.716 +0.706 +0.623 +0.990 +0.312 +0.340 +0.079 +0.443 +0.261 +0.343 +0.835 +0.935 +0.186 +0.373 +0.929 +0.062 +0.092 +0.163 +0.595 +0.150 +0.968 +0.447 +0.502 +0.247 +0.471 +0.662 +0.751 +0.754 +0.578 +0.904 +0.817 +0.757 +0.056 +0.007 +0.212 +0.664 +0.411 +0.402 +0.885 +0.896 +0.909 +0.314 +0.691 +0.272 +0.191 +0.185 +0.342 +0.430 +0.831 +0.120 +0.735 +0.531 +0.288 +0.493 +0.300 +0.596 +0.434 +0.164 +0.117 +0.547 +0.902 +0.344 +0.733 +0.659 +0.932 +0.821 +0.567 +0.657 +0.898 +0.400 +0.327 +0.011 +0.827 +0.801 +0.104 +0.577 +0.464 +0.119 +0.981 +0.215 +0.067 +0.595 +0.739 +0.032 +0.659 +0.532 +0.103 +0.173 +0.568 +0.296 +0.938 +0.819 +0.984 +0.260 +0.970 +0.431 +0.348 +0.050 +0.053 +0.692 +0.458 +0.227 +0.614 +0.253 +0.578 +0.359 +0.824 +0.821 +0.477 +0.351 +0.363 +0.806 +0.328 +0.209 +0.085 +0.467 +0.482 +0.841 +0.221 +0.381 +0.808 +0.824 +0.385 +0.459 +0.303 +0.933 +0.119 +0.934 +0.684 +0.532 +0.033 +0.476 +0.408 +0.161 +0.656 +0.971 +0.562 +0.715 +0.068 +0.418 +0.116 +0.613 +0.938 +0.662 +0.077 +0.355 +0.551 +0.403 +0.834 +0.815 +0.612 +0.373 +0.255 +0.106 +0.355 +0.413 +0.676 +0.658 +0.070 +0.395 +0.182 +0.157 +0.827 +0.042 +0.419 +0.168 +0.879 +0.557 +0.231 +0.502 +0.731 +0.958 +0.220 +0.886 +0.934 +0.916 +0.635 +0.630 +0.403 +0.752 +0.531 +0.677 +0.428 +0.731 +0.824 +0.146 +0.833 +0.540 +0.845 +0.431 +0.379 +0.915 +0.251 +0.844 +0.484 +0.514 +0.308 +0.573 +0.325 +0.039 +0.272 +0.006 +0.978 +0.966 +0.395 +0.728 +0.346 +0.671 +0.805 +0.947 +0.400 +0.783 +0.266 +0.990 +0.026 +0.604 +0.660 +0.688 +0.120 +0.939 +0.181 +0.623 +0.223 +0.307 +0.546 +0.417 +0.160 +0.171 +0.418 +0.757 +0.898 +0.084 +0.393 +0.100 +0.017 +0.662 +0.602 +0.163 +0.234 +0.024 +0.835 +0.975 +0.135 +0.231 +0.868 +0.926 +0.420 +0.051 +0.039 +0.574 +0.393 +0.029 +0.583 +0.011 +0.787 +0.306 +0.040 +0.588 +0.398 +0.974 +0.544 +0.275 +0.709 +0.271 +0.904 +0.375 +0.550 +0.051 +0.426 +0.832 +0.806 +0.224 +0.226 +0.817 +0.930 +0.095 +0.450 +0.337 +0.871 +0.084 +0.211 +0.752 +0.051 +0.493 +0.442 +0.334 +0.395 +0.530 +0.161 +0.572 +0.805 +0.760 +0.154 +0.149 +0.268 +0.361 +0.408 +0.680 +0.057 +0.035 +0.392 +0.697 +0.193 +0.642 +0.260 +0.886 +0.896 +0.297 +0.230 +0.411 +0.241 +0.672 +0.826 +0.673 +0.824 +0.397 +0.156 +0.738 +0.360 +0.671 +0.271 +0.081 +0.993 +0.156 +0.988 +0.977 +0.794 +0.659 +0.578 +0.866 +0.289 +0.468 +0.619 +0.411 +0.427 +0.330 +0.564 +0.851 +0.202 +0.934 +0.689 +0.823 +0.556 +0.780 +0.016 +0.818 +0.040 +0.890 +0.992 +0.294 +0.210 +0.765 +0.253 +0.866 +0.103 +0.126 +0.979 +0.674 +0.847 +0.324 +0.676 +0.594 +0.603 +0.683 +0.575 +0.429 +0.276 +0.769 +0.226 +0.692 +0.233 +0.625 +0.747 +0.219 +0.060 +0.131 +0.606 +0.849 +0.045 +0.734 +0.341 +0.479 +0.929 +0.332 +0.465 +0.014 +0.082 +0.259 +0.028 +0.631 +0.426 +0.548 +0.175 +0.296 +0.664 +0.965 +0.050 +0.890 +0.577 +0.564 +0.500 +0.069 +0.090 +0.601 +0.341 +0.917 +0.407 +0.143 +0.715 +0.293 +0.525 +0.698 +0.900 +0.792 +0.676 +0.680 +0.946 +0.296 +0.001 +0.272 +0.218 +0.662 +0.634 +0.593 +0.016 +0.729 +0.324 +0.665 +0.557 +0.343 +0.135 +0.094 +0.832 +0.918 +0.650 +0.103 +0.402 +0.729 +0.780 +0.118 +0.000 +0.712 +0.357 +0.254 +0.013 +0.540 +0.851 +0.958 +0.566 +0.514 +0.085 +0.549 +0.380 +0.607 +0.389 +0.240 +0.095 +0.315 +0.097 +0.177 +0.987 +0.444 +0.532 +0.874 +0.996 +0.583 +0.812 +0.327 +0.306 +0.403 +0.673 +0.682 +0.315 +0.133 +0.632 +0.128 +0.578 +0.693 +0.701 +0.753 +0.873 +0.500 +0.730 +0.619 +0.187 +0.026 +0.285 +0.443 +0.617 +0.850 +0.196 +0.126 +0.963 +0.108 +0.478 +0.586 +0.541 +0.086 +0.057 +0.105 +0.586 +0.544 +0.234 +0.638 +0.820 +0.042 +0.498 +0.689 +0.252 +0.308 +0.614 +0.898 +0.810 +0.583 +0.730 +0.365 +0.641 +0.466 +0.190 +0.702 +0.556 +0.359 +0.911 +0.021 +0.316 +0.057 +0.767 +0.702 +0.331 +0.676 +0.396 +0.756 +0.454 +0.412 +0.935 +0.251 +0.120 +0.585 +0.969 +0.378 +0.062 +0.339 +0.506 +0.162 +0.658 +0.998 +0.452 +0.355 +0.401 +0.115 +0.883 +0.415 +0.387 +0.660 +0.442 +0.648 +0.061 +0.814 +0.941 +0.649 +0.955 +0.151 +0.477 +0.602 +0.582 +0.401 +0.338 +0.127 +0.392 +0.163 +0.734 +0.209 +0.059 +0.552 +0.595 +0.872 +0.577 +0.345 +0.803 +0.540 +0.079 +0.566 +0.908 +0.396 +0.296 +0.143 +0.151 +0.433 +0.596 +0.081 +0.940 +0.755 +0.587 +0.828 +0.080 +0.477 +0.630 +0.829 +0.783 +0.277 +0.941 +0.124 +0.875 +0.971 +0.177 +0.722 +0.040 +0.405 +0.516 +0.581 +0.936 +0.672 +0.481 +0.810 +0.951 +0.022 +0.983 +0.086 +0.814 +0.280 +0.132 +0.440 +0.644 +0.381 +0.055 +0.599 +0.902 +0.383 +0.216 +0.443 +0.048 +0.820 +0.826 +0.588 +0.353 +0.800 +0.555 +0.826 +0.631 +0.784 +0.599 +0.414 +0.958 +0.541 +0.605 +0.220 +0.626 +0.572 +0.185 +0.060 +0.604 +0.764 +0.523 +0.227 +0.667 +0.080 +0.442 +0.163 +0.184 +0.202 +0.387 +0.051 +0.398 +0.512 +0.483 +0.383 +0.839 +0.145 +0.506 +0.062 +0.071 +0.574 +0.566 +0.878 +0.558 +0.960 +0.049 +0.098 +0.044 +0.185 +0.541 +0.646 +0.046 +0.946 +0.842 +0.297 +0.077 +0.170 +0.127 +0.123 +0.519 +0.246 +0.358 +0.990 +0.684 +0.949 +0.143 +0.382 +0.555 +0.077 +0.004 +0.670 +0.642 +0.411 +0.490 +0.415 +0.024 +0.335 +0.177 +0.098 +0.957 +0.599 +0.729 +0.315 +0.393 +0.237 +0.097 +0.179 +0.798 +0.678 +0.547 +0.475 +0.922 +0.073 +0.281 +0.350 +0.782 +0.993 +0.241 +0.874 +0.831 +0.225 +0.399 +0.410 +0.978 +0.181 +0.799 +0.334 +0.731 +0.420 +0.578 +0.833 +0.804 +0.866 +0.060 +0.692 +0.140 +0.416 +0.549 +0.403 +0.520 +0.997 +0.135 +0.675 +0.396 +0.133 +0.159 +0.949 +0.880 +0.907 +0.992 +0.208 +0.355 +0.669 +0.484 +0.418 +0.358 +0.594 +0.576 +0.161 +0.472 +0.553 +0.570 +0.210 +0.742 +0.025 +0.355 +0.780 +0.564 +0.261 +0.695 +0.567 +0.796 +0.735 +0.610 +0.488 +0.133 +0.261 +0.419 +0.599 +0.514 +0.288 +0.006 +0.496 +0.286 +0.735 +0.024 +0.585 +0.941 +0.174 +0.472 +0.091 +0.626 +0.551 +0.407 +0.521 +0.897 +0.196 +0.023 +0.862 +0.577 +0.891 +0.597 +0.810 +0.430 +0.750 +0.913 +0.572 +0.181 +0.269 +0.199 +0.247 +0.306 +0.555 +0.588 +0.426 +0.615 +0.082 +0.088 +0.172 +0.518 +0.214 +0.283 +0.400 +0.812 +0.014 +0.649 +0.669 +0.799 +0.933 +0.020 +0.154 +0.886 +0.459 +0.565 +0.663 +0.679 +0.932 +0.997 +0.720 +0.287 +0.880 +0.049 +0.231 +0.897 +0.256 +0.221 +0.138 +0.859 +0.500 +0.675 +0.239 +0.758 +0.760 +0.313 +0.411 +0.210 +0.977 +0.646 +0.935 +0.303 +0.782 +0.766 +0.692 +0.966 +0.394 +0.131 +0.672 +0.729 +0.575 +0.207 +0.504 +0.312 +0.427 +0.645 +0.745 +0.232 +0.405 +0.269 +0.199 +0.783 +0.909 +0.370 +0.751 +0.897 +0.846 +0.183 +0.956 +0.377 +0.339 +0.062 +0.486 +0.109 +0.249 +0.317 +0.809 +0.875 +0.302 +0.859 +0.857 +0.765 +0.147 +0.690 +0.945 +0.683 +0.332 +0.501 +0.646 +0.511 +0.120 +0.312 +0.802 +0.861 +0.137 +0.952 +0.329 +0.662 +0.752 +0.812 +0.946 +0.313 +0.859 +0.132 +0.705 +0.742 +0.678 +0.252 +0.873 +0.169 +0.054 +0.717 +0.475 +0.849 +0.383 +0.168 +0.836 +0.549 +0.189 +0.720 +0.511 +0.604 +0.461 +0.829 +0.830 +0.709 +0.105 +0.839 +0.671 +0.697 +0.363 +0.879 +0.725 +0.113 +0.408 +0.886 +0.047 +0.961 +0.679 +0.631 +0.271 +0.249 +0.942 +0.298 +0.776 +0.393 +0.139 +0.011 +0.544 +0.976 +0.369 +0.357 +0.344 +0.521 +0.441 +0.880 +0.506 +0.508 +0.138 +0.983 +0.250 +0.073 +0.663 +0.595 +0.415 +0.422 +0.785 +0.196 +0.820 +0.649 +0.419 +0.458 +0.257 +0.752 +0.513 +0.321 +0.596 +0.996 +0.435 +0.921 +0.345 +0.055 +0.204 +0.419 +0.220 +0.000 +0.915 +0.844 +0.742 +0.168 +0.120 +0.068 +0.724 +0.929 +0.431 +0.119 +0.512 +0.349 +0.375 +0.621 +0.719 +0.111 +0.036 +0.994 +0.233 +0.054 +0.190 +0.039 +0.392 +0.802 +0.341 +0.444 +0.676 +0.509 +0.861 +0.866 +0.010 +0.778 +0.461 +0.989 +0.483 +0.987 +0.755 +0.249 +0.538 +0.992 +0.858 +0.097 +0.706 +0.346 +0.007 +0.403 +0.235 +0.819 +0.337 +0.828 +0.001 +0.308 +0.241 +0.234 +0.068 +0.187 +0.556 +0.291 +0.417 +0.377 +0.876 +0.905 +0.493 +0.330 +0.107 +0.953 +0.060 +0.298 +0.298 +0.320 +0.164 +0.783 +0.122 +0.635 +0.124 +0.071 +0.909 +0.190 +0.821 +0.545 +0.078 +0.955 +0.247 +0.860 +0.169 +0.854 +0.013 +0.512 +0.769 +0.935 +0.909 +0.820 +0.888 +0.199 +0.284 +0.289 +0.377 +0.393 +0.545 +0.160 +0.691 +0.168 +0.310 +0.503 +0.796 +0.739 +0.646 +0.156 +0.547 +0.087 +0.377 +0.414 +0.040 +0.274 +0.956 +0.954 +0.352 +0.043 +0.179 +0.392 +0.948 +0.141 +0.764 +0.109 +0.981 +0.382 +0.775 +0.343 +0.628 +0.192 +0.146 +0.938 +0.918 +0.887 +0.625 +0.808 +0.069 +0.335 +0.314 +0.636 +0.356 +0.149 +0.596 +0.814 +0.382 +0.132 +0.197 +0.237 +0.300 +0.643 +0.601 +0.696 +0.363 +0.824 +0.204 +0.469 +0.801 +0.180 +0.110 +0.884 +0.343 +0.684 +0.025 +0.690 +0.316 +0.056 +0.610 +0.241 +0.387 +0.081 +0.147 +0.298 +0.090 +0.911 +0.006 +0.484 +0.123 +0.308 +0.926 +0.162 +0.966 +0.206 +0.742 +0.547 +0.884 +0.554 +0.535 +0.195 +0.166 +0.916 +0.328 +0.372 +0.760 +0.339 +0.952 +0.738 +0.597 +0.881 +0.050 +0.257 +0.465 +0.227 +0.744 +0.919 +0.424 +0.342 +0.962 +0.445 +0.489 +0.352 +0.910 +0.569 +0.762 +0.812 +0.342 +0.434 +0.848 +0.363 +0.794 +0.941 +0.623 +0.742 +0.805 +0.845 +0.395 +0.051 +0.402 +0.674 +0.450 +0.749 +0.357 +0.635 +0.186 +0.749 +0.546 +0.204 +0.292 +0.749 +0.483 +0.216 +0.967 +0.715 +0.957 +0.427 +0.475 +0.898 +0.525 +0.707 +0.252 +0.447 +0.123 +0.206 +0.001 +0.039 +0.977 +0.242 +0.663 +0.839 +0.551 +0.153 +0.728 +0.600 +0.731 +0.770 +0.975 +0.574 +0.342 +0.648 +0.068 +0.897 +0.119 +0.328 +0.816 +0.597 +0.394 +0.473 +0.855 +0.340 +0.870 +0.088 +0.777 +0.848 +0.182 +0.430 +0.165 +0.707 +0.535 +0.635 +0.196 +0.212 +0.041 +0.322 +0.560 +0.858 +0.667 +0.435 +0.953 +0.719 +0.930 +0.528 +0.259 +0.053 +0.726 +0.121 +0.303 +0.532 +0.564 +0.601 +0.166 +0.380 +0.617 +0.970 +0.728 +0.923 +0.762 +0.592 +0.192 +0.667 +0.623 +0.602 +0.490 +0.529 +0.334 +0.519 +0.198 +0.805 +0.186 +0.085 +0.436 +0.658 +0.438 +0.277 +0.558 +0.347 +0.933 +0.923 +0.502 +0.328 +0.737 +0.037 +0.475 +0.336 +0.921 +0.012 +0.553 +0.741 +0.485 +0.085 +0.972 +0.518 +0.614 +0.237 +0.483 +0.429 +0.075 +0.106 +0.837 +0.240 +0.195 +0.505 +0.769 +0.062 +0.577 +0.119 +0.036 +0.053 +0.834 +0.118 +0.045 +0.438 +0.844 +0.262 +0.422 +0.040 +0.449 +0.578 +0.571 +0.332 +0.316 +0.107 +0.367 +0.099 +0.767 +0.966 +0.970 +0.865 +0.601 +0.701 +0.285 +0.631 +0.456 +0.482 +0.758 +0.282 +0.318 +0.925 +0.056 +0.486 +0.913 +0.610 +0.546 +0.250 +0.343 +0.852 +0.559 +0.515 +0.100 +0.016 +0.964 +0.377 +0.603 +0.080 +0.683 +0.933 +0.576 +0.126 +0.582 +0.766 +0.406 +0.880 +0.649 +0.899 +0.624 +0.131 +0.310 +0.202 +0.909 +0.760 +0.676 +0.301 +0.184 +0.756 +0.474 +0.226 +0.617 +0.040 +0.326 +0.469 +0.148 +0.985 +0.209 +0.130 +0.204 +0.769 +0.426 +0.565 +0.021 +0.034 +0.446 +0.817 +0.885 +0.087 +0.538 +0.913 +0.388 +0.830 +0.134 +0.935 +0.751 +0.941 +0.677 +0.363 +0.938 +0.276 +0.332 +0.701 +0.765 +0.929 +0.205 +0.798 +0.739 +0.064 +0.387 +0.283 +0.304 +0.983 +0.643 +0.718 +0.977 +0.377 +0.802 +0.435 +0.870 +0.181 +0.948 +0.219 +0.326 +0.756 +0.394 +0.608 +0.445 +0.742 +0.228 +0.058 +0.300 +0.474 +0.168 +0.355 +0.400 +0.057 +0.583 +0.884 +0.152 +0.598 +0.665 +0.419 +0.701 +0.411 +0.505 +0.007 +0.692 +0.661 +0.038 +0.368 +0.979 +0.421 +0.502 +0.910 +0.717 +0.582 +0.798 +0.864 +0.457 +0.475 +0.386 +0.967 +0.697 +0.083 +0.863 +0.481 +0.069 +0.550 +0.417 +0.878 +0.204 +0.826 +0.558 +0.055 +0.972 +0.513 +0.291 +0.469 +0.858 +0.305 +0.193 +0.129 +0.298 +0.780 +0.472 +0.227 +0.166 +0.333 +0.940 +0.343 +0.988 +0.485 +0.179 +0.891 +0.460 +0.710 +0.984 +0.050 +0.097 +0.720 +0.282 +0.184 +0.039 +0.022 +0.774 +0.134 +0.468 +0.606 +0.428 +0.027 +0.784 +0.011 +0.762 +0.893 +0.433 +0.536 +0.477 +0.389 +0.887 +0.894 +0.336 +0.615 +0.927 +0.962 +0.509 +0.453 +0.279 +0.959 +0.143 +0.580 +0.283 +0.279 +0.405 +0.999 +0.055 +0.579 +0.083 +0.155 +0.440 +0.510 +0.259 +0.316 +0.087 +0.303 +0.967 +0.945 +0.335 +0.571 +0.910 +0.006 +0.811 +0.948 +0.091 +0.161 +0.961 +0.538 +0.014 +0.258 +0.450 +0.693 +0.733 +0.995 +0.288 +0.538 +0.533 +0.406 +0.169 +0.977 +0.853 +0.498 +0.603 +0.227 +0.544 +0.695 +0.515 +0.990 +0.611 +0.371 +0.772 +0.170 +0.385 +0.347 +0.432 +0.782 +0.484 +0.794 +0.576 +0.342 +0.810 +0.238 +0.224 +0.505 +0.261 +0.009 +0.648 +0.511 +0.446 +0.967 +0.571 +0.863 +0.139 +0.569 +0.868 +0.583 +0.718 +0.372 +0.092 +0.458 +0.394 +0.867 +0.244 +0.967 +0.958 +0.248 +0.348 +0.896 +0.967 +0.443 +0.321 +0.526 +0.688 +0.273 +0.736 +0.052 +0.049 +0.386 +0.353 +0.316 +0.491 +0.321 +0.192 +0.309 +0.975 +0.411 +0.445 +0.577 +0.983 +0.242 +0.681 +0.716 +0.607 +0.089 +0.677 +0.064 +0.596 +0.740 +0.211 +0.842 +0.030 +0.844 +0.460 +0.218 +0.134 +0.953 +0.073 +0.199 +0.348 +0.620 +0.545 +0.126 +0.290 +0.239 +0.214 +0.411 +0.704 +0.217 +0.367 +0.391 +0.976 +0.955 +0.800 +0.242 +0.068 +0.317 +0.267 +0.506 +0.778 +0.858 +0.845 +0.360 +0.687 +0.313 +0.061 +0.702 +0.010 +0.409 +0.329 +0.693 +0.766 +0.304 +0.812 +0.294 +0.446 +0.727 +0.693 +0.987 +0.225 +0.662 +0.610 +0.915 +0.156 +0.752 +0.568 +0.275 +0.474 +0.334 +0.579 +0.889 +0.569 +0.632 +0.519 +0.051 +0.233 +0.492 +0.310 +0.182 +0.504 +0.323 +0.258 +0.464 +0.133 +0.928 +0.868 +0.708 +0.106 +0.776 +0.808 +0.768 +0.114 +0.644 +0.834 +0.577 +0.646 +0.458 +0.529 +0.034 +0.639 +0.081 +0.435 +0.293 +0.545 +0.133 +0.692 +0.795 +0.163 +0.104 +0.146 +0.877 +0.528 +0.467 +0.107 +0.650 +0.868 +0.828 +0.626 +0.075 +0.112 +0.049 +0.257 +0.136 +0.260 +0.385 +0.943 +0.610 +0.835 +0.710 +0.113 +0.736 +0.275 +0.639 +0.341 +0.998 +0.264 +0.279 +0.931 +0.025 +0.697 +0.725 +0.836 +0.974 +0.976 +0.065 +0.822 +0.646 +0.647 +0.128 +0.650 +0.982 +0.912 +0.439 +0.040 +0.362 +0.826 +0.701 +0.459 +0.055 +0.427 +0.469 +0.929 +0.641 +0.312 +0.525 +0.066 +0.973 +0.360 +0.478 +0.355 +0.683 +0.662 +0.715 +0.268 +0.316 +0.125 +0.656 +0.040 +0.598 +0.870 +0.891 +0.449 +0.459 +0.077 +0.671 +0.821 +0.245 +0.421 +0.839 +0.877 +0.243 +0.551 +0.192 +0.102 +0.911 +0.821 +0.760 +0.049 +0.844 +0.438 +0.342 +0.546 +0.091 +0.757 +0.582 +0.187 +0.188 +0.184 +0.057 +0.588 +0.840 +0.017 +0.049 +0.573 +0.819 +0.327 +0.350 +0.596 +0.274 +0.027 +0.735 +0.626 +0.581 +0.742 +0.675 +0.206 +0.352 +0.126 +0.130 +0.676 +0.859 +0.963 +0.628 +0.184 +0.106 +0.813 +0.579 +0.618 +0.781 +0.703 +0.834 +0.056 +0.748 +0.851 +0.286 +0.640 +0.314 +0.958 +0.033 +0.580 +0.167 +0.712 +0.176 +0.235 +0.494 +0.915 +0.212 +0.144 +0.759 +0.501 +0.912 +0.147 +0.646 +0.233 +0.157 +0.683 +0.594 +0.755 +0.309 +0.448 +0.405 +0.984 +0.292 +0.295 +0.355 +0.565 +0.252 +0.915 +0.266 +0.896 +0.103 +0.657 +0.907 +0.169 +0.968 +0.453 +0.946 +0.677 +0.302 +0.188 +0.304 +0.093 +0.144 +0.105 +0.423 +0.796 +0.141 +0.130 +0.643 +0.197 +0.098 +0.167 +0.924 +0.821 +0.354 +0.656 +0.537 +0.677 +0.683 +0.629 +0.266 +0.039 +0.456 +0.891 +0.569 +0.832 +0.992 +0.164 +0.692 +0.674 +0.980 +0.986 +0.687 +0.244 +0.799 +0.107 +0.781 +0.910 +0.383 +0.631 +0.430 +0.438 +0.310 +0.762 +0.936 +0.941 +0.175 +0.875 +0.737 +0.253 +0.067 +0.268 +0.912 +0.633 +0.721 +0.257 +0.719 +0.028 +0.375 +0.627 +0.146 +0.823 +0.235 +0.652 +0.405 +0.596 +0.107 +0.017 +0.910 +0.270 +0.820 +0.999 +0.211 +0.265 +0.663 +0.965 +0.916 +0.503 +0.795 +0.136 +0.977 +0.964 +0.164 +0.299 +0.100 +0.272 +0.816 +0.378 +0.088 +0.342 +0.981 +0.630 +0.416 +0.167 +0.986 +0.821 +0.281 +0.070 +0.057 +0.120 +0.581 +0.929 +0.497 +0.843 +0.446 +0.709 +0.732 +0.174 +0.361 +0.637 +0.071 +0.362 +0.535 +0.996 +0.475 +0.816 +0.432 +0.796 +0.595 +0.887 +0.411 +0.604 +0.630 +0.417 +0.144 +0.094 +0.017 +0.874 +0.236 +0.212 +0.736 +0.320 +0.506 +0.163 +0.960 +0.852 +0.864 +0.407 +0.762 +0.144 +0.009 +0.020 +0.587 +0.455 +0.518 +0.007 +0.423 +0.718 +0.443 +0.732 +0.233 +0.813 +0.588 +0.211 +0.237 +0.628 +0.830 +0.416 +0.258 +0.867 +0.669 +0.448 +0.488 +0.204 +0.627 +0.359 +0.996 +0.480 +0.486 +0.566 +0.917 +0.801 +0.451 +0.719 +0.586 +0.492 +0.192 +0.464 +0.246 +0.915 +0.335 +0.952 +0.533 +0.632 +0.543 +0.129 +0.376 +0.099 +0.623 +0.447 +0.778 +0.220 +0.165 +0.406 +0.927 +0.710 +0.419 +0.517 +0.084 +0.985 +0.666 +0.285 +0.586 +0.109 +0.966 +0.175 +0.648 +0.245 +0.718 +0.777 +0.673 +0.614 +0.892 +0.079 +0.331 +0.248 +0.988 +0.389 +0.733 +0.685 +0.212 +0.239 +0.281 +0.411 +0.234 +0.020 +0.879 +0.966 +0.342 +0.339 +0.007 +0.680 +0.825 +0.147 +0.160 +0.790 +0.616 +0.045 +0.623 +0.309 +0.369 +0.770 +0.811 +0.400 +0.947 +0.406 +0.772 +0.484 +0.898 +0.908 +0.087 +0.936 +0.826 +0.791 +0.201 +0.805 +0.850 +0.289 +0.952 +0.050 +0.150 +0.538 +0.576 +0.645 +0.017 +0.960 +0.045 +0.143 +0.014 +0.567 +0.932 +0.666 +0.823 +0.013 +0.542 +0.460 +0.499 +0.072 +0.684 +0.503 +0.765 +0.485 +0.149 +0.648 +0.172 +0.872 +0.613 +0.157 +0.962 +0.518 +0.073 +0.627 +0.253 +0.804 +0.818 +0.979 +0.502 +0.455 +0.753 +0.132 +0.547 +0.546 +0.089 +0.418 +0.853 +0.856 +0.099 +0.091 +0.263 +0.875 +0.129 +0.720 +0.101 +0.623 +0.356 +0.789 +0.234 +0.639 +0.236 +0.714 +0.874 +0.127 +0.867 +0.594 +0.127 +0.428 +0.161 +0.700 +0.759 +0.106 +0.506 +0.815 +0.936 +0.514 +0.950 +0.536 +0.394 +0.848 +0.493 +0.472 +0.490 +0.847 +0.832 +0.404 +0.333 +0.492 +0.573 +0.238 +0.794 +0.486 +0.334 +0.107 +0.239 +0.949 +0.305 +0.169 +0.461 +0.307 +0.035 +0.323 +0.544 +0.552 +0.357 +0.447 +0.966 +0.377 +0.258 +0.243 +0.458 +0.760 +0.206 +0.709 +0.021 +0.419 +0.776 +0.339 +0.564 +0.205 +0.742 +0.052 +0.853 +0.198 +0.874 +0.353 +0.723 +0.319 +0.906 +0.127 +0.179 +0.493 +0.082 +0.196 +0.944 +0.976 +0.376 +0.923 +0.545 +0.270 +0.702 +0.659 +0.989 +0.265 +0.672 +0.052 +0.114 +0.004 +0.999 +0.802 +0.787 +0.510 +0.300 +0.151 +0.285 +0.376 +0.756 +0.540 +0.077 +0.018 +0.102 +0.410 +0.933 +0.637 +0.802 +0.466 +0.362 +0.108 +0.876 +0.783 +0.168 +0.512 +0.487 +0.625 +0.946 +0.580 +0.540 +0.050 +0.063 +0.187 +0.032 +0.743 +0.723 +0.136 +0.836 +0.155 +0.301 +0.249 +0.372 +0.255 +0.489 +0.407 +0.987 +0.056 +0.142 +0.053 +0.626 +0.055 +0.836 +0.512 +0.527 +0.562 +0.171 +0.198 +0.319 +0.593 +0.210 +0.116 +0.970 +0.022 +0.803 +0.788 +0.297 +0.136 +0.146 +0.697 +0.643 +0.783 +0.937 +0.692 +0.156 +0.243 +0.842 +0.209 +0.073 +0.703 +0.403 +0.920 +0.650 +0.090 +0.948 +0.259 +0.337 +0.402 +0.051 +0.497 +0.123 +0.401 +0.351 +0.773 +0.803 +0.153 +0.367 +0.373 +0.151 +0.798 +0.707 +0.190 +0.367 +0.106 +0.628 +0.121 +0.986 +0.781 +0.390 +0.579 +0.911 +0.554 +0.174 +0.379 +0.843 +0.644 +0.651 +0.867 +0.894 +0.920 +0.095 +0.544 +0.741 +0.757 +0.607 +0.167 +0.797 +0.720 +0.086 +0.464 +0.174 +0.337 +0.018 +0.400 +0.550 +0.620 +0.663 +0.836 +0.215 +0.120 +0.852 +0.157 +0.975 +0.843 +0.501 +0.981 +0.097 +0.303 +0.638 +0.350 +0.928 +0.066 +0.549 +0.591 +0.653 +0.256 +0.365 +0.844 +0.195 +0.827 +0.563 +0.762 +0.714 +0.208 +0.569 +0.868 +0.432 +0.967 +0.452 +0.143 +0.389 +0.952 +0.320 +0.868 +0.881 +0.350 +0.083 +0.725 +0.312 +0.783 +0.443 +0.485 +0.778 +0.382 +0.443 +0.376 +0.367 +0.526 +0.809 +0.362 +0.524 +0.671 +0.269 +0.278 +0.933 +0.724 +0.577 +0.648 +0.029 +0.618 +0.510 +0.546 +0.359 +0.081 +0.193 +0.951 +0.409 +0.467 +0.053 +0.038 +0.717 +0.538 +0.509 +0.243 +0.745 +0.168 +0.190 +0.461 +0.286 +0.247 +0.645 +0.651 +0.825 +0.418 +0.073 +0.274 +0.980 +0.596 +0.710 +0.007 +0.518 +0.038 +0.569 +0.921 +0.968 +0.541 +0.458 +0.945 +0.674 +0.065 +0.466 +0.240 +0.470 +0.455 +0.582 +0.357 +0.164 +0.504 +0.830 +0.664 +0.338 +0.936 +0.912 +0.209 +0.039 +0.741 +0.828 +0.968 +0.890 +0.601 +0.530 +0.028 +0.247 +0.460 +0.629 +0.557 +0.743 +0.280 +0.565 +0.088 +0.414 +0.750 +0.693 +0.991 +0.278 +0.498 +0.330 +0.441 +0.857 +0.684 +0.882 +0.204 +0.873 +0.060 +0.889 +0.329 +0.315 +0.512 +0.941 +0.048 +0.352 +0.857 +0.396 +0.199 +0.425 +0.386 +0.915 +0.762 +0.505 +0.988 +0.465 +0.058 +0.262 +0.436 +0.157 +0.948 +0.767 +0.268 +0.591 +0.721 +0.941 +0.882 +0.515 +0.775 +0.562 +0.553 +0.115 +0.126 +0.530 +0.269 +0.021 +0.723 +0.032 +0.595 +0.539 +0.969 +0.484 +0.064 +0.552 +0.841 +0.997 +0.430 +0.320 +0.907 +0.191 +0.615 +0.406 +0.041 +0.347 +0.265 +0.686 +0.949 +0.268 +0.811 +0.055 +0.538 +0.071 +0.934 +0.766 +0.658 +0.824 +0.648 +0.969 +0.714 +0.925 +0.517 +0.117 +0.528 +0.127 +0.592 +0.482 +0.623 +0.010 +0.195 +0.491 +0.951 +0.384 +0.424 +0.715 +0.604 +0.299 +0.411 +0.218 +0.966 +0.131 +0.372 +0.026 +0.222 +0.780 +0.201 +0.080 +0.474 +0.010 +0.085 +0.808 +0.771 +0.915 +0.855 +0.683 +0.052 +0.783 +0.846 +0.799 +0.508 +0.233 +0.845 +0.727 +0.981 +0.156 +0.226 +0.212 +0.793 +0.187 +0.067 +0.999 +0.025 +0.835 +0.365 +0.842 +0.555 +0.902 +0.926 +0.009 +0.075 +0.250 +0.669 +0.888 +0.460 +0.607 +0.188 +0.805 +0.967 +0.269 +0.495 +0.086 +0.559 +0.503 +0.144 +0.442 +0.334 +0.522 +0.586 +0.008 +0.761 +0.713 +0.366 +0.053 +0.755 +0.955 +0.240 +0.324 +0.184 +0.577 +0.155 +0.356 +0.697 +0.560 +0.386 +0.420 +0.562 +0.006 +0.079 +0.881 +0.078 +0.707 +0.920 +0.271 +0.370 +0.569 +0.882 +0.942 +0.740 +0.087 +0.669 +0.410 +0.471 +0.049 +0.191 +0.791 +0.454 +0.302 +0.384 +0.854 +0.749 +0.272 +0.118 +0.252 +0.921 +0.582 +0.533 +0.596 +0.366 +0.807 +0.523 +0.117 +0.129 +0.537 +0.937 +0.308 +0.870 +0.764 +0.248 +0.036 +0.102 +0.728 +0.200 +0.371 +0.502 +0.725 +0.113 +0.406 +0.485 +0.541 +0.818 +0.172 +0.808 +0.693 +0.556 +0.700 +0.412 +0.138 +0.666 +0.525 +0.639 +0.338 +0.907 +0.587 +0.085 +0.450 +0.756 +0.794 +0.495 +0.895 +0.268 +0.411 +0.899 +0.896 +0.686 +0.859 +0.089 +0.865 +0.321 +0.178 +0.239 +0.418 +0.251 +0.321 +0.135 +0.296 +0.572 +0.442 +0.347 +0.746 +0.894 +0.757 +0.332 +0.744 +0.152 +0.130 +0.346 +0.808 +0.246 +0.063 +0.132 +0.859 +0.634 +0.253 +0.555 +0.528 +0.655 +0.233 +0.864 +0.309 +0.694 +0.016 +0.373 +0.352 +0.543 +0.073 +0.790 +0.218 +0.677 +0.803 +0.562 +0.741 +0.662 +0.356 +0.809 +0.295 +0.571 +0.745 +0.379 +0.303 +0.459 +0.818 +0.930 +0.545 +0.661 +0.903 +0.099 +0.232 +0.709 +0.824 +0.857 +0.415 +0.089 +0.438 +0.231 +0.291 +0.772 +0.076 +0.828 +0.428 +0.596 +0.090 +0.337 +0.090 +0.962 +0.807 +0.460 +0.224 +0.933 +0.345 +0.161 +0.563 +0.944 +0.004 +0.238 +0.938 +0.171 +0.113 +0.570 +0.862 +0.477 +0.995 +0.679 +0.074 +0.993 +0.408 +0.287 +0.558 +0.304 +0.086 +0.001 +0.450 +0.837 +0.572 +0.999 +0.287 +0.280 +0.160 +0.164 +0.285 +0.793 +0.906 +0.975 +0.232 +0.881 +0.328 +0.113 +0.870 +0.636 +0.165 +0.430 +0.287 +0.737 +0.453 +0.989 +0.055 +0.355 +0.482 +0.503 +0.316 +0.729 +0.875 +0.939 +0.805 +0.851 +0.551 +0.826 +0.304 +0.398 +0.317 +0.467 +0.286 +0.962 +0.313 +0.098 +0.527 +0.057 +0.111 +0.219 +0.464 +0.433 +0.800 +0.739 +0.426 +0.334 +0.316 +0.774 +0.052 +0.013 +0.796 +0.477 +0.131 +0.236 +0.933 +0.860 +0.710 +0.482 +0.520 +0.975 +0.966 +0.215 +0.811 +0.197 +0.214 +0.676 +0.405 +0.932 +0.089 +0.512 +0.919 +0.904 +0.157 +0.367 +0.619 +0.159 +0.968 +0.915 +0.167 +0.121 +0.085 +0.909 +0.194 +0.416 +0.449 +0.599 +0.072 +0.227 +0.931 +0.908 +0.781 +0.019 +0.327 +0.628 +0.275 +0.910 +0.634 +0.072 +0.909 +0.958 +0.078 +0.532 +0.303 +0.393 +0.180 +0.578 +0.958 +0.503 +0.083 +0.620 +0.340 +0.856 +0.435 +0.193 +0.191 +0.769 +0.266 +0.767 +0.206 +0.322 +0.465 +0.943 +0.932 +0.207 +0.358 +0.149 +0.510 +0.461 +0.901 +0.930 +0.384 +0.492 +0.896 +0.807 +0.004 +0.320 +0.422 +0.230 +0.200 +0.038 +0.643 +0.263 +0.052 +0.231 +0.802 +0.851 +0.483 +0.416 +0.978 +0.201 +0.215 +0.292 +0.173 +0.379 +0.911 +0.615 +0.412 +0.934 +0.048 +0.210 +0.566 +0.616 +0.040 +0.403 +0.649 +0.698 +0.638 +0.321 +0.094 +0.427 +0.129 +0.948 +0.475 +0.317 +0.766 +0.498 +0.310 +0.812 +0.867 +0.673 +0.047 +0.566 +0.763 +0.137 +0.229 +0.882 +0.020 +0.753 +0.474 +0.447 +0.007 +0.819 +0.360 +0.541 +0.677 +0.703 +0.956 +0.531 +0.400 +0.954 +0.642 +0.298 +0.126 +0.843 +0.362 +0.660 +0.471 +0.199 +0.602 +0.061 +0.413 +0.547 +0.436 +0.749 +0.828 +0.771 +0.039 +0.194 +0.537 +0.935 +0.835 +0.843 +0.303 +0.429 +0.471 +0.157 +0.031 +0.947 +0.241 +0.240 +0.083 +0.929 +0.580 +0.667 +0.912 +0.346 +0.622 diff --git a/examples/regression/train.conf b/examples/regression/train.conf new file mode 100644 index 0000000..7c3838a --- /dev/null +++ b/examples/regression/train.conf @@ -0,0 +1,114 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , lambdarank task +# alias: application, app +objective = regression + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = l2 + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# column in data to use as label +label_column = 0 + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# forced bin thresholds +# forcedbins_filename = forced_bins.json + +# training data +# if existing weight file, should name to "regression.train.weight" +# alias: train_data, train +data = regression.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "regression.test.weight" +# alias: valid, test, test_data, +valid_data = regression.test + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.05 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 31 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# number of threads for multi-threading. One thread will use one CPU, default is set to #cpu. +# num_threads = 8 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 0.9 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 5 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.8 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 100 + +# minimal sum hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = LightGBM_model.txt + +# support continuous train from trained gbdt model +# input_model= trained_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + + +# number of machines in distributed training, alias: num_machine +num_machines = 1 + +# local listening port in distributed training, alias: local_port +local_listen_port = 12400 + +# machines list file for distributed training, alias: mlist +machine_list_file = mlist.txt diff --git a/examples/xendcg/README.md b/examples/xendcg/README.md new file mode 100644 index 0000000..122817d --- /dev/null +++ b/examples/xendcg/README.md @@ -0,0 +1,33 @@ +XE_NDCG Ranking Example +======================= + +Here is an example for LightGBM to train a ranking model with the [XE_NDCG loss](https://arxiv.org/abs/1911.09798). + +***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) +for the following commands to work. The `lightgbm` binary must be built and available at the root of this project.*** + +Training +-------- + +Run the following command in this folder: + +```bash +"../../lightgbm" config=train.conf +``` + +Prediction +---------- + +You should finish training first. + +Run the following command in this folder: + +```bash +"../../lightgbm" config=predict.conf +``` + +Data Format +----------- + +To learn more about the query format used in this example, check out the +[query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data). diff --git a/examples/xendcg/predict.conf b/examples/xendcg/predict.conf new file mode 100644 index 0000000..4c781b4 --- /dev/null +++ b/examples/xendcg/predict.conf @@ -0,0 +1,5 @@ +task = predict + +data = rank.test + +input_model= LightGBM_model.txt diff --git a/examples/xendcg/rank.test b/examples/xendcg/rank.test new file mode 100644 index 0000000..25a96ce --- /dev/null +++ b/examples/xendcg/rank.test @@ -0,0 +1,768 @@ +2 1:0.74 6:0.87 8:0.75 9:0.80 11:0.88 12:0.37 17:0.66 20:0.97 21:0.30 27:0.15 28:0.95 30:0.54 32:0.80 34:0.21 36:0.62 37:0.40 39:0.43 41:0.88 43:0.90 60:0.87 66:0.47 69:0.47 70:0.62 74:0.97 77:0.96 78:0.96 81:0.84 83:0.85 85:0.98 91:0.48 96:0.86 98:0.73 100:0.91 101:0.85 104:0.95 106:0.81 108:0.36 111:0.94 114:0.86 117:0.86 120:0.77 122:0.57 123:0.35 124:0.66 126:0.54 127:0.68 129:0.81 133:0.67 135:0.78 140:0.45 144:0.85 145:0.67 146:0.95 147:0.96 149:0.97 150:0.89 151:0.94 152:0.97 153:0.80 154:0.89 155:0.67 158:0.92 159:0.82 161:0.88 162:0.78 164:0.70 165:0.84 167:0.47 169:0.86 172:0.86 173:0.25 176:0.73 177:0.38 179:0.24 181:0.89 186:0.96 187:0.39 189:0.90 192:0.59 195:0.92 201:0.74 202:0.59 206:0.81 208:0.64 212:0.39 215:0.72 216:0.48 222:0.76 232:0.97 235:0.42 238:0.83 241:0.12 242:0.51 243:0.59 245:0.94 247:0.67 248:0.84 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 261:0.94 265:0.74 266:0.80 267:0.65 268:0.64 271:0.21 276:0.88 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.99 300:0.70 +3 1:0.74 6:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.47 20:0.99 21:0.30 27:0.15 28:0.95 30:0.17 34:0.80 36:1.00 37:0.81 39:0.43 41:0.95 43:0.77 55:0.71 60:0.97 66:0.34 69:0.80 70:0.68 74:0.79 78:0.99 81:0.84 83:0.84 85:0.80 91:0.38 96:0.91 98:0.43 100:0.97 101:0.73 104:0.98 108:0.64 111:0.98 114:0.86 117:0.86 120:0.84 122:0.67 123:0.62 124:0.77 126:0.54 127:0.37 129:0.59 133:0.76 135:0.60 138:0.98 140:0.45 144:0.85 145:0.74 146:0.67 147:0.72 149:0.66 150:0.89 151:0.97 152:0.93 153:0.89 154:0.69 155:0.67 158:0.92 159:0.64 161:0.88 162:0.65 163:0.81 164:0.82 165:0.84 167:0.53 169:0.96 172:0.37 173:0.71 176:0.73 177:0.38 179:0.67 181:0.89 186:0.98 187:0.87 189:0.83 192:0.59 201:0.74 202:0.77 208:0.64 212:0.19 215:0.72 216:0.54 222:0.76 232:0.95 235:0.42 238:0.90 241:0.46 242:0.63 243:0.50 245:0.57 247:0.47 248:0.90 253:0.92 255:0.79 256:0.81 259:0.21 260:0.84 261:0.96 265:0.45 266:0.75 267:0.65 268:0.79 271:0.21 276:0.47 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43 +2 1:0.74 6:0.80 8:0.60 11:0.88 12:0.37 17:0.61 20:0.91 27:0.34 28:0.95 30:0.21 32:0.78 34:0.63 36:0.93 37:0.36 39:0.43 43:0.87 60:0.87 66:0.29 69:0.54 70:0.87 74:0.85 77:0.89 78:0.90 81:0.98 83:0.84 85:0.90 91:0.25 98:0.38 100:0.84 101:0.78 104:0.80 108:0.84 111:0.88 114:0.98 123:0.83 124:0.79 126:0.54 127:0.34 129:0.81 133:0.76 135:0.98 144:0.85 145:0.99 146:0.62 147:0.67 149:0.79 150:0.99 152:0.85 154:0.85 155:0.67 158:0.92 159:0.64 161:0.88 163:0.90 165:0.92 167:0.47 169:0.81 172:0.45 173:0.39 176:0.73 177:0.38 178:0.55 179:0.50 181:0.89 186:0.90 187:0.21 189:0.83 192:0.59 195:0.87 201:0.74 208:0.64 212:0.27 215:0.96 216:0.84 222:0.76 232:0.85 235:0.05 238:0.81 241:0.12 242:0.18 243:0.37 245:0.69 247:0.67 253:0.78 259:0.21 261:0.88 265:0.40 266:0.75 267:0.28 271:0.21 276:0.59 279:0.86 282:0.86 283:0.82 290:0.98 297:0.36 300:0.70 +0 1:0.74 6:0.84 7:0.81 8:0.66 9:0.80 11:0.88 12:0.37 17:0.84 20:0.92 21:0.05 27:0.27 28:0.95 30:0.73 32:0.80 34:0.29 36:0.72 37:0.35 39:0.43 41:0.82 43:0.96 60:0.87 66:0.27 69:0.10 70:0.40 74:0.96 77:0.70 78:0.86 81:0.95 83:0.85 85:0.98 91:0.15 96:0.77 98:0.51 100:0.83 101:0.85 104:0.79 106:0.81 108:0.90 111:0.84 114:0.95 117:0.86 120:0.65 121:0.99 122:0.57 123:0.09 124:0.71 126:0.54 127:0.71 129:0.81 133:0.67 135:0.63 140:0.45 144:0.85 145:0.70 146:0.80 147:0.83 149:0.97 150:0.98 151:0.77 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.78 161:0.88 162:0.86 164:0.57 165:0.90 167:0.48 169:0.81 172:0.73 173:0.10 176:0.73 177:0.38 179:0.33 181:0.89 186:0.86 187:0.21 189:0.86 192:0.59 195:0.90 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.73 215:0.91 216:0.70 220:0.62 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.82 241:0.21 242:0.57 243:0.46 245:0.91 247:0.55 248:0.71 253:0.85 255:0.79 256:0.45 259:0.21 260:0.66 261:0.87 265:0.44 266:0.80 267:0.84 268:0.46 271:0.21 276:0.76 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.83 9:0.80 11:0.88 12:0.37 17:0.43 20:0.83 21:0.22 27:0.42 28:0.95 30:0.76 32:0.80 34:0.30 36:0.83 37:0.55 39:0.43 41:0.82 43:0.86 60:0.99 66:0.93 69:0.60 70:0.33 74:0.96 77:0.89 78:0.91 81:0.88 83:0.88 85:0.97 91:0.35 96:0.73 98:0.76 100:0.81 101:0.86 104:0.92 106:0.81 108:0.45 111:0.88 114:0.90 117:0.86 120:0.60 121:0.90 122:0.57 123:0.44 126:0.54 127:0.25 129:0.05 135:0.78 140:0.45 144:0.85 145:0.74 146:0.79 147:0.82 149:0.97 150:0.93 151:0.63 152:0.79 153:0.48 154:0.83 155:0.67 158:0.92 159:0.35 161:0.88 162:0.86 164:0.57 165:0.86 167:0.48 169:0.79 172:0.82 173:0.60 176:0.73 177:0.38 179:0.27 181:0.89 186:0.91 187:0.39 189:0.92 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.92 215:0.79 216:0.66 220:0.82 222:0.76 230:0.87 232:0.94 233:0.76 235:0.31 238:0.82 241:0.21 242:0.63 243:0.94 247:0.39 248:0.60 253:0.81 255:0.79 256:0.45 259:0.21 260:0.60 261:0.84 265:0.76 266:0.27 267:0.65 268:0.46 271:0.21 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.97 300:0.43 +1 1:0.74 11:0.88 12:0.37 17:0.62 20:0.80 21:0.40 27:0.33 28:0.95 30:0.70 32:0.80 34:0.63 36:0.91 37:0.78 39:0.43 43:0.86 66:0.46 69:0.60 70:0.43 74:0.94 77:0.89 78:0.85 81:0.87 83:0.76 85:0.98 91:0.09 98:0.55 100:0.73 101:0.84 104:0.84 106:0.81 108:0.46 111:0.83 114:0.82 117:0.86 122:0.57 123:0.44 124:0.58 126:0.54 127:0.54 129:0.59 133:0.47 135:0.51 144:0.85 145:0.99 146:0.84 147:0.87 149:0.96 150:0.92 152:0.77 154:0.83 155:0.67 159:0.78 161:0.88 165:0.86 167:0.56 169:0.72 172:0.77 173:0.40 176:0.73 177:0.38 178:0.55 179:0.32 181:0.89 186:0.86 187:0.39 192:0.59 195:0.92 201:0.74 206:0.81 212:0.50 215:0.67 216:0.62 222:0.76 232:0.89 235:0.68 241:0.56 242:0.58 243:0.59 245:0.93 247:0.47 253:0.76 259:0.21 261:0.79 265:0.56 266:0.75 267:0.65 271:0.21 276:0.76 282:0.88 290:0.82 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.86 8:0.70 9:0.80 11:0.88 12:0.37 17:0.15 20:0.93 21:0.76 27:0.15 28:0.95 30:0.45 34:0.63 36:0.70 37:0.88 39:0.43 41:0.82 43:0.83 55:0.84 66:0.24 69:0.69 70:0.73 74:0.95 78:0.98 81:0.50 83:0.77 85:0.91 91:0.06 96:0.82 98:0.52 100:0.92 101:0.75 108:0.52 111:0.95 114:0.66 120:0.72 122:0.67 123:0.83 124:0.74 126:0.54 127:0.42 129:0.81 133:0.67 135:0.83 140:0.90 144:0.85 146:0.89 147:0.91 149:0.88 150:0.65 151:0.90 152:0.90 153:0.69 154:0.78 155:0.67 159:0.85 161:0.88 162:0.48 163:0.75 164:0.62 165:0.65 167:0.51 169:0.90 172:0.70 173:0.40 176:0.73 177:0.38 178:0.50 179:0.24 181:0.89 186:0.97 187:0.95 189:0.88 191:0.70 192:0.59 201:0.74 202:0.67 208:0.64 212:0.16 215:0.46 216:0.68 222:0.76 232:0.99 235:0.97 238:0.83 241:0.39 242:0.76 243:0.44 245:0.93 247:0.47 248:0.79 253:0.87 255:0.79 256:0.45 259:0.21 260:0.73 261:0.94 265:0.49 266:0.80 267:0.91 268:0.55 271:0.21 276:0.84 285:0.62 290:0.66 297:0.36 300:0.70 +0 1:0.74 6:0.86 7:0.81 8:0.68 9:0.80 11:0.88 12:0.37 17:0.73 20:0.95 21:0.30 27:0.25 28:0.95 30:0.57 34:0.37 36:0.55 37:0.55 39:0.43 41:0.88 43:0.85 60:0.97 66:0.93 69:0.61 70:0.64 74:0.94 78:0.95 81:0.84 83:0.87 85:0.95 91:0.38 96:0.82 98:0.88 100:0.88 101:0.85 108:0.47 111:0.92 114:0.86 120:0.72 121:0.97 122:0.57 123:0.45 126:0.54 127:0.41 129:0.05 135:0.65 140:0.45 144:0.85 146:0.85 147:0.88 149:0.94 150:0.89 151:0.87 152:0.88 153:0.48 154:0.82 155:0.67 158:0.87 159:0.42 161:0.88 162:0.86 164:0.67 165:0.84 167:0.47 169:0.86 172:0.81 173:0.64 176:0.73 177:0.38 179:0.40 181:0.89 186:0.94 187:0.39 189:0.87 192:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.60 215:0.72 216:0.38 222:0.76 230:0.87 233:0.76 235:0.42 238:0.82 241:0.15 242:0.44 243:0.93 247:0.67 248:0.79 253:0.88 255:0.79 256:0.58 259:0.21 260:0.73 261:0.92 265:0.88 266:0.54 267:0.23 268:0.59 271:0.21 276:0.70 279:0.86 283:0.71 285:0.62 290:0.86 297:0.36 300:0.55 +2 1:0.74 6:0.86 8:0.77 9:0.80 11:0.88 12:0.37 17:0.13 20:0.80 21:0.74 27:0.15 28:0.95 30:0.53 34:0.55 36:0.43 37:0.88 39:0.43 41:0.90 43:0.83 55:0.84 66:0.15 69:0.68 70:0.76 74:0.95 78:0.95 81:0.53 83:0.77 85:0.91 91:0.10 96:0.82 98:0.49 100:0.93 101:0.74 108:0.85 111:0.93 114:0.67 120:0.72 122:0.67 123:0.92 124:0.87 126:0.54 127:0.42 129:0.89 133:0.87 135:0.85 140:0.80 144:0.85 146:0.38 147:0.45 149:0.88 150:0.67 151:0.87 152:0.90 153:0.48 154:0.78 155:0.67 159:0.88 161:0.88 162:0.49 163:0.75 164:0.72 165:0.65 167:0.49 169:0.90 172:0.70 173:0.36 176:0.73 177:0.38 178:0.42 179:0.23 181:0.89 186:0.95 187:0.95 189:0.92 192:0.59 201:0.74 202:0.76 208:0.64 212:0.15 215:0.46 216:0.68 220:0.87 222:0.76 232:0.99 235:0.95 238:0.85 241:0.32 242:0.74 243:0.16 245:0.88 247:0.60 248:0.79 253:0.87 255:0.79 256:0.64 259:0.21 260:0.73 261:0.94 265:0.49 266:0.87 267:0.84 268:0.65 271:0.21 276:0.85 285:0.62 290:0.67 297:0.36 300:0.70 +1 1:0.74 11:0.88 12:0.37 17:0.43 21:0.54 27:0.45 28:0.95 30:0.95 34:0.81 36:0.19 37:0.50 39:0.43 43:0.82 66:0.54 69:0.69 74:0.43 81:0.70 83:0.74 85:0.72 91:0.12 98:0.09 101:0.72 108:0.52 114:0.77 123:0.50 126:0.54 127:0.06 129:0.05 135:0.60 144:0.85 145:0.49 146:0.13 147:0.18 149:0.46 150:0.80 154:0.78 155:0.67 159:0.08 161:0.88 165:0.79 167:0.47 172:0.10 173:0.67 176:0.73 177:0.38 178:0.55 179:0.08 181:0.89 187:0.68 192:0.59 201:0.74 215:0.58 216:0.77 222:0.76 235:0.68 241:0.15 243:0.63 253:0.59 259:0.21 265:0.09 267:0.28 271:0.21 290:0.77 297:0.36 300:0.08 +2 1:0.74 6:0.87 8:0.72 9:0.80 11:0.88 12:0.37 17:0.66 20:0.93 21:0.59 27:0.15 28:0.95 30:0.45 34:0.58 36:0.74 37:0.40 39:0.43 41:0.90 43:0.90 60:0.87 66:0.87 69:0.49 70:0.48 74:0.85 78:0.93 81:0.66 83:0.82 85:0.90 91:0.37 96:0.85 98:0.76 100:0.86 101:0.80 104:0.94 106:0.81 108:0.38 111:0.90 114:0.74 117:0.86 120:0.74 122:0.43 123:0.36 126:0.54 127:0.25 129:0.05 135:0.39 140:0.45 144:0.85 145:0.79 146:0.83 147:0.86 149:0.86 150:0.77 151:0.91 152:0.97 153:0.72 154:0.89 155:0.67 158:0.92 159:0.39 161:0.88 162:0.58 164:0.72 165:0.78 167:0.46 169:0.86 172:0.63 173:0.45 176:0.73 177:0.38 179:0.51 181:0.89 186:0.93 187:0.68 189:0.86 192:0.59 201:0.74 202:0.70 206:0.81 208:0.64 212:0.27 215:0.55 216:0.48 222:0.76 232:0.96 235:0.74 238:0.82 241:0.10 242:0.40 243:0.88 247:0.60 248:0.81 253:0.87 255:0.79 256:0.68 259:0.21 260:0.75 261:0.94 265:0.76 266:0.54 267:0.52 268:0.69 271:0.21 276:0.52 279:0.86 283:0.82 285:0.62 290:0.74 297:0.36 300:0.43 +1 11:0.88 12:0.37 17:0.47 21:0.78 27:0.45 28:0.95 30:0.95 34:0.69 36:0.23 37:0.50 39:0.43 43:0.79 66:0.54 69:0.77 74:0.42 85:0.72 91:0.07 98:0.09 101:0.72 108:0.60 123:0.58 127:0.06 129:0.05 135:0.51 144:0.85 145:0.56 146:0.13 147:0.18 149:0.43 154:0.72 159:0.08 161:0.88 167:0.47 172:0.10 173:0.75 177:0.38 178:0.55 179:0.08 181:0.89 187:0.68 216:0.77 222:0.76 235:0.98 241:0.12 243:0.63 253:0.37 259:0.21 265:0.09 267:0.36 271:0.21 300:0.08 +0 12:0.79 17:0.13 21:0.74 27:0.41 30:0.62 34:0.39 36:0.61 37:0.53 39:0.29 43:0.41 55:0.84 66:0.19 69:0.42 70:0.63 74:0.89 76:0.85 77:0.70 81:0.32 91:0.35 96:0.68 98:0.27 108:0.76 114:0.61 117:0.86 122:0.67 123:0.75 124:0.91 126:0.32 127:0.50 129:0.97 133:0.92 135:0.75 140:0.45 145:0.95 146:0.05 147:0.05 149:0.47 150:0.29 151:0.46 153:0.46 154:0.31 158:0.84 159:0.88 162:0.86 163:0.96 172:0.37 173:0.15 175:0.75 176:0.56 177:0.05 179:0.58 187:0.87 190:0.77 195:0.97 202:0.49 212:0.18 216:0.59 220:0.99 222:0.35 232:0.98 235:0.88 241:0.01 242:0.82 243:0.10 244:0.61 245:0.60 247:0.30 248:0.47 253:0.66 256:0.58 257:0.65 259:0.21 265:0.29 266:0.87 267:0.94 268:0.58 271:0.37 276:0.59 277:0.69 282:0.84 285:0.50 290:0.61 300:0.55 +1 12:0.79 17:0.40 21:0.78 27:0.45 30:0.76 34:0.74 36:0.18 37:0.50 39:0.29 43:0.28 55:0.96 66:0.13 69:0.74 70:0.15 74:0.48 91:0.61 98:0.06 108:0.57 122:0.57 123:0.54 124:0.75 127:0.20 129:0.81 133:0.67 135:0.90 145:0.45 146:0.05 147:0.05 149:0.19 154:0.21 159:0.53 163:0.99 172:0.05 173:0.58 175:0.75 177:0.05 178:0.55 179:0.95 187:0.68 212:0.95 216:0.77 222:0.35 235:0.80 241:0.43 242:0.82 243:0.34 244:0.61 245:0.37 247:0.12 253:0.40 257:0.65 259:0.21 265:0.06 266:0.12 267:0.82 271:0.37 276:0.18 277:0.69 300:0.13 +1 7:0.78 12:0.79 17:0.53 21:0.71 27:0.55 30:0.37 32:0.75 34:0.94 36:0.21 37:0.66 39:0.29 43:0.30 55:0.42 66:0.06 69:0.30 70:0.66 74:0.77 76:0.85 77:0.70 81:0.44 91:0.63 98:0.34 108:0.78 114:0.68 123:0.52 124:0.75 126:0.54 127:0.57 129:0.05 133:0.74 135:0.21 145:0.45 146:0.21 147:0.27 149:0.47 150:0.54 154:0.84 159:0.46 172:0.51 173:0.34 176:0.73 177:0.38 178:0.55 179:0.65 187:0.68 195:0.70 212:0.75 215:0.48 216:0.18 222:0.35 230:0.81 233:0.76 235:0.88 241:0.05 242:0.73 243:0.31 245:0.62 247:0.55 253:0.63 254:0.84 259:0.21 265:0.26 266:0.54 267:0.94 271:0.37 276:0.52 277:0.69 282:0.83 290:0.68 297:0.36 300:0.55 +2 7:0.78 12:0.79 17:0.77 21:0.47 25:0.88 27:0.72 30:0.21 34:0.74 36:0.66 39:0.29 43:0.45 55:0.98 66:0.16 69:0.33 70:0.86 74:0.63 76:0.85 81:0.48 91:0.29 98:0.33 104:0.54 108:0.26 123:0.69 124:0.84 126:0.32 127:0.98 129:0.93 133:0.84 135:0.60 145:0.56 146:0.26 147:0.32 149:0.47 150:0.38 154:0.34 159:0.76 172:0.21 173:0.21 175:0.75 177:0.05 178:0.55 179:0.88 187:0.39 202:0.36 212:0.42 215:0.63 216:0.02 222:0.35 230:0.81 233:0.76 235:0.60 241:0.01 242:0.45 243:0.44 244:0.61 245:0.47 247:0.47 253:0.49 257:0.65 259:0.21 265:0.16 266:0.38 267:0.87 271:0.37 276:0.34 277:0.69 297:1.00 300:0.55 +1 8:0.65 12:0.79 17:0.79 21:0.05 27:0.59 30:0.58 34:0.34 36:0.75 37:0.29 39:0.29 43:0.28 55:0.96 66:0.06 69:0.73 70:0.62 74:0.92 77:0.70 81:0.88 91:0.58 98:0.22 108:0.75 114:0.77 117:0.86 122:0.67 123:0.94 124:0.98 126:0.32 127:0.74 129:1.00 133:0.98 135:0.80 145:0.45 146:0.05 147:0.05 149:0.56 150:0.74 154:0.21 158:0.84 159:0.91 163:0.97 172:0.32 173:0.39 175:0.75 176:0.56 177:0.05 178:0.55 179:0.43 187:0.21 195:0.98 212:0.14 216:0.52 222:0.35 232:0.94 235:0.05 241:0.02 242:0.82 243:0.08 244:0.61 245:0.63 247:0.30 253:0.73 257:0.65 259:0.21 265:0.15 266:0.94 267:0.87 271:0.37 276:0.75 277:0.69 282:0.84 290:0.77 300:0.70 +0 12:0.79 17:0.61 21:0.59 27:0.72 30:0.45 34:0.66 36:0.94 37:0.32 39:0.29 43:0.44 55:0.84 66:0.29 69:0.21 70:0.57 74:0.40 81:0.38 91:0.44 98:0.53 104:0.82 106:0.81 108:0.89 123:0.88 124:0.79 126:0.32 127:0.62 129:0.89 133:0.78 135:0.83 146:0.39 147:0.46 149:0.48 150:0.34 154:0.34 159:0.78 163:0.94 172:0.37 173:0.13 175:0.75 177:0.05 178:0.55 179:0.71 187:0.21 206:0.81 212:0.37 215:0.55 216:0.37 222:0.35 235:0.68 241:0.03 242:0.78 243:0.27 244:0.61 245:0.62 247:0.39 253:0.35 257:0.65 259:0.21 265:0.54 266:0.80 267:0.76 271:0.37 276:0.52 277:0.69 283:0.71 297:0.36 300:0.43 +2 7:0.78 12:0.79 17:0.85 21:0.78 27:0.64 30:0.11 34:0.70 36:0.96 37:0.82 39:0.29 43:0.26 55:0.52 66:0.47 69:0.80 70:0.54 74:0.40 91:0.35 98:0.44 108:0.64 123:0.61 124:0.52 127:0.37 129:0.05 133:0.39 135:0.34 145:0.40 146:0.59 147:0.65 149:0.28 154:0.18 159:0.53 163:0.88 172:0.21 173:0.78 177:0.38 178:0.55 179:0.91 187:0.21 212:0.30 216:0.39 222:0.35 230:0.81 233:0.76 235:0.22 241:0.05 242:0.77 243:0.59 244:0.61 245:0.46 247:0.30 253:0.36 259:0.21 265:0.46 266:0.27 267:0.73 271:0.37 276:0.23 300:0.33 +0 8:0.90 12:0.79 17:0.08 21:0.78 27:0.41 30:0.95 34:0.34 36:0.39 37:0.53 39:0.29 43:0.37 55:1.00 66:0.20 69:0.52 74:0.30 91:0.18 98:0.05 108:0.40 123:0.39 124:0.61 127:0.57 129:0.59 133:0.47 135:0.78 145:0.95 146:0.05 147:0.05 149:0.15 154:0.28 159:0.92 172:0.05 173:0.17 175:0.75 177:0.05 178:0.55 179:1.00 187:0.87 191:0.76 202:0.54 216:0.59 222:0.35 235:0.60 241:0.03 243:0.40 244:0.61 245:0.36 253:0.27 257:0.65 259:0.21 265:0.05 267:0.97 271:0.37 277:0.69 300:0.08 +0 8:0.72 12:0.79 17:0.47 21:0.78 27:0.60 30:0.21 34:0.59 36:0.91 37:0.44 39:0.29 43:0.07 55:0.59 66:0.20 69:0.99 70:0.37 74:0.30 91:0.35 98:0.09 108:0.97 123:0.97 124:0.73 127:0.18 129:0.81 133:0.67 135:0.54 146:0.05 147:0.05 149:0.08 154:0.07 159:0.88 163:0.90 172:0.10 173:0.92 175:0.75 177:0.05 178:0.55 179:0.80 187:0.68 202:0.63 212:0.42 216:0.46 222:0.35 235:0.22 241:0.52 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.27 257:0.65 259:0.21 265:0.09 266:0.23 267:0.59 271:0.37 276:0.14 277:0.69 300:0.25 +0 12:0.79 17:0.45 21:0.78 27:0.30 30:0.95 34:0.64 36:0.76 37:0.64 39:0.29 43:0.36 55:0.99 66:0.20 69:0.56 74:0.34 91:0.27 98:0.07 108:0.43 123:0.41 124:0.61 127:0.58 129:0.59 133:0.47 135:0.98 145:0.65 146:0.05 147:0.05 149:0.17 154:0.27 159:0.67 172:0.05 173:0.44 175:0.75 177:0.05 178:0.55 179:1.00 187:0.95 202:0.53 216:0.75 222:0.35 235:0.95 241:0.01 243:0.40 244:0.61 245:0.36 253:0.31 257:0.65 259:0.21 265:0.07 267:0.73 271:0.37 277:0.69 300:0.08 +2 7:0.78 12:0.79 17:0.43 21:0.54 27:0.26 30:0.62 34:0.78 36:0.88 37:0.68 39:0.29 43:0.29 55:0.98 66:0.30 69:0.73 70:0.34 74:0.63 76:0.85 81:0.42 91:0.33 98:0.33 104:0.99 106:0.81 108:0.68 122:0.51 123:0.66 124:0.78 126:0.32 127:0.68 129:0.89 132:0.97 133:0.78 135:0.49 145:0.56 146:0.05 147:0.05 149:0.29 150:0.36 154:0.21 159:0.74 163:0.96 172:0.16 173:0.60 175:0.75 177:0.05 178:0.55 179:0.95 187:0.87 206:0.81 212:0.30 215:0.58 216:0.90 222:0.35 230:0.81 233:0.76 235:0.60 242:0.33 243:0.23 244:0.61 245:0.40 247:0.39 253:0.49 257:0.65 259:0.21 265:0.35 266:0.27 267:0.88 271:0.37 276:0.28 277:0.69 297:0.36 300:0.25 +1 12:0.79 17:0.57 21:0.78 27:0.16 30:0.55 32:0.75 34:0.39 36:0.21 37:0.55 39:0.29 43:0.36 55:0.92 66:0.64 69:0.94 70:0.53 74:0.63 91:0.58 98:0.64 104:0.89 108:0.88 122:0.67 123:0.88 124:0.55 127:0.72 129:0.05 133:0.74 135:0.42 145:0.42 146:0.90 147:0.05 149:0.37 154:0.27 159:0.79 163:0.86 172:0.54 173:0.93 177:0.05 178:0.55 179:0.75 187:0.87 195:0.90 212:0.18 216:0.94 222:0.35 235:0.42 241:0.02 242:0.71 243:0.13 244:0.61 245:0.51 247:0.47 253:0.49 257:0.65 259:0.21 265:0.65 266:0.75 267:0.59 271:0.37 276:0.49 300:0.43 +2 12:0.79 17:0.09 21:0.30 27:0.30 30:0.11 34:0.62 36:0.45 37:0.64 39:0.29 43:0.41 55:0.52 66:0.48 69:0.88 70:0.69 74:0.48 81:0.60 91:0.12 98:0.48 108:0.76 123:0.75 124:0.74 126:0.32 127:0.60 129:0.05 133:0.74 135:0.95 146:0.72 147:0.05 149:0.38 150:0.45 154:0.31 159:0.71 163:0.94 172:0.37 173:0.84 177:0.05 178:0.55 179:0.82 187:0.68 212:0.28 215:0.72 216:0.75 222:0.35 235:0.31 241:0.03 242:0.29 243:0.16 244:0.61 245:0.49 247:0.55 253:0.40 257:0.65 259:0.21 265:0.50 266:0.63 267:0.65 271:0.37 276:0.40 297:0.36 300:0.43 +1 7:0.78 12:0.79 17:0.49 21:0.78 27:0.42 30:0.32 32:0.75 34:0.26 36:0.99 37:0.31 39:0.29 43:0.40 55:0.71 66:0.10 69:0.77 70:0.75 74:0.46 91:0.35 98:0.29 104:0.93 106:0.81 108:0.30 123:0.88 124:0.89 127:0.76 129:0.59 133:0.86 135:0.82 145:0.45 146:0.40 147:0.45 149:0.57 154:0.46 159:0.70 172:0.27 173:0.69 177:0.05 178:0.55 179:0.67 187:0.68 195:0.86 206:0.81 212:0.21 216:0.75 222:0.35 230:0.81 233:0.76 235:0.22 241:0.43 242:0.74 243:0.34 245:0.62 247:0.39 253:0.40 254:0.90 257:0.65 259:0.21 265:0.26 266:0.87 267:0.79 271:0.37 276:0.57 277:0.69 283:0.71 300:0.55 +0 12:0.79 17:0.43 21:0.30 27:0.30 30:0.76 34:0.69 36:0.74 37:0.64 39:0.29 43:0.36 55:0.96 66:0.20 69:0.52 70:0.22 74:0.35 81:0.60 91:0.22 98:0.23 104:0.98 106:0.81 108:0.64 123:0.61 124:0.81 126:0.32 127:0.44 129:0.89 133:0.78 135:0.86 146:0.05 147:0.05 149:0.28 150:0.45 154:0.27 159:0.64 163:0.96 172:0.10 173:0.40 175:0.75 177:0.05 178:0.55 179:0.95 187:0.87 206:0.81 212:0.42 215:0.72 216:0.75 222:0.35 235:0.31 241:0.18 242:0.75 243:0.26 244:0.61 245:0.39 247:0.30 253:0.31 257:0.65 259:0.21 265:0.25 266:0.23 267:0.84 271:0.37 276:0.24 277:0.69 297:0.36 300:0.18 +0 12:0.79 17:0.08 21:0.78 27:0.30 30:0.62 34:0.71 36:0.71 37:0.64 39:0.29 43:0.36 55:0.96 66:0.30 69:0.52 70:0.34 74:0.45 91:0.38 98:0.34 108:0.84 123:0.84 124:0.78 127:0.49 129:0.89 133:0.78 135:0.76 146:0.05 147:0.05 149:0.29 154:0.27 159:0.62 163:0.97 172:0.16 173:0.42 175:0.75 177:0.05 178:0.55 179:0.93 187:0.68 202:0.76 212:0.30 216:0.75 222:0.35 235:0.22 241:0.44 242:0.33 243:0.23 244:0.61 245:0.40 247:0.39 253:0.39 257:0.65 259:0.21 265:0.36 266:0.27 267:0.76 271:0.37 276:0.27 277:0.69 300:0.25 +2 8:0.70 12:0.79 17:0.28 21:0.78 27:0.63 30:0.45 34:0.40 36:0.97 37:0.28 39:0.29 43:0.45 55:0.84 66:0.06 69:0.05 70:0.59 74:0.34 91:0.58 98:0.22 108:0.97 120:0.80 123:0.97 124:0.98 127:0.84 129:1.00 133:0.98 135:0.75 140:0.45 146:0.34 147:0.40 149:0.61 151:0.60 153:0.85 154:0.34 159:0.95 162:0.72 163:0.94 164:0.88 172:0.32 173:0.05 175:0.75 177:0.05 179:0.31 187:0.39 190:0.77 191:0.75 202:0.82 212:0.30 216:0.42 220:0.82 222:0.35 235:0.05 241:0.07 242:0.81 243:0.09 244:0.61 245:0.73 247:0.39 248:0.72 253:0.31 256:0.83 257:0.65 259:0.21 260:0.81 265:0.24 266:0.96 267:0.36 268:0.85 271:0.37 276:0.84 277:0.69 283:0.71 285:0.50 300:0.70 +2 12:0.79 17:0.62 21:0.78 27:0.19 30:0.41 34:0.36 36:0.46 37:0.43 39:0.29 43:0.43 55:0.84 66:0.20 69:0.05 70:0.72 74:0.39 91:0.14 98:0.21 104:0.98 106:0.81 108:0.05 123:0.77 124:0.84 127:0.88 129:0.93 133:0.84 135:0.85 146:0.50 147:0.56 149:0.51 154:0.33 159:0.90 163:0.86 172:0.27 173:0.06 175:0.75 177:0.05 178:0.55 179:0.79 187:0.68 206:0.81 212:0.30 216:0.88 222:0.35 241:0.05 242:0.77 243:0.40 244:0.61 245:0.55 247:0.39 253:0.35 257:0.65 259:0.21 265:0.22 266:0.71 267:0.65 271:0.37 276:0.33 277:0.69 283:0.71 300:0.55 +1 12:0.79 17:0.43 21:0.13 27:0.49 30:0.36 34:0.44 36:0.99 37:0.42 39:0.29 43:0.45 55:0.92 66:0.07 69:0.07 70:0.73 74:0.34 81:0.76 91:0.35 98:0.22 108:0.58 123:0.96 124:0.96 126:0.32 127:0.84 129:0.99 133:0.96 135:0.87 146:0.26 147:0.32 149:0.55 150:0.56 154:0.34 159:0.91 163:0.96 172:0.27 173:0.06 175:0.75 177:0.05 178:0.55 179:0.58 187:0.68 212:0.16 215:0.84 216:0.94 222:0.35 235:0.12 241:0.03 242:0.80 243:0.15 244:0.61 245:0.58 247:0.39 253:0.31 257:0.65 259:0.21 265:0.23 266:0.94 267:0.65 271:0.37 276:0.65 277:0.69 283:0.71 297:0.36 300:0.55 +1 6:0.95 7:0.81 8:0.81 12:0.37 17:0.40 20:0.78 21:0.47 27:0.21 28:0.83 30:0.09 34:0.25 36:0.91 37:0.74 43:0.52 55:0.45 66:0.13 69:0.15 70:0.95 78:0.76 81:0.88 91:0.63 98:0.32 100:0.80 104:0.84 106:0.81 108:0.87 111:0.76 120:0.83 123:0.86 124:0.97 126:0.54 127:0.84 129:0.99 133:0.97 135:0.76 140:0.45 146:0.26 147:0.32 149:0.68 150:0.77 151:0.74 152:0.97 153:0.81 154:0.41 159:0.93 161:0.72 162:0.59 164:0.88 167:0.59 169:0.83 172:0.59 173:0.07 177:0.05 179:0.29 181:0.66 186:0.76 187:0.39 191:0.76 202:0.85 206:0.81 212:0.15 215:0.63 216:0.95 220:0.93 230:0.87 233:0.76 235:0.52 241:0.18 242:0.82 243:0.09 245:0.78 247:0.12 248:0.75 256:0.85 259:0.21 260:0.84 261:0.87 265:0.35 266:0.96 267:0.79 268:0.85 271:0.47 276:0.85 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.89 8:0.72 12:0.37 17:0.34 20:0.88 21:0.47 27:0.36 28:0.83 30:0.60 34:0.19 36:0.99 37:0.64 43:0.33 55:0.71 66:0.16 69:0.67 70:0.58 78:0.75 81:0.88 91:0.68 98:0.33 100:0.79 104:0.89 106:0.81 108:0.67 111:0.75 120:0.91 123:0.64 124:0.93 126:0.54 127:0.40 129:0.98 133:0.93 135:0.97 140:0.45 146:0.38 147:0.44 149:0.63 150:0.77 151:0.82 152:0.83 153:0.94 154:0.25 159:0.88 161:0.72 162:0.83 164:0.86 167:0.59 169:0.77 172:0.51 173:0.33 177:0.05 179:0.33 181:0.66 186:0.76 187:0.39 189:0.91 202:0.76 206:0.81 212:0.16 215:0.63 216:0.72 235:0.52 238:0.92 241:0.21 242:0.82 243:0.11 245:0.74 247:0.12 248:0.88 256:0.81 259:0.21 260:0.92 261:0.78 265:0.35 266:0.95 267:0.84 268:0.82 271:0.47 276:0.76 283:0.82 285:0.62 297:0.36 300:0.55 +2 12:0.37 17:0.20 21:0.59 25:0.88 27:0.07 28:0.83 30:0.62 32:0.80 34:0.88 36:0.40 37:0.91 43:0.52 55:0.71 66:0.64 69:0.19 70:0.54 81:0.85 91:0.29 98:0.74 104:0.58 106:0.81 108:0.15 120:0.54 123:0.15 124:0.44 126:0.54 127:0.69 129:0.59 133:0.47 135:0.72 140:0.80 145:0.59 146:0.75 147:0.79 149:0.53 150:0.66 151:0.47 153:0.48 154:0.41 159:0.49 161:0.72 162:0.62 164:0.57 167:0.70 172:0.45 173:0.26 177:0.05 178:0.42 179:0.83 181:0.66 187:0.98 195:0.68 202:0.50 206:0.81 212:0.28 215:0.55 216:0.68 220:0.82 235:0.68 241:0.57 242:0.82 243:0.69 245:0.55 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 265:0.74 266:0.63 267:0.44 268:0.46 271:0.47 276:0.39 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.96 8:0.97 12:0.37 17:0.03 20:0.75 21:0.40 27:0.39 28:0.83 30:0.45 34:0.25 36:0.71 37:0.98 43:0.48 55:0.59 66:0.77 69:0.35 70:0.33 78:0.91 81:0.90 91:0.88 98:0.94 100:0.90 104:0.58 108:0.27 111:0.90 120:0.82 123:0.26 126:0.54 127:0.60 129:0.05 135:0.58 140:0.45 146:0.61 147:0.67 149:0.45 150:0.81 151:0.66 152:0.74 153:0.48 154:0.37 159:0.23 161:0.72 162:0.74 164:0.94 167:0.95 169:0.88 172:0.37 173:0.62 177:0.05 179:0.92 181:0.66 186:0.91 189:0.97 191:0.85 202:0.90 212:0.52 215:0.67 216:0.40 220:0.82 235:0.42 238:1.00 241:0.98 242:0.82 243:0.79 247:0.12 248:0.74 256:0.94 259:0.21 260:0.83 261:0.77 265:0.94 266:0.27 267:0.73 268:0.93 271:0.47 276:0.31 283:0.60 285:0.62 297:0.36 300:0.25 +2 7:0.81 8:0.96 12:0.37 17:0.70 20:0.81 21:0.47 27:0.38 28:0.83 30:0.91 32:0.80 34:0.74 36:0.62 37:0.92 43:0.51 55:0.96 66:0.10 69:0.19 70:0.13 78:0.74 81:0.88 91:0.88 98:0.09 100:0.73 108:0.15 111:0.73 120:0.54 123:0.15 124:0.82 126:0.54 127:0.93 129:0.89 133:0.78 135:0.68 140:0.80 145:0.74 146:0.05 147:0.05 149:0.30 150:0.77 151:0.47 152:0.78 153:0.48 154:0.40 159:0.38 161:0.72 162:0.55 163:0.89 164:0.72 167:0.93 169:0.72 172:0.05 173:0.35 177:0.05 178:0.42 179:0.98 181:0.66 186:0.75 191:0.75 195:0.58 202:0.69 212:0.61 215:0.63 216:0.11 220:0.82 230:0.65 233:0.63 235:0.52 241:0.88 242:0.82 243:0.29 245:0.37 247:0.12 248:0.47 256:0.64 259:0.21 260:0.54 261:0.74 265:0.09 266:0.17 267:0.36 268:0.65 271:0.47 276:0.23 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.93 7:0.81 8:0.86 12:0.37 17:0.78 20:0.97 21:0.05 27:0.19 28:0.83 30:0.85 32:0.80 34:0.53 36:0.66 37:0.73 43:0.42 55:0.45 66:0.96 69:0.48 70:0.28 78:0.81 81:0.95 91:0.40 98:0.90 100:0.87 104:0.80 106:0.81 108:0.37 111:0.82 120:0.69 123:0.36 126:0.54 127:0.47 129:0.05 135:0.85 140:0.45 145:0.80 146:0.82 147:0.85 149:0.85 150:0.93 151:0.66 152:0.92 153:0.48 154:0.32 159:0.38 161:0.72 162:0.86 164:0.57 167:0.75 169:0.85 172:0.89 173:0.55 177:0.05 179:0.29 181:0.66 186:0.82 187:0.39 189:0.94 195:0.63 202:0.36 206:0.81 212:0.93 215:0.91 216:0.81 230:0.65 233:0.63 235:0.05 238:0.93 241:0.68 242:0.82 243:0.96 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.88 265:0.90 266:0.27 267:0.76 268:0.46 271:0.47 276:0.81 285:0.62 297:0.36 300:0.33 +4 6:0.95 8:0.91 12:0.37 17:0.09 20:0.99 21:0.54 25:0.88 27:0.07 28:0.83 30:0.76 34:0.80 36:0.42 37:0.91 43:0.28 55:0.45 66:0.80 69:0.77 70:0.28 78:0.90 81:0.87 91:0.99 98:0.68 100:0.99 104:0.58 108:0.60 111:0.98 120:0.99 123:0.58 126:0.54 127:0.21 129:0.05 135:0.90 140:0.45 146:0.60 147:0.66 149:0.43 150:0.72 151:0.90 152:0.99 153:0.99 154:0.21 159:0.22 161:0.72 162:0.75 164:0.99 167:0.95 169:0.97 172:0.45 173:0.87 177:0.05 179:0.64 181:0.66 186:0.90 189:0.96 191:0.95 202:0.98 212:0.71 215:0.58 216:0.68 235:0.60 238:0.99 241:1.00 242:0.82 243:0.82 247:0.12 248:0.99 256:0.98 259:0.21 260:0.99 261:0.99 265:0.69 266:0.27 267:0.76 268:0.99 271:0.47 276:0.35 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.93 7:0.81 8:0.75 12:0.37 17:0.69 20:0.77 21:0.68 27:0.38 28:0.83 30:0.54 32:0.80 34:0.88 36:0.95 37:0.69 43:0.35 55:0.45 66:0.94 69:0.65 70:0.58 78:0.80 81:0.91 91:0.68 98:0.91 100:0.83 104:0.92 106:0.81 108:0.50 111:0.80 120:0.85 123:0.48 126:0.54 127:0.50 129:0.05 135:0.97 140:0.45 145:0.85 146:0.90 147:0.92 149:0.76 150:0.82 151:0.75 152:0.75 153:0.84 154:0.26 159:0.50 161:0.72 162:0.81 164:0.80 167:0.74 169:0.80 172:0.84 173:0.65 177:0.05 179:0.39 181:0.66 186:0.81 187:0.87 189:0.94 195:0.72 202:0.70 206:0.81 212:0.74 215:0.49 216:0.59 230:0.65 233:0.63 235:0.84 238:0.84 241:0.65 242:0.82 243:0.94 247:0.12 248:0.79 256:0.79 259:0.21 260:0.85 261:0.76 265:0.91 266:0.54 267:0.44 268:0.76 271:0.47 276:0.74 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 12:0.37 17:0.22 21:0.59 25:0.88 27:0.07 28:0.83 30:0.72 32:0.80 34:0.92 36:0.32 37:0.91 43:0.52 55:0.71 66:0.84 69:0.21 70:0.22 81:0.93 91:0.25 98:0.97 104:0.58 106:0.81 108:0.17 123:0.16 126:0.54 127:0.74 129:0.05 135:0.68 145:0.61 146:0.80 147:0.83 149:0.56 150:0.89 154:0.41 159:0.35 161:0.72 167:0.63 172:0.54 173:0.38 177:0.05 178:0.55 179:0.82 181:0.66 187:0.98 195:0.60 206:0.81 212:0.23 215:0.55 216:0.68 230:0.65 233:0.63 235:0.74 241:0.37 242:0.82 243:0.85 247:0.12 259:0.21 265:0.97 266:0.54 267:0.52 271:0.47 276:0.45 297:0.36 300:0.18 +2 7:0.81 12:0.37 17:0.30 21:0.22 25:0.88 27:0.07 28:0.83 30:0.61 32:0.80 34:0.81 36:0.43 37:0.91 43:0.52 55:0.71 66:0.78 69:0.10 70:0.53 81:0.98 91:0.46 98:0.85 104:0.58 106:0.81 108:0.09 123:0.09 124:0.39 126:0.54 127:0.76 129:0.59 133:0.47 135:0.65 145:0.67 146:0.86 147:0.89 149:0.70 150:0.97 154:0.41 159:0.53 161:0.72 167:0.65 172:0.73 173:0.18 177:0.05 178:0.55 179:0.58 181:0.66 187:0.99 195:0.70 206:0.81 212:0.58 215:0.79 216:0.68 230:0.87 233:0.76 235:0.31 241:0.43 242:0.82 243:0.80 245:0.67 247:0.12 259:0.21 265:0.85 266:0.63 267:0.44 271:0.47 276:0.63 283:0.60 297:0.36 300:0.43 +1 7:0.81 8:0.96 12:0.37 17:0.94 20:0.77 21:0.68 27:0.72 28:0.83 30:0.68 32:0.80 34:0.55 36:0.46 37:0.98 43:0.33 55:0.84 66:0.79 69:0.68 70:0.31 78:0.72 81:0.91 91:0.80 98:0.85 100:0.73 104:0.57 108:0.52 111:0.72 123:0.50 126:0.54 127:0.36 129:0.05 135:0.47 140:0.45 145:0.96 146:0.76 147:0.80 149:0.41 150:0.82 152:0.75 153:0.48 154:0.25 159:0.32 161:0.72 162:0.62 163:0.92 164:0.57 167:0.93 169:0.72 172:0.41 173:0.79 177:0.05 179:0.85 181:0.66 186:0.73 195:0.63 202:0.50 212:0.61 215:0.49 216:0.01 220:0.99 230:0.65 233:0.63 235:0.84 241:0.93 242:0.82 243:0.80 247:0.12 256:0.45 259:0.21 261:0.73 265:0.85 266:0.27 267:0.52 268:0.46 271:0.47 276:0.35 285:0.62 297:0.36 300:0.25 +2 6:0.95 7:0.81 8:0.86 12:0.37 17:0.12 20:0.91 21:0.54 27:0.24 28:0.83 30:0.28 32:0.80 34:0.75 36:0.60 37:0.80 43:0.32 55:0.71 66:0.60 69:0.69 70:0.76 78:0.76 81:0.87 91:0.73 98:0.76 100:0.80 104:0.80 106:0.81 108:0.53 111:0.76 120:0.86 123:0.51 124:0.51 126:0.54 127:0.38 129:0.59 133:0.47 135:0.56 140:0.45 145:0.67 146:0.88 147:0.90 149:0.71 150:0.72 151:0.76 152:0.91 153:0.87 154:0.24 159:0.57 161:0.72 162:0.81 164:0.89 167:0.69 169:0.78 172:0.73 173:0.62 177:0.05 179:0.39 181:0.66 186:0.77 187:0.21 189:0.96 195:0.81 202:0.81 206:0.81 212:0.70 215:0.58 216:0.67 220:0.62 230:0.87 233:0.76 235:0.60 238:0.93 241:0.53 242:0.82 243:0.67 245:0.86 247:0.12 248:0.81 256:0.86 259:0.21 260:0.87 261:0.81 265:0.76 266:0.75 267:0.23 268:0.86 271:0.47 276:0.70 283:0.82 285:0.62 297:0.36 300:0.70 +1 12:0.37 17:0.45 21:0.30 27:0.45 28:0.83 30:0.62 32:0.80 34:0.45 36:0.18 37:0.50 43:0.32 55:0.59 66:0.47 69:0.68 70:0.34 81:0.92 91:0.18 98:0.31 108:0.65 123:0.63 124:0.52 126:0.54 127:0.16 129:0.59 133:0.47 135:0.99 145:0.47 146:0.22 147:0.28 149:0.36 150:0.84 154:0.24 159:0.26 161:0.72 163:0.75 167:0.60 172:0.21 173:0.65 177:0.05 178:0.55 179:0.65 181:0.66 187:0.68 195:0.75 212:0.30 215:0.72 216:0.77 235:0.31 241:0.24 242:0.82 243:0.37 245:0.46 247:0.12 259:0.21 265:0.33 266:0.27 267:0.99 271:0.47 276:0.26 283:0.60 297:0.36 300:0.25 +1 6:0.92 8:0.93 12:0.37 17:0.30 20:0.85 21:0.78 25:0.88 27:0.72 28:0.83 34:0.52 36:0.22 37:0.99 78:0.75 91:0.90 100:0.79 104:0.58 111:0.75 120:0.69 135:0.78 140:0.45 151:0.66 152:0.81 153:0.48 161:0.72 162:0.86 164:0.57 167:0.93 169:0.77 175:0.75 181:0.66 186:0.75 189:0.94 202:0.36 216:0.09 238:0.92 241:0.90 244:0.61 248:0.63 256:0.45 257:0.65 259:0.21 260:0.70 261:0.77 267:0.44 268:0.46 271:0.47 277:0.69 285:0.62 +1 8:0.87 12:0.37 17:0.04 20:0.84 21:0.78 27:0.27 28:0.83 34:0.36 36:0.68 37:0.99 78:0.74 91:0.85 100:0.79 111:0.74 120:0.84 135:0.44 140:0.96 151:0.74 152:0.80 153:0.83 161:0.72 162:0.46 164:0.83 167:0.92 169:0.77 175:0.75 178:0.54 181:0.66 186:0.75 187:0.21 202:0.96 216:0.59 220:0.74 235:0.12 241:0.85 244:0.61 248:0.76 256:0.78 257:0.65 260:0.85 261:0.76 267:0.28 268:0.79 271:0.47 277:0.69 283:0.60 285:0.62 +3 6:0.95 7:0.81 8:0.87 12:0.37 17:0.38 20:0.99 21:0.30 27:0.21 28:0.83 30:0.54 34:0.39 36:0.90 37:0.74 43:0.52 55:0.52 66:0.09 69:0.10 70:0.65 78:0.79 81:0.92 91:0.77 98:0.52 100:0.87 104:0.84 106:0.81 108:0.96 111:0.81 120:0.95 123:0.76 124:0.91 126:0.54 127:0.83 129:0.97 133:0.92 135:0.32 140:0.45 146:0.82 147:0.85 149:0.91 150:0.84 151:0.83 152:0.97 153:0.96 154:0.41 159:0.94 161:0.72 162:0.70 164:0.93 167:0.71 169:0.83 172:0.95 173:0.06 177:0.05 179:0.11 181:0.66 186:0.79 187:0.39 189:0.96 191:0.75 202:0.89 206:0.81 212:0.71 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.59 242:0.82 243:0.19 245:0.98 247:0.12 248:0.93 256:0.91 259:0.21 260:0.95 261:0.87 265:0.49 266:0.92 267:0.96 268:0.92 271:0.47 276:0.98 283:0.82 285:0.62 297:0.36 300:0.70 +1 12:0.37 17:0.38 21:0.47 27:0.45 28:0.83 30:0.45 32:0.80 34:0.83 36:0.17 37:0.50 43:0.32 55:0.92 66:0.53 69:0.68 70:0.57 81:0.88 91:0.25 98:0.50 108:0.52 123:0.50 124:0.52 126:0.54 127:0.32 129:0.59 133:0.47 135:0.96 145:0.50 146:0.71 147:0.75 149:0.51 150:0.77 154:0.24 159:0.57 161:0.72 167:0.66 172:0.45 173:0.59 177:0.05 178:0.55 179:0.67 181:0.66 187:0.68 195:0.83 212:0.13 215:0.63 216:0.77 235:0.52 241:0.47 242:0.82 243:0.62 245:0.64 247:0.12 259:0.21 265:0.51 266:0.80 267:0.96 271:0.47 276:0.45 283:0.60 297:0.36 300:0.43 +2 12:0.37 17:0.28 21:0.47 27:0.36 28:0.83 30:0.26 34:0.42 36:0.98 37:0.64 43:0.27 55:0.71 66:0.11 69:0.79 70:0.88 81:0.88 91:0.06 98:0.36 104:0.89 106:0.81 108:0.91 120:0.91 123:0.91 124:0.93 126:0.54 127:0.38 129:0.98 133:0.93 135:0.80 140:0.45 146:0.78 147:0.81 149:0.78 150:0.77 151:0.82 153:0.94 154:0.20 159:0.90 161:0.72 162:0.83 164:0.86 167:0.54 172:0.70 173:0.43 177:0.05 179:0.15 181:0.66 187:0.39 202:0.76 206:0.81 212:0.42 215:0.63 216:0.72 235:0.52 241:0.03 242:0.82 243:0.21 245:0.93 247:0.12 248:0.88 256:0.81 259:0.21 260:0.92 265:0.39 266:0.91 267:0.69 268:0.82 271:0.47 276:0.92 283:0.82 285:0.62 297:0.36 300:0.84 +1 8:0.73 12:0.06 17:0.26 21:0.47 27:0.45 28:0.83 30:0.84 34:0.70 36:0.17 37:0.50 43:0.32 55:0.59 66:0.51 69:0.69 70:0.28 81:0.92 91:0.46 98:0.65 108:0.79 123:0.78 124:0.65 126:0.54 127:0.52 129:0.81 133:0.67 135:0.74 145:0.50 146:0.72 147:0.76 149:0.73 150:0.86 154:0.24 159:0.70 161:0.93 167:0.45 172:0.76 173:0.57 177:0.05 178:0.55 179:0.35 181:0.91 187:0.68 212:0.76 215:0.63 216:0.77 235:0.60 241:0.07 242:0.82 243:0.40 245:0.86 247:0.12 259:0.21 265:0.66 266:0.63 267:0.69 271:0.17 276:0.77 297:0.36 300:0.43 +1 12:0.06 17:0.38 21:0.30 27:0.45 28:0.83 30:0.11 34:0.75 36:0.18 37:0.50 43:0.32 55:0.59 66:0.16 69:0.68 70:0.54 81:0.95 91:0.20 98:0.30 108:0.82 123:0.65 124:0.71 126:0.54 127:0.37 129:0.81 133:0.67 135:0.81 145:0.47 146:0.05 147:0.05 149:0.30 150:0.92 154:0.24 159:0.50 161:0.93 163:0.96 167:0.46 172:0.16 173:0.66 177:0.05 178:0.55 179:0.91 181:0.91 187:0.68 212:0.30 215:0.72 216:0.77 235:0.42 241:0.18 242:0.82 243:0.23 245:0.43 247:0.12 259:0.21 265:0.24 266:0.27 267:0.44 271:0.17 276:0.26 297:0.36 300:0.33 +1 6:0.79 8:0.58 12:0.06 17:0.45 20:0.85 21:0.13 27:0.40 28:0.83 30:0.45 34:0.53 36:0.97 37:0.38 43:0.52 55:0.84 66:0.15 69:0.07 70:0.66 78:0.80 81:0.97 91:0.44 98:0.46 100:0.79 108:0.94 111:0.78 120:0.80 123:0.93 124:0.90 126:0.54 127:0.77 129:0.96 133:0.90 135:0.81 140:0.45 146:0.55 147:0.61 149:0.76 150:0.96 151:0.72 152:0.80 153:0.78 154:0.41 159:0.87 161:0.93 162:0.79 163:0.94 164:0.76 167:0.49 169:0.77 172:0.57 173:0.07 177:0.05 179:0.33 181:0.91 186:0.80 187:0.68 189:0.81 202:0.66 212:0.22 215:0.84 216:0.25 235:0.12 238:0.82 241:0.35 242:0.82 243:0.29 245:0.84 247:0.12 248:0.73 256:0.68 259:0.21 260:0.81 261:0.81 265:0.48 266:0.92 267:0.23 268:0.71 271:0.17 276:0.82 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.90 7:0.81 8:0.76 12:0.06 17:0.15 20:0.87 21:0.30 27:0.50 28:0.83 30:0.12 32:0.80 34:0.67 36:0.91 37:0.60 43:0.51 55:0.84 66:0.10 69:0.15 70:0.91 78:0.86 81:0.95 91:0.10 98:0.35 100:0.90 104:0.84 106:0.81 108:0.96 111:0.86 120:0.75 123:0.95 124:0.97 126:0.54 127:0.95 129:1.00 133:0.98 135:0.70 140:0.45 145:0.63 146:0.29 147:0.35 149:0.74 150:0.92 151:0.73 152:0.82 153:0.78 154:0.40 159:0.94 161:0.93 162:0.78 164:0.70 167:0.45 169:0.87 172:0.71 173:0.07 177:0.05 179:0.20 181:0.91 186:0.86 187:0.39 189:0.91 191:0.70 195:0.99 202:0.59 206:0.81 212:0.37 215:0.72 216:0.86 230:0.87 233:0.76 235:0.31 238:0.91 241:0.03 242:0.82 243:0.07 245:0.86 247:0.12 248:0.68 256:0.58 259:0.21 260:0.76 261:0.88 265:0.24 266:0.97 267:0.65 268:0.64 271:0.17 276:0.92 283:0.82 285:0.62 297:0.36 300:0.84 +2 6:0.86 7:0.81 8:0.76 12:0.06 17:0.45 20:0.95 21:0.13 25:0.88 27:0.45 28:0.83 30:0.56 32:0.80 34:0.18 36:0.61 37:0.38 43:0.48 55:0.71 66:0.12 69:0.30 70:0.42 78:0.85 81:0.97 91:0.83 98:0.25 100:0.86 104:0.58 108:0.78 111:0.84 120:0.88 123:0.88 124:0.88 126:0.54 127:0.50 129:0.95 132:0.97 133:0.87 135:0.81 140:0.45 145:0.56 146:0.28 147:0.35 149:0.45 150:0.96 151:0.73 152:0.94 153:0.78 154:0.37 159:0.71 161:0.93 162:0.65 163:0.98 164:0.92 167:0.79 169:0.84 172:0.21 173:0.19 177:0.05 179:0.78 181:0.91 186:0.85 189:0.88 191:0.88 195:0.88 202:0.89 212:0.23 215:0.84 216:0.41 220:0.62 230:0.87 233:0.76 235:0.12 238:0.86 241:0.87 242:0.82 243:0.25 245:0.49 247:0.12 248:0.83 256:0.89 259:0.21 260:0.89 261:0.91 265:0.23 266:0.71 267:0.52 268:0.90 271:0.17 276:0.44 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.84 7:0.81 8:0.64 12:0.06 17:0.55 20:0.80 21:0.13 25:0.88 27:0.33 28:0.83 30:0.66 34:0.77 36:0.72 37:0.59 43:0.52 55:0.71 66:0.93 69:0.08 70:0.48 78:0.83 81:1.00 91:0.33 98:0.97 100:0.88 104:0.58 108:0.07 111:0.84 123:0.07 126:0.54 127:0.73 129:0.05 135:0.76 145:0.93 146:0.93 147:0.94 149:0.74 150:1.00 152:0.94 154:0.41 159:0.55 161:0.93 167:0.49 169:0.90 172:0.80 173:0.16 177:0.05 178:0.55 179:0.50 181:0.91 186:0.83 187:0.21 189:0.83 212:0.71 215:0.84 216:0.46 230:0.87 233:0.76 235:0.22 238:0.90 241:0.39 242:0.82 243:0.93 247:0.12 259:0.21 261:0.93 265:0.97 266:0.54 267:0.52 271:0.17 276:0.70 297:0.36 300:0.43 +3 6:0.91 7:0.81 8:0.84 12:0.06 17:0.03 20:0.94 21:0.30 27:0.21 28:0.83 30:0.28 34:0.71 36:0.83 37:0.74 43:0.52 55:0.84 66:0.08 69:0.10 70:0.91 78:0.90 81:0.95 91:0.77 98:0.25 100:0.97 104:0.84 106:0.81 108:0.95 111:0.94 120:0.93 123:0.95 124:0.97 126:0.54 127:0.82 129:1.00 133:0.97 135:0.24 140:0.45 146:0.48 147:0.55 149:0.70 150:0.92 151:0.80 152:0.97 153:0.93 154:0.41 159:0.93 161:0.93 162:0.56 164:0.91 167:0.50 169:0.94 172:0.48 173:0.06 177:0.05 179:0.28 181:0.91 186:0.90 187:0.39 189:0.93 191:0.78 202:0.89 206:0.81 212:0.16 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.43 242:0.82 243:0.13 245:0.78 247:0.12 248:0.90 256:0.88 259:0.21 260:0.93 261:0.97 265:0.27 266:0.97 267:0.84 268:0.88 271:0.17 276:0.86 283:0.82 285:0.62 297:0.36 300:0.84 +2 6:0.86 8:0.75 12:0.06 17:0.11 20:0.95 21:0.78 27:0.35 28:0.83 30:0.89 34:0.76 36:0.95 37:0.40 43:0.26 55:0.84 66:0.47 69:0.81 70:0.20 78:0.84 91:0.62 98:0.39 100:0.85 108:0.65 111:0.83 120:0.79 123:0.63 124:0.52 127:0.21 129:0.59 133:0.47 135:0.69 140:0.87 146:0.51 147:0.57 149:0.31 151:0.66 152:0.88 153:0.48 154:0.19 159:0.35 161:0.93 162:0.58 163:0.87 164:0.77 167:0.59 169:0.83 172:0.21 173:0.80 177:0.05 178:0.48 179:0.80 181:0.91 186:0.84 187:0.68 189:0.87 191:0.76 202:0.73 212:0.30 216:0.58 220:0.87 235:0.42 238:0.84 241:0.64 242:0.82 243:0.59 245:0.46 247:0.12 248:0.72 256:0.71 259:0.21 260:0.80 261:0.88 265:0.41 266:0.27 267:0.52 268:0.72 271:0.17 276:0.27 285:0.62 300:0.18 +3 6:0.86 7:0.81 8:0.88 12:0.06 17:0.26 20:0.95 21:0.78 25:0.88 27:0.33 28:0.83 30:0.76 34:0.82 36:0.57 37:0.59 43:0.48 55:0.84 66:0.72 69:0.44 70:0.22 78:0.85 91:0.93 98:0.92 100:0.94 104:0.58 108:0.34 111:0.89 120:0.93 123:0.32 127:0.54 129:0.05 135:0.26 140:0.45 145:0.93 146:0.55 147:0.61 149:0.38 151:0.81 152:0.94 153:0.94 154:0.36 159:0.20 161:0.93 162:0.79 163:0.75 164:0.94 167:0.80 169:0.91 172:0.27 173:0.74 177:0.05 179:0.96 181:0.91 186:0.84 189:0.89 191:0.89 202:0.89 212:0.42 216:0.46 230:0.87 233:0.76 235:0.97 238:0.96 241:0.96 242:0.82 243:0.75 247:0.12 248:0.89 256:0.92 259:0.21 260:0.93 261:0.93 265:0.92 266:0.23 267:0.18 268:0.93 271:0.17 276:0.26 283:0.82 285:0.62 300:0.18 +2 7:0.81 12:0.06 17:0.83 21:0.22 27:0.58 28:0.83 30:0.08 32:0.80 34:0.74 36:0.35 37:0.39 43:0.51 55:0.45 66:0.36 69:0.12 70:0.89 78:0.83 81:0.96 91:0.42 98:0.54 104:0.92 106:0.81 108:0.66 111:0.83 120:0.89 123:0.64 124:0.77 126:0.54 127:0.79 129:0.89 133:0.78 135:0.81 140:0.45 145:0.45 146:0.18 147:0.23 149:0.55 150:0.94 151:0.74 153:0.84 154:0.40 159:0.56 161:0.93 162:0.83 164:0.82 167:0.54 169:0.83 172:0.41 173:0.19 177:0.05 179:0.76 181:0.91 187:0.68 195:0.73 202:0.72 206:0.81 212:0.18 215:0.79 216:0.79 230:0.87 233:0.76 235:0.22 241:0.54 242:0.82 243:0.21 245:0.59 247:0.12 248:0.84 256:0.80 260:0.89 265:0.56 266:0.63 267:0.23 268:0.78 271:0.17 276:0.49 283:0.82 285:0.62 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.79 17:0.69 21:0.05 27:0.65 30:0.62 34:0.33 36:0.38 37:0.46 39:0.41 43:0.91 60:0.87 66:0.29 69:0.41 70:0.54 74:0.78 81:0.95 83:0.86 85:0.88 91:0.46 98:0.43 101:0.80 104:0.84 106:0.81 108:0.70 114:0.95 117:0.86 122:0.57 123:0.68 124:0.72 126:0.54 127:0.53 129:0.81 133:0.67 135:0.54 146:0.13 147:0.18 149:0.77 150:0.98 154:0.90 155:0.67 158:0.92 159:0.46 165:0.90 172:0.27 173:0.43 176:0.73 177:0.38 178:0.55 179:0.81 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.14 215:0.91 216:0.54 222:0.25 232:0.91 235:0.12 242:0.45 243:0.26 245:0.56 247:0.47 253:0.76 259:0.21 265:0.45 266:0.71 267:0.52 271:0.11 276:0.36 279:0.86 283:0.82 290:0.95 297:0.36 300:0.43 +0 1:0.74 12:0.79 17:0.95 21:0.13 27:0.63 34:0.36 36:0.59 37:0.38 39:0.41 81:0.92 83:0.77 91:0.63 114:0.92 126:0.54 135:0.79 150:0.96 155:0.67 165:0.88 175:0.91 176:0.73 178:0.55 187:0.39 192:0.59 201:0.74 202:0.50 215:0.84 216:0.69 222:0.25 235:0.22 241:0.54 244:0.83 253:0.69 257:0.84 259:0.21 267:0.36 271:0.11 277:0.87 290:0.92 297:0.36 +0 8:0.92 12:0.79 17:0.84 20:0.94 21:0.78 27:0.50 34:0.33 36:0.21 37:0.66 39:0.41 60:0.87 74:0.47 78:0.92 83:0.73 91:0.82 96:0.70 100:0.73 104:0.54 111:0.89 120:0.82 135:0.49 140:0.45 151:0.63 152:0.92 153:0.45 155:0.67 158:0.92 162:0.74 164:0.85 169:0.72 175:0.91 186:0.92 190:0.77 191:0.87 192:0.59 202:0.78 208:0.64 216:0.13 220:0.74 222:0.25 235:0.02 241:0.80 244:0.83 248:0.74 253:0.44 256:0.83 257:0.84 259:0.21 260:0.83 261:0.90 267:0.19 268:0.82 271:0.11 277:0.87 279:0.86 283:0.82 285:0.62 +1 1:0.74 11:0.57 12:0.79 17:0.82 21:0.54 27:0.42 30:0.72 34:0.58 36:0.59 37:0.25 39:0.41 43:0.98 60:0.87 66:0.33 69:0.19 70:0.29 74:0.76 81:0.71 83:0.84 85:0.90 91:0.18 98:0.23 101:0.80 108:0.84 114:0.77 122:0.43 123:0.83 124:0.71 126:0.54 127:0.67 129:0.81 133:0.67 135:0.28 145:0.47 146:0.28 147:0.34 149:0.81 150:0.80 154:0.98 155:0.67 158:0.87 159:0.59 165:0.79 172:0.32 173:0.20 176:0.73 177:0.38 178:0.55 179:0.81 187:0.21 192:0.59 201:0.74 208:0.64 212:0.39 215:0.58 216:0.57 222:0.25 235:0.68 241:0.01 242:0.63 243:0.39 245:0.58 247:0.30 253:0.67 259:0.21 265:0.25 266:0.54 267:0.59 271:0.11 276:0.37 279:0.86 283:0.71 290:0.77 297:0.36 300:0.25 +2 1:0.74 11:0.57 12:0.79 17:0.73 21:0.22 27:0.70 30:0.66 34:0.61 36:0.52 37:0.25 39:0.41 43:0.90 60:0.95 66:0.85 69:0.44 70:0.33 74:0.88 81:0.88 83:0.89 85:0.90 91:0.48 98:0.61 101:0.85 104:0.83 106:0.81 108:0.34 114:0.90 117:0.86 122:0.57 123:0.33 126:0.54 127:0.18 129:0.05 135:0.30 146:0.36 147:0.42 149:0.88 150:0.93 154:0.89 155:0.67 158:0.92 159:0.14 165:0.86 172:0.57 173:0.72 176:0.73 177:0.38 178:0.55 179:0.41 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.92 215:0.79 216:0.48 222:0.25 232:0.91 235:0.31 241:0.18 242:0.52 243:0.86 247:0.47 253:0.76 259:0.21 265:0.62 266:0.17 267:0.23 271:0.11 276:0.46 279:0.86 283:0.82 290:0.90 297:0.36 300:0.25 +0 1:0.74 11:0.57 12:0.79 17:0.89 21:0.47 27:0.72 30:0.21 34:0.37 36:0.62 37:0.71 39:0.41 43:0.80 66:0.72 69:0.73 70:0.37 74:0.65 81:0.75 83:0.74 85:0.72 91:0.44 98:0.15 101:0.71 108:0.56 114:0.80 122:0.67 123:0.53 126:0.54 127:0.09 129:0.05 135:0.87 146:0.24 147:0.30 149:0.48 150:0.83 154:0.75 155:0.67 159:0.11 165:0.80 172:0.27 173:0.68 176:0.73 177:0.38 178:0.55 179:0.11 187:0.39 192:0.59 201:0.74 212:0.78 215:0.63 216:0.18 222:0.25 235:0.60 241:0.07 242:0.75 243:0.75 247:0.30 253:0.65 259:0.21 265:0.17 266:0.17 267:0.52 271:0.11 276:0.23 290:0.80 297:0.36 300:0.25 +3 1:0.74 6:1.00 8:1.00 9:0.80 11:0.57 12:0.79 17:0.70 20:0.94 27:0.72 30:0.15 39:0.41 41:0.96 43:0.94 55:0.52 60:0.99 66:0.24 69:0.23 70:0.68 74:0.72 78:0.99 81:0.98 83:0.89 85:0.72 91:0.69 96:0.96 98:0.18 100:0.99 101:0.69 104:0.54 108:0.88 111:1.00 114:0.98 120:0.93 122:0.67 123:0.87 124:0.80 126:0.54 127:0.69 129:0.05 133:0.74 140:0.45 146:0.13 147:0.18 149:0.58 150:0.99 151:0.98 152:0.94 153:0.92 154:0.94 155:0.67 158:0.92 159:0.82 162:0.79 163:0.80 164:0.87 165:0.92 169:0.99 172:0.21 173:0.12 176:0.73 177:0.38 179:0.86 186:0.99 189:1.00 191:0.80 192:0.59 201:0.74 202:0.80 208:0.64 212:0.30 215:0.96 216:0.11 222:0.25 232:0.76 235:0.05 238:0.97 241:0.81 242:0.80 243:0.28 245:0.50 247:0.30 248:0.96 253:0.96 255:0.79 256:0.85 259:0.21 260:0.94 261:0.96 265:0.19 266:0.54 268:0.85 271:0.11 276:0.33 277:0.69 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.43 +1 11:0.57 12:0.79 17:0.49 21:0.05 27:0.70 30:0.54 34:0.30 36:0.36 37:0.25 39:0.41 43:0.81 55:0.52 66:0.90 69:0.45 70:0.51 74:0.96 81:0.82 85:0.72 91:0.69 98:0.63 101:0.73 108:0.35 114:0.77 117:0.86 122:0.67 123:0.34 126:0.32 127:0.18 129:0.05 135:0.28 146:0.63 147:0.68 149:0.70 150:0.62 154:0.76 158:0.85 159:0.23 172:0.71 173:0.49 176:0.56 177:0.38 178:0.55 179:0.28 187:0.68 212:0.93 216:0.48 222:0.25 232:0.86 235:0.05 241:0.15 242:0.76 243:0.90 247:0.39 253:0.75 259:0.21 265:0.64 266:0.17 267:0.82 271:0.11 276:0.61 290:0.77 300:0.43 +1 1:0.74 11:0.57 12:0.79 17:0.53 20:0.94 21:0.30 27:0.42 30:0.87 34:0.66 36:0.51 37:0.25 39:0.41 43:0.98 60:0.97 66:0.79 69:0.12 70:0.20 74:0.78 78:0.95 81:0.84 83:0.83 85:0.88 91:0.56 98:0.40 100:0.73 101:0.81 108:0.10 111:0.92 114:0.86 120:0.54 122:0.43 123:0.10 126:0.54 127:0.13 129:0.05 135:0.21 140:0.45 145:0.47 146:0.37 147:0.44 149:0.85 150:0.90 151:0.47 152:0.97 153:0.46 154:0.98 155:0.67 158:0.87 159:0.14 162:0.86 164:0.65 165:0.84 169:0.72 172:0.41 173:0.28 176:0.73 177:0.38 179:0.34 186:0.95 187:0.21 190:0.77 192:0.59 201:0.74 202:0.49 208:0.64 212:0.95 215:0.72 216:0.57 220:0.99 222:0.25 235:0.42 241:0.05 242:0.63 243:0.80 247:0.30 248:0.47 253:0.72 256:0.58 259:0.21 260:0.54 261:0.90 265:0.42 266:0.12 267:0.44 268:0.58 271:0.11 276:0.31 279:0.86 283:0.71 285:0.50 290:0.86 297:0.36 300:0.18 +1 1:0.74 11:0.57 12:0.79 17:0.79 20:0.93 21:0.64 27:0.59 30:0.58 34:0.28 36:0.53 37:0.31 39:0.41 43:0.85 55:0.71 60:0.87 66:0.80 69:0.63 70:0.33 74:0.84 78:0.94 81:0.62 83:0.83 85:0.80 91:0.33 98:0.51 100:0.73 101:0.77 108:0.48 111:0.91 114:0.72 122:0.67 123:0.46 126:0.54 127:0.15 129:0.05 135:0.84 146:0.45 147:0.52 149:0.72 150:0.75 152:0.87 154:0.81 155:0.67 158:0.87 159:0.16 165:0.70 169:0.72 172:0.45 173:0.74 176:0.73 177:0.38 178:0.55 179:0.43 186:0.94 187:0.39 192:0.59 201:0.74 208:0.64 212:0.90 215:0.53 216:0.70 222:0.25 235:0.80 241:0.12 242:0.72 243:0.82 247:0.39 253:0.68 259:0.21 261:0.89 265:0.52 266:0.17 267:0.44 271:0.11 276:0.35 279:0.86 283:0.71 290:0.72 297:0.36 300:0.25 +0 1:0.74 8:0.78 11:0.57 12:0.79 17:0.79 27:0.39 30:0.21 34:0.75 36:0.42 37:0.64 39:0.41 43:0.98 66:0.24 69:0.37 70:0.67 74:0.74 76:0.94 77:0.89 81:0.98 83:0.80 85:0.80 91:0.27 98:0.19 101:0.69 108:0.97 114:0.98 122:0.43 123:0.97 124:0.85 126:0.54 127:0.85 129:0.81 133:0.83 135:0.89 145:0.96 146:0.13 147:0.18 149:0.72 150:0.99 154:0.98 155:0.67 159:0.93 163:0.86 165:0.93 172:0.21 173:0.10 176:0.73 177:0.38 178:0.55 179:0.89 187:0.39 192:0.59 195:0.99 201:0.74 212:0.19 215:0.96 216:0.19 222:0.25 235:0.12 241:0.61 242:0.45 243:0.28 245:0.48 247:0.47 253:0.77 259:0.21 265:0.21 266:0.47 267:0.76 271:0.11 276:0.29 282:0.84 290:0.98 297:1.00 300:0.43 +1 1:0.74 11:0.57 12:0.79 17:0.83 21:0.47 27:0.42 30:0.87 34:0.80 36:0.53 37:0.25 39:0.41 43:0.98 60:0.87 66:0.45 69:0.17 70:0.27 74:0.78 81:0.75 83:0.84 85:0.90 91:0.33 98:0.35 101:0.83 108:0.65 114:0.80 122:0.43 123:0.63 124:0.57 126:0.54 127:0.44 129:0.59 133:0.47 135:0.26 145:0.47 146:0.28 147:0.34 149:0.85 150:0.83 154:0.98 155:0.67 158:0.87 159:0.31 165:0.80 172:0.32 173:0.35 176:0.73 177:0.38 178:0.55 179:0.82 187:0.21 192:0.59 201:0.74 208:0.64 212:0.50 215:0.63 216:0.57 222:0.25 235:0.60 241:0.02 242:0.63 243:0.44 245:0.59 247:0.30 253:0.69 259:0.21 265:0.38 266:0.47 267:0.23 271:0.11 276:0.27 279:0.86 283:0.71 290:0.80 297:0.36 300:0.25 +1 1:0.74 8:0.59 11:0.57 12:0.79 17:0.75 20:0.94 21:0.13 27:0.68 30:0.76 34:0.89 36:0.37 37:0.32 39:0.41 43:0.81 66:0.72 69:0.71 70:0.22 74:0.60 78:0.99 81:0.92 83:0.77 85:0.80 91:0.42 98:0.43 100:0.94 101:0.79 108:0.54 111:0.97 114:0.92 122:0.43 123:0.52 126:0.54 127:0.14 129:0.05 135:0.81 145:0.42 146:0.31 147:0.38 149:0.66 150:0.96 152:0.87 154:0.76 155:0.67 159:0.13 165:0.88 169:0.92 172:0.27 173:0.90 176:0.73 177:0.38 178:0.55 179:0.57 186:0.99 187:0.21 192:0.59 201:0.74 212:0.78 215:0.84 216:0.63 222:0.25 235:0.22 242:0.45 243:0.75 247:0.35 253:0.71 259:0.21 261:0.94 265:0.45 266:0.17 267:0.28 271:0.11 276:0.22 290:0.92 297:0.36 300:0.18 +2 1:0.74 11:0.57 12:0.79 17:0.66 21:0.05 27:0.65 30:0.62 34:0.34 36:0.37 37:0.46 39:0.41 43:0.91 60:0.87 66:0.29 69:0.41 70:0.54 74:0.78 81:0.95 83:0.86 85:0.88 91:0.46 98:0.43 101:0.80 104:0.84 106:0.81 108:0.70 114:0.95 117:0.86 122:0.57 123:0.68 124:0.72 126:0.54 127:0.53 129:0.81 133:0.67 135:0.44 146:0.13 147:0.18 149:0.77 150:0.98 154:0.90 155:0.67 158:0.92 159:0.46 165:0.90 172:0.27 173:0.43 176:0.73 177:0.38 178:0.55 179:0.81 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.14 215:0.91 216:0.54 222:0.25 232:0.91 235:0.12 241:0.01 242:0.45 243:0.26 245:0.56 247:0.47 253:0.76 259:0.21 265:0.45 266:0.71 267:0.52 271:0.11 276:0.36 279:0.86 283:0.82 290:0.95 297:0.36 300:0.43 +1 1:0.74 11:0.57 12:0.79 17:0.97 20:0.93 21:0.13 27:0.63 30:0.68 32:0.80 34:0.53 36:0.47 37:0.38 39:0.41 43:0.92 66:0.79 69:0.38 70:0.21 74:0.81 77:0.70 78:0.98 81:0.92 83:0.77 85:0.85 91:0.46 98:0.45 100:0.73 101:0.83 108:0.30 111:0.95 114:0.92 122:0.43 123:0.28 126:0.54 127:0.14 129:0.05 135:0.83 145:0.41 146:0.26 147:0.32 149:0.79 150:0.96 152:0.87 154:0.91 155:0.67 159:0.11 165:0.88 169:0.72 172:0.41 173:0.75 176:0.73 177:0.38 178:0.55 179:0.39 186:0.98 187:0.39 192:0.59 195:0.53 201:0.74 212:0.95 215:0.84 216:0.69 222:0.25 235:0.22 241:0.12 242:0.45 243:0.80 247:0.39 253:0.75 259:0.21 261:0.92 265:0.47 266:0.12 267:0.19 271:0.11 276:0.35 282:0.88 290:0.92 297:0.36 299:0.88 300:0.18 +0 7:0.72 8:0.93 9:0.76 12:0.37 17:0.92 21:0.74 27:0.29 28:0.92 29:0.86 32:0.69 34:0.21 36:0.66 37:0.83 39:0.52 43:0.18 48:0.91 66:0.36 69:0.29 71:0.88 74:0.48 76:0.85 77:0.70 81:0.23 83:0.60 91:0.73 96:0.77 98:0.14 104:0.58 108:0.22 120:0.65 123:0.22 126:0.26 127:0.09 129:0.05 135:0.28 139:0.64 140:0.45 145:0.77 146:0.05 147:0.05 149:0.07 150:0.23 151:0.65 153:0.48 154:0.12 155:0.58 159:0.05 161:0.86 162:0.74 164:0.64 167:0.82 172:0.05 173:0.75 175:0.91 177:0.05 181:0.47 190:0.77 192:0.59 195:0.54 202:0.56 204:0.85 208:0.54 215:0.36 216:0.34 220:0.74 222:0.60 230:0.75 232:0.77 233:0.76 235:0.88 241:0.95 243:0.51 244:0.83 248:0.67 253:0.58 255:0.74 256:0.58 257:0.84 259:0.21 260:0.64 265:0.15 267:0.23 268:0.59 271:0.44 277:0.87 281:0.89 282:0.77 285:0.56 297:0.36 +2 1:0.74 6:0.92 8:0.86 11:0.64 12:0.37 17:0.88 18:0.69 20:0.85 21:0.68 27:0.35 28:0.92 29:0.86 30:0.74 32:0.69 34:0.88 36:0.21 37:0.73 39:0.52 43:0.71 45:0.96 48:0.91 64:0.77 66:0.19 69:0.39 70:0.80 71:0.88 74:0.73 77:0.70 78:0.98 81:0.41 83:0.60 86:0.67 91:0.29 98:0.22 99:0.83 100:0.94 101:0.52 104:0.88 108:0.95 111:0.95 114:0.56 122:0.51 123:0.95 124:0.95 126:0.54 127:0.93 129:0.59 133:0.95 135:0.26 139:0.69 145:0.70 146:0.28 147:0.35 149:0.83 150:0.65 152:0.81 154:0.61 155:0.67 159:0.94 161:0.86 165:0.70 167:0.45 169:0.92 172:0.63 173:0.10 176:0.73 177:0.70 178:0.55 179:0.35 181:0.47 186:0.97 187:0.39 189:0.93 191:0.77 192:0.87 195:0.99 201:0.93 208:0.64 212:0.30 215:0.37 216:0.53 222:0.60 232:0.91 235:0.88 238:0.86 242:0.58 243:0.12 244:0.61 245:0.79 247:0.55 253:0.43 259:0.21 261:0.90 265:0.24 266:0.91 267:0.59 271:0.44 276:0.80 277:0.69 281:0.91 282:0.77 290:0.56 297:0.36 300:0.98 +2 1:0.74 6:0.87 7:0.81 8:0.78 9:0.80 11:0.64 12:0.37 17:0.97 18:0.83 20:0.91 21:0.64 27:0.61 28:0.92 29:0.86 30:0.41 31:0.91 32:0.78 34:0.36 36:0.92 37:0.55 39:0.52 41:0.88 43:0.59 44:0.93 45:0.81 48:0.98 64:0.77 66:0.20 69:0.41 70:0.54 71:0.88 74:0.71 77:0.70 78:0.96 79:0.92 81:0.42 83:0.91 86:0.83 91:0.77 96:0.75 98:0.45 99:0.83 100:0.95 101:0.52 108:0.31 111:0.95 114:0.57 120:0.76 121:0.90 122:0.82 123:0.30 124:0.80 126:0.54 127:0.85 129:0.59 133:0.74 135:0.51 137:0.91 139:0.90 140:0.94 145:0.56 146:0.47 147:0.54 149:0.51 150:0.66 151:0.51 152:0.92 153:0.72 154:0.74 155:0.67 159:0.48 161:0.86 162:0.65 164:0.79 165:0.70 167:0.78 169:0.92 172:0.27 173:0.44 176:0.73 177:0.70 179:0.78 181:0.47 186:0.96 189:0.89 190:0.77 191:0.86 192:0.87 195:0.67 196:0.94 201:0.93 202:0.80 204:0.89 208:0.64 212:0.30 215:0.38 216:0.35 219:0.89 220:0.74 222:0.60 230:0.87 233:0.76 235:0.84 238:0.87 241:0.80 242:0.33 243:0.40 245:0.58 247:0.60 248:0.52 253:0.60 254:0.96 255:0.95 256:0.82 259:0.21 260:0.69 261:0.93 265:0.47 266:0.54 267:0.23 268:0.82 271:0.44 276:0.47 281:0.91 282:0.86 284:0.97 285:0.62 290:0.57 292:0.91 297:0.36 300:0.43 +1 1:0.69 6:0.84 8:0.89 11:0.64 12:0.37 17:0.36 18:0.62 20:0.87 21:0.30 27:0.05 28:0.92 29:0.86 30:0.72 34:0.93 36:0.74 37:0.95 39:0.52 43:0.71 45:0.83 66:0.33 69:0.83 70:0.48 71:0.88 74:0.61 78:0.89 81:0.48 83:0.60 86:0.72 91:0.60 98:0.46 99:0.83 100:0.89 101:0.52 104:0.58 108:0.25 111:0.88 114:0.63 122:0.51 123:0.66 124:0.61 126:0.44 127:0.50 129:0.05 133:0.43 135:0.93 139:0.69 146:0.41 147:0.39 149:0.68 150:0.57 152:0.99 154:0.63 155:0.58 159:0.36 161:0.86 165:0.70 167:0.66 169:0.91 172:0.32 173:0.94 176:0.66 177:0.38 178:0.55 179:0.78 181:0.47 186:0.89 187:0.39 189:0.84 191:0.83 192:0.59 201:0.68 202:0.46 208:0.54 212:0.23 215:0.44 216:0.72 222:0.60 232:0.77 235:0.42 238:0.87 241:0.68 242:0.55 243:0.50 245:0.64 247:0.47 253:0.48 254:0.92 257:0.65 259:0.21 261:0.94 265:0.35 266:0.54 267:0.52 271:0.44 276:0.35 290:0.63 297:0.36 300:0.43 +2 6:0.82 7:0.72 8:0.84 9:0.76 12:0.37 17:0.94 20:0.89 21:0.74 27:0.13 28:0.92 29:0.86 32:0.69 34:0.22 36:0.49 37:0.85 39:0.52 43:0.18 48:0.91 66:0.36 69:0.29 71:0.88 74:0.48 76:0.85 77:0.70 78:0.85 81:0.23 83:0.60 91:0.76 96:0.75 98:0.14 100:0.89 104:0.58 108:0.22 111:0.85 120:0.63 123:0.22 126:0.26 127:0.09 129:0.05 135:0.28 139:0.64 140:0.45 145:0.77 146:0.05 147:0.05 149:0.07 150:0.23 151:0.65 152:0.83 153:0.48 154:0.12 155:0.58 159:0.05 161:0.86 162:0.62 164:0.54 167:0.81 169:0.86 172:0.05 173:0.74 175:0.91 177:0.05 181:0.47 186:0.85 189:0.84 190:0.77 192:0.59 195:0.54 202:0.50 204:0.85 208:0.54 215:0.36 216:0.32 220:0.62 222:0.60 230:0.75 232:0.77 233:0.76 235:0.88 238:0.89 241:0.90 243:0.51 244:0.83 248:0.63 253:0.55 255:0.74 256:0.45 257:0.84 259:0.21 260:0.63 261:0.85 265:0.15 267:0.36 268:0.46 271:0.44 277:0.87 281:0.89 282:0.77 285:0.56 297:0.36 +2 1:0.74 6:0.81 7:0.81 8:0.62 11:0.85 12:0.37 17:0.99 18:0.92 20:0.82 21:0.59 27:0.28 28:0.92 29:0.86 30:0.28 32:0.78 34:0.87 36:0.54 37:0.37 39:0.52 43:0.61 44:0.93 45:0.92 64:0.77 66:0.68 69:0.27 70:0.87 71:0.88 74:0.89 77:0.70 78:0.96 81:0.50 83:0.91 86:0.93 91:0.38 98:0.72 99:0.83 100:0.96 101:0.87 104:0.96 106:0.81 108:0.21 111:0.95 114:0.58 117:0.86 121:0.90 122:0.51 123:0.20 124:0.48 126:0.54 127:0.75 129:0.05 133:0.60 135:0.88 139:0.95 145:0.87 146:0.86 147:0.88 149:0.82 150:0.72 152:0.83 154:0.86 155:0.67 159:0.67 161:0.86 165:0.93 167:0.45 169:0.91 172:0.85 173:0.22 176:0.73 177:0.70 178:0.55 179:0.35 181:0.47 186:0.96 187:0.21 189:0.85 192:0.87 195:0.81 201:0.93 204:0.89 206:0.81 208:0.64 212:0.71 215:0.39 216:0.51 222:0.60 230:0.87 232:0.97 233:0.76 235:0.88 238:0.89 241:0.01 242:0.27 243:0.72 245:0.84 247:0.88 253:0.48 254:0.92 259:0.21 261:0.92 265:0.73 266:0.75 267:0.23 271:0.44 276:0.80 281:0.91 282:0.86 290:0.58 297:0.36 300:0.84 +2 1:0.69 6:0.84 8:0.66 11:0.64 12:0.37 17:0.59 18:0.71 20:0.92 21:0.13 27:0.05 28:0.92 29:0.86 30:0.72 34:0.93 36:0.73 37:0.95 39:0.52 43:0.71 45:0.92 66:0.93 69:0.32 70:0.47 71:0.88 74:0.83 78:0.95 81:0.78 83:0.60 86:0.82 91:0.40 98:0.92 99:0.95 100:0.95 101:0.52 104:0.58 108:0.25 111:0.94 114:0.75 122:0.51 123:0.24 126:0.44 127:0.54 129:0.05 135:0.73 139:0.78 146:0.72 147:0.76 149:0.88 150:0.74 152:0.99 154:0.76 155:0.58 159:0.29 161:0.86 165:0.70 167:0.57 169:0.91 172:0.80 173:0.50 176:0.66 177:0.70 178:0.55 179:0.46 181:0.47 186:0.94 187:0.39 189:0.87 192:0.59 201:0.68 208:0.54 212:0.92 215:0.61 216:0.72 222:0.60 232:0.77 235:0.42 238:0.90 241:0.57 242:0.55 243:0.93 247:0.60 253:0.56 254:0.84 259:0.21 261:0.94 265:0.92 266:0.27 267:0.65 271:0.44 276:0.70 290:0.75 297:0.36 300:0.55 +0 1:0.69 11:0.64 12:0.37 17:0.36 18:0.63 21:0.13 27:0.45 28:0.92 29:0.86 30:0.60 34:0.73 36:0.17 37:0.50 39:0.52 43:0.64 45:0.83 66:0.34 69:0.68 70:0.79 71:0.88 74:0.54 81:0.64 83:0.60 86:0.64 91:0.14 98:0.37 99:0.83 101:0.52 108:0.52 114:0.75 122:0.51 123:0.49 124:0.68 126:0.44 127:0.46 129:0.05 133:0.60 135:0.89 139:0.62 145:0.49 146:0.53 147:0.59 149:0.64 150:0.62 154:0.54 155:0.58 159:0.59 161:0.86 165:0.70 167:0.51 172:0.37 173:0.62 176:0.66 177:0.70 178:0.55 179:0.71 181:0.47 187:0.68 192:0.59 201:0.68 208:0.54 212:0.42 215:0.61 216:0.77 222:0.60 235:0.22 241:0.41 242:0.45 243:0.50 244:0.61 245:0.61 247:0.55 253:0.54 259:0.21 265:0.39 266:0.71 267:0.52 271:0.44 276:0.42 290:0.75 297:0.36 300:0.70 +2 7:0.72 8:0.85 9:0.76 12:0.37 17:0.89 20:0.79 21:0.74 27:0.29 28:0.92 29:0.86 32:0.69 34:0.29 36:0.44 37:0.93 39:0.52 43:0.18 48:0.91 66:0.36 69:0.29 71:0.88 74:0.48 76:0.85 77:0.70 78:0.90 81:0.23 83:0.60 91:0.82 96:0.93 98:0.14 100:0.73 108:0.22 111:0.87 120:0.85 123:0.22 126:0.26 127:0.09 129:0.05 135:0.24 139:0.64 140:0.80 145:0.77 146:0.05 147:0.05 149:0.07 150:0.23 151:0.93 152:0.77 153:0.87 154:0.12 155:0.58 159:0.05 161:0.86 162:0.78 164:0.76 167:0.82 169:0.72 172:0.05 173:0.74 175:0.91 177:0.05 181:0.47 186:0.90 190:0.77 191:0.93 192:0.59 195:0.54 202:0.79 204:0.85 208:0.54 215:0.36 216:0.14 220:0.62 222:0.60 230:0.75 232:0.76 233:0.76 235:0.88 241:0.94 243:0.51 244:0.83 248:0.88 253:0.87 255:0.74 256:0.82 257:0.84 259:0.21 260:0.82 261:0.80 265:0.15 267:0.59 268:0.83 271:0.44 277:0.87 281:0.89 282:0.77 285:0.56 297:0.36 +3 1:0.69 6:0.88 7:0.81 8:0.73 9:0.76 11:0.64 12:0.37 17:0.95 20:0.86 21:0.13 27:0.61 28:0.92 29:0.86 30:0.76 32:0.69 34:0.86 36:0.84 37:0.55 39:0.52 43:0.72 45:0.90 66:0.90 69:0.08 70:0.37 71:0.88 74:0.84 77:0.70 78:0.91 81:0.64 83:0.60 91:0.57 96:0.69 97:0.89 98:0.94 99:0.83 100:0.90 101:0.52 104:0.86 106:0.81 108:0.07 111:0.90 114:0.75 117:0.86 120:0.55 121:0.90 122:0.51 123:0.07 126:0.44 127:0.63 129:0.05 135:0.60 139:0.74 140:0.45 145:0.45 146:0.68 147:0.72 149:0.87 150:0.62 151:0.51 152:0.92 153:0.48 154:0.62 155:0.67 158:0.78 159:0.26 161:0.86 162:0.86 164:0.54 165:0.70 167:0.47 169:0.92 172:0.71 173:0.35 175:0.75 176:0.66 177:0.38 179:0.61 181:0.47 186:0.91 187:0.21 189:0.87 190:0.77 191:0.76 192:0.87 195:0.56 201:0.68 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.61 216:0.35 220:0.87 222:0.60 230:0.87 232:0.90 233:0.76 235:0.22 238:0.88 241:0.18 242:0.62 243:0.90 244:0.61 247:0.39 248:0.50 253:0.57 255:0.74 256:0.45 257:0.65 259:0.21 260:0.55 261:0.93 265:0.94 266:0.27 267:0.36 268:0.46 271:0.44 276:0.59 277:0.69 279:0.82 281:0.91 282:0.77 283:0.78 285:0.56 290:0.75 297:0.36 300:0.33 +2 1:0.74 6:0.81 7:0.72 8:0.63 9:0.80 11:0.64 12:0.37 17:0.83 18:0.77 20:0.96 21:0.40 27:0.49 28:0.92 29:0.86 30:0.66 31:0.86 32:0.80 34:0.86 36:0.81 37:0.32 39:0.52 43:0.68 44:0.93 45:0.89 48:0.97 64:0.77 66:0.69 69:0.21 70:0.71 71:0.88 74:0.81 77:0.70 78:0.94 79:0.86 81:0.57 83:0.60 86:0.80 91:0.48 96:0.68 97:0.89 98:0.81 99:0.83 100:0.94 101:0.52 108:0.17 111:0.93 114:0.62 120:0.55 122:0.51 123:0.16 124:0.44 126:0.54 127:0.58 129:0.05 133:0.43 135:0.32 137:0.86 139:0.85 140:0.45 145:0.61 146:0.78 147:0.82 149:0.79 150:0.73 151:0.50 152:0.89 153:0.48 154:0.70 155:0.67 158:0.79 159:0.44 161:0.86 162:0.86 164:0.57 165:0.70 167:0.46 169:0.92 172:0.68 173:0.29 176:0.73 177:0.70 179:0.56 181:0.47 186:0.93 187:0.21 189:0.83 192:0.87 195:0.70 196:0.89 201:0.93 202:0.36 204:0.85 208:0.64 212:0.51 215:0.42 216:0.31 220:0.91 222:0.60 230:0.75 233:0.76 235:0.68 238:0.88 241:0.10 242:0.45 243:0.72 245:0.75 247:0.74 248:0.49 253:0.50 254:0.97 255:0.95 256:0.45 259:0.21 260:0.55 261:0.92 265:0.81 266:0.54 267:0.36 268:0.46 271:0.44 276:0.60 279:0.82 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.62 297:0.36 300:0.70 +2 1:0.74 6:0.89 8:0.83 9:0.80 11:0.64 12:0.37 17:0.75 18:0.79 20:0.83 21:0.74 27:0.34 28:0.92 29:0.86 30:0.60 31:0.90 32:0.78 34:0.66 36:0.92 37:0.57 39:0.52 43:0.58 44:0.93 45:0.88 64:0.77 66:0.51 69:0.61 70:0.54 71:0.88 74:0.75 77:0.70 78:0.96 79:0.91 81:0.40 83:0.60 86:0.87 91:0.15 96:0.68 98:0.62 99:0.83 100:0.91 101:0.52 108:0.47 111:0.93 114:0.54 120:0.54 122:0.51 123:0.45 124:0.74 126:0.54 127:0.36 129:0.59 133:0.77 135:0.97 137:0.90 139:0.88 140:0.45 145:0.96 146:0.81 147:0.84 149:0.59 150:0.65 151:0.48 152:0.85 153:0.48 154:0.80 155:0.67 159:0.60 161:0.86 162:0.86 163:0.75 164:0.57 165:0.70 167:0.53 169:0.87 172:0.59 173:0.50 176:0.73 177:0.70 179:0.50 181:0.47 186:0.95 187:0.21 189:0.90 190:0.89 192:0.87 195:0.82 196:0.92 201:0.93 202:0.50 208:0.64 212:0.18 215:0.36 216:0.76 220:0.93 222:0.60 235:0.97 238:0.85 241:0.46 242:0.24 243:0.61 245:0.67 247:0.74 248:0.48 253:0.43 254:0.84 255:0.95 256:0.58 259:0.21 260:0.54 261:0.90 265:0.63 266:0.75 267:0.99 268:0.59 271:0.44 276:0.61 282:0.86 284:0.91 285:0.62 290:0.54 297:0.36 300:0.43 +1 1:0.69 8:0.86 11:0.64 12:0.37 17:0.32 18:0.62 21:0.30 27:0.05 28:0.92 29:0.86 30:0.72 34:0.93 36:0.74 37:0.95 39:0.52 43:0.71 45:0.83 66:0.33 69:0.83 70:0.48 71:0.88 74:0.61 81:0.48 83:0.60 86:0.72 91:0.57 98:0.46 99:0.83 101:0.52 104:0.58 108:0.25 114:0.63 122:0.51 123:0.66 124:0.61 126:0.44 127:0.50 129:0.05 133:0.43 135:0.93 139:0.69 146:0.41 147:0.39 149:0.68 150:0.57 154:0.63 155:0.58 159:0.36 161:0.86 165:0.70 167:0.66 172:0.32 173:0.94 176:0.66 177:0.38 178:0.55 179:0.78 181:0.47 187:0.39 192:0.59 201:0.68 202:0.36 208:0.54 212:0.23 215:0.44 216:0.72 222:0.60 232:0.77 235:0.42 241:0.68 242:0.55 243:0.50 245:0.64 247:0.47 253:0.48 254:0.92 257:0.65 259:0.21 265:0.35 266:0.54 267:0.52 271:0.44 276:0.35 290:0.63 297:0.36 300:0.43 +1 1:0.74 6:0.89 8:0.69 11:0.64 12:0.37 17:0.53 18:0.80 20:0.89 21:0.64 27:0.45 28:0.92 29:0.86 30:0.42 32:0.80 34:0.81 36:0.18 37:0.50 39:0.52 43:0.60 44:0.93 45:0.83 64:0.77 66:0.54 69:0.71 70:0.72 71:0.88 74:0.76 77:0.70 78:0.93 81:0.42 83:0.60 86:0.86 91:0.15 97:0.89 98:0.62 99:0.83 100:0.89 101:0.52 108:0.54 111:0.91 114:0.57 122:0.51 123:0.52 124:0.51 126:0.54 127:0.36 129:0.59 133:0.46 135:0.73 139:0.91 145:0.49 146:0.74 147:0.78 149:0.41 150:0.66 152:0.92 154:0.75 155:0.67 158:0.91 159:0.50 161:0.86 163:0.81 165:0.70 167:0.47 169:0.91 172:0.54 173:0.68 176:0.73 177:0.70 178:0.55 179:0.60 181:0.47 186:0.93 187:0.68 189:0.87 192:0.87 195:0.75 201:0.93 208:0.64 212:0.35 215:0.38 216:0.77 219:0.95 222:0.60 235:0.84 238:0.84 241:0.15 242:0.24 243:0.63 245:0.71 247:0.60 253:0.44 254:0.74 259:0.21 261:0.93 265:0.63 266:0.63 267:0.52 271:0.44 276:0.49 279:0.86 282:0.88 283:0.77 290:0.57 292:0.91 297:0.36 300:0.55 +2 1:0.69 6:0.84 8:0.65 11:0.64 12:0.37 17:0.45 18:0.71 20:0.91 21:0.30 27:0.05 28:0.92 29:0.86 30:0.72 34:0.89 36:0.71 37:0.95 39:0.52 43:0.71 45:0.92 66:0.93 69:0.32 70:0.46 71:0.88 74:0.83 78:0.87 81:0.48 83:0.60 86:0.82 91:0.42 98:0.94 99:0.83 100:0.85 101:0.52 104:0.58 108:0.25 111:0.85 114:0.63 122:0.51 123:0.24 126:0.44 127:0.62 129:0.05 135:0.49 139:0.78 146:0.73 147:0.78 149:0.88 150:0.57 152:0.99 154:0.76 155:0.58 159:0.30 161:0.86 165:0.70 167:0.61 169:0.91 172:0.80 173:0.50 176:0.66 177:0.70 178:0.55 179:0.48 181:0.47 186:0.87 187:0.39 189:0.85 192:0.59 201:0.68 208:0.54 212:0.92 215:0.44 216:0.72 222:0.60 232:0.77 235:0.42 238:0.84 241:0.63 242:0.55 243:0.93 247:0.60 253:0.51 254:0.84 259:0.21 261:0.94 265:0.94 266:0.27 267:0.52 271:0.44 276:0.70 290:0.63 297:0.36 300:0.55 +0 1:0.69 8:0.84 11:0.64 12:0.06 17:0.45 18:0.95 21:0.47 27:0.07 29:0.62 30:0.27 34:1.00 36:0.26 37:0.63 39:0.74 43:0.68 44:0.90 45:0.66 48:0.91 55:0.59 64:0.77 66:0.23 69:0.89 70:0.92 71:0.90 74:0.79 76:0.94 77:0.70 81:0.33 86:0.94 91:0.07 96:0.85 98:0.55 101:0.52 108:0.90 114:0.59 117:0.86 122:0.77 123:0.89 124:0.87 126:0.44 127:0.80 129:0.59 133:0.86 135:0.99 139:0.94 140:0.45 145:0.45 146:0.70 147:0.41 149:0.54 150:0.48 151:0.60 153:0.48 154:0.56 155:0.58 158:0.74 159:0.79 162:0.81 172:0.73 173:0.81 176:0.66 177:0.38 179:0.27 187:0.68 190:0.89 191:0.70 192:0.51 195:0.91 201:0.68 202:0.70 208:0.54 212:0.67 216:0.95 220:0.74 222:0.76 232:0.82 235:0.60 241:0.18 242:0.44 243:0.25 245:0.89 247:0.93 248:0.61 253:0.80 254:0.84 256:0.73 257:0.65 259:0.21 265:0.56 266:0.80 267:0.82 268:0.75 271:0.50 276:0.86 281:0.89 282:0.77 285:0.47 290:0.59 300:0.84 +1 1:0.69 9:0.76 11:0.64 12:0.06 17:0.72 18:0.96 21:0.22 22:0.93 27:0.07 29:0.62 30:0.10 31:0.90 34:0.69 36:0.47 37:0.63 39:0.74 43:0.68 44:0.90 45:0.73 47:0.93 48:0.91 55:0.71 64:0.77 66:0.96 69:0.54 70:0.92 71:0.90 74:0.78 76:0.85 77:0.70 79:0.90 81:0.43 83:0.60 86:0.94 91:0.09 96:0.84 98:0.93 101:0.52 108:0.42 114:0.67 117:0.86 122:0.86 123:0.40 126:0.44 127:0.56 129:0.05 135:0.92 137:0.90 139:0.93 140:0.45 145:0.67 146:0.98 147:0.98 149:0.59 150:0.53 151:0.65 153:0.48 154:0.57 155:0.58 158:0.75 159:0.76 162:0.86 172:0.91 173:0.35 176:0.66 177:0.70 179:0.28 187:0.68 190:0.89 192:0.51 195:0.90 196:0.94 201:0.68 202:0.50 208:0.54 212:0.50 216:0.95 220:0.62 222:0.76 232:0.82 235:0.31 241:0.21 242:0.18 243:0.96 244:0.61 247:0.90 248:0.63 253:0.81 255:0.74 256:0.58 259:0.21 262:0.93 265:0.93 266:0.63 267:0.65 268:0.59 271:0.50 276:0.83 281:0.89 282:0.77 284:0.87 285:0.56 290:0.67 300:0.70 +1 1:0.69 8:0.68 9:0.76 11:0.64 12:0.06 17:0.55 18:0.96 21:0.76 22:0.93 27:0.07 29:0.62 30:0.13 31:0.90 34:1.00 36:0.22 37:0.63 39:0.74 43:0.68 44:0.90 45:0.66 47:0.93 48:0.91 55:0.59 64:0.77 66:0.47 69:0.87 70:0.97 71:0.90 74:0.80 76:0.85 77:0.70 79:0.90 81:0.28 86:0.94 91:0.53 96:0.78 98:0.63 101:0.52 108:0.74 114:0.53 117:0.86 120:0.59 122:0.77 123:0.72 124:0.88 126:0.44 127:0.91 129:0.05 133:0.91 135:1.00 137:0.90 139:0.95 140:0.45 145:0.65 146:0.92 147:0.41 149:0.51 150:0.47 151:0.60 153:0.48 154:0.45 155:0.58 158:0.78 159:0.84 162:0.76 164:0.65 172:0.83 173:0.74 176:0.66 177:0.38 179:0.30 187:0.68 190:0.89 192:0.51 195:0.93 196:0.94 201:0.68 202:0.76 204:0.85 208:0.54 212:0.58 216:0.95 219:0.92 220:0.99 222:0.76 232:0.84 235:0.97 241:0.12 242:0.30 243:0.26 245:0.83 247:0.93 248:0.62 253:0.72 254:0.95 255:0.74 256:0.79 257:0.65 259:0.21 260:0.58 262:0.93 265:0.64 266:0.84 267:0.89 268:0.80 271:0.50 276:0.85 279:0.82 281:0.88 282:0.77 284:0.96 285:0.62 290:0.53 292:0.91 300:0.84 +0 1:0.74 8:0.69 9:0.80 11:0.83 12:0.06 17:0.95 18:0.74 21:0.54 27:0.72 29:0.62 30:0.89 31:0.87 34:0.20 36:0.25 39:0.74 41:0.82 43:0.74 44:0.90 45:0.66 48:0.91 64:0.77 66:0.30 69:0.84 70:0.20 71:0.90 74:0.57 76:0.99 77:0.70 79:0.87 81:0.53 83:0.91 85:0.72 86:0.78 91:0.85 96:0.69 98:0.12 99:0.83 101:0.97 104:0.58 108:0.69 114:0.59 120:0.56 122:0.57 123:0.67 124:0.71 126:0.54 127:0.46 129:0.05 133:0.60 135:0.47 137:0.87 139:0.83 140:0.45 145:0.61 146:0.17 147:0.05 149:0.56 150:0.73 151:0.52 153:0.69 154:0.65 155:0.67 159:0.34 162:0.57 163:0.75 164:0.62 165:1.00 172:0.16 173:0.95 176:0.73 177:0.05 179:0.93 192:0.87 195:0.63 196:0.89 201:0.93 202:0.56 204:0.85 208:0.64 212:0.30 215:0.39 216:0.03 220:0.87 222:0.76 232:0.75 235:0.88 241:0.83 242:0.33 243:0.23 245:0.43 247:0.39 248:0.52 253:0.45 255:0.95 256:0.45 257:0.84 259:0.21 260:0.56 265:0.13 266:0.27 267:0.28 268:0.55 271:0.50 276:0.23 281:0.91 282:0.77 284:0.90 285:0.62 290:0.59 297:0.36 300:0.18 +1 12:0.06 17:0.91 21:0.47 27:0.36 29:0.62 32:0.69 34:0.70 36:0.25 37:0.92 39:0.74 43:0.15 44:0.90 48:0.91 64:0.77 66:0.36 69:0.53 71:0.90 81:0.33 91:0.51 98:0.08 108:0.41 117:0.86 123:0.39 126:0.44 127:0.06 129:0.05 135:0.66 139:0.71 145:0.65 146:0.05 147:0.05 149:0.07 150:0.27 154:0.11 159:0.05 172:0.05 173:0.55 175:0.91 177:0.05 178:0.55 187:0.39 192:0.51 195:0.67 201:0.68 215:0.41 216:0.23 222:0.76 232:0.88 235:0.60 241:0.30 243:0.51 244:0.83 253:0.20 257:0.84 259:0.21 265:0.08 267:0.73 271:0.50 277:0.87 281:0.91 297:0.36 +2 1:0.74 9:0.80 11:0.57 12:0.06 17:0.55 18:0.86 21:0.30 27:0.07 29:0.62 30:0.21 31:0.89 34:0.66 36:0.22 37:0.63 39:0.74 41:0.82 43:0.85 44:0.87 55:0.71 60:0.87 64:0.77 66:0.51 69:0.61 70:0.92 71:0.90 74:0.84 79:0.88 81:0.57 83:0.91 85:0.72 86:0.88 91:0.20 96:0.72 98:0.68 101:0.87 108:0.72 114:0.65 120:0.59 122:0.77 123:0.70 124:0.74 126:0.54 127:0.74 129:0.05 133:0.74 135:0.95 137:0.89 139:0.90 140:0.45 145:0.91 146:0.61 147:0.67 149:0.73 150:0.76 151:0.61 153:0.48 154:0.82 155:0.67 158:0.91 159:0.79 162:0.86 164:0.57 165:0.93 172:0.82 173:0.42 176:0.73 177:0.70 179:0.31 187:0.95 192:0.87 195:0.90 196:0.92 201:0.93 202:0.36 208:0.64 212:0.48 215:0.44 216:0.95 219:0.95 220:0.74 222:0.76 235:0.52 241:0.21 242:0.70 243:0.24 245:0.88 247:0.74 248:0.59 253:0.63 255:0.95 256:0.45 259:0.21 260:0.60 265:0.68 266:0.80 267:0.79 268:0.46 271:0.50 276:0.83 277:0.69 279:0.86 283:0.78 284:0.89 285:0.62 290:0.65 292:0.91 297:0.36 300:0.70 +1 1:0.74 12:0.06 17:0.83 18:0.75 21:0.30 27:0.36 29:0.62 30:0.56 32:0.80 34:0.74 36:0.24 37:0.92 39:0.74 43:0.15 44:0.93 48:0.91 55:0.84 64:0.77 66:0.91 69:0.54 70:0.39 71:0.90 74:0.78 76:0.85 77:0.89 81:0.60 83:0.60 86:0.74 91:0.54 98:0.88 99:0.95 108:0.41 114:0.65 117:0.86 122:0.77 123:0.39 126:0.54 127:0.42 129:0.05 135:0.96 139:0.82 145:0.69 146:0.89 147:0.91 149:0.31 150:0.75 154:0.54 155:0.67 158:0.75 159:0.48 165:0.70 172:0.74 173:0.52 176:0.73 177:0.70 178:0.55 179:0.52 187:0.39 192:0.87 195:0.72 201:0.93 208:0.64 212:0.42 215:0.44 216:0.23 222:0.76 232:0.88 235:0.68 241:0.41 242:0.73 243:0.91 244:0.61 247:0.55 253:0.51 254:0.84 259:0.21 265:0.88 266:0.63 267:0.36 271:0.50 276:0.63 281:0.91 282:0.88 290:0.65 297:0.36 300:0.33 +1 12:0.06 17:0.36 18:0.75 21:0.76 27:0.45 29:0.62 30:0.19 34:0.79 36:0.17 37:0.50 39:0.74 43:0.13 55:0.59 66:0.29 69:0.84 70:0.97 71:0.90 74:0.75 77:0.70 81:0.23 86:0.81 91:0.25 98:0.34 108:0.70 114:0.53 122:0.77 123:0.68 124:0.87 126:0.26 127:0.58 129:0.05 133:0.85 135:0.90 139:0.77 145:0.50 146:0.63 147:0.30 149:0.25 150:0.23 154:0.63 159:0.78 172:0.61 173:0.72 176:0.61 177:0.05 178:0.55 179:0.41 187:0.68 195:0.91 212:0.40 216:0.77 222:0.76 235:0.95 241:0.63 242:0.75 243:0.12 245:0.77 247:0.76 253:0.43 254:0.84 257:0.84 259:0.21 265:0.36 266:0.91 267:0.84 271:0.50 276:0.73 282:0.73 290:0.53 300:0.84 +0 1:0.74 9:0.80 11:0.64 12:0.06 17:0.24 18:0.76 21:0.47 27:0.06 29:0.62 30:0.33 31:0.87 34:0.61 36:0.95 37:0.82 39:0.74 43:0.29 44:0.90 45:0.66 48:0.91 55:0.71 64:0.77 66:0.63 69:0.93 70:0.98 71:0.90 74:0.60 76:0.85 77:0.70 79:0.87 81:0.44 86:0.80 91:0.27 96:0.69 98:0.48 101:0.52 108:0.86 114:0.60 120:0.55 122:0.51 123:0.85 124:0.51 126:0.54 127:0.28 129:0.05 133:0.60 135:0.83 137:0.87 139:0.82 140:0.45 145:0.77 146:0.78 147:0.05 149:0.24 150:0.63 151:0.49 153:0.87 154:0.46 155:0.67 159:0.64 162:0.67 164:0.78 172:0.65 173:0.91 176:0.73 177:0.05 179:0.43 187:0.39 192:0.87 195:0.90 196:0.89 201:0.93 202:0.71 208:0.64 212:0.61 215:0.41 216:0.92 220:0.93 222:0.76 235:0.68 241:0.44 242:0.13 243:0.10 245:0.67 247:0.86 248:0.51 253:0.46 254:0.86 255:0.95 256:0.68 257:0.84 259:0.21 260:0.55 265:0.50 266:0.80 267:0.73 268:0.73 271:0.50 276:0.57 281:0.91 282:0.77 284:0.96 285:0.62 290:0.60 297:0.36 300:0.94 +1 8:0.61 9:0.80 11:0.83 12:0.06 17:0.57 18:0.93 21:0.78 27:0.39 29:0.62 30:0.09 31:0.92 34:0.36 36:0.97 37:0.31 39:0.74 41:0.93 43:0.86 45:0.81 55:0.84 60:0.95 66:0.13 69:0.30 70:1.00 71:0.90 74:0.86 79:0.91 83:0.91 85:0.85 86:0.95 91:0.01 96:0.77 98:0.22 101:0.97 108:0.24 120:0.65 122:0.77 123:0.23 124:0.98 127:0.95 129:0.59 133:0.98 135:0.39 137:0.92 139:0.95 140:0.45 146:0.90 147:0.91 149:0.87 151:0.72 153:0.48 154:0.83 155:0.67 158:0.91 159:0.98 162:0.74 164:0.77 172:0.73 173:0.06 177:0.70 179:0.21 187:0.21 192:0.87 196:0.95 202:0.70 208:0.64 212:0.30 216:0.44 219:0.95 220:0.82 222:0.76 235:0.05 241:0.24 242:0.37 243:0.34 245:0.83 247:0.84 248:0.69 253:0.78 255:0.95 256:0.73 259:0.21 260:0.65 265:0.23 266:1.00 267:0.76 268:0.74 271:0.50 276:0.91 279:0.86 283:0.78 284:0.96 285:0.62 292:0.91 300:1.00 +0 1:0.69 9:0.76 11:0.64 12:0.06 17:0.77 18:0.73 21:0.13 27:0.72 29:0.62 30:0.33 34:0.78 36:0.41 37:0.83 39:0.74 43:0.71 45:0.77 55:0.45 66:0.46 69:0.08 70:0.75 71:0.90 74:0.95 76:0.99 77:0.70 81:0.55 83:0.60 86:0.81 87:0.98 91:0.49 96:0.77 97:0.89 98:0.58 101:0.52 108:0.07 114:0.75 120:0.65 122:0.77 123:0.07 124:0.69 126:0.44 127:0.62 129:0.81 132:0.97 133:0.67 135:0.74 139:0.77 140:0.45 145:0.80 146:0.66 147:0.71 149:0.58 150:0.57 151:0.65 153:0.48 154:0.76 155:0.58 158:0.78 159:0.54 162:0.86 164:0.64 172:0.79 173:0.16 176:0.66 177:0.70 179:0.30 187:0.39 190:0.77 192:0.59 195:0.73 201:0.68 202:0.50 208:0.54 212:0.91 215:0.61 216:0.28 220:0.62 222:0.76 232:0.72 235:0.22 241:0.53 242:0.52 243:0.58 245:0.91 247:0.76 248:0.67 253:0.80 254:0.90 255:0.74 256:0.58 259:0.21 260:0.64 265:0.59 266:0.54 267:0.87 268:0.59 271:0.50 276:0.80 279:0.82 282:0.73 283:0.78 285:0.56 290:0.75 297:0.36 300:0.70 +0 12:0.06 17:0.88 18:0.81 21:0.76 27:0.69 29:0.62 30:0.45 34:0.86 36:0.25 37:0.45 39:0.74 43:0.17 55:0.92 66:0.24 69:0.45 70:0.61 71:0.90 74:0.50 76:0.85 77:0.70 81:0.23 86:0.75 91:0.17 98:0.32 108:0.35 114:0.53 117:0.86 122:0.80 123:0.33 124:0.88 126:0.26 127:0.73 129:0.05 133:0.85 135:0.28 139:0.72 145:0.79 146:0.56 147:0.62 149:0.22 150:0.23 154:0.11 159:0.76 172:0.27 173:0.28 176:0.61 177:0.70 178:0.55 179:0.79 187:0.21 195:0.88 212:0.13 216:0.18 222:0.76 232:0.95 235:0.95 241:0.18 242:0.22 243:0.44 244:0.83 245:0.51 247:0.74 253:0.36 259:0.21 265:0.34 266:0.80 267:0.19 271:0.50 276:0.46 282:0.73 290:0.53 300:0.43 +1 8:0.95 12:0.06 17:0.02 18:0.93 21:0.64 27:0.03 29:0.62 30:0.86 31:0.99 34:0.82 36:0.91 37:0.94 39:0.74 43:0.16 44:0.87 48:1.00 55:0.59 64:0.77 66:0.49 69:0.48 70:0.32 71:0.90 79:0.99 81:0.24 86:0.87 91:1.00 98:0.49 108:0.37 120:0.89 122:0.86 123:0.35 124:0.72 126:0.26 127:0.37 129:0.89 133:0.78 135:0.65 137:0.99 139:0.88 140:0.99 145:0.82 146:0.65 147:0.70 149:0.26 150:0.24 151:0.65 153:0.93 154:0.11 159:0.54 162:0.47 164:0.93 172:0.45 173:0.40 175:0.91 177:0.05 178:0.53 179:0.67 190:0.77 191:0.98 192:0.51 195:0.82 196:0.99 202:1.00 212:0.19 216:0.84 219:0.89 220:0.91 222:0.76 235:0.80 241:0.98 242:0.77 243:0.60 244:0.83 245:0.55 247:0.39 248:0.81 253:0.20 255:0.74 256:0.99 257:0.84 259:0.21 260:0.85 265:0.50 266:0.71 267:0.59 268:0.98 271:0.50 276:0.47 277:0.87 281:0.91 283:0.78 284:1.00 285:0.56 292:0.91 300:0.33 +1 1:0.74 8:0.60 9:0.80 11:0.64 12:0.06 17:0.20 18:0.82 21:0.54 27:0.60 29:0.62 30:0.38 31:0.91 34:0.77 36:0.69 37:0.41 39:0.74 43:0.64 45:0.66 55:0.84 64:0.77 66:0.83 69:0.37 70:0.63 71:0.90 74:0.75 79:0.91 81:0.41 86:0.86 91:0.08 96:0.76 98:0.81 101:0.52 108:0.29 114:0.59 120:0.64 122:0.80 123:0.28 126:0.54 127:0.30 129:0.05 135:0.83 137:0.91 139:0.87 140:0.45 146:0.56 147:0.62 149:0.34 150:0.63 151:0.72 153:0.48 154:0.91 155:0.67 158:0.91 159:0.20 162:0.86 164:0.67 172:0.51 173:0.60 176:0.73 177:0.70 179:0.71 187:0.39 192:0.87 196:0.93 201:0.93 202:0.50 208:0.64 212:0.57 215:0.39 216:0.59 219:0.95 220:0.62 222:0.76 232:0.96 235:0.74 241:0.57 242:0.29 243:0.84 247:0.55 248:0.67 253:0.70 254:0.74 255:0.95 256:0.58 259:0.21 260:0.65 265:0.81 266:0.38 267:0.69 268:0.59 271:0.50 276:0.40 279:0.86 283:0.78 284:0.92 285:0.62 290:0.59 292:0.91 297:0.36 300:0.43 +1 1:0.74 9:0.80 11:0.64 12:0.06 17:0.47 18:0.95 21:0.54 27:0.55 29:0.62 30:0.33 31:0.86 34:0.40 36:0.28 37:0.62 39:0.74 43:0.61 45:0.83 55:0.71 64:0.77 66:0.93 69:0.33 70:0.62 71:0.90 74:0.79 79:0.86 81:0.41 86:0.96 91:0.20 96:0.68 98:0.93 101:0.52 104:0.95 108:0.26 114:0.59 120:0.53 122:0.80 123:0.25 126:0.54 127:0.57 129:0.05 135:0.91 137:0.86 139:0.95 140:0.45 146:0.84 147:0.87 149:0.66 150:0.63 151:0.47 153:0.48 154:0.90 155:0.67 158:0.78 159:0.40 162:0.86 164:0.57 172:0.82 173:0.40 176:0.73 177:0.70 179:0.43 187:0.68 192:0.87 196:0.88 201:0.93 202:0.36 208:0.64 212:0.88 215:0.39 216:0.54 219:0.92 220:0.96 222:0.76 232:0.96 235:0.74 241:0.27 242:0.15 243:0.94 247:0.84 248:0.47 253:0.46 254:0.80 255:0.95 256:0.45 259:0.21 260:0.54 265:0.93 266:0.27 267:0.82 268:0.46 271:0.50 276:0.73 279:0.82 284:0.89 285:0.62 290:0.59 292:0.91 297:0.36 300:0.43 +0 12:0.06 17:0.89 18:0.91 21:0.78 27:0.72 29:0.62 30:0.66 34:0.36 36:0.65 39:0.74 43:0.12 55:0.59 66:0.85 69:0.25 70:0.40 71:0.90 74:0.44 86:0.93 91:0.85 98:0.87 108:0.20 122:0.86 123:0.19 127:0.39 129:0.05 135:0.28 139:0.90 146:0.55 147:0.61 149:0.11 154:0.71 159:0.20 172:0.57 173:0.55 177:0.70 178:0.55 179:0.71 202:0.36 212:0.83 216:0.03 222:0.76 235:0.05 241:0.82 242:0.52 243:0.86 247:0.67 253:0.33 254:1.00 259:0.21 265:0.87 266:0.27 267:0.23 271:0.50 276:0.46 300:0.33 +2 1:0.74 11:0.64 12:0.06 17:0.22 18:0.74 21:0.59 27:0.45 29:0.62 30:0.45 34:0.82 36:0.18 37:0.50 39:0.74 43:0.63 45:0.73 55:0.59 64:0.77 66:0.61 69:0.71 70:0.78 71:0.90 74:0.74 81:0.43 83:0.60 86:0.80 91:0.35 97:0.89 98:0.55 99:0.83 101:0.52 108:0.54 114:0.58 122:0.77 123:0.51 124:0.50 126:0.54 127:0.34 129:0.05 133:0.43 135:0.56 139:0.84 145:0.45 146:0.67 147:0.72 149:0.41 150:0.66 154:0.76 155:0.67 158:0.91 159:0.48 165:0.70 172:0.59 173:0.68 176:0.73 177:0.70 178:0.55 179:0.54 187:0.68 192:0.87 201:0.93 208:0.64 212:0.39 215:0.39 216:0.77 219:0.95 222:0.76 235:0.80 241:0.43 242:0.74 243:0.67 245:0.74 247:0.60 253:0.45 254:0.74 259:0.21 265:0.56 266:0.75 267:0.59 271:0.50 276:0.56 279:0.86 283:0.77 290:0.58 292:0.91 297:0.36 300:0.70 +0 7:0.72 11:0.64 12:0.06 17:0.88 18:0.74 21:0.54 22:0.93 27:0.54 29:0.62 30:0.15 31:0.86 34:0.25 36:0.86 37:0.46 39:0.74 43:0.29 45:0.73 47:0.93 48:0.97 55:0.52 60:0.87 64:0.77 66:0.54 69:0.19 70:0.98 71:0.90 74:0.77 76:0.85 79:0.86 81:0.31 83:0.91 86:0.82 91:0.31 98:0.59 101:0.52 108:0.15 120:0.53 122:0.77 123:0.15 124:0.63 126:0.44 127:0.59 129:0.05 132:0.97 133:0.60 135:0.32 137:0.86 139:0.87 140:0.45 146:0.73 147:0.77 149:0.35 150:0.25 151:0.47 153:0.48 154:0.90 155:0.67 158:0.91 159:0.60 162:0.86 164:0.54 172:0.57 173:0.19 177:0.70 179:0.64 187:0.39 190:0.77 192:0.87 196:0.88 201:0.68 202:0.36 204:0.89 208:0.64 212:0.30 215:0.39 216:0.49 219:0.95 220:0.96 222:0.76 230:0.74 233:0.73 235:0.68 241:0.12 242:0.21 243:0.62 245:0.67 247:0.82 248:0.47 253:0.43 254:0.86 255:0.74 256:0.45 260:0.53 262:0.93 265:0.60 266:0.75 267:0.99 268:0.46 271:0.50 276:0.54 279:0.86 281:0.91 283:0.78 284:0.87 285:0.56 292:0.91 297:0.36 300:0.84 +1 1:0.69 7:0.72 8:0.62 9:0.76 11:0.64 12:0.06 17:0.91 18:0.90 21:0.05 22:0.93 27:0.54 29:0.62 30:0.70 31:0.91 34:0.98 36:0.90 37:0.29 39:0.74 43:0.68 44:0.90 45:0.81 47:0.93 48:0.97 55:0.71 64:0.77 66:0.92 69:0.19 70:0.56 71:0.90 74:0.84 76:0.97 77:0.70 79:0.91 81:0.61 83:0.60 86:0.92 87:0.98 91:0.54 96:0.76 98:0.95 99:0.83 101:0.52 108:0.15 114:0.85 122:0.80 123:0.15 126:0.44 127:0.66 129:0.05 135:0.56 137:0.91 139:0.93 140:0.45 145:0.63 146:0.79 147:0.82 149:0.64 150:0.61 151:0.70 153:0.69 154:0.59 155:0.58 158:0.78 159:0.35 162:0.86 165:0.70 172:0.77 173:0.36 176:0.66 177:0.70 179:0.54 187:0.21 190:0.77 192:0.59 195:0.64 196:0.94 201:0.68 202:0.46 208:0.54 212:0.92 216:0.39 219:0.92 220:0.62 222:0.76 230:0.75 232:0.72 233:0.76 235:0.12 241:0.53 242:0.33 243:0.92 244:0.61 247:0.76 248:0.65 253:0.76 255:0.74 256:0.45 259:0.21 262:0.93 265:0.95 266:0.23 267:0.59 268:0.55 271:0.50 276:0.66 279:0.82 281:0.91 282:0.77 284:0.87 285:0.56 290:0.85 292:0.95 300:0.55 +1 1:0.74 11:0.64 12:0.06 17:0.64 18:0.88 21:0.22 22:0.93 25:0.88 27:0.30 29:0.62 30:0.15 34:0.96 36:0.22 37:0.56 39:0.74 43:0.63 44:0.90 45:0.77 47:0.93 55:0.59 64:0.77 66:0.27 69:0.25 70:0.98 71:0.90 74:0.65 77:0.70 81:0.61 83:0.60 86:0.91 91:0.20 97:0.89 98:0.46 99:0.83 101:0.52 104:0.58 108:0.20 114:0.71 117:0.86 122:0.80 123:0.19 124:0.78 126:0.54 127:0.69 129:0.59 133:0.73 135:0.66 139:0.92 145:0.96 146:0.55 147:0.61 149:0.45 150:0.75 154:0.62 155:0.67 158:0.79 159:0.55 165:0.70 172:0.41 173:0.26 176:0.73 177:0.70 178:0.55 179:0.66 187:0.98 192:0.87 195:0.72 201:0.93 204:0.85 208:0.64 212:0.30 215:0.51 216:0.82 219:0.92 222:0.76 232:0.83 235:0.42 241:0.35 242:0.21 243:0.46 245:0.66 247:0.79 253:0.53 254:0.80 259:0.21 262:0.93 265:0.47 266:0.75 267:0.76 271:0.50 276:0.56 279:0.82 281:0.91 282:0.77 290:0.71 292:0.95 297:0.36 300:0.84 +1 1:0.69 7:0.81 11:0.64 12:0.06 17:0.81 18:0.84 21:0.22 27:0.31 29:0.62 30:0.60 32:0.80 34:0.94 36:0.97 37:0.26 39:0.74 43:0.69 44:0.93 45:0.66 48:0.99 55:0.59 64:0.77 66:0.95 69:0.50 70:0.55 71:0.90 74:0.90 76:1.00 77:0.96 81:0.51 86:0.85 91:0.71 96:0.72 98:0.92 101:0.52 108:0.38 114:0.67 117:0.86 121:0.90 122:0.77 123:0.37 126:0.44 127:0.53 129:0.05 135:0.93 139:0.91 140:0.45 145:0.63 146:0.92 147:0.94 149:0.59 150:0.55 151:0.51 153:0.48 154:0.59 155:0.67 159:0.54 162:0.86 172:0.86 173:0.46 175:0.75 176:0.66 177:0.38 179:0.36 187:0.21 190:0.89 192:0.87 195:0.75 201:0.68 202:0.36 204:0.89 208:0.64 212:0.61 216:0.38 219:0.89 220:0.74 222:0.76 230:0.87 232:0.79 233:0.76 235:0.42 241:0.24 242:0.73 243:0.95 244:0.61 247:0.55 248:0.51 253:0.60 256:0.45 257:0.65 259:0.21 265:0.92 266:0.38 267:0.73 268:0.46 271:0.50 276:0.77 277:0.69 281:0.91 282:0.88 285:0.47 290:0.67 292:0.91 300:0.43 +1 9:0.76 11:0.64 12:0.06 17:0.66 18:0.83 21:0.64 27:0.66 29:0.62 30:0.55 31:0.90 34:0.73 36:0.21 37:0.69 39:0.74 43:0.68 44:0.87 45:0.73 55:0.71 64:0.77 66:0.92 69:0.61 70:0.46 71:0.90 74:0.74 79:0.90 81:0.24 83:0.60 86:0.83 91:0.38 96:0.75 97:0.89 98:0.93 101:0.52 108:0.47 122:0.75 123:0.45 126:0.26 127:0.57 129:0.05 135:0.80 137:0.90 139:0.84 140:0.80 145:0.89 146:0.88 147:0.90 149:0.53 150:0.24 151:0.65 153:0.48 154:0.57 155:0.58 158:0.78 159:0.47 162:0.62 172:0.79 173:0.64 177:0.70 178:0.42 179:0.48 187:0.68 190:0.77 192:0.51 195:0.70 196:0.92 202:0.50 208:0.54 212:0.81 216:0.41 219:0.92 220:0.62 222:0.76 235:0.80 241:0.46 242:0.36 243:0.93 244:0.61 247:0.84 248:0.63 253:0.63 255:0.74 256:0.45 259:0.21 265:0.93 266:0.27 267:0.52 268:0.46 271:0.50 276:0.69 279:0.82 284:0.87 285:0.56 292:0.91 300:0.43 +1 12:0.92 17:0.51 21:0.78 27:0.45 28:0.93 34:0.76 36:0.19 37:0.50 43:0.28 66:0.36 69:0.79 91:0.22 98:0.07 108:0.63 123:0.60 127:0.06 129:0.05 135:0.30 145:0.47 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 161:0.76 167:0.54 172:0.05 173:0.79 177:0.05 178:0.55 181:0.82 187:0.68 216:0.77 235:0.97 241:0.05 243:0.51 259:0.21 265:0.08 267:0.92 +2 8:0.65 12:0.92 17:0.81 21:0.78 27:0.52 28:0.93 30:0.76 34:0.79 36:0.67 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.48 98:0.22 108:0.65 123:0.63 124:0.52 127:0.24 129:0.59 133:0.47 135:0.51 146:0.05 147:0.05 149:0.24 154:0.25 159:0.23 161:0.76 163:0.96 167:0.54 172:0.10 173:0.79 177:0.05 178:0.55 179:0.97 181:0.82 187:0.39 212:0.95 216:0.13 235:0.22 241:0.05 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.23 266:0.12 267:0.28 276:0.14 300:0.13 +1 12:0.92 17:0.81 21:0.78 27:0.52 28:0.93 30:0.76 34:0.22 36:0.73 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.54 98:0.18 108:0.73 123:0.72 124:0.52 127:0.28 129:0.59 133:0.47 135:0.70 146:0.05 147:0.05 149:0.22 154:0.25 159:0.33 161:0.76 163:0.96 167:0.61 172:0.10 173:0.71 177:0.05 178:0.55 179:0.98 181:0.82 187:0.39 202:0.59 212:0.95 216:0.13 235:0.12 241:0.35 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.20 266:0.12 267:0.44 276:0.14 300:0.13 +1 12:0.92 17:0.30 21:0.78 27:0.45 28:0.93 34:0.88 36:0.18 37:0.50 43:0.28 66:0.36 69:0.78 91:0.20 98:0.07 108:0.62 123:0.59 127:0.06 129:0.05 135:0.92 145:0.54 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 161:0.76 167:0.59 172:0.05 173:0.74 177:0.05 178:0.55 181:0.82 187:0.68 216:0.77 235:0.97 241:0.27 243:0.51 259:0.21 265:0.07 267:0.92 +1 12:0.92 17:0.70 20:0.85 21:0.30 27:0.34 28:0.93 30:0.62 34:0.56 36:0.73 37:0.28 43:0.40 55:0.96 66:0.30 69:0.54 70:0.34 78:0.83 81:0.69 91:0.48 98:0.23 100:0.73 106:0.81 108:0.41 111:0.81 123:0.39 124:0.78 126:0.54 127:0.23 129:0.89 133:0.78 135:0.61 145:0.88 146:0.38 147:0.45 149:0.36 150:0.50 152:0.81 154:0.30 159:0.48 161:0.76 163:0.89 167:0.57 169:0.72 172:0.16 173:0.40 177:0.05 178:0.55 179:0.83 181:0.82 186:0.83 187:0.21 206:0.81 212:0.30 215:0.72 216:0.94 235:0.31 241:0.18 242:0.82 243:0.48 245:0.40 247:0.12 259:0.21 261:0.81 265:0.25 266:0.27 267:0.69 276:0.28 297:0.36 300:0.25 +1 8:0.77 12:0.92 17:0.79 21:0.78 27:0.55 28:0.93 30:0.62 34:0.58 36:0.62 37:0.29 43:0.44 55:0.71 66:0.61 69:0.45 70:0.34 91:0.37 98:0.69 108:0.35 123:0.33 124:0.43 127:0.59 129:0.59 133:0.47 135:0.79 146:0.65 147:0.70 149:0.38 154:0.33 159:0.41 161:0.76 163:0.94 167:0.59 172:0.27 173:0.51 177:0.05 178:0.55 179:0.94 181:0.82 187:0.21 212:0.61 216:0.23 235:0.05 241:0.27 242:0.82 243:0.68 245:0.42 247:0.12 259:0.21 265:0.69 266:0.23 267:0.85 276:0.27 300:0.25 +2 12:0.92 17:0.82 21:0.30 25:0.88 27:0.59 28:0.93 34:0.68 36:0.38 37:0.43 43:0.14 66:0.36 69:0.95 81:0.69 91:0.46 98:0.06 108:0.91 120:0.53 123:0.91 126:0.54 127:0.05 129:0.05 135:0.73 140:0.45 145:0.52 146:0.05 147:0.05 149:0.07 150:0.50 151:0.46 153:0.48 154:0.10 159:0.05 161:0.76 162:0.86 164:0.57 167:0.55 172:0.05 173:1.00 177:0.05 181:0.82 187:0.21 202:0.36 215:0.72 216:0.27 220:0.87 235:0.31 241:0.07 243:0.51 248:0.46 256:0.45 259:0.21 260:0.53 265:0.07 267:0.69 268:0.46 285:0.62 297:0.36 +1 8:0.63 12:0.92 17:0.67 21:0.78 27:0.55 28:0.93 30:0.62 34:0.55 36:0.54 37:0.29 43:0.44 55:0.71 66:0.30 69:0.45 70:0.34 91:0.42 98:0.30 108:0.35 123:0.34 124:0.71 127:0.84 129:0.81 133:0.67 135:0.51 146:0.41 147:0.48 149:0.34 154:0.33 159:0.62 161:0.76 163:0.94 167:0.62 172:0.16 173:0.40 177:0.05 178:0.55 179:0.95 181:0.82 187:0.21 202:0.36 212:0.30 216:0.23 235:0.12 241:0.37 242:0.82 243:0.48 245:0.43 247:0.12 259:0.21 265:0.32 266:0.27 267:0.85 276:0.25 300:0.25 +2 8:0.60 12:0.92 17:0.59 20:0.82 21:0.78 27:0.55 28:0.93 30:0.89 34:0.66 36:0.67 37:0.29 43:0.44 55:0.71 66:0.61 69:0.45 70:0.20 78:0.86 91:0.29 98:0.61 100:0.73 108:0.35 111:0.84 123:0.33 124:0.43 127:0.56 129:0.59 133:0.47 135:0.82 146:0.54 147:0.60 149:0.39 152:0.98 154:0.33 159:0.37 161:0.76 163:0.79 167:0.66 169:0.82 172:0.27 173:0.54 177:0.05 178:0.55 179:0.94 181:0.82 186:0.87 187:0.21 212:0.30 216:0.23 235:0.05 241:0.50 242:0.82 243:0.68 245:0.42 247:0.12 259:0.21 261:0.90 265:0.62 266:0.27 267:0.95 276:0.25 300:0.18 +2 8:0.62 12:0.92 17:0.73 21:0.78 27:0.72 28:0.93 30:0.62 34:0.19 36:0.58 37:0.29 43:0.44 55:0.71 66:0.61 69:0.46 70:0.23 91:0.71 98:0.68 108:0.35 123:0.34 124:0.43 127:0.75 129:0.59 133:0.47 135:0.70 146:0.59 147:0.65 149:0.38 154:0.33 159:0.39 161:0.76 163:0.94 167:0.90 172:0.27 173:0.56 177:0.05 178:0.55 179:0.95 181:0.82 202:0.60 212:0.61 216:0.03 235:0.22 241:0.82 242:0.82 243:0.68 245:0.42 247:0.12 259:0.21 265:0.68 266:0.23 267:0.79 276:0.26 300:0.18 +2 12:0.92 17:0.75 21:0.64 25:0.88 27:0.59 28:0.93 30:0.95 32:0.80 34:0.29 36:0.33 37:0.43 43:0.50 55:0.99 66:0.20 69:0.32 81:0.55 91:0.35 98:0.06 108:0.25 123:0.24 124:0.61 126:0.54 127:0.92 129:0.59 133:0.47 135:0.68 140:0.45 145:1.00 146:0.05 147:0.05 149:0.17 150:0.42 153:0.48 154:0.39 159:0.78 161:0.76 162:0.86 164:0.57 167:0.58 172:0.05 173:0.19 177:0.05 179:1.00 181:0.82 187:0.68 195:0.89 202:0.36 215:0.53 216:0.27 220:0.97 235:0.74 241:0.21 243:0.40 245:0.36 256:0.45 259:0.21 265:0.06 267:0.28 268:0.46 285:0.62 297:0.36 300:0.08 +4 6:0.96 8:0.85 12:0.92 17:0.76 20:0.99 21:0.47 25:0.88 27:0.59 28:0.93 30:0.76 32:0.80 34:0.25 36:0.29 37:0.43 43:0.50 55:0.96 66:0.13 69:0.27 70:0.15 78:0.99 81:0.63 91:0.81 98:0.07 100:1.00 108:0.21 111:1.00 120:0.81 123:0.20 124:0.75 126:0.54 127:0.85 129:0.81 133:0.67 135:0.63 140:0.80 145:1.00 146:0.05 147:0.05 149:0.22 150:0.46 151:0.73 152:0.98 153:0.78 154:0.39 159:0.59 161:0.76 162:0.52 163:0.90 164:0.82 167:0.91 169:0.99 172:0.05 173:0.26 177:0.05 178:0.42 179:0.99 181:0.82 186:0.98 189:0.98 191:0.81 195:0.74 202:0.82 212:0.95 215:0.63 216:0.27 220:0.62 235:0.52 238:0.98 241:0.88 242:0.82 243:0.34 245:0.37 247:0.12 248:0.74 256:0.75 259:0.21 260:0.82 261:0.98 265:0.08 266:0.12 267:0.76 268:0.78 276:0.17 283:0.60 285:0.62 297:0.36 300:0.13 +2 12:0.92 17:0.84 21:0.59 25:0.88 27:0.59 28:0.93 30:0.95 32:0.80 34:0.21 36:0.42 37:0.43 43:0.50 55:0.99 66:0.20 69:0.30 81:0.58 91:0.40 98:0.06 108:0.24 123:0.23 124:0.61 126:0.54 127:0.86 129:0.59 133:0.47 135:0.65 140:0.45 145:1.00 146:0.05 147:0.05 149:0.17 150:0.43 153:0.69 154:0.39 159:0.81 161:0.76 162:0.86 164:0.62 167:0.58 172:0.05 173:0.16 177:0.05 179:1.00 181:0.82 187:0.68 195:0.92 202:0.46 215:0.55 216:0.27 220:0.97 235:0.68 241:0.21 243:0.40 245:0.36 256:0.45 259:0.21 265:0.06 267:0.69 268:0.55 285:0.62 297:0.36 300:0.08 +2 12:0.92 17:0.90 21:0.54 25:0.88 27:0.59 28:0.93 32:0.80 34:0.81 36:0.58 37:0.43 43:0.11 66:0.36 69:0.97 81:0.61 91:0.27 98:0.06 108:0.94 123:0.93 126:0.54 127:0.05 129:0.05 135:0.61 145:0.98 146:0.05 147:0.05 149:0.06 150:0.45 154:0.09 159:0.05 161:0.76 167:0.56 172:0.05 173:1.00 177:0.05 178:0.55 181:0.82 187:0.21 195:0.93 215:0.58 216:0.27 235:0.60 241:0.12 243:0.51 259:0.21 265:0.06 267:0.59 297:0.36 +2 8:0.71 12:0.92 17:0.79 20:0.97 21:0.78 27:0.52 28:0.93 30:0.76 34:0.26 36:0.65 37:0.39 43:0.34 55:0.59 66:0.64 69:0.64 70:0.15 78:0.83 91:0.68 98:0.43 100:0.73 108:0.49 111:0.81 123:0.47 127:0.14 129:0.05 135:0.49 146:0.30 147:0.37 149:0.26 152:0.97 154:0.25 159:0.13 161:0.76 163:0.90 167:0.90 169:0.72 172:0.16 173:0.86 177:0.05 178:0.55 179:0.80 181:0.82 186:0.83 191:0.75 202:0.65 212:0.95 216:0.13 241:0.82 242:0.82 243:0.69 247:0.12 259:0.21 261:0.86 265:0.45 266:0.12 267:0.82 276:0.19 300:0.13 +2 12:0.92 17:0.92 21:0.59 25:0.88 27:0.59 28:0.93 32:0.80 34:0.91 36:0.27 37:0.43 43:0.20 66:0.36 69:0.93 81:0.58 91:0.27 98:0.07 108:0.87 123:0.86 126:0.54 127:0.06 129:0.05 135:0.54 145:0.98 146:0.05 147:0.05 149:0.08 150:0.43 154:0.13 159:0.05 161:0.76 167:0.57 172:0.05 173:0.99 177:0.05 178:0.55 181:0.82 187:0.21 195:0.77 215:0.55 216:0.27 235:0.68 241:0.18 243:0.51 259:0.21 265:0.07 267:0.44 297:0.36 +0 8:0.66 12:0.92 17:1.00 21:0.78 27:0.54 28:0.93 34:0.92 36:0.59 37:0.33 43:0.21 66:0.36 69:0.93 91:0.05 98:0.05 108:0.85 123:0.84 127:0.05 129:0.05 135:0.54 145:0.39 146:0.05 147:0.05 149:0.08 154:0.14 159:0.05 161:0.76 167:0.59 172:0.05 173:0.71 177:0.05 178:0.55 181:0.82 187:0.21 202:0.59 216:0.31 235:0.31 241:0.27 243:0.51 259:0.21 265:0.05 267:0.98 +2 12:0.92 17:0.84 21:0.64 25:0.88 27:0.59 28:0.93 30:0.76 32:0.80 34:0.88 36:0.19 37:0.43 43:0.50 55:0.71 66:0.36 69:0.32 70:0.15 81:0.55 91:0.17 98:0.20 108:0.83 123:0.82 124:0.52 126:0.54 127:0.79 129:0.59 133:0.47 135:0.68 145:1.00 146:0.05 147:0.05 149:0.21 150:0.42 154:0.39 159:0.56 161:0.76 163:0.90 167:0.60 172:0.10 173:0.31 177:0.05 178:0.55 179:0.99 181:0.82 187:0.68 195:0.73 212:0.95 215:0.53 216:0.27 235:0.74 241:0.30 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.22 266:0.12 267:0.82 276:0.13 297:0.36 300:0.13 +1 12:0.92 17:0.85 21:0.78 27:0.52 28:0.93 30:0.76 34:0.18 36:0.59 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.44 98:0.15 108:0.85 123:0.84 124:0.52 127:0.44 129:0.59 133:0.47 135:0.78 146:0.05 147:0.05 149:0.20 154:0.25 159:0.56 161:0.76 163:0.97 167:0.58 172:0.10 173:0.60 177:0.05 178:0.55 179:0.99 181:0.82 187:0.39 202:0.46 212:0.95 216:0.13 235:0.12 241:0.21 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.17 266:0.12 267:0.28 276:0.13 300:0.13 +2 12:0.92 17:0.79 21:0.30 25:0.88 27:0.59 28:0.93 30:0.95 34:0.44 36:0.47 37:0.43 43:0.50 55:0.99 66:0.20 69:0.23 81:0.69 91:0.42 98:0.06 108:0.18 120:0.53 123:0.18 124:0.61 126:0.54 127:0.86 129:0.59 133:0.47 135:0.75 140:0.45 145:0.52 146:0.05 147:0.05 149:0.17 150:0.50 151:0.46 153:0.48 154:0.39 159:0.80 161:0.76 162:0.86 164:0.57 167:0.59 172:0.05 173:0.14 177:0.05 179:1.00 181:0.82 187:0.68 202:0.36 215:0.72 216:0.27 220:0.87 235:0.31 241:0.27 243:0.40 245:0.36 248:0.46 256:0.45 259:0.21 260:0.53 265:0.06 267:0.44 268:0.46 285:0.62 297:0.36 300:0.08 +2 8:0.60 12:0.92 17:0.70 21:0.78 27:0.55 28:0.93 30:0.62 34:0.90 36:0.65 37:0.29 43:0.44 55:0.71 66:0.30 69:0.45 70:0.34 91:0.48 98:0.26 108:0.35 123:0.34 124:0.71 127:0.78 129:0.81 133:0.67 135:0.78 146:0.41 147:0.48 149:0.33 154:0.33 159:0.71 161:0.76 163:0.94 167:0.58 172:0.16 173:0.33 177:0.05 178:0.55 179:0.95 181:0.82 187:0.21 202:0.36 212:0.30 216:0.23 235:0.12 241:0.21 242:0.82 243:0.48 245:0.43 247:0.12 259:0.21 265:0.28 266:0.27 267:0.84 276:0.23 300:0.25 +2 8:0.67 12:0.92 17:0.84 21:0.78 27:0.52 28:0.93 30:0.76 34:0.18 36:0.67 37:0.39 43:0.34 55:0.59 66:0.36 69:0.65 70:0.15 91:0.64 98:0.16 108:0.84 123:0.84 124:0.52 127:0.49 129:0.59 133:0.47 135:0.70 146:0.05 147:0.05 149:0.20 154:0.25 159:0.55 161:0.76 163:0.97 167:0.55 172:0.10 173:0.62 177:0.05 178:0.55 179:0.99 181:0.82 187:0.39 202:0.50 212:0.95 216:0.13 235:0.22 241:0.10 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.17 266:0.12 267:0.28 276:0.13 300:0.13 +2 8:0.63 12:0.92 17:0.78 21:0.78 27:0.55 28:0.93 30:0.62 34:0.90 36:0.53 37:0.29 43:0.44 55:0.71 66:0.30 69:0.45 70:0.34 91:0.53 98:0.31 108:0.35 123:0.34 124:0.71 127:0.67 129:0.81 133:0.67 135:0.71 146:0.41 147:0.48 149:0.35 154:0.33 159:0.57 161:0.76 163:0.94 167:0.58 172:0.16 173:0.41 177:0.05 178:0.55 179:0.95 181:0.82 187:0.21 212:0.30 216:0.23 235:0.12 241:0.21 242:0.82 243:0.48 245:0.43 247:0.12 259:0.21 265:0.34 266:0.27 267:0.84 276:0.26 300:0.25 +1 1:0.74 7:0.81 11:0.92 12:0.06 17:0.79 18:1.00 21:0.13 22:0.93 27:0.31 28:0.85 29:0.71 30:0.60 32:0.80 34:0.56 36:0.24 37:0.81 39:0.50 43:0.96 44:0.93 45:0.92 46:0.99 47:0.93 48:0.91 55:0.52 60:0.87 64:0.77 66:0.99 69:0.15 70:0.52 71:0.75 74:0.95 77:0.70 81:0.77 83:0.91 85:0.96 86:1.00 91:0.58 98:0.99 101:0.87 104:0.96 106:0.81 107:1.00 108:0.12 110:0.99 114:0.79 117:0.86 121:0.90 122:0.86 123:0.12 125:0.88 126:0.54 127:0.86 129:0.05 135:0.89 139:1.00 144:0.85 145:0.84 146:0.97 147:0.98 149:0.98 150:0.85 154:0.96 155:0.67 158:0.92 159:0.70 160:0.88 161:0.63 165:0.93 167:0.45 172:0.98 173:0.15 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 187:0.87 192:0.87 195:0.82 201:0.93 204:0.89 206:0.81 208:0.64 212:0.90 215:0.61 216:0.59 219:0.95 222:0.25 230:0.87 233:0.76 235:0.31 241:0.12 242:0.63 243:0.99 247:0.67 253:0.59 259:0.21 262:0.93 265:0.99 266:0.27 267:0.65 271:0.33 276:0.95 279:0.86 281:0.91 282:0.88 283:0.82 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.92 7:0.72 8:0.85 9:0.80 11:0.92 12:0.06 17:0.95 18:0.99 20:0.79 21:0.13 22:0.93 27:0.29 28:0.85 29:0.71 30:0.33 31:0.92 32:0.80 34:0.36 36:0.29 37:0.88 39:0.50 41:0.82 43:0.97 44:0.93 45:0.91 46:0.99 47:0.93 48:0.91 55:0.52 60:0.87 64:0.77 66:0.68 69:0.10 70:0.72 71:0.75 74:0.93 77:0.89 78:0.91 79:0.92 81:0.77 83:0.91 85:0.93 86:0.99 91:0.56 96:0.82 98:0.77 100:0.87 101:0.87 104:1.00 106:0.81 107:1.00 108:0.76 110:0.99 111:0.88 114:0.79 117:0.86 120:0.72 122:0.86 123:0.74 124:0.50 125:0.99 126:0.54 127:0.79 129:0.59 133:0.66 135:0.70 137:0.92 139:1.00 140:0.45 144:0.85 145:0.52 146:0.59 147:0.64 149:0.96 150:0.85 151:0.61 152:0.77 153:0.48 154:0.97 155:0.67 158:0.92 159:0.65 160:0.97 161:0.63 162:0.86 164:0.65 165:0.93 167:0.45 169:0.84 172:0.96 173:0.14 176:0.73 177:0.70 179:0.16 181:0.55 186:0.91 187:0.87 189:0.94 190:0.77 191:0.90 192:0.87 195:0.79 196:0.97 201:0.93 202:0.60 204:0.85 206:0.81 208:0.64 212:0.91 215:0.61 216:0.61 219:0.95 220:0.74 222:0.25 230:0.75 233:0.76 235:0.31 238:0.84 241:0.24 242:0.70 243:0.25 245:0.97 247:0.84 248:0.59 253:0.85 255:0.95 256:0.68 259:0.21 260:0.69 261:0.82 262:0.93 265:0.77 266:0.71 267:0.28 268:0.69 271:0.33 276:0.94 279:0.86 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.81 11:0.91 12:0.06 17:0.22 18:0.84 21:0.30 22:0.93 27:0.10 28:0.85 29:0.71 30:0.89 32:0.80 34:0.75 36:0.54 37:0.73 39:0.50 43:0.96 44:0.93 45:0.94 46:0.98 47:0.93 48:0.99 64:0.77 66:0.80 69:0.21 70:0.46 71:0.75 74:0.89 77:0.70 81:0.70 83:0.91 86:0.89 91:0.31 97:0.97 98:0.69 99:0.95 101:0.52 104:0.89 106:0.81 107:1.00 108:0.17 110:0.99 114:0.65 117:0.86 122:0.51 123:0.16 124:0.40 125:0.88 126:0.54 127:0.50 129:0.05 133:0.59 135:0.90 139:0.94 144:0.85 145:0.56 146:0.89 147:0.91 149:0.95 150:0.82 154:0.96 155:0.67 158:0.92 159:0.70 160:0.88 161:0.63 165:0.93 167:0.45 172:0.91 173:0.15 176:0.73 177:0.70 178:0.55 179:0.25 181:0.55 187:0.95 192:0.87 195:0.87 201:0.93 204:0.89 206:0.81 208:0.64 212:0.69 215:0.44 216:0.87 219:0.95 222:0.25 230:0.87 233:0.76 235:0.68 241:0.05 242:0.42 243:0.82 245:0.82 247:0.74 253:0.52 259:0.21 262:0.93 265:0.70 266:0.71 267:0.28 271:0.33 276:0.84 279:0.86 281:0.91 282:0.88 283:0.82 289:0.98 290:0.65 292:0.95 297:0.36 300:0.94 +2 1:0.74 6:0.95 7:0.63 8:0.88 9:0.80 11:0.92 12:0.06 17:0.62 18:0.99 20:0.78 21:0.13 27:0.34 28:0.85 29:0.71 30:0.72 31:0.89 32:0.80 34:0.73 36:0.18 37:0.71 39:0.50 43:0.94 44:0.93 45:0.92 46:0.99 48:0.91 60:0.87 64:0.77 66:0.31 69:0.12 70:0.62 71:0.75 74:0.95 77:0.70 78:0.91 79:0.88 81:0.76 83:0.91 85:0.98 86:1.00 91:0.62 96:0.85 97:0.99 98:0.57 99:0.83 100:0.89 101:0.87 104:0.93 106:0.81 107:1.00 108:0.56 110:0.99 111:0.90 114:0.79 117:0.86 120:0.76 122:0.57 123:0.54 124:0.84 125:1.00 126:0.54 127:0.86 129:0.93 133:0.84 135:0.49 137:0.89 139:1.00 140:0.80 144:0.85 145:0.49 146:0.31 147:0.37 149:0.99 150:0.83 151:0.61 152:0.76 153:0.77 154:0.94 155:0.67 158:0.92 159:0.78 160:0.97 161:0.63 162:0.86 164:0.66 165:0.70 167:0.45 169:0.87 172:0.90 173:0.11 176:0.73 177:0.70 179:0.16 181:0.55 186:0.92 187:0.68 189:0.96 190:0.77 191:0.70 192:0.87 195:0.88 196:0.94 201:0.93 202:0.54 206:0.81 208:0.64 212:0.85 215:0.61 216:0.55 219:0.95 222:0.25 230:0.64 233:0.76 235:0.31 238:0.87 241:0.03 242:0.24 243:0.19 245:0.97 247:0.82 248:0.59 253:0.88 255:0.95 256:0.58 259:0.21 260:0.73 261:0.82 265:0.58 266:0.84 267:0.36 268:0.63 271:0.33 276:0.94 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.66 8:0.94 9:0.80 11:0.93 12:0.06 17:0.59 18:0.99 20:0.76 21:0.30 22:0.93 27:0.26 28:0.85 29:0.71 30:0.84 31:0.94 32:0.80 34:0.55 36:0.28 37:0.86 39:0.50 43:0.95 44:0.93 45:0.99 46:1.00 47:0.93 48:0.97 64:0.77 66:0.23 69:0.21 70:0.45 71:0.75 74:0.96 77:0.89 78:0.85 79:0.93 81:0.66 83:0.91 86:0.99 91:0.48 96:0.80 97:0.97 98:0.60 99:0.95 100:0.88 101:0.87 104:0.96 106:0.81 107:1.00 108:0.85 110:0.99 111:0.84 114:0.65 117:0.86 120:0.69 122:0.51 123:0.16 124:0.80 125:0.99 126:0.54 127:0.91 129:0.81 133:0.78 135:0.90 137:0.94 139:0.99 140:0.45 144:0.85 145:0.74 146:0.83 147:0.86 149:0.99 150:0.80 151:0.86 152:0.88 153:0.48 154:0.95 155:0.67 158:0.92 159:0.76 160:0.96 161:0.63 162:0.86 164:0.57 165:0.93 167:0.45 169:0.95 172:0.91 173:0.15 176:0.73 177:0.70 179:0.14 181:0.55 186:0.88 187:0.87 192:0.87 195:0.88 196:0.98 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.89 215:0.44 216:0.57 219:0.95 222:0.25 230:0.69 233:0.73 235:0.60 241:0.15 242:0.36 243:0.43 245:0.98 247:0.86 248:0.77 253:0.87 255:0.95 256:0.45 260:0.70 261:0.95 262:0.93 265:0.47 266:0.84 267:0.69 268:0.46 271:0.33 276:0.95 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 300:0.84 +1 1:0.74 11:0.91 12:0.06 17:0.26 18:0.85 21:0.71 27:0.45 28:0.85 29:0.71 30:0.87 32:0.80 34:0.86 36:0.21 37:0.50 39:0.50 43:0.47 44:0.93 45:0.98 46:0.99 48:0.91 64:0.77 66:0.31 69:0.63 70:0.46 71:0.75 74:0.85 77:0.70 81:0.41 83:0.60 86:0.87 91:0.06 98:0.52 99:0.83 101:0.52 107:1.00 108:0.93 110:0.99 114:0.55 122:0.51 123:0.92 124:0.89 125:0.88 126:0.54 127:0.72 129:0.59 133:0.90 135:0.74 139:0.89 144:0.85 145:0.50 146:0.39 147:0.45 149:0.90 150:0.65 154:0.76 155:0.67 159:0.85 160:0.88 161:0.63 165:0.70 167:0.45 172:0.88 173:0.37 176:0.73 177:0.70 178:0.55 179:0.17 181:0.55 187:0.68 192:0.87 195:0.95 201:0.93 208:0.64 212:0.71 215:0.37 216:0.77 222:0.25 235:0.95 241:0.15 242:0.52 243:0.13 245:0.94 247:0.82 253:0.45 254:0.74 259:0.21 265:0.53 266:0.92 267:0.36 271:0.33 276:0.93 281:0.91 282:0.88 289:0.98 290:0.55 297:0.36 300:0.94 +2 1:0.74 6:0.95 7:0.72 8:0.94 9:0.80 11:0.92 12:0.06 17:0.51 18:0.99 20:0.89 21:0.30 22:0.93 27:0.26 28:0.85 29:0.71 30:0.53 31:0.98 32:0.80 34:0.68 36:0.25 37:0.86 39:0.50 41:0.94 43:0.96 44:0.93 45:0.91 46:0.98 47:0.93 48:0.97 55:0.45 60:1.00 64:0.77 66:0.43 69:0.19 70:0.73 71:0.75 74:0.90 77:0.70 78:0.97 79:0.98 81:0.63 83:0.91 85:0.90 86:0.99 91:0.60 96:0.93 97:0.89 98:0.61 100:0.97 101:0.87 104:0.96 106:0.81 107:1.00 108:0.15 110:0.99 111:0.97 114:0.65 117:0.86 120:0.87 122:0.86 123:0.15 124:0.85 125:0.99 126:0.54 127:0.90 129:0.81 133:0.86 135:0.91 137:0.98 139:0.99 140:0.45 144:0.85 145:0.72 146:0.83 147:0.86 149:0.94 150:0.78 151:0.95 152:0.88 153:0.83 154:0.96 155:0.67 158:0.92 159:0.75 160:0.99 161:0.63 162:0.81 164:0.80 165:0.93 167:0.45 169:0.95 172:0.89 173:0.15 176:0.73 177:0.70 179:0.20 181:0.55 186:0.96 187:0.87 189:0.97 191:0.82 192:0.87 195:0.87 196:0.99 201:0.93 202:0.71 204:0.85 206:0.81 208:0.64 212:0.83 215:0.44 216:0.57 219:0.95 222:0.25 230:0.75 233:0.76 235:0.52 238:0.95 241:0.18 242:0.63 243:0.57 245:0.93 247:0.79 248:0.92 253:0.95 255:0.95 256:0.78 260:0.88 261:0.95 262:0.93 265:0.62 266:0.75 267:0.59 268:0.77 271:0.33 276:0.91 279:0.86 281:0.91 282:0.88 283:0.82 284:0.97 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.81 8:0.85 9:0.80 11:0.92 12:0.06 17:0.89 18:0.89 20:0.85 21:0.22 22:0.93 27:0.42 28:0.85 29:0.71 30:0.86 31:0.88 32:0.80 34:0.96 36:0.27 37:0.77 39:0.50 41:0.82 43:0.95 44:0.93 45:0.95 46:1.00 47:0.93 48:0.98 60:0.87 64:0.77 66:0.96 69:0.19 70:0.45 71:0.75 74:0.89 77:0.89 78:0.79 79:0.88 81:0.70 83:0.91 85:0.80 86:0.95 91:0.46 96:0.84 97:0.94 98:0.96 100:0.82 101:0.87 104:0.97 106:0.81 107:1.00 108:0.15 110:0.99 111:0.79 114:0.71 117:0.86 120:0.74 122:0.51 123:0.15 125:0.99 126:0.54 127:0.71 129:0.05 135:0.73 137:0.88 139:0.96 140:0.80 144:0.85 145:0.54 146:0.91 147:0.93 149:0.93 150:0.82 151:0.56 152:0.80 153:0.72 154:0.95 155:0.67 158:0.91 159:0.52 160:0.97 161:0.63 162:0.86 164:0.68 165:0.93 167:0.44 169:0.80 172:0.89 173:0.24 176:0.73 177:0.70 179:0.33 181:0.55 186:0.80 187:0.68 189:0.83 190:0.77 191:0.70 192:0.87 195:0.70 196:0.92 201:0.93 202:0.55 206:0.81 208:0.64 212:0.72 215:0.51 216:0.51 219:0.95 222:0.25 232:0.98 235:0.42 238:0.87 241:0.01 242:0.50 243:0.96 247:0.67 248:0.55 253:0.85 255:0.95 256:0.58 259:0.21 260:0.70 261:0.80 262:0.93 265:0.96 266:0.38 267:0.19 268:0.63 271:0.33 276:0.81 279:0.86 281:0.91 282:0.88 283:0.82 284:0.90 285:0.62 289:0.98 290:0.71 292:0.91 297:0.36 299:0.97 300:0.70 +2 1:0.74 7:0.81 11:0.92 12:0.06 17:0.67 18:0.92 21:0.13 22:0.93 27:0.36 28:0.85 29:0.71 30:0.75 32:0.80 34:0.79 36:0.28 37:0.79 39:0.50 43:0.97 44:0.93 45:0.94 46:0.98 47:0.93 48:0.91 60:0.95 64:0.77 66:0.23 69:0.10 70:0.71 71:0.75 74:0.92 77:0.70 81:0.77 83:0.91 85:0.90 86:0.98 91:0.11 98:0.33 101:0.87 104:0.93 106:0.81 107:1.00 108:0.78 110:0.99 114:0.79 117:0.86 121:0.90 122:0.51 123:0.76 124:0.90 125:0.88 126:0.54 127:0.95 129:0.81 133:0.90 135:0.99 139:0.99 144:0.85 145:0.44 146:0.52 147:0.58 149:0.95 150:0.85 154:0.97 155:0.67 158:0.92 159:0.85 160:0.88 161:0.63 165:0.93 167:0.44 172:0.73 173:0.09 176:0.73 177:0.70 178:0.55 179:0.28 181:0.55 187:0.87 191:0.78 192:0.87 195:0.94 201:0.93 204:0.89 206:0.81 208:0.64 212:0.72 215:0.61 216:0.62 219:0.95 222:0.25 230:0.87 233:0.76 235:0.31 241:0.01 242:0.37 243:0.36 245:0.88 247:0.76 253:0.58 259:0.21 262:0.93 265:0.35 266:0.89 267:0.23 271:0.33 276:0.85 279:0.86 281:0.91 282:0.88 283:0.82 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.84 7:0.81 8:0.69 9:0.80 11:0.91 12:0.06 17:0.15 18:0.85 20:0.84 21:0.40 22:0.93 27:0.21 28:0.85 29:0.71 30:0.21 31:0.87 32:0.80 34:0.66 36:0.23 37:0.71 39:0.50 41:0.88 43:0.56 44:0.93 45:0.81 46:0.95 47:0.93 48:0.97 64:0.77 66:0.16 69:0.95 70:0.86 71:0.75 74:0.74 77:0.89 78:0.95 79:0.87 81:0.57 83:0.91 86:0.81 91:0.42 96:0.70 97:0.94 98:0.35 100:0.95 101:0.52 104:0.91 106:0.81 107:0.99 108:0.91 110:0.96 111:0.94 114:0.62 117:0.86 120:0.56 121:0.97 122:0.86 123:0.48 124:0.73 125:0.98 126:0.54 127:0.64 129:0.05 133:0.60 135:0.84 137:0.87 139:0.92 140:0.45 144:0.85 145:0.65 146:0.41 147:0.66 149:0.42 150:0.76 151:0.52 152:0.92 153:0.48 154:0.30 155:0.67 158:0.91 159:0.75 160:0.98 161:0.63 162:0.71 164:0.72 165:0.93 167:0.46 169:0.87 172:0.41 173:0.98 176:0.73 177:0.05 179:0.69 181:0.55 186:0.94 187:0.87 189:0.86 191:0.88 192:0.87 195:0.89 196:0.91 201:0.93 202:0.63 204:0.89 206:0.81 208:0.64 212:0.23 215:0.42 216:0.79 219:0.95 220:0.91 222:0.25 230:0.87 233:0.76 235:0.60 238:0.90 241:0.32 242:0.37 243:0.37 245:0.68 247:0.67 248:0.52 253:0.51 254:0.74 255:0.95 256:0.64 257:0.84 259:0.21 260:0.57 261:0.90 262:0.93 265:0.25 266:0.75 267:0.19 268:0.66 271:0.33 276:0.38 277:0.87 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 289:0.98 290:0.62 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.94 7:0.81 8:0.90 9:0.80 11:0.92 12:0.06 17:0.70 18:1.00 20:0.85 21:0.13 22:0.93 27:0.31 28:0.85 29:0.71 30:0.52 31:0.95 32:0.80 34:0.49 36:0.30 37:0.81 39:0.50 41:0.82 43:0.96 44:0.93 45:0.93 46:0.99 47:0.93 48:0.91 55:0.52 60:0.97 64:0.77 66:0.61 69:0.15 70:0.57 71:0.75 74:0.95 77:0.70 78:0.92 79:0.95 81:0.77 83:0.91 85:0.96 86:1.00 91:0.68 96:0.85 98:0.80 100:0.90 101:0.87 104:0.97 106:0.81 107:1.00 108:0.12 110:0.99 111:0.91 114:0.79 117:0.86 120:0.75 121:0.90 122:0.86 123:0.12 124:0.63 125:0.99 126:0.54 127:0.88 129:0.59 133:0.66 135:0.91 137:0.95 139:1.00 140:0.45 144:0.85 145:0.80 146:0.93 147:0.95 149:0.98 150:0.85 151:0.93 152:0.85 153:0.78 154:0.96 155:0.67 158:0.92 159:0.75 160:0.96 161:0.63 162:0.57 164:0.65 165:0.93 167:0.45 169:0.85 172:0.97 173:0.13 176:0.73 177:0.70 179:0.14 181:0.55 186:0.92 187:0.87 189:0.95 191:0.73 192:0.87 195:0.85 196:0.98 201:0.93 202:0.59 204:0.89 206:0.81 208:0.64 212:0.89 215:0.61 216:0.59 219:0.95 222:0.25 230:0.87 233:0.76 235:0.31 238:0.86 241:0.24 242:0.62 243:0.67 245:0.98 247:0.82 248:0.83 253:0.90 255:0.95 256:0.45 259:0.21 260:0.76 261:0.88 262:0.93 265:0.80 266:0.71 267:0.59 268:0.58 271:0.33 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.91 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +0 1:0.74 11:0.91 12:0.06 17:0.34 18:0.87 21:0.77 27:0.45 28:0.85 29:0.71 30:0.61 34:0.90 36:0.18 37:0.50 39:0.50 43:0.47 45:0.93 46:0.98 48:0.91 64:0.77 66:0.15 69:0.63 70:0.87 71:0.75 74:0.77 81:0.40 83:0.60 86:0.90 91:0.03 97:0.89 98:0.42 99:0.83 101:0.52 107:1.00 108:0.88 110:0.99 114:0.53 122:0.51 123:0.88 124:0.86 125:0.88 126:0.54 127:0.51 129:0.59 133:0.84 135:0.78 139:0.91 144:0.85 145:0.42 146:0.55 147:0.61 149:0.74 150:0.65 154:0.76 155:0.67 158:0.91 159:0.73 160:0.88 161:0.63 165:0.70 167:0.45 172:0.51 173:0.47 176:0.73 177:0.70 178:0.55 179:0.34 181:0.55 187:0.68 192:0.87 201:0.93 208:0.64 212:0.48 215:0.36 216:0.77 219:0.95 222:0.25 235:0.99 241:0.12 242:0.39 243:0.28 245:0.84 247:0.76 253:0.43 254:0.74 259:0.21 265:0.44 266:0.80 267:0.19 271:0.33 276:0.77 279:0.86 281:0.91 283:0.77 289:0.98 290:0.53 292:0.91 297:0.36 300:0.84 +2 1:0.74 6:0.85 7:0.81 8:0.74 9:0.80 11:0.92 12:0.06 17:0.77 18:0.98 20:0.84 21:0.30 27:0.31 28:0.85 29:0.71 30:0.71 31:0.87 32:0.80 34:0.55 36:0.38 37:0.84 39:0.50 41:0.82 43:0.96 44:0.93 45:0.98 46:1.00 48:0.91 60:0.87 64:0.77 66:0.60 69:0.19 70:0.67 71:0.75 74:0.96 77:0.70 78:0.94 79:0.87 81:0.63 83:0.91 85:0.91 86:0.99 91:0.54 96:0.88 97:0.99 98:0.83 100:0.90 101:0.87 104:0.95 106:0.81 107:1.00 108:0.79 110:0.99 111:0.92 114:0.65 117:0.86 120:0.80 121:0.90 122:0.86 123:0.77 124:0.64 125:0.99 126:0.54 127:0.85 129:0.59 133:0.66 135:0.93 137:0.87 139:0.99 140:0.80 144:0.85 145:0.65 146:0.68 147:0.73 149:0.98 150:0.78 151:0.52 152:0.82 153:0.72 154:0.96 155:0.67 158:0.92 159:0.79 160:0.98 161:0.63 162:0.81 164:0.70 165:0.93 167:0.45 169:0.85 172:0.97 173:0.13 176:0.73 177:0.70 179:0.14 181:0.55 186:0.94 187:0.68 189:0.87 190:0.77 191:0.75 192:0.87 195:0.89 196:0.91 201:0.93 202:0.61 204:0.89 206:0.81 208:0.64 212:0.86 215:0.44 216:0.28 219:0.95 222:0.25 230:0.87 232:0.96 233:0.76 235:0.52 238:0.85 241:0.15 242:0.27 243:0.23 245:0.98 247:0.79 248:0.51 253:0.90 255:0.95 256:0.64 259:0.21 260:0.77 261:0.88 265:0.83 266:0.63 267:0.36 268:0.66 271:0.33 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.88 7:0.81 8:0.92 9:0.80 11:0.92 12:0.06 17:0.64 18:0.92 20:0.77 21:0.13 27:0.18 28:0.85 29:0.71 30:0.62 31:0.89 32:0.80 34:0.39 36:0.20 37:0.69 39:0.50 41:0.82 43:0.97 44:0.93 45:0.92 46:0.98 48:0.99 64:0.77 66:0.26 69:0.10 70:0.74 71:0.75 74:0.85 77:0.70 78:0.95 79:0.88 81:0.77 83:0.91 85:0.80 86:0.95 91:0.42 96:0.72 98:0.47 100:0.90 101:0.87 107:1.00 108:0.80 110:0.99 111:0.93 114:0.79 120:0.59 121:0.90 122:0.51 123:0.78 124:0.84 125:0.99 126:0.54 127:0.84 129:0.05 133:0.81 135:0.99 137:0.89 139:0.96 140:0.45 144:0.85 145:0.77 146:0.63 147:0.68 149:0.90 150:0.85 151:0.61 152:0.82 153:0.48 154:0.97 155:0.67 159:0.74 160:0.96 161:0.63 162:0.86 164:0.57 165:0.93 167:0.45 169:0.82 172:0.67 173:0.11 176:0.73 177:0.70 179:0.36 181:0.55 186:0.96 187:0.39 189:0.97 192:0.87 195:0.86 196:0.93 201:0.93 202:0.36 204:0.89 208:0.64 212:0.51 215:0.61 216:0.47 220:0.74 222:0.25 230:0.87 233:0.76 235:0.31 238:0.86 241:0.27 242:0.33 243:0.40 245:0.85 247:0.79 248:0.59 253:0.68 255:0.95 256:0.45 259:0.21 260:0.60 261:0.84 265:0.48 266:0.91 267:0.44 268:0.46 271:0.33 276:0.79 277:0.69 281:0.91 282:0.88 284:0.89 285:0.62 289:0.98 290:0.79 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.88 7:0.81 8:0.66 9:0.76 11:0.92 12:0.06 17:0.61 18:0.92 20:0.87 21:0.22 27:0.18 28:0.85 29:0.71 30:0.76 32:0.80 34:0.24 36:0.30 37:0.69 39:0.50 43:0.97 44:0.93 45:0.95 46:0.99 48:0.99 64:0.77 66:0.69 69:0.12 70:0.57 71:0.75 74:0.90 77:0.70 78:0.81 81:0.70 83:0.91 85:0.80 86:0.95 91:0.31 96:0.80 98:0.75 100:0.83 101:0.87 107:1.00 108:0.10 110:0.99 111:0.80 114:0.71 120:0.69 121:0.90 122:0.51 123:0.10 124:0.48 125:1.00 126:0.54 127:0.86 129:0.59 133:0.61 135:0.99 139:0.96 140:0.45 144:0.85 145:0.80 146:0.88 147:0.90 149:0.95 150:0.82 151:0.81 152:0.82 153:0.48 154:0.97 155:0.67 159:0.68 160:0.96 161:0.63 162:0.86 164:0.54 165:0.93 167:0.45 169:0.82 172:0.89 173:0.14 176:0.73 177:0.70 179:0.30 181:0.55 186:0.81 187:0.39 189:0.84 190:0.77 192:0.87 195:0.81 201:0.93 202:0.36 204:0.89 208:0.64 212:0.60 215:0.51 216:0.47 222:0.25 230:0.87 233:0.76 235:0.42 238:0.87 241:0.30 242:0.50 243:0.73 245:0.88 247:0.67 248:0.73 253:0.83 255:0.74 256:0.45 259:0.21 260:0.68 261:0.84 265:0.76 266:0.63 267:0.44 268:0.46 271:0.33 276:0.84 281:0.91 282:0.88 285:0.56 289:0.98 290:0.71 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.96 7:0.72 8:0.61 9:0.80 11:0.92 12:0.06 17:0.47 18:0.99 20:0.80 21:0.30 22:0.93 27:0.26 28:0.85 29:0.71 30:0.54 31:0.94 32:0.80 34:0.73 36:0.21 37:0.86 39:0.50 41:0.82 43:0.96 44:0.93 45:0.95 46:1.00 47:0.93 48:0.97 55:0.45 60:0.87 64:0.77 66:0.99 69:0.19 70:0.57 71:0.75 74:0.93 77:0.70 78:0.82 79:0.93 81:0.63 83:0.91 85:0.90 86:0.99 91:0.58 96:0.80 98:0.99 100:0.87 101:0.87 104:0.96 106:0.81 107:1.00 108:0.15 110:0.99 111:0.81 114:0.65 117:0.86 120:0.69 122:0.86 123:0.15 125:1.00 126:0.54 127:0.88 129:0.05 135:0.90 137:0.94 139:0.99 140:0.45 144:0.85 145:0.74 146:0.95 147:0.96 149:0.96 150:0.78 151:0.87 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.62 160:0.96 161:0.63 162:0.86 164:0.57 165:0.93 167:0.45 169:0.98 172:0.97 173:0.20 176:0.73 177:0.70 179:0.18 181:0.55 186:0.73 187:0.87 192:0.87 195:0.77 196:0.98 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.88 215:0.44 216:0.57 219:0.95 222:0.25 230:0.75 233:0.76 235:0.52 241:0.21 242:0.57 243:0.99 247:0.67 248:0.77 253:0.86 255:0.95 256:0.45 260:0.70 261:0.95 262:0.93 265:0.99 266:0.38 267:0.59 268:0.46 271:0.33 276:0.93 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.79 7:0.72 8:0.58 9:0.80 11:0.91 12:0.06 17:0.09 18:0.88 20:0.90 21:0.30 22:0.93 27:0.11 28:0.85 29:0.71 30:0.76 31:0.87 32:0.80 34:0.66 36:0.57 37:0.82 39:0.50 41:0.82 43:0.60 44:0.93 45:0.95 46:0.99 47:0.93 48:0.91 64:0.77 66:0.96 69:0.56 70:0.53 71:0.75 74:0.86 77:0.70 78:0.86 79:0.87 81:0.67 83:0.91 86:0.88 91:0.56 96:0.70 97:0.94 98:0.93 100:0.86 101:0.52 104:0.92 106:0.81 107:1.00 108:0.43 110:0.99 111:0.84 114:0.65 117:0.86 120:0.56 122:0.51 123:0.42 125:0.97 126:0.54 127:0.56 129:0.05 135:0.86 137:0.87 139:0.91 140:0.45 144:0.85 145:0.74 146:0.95 147:0.96 149:0.86 150:0.80 151:0.53 152:0.85 153:0.72 154:0.70 155:0.67 158:0.79 159:0.62 160:0.96 161:0.63 162:0.76 164:0.64 165:0.93 167:0.45 169:0.83 172:0.90 173:0.48 176:0.73 177:0.70 179:0.30 181:0.55 186:0.86 187:0.95 189:0.81 192:0.87 195:0.80 196:0.90 201:0.93 202:0.58 204:0.85 206:0.81 208:0.64 212:0.60 215:0.44 216:0.74 219:0.89 220:0.87 222:0.25 230:0.75 233:0.76 235:0.60 238:0.87 241:0.30 242:0.45 243:0.96 247:0.67 248:0.52 253:0.54 254:0.74 255:0.95 256:0.58 259:0.21 260:0.56 261:0.87 262:0.93 265:0.93 266:0.63 267:0.23 268:0.62 271:0.33 276:0.82 279:0.82 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 289:0.98 290:0.65 292:0.95 297:0.36 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.86 9:0.80 11:0.92 12:0.06 17:0.66 18:0.98 20:0.94 21:0.13 22:0.93 27:0.19 28:0.85 29:0.71 30:0.84 31:0.98 32:0.80 34:0.69 36:0.25 37:0.93 39:0.50 41:0.96 43:0.97 44:0.93 45:0.99 46:0.99 47:0.93 48:0.98 60:1.00 64:0.77 66:0.27 69:0.10 70:0.50 71:0.75 74:0.95 77:0.89 78:0.98 79:0.98 81:0.77 83:0.91 85:0.91 86:0.99 91:0.66 96:0.94 97:0.89 98:0.36 100:0.98 101:0.87 104:0.82 106:0.81 107:1.00 108:0.83 110:0.99 111:0.98 114:0.79 117:0.86 120:0.89 121:0.97 122:0.86 123:0.83 124:0.87 125:0.99 126:0.54 127:0.93 129:0.89 133:0.87 135:0.89 137:0.98 139:0.99 140:0.45 144:0.85 145:0.80 146:0.42 147:0.49 149:0.99 150:0.85 151:0.96 152:0.94 153:0.86 154:0.97 155:0.67 158:0.92 159:0.88 160:0.99 161:0.63 162:0.82 164:0.86 165:0.93 167:0.44 169:0.97 172:0.94 173:0.08 176:0.73 177:0.70 179:0.13 181:0.55 186:0.98 187:0.21 189:0.94 191:0.91 192:0.87 195:0.96 196:0.99 201:0.93 202:0.77 204:0.89 206:0.81 208:0.64 212:0.90 215:0.61 216:0.61 219:0.95 220:0.74 222:0.25 230:0.87 233:0.76 235:0.31 238:0.95 241:0.02 242:0.39 243:0.12 245:0.98 247:0.82 248:0.92 253:0.97 255:0.95 256:0.83 259:0.21 260:0.89 261:0.97 262:0.93 265:0.38 266:0.87 267:0.79 268:0.83 271:0.33 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 289:0.98 290:0.79 292:0.95 297:0.36 299:0.97 300:0.98 +1 10:0.99 17:0.88 21:0.13 27:0.39 29:0.71 30:0.44 34:0.19 36:0.17 37:0.37 39:0.80 43:0.19 48:0.91 55:0.42 66:0.93 69:0.15 70:0.53 71:0.83 74:0.28 75:0.96 81:0.42 83:0.56 91:0.31 98:0.91 102:0.95 108:0.12 122:0.95 123:0.12 126:0.24 127:0.50 129:0.05 131:0.61 135:0.56 139:0.67 145:0.52 146:0.80 147:0.83 149:0.42 150:0.46 154:0.21 155:0.49 157:0.61 159:0.36 166:0.61 170:0.87 172:0.82 173:0.29 174:0.98 175:0.75 177:0.70 178:0.55 179:0.42 182:0.61 187:0.39 192:0.45 193:0.98 195:0.61 197:0.99 204:0.79 208:0.50 212:0.83 216:0.28 222:0.35 226:0.96 227:0.61 229:0.61 231:0.62 235:0.12 236:0.92 239:0.99 241:0.46 242:0.79 243:0.93 244:0.83 246:0.61 247:0.55 253:0.25 254:0.95 257:0.65 259:0.21 265:0.91 266:0.38 267:0.44 271:0.73 276:0.71 277:0.69 281:0.91 287:0.61 300:0.43 +0 8:0.68 10:0.98 11:0.46 17:0.62 18:0.63 21:0.78 23:0.98 27:0.63 29:0.71 30:0.27 34:0.92 36:0.48 37:0.26 39:0.80 43:0.57 45:0.66 55:0.52 62:0.98 66:0.84 69:0.12 70:0.72 71:0.83 74:0.29 86:0.66 91:0.69 98:0.76 101:0.47 108:0.10 122:0.95 123:0.10 124:0.37 127:0.47 128:0.96 129:0.05 131:0.61 133:0.36 135:0.82 139:0.65 140:0.92 146:0.83 147:0.86 149:0.41 151:0.53 153:0.46 154:0.45 157:0.61 159:0.52 162:0.50 166:0.61 168:0.96 170:0.87 172:0.74 173:0.17 174:0.96 177:0.88 178:0.51 179:0.52 182:0.61 187:0.39 190:0.95 197:0.98 202:0.79 205:0.98 212:0.73 216:0.44 220:0.87 222:0.35 227:0.61 229:0.61 231:0.63 235:0.12 239:0.97 241:0.37 242:0.72 243:0.85 244:0.83 245:0.58 246:0.61 247:0.76 248:0.57 253:0.26 256:0.73 259:0.21 265:0.76 266:0.47 267:0.82 268:0.72 271:0.73 276:0.61 285:0.46 287:0.61 300:0.55 +0 10:0.84 17:0.45 18:0.64 21:0.78 27:0.34 29:0.71 30:0.76 34:0.52 36:0.41 37:0.46 39:0.80 43:0.12 55:0.71 66:0.36 69:0.76 70:0.15 71:0.83 86:0.64 91:0.09 98:0.15 108:0.60 122:0.93 123:0.57 124:0.52 127:0.18 129:0.05 131:0.61 133:0.39 135:0.92 139:0.63 145:0.65 146:0.28 147:0.34 149:0.10 154:0.09 157:0.61 159:0.49 163:0.98 166:0.61 170:0.87 172:0.10 173:0.60 174:0.79 175:0.91 177:0.38 178:0.55 179:0.93 182:0.61 187:0.87 197:0.83 212:0.95 216:0.53 222:0.35 227:0.61 229:0.61 231:0.61 235:0.31 239:0.83 241:0.24 242:0.82 243:0.51 244:0.93 245:0.37 246:0.61 247:0.12 253:0.20 257:0.84 259:0.21 265:0.16 266:0.12 267:0.19 271:0.73 276:0.14 277:0.87 287:0.61 300:0.13 +0 8:0.65 10:0.98 17:0.69 18:0.68 21:0.78 23:0.98 27:0.49 29:0.71 30:0.33 34:0.47 36:0.88 37:0.35 39:0.80 43:0.19 55:0.92 62:0.99 66:0.05 69:0.35 70:0.70 71:0.83 74:0.27 75:0.96 86:0.67 91:0.17 98:0.19 108:0.99 120:0.78 122:0.95 123:0.99 124:1.00 127:0.98 128:0.99 129:0.93 131:0.80 133:1.00 135:0.18 139:0.65 140:0.80 146:0.85 147:0.88 149:0.43 151:0.70 153:0.72 154:0.08 157:0.61 159:0.99 162:0.51 163:0.94 164:0.66 166:0.61 168:0.99 170:0.87 172:0.57 173:0.06 174:1.00 175:0.75 177:0.70 178:0.42 179:0.12 182:0.61 187:0.68 190:0.95 191:0.70 197:0.99 202:0.82 205:0.98 212:0.15 216:0.56 220:0.74 222:0.35 226:0.96 227:0.82 229:0.61 231:0.62 235:0.05 239:0.98 241:0.46 242:0.62 243:0.11 244:0.83 245:0.89 246:0.61 247:0.95 248:0.79 253:0.25 254:0.74 256:0.73 257:0.65 259:0.21 260:0.78 265:0.21 266:0.98 267:0.84 268:0.76 271:0.73 276:0.97 277:0.87 285:0.50 287:0.61 300:0.84 +1 10:0.85 11:0.50 17:0.64 18:0.78 21:0.13 27:0.24 29:0.71 30:0.90 34:0.75 36:0.20 37:0.65 39:0.80 43:0.58 45:0.77 55:0.45 64:0.77 66:0.96 69:0.17 70:0.36 71:0.83 74:0.44 81:0.36 86:0.76 91:0.22 98:0.94 101:0.47 106:0.81 108:0.14 123:0.13 126:0.32 127:0.60 129:0.05 131:0.61 135:0.87 139:0.72 144:0.57 146:0.82 147:0.85 149:0.67 150:0.37 154:0.46 157:0.91 159:0.38 166:0.72 170:0.87 172:0.91 173:0.31 174:0.79 177:0.88 178:0.55 179:0.28 182:0.80 187:0.39 192:0.46 197:0.82 201:0.58 206:0.81 212:0.90 215:0.84 216:0.76 222:0.35 227:0.61 229:0.61 231:0.65 235:0.22 239:0.84 241:0.18 242:0.77 243:0.97 244:0.83 246:0.61 247:0.84 253:0.37 259:0.21 265:0.94 266:0.38 267:0.52 271:0.73 276:0.83 287:0.61 297:0.36 300:0.55 +0 8:0.73 10:0.87 17:0.08 18:0.63 21:0.13 27:0.11 29:0.71 30:0.91 31:0.90 34:0.30 36:0.91 37:0.84 39:0.80 43:0.07 44:0.88 48:0.91 55:0.92 64:0.77 66:0.43 69:0.96 70:0.22 71:0.83 74:0.26 79:0.90 81:0.30 86:0.67 91:0.18 98:0.62 102:0.96 108:0.86 123:0.85 124:0.76 126:0.24 127:0.31 129:0.05 131:0.61 133:0.74 135:0.96 137:0.90 139:0.72 140:0.80 145:0.65 146:0.63 147:0.27 149:0.12 150:0.34 151:0.57 153:0.48 154:0.17 157:0.61 159:0.70 162:0.62 163:0.94 166:0.61 170:0.87 172:0.48 173:0.97 174:0.88 175:0.75 177:0.38 178:0.42 179:0.53 182:0.61 187:0.95 190:0.95 193:0.97 195:0.92 196:0.90 197:0.85 202:0.50 212:0.61 216:0.68 220:0.74 222:0.35 227:0.61 229:0.61 231:0.63 235:0.12 239:0.90 241:0.12 242:0.33 243:0.25 244:0.83 245:0.63 246:0.61 247:0.76 248:0.56 253:0.24 254:0.80 256:0.45 257:0.84 259:0.21 265:0.63 266:0.54 267:0.18 268:0.46 271:0.73 276:0.57 277:0.87 281:0.86 284:0.88 285:0.46 287:0.61 300:0.25 +0 8:0.79 10:0.97 11:0.46 17:0.72 18:0.57 21:0.30 23:0.99 27:0.53 29:0.71 30:0.08 33:0.97 34:0.78 36:0.49 37:0.49 39:0.80 43:0.20 45:0.66 55:0.96 62:0.99 66:0.12 69:0.17 70:1.00 71:0.83 74:0.26 75:0.96 81:0.38 86:0.59 91:0.02 98:0.14 101:0.47 108:0.14 122:0.95 123:0.13 124:0.99 126:0.24 127:0.99 128:0.98 129:0.96 131:0.61 133:0.99 135:0.19 139:0.57 140:0.80 146:0.72 147:0.76 149:0.38 150:0.42 151:0.63 153:0.72 154:0.09 157:0.61 159:0.99 162:0.54 166:0.61 168:0.98 170:0.87 172:0.61 173:0.05 174:0.99 175:0.91 177:0.38 178:0.42 179:0.28 182:0.61 187:0.39 190:0.95 191:0.70 193:0.96 197:0.98 202:0.78 205:0.99 212:0.17 216:0.87 220:0.74 222:0.35 226:0.96 227:0.61 229:0.61 231:0.61 235:0.31 236:0.92 239:0.97 241:0.32 242:0.61 243:0.33 244:0.83 245:0.71 246:0.61 247:0.91 248:0.68 253:0.24 256:0.75 257:0.84 259:0.21 265:0.14 266:1.00 267:0.96 268:0.75 271:0.73 276:0.86 277:0.87 285:0.46 287:0.61 291:0.97 300:1.00 +0 10:0.87 17:0.12 18:0.59 21:0.78 27:0.45 29:0.71 30:0.11 34:0.79 36:0.31 37:0.50 39:0.80 43:0.09 55:0.52 66:0.47 69:0.93 70:0.54 71:0.83 74:0.26 86:0.63 91:0.15 98:0.21 108:0.85 122:0.80 123:0.84 124:0.65 127:0.19 129:0.05 131:0.61 133:0.59 135:0.89 139:0.61 145:0.40 146:0.38 147:0.05 149:0.08 154:0.16 157:0.61 159:0.47 163:0.75 166:0.61 170:0.87 172:0.21 173:0.90 174:0.86 175:0.75 177:0.05 178:0.55 179:0.75 182:0.61 187:0.68 197:0.85 212:0.30 216:0.77 222:0.35 227:0.61 229:0.61 231:0.61 235:0.42 239:0.85 241:0.30 242:0.33 243:0.23 244:0.61 245:0.42 246:0.61 247:0.39 253:0.24 254:0.99 257:0.93 259:0.21 265:0.23 266:0.27 267:0.73 271:0.73 276:0.25 277:0.69 287:0.61 300:0.33 +0 8:0.64 10:0.99 17:0.24 21:0.30 23:1.00 27:0.21 29:0.71 30:0.33 33:0.97 34:0.53 36:0.79 37:0.74 39:0.80 43:0.20 55:0.59 62:1.00 66:0.26 69:0.94 70:0.73 71:0.83 74:0.26 75:0.96 81:0.38 91:0.86 98:0.66 108:0.87 122:0.95 123:0.66 124:0.77 126:0.24 127:0.96 128:1.00 129:0.05 131:0.61 133:0.74 135:0.17 140:0.45 146:0.80 147:0.63 149:0.46 150:0.42 151:0.84 153:0.98 154:0.13 157:0.61 159:0.80 162:0.69 166:0.61 168:1.00 170:0.87 172:0.76 173:0.93 174:0.99 175:0.91 177:0.05 179:0.31 182:0.61 187:0.39 190:0.95 191:0.78 193:0.97 197:0.99 202:0.90 205:1.00 212:0.72 216:0.95 222:0.35 226:0.98 227:0.61 229:0.61 231:0.61 235:0.31 236:0.92 239:0.99 241:0.52 242:0.81 243:0.25 244:0.83 245:0.90 246:0.61 247:0.47 248:0.96 253:0.24 254:0.92 256:0.92 257:0.93 259:0.21 265:0.53 266:0.87 267:0.85 268:0.92 271:0.73 276:0.84 277:0.95 285:0.46 287:0.61 291:0.97 300:0.70 +0 10:0.85 17:0.36 18:0.68 21:0.78 27:0.45 29:0.71 30:0.91 34:0.88 36:0.20 37:0.50 39:0.80 43:0.11 55:0.71 66:0.69 69:0.79 70:0.13 71:0.83 74:0.27 86:0.69 91:0.12 98:0.32 108:0.62 122:0.93 123:0.60 127:0.12 129:0.05 131:0.61 135:0.54 139:0.66 145:0.50 146:0.40 147:0.47 149:0.08 154:0.18 157:0.61 159:0.15 163:0.81 166:0.61 170:0.87 172:0.21 173:0.80 174:0.79 175:0.75 177:0.70 178:0.55 179:0.50 182:0.61 187:0.68 197:0.81 212:0.61 216:0.77 222:0.35 227:0.61 229:0.61 231:0.61 235:0.84 239:0.83 241:0.10 242:0.63 243:0.73 244:0.83 246:0.61 247:0.30 253:0.25 254:0.98 257:0.65 259:0.21 265:0.35 266:0.17 267:0.36 271:0.73 276:0.23 277:0.69 283:0.67 287:0.61 300:0.13 +0 10:0.99 17:0.76 18:0.63 21:0.13 23:0.95 27:0.38 29:0.71 30:0.21 33:0.97 34:0.50 36:0.63 37:0.58 39:0.80 43:0.16 55:0.45 62:0.98 66:0.94 69:0.54 70:0.68 71:0.83 75:0.96 81:0.42 86:0.64 91:0.70 98:0.94 102:0.95 108:0.42 122:0.95 123:0.40 126:0.24 127:0.62 128:0.97 129:0.05 131:0.61 135:0.79 139:0.63 140:0.45 145:0.79 146:0.89 147:0.91 149:0.39 150:0.46 151:0.61 153:0.48 154:0.47 157:0.61 159:0.48 162:0.86 166:0.61 168:0.97 170:0.87 172:0.83 173:0.56 174:0.98 175:0.75 177:0.70 179:0.43 182:0.61 187:0.68 190:0.95 193:0.97 195:0.68 197:0.99 202:0.36 205:0.95 212:0.71 216:0.60 220:0.62 222:0.35 226:0.98 227:0.61 229:0.61 231:0.61 235:0.12 236:0.92 239:0.99 241:0.24 242:0.78 243:0.94 244:0.83 246:0.61 247:0.60 248:0.59 253:0.20 254:0.86 256:0.45 257:0.65 259:0.21 265:0.94 266:0.54 267:0.85 268:0.46 271:0.73 276:0.73 277:0.69 285:0.46 287:0.61 291:0.97 300:0.55 +1 10:0.85 11:0.50 17:0.61 18:0.79 21:0.13 27:0.24 29:0.71 30:0.94 34:0.61 36:0.22 37:0.65 39:0.80 43:0.60 45:0.73 55:0.45 64:0.77 66:0.96 69:0.17 70:0.27 71:0.83 74:0.41 81:0.36 86:0.77 91:0.27 98:0.95 101:0.47 106:0.81 108:0.14 123:0.13 126:0.32 127:0.67 129:0.05 131:0.61 135:0.71 139:0.72 144:0.57 146:0.79 147:0.82 149:0.65 150:0.37 154:0.49 157:0.87 159:0.35 166:0.72 170:0.87 172:0.90 173:0.34 174:0.79 177:0.88 178:0.55 179:0.31 182:0.77 187:0.39 192:0.46 197:0.82 201:0.58 206:0.81 212:0.92 215:0.84 216:0.76 222:0.35 227:0.61 229:0.61 231:0.65 235:0.22 239:0.84 241:0.18 242:0.78 243:0.96 244:0.83 246:0.61 247:0.82 253:0.36 259:0.21 265:0.95 266:0.27 267:0.65 271:0.73 276:0.82 287:0.61 297:0.36 300:0.55 +0 8:0.78 10:0.85 17:0.32 18:0.57 21:0.78 27:0.21 29:0.71 30:0.13 34:0.49 36:0.77 37:0.74 39:0.80 43:0.19 55:0.92 66:0.10 69:0.40 70:0.97 71:0.83 74:0.30 86:0.59 91:0.76 96:0.74 98:0.20 108:0.31 120:0.89 122:0.41 123:0.30 124:0.95 127:0.96 129:0.81 131:0.85 133:0.94 135:0.23 139:0.57 140:0.87 146:0.64 147:0.69 149:0.21 151:0.79 153:0.91 154:0.12 157:0.61 159:0.94 162:0.47 163:0.86 164:0.79 166:0.61 170:0.87 172:0.21 173:0.10 174:0.86 175:0.91 177:0.38 178:0.48 179:0.68 182:0.61 187:0.39 190:0.95 191:0.76 197:0.85 202:0.91 212:0.12 216:0.95 222:0.35 227:0.86 229:0.61 231:0.69 235:0.42 239:0.84 241:0.03 242:0.13 243:0.29 244:0.93 245:0.55 246:0.61 247:0.84 248:0.84 253:0.53 256:0.77 257:0.84 259:0.21 260:0.89 265:0.22 266:0.95 267:0.85 268:0.76 271:0.73 276:0.58 277:0.87 283:0.82 285:0.50 287:0.61 300:0.84 +0 8:0.86 10:0.90 17:0.72 18:0.79 21:0.13 27:0.24 29:0.71 30:0.19 31:0.93 34:0.84 36:0.19 37:0.65 39:0.80 43:0.19 55:0.71 64:0.77 66:0.08 69:0.15 70:0.63 71:0.83 74:0.27 79:0.93 81:0.30 86:0.76 91:0.61 98:0.23 108:0.12 122:0.95 123:0.87 124:0.92 126:0.24 127:0.94 129:0.89 131:0.61 133:0.91 135:0.89 137:0.93 139:0.72 140:0.80 145:0.80 146:0.36 147:0.43 149:0.21 150:0.34 151:0.61 153:0.48 154:0.23 157:0.61 159:0.82 162:0.55 166:0.61 170:0.87 172:0.16 173:0.10 174:0.93 175:0.91 177:0.38 178:0.42 179:0.71 182:0.61 187:0.39 190:0.95 196:0.91 197:0.91 202:0.70 212:0.12 216:0.76 220:0.74 222:0.35 227:0.61 229:0.61 231:0.62 235:0.12 239:0.89 241:0.24 242:0.33 243:0.25 244:0.83 245:0.56 246:0.61 247:0.79 248:0.62 253:0.25 254:0.94 256:0.64 257:0.84 259:0.21 265:0.21 266:0.92 267:0.59 268:0.67 271:0.73 276:0.54 277:0.95 284:0.94 285:0.46 287:0.61 300:0.43 +2 1:0.67 8:0.95 9:0.75 10:0.85 11:0.50 17:0.69 18:0.77 21:0.05 27:0.24 29:0.71 30:0.67 31:0.92 34:0.45 36:0.19 37:0.65 39:0.80 43:0.59 45:0.95 55:0.45 64:0.77 66:0.98 69:0.17 70:0.56 71:0.83 74:0.78 79:0.92 81:0.59 83:0.56 86:0.75 91:0.67 96:0.87 98:0.96 99:0.83 101:0.47 106:0.81 108:0.14 114:0.95 117:0.86 120:0.79 123:0.13 126:0.51 127:0.72 129:0.05 131:0.85 135:0.80 137:0.92 139:0.71 140:0.80 144:0.57 146:0.92 147:0.94 149:0.90 150:0.52 151:0.80 153:0.78 154:0.37 155:0.64 157:0.98 159:0.55 162:0.79 164:0.71 165:0.65 166:0.79 170:0.87 172:0.96 173:0.21 174:0.79 176:0.70 177:0.88 179:0.19 182:0.94 187:0.39 190:0.89 192:0.98 196:0.91 197:0.81 201:0.64 202:0.60 206:0.81 208:0.61 212:0.91 215:0.91 216:0.76 222:0.35 227:0.86 229:0.97 231:0.70 232:0.96 235:0.22 239:0.84 241:0.35 242:0.57 243:0.98 244:0.61 246:0.90 247:0.90 248:0.73 253:0.89 254:0.84 255:0.77 256:0.58 259:0.21 260:0.79 265:0.96 266:0.27 267:0.73 268:0.65 271:0.73 276:0.92 284:0.91 285:0.60 287:0.92 290:0.95 297:0.36 300:0.55 +0 8:0.83 10:0.79 17:0.55 18:0.57 21:0.78 27:0.52 29:0.71 30:0.76 34:0.33 36:0.62 37:0.43 39:0.80 43:0.06 55:0.99 66:0.09 69:0.98 70:0.22 71:0.83 74:0.25 86:0.59 91:0.02 98:0.05 108:0.96 122:0.77 123:0.96 124:0.86 127:0.43 129:0.93 131:0.61 133:0.84 135:0.81 139:0.58 145:0.56 146:0.05 147:0.05 149:0.05 154:0.06 157:0.61 159:0.97 163:1.00 166:0.61 170:0.87 172:0.05 173:0.80 174:0.79 175:0.97 177:0.05 178:0.55 179:0.95 182:0.61 187:0.68 197:0.79 202:0.36 212:0.42 216:0.79 222:0.35 227:0.61 229:0.61 231:0.61 235:0.60 239:0.79 241:0.05 242:0.45 243:0.26 244:0.93 245:0.38 246:0.61 247:0.35 253:0.22 257:0.93 259:0.21 265:0.05 266:0.23 267:0.44 271:0.73 276:0.24 277:0.95 287:0.61 300:0.18 +0 6:0.90 8:0.81 9:0.76 11:0.81 12:0.69 17:0.62 18:0.97 20:0.84 21:0.78 22:0.93 28:0.68 29:0.71 30:0.75 31:0.93 34:0.36 36:0.56 37:0.98 39:0.56 43:0.61 45:0.98 46:0.88 47:0.93 66:0.78 69:0.76 70:0.41 71:0.81 74:0.90 78:0.75 79:0.94 83:0.60 86:0.98 91:0.51 96:0.83 97:0.94 98:0.78 100:0.79 101:0.52 107:0.97 108:0.78 110:0.96 111:0.75 117:0.86 122:0.80 123:0.77 124:0.41 125:0.88 127:0.46 129:0.59 131:0.86 133:0.46 135:0.24 137:0.93 139:0.98 140:0.80 144:0.76 145:0.83 146:0.41 147:0.48 149:0.94 151:0.83 152:0.81 153:0.69 154:0.67 155:0.58 157:0.87 158:0.79 159:0.70 160:0.88 161:0.79 162:0.59 166:0.61 167:0.72 169:0.77 172:0.95 173:0.64 177:0.70 178:0.42 179:0.17 181:0.55 182:0.78 186:0.76 187:0.39 189:0.92 190:0.77 191:0.70 192:0.51 196:0.97 202:0.55 208:0.54 212:0.90 216:0.95 219:0.92 222:0.84 227:0.94 229:0.87 231:0.70 232:0.85 235:0.05 238:0.94 241:0.54 242:0.61 243:0.19 245:0.95 246:0.61 247:0.55 248:0.75 253:0.84 254:0.92 255:0.74 256:0.45 259:0.21 261:0.78 262:0.93 265:0.78 266:0.54 267:0.69 268:0.55 276:0.91 279:0.82 284:0.87 285:0.56 287:0.95 289:0.94 292:0.95 300:0.70 +1 1:0.74 6:0.87 7:0.66 8:0.67 9:0.80 11:0.87 12:0.69 17:0.98 18:0.81 20:0.91 21:0.40 27:0.31 28:0.68 29:0.71 30:0.33 31:0.98 32:0.80 34:0.97 36:0.78 37:0.32 39:0.56 41:0.96 43:0.95 44:0.93 45:0.73 46:0.95 48:0.91 60:0.87 64:0.77 66:0.45 69:0.19 70:0.89 71:0.81 74:0.80 76:0.85 77:0.70 78:0.79 79:0.98 81:0.61 83:0.91 85:0.80 86:0.92 91:0.69 96:0.93 98:0.34 100:0.85 101:0.97 104:0.88 106:0.81 107:0.97 108:0.15 110:0.96 111:0.79 114:0.62 120:0.95 123:0.15 124:0.67 125:0.97 126:0.54 127:0.85 129:0.59 131:0.86 133:0.61 135:0.72 137:0.98 139:0.94 140:0.93 144:0.76 145:0.87 146:0.42 147:0.49 149:0.81 150:0.78 151:0.96 152:0.86 153:0.48 154:0.95 155:0.67 157:0.81 158:0.87 159:0.57 160:0.99 161:0.79 162:0.79 164:0.91 165:0.93 166:0.80 167:0.64 169:0.82 172:0.61 173:0.23 176:0.73 177:0.70 179:0.57 181:0.55 182:0.78 186:0.79 187:0.21 189:0.89 192:0.87 195:0.73 196:0.99 201:0.93 202:0.89 206:0.81 208:0.64 212:0.78 215:0.42 216:0.47 219:0.95 220:0.62 222:0.84 227:0.94 229:0.88 230:0.69 231:0.71 233:0.76 235:0.68 238:0.94 241:0.21 242:0.58 243:0.58 245:0.77 246:0.93 247:0.55 248:0.90 253:0.94 255:0.95 256:0.93 259:0.21 260:0.93 261:0.85 265:0.36 266:0.63 267:0.52 268:0.92 276:0.59 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 287:0.95 289:0.96 290:0.62 292:0.91 297:0.36 299:0.88 300:0.84 +1 6:0.85 7:0.66 11:0.81 12:0.69 17:0.62 18:0.63 20:0.76 21:0.40 27:0.67 28:0.68 29:0.71 30:0.08 32:0.78 34:0.92 36:0.25 37:0.44 39:0.56 43:0.12 44:0.93 45:0.66 46:0.88 66:0.64 69:0.73 70:0.74 71:0.81 74:0.54 77:0.70 78:0.72 81:0.31 86:0.74 91:0.44 98:0.46 100:0.73 101:0.52 107:0.93 108:0.56 110:0.93 111:0.72 123:0.53 124:0.42 125:0.88 126:0.26 127:0.20 129:0.05 131:0.61 133:0.37 135:0.74 139:0.80 144:0.76 145:0.67 146:0.48 147:0.54 149:0.11 150:0.29 152:0.79 154:0.69 157:0.76 159:0.25 160:0.88 161:0.79 166:0.61 167:0.63 169:0.75 172:0.32 173:0.79 177:0.70 178:0.55 179:0.71 181:0.55 182:0.73 186:0.73 187:0.39 195:0.66 212:0.23 215:0.42 216:0.56 222:0.84 227:0.61 229:0.61 230:0.69 231:0.63 233:0.73 235:0.42 241:0.15 242:0.73 243:0.69 245:0.43 246:0.61 247:0.35 253:0.37 254:0.95 259:0.21 261:0.76 265:0.48 266:0.38 267:0.52 276:0.29 282:0.86 287:0.61 289:0.93 297:0.36 300:0.43 +1 1:0.74 7:0.81 9:0.80 11:0.87 12:0.69 17:0.98 18:0.84 21:0.54 22:0.93 27:0.28 28:0.68 29:0.71 30:0.62 31:0.87 32:0.80 34:0.52 36:0.33 37:0.37 39:0.56 41:0.82 43:0.97 44:0.93 45:0.73 46:0.96 47:0.93 60:0.87 64:0.77 66:0.79 69:0.27 70:0.53 71:0.81 74:0.85 77:0.70 79:0.87 81:0.54 83:0.91 85:0.88 86:0.93 91:0.31 96:0.69 98:0.62 101:0.97 107:0.97 108:0.21 110:0.96 114:0.59 117:0.86 120:0.55 121:0.99 123:0.20 124:0.39 125:0.98 126:0.54 127:0.30 129:0.05 131:0.86 133:0.43 135:0.51 137:0.87 139:0.96 140:0.45 144:0.76 145:0.88 146:0.75 147:0.79 149:0.91 150:0.75 151:0.52 153:0.48 154:0.97 155:0.67 157:0.82 158:0.92 159:0.47 160:0.96 161:0.79 162:0.86 164:0.57 165:0.93 166:0.79 167:0.56 172:0.75 173:0.24 176:0.73 177:0.70 179:0.39 181:0.55 182:0.78 187:0.21 192:0.87 195:0.78 196:0.91 201:0.93 202:0.36 204:0.89 208:0.64 212:0.82 215:0.39 216:0.51 219:0.95 220:0.87 222:0.84 227:0.94 229:0.87 230:0.86 231:0.70 232:0.98 233:0.73 235:0.88 241:0.01 242:0.45 243:0.80 245:0.68 246:0.93 247:0.67 248:0.51 253:0.50 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.63 266:0.47 267:0.52 268:0.46 276:0.60 279:0.86 281:0.89 282:0.88 283:0.82 284:0.89 285:0.62 287:0.95 289:0.96 290:0.59 292:0.95 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.86 7:0.66 8:0.70 9:0.80 11:0.81 12:0.69 17:0.98 18:0.78 20:0.89 27:0.20 28:0.68 29:0.71 30:0.41 31:0.94 32:0.80 34:0.42 36:0.43 37:0.70 39:0.56 41:0.82 43:0.58 44:0.93 45:0.81 46:0.88 60:0.87 64:0.77 66:0.86 69:0.75 70:0.36 71:0.81 74:0.78 77:0.70 78:0.84 79:0.93 81:0.90 83:0.91 86:0.88 91:0.56 96:0.80 98:0.71 100:0.91 101:0.52 104:0.91 106:0.81 107:0.96 108:0.58 110:0.96 111:0.86 114:0.98 120:0.69 123:0.56 125:1.00 126:0.54 127:0.22 129:0.05 131:0.86 135:0.47 137:0.94 139:0.93 140:0.45 144:0.76 145:0.70 146:0.54 147:0.60 149:0.27 150:0.94 151:0.87 152:0.85 153:0.48 154:0.72 155:0.67 157:0.81 158:0.92 159:0.19 160:0.96 161:0.79 162:0.86 164:0.57 165:0.93 166:0.80 167:0.55 169:0.88 172:0.59 173:0.91 176:0.73 177:0.70 179:0.50 181:0.55 182:0.78 186:0.84 187:0.21 189:0.88 192:0.87 195:0.56 196:0.96 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.78 215:0.96 216:0.03 219:0.95 222:0.84 227:0.94 229:0.87 230:0.69 231:0.70 232:0.79 233:0.76 235:0.12 238:0.94 242:0.33 243:0.86 246:0.93 247:0.67 248:0.77 253:0.85 254:0.80 255:0.95 256:0.45 259:0.21 260:0.70 261:0.90 265:0.72 266:0.38 267:0.69 268:0.46 276:0.43 279:0.86 281:0.89 282:0.88 283:0.82 284:0.89 285:0.62 287:0.95 289:0.96 290:0.98 292:0.95 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.94 7:0.66 8:0.89 9:0.80 11:0.87 12:0.69 17:0.98 18:0.75 20:0.85 21:0.22 22:0.93 27:0.35 28:0.68 29:0.71 30:0.62 31:0.95 32:0.75 34:0.79 36:0.46 37:0.60 39:0.56 41:0.88 43:0.80 44:0.93 45:0.73 46:0.94 47:0.93 64:0.77 66:0.36 69:0.41 70:0.54 71:0.81 74:0.69 77:0.70 78:0.77 79:0.95 81:0.70 83:0.91 85:0.72 86:0.85 91:0.64 96:0.85 98:0.45 100:0.82 101:0.97 104:0.98 106:0.81 107:0.96 108:0.56 110:0.95 111:0.77 114:0.71 117:0.86 120:0.78 122:0.57 123:0.54 124:0.70 125:0.99 126:0.54 127:0.51 129:0.59 131:0.86 133:0.66 135:0.51 137:0.95 139:0.88 140:0.80 144:0.76 145:0.70 146:0.22 147:0.27 149:0.62 150:0.82 151:0.93 152:0.80 153:0.77 154:0.81 155:0.67 157:0.80 159:0.36 160:0.97 161:0.79 162:0.86 164:0.72 165:0.93 166:0.80 167:0.59 169:0.79 172:0.32 173:0.50 176:0.73 177:0.70 179:0.80 181:0.55 182:0.78 186:0.78 187:0.39 189:0.95 192:0.87 195:0.60 196:0.96 201:0.93 202:0.60 204:0.85 206:0.81 208:0.64 212:0.42 215:0.51 216:0.17 220:0.62 222:0.84 227:0.94 229:0.87 230:0.69 231:0.70 232:0.75 233:0.76 235:0.42 238:0.93 241:0.05 242:0.45 243:0.34 245:0.55 246:0.93 247:0.60 248:0.82 253:0.84 255:0.95 256:0.64 259:0.21 260:0.78 261:0.80 262:0.93 265:0.47 266:0.47 267:0.89 268:0.69 276:0.37 281:0.89 282:0.83 283:0.82 284:0.93 285:0.62 287:0.95 289:0.96 290:0.71 297:0.36 300:0.43 +3 6:0.98 7:0.66 8:0.98 9:0.80 12:0.69 17:0.06 20:0.99 21:0.74 25:0.88 27:0.13 28:0.68 29:0.71 30:0.45 31:0.99 32:0.64 34:0.94 36:0.42 37:0.82 39:0.56 41:0.94 43:0.13 46:0.88 55:0.52 66:0.49 69:0.70 70:0.25 71:0.81 78:0.88 79:0.99 81:0.23 83:0.91 91:1.00 96:0.95 98:0.39 100:0.98 104:0.58 107:0.90 108:0.69 110:0.90 111:0.97 120:1.00 123:0.67 124:0.48 125:0.96 126:0.26 127:0.36 129:0.59 131:0.85 133:0.47 135:0.24 137:0.99 140:0.98 144:0.76 145:0.72 146:0.05 147:0.05 149:0.11 150:0.23 151:0.96 152:0.98 153:0.48 154:0.10 155:0.67 157:0.61 159:0.27 160:0.98 161:0.79 162:0.74 163:0.96 164:0.99 166:0.61 167:0.99 169:0.96 172:0.16 173:0.85 175:0.91 177:0.05 179:0.96 181:0.55 182:0.77 186:0.88 189:0.99 191:0.97 192:0.87 195:0.58 202:0.99 208:0.64 212:0.61 215:0.36 216:0.75 222:0.84 227:0.93 229:0.87 230:0.69 231:0.70 233:0.73 235:0.88 238:0.99 241:0.96 242:0.82 243:0.29 244:0.83 245:0.39 246:0.61 247:0.12 248:0.94 253:0.92 255:0.95 256:0.99 257:0.84 259:0.21 260:0.99 261:0.99 265:0.41 266:0.17 267:0.73 268:0.99 276:0.17 277:0.87 283:0.82 284:0.98 285:0.62 287:0.94 289:0.95 297:0.36 300:0.18 +0 11:0.81 12:0.69 17:0.69 18:0.95 21:0.78 22:0.93 28:0.68 29:0.71 30:0.75 34:0.61 36:0.68 37:0.98 39:0.56 43:0.61 45:0.97 46:0.88 47:0.93 66:0.35 69:0.76 70:0.48 71:0.81 74:0.85 83:0.60 86:0.96 91:0.22 97:0.89 98:0.66 101:0.52 107:0.97 108:0.85 110:0.96 117:0.86 122:0.80 123:0.84 124:0.71 125:0.88 127:0.48 129:0.59 131:0.61 133:0.66 135:0.28 139:0.96 144:0.76 145:0.83 146:0.41 147:0.48 149:0.90 154:0.67 155:0.58 157:0.86 158:0.79 159:0.72 160:0.88 161:0.79 166:0.61 167:0.63 172:0.80 173:0.63 177:0.70 178:0.55 179:0.23 181:0.55 182:0.77 187:0.39 192:0.51 208:0.54 212:0.75 216:0.95 219:0.92 222:0.84 227:0.61 229:0.61 231:0.69 232:0.85 235:0.42 241:0.15 242:0.59 243:0.25 245:0.94 246:0.61 247:0.55 253:0.45 254:0.92 259:0.21 262:0.93 265:0.67 266:0.63 267:0.28 276:0.85 277:0.69 279:0.82 287:0.61 289:0.93 292:0.95 300:0.70 +0 11:0.81 12:0.69 17:0.53 18:0.62 21:0.78 27:0.45 28:0.68 29:0.71 30:0.76 34:0.89 36:0.18 37:0.50 39:0.56 43:0.58 45:0.66 46:0.88 66:0.64 69:0.82 70:0.15 71:0.81 74:0.35 86:0.67 91:0.06 98:0.16 101:0.52 107:0.90 108:0.67 110:0.90 122:0.57 123:0.65 125:0.88 127:0.09 129:0.05 131:0.61 135:0.44 139:0.65 144:0.76 145:0.49 146:0.26 147:0.32 149:0.27 154:0.47 157:0.76 159:0.11 160:0.88 161:0.79 163:0.97 166:0.61 167:0.67 172:0.16 173:0.79 177:0.70 178:0.55 179:0.18 181:0.55 182:0.72 187:0.68 212:0.95 216:0.77 222:0.84 227:0.61 229:0.61 231:0.61 235:0.99 241:0.32 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.29 259:0.21 265:0.18 266:0.12 267:0.44 276:0.14 287:0.61 289:0.88 300:0.13 +0 11:0.81 12:0.69 17:0.34 18:0.62 21:0.78 27:0.45 28:0.68 29:0.71 30:0.76 34:0.86 36:0.17 37:0.50 39:0.56 43:0.75 45:0.66 46:0.88 66:0.64 69:0.80 70:0.15 71:0.81 74:0.40 86:0.72 91:0.11 98:0.12 101:0.52 107:0.89 108:0.64 110:0.89 122:0.80 123:0.61 125:0.88 127:0.08 129:0.05 131:0.61 135:0.90 139:0.69 144:0.76 145:0.50 146:0.22 147:0.27 149:0.48 154:0.65 157:0.75 159:0.10 160:0.88 161:0.79 163:0.97 166:0.61 167:0.70 172:0.16 173:0.66 177:0.70 178:0.55 179:0.10 181:0.55 182:0.72 187:0.68 212:0.95 216:0.77 222:0.84 227:0.61 229:0.61 231:0.61 235:0.68 241:0.47 242:0.82 243:0.69 246:0.61 247:0.12 253:0.31 254:0.93 259:0.21 265:0.13 266:0.12 267:0.44 276:0.15 287:0.61 289:0.88 300:0.13 +0 1:0.69 8:0.92 9:0.76 11:0.81 12:0.69 17:0.85 18:0.91 20:0.74 21:0.05 22:0.93 27:0.06 28:0.68 29:0.71 30:0.73 31:0.90 34:0.31 36:0.49 37:0.87 39:0.56 43:0.72 44:0.90 45:0.91 46:0.88 47:0.93 64:0.77 66:0.44 69:0.93 70:0.54 71:0.81 74:0.81 77:0.70 78:0.72 79:0.90 81:0.68 83:0.60 86:0.93 91:0.18 96:0.75 97:0.89 98:0.35 99:0.83 100:0.73 101:0.52 107:0.96 108:0.07 110:0.95 111:0.72 114:0.85 117:0.86 122:0.80 123:0.07 124:0.67 125:0.88 126:0.44 127:0.70 129:0.05 131:0.86 133:0.66 135:0.26 137:0.90 139:0.94 140:0.45 144:0.76 145:0.44 146:0.49 147:0.57 149:0.85 150:0.64 151:0.65 152:0.76 153:0.48 154:0.45 155:0.58 157:0.84 158:0.79 159:0.66 160:0.88 161:0.79 162:0.86 165:0.70 166:0.80 167:0.59 169:0.72 172:0.59 173:0.97 176:0.66 177:0.38 179:0.56 181:0.55 182:0.78 186:0.73 187:0.39 190:0.77 192:0.51 195:0.81 196:0.94 201:0.68 202:0.36 204:0.85 208:0.54 212:0.74 216:0.54 219:0.92 220:0.62 222:0.84 227:0.94 229:0.87 231:0.70 232:0.85 235:0.12 241:0.05 242:0.59 243:0.57 245:0.76 246:0.93 247:0.55 248:0.63 253:0.74 254:0.90 255:0.74 256:0.45 257:0.65 259:0.21 261:0.76 262:0.93 265:0.37 266:0.71 267:0.52 268:0.46 276:0.53 277:0.69 279:0.82 281:0.89 282:0.77 284:0.87 285:0.56 287:0.95 289:0.93 290:0.85 292:0.95 300:0.55 +1 6:0.98 7:0.66 8:0.92 9:0.80 12:0.69 17:0.47 20:0.76 21:0.54 25:0.88 27:0.13 28:0.68 29:0.71 30:0.32 31:0.86 32:0.64 34:0.98 36:0.66 37:0.82 39:0.56 41:0.82 43:0.12 46:0.88 55:0.84 66:0.13 69:0.71 70:0.68 71:0.81 78:0.80 79:0.86 81:0.27 83:0.91 91:0.40 96:0.68 98:0.41 100:0.84 104:0.58 107:0.91 108:0.91 110:0.91 111:0.80 120:0.82 123:0.70 124:0.85 125:0.97 126:0.26 127:0.67 129:0.93 131:0.86 133:0.84 135:0.63 137:0.86 140:0.90 144:0.76 145:0.70 146:0.48 147:0.54 149:0.24 150:0.26 151:0.47 152:0.98 153:0.48 154:0.10 155:0.67 157:0.61 159:0.76 160:0.96 161:0.79 162:0.86 163:0.92 164:0.75 166:0.61 167:0.74 169:0.96 172:0.32 173:0.57 175:0.91 177:0.05 179:0.66 181:0.55 182:0.78 186:0.80 187:0.39 190:0.89 192:0.87 195:0.89 202:0.68 208:0.64 212:0.21 215:0.39 216:0.75 222:0.84 227:0.94 229:0.87 230:0.69 231:0.70 233:0.73 235:0.60 241:0.59 242:0.82 243:0.34 244:0.83 245:0.64 246:0.61 247:0.12 248:0.47 253:0.31 255:0.95 256:0.75 257:0.84 259:0.21 260:0.76 261:0.99 265:0.29 266:0.84 267:0.82 268:0.76 276:0.56 277:0.87 284:0.89 285:0.62 287:0.95 289:0.96 297:0.36 300:0.43 +2 1:0.74 6:0.82 8:0.64 9:0.80 11:0.87 12:0.69 17:0.61 18:0.86 20:0.80 21:0.13 27:0.61 28:0.68 29:0.71 30:0.93 31:0.89 34:0.62 36:0.71 37:0.41 39:0.56 41:0.82 43:0.97 45:0.66 46:0.99 64:0.77 66:0.86 69:0.10 70:0.19 71:0.81 74:0.78 78:0.81 79:0.89 81:0.77 83:0.91 85:0.88 86:0.95 91:0.22 96:0.73 98:0.41 100:0.83 101:0.97 107:0.96 108:0.09 110:0.96 111:0.80 114:0.79 120:0.59 122:0.57 123:0.09 125:0.97 126:0.54 127:0.13 129:0.05 131:0.86 135:0.60 137:0.89 139:0.93 140:0.80 144:0.76 146:0.28 147:0.34 149:0.91 150:0.86 151:0.61 152:0.79 153:0.48 154:0.97 155:0.67 157:0.82 159:0.12 160:0.96 161:0.79 162:0.86 164:0.57 165:0.93 166:0.80 167:0.65 169:0.80 172:0.59 173:0.43 176:0.73 177:0.70 179:0.20 181:0.55 182:0.78 186:0.81 187:0.39 189:0.84 192:0.87 196:0.93 201:0.93 202:0.50 208:0.64 212:0.95 215:0.61 216:0.45 220:0.87 222:0.84 227:0.94 229:0.87 231:0.70 235:0.31 238:0.88 241:0.24 242:0.45 243:0.86 246:0.93 247:0.47 248:0.59 253:0.66 255:0.95 256:0.58 259:0.21 260:0.60 261:0.81 265:0.43 266:0.12 267:0.36 268:0.59 276:0.48 284:0.92 285:0.62 287:0.95 289:0.96 290:0.79 297:0.36 300:0.18 +2 6:0.84 7:0.66 8:0.65 11:0.81 12:0.69 17:0.47 18:0.62 20:0.79 21:0.40 27:0.31 28:0.68 29:0.71 30:0.21 32:0.75 34:0.97 36:0.26 37:0.55 39:0.56 43:0.59 44:0.93 45:0.66 46:0.88 66:0.72 69:0.75 70:0.37 71:0.81 74:0.56 77:0.70 78:0.78 81:0.31 86:0.67 91:0.15 98:0.53 100:0.81 101:0.52 104:0.95 106:0.81 107:0.92 108:0.58 110:0.92 111:0.77 122:0.41 123:0.56 125:0.88 126:0.26 127:0.16 129:0.05 131:0.61 135:0.88 139:0.80 144:0.76 145:0.63 146:0.54 147:0.60 149:0.30 150:0.29 152:0.88 154:0.48 155:0.58 157:0.76 159:0.19 160:0.88 161:0.79 163:0.81 166:0.61 167:0.65 169:0.77 172:0.27 173:0.82 177:0.70 178:0.55 179:0.70 181:0.55 182:0.73 186:0.78 187:0.39 189:0.85 192:0.51 195:0.67 204:0.85 206:0.81 208:0.54 212:0.42 215:0.42 216:0.85 222:0.84 227:0.61 229:0.61 230:0.69 231:0.63 233:0.76 235:0.42 238:0.89 241:0.24 242:0.45 243:0.75 244:0.61 246:0.61 247:0.35 253:0.37 259:0.21 261:0.80 265:0.54 266:0.23 267:0.23 276:0.21 281:0.91 282:0.84 283:0.82 287:0.61 289:0.93 297:0.36 300:0.25 +1 1:0.69 8:0.66 9:0.76 11:0.81 12:0.69 17:0.91 18:0.97 20:0.76 21:0.40 22:0.93 27:0.42 28:0.68 29:0.71 30:0.67 31:0.93 34:0.97 36:0.85 37:0.47 39:0.56 43:0.71 45:0.99 46:0.88 47:0.93 64:0.77 66:0.35 69:0.25 70:0.71 71:0.81 74:0.89 77:0.70 78:0.77 79:0.94 81:0.39 83:0.60 86:0.97 91:0.23 96:0.83 97:0.94 98:0.60 99:0.83 100:0.73 101:0.52 107:0.98 108:0.94 110:0.96 111:0.76 114:0.61 117:0.86 122:0.80 123:0.94 124:0.92 125:0.88 126:0.44 127:0.94 129:0.81 131:0.86 133:0.93 135:0.47 137:0.93 139:0.97 140:0.45 144:0.76 145:0.49 146:0.80 147:0.83 149:0.93 150:0.54 151:0.81 152:0.75 153:0.48 154:0.74 155:0.58 157:0.87 158:0.79 159:0.92 160:0.88 161:0.79 162:0.79 165:0.70 166:0.80 167:0.57 169:0.72 172:0.93 173:0.09 176:0.66 177:0.70 179:0.15 181:0.55 182:0.78 186:0.78 187:0.68 190:0.77 192:0.51 195:0.98 196:0.97 201:0.68 202:0.68 204:0.85 208:0.54 212:0.58 216:0.72 219:0.92 220:0.87 222:0.84 227:0.94 229:0.88 231:0.71 232:0.99 235:0.52 241:0.01 242:0.54 243:0.22 245:0.95 246:0.93 247:0.86 248:0.75 253:0.84 254:0.86 255:0.74 256:0.71 259:0.21 261:0.75 262:0.93 265:0.61 266:0.94 267:0.73 268:0.73 276:0.95 279:0.82 281:0.91 282:0.71 284:0.95 285:0.56 287:0.95 289:0.94 290:0.61 292:0.95 300:0.84 +2 1:0.74 6:0.82 8:0.65 11:0.87 12:0.69 17:0.70 18:0.86 20:0.80 21:0.13 27:0.61 28:0.68 29:0.71 30:0.93 34:0.62 36:0.66 37:0.41 39:0.56 43:0.97 45:0.66 46:0.99 64:0.77 66:0.86 69:0.10 70:0.19 71:0.81 74:0.78 78:0.78 81:0.77 83:0.91 85:0.88 86:0.95 91:0.10 98:0.43 100:0.80 101:0.97 107:0.96 108:0.09 110:0.96 111:0.77 114:0.79 122:0.57 123:0.09 125:0.88 126:0.54 127:0.14 129:0.05 131:0.61 135:0.54 139:0.93 144:0.76 146:0.28 147:0.35 149:0.91 150:0.86 152:0.79 154:0.97 155:0.67 157:0.82 159:0.12 160:0.88 161:0.79 165:0.93 166:0.80 167:0.62 169:0.80 172:0.59 173:0.43 176:0.73 177:0.70 178:0.55 179:0.22 181:0.55 182:0.77 186:0.79 187:0.39 189:0.85 192:0.87 201:0.93 208:0.64 212:0.95 215:0.61 216:0.45 222:0.84 227:0.61 229:0.61 231:0.70 235:0.31 238:0.87 241:0.12 242:0.45 243:0.86 246:0.93 247:0.47 253:0.57 259:0.21 261:0.81 265:0.45 266:0.12 267:0.36 276:0.48 287:0.61 289:0.95 290:0.79 297:0.36 300:0.18 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.64 12:0.20 17:0.34 20:0.86 21:0.22 27:0.16 28:0.68 30:0.85 32:0.80 34:0.96 36:0.22 37:0.78 39:0.44 41:0.94 43:0.98 60:0.95 66:0.54 69:0.10 70:0.40 74:0.91 77:0.70 78:0.87 81:0.84 83:0.88 85:0.95 91:0.54 96:0.93 98:0.62 100:0.89 101:0.84 104:0.82 108:0.56 111:0.87 114:0.90 120:0.87 121:0.97 122:0.43 123:0.54 124:0.68 126:0.54 127:0.85 129:0.89 131:0.99 133:0.78 135:0.98 140:0.45 144:0.57 145:0.45 146:0.13 147:0.18 149:0.92 150:0.90 151:0.97 152:0.81 153:0.89 154:0.98 155:0.67 157:0.98 158:0.92 159:0.57 161:0.48 162:0.79 164:0.82 165:0.86 166:0.97 167:0.62 169:0.87 172:0.59 173:0.17 176:0.73 177:0.38 179:0.66 181:0.82 182:0.96 186:0.87 187:0.39 189:0.95 191:0.86 192:0.59 195:0.71 201:0.74 202:0.74 204:0.89 208:0.64 212:0.19 215:0.79 216:0.60 222:0.48 227:0.84 229:0.98 230:0.84 231:0.96 232:0.86 233:0.69 235:0.31 238:0.91 241:0.51 242:0.63 243:0.18 245:0.63 246:0.84 247:0.30 248:0.92 253:0.95 255:0.79 256:0.77 259:0.21 260:0.88 261:0.86 265:0.63 266:0.63 267:0.91 268:0.79 271:0.60 276:0.56 279:0.86 282:0.88 283:0.82 285:0.62 287:0.87 290:0.90 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.82 7:0.81 8:0.63 9:0.80 11:0.64 12:0.20 17:0.43 20:0.85 21:0.30 27:0.21 28:0.68 30:0.76 34:0.66 36:0.93 37:0.74 39:0.44 41:0.94 43:0.98 60:0.95 66:0.59 69:0.12 70:0.56 74:0.96 78:0.82 81:0.80 83:0.88 85:0.98 91:0.62 96:0.93 98:0.68 100:0.84 101:0.87 104:0.84 106:0.81 108:0.69 111:0.82 114:0.86 117:0.86 120:0.88 121:0.97 122:0.43 123:0.67 124:0.52 126:0.54 127:0.41 129:0.59 131:0.99 133:0.47 135:0.20 140:0.45 144:0.57 146:0.63 147:0.68 149:0.98 150:0.86 151:0.97 152:0.81 153:0.90 154:0.98 155:0.67 157:0.99 158:0.92 159:0.52 161:0.48 162:0.68 164:0.83 165:0.84 166:0.97 167:0.56 169:0.82 172:0.81 173:0.17 176:0.73 177:0.38 179:0.29 181:0.82 182:0.96 186:0.82 187:0.39 189:0.84 191:0.75 192:0.59 201:0.74 202:0.76 204:0.89 206:0.81 208:0.64 212:0.74 215:0.72 216:0.95 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.88 233:0.76 235:0.42 238:0.87 241:0.32 242:0.63 243:0.39 245:0.92 246:0.84 247:0.39 248:0.93 253:0.95 255:0.79 256:0.77 259:0.21 260:0.88 261:0.81 265:0.69 266:0.54 267:0.89 268:0.78 271:0.60 276:0.79 279:0.86 283:0.82 285:0.62 287:0.87 290:0.86 297:0.36 300:0.70 +2 1:0.74 6:0.89 8:0.76 9:0.80 11:0.64 12:0.20 17:0.67 20:0.82 21:0.13 27:0.26 28:0.68 30:0.76 34:0.56 36:0.57 37:0.73 39:0.44 41:0.92 43:0.95 60:0.97 66:0.54 69:0.21 70:0.63 74:0.97 78:0.86 81:0.88 83:0.88 85:0.99 91:0.58 96:0.89 98:0.75 100:0.89 101:0.87 104:0.87 108:0.58 111:0.86 114:0.92 120:0.81 122:0.43 123:0.55 124:0.63 126:0.54 127:0.73 129:0.81 131:0.99 133:0.67 135:0.75 140:0.45 144:0.57 146:0.13 147:0.18 149:0.99 150:0.93 151:0.95 152:0.82 153:0.84 154:0.95 155:0.67 157:0.99 158:0.92 159:0.60 161:0.48 162:0.58 164:0.77 165:0.88 166:0.97 167:0.74 169:0.86 172:0.87 173:0.21 176:0.73 177:0.38 179:0.27 181:0.82 182:0.96 186:0.86 187:0.39 189:0.91 191:0.88 192:0.59 201:0.74 202:0.73 208:0.64 212:0.81 215:0.84 216:0.43 222:0.48 227:0.84 229:0.98 231:0.96 232:0.89 235:0.22 238:0.90 241:0.70 242:0.63 243:0.08 245:0.92 246:0.84 247:0.30 248:0.88 253:0.93 255:0.79 256:0.68 259:0.21 260:0.82 261:0.86 265:0.76 266:0.54 267:0.44 268:0.72 271:0.60 276:0.85 279:0.86 283:0.82 285:0.62 287:0.87 290:0.92 297:0.36 300:0.84 +2 1:0.74 6:0.93 8:0.77 9:0.80 11:0.64 12:0.20 17:0.09 20:0.93 21:0.30 27:0.04 28:0.68 30:0.74 32:0.80 34:0.96 36:0.27 37:0.88 39:0.44 41:0.93 43:0.83 60:0.87 66:0.49 69:0.67 70:0.36 74:0.83 77:0.89 78:0.93 81:0.80 83:0.84 85:0.93 91:0.63 96:0.84 98:0.48 100:0.95 101:0.83 104:0.95 108:0.63 111:0.93 114:0.86 120:0.74 122:0.43 123:0.61 124:0.66 126:0.54 127:0.27 129:0.81 131:0.99 133:0.67 135:0.99 140:0.45 144:0.57 145:0.76 146:0.13 147:0.18 149:0.87 150:0.86 151:0.81 152:1.00 153:0.72 154:0.78 155:0.67 157:0.98 158:0.87 159:0.37 161:0.48 162:0.77 164:0.79 165:0.84 166:0.97 167:0.73 169:0.94 172:0.48 173:0.69 176:0.73 177:0.38 179:0.54 181:0.82 182:0.96 186:0.93 187:0.39 189:0.88 191:0.90 192:0.59 195:0.68 201:0.74 202:0.70 208:0.64 212:0.48 215:0.72 216:0.55 220:0.87 222:0.48 227:0.84 229:0.98 231:0.96 232:0.96 235:0.42 238:0.92 241:0.69 242:0.58 243:0.20 245:0.64 246:0.84 247:0.47 248:0.81 253:0.86 255:0.79 256:0.71 259:0.21 260:0.75 261:0.98 265:0.49 266:0.38 267:0.69 268:0.74 271:0.60 276:0.50 279:0.86 282:0.88 283:0.71 285:0.62 287:0.87 290:0.86 297:0.36 299:0.97 300:0.33 +2 1:0.74 6:0.93 8:0.89 9:0.80 11:0.64 12:0.20 17:0.22 20:0.99 21:0.22 27:0.04 28:0.68 30:0.86 32:0.80 34:0.94 36:0.23 37:0.88 39:0.44 41:0.96 43:0.83 60:0.99 66:0.15 69:0.67 70:0.21 74:0.83 77:0.89 78:0.96 81:0.84 83:0.87 85:0.93 91:0.72 96:0.93 98:0.22 100:0.99 101:0.82 104:0.95 108:0.64 111:0.98 114:0.90 120:0.88 122:0.43 123:0.77 124:0.82 126:0.54 127:0.31 129:0.89 131:0.99 133:0.78 135:0.98 140:0.45 144:0.57 145:0.72 146:0.13 147:0.18 149:0.86 150:0.90 151:0.98 152:1.00 153:0.91 154:0.78 155:0.67 157:0.98 158:0.92 159:0.42 161:0.48 162:0.77 163:0.81 164:0.86 165:0.86 166:0.97 167:0.79 169:0.94 172:0.21 173:0.67 176:0.73 177:0.38 179:0.63 181:0.82 182:0.96 186:0.96 187:0.39 189:0.96 191:0.92 192:0.59 195:0.70 201:0.74 202:0.78 208:0.64 212:0.42 215:0.79 216:0.55 222:0.48 227:0.84 229:0.98 231:0.96 232:0.96 235:0.31 238:0.98 241:0.76 242:0.63 243:0.22 245:0.59 246:0.84 247:0.30 248:0.93 253:0.94 255:0.79 256:0.81 259:0.21 260:0.88 261:0.98 265:0.21 266:0.38 267:0.59 268:0.82 271:0.60 276:0.46 279:0.86 282:0.88 283:0.82 285:0.62 287:0.87 290:0.90 297:0.36 299:0.97 300:0.18 +1 1:0.74 11:0.64 12:0.20 17:0.64 21:0.68 27:0.45 28:0.68 30:0.84 32:0.80 34:0.98 36:0.28 37:0.50 39:0.44 43:0.81 66:0.64 69:0.71 70:0.44 74:0.93 77:0.70 81:0.55 83:0.73 85:0.97 91:0.06 98:0.53 101:0.86 108:0.54 114:0.70 122:0.43 123:0.51 124:0.47 126:0.54 127:0.40 129:0.59 131:0.61 133:0.47 135:0.66 144:0.57 145:0.49 146:0.68 147:0.73 149:0.96 150:0.69 154:0.77 155:0.67 157:0.99 159:0.54 161:0.48 165:0.70 166:0.97 167:0.52 172:0.77 173:0.66 176:0.73 177:0.38 178:0.55 179:0.36 181:0.82 182:0.96 187:0.68 192:0.59 195:0.77 201:0.74 212:0.88 215:0.49 216:0.77 222:0.48 227:0.61 229:0.61 231:0.96 235:0.84 241:0.10 242:0.63 243:0.69 245:0.86 246:0.84 247:0.30 253:0.72 259:0.21 265:0.55 266:0.54 267:0.28 271:0.60 276:0.61 282:0.88 287:0.61 290:0.70 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.89 8:0.75 9:0.80 11:0.64 12:0.20 17:0.64 20:0.84 21:0.22 27:0.26 28:0.68 30:0.72 34:0.55 36:0.84 37:0.73 39:0.44 41:0.95 43:0.94 60:0.95 66:0.61 69:0.25 70:0.66 74:0.96 78:0.85 81:0.84 83:0.89 85:0.99 91:0.60 96:0.92 98:0.84 100:0.87 101:0.87 104:0.87 108:0.20 111:0.84 114:0.90 120:0.86 122:0.43 123:0.19 124:0.50 126:0.54 127:0.69 129:0.59 131:0.99 133:0.47 135:0.93 140:0.45 144:0.57 146:0.90 147:0.92 149:0.98 150:0.90 151:0.87 152:0.82 153:0.48 154:0.94 155:0.67 157:0.99 158:0.87 159:0.60 161:0.48 162:0.57 164:0.83 165:0.86 166:0.97 167:0.72 169:0.86 172:0.87 173:0.24 176:0.73 177:0.38 179:0.28 181:0.82 182:0.96 186:0.85 187:0.39 189:0.90 191:0.91 192:0.59 201:0.74 202:0.80 208:0.64 212:0.68 215:0.79 216:0.43 220:0.62 222:0.48 227:0.84 229:0.98 231:0.96 232:0.89 235:0.31 238:0.90 241:0.68 242:0.62 243:0.68 245:0.94 246:0.84 247:0.47 248:0.91 253:0.95 255:0.79 256:0.79 259:0.21 260:0.86 261:0.86 265:0.84 266:0.54 267:0.65 268:0.79 271:0.60 276:0.84 279:0.86 283:0.71 285:0.62 287:0.87 290:0.90 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.20 17:0.43 21:0.30 27:0.45 28:0.68 30:0.85 32:0.80 34:0.85 36:0.23 37:0.50 39:0.44 43:0.82 66:0.54 69:0.68 70:0.42 74:0.94 77:0.70 81:0.80 83:0.75 85:0.98 91:0.22 98:0.55 101:0.86 108:0.52 114:0.86 122:0.57 123:0.50 124:0.52 126:0.54 127:0.36 129:0.59 131:0.61 133:0.47 135:0.69 144:0.57 145:0.47 146:0.68 147:0.73 149:0.97 150:0.86 154:0.77 155:0.67 157:0.99 159:0.50 161:0.48 165:0.84 166:0.97 167:0.52 172:0.76 173:0.66 176:0.73 177:0.38 178:0.55 179:0.32 181:0.82 182:0.96 187:0.68 192:0.59 195:0.76 201:0.74 212:0.76 215:0.72 216:0.77 222:0.48 227:0.61 229:0.61 231:0.96 235:0.42 241:0.10 242:0.63 243:0.63 245:0.89 246:0.84 247:0.39 253:0.77 259:0.21 265:0.57 266:0.54 267:0.44 271:0.60 276:0.67 282:0.88 287:0.61 290:0.86 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.87 7:0.81 8:0.68 9:0.80 11:0.64 12:0.20 17:0.45 20:0.86 21:0.40 27:0.10 28:0.68 30:0.86 32:0.80 34:0.99 36:0.36 37:0.84 39:0.44 41:0.95 43:0.98 60:0.87 66:0.27 69:0.15 70:0.32 74:0.89 77:0.89 78:0.84 81:0.75 83:0.88 85:0.93 91:0.73 96:0.92 98:0.40 100:0.85 101:0.84 104:0.96 108:0.64 111:0.84 114:0.82 120:0.85 121:1.00 122:0.43 123:0.62 124:0.74 126:0.54 127:0.31 129:0.81 131:0.99 133:0.67 135:0.63 138:0.97 140:0.45 144:0.57 145:0.79 146:0.20 147:0.25 149:0.90 150:0.83 151:0.96 152:0.82 153:0.86 154:0.98 155:0.67 157:0.98 158:0.92 159:0.32 161:0.48 162:0.77 164:0.82 165:0.83 166:0.97 167:0.80 169:0.84 172:0.32 173:0.27 176:0.73 177:0.38 179:0.63 181:0.82 182:0.96 186:0.84 187:0.87 189:0.89 191:0.90 192:0.59 195:0.66 201:0.74 202:0.74 204:0.89 208:0.64 212:0.42 215:0.67 216:0.71 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.97 233:0.76 235:0.52 238:0.89 241:0.76 242:0.63 243:0.36 245:0.63 246:0.84 247:0.30 248:0.92 253:0.94 255:0.79 256:0.75 259:0.21 260:0.86 261:0.84 265:0.42 266:0.38 267:0.89 268:0.78 271:0.60 276:0.43 279:0.86 282:0.88 283:0.82 285:0.62 287:0.87 290:0.82 297:0.36 299:0.97 300:0.33 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.20 17:0.53 21:0.40 27:0.10 28:0.68 30:0.86 32:0.80 34:0.99 36:0.35 37:0.84 39:0.44 41:0.88 43:0.98 66:0.27 69:0.15 70:0.32 74:0.87 77:0.89 81:0.75 83:0.80 85:0.93 91:0.63 96:0.88 98:0.40 101:0.84 104:0.96 108:0.64 114:0.82 120:0.80 121:1.00 122:0.43 123:0.62 124:0.74 126:0.54 127:0.31 129:0.81 131:0.99 133:0.67 135:0.61 140:0.45 144:0.57 145:0.79 146:0.20 147:0.25 149:0.90 150:0.83 151:0.93 153:0.77 154:0.98 155:0.67 157:0.98 159:0.32 161:0.48 162:0.86 164:0.69 165:0.83 166:0.97 167:0.75 172:0.32 173:0.27 176:0.73 177:0.38 179:0.63 181:0.82 182:0.96 187:0.95 192:0.59 195:0.66 201:0.74 202:0.54 204:0.89 208:0.64 212:0.42 215:0.67 216:0.71 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.97 233:0.76 235:0.52 241:0.72 242:0.63 243:0.36 245:0.63 246:0.84 247:0.30 248:0.87 253:0.91 255:0.79 256:0.58 259:0.21 260:0.81 265:0.42 266:0.38 267:0.65 268:0.63 271:0.60 276:0.43 282:0.88 285:0.62 287:0.87 290:0.82 297:0.36 299:0.97 300:0.33 +3 1:0.74 6:0.91 7:0.81 8:0.79 9:0.80 11:0.64 12:0.20 17:0.59 20:0.87 21:0.47 27:0.09 28:0.68 30:0.75 34:0.98 36:0.33 37:0.82 39:0.44 41:0.98 43:0.84 60:0.95 66:0.27 69:0.64 70:0.48 74:0.91 78:0.88 81:0.83 83:0.89 85:0.97 91:0.58 96:0.95 98:0.57 100:0.92 101:0.85 104:0.91 108:0.49 111:0.89 114:0.80 120:0.91 121:0.99 122:0.43 123:0.47 124:0.81 126:0.54 127:0.51 129:0.89 131:0.99 133:0.78 135:0.97 140:0.45 144:0.57 145:0.61 146:0.78 147:0.82 149:0.94 150:0.89 151:0.97 152:0.82 153:0.90 154:0.81 155:0.67 157:0.98 158:0.92 159:0.67 161:0.48 162:0.81 163:0.81 164:0.88 165:0.86 166:0.97 167:0.69 169:0.90 172:0.59 173:0.52 176:0.73 177:0.38 179:0.40 181:0.82 182:0.96 186:0.88 187:0.39 189:0.92 191:0.92 192:0.59 201:0.74 202:0.81 204:0.89 208:0.64 212:0.30 215:0.63 216:0.94 222:0.48 227:0.84 229:0.98 230:0.87 231:0.96 232:0.93 233:0.76 235:0.84 238:0.93 241:0.64 242:0.62 243:0.46 245:0.82 246:0.84 247:0.39 248:0.95 253:0.97 255:0.79 256:0.85 259:0.21 260:0.92 261:0.87 265:0.58 266:0.80 267:0.95 268:0.86 271:0.60 276:0.72 279:0.86 283:0.82 285:0.62 287:0.87 290:0.80 297:0.36 300:0.55 +0 12:0.98 17:0.57 21:0.78 27:0.44 28:0.57 30:0.95 34:0.64 36:0.65 37:0.43 43:0.27 55:0.52 66:0.54 69:0.79 91:0.27 98:0.14 108:0.63 123:0.60 127:0.09 129:0.05 135:0.42 145:0.39 146:0.20 147:0.25 149:0.18 154:0.20 159:0.10 161:0.69 167:0.49 172:0.10 173:0.86 177:0.05 178:0.55 179:0.20 181:0.87 187:0.39 202:0.50 216:0.73 235:0.12 241:0.03 243:0.63 259:0.21 265:0.15 267:0.87 300:0.08 +0 6:0.80 8:0.59 12:0.98 17:0.79 20:0.83 21:0.78 27:0.44 28:0.57 34:0.37 36:0.34 37:0.43 43:0.28 66:0.36 69:0.78 78:0.81 91:0.18 98:0.08 100:0.73 108:0.61 111:0.79 123:0.59 127:0.06 129:0.05 135:0.37 145:0.39 146:0.05 147:0.05 149:0.12 152:0.85 154:0.20 159:0.05 161:0.69 167:0.54 169:0.96 172:0.05 173:0.84 177:0.05 178:0.55 181:0.87 186:0.82 187:0.39 202:0.60 216:0.73 235:0.12 241:0.21 243:0.51 259:0.21 261:0.96 265:0.08 267:0.91 +0 12:0.98 17:0.76 21:0.78 27:0.72 28:0.57 34:0.95 36:0.17 43:0.30 66:0.36 69:0.74 91:0.31 98:0.10 108:0.58 123:0.55 127:0.07 129:0.05 135:0.21 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 161:0.69 167:0.55 172:0.05 173:0.94 177:0.05 178:0.55 181:0.87 187:0.68 216:0.03 235:0.60 241:0.27 243:0.51 259:0.21 265:0.11 267:0.23 +1 12:0.98 17:0.76 21:0.78 27:0.49 28:0.57 30:0.76 34:0.80 36:0.28 37:0.46 43:0.26 55:0.71 66:0.36 69:0.82 70:0.22 91:0.33 98:0.19 108:0.67 123:0.65 124:0.57 127:0.36 129:0.59 133:0.47 135:0.49 146:0.30 147:0.36 149:0.26 154:0.18 159:0.56 161:0.69 163:0.80 167:0.52 172:0.16 173:0.78 177:0.05 178:0.55 179:0.94 181:0.87 187:0.39 202:0.72 212:0.42 216:0.73 235:0.74 241:0.15 242:0.82 243:0.51 245:0.43 247:0.12 259:0.21 265:0.21 266:0.23 267:0.96 276:0.15 300:0.18 +1 12:0.98 17:0.53 21:0.78 27:0.49 28:0.57 30:0.45 34:0.70 36:0.28 37:0.46 43:0.29 55:0.59 66:0.49 69:0.76 70:0.25 91:0.35 98:0.28 108:0.81 123:0.80 124:0.48 127:0.27 129:0.59 133:0.47 135:0.63 146:0.05 147:0.05 149:0.23 154:0.21 159:0.46 161:0.69 163:0.98 167:0.54 172:0.16 173:0.73 177:0.05 178:0.55 179:0.95 181:0.87 187:0.39 202:0.74 212:0.61 216:0.73 235:0.68 241:0.21 242:0.82 243:0.29 245:0.39 247:0.12 259:0.21 265:0.31 266:0.17 267:0.97 276:0.16 300:0.18 +0 12:0.98 17:0.92 21:0.78 27:0.72 28:0.57 34:0.86 36:0.31 43:0.30 66:0.36 69:0.74 91:0.37 98:0.10 108:0.57 123:0.54 127:0.07 129:0.05 135:0.18 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 161:0.69 167:0.55 172:0.05 173:0.93 177:0.05 178:0.55 181:0.87 187:0.68 216:0.02 235:0.02 241:0.27 243:0.51 259:0.21 265:0.11 267:0.36 +2 7:0.78 8:0.59 9:0.80 12:0.86 17:0.16 21:0.78 25:0.88 27:0.03 28:0.47 30:0.17 32:0.77 34:0.58 36:0.87 37:0.66 39:0.95 41:0.82 43:0.35 55:0.45 66:0.91 69:0.07 70:0.85 74:0.82 77:0.70 83:0.76 91:0.88 96:0.86 98:0.92 104:0.58 108:0.06 120:0.96 123:0.06 127:0.53 129:0.05 135:0.51 140:0.99 145:0.69 146:0.87 147:0.89 149:0.39 151:0.87 153:0.90 154:0.77 155:0.67 159:0.44 161:0.65 162:0.56 164:0.95 167:0.90 172:0.74 173:0.17 177:0.38 179:0.55 181:0.86 191:0.91 192:0.59 195:0.66 202:0.94 208:0.64 212:0.42 216:0.47 220:0.62 222:0.60 230:0.81 233:0.76 235:0.05 241:0.89 242:0.24 243:0.91 247:0.60 248:0.81 253:0.87 254:0.74 255:0.79 256:0.94 259:0.21 260:0.96 265:0.92 266:0.54 267:0.44 268:0.94 271:0.29 276:0.62 282:0.85 283:0.71 285:0.62 300:0.55 +2 6:0.88 8:0.93 9:0.80 12:0.86 17:0.12 20:0.98 21:0.78 25:0.88 27:0.03 28:0.47 34:0.75 36:0.64 37:0.66 39:0.95 41:0.82 78:0.92 83:0.74 91:0.94 96:0.93 100:0.97 104:0.58 111:0.96 120:0.96 135:0.56 138:0.97 140:0.93 151:0.96 152:0.99 153:0.95 155:0.67 161:0.65 162:0.62 164:0.94 167:0.74 169:0.95 175:0.91 181:0.86 186:0.92 187:0.21 189:0.96 191:0.94 192:0.59 202:0.92 208:0.64 216:0.47 222:0.60 235:0.05 238:0.96 241:0.69 244:0.83 248:0.92 253:0.90 255:0.79 256:0.92 257:0.84 259:0.21 260:0.96 261:0.96 267:0.36 268:0.93 271:0.29 277:0.87 285:0.62 +2 8:0.88 9:0.80 12:0.86 17:0.85 21:0.05 27:0.72 28:0.47 32:0.75 34:0.28 36:0.41 37:0.61 39:0.95 41:0.82 74:0.45 77:0.70 81:0.84 83:0.73 91:0.93 96:0.93 120:0.95 126:0.32 135:0.51 138:0.96 140:0.80 145:0.70 150:0.64 151:0.98 153:0.94 155:0.67 161:0.65 162:0.72 164:0.94 167:0.91 175:0.91 181:0.86 191:0.89 192:0.59 195:0.52 202:0.90 208:0.64 215:0.91 216:0.12 222:0.60 235:0.05 241:0.94 244:0.83 248:0.93 253:0.89 255:0.79 256:0.92 257:0.84 259:0.21 260:0.95 267:0.36 268:0.92 271:0.29 277:0.87 282:0.83 285:0.62 297:0.36 +2 8:0.74 9:0.80 12:0.86 17:0.62 21:0.05 27:0.18 28:0.47 34:0.63 36:0.49 37:0.82 39:0.95 41:0.82 81:0.84 83:0.74 91:0.90 96:0.93 104:0.96 106:0.81 120:0.92 126:0.32 135:0.83 140:0.80 150:0.64 151:0.91 153:0.94 155:0.67 161:0.65 162:0.79 164:0.92 167:0.84 175:0.91 181:0.86 187:0.95 191:0.92 192:0.59 202:0.87 206:0.81 208:0.64 215:0.91 216:0.53 220:0.62 222:0.60 235:0.05 241:0.77 244:0.83 248:0.92 253:0.90 255:0.79 256:0.91 257:0.84 259:0.21 260:0.93 267:0.28 268:0.91 271:0.29 277:0.87 283:0.71 285:0.62 297:0.36 +2 7:0.78 8:0.63 12:0.86 17:0.66 21:0.05 25:0.88 27:0.03 28:0.47 30:0.76 32:0.77 34:0.36 36:0.47 37:0.66 39:0.95 43:0.36 55:0.52 66:0.80 69:0.07 70:0.28 74:0.77 77:0.70 78:0.99 81:0.45 91:0.76 96:0.83 98:0.75 104:0.58 108:0.06 111:0.98 114:0.77 120:0.67 123:0.06 126:0.32 127:0.24 129:0.05 135:0.37 140:0.80 145:0.69 146:0.36 147:0.43 149:0.27 150:0.36 151:0.64 153:0.78 154:0.87 158:0.84 159:0.14 161:0.65 162:0.56 164:0.65 167:0.65 169:0.96 172:0.45 173:0.47 176:0.56 177:0.38 178:0.42 179:0.71 181:0.86 187:0.21 190:0.77 191:0.78 195:0.53 202:0.73 212:0.95 216:0.47 220:0.62 222:0.60 230:0.81 232:0.84 233:0.76 235:0.05 238:0.90 241:0.53 242:0.58 243:0.82 247:0.47 248:0.69 253:0.84 254:0.74 256:0.71 259:0.21 260:0.67 265:0.75 266:0.12 267:0.36 268:0.71 271:0.29 276:0.38 282:0.85 285:0.62 290:0.77 300:0.25 +2 6:0.92 8:0.83 9:0.80 12:0.86 17:0.26 20:0.96 21:0.05 27:0.12 28:0.47 34:0.73 36:0.92 37:0.67 39:0.95 41:0.90 43:0.33 66:0.36 69:0.62 74:0.43 78:0.97 81:0.84 83:0.77 91:0.93 96:0.94 98:0.12 100:0.98 104:0.84 108:0.47 111:0.97 120:0.95 123:0.46 126:0.32 127:0.08 129:0.05 135:0.58 138:0.96 140:0.87 145:0.80 146:0.05 147:0.05 149:0.13 150:0.64 151:0.98 152:0.98 153:0.92 154:0.24 155:0.67 159:0.05 161:0.65 162:0.69 164:0.93 167:0.66 169:0.96 172:0.05 173:0.93 175:0.75 177:0.05 181:0.86 186:0.96 187:0.87 189:0.93 191:0.91 192:0.59 202:0.89 208:0.64 215:0.91 216:0.68 220:0.62 222:0.60 235:0.05 238:0.95 241:0.55 243:0.51 244:0.61 248:0.94 253:0.90 255:0.79 256:0.91 257:0.65 259:0.21 260:0.95 261:0.97 265:0.13 267:0.87 268:0.91 271:0.29 277:0.69 283:0.71 285:0.62 297:0.36 +2 6:0.92 8:0.73 9:0.80 12:0.86 17:0.28 20:0.96 21:0.05 27:0.12 28:0.47 30:0.95 34:0.70 36:0.89 37:0.67 39:0.95 41:0.90 43:0.73 55:0.59 66:0.54 69:0.62 74:0.41 78:0.95 81:0.84 83:0.78 91:0.95 96:0.94 98:0.30 100:0.94 104:0.84 108:0.47 111:0.93 120:0.95 123:0.46 126:0.32 127:0.12 129:0.05 135:0.51 138:0.97 140:0.90 145:0.80 146:0.24 147:0.30 149:0.36 150:0.64 151:0.96 152:0.98 153:0.89 154:0.63 155:0.67 159:0.11 161:0.65 162:0.67 164:0.92 167:0.69 169:0.96 172:0.10 173:0.87 177:0.38 179:0.80 181:0.86 186:0.94 187:0.87 189:0.93 191:0.91 192:0.59 202:0.89 208:0.64 215:0.91 216:0.68 220:0.62 222:0.60 235:0.05 238:0.89 241:0.60 243:0.63 248:0.94 253:0.91 254:0.98 255:0.79 256:0.91 259:0.21 260:0.95 261:0.97 265:0.32 267:0.28 268:0.91 271:0.29 283:0.71 285:0.62 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.86 17:0.43 21:0.59 27:0.45 28:0.47 30:0.76 34:0.80 36:0.18 37:0.50 39:0.95 43:0.82 55:0.59 66:0.20 69:0.70 70:0.22 74:0.48 81:0.55 83:0.73 85:0.72 91:0.20 98:0.14 101:0.70 108:0.53 114:0.74 122:0.41 123:0.89 124:0.57 126:0.54 127:0.47 129:0.59 133:0.47 135:0.49 145:0.49 146:0.23 147:0.29 149:0.48 150:0.71 154:0.77 155:0.67 159:0.68 161:0.65 163:0.80 165:0.78 167:0.63 172:0.16 173:0.58 176:0.73 177:0.38 178:0.55 179:0.95 181:0.86 187:0.68 192:0.59 201:0.74 212:0.42 215:0.55 216:0.77 222:0.60 235:0.74 241:0.49 242:0.45 243:0.51 245:0.43 247:0.35 253:0.58 259:0.21 265:0.15 266:0.23 267:0.44 271:0.29 276:0.13 290:0.74 297:0.36 300:0.18 +1 1:0.74 6:0.88 7:0.78 8:0.59 9:0.80 12:0.86 17:0.59 20:0.98 21:0.40 25:0.88 27:0.03 28:0.47 30:0.13 32:0.77 34:0.75 36:0.66 37:0.66 39:0.95 43:0.91 55:0.59 66:0.35 69:0.17 70:0.79 74:0.82 77:0.70 78:0.94 81:0.64 91:0.69 96:0.88 98:0.77 100:0.93 104:0.58 108:0.14 111:0.92 114:0.82 120:0.88 123:0.79 124:0.61 126:0.54 127:0.74 129:0.05 133:0.39 135:0.54 138:0.97 140:0.80 145:0.69 146:0.86 147:0.89 149:0.89 150:0.69 151:0.92 152:0.99 153:0.86 154:0.90 155:0.67 158:0.87 159:0.73 161:0.65 162:0.83 164:0.87 167:0.61 169:0.95 172:0.76 173:0.14 176:0.73 177:0.38 179:0.33 181:0.86 186:0.93 187:0.21 189:0.82 191:0.77 192:0.59 195:0.86 201:0.74 202:0.79 208:0.64 212:0.58 215:0.67 216:0.47 222:0.60 230:0.81 233:0.76 235:0.52 238:0.88 241:0.41 242:0.77 243:0.51 245:0.94 247:0.55 248:0.89 253:0.90 255:0.79 256:0.83 259:0.21 260:0.88 261:0.96 265:0.67 266:0.63 267:0.69 268:0.84 271:0.29 276:0.82 277:0.69 279:0.86 282:0.85 283:0.71 285:0.62 290:0.82 297:0.36 300:0.70 +1 1:0.74 11:0.57 12:0.86 17:0.57 21:0.47 27:0.45 28:0.47 30:0.21 32:0.78 34:0.83 36:0.18 37:0.50 39:0.95 43:0.82 55:0.59 66:0.61 69:0.69 70:0.82 74:0.79 77:0.70 81:0.62 83:0.74 85:0.72 91:0.14 98:0.28 101:0.71 108:0.53 114:0.80 123:0.50 124:0.47 126:0.54 127:0.42 129:0.05 133:0.39 135:0.82 145:0.44 146:0.41 147:0.47 149:0.53 150:0.76 154:0.77 155:0.67 159:0.57 161:0.65 165:0.80 167:0.53 172:0.48 173:0.64 176:0.73 177:0.38 178:0.55 179:0.72 181:0.86 187:0.68 192:0.59 195:0.79 201:0.74 212:0.76 215:0.63 216:0.77 222:0.60 235:0.60 241:0.10 242:0.22 243:0.68 245:0.62 247:0.60 253:0.70 259:0.21 265:0.30 266:0.38 267:0.23 271:0.29 276:0.25 282:0.86 290:0.80 297:0.36 300:0.55 +3 6:0.92 8:0.83 9:0.80 12:0.86 17:0.55 20:0.77 21:0.22 27:0.12 28:0.47 30:0.33 34:0.68 36:0.77 37:0.67 39:0.95 41:0.82 43:0.28 55:0.59 66:0.86 69:0.62 70:0.65 74:0.74 78:0.96 81:0.35 83:0.76 91:0.89 96:0.95 98:0.88 100:0.97 104:0.84 108:0.47 111:0.97 114:0.72 120:0.91 123:0.46 126:0.32 127:0.42 129:0.05 135:0.71 138:0.97 140:0.92 145:0.80 146:0.78 147:0.81 149:0.36 150:0.31 151:0.97 152:0.98 153:0.90 154:0.74 155:0.67 158:0.84 159:0.34 161:0.65 162:0.60 164:0.86 167:0.67 169:0.96 172:0.61 173:0.72 176:0.56 177:0.38 179:0.67 181:0.86 186:0.95 187:0.68 189:0.94 191:0.87 192:0.59 202:0.89 208:0.64 212:0.69 216:0.68 220:0.82 222:0.60 235:0.22 238:0.95 241:0.57 242:0.54 243:0.87 247:0.67 248:0.91 253:0.95 254:0.86 255:0.79 256:0.90 259:0.21 260:0.91 261:0.97 265:0.88 266:0.38 267:0.79 268:0.90 271:0.29 276:0.49 285:0.62 290:0.72 300:0.43 +2 1:0.74 6:0.83 8:0.74 9:0.80 12:0.86 17:0.38 20:0.91 21:0.47 27:0.10 28:0.47 30:0.54 34:0.56 36:0.90 37:0.69 39:0.95 41:0.93 43:0.41 55:0.71 66:0.36 69:0.36 70:0.71 74:0.52 78:0.86 81:0.59 83:0.78 91:0.79 96:0.95 98:0.45 100:0.84 108:0.81 111:0.84 114:0.80 120:0.95 123:0.80 124:0.78 126:0.54 127:0.87 129:0.89 133:0.78 135:0.82 138:0.99 140:0.80 146:0.44 147:0.51 149:0.59 150:0.66 151:0.93 152:0.85 153:0.81 154:0.31 155:0.67 158:0.87 159:0.78 161:0.65 162:0.64 164:0.95 167:0.71 169:0.82 172:0.51 173:0.21 175:0.75 176:0.73 177:0.05 179:0.65 181:0.86 186:0.86 187:0.39 189:0.85 191:0.89 192:0.59 201:0.74 202:0.93 208:0.64 212:0.35 215:0.63 216:0.66 220:0.62 222:0.60 235:0.60 238:0.83 241:0.64 242:0.81 243:0.28 244:0.61 245:0.68 247:0.35 248:0.95 253:0.93 255:0.79 256:0.94 257:0.65 259:0.21 260:0.95 261:0.86 265:0.47 266:0.80 267:0.28 268:0.95 271:0.29 276:0.56 277:0.69 279:0.86 283:0.71 285:0.62 290:0.80 297:0.36 300:0.55 +0 7:0.78 8:0.62 12:0.86 17:0.57 21:0.30 27:0.21 28:0.47 30:0.33 34:0.73 36:0.89 37:0.74 39:0.95 43:0.45 55:0.84 66:0.09 69:0.95 70:0.81 74:0.34 81:0.60 91:0.68 98:0.33 104:0.84 106:0.81 108:0.97 120:0.94 123:0.98 124:0.98 126:0.32 127:0.87 129:0.59 133:0.98 135:0.20 140:0.45 146:0.19 147:0.58 149:0.76 150:0.45 151:0.83 153:0.96 154:0.24 159:0.96 161:0.65 162:0.56 163:0.92 164:0.90 167:0.63 172:0.73 173:0.72 177:0.05 179:0.16 181:0.86 187:0.39 190:0.77 191:0.77 202:0.89 206:0.81 212:0.16 215:0.72 216:0.95 222:0.60 230:0.81 233:0.76 235:0.31 241:0.49 242:0.82 243:0.11 245:0.91 247:0.39 248:0.92 253:0.31 254:0.86 256:0.88 257:0.65 259:0.21 260:0.95 265:0.35 266:0.97 267:0.96 268:0.88 271:0.29 276:0.95 283:0.82 285:0.50 297:0.36 300:0.84 +2 6:0.97 8:0.85 11:0.57 12:0.01 17:0.76 20:0.79 21:0.78 27:0.07 28:0.66 30:0.76 34:0.40 36:0.71 37:0.96 39:0.33 43:0.94 66:0.86 69:0.12 70:0.40 74:0.96 78:0.84 85:0.98 89:0.98 91:0.22 98:0.55 100:0.85 101:0.86 108:0.10 111:0.83 122:0.43 123:0.10 124:0.37 127:0.86 129:0.59 133:0.47 135:0.78 141:0.91 146:0.77 147:0.81 149:0.98 152:0.96 154:0.94 159:0.73 161:0.52 167:0.56 169:0.98 172:0.86 173:0.13 177:0.38 178:0.55 179:0.39 181:0.89 186:0.85 187:0.39 189:0.92 202:0.36 212:0.94 216:0.41 222:0.60 223:0.92 225:0.96 232:0.75 234:0.91 238:0.84 240:0.95 241:0.18 242:0.62 243:0.86 245:0.75 247:0.39 253:0.68 259:0.21 261:0.98 265:0.56 266:0.23 267:0.28 271:0.79 274:0.91 276:0.59 300:0.43 +1 1:0.74 11:0.57 12:0.01 17:0.94 27:0.72 28:0.66 30:0.62 34:0.25 36:0.28 39:0.33 43:0.88 66:0.75 69:0.52 70:0.23 74:0.69 81:0.94 83:0.80 85:0.80 89:0.96 91:0.78 98:0.69 101:0.81 104:0.58 108:0.40 114:0.98 122:0.51 123:0.38 126:0.54 127:0.21 129:0.05 135:0.34 141:0.91 146:0.30 147:0.36 149:0.68 150:0.97 154:0.86 155:0.67 159:0.12 161:0.52 165:0.92 167:0.86 172:0.32 173:0.91 176:0.73 177:0.38 178:0.55 179:0.79 181:0.89 192:0.59 201:0.74 212:0.84 215:0.96 222:0.60 223:0.93 225:0.97 232:0.82 234:0.91 235:0.05 240:0.95 241:0.78 242:0.33 243:0.77 247:0.39 253:0.76 259:0.21 265:0.69 266:0.17 267:0.23 271:0.79 274:0.91 276:0.25 290:0.98 297:0.36 300:0.18 +4 1:0.74 7:0.81 8:0.95 9:0.80 11:0.57 12:0.01 17:0.53 27:0.27 28:0.66 30:0.91 32:0.80 34:0.18 36:0.56 37:0.90 39:0.33 41:0.98 43:0.90 66:0.27 69:0.44 70:0.13 74:0.70 76:0.85 77:0.89 81:0.94 83:0.89 85:0.80 89:0.96 91:0.93 96:0.97 98:0.11 101:0.77 104:0.58 108:0.34 114:0.98 120:0.93 121:0.90 122:0.43 123:0.33 124:0.61 126:0.54 127:0.78 129:0.59 133:0.47 135:0.77 140:0.45 141:0.97 145:0.96 146:0.13 147:0.18 149:0.62 150:0.97 151:0.99 153:0.96 154:0.89 155:0.67 159:0.43 161:0.52 162:0.58 163:0.88 164:0.90 165:0.92 167:0.89 172:0.10 173:0.52 176:0.73 177:0.38 179:0.98 181:0.89 191:0.89 192:0.59 195:0.63 201:0.74 202:0.88 204:0.89 208:0.64 212:0.61 215:0.96 216:0.39 222:0.60 223:0.93 225:0.97 230:0.87 232:0.82 233:0.76 234:0.98 235:0.05 240:0.99 241:0.85 242:0.63 243:0.46 245:0.40 247:0.30 248:0.97 253:0.97 255:0.79 256:0.87 259:0.21 260:0.93 265:0.11 266:0.17 267:0.52 268:0.88 271:0.79 274:0.99 276:0.12 282:0.88 285:0.62 290:0.98 297:0.36 299:0.97 300:0.13 +1 1:0.74 7:0.81 8:0.75 9:0.80 11:0.57 12:0.01 17:0.43 20:0.82 21:0.30 25:0.88 27:0.71 28:0.66 30:0.88 34:0.87 36:0.27 37:0.60 39:0.33 41:0.88 43:0.98 55:0.42 66:0.98 69:0.19 70:0.31 74:0.85 78:0.81 81:0.81 83:0.78 85:0.90 89:0.98 91:0.54 96:0.82 98:0.90 100:0.78 101:0.83 108:0.15 111:0.80 114:0.68 117:0.86 120:0.72 121:0.90 123:0.15 126:0.54 127:0.46 129:0.05 135:0.87 138:0.95 140:0.45 141:0.97 146:0.79 147:0.82 149:0.96 150:0.87 151:0.87 152:0.78 153:0.48 154:0.98 155:0.67 159:0.35 161:0.52 162:0.86 164:0.69 165:0.81 167:0.58 169:0.80 172:0.94 173:0.33 176:0.56 177:0.38 179:0.19 181:0.89 186:0.77 187:0.68 192:0.59 201:0.74 202:0.53 204:0.89 208:0.64 212:0.93 215:0.67 216:0.56 220:0.87 222:0.60 223:0.93 225:0.97 230:0.87 232:0.81 233:0.76 234:1.00 235:0.68 238:0.87 240:0.99 241:0.30 242:0.78 243:0.98 247:0.60 248:0.79 253:0.85 255:0.79 256:0.58 260:0.72 261:0.76 265:0.90 266:0.38 267:0.69 268:0.62 271:0.79 274:0.98 276:0.88 285:0.62 286:0.99 290:0.68 297:0.36 300:0.70 +1 1:0.74 11:0.57 12:0.01 17:0.79 21:0.13 27:0.40 28:0.66 30:0.87 34:0.80 36:0.53 37:0.69 39:0.33 43:0.93 66:0.54 69:0.33 70:0.27 74:0.69 81:0.86 83:0.77 85:0.88 89:0.97 91:0.23 98:0.62 101:0.83 108:0.26 114:0.92 122:0.43 123:0.25 124:0.48 126:0.54 127:0.47 129:0.59 133:0.47 135:0.44 141:0.91 146:0.41 147:0.48 149:0.83 150:0.91 154:0.92 155:0.67 159:0.24 161:0.52 163:0.75 165:0.88 167:0.61 172:0.32 173:0.56 176:0.73 177:0.38 178:0.55 179:0.88 181:0.89 187:0.68 192:0.59 201:0.74 212:0.42 215:0.84 216:0.19 222:0.60 223:0.93 225:0.97 232:0.77 234:0.91 235:0.22 240:0.95 241:0.39 242:0.63 243:0.63 245:0.50 247:0.30 253:0.73 259:0.21 265:0.63 266:0.27 267:0.28 271:0.79 274:0.91 276:0.29 290:0.92 297:0.36 300:0.25 +2 1:0.74 6:0.86 8:0.73 9:0.80 11:0.57 12:0.01 17:0.85 20:0.85 21:0.64 27:0.72 28:0.66 30:0.62 34:0.34 36:0.43 39:0.33 41:0.88 43:0.96 60:0.87 66:0.75 69:0.29 70:0.23 74:0.76 78:0.78 81:0.56 83:0.83 85:0.80 89:0.96 91:0.72 96:0.78 98:0.89 100:0.80 101:0.81 104:0.58 108:0.22 111:0.77 114:0.72 120:0.66 122:0.51 123:0.22 126:0.54 127:0.44 129:0.05 135:0.24 140:0.45 141:0.97 146:0.34 147:0.40 149:0.70 150:0.70 151:0.77 152:0.80 153:0.48 154:0.96 155:0.67 158:0.92 159:0.13 161:0.52 162:0.74 164:0.67 165:0.70 167:0.87 169:0.78 172:0.32 173:0.80 176:0.73 177:0.38 179:0.92 181:0.89 186:0.78 189:0.87 192:0.59 201:0.74 202:0.56 208:0.64 212:0.30 215:0.53 216:0.07 220:0.62 222:0.60 223:0.93 225:0.97 232:0.82 234:0.99 235:0.80 238:0.87 240:0.99 241:0.80 242:0.33 243:0.77 247:0.39 248:0.71 253:0.80 255:0.79 256:0.58 259:0.21 260:0.67 261:0.78 265:0.89 266:0.27 267:0.17 268:0.59 271:0.79 274:0.98 276:0.22 279:0.86 283:0.82 285:0.62 290:0.72 297:0.36 300:0.18 +1 11:0.57 12:0.01 17:0.45 21:0.78 27:0.45 28:0.66 30:0.68 34:0.76 36:0.19 37:0.50 39:0.33 43:0.78 66:0.36 69:0.78 70:0.61 74:0.73 85:0.91 89:0.97 91:0.14 98:0.26 101:0.77 108:0.70 122:0.43 123:0.68 124:0.83 127:0.53 129:0.81 133:0.83 135:0.73 141:0.91 145:0.47 146:0.13 147:0.18 149:0.82 154:0.71 159:0.80 161:0.52 163:0.88 167:0.63 172:0.41 173:0.60 177:0.38 178:0.55 179:0.71 181:0.89 187:0.68 212:0.29 216:0.77 222:0.60 223:0.92 225:0.96 234:0.91 235:0.88 240:0.95 241:0.46 242:0.54 243:0.21 245:0.56 247:0.39 253:0.54 259:0.21 265:0.29 266:0.71 267:0.88 271:0.79 274:0.91 276:0.45 300:0.55 +1 6:0.90 8:0.78 9:0.80 12:0.01 17:0.59 20:0.94 21:0.78 27:0.15 28:0.66 34:0.47 36:0.22 37:0.72 39:0.33 41:0.88 78:0.92 83:0.78 89:0.95 91:0.91 96:0.87 100:0.94 111:0.92 120:0.79 135:0.76 140:0.45 141:0.97 151:0.93 152:0.88 153:0.78 155:0.67 161:0.52 162:0.60 164:0.71 167:0.91 169:0.92 175:0.91 181:0.89 186:0.92 189:0.91 192:0.59 202:0.65 208:0.64 216:0.05 222:0.60 223:0.93 225:0.97 232:0.72 234:0.98 238:0.91 240:0.99 241:0.96 244:0.83 248:0.86 253:0.82 255:0.79 256:0.58 257:0.84 259:0.21 260:0.80 261:0.93 267:0.36 268:0.65 271:0.79 274:0.98 277:0.87 285:0.62 +0 1:0.74 9:0.80 11:0.57 12:0.01 17:0.83 27:0.27 28:0.66 30:0.89 34:0.30 36:0.36 37:0.90 39:0.33 41:0.82 43:0.78 66:0.75 69:0.77 70:0.20 74:0.58 81:0.94 83:0.81 85:0.85 89:0.97 91:0.57 96:0.80 98:0.52 101:0.81 104:0.58 108:0.60 114:0.98 120:0.69 122:0.43 123:0.58 126:0.54 127:0.16 129:0.05 135:0.47 140:0.80 141:0.97 146:0.40 147:0.46 149:0.71 150:0.97 151:0.87 153:0.48 154:0.71 155:0.67 159:0.15 161:0.52 162:0.54 164:0.57 165:0.92 167:0.63 172:0.32 173:0.93 176:0.73 177:0.38 178:0.42 179:0.61 181:0.89 187:0.39 192:0.59 201:0.74 202:0.56 208:0.64 212:0.30 215:0.96 216:0.39 222:0.60 223:0.93 225:0.97 232:0.82 234:0.99 235:0.05 240:0.99 241:0.47 242:0.63 243:0.77 247:0.30 248:0.78 253:0.83 255:0.79 256:0.45 259:0.21 260:0.70 265:0.54 266:0.27 267:0.98 268:0.46 271:0.79 274:0.97 276:0.22 285:0.62 290:0.98 297:0.36 300:0.18 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.01 17:0.76 21:0.05 27:0.72 28:0.66 30:0.85 32:0.80 34:0.70 36:0.69 39:0.33 41:0.90 43:0.93 66:0.49 69:0.32 70:0.28 74:0.88 76:0.94 77:0.70 81:0.90 83:0.80 85:0.94 89:0.97 91:0.72 96:0.80 98:0.70 101:0.85 104:0.54 108:0.25 114:0.95 120:0.69 121:0.99 122:0.43 123:0.24 124:0.56 126:0.54 127:0.82 129:0.59 133:0.47 135:0.42 140:0.45 141:0.97 145:0.88 146:0.59 147:0.65 149:0.92 150:0.94 151:0.81 153:0.72 154:0.92 155:0.67 159:0.38 161:0.52 162:0.83 164:0.72 165:0.90 167:0.87 172:0.48 173:0.44 176:0.73 177:0.38 179:0.74 181:0.89 192:0.59 195:0.62 201:0.74 202:0.59 204:0.89 208:0.64 212:0.48 215:0.91 216:0.02 220:0.62 222:0.60 223:0.93 225:0.97 230:0.87 232:0.78 233:0.76 234:0.99 235:0.12 240:0.99 241:0.79 242:0.63 243:0.60 245:0.71 247:0.30 248:0.77 253:0.87 255:0.79 256:0.64 259:0.21 260:0.70 265:0.70 266:0.38 267:0.23 268:0.66 271:0.79 274:0.98 276:0.50 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.25 +1 1:0.74 11:0.57 12:0.01 17:0.32 21:0.13 27:0.45 28:0.66 30:0.68 34:0.70 36:0.17 37:0.50 39:0.33 43:0.82 60:0.87 66:0.35 69:0.68 70:0.41 74:0.87 81:0.86 83:0.85 85:0.94 89:0.97 91:0.20 98:0.53 101:0.82 108:0.75 114:0.92 122:0.57 123:0.74 124:0.70 126:0.54 127:0.45 129:0.59 133:0.64 135:0.89 141:0.91 145:0.52 146:0.52 147:0.58 149:0.90 150:0.91 154:0.77 155:0.67 158:0.92 159:0.60 161:0.52 163:0.81 165:0.88 167:0.57 172:0.51 173:0.61 176:0.73 177:0.38 178:0.55 179:0.54 181:0.89 187:0.68 192:0.59 201:0.74 208:0.64 212:0.30 215:0.84 216:0.77 222:0.60 223:0.93 225:0.97 234:0.91 235:0.22 240:0.95 241:0.24 242:0.55 243:0.43 245:0.74 247:0.47 253:0.77 259:0.21 265:0.55 266:0.71 267:0.36 271:0.79 274:0.91 276:0.59 279:0.86 283:0.82 290:0.92 297:0.36 300:0.33 +0 1:0.74 6:0.97 8:0.96 9:0.80 11:0.57 12:0.01 17:0.24 20:0.97 27:0.07 28:0.66 30:0.95 34:0.42 36:0.79 37:0.96 39:0.33 41:0.98 43:0.88 60:0.87 66:0.54 69:0.50 74:0.57 78:0.95 81:0.94 83:0.90 85:0.72 89:0.96 91:0.94 96:0.98 98:0.17 100:0.99 101:0.77 108:0.38 111:0.99 114:0.98 120:0.96 123:0.37 126:0.54 127:0.10 129:0.05 135:0.32 140:0.45 141:0.97 146:0.13 147:0.18 149:0.50 150:0.97 151:0.99 152:0.96 153:0.97 154:0.87 155:0.67 158:0.92 159:0.08 161:0.52 162:0.66 164:0.92 165:0.92 167:0.90 169:0.98 172:0.10 173:0.89 176:0.73 177:0.38 179:0.20 181:0.89 186:0.94 189:0.98 191:0.86 192:0.59 201:0.74 202:0.89 208:0.64 215:0.96 216:0.41 222:0.60 223:0.93 225:0.97 232:0.75 234:0.98 235:0.05 238:0.98 240:0.99 241:0.89 243:0.63 248:0.98 253:0.97 255:0.79 256:0.90 259:0.21 260:0.96 261:0.98 265:0.18 267:0.96 268:0.90 271:0.79 274:1.00 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.01 17:0.90 21:0.71 27:0.72 28:0.66 30:0.91 34:0.34 36:0.50 39:0.33 43:0.87 55:0.52 66:0.69 69:0.53 70:0.13 74:0.63 81:0.50 83:0.73 85:0.72 89:0.96 91:0.79 98:0.51 101:0.77 104:0.54 108:0.41 114:0.68 122:0.57 123:0.39 126:0.54 127:0.15 129:0.05 135:0.26 141:0.91 146:0.22 147:0.28 149:0.50 150:0.66 154:0.85 155:0.67 159:0.11 161:0.52 165:0.69 167:0.82 172:0.21 173:0.98 176:0.73 177:0.38 178:0.55 179:0.75 181:0.89 187:0.21 192:0.59 201:0.74 212:0.61 215:0.48 222:0.60 223:0.92 225:0.96 232:0.78 234:0.91 235:0.88 240:0.95 241:0.76 242:0.63 243:0.73 247:0.30 253:0.58 259:0.21 265:0.52 266:0.17 267:0.17 271:0.79 274:0.91 276:0.21 290:0.68 297:0.36 300:0.13 +1 1:0.74 11:0.57 12:0.01 17:0.96 27:0.72 28:0.66 30:0.62 34:0.24 36:0.32 39:0.33 43:0.88 66:0.75 69:0.52 70:0.23 74:0.69 81:0.94 83:0.80 85:0.80 89:0.96 91:0.78 98:0.69 101:0.81 104:0.58 108:0.40 114:0.98 122:0.51 123:0.38 126:0.54 127:0.21 129:0.05 135:0.37 141:0.91 146:0.30 147:0.36 149:0.68 150:0.97 154:0.86 155:0.67 159:0.12 161:0.52 165:0.92 167:0.85 172:0.32 173:0.91 176:0.73 177:0.38 178:0.55 179:0.79 181:0.89 192:0.59 201:0.74 212:0.84 215:0.96 222:0.60 223:0.93 225:0.97 232:0.82 234:0.91 235:0.05 240:0.95 241:0.78 242:0.33 243:0.77 247:0.39 253:0.76 259:0.21 265:0.69 266:0.17 267:0.23 271:0.79 274:0.91 276:0.25 290:0.98 297:0.36 300:0.18 +1 1:0.74 9:0.80 11:0.57 12:0.01 17:0.57 21:0.05 27:0.40 28:0.66 30:0.91 34:0.67 36:0.54 37:0.69 39:0.33 41:0.88 43:0.92 66:0.69 69:0.37 70:0.13 74:0.56 81:0.90 83:0.80 85:0.80 89:0.96 91:0.54 96:0.85 98:0.87 101:0.80 108:0.29 114:0.95 120:0.76 122:0.43 123:0.28 126:0.54 127:0.39 129:0.05 135:0.34 140:0.45 141:0.97 146:0.44 147:0.51 149:0.68 150:0.94 151:0.87 153:0.48 154:0.91 155:0.67 159:0.16 161:0.52 162:0.86 163:0.75 164:0.67 165:0.90 167:0.74 172:0.21 173:0.74 176:0.73 177:0.38 179:0.97 181:0.89 187:0.68 192:0.59 201:0.74 202:0.50 208:0.64 212:0.61 215:0.91 216:0.19 220:0.62 222:0.60 223:0.93 225:0.97 232:0.77 234:1.00 235:0.12 240:0.99 241:0.69 242:0.63 243:0.73 247:0.30 248:0.83 253:0.84 255:0.79 256:0.58 259:0.21 260:0.77 265:0.87 266:0.17 267:0.73 268:0.59 271:0.79 274:0.98 276:0.13 285:0.62 290:0.95 297:0.36 300:0.13 +1 1:0.74 6:0.86 7:0.81 9:0.80 11:0.57 12:0.01 17:0.66 20:0.80 21:0.05 27:0.40 28:0.66 30:0.91 34:0.67 36:0.49 37:0.69 39:0.33 41:0.88 43:0.92 66:0.69 69:0.33 70:0.13 74:0.72 76:0.85 77:0.70 78:0.88 81:0.90 83:0.81 85:0.80 89:0.96 91:0.61 96:0.88 98:0.83 100:0.73 101:0.80 108:0.26 111:0.85 114:0.95 120:0.79 121:0.90 122:0.43 123:0.25 126:0.54 127:0.32 129:0.05 135:0.34 140:0.45 141:0.97 145:0.93 146:0.41 147:0.48 149:0.68 150:0.94 151:0.93 152:0.84 153:0.78 154:0.92 155:0.67 159:0.15 161:0.52 162:0.61 163:0.75 164:0.71 165:0.90 167:0.83 169:0.79 172:0.21 173:0.70 176:0.73 177:0.38 179:0.96 181:0.89 186:0.88 187:0.39 191:0.84 192:0.59 195:0.54 201:0.74 202:0.65 204:0.89 208:0.64 212:0.61 215:0.91 216:0.19 222:0.60 223:0.93 225:0.97 230:0.87 232:0.77 233:0.76 234:0.98 235:0.12 240:0.99 241:0.76 242:0.63 243:0.73 247:0.30 248:0.87 253:0.88 255:0.79 256:0.58 259:0.21 260:0.80 261:0.84 265:0.83 266:0.17 267:0.44 268:0.65 271:0.79 274:0.98 276:0.13 282:0.84 285:0.62 290:0.95 297:0.36 300:0.13 +1 1:0.74 6:0.88 8:0.74 9:0.80 11:0.57 12:0.01 17:0.88 20:0.94 21:0.71 27:0.33 28:0.66 30:0.76 32:0.80 34:0.45 36:0.41 37:0.70 39:0.33 41:0.90 43:0.86 66:0.36 69:0.60 70:0.22 74:0.74 76:0.99 77:0.98 78:0.81 81:0.50 83:0.76 85:0.80 89:0.96 91:0.73 96:0.87 98:0.32 100:0.85 101:0.78 104:0.58 108:0.65 111:0.81 114:0.68 120:0.73 122:0.51 123:0.63 124:0.57 126:0.54 127:0.41 129:0.59 133:0.47 135:0.30 140:0.80 141:0.97 145:0.85 146:0.13 147:0.18 149:0.65 150:0.66 151:0.81 152:0.91 153:0.72 154:0.84 155:0.67 159:0.26 161:0.52 162:0.84 163:0.88 164:0.78 165:0.69 167:0.89 169:0.83 172:0.16 173:0.78 176:0.73 177:0.38 179:0.95 181:0.89 186:0.82 189:0.89 192:0.59 195:0.58 201:0.74 202:0.73 208:0.64 212:0.42 215:0.48 216:0.22 220:0.82 222:0.60 223:0.93 225:0.97 232:0.82 234:0.98 235:0.88 238:0.90 240:0.99 241:0.86 242:0.45 243:0.40 245:0.43 247:0.35 248:0.80 253:0.87 255:0.79 256:0.77 259:0.21 260:0.74 261:0.85 265:0.34 266:0.23 267:0.28 268:0.79 271:0.79 274:0.98 276:0.16 282:0.88 285:0.62 290:0.68 297:0.36 299:0.88 300:0.18 +1 1:0.68 10:0.96 11:0.81 12:0.69 17:0.51 18:0.92 21:0.13 22:0.93 27:0.25 29:0.77 30:0.57 33:0.97 34:0.24 36:0.24 37:0.89 39:0.51 43:0.48 45:0.94 47:0.93 64:0.77 66:0.33 69:0.55 70:0.62 71:0.91 74:0.48 81:0.76 83:0.69 86:0.88 91:0.20 98:0.66 99:0.95 101:0.65 104:0.54 108:0.42 114:0.92 122:0.93 123:0.40 124:0.79 126:0.54 127:0.80 129:0.59 131:0.61 133:0.78 135:0.32 139:0.83 144:0.85 146:0.89 147:0.91 149:0.66 150:0.63 154:0.60 155:0.52 157:0.99 159:0.78 165:0.81 166:0.86 170:0.82 172:0.73 173:0.37 174:0.95 176:0.70 177:0.88 178:0.55 179:0.35 182:1.00 187:0.21 192:0.54 197:0.96 201:0.67 208:0.64 212:0.28 215:0.84 216:0.36 222:0.48 227:0.61 229:0.61 231:0.78 235:0.42 236:0.95 239:0.94 241:0.05 242:0.18 243:0.50 244:0.61 245:0.88 246:1.00 247:0.94 253:0.63 254:0.86 259:0.21 262:0.93 265:0.66 266:0.80 267:0.59 271:0.61 276:0.81 287:0.61 290:0.92 291:0.97 297:0.36 300:0.70 +2 1:0.61 8:0.77 9:0.72 10:0.94 11:0.55 12:0.69 17:0.15 18:0.95 21:0.54 23:0.99 27:0.14 29:0.77 30:0.45 31:0.92 34:0.96 36:0.71 37:0.82 39:0.51 43:0.27 44:0.88 45:0.93 48:0.91 55:0.52 62:0.98 64:0.77 66:0.48 69:0.39 70:0.73 71:0.91 74:0.33 75:0.97 76:0.85 79:0.92 81:0.53 83:0.56 86:0.87 91:0.70 96:0.89 97:0.89 98:0.45 99:0.83 101:0.47 102:0.95 104:0.89 108:0.87 114:0.67 120:0.61 122:0.93 123:0.86 124:0.83 126:0.51 127:0.72 128:0.97 129:0.05 131:0.90 133:0.86 135:0.20 137:0.92 138:0.98 139:0.87 140:0.87 144:0.85 145:0.56 146:0.43 147:0.49 149:0.51 150:0.45 151:0.56 153:0.72 154:0.48 155:0.65 157:0.86 158:0.73 159:0.76 162:0.73 164:0.54 165:0.65 166:0.77 168:0.97 170:0.82 172:0.82 173:0.23 174:0.95 176:0.59 177:0.88 179:0.29 182:0.79 187:0.68 190:0.95 192:0.87 193:0.97 195:0.88 196:0.94 197:0.94 201:0.60 202:0.83 204:0.80 205:0.98 208:0.64 212:0.80 216:0.84 219:0.91 220:0.82 222:0.48 226:0.95 227:0.95 229:0.88 231:0.78 235:0.74 236:0.94 239:0.95 241:0.63 242:0.29 243:0.27 244:0.61 245:0.85 246:0.92 247:0.92 248:0.58 253:0.84 254:0.99 255:0.71 256:0.85 259:0.21 260:0.62 265:0.47 266:0.84 267:0.94 268:0.87 271:0.61 276:0.83 277:0.69 279:0.78 281:0.91 284:0.97 285:0.62 287:0.94 290:0.67 292:0.86 300:0.84 +1 1:0.65 7:0.81 8:0.63 10:0.93 11:0.81 12:0.69 17:0.81 18:0.89 21:0.40 27:0.53 29:0.77 30:0.55 32:0.71 34:0.86 36:0.75 37:0.28 39:0.51 43:0.44 44:0.92 45:0.90 64:0.77 66:0.92 69:0.61 70:0.81 71:0.91 74:0.64 77:0.70 81:0.71 83:0.69 86:0.83 91:0.20 97:0.89 98:0.90 99:0.95 101:0.65 102:0.97 104:0.94 106:0.81 108:0.46 114:0.82 121:0.90 122:0.76 123:0.44 126:0.54 127:0.46 129:0.05 131:0.61 132:0.97 135:0.96 139:0.88 144:0.85 145:0.85 146:0.85 147:0.87 149:0.66 150:0.56 154:0.47 155:0.57 157:0.99 158:0.75 159:0.41 165:0.81 166:0.85 170:0.82 172:0.79 173:0.66 174:0.91 176:0.70 177:0.88 178:0.55 179:0.45 182:1.00 187:0.39 192:0.85 193:1.00 195:0.65 197:0.94 201:0.65 204:0.84 206:0.81 208:0.64 212:0.70 215:0.67 216:0.34 222:0.48 227:0.61 229:0.61 230:0.87 231:0.78 233:0.76 235:0.68 236:0.95 239:0.96 241:0.07 242:0.13 243:0.93 244:0.83 246:1.00 247:0.93 253:0.61 254:0.74 259:0.21 265:0.90 266:0.54 267:0.79 271:0.61 276:0.68 279:0.76 281:0.91 282:0.80 283:0.82 287:0.61 290:0.82 297:0.36 300:0.70 +2 7:0.69 8:0.88 9:0.75 10:0.88 11:0.81 12:0.69 17:0.24 18:0.73 21:0.47 23:0.97 25:0.88 27:0.25 29:0.77 30:0.75 31:0.93 32:0.65 34:0.63 36:0.71 37:0.55 39:0.51 43:0.26 45:0.90 62:0.96 66:0.68 69:0.80 70:0.41 71:0.91 74:0.60 77:0.70 79:0.93 81:0.30 83:0.69 86:0.72 87:0.98 91:0.95 96:0.99 97:1.00 98:0.70 101:0.65 104:0.58 108:0.64 120:0.98 122:0.55 123:0.62 124:0.44 126:0.24 127:0.34 128:0.96 129:0.05 131:0.90 133:0.39 135:0.65 137:0.93 139:0.69 140:0.45 144:0.85 145:0.72 146:0.72 147:0.77 149:0.56 150:0.33 151:0.99 153:0.97 154:0.50 155:0.58 157:0.99 158:0.76 159:0.40 162:0.68 164:0.97 166:0.74 168:0.96 170:0.82 172:0.67 173:0.85 174:0.83 177:0.88 179:0.48 182:1.00 190:0.95 191:0.94 192:0.86 195:0.68 196:0.90 197:0.86 202:0.95 204:0.80 205:0.97 208:0.64 212:0.39 215:0.63 216:0.49 219:0.87 220:0.62 222:0.48 227:0.95 229:1.00 230:0.71 231:0.78 232:0.80 233:0.76 235:0.52 239:0.87 241:0.91 242:0.42 243:0.72 244:0.61 245:0.74 246:0.61 247:0.76 248:0.99 253:0.98 254:0.88 255:0.73 256:0.96 259:0.21 260:0.98 265:0.71 266:0.47 267:0.82 268:0.96 271:0.61 276:0.60 279:0.82 282:0.74 283:0.82 284:0.95 285:0.62 287:1.00 292:0.88 297:0.36 300:0.43 +1 1:0.63 7:0.65 8:0.78 10:0.96 11:0.81 12:0.69 17:0.26 18:0.96 21:0.40 23:0.95 27:0.15 29:0.77 30:0.37 34:0.70 36:0.53 37:0.58 39:0.51 43:0.57 44:0.85 45:0.95 62:0.97 64:0.77 66:0.67 69:0.23 70:0.83 71:0.91 74:0.42 75:0.97 76:0.85 81:0.56 83:0.69 86:0.91 91:0.29 97:0.99 98:0.85 99:0.83 101:0.65 108:0.73 114:0.70 120:0.61 122:0.93 123:0.71 124:0.50 126:0.51 127:0.78 128:0.96 129:0.59 131:0.83 133:0.60 135:0.95 139:0.89 140:0.45 144:0.85 145:0.59 146:0.54 147:0.60 149:0.69 150:0.47 151:0.53 153:0.48 154:0.49 155:0.52 157:0.98 158:0.76 159:0.74 162:0.86 164:0.66 165:0.65 166:0.78 168:0.96 170:0.82 172:0.90 173:0.17 174:0.96 176:0.59 177:0.88 179:0.26 182:0.94 187:0.87 190:0.98 191:0.73 192:0.55 193:0.98 195:0.85 197:0.96 201:0.61 202:0.56 204:0.80 205:0.95 208:0.64 212:0.60 216:0.50 219:0.91 220:0.91 222:0.48 226:0.96 227:0.90 229:0.61 230:0.67 231:0.78 233:0.65 235:0.60 236:0.94 239:0.96 241:0.24 242:0.23 243:0.31 244:0.61 245:0.91 246:0.92 247:0.97 248:0.57 253:0.53 254:0.86 256:0.64 259:0.21 260:0.61 265:0.85 266:0.54 267:0.59 268:0.65 271:0.61 276:0.87 279:0.82 281:0.86 283:0.82 285:0.50 287:0.61 290:0.70 292:0.88 300:0.70 +1 10:0.87 11:0.81 12:0.69 17:0.47 18:0.94 21:0.22 27:0.45 29:0.77 30:0.10 34:0.75 36:0.18 37:0.50 39:0.51 43:0.26 45:0.87 55:0.92 64:0.77 66:0.51 69:0.79 70:0.95 71:0.91 74:0.48 81:0.28 86:0.74 91:0.06 98:0.46 101:0.65 108:0.63 122:0.92 123:0.61 124:0.91 126:0.24 127:0.67 129:0.05 131:0.61 133:0.94 135:0.89 139:0.71 144:0.85 145:0.54 146:0.86 147:0.05 149:0.44 150:0.31 154:0.50 157:0.96 159:0.87 166:0.72 170:0.82 172:0.78 173:0.56 174:0.83 177:0.05 178:0.55 179:0.36 182:0.88 187:0.68 197:0.83 212:0.47 216:0.77 222:0.48 227:0.61 229:0.61 231:0.70 235:0.22 239:0.86 241:0.32 242:0.60 243:0.07 244:0.61 245:0.70 246:0.61 247:0.88 253:0.40 254:0.86 257:0.93 259:0.21 265:0.48 266:0.89 267:0.73 271:0.61 276:0.78 287:0.61 300:0.84 +3 1:0.58 7:0.64 8:0.95 9:0.72 12:0.69 17:0.11 21:0.47 23:1.00 27:0.19 29:0.77 31:0.96 34:0.71 36:0.39 37:0.69 39:0.51 43:0.10 44:0.88 62:0.97 64:0.77 66:0.36 69:0.51 71:0.91 79:0.95 81:0.55 83:0.69 91:1.00 96:0.88 98:0.10 99:0.83 102:0.95 104:0.58 108:0.39 120:0.95 123:0.38 126:0.51 127:0.07 128:0.98 129:0.05 131:0.90 135:0.24 137:0.96 139:0.69 140:0.90 144:0.85 145:0.91 146:0.05 147:0.05 149:0.06 150:0.44 151:0.57 153:0.46 154:0.09 155:0.63 157:0.61 159:0.05 162:0.58 164:0.96 165:0.65 166:0.79 168:0.98 170:0.82 172:0.05 173:0.66 175:0.97 177:0.05 178:0.42 182:1.00 190:0.95 191:0.96 192:0.86 193:0.97 195:0.59 196:0.91 201:0.60 202:0.96 204:0.82 205:1.00 208:0.61 215:0.63 216:0.56 220:0.62 222:0.48 227:0.96 229:1.00 230:0.64 231:0.79 233:0.66 235:0.68 236:0.94 239:0.87 241:0.95 243:0.51 244:0.97 246:0.92 248:0.68 253:0.83 255:0.71 256:0.97 257:0.93 259:0.21 260:0.95 265:0.10 267:0.87 268:0.96 271:0.61 277:0.95 281:0.91 283:0.67 284:0.99 285:0.62 287:1.00 297:0.36 +3 1:0.59 8:0.91 9:0.75 11:0.55 12:0.69 17:0.49 18:0.71 21:0.40 23:0.97 27:0.25 29:0.77 30:0.45 31:0.93 34:0.67 36:0.42 37:0.89 39:0.51 41:0.82 43:0.56 45:0.77 66:0.45 69:0.43 70:0.47 71:0.91 74:0.48 79:0.92 81:0.50 83:0.69 86:0.60 91:0.94 96:0.98 97:0.97 98:0.26 99:0.83 101:0.47 104:0.54 108:0.33 114:0.76 120:0.97 122:0.55 123:0.32 124:0.67 126:0.32 127:0.54 128:0.97 129:0.05 131:0.90 133:0.62 135:0.49 137:0.93 139:0.59 140:0.99 144:0.85 145:0.54 146:0.28 147:0.35 149:0.47 150:0.44 151:0.72 153:0.93 154:0.45 155:0.65 157:0.87 158:0.75 159:0.38 162:0.68 164:0.95 165:0.65 166:0.80 168:0.97 170:0.82 172:0.32 173:0.51 175:0.91 176:0.62 177:0.38 179:0.84 182:1.00 190:0.95 191:0.95 192:0.98 196:0.87 201:0.56 202:0.93 205:0.98 208:0.64 212:0.50 215:0.67 216:0.36 220:0.62 222:0.48 227:0.96 229:1.00 231:0.79 235:0.52 241:0.83 242:0.53 243:0.58 244:0.93 245:0.52 246:0.96 247:0.47 248:0.68 253:0.97 255:0.74 256:0.94 257:0.84 259:0.21 260:0.97 265:0.28 266:0.47 267:0.73 268:0.94 271:0.61 276:0.33 277:0.87 279:0.79 283:0.82 284:0.94 285:0.62 287:1.00 290:0.76 297:0.36 300:0.33 +2 8:0.66 9:0.73 10:0.96 11:0.87 12:0.69 17:0.75 18:0.93 21:0.78 27:0.41 29:0.77 30:0.33 31:0.86 34:0.59 36:0.74 37:0.38 39:0.51 43:0.83 45:0.83 66:0.93 69:0.42 70:0.76 71:0.91 74:0.67 79:0.86 83:0.56 85:0.80 86:0.90 91:0.77 96:0.80 98:0.92 101:0.96 104:0.58 108:0.32 120:0.69 122:0.92 123:0.31 127:0.53 129:0.05 131:0.88 132:0.97 135:0.74 137:0.86 139:0.87 140:0.90 144:0.85 146:0.80 147:0.83 149:0.83 151:0.85 153:0.48 154:0.79 155:0.56 157:0.99 159:0.35 162:0.52 164:0.55 166:0.61 170:0.82 172:0.82 173:0.53 174:0.88 177:0.88 178:0.50 179:0.42 182:0.97 190:0.95 191:0.73 192:0.58 196:0.88 197:0.92 202:0.67 208:0.50 212:0.84 216:0.18 222:0.48 227:0.94 229:0.94 231:0.77 232:0.79 235:0.31 239:0.95 241:0.83 242:0.19 243:0.94 246:0.61 247:0.90 248:0.76 253:0.80 255:0.72 256:0.58 260:0.70 265:0.92 266:0.38 267:0.91 268:0.59 271:0.61 276:0.72 284:0.87 285:0.60 287:0.97 300:0.55 +2 10:0.97 11:0.81 12:0.69 17:0.57 18:0.97 21:0.40 22:0.93 27:0.15 29:0.77 30:0.55 31:0.87 32:0.66 34:0.52 36:0.54 37:0.58 39:0.51 43:0.57 44:0.86 45:0.95 47:0.93 48:0.91 64:0.77 66:0.97 69:0.19 70:0.62 71:0.91 74:0.61 76:0.85 77:0.70 79:0.87 81:0.27 83:0.69 86:0.93 91:0.53 97:0.89 98:0.95 101:0.65 104:0.87 108:0.15 122:0.93 123:0.15 126:0.24 127:0.67 129:0.05 131:0.80 135:0.65 137:0.87 139:0.90 140:0.80 144:0.85 145:0.92 146:0.91 147:0.93 149:0.72 150:0.30 151:0.50 153:0.48 154:0.53 155:0.50 157:0.99 158:0.75 159:0.52 162:0.57 166:0.72 170:0.82 172:0.92 173:0.24 174:0.96 177:0.88 178:0.42 179:0.27 182:0.96 187:0.68 190:0.98 192:0.46 193:0.97 195:0.70 196:0.91 197:0.97 202:0.54 204:0.80 208:0.61 212:0.89 216:0.50 220:0.91 222:0.48 227:0.83 229:0.61 231:0.75 232:0.91 235:0.42 239:0.96 241:0.30 242:0.27 243:0.97 244:0.61 246:0.61 247:0.97 248:0.50 253:0.47 254:0.80 256:0.45 259:0.21 262:0.93 265:0.95 266:0.27 267:0.85 268:0.46 271:0.61 276:0.85 279:0.76 281:0.91 282:0.75 283:0.82 284:0.87 285:0.46 287:0.61 300:0.55 +1 1:0.61 7:0.64 8:0.62 9:0.73 10:0.93 11:0.55 12:0.69 17:0.64 18:0.87 20:0.95 21:0.54 23:0.99 27:0.37 29:0.77 30:0.66 31:0.91 32:0.63 34:0.64 36:0.98 37:0.29 39:0.51 43:0.29 44:0.88 45:0.87 48:0.91 62:0.97 64:0.77 66:0.63 69:0.49 70:0.61 71:0.91 78:0.95 79:0.91 81:0.45 83:0.69 86:0.82 91:0.83 96:0.80 98:0.79 99:0.83 100:0.96 101:0.47 102:0.94 104:0.58 108:0.62 111:0.95 120:0.89 122:0.93 123:0.60 124:0.47 126:0.32 127:0.64 128:0.97 129:0.59 131:0.90 133:0.46 135:0.42 137:0.91 138:0.97 139:0.81 140:0.93 144:0.85 145:0.76 146:0.26 147:0.32 149:0.46 150:0.40 151:0.85 152:0.88 153:0.48 154:0.48 155:0.64 157:0.84 159:0.43 162:0.68 164:0.88 165:0.65 166:0.72 168:0.97 169:0.94 170:0.82 172:0.59 173:0.55 174:0.91 175:0.75 177:0.70 178:0.50 179:0.67 182:1.00 186:0.95 190:0.95 191:0.93 192:0.86 193:0.97 195:0.67 196:0.92 197:0.93 201:0.58 202:0.88 204:0.81 205:0.99 208:0.61 212:0.41 216:0.36 220:0.74 222:0.48 227:0.95 229:1.00 230:0.64 231:0.78 232:0.80 233:0.66 235:0.74 236:0.92 239:0.92 241:0.95 242:0.33 243:0.31 244:0.83 245:0.71 246:0.92 247:0.67 248:0.82 253:0.73 254:0.90 255:0.71 256:0.89 257:0.65 259:0.21 260:0.89 261:0.94 265:0.79 266:0.63 267:0.52 268:0.90 271:0.61 276:0.53 277:0.69 281:0.91 283:0.67 284:0.98 285:0.62 287:1.00 300:0.55 +0 1:0.67 9:0.75 10:0.93 11:0.81 12:0.69 17:0.88 18:0.91 21:0.22 22:0.93 23:0.98 27:0.72 29:0.77 30:0.38 31:0.91 33:0.97 34:0.34 36:0.56 39:0.51 43:0.40 45:0.89 47:0.93 62:0.97 64:0.77 66:0.36 69:0.17 70:0.78 71:0.91 74:0.51 75:0.98 79:0.91 81:0.74 83:0.69 86:0.84 91:0.73 96:0.87 97:0.94 98:0.59 99:0.95 101:0.65 104:0.95 106:0.81 108:0.84 114:0.88 117:0.86 120:0.79 122:0.93 123:0.83 124:0.77 126:0.54 127:0.86 128:0.97 129:0.59 131:0.90 133:0.74 135:0.70 137:0.91 139:0.83 140:0.45 144:0.85 146:0.43 147:0.50 149:0.42 150:0.60 151:0.65 153:0.83 154:0.66 155:0.65 157:0.97 158:0.81 159:0.68 162:0.83 164:0.75 165:0.81 166:0.85 168:0.97 170:0.82 172:0.63 173:0.16 174:0.93 176:0.70 177:0.88 179:0.50 182:1.00 187:0.39 190:0.95 192:0.98 196:0.93 197:0.93 201:0.66 202:0.66 205:0.97 206:0.81 208:0.64 212:0.51 215:0.79 216:0.23 219:0.94 220:0.82 222:0.48 226:0.95 227:0.96 229:1.00 231:0.79 232:0.97 235:0.52 236:0.95 239:0.94 241:0.30 242:0.32 243:0.31 244:0.61 245:0.78 246:1.00 247:0.76 248:0.67 253:0.84 254:0.84 255:0.78 256:0.68 259:0.21 260:0.79 262:0.93 265:0.60 266:0.75 267:0.65 268:0.72 271:0.61 276:0.69 279:0.79 283:0.66 284:0.94 285:0.62 287:1.00 290:0.88 291:0.97 292:0.87 297:0.36 300:0.70 +1 1:0.64 7:0.69 8:0.68 9:0.74 10:0.96 11:0.87 12:0.69 17:0.64 18:0.94 27:0.28 29:0.77 30:0.60 31:0.89 32:0.66 34:0.87 36:0.96 37:0.48 39:0.51 43:0.67 44:0.86 45:0.96 48:0.91 60:0.87 66:0.98 69:0.05 70:0.62 71:0.91 74:0.80 75:0.99 77:0.89 79:0.89 81:0.60 83:0.69 85:0.72 86:0.91 91:0.79 96:0.88 97:0.99 98:0.97 99:0.83 101:0.96 104:0.91 106:0.81 108:0.05 114:0.92 117:0.86 120:0.79 122:0.55 123:0.05 126:0.32 127:0.76 129:0.05 131:0.88 132:0.97 135:0.81 137:0.89 139:0.91 140:0.45 144:0.85 145:0.65 146:0.93 147:0.94 149:0.81 150:0.56 151:0.93 153:0.80 154:0.56 155:0.57 157:1.00 158:0.92 159:0.56 162:0.81 164:0.68 165:0.65 166:0.80 170:0.82 172:0.94 173:0.13 174:0.94 176:0.62 177:0.88 179:0.24 182:0.98 187:0.39 190:0.95 191:0.70 192:0.86 193:0.96 195:0.72 196:0.92 197:0.95 201:0.60 202:0.61 204:0.83 206:0.81 208:0.64 212:0.86 215:0.96 216:0.48 219:0.95 222:0.48 226:0.98 227:0.94 229:0.94 230:0.71 231:0.78 232:0.98 233:0.76 235:0.05 239:0.97 241:0.27 242:0.15 243:0.98 246:0.97 247:0.96 248:0.86 253:0.90 255:0.72 256:0.64 260:0.80 265:0.97 266:0.27 267:0.84 268:0.66 271:0.61 276:0.88 279:0.82 281:0.86 282:0.75 283:0.82 284:0.87 285:0.60 287:0.97 290:0.92 292:0.95 297:0.36 300:0.55 +1 1:0.60 7:0.69 8:0.81 9:0.71 10:0.96 11:0.81 12:0.69 17:0.30 18:0.97 21:0.22 27:0.15 29:0.77 30:0.45 31:0.89 32:0.66 34:0.53 36:0.50 37:0.58 39:0.51 43:0.57 44:0.86 45:0.95 48:0.91 64:0.77 66:0.97 69:0.17 70:0.76 71:0.91 74:0.59 77:0.70 79:0.88 81:0.37 83:0.69 86:0.91 91:0.54 96:0.73 97:0.97 98:0.96 101:0.65 104:0.97 108:0.14 114:0.69 120:0.58 122:0.93 123:0.13 126:0.32 127:0.69 129:0.05 131:0.88 135:0.88 137:0.89 138:0.96 139:0.89 140:0.80 144:0.85 145:0.92 146:0.92 147:0.94 149:0.73 150:0.42 151:0.63 153:0.72 154:0.47 155:0.63 157:0.99 158:0.76 159:0.55 162:0.59 164:0.54 166:0.75 170:0.82 172:0.92 173:0.21 174:0.95 176:0.56 177:0.88 178:0.42 179:0.27 182:0.98 187:0.87 190:0.95 191:0.70 192:0.86 193:0.97 195:0.72 196:0.91 197:0.97 201:0.57 202:0.62 204:0.82 208:0.64 212:0.78 216:0.50 219:0.87 220:0.82 222:0.48 227:0.94 229:0.94 230:0.71 231:0.78 233:0.76 235:0.31 239:0.95 241:0.51 242:0.27 243:0.97 244:0.61 246:0.61 247:0.97 248:0.62 253:0.60 254:0.84 255:0.70 256:0.58 259:0.21 260:0.58 265:0.96 266:0.38 267:0.96 268:0.61 271:0.61 276:0.86 279:0.81 281:0.91 282:0.75 283:0.82 284:0.90 285:0.60 286:0.99 287:0.97 290:0.69 292:0.88 300:0.55 +0 1:0.68 10:0.96 11:0.81 12:0.69 17:0.97 18:0.91 21:0.05 27:0.67 29:0.77 30:0.75 34:0.34 36:0.40 37:0.74 39:0.51 43:0.56 45:0.94 64:0.77 66:0.60 69:0.47 70:0.66 71:0.91 74:0.52 81:0.81 83:0.69 86:0.89 91:0.29 97:0.89 98:0.69 101:0.65 104:0.91 106:0.81 108:0.80 114:0.95 117:0.86 122:0.93 123:0.79 124:0.63 126:0.54 127:0.77 129:0.59 131:0.61 133:0.64 135:0.82 139:0.85 144:0.85 146:0.32 147:0.38 149:0.71 150:0.66 154:0.60 155:0.53 157:0.99 158:0.75 159:0.65 165:0.93 166:0.86 170:0.82 172:0.79 173:0.38 174:0.95 176:0.73 177:0.88 178:0.55 179:0.40 182:1.00 187:0.39 192:0.55 197:0.96 201:0.67 206:0.81 208:0.64 212:0.61 215:0.91 216:0.16 222:0.48 227:0.61 229:0.61 231:0.78 232:0.95 235:0.42 236:0.97 239:0.94 241:0.05 242:0.41 243:0.29 244:0.61 245:0.85 246:1.00 247:0.82 253:0.67 259:0.21 265:0.69 266:0.75 267:0.28 271:0.61 276:0.75 279:0.76 283:0.82 287:0.61 290:0.95 297:0.36 300:0.84 +1 1:0.69 10:0.93 11:0.55 12:0.69 17:0.40 18:0.94 22:0.93 27:0.25 29:0.77 30:0.38 33:0.97 34:0.69 36:0.25 37:0.89 39:0.51 43:0.43 45:0.88 47:0.93 64:0.77 66:0.80 69:0.61 70:0.65 71:0.91 75:0.96 81:0.82 83:0.69 86:0.84 91:0.37 97:0.89 98:0.83 101:0.47 104:0.54 108:0.46 114:0.98 122:0.93 123:0.44 124:0.39 126:0.54 127:0.55 129:0.05 131:0.61 133:0.43 135:0.86 139:0.81 144:0.85 146:0.88 147:0.90 149:0.56 150:0.71 154:0.26 155:0.53 157:0.85 159:0.54 165:0.93 166:0.86 170:0.82 172:0.77 173:0.58 174:0.92 175:0.75 176:0.73 177:0.70 178:0.55 179:0.48 182:1.00 187:0.21 192:0.55 197:0.94 201:0.68 208:0.64 212:0.29 215:0.96 216:0.36 219:0.90 222:0.48 226:0.98 227:0.61 229:0.61 231:0.78 235:0.31 236:0.97 239:0.93 241:0.15 242:0.38 243:0.82 244:0.83 245:0.70 246:1.00 247:0.79 253:0.69 254:0.86 257:0.65 259:0.21 262:0.93 265:0.83 266:0.54 267:0.52 271:0.61 276:0.68 277:0.69 279:0.76 287:0.61 290:0.98 291:0.97 292:0.95 297:0.36 300:0.43 +0 1:0.61 7:0.64 8:0.61 10:0.91 11:0.81 12:0.69 17:0.49 18:0.78 21:0.22 22:0.93 27:0.21 29:0.77 30:0.60 32:0.62 33:0.97 34:0.97 36:0.71 37:0.66 39:0.51 43:0.26 44:0.88 45:0.88 47:0.93 66:0.51 69:0.83 70:0.60 71:0.91 74:0.39 81:0.53 83:0.56 86:0.75 91:0.51 98:0.38 99:0.83 101:0.65 102:0.95 104:0.95 106:0.81 108:0.82 114:0.82 117:0.86 122:0.93 123:0.81 124:0.56 126:0.32 127:0.26 129:0.59 131:0.61 133:0.47 135:0.89 139:0.74 144:0.85 145:0.70 146:0.47 147:0.54 149:0.25 150:0.48 154:0.35 155:0.48 157:0.98 158:0.72 159:0.45 165:0.65 166:0.80 170:0.82 172:0.59 173:0.81 174:0.89 176:0.62 177:0.88 178:0.55 179:0.41 182:0.97 187:0.68 192:0.47 195:0.77 197:0.89 201:0.58 206:0.81 208:0.50 212:0.73 215:0.79 216:0.54 222:0.48 227:0.61 229:0.61 230:0.64 231:0.76 232:0.98 233:0.66 235:0.31 239:0.90 241:0.47 242:0.24 243:0.43 244:0.61 245:0.79 246:0.97 247:0.86 253:0.58 254:0.97 259:0.21 262:0.93 265:0.40 266:0.54 267:0.28 271:0.61 276:0.54 287:0.61 290:0.82 291:0.97 297:0.36 300:0.55 +1 7:0.66 8:0.86 9:0.70 10:0.95 11:0.81 12:0.69 17:0.34 18:0.97 21:0.59 23:0.96 27:0.15 29:0.77 30:0.44 31:0.87 32:0.63 34:0.56 36:0.62 37:0.58 39:0.51 43:0.57 44:0.88 45:0.95 48:0.91 62:0.96 64:0.77 66:0.45 69:0.25 70:0.75 71:0.91 74:0.49 75:0.98 76:0.94 79:0.87 81:0.26 83:0.69 86:0.89 91:0.44 96:0.69 97:0.94 98:0.58 101:0.65 102:0.96 108:0.85 122:0.93 123:0.84 124:0.69 126:0.24 127:0.73 128:0.95 129:0.59 131:0.84 133:0.67 135:0.86 137:0.87 139:0.90 140:0.45 144:0.85 145:0.58 146:0.78 147:0.82 149:0.74 150:0.28 151:0.51 153:0.69 154:0.22 155:0.57 157:0.99 158:0.75 159:0.72 162:0.86 166:0.72 168:0.95 170:0.82 172:0.84 173:0.18 174:0.95 177:0.88 179:0.26 182:0.96 187:0.87 190:0.89 192:0.58 193:0.96 195:0.84 196:0.90 197:0.96 202:0.46 204:0.85 205:0.95 208:0.64 212:0.72 216:0.50 219:0.94 220:0.96 222:0.48 226:0.95 227:0.91 229:0.88 230:0.68 231:0.76 233:0.65 235:0.68 239:0.96 241:0.01 242:0.22 243:0.36 244:0.61 245:0.94 246:0.61 247:0.96 248:0.50 253:0.41 254:0.84 255:0.69 256:0.45 259:0.21 265:0.60 266:0.63 267:0.82 268:0.55 271:0.61 276:0.86 279:0.81 281:0.86 283:0.82 284:0.90 285:0.60 287:0.94 292:0.86 300:0.55 +2 1:0.69 8:0.88 9:0.79 10:0.86 11:0.81 12:0.69 17:0.96 18:0.84 20:0.94 23:0.97 27:0.25 29:0.77 30:0.13 31:0.95 34:0.77 36:0.76 37:0.84 39:0.51 41:0.82 43:0.29 44:0.88 45:0.87 62:0.97 64:0.77 66:0.72 69:0.77 70:0.83 71:0.91 74:0.49 78:0.97 79:0.94 81:0.82 83:0.69 86:0.70 91:0.61 96:0.88 97:0.94 98:0.85 100:0.96 101:0.65 102:0.95 104:0.82 106:0.81 108:0.61 111:0.96 114:0.98 117:0.86 120:0.80 123:0.58 124:0.43 126:0.54 127:0.62 128:0.98 129:0.05 131:0.90 133:0.45 135:0.96 137:0.95 139:0.74 140:0.45 144:0.85 145:0.67 146:0.89 147:0.43 149:0.48 150:0.71 151:0.91 152:0.88 153:0.72 154:0.38 155:0.66 157:0.98 158:0.75 159:0.56 162:0.86 164:0.72 165:0.93 166:0.86 168:0.98 169:0.94 170:0.82 172:0.79 173:0.77 174:0.88 176:0.73 177:0.70 179:0.43 182:1.00 186:0.96 187:0.21 192:0.99 193:0.97 195:0.74 196:0.92 197:0.89 201:0.68 202:0.58 204:0.83 205:0.95 206:0.81 208:0.64 212:0.41 215:0.96 216:0.40 222:0.48 227:0.96 229:1.00 231:0.79 232:0.89 235:0.31 236:0.97 239:0.89 241:0.07 242:0.18 243:0.23 244:0.83 245:0.82 246:1.00 247:0.92 248:0.81 253:0.86 254:0.80 255:0.94 256:0.64 257:0.65 259:0.21 260:0.80 261:0.94 265:0.85 266:0.63 267:0.96 268:0.66 271:0.61 276:0.73 279:0.78 281:0.91 283:0.82 284:0.91 285:0.62 287:1.00 290:0.98 297:0.36 300:0.55 +2 8:0.89 9:0.74 11:0.55 12:0.69 17:0.20 18:0.79 21:0.40 25:0.88 27:0.25 29:0.77 30:0.62 31:0.92 34:0.85 36:0.57 37:0.55 39:0.51 43:0.25 45:0.77 55:0.52 64:0.77 66:0.36 69:0.65 70:0.40 71:0.91 74:0.37 79:0.92 81:0.27 83:0.56 86:0.63 87:0.98 91:0.77 96:0.90 98:0.35 101:0.47 104:0.58 108:0.73 120:0.86 122:0.55 123:0.71 124:0.60 126:0.24 127:0.47 129:0.59 131:0.88 133:0.47 135:0.61 137:0.92 139:0.62 140:0.93 144:0.76 146:0.33 147:0.40 149:0.33 150:0.30 151:0.85 153:0.48 154:0.21 155:0.56 157:0.88 159:0.35 162:0.67 164:0.80 166:0.72 170:0.82 172:0.32 173:0.76 175:0.91 177:0.38 179:0.80 182:0.89 187:0.39 190:0.95 191:0.86 192:0.58 196:0.88 202:0.79 208:0.50 212:0.42 216:0.49 219:0.87 220:0.91 222:0.48 227:0.94 229:0.94 231:0.74 235:0.42 241:0.74 242:0.29 243:0.48 244:0.93 245:0.62 246:0.61 247:0.55 248:0.89 253:0.84 255:0.73 256:0.81 257:0.84 259:0.21 260:0.86 265:0.37 266:0.54 267:0.73 268:0.82 271:0.61 276:0.30 277:0.87 283:0.67 284:0.96 285:0.60 287:0.97 292:0.88 300:0.33 +0 1:0.56 8:0.60 9:0.74 10:0.95 11:0.55 12:0.69 17:0.84 18:0.93 21:0.71 23:1.00 27:0.28 29:0.77 30:0.72 31:1.00 34:0.67 36:0.55 37:0.26 39:0.51 43:0.56 45:0.89 62:1.00 64:0.77 66:0.82 69:0.29 70:0.45 71:0.91 75:0.96 79:1.00 81:0.48 83:0.56 86:0.87 91:0.83 96:0.74 97:0.97 98:0.71 99:0.83 101:0.47 108:0.22 120:0.95 122:0.93 123:0.22 124:0.37 126:0.51 127:0.62 128:1.00 129:0.05 131:0.90 133:0.37 135:0.49 137:1.00 139:0.85 140:0.95 144:0.76 146:0.61 147:0.67 149:0.69 150:0.38 151:0.82 153:0.94 154:0.45 155:0.58 157:0.85 159:0.36 162:0.79 164:0.94 165:0.65 166:0.79 168:1.00 170:0.82 172:0.70 173:0.41 174:0.93 175:0.75 177:0.70 179:0.61 182:0.79 190:0.89 191:0.70 192:0.86 196:0.99 197:0.96 201:0.54 202:0.93 205:1.00 208:0.61 212:0.87 215:0.48 216:0.21 219:0.90 220:0.74 222:0.48 226:0.96 227:0.95 229:0.88 231:0.78 235:0.95 236:0.94 239:0.94 241:0.86 242:0.62 243:0.83 244:0.83 245:0.56 246:0.92 247:0.47 248:0.96 253:0.51 254:0.97 255:0.78 256:0.95 257:0.65 259:0.21 260:0.94 265:0.72 266:0.38 267:0.59 268:0.96 271:0.61 276:0.53 277:0.69 279:0.79 283:0.67 284:1.00 285:0.62 287:0.94 292:0.88 297:0.36 300:0.43 +1 8:0.74 10:0.92 11:0.49 17:0.32 18:0.69 21:0.54 23:0.95 27:0.41 29:0.71 30:0.09 33:0.97 34:0.68 36:0.73 37:0.44 39:0.74 43:0.12 44:0.85 45:0.90 55:0.71 62:0.97 66:0.86 69:0.55 70:0.95 71:0.65 74:0.43 75:0.95 81:0.25 86:0.64 91:0.49 98:0.86 101:0.52 102:0.94 108:0.42 122:0.98 123:0.41 124:0.38 126:0.22 127:0.63 128:0.97 129:0.05 133:0.43 135:0.80 139:0.63 140:0.45 145:0.92 146:0.96 147:0.97 149:0.17 150:0.27 151:0.75 153:0.48 154:0.15 159:0.74 162:0.62 168:0.97 170:0.90 172:0.93 173:0.39 174:0.98 177:0.99 179:0.22 187:0.68 190:0.99 192:0.46 193:0.95 195:0.90 197:0.98 202:0.50 205:0.95 212:0.78 216:0.75 220:0.62 222:0.25 226:0.96 232:0.95 235:0.60 236:0.91 239:0.91 241:0.03 242:0.14 243:0.86 244:0.83 245:0.88 247:0.97 248:0.69 253:0.37 254:0.97 255:0.70 256:0.45 259:0.21 264:0.90 265:0.86 266:0.75 267:0.69 268:0.46 271:0.95 275:0.91 276:0.88 285:0.47 291:0.97 294:0.93 300:0.84 +0 1:0.59 8:0.79 9:0.72 10:0.83 11:0.46 17:0.36 18:0.67 21:0.05 27:0.54 29:0.71 30:0.32 32:0.63 34:0.45 36:0.98 37:0.46 39:0.74 43:0.10 45:0.87 55:0.71 66:0.49 69:0.64 70:0.77 71:0.65 74:0.47 76:0.94 77:0.70 81:0.35 83:0.54 86:0.61 91:0.73 96:0.85 97:0.94 98:0.71 99:0.83 101:0.47 108:0.87 114:0.81 117:0.86 120:0.73 122:0.90 123:0.86 124:0.72 126:0.26 127:0.68 129:0.05 133:0.72 135:0.81 139:0.59 140:0.80 145:0.91 146:0.85 147:0.87 149:0.25 150:0.34 151:0.81 153:0.78 154:0.10 155:0.52 158:0.73 159:0.80 162:0.69 164:0.68 165:0.63 170:0.90 172:0.85 173:0.45 174:0.88 176:0.59 177:0.99 178:0.42 179:0.26 187:0.39 190:0.99 191:0.79 192:0.58 195:0.93 197:0.91 201:0.56 202:0.65 208:0.49 212:0.56 215:0.91 216:0.67 220:0.62 222:0.25 232:0.89 235:0.12 239:0.82 241:0.35 242:0.59 243:0.40 244:0.98 245:0.90 247:0.90 248:0.80 253:0.81 254:0.88 255:0.71 256:0.64 259:0.21 260:0.73 264:0.90 265:0.71 266:0.75 267:0.85 268:0.67 271:0.95 275:0.93 276:0.86 277:0.95 279:0.75 282:0.72 283:0.67 285:0.47 290:0.81 294:0.92 297:0.36 300:0.70 +0 8:0.81 10:0.91 11:0.49 17:0.93 18:0.64 21:0.13 27:0.53 29:0.71 30:0.31 34:0.92 36:0.87 37:0.82 39:0.74 43:0.13 45:0.83 55:0.59 66:0.48 69:0.08 70:0.92 71:0.65 74:0.44 75:0.95 76:0.94 77:0.89 81:0.27 86:0.60 87:0.98 91:0.53 98:0.55 101:0.52 102:0.94 108:0.07 122:0.92 123:0.07 124:0.80 126:0.22 127:0.89 129:0.05 133:0.81 135:0.90 139:0.59 145:0.72 146:0.79 147:0.82 149:0.19 150:0.30 154:0.15 159:0.76 170:0.90 172:0.81 173:0.10 174:0.97 177:0.99 178:0.55 179:0.33 187:0.21 192:0.49 193:0.95 195:0.87 197:0.98 212:0.69 216:0.32 222:0.25 226:0.96 232:0.72 235:0.12 236:0.91 239:0.90 241:0.05 242:0.14 243:0.60 244:0.97 245:0.86 247:0.94 253:0.37 254:0.94 259:0.21 264:0.90 265:0.56 266:0.80 267:0.82 271:0.95 275:0.91 276:0.82 282:0.72 294:0.93 300:0.84 +0 8:0.86 10:0.90 11:0.49 17:0.79 18:0.62 21:0.30 23:0.95 27:0.71 29:0.71 30:0.20 32:0.64 34:0.36 36:0.96 37:0.70 39:0.74 43:0.11 45:0.92 55:0.71 62:0.96 66:0.12 69:0.97 70:0.96 71:0.65 74:0.40 75:0.95 76:0.94 77:0.70 81:0.26 86:0.61 91:0.42 98:0.38 101:0.65 102:0.94 108:0.90 122:0.93 123:0.90 124:0.96 126:0.22 127:0.92 128:0.96 129:0.05 133:0.95 135:0.89 139:0.59 140:0.45 145:0.94 146:0.88 147:0.79 149:0.21 150:0.28 151:0.53 153:0.48 154:0.06 159:0.93 162:0.86 168:0.96 170:0.90 172:0.76 173:0.93 174:0.99 177:0.99 179:0.17 187:0.21 190:1.00 193:0.95 195:0.99 197:0.98 202:0.36 205:0.95 212:0.29 216:0.12 220:0.82 222:0.25 226:0.96 235:0.31 236:0.91 239:0.90 241:0.24 242:0.16 243:0.19 244:0.97 245:0.92 247:0.98 248:0.53 251:1.00 253:0.35 254:0.80 256:0.45 257:0.65 259:0.21 264:0.90 265:0.40 266:0.92 267:0.97 268:0.46 271:0.95 275:0.96 276:0.94 277:0.87 282:0.73 285:0.45 294:0.94 300:0.94 +0 8:0.83 9:0.71 10:0.80 11:0.46 17:0.18 18:0.58 21:0.30 27:0.21 29:0.71 30:0.16 31:0.89 34:0.20 36:0.76 37:0.74 39:0.74 43:0.09 45:0.95 55:0.84 66:0.08 69:0.85 70:0.99 71:0.65 74:0.51 76:0.94 79:0.89 81:0.26 83:0.56 86:0.58 91:0.87 96:0.99 98:0.33 101:0.47 108:0.71 114:0.71 117:0.86 120:0.79 122:0.90 123:0.69 124:0.99 126:0.22 127:0.95 129:0.59 133:0.99 135:0.23 137:0.89 139:0.56 140:0.97 146:0.97 147:0.36 149:0.26 150:0.28 151:0.93 153:0.98 154:0.05 155:0.54 158:0.73 159:0.98 162:0.55 164:0.68 170:0.90 172:0.84 173:0.33 174:0.89 175:0.97 176:0.59 177:0.05 179:0.11 187:0.39 190:0.99 191:0.87 192:0.87 196:0.87 197:0.80 202:0.95 208:0.50 212:0.27 216:0.95 222:0.25 232:0.85 235:0.31 239:0.79 241:0.64 242:0.75 243:0.06 244:0.98 245:0.95 247:0.96 248:0.85 253:0.98 254:0.80 255:0.70 256:0.95 257:0.99 259:0.21 260:0.78 264:0.90 265:0.35 266:0.98 267:1.00 268:0.95 271:0.95 275:0.96 276:0.98 277:0.95 284:0.91 285:0.55 290:0.71 294:0.93 300:0.94 +1 10:0.90 11:0.46 17:0.78 18:0.62 21:0.40 23:0.95 27:0.39 29:0.71 30:0.26 33:0.97 34:0.71 36:0.66 37:0.59 39:0.74 43:0.11 45:0.77 55:0.45 62:0.96 66:0.36 69:0.17 70:0.89 71:0.65 74:0.53 81:0.30 86:0.60 91:0.42 98:0.76 101:0.47 108:0.14 114:0.70 122:0.90 123:0.13 124:0.77 126:0.26 127:0.83 128:0.96 129:0.05 133:0.74 135:0.87 139:0.58 140:0.45 146:0.95 147:0.96 149:0.18 150:0.31 151:0.57 153:0.48 154:0.11 159:0.81 162:0.86 168:0.96 170:0.90 172:0.90 173:0.11 174:0.97 176:0.59 177:0.99 179:0.18 187:0.87 190:1.00 192:0.45 197:0.97 201:0.54 202:0.36 205:0.95 212:0.83 216:0.62 220:0.74 222:0.25 232:0.83 235:0.52 236:0.91 239:0.88 241:0.44 242:0.70 243:0.51 244:0.97 245:0.95 247:0.94 248:0.55 253:0.54 254:0.90 256:0.45 259:0.21 264:0.90 265:0.76 266:0.63 267:0.59 268:0.46 271:0.95 275:0.91 276:0.93 285:0.45 290:0.70 291:0.97 294:0.93 300:0.70 +1 8:0.78 9:0.73 10:0.92 11:0.42 17:0.40 18:0.63 21:0.54 23:0.95 27:0.41 29:0.71 30:0.27 33:0.97 34:0.87 36:0.22 37:0.44 39:0.74 43:0.12 45:0.66 55:0.59 62:0.97 66:0.63 69:0.47 70:0.97 71:0.65 74:0.38 75:0.95 77:0.89 81:0.25 83:0.60 86:0.60 91:0.33 98:0.76 101:0.45 102:0.94 108:0.36 122:0.98 123:0.34 124:0.65 126:0.22 127:0.87 128:0.97 129:0.05 133:0.73 135:0.47 139:0.59 140:0.45 143:0.99 145:0.89 146:0.96 147:0.97 149:0.16 150:0.27 151:0.75 153:0.48 154:0.13 155:0.58 159:0.85 162:0.86 168:0.97 170:0.90 172:0.91 173:0.23 174:0.99 175:0.75 177:0.99 179:0.24 187:0.68 190:0.98 192:0.58 193:0.95 195:0.94 197:0.99 202:0.36 205:0.95 208:0.54 212:0.48 216:0.75 220:0.62 222:0.25 226:0.96 235:0.60 236:0.91 239:0.91 241:0.01 242:0.27 243:0.69 244:0.98 245:0.91 247:0.84 248:0.70 253:0.34 254:0.94 255:0.73 256:0.45 257:0.65 259:0.21 264:0.90 265:0.76 266:0.87 267:0.52 268:0.46 271:0.95 275:0.91 276:0.88 277:0.69 282:0.72 285:0.55 291:0.97 294:0.93 300:0.94 +0 1:0.58 7:0.65 8:0.73 10:0.84 11:0.46 17:0.76 18:0.60 21:0.13 27:0.49 29:0.71 30:0.11 34:0.88 36:0.99 37:0.64 39:0.74 43:0.09 45:0.73 55:0.42 66:0.29 69:0.87 70:0.98 71:0.65 74:0.37 75:0.95 76:0.97 77:0.70 81:0.41 83:0.54 86:0.58 91:0.57 98:0.46 99:0.83 101:0.47 102:0.94 108:0.82 114:0.80 122:0.97 123:0.81 124:0.74 126:0.32 127:0.57 129:0.59 133:0.67 135:0.72 139:0.57 145:0.47 146:0.33 147:0.51 149:0.11 150:0.37 154:0.12 155:0.48 158:0.73 159:0.60 165:0.63 170:0.90 172:0.37 173:0.88 174:0.90 175:0.97 176:0.59 177:0.70 178:0.55 179:0.70 187:0.68 192:0.57 193:0.95 195:0.79 197:0.91 201:0.60 204:0.78 208:0.49 212:0.16 215:0.84 216:0.43 222:0.25 226:0.95 230:0.67 232:0.88 233:0.65 235:0.31 236:0.91 239:0.86 241:0.01 242:0.16 243:0.38 244:0.99 245:0.67 247:0.84 253:0.57 257:0.97 259:0.21 264:0.90 265:0.48 266:0.75 267:0.73 271:0.95 275:0.91 276:0.49 277:0.95 282:0.72 290:0.80 294:0.92 297:0.36 300:0.84 +0 8:0.75 10:0.91 11:0.46 17:0.53 18:0.58 21:0.40 23:0.98 27:0.44 29:0.71 30:0.10 34:0.59 36:0.42 37:0.62 39:0.74 43:0.06 45:0.91 55:0.71 62:0.97 66:0.09 69:0.71 70:0.99 71:0.65 74:0.36 75:0.95 76:0.85 81:0.30 86:0.57 91:0.53 96:0.80 98:0.25 101:0.47 102:0.94 108:0.54 122:0.98 123:0.99 124:0.99 126:0.26 127:0.87 128:0.98 129:0.05 133:0.99 135:0.81 139:0.56 140:0.45 145:0.42 146:0.89 147:0.91 149:0.15 150:0.32 151:0.85 153:0.48 154:0.09 158:0.73 159:0.97 162:0.59 168:0.98 170:0.90 172:0.78 173:0.19 174:0.99 175:0.91 177:0.96 179:0.15 187:0.21 190:0.99 191:0.76 192:0.57 193:0.95 195:1.00 197:0.99 201:0.54 202:0.73 205:0.98 212:0.18 216:0.44 220:0.91 222:0.25 226:0.98 232:0.80 235:0.52 236:0.91 239:0.90 241:0.21 242:0.24 243:0.31 244:0.98 245:0.88 247:0.98 248:0.82 253:0.72 254:0.93 255:0.71 256:0.71 257:0.84 259:0.21 264:0.90 265:0.26 266:0.98 267:0.69 268:0.73 271:0.95 275:0.97 276:0.95 277:0.98 285:0.50 294:0.93 300:0.94 +1 8:0.77 10:0.92 11:0.46 17:0.16 18:0.59 21:0.13 23:0.99 27:0.34 29:0.71 30:0.36 31:0.89 34:0.30 36:0.93 37:0.54 39:0.74 43:0.12 45:0.81 55:0.59 62:0.98 66:0.80 69:0.37 70:0.89 71:0.65 74:0.36 75:0.95 77:0.70 79:0.89 81:0.33 82:0.98 86:0.58 88:0.98 91:0.70 96:0.74 98:0.72 101:0.47 102:0.94 108:0.29 122:0.98 123:0.28 124:0.40 126:0.26 127:0.36 128:0.99 129:0.05 133:0.59 135:0.73 137:0.89 139:0.56 140:0.45 145:0.96 146:0.95 147:0.96 149:0.17 150:0.35 151:0.75 153:0.48 154:0.16 159:0.77 162:0.66 168:0.99 170:0.90 172:0.90 173:0.17 174:0.99 175:0.75 177:0.99 179:0.22 187:0.87 190:0.99 192:0.48 193:0.95 195:0.94 196:0.87 197:0.99 201:0.55 202:0.82 205:0.99 212:0.70 216:0.85 220:0.74 222:0.25 226:0.95 235:0.22 236:0.91 239:0.91 241:0.41 242:0.71 243:0.82 244:0.97 245:0.81 247:0.92 248:0.88 253:0.53 254:0.93 255:0.71 256:0.85 257:0.65 259:0.21 264:0.90 265:0.72 266:0.80 267:0.44 268:0.84 271:0.95 275:0.94 276:0.84 277:0.69 282:0.73 284:0.87 285:0.55 294:0.95 300:0.94 +0 8:0.84 10:0.93 17:0.62 18:0.72 21:0.05 23:0.98 27:0.72 29:0.71 30:0.54 33:0.97 34:0.81 36:0.74 37:0.79 39:0.74 43:0.06 55:0.71 62:0.98 66:0.20 69:0.84 70:0.61 71:0.65 74:0.26 75:0.95 81:0.28 86:0.63 91:0.87 98:0.58 108:0.70 122:0.98 123:0.68 124:0.85 126:0.22 127:0.91 128:0.98 129:0.05 133:0.81 135:0.79 139:0.61 140:0.45 146:0.92 147:0.72 149:0.20 150:0.31 151:0.64 153:0.82 154:0.09 159:0.87 162:0.83 168:0.98 170:0.90 172:0.82 173:0.66 174:1.00 175:0.99 177:0.38 179:0.19 187:0.21 190:1.00 197:1.00 202:0.64 205:0.98 212:0.71 216:0.22 220:0.62 222:0.25 226:0.96 235:0.05 236:0.91 239:0.92 241:0.21 242:0.80 243:0.21 244:0.99 245:0.96 247:0.60 248:0.66 253:0.24 254:0.80 256:0.68 257:0.99 259:0.21 264:0.90 265:0.59 266:0.54 267:0.52 268:0.70 271:0.95 276:0.93 277:0.98 285:0.45 291:0.97 300:0.55 +0 8:0.64 9:0.70 10:0.81 11:0.46 17:0.64 18:0.92 21:0.22 27:0.30 29:0.71 30:0.11 31:0.90 34:0.56 36:0.87 37:0.57 39:0.74 43:0.21 45:0.95 55:0.59 64:0.77 66:0.51 69:0.84 70:0.92 71:0.65 74:0.51 79:0.90 81:0.34 83:0.54 86:0.78 91:0.25 96:0.75 97:0.89 98:0.72 101:0.47 108:0.94 117:0.86 120:0.57 122:0.90 123:0.94 124:0.91 126:0.26 127:0.96 129:0.59 133:0.94 135:0.82 137:0.90 138:0.96 139:0.73 140:0.45 146:0.75 147:0.45 149:0.37 150:0.36 151:0.53 153:0.48 154:0.16 155:0.52 158:0.73 159:0.92 162:0.86 164:0.65 170:0.90 172:0.96 173:0.55 174:0.83 175:0.75 177:0.88 179:0.14 187:0.39 190:1.00 192:0.86 196:0.90 197:0.81 201:0.55 202:0.62 208:0.50 212:0.61 216:0.67 219:0.88 220:0.93 222:0.25 235:0.31 239:0.80 241:0.21 242:0.54 243:0.16 244:0.97 245:0.94 247:0.98 248:0.52 253:0.59 255:0.70 256:0.68 257:0.93 259:0.21 260:0.57 264:0.90 265:0.72 266:0.94 267:1.00 268:0.71 271:0.95 275:0.94 276:0.96 277:0.69 279:0.75 283:0.65 284:0.94 285:0.55 292:0.88 294:0.94 300:0.84 +0 10:0.80 11:0.42 17:0.69 18:0.66 21:0.78 27:0.72 29:0.71 30:0.43 34:0.31 36:0.85 39:0.74 43:0.06 45:0.87 66:0.11 69:0.99 70:0.64 71:0.65 74:0.27 86:0.60 91:0.49 98:0.19 101:0.45 108:0.91 122:0.83 123:0.90 124:0.94 127:0.68 129:0.05 133:0.94 135:0.76 139:0.58 146:0.38 147:0.05 149:0.10 154:0.06 159:0.95 163:0.88 170:0.90 172:0.27 173:0.99 174:0.83 175:0.99 177:0.05 178:0.55 179:0.59 187:0.21 197:0.82 212:0.16 216:0.18 222:0.25 235:0.05 239:0.80 241:0.24 242:0.24 243:0.10 244:0.99 245:0.60 247:0.88 253:0.24 257:0.99 259:0.21 264:0.90 265:0.20 266:0.92 267:0.44 271:0.95 275:0.96 276:0.62 277:0.99 294:0.92 300:0.55 +1 1:0.57 10:0.83 11:0.46 17:0.38 18:0.82 21:0.59 27:0.45 29:0.71 30:0.38 34:0.80 36:0.17 37:0.50 39:0.74 43:0.08 45:0.77 55:0.92 64:0.77 66:0.33 69:0.91 70:0.90 71:0.65 74:0.35 77:0.70 81:0.42 83:0.56 86:0.68 91:0.48 97:0.94 98:0.57 99:0.95 101:0.47 108:0.82 114:0.66 122:0.91 123:0.81 124:0.83 126:0.32 127:0.46 129:0.05 133:0.81 135:0.94 139:0.67 145:0.47 146:0.82 147:0.28 149:0.09 150:0.36 154:0.15 155:0.46 158:0.73 159:0.70 163:0.90 165:0.65 170:0.90 172:0.48 173:0.88 174:0.86 175:0.91 176:0.59 177:0.88 178:0.55 179:0.56 187:0.68 192:0.47 195:0.87 197:0.85 201:0.56 208:0.50 212:0.27 215:0.55 216:0.77 219:0.88 222:0.25 235:0.80 239:0.82 241:0.24 242:0.18 243:0.28 244:0.97 245:0.65 247:0.84 253:0.51 254:0.94 257:0.93 259:0.21 264:0.90 265:0.59 266:0.80 267:0.65 271:0.95 276:0.60 277:0.87 279:0.75 282:0.72 283:0.67 290:0.66 292:0.88 297:0.36 300:0.70 +0 10:0.80 11:0.42 17:0.47 18:0.61 21:0.78 27:0.34 29:0.71 30:0.45 34:0.75 36:0.91 37:0.46 39:0.74 43:0.11 45:0.73 66:0.64 69:0.83 70:0.33 71:0.65 74:0.27 86:0.61 91:0.12 98:0.37 101:0.45 108:0.68 122:0.80 123:0.66 124:0.42 127:0.16 129:0.05 133:0.36 135:0.92 139:0.59 145:0.59 146:0.49 147:0.05 149:0.09 154:0.10 159:0.26 170:0.90 172:0.32 173:0.83 174:0.79 175:0.97 177:0.05 178:0.55 179:0.59 187:0.87 197:0.83 212:0.52 216:0.53 222:0.25 235:0.22 239:0.80 241:0.21 242:0.24 243:0.21 244:0.98 245:0.43 247:0.47 253:0.25 254:0.92 257:0.99 259:0.21 264:0.90 265:0.39 266:0.27 267:0.23 271:0.95 275:0.93 276:0.28 277:0.95 294:0.93 300:0.25 +0 8:0.85 10:0.83 11:0.42 17:0.94 18:0.59 21:0.78 27:0.68 29:0.71 30:0.30 34:0.42 36:0.26 37:0.38 39:0.74 43:0.07 45:0.66 55:0.71 66:0.12 69:0.94 70:0.92 71:0.65 74:0.26 86:0.58 91:0.78 98:0.23 101:0.45 108:0.78 122:0.98 123:0.88 124:0.74 127:0.31 129:0.05 133:0.60 135:0.56 139:0.56 145:0.63 146:0.46 147:0.51 149:0.08 154:0.07 159:0.70 170:0.90 172:0.27 173:0.92 174:0.92 175:0.99 177:0.05 178:0.55 179:0.68 191:0.73 197:0.91 202:0.79 212:0.23 216:0.30 222:0.25 235:0.88 239:0.82 241:0.82 242:0.42 243:0.45 244:0.99 245:0.59 247:0.67 253:0.23 257:0.99 259:0.21 264:0.90 265:0.25 266:0.71 267:0.28 271:0.95 275:0.91 276:0.39 277:0.98 294:0.92 300:0.70 +0 10:0.80 11:0.42 17:0.79 18:0.65 21:0.78 27:0.72 29:0.71 30:0.33 34:0.47 36:0.92 39:0.74 43:0.10 45:0.81 66:0.19 69:0.95 70:0.76 71:0.65 74:0.28 86:0.60 91:0.60 98:0.19 101:0.45 108:0.89 122:0.97 123:0.89 124:0.88 127:0.38 129:0.05 133:0.86 135:0.88 139:0.59 145:0.67 146:0.47 147:0.05 149:0.10 154:0.06 159:0.86 163:0.97 170:0.90 172:0.27 173:0.85 174:0.79 175:0.97 177:0.05 178:0.55 179:0.65 197:0.81 202:0.72 212:0.18 216:0.09 222:0.25 235:0.88 239:0.79 241:0.78 242:0.18 243:0.13 244:0.98 245:0.55 247:0.79 253:0.25 254:0.96 257:0.99 264:0.90 265:0.20 266:0.80 267:0.94 271:0.95 275:0.95 276:0.46 277:0.95 294:0.93 300:0.55 +1 1:0.56 10:0.88 11:0.49 17:0.30 18:0.75 21:0.47 27:0.45 29:0.71 30:0.18 34:0.89 36:0.17 37:0.50 39:0.74 43:0.06 44:0.86 45:0.83 55:0.52 64:0.77 66:0.12 69:0.69 70:0.96 71:0.65 74:0.39 75:0.95 77:0.70 81:0.40 83:0.54 86:0.68 91:0.35 97:0.89 98:0.44 99:0.83 101:0.65 102:0.94 108:0.52 122:0.90 123:0.73 124:0.79 126:0.32 127:0.46 129:0.05 133:0.73 135:0.93 139:0.69 145:0.47 146:0.56 147:0.62 149:0.07 150:0.37 154:0.15 155:0.46 159:0.66 165:0.63 170:0.90 172:0.54 173:0.58 174:0.95 175:0.91 177:0.96 178:0.55 179:0.42 187:0.68 192:0.45 195:0.84 197:0.95 201:0.57 208:0.49 212:0.48 216:0.77 219:0.88 222:0.25 226:0.95 235:0.68 236:0.91 239:0.88 241:0.21 242:0.16 243:0.45 244:0.97 245:0.79 247:0.90 253:0.34 254:0.90 257:0.84 259:0.21 264:0.90 265:0.38 266:0.87 267:0.44 271:0.95 276:0.70 277:0.95 279:0.74 282:0.73 292:0.87 300:0.84 +1 8:0.78 10:0.89 11:0.46 17:0.08 18:0.61 21:0.71 23:0.99 27:0.02 29:0.71 30:0.26 34:0.89 36:0.95 37:0.91 39:0.74 43:0.10 45:0.83 55:0.71 62:0.97 66:0.95 69:0.67 70:0.90 71:0.65 74:0.37 75:0.95 77:0.70 81:0.27 86:0.59 91:0.22 96:0.77 98:0.85 101:0.47 108:0.51 114:0.63 122:0.98 123:0.49 126:0.26 127:0.36 128:0.97 129:0.05 135:0.79 139:0.57 140:0.45 145:0.49 146:0.93 147:0.94 149:0.13 150:0.27 151:0.63 153:0.45 154:0.11 158:0.73 159:0.56 162:0.65 168:0.97 170:0.90 172:0.86 173:0.60 174:0.97 176:0.59 177:0.99 179:0.30 187:0.99 190:0.99 192:0.56 193:0.95 195:0.82 197:0.97 201:0.53 202:0.76 205:0.99 212:0.61 216:0.93 220:0.93 222:0.25 226:0.95 232:0.98 235:0.88 236:0.91 239:0.89 241:0.44 242:0.18 243:0.95 244:0.97 247:0.92 248:0.71 253:0.64 254:0.86 255:0.70 256:0.78 259:0.21 264:0.90 265:0.85 266:0.54 267:0.69 268:0.77 271:0.95 275:0.91 276:0.77 282:0.72 285:0.50 290:0.63 294:0.93 300:0.70 +1 8:0.78 10:0.87 17:0.03 18:0.61 21:0.47 23:1.00 27:0.30 29:0.71 30:0.32 31:0.90 33:0.97 34:0.86 36:0.92 37:0.60 39:0.74 43:0.06 55:0.84 62:0.97 66:0.80 69:0.57 70:0.63 71:0.65 74:0.30 75:0.95 79:0.90 81:0.30 86:0.59 91:0.71 96:0.74 98:0.76 102:0.94 108:0.44 117:0.86 122:0.98 123:0.42 124:0.38 126:0.26 127:0.35 128:0.99 129:0.05 133:0.36 135:0.61 137:0.90 139:0.58 140:0.45 145:0.59 146:0.81 147:0.84 149:0.07 150:0.31 151:0.75 153:0.80 154:0.11 159:0.45 162:0.58 163:0.75 168:0.99 170:0.90 172:0.67 173:0.56 174:0.94 175:0.97 177:0.88 179:0.55 187:0.87 190:1.00 192:0.55 193:0.95 195:0.72 196:0.87 197:0.96 201:0.54 202:0.87 205:1.00 212:0.30 216:0.82 220:0.93 222:0.25 226:0.95 232:0.99 235:0.60 236:0.91 239:0.87 241:0.46 242:0.31 243:0.82 244:0.99 245:0.55 247:0.74 248:0.85 253:0.52 254:0.74 255:0.71 256:0.88 257:0.93 259:0.21 264:0.90 265:0.76 266:0.54 267:0.91 268:0.88 271:0.95 276:0.57 277:0.95 284:0.87 285:0.55 291:0.97 300:0.43 +1 8:0.79 10:0.80 11:0.42 12:0.51 17:0.55 18:0.99 21:0.54 22:0.93 27:0.72 29:0.52 30:0.31 31:0.93 34:0.86 36:0.19 37:0.33 39:0.91 43:0.06 45:0.73 47:0.93 55:0.84 64:0.77 66:0.10 69:0.99 70:0.72 71:0.73 74:0.25 79:0.93 81:0.26 86:0.79 91:0.03 98:0.38 101:0.45 108:0.96 122:0.95 123:0.96 124:0.98 126:0.22 127:0.97 129:0.05 131:0.61 133:0.98 135:0.73 137:0.93 139:0.76 140:0.45 146:0.83 147:0.94 149:0.17 150:0.28 151:0.65 153:0.48 154:0.05 157:0.61 159:0.98 162:0.86 163:0.94 166:0.61 170:0.97 172:0.86 173:0.98 174:0.95 175:0.97 177:0.70 179:0.12 182:0.61 187:0.95 190:1.00 196:0.92 197:0.89 202:0.36 212:0.21 216:0.16 219:0.87 222:0.35 227:0.61 229:0.61 231:0.62 235:0.60 239:0.79 241:0.02 242:0.73 243:0.19 244:0.98 245:0.94 246:0.61 247:0.94 248:0.62 253:0.22 254:0.90 256:0.45 257:0.97 259:0.21 262:0.93 264:0.95 265:0.40 266:0.98 267:0.44 268:0.46 271:0.22 275:0.93 276:0.97 277:0.98 284:0.87 285:0.45 287:0.61 292:0.88 294:0.91 300:0.84 +0 8:0.61 10:0.80 12:0.51 17:0.11 18:0.99 21:0.05 27:0.58 29:0.52 30:0.27 31:0.90 34:0.30 36:0.51 37:0.38 39:0.91 43:0.06 55:0.84 64:0.77 66:0.16 69:0.23 70:0.76 71:0.73 74:0.25 79:0.90 81:0.30 86:0.84 91:0.15 98:0.49 108:0.18 122:0.95 123:0.18 124:0.97 126:0.22 127:0.97 129:0.05 131:0.61 133:0.97 135:0.90 137:0.90 139:0.80 140:0.45 146:0.99 147:0.99 149:0.20 150:0.33 151:0.57 153:0.48 154:0.08 157:0.61 159:0.97 162:0.62 166:0.61 170:0.97 172:0.92 173:0.06 174:0.91 175:0.91 177:0.96 179:0.12 182:0.61 187:0.21 190:1.00 196:0.91 197:0.87 202:0.50 212:0.19 216:0.67 219:0.87 220:0.74 222:0.35 227:0.61 229:0.61 231:0.62 235:0.05 239:0.79 241:0.07 242:0.78 243:0.37 244:0.99 245:0.97 246:0.61 247:0.95 248:0.55 253:0.22 254:0.80 256:0.45 257:0.84 259:0.21 264:0.95 265:0.50 266:0.95 267:0.87 268:0.46 271:0.22 275:0.93 276:0.98 277:0.87 284:0.87 285:0.45 287:0.61 292:0.88 294:0.91 300:0.84 +1 8:0.86 10:0.79 11:0.55 12:0.51 17:0.72 18:0.97 21:0.74 27:0.41 29:0.52 30:0.17 31:0.86 34:0.28 36:0.33 37:0.50 39:0.91 43:0.07 45:0.88 55:0.71 64:0.77 66:0.26 69:0.98 70:0.95 71:0.73 74:0.28 79:0.86 81:0.24 86:0.76 91:0.20 98:0.48 101:0.65 108:0.95 122:0.95 123:0.95 124:0.95 126:0.22 127:0.75 129:0.59 131:0.61 133:0.95 135:0.86 137:0.86 139:0.72 140:0.45 144:0.76 146:0.98 147:0.54 149:0.18 150:0.26 151:0.47 153:0.48 154:0.06 157:0.84 159:0.95 162:0.86 166:0.61 170:0.97 172:0.92 173:0.87 174:0.92 175:0.75 177:0.38 179:0.13 182:0.76 187:0.39 190:1.00 196:0.88 197:0.82 202:0.36 212:0.55 216:0.88 220:0.97 222:0.35 227:0.61 229:0.61 231:0.64 235:0.88 239:0.79 241:0.10 242:0.63 243:0.07 244:0.98 245:0.95 246:0.61 247:0.99 248:0.47 253:0.26 256:0.45 257:0.99 259:0.21 264:0.95 265:0.49 266:0.96 267:0.69 268:0.46 271:0.22 275:0.93 276:0.96 277:0.69 284:0.87 285:0.45 287:0.61 294:0.91 300:0.94 +0 8:0.61 10:0.79 12:0.51 17:0.57 18:0.57 21:0.59 27:0.39 29:0.52 30:0.20 34:0.56 36:0.68 37:0.33 39:0.91 43:0.14 55:0.71 66:0.05 69:1.00 70:0.92 71:0.73 74:0.35 81:0.24 86:0.57 91:0.01 96:0.68 98:0.17 108:1.00 114:0.62 122:0.90 123:0.99 124:1.00 126:0.22 127:0.96 129:0.05 131:0.80 133:1.00 135:0.66 139:0.55 140:0.45 146:0.54 147:0.72 149:0.24 150:0.25 151:0.47 153:0.69 154:0.10 157:0.61 159:1.00 162:0.86 166:0.72 170:0.97 172:0.80 173:0.95 174:0.97 175:0.99 176:0.55 177:0.38 179:0.09 182:0.61 187:0.68 190:1.00 197:0.82 202:0.46 212:0.21 216:0.88 220:0.97 222:0.35 227:0.82 229:0.61 231:0.63 232:0.86 235:0.68 239:0.79 241:0.03 242:0.77 243:0.08 244:0.99 245:0.95 246:0.61 247:0.99 248:0.47 253:0.47 256:0.45 257:0.99 259:0.21 264:0.95 265:0.19 266:1.00 267:0.79 268:0.55 271:0.22 275:0.96 276:0.99 277:0.99 285:0.45 287:0.61 290:0.62 294:0.91 300:0.99 +0 8:0.88 10:0.80 11:0.49 12:0.51 17:0.70 18:0.97 21:0.78 27:0.54 29:0.52 30:0.10 31:0.86 34:0.44 36:0.30 37:0.71 39:0.91 43:0.06 45:0.81 55:0.84 66:0.05 69:0.88 70:0.97 71:0.73 74:0.25 79:0.86 86:0.74 91:0.12 98:0.16 101:0.47 108:0.76 122:0.95 123:0.74 124:1.00 127:0.95 129:0.59 131:0.61 133:1.00 135:0.71 137:0.86 139:0.71 140:0.94 144:0.57 145:0.59 146:0.78 147:0.72 149:0.14 151:0.47 153:0.48 154:0.05 157:0.77 159:0.99 162:0.47 163:0.94 166:0.61 170:0.97 172:0.48 173:0.30 174:0.95 175:0.97 177:0.38 178:0.53 179:0.13 182:0.72 187:0.21 190:1.00 191:0.82 196:0.87 197:0.88 202:0.75 212:0.12 216:0.58 220:0.99 222:0.35 227:0.61 229:0.61 231:0.64 235:0.98 239:0.80 241:0.05 242:0.57 243:0.10 244:0.98 245:0.87 246:0.61 247:0.99 248:0.47 253:0.22 254:0.96 256:0.45 257:0.99 259:0.21 264:0.95 265:0.17 266:0.99 267:0.84 268:0.46 271:0.22 275:0.97 276:0.97 277:0.95 284:0.87 285:0.45 287:0.61 294:0.91 300:0.98 +1 8:0.73 10:0.80 11:0.56 12:0.51 17:0.30 18:0.98 21:0.30 27:0.62 29:0.52 30:0.32 31:0.88 34:0.76 36:0.22 37:0.31 39:0.91 43:0.07 45:0.92 55:0.84 64:0.77 66:0.06 69:0.98 70:0.93 71:0.73 74:0.26 79:0.88 81:0.28 86:0.79 91:0.04 98:0.30 101:0.71 108:0.99 122:0.95 123:0.95 124:0.99 126:0.22 127:0.99 129:0.81 131:0.61 133:0.99 135:0.26 137:0.88 139:0.76 140:0.45 144:0.57 146:0.97 147:0.36 149:0.19 150:0.30 151:0.51 153:0.48 154:0.06 157:0.75 159:0.98 162:0.62 163:0.75 166:0.61 170:0.97 172:0.82 173:0.75 174:0.94 175:0.91 177:0.05 179:0.11 182:0.71 187:0.68 190:1.00 196:0.89 197:0.84 202:0.50 212:0.14 216:0.41 219:0.87 220:0.87 222:0.35 227:0.61 229:0.61 231:0.64 235:0.31 239:0.79 241:0.02 242:0.72 243:0.05 244:0.83 245:0.95 246:0.61 247:0.98 248:0.51 253:0.24 254:0.93 256:0.45 257:0.99 259:0.21 264:0.95 265:0.29 266:0.98 267:0.76 268:0.46 271:0.22 275:0.96 276:0.98 277:0.87 284:0.87 285:0.45 287:0.61 292:0.88 294:0.92 300:0.94 +1 8:0.83 10:0.79 11:0.42 12:0.51 17:0.12 18:0.97 21:0.71 27:0.62 29:0.52 30:0.27 34:0.21 36:0.52 37:0.45 39:0.91 43:0.06 45:0.73 55:0.84 64:0.77 66:0.06 69:0.41 70:0.89 71:0.73 74:0.26 81:0.25 86:0.75 91:0.06 98:0.29 101:0.45 108:0.99 122:0.95 123:0.95 124:0.99 126:0.22 127:0.97 129:0.59 131:0.61 133:0.99 135:0.66 139:0.73 146:0.90 147:0.92 149:0.16 150:0.26 154:0.09 157:0.61 159:0.97 163:0.94 166:0.61 170:0.97 172:0.74 173:0.07 174:0.83 175:0.97 177:0.88 178:0.55 179:0.13 182:0.61 187:0.87 197:0.82 202:0.36 212:0.18 216:0.62 219:0.87 222:0.35 227:0.61 229:0.61 231:0.62 235:0.84 239:0.79 241:0.01 242:0.60 243:0.15 244:0.99 245:0.92 246:0.61 247:0.98 253:0.24 257:0.93 259:0.21 264:0.95 265:0.26 266:0.98 267:0.85 271:0.22 275:0.91 276:0.97 277:0.95 287:0.61 292:0.88 294:0.91 300:0.94 +0 10:0.80 11:0.53 12:0.51 17:0.49 18:0.73 21:0.78 27:0.72 29:0.52 30:0.09 34:0.17 36:0.46 39:0.91 43:0.06 45:0.73 55:0.84 66:0.06 69:0.72 70:0.99 71:0.73 74:0.26 86:0.62 91:0.01 98:0.14 101:0.47 108:0.99 123:0.99 124:1.00 127:0.95 129:0.98 131:0.61 132:0.97 133:1.00 135:0.86 139:0.60 144:0.76 146:0.64 147:0.69 149:0.09 154:0.05 157:0.75 159:0.99 166:0.61 170:0.97 172:0.51 173:0.11 174:0.95 175:0.99 177:0.70 178:0.55 179:0.15 182:0.72 187:0.68 197:0.86 212:0.16 216:0.16 222:0.35 227:0.61 229:0.61 231:0.64 235:0.22 239:0.79 241:0.02 242:0.14 243:0.11 244:0.98 245:0.83 246:0.61 247:1.00 253:0.23 254:0.98 257:0.97 259:0.21 264:0.95 265:0.15 266:1.00 267:0.44 271:0.22 275:0.97 276:0.95 277:0.98 287:0.61 294:0.92 300:0.98 +1 8:0.82 10:0.80 11:0.49 12:0.51 17:0.36 18:0.99 21:0.40 27:0.62 29:0.52 30:0.37 34:0.30 36:0.20 37:0.69 39:0.91 43:0.06 45:0.89 55:0.84 64:0.77 66:0.16 69:0.95 70:0.76 71:0.73 74:0.28 81:0.27 86:0.83 91:0.29 98:0.65 101:0.47 108:0.90 122:0.95 123:0.95 124:0.74 126:0.22 127:0.92 129:0.05 131:0.61 133:0.67 135:0.71 139:0.79 144:0.57 146:0.97 147:0.98 149:0.19 150:0.30 154:0.08 157:0.61 159:0.92 166:0.61 170:0.97 172:0.91 173:0.82 174:0.83 177:0.96 178:0.55 179:0.15 182:0.61 187:0.39 197:0.80 212:0.30 216:0.73 222:0.35 227:0.61 229:0.61 231:0.64 235:0.42 239:0.79 241:0.02 242:0.55 243:0.40 244:0.93 245:0.99 246:0.61 247:0.98 253:0.26 254:0.99 257:0.84 259:0.21 264:0.95 265:0.63 266:0.63 267:0.44 271:0.22 275:0.91 276:0.95 277:0.87 287:0.61 294:0.92 300:0.70 +1 8:0.84 10:0.79 11:0.52 12:0.51 17:0.22 18:0.99 21:0.59 27:0.45 29:0.52 30:0.21 31:0.89 34:0.24 36:0.30 37:0.39 39:0.91 43:0.06 44:0.85 45:0.85 55:0.84 64:0.77 66:0.10 69:0.98 70:0.90 71:0.73 74:0.27 79:0.88 81:0.26 86:0.79 91:0.17 98:0.37 101:0.65 108:0.97 122:0.95 123:0.96 124:0.96 126:0.22 127:0.79 129:0.81 131:0.61 133:0.96 135:0.89 137:0.89 139:0.76 140:0.45 144:0.57 145:0.83 146:0.83 147:0.72 149:0.18 150:0.28 151:0.52 153:0.72 154:0.06 157:0.75 159:0.95 162:0.65 166:0.61 170:0.97 172:0.80 173:0.88 174:0.89 175:0.75 177:0.05 179:0.13 182:0.72 187:0.68 190:1.00 195:0.99 196:0.90 197:0.82 202:0.61 212:0.19 216:0.84 219:0.87 220:0.87 222:0.35 227:0.61 229:0.61 231:0.64 235:0.68 239:0.79 241:0.15 242:0.73 243:0.10 244:0.93 245:0.96 246:0.61 247:0.99 248:0.52 253:0.25 254:0.94 256:0.58 257:0.99 259:0.21 264:0.95 265:0.39 266:0.92 267:0.69 268:0.62 271:0.22 275:0.93 276:0.96 277:0.95 284:0.92 285:0.45 287:0.61 292:0.88 294:0.91 300:0.94 +2 10:0.81 11:0.52 12:0.51 17:0.62 18:0.68 21:0.78 27:0.45 29:0.52 30:0.56 34:0.86 36:0.20 37:0.50 39:0.91 43:0.14 45:0.77 66:0.74 69:0.73 70:0.42 71:0.73 74:0.27 86:0.66 91:0.25 98:0.35 101:0.52 108:0.56 123:0.54 124:0.39 127:0.20 129:0.05 131:0.61 133:0.36 135:0.51 139:0.63 144:0.57 145:0.52 146:0.45 147:0.51 149:0.10 154:0.14 157:0.81 159:0.33 166:0.61 170:0.97 172:0.51 173:0.72 174:0.79 177:0.99 178:0.55 179:0.52 182:0.74 187:0.68 197:0.84 212:0.87 216:0.77 222:0.35 227:0.61 229:0.61 231:0.63 235:0.68 239:0.81 241:0.12 242:0.24 243:0.77 244:0.97 245:0.49 246:0.61 247:0.74 253:0.24 254:0.95 259:0.21 264:0.95 265:0.37 266:0.23 267:0.28 271:0.22 275:0.91 276:0.33 287:0.61 294:0.92 300:0.33 +1 8:0.71 10:0.79 11:0.42 12:0.51 17:0.43 18:0.96 21:0.54 27:0.72 29:0.52 30:0.14 34:0.68 36:0.17 37:0.47 39:0.91 43:0.06 45:0.81 55:0.92 66:0.05 69:0.98 70:0.95 71:0.73 74:0.25 81:0.25 86:0.73 91:0.04 98:0.18 101:0.45 104:0.95 106:0.81 108:0.98 120:0.59 122:0.95 123:0.99 124:1.00 126:0.22 127:0.97 129:0.59 131:0.81 133:1.00 135:0.71 139:0.70 140:0.45 146:0.75 147:0.28 149:0.15 150:0.26 151:0.51 153:0.48 154:0.06 157:0.61 159:0.99 162:0.70 164:0.70 166:0.72 170:0.97 172:0.67 173:0.71 174:0.94 175:0.97 177:0.05 179:0.11 182:0.61 187:0.95 190:1.00 197:0.82 202:0.63 206:0.81 212:0.13 215:0.58 216:0.06 220:0.96 222:0.35 227:0.83 229:0.61 231:0.65 235:0.60 239:0.79 241:0.12 242:0.52 243:0.05 244:0.99 245:0.92 246:0.61 247:0.99 248:0.52 253:0.23 256:0.64 257:0.99 259:0.21 260:0.59 264:0.95 265:0.16 266:0.98 267:0.85 268:0.65 271:0.22 275:0.93 276:0.98 277:0.95 283:0.67 285:0.45 287:0.61 294:0.91 297:0.36 300:0.94 +1 8:0.86 10:0.80 11:0.55 12:0.51 17:0.22 18:0.99 21:0.59 27:0.62 29:0.52 30:0.53 34:0.25 36:0.21 37:0.69 39:0.91 43:0.08 45:0.77 55:0.84 64:0.77 66:0.19 69:0.32 70:0.69 71:0.73 74:0.28 81:0.26 86:0.81 91:0.22 98:0.65 101:0.52 108:0.25 122:0.95 123:0.93 124:0.84 126:0.22 127:0.93 129:0.05 131:0.61 133:0.80 135:0.30 139:0.77 144:0.76 146:0.97 147:0.97 149:0.19 150:0.28 154:0.09 157:0.76 159:0.93 163:0.94 166:0.61 170:0.97 172:0.86 173:0.09 174:0.83 177:0.99 178:0.55 179:0.17 182:0.72 187:0.39 197:0.85 212:0.26 216:0.73 222:0.35 227:0.61 229:0.61 231:0.63 235:0.68 239:0.80 241:0.05 242:0.76 243:0.43 244:0.97 245:0.96 246:0.61 247:0.95 253:0.25 254:0.92 259:0.21 264:0.95 265:0.58 266:0.80 267:0.52 271:0.22 275:0.93 276:0.94 277:0.99 287:0.61 294:0.92 300:0.84 +0 10:0.81 11:0.53 12:0.51 17:0.03 18:0.59 21:0.78 27:0.62 29:0.52 30:0.09 34:0.26 36:0.20 37:0.45 39:0.91 43:0.13 45:0.73 55:0.71 66:0.09 69:0.74 70:0.98 71:0.73 74:0.29 86:0.58 91:0.04 98:0.24 101:0.47 108:0.96 123:0.95 124:0.93 127:0.91 129:0.05 131:0.61 133:0.92 135:0.66 139:0.56 144:0.76 146:0.28 147:0.05 149:0.14 154:0.10 157:0.84 159:0.93 166:0.61 170:0.97 172:0.37 173:0.38 174:0.88 175:0.99 177:0.05 178:0.55 179:0.53 182:0.75 187:0.68 197:0.88 212:0.18 216:0.62 222:0.35 227:0.61 229:0.61 231:0.62 235:0.52 239:0.80 241:0.01 242:0.71 243:0.09 244:0.99 245:0.67 246:0.61 247:0.82 253:0.26 257:0.99 259:0.21 264:0.95 265:0.26 266:0.95 267:0.73 271:0.22 275:0.91 276:0.68 277:0.99 287:0.61 294:0.92 300:0.84 +0 8:0.88 10:0.80 11:0.48 12:0.51 17:0.28 18:0.97 21:0.59 22:0.93 27:0.44 29:0.52 30:0.37 31:0.86 34:0.31 36:0.29 37:0.57 39:0.91 43:0.06 45:0.73 47:0.93 55:0.84 64:0.77 66:0.07 69:0.82 70:0.77 71:0.73 74:0.25 79:0.86 81:0.26 86:0.76 91:0.31 98:0.31 101:0.45 108:0.67 122:0.95 123:0.95 124:0.98 126:0.22 127:0.93 129:0.81 131:0.61 133:0.98 135:0.98 137:0.86 139:0.73 140:0.45 144:0.57 145:0.38 146:0.90 147:0.26 149:0.17 150:0.28 151:0.48 153:0.77 154:0.05 157:0.78 159:0.96 162:0.66 163:0.94 166:0.61 170:0.97 172:0.70 173:0.37 174:0.91 175:0.97 177:0.05 179:0.15 182:0.73 187:0.87 190:1.00 191:0.77 196:0.88 197:0.84 202:0.55 212:0.23 216:0.68 219:0.87 220:0.97 222:0.35 227:0.61 229:0.61 231:0.63 235:0.68 239:0.80 241:0.03 242:0.63 243:0.06 244:0.98 245:0.91 246:0.61 247:0.97 248:0.47 253:0.23 254:0.88 256:0.45 257:0.99 259:0.21 262:0.93 264:0.95 265:0.30 266:0.96 267:0.94 268:0.57 271:0.22 275:0.96 276:0.95 277:0.95 284:0.91 285:0.45 287:0.61 292:0.88 294:0.92 300:0.84 +1 8:0.81 10:0.79 11:0.42 12:0.51 17:0.22 18:0.99 21:0.47 27:0.62 29:0.52 30:0.38 34:0.36 36:0.18 37:0.69 39:0.91 43:0.06 45:0.66 55:0.71 64:0.77 66:0.23 69:0.96 70:0.68 71:0.73 74:0.25 81:0.30 86:0.86 91:0.22 98:0.66 101:0.45 108:0.24 122:0.95 123:0.92 124:0.77 126:0.26 127:0.92 129:0.05 131:0.61 133:0.74 135:0.75 139:0.81 146:0.98 147:0.99 149:0.15 150:0.31 154:0.06 157:0.61 159:0.93 166:0.72 170:0.97 172:0.94 173:0.85 174:0.83 175:0.91 177:0.88 178:0.55 179:0.13 182:0.61 187:0.39 192:0.45 197:0.80 201:0.54 212:0.54 215:0.63 216:0.73 222:0.35 227:0.61 229:0.61 231:0.64 235:0.60 239:0.78 241:0.05 242:0.31 243:0.49 244:0.98 245:0.99 246:0.61 247:0.97 253:0.23 254:0.84 257:0.93 259:0.21 264:0.95 265:0.66 266:0.80 267:0.44 271:0.22 275:0.91 276:0.97 277:0.87 287:0.61 294:0.91 297:0.36 300:0.70 +2 1:0.67 7:0.70 10:0.99 11:0.92 12:0.51 17:0.69 18:0.89 21:0.05 22:0.93 27:0.64 29:0.86 30:0.94 33:0.97 34:0.97 36:0.46 37:0.36 39:0.23 43:0.84 44:0.92 45:0.85 47:0.93 48:0.91 64:0.77 66:0.93 69:0.12 70:0.19 71:0.64 74:0.81 76:0.85 77:0.89 81:0.75 83:0.91 85:0.90 86:0.97 91:0.42 98:0.88 99:0.95 101:1.00 102:0.97 108:0.10 114:0.82 117:0.86 122:0.89 123:0.10 126:0.51 127:0.41 129:0.05 135:0.61 139:0.97 144:0.85 145:0.59 146:0.63 147:0.68 149:0.95 150:0.67 154:0.80 155:0.55 159:0.23 165:0.81 170:0.82 172:0.81 173:0.39 174:0.92 176:0.59 177:0.88 178:0.55 179:0.40 187:0.68 192:0.55 193:0.99 195:0.58 197:0.95 201:0.64 204:0.82 208:0.64 212:0.87 216:0.17 222:0.60 230:0.73 232:0.87 233:0.76 235:0.22 236:0.95 239:0.99 241:0.41 242:0.39 243:0.93 247:0.60 253:0.67 259:0.21 262:0.93 265:0.88 266:0.27 267:0.28 271:0.46 276:0.68 281:0.91 282:0.73 290:0.82 291:0.97 300:0.33 +2 1:0.66 9:0.70 10:0.95 11:0.88 12:0.51 18:0.70 21:0.30 23:0.95 27:0.15 29:0.86 30:0.91 31:0.87 34:0.95 36:0.37 37:0.47 39:0.23 43:0.78 62:0.96 64:0.77 66:0.82 69:0.77 70:0.21 71:0.64 74:0.51 79:0.87 81:0.79 83:0.91 85:0.80 86:0.81 91:0.27 98:0.65 101:0.87 108:0.60 114:0.86 122:0.65 123:0.58 126:0.54 127:0.19 128:0.95 129:0.05 135:0.54 137:0.87 138:0.95 139:0.77 140:0.45 144:0.85 145:0.94 146:0.49 147:0.55 149:0.78 150:0.64 151:0.54 153:0.48 154:0.71 155:0.53 159:0.18 162:0.86 165:0.93 168:0.95 170:0.82 172:0.48 173:0.93 174:0.88 176:0.73 177:0.88 179:0.56 187:0.87 190:0.89 192:0.51 196:0.89 197:0.90 201:0.65 202:0.36 205:0.95 208:0.64 212:0.84 215:0.72 216:0.94 220:0.91 222:0.60 235:0.60 236:0.97 239:0.94 241:0.24 242:0.33 243:0.83 247:0.47 248:0.53 253:0.62 255:0.69 256:0.45 259:0.21 265:0.66 266:0.23 267:0.52 268:0.46 271:0.46 276:0.30 284:0.88 285:0.50 286:0.99 290:0.86 297:0.36 298:0.99 300:0.25 +2 10:0.94 11:0.92 12:0.51 17:0.43 18:0.88 21:0.78 23:0.96 27:0.70 29:0.86 30:0.30 33:0.97 34:0.44 36:1.00 37:0.62 39:0.23 43:0.85 45:0.73 62:0.96 66:0.61 69:0.12 70:0.76 71:0.64 74:0.58 75:0.96 85:0.72 86:0.94 91:0.04 98:0.34 101:0.87 108:0.10 122:0.82 123:0.10 124:0.65 127:0.83 128:0.96 129:0.05 133:0.72 135:0.74 139:0.89 140:0.45 144:0.85 146:0.43 147:0.49 149:0.71 151:0.52 153:0.69 154:0.81 159:0.57 162:0.86 168:0.96 170:0.82 172:0.65 173:0.18 174:0.89 177:0.88 179:0.61 187:0.99 190:0.95 197:0.91 202:0.46 205:0.95 212:0.82 216:0.33 220:0.87 222:0.60 226:0.96 235:0.05 239:0.93 241:0.55 242:0.13 243:0.68 245:0.65 247:0.91 248:0.52 253:0.45 256:0.45 259:0.21 265:0.37 266:0.54 267:0.59 268:0.55 271:0.46 276:0.55 285:0.46 291:0.97 300:0.70 +2 1:0.68 7:0.81 10:0.98 11:0.92 12:0.51 17:0.72 18:0.86 21:0.05 22:0.93 27:0.64 29:0.86 30:0.93 32:0.80 33:0.97 34:1.00 36:0.37 37:0.36 39:0.23 43:0.88 44:0.93 45:0.89 47:0.93 48:0.91 64:0.77 66:0.91 69:0.15 70:0.21 71:0.64 74:0.78 76:0.94 77:0.89 81:0.83 83:0.91 85:0.80 86:0.94 91:0.42 98:0.87 101:1.00 102:0.98 104:0.87 106:0.81 108:0.12 114:0.95 117:0.86 121:0.90 122:0.89 123:0.12 126:0.54 127:0.38 129:0.05 135:0.82 139:0.95 144:0.85 145:0.61 146:0.62 147:0.67 149:0.92 150:0.78 154:0.86 155:0.57 159:0.23 165:0.93 170:0.82 172:0.75 173:0.41 174:0.91 176:0.73 177:0.88 178:0.55 179:0.48 187:0.68 192:0.58 193:0.99 195:0.59 197:0.95 201:0.67 204:0.83 206:0.81 208:0.64 212:0.73 215:0.91 216:0.17 222:0.60 230:0.83 232:0.91 233:0.76 235:0.31 236:0.97 239:0.99 241:0.21 242:0.38 243:0.91 247:0.76 253:0.72 259:0.21 262:0.93 265:0.87 266:0.27 267:0.36 271:0.46 276:0.62 281:0.91 282:0.88 290:0.95 291:0.97 297:0.36 299:0.88 300:0.33 +0 1:0.64 10:0.93 11:0.87 12:0.51 17:0.78 18:0.84 21:0.30 22:0.93 27:0.72 29:0.86 30:0.75 33:0.97 34:0.36 36:0.19 39:0.23 43:0.38 45:0.95 47:0.93 64:0.77 66:0.69 69:0.58 70:0.59 71:0.64 74:0.73 81:0.69 83:0.69 86:0.91 91:0.18 97:0.94 98:0.76 99:0.95 101:0.65 104:0.89 106:0.81 108:0.79 114:0.84 117:0.86 122:0.65 123:0.78 124:0.47 126:0.51 127:0.69 129:0.59 133:0.60 135:0.96 139:0.89 144:0.85 146:0.25 147:0.31 149:0.76 150:0.58 154:0.73 155:0.49 158:0.81 159:0.68 165:0.81 170:0.82 172:0.84 173:0.47 174:0.90 176:0.70 177:0.88 178:0.55 179:0.36 187:0.87 192:0.49 197:0.90 201:0.62 202:0.36 206:0.81 208:0.61 212:0.57 215:0.72 216:0.08 219:0.94 222:0.60 232:0.93 235:0.52 239:0.91 241:0.12 242:0.24 243:0.25 245:0.82 247:0.92 253:0.65 254:0.90 259:0.21 262:0.93 265:0.76 266:0.80 267:0.85 271:0.46 276:0.77 279:0.78 283:0.67 290:0.84 291:0.97 292:0.88 297:0.36 300:0.70 +1 1:0.64 8:0.69 10:0.92 11:0.92 12:0.51 17:0.86 18:0.73 27:0.47 29:0.86 30:0.44 34:0.97 36:0.57 37:0.48 39:0.23 43:0.74 45:0.95 66:0.15 69:0.35 70:0.81 71:0.64 74:0.65 81:0.64 83:0.56 85:0.72 86:0.79 91:0.71 97:0.94 98:0.41 99:0.83 101:1.00 104:0.84 108:0.96 114:0.95 122:0.83 123:0.79 124:0.87 126:0.32 127:0.92 129:0.59 133:0.86 135:0.80 139:0.75 144:0.85 146:0.81 147:0.84 149:0.82 150:0.60 154:0.58 155:0.49 158:0.77 159:0.91 165:0.65 170:0.82 172:0.76 173:0.11 174:0.93 176:0.63 177:0.88 178:0.55 179:0.24 187:0.21 192:0.48 197:0.88 201:0.60 208:0.50 212:0.61 215:0.96 216:0.17 222:0.60 232:0.89 235:0.05 239:0.90 241:0.21 242:0.45 243:0.37 245:0.92 247:0.88 253:0.68 259:0.21 265:0.36 266:0.95 267:0.28 271:0.46 276:0.88 277:0.87 279:0.78 283:0.82 290:0.95 297:0.36 300:0.94 +1 7:0.75 10:0.85 11:0.87 12:0.51 17:0.85 18:0.68 21:0.78 27:0.70 29:0.86 30:0.75 32:0.72 34:0.77 36:0.66 37:0.34 39:0.23 43:0.67 44:0.92 45:0.90 66:0.68 69:0.56 70:0.45 71:0.64 74:0.73 77:0.70 83:0.69 86:0.73 91:0.09 97:0.94 98:0.43 101:0.65 104:0.88 108:0.43 122:0.82 123:0.41 124:0.47 127:0.67 129:0.05 133:0.60 135:0.39 139:0.85 144:0.85 145:0.47 146:0.58 147:0.63 149:0.72 154:0.56 155:0.54 158:0.82 159:0.62 170:0.82 172:0.67 173:0.49 174:0.83 177:0.88 178:0.55 179:0.60 187:0.39 192:0.56 195:0.77 197:0.86 204:0.83 208:0.61 212:0.77 216:0.50 219:0.94 222:0.60 230:0.77 232:0.91 233:0.66 235:0.05 239:0.85 241:0.43 242:0.42 243:0.72 244:0.61 245:0.65 247:0.76 253:0.52 259:0.21 265:0.45 266:0.54 267:0.44 271:0.46 276:0.50 279:0.78 281:0.86 282:0.80 283:0.82 292:0.95 300:0.55 +0 1:0.63 10:0.81 11:0.84 12:0.51 17:0.69 21:0.40 27:0.29 29:0.86 30:0.67 32:0.72 34:0.95 36:0.22 37:0.68 39:0.23 43:0.27 44:0.92 45:0.88 64:0.77 66:0.71 69:0.91 70:0.47 71:0.64 74:0.64 77:0.99 81:0.67 83:0.69 91:0.08 97:0.94 98:0.15 99:0.95 101:0.47 108:0.81 114:0.82 122:0.55 123:0.80 124:0.47 126:0.51 127:0.42 129:0.05 133:0.74 135:0.89 139:0.77 144:0.85 145:0.99 146:0.29 147:0.05 149:0.61 150:0.56 154:0.20 155:0.49 158:0.81 159:0.77 165:0.81 170:0.82 172:0.65 173:0.83 174:0.79 175:0.91 176:0.70 177:0.05 178:0.55 179:0.57 187:0.99 192:0.48 195:0.92 197:0.80 201:0.61 208:0.61 212:0.86 215:0.67 216:0.38 219:0.94 222:0.60 235:0.60 239:0.80 241:0.24 242:0.54 243:0.11 244:0.83 245:0.55 247:0.55 253:0.61 257:0.93 259:0.21 265:0.17 266:0.38 267:0.73 271:0.46 276:0.40 277:0.87 279:0.78 282:0.80 283:0.67 290:0.82 292:0.88 297:0.36 300:0.43 +1 1:0.61 10:0.98 11:0.87 12:0.51 17:0.55 18:0.75 21:0.22 27:0.34 29:0.86 30:0.52 34:0.91 36:0.21 37:0.50 39:0.23 43:0.57 45:0.96 66:0.94 69:0.71 70:0.64 71:0.64 74:0.82 81:0.57 83:0.56 86:0.80 91:0.51 97:0.89 98:0.88 99:0.83 101:0.65 108:0.54 114:0.85 122:0.67 123:0.52 124:0.36 126:0.32 127:0.59 129:0.05 133:0.36 135:0.39 139:0.76 144:0.85 145:0.50 146:0.95 147:0.96 149:0.72 150:0.52 154:0.44 155:0.48 158:0.77 159:0.68 165:0.65 170:0.82 172:0.94 173:0.61 174:0.97 176:0.63 177:0.88 178:0.55 179:0.22 187:0.87 192:0.47 197:0.97 201:0.58 208:0.50 212:0.80 215:0.79 216:0.37 222:0.60 235:0.31 239:0.97 241:0.49 242:0.18 243:0.94 245:0.74 247:0.93 253:0.68 254:0.98 259:0.21 265:0.88 266:0.54 267:0.44 271:0.46 276:0.88 279:0.76 283:0.82 290:0.85 297:0.36 300:0.70 +2 1:0.64 10:0.98 11:0.92 12:0.51 17:0.85 18:0.82 21:0.47 22:0.93 27:0.72 29:0.86 30:0.58 33:0.97 34:0.52 36:0.22 39:0.23 43:0.89 45:0.89 47:0.93 60:0.95 64:0.77 66:0.36 69:0.49 70:0.75 71:0.64 74:0.80 75:0.99 81:0.76 83:0.91 85:0.88 86:0.90 91:0.06 98:0.59 101:0.96 104:0.96 106:0.81 108:0.78 114:0.80 117:0.86 122:0.95 123:0.77 124:0.82 126:0.54 127:0.88 129:0.59 133:0.81 135:0.98 139:0.91 144:0.85 146:0.54 147:0.60 149:0.92 150:0.59 154:0.88 155:0.50 158:0.86 159:0.81 165:0.93 170:0.82 172:0.79 173:0.28 174:0.97 176:0.73 177:0.88 178:0.55 179:0.30 187:0.87 192:0.49 197:0.96 201:0.64 206:0.81 208:0.64 212:0.59 215:0.63 216:0.11 219:0.95 222:0.60 226:0.96 232:0.98 235:0.74 236:0.97 239:0.98 242:0.24 243:0.32 245:0.88 247:0.95 253:0.65 259:0.21 262:0.93 265:0.60 266:0.91 267:0.84 271:0.46 276:0.83 279:0.81 283:0.67 290:0.80 291:0.97 292:0.88 297:0.36 300:0.94 +2 7:0.81 10:0.96 11:0.92 12:0.51 17:0.72 18:0.86 21:0.78 27:0.42 29:0.86 30:0.45 32:0.80 34:0.78 36:0.87 37:0.48 39:0.23 43:0.77 44:0.93 45:0.77 60:0.87 66:0.16 69:0.80 70:0.73 71:0.64 74:0.79 75:0.99 77:0.70 83:0.91 85:0.80 86:0.90 91:0.11 98:0.53 101:0.87 102:0.98 108:0.63 121:0.90 122:0.57 123:0.74 124:0.73 127:0.42 129:0.05 133:0.73 135:0.99 139:0.94 144:0.85 145:0.95 146:0.49 147:0.55 149:0.80 154:0.70 155:0.56 158:0.86 159:0.61 170:0.82 172:0.68 173:0.74 174:0.89 177:0.88 178:0.55 179:0.40 187:0.39 192:0.57 193:0.99 195:0.83 197:0.90 204:0.84 208:0.64 212:0.70 216:0.24 219:0.95 222:0.60 226:0.96 230:0.83 233:0.66 235:0.88 239:0.98 241:0.47 242:0.12 243:0.60 245:0.76 247:0.95 253:0.55 259:0.21 265:0.34 266:0.80 267:0.85 271:0.46 276:0.70 277:0.69 279:0.80 281:0.86 282:0.88 283:0.67 292:0.88 299:0.88 300:0.55 +2 1:0.68 7:0.81 10:0.98 11:0.92 12:0.51 17:0.70 18:0.86 21:0.05 22:0.93 27:0.64 29:0.86 30:0.93 32:0.80 33:0.97 34:1.00 36:0.33 37:0.36 39:0.23 43:0.88 44:0.93 45:0.89 47:0.93 48:0.91 64:0.77 66:0.91 69:0.15 70:0.21 71:0.64 74:0.78 76:0.94 77:0.89 81:0.83 83:0.91 85:0.80 86:0.94 91:0.46 98:0.87 101:1.00 102:0.98 104:0.87 106:0.81 108:0.12 114:0.95 117:0.86 121:0.90 122:0.89 123:0.12 126:0.54 127:0.39 129:0.05 135:0.81 139:0.95 144:0.85 145:0.63 146:0.62 147:0.67 149:0.92 150:0.78 154:0.86 155:0.56 159:0.23 165:0.93 170:0.82 172:0.75 173:0.41 174:0.91 176:0.73 177:0.88 178:0.55 179:0.48 187:0.68 192:0.57 193:0.99 195:0.60 197:0.95 201:0.67 204:0.83 206:0.81 208:0.64 212:0.73 215:0.91 216:0.17 222:0.60 230:0.83 232:0.91 233:0.76 235:0.31 236:0.97 239:0.99 241:0.21 242:0.38 243:0.91 247:0.76 253:0.72 259:0.21 262:0.93 265:0.87 266:0.27 267:0.36 271:0.46 276:0.62 281:0.91 282:0.88 290:0.95 291:0.97 297:0.36 299:0.88 300:0.33 +2 1:0.65 7:0.75 10:0.87 11:0.88 12:0.51 17:0.70 18:0.72 21:0.22 27:0.13 29:0.86 30:0.62 32:0.67 34:0.98 36:0.28 37:0.85 39:0.23 43:0.59 45:0.85 64:0.77 66:0.72 69:0.88 70:0.75 71:0.64 74:0.59 77:0.89 81:0.71 83:0.69 86:0.80 91:0.38 98:0.49 99:0.95 101:0.96 108:0.75 114:0.88 122:0.82 123:0.74 124:0.43 126:0.51 127:0.24 129:0.05 133:0.59 135:0.87 139:0.81 144:0.85 145:0.77 146:0.64 147:0.69 149:0.43 150:0.60 154:0.51 155:0.54 159:0.42 165:0.81 170:0.82 172:0.59 173:0.88 174:0.83 176:0.70 177:0.88 178:0.55 179:0.49 187:0.39 192:0.56 195:0.82 197:0.83 201:0.63 204:0.82 208:0.61 212:0.61 215:0.79 216:0.34 222:0.60 230:0.77 233:0.66 235:0.42 239:0.86 241:0.53 242:0.16 243:0.75 245:0.53 247:0.82 253:0.63 254:0.86 259:0.21 265:0.51 266:0.54 267:0.44 271:0.46 276:0.49 281:0.86 282:0.75 290:0.88 297:0.36 300:0.70 +1 1:0.74 11:0.57 12:0.06 17:0.07 20:0.85 21:0.64 26:0.96 27:0.55 30:0.21 32:0.75 34:0.86 36:0.96 37:0.43 39:0.55 43:0.67 55:0.42 58:0.93 66:0.30 69:0.78 70:0.91 74:0.97 77:0.70 78:0.89 81:0.62 85:0.72 87:0.98 91:0.11 98:0.71 100:0.91 101:0.70 108:0.91 111:0.89 114:0.72 117:0.86 122:0.67 123:0.59 124:0.76 126:0.54 127:0.73 129:0.05 133:0.74 135:0.61 141:0.91 145:0.44 146:0.93 147:0.05 149:0.77 150:0.68 152:0.85 154:0.61 155:0.67 159:0.79 169:0.88 172:0.85 173:0.63 176:0.73 177:0.05 178:0.55 179:0.21 186:0.89 187:0.99 192:0.59 195:0.91 199:1.00 201:0.74 212:0.73 215:0.53 216:0.59 222:0.90 223:0.96 224:0.93 232:0.70 234:0.91 235:0.84 241:0.07 242:0.72 243:0.06 245:0.94 247:0.67 253:0.75 257:0.65 259:0.21 261:0.88 265:0.42 266:0.84 267:0.28 271:0.55 274:0.91 276:0.90 282:0.83 290:0.72 297:0.36 300:0.84 +1 12:0.06 17:0.59 21:0.54 26:0.96 27:0.06 30:0.76 34:0.33 36:0.59 37:0.90 39:0.55 43:0.38 55:0.84 58:0.93 66:0.45 69:0.90 70:0.43 74:0.81 77:0.70 81:0.38 91:0.14 98:0.50 108:0.80 114:0.65 117:0.86 122:0.67 123:0.79 124:0.81 126:0.32 127:0.44 129:0.59 133:0.82 135:0.60 141:0.91 145:0.94 146:0.84 147:0.05 149:0.40 150:0.33 154:0.52 159:0.78 163:0.94 172:0.48 173:0.81 176:0.56 177:0.05 178:0.55 179:0.63 187:0.87 195:0.94 199:0.97 212:0.23 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.41 242:0.70 243:0.12 245:0.58 247:0.60 253:0.63 254:0.80 257:0.65 259:0.21 265:0.52 266:0.80 267:0.82 271:0.55 274:0.91 276:0.54 282:0.84 290:0.65 300:0.43 +2 1:0.74 7:0.78 8:0.97 9:0.80 12:0.06 17:0.36 21:0.22 25:0.88 26:0.96 27:0.03 30:0.58 32:0.80 34:0.94 36:0.28 37:0.98 39:0.55 41:0.82 43:0.43 55:0.92 58:0.98 66:0.49 69:0.23 70:0.33 74:0.75 76:0.94 77:0.89 81:0.88 83:0.76 91:0.44 96:0.73 98:0.75 104:0.58 108:0.18 114:0.90 117:0.86 120:0.60 123:0.18 124:0.52 126:0.54 127:0.54 129:0.05 133:0.39 135:0.79 140:0.45 141:0.98 145:0.65 146:0.67 147:0.71 149:0.35 150:0.93 151:0.62 153:0.48 154:0.70 155:0.67 158:0.85 159:0.36 162:0.86 163:0.86 164:0.57 165:0.86 172:0.32 173:0.36 176:0.73 177:0.38 179:0.86 187:0.68 192:0.59 195:0.62 199:0.97 201:0.74 202:0.36 208:0.64 212:0.37 215:0.79 216:0.54 220:0.82 222:0.90 223:0.96 224:1.00 230:0.81 232:0.97 233:0.76 234:1.00 235:0.42 241:0.57 242:0.58 243:0.60 245:0.55 247:0.47 248:0.60 253:0.76 254:0.86 255:0.79 256:0.45 259:0.21 260:0.60 265:0.75 266:0.38 267:0.84 268:0.46 271:0.55 274:0.98 276:0.37 282:0.88 285:0.62 290:0.90 297:0.36 299:0.88 300:0.25 +1 8:0.87 12:0.06 17:0.55 21:0.59 26:0.96 27:0.06 30:0.89 34:0.67 36:0.79 37:0.90 39:0.55 43:0.44 55:0.96 58:0.98 66:0.16 69:0.25 70:0.20 74:0.84 76:0.85 77:0.70 81:0.35 91:0.18 96:0.69 98:0.13 108:0.20 114:0.64 117:0.86 122:0.67 123:0.19 124:0.86 126:0.32 127:0.20 129:0.93 133:0.84 135:0.58 140:0.45 141:0.98 145:0.74 146:0.24 147:0.30 149:0.35 150:0.31 151:0.48 153:0.72 154:0.33 158:0.84 159:0.54 162:0.67 163:0.79 172:0.10 173:0.14 175:0.75 176:0.56 177:0.05 179:0.79 187:0.95 190:0.77 195:0.91 199:0.97 202:0.53 212:0.30 216:0.76 220:0.97 222:0.90 223:0.96 224:0.98 232:0.95 234:0.98 235:0.68 241:0.52 242:0.63 243:0.37 244:0.61 245:0.40 247:0.35 248:0.48 253:0.65 256:0.45 257:0.65 259:0.21 265:0.14 266:0.27 267:0.59 268:0.57 271:0.55 274:0.97 276:0.29 277:0.69 282:0.84 285:0.50 290:0.64 300:0.18 +1 8:0.87 12:0.06 17:0.43 20:0.93 21:0.78 26:0.96 27:0.02 34:0.55 36:0.70 37:0.98 39:0.55 58:0.98 74:0.45 78:0.72 91:0.23 96:0.69 100:0.73 111:0.72 117:0.86 135:0.56 140:0.45 141:0.98 145:0.93 151:0.48 152:0.97 153:0.48 158:0.84 162:0.86 169:0.72 175:0.91 186:0.73 187:0.39 190:0.77 199:0.98 202:0.36 216:0.73 220:0.96 222:0.90 223:0.96 224:0.99 232:0.87 234:0.99 235:0.22 241:0.37 244:0.83 248:0.48 253:0.41 256:0.45 257:0.84 259:0.21 261:0.73 267:0.76 268:0.46 271:0.55 274:0.98 277:0.87 285:0.50 +1 12:0.06 17:0.20 21:0.64 26:0.96 27:0.61 30:0.44 32:0.75 34:0.55 36:0.94 37:0.37 39:0.55 43:0.69 55:0.42 58:0.93 66:0.12 69:0.76 70:0.78 74:0.97 81:0.52 91:0.12 98:0.54 108:0.90 114:0.72 117:0.86 122:0.67 123:0.57 124:0.78 126:0.54 127:0.73 129:0.59 133:0.82 135:0.86 141:0.91 145:0.42 146:0.81 147:0.05 149:0.77 150:0.56 154:0.62 159:0.76 172:0.86 173:0.63 176:0.73 177:0.05 178:0.55 179:0.26 187:0.99 195:0.89 199:0.99 212:0.74 215:0.53 216:0.87 222:0.90 223:0.96 224:0.93 232:0.84 234:0.91 235:0.80 241:0.10 242:0.78 243:0.06 245:0.88 247:0.67 253:0.74 257:0.65 259:0.21 265:0.46 266:0.87 267:0.36 271:0.55 274:0.91 276:0.86 290:0.72 297:0.36 300:0.84 +1 1:0.74 11:0.57 12:0.06 17:0.24 21:0.47 26:0.96 27:0.56 30:0.26 32:0.78 34:0.75 36:0.32 37:0.60 39:0.55 43:0.65 55:0.45 58:0.93 66:0.90 69:0.83 70:0.82 74:0.92 77:0.70 81:0.70 85:0.80 91:0.15 98:0.85 101:0.74 104:0.90 106:0.81 108:0.67 114:0.80 122:0.67 123:0.65 124:0.36 126:0.54 127:0.50 129:0.05 133:0.39 135:0.47 141:0.91 145:0.67 146:0.93 147:0.05 149:0.69 150:0.75 154:0.62 155:0.67 159:0.62 172:0.87 173:0.79 176:0.73 177:0.05 178:0.55 179:0.32 187:0.39 192:0.59 195:0.79 199:0.99 201:0.74 206:0.81 212:0.60 215:0.63 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.10 242:0.40 243:0.07 245:0.67 247:0.67 253:0.75 257:0.65 259:0.21 265:0.85 266:0.71 267:0.44 271:0.55 274:0.91 276:0.79 282:0.86 290:0.80 297:0.36 300:0.84 +2 6:0.98 7:0.78 8:0.92 12:0.06 17:0.38 20:0.85 21:0.40 25:0.88 26:0.95 27:0.03 30:0.45 32:0.75 34:0.92 36:0.55 37:0.98 39:0.55 43:0.43 55:0.84 58:0.93 66:0.82 69:0.23 70:0.47 74:0.57 78:0.92 81:0.62 91:0.40 98:0.95 100:0.98 104:0.58 106:0.81 108:0.18 111:0.97 123:0.18 126:0.32 127:0.65 129:0.05 135:0.92 141:0.91 145:0.77 146:0.79 147:0.82 149:0.47 150:0.45 152:1.00 154:0.33 159:0.34 163:0.75 169:0.97 172:0.48 173:0.39 177:0.38 178:0.55 179:0.85 186:0.92 187:0.68 195:0.59 199:0.95 206:0.81 212:0.30 215:0.67 216:0.54 222:0.90 223:0.93 224:0.93 230:0.81 233:0.76 234:0.91 235:0.42 241:0.50 242:0.53 243:0.83 244:0.61 247:0.55 253:0.46 259:0.21 261:0.98 265:0.95 266:0.47 267:0.79 271:0.55 274:0.91 276:0.40 297:0.36 300:0.33 +1 8:0.82 12:0.06 17:0.61 20:0.96 21:0.78 26:0.96 27:0.02 34:0.58 36:0.71 37:0.98 39:0.55 58:0.99 74:0.45 78:0.72 91:0.23 96:0.69 100:0.73 111:0.72 117:0.86 135:0.58 140:0.45 141:0.98 145:0.93 151:0.48 152:0.97 153:0.46 158:0.84 162:0.86 169:0.72 175:0.91 186:0.73 187:0.39 190:0.77 199:0.98 202:0.49 216:0.73 220:0.96 222:0.90 223:0.96 224:0.99 232:0.87 234:0.99 235:0.22 241:0.51 244:0.83 248:0.48 253:0.42 256:0.58 257:0.84 259:0.21 261:0.73 267:0.76 268:0.58 271:0.55 274:0.98 277:0.87 285:0.50 +1 7:0.78 8:0.98 12:0.06 17:0.40 21:0.78 25:0.88 26:0.94 27:0.03 30:0.58 32:0.75 34:0.88 36:0.81 37:0.98 39:0.55 43:0.43 55:0.92 58:0.93 66:0.32 69:0.82 70:0.54 74:0.44 91:0.46 98:0.60 104:0.58 106:0.81 108:0.78 123:0.77 124:0.73 127:0.87 129:0.59 133:0.64 135:0.92 141:0.91 145:0.52 146:0.59 147:0.62 149:0.45 154:0.59 159:0.57 163:0.86 172:0.41 173:0.84 177:0.05 178:0.55 179:0.72 187:0.68 191:0.78 195:0.71 199:0.94 202:0.46 206:0.81 212:0.23 216:0.54 222:0.90 223:0.92 224:0.93 230:0.81 233:0.69 234:0.91 235:0.22 241:0.27 242:0.73 243:0.32 245:0.68 247:0.39 253:0.38 254:0.86 257:0.65 259:0.21 265:0.61 266:0.63 267:0.87 271:0.55 274:0.91 276:0.55 300:0.43 +2 6:0.98 7:0.78 8:0.92 12:0.06 17:0.36 20:0.86 21:0.40 25:0.88 26:0.94 27:0.03 30:0.11 32:0.75 34:0.92 36:0.45 37:0.98 39:0.55 43:0.43 55:0.45 58:0.93 66:0.36 69:0.23 70:0.69 74:0.41 78:0.92 81:0.62 91:0.58 98:0.58 100:0.97 104:0.58 108:0.18 111:0.95 120:0.73 123:0.18 124:0.60 126:0.32 127:0.71 129:0.05 133:0.39 135:0.95 140:0.45 141:0.91 145:0.54 146:0.61 147:0.67 149:0.48 150:0.45 151:0.66 152:1.00 153:0.48 154:0.33 159:0.49 162:0.71 164:0.75 169:0.97 172:0.32 173:0.29 177:0.38 179:0.84 186:0.91 187:0.68 190:0.77 191:0.81 195:0.67 199:0.94 202:0.66 212:0.14 215:0.67 216:0.54 220:0.91 222:0.90 223:0.92 224:0.93 230:0.81 233:0.76 234:0.91 235:0.42 241:0.70 242:0.75 243:0.51 244:0.61 245:0.62 247:0.39 248:0.66 253:0.36 256:0.64 259:0.21 260:0.74 261:0.98 265:0.59 266:0.71 267:0.92 268:0.69 271:0.55 274:0.91 276:0.39 285:0.50 297:0.36 300:0.43 +1 1:0.74 7:0.81 12:0.06 17:0.69 21:0.40 26:0.96 27:0.11 30:0.62 32:0.75 34:0.88 36:0.71 37:0.94 39:0.55 43:0.31 55:0.52 58:0.93 66:0.75 69:0.49 70:0.23 74:0.65 77:0.99 81:0.76 91:0.27 98:0.74 108:0.38 114:0.82 121:0.97 123:0.36 126:0.54 127:0.24 129:0.05 135:0.30 141:0.91 145:0.98 146:0.56 147:0.62 149:0.24 150:0.79 154:0.79 155:0.67 159:0.20 163:0.75 172:0.32 173:0.66 176:0.73 177:0.38 178:0.55 179:0.84 187:0.39 192:0.59 195:0.58 199:0.97 201:0.74 204:0.89 208:0.64 212:0.30 215:0.67 216:0.40 222:0.90 223:0.96 224:0.93 230:0.87 232:0.72 233:0.76 234:0.91 235:0.52 241:0.27 242:0.63 243:0.77 247:0.35 253:0.67 254:0.86 259:0.21 265:0.74 266:0.27 267:0.69 271:0.55 274:0.91 276:0.27 282:0.84 290:0.82 297:0.36 300:0.18 +1 8:0.86 12:0.06 17:0.93 21:0.13 26:0.96 27:0.06 30:0.85 34:0.53 36:0.54 37:0.87 39:0.55 43:0.45 55:0.84 58:0.98 66:0.87 69:0.07 70:0.29 74:0.94 76:0.85 77:0.89 81:0.69 91:0.31 96:0.74 98:0.93 108:0.06 114:0.74 117:0.86 122:0.67 123:0.06 126:0.32 127:0.58 129:0.05 135:0.42 140:0.45 141:0.98 145:0.44 146:0.87 147:0.89 149:0.60 150:0.50 151:0.58 153:0.48 154:0.34 159:0.44 162:0.86 163:0.94 172:0.63 173:0.18 176:0.56 177:0.38 179:0.70 187:0.68 190:0.77 195:0.65 199:0.98 202:0.36 212:0.48 216:0.54 220:0.74 222:0.90 223:0.96 224:1.00 232:0.82 234:1.00 235:0.12 241:0.18 242:0.80 243:0.88 244:0.61 247:0.35 248:0.56 253:0.80 256:0.45 259:0.21 265:0.93 266:0.63 267:0.36 268:0.46 271:0.55 274:0.97 276:0.53 282:0.84 285:0.50 290:0.74 300:0.33 +1 12:0.06 17:0.49 21:0.64 26:0.96 27:0.24 30:0.90 34:0.61 36:0.34 37:0.80 39:0.55 43:0.29 55:0.52 58:0.93 66:0.63 69:0.95 70:0.29 74:0.89 76:0.85 77:0.89 81:0.33 91:0.22 98:0.36 108:0.91 114:0.63 117:0.86 122:0.67 123:0.91 124:0.64 126:0.32 127:0.70 129:0.05 133:0.74 135:0.26 141:0.91 145:0.74 146:0.50 147:0.05 149:0.50 150:0.30 154:0.21 159:0.77 172:0.75 173:0.95 176:0.56 177:0.05 178:0.55 179:0.47 187:0.68 195:0.89 199:0.99 212:0.81 216:0.67 222:0.90 223:0.96 224:0.93 232:0.90 234:0.91 235:0.74 241:0.07 242:0.77 243:0.08 244:0.61 245:0.73 247:0.47 253:0.67 257:0.65 259:0.21 265:0.38 266:0.47 267:0.19 271:0.55 274:0.91 276:0.59 277:0.69 282:0.84 290:0.63 300:0.43 +1 1:0.74 11:0.57 12:0.06 17:0.22 21:0.47 26:0.96 27:0.56 30:0.43 32:0.78 34:0.75 36:0.43 37:0.60 39:0.55 43:0.73 55:0.45 58:0.93 66:0.49 69:0.57 70:0.86 74:0.96 77:0.70 81:0.70 85:0.80 91:0.17 98:0.61 101:0.74 104:0.90 106:0.81 108:0.65 114:0.80 122:0.67 123:0.62 124:0.73 126:0.54 127:0.49 129:0.59 133:0.76 135:0.34 141:0.91 145:0.67 146:0.32 147:0.38 149:0.74 150:0.75 154:0.82 155:0.67 159:0.58 172:0.75 173:0.51 176:0.73 177:0.38 178:0.55 179:0.35 187:0.39 192:0.59 195:0.76 199:0.99 201:0.74 206:0.81 212:0.77 215:0.63 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.21 242:0.59 243:0.18 245:0.82 247:0.60 253:0.76 259:0.21 265:0.62 266:0.71 267:0.52 271:0.55 274:0.91 276:0.75 282:0.86 290:0.80 297:0.36 300:0.84 +1 1:0.74 12:0.06 17:0.30 21:0.47 26:0.96 27:0.56 30:0.41 32:0.78 34:0.70 36:0.31 37:0.60 39:0.55 43:0.29 55:0.45 58:0.93 66:0.89 69:0.83 70:0.77 74:0.94 77:0.70 81:0.70 91:0.10 98:0.83 104:0.90 106:0.81 108:0.67 114:0.80 122:0.67 123:0.65 124:0.36 126:0.54 127:0.46 129:0.05 133:0.39 135:0.56 141:0.91 145:0.67 146:0.92 147:0.05 149:0.57 150:0.75 154:0.62 155:0.67 159:0.61 172:0.86 173:0.79 176:0.73 177:0.05 178:0.55 179:0.34 187:0.39 192:0.59 195:0.79 199:0.99 201:0.74 206:0.81 212:0.61 215:0.63 216:0.76 222:0.90 223:0.96 224:0.93 234:0.91 235:0.60 241:0.15 242:0.60 243:0.07 245:0.66 247:0.67 253:0.75 254:0.74 257:0.65 259:0.21 265:0.83 266:0.71 267:0.44 271:0.55 274:0.91 276:0.77 282:0.86 290:0.80 297:0.36 300:0.70 +0 1:0.74 7:0.66 9:0.80 12:0.06 17:0.59 21:0.59 27:0.72 29:0.71 30:0.33 31:0.86 34:0.79 36:0.26 37:0.64 39:0.74 43:0.17 55:0.71 64:0.77 66:0.60 69:0.38 70:0.75 71:0.73 79:0.86 81:0.45 83:0.60 91:0.54 96:0.68 98:0.77 99:0.95 108:0.30 114:0.58 120:0.56 123:0.28 124:0.50 126:0.54 127:0.67 129:0.59 133:0.47 135:0.26 137:0.86 140:0.45 146:0.74 147:0.78 149:0.35 150:0.67 151:0.47 153:0.48 154:0.12 155:0.67 159:0.45 162:0.81 164:0.68 165:0.70 172:0.63 173:0.43 175:0.91 176:0.73 177:0.05 179:0.60 187:0.39 190:0.89 192:0.87 201:0.93 202:0.61 208:0.64 212:0.73 215:0.39 216:0.20 220:0.96 222:0.35 230:0.69 233:0.73 235:0.88 241:0.46 242:0.82 243:0.67 244:0.83 245:0.78 247:0.12 248:0.47 253:0.40 255:0.95 256:0.64 257:0.84 259:0.21 260:0.55 265:0.77 266:0.47 267:0.36 268:0.66 271:0.88 276:0.61 277:0.87 283:0.78 284:0.91 285:0.62 290:0.58 297:0.36 300:0.55 +1 12:0.06 17:0.89 21:0.78 27:0.66 29:0.71 30:0.30 34:0.55 36:0.29 37:0.35 39:0.74 43:0.12 55:0.92 66:0.12 69:0.92 70:0.60 71:0.73 74:0.32 91:0.22 98:0.25 104:0.89 106:0.81 108:0.83 120:0.53 123:0.82 124:0.89 127:0.49 129:0.05 133:0.87 135:0.61 140:0.45 145:0.87 146:0.47 147:0.33 149:0.18 151:0.46 153:0.48 154:0.19 159:0.75 162:0.86 163:0.96 164:0.53 172:0.16 173:0.87 175:0.75 177:0.05 179:0.77 187:0.21 190:0.89 202:0.36 206:0.81 212:0.23 216:0.19 220:0.97 222:0.35 235:0.97 241:0.21 242:0.76 243:0.25 244:0.61 245:0.50 247:0.39 248:0.46 253:0.27 254:0.88 256:0.45 257:0.84 259:0.21 260:0.53 265:0.27 266:0.63 267:0.59 268:0.46 271:0.88 276:0.44 277:0.69 285:0.47 300:0.43 +0 12:0.06 17:0.84 21:0.78 27:0.58 29:0.71 30:0.95 34:0.94 36:0.66 37:0.29 39:0.74 43:0.10 55:0.98 66:0.20 69:0.83 71:0.73 74:0.30 91:0.35 98:0.08 108:0.67 123:0.65 124:0.61 127:0.22 129:0.59 133:0.47 135:0.58 146:0.05 147:0.05 149:0.07 154:0.08 159:0.33 172:0.05 173:0.85 175:0.91 177:0.05 178:0.55 179:0.99 187:0.68 216:0.36 222:0.35 235:0.60 241:0.10 243:0.40 244:0.83 245:0.36 253:0.26 257:0.84 259:0.21 265:0.08 267:0.44 271:0.88 277:0.87 300:0.08 +1 1:0.69 7:0.66 11:0.83 12:0.06 17:0.72 18:0.61 21:0.68 27:0.57 29:0.71 30:0.43 32:0.64 34:0.24 36:0.48 37:0.61 39:0.74 43:0.68 45:0.91 55:0.71 66:0.93 69:0.88 70:0.66 71:0.73 74:0.86 76:0.85 77:0.70 81:0.29 85:0.72 86:0.68 91:0.09 98:0.82 101:0.87 104:0.87 106:0.81 108:0.77 114:0.54 117:0.86 122:0.75 123:0.75 124:0.36 126:0.44 127:0.52 129:0.05 133:0.43 135:0.63 139:0.66 145:0.77 146:0.97 147:0.18 149:0.85 150:0.47 154:0.37 155:0.58 159:0.80 172:0.96 173:0.77 176:0.61 177:0.38 178:0.55 179:0.17 187:0.21 192:0.59 195:0.93 201:0.68 204:0.85 206:0.81 208:0.54 212:0.90 215:0.37 216:0.86 222:0.35 230:0.69 232:0.83 233:0.73 235:0.84 241:0.30 242:0.33 243:0.07 245:0.85 247:0.82 253:0.46 257:0.65 265:0.82 266:0.75 267:0.82 271:0.88 276:0.92 282:0.73 290:0.54 297:0.36 300:0.70 +0 8:0.67 12:0.06 17:0.89 18:0.97 21:0.78 27:0.56 29:0.71 30:0.87 34:0.61 36:0.30 37:0.26 39:0.74 43:0.06 55:0.52 66:0.84 69:0.95 70:0.27 71:0.73 74:0.38 86:0.94 91:0.44 98:0.66 108:0.91 122:0.86 123:0.90 124:0.39 127:0.50 129:0.05 133:0.60 135:0.70 139:0.92 145:0.72 146:0.24 147:0.26 149:0.36 154:0.28 159:0.82 172:0.95 173:0.90 177:0.38 178:0.55 179:0.18 187:0.21 202:0.36 212:0.92 216:0.35 222:0.35 235:0.60 241:0.21 242:0.81 243:0.07 245:0.87 247:0.60 253:0.31 254:0.96 257:0.65 259:0.21 265:0.67 266:0.75 267:0.36 271:0.88 276:0.88 277:0.87 300:0.55 +0 12:0.06 17:0.59 18:0.64 21:0.78 27:0.45 29:0.71 30:0.95 34:0.83 36:0.17 37:0.50 39:0.74 43:0.11 55:0.71 66:0.54 69:0.78 71:0.73 86:0.64 91:0.25 98:0.19 108:0.61 123:0.59 127:0.10 129:0.05 135:0.90 139:0.62 145:0.59 146:0.29 147:0.35 149:0.08 154:0.09 159:0.12 172:0.10 173:0.74 175:0.75 177:0.38 178:0.55 179:0.49 187:0.68 216:0.77 222:0.35 235:1.00 241:0.21 243:0.63 244:0.83 253:0.20 257:0.65 259:0.21 265:0.21 267:0.88 271:0.88 277:0.69 300:0.08 +0 12:0.06 17:0.75 18:0.78 21:0.78 27:0.72 29:0.71 30:0.68 34:0.50 36:0.51 39:0.74 43:0.13 48:0.91 55:0.92 66:0.20 69:0.79 70:0.43 71:0.73 74:0.36 86:0.74 91:0.37 98:0.27 108:0.63 122:0.83 123:0.60 124:0.85 127:0.35 129:0.59 132:0.97 133:0.82 135:0.61 139:0.75 145:0.54 146:0.35 147:0.05 149:0.15 154:0.27 159:0.47 163:0.86 172:0.16 173:0.80 175:0.75 177:0.05 178:0.55 179:0.84 187:0.21 212:0.19 216:0.05 222:0.35 235:0.99 241:0.47 242:0.45 243:0.19 244:0.61 245:0.43 247:0.47 253:0.30 254:0.74 257:0.84 259:0.21 265:0.29 266:0.47 267:0.59 271:0.88 276:0.34 277:0.69 281:0.91 300:0.33 +1 11:0.57 12:0.06 17:0.66 18:0.81 21:0.05 25:0.88 27:0.63 29:0.71 30:0.53 34:0.47 36:0.63 37:0.63 39:0.74 43:0.75 66:0.69 69:0.82 70:0.68 71:0.73 74:0.61 81:0.40 85:0.88 86:0.92 91:0.17 98:0.75 101:0.87 104:0.54 108:0.67 123:0.64 124:0.43 126:0.26 127:0.78 129:0.05 133:0.37 135:0.85 139:0.89 146:0.74 147:0.35 149:0.84 150:0.34 154:0.67 159:0.47 172:0.70 173:0.89 177:0.05 178:0.55 179:0.59 187:0.95 212:0.61 215:0.78 216:0.21 222:0.35 235:0.05 241:0.10 242:0.45 243:0.25 245:0.75 247:0.67 253:0.39 257:0.84 259:0.21 265:0.76 266:0.63 267:0.52 271:0.88 276:0.59 297:0.36 300:0.55 +0 11:0.57 12:0.06 17:0.49 18:0.70 21:0.05 27:0.14 29:0.71 30:0.09 34:0.42 36:0.69 37:0.78 39:0.74 43:0.70 66:0.23 69:0.87 70:0.94 71:0.73 74:0.51 81:0.40 85:0.85 86:0.81 91:0.07 98:0.60 101:0.87 104:0.58 108:0.74 123:0.84 124:0.82 126:0.26 127:0.77 129:0.05 133:0.81 135:0.92 139:0.77 146:0.71 147:0.31 149:0.76 150:0.34 154:0.60 159:0.71 172:0.63 173:0.84 177:0.05 178:0.55 179:0.46 187:0.68 212:0.30 215:0.78 216:0.63 222:0.35 235:0.05 241:0.18 242:0.73 243:0.20 245:0.77 247:0.47 253:0.36 257:0.84 259:0.21 265:0.51 266:0.75 267:0.84 271:0.88 276:0.73 277:0.87 297:0.36 300:0.70 +0 1:0.74 7:0.66 12:0.06 17:0.53 21:0.59 27:0.72 29:0.71 30:0.45 34:0.99 36:0.42 37:0.64 39:0.74 43:0.17 55:0.84 64:0.77 66:0.43 69:0.38 70:0.57 71:0.73 81:0.45 83:0.60 91:0.17 98:0.65 99:0.95 108:0.72 114:0.58 120:0.53 123:0.71 124:0.59 126:0.54 127:0.59 129:0.59 133:0.47 135:0.56 140:0.45 146:0.61 147:0.66 149:0.28 150:0.67 151:0.46 153:0.46 154:0.12 155:0.67 159:0.50 162:0.62 164:0.53 165:0.70 172:0.45 173:0.38 175:0.91 176:0.73 177:0.05 179:0.70 187:0.39 190:0.89 191:0.70 192:0.87 201:0.93 202:0.49 208:0.64 212:0.37 215:0.39 216:0.20 220:0.97 222:0.35 230:0.69 233:0.73 235:0.88 241:0.32 242:0.82 243:0.47 244:0.83 245:0.72 247:0.12 248:0.46 253:0.40 256:0.45 257:0.84 259:0.21 260:0.53 265:0.66 266:0.63 267:0.95 268:0.45 271:0.88 276:0.51 277:0.87 283:0.78 285:0.47 290:0.58 297:0.36 300:0.43 +0 12:0.06 17:0.28 18:0.61 21:0.54 27:0.45 29:0.71 30:0.56 34:0.67 36:0.18 37:0.50 39:0.74 43:0.13 55:0.92 66:0.51 69:0.80 70:0.46 71:0.73 74:0.41 81:0.24 86:0.63 91:0.12 98:0.42 108:0.64 123:0.61 124:0.77 126:0.26 127:0.58 129:0.05 133:0.81 135:0.92 139:0.61 145:0.50 146:0.74 147:0.05 149:0.18 150:0.24 154:0.28 159:0.79 172:0.41 173:0.65 177:0.05 178:0.55 179:0.79 187:0.68 212:0.13 215:0.39 216:0.77 222:0.35 235:0.60 241:0.46 242:0.24 243:0.15 244:0.61 245:0.48 247:0.74 253:0.32 254:0.95 257:0.84 259:0.21 265:0.44 266:0.75 267:0.44 271:0.88 276:0.41 297:0.36 300:0.33 +1 11:0.57 12:0.06 17:0.84 18:0.95 21:0.68 22:0.93 27:0.57 29:0.71 30:0.09 32:0.80 34:0.80 36:0.32 37:0.38 39:0.74 43:0.68 44:0.93 47:0.93 64:0.77 66:0.97 69:0.44 70:0.94 71:0.73 74:0.73 77:0.70 81:0.29 85:0.95 86:0.97 91:0.08 98:0.93 101:0.87 104:0.90 106:0.81 108:0.34 123:0.33 124:0.36 126:0.44 127:0.68 129:0.05 133:0.37 135:0.97 139:0.97 145:0.97 146:0.99 147:0.99 149:0.91 150:0.24 154:0.57 159:0.82 172:0.98 173:0.22 177:0.70 178:0.55 179:0.15 187:0.21 192:0.51 195:0.93 201:0.68 206:0.81 212:0.72 215:0.37 216:0.72 222:0.35 235:0.84 242:0.45 243:0.97 245:0.80 247:0.82 253:0.42 259:0.21 262:0.93 265:0.93 266:0.63 267:0.59 271:0.88 276:0.94 282:0.88 297:0.36 299:0.88 300:0.70 +0 11:0.83 12:0.06 17:0.40 18:0.80 21:0.05 27:0.14 29:0.71 30:0.09 34:0.47 36:0.62 37:0.78 39:0.74 43:0.75 45:0.77 66:0.74 69:0.79 70:0.97 71:0.73 74:0.63 81:0.40 85:0.72 86:0.91 91:0.20 98:0.82 101:0.87 104:0.58 108:0.63 123:0.61 124:0.42 126:0.26 127:0.69 129:0.05 133:0.37 135:0.97 139:0.88 146:0.84 147:0.31 149:0.59 150:0.34 154:0.69 159:0.52 172:0.77 173:0.82 177:0.05 178:0.55 179:0.49 187:0.95 212:0.71 215:0.78 216:0.63 222:0.35 235:0.05 241:0.32 242:0.63 243:0.22 245:0.79 247:0.67 253:0.39 257:0.84 259:0.21 265:0.82 266:0.75 267:0.87 271:0.88 276:0.68 297:0.36 300:0.84 +0 1:0.74 7:0.66 11:0.64 12:0.06 17:0.95 18:0.82 21:0.59 27:0.72 29:0.71 30:0.60 34:0.68 36:0.28 37:0.64 39:0.74 43:0.17 45:0.66 55:0.59 64:0.77 66:0.67 69:0.38 70:0.79 71:0.73 74:0.39 81:0.45 83:0.60 86:0.81 91:0.27 98:0.66 99:0.95 101:0.52 108:0.30 114:0.58 123:0.28 124:0.58 126:0.54 127:0.75 129:0.05 133:0.73 135:0.44 139:0.77 146:0.82 147:0.85 149:0.42 150:0.67 154:0.63 155:0.67 159:0.68 165:0.70 172:0.80 173:0.28 176:0.73 177:0.70 178:0.55 179:0.41 187:0.21 192:0.87 201:0.93 208:0.64 212:0.68 215:0.39 216:0.20 222:0.35 230:0.69 233:0.73 235:0.88 241:0.44 242:0.73 243:0.72 245:0.76 247:0.76 253:0.40 254:0.80 259:0.21 265:0.66 266:0.71 267:0.59 271:0.88 276:0.75 290:0.58 297:0.36 300:0.84 +0 1:0.74 12:0.06 17:0.78 18:0.70 21:0.64 27:0.72 29:0.71 30:0.76 34:0.61 36:0.23 39:0.74 43:0.12 55:0.59 64:0.77 66:0.64 69:0.76 70:0.15 71:0.73 81:0.42 83:0.60 86:0.68 91:0.82 98:0.60 99:0.83 108:0.59 114:0.57 122:0.85 123:0.57 126:0.54 127:0.18 129:0.05 135:0.44 139:0.66 146:0.44 147:0.50 149:0.10 150:0.66 154:0.09 155:0.67 159:0.16 163:0.98 165:0.70 172:0.16 173:0.93 175:0.75 176:0.73 177:0.38 178:0.55 179:0.92 187:0.21 192:0.87 201:0.93 208:0.64 212:0.95 215:0.38 216:0.08 222:0.35 235:0.84 241:0.81 242:0.82 243:0.69 244:0.83 247:0.12 253:0.38 257:0.65 259:0.21 265:0.61 266:0.12 267:0.36 271:0.88 276:0.15 277:0.69 290:0.57 297:0.36 300:0.13 +0 1:0.74 7:0.66 9:0.80 12:0.06 17:0.64 21:0.59 27:0.72 29:0.71 30:0.55 31:0.86 34:0.98 36:0.19 37:0.64 39:0.74 43:0.17 55:0.71 64:0.77 66:0.25 69:0.38 70:0.57 71:0.73 79:0.86 81:0.45 83:0.60 91:0.37 96:0.67 98:0.63 99:0.95 108:0.71 114:0.58 120:0.64 123:0.28 124:0.57 126:0.54 127:0.63 129:0.59 133:0.47 135:0.56 137:0.86 140:0.80 146:0.62 147:0.67 149:0.27 150:0.67 151:0.47 153:0.48 154:0.12 155:0.67 159:0.45 162:0.86 164:0.65 165:0.70 172:0.45 173:0.43 175:0.91 176:0.73 177:0.05 179:0.73 187:0.39 190:0.89 192:0.87 201:0.93 202:0.50 208:0.64 212:0.59 215:0.39 216:0.20 220:0.62 222:0.35 230:0.69 233:0.73 235:0.88 241:0.41 242:0.82 243:0.45 244:0.83 245:0.70 247:0.12 248:0.46 253:0.40 255:0.95 256:0.58 257:0.84 259:0.21 260:0.61 265:0.63 266:0.54 267:0.36 268:0.59 271:0.88 276:0.50 277:0.87 284:0.89 285:0.62 290:0.58 297:0.36 300:0.43 +2 11:0.57 12:0.06 17:0.66 18:0.90 21:0.78 27:0.51 29:0.71 30:0.33 34:0.53 36:0.42 37:0.33 39:0.74 43:0.73 66:0.72 69:0.86 70:0.80 71:0.73 74:0.63 85:0.91 86:0.94 91:0.10 98:0.68 101:0.87 108:0.81 122:0.65 123:0.80 124:0.43 127:0.25 129:0.59 133:0.46 135:0.99 139:0.92 145:0.70 146:0.45 147:0.52 149:0.85 154:0.63 159:0.62 172:0.84 173:0.76 177:0.70 178:0.55 179:0.22 187:0.21 212:0.47 216:0.85 222:0.35 235:0.80 241:0.03 242:0.29 243:0.25 245:0.88 247:0.79 253:0.39 259:0.21 265:0.68 266:0.63 267:0.99 271:0.88 276:0.78 300:0.55 +0 11:0.57 12:0.06 17:0.49 18:0.74 21:0.05 27:0.14 29:0.71 30:0.09 34:0.49 36:0.77 37:0.78 39:0.74 43:0.79 55:0.71 66:0.59 69:0.08 70:0.91 71:0.73 74:0.57 81:0.40 85:0.85 86:0.85 91:0.06 98:0.79 101:0.87 104:0.58 108:0.07 123:0.07 124:0.52 126:0.26 127:0.87 129:0.05 133:0.37 135:0.92 139:0.81 146:0.92 147:0.93 149:0.83 150:0.34 154:0.72 159:0.72 172:0.78 173:0.11 177:0.70 178:0.55 179:0.42 187:0.68 212:0.30 215:0.78 216:0.63 222:0.35 235:0.05 241:0.18 242:0.75 243:0.67 245:0.90 247:0.47 253:0.38 259:0.21 265:0.79 266:0.75 267:0.79 271:0.88 276:0.76 297:0.36 300:0.70 +1 11:0.57 12:0.06 17:0.40 18:0.81 21:0.05 27:0.14 29:0.71 30:0.26 34:0.63 36:0.75 37:0.78 39:0.74 43:0.75 66:0.75 69:0.82 70:0.91 71:0.73 74:0.61 81:0.40 85:0.88 86:0.92 91:0.15 98:0.83 101:0.87 104:0.58 108:0.67 123:0.65 124:0.41 126:0.26 127:0.73 129:0.05 133:0.37 135:0.93 139:0.89 146:0.89 147:0.31 149:0.85 150:0.34 154:0.67 159:0.60 172:0.79 173:0.82 177:0.05 178:0.55 179:0.47 187:0.95 212:0.52 215:0.78 216:0.63 222:0.35 235:0.05 241:0.07 242:0.63 243:0.21 245:0.79 247:0.67 253:0.39 257:0.84 259:0.21 265:0.83 266:0.71 267:0.76 271:0.88 276:0.70 297:0.36 300:0.84 +0 1:0.74 8:0.59 12:0.06 17:0.78 18:0.70 21:0.64 27:0.53 29:0.71 30:0.76 34:0.61 36:0.24 37:0.24 39:0.74 43:0.12 55:0.59 64:0.77 66:0.64 69:0.76 70:0.15 71:0.73 81:0.42 83:0.60 86:0.68 91:0.86 98:0.60 99:0.83 108:0.59 114:0.57 122:0.85 123:0.57 126:0.54 127:0.18 129:0.05 135:0.49 139:0.66 146:0.44 147:0.50 149:0.10 150:0.66 154:0.09 155:0.67 159:0.16 163:0.98 165:0.70 172:0.16 173:0.93 175:0.75 176:0.73 177:0.38 178:0.55 179:0.92 191:0.73 192:0.87 201:0.93 208:0.64 212:0.95 215:0.38 216:0.14 222:0.35 235:0.84 241:0.94 242:0.82 243:0.69 244:0.83 247:0.12 253:0.38 257:0.65 259:0.21 265:0.61 266:0.12 267:0.59 271:0.88 276:0.15 277:0.69 290:0.57 297:0.36 300:0.13 +2 7:0.66 8:0.66 11:0.92 12:0.06 17:0.83 18:0.83 21:0.47 27:0.53 29:0.71 30:0.61 32:0.64 34:0.74 36:0.31 37:0.45 39:0.74 43:0.63 45:0.88 66:0.86 69:0.89 70:0.44 71:0.73 74:0.76 81:0.25 85:0.93 86:0.93 91:0.37 98:0.68 101:0.97 104:0.91 106:0.81 108:0.79 120:0.53 123:0.77 124:0.38 126:0.26 127:0.54 129:0.05 133:0.59 135:0.69 139:0.90 140:0.45 145:0.67 146:0.83 147:0.05 149:0.88 150:0.25 151:0.46 153:0.48 154:0.53 159:0.63 162:0.86 164:0.53 172:0.91 173:0.90 177:0.05 179:0.26 187:0.21 190:0.89 195:0.83 202:0.36 206:0.81 212:0.91 215:0.41 216:0.27 220:0.99 222:0.35 230:0.69 233:0.73 235:0.52 241:0.32 242:0.40 243:0.06 245:0.76 247:0.60 248:0.46 253:0.43 256:0.45 257:0.84 259:0.21 260:0.53 265:0.69 266:0.47 267:0.59 268:0.46 271:0.88 276:0.82 283:0.78 285:0.47 297:0.36 300:0.55 +1 1:0.74 11:0.88 12:0.37 17:0.38 21:0.59 27:0.45 28:0.92 30:0.58 32:0.80 34:0.70 36:0.19 37:0.50 39:0.53 43:0.82 60:0.87 66:0.51 69:0.70 70:0.62 74:0.92 77:0.70 81:0.60 83:0.84 85:0.96 91:0.05 98:0.64 101:0.82 108:0.66 114:0.74 122:0.57 123:0.64 124:0.78 126:0.54 127:0.56 129:0.89 133:0.83 135:0.86 144:0.85 145:0.49 146:0.24 147:0.30 149:0.92 150:0.74 154:0.77 155:0.67 158:0.87 159:0.77 161:0.85 165:0.78 167:0.45 172:0.71 173:0.53 176:0.73 177:0.38 178:0.55 179:0.43 181:0.64 187:0.68 192:0.59 195:0.91 201:0.74 208:0.64 212:0.16 215:0.55 216:0.77 222:0.76 235:0.74 241:0.24 242:0.54 243:0.20 245:0.75 247:0.47 253:0.73 259:0.21 265:0.65 266:0.91 267:0.59 271:0.38 276:0.72 279:0.86 282:0.88 283:0.71 290:0.74 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 8:0.90 9:0.80 11:0.88 12:0.37 17:0.15 21:0.59 27:0.20 28:0.92 30:0.26 32:0.80 34:0.58 36:0.85 37:0.80 39:0.53 41:0.82 43:0.75 55:0.71 60:0.87 66:0.34 69:0.78 70:0.81 74:0.97 76:0.97 77:0.99 81:0.60 83:0.75 85:0.99 91:0.27 96:0.70 98:0.61 101:0.83 108:0.62 114:0.74 120:0.54 121:0.90 122:0.57 123:0.59 124:0.78 126:0.54 127:0.65 129:0.89 133:0.78 135:0.44 140:0.90 144:0.85 145:1.00 146:0.96 147:0.97 149:0.95 150:0.74 151:0.49 153:0.72 154:0.67 155:0.67 158:0.87 159:0.90 161:0.85 162:0.67 164:0.57 165:0.78 167:0.46 172:0.90 173:0.48 176:0.73 177:0.38 179:0.16 181:0.64 187:0.68 190:0.77 192:0.59 195:0.98 201:0.74 202:0.65 204:0.89 208:0.64 212:0.67 215:0.55 216:0.65 220:0.96 222:0.76 230:0.87 233:0.76 235:0.74 241:0.37 242:0.36 243:0.50 245:0.97 247:0.67 248:0.49 253:0.76 255:0.79 256:0.64 259:0.21 260:0.55 265:0.62 266:0.89 267:0.91 268:0.66 271:0.38 276:0.93 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.97 300:0.94 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.37 17:0.30 21:0.59 27:0.16 28:0.92 30:0.52 32:0.80 34:0.39 36:0.71 37:0.83 39:0.53 41:0.82 43:0.79 60:0.87 66:0.98 69:0.75 70:0.54 74:0.98 76:0.97 77:0.89 81:0.64 83:0.81 85:0.99 91:0.25 96:0.69 98:0.84 101:0.86 104:0.78 108:0.58 114:0.74 120:0.55 121:0.90 122:0.57 123:0.56 126:0.54 127:0.34 129:0.05 135:0.44 140:0.45 144:0.85 145:0.86 146:0.95 147:0.96 149:0.97 150:0.76 151:0.50 153:0.72 154:0.72 155:0.67 158:0.92 159:0.62 161:0.85 162:0.67 164:0.64 165:0.78 167:0.53 172:0.94 173:0.65 176:0.73 177:0.38 179:0.17 181:0.64 187:0.39 192:0.59 195:0.86 201:0.74 202:0.53 204:0.89 208:0.64 212:0.84 215:0.55 216:0.61 220:0.97 222:0.76 230:0.87 232:0.83 233:0.76 235:0.80 241:0.61 242:0.45 243:0.98 247:0.60 248:0.50 253:0.76 255:0.79 256:0.45 259:0.21 260:0.55 265:0.84 266:0.38 267:0.19 268:0.57 271:0.38 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 290:0.74 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.94 7:0.76 8:0.83 9:0.80 11:0.88 12:0.37 17:0.64 20:0.91 21:0.59 27:0.32 28:0.92 30:0.29 32:0.80 34:0.73 36:0.85 37:0.70 39:0.53 41:0.93 43:0.76 55:0.84 60:0.97 66:0.54 69:0.81 70:0.73 74:0.93 77:0.70 78:0.81 81:0.60 83:0.84 85:0.96 91:0.11 96:0.84 98:0.68 100:0.86 101:0.81 108:0.66 111:0.81 114:0.74 117:0.86 120:0.75 122:0.57 123:0.64 124:0.55 126:0.54 127:0.40 129:0.59 133:0.47 135:0.65 140:0.45 144:0.85 145:0.92 146:0.93 147:0.94 149:0.90 150:0.74 151:0.87 152:0.93 153:0.48 154:0.68 155:0.67 158:0.87 159:0.76 161:0.85 162:0.86 164:0.78 165:0.78 167:0.45 169:0.87 172:0.84 173:0.66 176:0.73 177:0.38 179:0.24 181:0.64 186:0.81 187:0.98 189:0.94 191:0.70 192:0.59 195:0.93 201:0.74 202:0.65 208:0.64 212:0.30 215:0.55 216:0.55 220:0.96 222:0.76 230:0.78 232:1.00 233:0.69 235:0.74 238:0.93 241:0.15 242:0.31 243:0.62 245:0.94 247:0.67 248:0.82 253:0.88 255:0.79 256:0.73 259:0.21 260:0.76 261:0.92 265:0.69 266:0.80 267:0.79 268:0.73 271:0.38 276:0.83 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.88 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.37 17:0.53 21:0.47 27:0.27 28:0.92 30:0.53 32:0.80 34:0.47 36:0.65 37:0.73 39:0.53 41:0.82 43:0.95 60:0.97 66:0.74 69:0.33 70:0.56 74:0.98 76:0.97 77:0.99 81:0.79 83:0.84 85:0.98 91:0.40 96:0.85 98:0.83 101:0.86 104:0.80 106:0.81 108:0.26 114:0.80 117:0.86 120:0.69 121:0.99 122:0.57 123:0.25 124:0.43 126:0.54 127:0.65 129:0.59 133:0.47 135:0.24 140:0.45 144:0.85 145:0.74 146:0.93 147:0.95 149:0.98 150:0.86 151:0.87 153:0.48 154:0.95 155:0.67 158:0.92 159:0.69 161:0.85 162:0.86 164:0.57 165:0.84 167:0.46 172:0.91 173:0.23 176:0.73 177:0.38 179:0.26 181:0.64 187:0.39 192:0.59 195:0.84 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.81 215:0.63 216:0.54 222:0.76 230:0.87 232:0.86 233:0.76 235:0.80 241:0.32 242:0.52 243:0.77 245:0.92 247:0.60 248:0.78 253:0.90 255:0.79 256:0.58 259:0.21 260:0.70 265:0.83 266:0.54 267:0.44 268:0.59 271:0.38 276:0.86 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.99 300:0.43 +2 1:0.74 6:0.94 8:0.88 9:0.80 11:0.88 12:0.37 17:0.62 20:0.86 21:0.40 27:0.33 28:0.92 30:0.70 34:0.75 36:0.73 37:0.78 39:0.53 41:0.94 43:0.86 60:0.95 66:0.23 69:0.58 70:0.72 74:0.92 76:0.85 78:0.86 81:0.81 83:0.84 85:0.99 91:0.53 96:0.92 98:0.39 100:0.95 101:0.83 104:0.84 106:0.81 108:0.96 111:0.90 114:0.82 117:0.86 120:0.83 122:0.57 123:0.95 124:0.95 126:0.54 127:0.77 129:0.98 133:0.95 135:0.47 140:0.45 144:0.85 145:0.87 146:0.13 147:0.18 149:0.94 150:0.87 151:0.96 152:0.81 153:0.87 154:0.84 155:0.67 158:0.92 159:0.91 161:0.85 162:0.83 164:0.78 165:0.86 167:0.57 169:0.93 172:0.74 173:0.24 176:0.73 177:0.38 179:0.25 181:0.64 186:0.86 187:0.39 189:0.95 192:0.59 201:0.74 202:0.69 206:0.81 208:0.64 212:0.35 215:0.67 216:0.62 222:0.76 232:0.89 235:0.68 238:0.97 241:0.65 242:0.59 243:0.08 245:0.84 247:0.47 248:0.90 253:0.94 255:0.79 256:0.73 259:0.21 260:0.84 261:0.91 265:0.41 266:0.91 267:0.73 268:0.75 271:0.38 276:0.87 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.84 +2 1:0.74 7:0.81 8:0.95 11:0.88 12:0.37 17:0.28 21:0.40 27:0.13 28:0.92 30:0.58 32:0.80 34:0.62 36:0.64 37:0.81 39:0.53 43:0.85 60:0.87 66:0.75 69:0.61 70:0.55 74:0.98 77:0.70 78:0.97 81:0.72 83:0.87 85:0.99 91:0.03 98:0.82 101:0.86 104:0.99 106:0.81 108:0.46 111:0.99 114:0.82 117:0.86 121:0.90 122:0.57 123:0.44 124:0.42 126:0.54 127:0.41 129:0.59 133:0.47 135:0.44 144:0.85 145:0.40 146:0.96 147:0.97 149:0.98 150:0.82 154:0.83 155:0.67 158:0.92 159:0.71 161:0.85 165:0.83 167:0.44 169:1.00 172:0.92 173:0.43 176:0.73 177:0.38 178:0.55 179:0.20 181:0.64 187:0.87 192:0.59 195:0.89 201:0.74 204:0.89 206:0.81 208:0.64 212:0.54 215:0.67 216:0.80 222:0.76 230:0.87 232:0.99 233:0.76 235:0.52 238:0.97 241:0.01 242:0.52 243:0.77 245:0.93 247:0.47 253:0.77 259:0.21 265:0.82 266:0.63 267:0.59 271:0.38 276:0.88 279:0.86 282:0.88 283:0.82 290:0.82 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.90 7:0.81 8:0.80 9:0.80 11:0.88 12:0.37 17:0.78 20:0.97 21:0.05 27:0.18 28:0.92 30:0.53 32:0.80 34:0.40 36:0.58 37:0.49 39:0.53 41:0.88 43:0.95 60:0.97 66:0.35 69:0.15 70:0.80 74:0.99 77:0.96 78:0.88 81:0.91 83:0.86 85:0.98 91:0.35 96:0.87 98:0.69 100:0.92 101:0.86 104:0.79 106:0.81 108:0.12 111:0.89 114:0.95 117:0.86 120:0.79 121:0.90 122:0.57 123:0.12 124:0.66 126:0.54 127:0.74 129:0.81 133:0.67 135:0.19 140:0.45 144:0.85 145:0.88 146:0.89 147:0.91 149:0.98 150:0.95 151:0.92 152:0.91 153:0.72 154:0.95 155:0.67 158:0.92 159:0.74 161:0.85 162:0.86 164:0.73 165:0.90 167:0.48 169:0.90 172:0.84 173:0.13 176:0.73 177:0.38 179:0.23 181:0.64 186:0.87 187:0.21 189:0.92 192:0.59 195:0.86 201:0.74 202:0.59 204:0.89 206:0.81 208:0.64 212:0.70 215:0.91 216:0.63 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.93 241:0.47 242:0.42 243:0.51 245:0.95 247:0.60 248:0.85 253:0.92 255:0.79 256:0.64 259:0.21 260:0.80 261:0.94 265:0.70 266:0.63 267:0.65 268:0.68 271:0.38 276:0.88 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.97 300:0.84 +1 1:0.74 11:0.88 12:0.37 17:0.40 21:0.54 27:0.45 28:0.92 30:0.54 32:0.72 34:0.78 36:0.18 37:0.50 39:0.53 43:0.82 66:0.88 69:0.70 70:0.71 74:0.78 81:0.63 83:0.74 85:0.91 91:0.08 98:0.51 101:0.78 108:0.53 114:0.77 122:0.57 123:0.51 126:0.54 127:0.15 129:0.05 135:0.79 144:0.85 145:0.49 146:0.79 147:0.83 149:0.83 150:0.76 154:0.77 155:0.67 159:0.35 161:0.85 165:0.79 167:0.56 172:0.67 173:0.54 176:0.73 177:0.38 178:0.55 179:0.23 181:0.64 187:0.68 192:0.59 195:0.89 201:0.74 212:0.30 215:0.58 216:0.77 222:0.76 235:0.68 241:0.65 242:0.44 243:0.89 247:0.60 253:0.68 259:0.21 265:0.53 266:0.38 267:0.73 271:0.38 276:0.54 290:0.77 297:0.36 300:0.55 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.37 17:0.06 21:0.13 27:0.16 28:0.92 30:0.61 32:0.80 34:0.74 36:0.73 37:0.69 39:0.53 41:0.97 43:0.82 60:1.00 66:0.74 69:0.69 70:0.57 74:0.98 77:0.70 81:0.86 83:0.83 85:0.98 91:0.53 96:0.91 98:0.73 101:0.85 104:0.99 106:0.81 108:0.52 114:0.92 117:0.86 120:0.84 121:0.90 122:0.57 123:0.50 124:0.42 126:0.54 127:0.28 129:0.59 133:0.47 135:0.30 138:0.96 140:0.45 144:0.85 145:0.40 146:0.95 147:0.96 149:0.97 150:0.92 151:0.99 153:0.95 154:0.77 155:0.67 158:0.92 159:0.71 161:0.85 162:0.68 164:0.86 165:0.88 167:0.51 172:0.91 173:0.48 176:0.73 177:0.38 179:0.17 181:0.64 187:0.87 192:0.59 195:0.93 201:0.74 202:0.81 204:0.89 206:0.81 208:0.64 212:0.55 215:0.84 216:0.80 222:0.76 230:0.87 232:0.99 233:0.76 235:0.22 241:0.56 242:0.54 243:0.77 245:0.93 247:0.47 248:0.90 253:0.94 255:0.79 256:0.83 259:0.21 260:0.84 265:0.74 266:0.80 267:0.73 268:0.84 271:0.38 276:0.86 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.93 7:0.76 8:0.88 9:0.80 11:0.88 12:0.37 17:0.34 20:0.86 21:0.74 27:0.16 28:0.92 30:0.20 32:0.80 34:0.33 36:0.80 37:0.83 39:0.53 41:0.93 43:0.76 60:0.99 66:0.12 69:0.77 70:0.85 74:0.98 76:0.85 77:0.70 78:0.81 81:0.48 83:0.77 85:1.00 91:0.06 96:0.71 98:0.37 100:0.88 101:0.83 108:0.60 111:0.83 114:0.67 120:0.56 122:0.57 123:0.58 124:0.96 126:0.54 127:0.79 129:0.99 133:0.97 135:0.69 140:0.80 144:0.85 145:0.79 146:0.95 147:0.96 149:0.98 150:0.65 151:0.50 152:0.94 153:0.45 154:0.68 155:0.67 158:0.87 159:0.96 161:0.85 162:0.61 164:0.78 165:0.65 167:0.45 169:0.84 172:0.90 173:0.31 176:0.73 177:0.38 179:0.11 181:0.64 186:0.81 187:0.68 189:0.93 190:0.77 192:0.59 195:0.99 201:0.74 202:0.75 208:0.64 212:0.48 215:0.46 216:0.61 220:0.97 222:0.76 230:0.79 233:0.76 235:0.95 238:0.94 241:0.21 242:0.29 243:0.32 245:0.97 247:0.67 248:0.51 253:0.77 255:0.79 256:0.79 259:0.21 260:0.57 261:0.90 265:0.39 266:0.95 267:0.92 268:0.76 271:0.38 276:0.98 279:0.86 282:0.88 283:0.71 285:0.62 290:0.67 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.81 8:0.91 9:0.80 11:0.88 12:0.37 17:0.15 20:0.93 21:0.40 27:0.29 28:0.92 30:0.54 32:0.80 34:0.67 36:0.64 37:0.99 39:0.53 41:0.96 43:0.76 60:0.99 66:0.94 69:0.82 70:0.47 74:0.98 76:0.94 77:0.70 78:0.87 81:0.77 83:0.82 85:0.99 91:0.44 96:0.88 98:0.81 100:0.94 101:0.86 104:0.98 106:0.81 108:0.72 111:0.90 114:0.82 117:0.86 120:0.78 121:0.90 122:0.57 123:0.70 124:0.36 126:0.54 127:0.34 129:0.59 133:0.47 135:0.24 140:0.45 144:0.85 145:0.42 146:0.13 147:0.18 149:0.98 150:0.84 151:0.78 152:0.86 153:0.46 154:0.67 155:0.67 158:0.92 159:0.74 161:0.85 162:0.74 164:0.84 165:0.81 167:0.54 169:0.92 172:0.97 173:0.66 176:0.73 177:0.38 179:0.14 181:0.64 186:0.87 187:0.87 189:0.96 191:0.70 192:0.59 195:0.93 201:0.74 202:0.77 204:0.89 206:0.81 208:0.64 212:0.88 215:0.67 216:0.45 220:0.87 222:0.76 230:0.87 233:0.76 235:0.60 238:0.95 241:0.63 242:0.45 243:0.07 245:0.86 247:0.60 248:0.85 253:0.92 255:0.79 256:0.83 259:0.21 260:0.79 261:0.94 265:0.81 266:0.47 267:0.76 268:0.81 271:0.38 276:0.93 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 11:0.88 12:0.37 17:0.30 21:0.54 27:0.05 28:0.92 30:0.62 32:0.80 34:0.77 36:0.86 37:0.79 39:0.53 43:0.84 66:0.60 69:0.64 70:0.56 74:0.96 76:0.85 77:0.89 81:0.63 83:0.74 85:0.98 91:0.05 98:0.80 101:0.86 108:0.49 114:0.77 121:0.90 122:0.57 123:0.47 124:0.51 126:0.54 127:0.46 129:0.59 133:0.47 135:0.75 144:0.85 145:0.97 146:0.93 147:0.94 149:0.96 150:0.76 154:0.81 155:0.67 159:0.66 161:0.85 165:0.79 167:0.45 172:0.84 173:0.53 176:0.73 177:0.38 178:0.55 179:0.27 181:0.64 187:0.68 192:0.59 195:0.87 201:0.74 204:0.89 208:0.64 212:0.55 215:0.58 216:0.90 222:0.76 230:0.87 233:0.76 235:0.68 241:0.24 242:0.54 243:0.67 245:0.93 247:0.47 253:0.75 259:0.21 265:0.80 266:0.63 267:0.94 271:0.38 276:0.82 282:0.88 290:0.77 297:0.36 299:0.88 300:0.70 +2 11:0.88 12:0.37 17:0.61 21:0.47 27:0.21 28:0.92 30:0.33 34:0.68 36:0.73 37:0.74 39:0.53 43:0.83 66:0.98 69:0.17 70:0.74 74:0.98 76:0.85 81:0.42 85:0.98 91:0.23 96:0.79 98:0.94 101:0.85 108:0.14 114:0.55 117:0.86 122:0.57 123:0.13 126:0.32 127:0.61 129:0.05 135:0.17 140:0.45 144:0.85 146:0.98 147:0.99 149:0.97 150:0.35 151:0.61 153:0.48 154:0.79 158:0.82 159:0.79 161:0.85 162:0.69 167:0.47 172:0.95 173:0.11 176:0.53 177:0.38 179:0.20 181:0.64 187:0.39 190:0.77 202:0.67 212:0.50 216:0.95 220:0.93 222:0.76 232:0.90 235:0.52 241:0.43 242:0.41 243:0.98 247:0.60 248:0.61 253:0.84 256:0.68 259:0.21 265:0.94 266:0.54 267:0.44 268:0.70 271:0.38 276:0.90 285:0.50 290:0.55 300:0.70 +0 1:0.74 6:0.92 7:0.81 8:0.83 9:0.80 11:0.88 12:0.37 17:0.76 20:0.91 21:0.05 27:0.27 28:0.92 30:0.41 32:0.80 34:0.34 36:0.74 37:0.35 39:0.53 41:0.88 43:0.96 60:1.00 66:0.68 69:0.10 70:0.73 74:0.99 77:0.89 78:0.87 81:0.91 83:0.82 85:0.99 91:0.53 96:0.86 98:0.87 100:0.92 101:0.86 104:0.79 106:0.81 108:0.80 111:0.88 114:0.95 117:0.86 120:0.77 121:0.99 122:0.57 123:0.79 124:0.46 126:0.54 127:0.92 129:0.59 133:0.47 135:0.84 140:0.45 144:0.85 145:0.74 146:0.86 147:0.89 149:0.98 150:0.95 151:0.87 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.85 161:0.85 162:0.86 164:0.69 165:0.90 167:0.46 169:0.89 172:0.96 173:0.09 176:0.73 177:0.38 179:0.17 181:0.64 186:0.87 187:0.21 189:0.93 192:0.59 195:0.93 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.55 215:0.91 216:0.70 220:0.62 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.92 241:0.35 242:0.43 243:0.31 245:0.98 247:0.60 248:0.84 253:0.91 255:0.79 256:0.58 259:0.21 260:0.78 261:0.93 265:0.87 266:0.63 267:0.36 268:0.63 271:0.38 276:0.94 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.70 +0 8:0.66 9:0.73 10:0.83 12:0.20 17:0.32 21:0.78 23:0.98 26:0.98 27:0.03 29:0.91 30:0.91 31:0.93 34:0.80 36:0.91 37:0.90 39:0.42 43:0.12 55:0.99 58:0.98 62:0.95 66:0.27 69:0.92 70:0.13 71:0.66 79:0.93 83:0.56 89:0.96 91:0.17 98:0.22 108:0.84 122:0.77 123:0.83 124:0.72 127:0.31 128:0.98 129:0.05 133:0.62 135:0.61 137:0.93 140:0.96 141:0.99 145:0.42 146:0.46 147:0.05 149:0.09 151:0.85 153:0.48 154:0.09 155:0.55 159:0.75 162:0.48 163:0.99 168:0.98 170:0.77 172:0.10 173:0.83 174:0.83 175:0.91 177:0.05 178:0.53 179:0.96 187:0.39 190:0.89 192:0.49 197:0.82 199:0.94 202:0.78 205:0.98 208:0.50 212:0.61 216:0.88 220:0.97 222:0.76 223:0.98 224:0.98 225:0.99 234:0.97 235:0.80 239:0.83 240:0.99 241:0.39 242:0.63 243:0.29 244:0.93 245:0.38 247:0.30 248:0.77 253:0.20 255:0.71 256:0.64 257:0.93 259:0.21 265:0.24 266:0.17 267:0.28 268:0.64 271:0.35 274:0.97 276:0.21 277:0.87 284:0.88 285:0.50 300:0.13 +0 10:0.91 12:0.20 17:0.95 18:0.71 21:0.59 26:0.95 27:0.72 29:0.91 30:0.87 32:0.66 34:0.73 36:0.77 39:0.42 43:0.20 55:0.84 58:0.93 66:0.72 69:0.23 70:0.27 71:0.66 81:0.28 86:0.68 89:0.98 91:0.11 98:0.77 102:0.96 108:0.18 122:0.94 123:0.18 124:0.40 126:0.24 127:0.70 129:0.05 133:0.37 135:0.47 139:0.66 141:0.91 145:0.58 146:0.77 147:0.81 149:0.20 150:0.30 154:0.25 159:0.49 163:0.94 170:0.77 172:0.45 173:0.29 174:0.89 175:0.75 177:0.70 178:0.55 179:0.86 187:0.68 195:0.68 197:0.92 199:0.95 212:0.30 215:0.55 222:0.76 223:0.93 224:0.93 225:0.97 234:0.91 235:0.74 239:0.91 240:0.95 241:0.18 242:0.33 243:0.75 244:0.83 245:0.47 247:0.60 253:0.20 254:0.92 257:0.65 265:0.77 266:0.54 267:0.36 271:0.35 274:0.91 276:0.38 277:0.69 297:0.36 300:0.25 +0 10:0.89 12:0.20 17:0.93 18:0.68 21:0.78 26:0.94 27:0.72 29:0.91 30:0.33 32:0.65 34:0.70 36:0.73 39:0.42 43:0.20 55:0.71 58:0.93 66:0.54 69:0.80 70:0.49 71:0.66 86:0.66 89:0.98 91:0.10 98:0.53 108:0.64 123:0.61 124:0.55 127:0.39 129:0.05 133:0.62 135:0.54 139:0.65 141:0.91 145:0.70 146:0.59 147:0.05 149:0.16 154:0.19 159:0.44 170:0.77 172:0.32 173:0.83 174:0.88 175:0.91 177:0.05 178:0.55 179:0.86 187:0.68 193:0.96 195:0.72 197:0.89 199:0.94 212:0.19 222:0.76 223:0.92 224:0.93 225:0.96 234:0.91 235:0.52 239:0.89 240:0.95 241:0.18 242:0.19 243:0.19 244:0.83 245:0.45 247:0.55 253:0.20 254:0.94 257:0.93 265:0.55 266:0.47 267:0.44 271:0.35 274:0.91 276:0.33 277:0.87 300:0.33 +0 1:0.69 10:0.99 11:0.46 12:0.20 17:0.98 18:0.97 21:0.54 22:0.93 26:0.98 27:0.69 29:0.91 30:0.75 32:0.65 33:0.97 34:0.95 36:0.31 37:0.41 39:0.42 43:0.58 45:0.97 47:0.93 58:0.93 64:0.77 66:0.26 69:0.74 70:0.44 71:0.66 75:0.97 81:0.61 83:0.56 86:0.97 89:0.99 91:0.18 97:0.89 98:0.58 99:0.99 101:0.47 108:0.80 122:0.93 123:0.55 124:0.57 126:0.32 127:0.33 129:0.05 133:0.43 135:0.94 139:0.97 141:0.91 145:0.67 146:0.72 147:0.76 149:0.92 150:0.56 154:0.47 155:0.54 159:0.50 165:0.65 170:0.77 172:0.86 173:0.71 174:0.98 175:0.75 177:0.70 178:0.55 179:0.18 187:0.21 192:0.48 193:0.98 195:0.78 197:0.99 199:0.99 201:0.65 204:0.83 208:0.50 212:0.93 216:0.41 219:0.91 222:0.76 223:0.98 224:0.93 225:0.98 226:0.96 234:0.91 235:0.97 236:0.92 239:0.99 240:0.95 241:0.03 242:0.59 243:0.46 244:0.83 245:0.96 247:0.60 253:0.20 257:0.65 259:0.21 262:0.93 265:0.47 266:0.38 267:0.59 271:0.35 274:0.91 276:0.86 277:0.69 279:0.75 281:0.91 291:0.97 292:0.88 300:0.55 +0 12:0.20 17:0.22 18:0.88 21:0.68 26:0.98 27:0.69 29:0.91 30:0.14 34:0.93 36:0.83 37:0.77 39:0.42 43:0.15 48:0.98 55:0.92 58:0.93 64:0.77 66:0.31 69:0.63 70:0.82 71:0.66 81:0.27 83:0.56 86:0.84 89:0.95 91:0.37 98:0.42 108:0.48 122:0.92 123:0.46 124:0.84 126:0.24 127:0.67 129:0.59 133:0.82 135:0.56 139:0.83 141:0.91 145:0.38 146:0.62 147:0.67 149:0.22 150:0.29 154:0.21 155:0.50 159:0.67 170:0.77 172:0.37 173:0.53 175:0.91 177:0.38 178:0.55 179:0.74 187:0.68 192:0.45 193:0.98 199:0.98 202:0.36 204:0.79 208:0.50 212:0.12 216:0.37 222:0.76 223:0.98 224:0.93 225:0.95 228:0.99 234:0.91 235:0.80 239:0.86 240:0.95 241:0.12 242:0.43 243:0.49 244:0.83 245:0.56 247:0.60 253:0.20 254:0.86 257:0.84 259:0.21 265:0.44 266:0.87 267:0.59 271:0.35 274:0.91 276:0.50 277:0.87 281:0.91 300:0.55 +1 10:0.92 11:0.46 12:0.20 17:0.94 18:0.99 21:0.22 26:0.98 27:0.16 29:0.91 30:0.45 34:0.36 36:0.45 37:0.99 39:0.42 43:0.56 44:0.88 45:0.83 48:0.91 55:0.59 58:0.93 64:0.77 66:0.94 69:0.74 70:0.56 71:0.66 81:0.33 86:0.98 89:0.98 91:0.14 98:0.93 101:0.47 108:0.57 122:0.92 123:0.55 124:0.36 126:0.24 127:0.83 129:0.05 133:0.37 135:0.26 139:0.97 141:0.91 145:0.41 146:0.97 147:0.05 149:0.65 150:0.37 154:0.44 159:0.74 170:0.77 172:0.94 173:0.64 174:0.92 175:0.75 177:0.05 178:0.55 179:0.25 187:0.21 195:0.85 197:0.91 199:0.99 212:0.77 216:0.08 222:0.76 223:0.98 224:0.93 225:0.97 228:0.99 234:0.91 235:0.22 239:0.91 240:0.95 241:0.37 242:0.71 243:0.06 244:0.83 245:0.73 247:0.79 253:0.20 257:0.93 259:0.21 265:0.93 266:0.63 267:0.59 271:0.35 274:0.91 276:0.88 277:0.69 281:0.86 300:0.55 +0 10:0.93 12:0.20 17:0.92 18:0.68 21:0.59 26:0.94 27:0.72 29:0.91 30:0.56 32:0.66 34:0.90 36:0.77 39:0.42 43:0.20 55:0.59 58:0.93 66:0.84 69:0.21 70:0.30 71:0.66 74:0.33 81:0.26 86:0.66 89:0.98 91:0.07 98:0.85 102:0.96 108:0.17 122:0.95 123:0.16 126:0.24 127:0.36 129:0.05 135:0.42 139:0.65 141:0.91 145:0.82 146:0.79 147:0.82 149:0.17 150:0.28 154:0.27 159:0.35 170:0.77 172:0.54 173:0.32 174:0.90 177:0.88 178:0.55 179:0.72 187:0.68 195:0.64 197:0.93 199:0.94 212:0.23 222:0.76 223:0.92 224:0.93 225:0.98 234:0.91 235:0.68 236:0.92 239:0.92 240:0.95 241:0.12 242:0.24 243:0.85 244:0.83 247:0.67 253:0.30 254:0.74 265:0.85 266:0.54 267:0.36 271:0.35 274:0.91 276:0.44 300:0.25 +0 1:0.56 8:0.80 9:0.72 10:0.99 11:0.46 12:0.20 17:0.62 18:0.99 21:0.71 22:0.93 23:0.98 26:0.98 27:0.54 29:0.91 30:0.20 31:0.98 33:0.97 34:0.64 36:0.44 37:0.62 39:0.42 43:0.66 44:0.88 45:0.99 47:0.93 58:0.99 62:0.99 64:0.77 66:0.08 69:0.99 70:0.94 71:0.66 75:0.97 79:0.98 81:0.41 83:0.56 86:0.98 89:0.99 91:0.05 97:0.99 98:0.30 99:0.83 101:0.47 102:0.96 108:0.98 122:0.93 123:0.94 124:0.97 126:0.32 127:0.95 128:0.97 129:0.05 133:0.97 135:0.71 137:0.98 139:0.98 140:0.80 141:0.99 145:0.40 146:0.86 147:0.91 149:0.86 150:0.37 151:0.82 153:0.78 154:0.56 155:0.55 159:0.96 162:0.73 165:0.65 168:0.97 170:0.77 172:0.94 173:0.99 174:0.99 175:0.75 177:0.38 179:0.12 187:0.39 190:0.89 192:0.49 195:0.99 196:0.99 197:0.99 199:1.00 201:0.54 202:0.74 205:0.98 208:0.50 212:0.69 216:0.80 219:0.91 222:0.76 223:0.98 224:0.99 225:0.99 226:0.98 234:0.99 235:0.88 236:0.94 239:0.99 240:0.99 241:0.51 242:0.37 243:0.23 244:0.83 245:0.97 247:0.82 248:0.74 253:0.20 255:0.71 256:0.77 257:0.84 259:0.21 262:0.93 265:0.28 266:0.98 267:0.28 268:0.78 271:0.35 274:0.99 276:0.98 277:0.87 279:0.81 284:0.97 285:0.50 291:0.97 292:0.95 300:0.98 +0 7:0.69 10:0.92 12:0.20 17:0.91 18:0.68 21:0.64 26:0.94 27:0.72 29:0.91 30:0.94 34:0.53 36:0.83 39:0.42 43:0.20 55:0.84 58:0.93 66:0.80 69:0.23 70:0.18 71:0.66 74:0.41 77:0.70 81:0.26 86:0.66 89:0.98 91:0.05 98:0.66 102:0.96 108:0.18 122:0.95 123:0.18 126:0.24 127:0.20 129:0.05 135:0.54 139:0.65 141:0.91 145:0.82 146:0.82 147:0.85 149:0.19 150:0.28 154:0.20 159:0.38 163:0.94 170:0.77 172:0.45 173:0.19 174:0.89 177:0.88 178:0.55 179:0.61 187:0.68 195:0.81 197:0.92 199:0.94 212:0.55 222:0.76 223:0.92 224:0.93 225:0.98 230:0.71 233:0.76 234:0.91 235:0.74 236:0.92 239:0.91 240:0.95 241:0.21 242:0.40 243:0.82 244:0.83 247:0.55 253:0.36 254:0.96 265:0.67 266:0.27 267:0.28 271:0.35 274:0.91 276:0.38 282:0.75 300:0.18 +0 12:0.20 17:0.34 18:0.71 21:0.68 26:0.98 27:0.15 29:0.91 30:0.76 34:0.85 36:0.95 37:0.77 39:0.42 43:0.17 48:0.98 55:0.96 58:0.93 64:0.77 66:0.20 69:0.55 70:0.22 71:0.66 81:0.33 83:0.56 86:0.68 89:0.95 91:0.27 98:0.40 108:0.43 120:0.57 122:0.43 123:0.41 124:0.73 126:0.32 127:0.58 129:0.81 133:0.67 135:0.72 139:0.72 140:0.45 141:0.91 145:0.38 146:0.47 147:0.53 149:0.14 150:0.33 151:0.50 153:0.48 154:0.11 155:0.50 159:0.50 162:0.86 163:0.96 164:0.55 170:0.77 172:0.10 173:0.56 175:0.97 177:0.05 179:0.96 187:0.68 190:0.95 192:0.46 193:0.98 199:0.96 201:0.54 202:0.36 204:0.79 208:0.50 212:0.42 215:0.49 216:0.56 220:0.91 222:0.76 223:0.98 224:0.93 225:0.95 228:0.99 234:0.91 235:0.88 239:0.86 240:0.95 241:0.32 242:0.45 243:0.40 244:0.93 245:0.40 247:0.35 248:0.50 253:0.20 256:0.45 257:0.93 259:0.21 260:0.57 265:0.42 266:0.23 267:0.59 268:0.46 271:0.35 274:0.91 276:0.24 277:0.95 281:0.91 283:0.67 285:0.46 297:0.36 300:0.18 +2 1:0.74 6:0.91 7:0.78 8:0.85 9:0.80 12:0.20 17:0.38 20:0.77 21:0.30 27:0.14 28:0.59 30:0.45 34:0.79 36:0.37 37:0.67 39:0.59 41:0.82 43:0.34 55:0.45 66:0.36 69:0.59 70:0.33 74:0.78 78:0.95 81:0.79 83:0.75 91:0.72 96:0.73 98:0.45 100:0.93 108:0.45 111:0.94 114:0.86 120:0.60 122:0.67 123:0.43 124:0.58 126:0.54 127:0.33 129:0.59 133:0.47 135:0.75 140:0.80 145:0.58 146:0.35 147:0.42 149:0.40 150:0.86 151:0.62 152:0.91 153:0.48 154:0.26 155:0.67 159:0.24 161:0.88 162:0.58 163:0.93 164:0.57 165:0.83 167:0.66 169:0.91 172:0.21 173:0.75 175:0.75 176:0.73 177:0.05 178:0.42 179:0.87 181:0.86 186:0.95 187:0.68 191:0.76 192:0.59 201:0.74 202:0.53 208:0.64 212:0.52 215:0.72 216:0.53 220:0.82 222:0.35 230:0.81 233:0.76 235:0.68 241:0.64 242:0.82 243:0.51 244:0.61 245:0.50 247:0.12 248:0.60 253:0.76 255:0.79 256:0.45 257:0.65 260:0.60 261:0.92 265:0.47 266:0.27 267:0.76 268:0.46 271:0.35 276:0.29 277:0.69 285:0.62 290:0.86 297:0.36 300:0.25 +2 1:0.74 7:0.81 9:0.80 12:0.20 17:0.32 21:0.30 27:0.03 28:0.59 30:0.76 32:0.78 34:0.94 36:0.25 37:0.95 39:0.59 43:0.39 55:0.71 66:0.72 69:0.44 70:0.22 74:0.85 76:0.94 77:0.89 81:0.77 91:0.48 96:0.71 98:0.63 108:0.34 114:0.86 117:0.86 120:0.58 121:0.90 122:0.67 123:0.33 126:0.54 127:0.19 129:0.05 135:0.77 140:0.80 145:0.49 146:0.52 147:0.58 149:0.35 150:0.80 151:0.58 153:0.48 154:0.30 155:0.67 158:0.85 159:0.19 161:0.88 162:0.62 164:0.57 167:0.73 172:0.27 173:0.57 175:0.75 176:0.73 177:0.05 178:0.42 179:0.81 181:0.86 187:0.95 192:0.59 195:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.42 215:0.72 216:0.76 220:0.87 222:0.35 230:0.87 232:1.00 233:0.76 235:0.60 241:0.71 242:0.82 243:0.75 244:0.61 247:0.12 248:0.57 253:0.76 255:0.79 256:0.45 257:0.65 259:0.21 260:0.59 265:0.64 266:0.23 267:0.52 268:0.46 271:0.35 276:0.25 277:0.69 282:0.86 285:0.62 290:0.86 297:0.36 300:0.18 +2 6:0.94 7:0.81 8:0.86 9:0.80 12:0.20 17:0.88 20:0.82 21:0.64 27:0.48 28:0.59 30:0.95 32:0.75 34:0.19 36:0.25 37:0.84 39:0.59 41:0.82 43:0.39 55:0.71 66:0.54 69:0.45 74:0.66 76:0.94 78:0.97 81:0.35 83:0.74 91:0.73 96:0.88 98:0.21 100:0.97 108:0.35 111:0.96 117:0.86 120:0.65 121:0.97 123:0.33 126:0.32 127:0.11 129:0.05 135:0.63 140:0.80 145:0.84 146:0.28 147:0.34 149:0.22 150:0.31 151:0.73 152:0.79 153:0.48 154:0.30 155:0.67 158:0.85 159:0.12 161:0.88 162:0.82 164:0.76 167:0.69 169:0.95 172:0.10 173:0.44 177:0.38 179:0.59 181:0.86 186:0.97 187:0.21 189:0.95 190:0.77 191:0.82 192:0.59 195:0.72 202:0.70 204:0.89 208:0.64 215:0.53 216:0.21 220:0.96 222:0.35 230:0.87 232:0.83 233:0.76 235:0.80 238:0.92 241:0.67 243:0.63 244:0.61 248:0.68 253:0.85 255:0.79 256:0.75 259:0.21 260:0.65 261:0.89 265:0.23 267:0.76 268:0.76 271:0.35 285:0.62 297:0.36 300:0.08 +1 1:0.74 7:0.81 8:0.62 9:0.80 11:0.57 12:0.20 17:0.79 20:0.82 21:0.54 27:0.49 28:0.59 30:0.62 34:0.99 36:0.90 37:0.34 39:0.59 41:0.95 43:0.94 55:0.42 60:0.87 66:0.87 69:0.30 70:0.39 74:0.90 76:0.85 77:0.70 78:0.84 81:0.70 83:0.84 85:0.91 91:0.72 96:0.79 98:0.88 100:0.87 101:0.85 108:0.24 111:0.84 114:0.77 120:0.68 121:0.90 122:0.43 123:0.23 126:0.54 127:0.43 129:0.05 135:0.47 140:0.87 145:0.98 146:0.62 147:0.67 149:0.91 150:0.81 151:0.64 152:0.79 153:0.46 154:0.94 155:0.67 158:0.87 159:0.23 161:0.88 162:0.55 164:0.78 165:0.81 167:0.76 169:0.85 172:0.63 173:0.54 176:0.73 177:0.38 178:0.48 179:0.65 181:0.86 186:0.85 187:0.21 191:0.70 192:0.59 195:0.59 201:0.74 202:0.75 204:0.89 208:0.64 212:0.86 215:0.58 216:0.36 220:0.91 222:0.35 230:0.87 232:0.88 233:0.76 235:0.88 241:0.75 242:0.51 243:0.88 247:0.55 248:0.75 253:0.85 255:0.79 256:0.77 259:0.21 260:0.69 261:0.82 265:0.88 266:0.27 267:0.52 268:0.73 271:0.35 276:0.52 279:0.86 282:0.84 283:0.71 285:0.62 290:0.77 297:0.36 300:0.33 +2 1:0.74 6:0.86 7:0.81 8:0.66 9:0.80 11:0.57 12:0.20 17:0.89 20:0.82 21:0.40 27:0.44 28:0.59 30:0.66 32:0.80 34:0.87 36:0.76 37:0.55 39:0.59 41:0.88 43:0.94 55:0.42 66:0.88 69:0.19 70:0.33 74:0.91 77:0.70 78:0.93 81:0.70 83:0.75 85:0.93 91:0.70 96:0.72 98:0.95 100:0.87 101:0.84 106:0.81 108:0.15 111:0.91 114:0.82 117:0.86 120:0.59 121:0.97 122:0.57 123:0.15 126:0.54 127:0.65 129:0.05 135:0.68 140:0.45 145:0.89 146:0.87 147:0.90 149:0.88 150:0.81 151:0.56 152:0.84 153:0.77 154:0.94 155:0.67 158:0.84 159:0.45 161:0.88 162:0.86 163:0.81 164:0.69 165:0.81 167:0.49 169:0.90 172:0.67 173:0.28 176:0.73 177:0.38 179:0.67 181:0.86 186:0.93 187:0.21 189:0.86 191:0.77 192:0.59 195:0.67 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.41 215:0.67 216:0.28 220:0.91 222:0.35 230:0.87 232:0.92 233:0.76 235:0.60 238:0.83 241:0.10 242:0.59 243:0.89 247:0.39 248:0.58 253:0.78 255:0.79 256:0.58 259:0.21 260:0.59 261:0.92 265:0.95 266:0.54 267:0.44 268:0.63 271:0.35 276:0.56 282:0.83 285:0.62 290:0.82 297:0.36 299:0.88 300:0.33 +0 8:0.98 12:0.20 17:0.62 21:0.74 27:0.09 28:0.59 32:0.80 34:0.24 36:0.20 37:0.96 39:0.59 74:0.47 77:0.70 81:0.28 91:0.97 114:0.61 120:0.58 126:0.32 135:0.26 140:0.99 145:0.72 150:0.27 151:0.52 153:0.48 161:0.88 162:0.45 164:0.56 167:0.86 175:0.91 176:0.56 178:0.55 181:0.86 190:0.77 191:0.97 195:0.64 202:0.93 216:0.31 220:0.87 222:0.35 235:0.88 241:0.89 244:0.83 248:0.52 253:0.48 256:0.45 257:0.84 259:0.21 260:0.59 267:0.98 268:0.46 271:0.35 277:0.87 282:0.88 285:0.50 290:0.61 299:0.88 +2 1:0.74 6:0.83 7:0.78 8:0.65 9:0.80 11:0.57 12:0.20 17:0.82 20:0.85 21:0.05 27:0.70 28:0.59 30:0.32 34:0.37 36:0.32 37:0.37 39:0.59 41:0.88 43:0.94 55:0.45 60:0.87 66:0.89 69:0.23 70:0.88 74:0.96 76:0.98 77:0.70 78:0.93 81:0.88 83:0.87 85:0.80 91:0.56 96:0.84 98:0.89 100:0.88 101:0.78 108:0.18 111:0.91 114:0.95 120:0.77 122:0.67 123:0.18 126:0.54 127:0.45 129:0.05 135:0.32 140:0.45 145:0.56 146:0.70 147:0.74 149:0.81 150:0.93 151:0.83 152:0.80 153:0.78 154:0.94 155:0.67 158:0.92 159:0.27 161:0.88 162:0.81 164:0.73 165:0.90 167:0.63 169:0.86 172:0.68 173:0.43 176:0.73 177:0.38 179:0.60 181:0.86 186:0.93 187:0.39 189:0.85 192:0.59 195:0.59 201:0.74 202:0.62 208:0.64 212:0.77 215:0.91 216:0.37 220:0.62 222:0.35 230:0.81 233:0.76 235:0.22 238:0.83 241:0.59 242:0.74 243:0.89 247:0.55 248:0.81 253:0.89 255:0.79 256:0.64 259:0.21 260:0.78 261:0.87 265:0.89 266:0.27 267:0.52 268:0.67 271:0.35 276:0.55 279:0.86 282:0.84 283:0.82 285:0.62 290:0.95 297:0.36 300:0.70 +1 6:0.87 7:0.78 8:0.81 9:0.80 12:0.20 17:0.16 20:0.86 21:0.71 27:0.38 28:0.59 32:0.75 34:0.47 36:0.30 37:0.42 39:0.59 78:0.96 81:0.31 91:0.96 96:0.77 100:0.97 111:0.96 120:0.83 126:0.32 135:0.75 140:0.94 145:0.70 150:0.29 151:0.59 152:0.82 153:0.78 155:0.67 161:0.88 162:0.50 164:0.92 167:0.88 169:0.96 175:0.91 178:0.50 181:0.86 186:0.96 189:0.89 190:0.77 191:0.94 192:0.59 195:0.79 202:0.94 208:0.64 215:0.48 216:0.54 220:0.82 222:0.35 230:0.81 233:0.69 235:0.88 238:0.92 241:0.99 244:0.83 248:0.69 253:0.62 255:0.79 256:0.90 257:0.84 259:0.21 260:0.83 261:0.92 267:0.91 268:0.90 271:0.35 277:0.87 285:0.62 297:0.36 +2 1:0.74 6:0.91 7:0.78 8:0.82 9:0.80 12:0.20 17:0.38 20:0.92 21:0.54 25:0.88 27:0.63 28:0.59 30:0.95 34:0.68 36:0.32 37:0.56 39:0.59 41:0.94 43:0.25 55:0.92 66:0.54 69:0.82 74:0.73 76:0.85 77:0.70 78:0.95 81:0.61 83:0.78 87:0.98 91:0.72 96:0.87 98:0.33 100:0.93 108:0.66 111:0.93 114:0.77 120:0.81 123:0.64 126:0.54 127:0.12 129:0.05 135:0.49 140:0.87 145:0.91 146:0.45 147:0.51 149:0.17 150:0.68 151:0.91 152:0.88 153:0.72 154:0.18 155:0.67 158:0.84 159:0.16 161:0.88 162:0.75 164:0.84 167:0.85 169:0.91 172:0.10 173:0.81 175:0.75 176:0.73 177:0.05 178:0.48 179:0.86 181:0.86 186:0.94 189:0.92 191:0.83 192:0.59 195:0.80 201:0.74 202:0.76 208:0.64 215:0.58 216:0.43 222:0.35 230:0.81 232:0.79 233:0.76 235:0.68 238:0.86 241:0.85 243:0.63 244:0.61 248:0.85 253:0.87 255:0.79 256:0.80 257:0.65 259:0.21 260:0.82 261:0.93 265:0.35 267:0.23 268:0.80 271:0.35 277:0.69 282:0.84 285:0.62 290:0.77 297:0.36 300:0.08 +2 1:0.74 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.97 21:0.54 25:0.88 27:0.72 28:0.59 30:0.15 32:0.80 34:0.64 36:0.60 39:0.59 41:0.95 43:0.94 55:0.45 60:0.99 66:0.93 69:0.23 70:0.66 74:0.98 77:0.70 81:0.62 83:0.83 85:0.96 91:0.78 96:0.93 98:0.98 101:0.87 104:0.76 108:0.18 114:0.77 120:0.87 121:0.90 122:0.43 123:0.18 126:0.54 127:0.84 129:0.05 135:0.28 140:0.45 145:0.63 146:0.67 147:0.72 149:0.96 150:0.75 151:0.94 153:0.82 154:0.94 155:0.67 158:0.92 159:0.26 161:0.88 162:0.80 164:0.86 165:0.79 167:0.84 172:0.82 173:0.51 176:0.73 177:0.38 179:0.48 181:0.86 191:0.81 192:0.59 195:0.56 201:0.74 202:0.78 204:0.89 208:0.64 212:0.88 215:0.58 216:0.18 220:0.62 222:0.35 230:0.84 232:0.84 233:0.69 235:0.74 241:0.82 242:0.41 243:0.94 247:0.60 248:0.92 253:0.95 255:0.79 256:0.82 259:0.21 260:0.88 265:0.98 266:0.27 267:0.28 268:0.83 271:0.35 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.77 297:0.36 299:0.88 300:0.43 +1 1:0.74 6:0.86 7:0.81 8:0.71 9:0.80 11:0.57 12:0.20 17:0.90 20:0.87 21:0.40 27:0.44 28:0.59 30:0.21 32:0.80 34:0.47 36:0.93 37:0.55 39:0.59 41:0.95 43:0.94 55:0.42 66:0.10 69:0.19 70:0.50 74:0.78 77:0.70 78:0.96 81:0.70 83:0.77 85:0.80 91:0.85 96:0.80 98:0.42 100:0.93 101:0.76 108:0.79 111:0.94 114:0.82 120:0.78 121:0.97 123:0.60 124:0.73 126:0.54 127:0.85 129:0.81 133:0.67 135:0.68 140:0.87 145:0.89 146:0.38 147:0.45 149:0.68 150:0.81 151:0.69 152:0.84 153:0.84 154:0.94 155:0.67 159:0.50 161:0.88 162:0.55 163:0.75 164:0.87 165:0.81 167:0.84 169:0.90 172:0.21 173:0.27 176:0.73 177:0.38 178:0.48 179:0.89 181:0.86 186:0.96 189:0.89 191:0.89 192:0.59 195:0.68 201:0.74 202:0.85 204:0.89 208:0.64 212:0.16 215:0.67 216:0.28 220:0.91 222:0.35 230:0.87 233:0.76 235:0.60 238:0.86 241:0.84 242:0.40 243:0.39 245:0.51 247:0.55 248:0.73 253:0.84 255:0.79 256:0.83 259:0.21 260:0.78 261:0.92 265:0.35 266:0.54 267:0.69 268:0.84 271:0.35 276:0.36 282:0.83 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.57 12:0.20 17:0.49 21:0.13 27:0.45 28:0.59 30:0.68 34:0.80 36:0.18 37:0.50 39:0.59 43:0.81 55:0.59 66:0.32 69:0.68 70:0.24 74:0.51 81:0.84 83:0.77 85:0.80 91:0.22 98:0.21 101:0.75 108:0.81 114:0.92 123:0.80 124:0.72 126:0.54 127:0.34 129:0.59 133:0.64 135:0.65 145:0.47 146:0.17 147:0.22 149:0.64 150:0.90 154:0.76 155:0.67 159:0.47 161:0.88 163:0.88 165:0.88 167:0.56 172:0.21 173:0.67 176:0.73 177:0.38 178:0.55 179:0.84 181:0.86 187:0.68 192:0.59 201:0.74 212:0.42 215:0.84 216:0.77 222:0.35 235:0.31 241:0.41 242:0.45 243:0.32 245:0.48 247:0.39 253:0.70 259:0.21 265:0.23 266:0.38 267:0.28 271:0.35 276:0.29 277:0.69 290:0.92 297:0.36 300:0.18 +2 6:0.86 7:0.81 8:0.84 9:0.80 11:0.57 12:0.20 17:0.73 20:0.90 21:0.47 27:0.52 28:0.59 30:0.62 32:0.80 34:0.44 36:0.53 37:0.70 39:0.59 41:0.92 43:0.89 55:0.42 66:0.75 69:0.47 70:0.34 74:0.76 76:0.85 77:0.70 78:0.92 81:0.44 83:0.79 85:0.80 91:0.95 96:0.90 98:0.79 100:0.92 101:0.79 108:0.36 111:0.91 120:0.85 121:0.97 122:0.41 123:0.34 126:0.32 127:0.28 129:0.05 135:0.42 140:0.96 145:0.65 146:0.49 147:0.55 149:0.67 150:0.36 151:0.86 152:0.85 153:0.48 154:0.88 155:0.67 159:0.18 161:0.88 162:0.48 164:0.89 167:0.86 169:0.89 172:0.32 173:0.73 177:0.38 178:0.48 179:0.87 181:0.86 186:0.92 189:0.88 191:0.94 192:0.59 195:0.55 202:0.94 204:0.89 208:0.64 212:0.30 215:0.63 216:0.26 220:0.93 222:0.35 230:0.87 233:0.76 235:0.60 238:0.86 241:0.90 242:0.33 243:0.77 247:0.39 248:0.87 253:0.90 255:0.79 256:0.88 259:0.21 260:0.85 261:0.90 265:0.79 266:0.27 267:0.59 268:0.87 271:0.35 276:0.19 282:0.88 285:0.62 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.85 7:0.81 8:0.64 12:0.20 17:0.47 20:0.78 21:0.40 27:1.00 28:0.59 32:0.78 34:0.78 36:0.40 37:0.62 39:0.59 74:0.58 76:0.85 77:0.70 78:0.92 81:0.68 91:0.53 100:0.87 111:0.90 114:0.82 121:0.97 126:0.54 135:0.66 145:0.50 150:0.73 152:0.77 155:0.67 161:0.88 167:0.61 169:0.83 175:0.91 176:0.73 178:0.55 181:0.86 186:0.92 187:0.39 189:0.88 192:0.59 195:0.62 201:0.74 204:0.89 208:0.64 215:0.67 216:0.27 222:0.35 230:0.87 233:0.76 235:0.60 238:0.84 241:0.54 244:0.83 253:0.65 257:0.84 259:0.21 261:0.81 267:0.23 271:0.35 277:0.87 282:0.86 290:0.82 297:0.36 +2 1:0.74 6:0.89 7:0.78 8:0.84 9:0.80 12:0.20 17:0.28 20:0.96 21:0.47 27:0.14 28:0.59 30:0.95 34:0.19 36:0.76 37:0.67 39:0.59 41:0.97 43:0.35 55:0.45 66:0.64 69:0.57 74:0.63 78:0.93 81:0.66 83:0.77 91:0.94 96:0.85 98:0.13 100:0.94 108:0.44 111:0.93 114:0.80 120:0.86 122:0.67 123:0.42 126:0.54 127:0.08 129:0.05 135:0.44 140:0.93 145:0.58 146:0.21 147:0.26 149:0.27 150:0.78 151:0.59 152:0.89 153:0.48 154:0.26 155:0.67 159:0.10 161:0.88 162:0.49 163:0.90 164:0.93 165:0.80 167:0.86 169:0.92 172:0.16 173:0.49 175:0.75 176:0.73 177:0.05 178:0.52 179:0.11 181:0.86 186:0.93 189:0.90 190:0.77 191:0.96 192:0.59 201:0.74 202:0.96 208:0.64 212:0.95 215:0.63 216:0.53 220:0.91 222:0.35 230:0.81 233:0.76 235:0.68 238:0.89 241:0.91 242:0.82 243:0.69 244:0.61 247:0.12 248:0.74 253:0.84 255:0.79 256:0.94 257:0.65 260:0.86 261:0.93 265:0.14 266:0.12 267:0.95 268:0.93 271:0.35 276:0.19 277:0.69 285:0.62 290:0.80 297:0.36 300:0.08 +0 1:0.74 12:0.20 17:0.53 21:0.54 27:0.45 28:0.59 30:0.38 34:0.85 36:0.20 37:0.50 39:0.59 43:0.29 55:0.71 66:0.29 69:0.70 70:0.63 74:0.77 81:0.59 91:0.18 98:0.44 108:0.77 114:0.77 122:0.57 123:0.76 124:0.73 126:0.54 127:0.32 129:0.59 133:0.64 135:0.65 145:0.49 146:0.26 147:0.32 149:0.38 150:0.67 154:0.75 155:0.67 158:0.86 159:0.45 161:0.88 163:0.90 167:0.52 172:0.27 173:0.69 176:0.73 177:0.38 178:0.55 179:0.73 181:0.86 187:0.68 192:0.59 201:0.74 208:0.64 212:0.42 215:0.58 216:0.77 222:0.35 235:0.74 241:0.24 242:0.29 243:0.34 245:0.56 247:0.60 253:0.67 254:0.74 259:0.21 265:0.46 266:0.54 267:0.28 271:0.35 276:0.39 279:0.86 283:0.67 290:0.77 297:0.36 300:0.43 +0 1:0.74 8:0.58 9:0.80 12:0.20 17:0.38 20:0.79 21:0.13 27:0.35 28:0.59 30:0.45 34:0.34 36:0.93 37:0.49 39:0.59 43:0.29 55:0.45 66:0.77 69:0.63 70:0.33 74:0.73 78:0.86 81:0.86 91:0.25 96:0.74 98:0.30 100:0.73 104:0.90 106:0.81 108:0.48 111:0.83 114:0.92 117:0.86 120:0.76 122:0.41 123:0.46 126:0.54 127:0.12 129:0.05 135:0.78 140:0.80 146:0.52 147:0.58 149:0.19 150:0.88 151:0.69 152:0.77 153:0.77 154:0.79 155:0.67 158:0.87 159:0.18 161:0.88 162:0.86 164:0.69 167:0.50 169:0.72 172:0.37 173:0.48 176:0.73 177:0.38 179:0.25 181:0.86 186:0.86 187:0.87 190:0.77 192:0.59 201:0.74 202:0.54 206:0.81 208:0.64 212:0.52 215:0.84 216:0.84 222:0.35 232:0.98 235:0.22 241:0.15 242:0.24 243:0.79 247:0.47 248:0.65 253:0.79 254:0.74 255:0.79 256:0.58 259:0.21 260:0.77 261:0.80 265:0.32 266:0.27 267:0.44 268:0.63 271:0.35 276:0.30 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.25 +2 1:0.74 7:0.81 9:0.80 12:0.20 17:0.38 21:0.40 27:1.00 28:0.59 32:0.78 34:0.52 36:0.34 37:0.62 39:0.59 74:0.58 76:0.85 77:0.70 81:0.68 91:0.54 96:0.70 114:0.82 120:0.57 121:0.97 126:0.54 135:0.51 140:0.45 145:0.50 150:0.73 151:0.54 153:0.48 155:0.67 161:0.88 162:0.86 164:0.57 167:0.71 175:0.91 176:0.73 181:0.86 187:0.39 192:0.59 195:0.64 201:0.74 202:0.36 204:0.89 208:0.64 215:0.67 216:0.27 220:0.91 222:0.35 230:0.87 233:0.76 235:0.60 241:0.69 244:0.83 248:0.53 253:0.66 255:0.79 256:0.45 257:0.84 259:0.21 260:0.57 267:0.36 268:0.46 271:0.35 277:0.87 282:0.86 285:0.62 290:0.82 297:0.36 +0 1:0.74 9:0.80 11:0.57 12:0.20 17:0.76 21:0.13 27:0.44 28:0.59 30:0.70 32:0.78 34:0.76 36:0.39 37:0.86 39:0.59 43:0.84 55:0.45 66:0.83 69:0.63 70:0.62 74:0.96 77:0.99 81:0.84 83:0.77 85:0.85 91:0.60 96:0.89 98:0.86 101:0.76 106:0.81 108:0.67 114:0.92 117:0.86 120:0.82 122:0.57 123:0.64 124:0.39 126:0.54 127:0.60 129:0.59 133:0.47 135:0.79 140:0.45 145:0.76 146:0.31 147:0.38 149:0.76 150:0.90 151:0.75 153:0.84 154:0.80 155:0.67 159:0.66 161:0.88 162:0.83 164:0.83 165:0.88 167:0.53 172:0.90 173:0.54 176:0.73 177:0.38 179:0.27 181:0.86 187:0.21 192:0.59 195:0.84 201:0.74 202:0.75 206:0.81 208:0.64 212:0.59 215:0.84 216:0.62 220:0.74 222:0.35 232:0.87 235:0.31 241:0.27 242:0.54 243:0.14 245:0.86 247:0.60 248:0.88 253:0.93 255:0.79 256:0.79 259:0.21 260:0.82 265:0.86 266:0.63 267:0.69 268:0.81 271:0.35 276:0.84 282:0.86 285:0.62 290:0.92 297:0.36 300:0.84 +2 1:0.56 8:0.60 10:0.94 11:0.57 12:0.86 17:0.36 18:0.96 21:0.71 27:0.48 29:0.86 30:0.44 34:0.88 36:0.91 37:0.42 39:0.51 43:0.65 45:0.98 46:0.93 64:0.77 66:0.43 69:0.81 70:0.77 71:0.57 74:0.94 81:0.42 83:0.56 86:0.97 91:0.20 97:0.89 98:0.71 99:0.83 101:0.96 107:0.97 108:0.65 110:0.97 114:0.66 122:0.86 123:0.63 124:0.68 125:0.88 126:0.32 127:0.49 129:0.59 133:0.67 135:0.56 139:0.96 146:0.96 147:0.59 149:0.91 150:0.37 154:0.27 155:0.46 158:0.76 159:0.84 160:0.88 165:0.65 170:0.82 172:0.94 173:0.59 174:0.96 176:0.63 177:0.70 178:0.55 179:0.13 187:0.21 192:0.44 197:0.92 201:0.54 208:0.50 212:0.78 216:0.70 219:0.91 222:0.35 235:0.88 239:0.92 241:0.27 242:0.18 243:0.15 244:0.61 245:0.99 247:0.97 253:0.67 257:0.65 259:0.21 265:0.71 266:0.54 267:0.59 271:0.38 276:0.96 279:0.75 289:0.94 290:0.66 292:0.88 300:0.70 +4 1:0.64 7:0.69 9:0.70 10:0.98 11:0.52 12:0.86 17:0.40 18:0.73 21:0.47 23:0.96 27:0.72 29:0.86 30:0.40 31:0.87 33:0.97 34:0.71 36:0.85 39:0.51 43:0.58 45:0.87 46:0.96 62:0.96 64:0.77 66:0.35 69:0.25 70:0.81 71:0.57 74:0.61 75:0.96 76:0.85 79:0.87 81:0.71 83:0.69 86:0.84 91:0.35 96:0.69 98:0.62 99:0.95 101:0.65 102:0.95 107:0.95 108:0.75 110:0.94 114:0.80 120:0.56 122:0.95 123:0.73 124:0.71 125:0.97 126:0.54 127:0.74 128:0.95 129:0.59 133:0.67 135:0.56 137:0.87 139:0.79 140:0.45 145:0.49 146:0.34 147:0.40 149:0.39 150:0.56 151:0.52 153:0.48 154:0.83 155:0.55 159:0.52 160:0.96 162:0.86 164:0.57 165:0.81 168:0.95 170:0.82 172:0.54 173:0.28 174:0.94 176:0.73 177:0.88 179:0.58 187:0.21 192:0.57 195:0.70 196:0.89 197:0.96 201:0.64 202:0.36 205:0.95 208:0.64 212:0.49 215:0.63 216:0.09 220:0.93 222:0.35 226:0.96 230:0.71 233:0.76 235:0.74 236:0.97 239:0.97 241:0.24 242:0.22 243:0.37 245:0.77 247:0.76 248:0.52 253:0.61 254:0.86 255:0.70 256:0.45 259:0.21 260:0.56 265:0.63 266:0.71 267:0.36 268:0.46 271:0.38 276:0.62 284:0.89 285:0.62 289:0.95 290:0.80 291:0.97 297:0.36 300:0.70 +3 1:0.66 10:0.87 11:0.52 12:0.86 17:0.43 18:0.64 21:0.13 27:0.72 29:0.86 30:0.68 34:0.90 36:0.62 37:0.92 39:0.51 43:0.68 45:0.73 46:0.96 55:0.71 64:0.77 66:0.79 69:0.52 70:0.31 71:0.57 74:0.41 81:0.70 83:0.69 86:0.70 91:0.27 98:0.80 99:0.95 101:0.65 107:0.92 108:0.40 110:0.92 114:0.92 122:0.41 123:0.38 125:0.88 126:0.51 127:0.28 129:0.05 135:0.37 139:0.67 146:0.55 147:0.61 149:0.47 150:0.58 154:0.58 155:0.50 159:0.20 160:0.88 165:0.81 170:0.82 172:0.41 173:0.73 174:0.83 176:0.70 177:0.88 178:0.55 179:0.80 187:0.39 192:0.50 197:0.88 201:0.63 208:0.61 212:0.42 215:0.84 216:0.24 222:0.35 235:0.31 239:0.87 241:0.41 242:0.19 243:0.80 244:0.61 247:0.55 253:0.63 259:0.21 265:0.80 266:0.27 267:0.52 271:0.38 276:0.32 289:0.95 290:0.92 297:0.36 300:0.25 +2 1:0.66 9:0.74 10:0.89 11:0.52 12:0.86 17:0.73 18:0.69 21:0.13 27:0.72 29:0.86 30:0.72 31:0.92 34:0.37 36:0.40 39:0.51 43:0.69 45:0.83 46:0.96 64:0.77 66:0.60 69:0.75 70:0.37 71:0.57 74:0.50 79:0.91 81:0.70 83:0.69 86:0.75 91:0.83 96:0.77 98:0.79 99:0.95 101:0.65 107:0.94 108:0.59 110:0.94 114:0.92 120:0.65 122:0.65 123:0.56 124:0.48 125:0.99 126:0.51 127:0.87 129:0.05 133:0.43 135:0.26 137:0.92 139:0.72 140:0.45 146:0.66 147:0.42 149:0.65 150:0.58 151:0.76 153:0.48 154:0.58 155:0.64 159:0.36 160:0.96 162:0.86 164:0.56 165:0.81 170:0.82 172:0.45 173:0.89 174:0.83 175:0.75 176:0.70 177:0.38 179:0.83 190:0.77 192:0.97 196:0.90 197:0.88 201:0.63 202:0.36 208:0.61 212:0.23 215:0.84 216:0.10 220:0.62 222:0.35 235:0.31 239:0.87 241:0.86 242:0.24 243:0.33 244:0.61 245:0.60 247:0.74 248:0.70 253:0.75 255:0.73 256:0.45 257:0.84 260:0.66 265:0.79 266:0.38 267:0.23 268:0.46 271:0.38 276:0.42 277:0.69 284:0.88 285:0.60 289:0.95 290:0.92 297:0.36 300:0.33 +3 1:0.65 7:0.69 10:0.84 11:0.52 12:0.86 17:0.47 18:0.83 21:0.22 27:0.72 29:0.86 30:0.60 32:0.72 34:0.69 36:0.65 39:0.51 43:0.77 44:0.92 45:0.88 46:0.98 64:0.77 66:0.90 69:0.17 70:0.51 71:0.57 74:0.83 76:0.85 77:0.89 81:0.68 83:0.69 86:0.88 91:0.46 97:0.99 98:0.95 99:0.95 101:0.65 107:0.96 108:0.14 110:0.96 114:0.88 122:0.65 123:0.13 125:0.88 126:0.51 127:0.68 129:0.05 135:0.54 139:0.90 145:0.98 146:0.74 147:0.78 149:0.85 150:0.56 154:0.69 155:0.50 158:0.81 159:0.31 160:0.88 165:0.81 170:0.82 172:0.73 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.60 192:0.49 195:0.57 197:0.83 201:0.63 208:0.61 212:0.90 215:0.79 216:0.09 219:0.94 222:0.35 230:0.71 233:0.76 235:0.42 239:0.83 241:0.84 242:0.24 243:0.91 244:0.61 247:0.84 253:0.69 259:0.21 265:0.95 266:0.27 267:0.23 271:0.38 276:0.59 279:0.81 282:0.80 283:0.67 289:0.95 290:0.88 292:0.88 297:0.36 300:0.43 +1 10:0.97 11:0.52 12:0.86 17:0.38 18:0.70 21:0.78 27:0.54 29:0.86 30:0.13 34:0.49 36:0.64 37:0.72 39:0.51 43:0.19 45:0.81 46:0.93 55:0.52 66:0.12 69:0.73 70:0.98 71:0.57 74:0.66 86:0.82 91:0.44 98:0.25 101:0.65 107:0.94 108:0.56 110:0.94 122:0.94 123:0.84 124:0.94 125:0.88 127:0.75 129:0.05 133:0.94 135:0.81 139:0.78 146:0.28 147:0.05 149:0.19 154:0.71 159:0.84 160:0.88 170:0.82 172:0.48 173:0.52 174:0.94 177:0.05 178:0.55 179:0.44 187:0.39 197:0.94 212:0.42 216:0.32 222:0.35 235:0.12 239:0.96 241:0.07 242:0.14 243:0.08 245:0.69 247:0.90 253:0.49 254:0.74 257:0.93 259:0.21 265:0.15 266:0.92 267:0.44 271:0.38 276:0.73 277:0.87 289:0.91 300:0.94 +0 1:0.67 10:0.98 11:0.81 12:0.86 17:0.55 18:0.79 21:0.22 27:0.64 29:0.86 30:0.84 32:0.80 34:0.34 36:0.90 37:0.49 39:0.51 43:0.88 44:0.93 45:0.85 46:0.94 60:0.87 64:0.77 66:0.51 69:0.51 70:0.37 71:0.57 74:0.85 75:0.99 76:0.85 77:0.89 81:0.79 83:0.91 85:0.85 86:0.90 91:0.35 98:0.38 101:0.87 102:0.98 104:0.97 106:0.81 107:0.95 108:0.39 110:0.95 114:0.90 117:0.86 122:0.65 123:0.37 124:0.73 125:0.88 126:0.54 127:0.30 129:0.05 133:0.76 135:0.76 139:0.93 145:0.98 146:0.70 147:0.75 149:0.92 150:0.63 154:0.87 155:0.52 158:0.86 159:0.70 160:0.88 165:0.93 170:0.82 172:0.68 173:0.29 174:0.90 176:0.73 177:0.88 178:0.55 179:0.34 187:0.39 192:0.51 193:0.97 195:0.93 197:0.94 201:0.66 206:0.81 208:0.64 212:0.59 215:0.79 216:0.41 219:0.95 222:0.35 226:0.96 232:0.99 235:0.52 236:0.97 239:0.98 241:0.27 242:0.16 243:0.61 245:0.75 247:0.91 253:0.71 259:0.21 265:0.40 266:0.71 267:0.59 271:0.38 276:0.66 279:0.80 282:0.88 283:0.67 289:0.95 290:0.90 292:0.88 297:0.36 299:0.88 300:0.55 +2 1:0.66 10:0.87 11:0.52 12:0.86 17:0.59 18:0.79 21:0.13 27:0.72 29:0.86 30:0.74 34:0.49 36:0.95 37:0.92 39:0.51 43:0.73 45:0.87 46:0.97 64:0.77 66:0.87 69:0.65 70:0.47 71:0.57 74:0.58 81:0.70 83:0.69 86:0.84 91:0.35 98:0.85 99:0.95 101:0.65 107:0.95 108:0.50 110:0.95 114:0.92 122:0.65 123:0.48 125:0.88 126:0.51 127:0.36 129:0.05 135:0.56 139:0.81 146:0.75 147:0.79 149:0.72 150:0.58 154:0.63 155:0.50 159:0.31 160:0.88 163:0.90 165:0.81 170:0.82 172:0.63 173:0.76 174:0.83 176:0.70 177:0.88 178:0.55 179:0.62 187:0.21 192:0.50 197:0.86 201:0.63 202:0.36 208:0.61 212:0.37 215:0.84 216:0.24 222:0.35 235:0.31 239:0.85 241:0.63 242:0.40 243:0.88 244:0.61 247:0.74 253:0.65 259:0.21 265:0.85 266:0.47 267:0.52 271:0.38 276:0.50 289:0.95 290:0.92 297:0.36 300:0.43 +2 9:0.72 10:0.90 11:0.52 12:0.86 17:0.24 18:0.72 21:0.78 27:0.72 29:0.86 30:0.87 31:0.90 34:0.78 36:0.89 39:0.51 43:0.69 45:0.81 46:0.98 66:0.45 69:0.37 70:0.27 71:0.57 74:0.54 79:0.89 83:0.69 86:0.81 91:0.18 96:0.73 98:0.55 101:0.65 107:0.93 108:0.29 110:0.93 120:0.61 122:0.65 123:0.28 124:0.56 125:0.99 127:0.48 129:0.05 133:0.45 135:0.34 137:0.90 139:0.77 140:0.45 146:0.33 147:0.40 149:0.44 151:0.63 153:0.69 154:0.81 155:0.63 159:0.21 160:0.96 162:0.86 163:0.81 164:0.62 170:0.82 172:0.27 173:0.65 174:0.79 177:0.88 179:0.88 187:0.21 190:0.77 192:0.87 196:0.90 197:0.84 202:0.46 208:0.61 212:0.42 216:0.08 220:0.82 222:0.35 235:0.02 239:0.89 241:0.56 242:0.45 243:0.58 245:0.53 247:0.47 248:0.61 253:0.53 254:0.74 255:0.71 256:0.45 259:0.21 260:0.61 265:0.56 266:0.27 267:0.23 268:0.55 271:0.38 276:0.26 284:0.90 285:0.60 289:0.95 300:0.25 +1 1:0.66 10:0.88 11:0.52 12:0.86 17:0.51 18:0.74 21:0.13 27:0.72 29:0.86 30:0.57 34:0.66 36:0.93 37:0.92 39:0.51 43:0.61 45:0.87 46:0.96 64:0.77 66:0.89 69:0.69 70:0.62 71:0.57 74:0.56 81:0.70 83:0.69 86:0.80 91:0.27 98:0.90 99:0.95 101:0.65 107:0.95 108:0.53 110:0.95 114:0.92 122:0.65 123:0.51 125:0.88 126:0.51 127:0.46 129:0.05 135:0.21 139:0.76 146:0.88 147:0.91 149:0.64 150:0.58 154:0.50 155:0.50 159:0.47 160:0.88 163:0.90 165:0.81 170:0.82 172:0.70 173:0.71 174:0.88 176:0.70 177:0.88 178:0.55 179:0.58 187:0.39 192:0.50 197:0.89 201:0.63 202:0.36 208:0.61 212:0.28 215:0.84 216:0.24 222:0.35 235:0.31 239:0.87 241:0.57 242:0.29 243:0.90 244:0.61 247:0.84 253:0.64 259:0.21 265:0.90 266:0.63 267:0.79 271:0.38 276:0.58 289:0.95 290:0.92 297:0.36 300:0.55 +2 8:0.88 9:0.76 12:0.06 17:0.30 18:0.71 21:0.54 27:0.16 29:0.77 30:0.58 34:0.80 36:0.56 37:0.79 39:0.31 43:0.15 55:0.84 66:0.49 69:0.84 70:0.60 71:0.71 74:0.39 81:0.26 83:0.91 86:0.59 91:0.56 96:0.76 97:0.94 98:0.48 108:0.69 114:0.56 120:0.63 122:0.82 123:0.67 124:0.64 126:0.26 127:0.43 129:0.05 131:0.92 133:0.60 135:0.68 139:0.69 140:0.80 144:0.76 146:0.55 147:0.30 149:0.14 150:0.26 151:0.65 153:0.48 154:0.39 155:0.67 157:0.61 158:0.79 159:0.46 162:0.74 163:0.86 164:0.54 166:0.87 172:0.32 173:0.88 176:0.55 177:0.38 178:0.42 179:0.84 182:0.61 187:0.39 190:0.77 192:0.87 202:0.65 208:0.64 212:0.37 216:0.74 219:0.92 220:0.62 222:0.90 227:0.96 229:0.61 231:0.88 235:0.60 241:0.66 242:0.40 243:0.29 244:0.61 245:0.48 246:0.61 247:0.55 248:0.63 253:0.46 254:0.90 255:0.74 256:0.68 257:0.65 259:0.21 260:0.63 265:0.50 266:0.47 267:0.36 268:0.69 271:0.18 276:0.34 279:0.82 283:0.78 285:0.56 287:0.61 290:0.56 292:0.95 300:0.43 +1 8:0.97 12:0.06 17:0.28 21:0.78 27:0.26 29:0.77 30:0.72 32:0.77 34:0.89 36:0.30 37:0.90 39:0.31 43:0.16 44:0.93 55:0.52 66:0.84 69:0.47 70:0.22 71:0.71 74:0.52 77:0.70 91:0.33 98:0.79 104:0.86 108:0.36 120:0.65 123:0.34 127:0.28 129:0.05 131:0.61 135:0.30 139:0.78 140:0.45 144:0.76 145:0.92 146:0.55 147:0.61 149:0.27 151:0.55 153:0.48 154:0.11 155:0.58 157:0.61 159:0.20 162:0.86 164:0.56 166:0.61 172:0.54 173:0.68 175:0.91 177:0.05 179:0.65 182:0.75 187:0.39 190:0.89 192:0.51 195:0.56 202:0.50 204:0.85 208:0.54 212:0.92 216:0.47 220:0.62 222:0.90 227:0.61 229:0.61 231:0.69 235:0.31 241:0.52 242:0.82 243:0.85 244:0.83 246:0.61 247:0.12 248:0.56 253:0.36 256:0.58 257:0.84 259:0.21 260:0.62 265:0.79 266:0.17 267:0.19 268:0.59 271:0.18 276:0.45 277:0.87 281:0.89 282:0.85 285:0.47 287:0.61 300:0.18 +1 8:0.96 11:0.81 12:0.06 17:0.64 18:0.95 21:0.13 27:0.55 29:0.77 30:0.21 31:0.86 34:0.74 36:0.80 37:0.94 39:0.31 43:0.57 45:0.66 55:0.71 66:0.75 69:0.41 70:0.90 71:0.71 74:0.43 79:0.88 81:0.53 86:0.79 91:0.82 96:0.69 98:0.84 101:0.52 104:0.58 108:0.31 120:0.58 122:0.83 123:0.30 124:0.43 126:0.26 127:0.87 129:0.05 131:0.97 133:0.60 135:0.82 137:0.86 139:0.75 140:0.90 144:0.76 146:0.91 147:0.93 149:0.58 150:0.40 151:0.50 153:0.45 154:0.73 157:0.80 159:0.65 162:0.50 164:0.53 166:0.61 172:0.86 173:0.33 177:0.70 178:0.50 179:0.36 182:0.74 190:0.89 191:0.86 196:0.88 202:0.63 212:0.58 215:0.61 216:0.14 220:0.74 222:0.90 227:0.98 229:0.61 231:0.92 235:0.12 241:0.79 242:0.33 243:0.77 245:0.81 246:0.61 247:0.79 248:0.51 253:0.33 254:0.92 256:0.64 259:0.21 260:0.57 265:0.84 266:0.54 267:0.52 268:0.55 271:0.18 276:0.80 284:0.86 285:0.62 287:0.61 297:0.36 300:0.70 +2 11:0.81 12:0.06 17:0.61 18:0.77 21:0.74 27:0.49 29:0.77 30:0.41 34:0.94 36:0.68 37:0.86 39:0.31 43:0.16 45:0.73 55:0.59 66:0.77 69:0.50 70:0.60 71:0.71 74:0.46 77:0.70 81:0.23 83:0.60 86:0.76 91:0.31 97:0.89 98:0.78 101:0.52 108:0.39 114:0.53 123:0.37 124:0.39 126:0.26 127:0.49 129:0.05 131:0.61 133:0.37 135:0.49 139:0.73 144:0.76 145:0.77 146:0.69 147:0.73 149:0.18 150:0.23 154:0.82 155:0.58 157:0.61 158:0.72 159:0.35 166:0.87 172:0.57 173:0.60 176:0.55 177:0.70 178:0.55 179:0.72 182:0.61 187:0.21 192:0.59 195:0.59 208:0.54 212:0.42 216:0.27 222:0.90 227:0.61 229:0.61 231:0.88 235:0.88 241:0.41 242:0.19 243:0.79 245:0.51 246:0.61 247:0.74 253:0.35 254:0.86 259:0.21 265:0.78 266:0.27 267:0.28 271:0.18 276:0.47 279:0.82 282:0.71 283:0.78 287:0.61 290:0.53 300:0.43 +0 8:0.61 11:0.81 12:0.06 17:0.32 18:0.84 21:0.78 27:0.45 29:0.77 30:0.21 34:0.86 36:0.23 37:0.50 39:0.31 43:0.11 45:0.66 55:0.42 66:0.80 69:0.80 70:0.67 71:0.71 74:0.40 86:0.81 91:0.10 98:0.35 101:0.52 108:0.63 122:0.86 123:0.61 127:0.12 129:0.05 131:0.61 135:0.83 139:0.77 144:0.76 145:0.47 146:0.48 147:0.54 149:0.10 154:0.67 157:0.80 159:0.17 166:0.61 172:0.45 173:0.77 177:0.70 178:0.55 179:0.24 182:0.74 187:0.68 212:0.82 216:0.77 222:0.90 227:0.61 229:0.61 231:0.73 235:0.74 241:0.39 242:0.58 243:0.82 246:0.61 247:0.47 253:0.32 254:0.74 259:0.21 265:0.37 266:0.23 267:0.52 271:0.18 276:0.31 287:0.61 300:0.43 +0 12:0.06 17:0.59 18:0.69 21:0.78 27:0.45 29:0.77 30:0.58 34:0.80 36:0.21 37:0.50 39:0.31 43:0.10 55:0.59 66:0.61 69:0.86 70:0.60 71:0.71 74:0.26 86:0.59 91:0.15 98:0.19 108:0.73 123:0.71 124:0.51 127:0.23 129:0.05 131:0.61 133:0.62 135:0.89 139:0.58 145:0.47 146:0.39 147:0.05 149:0.14 154:0.22 157:0.61 159:0.66 166:0.61 172:0.37 173:0.71 175:0.75 177:0.05 178:0.55 179:0.70 182:0.61 187:0.68 212:0.71 216:0.77 222:0.90 227:0.61 229:0.61 231:0.67 235:0.98 241:0.59 242:0.40 243:0.18 244:0.61 245:0.45 246:0.61 247:0.55 253:0.23 254:0.98 257:0.84 259:0.21 265:0.21 266:0.27 267:0.36 271:0.18 276:0.23 277:0.69 287:0.61 300:0.43 +2 8:0.85 9:0.76 12:0.06 17:0.28 18:0.81 21:0.54 27:0.16 29:0.77 30:0.33 34:0.84 36:0.68 37:0.79 39:0.31 43:0.15 55:0.59 66:0.71 69:0.84 70:0.76 71:0.71 74:0.29 81:0.26 83:0.60 86:0.61 91:0.49 96:0.78 98:0.72 104:0.84 106:0.81 108:0.69 114:0.56 120:0.67 122:0.80 123:0.67 124:0.42 126:0.26 127:0.45 129:0.05 131:0.92 133:0.43 135:0.68 139:0.60 140:0.45 146:0.80 147:0.30 149:0.19 150:0.26 151:0.74 153:0.82 154:0.36 155:0.58 157:0.61 158:0.71 159:0.52 162:0.86 163:0.75 164:0.65 166:0.87 172:0.57 173:0.85 176:0.55 177:0.38 179:0.68 182:0.61 187:0.68 190:0.77 191:0.73 192:0.59 202:0.62 206:0.81 208:0.54 212:0.29 216:0.74 219:0.87 220:0.62 222:0.90 227:0.96 229:0.61 231:0.90 235:0.60 241:0.18 242:0.18 243:0.21 244:0.61 245:0.60 246:0.61 247:0.79 248:0.68 253:0.48 254:0.92 255:0.74 256:0.68 257:0.65 259:0.21 260:0.65 265:0.73 266:0.63 267:0.97 268:0.70 271:0.18 276:0.48 283:0.78 285:0.56 287:0.61 290:0.56 292:0.91 300:0.55 +0 11:0.81 12:0.06 17:0.45 18:0.87 21:0.30 27:0.26 29:0.77 30:0.90 32:0.77 34:0.70 36:0.28 37:0.90 39:0.31 43:0.63 44:0.93 45:0.66 55:0.52 66:0.69 69:0.47 70:0.28 71:0.71 74:0.56 77:0.70 81:0.36 86:0.70 91:0.53 98:0.64 101:0.52 104:0.86 108:0.36 120:0.57 122:0.86 123:0.34 124:0.42 126:0.26 127:0.35 129:0.05 131:0.61 133:0.37 135:0.84 139:0.82 140:0.45 144:0.76 145:0.92 146:0.56 147:0.62 149:0.38 150:0.32 151:0.48 153:0.77 154:0.53 155:0.58 157:0.81 159:0.31 162:0.86 164:0.62 166:0.61 172:0.54 173:0.56 177:0.70 179:0.66 182:0.78 187:0.39 190:0.89 192:0.51 195:0.62 202:0.54 204:0.85 208:0.54 212:0.61 215:0.44 216:0.47 220:0.87 222:0.90 227:0.61 229:0.61 231:0.80 235:0.31 241:0.57 242:0.45 243:0.73 244:0.61 245:0.59 246:0.61 247:0.67 248:0.49 253:0.38 256:0.58 259:0.21 260:0.56 265:0.65 266:0.47 267:0.36 268:0.63 271:0.18 276:0.43 281:0.89 282:0.85 285:0.47 287:0.61 297:0.36 300:0.33 +1 8:0.88 11:0.81 12:0.06 17:0.34 18:0.94 21:0.78 27:0.26 29:0.77 30:0.43 32:0.77 34:0.91 36:0.26 37:0.90 39:0.31 43:0.68 44:0.93 45:0.83 55:0.52 66:0.89 69:0.57 70:0.64 71:0.71 74:0.69 77:0.70 86:0.87 91:0.57 98:0.80 101:0.52 108:0.44 122:0.86 123:0.42 127:0.29 129:0.05 131:0.61 135:0.81 139:0.90 144:0.76 145:0.92 146:0.74 147:0.78 149:0.69 154:0.57 155:0.58 157:0.95 159:0.31 166:0.61 172:0.70 173:0.64 177:0.70 178:0.55 179:0.47 182:0.88 187:0.39 192:0.51 195:0.64 202:0.50 204:0.85 208:0.54 212:0.61 216:0.47 222:0.90 227:0.61 229:0.61 231:0.87 235:0.22 241:0.51 242:0.39 243:0.90 244:0.61 246:0.61 247:0.55 253:0.41 259:0.21 265:0.80 266:0.38 267:0.65 271:0.18 276:0.57 281:0.89 282:0.85 287:0.61 300:0.55 +1 11:0.81 12:0.06 17:0.38 18:0.84 21:0.30 27:0.26 29:0.77 30:0.91 32:0.77 34:0.69 36:0.31 37:0.90 39:0.31 43:0.63 44:0.93 45:0.66 55:0.52 66:0.68 69:0.47 70:0.25 71:0.71 74:0.56 77:0.70 81:0.36 86:0.70 91:0.42 98:0.61 101:0.52 104:0.86 106:0.81 108:0.36 120:0.54 122:0.86 123:0.34 124:0.43 126:0.26 127:0.33 129:0.05 131:0.61 133:0.37 135:0.83 139:0.82 140:0.45 144:0.76 145:0.92 146:0.55 147:0.61 149:0.36 150:0.32 151:0.47 153:0.46 154:0.53 155:0.58 157:0.81 159:0.31 162:0.86 164:0.54 166:0.61 172:0.51 173:0.56 177:0.70 179:0.68 182:0.78 187:0.68 190:0.89 192:0.51 195:0.62 202:0.49 204:0.85 206:0.81 208:0.54 212:0.58 215:0.44 216:0.47 220:0.91 222:0.90 227:0.61 229:0.61 231:0.80 235:0.31 241:0.51 242:0.38 243:0.72 244:0.61 245:0.58 246:0.61 247:0.67 248:0.47 253:0.38 256:0.58 259:0.21 260:0.54 265:0.62 266:0.47 267:0.52 268:0.58 271:0.18 276:0.41 281:0.89 282:0.85 285:0.47 287:0.61 297:0.36 300:0.33 +2 1:0.69 7:0.66 8:0.60 11:0.87 12:0.06 17:0.93 18:0.79 21:0.22 27:0.72 29:0.77 30:0.45 32:0.80 34:0.55 36:0.66 39:0.31 43:0.77 44:0.93 45:0.73 66:0.85 69:0.25 70:0.74 71:0.71 74:0.64 76:0.85 77:0.70 81:0.63 83:0.60 85:0.72 86:0.83 91:0.58 98:0.91 99:0.83 101:0.87 108:0.20 114:0.62 122:0.57 123:0.19 126:0.44 127:0.50 129:0.05 131:0.61 135:0.74 139:0.86 144:0.76 145:0.58 146:0.72 147:0.76 149:0.61 150:0.61 154:0.89 155:0.58 157:0.81 159:0.29 165:0.70 166:0.87 172:0.57 173:0.44 176:0.55 177:0.70 178:0.55 179:0.75 182:0.78 192:0.59 195:0.61 201:0.68 208:0.54 212:0.58 215:0.51 216:0.03 222:0.90 227:0.61 229:0.61 230:0.69 231:0.90 233:0.76 235:0.31 241:0.78 242:0.22 243:0.86 246:0.61 247:0.74 253:0.47 259:0.21 265:0.91 266:0.54 267:0.28 271:0.18 276:0.45 282:0.88 287:0.61 290:0.62 297:0.36 299:0.88 300:0.55 +2 11:0.81 12:0.06 17:0.62 18:0.95 21:0.78 27:0.55 29:0.77 30:0.44 31:0.88 34:0.81 36:0.72 37:0.94 39:0.31 43:0.17 45:0.66 55:0.71 66:0.29 69:0.30 70:0.85 71:0.71 74:0.44 79:0.90 86:0.79 91:0.20 98:0.56 101:0.52 104:0.58 108:0.70 122:0.86 123:0.68 124:0.89 127:0.90 129:0.05 131:0.83 133:0.90 135:0.97 137:0.88 139:0.75 140:0.45 144:0.76 146:0.61 147:0.66 149:0.35 151:0.55 153:0.48 154:0.79 157:0.77 159:0.90 162:0.86 166:0.61 172:0.79 173:0.11 177:0.70 179:0.25 182:0.72 187:0.39 190:0.89 196:0.89 202:0.36 212:0.26 216:0.14 220:0.62 222:0.90 227:0.89 229:0.61 231:0.73 235:0.02 241:0.15 242:0.24 243:0.20 245:0.89 246:0.61 247:0.76 248:0.54 253:0.33 254:0.93 256:0.45 259:0.21 265:0.57 266:0.95 267:0.52 268:0.46 271:0.18 276:0.88 277:0.69 284:0.86 285:0.47 287:0.61 300:0.84 +3 7:0.66 11:0.64 12:0.06 17:0.77 18:0.80 21:0.78 27:0.71 29:0.77 30:0.72 34:0.95 36:0.95 37:0.65 39:0.31 43:0.69 45:0.66 55:0.71 66:0.59 69:0.45 70:0.57 71:0.71 74:0.31 77:0.70 83:0.60 86:0.61 91:0.44 97:0.89 98:0.65 101:0.52 108:0.35 123:0.34 124:0.61 127:0.53 129:0.59 131:0.61 133:0.66 135:0.83 139:0.59 145:0.79 146:0.72 147:0.76 149:0.43 154:0.65 155:0.58 157:0.61 158:0.72 159:0.51 166:0.61 172:0.61 173:0.43 177:0.70 178:0.55 179:0.59 182:0.61 187:0.21 192:0.59 195:0.74 202:0.36 208:0.54 212:0.68 216:0.15 222:0.90 227:0.61 229:0.61 230:0.69 231:0.86 233:0.76 235:0.80 241:0.24 242:0.18 243:0.67 245:0.69 246:0.61 247:0.86 253:0.27 254:0.99 259:0.21 265:0.65 266:0.54 267:0.65 271:0.18 276:0.60 279:0.82 282:0.71 283:0.78 287:0.61 300:0.55 +3 6:0.93 7:0.81 8:0.91 12:0.20 17:0.01 20:0.94 21:0.64 27:0.04 28:0.73 32:0.80 34:0.47 36:0.30 37:0.88 43:0.27 66:0.36 69:0.80 78:0.82 81:0.61 91:0.98 98:0.09 100:0.88 108:0.64 111:0.83 120:0.87 123:0.62 126:0.54 127:0.07 129:0.05 135:0.18 140:0.94 145:0.69 146:0.05 147:0.05 149:0.11 150:0.45 151:0.77 152:0.97 153:0.88 154:0.19 159:0.05 161:0.60 162:0.46 164:0.97 167:0.84 169:0.94 172:0.05 173:0.93 177:0.05 178:0.53 181:0.89 186:0.82 189:0.91 191:0.99 195:0.61 202:0.99 215:0.53 216:0.82 220:0.99 230:0.87 233:0.76 235:0.74 238:0.93 241:0.99 243:0.51 248:0.82 256:0.96 259:0.21 260:0.88 261:0.97 265:0.09 267:0.36 268:0.96 271:0.94 285:0.62 297:0.36 +3 6:0.96 8:0.95 12:0.20 17:0.34 20:0.95 21:0.30 25:0.88 27:0.09 28:0.73 30:0.71 34:0.20 36:0.63 37:0.96 43:0.39 55:0.84 66:0.53 69:0.56 70:0.29 78:0.85 81:0.75 91:0.93 98:0.72 100:0.91 104:0.54 108:0.70 111:0.86 120:0.92 123:0.68 124:0.51 126:0.54 127:0.49 129:0.59 133:0.47 135:0.63 140:0.45 146:0.55 147:0.61 149:0.44 150:0.56 151:0.83 152:0.88 153:0.96 154:0.29 159:0.50 161:0.60 162:0.78 163:0.94 164:0.93 167:0.82 169:0.89 172:0.37 173:0.54 177:0.05 179:0.83 181:0.89 186:0.85 189:0.97 191:0.94 202:0.87 212:0.61 215:0.72 216:0.44 220:0.74 235:0.31 238:0.96 241:0.91 242:0.82 243:0.37 245:0.57 247:0.12 248:0.88 256:0.89 259:0.21 260:0.92 261:0.92 265:0.72 266:0.38 267:0.36 268:0.91 271:0.94 276:0.40 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.83 7:0.81 8:0.63 12:0.20 17:0.02 20:0.86 21:0.71 27:0.15 28:0.73 30:0.95 32:0.80 34:0.26 36:0.43 37:0.27 43:0.34 55:0.71 66:0.54 69:0.66 78:0.87 81:0.55 91:0.96 98:0.43 100:0.90 104:0.58 108:0.50 111:0.86 120:0.86 123:0.48 126:0.54 127:0.14 129:0.05 135:0.28 140:0.90 145:0.74 146:0.26 147:0.32 149:0.21 150:0.42 151:0.73 152:0.81 153:0.80 154:0.25 159:0.11 161:0.60 162:0.54 164:0.94 167:0.81 169:0.87 172:0.10 173:0.96 177:0.05 178:0.50 179:0.93 181:0.89 186:0.87 189:0.85 191:0.92 195:0.54 202:0.93 215:0.48 216:0.36 220:0.62 230:0.87 233:0.76 235:0.84 238:0.91 241:0.84 243:0.63 248:0.81 256:0.96 259:0.21 260:0.87 261:0.88 265:0.45 267:0.44 268:0.92 271:0.94 283:0.82 285:0.62 297:0.36 300:0.08 +1 7:0.81 12:0.20 17:0.51 21:0.54 27:0.15 28:0.73 30:0.11 32:0.80 34:0.75 36:0.30 37:0.79 43:0.37 55:0.52 66:0.49 69:0.61 70:0.85 81:0.67 91:0.22 98:0.45 104:0.88 106:0.81 108:0.81 123:0.80 124:0.78 126:0.54 127:0.34 129:0.93 133:0.84 135:0.99 145:0.86 146:0.38 147:0.44 149:0.52 150:0.49 154:0.28 159:0.58 161:0.60 167:0.48 172:0.48 173:0.50 177:0.05 178:0.55 179:0.60 181:0.89 187:0.68 195:0.83 206:0.81 212:0.48 215:0.58 216:0.92 230:0.65 233:0.63 235:0.60 241:0.21 242:0.82 243:0.27 245:0.55 247:0.12 259:0.21 265:0.47 266:0.75 267:0.97 271:0.94 276:0.52 283:0.82 297:0.36 300:0.55 +2 8:0.94 12:0.20 17:0.09 21:0.78 27:0.03 28:0.73 34:0.61 36:0.23 37:0.90 91:0.99 120:0.82 135:0.88 140:1.00 151:0.66 153:0.48 161:0.60 162:0.46 164:0.91 167:0.83 175:0.75 178:0.55 181:0.89 191:0.99 202:0.99 216:0.79 220:0.62 241:0.93 244:0.61 248:0.74 256:0.92 257:0.65 259:0.21 260:0.82 267:0.91 268:0.89 271:0.94 277:0.69 285:0.62 +0 7:0.81 8:0.90 12:0.20 17:0.40 20:0.74 21:0.64 27:0.65 28:0.73 30:0.73 32:0.80 37:0.33 43:0.27 55:0.52 66:0.44 69:0.80 70:0.37 78:0.76 81:0.76 91:0.93 98:0.34 100:0.80 108:0.64 111:0.76 120:0.97 123:0.61 124:0.58 126:0.54 127:0.26 129:0.59 133:0.47 140:0.45 145:0.59 146:0.45 147:0.51 149:0.50 150:0.57 151:0.84 152:0.74 153:0.98 154:0.19 159:0.41 161:0.60 162:0.60 164:0.94 167:0.81 169:0.78 172:0.41 173:0.80 177:0.05 179:0.57 181:0.89 186:0.77 191:0.88 195:0.75 202:0.92 212:0.72 215:0.53 216:0.34 230:0.87 233:0.76 235:0.80 241:0.86 242:0.82 243:0.57 245:0.68 247:0.12 248:0.96 256:0.92 260:0.97 261:0.74 265:0.37 266:0.47 268:0.93 271:0.94 276:0.34 283:0.82 285:0.62 297:0.36 300:0.33 +2 6:0.86 7:0.81 8:0.72 12:0.20 17:0.24 20:0.97 21:0.54 25:0.88 27:0.12 28:0.73 30:0.08 32:0.80 34:0.61 36:0.54 37:0.53 43:0.33 55:0.45 66:0.36 69:0.68 70:0.86 78:0.81 81:0.67 91:0.92 98:0.59 100:0.85 104:0.58 108:0.75 111:0.81 120:0.96 123:0.74 124:0.67 126:0.54 127:0.37 129:0.81 133:0.67 135:0.96 140:0.45 145:0.72 146:0.31 147:0.37 149:0.51 150:0.49 151:0.82 152:0.92 153:0.94 154:0.24 159:0.48 161:0.60 162:0.66 164:0.96 167:0.83 169:0.82 172:0.37 173:0.67 177:0.05 179:0.70 181:0.89 186:0.82 189:0.88 191:0.96 195:0.77 202:0.93 212:0.22 215:0.58 216:0.19 220:0.96 230:0.87 233:0.76 235:0.60 238:0.90 241:0.92 242:0.82 243:0.44 245:0.59 247:0.12 248:0.94 256:0.94 259:0.21 260:0.96 261:0.88 265:0.60 266:0.63 267:0.99 268:0.94 271:0.94 276:0.45 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.86 7:0.81 8:0.68 12:0.20 17:0.30 20:0.85 21:0.40 27:0.21 28:0.73 30:0.45 34:0.71 36:0.68 37:0.74 43:0.52 55:0.71 66:0.91 69:0.15 70:0.39 78:0.80 81:0.72 91:0.84 98:0.86 100:0.83 104:0.91 106:0.81 108:0.12 111:0.80 120:0.93 123:0.12 126:0.54 127:0.38 129:0.05 135:0.54 140:0.45 146:0.89 147:0.91 149:0.72 150:0.54 151:0.82 152:0.80 153:0.95 154:0.41 159:0.49 161:0.60 162:0.72 164:0.94 167:0.64 169:0.81 172:0.76 173:0.19 177:0.05 179:0.46 181:0.89 186:0.80 187:0.39 189:0.88 191:0.83 202:0.90 206:0.81 212:0.35 215:0.67 216:0.95 220:0.62 230:0.87 233:0.76 235:0.42 238:0.91 241:0.65 242:0.82 243:0.92 247:0.12 248:0.90 256:0.91 259:0.21 260:0.94 261:0.82 265:0.86 266:0.54 267:0.89 268:0.92 271:0.94 276:0.65 283:0.82 285:0.62 297:0.36 300:0.33 +1 8:0.86 12:0.20 17:0.30 21:0.22 27:0.45 28:0.73 30:0.38 34:0.86 36:0.24 37:0.50 43:0.32 55:0.52 66:0.43 69:0.69 70:0.53 81:0.78 91:0.14 98:0.65 108:0.52 123:0.50 124:0.70 126:0.54 127:0.47 129:0.81 133:0.67 135:0.78 145:0.50 146:0.82 147:0.85 149:0.68 150:0.58 154:0.24 159:0.63 161:0.60 167:0.48 172:0.61 173:0.60 177:0.05 178:0.55 179:0.46 181:0.89 187:0.68 212:0.51 215:0.79 216:0.77 235:0.22 241:0.21 242:0.82 243:0.57 245:0.80 247:0.12 259:0.21 265:0.66 266:0.75 267:0.59 271:0.94 276:0.68 283:0.60 297:0.36 300:0.43 +1 6:0.83 7:0.81 8:0.64 12:0.20 17:0.49 20:0.81 21:0.71 27:0.28 28:0.73 30:0.95 32:0.80 34:0.29 36:0.36 37:0.43 43:0.34 55:0.71 66:0.54 69:0.66 78:0.84 81:0.55 91:0.76 98:0.43 100:0.86 104:0.72 108:0.50 111:0.83 120:0.62 123:0.48 126:0.54 127:0.14 129:0.05 135:0.30 140:0.80 145:0.74 146:0.26 147:0.32 149:0.21 150:0.42 151:0.57 152:0.78 153:0.80 154:0.25 159:0.11 161:0.60 162:0.48 164:0.70 167:0.82 169:0.84 172:0.10 173:0.96 177:0.05 178:0.42 179:0.93 181:0.89 186:0.84 189:0.85 191:0.77 195:0.54 202:0.76 215:0.48 216:0.24 220:0.62 230:0.87 233:0.76 235:0.84 238:0.89 241:0.89 243:0.63 248:0.55 256:0.58 259:0.21 260:0.62 261:0.83 265:0.45 267:0.23 268:0.64 271:0.94 283:0.60 285:0.62 297:0.36 300:0.08 +1 8:0.65 12:0.20 17:0.67 21:0.30 27:0.45 28:0.73 30:0.42 32:0.80 34:0.58 36:0.28 37:0.50 43:0.32 55:0.59 66:0.62 69:0.69 70:0.68 81:0.75 91:0.27 98:0.70 108:0.52 120:0.56 123:0.50 124:0.50 126:0.54 127:0.48 129:0.59 133:0.47 135:0.79 140:0.45 145:0.39 146:0.88 147:0.90 149:0.79 150:0.56 151:0.50 153:0.69 154:0.24 159:0.66 161:0.60 162:0.86 164:0.62 167:0.52 172:0.83 173:0.58 177:0.05 179:0.30 181:0.89 187:0.68 195:0.84 202:0.46 212:0.71 215:0.72 216:0.77 220:0.74 235:0.31 241:0.41 242:0.82 243:0.68 245:0.91 247:0.12 248:0.49 256:0.45 259:0.21 260:0.57 265:0.70 266:0.75 267:0.82 268:0.55 271:0.94 276:0.80 285:0.62 297:0.36 300:0.55 +2 6:0.93 7:0.81 8:0.80 12:0.20 17:0.01 20:0.89 21:0.22 27:0.04 28:0.73 30:0.58 32:0.80 34:0.71 36:0.45 37:0.88 43:0.35 55:0.59 66:0.80 69:0.62 70:0.33 78:0.89 81:0.78 91:0.84 98:0.79 100:0.98 104:0.91 108:0.47 111:0.95 120:0.98 123:0.46 126:0.54 127:0.27 129:0.05 135:0.51 140:0.45 145:0.70 146:0.61 147:0.66 149:0.48 150:0.58 151:0.85 152:0.97 153:0.99 154:0.27 159:0.22 161:0.60 162:0.69 163:0.75 164:0.99 167:0.76 169:0.94 172:0.45 173:0.79 177:0.05 179:0.75 181:0.89 186:0.89 187:0.39 189:0.94 191:0.98 195:0.58 202:0.98 212:0.37 215:0.79 216:0.82 220:0.62 230:0.87 233:0.76 235:0.22 238:0.98 241:0.76 242:0.82 243:0.82 247:0.12 248:0.98 256:0.99 259:0.21 260:0.98 261:0.97 265:0.79 266:0.27 267:0.36 268:0.99 271:0.94 276:0.38 283:0.82 285:0.62 297:0.36 300:0.25 +1 7:0.81 8:0.92 12:0.20 17:0.45 21:0.47 27:0.34 28:0.73 30:0.44 32:0.80 34:0.86 36:0.54 37:0.57 43:0.23 55:0.52 66:0.97 69:0.87 70:0.72 81:0.83 91:0.78 98:0.85 108:0.74 120:0.96 123:0.73 126:0.54 127:0.35 129:0.05 135:0.26 140:0.45 145:0.79 146:0.95 147:0.96 149:0.77 150:0.63 151:0.84 153:0.97 154:0.16 159:0.64 161:0.60 162:0.79 164:0.93 167:0.66 172:0.92 173:0.81 177:0.05 179:0.21 181:0.89 187:0.21 191:0.92 195:0.88 202:0.87 212:0.86 215:0.63 216:0.64 230:0.87 233:0.76 235:0.60 241:0.67 242:0.82 243:0.97 247:0.12 248:0.95 256:0.89 259:0.21 260:0.97 265:0.85 266:0.47 267:0.52 268:0.91 271:0.94 276:0.85 283:0.60 285:0.62 297:0.36 300:0.70 +2 6:0.86 7:0.81 8:0.66 12:0.20 17:0.13 20:0.74 21:0.40 27:0.21 28:0.73 30:0.28 34:0.62 36:0.94 37:0.74 43:0.52 55:0.59 66:0.96 69:0.12 70:0.57 78:0.90 81:0.72 91:0.88 98:0.93 100:0.93 104:0.84 106:0.81 108:0.10 111:0.88 120:0.91 123:0.10 126:0.54 127:0.58 129:0.05 135:0.60 140:0.45 146:0.97 147:0.97 149:0.79 150:0.54 151:0.74 152:0.80 153:0.84 154:0.41 159:0.69 161:0.60 162:0.73 164:0.94 167:0.61 169:0.81 172:0.88 173:0.13 177:0.05 179:0.32 181:0.89 186:0.97 187:0.39 189:0.83 191:0.85 202:0.90 206:0.81 212:0.54 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.99 241:0.62 242:0.82 243:0.96 247:0.12 248:0.87 256:0.92 259:0.21 260:0.92 261:0.82 265:0.93 266:0.63 267:0.84 268:0.93 271:0.94 276:0.81 283:0.82 285:0.62 297:0.36 300:0.43 +0 7:0.81 8:0.81 12:0.20 17:0.38 20:0.74 21:0.54 27:0.39 28:0.73 30:0.76 32:0.80 37:0.29 43:0.30 55:0.52 66:0.80 69:0.73 70:0.21 78:0.72 81:0.81 91:0.88 98:0.51 100:0.73 108:0.56 111:0.72 120:0.94 123:0.54 126:0.54 127:0.15 129:0.05 140:0.87 145:0.63 146:0.32 147:0.39 149:0.47 150:0.61 151:0.80 152:0.73 153:0.93 154:0.22 159:0.13 161:0.60 162:0.52 164:0.90 167:0.79 169:0.72 172:0.45 173:0.96 177:0.05 178:0.48 179:0.43 181:0.89 186:0.73 191:0.86 195:0.57 202:0.90 212:0.90 215:0.58 216:0.18 230:0.87 233:0.76 235:0.68 241:0.81 242:0.82 243:0.82 247:0.12 248:0.91 256:0.86 259:0.21 260:0.94 261:0.73 265:0.53 266:0.17 268:0.87 271:0.94 276:0.38 283:0.82 285:0.62 297:0.36 300:0.18 +1 6:0.82 8:0.67 12:0.20 17:0.01 20:0.87 21:0.22 27:0.31 28:0.73 30:0.91 34:0.28 36:0.79 37:0.35 43:0.51 55:0.84 66:0.69 69:0.12 70:0.13 78:0.79 81:0.78 91:0.95 98:0.29 100:0.81 104:0.58 108:0.10 111:0.79 120:0.97 123:0.10 126:0.54 127:0.12 129:0.05 135:0.24 140:0.80 146:0.44 147:0.50 149:0.34 150:0.58 151:0.70 152:0.82 153:0.48 154:0.40 159:0.16 161:0.60 162:0.51 163:0.86 164:0.97 167:0.81 169:0.79 172:0.21 173:0.14 177:0.05 178:0.42 179:0.43 181:0.89 186:0.80 189:0.84 191:0.86 202:0.98 212:0.61 215:0.79 216:0.65 235:0.22 238:0.87 241:0.84 242:0.82 243:0.73 247:0.12 248:0.96 256:0.96 259:0.21 260:0.97 261:0.82 265:0.31 266:0.17 267:0.82 268:0.97 271:0.94 276:0.22 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.91 8:0.79 12:0.20 17:0.34 20:0.74 21:0.68 27:0.33 28:0.73 30:0.62 34:0.22 36:0.42 37:0.73 43:0.50 55:0.84 66:0.75 69:0.32 70:0.34 78:0.90 81:0.74 91:0.96 98:0.82 100:0.94 108:0.25 111:0.89 120:0.93 123:0.24 126:0.54 127:0.31 129:0.05 135:0.37 140:0.45 146:0.63 147:0.68 149:0.41 150:0.55 151:0.84 152:0.74 153:0.97 154:0.39 159:0.23 161:0.60 162:0.65 163:0.75 164:0.96 167:0.81 169:0.88 172:0.32 173:0.50 177:0.05 179:0.89 181:0.89 186:0.96 189:0.92 191:0.88 202:0.95 212:0.30 215:0.49 216:0.44 235:0.84 238:0.91 241:0.86 242:0.82 243:0.77 247:0.12 248:0.90 256:0.94 259:0.21 260:0.93 261:0.77 265:0.82 266:0.27 267:0.89 268:0.96 271:0.94 276:0.28 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.95 8:0.96 12:0.20 17:0.12 20:0.97 21:0.78 27:0.07 28:0.73 30:0.62 34:0.42 36:0.51 37:0.96 43:0.39 55:0.84 66:0.75 69:0.54 70:0.34 78:0.85 91:1.00 98:0.78 100:0.92 108:0.42 111:0.87 120:0.90 123:0.40 127:0.26 129:0.05 135:0.51 140:0.94 146:0.58 147:0.64 149:0.39 151:0.78 152:0.89 153:0.89 154:0.30 159:0.21 161:0.60 162:0.51 163:0.75 164:0.95 167:0.82 169:0.90 172:0.32 173:0.73 177:0.05 178:0.53 179:0.87 181:0.89 186:0.85 189:0.96 191:0.97 202:0.96 212:0.30 216:0.61 235:0.05 238:0.95 241:0.92 242:0.82 243:0.77 247:0.12 248:0.85 256:0.97 259:0.21 260:0.90 261:0.92 265:0.78 266:0.27 267:0.88 268:0.94 271:0.94 276:0.28 285:0.62 300:0.25 +0 12:0.37 17:0.49 18:0.63 21:0.78 27:0.45 28:0.98 29:0.91 30:0.76 34:0.84 36:0.19 37:0.50 39:0.28 43:0.10 55:0.42 66:0.64 69:0.80 70:0.15 71:0.63 74:0.35 86:0.67 91:0.25 98:0.18 108:0.64 122:0.67 123:0.61 127:0.10 129:0.05 135:0.86 139:0.65 145:0.49 146:0.28 147:0.35 149:0.06 154:0.47 159:0.12 161:0.99 163:0.97 167:0.50 172:0.16 173:0.74 177:0.70 178:0.55 179:0.23 181:0.64 187:0.68 212:0.95 216:0.77 222:0.60 235:0.60 241:0.21 242:0.82 243:0.69 244:0.61 247:0.12 253:0.29 254:0.74 259:0.21 265:0.19 266:0.12 267:0.44 271:0.88 276:0.14 300:0.13 +1 12:0.37 17:0.94 18:0.98 21:0.64 27:0.55 28:0.98 29:0.91 30:0.62 31:0.88 34:0.83 36:0.22 37:0.49 39:0.28 43:0.15 44:0.87 55:0.45 64:0.77 66:0.18 69:0.54 70:0.60 71:0.63 74:0.34 79:0.89 81:0.30 86:0.94 91:0.58 98:0.67 108:0.41 120:0.55 122:0.86 123:0.66 124:0.52 126:0.44 127:0.35 129:0.59 133:0.47 135:0.56 137:0.88 139:0.93 140:0.45 145:0.65 146:0.49 147:0.56 149:0.40 150:0.24 151:0.49 153:0.48 154:0.11 159:0.47 161:0.99 162:0.74 164:0.54 167:0.56 172:0.67 173:0.50 175:0.75 177:0.38 179:0.43 181:0.64 187:0.68 190:0.89 192:0.51 195:0.74 196:0.92 201:0.68 202:0.56 212:0.78 215:0.38 216:0.30 219:0.89 220:0.91 222:0.60 235:0.80 241:0.49 242:0.79 243:0.63 244:0.83 245:0.82 247:0.47 248:0.48 253:0.28 255:0.74 256:0.58 257:0.65 259:0.21 260:0.54 265:0.41 266:0.63 267:0.19 268:0.59 271:0.88 276:0.65 277:0.69 284:0.91 285:0.56 292:0.91 297:0.36 300:0.55 +1 8:0.68 11:0.64 12:0.37 17:0.79 18:0.79 20:0.87 21:0.77 27:0.59 28:0.98 29:0.91 30:0.21 34:0.74 36:0.63 37:0.27 39:0.28 43:0.64 44:0.90 45:0.66 55:0.42 66:0.16 69:0.71 70:0.76 71:0.63 74:0.75 76:0.98 77:0.98 78:0.89 81:0.23 86:0.80 91:0.85 96:0.68 98:0.45 100:0.73 101:0.52 108:0.70 111:0.86 114:0.53 122:0.80 123:0.52 124:0.75 126:0.26 127:0.80 129:0.59 133:0.76 135:0.39 139:0.80 140:0.96 145:0.98 146:0.48 147:0.05 149:0.44 150:0.23 151:0.46 152:0.82 153:0.48 154:0.53 159:0.49 161:0.99 162:0.47 167:0.83 169:0.72 172:0.48 173:0.76 175:0.75 176:0.61 177:0.05 178:0.54 179:0.71 181:0.64 186:0.89 190:0.89 195:0.70 202:0.85 212:0.68 216:0.24 220:0.99 222:0.60 235:0.97 241:0.84 242:0.37 243:0.12 244:0.61 245:0.60 247:0.47 248:0.46 253:0.43 256:0.68 257:0.84 259:0.21 261:0.86 265:0.25 266:0.54 267:0.44 268:0.69 271:0.88 276:0.50 277:0.69 282:0.77 285:0.47 290:0.53 300:0.55 +2 1:0.69 7:0.66 9:0.76 11:0.64 12:0.37 17:0.94 18:0.97 21:0.40 22:0.93 27:0.71 28:0.98 29:0.91 30:0.10 31:0.87 34:0.25 36:0.29 37:0.40 39:0.28 43:0.62 44:0.90 45:0.81 47:0.93 64:0.77 66:0.97 69:0.66 70:0.83 71:0.63 74:0.92 77:1.00 79:0.87 81:0.45 83:0.60 86:0.97 91:0.61 96:0.69 97:0.89 98:0.89 99:0.83 101:0.52 108:0.50 114:0.61 117:0.86 122:0.80 123:0.48 126:0.44 127:0.43 129:0.05 135:0.51 137:0.87 139:0.97 140:0.45 145:0.96 146:0.88 147:0.90 149:0.83 150:0.56 151:0.50 153:0.77 154:0.51 155:0.58 158:0.79 159:0.46 161:0.99 162:0.86 165:0.70 167:0.59 172:0.92 173:0.68 175:0.75 176:0.66 177:0.38 179:0.23 181:0.64 187:0.39 190:0.77 192:0.51 195:0.72 196:0.90 201:0.68 202:0.55 204:0.85 208:0.54 212:0.91 216:0.12 219:0.92 220:0.91 222:0.60 230:0.69 232:0.80 233:0.73 235:0.52 241:0.54 242:0.54 243:0.97 244:0.61 247:0.60 248:0.50 253:0.52 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.89 266:0.47 267:0.69 268:0.64 271:0.88 276:0.85 277:0.69 279:0.82 281:0.91 282:0.77 284:0.92 285:0.56 290:0.61 292:0.95 300:0.55 +1 8:0.65 12:0.37 17:0.69 18:0.63 20:0.87 21:0.30 27:0.12 28:0.98 29:0.91 30:0.72 34:0.85 36:0.69 37:0.82 39:0.28 43:0.16 55:0.59 66:0.84 69:0.40 70:0.29 71:0.63 74:0.64 76:0.85 77:0.70 78:0.90 81:0.36 86:0.64 87:0.98 91:0.29 98:0.89 100:0.92 108:0.31 111:0.90 114:0.63 117:0.86 122:0.77 123:0.30 126:0.26 127:0.44 129:0.05 135:0.61 139:0.62 145:0.58 146:0.78 147:0.82 149:0.26 150:0.32 152:0.82 154:0.11 159:0.34 161:0.99 167:0.53 169:0.89 172:0.54 173:0.49 176:0.61 177:0.70 178:0.55 179:0.76 181:0.64 186:0.90 187:0.39 195:0.66 212:0.81 216:0.54 222:0.60 232:0.72 235:0.31 241:0.37 242:0.76 243:0.85 244:0.83 247:0.39 253:0.48 259:0.21 261:0.90 265:0.89 266:0.27 267:0.65 271:0.88 276:0.44 282:0.73 290:0.63 300:0.25 +1 7:0.72 8:0.60 12:0.37 17:0.95 18:0.87 21:0.40 22:0.93 27:0.57 28:0.98 29:0.91 30:0.68 31:0.86 34:0.69 36:0.23 37:0.40 39:0.28 43:0.18 44:0.87 47:0.93 48:0.91 55:0.71 64:0.77 66:0.54 69:0.27 70:0.43 71:0.63 74:0.32 79:0.86 81:0.36 86:0.81 91:0.46 98:0.74 108:0.21 122:0.86 123:0.20 124:0.48 126:0.26 127:0.70 129:0.05 133:0.39 135:0.54 137:0.86 139:0.84 140:0.45 145:0.47 146:0.61 147:0.66 149:0.20 150:0.32 151:0.47 153:0.69 154:0.12 159:0.35 161:0.99 162:0.86 163:0.81 167:0.45 172:0.32 173:0.42 175:0.75 177:0.38 179:0.91 181:0.64 187:0.21 190:0.89 192:0.51 195:0.58 196:0.88 202:0.46 212:0.42 216:0.11 219:0.89 220:0.91 222:0.60 230:0.74 233:0.66 235:0.52 241:0.01 242:0.63 243:0.63 244:0.83 245:0.50 247:0.39 248:0.47 253:0.27 256:0.45 257:0.65 259:0.21 262:0.93 265:0.74 266:0.38 267:0.23 268:0.55 271:0.88 276:0.33 277:0.69 281:0.86 284:0.86 285:0.47 292:0.91 300:0.33 +1 8:0.71 11:0.64 12:0.37 17:0.85 18:0.61 21:0.05 27:0.34 28:0.98 29:0.91 30:0.09 32:0.64 34:0.49 36:0.35 37:0.37 39:0.28 43:0.48 44:0.90 45:0.66 55:0.45 66:0.77 69:0.86 70:0.95 71:0.63 74:0.77 76:1.00 77:1.00 81:0.62 86:0.66 91:0.57 98:0.67 101:0.52 108:0.73 114:0.81 117:0.86 122:0.77 123:0.71 124:0.39 126:0.26 127:0.30 129:0.05 133:0.39 135:0.68 139:0.77 145:1.00 146:0.76 147:0.23 149:0.35 150:0.45 154:0.37 155:0.58 158:0.74 159:0.44 161:0.99 167:0.53 172:0.71 173:0.89 175:0.75 176:0.61 177:0.05 178:0.55 179:0.43 181:0.64 187:0.39 192:0.51 195:0.80 204:0.85 208:0.54 212:0.75 216:0.28 222:0.60 232:0.95 235:0.05 241:0.35 242:0.80 243:0.16 244:0.61 245:0.67 247:0.39 253:0.57 257:0.84 259:0.21 265:0.67 266:0.54 267:0.36 271:0.88 276:0.60 277:0.69 281:0.91 282:0.77 290:0.81 300:0.84 +2 1:0.69 12:0.37 17:0.96 18:0.68 21:0.64 27:0.34 28:0.98 29:0.91 30:0.74 34:0.21 36:0.38 37:0.37 39:0.28 43:0.11 44:0.87 55:0.59 64:0.77 66:0.77 69:0.95 70:0.59 71:0.63 74:0.66 76:0.85 77:0.98 81:0.32 86:0.71 91:0.25 96:0.75 98:0.47 108:0.91 114:0.56 117:0.86 122:0.77 123:0.90 124:0.41 126:0.44 127:0.54 129:0.05 133:0.62 135:0.83 139:0.72 140:0.45 145:0.70 146:0.78 147:0.05 149:0.17 150:0.48 151:0.55 153:0.48 154:0.15 155:0.58 158:0.74 159:0.78 161:0.99 162:0.86 167:0.52 172:0.79 173:0.96 175:0.75 176:0.66 177:0.05 179:0.45 181:0.64 187:0.39 190:0.89 192:0.51 195:0.92 201:0.68 202:0.36 208:0.54 212:0.87 216:0.28 220:0.62 222:0.60 232:1.00 235:0.84 241:0.30 242:0.63 243:0.08 244:0.61 245:0.67 247:0.55 248:0.54 253:0.57 254:0.74 256:0.45 257:0.84 259:0.21 265:0.49 266:0.47 267:0.23 268:0.46 271:0.88 276:0.55 277:0.69 282:0.77 285:0.47 290:0.56 300:0.70 +1 8:0.67 12:0.37 17:0.64 18:0.63 21:0.13 27:0.12 28:0.98 29:0.91 30:0.56 34:0.83 36:0.68 37:0.82 39:0.28 43:0.13 55:0.59 66:0.74 69:0.64 70:0.30 71:0.63 74:0.62 76:0.85 77:0.70 81:0.52 86:0.64 87:0.98 91:0.31 98:0.78 108:0.49 114:0.72 122:0.77 123:0.46 124:0.39 126:0.26 127:0.40 129:0.05 133:0.37 135:0.66 139:0.62 145:0.58 146:0.72 147:0.77 149:0.25 150:0.40 154:0.10 159:0.36 161:0.99 167:0.55 172:0.51 173:0.71 176:0.61 177:0.70 178:0.55 179:0.74 181:0.64 187:0.39 195:0.68 212:0.73 216:0.54 222:0.60 232:0.72 235:0.12 241:0.46 242:0.76 243:0.77 244:0.83 245:0.49 247:0.39 253:0.53 259:0.21 265:0.78 266:0.38 267:0.59 271:0.88 276:0.44 282:0.73 290:0.72 300:0.25 +1 1:0.69 7:0.66 11:0.64 12:0.37 17:0.89 18:0.78 21:0.68 27:0.69 28:0.98 29:0.91 30:0.45 34:1.00 36:0.46 37:0.24 39:0.28 43:0.61 44:0.90 45:0.66 48:0.98 55:0.45 64:0.77 66:0.82 69:0.76 70:0.47 71:0.63 74:0.66 76:0.98 77:0.96 81:0.32 86:0.81 91:0.54 98:0.62 101:0.52 108:0.60 114:0.55 117:0.86 122:0.80 123:0.57 126:0.44 127:0.18 129:0.05 132:0.97 135:0.56 139:0.84 145:0.98 146:0.51 147:0.57 149:0.30 150:0.48 154:0.51 155:0.58 159:0.18 161:0.99 167:0.51 172:0.48 173:0.90 175:0.75 176:0.66 177:0.38 178:0.55 179:0.53 181:0.64 187:0.39 192:0.51 195:0.60 201:0.68 204:0.85 208:0.54 212:0.50 216:0.39 222:0.60 230:0.69 232:0.93 233:0.73 235:0.97 241:0.27 242:0.33 243:0.83 244:0.61 247:0.55 253:0.41 257:0.65 265:0.63 266:0.38 267:0.65 271:0.88 276:0.38 277:0.69 281:0.88 282:0.77 290:0.55 300:0.33 +1 12:0.37 17:0.89 18:0.61 21:0.64 27:0.34 28:0.98 29:0.91 30:0.87 32:0.64 34:0.39 36:0.64 37:0.37 39:0.28 43:0.11 55:0.59 66:0.86 69:0.95 70:0.36 71:0.63 74:0.63 76:0.85 77:0.96 81:0.26 86:0.66 91:0.40 96:0.80 98:0.46 108:0.90 114:0.56 117:0.86 122:0.77 123:0.89 124:0.37 126:0.26 127:0.30 129:0.05 133:0.39 135:0.82 139:0.64 140:0.45 145:1.00 146:0.85 147:0.05 149:0.18 150:0.25 151:0.60 153:0.48 154:0.15 158:0.75 159:0.78 161:0.99 162:0.86 167:0.49 172:0.78 173:0.89 175:0.75 176:0.61 177:0.05 179:0.37 181:0.64 187:0.39 190:0.89 195:0.95 202:0.36 212:0.89 216:0.28 222:0.60 232:0.98 235:0.80 241:0.18 242:0.77 243:0.09 244:0.61 245:0.60 247:0.47 248:0.59 253:0.69 254:0.74 256:0.45 257:0.84 259:0.21 265:0.47 266:0.38 267:0.19 268:0.46 271:0.88 276:0.52 277:0.69 282:0.73 285:0.47 290:0.56 300:0.55 +0 1:0.69 11:0.85 12:0.37 17:1.00 18:0.96 21:0.30 22:0.93 27:0.72 28:0.98 29:0.91 30:0.13 34:0.75 36:0.49 39:0.28 43:0.71 44:0.90 45:0.89 47:0.93 48:0.91 55:0.42 64:0.77 66:0.90 69:0.85 70:0.92 71:0.63 74:0.89 76:0.85 77:0.70 81:0.52 83:0.60 86:0.94 91:0.18 97:0.89 98:0.83 99:0.83 101:0.87 104:0.74 108:0.71 114:0.63 117:0.86 122:0.77 123:0.69 124:0.37 126:0.44 127:0.81 129:0.05 133:0.43 135:0.74 139:0.94 145:0.58 146:0.91 147:0.18 149:0.77 150:0.58 154:0.53 155:0.58 158:0.79 159:0.66 161:0.99 165:0.70 167:0.45 172:0.93 173:0.84 176:0.66 177:0.38 178:0.55 179:0.25 181:0.64 187:0.39 192:0.51 195:0.79 201:0.68 204:0.85 208:0.54 212:0.86 216:0.04 219:0.92 222:0.60 232:0.90 235:0.42 242:0.43 243:0.08 245:0.81 247:0.79 253:0.51 254:0.96 257:0.65 259:0.21 262:0.93 265:0.83 266:0.54 267:0.84 271:0.88 276:0.86 279:0.82 281:0.89 282:0.77 290:0.63 292:0.95 300:0.84 +2 12:0.37 17:0.93 18:0.58 21:0.78 27:0.34 28:0.98 29:0.91 30:0.21 34:0.21 36:0.41 37:0.37 39:0.28 43:0.11 55:0.42 66:0.20 69:0.95 70:0.37 71:0.63 74:0.39 76:0.85 86:0.63 91:0.11 98:0.09 108:0.90 122:0.55 123:0.90 124:0.73 127:0.28 129:0.05 133:0.62 135:0.84 139:0.61 145:0.89 146:0.19 147:0.05 149:0.08 154:0.15 159:0.71 161:0.99 163:0.81 167:0.47 172:0.10 173:0.93 175:0.75 177:0.05 178:0.55 179:0.91 181:0.64 187:0.39 212:0.42 216:0.28 222:0.60 235:0.97 241:0.07 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.31 254:0.84 257:0.84 259:0.21 265:0.08 266:0.23 267:0.23 271:0.88 276:0.15 277:0.69 300:0.25 +1 12:0.37 17:0.20 18:0.85 21:0.64 27:0.45 28:0.98 29:0.91 30:0.08 32:0.68 34:0.66 36:0.18 37:0.50 39:0.28 43:0.57 44:0.90 64:0.77 66:0.24 69:0.70 70:0.95 71:0.63 74:0.41 81:0.30 86:0.89 91:0.23 98:0.33 108:0.53 122:0.75 123:0.69 124:0.71 126:0.44 127:0.35 129:0.05 133:0.60 135:0.89 139:0.90 145:0.47 146:0.41 147:0.48 149:0.38 150:0.24 154:0.70 159:0.50 161:0.99 163:0.79 167:0.54 172:0.32 173:0.67 177:0.70 178:0.55 179:0.69 181:0.64 187:0.68 192:0.51 195:0.75 201:0.68 212:0.22 215:0.38 216:0.77 219:0.92 222:0.60 235:0.80 241:0.41 242:0.22 243:0.48 245:0.60 247:0.67 253:0.32 254:0.80 259:0.21 265:0.33 266:0.54 267:0.36 271:0.88 276:0.42 277:0.69 283:0.71 292:0.91 297:0.36 300:0.70 +0 1:0.69 7:0.81 12:0.37 17:0.90 18:0.92 21:0.40 22:0.93 27:0.55 28:0.98 29:0.91 30:0.55 34:0.97 36:0.33 37:0.32 39:0.28 43:0.09 44:0.90 47:0.93 48:0.99 55:0.42 64:0.77 66:0.92 69:0.78 70:0.51 71:0.63 74:0.82 76:0.98 77:0.89 81:0.42 86:0.93 91:0.25 98:0.60 108:0.61 114:0.61 117:0.86 122:0.85 123:0.59 126:0.44 127:0.17 129:0.05 135:0.95 139:0.95 145:0.83 146:0.68 147:0.72 149:0.08 150:0.52 154:0.49 155:0.67 158:0.78 159:0.26 161:0.99 167:0.47 172:0.77 173:0.79 175:0.75 176:0.66 177:0.38 178:0.55 179:0.21 181:0.64 187:0.39 192:0.87 195:0.73 201:0.68 204:0.89 208:0.64 212:0.89 216:0.81 219:0.92 222:0.60 230:0.86 232:0.83 233:0.73 235:0.52 241:0.03 242:0.45 243:0.92 244:0.61 247:0.67 253:0.49 254:0.74 257:0.65 259:0.21 262:0.93 265:0.61 266:0.38 267:0.65 271:0.88 276:0.66 277:0.69 279:0.82 281:0.89 282:0.77 290:0.61 292:0.91 300:0.43 +2 6:0.85 8:0.70 11:0.64 12:0.37 17:0.93 18:0.61 20:0.87 21:0.05 27:0.34 28:0.98 29:0.91 30:0.42 32:0.64 34:0.75 36:0.31 37:0.37 39:0.28 43:0.48 45:0.66 55:0.71 66:0.72 69:0.86 70:0.85 71:0.63 74:0.68 76:0.85 77:0.96 78:0.87 81:0.62 86:0.66 91:0.54 96:0.75 98:0.59 100:0.89 101:0.52 108:0.73 111:0.86 114:0.81 117:0.86 122:0.77 123:0.71 124:0.41 126:0.26 127:0.26 129:0.05 133:0.39 135:0.76 139:0.64 140:0.45 145:1.00 146:0.67 147:0.23 149:0.34 150:0.45 151:0.55 152:0.94 153:0.48 154:0.37 158:0.74 159:0.38 161:0.99 162:0.86 167:0.52 169:0.90 172:0.61 173:0.90 175:0.75 176:0.61 177:0.05 179:0.50 181:0.64 186:0.87 187:0.39 190:0.89 195:0.73 202:0.36 212:0.73 216:0.28 220:0.62 222:0.60 232:0.95 235:0.05 241:0.32 242:0.78 243:0.19 244:0.61 245:0.62 247:0.39 248:0.54 253:0.65 256:0.45 257:0.84 259:0.21 261:0.93 265:0.60 266:0.54 267:0.52 268:0.46 271:0.88 276:0.50 277:0.69 282:0.73 285:0.47 290:0.81 300:0.70 +0 12:0.37 17:0.98 18:0.98 21:0.54 22:0.93 27:0.55 28:0.98 29:0.91 30:0.72 34:0.75 36:0.30 37:0.49 39:0.28 43:0.14 44:0.87 47:0.93 48:0.91 55:0.45 64:0.77 66:0.13 69:0.83 70:0.52 71:0.63 74:0.31 81:0.40 86:0.94 91:0.15 98:0.58 108:0.46 122:0.86 123:0.66 124:0.50 126:0.44 127:0.29 129:0.05 133:0.39 135:0.34 139:0.93 145:0.54 146:0.66 147:0.51 149:0.38 150:0.32 154:0.10 159:0.40 161:0.99 167:0.55 172:0.65 173:0.86 175:0.75 177:0.05 178:0.55 179:0.42 181:0.64 187:0.68 192:0.51 195:0.71 201:0.68 212:0.82 215:0.39 216:0.30 222:0.60 235:0.88 241:0.46 242:0.80 243:0.68 244:0.83 245:0.78 247:0.39 253:0.27 257:0.84 259:0.21 262:0.93 265:0.38 266:0.47 267:0.36 271:0.88 276:0.60 277:0.69 281:0.89 297:0.36 300:0.55 +1 1:0.74 11:0.83 12:0.37 17:0.93 18:0.81 21:0.30 27:0.72 28:0.57 29:0.71 30:0.40 34:0.88 36:0.74 39:0.84 43:0.80 45:0.66 55:0.59 64:0.77 66:0.62 69:0.72 70:0.54 71:0.77 74:0.71 76:0.85 81:0.57 83:0.91 85:0.80 86:0.87 91:0.29 98:0.77 101:0.87 104:0.58 108:0.65 114:0.65 122:0.77 123:0.63 124:0.55 126:0.54 127:0.71 129:0.59 133:0.66 135:0.23 139:0.83 145:0.92 146:0.20 147:0.18 149:0.75 150:0.76 154:0.74 155:0.67 159:0.46 161:0.86 165:0.93 167:0.53 172:0.67 173:0.78 176:0.73 177:0.38 178:0.55 179:0.58 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.58 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.32 242:0.16 243:0.15 245:0.71 247:0.90 253:0.50 257:0.65 259:0.21 265:0.77 266:0.27 267:0.18 271:0.88 276:0.63 290:0.65 297:0.36 300:0.43 +1 1:0.74 8:0.78 9:0.80 11:0.92 12:0.37 17:0.76 18:0.95 21:0.05 22:0.93 27:0.54 28:0.57 29:0.71 30:0.68 31:0.91 32:0.79 34:0.50 36:0.94 37:0.65 39:0.84 41:0.82 43:0.78 44:0.93 45:0.83 47:0.93 60:0.95 64:0.77 66:0.95 69:0.78 70:0.45 71:0.77 74:0.97 76:0.85 77:0.96 78:0.93 79:0.90 81:0.76 83:0.91 85:0.97 86:0.99 91:0.48 96:0.75 97:0.94 98:0.80 101:0.97 106:0.81 108:0.62 111:0.96 114:0.89 117:0.86 120:0.64 122:0.57 123:0.60 124:0.36 126:0.54 127:0.34 129:0.05 133:0.37 135:0.87 137:0.91 139:0.99 140:0.45 145:0.91 146:0.92 147:0.05 149:0.98 150:0.85 151:0.72 153:0.48 154:0.70 155:0.67 158:0.92 159:0.59 161:0.86 162:0.86 164:0.66 165:0.93 167:0.56 169:0.95 172:0.96 173:0.71 176:0.73 177:0.05 179:0.15 181:0.74 187:0.87 191:0.75 192:0.87 195:0.85 196:0.96 201:0.93 202:0.56 206:0.81 208:0.64 212:0.93 215:0.78 216:0.62 219:0.95 220:0.62 222:0.48 232:0.96 235:0.22 238:0.95 241:0.44 242:0.27 243:0.06 245:0.76 247:0.90 248:0.67 253:0.81 255:0.95 256:0.64 257:0.84 260:0.65 262:0.93 265:0.80 266:0.38 267:0.98 268:0.65 271:0.88 276:0.91 279:0.86 282:0.87 283:0.82 284:0.89 285:0.62 290:0.89 292:0.95 297:0.36 300:0.55 +2 1:0.74 9:0.80 11:0.92 12:0.37 17:0.95 18:0.81 21:0.13 27:1.00 28:0.57 29:0.71 30:0.43 31:0.99 32:0.80 34:0.24 36:0.54 39:0.84 41:0.96 43:0.89 44:0.93 45:0.73 60:0.87 64:0.77 66:0.13 69:0.17 70:0.79 71:0.77 74:0.85 77:0.96 79:0.99 81:0.68 83:0.91 85:0.80 86:0.91 91:0.90 96:0.96 98:0.31 101:0.97 108:0.14 114:0.79 120:0.92 122:0.77 123:0.90 124:0.72 126:0.54 127:0.98 129:0.59 133:0.66 135:0.63 137:0.99 139:0.93 140:0.45 145:0.52 146:0.39 147:0.46 149:0.68 150:0.81 151:0.99 153:0.97 154:0.90 155:0.67 158:0.91 159:0.80 161:0.86 162:0.77 164:0.89 165:0.93 167:0.83 172:0.48 173:0.12 176:0.73 177:0.70 179:0.68 181:0.74 192:0.87 195:0.89 196:0.99 201:0.93 202:0.87 208:0.64 212:0.57 215:0.61 216:0.13 219:0.95 222:0.48 235:0.31 241:0.81 242:0.29 243:0.51 245:0.72 247:0.79 248:0.96 253:0.97 255:0.95 256:0.90 259:0.21 260:0.92 265:0.23 266:0.71 267:0.76 268:0.91 271:0.88 276:0.39 277:0.69 279:0.86 282:0.88 283:0.78 284:0.99 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.70 +2 6:0.96 7:0.72 8:0.90 9:0.76 12:0.37 17:0.96 20:0.91 21:0.40 27:0.09 28:0.57 29:0.71 30:0.86 32:0.77 34:0.61 36:0.80 37:0.86 39:0.84 43:0.15 44:0.93 55:0.45 66:0.68 69:0.52 70:0.32 71:0.77 74:0.70 76:0.99 77:0.70 78:0.86 81:0.27 83:0.60 91:0.77 96:0.77 98:0.43 100:0.94 104:0.58 108:0.85 111:0.89 120:0.68 123:0.85 124:0.64 126:0.26 127:0.59 129:0.59 133:0.82 135:0.75 139:0.74 140:0.80 145:0.77 146:0.36 147:0.43 149:0.38 150:0.26 151:0.65 152:0.96 153:0.48 154:0.18 155:0.58 158:0.75 159:0.66 161:0.86 162:0.69 164:0.86 167:0.55 169:0.94 172:0.86 173:0.41 175:0.75 177:0.38 178:0.42 179:0.31 181:0.74 186:0.86 187:0.39 189:0.97 190:0.77 191:0.91 192:0.59 195:0.84 202:0.85 204:0.85 208:0.54 212:0.86 215:0.42 216:0.67 220:0.82 222:0.48 230:0.75 232:0.90 233:0.76 235:0.42 238:0.95 241:0.39 242:0.77 243:0.18 244:0.61 245:0.79 247:0.60 248:0.67 253:0.67 254:0.84 255:0.74 256:0.88 257:0.65 259:0.21 260:0.66 261:0.96 265:0.45 266:0.80 267:0.44 268:0.88 271:0.88 276:0.79 277:0.69 282:0.85 283:0.78 285:0.56 297:0.36 300:0.55 +2 6:0.97 8:0.99 9:0.80 11:0.57 12:0.37 17:0.67 18:0.67 20:0.82 21:0.78 27:0.04 28:0.57 29:0.71 30:0.54 31:0.95 34:0.37 36:0.66 37:0.94 39:0.84 41:0.94 43:0.94 55:0.45 66:0.12 69:0.10 70:0.80 71:0.77 74:0.83 76:0.85 77:0.70 78:0.81 79:0.94 83:0.91 85:0.72 86:0.77 91:0.78 96:0.88 98:0.46 100:0.85 101:0.87 108:0.09 111:0.81 120:0.79 122:0.77 123:0.80 124:0.73 127:0.50 129:0.05 133:0.59 135:0.61 137:0.95 139:0.74 140:0.45 145:0.39 146:0.45 147:0.52 149:0.68 151:0.87 152:0.92 153:0.48 154:0.94 155:0.67 158:0.74 159:0.60 161:0.86 162:0.48 164:0.81 167:0.75 169:0.91 172:0.48 173:0.14 177:0.70 179:0.58 181:0.74 186:0.81 187:0.21 189:1.00 191:0.88 192:0.87 195:0.78 196:0.92 202:0.91 208:0.64 212:0.55 216:0.87 220:0.87 222:0.48 232:0.83 238:0.93 241:0.74 242:0.80 243:0.50 245:0.74 247:0.39 248:0.81 253:0.89 255:0.95 256:0.92 259:0.21 260:0.79 261:0.93 265:0.33 266:0.71 267:0.17 268:0.81 271:0.88 276:0.55 277:0.87 282:0.73 284:0.97 285:0.62 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.83 12:0.37 17:0.97 18:0.91 21:0.13 27:0.72 28:0.57 29:0.71 30:0.86 31:0.99 32:0.68 34:0.59 36:0.72 39:0.84 41:0.96 43:0.91 44:0.90 45:0.66 64:0.77 66:0.72 69:0.39 70:0.41 71:0.77 74:0.89 79:0.99 81:0.68 83:0.91 85:0.94 86:0.97 91:0.89 96:0.97 98:0.73 101:0.97 104:0.58 108:0.65 114:0.79 120:0.94 121:0.97 122:0.57 123:0.62 124:0.45 126:0.54 127:0.82 129:0.81 133:0.67 135:0.20 137:0.99 139:0.97 140:0.45 145:0.56 146:0.17 147:0.22 149:0.96 150:0.81 151:0.99 153:0.97 154:0.91 155:0.67 159:0.40 161:0.86 162:0.85 164:0.92 165:0.93 167:0.84 172:0.82 173:0.49 176:0.73 177:0.70 179:0.42 181:0.74 192:0.87 195:0.62 196:1.00 201:0.93 202:0.87 204:0.89 208:0.64 212:0.89 215:0.61 216:0.15 222:0.48 228:0.99 230:0.87 232:0.77 233:0.76 235:0.31 241:0.85 242:0.33 243:0.15 245:0.78 247:0.84 248:0.96 253:0.98 255:0.95 256:0.89 259:0.21 260:0.94 265:0.73 266:0.27 267:0.23 268:0.92 271:0.88 276:0.73 281:0.91 284:0.99 285:0.62 290:0.79 297:0.36 300:0.55 +1 1:0.74 7:0.72 8:0.88 9:0.80 12:0.37 17:0.83 18:0.69 20:0.75 21:0.59 27:0.71 28:0.57 29:0.71 30:0.45 31:0.97 32:0.79 34:0.59 36:0.47 37:0.77 39:0.84 41:0.82 43:0.17 44:0.93 55:0.52 64:0.77 66:0.49 69:0.83 70:0.25 71:0.77 74:0.61 76:0.99 77:0.70 78:0.89 79:0.97 81:0.47 83:0.91 86:0.70 91:0.83 96:0.96 98:0.23 100:0.92 108:0.67 111:0.87 114:0.58 120:0.88 122:0.80 123:0.65 124:0.48 126:0.54 127:0.22 129:0.05 133:0.39 135:0.61 137:0.97 139:0.78 140:0.94 145:0.76 146:0.30 147:0.05 149:0.09 150:0.71 151:0.96 152:0.74 153:0.89 154:0.45 155:0.67 158:0.74 159:0.32 161:0.86 162:0.79 163:0.88 164:0.82 165:0.93 167:0.83 169:0.81 172:0.16 173:0.87 175:0.75 176:0.73 177:0.05 179:0.92 181:0.74 186:0.99 191:0.70 192:0.87 195:0.72 196:0.95 201:0.93 202:0.88 204:0.85 208:0.64 212:0.61 215:0.39 216:0.22 220:0.93 222:0.48 230:0.75 232:0.82 233:0.76 235:0.88 241:0.81 242:0.63 243:0.29 244:0.61 245:0.39 247:0.30 248:0.86 253:0.93 254:0.90 255:0.95 256:0.89 257:0.84 259:0.21 260:0.87 261:0.78 265:0.25 266:0.17 267:0.23 268:0.92 271:0.88 276:0.15 277:0.69 282:0.87 284:0.96 285:0.62 290:0.58 297:0.36 300:0.18 +0 1:0.74 7:0.66 8:0.71 9:0.80 11:0.64 12:0.37 17:0.79 18:0.73 21:0.13 27:0.68 28:0.57 29:0.71 30:0.14 31:0.97 32:0.79 34:0.58 36:0.85 37:0.73 39:0.84 43:0.96 44:0.93 45:0.66 48:0.91 55:0.84 64:0.77 66:0.31 69:0.17 70:0.98 71:0.77 74:0.78 76:0.94 77:0.89 79:0.97 81:0.75 83:0.91 86:0.80 91:0.68 96:0.94 97:0.89 98:0.58 99:0.95 101:0.52 108:0.80 114:0.79 120:0.61 122:0.77 123:0.79 124:0.72 126:0.54 127:0.96 129:0.59 133:0.61 135:0.49 137:0.97 138:0.98 139:0.90 140:0.96 145:0.79 146:0.17 147:0.22 149:0.67 150:0.84 151:0.63 153:0.78 154:0.96 155:0.67 158:0.87 159:0.60 161:0.86 162:0.61 163:0.81 164:0.66 165:0.93 167:0.81 172:0.37 173:0.21 176:0.73 177:0.70 179:0.77 181:0.74 190:0.77 192:0.87 195:0.75 196:0.97 201:0.93 202:0.87 204:0.85 208:0.64 212:0.18 215:0.61 216:0.13 219:0.95 220:0.62 222:0.48 230:0.69 232:0.72 233:0.76 235:0.52 241:0.78 242:0.43 243:0.28 245:0.64 247:0.74 248:0.61 253:0.93 255:0.95 256:0.88 259:0.21 260:0.61 265:0.59 266:0.84 267:0.28 268:0.88 271:0.88 276:0.47 279:0.86 281:0.89 282:0.87 283:0.71 284:0.96 285:0.62 290:0.79 292:0.91 297:0.36 300:0.84 +1 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 12:0.37 17:0.66 20:0.76 21:0.47 27:0.04 28:0.57 29:0.71 30:0.14 31:0.91 32:0.80 34:0.61 36:0.39 37:0.94 39:0.84 41:1.00 43:0.14 44:0.93 55:0.59 64:0.77 66:0.71 69:0.63 70:0.82 71:0.77 74:0.79 76:0.99 77:0.96 78:0.90 79:0.90 81:0.52 83:0.91 91:0.82 96:0.79 98:0.60 100:0.92 108:0.48 111:0.91 114:0.60 120:0.73 121:0.90 122:0.77 123:0.46 124:0.43 126:0.54 127:0.39 129:0.81 133:0.67 135:0.54 137:0.91 139:0.82 140:0.45 145:0.89 146:0.70 147:0.75 149:0.28 150:0.74 151:0.60 152:0.92 153:0.45 154:0.10 155:0.67 158:0.75 159:0.49 161:0.86 162:0.57 164:0.84 165:0.93 167:0.73 169:0.91 172:0.57 173:0.61 175:0.91 176:0.73 177:0.05 179:0.66 181:0.74 186:0.88 187:0.68 191:0.92 192:0.87 195:0.75 196:0.92 201:0.93 202:0.86 204:0.89 208:0.64 212:0.29 215:0.41 216:0.87 220:0.87 222:0.48 230:0.87 232:0.85 233:0.76 235:0.80 238:0.93 241:0.72 242:0.82 243:0.75 244:0.83 245:0.52 247:0.12 248:0.64 253:0.76 255:0.95 256:1.00 257:0.84 259:0.21 260:0.68 261:0.93 265:0.61 266:0.54 267:0.65 268:0.85 271:0.88 276:0.48 277:0.87 281:0.91 282:0.88 284:0.97 285:0.62 290:0.60 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.83 12:0.37 17:0.97 18:0.79 21:0.30 27:0.72 28:0.57 29:0.71 30:0.28 34:0.89 36:0.92 39:0.84 43:0.80 45:0.66 64:0.77 66:0.54 69:0.72 70:0.76 71:0.77 74:0.67 76:0.85 81:0.57 83:0.91 85:0.80 86:0.85 91:0.14 98:0.80 101:0.87 104:0.58 108:0.65 114:0.65 122:0.57 123:0.63 124:0.63 126:0.54 127:0.83 129:0.59 133:0.66 135:0.47 139:0.81 145:0.97 146:0.20 147:0.18 149:0.75 150:0.76 154:0.74 155:0.67 159:0.47 161:0.86 165:0.93 167:0.53 172:0.59 173:0.78 176:0.73 177:0.38 178:0.55 179:0.66 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.42 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.32 242:0.13 243:0.18 245:0.68 247:0.86 253:0.50 257:0.65 259:0.21 265:0.80 266:0.54 267:0.18 271:0.88 276:0.58 290:0.65 297:0.36 300:0.55 +2 1:0.74 7:0.72 9:0.80 11:0.64 12:0.37 17:0.84 18:0.83 21:0.30 27:0.13 28:0.57 29:0.71 30:0.09 31:0.87 32:0.69 34:0.77 36:0.65 37:0.94 39:0.84 43:0.58 45:0.77 55:0.52 64:0.77 66:0.63 69:0.68 70:0.98 71:0.77 74:0.82 77:0.89 79:0.87 81:0.57 83:0.91 86:0.89 91:0.14 96:0.82 97:0.89 98:0.71 99:0.83 101:0.52 104:0.58 108:0.52 114:0.65 120:0.56 122:0.77 123:0.50 124:0.48 126:0.54 127:0.46 129:0.05 133:0.43 135:0.68 137:0.87 139:0.88 140:0.45 145:0.92 146:0.84 147:0.87 149:0.43 150:0.73 151:0.53 153:0.48 154:0.65 155:0.67 158:0.79 159:0.59 161:0.86 162:0.86 164:0.64 165:0.70 167:0.60 172:0.73 173:0.63 176:0.73 177:0.70 179:0.44 181:0.74 187:0.39 190:0.89 191:0.94 192:0.87 195:0.80 196:0.90 201:0.93 202:0.53 204:0.89 208:0.64 212:0.60 215:0.44 216:0.67 220:0.87 222:0.48 230:0.74 233:0.73 235:0.60 241:0.55 242:0.42 243:0.69 245:0.83 247:0.67 248:0.52 253:0.79 254:0.86 255:0.95 256:0.58 259:0.21 260:0.56 265:0.72 266:0.71 267:0.44 268:0.62 271:0.88 276:0.66 279:0.82 281:0.89 282:0.77 283:0.82 284:0.91 285:0.62 290:0.65 297:0.36 300:0.84 +2 1:0.74 7:0.66 11:0.64 12:0.37 17:0.97 18:0.67 27:0.72 28:0.57 29:0.71 30:0.45 32:0.80 34:0.42 36:0.54 39:0.84 43:0.71 44:0.93 45:0.66 55:0.84 60:0.87 64:0.77 66:0.52 69:0.27 70:0.50 71:0.77 74:0.73 76:0.98 77:0.96 81:0.83 83:0.91 86:0.70 91:0.54 97:0.97 98:0.29 101:0.52 104:0.58 108:0.21 114:0.98 122:0.77 123:0.20 124:0.50 126:0.54 127:0.95 129:0.05 133:0.39 135:0.37 139:0.87 145:0.58 146:0.42 147:0.49 149:0.40 150:0.89 154:0.61 155:0.67 158:0.92 159:0.68 161:0.86 165:0.93 167:0.79 172:0.27 173:0.22 175:0.75 176:0.73 177:0.38 178:0.55 179:0.94 181:0.74 192:0.87 195:0.82 201:0.93 204:0.85 208:0.64 212:0.23 215:0.96 216:0.01 219:0.95 222:0.48 230:0.69 232:0.77 233:0.73 235:0.12 241:0.77 242:0.73 243:0.61 244:0.61 245:0.48 247:0.35 253:0.67 257:0.65 259:0.21 265:0.31 266:0.38 267:0.44 271:0.88 276:0.17 277:0.69 279:0.86 281:0.91 282:0.88 283:0.82 290:0.98 292:0.95 297:0.36 299:0.88 300:0.33 +2 6:0.96 8:0.93 9:0.80 12:0.37 17:0.61 20:0.97 21:0.74 27:0.09 28:0.57 29:0.71 30:0.14 31:0.92 32:0.64 34:0.21 36:0.66 37:0.86 39:0.84 41:1.00 43:0.15 55:0.84 66:0.10 69:0.55 70:0.94 71:0.77 74:0.55 76:0.85 78:0.92 79:0.94 81:0.23 83:0.91 91:0.98 96:0.87 98:0.24 100:0.97 104:0.58 108:0.42 111:0.95 120:0.97 122:0.77 123:0.94 124:0.88 126:0.26 127:0.88 129:0.59 133:0.86 135:0.51 137:0.92 140:1.00 145:0.74 146:0.36 147:0.43 149:0.22 150:0.23 151:0.46 152:0.96 153:0.84 154:0.11 155:0.67 159:0.88 161:0.86 162:0.53 163:0.98 164:0.98 167:0.83 169:0.94 172:0.27 173:0.26 175:0.75 177:0.38 179:0.77 181:0.74 186:0.91 189:0.97 190:0.89 191:0.98 192:0.87 195:0.96 202:0.99 208:0.64 212:0.18 215:0.36 216:0.67 220:0.62 222:0.48 235:0.88 238:0.96 241:0.82 242:0.78 243:0.40 244:0.83 245:0.55 247:0.39 248:0.46 253:0.78 255:0.95 256:1.00 257:0.65 259:0.21 260:0.95 261:0.96 265:0.18 266:0.84 267:0.91 268:0.99 271:0.88 276:0.48 277:0.87 283:0.78 284:0.92 285:0.62 297:0.36 300:0.70 +2 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 12:0.37 17:0.78 20:0.96 21:0.47 27:0.04 28:0.57 29:0.71 30:0.28 31:0.91 32:0.80 34:0.76 36:0.65 37:0.94 39:0.84 41:0.92 43:0.14 44:0.93 55:0.45 64:0.77 66:0.79 69:0.61 70:0.65 71:0.77 74:0.79 76:0.99 77:0.96 78:0.89 79:0.91 81:0.52 83:0.91 91:0.65 96:0.78 98:0.77 100:0.93 108:0.46 111:0.90 114:0.60 120:0.59 121:0.90 122:0.77 123:0.45 124:0.38 126:0.54 127:0.35 129:0.59 133:0.47 135:0.65 137:0.91 139:0.82 140:0.45 145:0.89 146:0.81 147:0.85 149:0.29 150:0.74 151:0.56 152:0.92 153:0.48 154:0.10 155:0.67 158:0.75 159:0.45 161:0.86 162:0.60 163:0.81 164:0.78 165:0.93 167:0.77 169:0.91 172:0.61 173:0.60 175:0.91 176:0.73 177:0.05 179:0.61 181:0.74 186:0.89 187:0.39 189:0.98 190:0.77 191:0.76 192:0.87 195:0.74 196:0.92 201:0.93 202:0.77 204:0.89 208:0.64 212:0.37 215:0.41 216:0.87 220:0.87 222:0.48 230:0.87 232:0.85 233:0.76 235:0.80 238:0.92 241:0.76 242:0.82 243:0.80 244:0.83 245:0.53 247:0.12 248:0.58 253:0.73 255:0.95 256:0.75 257:0.84 259:0.21 260:0.59 261:0.93 265:0.77 266:0.47 267:0.82 268:0.77 271:0.88 276:0.51 277:0.87 281:0.91 282:0.88 284:0.96 285:0.62 290:0.60 297:0.36 299:0.88 300:0.43 +1 1:0.74 8:0.81 11:0.85 12:0.37 17:0.43 18:0.80 20:0.81 21:0.54 27:0.45 28:0.57 29:0.71 30:0.12 34:0.81 36:0.30 37:0.50 39:0.84 43:0.81 45:0.83 55:0.71 64:0.77 66:0.27 69:0.70 70:0.98 71:0.77 74:0.80 77:0.70 78:0.72 81:0.44 83:0.60 86:0.82 91:0.03 97:0.89 98:0.44 99:0.83 100:0.80 101:0.87 108:0.53 111:0.74 114:0.59 122:0.77 123:0.51 124:0.91 126:0.54 127:0.68 129:0.05 133:0.91 135:0.83 139:0.85 145:0.50 146:0.84 147:0.87 149:0.68 150:0.66 152:0.78 154:0.77 155:0.67 158:0.91 159:0.86 161:0.86 165:0.70 167:0.65 169:0.78 172:0.63 173:0.44 176:0.73 177:0.70 178:0.55 179:0.38 181:0.74 186:0.73 187:0.68 192:0.87 195:0.96 201:0.93 208:0.64 212:0.22 215:0.39 216:0.77 219:0.95 222:0.48 235:0.74 241:0.63 242:0.44 243:0.46 245:0.76 247:0.91 253:0.46 259:0.21 261:0.76 265:0.46 266:0.95 267:0.73 271:0.88 276:0.76 279:0.86 282:0.73 283:0.78 290:0.59 292:0.91 297:0.36 300:0.94 +2 1:0.74 9:0.80 11:0.57 12:0.37 17:0.96 18:0.85 25:0.88 27:0.72 28:0.57 29:0.71 30:0.66 31:0.99 32:0.69 34:0.21 36:0.55 39:0.84 41:0.94 43:0.97 60:0.95 64:0.77 66:0.10 69:0.07 70:0.70 71:0.77 74:0.84 77:0.98 79:0.98 81:0.83 83:0.91 85:0.90 86:0.94 87:0.98 91:0.70 96:0.94 98:0.31 101:0.87 104:0.54 108:0.06 114:0.98 120:0.90 122:0.57 123:0.90 124:0.72 126:0.54 127:0.99 129:0.59 133:0.66 135:0.75 137:0.99 139:0.94 140:0.45 145:0.49 146:0.37 147:0.43 149:0.90 150:0.89 151:0.97 153:0.90 154:0.97 155:0.67 158:0.91 159:0.80 161:0.86 162:0.86 164:0.80 165:0.93 167:0.81 172:0.54 173:0.09 176:0.73 177:0.70 179:0.60 181:0.74 192:0.87 195:0.89 196:0.99 201:0.93 202:0.71 208:0.64 212:0.74 215:0.96 216:0.08 219:0.95 222:0.48 228:0.99 232:0.74 235:0.12 241:0.79 242:0.28 243:0.50 245:0.78 247:0.74 248:0.94 253:0.96 255:0.95 256:0.79 259:0.21 260:0.90 265:0.22 266:0.71 267:0.79 268:0.78 271:0.88 276:0.44 277:0.69 279:0.86 282:0.77 283:0.78 284:0.97 285:0.62 290:0.98 292:0.91 297:0.36 300:0.70 +1 1:0.74 11:0.83 12:0.37 17:0.61 18:0.74 21:0.59 27:0.45 28:0.57 29:0.71 30:0.28 32:0.80 34:0.85 36:0.19 37:0.50 39:0.84 43:0.81 44:0.93 45:0.66 60:0.87 64:0.77 66:0.23 69:0.71 70:0.75 71:0.77 74:0.70 77:0.70 81:0.46 83:0.91 85:0.72 86:0.80 91:0.14 98:0.40 101:0.97 108:0.54 114:0.58 122:0.77 123:0.52 124:0.79 126:0.54 127:0.49 129:0.59 133:0.74 135:0.93 139:0.88 145:0.47 146:0.65 147:0.70 149:0.66 150:0.70 154:0.76 155:0.67 158:0.91 159:0.70 161:0.86 165:0.93 167:0.52 172:0.32 173:0.58 176:0.73 177:0.70 178:0.55 179:0.68 181:0.74 187:0.68 192:0.87 195:0.87 201:0.93 208:0.64 212:0.16 215:0.39 216:0.77 219:0.95 222:0.48 235:0.80 241:0.27 242:0.16 243:0.43 245:0.62 247:0.84 253:0.44 259:0.21 265:0.42 266:0.87 267:0.73 271:0.88 276:0.51 279:0.86 282:0.88 283:0.78 290:0.58 292:0.91 297:0.36 299:0.88 300:0.55 +0 9:0.80 11:0.83 12:0.37 17:0.90 18:0.92 20:0.76 21:0.78 25:0.88 27:0.72 28:0.57 29:0.71 30:0.85 31:0.98 34:0.68 36:0.30 39:0.84 41:0.94 43:0.96 45:0.83 66:0.46 69:0.05 70:0.43 71:0.77 74:0.86 78:0.72 79:0.97 83:0.91 85:0.72 86:0.95 87:0.98 91:0.80 96:0.92 98:0.34 100:0.87 101:0.97 104:0.73 108:0.05 111:0.79 120:0.87 122:0.76 123:0.05 124:0.76 127:0.99 129:0.59 133:0.74 135:0.91 137:0.98 138:0.98 139:0.94 140:0.45 146:0.59 147:0.65 149:0.92 151:0.98 152:0.75 153:0.94 154:0.96 155:0.67 158:0.78 159:0.78 161:0.86 162:0.82 164:0.85 167:0.84 169:0.84 172:0.67 173:0.06 177:0.70 179:0.52 181:0.74 186:0.73 192:0.87 196:0.99 202:0.81 208:0.64 212:0.73 216:0.07 219:0.92 222:0.48 228:0.99 232:0.70 241:0.83 242:0.16 243:0.59 245:0.77 247:0.88 248:0.93 253:0.94 255:0.95 256:0.85 259:0.21 260:0.86 261:0.75 265:0.37 266:0.75 267:0.87 268:0.86 271:0.88 276:0.61 279:0.86 283:0.78 284:0.98 285:0.62 292:0.91 300:0.55 +2 1:0.74 8:0.92 9:0.80 11:0.92 12:0.37 17:0.92 18:0.84 25:0.88 27:0.72 28:0.57 29:0.71 30:0.71 31:0.96 32:0.80 34:0.20 36:0.50 39:0.84 41:0.94 43:0.97 44:0.93 45:0.73 60:0.87 64:0.77 66:0.09 69:0.07 70:0.71 71:0.77 74:0.89 77:0.99 78:0.94 79:0.95 81:0.83 83:0.91 85:0.88 86:0.92 87:0.98 91:0.58 96:0.86 97:0.97 98:0.31 101:0.97 104:0.58 108:0.06 111:0.96 114:0.98 120:0.79 122:0.57 123:0.90 124:0.67 126:0.54 127:0.99 129:0.59 133:0.66 135:0.47 137:0.96 139:0.95 140:0.45 145:0.50 146:0.39 147:0.45 149:0.90 150:0.89 151:0.93 153:0.77 154:0.97 155:0.67 158:0.92 159:0.80 161:0.86 162:0.86 164:0.77 165:0.93 167:0.80 169:0.97 172:0.65 173:0.09 176:0.73 177:0.70 179:0.56 181:0.74 192:0.87 195:0.89 196:0.98 201:0.93 202:0.64 208:0.64 212:0.78 215:0.96 216:0.06 219:0.95 220:0.74 222:0.48 228:0.99 232:0.78 235:0.12 238:0.95 241:0.78 242:0.18 243:0.60 245:0.79 247:0.84 248:0.85 253:0.90 255:0.95 256:0.94 259:0.21 260:0.79 265:0.23 266:0.71 267:0.79 268:0.73 271:0.88 276:0.47 277:0.69 279:0.86 282:0.88 283:0.82 284:0.95 285:0.62 290:0.98 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.92 8:0.85 9:0.80 11:0.57 12:0.37 17:0.94 18:0.76 20:0.81 25:0.88 27:1.00 28:0.57 29:0.71 30:0.43 31:0.99 32:0.79 34:0.21 36:0.54 39:0.84 41:0.94 43:0.93 44:0.93 64:0.77 66:0.36 69:0.07 70:0.92 71:0.77 74:0.83 77:0.98 78:0.85 79:0.99 81:0.83 83:0.91 85:0.80 86:0.87 87:0.98 91:0.72 96:0.95 97:0.89 98:0.49 100:0.86 101:0.87 104:0.73 108:0.91 111:0.84 114:0.98 120:0.91 122:0.75 123:0.90 124:0.60 126:0.54 127:0.99 129:0.59 133:0.46 135:0.86 137:0.99 138:0.97 139:0.90 140:0.45 145:0.50 146:0.46 147:0.52 149:0.74 150:0.89 151:0.99 152:0.78 153:0.95 154:0.93 155:0.67 158:0.78 159:0.81 161:0.86 162:0.86 164:0.83 165:0.93 167:0.81 169:0.84 172:0.54 173:0.09 176:0.73 177:0.70 179:0.63 181:0.74 186:0.85 189:0.93 192:0.87 195:0.89 196:0.99 201:0.93 202:0.74 208:0.64 212:0.55 215:0.96 216:0.09 219:0.92 222:0.48 232:0.70 235:0.12 238:0.91 241:0.78 242:0.24 243:0.50 245:0.81 247:0.74 248:0.95 253:0.96 255:0.95 256:0.81 259:0.21 260:0.91 261:0.82 265:0.50 266:0.75 267:0.73 268:0.82 271:0.88 276:0.44 279:0.86 282:0.87 283:0.78 284:0.97 285:0.62 290:0.98 292:0.91 297:0.36 300:0.84 +1 1:0.74 11:0.83 12:0.37 17:0.96 18:0.77 21:0.30 27:0.72 28:0.57 29:0.71 30:0.33 34:0.87 36:0.76 39:0.84 43:0.80 45:0.66 55:0.71 64:0.77 66:0.63 69:0.72 70:0.78 71:0.77 74:0.71 76:0.94 81:0.57 83:0.91 85:0.80 86:0.83 91:0.20 98:0.80 101:0.87 104:0.58 108:0.65 114:0.65 122:0.77 123:0.63 124:0.52 126:0.54 127:0.74 129:0.59 133:0.66 135:0.51 139:0.79 145:0.90 146:0.20 147:0.18 149:0.76 150:0.76 154:0.74 155:0.67 159:0.50 161:0.86 165:0.93 167:0.55 172:0.70 173:0.76 176:0.73 177:0.38 178:0.55 179:0.55 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.54 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.39 242:0.45 243:0.15 245:0.72 247:0.88 253:0.50 257:0.65 259:0.21 265:0.80 266:0.54 267:0.19 271:0.88 276:0.65 290:0.65 297:0.36 300:0.55 +2 6:0.96 8:0.95 9:0.80 12:0.37 17:0.43 20:0.97 21:0.68 27:0.13 28:0.57 29:0.71 30:0.76 31:0.95 34:0.26 36:0.60 37:0.70 39:0.84 41:1.00 43:0.08 44:0.90 48:0.91 55:0.71 66:0.36 69:0.91 70:0.15 71:0.77 74:0.48 76:0.85 77:0.89 78:0.91 79:0.95 81:0.23 83:0.91 91:0.98 96:0.98 98:0.19 100:0.96 108:0.82 111:0.93 114:0.55 120:0.83 122:0.77 123:0.81 124:0.52 126:0.26 127:0.14 129:0.59 133:0.47 135:0.44 137:0.95 139:0.72 140:0.99 145:0.86 146:0.28 147:0.34 149:0.08 150:0.23 151:0.75 152:0.89 153:0.91 154:0.07 155:0.67 159:0.22 161:0.86 162:0.54 163:0.99 164:0.90 167:0.86 169:0.94 172:0.10 173:0.95 175:0.91 176:0.61 177:0.05 178:0.48 179:0.83 181:0.74 186:0.91 189:0.97 191:0.98 192:0.87 195:0.80 196:0.91 202:0.97 208:0.64 212:0.95 216:0.73 220:0.62 222:0.48 232:0.72 235:0.80 238:0.94 241:0.96 242:0.82 243:0.51 244:0.83 245:0.37 247:0.12 248:0.76 253:0.95 255:0.95 256:1.00 257:0.84 259:0.21 260:0.80 261:0.95 265:0.21 266:0.12 267:0.65 268:0.97 271:0.88 276:0.16 277:0.87 281:0.91 282:0.77 284:0.97 285:0.62 290:0.55 300:0.13 +1 1:0.74 11:0.83 12:0.37 17:0.96 18:0.81 21:0.30 27:0.72 28:0.57 29:0.71 30:0.14 34:0.84 36:0.88 39:0.84 43:0.80 45:0.66 64:0.77 66:0.62 69:0.72 70:0.84 71:0.77 74:0.68 81:0.57 83:0.91 85:0.80 86:0.86 91:0.33 98:0.80 101:0.87 104:0.58 108:0.65 114:0.65 122:0.77 123:0.63 124:0.55 126:0.54 127:0.67 129:0.59 133:0.66 135:0.26 139:0.82 145:0.95 146:0.20 147:0.18 149:0.75 150:0.76 154:0.74 155:0.67 159:0.51 161:0.86 165:0.93 167:0.56 172:0.67 173:0.75 176:0.73 177:0.38 178:0.55 179:0.57 181:0.74 187:0.21 192:0.87 201:0.93 202:0.36 208:0.64 212:0.49 215:0.44 216:0.15 222:0.48 228:0.99 232:0.77 235:0.52 241:0.43 242:0.16 243:0.15 245:0.71 247:0.90 253:0.50 257:0.65 259:0.21 265:0.80 266:0.54 267:0.28 271:0.88 276:0.63 290:0.65 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.37 17:0.72 21:0.13 27:0.45 28:0.85 30:0.19 34:0.64 36:0.19 37:0.50 39:0.74 43:0.82 66:0.35 69:0.68 70:0.80 74:0.79 81:0.80 83:0.77 85:0.85 91:0.14 98:0.39 101:0.76 108:0.82 114:0.92 122:0.57 123:0.81 124:0.72 126:0.54 127:0.41 129:0.81 133:0.67 135:0.83 145:0.49 146:0.31 147:0.38 149:0.75 150:0.87 154:0.77 155:0.67 159:0.55 161:0.54 165:0.88 167:0.53 172:0.45 173:0.64 176:0.73 177:0.38 178:0.55 179:0.60 181:0.82 187:0.68 192:0.59 201:0.74 212:0.57 215:0.84 216:0.77 222:0.48 235:0.22 241:0.24 242:0.33 243:0.36 245:0.69 247:0.60 253:0.75 259:0.21 265:0.41 266:0.63 267:0.36 271:0.88 276:0.51 290:0.92 297:0.36 300:0.55 +2 1:0.74 7:0.78 8:0.79 9:0.80 12:0.37 17:0.75 20:0.75 21:0.71 27:1.00 28:0.85 30:0.28 34:0.50 36:0.43 37:0.70 39:0.74 41:0.99 43:0.43 55:0.59 66:0.29 69:0.76 70:0.70 74:0.94 76:0.99 77:0.89 78:0.88 81:0.47 83:0.90 87:0.98 91:0.95 96:1.00 98:0.59 100:0.73 108:0.59 111:0.89 114:0.68 120:0.97 122:0.67 123:0.57 124:0.79 126:0.54 127:0.85 129:0.05 133:0.74 135:0.42 140:0.45 145:0.99 146:0.68 147:0.33 149:0.54 150:0.65 151:1.00 152:0.74 153:0.98 154:0.60 155:0.67 158:0.84 159:0.58 161:0.54 162:0.84 164:0.95 165:0.69 167:0.88 169:0.90 172:0.37 173:0.76 176:0.73 177:0.05 179:0.74 181:0.82 186:0.83 191:0.78 192:0.59 195:0.74 201:0.74 202:0.96 208:0.64 212:0.48 215:0.48 216:0.38 222:0.48 230:0.81 232:0.72 233:0.69 235:0.88 238:0.90 241:0.96 242:0.78 243:0.33 245:0.62 247:0.39 248:0.99 253:1.00 254:0.80 255:0.79 256:0.98 257:0.65 260:0.97 261:0.75 265:0.60 266:0.75 267:0.89 268:0.98 271:0.88 276:0.51 282:0.84 285:0.62 290:0.68 297:0.36 300:0.43 +0 1:0.74 8:0.63 11:0.57 12:0.37 17:0.62 20:0.76 21:0.54 27:0.45 28:0.85 30:0.09 32:0.80 34:0.91 36:0.28 37:0.50 39:0.74 43:0.82 55:0.71 60:0.87 66:0.53 69:0.69 70:0.99 74:0.95 77:0.70 78:0.77 81:0.57 83:0.84 85:0.80 91:0.14 98:0.46 100:0.73 101:0.72 108:0.53 111:0.76 114:0.77 122:0.67 123:0.50 124:0.84 126:0.54 127:0.56 129:0.81 133:0.89 135:0.88 145:0.44 146:0.77 147:0.80 149:0.77 150:0.73 152:0.75 154:0.77 155:0.67 158:0.87 159:0.77 161:0.54 165:0.79 167:0.53 169:0.75 172:0.68 173:0.52 176:0.73 177:0.38 178:0.55 179:0.47 181:0.82 186:0.78 187:0.68 192:0.59 195:0.90 201:0.74 208:0.64 212:0.50 215:0.58 216:0.77 222:0.48 235:0.68 241:0.21 242:0.74 243:0.62 245:0.65 247:0.67 253:0.75 259:0.21 261:0.75 265:0.48 266:0.92 267:0.82 271:0.88 276:0.68 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.94 +1 1:0.74 7:0.81 8:0.94 9:0.80 11:0.57 12:0.37 17:0.85 21:0.30 27:0.47 28:0.85 30:0.94 32:0.80 34:0.50 36:0.57 37:0.79 39:0.74 41:1.00 43:0.57 55:0.45 66:0.77 69:0.94 70:0.23 74:0.98 76:0.85 77:0.98 81:0.74 83:0.82 85:0.85 91:0.94 96:0.97 98:0.47 101:0.74 108:0.88 114:0.68 117:0.86 120:0.88 121:0.90 122:0.67 123:0.88 124:0.43 126:0.54 127:0.78 129:0.59 133:0.64 135:0.42 138:0.97 140:0.92 145:0.82 146:0.77 147:0.46 149:0.77 150:0.83 151:0.77 153:0.98 154:0.46 155:0.67 158:0.85 159:0.79 161:0.54 162:0.81 164:0.90 165:0.81 167:0.69 172:0.92 173:0.94 176:0.56 177:0.05 179:0.26 181:0.82 187:0.21 190:0.77 191:0.88 192:0.59 195:0.90 201:0.74 202:0.89 204:0.89 208:0.64 212:0.88 215:0.67 216:0.65 222:0.48 230:0.84 232:0.82 233:0.69 235:0.60 241:0.66 242:0.80 243:0.08 245:0.87 247:0.47 248:0.94 253:0.98 255:0.79 256:1.00 257:0.65 260:0.89 265:0.49 266:0.75 267:0.87 268:0.93 271:0.88 276:0.78 282:0.88 285:0.62 290:0.68 297:0.36 299:0.88 300:0.84 +3 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 11:0.57 12:0.37 17:0.96 20:0.85 21:0.30 27:0.05 28:0.85 30:0.94 34:0.25 36:0.45 37:0.94 39:0.74 41:1.00 43:0.31 55:0.45 66:0.06 69:0.96 70:0.22 74:0.98 76:0.94 77:0.70 78:0.86 81:0.74 83:0.82 85:0.72 91:0.73 96:0.97 98:0.38 100:0.89 101:0.69 108:0.92 111:0.86 114:0.86 117:0.86 120:0.88 121:0.90 122:0.67 123:0.77 124:0.51 126:0.54 127:0.67 129:0.05 133:0.82 135:0.37 140:0.87 145:0.67 146:0.69 147:0.33 149:0.71 150:0.83 151:0.69 152:0.93 153:0.91 154:0.22 155:0.67 158:0.85 159:0.81 161:0.54 162:0.77 164:0.89 165:0.84 167:0.61 169:0.87 172:0.92 173:0.97 176:0.73 177:0.05 179:0.24 181:0.82 186:0.86 187:0.21 189:0.98 190:0.77 191:0.85 192:0.59 195:0.93 201:0.74 202:0.86 204:0.89 208:0.64 212:0.86 215:0.72 216:0.72 222:0.48 230:0.87 232:0.83 233:0.76 235:0.52 238:0.90 241:0.52 242:0.81 243:0.08 245:0.83 247:0.47 248:0.93 253:0.98 255:0.79 256:1.00 257:0.65 259:0.21 260:0.88 261:0.91 265:0.39 266:0.87 267:0.79 268:0.90 271:0.88 276:0.84 277:0.69 282:0.84 285:0.62 290:0.86 297:0.36 300:0.84 +1 1:0.74 6:0.92 7:0.81 8:0.84 9:0.80 12:0.37 17:0.57 20:0.85 21:0.40 27:0.59 28:0.85 30:0.32 34:0.93 36:0.59 37:0.60 39:0.74 41:0.97 43:0.38 55:0.45 66:0.36 69:0.49 70:0.61 74:0.93 76:0.97 77:0.70 78:0.80 81:0.70 83:0.88 91:0.80 96:0.98 98:0.22 100:0.81 108:0.88 111:0.79 114:0.66 117:0.86 120:0.93 121:0.90 122:0.67 123:0.88 124:0.77 126:0.54 127:0.70 129:0.89 133:0.78 135:0.54 138:0.97 140:0.45 145:0.89 146:0.33 147:0.40 149:0.52 150:0.80 151:0.99 152:0.80 153:0.96 154:0.28 155:0.67 158:0.85 159:0.69 161:0.54 162:0.75 164:0.88 165:0.80 167:0.70 169:0.79 172:0.48 173:0.37 175:0.75 176:0.56 177:0.05 179:0.66 181:0.82 186:0.81 187:0.39 189:0.94 191:0.79 192:0.59 195:0.84 201:0.74 202:0.88 204:0.89 208:0.64 212:0.59 215:0.63 216:0.57 222:0.48 230:0.87 232:0.81 233:0.76 235:0.68 238:0.84 241:0.67 242:0.82 243:0.29 244:0.61 245:0.66 247:0.12 248:0.96 253:0.99 255:0.79 256:0.91 257:0.65 259:0.21 260:0.93 261:0.81 265:0.24 266:0.63 267:0.85 268:0.91 271:0.88 276:0.52 277:0.69 282:0.84 285:0.62 290:0.66 297:0.36 300:0.43 +2 6:0.97 8:0.96 9:0.80 12:0.37 17:0.36 20:0.88 21:0.78 27:0.05 28:0.85 34:0.52 36:0.28 37:0.94 39:0.74 41:1.00 78:0.88 83:0.82 91:0.99 96:0.97 100:0.91 111:0.88 120:0.95 135:0.86 138:0.99 140:0.95 151:0.78 152:0.93 153:0.78 155:0.67 161:0.54 162:0.49 164:0.98 167:0.87 169:0.87 175:0.91 178:0.53 181:0.82 186:0.88 189:0.98 191:0.98 192:0.59 202:0.99 208:0.64 216:0.72 220:0.96 222:0.48 238:0.91 241:0.89 244:0.83 248:0.95 253:0.95 255:0.79 256:1.00 257:0.84 259:0.21 260:0.96 261:0.91 267:0.95 268:0.98 271:0.88 277:0.87 285:0.62 +2 1:0.74 6:0.96 7:0.81 8:0.95 9:0.80 11:0.57 12:0.37 17:0.96 20:0.86 21:0.68 27:0.09 28:0.85 30:0.85 32:0.80 34:0.50 36:0.74 37:0.86 39:0.74 41:1.00 43:0.85 55:0.45 60:0.87 66:0.82 69:0.44 70:0.28 74:0.95 77:0.96 78:0.85 81:0.58 83:0.89 85:0.85 91:0.69 96:0.96 98:0.34 100:0.90 101:0.72 104:0.58 106:0.81 108:0.95 111:0.86 114:0.70 117:0.86 120:0.89 121:0.97 122:0.43 123:0.95 124:0.44 126:0.54 127:0.87 129:0.59 133:0.86 135:0.81 140:0.45 145:0.69 146:0.37 147:0.43 149:0.65 150:0.73 151:0.98 152:0.98 153:0.92 154:0.81 155:0.67 158:0.92 159:0.89 161:0.54 162:0.85 164:0.86 165:0.79 167:0.63 169:0.91 172:0.97 173:0.17 176:0.73 177:0.38 179:0.16 181:0.82 186:0.85 187:0.21 189:0.98 191:0.83 192:0.59 195:0.97 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.91 215:0.49 216:0.67 222:0.48 230:0.87 232:0.88 233:0.76 235:0.97 238:0.92 241:0.56 242:0.61 243:0.09 245:0.87 247:0.67 248:0.94 253:0.97 255:0.79 256:1.00 259:0.21 260:0.90 261:0.95 265:0.37 266:0.87 267:0.88 268:0.86 271:0.88 276:0.93 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.89 7:0.81 8:0.81 9:0.80 11:0.57 12:0.37 17:0.85 20:0.77 21:0.47 27:0.61 28:0.85 30:0.56 32:0.80 34:0.86 36:0.77 37:0.57 39:0.74 41:0.99 43:0.97 60:0.97 66:0.27 69:0.21 70:0.73 74:0.96 77:0.89 78:0.89 81:0.65 83:0.87 85:0.96 91:0.83 96:0.97 98:0.68 100:0.89 101:0.86 104:0.58 108:0.72 111:0.91 114:0.80 120:0.95 121:0.90 122:0.57 123:0.16 124:0.58 126:0.54 127:0.94 129:0.59 132:0.97 133:0.47 135:0.66 140:0.80 145:0.70 146:0.67 147:0.72 149:0.95 150:0.78 151:0.98 152:0.75 153:0.92 154:0.97 155:0.67 158:0.92 159:0.48 161:0.54 162:0.79 164:0.95 165:0.80 167:0.85 169:0.92 172:0.70 173:0.30 176:0.73 177:0.38 178:0.42 179:0.48 181:0.82 186:0.91 189:0.90 191:0.73 192:0.59 195:0.66 201:0.74 202:0.91 204:0.89 208:0.64 212:0.82 215:0.63 216:0.33 220:0.93 222:0.48 230:0.87 233:0.76 235:0.68 238:0.93 241:0.83 242:0.44 243:0.46 245:0.89 247:0.67 248:0.96 253:0.98 255:0.79 256:0.95 260:0.95 261:0.79 265:0.68 266:0.54 267:0.44 268:0.94 271:0.88 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.96 8:0.93 9:0.80 12:0.37 17:0.61 20:0.93 21:0.71 27:0.09 28:0.85 30:0.45 32:0.80 34:0.21 36:0.66 37:0.86 39:0.74 41:1.00 43:0.40 55:0.71 66:0.27 69:0.43 70:0.25 74:0.66 76:0.85 77:0.70 78:0.90 81:0.44 83:0.82 91:0.98 96:0.97 98:0.19 100:0.94 104:0.58 108:0.33 111:0.91 114:0.68 120:0.99 123:0.95 124:0.61 126:0.54 127:0.91 129:0.05 133:0.39 135:0.51 138:0.99 140:0.93 145:0.74 146:0.27 147:0.33 149:0.24 150:0.61 151:0.73 152:0.98 153:0.84 154:0.31 155:0.67 159:0.89 161:0.54 162:0.53 163:0.90 164:0.99 167:0.85 169:0.91 172:0.10 173:0.17 176:0.73 177:0.38 179:0.98 181:0.82 186:0.90 189:0.97 190:0.77 191:0.98 192:0.59 195:0.96 201:0.74 202:0.99 208:0.64 212:0.61 215:0.48 216:0.67 220:0.62 222:0.48 235:0.88 238:0.93 241:0.82 242:0.63 243:0.46 244:0.61 245:0.40 247:0.30 248:0.95 253:0.96 255:0.79 256:1.00 259:0.21 260:0.99 261:0.95 265:0.14 266:0.17 267:0.91 268:0.99 271:0.88 276:0.13 277:0.69 282:0.88 283:0.71 285:0.62 290:0.68 297:0.36 299:0.88 300:0.18 +3 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 12:0.37 17:0.78 20:0.90 21:0.59 27:0.04 28:0.85 30:0.88 32:0.80 34:0.76 36:0.65 37:0.94 39:0.74 41:0.92 43:0.38 55:0.45 66:0.67 69:0.50 70:0.36 74:0.97 77:0.70 78:0.86 81:0.58 83:0.74 91:0.65 96:0.87 98:0.55 100:0.88 108:0.66 111:0.85 114:0.74 117:0.86 120:0.59 121:0.99 122:0.67 123:0.63 124:0.55 126:0.54 127:0.61 129:0.89 133:0.78 135:0.65 140:0.45 145:0.89 146:0.05 147:0.05 149:0.65 150:0.72 151:0.56 152:0.85 153:0.69 154:0.29 155:0.67 158:0.85 159:0.53 161:0.54 162:0.59 164:0.78 165:0.78 167:0.78 169:0.85 172:0.70 173:0.48 175:0.75 176:0.73 177:0.05 179:0.54 181:0.82 186:0.86 187:0.39 189:0.98 190:0.77 191:0.76 192:0.59 195:0.74 201:0.74 202:0.77 204:0.89 208:0.64 212:0.74 215:0.55 216:0.87 220:0.93 222:0.48 230:0.87 232:0.85 233:0.76 235:0.80 238:0.89 241:0.76 242:0.82 243:0.09 244:0.61 245:0.64 247:0.12 248:0.58 253:0.91 255:0.79 256:0.75 257:0.65 259:0.21 260:0.60 261:0.88 265:0.57 266:0.71 267:0.82 268:0.77 271:0.88 276:0.63 277:0.69 282:0.88 285:0.62 290:0.74 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.89 7:0.81 8:0.84 9:0.80 11:0.57 12:0.37 17:0.97 20:0.80 21:0.22 27:0.63 28:0.85 30:0.41 32:0.80 34:0.26 36:0.77 37:0.85 39:0.74 41:0.98 43:0.93 60:0.95 66:0.54 69:0.32 70:0.60 74:0.93 77:0.89 78:0.78 81:0.74 83:0.89 85:0.88 91:0.88 96:0.96 98:0.53 100:0.80 101:0.81 104:0.58 108:0.25 111:0.78 114:0.90 120:0.93 121:0.90 122:0.67 123:0.24 124:0.50 126:0.54 127:0.86 129:0.59 133:0.47 135:0.21 140:0.45 145:0.76 146:0.50 147:0.56 149:0.81 150:0.84 151:0.99 152:0.77 153:0.96 154:0.93 155:0.67 158:0.92 159:0.43 161:0.54 162:0.84 164:0.90 165:0.86 167:0.86 169:0.79 172:0.48 173:0.40 176:0.73 177:0.38 179:0.78 181:0.82 186:0.79 189:0.90 191:0.78 192:0.59 195:0.61 201:0.74 202:0.83 204:0.89 208:0.64 212:0.78 215:0.79 216:0.11 222:0.48 230:0.87 232:0.77 233:0.76 235:0.31 238:0.88 241:0.86 242:0.45 243:0.63 245:0.66 247:0.47 248:0.96 253:0.97 255:0.79 256:0.86 259:0.21 260:0.93 261:0.78 265:0.55 266:0.38 267:0.17 268:0.88 271:0.88 276:0.40 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.43 +2 6:0.96 8:0.94 9:0.80 12:0.37 17:0.78 20:0.74 21:0.47 27:0.24 28:0.85 30:0.37 34:0.79 36:0.71 37:0.77 39:0.74 41:0.97 43:0.75 55:0.42 60:0.87 66:0.80 69:0.77 70:0.50 74:0.92 78:0.99 81:0.37 83:0.79 91:0.87 96:0.98 98:0.69 100:0.98 108:0.60 111:0.98 114:0.66 117:0.86 120:0.81 122:0.67 123:0.58 124:0.38 126:0.32 127:0.40 129:0.05 133:0.39 135:0.76 138:0.98 140:0.96 146:0.68 147:0.05 149:0.66 150:0.33 151:0.82 152:0.74 153:0.88 154:0.66 155:0.67 158:0.92 159:0.39 161:0.54 162:0.82 164:0.90 167:0.73 169:0.96 172:0.65 173:0.84 176:0.56 177:0.05 179:0.60 181:0.82 186:1.00 187:0.21 189:0.97 191:0.93 192:0.59 202:0.93 208:0.64 212:0.84 216:0.64 220:0.62 222:0.48 232:0.83 235:0.52 238:0.80 241:0.71 242:0.79 243:0.11 245:0.54 247:0.39 248:0.89 253:0.99 255:0.79 256:0.96 257:0.65 259:0.21 260:0.82 261:0.77 265:0.70 266:0.38 267:0.52 268:0.96 271:0.88 276:0.51 279:0.86 283:0.82 285:0.62 290:0.66 300:0.43 +2 1:0.74 6:0.95 8:0.93 9:0.80 12:0.37 17:0.51 20:0.81 21:0.59 27:0.07 28:0.85 34:0.39 36:0.79 37:0.88 39:0.74 41:1.00 43:0.41 66:0.36 69:0.39 74:0.65 76:0.85 77:0.70 78:0.85 81:0.51 83:0.82 91:0.98 96:0.98 98:0.06 100:0.86 108:0.30 111:0.84 114:0.74 120:0.96 123:0.29 126:0.54 127:0.05 129:0.05 135:0.58 138:0.99 140:0.90 145:0.77 146:0.05 147:0.05 149:0.14 150:0.62 151:0.92 152:0.80 153:0.83 154:0.31 155:0.67 159:0.05 161:0.54 162:0.49 164:0.98 167:0.85 169:0.83 172:0.05 173:0.15 175:0.75 176:0.73 177:0.05 178:0.50 181:0.82 186:0.85 189:0.96 191:0.98 192:0.59 195:0.96 201:0.74 202:0.99 208:0.64 215:0.55 216:0.81 220:0.93 222:0.48 235:0.74 238:0.88 241:0.83 243:0.51 244:0.61 248:0.95 253:0.97 255:0.79 256:1.00 257:0.65 259:0.21 260:0.96 261:0.83 265:0.06 267:0.82 268:0.98 271:0.88 277:0.69 282:0.84 285:0.62 290:0.74 297:0.36 +2 1:0.74 6:0.97 7:0.81 8:0.97 9:0.80 11:0.57 12:0.37 17:0.93 20:0.86 21:0.47 27:0.05 28:0.85 30:0.94 32:0.80 34:0.24 36:0.46 37:0.94 39:0.74 41:1.00 43:0.31 55:0.45 66:0.06 69:0.96 70:0.24 74:0.98 76:0.97 77:0.70 78:0.85 81:0.68 83:0.84 85:0.72 91:0.71 96:0.97 98:0.39 100:0.88 101:0.69 108:0.92 111:0.85 114:0.80 117:0.86 120:0.91 121:0.97 122:0.67 123:0.77 124:0.52 126:0.54 127:0.67 129:0.05 133:0.82 135:0.37 138:0.98 140:0.80 145:0.84 146:0.69 147:0.33 149:0.71 150:0.79 151:0.87 152:0.93 153:0.93 154:0.22 155:0.67 158:0.85 159:0.81 161:0.54 162:0.77 164:0.94 165:0.79 167:0.65 169:0.87 172:0.92 173:0.97 176:0.73 177:0.05 179:0.24 181:0.82 186:0.85 187:0.39 189:0.98 191:0.90 192:0.59 195:0.93 201:0.74 202:0.90 204:0.89 208:0.64 212:0.86 215:0.63 216:0.72 222:0.48 230:0.87 232:0.83 233:0.76 235:0.74 238:0.89 241:0.59 242:0.81 243:0.08 245:0.84 247:0.47 248:0.95 253:0.98 255:0.79 256:1.00 257:0.65 259:0.21 260:0.91 261:0.91 265:0.39 266:0.87 267:0.84 268:0.93 271:0.88 276:0.85 277:0.69 282:0.88 285:0.62 290:0.80 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.94 7:0.81 8:0.89 9:0.80 11:0.57 12:0.37 17:0.85 20:0.76 21:0.47 27:0.29 28:0.85 30:0.13 32:0.80 34:0.40 36:0.95 37:0.79 39:0.74 41:0.98 43:0.98 55:0.52 60:0.98 66:0.24 69:0.17 70:0.97 74:0.99 76:0.94 77:0.96 78:0.81 81:0.61 83:0.88 85:0.97 87:0.98 91:0.66 96:0.96 98:0.56 100:0.90 101:0.82 104:0.82 106:0.81 108:0.14 111:0.81 114:0.80 117:0.86 120:0.91 121:0.97 122:0.67 123:0.91 124:0.82 126:0.54 127:0.98 129:0.81 132:0.97 133:0.86 135:0.76 138:0.97 140:0.45 145:0.70 146:0.83 147:0.86 149:0.96 150:0.76 151:0.89 152:0.75 153:0.48 154:0.98 155:0.67 158:0.92 159:0.84 161:0.54 162:0.86 164:0.94 165:0.80 167:0.63 169:0.80 172:0.87 173:0.11 176:0.73 177:0.38 179:0.24 181:0.82 186:0.91 187:0.21 189:0.95 191:0.77 192:0.59 195:0.93 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.72 215:0.63 216:0.60 220:0.93 222:0.48 230:0.87 232:0.76 233:0.76 235:0.60 238:0.90 241:0.56 242:0.60 243:0.58 245:0.90 247:0.60 248:0.96 253:0.98 255:0.79 256:0.93 259:0.21 260:0.92 261:0.78 265:0.52 266:0.89 267:0.79 268:0.94 271:0.88 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.97 300:0.94 +3 6:0.96 7:0.81 8:0.95 9:0.80 12:0.37 17:0.84 20:0.94 21:0.78 27:0.06 28:0.85 30:0.37 32:0.75 34:0.68 36:0.81 37:0.91 39:0.74 41:0.94 43:0.40 55:0.71 66:0.35 69:0.42 70:0.94 74:0.81 78:0.90 83:0.74 91:0.97 96:0.89 98:0.18 100:0.93 108:0.97 111:0.90 120:0.88 121:0.90 122:0.67 123:0.96 124:0.89 127:0.93 129:0.96 133:0.90 135:0.87 140:0.99 145:0.72 146:0.25 147:0.31 149:0.39 151:0.69 152:0.88 153:0.48 154:0.31 155:0.67 159:0.92 161:0.54 162:0.48 164:0.93 167:0.88 169:0.91 172:0.45 173:0.13 175:0.75 177:0.05 178:0.53 179:0.71 181:0.82 186:0.90 189:0.97 190:0.77 191:0.98 192:0.59 195:0.98 202:0.96 204:0.89 208:0.64 212:0.50 216:0.71 220:0.97 222:0.48 230:0.87 233:0.76 235:0.84 238:0.91 241:0.95 242:0.82 243:0.19 244:0.61 245:0.56 247:0.12 248:0.74 253:0.90 255:0.79 256:0.93 257:0.65 259:0.21 260:0.88 261:0.93 265:0.20 266:0.80 267:0.76 268:0.92 271:0.88 276:0.46 277:0.69 283:0.71 285:0.62 300:0.84 +3 6:0.96 8:0.95 9:0.80 12:0.37 17:0.43 20:0.98 21:0.64 27:0.13 28:0.85 30:0.84 34:0.26 36:0.60 37:0.70 39:0.74 41:1.00 43:0.39 55:0.45 66:0.82 69:0.45 70:0.37 74:0.98 76:1.00 77:0.96 78:0.93 81:0.36 83:0.84 87:0.98 91:0.98 96:1.00 98:0.58 100:0.98 108:0.83 111:0.96 114:0.63 120:0.91 122:0.67 123:0.82 124:0.39 126:0.32 127:0.90 129:0.81 133:0.67 135:0.44 138:0.99 140:0.94 145:0.86 146:0.20 147:0.25 149:0.70 150:0.32 151:0.77 152:0.90 153:0.99 154:0.30 155:0.67 158:0.85 159:0.64 161:0.54 162:0.71 164:0.93 167:0.88 169:0.97 172:0.86 173:0.39 175:0.75 176:0.56 177:0.05 179:0.40 181:0.82 186:0.93 189:0.97 191:0.98 192:0.59 195:0.80 202:0.97 208:0.64 212:0.84 216:0.73 220:0.62 222:0.48 232:0.72 235:0.80 238:0.97 241:0.96 242:0.82 243:0.11 244:0.61 245:0.71 247:0.12 248:0.96 253:1.00 255:0.79 256:1.00 257:0.65 259:0.21 260:0.92 261:0.97 265:0.59 266:0.63 267:0.65 268:0.98 271:0.88 276:0.70 277:0.69 282:0.84 285:0.62 290:0.63 300:0.70 +2 1:0.74 6:0.96 7:0.81 8:0.90 9:0.80 11:0.57 12:0.37 17:0.96 20:0.91 21:0.30 27:0.09 28:0.85 30:0.44 32:0.80 34:0.61 36:0.80 37:0.86 39:0.74 41:0.96 43:0.60 55:0.45 60:0.87 66:0.30 69:0.37 70:0.52 74:0.99 76:0.94 77:0.70 78:0.86 81:0.69 83:0.81 85:0.72 91:0.77 96:0.93 98:0.60 100:0.91 101:0.71 104:0.58 108:0.85 111:0.87 114:0.86 120:0.82 121:0.99 122:0.67 123:0.67 124:0.65 126:0.54 127:0.63 129:0.59 133:0.64 135:0.75 138:0.97 140:0.80 145:0.77 146:0.27 147:0.33 149:0.84 150:0.81 151:0.77 152:0.98 153:0.48 154:0.49 155:0.67 158:0.92 159:0.67 161:0.54 162:0.81 164:0.90 165:0.84 167:0.56 169:0.91 172:0.90 173:0.27 176:0.73 177:0.38 179:0.18 181:0.82 186:0.86 187:0.39 189:0.96 190:0.77 191:0.91 192:0.59 195:0.84 201:0.74 202:0.85 204:0.89 208:0.64 212:0.93 215:0.72 216:0.67 220:0.62 222:0.48 230:0.87 232:0.90 233:0.76 235:0.42 238:0.92 241:0.39 242:0.58 243:0.07 245:0.97 247:0.67 248:0.89 253:0.96 255:0.79 256:0.89 259:0.21 260:0.83 261:0.95 265:0.45 266:0.54 267:0.44 268:0.90 271:0.88 276:0.91 277:0.69 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.78 8:0.98 9:0.80 12:0.37 17:0.34 21:0.54 27:0.04 28:0.85 30:0.93 34:0.55 36:0.38 37:0.94 39:0.74 41:1.00 43:0.38 55:0.45 66:0.79 69:0.49 70:0.27 74:0.99 76:0.85 78:0.92 81:0.54 83:0.82 91:0.99 96:0.98 98:0.69 108:0.78 111:0.92 114:0.77 120:0.94 122:0.67 123:0.77 124:0.41 126:0.54 127:0.84 129:0.81 133:0.67 135:0.32 138:1.00 140:0.93 145:0.70 146:0.05 147:0.05 149:0.80 150:0.64 151:0.77 153:0.80 154:0.29 155:0.67 159:0.60 161:0.54 162:0.47 164:0.96 167:0.89 169:0.90 172:0.91 173:0.44 175:0.75 176:0.73 177:0.05 178:0.52 179:0.29 181:0.82 190:0.77 191:0.99 192:0.59 201:0.74 202:0.99 208:0.64 212:0.89 215:0.58 216:0.87 220:0.96 222:0.48 230:0.81 233:0.69 235:0.68 241:1.00 242:0.82 243:0.06 244:0.61 245:0.83 247:0.12 248:0.95 253:0.99 255:0.79 256:1.00 257:0.65 259:0.21 260:0.94 265:0.70 266:0.63 267:0.59 268:0.97 271:0.88 276:0.82 277:0.69 285:0.62 290:0.77 297:0.36 300:0.70 +2 1:0.74 6:0.95 7:0.81 8:0.88 9:0.80 11:0.57 12:0.37 17:0.59 20:0.91 21:0.30 27:0.39 28:0.85 30:0.14 32:0.80 34:0.49 36:0.78 37:0.71 39:0.74 41:0.99 43:0.80 60:0.87 66:0.11 69:0.73 70:0.95 74:0.92 77:0.89 78:0.81 81:0.69 83:0.89 85:0.94 91:0.93 96:0.97 98:0.42 100:0.82 101:0.79 108:0.90 111:0.80 114:0.86 120:0.94 121:0.90 122:0.57 123:0.92 124:0.80 126:0.54 127:0.91 129:0.89 133:0.83 135:0.44 138:0.97 140:0.90 145:0.69 146:0.28 147:0.37 149:0.81 150:0.81 151:0.99 152:0.85 153:0.94 154:0.74 155:0.67 158:0.87 159:0.84 161:0.54 162:0.52 164:0.95 165:0.84 167:0.86 169:0.80 172:0.73 173:0.53 176:0.73 177:0.05 178:0.50 179:0.43 181:0.82 186:0.81 189:0.96 191:0.84 192:0.59 195:0.93 201:0.74 202:0.96 204:0.89 208:0.64 212:0.51 215:0.72 216:0.49 220:0.87 222:0.48 230:0.84 233:0.69 235:0.42 238:0.86 241:0.84 242:0.29 243:0.11 245:0.79 247:0.67 248:0.97 253:0.98 255:0.79 256:0.95 257:0.65 259:0.21 260:0.94 261:0.84 265:0.42 266:0.80 267:0.44 268:0.95 271:0.88 276:0.70 279:0.86 282:0.83 283:0.71 285:0.62 290:0.86 297:0.36 299:0.97 300:0.84 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.37 17:0.96 21:0.40 27:0.09 28:0.85 30:0.43 32:0.80 34:0.61 36:0.74 37:0.86 39:0.74 41:0.88 43:0.60 55:0.45 60:0.87 66:0.31 69:0.40 70:0.59 74:0.99 76:0.94 77:0.70 81:0.72 83:0.83 85:0.85 91:0.56 96:0.90 98:0.60 101:0.75 104:0.58 106:0.81 108:0.85 114:0.82 117:0.86 120:0.79 121:0.99 122:0.67 123:0.67 124:0.65 126:0.54 127:0.65 129:0.59 133:0.64 135:0.88 140:0.80 145:0.74 146:0.27 147:0.33 149:0.87 150:0.82 151:0.94 153:0.84 154:0.49 155:0.67 158:0.92 159:0.69 161:0.54 162:0.84 164:0.75 165:0.81 167:0.55 172:0.91 173:0.28 176:0.73 177:0.38 179:0.17 181:0.82 187:0.39 192:0.59 195:0.85 201:0.74 202:0.67 204:0.89 206:0.81 208:0.64 212:0.92 215:0.67 216:0.67 222:0.48 230:0.87 232:0.93 233:0.76 235:0.68 241:0.35 242:0.58 243:0.07 245:0.97 247:0.67 248:0.84 253:0.94 255:0.79 256:0.68 259:0.21 260:0.80 265:0.54 266:0.71 267:0.23 268:0.73 271:0.88 276:0.92 277:0.69 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.94 +2 1:0.74 8:0.97 9:0.80 11:0.57 12:0.37 17:0.84 20:0.74 21:0.30 27:0.67 28:0.85 30:0.62 32:0.80 34:0.52 36:0.66 37:0.63 39:0.74 41:1.00 43:0.96 55:0.45 66:0.67 69:0.19 70:0.62 74:0.95 76:0.94 77:0.98 78:0.90 81:0.78 83:0.86 85:0.80 91:0.88 96:0.97 98:0.51 100:0.73 101:0.78 108:0.15 111:0.87 114:0.86 120:0.92 122:0.67 123:0.15 124:0.45 126:0.54 127:0.90 129:0.59 133:0.47 135:0.47 140:0.45 145:0.47 146:0.42 147:0.49 149:0.79 150:0.85 151:0.99 152:0.74 153:0.97 154:0.96 155:0.67 159:0.37 161:0.54 162:0.66 164:0.90 165:0.83 167:0.84 169:0.72 172:0.57 173:0.36 176:0.73 177:0.38 179:0.75 181:0.82 186:0.96 191:0.73 192:0.59 195:0.60 201:0.74 202:0.89 208:0.64 212:0.71 215:0.72 216:0.39 222:0.48 232:0.72 235:0.68 241:0.80 242:0.74 243:0.72 245:0.66 247:0.39 248:0.96 253:0.98 255:0.79 256:1.00 259:0.21 260:0.92 261:0.75 265:0.53 266:0.54 267:0.95 268:0.91 271:0.88 276:0.42 282:0.88 285:0.62 290:0.86 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.97 7:0.81 8:0.97 9:0.80 12:0.37 17:0.93 20:0.76 21:0.30 27:0.02 28:0.85 30:0.95 32:0.80 34:0.26 36:0.62 37:0.98 39:0.74 41:1.00 43:0.32 55:0.45 66:0.72 69:0.65 70:0.15 74:0.90 76:0.94 77:0.89 78:0.92 81:0.74 83:0.81 91:0.73 96:0.96 98:0.21 100:0.90 108:0.95 111:0.91 114:0.68 117:0.86 120:0.86 121:0.90 122:0.67 123:0.95 124:0.47 126:0.54 127:0.89 129:0.89 133:0.78 135:0.69 138:0.95 140:0.45 145:0.80 146:0.19 147:0.24 149:0.45 150:0.83 151:0.87 152:0.75 153:0.77 154:0.23 155:0.67 158:0.85 159:0.87 161:0.54 162:0.82 164:0.87 165:0.81 167:0.69 169:0.88 172:0.80 173:0.38 175:0.75 176:0.56 177:0.05 179:0.46 181:0.82 186:0.92 187:0.21 189:0.97 191:0.85 192:0.59 195:0.96 201:0.74 202:0.85 204:0.89 208:0.64 212:0.88 215:0.67 216:0.81 220:0.87 222:0.48 230:0.87 232:0.84 233:0.76 235:0.60 238:0.89 241:0.65 242:0.82 243:0.12 244:0.61 245:0.70 247:0.12 248:0.93 253:0.97 255:0.79 256:1.00 257:0.65 259:0.21 260:0.87 261:0.80 265:0.23 266:0.54 267:0.92 268:0.89 271:0.88 276:0.63 277:0.69 282:0.88 285:0.62 286:0.99 290:0.68 297:0.36 299:0.88 300:0.43 +1 1:0.68 7:0.65 10:0.92 11:0.88 12:0.51 17:0.06 18:0.79 21:0.05 27:0.49 29:0.62 30:0.68 32:0.71 34:0.96 36:0.75 37:0.47 39:0.51 43:0.41 44:0.92 45:0.94 56:0.97 64:0.77 66:0.92 69:0.54 70:0.45 71:0.79 74:0.59 77:0.70 80:0.98 81:0.81 82:0.99 83:0.69 86:0.82 88:0.98 91:0.40 98:0.90 99:0.99 101:0.70 102:0.97 108:0.42 114:0.95 122:0.91 123:0.40 126:0.54 127:0.46 129:0.05 131:0.61 135:0.96 139:0.83 144:0.92 145:0.58 146:0.82 147:0.85 149:0.50 150:0.64 154:0.57 155:0.57 157:0.91 159:0.38 165:0.88 166:0.94 170:0.90 172:0.79 173:0.61 174:0.88 176:0.70 177:0.99 178:0.55 179:0.44 182:0.93 187:0.87 192:0.87 193:0.95 195:0.64 197:0.90 201:0.68 204:0.84 208:0.64 212:0.86 215:0.91 216:0.56 222:0.48 227:0.61 229:0.61 230:0.67 231:0.90 233:0.66 235:0.68 236:0.95 239:0.93 241:0.05 242:0.17 243:0.93 244:0.61 246:0.92 247:0.79 253:0.67 254:0.80 259:0.21 264:0.98 265:0.90 266:0.38 267:0.79 271:0.89 275:0.95 276:0.67 281:0.86 282:0.80 287:0.61 290:0.95 294:0.98 297:0.36 300:0.43 +1 1:0.61 8:0.59 9:0.76 10:0.89 11:0.88 12:0.51 17:0.32 18:0.83 20:0.91 21:0.54 23:0.98 27:0.68 29:0.62 30:0.53 31:0.96 34:0.30 36:0.94 37:0.27 39:0.51 43:0.23 45:0.95 56:0.97 62:0.97 64:0.77 66:0.36 69:0.47 70:0.84 71:0.79 74:0.49 75:0.97 78:0.99 79:0.95 81:0.69 83:0.69 86:0.74 91:0.40 96:0.86 97:0.98 98:0.61 99:0.99 100:0.73 101:0.97 108:0.36 111:0.95 114:0.74 122:0.91 123:0.34 124:0.79 126:0.54 127:0.45 128:0.98 129:0.05 131:0.97 133:0.80 135:0.99 137:0.96 139:0.75 140:0.87 143:0.99 144:0.92 145:0.83 146:0.89 147:0.91 149:0.31 150:0.48 151:0.85 152:0.85 153:0.48 154:0.61 155:0.65 157:0.91 158:0.77 159:0.76 162:0.55 165:0.88 166:0.94 168:0.98 169:0.72 170:0.90 172:0.75 173:0.26 174:0.86 176:0.63 177:0.99 178:0.48 179:0.29 182:0.94 186:0.98 187:0.87 190:0.89 191:0.70 192:0.99 196:0.93 197:0.88 201:0.63 202:0.69 205:0.98 208:0.64 212:0.55 216:0.42 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 231:0.91 235:0.95 236:0.94 239:0.90 241:0.21 242:0.18 243:0.51 244:0.61 245:0.84 246:0.92 247:0.93 248:0.83 253:0.82 254:0.90 255:0.94 256:0.64 259:0.21 261:0.90 264:0.98 265:0.62 266:0.80 267:0.69 268:0.65 271:0.89 275:0.95 276:0.79 279:0.79 284:0.94 285:0.61 287:0.94 290:0.74 292:0.95 294:0.97 295:0.99 300:0.84 +0 1:0.62 6:0.84 7:0.64 8:0.58 9:0.74 10:0.83 11:0.86 12:0.51 17:0.59 18:0.65 20:0.89 21:0.54 23:0.95 27:0.21 29:0.62 30:0.89 31:0.92 34:0.93 36:0.89 37:0.78 39:0.51 43:0.06 44:0.86 45:0.77 56:0.97 62:0.96 64:0.77 66:0.75 69:0.29 70:0.20 71:0.79 74:0.38 75:0.95 77:0.70 78:1.00 79:0.91 81:0.71 82:0.98 83:0.69 86:0.63 88:0.98 91:0.63 96:0.77 97:0.97 98:0.78 99:0.99 100:0.92 101:0.64 102:0.94 108:0.22 111:0.98 114:0.72 122:0.67 123:0.22 126:0.54 127:0.27 128:0.97 129:0.05 131:0.96 135:0.86 137:0.92 139:0.68 140:0.45 143:0.99 144:0.92 145:0.56 146:0.46 147:0.52 149:0.05 150:0.52 151:0.75 152:0.84 153:0.48 154:0.17 155:0.65 157:0.81 158:0.73 159:0.17 162:0.86 165:0.79 166:0.94 168:0.97 169:0.90 170:0.90 172:0.32 173:0.60 174:0.79 176:0.62 177:0.99 179:0.87 182:0.94 186:1.00 187:0.39 189:0.86 190:0.98 192:0.99 195:0.55 196:0.89 197:0.84 201:0.64 202:0.36 205:0.95 208:0.64 212:0.61 215:0.58 216:0.50 219:0.88 220:0.62 222:0.48 226:0.96 227:0.95 229:0.97 230:0.64 231:0.91 232:0.90 233:0.76 235:0.88 236:0.92 238:0.81 239:0.86 241:0.18 242:0.33 243:0.77 244:0.97 246:0.92 247:0.39 248:0.69 253:0.68 254:0.74 255:0.78 256:0.45 259:0.21 261:0.92 264:0.98 265:0.79 266:0.23 267:0.73 268:0.46 271:0.89 275:0.91 276:0.14 279:0.79 282:0.73 284:0.88 285:0.61 287:0.94 290:0.72 292:0.88 294:0.95 295:0.98 297:0.36 300:0.18 +1 1:0.66 8:0.61 10:0.94 11:0.88 12:0.51 17:0.16 18:0.90 21:0.13 27:0.49 29:0.62 30:0.85 32:0.71 34:0.98 36:0.99 37:0.47 39:0.51 43:0.57 44:0.92 45:0.98 56:0.97 64:0.77 66:0.52 69:0.54 70:0.44 71:0.79 74:0.71 75:0.97 77:0.70 80:0.98 81:0.75 82:0.99 83:0.69 86:0.87 88:0.98 91:0.31 97:0.99 98:0.71 99:0.99 101:0.70 102:0.97 108:0.42 114:0.88 122:0.91 123:0.40 124:0.55 126:0.54 127:0.66 129:0.59 131:0.61 133:0.46 135:0.97 139:0.88 144:0.92 145:0.61 146:0.75 147:0.79 149:0.83 150:0.58 154:0.57 155:0.55 157:0.96 158:0.77 159:0.51 165:0.88 166:0.94 170:0.90 172:0.82 173:0.55 174:0.91 176:0.63 177:0.99 178:0.55 179:0.31 182:0.93 187:0.39 192:0.58 193:0.95 195:0.69 197:0.93 201:0.66 204:0.82 208:0.64 212:0.84 216:0.56 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 231:0.91 235:0.68 236:0.94 239:0.95 241:0.05 242:0.17 243:0.62 244:0.61 245:0.94 246:0.92 247:0.82 253:0.66 254:0.84 259:0.21 264:0.98 265:0.71 266:0.63 267:0.79 271:0.89 275:0.96 276:0.80 279:0.81 281:0.86 282:0.79 287:0.61 290:0.88 292:0.95 294:0.99 295:0.99 300:0.70 +0 1:0.65 10:0.93 11:0.92 12:0.51 17:0.22 18:0.96 21:0.22 22:0.93 27:0.43 29:0.62 30:0.75 33:0.97 34:0.77 36:0.43 37:0.98 39:0.51 43:0.75 44:0.88 45:0.98 47:0.93 56:0.97 64:0.77 66:0.59 69:0.56 70:0.36 71:0.79 74:0.62 77:0.70 80:0.98 81:0.74 82:0.99 83:0.69 85:0.72 86:0.86 88:0.99 91:0.29 98:0.67 99:0.99 101:1.00 102:0.96 108:0.75 114:0.85 117:0.86 122:0.91 123:0.73 124:0.52 126:0.54 127:0.54 129:0.05 131:0.61 133:0.43 135:0.42 139:0.85 144:0.92 145:0.72 146:0.69 147:0.74 149:0.81 150:0.56 154:0.67 155:0.54 157:0.90 159:0.54 165:0.88 166:0.94 170:0.90 172:0.90 173:0.53 174:0.89 176:0.63 177:0.99 178:0.55 179:0.20 182:0.93 187:0.95 192:0.57 193:0.95 195:0.76 197:0.91 201:0.66 204:0.82 208:0.64 212:0.88 216:0.64 222:0.48 227:0.61 229:0.61 231:0.90 232:0.94 235:0.74 236:0.94 239:0.93 241:0.05 242:0.45 243:0.40 245:0.97 246:0.92 247:0.86 253:0.62 259:0.21 262:0.93 264:0.98 265:0.68 266:0.71 267:0.88 271:0.89 275:0.94 276:0.88 277:0.87 281:0.86 282:0.75 287:0.61 290:0.85 291:0.97 294:0.98 300:0.70 +1 1:0.68 6:0.84 7:0.65 8:0.63 10:0.92 11:0.88 12:0.51 17:0.04 18:0.79 20:0.93 21:0.05 27:0.49 29:0.62 30:0.68 32:0.71 34:0.97 36:0.75 37:0.47 39:0.51 43:0.41 44:0.92 45:0.94 56:0.97 64:0.77 66:0.92 69:0.54 70:0.45 71:0.79 74:0.63 75:0.97 77:0.70 78:1.00 80:0.98 81:0.81 82:0.99 83:0.69 86:0.82 88:0.98 91:0.37 97:0.98 98:0.90 99:0.99 100:0.98 101:0.70 102:0.97 108:0.42 111:0.99 114:0.95 122:0.91 123:0.40 126:0.54 127:0.46 129:0.05 131:0.61 135:0.97 139:0.85 144:0.92 145:0.58 146:0.82 147:0.85 149:0.50 150:0.64 152:0.97 154:0.57 155:0.57 157:0.91 158:0.77 159:0.38 165:0.88 166:0.94 169:0.94 170:0.90 172:0.79 173:0.61 174:0.88 176:0.70 177:0.99 178:0.55 179:0.44 182:0.93 186:1.00 187:0.68 189:0.86 192:0.87 193:0.95 195:0.64 197:0.90 201:0.68 204:0.84 208:0.64 212:0.86 215:0.91 216:0.56 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 230:0.67 231:0.90 233:0.66 235:0.68 236:0.95 238:0.85 239:0.94 241:0.05 242:0.17 243:0.93 244:0.61 246:0.92 247:0.79 253:0.68 254:0.80 259:0.21 261:0.96 264:0.98 265:0.90 266:0.38 267:0.76 271:0.89 275:0.95 276:0.67 279:0.79 281:0.86 282:0.80 283:0.82 287:0.61 290:0.95 292:0.95 294:0.98 295:0.99 297:0.36 300:0.43 +0 1:0.66 6:0.84 8:0.63 9:0.76 10:0.92 11:0.88 12:0.51 17:0.03 18:0.87 20:0.94 21:0.13 23:0.98 27:0.49 29:0.62 30:0.73 31:0.96 34:0.74 36:1.00 37:0.47 39:0.51 43:0.57 44:0.88 45:0.97 56:0.97 62:0.98 64:0.77 66:0.46 69:0.54 70:0.52 71:0.79 74:0.60 75:0.97 77:0.89 78:0.97 79:0.95 80:0.98 81:0.75 82:0.99 83:0.69 86:0.80 88:0.99 91:0.42 96:0.86 97:0.99 98:0.67 99:0.99 100:0.73 101:0.96 102:0.96 108:0.42 111:0.94 114:0.88 122:0.98 123:0.40 124:0.69 126:0.54 127:0.85 128:0.98 129:0.05 131:0.97 133:0.67 135:1.00 137:0.96 139:0.83 140:0.92 143:0.99 144:0.92 145:0.70 146:0.86 147:0.88 149:0.70 150:0.58 151:0.85 152:0.97 153:0.48 154:0.57 155:0.65 157:0.95 158:0.77 159:0.73 162:0.53 165:0.88 166:0.94 168:0.98 169:0.94 170:0.90 172:0.79 173:0.41 174:0.91 176:0.63 177:0.99 178:0.48 179:0.33 182:0.94 186:0.97 187:0.39 190:0.89 192:0.99 193:0.95 195:0.84 196:0.95 197:0.91 201:0.66 202:0.70 204:0.84 205:0.98 208:0.64 212:0.61 216:0.56 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 231:0.91 235:0.68 236:0.94 239:0.94 241:0.37 242:0.16 243:0.58 244:0.61 245:0.91 246:0.92 247:0.88 248:0.79 253:0.84 254:0.84 255:0.94 256:0.64 259:0.21 261:0.96 264:0.98 265:0.68 266:0.84 267:0.59 268:0.65 271:0.89 275:0.96 276:0.81 279:0.81 281:0.86 282:0.75 284:0.94 285:0.61 287:0.94 290:0.88 292:0.95 294:0.98 295:0.99 300:0.70 +0 1:0.67 7:0.64 8:0.58 9:0.73 10:0.89 11:0.88 12:0.51 17:0.34 18:0.79 21:0.05 22:0.93 23:0.96 27:0.58 29:0.62 30:0.87 31:0.92 32:0.71 33:0.97 34:0.86 36:0.96 37:0.36 39:0.51 43:0.57 44:0.92 45:0.93 47:0.93 56:0.97 62:0.97 64:0.77 66:0.83 69:0.79 70:0.36 71:0.79 74:0.56 75:0.97 77:0.70 79:0.92 80:0.98 81:0.76 82:0.99 83:0.69 86:0.73 88:0.98 91:0.44 96:0.78 97:0.99 98:0.62 99:0.99 101:0.70 102:0.97 108:0.63 114:0.92 117:0.86 122:0.91 123:0.60 124:0.37 126:0.54 127:0.30 128:0.97 129:0.05 131:0.61 133:0.36 135:0.75 137:0.92 139:0.81 140:0.45 144:0.92 145:0.76 146:0.59 147:0.05 149:0.65 150:0.60 151:0.77 153:0.69 154:0.46 155:0.58 157:0.87 158:0.77 159:0.31 162:0.86 165:0.88 166:0.94 168:0.97 170:0.90 172:0.71 173:0.88 174:0.89 175:0.75 176:0.63 177:0.05 179:0.45 182:0.93 187:0.39 190:0.99 192:0.87 193:0.95 195:0.66 196:0.93 197:0.91 201:0.67 202:0.46 204:0.81 205:0.95 208:0.64 212:0.84 216:0.63 219:0.91 220:0.62 222:0.48 226:0.98 227:0.61 229:0.61 230:0.64 231:0.91 232:0.83 233:0.76 235:0.60 236:0.94 239:0.93 241:0.32 242:0.33 243:0.10 244:0.83 245:0.57 246:0.92 247:0.76 248:0.71 253:0.77 255:0.72 256:0.45 257:0.99 259:0.21 262:0.93 264:0.98 265:0.63 266:0.47 267:0.44 268:0.55 271:0.89 275:0.93 276:0.55 277:0.69 279:0.81 281:0.86 282:0.80 284:0.90 285:0.50 287:0.61 290:0.92 291:0.97 292:0.95 294:0.98 295:0.99 300:0.43 +0 1:0.65 7:0.64 8:0.58 10:0.92 11:0.88 12:0.51 17:0.22 18:0.95 21:0.22 22:0.93 27:0.43 29:0.62 30:0.75 32:0.63 33:0.97 34:0.37 36:0.84 37:0.98 39:0.51 43:0.48 44:0.88 45:0.98 47:0.93 56:0.97 64:0.77 66:0.59 69:0.56 70:0.36 71:0.79 74:0.63 75:0.97 77:0.70 80:0.98 81:0.74 82:0.99 83:0.69 86:0.84 88:0.98 91:0.27 97:0.99 98:0.52 99:0.99 101:0.70 102:0.96 108:0.83 114:0.85 117:0.86 122:0.91 123:0.82 124:0.64 126:0.54 127:0.52 129:0.05 131:0.61 133:0.66 135:0.98 139:0.85 144:0.92 145:0.74 146:0.35 147:0.42 149:0.55 150:0.56 154:0.61 155:0.54 157:0.90 158:0.77 159:0.63 165:0.88 166:0.94 170:0.90 172:0.89 173:0.46 174:0.86 176:0.63 177:0.99 178:0.55 179:0.21 182:0.93 187:0.87 192:0.56 193:0.95 195:0.83 197:0.89 201:0.66 204:0.81 208:0.64 212:0.90 216:0.64 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 230:0.64 231:0.90 232:0.94 233:0.76 235:0.74 236:0.94 239:0.94 241:0.07 242:0.50 243:0.28 244:0.61 245:0.94 246:0.92 247:0.84 253:0.63 254:0.80 259:0.21 262:0.93 264:0.98 265:0.53 266:0.63 267:0.76 271:0.89 275:0.94 276:0.86 277:0.95 279:0.81 281:0.86 282:0.75 287:0.61 290:0.85 291:0.97 292:0.95 294:0.98 295:0.99 300:0.70 +0 1:0.64 7:0.64 10:0.89 11:0.86 12:0.51 17:0.77 18:0.81 21:0.47 27:0.25 29:0.62 30:0.87 32:0.65 34:0.88 36:0.41 37:0.83 39:0.51 43:0.05 44:0.88 45:0.93 56:0.97 64:0.77 66:0.92 69:0.64 70:0.32 71:0.79 74:0.47 77:0.70 80:0.98 81:0.73 82:0.98 83:0.69 86:0.73 88:0.98 91:0.20 98:0.85 99:0.99 101:0.64 102:0.95 104:0.76 108:0.49 114:0.74 122:0.67 123:0.46 126:0.54 127:0.36 129:0.05 131:0.61 135:0.56 139:0.74 144:0.92 145:0.52 146:0.75 147:0.79 149:0.05 150:0.54 154:0.17 155:0.57 157:0.95 159:0.31 165:0.79 166:0.94 170:0.90 172:0.79 173:0.74 174:0.89 176:0.62 177:0.99 178:0.55 179:0.41 182:0.93 187:0.68 192:0.87 193:0.95 195:0.61 197:0.92 201:0.66 204:0.84 208:0.64 212:0.69 215:0.63 216:0.40 222:0.48 227:0.61 229:0.61 230:0.64 231:0.90 232:0.86 233:0.66 235:0.84 236:0.92 239:0.90 241:0.21 242:0.24 243:0.92 244:0.93 246:0.92 247:0.60 253:0.55 254:0.93 259:0.21 264:0.98 265:0.85 266:0.38 267:0.52 271:0.89 275:0.96 276:0.66 281:0.86 282:0.74 287:0.61 290:0.74 294:0.97 297:0.36 300:0.43 +0 1:0.67 7:0.64 8:0.79 9:0.75 10:0.90 11:0.88 12:0.51 17:0.85 18:0.82 21:0.30 22:0.93 23:0.97 27:0.67 29:0.62 30:0.33 31:0.93 33:0.97 34:0.40 36:0.89 39:0.51 43:0.62 45:0.96 47:0.93 56:0.97 62:0.97 64:0.77 66:0.07 69:0.27 70:0.97 71:0.79 74:0.52 75:0.97 79:0.92 80:0.99 81:0.75 82:0.98 83:0.69 86:0.76 88:0.99 91:0.12 96:0.79 97:1.00 98:0.28 99:1.00 101:0.97 102:0.94 108:0.99 114:0.82 117:0.86 122:0.90 123:0.98 124:0.97 126:0.54 127:1.00 128:0.97 129:0.59 131:0.96 133:0.97 135:0.34 137:0.93 139:0.77 140:0.45 143:0.99 144:0.92 145:0.58 146:0.67 147:0.72 149:0.55 150:0.58 151:0.80 153:0.72 154:0.52 155:0.65 157:0.90 158:0.76 159:0.97 162:0.76 165:0.88 166:0.94 168:0.97 170:0.90 172:0.70 173:0.06 174:0.89 176:0.63 177:0.99 179:0.25 182:0.94 187:0.21 190:0.89 192:0.99 193:0.96 195:1.00 196:0.92 197:0.88 201:0.67 202:0.58 204:0.81 205:0.95 208:0.64 212:0.55 216:0.03 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 230:0.64 231:0.91 232:0.70 233:0.76 235:0.88 236:0.94 239:0.92 241:0.30 242:0.14 243:0.32 244:0.83 245:0.82 246:0.92 247:0.98 248:0.73 253:0.77 255:0.78 256:0.58 259:0.21 262:0.93 264:0.98 265:0.15 266:0.98 267:0.23 268:0.62 271:0.89 275:0.97 276:0.88 277:0.99 279:0.81 281:0.91 284:0.93 285:0.61 287:0.94 290:0.82 291:0.97 292:0.88 294:0.98 295:0.99 300:0.98 +0 1:0.66 8:0.64 9:0.74 10:0.92 11:0.88 12:0.51 17:0.22 18:0.81 21:0.13 23:0.95 27:0.35 29:0.62 30:0.75 31:0.92 34:0.26 36:0.84 37:0.43 39:0.51 43:0.28 44:0.88 45:0.99 56:0.97 62:0.97 64:0.77 66:0.23 69:0.89 70:0.56 71:0.79 74:0.58 75:0.97 77:0.70 79:0.91 80:0.98 81:0.75 82:0.99 83:0.69 86:0.79 88:0.99 91:0.44 96:0.77 97:0.99 98:0.34 99:0.99 101:0.97 102:0.96 108:0.78 114:0.88 122:0.91 123:0.76 124:0.90 126:0.54 127:0.62 128:0.97 129:0.59 131:0.96 133:0.88 135:1.00 137:0.92 139:0.82 140:0.80 143:0.99 144:0.92 145:0.83 146:0.69 147:0.05 149:0.73 150:0.58 151:0.75 153:0.48 154:0.22 155:0.65 157:0.96 158:0.77 159:0.83 162:0.49 165:0.88 166:0.94 168:0.97 170:0.90 172:0.78 173:0.77 174:0.95 176:0.63 177:0.05 178:0.42 179:0.21 182:0.94 187:0.68 190:0.98 191:0.73 192:0.99 193:0.95 195:0.94 196:0.93 197:0.92 201:0.66 202:0.64 204:0.82 205:0.95 208:0.64 212:0.68 216:0.70 219:0.91 220:0.62 222:0.48 226:0.98 227:0.95 229:0.97 231:0.91 235:0.68 236:0.94 239:0.94 241:0.41 242:0.21 243:0.06 244:0.61 245:0.91 246:0.92 247:0.90 248:0.69 253:0.76 254:0.96 255:0.78 256:0.45 257:0.99 259:0.21 264:0.98 265:0.36 266:0.80 267:0.59 268:0.46 271:0.89 275:0.97 276:0.88 279:0.81 281:0.86 282:0.75 284:0.88 285:0.61 287:0.94 290:0.88 292:0.95 294:0.98 295:0.99 300:0.84 +0 1:0.56 10:0.82 11:0.91 12:0.51 17:0.34 18:0.78 21:0.64 27:0.45 29:0.62 30:0.66 32:0.62 34:0.71 36:0.24 37:0.50 39:0.51 43:0.22 45:0.89 64:0.77 66:0.30 69:0.69 70:0.61 71:0.79 74:0.32 81:0.36 83:0.54 86:0.64 91:0.12 98:0.49 99:0.83 101:0.87 108:0.86 114:0.63 122:0.67 123:0.51 124:0.59 126:0.26 127:0.40 129:0.05 131:0.61 133:0.39 135:0.74 139:0.63 144:0.92 145:0.49 146:0.67 147:0.72 149:0.25 150:0.34 154:0.14 155:0.46 157:0.79 159:0.58 165:0.63 166:0.61 170:0.90 172:0.48 173:0.63 174:0.79 176:0.59 177:0.99 178:0.55 179:0.60 182:0.73 187:0.68 192:0.44 195:0.81 197:0.82 201:0.54 208:0.49 212:0.57 216:0.77 222:0.48 227:0.61 229:0.61 231:0.65 235:0.80 239:0.82 241:0.18 242:0.33 243:0.48 244:0.97 245:0.75 246:0.61 247:0.74 253:0.48 259:0.21 264:0.98 265:0.17 266:0.63 267:0.52 271:0.89 275:0.91 276:0.40 287:0.61 290:0.63 294:0.93 300:0.55 +0 1:0.66 10:0.88 11:0.88 12:0.51 17:0.55 18:0.71 21:0.13 27:0.54 29:0.62 30:0.60 34:0.97 36:0.76 37:0.41 39:0.51 43:0.09 45:0.85 56:0.97 64:0.77 66:0.69 69:0.51 70:0.67 71:0.79 74:0.46 75:0.97 81:0.75 83:0.69 86:0.72 91:0.31 97:0.98 98:0.69 99:0.99 101:0.87 108:0.39 114:0.88 122:0.41 123:0.38 124:0.42 126:0.54 127:0.43 129:0.05 131:0.61 133:0.36 135:0.96 139:0.73 144:0.92 145:0.59 146:0.61 147:0.66 149:0.11 150:0.58 154:0.60 155:0.51 157:0.87 158:0.77 159:0.34 165:0.88 166:0.94 170:0.90 172:0.54 173:0.61 174:0.79 176:0.63 177:0.99 178:0.55 179:0.70 182:0.93 187:0.87 192:0.55 197:0.84 201:0.66 208:0.64 212:0.42 216:0.55 219:0.91 222:0.48 226:0.98 227:0.61 229:0.61 231:0.90 235:0.68 236:0.94 239:0.89 241:0.32 242:0.12 243:0.73 244:0.61 245:0.59 246:0.92 247:0.82 253:0.62 254:0.90 259:0.21 264:0.98 265:0.69 266:0.38 267:0.88 271:0.89 275:0.93 276:0.45 279:0.79 287:0.61 290:0.88 292:0.95 294:0.97 295:0.99 300:0.55 +0 1:0.61 7:0.64 8:0.89 9:0.73 10:0.92 11:0.88 12:0.51 17:0.73 18:0.78 21:0.59 23:0.95 27:0.40 29:0.62 30:0.56 31:0.91 34:0.98 36:0.88 37:0.88 39:0.51 43:0.05 44:0.88 45:0.92 56:0.97 62:0.97 64:0.77 66:0.91 69:0.64 70:0.62 71:0.79 74:0.58 75:0.97 77:0.70 79:0.91 80:0.98 81:0.65 82:0.99 83:0.69 86:0.79 88:0.98 91:0.48 96:0.77 97:0.99 98:0.83 99:0.99 101:0.64 102:0.96 108:0.49 114:0.71 122:0.91 123:0.47 126:0.54 127:0.33 128:0.97 129:0.05 131:0.61 135:0.84 137:0.91 139:0.82 140:0.90 144:0.92 145:0.77 146:0.80 147:0.83 149:0.05 150:0.46 151:0.75 153:0.48 154:0.50 155:0.57 157:0.93 158:0.76 159:0.36 162:0.51 165:0.79 166:0.94 168:0.97 170:0.90 172:0.74 173:0.70 174:0.88 176:0.63 177:0.99 178:0.50 179:0.46 182:0.93 187:0.39 190:0.99 191:0.87 192:0.87 193:0.95 195:0.69 196:0.93 197:0.90 201:0.62 202:0.60 204:0.81 205:0.95 208:0.64 212:0.71 216:0.41 219:0.91 220:0.62 222:0.48 226:0.96 227:0.61 229:0.61 230:0.64 231:0.90 232:0.87 233:0.76 235:0.97 236:0.94 239:0.93 241:0.37 242:0.24 243:0.91 244:0.83 246:0.92 247:0.67 248:0.69 253:0.72 254:0.74 255:0.72 256:0.45 259:0.21 264:0.98 265:0.83 266:0.47 267:0.44 268:0.46 271:0.89 275:0.95 276:0.60 279:0.81 281:0.86 282:0.75 284:0.87 285:0.50 287:0.61 290:0.71 292:0.88 294:0.98 295:0.98 300:0.55 +0 1:0.63 8:0.61 10:0.87 11:0.86 12:0.51 17:0.24 18:0.95 20:0.92 21:0.47 27:0.20 29:0.62 30:0.70 34:0.36 36:0.70 37:0.68 39:0.51 43:0.08 44:0.86 45:0.97 56:0.97 64:0.77 66:0.97 69:0.69 70:0.55 71:0.79 74:0.51 75:0.95 77:0.89 78:0.96 80:0.98 81:0.67 82:0.98 83:0.69 86:0.76 88:0.98 91:0.23 97:0.97 98:0.86 99:0.99 100:0.73 101:0.64 102:0.94 108:0.52 111:0.93 114:0.72 122:0.91 123:0.50 126:0.54 127:0.37 129:0.05 131:0.61 135:0.85 139:0.77 144:0.92 145:0.72 146:0.89 147:0.91 149:0.32 150:0.50 152:0.95 154:0.16 155:0.57 157:0.94 158:0.73 159:0.48 165:0.79 166:0.94 169:0.72 170:0.90 172:0.93 173:0.67 174:0.88 176:0.60 177:0.99 178:0.55 179:0.19 182:0.93 186:0.96 187:0.87 192:0.86 193:0.95 195:0.75 197:0.90 201:0.64 202:0.36 204:0.85 208:0.64 212:0.85 216:0.83 219:0.88 222:0.48 226:0.95 227:0.61 229:0.61 231:0.90 232:0.91 235:0.80 236:0.92 239:0.89 241:0.03 242:0.28 243:0.97 244:0.97 246:0.92 247:0.86 253:0.55 254:0.74 259:0.21 261:0.91 264:0.98 265:0.86 266:0.54 267:0.82 271:0.89 275:0.96 276:0.87 279:0.79 281:0.91 282:0.73 287:0.61 290:0.72 292:0.87 294:0.97 295:0.98 300:0.70 +1 7:0.69 10:0.94 11:0.46 12:0.20 17:0.86 21:0.40 27:0.39 29:0.53 30:0.37 32:0.67 34:0.69 36:0.18 37:0.69 39:0.32 43:0.65 45:0.66 55:0.59 66:0.74 69:0.83 70:0.59 71:0.71 74:0.51 76:0.94 77:0.89 81:0.32 83:0.56 91:0.38 98:0.83 101:0.47 108:0.68 114:0.69 122:0.83 123:0.66 124:0.42 126:0.24 127:0.78 129:0.05 131:0.61 133:0.43 135:0.63 139:0.67 144:0.57 145:0.88 146:0.89 147:0.63 149:0.42 150:0.35 154:0.55 155:0.49 157:0.61 159:0.61 166:0.82 170:0.99 172:0.84 173:0.83 174:0.92 175:0.75 176:0.59 177:0.38 178:0.55 179:0.38 182:0.61 187:0.39 192:0.46 195:0.78 197:0.92 204:0.78 208:0.50 212:0.80 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.42 239:0.92 241:0.24 242:0.30 243:0.23 244:0.83 245:0.86 246:0.61 247:0.76 253:0.53 257:0.84 259:0.21 265:0.83 266:0.47 267:0.52 271:0.16 276:0.78 277:0.69 281:0.91 282:0.72 287:0.61 290:0.69 300:0.55 +0 8:0.85 10:0.91 11:0.55 12:0.20 17:0.67 18:0.62 21:0.71 27:0.39 29:0.53 30:0.13 32:0.67 34:1.00 36:0.18 37:0.69 39:0.32 43:0.65 45:0.81 66:0.71 69:0.41 70:0.84 71:0.71 74:0.49 76:0.94 77:0.89 81:0.30 86:0.65 91:0.40 98:0.80 101:0.65 108:0.32 114:0.62 117:0.86 122:0.57 123:0.31 124:0.43 126:0.24 127:0.64 129:0.05 131:0.61 133:0.39 135:0.98 139:0.63 144:0.57 145:0.74 146:0.79 147:0.83 149:0.57 150:0.33 154:0.50 157:0.78 158:0.72 159:0.48 166:0.82 170:0.99 172:0.71 173:0.43 174:0.86 176:0.59 177:0.88 178:0.55 179:0.55 182:0.73 187:0.68 195:0.70 197:0.87 212:0.68 216:0.22 222:0.60 227:0.61 229:0.61 231:0.79 232:0.72 235:0.88 239:0.89 241:0.24 242:0.21 243:0.75 244:0.61 245:0.76 246:0.61 247:0.86 253:0.48 254:0.74 259:0.21 265:0.80 266:0.63 267:0.59 271:0.16 276:0.63 282:0.72 287:0.61 290:0.62 300:0.70 +0 7:0.69 8:0.77 10:0.87 11:0.46 12:0.20 17:0.79 21:0.40 27:0.39 29:0.53 30:0.87 32:0.67 34:0.63 36:0.17 37:0.69 39:0.32 43:0.65 45:0.66 55:0.71 66:0.60 69:0.35 70:0.42 71:0.71 74:0.50 76:0.94 77:0.89 81:0.32 83:0.56 91:0.38 98:0.74 101:0.47 108:0.70 114:0.69 122:0.83 123:0.68 124:0.63 126:0.24 127:0.73 129:0.81 131:0.61 133:0.67 135:0.83 139:0.67 144:0.57 145:0.88 146:0.62 147:0.67 149:0.47 150:0.35 154:0.55 155:0.49 157:0.61 159:0.63 166:0.82 170:0.99 172:0.77 173:0.29 174:0.86 175:0.91 176:0.59 177:0.38 178:0.55 179:0.42 182:0.61 187:0.21 192:0.46 195:0.80 197:0.86 204:0.78 208:0.50 212:0.76 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.42 239:0.86 241:0.24 242:0.70 243:0.28 244:0.83 245:0.83 246:0.61 247:0.74 253:0.53 257:0.84 259:0.21 265:0.74 266:0.71 267:0.65 271:0.16 276:0.75 277:0.87 281:0.91 282:0.72 287:0.61 290:0.69 300:0.70 +2 1:0.65 7:0.69 8:0.79 9:0.72 10:0.84 11:0.55 12:0.20 17:0.93 18:0.93 21:0.13 27:0.59 29:0.53 30:0.40 32:0.67 34:0.31 36:0.48 37:0.74 39:0.32 43:0.66 45:0.99 55:0.84 66:1.00 69:0.10 70:0.58 71:0.71 74:0.86 76:0.85 77:0.98 81:0.66 83:0.56 86:0.93 91:0.49 96:0.75 97:0.94 98:1.00 99:0.83 101:0.87 104:0.54 108:0.09 114:0.78 120:0.63 122:0.83 123:0.09 126:0.32 127:0.99 129:0.05 131:0.88 135:0.34 139:0.91 140:0.45 144:0.57 145:0.96 146:1.00 147:1.00 149:0.94 150:0.61 151:0.70 153:0.69 154:0.22 155:0.55 157:0.94 158:0.73 159:0.93 162:0.86 163:0.81 164:0.62 165:0.65 166:0.83 170:0.99 172:0.99 173:0.07 174:0.83 176:0.59 177:0.88 179:0.11 182:0.84 187:0.21 190:0.89 192:0.58 195:0.98 197:0.82 201:0.61 202:0.46 208:0.50 212:0.39 215:0.84 216:0.01 220:0.74 222:0.60 227:0.96 229:0.61 230:0.71 231:0.87 232:0.74 233:0.76 235:0.31 239:0.84 241:0.64 242:0.50 243:1.00 244:0.61 246:0.61 247:0.95 248:0.65 253:0.79 255:0.71 256:0.45 259:0.21 260:0.64 265:1.00 266:0.54 267:0.18 268:0.55 271:0.16 276:0.98 279:0.78 282:0.72 283:0.67 285:0.50 287:0.61 290:0.78 297:0.36 300:0.55 +0 1:0.64 10:0.81 11:0.55 12:0.20 17:1.00 18:0.96 27:0.72 29:0.53 30:0.21 34:0.24 36:0.28 39:0.32 43:0.62 45:0.99 55:0.45 66:0.75 69:0.61 70:0.71 71:0.71 74:0.90 81:0.65 83:0.56 86:0.96 91:0.23 97:0.89 98:0.76 99:0.83 101:0.87 104:0.87 106:0.81 108:0.46 114:0.84 117:0.86 122:0.83 123:0.45 124:0.64 126:0.32 127:0.55 129:0.05 131:0.61 132:0.97 133:0.88 135:0.60 139:0.94 144:0.57 146:0.99 147:0.99 149:0.97 150:0.61 154:0.46 155:0.49 157:0.94 158:0.73 159:0.92 165:0.65 166:0.83 170:0.99 172:0.99 173:0.22 174:0.79 176:0.59 177:0.88 178:0.55 179:0.09 182:0.84 187:0.39 192:0.48 197:0.80 201:0.60 206:0.81 208:0.50 212:0.93 215:0.96 216:0.15 222:0.60 227:0.61 229:0.61 231:0.87 232:0.95 235:0.05 239:0.81 241:0.05 242:0.32 243:0.77 244:0.61 245:0.98 246:0.61 247:0.97 253:0.71 259:0.21 265:0.76 266:0.87 267:0.18 271:0.16 276:0.99 279:0.76 283:0.82 287:0.61 290:0.84 297:0.36 300:0.94 +0 7:0.69 10:0.87 11:0.46 12:0.20 17:0.82 21:0.59 27:0.39 29:0.53 30:0.86 32:0.65 34:0.83 36:0.17 37:0.69 39:0.32 43:0.65 45:0.66 55:0.71 66:0.77 69:0.38 70:0.40 71:0.71 74:0.50 76:0.94 77:0.70 81:0.29 83:0.56 91:0.42 98:0.91 101:0.47 108:0.30 114:0.64 122:0.83 123:0.28 124:0.40 126:0.24 127:0.77 129:0.05 131:0.61 133:0.43 135:0.85 139:0.67 144:0.57 145:0.74 146:0.93 147:0.95 149:0.48 150:0.32 154:0.55 155:0.49 157:0.61 159:0.63 166:0.82 170:0.99 172:0.83 173:0.31 174:0.86 175:0.75 176:0.59 177:0.70 178:0.55 179:0.42 182:0.61 187:0.39 192:0.46 195:0.80 197:0.86 204:0.78 208:0.50 212:0.75 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.68 239:0.86 241:0.15 242:0.60 243:0.79 244:0.83 245:0.82 246:0.61 247:0.76 253:0.50 257:0.65 259:0.21 265:0.91 266:0.47 267:0.65 271:0.16 276:0.76 277:0.69 281:0.91 282:0.72 287:0.61 290:0.64 300:0.55 +1 10:0.90 11:0.55 12:0.20 17:0.72 18:0.62 21:0.68 27:0.39 29:0.53 30:0.11 32:0.67 33:0.97 34:1.00 36:0.18 37:0.69 39:0.32 43:0.65 45:0.77 66:0.67 69:0.41 70:0.92 71:0.71 74:0.48 75:0.97 76:0.94 77:0.70 81:0.38 86:0.66 91:0.42 98:0.77 101:0.65 108:0.32 114:0.63 117:0.86 122:0.57 123:0.31 124:0.45 126:0.32 127:0.57 129:0.05 131:0.61 133:0.39 135:0.98 139:0.64 144:0.57 145:0.79 146:0.74 147:0.78 149:0.56 150:0.39 154:0.55 157:0.78 158:0.73 159:0.44 166:0.82 170:0.99 172:0.65 173:0.46 174:0.86 176:0.59 177:0.88 178:0.55 179:0.60 182:0.73 187:0.68 192:0.44 193:0.97 195:0.68 197:0.89 201:0.56 212:0.49 216:0.22 222:0.60 226:0.95 227:0.61 229:0.61 231:0.79 232:0.72 235:0.88 236:0.94 239:0.92 241:0.32 242:0.18 243:0.72 244:0.83 245:0.73 246:0.61 247:0.86 253:0.49 259:0.21 265:0.77 266:0.54 267:0.69 271:0.16 276:0.58 282:0.72 287:0.61 290:0.63 291:0.97 300:0.70 +2 10:0.89 11:0.55 12:0.20 17:0.96 18:0.76 21:0.40 27:0.64 29:0.53 30:0.55 34:0.66 36:0.24 39:0.32 43:0.60 45:0.81 55:0.71 66:0.95 69:0.35 70:0.64 71:0.71 74:0.57 76:1.00 77:0.96 81:0.37 86:0.77 91:0.22 98:0.94 101:0.65 108:0.27 114:0.69 117:0.86 122:0.83 123:0.26 126:0.24 127:0.60 129:0.05 131:0.61 135:0.56 139:0.74 144:0.57 145:0.91 146:0.84 147:0.87 149:0.67 150:0.42 154:0.50 157:0.83 158:0.73 159:0.41 166:0.82 170:0.99 172:0.86 173:0.42 174:0.83 176:0.59 177:0.88 178:0.55 179:0.37 182:0.75 187:0.39 192:0.46 193:0.98 195:0.65 197:0.86 212:0.93 216:0.27 222:0.60 227:0.61 229:0.61 231:0.83 232:0.95 235:0.52 239:0.90 241:0.18 242:0.70 243:0.95 244:0.61 246:0.61 247:0.84 253:0.54 254:0.80 259:0.21 265:0.94 266:0.27 267:0.44 271:0.16 276:0.77 282:0.72 287:0.61 290:0.69 300:0.55 +1 8:0.88 10:0.85 11:0.55 12:0.20 17:0.85 18:0.91 21:0.74 27:0.66 29:0.53 30:0.36 34:0.21 36:0.31 37:0.30 39:0.32 43:0.75 45:0.91 66:0.95 69:0.37 70:0.83 71:0.71 74:0.60 81:0.27 86:0.88 91:0.23 98:0.90 101:0.65 102:0.95 108:0.29 122:0.75 123:0.28 126:0.24 127:0.46 129:0.05 131:0.61 135:0.58 139:0.85 144:0.57 145:0.87 146:0.84 147:0.87 149:0.83 150:0.29 154:0.67 157:0.89 159:0.41 166:0.61 170:0.99 172:0.86 173:0.42 174:0.79 177:0.88 178:0.55 179:0.34 182:0.79 187:0.68 193:0.97 195:0.65 197:0.85 212:0.88 216:0.08 222:0.60 227:0.61 229:0.61 231:0.82 235:0.88 236:0.92 239:0.89 241:0.21 242:0.23 243:0.95 244:0.61 246:0.61 247:0.82 253:0.46 259:0.21 265:0.90 266:0.27 267:0.28 271:0.16 276:0.76 287:0.61 300:0.70 +2 1:0.66 7:0.69 8:0.79 9:0.73 10:0.82 11:0.50 12:0.20 17:0.92 18:0.77 21:0.05 27:0.27 29:0.53 30:0.95 32:0.67 34:0.17 36:0.18 37:0.57 39:0.32 43:0.58 45:0.81 55:0.71 66:0.80 69:0.44 70:0.15 71:0.71 74:0.55 76:0.94 77:0.89 81:0.66 83:0.56 86:0.77 91:0.67 96:0.85 98:0.81 99:0.83 101:0.47 108:0.34 114:0.76 117:0.86 120:0.65 122:0.83 123:0.32 124:0.39 126:0.32 127:0.56 129:0.05 131:0.88 133:0.62 135:0.34 138:0.95 139:0.74 140:0.45 144:0.57 145:0.40 146:0.89 147:0.91 149:0.62 150:0.62 151:0.82 153:0.78 154:0.46 155:0.56 157:0.87 158:0.72 159:0.60 162:0.86 164:0.69 165:0.65 166:0.83 170:0.99 172:0.76 173:0.36 174:0.79 175:0.91 176:0.59 177:0.38 179:0.50 182:0.77 187:0.39 190:0.89 191:0.77 192:0.58 195:0.77 197:0.81 201:0.61 202:0.62 208:0.50 212:0.89 215:0.84 216:0.35 220:0.62 222:0.60 227:0.96 229:0.61 230:0.71 231:0.84 232:0.95 233:0.76 235:0.22 239:0.82 241:0.39 242:0.63 243:0.82 244:0.83 245:0.60 246:0.61 247:0.60 248:0.77 253:0.82 255:0.72 256:0.68 257:0.84 259:0.21 260:0.66 265:0.81 266:0.38 267:0.23 268:0.70 271:0.16 276:0.67 277:0.87 282:0.72 285:0.50 286:0.99 287:0.61 290:0.76 297:0.36 298:0.99 300:0.18 +2 1:0.65 7:0.69 8:0.82 9:0.72 10:0.84 11:0.55 12:0.20 17:0.95 18:0.93 21:0.13 27:0.59 29:0.53 30:0.40 32:0.67 34:0.26 36:0.49 37:0.74 39:0.32 43:0.66 45:0.99 55:0.84 66:1.00 69:0.10 70:0.58 71:0.71 74:0.88 76:0.85 77:0.98 81:0.66 83:0.56 86:0.94 91:0.72 96:0.74 98:1.00 99:0.83 101:0.87 104:0.54 108:0.09 114:0.78 120:0.62 122:0.83 123:0.09 126:0.32 127:1.00 129:0.05 131:0.88 135:0.32 139:0.92 140:0.45 144:0.57 145:0.96 146:1.00 147:1.00 149:0.95 150:0.61 151:0.65 153:0.48 154:0.21 155:0.55 157:0.94 159:0.93 162:0.86 163:0.81 164:0.56 165:0.65 166:0.83 170:0.99 172:0.99 173:0.07 174:0.83 176:0.59 177:0.88 179:0.11 182:0.85 190:0.89 192:0.57 195:0.98 197:0.82 201:0.61 202:0.36 208:0.50 212:0.39 215:0.84 216:0.01 220:0.74 222:0.60 227:0.96 229:0.61 230:0.71 231:0.87 232:0.74 233:0.76 235:0.31 239:0.84 241:0.80 242:0.50 243:1.00 244:0.61 246:0.61 247:0.95 248:0.63 253:0.78 255:0.71 256:0.45 259:0.21 260:0.63 265:1.00 266:0.54 267:0.19 268:0.46 271:0.16 276:0.98 282:0.72 285:0.50 287:0.61 290:0.78 297:0.36 300:0.55 +1 8:0.67 10:0.95 11:0.55 12:0.20 17:0.75 18:0.61 21:0.68 27:0.39 29:0.53 30:0.20 32:0.71 33:0.97 34:1.00 36:0.17 37:0.69 39:0.32 43:0.65 45:0.77 55:0.52 66:0.72 69:0.41 70:0.94 71:0.71 74:0.51 75:0.97 76:0.94 77:0.89 81:0.38 86:0.68 91:0.42 98:0.79 101:0.65 102:0.97 108:0.32 114:0.63 117:0.86 122:0.90 123:0.31 124:0.43 126:0.32 127:0.70 129:0.05 131:0.61 133:0.39 135:0.98 139:0.65 144:0.57 145:0.76 146:0.77 147:0.80 149:0.36 150:0.39 154:0.49 157:0.78 158:0.73 159:0.46 166:0.82 170:0.99 172:0.73 173:0.46 174:0.90 176:0.59 177:0.88 178:0.55 179:0.54 182:0.73 187:0.68 192:0.44 193:0.97 195:0.68 197:0.92 201:0.56 212:0.61 216:0.22 222:0.60 226:0.95 227:0.61 229:0.61 231:0.79 232:0.72 235:0.88 236:0.94 239:0.96 241:0.30 242:0.27 243:0.75 244:0.61 245:0.76 246:0.61 247:0.88 253:0.50 254:0.86 259:0.21 265:0.79 266:0.54 267:0.59 271:0.16 276:0.64 282:0.72 287:0.61 290:0.63 291:0.97 300:0.84 +1 7:0.69 8:0.77 10:0.91 11:0.46 12:0.20 17:0.85 21:0.40 27:0.39 29:0.53 30:0.45 32:0.67 34:0.71 36:0.17 37:0.69 39:0.32 43:0.65 45:0.66 55:0.71 66:0.72 69:0.83 70:0.57 71:0.71 74:0.50 76:0.94 77:0.89 81:0.32 83:0.56 91:0.38 98:0.82 101:0.47 108:0.68 114:0.69 122:0.83 123:0.66 124:0.43 126:0.24 127:0.76 129:0.05 131:0.61 133:0.43 135:0.60 139:0.67 144:0.57 145:0.88 146:0.87 147:0.63 149:0.46 150:0.35 154:0.55 155:0.49 157:0.61 159:0.58 166:0.82 170:0.99 172:0.82 173:0.85 174:0.89 175:0.75 176:0.59 177:0.38 178:0.55 179:0.41 182:0.61 187:0.39 192:0.46 195:0.77 197:0.90 204:0.78 208:0.50 212:0.82 216:0.22 222:0.60 227:0.61 229:0.61 230:0.71 231:0.74 232:0.72 233:0.76 235:0.42 239:0.90 241:0.21 242:0.62 243:0.25 244:0.83 245:0.85 246:0.61 247:0.76 253:0.53 257:0.84 259:0.21 265:0.82 266:0.47 267:0.59 271:0.16 276:0.76 277:0.69 281:0.91 282:0.72 287:0.61 290:0.69 300:0.55 +1 1:0.74 6:0.94 8:0.90 9:0.80 11:0.78 12:0.69 17:0.47 20:0.98 21:0.47 26:0.95 27:0.12 28:0.78 30:0.57 32:0.80 34:0.96 36:0.47 37:0.78 39:0.41 41:0.99 43:0.77 58:1.00 60:0.99 66:0.20 69:0.80 70:0.76 74:0.94 77:0.70 78:0.78 81:0.65 83:0.89 85:0.97 91:0.75 96:0.98 98:0.50 100:0.83 101:0.84 108:0.83 111:0.78 114:0.80 120:0.94 122:0.57 123:0.62 124:0.65 126:0.54 127:0.41 129:0.81 131:0.90 133:0.67 135:0.20 140:0.45 141:0.97 144:0.76 145:0.54 146:0.75 147:0.79 149:0.93 150:0.78 151:0.99 152:0.90 153:0.96 154:0.69 155:0.67 157:0.90 158:0.92 159:0.67 161:0.72 162:0.73 164:0.92 165:0.80 166:0.84 167:0.75 169:0.81 172:0.79 173:0.71 176:0.73 177:0.38 179:0.28 181:0.55 182:0.82 186:0.79 187:0.68 189:0.95 191:0.93 192:0.59 195:0.86 199:1.00 201:0.74 202:0.90 208:0.64 212:0.78 215:0.63 216:0.81 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 231:0.77 234:0.98 235:0.60 238:0.93 241:0.64 242:0.51 243:0.40 245:0.89 246:0.93 247:0.60 248:0.97 253:0.99 255:0.79 256:0.93 259:0.21 260:0.95 261:0.83 265:0.42 266:0.75 267:0.91 268:0.93 271:0.87 274:1.00 276:0.78 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.80 297:0.36 299:0.88 300:0.84 +2 8:0.85 9:0.80 12:0.69 17:0.04 21:0.22 25:0.88 26:0.95 27:0.10 28:0.78 30:0.44 34:0.49 36:0.64 37:0.87 39:0.41 41:0.92 43:0.41 55:0.59 58:0.99 66:0.67 69:0.86 70:0.67 74:0.94 78:0.83 81:0.77 83:0.78 87:0.98 91:0.40 96:0.91 98:0.70 104:0.54 108:0.72 111:0.82 114:0.55 120:0.77 122:0.67 123:0.70 124:0.51 126:0.32 127:0.47 129:0.05 131:0.89 133:0.62 135:0.54 140:0.90 141:0.97 144:0.76 146:0.82 147:0.43 149:0.69 150:0.57 151:0.87 153:0.48 154:0.31 155:0.67 157:0.61 158:0.83 159:0.57 161:0.72 162:0.73 164:0.75 166:0.77 167:0.90 169:0.99 172:0.76 173:0.86 176:0.53 177:0.05 178:0.42 179:0.41 181:0.55 182:0.82 187:0.68 191:0.90 192:0.59 199:0.96 202:0.74 208:0.64 212:0.73 216:0.70 220:0.82 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.76 234:0.98 235:0.31 241:0.77 242:0.81 243:0.17 244:0.61 245:0.77 246:0.61 247:0.39 248:0.84 253:0.94 255:0.79 256:0.78 257:0.65 259:0.21 260:0.77 265:0.70 266:0.75 267:0.18 268:0.78 271:0.87 274:0.98 276:0.71 285:0.62 287:0.95 290:0.55 300:0.55 +2 1:0.74 6:0.98 8:0.93 9:0.80 11:0.78 12:0.69 17:0.13 20:0.77 21:0.54 26:0.95 27:0.30 28:0.78 30:0.33 34:0.97 36:0.59 37:0.93 39:0.41 41:0.96 43:0.58 55:0.71 58:0.99 66:0.67 69:0.83 70:0.88 74:0.96 78:0.82 81:0.68 83:0.82 85:0.80 87:0.98 91:0.53 96:0.94 98:0.79 100:0.91 101:0.71 108:0.68 111:0.85 114:0.77 120:0.87 122:0.67 123:0.66 124:0.57 126:0.54 127:0.74 129:0.05 131:0.89 133:0.74 135:0.47 140:0.45 141:0.97 144:0.76 146:0.98 147:0.44 149:0.75 150:0.79 151:0.97 152:0.78 153:0.90 154:0.47 155:0.67 157:0.78 158:0.82 159:0.86 161:0.72 162:0.81 164:0.85 165:0.79 166:0.84 167:0.91 169:0.90 172:0.89 173:0.63 176:0.73 177:0.05 179:0.27 181:0.55 182:0.82 186:0.83 187:0.87 189:0.99 191:0.92 192:0.59 199:0.98 201:0.74 202:0.78 208:0.64 212:0.54 215:0.58 216:0.46 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 231:0.77 232:0.70 234:0.99 235:0.74 238:0.96 241:0.78 242:0.78 243:0.11 245:0.87 246:0.93 247:0.39 248:0.93 253:0.96 255:0.79 256:0.83 257:0.65 259:0.21 260:0.88 261:0.84 265:0.79 266:0.89 267:0.79 268:0.83 271:0.87 274:0.99 276:0.85 285:0.62 287:0.95 290:0.77 297:0.36 300:0.84 +2 1:0.74 6:0.97 7:0.81 8:0.81 11:0.78 12:0.69 17:0.32 20:0.77 21:0.30 26:0.95 27:0.21 28:0.78 30:0.10 34:0.34 36:0.72 37:0.74 39:0.41 43:0.98 55:0.71 58:0.93 66:0.13 69:0.12 70:0.94 74:0.98 78:0.75 81:0.75 83:0.75 85:1.00 91:0.01 98:0.32 100:0.79 101:0.80 104:0.84 106:0.81 108:0.99 111:0.75 114:0.86 121:1.00 122:0.67 123:0.99 124:0.98 126:0.54 127:0.98 129:1.00 131:0.61 133:0.98 135:0.20 141:0.91 144:0.76 146:0.57 147:0.63 149:0.96 150:0.84 152:0.91 154:0.98 155:0.67 157:0.90 159:0.98 161:0.72 165:0.84 166:0.84 167:0.70 169:0.83 172:0.94 173:0.05 176:0.73 177:0.38 178:0.55 179:0.10 181:0.55 182:0.81 186:0.76 187:0.39 192:0.59 199:1.00 201:0.74 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 230:0.87 231:0.77 233:0.76 234:0.91 235:0.42 241:0.50 242:0.31 243:0.10 245:0.97 246:0.93 247:0.67 253:0.78 259:0.21 261:0.84 265:0.34 266:0.95 267:0.44 271:0.87 274:0.91 276:0.99 283:0.71 287:0.61 290:0.86 297:0.36 300:0.94 +0 1:0.74 7:0.76 11:0.78 12:0.69 17:0.43 21:0.13 26:0.95 27:0.45 28:0.78 30:0.27 32:0.80 34:0.67 36:0.18 37:0.50 39:0.41 43:0.82 55:0.71 58:0.93 60:0.87 66:0.32 69:0.61 70:0.84 74:0.86 77:0.70 81:0.85 83:0.87 85:0.88 91:0.10 98:0.59 101:0.78 108:0.46 114:0.92 123:0.78 124:0.69 126:0.54 127:0.47 129:0.05 131:0.61 133:0.62 135:0.72 141:0.91 144:0.76 145:0.39 146:0.59 147:0.64 149:0.85 150:0.91 154:0.77 155:0.67 157:0.84 158:0.92 159:0.59 161:0.72 165:0.88 166:0.85 167:0.69 172:0.51 173:0.53 176:0.73 177:0.38 178:0.55 179:0.52 181:0.55 182:0.81 187:0.68 192:0.59 195:0.79 199:0.98 201:0.74 208:0.64 212:0.49 215:0.84 216:0.77 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 230:0.79 231:0.77 233:0.76 234:0.91 235:0.22 241:0.46 242:0.57 243:0.49 245:0.76 246:0.93 247:0.67 253:0.76 259:0.21 265:0.44 266:0.71 267:0.23 271:0.87 274:0.91 276:0.63 277:0.69 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.70 +3 1:0.74 8:0.92 9:0.80 11:0.78 12:0.69 17:0.30 21:0.22 25:0.88 26:0.95 27:0.10 28:0.78 30:0.43 34:0.97 36:0.68 37:0.87 39:0.41 41:0.94 43:0.67 55:0.84 58:0.99 66:0.51 69:0.86 70:0.79 74:0.89 78:0.72 81:0.85 83:0.80 85:0.72 87:0.98 91:0.68 96:0.92 98:0.66 101:0.71 104:0.54 108:0.72 111:0.82 114:0.90 120:0.80 122:0.67 123:0.71 124:0.65 126:0.54 127:0.41 129:0.05 131:0.89 133:0.62 135:0.49 140:0.87 141:0.97 144:0.76 146:0.79 147:0.43 149:0.65 150:0.90 151:0.87 153:0.48 154:0.57 155:0.67 157:0.76 158:0.82 159:0.55 161:0.72 162:0.68 164:0.81 165:0.86 166:0.84 167:0.91 169:0.99 172:0.57 173:0.86 176:0.73 177:0.05 178:0.42 179:0.56 181:0.55 182:0.82 187:0.68 191:0.92 192:0.59 199:0.97 201:0.74 202:0.79 208:0.64 212:0.28 215:0.79 216:0.70 220:0.62 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.76 234:0.98 235:0.42 241:0.78 242:0.79 243:0.23 245:0.69 246:0.93 247:0.39 248:0.87 253:0.94 255:0.79 256:0.81 257:0.65 259:0.21 260:0.81 265:0.67 266:0.75 267:0.65 268:0.82 271:0.87 274:0.99 276:0.58 285:0.62 287:0.95 290:0.90 297:0.36 300:0.70 +2 1:0.74 6:0.97 7:0.81 8:0.89 9:0.80 11:0.78 12:0.69 17:0.16 20:0.98 21:0.30 26:0.95 27:0.21 28:0.78 30:0.11 34:0.30 36:0.74 37:0.74 39:0.41 41:0.98 43:0.98 55:0.71 58:1.00 60:0.95 66:0.13 69:0.12 70:0.94 74:0.98 78:0.77 81:0.75 83:0.90 85:1.00 91:0.72 96:0.98 98:0.32 100:0.85 101:0.80 104:0.84 106:0.81 108:0.99 111:0.79 114:0.86 117:0.86 120:0.96 121:1.00 122:0.57 123:0.99 124:0.98 126:0.54 127:0.98 129:1.00 131:0.89 133:0.98 135:0.20 140:0.45 141:0.97 144:0.76 146:0.56 147:0.62 149:0.96 150:0.84 151:1.00 152:0.91 153:0.98 154:0.98 155:0.67 157:0.90 158:0.92 159:0.98 161:0.72 162:0.65 164:0.93 165:0.84 166:0.84 167:0.74 169:0.83 172:0.94 173:0.05 176:0.73 177:0.38 179:0.10 181:0.55 182:0.82 186:0.78 187:0.39 189:0.97 191:0.73 192:0.59 199:1.00 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 230:0.87 231:0.77 232:0.91 233:0.76 234:0.98 235:0.42 238:0.97 241:0.61 242:0.31 243:0.10 245:0.97 246:0.93 247:0.67 248:0.98 253:0.99 255:0.79 256:0.92 259:0.21 260:0.96 261:0.84 265:0.34 266:0.95 267:0.65 268:0.92 271:0.87 274:1.00 276:0.99 279:0.86 283:0.82 285:0.62 287:0.95 290:0.86 297:0.36 300:0.94 +1 1:0.74 6:0.85 8:0.76 11:0.78 12:0.69 17:0.32 20:0.76 21:0.40 26:0.95 27:0.45 28:0.78 30:0.11 32:0.80 34:0.67 36:0.23 37:0.50 39:0.41 43:0.82 58:0.93 66:0.32 69:0.69 70:0.95 74:0.83 77:0.70 78:0.80 81:0.70 83:0.75 85:0.90 91:0.09 98:0.39 100:0.81 101:0.77 108:0.52 111:0.79 114:0.82 122:0.43 123:0.50 124:0.87 126:0.54 127:0.54 129:0.89 131:0.61 133:0.87 135:0.86 141:0.91 144:0.76 145:0.41 146:0.68 147:0.72 149:0.81 150:0.81 152:0.75 154:0.77 155:0.67 157:0.84 159:0.76 161:0.72 165:0.83 166:0.84 167:0.70 169:0.79 172:0.54 173:0.53 176:0.73 177:0.38 178:0.55 179:0.51 181:0.55 182:0.81 186:0.81 187:0.68 189:0.87 192:0.59 195:0.90 199:0.98 201:0.74 212:0.19 215:0.67 216:0.77 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 231:0.76 234:0.91 235:0.52 238:0.85 241:0.51 242:0.33 243:0.49 245:0.68 246:0.93 247:0.67 253:0.72 259:0.21 261:0.75 265:0.41 266:0.92 267:0.28 271:0.87 274:0.91 276:0.65 282:0.88 287:0.61 290:0.82 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.81 8:0.91 9:0.80 11:0.78 12:0.69 17:0.16 20:0.92 21:0.54 26:0.95 27:0.11 28:0.78 30:0.09 32:0.80 34:0.44 36:0.95 37:0.80 39:0.41 41:0.98 43:0.89 55:0.52 58:1.00 60:0.98 66:0.18 69:0.50 70:0.97 74:0.94 77:0.70 78:0.81 81:0.61 83:0.89 85:0.90 91:0.76 96:0.98 98:0.37 100:0.84 101:0.75 108:0.80 111:0.80 114:0.77 120:0.94 121:0.99 122:0.67 123:0.79 124:0.93 126:0.54 127:0.84 129:0.96 131:0.89 133:0.93 135:0.78 140:0.45 141:0.97 144:0.76 145:0.54 146:0.13 147:0.18 149:0.77 150:0.75 151:0.99 152:0.91 153:0.95 154:0.88 155:0.67 157:0.83 158:0.92 159:0.87 161:0.72 162:0.72 164:0.92 165:0.79 166:0.84 167:0.79 169:0.81 172:0.61 173:0.23 176:0.73 177:0.38 179:0.34 181:0.55 182:0.82 186:0.81 187:0.68 189:0.96 191:0.93 192:0.59 195:0.95 199:1.00 201:0.74 202:0.91 204:0.89 208:0.64 212:0.29 215:0.58 216:0.86 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 230:0.87 231:0.77 233:0.76 234:0.98 235:0.68 238:0.92 241:0.70 242:0.53 243:0.10 245:0.81 246:0.93 247:0.60 248:0.97 253:0.99 255:0.79 256:0.94 259:0.21 260:0.95 261:0.85 265:0.39 266:0.92 267:0.69 268:0.94 271:0.87 274:1.00 276:0.82 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.77 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.85 9:0.80 11:0.78 12:0.69 17:0.03 20:0.90 21:0.68 26:0.95 27:0.06 28:0.78 30:0.57 32:0.80 34:0.19 36:0.73 37:0.85 39:0.41 41:0.97 43:0.59 55:0.59 58:1.00 60:0.87 66:0.60 69:0.93 70:0.74 74:0.94 77:0.70 78:0.78 81:0.55 83:0.83 85:0.80 91:0.76 96:0.98 98:0.58 100:0.81 101:0.71 104:0.58 108:0.87 111:0.78 114:0.70 117:0.86 120:0.95 121:0.90 122:0.67 123:0.86 124:0.73 126:0.54 127:0.61 129:0.59 131:0.89 133:0.82 135:0.84 138:0.98 140:0.90 141:0.97 144:0.76 145:0.96 146:0.93 147:0.29 149:0.71 150:0.69 151:0.98 152:0.85 153:0.98 154:0.47 155:0.67 157:0.78 158:0.92 159:0.86 161:0.72 162:0.75 164:0.93 165:0.69 166:0.84 167:0.75 169:0.79 172:0.86 173:0.84 176:0.73 177:0.05 179:0.28 181:0.55 182:0.82 186:0.79 187:0.68 189:0.94 191:0.88 192:0.59 195:0.96 199:1.00 201:0.74 202:0.91 204:0.89 208:0.64 212:0.68 215:0.49 216:0.88 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 230:0.87 231:0.77 232:0.95 233:0.76 234:0.98 235:0.88 238:0.90 241:0.65 242:0.80 243:0.09 245:0.84 246:0.93 247:0.60 248:0.96 253:0.99 255:0.79 256:0.93 257:0.65 259:0.21 260:0.95 261:0.82 265:0.59 266:0.89 267:0.76 268:0.93 271:0.87 274:1.00 276:0.83 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.70 297:0.36 299:0.88 300:0.94 +4 6:0.98 8:0.97 9:0.80 12:0.69 20:0.93 21:0.78 26:0.95 27:0.03 28:0.78 34:0.77 36:0.26 37:0.97 39:0.41 41:1.00 58:1.00 60:0.87 74:0.47 78:0.94 83:0.90 87:0.98 91:0.98 96:1.00 100:0.99 111:0.99 120:0.98 131:0.89 135:0.81 138:0.99 140:0.45 141:0.97 144:0.76 151:1.00 152:0.86 153:0.99 155:0.67 157:0.61 158:0.92 161:0.72 162:0.68 164:0.97 166:0.61 167:0.97 169:1.00 175:0.91 181:0.55 182:0.82 186:0.94 189:0.99 191:0.97 192:0.59 199:1.00 202:0.97 208:0.64 216:0.54 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.72 234:0.98 238:0.99 241:0.95 244:0.83 246:0.61 248:0.99 253:0.99 255:0.79 256:0.98 257:0.84 259:0.21 260:0.98 261:0.98 267:0.91 268:0.98 271:0.87 274:1.00 277:0.87 279:0.86 283:0.82 285:0.62 287:0.95 +1 1:0.74 6:0.94 7:0.81 8:0.87 9:0.80 11:0.78 12:0.69 17:0.51 20:0.85 21:0.64 26:0.95 27:0.05 28:0.78 30:0.17 32:0.80 34:0.30 36:0.75 37:0.78 39:0.41 41:0.98 43:0.70 58:1.00 60:0.99 66:0.35 69:0.86 70:0.92 74:0.94 77:0.70 78:0.78 81:0.55 83:0.85 85:0.94 91:0.85 96:0.96 98:0.42 100:0.81 101:0.80 108:0.72 111:0.77 114:0.72 117:0.86 120:0.90 121:0.90 122:0.67 123:0.71 124:0.77 126:0.54 127:0.70 129:0.81 131:0.89 133:0.76 135:0.73 140:0.87 141:0.97 144:0.76 145:0.79 146:0.72 147:0.69 149:0.86 150:0.70 151:0.98 152:0.90 153:0.97 154:0.60 155:0.67 157:0.87 158:0.92 159:0.79 161:0.72 162:0.70 164:0.90 165:0.70 166:0.84 167:0.72 169:0.78 172:0.67 173:0.76 176:0.73 177:0.05 179:0.42 181:0.55 182:0.82 186:0.79 187:0.87 189:0.95 191:0.90 192:0.59 195:0.91 199:0.99 201:0.74 202:0.88 204:0.89 208:0.64 212:0.68 215:0.53 216:0.83 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 230:0.87 231:0.77 232:0.97 233:0.76 234:0.98 235:0.80 238:0.89 241:0.55 242:0.41 243:0.19 245:0.82 246:0.93 247:0.60 248:0.94 253:0.97 255:0.79 256:0.90 257:0.65 259:0.21 260:0.91 261:0.82 265:0.44 266:0.84 267:0.76 268:0.90 271:0.87 274:1.00 276:0.70 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.72 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.98 8:0.93 9:0.80 11:0.78 12:0.69 17:0.11 20:0.79 21:0.54 26:0.95 27:0.30 28:0.78 30:0.28 34:0.93 36:0.72 37:0.93 39:0.41 41:0.98 43:0.41 55:0.59 58:1.00 66:0.30 69:0.83 70:0.82 74:0.95 78:0.85 81:0.68 83:0.88 85:0.72 87:0.98 91:0.76 96:0.98 98:0.71 100:0.93 101:0.69 108:0.91 111:0.88 114:0.77 120:0.93 122:0.67 123:0.66 124:0.76 126:0.54 127:0.67 129:0.59 131:0.90 133:0.76 135:0.68 138:0.98 140:0.45 141:0.97 144:0.76 146:0.95 147:0.44 149:0.72 150:0.79 151:0.99 152:0.78 153:0.95 154:0.33 155:0.67 157:0.76 159:0.82 161:0.72 162:0.82 164:0.91 165:0.79 166:0.84 167:0.93 169:0.90 172:0.76 173:0.67 176:0.73 177:0.05 179:0.29 181:0.55 182:0.82 186:0.85 187:0.95 189:0.99 191:0.90 192:0.59 199:0.99 201:0.74 202:0.87 208:0.64 212:0.51 215:0.58 216:0.46 222:0.35 223:0.93 224:0.99 227:0.94 229:0.90 231:0.77 232:0.70 234:0.99 235:0.74 238:0.97 241:0.81 242:0.79 243:0.12 245:0.89 246:0.93 247:0.39 248:0.97 253:0.98 255:0.79 256:0.91 257:0.65 259:0.21 260:0.94 261:0.84 265:0.52 266:0.80 267:0.52 268:0.91 271:0.87 274:1.00 276:0.83 285:0.62 287:0.95 290:0.77 297:0.36 300:0.70 +2 1:0.74 7:0.81 8:0.95 9:0.80 12:0.69 21:0.71 26:0.95 27:0.06 28:0.78 30:0.19 32:0.80 34:0.21 36:0.93 37:0.85 39:0.41 41:0.96 43:0.41 55:0.71 58:1.00 66:0.35 69:0.97 70:0.74 74:0.74 77:0.70 81:0.47 83:0.81 91:0.89 96:0.95 98:0.33 104:0.58 108:0.94 114:0.54 120:0.97 121:0.90 123:0.94 124:0.90 126:0.54 127:0.81 129:0.05 131:0.89 133:0.91 135:0.73 140:0.95 141:0.97 144:0.76 145:0.67 146:0.71 147:0.05 149:0.42 150:0.61 151:0.96 153:0.88 154:0.31 155:0.67 157:0.61 158:0.82 159:0.86 161:0.72 162:0.55 164:0.97 166:0.84 167:0.75 172:0.45 173:0.98 176:0.53 177:0.05 178:0.48 179:0.70 181:0.55 182:0.80 187:0.21 191:0.95 192:0.59 195:0.95 199:1.00 201:0.74 202:0.97 204:0.89 208:0.64 212:0.22 215:0.48 216:0.88 220:0.62 222:0.35 223:0.93 224:0.98 227:0.94 229:0.89 230:0.87 231:0.77 233:0.76 234:0.97 235:0.88 241:0.63 242:0.14 243:0.11 244:0.61 245:0.54 246:0.61 247:0.67 248:0.91 253:0.95 255:0.79 256:0.97 257:0.65 259:0.21 260:0.97 265:0.36 266:0.84 267:0.88 268:0.97 271:0.87 274:1.00 276:0.55 282:0.83 283:0.71 285:0.62 287:0.94 290:0.54 297:0.36 299:0.88 300:0.55 +3 1:0.74 7:0.81 11:0.78 12:0.69 17:0.51 21:0.30 26:0.95 27:0.04 28:0.78 30:0.33 32:0.80 34:0.69 36:0.30 37:0.99 39:0.41 43:0.59 58:0.93 66:0.49 69:0.93 70:0.88 74:0.80 77:0.70 81:0.75 83:0.75 85:0.95 91:0.15 98:0.43 101:0.78 108:0.95 114:0.86 121:0.99 122:0.57 123:0.94 124:0.79 126:0.54 127:0.38 129:0.93 131:0.61 133:0.84 135:0.82 141:0.91 144:0.76 145:0.47 146:0.55 147:0.61 149:0.71 150:0.84 154:0.49 155:0.67 157:0.87 159:0.83 161:0.72 165:0.84 166:0.84 167:0.62 172:0.73 173:0.81 176:0.73 177:0.38 178:0.55 179:0.34 181:0.55 182:0.81 187:0.87 192:0.59 195:0.96 199:0.98 201:0.74 204:0.89 208:0.64 212:0.59 215:0.72 216:0.57 222:0.35 223:0.93 224:0.93 227:0.61 229:0.61 230:0.87 231:0.76 233:0.76 234:0.91 235:0.42 241:0.21 242:0.37 243:0.23 245:0.77 246:0.93 247:0.60 253:0.72 259:0.21 265:0.45 266:0.89 267:0.44 271:0.87 274:0.91 276:0.72 282:0.88 287:0.61 290:0.86 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.97 7:0.81 8:0.78 9:0.80 11:0.78 12:0.69 17:0.30 20:0.77 21:0.30 26:0.95 27:0.21 28:0.78 30:0.10 34:0.30 36:0.71 37:0.74 39:0.41 41:0.82 43:0.98 55:0.71 58:0.98 66:0.13 69:0.12 70:0.94 74:0.98 78:0.75 81:0.75 83:0.76 85:1.00 91:0.01 96:0.82 98:0.32 100:0.79 101:0.80 108:0.99 111:0.75 114:0.86 117:0.86 120:0.58 121:1.00 122:0.57 123:0.99 124:0.98 126:0.54 127:0.98 129:1.00 131:0.89 133:0.98 135:0.21 140:0.87 141:0.97 144:0.76 146:0.57 147:0.63 149:0.96 150:0.84 151:0.58 152:0.91 153:0.48 154:0.98 155:0.67 157:0.90 159:0.98 161:0.72 162:0.74 164:0.57 165:0.84 166:0.84 167:0.74 169:0.83 172:0.94 173:0.05 176:0.73 177:0.38 179:0.10 181:0.55 182:0.82 186:0.76 187:0.39 190:0.77 192:0.59 199:1.00 201:0.74 202:0.56 204:0.89 208:0.64 212:0.42 215:0.72 216:0.95 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 230:0.87 231:0.77 232:0.91 233:0.76 234:0.98 235:0.42 241:0.61 242:0.31 243:0.10 245:0.97 246:0.93 247:0.67 248:0.57 253:0.88 255:0.79 256:0.58 259:0.21 260:0.59 261:0.84 265:0.34 266:0.95 267:0.59 268:0.59 271:0.87 274:0.97 276:0.99 285:0.62 287:0.94 290:0.86 297:0.36 300:0.94 +4 1:0.74 6:0.99 8:0.96 9:0.80 11:0.51 12:0.69 17:0.02 20:0.81 21:0.54 25:0.88 26:0.95 27:0.10 28:0.78 30:0.19 34:0.96 36:0.68 37:0.87 39:0.41 41:1.00 43:0.41 55:0.59 58:1.00 60:0.97 66:0.07 69:0.95 70:0.88 74:0.94 78:0.94 81:0.68 83:0.90 87:0.98 91:0.95 96:1.00 98:0.33 100:0.99 104:0.54 108:0.91 111:0.99 114:0.77 120:0.99 122:0.67 123:0.66 124:0.90 126:0.54 127:0.77 129:0.05 131:0.90 133:0.89 135:0.49 138:0.99 140:0.45 141:0.97 144:0.76 146:0.66 147:0.44 149:0.68 150:0.79 151:1.00 152:0.86 153:0.99 154:0.21 155:0.67 157:0.61 158:0.92 159:0.86 161:0.72 162:0.67 164:0.98 165:0.79 166:0.84 167:0.97 169:0.94 172:0.57 173:0.92 176:0.73 177:0.05 179:0.34 181:0.55 182:0.82 186:0.94 187:0.39 189:0.99 191:0.98 192:0.59 199:1.00 201:0.74 202:0.97 208:0.64 212:0.42 215:0.58 216:0.70 222:0.35 223:0.93 224:0.98 227:0.94 229:0.90 231:0.77 232:0.76 234:0.98 235:0.74 238:0.99 241:0.94 242:0.81 243:0.13 245:0.83 246:0.93 247:0.39 248:1.00 253:1.00 254:0.86 255:0.79 256:0.98 257:0.65 259:0.21 260:0.99 261:0.93 265:0.32 266:0.94 267:0.82 268:0.98 271:0.87 274:1.00 276:0.81 277:0.69 279:0.86 283:0.82 285:0.62 287:0.95 290:0.77 297:0.36 300:0.70 +1 1:0.74 6:0.81 7:0.64 8:0.68 11:0.86 12:0.86 17:0.62 18:0.67 20:0.94 21:0.40 27:0.72 29:0.62 30:0.76 32:0.63 34:0.45 36:0.67 37:0.27 39:0.54 43:0.71 45:0.73 48:0.91 64:0.77 66:0.72 69:0.27 70:0.22 71:0.92 74:0.48 76:0.94 77:0.70 78:0.97 81:0.54 83:0.60 86:0.77 91:0.87 98:0.55 99:0.83 100:0.89 101:0.87 108:0.21 111:0.94 114:0.62 122:0.51 123:0.20 126:0.54 127:0.16 129:0.05 131:0.61 132:0.97 135:0.32 139:0.73 144:0.57 145:0.44 146:0.24 147:0.30 149:0.36 150:0.71 152:0.88 154:0.88 155:0.67 157:0.61 159:0.11 165:0.70 166:0.93 169:0.87 172:0.27 173:0.76 176:0.73 177:0.70 178:0.55 179:0.71 182:0.61 186:0.97 187:0.21 189:0.84 192:0.87 195:0.53 201:0.93 204:0.85 208:0.64 212:0.95 215:0.42 216:0.14 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 233:0.76 235:0.60 238:0.81 241:0.78 242:0.45 243:0.75 246:0.61 247:0.35 253:0.46 254:0.80 259:0.21 261:0.91 265:0.56 266:0.12 267:0.23 276:0.21 281:0.91 282:0.71 287:0.61 290:0.62 297:0.36 300:0.18 +1 7:0.63 8:0.61 11:0.75 12:0.86 17:0.28 18:0.97 21:0.59 27:0.61 29:0.62 30:0.53 34:0.53 36:0.25 37:0.27 39:0.54 43:0.18 44:0.85 45:0.96 48:0.91 55:0.59 66:0.98 69:0.19 70:0.37 71:0.92 74:0.89 76:0.85 77:0.70 81:0.24 86:0.99 91:0.46 96:0.67 98:0.97 101:0.52 108:0.15 114:0.54 122:0.80 123:0.15 126:0.26 127:0.76 129:0.05 131:0.83 135:0.21 139:0.99 140:0.45 144:0.57 145:0.79 146:0.88 147:0.90 149:0.07 150:0.24 151:0.46 153:0.48 154:0.78 155:0.58 157:0.61 159:0.47 162:0.86 166:0.75 172:0.94 173:0.28 176:0.54 177:0.70 179:0.23 182:0.61 187:0.21 190:0.89 192:0.51 195:0.67 202:0.36 204:0.85 208:0.54 212:0.94 216:0.26 219:0.86 220:0.97 222:0.76 227:0.84 229:0.61 230:0.63 231:0.89 233:0.73 235:0.68 241:0.32 242:0.44 243:0.98 246:0.61 247:0.76 248:0.46 253:0.46 254:0.84 256:0.45 259:0.21 265:0.97 266:0.27 267:0.28 268:0.46 276:0.89 281:0.89 282:0.71 285:0.47 287:0.61 290:0.54 292:0.91 300:0.43 +2 1:0.69 6:0.79 7:0.81 8:0.61 11:0.86 12:0.86 17:0.53 18:0.92 20:0.89 21:0.54 27:0.61 29:0.62 30:0.10 32:0.80 34:0.83 36:0.23 37:0.27 39:0.54 43:0.58 44:0.93 45:0.83 48:0.97 55:0.42 66:0.12 69:0.21 70:1.00 71:0.92 74:0.81 76:0.85 77:0.89 78:0.98 81:0.33 86:0.86 91:0.20 98:0.30 100:0.94 101:0.87 108:0.61 111:0.96 114:0.57 122:0.57 123:0.92 124:0.94 126:0.44 127:0.99 129:0.98 131:0.61 133:0.94 135:0.28 139:0.92 144:0.57 145:0.80 146:0.32 147:0.38 149:0.33 150:0.49 152:0.96 154:0.86 155:0.67 157:0.61 158:0.78 159:0.94 166:0.87 169:0.91 172:0.71 173:0.07 176:0.55 177:0.70 178:0.55 179:0.28 182:0.61 186:0.98 187:0.21 189:0.83 191:0.75 192:0.87 195:0.99 201:0.68 204:0.89 208:0.64 212:0.40 215:0.39 216:0.26 219:0.92 222:0.76 227:0.61 229:0.61 230:0.86 231:0.89 233:0.73 235:0.68 238:0.82 241:0.07 242:0.21 243:0.19 245:0.84 246:0.61 247:0.91 253:0.45 254:0.74 259:0.21 261:0.94 265:0.27 266:0.96 267:0.19 276:0.86 279:0.86 281:0.89 282:0.88 283:0.77 287:0.61 290:0.57 292:0.91 297:0.36 300:0.98 +1 1:0.74 11:0.85 12:0.86 17:0.72 18:0.95 21:0.13 22:0.93 27:0.62 29:0.62 30:0.60 34:0.98 36:0.35 39:0.54 43:0.80 45:0.73 47:0.93 64:0.77 66:0.96 69:0.46 70:0.50 71:0.92 74:0.82 81:0.80 83:0.91 85:0.94 86:0.97 87:0.98 91:0.11 98:0.94 101:0.97 106:0.81 108:0.35 114:0.79 117:0.86 122:0.57 123:0.34 126:0.54 127:0.63 129:0.05 131:0.61 135:0.65 139:0.96 144:0.57 146:0.92 147:0.93 149:0.95 150:0.87 154:0.74 155:0.67 157:0.99 159:0.54 165:0.93 166:0.93 172:0.91 173:0.44 176:0.73 177:0.70 178:0.55 179:0.29 182:0.98 187:0.39 192:0.87 201:0.93 206:0.81 208:0.64 212:0.81 215:0.61 216:0.18 222:0.76 227:0.61 228:0.99 229:0.61 231:0.90 232:0.70 235:0.42 241:0.39 242:0.21 243:0.96 246:0.93 247:0.91 253:0.57 259:0.21 262:0.93 265:0.94 266:0.54 267:0.44 276:0.83 287:0.61 290:0.79 297:0.36 300:0.43 +1 1:0.69 8:0.66 12:0.86 17:0.36 18:0.65 21:0.54 27:0.34 29:0.62 30:0.37 34:0.81 36:0.99 37:0.46 39:0.54 43:0.14 55:0.71 66:0.33 69:0.91 70:0.88 71:0.92 74:0.30 81:0.33 86:0.59 91:0.18 98:0.51 104:0.92 106:0.81 108:0.94 114:0.55 117:0.86 122:0.51 123:0.94 124:0.86 126:0.44 127:0.88 129:0.59 131:0.61 133:0.86 135:0.77 139:0.57 146:0.82 147:0.84 149:0.26 150:0.48 154:0.55 155:0.58 157:0.61 158:0.72 159:0.88 166:0.87 172:0.78 173:0.78 176:0.55 177:0.38 178:0.55 179:0.29 182:0.61 187:0.68 192:0.59 201:0.68 206:0.81 208:0.54 212:0.49 215:0.39 216:0.71 222:0.76 227:0.61 229:0.61 231:0.86 232:0.82 235:0.74 241:0.03 242:0.18 243:0.34 244:0.61 245:0.87 246:0.61 247:0.91 253:0.36 254:0.74 257:0.65 259:0.21 265:0.52 266:0.94 267:0.76 276:0.85 279:0.82 283:0.78 287:0.61 290:0.55 297:0.36 300:0.84 +1 11:0.75 12:0.86 17:0.36 18:0.87 21:0.78 22:0.93 27:0.67 29:0.62 30:0.38 31:0.90 32:0.62 34:0.95 36:0.68 37:0.26 39:0.54 43:0.72 44:0.85 45:0.66 47:0.93 66:0.83 69:0.29 70:0.58 71:0.92 74:0.46 77:0.70 79:0.93 86:0.77 91:0.66 98:0.78 101:0.52 104:0.87 106:0.81 108:0.22 120:0.71 122:0.75 123:0.22 127:0.26 129:0.05 131:0.90 135:0.54 137:0.90 139:0.74 140:0.45 144:0.57 145:0.63 146:0.53 147:0.59 149:0.38 151:0.81 153:0.48 154:0.86 157:0.61 159:0.19 162:0.74 164:0.53 166:0.61 172:0.51 173:0.53 177:0.70 179:0.67 182:0.61 187:0.21 190:0.77 191:0.77 192:0.51 195:0.55 196:0.90 202:0.56 206:0.81 212:0.86 216:0.28 219:0.87 220:0.87 222:0.76 227:0.88 229:0.61 231:0.86 235:0.95 241:0.54 242:0.29 243:0.84 246:0.61 247:0.60 248:0.74 253:0.34 254:0.84 255:0.74 256:0.58 259:0.21 260:0.61 262:0.93 265:0.78 266:0.23 267:0.28 268:0.59 276:0.39 282:0.71 283:0.82 284:0.86 285:0.56 287:0.61 292:0.95 300:0.43 +0 11:0.86 12:0.86 17:0.66 18:0.71 21:0.78 27:0.69 29:0.62 30:0.38 34:0.44 36:0.88 37:0.53 39:0.54 43:0.72 45:0.77 66:0.54 69:0.05 70:0.63 71:0.92 74:0.45 83:0.60 86:0.75 91:0.23 97:0.89 98:0.64 101:0.87 108:0.05 122:0.51 123:0.05 124:0.50 127:0.51 129:0.05 131:0.61 133:0.43 135:0.85 139:0.72 144:0.57 146:0.49 147:0.55 149:0.49 154:0.77 155:0.58 157:0.61 158:0.72 159:0.29 166:0.61 172:0.41 173:0.24 177:0.70 178:0.55 179:0.80 182:0.61 187:0.39 192:0.59 208:0.54 212:0.42 216:0.30 222:0.76 227:0.61 229:0.61 231:0.79 232:0.72 235:0.05 241:0.21 242:0.13 243:0.63 245:0.59 246:0.61 247:0.74 253:0.33 254:0.80 259:0.21 265:0.65 266:0.54 267:0.65 276:0.36 279:0.82 283:0.82 287:0.61 300:0.43 +1 1:0.74 6:0.83 7:0.81 8:0.66 9:0.80 11:0.85 12:0.86 17:0.78 18:0.99 20:0.92 21:0.22 27:0.66 29:0.62 30:0.26 31:0.89 32:0.80 34:0.90 36:0.63 37:0.55 39:0.54 41:0.88 43:0.78 44:0.93 45:0.66 48:0.97 55:0.52 64:0.77 66:0.97 69:0.30 70:0.73 71:0.92 74:0.65 77:0.89 78:0.99 79:0.88 81:0.75 83:0.91 85:0.72 86:0.84 91:0.33 96:0.72 98:0.84 100:0.95 101:0.97 104:0.87 108:0.24 111:0.98 114:0.71 120:0.59 121:0.90 122:0.86 123:0.23 126:0.54 127:0.34 129:0.05 131:0.96 135:0.34 137:0.89 139:0.90 140:0.45 144:0.57 145:0.63 146:0.92 147:0.93 149:0.70 150:0.84 151:0.56 152:0.87 153:0.48 154:0.70 155:0.67 157:0.84 159:0.53 162:0.86 164:0.67 165:0.93 166:0.93 169:0.93 172:0.93 173:0.24 176:0.73 177:0.70 179:0.18 182:0.99 186:0.99 187:0.21 189:0.84 192:0.87 195:0.80 196:0.92 201:0.93 202:0.50 204:0.89 208:0.64 212:0.94 215:0.51 216:0.14 219:0.87 220:0.82 222:0.76 227:0.92 229:0.99 230:0.87 231:0.90 233:0.76 235:0.60 238:0.84 241:0.05 242:0.21 243:0.97 246:0.93 247:0.92 248:0.58 253:0.58 255:0.95 256:0.58 259:0.21 260:0.59 261:0.94 265:0.84 266:0.27 267:0.69 268:0.59 276:0.87 281:0.91 282:0.88 283:0.78 284:0.92 285:0.62 287:0.95 290:0.71 292:0.91 297:0.36 299:0.88 300:0.55 +1 1:0.69 11:0.85 12:0.86 17:0.26 18:0.80 21:0.68 27:0.45 29:0.62 30:0.61 34:0.71 36:0.17 37:0.50 39:0.54 43:0.82 44:0.85 45:0.83 60:0.87 64:0.77 66:0.27 69:0.70 70:0.65 71:0.92 74:0.64 77:0.70 81:0.32 83:0.91 85:0.72 86:0.83 91:0.09 98:0.17 99:0.83 101:0.97 108:0.92 114:0.54 122:0.85 123:0.91 124:0.87 126:0.44 127:0.54 129:0.05 131:0.61 133:0.86 135:0.66 139:0.85 144:0.57 145:0.50 146:0.24 147:0.30 149:0.66 150:0.51 154:0.77 155:0.67 157:0.82 158:0.91 159:0.77 165:0.70 166:0.83 172:0.41 173:0.53 176:0.55 177:0.70 178:0.55 179:0.63 182:0.75 187:0.68 192:0.87 195:0.91 201:0.68 208:0.64 212:0.59 216:0.77 219:0.95 222:0.76 227:0.61 229:0.61 231:0.84 235:0.84 241:0.51 242:0.51 243:0.39 245:0.60 246:0.61 247:0.67 253:0.40 259:0.21 265:0.18 266:0.54 267:0.36 276:0.54 277:0.69 279:0.86 282:0.71 283:0.78 287:0.61 290:0.54 292:0.91 300:0.55 +1 1:0.69 6:0.82 8:0.63 9:0.76 11:0.75 12:0.86 17:0.24 18:0.93 20:0.88 27:0.51 29:0.62 30:0.10 31:0.91 34:0.21 36:0.87 37:0.29 39:0.54 43:0.68 44:0.85 45:0.73 64:0.77 66:0.53 69:0.32 70:0.94 71:0.92 74:0.59 76:0.85 77:0.70 78:0.99 79:0.96 81:0.77 83:0.60 86:0.91 91:0.80 96:0.87 97:0.94 98:0.91 99:0.83 100:0.87 101:0.52 108:0.45 111:0.96 114:0.78 122:0.75 123:0.43 124:0.52 126:0.44 127:0.81 129:0.59 131:0.88 132:0.97 133:0.46 135:0.37 137:0.91 139:0.88 140:0.80 144:0.57 145:0.39 146:0.21 147:0.27 149:0.38 150:0.74 151:0.81 152:0.83 153:0.48 154:0.94 155:0.58 157:0.61 158:0.72 159:0.31 162:0.70 165:0.70 166:0.83 169:0.85 172:0.54 173:0.50 176:0.55 177:0.70 178:0.42 179:0.67 182:0.61 186:0.99 189:0.84 190:0.77 191:0.70 192:0.51 195:0.62 196:0.93 201:0.68 202:0.65 208:0.54 212:0.61 216:0.08 219:0.87 220:0.74 222:0.76 227:0.87 229:0.61 231:0.84 232:0.72 235:0.12 238:0.82 241:0.82 242:0.16 243:0.40 245:0.74 246:0.61 247:0.74 248:0.83 253:0.68 254:0.74 255:0.74 256:0.68 261:0.90 265:0.91 266:0.38 267:0.79 268:0.68 276:0.53 279:0.82 282:0.71 284:0.86 285:0.56 287:0.61 290:0.78 292:0.91 300:0.70 +1 1:0.74 6:0.79 7:0.64 8:0.59 11:0.64 12:0.86 17:0.47 18:0.80 20:0.89 21:0.47 27:0.61 29:0.62 30:0.09 34:0.99 36:0.81 37:0.27 39:0.54 43:0.72 44:0.85 45:0.85 48:0.98 64:0.77 66:0.67 69:0.21 70:0.93 71:0.92 74:0.35 76:0.94 77:0.70 78:0.99 81:0.48 83:0.60 86:0.58 91:0.54 97:0.97 98:0.75 99:0.83 100:0.92 101:0.52 108:0.17 111:0.96 114:0.58 122:0.55 123:0.16 124:0.50 126:0.54 127:0.75 129:0.05 131:0.61 133:0.66 135:0.76 139:0.59 144:0.57 145:0.74 146:0.82 147:0.85 149:0.78 150:0.68 152:0.96 154:0.59 155:0.67 157:0.61 158:0.72 159:0.58 165:0.70 166:0.93 169:0.91 172:0.78 173:0.23 176:0.66 177:0.70 178:0.55 179:0.45 182:0.61 186:0.99 187:0.21 189:0.81 192:0.87 195:0.75 201:0.93 204:0.85 208:0.64 212:0.68 215:0.39 216:0.26 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 232:0.86 233:0.73 235:0.74 238:0.82 241:0.15 242:0.18 243:0.72 245:0.78 246:0.61 247:0.84 253:0.41 254:0.90 259:0.21 261:0.94 265:0.75 266:0.54 267:0.85 276:0.73 279:0.82 281:0.89 282:0.70 283:0.78 287:0.61 290:0.58 297:0.36 300:0.70 +1 1:0.74 6:0.79 7:0.81 8:0.59 9:0.80 11:0.85 12:0.86 17:0.70 18:0.93 20:0.91 21:0.47 27:0.61 29:0.62 30:0.21 31:0.87 32:0.62 34:0.98 36:0.59 37:0.27 39:0.54 43:0.58 44:0.85 45:0.81 48:0.91 55:0.71 64:0.77 66:0.33 69:0.19 70:0.90 71:0.92 74:0.50 76:0.94 77:0.89 78:0.99 79:0.87 81:0.50 83:0.60 86:0.62 91:0.60 96:0.70 98:0.48 99:0.83 100:0.93 101:0.87 108:0.15 111:0.96 114:0.60 120:0.56 122:0.86 123:0.15 124:0.87 126:0.54 127:0.81 129:0.59 131:0.96 133:0.86 135:0.63 137:0.87 139:0.76 140:0.80 144:0.57 145:0.79 146:0.76 147:0.80 149:0.50 150:0.68 151:0.52 152:0.96 153:0.48 154:0.60 155:0.67 157:0.61 159:0.78 162:0.80 164:0.72 165:0.70 166:0.93 169:0.90 172:0.68 173:0.13 176:0.73 177:0.70 178:0.42 179:0.40 182:0.61 186:0.98 187:0.21 189:0.81 191:0.70 192:0.87 195:0.90 196:0.89 201:0.93 202:0.60 204:0.89 208:0.64 212:0.37 215:0.41 216:0.26 219:0.87 220:0.87 222:0.76 227:0.92 229:0.61 230:0.86 231:0.90 233:0.73 235:0.68 238:0.83 241:0.27 242:0.27 243:0.50 244:0.61 245:0.80 246:0.61 247:0.93 248:0.52 253:0.46 255:0.95 256:0.64 259:0.21 260:0.57 261:0.94 265:0.49 266:0.87 267:0.69 268:0.65 276:0.77 281:0.89 282:0.71 283:0.78 284:0.94 285:0.62 287:0.61 290:0.60 292:0.91 297:0.36 300:0.70 +3 1:0.74 6:0.80 7:0.64 8:0.59 11:0.42 12:0.86 17:0.90 18:0.76 20:0.96 21:0.40 22:0.93 27:0.06 29:0.62 30:0.33 32:0.78 34:0.99 36:0.25 37:0.60 39:0.54 43:0.10 44:0.93 47:0.93 48:0.91 55:0.45 64:0.77 66:0.79 69:0.78 70:0.49 71:0.92 74:0.54 76:0.85 77:0.70 78:0.99 81:0.52 86:0.73 91:0.40 98:0.28 100:0.93 104:0.88 106:0.81 108:0.62 111:0.96 114:0.62 117:0.86 122:0.57 123:0.59 126:0.54 127:0.12 129:0.05 131:0.61 135:0.96 139:0.80 144:0.57 145:0.44 146:0.59 147:0.65 149:0.06 150:0.66 152:0.89 154:0.69 155:0.67 157:0.61 159:0.22 166:0.93 169:0.91 172:0.41 173:0.59 176:0.73 177:0.70 178:0.55 179:0.20 182:0.61 186:0.99 187:0.68 189:0.82 192:0.87 195:0.94 201:0.93 206:0.81 208:0.64 212:0.19 215:0.42 216:0.85 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 232:0.96 233:0.73 235:0.60 238:0.82 241:0.27 242:0.45 243:0.80 246:0.61 247:0.39 253:0.47 254:0.84 259:0.21 261:0.93 262:0.93 265:0.30 266:0.47 267:0.84 276:0.31 281:0.88 282:0.86 287:0.61 290:0.62 297:0.36 300:0.33 +3 1:0.74 7:0.64 8:0.64 9:0.80 11:0.75 12:0.86 17:0.53 18:0.81 21:0.64 27:0.54 29:0.62 30:0.53 31:0.88 32:0.79 34:0.99 36:0.18 37:0.35 39:0.54 43:0.18 44:0.93 45:0.66 48:0.91 55:0.42 64:0.77 66:0.60 69:0.80 70:0.95 71:0.92 74:0.74 76:0.85 77:0.89 79:0.87 81:0.39 86:0.82 91:0.04 96:0.70 98:0.27 101:0.52 108:0.64 114:0.57 120:0.57 122:0.77 123:0.61 124:0.82 126:0.54 127:0.94 129:0.05 131:0.96 133:0.89 135:0.70 137:0.88 139:0.89 140:0.80 144:0.57 145:1.00 146:0.76 147:0.05 149:0.25 150:0.62 151:0.54 153:0.46 154:0.63 155:0.67 157:0.61 158:0.87 159:0.93 162:0.57 164:0.54 166:0.93 172:0.85 173:0.45 176:0.73 177:0.05 178:0.42 179:0.33 182:0.61 187:0.21 192:0.87 195:0.99 196:0.91 201:0.93 202:0.49 208:0.64 212:0.84 215:0.38 216:0.55 219:0.95 220:0.82 222:0.76 227:0.92 229:0.61 230:0.64 231:0.90 232:0.72 233:0.73 235:0.84 241:0.15 242:0.51 243:0.07 245:0.80 246:0.61 247:0.90 248:0.53 253:0.49 254:0.80 255:0.95 256:0.45 257:0.84 259:0.21 260:0.57 265:0.29 266:0.80 267:0.59 268:0.45 276:0.80 279:0.86 281:0.88 282:0.87 283:0.71 284:0.87 285:0.62 287:0.61 290:0.57 292:0.91 297:0.36 300:0.99 +2 7:0.64 8:0.63 11:0.75 12:0.86 17:0.45 18:0.73 21:0.30 22:0.93 27:0.39 29:0.62 30:0.76 31:0.87 32:0.63 34:0.31 36:0.54 37:0.43 39:0.54 43:0.85 44:0.85 45:0.66 47:0.93 48:0.98 55:0.45 64:0.77 66:0.72 69:0.51 70:0.22 71:0.92 74:0.45 79:0.94 81:0.42 86:0.76 91:0.53 98:0.20 101:0.52 108:0.39 122:0.57 123:0.37 126:0.44 127:0.10 129:0.05 131:0.81 135:0.51 137:0.87 139:0.73 140:0.45 144:0.57 145:0.65 146:0.25 147:0.31 149:0.59 150:0.33 151:0.60 153:0.48 154:0.82 157:0.61 159:0.11 162:0.82 166:0.85 172:0.27 173:0.55 177:0.70 179:0.17 182:0.61 187:0.68 190:0.89 191:0.70 192:0.51 195:0.66 196:0.89 201:0.68 202:0.63 212:0.78 215:0.44 216:0.44 219:0.86 220:0.74 222:0.76 227:0.83 229:0.61 230:0.64 231:0.85 233:0.73 235:0.42 241:0.57 242:0.45 243:0.75 246:0.61 247:0.35 248:0.61 253:0.33 254:0.84 256:0.68 259:0.21 262:0.93 265:0.22 266:0.17 267:0.19 268:0.69 276:0.24 281:0.89 284:0.86 285:0.47 287:0.61 292:0.95 297:0.36 300:0.18 +1 1:0.69 7:0.63 8:0.77 9:0.76 11:0.64 12:0.86 17:0.04 18:0.88 21:0.05 22:0.93 27:0.66 29:0.62 30:0.55 31:0.91 32:0.78 34:0.56 36:0.69 37:0.31 39:0.54 43:0.56 44:0.93 45:0.73 47:0.93 55:0.59 64:0.77 66:0.25 69:0.08 70:0.60 71:0.92 74:0.48 77:0.70 79:0.95 81:0.68 83:0.60 86:0.61 91:0.74 96:0.88 98:0.50 99:0.83 101:0.52 108:0.61 114:0.70 122:0.85 123:0.59 124:0.74 126:0.44 127:0.72 129:0.81 131:0.88 132:0.97 133:0.67 135:0.60 137:0.91 139:0.75 140:0.45 144:0.57 145:0.58 146:0.43 147:0.50 149:0.40 150:0.64 151:0.81 153:0.48 154:0.56 155:0.58 157:0.61 159:0.39 162:0.82 165:0.70 166:0.83 172:0.32 173:0.24 175:0.75 176:0.55 177:0.38 179:0.75 182:0.61 187:0.21 190:0.77 192:0.51 195:0.61 196:0.91 201:0.68 202:0.70 204:0.85 208:0.54 212:0.59 216:0.44 219:0.86 220:0.62 222:0.76 227:0.87 229:0.61 230:0.63 231:0.82 232:0.78 233:0.73 235:0.12 241:0.59 242:0.31 243:0.40 244:0.61 245:0.65 246:0.61 247:0.67 248:0.81 253:0.62 255:0.74 256:0.75 257:0.65 262:0.93 265:0.52 266:0.47 267:0.36 268:0.76 276:0.49 277:0.69 281:0.91 282:0.86 284:0.86 285:0.56 287:0.61 290:0.70 292:0.95 300:0.43 +2 1:0.74 6:0.80 7:0.63 8:0.60 11:0.92 12:0.86 17:0.82 18:0.96 20:0.85 22:0.93 27:0.48 29:0.62 30:0.36 32:0.80 34:0.95 36:0.63 37:0.29 39:0.54 43:0.87 44:0.93 45:0.73 47:0.93 48:0.97 60:0.87 64:0.77 66:0.95 69:0.55 70:0.77 71:0.92 74:0.75 76:0.85 77:0.70 78:1.00 81:0.88 83:0.91 85:0.80 86:0.92 91:0.33 98:0.92 100:0.96 101:0.97 104:0.96 106:0.81 108:0.42 111:0.98 114:0.98 117:0.86 122:0.86 123:0.40 126:0.54 127:0.52 129:0.05 131:0.61 132:0.97 135:0.65 139:0.94 144:0.57 145:0.44 146:0.80 147:0.84 149:0.82 150:0.93 152:0.91 154:0.85 155:0.67 157:0.95 158:0.92 159:0.36 165:0.93 166:0.93 169:0.91 172:0.86 173:0.65 176:0.73 177:0.70 178:0.55 179:0.36 182:0.98 186:1.00 187:0.39 189:0.81 192:0.87 195:0.67 201:0.93 206:0.81 208:0.64 212:0.88 215:0.96 216:0.20 219:0.95 222:0.76 227:0.61 229:0.61 230:0.63 231:0.90 232:0.98 233:0.76 235:0.12 238:0.83 241:0.27 242:0.23 243:0.95 246:0.93 247:0.86 253:0.67 261:0.93 262:0.93 265:0.92 266:0.54 267:0.59 276:0.75 279:0.86 281:0.91 282:0.88 283:0.82 287:0.61 290:0.98 292:0.95 297:0.36 299:0.88 300:0.70 +0 11:0.85 12:0.86 17:0.51 18:0.75 21:0.78 27:0.45 29:0.62 30:0.76 34:0.79 36:0.17 37:0.50 39:0.54 43:0.80 45:0.66 66:0.25 69:0.73 70:0.29 71:0.92 74:0.43 85:0.72 86:0.74 91:0.14 98:0.13 101:0.97 108:0.56 122:0.82 123:0.82 124:0.71 127:0.33 129:0.05 131:0.61 133:0.60 135:0.68 139:0.71 144:0.57 145:0.49 146:0.17 147:0.22 149:0.61 154:0.75 157:0.84 159:0.48 163:0.97 166:0.61 172:0.16 173:0.71 177:0.70 178:0.55 179:0.87 182:0.76 187:0.68 212:0.52 216:0.77 222:0.76 227:0.61 229:0.61 231:0.71 235:0.88 241:0.27 242:0.55 243:0.45 245:0.45 246:0.61 247:0.39 253:0.33 259:0.21 265:0.10 266:0.27 267:0.28 276:0.24 277:0.69 287:0.61 300:0.25 +2 1:0.74 7:0.63 9:0.76 11:0.64 12:0.86 17:0.55 18:0.71 21:0.30 27:0.02 29:0.62 30:0.21 31:0.88 32:0.80 34:0.98 36:0.40 37:0.95 39:0.54 43:0.57 44:0.93 45:0.66 55:0.42 64:0.77 66:0.27 69:0.83 70:0.86 71:0.92 74:0.49 77:0.70 79:0.90 81:0.59 83:0.60 86:0.59 91:0.25 96:0.75 97:0.89 98:0.16 99:0.83 101:0.52 108:0.68 114:0.65 122:0.80 123:0.66 124:0.70 126:0.54 127:0.17 129:0.05 131:0.88 133:0.60 135:0.80 137:0.88 139:0.75 140:0.45 144:0.57 145:0.47 146:0.31 147:0.38 149:0.28 150:0.74 151:0.65 153:0.48 154:0.46 155:0.67 157:0.61 158:0.72 159:0.48 162:0.86 165:0.70 166:0.93 172:0.21 173:0.68 176:0.73 177:0.70 179:0.53 182:0.61 187:0.68 190:0.77 192:0.87 195:0.93 196:0.89 201:0.93 202:0.36 204:0.85 208:0.64 212:0.16 215:0.44 216:0.87 219:0.87 220:0.62 222:0.76 227:0.87 229:0.61 230:0.63 231:0.90 233:0.73 235:0.52 241:0.49 242:0.40 243:0.46 244:0.61 245:0.50 246:0.61 247:0.47 248:0.63 253:0.52 255:0.74 256:0.45 259:0.21 265:0.17 266:0.54 267:0.44 268:0.46 276:0.31 279:0.82 281:0.91 282:0.88 284:0.86 285:0.56 287:0.61 290:0.65 292:0.95 297:0.36 300:0.55 +3 1:0.69 7:0.64 8:0.79 11:0.86 12:0.86 17:0.86 18:0.88 21:0.05 27:0.54 29:0.62 30:0.13 32:0.63 34:0.74 36:0.31 37:0.35 39:0.54 43:0.42 45:0.87 55:0.59 66:0.26 69:0.79 70:0.95 71:0.92 74:0.52 76:0.94 77:0.70 81:0.73 83:0.60 86:0.72 91:0.27 96:0.73 97:0.89 98:0.69 99:0.83 101:0.87 108:0.63 114:0.70 122:0.77 123:0.85 124:0.64 126:0.44 127:0.92 129:0.05 131:0.83 133:0.60 135:0.65 139:0.70 140:0.45 144:0.57 145:0.39 146:0.81 147:0.05 149:0.66 150:0.69 151:0.53 153:0.72 154:0.63 155:0.58 157:0.61 158:0.72 159:0.76 162:0.66 165:0.70 166:0.88 172:0.88 173:0.69 176:0.55 177:0.05 179:0.25 182:0.61 187:0.21 190:0.89 191:0.82 192:0.59 195:0.86 201:0.68 202:0.61 204:0.85 208:0.54 212:0.86 215:0.78 216:0.55 220:0.74 222:0.76 227:0.84 229:0.61 230:0.64 231:0.89 232:0.72 233:0.76 235:0.12 241:0.64 242:0.51 243:0.06 245:0.94 246:0.61 247:0.82 248:0.52 253:0.52 254:0.84 256:0.58 257:0.84 259:0.21 265:0.58 266:0.80 267:0.36 268:0.62 276:0.88 277:0.69 279:0.82 282:0.71 283:0.78 285:0.47 287:0.61 290:0.70 297:0.36 300:0.94 +1 1:0.74 7:0.63 11:0.64 12:0.86 17:0.59 18:0.70 21:0.05 22:0.93 27:0.72 29:0.62 30:0.33 34:0.92 36:0.76 39:0.54 43:0.79 47:0.93 60:0.87 64:0.77 66:0.79 69:0.74 70:0.49 71:0.92 74:0.55 76:0.94 81:0.83 83:0.91 85:0.72 86:0.74 91:0.35 98:0.60 101:0.87 108:0.57 114:0.89 117:0.86 122:0.57 123:0.55 126:0.54 127:0.18 129:0.05 131:0.61 135:0.56 139:0.80 144:0.57 145:0.74 146:0.40 147:0.47 149:0.55 150:0.89 154:0.72 155:0.67 157:0.87 158:0.92 159:0.15 165:0.93 166:0.93 172:0.41 173:0.93 176:0.73 177:0.70 178:0.55 179:0.59 182:0.98 187:0.68 192:0.87 201:0.93 208:0.64 212:0.42 215:0.78 216:0.08 219:0.95 222:0.76 227:0.61 229:0.61 230:0.63 231:0.89 232:0.99 233:0.76 235:0.22 241:0.12 242:0.19 243:0.80 246:0.93 247:0.55 253:0.60 259:0.21 262:0.93 265:0.61 266:0.38 267:0.52 276:0.31 279:0.86 283:0.82 287:0.61 290:0.89 292:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.64 8:0.63 11:0.92 12:0.86 17:0.94 18:0.99 21:0.13 27:0.66 29:0.62 30:0.40 34:0.89 36:0.53 37:0.55 39:0.54 43:0.74 44:0.85 45:0.92 48:0.99 55:0.42 64:0.77 66:0.74 69:0.10 70:0.73 71:0.92 74:0.92 76:0.97 77:0.96 81:0.73 83:0.60 85:0.72 86:0.99 91:0.12 97:0.89 98:0.70 101:0.97 104:0.87 108:0.09 114:0.79 122:0.80 123:0.09 124:0.44 126:0.54 127:0.45 129:0.05 131:0.61 133:0.60 135:0.44 139:0.98 144:0.57 145:0.74 146:0.89 147:0.91 149:0.67 150:0.80 154:0.88 155:0.67 157:0.83 158:0.72 159:0.67 166:0.93 172:0.96 173:0.11 176:0.73 177:0.70 178:0.55 179:0.14 182:0.75 187:0.21 192:0.87 195:0.86 201:0.93 204:0.85 208:0.64 212:0.91 215:0.61 216:0.14 219:0.87 222:0.76 227:0.61 229:0.61 230:0.64 231:0.90 232:0.97 233:0.73 235:0.31 241:0.01 242:0.30 243:0.77 245:0.95 246:0.61 247:0.97 253:0.58 259:0.21 265:0.71 266:0.75 267:0.52 276:0.93 279:0.82 281:0.91 282:0.71 287:0.61 290:0.79 292:0.91 297:0.36 300:0.84 +2 8:0.71 11:0.85 12:0.86 17:0.59 18:0.86 21:0.13 27:0.49 29:0.62 30:0.14 32:0.62 34:0.61 36:0.43 37:0.29 39:0.54 43:0.18 45:0.73 55:0.52 66:0.64 69:0.10 70:0.94 71:0.92 74:0.29 81:0.45 86:0.61 91:0.72 96:0.68 98:0.50 101:0.87 108:0.09 120:0.61 122:0.57 123:0.09 124:0.55 126:0.26 127:0.60 129:0.05 131:0.91 133:0.73 135:0.42 139:0.59 140:0.45 145:0.54 146:0.58 147:0.64 149:0.18 150:0.37 151:0.53 153:0.82 154:0.81 157:0.61 158:0.71 159:0.53 162:0.65 164:0.52 166:0.77 172:0.54 173:0.17 177:0.70 179:0.73 182:0.61 187:0.39 190:0.89 191:0.76 195:0.72 202:0.63 212:0.69 215:0.61 216:0.31 220:0.74 222:0.76 227:0.89 229:0.61 231:0.88 235:0.12 241:0.62 242:0.18 243:0.69 245:0.51 246:0.61 247:0.82 248:0.52 253:0.28 254:0.92 256:0.58 259:0.21 260:0.55 265:0.51 266:0.47 267:0.52 268:0.64 276:0.47 283:0.78 285:0.56 287:0.61 297:0.36 300:0.70 +1 1:0.74 7:0.64 8:0.66 11:0.86 12:0.86 17:0.55 18:0.67 21:0.40 27:0.72 29:0.62 30:0.76 32:0.63 34:0.33 36:0.67 37:0.27 39:0.54 43:0.71 45:0.73 48:0.91 64:0.77 66:0.72 69:0.27 70:0.22 71:0.92 74:0.48 76:0.94 77:0.70 81:0.54 83:0.60 86:0.77 91:0.89 98:0.55 99:0.83 101:0.87 108:0.21 114:0.62 122:0.51 123:0.20 126:0.54 127:0.16 129:0.05 131:0.61 132:0.97 135:0.32 139:0.73 144:0.57 145:0.44 146:0.24 147:0.30 149:0.36 150:0.71 154:0.88 155:0.67 157:0.61 159:0.11 165:0.70 166:0.93 172:0.27 173:0.76 176:0.73 177:0.70 178:0.55 179:0.71 182:0.61 192:0.87 195:0.53 201:0.93 202:0.50 204:0.85 208:0.64 212:0.95 215:0.42 216:0.14 222:0.76 227:0.61 229:0.61 230:0.64 231:0.89 233:0.76 235:0.60 241:0.90 242:0.45 243:0.75 246:0.61 247:0.35 253:0.46 254:0.80 259:0.21 265:0.56 266:0.12 267:0.18 276:0.21 281:0.91 282:0.71 287:0.61 290:0.62 297:0.36 300:0.18 +2 1:0.74 6:0.96 7:0.81 8:0.94 9:0.80 11:0.64 12:0.86 17:0.92 20:0.84 21:0.40 27:0.41 28:0.93 30:0.30 32:0.80 34:0.18 36:0.23 37:0.92 39:0.56 41:0.94 43:0.59 55:0.59 66:0.67 69:0.93 70:0.80 74:0.67 77:0.89 78:0.94 81:0.68 83:0.82 85:0.80 91:0.92 96:0.93 98:0.32 100:0.95 101:0.74 104:0.58 108:0.85 111:0.93 114:0.82 120:0.92 121:0.90 123:0.84 124:0.46 126:0.54 127:0.21 129:0.05 131:0.89 133:0.62 135:0.44 140:0.45 144:0.57 145:0.79 146:0.44 147:0.05 149:0.48 150:0.80 151:0.98 152:0.80 153:0.93 154:0.48 155:0.67 157:0.81 159:0.36 161:0.77 162:0.79 164:0.87 165:0.83 166:0.84 167:0.81 169:0.93 172:0.48 173:0.98 176:0.73 177:0.05 179:0.53 181:0.60 182:0.86 186:0.94 189:0.97 191:0.94 192:0.59 195:0.76 201:0.74 202:0.80 204:0.89 208:0.64 212:0.81 215:0.67 216:0.20 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.52 238:0.90 241:0.84 242:0.63 243:0.15 245:0.49 246:0.91 247:0.47 248:0.93 253:0.92 255:0.79 256:0.88 257:0.65 259:0.21 260:0.92 261:0.89 265:0.35 266:0.27 267:0.69 268:0.84 271:0.78 276:0.40 282:0.88 285:0.62 287:0.93 290:0.82 297:0.36 299:0.97 300:0.55 +2 1:0.74 6:0.95 7:0.81 8:0.93 9:0.80 11:0.64 12:0.86 17:0.72 20:0.77 21:0.54 27:0.08 28:0.93 30:0.40 32:0.80 34:0.55 36:0.36 37:0.98 39:0.56 41:0.90 43:0.95 66:0.49 69:0.23 70:0.88 74:0.95 76:0.85 77:0.70 78:0.89 81:0.66 83:0.78 85:0.96 91:0.53 96:0.83 98:0.68 100:0.91 101:0.85 104:0.58 108:0.74 111:0.89 114:0.77 120:0.73 121:0.90 122:0.43 123:0.73 124:0.57 126:0.54 127:0.72 129:0.59 131:0.89 133:0.47 135:0.42 140:0.45 144:0.57 145:0.69 146:0.70 147:0.75 149:0.95 150:0.77 151:0.90 152:0.91 153:0.69 154:0.95 155:0.67 157:0.92 159:0.55 161:0.77 162:0.86 164:0.74 165:0.79 166:0.84 167:0.59 169:0.94 172:0.78 173:0.25 176:0.73 177:0.38 179:0.36 181:0.60 182:0.86 186:0.89 187:0.39 189:0.97 192:0.59 195:0.74 201:0.74 202:0.60 204:0.89 208:0.64 212:0.80 215:0.58 216:0.45 220:0.93 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.80 238:0.87 241:0.59 242:0.24 243:0.45 245:0.92 246:0.91 247:0.67 248:0.81 253:0.88 255:0.79 256:0.64 259:0.21 260:0.74 261:0.95 265:0.69 266:0.54 267:0.76 268:0.69 271:0.78 276:0.78 282:0.88 285:0.62 287:0.93 290:0.77 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.85 7:0.81 8:0.89 9:0.80 11:0.64 12:0.86 17:0.73 20:0.92 21:0.22 27:0.34 28:0.93 30:0.67 32:0.80 34:0.31 36:0.73 37:0.72 39:0.56 41:0.99 43:0.97 60:0.87 66:0.64 69:0.23 70:0.65 74:0.96 76:0.98 77:0.96 78:0.93 81:0.85 83:0.89 85:0.94 91:0.88 96:0.98 98:0.66 100:0.91 101:0.84 108:0.76 111:0.91 114:0.69 120:0.96 121:0.90 122:0.43 123:0.75 124:0.48 126:0.54 127:0.97 129:0.59 131:0.89 133:0.47 135:0.42 138:0.98 140:0.45 144:0.57 145:0.65 146:0.59 147:0.64 149:0.98 150:0.90 151:0.98 152:0.86 153:0.93 154:0.97 155:0.67 157:0.91 158:0.92 159:0.55 161:0.77 162:0.78 164:0.96 165:0.86 166:0.84 167:0.82 169:0.89 172:0.88 173:0.27 176:0.56 177:0.38 179:0.31 181:0.60 182:0.86 186:0.92 189:0.87 191:0.93 192:0.59 195:0.73 201:0.74 202:0.94 204:0.89 208:0.64 212:0.87 215:0.72 216:0.42 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.72 233:0.76 235:0.74 238:0.85 241:0.89 242:0.28 243:0.33 245:0.94 246:0.91 247:0.60 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.96 261:0.91 265:0.67 266:0.54 267:0.73 268:0.96 271:0.78 276:0.81 279:0.86 282:0.88 283:0.82 285:0.62 286:0.99 287:0.93 290:0.69 297:0.36 299:0.97 300:0.84 +0 1:0.74 7:0.78 8:0.61 9:0.80 12:0.86 17:0.73 21:0.47 27:0.72 28:0.93 32:0.80 34:0.49 36:0.32 39:0.56 41:0.82 74:0.57 76:0.94 77:0.96 81:0.67 83:0.74 91:0.84 96:0.70 104:0.58 114:0.80 120:0.56 126:0.54 131:0.89 135:0.77 140:0.45 144:0.57 145:0.70 150:0.79 151:0.52 153:0.48 155:0.67 157:0.61 161:0.77 162:0.86 164:0.57 165:0.80 166:0.84 167:0.81 175:0.91 176:0.73 181:0.60 182:0.86 192:0.59 195:0.57 201:0.74 202:0.36 204:0.89 208:0.64 215:0.63 216:0.33 220:0.93 222:0.60 227:0.92 229:0.92 230:0.81 231:0.77 232:0.81 233:0.69 235:0.68 241:0.86 244:0.83 246:0.91 248:0.52 253:0.64 255:0.79 256:0.45 257:0.84 259:0.21 260:0.56 267:0.44 268:0.46 271:0.78 277:0.87 282:0.88 285:0.62 287:0.93 290:0.80 297:0.36 299:0.99 +1 7:0.81 12:0.86 17:0.83 21:0.40 27:0.72 28:0.93 32:0.80 34:0.37 36:0.48 39:0.56 74:0.59 77:0.70 81:0.47 91:0.83 104:0.58 114:0.55 121:1.00 126:0.32 131:0.61 135:0.54 144:0.57 145:0.45 150:0.38 155:0.67 157:0.61 161:0.77 166:0.76 167:0.80 175:0.91 176:0.53 178:0.55 181:0.60 182:0.73 191:0.77 192:0.59 195:0.52 204:0.89 208:0.64 222:0.60 227:0.61 229:0.61 230:0.87 231:0.70 232:0.81 233:0.76 235:0.42 241:0.83 244:0.83 246:0.61 253:0.47 257:0.84 259:0.21 267:0.23 271:0.78 277:0.87 282:0.88 287:0.61 290:0.55 299:0.88 +1 1:0.74 11:0.64 12:0.86 17:0.36 21:0.77 27:0.45 28:0.93 30:0.11 34:0.73 36:0.18 37:0.50 39:0.56 43:0.82 66:0.29 69:0.71 70:0.93 74:0.77 81:0.42 83:0.72 85:0.85 91:0.14 98:0.35 101:0.76 108:0.66 114:0.64 122:0.67 123:0.64 124:0.70 126:0.54 127:0.43 129:0.81 131:0.61 133:0.67 135:0.92 144:0.57 145:0.49 146:0.13 147:0.18 149:0.68 150:0.62 154:0.77 155:0.67 157:0.84 159:0.59 161:0.77 165:0.63 166:0.84 167:0.53 172:0.37 173:0.64 176:0.73 177:0.38 178:0.55 179:0.65 181:0.60 182:0.84 187:0.68 192:0.59 201:0.74 212:0.37 215:0.44 216:0.77 222:0.60 227:0.61 229:0.61 231:0.76 235:0.98 241:0.44 242:0.51 243:0.20 245:0.65 246:0.90 247:0.47 253:0.61 259:0.21 265:0.37 266:0.71 267:0.85 271:0.78 276:0.42 287:0.61 290:0.64 297:0.36 300:0.70 +2 1:0.74 6:0.90 7:0.81 8:0.81 9:0.80 11:0.64 12:0.86 17:0.81 20:0.86 21:0.59 27:0.42 28:0.93 30:0.54 32:0.80 34:0.77 36:0.75 37:0.68 39:0.56 41:0.99 43:0.97 60:0.99 66:0.72 69:0.29 70:0.77 74:0.95 77:0.89 78:0.91 81:0.64 83:0.89 85:0.95 91:0.91 96:0.97 98:0.89 100:0.88 101:0.85 104:0.75 108:0.57 111:0.89 114:0.63 120:0.94 121:0.90 122:0.43 123:0.55 124:0.43 126:0.54 127:0.96 129:0.59 131:0.89 133:0.47 135:0.58 138:0.96 140:0.45 144:0.57 145:0.77 146:0.26 147:0.32 149:0.94 150:0.77 151:0.99 152:0.81 153:0.97 154:0.97 155:0.67 157:0.92 158:0.92 159:0.51 161:0.77 162:0.80 164:0.95 165:0.80 166:0.84 167:0.78 169:0.86 172:0.80 173:0.33 176:0.56 177:0.38 179:0.47 181:0.60 182:0.86 186:0.91 189:0.91 191:0.85 192:0.59 195:0.68 201:0.74 202:0.90 204:0.89 208:0.64 212:0.74 215:0.53 216:0.32 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.83 233:0.76 235:0.88 238:0.84 241:0.79 242:0.32 243:0.23 245:0.83 246:0.91 247:0.67 248:0.97 253:0.98 255:0.79 256:0.92 259:0.21 260:0.95 261:0.87 265:0.89 266:0.27 267:0.65 268:0.93 271:0.78 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.63 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.97 9:0.80 11:0.64 12:0.86 17:0.85 20:0.98 21:0.30 27:0.10 28:0.93 30:0.18 32:0.80 34:0.63 36:0.47 37:0.96 39:0.56 41:0.99 43:0.90 60:0.99 66:0.64 69:0.17 70:0.82 74:0.86 77:0.70 78:0.93 81:0.76 83:0.89 85:0.93 91:0.89 96:0.97 98:0.63 100:0.93 101:0.81 104:0.58 108:0.84 111:0.92 114:0.68 120:0.96 121:0.97 122:0.43 123:0.83 124:0.51 126:0.54 127:0.98 129:0.81 131:0.89 133:0.67 135:0.18 138:0.97 140:0.94 144:0.57 145:0.77 146:0.42 147:0.49 149:0.85 150:0.84 151:0.96 152:0.92 153:0.86 154:0.89 155:0.67 157:0.90 158:0.92 159:0.70 161:0.77 162:0.69 164:0.95 165:0.81 166:0.84 167:0.83 169:0.91 172:0.75 173:0.16 176:0.56 177:0.38 178:0.48 179:0.52 181:0.60 182:0.86 186:0.93 189:0.94 191:0.97 192:0.59 195:0.83 201:0.74 202:0.93 204:0.89 208:0.64 212:0.70 215:0.67 216:0.52 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.60 238:0.88 241:0.93 242:0.39 243:0.25 245:0.77 246:0.91 247:0.60 248:0.97 253:0.98 255:0.79 256:0.94 259:0.21 260:0.96 261:0.93 265:0.64 266:0.75 267:0.52 268:0.94 271:0.78 276:0.67 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.68 297:0.36 299:0.88 300:0.55 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.86 17:0.04 21:0.54 27:0.08 28:0.93 30:0.68 32:0.80 34:0.42 36:0.35 37:0.98 39:0.56 41:0.88 43:0.95 66:0.79 69:0.23 70:0.31 74:0.78 76:0.85 77:0.89 81:0.66 83:0.74 85:0.85 91:0.68 96:0.71 98:0.76 101:0.82 104:0.58 108:0.18 114:0.77 120:0.58 121:0.90 122:0.41 123:0.18 126:0.54 127:0.25 129:0.05 131:0.89 135:0.32 140:0.45 144:0.57 145:0.79 146:0.44 147:0.50 149:0.79 150:0.77 151:0.54 153:0.83 154:0.95 155:0.67 157:0.86 159:0.16 161:0.77 162:0.71 164:0.72 165:0.79 166:0.84 167:0.60 172:0.41 173:0.55 176:0.73 177:0.38 179:0.76 181:0.60 182:0.86 187:0.39 191:0.95 192:0.59 195:0.55 201:0.74 202:0.63 204:0.89 208:0.64 212:0.78 215:0.58 216:0.45 220:0.93 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.80 241:0.60 242:0.45 243:0.80 246:0.91 247:0.39 248:0.56 253:0.70 255:0.79 256:0.58 259:0.21 260:0.58 265:0.76 266:0.23 267:0.23 268:0.66 271:0.78 276:0.24 282:0.88 285:0.62 287:0.93 290:0.77 297:0.36 299:0.97 300:0.25 +3 1:0.74 6:0.91 7:0.76 8:0.82 9:0.80 11:0.64 12:0.86 17:0.92 20:0.95 21:0.68 27:0.19 28:0.93 30:0.28 32:0.80 34:0.85 36:0.40 37:0.74 39:0.56 41:0.98 43:0.80 60:0.95 66:0.79 69:0.51 70:0.80 74:0.95 76:0.94 77:0.89 78:0.96 81:0.53 83:0.89 85:0.94 91:0.88 96:0.96 98:0.81 100:0.97 101:0.84 104:0.58 108:0.39 111:0.96 114:0.70 120:0.94 122:0.43 123:0.38 124:0.39 126:0.54 127:0.46 129:0.05 131:0.89 133:0.39 135:0.34 140:0.45 144:0.57 145:0.88 146:0.84 147:0.86 149:0.92 150:0.68 151:0.99 152:0.91 153:0.96 154:0.75 155:0.67 157:0.91 158:0.92 159:0.48 161:0.77 162:0.80 164:0.94 165:0.69 166:0.84 167:0.81 169:0.95 172:0.82 173:0.50 176:0.73 177:0.38 179:0.38 181:0.60 182:0.86 186:0.96 189:0.92 191:0.92 192:0.59 195:0.73 201:0.74 202:0.89 208:0.64 212:0.67 215:0.49 216:0.33 222:0.60 227:0.92 229:0.92 230:0.79 231:0.77 232:0.81 233:0.76 235:0.88 238:0.93 241:0.87 242:0.43 243:0.80 245:0.78 246:0.91 247:0.67 248:0.96 253:0.98 255:0.79 256:0.91 259:0.21 260:0.94 261:0.95 265:0.81 266:0.38 267:0.28 268:0.92 271:0.78 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.70 297:0.36 299:0.97 300:0.55 +1 1:0.74 6:0.92 8:0.87 9:0.80 12:0.86 17:0.47 20:0.76 21:0.30 27:0.16 28:0.93 30:0.76 32:0.80 34:0.28 36:0.76 37:0.59 39:0.56 41:0.92 43:0.34 55:0.92 66:0.25 69:0.59 70:0.29 74:0.68 77:0.89 78:0.93 81:0.78 83:0.78 91:0.65 96:0.95 98:0.33 100:0.91 108:0.45 111:0.91 114:0.86 117:0.86 120:0.75 122:0.51 123:0.43 124:0.71 126:0.54 127:0.55 129:0.59 131:0.89 133:0.64 135:0.66 138:0.96 140:0.80 144:0.57 145:0.49 146:0.57 147:0.63 149:0.31 150:0.85 151:0.87 152:0.75 153:0.95 154:0.26 155:0.67 157:0.61 158:0.83 159:0.72 161:0.77 162:0.81 163:0.93 164:0.81 165:0.84 166:0.84 167:0.61 169:0.89 172:0.16 173:0.43 176:0.73 177:0.38 179:0.91 181:0.60 182:0.86 186:0.93 187:0.21 189:0.93 191:0.82 192:0.59 195:0.87 201:0.74 202:0.81 208:0.64 212:0.23 215:0.72 216:0.79 222:0.60 227:0.92 229:0.92 231:0.77 232:0.83 235:0.52 238:0.88 241:0.62 242:0.55 243:0.45 244:0.61 245:0.45 246:0.91 247:0.39 248:0.84 253:0.94 255:0.79 256:0.84 259:0.21 260:0.76 261:0.78 265:0.36 266:0.38 267:0.59 268:0.86 271:0.78 276:0.27 282:0.88 285:0.62 287:0.93 290:0.86 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.92 7:0.81 8:0.85 9:0.80 12:0.86 17:0.90 20:0.82 21:0.05 27:0.72 28:0.93 32:0.80 34:0.59 36:0.86 37:1.00 39:0.56 41:0.88 74:0.59 76:0.85 77:0.89 78:0.90 81:0.87 83:0.81 91:0.85 96:0.88 100:0.89 104:0.74 111:0.89 114:0.95 120:0.79 121:0.90 126:0.54 131:0.89 135:0.18 140:0.45 144:0.57 145:0.85 150:0.93 151:0.94 152:0.79 153:0.80 155:0.67 157:0.61 161:0.77 162:0.79 164:0.71 165:0.90 166:0.85 167:0.81 169:0.87 175:0.91 176:0.73 181:0.60 182:0.86 186:0.90 189:0.93 192:0.59 195:0.65 201:0.74 202:0.60 204:0.89 208:0.64 215:0.91 216:0.03 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.83 233:0.76 235:0.12 238:0.86 241:0.86 244:0.83 246:0.91 248:0.87 253:0.87 255:0.79 256:0.58 257:0.84 259:0.21 260:0.80 261:0.84 267:0.82 268:0.65 271:0.78 277:0.87 282:0.88 285:0.62 287:0.93 290:0.95 297:0.36 299:0.97 +2 1:0.74 6:0.87 7:0.81 8:0.78 9:0.80 11:0.64 12:0.86 17:0.72 20:0.90 21:0.40 27:0.44 28:0.93 30:0.45 32:0.80 34:0.49 36:0.93 37:0.70 39:0.56 41:0.95 43:0.80 60:0.87 66:0.94 69:0.64 70:0.53 74:0.93 77:0.70 78:0.88 81:0.71 83:0.85 85:0.95 91:0.92 96:0.94 98:0.88 100:0.86 101:0.86 108:0.49 111:0.86 114:0.82 120:0.93 121:0.97 122:0.43 123:0.46 126:0.54 127:0.42 129:0.05 131:0.89 135:0.37 140:0.87 144:0.57 145:0.76 146:0.72 147:0.76 149:0.94 150:0.81 151:0.99 152:0.84 153:0.95 154:0.75 155:0.67 157:0.92 158:0.92 159:0.29 161:0.77 162:0.77 164:0.92 165:0.81 166:0.84 167:0.80 169:0.83 172:0.83 173:0.78 176:0.73 177:0.38 179:0.37 181:0.60 182:0.86 186:0.88 189:0.89 191:0.84 192:0.59 195:0.61 201:0.74 202:0.88 204:0.89 208:0.64 212:0.93 215:0.67 216:0.29 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.72 233:0.76 235:0.60 238:0.83 241:0.83 242:0.44 243:0.94 246:0.91 247:0.47 248:0.93 253:0.96 255:0.79 256:0.89 259:0.21 260:0.93 261:0.87 265:0.88 266:0.23 267:0.59 268:0.91 271:0.78 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.93 290:0.82 297:0.36 299:0.88 300:0.43 +2 1:0.74 7:0.81 9:0.80 12:0.86 17:0.79 27:0.19 28:0.93 32:0.80 34:0.18 36:0.21 37:0.74 39:0.56 41:0.82 74:0.59 77:0.70 81:0.92 83:0.81 91:0.64 96:0.83 104:0.58 114:0.98 120:0.72 121:0.90 126:0.54 131:0.89 135:0.60 140:0.45 144:0.57 145:0.44 150:0.96 151:0.90 153:0.69 155:0.67 157:0.61 161:0.77 162:0.86 164:0.62 165:0.92 166:0.85 167:0.54 175:0.91 176:0.73 181:0.60 182:0.86 187:0.21 191:0.85 192:0.59 195:0.71 201:0.74 202:0.46 204:0.89 208:0.64 215:0.96 216:0.33 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 232:0.81 233:0.76 235:0.05 241:0.49 244:0.83 246:0.91 248:0.80 253:0.83 255:0.79 256:0.45 257:0.84 259:0.21 260:0.73 267:0.36 268:0.55 271:0.78 277:0.87 282:0.88 285:0.62 287:0.93 290:0.98 297:0.36 299:0.88 +1 1:0.74 6:0.97 7:0.81 8:0.97 9:0.80 11:0.64 12:0.86 17:0.87 20:0.75 21:0.54 27:0.27 28:0.93 30:0.14 32:0.80 34:0.21 36:0.23 37:0.95 39:0.56 41:0.93 43:0.94 66:0.30 69:0.30 70:0.94 74:0.91 76:0.85 77:0.70 78:1.00 81:0.63 83:0.77 85:0.95 91:0.87 96:0.82 98:0.26 100:0.97 101:0.77 104:0.58 108:0.24 111:0.99 114:0.77 120:0.81 121:0.97 123:0.23 124:0.95 126:0.54 127:0.99 129:0.81 131:0.89 133:0.96 135:0.24 140:0.45 144:0.57 145:0.70 146:0.67 147:0.72 149:0.93 150:0.76 151:0.87 152:0.74 153:0.48 154:0.94 155:0.67 157:0.89 159:0.91 161:0.77 162:0.85 164:0.85 165:0.79 166:0.84 167:0.80 169:0.95 172:0.71 173:0.10 176:0.73 177:0.38 179:0.36 181:0.60 182:0.86 186:1.00 189:0.97 191:0.94 192:0.59 195:0.97 201:0.74 202:0.76 204:0.89 208:0.64 212:0.58 215:0.58 216:0.26 220:0.96 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.74 238:0.90 241:0.82 242:0.45 243:0.48 245:0.74 246:0.91 247:0.60 248:0.79 253:0.86 255:0.79 256:0.81 259:0.21 260:0.81 261:0.80 265:0.28 266:0.92 267:0.69 268:0.82 271:0.78 276:0.81 282:0.88 283:0.71 285:0.62 287:0.93 290:0.77 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.95 7:0.81 8:0.97 9:0.80 11:0.64 12:0.86 17:0.43 20:0.95 21:0.54 27:0.08 28:0.93 30:0.42 32:0.80 34:0.34 36:0.48 37:0.98 39:0.56 41:0.99 43:0.95 66:0.51 69:0.23 70:0.85 74:0.95 76:0.85 77:0.70 78:0.95 81:0.66 83:0.83 85:0.96 91:0.93 96:0.96 98:0.72 100:0.96 101:0.86 104:0.58 108:0.72 111:0.95 114:0.77 120:0.96 121:0.90 122:0.43 123:0.70 124:0.56 126:0.54 127:0.81 129:0.59 131:0.89 133:0.47 135:0.34 138:0.98 140:0.87 144:0.57 145:0.69 146:0.63 147:0.69 149:0.96 150:0.77 151:0.94 152:0.91 153:0.81 154:0.95 155:0.67 157:0.92 159:0.52 161:0.77 162:0.76 164:0.96 165:0.79 166:0.84 167:0.82 169:0.94 172:0.78 173:0.28 176:0.73 177:0.38 179:0.38 181:0.60 182:0.86 186:0.95 189:0.96 191:0.97 192:0.59 195:0.71 201:0.74 202:0.93 204:0.89 208:0.64 212:0.82 215:0.58 216:0.45 220:0.62 222:0.60 227:0.92 229:0.92 230:0.87 231:0.77 233:0.76 235:0.80 238:0.92 241:0.89 242:0.24 243:0.44 245:0.92 246:0.91 247:0.67 248:0.95 253:0.97 255:0.79 256:0.95 259:0.21 260:0.96 261:0.95 265:0.72 266:0.54 267:0.59 268:0.95 271:0.78 276:0.78 282:0.88 283:0.71 285:0.62 287:0.93 290:0.77 297:0.36 299:0.88 300:0.84 +0 1:0.74 11:0.64 12:0.86 17:0.32 21:0.54 27:0.45 28:0.93 30:0.58 34:0.86 36:0.18 37:0.50 39:0.56 43:0.82 55:0.84 60:0.87 66:0.10 69:0.69 70:0.60 74:0.62 81:0.59 83:0.86 85:0.72 91:0.17 98:0.18 101:0.68 108:0.52 114:0.77 122:0.43 123:0.70 124:0.82 126:0.54 127:0.64 129:0.59 131:0.61 133:0.76 135:0.81 144:0.57 145:0.49 146:0.26 147:0.32 149:0.56 150:0.74 154:0.77 155:0.67 157:0.76 158:0.87 159:0.84 161:0.77 163:0.79 165:0.79 166:0.84 167:0.48 172:0.16 173:0.45 176:0.73 177:0.38 178:0.55 179:0.88 181:0.60 182:0.85 187:0.68 192:0.59 201:0.74 208:0.64 212:0.16 215:0.58 216:0.77 222:0.60 227:0.61 229:0.61 231:0.76 235:0.68 241:0.21 242:0.40 243:0.39 245:0.48 246:0.91 247:0.55 253:0.63 259:0.21 265:0.14 266:0.54 267:0.52 271:0.78 276:0.32 279:0.86 283:0.71 287:0.61 290:0.77 297:0.36 300:0.43 +0 12:0.86 17:0.47 18:0.69 21:0.78 27:0.45 29:0.53 30:0.45 34:0.67 36:0.19 37:0.50 39:0.37 43:0.12 55:0.45 66:0.27 69:0.81 70:0.25 71:0.95 74:0.26 86:0.67 91:0.22 98:0.10 108:0.66 122:0.57 123:0.63 124:0.72 127:0.17 129:0.05 133:0.62 135:0.54 139:0.65 145:0.47 146:0.19 147:0.05 149:0.08 154:0.47 159:0.36 163:0.93 172:0.10 173:0.73 175:0.75 177:0.05 178:0.55 179:0.84 187:0.68 212:0.61 216:0.77 222:0.60 235:0.74 241:0.47 242:0.63 243:0.29 244:0.61 245:0.38 247:0.30 253:0.23 254:0.84 257:0.84 259:0.21 265:0.11 266:0.17 267:0.76 271:0.43 276:0.19 277:0.69 300:0.18 +2 1:0.74 9:0.80 11:0.91 12:0.86 17:0.34 18:0.87 21:0.64 27:0.48 29:0.53 30:0.26 31:0.86 32:0.78 34:0.67 36:0.29 37:0.55 39:0.37 43:0.63 44:0.93 45:0.91 64:0.77 66:0.09 69:0.56 70:0.97 71:0.95 74:0.81 77:0.70 79:0.86 81:0.44 83:0.60 86:0.90 91:0.07 96:0.68 97:0.94 98:0.40 99:0.83 101:0.52 108:0.43 114:0.57 120:0.53 122:0.51 123:0.89 124:0.87 126:0.54 127:0.89 129:0.81 133:0.86 135:0.42 137:0.86 139:0.90 140:0.45 144:0.85 145:0.65 146:0.70 147:0.74 149:0.74 150:0.66 151:0.47 153:0.48 154:0.70 155:0.67 158:0.78 159:0.86 162:0.86 164:0.57 165:0.70 172:0.65 173:0.30 176:0.73 177:0.70 179:0.38 187:0.39 192:0.87 195:0.94 196:0.88 201:0.93 202:0.36 208:0.64 212:0.54 215:0.38 216:0.92 220:0.96 222:0.60 228:0.99 235:0.88 241:0.64 242:0.19 243:0.45 245:0.82 247:0.88 248:0.47 253:0.45 254:0.84 255:0.95 256:0.45 259:0.21 260:0.54 265:0.36 266:0.92 267:0.91 268:0.46 271:0.43 276:0.78 279:0.82 282:0.86 283:0.82 284:0.89 285:0.62 290:0.57 297:0.36 300:0.98 +0 1:0.69 7:0.72 11:0.91 12:0.86 17:0.98 18:0.77 27:0.53 29:0.53 30:0.31 32:0.80 34:0.50 36:0.64 37:0.27 39:0.37 43:0.63 44:0.93 45:0.92 55:0.52 66:0.54 69:0.69 70:0.87 71:0.95 74:0.88 77:0.99 81:0.83 83:0.60 86:0.82 91:0.27 97:0.89 98:0.72 99:0.83 101:0.52 104:0.86 106:0.81 108:0.68 114:0.95 117:0.86 123:0.66 124:0.64 126:0.44 127:0.37 129:0.59 133:0.61 135:0.54 139:0.85 144:0.85 145:0.97 146:0.40 147:0.46 149:0.85 150:0.80 154:0.75 155:0.58 158:0.79 159:0.70 165:0.70 172:0.89 173:0.53 176:0.66 177:0.70 178:0.55 179:0.18 187:0.21 192:0.59 195:0.90 201:0.68 204:0.85 206:0.81 208:0.54 212:0.80 215:0.96 216:0.30 222:0.60 230:0.75 232:0.92 233:0.76 235:0.05 241:0.01 242:0.53 243:0.36 245:0.94 247:0.88 253:0.65 254:0.74 259:0.21 265:0.72 266:0.47 267:0.28 271:0.43 276:0.89 279:0.82 282:0.88 283:0.82 290:0.95 297:0.36 300:0.70 +0 1:0.69 8:0.72 9:0.76 11:0.91 12:0.86 17:0.75 18:0.58 20:0.92 27:0.29 29:0.53 30:0.43 34:0.20 36:0.31 37:0.53 39:0.37 43:0.61 45:0.92 66:0.11 69:0.77 70:0.91 71:0.95 74:0.72 78:0.97 81:0.83 83:0.60 86:0.60 91:0.17 96:0.80 97:0.89 98:0.34 99:0.83 100:0.92 101:0.52 108:0.70 111:0.95 114:0.95 120:0.69 122:0.51 123:0.90 124:0.89 126:0.44 127:0.45 129:0.93 133:0.90 135:0.47 139:0.59 140:0.45 144:0.85 146:0.28 147:0.35 149:0.83 150:0.80 151:0.81 152:0.86 153:0.48 154:0.50 155:0.58 158:0.79 159:0.83 162:0.86 164:0.54 165:0.70 169:0.90 172:0.65 173:0.54 175:0.75 176:0.66 177:0.38 179:0.32 186:0.97 187:0.68 190:0.77 192:0.59 201:0.68 202:0.36 208:0.54 212:0.50 215:0.96 216:0.21 222:0.60 235:0.05 241:0.05 242:0.33 243:0.20 244:0.61 245:0.79 247:0.76 248:0.73 253:0.81 255:0.74 256:0.45 257:0.65 259:0.21 260:0.68 261:0.93 265:0.22 266:0.89 267:0.28 268:0.46 271:0.43 276:0.77 277:0.69 279:0.82 283:0.82 285:0.56 290:0.95 297:0.36 300:0.84 +0 1:0.69 7:0.72 9:0.76 11:0.91 12:0.86 17:0.79 18:0.69 21:0.05 27:0.24 29:0.53 30:0.45 34:0.73 36:0.51 37:0.61 39:0.37 43:0.69 45:0.66 66:0.27 69:0.54 70:0.25 71:0.95 74:0.53 81:0.75 83:0.60 86:0.67 91:0.53 96:0.80 97:0.89 98:0.19 99:0.83 101:0.52 108:0.41 114:0.85 120:0.69 122:0.57 123:0.65 124:0.61 126:0.44 127:0.17 129:0.05 133:0.39 135:0.89 139:0.65 140:0.45 144:0.85 146:0.13 147:0.18 149:0.36 150:0.71 151:0.81 153:0.48 154:0.58 155:0.58 158:0.79 159:0.29 162:0.86 163:0.88 164:0.54 165:0.70 172:0.10 173:0.48 175:0.75 176:0.66 177:0.38 179:0.85 187:0.39 190:0.77 192:0.59 201:0.68 202:0.36 204:0.85 208:0.54 212:0.61 215:0.78 216:0.61 222:0.60 230:0.75 233:0.76 235:0.12 241:0.30 242:0.63 243:0.46 244:0.61 245:0.40 247:0.30 248:0.73 253:0.76 255:0.74 256:0.45 257:0.65 259:0.21 260:0.68 265:0.09 266:0.17 267:0.95 268:0.46 271:0.43 276:0.15 277:0.87 279:0.82 283:0.82 285:0.56 290:0.85 297:0.36 300:0.18 +0 1:0.69 11:0.91 12:0.86 17:0.76 18:0.86 21:0.40 27:0.19 29:0.53 30:0.13 34:0.44 36:0.36 37:0.38 39:0.37 43:0.63 44:0.87 45:0.77 55:0.59 66:0.67 69:0.35 70:0.96 71:0.95 74:0.45 81:0.42 83:0.60 86:0.80 91:0.03 98:0.82 99:0.83 101:0.52 106:0.81 108:0.27 114:0.61 117:0.86 122:0.51 123:0.26 124:0.45 126:0.44 127:0.55 129:0.05 133:0.37 135:0.92 139:0.78 144:0.85 145:0.83 146:0.85 147:0.88 149:0.56 150:0.55 154:0.53 155:0.58 159:0.52 165:0.70 172:0.70 173:0.33 176:0.66 177:0.70 178:0.55 179:0.53 187:0.39 192:0.59 195:0.77 201:0.68 206:0.81 208:0.54 212:0.40 215:0.42 216:0.87 222:0.60 228:0.99 235:0.52 241:0.54 242:0.28 243:0.72 244:0.61 245:0.78 247:0.76 253:0.45 259:0.21 265:0.82 266:0.75 267:0.65 271:0.43 276:0.64 290:0.61 297:0.36 300:0.84 +0 1:0.69 7:0.72 9:0.76 11:0.91 12:0.86 17:0.66 18:0.66 20:0.94 21:0.22 27:0.72 29:0.53 30:0.76 32:0.79 34:0.47 36:0.26 39:0.37 43:0.72 44:0.93 45:0.83 66:0.48 69:0.12 70:0.36 71:0.95 74:0.74 77:0.70 78:0.86 81:0.57 83:0.60 86:0.76 91:0.37 96:0.71 97:0.89 98:0.23 99:0.83 100:0.73 101:0.52 104:0.88 106:0.81 108:0.10 111:0.83 114:0.67 117:0.86 120:0.57 122:0.51 123:0.10 124:0.56 126:0.44 127:0.16 129:0.05 132:0.97 133:0.43 135:0.72 139:0.81 140:0.45 144:0.85 145:1.00 146:0.33 147:0.40 149:0.62 150:0.59 151:0.53 152:0.87 153:0.48 154:0.84 155:0.58 158:0.78 159:0.27 162:0.86 164:0.54 165:0.70 169:0.72 172:0.37 173:0.16 176:0.66 177:0.70 179:0.40 186:0.86 187:0.68 190:0.77 192:0.59 195:0.77 201:0.68 202:0.36 204:0.85 206:0.81 208:0.54 212:0.78 215:0.51 216:0.20 220:0.82 222:0.60 230:0.75 232:0.93 233:0.76 235:0.31 241:0.07 242:0.45 243:0.60 245:0.61 247:0.47 248:0.53 253:0.55 254:0.80 255:0.74 256:0.45 259:0.21 260:0.57 261:0.85 265:0.25 266:0.27 267:0.36 268:0.46 271:0.43 276:0.29 279:0.82 282:0.87 283:0.78 285:0.56 290:0.67 297:0.36 300:0.33 +0 1:0.69 8:0.62 9:0.76 11:0.91 12:0.86 17:0.64 18:0.63 21:0.47 27:0.57 29:0.53 30:0.28 34:0.58 36:0.74 37:0.27 39:0.37 43:0.71 45:0.81 66:0.29 69:0.29 70:0.97 71:0.95 74:0.56 81:0.38 83:0.60 86:0.64 91:0.54 96:0.86 97:0.99 98:0.35 99:0.83 101:0.52 104:0.86 106:0.81 108:0.56 114:0.59 117:0.86 120:0.77 123:0.53 124:0.84 126:0.44 127:0.75 129:0.89 133:0.83 135:0.34 138:0.97 139:0.62 140:0.45 144:0.85 146:0.13 147:0.18 149:0.61 150:0.54 151:0.84 153:0.72 154:0.61 155:0.58 158:0.79 159:0.74 162:0.84 163:0.88 164:0.71 165:0.70 172:0.37 173:0.18 175:0.75 176:0.66 177:0.38 179:0.73 187:0.39 190:0.77 191:0.80 192:0.59 201:0.68 202:0.66 206:0.81 208:0.54 212:0.27 215:0.41 216:0.17 220:0.93 222:0.60 232:0.92 235:0.60 241:0.27 242:0.40 243:0.20 244:0.61 245:0.58 247:0.74 248:0.83 253:0.78 255:0.74 256:0.71 257:0.65 259:0.21 260:0.73 265:0.37 266:0.71 267:0.28 268:0.73 271:0.43 276:0.49 277:0.69 279:0.82 283:0.82 285:0.56 290:0.59 297:0.36 300:0.84 +0 1:0.69 11:0.91 12:0.86 17:0.49 21:0.13 27:0.25 29:0.53 30:0.95 32:0.75 34:0.24 36:0.56 37:0.90 39:0.37 43:0.60 44:0.93 45:0.66 66:0.54 69:0.79 71:0.95 74:0.50 77:0.70 81:0.65 83:0.60 91:0.42 98:0.12 99:0.83 101:0.52 104:0.86 106:0.81 108:0.63 114:0.75 117:0.86 123:0.61 126:0.44 127:0.08 129:0.05 135:0.39 139:0.73 144:0.85 145:0.61 146:0.13 147:0.18 149:0.28 150:0.63 154:0.49 155:0.58 159:0.08 165:0.70 172:0.10 173:0.95 175:0.75 176:0.66 177:0.38 178:0.55 179:0.10 187:0.39 192:0.59 195:0.56 201:0.68 206:0.81 208:0.54 215:0.61 216:0.87 222:0.60 232:0.92 235:0.22 241:0.05 243:0.63 244:0.61 253:0.54 257:0.65 259:0.21 265:0.12 267:0.82 271:0.43 277:0.69 282:0.83 290:0.75 297:0.36 300:0.08 +1 1:0.74 9:0.80 11:0.91 12:0.86 17:0.49 18:0.89 21:0.59 27:0.48 29:0.53 30:0.40 31:0.86 32:0.79 34:0.40 36:0.21 37:0.55 39:0.37 43:0.63 44:0.93 45:0.91 64:0.77 66:0.45 69:0.25 70:0.95 71:0.95 74:0.78 77:0.70 79:0.86 81:0.46 83:0.60 86:0.90 91:0.15 96:0.68 98:0.56 99:0.83 101:0.52 108:0.20 114:0.58 120:0.54 122:0.51 123:0.19 124:0.81 126:0.54 127:0.90 129:0.05 133:0.81 135:0.61 137:0.86 139:0.90 140:0.45 144:0.85 145:0.59 146:0.79 147:0.83 149:0.77 150:0.67 151:0.48 153:0.48 154:0.71 155:0.67 159:0.75 162:0.86 164:0.57 165:0.70 172:0.71 173:0.17 176:0.73 177:0.70 179:0.44 187:0.39 192:0.87 195:0.85 196:0.88 201:0.93 202:0.36 208:0.64 212:0.37 215:0.39 216:0.92 220:0.93 222:0.60 228:0.99 235:0.84 241:0.56 242:0.36 243:0.58 245:0.79 247:0.79 248:0.48 253:0.46 254:0.80 255:0.95 256:0.45 259:0.21 260:0.54 265:0.58 266:0.75 267:0.36 268:0.46 271:0.43 276:0.73 282:0.87 283:0.82 284:0.89 285:0.62 290:0.58 297:0.36 300:0.94 +0 8:0.66 12:0.79 17:0.55 21:0.59 27:0.45 28:0.49 30:0.17 32:0.80 34:0.79 36:0.20 37:0.50 43:0.32 55:0.59 66:0.47 69:0.70 70:0.73 81:0.82 91:0.09 98:0.71 108:0.82 123:0.81 124:0.65 126:0.54 127:0.58 129:0.81 133:0.67 135:0.82 145:0.47 146:0.84 147:0.87 149:0.70 150:0.62 154:0.24 159:0.78 161:0.55 167:0.64 172:0.71 173:0.52 177:0.05 178:0.55 179:0.40 181:0.68 187:0.68 195:0.91 212:0.21 215:0.55 216:0.77 235:0.68 241:0.24 242:0.82 243:0.45 245:0.84 247:0.12 259:0.21 265:0.71 266:0.71 267:0.69 271:0.83 276:0.75 283:0.60 297:0.36 300:0.70 +1 6:0.86 7:0.81 8:0.78 12:0.79 17:0.91 20:0.75 21:0.77 27:0.56 28:0.49 30:0.95 32:0.80 34:0.97 36:0.81 37:0.57 43:0.45 55:0.99 66:0.20 69:0.51 78:0.83 81:0.61 91:0.97 98:0.07 100:0.81 108:0.39 111:0.82 120:0.96 123:0.37 124:0.61 126:0.54 127:0.32 129:0.59 133:0.47 135:0.73 140:0.90 145:0.79 146:0.05 147:0.05 149:0.18 150:0.45 151:0.83 152:0.74 153:0.95 154:0.34 159:0.53 161:0.55 162:0.49 164:0.97 167:0.96 169:0.83 172:0.05 173:0.41 177:0.05 178:0.50 179:1.00 181:0.68 186:0.80 191:0.94 195:0.82 202:0.98 215:0.43 216:0.56 230:0.65 233:0.63 235:0.98 238:0.90 241:0.87 243:0.40 245:0.36 248:0.94 256:0.98 259:0.21 260:0.96 261:0.75 265:0.07 267:1.00 268:0.96 271:0.83 285:0.62 297:0.36 300:0.08 +0 12:0.79 17:0.53 20:0.75 21:0.78 27:0.15 28:0.49 34:0.71 36:0.34 37:0.85 78:0.72 91:0.56 100:0.73 104:0.81 106:0.81 111:0.72 120:0.60 135:0.60 140:0.45 151:0.54 152:0.74 153:0.48 161:0.55 162:0.86 164:0.57 167:0.85 169:0.72 175:0.75 181:0.68 186:0.73 187:0.21 202:0.36 206:0.81 216:0.79 220:0.62 235:0.68 241:0.74 244:0.61 248:0.54 256:0.45 257:0.65 259:0.21 260:0.61 261:0.73 267:0.89 268:0.46 271:0.83 277:0.69 285:0.62 +4 6:0.96 8:0.98 12:0.79 17:0.16 20:0.86 21:0.68 27:0.06 28:0.49 32:0.80 34:0.89 36:0.38 37:0.92 78:0.85 81:0.77 91:0.90 100:0.90 111:0.86 120:0.96 126:0.54 135:0.32 140:0.45 145:0.76 150:0.57 151:0.84 152:0.84 153:0.98 161:0.55 162:0.56 164:0.96 167:0.92 169:0.90 175:0.75 181:0.68 186:0.85 187:0.68 189:0.99 191:0.97 195:0.62 202:0.95 215:0.49 216:0.75 235:0.80 238:0.95 241:0.78 244:0.61 248:0.94 256:0.94 257:0.65 259:0.21 260:0.96 261:0.89 267:0.23 268:0.94 271:0.83 277:0.69 283:0.82 285:0.62 297:0.36 +2 8:0.95 12:0.79 17:0.13 21:0.78 27:0.72 28:0.49 34:0.64 36:0.27 37:0.94 91:0.88 104:0.58 120:0.75 135:0.78 140:0.45 151:0.71 153:0.72 161:0.55 162:0.67 164:0.75 167:0.96 175:0.75 181:0.68 202:0.68 216:0.32 241:0.88 244:0.61 248:0.68 256:0.68 257:0.65 259:0.21 260:0.76 267:0.44 268:0.70 271:0.83 277:0.69 283:0.60 285:0.62 +4 6:0.96 7:0.81 8:0.96 12:0.79 17:0.24 20:0.75 21:0.68 27:0.06 28:0.49 30:0.95 32:0.80 34:0.67 36:0.71 37:0.92 43:0.50 55:0.96 66:0.20 69:0.30 78:0.94 81:0.77 91:0.96 98:0.11 100:0.97 108:0.24 111:0.98 120:0.99 123:0.23 124:0.61 126:0.54 127:0.44 129:0.59 133:0.47 135:0.23 140:0.45 145:0.63 146:0.05 147:0.05 149:0.22 150:0.57 151:0.90 152:0.84 153:0.99 154:0.39 159:0.20 161:0.55 162:0.58 164:0.99 167:0.99 169:0.90 172:0.05 173:0.61 177:0.05 179:1.00 181:0.68 186:0.92 189:0.97 191:0.98 195:0.55 202:0.99 215:0.49 216:0.75 230:0.87 233:0.76 235:0.80 238:0.98 241:1.00 243:0.40 245:0.36 248:0.98 256:0.99 259:0.21 260:0.99 261:0.89 265:0.12 267:0.82 268:0.99 271:0.83 283:0.82 285:0.62 297:0.36 300:0.08 +1 6:0.91 8:0.86 12:0.79 17:0.24 20:0.90 21:0.78 27:0.37 28:0.49 34:0.55 36:0.25 37:0.90 78:0.76 91:0.91 100:0.79 104:0.58 111:0.75 120:0.76 135:0.72 140:0.80 151:0.66 152:0.84 153:0.48 161:0.55 162:0.55 164:0.79 167:0.97 169:0.76 175:0.75 178:0.42 181:0.68 186:0.76 189:0.92 191:0.90 202:0.77 216:0.29 220:0.62 238:0.90 241:0.89 244:0.61 248:0.68 256:0.73 257:0.65 259:0.21 260:0.77 261:0.78 267:0.44 268:0.74 271:0.83 277:0.69 285:0.62 +2 12:0.79 17:0.47 21:0.78 27:0.21 28:0.49 30:0.21 34:0.42 36:0.73 37:0.74 43:0.42 55:0.71 66:0.15 69:0.51 70:0.82 91:0.29 98:0.36 108:0.92 120:0.76 123:0.92 124:0.95 127:0.60 129:0.99 133:0.95 135:0.26 140:0.90 146:0.05 147:0.05 149:0.73 151:0.73 153:0.80 154:0.32 159:0.93 161:0.55 162:0.49 164:0.79 167:0.69 172:0.70 173:0.14 177:0.05 178:0.50 179:0.21 181:0.68 187:0.39 202:0.84 212:0.19 216:0.95 235:0.42 241:0.46 242:0.82 243:0.06 245:0.86 247:0.12 248:0.68 256:0.78 259:0.21 260:0.77 265:0.38 266:0.95 267:0.19 268:0.74 271:0.83 276:0.89 283:0.82 285:0.62 300:0.70 +2 6:0.81 8:0.62 12:0.79 17:0.43 20:0.96 21:0.78 27:0.21 28:0.49 30:0.28 34:0.45 36:0.74 37:0.74 43:0.48 55:0.71 66:0.15 69:0.36 70:0.81 78:0.75 91:0.56 98:0.36 100:0.78 104:0.84 106:0.81 108:0.92 111:0.75 120:0.92 123:0.92 124:0.95 127:0.61 129:0.99 133:0.95 135:0.24 140:0.45 146:0.28 147:0.34 149:0.74 151:0.82 152:0.90 153:0.95 154:0.36 159:0.93 161:0.55 162:0.58 164:0.89 167:0.70 169:0.76 172:0.70 173:0.09 177:0.05 179:0.21 181:0.68 186:0.76 187:0.39 189:0.83 191:0.73 202:0.86 206:0.81 212:0.21 216:0.95 235:0.42 238:0.87 241:0.47 242:0.82 243:0.08 245:0.87 247:0.12 248:0.88 256:0.85 259:0.21 260:0.92 261:0.78 265:0.38 266:0.95 267:0.44 268:0.86 271:0.83 276:0.89 283:0.82 285:0.62 300:0.70 +0 12:0.79 17:0.66 21:0.78 27:0.10 28:0.49 34:0.33 36:0.52 37:0.28 91:0.86 104:0.58 106:0.81 120:0.90 135:0.49 140:0.45 151:0.80 153:0.93 161:0.55 162:0.63 164:0.91 167:0.87 175:0.75 181:0.68 187:0.68 202:0.87 206:0.81 216:0.23 235:0.05 241:0.75 244:0.61 248:0.86 256:0.88 257:0.65 259:0.21 260:0.91 267:0.23 268:0.89 271:0.83 277:0.69 285:0.62 +0 8:0.75 12:0.79 17:0.69 21:0.47 27:0.45 28:0.49 30:0.20 32:0.80 34:0.80 36:0.20 37:0.50 43:0.32 55:0.42 66:0.72 69:0.69 70:0.92 81:0.86 91:0.11 98:0.55 108:0.52 123:0.50 124:0.45 126:0.54 127:0.49 129:0.81 133:0.67 135:0.79 145:0.41 146:0.77 147:0.80 149:0.69 150:0.72 154:0.24 159:0.67 161:0.55 167:0.69 172:0.73 173:0.59 177:0.05 178:0.55 179:0.49 181:0.68 187:0.68 195:0.84 212:0.72 215:0.63 216:0.77 235:0.52 241:0.44 242:0.82 243:0.75 245:0.68 247:0.12 259:0.21 265:0.56 266:0.71 267:0.23 271:0.83 276:0.61 297:0.36 300:0.84 +1 8:1.00 12:0.79 17:0.53 20:0.74 21:0.78 27:0.42 28:0.49 34:0.62 36:0.24 37:1.00 78:0.83 91:0.90 100:0.86 104:0.58 111:0.83 120:0.60 135:0.72 140:0.87 151:0.54 152:0.73 153:0.48 161:0.55 162:0.54 164:0.57 167:0.95 169:0.84 175:0.75 178:0.48 181:0.68 186:0.83 191:0.89 202:0.56 216:0.17 220:0.62 241:0.84 244:0.61 248:0.54 256:0.45 257:0.65 259:0.21 260:0.61 261:0.74 267:0.59 268:0.46 271:0.83 277:0.69 285:0.62 +1 6:0.96 8:0.97 12:0.79 17:0.38 20:0.73 21:0.78 27:0.18 28:0.49 34:0.62 36:0.51 37:0.96 78:0.82 91:0.33 100:0.91 111:0.83 120:0.86 135:0.69 140:0.45 151:0.76 152:0.76 153:0.87 161:0.55 162:0.61 164:0.89 167:0.84 169:0.76 175:0.75 181:0.68 186:0.86 187:0.87 191:0.89 202:0.84 216:0.52 235:0.12 241:0.73 244:0.61 248:0.81 256:0.85 257:0.65 259:0.21 260:0.87 261:0.75 267:0.65 268:0.86 271:0.83 277:0.69 283:0.60 285:0.62 +2 6:0.96 8:0.93 12:0.79 17:0.38 20:0.74 21:0.71 27:0.06 28:0.49 32:0.80 34:0.71 36:0.63 37:0.92 78:0.81 81:0.74 91:0.82 100:0.80 111:0.80 120:0.82 126:0.54 135:0.65 140:0.94 145:0.69 150:0.55 151:0.75 152:0.84 153:0.85 161:0.55 162:0.49 164:0.81 167:0.91 169:0.90 175:0.75 178:0.52 181:0.68 186:0.82 187:0.68 191:0.95 195:0.62 202:0.85 215:0.48 216:0.75 235:0.84 241:0.77 244:0.61 248:0.74 256:0.75 257:0.65 259:0.21 260:0.83 261:0.89 267:0.19 268:0.76 271:0.83 277:0.69 285:0.62 297:0.36 +1 6:0.96 8:0.95 12:0.79 17:0.32 20:0.79 21:0.78 27:0.18 28:0.49 34:0.33 36:0.61 37:0.96 78:0.75 91:0.78 100:0.79 111:0.75 120:0.59 135:0.61 140:0.93 151:0.51 152:0.76 153:0.45 161:0.55 162:0.51 164:0.84 167:0.98 169:0.76 175:0.75 178:0.52 181:0.68 186:0.76 189:0.98 191:0.90 202:0.84 216:0.52 220:0.91 235:0.12 238:0.91 241:0.94 244:0.61 248:0.53 256:0.83 257:0.65 259:0.21 260:0.60 261:0.75 267:0.73 268:0.80 271:0.83 277:0.69 285:0.62 +3 6:0.96 7:0.81 8:0.95 12:0.79 17:0.45 20:0.83 21:0.68 27:0.06 28:0.49 30:0.95 32:0.80 34:0.85 36:0.71 37:0.92 43:0.50 55:0.96 66:0.20 69:0.30 78:0.88 81:0.77 91:0.94 98:0.11 100:0.94 108:0.24 111:0.90 120:0.97 123:0.23 124:0.61 126:0.54 127:0.28 129:0.59 133:0.47 135:0.26 140:0.80 145:0.63 146:0.05 147:0.05 149:0.22 150:0.57 151:0.84 152:0.84 153:0.98 154:0.39 159:0.20 161:0.55 162:0.55 164:0.97 167:0.96 169:0.90 172:0.05 173:0.54 177:0.05 178:0.42 179:0.99 181:0.68 186:0.88 187:0.21 189:0.96 191:0.97 195:0.57 202:0.96 215:0.49 216:0.75 230:0.87 233:0.76 235:0.80 238:0.95 241:0.86 243:0.40 245:0.36 248:0.95 256:0.95 259:0.21 260:0.97 261:0.89 265:0.11 267:0.76 268:0.96 271:0.83 283:0.82 285:0.62 297:0.36 300:0.08 +2 7:0.81 8:0.93 12:0.79 17:0.24 21:0.59 27:0.06 28:0.49 32:0.80 34:0.78 36:0.35 37:0.92 81:0.82 91:0.84 120:0.84 126:0.54 135:0.37 140:0.90 145:0.72 150:0.62 151:0.74 153:0.83 161:0.55 162:0.52 164:0.89 167:0.89 175:0.75 178:0.50 181:0.68 187:0.39 191:0.93 195:0.62 202:0.89 215:0.55 216:0.75 220:0.97 230:0.65 233:0.63 235:0.68 241:0.76 244:0.61 248:0.76 256:0.86 257:0.65 259:0.21 260:0.85 267:0.18 268:0.86 271:0.83 277:0.69 285:0.62 297:0.36 +0 8:0.96 12:0.51 17:0.15 21:0.71 25:0.88 27:0.12 28:0.66 30:0.21 34:0.30 36:0.83 37:0.79 39:0.92 43:0.24 55:0.52 66:0.36 69:0.30 70:0.50 74:0.63 76:0.85 81:0.38 91:0.94 98:0.31 104:0.58 108:0.24 120:0.97 123:0.23 124:0.82 126:0.32 127:0.54 129:0.05 133:0.82 135:0.34 140:0.45 145:0.77 146:0.44 147:0.51 149:0.20 150:0.34 151:0.80 153:0.94 154:0.81 159:0.61 161:0.68 162:0.80 164:0.96 167:0.99 172:0.27 173:0.24 177:0.38 179:0.86 181:0.56 190:0.77 191:0.95 202:0.92 212:0.16 215:0.48 216:0.53 220:0.62 222:0.48 235:0.84 241:0.90 242:0.40 243:0.51 245:0.45 247:0.47 248:0.96 253:0.49 254:0.86 256:0.94 259:0.21 260:0.97 265:0.33 266:0.54 267:0.76 268:0.95 276:0.36 283:0.71 285:0.50 297:0.36 300:0.33 +4 1:0.74 6:0.99 7:0.81 8:0.98 9:0.80 11:0.57 12:0.51 17:0.11 20:0.99 21:0.64 25:0.88 27:0.09 28:0.66 30:0.89 32:0.80 34:0.28 36:0.39 37:0.95 39:0.92 41:1.00 43:0.46 60:1.00 66:0.16 69:0.95 70:0.20 74:0.70 77:0.70 78:0.98 81:0.70 83:0.90 85:0.85 87:0.98 91:0.96 96:0.99 98:0.06 100:1.00 101:0.72 104:0.58 108:0.90 111:1.00 114:0.72 120:0.97 121:0.90 122:0.43 123:0.90 124:0.75 126:0.54 127:0.29 129:0.81 133:0.67 135:0.23 140:0.45 145:0.90 146:0.13 147:0.18 149:0.36 150:0.80 151:0.99 152:1.00 153:0.94 154:0.35 155:0.67 158:0.92 159:0.81 161:0.68 162:0.75 163:0.88 164:0.97 165:0.80 167:0.99 169:0.99 172:0.10 173:0.87 176:0.73 177:0.38 179:0.89 181:0.56 186:0.97 189:0.99 191:0.92 192:0.59 195:0.97 201:0.74 202:0.94 204:0.89 208:0.64 212:0.30 215:0.53 216:0.68 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.88 238:1.00 241:0.93 242:0.63 243:0.37 245:0.43 247:0.30 248:0.99 253:0.99 255:0.79 256:0.96 259:0.21 260:0.97 261:1.00 265:0.06 266:0.27 267:0.76 268:0.96 276:0.22 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.88 300:0.18 +2 1:0.74 6:0.99 7:0.81 8:0.94 9:0.80 11:0.57 12:0.51 17:0.04 20:0.78 21:0.40 25:0.88 27:0.09 28:0.66 30:0.87 32:0.80 34:0.33 36:0.93 37:0.95 39:0.92 41:0.95 43:0.57 60:0.87 66:0.54 69:0.94 70:0.27 74:0.74 77:0.70 78:0.84 81:0.85 83:0.83 85:0.88 87:0.98 91:0.15 96:0.90 98:0.21 100:0.95 101:0.76 104:0.58 108:0.89 111:0.90 114:0.82 120:0.83 121:0.90 122:0.43 123:0.88 124:0.48 126:0.54 127:0.17 129:0.59 133:0.47 135:0.58 140:0.90 145:0.90 146:0.38 147:0.45 149:0.53 150:0.91 151:0.85 152:1.00 153:0.46 154:0.45 155:0.67 158:0.87 159:0.42 161:0.68 162:0.53 164:0.81 165:0.86 167:0.76 169:0.99 172:0.32 173:0.94 176:0.73 177:0.38 178:0.50 179:0.58 181:0.56 186:0.84 187:0.87 191:0.89 192:0.59 195:0.90 201:0.74 202:0.79 204:0.89 208:0.64 212:0.19 215:0.67 216:0.68 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.68 241:0.62 242:0.63 243:0.63 245:0.50 247:0.30 248:0.89 253:0.90 255:0.79 256:0.77 259:0.21 260:0.83 261:1.00 265:0.23 266:0.47 267:0.69 268:0.76 276:0.20 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.25 +1 1:0.74 8:0.95 9:0.80 11:0.57 12:0.51 17:0.79 21:0.59 25:0.88 27:0.72 28:0.66 30:0.91 34:0.73 36:0.69 37:0.95 39:0.92 41:0.96 43:0.88 66:0.27 69:0.54 70:0.13 74:0.51 81:0.74 83:0.80 85:0.80 87:0.98 91:0.56 96:0.90 98:0.10 101:0.76 104:0.54 108:0.42 114:0.74 120:0.83 122:0.43 123:0.40 124:0.61 126:0.54 127:0.70 129:0.59 133:0.47 135:0.69 140:0.87 145:0.77 146:0.13 147:0.18 149:0.61 150:0.83 151:0.87 153:0.48 154:0.87 155:0.67 159:0.46 161:0.68 162:0.64 163:0.88 164:0.83 165:0.81 167:0.79 172:0.10 173:0.59 176:0.73 177:0.38 178:0.48 179:0.98 181:0.56 187:0.39 192:0.59 201:0.74 202:0.77 208:0.64 212:0.61 215:0.55 216:0.21 220:0.93 222:0.48 232:0.76 235:0.84 241:0.68 242:0.63 243:0.46 245:0.40 247:0.30 248:0.89 253:0.87 255:0.79 256:0.78 259:0.21 260:0.84 265:0.11 266:0.17 267:0.19 268:0.78 276:0.12 285:0.62 290:0.74 297:0.36 300:0.13 +0 1:0.74 12:0.51 17:0.53 21:0.78 27:0.45 28:0.66 30:0.21 34:0.73 36:0.36 37:0.50 39:0.92 43:0.22 55:0.45 66:0.36 69:0.72 70:0.37 74:0.42 81:0.42 91:0.20 98:0.32 108:0.79 114:0.63 123:0.78 124:0.57 126:0.54 127:0.23 129:0.05 133:0.39 135:0.83 145:0.50 146:0.18 147:0.23 149:0.17 150:0.60 154:0.73 155:0.67 159:0.47 161:0.68 163:0.89 167:0.65 172:0.16 173:0.63 176:0.73 177:0.38 178:0.55 179:0.88 181:0.56 187:0.68 192:0.59 201:0.74 212:0.42 215:0.43 216:0.77 222:0.48 235:1.00 241:0.21 242:0.45 243:0.40 245:0.43 247:0.35 253:0.50 254:0.80 259:0.21 265:0.34 266:0.23 267:0.94 276:0.19 277:0.69 290:0.63 297:0.36 300:0.25 +1 1:0.74 7:0.78 8:0.92 9:0.80 12:0.51 17:0.51 20:0.82 27:0.51 28:0.66 30:0.91 32:0.75 34:0.29 36:0.80 37:0.70 39:0.92 43:0.87 66:0.69 69:0.55 70:0.13 74:0.76 76:0.97 77:0.89 78:0.72 81:0.96 91:0.82 96:0.93 98:0.34 100:0.80 104:0.76 108:0.43 111:0.75 114:0.77 120:0.88 122:0.43 123:0.41 126:0.54 127:0.12 129:0.05 135:0.18 138:0.95 140:0.45 145:0.74 146:0.36 147:0.43 149:0.67 150:0.98 151:0.98 152:0.79 153:0.94 154:0.84 155:0.67 158:0.84 159:0.14 161:0.68 162:0.72 163:0.81 164:0.88 167:0.97 169:0.78 172:0.21 173:0.61 176:0.56 177:0.38 179:0.52 181:0.56 186:0.73 191:0.76 192:0.59 195:0.72 201:0.74 202:0.82 204:0.89 208:0.64 212:0.61 215:0.91 216:0.29 222:0.48 230:0.81 232:0.72 233:0.69 235:0.05 241:0.83 242:0.63 243:0.73 247:0.30 248:0.96 253:0.93 255:0.79 256:0.84 259:0.21 260:0.89 261:0.76 265:0.36 266:0.17 267:0.59 268:0.85 276:0.13 279:0.86 282:0.83 283:0.71 285:0.62 286:0.99 290:0.77 297:0.36 298:0.99 300:0.13 +2 1:0.74 7:0.81 8:0.86 9:0.80 11:0.57 12:0.51 17:0.67 20:0.83 21:0.13 25:0.88 27:0.68 28:0.66 30:0.89 34:0.67 36:0.74 37:0.56 39:0.92 41:0.82 43:0.48 66:0.75 69:0.95 70:0.20 74:0.55 78:0.79 81:0.90 83:0.79 85:0.85 87:0.98 91:0.04 96:0.80 98:0.29 100:0.73 101:0.75 104:0.58 108:0.90 111:0.77 114:0.92 120:0.76 121:0.90 122:0.43 123:0.90 126:0.54 127:0.12 129:0.05 135:0.30 140:0.80 146:0.49 147:0.55 149:0.44 150:0.95 151:0.87 152:0.79 153:0.48 154:0.37 155:0.67 159:0.18 161:0.68 162:0.86 164:0.67 165:0.88 167:0.70 169:0.72 172:0.32 173:0.98 176:0.73 177:0.38 179:0.27 181:0.56 186:0.80 187:0.39 191:0.70 192:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.30 215:0.84 216:0.37 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.22 241:0.43 242:0.63 243:0.77 247:0.30 248:0.78 253:0.82 255:0.79 256:0.58 259:0.21 260:0.76 261:0.78 265:0.31 266:0.27 267:0.87 268:0.59 276:0.19 285:0.62 290:0.92 297:0.36 300:0.18 +0 12:0.51 17:0.34 21:0.59 27:0.45 28:0.66 30:0.62 34:0.86 36:0.19 37:0.50 39:0.92 43:0.30 55:0.98 66:0.30 69:0.87 70:0.34 74:0.44 81:0.52 91:0.14 98:0.24 108:0.74 122:0.43 123:0.72 124:0.71 126:0.32 127:0.28 129:0.59 133:0.64 135:0.65 145:0.49 146:0.37 147:0.05 149:0.24 150:0.40 154:0.48 159:0.52 161:0.68 163:0.88 167:0.62 172:0.16 173:0.84 177:0.05 178:0.55 179:0.88 181:0.56 187:0.68 212:0.30 215:0.55 216:0.77 222:0.48 235:0.68 241:0.10 242:0.63 243:0.23 245:0.43 247:0.35 253:0.38 254:0.84 257:0.65 259:0.21 265:0.26 266:0.27 267:0.95 276:0.26 283:0.71 297:0.36 300:0.25 +2 1:0.74 8:0.96 9:0.80 11:0.57 12:0.51 17:0.83 20:0.82 21:0.59 25:0.88 27:0.72 28:0.66 30:0.91 34:0.73 36:0.69 37:0.95 39:0.92 41:0.94 43:0.88 66:0.27 69:0.54 70:0.13 74:0.51 78:0.72 81:0.74 83:0.79 85:0.80 87:0.98 91:0.54 96:0.86 98:0.10 100:0.73 101:0.76 104:0.54 108:0.42 111:0.72 114:0.74 120:0.77 122:0.43 123:0.40 124:0.61 126:0.54 127:0.70 129:0.59 133:0.47 135:0.69 140:0.87 145:0.77 146:0.13 147:0.18 149:0.61 150:0.83 151:0.87 152:0.79 153:0.48 154:0.87 155:0.67 159:0.46 161:0.68 162:0.57 163:0.88 164:0.78 165:0.81 167:0.80 169:0.72 172:0.10 173:0.59 176:0.73 177:0.38 178:0.48 179:0.98 181:0.56 186:0.73 187:0.21 191:0.87 192:0.59 201:0.74 202:0.73 208:0.64 212:0.61 215:0.55 216:0.21 220:0.93 222:0.48 232:0.76 235:0.84 241:0.68 242:0.63 243:0.46 245:0.40 247:0.30 248:0.85 253:0.83 255:0.79 256:0.71 259:0.21 260:0.78 261:0.73 265:0.11 266:0.17 267:0.65 268:0.72 276:0.12 285:0.62 290:0.74 297:0.36 300:0.13 +2 1:0.74 7:0.81 8:0.63 9:0.80 11:0.57 12:0.51 17:0.16 20:0.94 21:0.40 27:0.21 28:0.66 30:0.57 34:0.47 36:0.87 37:0.74 39:0.92 41:0.92 43:0.97 60:0.95 66:0.82 69:0.17 70:0.64 74:0.97 78:0.75 81:0.78 83:0.82 85:0.97 91:0.27 96:0.85 98:0.82 100:0.73 101:0.85 104:0.93 106:0.81 108:0.14 111:0.74 114:0.82 117:0.86 120:0.76 121:0.90 122:0.57 123:0.13 124:0.39 126:0.54 127:0.54 129:0.05 133:0.39 135:0.17 140:0.45 146:0.91 147:0.92 149:0.97 150:0.85 151:0.86 152:0.87 153:0.86 154:0.97 155:0.67 158:0.87 159:0.61 161:0.68 162:0.73 164:0.78 165:0.83 167:0.70 169:0.72 172:0.86 173:0.17 176:0.73 177:0.38 179:0.34 181:0.56 186:0.75 187:0.39 192:0.59 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.70 215:0.67 216:0.95 220:0.62 222:0.48 230:0.84 232:0.95 233:0.69 235:0.52 241:0.41 242:0.53 243:0.83 245:0.80 247:0.47 248:0.83 253:0.90 255:0.79 256:0.68 259:0.21 260:0.77 261:0.77 265:0.82 266:0.63 267:0.69 268:0.73 276:0.78 279:0.86 283:0.71 285:0.62 290:0.82 297:0.36 300:0.55 +0 8:0.96 9:0.80 12:0.51 17:0.53 21:0.05 27:0.17 28:0.66 34:0.29 36:0.87 37:0.92 39:0.92 41:0.82 81:0.94 83:0.73 91:0.62 96:0.74 104:0.58 120:0.88 126:0.32 135:0.34 140:0.97 150:0.89 151:0.69 153:0.92 155:0.67 161:0.68 162:0.68 164:0.88 167:0.89 175:0.91 181:0.56 187:0.39 190:0.77 191:0.92 192:0.59 202:0.83 208:0.64 215:0.91 216:0.43 220:0.62 222:0.48 235:0.05 241:0.75 244:0.83 248:0.65 253:0.55 255:0.79 256:0.86 257:0.84 259:0.21 260:0.88 267:0.19 268:0.85 277:0.87 285:0.62 297:0.36 +2 1:0.74 6:0.99 7:0.81 8:0.80 11:0.57 12:0.51 17:0.11 20:0.79 21:0.22 25:0.88 27:0.09 28:0.66 30:0.76 32:0.80 34:0.39 36:0.87 37:0.95 39:0.92 43:0.85 66:0.10 69:0.64 70:0.44 74:0.97 77:0.70 78:0.72 81:0.91 83:0.78 85:1.00 87:0.98 91:0.18 98:0.38 100:0.82 101:0.86 104:0.58 108:0.95 111:0.76 114:0.90 121:0.90 122:0.57 123:0.95 124:0.95 126:0.54 127:0.92 129:0.99 133:0.95 135:0.78 145:0.79 146:0.78 147:0.82 149:0.99 150:0.95 152:1.00 154:0.81 155:0.67 159:0.92 161:0.68 163:0.81 165:0.90 167:0.75 169:0.99 172:0.73 173:0.29 176:0.73 177:0.38 178:0.55 179:0.18 181:0.56 186:0.73 187:0.21 192:0.59 195:0.98 201:0.74 202:0.54 204:0.89 208:0.64 212:0.37 215:0.79 216:0.68 222:0.48 230:0.84 232:0.80 233:0.69 235:0.52 241:0.61 242:0.63 243:0.28 245:0.92 247:0.39 253:0.79 259:0.21 261:1.00 265:0.40 266:0.80 267:0.73 276:0.93 282:0.88 290:0.90 297:0.36 299:0.88 300:0.55 +0 1:0.74 8:0.90 9:0.80 12:0.51 17:0.64 21:0.22 27:0.72 28:0.66 34:0.63 36:0.90 37:0.86 39:0.92 81:0.86 91:0.80 96:0.73 104:0.73 114:0.90 120:0.77 126:0.54 135:0.20 140:0.87 150:0.88 151:0.62 153:0.48 155:0.67 161:0.68 162:0.86 164:0.71 167:0.99 175:0.91 176:0.73 181:0.56 190:0.77 192:0.59 201:0.74 202:0.56 208:0.64 215:0.79 216:0.10 222:0.48 235:0.31 241:0.90 244:0.83 248:0.60 253:0.70 255:0.79 256:0.64 257:0.84 259:0.21 260:0.77 267:0.17 268:0.65 277:0.87 285:0.62 290:0.90 297:0.36 +2 1:0.74 6:0.91 8:0.84 9:0.80 12:0.51 17:0.69 20:0.99 21:0.30 25:0.88 27:0.55 28:0.66 34:0.47 36:0.51 37:0.35 39:0.92 41:0.95 78:0.79 81:0.82 83:0.81 87:0.98 91:0.72 96:0.91 100:0.83 104:0.58 111:0.79 114:0.86 120:0.85 126:0.54 135:0.37 140:0.45 150:0.88 151:0.92 152:0.96 153:0.72 155:0.67 161:0.68 162:0.86 164:0.82 165:0.84 167:0.97 169:0.79 175:0.91 176:0.73 181:0.56 186:0.79 189:0.93 192:0.59 201:0.74 202:0.69 208:0.64 215:0.72 216:0.43 220:0.87 222:0.48 232:0.80 235:0.42 238:0.92 241:0.83 244:0.83 248:0.91 253:0.88 255:0.79 256:0.78 257:0.84 259:0.21 260:0.85 261:0.83 267:0.23 268:0.77 277:0.87 285:0.62 290:0.86 297:0.36 +2 1:0.74 6:0.99 7:0.81 8:0.92 9:0.80 11:0.57 12:0.51 17:0.16 20:0.81 21:0.30 25:0.88 27:0.09 28:0.66 30:0.87 32:0.80 34:0.44 36:0.73 37:0.95 39:0.92 41:0.94 43:0.68 60:0.87 66:0.24 69:0.89 70:0.27 74:0.77 77:0.70 78:0.85 81:0.89 83:0.83 85:0.90 87:0.98 91:0.23 96:0.90 98:0.17 100:0.94 101:0.78 104:0.58 108:0.87 111:0.89 114:0.86 120:0.82 121:0.90 122:0.43 123:0.87 124:0.81 126:0.54 127:0.30 129:0.89 133:0.78 135:0.30 140:0.87 145:0.90 146:0.13 147:0.18 149:0.67 150:0.93 151:0.87 152:1.00 153:0.48 154:0.58 155:0.67 158:0.87 159:0.57 161:0.68 162:0.61 163:0.88 164:0.80 165:0.88 167:0.75 169:0.99 172:0.21 173:0.86 176:0.73 177:0.38 178:0.48 179:0.74 181:0.56 186:0.85 187:0.87 189:0.97 191:0.79 192:0.59 195:0.85 201:0.74 202:0.74 204:0.89 208:0.64 212:0.50 215:0.72 216:0.68 220:0.62 222:0.48 230:0.84 232:0.80 233:0.69 235:0.60 238:0.97 241:0.61 242:0.63 243:0.28 245:0.50 247:0.30 248:0.89 253:0.90 255:0.79 256:0.73 259:0.21 260:0.83 261:1.00 265:0.18 266:0.38 267:0.91 268:0.75 276:0.36 279:0.86 282:0.88 283:0.71 285:0.62 290:0.86 297:0.36 299:0.88 300:0.25 +1 1:0.60 7:0.70 8:0.76 10:0.86 11:0.75 12:0.20 17:0.62 18:0.78 21:0.22 27:0.59 29:0.71 30:0.21 32:0.67 34:0.56 36:0.23 37:0.27 39:0.38 43:0.66 45:0.87 46:0.88 55:0.52 66:0.94 69:0.12 70:0.88 71:0.78 74:0.61 77:0.70 81:0.51 86:0.68 91:0.33 98:0.93 99:0.83 101:0.87 107:0.91 108:0.10 110:0.93 114:0.85 122:0.83 123:0.10 125:0.88 126:0.32 127:0.58 129:0.05 131:0.61 135:0.26 139:0.66 144:0.76 145:0.44 146:0.81 147:0.84 149:0.58 150:0.45 154:0.55 155:0.51 157:0.95 158:0.73 159:0.37 160:0.88 165:0.65 166:0.86 170:0.90 172:0.85 173:0.28 174:0.89 176:0.63 177:0.88 178:0.55 179:0.38 182:1.00 187:0.21 192:0.51 195:0.60 197:0.92 201:0.57 204:0.81 208:0.50 212:0.88 215:0.79 216:0.40 222:0.76 227:0.61 229:0.61 230:0.73 231:0.86 233:0.76 235:0.42 239:0.85 241:0.24 242:0.39 243:0.95 244:0.83 246:0.84 247:0.82 253:0.62 259:0.21 265:0.93 266:0.27 267:0.23 271:0.57 276:0.75 279:0.75 282:0.75 283:0.66 287:0.61 289:0.91 290:0.85 297:0.36 300:0.70 +0 1:0.59 10:0.88 11:0.75 12:0.20 17:0.43 18:0.70 21:0.64 27:0.34 29:0.71 30:0.43 34:0.91 36:0.53 37:0.48 39:0.38 43:0.37 45:0.87 46:0.88 66:0.89 69:0.86 70:0.84 71:0.78 74:0.38 81:0.47 86:0.71 91:0.10 98:0.61 99:0.83 101:0.87 107:0.90 108:0.73 110:0.92 114:0.69 122:0.83 123:0.71 125:0.88 126:0.32 127:0.18 129:0.05 131:0.61 135:0.24 139:0.69 144:0.76 146:0.79 147:0.82 149:0.56 150:0.40 154:0.28 155:0.47 157:0.97 159:0.35 160:0.88 165:0.65 166:0.85 170:0.90 172:0.70 173:0.83 174:0.88 176:0.63 177:0.88 178:0.55 179:0.27 182:1.00 187:0.68 192:0.46 197:0.88 201:0.56 208:0.50 212:0.61 215:0.53 216:0.88 222:0.76 227:0.61 229:0.61 231:0.84 235:0.88 239:0.87 241:0.01 242:0.19 243:0.90 244:0.83 246:0.84 247:0.67 253:0.52 259:0.21 265:0.62 266:0.63 267:0.97 271:0.57 276:0.57 287:0.61 289:0.91 290:0.69 297:0.36 300:0.70 +0 10:0.85 11:0.49 12:0.20 17:0.67 18:0.62 21:0.54 27:0.52 29:0.71 30:0.33 34:0.36 36:0.48 37:0.43 39:0.38 43:0.09 45:0.66 46:0.88 55:0.52 66:0.60 69:0.65 70:0.83 71:0.78 74:0.36 76:0.99 77:0.89 81:0.29 86:0.67 91:0.29 96:0.74 98:0.53 101:0.47 107:0.90 108:0.50 110:0.91 114:0.64 122:0.83 123:0.48 124:0.58 125:0.88 126:0.24 127:0.46 129:0.05 131:0.85 133:0.60 135:0.76 139:0.64 140:0.45 144:0.57 145:0.70 146:0.78 147:0.82 149:0.18 150:0.32 151:0.57 153:0.48 154:0.36 157:0.76 158:0.72 159:0.70 160:0.88 162:0.86 166:0.78 170:0.90 172:0.63 173:0.51 174:0.79 176:0.56 177:0.88 179:0.55 182:0.72 187:0.87 190:0.98 195:0.88 197:0.82 202:0.36 212:0.30 216:0.79 220:0.74 222:0.76 227:0.82 229:0.61 231:0.79 235:0.60 239:0.84 241:0.10 242:0.55 243:0.67 244:0.83 245:0.70 246:0.61 247:0.79 248:0.56 253:0.58 254:0.88 256:0.45 259:0.21 265:0.54 266:0.84 267:0.17 268:0.46 271:0.57 276:0.59 282:0.71 285:0.46 287:0.61 289:0.89 290:0.64 300:0.70 +1 1:0.61 10:0.94 11:0.82 12:0.20 17:0.38 18:0.73 21:0.13 27:0.63 29:0.71 30:0.45 34:0.68 36:0.70 37:0.66 39:0.38 43:0.92 45:0.81 46:0.95 66:0.19 69:0.12 70:0.54 71:0.78 74:0.63 81:0.53 85:0.88 86:0.84 91:0.53 97:0.89 98:0.25 99:0.83 101:0.87 104:0.93 107:0.91 108:0.91 110:0.93 114:0.88 122:0.55 123:0.90 124:0.82 125:0.88 126:0.32 127:0.61 129:0.89 131:0.61 133:0.78 135:0.83 139:0.80 144:0.76 146:0.24 147:0.30 149:0.84 150:0.46 154:0.91 155:0.48 157:1.00 158:0.77 159:0.77 160:0.88 165:0.65 166:0.86 170:0.90 172:0.41 173:0.11 174:0.88 176:0.63 177:0.88 178:0.55 179:0.54 182:1.00 187:0.39 192:0.47 197:0.88 201:0.58 208:0.50 212:0.56 215:0.84 216:0.29 222:0.76 227:0.61 229:0.61 231:0.85 232:0.95 235:0.31 239:0.93 241:0.24 242:0.21 243:0.33 245:0.75 246:0.84 247:0.67 253:0.64 259:0.21 265:0.27 266:0.84 267:0.82 271:0.57 276:0.52 279:0.76 283:0.82 287:0.61 289:0.91 290:0.88 297:0.36 300:0.55 +0 10:0.85 11:0.52 12:0.20 17:0.32 18:0.64 21:0.78 27:0.45 29:0.71 30:0.40 34:0.64 36:0.27 37:0.50 39:0.38 43:0.31 45:0.73 46:0.88 55:0.59 66:0.35 69:0.76 70:0.79 71:0.78 74:0.31 86:0.66 91:0.04 98:0.42 101:0.47 107:0.89 108:0.59 110:0.90 123:0.57 124:0.83 125:0.88 127:0.75 129:0.59 131:0.61 133:0.85 135:0.66 139:0.65 144:0.76 145:0.44 146:0.93 147:0.94 149:0.52 154:0.23 157:0.77 159:0.93 160:0.88 166:0.61 170:0.90 172:0.90 173:0.39 174:0.83 177:0.88 178:0.55 179:0.18 182:0.72 187:0.68 197:0.84 212:0.81 216:0.77 222:0.76 227:0.61 229:0.61 231:0.71 235:0.68 239:0.84 241:0.07 242:0.80 243:0.51 244:0.83 245:0.94 246:0.61 247:0.76 253:0.28 259:0.21 265:0.44 266:0.92 267:0.52 271:0.57 276:0.92 287:0.61 289:0.89 300:0.98 +0 8:0.86 10:0.89 11:0.49 12:0.20 17:0.72 18:0.70 21:0.78 27:0.10 29:0.71 30:0.21 34:0.45 36:0.54 37:0.86 39:0.38 43:0.25 45:0.73 46:0.88 55:0.59 66:0.88 69:0.50 70:0.77 71:0.78 74:0.27 86:0.70 91:0.40 98:0.80 101:0.47 107:0.90 108:0.38 110:0.91 122:0.94 123:0.37 125:0.88 127:0.29 129:0.05 131:0.61 135:0.66 139:0.68 144:0.57 145:0.54 146:0.78 147:0.81 149:0.24 154:0.49 157:0.80 159:0.34 160:0.88 166:0.61 170:0.90 172:0.65 173:0.54 174:0.94 177:0.88 178:0.55 179:0.53 182:0.74 187:0.39 191:0.70 197:0.96 202:0.53 212:0.59 216:0.55 222:0.76 227:0.61 229:0.61 231:0.71 235:0.22 239:0.88 241:0.51 242:0.15 243:0.88 244:0.83 246:0.61 247:0.84 253:0.25 254:0.80 259:0.21 265:0.80 266:0.47 267:0.65 271:0.57 276:0.54 287:0.61 289:0.89 300:0.55 +0 8:0.72 10:0.83 11:0.49 12:0.20 17:0.92 18:0.62 21:0.78 27:0.72 29:0.71 30:0.68 34:0.37 36:0.57 37:0.23 39:0.38 43:0.26 45:0.66 46:0.88 55:0.71 66:0.20 69:0.84 70:0.43 71:0.78 74:0.28 86:0.64 91:0.48 98:0.20 101:0.47 107:0.89 108:0.69 110:0.90 122:0.77 123:0.87 124:0.74 125:0.88 127:0.48 129:0.05 131:0.61 133:0.60 135:0.20 139:0.62 144:0.57 145:0.65 146:0.25 147:0.26 149:0.22 154:0.19 157:0.76 159:0.64 160:0.88 163:0.80 166:0.61 170:0.90 172:0.16 173:0.79 174:0.83 175:0.75 177:0.05 178:0.55 179:0.88 182:0.72 197:0.84 202:0.36 212:0.19 216:0.06 222:0.76 227:0.61 229:0.61 231:0.70 235:0.80 239:0.83 241:0.77 242:0.19 243:0.32 244:0.93 245:0.49 246:0.61 247:0.55 253:0.26 257:0.93 259:0.21 265:0.17 266:0.47 267:0.19 271:0.57 276:0.24 277:0.87 287:0.61 289:0.89 300:0.33 +0 1:0.56 10:0.90 11:0.52 12:0.20 17:0.62 18:0.74 21:0.74 27:0.63 29:0.71 30:0.36 34:0.52 36:0.87 37:0.79 39:0.38 43:0.10 45:0.73 46:0.88 55:0.45 64:0.77 66:0.36 69:0.45 70:0.91 71:0.78 74:0.36 81:0.48 83:0.56 86:0.76 91:0.12 98:0.66 99:0.83 101:0.47 107:0.91 108:0.35 110:0.92 123:0.33 124:0.68 125:0.88 126:0.51 127:0.66 129:0.59 131:0.61 133:0.60 135:0.58 139:0.72 144:0.76 146:0.75 147:0.79 149:0.16 150:0.36 154:0.52 155:0.46 157:0.80 159:0.56 160:0.88 163:0.81 165:0.65 166:0.80 170:0.90 172:0.57 173:0.41 174:0.86 177:0.88 178:0.55 179:0.55 182:0.81 187:0.39 192:0.44 197:0.89 201:0.56 208:0.50 212:0.27 215:0.46 216:0.41 222:0.76 227:0.61 229:0.61 231:0.82 235:0.97 236:0.94 239:0.89 241:0.10 242:0.37 243:0.51 244:0.83 245:0.76 246:0.91 247:0.79 253:0.32 254:0.80 259:0.21 265:0.67 266:0.75 267:0.36 271:0.57 276:0.64 287:0.61 289:0.91 297:0.36 300:0.84 +0 1:0.62 10:0.90 11:0.75 12:0.20 17:0.16 18:0.74 21:0.47 27:0.47 29:0.71 30:0.68 34:0.82 36:0.19 37:0.66 39:0.38 43:0.61 44:0.88 45:0.85 46:0.88 66:0.36 69:0.40 70:0.52 71:0.78 74:0.49 77:0.96 81:0.51 86:0.75 91:0.18 97:0.97 98:0.31 99:0.83 101:0.96 102:0.96 104:0.90 106:0.81 107:0.90 108:0.63 110:0.92 114:0.76 117:0.86 122:0.85 123:0.61 124:0.60 125:0.88 126:0.32 127:0.17 129:0.05 131:0.61 133:0.45 135:0.51 139:0.76 144:0.76 145:1.00 146:0.47 147:0.53 149:0.62 150:0.45 154:0.50 155:0.48 157:0.97 158:0.76 159:0.32 160:0.88 165:0.65 166:0.85 170:0.90 172:0.41 173:0.28 174:0.86 176:0.63 177:0.88 178:0.55 179:0.31 182:1.00 187:0.39 192:0.47 195:0.88 197:0.88 201:0.58 206:0.81 208:0.50 212:0.69 215:0.63 216:0.82 222:0.76 227:0.61 229:0.61 231:0.85 232:0.84 235:0.74 239:0.91 241:0.05 242:0.31 243:0.49 244:0.83 245:0.71 246:0.84 247:0.60 253:0.56 259:0.21 265:0.33 266:0.47 267:0.59 271:0.57 276:0.48 277:0.69 279:0.79 282:0.72 283:0.67 287:0.61 289:0.91 290:0.76 297:0.36 300:0.43 +1 1:0.60 8:0.59 10:0.85 11:0.75 12:0.20 17:0.57 18:0.72 21:0.30 27:0.59 29:0.71 30:0.26 34:0.80 36:0.47 37:0.27 39:0.38 43:0.66 45:0.81 46:0.88 55:0.59 66:0.67 69:0.15 70:0.75 71:0.78 74:0.52 81:0.50 86:0.65 91:0.08 97:0.89 98:0.86 99:0.83 101:0.87 107:0.92 108:0.12 110:0.93 114:0.82 122:0.83 123:0.12 124:0.47 125:0.88 126:0.32 127:0.65 129:0.05 131:0.61 133:0.45 135:0.47 139:0.64 144:0.76 146:0.90 147:0.92 149:0.55 150:0.43 154:0.55 155:0.47 157:0.98 158:0.77 159:0.59 160:0.88 165:0.65 166:0.85 170:0.90 172:0.79 173:0.18 174:0.86 176:0.63 177:0.88 178:0.55 179:0.41 182:1.00 187:0.21 192:0.46 197:0.89 201:0.57 208:0.50 212:0.68 215:0.72 216:0.40 222:0.76 227:0.61 229:0.61 231:0.85 235:0.52 239:0.84 241:0.30 242:0.41 243:0.71 244:0.83 245:0.87 246:0.84 247:0.94 253:0.59 259:0.21 265:0.86 266:0.63 267:0.28 271:0.57 276:0.75 279:0.76 283:0.82 287:0.61 289:0.91 290:0.82 297:0.36 300:0.55 +2 1:0.63 8:0.67 10:0.91 11:0.75 12:0.20 17:0.47 18:0.82 22:0.93 27:0.27 29:0.71 30:0.17 33:0.97 34:0.73 36:0.76 37:0.69 39:0.38 43:0.57 45:0.87 46:0.88 47:0.93 66:0.92 69:0.37 70:0.91 71:0.78 74:0.42 75:0.96 81:0.57 83:0.56 86:0.78 87:0.98 91:0.15 97:0.89 98:0.94 99:0.83 101:0.96 107:0.92 108:0.29 110:0.93 114:0.95 122:0.89 123:0.28 125:0.88 126:0.32 127:0.63 129:0.05 131:0.61 135:0.56 139:0.77 144:0.76 146:0.87 147:0.90 149:0.62 150:0.51 154:0.46 155:0.49 157:0.99 159:0.45 160:0.88 165:0.65 166:0.86 170:0.90 172:0.78 173:0.41 174:0.90 176:0.63 177:0.88 178:0.55 179:0.52 182:1.00 187:0.68 192:0.48 197:0.93 201:0.60 208:0.61 212:0.57 215:0.96 216:0.44 219:0.90 222:0.76 226:0.96 227:0.61 229:0.61 231:0.86 232:0.75 235:0.12 239:0.91 241:0.39 242:0.14 243:0.92 244:0.83 246:0.84 247:0.92 253:0.65 259:0.21 262:0.93 265:0.94 266:0.38 267:0.36 271:0.57 276:0.66 279:0.75 287:0.61 289:0.91 290:0.95 291:0.97 292:0.88 297:0.36 300:0.70 +1 1:0.63 10:0.86 11:0.64 12:0.20 17:0.34 18:0.62 22:0.93 27:0.27 29:0.71 30:0.45 33:0.97 34:0.79 36:0.68 37:0.69 39:0.38 43:0.59 45:0.73 46:0.88 47:0.93 55:0.52 66:0.82 69:0.37 70:0.61 71:0.78 74:0.44 75:0.96 81:0.57 83:0.56 86:0.67 87:0.98 91:0.08 97:0.94 98:0.83 99:0.83 101:0.65 107:0.90 108:0.29 110:0.91 114:0.95 122:0.41 123:0.28 125:0.88 126:0.32 127:0.33 129:0.05 131:0.61 135:0.66 139:0.68 144:0.76 146:0.51 147:0.57 149:0.27 150:0.51 154:0.56 155:0.49 157:0.96 159:0.18 160:0.88 165:0.65 166:0.86 170:0.90 172:0.48 173:0.65 174:0.79 176:0.63 177:0.88 178:0.55 179:0.76 182:1.00 187:0.68 192:0.48 197:0.82 201:0.60 208:0.61 212:0.75 215:0.96 216:0.44 219:0.90 222:0.76 226:0.96 227:0.61 229:0.61 231:0.83 232:0.75 235:0.12 239:0.87 241:0.46 242:0.33 243:0.83 246:0.84 247:0.60 253:0.65 254:0.99 259:0.21 262:0.93 265:0.83 266:0.27 267:0.23 271:0.57 276:0.31 279:0.78 287:0.61 289:0.90 290:0.95 291:0.97 292:0.88 297:0.36 300:0.43 +0 1:0.63 8:0.79 10:0.92 11:0.64 12:0.20 17:0.34 18:0.70 27:0.27 29:0.71 30:0.14 34:0.75 36:0.78 37:0.69 39:0.38 43:0.58 45:0.83 46:0.88 55:0.52 66:0.69 69:0.08 70:0.97 71:0.78 74:0.56 81:0.57 86:0.78 87:0.98 91:0.37 98:0.85 99:0.83 101:0.65 107:0.91 108:0.07 110:0.93 114:0.95 117:0.86 122:0.55 123:0.07 124:0.44 125:0.88 126:0.32 127:0.71 129:0.05 131:0.61 133:0.39 135:0.66 139:0.73 144:0.76 146:0.84 147:0.87 149:0.38 150:0.51 154:0.63 155:0.49 157:1.00 158:0.72 159:0.49 160:0.88 165:0.65 166:0.86 170:0.90 172:0.75 173:0.18 174:0.88 176:0.63 177:0.88 178:0.55 179:0.51 182:1.00 187:0.39 192:0.48 197:0.90 201:0.60 208:0.50 212:0.57 215:0.96 216:0.44 222:0.76 227:0.61 229:0.61 231:0.85 232:0.75 235:0.12 239:0.89 241:0.41 242:0.13 243:0.73 245:0.80 246:0.84 247:0.90 253:0.67 254:0.95 259:0.21 265:0.85 266:0.63 267:0.59 271:0.57 276:0.68 287:0.61 289:0.91 290:0.95 297:0.36 300:0.84 +0 1:0.64 8:0.61 10:0.96 11:0.49 12:0.20 17:0.24 18:0.96 22:0.93 27:0.27 29:0.71 30:0.43 33:0.97 34:0.37 36:0.96 37:0.31 39:0.38 43:0.57 45:0.95 46:0.88 47:0.93 64:0.77 66:0.25 69:0.15 70:0.85 71:0.78 74:0.25 81:0.55 83:0.56 86:0.90 91:0.37 98:0.70 99:0.83 101:0.47 107:0.91 108:0.74 110:0.92 122:0.93 123:0.72 124:0.84 125:0.88 126:0.32 127:0.95 129:0.89 131:0.61 133:0.83 135:0.69 139:0.87 144:0.57 146:0.75 147:0.79 149:0.76 150:0.49 154:0.49 155:0.49 157:0.90 159:0.82 160:0.88 165:0.65 166:0.74 170:0.90 172:0.76 173:0.11 174:0.97 175:0.75 177:0.70 178:0.55 179:0.27 182:0.82 187:0.39 192:0.45 197:0.98 201:0.60 208:0.50 212:0.52 216:0.53 219:0.87 222:0.76 227:0.61 229:0.61 231:0.76 235:0.05 236:0.92 239:0.95 241:0.15 242:0.16 243:0.40 244:0.83 245:0.91 246:0.91 247:0.97 253:0.23 257:0.65 259:0.21 262:0.93 265:0.70 266:0.80 267:0.73 271:0.57 276:0.87 277:0.69 287:0.61 289:0.90 291:0.97 292:0.88 300:0.84 +0 10:0.83 11:0.49 12:0.20 17:0.88 18:0.62 21:0.78 27:0.72 29:0.71 30:0.21 34:0.75 36:0.91 37:0.23 39:0.38 43:0.26 45:0.66 46:0.88 66:0.36 69:0.84 70:0.37 71:0.78 74:0.28 86:0.64 91:0.07 98:0.16 101:0.47 107:0.89 108:0.69 110:0.89 122:0.85 123:0.67 124:0.57 125:0.88 127:0.14 129:0.05 131:0.61 133:0.37 135:0.37 139:0.62 144:0.57 145:0.59 146:0.25 147:0.26 149:0.21 154:0.19 157:0.76 159:0.23 160:0.88 163:0.88 166:0.61 170:0.90 172:0.16 173:0.81 174:0.79 175:0.75 177:0.05 178:0.55 179:0.61 182:0.72 187:0.21 197:0.82 212:0.42 216:0.06 222:0.76 227:0.61 229:0.61 231:0.67 235:0.31 239:0.83 241:0.05 242:0.45 243:0.40 244:0.93 245:0.43 246:0.61 247:0.35 253:0.26 257:0.93 259:0.21 265:0.18 266:0.23 267:0.18 271:0.57 276:0.17 277:0.69 287:0.61 289:0.88 300:0.25 +1 1:0.63 8:0.59 10:0.92 11:0.64 12:0.20 17:0.72 18:0.75 27:0.27 29:0.71 30:0.17 34:0.39 36:0.82 37:0.69 39:0.38 43:0.24 45:0.85 46:0.88 55:0.59 66:0.96 69:0.37 70:0.88 71:0.78 74:0.49 81:0.57 86:0.79 87:0.98 91:0.09 98:0.97 99:0.83 101:0.65 107:0.92 108:0.29 110:0.94 114:0.95 117:0.86 122:0.41 123:0.28 125:0.88 126:0.32 127:0.79 129:0.05 131:0.61 135:0.56 139:0.73 144:0.76 146:0.95 147:0.96 149:0.37 150:0.51 154:0.47 155:0.49 157:0.98 158:0.72 159:0.61 160:0.88 165:0.65 166:0.86 170:0.90 172:0.91 173:0.32 174:0.94 176:0.63 177:0.88 178:0.55 179:0.31 182:1.00 187:0.39 192:0.48 197:0.95 201:0.60 208:0.50 212:0.80 215:0.96 216:0.44 222:0.76 227:0.61 229:0.61 231:0.87 232:0.75 235:0.12 239:0.90 241:0.37 242:0.16 243:0.96 244:0.61 246:0.84 247:0.98 253:0.66 254:0.98 259:0.21 265:0.97 266:0.54 267:0.28 271:0.57 276:0.83 287:0.61 289:0.92 290:0.95 297:0.36 300:0.70 +0 8:0.68 10:0.86 11:0.49 12:0.20 17:0.64 18:0.60 21:0.78 27:0.45 29:0.71 30:0.76 34:0.79 36:0.22 37:0.50 39:0.38 43:0.08 45:0.66 46:0.88 55:0.59 66:0.52 69:0.83 70:0.29 71:0.78 74:0.27 86:0.68 91:0.09 98:0.19 101:0.47 107:0.89 108:0.68 110:0.89 122:0.43 123:0.66 124:0.50 125:0.88 127:0.33 129:0.05 131:0.61 133:0.36 135:0.77 139:0.64 144:0.57 145:0.69 146:0.33 147:0.40 149:0.07 154:0.28 157:0.76 159:0.64 160:0.88 166:0.61 170:0.90 172:0.27 173:0.74 174:0.79 177:0.88 178:0.55 179:0.87 182:0.72 187:0.68 197:0.81 202:0.36 212:0.52 216:0.77 222:0.76 227:0.61 229:0.61 231:0.67 235:1.00 239:0.84 241:0.39 242:0.24 243:0.61 244:0.61 245:0.48 246:0.61 247:0.47 253:0.25 254:0.99 259:0.21 265:0.21 266:0.27 267:0.28 271:0.57 276:0.15 287:0.61 289:0.88 300:0.25 +0 1:0.63 10:0.91 11:0.49 12:0.20 17:0.38 18:0.78 21:0.05 22:0.93 27:0.43 29:0.71 30:0.28 33:0.97 34:0.69 36:0.48 37:0.48 39:0.38 43:0.57 45:0.81 46:0.88 47:0.93 64:0.77 66:0.54 69:0.79 70:0.89 71:0.78 74:0.27 81:0.53 83:0.56 86:0.76 91:0.35 98:0.42 99:0.83 101:0.47 104:0.77 107:0.90 108:0.63 110:0.91 122:0.93 123:0.61 124:0.64 125:0.88 126:0.32 127:0.75 129:0.05 131:0.61 133:0.60 135:0.58 139:0.72 144:0.57 146:0.49 147:0.30 149:0.29 150:0.47 154:0.45 155:0.49 157:0.84 159:0.53 160:0.88 165:0.65 166:0.74 170:0.90 172:0.51 173:0.83 174:0.88 177:0.70 178:0.55 179:0.73 182:0.82 187:0.68 192:0.45 197:0.89 201:0.59 208:0.50 212:0.71 216:0.40 222:0.76 227:0.61 229:0.61 231:0.76 235:0.12 236:0.92 239:0.89 241:0.24 242:0.28 243:0.27 244:0.83 245:0.62 246:0.91 247:0.79 253:0.24 254:0.88 257:0.65 259:0.21 262:0.93 265:0.44 266:0.47 267:0.36 271:0.57 276:0.45 287:0.61 289:0.90 291:0.97 300:0.70 +1 7:0.66 10:0.91 11:0.49 12:0.20 17:0.49 18:0.87 21:0.68 26:0.99 27:0.45 28:0.70 29:0.71 30:0.21 34:0.80 36:0.17 37:0.50 39:0.61 43:0.34 45:0.94 58:0.93 66:0.26 69:0.63 70:0.90 71:0.87 74:0.61 81:0.27 86:0.90 89:0.98 91:0.14 98:0.43 101:0.65 108:0.48 123:0.46 124:0.90 126:0.24 127:0.75 129:0.81 133:0.89 135:0.78 139:0.84 141:0.91 145:0.47 146:0.84 147:0.87 149:0.57 150:0.29 154:0.47 159:0.88 161:0.68 167:0.63 170:0.77 172:0.84 173:0.34 174:0.91 177:0.88 178:0.55 179:0.19 181:0.64 187:0.68 197:0.90 199:0.97 212:0.60 215:0.49 216:0.77 222:0.21 223:0.99 224:0.93 225:0.97 230:0.68 233:0.66 234:0.91 235:0.84 239:0.89 240:0.95 241:0.12 242:0.42 243:0.46 244:0.61 245:0.93 247:0.91 253:0.46 254:0.94 259:0.21 265:0.45 266:0.87 267:0.28 271:0.57 274:0.91 276:0.92 297:0.36 300:0.84 +1 8:0.96 9:0.80 12:0.20 17:0.02 20:0.84 21:0.13 23:0.98 26:1.00 27:0.13 28:0.70 29:0.71 31:0.99 34:0.62 36:0.57 37:0.94 39:0.61 41:0.88 58:1.00 60:0.87 62:0.98 71:0.87 74:0.47 75:0.99 78:0.72 79:0.99 81:0.30 83:0.69 89:0.95 91:0.99 96:0.97 100:0.73 104:0.58 111:0.72 120:0.96 126:0.24 128:0.99 135:0.44 137:0.99 139:0.74 140:0.99 141:1.00 150:0.33 151:0.86 152:0.82 153:0.78 155:0.66 158:0.92 161:0.68 162:0.73 164:0.94 167:1.00 168:0.99 169:0.72 170:0.77 175:0.99 181:0.64 186:0.73 191:0.86 192:0.99 196:0.96 199:0.99 202:0.91 205:0.98 208:0.64 215:0.84 216:0.52 219:0.95 220:0.62 222:0.21 223:0.99 224:0.98 225:0.99 226:0.98 234:0.97 235:0.12 239:0.90 240:1.00 241:0.99 244:0.98 248:0.84 253:0.95 255:0.95 256:0.93 257:0.97 259:0.21 260:0.96 261:0.73 267:0.69 268:0.93 271:0.57 274:1.00 277:0.98 279:0.80 283:0.82 284:0.99 285:0.62 292:0.95 297:0.36 +2 1:0.67 6:0.97 8:0.89 9:0.80 10:0.89 11:0.57 12:0.20 17:0.34 18:0.76 20:0.89 21:0.05 23:0.97 26:1.00 27:0.38 28:0.70 29:0.71 30:0.15 31:0.99 34:0.28 36:0.77 37:0.72 39:0.61 41:0.82 43:0.58 45:0.88 58:1.00 62:0.97 64:0.77 66:0.92 69:0.56 70:0.88 71:0.87 74:0.61 78:0.93 79:0.99 81:0.69 83:0.69 86:0.84 89:0.97 91:0.72 96:0.96 97:0.94 98:0.91 99:0.95 100:0.99 101:0.96 108:0.43 111:0.98 114:0.95 120:0.90 123:0.41 126:0.51 127:0.52 128:0.98 129:0.05 135:0.56 137:0.99 139:0.83 140:0.95 141:1.00 146:0.86 147:0.88 149:0.39 150:0.56 151:0.91 152:0.99 153:0.85 154:0.54 155:0.66 158:0.81 159:0.43 161:0.68 162:0.83 164:0.87 165:0.81 167:0.82 168:0.98 169:0.99 170:0.77 172:0.79 173:0.61 174:0.83 176:0.70 177:0.88 179:0.48 181:0.64 186:0.92 187:0.39 189:0.98 191:0.88 192:0.99 196:0.98 197:0.85 199:0.99 201:0.64 202:0.83 205:0.97 208:0.64 212:0.59 215:0.91 216:0.65 219:0.94 220:0.62 222:0.21 223:0.99 224:0.99 225:0.99 234:0.99 235:0.31 238:0.98 239:0.87 240:1.00 241:0.71 242:0.38 243:0.92 247:0.76 248:0.81 251:1.00 253:0.96 254:0.90 255:0.95 256:0.87 259:0.21 260:0.91 261:0.99 265:0.91 266:0.54 267:0.23 268:0.88 271:0.57 274:0.99 276:0.68 279:0.78 283:0.67 284:0.99 285:0.62 290:0.95 292:0.88 297:0.36 300:0.70 +1 1:0.68 8:0.64 9:0.76 10:0.87 11:0.49 12:0.20 18:0.62 20:0.89 21:0.05 23:0.97 26:1.00 27:0.13 28:0.70 29:0.71 30:0.91 31:0.94 34:0.37 36:0.52 37:0.94 39:0.61 41:0.82 43:0.63 45:0.73 58:0.98 62:0.96 64:0.77 66:0.69 69:0.79 70:0.13 71:0.87 74:0.36 78:0.72 79:0.94 81:0.80 83:0.69 86:0.69 89:0.96 91:0.62 96:0.82 98:0.18 100:0.73 101:0.65 104:0.58 108:0.63 111:0.72 114:0.95 120:0.81 122:0.89 123:0.61 126:0.54 127:0.10 128:0.97 129:0.05 135:0.42 137:0.94 139:0.66 140:0.45 141:1.00 146:0.20 147:0.25 149:0.45 150:0.64 151:0.84 152:0.87 153:0.82 154:0.52 155:0.66 159:0.10 161:0.68 162:0.73 164:0.80 165:0.93 167:0.80 168:0.97 169:0.72 170:0.77 172:0.21 173:0.99 174:0.79 176:0.73 177:0.88 179:0.16 181:0.64 186:0.73 187:0.21 191:0.70 192:0.99 196:0.89 197:0.82 199:0.95 201:0.67 202:0.72 205:0.95 208:0.64 212:0.61 215:0.91 216:0.52 220:0.62 222:0.21 223:0.99 224:0.98 225:0.99 234:0.97 235:0.42 236:0.97 239:0.85 240:1.00 241:0.68 242:0.63 243:0.73 244:0.61 247:0.30 248:0.75 253:0.81 254:0.84 255:0.79 256:0.75 259:0.21 260:0.81 261:0.73 265:0.20 266:0.17 267:0.28 268:0.76 271:0.57 274:0.98 276:0.20 284:0.93 285:0.62 290:0.95 297:0.36 300:0.13 +2 1:0.60 8:0.71 9:0.79 10:0.98 11:0.57 12:0.20 17:0.57 18:0.99 21:0.30 22:0.93 23:0.98 26:1.00 27:0.50 28:0.70 29:0.71 30:0.60 31:0.98 32:0.71 33:0.97 34:0.66 36:0.81 37:0.60 39:0.61 43:0.39 44:0.92 45:1.00 47:0.93 58:0.99 62:0.99 64:0.77 66:0.15 69:0.53 70:0.75 71:0.87 74:0.95 75:0.98 77:0.89 79:0.98 81:0.49 83:0.69 86:0.99 89:1.00 91:0.18 96:0.93 97:1.00 98:0.56 99:0.83 101:0.96 102:0.97 108:0.99 114:0.80 117:0.86 122:0.91 123:0.99 124:0.94 126:0.32 127:0.97 128:0.99 129:0.96 133:0.94 135:0.61 137:0.98 139:0.99 140:0.90 141:1.00 145:0.70 146:0.70 147:0.74 149:0.92 150:0.43 151:0.91 153:0.85 154:0.19 155:0.64 158:0.82 159:0.98 161:0.68 162:0.81 165:0.65 167:0.54 168:0.99 170:0.77 172:0.99 173:0.09 174:0.99 176:0.62 177:0.88 179:0.09 181:0.64 187:0.39 190:0.89 192:0.58 195:1.00 196:0.99 197:0.97 199:1.00 201:0.57 202:0.75 204:0.81 205:0.98 208:0.64 212:0.60 216:0.86 219:0.94 222:0.21 223:0.99 224:0.99 225:0.99 226:0.98 234:0.99 235:0.42 239:0.98 240:1.00 242:0.27 243:0.10 245:1.00 247:0.99 248:0.87 253:0.96 254:0.84 255:0.78 256:0.80 259:0.21 262:0.93 265:0.57 266:0.87 267:0.82 268:0.81 271:0.57 274:0.99 276:1.00 279:0.82 281:0.91 282:0.79 283:0.82 284:0.98 285:0.60 290:0.80 291:0.97 292:0.95 300:0.84 +1 1:0.69 9:0.79 10:0.87 11:0.49 12:0.20 17:0.08 18:0.93 23:0.96 26:1.00 27:0.31 28:0.70 29:0.71 30:0.73 31:0.95 34:0.75 36:0.45 37:0.70 39:0.61 41:0.82 43:0.56 45:0.92 58:0.98 62:0.96 64:0.77 66:0.92 69:0.25 70:0.56 71:0.87 74:0.69 79:0.94 81:0.81 83:0.69 86:0.91 89:0.96 91:0.56 96:0.83 98:0.96 101:0.65 108:0.20 114:0.98 120:0.78 122:0.86 123:0.19 126:0.54 127:0.71 128:0.98 129:0.05 135:0.68 137:0.95 139:0.87 140:0.80 141:1.00 146:0.69 147:0.73 149:0.66 150:0.68 151:0.90 153:0.69 154:0.54 155:0.66 159:0.27 161:0.68 162:0.86 164:0.70 165:0.93 167:0.81 168:0.98 170:0.77 172:0.78 173:0.50 174:0.79 176:0.73 177:0.88 179:0.53 181:0.64 187:0.68 192:0.99 196:0.95 197:0.83 199:0.97 201:0.68 202:0.55 205:0.95 208:0.64 212:0.89 215:0.96 216:0.62 220:0.62 222:0.21 223:0.99 224:0.99 225:0.99 234:0.98 235:0.31 236:0.97 239:0.86 240:1.00 241:0.69 242:0.45 243:0.92 244:0.61 247:0.79 248:0.80 253:0.85 254:0.93 255:0.94 256:0.58 259:0.21 260:0.78 265:0.96 266:0.38 267:0.23 268:0.64 271:0.57 274:0.97 276:0.64 283:0.67 284:0.90 285:0.62 290:0.98 297:0.36 300:0.55 +2 1:0.64 6:0.95 8:0.70 9:0.79 10:0.97 11:0.57 12:0.20 17:0.64 18:1.00 20:0.82 21:0.30 22:0.93 23:0.99 26:1.00 27:0.21 28:0.70 29:0.71 30:0.45 31:1.00 34:0.25 36:0.66 37:0.74 39:0.61 41:0.82 43:0.74 45:1.00 47:0.93 58:1.00 62:0.99 64:0.77 66:0.17 69:0.15 70:0.82 71:0.87 74:0.96 75:0.97 78:0.72 79:0.99 81:0.65 83:0.69 86:1.00 89:0.99 91:0.70 96:0.98 97:1.00 98:0.59 99:0.95 100:0.90 101:0.96 108:0.98 111:0.82 114:0.82 117:0.86 120:0.72 122:0.86 123:0.98 124:0.95 126:0.51 127:1.00 128:0.99 129:0.97 133:0.96 135:0.51 137:1.00 139:0.99 140:0.98 141:1.00 146:0.91 147:0.93 149:0.96 150:0.52 151:0.69 152:0.87 153:0.96 154:0.12 155:0.65 158:0.77 159:0.98 161:0.68 162:0.81 164:0.70 165:0.81 167:0.71 168:0.99 169:0.84 170:0.77 172:0.99 173:0.05 174:0.99 176:0.63 177:0.88 179:0.08 181:0.64 186:0.73 187:0.39 190:0.89 191:0.73 192:0.98 193:0.96 196:1.00 197:0.97 199:1.00 201:0.62 202:0.85 204:0.80 205:0.99 208:0.64 212:0.54 216:0.95 219:0.91 222:0.21 223:1.00 224:0.99 225:0.99 226:0.98 234:0.99 235:0.52 236:0.94 239:0.97 240:1.00 241:0.44 242:0.32 243:0.15 245:1.00 247:0.99 248:0.64 253:0.99 254:0.86 255:0.79 256:0.89 259:0.21 260:0.72 261:0.84 262:0.93 265:0.60 266:0.89 267:0.28 268:0.89 271:0.57 274:1.00 276:1.00 279:0.82 281:0.91 284:0.99 285:0.62 290:0.82 292:0.95 300:0.84 +2 1:0.64 6:0.95 8:0.84 9:0.80 10:0.98 11:0.57 12:0.20 17:0.55 18:1.00 20:0.89 21:0.30 22:0.93 23:1.00 26:1.00 27:0.21 28:0.70 29:0.71 30:0.45 31:1.00 33:0.97 34:0.28 36:0.72 37:0.74 39:0.61 43:0.73 45:1.00 47:0.93 58:1.00 62:1.00 64:0.77 66:0.17 69:0.15 70:0.82 71:0.87 74:0.96 75:0.97 78:0.81 79:1.00 81:0.65 83:0.69 86:0.99 89:0.99 91:0.75 96:0.99 97:1.00 98:0.60 99:0.95 100:0.83 101:0.96 108:0.98 111:0.81 114:0.82 117:0.86 120:0.86 122:0.86 123:0.98 124:0.96 126:0.51 127:0.98 128:1.00 129:0.98 133:0.97 135:0.49 137:1.00 139:0.99 140:0.98 141:1.00 146:0.93 147:0.95 149:0.96 150:0.52 151:0.90 152:0.87 153:0.98 154:0.11 155:0.66 158:0.82 159:0.98 161:0.68 162:0.79 164:0.81 165:0.81 167:0.65 168:1.00 169:0.83 170:0.77 172:0.99 173:0.05 174:0.99 176:0.63 177:0.88 179:0.08 181:0.64 186:0.82 187:0.39 190:0.77 191:0.80 192:0.99 193:0.96 196:1.00 197:0.97 199:1.00 201:0.62 202:0.93 204:0.80 205:1.00 208:0.64 212:0.54 216:0.95 219:0.94 222:0.21 223:1.00 224:0.99 225:0.99 226:0.98 234:0.99 235:0.52 236:0.94 239:0.97 240:1.00 241:0.21 242:0.32 243:0.16 245:1.00 247:0.99 248:0.85 253:1.00 254:0.86 255:0.94 256:0.96 259:0.21 260:0.87 261:0.84 262:0.93 265:0.61 266:0.89 267:0.82 268:0.96 271:0.57 274:1.00 276:1.00 279:0.82 281:0.91 283:0.82 284:1.00 285:0.62 290:0.82 291:0.97 292:0.95 300:0.84 +3 1:0.64 6:0.97 8:0.84 9:0.80 10:0.84 11:0.54 12:0.20 17:0.26 18:0.88 20:0.99 21:0.59 23:1.00 26:1.00 27:0.38 28:0.70 29:0.71 30:0.58 31:1.00 32:0.80 34:0.19 36:0.71 37:0.72 39:0.61 41:0.97 43:0.55 44:0.93 45:0.93 58:1.00 62:1.00 64:0.77 66:0.94 69:0.62 70:0.71 71:0.87 74:0.74 76:0.85 77:1.00 78:0.92 79:1.00 81:0.64 83:0.69 86:0.86 89:0.96 91:0.95 96:1.00 97:1.00 98:0.95 99:0.99 100:0.99 101:0.87 102:0.98 108:0.47 111:0.98 114:0.73 120:0.98 122:0.86 123:0.46 126:0.51 127:0.64 128:1.00 129:0.05 135:0.49 137:1.00 139:0.91 140:0.98 141:1.00 145:0.84 146:0.89 147:0.91 149:0.67 150:0.49 151:0.94 152:0.99 153:0.97 154:0.44 155:0.67 158:0.82 159:0.49 161:0.68 162:0.84 164:0.97 165:0.81 167:0.97 168:1.00 169:0.99 170:0.77 172:0.84 173:0.65 174:0.79 176:0.70 177:0.88 179:0.41 181:0.64 186:0.92 189:0.98 191:0.93 192:0.99 193:0.98 195:0.70 196:1.00 197:0.81 199:1.00 201:0.62 202:0.96 204:0.85 205:1.00 208:0.64 212:0.73 215:0.55 216:0.65 219:0.94 222:0.21 223:1.00 224:0.99 225:0.99 234:0.99 235:0.98 238:0.98 239:0.92 240:1.00 241:0.85 242:0.41 243:0.94 244:0.61 247:0.76 248:0.95 251:1.00 253:1.00 255:1.00 256:0.98 259:0.21 260:0.98 261:0.99 265:0.95 266:0.54 267:0.59 268:0.98 271:0.57 274:1.00 276:0.74 279:0.82 281:0.91 282:0.88 283:0.82 284:1.00 285:0.62 290:0.73 292:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.67 8:0.96 9:0.79 10:0.85 11:0.45 12:0.20 17:0.11 18:0.81 21:0.05 23:0.99 26:1.00 27:0.28 28:0.70 29:0.71 30:0.76 31:1.00 34:0.49 36:0.56 37:0.84 39:0.61 43:0.33 45:0.83 58:1.00 62:0.96 64:0.77 66:0.83 69:0.73 70:0.36 71:0.87 74:0.50 79:1.00 81:0.64 83:0.69 86:0.77 87:0.98 89:0.96 91:0.94 96:0.99 97:0.98 98:0.74 99:0.83 101:0.47 108:0.56 114:0.92 120:0.72 122:0.86 123:0.54 126:0.51 127:0.24 128:1.00 129:0.05 135:0.21 137:1.00 139:0.76 140:0.99 141:1.00 146:0.54 147:0.60 149:0.44 150:0.58 151:0.86 153:0.91 154:0.25 155:0.66 158:0.75 159:0.20 161:0.68 162:0.83 164:0.65 165:0.65 167:0.99 168:1.00 170:0.77 172:0.51 173:0.90 174:0.79 175:0.75 176:0.63 177:0.70 179:0.63 181:0.64 190:0.77 191:0.89 192:0.98 196:0.98 197:0.82 199:1.00 201:0.64 202:0.92 205:0.99 208:0.64 212:0.57 216:0.55 219:0.90 220:0.62 222:0.21 223:1.00 224:0.99 225:0.99 228:0.99 234:0.99 235:0.22 236:0.94 239:0.83 240:1.00 241:0.91 242:0.60 243:0.84 244:0.83 247:0.35 248:0.77 251:1.00 253:0.98 254:0.90 255:0.79 256:0.94 257:0.65 259:0.21 260:0.73 265:0.75 266:0.38 267:0.59 268:0.95 271:0.57 274:1.00 276:0.39 277:0.69 279:0.80 284:1.00 285:0.62 290:0.92 292:0.88 300:0.33 +1 1:0.64 6:0.92 8:0.72 9:0.79 10:0.91 11:0.81 12:0.20 17:0.22 18:0.88 20:0.90 21:0.30 23:0.97 26:1.00 27:0.50 28:0.70 29:0.71 30:0.40 31:0.99 34:0.70 36:0.88 37:0.46 39:0.61 41:0.82 43:0.77 45:0.93 58:1.00 62:0.97 64:0.77 66:0.94 69:0.23 70:0.78 71:0.87 74:0.78 78:0.79 79:0.98 81:0.64 83:0.69 85:0.72 86:0.93 89:0.98 91:0.51 96:0.94 97:0.98 98:0.94 99:0.95 100:0.84 101:0.96 108:0.18 111:0.79 114:0.84 120:0.87 122:0.65 123:0.18 126:0.51 127:0.63 128:0.98 129:0.05 135:0.66 137:0.99 139:0.93 140:0.95 141:1.00 146:0.82 147:0.85 149:0.90 150:0.49 151:0.62 152:0.91 153:0.78 154:0.62 155:0.66 158:0.81 159:0.38 161:0.68 162:0.86 164:0.87 165:0.81 167:0.79 168:0.98 169:0.81 170:0.77 172:0.83 173:0.36 174:0.88 176:0.70 177:0.88 179:0.43 181:0.64 186:0.80 187:0.39 190:0.89 192:0.99 196:0.99 197:0.88 199:0.99 201:0.62 202:0.80 204:0.81 205:0.97 208:0.64 212:0.82 215:0.72 216:0.47 219:0.94 220:0.87 222:0.21 223:0.99 224:0.99 225:0.99 234:0.98 235:0.60 239:0.89 240:1.00 241:0.68 242:0.29 243:0.94 247:0.84 248:0.60 253:0.95 255:0.94 256:0.86 259:0.21 260:0.87 261:0.83 265:0.94 266:0.27 267:0.23 268:0.86 271:0.57 274:0.99 276:0.72 279:0.81 281:0.91 283:0.67 284:0.98 285:0.62 290:0.84 292:0.88 297:0.36 300:0.70 +2 1:0.62 6:0.84 7:0.70 8:0.77 9:0.80 10:0.86 11:0.49 12:0.20 17:0.66 18:0.91 20:0.92 21:0.47 23:0.99 26:1.00 27:0.55 28:0.70 29:0.71 30:0.76 31:1.00 34:0.61 36:0.41 37:0.69 39:0.61 43:0.63 44:0.88 45:0.94 58:1.00 62:0.96 64:0.77 66:0.32 69:0.19 70:0.61 71:0.87 74:0.70 77:0.96 78:0.83 79:0.99 81:0.54 83:0.69 86:0.88 89:0.96 91:0.93 96:0.98 97:0.94 98:0.46 99:0.83 100:0.87 101:0.65 104:0.75 108:0.77 111:0.83 114:0.76 120:0.90 122:0.86 123:0.76 124:0.73 126:0.51 127:0.63 128:0.99 129:0.05 133:0.65 135:0.75 137:1.00 139:0.89 140:0.96 141:1.00 145:0.89 146:0.31 147:0.37 149:0.70 150:0.46 151:0.93 152:0.86 153:0.88 154:0.54 155:0.66 158:0.76 159:0.63 161:0.68 162:0.82 164:0.88 165:0.65 167:0.99 168:0.99 169:0.85 170:0.77 172:0.61 173:0.18 174:0.79 176:0.63 177:0.88 179:0.44 181:0.64 186:0.84 189:0.86 190:0.77 191:0.79 192:0.99 195:0.82 196:1.00 197:0.85 199:1.00 201:0.60 202:0.89 204:0.83 205:0.99 208:0.64 212:0.70 215:0.63 216:0.51 219:0.91 220:0.93 222:0.21 223:1.00 224:0.99 225:0.99 230:0.73 233:0.66 234:0.98 235:0.68 238:0.91 239:0.86 240:1.00 241:0.89 242:0.51 243:0.33 244:0.83 245:0.84 247:0.67 248:0.89 253:0.98 255:0.94 256:0.92 259:0.21 260:0.91 261:0.86 265:0.47 266:0.63 267:0.44 268:0.93 271:0.57 274:1.00 276:0.66 277:0.69 279:0.80 281:0.91 282:0.74 283:0.67 284:0.99 285:0.62 290:0.76 292:0.95 297:0.36 300:0.84 +3 1:0.64 6:0.94 8:0.75 9:0.80 10:0.98 11:0.57 12:0.20 17:0.62 18:1.00 20:0.83 21:0.30 22:0.93 23:0.99 26:1.00 27:0.21 28:0.70 29:0.71 30:0.45 31:0.99 33:0.97 34:0.21 36:0.70 37:0.74 39:0.61 43:0.72 45:1.00 47:0.93 58:1.00 62:1.00 64:0.77 66:0.17 69:0.15 70:0.82 71:0.87 74:0.96 75:0.97 78:0.79 79:0.99 81:0.65 83:0.69 86:0.99 89:0.99 91:0.35 96:0.97 97:1.00 98:0.60 99:0.95 100:0.89 101:0.96 108:0.98 111:0.82 114:0.82 117:0.86 120:0.77 122:0.86 123:0.98 124:0.96 126:0.51 127:0.99 128:1.00 129:0.98 133:0.97 135:0.54 137:0.99 139:0.99 140:0.97 141:1.00 146:0.93 147:0.95 149:0.96 150:0.52 151:0.66 152:0.91 153:0.92 154:0.11 155:0.66 158:0.82 159:0.98 161:0.68 162:0.77 164:0.71 165:0.81 167:0.67 168:1.00 169:0.84 170:0.77 172:0.99 173:0.05 174:0.99 176:0.63 177:0.88 179:0.08 181:0.64 186:0.80 187:0.39 190:0.89 192:0.99 193:0.96 196:1.00 197:0.97 199:1.00 201:0.62 202:0.82 204:0.80 205:0.99 208:0.64 212:0.54 216:0.95 219:0.94 222:0.21 223:0.99 224:0.99 225:0.99 226:0.98 234:0.99 235:0.52 236:0.94 239:0.97 240:1.00 241:0.30 242:0.32 243:0.16 245:1.00 247:0.99 248:0.63 253:0.99 254:0.86 255:0.94 256:0.85 259:0.21 260:0.78 261:0.85 262:0.93 265:0.61 266:0.89 267:0.73 268:0.87 271:0.57 274:0.99 276:1.00 279:0.82 281:0.91 283:0.82 284:0.99 285:0.62 290:0.82 291:0.97 292:0.95 300:0.84 +2 1:0.67 6:0.98 8:0.94 9:0.80 10:0.89 11:0.57 12:0.20 17:0.34 18:0.76 20:0.90 21:0.05 23:0.99 26:1.00 27:0.38 28:0.70 29:0.71 30:0.15 31:1.00 34:0.28 36:0.77 37:0.72 39:0.61 41:0.88 43:0.58 45:0.88 58:1.00 62:0.97 64:0.77 66:0.92 69:0.56 70:0.88 71:0.87 74:0.61 78:0.92 79:1.00 81:0.69 83:0.69 86:0.84 89:0.97 91:0.74 96:0.98 97:0.99 98:0.91 99:0.95 100:0.98 101:0.96 108:0.43 111:0.97 114:0.95 120:0.94 123:0.41 126:0.51 127:0.52 128:0.99 129:0.05 135:0.51 137:1.00 139:0.83 140:0.96 141:1.00 146:0.86 147:0.88 149:0.39 150:0.56 151:0.91 152:0.99 153:0.88 154:0.54 155:0.67 158:0.81 159:0.43 161:0.68 162:0.85 164:0.91 165:0.81 167:0.80 168:0.99 169:0.99 170:0.77 172:0.79 173:0.61 174:0.83 176:0.70 177:0.88 179:0.48 181:0.64 186:0.92 187:0.21 189:0.98 191:0.88 192:0.99 196:0.99 197:0.85 199:0.99 201:0.64 202:0.87 205:0.99 208:0.64 212:0.59 215:0.91 216:0.65 219:0.94 220:0.62 222:0.21 223:1.00 224:0.99 225:0.99 234:0.99 235:0.31 238:0.98 239:0.87 240:1.00 241:0.69 242:0.38 243:0.92 247:0.76 248:0.85 251:1.00 253:0.98 254:0.90 255:1.00 256:0.91 259:0.21 260:0.94 261:0.98 265:0.91 266:0.54 267:0.23 268:0.92 271:0.57 274:1.00 276:0.68 279:0.81 283:0.67 284:0.99 285:0.62 290:0.95 292:0.88 297:0.36 300:0.70 +1 1:0.69 7:0.70 9:0.75 10:0.98 11:0.78 12:0.20 17:0.78 18:0.93 21:0.47 23:0.96 26:1.00 27:0.52 28:0.70 29:0.71 30:0.88 31:0.92 32:0.71 34:0.22 36:0.23 37:0.43 39:0.61 41:0.82 43:0.61 44:0.92 45:0.98 58:0.99 62:0.97 64:0.77 66:0.84 69:0.82 70:0.40 71:0.87 74:0.87 77:0.89 79:0.92 81:0.83 83:0.69 85:0.72 86:0.97 89:1.00 91:0.31 96:0.78 97:0.94 98:0.79 99:0.99 101:1.00 102:0.97 108:0.67 114:0.80 120:0.66 122:0.85 123:0.65 124:0.40 126:0.54 127:0.57 128:0.95 129:0.05 133:0.73 135:0.66 137:0.92 139:0.97 140:0.90 141:1.00 145:0.82 146:0.94 147:0.22 149:0.67 150:0.65 151:0.58 153:0.48 154:0.47 155:0.65 158:0.77 159:0.73 161:0.68 162:0.62 164:0.67 165:1.00 167:0.65 168:0.95 170:0.77 172:0.95 173:0.73 174:0.97 176:0.73 177:0.70 178:0.48 179:0.19 181:0.64 187:0.39 190:0.89 192:0.98 193:0.98 195:0.88 196:0.96 197:0.95 199:0.99 201:0.68 202:0.60 204:0.85 205:0.95 208:0.64 212:0.77 215:0.63 216:0.79 219:0.91 220:0.62 222:0.21 223:0.99 224:0.99 225:0.99 230:0.73 233:0.66 234:0.98 235:0.99 236:0.97 239:0.97 240:1.00 241:0.21 242:0.17 243:0.09 245:0.83 247:0.93 248:0.56 253:0.83 255:0.77 256:0.58 257:0.65 259:0.21 260:0.67 265:0.79 266:0.87 267:0.17 268:0.59 271:0.57 274:0.98 276:0.90 279:0.81 281:0.86 282:0.80 283:0.82 284:0.92 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 +2 1:0.65 6:0.96 8:0.93 9:0.79 10:0.86 11:0.49 12:0.20 17:0.38 18:0.88 20:0.90 21:0.22 23:1.00 26:1.00 27:0.31 28:0.70 29:0.71 30:0.88 31:1.00 34:0.44 36:0.79 37:0.70 39:0.61 43:0.65 44:0.88 45:0.87 58:1.00 62:1.00 64:0.77 66:0.85 69:0.15 70:0.21 71:0.87 74:0.67 75:0.97 76:0.85 77:0.89 78:0.83 79:1.00 81:0.66 83:0.91 86:0.85 89:0.96 91:0.95 96:1.00 97:0.99 98:0.89 99:0.95 100:0.91 101:0.65 102:0.96 108:0.12 111:0.86 114:0.85 120:0.89 122:0.86 123:0.12 126:0.51 127:0.45 128:1.00 129:0.05 135:0.63 137:1.00 138:1.00 139:0.88 140:0.97 141:1.00 145:0.76 146:0.36 147:0.42 149:0.64 150:0.54 151:0.94 152:0.92 153:0.98 154:0.55 155:0.66 158:0.77 159:0.14 161:0.68 162:0.80 164:0.83 165:0.81 167:0.99 168:1.00 169:0.87 170:0.77 172:0.57 173:0.68 174:0.79 175:0.75 176:0.63 177:0.70 179:0.73 181:0.64 186:0.84 189:0.97 190:0.77 191:0.88 192:0.98 193:0.98 195:0.54 196:1.00 197:0.85 199:1.00 201:0.63 202:0.97 204:0.85 205:1.00 208:0.64 212:0.95 216:0.62 219:0.91 220:0.62 222:0.21 223:1.00 224:0.99 225:0.99 226:0.98 234:0.99 235:0.42 236:0.94 238:0.94 239:0.93 240:1.00 241:0.92 242:0.52 243:0.86 244:0.83 247:0.47 248:0.84 253:1.00 255:0.79 256:0.98 257:0.65 259:0.21 260:0.89 261:0.87 265:0.89 266:0.12 267:0.94 268:0.98 271:0.57 274:1.00 276:0.46 277:0.69 279:0.81 281:0.91 282:0.75 284:1.00 285:0.62 290:0.85 292:0.95 300:0.18 +1 12:0.99 17:0.87 21:0.40 27:0.44 28:0.57 34:0.49 36:0.17 37:0.46 81:0.66 91:0.17 106:0.81 126:0.54 135:0.81 150:0.48 161:0.98 167:0.61 175:0.75 178:0.55 181:0.98 187:0.39 206:0.81 215:0.67 216:0.55 235:0.42 241:0.59 244:0.61 257:0.65 259:0.21 267:0.44 277:0.69 297:0.36 +0 12:0.99 17:0.99 21:0.78 27:0.67 28:0.57 30:0.95 34:0.24 36:0.42 37:0.29 43:0.05 55:0.99 66:0.20 69:1.00 98:0.05 108:1.00 123:1.00 124:0.61 127:0.10 129:0.59 133:0.47 135:0.26 146:0.05 147:0.05 149:0.05 154:0.05 159:0.47 161:0.98 167:0.46 172:0.05 173:0.98 177:0.05 178:0.55 179:0.48 181:0.98 187:0.68 216:0.60 235:0.12 241:0.01 243:0.40 245:0.36 259:0.21 265:0.05 267:0.76 300:0.08 +0 12:0.99 17:1.00 21:0.78 27:0.56 28:0.57 34:0.33 36:0.74 37:0.44 43:0.06 66:0.36 69:0.99 98:0.05 108:0.99 123:0.99 127:0.05 129:0.05 135:0.20 146:0.05 147:0.05 149:0.05 154:0.06 159:0.05 161:0.98 167:0.45 172:0.05 173:0.88 177:0.05 178:0.55 181:0.98 187:0.21 216:0.16 235:0.12 243:0.51 259:0.21 265:0.05 267:0.82 +1 12:0.99 17:0.76 21:0.78 27:0.51 28:0.57 30:0.95 34:0.94 36:0.73 37:0.62 43:0.26 55:0.59 66:0.64 69:0.80 91:0.37 98:0.20 108:0.64 123:0.62 127:0.10 129:0.05 135:0.28 145:0.45 146:0.27 147:0.33 149:0.23 154:0.19 159:0.12 161:0.98 163:0.97 167:0.48 172:0.16 173:0.84 177:0.05 178:0.55 179:0.31 181:0.98 187:0.39 212:0.95 216:0.28 235:0.05 241:0.12 242:0.82 243:0.69 247:0.12 259:0.21 265:0.22 266:0.12 267:0.69 276:0.19 300:0.08 +0 12:0.99 17:0.89 21:0.78 27:0.52 28:0.57 30:0.95 34:0.88 36:0.75 37:0.48 43:0.28 55:0.98 66:0.20 69:0.78 91:0.12 98:0.08 108:0.61 123:0.59 124:0.61 127:0.22 129:0.59 133:0.47 135:0.47 146:0.05 147:0.05 149:0.17 154:0.20 159:0.29 161:0.98 167:0.47 172:0.05 173:0.83 177:0.05 178:0.55 179:0.99 181:0.98 187:0.68 216:0.20 235:0.31 241:0.07 243:0.40 245:0.36 259:0.21 265:0.08 267:0.28 300:0.08 +0 12:0.99 17:0.98 20:0.95 21:0.78 27:0.50 28:0.57 34:0.50 36:0.68 37:0.42 43:0.34 66:0.36 69:0.65 78:0.77 91:0.02 98:0.06 100:0.80 108:0.49 111:0.76 123:0.47 127:0.05 129:0.05 135:0.34 146:0.05 147:0.05 149:0.13 152:0.97 154:0.26 159:0.05 161:0.98 167:0.45 169:0.78 172:0.05 173:0.49 177:0.05 178:0.55 181:0.98 186:0.77 187:0.68 216:0.92 235:0.68 243:0.51 259:0.21 261:0.86 265:0.06 267:0.82 +0 12:0.99 17:0.22 21:0.78 27:0.72 28:0.57 34:0.29 36:0.62 37:0.79 43:0.29 66:0.36 69:0.75 91:0.46 98:0.06 108:0.58 123:0.55 127:0.05 129:0.05 135:0.30 146:0.05 147:0.05 149:0.12 154:0.21 159:0.05 161:0.98 167:0.64 172:0.05 173:0.47 177:0.05 178:0.55 181:0.98 187:0.87 216:0.46 235:0.05 241:0.64 243:0.51 265:0.06 267:0.36 +0 12:0.99 17:1.00 20:0.95 21:0.78 27:0.50 28:0.57 34:0.42 36:0.64 37:0.42 43:0.23 66:0.36 69:0.88 78:0.72 91:0.02 98:0.06 100:0.80 108:0.77 111:0.74 123:0.75 127:0.05 129:0.05 135:0.54 146:0.05 147:0.05 149:0.09 152:0.97 154:0.16 159:0.05 161:0.98 167:0.45 169:0.78 172:0.05 173:0.82 177:0.05 178:0.55 181:0.98 186:0.73 187:0.68 216:0.92 235:0.02 243:0.51 259:0.21 261:0.86 265:0.06 267:0.79 +0 8:0.58 12:0.99 17:0.88 20:0.96 21:0.78 27:0.52 28:0.57 34:0.45 36:0.44 37:0.30 78:0.77 91:0.54 100:0.85 111:0.79 120:0.65 135:0.58 140:0.45 151:0.56 152:0.89 153:0.72 161:0.98 162:0.71 164:0.73 167:0.57 169:0.83 175:0.75 181:0.98 186:0.78 187:0.21 202:0.63 216:0.65 220:0.62 235:0.05 241:0.53 244:0.61 248:0.59 256:0.64 257:0.65 259:0.21 260:0.66 261:0.89 267:0.76 268:0.66 277:0.69 285:0.62 +3 1:0.57 8:0.95 9:0.75 10:0.87 11:0.86 12:0.37 17:0.01 18:0.62 21:0.40 23:0.98 27:0.24 29:0.77 30:0.43 31:1.00 34:0.30 36:0.70 37:0.72 39:0.82 43:0.21 45:0.87 46:0.97 62:0.96 66:0.91 69:0.25 70:0.53 71:0.80 74:0.44 79:1.00 81:0.35 83:0.69 86:0.62 91:0.97 96:1.00 97:1.00 98:0.99 99:0.83 101:0.52 104:0.76 107:0.94 108:0.20 110:0.93 114:0.72 120:0.99 122:0.90 123:0.19 125:0.97 126:0.26 127:0.90 128:0.98 129:0.05 131:0.94 135:0.28 137:1.00 138:0.99 139:0.60 140:1.00 143:1.00 144:0.92 146:0.79 147:0.83 149:0.29 150:0.34 151:0.83 153:0.99 154:0.52 155:0.64 157:0.61 158:0.73 159:0.35 160:1.00 162:0.81 164:0.98 165:0.63 166:0.61 168:0.98 170:0.90 172:0.74 173:0.42 174:0.86 175:0.75 176:0.59 177:0.99 179:0.62 182:0.86 190:0.89 191:0.94 192:1.00 196:0.89 197:0.90 201:0.54 202:0.96 205:0.98 208:0.64 212:0.60 215:0.67 216:0.58 222:0.60 227:0.97 229:0.92 231:0.86 232:0.82 235:0.52 239:0.86 241:0.96 242:0.32 243:0.91 244:0.83 246:0.61 247:0.86 248:0.75 253:0.99 254:0.84 255:0.78 256:0.97 257:0.65 259:0.21 260:0.99 264:0.96 265:0.99 266:0.47 267:0.65 268:0.98 271:0.62 275:0.93 276:0.62 277:0.69 279:0.80 283:0.82 284:0.99 285:0.62 287:0.95 289:0.93 290:0.72 294:0.95 297:0.36 298:0.99 300:0.43 +0 1:0.60 7:0.65 8:0.89 9:0.72 10:0.96 11:0.84 12:0.37 17:0.97 18:0.68 21:0.22 23:0.97 27:0.68 29:0.77 30:0.54 32:0.66 34:0.88 36:0.62 37:0.89 39:0.82 43:0.15 45:0.98 46:0.99 56:0.97 62:0.98 66:0.60 69:0.82 70:0.75 71:0.80 74:0.61 75:0.96 77:0.70 80:0.98 81:0.54 82:0.99 83:0.56 86:0.61 88:0.98 91:0.15 96:0.78 97:0.94 98:0.85 99:0.95 101:0.47 102:0.96 104:0.95 106:0.81 107:0.97 108:0.67 110:0.96 114:0.82 120:0.67 122:0.93 123:0.65 124:0.52 125:0.99 126:0.43 127:0.74 128:0.97 129:0.05 131:0.61 133:0.43 135:0.37 139:0.60 140:0.45 143:0.99 144:0.85 145:0.69 146:0.92 147:0.66 149:0.33 150:0.43 151:0.75 153:0.48 154:0.37 155:0.55 157:0.61 158:0.75 159:0.65 160:0.97 162:0.86 164:0.66 165:0.65 166:0.61 168:0.97 170:0.90 172:0.94 173:0.80 174:0.96 175:0.75 176:0.62 177:0.88 179:0.18 182:0.61 187:0.21 190:0.95 192:0.99 193:0.95 195:0.80 197:0.98 201:0.62 202:0.50 204:0.80 205:0.97 206:0.81 208:0.50 212:0.90 215:0.79 216:0.34 220:0.62 222:0.60 226:0.95 227:0.61 229:0.61 230:0.68 231:0.73 233:0.76 235:0.52 236:0.92 239:0.96 241:0.24 242:0.12 243:0.39 244:0.93 245:0.98 246:0.61 247:0.99 248:0.72 253:0.78 254:0.74 255:0.74 256:0.58 257:0.93 259:0.21 260:0.67 264:0.96 265:0.85 266:0.47 267:0.36 268:0.59 271:0.62 275:0.99 276:0.93 277:0.69 279:0.75 282:0.75 283:0.66 285:0.55 287:0.61 289:0.94 290:0.82 294:0.99 295:0.98 297:0.36 300:0.70 +1 1:0.66 2:0.99 7:0.69 8:0.64 9:0.74 10:0.95 11:0.92 12:0.37 17:0.98 18:0.86 27:0.58 29:0.77 30:0.55 31:0.93 32:0.64 34:0.87 36:0.66 37:0.50 39:0.82 43:0.21 44:0.88 45:0.98 46:0.97 56:0.97 64:0.77 66:0.35 69:0.15 70:0.76 71:0.80 74:0.64 77:0.89 79:0.92 80:0.99 81:0.69 83:0.69 86:0.86 91:0.75 96:0.79 97:0.94 98:0.81 99:0.95 101:0.65 107:0.97 108:0.12 110:0.95 114:0.89 120:0.67 122:0.41 123:0.80 124:0.60 125:0.96 126:0.51 127:0.91 129:0.59 131:0.84 133:0.47 135:0.30 137:0.93 138:0.95 139:0.83 140:0.45 143:0.99 144:0.92 145:0.93 146:0.88 147:0.90 149:0.42 150:0.56 151:0.92 153:0.77 154:0.48 155:0.63 157:0.91 159:0.79 160:0.96 162:0.86 164:0.64 165:0.65 166:0.77 170:0.90 172:0.94 173:0.12 174:0.95 176:0.62 177:0.99 179:0.15 182:0.80 190:0.89 191:0.70 192:1.00 193:0.97 195:0.90 196:0.94 197:0.94 201:0.67 202:0.54 204:0.82 208:0.64 212:0.88 215:0.91 216:0.08 219:0.88 222:0.60 227:0.89 229:0.61 230:0.71 231:0.84 233:0.76 235:0.42 239:0.93 241:0.79 242:0.12 243:0.57 244:0.61 245:0.99 246:0.61 247:1.00 248:0.82 253:0.80 254:0.74 255:0.78 256:0.58 259:0.21 260:0.68 264:0.96 265:0.65 266:0.47 267:0.59 268:0.63 271:0.62 275:0.99 276:0.95 279:0.78 281:0.91 282:0.73 284:0.91 285:0.60 286:0.99 287:0.61 289:0.93 290:0.89 292:0.88 294:0.99 295:0.98 297:0.36 298:0.99 300:0.70 +2 1:0.63 7:0.66 8:0.72 9:0.73 10:0.81 11:0.86 12:0.37 17:0.91 18:0.92 21:0.13 27:0.49 29:0.77 30:0.57 31:0.95 32:0.65 34:0.67 36:0.75 37:0.78 39:0.82 43:0.57 44:0.88 45:0.92 46:0.98 48:0.91 56:0.97 64:0.77 66:0.54 69:0.86 70:0.59 71:0.80 74:0.57 77:0.70 79:0.95 80:0.99 81:0.63 82:0.98 83:0.56 86:0.83 88:0.98 91:0.23 96:0.80 97:0.89 98:0.72 99:0.95 101:0.52 104:0.81 106:0.81 107:0.95 108:0.12 110:0.94 114:0.82 117:0.86 120:0.68 122:0.57 123:0.12 124:0.52 125:0.97 126:0.51 127:0.56 129:0.05 131:0.84 133:0.45 135:0.60 137:0.95 138:0.95 139:0.80 140:0.45 143:0.99 144:0.85 145:0.49 146:0.44 147:0.75 149:0.59 150:0.49 151:0.70 153:0.46 154:0.19 155:0.58 157:0.61 158:0.73 159:0.44 160:0.96 162:0.86 164:0.66 165:0.65 166:0.77 170:0.90 172:0.86 173:0.95 174:0.79 175:0.75 176:0.62 177:0.96 179:0.26 182:0.61 187:0.39 190:0.98 191:0.82 192:1.00 193:0.95 195:0.65 196:0.94 197:0.81 201:0.65 202:0.56 204:0.82 206:0.81 208:0.61 212:0.93 215:0.79 216:0.30 220:0.74 222:0.60 227:0.89 229:0.61 230:0.68 231:0.67 232:0.87 233:0.65 235:0.52 239:0.82 241:0.02 242:0.16 243:0.63 244:0.83 245:0.95 246:0.61 247:0.98 248:0.65 253:0.79 254:0.92 255:0.74 256:0.64 257:0.84 259:0.21 260:0.69 264:0.96 265:0.72 266:0.27 267:0.36 268:0.64 271:0.62 275:0.97 276:0.83 277:0.87 279:0.74 281:0.86 282:0.74 283:0.82 284:0.93 285:0.60 286:0.99 287:0.61 289:0.93 290:0.82 294:0.98 297:0.36 298:0.99 300:0.55 +1 1:0.66 7:0.65 8:0.83 9:0.74 10:0.96 11:0.87 12:0.37 17:0.97 18:0.93 21:0.13 23:0.97 27:0.68 29:0.77 30:0.62 31:0.91 32:0.71 34:0.75 36:0.68 37:0.89 39:0.82 43:0.23 44:0.92 45:0.99 46:0.99 48:0.91 56:0.97 62:0.98 64:0.77 66:0.99 69:0.15 70:0.64 71:0.80 74:0.71 75:0.97 77:0.70 79:0.90 80:0.99 81:0.66 82:0.99 83:0.69 86:0.89 88:0.98 91:0.11 96:0.79 97:1.00 98:0.98 99:0.99 101:0.96 102:0.97 104:0.95 106:0.81 107:0.97 108:0.12 110:0.96 114:0.88 120:0.68 122:0.86 123:0.12 125:0.98 126:0.51 127:0.83 128:0.97 129:0.05 131:0.61 135:0.26 137:0.91 139:0.89 140:0.87 143:0.99 144:0.85 145:0.69 146:0.97 147:0.97 149:0.46 150:0.51 151:0.65 153:0.48 154:0.53 155:0.64 157:0.61 158:0.76 159:0.69 160:0.97 162:0.86 164:0.66 165:0.70 166:0.61 168:0.97 170:0.90 172:0.98 173:0.15 174:0.95 176:0.63 177:0.99 179:0.15 182:0.61 187:0.21 190:0.95 191:0.85 192:1.00 193:0.95 195:0.83 196:0.93 197:0.97 201:0.66 202:0.58 204:0.81 205:0.97 206:0.81 208:0.61 212:0.91 215:0.84 216:0.34 219:0.91 220:0.91 222:0.60 226:0.96 227:0.61 229:0.61 230:0.68 231:0.75 233:0.76 235:0.52 236:0.94 239:0.96 241:0.15 242:0.12 243:0.99 244:0.83 246:0.61 247:1.00 248:0.63 253:0.82 255:0.78 256:0.64 259:0.21 260:0.68 264:0.96 265:0.98 266:0.54 267:0.28 268:0.66 271:0.62 275:0.99 276:0.95 279:0.81 281:0.86 282:0.79 283:0.82 284:0.93 285:0.60 287:0.61 289:0.94 290:0.88 292:0.88 294:0.99 295:0.98 297:0.36 300:0.55 +0 1:0.60 7:0.65 8:0.59 9:0.72 10:0.96 11:0.84 12:0.37 17:0.95 18:0.68 21:0.22 23:0.97 27:0.68 29:0.77 30:0.53 32:0.66 34:0.90 36:0.55 37:0.89 39:0.82 43:0.17 45:0.97 46:0.99 56:0.97 62:0.98 66:0.98 69:0.15 70:0.72 71:0.80 74:0.59 75:0.96 77:0.70 80:0.98 81:0.54 82:0.99 83:0.56 86:0.61 88:0.98 91:0.22 96:0.79 97:0.97 98:0.97 99:0.95 101:0.47 102:0.96 104:0.95 106:0.81 107:0.97 108:0.12 110:0.96 114:0.82 120:0.67 122:0.93 123:0.12 125:0.99 126:0.43 127:0.78 128:0.97 129:0.05 131:0.61 135:0.49 139:0.60 140:0.45 143:0.99 144:0.85 145:0.69 146:0.95 147:0.96 149:0.34 150:0.43 151:0.75 153:0.48 154:0.37 155:0.55 157:0.61 158:0.75 159:0.61 160:0.97 162:0.86 164:0.68 165:0.65 166:0.61 168:0.97 170:0.90 172:0.96 173:0.18 174:0.96 175:0.75 176:0.62 177:0.99 179:0.19 182:0.61 187:0.21 190:0.95 192:0.99 193:0.95 195:0.77 197:0.98 201:0.62 202:0.53 204:0.80 205:0.97 206:0.81 208:0.50 212:0.89 215:0.79 216:0.34 220:0.82 222:0.60 226:0.95 227:0.61 229:0.61 230:0.68 231:0.73 233:0.76 235:0.52 236:0.92 239:0.96 241:0.35 242:0.13 243:0.98 244:0.93 246:0.61 247:0.99 248:0.73 253:0.78 254:0.74 255:0.74 256:0.58 257:0.65 259:0.21 260:0.68 264:0.96 265:0.97 266:0.47 267:0.36 268:0.62 271:0.62 275:0.99 276:0.92 277:0.69 279:0.77 282:0.75 283:0.82 285:0.55 287:0.61 289:0.94 290:0.82 294:0.99 295:0.98 297:0.36 300:0.55 +0 1:0.63 8:0.91 9:0.72 10:0.95 11:0.92 12:0.37 17:0.95 18:0.94 21:0.22 23:0.97 27:0.68 29:0.77 30:0.26 31:0.89 34:0.80 36:0.57 37:0.89 39:0.82 43:0.20 44:0.88 45:0.96 46:0.97 48:0.91 56:0.97 62:0.98 64:0.77 66:0.99 69:0.17 70:0.75 71:0.80 74:0.42 75:0.96 76:0.85 77:0.70 79:0.89 80:0.99 81:0.54 82:0.99 83:0.60 86:0.86 88:0.98 91:0.15 96:0.76 97:0.97 98:0.98 99:0.95 101:0.96 102:0.96 107:0.96 108:0.14 110:0.94 114:0.73 122:0.99 123:0.13 125:0.97 126:0.43 127:0.80 128:0.97 129:0.05 131:0.61 135:0.19 137:0.89 139:0.85 140:0.80 143:0.99 144:0.92 145:0.69 146:0.96 147:0.97 149:0.29 150:0.44 151:0.61 153:0.48 154:0.31 155:0.57 157:0.78 158:0.72 159:0.67 160:0.96 162:0.86 165:0.65 166:0.61 168:0.97 170:0.90 172:0.97 173:0.16 174:0.95 176:0.59 177:0.99 179:0.17 182:0.73 187:0.21 190:0.98 191:0.79 192:0.98 193:0.95 195:0.82 196:0.92 197:0.97 201:0.62 202:0.50 204:0.80 205:0.97 208:0.54 212:0.91 216:0.34 219:0.90 220:0.74 222:0.60 226:0.96 227:0.61 229:0.61 231:0.77 235:0.52 236:0.92 239:0.95 241:0.10 242:0.12 243:0.99 244:0.83 246:0.61 247:1.00 248:0.59 253:0.65 254:0.94 255:0.72 256:0.58 259:0.21 264:0.96 265:0.98 266:0.54 267:0.28 268:0.59 271:0.62 275:0.99 276:0.93 279:0.77 281:0.86 282:0.72 284:0.88 285:0.55 287:0.61 289:0.92 290:0.73 292:0.88 294:0.99 295:0.98 300:0.55 +2 1:0.58 2:1.00 8:0.96 9:0.74 10:0.82 11:0.84 12:0.37 17:0.30 18:0.63 21:0.05 27:0.24 29:0.77 30:0.60 31:0.88 34:0.76 36:0.81 37:0.72 39:0.82 43:0.23 45:0.81 46:0.96 56:0.97 66:0.86 69:0.42 70:0.67 71:0.80 74:0.36 79:0.87 81:0.34 83:0.56 86:0.62 91:0.73 96:0.88 98:0.88 101:0.47 104:0.76 107:0.92 108:0.32 110:0.92 120:0.79 122:0.83 123:0.31 125:0.97 126:0.26 127:0.43 129:0.05 131:0.95 135:0.30 137:0.88 138:0.96 139:0.61 140:0.87 143:0.99 144:0.92 146:0.64 147:0.69 149:0.24 150:0.38 151:0.75 153:0.48 154:0.22 155:0.63 157:0.61 159:0.24 160:0.98 162:0.76 164:0.78 166:0.77 170:0.90 172:0.59 173:0.64 174:0.79 175:0.75 177:0.99 178:0.42 179:0.70 182:0.88 187:0.95 190:0.98 191:0.84 192:0.99 196:0.87 197:0.82 201:0.55 202:0.74 208:0.61 212:0.72 216:0.58 220:0.74 222:0.60 227:0.97 229:0.94 231:0.88 232:0.82 235:0.12 239:0.81 241:0.70 242:0.19 243:0.86 244:0.93 246:0.61 247:0.79 248:0.74 253:0.82 254:0.95 255:0.78 256:0.79 257:0.65 259:0.21 260:0.79 264:0.96 265:0.88 266:0.38 267:0.36 268:0.78 271:0.62 275:0.93 276:0.44 277:0.69 279:0.77 284:0.88 285:0.62 287:0.96 289:0.93 294:0.95 295:0.98 300:0.55 +0 1:0.60 7:0.65 9:0.72 10:0.95 11:0.91 12:0.37 17:0.96 18:0.68 21:0.22 23:0.97 27:0.68 29:0.77 30:0.61 32:0.66 34:0.75 36:0.55 37:0.89 39:0.82 43:0.17 45:0.98 46:0.99 56:0.97 62:0.98 66:0.99 69:0.15 70:0.68 71:0.80 74:0.64 75:0.96 77:0.70 80:0.98 81:0.54 82:0.99 83:0.56 86:0.61 88:0.98 91:0.11 96:0.78 97:0.97 98:0.98 99:0.95 101:0.52 102:0.96 104:0.95 106:0.81 107:0.97 108:0.12 110:0.96 114:0.82 120:0.67 122:0.86 123:0.12 125:0.99 126:0.43 127:0.83 128:0.97 129:0.05 131:0.61 135:0.30 139:0.60 140:0.45 143:0.99 144:0.92 145:0.69 146:0.97 147:0.98 149:0.40 150:0.43 151:0.75 153:0.48 154:0.14 155:0.55 157:0.85 158:0.75 159:0.71 160:0.97 162:0.86 164:0.66 165:0.65 166:0.61 168:0.97 170:0.90 172:0.97 173:0.14 174:0.95 176:0.62 177:0.99 179:0.16 182:0.76 187:0.21 190:0.95 191:0.76 192:0.99 193:0.95 195:0.85 197:0.97 201:0.62 202:0.50 204:0.80 205:0.97 206:0.81 208:0.50 212:0.88 215:0.79 216:0.34 220:0.62 222:0.60 226:0.95 227:0.61 229:0.61 230:0.68 231:0.80 233:0.76 235:0.52 236:0.92 239:0.95 241:0.18 242:0.17 243:0.99 244:0.83 246:0.61 247:0.99 248:0.72 253:0.79 254:0.93 255:0.74 256:0.58 259:0.21 260:0.67 264:0.96 265:0.98 266:0.54 267:0.28 268:0.59 271:0.62 275:0.99 276:0.94 279:0.77 282:0.75 283:0.82 285:0.55 287:0.61 289:0.94 290:0.82 294:0.99 295:0.98 297:0.36 300:0.70 +1 1:0.65 10:0.99 11:0.64 12:0.95 17:0.77 18:0.71 21:0.30 26:0.94 27:0.15 29:0.62 30:0.62 34:0.89 36:0.34 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.98 69:0.19 70:0.61 74:0.53 75:0.95 80:0.99 81:0.45 82:0.98 83:0.54 86:0.76 88:0.99 91:0.11 98:0.99 99:0.95 101:0.69 102:0.95 108:0.15 122:0.99 123:0.15 126:0.31 127:0.89 129:0.05 131:0.61 135:0.23 139:0.72 141:0.91 144:0.76 145:0.50 146:0.94 147:0.95 149:0.81 150:0.42 154:0.58 155:0.51 157:0.86 159:0.60 165:0.63 166:0.75 170:0.79 172:0.95 173:0.22 174:0.99 177:0.96 178:0.55 179:0.22 182:0.77 187:0.39 192:0.46 193:0.97 195:0.73 197:0.99 199:0.94 201:0.61 204:0.79 208:0.49 212:0.90 216:0.38 222:0.20 223:0.92 224:0.93 226:0.96 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.92 239:0.99 241:0.01 242:0.31 243:0.98 244:0.61 246:0.90 247:0.95 251:1.00 253:0.42 254:0.94 259:0.21 264:0.93 265:0.99 266:0.54 267:0.65 271:0.24 274:0.91 275:0.99 276:0.90 287:0.61 294:1.00 300:0.70 +1 1:0.67 8:0.93 10:1.00 11:0.55 12:0.95 17:0.89 18:0.64 21:0.30 26:0.94 27:0.15 29:0.62 30:0.95 33:0.97 34:0.74 36:0.46 37:0.64 39:0.97 43:0.57 45:0.94 55:0.52 56:0.97 58:0.93 66:1.00 69:0.21 70:0.19 74:0.49 75:0.96 80:0.99 81:0.48 82:0.98 83:0.54 86:0.72 88:0.99 91:0.04 97:0.89 98:1.00 99:0.95 101:0.46 102:0.95 108:0.17 122:0.98 123:0.16 126:0.31 127:0.96 129:0.05 131:0.61 135:0.24 139:0.68 141:0.91 144:0.76 145:0.52 146:1.00 147:1.00 149:0.87 150:0.44 154:0.58 155:0.52 157:0.84 159:0.91 165:0.63 166:0.75 170:0.79 172:1.00 173:0.08 174:1.00 177:0.96 178:0.55 179:0.09 182:0.77 187:0.39 192:0.47 193:0.96 195:0.98 197:1.00 199:0.94 201:0.62 204:0.82 208:0.49 212:0.94 216:0.38 222:0.20 223:0.92 224:0.93 226:0.96 227:0.61 229:0.61 231:0.64 234:0.91 235:0.68 236:0.92 239:0.99 241:0.02 242:0.80 243:1.00 244:0.61 246:0.90 247:0.93 251:1.00 253:0.40 254:0.94 259:0.21 264:0.93 265:1.00 266:0.38 267:0.19 271:0.24 274:0.91 275:0.98 276:1.00 279:0.75 287:0.61 291:0.97 294:0.99 295:0.98 300:0.55 +0 7:0.66 10:0.97 11:0.55 12:0.95 17:0.95 18:0.58 21:0.47 26:0.99 27:0.69 29:0.62 30:0.21 32:0.64 34:0.19 36:0.78 37:0.63 39:0.97 43:0.15 45:0.95 55:0.52 58:0.98 66:0.59 69:0.83 70:0.78 74:0.37 81:0.29 86:0.59 91:0.29 98:0.80 101:0.46 104:0.76 108:0.68 120:0.56 123:0.65 124:0.60 126:0.23 127:0.79 129:0.05 131:0.61 133:0.65 135:0.97 139:0.58 140:0.45 141:0.99 144:0.76 145:0.61 146:0.95 147:0.50 149:0.23 150:0.32 151:0.49 153:0.48 154:0.28 157:0.85 159:0.78 162:0.62 164:0.55 166:0.61 170:0.79 172:0.93 173:0.72 174:0.97 175:0.75 177:0.38 179:0.18 182:0.76 187:0.39 190:0.98 195:0.90 197:0.97 199:0.99 202:0.50 212:0.89 215:0.63 216:0.10 220:0.93 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 230:0.68 231:0.63 233:0.76 234:0.98 235:0.52 239:0.95 241:0.10 242:0.36 243:0.07 244:0.61 245:0.96 246:0.61 247:0.88 248:0.48 253:0.33 254:0.93 256:0.45 257:0.93 259:0.21 260:0.56 264:0.93 265:0.80 266:0.71 267:0.28 268:0.46 271:0.24 274:0.97 275:0.99 276:0.92 277:0.69 285:0.45 287:0.61 294:0.99 297:0.36 300:0.84 +1 1:0.58 10:0.96 11:0.64 12:0.95 17:0.88 18:0.76 21:0.30 26:0.94 27:0.28 29:0.62 30:0.87 34:0.92 36:0.27 37:0.24 39:0.97 43:0.57 45:0.91 56:0.97 58:0.93 66:0.84 69:0.21 70:0.41 74:0.43 80:0.99 81:0.39 82:0.98 83:0.54 86:0.84 88:0.99 91:0.25 98:0.79 99:0.83 101:0.69 102:0.95 108:0.17 122:0.99 123:0.16 124:0.37 126:0.31 127:0.57 129:0.05 131:0.61 133:0.36 135:0.56 139:0.78 141:0.91 144:0.76 145:0.77 146:0.76 147:0.80 149:0.44 150:0.37 154:0.56 155:0.52 157:0.83 159:0.44 165:0.63 166:0.75 170:0.79 172:0.74 173:0.29 174:0.93 175:0.75 177:0.88 178:0.55 179:0.55 182:0.77 187:0.87 192:0.46 193:0.96 195:0.66 197:0.96 199:0.94 201:0.56 204:0.82 208:0.49 212:0.58 216:0.34 222:0.20 223:0.92 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 236:0.92 239:0.95 241:0.30 242:0.30 243:0.85 244:0.61 245:0.58 246:0.89 247:0.79 253:0.37 254:0.84 257:0.65 259:0.21 264:0.93 265:0.79 266:0.47 267:0.19 271:0.24 274:0.91 275:0.96 276:0.60 277:0.69 287:0.61 294:0.99 300:0.55 +0 1:0.66 10:0.99 11:0.64 12:0.95 17:0.77 18:0.71 21:0.22 26:0.98 27:0.15 29:0.62 30:0.60 32:0.67 34:0.79 36:0.56 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.33 69:0.19 70:0.59 74:0.52 75:0.95 80:0.99 81:0.58 82:0.99 83:0.54 86:0.77 88:0.98 91:0.17 98:0.71 99:0.95 101:0.69 102:0.96 104:0.89 106:0.81 108:0.15 122:0.99 123:0.74 124:0.60 126:0.43 127:0.91 129:0.59 131:0.61 133:0.47 135:0.21 139:0.72 141:0.91 144:0.76 145:0.50 146:0.76 147:0.80 149:0.82 150:0.50 154:0.58 155:0.51 157:0.86 159:0.57 165:0.63 166:0.75 170:0.79 172:0.89 173:0.23 174:0.99 177:0.96 178:0.55 179:0.21 182:0.77 187:0.39 192:0.49 193:0.97 195:0.71 197:0.99 199:0.97 201:0.65 204:0.79 206:0.81 208:0.49 212:0.93 215:0.79 216:0.38 222:0.20 223:0.98 224:0.93 226:0.95 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.94 239:0.99 242:0.31 243:0.57 244:0.61 245:0.98 246:0.90 247:0.97 251:1.00 253:0.42 254:0.94 259:0.21 264:0.93 265:0.70 266:0.54 267:0.73 271:0.24 274:0.91 275:0.99 276:0.91 283:0.82 287:0.61 294:1.00 297:0.36 300:0.84 +0 7:0.66 10:0.97 11:0.55 12:0.95 17:0.91 18:0.61 21:0.47 26:0.99 27:0.69 29:0.62 30:0.14 32:0.64 34:0.40 36:0.39 37:0.63 39:0.97 43:0.20 45:0.95 58:0.98 66:0.93 69:0.82 70:0.83 74:0.34 81:0.34 86:0.64 91:0.37 98:0.89 101:0.46 104:0.76 108:0.67 120:0.65 123:0.65 124:0.36 126:0.23 127:0.81 129:0.05 131:0.61 133:0.38 135:0.60 139:0.61 140:0.45 141:0.99 144:0.76 145:0.41 146:0.96 147:0.50 149:0.25 150:0.38 151:0.60 153:0.48 154:0.33 157:0.85 159:0.72 162:0.86 164:0.55 166:0.61 170:0.79 172:0.96 173:0.76 174:0.97 177:0.38 179:0.19 182:0.76 187:0.21 190:0.98 195:0.84 197:0.96 199:0.99 202:0.36 212:0.92 215:0.63 216:0.10 220:0.62 222:0.20 223:0.99 224:1.00 227:0.61 229:0.61 230:0.68 231:0.63 233:0.76 234:1.00 235:0.60 239:0.95 241:0.18 242:0.36 243:0.07 244:0.61 245:0.85 246:0.61 247:0.88 248:0.59 253:0.30 254:0.93 256:0.45 257:0.93 259:0.21 260:0.66 264:0.93 265:0.89 266:0.71 267:0.19 268:0.46 271:0.24 274:0.97 275:0.98 276:0.92 283:0.67 285:0.45 287:0.61 294:0.99 297:0.36 300:0.84 +0 10:0.86 11:0.64 12:0.95 17:0.16 18:0.71 21:0.78 26:0.97 27:0.20 29:0.62 30:0.66 33:0.97 34:0.75 36:0.61 37:0.66 39:0.97 43:0.64 45:0.73 55:0.59 58:0.93 66:0.85 69:0.47 70:0.53 74:0.40 82:0.98 86:0.77 88:0.99 91:0.20 98:0.84 101:0.52 102:0.95 108:0.36 123:0.35 127:0.35 129:0.05 131:0.61 135:0.34 139:0.72 141:0.91 144:0.76 145:0.54 146:0.60 147:0.65 149:0.35 154:0.53 157:0.76 159:0.22 166:0.61 170:0.79 172:0.57 173:0.69 174:0.79 177:0.96 178:0.55 179:0.68 182:0.73 187:0.68 193:0.94 195:0.57 197:0.85 199:0.95 212:0.88 216:0.60 222:0.20 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.12 239:0.88 241:0.27 242:0.22 243:0.86 244:0.61 246:0.61 247:0.74 253:0.35 254:0.97 259:0.21 264:0.93 265:0.84 266:0.23 267:0.65 271:0.24 274:0.91 275:0.91 276:0.44 287:0.61 291:0.97 294:0.96 300:0.43 +0 10:0.91 11:0.64 12:0.95 17:0.61 18:0.77 21:0.78 26:0.97 27:0.02 29:0.62 30:0.61 33:0.97 34:0.53 36:0.51 37:0.95 39:0.97 43:0.62 45:0.85 58:0.93 66:0.10 69:0.60 70:0.48 74:0.32 86:0.75 91:0.14 98:0.31 101:0.69 108:0.46 122:0.57 123:0.79 124:0.69 127:0.20 129:0.05 131:0.61 133:0.66 135:0.70 139:0.72 141:0.91 144:0.76 145:0.41 146:0.53 147:0.59 149:0.63 154:0.51 157:0.80 159:0.54 166:0.61 170:0.79 172:0.51 173:0.39 174:0.88 175:0.91 177:0.70 178:0.55 179:0.35 182:0.74 187:0.68 197:0.91 199:0.96 202:0.36 212:0.59 216:0.87 222:0.20 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.31 239:0.89 241:0.57 242:0.14 243:0.58 244:0.83 245:0.70 246:0.61 247:0.79 253:0.29 257:0.84 259:0.21 264:0.93 265:0.29 266:0.75 267:0.28 271:0.24 274:0.91 275:0.95 276:0.54 277:0.95 287:0.61 291:0.97 294:0.97 300:0.43 +1 1:0.67 8:0.92 9:0.72 10:1.00 11:0.55 12:0.95 17:0.89 18:0.64 21:0.30 23:0.96 26:0.94 27:0.15 29:0.62 30:0.95 33:0.97 34:0.44 36:0.50 37:0.64 39:0.97 43:0.57 45:0.94 55:0.52 56:0.97 58:0.93 62:0.97 66:1.00 69:0.21 70:0.19 74:0.49 75:0.95 80:0.99 81:0.48 82:0.98 83:0.54 86:0.72 88:0.99 91:0.14 98:1.00 99:0.95 101:0.46 102:0.95 108:0.17 122:0.98 123:0.16 126:0.31 127:0.95 128:0.96 129:0.05 131:0.82 135:0.34 139:0.68 140:0.45 141:0.91 143:0.99 144:0.76 145:0.52 146:1.00 147:1.00 149:0.87 150:0.44 151:0.58 153:0.69 154:0.58 155:0.54 157:0.84 159:0.91 162:0.86 165:0.63 166:0.75 168:0.96 170:0.79 172:1.00 173:0.08 174:1.00 177:0.96 179:0.09 182:0.77 187:0.39 190:0.95 191:0.76 192:0.48 193:0.96 195:0.98 197:1.00 199:0.94 201:0.62 202:0.46 204:0.82 205:0.95 208:0.49 212:0.94 216:0.38 220:0.87 222:0.20 223:0.92 224:0.93 226:0.96 227:0.87 229:0.87 231:0.64 234:0.91 235:0.68 236:0.92 239:0.99 241:0.15 242:0.80 243:1.00 244:0.61 246:0.90 247:0.93 248:0.57 251:1.00 253:0.40 254:0.94 255:0.70 256:0.45 259:0.21 264:0.93 265:1.00 266:0.38 267:0.19 268:0.55 271:0.24 274:0.91 275:0.98 276:1.00 285:0.49 287:0.92 291:0.97 294:0.99 300:0.55 +1 7:0.66 8:0.74 10:0.88 11:0.55 12:0.95 17:0.13 18:0.63 21:0.40 26:0.99 27:0.21 29:0.62 30:0.15 34:0.56 36:0.77 37:0.74 39:0.97 43:0.57 45:0.73 55:0.52 58:0.99 66:0.54 69:0.12 70:0.90 74:0.39 80:0.98 81:0.30 86:0.65 91:0.44 98:0.50 101:0.46 104:0.84 106:0.81 108:0.10 120:0.74 123:0.10 124:0.52 126:0.23 127:0.44 129:0.05 131:0.61 133:0.45 135:0.19 139:0.63 140:0.80 141:0.99 144:0.76 146:0.65 147:0.70 149:0.37 150:0.33 151:0.65 153:0.48 154:0.47 157:0.78 159:0.56 162:0.70 164:0.71 166:0.61 170:0.79 172:0.57 173:0.16 174:0.83 175:0.75 177:0.88 178:0.42 179:0.59 182:0.73 187:0.39 190:0.98 197:0.87 199:0.98 202:0.63 206:0.81 212:0.59 215:0.67 216:0.95 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 230:0.68 231:0.62 233:0.76 234:0.99 235:0.42 239:0.87 241:0.30 242:0.12 243:0.62 244:0.83 245:0.75 246:0.61 247:0.91 248:0.66 253:0.34 256:0.64 257:0.65 259:0.21 260:0.74 264:0.93 265:0.52 266:0.63 267:0.69 268:0.65 271:0.24 274:0.98 275:0.95 276:0.52 277:0.69 283:0.82 285:0.45 287:0.61 294:0.96 297:0.36 300:0.70 +0 1:0.58 10:0.95 11:0.64 12:0.95 17:0.76 18:0.78 21:0.30 26:0.99 27:0.43 29:0.62 30:0.40 34:0.69 36:0.42 37:0.29 39:0.97 43:0.61 45:0.92 56:0.97 58:0.98 66:0.63 69:0.48 70:0.96 74:0.35 80:0.99 81:0.39 82:0.98 83:0.54 86:0.78 88:0.99 91:0.22 98:0.67 99:0.83 101:0.69 102:0.95 108:0.37 120:0.65 122:0.99 123:0.36 124:0.64 126:0.31 127:0.84 129:0.05 131:0.61 133:0.72 135:0.20 139:0.74 140:0.45 141:0.99 144:0.76 145:0.89 146:0.90 147:0.92 149:0.75 150:0.37 151:0.60 153:0.48 154:0.51 155:0.51 157:0.83 159:0.79 162:0.86 164:0.55 165:0.63 166:0.75 170:0.79 172:0.82 173:0.30 174:0.94 177:0.96 179:0.38 182:0.77 187:0.39 190:0.98 192:0.46 193:0.97 195:0.90 197:0.96 199:0.95 201:0.56 202:0.36 204:0.81 208:0.49 212:0.54 216:0.46 220:0.62 222:0.20 223:0.99 224:1.00 227:0.61 229:0.61 231:0.64 234:1.00 235:0.42 236:0.92 239:0.95 241:0.41 242:0.16 243:0.69 244:0.83 245:0.81 246:0.89 247:0.96 248:0.59 253:0.31 256:0.45 259:0.21 260:0.66 264:0.93 265:0.68 266:0.87 267:0.52 268:0.46 271:0.24 274:0.97 275:0.98 276:0.78 285:0.45 287:0.61 294:0.99 300:0.94 +0 10:0.92 11:0.64 12:0.95 17:0.51 18:0.63 21:0.59 26:0.98 27:0.67 29:0.62 30:0.68 32:0.64 34:0.78 36:0.45 37:0.57 39:0.97 43:0.39 45:0.77 58:0.93 66:0.86 69:0.37 70:0.21 74:0.36 81:0.28 86:0.72 91:0.38 98:0.81 101:0.69 104:0.97 106:0.81 108:0.29 123:0.28 126:0.23 127:0.29 129:0.05 131:0.61 135:0.68 139:0.67 141:0.91 144:0.76 145:0.56 146:0.56 147:0.62 149:0.29 150:0.30 154:0.56 157:0.78 159:0.20 166:0.61 170:0.79 172:0.61 173:0.60 174:0.88 177:0.96 178:0.55 179:0.59 182:0.73 187:0.87 195:0.55 197:0.90 199:0.97 206:0.81 212:0.95 215:0.55 216:0.36 222:0.20 223:0.98 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.68 239:0.89 241:0.03 242:0.31 243:0.87 244:0.61 246:0.61 247:0.76 253:0.32 254:0.95 259:0.21 264:0.93 265:0.81 266:0.12 267:0.28 271:0.24 274:0.91 275:0.93 276:0.50 283:0.82 287:0.61 294:0.96 297:0.36 300:0.18 +0 10:0.89 11:0.55 12:0.95 17:0.32 18:0.75 21:0.78 26:0.96 27:0.21 29:0.62 30:0.17 34:0.62 36:0.85 37:0.74 39:0.97 43:0.26 45:0.77 55:0.45 58:0.93 66:0.48 69:0.74 70:0.90 74:0.44 86:0.68 91:0.23 98:0.46 101:0.46 108:0.57 122:0.95 123:0.54 124:0.57 127:0.35 129:0.05 131:0.61 133:0.45 135:0.20 139:0.66 141:0.91 144:0.76 146:0.64 147:0.69 149:0.31 154:0.46 157:0.79 159:0.55 166:0.61 170:0.79 172:0.59 173:0.67 174:0.88 175:0.75 177:0.88 178:0.55 179:0.47 182:0.73 187:0.39 197:0.89 199:0.95 202:0.50 212:0.60 216:0.95 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.52 239:0.88 241:0.18 242:0.24 243:0.60 244:0.61 245:0.81 246:0.61 247:0.88 253:0.37 254:0.93 257:0.65 259:0.21 264:0.93 265:0.48 266:0.75 267:0.36 271:0.24 274:0.91 275:0.95 276:0.57 277:0.69 287:0.61 294:0.96 300:0.84 +0 1:0.66 10:0.99 11:0.64 12:0.95 17:0.81 18:0.72 21:0.22 26:0.98 27:0.15 29:0.62 30:0.58 32:0.67 34:0.89 36:0.55 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.98 69:0.19 70:0.59 74:0.54 75:0.95 80:0.99 81:0.58 82:0.99 83:0.54 86:0.77 88:0.98 91:0.25 98:0.99 99:0.95 101:0.69 102:0.96 104:0.89 106:0.81 108:0.15 122:0.99 123:0.15 126:0.43 127:0.89 129:0.05 131:0.61 135:0.23 139:0.72 141:0.91 144:0.76 145:0.50 146:0.92 147:0.94 149:0.83 150:0.50 154:0.58 155:0.51 157:0.86 159:0.54 165:0.63 166:0.75 170:0.79 172:0.96 173:0.24 174:0.99 177:0.96 178:0.55 179:0.20 182:0.77 187:0.39 192:0.49 193:0.97 195:0.69 197:1.00 199:0.97 201:0.65 204:0.79 206:0.81 208:0.49 212:0.92 215:0.79 216:0.38 222:0.20 223:0.98 224:0.93 226:0.95 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.94 239:0.99 242:0.30 243:0.98 244:0.61 246:0.90 247:0.98 251:1.00 253:0.43 254:0.94 259:0.21 264:0.93 265:0.99 266:0.38 267:0.69 271:0.24 274:0.91 275:0.99 276:0.91 283:0.82 287:0.61 294:1.00 297:0.36 300:0.70 +1 1:0.58 10:0.95 11:0.64 12:0.95 17:0.81 18:0.78 21:0.30 26:0.96 27:0.43 29:0.62 30:0.27 34:0.81 36:0.39 37:0.29 39:0.97 43:0.62 45:0.92 56:0.97 58:0.93 66:0.53 69:0.48 70:0.95 74:0.40 80:0.99 81:0.39 82:0.98 83:0.54 86:0.78 88:0.99 91:0.15 98:0.66 99:0.83 101:0.69 102:0.95 108:0.37 122:0.90 123:0.36 124:0.64 126:0.31 127:0.82 129:0.05 131:0.61 133:0.60 135:0.21 139:0.74 141:0.91 144:0.76 145:0.89 146:0.90 147:0.92 149:0.75 150:0.37 154:0.51 155:0.51 157:0.83 159:0.79 165:0.63 166:0.75 170:0.79 172:0.82 173:0.29 174:0.94 177:0.96 178:0.55 179:0.33 182:0.77 187:0.39 192:0.46 193:0.97 195:0.91 197:0.96 199:0.95 201:0.56 204:0.81 208:0.49 212:0.54 216:0.46 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 236:0.92 239:0.95 241:0.15 242:0.16 243:0.62 244:0.83 245:0.90 246:0.89 247:0.97 253:0.35 259:0.21 264:0.93 265:0.67 266:0.84 267:0.52 271:0.24 274:0.91 275:0.98 276:0.82 287:0.61 294:0.99 300:0.84 +1 1:0.58 10:0.92 11:0.64 12:0.95 17:0.03 18:0.65 21:0.40 26:0.96 27:0.26 29:0.62 30:0.66 34:0.67 36:0.36 37:0.58 39:0.97 43:0.60 45:0.83 55:0.71 56:0.97 58:0.93 66:0.92 69:0.23 70:0.49 74:0.35 81:0.38 83:0.54 86:0.67 91:0.12 98:0.94 99:0.83 101:0.69 108:0.18 122:0.98 123:0.18 126:0.31 127:0.62 129:0.05 131:0.61 135:0.30 139:0.66 141:0.91 144:0.76 146:0.92 147:0.93 149:0.50 150:0.36 154:0.52 155:0.47 157:0.81 159:0.53 165:0.63 166:0.75 170:0.79 172:0.79 173:0.25 174:0.95 177:0.96 178:0.55 179:0.50 182:0.77 187:0.87 192:0.44 197:0.97 199:0.95 201:0.55 208:0.49 212:0.48 216:0.90 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.52 236:0.92 239:0.90 241:0.07 242:0.31 243:0.92 244:0.83 246:0.89 247:0.94 253:0.32 259:0.21 264:0.93 265:0.94 266:0.47 267:0.28 271:0.24 274:0.91 275:0.96 276:0.68 287:0.61 294:0.97 300:0.43 +1 1:0.62 10:0.95 11:0.64 12:0.95 17:0.87 18:0.76 21:0.30 26:0.94 27:0.28 29:0.62 30:0.87 34:0.96 36:0.33 37:0.24 39:0.97 43:0.56 44:0.88 45:0.90 56:0.97 58:0.93 64:0.77 66:0.83 69:0.44 70:0.36 74:0.43 80:1.00 81:0.61 82:0.99 83:0.60 86:0.84 88:0.99 91:0.23 98:0.78 99:0.95 101:0.69 102:0.96 108:0.34 122:0.97 123:0.33 124:0.37 126:0.43 127:0.64 129:0.05 131:0.61 133:0.36 135:0.44 139:0.84 141:0.91 144:0.76 145:0.79 146:0.76 147:0.80 149:0.34 150:0.50 154:0.55 155:0.55 157:0.82 159:0.45 165:0.70 166:0.75 170:0.79 172:0.71 173:0.48 174:0.93 175:0.75 177:0.88 178:0.55 179:0.59 182:0.77 187:0.87 192:0.51 193:0.98 195:0.66 197:0.95 199:0.94 201:0.61 204:0.84 208:0.54 212:0.61 216:0.34 222:0.20 223:0.92 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.52 236:0.94 239:0.95 241:0.46 242:0.24 243:0.84 244:0.61 245:0.57 246:0.89 247:0.79 253:0.37 254:0.86 257:0.65 259:0.21 264:0.93 265:0.78 266:0.63 267:0.44 271:0.24 274:0.91 275:0.96 276:0.57 277:0.69 281:0.86 287:0.61 294:0.99 300:0.43 +0 1:0.58 10:0.87 11:0.64 12:0.95 17:0.26 18:0.66 21:0.40 26:0.99 27:0.20 29:0.62 30:0.58 34:0.69 36:0.51 37:0.66 39:0.97 43:0.63 45:0.77 56:0.97 58:0.98 66:0.80 69:0.52 70:0.44 74:0.38 80:0.99 81:0.38 82:0.98 83:0.54 86:0.68 88:0.99 91:0.14 98:0.78 99:0.83 101:0.69 102:0.95 108:0.40 120:0.56 122:0.90 123:0.38 126:0.31 127:0.27 129:0.05 131:0.61 135:0.42 139:0.66 140:0.45 141:0.99 144:0.76 145:0.41 146:0.60 147:0.66 149:0.41 150:0.36 151:0.49 153:0.48 154:0.53 155:0.49 157:0.78 159:0.22 162:0.86 164:0.55 165:0.63 166:0.75 170:0.79 172:0.45 173:0.69 174:0.83 175:0.75 177:0.88 179:0.75 182:0.77 187:0.68 190:0.98 192:0.45 193:0.97 195:0.58 197:0.88 199:0.94 201:0.55 202:0.36 204:0.79 208:0.49 212:0.55 216:0.60 220:0.93 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 231:0.64 234:0.98 235:0.52 236:0.92 239:0.90 241:0.52 242:0.40 243:0.82 244:0.83 246:0.89 247:0.55 248:0.48 253:0.34 256:0.45 257:0.65 259:0.21 260:0.56 264:0.93 265:0.79 266:0.27 267:0.65 268:0.46 271:0.24 274:0.97 275:0.93 276:0.33 277:0.69 285:0.45 287:0.61 294:0.97 300:0.33 +1 1:0.58 10:0.95 11:0.64 12:0.95 17:0.78 18:0.78 21:0.30 26:0.96 27:0.43 29:0.62 30:0.38 34:0.83 36:0.30 37:0.29 39:0.97 43:0.62 45:0.92 56:0.97 58:0.93 66:0.62 69:0.48 70:0.93 74:0.36 80:0.99 81:0.39 82:0.98 83:0.54 86:0.78 88:0.99 91:0.18 98:0.72 99:0.83 101:0.69 102:0.95 108:0.37 122:0.99 123:0.36 124:0.65 126:0.31 127:0.87 129:0.05 131:0.61 133:0.72 135:0.18 139:0.74 141:0.91 144:0.76 145:0.89 146:0.90 147:0.92 149:0.75 150:0.37 154:0.51 155:0.51 157:0.83 159:0.74 165:0.63 166:0.75 170:0.79 172:0.82 173:0.34 174:0.94 177:0.96 178:0.55 179:0.37 182:0.77 187:0.39 192:0.46 193:0.97 195:0.87 197:0.96 199:0.95 201:0.56 204:0.81 208:0.49 212:0.59 216:0.46 222:0.20 223:0.96 224:0.93 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 236:0.92 239:0.95 241:0.15 242:0.16 243:0.68 244:0.83 245:0.82 246:0.89 247:0.97 253:0.32 259:0.21 264:0.93 265:0.73 266:0.84 267:0.52 271:0.24 274:0.91 275:0.98 276:0.79 287:0.61 294:0.99 300:0.84 +0 1:0.58 10:0.91 11:0.64 12:0.95 17:0.36 18:0.66 21:0.40 26:0.99 27:0.20 29:0.62 30:0.38 34:0.70 36:0.50 37:0.66 39:0.97 43:0.63 45:0.77 56:0.97 58:0.98 66:0.83 69:0.52 70:0.63 74:0.40 80:0.99 81:0.38 82:0.98 83:0.54 86:0.68 88:0.99 91:0.14 98:0.88 99:0.83 101:0.69 102:0.95 108:0.40 120:0.56 122:0.90 123:0.38 126:0.31 127:0.41 129:0.05 131:0.61 135:0.42 139:0.66 140:0.45 141:0.99 144:0.76 145:0.41 146:0.68 147:0.73 149:0.42 150:0.36 151:0.49 153:0.48 154:0.52 155:0.49 157:0.78 159:0.27 162:0.86 164:0.55 165:0.63 166:0.75 170:0.79 172:0.51 173:0.69 174:0.83 177:0.96 179:0.77 182:0.77 187:0.68 190:0.98 192:0.45 193:0.97 195:0.58 197:0.87 199:0.94 201:0.55 202:0.36 204:0.79 208:0.49 212:0.42 216:0.60 220:0.93 222:0.20 223:0.99 224:0.99 227:0.61 229:0.61 231:0.64 234:0.98 235:0.52 236:0.92 239:0.91 241:0.52 242:0.45 243:0.84 244:0.61 246:0.89 247:0.60 248:0.48 253:0.35 254:0.97 256:0.45 259:0.21 260:0.56 264:0.93 265:0.88 266:0.27 267:0.65 268:0.46 271:0.24 274:0.97 275:0.93 276:0.41 285:0.45 287:0.61 294:0.97 300:0.43 +0 1:0.66 10:0.99 11:0.64 12:0.95 17:0.78 18:0.72 21:0.22 26:0.98 27:0.15 29:0.62 30:0.60 32:0.67 34:0.89 36:0.61 37:0.64 39:0.97 43:0.57 45:0.97 56:0.97 58:0.93 66:0.43 69:0.19 70:0.58 74:0.52 75:0.95 80:0.99 81:0.58 82:0.99 83:0.54 86:0.77 88:0.98 91:0.18 98:0.69 99:0.95 101:0.69 102:0.96 104:0.89 106:0.81 108:0.15 122:0.99 123:0.15 124:0.60 126:0.43 127:0.71 129:0.59 131:0.61 133:0.47 135:0.20 139:0.72 141:0.91 144:0.76 145:0.50 146:0.76 147:0.80 149:0.83 150:0.50 154:0.58 155:0.51 157:0.86 159:0.55 165:0.63 166:0.75 170:0.79 172:0.89 173:0.23 174:0.99 177:0.96 178:0.55 179:0.19 182:0.77 187:0.39 192:0.49 193:0.97 195:0.71 197:1.00 199:0.97 201:0.65 204:0.79 206:0.81 208:0.49 212:0.93 215:0.79 216:0.38 222:0.20 223:0.98 224:0.93 226:0.95 227:0.61 229:0.61 231:0.64 234:0.91 235:0.60 236:0.94 239:0.99 242:0.31 243:0.57 244:0.61 245:0.98 246:0.90 247:0.97 251:1.00 253:0.42 254:0.94 259:0.21 264:0.93 265:0.70 266:0.54 267:0.69 271:0.24 274:0.91 275:0.99 276:0.91 283:0.82 287:0.61 294:1.00 297:0.36 300:0.70 +3 6:0.90 8:0.97 12:0.37 20:0.79 21:0.78 27:0.03 28:0.96 29:0.62 34:0.75 36:0.98 37:0.93 39:0.98 71:0.70 78:0.82 91:1.00 96:0.76 100:0.86 111:0.82 135:0.26 140:1.00 145:0.76 151:0.53 152:0.97 153:0.72 161:0.79 162:0.45 167:0.86 169:0.96 175:0.97 178:0.55 181:0.59 186:0.82 190:0.89 191:0.99 202:0.99 216:0.76 220:0.74 222:0.21 235:0.97 241:0.98 244:0.93 248:0.55 253:0.53 256:0.75 257:0.93 259:0.21 261:0.98 267:0.91 268:0.69 271:0.48 277:0.95 285:0.47 +4 1:0.74 6:0.90 7:0.66 8:0.92 9:0.80 11:0.57 12:0.37 18:0.72 20:0.84 21:0.13 27:0.03 28:0.96 29:0.62 30:0.95 31:0.99 34:0.88 36:0.79 37:0.93 39:0.98 41:0.96 43:0.83 48:0.91 64:0.77 66:0.75 69:0.65 70:0.13 71:0.70 74:0.54 78:0.97 79:0.99 81:0.65 83:0.91 85:0.80 86:0.83 91:0.79 96:0.96 98:0.21 100:0.98 101:0.87 104:0.93 108:0.50 111:0.98 114:0.79 120:0.92 122:0.57 123:0.48 126:0.54 127:0.10 129:0.05 135:0.63 137:0.99 139:0.81 140:0.45 146:0.23 147:0.29 149:0.76 150:0.79 151:0.97 152:0.97 153:0.89 154:0.79 155:0.67 159:0.11 161:0.79 162:0.69 164:0.86 165:0.93 167:0.79 169:0.96 172:0.32 173:0.79 176:0.73 177:0.70 179:0.15 181:0.59 186:0.97 187:0.68 189:0.98 191:0.97 192:0.87 196:0.98 201:0.93 202:0.80 208:0.64 212:0.84 215:0.61 216:0.76 222:0.21 230:0.69 233:0.76 235:0.31 238:0.97 241:0.77 242:0.63 243:0.77 247:0.35 248:0.96 253:0.94 255:0.95 256:0.83 259:0.21 260:0.92 261:0.98 265:0.23 266:0.17 267:0.59 268:0.83 271:0.48 276:0.23 281:0.91 284:0.98 285:0.62 290:0.79 297:0.36 300:0.13 +1 1:0.74 11:0.57 12:0.37 17:0.61 18:0.71 21:0.68 27:0.45 28:0.96 29:0.62 30:0.95 32:0.80 34:0.85 36:0.20 37:0.50 39:0.98 43:0.81 44:0.93 64:0.77 66:0.75 69:0.71 70:0.13 71:0.70 74:0.63 77:0.70 81:0.44 83:0.91 85:0.80 86:0.82 91:0.23 98:0.16 101:0.87 108:0.54 114:0.56 122:0.57 123:0.52 126:0.54 127:0.09 129:0.05 135:0.78 139:0.85 145:0.49 146:0.22 147:0.28 149:0.75 150:0.69 154:0.76 155:0.67 159:0.11 161:0.79 165:0.93 167:0.50 172:0.32 173:0.72 176:0.73 177:0.70 178:0.55 179:0.10 181:0.59 187:0.68 192:0.87 195:0.72 201:0.93 208:0.64 212:0.84 215:0.37 216:0.77 222:0.21 235:0.88 241:0.18 242:0.63 243:0.77 247:0.35 253:0.41 259:0.21 265:0.17 266:0.17 267:0.36 271:0.48 276:0.24 282:0.88 290:0.56 297:0.36 299:0.88 300:0.13 +3 1:0.74 8:0.68 9:0.80 11:0.57 12:0.37 17:0.64 18:0.96 20:0.80 21:0.68 28:0.96 29:0.62 30:0.91 31:0.91 32:0.80 34:0.61 36:0.29 37:0.97 39:0.98 41:0.82 43:0.80 44:0.93 60:0.87 64:0.77 66:0.26 69:0.73 70:0.30 71:0.70 74:0.96 77:0.70 78:0.84 79:0.90 81:0.44 83:0.91 85:0.99 86:0.99 91:0.22 96:0.75 98:0.57 100:0.73 101:0.87 104:0.80 108:0.57 111:0.82 114:0.56 120:0.63 122:0.57 123:0.93 124:0.81 126:0.54 127:0.75 129:0.93 133:0.84 135:0.54 137:0.91 139:0.99 140:0.80 145:0.65 146:0.81 147:0.84 149:0.99 150:0.69 151:0.72 152:0.77 153:0.48 154:0.74 155:0.67 158:0.91 159:0.89 161:0.79 162:0.53 164:0.57 165:0.93 167:0.46 169:0.72 172:0.90 173:0.44 176:0.73 177:0.70 178:0.42 179:0.17 181:0.59 186:0.84 187:0.21 192:0.87 195:0.97 196:0.96 201:0.93 202:0.58 208:0.64 212:0.49 215:0.37 216:0.98 219:0.95 220:0.62 222:0.21 235:0.88 241:0.01 242:0.45 243:0.50 245:0.96 247:0.55 248:0.67 253:0.79 255:0.95 256:0.45 259:0.21 260:0.64 261:0.79 265:0.39 266:0.75 267:0.92 268:0.46 271:0.48 276:0.91 279:0.86 282:0.88 283:0.78 284:0.89 285:0.62 290:0.56 292:0.91 297:0.36 299:0.88 300:0.94 +3 1:0.74 6:0.87 7:0.81 8:0.69 9:0.80 11:0.57 12:0.37 17:0.06 18:0.95 20:0.93 21:0.13 22:0.93 27:0.18 28:0.96 29:0.62 30:0.91 31:0.92 32:0.80 34:0.87 36:0.36 37:0.46 39:0.98 41:0.88 43:0.93 44:0.93 47:0.93 60:0.98 64:0.77 66:0.48 69:0.32 70:0.33 71:0.70 74:0.95 77:0.89 78:0.87 79:0.91 81:0.65 83:0.91 85:0.99 86:0.99 91:0.17 96:0.77 98:0.57 100:0.88 101:0.87 104:0.77 106:0.81 108:0.76 111:0.86 114:0.79 117:0.86 120:0.65 121:1.00 122:0.57 123:0.74 124:0.76 126:0.54 127:0.64 129:0.89 133:0.78 135:0.94 137:0.92 139:0.99 140:0.45 145:0.80 146:0.57 147:0.63 149:0.99 150:0.79 151:0.72 152:0.87 153:0.48 154:0.93 155:0.67 158:0.92 159:0.77 161:0.79 162:0.74 164:0.67 165:0.93 167:0.47 169:0.86 172:0.89 173:0.17 176:0.73 177:0.70 179:0.20 181:0.59 186:0.87 187:0.87 189:0.88 191:0.84 192:0.87 195:0.91 196:0.96 201:0.93 202:0.56 204:0.89 206:0.81 208:0.64 212:0.77 215:0.61 216:0.75 219:0.95 220:0.62 222:0.21 230:0.87 233:0.76 235:0.31 238:0.88 241:0.03 242:0.44 243:0.19 245:0.94 247:0.74 248:0.70 253:0.82 255:0.95 256:0.58 259:0.21 260:0.66 261:0.88 262:0.93 265:0.58 266:0.71 267:0.65 268:0.59 271:0.48 276:0.89 279:0.86 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 290:0.79 292:0.95 297:0.36 299:0.97 300:0.94 +3 1:0.74 11:0.57 12:0.37 17:0.55 18:0.96 21:0.68 27:0.48 28:0.96 29:0.62 30:0.91 32:0.80 34:0.45 36:0.28 37:0.55 39:0.98 43:0.80 44:0.93 60:0.99 64:0.77 66:0.26 69:0.73 70:0.30 71:0.70 74:0.96 77:0.70 81:0.44 83:0.91 85:0.99 86:0.99 91:0.09 97:0.89 98:0.57 101:0.87 108:0.57 114:0.56 122:0.57 123:0.93 124:0.81 126:0.54 127:0.75 129:0.93 133:0.84 135:0.44 139:0.99 145:0.65 146:0.81 147:0.84 149:0.99 150:0.69 154:0.74 155:0.67 158:0.92 159:0.89 161:0.79 165:0.93 167:0.55 172:0.90 173:0.44 176:0.73 177:0.70 178:0.55 179:0.17 181:0.59 187:0.39 192:0.87 195:0.97 201:0.93 202:0.36 208:0.64 212:0.49 215:0.37 216:0.92 219:0.95 222:0.21 235:0.88 241:0.41 242:0.45 243:0.50 245:0.96 247:0.55 253:0.48 259:0.21 265:0.39 266:0.75 267:0.76 271:0.48 276:0.91 279:0.86 282:0.88 283:0.82 290:0.56 292:0.95 297:0.36 299:0.88 300:0.94 +0 1:0.74 11:0.57 12:0.37 17:0.45 18:0.72 21:0.13 27:0.45 28:0.96 29:0.62 30:0.91 32:0.80 34:0.61 36:0.17 37:0.50 39:0.98 43:0.82 44:0.93 64:0.77 66:0.64 69:0.68 70:0.19 71:0.70 74:0.63 77:0.70 81:0.65 83:0.91 85:0.80 86:0.83 91:0.14 98:0.15 101:0.87 108:0.52 114:0.79 122:0.57 123:0.50 124:0.42 126:0.54 127:0.33 129:0.05 133:0.37 135:0.58 139:0.85 145:0.41 146:0.22 147:0.27 149:0.75 150:0.79 154:0.77 155:0.67 159:0.41 161:0.79 165:0.93 167:0.54 172:0.32 173:0.70 176:0.73 177:0.70 178:0.55 179:0.87 181:0.59 187:0.68 192:0.87 195:0.69 201:0.93 208:0.64 212:0.73 215:0.61 216:0.77 222:0.21 235:0.31 241:0.37 242:0.55 243:0.69 245:0.43 247:0.35 253:0.56 259:0.21 265:0.16 266:0.23 267:0.28 271:0.48 276:0.13 282:0.88 290:0.79 297:0.36 299:0.88 300:0.18 +3 1:0.74 6:0.94 7:0.81 8:0.85 9:0.80 11:0.57 12:0.37 17:0.32 18:0.90 20:0.96 21:0.30 22:0.93 27:0.21 28:0.96 29:0.62 30:0.73 31:0.98 34:0.50 36:0.64 37:0.74 39:0.98 41:0.95 43:0.97 47:0.93 48:0.91 60:0.95 64:0.77 66:0.17 69:0.15 70:0.63 71:0.70 74:0.83 78:0.91 79:0.98 81:0.54 83:0.91 85:0.94 86:0.96 91:0.31 96:0.93 98:0.22 100:0.97 101:0.87 104:0.84 106:0.81 108:0.12 111:0.94 114:0.65 117:0.86 120:0.87 121:0.90 122:0.57 123:0.12 124:0.95 126:0.54 127:0.77 129:0.96 133:0.94 135:0.24 137:0.98 139:0.97 140:0.45 146:0.60 147:0.65 149:0.92 150:0.75 151:0.97 152:0.92 153:0.90 154:0.97 155:0.67 158:0.92 159:0.91 161:0.79 162:0.63 164:0.83 165:0.93 167:0.54 169:0.95 172:0.54 173:0.07 176:0.73 177:0.70 179:0.40 181:0.59 186:0.91 187:0.39 189:0.95 191:0.75 192:0.87 196:0.99 201:0.93 202:0.78 204:0.89 206:0.81 208:0.64 212:0.22 215:0.44 216:0.95 219:0.95 222:0.21 230:0.87 233:0.76 235:0.52 238:0.96 241:0.37 242:0.32 243:0.38 245:0.73 247:0.74 248:0.92 253:0.94 255:0.95 256:0.79 259:0.21 260:0.88 261:0.94 262:0.93 265:0.24 266:0.92 267:0.52 268:0.80 271:0.48 276:0.75 279:0.86 281:0.91 283:0.82 284:0.97 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:1.00 7:0.66 8:0.93 9:0.80 11:0.57 12:0.37 17:0.34 18:0.96 20:0.76 21:0.13 27:0.02 28:0.96 29:0.62 30:0.93 31:0.91 32:0.80 34:0.47 36:0.20 37:0.92 39:0.98 41:0.82 43:0.89 44:0.93 48:0.91 60:0.87 64:0.77 66:0.97 69:0.50 70:0.20 71:0.70 74:0.96 77:0.70 78:0.95 79:0.90 81:0.65 83:0.91 85:0.98 86:0.99 91:0.42 96:0.75 98:0.69 100:0.98 101:0.87 108:0.39 111:0.97 114:0.79 120:0.63 122:0.57 123:0.37 126:0.54 127:0.21 129:0.05 135:0.85 137:0.91 139:0.99 140:0.87 145:0.41 146:0.90 147:0.92 149:0.99 150:0.79 151:0.72 152:0.82 153:0.48 154:0.87 155:0.67 158:0.91 159:0.50 161:0.79 162:0.50 164:0.57 165:0.93 167:0.60 169:0.89 172:0.93 173:0.33 176:0.73 177:0.70 178:0.48 179:0.13 181:0.59 186:0.94 187:0.39 191:0.77 192:0.87 195:0.88 196:0.96 201:0.93 202:0.61 208:0.64 212:0.87 215:0.61 216:0.99 219:0.95 220:0.62 222:0.21 230:0.69 233:0.76 235:0.31 241:0.55 242:0.50 243:0.97 247:0.35 248:0.67 253:0.81 255:0.95 256:0.45 259:0.21 260:0.64 261:0.85 265:0.69 266:0.27 267:0.89 268:0.46 271:0.48 276:0.87 279:0.86 281:0.91 282:0.88 283:0.78 284:0.89 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.43 +1 1:0.74 6:1.00 7:0.66 8:0.96 9:0.80 11:0.57 12:0.37 17:0.13 18:0.89 20:0.87 21:0.22 27:0.02 28:0.96 29:0.62 30:0.91 31:0.95 34:0.49 36:0.30 37:0.92 39:0.98 41:0.88 43:0.82 44:0.87 48:0.91 60:0.87 64:0.77 66:0.79 69:0.68 70:0.22 71:0.70 74:0.85 78:0.82 79:0.94 81:0.59 83:0.91 85:0.98 86:0.96 91:0.33 96:0.83 98:0.63 100:0.88 101:0.87 108:0.86 111:0.83 114:0.71 120:0.73 122:0.57 123:0.86 124:0.41 126:0.54 127:0.54 129:0.81 133:0.67 135:0.92 137:0.95 139:0.97 140:0.45 145:0.44 146:0.17 147:0.22 149:0.95 150:0.77 151:0.87 152:0.82 153:0.48 154:0.78 155:0.67 158:0.91 159:0.75 161:0.79 162:0.55 164:0.69 165:0.93 167:0.67 169:0.88 172:0.91 173:0.53 176:0.73 177:0.70 179:0.25 181:0.59 186:0.83 187:0.39 189:1.00 191:0.73 192:0.87 195:0.89 196:0.97 201:0.93 202:0.65 208:0.64 212:0.75 215:0.51 216:0.99 219:0.95 220:0.74 222:0.21 230:0.69 233:0.76 235:0.42 238:0.94 241:0.66 242:0.45 243:0.11 245:0.84 247:0.55 248:0.80 253:0.86 255:0.95 256:0.58 259:0.21 260:0.74 261:0.85 265:0.63 266:0.75 267:0.65 268:0.62 271:0.48 276:0.81 279:0.86 281:0.91 283:0.78 284:0.93 285:0.62 290:0.71 292:0.91 297:0.36 300:0.43 +2 1:0.74 9:0.80 11:0.57 12:0.37 17:0.04 18:0.77 21:0.05 27:0.33 28:0.96 29:0.62 30:0.94 31:0.94 34:0.50 36:0.23 37:0.41 39:0.98 41:0.82 43:0.86 60:0.97 64:0.77 66:0.33 69:0.57 70:0.19 71:0.70 74:0.70 79:0.93 81:0.72 83:0.91 85:0.88 86:0.88 91:0.40 96:0.80 98:0.21 101:0.87 108:0.68 114:0.89 120:0.69 122:0.57 123:0.65 124:0.72 126:0.54 127:0.39 129:0.81 133:0.67 135:0.99 137:0.94 139:0.89 140:0.45 145:0.93 146:0.17 147:0.22 149:0.84 150:0.83 151:0.87 153:0.48 154:0.84 155:0.67 158:0.92 159:0.60 161:0.79 162:0.86 163:0.90 164:0.57 165:0.93 167:0.48 172:0.32 173:0.47 176:0.73 177:0.70 179:0.74 181:0.59 187:0.39 192:0.87 196:0.95 201:0.93 202:0.36 208:0.64 212:0.23 215:0.78 216:0.88 219:0.95 222:0.21 235:0.22 241:0.07 242:0.55 243:0.33 245:0.58 247:0.35 248:0.77 253:0.82 255:0.95 256:0.45 259:0.21 260:0.70 265:0.23 266:0.71 267:0.82 268:0.46 271:0.48 276:0.33 279:0.86 283:0.82 284:0.89 285:0.62 290:0.89 292:0.95 297:0.36 300:0.25 +1 1:0.74 6:0.89 7:0.81 8:0.69 9:0.80 11:0.57 12:0.37 17:0.20 18:0.78 20:0.93 21:0.54 22:0.93 27:0.07 28:0.96 29:0.62 30:0.89 31:0.97 32:0.80 34:0.97 36:0.21 37:0.63 39:0.98 41:0.96 43:0.77 44:0.93 47:0.93 60:1.00 64:0.77 66:0.20 69:0.80 70:0.29 71:0.70 74:0.77 77:0.70 78:0.82 79:0.96 81:0.48 83:0.91 85:0.85 86:0.89 91:0.53 96:0.88 98:0.14 100:0.86 101:0.87 104:0.79 106:0.81 108:0.63 111:0.82 114:0.59 120:0.80 121:0.90 122:0.57 123:0.61 124:0.74 126:0.54 127:0.28 129:0.81 133:0.67 135:0.97 137:0.97 139:0.94 140:0.80 145:0.50 146:0.22 147:0.28 149:0.74 150:0.72 151:0.87 152:0.86 153:0.48 154:0.70 155:0.67 158:0.92 159:0.44 161:0.79 162:0.61 163:0.97 164:0.84 165:0.93 167:0.61 169:0.84 172:0.21 173:0.79 176:0.73 177:0.70 178:0.42 179:0.69 181:0.59 186:0.82 187:0.68 189:0.91 192:0.87 195:0.78 196:0.98 201:0.93 202:0.79 204:0.89 206:0.81 208:0.64 212:0.42 215:0.39 216:0.95 219:0.95 220:0.82 222:0.21 230:0.86 233:0.73 235:0.80 238:0.90 241:0.57 242:0.45 243:0.40 245:0.56 247:0.39 248:0.87 253:0.89 255:0.95 256:0.80 259:0.21 260:0.81 261:0.85 262:0.93 265:0.15 266:0.27 267:0.52 268:0.79 271:0.48 276:0.33 279:0.86 281:0.89 282:0.88 283:0.82 284:0.97 285:0.62 290:0.59 292:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.90 7:0.66 8:0.93 9:0.80 12:0.37 20:0.81 21:0.22 27:0.03 28:0.96 29:0.62 31:0.96 34:0.80 36:0.88 37:0.93 39:0.98 41:0.94 48:0.91 64:0.77 71:0.70 78:0.93 79:0.96 81:0.59 83:0.91 91:0.76 96:0.87 100:0.97 104:0.91 111:0.95 114:0.71 120:0.78 126:0.54 135:0.71 137:0.96 139:0.64 140:0.45 145:0.59 150:0.77 151:0.87 152:0.97 153:0.48 155:0.67 161:0.79 162:0.72 164:0.79 165:0.93 167:0.76 169:0.96 175:0.97 176:0.73 181:0.59 186:0.93 187:0.87 189:0.97 191:0.96 192:0.87 196:0.89 201:0.93 202:0.71 208:0.64 215:0.51 216:0.76 220:0.82 222:0.21 230:0.69 233:0.76 235:0.42 238:0.97 241:0.75 244:0.93 248:0.85 253:0.82 255:0.95 256:0.73 257:0.93 259:0.21 260:0.79 261:0.98 267:0.59 268:0.74 271:0.48 277:0.95 281:0.91 284:0.96 285:0.62 290:0.71 297:0.36 +1 1:0.74 11:0.57 12:0.37 17:0.78 18:0.92 27:0.39 28:0.96 29:0.62 30:0.90 34:0.45 36:0.78 37:0.27 39:0.98 43:0.85 60:0.95 64:0.77 66:0.59 69:0.61 70:0.28 71:0.70 74:0.86 81:0.79 83:0.91 85:0.94 86:0.97 91:0.03 98:0.53 101:0.87 108:0.47 114:0.98 122:0.57 123:0.45 124:0.63 126:0.54 127:0.42 129:0.81 133:0.67 135:0.97 139:0.97 146:0.57 147:0.63 149:0.96 150:0.86 154:0.82 155:0.67 158:0.92 159:0.43 161:0.79 165:0.93 167:0.50 172:0.71 173:0.64 176:0.73 177:0.70 178:0.55 179:0.42 181:0.59 187:0.39 192:0.87 201:0.93 208:0.64 212:0.84 215:0.96 216:0.42 219:0.95 222:0.21 235:0.12 241:0.18 242:0.45 243:0.67 245:0.79 247:0.55 253:0.68 259:0.21 265:0.55 266:0.54 267:0.69 271:0.48 276:0.66 279:0.86 283:0.82 290:0.98 292:0.95 297:0.36 300:0.55 +0 8:0.80 10:0.82 12:0.37 17:0.88 18:0.68 21:0.78 27:0.63 29:0.98 30:0.94 34:0.50 36:0.61 37:0.46 39:0.89 43:0.20 46:0.88 55:0.84 66:0.80 69:0.93 70:0.19 71:0.57 74:0.29 86:0.57 91:0.20 98:0.71 107:0.93 108:0.85 110:0.92 122:0.83 123:0.84 124:0.39 125:0.88 127:0.54 129:0.05 131:0.61 133:0.62 135:0.30 139:0.56 145:0.59 146:0.94 147:0.05 149:0.37 154:0.13 157:0.61 159:0.80 160:0.88 163:0.94 166:0.61 170:0.97 172:0.77 173:0.86 174:0.79 175:0.91 177:0.05 178:0.55 179:0.48 182:0.61 187:0.39 197:0.82 212:0.51 216:0.42 222:0.48 227:0.61 229:0.61 231:0.75 235:0.52 239:0.82 241:0.07 242:0.58 243:0.09 244:0.93 245:0.60 246:0.61 247:0.60 253:0.26 257:0.93 259:0.21 265:0.71 266:0.63 267:0.19 271:0.46 276:0.66 277:0.87 287:0.61 289:0.90 300:0.18 +1 10:0.96 12:0.37 17:0.95 21:0.78 27:0.36 29:0.98 30:0.14 34:0.59 36:0.37 37:0.82 39:0.89 43:0.10 46:0.88 48:0.91 55:0.84 66:0.11 69:0.99 70:0.82 71:0.57 74:0.25 91:0.05 98:0.30 107:0.89 108:0.98 110:0.89 122:0.95 123:0.98 124:0.98 125:0.88 127:0.95 129:0.05 131:0.61 133:0.98 135:0.44 139:0.56 145:0.50 146:0.89 147:0.05 149:0.30 154:0.08 157:0.61 159:0.95 160:0.88 163:0.86 166:0.61 170:0.97 172:0.63 173:0.99 174:0.99 175:0.75 177:0.05 178:0.55 179:0.24 182:0.61 187:0.68 197:0.99 202:0.50 212:0.13 216:0.13 222:0.48 227:0.61 229:0.61 231:0.62 235:0.42 239:0.95 241:0.46 242:0.76 243:0.06 244:0.93 245:0.80 246:0.61 247:0.74 253:0.23 257:0.93 259:0.21 265:0.33 266:0.98 267:0.36 271:0.46 276:0.89 277:0.69 281:0.91 287:0.61 289:0.88 300:0.70 +0 7:0.64 8:0.60 10:0.82 11:0.45 12:0.37 17:0.98 18:0.97 21:0.78 27:0.63 29:0.98 30:0.86 34:0.59 36:0.71 37:0.28 39:0.89 43:0.10 45:0.77 46:0.88 55:0.59 66:0.29 69:0.89 70:0.41 71:0.57 74:0.31 83:0.56 86:0.62 91:0.15 98:0.70 101:0.47 107:0.96 108:0.78 110:0.97 122:0.92 123:0.42 124:0.58 125:0.88 127:0.61 129:0.05 131:0.61 133:0.45 135:0.21 139:0.61 146:0.76 147:0.83 149:0.23 154:0.36 155:0.50 157:0.61 159:0.58 160:0.88 166:0.61 170:0.97 172:0.73 173:0.92 174:0.79 177:0.70 178:0.55 179:0.39 182:0.61 187:0.21 192:0.49 197:0.80 204:0.80 208:0.50 212:0.61 216:0.38 222:0.48 227:0.61 229:0.61 230:0.65 231:0.91 233:0.76 235:0.22 239:0.81 241:0.03 242:0.70 243:0.47 244:0.83 245:0.90 246:0.61 247:0.55 253:0.28 254:0.88 257:0.65 259:0.21 265:0.65 266:0.63 267:0.44 271:0.46 276:0.76 277:0.69 287:0.61 289:0.95 300:0.55 +0 8:0.65 10:0.82 12:0.37 17:1.00 18:0.61 21:0.78 27:0.71 29:0.98 30:0.33 34:0.33 36:0.76 37:0.27 39:0.89 43:0.07 46:0.88 55:1.00 66:0.12 69:0.87 70:0.69 71:0.57 74:0.25 86:0.57 91:0.01 98:0.08 107:0.88 108:0.99 110:0.88 122:0.95 123:0.99 124:0.91 125:0.88 127:0.85 129:0.96 131:0.61 133:0.90 135:0.75 139:0.55 146:0.05 147:0.05 149:0.07 154:0.07 157:0.61 159:0.99 160:0.88 163:0.90 166:0.61 170:0.97 172:0.10 173:0.31 174:0.89 175:0.97 177:0.05 178:0.55 179:0.92 182:0.61 187:0.87 197:0.82 202:0.53 212:0.19 216:0.17 222:0.48 227:0.61 229:0.61 231:0.61 235:0.42 239:0.81 241:0.02 242:0.63 243:0.19 244:0.97 245:0.42 246:0.61 247:0.39 253:0.22 257:0.93 259:0.21 265:0.08 266:0.47 267:0.98 271:0.46 276:0.33 277:0.95 287:0.61 289:0.88 300:0.43 +0 8:0.81 10:0.81 11:0.45 12:0.37 17:1.00 18:0.57 21:0.78 27:1.00 29:0.98 30:0.45 34:0.96 36:0.47 39:0.89 43:0.07 45:0.66 46:0.88 55:1.00 66:0.13 69:0.99 70:0.50 71:0.57 74:0.25 86:0.56 91:0.03 98:0.05 101:0.47 107:0.88 108:0.98 110:0.89 122:0.55 123:0.98 124:0.89 125:0.88 127:0.43 129:0.05 131:0.61 133:0.87 135:0.89 139:0.55 146:0.13 147:0.05 149:0.07 154:0.06 157:0.61 159:0.97 160:0.88 163:0.90 166:0.61 170:0.97 172:0.10 173:0.90 174:0.83 175:0.91 177:0.05 178:0.55 179:0.90 182:0.61 187:0.39 197:0.81 202:0.36 212:0.23 216:0.05 222:0.48 227:0.61 229:0.61 231:0.62 235:0.05 239:0.81 241:0.01 242:0.24 243:0.21 244:0.93 245:0.40 246:0.61 247:0.47 253:0.22 257:0.93 259:0.21 265:0.05 266:0.38 267:0.99 271:0.46 276:0.30 277:0.87 287:0.61 289:0.88 300:0.33 +1 8:0.81 10:0.85 11:0.45 12:0.37 17:0.88 18:0.64 21:0.78 27:0.54 29:0.98 30:0.45 34:0.68 36:0.92 37:0.49 39:0.89 43:0.10 45:0.66 46:0.88 55:0.92 66:0.09 69:0.76 70:0.72 71:0.57 74:0.25 86:0.57 91:0.01 98:0.29 101:0.47 107:0.92 108:0.96 110:0.94 123:0.95 124:0.97 125:0.88 127:0.84 129:0.59 131:0.61 133:0.97 135:0.71 139:0.56 146:0.45 147:0.05 149:0.21 154:0.08 157:0.61 159:0.90 160:0.88 163:0.94 166:0.61 170:0.97 172:0.32 173:0.47 174:0.89 175:0.75 177:0.05 178:0.55 179:0.47 182:0.61 187:0.21 191:0.90 197:0.86 212:0.13 216:0.22 222:0.48 227:0.61 229:0.61 231:0.79 239:0.84 241:0.10 242:0.57 243:0.08 244:0.83 245:0.64 246:0.61 247:0.84 253:0.23 254:0.98 257:0.93 259:0.21 265:0.31 266:0.96 267:0.82 271:0.46 276:0.73 277:0.87 287:0.61 289:0.91 300:0.70 +0 7:0.64 8:0.66 10:0.83 11:0.45 12:0.37 17:0.87 18:0.72 21:0.78 27:0.72 29:0.98 30:0.76 34:0.45 36:0.21 39:0.89 43:0.10 44:0.85 45:0.66 46:0.88 48:0.91 55:0.84 66:0.72 69:0.27 70:0.22 71:0.57 74:0.30 76:0.94 83:0.56 86:0.58 91:0.86 98:0.62 101:0.47 107:0.91 108:0.21 110:0.93 122:0.65 123:0.20 125:0.88 127:0.18 129:0.05 131:0.61 135:0.44 139:0.57 145:0.50 146:0.49 147:0.55 149:0.08 154:0.45 155:0.49 157:0.61 159:0.18 160:0.88 166:0.61 170:0.97 172:0.27 173:0.44 174:0.79 177:0.88 178:0.55 179:0.80 182:0.61 192:0.48 193:0.96 195:0.60 197:0.84 204:0.79 208:0.50 212:0.42 216:0.02 222:0.48 227:0.61 229:0.61 230:0.65 231:0.79 233:0.76 235:0.88 239:0.85 241:0.84 242:0.45 243:0.75 244:0.83 246:0.61 247:0.35 253:0.27 254:0.97 259:0.21 265:0.63 266:0.23 267:0.36 271:0.46 276:0.24 281:0.91 287:0.61 289:0.91 300:0.18 +0 8:0.80 10:0.82 12:0.37 17:0.88 18:0.68 21:0.78 27:0.63 29:0.98 30:0.90 34:0.56 36:0.52 37:0.46 39:0.89 43:0.21 46:0.88 55:0.84 66:0.82 69:0.93 70:0.19 71:0.57 74:0.30 86:0.57 91:0.20 98:0.70 107:0.93 108:0.85 110:0.93 122:0.82 123:0.84 124:0.39 125:0.88 127:0.52 129:0.05 131:0.61 133:0.62 135:0.28 139:0.56 145:0.59 146:0.94 147:0.05 149:0.47 154:0.14 157:0.61 159:0.80 160:0.88 163:0.92 166:0.61 170:0.97 172:0.80 173:0.86 174:0.79 175:0.91 177:0.05 178:0.55 179:0.43 182:0.61 187:0.39 197:0.82 212:0.35 216:0.42 222:0.48 227:0.61 229:0.61 231:0.76 235:0.52 239:0.82 241:0.07 242:0.42 243:0.08 244:0.93 245:0.62 246:0.61 247:0.60 253:0.27 257:0.93 259:0.21 265:0.71 266:0.63 267:0.19 271:0.46 276:0.69 277:0.87 287:0.61 289:0.90 300:0.18 +0 9:0.73 10:0.92 11:0.64 12:0.37 17:0.85 18:0.98 21:0.68 23:0.95 27:0.72 29:0.98 30:0.45 31:0.93 34:0.64 36:0.47 39:0.89 43:0.58 44:0.85 45:0.77 46:0.94 62:0.97 64:0.77 66:0.94 69:0.27 70:0.53 71:0.57 74:0.37 75:0.96 79:0.93 81:0.39 83:0.56 86:0.88 91:0.20 98:0.83 101:0.87 107:0.94 108:0.21 110:0.96 122:0.92 123:0.20 125:0.88 126:0.24 127:0.32 128:0.97 129:0.05 131:0.92 132:0.97 135:0.69 137:0.93 139:0.83 140:0.45 144:0.57 145:0.45 146:0.77 147:0.80 149:0.53 150:0.44 151:0.85 153:0.48 154:0.55 155:0.56 157:0.86 159:0.33 160:0.88 162:0.86 166:0.88 168:0.97 170:0.97 172:0.85 173:0.36 174:0.89 177:0.88 179:0.29 182:0.76 187:0.87 190:0.95 192:0.49 195:0.62 196:0.94 197:0.92 202:0.36 205:0.95 208:0.50 212:0.84 216:0.12 219:0.86 222:0.48 226:0.98 227:0.87 229:0.61 231:0.89 235:0.88 239:0.92 241:0.01 242:0.72 243:0.95 246:0.61 247:0.74 248:0.76 253:0.33 254:0.97 255:0.72 256:0.45 259:0.21 265:0.83 266:0.38 267:0.19 268:0.46 271:0.46 276:0.74 279:0.76 284:0.87 285:0.50 287:0.61 289:0.94 292:0.95 300:0.55 +1 10:0.84 11:0.45 12:0.37 17:0.62 18:0.59 21:0.78 27:1.00 29:0.98 30:0.36 34:0.30 36:0.62 37:0.27 39:0.89 43:0.28 45:0.83 46:0.88 55:0.71 66:0.30 69:0.95 70:0.92 71:0.57 74:0.30 86:0.57 91:0.20 98:0.34 101:0.47 107:0.92 108:0.94 110:0.92 122:0.83 123:0.94 124:0.91 125:0.88 127:0.64 129:0.05 131:0.61 133:0.91 135:0.93 139:0.55 146:0.65 147:0.18 149:0.35 154:0.20 157:0.61 159:0.90 160:0.88 166:0.61 170:0.97 172:0.65 173:0.85 174:0.89 175:0.75 177:0.38 178:0.55 179:0.39 182:0.61 187:0.68 197:0.86 212:0.36 216:0.37 222:0.48 227:0.61 229:0.61 231:0.73 235:0.52 239:0.84 241:0.05 242:0.59 243:0.11 244:0.93 245:0.75 246:0.61 247:0.84 253:0.27 257:0.84 259:0.21 265:0.36 266:0.96 267:0.76 271:0.46 276:0.75 277:0.87 287:0.61 289:0.90 300:0.84 +0 10:0.81 11:0.49 12:0.37 17:0.98 18:0.95 21:0.54 22:0.93 27:0.61 29:0.98 30:0.21 34:0.95 36:0.60 37:0.42 39:0.89 43:0.11 45:0.73 46:0.88 47:0.93 55:0.92 64:0.77 66:0.06 69:0.98 70:0.93 71:0.57 74:0.26 81:0.33 86:0.61 91:0.01 98:0.21 101:0.65 107:0.94 108:0.97 110:0.96 122:0.92 123:0.83 124:0.98 125:0.88 126:0.24 127:0.89 129:0.05 131:0.61 133:0.98 135:0.85 139:0.59 146:0.51 147:0.05 149:0.24 150:0.37 154:0.08 157:0.61 159:0.95 160:0.88 163:0.90 166:0.88 170:0.97 172:0.37 173:0.94 174:0.79 177:0.05 178:0.55 179:0.32 182:0.61 187:0.98 197:0.79 212:0.13 216:0.49 219:0.86 222:0.48 227:0.61 229:0.61 231:0.88 235:0.60 239:0.80 241:0.01 242:0.75 243:0.06 244:0.61 245:0.71 246:0.61 247:0.82 253:0.23 254:0.97 257:0.93 259:0.21 262:0.93 265:0.17 266:0.97 267:0.99 271:0.46 276:0.84 277:0.87 287:0.61 289:0.94 292:0.88 300:0.84 +0 10:0.81 12:0.37 17:0.66 18:0.57 21:0.78 27:0.66 29:0.98 30:0.33 34:0.64 36:0.77 37:0.49 39:0.89 43:0.05 46:0.88 55:0.99 66:0.12 69:0.97 70:0.69 71:0.57 74:0.24 86:0.56 91:0.01 98:0.06 107:0.88 108:0.96 110:0.89 123:0.96 124:0.91 125:0.88 127:0.23 129:0.96 131:0.61 133:0.90 135:0.58 139:0.55 146:0.05 147:0.05 149:0.06 154:0.05 157:0.61 159:0.95 160:0.88 163:0.90 166:0.61 170:0.97 172:0.10 173:0.69 174:0.83 175:0.97 177:0.05 178:0.55 179:0.73 182:0.61 187:0.39 197:0.80 212:0.19 216:0.21 222:0.48 227:0.61 229:0.61 231:0.62 235:0.98 239:0.80 241:0.05 242:0.19 243:0.19 244:0.97 245:0.42 246:0.61 247:0.55 253:0.20 257:0.93 259:0.21 265:0.06 266:0.47 267:0.23 271:0.46 276:0.33 277:0.95 287:0.61 289:0.88 300:0.43 +2 10:0.83 12:0.37 17:0.59 18:0.60 21:0.78 27:0.24 29:0.98 30:0.91 34:0.39 36:0.37 37:0.80 39:0.89 43:0.09 46:0.88 55:0.99 66:0.10 69:0.72 70:0.13 71:0.57 74:0.24 86:0.57 91:0.03 98:0.06 107:0.89 108:0.55 110:0.90 122:0.91 123:0.52 124:0.82 125:0.88 127:0.41 129:0.89 131:0.61 133:0.78 135:0.23 139:0.55 145:0.80 146:0.05 147:0.05 149:0.08 154:0.08 157:0.61 159:0.81 160:0.88 163:0.99 166:0.61 170:0.97 172:0.05 173:0.47 174:0.79 175:0.97 177:0.05 178:0.55 179:0.97 182:0.61 187:0.68 197:0.84 212:0.61 216:0.67 222:0.48 227:0.61 229:0.61 231:0.65 235:0.84 239:0.82 241:0.10 242:0.63 243:0.29 244:0.97 245:0.37 246:0.61 247:0.30 253:0.22 257:0.93 259:0.21 265:0.06 266:0.17 267:0.19 271:0.46 276:0.23 277:0.95 287:0.61 289:0.88 300:0.13 +0 1:0.74 7:0.81 8:0.69 11:0.64 12:0.86 17:0.36 18:0.92 21:0.13 26:0.96 27:0.40 29:0.77 30:0.27 32:0.79 34:0.31 36:0.21 37:0.74 43:0.69 44:0.93 45:0.73 48:0.91 55:0.71 58:0.93 64:0.77 66:0.19 69:0.54 70:0.91 71:0.55 74:0.74 77:0.70 81:0.68 83:0.60 86:0.90 91:0.44 97:0.89 98:0.45 99:0.83 101:0.52 104:0.95 106:0.81 108:0.41 114:0.79 122:0.86 123:0.80 124:0.80 126:0.54 127:0.61 129:0.59 133:0.77 135:1.00 139:0.93 141:0.91 145:0.67 146:0.58 147:0.64 149:0.45 150:0.79 154:0.70 155:0.67 158:0.78 159:0.67 165:0.70 172:0.51 173:0.42 176:0.73 177:0.70 178:0.55 179:0.50 187:0.87 192:0.87 195:0.82 199:0.97 201:0.93 204:0.89 206:0.81 208:0.64 212:0.48 215:0.61 216:0.36 222:0.60 223:0.96 224:0.93 230:0.86 233:0.73 234:0.91 235:0.31 241:0.07 242:0.43 243:0.45 245:0.77 247:0.86 253:0.57 254:0.74 259:0.21 265:0.41 266:0.84 267:0.36 271:0.64 274:0.91 276:0.67 279:0.82 281:0.88 282:0.87 283:0.78 290:0.79 297:0.36 300:0.70 +1 1:0.69 7:0.72 8:0.68 11:0.64 12:0.86 17:0.53 18:0.84 21:0.30 26:0.96 27:0.06 29:0.77 30:0.43 32:0.69 34:0.95 36:0.17 37:0.86 43:0.63 45:0.92 58:0.93 66:0.43 69:0.57 70:0.81 71:0.55 74:0.82 77:0.70 81:0.41 83:0.60 86:0.87 91:0.23 97:0.89 98:0.66 99:0.83 101:0.52 104:0.91 106:0.81 108:0.44 114:0.63 117:0.86 122:0.51 123:0.42 124:0.60 126:0.44 127:0.46 129:0.59 133:0.46 135:1.00 139:0.83 141:0.91 145:0.61 146:0.88 147:0.90 149:0.80 150:0.55 154:0.81 155:0.58 158:0.78 159:0.70 163:0.81 165:0.70 172:0.73 173:0.41 176:0.66 177:0.70 178:0.55 179:0.32 187:0.87 192:0.59 195:0.88 199:0.99 201:0.68 204:0.85 206:0.81 208:0.54 212:0.37 215:0.44 216:0.76 222:0.60 223:0.96 224:0.93 230:0.74 233:0.73 234:0.91 235:0.42 241:0.56 242:0.39 243:0.57 245:0.92 247:0.84 253:0.51 254:0.84 259:0.21 265:0.66 266:0.54 267:0.88 271:0.64 274:0.91 276:0.78 279:0.82 282:0.77 283:0.78 290:0.63 297:0.36 300:0.70 +2 1:0.74 8:0.91 9:0.76 11:0.64 12:0.86 17:0.85 18:0.72 21:0.30 22:0.93 26:0.96 27:0.60 29:0.77 30:0.09 34:0.18 36:0.50 37:0.59 43:0.87 45:0.83 47:0.93 55:0.71 58:0.98 64:0.77 66:0.11 69:0.56 70:0.97 71:0.55 74:0.87 81:0.55 83:0.60 86:0.80 91:0.17 96:0.75 97:0.97 98:0.31 99:0.83 101:0.52 104:0.96 106:0.81 108:0.43 114:0.65 117:0.86 120:0.63 122:0.77 123:0.84 124:0.92 126:0.54 127:0.54 129:0.81 133:0.91 135:0.98 139:0.84 140:0.45 141:0.98 146:0.61 147:0.66 149:0.76 150:0.72 151:0.65 153:0.48 154:0.85 155:0.67 158:0.91 159:0.82 162:0.86 164:0.54 165:0.70 172:0.59 173:0.31 176:0.73 177:0.70 179:0.29 187:0.95 190:0.77 192:0.87 199:0.98 201:0.93 202:0.36 206:0.81 208:0.64 212:0.59 215:0.44 216:0.20 219:0.95 220:0.62 222:0.60 223:0.96 224:1.00 234:1.00 235:0.52 241:0.01 242:0.45 243:0.36 245:0.83 247:0.76 248:0.63 253:0.72 255:0.74 256:0.45 259:0.21 260:0.63 262:0.93 265:0.31 266:0.89 267:0.23 268:0.46 271:0.64 274:0.97 276:0.82 277:0.69 279:0.86 283:0.78 285:0.56 290:0.65 292:0.91 297:0.36 300:0.84 +2 1:0.74 8:0.78 9:0.80 11:0.64 12:0.86 17:0.40 18:0.80 21:0.47 22:0.93 26:0.96 27:0.56 29:0.77 30:0.10 31:0.89 34:0.42 36:0.88 37:0.71 43:0.61 45:0.90 47:0.93 55:0.59 58:0.99 64:0.77 66:0.23 69:0.47 70:0.96 71:0.55 74:0.87 79:0.88 81:0.47 83:0.60 86:0.88 91:0.38 96:0.72 97:0.97 98:0.59 99:0.83 101:0.52 104:0.93 106:0.81 108:0.90 114:0.60 117:0.86 120:0.59 122:0.75 123:0.89 124:0.88 126:0.54 127:0.57 129:0.93 133:0.87 135:1.00 137:0.89 139:0.89 140:0.45 141:0.98 145:0.41 146:0.26 147:0.32 149:0.63 150:0.67 151:0.59 153:0.84 154:0.86 155:0.67 158:0.87 159:0.84 162:0.66 164:0.74 165:0.70 172:0.74 173:0.21 176:0.73 177:0.70 179:0.24 187:0.68 192:0.87 196:0.92 199:0.99 201:0.93 202:0.66 206:0.81 208:0.64 212:0.30 215:0.41 216:0.41 219:0.95 220:0.82 222:0.60 223:0.96 224:0.98 234:0.98 235:0.68 241:0.15 242:0.24 243:0.15 245:0.90 247:0.92 248:0.59 253:0.62 254:0.80 255:0.95 256:0.64 259:0.21 260:0.60 262:0.93 265:0.60 266:0.91 267:0.73 268:0.68 271:0.64 274:0.98 276:0.87 279:0.86 283:0.71 284:0.95 285:0.62 290:0.60 292:0.91 297:0.36 300:0.84 +0 11:0.64 12:0.86 17:0.22 21:0.78 26:0.95 27:0.45 29:0.77 30:0.30 34:0.76 36:0.19 37:0.50 43:0.63 45:0.77 58:0.93 66:0.19 69:0.72 70:0.80 71:0.55 74:0.48 91:0.15 98:0.23 101:0.52 108:0.92 122:0.51 123:0.53 124:0.67 127:0.59 129:0.59 133:0.64 135:0.87 141:0.91 145:0.49 146:0.44 147:0.51 149:0.42 154:0.53 159:0.78 172:0.37 173:0.54 175:0.75 177:0.38 178:0.55 179:0.80 187:0.68 199:0.96 212:0.52 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.31 241:0.27 242:0.24 243:0.39 244:0.61 245:0.56 247:0.67 253:0.34 257:0.65 259:0.21 265:0.19 266:0.54 267:0.65 271:0.64 274:0.91 276:0.36 277:0.69 300:0.55 +1 1:0.69 7:0.72 11:0.64 12:0.86 17:0.67 18:0.86 21:0.30 26:0.96 27:0.06 29:0.77 30:0.33 32:0.69 34:0.95 36:0.17 37:0.86 43:0.63 45:0.91 58:0.93 66:0.44 69:0.69 70:0.85 71:0.55 74:0.79 77:0.70 81:0.41 83:0.60 86:0.87 91:0.14 98:0.58 99:0.83 101:0.52 104:0.91 106:0.81 108:0.52 114:0.63 117:0.86 122:0.51 123:0.50 124:0.68 126:0.44 127:0.35 129:0.59 133:0.66 135:0.99 139:0.83 141:0.91 145:0.61 146:0.85 147:0.87 149:0.77 150:0.55 154:0.75 155:0.58 159:0.68 165:0.70 172:0.73 173:0.53 176:0.66 177:0.70 178:0.55 179:0.29 187:0.95 192:0.59 195:0.90 199:0.98 201:0.68 204:0.85 206:0.81 208:0.54 212:0.39 215:0.44 216:0.76 222:0.60 223:0.96 224:0.93 230:0.74 233:0.73 234:0.91 235:0.42 241:0.64 242:0.32 243:0.57 245:0.87 247:0.84 253:0.50 254:0.84 259:0.21 265:0.60 266:0.54 267:0.89 271:0.64 274:0.91 276:0.77 282:0.77 290:0.63 297:0.36 300:0.70 +2 1:0.74 7:0.72 8:0.70 11:0.64 12:0.86 17:0.82 18:0.70 21:0.30 22:0.93 26:0.96 27:0.26 29:0.77 30:0.53 32:0.69 34:0.63 36:0.89 37:0.84 43:0.68 45:0.73 47:0.93 48:0.97 55:0.84 58:0.93 64:0.77 66:0.91 69:0.58 70:0.68 71:0.55 74:0.77 76:0.85 77:0.70 81:0.55 83:0.60 86:0.70 91:0.44 98:0.85 99:0.83 101:0.52 108:0.44 114:0.65 122:0.77 123:0.42 126:0.54 127:0.35 129:0.05 135:1.00 139:0.71 141:0.91 145:0.61 146:0.92 147:0.93 149:0.49 150:0.72 154:0.57 155:0.67 159:0.54 165:0.70 172:0.75 173:0.50 176:0.73 177:0.70 178:0.55 179:0.46 187:0.87 192:0.87 195:0.80 199:0.96 201:0.93 204:0.85 208:0.64 212:0.29 215:0.44 216:0.42 222:0.60 223:0.96 224:0.93 230:0.74 233:0.73 234:0.91 235:0.52 241:0.10 242:0.30 243:0.91 244:0.61 247:0.74 253:0.51 259:0.21 262:0.93 265:0.85 266:0.63 267:0.52 271:0.64 274:0.91 276:0.64 281:0.89 282:0.77 290:0.65 297:0.36 300:0.55 +2 1:0.74 8:0.83 9:0.80 11:0.85 12:0.86 17:0.51 18:0.75 21:0.30 22:0.93 26:0.96 27:0.56 29:0.77 30:0.15 31:0.87 34:0.18 36:0.61 37:0.71 43:0.14 45:0.85 47:0.93 55:0.71 58:0.98 64:0.77 66:0.13 69:0.47 70:0.98 71:0.55 74:0.87 79:0.87 81:0.55 83:0.91 86:0.83 91:0.38 96:0.69 97:0.97 98:0.31 99:0.83 101:0.87 104:0.93 106:0.81 108:0.36 114:0.65 117:0.86 120:0.55 122:0.77 123:0.34 124:0.97 126:0.54 127:0.83 129:0.95 133:0.97 135:1.00 137:0.87 139:0.85 140:0.45 141:0.98 145:0.41 146:0.90 147:0.92 149:0.39 150:0.72 151:0.52 153:0.48 154:0.88 155:0.67 158:0.91 159:0.95 162:0.86 164:0.57 165:0.70 172:0.79 173:0.10 176:0.73 177:0.70 179:0.16 187:0.68 192:0.87 196:0.89 199:0.99 201:0.93 202:0.36 206:0.81 208:0.64 212:0.22 215:0.44 216:0.41 219:0.95 220:0.87 222:0.60 223:0.96 224:0.99 234:0.98 235:0.52 241:0.15 242:0.24 243:0.34 245:0.90 247:0.96 248:0.51 253:0.54 254:0.74 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.33 266:0.98 267:0.97 268:0.46 271:0.64 274:0.97 276:0.94 279:0.86 283:0.78 284:0.89 285:0.62 290:0.65 292:0.95 297:0.36 300:0.94 +2 1:0.74 6:0.91 8:0.89 11:0.85 12:0.86 17:0.51 18:0.75 20:0.94 21:0.30 22:0.93 26:0.96 27:0.56 29:0.77 30:0.15 31:0.87 34:0.17 36:0.63 37:0.71 43:0.14 45:0.85 47:0.93 55:0.71 58:0.93 64:0.77 66:0.13 69:0.45 70:0.98 71:0.55 74:0.86 78:0.97 79:0.87 81:0.55 83:0.60 86:0.83 91:0.33 97:0.94 98:0.31 99:0.83 100:0.94 101:0.87 104:0.84 106:0.81 108:0.35 111:0.95 114:0.65 117:0.86 122:0.77 123:0.34 124:0.97 126:0.54 127:0.81 129:0.95 133:0.97 135:1.00 137:0.87 139:0.79 140:0.80 141:0.91 145:0.41 146:0.90 147:0.92 149:0.39 150:0.72 151:0.48 152:0.95 153:0.48 154:0.88 155:0.67 158:0.79 159:0.95 162:0.62 165:0.70 169:0.91 172:0.79 173:0.10 176:0.73 177:0.70 178:0.42 179:0.16 186:0.97 187:0.39 189:0.93 190:0.89 192:0.87 196:0.88 199:0.99 201:0.93 202:0.50 206:0.81 208:0.64 212:0.22 215:0.44 216:0.41 220:0.87 222:0.60 223:0.96 224:0.93 234:0.91 235:0.52 238:0.88 241:0.05 242:0.24 243:0.34 245:0.90 247:0.96 248:0.48 253:0.52 254:0.74 256:0.45 259:0.21 261:0.94 262:0.93 265:0.33 266:0.98 267:0.97 268:0.46 271:0.64 274:0.91 276:0.94 279:0.82 283:0.82 284:0.86 285:0.47 290:0.65 297:0.36 300:0.94 +2 1:0.74 6:0.87 7:0.81 8:0.78 11:0.64 12:0.86 17:0.40 18:0.95 20:0.93 26:0.96 27:0.40 29:0.77 30:0.45 32:0.80 34:0.36 36:0.18 37:0.74 43:0.69 44:0.93 45:0.83 55:0.71 58:0.93 64:0.77 66:0.44 69:0.52 70:0.83 71:0.55 74:0.83 77:0.70 78:0.99 81:0.84 83:0.91 86:0.94 91:0.48 97:0.94 98:0.70 100:0.99 101:0.52 104:0.92 106:0.81 108:0.77 111:0.99 114:0.98 121:0.90 122:0.86 123:0.75 124:0.67 126:0.54 127:0.62 129:0.59 133:0.61 135:0.99 139:0.95 141:0.91 145:0.63 146:0.79 147:0.82 149:0.47 150:0.89 152:0.96 154:0.69 155:0.67 158:0.79 159:0.71 165:0.93 169:0.97 172:0.75 173:0.38 176:0.73 177:0.70 178:0.55 179:0.35 186:0.98 187:0.87 189:0.89 192:0.87 195:0.86 199:0.98 201:0.93 204:0.89 206:0.81 208:0.64 212:0.55 215:0.96 216:0.36 222:0.60 223:0.96 224:0.93 230:0.87 233:0.76 234:0.91 235:0.12 238:0.96 241:0.01 242:0.37 243:0.45 245:0.88 247:0.84 253:0.68 254:0.74 259:0.21 261:0.97 265:0.70 266:0.75 267:0.44 271:0.64 274:0.91 276:0.79 279:0.82 281:0.91 282:0.88 283:0.82 290:0.98 297:0.36 299:0.88 300:0.70 +2 1:0.74 9:0.80 11:0.64 12:0.86 17:0.81 18:0.76 21:0.68 26:0.96 27:0.55 29:0.77 30:0.08 31:0.91 34:0.25 36:0.76 37:0.62 43:0.48 45:0.77 55:0.59 58:0.98 64:0.77 66:0.36 69:0.55 70:0.98 71:0.55 74:0.69 79:0.90 81:0.41 83:0.60 86:0.82 91:0.27 96:0.75 97:0.89 98:0.59 99:0.83 101:0.52 108:0.81 114:0.56 120:0.63 122:0.51 123:0.80 124:0.70 126:0.54 127:0.42 129:0.59 133:0.66 135:1.00 137:0.91 139:0.84 140:0.45 141:0.98 145:0.96 146:0.67 147:0.72 149:0.50 150:0.65 151:0.72 153:0.48 154:0.72 155:0.67 158:0.91 159:0.64 162:0.86 164:0.57 165:0.70 172:0.63 173:0.42 176:0.73 177:0.70 179:0.40 187:0.39 192:0.87 196:0.93 199:0.98 201:0.93 202:0.36 208:0.64 212:0.48 215:0.37 216:0.19 219:0.95 220:0.62 222:0.60 223:0.96 224:1.00 234:0.99 235:0.88 241:0.61 242:0.21 243:0.43 245:0.82 247:0.91 248:0.67 253:0.67 254:0.74 255:0.95 256:0.45 259:0.21 260:0.64 265:0.60 266:0.63 267:0.99 268:0.46 271:0.64 274:0.97 276:0.70 279:0.86 283:0.78 284:0.89 285:0.62 290:0.56 292:0.91 297:0.36 300:0.84 +2 1:0.74 8:0.89 9:0.80 11:0.64 12:0.86 17:0.61 18:0.76 21:0.40 22:0.93 26:0.96 27:0.56 29:0.77 30:0.20 31:0.90 34:0.21 36:0.91 37:0.71 43:0.57 45:0.85 47:0.93 55:0.59 58:0.98 64:0.77 66:0.25 69:0.46 70:0.94 71:0.55 74:0.86 79:0.89 81:0.51 83:0.60 86:0.85 91:0.38 96:0.73 97:0.94 98:0.41 99:0.83 101:0.52 104:0.93 106:0.81 108:0.35 114:0.62 117:0.86 120:0.61 122:0.77 123:0.34 124:0.95 126:0.54 127:0.69 129:0.81 133:0.95 135:1.00 137:0.90 139:0.86 140:0.45 141:0.98 145:0.41 146:0.87 147:0.89 149:0.35 150:0.69 151:0.63 153:0.72 154:0.87 155:0.67 158:0.91 159:0.89 162:0.86 164:0.69 165:0.70 172:0.75 173:0.17 176:0.73 177:0.70 179:0.26 187:0.68 192:0.87 196:0.92 199:0.99 201:0.93 202:0.53 206:0.81 208:0.64 212:0.39 215:0.42 216:0.41 219:0.95 220:0.74 222:0.60 223:0.96 224:0.99 234:0.98 235:0.60 241:0.01 242:0.17 243:0.44 245:0.83 247:0.91 248:0.61 253:0.65 254:0.80 255:0.95 256:0.58 259:0.21 260:0.61 262:0.93 265:0.43 266:0.96 267:0.59 268:0.62 271:0.64 274:0.98 276:0.86 279:0.86 283:0.77 284:0.93 285:0.62 290:0.62 292:0.91 297:0.36 300:0.70 +0 1:0.69 11:0.64 12:0.86 17:0.61 21:0.64 26:0.96 27:0.45 29:0.77 30:0.21 32:0.69 34:0.75 36:0.20 37:0.50 43:0.65 45:0.81 58:0.93 66:0.09 69:0.69 70:0.79 71:0.55 74:0.62 77:0.70 81:0.33 83:0.60 91:0.20 98:0.29 99:0.83 101:0.52 108:0.87 114:0.56 122:0.55 123:0.68 124:0.79 126:0.44 127:0.46 129:0.81 133:0.76 135:0.89 141:0.91 145:0.47 146:0.32 147:0.38 149:0.58 150:0.51 154:0.54 155:0.58 159:0.62 165:0.70 172:0.37 173:0.61 175:0.75 176:0.66 177:0.38 178:0.55 179:0.64 187:0.68 192:0.59 195:0.82 199:0.97 201:0.68 208:0.54 212:0.59 215:0.38 216:0.77 222:0.60 223:0.95 224:0.93 234:0.91 235:0.84 241:0.18 242:0.24 243:0.40 244:0.61 245:0.63 247:0.60 253:0.41 257:0.65 259:0.21 265:0.26 266:0.71 267:1.00 271:0.64 274:0.91 276:0.51 277:0.69 282:0.77 290:0.56 297:0.36 300:0.55 +0 1:0.74 11:0.78 12:0.86 17:0.51 18:0.72 21:0.54 26:0.96 27:0.45 28:0.73 29:0.77 30:0.91 32:0.80 34:0.52 36:0.22 37:0.50 39:0.66 43:0.82 44:0.93 58:0.93 64:0.77 66:0.36 69:0.69 70:0.19 74:0.60 77:0.70 81:0.52 83:0.91 85:0.80 86:0.81 91:0.17 98:0.24 101:0.87 108:0.79 114:0.59 122:0.57 123:0.78 124:0.58 126:0.54 127:0.33 129:0.59 131:0.61 133:0.46 135:0.84 139:0.84 141:0.91 144:0.76 145:0.45 146:0.17 147:0.22 149:0.69 150:0.73 154:0.77 155:0.67 157:0.81 159:0.44 161:0.58 163:0.90 165:0.93 166:0.85 167:0.61 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.87 181:0.64 182:0.84 187:0.68 192:0.87 195:0.71 199:0.95 201:0.93 208:0.64 212:0.52 215:0.39 216:0.77 222:0.48 223:0.95 224:0.93 227:0.61 229:0.61 231:0.77 234:0.91 235:0.74 241:0.12 242:0.55 243:0.45 245:0.50 246:0.96 247:0.39 253:0.43 259:0.21 265:0.26 266:0.27 267:0.28 271:0.64 274:0.91 276:0.16 282:0.88 287:0.61 290:0.59 297:0.36 299:0.88 300:0.18 +3 1:0.69 6:0.93 7:0.81 8:0.78 9:0.80 11:0.81 12:0.86 17:0.20 18:0.83 20:0.99 21:0.64 26:0.96 27:0.09 28:0.73 29:0.77 30:0.45 31:0.98 32:0.80 34:0.97 36:0.77 37:0.86 39:0.66 41:0.92 43:0.71 44:0.93 45:0.87 58:0.99 64:0.77 66:0.43 69:0.39 70:0.66 74:0.63 77:0.70 78:0.84 79:0.98 81:0.34 83:0.91 86:0.89 91:0.67 96:0.93 98:0.48 99:0.83 100:0.86 101:0.52 108:0.82 111:0.83 114:0.55 120:0.83 121:0.90 122:0.80 123:0.81 124:0.77 126:0.44 127:0.75 129:0.81 131:0.90 133:0.74 135:0.76 137:0.98 139:0.93 140:0.45 141:0.98 144:0.76 145:0.65 146:0.37 147:0.44 149:0.59 150:0.52 151:0.97 152:0.99 153:0.89 154:0.76 155:0.67 157:0.61 159:0.60 161:0.58 162:0.85 164:0.81 165:0.70 166:0.75 167:0.90 169:0.92 172:0.54 173:0.34 176:0.55 177:0.70 179:0.62 181:0.64 182:0.85 186:0.84 187:0.21 189:0.90 191:0.78 192:0.87 195:0.78 196:0.99 199:0.98 201:0.68 202:0.75 204:0.89 208:0.64 212:0.52 216:0.45 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.87 231:0.78 232:0.72 233:0.76 234:0.98 235:0.80 238:0.89 241:0.77 242:0.33 243:0.31 245:0.69 246:0.61 247:0.76 248:0.90 253:0.90 254:0.80 255:0.95 256:0.81 259:0.21 260:0.84 261:0.94 265:0.50 266:0.80 267:0.73 268:0.82 271:0.64 274:0.99 276:0.60 281:0.91 282:0.88 284:0.98 285:0.62 287:0.97 290:0.55 299:0.88 300:0.55 +3 1:0.74 8:0.77 9:0.80 11:0.64 12:0.86 17:0.15 18:0.79 21:0.30 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.14 31:0.98 34:0.42 36:0.97 37:0.80 39:0.66 41:0.90 43:0.66 45:0.87 55:0.71 58:0.99 64:0.77 66:0.19 69:0.34 70:0.94 74:0.31 79:0.98 81:0.66 83:0.91 86:0.83 91:0.53 96:0.94 97:0.99 98:0.41 99:0.83 101:0.52 104:0.58 108:0.94 114:0.65 120:0.83 123:0.93 124:0.91 126:0.54 127:0.94 129:0.81 131:0.90 133:0.91 135:0.56 137:0.98 139:0.83 140:0.45 141:0.98 144:0.76 146:0.68 147:0.73 149:0.65 150:0.77 151:0.97 153:0.90 154:0.22 155:0.67 157:0.61 158:0.72 159:0.87 161:0.58 162:0.69 163:0.81 164:0.79 165:0.70 166:0.85 167:0.63 172:0.59 173:0.14 176:0.73 177:0.70 179:0.39 181:0.64 182:0.85 187:0.39 191:0.76 192:0.87 196:0.97 199:0.99 201:0.93 202:0.80 208:0.64 212:0.21 215:0.44 216:0.82 219:0.92 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 231:0.78 232:0.86 234:0.98 235:0.52 241:0.21 242:0.71 243:0.31 245:0.79 246:0.61 247:0.67 248:0.90 253:0.87 254:0.88 255:0.95 256:0.79 259:0.21 260:0.84 265:0.43 266:0.95 267:0.69 268:0.82 271:0.64 274:0.99 276:0.78 279:0.82 284:0.98 285:0.62 287:0.97 290:0.65 292:0.91 297:0.36 300:0.84 +3 1:0.74 6:0.98 7:0.81 8:0.97 9:0.80 11:0.87 12:0.86 17:0.20 18:0.88 20:0.85 21:0.22 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.29 31:1.00 32:0.78 34:0.45 36:0.96 37:0.80 39:0.66 41:0.99 43:0.82 44:0.93 45:0.92 58:1.00 64:0.77 66:0.54 69:0.35 70:0.81 74:0.75 77:0.70 78:0.94 79:1.00 81:0.80 83:0.91 85:0.72 86:0.94 91:0.91 96:0.99 97:0.99 98:0.77 100:0.98 101:0.97 104:0.58 108:0.27 111:0.97 114:0.71 120:0.97 121:0.90 123:0.26 124:0.70 126:0.54 127:0.92 129:0.89 131:0.90 133:0.78 135:0.85 137:1.00 138:0.99 139:0.96 140:0.45 141:0.98 144:0.76 145:0.74 146:0.96 147:0.97 149:0.78 150:0.87 151:1.00 152:1.00 153:0.98 154:0.80 155:0.67 157:0.76 158:0.72 159:0.83 161:0.58 162:0.79 164:0.95 165:0.93 166:0.85 167:0.76 169:0.98 172:0.85 173:0.17 176:0.73 177:0.70 179:0.31 181:0.64 182:0.85 186:0.94 187:0.21 189:0.99 191:0.95 192:0.87 195:0.92 196:1.00 199:1.00 201:0.93 202:0.93 204:0.89 208:0.64 212:0.23 215:0.51 216:0.82 219:0.92 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.87 231:0.78 232:0.83 233:0.76 234:0.98 235:0.60 238:0.98 241:0.65 242:0.32 243:0.63 245:0.89 246:0.96 247:0.67 248:0.99 253:0.99 255:0.95 256:0.95 259:0.21 260:0.96 261:0.98 265:0.77 266:0.87 267:0.73 268:0.95 271:0.64 274:1.00 276:0.84 279:0.82 281:0.91 282:0.86 284:1.00 285:0.62 287:0.97 290:0.71 292:0.95 297:0.36 300:0.70 +3 1:0.74 6:0.94 8:0.83 9:0.80 11:0.64 12:0.86 17:0.40 18:0.79 20:0.82 21:0.30 25:0.88 26:0.96 27:0.61 28:0.73 29:0.77 30:0.28 31:0.96 34:0.21 36:0.93 37:0.44 39:0.66 41:0.82 43:0.24 45:0.97 58:0.99 64:0.77 66:0.12 69:0.95 70:0.90 74:0.48 78:0.78 79:0.95 81:0.66 83:0.91 86:0.83 91:0.25 96:0.86 97:0.99 98:0.32 99:0.83 100:0.81 101:0.52 104:0.72 108:0.98 111:0.77 114:0.65 120:0.82 123:0.98 124:0.99 126:0.54 127:0.94 129:0.98 131:0.90 133:0.99 135:0.92 137:0.96 139:0.85 140:0.45 141:0.98 144:0.76 146:0.71 147:0.05 149:0.64 150:0.77 151:0.87 152:0.98 153:0.48 154:0.17 155:0.67 157:0.61 158:0.86 159:0.97 161:0.58 162:0.84 163:0.81 164:0.73 165:0.70 166:0.85 167:0.64 169:0.76 172:0.78 173:0.64 176:0.73 177:0.05 179:0.16 181:0.64 182:0.84 186:0.78 187:0.21 189:0.98 192:0.87 196:0.96 199:1.00 201:0.93 202:0.74 208:0.64 212:0.14 215:0.44 216:0.64 219:0.95 220:0.87 222:0.48 223:0.96 224:0.99 227:0.96 229:0.91 231:0.78 232:0.84 234:0.99 235:0.52 238:0.90 241:0.27 242:0.21 243:0.05 245:0.87 246:0.61 247:0.88 248:0.79 253:0.78 254:0.86 255:0.95 256:0.80 257:0.84 259:0.21 260:0.74 261:0.79 265:0.34 266:0.99 267:0.85 268:0.80 271:0.64 274:0.99 276:0.94 279:0.86 283:0.78 284:0.96 285:0.62 287:0.97 290:0.65 292:0.87 297:0.36 300:0.84 +2 1:0.74 6:0.94 8:0.75 9:0.80 11:0.64 12:0.86 17:0.11 18:0.76 20:0.87 21:0.59 25:0.88 26:0.96 27:0.61 28:0.73 29:0.77 30:0.08 31:0.98 34:0.62 36:0.23 37:0.44 39:0.66 41:0.96 43:0.58 45:0.81 58:1.00 64:0.77 66:0.16 69:0.77 70:1.00 74:0.30 78:0.75 79:0.98 81:0.46 83:0.91 86:0.82 91:0.53 96:0.94 98:0.16 99:0.83 100:0.78 101:0.52 104:0.72 108:0.60 111:0.74 114:0.58 120:0.87 123:0.57 124:0.96 126:0.54 127:0.92 129:0.05 131:0.90 133:0.95 135:0.88 137:0.98 139:0.78 140:0.80 141:0.98 144:0.76 146:0.50 147:0.56 149:0.50 150:0.67 151:0.96 152:0.98 153:0.86 154:0.68 155:0.67 157:0.61 159:0.95 161:0.58 162:0.51 164:0.86 165:0.70 166:0.84 167:0.64 169:0.76 172:0.41 173:0.34 176:0.73 177:0.70 178:0.42 179:0.56 181:0.64 182:0.85 186:0.76 187:0.87 189:0.91 191:0.70 192:0.87 196:0.96 199:0.99 201:0.93 202:0.90 208:0.64 212:0.12 215:0.39 216:0.64 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 231:0.78 232:0.84 234:0.97 235:0.80 238:0.85 241:0.27 242:0.71 243:0.37 245:0.60 246:0.61 247:0.60 248:0.91 253:0.88 254:0.88 255:0.95 256:0.87 259:0.21 260:0.86 261:0.79 265:0.17 266:0.96 267:0.69 268:0.87 271:0.64 274:0.99 276:0.66 284:0.98 285:0.62 287:0.97 290:0.58 297:0.36 300:0.98 +2 6:0.96 8:0.82 9:0.80 11:0.64 12:0.86 17:0.28 18:0.61 20:0.99 21:0.13 25:0.88 26:0.96 27:0.31 28:0.73 29:0.77 30:0.15 31:1.00 34:0.21 36:0.81 37:0.54 39:0.66 41:0.99 43:0.15 45:0.66 55:0.59 58:1.00 60:0.95 66:0.33 69:0.84 70:0.84 74:0.48 78:0.77 79:1.00 81:0.39 83:0.91 86:0.70 91:0.65 96:0.98 98:0.56 100:0.81 101:0.52 104:0.74 108:0.38 111:0.77 120:0.95 123:0.67 124:0.61 126:0.26 127:0.63 129:0.05 131:0.90 133:0.37 135:0.47 137:1.00 139:0.78 140:0.45 141:0.98 144:0.76 146:0.50 147:0.52 149:0.19 150:0.34 151:0.99 152:0.90 153:0.96 154:0.58 155:0.67 157:0.61 158:0.91 159:0.38 161:0.58 162:0.50 163:0.90 164:0.95 166:0.75 167:0.94 169:0.79 172:0.27 173:0.95 177:0.05 179:0.85 181:0.64 182:0.85 186:0.78 189:0.97 191:0.90 192:0.87 196:0.98 199:1.00 202:0.98 208:0.64 212:0.30 215:0.61 216:0.17 219:0.95 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 231:0.78 234:0.97 235:0.12 238:0.93 241:0.82 242:0.73 243:0.50 245:0.60 246:0.61 247:0.39 248:0.97 253:0.95 254:0.88 255:0.95 256:0.98 257:0.84 260:0.94 261:0.81 265:0.52 266:0.54 267:0.44 268:0.97 271:0.64 274:1.00 276:0.37 279:0.86 283:0.78 284:1.00 285:0.62 287:0.97 292:0.91 297:0.36 300:0.55 +1 1:0.69 6:0.84 8:0.65 9:0.80 11:0.87 12:0.86 17:0.59 18:0.98 20:0.95 21:0.47 22:0.93 26:0.96 27:0.21 28:0.73 29:0.77 30:0.66 31:0.97 34:0.76 36:0.86 37:0.74 39:0.66 41:0.82 43:0.92 45:0.99 47:0.93 58:0.98 60:0.87 64:0.77 66:0.27 69:0.19 70:0.66 74:0.75 78:0.76 79:0.97 81:0.41 83:0.91 85:0.88 86:0.99 91:0.25 96:0.91 97:0.94 98:0.55 99:0.83 100:0.78 101:0.97 104:0.84 106:0.81 108:0.95 111:0.75 114:0.57 117:0.86 120:0.80 122:0.85 123:0.95 124:0.87 126:0.44 127:0.83 129:0.89 131:0.90 133:0.87 135:0.23 137:0.97 139:0.99 140:0.45 141:0.98 144:0.76 146:0.90 147:0.92 149:0.95 150:0.55 151:0.96 152:0.91 153:0.87 154:0.91 155:0.67 157:0.82 158:0.92 159:0.91 161:0.58 162:0.74 164:0.68 165:0.70 166:0.75 167:0.61 169:0.76 172:0.92 173:0.08 176:0.55 177:0.70 179:0.14 181:0.64 182:0.85 186:0.77 187:0.39 189:0.86 191:0.70 192:0.87 196:0.99 199:0.99 201:0.68 202:0.72 204:0.85 206:0.81 208:0.64 212:0.67 216:0.95 219:0.95 222:0.48 223:0.96 224:0.98 227:0.96 229:0.91 231:0.78 232:0.92 234:0.97 235:0.60 238:0.86 241:0.15 242:0.36 243:0.38 245:0.98 246:0.61 247:0.82 248:0.85 253:0.88 255:0.95 256:0.73 259:0.21 260:0.79 261:0.79 262:0.93 265:0.56 266:0.89 267:0.44 268:0.76 271:0.64 274:0.98 276:0.96 279:0.86 281:0.89 283:0.82 284:0.96 285:0.62 287:0.97 290:0.57 292:0.95 300:0.94 +2 1:0.74 6:0.93 7:0.63 8:0.91 9:0.80 11:0.64 12:0.86 17:0.55 18:0.73 20:0.99 21:0.30 26:0.96 27:0.09 28:0.73 29:0.77 30:0.62 31:1.00 32:0.80 34:0.76 36:0.87 37:0.86 39:0.66 41:0.98 43:0.17 44:0.93 45:0.73 58:1.00 64:0.77 66:0.75 69:0.30 70:0.34 74:0.49 77:0.70 78:0.91 79:1.00 81:0.67 83:0.91 86:0.85 91:0.91 96:0.98 97:0.89 98:0.75 100:0.96 101:0.52 108:0.24 111:0.93 114:0.65 120:0.95 122:0.57 123:0.23 126:0.54 127:0.24 129:0.05 131:0.90 135:0.23 137:1.00 139:0.90 140:0.45 141:0.98 144:0.76 145:0.56 146:0.31 147:0.38 149:0.07 150:0.80 151:0.99 152:0.99 153:0.97 154:0.82 155:0.67 157:0.61 158:0.72 159:0.13 161:0.58 162:0.78 164:0.92 165:0.93 166:0.85 167:0.97 169:0.92 172:0.32 173:0.76 176:0.73 177:0.70 179:0.84 181:0.64 182:0.85 186:0.91 189:0.95 191:0.94 192:0.87 195:0.53 196:1.00 199:0.99 201:0.93 202:0.90 204:0.85 208:0.64 212:0.95 215:0.44 216:0.45 219:0.92 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 230:0.63 231:0.78 232:0.72 233:0.73 234:0.98 235:0.52 238:0.95 241:0.95 242:0.63 243:0.77 246:0.96 247:0.35 248:0.97 253:0.96 254:0.90 255:0.95 256:0.93 259:0.21 260:0.95 261:0.94 265:0.75 266:0.12 267:0.79 268:0.93 271:0.64 274:1.00 276:0.27 279:0.82 281:0.91 282:0.88 284:0.99 285:0.62 287:0.97 290:0.65 292:0.91 297:0.36 300:0.25 +2 6:0.98 7:0.63 8:0.97 9:0.80 11:0.64 12:0.86 17:0.09 18:0.80 20:0.99 21:0.59 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.13 31:1.00 32:0.62 34:0.52 36:0.64 37:0.80 39:0.66 41:0.99 43:0.62 45:0.90 55:0.59 58:1.00 60:0.97 66:0.32 69:0.51 70:0.96 74:0.49 78:0.92 79:1.00 81:0.26 83:0.91 86:0.86 91:0.99 96:0.99 97:0.94 98:0.55 100:0.98 101:0.52 104:0.58 108:0.94 111:0.96 120:0.99 123:0.94 124:0.92 126:0.26 127:0.93 129:0.81 131:0.90 133:0.92 135:0.65 137:1.00 138:0.99 139:0.88 140:0.45 141:0.98 144:0.76 145:0.89 146:0.55 147:0.61 149:0.60 150:0.25 151:1.00 152:1.00 153:0.99 154:0.80 155:0.67 157:0.61 158:0.92 159:0.88 161:0.58 162:0.79 164:0.97 166:0.75 167:0.95 169:0.98 172:0.77 173:0.23 177:0.70 179:0.30 181:0.64 182:0.85 186:0.92 189:0.99 191:0.98 192:0.87 195:0.96 196:1.00 199:1.00 202:0.98 208:0.64 212:0.21 215:0.39 216:0.82 219:0.95 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.63 231:0.78 233:0.73 234:0.99 235:0.74 238:0.98 241:0.85 242:0.63 243:0.23 245:0.83 246:0.61 247:0.60 248:0.99 253:0.99 254:0.92 255:0.95 256:0.99 259:0.21 260:0.98 261:0.98 265:0.56 266:0.92 267:0.65 268:0.99 271:0.64 274:1.00 276:0.85 279:0.86 283:0.82 284:1.00 285:0.62 287:0.97 292:0.95 297:0.36 300:0.94 +2 9:0.80 11:0.81 12:0.86 17:0.07 18:0.88 21:0.30 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.74 31:0.95 34:0.50 36:0.86 37:0.80 39:0.66 41:0.82 43:0.72 45:0.87 58:0.98 66:0.87 69:0.17 70:0.44 74:0.44 79:0.94 81:0.31 83:0.91 86:0.91 91:0.31 96:0.84 98:0.94 101:0.52 104:0.58 108:0.14 120:0.81 122:0.80 123:0.13 126:0.26 127:0.62 129:0.05 131:0.90 135:0.61 137:0.95 139:0.88 140:0.80 141:0.98 144:0.76 146:0.74 147:0.78 149:0.78 150:0.29 151:0.92 153:0.72 154:0.68 155:0.67 157:0.61 159:0.30 161:0.58 162:0.78 164:0.65 166:0.75 167:0.74 172:0.63 173:0.38 177:0.70 179:0.71 181:0.64 182:0.85 187:0.87 192:0.87 196:0.96 199:0.96 202:0.59 208:0.64 212:0.48 215:0.44 216:0.82 222:0.48 223:0.96 224:1.00 227:0.96 229:0.92 231:0.77 232:0.86 234:1.00 235:0.31 241:0.60 242:0.58 243:0.88 246:0.61 247:0.39 248:0.81 253:0.78 254:0.80 255:0.95 256:0.58 259:0.21 260:0.76 265:0.94 266:0.38 267:0.69 268:0.64 271:0.64 274:0.98 276:0.49 284:0.91 285:0.62 287:0.97 297:0.36 300:0.43 +0 1:0.74 7:0.63 11:0.78 12:0.86 17:0.40 18:0.72 21:0.68 26:0.96 27:0.45 28:0.73 29:0.77 30:0.32 34:0.71 36:0.19 37:0.50 39:0.66 43:0.82 55:0.59 58:0.93 60:0.87 64:0.77 66:0.64 69:0.63 70:0.72 74:0.60 81:0.45 83:0.91 85:0.80 86:0.80 91:0.08 98:0.69 101:0.87 108:0.71 114:0.56 123:0.69 124:0.46 126:0.54 127:0.41 129:0.59 131:0.61 133:0.46 135:0.79 139:0.84 141:0.91 144:0.76 145:0.49 146:0.33 147:0.39 149:0.71 150:0.70 154:0.77 155:0.67 157:0.81 158:0.91 159:0.50 161:0.58 165:0.93 166:0.84 167:0.61 172:0.61 173:0.60 176:0.73 177:0.70 178:0.55 179:0.58 181:0.64 182:0.84 187:0.68 192:0.87 199:0.98 201:0.93 208:0.64 212:0.30 215:0.37 216:0.77 219:0.95 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 230:0.64 231:0.76 233:0.76 234:0.91 235:0.88 241:0.15 242:0.57 243:0.29 245:0.71 246:0.96 247:0.67 253:0.41 259:0.21 265:0.69 266:0.71 267:0.28 271:0.64 274:0.91 276:0.54 279:0.86 283:0.78 287:0.61 290:0.56 292:0.91 297:0.36 300:0.55 +2 1:0.69 6:0.93 8:0.95 9:0.80 11:0.64 12:0.86 17:0.24 18:0.90 20:0.74 21:0.13 26:0.96 27:0.09 28:0.73 29:0.77 30:0.67 31:0.96 34:0.92 36:0.89 37:0.86 39:0.66 41:0.88 43:0.71 44:0.90 45:0.91 58:0.98 64:0.77 66:0.47 69:0.32 70:0.63 74:0.35 77:0.70 78:0.72 79:0.95 81:0.74 83:0.91 86:0.96 91:0.38 96:0.86 97:0.89 98:0.44 99:0.83 100:0.73 101:0.52 108:0.25 111:0.72 114:0.64 120:0.75 122:0.80 123:0.24 124:0.66 126:0.44 127:0.77 129:0.59 131:0.90 133:0.66 135:0.54 137:0.96 139:0.96 140:0.45 141:0.98 144:0.76 145:0.79 146:0.61 147:0.66 149:0.62 150:0.70 151:0.92 152:0.99 153:0.72 154:0.80 155:0.67 157:0.61 158:0.72 159:0.65 161:0.58 162:0.86 164:0.70 165:0.70 166:0.75 167:0.75 169:0.92 172:0.63 173:0.25 176:0.55 177:0.70 179:0.54 181:0.64 182:0.85 186:0.73 187:0.39 192:0.87 195:0.80 196:0.98 199:0.96 201:0.68 202:0.59 208:0.64 212:0.57 216:0.45 219:0.92 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 231:0.78 232:0.72 234:0.99 235:0.22 241:0.64 242:0.32 243:0.59 245:0.78 246:0.61 247:0.60 248:0.83 253:0.80 254:0.86 255:0.95 256:0.64 259:0.21 260:0.76 261:0.94 265:0.46 266:0.75 267:0.59 268:0.68 271:0.64 274:0.98 276:0.61 279:0.82 282:0.71 284:0.94 285:0.62 287:0.97 290:0.64 292:0.91 300:0.70 +2 6:0.94 7:0.76 8:0.86 9:0.80 12:0.86 17:0.53 20:0.91 21:0.54 25:0.88 27:0.57 28:0.73 30:0.68 32:0.72 34:0.49 36:0.37 37:0.62 39:0.73 41:0.90 43:0.45 55:0.59 66:0.31 69:0.17 70:0.57 78:0.88 81:0.37 83:0.77 91:0.86 96:0.84 98:0.44 100:0.92 104:0.54 108:0.78 111:0.89 120:0.93 123:0.77 124:0.72 126:0.32 127:0.75 129:0.81 133:0.67 135:0.94 140:0.97 144:0.85 145:0.87 146:0.30 147:0.36 149:0.49 150:0.33 151:0.87 152:0.85 153:0.90 154:0.34 155:0.67 159:0.63 161:0.74 162:0.82 164:0.91 167:0.87 169:0.90 172:0.37 173:0.18 175:0.75 177:0.05 179:0.75 181:0.91 186:0.88 189:0.95 191:0.79 192:0.59 195:0.81 202:0.85 208:0.64 212:0.18 215:0.58 216:0.49 222:0.35 230:0.79 233:0.76 235:0.60 238:0.91 241:0.88 242:0.82 243:0.28 244:0.61 245:0.64 247:0.12 248:0.82 253:0.78 255:0.79 256:0.89 257:0.65 259:0.21 260:0.93 261:0.90 265:0.46 266:0.71 267:0.65 268:0.90 271:0.64 276:0.44 277:0.69 283:0.82 285:0.62 297:0.36 300:0.55 +1 1:0.74 7:0.76 11:0.88 12:0.86 17:0.30 21:0.47 27:0.45 28:0.73 30:0.31 32:0.80 34:0.88 36:0.19 37:0.50 39:0.73 43:0.82 55:0.59 60:0.87 66:0.24 69:0.61 70:0.81 74:0.79 77:0.70 81:0.63 83:0.84 85:0.80 91:0.17 98:0.57 101:0.72 108:0.90 114:0.80 123:0.45 124:0.78 126:0.54 127:0.60 129:0.81 133:0.76 135:0.87 144:0.85 145:0.42 146:0.84 147:0.87 149:0.79 150:0.77 154:0.77 155:0.67 158:0.87 159:0.77 161:0.74 165:0.80 167:0.59 172:0.63 173:0.43 176:0.73 177:0.38 178:0.55 179:0.36 181:0.91 187:0.68 192:0.59 195:0.90 201:0.74 208:0.64 212:0.51 215:0.63 216:0.77 222:0.35 230:0.79 233:0.76 235:0.60 241:0.44 242:0.57 243:0.44 245:0.85 247:0.67 253:0.69 259:0.21 265:0.46 266:0.87 267:0.36 271:0.64 276:0.78 279:0.86 282:0.88 283:0.71 290:0.80 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.97 7:0.81 8:0.93 9:0.80 12:0.86 17:0.32 20:0.98 21:0.40 27:0.29 28:0.73 30:0.66 32:0.80 34:0.49 36:0.85 37:0.81 39:0.73 41:0.95 43:0.42 55:0.59 60:0.87 66:0.68 69:0.73 70:0.64 74:0.70 76:0.98 77:0.70 78:0.92 81:0.67 83:0.81 91:0.92 96:0.88 98:0.84 100:0.96 104:0.79 108:0.56 111:0.93 114:0.82 120:0.95 121:0.90 123:0.54 124:0.43 126:0.54 127:0.78 129:0.05 133:0.39 135:0.54 138:0.99 140:0.80 144:0.85 145:0.63 146:0.75 147:0.33 149:0.57 150:0.80 151:0.92 152:0.90 153:0.90 154:0.62 155:0.67 158:0.87 159:0.41 161:0.74 162:0.76 164:0.95 165:0.83 167:0.88 169:0.94 172:0.61 173:0.83 176:0.73 177:0.05 179:0.69 181:0.91 186:0.92 189:0.98 191:0.87 192:0.59 195:0.63 201:0.74 202:0.91 204:0.89 208:0.64 212:0.61 215:0.67 216:0.50 220:0.62 222:0.35 230:0.87 233:0.76 235:0.52 238:0.94 241:0.90 242:0.79 243:0.25 245:0.68 247:0.39 248:0.89 253:0.88 254:0.84 255:0.79 256:0.93 257:0.65 259:0.21 260:0.95 261:0.94 265:0.84 266:0.38 267:0.65 268:0.94 271:0.64 276:0.55 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.82 8:0.87 9:0.80 11:0.88 12:0.86 17:0.18 20:0.87 21:0.22 27:0.38 28:0.73 30:0.11 34:0.34 36:0.64 37:0.90 39:0.73 41:0.95 43:0.67 55:0.71 60:0.87 66:0.13 69:0.54 70:0.84 74:0.69 78:0.77 81:0.77 83:0.81 85:0.72 91:0.85 96:0.93 98:0.43 100:0.80 101:0.72 108:0.78 111:0.76 114:0.90 120:0.93 122:0.43 123:0.66 124:0.73 126:0.54 127:0.51 129:0.81 133:0.67 135:0.23 140:0.93 144:0.85 146:0.30 147:0.36 149:0.46 150:0.85 151:0.87 152:0.83 153:0.84 154:0.63 155:0.67 158:0.87 159:0.46 161:0.74 162:0.79 164:0.92 165:0.86 167:0.75 169:0.78 172:0.27 173:0.56 176:0.73 177:0.38 179:0.81 181:0.91 186:0.78 187:0.39 189:0.84 191:0.89 192:0.59 201:0.74 202:0.87 208:0.64 212:0.28 215:0.79 216:0.79 220:0.62 222:0.35 235:0.31 238:0.87 241:0.72 242:0.45 243:0.34 245:0.56 247:0.47 248:0.88 253:0.93 255:0.79 256:0.91 259:0.21 260:0.92 261:0.79 265:0.25 266:0.63 267:0.84 268:0.91 271:0.64 276:0.39 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.55 +3 6:0.89 7:0.81 8:0.86 9:0.80 11:0.88 12:0.86 17:0.05 20:0.98 21:0.13 25:0.88 27:0.24 28:0.73 30:0.21 32:0.80 34:0.30 36:0.79 37:0.71 39:0.73 41:0.97 43:0.60 55:0.71 66:0.44 69:0.90 70:0.86 74:0.65 76:0.94 77:0.70 78:0.95 81:0.67 83:0.83 85:0.72 91:0.92 96:0.95 98:0.69 100:0.98 101:0.71 104:0.76 108:0.79 111:0.97 120:0.97 121:0.90 123:0.78 124:0.65 126:0.32 127:0.94 129:0.05 133:0.62 135:0.17 140:0.90 144:0.85 145:0.47 146:0.79 147:0.05 149:0.74 150:0.48 151:0.98 152:0.90 153:0.97 154:0.51 155:0.67 159:0.63 161:0.74 162:0.77 164:0.97 167:0.89 169:0.97 172:0.74 173:0.93 177:0.05 179:0.40 181:0.91 186:0.95 189:0.91 191:0.91 192:0.59 195:0.76 202:0.94 204:0.89 208:0.64 212:0.77 215:0.84 216:0.57 220:0.74 222:0.35 230:0.87 233:0.76 235:0.12 238:0.95 241:0.97 242:0.80 243:0.07 245:0.87 247:0.39 248:0.94 253:0.94 255:0.79 256:0.96 257:0.65 259:0.21 260:0.97 261:0.96 265:0.69 266:0.54 267:0.59 268:0.96 271:0.64 276:0.78 282:0.88 283:0.71 285:0.62 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.93 7:0.76 8:0.84 9:0.80 11:0.88 12:0.86 17:0.43 20:0.99 21:0.30 27:0.21 28:0.73 30:0.14 34:0.28 36:0.88 37:0.74 39:0.73 41:0.99 43:0.98 55:0.71 60:0.97 66:0.13 69:0.12 70:0.88 74:0.90 76:0.97 78:0.88 81:0.72 83:0.90 85:0.96 91:0.72 96:0.98 98:0.45 100:0.96 101:0.73 104:0.84 106:0.81 108:0.97 111:0.92 114:0.86 117:0.86 120:0.98 123:0.97 124:0.96 126:0.54 127:0.99 129:0.99 133:0.96 135:0.19 138:0.98 140:0.45 144:0.85 146:0.49 147:0.55 149:0.91 150:0.82 151:1.00 152:0.91 153:0.98 154:0.98 155:0.67 158:0.92 159:0.98 161:0.74 162:0.73 164:0.95 165:0.84 167:0.67 169:0.94 172:0.95 173:0.05 176:0.73 177:0.38 179:0.10 181:0.91 186:0.88 187:0.39 189:0.94 191:0.80 192:0.59 201:0.74 202:0.92 206:0.81 208:0.64 212:0.49 215:0.72 216:0.95 222:0.35 230:0.79 232:0.92 233:0.76 235:0.42 238:0.96 241:0.62 242:0.73 243:0.08 245:0.99 247:0.60 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.93 265:0.47 266:0.94 267:0.36 268:0.94 271:0.64 276:0.99 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94 +2 6:0.88 8:0.73 9:0.80 12:0.86 17:0.02 20:0.97 21:0.30 25:0.88 27:0.39 28:0.73 30:0.76 34:0.30 36:0.89 37:0.45 39:0.73 41:0.97 43:0.43 55:0.59 66:0.72 69:0.35 70:0.22 74:0.40 78:0.85 81:0.52 83:0.84 91:0.85 96:0.96 98:0.92 100:0.87 104:0.58 108:0.27 111:0.85 120:0.97 122:0.41 123:0.26 126:0.32 127:0.54 129:0.05 135:0.28 140:0.87 144:0.85 146:0.44 147:0.51 149:0.27 150:0.40 151:0.98 152:0.89 153:0.97 154:0.72 155:0.67 159:0.16 161:0.74 162:0.83 164:0.96 167:0.88 169:0.85 172:0.27 173:0.75 177:0.38 179:0.96 181:0.91 186:0.85 189:0.90 191:0.77 192:0.59 202:0.92 208:0.64 212:0.42 215:0.72 216:0.46 222:0.35 235:0.31 238:0.88 241:0.93 242:0.45 243:0.75 247:0.35 248:0.95 253:0.93 254:0.94 255:0.79 256:0.95 259:0.21 260:0.97 261:0.88 265:0.92 266:0.23 267:0.19 268:0.95 271:0.64 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +1 1:0.74 7:0.76 11:0.88 12:0.86 17:0.47 21:0.40 27:0.45 28:0.73 30:0.15 34:0.86 36:0.28 37:0.50 39:0.73 43:0.82 66:0.43 69:0.61 70:0.87 74:0.71 81:0.67 83:0.75 85:0.90 91:0.06 98:0.71 101:0.78 108:0.47 114:0.82 122:0.41 123:0.45 124:0.60 126:0.54 127:0.53 129:0.59 133:0.47 135:0.93 144:0.85 145:0.50 146:0.88 147:0.90 149:0.81 150:0.80 154:0.77 155:0.67 159:0.68 161:0.74 165:0.83 167:0.54 172:0.61 173:0.49 176:0.73 177:0.38 178:0.55 179:0.48 181:0.91 187:0.68 192:0.59 201:0.74 212:0.29 215:0.67 216:0.77 222:0.35 230:0.79 233:0.76 235:0.52 241:0.27 242:0.31 243:0.57 245:0.85 247:0.60 253:0.68 259:0.21 265:0.71 266:0.75 267:0.28 271:0.64 276:0.67 290:0.82 297:0.36 300:0.70 +2 1:0.74 7:0.76 8:0.75 9:0.80 11:0.88 12:0.86 17:0.40 21:0.40 27:0.21 28:0.73 30:0.18 34:0.61 36:0.93 37:0.74 39:0.73 41:0.92 43:0.97 55:0.71 60:0.87 66:0.16 69:0.17 70:0.96 74:0.89 76:0.85 81:0.67 83:0.81 85:0.93 91:0.23 96:0.85 98:0.41 101:0.75 104:0.89 106:0.81 108:0.95 114:0.82 117:0.86 120:0.82 123:0.93 124:0.91 126:0.54 127:0.76 129:0.93 133:0.91 135:0.54 140:0.80 144:0.85 146:0.38 147:0.45 149:0.89 150:0.80 151:0.87 153:0.91 154:0.97 155:0.67 158:0.87 159:0.90 161:0.74 162:0.67 164:0.80 165:0.83 167:0.68 172:0.77 173:0.08 176:0.73 177:0.38 179:0.19 181:0.91 187:0.39 192:0.59 201:0.74 202:0.74 206:0.81 208:0.64 212:0.67 215:0.67 216:0.95 220:0.62 222:0.35 230:0.78 232:0.95 233:0.69 235:0.52 241:0.64 242:0.71 243:0.16 245:0.92 247:0.55 248:0.84 253:0.89 255:0.79 256:0.71 259:0.21 260:0.82 265:0.37 266:0.94 267:0.36 268:0.76 271:0.64 276:0.91 279:0.86 283:0.71 285:0.62 290:0.82 297:0.36 300:0.98 +1 6:0.92 7:0.76 8:0.97 9:0.80 11:0.82 12:0.86 17:0.09 20:0.98 21:0.59 25:0.88 27:0.08 28:0.73 30:0.17 32:0.72 34:0.52 36:0.64 37:0.80 39:0.73 41:0.98 43:0.36 55:0.84 66:0.16 69:0.55 70:0.86 74:0.89 76:0.94 77:0.70 78:0.92 81:0.42 83:0.87 91:0.99 96:0.97 98:0.59 100:0.95 104:0.58 108:0.42 111:0.93 120:0.99 122:0.43 123:0.93 124:0.88 126:0.32 127:0.92 129:0.93 133:0.90 135:0.65 138:0.99 140:0.95 144:0.85 145:0.89 146:0.93 147:0.95 149:0.68 150:0.35 151:0.98 152:0.93 153:0.98 154:0.63 155:0.67 159:0.88 161:0.74 162:0.73 164:0.99 167:0.87 169:0.92 172:0.80 173:0.26 177:0.38 179:0.27 181:0.91 186:0.92 189:0.93 191:0.98 192:0.59 195:0.96 202:0.98 208:0.64 212:0.22 215:0.55 216:0.82 220:0.74 222:0.35 230:0.78 233:0.69 235:0.74 238:0.93 241:0.85 242:0.21 243:0.50 245:0.88 247:0.67 248:0.97 253:0.98 254:0.74 255:0.79 256:0.98 259:0.21 260:0.99 261:0.94 265:0.60 266:0.87 267:0.65 268:0.99 271:0.64 276:0.87 282:0.79 283:0.71 285:0.62 297:0.36 300:0.84 +1 12:0.06 17:0.40 21:0.64 27:0.45 28:0.64 30:0.58 32:0.80 34:0.78 36:0.18 37:0.50 43:0.32 55:0.52 66:0.80 69:0.70 70:0.33 81:0.81 91:0.12 98:0.48 108:0.53 123:0.51 126:0.54 127:0.15 129:0.05 135:0.39 145:0.47 146:0.59 147:0.64 149:0.46 150:0.60 154:0.24 159:0.21 161:0.68 167:0.48 172:0.45 173:0.69 177:0.05 178:0.55 179:0.40 181:0.64 187:0.68 195:0.73 212:0.71 215:0.53 216:0.77 235:0.80 241:0.12 242:0.82 243:0.82 247:0.12 259:0.21 265:0.50 266:0.27 267:0.44 271:0.63 276:0.36 283:0.60 297:0.36 300:0.25 +1 6:0.83 7:0.81 8:0.63 12:0.06 17:0.26 20:0.97 21:0.47 27:0.07 28:0.64 30:0.33 32:0.80 34:0.25 36:0.89 37:0.32 43:0.35 55:0.52 66:0.12 69:0.63 70:0.49 78:0.92 81:0.87 91:0.95 98:0.27 100:0.93 108:0.48 111:0.91 120:0.84 123:0.71 124:0.66 126:0.54 127:0.41 129:0.81 133:0.67 135:0.65 140:0.87 145:0.45 146:0.31 147:0.37 149:0.43 150:0.73 151:0.73 152:0.89 153:0.81 154:0.27 159:0.46 161:0.68 162:0.49 164:0.94 167:0.82 169:0.90 172:0.27 173:0.63 177:0.05 178:0.48 179:0.86 181:0.64 186:0.91 189:0.85 191:0.91 195:0.71 202:0.96 212:0.42 215:0.63 216:0.52 220:0.93 230:0.87 233:0.76 235:0.52 238:0.88 241:0.81 242:0.82 243:0.58 245:0.47 247:0.12 248:0.76 256:0.93 259:0.21 260:0.85 261:0.92 265:0.26 266:0.38 267:0.91 268:0.92 271:0.63 276:0.30 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.97 8:0.95 12:0.06 17:0.06 20:0.96 21:0.78 27:0.12 28:0.64 34:0.52 36:0.37 37:0.88 78:0.95 91:0.89 100:0.98 104:0.89 106:0.81 111:0.97 120:0.97 135:0.68 140:0.87 151:0.81 152:0.89 153:0.94 161:0.68 162:0.55 164:0.99 167:0.74 169:0.97 175:0.75 178:0.48 181:0.64 186:0.95 187:0.68 189:0.98 191:0.98 202:0.99 206:0.81 216:0.91 220:0.62 238:0.97 241:0.74 244:0.61 248:0.95 256:0.99 257:0.65 259:0.21 260:0.97 261:0.96 267:0.79 268:0.99 271:0.63 277:0.69 283:0.82 285:0.62 +2 6:0.83 7:0.81 8:0.62 12:0.06 17:0.01 20:0.98 21:0.47 27:0.04 28:0.64 30:0.95 32:0.80 34:0.28 36:0.74 37:0.60 43:0.47 55:0.84 66:0.64 69:0.39 78:0.91 81:0.87 91:0.98 98:0.89 100:0.89 104:0.58 108:0.30 111:0.90 120:0.97 123:0.29 126:0.54 127:0.44 129:0.05 135:0.28 140:0.45 145:0.94 146:0.50 147:0.56 149:0.27 150:0.73 151:0.79 152:0.91 153:0.91 154:0.36 159:0.18 161:0.68 162:0.71 163:0.86 164:0.98 167:0.85 169:0.86 172:0.16 173:0.72 177:0.05 179:0.99 181:0.64 186:0.91 189:0.85 191:0.87 195:0.54 202:0.97 212:0.95 215:0.63 216:0.61 220:0.62 230:0.87 233:0.76 235:0.52 238:0.84 241:0.96 242:0.82 243:0.69 247:0.12 248:0.95 256:0.98 259:0.21 260:0.97 261:0.91 265:0.89 266:0.12 267:0.79 268:0.98 271:0.63 276:0.19 283:0.60 285:0.62 297:0.36 300:0.08 +2 6:0.83 7:0.81 8:0.63 12:0.06 17:0.38 20:0.90 21:0.30 27:0.21 28:0.64 30:0.52 34:0.47 36:0.93 37:0.74 43:0.52 55:0.71 66:0.44 69:0.12 70:0.63 78:0.82 81:0.90 91:0.76 98:0.65 100:0.81 106:0.81 108:0.77 111:0.81 120:0.93 123:0.75 124:0.85 126:0.54 127:0.76 129:0.95 133:0.87 135:0.28 140:0.45 146:0.80 147:0.84 149:0.83 150:0.82 151:0.82 152:0.84 153:0.95 154:0.41 159:0.88 161:0.68 162:0.75 164:0.90 167:0.59 169:0.80 172:0.87 173:0.08 177:0.05 179:0.22 181:0.64 186:0.81 187:0.39 189:0.85 191:0.76 202:0.83 206:0.81 212:0.49 215:0.72 216:0.95 230:0.87 233:0.76 235:0.42 238:0.84 241:0.55 242:0.82 243:0.29 245:0.91 247:0.12 248:0.89 256:0.86 259:0.21 260:0.93 261:0.82 265:0.66 266:0.91 267:0.76 268:0.87 271:0.63 276:0.90 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.86 7:0.81 8:0.77 12:0.06 17:0.01 20:0.82 21:0.30 27:0.30 28:0.64 30:0.21 32:0.80 34:0.67 36:0.53 37:0.32 43:0.41 55:0.52 66:0.80 69:0.52 70:0.50 78:0.89 81:0.97 91:0.92 98:0.81 100:0.87 104:0.91 106:0.81 108:0.40 111:0.87 120:0.96 123:0.39 126:0.54 127:0.30 129:0.05 135:0.65 140:0.45 145:0.52 146:0.68 147:0.72 149:0.47 150:0.95 151:0.80 152:0.86 153:0.93 154:0.31 159:0.26 161:0.68 162:0.78 163:0.90 164:0.99 167:0.74 169:0.83 172:0.45 173:0.66 177:0.05 179:0.78 181:0.64 186:0.88 187:0.21 189:0.88 191:0.88 195:0.61 202:0.97 206:0.81 212:0.37 215:0.72 216:0.64 220:0.82 230:0.87 233:0.76 235:0.52 238:0.84 241:0.74 242:0.82 243:0.82 247:0.12 248:0.94 256:0.99 259:0.21 260:0.96 261:0.87 265:0.81 266:0.38 267:0.65 268:0.99 271:0.63 276:0.36 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.06 17:0.30 21:0.40 27:0.45 28:0.64 30:0.55 32:0.80 34:0.69 36:0.19 37:0.50 43:0.32 55:0.59 66:0.52 69:0.70 70:0.43 81:0.89 91:0.25 98:0.57 108:0.53 123:0.51 124:0.65 126:0.54 127:0.41 129:0.81 133:0.67 135:0.92 145:0.52 146:0.76 147:0.80 149:0.53 150:0.78 154:0.24 159:0.61 161:0.68 163:0.86 167:0.55 172:0.48 173:0.62 177:0.05 178:0.55 179:0.67 181:0.64 187:0.68 195:0.82 212:0.18 215:0.67 216:0.77 235:0.52 241:0.44 242:0.82 243:0.61 245:0.61 247:0.12 259:0.21 265:0.58 266:0.80 267:0.59 271:0.63 276:0.49 297:0.36 300:0.33 +1 6:0.81 7:0.81 8:0.61 12:0.06 17:0.02 20:0.95 21:0.59 27:0.06 28:0.64 30:0.76 32:0.80 34:0.68 36:0.43 37:0.26 43:0.44 55:0.71 66:0.72 69:0.49 70:0.22 78:0.92 81:0.83 91:0.96 98:0.66 100:0.89 104:0.74 108:0.38 111:0.90 120:0.90 123:0.36 126:0.54 127:0.19 129:0.05 135:0.39 140:0.87 145:0.72 146:0.55 147:0.61 149:0.36 150:0.63 151:0.74 152:0.88 153:0.83 154:0.33 159:0.20 161:0.68 162:0.64 163:0.81 164:0.95 167:0.82 169:0.86 172:0.27 173:0.61 177:0.05 178:0.48 179:0.83 181:0.64 186:0.92 189:0.83 191:0.83 195:0.59 202:0.93 212:0.42 215:0.55 216:0.39 220:0.62 230:0.65 233:0.63 235:0.74 238:0.83 241:0.83 242:0.82 243:0.75 247:0.12 248:0.86 256:0.94 259:0.21 260:0.91 261:0.92 265:0.66 266:0.23 267:0.95 268:0.94 271:0.63 276:0.24 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.90 7:0.81 8:0.76 12:0.06 17:0.07 20:0.98 21:0.13 27:0.07 28:0.64 30:0.45 32:0.80 34:0.21 36:0.49 37:0.68 43:0.49 55:0.52 66:0.85 69:0.21 70:0.69 78:0.91 81:0.93 91:0.98 98:0.90 100:0.91 104:0.76 108:0.17 111:0.90 120:0.97 123:0.16 126:0.54 127:0.47 129:0.05 135:0.47 140:0.45 145:0.77 146:0.78 147:0.81 149:0.61 150:0.88 151:0.80 152:0.90 153:0.93 154:0.38 159:0.34 161:0.68 162:0.75 164:0.99 167:0.83 169:0.89 172:0.57 173:0.35 177:0.05 179:0.74 181:0.64 186:0.91 189:0.91 191:0.90 195:0.65 202:0.98 212:0.49 215:0.84 216:0.64 220:0.74 230:0.87 233:0.76 235:0.12 238:0.86 241:0.85 242:0.82 243:0.86 247:0.12 248:0.96 256:0.99 259:0.21 260:0.97 261:0.92 265:0.90 266:0.54 267:0.98 268:0.99 271:0.63 276:0.45 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.85 7:0.81 8:0.67 12:0.06 20:0.98 21:0.64 25:0.88 27:0.13 28:0.64 30:0.76 32:0.80 34:0.37 36:0.55 37:0.29 43:0.46 55:0.71 66:0.80 69:0.47 70:0.37 78:0.93 81:0.91 91:0.99 98:0.90 100:0.93 104:0.58 108:0.36 111:0.93 120:0.99 123:0.35 126:0.54 127:0.48 129:0.05 135:0.26 140:0.45 145:0.94 146:0.71 147:0.75 149:0.48 150:0.83 151:0.84 152:0.93 153:0.98 154:0.35 159:0.28 161:0.68 162:0.65 164:0.99 167:0.85 169:0.92 172:0.45 173:0.64 177:0.05 179:0.85 181:0.64 186:0.92 189:0.87 191:0.95 195:0.57 202:0.98 212:0.55 215:0.53 216:0.72 220:0.62 230:0.65 233:0.63 235:0.80 238:0.90 241:0.96 242:0.82 243:0.82 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.93 265:0.90 266:0.38 267:0.65 268:0.99 271:0.63 276:0.37 283:0.82 285:0.62 297:0.36 300:0.33 +0 12:0.20 17:0.93 21:0.78 27:0.58 30:0.95 37:0.35 39:0.40 43:0.06 55:1.00 66:0.20 69:0.99 74:0.26 91:0.01 98:0.05 108:0.99 123:0.99 124:0.61 127:0.23 129:0.59 133:0.47 146:0.05 147:0.05 149:0.05 154:0.06 159:0.99 172:0.05 173:0.78 175:0.75 177:0.05 178:0.55 179:0.99 187:0.21 202:0.36 216:0.11 222:0.25 235:0.60 243:0.40 244:0.61 245:0.36 253:0.23 257:0.65 259:0.21 265:0.05 277:0.69 300:0.08 +0 12:0.20 17:0.83 21:0.78 27:0.61 30:0.95 37:0.28 39:0.40 43:0.06 55:1.00 66:0.20 69:0.99 74:0.28 98:0.05 108:0.98 123:0.98 124:0.61 127:0.29 129:0.59 133:0.47 146:0.05 147:0.05 149:0.05 154:0.06 159:0.97 172:0.05 173:0.87 175:0.75 177:0.05 178:0.55 179:0.99 187:0.68 216:0.64 222:0.25 241:0.03 243:0.40 244:0.61 245:0.36 253:0.25 257:0.65 265:0.05 277:0.69 300:0.08 +0 12:0.20 17:0.89 21:0.78 27:0.63 30:0.95 34:0.36 36:0.42 37:0.30 39:0.40 43:0.05 55:1.00 66:0.20 69:1.00 74:0.26 98:0.05 108:1.00 123:1.00 124:0.61 127:0.23 129:0.59 133:0.47 135:0.70 146:0.05 147:0.05 149:0.05 154:0.05 159:0.99 172:0.05 173:0.87 175:0.75 177:0.05 178:0.55 179:0.99 187:0.68 202:0.36 216:0.59 222:0.25 243:0.40 244:0.61 245:0.36 253:0.24 257:0.65 259:0.21 265:0.05 267:0.44 277:0.69 300:0.08 +0 12:0.20 17:0.72 21:0.78 27:0.72 30:0.76 34:0.31 36:0.33 39:0.40 43:0.20 55:0.59 66:0.36 69:0.72 70:0.15 74:0.41 91:0.06 98:0.15 108:0.55 122:0.57 123:0.52 124:0.52 127:0.47 129:0.05 133:0.39 135:0.30 146:0.24 147:0.30 149:0.08 154:0.58 159:0.59 163:0.98 172:0.10 173:0.66 177:0.38 178:0.55 179:0.99 187:0.21 212:0.95 216:0.04 222:0.25 235:0.12 241:0.24 242:0.82 243:0.51 245:0.37 247:0.12 253:0.36 254:0.98 259:0.21 265:0.16 266:0.12 267:0.23 276:0.13 300:0.13 +1 12:0.20 17:0.77 21:0.78 27:0.58 30:0.76 34:0.31 36:0.46 37:0.24 39:0.40 43:0.27 55:0.92 66:0.36 69:0.76 70:0.15 74:0.40 91:0.38 98:0.19 108:0.59 122:0.57 123:0.57 124:0.52 127:0.65 129:0.05 133:0.39 135:0.78 146:0.46 147:0.53 149:0.18 154:0.20 159:0.89 163:0.98 172:0.10 173:0.48 177:0.38 178:0.55 179:0.99 187:0.39 212:0.95 216:0.40 222:0.25 235:0.60 241:0.07 242:0.82 243:0.51 244:0.61 245:0.37 247:0.12 253:0.36 259:0.21 265:0.21 266:0.12 267:0.59 276:0.13 300:0.13 +0 12:0.20 17:0.69 21:0.78 27:0.71 30:0.95 34:0.40 36:0.68 37:0.25 39:0.40 43:0.44 55:1.00 66:0.20 69:0.05 74:0.42 96:0.67 98:0.05 108:0.05 123:0.05 124:0.61 127:0.93 129:0.59 133:0.47 135:0.66 140:0.45 146:0.05 147:0.05 149:0.15 151:0.46 153:0.48 154:0.33 159:1.00 162:0.86 172:0.05 173:0.05 175:0.75 177:0.05 179:1.00 187:0.95 190:0.77 202:0.36 216:0.16 220:1.00 222:0.25 241:0.05 243:0.40 244:0.61 245:0.36 248:0.46 253:0.37 256:0.45 257:0.65 259:0.21 265:0.05 267:0.18 268:0.46 277:0.69 285:0.50 300:0.08 diff --git a/examples/xendcg/rank.test.query b/examples/xendcg/rank.test.query new file mode 100644 index 0000000..f924152 --- /dev/null +++ b/examples/xendcg/rank.test.query @@ -0,0 +1,50 @@ +12 +19 +18 +10 +15 +15 +22 +23 +18 +16 +16 +11 +6 +13 +17 +21 +20 +16 +13 +16 +21 +15 +10 +19 +10 +13 +18 +17 +23 +24 +16 +13 +17 +24 +17 +10 +17 +15 +18 +16 +9 +9 +21 +14 +13 +13 +13 +10 +10 +6 diff --git a/examples/xendcg/rank.train b/examples/xendcg/rank.train new file mode 100644 index 0000000..6312ba3 --- /dev/null +++ b/examples/xendcg/rank.train @@ -0,0 +1,3005 @@ +0 10:0.89 11:0.75 12:0.01 17:0.45 18:0.91 21:0.78 27:0.72 29:0.77 30:0.76 39:0.65 43:0.79 44:0.88 45:0.88 66:0.64 69:0.25 70:0.41 71:0.83 74:0.79 77:0.70 83:0.56 85:0.72 86:0.93 91:0.35 98:0.70 101:0.96 108:0.20 122:0.86 123:0.19 124:0.47 127:0.43 129:0.05 133:0.45 139:0.92 145:0.47 146:0.58 147:0.63 149:0.84 154:0.73 155:0.50 159:0.31 170:0.90 172:0.67 173:0.40 174:0.79 177:0.88 178:0.55 179:0.52 187:0.21 192:0.48 195:0.59 197:0.83 204:0.80 208:0.50 212:0.82 216:0.29 222:0.48 235:0.22 239:0.88 241:0.21 242:0.40 243:0.69 245:0.77 247:0.76 253:0.55 265:0.70 266:0.27 271:0.34 276:0.58 281:0.91 282:0.75 300:0.43 +1 1:0.69 11:0.64 12:0.51 17:0.53 18:0.75 21:0.77 27:0.45 29:0.71 30:0.45 34:0.81 36:0.21 37:0.50 39:0.48 43:0.10 45:0.66 55:0.52 64:0.77 66:0.52 69:0.71 70:0.33 71:0.97 74:0.50 81:0.37 83:0.60 86:0.80 91:0.08 98:0.24 99:0.83 101:0.52 108:0.54 114:0.53 122:0.80 123:0.52 124:0.50 126:0.54 127:0.17 129:0.05 133:0.43 135:0.56 139:0.77 145:0.49 146:0.31 147:0.38 149:0.08 150:0.58 154:0.72 155:0.58 159:0.25 165:0.70 172:0.27 173:0.72 176:0.73 177:0.70 178:0.55 179:0.61 187:0.68 192:0.59 201:0.74 208:0.64 212:0.52 215:0.36 216:0.77 222:0.48 235:0.99 241:0.21 242:0.55 243:0.61 245:0.48 247:0.39 253:0.36 254:0.90 259:0.21 265:0.26 266:0.27 267:0.69 271:0.60 276:0.25 290:0.53 297:0.36 300:0.25 +0 1:0.69 11:0.64 12:0.51 17:0.34 18:0.85 21:0.13 27:0.18 29:0.71 30:0.17 34:0.47 36:0.30 37:0.85 39:0.48 43:0.65 45:0.77 66:0.20 69:0.10 70:0.68 71:0.97 74:0.59 81:0.62 83:0.60 86:0.85 91:0.31 97:0.89 98:0.50 99:0.83 101:0.52 104:0.84 108:0.09 114:0.75 122:0.82 123:0.70 124:0.67 126:0.44 127:0.52 129:0.59 133:0.61 135:0.34 139:0.81 146:0.59 147:0.65 149:0.46 150:0.61 154:0.83 155:0.58 158:0.79 159:0.56 165:0.70 172:0.41 173:0.15 176:0.66 177:0.70 178:0.55 179:0.73 187:0.21 192:0.59 201:0.68 208:0.54 212:0.19 215:0.61 216:0.82 222:0.48 235:0.22 241:0.50 242:0.33 243:0.57 245:0.60 247:0.67 253:0.54 254:0.90 259:0.21 265:0.48 266:0.71 267:0.19 271:0.60 276:0.47 279:0.82 283:0.82 290:0.75 297:0.36 300:0.43 +1 11:0.64 12:0.51 17:0.11 18:0.82 21:0.78 27:0.45 29:0.71 30:0.45 34:0.82 36:0.27 37:0.50 39:0.48 43:0.12 45:0.66 55:0.59 66:0.63 69:0.72 70:0.61 71:0.97 74:0.41 86:0.83 91:0.17 98:0.47 101:0.52 108:0.56 122:0.51 123:0.53 124:0.45 127:0.29 129:0.05 133:0.37 135:0.39 139:0.79 145:0.50 146:0.57 147:0.63 149:0.11 154:0.66 159:0.43 172:0.41 173:0.71 177:0.70 178:0.55 179:0.74 187:0.68 202:0.36 212:0.50 216:0.77 222:0.48 235:0.68 241:0.32 242:0.14 243:0.68 245:0.54 247:0.67 253:0.32 254:0.96 259:0.21 265:0.49 266:0.47 267:0.44 271:0.60 276:0.32 300:0.43 +0 1:0.69 7:0.72 11:0.64 12:0.51 17:0.76 18:0.61 21:0.47 27:0.72 29:0.71 30:0.74 32:0.69 34:0.55 36:0.78 39:0.48 43:0.72 45:0.87 66:0.43 69:0.81 70:0.37 71:0.97 74:0.75 77:0.98 81:0.37 83:0.60 86:0.71 91:0.72 98:0.63 99:0.83 101:0.52 104:0.58 108:0.66 114:0.59 122:0.75 123:0.63 124:0.59 126:0.44 127:0.86 129:0.05 133:0.43 135:0.32 139:0.68 145:1.00 146:0.53 147:0.48 149:0.75 150:0.54 154:0.58 155:0.58 158:0.74 159:0.37 165:0.70 172:0.45 173:0.94 176:0.66 177:0.38 178:0.55 179:0.74 192:0.59 195:0.59 201:0.68 204:0.85 208:0.54 212:0.37 215:0.41 216:0.03 222:0.48 230:0.74 232:0.78 233:0.73 235:0.60 241:0.79 242:0.51 243:0.47 245:0.72 247:0.60 253:0.46 254:0.95 257:0.65 259:0.21 265:0.64 266:0.63 267:0.36 271:0.60 276:0.48 282:0.77 290:0.59 297:0.36 300:0.33 +1 1:0.69 7:0.72 11:0.64 12:0.51 17:0.06 18:0.91 21:0.22 22:0.93 27:0.11 29:0.71 30:0.21 34:0.92 36:0.26 37:0.91 39:0.48 43:0.60 45:0.73 47:0.93 55:0.59 66:0.75 69:0.10 70:0.64 71:0.97 74:0.59 81:0.54 83:0.60 86:0.88 91:0.53 97:0.89 98:0.81 99:0.83 101:0.52 108:0.09 114:0.67 122:0.86 123:0.09 124:0.39 126:0.44 127:0.57 129:0.05 133:0.37 135:0.94 139:0.84 145:0.59 146:0.68 147:0.72 149:0.37 150:0.58 154:0.70 155:0.58 158:0.79 159:0.33 165:0.70 172:0.54 173:0.29 176:0.66 177:0.70 178:0.55 179:0.77 187:0.39 192:0.59 201:0.68 204:0.85 208:0.54 212:0.49 215:0.51 216:0.75 222:0.48 230:0.75 233:0.76 235:0.31 241:0.37 242:0.52 243:0.77 245:0.50 247:0.47 253:0.51 254:0.97 259:0.21 262:0.93 265:0.81 266:0.47 267:0.44 271:0.60 276:0.46 279:0.82 283:0.82 290:0.67 297:0.36 300:0.43 +0 1:0.69 12:0.51 17:0.69 18:0.60 21:0.05 25:0.88 27:0.72 29:0.71 30:0.76 34:0.29 36:0.55 39:0.48 43:0.10 55:0.84 66:0.36 69:0.90 70:0.15 71:0.97 74:0.34 81:0.79 83:0.60 86:0.62 87:0.98 91:0.76 98:0.26 99:0.83 104:0.58 108:0.80 114:0.85 122:0.80 123:0.79 124:0.52 126:0.44 127:0.23 129:0.05 133:0.39 135:0.51 139:0.60 146:0.32 147:0.05 149:0.08 150:0.75 154:0.08 155:0.58 159:0.33 163:0.98 165:0.70 172:0.10 173:0.97 175:0.75 176:0.66 177:0.05 178:0.55 179:0.96 192:0.59 201:0.68 208:0.54 212:0.95 215:0.78 216:0.01 222:0.48 232:0.79 235:0.12 241:0.81 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.58 257:0.84 259:0.21 265:0.28 266:0.12 267:0.52 271:0.60 276:0.15 277:0.69 290:0.85 297:0.36 300:0.13 +1 1:0.69 11:0.64 12:0.51 17:0.86 18:0.64 21:0.05 22:0.93 27:0.72 29:0.71 30:0.76 34:0.33 36:0.37 39:0.48 43:0.71 45:0.81 47:0.93 66:0.80 69:0.32 70:0.28 71:0.97 74:0.59 81:0.71 83:0.60 86:0.75 91:0.62 98:0.79 99:0.83 101:0.52 104:0.54 108:0.25 114:0.85 122:0.51 123:0.24 126:0.44 127:0.28 129:0.05 135:0.34 139:0.71 145:0.45 146:0.48 147:0.54 149:0.60 150:0.67 154:0.72 155:0.58 159:0.17 165:0.70 172:0.45 173:0.61 176:0.66 177:0.70 178:0.55 179:0.76 187:0.21 192:0.59 201:0.68 208:0.54 212:0.71 215:0.78 216:0.11 222:0.48 232:0.74 235:0.12 241:0.49 242:0.40 243:0.82 247:0.47 253:0.58 254:0.97 259:0.21 262:0.93 265:0.79 266:0.27 267:0.23 271:0.60 276:0.33 290:0.85 297:0.36 300:0.25 +1 1:0.69 9:0.76 11:0.64 12:0.51 17:0.47 18:0.90 21:0.05 27:0.02 29:0.71 30:0.37 32:0.69 34:0.75 36:0.22 37:0.92 39:0.48 43:0.69 45:0.77 55:0.59 66:0.48 69:0.38 70:0.85 71:0.97 74:0.61 77:0.70 81:0.71 83:0.60 86:0.83 91:0.38 96:0.76 97:0.89 98:0.46 99:0.83 101:0.52 108:0.69 114:0.85 120:0.63 122:0.86 123:0.67 124:0.57 126:0.44 127:0.23 129:0.05 133:0.37 135:0.88 139:0.82 140:0.80 145:0.56 146:0.49 147:0.55 149:0.63 150:0.67 151:0.65 153:0.48 154:0.59 155:0.58 158:0.79 159:0.44 162:0.86 164:0.54 165:0.70 172:0.51 173:0.29 176:0.66 177:0.70 179:0.43 187:0.39 190:0.77 192:0.59 195:0.81 201:0.68 202:0.50 208:0.54 212:0.50 215:0.78 216:0.99 219:0.89 220:0.96 222:0.48 235:0.12 241:0.53 242:0.71 243:0.44 244:0.61 245:0.75 247:0.55 248:0.63 253:0.68 255:0.74 256:0.58 259:0.21 260:0.63 265:0.48 266:0.71 267:0.79 268:0.59 271:0.60 276:0.53 277:0.87 279:0.82 282:0.77 283:0.82 285:0.56 290:0.85 292:0.95 297:0.36 300:0.70 +0 1:0.69 7:0.72 8:0.67 9:0.76 11:0.64 12:0.51 17:0.59 18:0.61 21:0.47 27:0.72 29:0.71 30:0.74 32:0.69 34:0.49 36:0.81 39:0.48 43:0.72 45:0.87 66:0.43 69:0.81 70:0.37 71:0.97 74:0.75 77:0.98 81:0.37 83:0.60 86:0.71 91:0.76 96:0.78 98:0.63 99:0.83 101:0.52 104:0.58 108:0.66 114:0.59 120:0.66 122:0.75 123:0.63 124:0.59 126:0.44 127:0.86 129:0.05 133:0.43 135:0.30 139:0.68 140:0.45 145:1.00 146:0.53 147:0.48 149:0.75 150:0.54 151:0.65 153:0.48 154:0.58 155:0.58 158:0.74 159:0.37 162:0.86 164:0.65 165:0.70 172:0.45 173:0.94 176:0.66 177:0.38 179:0.74 190:0.77 192:0.59 195:0.60 201:0.68 202:0.53 204:0.85 208:0.54 212:0.37 215:0.41 216:0.02 220:0.74 222:0.48 230:0.74 232:0.78 233:0.73 235:0.60 241:0.82 242:0.51 243:0.47 245:0.72 247:0.60 248:0.68 253:0.71 254:0.95 255:0.74 256:0.58 257:0.65 259:0.21 260:0.65 265:0.64 266:0.63 267:0.36 268:0.62 271:0.60 276:0.48 282:0.77 285:0.56 290:0.59 297:0.36 300:0.33 +1 1:0.74 11:0.64 12:0.51 17:0.62 18:0.87 21:0.05 27:0.53 29:0.71 30:0.76 32:0.75 34:0.42 36:0.25 37:0.86 39:0.48 43:0.82 44:0.93 45:0.73 48:0.91 55:0.52 64:0.77 66:0.61 69:0.47 70:0.36 71:0.97 74:0.68 77:0.70 81:0.79 83:0.91 86:0.96 91:0.27 98:0.38 101:0.52 104:0.91 106:0.81 108:0.36 114:0.89 117:0.86 122:0.82 123:0.35 124:0.47 126:0.54 127:0.49 129:0.59 133:0.46 135:0.75 139:0.96 145:0.65 146:0.38 147:0.45 149:0.57 150:0.87 154:0.81 155:0.67 159:0.39 165:0.93 172:0.48 173:0.54 176:0.73 177:0.70 178:0.55 179:0.75 187:0.39 192:0.87 195:0.63 201:0.93 206:0.81 208:0.64 212:0.76 215:0.78 216:0.65 219:0.89 222:0.48 232:0.94 235:0.22 241:0.10 242:0.38 243:0.68 245:0.62 247:0.47 253:0.61 254:0.86 259:0.21 265:0.40 266:0.38 267:0.65 271:0.60 276:0.28 281:0.91 282:0.84 290:0.89 292:0.91 297:0.36 300:0.33 +0 7:0.66 11:0.64 12:0.51 17:0.36 18:0.94 21:0.54 27:0.20 29:0.71 30:0.13 34:0.88 36:0.37 37:0.60 39:0.48 43:0.14 45:0.66 55:0.59 64:0.77 66:0.59 69:0.49 70:0.98 71:0.97 74:0.56 76:0.85 81:0.31 86:0.92 91:0.29 98:0.45 101:0.52 104:0.94 106:0.81 108:0.38 120:0.54 122:0.86 123:0.36 124:0.78 126:0.44 127:0.94 129:0.05 133:0.86 135:0.80 139:0.90 140:0.45 145:0.61 146:0.84 147:0.86 149:0.29 150:0.25 151:0.46 153:0.48 154:0.76 159:0.87 162:0.86 164:0.53 172:0.71 173:0.23 177:0.70 179:0.52 187:0.68 190:0.89 192:0.51 201:0.68 202:0.36 206:0.81 212:0.30 215:0.39 216:0.84 219:0.89 220:0.93 222:0.48 230:0.69 233:0.76 235:0.68 241:0.41 242:0.51 243:0.67 245:0.67 247:0.90 248:0.46 253:0.37 254:0.96 256:0.45 259:0.21 260:0.54 265:0.47 266:0.92 267:0.44 268:0.46 271:0.60 276:0.66 283:0.78 285:0.47 292:0.91 297:0.36 300:0.94 +1 1:0.74 7:0.66 11:0.64 12:0.51 17:0.49 18:0.97 21:0.30 22:0.93 27:0.20 29:0.71 30:0.42 32:0.80 34:0.95 36:0.32 37:0.60 39:0.48 43:0.14 44:0.93 45:0.77 47:0.93 55:0.71 64:0.77 66:0.49 69:0.49 70:0.85 71:0.97 74:0.72 76:0.85 77:0.70 81:0.59 83:0.60 86:0.96 91:0.25 98:0.76 99:0.83 101:0.52 104:0.99 106:0.81 108:0.38 114:0.65 117:0.86 122:0.86 123:0.36 124:0.65 126:0.54 127:0.88 129:0.05 133:0.66 135:0.70 139:0.96 145:0.88 146:0.91 147:0.92 149:0.34 150:0.74 154:0.85 155:0.67 159:0.73 165:0.70 172:0.77 173:0.35 176:0.73 177:0.70 178:0.55 179:0.39 187:0.68 192:0.87 195:0.86 201:0.93 206:0.81 208:0.64 212:0.54 215:0.44 216:0.84 222:0.48 230:0.69 232:0.99 233:0.76 235:0.60 241:0.21 242:0.70 243:0.60 245:0.87 247:0.84 253:0.50 254:0.80 259:0.21 262:0.93 265:0.76 266:0.80 267:0.87 271:0.60 276:0.79 282:0.88 290:0.65 297:0.36 300:0.70 +1 1:0.74 7:0.66 9:0.76 11:0.64 12:0.51 17:0.34 18:0.96 21:0.22 27:0.20 29:0.71 30:0.40 32:0.75 34:0.94 36:0.19 37:0.60 39:0.48 43:0.69 44:0.93 45:0.81 55:0.71 64:0.77 66:0.10 69:0.48 70:0.86 71:0.97 74:0.75 76:0.85 77:0.70 81:0.63 83:0.60 86:0.94 91:0.37 96:0.80 97:0.99 98:0.43 99:0.83 101:0.52 104:0.98 106:0.81 108:0.37 114:0.71 117:0.86 120:0.69 122:0.86 123:0.82 124:0.90 126:0.54 127:0.80 129:0.59 133:0.89 135:0.69 139:0.94 140:0.45 145:0.70 146:0.59 147:0.65 149:0.63 150:0.76 151:0.81 153:0.48 154:0.79 155:0.67 158:0.78 159:0.77 162:0.86 164:0.54 165:0.70 172:0.63 173:0.31 176:0.73 177:0.70 179:0.37 187:0.68 190:0.77 192:0.87 195:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.48 215:0.51 216:0.84 219:0.89 222:0.48 230:0.69 232:0.99 233:0.76 235:0.42 241:0.41 242:0.61 243:0.44 245:0.81 247:0.91 248:0.73 253:0.80 254:0.93 255:0.74 256:0.45 259:0.21 260:0.68 265:0.36 266:0.80 267:0.44 268:0.46 271:0.60 276:0.79 277:0.69 279:0.82 282:0.83 283:0.78 285:0.56 290:0.71 292:0.91 297:0.36 300:0.70 +1 11:0.83 12:0.51 17:0.32 18:0.70 21:0.78 27:0.28 29:0.86 30:0.85 34:0.73 36:0.90 37:0.65 39:0.55 43:0.57 45:0.77 66:0.80 69:0.94 70:0.28 71:0.79 74:0.50 85:0.85 86:0.77 91:0.02 98:0.32 101:0.87 108:0.88 122:0.82 123:0.87 124:0.38 127:0.25 129:0.05 133:0.37 135:0.90 139:0.74 146:0.68 147:0.05 149:0.66 154:0.46 159:0.71 163:0.81 172:0.65 173:0.87 177:0.05 178:0.55 179:0.47 187:0.87 212:0.30 216:0.44 222:0.48 241:0.10 242:0.33 243:0.11 245:0.54 247:0.60 253:0.35 257:0.84 259:0.21 265:0.35 266:0.63 267:0.23 271:0.56 276:0.34 300:0.33 +1 11:0.57 12:0.51 17:0.28 18:0.60 21:0.78 27:0.28 29:0.86 30:0.91 34:0.50 36:0.79 37:0.65 39:0.55 43:0.65 66:0.49 69:0.90 70:0.13 71:0.79 74:0.37 85:0.72 86:0.69 91:0.02 98:0.08 101:0.87 108:0.80 122:0.41 123:0.79 124:0.48 127:0.17 129:0.05 133:0.37 135:0.71 139:0.67 146:0.17 147:0.05 149:0.41 154:0.54 159:0.46 163:0.97 172:0.16 173:0.81 177:0.05 178:0.55 179:0.83 187:0.87 212:0.61 216:0.44 222:0.48 241:0.12 242:0.63 243:0.29 245:0.39 247:0.30 253:0.30 257:0.84 259:0.21 265:0.08 266:0.17 267:0.23 271:0.56 276:0.12 300:0.13 +1 11:0.57 12:0.51 17:0.40 18:0.66 21:0.78 27:0.66 29:0.86 30:0.95 34:0.85 36:0.88 37:0.57 39:0.55 43:0.85 66:0.64 69:0.61 71:0.79 74:0.44 85:0.72 86:0.75 91:0.22 98:0.17 101:0.87 108:0.46 122:0.57 123:0.45 127:0.10 129:0.05 135:0.66 139:0.72 146:0.17 147:0.22 149:0.60 154:0.82 159:0.09 163:0.90 172:0.16 173:0.92 177:0.70 178:0.55 179:0.16 187:0.98 212:0.95 216:0.23 222:0.48 235:0.22 241:0.44 242:0.82 243:0.69 247:0.12 253:0.33 259:0.21 265:0.19 266:0.12 267:0.52 271:0.56 276:0.19 300:0.08 +1 11:0.57 12:0.51 17:0.47 18:0.74 21:0.78 27:0.28 29:0.86 30:0.87 34:0.44 36:0.79 37:0.65 39:0.55 43:0.56 66:0.79 69:0.95 70:0.24 71:0.79 74:0.55 85:0.93 86:0.84 91:0.02 98:0.32 101:0.87 108:0.89 122:0.57 123:0.89 124:0.40 127:0.21 129:0.05 133:0.59 135:0.89 139:0.81 146:0.80 147:0.05 149:0.77 154:0.45 159:0.77 163:0.75 172:0.75 173:0.82 177:0.05 178:0.55 179:0.27 187:0.87 212:0.39 216:0.44 222:0.48 241:0.15 242:0.41 243:0.09 245:0.59 247:0.60 253:0.37 257:0.84 259:0.21 265:0.34 266:0.54 267:0.23 271:0.56 276:0.53 300:0.33 +1 8:0.60 11:0.83 12:0.51 17:0.49 18:0.60 21:0.78 27:0.28 29:0.86 30:0.57 34:0.23 36:0.81 37:0.65 39:0.55 43:0.31 45:0.93 66:0.07 69:0.99 70:0.80 71:0.79 74:0.53 85:0.80 86:0.64 91:0.08 98:0.15 101:0.87 108:0.98 122:0.51 123:0.98 124:0.98 127:0.84 129:0.81 133:0.98 135:0.19 139:0.63 146:0.34 147:0.05 149:0.66 154:0.12 159:0.97 163:0.88 172:0.37 173:0.94 177:0.05 178:0.55 179:0.32 187:0.39 212:0.12 216:0.44 222:0.48 235:0.31 241:0.10 242:0.31 243:0.07 245:0.71 247:0.86 253:0.36 257:0.84 259:0.21 265:0.16 266:0.98 267:0.82 271:0.56 276:0.83 277:0.69 300:0.84 +1 11:0.57 12:0.37 17:0.83 21:0.78 27:0.29 30:0.95 34:0.52 36:0.21 37:0.26 43:0.79 66:0.54 69:0.74 74:0.42 85:0.72 91:0.12 98:0.07 101:0.68 108:0.57 123:0.55 127:0.05 129:0.05 135:0.90 146:0.13 147:0.18 149:0.44 154:0.73 159:0.08 172:0.10 173:0.53 177:0.38 178:0.55 179:0.08 187:0.21 216:0.26 222:0.84 241:0.24 243:0.63 253:0.37 259:0.21 265:0.07 267:0.23 271:0.42 300:0.08 +1 11:0.57 12:0.37 17:0.22 21:0.78 27:0.32 30:0.20 34:0.44 36:0.57 37:0.66 43:0.64 55:0.71 66:0.12 69:0.57 70:1.00 74:0.65 85:0.80 91:0.02 98:0.12 101:0.47 108:0.97 122:0.67 123:0.97 124:0.94 127:0.15 129:0.89 133:0.93 135:0.73 146:0.17 147:0.22 149:0.44 154:0.82 159:0.95 163:0.87 172:0.37 173:0.06 177:0.38 178:0.55 179:0.14 187:0.68 212:0.12 216:0.80 222:0.84 235:0.42 241:0.07 242:0.72 243:0.13 245:0.67 247:0.55 253:0.50 259:0.21 265:0.12 266:0.97 267:0.82 271:0.42 276:0.65 277:0.69 300:0.99 +0 11:0.57 12:0.37 17:0.69 21:0.78 27:0.64 30:0.95 34:0.64 36:0.97 37:0.39 43:0.90 66:0.54 69:0.45 74:0.45 85:0.72 91:0.25 98:0.13 101:0.75 108:0.35 123:0.33 127:0.08 129:0.05 135:0.77 146:0.13 147:0.18 149:0.51 154:0.89 159:0.08 172:0.10 173:0.72 177:0.38 178:0.55 179:0.12 187:0.68 216:0.19 222:0.84 235:0.05 241:0.41 243:0.63 253:0.39 259:0.21 265:0.14 267:0.17 271:0.42 300:0.08 +1 11:0.57 12:0.37 21:0.78 27:0.32 30:0.95 34:0.69 36:0.98 37:0.66 43:0.76 66:0.54 69:0.81 74:0.41 85:0.72 91:0.08 98:0.10 101:0.73 108:0.65 123:0.63 127:0.07 129:0.05 135:0.79 146:0.13 147:0.18 149:0.40 154:0.68 159:0.08 172:0.10 173:0.90 177:0.38 178:0.55 179:0.09 187:0.68 216:0.80 222:0.84 235:0.52 241:0.05 243:0.63 253:0.36 259:0.21 265:0.11 267:0.36 271:0.42 300:0.08 +1 11:0.57 12:0.37 17:0.40 21:0.78 27:0.72 30:0.95 34:0.90 36:0.95 37:0.45 43:0.76 66:0.54 69:0.81 74:0.41 85:0.72 91:0.07 98:0.11 101:0.74 108:0.65 123:0.63 127:0.08 129:0.05 135:0.61 146:0.13 147:0.18 149:0.41 154:0.68 159:0.08 172:0.10 173:0.94 177:0.38 178:0.55 179:0.10 187:0.21 216:0.12 222:0.84 235:0.68 241:0.43 243:0.63 253:0.36 259:0.21 265:0.12 267:0.23 271:0.42 300:0.08 +1 11:0.57 12:0.37 17:0.67 21:0.78 27:0.64 30:0.76 34:0.31 36:0.96 37:0.39 43:0.87 66:0.64 69:0.53 70:0.15 74:0.45 85:0.72 91:0.18 98:0.13 101:0.71 108:0.41 122:0.43 123:0.39 127:0.08 129:0.05 135:0.87 146:0.20 147:0.26 149:0.50 154:0.85 159:0.10 163:0.90 172:0.16 173:0.46 177:0.38 178:0.55 179:0.11 187:0.68 212:0.95 216:0.19 222:0.84 235:0.05 241:0.37 242:0.82 243:0.69 247:0.12 253:0.39 259:0.21 265:0.14 266:0.12 267:0.18 271:0.42 276:0.15 300:0.13 +1 11:0.57 12:0.37 17:0.30 21:0.78 27:0.32 30:0.27 34:0.30 36:0.47 37:0.66 43:0.71 55:0.71 66:0.13 69:0.87 70:0.98 74:0.59 85:0.80 98:0.12 101:0.47 108:0.97 122:0.67 123:0.97 124:0.93 127:0.14 129:0.81 133:0.92 135:0.85 146:0.31 147:0.37 149:0.48 154:0.61 159:0.94 163:0.87 172:0.37 173:0.19 177:0.38 178:0.55 179:0.14 187:0.68 212:0.12 216:0.80 222:0.84 241:0.07 242:0.73 243:0.19 245:0.67 247:0.55 253:0.47 259:0.21 265:0.12 266:0.97 267:0.82 271:0.42 276:0.62 277:0.69 300:0.98 +2 11:0.57 12:0.37 17:0.51 21:0.78 27:0.64 30:0.76 34:0.61 36:1.00 37:0.39 43:0.81 66:0.64 69:0.59 70:0.15 74:0.54 85:0.72 91:0.15 98:0.61 101:0.75 108:0.45 122:0.57 123:0.43 127:0.18 129:0.05 135:0.74 146:0.36 147:0.42 149:0.49 154:0.76 159:0.14 163:0.93 172:0.16 173:0.84 177:0.38 178:0.55 179:0.92 187:0.68 212:0.95 216:0.19 222:0.84 235:0.05 241:0.43 242:0.82 243:0.69 247:0.12 253:0.44 259:0.21 265:0.62 266:0.12 267:0.28 271:0.42 276:0.13 300:0.13 +0 7:0.78 8:0.90 9:0.80 12:0.20 17:0.26 21:0.40 27:0.06 28:0.56 30:0.58 32:0.75 34:0.59 36:0.34 37:0.92 39:0.50 41:0.82 43:0.29 55:0.52 66:0.27 69:0.71 70:0.44 81:0.50 83:0.76 91:0.64 96:0.80 98:0.57 108:0.74 120:0.79 123:0.52 124:0.59 126:0.32 127:0.32 129:0.59 133:0.47 135:0.28 140:0.92 145:0.56 146:0.54 147:0.60 149:0.41 150:0.39 151:0.87 153:0.48 154:0.21 155:0.67 159:0.32 161:0.74 162:0.56 164:0.72 167:0.85 172:0.27 173:0.79 175:0.75 177:0.05 178:0.48 179:0.79 181:0.72 187:0.39 191:0.85 192:0.59 195:0.66 202:0.69 208:0.64 212:0.37 215:0.67 216:0.75 220:0.91 222:0.84 230:0.81 233:0.76 235:0.42 241:0.75 242:0.82 243:0.46 244:0.61 245:0.56 247:0.12 248:0.78 253:0.74 255:0.79 256:0.64 257:0.65 259:0.21 260:0.79 265:0.25 266:0.47 267:0.59 268:0.66 276:0.34 277:0.69 283:0.71 285:0.62 297:0.36 300:0.33 +1 11:0.57 12:0.20 17:0.67 21:0.78 27:0.45 28:0.56 30:0.58 34:0.96 36:0.21 37:0.50 39:0.50 43:0.76 66:0.36 69:0.76 70:0.24 74:0.53 85:0.85 91:0.17 98:0.37 101:0.76 108:0.59 122:0.41 123:0.57 124:0.59 127:0.18 129:0.59 133:0.47 135:0.73 145:0.49 146:0.55 147:0.61 149:0.68 154:0.67 159:0.35 161:0.74 163:0.81 167:0.63 172:0.27 173:0.69 177:0.38 178:0.55 179:0.55 181:0.72 187:0.68 212:0.37 216:0.77 222:0.84 235:0.98 241:0.24 242:0.40 243:0.51 245:0.56 247:0.47 253:0.44 259:0.21 265:0.39 266:0.47 267:0.89 276:0.35 300:0.18 +4 6:0.99 7:0.78 8:0.96 9:0.80 12:0.20 17:0.24 20:0.89 21:0.68 27:0.06 28:0.56 30:0.58 32:0.75 34:0.67 36:0.71 37:0.92 39:0.50 41:0.95 43:0.44 55:0.52 66:0.80 69:0.27 70:0.60 78:0.94 81:0.32 83:0.82 91:0.96 96:0.94 98:0.98 100:0.99 108:0.21 111:0.99 120:0.99 123:0.20 126:0.32 127:0.81 129:0.05 135:0.23 140:0.95 145:0.63 146:0.61 147:0.66 149:0.48 150:0.30 151:0.98 152:0.92 153:0.48 154:0.33 155:0.67 159:0.22 161:0.74 162:0.51 164:0.98 167:0.98 169:0.98 172:0.45 173:0.58 175:0.75 177:0.05 178:0.48 179:0.89 181:0.72 186:0.93 189:0.99 191:0.98 192:0.59 195:0.56 202:0.99 208:0.64 212:0.37 215:0.49 216:0.75 222:0.84 230:0.81 233:0.76 235:0.80 238:0.99 241:1.00 242:0.82 243:0.82 244:0.61 247:0.12 248:0.93 253:0.92 255:0.79 256:0.98 257:0.65 259:0.21 260:0.99 261:0.97 265:0.98 266:0.27 267:0.82 268:0.98 276:0.35 277:0.69 283:0.82 285:0.62 297:0.36 300:0.43 +1 7:0.78 11:0.57 12:0.20 17:0.47 21:0.47 27:0.21 28:0.56 30:0.26 34:0.58 36:0.56 37:0.74 39:0.50 43:0.89 55:0.59 66:0.64 69:0.15 70:0.81 74:0.73 81:0.44 85:0.94 91:0.33 98:0.84 101:0.79 106:0.81 108:0.78 120:0.73 123:0.77 124:0.48 126:0.32 127:0.68 129:0.59 133:0.47 135:0.17 140:0.45 146:0.83 147:0.86 149:0.93 150:0.36 151:0.63 153:0.72 154:0.87 159:0.82 161:0.74 162:0.66 164:0.79 167:0.68 172:0.94 173:0.10 177:0.38 179:0.18 181:0.72 187:0.39 190:0.77 202:0.73 206:0.81 212:0.71 215:0.63 216:0.95 220:0.93 222:0.84 230:0.81 233:0.76 235:0.52 241:0.43 242:0.77 243:0.33 245:0.98 247:0.67 248:0.66 253:0.54 256:0.71 259:0.21 260:0.74 265:0.84 266:0.63 267:0.36 268:0.75 276:0.93 283:0.71 285:0.50 297:0.36 300:0.70 +1 1:0.74 9:0.80 11:0.57 12:0.20 17:0.55 20:0.87 21:0.59 27:0.35 28:0.56 30:0.21 34:0.47 36:0.96 37:0.48 39:0.50 41:0.82 43:0.84 55:0.52 60:0.87 66:0.23 69:0.63 70:0.93 74:0.81 78:0.74 81:0.60 83:0.83 85:0.93 91:0.11 96:0.83 98:0.30 100:0.77 101:0.77 108:0.88 111:0.74 114:0.74 117:0.86 120:0.72 123:0.88 124:0.91 126:0.54 127:0.47 129:0.89 133:0.91 135:0.89 140:0.45 146:0.67 147:0.72 149:0.88 150:0.74 151:0.90 152:0.82 153:0.69 154:0.81 155:0.67 158:0.87 159:0.84 161:0.74 162:0.86 164:0.62 165:0.78 167:0.59 169:0.75 172:0.63 173:0.36 176:0.73 177:0.38 179:0.31 181:0.72 186:0.75 187:0.87 192:0.59 201:0.74 202:0.46 208:0.64 212:0.67 215:0.55 216:0.64 222:0.84 232:1.00 235:0.74 241:0.10 242:0.55 243:0.37 245:0.79 247:0.55 248:0.80 253:0.85 255:0.79 256:0.45 259:0.21 260:0.73 261:0.76 265:0.32 266:0.89 267:0.23 268:0.55 276:0.79 279:0.86 283:0.71 285:0.62 290:0.74 297:0.36 300:0.84 +4 6:0.99 8:0.95 9:0.80 11:0.57 12:0.20 17:0.22 20:0.82 21:0.68 27:0.06 28:0.56 30:0.33 32:0.75 34:0.86 36:0.39 37:0.92 39:0.50 41:0.82 43:0.75 66:0.68 69:0.83 70:0.49 74:0.40 78:0.84 81:0.32 83:0.77 85:0.72 91:0.85 96:0.85 98:0.51 100:0.94 101:0.73 108:0.68 111:0.88 120:0.90 123:0.66 124:0.41 126:0.32 127:0.45 129:0.05 133:0.39 135:0.44 140:0.93 145:0.76 146:0.46 147:0.05 149:0.53 150:0.30 151:0.93 152:0.92 153:0.48 154:0.66 155:0.67 159:0.34 161:0.74 162:0.60 164:0.87 167:0.88 169:0.98 172:0.37 173:0.94 177:0.05 179:0.87 181:0.72 186:0.84 187:0.87 189:0.99 191:0.96 192:0.59 195:0.62 202:0.84 208:0.64 212:0.42 215:0.49 216:0.75 222:0.84 235:0.80 238:0.97 241:0.76 242:0.63 243:0.19 245:0.45 247:0.39 248:0.83 253:0.80 255:0.79 256:0.83 257:0.65 259:0.21 260:0.90 261:0.97 265:0.53 266:0.38 267:0.23 268:0.84 276:0.25 283:0.71 285:0.62 297:0.36 300:0.33 +1 1:0.74 11:0.57 12:0.20 17:0.77 21:0.71 27:0.37 28:0.56 30:0.76 32:0.80 34:0.90 36:0.34 37:0.58 39:0.50 43:0.78 66:0.80 69:0.79 70:0.28 74:0.71 77:0.70 81:0.51 83:0.73 85:0.88 91:0.37 98:0.41 101:0.81 108:0.63 114:0.68 117:0.86 122:0.43 123:0.60 126:0.54 127:0.13 129:0.05 135:0.49 145:0.47 146:0.38 147:0.44 149:0.77 150:0.67 154:0.70 155:0.67 159:0.15 161:0.74 165:0.69 167:0.56 172:0.45 173:0.90 176:0.73 177:0.38 178:0.55 179:0.31 181:0.72 187:0.68 192:0.59 195:0.59 201:0.74 212:0.90 215:0.48 216:0.35 222:0.84 232:0.95 235:0.88 241:0.02 242:0.72 243:0.82 247:0.30 253:0.61 259:0.21 265:0.43 266:0.17 267:0.76 276:0.34 282:0.88 290:0.68 297:0.36 299:0.88 300:0.25 +0 1:0.74 6:0.89 7:0.81 8:0.62 9:0.80 11:0.57 12:0.20 17:0.43 20:0.82 21:0.30 27:0.21 28:0.56 30:0.19 34:0.45 36:0.74 37:0.74 39:0.50 41:0.92 43:0.98 55:0.71 60:0.87 66:0.29 69:0.12 70:0.88 74:0.95 78:0.79 81:0.77 83:0.82 85:0.99 91:0.56 96:0.82 98:0.65 100:0.89 101:0.82 108:0.83 111:0.82 114:0.86 120:0.77 121:0.97 123:0.82 124:0.89 126:0.54 127:0.88 129:0.93 133:0.90 135:0.24 140:0.80 146:0.91 147:0.92 149:0.98 150:0.85 151:0.82 152:0.95 153:0.46 154:0.98 155:0.67 158:0.92 159:0.94 161:0.74 162:0.50 164:0.80 165:0.84 167:0.69 169:0.83 172:0.96 173:0.06 176:0.73 177:0.38 178:0.42 179:0.12 181:0.72 186:0.80 187:0.39 189:0.92 191:0.73 192:0.59 201:0.74 202:0.86 204:0.89 208:0.64 212:0.55 215:0.72 216:0.95 220:0.87 222:0.84 230:0.87 233:0.76 235:0.42 238:0.96 241:0.47 242:0.71 243:0.27 245:0.99 247:0.67 248:0.79 253:0.88 255:0.79 256:0.79 259:0.21 260:0.78 261:0.87 265:0.66 266:0.80 267:0.44 268:0.79 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +3 6:0.99 7:0.78 8:0.95 9:0.80 12:0.20 17:0.45 20:0.83 21:0.68 27:0.06 28:0.56 30:0.58 32:0.75 34:0.85 36:0.71 37:0.92 39:0.50 41:0.94 43:0.44 55:0.52 66:0.80 69:0.27 70:0.60 78:0.86 81:0.32 83:0.82 91:0.94 96:0.94 98:0.87 100:0.93 108:0.21 111:0.88 120:0.98 123:0.20 126:0.32 127:0.39 129:0.05 135:0.26 140:0.95 145:0.63 146:0.61 147:0.66 149:0.48 150:0.30 151:0.98 152:0.92 153:0.48 154:0.33 155:0.67 159:0.22 161:0.74 162:0.58 164:0.97 167:0.95 169:0.98 172:0.45 173:0.52 175:0.75 177:0.05 179:0.82 181:0.72 186:0.86 187:0.21 189:0.97 191:0.97 192:0.59 195:0.58 202:0.96 208:0.64 212:0.37 215:0.49 216:0.75 222:0.84 230:0.81 233:0.76 235:0.80 238:0.95 241:0.86 242:0.82 243:0.82 244:0.61 247:0.12 248:0.93 253:0.91 255:0.79 256:0.96 257:0.65 259:0.21 260:0.98 261:0.97 265:0.87 266:0.27 267:0.76 268:0.96 276:0.35 277:0.69 283:0.82 285:0.62 297:0.36 300:0.43 +1 8:0.62 11:0.57 12:0.20 17:0.64 21:0.78 27:0.45 28:0.56 30:0.40 34:0.81 36:0.18 37:0.50 39:0.50 43:0.74 55:0.59 66:0.43 69:0.85 70:0.96 74:0.56 85:0.85 91:0.25 98:0.40 101:0.75 108:0.71 123:0.69 124:0.66 127:0.37 129:0.59 133:0.64 135:0.94 145:0.47 146:0.65 147:0.70 149:0.72 154:0.65 159:0.66 161:0.74 167:0.66 172:0.57 173:0.77 177:0.38 178:0.55 179:0.47 181:0.72 187:0.68 212:0.73 216:0.77 222:0.84 235:1.00 241:0.37 242:0.63 243:0.57 245:0.75 247:0.39 253:0.45 259:0.21 265:0.42 266:0.63 267:0.91 276:0.56 300:0.94 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.20 17:0.24 21:0.54 27:0.56 28:0.56 30:0.16 34:0.30 36:0.33 37:0.53 39:0.50 41:0.82 43:0.98 60:0.87 66:0.05 69:0.19 70:0.98 74:0.87 81:0.63 83:0.84 85:0.99 91:0.04 96:0.79 98:0.17 101:0.78 106:0.81 108:0.99 114:0.77 117:0.86 120:0.68 121:0.99 123:0.97 124:0.99 126:0.54 127:0.93 129:0.99 133:0.99 135:0.32 140:0.45 146:0.64 147:0.70 149:0.93 150:0.77 151:0.83 153:0.80 154:0.98 155:0.67 158:0.87 159:0.98 161:0.74 162:0.78 164:0.70 165:0.79 167:0.57 172:0.59 173:0.05 176:0.73 177:0.38 179:0.14 181:0.72 187:0.39 192:0.59 201:0.74 202:0.59 204:0.89 206:0.81 208:0.64 212:0.26 215:0.58 216:0.87 220:0.62 222:0.84 230:0.84 232:0.88 233:0.69 235:0.68 241:0.03 242:0.63 243:0.10 245:0.91 247:0.47 248:0.75 253:0.84 255:0.79 256:0.58 259:0.21 260:0.69 265:0.18 266:0.99 267:0.99 268:0.64 276:0.96 279:0.86 283:0.71 285:0.62 290:0.77 297:0.36 300:0.94 +1 7:0.78 8:0.73 11:0.57 12:0.20 17:0.43 21:0.47 27:0.21 28:0.56 30:0.26 34:0.52 36:0.55 37:0.74 39:0.50 43:0.88 55:0.59 66:0.64 69:0.15 70:0.81 74:0.73 81:0.44 85:0.94 91:0.35 98:0.84 101:0.79 108:0.78 120:0.56 123:0.77 124:0.48 126:0.32 127:0.69 129:0.59 133:0.47 135:0.17 140:0.80 146:0.83 147:0.86 149:0.93 150:0.36 151:0.49 153:0.48 154:0.86 159:0.82 161:0.74 162:0.49 164:0.56 167:0.68 172:0.94 173:0.10 177:0.38 178:0.42 179:0.18 181:0.72 187:0.39 190:0.77 202:0.66 212:0.71 215:0.63 216:0.95 220:0.93 222:0.84 230:0.81 233:0.76 235:0.52 241:0.43 242:0.77 243:0.33 245:0.98 247:0.67 248:0.49 253:0.54 256:0.45 259:0.21 260:0.56 265:0.84 266:0.63 267:0.28 268:0.46 276:0.93 285:0.50 297:0.36 300:0.70 +1 7:0.78 8:0.92 9:0.80 11:0.57 12:0.20 17:0.03 21:0.68 27:0.06 28:0.56 30:0.17 32:0.75 34:0.93 36:0.92 37:0.92 39:0.50 41:0.90 43:0.61 55:0.52 66:0.49 69:0.92 70:0.90 74:0.36 81:0.38 83:0.74 85:0.72 91:0.93 96:0.77 98:0.39 101:0.71 108:0.83 120:0.97 123:0.82 124:0.73 126:0.32 127:0.67 129:0.05 133:0.74 135:0.81 140:1.00 145:0.63 146:0.51 147:0.05 149:0.50 150:0.34 151:0.69 153:0.48 154:0.50 155:0.67 159:0.59 161:0.74 162:0.56 164:0.98 167:0.82 172:0.45 173:0.97 177:0.05 179:0.76 181:0.72 187:0.68 190:0.77 191:0.95 192:0.59 195:0.76 202:0.97 208:0.64 212:0.30 215:0.49 216:0.75 222:0.84 230:0.81 233:0.76 235:0.84 241:0.72 242:0.77 243:0.13 245:0.55 247:0.39 248:0.70 253:0.62 255:0.79 256:0.97 257:0.65 259:0.21 260:0.98 265:0.41 266:0.63 267:0.28 268:0.97 276:0.44 283:0.71 285:0.62 297:0.36 300:0.70 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.55 21:0.22 27:0.51 28:0.56 30:0.74 34:0.86 36:0.41 37:0.47 39:0.50 43:0.87 66:0.77 69:0.55 70:0.51 74:0.93 81:0.82 83:0.76 85:0.97 91:0.29 98:0.75 101:0.86 106:0.81 108:0.62 114:0.90 117:0.86 121:0.90 122:0.43 123:0.60 124:0.41 126:0.54 127:0.35 129:0.59 133:0.47 135:0.60 146:0.20 147:0.25 149:0.96 150:0.88 154:0.85 155:0.67 159:0.49 161:0.74 165:0.86 167:0.68 172:0.82 173:0.49 176:0.73 177:0.38 178:0.55 179:0.31 181:0.72 187:0.68 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.69 215:0.79 216:0.74 222:0.84 230:0.84 232:0.85 233:0.69 235:0.31 241:0.44 242:0.58 243:0.19 245:0.81 247:0.55 253:0.77 259:0.21 265:0.75 266:0.63 267:0.44 276:0.74 290:0.90 297:0.36 300:0.55 +0 6:0.97 8:0.95 9:0.80 12:0.20 17:0.09 20:0.89 21:0.05 27:0.32 28:0.56 30:0.28 32:0.75 34:0.37 36:0.25 37:0.97 39:0.50 41:0.90 43:0.44 55:0.71 66:0.87 69:0.08 70:0.79 78:0.77 81:0.81 83:0.79 91:0.89 96:0.90 98:0.98 100:0.84 108:0.07 111:0.78 120:0.94 123:0.07 126:0.32 127:0.84 129:0.05 135:0.39 140:0.94 145:0.74 146:0.84 147:0.86 149:0.59 150:0.60 151:0.96 152:0.84 153:0.48 154:0.33 155:0.67 159:0.40 161:0.74 162:0.51 164:0.92 167:0.94 169:0.82 172:0.63 173:0.24 175:0.75 177:0.05 179:0.74 181:0.72 186:0.77 189:0.98 191:0.95 192:0.59 195:0.59 202:0.93 208:0.64 212:0.48 215:0.91 216:0.37 222:0.84 235:0.05 238:0.96 241:0.84 242:0.82 243:0.88 244:0.61 247:0.12 248:0.87 253:0.85 255:0.79 256:0.89 257:0.65 259:0.21 260:0.94 261:0.82 265:0.98 266:0.54 267:0.87 268:0.90 276:0.52 277:0.69 283:0.82 285:0.62 297:0.36 300:0.55 +0 7:0.81 8:0.98 9:0.80 12:0.20 17:0.51 21:0.74 27:0.06 28:0.56 30:0.45 32:0.75 34:0.86 36:0.51 37:0.92 39:0.50 41:0.92 43:0.37 55:0.52 66:0.24 69:0.55 70:0.41 74:0.47 81:0.34 83:0.75 91:0.89 96:0.79 98:0.54 108:0.76 120:0.93 121:0.90 123:0.61 124:0.71 126:0.32 127:0.65 129:0.81 133:0.67 135:0.39 140:0.98 145:0.61 146:0.39 147:0.46 149:0.50 150:0.31 151:0.80 153:0.48 154:0.28 155:0.67 159:0.41 161:0.74 162:0.54 164:0.94 167:0.86 172:0.32 173:0.62 175:0.75 177:0.05 178:0.42 179:0.78 181:0.72 187:0.21 190:0.77 191:0.94 192:0.59 195:0.65 202:0.93 204:0.89 208:0.64 212:0.36 215:0.46 216:0.75 220:1.00 222:0.84 230:0.87 233:0.76 235:0.95 241:0.75 242:0.82 243:0.23 244:0.61 245:0.60 247:0.12 248:0.74 253:0.69 255:0.79 256:0.92 257:0.65 259:0.21 260:0.93 265:0.26 266:0.54 267:0.87 268:0.92 276:0.45 277:0.69 283:0.71 285:0.62 297:0.36 300:0.33 +1 7:0.78 8:0.97 9:0.80 12:0.20 17:0.08 21:0.59 27:0.04 28:0.56 30:0.58 34:0.39 36:0.68 37:0.94 39:0.50 41:0.90 43:0.39 55:0.52 66:0.61 69:0.46 70:0.44 81:0.36 83:0.79 91:0.98 96:0.92 98:0.28 108:0.75 120:1.00 123:0.73 124:0.47 126:0.32 127:0.80 129:0.59 133:0.47 135:0.18 140:0.99 145:0.59 146:0.21 147:0.27 149:0.41 150:0.32 151:0.97 153:0.48 154:0.29 155:0.67 159:0.41 161:0.74 162:0.54 164:1.00 167:0.96 172:0.37 173:0.55 175:0.75 177:0.05 179:0.89 181:0.72 191:0.99 192:0.59 202:1.00 208:0.64 212:0.71 215:0.55 216:0.87 222:0.84 230:0.81 233:0.76 235:0.68 241:0.93 242:0.82 243:0.29 244:0.61 245:0.52 247:0.12 248:0.88 253:0.88 255:0.79 256:0.99 257:0.65 259:0.21 260:1.00 265:0.30 266:0.27 267:0.79 268:0.99 276:0.17 277:0.69 283:0.82 285:0.62 297:0.36 300:0.33 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.20 17:0.47 21:0.30 27:0.21 28:0.56 30:0.19 34:0.42 36:0.73 37:0.74 39:0.50 41:0.94 43:0.98 55:0.71 60:0.87 66:0.30 69:0.12 70:0.88 74:0.95 81:0.77 83:0.83 85:0.99 91:0.29 96:0.94 98:0.65 101:0.82 106:0.81 108:0.83 114:0.86 117:0.86 120:0.92 121:0.97 123:0.82 124:0.89 126:0.54 127:0.87 129:0.93 133:0.90 135:0.26 140:0.45 146:0.91 147:0.92 149:0.98 150:0.85 151:0.98 153:0.93 154:0.98 155:0.67 158:0.92 159:0.94 161:0.74 162:0.59 164:0.86 165:0.84 167:0.68 172:0.96 173:0.06 176:0.73 177:0.38 179:0.12 181:0.72 187:0.39 192:0.59 201:0.74 202:0.84 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.95 222:0.84 230:0.87 232:0.88 233:0.76 235:0.42 241:0.46 242:0.71 243:0.27 245:0.99 247:0.67 248:0.93 253:0.96 255:0.79 256:0.84 259:0.21 260:0.92 265:0.66 266:0.80 267:0.19 268:0.84 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.47 21:0.05 27:0.61 28:0.56 30:0.21 37:0.58 39:0.50 43:0.98 66:0.85 69:0.07 70:0.64 74:0.71 81:0.91 83:0.79 85:0.85 91:0.23 98:0.72 101:0.78 106:0.81 108:0.06 114:0.95 117:0.86 121:0.97 123:0.06 126:0.54 127:0.22 129:0.05 146:0.76 147:0.80 149:0.80 150:0.95 154:0.98 155:0.67 159:0.32 161:0.74 165:0.90 167:0.61 172:0.57 173:0.15 176:0.73 177:0.38 178:0.55 179:0.54 181:0.72 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.49 215:0.91 216:0.92 222:0.84 230:0.87 232:0.88 233:0.76 235:0.12 241:0.18 242:0.52 243:0.86 247:0.47 253:0.75 265:0.72 266:0.54 276:0.46 290:0.95 297:0.36 300:0.43 +1 1:0.69 8:0.86 11:0.64 12:0.96 17:0.47 18:0.63 21:0.54 26:0.94 27:0.72 29:0.86 30:0.62 34:0.89 36:0.49 37:0.23 39:0.68 43:0.69 45:0.66 58:0.93 66:0.61 69:0.50 70:0.34 71:0.86 74:0.52 77:0.70 81:0.36 83:0.60 86:0.64 91:0.38 96:0.72 98:0.24 99:0.83 101:0.52 108:0.38 114:0.57 117:0.86 122:0.57 123:0.37 124:0.43 126:0.44 127:0.45 129:0.05 133:0.39 135:0.37 139:0.62 140:0.45 141:0.91 145:0.50 146:0.24 147:0.30 149:0.44 150:0.53 151:0.51 153:0.48 154:0.58 155:0.58 159:0.27 162:0.62 165:0.70 172:0.27 173:0.67 175:0.75 176:0.61 177:0.38 179:0.93 187:0.39 190:0.89 192:0.59 195:0.56 199:0.94 201:0.68 202:0.50 208:0.54 212:0.61 215:0.39 216:0.07 220:0.74 222:0.48 223:0.92 224:0.93 232:0.92 234:0.91 235:0.88 241:0.61 242:0.33 243:0.68 244:0.61 245:0.42 247:0.39 248:0.51 253:0.47 256:0.45 257:0.65 259:0.21 265:0.26 266:0.23 267:0.23 268:0.46 271:0.51 274:0.91 276:0.15 277:0.69 282:0.73 285:0.47 290:0.57 297:0.36 300:0.25 +1 11:0.64 12:0.96 17:0.43 18:0.84 21:0.30 26:0.96 27:1.00 29:0.86 30:0.08 31:0.87 34:0.87 36:0.47 37:0.40 39:0.68 43:0.68 45:0.88 55:0.71 58:0.98 64:0.77 66:0.31 69:0.50 70:1.00 71:0.86 74:0.70 79:0.87 81:0.30 86:0.82 91:0.03 98:0.21 101:0.52 104:0.58 108:0.39 122:0.86 123:0.37 124:0.97 126:0.26 127:0.99 129:0.05 133:0.98 135:0.26 137:0.87 139:0.80 140:0.45 141:0.98 146:0.83 147:0.86 149:0.84 150:0.28 151:0.48 153:0.48 154:0.71 159:0.97 162:0.86 172:0.81 173:0.09 177:0.70 179:0.26 187:0.87 190:0.89 196:0.88 199:0.99 202:0.36 212:0.39 216:0.06 219:0.89 220:0.87 222:0.48 223:0.95 224:0.99 234:0.99 235:0.31 241:0.15 242:0.24 243:0.49 245:0.78 247:0.88 248:0.48 253:0.41 254:0.99 256:0.45 259:0.21 265:0.23 266:0.99 267:0.23 268:0.46 271:0.51 274:0.97 276:0.88 284:0.86 285:0.47 292:0.91 300:0.98 +0 7:0.66 11:0.64 12:0.96 17:0.64 18:0.99 21:0.40 22:0.93 26:0.96 27:0.39 29:0.86 30:0.31 32:0.64 34:0.75 36:0.35 37:0.67 39:0.68 43:0.30 45:0.73 47:0.93 48:0.91 55:0.59 58:0.93 64:0.77 66:0.71 69:0.52 70:0.72 71:0.86 74:0.35 81:0.36 86:0.98 91:0.22 98:0.81 101:0.52 104:1.00 106:0.81 108:0.40 120:0.59 122:0.86 123:0.38 124:0.47 126:0.44 127:0.62 129:0.59 133:0.61 135:0.92 139:0.98 140:0.45 141:0.91 145:0.82 146:0.97 147:0.97 149:0.59 150:0.29 151:0.51 153:0.48 154:0.56 159:0.80 162:0.86 164:0.53 172:0.94 173:0.30 177:0.70 179:0.19 187:0.21 190:0.89 192:0.51 195:0.92 199:1.00 201:0.68 202:0.36 206:0.81 212:0.88 215:0.42 216:0.89 220:0.74 222:0.48 223:0.95 224:0.93 230:0.69 233:0.73 234:0.91 235:0.52 241:0.05 242:0.73 243:0.75 244:0.61 245:0.93 247:0.79 248:0.51 253:0.29 256:0.45 259:0.21 260:0.58 262:0.93 265:0.81 266:0.75 267:0.28 268:0.46 271:0.51 274:0.91 276:0.91 281:0.91 283:0.78 285:0.47 297:0.36 300:0.84 +2 1:0.69 7:0.72 9:0.76 11:0.64 12:0.96 17:0.89 18:0.69 21:0.22 26:0.94 27:1.00 29:0.86 30:0.44 32:0.69 34:0.96 36:0.64 37:0.40 39:0.68 43:0.72 45:0.95 58:0.93 66:0.51 69:0.15 70:0.84 71:0.86 74:0.91 76:0.85 77:0.70 81:0.47 83:0.60 86:0.67 91:0.70 96:0.76 97:0.89 98:0.89 99:0.83 101:0.52 104:0.58 106:0.81 108:0.65 114:0.63 117:0.86 120:0.64 122:0.51 123:0.62 124:0.56 126:0.44 127:0.90 129:0.59 133:0.46 135:0.58 139:0.65 140:0.45 141:0.91 145:0.54 146:0.41 147:0.47 149:0.94 150:0.56 151:0.70 153:0.69 154:0.62 155:0.58 158:0.78 159:0.67 162:0.86 164:0.55 165:0.70 172:0.85 173:0.16 176:0.61 177:0.70 179:0.29 187:0.21 190:0.77 192:0.59 195:0.80 199:0.94 201:0.68 202:0.46 204:0.85 206:0.81 208:0.54 212:0.77 215:0.44 216:0.06 220:0.62 222:0.48 223:0.92 224:0.93 230:0.75 233:0.76 234:0.91 235:0.42 241:0.50 242:0.41 243:0.45 244:0.61 245:0.95 247:0.82 248:0.65 253:0.75 255:0.74 256:0.45 259:0.21 260:0.64 265:0.89 266:0.71 267:0.65 268:0.55 271:0.51 274:0.91 276:0.85 279:0.82 282:0.77 283:0.78 285:0.56 290:0.63 297:0.36 300:0.84 +2 11:0.64 12:0.96 17:0.83 18:0.69 21:0.22 26:0.94 27:1.00 29:0.86 30:0.57 34:0.96 36:0.18 37:0.40 39:0.68 43:0.72 45:0.94 58:0.93 66:0.36 69:0.10 70:0.64 71:0.86 74:0.88 81:0.32 86:0.67 91:0.54 98:0.82 101:0.52 104:0.58 108:0.65 122:0.75 123:0.62 124:0.61 126:0.26 127:0.71 129:0.59 133:0.46 135:0.66 139:0.66 141:0.91 146:0.41 147:0.47 149:0.93 150:0.29 154:0.62 159:0.55 172:0.75 173:0.17 177:0.70 178:0.55 179:0.34 187:0.21 199:0.94 212:0.83 215:0.51 216:0.06 222:0.48 223:0.92 224:0.93 234:0.91 235:0.22 241:0.55 242:0.51 243:0.51 244:0.61 245:0.93 247:0.76 253:0.46 259:0.21 265:0.82 266:0.71 267:0.28 271:0.51 274:0.91 276:0.80 297:0.36 300:0.70 +2 1:0.69 7:0.72 8:0.62 9:0.76 11:0.64 12:0.96 17:0.87 18:0.69 21:0.22 26:0.94 27:1.00 29:0.86 30:0.52 32:0.69 34:0.90 36:0.48 37:0.40 39:0.68 43:0.72 45:0.97 58:0.93 66:0.61 69:0.17 70:0.80 71:0.86 74:0.93 77:0.70 81:0.51 83:0.60 86:0.67 91:0.85 96:0.91 97:0.89 98:0.92 99:0.83 101:0.52 104:0.58 108:0.65 114:0.63 120:0.90 122:0.51 123:0.62 124:0.50 126:0.44 127:0.98 129:0.59 133:0.46 135:0.51 138:0.97 139:0.65 140:0.45 141:0.91 145:0.92 146:0.41 147:0.47 149:0.95 150:0.57 151:0.95 153:0.92 154:0.62 155:0.58 158:0.79 159:0.73 162:0.83 164:0.84 165:0.70 172:0.92 173:0.15 176:0.61 177:0.70 179:0.23 190:0.77 191:0.70 192:0.59 195:0.84 199:0.94 201:0.68 202:0.81 204:0.85 208:0.54 212:0.78 215:0.44 216:0.06 222:0.48 223:0.92 224:0.93 230:0.75 233:0.76 234:0.91 235:0.52 241:0.86 242:0.41 243:0.37 244:0.61 245:0.97 247:0.79 248:0.87 253:0.93 255:0.74 256:0.84 259:0.21 260:0.87 265:0.92 266:0.75 267:0.28 268:0.86 271:0.51 274:0.91 276:0.90 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +1 1:0.69 7:0.72 11:0.64 12:0.96 17:0.88 18:0.79 21:0.22 26:0.95 27:1.00 29:0.86 30:0.52 32:0.69 34:0.98 36:0.55 37:0.40 39:0.68 43:0.72 45:0.95 58:0.93 66:0.80 69:0.15 70:0.66 71:0.86 74:0.92 77:0.70 81:0.47 83:0.60 86:0.86 91:0.51 97:0.89 98:0.86 99:0.83 101:0.52 104:0.58 106:0.81 108:0.12 114:0.63 117:0.86 122:0.51 123:0.12 124:0.39 126:0.44 127:0.69 129:0.05 133:0.43 135:0.63 139:0.81 141:0.91 145:0.58 146:0.88 147:0.90 149:0.94 150:0.56 154:0.69 155:0.58 158:0.78 159:0.55 165:0.70 172:0.90 173:0.20 176:0.61 177:0.70 178:0.55 179:0.28 187:0.21 192:0.59 195:0.73 199:0.95 201:0.68 204:0.85 206:0.81 208:0.54 212:0.83 215:0.44 216:0.06 222:0.48 223:0.93 224:0.93 230:0.75 233:0.76 234:0.91 235:0.42 241:0.39 242:0.38 243:0.82 245:0.88 247:0.91 253:0.51 254:0.88 259:0.21 265:0.86 266:0.47 267:0.28 271:0.51 274:0.91 276:0.84 279:0.82 282:0.77 283:0.78 290:0.63 297:0.36 300:0.55 +0 7:0.72 11:0.64 12:0.96 17:0.47 18:0.79 21:0.71 22:0.93 26:0.96 27:0.59 29:0.86 30:0.15 31:0.86 32:0.64 34:0.80 36:0.65 37:0.34 39:0.68 43:0.12 45:0.66 47:0.93 55:0.42 58:0.98 64:0.77 66:0.45 69:0.85 70:0.73 71:0.86 74:0.55 76:0.85 79:0.86 81:0.29 86:0.82 91:0.23 98:0.46 101:0.52 104:0.96 106:0.81 108:0.71 120:0.53 123:0.69 124:0.58 126:0.44 127:0.21 129:0.05 133:0.43 135:0.60 137:0.86 139:0.81 140:0.45 141:0.98 145:0.93 146:0.57 147:0.46 149:0.17 150:0.23 151:0.46 153:0.48 154:0.58 155:0.58 159:0.34 162:0.86 164:0.54 172:0.51 173:0.87 177:0.38 179:0.37 187:0.87 190:0.77 192:0.59 195:0.77 196:0.87 199:0.96 201:0.68 202:0.36 204:0.85 206:0.81 208:0.54 212:0.77 215:0.37 216:0.87 219:0.92 220:1.00 222:0.48 223:0.95 224:0.98 230:0.75 233:0.76 234:0.97 235:0.88 241:0.05 242:0.21 243:0.46 245:0.76 247:0.82 248:0.46 253:0.37 254:0.74 255:0.74 256:0.45 257:0.65 259:0.21 260:0.53 262:0.93 265:0.48 266:0.47 267:0.52 268:0.46 271:0.51 274:0.97 276:0.56 283:0.66 284:0.87 285:0.56 292:0.87 297:0.36 300:0.55 +0 7:0.66 11:0.64 12:0.96 17:0.92 18:0.92 21:0.05 26:0.96 27:0.72 29:0.86 30:0.72 32:0.64 34:0.63 36:0.54 39:0.68 43:0.71 45:0.66 55:0.71 58:0.93 64:0.77 66:0.49 69:0.29 70:0.65 71:0.86 74:0.37 81:0.72 86:0.89 91:0.10 98:0.68 101:0.52 108:0.83 120:0.69 123:0.82 124:0.75 126:0.44 127:0.75 129:0.05 133:0.73 135:0.44 139:0.85 140:0.45 141:0.91 145:0.49 146:0.53 147:0.59 149:0.61 150:0.50 151:0.60 153:0.48 154:0.61 159:0.72 162:0.86 164:0.53 172:0.79 173:0.20 177:0.70 179:0.34 187:0.68 190:0.89 192:0.51 195:0.85 199:0.98 201:0.68 202:0.36 212:0.81 215:0.78 216:0.01 222:0.48 223:0.95 224:0.93 228:0.99 230:0.69 233:0.73 234:0.91 235:0.22 242:0.45 243:0.31 244:0.61 245:0.86 247:0.76 248:0.59 253:0.30 256:0.45 259:0.21 260:0.66 265:0.68 266:0.71 267:0.52 268:0.46 271:0.51 274:0.91 276:0.81 277:0.69 283:0.82 285:0.47 297:0.36 300:0.84 +2 11:0.64 12:0.96 17:0.86 18:0.84 21:0.05 22:0.93 26:0.96 27:1.00 29:0.86 30:0.55 34:0.99 36:0.37 37:0.40 39:0.68 43:0.72 45:0.95 47:0.93 58:0.93 64:0.77 66:0.77 69:0.07 70:0.74 71:0.86 74:0.89 81:0.47 86:0.85 91:0.61 98:0.82 101:0.52 104:0.58 106:0.81 108:0.06 117:0.86 122:0.51 123:0.06 124:0.42 126:0.26 127:0.83 129:0.05 133:0.60 135:0.97 139:0.83 141:0.91 146:0.86 147:0.88 149:0.93 150:0.37 154:0.66 159:0.57 172:0.88 173:0.14 177:0.70 178:0.55 179:0.33 187:0.39 199:0.97 206:0.81 212:0.76 216:0.06 219:0.89 222:0.48 223:0.95 224:0.93 234:0.91 235:0.05 241:0.32 242:0.40 243:0.79 245:0.82 247:0.90 253:0.46 254:0.74 259:0.21 262:0.93 265:0.82 266:0.54 267:0.36 271:0.51 274:0.91 276:0.82 292:0.91 300:0.70 +1 7:0.72 11:0.64 12:0.96 17:0.76 18:0.78 21:0.40 22:0.93 26:0.96 27:0.18 29:0.86 30:0.62 32:0.68 34:0.85 36:0.31 37:0.93 39:0.68 43:0.10 44:0.90 45:0.73 47:0.93 55:0.42 58:0.93 64:0.77 66:0.72 69:0.49 70:0.54 71:0.86 74:0.68 81:0.36 86:0.89 91:0.53 98:0.34 101:0.52 104:0.80 106:0.81 108:0.38 122:0.51 123:0.36 124:0.40 126:0.44 127:0.32 129:0.05 133:0.37 135:0.37 139:0.89 141:0.91 145:0.94 146:0.29 147:0.36 149:0.06 150:0.29 154:0.85 155:0.58 159:0.24 172:0.48 173:0.66 177:0.70 178:0.55 179:0.73 187:0.21 192:0.59 195:0.60 199:0.96 201:0.68 204:0.85 206:0.81 208:0.54 212:0.86 215:0.42 216:0.43 219:0.89 222:0.48 223:0.95 224:0.93 230:0.75 233:0.76 234:0.91 235:0.52 241:0.30 242:0.29 243:0.75 245:0.48 247:0.60 253:0.41 254:0.74 259:0.21 262:0.93 265:0.36 266:0.23 267:0.65 271:0.51 274:0.91 276:0.25 292:0.95 297:0.36 300:0.43 +0 7:0.66 11:0.64 12:0.96 17:0.49 18:0.92 21:0.22 26:0.96 27:0.37 29:0.86 30:0.76 32:0.64 34:0.99 36:0.35 37:0.89 39:0.68 43:0.64 45:0.66 55:0.59 58:0.93 64:0.77 66:0.91 69:0.54 70:0.30 71:0.86 74:0.41 76:0.94 81:0.34 86:0.87 91:0.35 98:0.94 101:0.52 104:0.58 108:0.41 122:0.86 123:0.39 126:0.26 127:0.63 129:0.05 135:0.34 139:0.84 141:0.91 145:0.65 146:0.89 147:0.91 149:0.47 150:0.31 154:0.54 159:0.48 172:0.76 173:0.56 177:0.70 178:0.55 179:0.54 187:0.68 195:0.69 199:0.97 212:0.68 216:0.24 219:0.89 222:0.48 223:0.95 224:0.93 230:0.69 233:0.76 234:0.91 235:0.22 241:0.12 242:0.55 243:0.92 244:0.61 247:0.74 253:0.32 259:0.21 265:0.94 266:0.54 267:0.36 271:0.51 274:0.91 276:0.65 292:0.91 300:0.33 +0 1:0.69 7:0.81 10:1.00 11:0.83 12:0.51 17:0.97 18:0.99 22:0.93 27:0.70 29:0.91 30:0.87 32:0.80 33:0.97 34:0.75 36:0.80 37:0.35 39:0.90 43:0.96 44:0.93 45:0.99 47:0.93 48:0.91 60:0.95 64:0.77 66:0.99 69:0.08 70:0.39 71:0.92 74:0.98 75:0.99 77:0.98 81:0.79 83:0.91 85:0.94 86:1.00 91:0.60 98:0.97 101:0.96 102:0.98 104:0.82 106:0.81 108:0.07 114:0.98 117:0.86 121:0.90 122:0.65 123:0.07 126:0.54 127:0.74 129:0.05 131:0.61 135:0.34 139:1.00 144:0.57 145:0.74 146:0.90 147:0.92 149:0.99 150:0.62 154:0.96 155:0.58 157:0.91 158:0.92 159:0.50 165:0.93 166:0.74 170:0.82 172:0.98 173:0.18 174:0.98 176:0.73 177:0.88 178:0.55 179:0.14 182:0.82 187:0.21 192:0.86 193:1.00 195:0.70 197:0.99 201:0.68 204:0.84 206:0.81 208:0.64 212:0.94 215:0.96 216:0.35 219:0.95 222:0.48 226:0.98 227:0.61 229:0.61 230:0.87 231:0.63 232:0.88 233:0.76 235:0.22 236:0.97 239:1.00 241:0.15 242:0.21 243:0.99 246:0.89 247:0.97 253:0.78 259:0.21 262:0.93 265:0.97 266:0.27 267:0.65 271:0.42 276:0.96 279:0.82 281:0.91 282:0.88 283:0.82 287:0.61 290:0.98 291:0.97 292:0.95 297:0.36 299:0.97 300:0.55 +0 1:0.67 8:0.63 10:0.96 11:0.64 12:0.51 17:0.75 18:0.87 21:0.47 27:0.54 29:0.91 30:0.87 34:0.97 36:0.34 37:0.60 39:0.90 43:0.61 45:0.91 64:0.77 66:0.90 69:0.32 70:0.30 71:0.92 74:0.73 81:0.73 83:0.91 86:0.96 91:0.06 98:0.71 99:0.99 101:0.87 108:0.25 114:0.80 122:0.65 123:0.24 126:0.54 127:0.22 129:0.05 131:0.61 135:0.37 139:0.94 144:0.57 146:0.53 147:0.59 149:0.43 150:0.53 154:0.87 155:0.52 157:0.83 159:0.19 165:0.93 166:0.74 170:0.82 172:0.71 173:0.50 174:0.86 176:0.73 177:0.88 178:0.55 179:0.35 182:0.81 187:0.68 192:0.50 197:0.90 201:0.66 208:0.64 212:0.80 215:0.63 216:0.64 222:0.48 227:0.61 229:0.61 231:0.63 235:0.80 236:0.97 239:0.95 241:0.05 242:0.18 243:0.90 246:0.89 247:0.79 253:0.64 254:0.80 259:0.21 265:0.71 266:0.27 267:0.65 271:0.42 276:0.57 287:0.61 290:0.80 297:0.36 300:0.33 +0 1:0.58 8:0.70 10:0.96 11:0.64 12:0.51 17:0.62 18:0.95 20:0.88 21:0.77 27:0.45 29:0.91 30:0.36 34:0.85 36:0.39 37:0.50 39:0.90 43:0.58 44:0.88 45:0.91 48:0.91 64:0.77 66:0.49 69:0.63 70:0.64 71:0.92 74:0.62 78:0.98 81:0.62 83:0.69 86:0.92 91:0.08 98:0.61 99:0.95 100:0.73 101:0.87 102:0.96 108:0.48 111:0.95 114:0.64 122:0.92 123:0.46 124:0.56 126:0.54 127:0.41 129:0.05 131:0.61 133:0.39 135:0.42 139:0.90 144:0.57 145:0.49 146:0.71 147:0.75 149:0.48 150:0.43 152:0.85 154:0.73 155:0.47 157:0.86 159:0.50 165:0.81 166:0.74 169:0.72 170:0.82 172:0.75 173:0.60 174:0.93 176:0.73 177:0.88 178:0.55 179:0.33 182:0.81 186:0.98 187:0.68 192:0.45 195:0.73 197:0.94 201:0.58 208:0.64 212:0.73 215:0.44 216:0.77 222:0.48 227:0.61 229:0.61 231:0.63 235:1.00 236:0.97 239:0.95 241:0.21 242:0.42 243:0.60 245:0.91 246:0.89 247:0.82 253:0.53 254:0.80 259:0.21 261:0.91 265:0.62 266:0.54 267:0.28 271:0.42 276:0.76 281:0.86 287:0.61 290:0.64 297:0.36 300:0.55 +0 8:0.58 10:0.81 11:0.52 12:0.51 17:0.45 18:0.74 21:0.13 25:0.88 27:1.00 29:0.91 30:0.33 34:0.78 36:0.84 37:0.27 39:0.90 43:0.75 45:0.73 55:0.71 66:0.86 69:0.37 70:0.65 71:0.92 74:0.44 81:0.34 86:0.64 91:0.61 98:0.91 101:0.65 104:0.58 108:0.29 120:0.62 123:0.28 126:0.24 127:0.51 129:0.05 131:0.61 135:0.44 139:0.62 140:0.45 146:0.83 147:0.86 149:0.56 150:0.37 151:0.57 153:0.48 154:0.67 157:0.61 159:0.39 162:0.86 164:0.55 166:0.61 170:0.82 172:0.61 173:0.44 174:0.83 177:0.88 179:0.70 182:0.61 187:0.21 190:0.95 197:0.85 202:0.36 212:0.59 215:0.84 216:0.23 220:0.74 222:0.48 227:0.61 229:0.61 231:0.62 235:0.12 239:0.80 241:0.56 242:0.31 243:0.87 244:0.61 246:0.61 247:0.79 248:0.56 253:0.38 256:0.45 259:0.21 260:0.63 265:0.91 266:0.47 267:0.28 268:0.46 271:0.42 276:0.50 285:0.46 287:0.61 297:0.36 300:0.43 +0 10:0.80 11:0.46 12:0.51 17:0.83 18:0.69 21:0.78 27:0.34 29:0.91 30:0.55 34:0.56 36:0.54 37:0.46 39:0.90 43:0.21 45:0.66 55:0.99 66:0.07 69:0.96 70:0.60 71:0.92 74:0.36 86:0.61 91:0.12 98:0.13 101:0.47 108:0.91 122:0.93 123:0.98 124:0.96 127:0.86 129:0.59 131:0.61 133:0.95 135:0.51 139:0.59 145:0.54 146:0.13 147:0.05 149:0.19 154:0.14 157:0.61 159:0.96 163:0.90 166:0.61 170:0.82 172:0.10 173:0.75 174:0.86 175:0.91 177:0.05 178:0.55 179:0.76 182:0.61 187:0.95 197:0.80 212:0.18 216:0.53 222:0.48 227:0.61 229:0.61 231:0.61 235:0.60 239:0.79 241:0.18 242:0.31 243:0.13 244:0.83 245:0.48 246:0.61 247:0.76 253:0.32 257:0.93 259:0.21 265:0.06 266:0.84 267:0.87 271:0.42 276:0.50 277:0.87 287:0.61 300:0.55 +0 1:0.59 7:0.70 8:0.70 9:0.73 10:0.84 11:0.50 12:0.51 17:0.84 18:0.95 21:0.40 27:0.49 29:0.91 30:0.10 31:0.91 32:0.72 34:0.97 36:0.35 37:0.52 39:0.90 43:0.66 44:0.86 45:0.77 48:0.99 55:0.71 66:0.59 69:0.15 70:0.92 71:0.92 74:0.63 77:0.89 79:0.90 81:0.46 83:0.69 86:0.74 91:0.42 96:0.75 97:0.97 98:0.74 99:0.83 101:0.47 108:0.12 114:0.78 120:0.63 122:0.92 123:0.12 124:0.63 126:0.32 127:0.69 129:0.05 131:0.80 133:0.60 135:0.66 137:0.91 139:0.73 140:0.45 144:0.57 145:0.77 146:0.77 147:0.81 149:0.55 150:0.41 151:0.70 153:0.69 154:0.48 155:0.64 157:0.61 158:0.76 159:0.52 162:0.86 164:0.66 165:0.65 166:0.61 170:0.82 172:0.68 173:0.22 174:0.79 175:0.75 176:0.63 177:0.70 179:0.53 182:0.61 187:0.39 190:0.77 192:0.87 195:0.72 196:0.90 197:0.81 201:0.56 202:0.50 204:0.79 208:0.61 212:0.52 215:0.67 216:0.44 220:0.74 222:0.48 227:0.82 229:0.61 230:0.73 231:0.62 233:0.76 235:0.52 239:0.83 241:0.55 242:0.52 243:0.67 244:0.61 245:0.76 246:0.61 247:0.86 248:0.66 253:0.72 254:0.96 255:0.72 256:0.58 257:0.65 259:0.21 260:0.64 265:0.74 266:0.54 267:0.96 268:0.59 271:0.42 276:0.67 277:0.69 279:0.79 281:0.86 282:0.80 283:0.67 284:0.90 285:0.60 287:0.61 290:0.78 297:0.36 300:0.70 +1 1:0.61 6:0.83 7:0.70 8:0.65 10:0.91 11:0.64 12:0.51 17:0.94 18:0.93 20:0.93 21:0.22 27:0.64 29:0.91 30:0.45 32:0.67 34:0.98 36:0.25 37:0.27 39:0.90 43:0.61 44:0.86 45:0.96 66:0.97 69:0.17 70:0.72 71:0.92 74:0.85 76:0.97 77:0.89 78:0.99 81:0.50 83:0.91 86:0.84 91:0.51 97:0.94 98:0.96 99:0.83 100:0.89 101:0.87 102:0.94 104:0.93 106:0.81 108:0.14 111:0.96 114:0.85 117:0.86 122:0.91 123:0.13 126:0.32 127:0.71 129:0.05 131:0.61 135:0.96 139:0.82 144:0.57 145:0.98 146:0.96 147:0.97 149:0.81 150:0.44 152:0.94 154:0.58 155:0.64 157:0.80 158:0.77 159:0.66 165:0.65 166:0.61 169:0.95 170:0.82 172:0.93 173:0.16 174:0.89 176:0.63 177:0.88 178:0.55 179:0.25 182:0.74 186:0.99 187:0.68 189:0.84 192:0.87 193:0.98 195:0.80 197:0.90 201:0.58 204:0.85 206:0.81 208:0.64 212:0.68 215:0.79 216:0.18 222:0.48 227:0.61 229:0.61 230:0.72 231:0.63 232:0.95 233:0.65 235:0.31 238:0.82 239:0.91 241:0.07 242:0.16 243:0.97 246:0.61 247:0.97 253:0.69 254:0.80 259:0.21 261:0.95 265:0.96 266:0.38 267:0.93 271:0.42 276:0.87 279:0.78 281:0.86 282:0.76 283:0.82 287:0.61 290:0.85 297:0.36 300:0.70 +2 1:0.56 10:0.92 11:0.55 12:0.51 17:0.49 18:0.95 21:0.78 27:0.45 29:0.91 30:0.21 34:0.85 36:0.18 37:0.50 39:0.90 43:0.60 45:0.88 48:0.91 55:0.71 66:0.27 69:0.63 70:0.93 71:0.92 74:0.41 81:0.38 83:0.56 86:0.85 91:0.20 98:0.55 99:0.83 101:0.65 108:0.87 114:0.62 122:0.93 123:0.46 124:0.77 126:0.32 127:0.59 129:0.05 131:0.61 133:0.74 135:0.79 139:0.82 144:0.57 145:0.50 146:0.78 147:0.81 149:0.66 150:0.33 154:0.44 155:0.46 157:0.86 159:0.70 165:0.65 166:0.61 170:0.82 172:0.68 173:0.51 174:0.93 176:0.63 177:0.88 178:0.55 179:0.33 182:0.76 187:0.68 192:0.44 197:0.92 201:0.53 208:0.50 212:0.74 215:0.43 216:0.77 222:0.48 227:0.61 229:0.61 231:0.63 235:1.00 239:0.91 241:0.43 242:0.19 243:0.46 244:0.61 245:0.86 246:0.61 247:0.93 253:0.47 254:0.97 259:0.21 265:0.49 266:0.80 267:0.79 271:0.42 276:0.79 281:0.86 287:0.61 290:0.62 297:0.36 300:0.84 +2 1:0.67 6:0.83 7:0.70 8:0.61 10:1.00 11:0.82 12:0.51 17:0.95 18:0.98 20:0.89 21:0.22 22:0.93 27:0.64 29:0.91 30:0.72 33:0.97 34:0.98 36:0.33 37:0.27 39:0.90 43:0.61 44:0.88 45:0.99 47:0.93 64:0.77 66:0.99 69:0.15 70:0.53 71:0.92 74:0.86 75:0.99 76:0.97 78:0.99 81:0.71 83:0.91 85:0.72 86:0.99 91:0.42 97:0.97 98:0.99 99:0.95 100:0.98 101:0.96 102:0.96 104:0.96 106:0.81 108:0.12 111:0.98 114:0.90 117:0.86 122:0.93 123:0.12 126:0.54 127:0.88 129:0.05 131:0.61 135:0.98 139:0.99 144:0.57 145:0.97 146:0.97 147:0.98 149:0.89 150:0.56 152:0.94 154:0.86 155:0.64 157:0.91 158:0.84 159:0.70 165:0.81 166:0.74 169:0.95 170:0.82 172:0.97 173:0.15 174:0.99 176:0.73 177:0.88 178:0.55 179:0.17 182:0.82 186:0.99 187:0.68 189:0.87 191:0.70 192:0.87 193:0.98 195:0.82 197:0.99 201:0.66 204:0.85 206:0.81 208:0.64 212:0.87 215:0.79 216:0.18 219:0.95 222:0.48 226:0.95 227:0.61 229:0.61 230:0.72 231:0.63 232:0.98 233:0.65 235:0.52 236:0.97 238:0.91 239:1.00 241:0.12 242:0.24 243:0.99 246:0.89 247:0.94 253:0.71 259:0.21 261:0.95 262:0.93 265:0.99 266:0.38 267:0.44 271:0.42 276:0.94 279:0.81 281:0.86 283:0.82 287:0.61 290:0.90 291:0.97 292:0.87 297:0.36 300:0.55 +1 1:0.67 8:0.63 10:0.96 11:0.64 12:0.51 17:0.76 18:0.87 21:0.47 27:0.54 29:0.91 30:0.87 34:0.97 36:0.37 37:0.60 39:0.90 43:0.61 45:0.91 64:0.77 66:0.90 69:0.32 70:0.30 71:0.92 74:0.73 81:0.73 83:0.91 86:0.96 91:0.06 98:0.71 99:0.99 101:0.87 108:0.25 114:0.80 122:0.65 123:0.24 126:0.54 127:0.22 129:0.05 131:0.61 135:0.37 139:0.94 144:0.57 146:0.53 147:0.59 149:0.43 150:0.53 154:0.87 155:0.52 157:0.83 159:0.19 165:0.93 166:0.74 170:0.82 172:0.71 173:0.50 174:0.86 176:0.73 177:0.88 178:0.55 179:0.35 182:0.81 187:0.68 192:0.50 197:0.90 201:0.66 208:0.64 212:0.80 215:0.63 216:0.64 222:0.48 227:0.61 229:0.61 231:0.63 235:0.80 236:0.97 239:0.95 241:0.05 242:0.18 243:0.90 246:0.89 247:0.79 253:0.64 254:0.80 259:0.21 265:0.71 266:0.27 267:0.65 271:0.42 276:0.57 287:0.61 290:0.80 297:0.36 300:0.33 +0 7:0.69 8:0.89 10:0.82 11:0.55 12:0.51 17:0.16 18:0.73 21:0.47 27:0.28 29:0.91 30:0.29 32:0.65 34:0.55 36:0.95 37:0.72 39:0.90 43:0.61 45:0.91 55:0.59 66:0.53 69:0.84 70:0.84 71:0.92 74:0.73 81:0.29 86:0.65 91:0.46 98:0.75 101:0.65 104:0.82 106:0.81 108:0.69 120:0.56 123:0.67 124:0.74 126:0.24 127:0.81 129:0.05 131:0.61 133:0.76 135:0.70 139:0.63 140:0.45 144:0.57 145:0.44 146:0.96 147:0.67 149:0.77 150:0.32 151:0.49 153:0.72 154:0.22 157:0.76 159:0.85 162:0.56 164:0.63 166:0.61 170:0.82 172:0.92 173:0.67 174:0.79 177:0.38 179:0.19 182:0.72 187:0.39 190:0.95 195:0.95 197:0.80 202:0.58 206:0.81 212:0.59 215:0.63 216:0.82 220:0.93 222:0.48 227:0.61 229:0.61 230:0.71 231:0.61 232:0.88 233:0.76 235:0.52 239:0.82 241:0.15 242:0.28 243:0.22 244:0.61 245:0.95 246:0.61 247:0.96 248:0.49 253:0.52 254:0.99 256:0.45 257:0.84 259:0.21 260:0.56 265:0.75 266:0.63 267:0.94 268:0.57 271:0.42 276:0.92 285:0.46 287:0.61 297:0.36 300:0.70 +1 1:0.58 7:0.70 10:0.85 11:0.50 12:0.51 17:0.87 18:0.96 21:0.47 27:0.49 29:0.91 30:0.45 32:0.72 34:0.89 36:0.62 37:0.52 39:0.90 43:0.66 44:0.86 45:0.97 48:0.99 66:0.67 69:0.17 70:0.72 71:0.92 74:0.84 77:0.89 81:0.45 83:0.56 86:0.76 91:0.18 97:0.94 98:0.80 99:0.83 101:0.47 108:0.79 114:0.76 122:0.82 123:0.78 124:0.47 126:0.32 127:0.82 129:0.05 131:0.61 133:0.45 135:0.82 139:0.75 144:0.57 145:0.76 146:0.78 147:0.82 149:0.86 150:0.40 154:0.47 155:0.50 157:0.61 158:0.77 159:0.75 165:0.65 166:0.61 170:0.82 172:0.94 173:0.14 174:0.79 176:0.63 177:0.88 178:0.55 179:0.20 182:0.61 187:0.39 192:0.49 195:0.88 197:0.82 201:0.55 204:0.79 208:0.50 212:0.82 215:0.63 216:0.44 222:0.48 227:0.61 229:0.61 230:0.73 231:0.61 233:0.76 235:0.60 239:0.84 241:0.10 242:0.41 243:0.32 244:0.61 245:0.97 246:0.61 247:0.91 253:0.65 254:0.97 259:0.21 265:0.80 266:0.63 267:0.98 271:0.42 276:0.91 277:0.69 279:0.78 281:0.86 282:0.80 283:0.82 287:0.61 290:0.76 297:0.36 300:0.84 +0 1:0.58 7:0.70 10:0.88 11:0.55 12:0.51 17:0.78 18:0.90 21:0.71 27:0.32 29:0.91 30:0.41 32:0.67 34:0.97 36:0.96 37:0.85 39:0.90 43:0.60 44:0.88 45:0.81 48:0.99 55:0.71 64:0.77 66:0.92 69:0.33 70:0.68 71:0.92 74:0.69 77:0.98 81:0.57 83:0.69 86:0.78 91:0.29 97:0.89 98:0.92 99:0.95 101:0.65 102:0.96 108:0.26 114:0.67 122:0.92 123:0.25 126:0.51 127:0.55 129:0.05 131:0.61 135:0.71 139:0.77 144:0.57 145:1.00 146:0.88 147:0.90 149:0.46 150:0.41 154:0.61 155:0.50 157:0.80 158:0.76 159:0.46 165:0.81 166:0.72 170:0.82 172:0.78 173:0.36 174:0.83 176:0.70 177:0.88 178:0.55 179:0.50 182:0.75 187:0.21 192:0.49 195:0.69 197:0.85 201:0.57 204:0.79 208:0.61 212:0.68 215:0.48 216:0.55 222:0.48 227:0.61 229:0.61 230:0.73 231:0.62 233:0.66 235:0.95 239:0.89 241:0.32 242:0.19 243:0.92 244:0.61 246:0.61 247:0.92 253:0.57 254:0.92 259:0.21 265:0.92 266:0.54 267:0.94 271:0.42 276:0.67 279:0.75 281:0.86 282:0.75 283:0.67 287:0.61 290:0.67 297:0.36 300:0.55 +2 1:0.56 10:0.90 11:0.55 12:0.51 17:0.62 18:0.96 21:0.77 27:0.45 29:0.91 30:0.53 32:0.71 34:0.90 36:0.24 37:0.50 39:0.90 43:0.57 44:0.86 45:0.81 48:0.91 55:0.71 64:0.77 66:0.45 69:0.63 70:0.75 71:0.92 74:0.49 77:0.70 81:0.45 83:0.56 86:0.81 91:0.10 98:0.68 99:0.83 101:0.65 108:0.48 114:0.63 122:0.92 123:0.46 124:0.59 126:0.51 127:0.44 129:0.05 131:0.61 133:0.39 135:0.47 139:0.79 144:0.57 145:0.61 146:0.76 147:0.80 149:0.56 150:0.37 154:0.60 155:0.46 157:0.82 159:0.51 165:0.65 166:0.72 170:0.82 172:0.65 173:0.60 174:0.88 176:0.70 177:0.88 178:0.55 179:0.42 182:0.75 187:0.68 192:0.45 195:0.73 197:0.89 201:0.55 208:0.61 212:0.69 215:0.43 216:0.77 222:0.48 227:0.61 229:0.61 231:0.62 235:1.00 239:0.89 241:0.21 242:0.62 243:0.58 244:0.61 245:0.87 246:0.61 247:0.74 253:0.49 254:0.74 259:0.21 265:0.69 266:0.54 267:0.52 271:0.42 276:0.70 281:0.86 282:0.80 287:0.61 290:0.63 297:0.36 300:0.70 +1 1:0.69 7:0.81 8:0.60 10:1.00 11:0.83 12:0.51 17:0.94 18:0.99 22:0.93 27:0.71 29:0.91 30:0.90 32:0.77 33:0.97 34:0.86 36:0.59 37:0.27 39:0.90 43:0.96 44:0.93 45:0.98 47:0.93 60:0.87 64:0.77 66:0.99 69:0.08 70:0.37 71:0.92 74:0.98 75:0.99 77:0.70 81:0.79 83:0.91 85:0.97 86:0.99 91:0.23 97:0.89 98:0.98 101:0.96 102:0.98 104:0.93 106:0.81 108:0.07 114:0.98 117:0.86 121:0.90 122:0.65 123:0.07 126:0.54 127:0.85 129:0.05 131:0.61 132:0.97 135:0.65 139:0.99 144:0.57 145:0.45 146:0.95 147:0.96 149:1.00 150:0.62 154:0.96 155:0.58 157:0.91 158:0.92 159:0.62 165:0.93 166:0.74 170:0.82 172:0.98 173:0.14 174:0.97 176:0.73 177:0.88 178:0.55 179:0.15 182:0.82 187:0.21 192:0.86 193:1.00 195:0.75 197:0.98 201:0.68 204:0.84 206:0.81 208:0.64 212:0.90 215:0.96 216:0.16 219:0.95 222:0.48 226:0.98 227:0.61 229:0.61 230:0.87 231:0.63 232:0.95 233:0.76 235:0.22 236:0.97 239:1.00 241:0.12 242:0.30 243:0.99 246:0.89 247:0.91 253:0.78 262:0.93 265:0.98 266:0.47 267:0.69 271:0.42 276:0.95 279:0.81 281:0.91 282:0.85 283:0.82 287:0.61 290:0.98 291:0.97 292:0.95 297:0.36 300:0.70 +1 1:0.62 8:0.86 9:0.71 10:0.91 11:0.55 12:0.51 17:0.64 18:0.92 20:0.87 21:0.47 22:0.93 23:0.96 27:0.42 29:0.91 30:0.09 31:0.89 33:0.97 34:0.56 36:0.90 37:0.57 39:0.90 43:0.21 44:0.92 45:0.93 47:0.93 48:0.91 55:0.71 62:0.97 64:0.77 66:0.06 69:0.32 70:0.99 71:0.92 74:0.69 75:0.98 76:0.85 77:0.96 78:0.94 79:0.88 81:0.50 83:0.69 86:0.81 91:0.17 96:0.72 97:0.97 98:0.22 99:0.83 100:0.92 101:0.65 102:0.97 108:0.98 111:0.92 114:0.78 117:0.86 122:0.92 123:0.95 124:0.98 126:0.51 127:0.94 128:0.96 129:0.96 131:0.82 133:0.98 135:0.74 137:0.89 139:0.85 140:0.45 144:0.57 145:0.80 146:0.29 147:0.35 149:0.44 150:0.42 151:0.59 152:0.82 153:0.69 154:0.64 155:0.57 157:0.84 158:0.81 159:0.96 162:0.86 165:0.65 166:0.74 168:0.96 169:0.90 170:0.82 172:0.70 173:0.07 174:0.96 175:0.75 176:0.70 177:0.70 179:0.19 182:0.82 186:0.94 187:0.21 190:0.77 192:0.56 193:0.95 195:0.99 196:0.91 197:0.89 201:0.60 202:0.46 205:0.95 208:0.61 212:0.30 216:0.56 219:0.94 220:0.87 222:0.48 226:0.96 227:0.85 229:0.90 231:0.63 232:0.88 235:0.68 236:0.95 239:0.94 241:0.01 242:0.14 243:0.13 244:0.61 245:0.86 246:0.89 247:0.99 248:0.57 253:0.65 254:0.84 255:0.70 256:0.45 257:0.65 259:0.21 261:0.87 262:0.93 265:0.18 266:0.99 267:0.19 268:0.55 271:0.42 276:0.92 277:0.69 279:0.81 281:0.91 282:0.80 284:0.90 285:0.60 287:0.91 290:0.78 291:0.97 292:0.88 300:0.98 +0 1:0.58 6:0.81 8:0.74 10:0.96 11:0.50 12:0.51 17:0.43 18:0.94 20:0.94 21:0.47 27:0.33 29:0.91 30:0.10 34:0.71 36:0.79 37:0.88 39:0.90 43:0.50 45:0.97 55:0.71 64:0.77 66:0.06 69:0.27 70:0.97 71:0.92 74:0.62 75:0.97 78:0.87 81:0.53 83:0.56 86:0.90 91:0.18 97:0.94 98:0.22 99:0.83 100:0.92 101:0.47 108:0.99 111:0.88 122:0.83 123:0.99 124:0.99 126:0.51 127:1.00 129:0.98 131:0.61 133:0.99 135:0.28 139:0.88 144:0.57 146:0.85 147:0.88 149:0.70 150:0.41 152:0.97 154:0.64 155:0.47 157:0.88 159:0.98 165:0.65 166:0.74 169:0.90 170:0.82 172:0.77 173:0.06 174:0.99 177:0.88 178:0.55 179:0.11 182:0.81 186:0.87 187:0.39 192:0.46 197:0.96 201:0.60 208:0.50 212:0.30 215:0.63 216:0.49 219:0.91 222:0.48 226:0.96 227:0.61 229:0.61 231:0.63 235:0.68 236:0.95 239:0.96 241:0.10 242:0.16 243:0.17 244:0.61 245:0.95 246:0.89 247:1.00 253:0.47 254:0.86 259:0.21 261:0.93 265:0.24 266:0.98 267:0.36 271:0.42 276:0.98 277:0.69 279:0.78 287:0.61 292:0.88 297:0.36 300:0.98 +2 1:0.57 7:0.69 10:0.80 11:0.52 12:0.51 17:0.89 18:0.96 21:0.64 27:0.72 29:0.91 30:0.62 32:0.67 34:0.88 36:0.55 39:0.90 43:0.62 45:0.99 48:0.91 66:0.43 69:0.94 70:0.56 71:0.92 74:0.90 76:0.85 77:0.70 81:0.42 83:0.56 86:0.78 91:0.71 97:0.89 98:0.68 99:0.83 101:0.65 104:0.78 108:0.88 114:0.69 122:0.65 123:0.87 124:0.91 126:0.32 127:0.89 129:0.05 131:0.61 133:0.93 135:0.96 139:0.74 145:0.74 146:0.98 147:0.94 149:0.94 150:0.37 154:0.57 155:0.46 157:0.61 158:0.76 159:0.92 165:0.65 166:0.61 170:0.82 172:0.96 173:0.79 174:0.79 176:0.63 177:0.70 178:0.55 179:0.13 182:0.61 192:0.45 195:0.98 197:0.80 201:0.54 208:0.50 212:0.50 215:0.53 216:0.09 222:0.48 227:0.61 229:0.61 230:0.71 231:0.61 232:0.84 233:0.76 235:0.80 239:0.79 241:0.78 242:0.21 243:0.34 244:0.61 245:0.97 246:0.61 247:0.95 253:0.65 257:0.65 259:0.21 265:0.68 266:0.87 267:0.94 271:0.42 276:0.97 279:0.75 281:0.91 282:0.75 283:0.67 287:0.61 290:0.69 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.20 17:0.70 21:0.30 30:0.85 32:0.80 34:0.94 36:0.19 37:0.97 43:0.92 66:0.87 69:0.37 70:0.40 74:0.88 77:0.99 81:0.65 83:0.75 85:0.94 91:0.05 98:0.78 101:0.86 104:0.87 106:0.81 108:0.29 114:0.86 117:0.86 122:0.43 123:0.28 126:0.54 127:0.26 129:0.05 135:0.47 145:0.99 146:0.54 147:0.60 149:0.94 150:0.79 154:0.92 155:0.67 159:0.19 165:0.84 172:0.63 173:0.59 176:0.73 177:0.38 178:0.55 179:0.53 187:0.68 192:0.59 195:0.54 201:0.74 206:0.81 212:0.86 215:0.72 216:0.94 222:0.90 232:0.90 235:0.42 241:0.44 242:0.63 243:0.88 247:0.30 253:0.75 259:0.21 265:0.78 266:0.27 267:0.44 271:0.68 276:0.51 282:0.88 290:0.86 297:0.36 299:1.00 300:0.43 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.38 21:0.40 27:0.39 30:0.68 32:0.80 34:0.97 36:0.48 37:0.55 43:0.91 66:0.79 69:0.41 70:0.43 74:0.81 77:0.70 81:0.61 83:0.75 85:0.85 91:0.20 98:0.48 101:0.80 104:0.80 106:0.81 108:0.32 114:0.82 117:0.86 121:0.99 122:0.43 123:0.31 126:0.54 127:0.15 129:0.05 135:0.42 145:0.72 146:0.43 147:0.50 149:0.78 150:0.76 154:0.91 155:0.67 158:0.84 159:0.16 165:0.83 172:0.41 173:0.51 176:0.73 177:0.38 178:0.55 179:0.44 187:0.21 192:0.59 195:0.62 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.67 216:0.59 222:0.90 230:0.87 232:0.85 233:0.76 235:0.52 241:0.24 242:0.45 243:0.80 247:0.47 253:0.71 259:0.21 265:0.50 266:0.27 267:0.84 271:0.68 276:0.32 279:0.86 282:0.88 283:0.67 290:0.82 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.57 12:0.20 17:0.86 21:0.05 27:0.44 30:0.76 32:0.80 34:0.63 36:0.34 37:0.28 43:0.98 60:0.87 66:0.85 69:0.07 70:0.43 74:0.86 77:0.89 81:0.81 83:0.88 85:0.91 91:0.23 98:0.71 101:0.84 108:0.06 114:0.95 122:0.43 123:0.06 126:0.54 127:0.22 129:0.05 135:0.56 145:0.84 146:0.59 147:0.64 149:0.89 150:0.88 154:0.98 155:0.67 158:0.92 159:0.21 165:0.90 172:0.57 173:0.24 176:0.73 177:0.38 178:0.55 179:0.52 187:0.87 192:0.59 195:0.57 201:0.74 208:0.64 212:0.58 215:0.91 216:0.91 222:0.90 235:0.12 241:0.18 242:0.61 243:0.86 247:0.39 253:0.78 259:0.21 265:0.71 266:0.27 267:0.52 271:0.68 276:0.43 279:0.86 282:0.88 283:0.82 290:0.95 297:0.36 299:0.97 300:0.43 +1 11:0.57 12:0.20 17:0.87 21:0.78 27:0.44 30:0.95 34:0.55 36:0.26 37:0.28 43:0.87 55:0.45 66:0.90 69:0.52 70:0.13 74:0.97 85:0.72 91:0.02 98:0.27 101:0.72 108:0.40 122:0.67 123:0.38 127:0.11 129:0.05 135:0.56 146:0.36 147:0.43 149:0.74 154:0.85 159:0.14 172:0.71 173:0.48 177:0.38 178:0.55 179:0.10 187:0.95 212:0.93 216:0.91 222:0.90 235:0.12 241:0.15 242:0.80 243:0.90 247:0.39 253:0.69 259:0.21 265:0.30 266:0.17 267:0.36 271:0.68 276:0.61 300:0.18 +0 11:0.57 12:0.20 17:0.45 21:0.78 30:0.76 34:0.73 36:0.30 37:0.99 43:0.61 66:0.64 69:0.92 70:0.15 74:0.36 85:0.72 91:0.23 98:0.15 101:0.70 108:0.84 122:0.41 123:0.83 127:0.09 129:0.05 132:0.97 135:0.85 146:0.26 147:0.32 149:0.30 154:0.50 159:0.11 163:0.99 172:0.16 173:0.90 177:0.38 178:0.55 179:0.14 187:0.68 202:0.65 212:0.95 216:1.00 222:0.90 235:0.31 241:0.41 242:0.82 243:0.69 247:0.12 253:0.32 259:0.21 265:0.16 266:0.12 267:0.88 271:0.68 276:0.13 300:0.13 +1 11:0.57 12:0.51 17:0.78 21:0.78 27:0.63 30:0.95 34:0.85 36:0.59 37:0.23 39:0.26 43:0.86 66:0.54 69:0.59 74:0.44 85:0.72 91:0.70 98:0.13 101:0.75 104:0.89 106:0.81 108:0.45 123:0.43 127:0.08 129:0.05 135:0.47 146:0.13 147:0.18 149:0.49 154:0.83 159:0.08 172:0.10 173:0.84 177:0.38 178:0.55 179:0.12 187:0.68 206:0.81 216:0.11 222:0.35 235:0.12 241:0.61 243:0.63 253:0.39 259:0.21 265:0.14 267:0.59 271:0.11 300:0.08 +0 11:0.57 12:0.51 17:0.49 21:0.78 27:0.21 30:0.95 34:0.71 36:0.70 37:0.74 39:0.26 43:0.09 66:0.54 69:1.00 74:0.27 85:0.72 91:0.02 98:0.05 101:0.47 108:0.99 123:0.99 127:0.05 129:0.05 135:0.18 146:0.13 147:0.18 149:0.07 154:0.08 159:0.08 172:0.10 173:0.96 177:0.38 178:0.55 179:0.08 187:0.39 216:0.95 222:0.35 232:0.85 235:0.42 241:0.70 243:0.63 253:0.25 259:0.21 265:0.05 267:0.52 271:0.11 300:0.08 +2 1:0.74 11:0.57 12:0.51 17:0.69 21:0.22 27:0.51 30:0.87 34:0.97 36:0.32 37:0.46 39:0.26 43:0.40 66:0.82 69:0.95 70:0.20 74:0.48 81:0.93 83:0.76 85:0.90 91:0.17 98:0.32 101:0.76 106:0.81 108:0.91 114:0.90 117:0.86 122:0.43 123:0.90 126:0.54 127:0.12 129:0.05 135:0.66 145:0.82 146:0.66 147:0.71 149:0.55 150:0.96 154:0.31 155:0.67 159:0.25 163:0.81 165:0.86 172:0.48 173:0.94 176:0.73 177:0.38 178:0.55 179:0.20 187:0.39 192:0.59 201:0.74 206:0.81 212:0.61 215:0.79 216:0.12 222:0.35 235:0.31 241:0.37 242:0.63 243:0.83 247:0.30 253:0.67 259:0.21 265:0.35 266:0.23 267:0.44 271:0.11 276:0.37 290:0.90 297:0.36 300:0.18 +2 11:0.57 12:0.51 17:0.51 21:0.78 27:0.51 30:0.95 34:0.70 36:0.80 37:0.29 39:0.26 43:0.94 66:0.54 69:0.40 74:0.47 85:0.72 91:0.88 98:0.16 101:0.76 108:0.31 123:0.30 127:0.09 129:0.05 135:0.34 145:0.61 146:0.13 147:0.18 149:0.52 154:0.94 159:0.08 172:0.10 173:0.78 177:0.38 178:0.55 179:0.18 187:0.21 202:0.36 216:0.16 222:0.35 235:0.99 241:0.74 243:0.63 253:0.40 259:0.21 265:0.17 267:0.79 271:0.11 300:0.08 +2 1:0.74 7:0.78 8:0.58 12:0.51 17:0.69 20:0.99 21:0.30 27:0.38 30:0.95 32:0.75 34:0.97 36:0.25 37:0.24 39:0.26 43:0.61 66:0.54 69:0.92 74:0.68 76:0.85 77:0.89 78:0.99 81:0.92 83:0.74 91:0.44 98:0.08 100:0.92 108:0.84 111:0.97 114:0.61 123:0.83 126:0.54 127:0.06 129:0.05 135:0.71 145:0.59 146:0.13 147:0.18 149:0.29 150:0.95 152:0.90 154:0.51 155:0.67 158:0.84 159:0.08 165:0.80 169:0.90 172:0.10 173:0.95 176:0.54 177:0.38 178:0.55 179:0.08 186:0.99 187:0.39 192:0.59 195:0.80 201:0.74 204:0.89 208:0.64 215:0.63 216:0.48 222:0.35 230:0.81 233:0.69 235:0.60 241:0.21 243:0.63 253:0.55 259:0.21 261:0.93 265:0.09 267:0.36 271:0.11 279:0.86 282:0.83 283:0.71 290:0.61 297:0.36 300:0.08 +2 1:0.74 6:0.81 7:0.81 9:0.80 11:0.57 12:0.51 17:0.84 20:0.94 21:0.22 27:0.55 30:0.95 34:0.93 36:0.65 37:0.24 39:0.26 41:0.82 43:0.81 66:0.54 69:0.69 74:0.56 78:1.00 81:0.93 83:0.77 85:0.72 91:0.64 96:0.73 98:0.14 100:0.73 101:0.75 108:0.53 111:0.97 114:0.90 120:0.60 121:0.90 123:0.51 126:0.54 127:0.09 129:0.05 135:0.23 140:0.45 145:0.87 146:0.13 147:0.18 149:0.46 150:0.96 151:0.63 152:0.94 153:0.48 154:0.77 155:0.67 159:0.08 162:0.86 164:0.57 165:0.86 169:0.72 172:0.10 173:0.94 176:0.73 177:0.38 179:0.13 186:0.99 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 208:0.64 215:0.79 216:0.07 220:0.82 222:0.35 230:0.87 233:0.76 235:0.31 241:0.59 243:0.63 248:0.60 253:0.72 255:0.79 256:0.45 259:0.21 260:0.60 261:0.93 265:0.14 267:0.44 268:0.46 271:0.11 285:0.62 290:0.90 297:0.36 300:0.08 +1 11:0.57 12:0.51 17:0.77 21:0.78 27:0.72 30:0.91 34:0.90 36:0.49 37:0.93 39:0.26 43:0.84 66:0.27 69:0.65 70:0.13 74:0.52 85:0.80 91:0.44 98:0.11 101:0.77 108:0.49 122:0.43 123:0.47 124:0.61 127:0.27 129:0.59 133:0.47 135:0.34 146:0.13 147:0.18 149:0.62 154:0.80 159:0.27 163:0.88 172:0.10 173:0.75 177:0.38 178:0.55 179:0.94 187:0.39 212:0.61 216:0.07 222:0.35 235:0.12 241:0.24 242:0.63 243:0.46 245:0.40 247:0.30 253:0.43 259:0.21 265:0.11 266:0.17 267:0.79 271:0.11 276:0.12 300:0.13 +2 11:0.57 12:0.51 17:0.57 21:0.78 27:0.51 30:0.95 34:0.75 36:0.87 37:0.29 39:0.26 43:0.89 66:0.54 69:0.46 74:0.45 85:0.72 91:0.77 98:0.15 101:0.76 108:0.35 120:0.56 123:0.34 127:0.09 129:0.05 135:0.28 140:0.45 145:0.61 146:0.13 147:0.18 149:0.50 151:0.49 153:0.48 154:0.87 159:0.08 162:0.86 164:0.56 172:0.10 173:0.80 177:0.38 179:0.15 187:0.39 190:0.77 202:0.36 216:0.16 220:0.93 222:0.35 241:0.59 243:0.63 248:0.49 253:0.39 256:0.45 259:0.21 260:0.56 265:0.16 267:0.79 268:0.46 271:0.11 285:0.50 300:0.08 +1 8:0.63 11:0.57 12:0.51 17:0.36 21:0.78 27:0.21 30:0.95 34:0.66 36:0.83 37:0.74 39:0.26 43:0.09 66:0.54 69:1.00 74:0.27 85:0.72 91:0.15 98:0.05 101:0.47 108:0.99 123:0.99 127:0.05 129:0.05 135:0.18 146:0.13 147:0.18 149:0.07 154:0.08 159:0.08 172:0.10 173:0.96 177:0.38 178:0.55 179:0.08 187:0.39 202:0.76 216:0.95 222:0.35 232:0.85 235:0.42 241:0.64 243:0.63 253:0.25 259:0.21 265:0.05 267:0.59 271:0.11 300:0.08 +2 9:0.80 11:0.57 12:0.51 17:0.77 21:0.78 27:0.63 30:0.95 34:0.85 36:0.54 37:0.23 39:0.26 41:0.82 43:0.86 66:0.54 69:0.59 74:0.44 83:0.76 85:0.72 91:0.76 96:0.80 98:0.13 101:0.75 104:0.89 106:0.81 108:0.45 120:0.69 123:0.43 127:0.08 129:0.05 135:0.47 140:0.45 146:0.13 147:0.18 149:0.49 151:0.87 153:0.48 154:0.83 155:0.67 159:0.08 162:0.86 164:0.57 172:0.10 173:0.84 177:0.38 179:0.12 187:0.68 192:0.59 202:0.36 206:0.81 208:0.64 216:0.11 222:0.35 235:0.12 241:0.67 243:0.63 248:0.78 253:0.75 255:0.79 256:0.45 259:0.21 260:0.70 265:0.14 267:0.59 268:0.46 271:0.11 285:0.62 300:0.08 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.90 21:0.22 27:0.55 30:0.95 34:0.96 36:0.63 37:0.24 39:0.26 43:0.82 66:0.54 69:0.69 74:0.56 81:0.93 83:0.76 85:0.72 91:0.58 98:0.14 101:0.75 108:0.53 114:0.90 121:0.90 123:0.50 126:0.54 127:0.09 129:0.05 135:0.28 145:0.87 146:0.13 147:0.18 149:0.46 150:0.96 154:0.77 155:0.67 159:0.08 165:0.86 172:0.10 173:0.94 176:0.73 177:0.38 178:0.55 179:0.13 187:0.39 192:0.59 201:0.74 204:0.89 208:0.64 215:0.79 216:0.07 222:0.35 230:0.87 233:0.76 235:0.31 241:0.39 243:0.63 253:0.68 259:0.21 265:0.14 267:0.44 271:0.11 290:0.90 297:0.36 300:0.08 +2 1:0.74 11:0.57 12:0.51 17:0.78 21:0.30 27:0.16 30:0.62 34:0.93 36:0.54 37:0.37 39:0.26 43:0.73 66:0.75 69:0.86 70:0.23 74:0.48 81:0.90 83:0.75 85:0.80 91:0.60 98:0.41 101:0.75 106:0.81 108:0.73 114:0.86 117:0.86 122:0.43 123:0.71 126:0.54 127:0.13 129:0.05 135:0.26 146:0.50 147:0.56 149:0.57 150:0.94 154:0.63 155:0.67 159:0.18 165:0.84 172:0.32 173:0.90 176:0.73 177:0.38 178:0.55 179:0.47 187:0.39 192:0.59 201:0.74 206:0.81 212:0.30 215:0.72 216:0.06 222:0.35 235:0.42 241:0.15 242:0.63 243:0.77 247:0.35 253:0.65 265:0.43 266:0.27 267:0.28 271:0.11 276:0.25 290:0.86 297:0.36 300:0.18 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.83 21:0.47 27:0.37 30:0.95 32:0.80 34:0.99 36:0.65 37:0.32 39:0.26 43:0.81 66:0.54 69:0.72 74:0.65 77:0.70 81:0.82 83:0.74 85:0.72 91:0.64 98:0.13 101:0.75 108:0.55 114:0.80 121:0.97 123:0.52 126:0.54 127:0.08 129:0.05 135:0.34 145:0.76 146:0.13 147:0.18 149:0.45 150:0.88 154:0.75 155:0.67 159:0.08 165:0.80 172:0.10 173:0.91 176:0.73 177:0.38 178:0.55 179:0.11 187:0.39 192:0.59 195:0.59 201:0.74 204:0.89 208:0.64 215:0.63 216:0.30 222:0.35 230:0.87 233:0.76 235:0.60 241:0.10 243:0.63 253:0.65 259:0.21 265:0.13 267:0.28 271:0.11 282:0.88 290:0.80 297:0.36 299:0.88 300:0.08 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.77 21:0.13 27:0.55 30:0.95 34:0.95 36:0.59 37:0.24 39:0.26 43:0.82 66:0.54 69:0.67 74:0.56 81:0.96 83:0.77 85:0.72 91:0.44 98:0.14 101:0.75 108:0.51 114:0.92 121:0.90 123:0.49 126:0.54 127:0.09 129:0.05 135:0.21 145:0.87 146:0.13 147:0.18 149:0.46 150:0.98 154:0.78 155:0.67 159:0.08 165:0.88 172:0.10 173:0.92 176:0.73 177:0.38 178:0.55 179:0.13 187:0.39 192:0.59 201:0.74 204:0.89 208:0.64 215:0.84 216:0.07 222:0.35 230:0.87 233:0.76 235:0.22 241:0.41 243:0.63 253:0.70 259:0.21 265:0.14 267:0.65 271:0.11 290:0.92 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.51 17:0.77 18:0.78 21:0.47 27:0.63 29:0.56 30:0.94 32:0.80 34:1.00 36:0.32 37:0.29 39:0.82 43:0.87 44:0.93 60:0.87 64:0.77 66:0.80 69:0.55 70:0.18 71:0.92 74:0.77 77:0.70 81:0.49 83:0.91 85:0.85 86:0.89 91:0.68 98:0.67 101:0.87 108:0.42 114:0.60 122:0.57 123:0.41 126:0.54 127:0.20 129:0.05 135:0.58 139:0.92 145:0.58 146:0.38 147:0.45 149:0.85 150:0.72 154:0.86 155:0.67 158:0.92 159:0.15 165:0.93 172:0.45 173:0.82 176:0.73 177:0.70 178:0.55 179:0.62 187:0.21 192:0.87 195:0.54 201:0.93 208:0.64 212:0.55 215:0.41 216:0.25 219:0.95 222:0.25 235:0.68 241:0.50 242:0.58 243:0.82 247:0.35 253:0.47 259:0.21 265:0.67 266:0.27 267:0.44 271:0.47 276:0.26 279:0.86 282:0.88 283:0.82 290:0.60 292:0.95 297:0.36 299:0.88 300:0.18 +0 1:0.74 9:0.80 12:0.51 17:0.69 18:0.67 21:0.05 27:0.69 29:0.56 31:0.91 34:0.26 36:0.80 37:0.37 39:0.82 41:0.82 43:0.17 48:0.98 64:0.77 66:0.36 69:0.33 71:0.92 74:0.39 76:0.94 79:0.90 81:0.74 83:0.91 86:0.66 91:0.78 96:0.75 98:0.15 108:0.26 114:0.89 120:0.63 123:0.25 126:0.54 127:0.09 129:0.05 135:0.54 137:0.91 139:0.75 140:0.45 146:0.05 147:0.05 149:0.07 150:0.84 151:0.72 153:0.48 154:0.11 155:0.67 159:0.05 162:0.62 164:0.57 165:0.93 172:0.05 173:0.84 175:0.91 176:0.73 177:0.05 187:0.39 191:0.82 192:0.87 196:0.91 201:0.93 202:0.50 204:0.85 208:0.64 215:0.78 216:0.18 219:0.89 220:0.62 222:0.25 232:0.87 235:0.22 241:0.30 243:0.51 244:0.83 248:0.67 253:0.70 255:0.95 256:0.45 257:0.84 259:0.21 260:0.64 265:0.16 267:0.28 268:0.46 271:0.47 277:0.87 281:0.91 284:0.89 285:0.62 290:0.89 292:0.91 297:0.36 +2 1:0.69 8:0.68 9:0.80 11:0.64 12:0.51 17:0.61 18:0.98 21:0.22 22:0.93 27:0.30 29:0.56 30:0.09 31:0.88 34:0.55 36:0.94 37:0.57 39:0.82 41:0.82 43:0.64 45:0.85 47:0.93 55:0.59 64:0.77 66:0.36 69:0.10 70:0.98 71:0.92 74:0.75 79:0.88 81:0.44 83:0.91 86:0.97 91:0.67 96:0.71 97:0.89 98:0.54 101:0.52 108:0.95 114:0.67 120:0.56 122:0.86 123:0.95 124:0.88 126:0.44 127:0.93 129:0.59 133:0.89 135:0.75 137:0.88 139:0.97 140:0.45 146:0.79 147:0.82 149:0.65 150:0.53 151:0.53 153:0.48 154:0.84 155:0.67 158:0.86 159:0.90 162:0.86 164:0.70 172:0.89 173:0.07 176:0.66 177:0.70 179:0.20 187:0.39 190:0.77 192:0.87 196:0.92 201:0.68 202:0.59 208:0.64 212:0.49 216:0.67 219:0.95 220:0.87 222:0.25 232:0.86 235:0.31 241:0.21 242:0.72 243:0.25 245:0.93 247:0.76 248:0.52 253:0.56 254:0.84 255:0.95 256:0.64 259:0.21 260:0.57 262:0.93 265:0.55 266:0.92 267:0.99 268:0.67 271:0.47 276:0.92 279:0.86 283:0.67 284:0.94 285:0.62 290:0.67 292:0.91 300:0.94 +1 1:0.74 11:0.92 12:0.51 17:0.28 18:0.92 21:0.40 27:0.45 29:0.56 30:0.44 32:0.80 34:0.78 36:0.22 37:0.50 39:0.82 43:0.82 44:0.93 45:0.85 64:0.77 66:0.20 69:0.69 70:0.86 71:0.92 74:0.78 77:0.70 81:0.52 83:0.91 85:0.90 86:0.94 91:0.06 98:0.51 101:0.97 108:0.90 114:0.62 122:0.86 123:0.89 124:0.90 126:0.54 127:0.68 129:0.81 133:0.89 135:0.63 139:0.94 145:0.50 146:0.45 147:0.52 149:0.90 150:0.73 154:0.77 155:0.67 159:0.84 165:0.93 172:0.65 173:0.45 176:0.73 177:0.70 178:0.55 179:0.31 187:0.68 192:0.87 195:0.94 201:0.93 208:0.64 212:0.35 215:0.42 216:0.77 222:0.25 235:0.60 241:0.30 242:0.30 243:0.27 245:0.84 247:0.88 253:0.49 259:0.21 265:0.52 266:0.89 267:0.36 271:0.47 276:0.82 282:0.88 290:0.62 297:0.36 299:0.88 300:0.84 +1 1:0.69 9:0.80 11:0.64 12:0.51 17:0.75 18:0.68 21:0.13 27:0.54 29:0.56 30:0.95 31:0.88 34:0.69 36:0.68 37:0.79 39:0.82 41:0.82 43:0.72 45:0.66 64:0.77 66:0.54 69:0.15 71:0.92 74:0.39 79:0.88 81:0.54 83:0.91 86:0.70 91:0.65 96:0.71 98:0.15 99:0.83 101:0.52 108:0.12 114:0.75 120:0.57 123:0.12 126:0.44 127:0.09 129:0.05 135:0.44 137:0.88 139:0.68 140:0.45 146:0.13 147:0.18 149:0.36 150:0.58 151:0.56 153:0.48 154:0.62 155:0.67 159:0.08 162:0.86 164:0.57 165:0.70 172:0.10 173:0.57 175:0.75 176:0.66 177:0.38 179:0.16 187:0.21 192:0.87 196:0.88 201:0.68 202:0.36 208:0.64 216:0.41 220:0.82 222:0.25 232:0.84 235:0.22 241:0.35 243:0.63 244:0.61 248:0.55 253:0.55 255:0.95 256:0.45 257:0.65 259:0.21 260:0.58 265:0.16 267:0.96 268:0.46 271:0.47 277:0.69 284:0.89 285:0.62 290:0.75 300:0.08 +0 8:0.61 9:0.80 12:0.51 17:0.24 18:0.85 21:0.74 27:0.38 29:0.56 30:0.87 31:0.89 34:1.00 36:0.40 37:0.57 39:0.82 41:0.82 43:0.11 48:0.91 55:0.52 64:0.77 66:0.79 69:0.71 70:0.20 71:0.92 79:0.88 81:0.23 83:0.91 86:0.84 91:0.66 96:0.71 98:0.72 108:0.54 120:0.57 122:0.86 123:0.52 126:0.26 127:0.22 129:0.05 135:0.37 137:0.89 139:0.83 140:0.90 146:0.67 147:0.72 149:0.11 150:0.23 151:0.56 153:0.48 154:0.50 155:0.67 159:0.26 162:0.58 164:0.57 172:0.41 173:0.79 175:0.75 177:0.38 178:0.42 179:0.73 187:0.68 192:0.87 196:0.91 202:0.69 208:0.64 212:0.19 216:0.56 219:0.89 220:0.99 222:0.25 235:0.95 241:0.69 242:0.75 243:0.80 244:0.61 247:0.35 248:0.55 253:0.43 254:0.90 255:0.95 256:0.68 257:0.65 259:0.21 260:0.58 265:0.73 266:0.47 267:0.44 268:0.68 271:0.47 276:0.32 277:0.69 281:0.89 283:0.78 284:0.93 285:0.62 292:0.91 300:0.18 +1 1:0.69 11:0.57 12:0.51 17:0.36 18:0.96 22:0.93 27:0.64 29:0.56 30:0.21 34:0.75 36:0.86 37:0.29 39:0.82 43:0.94 47:0.93 55:0.45 64:0.77 66:0.91 69:0.19 70:0.59 71:0.92 74:0.59 81:0.71 83:0.60 85:0.80 86:0.97 91:0.77 97:0.89 98:0.93 99:0.83 101:0.87 108:0.15 114:0.95 117:0.86 122:0.86 123:0.15 126:0.44 127:0.58 129:0.05 135:0.51 139:0.96 146:0.78 147:0.81 149:0.77 150:0.67 154:0.94 155:0.58 158:0.79 159:0.34 165:0.70 172:0.76 173:0.36 176:0.66 177:0.70 178:0.55 179:0.53 187:0.39 192:0.51 201:0.68 208:0.54 212:0.91 216:0.12 219:0.92 222:0.25 232:0.88 235:0.05 241:0.24 242:0.74 243:0.92 247:0.60 253:0.64 259:0.21 262:0.93 265:0.93 266:0.27 267:0.44 271:0.47 276:0.64 279:0.82 290:0.95 292:0.95 300:0.43 +0 11:0.83 12:0.51 17:0.28 18:0.72 21:0.78 27:0.45 29:0.56 30:0.45 34:0.71 36:0.17 37:0.50 39:0.82 43:0.74 45:0.66 66:0.72 69:0.84 70:0.74 71:0.92 74:0.71 85:0.80 86:0.83 91:0.11 98:0.29 101:0.97 108:0.69 123:0.67 124:0.43 127:0.24 129:0.05 133:0.59 135:0.88 139:0.79 145:0.50 146:0.50 147:0.05 149:0.74 154:0.64 159:0.53 172:0.68 173:0.76 177:0.05 178:0.55 179:0.37 187:0.68 212:0.87 216:0.77 222:0.25 235:0.22 241:0.15 242:0.17 243:0.10 245:0.62 247:0.86 253:0.42 257:0.84 259:0.21 265:0.31 266:0.38 267:0.44 271:0.47 276:0.52 300:0.70 +2 1:0.74 11:0.83 12:0.51 17:0.32 18:0.84 21:0.13 27:0.39 29:0.56 30:0.55 34:0.31 36:0.23 39:0.82 43:0.94 45:0.77 60:0.97 64:0.77 66:0.12 69:0.25 70:0.60 71:0.92 74:0.77 81:0.67 83:0.91 85:0.80 86:0.91 91:0.54 98:0.47 101:0.87 108:0.75 114:0.79 122:0.57 123:0.19 124:0.69 126:0.54 127:0.55 129:0.59 133:0.66 135:0.42 139:0.91 146:0.51 147:0.57 149:0.78 150:0.80 154:0.94 155:0.67 158:0.92 159:0.46 165:0.93 172:0.61 173:0.30 176:0.73 177:0.70 178:0.55 179:0.51 187:0.39 192:0.87 201:0.93 202:0.36 208:0.64 212:0.75 215:0.61 216:0.36 219:0.95 222:0.25 235:0.31 241:0.39 242:0.19 243:0.32 245:0.78 247:0.90 253:0.57 259:0.21 265:0.43 266:0.54 267:0.52 271:0.47 276:0.64 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.57 12:0.51 18:0.74 21:0.05 22:0.93 27:0.30 29:0.56 30:0.95 31:0.89 34:0.79 36:0.76 37:0.31 39:0.82 41:0.82 43:0.95 47:0.93 60:0.87 64:0.77 66:0.75 69:0.17 70:0.13 71:0.92 74:0.67 79:0.88 81:0.74 83:0.91 85:0.80 86:0.85 91:0.66 96:0.72 98:0.65 101:0.87 104:0.96 106:0.81 108:0.14 114:0.89 117:0.86 120:0.59 122:0.57 123:0.13 126:0.54 127:0.19 129:0.05 135:0.79 137:0.89 139:0.87 140:0.80 146:0.26 147:0.32 149:0.80 150:0.84 151:0.61 153:0.48 154:0.95 155:0.67 158:0.91 159:0.11 162:0.62 164:0.57 165:0.93 172:0.32 173:0.73 176:0.73 177:0.70 178:0.42 179:0.74 187:0.87 192:0.87 196:0.92 201:0.93 202:0.50 206:0.81 208:0.64 212:0.95 215:0.78 216:0.80 219:0.95 220:0.74 222:0.25 232:0.98 235:0.22 241:0.49 242:0.63 243:0.77 247:0.35 248:0.59 253:0.66 255:0.95 256:0.45 259:0.21 260:0.60 262:0.93 265:0.65 266:0.12 267:0.79 268:0.46 271:0.47 276:0.20 279:0.86 283:0.78 284:0.89 285:0.62 290:0.89 292:0.91 297:0.36 300:0.13 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.69 18:0.77 21:0.68 22:0.93 27:0.58 29:0.56 30:0.33 31:0.86 34:0.20 36:0.58 37:0.47 39:0.82 41:0.82 43:0.89 47:0.93 60:0.95 64:0.77 66:0.20 69:0.53 70:0.49 71:0.92 74:0.57 79:0.86 81:0.44 83:0.91 85:0.72 86:0.81 91:0.06 96:0.68 98:0.20 101:0.87 104:1.00 106:0.81 108:0.41 114:0.56 117:0.86 120:0.53 122:0.57 123:0.89 124:0.70 126:0.54 127:0.67 129:0.05 133:0.59 135:0.72 137:0.86 139:0.84 140:0.45 146:0.31 147:0.38 149:0.63 150:0.70 151:0.47 153:0.48 154:0.87 155:0.67 158:0.91 159:0.73 162:0.62 163:0.75 164:0.57 165:0.93 172:0.21 173:0.37 176:0.73 177:0.70 179:0.90 187:0.95 192:0.87 196:0.88 201:0.93 202:0.50 206:0.81 208:0.64 212:0.19 215:0.37 216:0.45 219:0.95 220:0.96 222:0.25 232:1.00 235:0.88 242:0.19 243:0.49 245:0.48 247:0.55 248:0.47 253:0.40 255:0.95 256:0.45 259:0.21 260:0.54 262:0.93 265:0.20 266:0.47 267:0.88 268:0.46 271:0.47 276:0.23 277:0.87 279:0.86 283:0.78 284:0.89 285:0.62 290:0.56 292:0.91 297:0.36 300:0.33 +0 1:0.69 8:0.77 9:0.80 11:0.64 12:0.51 17:0.06 18:0.73 21:0.22 27:0.14 29:0.56 30:0.21 31:0.88 34:0.45 36:0.25 37:0.67 39:0.82 41:0.82 43:0.68 45:0.81 66:0.26 69:0.93 70:0.60 71:0.92 74:0.62 79:0.88 81:0.44 83:0.91 86:0.73 91:0.72 96:0.80 97:0.94 98:0.38 99:0.83 101:0.52 108:0.86 114:0.67 120:0.73 122:0.80 123:0.86 124:0.84 126:0.44 127:0.45 129:0.05 133:0.84 135:0.97 137:0.88 139:0.71 140:0.98 145:0.96 146:0.61 147:0.18 149:0.57 150:0.56 151:0.58 153:0.46 154:0.57 155:0.67 158:0.78 159:0.67 162:0.51 163:0.88 164:0.79 165:0.70 172:0.37 173:0.94 176:0.66 177:0.38 179:0.64 187:0.39 190:0.89 192:0.87 196:0.89 201:0.68 202:0.82 208:0.64 212:0.35 215:0.51 216:0.93 220:0.74 222:0.25 235:0.31 241:0.39 242:0.24 243:0.19 244:0.61 245:0.60 247:0.74 248:0.56 253:0.76 255:0.95 256:0.77 257:0.65 259:0.21 260:0.69 265:0.40 266:0.63 267:0.88 268:0.78 271:0.47 276:0.52 279:0.82 283:0.78 284:0.86 285:0.62 290:0.67 297:0.36 300:0.43 +1 1:0.74 11:0.83 12:0.51 17:0.49 18:0.95 21:0.13 27:0.58 29:0.56 30:0.54 34:0.37 36:0.84 37:0.27 39:0.82 43:0.92 45:0.66 55:0.59 60:0.87 64:0.77 66:0.88 69:0.38 70:0.44 71:0.92 74:0.66 81:0.76 83:0.91 85:0.72 86:0.94 91:0.44 97:0.89 98:0.82 99:0.83 101:0.97 108:0.30 114:0.79 122:0.86 123:0.28 126:0.54 127:0.31 129:0.05 135:0.70 139:0.94 146:0.79 147:0.83 149:0.78 150:0.84 154:0.91 155:0.67 158:0.92 159:0.35 165:1.00 172:0.67 173:0.42 176:0.73 177:0.70 178:0.55 179:0.54 187:0.39 192:0.87 201:0.93 208:0.64 212:0.61 215:0.61 216:0.39 219:0.95 222:0.25 235:0.52 241:0.07 242:0.77 243:0.89 247:0.47 253:0.56 259:0.21 265:0.82 266:0.47 267:0.28 271:0.47 276:0.55 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.33 +1 12:0.86 17:0.92 21:0.78 27:0.72 43:0.08 66:0.36 69:0.98 91:0.07 98:0.05 108:0.97 123:0.97 127:0.05 129:0.05 145:0.52 146:0.05 147:0.05 149:0.05 154:0.07 159:0.05 172:0.05 173:0.91 177:0.05 178:0.55 187:0.39 216:0.07 235:0.95 241:0.44 243:0.51 265:0.05 +1 12:0.86 17:0.16 21:0.78 27:0.72 30:0.62 34:0.36 36:0.25 43:0.08 55:0.99 66:0.07 69:0.98 70:0.34 91:0.01 98:0.05 108:0.96 123:0.96 124:0.89 127:0.20 129:0.95 133:0.87 135:0.78 146:0.05 147:0.05 149:0.10 154:0.07 159:0.92 163:0.99 172:0.05 173:0.84 177:0.05 178:0.55 179:0.78 187:0.68 212:0.30 216:0.04 241:0.03 242:0.82 243:0.23 245:0.39 247:0.12 259:0.21 265:0.05 266:0.27 267:0.76 276:0.29 300:0.25 +1 12:0.86 17:0.77 21:0.78 27:0.72 34:0.50 36:0.21 37:0.78 43:0.31 66:0.36 69:0.72 91:0.27 98:0.09 108:0.55 123:0.53 127:0.07 129:0.05 135:0.70 145:0.41 146:0.05 147:0.05 149:0.13 154:0.23 159:0.05 172:0.05 173:0.85 177:0.05 178:0.55 187:0.68 216:0.19 235:0.31 241:0.21 243:0.51 259:0.21 265:0.10 267:0.52 +1 12:0.86 17:0.40 21:0.78 27:0.21 34:0.25 36:0.80 37:0.59 43:0.06 66:0.36 69:0.99 91:0.02 98:0.05 108:0.99 123:0.99 127:0.05 129:0.05 135:0.61 146:0.05 147:0.05 149:0.05 154:0.06 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.87 216:0.67 235:0.12 241:0.03 243:0.51 259:0.21 265:0.05 267:0.23 +0 12:0.86 17:0.75 21:0.78 27:0.72 43:0.27 66:0.36 69:0.80 91:0.22 98:0.06 108:0.64 123:0.61 127:0.05 129:0.05 146:0.05 147:0.05 149:0.11 154:0.19 159:0.05 172:0.05 173:0.71 177:0.05 178:0.55 187:0.87 216:0.11 235:0.52 241:0.05 243:0.51 265:0.07 +0 12:0.86 17:0.64 21:0.78 27:0.72 43:0.27 66:0.36 69:0.81 91:0.37 98:0.06 108:0.65 123:0.62 127:0.05 129:0.05 146:0.05 147:0.05 149:0.11 154:0.19 159:0.05 172:0.05 173:0.72 177:0.05 178:0.55 187:0.87 216:0.11 235:0.74 241:0.07 243:0.51 265:0.07 +0 12:0.86 17:0.47 21:0.78 27:0.72 34:0.69 36:0.18 37:0.78 43:0.28 66:0.36 69:0.78 91:0.48 98:0.07 108:0.61 123:0.59 127:0.06 129:0.05 135:0.75 145:0.40 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 172:0.05 173:0.76 177:0.05 178:0.55 187:0.39 216:0.19 235:0.22 241:0.05 243:0.51 259:0.21 265:0.07 267:0.28 +1 12:0.86 17:0.57 21:0.78 27:0.72 34:0.58 36:0.83 43:0.21 66:0.36 69:0.92 91:0.44 98:0.07 108:0.83 123:0.82 127:0.05 129:0.05 135:0.80 146:0.05 147:0.05 149:0.08 154:0.14 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.39 216:0.42 235:0.68 241:0.15 243:0.51 259:0.21 265:0.07 267:0.59 +1 8:0.86 12:0.86 17:0.83 21:0.78 27:0.72 34:0.40 36:0.68 37:0.81 43:0.32 66:0.36 69:0.70 91:0.91 98:0.06 108:0.53 123:0.51 127:0.05 129:0.05 135:0.39 145:0.59 146:0.05 147:0.05 149:0.13 154:0.24 159:0.05 172:0.05 173:0.56 177:0.05 178:0.55 191:0.89 202:0.90 216:0.45 235:0.60 241:0.83 243:0.51 259:0.21 265:0.06 267:0.69 +1 12:0.86 17:0.94 21:0.78 27:0.57 30:0.95 34:0.82 36:0.57 37:0.44 43:0.24 55:0.59 66:0.54 69:0.85 91:0.42 98:0.16 108:0.72 123:0.70 127:0.09 129:0.05 135:0.51 145:0.59 146:0.23 147:0.29 149:0.16 154:0.17 159:0.11 172:0.10 173:0.90 177:0.05 178:0.55 179:0.33 187:0.68 216:0.22 235:0.22 241:0.07 243:0.63 259:0.21 265:0.18 267:0.28 300:0.08 +0 12:0.86 17:0.53 21:0.78 27:0.53 34:0.29 36:0.61 37:0.38 43:0.19 66:0.36 69:0.94 91:0.22 98:0.06 108:0.89 123:0.88 127:0.05 129:0.05 135:0.82 145:0.49 146:0.05 147:0.05 149:0.08 154:0.13 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.39 216:0.80 235:0.31 241:0.05 243:0.51 259:0.21 265:0.06 267:0.36 +2 7:0.81 12:0.86 17:0.72 21:0.05 27:0.55 30:0.45 32:0.80 34:0.29 36:0.23 37:0.31 43:0.48 55:0.45 66:0.27 69:0.29 70:0.25 81:0.77 91:0.25 98:0.19 106:0.81 108:0.22 123:0.22 124:0.61 126:0.54 127:0.52 129:0.59 133:0.47 135:0.61 145:0.41 146:0.24 147:0.30 149:0.29 150:0.57 154:0.37 159:0.43 163:0.79 172:0.10 173:0.34 177:0.05 178:0.55 179:0.98 187:0.39 195:0.65 206:0.81 212:0.61 215:0.91 216:0.58 230:0.87 233:0.76 235:0.05 241:0.05 242:0.82 243:0.46 245:0.40 247:0.12 265:0.21 266:0.17 267:0.52 276:0.14 283:0.60 297:0.36 300:0.18 +1 8:0.81 12:0.86 17:0.92 21:0.78 27:0.72 34:0.31 36:0.69 37:0.81 43:0.32 66:0.36 69:0.70 91:0.58 98:0.06 108:0.53 123:0.51 127:0.05 129:0.05 135:0.42 145:0.59 146:0.05 147:0.05 149:0.13 154:0.24 159:0.05 172:0.05 173:0.56 177:0.05 178:0.55 187:0.21 202:0.46 216:0.45 235:0.60 241:0.80 243:0.51 259:0.21 265:0.06 267:0.52 +0 12:0.86 17:0.91 21:0.78 27:0.43 34:0.56 36:0.43 37:0.28 43:0.28 66:0.36 69:0.78 91:0.11 98:0.08 108:0.61 123:0.59 127:0.06 129:0.05 135:0.30 145:0.40 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 172:0.05 173:0.86 177:0.05 178:0.55 187:0.39 216:0.18 235:0.31 241:0.12 243:0.51 259:0.21 265:0.09 267:0.65 +1 12:0.86 17:0.82 21:0.78 27:0.54 34:0.64 36:0.28 37:0.48 43:0.23 66:0.36 69:0.87 91:0.62 98:0.08 108:0.74 123:0.73 127:0.06 129:0.05 135:0.56 146:0.05 147:0.05 149:0.10 154:0.16 159:0.05 172:0.05 173:0.95 177:0.05 178:0.55 187:0.68 202:0.46 216:0.23 235:0.60 241:0.62 243:0.51 259:0.21 265:0.08 267:0.36 +0 12:0.86 17:0.96 21:0.78 27:0.72 34:0.49 36:0.50 43:0.20 66:0.36 69:0.94 91:0.23 98:0.06 108:0.88 123:0.87 127:0.05 129:0.05 135:0.54 145:0.44 146:0.05 147:0.05 149:0.08 154:0.13 159:0.05 172:0.05 173:0.95 177:0.05 178:0.55 187:0.68 216:0.14 235:0.52 243:0.51 259:0.21 265:0.06 267:0.28 +2 12:0.86 17:0.90 21:0.78 27:0.72 30:0.95 34:0.96 36:0.47 43:0.28 55:0.59 66:0.54 69:0.78 91:0.53 98:0.16 108:0.62 123:0.60 127:0.09 129:0.05 135:0.42 145:0.49 146:0.24 147:0.30 149:0.18 154:0.20 159:0.11 172:0.10 173:0.79 177:0.05 178:0.55 179:0.32 187:0.39 216:0.30 235:0.84 241:0.05 243:0.63 259:0.21 265:0.17 267:0.85 300:0.08 +2 1:0.74 6:0.87 8:0.75 9:0.80 11:0.92 12:0.20 17:0.90 18:1.00 20:0.99 21:0.71 22:0.93 27:0.57 28:0.57 29:0.94 30:0.40 31:0.86 32:0.80 34:0.95 36:0.25 37:0.38 39:0.47 41:0.82 43:0.85 44:0.93 45:0.96 47:0.93 60:0.95 64:0.77 66:0.81 69:0.44 70:0.77 71:0.71 74:0.98 77:0.98 78:0.96 79:0.86 81:0.44 83:0.91 85:0.88 86:1.00 91:0.15 96:0.68 98:0.90 100:0.97 101:0.97 106:0.81 108:0.34 111:0.96 114:0.55 117:0.86 120:0.54 122:0.85 123:0.33 124:0.40 126:0.54 127:0.77 129:0.59 133:0.46 135:0.96 137:0.86 139:1.00 140:0.45 145:0.94 146:0.98 147:0.98 149:0.96 150:0.70 151:0.48 152:0.91 153:0.48 154:0.90 155:0.67 158:0.92 159:0.80 161:0.97 162:0.86 164:0.57 165:0.93 167:0.48 169:0.95 172:0.98 173:0.25 176:0.73 177:0.70 179:0.14 181:0.45 186:0.95 187:0.39 189:0.89 192:0.87 195:0.91 196:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.80 215:0.37 216:0.72 219:0.95 220:0.93 222:0.35 232:0.84 235:0.95 238:0.92 241:0.21 242:0.28 243:0.83 245:0.98 247:0.92 248:0.48 253:0.49 255:0.95 256:0.45 259:0.21 260:0.54 261:0.94 262:0.93 265:0.90 266:0.54 267:0.99 268:0.46 271:0.38 276:0.96 279:0.86 282:0.88 283:0.82 284:0.89 285:0.62 290:0.55 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 8:0.60 11:0.64 12:0.20 17:0.78 18:0.86 21:0.22 27:0.43 28:0.57 29:0.94 30:0.53 32:0.80 34:0.99 36:0.17 37:0.25 39:0.47 43:0.79 44:0.93 45:0.81 48:0.91 55:0.42 64:0.77 66:0.91 69:0.76 70:0.48 71:0.71 74:0.86 77:0.89 81:0.70 83:0.60 86:0.94 91:0.48 97:0.89 98:0.75 99:0.83 101:0.52 108:0.59 114:0.71 122:0.57 123:0.57 126:0.54 127:0.24 129:0.05 135:0.87 139:0.97 145:0.96 146:0.70 147:0.74 149:0.55 150:0.80 154:0.72 155:0.67 158:0.87 159:0.28 161:0.97 165:0.70 167:0.49 172:0.75 173:0.85 176:0.73 177:0.70 178:0.55 179:0.35 181:0.45 187:0.21 192:0.87 195:0.65 201:0.93 204:0.89 208:0.64 212:0.76 215:0.51 216:0.33 219:0.95 222:0.35 230:0.86 233:0.76 235:0.52 241:0.27 242:0.30 243:0.91 247:0.67 253:0.54 259:0.21 265:0.75 266:0.54 267:0.52 271:0.38 276:0.60 279:0.86 281:0.88 282:0.88 283:0.71 290:0.71 292:0.91 297:0.36 300:0.43 +3 1:0.74 6:0.85 7:0.81 8:0.67 11:0.92 12:0.20 17:0.79 18:0.95 20:0.87 21:0.40 27:0.31 28:0.57 29:0.94 30:0.10 32:0.80 34:0.97 36:0.25 37:0.71 39:0.47 43:0.94 44:0.93 45:0.77 48:0.99 55:0.71 64:0.77 66:0.74 69:0.35 70:0.94 71:0.71 74:0.86 77:1.00 78:0.97 81:0.66 83:0.91 85:0.80 86:0.97 91:0.20 98:0.77 99:0.83 100:0.91 101:0.97 106:0.81 108:0.58 111:0.94 114:0.62 120:0.64 121:0.97 122:0.85 123:0.55 124:0.44 126:0.54 127:0.70 129:0.59 133:0.61 135:0.86 139:0.97 140:0.45 145:0.96 146:0.17 147:0.22 149:0.80 150:0.80 151:0.56 152:0.83 153:0.69 154:0.94 155:0.67 159:0.62 161:0.97 162:0.86 164:0.53 165:0.93 167:0.51 169:0.88 172:0.84 173:0.29 176:0.73 177:0.70 179:0.37 181:0.45 186:0.97 187:0.39 189:0.86 190:0.89 192:0.87 195:0.78 201:0.93 202:0.46 204:0.89 206:0.81 208:0.64 212:0.73 215:0.42 216:0.28 220:0.62 222:0.35 230:0.87 232:0.92 233:0.76 235:0.84 238:0.82 241:0.39 242:0.42 243:0.14 245:0.80 247:0.84 248:0.55 253:0.50 256:0.45 259:0.21 260:0.62 261:0.90 265:0.77 266:0.71 267:0.96 268:0.55 271:0.38 276:0.77 281:0.91 282:0.88 283:0.78 285:0.47 290:0.62 297:0.36 299:0.99 300:0.84 +2 1:0.74 7:0.81 11:0.83 12:0.20 17:0.57 18:0.96 21:0.68 22:0.93 27:0.57 28:0.57 29:0.94 30:0.75 32:0.80 34:0.45 36:0.37 37:0.61 39:0.47 43:0.86 44:0.93 45:0.97 47:0.93 64:0.77 66:0.90 69:0.60 70:0.46 71:0.71 74:0.96 77:0.70 81:0.45 83:0.91 85:0.94 86:0.99 91:0.06 98:0.82 101:0.87 106:0.81 108:0.72 114:0.56 117:0.86 121:0.90 122:0.57 123:0.70 124:0.37 126:0.54 127:0.49 129:0.59 133:0.61 135:0.44 139:0.99 145:0.77 146:0.17 147:0.22 149:0.96 150:0.70 154:0.84 155:0.67 159:0.73 161:0.97 165:0.93 167:0.50 172:0.97 173:0.42 176:0.73 177:0.70 178:0.55 179:0.15 181:0.45 187:0.21 192:0.87 195:0.89 201:0.93 204:0.89 206:0.81 208:0.64 212:0.90 215:0.37 216:0.86 222:0.35 230:0.87 232:0.85 233:0.76 235:0.88 241:0.32 242:0.39 243:0.08 245:0.86 247:0.74 253:0.49 262:0.93 265:0.82 266:0.63 267:0.94 271:0.38 276:0.93 281:0.91 282:0.88 290:0.56 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.86 7:0.72 8:0.70 11:0.64 12:0.20 17:0.73 18:0.95 20:0.82 21:0.22 22:0.93 27:0.43 28:0.57 29:0.94 30:0.33 32:0.80 34:0.99 36:0.64 37:0.38 39:0.47 43:0.09 44:0.93 45:0.83 47:0.93 48:0.91 55:0.71 64:0.77 66:0.68 69:0.65 70:0.89 71:0.71 74:0.83 77:0.70 78:0.99 81:0.67 83:0.60 86:0.96 91:0.25 97:0.89 98:0.79 99:0.83 100:0.93 101:0.52 106:0.81 108:0.63 111:0.96 114:0.71 117:0.86 122:0.86 123:0.61 124:0.45 126:0.54 127:0.35 129:0.59 133:0.46 135:0.98 139:0.97 145:0.42 146:0.29 147:0.35 149:0.23 150:0.78 152:0.83 154:0.79 155:0.67 158:0.87 159:0.61 161:0.97 165:0.70 167:0.51 169:0.88 172:0.79 173:0.53 176:0.73 177:0.70 178:0.55 179:0.32 181:0.45 186:0.98 187:0.39 192:0.87 195:0.85 201:0.93 204:0.85 206:0.81 208:0.64 212:0.59 215:0.51 216:0.41 219:0.95 222:0.35 230:0.75 232:0.93 233:0.76 235:0.42 241:0.39 242:0.60 243:0.29 245:0.86 247:0.76 253:0.54 254:0.74 259:0.21 261:0.90 262:0.93 265:0.80 266:0.47 267:0.97 271:0.38 276:0.74 279:0.86 281:0.89 282:0.88 283:0.71 290:0.71 292:0.91 297:0.36 300:0.70 +2 1:0.74 7:0.81 11:0.83 12:0.20 17:0.53 18:0.96 21:0.47 22:0.93 27:0.48 28:0.57 29:0.94 30:0.84 32:0.80 34:0.88 36:0.85 37:0.42 39:0.47 43:0.87 44:0.93 45:0.73 47:0.93 60:0.87 64:0.77 66:0.95 69:0.58 70:0.53 71:0.71 74:0.93 77:0.70 81:0.58 83:0.91 85:0.95 86:0.99 91:0.17 98:0.88 101:0.87 106:0.81 108:0.44 114:0.60 117:0.86 121:0.90 122:0.57 123:0.43 126:0.54 127:0.43 129:0.05 135:0.56 139:0.99 145:0.44 146:0.86 147:0.88 149:0.96 150:0.76 154:0.85 155:0.67 158:0.92 159:0.43 161:0.97 165:0.93 167:0.47 172:0.88 173:0.61 176:0.73 177:0.70 178:0.55 179:0.30 181:0.45 187:0.68 192:0.87 195:0.67 201:0.93 204:0.89 206:0.81 208:0.64 212:0.80 215:0.41 216:0.51 219:0.95 222:0.35 230:0.87 232:0.97 233:0.76 235:0.84 241:0.18 242:0.37 243:0.95 247:0.67 253:0.50 259:0.21 262:0.93 265:0.88 266:0.27 267:0.73 271:0.38 276:0.78 279:0.86 281:0.91 282:0.88 283:0.82 290:0.60 292:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.20 17:0.92 18:0.83 21:0.22 27:0.17 28:0.57 29:0.94 30:0.19 31:0.91 34:0.94 36:0.28 37:0.81 39:0.47 43:0.08 44:0.87 45:0.77 48:0.98 55:0.42 64:0.77 66:0.53 69:0.86 70:0.96 71:0.71 74:0.65 79:0.90 81:0.67 83:0.60 86:0.89 91:0.27 96:0.75 98:0.48 99:0.83 101:0.52 108:0.72 114:0.71 120:0.63 122:0.57 123:0.71 124:0.52 126:0.54 127:0.23 129:0.05 133:0.43 135:0.96 137:0.91 139:0.90 140:0.45 145:0.84 146:0.68 147:0.72 149:0.08 150:0.78 151:0.72 153:0.48 154:0.63 155:0.67 159:0.45 161:0.97 162:0.86 164:0.57 165:0.70 167:0.52 172:0.54 173:0.83 176:0.73 177:0.70 179:0.42 181:0.45 187:0.68 192:0.87 195:0.82 196:0.94 201:0.93 202:0.36 204:0.89 208:0.64 212:0.30 215:0.51 216:0.38 220:0.62 222:0.35 230:0.86 233:0.76 235:0.42 241:0.44 242:0.23 243:0.62 245:0.74 247:0.60 248:0.67 253:0.69 254:0.74 255:0.95 256:0.45 259:0.21 260:0.64 265:0.50 266:0.63 267:0.52 268:0.46 271:0.38 276:0.52 281:0.91 284:0.89 285:0.62 290:0.71 297:0.36 300:0.84 +2 1:0.74 8:0.83 11:0.92 12:0.20 17:0.38 18:0.88 20:0.82 21:0.47 27:0.45 28:0.57 29:0.94 30:0.37 34:0.84 36:0.19 37:0.50 39:0.47 43:0.82 45:0.73 55:0.84 64:0.77 66:0.48 69:0.69 70:0.90 71:0.71 74:0.56 78:0.98 81:0.52 83:0.91 85:0.72 86:0.89 91:0.17 98:0.43 100:0.73 101:0.97 108:0.52 111:0.94 114:0.60 122:0.86 123:0.50 124:0.73 126:0.54 127:0.46 129:0.05 133:0.73 135:0.89 139:0.86 145:0.50 146:0.68 147:0.72 149:0.66 150:0.74 152:0.79 154:0.77 155:0.67 159:0.69 161:0.97 163:0.81 165:0.93 167:0.48 169:0.72 172:0.51 173:0.56 176:0.73 177:0.70 178:0.55 179:0.62 181:0.45 186:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.22 215:0.41 216:0.77 222:0.35 235:0.68 241:0.24 242:0.63 243:0.59 245:0.62 247:0.47 253:0.45 259:0.21 261:0.86 265:0.45 266:0.80 267:0.52 271:0.38 276:0.54 290:0.60 297:0.36 300:0.84 +2 1:0.74 7:0.81 11:0.64 12:0.20 17:0.84 18:0.90 21:0.22 22:0.93 27:0.17 28:0.57 29:0.94 30:0.33 34:0.98 36:0.18 37:0.81 39:0.47 43:0.09 44:0.87 45:0.77 47:0.93 48:0.98 55:0.42 64:0.77 66:0.31 69:0.62 70:0.84 71:0.71 74:0.74 81:0.67 83:0.60 86:0.94 91:0.17 98:0.44 99:0.83 101:0.52 106:0.81 108:0.75 114:0.71 117:0.86 122:0.85 123:0.73 124:0.73 126:0.54 127:0.42 129:0.59 133:0.66 135:0.96 139:0.94 145:0.84 146:0.42 147:0.49 149:0.08 150:0.78 154:0.80 155:0.67 159:0.43 161:0.97 165:0.70 167:0.56 172:0.48 173:0.65 176:0.73 177:0.70 178:0.55 179:0.53 181:0.45 187:0.68 192:0.87 195:0.68 201:0.93 204:0.89 206:0.81 208:0.64 212:0.52 215:0.51 216:0.38 222:0.35 230:0.86 232:0.99 233:0.76 235:0.42 241:0.54 242:0.24 243:0.36 245:0.75 247:0.67 253:0.53 254:0.74 259:0.21 262:0.93 265:0.46 266:0.38 267:0.79 271:0.38 276:0.59 277:0.69 281:0.91 290:0.71 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.20 17:0.30 18:0.83 21:0.64 27:0.45 28:0.57 29:0.94 30:0.15 34:0.83 36:0.20 37:0.50 39:0.47 43:0.09 45:0.66 55:0.71 64:0.77 66:0.33 69:0.70 70:0.84 71:0.71 74:0.56 81:0.42 83:0.60 86:0.83 91:0.15 97:0.89 98:0.33 99:0.83 101:0.52 108:0.81 114:0.57 122:0.86 123:0.80 124:0.71 126:0.54 127:0.27 129:0.05 133:0.59 135:0.83 139:0.85 145:0.49 146:0.18 147:0.23 149:0.13 150:0.66 154:0.76 155:0.67 158:0.91 159:0.50 161:0.97 163:0.88 165:0.70 167:0.49 172:0.27 173:0.62 176:0.73 177:0.70 178:0.55 179:0.71 181:0.45 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.38 216:0.77 219:0.95 222:0.35 235:0.84 241:0.30 242:0.77 243:0.37 245:0.53 247:0.35 253:0.41 254:0.74 259:0.21 265:0.35 266:0.47 267:0.44 271:0.38 276:0.36 277:0.87 279:0.86 283:0.77 290:0.57 292:0.91 297:0.36 300:0.55 +2 1:0.74 6:0.84 8:0.62 9:0.80 11:0.64 12:0.20 17:0.13 18:0.82 20:0.87 21:0.71 27:0.17 28:0.57 29:0.94 30:0.45 31:0.91 34:1.00 36:0.42 37:0.65 39:0.47 43:0.92 44:0.87 45:0.66 64:0.77 66:0.36 69:0.45 70:0.25 71:0.71 74:0.58 78:0.96 79:0.90 81:0.41 83:0.60 86:0.88 91:0.54 96:0.75 97:0.89 98:0.16 99:0.83 100:0.90 101:0.52 108:0.35 111:0.94 114:0.55 120:0.63 122:0.86 123:0.33 124:0.69 126:0.54 127:0.39 129:0.05 133:0.59 135:0.89 137:0.91 139:0.90 140:0.80 145:0.82 146:0.21 147:0.27 149:0.64 150:0.65 151:0.72 152:0.94 153:0.48 154:0.91 155:0.67 158:0.91 159:0.39 161:0.97 162:0.49 164:0.57 165:0.70 167:0.59 169:0.88 172:0.21 173:0.49 176:0.73 177:0.70 178:0.42 179:0.89 181:0.45 186:0.96 187:0.95 189:0.83 191:0.85 192:0.87 195:0.65 196:0.94 201:0.93 202:0.64 208:0.64 212:0.23 215:0.37 216:0.59 219:0.95 220:0.62 222:0.35 235:0.95 238:0.82 241:0.61 242:0.55 243:0.51 245:0.45 247:0.39 248:0.67 253:0.62 255:0.95 256:0.45 259:0.21 260:0.64 261:0.93 265:0.17 266:0.38 267:0.96 268:0.46 271:0.38 276:0.26 279:0.86 283:0.78 284:0.89 285:0.62 290:0.55 292:0.91 297:0.36 300:0.18 +1 7:0.81 12:0.20 17:0.73 18:0.79 21:0.78 27:0.49 28:0.57 29:0.94 30:0.76 31:0.90 34:0.31 36:0.33 37:0.43 39:0.47 43:0.13 48:0.99 55:0.71 66:0.72 69:0.64 70:0.22 71:0.71 74:0.51 79:0.90 86:0.76 91:0.69 98:0.72 108:0.49 121:0.90 122:0.85 123:0.46 127:0.22 129:0.05 135:0.42 137:0.90 139:0.81 140:0.45 145:0.40 146:0.55 147:0.61 149:0.11 151:0.55 153:0.48 154:0.48 155:0.67 159:0.20 161:0.97 162:0.86 167:0.66 172:0.27 173:0.80 175:0.75 177:0.38 179:0.87 181:0.45 187:0.39 190:0.89 192:0.87 196:0.91 202:0.36 204:0.89 208:0.64 212:0.42 216:0.12 220:0.62 222:0.35 230:0.87 233:0.76 235:0.02 241:0.68 242:0.45 243:0.75 244:0.61 247:0.35 248:0.54 253:0.36 254:0.80 256:0.45 257:0.65 259:0.21 265:0.72 266:0.23 267:0.19 268:0.46 271:0.38 276:0.23 277:0.69 281:0.91 284:0.86 285:0.47 300:0.18 +2 1:0.74 11:0.83 12:0.20 17:0.77 18:0.92 21:0.59 27:0.54 28:0.57 29:0.94 30:0.21 34:0.99 36:0.19 37:0.54 39:0.47 43:0.79 44:0.90 45:0.77 48:0.99 55:0.59 64:0.77 66:0.94 69:0.75 70:0.77 71:0.71 74:0.75 77:0.70 81:0.46 83:0.60 85:0.80 86:0.97 91:0.29 98:0.87 99:0.83 101:0.87 108:0.58 114:0.58 122:0.82 123:0.56 126:0.54 127:0.40 129:0.05 135:0.81 139:0.97 145:0.45 146:0.90 147:0.92 149:0.76 150:0.67 154:0.73 155:0.67 159:0.49 161:0.97 165:0.70 167:0.46 172:0.83 173:0.75 176:0.73 177:0.70 178:0.55 179:0.36 181:0.45 187:0.39 192:0.87 195:0.74 201:0.93 208:0.64 212:0.68 215:0.39 216:0.37 222:0.35 235:0.84 241:0.10 242:0.18 243:0.94 247:0.88 253:0.45 259:0.21 265:0.87 266:0.47 267:0.44 271:0.38 276:0.72 281:0.89 282:0.77 290:0.58 297:0.36 300:0.55 +2 1:0.74 7:0.72 8:0.59 11:0.64 12:0.20 17:0.84 18:0.89 21:0.22 22:0.93 27:0.57 28:0.57 29:0.94 30:0.43 32:0.80 34:0.99 36:0.27 37:0.26 39:0.47 43:0.88 44:0.93 45:0.83 47:0.93 48:0.99 64:0.77 66:0.89 69:0.23 70:0.57 71:0.71 74:0.87 77:0.70 81:0.67 83:0.60 86:0.95 91:0.44 97:0.89 98:0.68 99:0.83 101:0.52 108:0.18 114:0.71 122:0.57 123:0.18 126:0.54 127:0.20 129:0.05 135:0.84 139:0.96 145:0.91 146:0.63 147:0.69 149:0.67 150:0.78 154:0.93 155:0.67 158:0.91 159:0.24 161:0.97 165:0.70 167:0.54 172:0.70 173:0.33 176:0.73 177:0.70 178:0.55 179:0.33 181:0.45 187:0.21 192:0.87 195:0.64 201:0.93 204:0.85 208:0.64 212:0.78 215:0.51 216:0.46 219:0.95 222:0.35 230:0.74 233:0.73 235:0.42 241:0.51 242:0.29 243:0.90 247:0.76 253:0.54 259:0.21 262:0.93 265:0.68 266:0.27 267:0.44 271:0.38 276:0.57 279:0.86 281:0.89 282:0.88 283:0.77 290:0.71 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.81 11:0.92 12:0.20 17:0.67 18:0.81 21:0.05 27:0.49 28:0.57 29:0.94 30:0.62 32:0.80 34:0.99 36:0.38 37:0.43 39:0.47 43:0.84 44:0.93 45:0.73 48:0.98 60:0.87 64:0.77 66:0.29 69:0.64 70:0.40 71:0.71 74:0.79 77:0.70 81:0.83 83:0.91 85:0.72 86:0.87 91:0.46 98:0.62 101:0.97 108:0.49 114:0.89 121:0.90 122:0.57 123:0.61 124:0.60 126:0.54 127:0.30 129:0.59 133:0.46 135:0.91 139:0.94 145:0.39 146:0.22 147:0.28 149:0.65 150:0.89 154:0.80 155:0.67 158:0.92 159:0.32 161:0.97 165:0.93 167:0.57 172:0.32 173:0.72 176:0.73 177:0.70 178:0.55 179:0.71 181:0.45 187:0.39 192:0.87 195:0.64 201:0.93 204:0.89 208:0.64 212:0.42 215:0.78 216:0.12 219:0.95 222:0.35 230:0.87 233:0.76 235:0.22 241:0.57 242:0.45 243:0.51 245:0.62 247:0.55 253:0.61 259:0.21 265:0.19 266:0.47 267:0.23 271:0.38 276:0.39 279:0.86 281:0.91 282:0.88 283:0.82 290:0.89 292:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 7:0.81 11:0.64 12:0.20 17:0.93 18:0.86 21:0.22 22:0.93 27:0.17 28:0.57 29:0.94 30:0.33 34:0.98 36:0.20 37:0.81 39:0.47 43:0.08 44:0.87 45:0.77 47:0.93 48:0.98 55:0.42 64:0.77 66:0.31 69:0.84 70:0.92 71:0.71 74:0.69 81:0.67 83:0.60 86:0.91 91:0.18 98:0.38 99:0.83 101:0.52 106:0.81 108:0.83 114:0.71 117:0.86 122:0.85 123:0.82 124:0.73 126:0.54 127:0.29 129:0.59 133:0.66 135:0.93 139:0.92 145:0.84 146:0.42 147:0.49 149:0.07 150:0.78 154:0.64 155:0.67 159:0.43 161:0.97 165:0.70 167:0.52 172:0.48 173:0.86 176:0.73 177:0.70 178:0.55 179:0.44 181:0.45 187:0.68 192:0.87 195:0.74 201:0.93 204:0.89 206:0.81 208:0.64 212:0.52 215:0.51 216:0.38 222:0.35 230:0.86 232:0.99 233:0.76 235:0.42 241:0.44 242:0.24 243:0.36 245:0.75 247:0.67 253:0.53 254:0.74 259:0.21 262:0.93 265:0.40 266:0.63 267:0.65 271:0.38 276:0.59 277:0.69 281:0.91 290:0.71 297:0.36 300:0.84 +1 1:0.64 10:0.89 11:0.57 12:0.20 17:0.77 18:0.92 26:0.94 27:0.41 28:0.76 29:0.53 30:0.70 34:0.47 36:0.35 37:0.29 39:0.58 43:0.75 45:0.95 58:0.93 66:0.97 69:0.15 70:0.50 71:0.83 74:0.87 81:0.60 83:0.56 86:0.94 91:0.33 98:0.97 99:0.83 101:0.96 108:0.12 114:0.95 122:0.86 123:0.12 126:0.32 127:0.76 129:0.05 135:0.49 139:0.92 141:0.91 146:0.90 147:0.92 149:0.91 150:0.56 154:0.66 155:0.49 159:0.50 161:0.67 165:0.65 167:0.63 170:0.94 172:0.92 173:0.23 174:0.83 176:0.63 177:0.88 178:0.55 179:0.29 181:0.68 187:0.39 192:0.48 197:0.87 199:0.94 201:0.60 208:0.50 212:0.83 215:0.96 216:0.46 222:0.35 223:0.92 224:0.93 234:0.91 235:0.05 239:0.88 241:0.27 242:0.22 243:0.97 247:0.95 253:0.73 259:0.21 265:0.97 266:0.27 267:0.28 271:0.13 274:0.91 276:0.85 290:0.95 297:0.36 300:0.55 +2 1:0.60 7:0.69 8:0.58 10:0.90 12:0.20 17:0.67 18:0.69 20:0.96 21:0.59 22:0.93 26:0.96 27:0.31 28:0.76 29:0.53 30:0.21 34:0.98 36:0.30 37:0.55 39:0.58 43:0.10 44:0.92 47:0.93 48:0.91 55:0.45 58:0.93 64:0.77 66:0.69 69:0.74 70:0.67 71:0.83 74:0.57 76:0.85 77:0.70 78:0.80 81:0.41 86:0.78 91:0.06 98:0.46 100:0.80 102:0.97 106:0.81 108:0.58 111:0.79 114:0.73 117:0.86 122:0.57 123:0.55 124:0.41 126:0.51 127:0.24 129:0.05 133:0.36 135:0.84 139:0.80 141:0.91 145:0.76 146:0.50 147:0.56 149:0.09 150:0.43 152:0.91 154:0.57 155:0.51 159:0.31 161:0.67 167:0.63 169:0.78 170:0.94 172:0.41 173:0.79 174:0.83 176:0.70 177:0.88 178:0.55 179:0.71 181:0.68 186:0.81 187:0.39 192:0.48 193:0.97 195:0.69 197:0.85 199:0.96 201:0.59 204:0.79 206:0.81 208:0.61 212:0.55 216:0.85 222:0.35 223:0.95 224:0.93 230:0.71 232:0.94 233:0.66 234:0.91 235:0.80 236:0.95 239:0.92 241:0.27 242:0.16 243:0.73 245:0.46 247:0.60 253:0.56 254:0.97 259:0.21 261:0.83 262:0.93 265:0.48 266:0.38 267:0.28 271:0.13 274:0.91 276:0.30 281:0.86 282:0.79 290:0.73 300:0.43 +0 10:0.89 11:0.52 12:0.20 17:0.43 18:0.64 20:0.87 21:0.78 26:0.94 27:0.45 28:0.76 29:0.53 30:0.91 34:0.63 36:0.18 37:0.50 39:0.58 43:0.78 45:0.73 58:0.93 66:0.69 69:0.74 70:0.13 71:0.83 74:0.42 78:0.76 86:0.74 91:0.18 98:0.12 100:0.73 101:0.65 108:0.57 111:0.75 122:0.65 123:0.55 127:0.08 129:0.05 135:0.82 139:0.71 141:0.91 145:0.50 146:0.19 147:0.25 149:0.61 152:0.82 154:0.71 159:0.10 161:0.67 167:0.63 169:0.72 170:0.94 172:0.21 173:0.72 174:0.79 177:0.88 178:0.55 179:0.09 181:0.68 186:0.77 187:0.68 197:0.83 199:0.94 212:0.61 216:0.77 222:0.35 223:0.91 224:0.93 234:0.91 235:0.68 239:0.88 241:0.27 242:0.63 243:0.73 247:0.30 253:0.36 254:0.80 259:0.21 261:0.77 265:0.13 266:0.17 267:0.23 271:0.13 274:0.91 276:0.21 300:0.13 +0 10:0.91 11:0.52 12:0.20 17:0.24 18:0.78 21:0.78 26:0.94 27:0.48 28:0.76 29:0.53 30:0.29 34:0.52 36:0.30 37:0.55 39:0.58 43:0.21 45:0.87 58:0.93 66:0.36 69:0.92 70:0.89 71:0.83 74:0.50 86:0.80 91:0.12 98:0.34 101:0.65 108:0.90 122:0.86 123:0.89 124:0.82 127:0.18 129:0.05 133:0.81 135:0.84 139:0.76 141:0.91 145:0.89 146:0.25 147:0.31 149:0.35 154:0.50 159:0.72 161:0.67 167:0.69 170:0.94 172:0.59 173:0.72 174:0.86 177:0.88 178:0.55 179:0.22 181:0.68 187:0.39 197:0.85 199:0.95 202:0.58 212:0.37 216:0.92 222:0.35 223:0.92 224:0.93 234:0.91 235:0.95 239:0.89 241:0.47 242:0.32 243:0.31 245:0.71 247:0.84 253:0.41 254:0.80 259:0.21 265:0.37 266:0.91 267:0.28 271:0.13 274:0.91 276:0.66 277:0.69 300:0.84 +4 1:0.57 6:0.99 8:0.98 9:0.74 11:0.46 12:0.20 17:0.73 18:0.66 20:0.99 21:0.59 23:0.98 26:0.96 27:0.15 28:0.76 29:0.53 30:0.28 31:0.96 34:0.94 36:0.30 37:0.99 39:0.58 43:0.35 45:0.77 55:0.71 58:0.99 62:0.96 64:0.77 66:0.67 69:0.87 70:0.95 71:0.83 74:0.46 78:0.96 79:0.96 81:0.43 83:0.69 86:0.68 91:0.67 96:0.90 98:0.36 99:0.83 100:1.00 101:0.47 108:0.93 111:1.00 114:0.71 122:0.83 123:0.93 124:0.45 126:0.32 127:0.47 128:0.97 129:0.59 133:0.47 135:0.49 137:0.96 139:0.66 140:0.96 141:0.98 146:0.22 147:0.28 149:0.31 150:0.38 151:0.62 152:0.92 153:0.48 154:0.26 155:0.63 159:0.79 161:0.67 162:0.74 165:0.65 167:0.89 168:0.97 169:1.00 170:0.94 172:0.57 173:0.74 175:0.91 176:0.63 177:0.38 179:0.67 181:0.68 186:0.96 187:0.21 189:0.99 190:0.89 191:0.90 192:0.57 193:0.96 196:0.90 199:0.96 201:0.55 202:0.79 205:0.98 208:0.61 212:0.55 216:0.22 220:0.97 222:0.35 223:0.95 224:0.98 232:0.76 234:0.97 235:0.74 238:0.99 239:0.84 241:0.77 242:0.72 243:0.27 244:0.83 245:0.66 247:0.47 248:0.61 253:0.86 255:0.73 256:0.84 257:0.84 259:0.21 261:0.99 265:0.38 266:0.63 267:0.44 268:0.83 271:0.13 274:0.99 276:0.29 277:0.87 284:0.98 285:0.60 290:0.71 300:0.84 +0 1:0.60 8:0.75 9:0.73 12:0.20 17:0.49 20:0.79 21:0.30 26:0.93 27:0.72 28:0.76 29:0.53 30:0.95 31:0.97 34:0.42 36:0.90 37:0.29 39:0.58 43:0.16 55:0.92 58:0.93 64:0.77 66:0.20 69:0.54 71:0.83 74:0.41 78:0.72 79:0.97 81:0.49 83:0.56 91:0.83 96:0.88 97:0.94 98:0.11 99:0.83 100:0.73 108:0.42 111:0.72 114:0.82 123:0.40 124:0.61 126:0.32 127:0.29 129:0.59 133:0.47 135:0.44 137:0.97 139:0.67 140:0.87 141:0.91 146:0.05 147:0.05 149:0.09 150:0.44 151:0.85 152:0.77 153:0.48 154:0.11 155:0.56 158:0.76 159:0.17 161:0.67 162:0.57 165:0.65 167:0.96 169:0.72 170:0.94 172:0.05 173:0.82 175:0.97 176:0.63 177:0.05 179:0.99 181:0.68 186:0.73 190:0.89 192:0.55 196:0.90 199:0.93 201:0.57 202:0.78 208:0.50 216:0.14 219:0.91 220:0.62 222:0.35 223:0.91 224:0.93 232:0.72 234:0.91 235:0.42 241:0.93 243:0.40 244:0.93 245:0.36 248:0.82 253:0.84 255:0.72 256:0.77 257:0.93 259:0.21 261:0.73 265:0.12 267:0.23 268:0.77 271:0.13 274:0.91 277:0.95 279:0.79 284:0.96 285:0.50 290:0.82 292:0.88 300:0.08 +1 1:0.67 6:0.79 8:0.58 10:0.96 11:0.57 12:0.20 17:0.84 18:0.95 20:0.92 26:0.95 27:0.41 28:0.76 29:0.53 30:0.58 34:0.64 36:0.47 37:0.29 39:0.58 43:0.75 45:0.95 58:0.93 64:0.77 66:0.80 69:0.47 70:0.72 71:0.83 74:0.90 78:0.87 81:0.74 83:0.69 86:0.97 91:0.29 97:0.94 98:0.83 99:0.95 100:0.85 101:1.00 108:0.36 111:0.85 114:0.97 122:0.86 123:0.35 124:0.40 126:0.51 127:0.57 129:0.05 133:0.45 135:0.39 139:0.96 141:0.91 146:0.86 147:0.89 149:0.89 150:0.64 152:0.95 154:0.57 155:0.52 158:0.82 159:0.53 161:0.67 165:0.81 167:0.64 169:0.81 170:0.94 172:0.92 173:0.45 174:0.91 176:0.70 177:0.88 178:0.55 179:0.24 181:0.68 186:0.87 187:0.39 189:0.81 192:0.51 197:0.91 199:0.96 201:0.65 208:0.61 212:0.85 215:0.96 216:0.46 219:0.94 222:0.35 223:0.93 224:0.93 234:0.91 235:0.12 238:0.86 239:0.95 241:0.30 242:0.24 243:0.82 245:0.90 247:0.93 253:0.75 259:0.21 261:0.89 265:0.83 266:0.63 267:0.28 271:0.13 274:0.91 276:0.85 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.84 +0 1:0.66 10:0.93 11:0.46 12:0.20 17:0.08 18:0.72 21:0.30 22:0.93 26:0.96 27:0.60 28:0.76 29:0.53 30:0.68 33:0.97 34:0.82 36:0.89 37:0.45 39:0.58 43:0.64 45:0.77 47:0.93 58:0.93 64:0.77 66:0.71 69:0.78 70:0.48 71:0.83 74:0.52 81:0.69 83:0.56 86:0.83 91:0.23 98:0.69 99:0.83 101:0.47 106:0.81 108:0.61 114:0.86 117:0.86 122:0.85 123:0.59 124:0.42 126:0.54 127:0.43 129:0.05 133:0.36 135:0.39 139:0.78 141:0.91 145:0.70 146:0.64 147:0.41 149:0.44 150:0.59 154:0.64 155:0.51 159:0.37 161:0.67 165:0.65 167:0.60 170:0.94 172:0.57 173:0.86 174:0.90 176:0.73 177:0.05 178:0.55 179:0.68 181:0.68 187:0.68 192:0.50 197:0.93 199:0.97 201:0.65 206:0.81 208:0.64 212:0.69 215:0.72 216:0.59 222:0.35 223:0.95 224:0.93 232:0.99 234:0.91 235:0.60 236:0.97 239:0.92 241:0.15 242:0.31 243:0.21 245:0.60 247:0.74 251:1.00 253:0.62 254:0.84 257:0.93 259:0.21 262:0.93 265:0.69 266:0.54 267:0.28 271:0.13 274:0.91 276:0.47 290:0.86 291:0.97 297:0.36 300:0.43 +0 10:0.94 11:0.52 12:0.20 17:0.11 18:0.86 21:0.47 26:0.96 27:0.56 28:0.76 29:0.53 30:0.62 33:0.97 34:0.64 36:0.27 37:0.60 39:0.58 43:0.59 45:0.81 55:0.45 58:0.93 66:0.49 69:0.82 70:0.63 71:0.83 74:0.43 81:0.28 86:0.84 91:0.07 98:0.60 101:0.87 102:0.95 108:0.67 122:0.95 123:0.65 124:0.56 126:0.24 127:0.27 129:0.05 133:0.36 135:0.44 139:0.80 141:0.91 145:0.65 146:0.70 147:0.75 149:0.58 150:0.30 154:0.48 159:0.41 161:0.67 167:0.59 170:0.94 172:0.67 173:0.84 174:0.96 177:0.88 178:0.55 179:0.33 181:0.68 187:0.39 195:0.72 197:0.95 199:0.98 212:0.70 216:0.76 222:0.35 223:0.95 224:0.93 234:0.91 235:0.52 236:0.92 239:0.94 241:0.10 242:0.63 243:0.60 244:0.61 245:0.86 247:0.82 253:0.37 259:0.21 265:0.61 266:0.54 267:0.44 271:0.13 274:0.91 276:0.68 291:0.97 300:0.55 +1 1:0.64 6:0.79 8:0.58 10:0.98 11:0.82 12:0.20 17:0.84 18:0.92 20:0.92 26:0.95 27:0.41 28:0.76 29:0.53 30:0.75 34:0.37 36:0.24 37:0.29 39:0.58 43:0.73 45:0.95 58:0.93 66:0.97 69:0.47 70:0.46 71:0.83 74:0.90 78:0.84 81:0.60 83:0.56 85:0.85 86:0.97 91:0.33 97:0.89 98:0.95 99:0.83 100:0.81 101:1.00 108:0.36 111:0.82 114:0.95 122:0.65 123:0.34 126:0.32 127:0.63 129:0.05 135:0.47 139:0.95 141:0.91 146:0.89 147:0.91 149:0.94 150:0.56 152:0.95 154:0.63 155:0.49 158:0.77 159:0.48 161:0.67 165:0.65 167:0.65 169:0.81 170:0.94 172:0.92 173:0.48 174:0.92 176:0.63 177:0.88 178:0.55 179:0.26 181:0.68 186:0.84 187:0.39 189:0.81 192:0.48 197:0.92 199:0.96 201:0.60 208:0.50 212:0.90 215:0.96 216:0.46 222:0.35 223:0.93 224:0.93 234:0.91 235:0.05 238:0.84 239:0.96 241:0.32 242:0.15 243:0.97 247:0.96 253:0.74 259:0.21 261:0.89 265:0.95 266:0.38 267:0.28 271:0.13 274:0.91 276:0.86 279:0.76 283:0.82 290:0.95 297:0.36 300:0.55 +4 1:0.57 8:0.95 9:0.75 11:0.46 12:0.20 17:0.81 18:0.66 20:0.80 21:0.59 23:0.99 26:0.96 27:0.18 28:0.76 29:0.53 30:0.28 31:0.99 34:0.47 36:0.23 37:0.98 39:0.58 43:0.35 45:0.77 55:0.71 58:0.99 62:0.96 64:0.77 66:0.67 69:0.87 70:0.95 71:0.83 74:0.46 78:0.85 79:0.98 81:0.43 83:0.69 86:0.68 91:0.93 96:0.92 98:0.36 99:0.83 100:0.88 101:0.47 108:0.93 111:0.85 114:0.71 122:0.83 123:0.93 124:0.45 126:0.32 127:0.48 128:0.99 129:0.59 133:0.47 135:0.37 137:0.99 138:0.98 139:0.70 140:0.98 141:0.98 146:0.22 147:0.28 149:0.31 150:0.38 151:0.60 152:0.77 153:0.82 154:0.26 155:0.64 159:0.79 161:0.67 162:0.76 165:0.65 167:0.97 168:0.99 169:0.86 170:0.94 172:0.57 173:0.74 175:0.91 176:0.63 177:0.38 178:0.48 179:0.67 181:0.68 186:0.85 190:0.89 191:0.83 192:0.58 193:0.96 196:0.93 199:0.96 201:0.55 202:0.86 205:0.99 208:0.61 212:0.55 216:0.27 219:0.90 220:0.82 222:0.35 223:0.95 224:0.98 234:0.97 235:0.74 239:0.84 241:0.98 242:0.72 243:0.27 244:0.83 245:0.66 247:0.47 248:0.58 253:0.89 255:0.74 256:0.89 257:0.84 259:0.21 261:0.80 265:0.38 266:0.63 267:0.28 268:0.90 271:0.13 274:0.99 276:0.29 277:0.87 284:0.99 285:0.60 290:0.71 292:0.88 300:0.84 +1 10:0.96 11:0.54 12:0.06 17:0.09 18:0.91 21:0.78 26:0.96 27:0.28 29:0.56 30:0.76 34:0.63 36:0.70 37:0.77 39:0.51 43:0.56 45:0.96 58:0.93 66:0.77 69:0.62 70:0.45 71:0.66 74:0.75 86:0.94 89:0.97 91:0.20 98:0.76 101:0.87 108:0.60 122:0.67 123:0.57 124:0.41 127:0.35 129:0.59 133:0.47 135:0.32 139:0.90 141:0.91 146:0.19 147:0.25 149:0.57 154:0.79 159:0.42 170:0.96 172:0.86 173:0.63 174:0.90 177:0.96 178:0.55 179:0.26 187:0.68 197:0.92 199:0.96 212:0.81 216:0.56 222:0.76 223:0.97 224:0.93 225:0.96 234:0.91 235:0.31 239:0.94 240:0.95 241:0.10 242:0.19 243:0.19 245:0.86 247:0.93 253:0.53 254:0.74 259:0.21 264:0.90 265:0.76 266:0.27 267:0.28 271:0.13 274:0.91 275:0.93 276:0.79 294:0.98 300:0.55 +1 8:0.83 10:0.93 11:0.54 12:0.06 17:0.04 18:0.70 21:0.22 26:0.95 27:0.28 29:0.56 30:0.54 34:0.62 36:0.85 37:0.77 39:0.51 43:0.10 45:0.85 56:0.97 58:0.93 66:0.60 69:0.48 70:0.51 71:0.66 74:0.51 81:0.28 86:0.81 89:0.99 91:0.57 98:0.74 101:0.69 108:0.37 122:0.99 123:0.36 124:0.50 126:0.23 127:0.41 129:0.59 133:0.47 135:0.58 139:0.77 140:0.45 141:0.91 143:1.00 146:0.75 147:0.79 149:0.16 150:0.31 151:0.72 153:0.78 154:0.84 159:0.43 162:0.70 170:0.96 172:0.57 173:0.50 174:0.83 177:0.96 179:0.60 187:0.68 190:0.98 191:0.75 197:0.87 199:0.94 202:0.69 212:0.30 216:0.56 220:0.82 222:0.76 223:0.94 224:0.93 225:1.00 234:0.91 235:0.22 239:0.91 240:1.00 241:0.64 242:0.59 243:0.67 245:0.73 247:0.60 248:0.70 253:0.41 254:0.74 256:0.68 259:0.21 264:0.90 265:0.74 266:0.75 267:0.76 268:0.72 271:0.13 274:0.91 275:0.97 276:0.53 285:0.45 294:0.98 300:0.43 +1 8:0.98 10:0.93 11:0.54 12:0.06 17:0.20 18:0.71 21:0.78 26:0.96 27:0.28 29:0.56 30:0.74 34:0.76 36:0.86 37:0.77 39:0.51 43:0.09 45:0.89 58:0.93 66:0.07 69:0.63 70:0.59 71:0.66 74:0.52 86:0.82 89:0.99 91:0.38 98:0.31 101:0.69 108:0.84 122:0.67 123:0.58 124:0.78 127:0.46 129:0.59 133:0.73 135:0.30 139:0.78 141:0.91 146:0.19 147:0.25 149:0.10 154:0.79 159:0.57 170:0.96 172:0.48 173:0.57 174:0.88 177:0.96 178:0.55 179:0.58 187:0.39 197:0.88 199:0.95 202:0.36 212:0.61 216:0.56 222:0.76 223:0.95 224:0.93 225:0.97 234:0.91 235:0.31 239:0.92 240:0.95 241:0.03 242:0.19 243:0.33 245:0.67 247:0.76 253:0.42 254:0.74 259:0.21 264:0.90 265:0.24 266:0.75 267:0.36 271:0.13 274:0.91 275:0.96 276:0.54 277:0.98 294:0.98 300:0.70 +1 2:0.99 10:0.94 11:0.54 12:0.06 18:0.72 21:0.78 26:0.95 27:0.52 29:0.56 30:0.73 32:0.74 34:0.89 36:0.49 37:0.57 39:0.51 43:0.09 44:0.93 45:0.85 58:0.93 66:0.86 69:0.19 70:0.47 71:0.66 74:0.63 77:0.70 82:0.99 86:0.84 88:0.98 89:0.99 91:0.49 98:0.87 101:0.69 102:0.98 108:0.15 122:0.67 123:0.15 127:0.40 129:0.05 135:0.39 139:0.85 140:0.80 141:0.91 143:0.99 145:0.96 146:0.62 147:0.68 149:0.07 151:0.65 153:0.48 154:0.85 159:0.23 162:0.54 170:0.96 172:0.59 173:0.45 174:0.83 177:0.96 178:0.42 179:0.69 187:0.87 190:0.98 195:0.56 197:0.87 199:0.95 202:0.65 212:0.72 216:0.57 222:0.76 223:0.96 224:0.93 225:1.00 234:0.91 235:0.31 239:0.95 240:1.00 241:0.64 242:0.19 243:0.86 247:0.60 248:0.65 253:0.47 254:0.80 256:0.58 259:0.21 264:0.90 265:0.87 266:0.27 267:0.52 268:0.59 271:0.13 274:0.91 275:0.96 276:0.43 282:0.83 285:0.45 294:0.99 295:0.98 300:0.43 +1 10:0.97 11:0.54 12:0.06 17:0.02 18:0.90 21:0.78 26:0.96 27:0.29 29:0.56 30:0.73 32:0.71 34:0.70 36:0.63 37:0.78 39:0.51 43:0.59 44:0.92 45:0.94 58:0.93 66:0.94 69:0.27 70:0.51 71:0.66 74:0.76 77:0.98 82:0.99 86:0.93 88:0.98 89:0.99 91:0.40 98:0.93 101:0.87 108:0.21 122:0.67 123:0.20 127:0.58 129:0.05 135:0.32 139:0.91 141:0.91 145:0.97 146:0.81 147:0.84 149:0.67 154:0.85 159:0.37 170:0.96 172:0.84 173:0.38 174:0.89 177:0.96 178:0.55 179:0.40 187:0.87 195:0.60 197:0.92 199:0.95 202:0.46 212:0.85 216:0.82 222:0.76 223:0.97 224:0.93 225:0.97 234:0.91 235:0.52 239:0.95 240:0.95 241:0.39 242:0.17 243:0.94 247:0.96 253:0.53 254:0.74 259:0.21 264:0.90 265:0.93 266:0.27 267:0.28 271:0.13 274:0.91 275:0.96 276:0.74 282:0.79 294:0.99 300:0.55 +2 1:0.66 7:0.70 10:0.96 11:0.54 12:0.06 17:0.69 18:0.69 21:0.30 22:0.93 26:0.97 29:0.56 30:0.41 32:0.80 33:0.97 34:0.95 36:0.74 37:0.98 39:0.51 43:0.08 44:0.93 45:0.85 47:0.93 55:0.52 56:0.97 58:0.93 64:0.77 66:0.27 69:0.81 70:0.60 71:0.66 74:0.64 77:0.70 80:1.00 81:0.79 82:0.99 83:0.91 86:0.80 88:0.99 89:0.98 91:0.05 98:0.40 101:0.69 102:0.98 106:0.81 108:0.65 114:0.86 117:0.86 122:0.98 123:0.75 124:0.58 126:0.54 127:0.22 129:0.05 133:0.38 135:0.32 139:0.86 141:0.91 145:0.65 146:0.42 147:0.49 149:0.12 150:0.60 154:0.66 155:0.57 159:0.36 165:0.93 170:0.96 172:0.63 173:0.82 174:0.96 176:0.73 177:0.96 178:0.55 179:0.29 187:0.68 192:0.86 193:0.98 195:0.75 197:0.96 199:0.98 201:0.66 204:0.84 206:0.81 208:0.64 212:0.80 215:0.72 216:0.95 222:0.76 223:0.98 224:0.93 225:0.99 230:0.73 232:0.99 233:0.76 234:0.91 235:0.68 236:0.97 239:0.97 240:0.95 241:0.53 242:0.39 243:0.59 245:0.84 247:0.74 253:0.64 254:0.74 259:0.21 262:0.93 264:0.90 265:0.35 266:0.63 267:0.96 271:0.13 274:0.91 275:0.96 276:0.61 277:0.95 281:0.91 282:0.88 290:0.86 291:0.97 294:0.99 297:0.36 299:0.88 300:0.55 +1 10:0.96 11:0.54 12:0.06 17:0.03 18:0.91 21:0.78 26:0.96 27:0.28 29:0.56 30:0.76 34:0.89 36:0.49 37:0.77 39:0.51 43:0.57 45:0.96 58:0.93 66:0.95 69:0.59 70:0.48 71:0.66 74:0.75 86:0.94 89:0.97 91:0.29 98:0.87 101:0.87 108:0.45 122:0.67 123:0.43 127:0.39 129:0.05 135:0.51 139:0.91 141:0.91 146:0.85 147:0.87 149:0.58 154:0.81 159:0.42 170:0.96 172:0.88 173:0.61 174:0.90 177:0.96 178:0.55 179:0.28 187:0.39 197:0.92 199:0.96 212:0.81 216:0.56 222:0.76 223:0.97 224:0.93 225:0.96 234:0.91 235:0.31 239:0.94 240:0.95 241:0.10 242:0.19 243:0.96 247:0.93 253:0.53 254:0.74 259:0.21 264:0.90 265:0.87 266:0.27 267:0.23 271:0.13 274:0.91 275:0.93 276:0.79 294:0.98 300:0.70 +1 2:0.99 8:0.78 10:0.97 11:0.54 12:0.06 17:0.70 18:0.80 26:0.97 27:0.61 29:0.56 30:0.66 34:0.33 36:0.76 37:0.82 39:0.51 43:0.11 45:0.98 56:0.97 58:0.93 66:0.07 69:0.76 70:0.71 71:0.66 74:0.68 81:0.31 86:0.92 89:1.00 91:0.15 98:0.21 101:0.69 108:0.79 122:0.99 123:0.77 124:0.96 126:0.23 127:0.93 129:0.99 133:0.96 135:0.28 139:0.88 141:0.91 146:0.51 147:0.47 149:0.22 150:0.35 154:0.65 159:0.91 170:0.96 172:0.54 173:0.45 174:0.95 177:0.05 178:0.55 179:0.20 187:0.39 197:0.92 199:0.97 202:0.36 212:0.52 216:0.46 222:0.76 223:0.97 224:0.93 225:0.99 234:0.91 235:0.02 239:0.96 240:0.95 241:0.07 242:0.22 243:0.09 245:0.89 247:0.76 253:0.50 254:0.84 257:0.97 259:0.21 264:0.90 265:0.23 266:0.94 267:0.79 271:0.13 274:0.91 275:0.99 276:0.91 294:1.00 295:0.98 300:0.84 +2 10:0.95 11:0.57 12:0.06 17:0.11 18:0.89 21:0.78 26:0.96 27:0.28 29:0.56 30:0.85 34:0.80 36:0.86 37:0.77 39:0.51 43:0.69 45:0.93 58:0.93 66:0.92 69:0.58 70:0.45 71:0.66 74:0.70 75:0.98 83:0.72 86:0.91 89:0.96 91:0.27 97:0.97 98:0.80 101:0.96 108:0.45 122:0.67 123:0.43 127:0.29 129:0.05 135:0.51 139:0.89 141:0.91 146:0.80 147:0.83 149:0.77 154:0.81 158:0.81 159:0.36 170:0.96 172:0.79 173:0.61 174:0.89 177:0.96 178:0.55 179:0.35 187:0.68 197:0.92 199:0.96 208:0.64 212:0.61 216:0.56 219:0.94 222:0.76 223:0.97 224:0.93 225:0.96 226:0.96 234:0.91 235:0.22 239:0.95 240:0.95 241:0.12 242:0.14 243:0.92 247:0.92 253:0.51 254:0.74 259:0.21 264:0.90 265:0.80 266:0.27 267:0.28 271:0.13 274:0.91 275:0.91 276:0.67 279:0.79 283:0.67 292:0.88 294:0.96 300:0.55 +2 10:0.95 11:0.54 12:0.06 17:0.04 18:0.72 21:0.78 26:0.96 27:0.29 29:0.56 30:0.73 32:0.80 34:0.50 36:0.39 37:0.78 39:0.51 43:0.11 44:0.93 45:0.85 58:0.93 66:0.44 69:0.17 70:0.68 71:0.66 74:0.63 77:0.70 82:0.99 86:0.83 88:0.99 89:0.99 91:0.23 98:0.57 101:0.69 102:0.98 108:0.59 122:0.67 123:0.56 124:0.58 127:0.70 129:0.59 133:0.47 135:0.24 139:0.85 141:0.91 145:0.87 146:0.29 147:0.35 149:0.09 154:0.82 159:0.27 170:0.96 172:0.41 173:0.43 174:0.86 177:0.96 178:0.55 179:0.76 187:0.87 195:0.55 197:0.89 199:0.95 212:0.72 216:0.82 222:0.76 223:0.96 224:0.93 225:0.97 234:0.91 235:0.05 239:0.96 240:0.95 241:0.24 242:0.19 243:0.46 245:0.68 247:0.67 253:0.48 254:0.74 259:0.21 264:0.90 265:0.58 266:0.47 267:0.65 271:0.13 274:0.91 275:0.96 276:0.40 282:0.88 294:0.99 299:0.88 300:0.70 +1 10:1.00 11:0.54 12:0.06 17:0.34 18:0.95 21:0.78 26:0.97 27:0.28 29:0.56 30:0.84 34:0.55 36:0.79 37:0.77 39:0.51 43:0.23 45:1.00 58:0.93 66:0.11 69:0.62 70:0.52 71:0.66 74:0.92 86:0.99 89:1.00 91:0.05 98:0.43 101:0.87 108:0.91 122:0.97 123:0.90 124:0.95 127:0.89 129:0.97 133:0.95 135:0.58 139:0.98 141:0.91 146:0.19 147:0.25 149:0.73 154:0.79 159:0.96 170:0.96 172:0.95 173:0.17 174:1.00 177:0.96 178:0.55 179:0.09 187:0.39 197:0.99 199:1.00 202:0.36 212:0.68 216:0.56 222:0.76 223:0.98 224:0.93 225:0.99 234:0.91 235:0.42 239:1.00 240:0.95 241:0.01 242:0.16 243:0.06 245:0.99 247:0.99 253:0.61 254:0.74 259:0.21 264:0.90 265:0.45 266:0.91 267:0.76 271:0.13 274:0.91 275:1.00 276:0.99 294:1.00 300:0.94 +1 10:0.98 11:0.81 12:0.06 17:0.01 18:0.88 21:0.78 26:0.97 27:0.52 29:0.56 30:0.73 34:0.71 36:0.37 37:0.57 39:0.51 43:0.84 45:0.88 58:0.93 60:0.87 66:0.94 69:0.21 70:0.47 71:0.66 74:0.75 75:0.99 83:0.91 85:0.80 86:0.92 89:0.99 91:0.25 98:0.93 101:0.97 108:0.17 122:0.91 123:0.16 127:0.58 129:0.05 135:0.42 139:0.91 141:0.91 145:0.50 146:0.81 147:0.84 149:0.86 154:0.85 158:0.86 159:0.37 170:0.96 172:0.84 173:0.34 174:0.92 177:0.96 178:0.55 179:0.40 187:0.87 197:0.95 199:0.96 202:0.36 208:0.64 212:0.85 216:0.57 219:0.95 222:0.76 223:0.97 224:0.93 225:0.98 226:0.96 234:0.91 235:0.42 239:0.98 240:0.95 241:0.47 242:0.17 243:0.94 247:0.94 253:0.53 259:0.21 264:0.90 265:0.93 266:0.27 267:0.44 271:0.13 274:0.91 275:0.97 276:0.74 279:0.80 283:0.67 292:0.88 294:1.00 295:0.98 300:0.43 +3 1:0.66 10:0.97 11:0.81 12:0.06 17:0.64 18:0.77 21:0.30 22:0.93 26:0.97 29:0.56 30:0.85 33:0.97 34:0.73 36:0.61 37:0.98 39:0.51 43:0.61 45:0.96 47:0.93 56:0.97 58:0.93 64:0.77 66:0.63 69:0.90 70:0.40 71:0.66 74:0.62 81:0.79 83:0.91 85:0.72 86:0.89 89:0.99 91:0.14 98:0.57 101:0.96 106:0.81 108:0.86 114:0.86 117:0.86 122:0.67 123:0.85 124:0.64 126:0.54 127:0.38 129:0.59 133:0.72 135:0.20 139:0.85 141:0.91 145:0.83 146:0.60 147:0.29 149:0.54 150:0.60 154:0.50 155:0.51 159:0.72 165:0.93 170:0.96 172:0.87 173:0.84 174:0.96 176:0.73 177:0.05 178:0.55 179:0.23 187:0.39 192:0.55 197:0.95 199:0.98 201:0.66 202:0.36 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.76 223:0.98 224:0.93 225:0.99 234:0.91 235:0.68 236:0.97 239:0.96 240:0.95 241:0.39 242:0.19 243:0.09 245:0.86 247:0.82 253:0.64 257:0.97 259:0.21 262:0.93 264:0.90 265:0.59 266:0.63 267:0.97 271:0.13 274:0.91 275:0.98 276:0.83 290:0.86 291:0.97 294:0.99 297:0.36 300:0.55 +3 10:0.96 11:0.54 12:0.06 17:0.16 18:0.84 21:0.78 26:0.97 27:0.72 29:0.56 30:0.53 32:0.80 34:0.49 36:0.95 39:0.51 43:0.24 44:0.93 45:0.92 58:0.93 66:0.45 69:0.58 70:0.89 71:0.66 74:0.72 77:0.70 82:0.99 86:0.90 88:0.99 89:0.99 91:0.20 98:0.42 101:0.87 102:0.98 108:0.72 122:0.91 123:0.71 124:0.80 127:0.78 129:0.59 133:0.80 135:0.76 139:0.90 141:0.91 145:0.94 146:0.39 147:0.45 149:0.32 154:0.79 159:0.77 170:0.96 172:0.73 173:0.41 174:0.93 177:0.96 178:0.55 179:0.41 187:0.87 195:0.88 197:0.93 199:0.96 212:0.71 216:0.49 222:0.76 223:0.97 224:0.93 225:0.98 234:0.91 235:0.22 239:0.97 240:0.95 241:0.24 242:0.16 243:0.37 245:0.80 247:0.96 253:0.52 254:0.74 259:0.21 264:0.90 265:0.44 266:0.80 267:0.52 271:0.13 274:0.91 275:0.97 276:0.74 282:0.88 294:0.99 299:0.88 300:0.84 +1 2:0.99 8:0.83 10:0.98 11:0.81 12:0.06 17:0.64 18:0.85 21:0.13 26:0.97 27:0.59 29:0.56 30:0.91 34:0.71 36:0.66 37:0.42 39:0.51 43:0.86 45:0.83 56:0.97 58:0.93 66:0.95 69:0.53 70:0.21 71:0.66 74:0.75 80:0.99 81:0.29 82:0.98 85:0.90 86:0.94 88:0.99 89:1.00 91:0.35 98:0.83 101:0.96 108:0.41 122:0.67 123:0.39 126:0.23 127:0.33 129:0.05 135:0.34 139:0.92 140:0.45 141:0.91 143:0.99 145:0.45 146:0.65 147:0.70 149:0.96 150:0.32 151:0.65 153:0.48 154:0.83 159:0.25 162:0.86 170:0.96 172:0.86 173:0.70 174:0.92 177:0.96 179:0.29 187:0.39 190:0.98 195:0.61 197:0.95 199:0.96 202:0.36 212:0.94 216:0.33 222:0.76 223:0.97 224:0.93 225:1.00 234:0.91 235:0.12 239:0.98 240:1.00 241:0.02 242:0.19 243:0.95 247:0.82 248:0.62 253:0.53 256:0.45 259:0.21 264:0.90 265:0.83 266:0.17 267:0.28 268:0.46 271:0.13 274:0.91 275:0.98 276:0.76 285:0.45 294:1.00 295:0.99 300:0.25 +2 1:0.66 7:0.70 10:0.96 11:0.54 12:0.06 17:0.67 18:0.69 21:0.30 22:0.93 26:0.97 29:0.56 30:0.41 32:0.80 33:0.97 34:0.94 36:0.78 37:0.98 39:0.51 43:0.08 44:0.93 45:0.85 47:0.93 55:0.52 56:0.97 58:0.93 64:0.77 66:0.47 69:0.81 70:0.60 71:0.66 74:0.64 77:0.70 80:1.00 81:0.79 82:0.99 83:0.91 86:0.80 88:0.99 89:0.98 91:0.03 98:0.33 101:0.69 102:0.98 106:0.81 108:0.65 114:0.86 117:0.86 122:0.98 123:0.63 124:0.65 126:0.54 127:0.23 129:0.05 133:0.59 135:0.28 139:0.86 141:0.91 145:0.65 146:0.42 147:0.49 149:0.12 150:0.60 154:0.66 155:0.57 159:0.37 165:0.93 170:0.96 172:0.63 173:0.82 174:0.96 176:0.73 177:0.96 178:0.55 179:0.30 187:0.68 192:0.86 193:0.98 195:0.75 197:0.97 199:0.98 201:0.66 204:0.84 206:0.81 208:0.64 212:0.80 215:0.72 216:0.95 222:0.76 223:0.98 224:0.93 225:0.99 230:0.73 232:0.99 233:0.76 234:0.91 235:0.68 236:0.97 239:0.97 240:0.95 241:0.58 242:0.55 243:0.59 245:0.77 247:0.74 253:0.64 254:0.74 259:0.21 262:0.93 264:0.90 265:0.35 266:0.63 267:0.84 271:0.13 274:0.91 275:0.95 276:0.62 281:0.91 282:0.88 290:0.86 291:0.97 294:0.99 297:0.36 299:0.88 300:0.55 +1 10:0.94 11:0.54 12:0.06 17:0.01 18:0.72 21:0.40 26:0.95 27:0.52 29:0.56 30:0.68 32:0.71 34:0.58 36:0.55 37:0.57 39:0.51 43:0.10 44:0.92 45:0.85 56:0.97 58:0.93 66:0.86 69:0.21 70:0.52 71:0.66 74:0.60 77:0.70 80:0.99 81:0.27 82:0.99 86:0.84 88:0.99 89:0.99 91:0.20 98:0.94 101:0.69 108:0.17 122:0.67 123:0.16 126:0.23 127:0.62 129:0.05 135:0.32 139:0.83 140:0.45 141:0.91 143:0.99 145:0.94 146:0.59 147:0.65 149:0.09 150:0.30 151:0.65 153:0.48 154:0.85 159:0.22 162:0.86 170:0.96 172:0.61 173:0.53 174:0.83 177:0.96 179:0.73 187:0.87 190:0.98 195:0.54 197:0.88 199:0.94 202:0.36 212:0.75 216:0.57 222:0.76 223:0.96 224:0.93 225:0.99 234:0.91 235:0.42 239:0.92 240:1.00 241:0.59 242:0.18 243:0.87 247:0.60 248:0.62 253:0.46 254:0.80 256:0.45 259:0.21 264:0.90 265:0.94 266:0.27 267:0.52 268:0.46 271:0.13 274:0.91 275:0.96 276:0.48 282:0.79 285:0.45 294:0.99 300:0.43 +1 10:0.95 11:0.81 12:0.06 17:0.05 18:0.75 21:0.78 26:0.95 27:0.29 29:0.56 30:0.88 34:0.76 36:0.39 37:0.78 39:0.51 43:0.75 45:0.77 58:0.93 66:0.20 69:0.21 70:0.28 71:0.66 74:0.58 85:0.80 86:0.86 89:0.99 91:0.42 98:0.56 101:0.96 108:0.58 122:0.67 123:0.55 124:0.75 127:0.74 129:0.81 133:0.67 135:0.23 139:0.82 140:0.45 141:0.91 143:0.99 145:0.50 146:0.29 147:0.35 149:0.80 151:0.70 153:0.72 154:0.82 159:0.40 162:0.57 170:0.96 172:0.37 173:0.34 174:0.86 177:0.96 179:0.65 187:0.87 190:0.98 197:0.89 199:0.95 202:0.66 212:0.75 216:0.82 220:0.74 222:0.76 223:0.96 224:0.93 225:1.00 234:0.91 235:0.22 239:0.93 240:1.00 241:0.64 242:0.19 243:0.37 245:0.73 247:0.67 248:0.68 253:0.45 256:0.58 259:0.21 264:0.90 265:0.58 266:0.47 267:0.44 268:0.65 271:0.13 274:0.91 275:0.97 276:0.55 285:0.45 294:0.99 300:0.33 +1 1:0.65 9:0.70 10:0.96 11:0.82 12:0.06 17:0.67 18:0.77 21:0.54 26:0.97 27:0.29 29:0.56 30:0.66 34:0.73 36:0.62 37:0.78 39:0.51 43:0.64 45:0.85 56:0.97 58:0.93 64:0.77 66:0.18 69:0.45 70:0.85 71:0.66 74:0.62 81:0.76 83:0.91 85:0.80 86:0.88 89:1.00 91:0.09 96:0.71 98:0.31 101:0.96 108:0.35 114:0.77 120:0.58 122:0.99 123:0.86 124:0.87 126:0.54 127:0.50 129:0.81 133:0.85 135:0.44 139:0.84 140:0.45 141:0.98 146:0.35 147:0.42 149:0.68 150:0.56 151:0.57 153:0.48 154:0.83 155:0.53 159:0.74 162:0.86 164:0.55 165:0.93 170:0.96 172:0.54 173:0.27 174:0.88 176:0.73 177:0.96 179:0.39 187:0.95 190:0.95 192:0.57 197:0.90 199:0.95 201:0.66 202:0.36 208:0.64 212:0.76 215:0.58 216:0.82 220:0.87 222:0.76 223:0.98 224:0.93 225:0.99 234:0.91 235:0.88 236:0.97 239:0.94 240:0.95 241:0.21 242:0.43 243:0.40 245:0.76 247:0.60 248:0.56 253:0.62 255:0.69 256:0.45 259:0.21 260:0.58 264:0.90 265:0.21 266:0.75 267:0.36 268:0.46 271:0.13 274:0.91 275:0.98 276:0.70 285:0.49 290:0.77 294:1.00 297:0.36 300:0.94 +1 8:0.84 10:0.97 11:0.54 12:0.06 17:0.66 18:0.80 26:0.97 27:0.61 29:0.56 30:0.66 34:0.40 36:0.83 37:0.82 39:0.51 43:0.11 45:0.98 56:1.00 58:0.93 66:0.07 69:0.84 70:0.71 71:0.66 74:0.68 81:0.38 86:0.92 89:1.00 91:0.23 98:0.21 101:0.69 108:0.82 122:0.99 123:0.81 124:0.97 126:0.23 127:0.94 129:0.99 133:0.96 135:0.28 139:0.88 140:0.45 141:0.91 143:0.99 146:0.51 147:0.05 149:0.24 150:0.43 151:0.73 153:0.83 154:0.65 159:0.91 162:0.64 170:0.96 172:0.54 173:0.56 174:0.95 177:0.05 179:0.20 187:0.39 190:0.98 197:0.92 199:0.97 202:0.63 212:0.52 216:0.46 222:0.76 223:0.97 224:0.93 225:1.00 234:0.91 235:0.12 239:0.96 240:1.00 241:0.41 242:0.22 243:0.06 245:0.89 247:0.76 248:0.69 253:0.50 254:0.84 256:0.58 257:0.97 259:0.21 264:0.90 265:0.23 266:0.94 267:0.88 268:0.64 271:0.13 274:0.91 275:0.99 276:0.91 285:0.45 294:1.00 300:0.84 +1 2:0.99 10:0.97 11:0.81 12:0.06 18:0.75 21:0.30 26:0.96 27:0.29 29:0.56 30:0.73 34:0.82 36:0.70 37:0.78 39:0.51 43:0.75 45:0.77 56:0.97 58:0.93 66:0.91 69:0.23 70:0.45 71:0.66 74:0.60 81:0.28 82:0.98 85:0.80 86:0.87 88:0.99 89:1.00 91:0.17 98:0.93 101:0.96 108:0.18 122:0.99 123:0.18 126:0.23 127:0.59 129:0.05 135:0.42 139:0.83 141:0.91 145:0.97 146:0.78 147:0.81 149:0.81 150:0.30 154:0.85 159:0.34 170:0.96 172:0.76 173:0.39 174:0.88 177:0.96 178:0.55 179:0.53 187:0.87 195:0.57 197:0.91 199:0.95 212:0.88 216:0.82 222:0.76 223:0.96 224:0.93 225:0.99 234:0.91 235:0.31 239:0.96 240:0.95 241:0.27 242:0.21 243:0.92 247:0.74 253:0.46 259:0.21 264:0.90 265:0.93 266:0.38 267:0.52 271:0.13 274:0.91 275:0.98 276:0.63 294:0.99 295:0.98 300:0.43 +1 1:0.64 10:0.86 11:0.52 12:0.20 17:0.87 18:0.90 21:0.30 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.51 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.48 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.68 83:0.69 86:0.90 91:0.27 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.84 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.69 146:0.82 147:0.85 149:0.73 150:0.56 154:0.55 155:0.49 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.49 195:0.80 197:0.82 201:0.62 206:0.81 208:0.61 212:0.50 215:0.72 216:0.17 219:0.94 222:0.35 232:0.87 235:0.52 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.65 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.84 292:0.88 297:0.36 300:0.84 +1 1:0.58 10:0.91 11:0.46 12:0.20 17:0.30 18:0.98 21:0.47 27:0.52 29:0.91 30:0.70 34:0.62 36:0.51 37:0.56 39:0.69 43:0.61 45:0.99 64:0.77 66:0.78 69:0.64 70:0.62 71:0.57 74:0.88 81:0.49 83:0.56 86:0.98 91:0.12 98:0.81 99:0.83 101:0.47 108:0.81 114:0.76 122:0.91 123:0.80 124:0.44 126:0.32 127:0.72 129:0.59 133:0.77 135:0.84 139:0.98 146:0.62 147:0.68 149:0.94 150:0.44 154:0.19 155:0.51 159:0.89 165:0.65 170:0.87 172:0.98 173:0.33 174:0.90 175:0.75 176:0.63 177:0.70 178:0.55 179:0.12 187:0.21 192:0.48 197:0.93 201:0.55 204:0.81 208:0.50 212:0.58 216:0.48 219:0.90 222:0.35 232:0.98 235:0.60 239:0.90 241:0.10 242:0.52 243:0.11 244:0.61 245:0.96 247:0.91 253:0.67 257:0.65 259:0.21 265:0.81 266:0.84 267:0.28 271:0.23 276:0.97 277:0.69 281:0.91 290:0.76 292:0.95 300:0.94 +0 10:0.91 11:0.52 12:0.20 17:0.28 18:0.59 21:0.54 27:0.52 29:0.91 30:0.33 34:0.44 36:0.40 37:0.56 39:0.69 43:0.25 45:0.90 55:0.52 66:0.75 69:0.90 70:0.74 71:0.57 74:0.84 76:0.85 81:0.27 86:0.63 91:0.23 96:0.71 98:0.83 101:0.65 108:0.80 114:0.72 122:0.83 123:0.79 124:0.47 126:0.24 127:0.71 129:0.05 133:0.77 135:0.91 139:0.61 140:0.45 146:0.99 147:0.68 149:0.67 150:0.29 151:0.52 153:0.48 154:0.13 159:0.88 162:0.86 170:0.87 172:0.96 173:0.75 174:0.90 175:0.75 176:0.62 177:0.38 179:0.16 187:0.21 190:0.95 197:0.93 202:0.36 212:0.58 216:0.48 220:0.87 222:0.35 232:0.98 235:0.60 239:0.90 241:0.35 242:0.63 243:0.13 244:0.61 245:0.93 247:0.91 248:0.51 253:0.69 254:0.99 256:0.45 257:0.84 259:0.21 265:0.83 266:0.87 267:0.52 268:0.46 271:0.23 276:0.94 277:0.69 285:0.46 290:0.72 300:0.70 +0 1:0.63 10:0.85 11:0.52 12:0.20 17:0.89 18:0.90 21:0.40 22:0.93 27:0.70 29:0.91 30:0.41 32:0.72 34:1.00 36:0.42 37:0.26 39:0.69 43:0.66 44:0.92 45:0.88 47:0.93 64:0.77 66:0.68 69:0.48 70:0.94 71:0.57 74:0.75 76:0.85 77:0.89 81:0.66 83:0.69 86:0.90 91:0.29 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.82 117:0.86 122:0.86 123:0.36 124:0.47 126:0.51 127:0.65 129:0.05 133:0.62 135:0.58 139:0.91 145:0.72 146:0.85 147:0.88 149:0.74 150:0.54 154:0.55 155:0.49 158:0.81 159:0.70 165:0.81 170:0.87 172:0.73 173:0.35 174:0.79 176:0.70 177:0.88 178:0.55 179:0.52 187:0.21 192:0.48 195:0.85 197:0.82 201:0.61 206:0.81 208:0.61 212:0.52 215:0.67 216:0.17 219:0.94 222:0.35 232:0.87 235:0.60 239:0.85 241:0.24 242:0.19 243:0.72 244:0.61 245:0.71 247:0.86 253:0.64 259:0.21 262:0.93 265:0.67 266:0.87 267:0.36 271:0.23 276:0.65 279:0.78 282:0.80 283:0.67 290:0.82 292:0.88 297:0.36 300:0.94 +0 1:0.64 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.30 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.50 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.48 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.68 83:0.69 86:0.90 91:0.27 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.84 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.69 146:0.82 147:0.85 149:0.73 150:0.56 154:0.55 155:0.49 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.49 195:0.80 197:0.82 201:0.62 206:0.81 208:0.61 212:0.50 215:0.72 216:0.17 219:0.94 222:0.35 232:0.87 235:0.52 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.65 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.84 292:0.88 297:0.36 300:0.84 +0 1:0.64 8:0.71 10:0.90 11:0.46 12:0.20 17:0.85 18:0.72 21:0.30 22:0.93 27:0.43 29:0.91 30:0.19 32:0.72 34:0.99 36:0.40 37:0.55 39:0.69 43:0.76 44:0.92 45:0.73 47:0.93 64:0.77 66:0.88 69:0.19 70:0.59 71:0.57 74:0.68 77:0.70 81:0.62 83:0.56 86:0.83 91:0.42 98:0.91 99:0.83 101:0.47 104:0.96 106:0.81 108:0.15 114:0.84 117:0.86 123:0.15 126:0.51 127:0.51 129:0.05 135:0.88 139:0.82 145:0.40 146:0.91 147:0.92 149:0.57 150:0.56 154:0.69 155:0.49 159:0.51 165:0.65 170:0.87 172:0.67 173:0.23 174:0.79 176:0.70 177:0.88 178:0.55 179:0.64 187:0.21 192:0.49 195:0.71 197:0.83 201:0.62 206:0.81 208:0.61 212:0.57 215:0.72 216:0.34 222:0.35 232:0.88 235:0.52 239:0.88 241:0.49 242:0.44 243:0.89 247:0.79 253:0.64 254:0.96 259:0.21 262:0.93 265:0.91 266:0.47 267:0.44 271:0.23 276:0.56 282:0.80 290:0.84 297:0.36 300:0.43 +0 1:0.66 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.13 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:0.99 36:0.52 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.47 70:0.82 71:0.57 74:0.74 76:0.85 77:0.89 81:0.72 83:0.69 86:0.90 91:0.27 97:0.94 98:0.68 99:0.95 101:0.87 104:0.96 106:0.81 108:0.36 114:0.92 117:0.86 122:0.65 123:0.34 124:0.51 126:0.51 127:0.60 129:0.05 133:0.60 135:0.69 139:0.91 145:0.61 146:0.82 147:0.85 149:0.73 150:0.61 154:0.55 155:0.50 158:0.81 159:0.62 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.52 187:0.21 192:0.50 195:0.79 197:0.82 201:0.63 206:0.81 208:0.61 212:0.50 215:0.84 216:0.17 219:0.94 222:0.35 232:0.87 235:0.31 239:0.85 241:0.27 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.68 259:0.21 262:0.93 265:0.68 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.92 292:0.88 297:0.36 300:0.70 +2 1:0.61 7:0.75 8:0.59 10:0.84 11:0.46 12:0.20 17:0.81 18:0.80 21:0.54 27:0.53 29:0.91 30:0.45 32:0.71 34:0.89 36:0.61 37:0.25 39:0.69 43:0.12 44:0.92 45:0.77 48:0.91 55:0.42 64:0.77 66:0.87 69:0.57 70:0.71 71:0.57 74:0.75 76:0.85 77:0.70 81:0.56 83:0.56 86:0.87 91:0.74 98:0.81 99:0.83 101:0.47 108:0.44 114:0.76 122:0.65 123:0.42 126:0.51 127:0.29 129:0.05 135:0.42 139:0.88 145:0.69 146:0.62 147:0.67 149:0.12 150:0.49 154:0.64 155:0.53 159:0.23 165:0.65 170:0.87 172:0.63 173:0.74 174:0.79 176:0.70 177:0.88 178:0.55 179:0.56 187:0.21 192:0.56 195:0.59 197:0.83 201:0.60 204:0.82 208:0.61 212:0.77 215:0.58 216:0.34 222:0.35 230:0.77 233:0.65 235:0.74 239:0.83 241:0.67 242:0.28 243:0.88 244:0.61 247:0.74 253:0.62 254:0.74 259:0.21 265:0.81 266:0.38 267:0.65 271:0.23 276:0.50 281:0.86 282:0.80 290:0.76 297:0.36 300:0.55 +0 10:0.82 11:0.52 12:0.20 17:0.75 18:0.58 21:0.78 27:0.58 29:0.91 30:0.76 34:0.17 36:0.35 37:0.57 39:0.69 43:0.23 45:0.73 66:0.54 69:0.96 70:0.22 71:0.57 74:0.31 86:0.62 91:0.27 98:0.14 101:0.65 108:0.93 122:0.65 123:0.93 124:0.45 127:0.12 129:0.05 133:0.36 135:0.34 139:0.61 146:0.28 147:0.05 149:0.20 154:0.16 159:0.27 170:0.87 172:0.21 173:0.97 174:0.83 177:0.05 178:0.55 179:0.42 187:0.39 197:0.81 212:0.42 216:0.59 222:0.35 235:0.42 239:0.82 241:0.05 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.28 257:0.93 259:0.21 265:0.15 266:0.23 267:0.52 271:0.23 276:0.16 300:0.18 +1 1:0.65 7:0.75 10:0.92 11:0.52 12:0.20 17:0.16 18:0.76 21:0.22 27:0.52 29:0.91 30:0.66 34:0.91 36:0.22 37:0.56 39:0.69 43:0.62 45:0.89 64:0.77 66:0.33 69:0.70 70:0.58 71:0.57 74:0.61 81:0.70 83:0.69 86:0.82 91:0.17 98:0.60 99:0.95 101:0.65 104:1.00 108:0.54 114:0.88 122:0.65 123:0.51 124:0.78 126:0.51 127:0.44 129:0.05 133:0.74 135:0.97 139:0.83 146:0.77 147:0.81 149:0.74 150:0.58 154:0.52 155:0.55 159:0.60 163:0.75 165:0.81 170:0.87 172:0.54 173:0.63 174:0.90 176:0.70 177:0.88 178:0.55 179:0.49 187:0.21 192:0.57 197:0.93 201:0.63 204:0.83 208:0.61 212:0.27 215:0.79 216:0.48 222:0.35 230:0.77 232:0.97 233:0.76 235:0.42 239:0.90 241:0.30 242:0.28 243:0.50 244:0.61 245:0.73 247:0.79 253:0.64 259:0.21 265:0.61 266:0.71 267:0.52 271:0.23 276:0.64 281:0.91 290:0.88 297:0.36 300:0.55 +0 1:0.65 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.22 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.53 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.47 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.70 83:0.69 86:0.90 91:0.25 97:0.94 98:0.66 99:0.95 101:0.87 104:0.96 106:0.81 108:0.36 114:0.88 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.65 146:0.82 147:0.85 149:0.73 150:0.58 154:0.55 155:0.50 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.37 174:0.79 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.49 195:0.80 197:0.82 201:0.63 206:0.81 208:0.61 212:0.50 215:0.79 216:0.17 219:0.94 222:0.35 232:0.87 235:0.42 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.67 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.88 292:0.88 297:0.36 300:0.84 +0 1:0.58 10:0.91 11:0.52 12:0.20 17:0.38 18:0.98 21:0.47 27:0.52 29:0.91 30:0.68 34:0.78 36:0.32 37:0.56 39:0.69 43:0.61 45:0.99 48:0.91 64:0.77 66:0.79 69:0.63 70:0.59 71:0.57 74:0.86 76:0.85 81:0.37 86:0.98 91:0.09 98:0.81 101:0.87 108:0.81 114:0.72 122:0.91 123:0.80 124:0.43 126:0.32 127:0.72 129:0.59 133:0.77 135:0.92 139:0.97 146:0.65 147:0.70 149:0.93 150:0.41 154:0.16 155:0.51 159:0.88 170:0.87 172:0.98 173:0.34 174:0.90 175:0.75 176:0.62 177:0.70 178:0.55 179:0.13 187:0.21 192:0.48 197:0.93 201:0.55 204:0.81 208:0.50 212:0.37 216:0.48 222:0.35 232:1.00 235:0.60 239:0.90 241:0.24 242:0.54 243:0.12 244:0.61 245:0.95 247:0.91 253:0.65 257:0.65 259:0.21 265:0.81 266:0.84 267:0.28 271:0.23 276:0.96 277:0.69 281:0.86 290:0.72 300:0.94 +0 1:0.64 10:0.86 11:0.52 12:0.20 17:0.88 18:0.90 21:0.30 22:0.93 27:0.70 29:0.91 30:0.33 32:0.72 34:1.00 36:0.51 37:0.26 39:0.69 43:0.66 44:0.92 45:0.87 47:0.93 64:0.77 66:0.63 69:0.48 70:0.89 71:0.57 74:0.74 76:0.85 77:0.89 81:0.68 83:0.69 86:0.90 91:0.27 97:0.94 98:0.67 99:0.95 101:0.87 104:0.96 106:0.81 108:0.37 114:0.84 117:0.86 122:0.65 123:0.35 124:0.51 126:0.51 127:0.52 129:0.05 133:0.60 135:0.69 139:0.91 145:0.69 146:0.82 147:0.85 149:0.73 150:0.56 154:0.55 155:0.49 158:0.81 159:0.63 165:0.81 170:0.87 172:0.70 173:0.38 174:0.79 176:0.70 177:0.88 178:0.55 179:0.51 187:0.21 192:0.49 195:0.80 197:0.82 201:0.62 206:0.81 208:0.61 212:0.50 215:0.72 216:0.17 219:0.94 222:0.35 232:0.87 235:0.52 239:0.85 241:0.24 242:0.14 243:0.69 244:0.61 245:0.72 247:0.84 253:0.65 259:0.21 262:0.93 265:0.67 266:0.75 267:0.44 271:0.23 276:0.66 279:0.78 282:0.80 283:0.67 290:0.84 292:0.88 297:0.36 300:0.84 +1 1:0.65 7:0.75 10:0.92 11:0.52 12:0.20 17:0.18 18:0.76 21:0.22 27:0.52 29:0.91 30:0.70 34:0.91 36:0.21 37:0.56 39:0.69 43:0.62 45:0.89 64:0.77 66:0.35 69:0.70 70:0.53 71:0.57 74:0.60 81:0.70 83:0.69 86:0.82 91:0.18 98:0.60 99:0.95 101:0.65 104:1.00 108:0.54 114:0.88 122:0.65 123:0.51 124:0.77 126:0.51 127:0.42 129:0.05 133:0.74 135:0.97 139:0.83 146:0.77 147:0.81 149:0.74 150:0.58 154:0.52 155:0.55 159:0.59 163:0.75 165:0.81 170:0.87 172:0.54 173:0.63 174:0.90 176:0.70 177:0.88 178:0.55 179:0.50 187:0.21 192:0.57 197:0.93 201:0.63 204:0.83 208:0.61 212:0.29 215:0.79 216:0.48 222:0.35 230:0.77 232:0.97 233:0.76 235:0.42 239:0.90 241:0.32 242:0.30 243:0.51 244:0.61 245:0.72 247:0.76 253:0.63 259:0.21 265:0.61 266:0.71 267:0.59 271:0.23 276:0.63 281:0.91 290:0.88 297:0.36 300:0.55 +1 1:0.74 6:0.81 7:0.81 8:0.61 11:0.83 12:0.20 17:0.64 18:0.94 20:0.83 21:0.40 27:0.52 29:0.71 30:0.30 32:0.80 34:0.71 36:0.39 37:0.37 39:0.44 43:0.92 44:0.93 45:0.87 48:0.97 64:0.77 66:0.53 69:0.40 70:0.71 71:0.84 74:0.84 77:0.70 78:0.97 81:0.62 83:0.91 85:0.72 86:0.96 91:0.29 98:0.85 100:0.95 101:0.97 104:0.98 106:0.81 108:0.60 111:0.96 114:0.62 121:0.99 122:0.80 123:0.58 124:0.55 126:0.54 127:0.76 129:0.59 133:0.46 135:0.95 139:0.97 145:0.69 146:0.37 147:0.43 149:0.85 150:0.78 152:0.86 154:0.91 155:0.67 159:0.50 165:0.93 169:0.91 172:0.76 173:0.42 176:0.73 177:0.70 178:0.55 179:0.41 186:0.97 187:0.95 192:0.87 195:0.69 201:0.93 204:0.89 206:0.81 208:0.64 212:0.77 215:0.42 216:0.37 222:0.48 230:0.87 233:0.76 235:0.80 241:0.41 242:0.16 243:0.40 245:0.90 247:0.92 253:0.50 259:0.21 261:0.94 265:0.85 266:0.54 267:0.52 271:0.27 276:0.75 281:0.91 282:0.88 283:0.82 290:0.62 297:0.36 299:0.88 300:0.55 +1 1:0.69 8:0.65 9:0.76 11:0.64 12:0.20 17:0.62 18:0.89 20:0.92 22:0.93 27:1.00 29:0.71 30:0.93 31:0.93 34:0.84 36:0.48 37:0.27 39:0.44 43:0.68 45:0.85 47:0.93 55:0.59 64:0.77 66:0.99 69:0.52 70:0.27 71:0.84 74:0.97 78:0.83 79:0.93 81:0.81 83:0.60 86:0.93 91:0.53 96:0.80 98:0.96 99:0.83 100:0.73 101:0.52 108:0.40 111:0.81 114:0.95 117:0.86 122:0.77 123:0.38 126:0.44 127:0.70 129:0.05 135:0.68 137:0.93 139:0.90 140:0.45 146:0.98 147:0.98 149:0.72 150:0.78 151:0.81 152:0.86 153:0.48 154:0.85 155:0.58 159:0.74 162:0.86 165:0.70 169:0.72 172:0.97 173:0.36 176:0.66 177:0.70 179:0.16 186:0.84 187:0.68 190:0.77 192:0.51 196:0.95 201:0.68 202:0.36 208:0.54 212:0.95 216:0.24 222:0.48 232:0.90 235:0.05 241:0.27 242:0.73 243:0.99 247:0.86 248:0.73 253:0.86 254:0.74 255:0.74 256:0.45 259:0.21 261:0.86 262:0.93 265:0.96 266:0.17 267:0.23 268:0.46 271:0.27 276:0.94 284:0.87 285:0.56 290:0.95 300:0.43 +1 1:0.69 7:0.72 8:0.64 9:0.76 11:0.85 12:0.20 17:0.32 18:0.90 21:0.22 27:0.58 29:0.71 30:0.56 32:0.69 34:0.97 36:0.44 37:0.35 39:0.44 43:0.65 45:0.88 66:0.64 69:0.56 70:0.70 71:0.84 74:0.87 76:0.85 77:0.70 81:0.52 83:0.60 86:0.94 91:0.58 96:0.71 97:0.89 98:0.61 99:0.83 101:0.87 104:0.88 106:0.81 108:0.43 114:0.67 117:0.86 120:0.57 122:0.80 123:0.41 124:0.50 126:0.44 127:0.41 129:0.59 133:0.61 135:0.70 139:0.91 140:0.45 145:0.47 146:0.61 147:0.66 149:0.36 150:0.58 151:0.53 153:0.48 154:0.79 155:0.58 158:0.78 159:0.40 162:0.86 164:0.54 165:0.70 172:0.67 173:0.60 176:0.66 177:0.70 179:0.51 187:0.68 190:0.77 192:0.59 195:0.65 201:0.68 202:0.36 204:0.85 206:0.81 208:0.54 212:0.82 215:0.51 216:0.48 220:0.82 222:0.48 230:0.74 232:0.90 233:0.73 235:0.31 241:0.21 242:0.16 243:0.69 245:0.68 247:0.86 248:0.53 253:0.57 254:0.90 255:0.74 256:0.45 259:0.21 260:0.57 265:0.62 266:0.54 267:0.59 268:0.46 271:0.27 276:0.60 279:0.82 282:0.77 283:0.78 285:0.56 290:0.67 297:0.36 300:0.70 +2 1:0.69 8:0.87 9:0.76 11:0.64 12:0.20 17:0.98 18:0.91 21:0.71 27:1.00 29:0.71 30:0.09 31:0.91 34:0.20 36:0.47 37:0.84 39:0.44 43:0.71 44:0.90 45:0.81 55:0.71 64:0.77 66:0.13 69:0.30 70:0.98 71:0.84 74:0.74 76:0.85 77:0.70 79:0.91 81:0.32 83:0.60 86:0.92 87:0.98 91:0.65 96:0.77 97:0.89 98:0.33 99:0.83 101:0.52 108:0.90 114:0.54 122:0.80 123:0.89 124:0.91 126:0.44 127:0.97 129:0.05 133:0.89 135:0.83 137:0.91 139:0.92 140:0.45 145:0.99 146:0.32 147:0.38 149:0.70 150:0.51 151:0.70 153:0.72 154:0.61 155:0.58 158:0.78 159:0.75 162:0.86 165:0.70 172:0.37 173:0.20 176:0.66 177:0.70 179:0.54 190:0.77 191:0.70 192:0.51 195:0.87 196:0.94 201:0.68 202:0.63 208:0.54 212:0.39 216:0.07 219:0.92 220:0.96 222:0.48 232:0.72 235:0.95 241:0.79 242:0.17 243:0.29 244:0.61 245:0.71 247:0.79 248:0.67 253:0.67 255:0.74 256:0.68 259:0.21 265:0.35 266:0.95 267:0.36 268:0.71 271:0.27 276:0.69 277:0.69 279:0.82 282:0.77 284:0.94 285:0.56 290:0.54 292:0.91 300:0.84 +1 1:0.74 7:0.81 9:0.76 11:0.85 12:0.20 17:0.70 18:0.83 21:0.30 27:0.48 29:0.71 30:0.33 32:0.69 34:0.95 36:0.92 37:0.48 39:0.44 43:0.70 45:0.81 55:0.59 64:0.77 66:0.31 69:0.42 70:0.89 71:0.84 74:0.81 76:0.85 77:0.70 81:0.59 83:0.60 86:0.81 91:0.44 96:0.71 97:0.89 98:0.54 99:0.83 101:0.87 104:0.94 106:0.81 108:0.32 114:0.65 117:0.86 120:0.57 121:0.90 122:0.80 123:0.31 124:0.70 126:0.54 127:0.55 129:0.05 133:0.66 135:0.93 139:0.84 140:0.45 145:0.83 146:0.63 147:0.68 149:0.63 150:0.74 151:0.53 153:0.48 154:0.60 155:0.67 158:0.78 159:0.52 162:0.52 164:0.54 165:0.70 172:0.48 173:0.40 176:0.73 177:0.70 179:0.58 187:0.68 190:0.77 192:0.87 195:0.74 201:0.93 202:0.58 204:0.89 206:0.81 208:0.64 212:0.52 215:0.44 216:0.40 220:0.82 222:0.48 230:0.87 232:0.95 233:0.76 235:0.52 241:0.53 242:0.24 243:0.49 244:0.61 245:0.74 247:0.79 248:0.53 253:0.54 255:0.74 256:0.45 259:0.21 260:0.57 265:0.56 266:0.63 267:0.96 268:0.46 271:0.27 276:0.60 279:0.82 281:0.91 282:0.77 283:0.71 285:0.56 290:0.65 297:0.36 300:0.70 +1 1:0.74 9:0.80 11:0.64 12:0.20 17:0.13 18:0.91 21:0.30 27:0.12 29:0.71 30:0.84 31:0.87 34:0.93 36:0.67 37:0.95 39:0.44 43:0.11 45:0.89 55:0.45 64:0.77 66:0.93 69:0.54 70:0.28 71:0.84 74:0.85 79:0.87 81:0.59 83:0.60 86:0.98 91:0.27 96:0.69 98:0.64 99:0.83 101:0.52 108:0.42 114:0.65 120:0.55 122:0.57 123:0.40 126:0.54 127:0.19 129:0.05 135:0.24 137:0.87 139:0.96 140:0.45 146:0.63 147:0.69 149:0.08 150:0.74 151:0.52 153:0.48 154:0.84 155:0.67 159:0.24 162:0.86 164:0.57 165:0.70 172:0.80 173:0.59 176:0.73 177:0.70 179:0.21 187:0.68 192:0.87 196:0.91 201:0.93 202:0.36 208:0.64 212:0.84 215:0.44 216:0.61 220:0.87 222:0.48 235:0.52 241:0.32 242:0.45 243:0.93 247:0.60 248:0.51 253:0.54 254:0.80 255:0.95 256:0.45 259:0.21 260:0.56 265:0.65 266:0.27 267:0.44 268:0.46 271:0.27 276:0.68 284:0.89 285:0.62 290:0.65 297:0.36 300:0.33 +1 1:0.74 11:0.85 12:0.20 17:0.30 18:0.80 21:0.30 27:0.45 29:0.71 30:0.08 34:0.88 36:0.18 37:0.50 39:0.44 43:0.26 44:0.87 45:0.73 64:0.77 66:0.30 69:0.69 70:1.00 71:0.84 74:0.63 81:0.59 83:0.60 86:0.86 91:0.14 97:0.89 98:0.27 99:0.83 101:0.87 108:0.86 114:0.65 122:0.77 123:0.85 124:0.77 126:0.54 127:0.33 129:0.05 133:0.73 135:0.85 139:0.88 145:0.52 146:0.41 147:0.48 149:0.22 150:0.74 154:0.76 155:0.67 158:0.91 159:0.61 165:0.70 172:0.32 173:0.57 176:0.73 177:0.70 178:0.55 179:0.67 187:0.68 192:0.87 195:0.86 201:0.93 208:0.64 212:0.22 215:0.44 216:0.77 219:0.95 222:0.48 235:0.52 241:0.21 242:0.38 243:0.44 245:0.55 247:0.67 253:0.50 254:0.74 259:0.21 265:0.29 266:0.71 267:0.65 271:0.27 276:0.43 277:0.69 279:0.86 283:0.77 290:0.65 292:0.91 297:0.36 300:0.94 +1 1:0.74 7:0.66 11:0.57 12:0.20 17:0.13 18:0.78 21:0.77 27:0.36 29:0.71 30:0.94 32:0.80 34:0.98 36:0.51 37:0.57 39:0.44 43:0.88 44:0.93 64:0.77 66:0.80 69:0.58 70:0.13 71:0.84 74:0.75 77:0.70 81:0.43 83:0.91 85:0.85 86:0.89 91:0.48 98:0.45 101:0.87 108:0.45 114:0.53 122:0.57 123:0.43 126:0.54 127:0.14 129:0.05 135:0.74 139:0.91 145:0.88 146:0.28 147:0.34 149:0.86 150:0.69 154:0.86 155:0.67 159:0.12 165:0.93 172:0.45 173:0.86 176:0.73 177:0.70 178:0.55 179:0.35 187:0.39 192:0.87 195:0.60 201:0.93 204:0.85 208:0.64 212:0.90 215:0.36 216:0.59 222:0.48 230:0.69 233:0.73 235:1.00 241:0.57 242:0.58 243:0.82 247:0.35 253:0.43 259:0.21 265:0.47 266:0.17 267:0.52 271:0.27 276:0.34 281:0.91 282:0.88 290:0.53 297:0.36 299:0.88 300:0.13 +1 1:0.74 11:0.83 12:0.20 17:0.89 18:0.95 21:0.22 22:0.93 27:0.62 29:0.71 30:0.55 32:0.80 34:0.89 36:0.43 37:0.27 39:0.44 43:0.76 44:0.93 45:0.98 47:0.93 55:0.42 64:0.77 66:0.99 69:0.78 70:0.29 71:0.84 74:0.94 77:0.70 81:0.67 83:0.91 85:0.72 86:0.99 91:0.17 98:0.94 101:0.97 104:0.82 106:0.81 108:0.62 114:0.71 117:0.86 122:0.57 123:0.59 126:0.54 127:0.60 129:0.05 135:0.74 139:0.99 145:0.50 146:0.99 147:1.00 149:0.51 150:0.80 154:0.67 155:0.67 159:0.89 163:0.90 165:0.93 172:0.98 173:0.49 176:0.73 177:0.70 178:0.55 179:0.14 187:0.21 192:0.87 195:0.97 201:0.93 206:0.81 208:0.64 212:0.47 215:0.51 216:0.60 222:0.48 232:0.87 235:0.42 241:0.10 242:0.50 243:0.99 247:0.35 253:0.55 259:0.21 262:0.93 265:0.94 266:0.27 267:0.52 271:0.27 276:0.95 282:0.88 290:0.71 297:0.36 299:0.88 300:0.33 +0 1:0.69 8:0.65 9:0.76 11:0.85 12:0.20 17:0.57 18:0.99 21:0.47 27:0.14 29:0.71 30:0.70 31:0.86 34:0.90 36:0.53 37:0.92 39:0.44 43:0.58 44:0.90 45:0.90 48:0.91 55:0.52 64:0.77 66:0.99 69:0.48 70:0.58 71:0.84 74:0.92 77:0.70 79:0.86 81:0.35 86:0.98 91:0.46 96:0.68 98:0.98 101:0.87 108:0.37 114:0.59 122:0.77 123:0.36 126:0.44 127:0.80 129:0.05 135:0.83 137:0.86 139:0.98 140:0.45 145:0.70 146:0.96 147:0.97 149:0.82 150:0.49 151:0.48 153:0.69 154:0.47 155:0.58 159:0.67 162:0.86 172:0.96 173:0.38 176:0.66 177:0.70 179:0.19 187:0.21 190:0.77 192:0.51 195:0.84 196:0.89 201:0.68 202:0.46 204:0.85 208:0.54 212:0.89 216:0.36 220:0.93 222:0.48 232:0.83 235:0.60 241:0.35 242:0.52 243:0.99 244:0.61 247:0.82 248:0.48 253:0.50 255:0.74 256:0.45 259:0.21 265:0.98 266:0.27 267:0.17 268:0.55 271:0.27 276:0.92 281:0.89 282:0.77 284:0.87 285:0.56 290:0.59 300:0.70 +1 1:0.69 8:0.63 11:0.64 12:0.20 17:0.91 18:0.97 21:0.71 27:0.70 29:0.71 30:0.12 34:0.91 36:0.75 37:0.38 39:0.44 43:0.15 44:0.87 45:0.93 48:0.91 55:0.42 64:0.77 66:0.98 69:0.39 70:0.78 71:0.84 74:0.95 81:0.29 86:0.99 91:0.42 98:0.89 101:0.52 108:0.30 114:0.54 122:0.80 123:0.29 126:0.44 127:0.43 129:0.05 135:0.44 139:0.99 145:0.72 146:0.96 147:0.97 149:0.08 150:0.47 154:0.58 155:0.58 159:0.68 172:0.95 173:0.25 176:0.66 177:0.70 178:0.55 179:0.17 187:0.87 192:0.51 195:0.88 201:0.68 208:0.54 212:0.58 216:0.13 222:0.48 235:0.88 241:0.15 242:0.21 243:0.98 247:0.88 253:0.48 254:0.74 259:0.21 265:0.89 266:0.27 267:0.36 271:0.27 276:0.91 281:0.91 290:0.54 300:0.55 +0 1:0.69 7:0.72 11:0.83 12:0.20 17:0.97 18:0.63 21:0.40 22:0.93 27:0.55 29:0.71 30:0.86 32:0.69 34:0.96 36:0.47 37:0.32 39:0.44 43:0.78 45:0.83 47:0.93 66:0.77 69:0.78 70:0.30 71:0.84 74:0.73 77:0.89 81:0.39 83:0.60 85:0.72 86:0.73 91:0.10 98:0.34 99:0.83 101:0.87 104:0.81 106:0.81 108:0.61 114:0.61 117:0.86 122:0.51 123:0.59 124:0.39 126:0.44 127:0.18 129:0.05 133:0.37 135:0.94 139:0.70 145:0.83 146:0.40 147:0.47 149:0.71 150:0.54 154:0.70 155:0.58 159:0.25 165:0.70 172:0.57 173:0.81 176:0.66 177:0.70 178:0.55 179:0.37 187:0.39 192:0.59 195:0.72 201:0.68 204:0.85 206:0.81 208:0.54 212:0.89 215:0.42 216:0.81 222:0.48 230:0.74 232:0.86 233:0.73 235:0.52 241:0.10 242:0.58 243:0.79 245:0.51 247:0.47 253:0.48 259:0.21 262:0.93 265:0.36 266:0.23 267:0.65 271:0.27 276:0.39 282:0.77 290:0.61 297:0.36 300:0.33 +1 1:0.69 8:0.64 9:0.76 11:0.64 12:0.20 17:0.40 18:0.86 20:0.89 21:0.05 27:0.48 29:0.71 30:0.15 31:0.90 34:0.95 36:0.89 37:0.49 39:0.44 43:0.13 45:0.66 64:0.77 66:0.63 69:0.51 70:0.68 71:0.84 74:0.61 78:0.72 79:0.90 81:0.71 83:0.60 86:0.88 91:0.58 96:0.75 97:0.89 98:0.46 100:0.73 101:0.52 108:0.39 111:0.72 114:0.85 122:0.86 123:0.38 124:0.45 126:0.44 127:0.26 129:0.05 133:0.37 135:0.54 137:0.90 139:0.87 140:0.45 146:0.41 147:0.48 149:0.12 150:0.64 151:0.65 152:0.83 153:0.48 154:0.84 155:0.58 158:0.78 159:0.27 162:0.86 169:0.72 172:0.41 173:0.61 176:0.66 177:0.70 179:0.70 186:0.73 187:0.39 190:0.77 192:0.51 196:0.93 201:0.68 202:0.36 208:0.54 212:0.50 216:0.36 219:0.92 220:0.62 222:0.48 232:0.91 235:0.12 241:0.41 242:0.53 243:0.68 245:0.54 247:0.55 248:0.63 253:0.68 254:0.80 255:0.74 256:0.45 259:0.21 261:0.73 265:0.47 266:0.38 267:0.52 268:0.46 271:0.27 276:0.34 279:0.82 284:0.87 285:0.56 290:0.85 292:0.87 300:0.43 +1 1:0.74 6:0.81 7:0.72 8:0.64 9:0.80 11:0.85 12:0.20 17:0.64 18:0.96 20:0.90 21:0.30 27:0.52 29:0.71 30:0.32 31:0.87 32:0.80 34:0.61 36:0.89 37:0.37 39:0.44 43:0.71 44:0.93 45:0.83 55:0.71 64:0.77 66:0.53 69:0.35 70:0.83 71:0.84 74:0.80 76:0.85 77:0.70 78:0.93 79:0.87 81:0.63 83:0.60 86:0.95 91:0.27 96:0.71 97:0.89 98:0.79 99:0.83 100:0.92 101:0.87 104:0.98 106:0.81 108:0.81 111:0.92 114:0.65 117:0.86 120:0.58 122:0.86 123:0.80 124:0.64 126:0.54 127:0.85 129:0.05 133:0.66 135:0.97 137:0.87 139:0.95 140:0.45 145:0.69 146:0.76 147:0.80 149:0.57 150:0.76 151:0.53 152:0.86 153:0.48 154:0.80 155:0.67 158:0.78 159:0.77 162:0.86 164:0.67 165:0.70 169:0.91 172:0.81 173:0.21 176:0.73 177:0.70 179:0.35 186:0.93 187:0.87 190:0.77 192:0.87 195:0.89 196:0.91 201:0.93 202:0.53 204:0.89 206:0.81 208:0.64 212:0.60 215:0.44 216:0.37 220:0.87 222:0.48 230:0.75 232:0.94 233:0.76 235:0.60 241:0.27 242:0.43 243:0.37 245:0.89 247:0.79 248:0.52 253:0.56 254:0.95 255:0.95 256:0.58 259:0.21 260:0.58 261:0.94 265:0.79 266:0.75 267:0.76 268:0.62 271:0.27 276:0.81 277:0.69 279:0.82 281:0.91 282:0.88 283:0.78 284:0.91 285:0.62 290:0.65 297:0.36 300:0.70 +1 8:0.82 11:0.85 12:0.20 17:0.88 18:1.00 20:0.84 21:0.68 27:0.14 29:0.71 30:0.73 34:0.97 36:0.31 37:0.92 39:0.44 43:0.58 44:0.87 45:0.92 48:1.00 55:0.52 64:0.77 66:0.48 69:0.51 70:0.42 71:0.84 74:0.96 76:0.85 78:0.92 81:0.25 86:1.00 91:0.23 98:0.72 100:0.94 101:0.87 108:0.93 111:0.92 122:0.86 123:0.93 124:0.85 126:0.26 127:0.89 129:0.59 133:0.88 135:0.70 139:1.00 145:0.93 146:0.89 147:0.91 149:0.82 150:0.25 152:0.85 154:0.17 155:0.58 159:0.90 169:0.89 172:0.99 173:0.20 177:0.70 178:0.55 179:0.10 186:0.92 187:0.68 191:0.70 192:0.51 195:0.97 202:0.36 204:0.85 208:0.54 212:0.93 216:0.36 222:0.48 235:0.95 241:0.51 242:0.42 243:0.22 245:0.99 247:0.94 253:0.48 254:0.74 259:0.21 261:0.92 265:0.72 266:0.54 267:0.44 271:0.27 276:0.99 281:0.89 300:0.70 +1 1:0.74 11:0.92 12:0.20 17:0.40 18:0.77 21:0.30 27:0.45 29:0.71 30:0.62 32:0.69 34:0.86 36:0.17 37:0.50 39:0.44 43:0.82 45:0.73 60:0.87 64:0.77 66:0.20 69:0.69 70:0.54 71:0.84 74:0.67 77:0.70 81:0.61 83:0.91 85:0.72 86:0.81 91:0.18 98:0.16 101:0.97 108:0.52 114:0.65 122:0.57 123:0.84 124:0.78 126:0.54 127:0.39 129:0.05 133:0.73 135:0.86 139:0.84 145:0.52 146:0.25 147:0.31 149:0.65 150:0.77 154:0.77 155:0.67 158:0.91 159:0.56 165:0.93 172:0.27 173:0.63 176:0.73 177:0.70 178:0.55 179:0.77 187:0.68 192:0.87 195:0.79 201:0.93 208:0.64 212:0.42 215:0.44 216:0.77 219:0.95 222:0.48 235:0.52 241:0.21 242:0.29 243:0.48 245:0.51 247:0.60 253:0.50 259:0.21 265:0.17 266:0.54 267:0.28 271:0.27 276:0.36 277:0.69 279:0.86 282:0.77 283:0.78 290:0.65 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.66 8:0.88 11:0.57 12:0.20 17:0.28 18:0.98 20:0.93 21:0.68 27:0.36 29:0.71 30:0.93 32:0.80 34:0.96 36:0.51 37:0.57 39:0.44 43:0.87 44:0.93 60:0.87 64:0.77 66:0.97 69:0.58 70:0.18 71:0.84 74:0.98 77:0.70 78:0.88 81:0.44 83:0.91 85:0.98 86:1.00 91:0.42 98:0.82 100:0.92 101:0.87 108:0.44 111:0.89 114:0.56 122:0.57 123:0.43 126:0.54 127:0.31 129:0.05 135:0.75 139:1.00 145:0.83 146:0.63 147:0.68 149:1.00 150:0.70 152:0.87 154:0.85 155:0.67 158:0.92 159:0.24 165:0.93 169:0.90 172:0.94 173:0.75 176:0.73 177:0.70 178:0.55 179:0.17 186:0.88 187:0.39 192:0.87 195:0.65 201:0.93 204:0.85 208:0.64 212:0.94 215:0.37 216:0.59 219:0.95 222:0.48 230:0.69 233:0.76 235:0.88 241:0.49 242:0.50 243:0.98 247:0.35 253:0.49 259:0.21 261:0.92 265:0.82 266:0.17 267:0.44 271:0.27 276:0.88 279:0.86 281:0.91 282:0.88 283:0.82 290:0.56 292:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.69 8:0.74 11:0.64 12:0.20 17:0.85 18:0.99 21:0.71 22:0.93 27:0.70 29:0.71 30:0.42 34:0.94 36:0.91 37:0.38 39:0.44 43:0.63 44:0.90 45:0.91 47:0.93 55:0.59 64:0.77 66:0.98 69:0.39 70:0.67 71:0.84 74:0.90 76:0.85 77:0.70 81:0.29 86:0.99 91:0.46 98:0.88 101:0.52 108:0.30 114:0.54 117:0.86 122:0.86 123:0.29 126:0.44 127:0.42 129:0.05 135:0.30 139:0.99 145:0.69 146:0.97 147:0.98 149:0.79 150:0.47 154:0.76 155:0.58 159:0.71 172:0.96 173:0.23 176:0.66 177:0.70 178:0.55 179:0.17 187:0.87 192:0.51 195:0.89 201:0.68 208:0.54 212:0.75 216:0.13 222:0.48 232:0.93 235:0.88 241:0.10 242:0.43 243:0.98 247:0.86 253:0.47 254:0.92 259:0.21 262:0.93 265:0.88 266:0.27 267:0.65 271:0.27 276:0.91 282:0.77 290:0.54 300:0.55 +1 1:0.74 7:0.81 11:0.64 12:0.20 17:0.93 18:0.90 21:0.59 27:0.14 29:0.71 30:0.60 32:0.80 34:0.94 36:0.56 37:0.92 39:0.44 43:0.69 44:0.93 45:0.95 48:0.99 64:0.77 66:0.96 69:0.32 70:0.39 71:0.84 74:0.95 76:0.85 77:0.98 81:0.48 83:0.60 86:0.95 91:0.20 97:0.89 98:0.93 99:0.95 101:0.52 104:0.94 106:0.81 108:0.25 114:0.58 117:0.86 122:0.75 123:0.24 126:0.54 127:0.56 129:0.05 135:0.56 139:0.96 145:0.84 146:0.80 147:0.84 149:0.87 150:0.68 154:0.82 155:0.67 158:0.78 159:0.36 165:0.70 172:0.90 173:0.42 176:0.73 177:0.70 178:0.55 179:0.29 187:0.68 192:0.87 195:0.63 201:0.93 204:0.89 206:0.81 208:0.64 212:0.95 215:0.39 216:0.36 222:0.48 230:0.86 232:0.95 233:0.73 235:0.97 241:0.10 242:0.43 243:0.96 247:0.82 253:0.49 254:0.80 259:0.21 265:0.93 266:0.12 267:0.52 271:0.27 276:0.83 279:0.82 281:0.89 282:0.88 283:0.78 290:0.58 297:0.36 300:0.33 +1 1:0.74 7:0.81 11:0.92 12:0.20 17:0.72 18:0.99 21:0.59 27:0.57 29:0.71 30:0.36 32:0.80 34:0.82 36:0.32 37:0.61 39:0.44 43:0.84 44:0.93 45:0.97 64:0.77 66:0.91 69:0.64 70:0.67 71:0.84 74:0.98 77:0.70 81:0.47 83:0.91 85:0.98 86:1.00 91:0.27 98:0.87 101:0.97 104:0.91 108:0.49 114:0.58 121:0.90 122:0.57 123:0.47 124:0.37 126:0.54 127:0.56 129:0.05 133:0.43 135:0.76 139:1.00 145:0.74 146:0.99 147:0.99 149:0.97 150:0.71 154:0.81 155:0.67 159:0.86 165:0.93 172:0.99 173:0.36 176:0.73 177:0.70 178:0.55 179:0.11 187:0.39 192:0.87 195:0.96 201:0.93 204:0.89 208:0.64 212:0.93 215:0.39 216:0.65 222:0.48 230:0.87 232:0.94 233:0.76 235:0.80 241:0.37 242:0.27 243:0.91 245:0.98 247:0.92 253:0.50 265:0.87 266:0.54 267:0.36 271:0.27 276:0.98 281:0.91 282:0.88 290:0.58 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.66 9:0.76 11:0.57 12:0.20 17:0.45 18:0.97 21:0.68 27:0.32 29:0.71 30:0.93 32:0.80 34:0.95 36:0.52 37:0.91 39:0.44 43:0.81 44:0.93 60:0.87 64:0.77 66:0.97 69:0.72 70:0.18 71:0.84 74:0.98 77:0.70 81:0.44 83:0.91 85:0.98 86:1.00 91:0.42 96:0.68 98:0.72 101:0.87 108:0.55 114:0.56 120:0.54 122:0.57 123:0.53 126:0.54 127:0.22 129:0.05 135:0.61 139:1.00 140:0.45 145:0.49 146:0.63 147:0.68 149:1.00 150:0.70 151:0.48 153:0.48 154:0.75 155:0.67 158:0.92 159:0.24 162:0.86 164:0.54 165:0.93 172:0.94 173:0.83 176:0.73 177:0.70 179:0.14 187:0.39 190:0.77 192:0.87 195:0.65 201:0.93 202:0.36 204:0.85 208:0.64 212:0.94 215:0.37 216:0.43 219:0.95 220:0.93 222:0.48 230:0.69 233:0.76 235:0.88 241:0.58 242:0.50 243:0.98 247:0.35 248:0.48 253:0.49 255:0.74 256:0.45 259:0.21 260:0.54 265:0.72 266:0.17 267:0.91 268:0.46 271:0.27 276:0.88 279:0.86 281:0.91 282:0.88 283:0.82 285:0.56 290:0.56 292:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.63 10:0.83 11:0.45 12:0.20 17:0.82 18:0.76 21:0.22 27:0.44 29:0.56 30:0.76 34:0.33 36:0.33 37:0.41 39:0.56 43:0.23 45:0.73 55:0.71 64:0.77 66:0.59 69:0.90 70:0.43 71:0.66 74:0.53 81:0.55 83:0.54 86:0.73 91:0.27 98:0.60 99:0.83 101:0.46 108:0.80 114:0.85 122:0.90 123:0.79 124:0.67 126:0.43 127:0.62 129:0.05 133:0.73 135:0.85 139:0.69 146:0.88 147:0.53 149:0.24 150:0.49 154:0.48 155:0.49 158:0.73 159:0.80 165:0.63 170:0.82 172:0.68 173:0.82 174:0.83 175:0.75 176:0.63 177:0.70 178:0.55 179:0.52 187:0.68 192:0.50 197:0.82 201:0.61 208:0.54 212:0.30 215:0.79 216:0.93 222:0.60 235:0.42 239:0.82 241:0.05 242:0.52 243:0.23 244:0.83 245:0.71 247:0.88 253:0.61 254:0.93 257:0.84 259:0.21 264:0.98 265:0.61 266:0.80 267:0.44 271:0.29 276:0.65 277:0.69 290:0.85 297:0.36 300:0.43 +0 10:0.80 11:0.49 12:0.20 17:0.57 18:0.88 21:0.78 27:0.64 29:0.56 30:0.26 34:0.28 36:0.31 37:0.46 39:0.56 43:0.58 45:0.90 48:0.91 66:0.15 69:0.25 70:1.00 71:0.66 74:0.63 76:0.85 86:0.82 91:0.40 98:0.22 101:0.52 108:0.20 122:0.90 123:0.19 124:0.95 127:0.79 129:0.95 133:0.95 135:0.86 139:0.80 146:0.61 147:0.66 149:0.67 154:0.47 159:0.92 170:0.82 172:0.57 173:0.08 174:0.79 175:0.91 177:0.70 178:0.55 179:0.34 187:0.68 197:0.79 212:0.37 216:0.41 222:0.60 235:0.68 239:0.80 242:0.24 243:0.36 244:0.83 245:0.75 247:0.91 253:0.47 257:0.84 259:0.21 264:0.98 265:0.24 266:0.97 267:0.59 271:0.29 276:0.80 277:0.87 281:0.91 300:0.98 +1 8:0.70 11:0.49 12:0.20 17:0.89 18:0.97 21:0.78 27:0.47 29:0.56 30:0.29 34:0.30 36:0.41 37:0.55 39:0.56 43:0.30 45:0.85 55:0.59 66:0.71 69:0.21 70:0.88 71:0.66 74:0.76 76:0.85 86:0.94 91:0.10 98:0.90 101:0.52 108:0.17 117:0.86 122:0.91 123:0.16 124:0.45 127:0.85 129:0.59 133:0.46 135:0.70 139:0.90 145:0.52 146:0.98 147:0.99 149:0.44 154:0.22 159:0.82 170:0.82 172:0.95 173:0.12 175:0.91 177:0.70 178:0.55 179:0.18 187:0.39 212:0.73 216:0.26 222:0.60 232:0.83 235:0.60 241:0.07 242:0.14 243:0.75 244:0.83 245:0.97 247:0.96 253:0.53 254:0.86 257:0.84 259:0.21 264:0.98 265:0.90 266:0.80 267:0.28 271:0.29 276:0.93 277:0.87 300:0.84 +1 1:0.58 10:0.80 11:0.49 12:0.20 17:0.66 18:0.85 21:0.74 27:0.55 29:0.56 30:0.08 34:0.63 36:0.66 37:0.40 39:0.56 43:0.23 45:0.77 48:0.91 55:0.42 64:0.77 66:0.30 69:0.58 70:1.00 71:0.66 74:0.54 77:0.70 81:0.43 83:0.54 86:0.80 91:0.02 98:0.25 99:0.83 101:0.52 108:0.97 114:0.64 117:0.86 122:0.95 123:0.97 124:0.83 126:0.43 127:0.96 129:0.05 133:0.83 135:0.95 139:0.77 145:0.45 146:0.49 147:0.55 149:0.23 150:0.39 154:0.50 155:0.47 159:0.93 165:0.63 170:0.82 172:0.59 173:0.22 174:0.79 175:0.91 176:0.63 177:0.70 178:0.55 179:0.50 187:1.00 192:0.46 195:0.98 197:0.79 201:0.56 208:0.54 212:0.68 215:0.46 216:0.61 222:0.60 232:0.99 235:0.98 239:0.79 241:0.05 242:0.37 243:0.40 244:0.83 245:0.76 247:0.94 253:0.51 254:0.74 257:0.84 259:0.21 264:0.98 265:0.27 266:0.87 267:0.18 271:0.29 276:0.65 277:0.95 281:0.91 282:0.73 290:0.64 297:0.36 300:0.98 +1 10:0.87 11:0.45 12:0.20 17:0.91 18:0.93 21:0.54 22:0.93 27:0.55 29:0.56 30:0.40 34:0.52 36:0.46 37:0.35 39:0.56 43:0.38 45:0.66 47:0.93 48:0.91 55:0.71 64:0.77 66:0.67 69:0.50 70:0.49 71:0.66 74:0.45 81:0.27 86:0.89 91:0.33 98:0.64 101:0.46 108:0.38 122:0.94 123:0.37 124:0.46 126:0.23 127:0.39 129:0.05 133:0.37 135:0.58 139:0.85 146:0.67 147:0.72 149:0.28 150:0.30 154:0.44 159:0.42 170:0.82 172:0.68 173:0.52 174:0.89 175:0.91 177:0.70 178:0.55 179:0.48 187:0.39 197:0.90 212:0.86 216:0.29 222:0.60 235:0.60 239:0.86 241:0.21 242:0.16 243:0.71 244:0.83 245:0.77 247:0.86 253:0.38 254:0.96 257:0.84 259:0.21 262:0.93 264:0.98 265:0.65 266:0.27 267:0.36 271:0.29 276:0.63 277:0.87 281:0.86 300:0.43 +0 8:0.59 10:0.81 11:0.49 12:0.20 17:0.78 18:0.88 21:0.78 27:0.60 29:0.56 30:0.43 34:0.74 36:0.42 37:0.24 39:0.56 43:0.56 45:0.77 55:0.92 66:0.13 69:0.74 70:0.91 71:0.66 74:0.55 86:0.82 91:0.37 98:0.44 101:0.69 108:0.58 122:0.91 123:0.94 124:0.91 127:0.73 129:0.59 133:0.91 135:0.87 139:0.78 146:0.85 147:0.87 149:0.42 154:0.45 159:0.87 163:0.92 170:0.82 172:0.61 173:0.49 174:0.79 175:0.91 177:0.70 178:0.55 179:0.36 187:0.21 197:0.81 212:0.16 216:0.37 222:0.60 232:0.72 235:0.22 239:0.81 241:0.05 242:0.27 243:0.40 244:0.83 245:0.80 247:0.86 253:0.44 257:0.84 259:0.21 264:0.98 265:0.46 266:0.91 267:0.19 271:0.29 276:0.80 277:0.95 300:0.94 +1 8:0.86 10:0.83 12:0.20 17:0.97 18:0.63 21:0.78 27:0.60 29:0.56 30:0.41 34:0.18 36:0.59 37:0.88 39:0.56 43:0.11 48:0.91 55:0.59 66:0.27 69:0.74 70:0.65 71:0.66 74:0.52 76:0.94 77:0.89 86:0.65 87:0.98 91:0.62 96:0.75 98:0.22 108:0.57 122:0.90 123:0.55 124:0.79 127:0.99 129:0.05 133:0.74 135:0.65 139:0.66 140:0.80 145:0.85 146:0.49 147:0.35 149:0.17 151:0.59 153:0.72 154:0.18 158:0.74 159:0.88 162:0.55 170:0.82 172:0.32 173:0.49 174:0.79 175:0.91 177:0.38 178:0.42 179:0.80 190:0.98 191:0.70 195:0.96 197:0.82 202:0.59 212:0.19 216:0.07 220:0.74 222:0.60 232:0.72 235:0.95 239:0.83 241:0.78 242:0.33 243:0.29 244:0.93 245:0.58 247:0.74 248:0.57 253:0.59 254:0.84 256:0.45 257:0.93 259:0.21 264:0.98 265:0.24 266:0.63 267:0.65 268:0.57 271:0.29 275:0.91 276:0.34 277:0.87 281:0.91 282:0.73 285:0.45 294:0.93 300:0.55 +1 8:0.76 9:0.71 10:0.84 11:0.45 12:0.20 17:0.47 18:0.67 21:0.40 27:0.21 29:0.56 30:0.13 34:0.49 36:0.78 37:0.74 39:0.56 43:0.22 45:0.66 55:0.45 66:0.13 69:0.12 70:0.94 71:0.66 74:0.83 76:0.94 81:0.28 83:0.54 86:0.66 91:0.64 96:0.94 98:0.33 101:0.46 108:0.97 114:0.74 117:0.86 120:0.65 122:0.90 123:0.97 124:0.95 126:0.23 127:0.88 129:0.96 133:0.95 135:0.65 139:0.64 140:0.97 146:0.83 147:0.85 149:0.45 150:0.30 151:0.75 153:0.90 154:0.15 155:0.53 158:0.73 159:0.94 162:0.66 164:0.55 170:0.82 172:0.84 173:0.06 174:0.93 175:0.97 176:0.60 177:0.38 179:0.14 187:0.39 190:0.95 191:0.75 192:0.57 197:0.84 202:0.83 208:0.49 212:0.60 216:0.95 220:0.62 222:0.60 232:0.85 235:0.42 239:0.83 241:0.35 242:0.77 243:0.23 244:0.93 245:0.95 247:0.82 248:0.70 253:0.95 255:0.70 256:0.84 257:0.93 259:0.21 260:0.66 264:0.98 265:0.35 266:0.96 267:0.28 268:0.85 271:0.29 276:0.95 277:0.95 285:0.49 290:0.74 300:0.94 +1 7:0.66 10:0.92 11:0.49 12:0.20 17:0.87 18:0.96 21:0.77 27:0.55 29:0.56 30:0.12 32:0.64 34:1.00 36:0.28 37:0.40 39:0.56 43:0.30 45:0.73 48:0.91 55:0.52 66:0.23 69:0.55 70:0.95 71:0.66 74:0.32 81:0.24 86:0.92 91:0.25 98:0.61 101:0.52 108:0.43 122:0.95 123:0.75 124:0.73 126:0.23 127:0.75 129:0.05 133:0.73 135:0.49 139:0.88 145:1.00 146:0.76 147:0.80 149:0.39 150:0.25 154:0.22 159:0.70 170:0.82 172:0.74 173:0.44 174:0.93 175:0.75 177:0.88 178:0.55 179:0.40 187:0.95 195:0.83 197:0.94 212:0.68 215:0.43 216:0.35 222:0.60 230:0.68 233:0.66 235:0.98 239:0.90 241:0.52 242:0.32 243:0.58 244:0.93 245:0.82 247:0.92 253:0.29 257:0.65 259:0.21 264:0.98 265:0.57 266:0.80 267:0.36 271:0.29 276:0.77 277:0.87 281:0.86 283:0.67 297:0.36 300:0.84 +2 7:0.81 12:0.79 17:0.88 20:0.89 27:0.52 28:0.51 30:0.95 32:0.80 34:0.73 36:0.26 37:0.32 43:0.38 55:0.59 66:0.96 69:0.57 70:0.13 78:0.81 81:1.00 91:0.56 98:0.77 100:0.80 106:0.81 108:0.86 111:0.80 120:0.69 123:0.85 124:0.36 126:0.54 127:0.57 129:0.59 133:0.47 135:0.78 140:0.45 145:0.50 146:0.05 147:0.05 149:0.80 150:0.99 151:0.66 152:0.93 153:0.48 154:0.28 159:0.85 161:0.87 162:0.86 164:0.57 167:0.68 169:0.76 172:0.97 173:0.30 177:0.05 179:0.16 181:0.64 186:0.81 187:0.21 195:0.95 202:0.36 206:0.81 212:0.94 215:0.96 216:0.32 230:0.87 233:0.76 235:0.02 241:0.56 242:0.82 243:0.05 245:0.78 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.83 265:0.77 266:0.23 267:0.28 268:0.46 271:0.15 276:0.91 283:0.82 285:0.62 297:0.36 300:0.18 +1 6:0.95 7:0.81 8:0.83 12:0.79 17:0.81 20:0.97 21:0.13 25:0.88 27:0.51 28:0.51 30:0.45 32:0.80 34:0.49 36:0.27 37:0.29 43:0.28 55:0.59 66:0.49 69:0.78 70:0.25 78:0.88 81:0.99 91:0.33 98:0.29 100:0.95 108:0.86 111:0.91 123:0.85 124:0.48 126:0.54 127:0.46 129:0.59 133:0.47 135:0.82 145:0.61 146:0.05 147:0.05 149:0.21 150:0.99 152:0.95 154:0.20 159:0.57 161:0.87 163:0.99 167:0.67 169:0.97 172:0.16 173:0.75 177:0.05 178:0.55 179:0.97 181:0.64 186:0.88 187:0.39 189:0.95 195:0.78 212:0.61 215:0.84 216:0.25 230:0.87 233:0.76 235:0.12 238:0.97 241:0.55 242:0.82 243:0.29 245:0.39 247:0.12 259:0.21 261:0.98 265:0.31 266:0.17 267:0.36 271:0.15 276:0.15 297:0.36 300:0.18 +1 12:0.79 17:0.40 21:0.13 27:0.29 28:0.51 30:0.74 34:0.75 36:0.24 37:0.92 43:0.33 55:0.84 66:0.87 69:0.66 70:0.28 81:0.99 91:0.29 98:0.92 106:0.81 108:0.50 120:0.72 123:0.48 126:0.54 127:0.53 129:0.05 135:0.74 140:0.45 146:0.95 147:0.96 149:0.53 150:0.99 151:0.70 153:0.69 154:0.25 159:0.61 161:0.87 162:0.86 163:0.94 164:0.62 167:0.67 172:0.63 173:0.60 177:0.05 179:0.69 181:0.64 187:0.21 202:0.46 206:0.81 212:0.61 215:0.84 216:0.93 235:0.12 241:0.54 242:0.82 243:0.88 247:0.12 248:0.66 256:0.45 259:0.21 260:0.73 265:0.92 266:0.38 267:0.85 268:0.55 271:0.15 276:0.54 285:0.62 297:0.36 300:0.25 +1 12:0.79 17:0.90 21:0.30 27:0.48 28:0.51 30:0.76 34:1.00 36:0.38 37:0.43 43:0.30 66:0.64 69:0.72 70:0.15 81:0.98 91:0.51 98:0.16 108:0.56 120:0.53 123:0.53 126:0.54 127:0.09 129:0.05 135:0.66 140:0.45 145:0.54 146:0.21 147:0.27 149:0.25 150:0.97 151:0.46 153:0.48 154:0.22 159:0.10 161:0.87 162:0.86 163:0.97 164:0.57 167:0.62 172:0.16 173:0.80 177:0.05 179:0.17 181:0.64 187:0.39 202:0.36 212:0.95 215:0.72 216:0.32 220:0.87 235:0.31 241:0.39 242:0.82 243:0.69 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.18 266:0.12 267:0.44 268:0.46 271:0.15 276:0.15 285:0.62 297:0.36 300:0.13 +1 12:0.79 17:0.45 21:0.59 25:0.88 27:0.72 28:0.51 30:0.95 32:0.80 34:0.91 36:0.57 43:0.52 55:0.59 66:0.64 69:0.19 81:0.93 91:0.71 98:0.78 108:0.15 123:0.15 126:0.54 127:0.26 129:0.05 135:0.44 145:0.47 146:0.28 147:0.34 149:0.30 150:0.88 154:0.41 159:0.12 161:0.87 167:0.57 172:0.16 173:0.77 177:0.05 178:0.55 179:0.97 181:0.64 187:0.21 195:0.53 212:0.95 215:0.55 216:0.04 235:0.68 241:0.18 242:0.82 243:0.69 247:0.12 259:0.21 265:0.78 266:0.12 267:0.23 271:0.15 276:0.19 297:0.36 300:0.08 +1 12:0.79 17:0.67 21:0.78 27:0.45 28:0.51 34:0.86 36:0.18 37:0.50 43:0.28 66:0.36 69:0.78 91:0.22 98:0.07 108:0.62 123:0.59 127:0.05 129:0.05 135:0.87 145:0.47 146:0.05 147:0.05 149:0.12 154:0.20 159:0.05 161:0.87 167:0.58 172:0.05 173:0.72 177:0.05 178:0.55 181:0.64 187:0.68 216:0.77 235:0.95 241:0.24 243:0.51 259:0.21 265:0.07 267:0.28 271:0.15 +2 12:0.79 17:0.47 20:0.83 21:0.13 25:0.88 27:0.72 28:0.51 30:0.91 32:0.80 34:0.29 36:0.50 43:0.52 55:0.52 66:0.69 69:0.07 70:0.13 78:0.77 81:0.99 91:0.82 98:0.93 100:0.82 108:0.06 111:0.78 120:0.56 123:0.06 126:0.54 127:0.56 129:0.05 135:0.37 140:0.45 145:0.39 146:0.43 147:0.49 149:0.35 150:0.99 151:0.50 152:0.80 153:0.72 154:0.41 159:0.16 161:0.87 162:0.67 164:0.64 167:0.89 169:0.82 172:0.21 173:0.52 177:0.05 179:0.98 181:0.64 186:0.78 195:0.53 202:0.53 212:0.61 215:0.84 216:0.04 220:0.74 235:0.12 241:0.82 242:0.82 243:0.73 247:0.12 248:0.50 256:0.45 259:0.21 260:0.57 261:0.80 265:0.93 266:0.17 267:0.17 268:0.57 271:0.15 276:0.15 283:0.60 285:0.62 297:0.36 300:0.13 +2 12:0.79 17:0.92 21:0.40 25:0.88 27:0.51 28:0.51 30:0.76 32:0.80 34:0.52 36:0.40 37:0.29 43:0.20 55:0.59 66:0.36 69:0.93 70:0.15 81:0.97 91:0.57 98:0.13 108:0.92 120:0.73 123:0.92 124:0.52 126:0.54 127:0.23 129:0.59 133:0.47 135:0.78 140:0.45 145:0.42 146:0.05 147:0.05 149:0.12 150:0.95 151:0.66 153:0.48 154:0.13 159:0.58 161:0.87 162:0.86 163:1.00 164:0.67 167:0.64 172:0.10 173:0.90 177:0.05 179:0.96 181:0.64 187:0.21 195:0.90 202:0.50 212:0.95 215:0.67 216:0.25 220:0.62 235:0.42 241:0.47 242:0.82 243:0.34 245:0.37 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 265:0.14 266:0.12 267:0.28 268:0.59 271:0.15 276:0.13 285:0.62 297:0.36 300:0.13 +2 7:0.81 12:0.79 17:0.95 20:0.81 27:0.58 28:0.51 30:0.95 32:0.80 34:0.47 36:0.33 37:0.59 43:0.40 55:0.99 66:0.20 69:0.52 78:0.72 81:1.00 91:0.27 98:0.06 100:0.73 106:0.81 108:0.40 111:0.72 123:0.38 124:0.61 126:0.54 127:0.23 129:0.59 133:0.47 135:0.82 145:0.50 146:0.05 147:0.05 149:0.17 150:0.99 152:0.82 154:0.30 159:0.64 161:0.87 167:0.59 169:0.72 172:0.05 173:0.28 177:0.05 178:0.55 179:0.99 181:0.64 186:0.73 187:0.21 195:0.94 206:0.81 215:0.96 216:0.24 230:0.87 233:0.76 235:0.02 241:0.27 243:0.40 245:0.36 259:0.21 261:0.78 265:0.06 267:0.28 271:0.15 283:0.82 297:0.36 300:0.08 +4 6:0.95 7:0.81 8:0.90 12:0.79 17:0.86 20:0.90 21:0.74 25:0.88 27:0.51 28:0.51 30:0.76 32:0.80 34:0.67 36:0.80 37:0.29 43:0.46 55:0.99 66:0.20 69:0.48 70:0.22 78:0.97 81:0.86 91:0.90 98:0.13 100:0.99 108:0.94 111:0.99 120:0.86 123:0.93 124:0.81 126:0.54 127:0.95 129:0.89 133:0.78 135:0.94 140:0.45 145:0.58 146:0.05 147:0.05 149:0.24 150:0.70 151:0.80 152:0.95 153:0.92 154:0.35 159:0.84 161:0.87 162:0.63 163:0.97 164:0.90 167:0.90 169:0.97 172:0.10 173:0.26 177:0.05 179:0.97 181:0.64 186:0.97 189:0.96 191:0.89 195:0.93 202:0.86 212:0.42 215:0.46 216:0.25 230:0.87 233:0.76 235:0.88 238:0.99 241:0.86 242:0.82 243:0.26 245:0.39 247:0.12 248:0.80 256:0.85 259:0.21 260:0.86 261:0.98 265:0.14 266:0.23 267:0.87 268:0.87 271:0.15 276:0.25 283:0.82 285:0.62 297:0.36 300:0.18 +1 7:0.81 12:0.79 17:0.96 21:0.40 27:0.58 28:0.51 30:0.45 32:0.80 34:1.00 36:0.38 37:0.59 43:0.40 55:0.71 66:0.49 69:0.54 70:0.25 81:0.97 91:0.33 98:0.31 108:0.87 123:0.87 124:0.48 126:0.54 127:0.61 129:0.59 133:0.47 135:0.75 145:0.50 146:0.05 147:0.05 149:0.22 150:0.95 154:0.30 159:0.68 161:0.87 163:0.97 167:0.57 172:0.16 173:0.41 177:0.05 178:0.55 179:0.98 181:0.64 187:0.21 195:0.84 212:0.61 215:0.67 216:0.24 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.29 245:0.39 247:0.12 259:0.21 265:0.33 266:0.17 267:0.28 271:0.15 276:0.15 297:0.36 300:0.18 +1 6:0.94 8:0.83 12:0.79 17:0.81 20:0.95 21:0.78 27:0.59 28:0.51 30:0.95 34:0.39 36:0.80 37:0.41 43:0.23 55:0.99 66:0.13 69:0.89 78:0.88 91:0.93 98:0.06 100:0.96 108:0.78 111:0.92 120:0.67 123:0.76 124:0.75 127:0.45 129:0.81 133:0.67 135:0.87 140:0.96 145:0.54 146:0.05 147:0.05 149:0.14 151:0.64 152:0.95 153:0.46 154:0.15 159:0.78 161:0.87 162:0.46 163:0.99 164:0.66 167:0.90 169:0.92 172:0.05 173:0.78 177:0.05 178:0.54 179:0.99 181:0.64 186:0.88 189:0.95 191:0.91 202:0.86 212:0.95 216:0.32 220:0.74 235:0.88 238:0.98 241:0.85 242:0.82 243:0.34 245:0.37 247:0.12 248:0.62 256:0.58 259:0.21 260:0.68 261:0.94 265:0.06 266:0.12 267:0.82 268:0.58 271:0.15 276:0.19 285:0.62 300:0.08 +3 6:0.92 8:0.83 12:0.79 17:0.24 20:0.85 21:0.05 25:0.88 27:0.64 28:0.51 30:0.45 34:0.22 36:0.32 37:0.43 43:0.52 55:0.45 66:0.27 69:0.05 70:0.25 78:0.85 81:0.99 91:0.84 98:0.16 100:0.89 108:0.05 111:0.85 120:0.76 123:0.83 124:0.61 126:0.54 127:0.96 129:0.59 133:0.47 135:0.83 140:0.45 146:0.19 147:0.24 149:0.28 150:0.99 151:0.66 152:0.82 153:0.48 154:0.41 159:0.59 161:0.87 162:0.75 163:0.80 164:0.87 167:0.88 169:0.86 172:0.10 173:0.12 177:0.05 179:0.98 181:0.64 186:0.85 189:0.94 202:0.80 212:0.61 215:0.91 216:0.23 220:0.74 235:0.05 238:0.96 241:0.80 242:0.82 243:0.46 245:0.40 247:0.12 248:0.68 256:0.81 259:0.21 260:0.77 261:0.86 265:0.13 266:0.17 267:0.73 268:0.83 271:0.15 276:0.13 283:0.60 285:0.62 297:0.36 300:0.18 +1 12:0.79 17:0.81 21:0.78 25:0.88 27:0.51 28:0.51 30:0.76 32:0.80 34:0.50 36:0.28 37:0.29 43:0.28 55:0.59 66:0.36 69:0.78 70:0.15 91:0.58 98:0.16 108:0.86 120:0.69 123:0.86 124:0.52 127:0.44 129:0.59 133:0.47 135:0.77 140:0.45 145:0.63 146:0.05 147:0.05 149:0.18 151:0.66 153:0.48 154:0.20 159:0.56 161:0.87 162:0.57 163:0.99 164:0.57 167:0.74 172:0.10 173:0.75 177:0.05 179:0.99 181:0.64 187:0.39 195:0.78 202:0.55 212:0.95 216:0.25 235:0.22 241:0.68 242:0.82 243:0.34 245:0.37 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.17 266:0.12 267:0.36 268:0.46 271:0.15 276:0.13 285:0.62 300:0.13 +1 1:0.63 10:0.85 11:0.86 12:0.20 17:0.47 18:0.78 21:0.54 22:0.93 27:0.02 29:0.77 30:0.43 34:0.91 36:0.54 37:0.99 39:0.69 43:0.23 45:0.85 46:0.96 47:0.93 56:0.97 64:0.77 66:0.89 69:0.71 70:0.87 71:0.71 74:0.42 81:0.58 83:0.54 86:0.74 91:0.31 98:0.79 99:0.95 101:0.70 104:0.88 106:0.81 107:0.93 108:0.54 110:0.90 114:0.71 117:0.86 123:0.51 125:0.88 126:0.54 127:0.27 129:0.05 135:0.98 139:0.70 144:0.85 146:0.65 147:0.70 149:0.29 150:0.46 154:0.38 155:0.49 159:0.25 160:0.88 165:0.63 170:0.82 172:0.70 173:0.84 174:0.83 176:0.63 177:0.99 178:0.55 179:0.46 187:0.68 192:0.55 197:0.86 201:0.65 206:0.81 208:0.64 212:0.78 215:0.55 216:0.85 222:0.35 232:0.91 235:0.95 236:0.94 239:0.84 241:0.64 242:0.19 243:0.90 244:0.83 247:0.84 253:0.53 254:0.93 259:0.21 262:0.93 264:0.93 265:0.79 266:0.38 267:0.52 271:0.40 275:0.93 276:0.56 283:0.82 289:0.92 290:0.71 294:0.94 297:0.36 300:0.70 +2 1:0.65 7:0.69 9:0.72 11:0.48 12:0.20 17:0.72 18:0.96 21:0.47 22:0.93 23:0.96 27:0.32 29:0.77 30:0.90 31:0.88 32:0.67 34:0.90 36:0.95 37:0.62 39:0.69 43:0.48 44:0.88 45:0.98 46:0.88 47:0.93 48:0.98 56:0.97 64:0.77 66:0.97 69:0.57 70:0.28 71:0.71 74:0.76 77:0.70 79:0.88 81:0.75 83:0.72 86:0.93 91:0.44 96:0.71 97:0.94 98:0.89 99:0.99 101:0.64 104:0.87 106:0.81 107:0.88 108:0.44 110:0.88 114:0.80 117:0.86 120:0.58 122:0.67 123:0.42 125:0.97 126:0.54 127:0.44 128:0.95 129:0.05 135:0.95 137:0.88 139:0.93 140:0.45 143:0.99 144:0.85 145:0.83 146:0.81 147:0.84 149:0.84 150:0.53 151:0.57 153:0.48 154:0.47 155:0.64 158:0.75 159:0.37 160:0.96 162:0.86 164:0.57 165:0.88 168:0.95 170:0.82 172:0.92 173:0.65 175:0.91 176:0.73 177:0.88 179:0.23 187:0.98 192:1.00 195:0.65 196:0.92 201:0.66 202:0.36 204:0.83 205:0.95 206:0.81 208:0.64 212:0.93 215:0.63 216:0.69 219:0.90 220:0.87 222:0.35 230:0.71 232:0.90 233:0.76 235:0.88 236:0.97 241:0.57 242:0.30 243:0.97 244:0.83 247:0.76 248:0.56 253:0.68 254:0.86 255:0.72 256:0.45 257:0.84 259:0.21 260:0.58 262:0.93 264:0.93 265:0.89 266:0.27 267:0.59 268:0.46 271:0.40 276:0.85 277:0.87 279:0.77 281:0.91 282:0.75 283:0.82 284:0.89 285:0.62 289:0.92 290:0.80 292:0.95 297:0.36 300:0.43 +2 1:0.64 7:0.69 10:0.90 11:0.87 12:0.20 17:0.34 18:0.83 21:0.05 27:0.06 29:0.77 30:0.85 32:0.71 34:0.94 36:0.71 37:0.90 39:0.69 43:0.57 44:0.92 45:0.89 46:0.95 64:0.77 66:0.43 69:0.89 70:0.40 71:0.71 74:0.56 77:0.70 81:0.57 82:0.98 83:0.60 86:0.81 88:0.98 91:0.11 98:0.51 99:0.95 101:0.87 104:0.98 106:0.81 107:0.92 108:0.07 110:0.90 114:0.89 122:0.67 123:0.07 124:0.68 125:0.88 126:0.32 127:0.43 129:0.05 133:0.62 135:0.98 139:0.82 144:0.85 145:0.80 146:0.31 147:0.65 149:0.42 150:0.48 154:0.52 155:0.52 159:0.49 160:0.88 165:0.69 170:0.82 172:0.51 173:0.95 174:0.83 176:0.62 177:0.96 178:0.55 179:0.57 187:0.87 192:0.57 195:0.74 197:0.85 201:0.62 204:0.81 206:0.81 208:0.54 212:0.61 215:0.91 216:0.76 222:0.35 230:0.71 233:0.66 235:0.22 239:0.87 241:0.46 242:0.13 243:0.57 244:0.61 245:0.71 247:0.88 253:0.63 254:0.88 257:0.65 259:0.21 264:0.93 265:0.53 266:0.71 267:0.69 271:0.40 275:0.93 276:0.55 277:0.69 281:0.86 282:0.79 289:0.89 290:0.89 294:0.96 297:0.36 300:0.43 +0 8:0.66 10:0.83 11:0.87 12:0.20 17:0.43 18:0.65 21:0.78 27:0.45 29:0.77 30:0.91 32:0.65 34:0.80 36:0.17 37:0.50 39:0.69 43:0.35 44:0.88 45:0.81 46:0.93 66:0.25 69:0.71 70:0.19 71:0.71 74:0.39 77:0.70 86:0.65 91:0.20 98:0.08 101:0.70 107:0.89 108:0.54 110:0.89 122:0.67 123:0.52 124:0.71 125:0.88 127:0.37 129:0.59 133:0.61 135:0.91 139:0.68 144:0.85 145:0.49 146:0.17 147:0.22 149:0.37 154:0.26 159:0.76 160:0.88 163:0.97 170:0.82 172:0.16 173:0.51 174:0.79 175:0.97 177:0.70 178:0.55 179:0.88 187:0.68 195:0.93 197:0.82 212:0.52 216:0.77 222:0.35 235:0.74 239:0.82 241:0.49 242:0.24 243:0.45 244:0.93 245:0.45 247:0.47 253:0.34 257:0.93 259:0.21 264:0.93 265:0.08 266:0.27 267:0.91 271:0.40 275:0.91 276:0.21 277:0.95 282:0.74 289:0.88 294:0.93 300:0.18 +1 1:0.67 7:0.69 10:0.89 11:0.87 12:0.20 17:0.24 18:0.84 21:0.22 22:0.93 27:0.24 29:0.77 30:0.86 32:0.74 33:0.97 34:0.96 36:0.47 37:0.80 39:0.69 43:0.41 44:0.93 45:0.95 46:0.95 47:0.93 56:0.97 64:0.77 66:0.12 69:0.63 70:0.45 71:0.71 74:0.61 77:0.70 80:0.99 81:0.77 82:0.99 83:0.72 86:0.76 88:0.98 91:0.25 98:0.32 99:0.99 101:0.87 102:0.98 107:0.95 108:0.85 110:0.92 114:0.90 117:0.86 122:0.91 123:0.46 124:0.80 125:0.88 126:0.54 127:0.34 129:0.59 133:0.74 135:0.42 139:0.83 144:0.85 145:0.50 146:0.52 147:0.58 149:0.71 150:0.57 154:0.31 155:0.56 159:0.60 160:0.88 165:0.88 170:0.82 172:0.61 173:0.51 174:0.88 176:0.73 177:0.99 178:0.55 179:0.29 187:0.39 192:0.87 193:0.96 195:0.85 197:0.90 201:0.67 204:0.83 208:0.64 212:0.60 215:0.79 216:0.67 222:0.35 230:0.71 232:0.88 233:0.76 235:0.68 236:0.97 239:0.93 241:0.10 242:0.18 243:0.32 244:0.93 245:0.85 247:0.90 253:0.65 259:0.21 262:0.93 264:0.93 265:0.31 266:0.71 267:0.18 271:0.40 275:0.95 276:0.75 281:0.91 282:0.83 289:0.92 290:0.90 291:0.97 294:0.98 297:0.36 300:0.70 +1 10:0.87 11:0.86 12:0.20 17:0.47 18:0.69 21:0.78 27:0.45 29:0.77 30:0.71 34:0.71 36:0.28 37:0.50 39:0.69 43:0.23 45:0.88 46:0.94 66:0.89 69:0.80 70:0.45 71:0.71 74:0.40 86:0.73 91:0.05 98:0.57 101:0.64 107:0.92 108:0.64 110:0.90 123:0.61 125:0.88 127:0.17 129:0.05 135:0.88 139:0.70 144:0.85 145:0.49 146:0.76 147:0.80 149:0.22 154:0.35 159:0.32 160:0.88 170:0.82 172:0.68 173:0.74 174:0.83 177:0.99 178:0.55 179:0.26 187:0.68 197:0.83 212:0.77 216:0.77 222:0.35 235:0.31 239:0.85 241:0.30 242:0.31 243:0.89 244:0.61 247:0.76 253:0.35 254:0.90 259:0.21 264:0.93 265:0.59 266:0.54 267:0.44 271:0.40 275:0.93 276:0.56 289:0.89 294:0.95 300:0.43 +1 1:0.62 7:0.69 10:0.88 11:0.86 12:0.20 17:0.53 18:0.69 21:0.22 22:0.93 27:0.18 29:0.77 30:0.87 32:0.71 34:0.96 36:0.18 37:0.79 39:0.69 43:0.23 44:0.92 45:0.88 46:0.95 47:0.93 48:0.91 64:0.77 66:0.36 69:0.90 70:0.30 71:0.71 74:0.51 76:0.85 77:0.70 80:0.99 81:0.53 82:0.99 83:0.69 86:0.71 88:0.98 91:0.27 98:0.51 99:0.95 101:0.70 102:0.97 104:0.97 106:0.81 107:0.94 108:0.80 110:0.91 114:0.82 117:0.86 122:0.67 123:0.79 124:0.69 125:0.88 126:0.32 127:0.30 129:0.05 133:0.62 135:0.95 139:0.78 144:0.85 145:0.69 146:0.62 147:0.29 149:0.27 150:0.44 154:0.46 155:0.52 159:0.44 160:0.88 165:0.69 170:0.82 172:0.41 173:0.95 174:0.86 176:0.62 177:0.96 178:0.55 179:0.59 187:0.87 192:0.57 193:0.95 195:0.77 197:0.86 201:0.60 204:0.81 206:0.81 208:0.61 212:0.18 215:0.79 216:0.72 222:0.35 230:0.71 232:0.99 233:0.76 235:0.42 239:0.91 241:0.67 242:0.18 243:0.40 244:0.61 245:0.63 247:0.74 253:0.59 254:0.95 257:0.65 259:0.21 262:0.93 264:0.93 265:0.52 266:0.71 267:0.65 271:0.40 275:0.94 276:0.47 281:0.91 282:0.80 289:0.90 290:0.82 294:0.97 297:0.36 300:0.33 +1 1:0.64 8:0.92 10:0.85 11:0.86 12:0.20 17:0.53 18:0.77 21:0.47 22:0.93 27:0.02 29:0.77 30:0.43 34:0.92 36:0.51 37:0.99 39:0.69 43:0.23 45:0.85 46:0.96 47:0.93 56:0.97 64:0.77 66:0.89 69:0.70 70:0.87 71:0.71 74:0.42 81:0.59 83:0.54 86:0.76 91:0.33 98:0.79 99:0.95 101:0.70 104:0.88 106:0.81 107:0.93 108:0.53 110:0.90 114:0.74 117:0.86 123:0.51 125:0.88 126:0.54 127:0.27 129:0.05 135:0.97 139:0.71 144:0.85 146:0.65 147:0.70 149:0.29 150:0.47 154:0.38 155:0.49 159:0.25 160:0.88 165:0.63 170:0.82 172:0.70 173:0.84 174:0.83 176:0.63 177:0.99 178:0.55 179:0.46 187:0.68 192:0.55 197:0.86 201:0.65 206:0.81 208:0.64 212:0.78 215:0.58 216:0.85 222:0.35 232:0.91 235:0.88 236:0.94 239:0.84 241:0.65 242:0.19 243:0.90 244:0.83 247:0.84 253:0.54 254:0.93 259:0.21 262:0.93 264:0.93 265:0.79 266:0.27 267:0.52 271:0.40 275:0.93 276:0.56 289:0.92 290:0.74 294:0.94 297:0.36 300:0.70 +2 1:0.62 7:0.69 10:0.96 11:0.86 12:0.20 17:0.36 18:0.64 21:0.22 22:0.93 27:0.18 29:0.77 30:0.56 32:0.65 34:0.94 36:0.18 37:0.79 39:0.69 43:0.12 44:0.88 45:0.97 46:0.99 47:0.93 48:0.91 64:0.77 66:0.98 69:0.82 70:0.37 71:0.71 74:0.45 76:0.85 77:0.70 80:0.99 81:0.53 83:0.69 86:0.69 91:0.31 98:0.87 99:0.95 101:0.70 104:0.97 106:0.81 107:0.98 108:0.66 110:0.94 114:0.82 117:0.86 122:0.98 123:0.64 125:0.88 126:0.32 127:0.39 129:0.05 135:0.90 139:0.73 144:0.85 145:0.63 146:0.95 147:0.96 149:0.09 150:0.44 154:0.46 155:0.52 159:0.63 160:0.88 165:0.69 170:0.82 172:0.94 173:0.75 174:0.97 176:0.62 177:0.99 178:0.55 179:0.18 187:0.87 192:0.57 193:0.95 195:0.87 197:0.97 201:0.60 204:0.81 206:0.81 208:0.61 212:0.91 215:0.79 216:0.72 222:0.35 230:0.71 232:0.99 233:0.76 235:0.42 239:0.95 241:0.63 242:0.44 243:0.98 244:0.61 247:0.67 253:0.59 254:0.95 259:0.21 262:0.93 264:0.93 265:0.87 266:0.47 267:0.36 271:0.40 275:0.99 276:0.89 281:0.91 282:0.74 289:0.91 290:0.82 294:0.99 297:0.36 300:0.55 +1 1:0.61 7:0.69 10:0.95 11:0.87 12:0.20 17:0.47 18:0.80 21:0.30 22:0.93 27:0.18 29:0.77 30:0.89 32:0.74 34:0.95 36:0.19 37:0.79 39:0.69 43:0.21 44:0.93 45:0.95 46:0.98 47:0.93 48:0.91 64:0.77 66:0.93 69:0.82 70:0.29 71:0.71 74:0.67 76:0.85 77:0.70 80:0.99 81:0.52 82:0.99 83:0.69 86:0.88 88:0.98 91:0.20 98:0.74 99:0.95 101:0.87 102:0.98 104:0.97 106:0.81 107:0.96 108:0.66 110:0.93 114:0.80 117:0.86 122:0.91 123:0.64 125:0.88 126:0.32 127:0.24 129:0.05 135:0.93 139:0.88 144:0.85 145:0.69 146:0.75 147:0.79 149:0.11 150:0.43 154:0.51 155:0.52 159:0.31 160:0.88 165:0.69 170:0.82 172:0.82 173:0.87 174:0.90 176:0.62 177:0.99 178:0.55 179:0.26 187:0.87 192:0.57 193:0.95 195:0.71 197:0.90 201:0.59 204:0.81 206:0.81 208:0.61 212:0.82 215:0.72 216:0.72 222:0.35 230:0.71 232:0.99 233:0.76 235:0.52 239:0.95 241:0.67 242:0.24 243:0.94 247:0.79 253:0.61 254:0.94 259:0.21 262:0.93 264:0.93 265:0.74 266:0.47 267:0.28 271:0.40 275:0.96 276:0.70 281:0.86 282:0.83 289:0.90 290:0.80 294:0.99 297:0.36 300:0.43 +1 1:0.67 7:0.69 10:0.89 11:0.87 12:0.20 17:0.20 18:0.84 21:0.22 22:0.93 27:0.24 29:0.77 30:0.86 32:0.74 33:0.97 34:0.96 36:0.55 37:0.80 39:0.69 43:0.41 44:0.93 45:0.95 46:0.95 47:0.93 56:0.97 64:0.77 66:0.12 69:0.63 70:0.43 71:0.71 74:0.61 77:0.70 80:0.99 81:0.77 82:0.99 83:0.72 86:0.76 88:0.98 91:0.20 98:0.32 99:0.99 101:0.87 102:0.98 107:0.95 108:0.85 110:0.92 114:0.90 117:0.86 122:0.91 123:0.46 124:0.80 125:0.88 126:0.54 127:0.34 129:0.59 133:0.74 135:0.44 139:0.83 144:0.85 145:0.50 146:0.52 147:0.58 149:0.71 150:0.57 154:0.31 155:0.56 159:0.60 160:0.88 165:0.88 170:0.82 172:0.61 173:0.51 174:0.88 176:0.73 177:0.99 178:0.55 179:0.29 187:0.39 192:0.87 193:0.96 195:0.85 197:0.90 201:0.67 204:0.83 208:0.64 212:0.60 215:0.79 216:0.67 222:0.35 230:0.71 232:0.88 233:0.76 235:0.68 236:0.97 239:0.93 241:0.10 242:0.18 243:0.32 244:0.93 245:0.85 247:0.90 253:0.65 259:0.21 262:0.93 264:0.93 265:0.32 266:0.71 267:0.18 271:0.40 275:0.95 276:0.75 281:0.91 282:0.83 289:0.92 290:0.90 291:0.97 294:0.98 297:0.36 300:0.70 +0 10:0.81 11:0.84 12:0.20 17:0.36 18:0.81 21:0.05 27:0.43 29:0.77 30:0.14 34:0.70 36:0.97 37:0.87 39:0.38 43:0.58 45:0.87 55:0.92 66:0.15 69:0.43 70:0.87 71:0.97 74:0.54 81:0.33 86:0.83 91:0.05 98:0.42 101:0.47 104:0.93 106:0.81 108:0.57 122:0.83 123:0.93 124:0.91 126:0.24 127:0.91 129:0.89 133:0.91 135:0.82 139:0.78 144:0.85 146:0.28 147:0.34 149:0.56 150:0.37 154:0.56 159:0.88 163:0.94 170:0.96 172:0.57 173:0.17 174:0.79 175:0.75 177:0.70 178:0.55 179:0.43 187:0.39 197:0.80 206:0.81 212:0.16 215:0.91 216:0.28 222:0.35 232:0.74 235:0.05 239:0.80 241:0.07 242:0.33 243:0.19 244:0.61 245:0.76 247:0.90 253:0.43 254:0.95 257:0.65 259:0.21 265:0.39 266:0.91 267:0.82 271:0.43 276:0.74 277:0.69 283:0.67 297:0.36 300:0.84 +2 1:0.65 10:0.97 11:0.84 12:0.20 17:0.69 18:0.78 21:0.22 27:0.72 29:0.77 30:0.17 33:0.97 34:0.85 36:0.62 37:0.47 39:0.38 43:0.65 45:0.77 55:0.71 64:0.77 66:0.33 69:0.77 70:0.96 71:0.97 74:0.49 75:0.96 81:0.63 83:0.56 86:0.79 91:0.15 98:0.52 99:0.83 101:0.47 102:0.95 108:0.61 114:0.88 122:0.95 123:0.58 124:0.83 126:0.51 127:0.70 129:0.05 133:0.80 135:0.34 139:0.75 144:0.85 145:0.41 146:0.76 147:0.46 149:0.61 150:0.57 154:0.45 155:0.50 159:0.73 165:0.65 170:0.96 172:0.65 173:0.68 174:0.97 176:0.70 177:0.70 178:0.55 179:0.42 187:0.68 192:0.47 195:0.85 197:0.97 201:0.63 208:0.61 212:0.71 216:0.17 222:0.35 226:0.98 232:0.80 235:0.42 236:0.95 239:0.96 241:0.12 242:0.57 243:0.23 245:0.79 247:0.86 253:0.62 254:0.99 257:0.65 259:0.21 265:0.53 266:0.80 267:0.44 271:0.43 276:0.74 290:0.88 291:0.97 300:0.84 +0 1:0.61 8:0.90 9:0.73 10:0.88 11:0.84 12:0.20 17:0.22 18:0.67 21:0.22 27:0.08 29:0.77 30:0.13 31:0.94 34:0.36 36:0.75 37:0.87 39:0.38 43:0.41 44:0.88 45:0.73 55:0.52 64:0.77 66:0.77 69:0.85 70:0.84 71:0.97 74:0.54 76:0.85 77:0.70 79:0.94 81:0.51 83:0.56 86:0.69 91:0.72 96:0.92 97:0.97 98:0.76 99:0.83 101:0.47 108:0.71 114:0.85 117:0.86 122:0.83 123:0.69 124:0.42 126:0.32 127:0.73 129:0.05 133:0.60 135:0.54 137:0.94 139:0.76 140:0.87 144:0.85 145:0.65 146:0.92 147:0.31 149:0.44 150:0.45 151:0.80 153:0.93 154:0.31 155:0.55 158:0.76 159:0.73 162:0.83 165:0.65 170:0.96 172:0.81 173:0.78 174:0.89 176:0.63 177:0.38 179:0.44 187:0.95 190:0.95 191:0.73 192:0.55 195:0.88 196:0.93 197:0.87 201:0.58 202:0.75 208:0.50 212:0.42 216:0.83 219:0.91 222:0.35 232:0.92 235:0.31 239:0.86 241:0.39 242:0.45 243:0.12 244:0.83 245:0.72 247:0.92 248:0.76 253:0.90 255:0.71 256:0.79 257:0.84 259:0.21 265:0.76 266:0.84 267:0.97 268:0.81 271:0.43 276:0.73 279:0.79 282:0.75 284:0.95 285:0.50 290:0.85 292:0.88 300:0.84 +0 10:0.87 11:0.84 12:0.20 17:0.30 18:0.62 21:0.59 27:0.08 29:0.77 30:0.13 34:0.75 36:0.74 37:0.87 39:0.38 43:0.13 45:0.73 55:0.59 66:0.71 69:0.92 70:0.94 71:0.97 74:0.43 81:0.28 86:0.70 91:0.02 98:0.49 101:0.47 104:0.96 106:0.81 108:0.85 114:0.64 122:0.83 123:0.84 124:0.56 126:0.24 127:0.62 129:0.05 133:0.80 135:0.26 139:0.68 144:0.85 145:0.94 146:0.73 147:0.05 149:0.19 150:0.31 154:0.46 158:0.72 159:0.71 170:0.96 172:0.76 173:0.92 174:0.86 176:0.59 177:0.05 178:0.55 179:0.48 187:1.00 193:0.97 197:0.85 206:0.81 212:0.77 216:0.83 222:0.35 232:0.99 235:0.68 239:0.88 241:0.12 242:0.16 243:0.08 245:0.64 247:0.93 253:0.50 254:0.84 257:0.93 259:0.21 265:0.50 266:0.75 267:0.96 271:0.43 276:0.68 290:0.64 300:0.70 +1 10:0.90 11:0.84 12:0.20 17:0.40 18:0.69 21:0.54 27:0.08 29:0.77 30:0.33 34:0.80 36:0.82 37:0.87 39:0.38 43:0.27 44:0.88 45:0.77 55:0.59 66:0.54 69:0.93 70:0.98 71:0.97 74:0.49 77:0.70 81:0.37 86:0.72 91:0.06 98:0.41 101:0.47 102:0.95 104:0.99 106:0.81 108:0.87 114:0.67 123:0.86 124:0.67 126:0.32 127:0.29 129:0.05 133:0.73 135:0.75 139:0.74 144:0.85 145:0.93 146:0.76 147:0.05 149:0.28 150:0.39 154:0.34 159:0.71 170:0.96 172:0.70 173:0.88 174:0.89 176:0.59 177:0.05 178:0.55 179:0.35 187:1.00 192:0.44 193:0.97 195:0.94 197:0.89 201:0.55 206:0.81 212:0.68 216:0.83 222:0.35 235:0.68 236:0.94 239:0.91 241:0.10 242:0.23 243:0.09 245:0.73 247:0.93 253:0.52 254:0.84 257:0.93 259:0.21 265:0.43 266:0.84 267:0.97 271:0.43 276:0.67 282:0.75 290:0.67 300:0.94 +2 8:0.72 9:0.73 10:0.92 11:0.84 12:0.20 17:0.55 18:0.63 21:0.40 23:0.98 27:0.72 29:0.77 30:0.21 31:0.91 34:0.47 36:0.67 39:0.38 43:0.58 45:0.66 62:0.97 66:0.80 69:0.19 70:0.50 71:0.97 74:0.38 79:0.90 81:0.32 83:0.56 86:0.72 91:0.79 96:0.75 98:0.93 101:0.47 102:0.95 108:0.15 122:0.77 123:0.15 126:0.24 127:0.58 128:0.97 129:0.05 135:0.21 137:0.91 139:0.67 140:0.80 144:0.85 145:0.45 146:0.76 147:0.80 149:0.30 150:0.36 151:0.71 153:0.72 154:0.55 155:0.64 159:0.32 162:0.53 163:0.81 168:0.97 170:0.96 172:0.45 173:0.37 174:0.86 177:0.88 178:0.42 179:0.87 190:0.77 192:0.57 193:0.97 195:0.58 196:0.89 197:0.89 202:0.71 205:0.98 208:0.61 212:0.55 216:0.08 220:0.74 222:0.35 235:0.42 236:0.92 239:0.92 241:0.86 242:0.16 243:0.82 244:0.61 247:0.60 248:0.67 251:1.00 253:0.57 254:0.97 255:0.73 256:0.64 259:0.21 265:0.93 266:0.27 267:0.19 268:0.66 271:0.43 276:0.36 284:0.91 285:0.60 300:0.33 +1 10:0.88 11:0.84 12:0.20 17:0.22 18:0.69 21:0.64 27:0.08 29:0.77 30:0.08 34:0.75 36:0.83 37:0.87 39:0.38 43:0.25 45:0.77 55:0.52 66:0.68 69:0.92 70:0.98 71:0.97 74:0.42 81:0.27 86:0.72 91:0.03 96:0.68 98:0.57 101:0.47 104:0.96 106:0.81 108:0.84 114:0.63 123:0.83 124:0.66 126:0.24 127:0.61 129:0.05 133:0.85 135:0.32 139:0.69 140:0.45 144:0.85 145:0.92 146:0.82 147:0.05 149:0.28 150:0.30 151:0.46 153:0.48 154:0.35 159:0.74 162:0.86 170:0.96 172:0.77 173:0.90 174:0.88 176:0.59 177:0.05 179:0.45 187:0.99 190:0.95 193:0.97 197:0.86 202:0.36 206:0.81 212:0.73 216:0.83 220:1.00 222:0.35 232:0.99 235:0.74 239:0.89 241:0.10 242:0.15 243:0.08 245:0.64 247:0.93 248:0.46 253:0.49 254:0.84 256:0.45 257:0.93 259:0.21 265:0.58 266:0.84 267:0.96 268:0.46 271:0.43 276:0.70 285:0.46 290:0.63 300:0.84 +2 1:0.60 8:0.88 10:0.88 11:0.84 12:0.20 17:0.07 18:0.75 21:0.30 27:0.43 29:0.77 30:0.21 34:0.75 36:0.82 37:0.87 39:0.38 43:0.58 45:0.73 64:0.77 66:0.80 69:0.66 70:0.50 71:0.97 74:0.41 75:0.96 81:0.49 83:0.56 86:0.75 91:0.25 98:0.74 99:0.83 101:0.47 108:0.50 114:0.82 122:0.91 123:0.48 126:0.32 127:0.24 129:0.05 135:0.39 139:0.71 144:0.85 146:0.66 147:0.71 149:0.31 150:0.44 154:0.49 155:0.47 159:0.25 165:0.65 170:0.96 172:0.45 173:0.77 174:0.83 176:0.63 177:0.88 178:0.55 179:0.71 187:0.21 192:0.45 197:0.86 201:0.57 208:0.50 212:0.55 216:0.28 222:0.35 226:0.96 232:0.74 235:0.42 239:0.89 241:0.47 242:0.40 243:0.82 244:0.61 247:0.55 253:0.58 254:0.94 259:0.21 265:0.75 266:0.38 267:0.28 271:0.43 276:0.35 290:0.82 300:0.33 +2 10:0.95 11:0.87 12:0.20 17:0.85 18:0.77 21:0.40 27:0.66 29:0.77 30:0.30 34:0.53 36:0.95 37:0.78 39:0.38 43:0.61 45:0.85 55:0.59 66:0.23 69:0.12 70:0.89 71:0.97 74:0.55 81:0.31 86:0.82 91:0.18 98:0.57 101:0.65 108:0.77 114:0.69 122:0.83 123:0.10 124:0.72 126:0.24 127:0.75 129:0.59 133:0.67 135:0.74 139:0.77 144:0.85 146:0.61 147:0.66 149:0.59 150:0.34 154:0.32 158:0.72 159:0.50 170:0.96 172:0.63 173:0.21 174:0.92 176:0.59 177:0.88 178:0.55 179:0.45 187:0.99 197:0.93 212:0.78 216:0.20 222:0.35 235:0.42 239:0.93 241:0.18 242:0.13 243:0.43 245:0.85 247:0.96 253:0.54 254:0.99 259:0.21 265:0.48 266:0.63 267:0.52 271:0.43 276:0.72 290:0.69 300:0.84 +0 10:1.00 11:0.87 12:0.20 17:0.62 18:0.74 21:0.47 23:0.95 27:0.53 29:0.77 30:0.60 33:0.97 34:0.70 36:0.55 37:0.38 39:0.38 43:0.39 45:0.81 55:0.52 62:0.97 66:0.90 69:0.96 70:0.62 71:0.97 74:0.34 75:0.97 81:0.38 86:0.69 91:0.07 98:0.85 101:0.87 102:0.95 104:1.00 106:0.81 108:0.92 120:0.56 122:0.94 123:0.92 124:0.39 126:0.32 127:0.71 128:0.95 129:0.05 133:0.85 135:0.72 139:0.66 140:0.45 144:0.85 145:0.47 146:1.00 147:0.18 149:0.74 150:0.39 151:0.52 153:0.48 154:0.18 159:0.93 162:0.86 164:0.56 168:0.95 170:0.96 172:1.00 173:0.83 174:1.00 177:0.38 179:0.09 187:0.68 190:0.89 192:0.45 193:0.96 195:0.99 197:1.00 201:0.55 202:0.36 205:0.95 206:0.81 212:0.93 215:0.63 216:0.80 220:0.93 222:0.35 226:0.96 235:0.60 236:0.94 239:1.00 241:0.07 242:0.24 243:0.05 245:0.97 247:0.98 248:0.51 253:0.30 254:0.74 255:0.69 256:0.45 257:0.84 259:0.21 260:0.56 265:0.85 266:0.84 267:0.96 268:0.46 271:0.43 276:0.99 283:0.67 285:0.50 291:0.97 297:0.36 300:0.94 +0 8:0.69 10:0.86 11:0.84 12:0.20 17:0.36 18:0.74 21:0.22 23:0.95 27:0.16 29:0.77 30:0.38 33:0.97 34:0.42 36:0.54 37:0.87 39:0.38 43:0.57 45:0.77 62:0.95 66:0.83 69:0.53 70:0.33 71:0.97 74:0.44 75:0.96 81:0.35 86:0.75 91:0.33 98:0.84 101:0.47 108:0.41 122:0.86 123:0.39 126:0.24 127:0.34 128:0.95 129:0.05 135:0.32 139:0.72 140:0.80 144:0.85 146:0.66 147:0.71 149:0.52 150:0.39 151:0.50 153:0.48 154:0.46 159:0.25 162:0.62 168:0.95 170:0.96 172:0.51 173:0.70 174:0.83 177:0.88 178:0.42 179:0.74 187:0.68 190:0.95 191:0.70 197:0.87 202:0.50 205:0.95 212:0.42 216:0.50 220:0.91 222:0.35 226:0.98 232:0.72 235:0.22 236:0.92 239:0.88 241:0.50 242:0.29 243:0.84 244:0.83 247:0.60 248:0.50 253:0.37 256:0.45 259:0.21 265:0.84 266:0.47 267:0.52 268:0.46 271:0.43 276:0.42 285:0.46 291:0.97 300:0.25 +2 1:0.62 9:0.70 10:0.89 11:0.84 12:0.20 17:0.34 18:0.63 21:0.47 23:0.96 27:0.08 29:0.77 30:0.21 31:0.86 33:0.97 34:0.61 36:0.87 37:0.87 39:0.38 43:0.56 45:0.73 55:0.59 62:0.96 64:0.77 66:0.63 69:0.90 70:0.96 71:0.97 74:0.50 75:0.98 79:0.86 81:0.57 83:0.56 86:0.69 91:0.06 96:0.68 97:0.89 98:0.38 99:0.83 101:0.47 104:0.99 106:0.81 108:0.89 114:0.78 122:0.83 123:0.88 124:0.67 126:0.51 127:0.32 128:0.95 129:0.59 133:0.80 135:0.65 137:0.86 139:0.75 140:0.45 144:0.85 145:0.94 146:0.29 147:0.05 149:0.38 150:0.50 151:0.49 153:0.69 154:0.17 155:0.50 158:0.81 159:0.75 162:0.86 165:0.65 168:0.95 170:0.96 172:0.73 173:0.80 174:0.89 176:0.70 177:0.05 179:0.37 187:1.00 190:0.77 192:0.48 193:0.97 196:0.88 197:0.88 201:0.60 202:0.46 205:0.95 206:0.81 208:0.61 212:0.73 216:0.83 219:0.94 220:0.97 222:0.35 226:0.96 235:0.68 236:0.95 239:0.92 241:0.05 242:0.17 243:0.09 245:0.67 247:0.93 248:0.49 253:0.57 254:0.99 255:0.69 256:0.45 257:0.93 259:0.21 265:0.40 266:0.80 267:0.96 268:0.55 271:0.43 276:0.66 279:0.78 284:0.90 285:0.60 290:0.78 291:0.97 292:0.88 300:0.84 +0 1:0.62 8:0.96 10:0.89 11:0.84 12:0.20 17:0.11 18:0.64 21:0.13 27:0.16 29:0.77 30:0.58 34:0.29 36:0.83 37:0.87 39:0.38 43:0.13 45:0.66 55:0.52 64:0.77 66:0.69 69:0.84 70:0.44 71:0.97 74:0.34 81:0.53 83:0.56 86:0.67 91:0.62 98:0.65 99:0.83 101:0.47 108:0.70 114:0.88 122:0.95 123:0.68 124:0.41 126:0.32 127:0.36 129:0.05 133:0.36 135:0.84 139:0.65 144:0.85 146:0.65 147:0.05 149:0.13 150:0.47 154:0.44 155:0.48 159:0.38 165:0.65 170:0.96 172:0.41 173:0.92 174:0.88 176:0.63 177:0.05 178:0.55 179:0.82 187:0.68 192:0.46 197:0.90 201:0.58 202:0.74 208:0.50 212:0.16 216:0.50 222:0.35 232:0.72 235:0.22 239:0.88 241:0.63 242:0.40 243:0.18 244:0.61 245:0.46 247:0.55 253:0.61 254:0.84 257:0.93 259:0.21 265:0.66 266:0.54 267:0.19 271:0.43 276:0.33 290:0.88 300:0.33 +1 10:0.93 11:0.84 12:0.20 17:0.13 18:0.95 21:0.78 27:0.43 29:0.77 30:0.60 34:0.75 36:0.50 37:0.87 39:0.38 43:0.29 45:0.91 55:0.59 66:0.35 69:0.93 70:0.77 71:0.97 74:0.51 75:0.97 86:0.84 91:0.31 98:0.61 101:0.47 104:0.93 108:0.91 122:0.92 123:0.90 124:0.85 127:0.96 129:0.05 133:0.86 135:0.37 139:0.80 144:0.85 146:0.76 147:0.60 149:0.66 154:0.49 159:0.83 170:0.96 172:0.83 173:0.89 174:0.91 177:0.70 178:0.55 179:0.26 187:0.39 197:0.93 212:0.71 216:0.28 222:0.35 226:0.95 232:0.74 235:0.31 239:0.93 241:0.51 242:0.58 243:0.22 244:0.83 245:0.90 247:0.94 253:0.41 257:0.65 259:0.21 265:0.62 266:0.80 267:0.28 271:0.43 276:0.88 277:0.87 283:0.65 300:0.70 +0 10:0.88 11:0.87 12:0.20 17:0.38 18:0.87 21:0.47 27:0.67 29:0.77 30:0.62 34:0.69 36:0.88 37:0.46 39:0.38 43:0.61 45:0.88 66:0.90 69:0.56 70:0.50 71:0.97 74:0.73 81:0.28 83:0.69 86:0.89 91:0.33 98:0.90 101:0.87 108:0.43 122:0.86 123:0.41 126:0.24 127:0.48 129:0.05 135:0.65 139:0.88 144:0.85 145:0.83 146:0.79 147:0.83 149:0.67 150:0.31 154:0.60 155:0.54 159:0.35 170:0.96 172:0.71 173:0.66 174:0.79 177:0.88 178:0.55 179:0.57 187:0.68 192:0.50 193:0.99 197:0.84 202:0.36 204:0.83 208:0.61 212:0.35 215:0.63 216:0.39 222:0.35 232:0.72 235:0.52 239:0.91 241:0.44 242:0.44 243:0.90 244:0.61 247:0.76 253:0.52 259:0.21 265:0.90 266:0.54 267:0.36 271:0.43 276:0.59 281:0.91 297:0.36 300:0.43 +0 1:0.59 8:0.88 10:0.84 11:0.84 12:0.20 17:0.51 18:0.92 21:0.40 22:0.93 27:0.42 29:0.77 30:0.45 34:0.37 36:0.50 37:0.49 39:0.38 43:0.27 44:0.88 45:0.89 47:0.93 55:0.42 64:0.77 66:0.88 69:0.90 70:0.62 71:0.97 74:0.63 77:0.70 81:0.48 83:0.56 86:0.85 91:0.07 97:0.89 98:0.72 99:0.83 101:0.47 104:0.94 106:0.81 108:0.80 114:0.78 117:0.86 122:0.92 123:0.79 124:0.37 126:0.32 127:0.38 129:0.05 133:0.59 135:0.71 139:0.87 144:0.85 145:0.76 146:0.95 147:0.05 149:0.38 150:0.42 154:0.31 155:0.51 158:0.77 159:0.77 165:0.65 170:0.96 172:0.91 173:0.81 174:0.79 175:0.75 176:0.63 177:0.05 178:0.55 179:0.23 187:0.95 192:0.48 195:0.93 197:0.83 201:0.56 202:0.36 204:0.80 206:0.81 208:0.50 212:0.54 216:0.37 219:0.91 222:0.35 232:0.97 235:0.52 239:0.83 241:0.05 242:0.39 243:0.06 244:0.61 245:0.70 247:0.88 253:0.60 254:0.86 257:0.93 259:0.21 262:0.93 265:0.73 266:0.75 267:0.96 271:0.43 276:0.83 277:0.69 279:0.76 281:0.91 282:0.75 290:0.78 292:0.95 300:0.84 +1 10:0.94 11:0.87 12:0.20 17:0.83 18:0.84 21:0.13 27:0.62 29:0.77 30:0.32 34:0.36 36:0.95 37:0.81 39:0.38 43:0.55 45:0.88 66:0.26 69:0.29 70:0.92 71:0.97 74:0.58 81:0.36 86:0.86 91:0.15 98:0.57 101:0.65 102:0.95 108:0.22 122:0.86 123:0.79 124:0.73 126:0.24 127:0.72 129:0.59 133:0.64 135:0.49 139:0.82 144:0.85 145:0.47 146:0.43 147:0.50 149:0.64 150:0.41 154:0.74 159:0.60 170:0.96 172:0.54 173:0.26 174:0.90 177:0.88 178:0.55 179:0.49 187:0.39 193:0.97 195:0.75 197:0.91 202:0.36 212:0.69 216:0.14 222:0.35 235:0.12 236:0.92 239:0.94 241:0.30 242:0.16 243:0.46 245:0.82 247:0.91 253:0.45 254:0.88 259:0.21 265:0.34 266:0.71 267:0.23 271:0.43 276:0.67 300:0.84 +1 8:0.88 10:0.89 12:0.20 17:0.40 21:0.78 23:0.97 27:0.35 29:0.77 30:0.62 34:0.83 36:0.56 37:0.82 39:0.38 43:0.20 55:0.59 62:0.97 66:0.61 69:0.80 70:0.34 71:0.97 74:0.37 83:0.56 91:0.37 98:0.46 102:0.95 108:0.63 123:0.61 124:0.43 127:0.45 128:0.97 129:0.05 133:0.39 135:0.37 139:0.67 140:0.80 144:0.85 145:0.40 146:0.38 147:0.05 149:0.11 151:0.57 153:0.48 154:0.29 155:0.50 159:0.31 162:0.54 168:0.97 170:0.96 172:0.27 173:0.93 174:0.86 175:0.91 177:0.05 178:0.42 179:0.93 187:0.39 190:0.95 191:0.78 192:0.47 193:0.97 195:0.60 197:0.89 202:0.65 204:0.80 205:0.97 208:0.50 212:0.30 216:0.74 220:0.82 222:0.35 235:0.74 239:0.91 241:0.54 242:0.33 243:0.23 244:0.83 245:0.42 247:0.39 248:0.58 253:0.33 254:0.84 256:0.58 257:0.93 259:0.21 265:0.48 266:0.27 267:0.44 268:0.59 271:0.43 276:0.21 277:0.87 281:0.91 285:0.46 300:0.25 +0 1:0.59 10:0.89 11:0.84 12:0.20 17:0.67 18:0.81 21:0.68 27:0.31 29:0.77 30:0.32 32:0.65 34:0.95 36:0.76 37:0.83 39:0.38 43:0.62 45:0.77 55:0.52 64:0.77 66:0.27 69:0.60 70:0.95 71:0.97 74:0.48 76:0.85 81:0.52 83:0.56 86:0.74 91:0.27 98:0.39 99:0.83 101:0.47 102:0.95 108:0.85 114:0.69 122:0.92 123:0.44 124:0.67 126:0.51 127:0.31 129:0.05 133:0.60 135:0.42 139:0.76 144:0.85 145:0.76 146:0.64 147:0.69 149:0.45 150:0.44 154:0.52 155:0.53 159:0.61 165:0.65 170:0.96 172:0.48 173:0.45 174:0.86 176:0.70 177:0.88 178:0.55 179:0.51 187:0.68 192:0.50 195:0.86 197:0.89 201:0.57 204:0.82 208:0.61 212:0.54 216:0.16 222:0.35 232:0.84 235:0.88 236:0.95 239:0.89 241:0.27 242:0.21 243:0.46 244:0.83 245:0.69 247:0.82 253:0.53 259:0.21 265:0.28 266:0.71 267:0.44 271:0.43 276:0.51 281:0.91 290:0.69 300:0.84 +0 10:0.88 11:0.87 12:0.20 17:0.36 18:0.87 21:0.47 27:0.67 29:0.77 30:0.62 34:0.75 36:0.89 37:0.46 39:0.38 43:0.61 45:0.88 66:0.90 69:0.56 70:0.50 71:0.97 74:0.73 81:0.28 83:0.69 86:0.89 91:0.42 98:0.90 101:0.87 108:0.43 122:0.86 123:0.41 126:0.24 127:0.48 129:0.05 135:0.54 139:0.88 144:0.85 145:0.83 146:0.79 147:0.83 149:0.67 150:0.31 154:0.60 155:0.54 159:0.35 170:0.96 172:0.71 173:0.66 174:0.79 177:0.88 178:0.55 179:0.57 187:0.68 192:0.50 193:0.99 197:0.84 202:0.46 204:0.83 208:0.61 212:0.35 215:0.63 216:0.39 222:0.35 232:0.72 235:0.52 239:0.91 241:0.49 242:0.44 243:0.90 244:0.61 247:0.76 253:0.52 259:0.21 265:0.90 266:0.54 267:0.36 271:0.43 276:0.59 281:0.91 297:0.36 300:0.43 +2 8:0.93 9:0.72 10:0.90 11:0.54 12:0.20 17:0.26 18:0.73 21:0.22 23:0.97 27:0.67 28:0.83 29:0.62 30:0.28 34:0.45 36:0.42 37:0.44 39:0.60 43:0.34 45:0.81 62:0.97 66:0.67 69:0.68 70:0.95 71:0.88 74:0.39 75:0.96 81:0.29 83:0.54 86:0.71 91:0.53 97:0.97 98:0.38 101:0.69 108:0.51 123:0.49 124:0.45 126:0.23 127:0.69 128:0.98 129:0.05 133:0.36 135:0.47 139:0.68 140:0.90 143:0.99 146:0.66 147:0.71 149:0.32 150:0.32 151:0.85 153:0.48 154:0.48 155:0.54 159:0.77 161:0.87 162:0.60 167:0.60 168:0.98 170:0.87 172:0.57 173:0.52 174:0.86 177:0.96 179:0.72 181:0.91 187:0.39 190:0.95 192:0.48 197:0.89 202:0.63 205:0.97 208:0.49 212:0.55 215:0.79 216:0.39 220:0.91 222:0.76 226:0.98 232:0.84 235:0.22 239:0.90 241:0.60 242:0.12 243:0.72 244:0.83 245:0.66 247:0.86 248:0.76 253:0.34 254:0.74 255:0.71 256:0.58 259:0.21 264:0.95 265:0.41 266:0.54 267:0.69 268:0.62 271:0.43 275:0.93 276:0.31 279:0.78 285:0.49 294:0.96 295:0.99 297:0.36 300:0.84 +0 6:0.95 7:0.66 8:0.89 9:0.73 10:0.95 11:0.54 12:0.20 17:0.28 18:0.81 20:0.90 21:0.59 23:0.99 27:0.21 28:0.83 29:0.62 30:0.32 31:0.89 33:0.97 34:0.45 36:0.75 37:0.74 39:0.60 43:0.10 45:0.85 55:0.59 62:0.99 66:0.29 69:0.25 70:0.99 71:0.88 74:0.66 75:0.97 76:0.85 78:0.80 79:0.89 81:0.32 83:0.60 86:0.72 91:0.63 96:0.86 97:0.99 98:0.24 100:0.83 101:0.69 108:0.20 111:0.80 114:0.69 117:0.86 120:0.58 122:0.90 123:0.19 124:0.89 126:0.31 127:0.91 128:0.99 129:0.05 133:0.89 135:0.28 137:0.89 139:0.70 140:0.97 143:1.00 146:0.82 147:0.85 149:0.22 150:0.32 151:0.57 152:0.97 153:0.72 154:0.50 155:0.58 158:0.76 159:0.96 161:0.87 162:0.56 164:0.56 167:0.62 168:0.99 169:0.86 170:0.87 172:0.74 173:0.07 174:0.96 176:0.62 177:0.96 179:0.32 181:0.91 186:0.81 187:0.39 190:0.89 191:0.70 192:0.97 193:0.96 196:0.89 197:0.94 201:0.54 202:0.86 205:0.99 208:0.64 212:0.68 216:0.95 220:0.82 222:0.76 226:0.98 230:0.68 232:0.91 233:0.76 235:0.74 236:0.92 239:0.94 241:0.63 242:0.13 243:0.47 244:0.61 245:0.85 247:0.98 248:0.56 253:0.85 254:0.99 255:0.74 256:0.84 259:0.21 260:0.58 261:0.90 264:0.95 265:0.26 266:0.94 267:0.65 268:0.85 271:0.43 275:0.95 276:0.71 279:0.79 284:0.86 285:0.62 290:0.69 291:0.97 294:0.97 295:0.99 300:0.99 +1 1:0.57 7:0.66 8:0.88 10:0.82 11:0.45 12:0.20 17:0.53 18:0.76 21:0.68 27:0.12 28:0.83 29:0.62 30:0.11 32:0.64 34:0.75 36:0.41 37:0.80 39:0.60 43:0.29 45:0.66 55:0.52 56:0.97 66:0.36 69:0.93 70:0.88 71:0.88 74:0.49 76:0.85 77:0.96 80:0.99 81:0.52 82:0.99 83:0.54 86:0.72 88:0.99 91:0.56 98:0.41 99:0.83 101:0.46 102:0.96 108:0.85 114:0.69 122:0.86 123:0.84 124:0.70 126:0.53 127:0.47 129:0.05 133:0.65 135:0.32 139:0.68 145:0.91 146:0.61 147:0.39 149:0.24 150:0.41 154:0.15 155:0.49 159:0.64 161:0.87 165:0.63 167:0.68 170:0.87 172:0.51 173:0.94 174:0.79 175:0.75 176:0.70 177:0.70 178:0.55 179:0.57 181:0.91 187:0.87 192:0.47 193:0.97 195:0.85 197:0.80 201:0.59 202:0.73 204:0.78 208:0.54 212:0.55 215:0.49 216:0.75 222:0.76 230:0.68 232:0.75 233:0.66 235:0.95 236:0.95 239:0.88 241:0.69 242:0.13 243:0.37 244:0.83 245:0.73 247:0.86 253:0.53 254:0.93 257:0.84 259:0.21 264:0.95 265:0.43 266:0.63 267:0.44 271:0.43 276:0.51 277:0.69 282:0.75 290:0.69 294:0.96 297:0.36 300:0.70 +1 1:0.63 8:0.71 10:0.99 11:0.57 12:0.20 17:0.69 18:0.97 21:0.22 27:0.24 28:0.83 29:0.62 30:0.33 32:0.71 33:0.97 34:0.44 36:0.73 37:0.45 39:0.60 43:0.58 44:0.88 45:0.99 56:0.97 66:0.92 69:0.52 70:0.81 71:0.88 74:0.86 75:0.98 77:0.70 81:0.64 82:0.99 83:0.54 86:0.96 88:0.98 91:0.15 97:0.89 98:0.95 99:0.83 101:0.97 102:0.97 104:0.82 106:0.81 108:0.63 114:0.88 117:0.86 122:0.97 123:0.61 124:0.37 126:0.53 127:0.96 129:0.59 133:0.73 135:0.34 139:0.93 145:0.88 146:0.36 147:0.42 149:0.83 150:0.54 154:0.67 155:0.49 158:0.81 159:0.94 161:0.87 165:0.63 167:0.45 170:0.87 172:0.99 173:0.15 174:0.99 176:0.70 177:0.96 178:0.55 179:0.11 181:0.91 187:0.39 192:0.50 195:0.99 197:0.98 201:0.65 206:0.81 208:0.54 212:0.75 215:0.79 216:0.53 222:0.76 226:0.96 232:0.91 235:0.52 236:0.95 239:0.99 241:0.02 242:0.12 243:0.07 245:0.95 247:1.00 253:0.70 254:0.80 259:0.21 264:0.95 265:0.95 266:0.80 267:0.36 271:0.43 275:0.99 276:0.98 279:0.77 282:0.80 283:0.67 290:0.88 291:0.97 294:1.00 295:0.98 297:0.36 300:0.84 +1 1:0.56 7:0.66 8:0.84 9:0.73 10:0.87 11:0.45 12:0.20 17:0.13 18:0.74 21:0.68 23:0.97 27:0.12 28:0.83 29:0.62 30:0.11 31:0.93 32:0.68 34:0.69 36:0.48 37:0.80 39:0.60 43:0.19 45:0.66 55:0.45 56:0.97 62:0.98 66:0.33 69:0.95 70:0.96 71:0.88 74:0.43 75:0.96 76:0.85 77:0.70 79:0.93 80:0.99 81:0.44 82:0.99 83:0.60 86:0.70 88:0.98 91:0.46 97:0.89 98:0.33 99:0.83 101:0.46 102:0.97 108:0.90 120:0.57 122:0.95 123:0.90 124:0.72 126:0.43 127:0.59 128:0.98 129:0.05 133:0.65 135:0.47 137:0.93 139:0.67 140:0.45 143:0.99 145:0.96 146:0.60 147:0.57 149:0.16 150:0.37 151:0.85 153:0.48 154:0.13 155:0.57 159:0.77 161:0.87 162:0.86 163:0.75 164:0.55 165:0.63 167:0.60 168:0.98 170:0.87 172:0.48 173:0.96 174:0.89 175:0.75 177:0.70 179:0.60 181:0.91 187:0.68 190:0.95 192:0.56 193:0.97 195:0.91 196:0.90 197:0.87 201:0.55 202:0.56 204:0.78 205:0.97 208:0.54 212:0.27 215:0.49 216:0.75 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.92 241:0.60 242:0.13 243:0.40 244:0.83 245:0.73 247:0.92 248:0.80 253:0.37 254:0.93 255:0.72 256:0.64 257:0.84 259:0.21 260:0.57 264:0.95 265:0.36 266:0.75 267:0.59 268:0.65 271:0.43 276:0.47 277:0.69 279:0.75 282:0.77 283:0.67 284:0.88 285:0.61 294:0.97 295:0.98 297:0.36 300:0.84 +3 1:0.65 7:0.75 8:0.86 9:0.79 10:1.00 11:0.54 12:0.20 17:0.91 18:0.91 20:0.85 21:0.05 23:0.99 27:0.18 28:0.83 29:0.62 30:0.45 31:0.93 32:0.72 33:0.97 34:0.55 36:0.30 37:0.49 39:0.60 43:0.77 45:0.99 55:0.45 56:0.97 62:1.00 66:0.08 69:0.21 70:0.82 71:0.88 74:0.87 75:0.98 76:1.00 77:0.89 78:0.80 79:0.93 80:1.00 81:0.68 82:0.99 83:0.91 86:0.92 88:0.99 91:0.33 96:0.93 97:0.99 98:0.34 99:0.83 100:0.90 101:0.87 102:0.97 104:0.94 106:0.81 108:0.17 111:0.84 114:0.95 117:0.86 120:0.86 122:0.98 123:0.96 124:0.97 126:0.53 127:0.98 128:0.99 129:0.05 133:0.97 135:0.17 137:0.93 139:0.88 140:0.45 143:1.00 145:0.87 146:0.80 147:0.83 149:0.90 150:0.59 151:0.95 152:0.85 153:0.86 154:0.48 155:0.66 158:0.82 159:0.95 161:0.87 162:0.86 164:0.79 165:0.63 167:0.45 168:0.99 169:0.86 170:0.87 172:0.91 173:0.07 174:1.00 176:0.70 177:0.96 179:0.11 181:0.91 186:0.81 187:0.21 190:0.77 192:1.00 193:0.99 195:0.99 196:0.95 197:0.99 201:0.66 202:0.70 204:0.79 205:0.99 206:0.81 208:0.64 212:0.70 215:0.91 216:0.63 219:0.90 220:0.62 222:0.76 226:0.98 230:0.77 232:0.82 233:0.76 235:0.31 236:0.95 239:1.00 242:0.19 243:0.33 245:0.98 247:1.00 248:0.91 253:0.95 254:1.00 255:0.95 256:0.78 259:0.21 260:0.86 261:0.85 264:0.95 265:0.29 266:0.75 267:0.94 268:0.78 271:0.43 275:0.97 276:0.98 277:0.87 279:0.81 282:0.80 283:0.82 284:0.92 285:0.62 290:0.95 291:0.97 292:0.95 294:1.00 295:0.99 297:0.36 300:0.94 +1 1:0.63 8:0.63 10:0.94 11:0.49 12:0.20 17:0.64 18:0.70 20:0.90 21:0.22 27:0.24 28:0.83 29:0.62 30:0.21 32:0.72 33:0.97 34:0.63 36:0.62 37:0.45 39:0.60 43:0.75 45:0.85 56:0.97 66:0.23 69:0.52 70:0.80 71:0.88 74:0.68 75:0.98 77:0.89 78:0.83 81:0.68 82:0.99 83:0.60 86:0.66 88:0.99 91:0.17 97:0.94 98:0.46 99:0.95 100:0.73 101:0.52 102:0.97 104:0.82 106:0.81 108:0.40 111:0.81 114:0.88 117:0.86 122:0.41 123:0.71 124:0.89 126:0.53 127:0.87 129:0.59 133:0.88 135:0.42 139:0.64 145:0.86 146:0.71 147:0.75 149:0.77 150:0.55 152:0.85 154:0.66 155:0.49 158:0.82 159:0.83 161:0.87 165:0.70 167:0.50 169:0.72 170:0.87 172:0.57 173:0.31 174:0.89 176:0.70 177:0.96 178:0.55 179:0.44 181:0.91 186:0.83 187:0.39 192:0.50 195:0.92 197:0.91 201:0.65 206:0.81 208:0.54 212:0.29 215:0.79 216:0.53 222:0.76 226:0.98 232:0.87 235:0.52 236:0.95 239:0.96 241:0.27 242:0.12 243:0.43 244:0.61 245:0.76 247:0.97 253:0.65 259:0.21 261:0.84 264:0.95 265:0.40 266:0.87 267:0.36 271:0.43 275:0.95 276:0.75 279:0.77 282:0.80 283:0.82 290:0.88 291:0.97 294:0.99 295:0.99 297:0.36 300:0.70 +1 1:0.62 10:0.95 11:0.57 12:0.20 17:0.30 18:0.87 21:0.30 27:0.45 28:0.83 29:0.62 30:0.28 32:0.65 34:0.87 36:0.22 37:0.50 39:0.60 43:0.23 45:0.89 56:0.97 64:0.77 66:0.35 69:0.64 70:0.89 71:0.88 74:0.50 75:0.97 77:0.70 81:0.53 83:0.54 86:0.89 91:0.17 97:0.89 98:0.45 99:0.83 101:0.96 104:0.95 108:0.49 122:0.90 123:0.47 124:0.79 126:0.43 127:0.57 129:0.59 133:0.74 135:0.88 139:0.86 145:0.50 146:0.69 147:0.73 149:0.39 150:0.47 154:0.67 155:0.48 159:0.70 161:0.87 165:0.63 167:0.54 170:0.87 172:0.68 173:0.51 174:0.93 177:0.96 178:0.55 179:0.37 181:0.91 187:0.68 192:0.46 195:0.86 197:0.94 201:0.61 208:0.54 212:0.56 216:0.77 219:0.91 222:0.76 226:0.95 235:0.52 236:0.94 239:0.94 241:0.46 242:0.12 243:0.51 245:0.84 247:0.98 253:0.41 254:0.92 259:0.21 264:0.95 265:0.47 266:0.84 267:0.44 271:0.43 275:0.97 276:0.76 279:0.77 282:0.74 292:0.87 294:0.99 295:0.98 300:0.84 +0 8:0.65 9:0.72 10:0.98 11:0.54 12:0.20 17:0.24 18:0.86 21:0.59 23:0.98 27:0.21 28:0.83 29:0.62 30:0.33 33:0.97 34:0.69 36:0.66 37:0.74 39:0.60 43:0.26 45:0.89 55:0.59 62:0.98 66:0.51 69:0.25 70:0.78 71:0.88 74:0.57 75:0.96 81:0.27 83:0.54 86:0.86 91:0.12 96:0.77 97:0.89 98:0.54 101:0.87 108:0.20 122:0.57 123:0.19 124:0.64 126:0.23 127:0.88 128:0.97 129:0.59 133:0.60 135:0.30 139:0.80 140:0.45 143:0.99 146:0.87 147:0.90 149:0.41 150:0.29 151:0.76 153:0.48 154:0.46 155:0.54 159:0.84 161:0.87 162:0.53 167:0.56 168:0.97 170:0.87 172:0.85 173:0.12 174:0.97 177:0.96 179:0.29 181:0.91 187:0.39 190:0.89 192:0.56 193:0.95 197:0.98 202:0.69 205:0.98 208:0.49 212:0.83 216:0.95 220:0.62 222:0.76 226:0.96 232:0.93 235:0.68 236:0.92 239:0.97 241:0.51 242:0.12 243:0.61 244:0.61 245:0.92 247:1.00 248:0.70 253:0.68 254:0.80 255:0.73 256:0.64 259:0.21 264:0.95 265:0.56 266:0.75 267:0.28 268:0.64 271:0.43 275:0.94 276:0.78 279:0.75 285:0.55 291:0.97 294:0.96 295:0.98 300:0.70 +2 7:0.69 8:0.94 9:0.71 10:0.98 11:0.49 12:0.20 17:0.47 18:0.90 21:0.54 23:0.98 27:0.21 28:0.83 29:0.62 30:0.43 31:0.89 33:0.97 34:0.67 36:0.64 37:0.74 39:0.60 43:0.34 45:0.85 55:0.52 62:0.99 66:0.59 69:0.19 70:0.75 71:0.88 74:0.57 75:0.96 76:0.85 79:0.89 81:0.33 83:0.54 86:0.90 91:0.53 98:0.63 101:0.69 104:0.84 106:0.81 108:0.94 120:0.67 123:0.93 124:0.63 126:0.31 127:0.79 128:0.97 129:0.05 133:0.66 135:0.20 137:0.89 139:0.83 140:0.97 143:0.99 146:0.92 147:0.94 149:0.42 150:0.34 151:0.59 153:0.46 154:0.53 155:0.53 159:0.91 161:0.87 162:0.53 164:0.75 167:0.55 168:0.97 170:0.87 172:0.96 173:0.08 174:0.99 177:0.96 178:0.42 179:0.14 181:0.91 187:0.39 190:0.98 192:0.55 193:0.96 196:0.91 197:0.99 201:0.54 202:0.79 205:0.99 206:0.81 208:0.49 212:0.85 215:0.58 216:0.95 220:0.82 222:0.76 226:0.96 230:0.68 232:0.93 233:0.65 235:0.68 236:0.92 239:0.98 241:0.50 242:0.13 243:0.39 244:0.61 245:0.98 247:1.00 248:0.57 253:0.44 254:0.90 255:0.70 256:0.81 259:0.21 260:0.67 264:0.95 265:0.64 266:0.87 267:0.65 268:0.76 271:0.43 275:0.94 276:0.95 277:0.69 283:0.82 284:0.86 285:0.61 291:0.97 294:0.96 297:0.36 300:0.94 +2 1:0.58 7:0.66 8:0.98 9:0.73 10:0.85 12:0.20 17:0.45 18:0.63 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.60 31:0.89 32:0.64 34:0.34 36:0.62 37:0.80 39:0.60 43:0.08 55:0.45 62:0.98 66:0.34 69:0.79 70:0.46 71:0.88 74:0.39 75:0.96 76:0.85 77:0.70 79:0.89 81:0.39 83:0.60 86:0.62 91:0.80 96:0.86 98:0.21 104:0.89 108:0.95 114:0.69 120:0.77 123:0.94 124:0.72 126:0.43 127:0.69 128:0.99 129:0.59 133:0.66 135:0.61 137:0.89 139:0.61 140:0.87 143:1.00 145:0.76 146:0.28 147:0.34 149:0.08 150:0.39 151:0.64 153:0.83 154:0.20 155:0.57 159:0.87 161:0.87 162:0.63 164:0.85 167:0.66 168:0.99 170:0.87 172:0.37 173:0.55 174:0.86 175:0.91 176:0.63 177:0.70 179:0.76 181:0.91 187:0.68 190:0.95 191:0.70 192:0.87 193:0.95 195:0.96 196:0.87 197:0.85 201:0.59 202:0.88 205:1.00 208:0.54 212:0.19 215:0.53 216:0.75 220:0.91 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.88 241:0.67 242:0.12 243:0.36 244:0.93 245:0.62 247:0.82 248:0.68 253:0.81 254:0.80 255:0.73 256:0.89 257:0.84 259:0.21 260:0.77 264:0.95 265:0.23 266:0.63 267:0.23 268:0.89 271:0.43 276:0.23 277:0.87 282:0.73 283:0.67 284:0.88 285:0.62 290:0.69 297:0.36 300:0.43 +3 1:0.56 7:0.69 8:0.96 9:0.74 10:0.84 11:0.45 12:0.20 17:0.13 18:0.58 21:0.64 23:1.00 27:0.56 28:0.83 29:0.62 30:0.45 31:0.98 32:0.65 34:0.71 36:0.44 39:0.60 43:0.57 45:0.73 55:0.52 62:0.99 66:0.36 69:0.98 70:0.74 71:0.88 74:0.44 75:0.97 77:0.70 79:0.98 81:0.45 83:0.91 86:0.60 91:0.94 96:0.98 97:0.97 98:0.20 99:0.83 101:0.46 104:0.79 108:0.96 114:0.69 120:0.93 123:0.96 124:0.70 126:0.43 127:0.97 128:1.00 129:0.05 133:0.60 135:0.39 137:0.98 138:0.99 139:0.68 140:1.00 143:1.00 145:0.47 146:0.48 147:0.25 149:0.28 150:0.38 151:0.76 153:0.91 154:0.45 155:0.65 159:0.90 161:0.87 162:0.54 164:0.95 165:0.63 167:0.74 168:1.00 170:0.87 172:0.37 173:0.99 174:0.86 175:0.91 176:0.63 177:0.38 179:0.82 181:0.91 187:0.98 190:0.89 192:0.99 195:0.97 196:0.92 197:0.86 201:0.54 202:0.98 204:0.79 205:1.00 208:0.64 212:0.22 215:0.53 216:0.72 219:0.91 220:0.74 222:0.76 226:0.98 230:0.71 233:0.66 235:0.84 236:0.94 239:0.88 241:0.74 242:0.22 243:0.31 244:0.93 245:0.60 247:0.76 248:0.77 253:0.96 255:0.78 256:0.98 257:0.93 259:0.21 260:0.93 264:0.95 265:0.21 266:0.75 267:0.99 268:0.98 271:0.43 276:0.29 277:0.87 279:0.79 282:0.74 284:0.99 285:0.62 290:0.69 292:0.95 294:0.95 295:0.99 297:0.36 300:0.55 +2 1:0.65 6:0.96 7:0.75 8:0.84 9:0.75 10:0.97 11:0.49 12:0.20 17:0.57 18:0.74 20:0.94 21:0.05 23:0.99 27:0.37 28:0.83 29:0.62 30:0.45 32:0.67 33:0.97 34:0.81 36:0.43 37:0.94 39:0.60 43:0.65 45:0.85 56:0.97 62:1.00 66:0.86 69:0.45 70:0.66 71:0.88 74:0.77 75:0.98 76:0.94 78:0.89 80:1.00 81:0.72 82:0.99 83:0.72 86:0.73 88:0.99 91:0.67 96:0.91 97:1.00 98:0.50 99:0.95 100:0.94 101:0.52 102:0.96 104:0.95 106:0.81 108:0.35 111:0.90 114:0.95 117:0.86 120:0.84 122:0.90 123:0.33 124:0.37 126:0.53 127:0.82 128:0.99 129:0.05 133:0.36 135:0.72 139:0.73 140:0.45 143:1.00 145:0.59 146:0.70 147:0.74 149:0.64 150:0.59 151:0.84 152:0.88 153:0.72 154:0.59 155:0.58 158:0.81 159:0.70 161:0.87 162:0.72 164:0.79 165:0.70 167:0.54 168:0.99 169:0.92 170:0.87 172:0.78 173:0.34 174:0.90 176:0.70 177:0.96 179:0.53 181:0.91 186:0.89 187:0.68 189:0.97 190:0.77 192:0.98 193:0.99 195:0.83 197:0.93 201:0.66 202:0.79 204:0.85 205:0.99 206:0.81 208:0.64 212:0.83 215:0.91 216:0.25 219:0.91 220:0.62 222:0.76 226:0.96 230:0.77 232:0.96 233:0.76 235:0.31 236:0.95 238:0.93 239:0.97 241:0.46 242:0.18 243:0.86 245:0.60 247:0.92 248:0.87 253:0.92 254:1.00 255:0.94 256:0.82 259:0.21 260:0.84 261:0.92 264:0.95 265:0.52 266:0.47 267:0.79 268:0.82 271:0.43 275:0.95 276:0.45 279:0.82 283:0.67 285:0.61 290:0.95 291:0.97 292:0.88 294:0.99 295:0.98 297:0.36 300:0.70 +3 1:0.58 7:0.66 8:0.98 9:0.73 10:0.85 12:0.20 17:0.45 18:0.64 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.45 31:0.89 32:0.64 34:0.31 36:0.62 37:0.80 39:0.60 43:0.08 55:0.45 62:0.98 66:0.34 69:0.97 70:0.65 71:0.88 74:0.39 75:0.96 76:0.85 77:0.70 79:0.89 81:0.39 83:0.60 86:0.63 91:0.78 96:0.86 98:0.23 104:0.89 108:0.95 114:0.69 120:0.77 123:0.94 124:0.72 126:0.43 127:0.74 128:0.99 129:0.05 133:0.65 135:0.54 137:0.89 139:0.61 140:0.87 143:1.00 145:0.76 146:0.53 147:0.34 149:0.08 150:0.39 151:0.64 153:0.83 154:0.19 155:0.57 159:0.87 161:0.87 162:0.64 164:0.85 167:0.66 168:0.99 170:0.87 172:0.41 173:0.98 174:0.86 175:0.75 176:0.63 177:0.70 179:0.73 181:0.91 187:0.68 190:0.95 191:0.70 192:0.87 193:0.95 195:0.96 196:0.87 197:0.85 201:0.59 202:0.88 205:1.00 208:0.54 212:0.27 215:0.53 216:0.75 220:0.91 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.88 241:0.67 242:0.12 243:0.33 244:0.93 245:0.66 247:0.86 248:0.68 253:0.81 254:0.80 255:0.73 256:0.89 257:0.84 259:0.21 260:0.77 264:0.95 265:0.25 266:0.63 267:0.23 268:0.89 271:0.43 276:0.27 277:0.69 282:0.73 283:0.67 284:0.88 285:0.62 290:0.69 297:0.36 300:0.55 +3 1:0.56 6:0.99 7:0.66 8:0.97 9:0.73 10:0.87 12:0.20 17:0.43 18:0.71 20:0.98 21:0.68 23:1.00 27:0.12 28:0.83 29:0.62 30:0.45 31:0.90 32:0.66 34:0.34 36:0.65 37:0.80 39:0.60 43:0.08 55:0.71 62:0.99 66:0.18 69:0.80 70:0.54 71:0.88 74:0.49 75:0.97 76:0.85 77:0.70 78:0.94 79:0.90 81:0.36 83:0.54 86:0.66 91:0.91 96:0.97 97:0.97 98:0.26 100:0.99 102:0.96 108:0.92 111:0.98 114:0.67 120:0.68 122:0.98 123:0.91 124:0.86 126:0.43 127:0.60 128:1.00 129:0.05 133:0.84 135:0.58 137:0.90 139:0.64 140:0.92 143:1.00 145:0.92 146:0.26 147:0.32 149:0.15 150:0.36 151:0.94 152:0.94 153:0.97 154:0.19 155:0.55 158:0.77 159:0.79 161:0.87 162:0.61 163:0.75 164:0.80 167:0.74 168:1.00 169:0.97 170:0.87 172:0.32 173:0.65 174:0.89 175:0.91 176:0.63 177:0.70 179:0.64 181:0.91 186:0.94 187:0.39 189:0.99 190:0.89 192:0.86 193:0.95 195:0.92 196:0.88 197:0.90 201:0.56 202:0.92 205:1.00 208:0.54 212:0.21 215:0.49 216:0.75 222:0.76 226:0.98 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 238:0.97 239:0.92 241:0.75 242:0.21 243:0.24 244:0.93 245:0.64 247:0.84 248:0.84 253:0.95 254:0.80 255:0.77 256:0.92 257:0.84 259:0.21 260:0.68 261:0.97 264:0.95 265:0.28 266:0.80 267:0.69 268:0.93 271:0.43 276:0.50 277:0.95 279:0.78 282:0.75 284:0.94 285:0.62 290:0.67 294:0.95 295:0.99 297:0.36 300:0.43 +2 6:0.95 7:0.69 8:0.89 9:0.74 10:0.98 11:0.54 12:0.20 17:0.47 18:0.87 20:0.91 21:0.54 23:0.99 27:0.21 28:0.83 29:0.62 30:0.44 33:0.97 34:0.68 36:0.77 37:0.74 39:0.60 43:0.33 45:0.88 55:0.52 62:0.99 66:0.51 69:0.19 70:0.76 71:0.88 74:0.58 75:0.97 76:0.85 78:0.82 81:0.33 83:0.60 86:0.85 91:0.49 96:0.85 97:0.94 98:0.52 100:0.93 101:0.87 104:0.84 106:0.81 108:0.93 111:0.87 120:0.74 122:0.98 123:0.93 124:0.69 126:0.31 127:0.90 128:0.99 129:0.05 133:0.74 135:0.18 139:0.79 140:0.97 143:1.00 146:0.90 147:0.92 149:0.41 150:0.34 151:0.80 152:0.97 153:0.46 154:0.52 155:0.58 159:0.90 161:0.87 162:0.56 164:0.75 167:0.51 168:0.99 169:0.86 170:0.87 172:0.94 173:0.09 174:0.99 177:0.96 179:0.16 181:0.91 186:0.83 187:0.39 190:0.77 192:0.98 193:0.96 197:0.98 201:0.54 202:0.76 205:0.99 206:0.81 208:0.54 212:0.87 215:0.58 216:0.95 220:0.74 222:0.76 226:0.98 230:0.68 232:0.93 233:0.65 235:0.68 236:0.92 239:0.98 241:0.37 242:0.12 243:0.43 244:0.61 245:0.97 247:1.00 248:0.75 253:0.82 254:0.90 255:0.78 256:0.79 259:0.21 260:0.74 261:0.90 264:0.95 265:0.53 266:0.87 267:0.73 268:0.75 271:0.43 275:0.95 276:0.92 277:0.69 279:0.77 283:0.66 285:0.61 291:0.97 294:0.97 295:0.99 297:0.36 300:0.98 +3 1:0.56 8:0.89 9:0.73 10:0.87 11:0.45 12:0.20 17:0.13 18:0.61 20:0.81 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.30 31:0.95 34:0.80 36:0.42 37:0.78 39:0.60 43:0.27 45:0.77 56:0.97 62:0.99 66:0.26 69:0.83 70:0.75 71:0.88 74:0.30 75:0.97 78:0.72 79:0.94 81:0.38 83:0.60 86:0.66 91:0.85 96:0.93 97:0.97 98:0.19 99:0.83 100:0.73 101:0.46 108:0.67 111:0.72 120:0.75 122:0.57 123:0.93 124:0.72 126:0.31 127:0.60 128:1.00 129:0.05 133:0.66 135:0.24 137:0.95 139:0.70 140:0.94 143:1.00 145:0.49 146:0.24 147:0.30 149:0.17 150:0.36 151:0.91 152:0.78 153:0.86 154:0.27 155:0.54 159:0.80 161:0.87 162:0.57 164:0.73 165:0.63 167:0.63 168:1.00 169:0.72 170:0.87 172:0.27 173:0.68 174:0.86 175:0.91 177:0.70 178:0.42 179:0.80 181:0.91 186:0.73 187:0.68 190:0.89 192:0.85 196:0.91 197:0.87 201:0.54 202:0.93 205:1.00 208:0.54 212:0.39 216:0.81 219:0.91 220:0.99 222:0.76 226:0.98 232:0.83 235:0.80 236:0.92 239:0.89 241:0.64 242:0.13 243:0.45 244:0.83 245:0.58 247:0.76 248:0.91 253:0.90 254:0.86 255:0.77 256:0.94 257:0.84 259:0.21 260:0.76 261:0.80 264:0.95 265:0.14 266:0.54 267:0.76 268:0.93 271:0.43 275:0.94 276:0.30 277:0.95 279:0.79 284:0.95 285:0.62 292:0.95 294:0.97 295:0.99 300:0.55 +2 8:0.92 9:0.74 10:0.86 11:0.45 12:0.20 17:0.12 18:0.63 21:0.68 23:0.99 27:0.05 28:0.83 29:0.62 30:0.15 31:0.87 34:0.24 36:0.43 37:0.78 39:0.60 43:0.14 45:0.66 55:0.45 62:0.98 66:0.33 69:0.96 70:0.84 71:0.88 74:0.37 75:0.97 79:0.87 81:0.25 83:0.60 86:0.62 91:0.86 96:0.92 97:0.94 98:0.33 101:0.46 104:0.94 106:0.81 108:0.92 120:0.73 122:0.75 123:0.92 124:0.71 126:0.23 127:0.45 128:1.00 129:0.05 133:0.65 135:0.23 137:0.87 139:0.61 140:0.98 143:1.00 146:0.63 147:0.24 149:0.11 150:0.27 151:0.76 153:0.72 154:0.16 155:0.58 158:0.77 159:0.76 161:0.87 162:0.57 163:0.80 164:0.83 167:0.56 168:1.00 170:0.87 172:0.27 173:0.98 174:0.86 175:0.75 177:0.38 179:0.82 181:0.91 187:0.87 190:0.89 192:0.98 196:0.87 197:0.86 202:0.88 205:0.99 206:0.81 208:0.54 212:0.15 215:0.49 216:0.83 220:0.62 222:0.76 226:0.98 232:0.88 235:0.80 239:0.88 241:0.53 242:0.14 243:0.28 244:0.93 245:0.53 247:0.67 248:0.70 253:0.87 255:0.78 256:0.88 257:0.93 259:0.21 260:0.74 264:0.95 265:0.35 266:0.63 267:0.36 268:0.88 271:0.43 275:0.91 276:0.29 277:0.69 279:0.77 283:0.66 284:0.86 285:0.62 294:0.95 295:0.99 297:0.36 300:0.55 +1 1:0.66 7:0.75 10:0.93 11:0.82 17:0.89 18:0.86 21:0.13 22:0.93 27:0.69 29:0.71 30:0.68 32:0.72 34:1.00 36:0.46 37:0.43 39:0.40 43:0.65 44:0.92 45:0.85 47:0.93 64:0.77 66:0.63 69:0.81 70:0.79 71:0.67 74:0.68 77:0.70 81:0.68 83:0.69 85:0.72 86:0.90 91:0.38 97:0.94 98:0.61 99:0.95 101:0.96 106:0.81 108:0.65 114:0.92 117:0.86 122:0.93 123:0.63 124:0.47 126:0.51 127:0.26 129:0.59 133:0.47 135:0.97 139:0.93 145:0.41 146:0.71 147:0.76 149:0.76 150:0.56 154:0.52 155:0.55 158:0.81 159:0.40 165:0.81 170:0.90 172:0.65 173:0.81 174:0.90 176:0.70 177:0.88 178:0.55 179:0.41 187:0.21 192:0.57 195:0.74 197:0.91 201:0.63 204:0.83 206:0.81 208:0.61 212:0.58 215:0.84 216:0.13 219:0.94 222:0.35 230:0.77 232:0.99 233:0.76 235:0.31 239:0.92 241:0.15 242:0.24 243:0.69 245:0.76 247:0.79 253:0.67 259:0.21 262:0.93 265:0.62 266:0.54 267:0.73 271:0.89 276:0.58 279:0.78 281:0.91 282:0.80 283:0.67 290:0.92 292:0.88 297:0.36 300:0.84 +1 1:0.60 8:0.81 9:0.70 10:0.98 11:0.52 17:0.12 18:0.82 21:0.74 23:0.96 27:0.45 29:0.71 30:0.09 31:0.86 32:0.66 34:0.59 36:0.19 37:0.50 39:0.40 43:0.24 44:0.88 45:0.81 55:0.45 62:0.96 64:0.77 66:0.29 69:0.71 70:0.94 71:0.67 74:0.76 75:0.98 79:0.86 81:0.52 83:0.56 86:0.88 91:0.23 97:0.94 98:0.46 99:0.95 101:0.65 102:0.96 108:0.54 120:0.54 122:0.83 123:0.85 124:0.82 126:0.51 127:0.55 128:0.95 129:0.59 133:0.80 135:0.89 137:0.86 139:0.87 140:0.45 145:0.54 146:0.72 147:0.76 149:0.27 150:0.41 151:0.47 153:0.48 154:0.60 155:0.48 159:0.77 162:0.86 164:0.56 165:0.65 168:0.95 170:0.90 172:0.75 173:0.54 174:0.94 177:0.88 179:0.27 187:0.68 190:0.77 192:0.46 195:0.90 196:0.88 197:0.95 201:0.59 202:0.36 205:0.95 208:0.50 212:0.71 215:0.46 216:0.77 219:0.94 220:0.99 222:0.35 226:0.95 235:0.99 236:0.95 239:0.97 241:0.35 242:0.23 243:0.48 245:0.87 247:0.97 248:0.47 253:0.53 254:0.90 255:0.68 256:0.45 259:0.21 260:0.54 265:0.43 266:0.87 267:0.76 268:0.46 271:0.89 276:0.83 279:0.79 283:0.65 284:0.88 285:0.60 292:0.86 297:0.36 300:0.70 +2 1:0.58 7:0.70 8:0.75 10:0.99 11:0.46 17:0.18 18:0.92 21:0.47 27:0.39 29:0.71 30:0.16 32:0.69 34:0.45 36:0.44 37:0.65 39:0.40 43:0.61 44:0.92 45:0.73 48:0.91 55:0.42 64:0.77 66:0.63 69:0.57 70:0.84 71:0.67 74:0.53 75:0.98 81:0.44 86:0.95 91:0.46 98:0.72 101:0.47 102:0.97 108:0.44 122:0.77 123:0.42 124:0.55 126:0.51 127:0.55 129:0.05 133:0.65 135:0.94 139:0.95 145:0.84 146:0.85 147:0.87 149:0.39 150:0.42 154:0.50 155:0.47 159:0.61 170:0.90 172:0.88 173:0.49 174:0.97 177:0.88 178:0.55 179:0.25 187:0.87 192:0.47 193:0.98 195:0.81 197:0.98 201:0.60 202:0.36 208:0.50 212:0.86 215:0.63 216:0.31 219:0.94 222:0.35 226:0.95 230:0.73 233:0.66 235:0.68 236:0.95 239:0.99 241:0.10 242:0.12 243:0.69 245:0.90 247:0.99 253:0.42 254:0.99 259:0.21 265:0.72 266:0.54 267:0.91 271:0.89 276:0.84 279:0.75 281:0.86 283:0.65 292:0.88 297:0.36 300:0.84 +2 10:0.97 11:0.82 17:0.69 18:0.94 21:0.78 27:0.72 29:0.71 30:0.76 34:1.00 36:0.37 37:0.37 39:0.40 43:0.77 45:0.89 66:0.47 69:0.32 70:0.59 71:0.67 74:0.62 83:0.69 85:0.72 86:0.96 91:0.60 97:0.94 98:0.55 101:1.00 108:0.25 122:0.65 123:0.24 124:0.68 127:0.87 129:0.59 133:0.67 135:0.65 139:0.95 145:0.98 146:0.70 147:0.74 149:0.85 154:0.61 158:0.81 159:0.64 170:0.90 172:0.65 173:0.26 174:0.92 177:0.88 178:0.55 179:0.53 187:0.68 197:0.95 208:0.61 212:0.68 216:0.27 219:0.94 222:0.35 235:0.88 239:0.96 241:0.61 242:0.13 243:0.59 245:0.80 247:0.90 253:0.47 259:0.21 265:0.57 266:0.71 267:0.52 271:0.89 276:0.60 279:0.79 283:0.67 292:0.88 300:0.84 +1 1:0.68 8:0.77 10:0.98 11:0.52 17:0.79 18:0.96 21:0.30 27:0.54 29:0.71 30:0.62 34:0.88 36:0.43 37:0.60 39:0.40 43:0.14 45:0.94 64:0.77 66:0.98 69:0.29 70:0.75 71:0.67 74:0.94 81:0.76 83:0.69 86:0.98 91:0.23 98:0.91 99:0.95 101:0.65 108:0.22 114:0.86 122:0.67 123:0.22 126:0.54 127:0.51 129:0.05 135:0.97 139:0.97 146:0.94 147:0.95 149:0.14 150:0.62 154:0.90 155:0.53 159:0.58 165:0.81 170:0.90 172:0.94 173:0.24 174:0.92 176:0.73 177:0.88 178:0.55 179:0.21 187:0.68 192:0.55 197:0.93 201:0.67 202:0.36 208:0.64 212:0.81 215:0.72 216:0.64 222:0.35 235:0.68 236:0.97 239:0.97 241:0.02 242:0.14 243:0.98 247:0.90 253:0.73 254:0.74 259:0.21 265:0.91 266:0.27 267:0.87 271:0.89 276:0.88 290:0.86 297:0.36 300:0.70 +1 1:0.64 7:0.75 10:0.88 11:0.75 17:0.34 18:0.68 21:0.54 27:0.02 29:0.71 30:0.87 32:0.80 34:0.99 36:0.21 37:0.91 39:0.40 43:0.75 44:0.93 45:0.73 48:0.91 64:0.77 66:0.82 69:0.83 70:0.32 71:0.67 74:0.67 77:0.70 81:0.73 83:0.91 85:0.72 86:0.77 91:0.14 98:0.39 101:0.87 102:0.98 108:0.68 114:0.77 122:0.65 123:0.66 126:0.54 127:0.13 129:0.05 135:0.92 139:0.85 145:0.49 146:0.45 147:0.51 149:0.58 150:0.54 154:0.66 155:0.55 159:0.16 165:0.93 170:0.90 172:0.48 173:0.87 174:0.79 176:0.73 177:0.88 178:0.55 179:0.26 187:0.68 192:0.57 193:0.96 195:0.72 197:0.82 201:0.63 204:0.83 208:0.64 212:0.61 215:0.58 216:0.81 222:0.35 230:0.77 233:0.66 235:0.80 236:0.97 239:0.93 241:0.57 242:0.33 243:0.83 247:0.60 253:0.60 259:0.21 265:0.41 266:0.38 267:0.96 271:0.89 276:0.34 281:0.91 282:0.88 290:0.77 297:0.36 299:0.88 300:0.33 +0 10:0.83 17:0.91 21:0.76 27:0.71 29:0.71 30:0.76 34:0.86 36:0.22 37:0.48 39:0.40 43:0.09 44:0.88 48:0.91 55:0.98 64:0.77 66:0.13 69:0.87 70:0.15 71:0.67 74:0.30 81:0.25 91:0.09 98:0.06 108:0.74 122:0.94 123:0.73 124:0.75 126:0.24 127:0.37 129:0.81 133:0.67 135:0.32 139:0.69 145:0.82 146:0.05 147:0.05 149:0.08 150:0.27 154:0.08 159:0.68 163:0.99 170:0.90 172:0.05 173:0.80 174:0.79 175:0.97 177:0.05 178:0.55 179:0.98 187:0.21 195:0.89 197:0.82 212:0.95 216:0.13 222:0.35 235:0.95 239:0.82 241:0.15 242:0.82 243:0.34 244:0.93 245:0.37 247:0.12 253:0.27 257:0.93 259:0.21 265:0.06 266:0.12 267:0.52 271:0.89 276:0.17 277:0.95 281:0.86 300:0.13 +2 8:0.80 9:0.72 10:0.87 11:0.57 17:0.75 18:0.84 21:0.22 23:0.95 27:0.53 29:0.71 30:0.33 31:0.92 32:0.67 34:0.55 36:0.98 37:0.77 39:0.40 43:0.58 44:0.88 45:0.81 48:0.91 55:0.59 62:0.96 64:0.77 66:0.90 69:0.60 70:0.63 71:0.67 74:0.70 75:0.97 79:0.91 81:0.32 83:0.56 86:0.89 91:0.51 96:0.77 97:0.89 98:0.75 101:0.87 108:0.46 122:0.83 123:0.44 126:0.24 127:0.24 128:0.96 129:0.05 135:0.80 137:0.92 139:0.88 140:0.87 145:0.67 146:0.63 147:0.68 149:0.30 150:0.36 151:0.76 153:0.48 154:0.52 155:0.55 158:0.76 159:0.23 162:0.54 168:0.96 170:0.90 172:0.73 173:0.73 174:0.79 177:0.88 178:0.48 179:0.38 187:0.39 190:0.89 191:0.70 192:0.55 195:0.64 196:0.94 197:0.82 202:0.64 205:0.95 208:0.61 212:0.94 216:0.33 219:0.91 220:0.62 222:0.35 226:0.98 235:0.22 239:0.89 241:0.41 242:0.24 243:0.91 244:0.61 247:0.84 248:0.70 253:0.75 254:0.86 255:0.71 256:0.58 259:0.21 265:0.75 266:0.17 267:0.82 268:0.59 271:0.89 276:0.62 279:0.78 281:0.91 284:0.88 285:0.60 292:0.95 300:0.43 +2 1:0.58 8:0.76 10:0.98 11:0.57 17:0.22 18:0.94 21:0.47 22:0.93 27:0.38 29:0.71 30:0.31 33:0.97 34:0.96 36:0.59 37:0.39 39:0.40 43:0.60 44:0.88 45:0.91 47:0.93 55:0.52 64:0.77 66:0.42 69:0.68 70:0.89 71:0.67 74:0.71 75:0.97 81:0.48 83:0.56 86:0.96 91:0.38 97:0.98 98:0.77 99:0.83 101:0.96 102:0.96 108:0.52 122:0.94 123:0.50 124:0.60 126:0.32 127:0.57 129:0.59 133:0.47 135:0.83 139:0.95 145:0.83 146:0.91 147:0.92 149:0.51 150:0.43 154:0.47 155:0.47 158:0.76 159:0.67 165:0.65 170:0.90 172:0.85 173:0.59 174:0.96 177:0.88 178:0.55 179:0.21 187:0.39 192:0.44 195:0.83 197:0.96 201:0.55 208:0.61 212:0.78 216:0.62 219:0.91 222:0.35 226:0.96 232:0.92 235:0.60 236:0.94 239:0.98 241:0.10 242:0.12 243:0.57 245:0.97 247:1.00 253:0.51 254:0.84 259:0.21 262:0.93 265:0.77 266:0.63 267:0.91 271:0.89 276:0.89 279:0.81 291:0.97 292:0.88 300:0.84 +0 8:0.80 10:0.89 11:0.52 17:0.76 18:0.99 21:0.30 27:0.42 29:0.71 30:0.15 31:0.90 34:0.44 36:0.40 37:0.55 39:0.40 43:0.14 44:0.88 45:0.73 55:0.71 64:0.77 66:0.23 69:0.98 70:0.90 71:0.67 74:0.56 77:0.70 79:0.89 81:0.31 86:0.98 91:0.54 98:0.47 101:0.65 108:0.97 122:0.92 123:0.97 124:0.97 126:0.24 127:0.94 129:0.89 133:0.97 135:0.84 137:0.90 139:0.97 140:0.45 145:0.42 146:0.62 147:0.56 149:0.47 150:0.35 151:0.53 153:0.77 154:0.10 159:0.97 162:0.86 170:0.90 172:0.96 173:0.88 174:0.97 177:0.38 179:0.10 187:0.39 190:0.95 195:1.00 196:0.94 197:0.88 202:0.54 212:0.48 216:0.34 219:0.90 220:0.87 222:0.35 232:0.80 235:0.31 239:0.88 241:0.03 242:0.55 243:0.06 244:0.61 245:0.98 247:1.00 248:0.54 253:0.44 254:0.74 256:0.58 257:0.84 259:0.21 265:0.49 266:0.95 267:0.65 268:0.63 271:0.89 276:0.99 282:0.75 284:0.93 285:0.46 292:0.88 300:0.94 +0 1:0.58 8:0.80 9:0.72 10:0.86 11:0.52 17:0.57 18:0.96 21:0.54 22:0.93 27:0.56 29:0.71 30:0.20 31:0.92 33:0.97 34:0.67 36:0.87 37:0.55 39:0.40 43:0.34 44:0.88 45:0.83 47:0.93 55:0.59 64:0.77 66:0.45 69:0.63 70:0.94 71:0.67 74:0.80 77:0.70 79:0.92 81:0.36 86:0.96 91:0.60 96:0.78 98:0.57 101:0.87 108:0.84 114:0.74 117:0.86 122:0.92 123:0.83 124:0.80 126:0.32 127:0.67 129:0.59 133:0.81 135:0.98 137:0.92 138:0.96 139:0.95 140:0.45 145:0.54 146:0.66 147:0.71 149:0.49 150:0.41 151:0.82 153:0.46 154:0.63 155:0.55 158:0.76 159:0.81 162:0.82 170:0.90 172:0.88 173:0.42 174:0.79 175:0.75 176:0.63 177:0.70 179:0.21 187:0.39 190:0.89 192:0.55 195:0.92 196:0.96 197:0.84 201:0.55 202:0.58 208:0.50 212:0.75 216:0.16 219:0.91 220:0.74 222:0.35 235:0.68 239:0.85 241:0.46 242:0.16 243:0.27 244:0.61 245:0.93 247:0.86 248:0.76 253:0.81 255:0.71 256:0.64 257:0.65 259:0.21 262:0.93 265:0.58 266:0.87 267:0.19 268:0.64 271:0.89 276:0.90 277:0.69 279:0.79 282:0.75 284:0.94 285:0.50 290:0.74 291:0.97 292:0.87 300:0.84 +1 1:0.59 10:0.92 11:0.46 17:0.24 18:0.87 21:0.68 27:0.45 29:0.71 30:0.36 34:0.81 36:0.21 37:0.50 39:0.40 43:0.39 44:0.88 45:0.73 55:0.52 64:0.77 66:0.46 69:0.71 70:0.87 71:0.67 74:0.83 81:0.38 86:0.91 91:0.17 98:0.63 101:0.47 108:0.54 114:0.69 122:0.83 123:0.52 124:0.73 126:0.51 127:0.53 129:0.59 133:0.73 135:0.86 139:0.89 145:0.49 146:0.85 147:0.88 149:0.37 150:0.40 154:0.65 155:0.47 158:0.81 159:0.71 170:0.90 172:0.82 173:0.58 174:0.88 176:0.70 177:0.88 178:0.55 179:0.26 187:0.68 192:0.46 195:0.87 197:0.87 201:0.57 208:0.61 212:0.73 215:0.49 216:0.77 219:0.94 222:0.35 235:0.88 239:0.90 241:0.27 242:0.19 243:0.58 245:0.89 247:0.95 253:0.62 254:0.93 259:0.21 265:0.64 266:0.80 267:0.97 271:0.89 276:0.84 279:0.78 283:0.65 290:0.69 292:0.86 297:0.36 300:0.84 +2 1:0.68 7:0.75 10:0.88 11:0.75 17:0.40 18:0.69 21:0.13 27:0.02 29:0.71 30:0.87 32:0.80 34:0.99 36:0.20 37:0.91 39:0.40 43:0.76 44:0.93 45:0.73 48:0.91 64:0.77 66:0.82 69:0.81 70:0.27 71:0.67 74:0.67 76:0.85 77:0.70 81:0.80 83:0.91 85:0.72 86:0.77 91:0.42 98:0.38 101:0.87 102:0.98 108:0.65 114:0.92 122:0.65 123:0.63 126:0.54 127:0.13 129:0.05 135:0.89 139:0.85 145:0.42 146:0.43 147:0.49 149:0.59 150:0.64 154:0.68 155:0.56 159:0.16 165:0.93 170:0.90 172:0.48 173:0.86 174:0.79 176:0.73 177:0.88 178:0.55 179:0.25 187:0.68 192:0.57 193:0.96 195:0.70 197:0.83 201:0.67 204:0.83 208:0.64 212:0.61 215:0.84 216:0.81 222:0.35 230:0.77 233:0.66 235:0.42 236:0.97 239:0.93 241:0.61 242:0.33 243:0.83 247:0.60 253:0.68 259:0.21 265:0.40 266:0.38 267:0.91 271:0.89 276:0.34 281:0.91 282:0.88 290:0.92 297:0.36 299:0.88 300:0.25 +0 1:0.61 7:0.70 9:0.74 11:0.52 12:0.01 17:0.11 18:0.76 21:0.54 27:0.05 29:0.56 30:0.87 31:0.92 32:0.72 34:0.78 36:0.75 37:0.79 39:0.93 43:0.74 44:0.92 45:0.83 64:0.77 66:0.45 69:0.62 70:0.20 71:0.90 74:0.70 77:0.70 79:0.92 81:0.59 83:0.69 86:0.82 91:0.20 96:0.78 97:0.94 98:0.38 99:0.95 101:0.87 108:0.59 114:0.76 120:0.66 122:0.65 123:0.57 124:0.67 126:0.51 127:0.21 129:0.59 133:0.61 135:0.92 137:0.92 139:0.86 140:0.45 145:0.45 146:0.17 147:0.22 149:0.65 150:0.44 151:0.76 153:0.48 154:0.64 155:0.64 158:0.81 159:0.34 162:0.86 164:0.72 165:0.81 170:0.94 172:0.32 173:0.59 175:0.75 176:0.70 177:0.70 179:0.61 187:0.68 190:0.77 192:0.97 193:0.97 195:0.77 196:0.94 201:0.60 202:0.58 204:0.79 208:0.61 212:0.30 215:0.58 216:0.90 219:0.94 220:0.96 222:0.35 230:0.73 233:0.66 235:0.74 239:0.84 241:0.30 242:0.33 243:0.37 244:0.61 245:0.52 247:0.55 248:0.71 253:0.79 255:0.73 256:0.64 257:0.65 259:0.21 260:0.67 265:0.40 266:0.38 267:0.94 268:0.66 271:0.22 276:0.36 277:0.69 279:0.78 282:0.80 283:0.67 284:0.94 285:0.60 290:0.76 292:0.88 297:0.36 300:0.18 +1 1:0.66 10:0.92 11:0.75 12:0.01 17:0.51 18:0.79 21:0.30 27:0.69 29:0.56 30:0.68 34:0.73 36:0.84 37:0.27 39:0.93 43:0.83 45:0.77 64:0.77 66:0.86 69:0.67 70:0.48 71:0.90 74:0.60 81:0.76 83:0.91 85:0.72 86:0.86 91:0.12 98:0.79 101:0.96 102:0.95 108:0.51 114:0.86 122:0.86 123:0.49 126:0.54 127:0.27 129:0.05 132:0.97 135:0.85 139:0.81 145:0.58 146:0.60 147:0.66 149:0.70 150:0.57 154:0.78 155:0.51 159:0.22 165:0.93 170:0.94 172:0.61 173:0.84 174:0.83 176:0.73 177:0.88 178:0.55 179:0.56 187:0.39 192:0.50 195:0.56 197:0.87 201:0.65 208:0.64 212:0.81 215:0.72 216:0.59 222:0.35 235:0.60 236:0.97 239:0.92 241:0.43 242:0.18 243:0.87 247:0.79 253:0.63 265:0.79 266:0.27 267:0.85 271:0.22 276:0.48 290:0.86 297:0.36 300:0.43 +1 8:0.89 9:0.79 12:0.01 17:0.36 20:0.99 21:0.78 23:0.96 27:0.21 29:0.56 31:0.94 34:0.40 36:0.88 37:0.74 39:0.93 41:0.82 43:0.14 66:0.36 69:0.65 71:0.90 78:0.83 79:0.93 83:0.91 91:0.61 96:0.80 98:0.06 100:0.73 108:0.49 111:0.81 120:0.69 123:0.47 127:0.05 128:0.98 129:0.05 135:0.20 137:0.94 140:0.97 146:0.05 147:0.05 149:0.07 151:0.86 152:0.90 153:0.48 154:0.10 155:0.66 159:0.05 162:0.46 164:0.57 168:0.98 169:0.72 170:0.94 172:0.05 173:0.46 175:0.97 177:0.05 178:0.54 186:0.84 187:0.39 192:0.99 202:0.78 205:0.95 208:0.64 216:0.95 222:0.35 235:0.42 241:0.32 243:0.51 244:0.93 248:0.77 253:0.74 255:0.94 256:0.45 257:0.93 259:0.21 260:0.70 261:0.85 265:0.06 267:0.65 268:0.46 271:0.22 277:0.95 284:0.89 285:0.62 +1 1:0.66 7:0.75 8:0.61 9:0.74 10:0.82 11:0.52 12:0.01 17:0.24 18:0.98 21:0.59 22:0.93 27:0.41 29:0.56 30:0.91 31:0.92 32:0.72 34:0.53 36:0.65 37:0.38 39:0.93 43:0.75 44:0.92 45:0.99 47:0.93 48:0.91 64:0.77 66:0.78 69:0.54 70:0.27 71:0.90 74:0.96 77:0.99 79:0.91 81:0.63 83:0.69 86:0.99 91:0.35 96:0.77 97:1.00 98:0.78 99:0.99 101:0.65 106:0.81 108:0.61 114:0.73 117:0.86 120:0.65 122:0.65 123:0.59 124:0.44 126:0.51 127:0.64 129:0.81 133:0.74 135:0.89 137:0.92 139:0.99 140:0.45 145:0.96 146:0.25 147:0.31 149:0.99 150:0.48 151:0.68 153:0.48 154:0.66 155:0.64 158:0.81 159:0.67 162:0.84 164:0.78 165:0.81 170:0.94 172:0.96 173:0.44 174:0.79 175:0.75 176:0.70 177:0.70 179:0.16 187:0.68 190:0.77 192:0.97 195:0.82 196:0.96 197:0.80 201:0.64 202:0.66 204:0.85 206:0.81 208:0.61 212:0.89 215:0.55 216:0.56 219:0.94 220:0.93 222:0.35 230:0.77 232:0.87 233:0.66 235:0.95 239:0.81 241:0.01 242:0.50 243:0.15 244:0.61 245:0.92 247:0.55 248:0.70 253:0.84 255:0.73 256:0.71 257:0.65 259:0.21 260:0.66 262:0.93 265:0.78 266:0.71 267:0.44 268:0.73 271:0.22 276:0.93 277:0.69 279:0.82 281:0.86 282:0.80 283:0.67 284:0.96 285:0.60 290:0.73 292:0.88 297:0.36 300:0.55 +0 1:0.62 7:0.75 10:0.90 11:0.52 12:0.01 17:0.79 18:0.82 21:0.47 27:0.65 29:0.56 30:0.90 32:0.72 34:0.95 36:0.55 37:0.27 39:0.93 43:0.75 44:0.92 45:0.90 48:0.91 64:0.77 66:0.45 69:0.50 70:0.23 71:0.90 74:0.75 76:0.85 77:0.70 81:0.60 83:0.69 86:0.88 91:0.40 97:0.98 98:0.59 99:0.95 101:0.65 108:0.64 114:0.78 122:0.65 123:0.62 124:0.58 126:0.51 127:0.42 129:0.59 133:0.47 135:0.96 139:0.92 145:0.99 146:0.29 147:0.36 149:0.80 150:0.45 154:0.54 155:0.53 158:0.81 159:0.33 165:0.81 170:0.94 172:0.48 173:0.60 174:0.83 176:0.70 177:0.88 178:0.55 179:0.63 187:0.39 192:0.56 195:0.62 197:0.87 201:0.60 204:0.82 208:0.61 212:0.42 215:0.63 216:0.26 219:0.94 222:0.35 230:0.77 232:0.95 233:0.76 235:0.68 239:0.88 241:0.21 242:0.45 243:0.46 245:0.74 247:0.47 253:0.63 254:0.94 259:0.21 265:0.60 266:0.54 267:0.59 271:0.22 276:0.47 279:0.81 281:0.91 282:0.80 283:0.67 290:0.78 292:0.88 297:0.36 300:0.25 +0 1:0.60 11:0.52 12:0.01 17:0.13 18:0.77 21:0.59 27:0.05 29:0.56 30:0.94 34:0.79 36:0.66 37:0.79 39:0.93 43:0.74 45:0.85 64:0.77 66:0.80 69:0.60 70:0.18 71:0.90 74:0.54 81:0.59 83:0.69 86:0.83 91:0.15 98:0.44 99:0.95 101:0.65 108:0.46 114:0.73 122:0.65 123:0.44 126:0.51 127:0.14 129:0.05 135:0.79 139:0.79 145:0.42 146:0.40 147:0.46 149:0.76 150:0.43 154:0.65 155:0.48 159:0.15 165:0.81 170:0.94 172:0.45 173:0.71 175:0.75 176:0.70 177:0.70 178:0.55 179:0.35 187:0.68 192:0.47 193:0.97 201:0.59 202:0.36 208:0.61 212:0.55 215:0.55 216:0.90 222:0.35 235:0.80 239:0.84 241:0.32 242:0.58 243:0.82 244:0.61 247:0.35 253:0.56 257:0.65 259:0.21 265:0.46 266:0.27 267:0.94 271:0.22 276:0.25 277:0.69 290:0.73 297:0.36 300:0.18 +1 7:0.75 10:0.88 11:0.81 12:0.01 17:0.66 18:0.74 21:0.78 27:0.16 29:0.56 30:0.85 34:0.68 36:0.55 37:0.69 39:0.93 43:0.62 45:0.88 66:0.12 69:0.96 70:0.41 71:0.90 74:0.56 83:0.69 85:0.72 86:0.79 91:0.08 98:0.14 101:0.87 108:0.93 122:0.65 123:0.93 124:0.93 127:0.58 129:0.05 133:0.92 135:0.89 139:0.80 145:0.74 146:0.18 147:0.05 149:0.68 154:0.26 155:0.54 159:0.87 163:0.97 170:0.94 172:0.27 173:0.90 174:0.88 177:0.05 178:0.55 179:0.58 187:0.87 192:0.56 197:0.87 202:0.36 204:0.83 208:0.61 212:0.23 216:0.74 222:0.35 230:0.77 233:0.66 235:0.22 239:0.87 242:0.33 243:0.10 245:0.60 247:0.82 253:0.44 257:0.93 259:0.21 265:0.14 266:0.80 267:0.69 271:0.22 276:0.59 277:0.69 281:0.86 300:0.43 +1 8:0.83 10:0.90 11:0.52 12:0.01 17:0.09 18:0.64 21:0.78 27:0.11 29:0.56 30:0.62 32:0.78 34:0.89 36:0.72 37:0.79 39:0.93 43:0.10 44:0.93 45:0.73 66:0.16 69:0.71 70:0.34 71:0.90 74:0.57 77:0.70 86:0.74 91:0.25 98:0.17 101:0.65 102:0.98 108:0.54 122:0.65 123:0.64 124:0.52 127:0.15 129:0.05 133:0.39 135:0.56 139:0.80 145:0.96 146:0.19 147:0.24 149:0.08 154:0.74 159:0.22 170:0.94 172:0.21 173:0.71 174:0.83 177:0.88 178:0.55 179:0.60 187:0.95 195:0.71 197:0.87 202:0.65 212:0.30 216:0.85 222:0.35 235:0.52 239:0.93 241:0.27 242:0.33 243:0.59 245:0.46 247:0.39 253:0.45 254:0.74 259:0.21 265:0.13 266:0.27 267:0.69 271:0.22 276:0.17 277:0.87 282:0.86 300:0.25 +1 1:0.64 10:0.91 11:0.75 12:0.01 17:0.85 18:0.76 21:0.47 27:0.12 29:0.56 30:0.62 34:0.67 36:0.83 37:0.55 39:0.93 43:0.78 45:0.77 64:0.77 66:0.87 69:0.75 70:0.62 71:0.90 74:0.58 81:0.73 83:0.91 85:0.72 86:0.84 91:0.08 98:0.57 101:0.96 108:0.58 114:0.80 122:0.86 123:0.55 126:0.54 127:0.17 129:0.05 135:0.51 139:0.79 145:0.45 146:0.67 147:0.72 149:0.66 150:0.54 154:0.70 155:0.50 159:0.26 165:0.93 170:0.94 172:0.63 173:0.74 174:0.83 176:0.73 177:0.88 178:0.55 179:0.31 187:0.39 192:0.49 197:0.86 201:0.64 208:0.64 212:0.77 215:0.63 216:0.51 222:0.35 235:0.74 236:0.97 239:0.90 241:0.24 242:0.16 243:0.88 247:0.82 253:0.60 259:0.21 265:0.58 266:0.38 267:0.59 271:0.22 276:0.50 290:0.80 297:0.36 300:0.55 +1 1:0.64 10:0.91 11:0.52 12:0.01 17:0.93 18:0.66 21:0.30 22:0.93 27:0.72 29:0.56 30:0.91 34:0.83 36:0.51 39:0.93 43:0.87 45:0.73 47:0.93 64:0.77 66:0.69 69:0.27 70:0.13 71:0.90 74:0.53 81:0.62 83:0.69 86:0.77 91:0.17 97:0.94 98:0.25 99:0.95 101:0.65 106:0.81 108:0.21 114:0.84 117:0.86 122:0.65 123:0.20 126:0.51 127:0.11 129:0.05 135:0.54 139:0.79 146:0.20 147:0.25 149:0.68 150:0.48 154:0.84 155:0.49 158:0.81 159:0.10 165:0.81 170:0.94 172:0.21 173:0.67 174:0.79 176:0.70 177:0.88 178:0.55 179:0.29 187:0.68 192:0.49 197:0.85 201:0.62 202:0.36 206:0.81 208:0.61 212:0.95 215:0.72 216:0.13 219:0.94 222:0.35 232:0.95 235:0.52 239:0.89 241:0.24 242:0.63 243:0.73 247:0.30 253:0.61 254:0.84 259:0.21 262:0.93 265:0.27 266:0.12 267:0.28 271:0.22 276:0.20 279:0.78 283:0.67 290:0.84 292:0.88 297:0.36 300:0.13 +0 8:0.77 10:0.80 11:0.46 12:0.01 17:0.66 18:0.58 21:0.78 27:0.25 29:0.56 30:0.09 34:0.17 36:0.31 37:0.60 39:0.93 43:0.13 45:0.66 55:0.45 66:0.32 69:0.97 70:1.00 71:0.90 74:0.44 86:0.61 91:0.75 96:0.74 98:0.15 101:0.47 108:0.96 120:0.67 122:0.83 123:0.96 124:0.92 127:0.54 129:0.05 133:0.93 135:0.61 139:0.59 140:0.98 145:0.77 146:0.44 147:0.05 149:0.18 151:0.61 153:0.48 154:0.10 159:0.95 162:0.45 164:0.64 170:0.94 172:0.51 173:0.85 174:0.79 175:0.91 177:0.05 178:0.54 179:0.54 190:0.95 191:0.94 197:0.79 202:0.93 212:0.49 216:0.34 220:0.62 222:0.35 235:0.60 239:0.79 241:0.79 242:0.74 243:0.10 244:0.83 245:0.60 247:0.67 248:0.62 253:0.53 256:0.73 257:0.93 259:0.21 260:0.68 265:0.16 266:0.91 267:0.52 268:0.62 271:0.22 276:0.59 277:0.95 285:0.50 300:0.98 +0 10:0.82 12:0.01 17:0.81 18:0.76 21:0.78 27:0.69 29:0.56 30:0.38 34:0.96 36:0.33 37:0.45 39:0.93 43:0.08 55:0.92 66:0.13 69:0.91 70:0.50 71:0.90 86:0.72 91:0.10 98:0.29 108:0.81 122:0.92 123:0.80 124:0.82 127:0.23 129:0.05 133:0.74 135:0.42 139:0.70 145:0.74 146:0.53 147:0.59 149:0.15 154:0.07 159:0.55 163:0.79 170:0.94 172:0.16 173:0.86 174:0.79 175:0.91 177:0.38 178:0.55 179:0.61 187:0.21 197:0.81 212:0.14 216:0.18 222:0.35 235:0.80 239:0.82 241:0.18 242:0.75 243:0.34 244:0.93 245:0.53 247:0.39 253:0.20 257:0.84 259:0.21 265:0.31 266:0.71 267:0.36 271:0.22 276:0.42 277:0.87 300:0.33 +1 1:0.65 7:0.75 8:0.66 10:0.82 11:0.52 12:0.01 17:0.09 18:0.93 21:0.54 27:0.41 29:0.56 30:0.91 32:0.72 34:0.64 36:0.40 37:0.38 39:0.93 43:0.75 44:0.92 45:0.98 48:0.91 64:0.77 66:0.42 69:0.58 70:0.28 71:0.90 74:0.88 77:0.70 81:0.64 83:0.69 86:0.96 91:0.11 97:0.99 98:0.33 99:0.99 101:0.65 108:0.79 114:0.76 122:0.65 123:0.77 124:0.77 126:0.51 127:0.57 129:0.81 133:0.74 135:0.92 139:0.97 145:0.76 146:0.41 147:0.48 149:0.96 150:0.50 154:0.66 155:0.57 158:0.81 159:0.62 165:0.81 170:0.94 172:0.78 173:0.50 174:0.79 175:0.75 176:0.70 177:0.70 178:0.55 179:0.29 187:0.87 192:0.86 195:0.79 197:0.81 201:0.63 202:0.36 204:0.85 208:0.61 212:0.89 215:0.58 216:0.56 219:0.94 222:0.35 230:0.77 232:0.87 233:0.66 235:0.88 239:0.82 241:0.01 242:0.50 243:0.37 244:0.61 245:0.89 247:0.55 253:0.67 257:0.65 259:0.21 265:0.35 266:0.54 267:0.36 271:0.22 276:0.77 277:0.69 279:0.81 281:0.86 282:0.80 283:0.67 290:0.76 292:0.88 297:0.36 300:0.70 +1 1:0.58 10:0.84 11:0.52 12:0.01 17:0.76 18:0.64 21:0.76 27:0.67 29:0.56 30:0.76 34:0.96 36:0.56 37:0.27 39:0.93 43:0.69 45:0.73 64:0.77 66:0.54 69:0.69 70:0.22 71:0.90 74:0.38 81:0.68 83:0.91 86:0.70 91:0.25 98:0.33 101:0.65 108:0.52 114:0.66 122:0.65 123:0.50 124:0.45 126:0.54 127:0.31 129:0.05 133:0.36 135:0.51 139:0.68 145:1.00 146:0.37 147:0.44 149:0.45 150:0.45 154:0.58 155:0.47 159:0.37 163:0.79 165:0.93 170:0.94 172:0.21 173:0.73 174:0.79 176:0.73 177:0.88 178:0.55 179:0.93 187:0.39 192:0.46 197:0.84 201:0.59 208:0.64 212:0.42 215:0.46 216:0.48 222:0.35 235:0.99 236:0.97 239:0.84 241:0.24 242:0.45 243:0.63 244:0.61 245:0.40 247:0.35 253:0.50 259:0.21 265:0.35 266:0.23 267:0.52 271:0.22 276:0.17 290:0.66 297:0.36 300:0.18 +0 8:0.61 10:0.90 11:0.52 12:0.01 17:0.13 18:0.72 21:0.40 27:0.13 29:0.56 30:0.45 32:0.78 34:0.73 36:0.19 37:0.79 39:0.93 43:0.14 44:0.93 45:0.73 66:0.13 69:0.80 70:0.50 71:0.90 74:0.54 75:0.96 77:0.70 81:0.30 86:0.76 91:0.10 98:0.32 101:0.65 102:0.98 108:0.64 122:0.65 123:0.69 124:0.69 126:0.24 127:0.28 129:0.05 133:0.59 135:0.95 139:0.81 145:0.61 146:0.19 147:0.05 149:0.10 150:0.33 154:0.68 159:0.33 170:0.94 172:0.21 173:0.87 174:0.83 177:0.05 178:0.55 179:0.83 187:0.68 193:0.97 195:0.64 197:0.87 212:0.23 216:0.83 222:0.35 226:0.96 235:0.42 236:0.92 239:0.94 241:0.15 242:0.24 243:0.21 245:0.45 247:0.47 253:0.43 254:0.74 257:0.93 259:0.21 265:0.14 266:0.38 267:0.69 271:0.22 276:0.29 277:0.95 282:0.86 300:0.33 +1 7:0.69 10:0.96 11:0.75 12:0.01 17:0.81 18:0.69 21:0.54 27:0.59 29:0.56 30:0.32 32:0.66 33:0.97 34:0.56 36:0.56 37:0.49 39:0.93 43:0.78 45:0.66 55:0.59 66:0.89 69:0.54 70:0.61 71:0.90 74:0.49 81:0.28 85:0.72 86:0.80 91:0.23 98:0.66 101:0.96 102:0.96 108:0.41 122:0.77 123:0.40 126:0.24 127:0.20 129:0.05 135:0.66 139:0.76 145:0.94 146:0.73 147:0.77 149:0.62 150:0.31 154:0.70 159:0.30 170:0.94 172:0.68 173:0.52 174:0.91 177:0.88 178:0.55 179:0.34 187:0.87 193:0.97 195:0.75 197:0.93 212:0.59 216:0.40 222:0.35 230:0.71 232:1.00 233:0.76 235:0.60 236:0.92 239:0.96 241:0.15 242:0.21 243:0.89 247:0.79 253:0.40 259:0.21 265:0.67 266:0.47 267:0.44 271:0.22 276:0.57 291:0.97 300:0.43 +1 8:0.70 10:0.90 11:0.81 12:0.01 17:0.69 18:0.76 21:0.78 27:0.09 29:0.56 30:0.30 32:0.80 34:0.20 36:0.47 37:0.82 39:0.93 43:0.79 44:0.93 45:0.77 66:0.09 69:0.75 70:0.93 71:0.90 74:0.62 77:0.89 85:0.72 86:0.82 91:0.33 98:0.19 101:0.96 102:0.98 108:0.58 122:0.65 123:0.93 124:0.93 127:0.45 129:0.05 133:0.92 135:0.77 139:0.84 145:1.00 146:0.25 147:0.31 149:0.68 154:0.73 159:0.87 170:0.94 172:0.27 173:0.45 174:0.86 177:0.88 178:0.55 179:0.52 187:0.68 195:0.98 197:0.85 202:0.54 212:0.23 216:0.56 222:0.35 235:0.42 239:0.93 241:0.46 242:0.16 243:0.31 245:0.61 247:0.88 253:0.47 259:0.21 265:0.12 266:0.89 267:0.28 271:0.22 276:0.61 277:0.69 282:0.88 299:0.88 300:0.84 +1 10:0.96 11:0.52 12:0.01 17:0.11 18:0.73 21:0.68 27:0.45 29:0.56 30:0.11 32:0.72 34:0.91 36:0.18 37:0.50 39:0.93 43:0.11 44:0.92 45:0.73 55:0.92 66:0.23 69:0.70 70:0.85 71:0.90 74:0.51 75:0.96 77:0.70 81:0.26 86:0.78 91:0.12 98:0.40 101:0.65 108:0.80 122:0.95 123:0.78 124:0.87 126:0.24 127:0.49 129:0.05 133:0.85 135:0.84 139:0.79 145:0.52 146:0.24 147:0.30 149:0.16 150:0.29 154:0.70 159:0.67 170:0.94 172:0.32 173:0.60 174:0.93 177:0.88 178:0.55 179:0.68 187:0.68 195:0.84 197:0.93 212:0.16 216:0.77 222:0.35 226:0.96 235:0.80 236:0.92 239:0.94 241:0.27 242:0.28 243:0.38 245:0.56 247:0.79 253:0.41 254:0.86 259:0.21 265:0.42 266:0.80 267:0.69 271:0.22 276:0.52 277:0.87 282:0.80 300:0.55 +0 10:0.90 11:0.75 12:0.01 17:0.20 18:0.81 20:0.89 21:0.78 27:0.45 29:0.56 30:0.36 32:0.72 34:0.78 36:0.17 37:0.50 39:0.93 43:0.80 44:0.92 45:0.81 66:0.19 69:0.73 70:0.56 71:0.90 74:0.69 77:0.70 78:0.86 85:0.72 86:0.84 91:0.11 98:0.58 100:0.73 101:0.96 108:0.56 111:0.84 122:0.83 123:0.84 124:0.73 127:0.37 129:0.59 133:0.73 135:0.87 139:0.85 145:0.52 146:0.72 147:0.76 149:0.77 152:0.87 154:0.75 159:0.69 169:0.72 170:0.94 172:0.61 173:0.58 174:0.83 177:0.88 178:0.55 179:0.46 186:0.86 187:0.68 195:0.89 197:0.86 212:0.27 216:0.77 222:0.35 235:0.95 239:0.89 241:0.24 242:0.15 243:0.59 245:0.71 247:0.90 253:0.50 259:0.21 261:0.85 265:0.45 266:0.80 267:0.44 271:0.22 276:0.65 282:0.80 300:0.43 +2 1:0.65 6:0.83 8:0.63 9:0.73 10:0.96 11:0.75 12:0.01 17:0.43 18:0.83 20:0.93 21:0.40 22:0.93 23:0.97 27:0.38 29:0.56 30:0.76 31:0.90 32:0.80 33:0.97 34:0.61 36:0.91 37:0.28 39:0.93 41:0.88 43:0.93 44:0.93 45:0.77 47:0.93 60:0.95 62:0.98 64:0.77 66:0.89 69:0.33 70:0.47 71:0.90 74:0.80 75:0.99 77:0.70 78:0.98 79:0.89 81:0.75 83:0.91 85:0.80 86:0.91 91:0.10 96:0.73 98:0.74 100:0.98 101:0.96 102:0.98 106:0.81 108:0.26 111:0.98 114:0.82 117:0.86 120:0.61 122:0.65 123:0.25 126:0.54 127:0.23 128:0.96 129:0.05 132:0.97 135:0.98 137:0.90 139:0.93 140:0.45 145:0.72 146:0.72 147:0.76 149:0.86 150:0.56 151:0.62 152:0.87 153:0.48 154:0.93 155:0.65 158:0.86 159:0.29 162:0.86 164:0.67 165:0.93 168:0.96 169:0.98 170:0.94 172:0.68 173:0.38 174:0.86 176:0.73 177:0.88 179:0.41 186:0.98 187:0.68 189:0.85 192:0.98 195:0.68 196:0.93 197:0.90 201:0.65 202:0.50 205:0.97 206:0.81 208:0.64 212:0.77 215:0.67 216:0.67 219:0.95 220:0.91 222:0.35 226:0.96 232:0.98 235:0.68 236:0.97 238:0.95 239:0.97 241:0.15 242:0.14 243:0.89 247:0.84 248:0.61 253:0.75 255:0.73 256:0.58 260:0.61 261:0.96 262:0.93 265:0.74 266:0.27 267:0.65 268:0.59 271:0.22 276:0.54 279:0.81 282:0.88 283:0.67 284:0.92 285:0.62 290:0.82 291:0.97 292:0.88 297:0.36 299:0.88 300:0.55 +1 1:0.66 7:0.81 8:0.67 10:0.98 11:0.75 12:0.20 17:0.73 18:0.87 21:0.30 22:0.93 27:0.43 29:0.94 30:0.40 32:0.80 33:0.97 34:0.89 36:0.44 37:0.44 39:0.36 43:0.78 44:0.93 45:0.90 47:0.93 48:0.91 64:0.77 66:0.54 69:0.79 70:0.94 71:0.86 74:0.86 75:0.99 76:0.85 77:0.70 81:0.77 83:0.69 86:0.92 91:0.12 97:0.94 98:0.41 99:0.95 101:0.65 102:0.98 104:0.96 106:0.81 108:0.62 114:0.86 117:0.86 122:0.95 123:0.60 124:0.83 126:0.54 127:0.46 129:0.05 131:0.61 133:0.88 135:0.91 139:0.96 144:0.76 145:0.41 146:0.80 147:0.83 149:0.91 150:0.67 154:0.70 155:0.57 157:0.87 158:0.86 159:0.83 165:0.81 166:0.96 170:0.90 172:0.84 173:0.57 174:0.98 176:0.73 177:0.88 178:0.55 179:0.26 182:0.87 187:0.39 192:0.58 193:0.99 195:0.95 197:0.98 201:0.65 202:0.49 204:0.83 206:0.81 208:0.64 212:0.60 215:0.72 216:0.48 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 230:0.83 231:0.94 232:0.97 233:0.76 235:0.60 236:0.97 239:0.99 241:0.07 242:0.21 243:0.63 245:0.81 246:0.96 247:0.93 253:0.70 259:0.21 262:0.93 265:0.43 266:0.92 267:0.18 271:0.32 276:0.83 279:0.80 281:0.91 282:0.88 283:0.67 287:0.61 290:0.86 291:0.97 292:0.88 297:0.36 300:0.94 +1 1:0.66 10:0.98 11:0.75 12:0.20 17:0.66 18:0.97 21:0.30 22:0.93 27:0.24 29:0.94 30:0.45 32:0.80 33:0.97 34:0.97 36:0.38 37:0.80 39:0.36 43:0.79 44:0.93 45:0.96 47:0.93 64:0.77 66:0.63 69:0.76 70:0.59 71:0.86 74:0.76 77:0.70 81:0.77 83:0.69 86:0.96 91:0.15 98:0.62 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.89 114:0.86 117:0.86 122:0.92 123:0.89 124:0.49 126:0.54 127:0.59 129:0.59 131:0.61 133:0.47 135:0.94 139:0.96 144:0.76 145:0.52 146:0.71 147:0.76 149:0.93 150:0.67 154:0.72 155:0.51 157:0.94 159:0.78 165:0.81 166:0.96 170:0.90 172:0.94 173:0.59 174:0.97 176:0.73 177:0.88 178:0.55 179:0.17 182:0.87 187:0.39 192:0.50 195:0.91 197:0.96 201:0.65 206:0.81 208:0.64 212:0.88 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.98 241:0.10 242:0.52 243:0.36 245:0.98 246:0.96 247:0.91 253:0.67 259:0.21 262:0.93 265:0.63 266:0.63 267:0.92 271:0.32 276:0.89 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.70 +2 1:0.67 8:0.67 10:0.98 11:0.75 12:0.20 17:0.55 18:0.95 21:0.22 22:0.93 27:0.24 29:0.94 30:0.60 32:0.80 33:0.97 34:0.89 36:0.47 37:0.80 39:0.36 43:0.79 44:0.93 45:0.97 47:0.93 64:0.77 66:0.68 69:0.75 70:0.56 71:0.86 74:0.80 75:0.99 77:0.70 81:0.79 83:0.69 86:0.95 91:0.31 97:0.94 98:0.41 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.96 114:0.90 117:0.86 122:0.92 123:0.95 124:0.50 126:0.54 127:0.76 129:0.59 131:0.61 133:0.60 135:0.98 139:0.96 144:0.76 145:0.69 146:0.66 147:0.71 149:0.92 150:0.72 154:0.72 155:0.52 157:0.93 158:0.86 159:0.90 165:0.81 166:0.96 170:0.90 172:0.95 173:0.45 174:0.97 176:0.73 177:0.88 178:0.55 179:0.17 182:0.87 187:0.21 192:0.51 195:0.98 197:0.94 201:0.66 206:0.81 208:0.64 212:0.89 215:0.79 216:0.67 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 231:0.94 232:0.95 235:0.52 236:0.97 239:0.98 241:0.46 242:0.51 243:0.31 245:0.96 246:0.96 247:0.90 253:0.70 259:0.21 262:0.93 265:0.43 266:0.63 267:0.94 271:0.32 276:0.82 279:0.80 282:0.88 283:0.67 287:0.61 290:0.90 291:0.97 292:0.88 297:0.36 300:0.70 +2 1:0.64 10:0.98 11:0.75 12:0.20 17:0.95 18:0.89 21:0.30 22:0.93 27:0.72 29:0.94 30:0.33 33:0.97 34:0.96 36:0.85 37:0.95 39:0.36 43:0.62 45:0.93 47:0.93 64:0.77 66:0.35 69:0.40 70:0.93 71:0.86 74:0.70 81:0.59 83:0.56 86:0.93 91:0.27 98:0.42 99:0.83 101:0.65 108:0.31 122:0.95 123:0.30 124:0.82 126:0.32 127:0.81 129:0.59 131:0.61 133:0.81 135:0.98 139:0.90 144:0.76 146:0.77 147:0.81 149:0.58 150:0.55 154:0.87 155:0.49 157:0.90 159:0.83 165:0.65 166:0.96 170:0.90 172:0.77 173:0.19 174:0.97 177:0.88 178:0.55 179:0.31 182:0.87 187:0.87 192:0.45 197:0.98 201:0.60 208:0.50 212:0.82 216:0.06 222:0.97 227:0.61 229:0.61 231:0.94 235:0.52 236:0.94 239:0.97 241:0.41 242:0.28 243:0.51 245:0.87 246:0.96 247:0.86 253:0.51 254:0.74 259:0.21 262:0.93 265:0.44 266:0.89 267:0.79 271:0.32 276:0.81 287:0.61 291:0.97 300:0.98 +2 1:0.64 7:0.81 10:0.98 11:0.75 12:0.20 17:0.61 18:0.95 21:0.54 22:0.93 27:0.24 29:0.94 30:0.68 32:0.80 33:0.97 34:0.97 36:0.69 37:0.80 39:0.36 43:0.81 44:0.93 45:0.98 47:0.93 64:0.77 66:0.35 69:0.70 70:0.59 71:0.86 74:0.78 77:0.70 81:0.72 83:0.69 86:0.96 91:0.20 98:0.47 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.92 114:0.77 117:0.86 122:0.92 123:0.91 124:0.69 126:0.54 127:0.68 129:0.81 131:0.61 133:0.67 135:0.90 139:0.97 144:0.76 145:0.70 146:0.26 147:0.32 149:0.91 150:0.58 154:0.75 155:0.64 157:0.95 159:0.86 165:0.81 166:0.96 170:0.90 172:0.90 173:0.45 174:0.98 176:0.73 177:0.88 178:0.55 179:0.17 182:0.87 187:0.39 192:0.87 193:0.99 195:0.95 197:0.96 201:0.63 204:0.85 206:0.81 208:0.64 212:0.90 215:0.58 216:0.67 222:0.97 227:0.61 229:0.61 230:0.83 231:0.94 232:0.95 233:0.66 235:0.80 236:0.97 239:0.99 241:0.05 242:0.27 243:0.16 245:0.98 246:0.96 247:0.82 253:0.64 259:0.21 262:0.93 265:0.49 266:0.75 267:0.52 271:0.32 276:0.88 281:0.86 282:0.88 287:0.61 290:0.77 291:0.97 297:0.36 300:0.94 +2 1:0.64 7:0.75 10:0.93 11:0.75 12:0.20 17:0.45 18:0.76 21:0.47 22:0.93 27:0.41 29:0.94 30:0.86 32:0.78 33:0.97 34:0.97 36:0.40 37:0.68 39:0.36 43:0.29 44:0.93 45:0.87 47:0.93 64:0.77 66:0.44 69:0.76 70:0.30 71:0.86 74:0.57 76:0.85 77:0.70 81:0.74 83:0.69 86:0.82 91:0.15 98:0.19 99:0.95 101:0.65 102:0.98 108:0.79 114:0.80 122:0.93 123:0.78 124:0.69 126:0.54 127:0.42 129:0.05 131:0.61 133:0.62 135:0.93 139:0.87 144:0.76 145:0.54 146:0.22 147:0.28 149:0.49 150:0.60 154:0.62 155:0.53 157:0.90 159:0.45 165:0.81 166:0.96 170:0.90 172:0.41 173:0.79 174:0.89 176:0.73 177:0.88 178:0.55 179:0.70 182:0.87 187:0.68 192:0.55 193:0.99 195:0.69 197:0.90 201:0.64 204:0.80 208:0.64 212:0.61 215:0.63 216:0.59 222:0.97 227:0.61 229:0.61 230:0.77 231:0.94 233:0.65 235:0.74 236:0.97 239:0.95 241:0.10 242:0.33 243:0.36 245:0.61 246:0.96 247:0.55 253:0.60 254:0.94 259:0.21 262:0.93 265:0.21 266:0.47 267:0.91 271:0.32 276:0.35 277:0.87 281:0.86 282:0.86 287:0.61 290:0.80 291:0.97 297:0.36 300:0.33 +1 1:0.60 8:0.87 9:0.74 10:0.98 11:0.75 12:0.20 17:0.59 18:0.94 21:0.30 22:0.93 23:0.98 27:0.19 29:0.94 30:0.75 31:0.96 33:0.97 34:0.67 36:0.26 37:0.73 39:0.36 43:0.63 44:0.88 45:0.94 47:0.93 62:0.99 64:0.77 66:0.09 69:0.51 70:0.31 71:0.86 74:0.51 75:0.99 79:0.95 81:0.55 83:0.69 86:0.94 91:0.64 97:0.99 98:0.49 99:0.83 101:0.65 102:0.96 108:0.66 122:0.93 123:0.38 124:0.44 126:0.32 127:0.34 128:0.99 129:0.05 131:0.98 133:0.39 135:0.92 137:0.96 139:0.95 140:0.45 144:0.76 145:0.47 146:0.44 147:0.50 149:0.87 150:0.50 151:0.91 153:0.72 154:0.52 155:0.56 157:0.94 158:0.86 159:0.31 162:0.86 165:0.65 166:0.96 168:0.99 170:0.90 172:0.79 173:0.61 174:0.96 177:0.88 179:0.33 182:0.88 187:0.87 190:0.89 191:0.81 192:0.49 193:0.98 195:0.60 196:0.98 197:0.98 201:0.57 202:0.61 204:0.81 205:0.98 208:0.64 212:0.88 216:0.81 219:0.95 222:0.97 226:0.98 227:0.99 229:0.94 231:0.95 235:0.42 236:0.94 239:0.99 241:0.61 242:0.59 243:0.27 244:0.83 245:0.84 246:0.96 247:0.55 248:0.85 253:0.41 255:0.72 256:0.68 259:0.21 262:0.93 265:0.44 266:0.38 267:0.84 268:0.70 271:0.32 276:0.67 279:0.82 281:0.86 283:0.67 284:0.94 285:0.50 287:0.97 291:0.97 292:0.95 300:0.33 +2 1:0.66 10:0.97 11:0.75 12:0.20 17:0.67 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.45 32:0.80 33:0.97 34:0.97 36:0.20 37:0.80 39:0.36 43:0.79 44:0.93 45:0.92 47:0.93 55:0.45 64:0.77 66:0.97 69:0.76 70:0.48 71:0.86 74:0.73 77:0.70 81:0.77 83:0.69 86:0.95 91:0.20 98:0.84 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.59 114:0.86 117:0.86 122:0.92 123:0.56 126:0.54 127:0.34 129:0.05 131:0.61 135:0.87 139:0.95 144:0.76 145:0.54 146:0.88 147:0.90 149:0.91 150:0.67 154:0.72 155:0.51 157:0.92 159:0.46 165:0.81 166:0.96 170:0.90 172:0.93 173:0.75 174:0.93 176:0.73 177:0.88 178:0.55 179:0.19 182:0.87 187:0.21 192:0.50 195:0.74 197:0.94 201:0.65 206:0.81 208:0.64 212:0.88 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.98 241:0.18 242:0.63 243:0.97 246:0.96 247:0.82 253:0.66 259:0.21 262:0.93 265:0.84 266:0.54 267:0.23 271:0.32 276:0.86 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.55 +1 1:0.66 10:0.99 11:0.75 12:0.20 17:0.70 18:0.99 21:0.30 22:0.93 27:0.24 29:0.94 30:0.45 32:0.80 33:0.97 34:0.97 36:0.21 37:0.80 39:0.36 43:0.79 44:0.93 45:0.96 47:0.93 64:0.77 66:0.63 69:0.76 70:0.56 71:0.86 74:0.77 77:0.70 81:0.77 83:0.69 86:0.97 91:0.14 98:0.72 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.79 114:0.86 117:0.86 122:0.92 123:0.77 124:0.50 126:0.54 127:0.44 129:0.59 131:0.61 133:0.47 135:0.93 139:0.97 144:0.76 145:0.63 146:0.71 147:0.76 149:0.94 150:0.67 154:0.72 155:0.51 157:0.94 159:0.62 165:0.81 166:0.96 170:0.90 172:0.94 173:0.69 174:0.96 176:0.73 177:0.88 178:0.55 179:0.16 182:0.87 187:0.21 192:0.50 195:0.82 197:0.97 201:0.65 206:0.81 208:0.64 212:0.89 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.99 241:0.07 242:0.53 243:0.36 245:0.98 246:0.96 247:0.82 253:0.67 259:0.21 262:0.93 265:0.72 266:0.38 267:0.36 271:0.32 276:0.92 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.55 +2 1:0.66 10:0.98 11:0.75 12:0.20 17:0.62 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.44 32:0.80 33:0.97 34:0.98 36:0.21 37:0.80 39:0.36 43:0.79 44:0.93 45:0.95 47:0.93 64:0.77 66:0.98 69:0.76 70:0.56 71:0.86 74:0.80 77:0.70 81:0.77 83:0.69 86:0.97 91:0.11 97:0.89 98:0.89 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.59 114:0.86 117:0.86 122:0.92 123:0.56 126:0.54 127:0.44 129:0.05 131:0.61 135:0.93 139:0.97 144:0.76 145:0.63 146:0.94 147:0.95 149:0.94 150:0.67 154:0.72 155:0.51 157:0.94 158:0.77 159:0.60 165:0.81 166:0.96 170:0.90 172:0.96 173:0.70 174:0.96 176:0.73 177:0.88 178:0.55 179:0.16 182:0.87 187:0.21 192:0.50 195:0.80 197:0.96 201:0.65 206:0.81 208:0.64 212:0.88 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.99 241:0.10 242:0.55 243:0.98 246:0.96 247:0.86 253:0.68 259:0.21 262:0.93 265:0.89 266:0.63 267:0.87 271:0.32 276:0.91 279:0.76 282:0.88 283:0.82 287:0.61 290:0.86 291:0.97 297:0.36 300:0.55 +1 1:0.67 8:0.63 10:0.98 11:0.75 12:0.20 17:0.59 18:0.94 21:0.22 22:0.93 27:0.24 29:0.94 30:0.60 32:0.80 33:0.97 34:0.87 36:0.47 37:0.80 39:0.36 43:0.79 44:0.93 45:0.97 47:0.93 64:0.77 66:0.67 69:0.75 70:0.55 71:0.86 74:0.75 77:0.70 81:0.79 83:0.69 86:0.95 91:0.25 98:0.42 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.96 114:0.90 117:0.86 122:0.92 123:0.95 124:0.50 126:0.54 127:0.78 129:0.59 131:0.61 133:0.60 135:0.99 139:0.94 144:0.76 145:0.69 146:0.66 147:0.71 149:0.92 150:0.72 154:0.72 155:0.52 157:0.93 159:0.90 165:0.81 166:0.96 170:0.90 172:0.95 173:0.45 174:0.96 176:0.73 177:0.88 178:0.55 179:0.18 182:0.87 187:0.39 192:0.51 195:0.98 197:0.94 201:0.66 206:0.81 208:0.64 212:0.89 215:0.79 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.52 236:0.97 239:0.98 241:0.46 242:0.52 243:0.31 245:0.96 246:0.96 247:0.90 253:0.68 259:0.21 262:0.93 265:0.44 266:0.71 267:0.94 271:0.32 276:0.82 282:0.88 287:0.61 290:0.90 291:0.97 297:0.36 300:0.70 +2 1:0.66 7:0.81 10:0.99 11:0.75 12:0.20 17:0.64 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.61 32:0.80 33:0.97 34:0.95 36:0.32 37:0.80 39:0.36 43:0.81 44:0.93 45:0.97 47:0.93 48:0.91 64:0.77 66:0.33 69:0.69 70:0.56 71:0.86 74:0.83 77:0.70 81:0.77 83:0.69 86:0.98 91:0.29 98:0.63 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.68 114:0.86 117:0.86 122:0.92 123:0.66 124:0.69 126:0.54 127:0.47 129:0.81 131:0.61 133:0.67 135:0.77 139:0.98 144:0.76 145:0.63 146:0.24 147:0.30 149:0.96 150:0.67 154:0.75 155:0.63 157:0.95 159:0.61 165:0.81 166:0.96 170:0.90 172:0.87 173:0.62 174:0.97 176:0.73 177:0.88 178:0.55 179:0.16 182:0.88 187:0.39 192:0.86 193:0.99 195:0.81 197:0.98 201:0.65 204:0.85 206:0.81 208:0.64 212:0.92 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 230:0.83 231:0.94 232:0.95 233:0.66 235:0.60 236:0.97 239:0.99 241:0.07 242:0.33 243:0.18 245:0.97 246:0.96 247:0.82 253:0.69 259:0.21 262:0.93 265:0.64 266:0.54 267:0.28 271:0.32 276:0.91 281:0.86 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.70 +2 1:0.68 10:0.91 11:0.75 12:0.20 17:0.06 18:0.67 21:0.13 22:0.93 27:0.36 29:0.94 30:0.95 33:0.97 34:0.47 36:0.39 37:0.64 39:0.36 43:0.91 45:0.73 47:0.93 64:0.77 66:0.69 69:0.41 71:0.86 74:0.57 75:0.99 81:0.81 83:0.69 86:0.77 91:0.53 97:0.94 98:0.17 99:0.95 101:0.65 104:0.96 106:0.81 108:0.31 114:0.92 117:0.86 122:0.93 123:0.30 126:0.54 127:0.10 129:0.05 131:0.61 135:0.73 139:0.82 144:0.76 146:0.18 147:0.23 149:0.70 150:0.77 154:0.90 155:0.52 157:0.79 158:0.86 159:0.09 165:0.81 166:0.96 170:0.90 172:0.21 173:0.69 174:0.79 176:0.73 177:0.88 178:0.55 179:0.13 182:0.87 187:0.68 192:0.54 197:0.85 201:0.67 206:0.81 208:0.64 212:0.95 215:0.84 216:0.72 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 231:0.94 232:0.97 235:0.42 236:0.97 239:0.93 241:0.27 242:0.63 243:0.73 246:0.96 247:0.30 253:0.66 259:0.21 262:0.93 265:0.18 266:0.12 267:0.23 271:0.32 276:0.23 279:0.80 283:0.67 287:0.61 290:0.92 291:0.97 292:0.88 297:0.36 300:0.08 +1 1:0.68 8:0.76 10:0.99 11:0.75 12:0.20 17:0.85 18:0.92 21:0.05 22:0.93 27:0.31 29:0.94 30:0.09 33:0.97 34:0.40 36:0.95 37:0.68 39:0.36 43:0.88 45:0.88 47:0.93 64:0.77 66:0.86 69:0.35 70:0.95 71:0.86 74:0.87 75:0.99 77:0.70 81:0.83 83:0.69 86:0.95 91:0.25 97:0.94 98:0.89 99:0.95 101:0.65 102:0.94 104:0.98 106:0.81 108:0.27 114:0.95 117:0.86 122:0.94 123:0.26 124:0.38 126:0.54 127:0.82 129:0.05 131:0.61 133:0.62 135:0.97 139:0.94 144:0.76 145:0.65 146:0.99 147:0.99 149:0.85 150:0.82 154:0.87 155:0.53 157:0.87 158:0.86 159:0.87 165:0.81 166:0.96 170:0.90 172:0.96 173:0.14 174:0.99 176:0.73 177:0.88 178:0.55 179:0.19 182:0.87 187:0.39 192:0.55 195:0.95 197:0.99 201:0.67 202:0.46 206:0.81 208:0.64 212:0.60 215:0.91 216:0.57 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 231:0.94 232:0.99 235:0.31 236:0.97 239:0.99 241:0.43 242:0.18 243:0.87 245:0.87 246:0.96 247:0.98 253:0.74 262:0.93 265:0.89 266:0.75 267:0.91 271:0.32 276:0.92 279:0.80 282:0.75 283:0.67 287:0.61 290:0.95 291:0.97 292:0.88 297:0.36 300:0.84 +2 1:0.68 7:0.70 8:0.71 9:0.74 10:0.97 11:0.75 12:0.20 17:0.06 18:0.83 21:0.13 23:0.96 27:0.19 29:0.94 30:0.74 31:0.90 34:0.79 36:0.75 37:0.73 39:0.36 43:0.96 45:0.90 62:0.98 64:0.77 66:0.77 69:0.15 70:0.53 71:0.86 74:0.85 75:0.99 79:0.90 81:0.81 83:0.69 86:0.92 91:0.17 96:0.74 97:0.94 98:0.76 99:0.95 101:0.65 102:0.94 108:0.59 114:0.92 120:0.62 122:0.94 123:0.57 124:0.41 126:0.54 127:0.56 128:0.96 129:0.59 131:0.98 133:0.47 135:0.70 137:0.90 139:0.93 140:0.80 144:0.76 145:0.67 146:0.18 147:0.23 149:0.92 150:0.77 151:0.69 153:0.48 154:0.96 155:0.65 157:0.89 158:0.86 159:0.40 162:0.51 164:0.57 165:0.81 166:0.96 168:0.96 170:0.90 172:0.81 173:0.27 174:0.94 176:0.73 177:0.88 178:0.42 179:0.41 182:0.88 187:0.87 192:0.98 193:0.98 195:0.63 196:0.94 197:0.96 201:0.67 202:0.60 204:0.83 205:0.95 208:0.64 212:0.90 215:0.84 216:0.81 219:0.95 220:0.74 222:0.97 226:0.96 227:0.99 229:0.93 230:0.73 231:0.94 233:0.66 235:0.42 236:0.97 239:0.98 241:0.12 242:0.18 243:0.19 245:0.81 246:0.96 247:0.94 248:0.64 253:0.80 255:0.74 256:0.45 259:0.21 260:0.63 265:0.76 266:0.47 267:0.18 268:0.46 271:0.32 276:0.70 279:0.80 281:0.86 283:0.67 284:0.89 285:0.62 287:0.97 290:0.92 292:0.88 297:0.36 300:0.70 +1 1:0.58 10:0.94 11:0.75 12:0.20 17:0.67 18:0.84 21:0.47 22:0.93 27:0.19 29:0.94 30:0.45 33:0.97 34:0.90 36:0.43 37:0.73 39:0.36 43:0.11 45:0.81 47:0.93 48:0.91 64:0.77 66:0.30 69:0.37 70:0.46 71:0.86 74:0.57 81:0.51 83:0.69 86:0.87 91:0.40 97:0.89 98:0.19 99:0.83 101:0.65 108:0.29 122:0.65 123:0.28 124:0.78 126:0.32 127:0.47 129:0.59 131:0.61 133:0.73 135:0.90 139:0.84 144:0.76 145:0.61 146:0.25 147:0.31 149:0.09 150:0.46 154:0.74 155:0.52 157:0.83 158:0.77 159:0.45 165:0.65 166:0.96 170:0.90 172:0.32 173:0.39 174:0.86 177:0.88 178:0.55 179:0.74 182:0.87 187:0.98 192:0.46 193:0.98 197:0.89 201:0.55 204:0.82 208:0.61 212:0.58 216:0.81 222:0.97 227:0.61 229:0.61 231:0.94 235:0.60 236:0.94 239:0.93 241:0.27 242:0.22 243:0.48 245:0.56 246:0.96 247:0.67 253:0.45 254:0.74 259:0.21 262:0.93 265:0.20 266:0.47 267:0.69 271:0.32 276:0.40 279:0.76 281:0.91 283:0.82 287:0.61 291:0.97 300:0.33 +2 1:0.66 7:0.81 8:0.77 10:0.98 11:0.75 12:0.20 17:0.92 18:0.93 21:0.30 22:0.93 27:0.34 29:0.94 30:0.36 32:0.80 33:0.97 34:0.76 36:0.99 37:0.64 39:0.36 43:0.83 44:0.93 45:0.92 47:0.93 48:0.91 64:0.77 66:0.60 69:0.37 70:0.85 71:0.86 74:0.83 75:0.99 77:0.89 81:0.77 83:0.69 86:0.93 91:0.18 97:0.99 98:0.85 99:0.95 101:0.65 102:0.98 104:0.99 106:0.81 108:0.29 114:0.86 117:0.86 122:0.94 123:0.28 124:0.51 126:0.54 127:0.46 129:0.59 131:0.61 133:0.47 135:0.93 139:0.96 144:0.76 145:0.89 146:0.95 147:0.96 149:0.85 150:0.67 154:0.79 155:0.57 157:0.92 158:0.86 159:0.69 165:0.81 166:0.96 170:0.90 172:0.87 173:0.23 174:0.98 176:0.73 177:0.88 178:0.55 179:0.23 182:0.87 187:0.68 192:0.58 193:0.99 195:0.88 197:0.98 201:0.65 202:0.36 204:0.84 206:0.81 208:0.64 212:0.74 215:0.72 216:0.51 219:0.95 222:0.97 226:0.96 227:0.61 229:0.61 230:0.83 231:0.94 232:0.99 233:0.66 235:0.60 236:0.97 239:0.99 241:0.03 242:0.27 243:0.67 245:0.95 246:0.96 247:0.94 253:0.69 259:0.21 262:0.93 265:0.85 266:0.63 267:0.59 271:0.32 276:0.85 279:0.82 281:0.91 282:0.88 283:0.67 287:0.61 290:0.86 291:0.97 292:0.95 297:0.36 300:0.70 +2 1:0.66 7:0.81 10:0.98 11:0.75 12:0.20 17:0.61 18:0.96 21:0.30 22:0.93 27:0.24 29:0.94 30:0.60 32:0.80 33:0.97 34:0.94 36:0.41 37:0.80 39:0.36 43:0.81 44:0.93 45:0.97 47:0.93 64:0.77 66:0.32 69:0.69 70:0.59 71:0.86 74:0.79 77:0.70 81:0.77 83:0.69 86:0.96 91:0.20 98:0.48 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.88 114:0.86 117:0.86 122:0.92 123:0.87 124:0.69 126:0.54 127:0.63 129:0.81 131:0.61 133:0.67 135:0.89 139:0.97 144:0.76 145:0.63 146:0.25 147:0.31 149:0.91 150:0.67 154:0.75 155:0.63 157:0.94 159:0.79 165:0.81 166:0.96 170:0.90 172:0.87 173:0.52 174:0.97 176:0.73 177:0.88 178:0.55 179:0.18 182:0.87 187:0.39 192:0.86 193:0.99 195:0.91 197:0.96 201:0.65 204:0.85 206:0.81 208:0.64 212:0.91 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 230:0.83 231:0.94 232:0.95 233:0.66 235:0.60 236:0.97 239:0.99 241:0.07 242:0.36 243:0.18 245:0.97 246:0.96 247:0.84 253:0.68 259:0.21 262:0.93 265:0.50 266:0.75 267:0.36 271:0.32 276:0.87 281:0.86 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.84 +2 1:0.64 7:0.75 10:0.96 11:0.75 12:0.20 17:0.84 18:0.82 21:0.47 22:0.93 27:0.58 29:0.94 30:0.74 32:0.78 33:0.97 34:0.67 36:0.46 37:0.71 39:0.36 43:0.19 44:0.93 45:0.95 47:0.93 64:0.77 66:0.23 69:0.84 70:0.39 71:0.86 74:0.74 76:0.85 77:0.70 81:0.74 83:0.69 86:0.89 91:0.15 97:0.89 98:0.39 99:0.95 101:0.65 102:0.98 104:0.99 106:0.81 108:0.89 114:0.80 117:0.86 122:0.93 123:0.89 124:0.87 126:0.54 127:0.68 129:0.93 131:0.61 133:0.87 135:0.21 139:0.91 144:0.76 145:0.77 146:0.47 147:0.05 149:0.09 150:0.60 154:0.52 155:0.53 157:0.92 158:0.76 159:0.84 165:0.81 166:0.96 170:0.90 172:0.63 173:0.66 174:0.94 176:0.73 177:0.05 178:0.55 179:0.34 182:0.87 187:0.68 192:0.55 193:0.99 195:0.94 197:0.91 201:0.64 204:0.80 206:0.81 208:0.64 212:0.30 215:0.63 216:0.49 222:0.97 227:0.61 229:0.61 230:0.77 231:0.94 232:0.99 233:0.65 235:0.74 236:0.97 239:0.97 241:0.52 242:0.33 243:0.07 245:0.83 246:0.96 247:0.55 253:0.64 254:0.74 257:0.93 259:0.21 262:0.93 265:0.41 266:0.84 267:0.44 271:0.32 276:0.78 279:0.75 281:0.86 282:0.86 283:0.67 287:0.61 290:0.80 291:0.97 297:0.36 300:0.55 +0 1:0.64 9:0.75 10:0.93 11:0.75 12:0.20 17:0.81 18:0.96 21:0.47 22:0.93 23:0.98 27:0.61 29:0.94 30:0.29 31:0.93 33:0.97 37:0.41 39:0.36 43:0.74 44:0.85 45:0.87 47:0.93 55:0.71 62:0.98 64:0.77 66:0.68 69:0.84 70:0.74 71:0.86 74:0.62 75:0.99 79:0.92 81:0.74 83:0.69 86:0.85 91:0.23 96:0.79 97:0.94 98:0.74 99:0.95 101:0.65 102:0.94 104:0.96 106:0.81 108:0.80 114:0.80 117:0.86 120:0.67 122:0.92 123:0.78 124:0.48 126:0.54 127:0.34 128:0.97 129:0.59 131:0.98 133:0.60 137:0.93 139:0.88 140:0.45 144:0.76 145:0.87 146:0.58 147:0.63 149:0.76 150:0.60 151:0.77 153:0.48 154:0.65 155:0.66 157:0.83 158:0.86 159:0.80 162:0.79 164:0.71 165:0.81 166:0.96 168:0.97 170:0.90 172:0.91 173:0.64 174:0.93 176:0.73 177:0.88 179:0.18 182:0.88 187:0.39 192:0.99 193:0.98 195:0.95 196:0.95 197:0.93 201:0.64 202:0.60 204:0.81 205:0.98 206:0.81 208:0.64 212:0.61 215:0.63 216:0.40 219:0.95 220:0.87 222:0.97 226:0.96 227:0.99 229:0.94 231:0.95 232:0.97 235:0.74 236:0.97 239:0.95 241:0.37 242:0.74 243:0.29 245:0.91 246:0.96 247:0.91 248:0.74 253:0.79 255:0.78 256:0.64 260:0.68 262:0.93 265:0.75 266:0.75 268:0.65 271:0.32 276:0.88 279:0.80 281:0.91 283:0.67 284:0.94 285:0.62 287:0.97 290:0.80 291:0.97 292:0.88 297:0.36 300:0.70 +2 1:0.66 10:0.98 11:0.75 12:0.20 17:0.66 18:0.98 21:0.30 22:0.93 27:0.24 29:0.94 30:0.40 32:0.80 33:0.97 34:0.95 36:0.23 37:0.80 39:0.36 43:0.79 44:0.93 45:0.95 47:0.93 64:0.77 66:0.59 69:0.76 70:0.59 71:0.86 74:0.75 77:0.70 81:0.77 83:0.69 86:0.96 91:0.12 98:0.60 99:0.95 101:0.65 102:0.98 104:0.93 106:0.81 108:0.85 114:0.86 117:0.86 122:0.92 123:0.84 124:0.59 126:0.54 127:0.48 129:0.59 131:0.61 133:0.60 135:0.95 139:0.95 144:0.76 145:0.54 146:0.78 147:0.82 149:0.93 150:0.67 154:0.72 155:0.51 157:0.93 159:0.68 165:0.81 166:0.96 170:0.90 172:0.92 173:0.66 174:0.94 176:0.73 177:0.88 178:0.55 179:0.18 182:0.87 187:0.21 192:0.50 195:0.86 197:0.94 201:0.65 206:0.81 208:0.64 212:0.86 215:0.72 216:0.67 222:0.97 227:0.61 229:0.61 231:0.94 232:0.95 235:0.60 236:0.97 239:0.98 241:0.15 242:0.63 243:0.39 245:0.95 246:0.96 247:0.82 253:0.67 259:0.21 262:0.93 265:0.61 266:0.71 267:0.93 271:0.32 276:0.90 282:0.88 287:0.61 290:0.86 291:0.97 297:0.36 300:0.70 +2 9:0.75 10:0.91 11:0.75 12:0.20 17:0.51 18:0.67 21:0.78 22:0.93 23:0.96 27:0.36 29:0.94 30:0.95 31:0.92 33:0.97 34:0.37 36:0.66 37:0.64 39:0.36 43:0.91 45:0.73 47:0.93 62:0.97 66:0.69 69:0.35 71:0.86 74:0.57 75:0.99 79:0.91 83:0.69 86:0.77 91:0.48 96:0.77 97:0.94 98:0.19 101:0.65 104:0.96 106:0.81 108:0.27 117:0.86 120:0.65 122:0.93 123:0.26 127:0.10 128:0.97 129:0.05 131:0.98 135:0.79 137:0.92 139:0.82 140:0.45 144:0.76 146:0.18 147:0.23 149:0.70 151:0.77 153:0.48 154:0.90 155:0.65 157:0.79 158:0.86 159:0.09 162:0.86 164:0.57 166:0.61 168:0.97 170:0.90 172:0.21 173:0.72 174:0.79 177:0.88 179:0.16 182:0.88 187:0.39 192:0.98 196:0.93 197:0.85 202:0.36 205:0.95 206:0.81 208:0.64 212:0.95 216:0.72 219:0.95 220:0.62 222:0.97 226:0.96 227:0.99 229:0.93 231:0.94 232:0.97 235:0.02 239:0.93 241:0.63 242:0.63 243:0.73 246:0.61 247:0.30 248:0.70 253:0.69 255:0.78 256:0.45 259:0.21 260:0.66 262:0.93 265:0.21 266:0.12 267:0.19 268:0.46 271:0.32 276:0.23 279:0.80 283:0.67 284:0.89 285:0.62 287:0.97 291:0.97 292:0.88 300:0.08 +0 11:0.82 17:0.22 18:0.64 21:0.78 27:0.45 29:0.77 30:0.76 32:0.69 34:0.77 36:0.21 37:0.50 39:0.98 43:0.60 55:0.42 66:0.77 69:0.70 70:0.29 71:0.89 74:0.57 77:0.70 86:0.74 91:0.10 98:0.39 108:0.53 122:0.51 123:0.51 127:0.13 129:0.05 135:0.61 139:0.71 144:0.85 145:0.52 146:0.48 147:0.54 149:0.42 154:0.72 159:0.17 163:0.90 172:0.37 173:0.70 177:0.70 178:0.55 179:0.37 187:0.68 195:0.71 212:0.52 216:0.77 222:0.20 235:0.22 241:0.10 242:0.24 243:0.79 247:0.47 253:0.38 254:0.92 259:0.21 265:0.41 266:0.27 267:0.44 271:0.47 276:0.22 282:0.77 300:0.25 +0 11:0.91 17:0.57 18:0.74 21:0.78 27:0.62 29:0.77 30:0.13 34:0.99 36:0.68 37:0.70 39:0.98 43:0.67 45:0.66 55:0.84 66:0.71 69:0.37 70:0.84 71:0.89 74:0.36 86:0.73 91:0.44 98:0.74 101:0.52 108:0.62 122:0.77 123:0.59 124:0.43 127:0.47 129:0.59 133:0.47 135:0.47 139:0.70 144:0.85 145:0.91 146:0.38 147:0.45 149:0.48 154:0.56 159:0.41 172:0.65 173:0.42 175:0.75 177:0.38 178:0.55 179:0.59 187:0.39 202:0.46 212:0.57 216:0.32 222:0.20 235:0.74 241:0.21 242:0.45 243:0.23 244:0.61 245:0.69 247:0.60 253:0.29 257:0.65 259:0.21 265:0.74 266:0.54 267:0.28 271:0.47 276:0.58 277:0.69 300:0.55 +0 1:0.69 11:0.91 17:0.81 18:0.73 21:0.74 27:0.52 29:0.77 30:0.71 32:0.78 34:0.97 36:0.26 37:0.34 39:0.98 43:0.64 44:0.93 45:0.81 66:0.63 69:0.33 70:0.40 71:0.89 74:0.73 77:0.70 81:0.31 83:0.60 86:0.85 91:0.58 97:0.98 98:0.36 99:0.83 101:0.52 108:0.26 114:0.54 122:0.51 123:0.25 124:0.45 126:0.44 127:0.27 129:0.05 133:0.43 135:0.42 139:0.86 144:0.85 145:0.50 146:0.51 147:0.57 149:0.40 150:0.51 154:0.85 155:0.58 158:0.78 159:0.47 165:0.70 172:0.41 173:0.26 176:0.66 177:0.70 178:0.55 179:0.72 187:0.68 192:0.59 195:0.82 201:0.68 208:0.54 212:0.61 215:0.36 216:0.40 222:0.20 235:0.95 241:0.10 242:0.33 243:0.68 245:0.54 247:0.55 253:0.42 254:0.92 259:0.21 265:0.38 266:0.38 267:0.65 271:0.47 276:0.26 279:0.82 282:0.86 283:0.78 290:0.54 297:0.36 300:0.33 +0 8:0.66 11:0.91 17:0.18 18:0.88 21:0.59 22:0.93 27:0.21 29:0.77 30:0.40 31:0.90 34:0.42 36:0.47 37:0.74 39:0.98 43:0.34 45:0.81 47:0.93 48:0.91 55:0.59 64:0.77 66:0.60 69:0.27 70:0.84 71:0.89 74:0.66 79:0.91 81:0.24 86:0.84 91:0.49 98:0.75 101:0.52 108:0.93 122:0.77 123:0.93 124:0.66 126:0.26 127:0.95 129:0.59 133:0.74 135:0.24 137:0.90 139:0.83 140:0.45 144:0.85 146:0.95 147:0.96 149:0.74 150:0.24 151:0.55 153:0.48 154:0.38 159:0.92 162:0.62 172:0.99 173:0.09 177:0.70 179:0.11 187:0.39 190:0.89 196:0.92 202:0.67 212:0.91 216:0.95 219:0.89 220:0.87 222:0.20 235:0.68 241:0.37 242:0.58 243:0.37 244:0.61 245:0.99 247:0.92 248:0.56 253:0.40 256:0.68 259:0.21 262:0.93 265:0.76 266:0.80 267:0.99 268:0.68 271:0.47 276:0.98 281:0.89 284:0.92 285:0.47 292:0.91 300:0.94 +0 11:0.91 17:0.15 18:0.72 21:0.13 22:0.93 27:0.43 29:0.77 30:0.21 34:0.95 36:0.55 37:0.66 39:0.98 43:0.63 45:0.81 47:0.93 55:0.45 64:0.77 66:0.96 69:0.65 70:0.77 71:0.89 74:0.65 81:0.36 86:0.69 91:0.35 98:0.89 101:0.52 108:0.49 122:0.75 123:0.47 126:0.26 127:0.43 129:0.05 135:0.68 139:0.67 144:0.85 146:0.84 147:0.87 149:0.68 150:0.32 154:0.53 159:0.41 172:0.90 173:0.70 177:0.70 178:0.55 179:0.27 187:0.68 212:0.90 216:0.68 222:0.20 235:0.12 241:0.37 242:0.30 243:0.96 244:0.61 247:0.74 253:0.40 259:0.21 262:0.93 265:0.89 266:0.27 267:0.36 271:0.47 276:0.82 300:0.55 +0 1:0.69 8:0.62 9:0.80 11:0.91 17:0.24 21:0.47 27:0.28 29:0.77 30:0.28 31:0.87 34:0.59 36:0.47 37:0.49 39:0.98 43:0.72 45:0.66 55:0.71 66:0.29 69:0.19 70:0.95 71:0.89 74:0.48 79:0.87 81:0.34 83:0.60 91:0.48 96:0.76 97:0.89 98:0.47 99:0.83 101:0.52 108:0.67 114:0.59 117:0.86 120:0.74 122:0.77 123:0.65 124:0.70 126:0.44 127:0.73 129:0.59 133:0.64 135:0.83 137:0.87 139:0.68 140:0.80 144:0.85 146:0.13 147:0.18 149:0.44 150:0.52 151:0.65 153:0.48 154:0.62 155:0.67 158:0.79 159:0.58 162:0.86 164:0.55 165:0.70 172:0.37 173:0.21 175:0.75 176:0.66 177:0.38 179:0.73 187:0.99 190:0.89 192:0.87 196:0.87 201:0.68 202:0.56 208:0.64 212:0.27 215:0.41 216:0.67 219:0.92 222:0.20 235:0.60 241:0.21 242:0.58 243:0.20 244:0.61 245:0.65 247:0.55 248:0.64 253:0.57 255:0.79 256:0.64 257:0.65 259:0.21 260:0.64 265:0.49 266:0.71 267:0.44 268:0.65 271:0.47 276:0.49 277:0.69 279:0.86 283:0.82 284:0.87 285:0.62 290:0.59 292:0.87 297:0.36 300:0.84 +1 8:0.75 11:0.91 17:0.82 18:0.93 21:0.47 22:0.93 27:0.40 29:0.77 30:0.70 31:0.93 34:0.44 36:0.45 37:0.31 39:0.98 43:0.57 44:0.87 45:0.77 47:0.93 48:0.91 55:0.45 64:0.77 66:0.43 69:0.25 70:0.57 71:0.89 74:0.46 79:0.95 81:0.26 86:0.88 91:0.78 98:0.56 101:0.52 108:0.81 123:0.80 124:0.76 126:0.26 127:0.90 129:0.05 133:0.74 135:0.78 137:0.93 139:0.88 140:0.80 144:0.85 145:0.90 146:0.82 147:0.85 149:0.66 150:0.25 151:0.60 153:0.48 154:0.67 159:0.81 162:0.56 172:0.92 173:0.14 177:0.70 178:0.42 179:0.17 187:0.21 190:0.89 191:0.82 195:0.90 196:0.95 202:0.72 212:0.88 216:0.36 219:0.89 220:0.93 222:0.20 235:0.52 241:0.49 242:0.38 243:0.36 245:0.97 247:0.88 248:0.61 253:0.34 254:0.99 256:0.68 259:0.21 262:0.93 265:0.57 266:0.75 267:0.28 268:0.70 271:0.47 276:0.94 277:0.69 281:0.89 284:0.93 285:0.47 292:0.95 300:0.70 +0 7:0.63 11:0.91 17:0.73 18:0.82 21:0.30 27:0.72 29:0.77 30:0.87 32:0.63 34:0.34 36:0.28 37:0.23 39:0.98 43:0.57 44:0.90 45:0.73 48:0.97 55:0.59 64:0.77 66:0.84 69:0.62 70:0.39 71:0.89 74:0.39 81:0.39 86:0.79 91:0.33 98:0.86 101:0.52 108:0.47 123:0.46 124:0.38 126:0.44 127:0.61 129:0.05 132:0.97 133:0.37 135:0.69 139:0.81 144:0.85 145:0.79 146:0.91 147:0.92 149:0.51 150:0.32 154:0.53 159:0.58 172:0.83 173:0.58 177:0.70 178:0.55 179:0.41 187:0.39 192:0.51 195:0.81 201:0.68 212:0.80 215:0.44 216:0.16 222:0.20 230:0.63 233:0.73 235:0.42 241:0.49 242:0.72 243:0.85 244:0.61 245:0.73 247:0.79 253:0.31 259:0.21 265:0.86 266:0.54 267:0.87 271:0.47 276:0.74 281:0.91 297:0.36 300:0.55 +0 8:0.66 11:0.91 17:0.49 18:0.94 21:0.13 27:0.65 29:0.77 30:0.14 31:0.89 34:0.64 36:0.94 37:0.36 39:0.98 43:0.62 45:0.66 55:0.84 64:0.77 66:0.16 69:0.12 70:0.99 71:0.89 74:0.38 79:0.89 81:0.36 86:0.92 91:0.31 98:0.24 101:0.52 108:0.10 122:0.86 123:0.10 124:0.97 126:0.26 127:0.96 129:0.05 133:0.97 135:0.88 137:0.89 139:0.91 140:0.45 144:0.85 146:0.78 147:0.82 149:0.36 150:0.32 151:0.51 153:0.48 154:0.67 159:0.95 162:0.86 172:0.70 173:0.06 177:0.70 179:0.26 187:0.95 190:0.89 192:0.51 196:0.92 202:0.50 212:0.30 216:0.27 219:0.92 220:0.74 222:0.20 235:0.12 241:0.07 242:0.41 243:0.37 245:0.82 247:0.96 248:0.52 253:0.30 254:0.97 256:0.58 259:0.21 265:0.26 266:0.98 267:0.89 268:0.59 271:0.47 276:0.87 283:0.78 284:0.88 285:0.47 292:0.91 300:0.99 +0 8:0.81 11:0.82 17:0.78 18:0.64 21:0.78 27:0.52 29:0.77 30:0.76 34:0.24 36:0.24 39:0.98 43:0.12 55:0.42 66:0.64 69:0.73 70:0.15 71:0.89 74:0.36 86:0.65 91:0.72 98:0.20 108:0.56 122:0.83 123:0.54 127:0.10 129:0.05 135:0.49 139:0.63 144:0.85 145:0.87 146:0.33 147:0.40 149:0.06 154:0.50 159:0.13 163:0.98 172:0.16 173:0.64 177:0.70 178:0.55 179:0.32 202:0.80 212:0.95 216:0.18 222:0.20 235:0.84 241:0.85 242:0.82 243:0.69 244:0.61 247:0.12 253:0.29 254:0.74 259:0.21 265:0.22 266:0.12 267:0.76 271:0.47 276:0.13 300:0.13 +0 9:0.76 17:0.04 21:0.78 27:0.16 29:0.77 30:0.95 31:0.90 34:0.80 36:0.71 37:0.79 39:0.98 43:0.64 55:0.59 66:0.54 69:0.48 71:0.89 74:0.26 79:0.91 91:0.73 96:0.78 98:0.24 108:0.37 120:0.68 123:0.35 127:0.11 129:0.05 135:0.30 137:0.90 138:0.98 140:0.90 144:0.85 146:0.21 147:0.27 149:0.31 151:0.81 153:0.48 154:0.53 155:0.58 159:0.10 162:0.53 164:0.76 172:0.10 173:0.76 175:0.75 177:0.38 178:0.50 179:0.66 187:0.39 190:0.77 192:0.59 202:0.86 208:0.54 216:0.69 220:0.82 222:0.20 235:0.80 241:0.64 243:0.63 244:0.61 248:0.76 253:0.55 254:0.93 255:0.79 256:0.85 257:0.65 259:0.21 260:0.64 265:0.26 267:0.44 268:0.83 271:0.47 277:0.69 284:0.91 285:0.62 300:0.08 +0 11:0.91 17:0.20 18:0.90 21:0.78 27:0.12 29:0.77 30:0.54 34:0.96 36:0.46 37:0.87 39:0.98 43:0.15 45:0.83 55:0.71 66:0.90 69:0.56 70:0.32 71:0.89 74:0.70 86:0.95 91:0.29 98:0.87 101:0.52 108:0.43 122:0.57 123:0.41 127:0.41 129:0.05 135:0.60 139:0.92 144:0.85 146:0.68 147:0.73 149:0.17 154:0.74 159:0.26 172:0.71 173:0.73 177:0.70 178:0.55 179:0.54 187:0.68 212:0.93 216:0.52 222:0.20 235:0.60 241:0.24 242:0.37 243:0.90 247:0.55 253:0.41 254:0.93 259:0.21 265:0.87 266:0.17 267:0.36 271:0.47 276:0.61 300:0.25 +0 11:0.91 17:0.36 18:0.59 21:0.13 22:0.93 27:0.27 29:0.77 30:0.11 34:0.37 36:0.76 37:0.35 39:0.98 43:0.59 45:0.66 47:0.93 55:0.42 64:0.77 66:0.67 69:0.92 70:0.93 71:0.89 74:0.49 81:0.55 86:0.68 91:0.15 98:0.58 101:0.52 106:0.81 108:0.34 117:0.86 122:0.75 123:0.33 124:0.45 126:0.44 127:0.65 129:0.05 133:0.43 135:0.73 139:0.65 144:0.85 146:0.23 147:0.73 149:0.30 150:0.39 154:0.47 158:0.71 159:0.57 172:0.57 173:0.98 177:0.38 178:0.55 179:0.71 187:0.95 192:0.51 201:0.68 206:0.81 212:0.27 215:0.61 216:0.75 222:0.20 235:0.22 241:0.15 242:0.28 243:0.72 245:0.66 247:0.67 253:0.35 254:0.96 257:0.65 259:0.21 262:0.93 265:0.59 266:0.63 267:0.59 271:0.47 276:0.40 277:0.69 297:0.36 300:0.70 +0 1:0.74 11:0.91 17:0.55 18:0.95 21:0.54 27:0.52 29:0.77 30:0.27 34:0.97 36:0.65 37:0.35 39:0.98 43:0.57 44:0.87 45:0.66 48:0.97 55:0.52 64:0.77 66:0.29 69:0.83 70:0.79 71:0.89 74:0.43 81:0.44 83:0.60 86:0.90 91:0.22 97:0.89 98:0.61 99:0.83 101:0.52 108:0.21 114:0.59 122:0.86 123:0.66 124:0.71 126:0.54 127:0.80 129:0.59 133:0.66 135:0.74 139:0.89 144:0.85 145:0.92 146:0.70 147:0.54 149:0.44 150:0.66 154:0.57 155:0.67 158:0.78 159:0.59 165:0.70 172:0.54 173:0.84 176:0.73 177:0.38 178:0.55 179:0.53 187:0.68 192:0.87 195:0.76 201:0.93 202:0.36 208:0.64 212:0.59 215:0.39 216:0.34 222:0.20 235:0.74 241:0.37 242:0.54 243:0.48 244:0.61 245:0.80 247:0.84 253:0.42 257:0.65 259:0.21 265:0.39 266:0.71 267:0.79 271:0.47 276:0.67 279:0.82 281:0.89 283:0.66 290:0.59 297:0.36 300:0.70 +0 11:0.82 17:0.30 18:0.83 21:0.78 27:0.45 29:0.77 30:0.43 34:0.87 36:0.20 37:0.50 39:0.98 43:0.49 55:0.52 66:0.20 69:0.85 70:0.88 71:0.89 74:0.44 86:0.87 91:0.12 98:0.45 108:0.71 122:0.77 123:0.79 124:0.70 127:0.27 129:0.59 133:0.66 135:0.88 139:0.83 144:0.85 145:0.47 146:0.51 147:0.40 149:0.30 154:0.48 159:0.52 172:0.68 173:0.81 177:0.38 178:0.55 179:0.27 187:0.68 212:0.80 216:0.77 222:0.20 235:0.42 241:0.47 242:0.31 243:0.23 245:0.85 247:0.82 253:0.33 254:0.88 257:0.65 259:0.21 265:0.35 266:0.54 267:0.44 271:0.47 276:0.73 300:0.84 +0 7:0.63 8:0.63 11:0.82 17:0.95 18:0.64 21:0.78 27:0.66 29:0.77 30:0.72 32:0.62 34:0.26 36:0.61 37:0.27 39:0.98 43:0.22 55:0.59 66:0.08 69:0.39 70:0.56 71:0.89 74:0.29 86:0.64 91:0.62 98:0.26 108:0.30 123:0.29 124:0.95 127:0.81 129:0.81 133:0.94 135:0.60 139:0.63 144:0.85 145:0.58 146:0.70 147:0.75 149:0.34 154:0.15 159:0.92 172:0.32 173:0.12 175:0.75 177:0.38 178:0.55 179:0.41 195:0.98 202:0.74 212:0.27 216:0.18 222:0.20 230:0.63 233:0.73 235:0.98 241:0.78 242:0.73 243:0.24 244:0.61 245:0.73 247:0.79 253:0.25 257:0.65 259:0.21 265:0.28 266:0.95 267:0.23 271:0.47 276:0.75 277:0.69 300:0.70 +1 8:0.62 11:0.91 17:0.45 18:0.83 21:0.05 22:0.93 27:0.26 29:0.77 30:0.14 31:0.90 34:0.89 36:0.73 37:0.84 39:0.98 43:0.56 45:0.73 47:0.93 55:0.71 64:0.77 66:0.53 69:0.61 70:0.95 71:0.89 74:0.39 79:0.90 81:0.43 86:0.77 91:0.58 98:0.64 101:0.52 108:0.47 122:0.75 123:0.45 124:0.65 126:0.26 127:0.54 129:0.05 133:0.66 135:0.54 137:0.90 139:0.77 140:0.45 144:0.85 145:0.38 146:0.77 147:0.81 149:0.45 150:0.36 151:0.55 153:0.48 154:0.45 159:0.59 162:0.86 172:0.63 173:0.55 177:0.70 179:0.54 187:0.39 190:0.89 196:0.90 202:0.36 212:0.29 216:0.58 219:0.89 220:0.62 222:0.20 235:0.05 241:0.32 242:0.16 243:0.62 244:0.61 245:0.74 247:0.90 248:0.54 253:0.31 256:0.45 259:0.21 262:0.93 265:0.64 266:0.75 267:0.36 268:0.46 271:0.47 276:0.62 284:0.86 285:0.47 292:0.91 300:0.84 +1 8:0.82 11:0.91 17:0.84 18:0.93 21:0.47 22:0.93 27:0.40 29:0.77 30:0.65 31:0.91 34:0.50 36:0.49 37:0.31 39:0.98 43:0.61 44:0.87 45:0.73 47:0.93 48:0.91 55:0.45 64:0.77 66:0.35 69:0.25 70:0.58 71:0.89 74:0.50 79:0.92 81:0.26 86:0.88 91:0.78 98:0.54 101:0.52 108:0.81 122:0.77 123:0.80 124:0.77 126:0.26 127:0.71 129:0.05 133:0.74 135:0.84 137:0.91 139:0.88 140:0.45 144:0.85 145:0.89 146:0.82 147:0.85 149:0.71 150:0.25 151:0.58 153:0.80 154:0.67 159:0.81 162:0.86 172:0.92 173:0.14 177:0.70 179:0.15 187:0.21 190:0.89 191:0.82 195:0.91 196:0.93 202:0.61 212:0.90 216:0.36 219:0.89 220:0.62 222:0.20 235:0.52 241:0.43 242:0.38 243:0.40 245:0.98 247:0.88 248:0.56 253:0.35 254:0.99 256:0.64 259:0.21 262:0.93 265:0.55 266:0.75 267:0.36 268:0.69 271:0.47 276:0.94 277:0.69 281:0.89 284:0.92 285:0.47 292:0.91 300:0.70 +0 11:0.82 17:0.97 18:0.67 21:0.78 27:0.72 29:0.77 30:0.45 32:0.62 34:0.86 36:0.33 39:0.98 43:0.16 55:0.71 66:0.16 69:0.86 70:0.61 71:0.89 74:0.33 77:0.89 86:0.66 91:0.66 98:0.20 108:0.84 122:0.75 123:0.71 124:0.84 127:0.80 129:0.59 133:0.82 135:0.81 139:0.64 144:0.85 145:1.00 146:0.31 147:0.05 149:0.13 154:0.20 159:0.68 163:0.97 172:0.21 173:0.84 175:0.75 177:0.05 178:0.55 179:0.87 195:0.83 202:0.36 212:0.30 222:0.20 235:0.12 241:0.78 242:0.33 243:0.17 244:0.61 245:0.47 247:0.60 253:0.28 254:0.74 257:0.84 259:0.21 265:0.21 266:0.54 267:0.18 271:0.47 276:0.37 277:0.69 282:0.71 300:0.43 +0 1:0.69 9:0.76 17:0.61 18:0.71 21:0.59 27:0.66 29:0.77 30:0.56 32:0.64 34:0.47 36:0.99 37:0.73 39:0.98 43:0.16 55:0.59 66:0.68 69:0.45 70:0.59 71:0.89 74:0.50 76:0.85 77:0.70 81:0.29 86:0.72 91:0.57 96:0.75 98:0.68 106:0.81 108:0.35 114:0.56 117:0.86 120:0.63 122:0.77 123:0.33 124:0.48 126:0.44 127:0.61 129:0.05 133:0.60 135:0.86 138:0.95 139:0.69 140:0.45 144:0.85 145:0.52 146:0.80 147:0.83 149:0.43 150:0.47 151:0.73 153:0.80 154:0.69 155:0.58 158:0.74 159:0.60 162:0.78 164:0.63 172:0.80 173:0.38 176:0.61 177:0.70 179:0.40 187:0.87 190:0.77 191:0.70 192:0.59 195:0.79 201:0.68 202:0.59 206:0.81 208:0.54 212:0.78 215:0.38 216:0.32 220:0.62 222:0.20 235:0.74 241:0.27 242:0.76 243:0.72 245:0.79 247:0.74 248:0.73 253:0.52 254:0.96 255:0.74 256:0.58 259:0.21 260:0.61 265:0.68 266:0.71 267:0.73 268:0.64 271:0.47 276:0.74 279:0.82 282:0.73 283:0.77 285:0.56 286:0.99 290:0.56 297:0.36 298:0.99 300:0.70 +0 8:0.68 11:0.91 17:0.73 18:0.95 21:0.64 22:0.93 27:0.40 29:0.77 30:0.55 31:0.94 34:0.67 36:0.48 37:0.31 39:0.98 43:0.57 44:0.87 45:0.81 47:0.93 48:0.98 55:0.45 64:0.77 66:0.48 69:0.32 70:0.81 71:0.89 74:0.45 79:0.96 81:0.24 86:0.91 91:0.65 98:0.43 101:0.52 108:0.86 122:0.86 123:0.86 124:0.88 126:0.26 127:0.95 129:0.59 133:0.91 135:0.81 137:0.94 139:0.90 140:0.45 144:0.85 145:0.88 146:0.80 147:0.83 149:0.63 150:0.24 151:0.64 153:0.83 154:0.46 159:0.89 162:0.75 172:0.92 173:0.12 177:0.70 179:0.18 187:0.21 190:0.89 191:0.89 195:0.96 196:0.96 202:0.71 212:0.77 216:0.36 219:0.89 222:0.20 235:0.80 241:0.32 242:0.28 243:0.25 244:0.61 245:0.92 247:0.91 248:0.65 253:0.33 256:0.79 259:0.21 262:0.93 265:0.45 266:0.89 267:0.52 268:0.75 271:0.47 276:0.93 277:0.69 281:0.89 284:0.95 285:0.47 292:0.95 300:0.84 +1 11:0.91 17:0.16 18:0.88 21:0.05 22:0.93 27:0.72 29:0.77 30:0.26 31:0.90 34:0.49 36:0.47 39:0.98 43:0.24 45:0.83 47:0.93 55:0.59 64:0.77 66:0.98 69:0.07 70:0.76 71:0.89 74:0.61 79:0.91 81:0.43 86:0.91 91:0.75 98:0.99 101:0.52 108:0.06 122:0.77 123:0.06 126:0.26 127:0.89 129:0.05 135:0.23 137:0.90 139:0.88 140:0.45 144:0.85 146:0.99 147:0.99 149:0.42 150:0.36 151:0.55 153:0.48 154:0.66 159:0.83 162:0.86 172:0.96 173:0.08 177:0.70 179:0.20 187:0.21 190:0.89 196:0.93 202:0.50 212:0.51 216:0.23 219:0.89 220:0.82 222:0.20 235:0.05 241:0.62 242:0.32 243:0.98 247:0.91 248:0.55 253:0.39 254:0.90 256:0.58 259:0.21 262:0.93 265:0.99 266:0.63 267:0.52 268:0.59 271:0.47 276:0.92 284:0.88 285:0.47 292:0.91 300:0.55 +0 8:0.59 11:0.57 12:0.01 17:0.34 21:0.78 27:0.45 28:0.96 30:0.84 34:0.84 36:0.21 37:0.50 39:0.83 43:0.78 66:0.95 69:0.77 70:0.21 74:0.92 85:0.98 91:0.25 98:0.74 101:0.85 108:0.61 122:0.43 123:0.58 127:0.23 129:0.05 135:0.69 145:0.50 146:0.93 147:0.94 149:0.96 154:0.71 159:0.56 161:0.48 163:0.81 167:0.65 172:0.88 173:0.64 177:0.38 178:0.55 179:0.20 181:0.92 187:0.68 212:0.48 216:0.77 222:0.76 235:0.31 241:0.37 242:0.63 243:0.95 247:0.30 253:0.65 259:0.21 265:0.74 266:0.47 267:0.44 271:0.42 276:0.79 300:0.18 +2 1:0.74 9:0.80 11:0.57 12:0.01 17:0.02 21:0.05 27:0.33 28:0.96 30:0.75 34:0.47 36:0.23 37:0.41 39:0.83 41:0.90 43:0.86 60:0.99 66:0.30 69:0.57 70:0.36 74:0.85 81:0.80 83:0.85 85:0.97 91:0.62 96:0.90 98:0.41 101:0.83 108:0.86 114:0.95 120:0.81 122:0.43 123:0.85 124:0.87 126:0.54 127:0.56 129:0.95 133:0.87 135:0.99 140:0.45 145:0.86 146:0.13 147:0.18 149:0.91 150:0.88 151:0.95 153:0.84 154:0.84 155:0.67 158:0.92 159:0.76 161:0.48 162:0.84 163:0.81 164:0.75 165:0.90 167:0.58 172:0.59 173:0.38 176:0.73 177:0.38 179:0.44 181:0.92 187:0.39 192:0.59 201:0.74 202:0.65 208:0.64 212:0.35 215:0.91 216:0.88 222:0.76 235:0.12 241:0.10 242:0.63 243:0.13 245:0.74 247:0.39 248:0.88 253:0.92 255:0.79 256:0.68 259:0.21 260:0.82 265:0.43 266:0.71 267:0.88 268:0.71 271:0.42 276:0.71 279:0.86 283:0.82 285:0.62 290:0.95 297:0.36 300:0.33 +2 6:0.98 8:0.89 9:0.80 11:0.57 12:0.01 17:0.05 20:0.98 21:0.78 27:0.09 28:0.96 30:0.89 32:0.80 34:0.49 36:0.68 37:0.78 39:0.83 41:0.99 43:0.92 60:0.99 66:0.23 69:0.41 70:0.28 74:0.85 77:0.70 78:0.84 83:0.90 85:0.94 91:0.63 96:0.98 98:0.20 100:0.90 101:0.79 108:0.32 111:0.86 120:0.95 122:0.43 123:0.31 124:0.79 127:0.65 129:0.89 133:0.78 135:0.92 138:0.99 140:0.45 145:0.74 146:0.37 147:0.44 149:0.88 151:0.97 152:0.99 153:0.90 154:0.91 155:0.67 158:0.92 159:0.79 161:0.48 162:0.69 164:0.94 167:0.67 169:0.98 172:0.32 173:0.22 177:0.38 179:0.71 181:0.92 186:0.85 187:0.68 189:0.95 191:0.73 192:0.59 195:0.92 202:0.90 208:0.64 212:0.61 216:0.89 220:0.62 222:0.76 235:0.74 238:0.94 241:0.46 242:0.63 243:0.43 245:0.62 247:0.30 248:0.98 253:0.98 255:0.79 256:0.92 259:0.21 260:0.96 261:0.99 265:0.21 266:0.47 267:0.59 268:0.92 271:0.42 276:0.45 279:0.86 282:0.88 283:0.82 285:0.62 299:0.88 300:0.33 +2 1:0.74 7:0.81 8:0.79 9:0.80 11:0.57 12:0.01 17:0.13 20:0.79 21:0.30 27:0.11 28:0.96 30:0.86 34:0.52 36:0.80 37:0.79 39:0.83 41:0.92 43:0.87 60:0.87 66:0.33 69:0.56 70:0.40 74:0.74 78:0.76 81:0.64 83:0.86 85:0.91 91:0.25 96:0.88 98:0.17 100:0.80 101:0.77 106:0.81 108:0.92 111:0.75 114:0.86 117:0.86 120:0.80 121:0.90 122:0.43 123:0.92 124:0.78 126:0.54 127:0.55 129:0.89 133:0.78 135:0.89 140:0.45 145:0.52 146:0.13 147:0.18 149:0.71 150:0.78 151:0.95 152:0.77 153:0.84 154:0.85 155:0.67 158:0.92 159:0.80 161:0.48 162:0.84 164:0.77 165:0.84 167:0.56 169:0.77 172:0.32 173:0.34 176:0.73 177:0.38 179:0.79 181:0.92 186:0.76 187:0.39 192:0.59 201:0.74 202:0.65 204:0.89 206:0.81 208:0.64 212:0.39 215:0.72 216:0.85 222:0.76 230:0.87 232:0.91 233:0.76 235:0.42 241:0.03 242:0.63 243:0.25 245:0.54 247:0.30 248:0.87 253:0.89 255:0.79 256:0.68 259:0.21 260:0.81 261:0.76 265:0.18 266:0.54 267:0.87 268:0.72 271:0.42 276:0.36 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43 +3 1:0.74 8:0.93 9:0.80 11:0.57 12:0.01 17:0.28 21:0.22 27:0.09 28:0.96 30:0.84 34:0.76 36:0.97 37:0.78 39:0.83 41:0.98 43:0.92 60:0.98 66:0.16 69:0.37 70:0.53 74:0.94 81:0.69 83:0.89 85:0.99 91:0.51 96:0.96 98:0.35 101:0.84 108:0.76 114:0.90 120:0.92 122:0.43 123:0.75 124:0.94 126:0.54 127:0.75 129:0.98 133:0.94 135:0.72 140:0.45 146:0.31 147:0.38 149:0.97 150:0.81 151:0.98 153:0.91 154:0.91 155:0.67 158:0.92 159:0.89 161:0.48 162:0.72 164:0.90 165:0.86 167:0.59 172:0.67 173:0.13 176:0.73 177:0.38 179:0.26 181:0.92 187:0.98 191:0.70 192:0.59 201:0.74 202:0.84 208:0.64 212:0.54 215:0.79 216:0.89 220:0.62 222:0.76 235:0.31 241:0.12 242:0.63 243:0.13 245:0.85 247:0.30 248:0.96 253:0.97 255:0.79 256:0.87 259:0.21 260:0.92 265:0.37 266:0.80 267:0.88 268:0.87 271:0.42 276:0.86 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.70 +1 1:0.74 7:0.81 8:0.93 9:0.80 11:0.57 12:0.01 17:0.02 21:0.77 27:0.09 28:0.96 30:0.73 34:0.97 36:0.65 37:0.78 39:0.83 41:0.98 43:0.92 60:0.95 66:0.27 69:0.45 70:0.47 74:0.83 78:0.81 81:0.40 83:0.88 85:0.91 91:0.60 96:0.96 98:0.19 101:0.77 108:0.35 111:0.83 114:0.64 120:0.92 121:0.90 122:0.43 123:0.34 124:0.84 126:0.54 127:0.67 129:0.81 133:0.83 135:0.97 140:0.45 145:0.89 146:0.37 147:0.44 149:0.85 150:0.62 151:0.97 153:0.91 154:0.91 155:0.67 158:0.92 159:0.82 161:0.48 162:0.55 164:0.92 165:0.63 167:0.69 169:0.98 172:0.32 173:0.23 176:0.73 177:0.38 179:0.76 181:0.92 187:0.98 191:0.76 192:0.59 201:0.74 202:0.91 204:0.89 208:0.64 212:0.42 215:0.44 216:0.89 222:0.76 230:0.87 232:0.94 233:0.76 235:0.98 241:0.51 242:0.63 243:0.46 245:0.55 247:0.30 248:0.96 253:0.96 255:0.79 256:0.90 259:0.21 260:0.92 265:0.20 266:0.63 267:0.76 268:0.90 271:0.42 276:0.42 279:0.86 283:0.82 285:0.62 290:0.64 297:0.36 300:0.43 +4 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.57 12:0.01 17:0.01 20:0.99 21:0.47 27:0.09 28:0.96 30:0.85 32:0.80 34:0.66 36:0.86 37:0.78 39:0.83 41:1.00 43:0.92 60:1.00 66:0.35 69:0.40 70:0.41 74:0.90 76:0.85 77:0.70 78:0.92 81:0.57 83:0.91 85:0.96 91:0.89 96:1.00 98:0.44 100:0.99 101:0.82 108:0.85 111:0.99 114:0.80 120:0.99 121:0.97 122:0.43 123:0.84 124:0.83 126:0.54 127:0.62 129:0.93 133:0.84 135:0.89 138:1.00 140:0.45 145:0.67 146:0.37 147:0.44 149:0.91 150:0.74 151:1.00 152:0.99 153:0.99 154:0.91 155:0.67 158:0.92 159:0.79 161:0.48 162:0.66 164:0.98 165:0.80 167:0.79 169:0.98 172:0.54 173:0.21 176:0.73 177:0.38 179:0.56 181:0.92 186:0.92 187:0.68 189:0.99 191:0.92 192:0.59 195:0.91 201:0.74 202:0.97 204:0.89 208:0.64 212:0.42 215:0.63 216:0.89 222:0.76 230:0.87 232:0.88 233:0.76 235:0.60 238:0.99 241:0.71 242:0.63 243:0.33 245:0.68 247:0.30 248:1.00 253:1.00 255:0.79 256:0.98 259:0.21 260:0.99 261:0.99 265:0.46 266:0.63 267:0.84 268:0.98 271:0.42 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.85 8:0.73 9:0.80 11:0.57 12:0.01 17:0.08 20:0.85 21:0.05 27:0.14 28:0.96 30:0.76 34:0.56 36:0.19 37:0.67 39:0.83 41:0.97 43:0.86 60:0.99 66:0.31 69:0.57 70:0.41 74:0.86 78:0.76 81:0.80 83:0.90 85:0.97 91:0.73 96:0.97 98:0.42 100:0.79 101:0.84 108:0.85 111:0.75 114:0.95 120:0.94 122:0.43 123:0.84 124:0.87 126:0.54 127:0.47 129:0.95 133:0.87 135:0.98 140:0.45 145:0.86 146:0.34 147:0.41 149:0.92 150:0.88 151:0.98 152:0.89 153:0.92 154:0.84 155:0.67 158:0.92 159:0.73 161:0.48 162:0.77 163:0.81 164:0.88 165:0.90 167:0.59 169:0.77 172:0.59 173:0.39 176:0.73 177:0.38 179:0.42 181:0.92 186:0.76 187:0.39 189:0.88 192:0.59 201:0.74 202:0.81 208:0.64 212:0.30 215:0.91 216:0.93 222:0.76 235:0.12 238:0.87 241:0.12 242:0.63 243:0.21 245:0.73 247:0.39 248:0.97 253:0.97 255:0.79 256:0.86 259:0.21 260:0.94 261:0.79 265:0.44 266:0.75 267:0.84 268:0.86 271:0.42 276:0.70 279:0.86 283:0.82 285:0.62 290:0.95 297:0.36 300:0.43 +1 7:0.81 9:0.80 11:0.57 12:0.01 17:0.36 21:0.13 27:0.43 28:0.96 30:0.85 34:0.19 36:0.98 37:0.84 39:0.83 41:0.97 43:0.85 60:0.95 66:0.46 69:0.62 70:0.44 74:0.90 78:0.91 81:0.56 83:0.89 85:0.96 91:0.78 96:0.97 98:0.29 101:0.82 108:0.47 111:0.97 114:0.74 120:0.93 121:0.90 122:0.43 123:0.45 124:0.67 126:0.32 127:0.65 129:0.81 133:0.67 135:0.85 140:0.45 145:0.39 146:0.53 147:0.59 149:0.93 150:0.42 151:0.98 153:0.93 154:0.81 155:0.67 158:0.92 159:0.77 161:0.48 162:0.81 164:0.89 167:0.78 169:0.97 172:0.59 173:0.44 176:0.56 177:0.38 179:0.56 181:0.92 187:0.21 191:0.77 192:0.59 202:0.83 204:0.89 208:0.64 212:0.76 216:0.50 222:0.76 230:0.87 232:0.74 233:0.76 235:0.12 238:0.98 241:0.70 242:0.63 243:0.58 245:0.75 247:0.30 248:0.96 253:0.98 255:0.79 256:0.88 260:0.94 265:0.31 266:0.71 267:0.36 268:0.87 271:0.42 276:0.44 279:0.86 283:0.82 285:0.62 290:0.74 300:0.70 +3 1:0.74 6:0.96 8:0.94 9:0.80 11:0.57 12:0.01 17:0.32 20:0.99 21:0.30 27:0.34 28:0.96 30:0.76 32:0.80 34:0.33 36:0.38 37:0.85 39:0.83 41:0.98 43:0.89 60:1.00 66:0.36 69:0.49 70:0.28 74:0.79 77:0.89 78:0.83 81:0.64 83:0.88 85:0.88 91:0.93 96:0.96 98:0.29 100:0.92 101:0.77 108:0.38 111:0.86 114:0.86 120:0.92 122:0.57 123:0.36 124:0.59 126:0.54 127:0.61 129:0.59 133:0.47 135:0.17 138:0.98 140:0.45 145:0.90 146:0.47 147:0.53 149:0.75 150:0.78 151:0.99 152:0.90 153:0.95 154:0.88 155:0.67 158:0.92 159:0.69 161:0.48 162:0.69 163:0.81 164:0.91 165:0.84 167:0.95 169:0.90 172:0.27 173:0.36 176:0.73 177:0.38 179:0.87 181:0.92 186:0.83 189:0.97 191:0.94 192:0.59 195:0.83 201:0.74 202:0.87 208:0.64 212:0.37 215:0.72 216:0.58 222:0.76 232:0.71 235:0.42 238:0.96 241:0.94 242:0.58 243:0.51 245:0.56 247:0.39 248:0.96 253:0.96 255:0.79 256:0.89 259:0.21 260:0.93 261:0.90 265:0.31 266:0.47 267:0.89 268:0.89 271:0.42 276:0.20 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.25 +3 1:0.74 6:0.93 7:0.81 8:0.83 9:0.80 11:0.57 12:0.01 17:0.22 20:0.99 21:0.30 27:0.21 28:0.96 30:0.75 34:0.49 36:0.80 37:0.74 39:0.83 41:0.99 43:0.98 60:0.99 66:0.11 69:0.12 70:0.54 74:0.98 78:0.79 81:0.64 83:0.90 85:1.00 91:0.70 96:0.99 98:0.44 100:0.86 101:0.86 106:0.81 108:0.97 111:0.81 114:0.86 117:0.86 120:0.97 121:0.90 122:0.57 123:0.86 124:0.95 126:0.54 127:0.83 129:0.99 133:0.96 135:0.30 138:0.99 140:0.45 146:0.59 147:0.65 149:0.99 150:0.78 151:0.99 152:0.96 153:0.97 154:0.98 155:0.67 158:0.92 159:0.94 161:0.48 162:0.77 164:0.94 165:0.84 167:0.65 169:0.82 172:0.92 173:0.06 176:0.73 177:0.38 179:0.13 181:0.92 186:0.79 187:0.39 189:0.94 191:0.70 192:0.59 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.76 230:0.87 232:0.87 233:0.76 235:0.42 238:0.95 241:0.39 242:0.62 243:0.13 245:0.96 247:0.60 248:0.99 253:0.99 255:0.79 256:0.92 259:0.21 260:0.97 261:0.85 265:0.45 266:0.91 267:0.59 268:0.92 271:0.42 276:0.96 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.01 17:0.81 21:0.22 27:0.08 28:0.96 30:0.74 32:0.80 34:0.37 36:0.33 37:0.81 39:0.83 41:0.93 43:0.92 66:0.97 69:0.35 70:0.20 74:1.00 77:0.70 81:0.69 83:0.81 85:1.00 91:0.57 96:0.90 98:0.93 101:0.87 108:0.65 114:0.90 120:0.82 121:0.90 122:0.43 123:0.63 124:0.36 126:0.54 127:0.84 129:0.89 133:0.78 135:0.85 140:0.45 145:0.42 146:0.26 147:0.32 149:1.00 150:0.81 151:0.92 153:0.72 154:0.92 155:0.67 159:0.92 161:0.48 162:0.86 164:0.77 165:0.86 167:0.72 172:1.00 173:0.11 176:0.73 177:0.38 179:0.08 181:0.92 187:0.68 192:0.59 195:0.98 201:0.74 202:0.64 204:0.89 208:0.64 212:0.94 215:0.79 216:0.79 220:0.82 222:0.76 230:0.84 233:0.69 235:0.31 241:0.59 242:0.63 243:0.05 245:0.99 247:0.30 248:0.89 253:0.94 255:0.79 256:0.71 259:0.21 260:0.83 265:0.93 266:0.63 267:0.59 268:0.72 271:0.42 276:1.00 282:0.88 285:0.62 290:0.90 297:0.36 299:0.88 300:0.55 +0 11:0.57 12:0.01 17:0.45 21:0.78 27:0.45 28:0.96 30:0.10 34:0.87 36:0.20 37:0.50 39:0.83 43:0.74 55:0.45 66:0.34 69:0.79 70:0.96 74:0.94 85:0.90 91:0.12 98:0.35 101:0.77 108:0.62 122:0.67 123:0.60 124:0.86 127:0.50 129:0.81 133:0.86 135:0.89 145:0.41 146:0.64 147:0.69 149:0.78 154:0.65 159:0.76 161:0.48 167:0.66 172:0.63 173:0.64 177:0.38 178:0.55 179:0.41 181:0.92 187:0.68 212:0.72 216:0.77 222:0.76 235:0.97 241:0.41 242:0.72 243:0.51 245:0.74 247:0.47 253:0.66 259:0.21 265:0.37 266:0.87 267:0.69 271:0.42 276:0.71 300:0.84 +1 1:0.60 10:0.92 11:0.49 17:0.24 21:0.22 27:0.65 29:0.62 30:0.17 34:0.47 36:0.72 37:0.29 39:0.94 43:0.07 45:0.99 55:0.71 56:0.97 66:0.07 69:0.99 70:0.94 71:0.78 74:0.24 81:0.46 83:0.56 91:0.10 98:0.30 99:0.95 101:0.65 108:0.99 122:0.98 123:0.99 124:0.99 126:0.32 127:1.00 129:0.05 131:0.61 133:0.99 135:0.30 146:0.98 147:0.62 149:0.26 150:0.39 154:0.06 155:0.48 157:0.61 159:0.99 165:0.65 166:0.61 170:0.90 172:0.94 173:0.89 174:1.00 175:0.75 177:0.38 178:0.55 179:0.09 182:0.61 187:0.39 192:0.45 197:1.00 201:0.59 208:0.50 212:0.56 216:0.43 222:0.35 227:0.61 229:0.61 231:0.61 235:0.42 239:0.91 241:0.03 242:0.41 243:0.05 244:0.97 245:0.99 246:0.61 247:1.00 253:0.20 254:0.92 257:0.99 259:0.21 264:0.96 265:0.32 266:0.97 267:0.52 271:0.75 275:1.00 276:1.00 277:0.69 287:0.61 294:0.95 300:0.94 +0 8:0.86 9:0.70 10:0.80 11:0.42 17:0.38 18:0.85 21:0.78 22:0.93 27:0.11 29:0.62 30:0.27 31:0.91 34:0.82 36:0.76 37:0.89 39:0.94 43:0.05 47:0.93 55:0.71 66:0.62 69:0.83 70:0.58 71:0.78 74:0.30 79:0.91 86:0.61 91:0.20 96:0.72 98:0.56 108:0.67 117:0.86 122:0.67 123:0.65 124:0.64 127:0.32 129:0.05 131:0.85 133:0.72 135:0.70 137:0.91 139:0.61 140:0.45 144:0.57 146:0.88 147:0.26 149:0.07 151:0.58 153:0.48 154:0.09 155:0.50 157:0.61 158:0.72 159:0.74 162:0.83 163:0.75 166:0.61 170:0.90 172:0.67 173:0.66 174:0.83 175:0.91 177:0.70 179:0.43 182:0.61 187:0.87 190:1.00 191:0.84 192:0.54 196:0.87 197:0.84 202:0.63 208:0.49 212:0.22 216:0.90 219:0.87 220:0.87 222:0.35 227:0.85 229:0.61 231:0.68 232:0.97 239:0.80 241:0.18 242:0.12 243:0.21 244:0.99 245:0.66 246:0.61 247:0.92 248:0.57 253:0.46 254:0.90 255:0.69 256:0.68 257:0.97 259:0.21 262:0.93 264:0.96 265:0.57 266:0.75 267:0.17 268:0.70 271:0.75 275:0.96 276:0.61 277:0.87 279:0.75 284:0.95 285:0.47 287:0.61 292:0.87 294:0.93 300:0.43 +0 1:0.56 11:0.42 17:0.38 18:0.68 21:0.54 27:0.12 29:0.62 30:0.30 34:0.88 36:0.55 37:0.81 39:0.94 43:0.05 55:0.71 56:0.97 66:0.51 69:0.60 70:0.80 71:0.78 74:0.28 81:0.31 86:0.59 91:0.11 98:0.38 106:0.81 108:0.46 114:0.63 122:0.55 123:0.44 124:0.64 126:0.32 127:0.29 129:0.05 131:0.61 133:0.60 135:0.80 139:0.58 144:0.57 146:0.47 147:0.53 149:0.05 150:0.31 154:0.09 155:0.46 157:0.61 159:0.41 166:0.72 170:0.90 172:0.41 173:0.59 175:0.97 176:0.55 177:0.88 178:0.55 179:0.67 182:0.61 187:0.98 192:0.47 201:0.57 202:0.36 206:0.81 208:0.49 212:0.39 215:0.58 216:0.90 222:0.35 227:0.61 229:0.61 231:0.64 235:0.74 241:0.12 242:0.13 243:0.61 244:0.99 245:0.55 246:0.61 247:0.76 253:0.48 254:0.88 257:0.93 259:0.21 264:0.96 265:0.40 266:0.54 267:0.91 271:0.75 275:0.93 276:0.40 277:0.95 287:0.61 290:0.63 294:0.93 297:0.36 300:0.55 +1 1:0.56 8:0.72 9:0.70 10:0.92 11:0.48 17:0.22 18:0.60 21:0.68 27:0.63 29:0.62 30:0.18 34:0.63 36:0.85 37:0.72 39:0.94 43:0.12 45:0.97 55:0.59 66:0.08 69:0.33 70:0.95 71:0.78 74:0.25 81:0.33 83:0.54 86:0.58 91:0.06 97:0.89 98:0.37 99:0.83 101:0.47 108:0.26 122:0.90 123:0.98 124:0.98 126:0.26 127:0.75 129:0.05 131:0.61 133:0.98 135:0.70 139:0.56 140:0.45 146:0.96 147:0.97 149:0.27 150:0.31 151:0.57 153:0.48 154:0.13 155:0.50 157:0.61 159:0.97 162:0.86 165:0.63 166:0.61 170:0.90 172:0.94 173:0.06 174:1.00 177:0.99 179:0.09 182:0.61 187:0.39 190:1.00 192:0.45 197:0.99 201:0.54 202:0.36 208:0.49 212:0.73 216:0.34 220:0.87 222:0.35 227:0.61 229:0.61 231:0.61 235:0.88 239:0.89 241:0.05 242:0.17 243:0.29 244:0.93 245:0.99 246:0.61 247:1.00 248:0.56 253:0.23 254:0.97 255:0.69 256:0.45 259:0.21 264:0.96 265:0.33 266:0.96 267:0.36 268:0.46 271:0.75 275:0.99 276:0.99 277:0.87 279:0.74 285:0.47 287:0.61 294:0.95 300:0.94 +1 1:0.56 8:0.64 9:0.70 10:0.92 11:0.48 17:0.11 18:0.60 21:0.68 27:0.63 29:0.62 30:0.17 34:0.45 36:0.91 37:0.72 39:0.94 43:0.12 45:0.97 55:0.71 66:0.24 69:0.33 70:0.97 71:0.78 74:0.25 81:0.33 83:0.54 86:0.58 91:0.11 97:0.89 98:0.40 99:0.83 101:0.47 108:0.26 122:0.90 123:0.25 124:0.98 126:0.26 127:1.00 129:0.05 131:0.61 133:0.98 135:0.56 139:0.56 140:0.45 146:0.98 147:0.99 149:0.30 150:0.31 151:0.53 153:0.48 154:0.13 155:0.49 157:0.61 159:0.98 162:0.86 165:0.63 166:0.61 170:0.90 172:0.98 173:0.06 174:0.99 177:0.99 179:0.10 182:0.61 187:0.39 190:1.00 192:0.45 197:0.99 201:0.54 202:0.36 208:0.49 212:0.69 216:0.34 220:0.91 222:0.35 227:0.61 229:0.61 231:0.61 235:0.88 239:0.89 241:0.03 242:0.16 243:0.44 244:0.93 245:0.98 246:0.61 247:1.00 248:0.53 253:0.23 254:0.97 255:0.69 256:0.45 259:0.21 264:0.96 265:0.42 266:0.99 267:0.44 268:0.46 271:0.75 275:0.99 276:0.99 279:0.74 285:0.47 287:0.61 294:0.95 300:0.98 +0 8:0.81 9:0.71 10:0.83 11:0.42 17:0.38 18:0.86 21:0.68 23:0.95 27:0.12 29:0.62 30:0.16 31:0.91 34:0.96 36:0.85 37:0.81 39:0.94 43:0.05 55:0.45 62:0.95 66:0.26 69:0.89 70:0.79 71:0.78 74:0.34 79:0.90 81:0.23 86:0.66 91:0.38 96:0.80 98:0.54 108:0.46 114:0.60 122:0.91 123:0.76 124:0.73 126:0.22 127:0.38 128:0.95 129:0.05 131:0.85 133:0.67 135:0.68 137:0.91 139:0.64 140:0.80 144:0.57 146:0.82 147:0.81 149:0.07 150:0.24 151:0.70 153:0.48 154:0.09 155:0.51 157:0.61 159:0.71 162:0.55 166:0.72 168:0.95 170:0.90 172:0.65 173:0.82 174:0.91 175:0.91 176:0.55 177:0.88 178:0.42 179:0.30 182:0.61 187:0.98 190:1.00 191:0.73 192:0.56 196:0.88 197:0.91 202:0.74 205:0.95 208:0.49 212:0.37 216:0.90 220:0.74 222:0.35 227:0.85 229:0.61 231:0.68 235:0.80 239:0.82 241:0.46 242:0.22 243:0.47 244:0.99 245:0.88 246:0.61 247:0.97 248:0.65 253:0.71 254:0.93 255:0.70 256:0.71 257:0.93 259:0.21 264:0.96 265:0.49 266:0.54 267:0.85 268:0.72 271:0.75 275:0.91 276:0.78 277:0.87 284:0.90 285:0.50 287:0.61 290:0.60 294:0.92 300:0.55 +0 1:0.56 8:0.98 9:0.70 10:0.80 11:0.42 17:0.24 18:0.78 21:0.47 27:0.13 29:0.62 30:0.33 31:0.89 34:0.63 36:0.99 37:0.79 39:0.94 43:0.05 55:0.84 64:0.77 66:0.64 69:0.82 70:0.46 71:0.78 74:0.29 79:0.89 81:0.28 86:0.60 91:0.35 96:0.73 98:0.68 108:0.66 114:0.67 122:0.99 123:0.64 124:0.49 126:0.26 127:0.29 129:0.05 131:0.85 133:0.59 135:0.96 137:0.89 139:0.60 140:0.80 144:0.57 145:0.56 146:0.86 147:0.23 149:0.06 150:0.31 151:0.58 153:0.71 154:0.08 155:0.51 157:0.61 158:0.72 159:0.55 162:0.56 163:0.86 166:0.78 170:0.90 172:0.54 173:0.76 174:0.83 175:0.91 176:0.59 177:0.38 178:0.42 179:0.59 182:0.61 187:0.87 190:1.00 191:0.84 192:0.55 196:0.87 197:0.84 201:0.54 202:0.69 208:0.49 212:0.29 216:0.91 219:0.87 220:0.87 222:0.35 227:0.85 229:0.61 231:0.68 235:0.60 239:0.80 241:0.44 242:0.18 243:0.21 244:1.00 245:0.56 246:0.61 247:0.82 248:0.59 253:0.56 254:0.80 255:0.69 256:0.64 257:0.99 259:0.21 264:0.96 265:0.69 266:0.54 267:0.91 268:0.66 271:0.75 275:0.96 276:0.51 277:0.87 279:0.74 284:0.94 285:0.47 287:0.61 290:0.67 292:0.87 294:0.93 300:0.33 +1 8:0.89 9:0.71 10:0.81 11:0.46 17:0.47 18:0.79 21:0.47 27:0.12 29:0.62 30:0.41 31:0.91 34:0.63 36:0.77 37:0.81 39:0.94 43:0.08 45:0.66 55:0.71 66:0.68 69:0.83 70:0.72 71:0.78 74:0.30 76:0.98 79:0.91 81:0.24 86:0.60 91:0.58 96:0.79 98:0.74 101:0.45 108:0.68 114:0.63 117:0.86 122:0.90 123:0.65 124:0.45 126:0.22 127:0.35 129:0.05 131:0.85 133:0.37 135:0.81 137:0.91 139:0.59 140:0.80 144:0.57 145:0.45 146:0.88 147:0.49 149:0.09 150:0.25 151:0.74 153:0.48 154:0.07 155:0.52 157:0.77 158:0.71 159:0.58 162:0.61 166:0.72 170:0.90 172:0.73 173:0.78 174:0.86 175:0.75 176:0.55 177:0.38 179:0.42 182:0.73 187:0.68 190:1.00 192:0.56 196:0.87 197:0.86 202:0.68 208:0.49 212:0.49 216:0.90 220:0.82 222:0.35 227:0.85 229:0.61 231:0.68 232:0.82 235:0.52 239:0.80 241:0.44 242:0.19 243:0.28 244:1.00 245:0.79 246:0.61 247:0.93 248:0.69 253:0.69 255:0.70 256:0.64 257:0.99 259:0.21 264:0.96 265:0.75 266:0.75 267:0.79 268:0.68 271:0.75 275:0.91 276:0.67 277:0.69 284:0.87 285:0.47 287:0.61 290:0.63 294:0.91 300:0.70 +0 1:0.59 8:0.73 10:0.80 11:0.49 17:0.69 18:0.96 21:0.05 27:0.49 29:0.62 30:0.09 34:0.44 36:0.88 37:0.47 39:0.94 43:0.05 45:0.66 55:0.42 64:0.77 66:0.06 69:0.99 70:0.98 71:0.78 74:0.38 81:0.31 86:0.72 91:0.03 98:0.25 101:0.45 108:0.97 114:0.78 117:0.86 123:0.97 124:1.00 126:0.26 127:0.98 129:0.05 131:0.61 133:1.00 135:0.39 139:0.69 144:0.57 146:0.96 147:0.39 149:0.10 150:0.35 154:0.08 155:0.47 157:0.61 159:0.99 166:0.78 170:0.90 172:0.82 173:0.81 174:0.94 175:0.91 176:0.59 177:0.38 178:0.55 179:0.10 182:0.61 187:0.39 192:0.47 197:0.83 201:0.56 202:0.60 208:0.49 212:0.29 216:0.61 222:0.35 227:0.61 229:0.61 231:0.68 232:0.89 235:0.12 239:0.80 241:0.35 242:0.24 243:0.06 244:0.99 245:0.96 246:0.61 247:1.00 253:0.57 257:0.99 259:0.21 264:0.96 265:0.27 266:0.98 267:0.52 271:0.75 275:1.00 276:0.99 277:0.87 287:0.61 290:0.78 294:0.95 300:0.94 +2 1:0.56 2:0.99 10:0.87 11:0.45 17:0.49 21:0.47 27:0.24 29:0.62 30:0.17 33:0.97 34:0.66 36:0.96 37:0.79 39:0.94 43:0.08 45:0.83 55:0.71 56:0.97 66:0.85 69:0.87 70:0.93 71:0.78 74:0.28 75:0.95 81:0.40 83:0.54 91:0.17 98:0.80 101:0.52 108:0.75 114:0.64 122:0.98 123:0.73 124:0.38 126:0.43 127:0.61 129:0.05 131:0.61 133:0.43 135:0.97 145:0.89 146:0.95 147:0.24 149:0.12 150:0.38 154:0.10 155:0.46 157:0.61 158:0.72 159:0.76 166:0.72 170:0.90 172:0.90 173:0.79 174:0.96 175:0.75 176:0.55 177:0.05 178:0.55 179:0.29 182:0.61 187:1.00 192:0.47 195:0.90 197:0.95 201:0.60 208:0.49 212:0.73 216:0.65 222:0.35 226:0.95 227:0.61 229:0.61 231:0.61 235:0.74 236:0.91 239:0.86 241:0.03 242:0.12 243:0.09 244:0.98 245:0.75 246:0.61 247:0.99 253:0.49 254:0.80 257:0.99 259:0.21 264:0.96 265:0.80 266:0.80 267:0.73 271:0.75 275:0.97 276:0.82 277:0.69 279:0.74 287:0.61 290:0.64 291:0.97 294:0.95 295:0.98 300:0.84 +0 1:0.64 8:0.78 9:0.70 10:0.79 11:0.42 17:0.61 18:0.98 21:0.13 22:0.93 27:0.49 29:0.62 30:0.28 31:0.87 34:0.52 36:0.86 37:0.47 39:0.94 43:0.05 47:0.93 55:0.42 64:0.77 66:0.07 69:1.00 70:0.96 71:0.78 74:0.41 79:0.87 81:0.34 86:0.75 91:0.04 96:0.70 98:0.21 108:0.39 114:0.72 117:0.86 122:0.94 123:0.94 124:0.99 126:0.26 127:0.96 129:0.05 131:0.85 133:0.99 135:0.37 137:0.87 139:0.72 140:0.45 144:0.57 146:0.71 147:0.73 149:0.07 150:0.38 151:0.52 153:0.72 154:0.05 155:0.49 157:0.61 159:0.98 162:0.67 166:0.78 170:0.90 172:0.80 173:1.00 174:0.86 175:0.75 176:0.56 177:0.70 179:0.12 182:0.61 187:0.39 190:1.00 192:0.51 196:0.88 197:0.80 201:0.60 202:0.53 208:0.49 212:0.30 216:0.61 220:0.93 222:0.35 227:0.85 229:0.61 231:0.68 232:0.89 235:0.42 239:0.79 241:0.24 242:0.17 243:0.23 244:0.98 245:0.92 246:0.61 247:1.00 248:0.52 253:0.54 254:0.97 255:0.69 256:0.45 257:0.97 259:0.21 262:0.93 264:0.96 265:0.17 266:0.97 267:0.19 268:0.57 271:0.75 275:1.00 276:0.98 277:0.95 284:0.90 285:0.47 287:0.61 290:0.72 294:0.94 300:0.94 +1 1:0.56 2:0.99 9:0.70 10:0.87 11:0.45 17:0.47 21:0.47 23:0.95 27:0.06 29:0.62 30:0.19 33:0.97 34:0.44 36:1.00 37:0.90 39:0.94 43:0.08 45:0.83 55:0.71 56:0.97 62:0.96 66:0.84 69:0.87 70:0.90 71:0.78 74:0.28 75:0.95 81:0.40 83:0.54 91:0.18 96:0.72 98:0.70 101:0.52 108:0.74 114:0.64 122:0.98 123:0.73 124:0.39 126:0.43 127:0.39 128:0.96 129:0.05 131:0.80 133:0.59 135:0.98 140:0.45 143:0.99 145:0.89 146:0.91 147:0.93 149:0.12 150:0.38 151:0.59 153:0.46 154:0.10 155:0.50 157:0.61 158:0.72 159:0.70 162:0.62 166:0.72 168:0.96 170:0.90 172:0.88 173:0.79 174:0.96 175:0.91 176:0.55 177:0.96 179:0.26 182:0.61 187:1.00 190:0.99 192:0.58 195:0.90 197:0.95 201:0.60 202:0.49 205:0.95 208:0.49 212:0.77 216:0.73 220:0.82 222:0.35 226:0.95 227:0.82 229:0.61 231:0.61 235:0.74 236:0.91 239:0.86 241:0.03 242:0.12 243:0.85 244:0.98 245:0.73 246:0.61 247:0.98 248:0.57 253:0.53 254:0.80 255:0.71 256:0.45 257:0.84 259:0.21 264:0.96 265:0.71 266:0.63 267:0.84 268:0.45 271:0.75 275:0.97 276:0.80 277:0.87 279:0.74 285:0.55 287:0.61 290:0.64 291:0.97 294:0.95 295:0.98 300:0.84 +0 8:0.86 9:0.70 10:0.82 11:0.42 17:0.53 18:0.83 21:0.77 27:0.12 29:0.62 30:0.10 31:0.89 34:0.94 36:0.91 37:0.81 39:0.94 43:0.05 55:0.92 66:0.17 69:0.85 70:0.90 71:0.78 74:0.34 79:0.88 81:0.23 86:0.66 91:0.51 96:0.75 98:0.41 108:0.71 114:0.58 120:0.59 122:0.90 123:0.69 124:0.90 126:0.22 127:0.60 129:0.59 131:0.85 133:0.89 135:0.66 137:0.89 139:0.63 140:0.95 144:0.57 146:0.79 147:0.63 149:0.08 150:0.24 151:0.57 153:0.48 154:0.08 155:0.52 157:0.61 159:0.84 162:0.58 163:0.81 164:0.66 166:0.72 170:0.90 172:0.59 173:0.68 174:0.89 175:0.97 176:0.55 177:0.70 178:0.50 179:0.32 182:0.61 187:0.98 190:1.00 191:0.73 192:0.86 196:0.87 197:0.89 202:0.78 208:0.50 212:0.30 216:0.90 220:0.99 222:0.35 227:0.85 229:0.61 231:0.68 235:0.97 239:0.81 241:0.27 242:0.24 243:0.33 244:0.99 245:0.83 246:0.61 247:0.96 248:0.56 253:0.56 254:0.93 255:0.69 256:0.78 257:0.97 259:0.21 260:0.59 264:0.96 265:0.43 266:0.84 267:0.93 268:0.78 271:0.75 275:0.91 276:0.81 277:0.95 284:0.92 285:0.50 287:0.61 290:0.58 294:0.92 300:0.70 +1 1:0.56 8:0.65 9:0.70 10:0.93 11:0.48 17:0.34 18:0.70 21:0.74 27:0.63 29:0.62 30:0.18 34:0.80 36:0.87 37:0.72 39:0.94 43:0.12 45:0.98 55:0.59 66:0.24 69:0.35 70:0.96 71:0.78 74:0.25 81:0.32 83:0.54 86:0.59 91:0.07 97:0.89 98:0.43 99:0.83 101:0.47 108:0.27 122:0.90 123:0.26 124:0.98 126:0.26 127:0.88 129:0.05 131:0.61 133:0.98 135:0.66 139:0.57 140:0.45 146:0.99 147:0.99 149:0.27 150:0.30 151:0.60 153:0.48 154:0.14 155:0.50 157:0.61 159:0.97 162:0.86 165:0.63 166:0.61 170:0.90 172:0.98 173:0.06 174:1.00 177:0.99 179:0.09 182:0.61 187:0.39 190:1.00 192:0.46 197:0.99 201:0.53 202:0.50 208:0.49 212:0.77 216:0.34 220:0.82 222:0.35 227:0.61 229:0.61 231:0.62 235:0.97 239:0.90 241:0.03 242:0.16 243:0.44 244:0.93 245:0.99 246:0.61 247:1.00 248:0.58 253:0.23 254:0.97 255:0.69 256:0.58 259:0.21 264:0.96 265:0.45 266:0.98 267:0.28 268:0.59 271:0.75 275:0.99 276:0.99 279:0.74 285:0.47 287:0.61 294:0.95 300:0.98 +1 1:0.56 9:0.70 10:0.86 11:0.45 17:0.49 21:0.47 23:0.95 27:0.06 29:0.62 30:0.28 33:0.97 34:0.55 36:0.98 37:0.90 39:0.94 43:0.08 45:0.83 55:0.71 56:0.97 62:0.95 66:0.84 69:0.87 70:0.85 71:0.78 74:0.27 81:0.40 83:0.54 91:0.18 96:0.69 98:0.79 101:0.52 108:0.75 114:0.64 122:0.98 123:0.73 124:0.38 126:0.43 127:0.63 128:0.95 129:0.05 131:0.80 133:0.43 135:0.98 140:0.80 143:0.98 145:0.89 146:0.95 147:0.24 149:0.13 150:0.38 151:0.50 153:0.46 154:0.13 155:0.47 157:0.61 159:0.75 162:0.56 166:0.72 168:0.95 170:0.90 172:0.89 173:0.80 174:0.96 175:0.75 176:0.55 177:0.05 178:0.42 179:0.31 182:0.61 187:1.00 190:0.99 192:0.50 195:0.90 197:0.95 201:0.60 202:0.50 205:0.95 208:0.49 212:0.75 216:0.73 220:0.96 222:0.35 227:0.82 229:0.61 231:0.61 235:0.74 236:0.91 239:0.85 241:0.12 242:0.12 243:0.10 244:0.97 245:0.74 246:0.61 247:0.98 248:0.49 253:0.50 254:0.90 255:0.69 256:0.45 257:0.99 259:0.21 264:0.96 265:0.79 266:0.80 267:0.89 268:0.45 271:0.75 275:0.97 276:0.81 277:0.69 285:0.55 287:0.61 290:0.64 291:0.97 294:0.94 300:0.70 +1 8:0.77 9:0.71 10:0.83 11:0.42 17:0.32 18:0.86 21:0.64 27:0.12 29:0.62 30:0.12 31:0.92 34:0.96 36:0.83 37:0.81 39:0.94 43:0.05 55:0.45 66:0.68 69:0.60 70:0.79 71:0.78 74:0.34 79:0.92 81:0.23 83:0.54 86:0.66 91:0.40 96:0.79 98:0.75 108:0.46 114:0.61 122:0.90 123:0.44 124:0.49 126:0.22 127:0.36 129:0.05 131:0.85 133:0.60 135:0.65 137:0.92 139:0.64 140:0.87 144:0.57 146:0.94 147:0.95 149:0.06 150:0.24 151:0.65 153:0.48 154:0.09 155:0.51 157:0.61 159:0.70 162:0.63 166:0.72 170:0.90 172:0.82 173:0.42 174:0.89 175:0.75 176:0.55 177:0.99 178:0.48 179:0.30 182:0.88 187:0.98 190:1.00 192:0.56 196:0.88 197:0.90 202:0.73 208:0.49 212:0.27 216:0.90 220:0.91 222:0.35 227:0.85 229:0.93 231:0.69 235:0.74 239:0.82 241:0.62 242:0.18 243:0.72 244:0.99 245:0.82 246:0.61 247:0.92 248:0.70 253:0.67 254:0.93 255:0.70 256:0.75 257:0.65 259:0.21 264:0.96 265:0.76 266:0.54 267:0.91 268:0.74 271:0.75 275:0.91 276:0.77 277:0.69 284:0.95 285:0.47 287:0.89 290:0.61 294:0.92 300:0.55 +0 8:0.94 9:0.70 10:0.79 11:0.42 17:0.34 18:0.80 21:0.54 27:0.12 29:0.62 30:0.42 31:0.91 34:0.96 36:0.74 37:0.81 39:0.94 43:0.05 55:0.84 66:0.20 69:0.92 70:0.54 71:0.78 74:0.31 76:0.97 77:0.70 79:0.91 81:0.24 86:0.62 91:0.82 96:0.92 98:0.57 108:0.46 114:0.62 122:0.90 123:0.83 124:0.75 126:0.22 127:0.44 129:0.05 131:0.85 133:0.64 135:0.80 137:0.91 139:0.60 140:0.99 144:0.57 145:0.83 146:0.81 147:0.75 149:0.08 150:0.25 151:0.59 153:0.72 154:0.06 155:0.49 157:0.61 158:0.71 159:0.68 162:0.73 166:0.72 170:0.90 172:0.51 173:0.91 174:0.79 175:0.91 176:0.55 177:0.88 179:0.39 182:0.61 187:0.87 190:1.00 191:0.83 192:0.51 195:0.88 196:0.87 197:0.80 202:0.92 208:0.49 212:0.19 216:0.90 219:0.86 220:0.82 222:0.35 227:0.85 229:0.61 231:0.68 235:0.60 239:0.79 241:0.62 242:0.41 243:0.40 244:0.99 245:0.85 246:0.61 247:0.84 248:0.62 253:0.86 254:0.97 255:0.69 256:0.94 257:0.93 259:0.21 264:0.96 265:0.47 266:0.63 267:0.99 268:0.94 271:0.75 275:0.91 276:0.73 277:0.87 279:0.74 282:0.71 284:0.96 285:0.47 287:0.61 290:0.62 292:0.86 294:0.92 300:0.43 +0 8:0.76 10:0.89 12:0.01 17:0.90 18:0.92 21:0.78 27:0.47 29:0.52 30:0.43 34:0.47 36:0.22 37:0.55 39:0.56 43:0.56 48:0.91 55:0.84 66:0.35 69:0.71 70:0.73 71:0.61 74:0.45 76:0.85 86:0.89 91:0.12 98:0.68 108:0.54 117:0.86 122:0.92 123:0.52 124:0.76 127:0.83 129:0.05 133:0.74 135:0.78 139:0.86 145:0.56 146:0.92 147:0.23 149:0.48 154:0.45 159:0.80 163:0.92 170:0.82 172:0.67 173:0.54 174:0.90 177:0.05 178:0.55 179:0.44 187:0.39 197:0.90 212:0.21 216:0.26 222:0.60 232:0.83 235:0.68 239:0.88 241:0.07 242:0.21 243:0.12 244:0.83 245:0.81 247:0.88 253:0.38 257:0.93 259:0.21 265:0.69 266:0.80 267:0.69 271:0.23 276:0.75 281:0.91 300:0.70 +0 1:0.59 8:0.69 10:0.88 11:0.52 12:0.01 17:0.86 18:0.98 21:0.40 27:0.54 29:0.52 30:0.42 34:0.36 36:0.20 37:0.54 39:0.56 43:0.31 45:0.73 48:0.91 55:0.71 64:0.77 66:0.71 69:0.23 70:0.66 71:0.61 74:0.65 76:1.00 81:0.40 86:0.97 91:0.31 98:0.88 101:0.65 108:0.18 114:0.78 122:0.92 123:0.18 124:0.44 126:0.32 127:0.83 129:0.59 133:0.47 135:0.54 139:0.96 145:0.38 146:0.96 147:0.97 149:0.46 150:0.44 154:0.21 155:0.49 158:0.76 159:0.75 170:0.82 172:0.90 173:0.16 174:0.88 176:0.63 177:0.88 178:0.55 179:0.28 187:0.87 192:0.47 197:0.87 201:0.56 204:0.78 208:0.50 212:0.58 216:0.19 219:0.91 222:0.60 235:0.52 239:0.87 241:0.24 242:0.42 243:0.75 244:0.61 245:0.93 247:0.96 253:0.60 254:0.97 259:0.21 265:0.88 266:0.63 267:0.69 271:0.23 276:0.86 279:0.75 281:0.86 290:0.78 292:0.87 300:0.70 +1 8:0.67 10:0.92 11:0.57 12:0.01 17:0.90 18:0.99 21:0.68 27:0.69 29:0.52 30:0.40 34:0.39 36:0.42 37:0.42 39:0.56 43:0.60 45:0.93 55:0.52 66:0.99 69:0.29 70:0.58 71:0.61 74:0.93 76:0.85 77:0.70 81:0.28 86:0.99 91:0.12 98:0.99 101:0.87 108:0.22 114:0.66 122:0.83 123:0.22 126:0.24 127:0.92 129:0.05 135:0.61 139:0.98 145:0.42 146:0.99 147:0.99 149:0.71 150:0.31 154:0.55 158:0.75 159:0.82 170:0.82 172:0.99 173:0.15 174:0.88 176:0.62 177:0.88 178:0.55 179:0.13 187:0.87 195:0.91 197:0.86 212:0.92 216:0.25 222:0.60 235:0.84 239:0.89 241:0.15 242:0.14 243:0.99 247:0.98 253:0.66 254:0.98 259:0.21 265:0.99 266:0.38 267:0.44 271:0.23 276:0.96 282:0.74 290:0.66 300:0.55 +0 8:0.81 10:0.88 11:0.52 12:0.01 17:0.87 18:0.91 21:0.78 27:0.47 29:0.52 30:0.17 34:0.47 36:0.19 37:0.55 39:0.56 43:0.23 45:0.73 48:0.91 55:0.92 66:0.53 69:0.71 70:0.94 71:0.61 74:0.54 76:0.85 86:0.89 91:0.15 98:0.76 101:0.65 108:0.85 117:0.86 122:0.92 123:0.84 124:0.76 127:0.86 129:0.59 133:0.80 135:0.71 139:0.86 145:0.56 146:0.77 147:0.23 149:0.34 154:0.53 159:0.80 170:0.82 172:0.77 173:0.55 174:0.89 177:0.05 178:0.55 179:0.41 187:0.39 197:0.88 212:0.18 216:0.26 222:0.60 232:0.83 235:0.68 239:0.87 241:0.15 242:0.15 243:0.11 244:0.61 245:0.78 247:0.96 253:0.43 254:0.74 257:0.93 259:0.21 265:0.76 266:0.80 267:0.79 271:0.23 276:0.77 281:0.91 300:0.84 +0 8:0.70 10:0.85 11:0.52 12:0.01 17:0.89 18:1.00 21:0.78 27:0.47 29:0.52 30:0.53 34:0.30 36:0.41 37:0.55 39:0.56 43:0.57 45:0.93 48:0.91 55:0.52 66:0.99 69:0.21 70:0.56 71:0.61 74:0.87 76:0.85 86:1.00 91:0.10 98:0.99 101:0.65 108:0.17 117:0.86 122:0.92 123:0.16 127:0.86 129:0.05 135:0.70 139:0.99 145:0.52 146:0.99 147:0.99 149:0.76 154:0.18 159:0.83 170:0.82 172:0.99 173:0.12 174:0.83 177:0.88 178:0.55 179:0.13 187:0.39 197:0.81 212:0.93 216:0.26 222:0.60 232:0.83 235:0.60 239:0.84 241:0.07 242:0.41 243:0.99 247:0.93 253:0.59 254:0.99 259:0.21 265:0.99 266:0.27 267:0.28 271:0.23 276:0.97 281:0.91 300:0.55 +1 8:0.63 10:0.97 11:0.57 12:0.01 17:0.53 18:0.99 21:0.30 22:0.93 27:0.32 29:0.52 30:0.53 34:0.55 36:0.39 37:0.46 39:0.56 43:0.38 44:0.88 45:0.97 47:0.93 48:0.91 55:0.59 64:0.77 66:0.99 69:0.77 70:0.72 71:0.61 74:0.66 81:0.34 86:0.99 91:0.64 98:0.91 101:0.96 108:0.61 122:0.92 123:0.58 126:0.24 127:0.51 129:0.05 135:0.23 139:0.99 145:0.63 146:0.99 147:0.99 149:0.79 150:0.37 154:0.35 159:0.87 170:0.82 172:0.99 173:0.51 174:0.97 177:0.88 178:0.55 179:0.12 187:0.68 195:0.97 197:0.96 212:0.80 216:0.73 219:0.90 222:0.60 235:0.31 239:0.96 241:0.10 242:0.40 243:0.99 247:0.97 253:0.49 254:0.94 259:0.21 262:0.93 265:0.91 266:0.71 267:0.52 271:0.23 276:0.96 281:0.91 292:0.95 300:0.70 +0 8:0.71 10:0.87 11:0.46 12:0.01 17:0.91 18:0.91 21:0.78 27:0.47 29:0.52 30:0.14 34:0.45 36:0.20 37:0.55 39:0.56 43:0.20 45:0.66 48:0.91 55:0.92 66:0.12 69:0.94 70:0.90 71:0.61 74:0.51 76:0.85 86:0.87 91:0.15 98:0.38 101:0.47 108:0.90 117:0.86 122:0.92 123:0.82 124:0.92 127:0.91 129:0.59 133:0.91 135:0.88 139:0.84 145:0.56 146:0.54 147:0.23 149:0.31 154:0.15 159:0.80 163:0.75 170:0.82 172:0.48 173:0.93 174:0.88 177:0.05 178:0.55 179:0.43 187:0.39 197:0.86 212:0.23 216:0.26 222:0.60 232:0.83 235:0.68 239:0.86 241:0.12 242:0.39 243:0.11 244:0.61 245:0.76 247:0.97 253:0.41 254:0.92 257:0.93 259:0.21 265:0.37 266:0.75 267:0.91 271:0.23 276:0.76 277:0.87 281:0.91 300:0.84 +1 8:0.71 9:0.73 10:0.90 11:0.52 12:0.01 17:0.76 18:0.99 21:0.30 27:0.65 29:0.52 30:0.17 31:0.97 34:0.34 36:0.40 37:0.52 39:0.56 43:0.66 45:0.92 55:0.71 64:0.77 66:0.35 69:0.10 70:0.83 71:0.61 74:0.69 79:0.97 81:0.34 83:0.56 86:0.98 91:0.54 96:0.84 97:0.89 98:0.75 101:0.65 108:0.90 122:0.92 123:0.09 124:0.60 126:0.24 127:0.92 129:0.59 133:0.47 135:0.76 137:0.97 139:0.98 140:0.92 146:0.97 147:0.97 149:0.81 150:0.37 151:0.85 153:0.48 154:0.16 155:0.56 158:0.76 159:0.86 162:0.86 170:0.82 172:0.91 173:0.08 174:0.92 177:0.88 179:0.18 187:0.39 190:0.89 191:0.73 192:0.55 196:0.99 197:0.88 202:0.73 208:0.50 212:0.47 216:0.17 219:0.91 220:0.62 222:0.60 235:0.31 239:0.88 241:0.53 242:0.54 243:0.51 244:0.61 245:0.98 247:0.91 248:0.82 253:0.83 255:0.72 256:0.81 259:0.21 265:0.74 266:0.63 267:0.84 268:0.81 271:0.23 276:0.93 279:0.75 284:0.98 285:0.50 292:0.88 300:0.55 +1 1:0.63 7:0.70 10:0.85 12:0.01 17:0.78 18:0.80 21:0.40 22:0.93 27:0.67 29:0.52 30:0.19 32:0.67 34:0.99 36:0.57 37:0.36 39:0.56 43:0.59 44:0.88 47:0.93 48:0.98 64:0.77 66:0.35 69:0.56 70:0.86 71:0.61 74:0.52 81:0.51 86:0.82 91:0.29 98:0.43 106:0.81 108:0.43 114:0.82 117:0.86 122:0.83 123:0.41 124:0.67 126:0.51 127:0.54 129:0.05 133:0.60 135:0.94 139:0.86 145:0.59 146:0.55 147:0.61 149:0.48 150:0.50 154:0.65 155:0.49 159:0.57 170:0.82 172:0.45 173:0.51 174:0.79 176:0.70 177:0.88 178:0.55 179:0.65 187:0.95 192:0.55 195:0.75 197:0.82 201:0.61 206:0.81 208:0.61 212:0.50 215:0.67 216:0.14 219:0.91 222:0.60 230:0.73 232:0.93 233:0.66 235:0.60 239:0.84 241:0.18 242:0.23 243:0.51 244:0.61 245:0.67 247:0.79 253:0.59 254:0.74 259:0.21 262:0.93 265:0.45 266:0.63 267:0.52 271:0.23 276:0.50 281:0.86 283:0.67 290:0.82 292:0.88 297:0.36 300:0.70 +1 1:0.58 8:0.63 10:0.87 11:0.46 12:0.01 17:0.94 18:0.96 21:0.47 22:0.93 27:0.55 29:0.52 30:0.30 34:0.97 36:0.57 37:0.50 39:0.56 43:0.58 44:0.88 45:0.94 47:0.93 64:0.77 66:0.97 69:0.69 70:0.79 71:0.61 74:0.84 77:0.70 81:0.49 83:0.56 86:0.97 91:0.29 97:0.89 98:0.88 99:0.83 101:0.47 108:0.52 114:0.76 117:0.86 122:0.91 123:0.50 126:0.32 127:0.42 129:0.05 135:0.93 139:0.97 145:0.96 146:0.97 147:0.98 149:0.74 150:0.44 154:0.47 155:0.51 158:0.76 159:0.70 165:0.65 170:0.82 172:0.93 173:0.54 174:0.83 176:0.63 177:0.88 178:0.55 179:0.20 187:0.21 192:0.48 195:0.90 197:0.87 201:0.55 204:0.80 208:0.50 212:0.57 216:0.16 219:0.91 222:0.60 232:0.88 235:0.60 239:0.86 242:0.33 243:0.97 244:0.61 247:0.82 253:0.65 254:0.86 259:0.21 262:0.93 265:0.88 266:0.38 267:0.59 271:0.23 276:0.87 279:0.75 281:0.86 282:0.75 290:0.76 292:0.88 300:0.55 +2 6:1.00 8:1.00 12:0.20 17:0.59 20:0.82 21:0.74 25:0.88 27:0.05 28:0.78 30:0.45 32:0.80 34:0.21 36:0.55 43:0.52 55:0.45 66:0.36 69:0.27 70:0.53 78:0.79 81:0.77 91:0.89 98:0.75 100:0.89 104:0.73 108:0.67 111:0.82 120:0.84 123:0.64 124:0.60 126:0.54 127:0.95 129:0.59 133:0.47 135:0.54 140:0.90 145:0.92 146:0.51 147:0.57 149:0.68 150:0.57 151:0.76 152:0.78 153:0.87 154:0.41 159:0.44 161:0.69 162:0.59 164:0.92 167:0.94 169:0.87 172:0.48 173:0.36 177:0.05 178:0.50 179:0.69 181:0.72 186:0.80 189:1.00 191:0.95 195:0.65 202:0.89 212:0.68 215:0.46 216:0.16 220:0.62 235:0.88 238:0.98 241:0.99 242:0.82 243:0.50 245:0.77 247:0.12 248:0.76 256:0.88 259:0.21 260:0.84 261:0.82 265:0.75 266:0.63 267:0.92 268:0.90 271:0.50 276:0.55 283:0.60 285:0.62 297:0.36 300:0.43 +1 12:0.20 17:0.53 21:0.59 27:0.45 28:0.78 30:0.11 32:0.80 34:0.91 36:0.18 37:0.50 43:0.32 55:0.59 66:0.30 69:0.70 70:0.54 81:0.87 91:0.27 98:0.30 108:0.53 123:0.51 124:0.61 126:0.54 127:0.21 129:0.59 133:0.47 135:0.75 145:0.44 146:0.48 147:0.54 149:0.33 150:0.72 154:0.24 159:0.44 161:0.69 163:0.88 167:0.68 172:0.16 173:0.60 177:0.05 178:0.55 179:0.80 181:0.72 187:0.68 195:0.83 212:0.30 215:0.55 216:0.77 235:0.68 241:0.53 242:0.82 243:0.48 245:0.47 247:0.12 259:0.21 265:0.32 266:0.27 267:0.28 271:0.50 276:0.25 283:0.60 297:0.36 300:0.33 +3 6:0.96 7:0.81 8:0.92 12:0.20 17:0.47 20:0.99 21:0.30 27:0.21 28:0.78 30:0.27 34:0.66 36:0.87 37:0.74 43:0.52 55:0.71 66:0.44 69:0.10 70:0.80 78:0.79 81:0.93 91:0.80 98:0.75 100:0.85 104:0.84 106:0.81 108:0.87 111:0.80 120:0.92 123:0.86 124:0.67 126:0.54 127:0.66 129:0.81 133:0.67 135:0.20 140:0.45 146:0.92 147:0.93 149:0.85 150:0.88 151:0.82 152:0.90 153:0.95 154:0.41 159:0.84 161:0.69 162:0.61 164:0.90 167:0.72 169:0.82 172:0.89 173:0.08 177:0.05 179:0.19 181:0.72 186:0.79 187:0.39 189:0.97 191:0.70 202:0.86 206:0.81 212:0.60 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.94 241:0.62 242:0.82 243:0.45 245:0.96 247:0.12 248:0.88 256:0.89 259:0.21 260:0.92 261:0.85 265:0.75 266:0.63 267:0.44 268:0.88 271:0.50 276:0.91 283:0.82 285:0.62 297:0.36 300:0.70 +1 12:0.20 17:0.64 21:0.54 27:0.45 28:0.78 30:0.76 34:0.80 36:0.17 37:0.50 43:0.32 55:0.92 66:0.36 69:0.69 70:0.15 81:0.89 91:0.35 98:0.34 108:0.53 123:0.50 124:0.52 126:0.54 127:0.19 129:0.59 133:0.47 135:0.56 145:0.52 146:0.46 147:0.52 149:0.23 150:0.77 154:0.24 159:0.32 161:0.69 163:0.96 167:0.56 172:0.10 173:0.66 177:0.05 178:0.55 179:0.94 181:0.72 187:0.68 212:0.95 215:0.58 216:0.77 235:0.60 241:0.12 242:0.82 243:0.51 245:0.37 247:0.12 259:0.21 265:0.36 266:0.12 267:0.28 271:0.50 276:0.18 297:0.36 300:0.13 +1 8:0.82 12:0.20 17:0.66 21:0.40 27:0.26 28:0.78 30:0.33 32:0.80 34:0.71 36:0.62 37:0.91 43:0.52 55:0.52 66:0.36 69:0.12 70:0.61 81:0.92 91:0.65 98:0.66 104:0.73 108:0.63 120:0.73 123:0.61 124:0.60 126:0.54 127:0.58 129:0.59 133:0.47 135:0.54 140:0.87 145:0.52 146:0.47 147:0.53 149:0.62 150:0.85 151:0.66 153:0.48 154:0.41 159:0.37 161:0.69 162:0.61 164:0.75 167:0.78 172:0.41 173:0.28 177:0.05 178:0.48 179:0.72 181:0.72 187:0.21 191:0.75 195:0.64 202:0.68 212:0.59 215:0.67 216:0.45 220:0.82 235:0.42 241:0.72 242:0.82 243:0.49 245:0.71 247:0.12 248:0.66 256:0.68 259:0.21 260:0.74 265:0.66 266:0.63 267:0.65 268:0.69 271:0.50 276:0.48 285:0.62 297:0.36 300:0.43 +4 6:0.98 8:0.97 12:0.20 17:0.06 20:0.99 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.50 36:0.74 37:0.82 43:0.47 55:0.59 66:0.33 69:0.46 70:0.81 78:0.92 81:0.77 91:1.00 98:0.56 100:0.99 104:0.58 108:0.81 111:0.98 120:0.99 123:0.80 124:0.78 126:0.54 127:0.90 129:0.89 133:0.78 135:0.32 140:0.45 145:0.97 146:0.25 147:0.31 149:0.67 150:0.57 151:0.91 152:1.00 153:1.00 154:0.35 159:0.59 161:0.69 162:0.69 164:0.99 167:0.94 169:0.96 172:0.54 173:0.42 177:0.05 179:0.59 181:0.72 186:0.91 189:0.99 191:0.97 195:0.76 202:0.99 212:0.40 215:0.46 216:0.87 235:0.88 238:0.99 241:0.98 242:0.82 243:0.29 245:0.73 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.57 266:0.63 267:0.79 268:0.99 271:0.50 276:0.64 283:0.82 285:0.62 297:0.36 300:0.55 +3 6:0.96 7:0.81 8:0.97 12:0.20 17:0.12 20:0.85 21:0.78 25:0.88 27:0.44 28:0.78 30:0.45 32:0.80 34:0.55 36:0.63 43:0.47 55:0.45 66:0.43 69:0.46 70:0.57 78:0.77 91:0.88 98:0.39 100:0.82 104:0.76 108:0.86 111:0.77 120:0.94 123:0.85 124:0.66 127:0.93 129:0.81 133:0.67 135:0.65 140:0.45 145:0.85 146:0.27 147:0.33 149:0.56 151:0.83 152:0.81 153:0.96 154:0.35 159:0.67 161:0.69 162:0.55 164:0.99 167:0.92 169:0.80 172:0.45 173:0.37 177:0.05 179:0.75 181:0.72 186:0.77 189:0.97 191:0.92 195:0.81 202:0.98 212:0.55 216:0.49 220:0.62 230:0.65 233:0.63 235:0.88 238:0.96 241:0.92 242:0.82 243:0.43 245:0.64 247:0.12 248:0.91 256:0.98 259:0.21 260:0.94 261:0.80 265:0.41 266:0.63 267:0.44 268:0.98 271:0.50 276:0.39 283:0.60 285:0.62 300:0.55 +1 12:0.20 17:0.18 21:0.78 25:0.88 27:0.13 28:0.78 30:0.21 34:0.37 36:0.92 37:0.82 43:0.52 55:0.52 66:0.63 69:0.05 70:0.80 91:0.58 98:0.80 104:0.58 108:0.05 120:0.80 123:0.05 124:0.52 127:0.86 129:0.81 133:0.67 135:0.34 140:0.45 146:0.87 147:0.90 149:0.75 151:0.74 153:0.83 154:0.41 159:0.62 161:0.69 162:0.85 164:0.82 167:0.86 172:0.73 173:0.10 177:0.05 179:0.53 181:0.72 187:0.21 202:0.70 212:0.54 216:0.87 235:0.02 241:0.77 242:0.82 243:0.69 245:0.75 247:0.12 248:0.72 256:0.77 259:0.21 260:0.81 265:0.80 266:0.80 267:0.44 268:0.78 271:0.50 276:0.68 283:0.60 285:0.62 300:0.55 +3 6:0.98 8:0.95 12:0.20 17:0.34 20:0.81 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.52 36:0.93 37:0.82 43:0.52 55:0.52 66:0.27 69:0.27 70:0.73 78:0.81 81:0.77 91:0.78 98:0.75 100:0.90 104:0.58 108:0.81 111:0.83 120:0.79 123:0.20 124:0.58 126:0.54 127:0.78 129:0.59 133:0.47 135:0.81 140:0.45 145:0.97 146:0.85 147:0.88 149:0.76 150:0.57 151:0.75 152:1.00 153:0.86 154:0.41 159:0.63 161:0.69 162:0.84 164:0.81 167:0.82 169:0.96 172:0.73 173:0.24 177:0.05 179:0.43 181:0.72 186:0.81 187:0.21 189:0.98 195:0.80 202:0.69 212:0.58 215:0.46 216:0.87 235:0.88 238:0.97 241:0.75 242:0.82 243:0.46 245:0.90 247:0.12 248:0.71 256:0.73 259:0.21 260:0.79 261:0.98 265:0.62 266:0.80 267:0.65 268:0.76 271:0.50 276:0.75 285:0.62 297:0.36 300:0.55 +1 7:0.81 8:0.77 12:0.20 17:0.40 21:0.54 25:0.88 27:0.44 28:0.78 30:0.38 32:0.80 34:0.80 36:0.56 43:0.52 55:0.45 66:0.19 69:0.17 70:0.71 81:0.89 91:0.68 98:0.74 104:0.76 106:0.81 108:0.14 120:0.53 123:0.55 124:0.75 126:0.54 127:0.84 129:0.81 133:0.67 135:0.39 140:0.87 145:0.72 146:0.27 147:0.33 149:0.67 150:0.77 151:0.46 153:0.48 154:0.41 159:0.59 161:0.69 162:0.54 164:0.57 167:0.67 172:0.37 173:0.20 177:0.05 178:0.48 179:0.64 181:0.72 187:0.87 195:0.76 202:0.56 206:0.81 212:0.60 215:0.58 216:0.49 220:0.87 230:0.87 233:0.76 235:0.60 241:0.51 242:0.82 243:0.40 245:0.74 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.20 266:0.71 267:0.91 268:0.46 271:0.50 276:0.59 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.98 8:0.93 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.94 98:0.71 100:0.85 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.86 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.79 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.98 8:0.97 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.87 98:0.71 100:0.82 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.75 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.28 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +3 6:1.00 7:0.81 8:1.00 12:0.20 17:0.11 20:0.99 21:0.13 27:0.07 28:0.78 30:0.45 32:0.80 34:0.67 36:0.82 37:0.84 43:0.47 55:0.45 66:0.33 69:0.37 70:0.42 78:0.83 81:0.96 91:0.96 98:0.55 100:0.93 104:0.58 108:0.68 111:0.88 120:0.94 123:0.66 124:0.71 126:0.54 127:0.88 129:0.81 133:0.67 135:0.30 140:0.45 145:0.86 146:0.19 147:0.25 149:0.47 150:0.93 151:0.81 152:0.95 153:0.94 154:0.36 159:0.51 161:0.69 162:0.58 164:0.97 167:0.92 169:0.89 172:0.27 173:0.39 177:0.05 179:0.87 181:0.72 186:0.84 189:1.00 191:0.94 195:0.70 202:0.96 212:0.15 215:0.84 216:0.63 220:0.74 230:0.87 233:0.76 235:0.12 238:0.98 241:0.91 242:0.82 243:0.37 245:0.53 247:0.12 248:0.92 256:0.95 259:0.21 260:0.95 261:0.92 265:0.56 266:0.63 267:0.99 268:0.96 271:0.50 276:0.36 283:0.60 285:0.62 297:0.36 300:0.33 +1 1:0.74 11:0.64 12:0.51 17:0.61 21:0.74 27:0.45 28:0.70 30:0.21 32:0.80 34:0.92 36:0.18 37:0.50 39:0.50 43:0.81 55:0.52 66:0.27 69:0.71 70:0.97 74:0.91 77:0.89 81:0.51 83:0.72 85:0.91 91:0.20 98:0.56 101:0.79 108:0.77 114:0.67 122:0.67 123:0.75 124:0.79 126:0.54 127:0.46 129:0.81 131:0.61 133:0.76 135:0.85 144:0.57 145:0.54 146:0.61 147:0.66 149:0.85 150:0.66 154:0.77 155:0.67 157:0.85 159:0.66 161:0.45 165:0.65 166:0.83 167:0.64 172:0.59 173:0.61 176:0.73 177:0.38 178:0.55 179:0.39 181:0.94 182:0.80 187:0.68 192:0.59 195:0.84 201:0.74 212:0.54 215:0.46 216:0.77 222:0.76 227:0.61 229:0.61 231:0.75 235:0.97 241:0.18 242:0.29 243:0.40 245:0.81 246:0.89 247:0.67 253:0.69 259:0.21 265:0.58 266:0.71 267:0.28 271:0.69 276:0.72 282:0.88 287:0.61 290:0.67 297:0.36 299:0.88 300:0.84 +3 1:0.74 7:0.81 8:0.83 11:0.64 12:0.51 17:0.77 21:0.30 27:0.21 28:0.70 30:0.12 34:0.56 36:0.49 37:0.74 39:0.50 43:0.98 66:0.48 69:0.12 70:0.95 74:0.99 81:0.75 83:0.75 85:1.00 91:0.03 98:0.64 101:0.85 104:0.84 106:0.81 108:0.96 114:0.86 120:0.65 121:0.97 122:0.67 123:0.96 124:0.87 126:0.54 127:0.87 129:0.96 131:0.81 133:0.90 135:0.24 140:0.45 144:0.57 146:0.56 147:0.62 149:0.99 150:0.84 151:0.61 153:0.48 154:0.98 155:0.67 157:0.91 159:0.94 161:0.45 162:0.86 164:0.55 165:0.84 166:0.84 167:0.56 172:0.99 173:0.06 176:0.73 177:0.38 179:0.10 181:0.94 182:0.81 187:0.39 190:0.77 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.84 215:0.72 216:0.95 220:0.62 222:0.76 227:0.85 229:0.61 230:0.87 231:0.76 233:0.76 235:0.42 242:0.58 243:0.08 245:0.99 246:0.89 247:0.67 248:0.59 253:0.79 256:0.45 259:0.21 260:0.65 265:0.65 266:0.89 267:0.44 268:0.46 271:0.69 276:0.99 283:0.71 285:0.50 287:0.61 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:1.00 8:0.97 9:0.80 11:0.64 12:0.51 17:0.55 20:0.93 21:0.22 27:0.03 28:0.70 30:0.95 34:0.53 36:0.40 37:1.00 39:0.50 41:0.93 43:0.79 66:0.54 69:0.76 74:0.42 78:0.83 81:0.80 83:0.81 85:0.72 91:0.83 96:0.92 98:0.10 100:0.92 101:0.73 104:0.58 108:0.59 111:0.86 114:0.90 120:0.86 123:0.56 126:0.54 127:0.07 129:0.05 131:0.89 135:0.42 140:0.45 144:0.57 146:0.13 147:0.18 149:0.43 150:0.87 151:0.98 152:1.00 153:0.92 154:0.72 155:0.67 157:0.77 159:0.08 161:0.45 162:0.57 164:0.84 165:0.86 166:0.84 167:0.86 169:0.97 172:0.10 173:0.84 176:0.73 177:0.38 179:0.09 181:0.94 182:0.81 186:0.83 187:0.68 189:0.99 191:0.95 192:0.59 201:0.74 202:0.81 208:0.64 215:0.79 216:0.76 222:0.76 227:0.92 229:0.89 231:0.76 235:0.31 238:0.97 241:0.74 243:0.63 246:0.89 248:0.92 253:0.89 255:0.79 256:0.77 259:0.21 260:0.87 261:0.99 265:0.11 267:0.91 268:0.81 271:0.69 285:0.62 287:0.92 290:0.90 297:0.36 300:0.08 +1 1:0.74 11:0.64 12:0.51 17:0.57 21:0.40 27:0.01 28:0.70 30:0.74 32:0.80 34:0.90 36:0.68 37:0.97 39:0.50 43:0.87 66:0.68 69:0.37 70:0.41 74:0.92 77:0.70 81:0.76 83:0.76 85:0.97 91:0.25 98:0.75 101:0.86 104:0.84 106:0.81 108:0.29 114:0.66 117:0.86 122:0.43 123:0.28 124:0.45 126:0.54 127:0.60 129:0.59 131:0.61 133:0.47 135:0.81 144:0.57 145:0.84 146:0.76 147:0.80 149:0.97 150:0.85 154:0.85 155:0.67 157:0.89 159:0.47 161:0.45 165:0.86 166:0.84 167:0.69 172:0.76 173:0.39 176:0.56 177:0.38 178:0.55 179:0.46 181:0.94 182:0.81 187:0.68 192:0.59 195:0.66 201:0.74 206:0.81 212:0.68 215:0.63 216:0.85 222:0.76 227:0.61 229:0.61 231:0.75 232:0.90 235:0.68 241:0.39 242:0.60 243:0.72 245:0.83 246:0.90 247:0.39 253:0.70 259:0.21 265:0.75 266:0.63 267:0.52 271:0.69 276:0.66 282:0.88 287:0.61 290:0.66 297:0.36 299:0.88 300:0.55 +4 1:0.74 6:1.00 8:0.99 9:0.80 11:0.64 12:0.51 17:0.28 20:0.97 21:0.13 27:0.03 28:0.70 30:0.95 34:0.52 36:0.61 37:1.00 39:0.50 41:1.00 43:0.78 60:0.99 66:0.54 69:0.77 74:0.55 78:0.96 81:0.84 83:0.91 85:0.72 91:1.00 96:1.00 98:0.09 100:0.99 101:0.71 104:0.58 108:0.60 111:0.99 114:0.92 120:1.00 123:0.58 126:0.54 127:0.06 129:0.05 131:0.89 135:0.66 138:1.00 140:0.45 144:0.57 146:0.13 147:0.18 149:0.43 150:0.91 151:1.00 152:1.00 153:1.00 154:0.71 155:0.67 157:0.76 158:0.92 159:0.08 161:0.45 162:0.69 164:0.99 165:0.88 166:0.84 167:1.00 169:0.97 172:0.10 173:0.75 176:0.73 177:0.38 179:0.08 181:0.94 182:0.81 186:0.95 189:1.00 191:0.97 192:0.59 201:0.74 202:0.98 208:0.64 215:0.84 216:0.76 222:0.76 227:0.92 229:0.89 231:0.76 235:0.22 238:1.00 241:1.00 243:0.63 246:0.90 248:1.00 253:1.00 255:0.79 256:0.99 259:0.21 260:1.00 261:0.99 265:0.09 267:0.94 268:0.99 271:0.69 279:0.86 283:0.82 285:0.62 287:0.92 290:0.92 297:0.36 300:0.08 +2 1:0.74 6:0.94 7:0.81 8:0.87 9:0.80 11:0.64 12:0.51 17:0.85 20:0.85 27:0.30 28:0.70 30:0.56 32:0.80 34:0.47 36:0.71 37:0.72 39:0.50 41:0.88 43:0.69 60:0.87 66:0.06 69:0.86 70:0.75 74:0.99 77:0.70 78:0.78 81:0.93 83:0.88 85:1.00 91:0.18 96:0.87 98:0.29 100:0.83 101:0.86 104:0.92 106:0.81 108:0.84 111:0.78 114:0.98 117:0.86 120:0.82 121:0.90 122:0.43 123:0.98 124:0.99 126:0.54 127:0.90 129:1.00 131:0.89 133:0.99 135:0.63 140:0.45 144:0.57 145:0.67 146:0.69 147:0.74 149:0.99 150:0.97 151:0.95 152:0.86 153:0.83 154:0.59 155:0.67 157:0.91 158:0.92 159:0.98 161:0.45 162:0.84 164:0.80 165:0.92 166:0.84 167:0.62 169:0.80 172:0.92 173:0.35 176:0.73 177:0.38 179:0.09 181:0.94 182:0.81 186:0.79 187:0.39 189:0.96 191:0.70 192:0.59 195:1.00 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.59 215:0.96 216:0.32 222:0.76 227:0.92 229:0.89 230:0.87 231:0.76 232:0.85 233:0.76 235:0.05 238:0.94 241:0.10 242:0.51 243:0.08 245:0.99 246:0.90 247:0.67 248:0.85 253:0.92 255:0.79 256:0.81 259:0.21 260:0.82 261:0.82 265:0.30 266:0.91 267:0.85 268:0.75 271:0.69 276:0.99 279:0.86 282:0.88 283:0.82 285:0.62 287:0.92 290:0.98 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 8:0.68 11:0.64 12:0.51 17:0.38 21:0.40 27:0.21 28:0.70 30:0.21 34:0.63 36:0.42 37:0.74 39:0.50 43:0.82 55:0.59 66:0.31 69:0.15 70:0.95 74:0.95 76:0.85 81:0.67 85:0.80 91:0.06 98:0.54 101:0.71 104:0.84 106:0.81 108:0.88 114:0.82 117:0.86 121:0.90 122:0.57 123:0.87 124:0.86 126:0.54 127:0.75 129:0.89 131:0.61 133:0.87 135:0.30 144:0.57 146:0.72 147:0.76 149:0.80 150:0.73 154:0.92 155:0.67 157:0.78 158:0.87 159:0.84 161:0.45 166:0.84 167:0.65 172:0.79 173:0.10 176:0.73 177:0.38 178:0.55 179:0.26 181:0.94 182:0.73 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.73 215:0.67 216:0.95 222:0.76 227:0.61 229:0.61 230:0.87 231:0.76 232:0.94 233:0.76 235:0.52 241:0.24 242:0.30 243:0.40 245:0.88 246:0.61 247:0.67 253:0.77 259:0.21 265:0.55 266:0.91 267:0.36 271:0.69 276:0.86 279:0.86 283:0.71 287:0.61 290:0.82 297:0.36 300:0.84 +1 1:0.74 11:0.64 12:0.51 17:0.57 21:0.59 27:0.45 28:0.70 30:0.17 32:0.80 34:0.90 36:0.21 37:0.50 39:0.50 43:0.82 66:0.30 69:0.70 70:0.92 74:0.86 77:0.89 81:0.63 83:0.73 85:0.91 91:0.18 98:0.49 101:0.80 108:0.53 114:0.74 122:0.57 123:0.51 124:0.77 126:0.54 127:0.42 129:0.81 131:0.61 133:0.76 135:0.81 144:0.57 145:0.47 146:0.67 147:0.72 149:0.85 150:0.76 154:0.77 155:0.67 157:0.85 159:0.59 161:0.45 165:0.78 166:0.83 167:0.65 172:0.54 173:0.63 176:0.73 177:0.38 178:0.55 179:0.45 181:0.94 182:0.80 187:0.68 192:0.59 195:0.80 201:0.74 212:0.49 215:0.55 216:0.77 222:0.76 227:0.61 229:0.61 231:0.75 235:0.80 241:0.21 242:0.39 243:0.48 245:0.75 246:0.89 247:0.60 253:0.70 259:0.21 265:0.51 266:0.71 267:0.23 271:0.69 276:0.66 282:0.88 287:0.61 290:0.74 297:0.36 299:0.88 300:0.70 +2 6:0.93 8:0.98 9:0.80 12:0.51 17:0.08 20:0.81 21:0.78 27:0.20 28:0.70 34:0.49 36:0.27 37:0.94 39:0.50 41:0.93 78:0.75 83:0.81 91:0.88 96:0.91 100:0.78 104:0.58 111:0.74 120:0.87 131:0.89 135:0.88 140:0.45 144:0.57 151:0.92 152:0.92 153:0.72 155:0.67 157:0.61 161:0.45 162:0.66 164:0.82 166:0.61 167:0.97 169:0.76 175:0.91 181:0.94 182:0.81 186:0.75 189:0.87 191:0.94 192:0.59 202:0.76 208:0.64 216:0.38 220:0.74 222:0.76 227:0.92 229:0.89 231:0.76 238:0.90 241:0.87 244:0.83 246:0.61 248:0.91 253:0.87 255:0.79 256:0.78 257:0.84 259:0.21 260:0.87 261:0.78 267:0.44 268:0.78 271:0.69 277:0.87 283:0.71 285:0.62 287:0.92 +3 1:0.74 6:0.88 7:0.81 9:0.80 11:0.64 12:0.51 17:0.75 20:0.73 21:0.30 27:0.21 28:0.70 30:0.13 34:0.62 36:0.50 37:0.74 39:0.50 41:0.82 43:0.98 60:0.87 66:0.48 69:0.12 70:0.95 74:1.00 78:0.72 81:0.75 83:0.84 85:1.00 91:0.03 96:0.77 98:0.64 100:0.73 101:0.86 104:0.84 106:0.81 108:0.96 111:0.72 114:0.86 117:0.86 120:0.65 121:0.97 122:0.67 123:0.96 124:0.87 126:0.54 127:0.87 129:0.96 131:0.89 133:0.90 135:0.23 140:0.45 144:0.57 146:0.56 147:0.62 149:0.99 150:0.84 151:0.77 152:0.91 153:0.48 154:0.98 155:0.67 157:0.91 158:0.87 159:0.94 161:0.45 162:0.86 164:0.57 165:0.84 166:0.84 167:0.54 169:0.78 172:0.99 173:0.06 176:0.73 177:0.38 179:0.10 181:0.94 182:0.81 186:0.73 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.84 215:0.72 216:0.95 220:0.62 222:0.76 227:0.92 229:0.89 230:0.87 231:0.76 232:0.90 233:0.76 235:0.42 242:0.58 243:0.08 245:0.99 246:0.89 247:0.67 248:0.71 253:0.85 255:0.79 256:0.45 259:0.21 260:0.66 261:0.81 265:0.65 266:0.89 267:0.44 268:0.46 271:0.69 276:0.99 279:0.86 283:0.71 285:0.62 287:0.91 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:1.00 8:0.95 9:0.80 11:0.64 12:0.51 17:0.55 20:0.75 21:0.22 27:0.03 28:0.70 30:0.58 34:0.74 36:0.66 37:1.00 39:0.50 41:0.98 43:0.79 66:0.27 69:0.74 70:0.44 74:0.55 78:0.83 81:0.80 83:0.88 85:0.85 91:0.81 96:0.96 98:0.40 100:0.91 101:0.78 104:0.58 108:0.71 111:0.84 114:0.90 120:0.94 122:0.43 123:0.69 124:0.73 126:0.54 127:0.32 129:0.81 131:0.89 133:0.67 135:0.39 140:0.80 144:0.57 146:0.13 147:0.18 149:0.69 150:0.87 151:0.98 152:1.00 153:0.91 154:0.73 155:0.67 157:0.82 159:0.38 161:0.45 162:0.80 163:0.86 164:0.91 165:0.86 166:0.84 167:0.90 169:0.97 172:0.21 173:0.79 176:0.73 177:0.38 178:0.42 179:0.80 181:0.94 182:0.81 186:0.84 187:0.21 189:0.93 191:0.89 192:0.59 201:0.74 202:0.85 208:0.64 212:0.16 215:0.79 216:0.76 222:0.76 227:0.92 229:0.89 231:0.76 235:0.31 238:0.95 241:0.76 242:0.58 243:0.29 245:0.51 246:0.89 247:0.47 248:0.96 253:0.95 255:0.79 256:0.88 259:0.21 260:0.94 261:0.99 265:0.42 266:0.54 267:0.96 268:0.89 271:0.69 276:0.34 285:0.62 287:0.92 290:0.90 297:0.36 300:0.33 +3 1:0.74 6:0.88 7:0.81 8:0.75 9:0.80 11:0.64 12:0.51 17:0.61 20:0.98 21:0.30 27:0.21 28:0.70 30:0.13 34:0.50 36:0.57 37:0.74 39:0.50 41:0.94 43:0.98 60:0.99 66:0.49 69:0.12 70:0.94 74:0.99 76:0.85 78:0.77 81:0.75 83:0.85 85:1.00 91:0.40 96:0.94 98:0.63 100:0.81 101:0.86 104:0.84 106:0.81 108:0.95 111:0.77 114:0.86 117:0.86 120:0.90 121:0.90 122:0.67 123:0.95 124:0.83 126:0.54 127:0.84 129:0.95 131:0.89 133:0.87 135:0.21 140:0.45 144:0.57 146:0.29 147:0.36 149:0.99 150:0.84 151:0.99 152:0.91 153:0.95 154:0.98 155:0.67 157:0.91 158:0.92 159:0.93 161:0.45 162:0.56 164:0.85 165:0.84 166:0.84 167:0.67 169:0.79 172:0.99 173:0.07 176:0.73 177:0.38 179:0.10 181:0.94 182:0.81 186:0.77 187:0.39 189:0.90 192:0.59 201:0.74 202:0.82 204:0.89 206:0.81 208:0.64 212:0.85 215:0.72 216:0.95 222:0.76 227:0.92 229:0.89 230:0.87 231:0.76 232:0.90 233:0.76 235:0.42 238:0.93 241:0.30 242:0.57 243:0.06 245:0.99 246:0.89 247:0.67 248:0.94 253:0.97 255:0.79 256:0.81 259:0.21 260:0.91 261:0.81 265:0.64 266:0.80 267:0.28 268:0.81 271:0.69 276:0.99 279:0.86 283:0.82 285:0.62 287:0.92 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:0.91 7:0.81 8:0.81 9:0.80 11:0.88 12:0.06 17:0.05 18:0.92 20:0.94 21:0.22 22:0.93 27:0.19 28:0.87 29:0.86 30:0.93 31:0.97 32:0.80 34:0.39 36:0.56 37:0.66 39:0.90 41:0.95 43:0.81 44:0.93 47:0.93 60:0.99 64:0.77 66:0.49 69:0.70 70:0.28 71:0.71 74:0.93 77:0.99 78:0.80 79:0.96 81:0.60 83:0.91 85:0.97 86:0.98 91:0.67 96:0.88 98:0.59 100:0.83 101:0.87 106:0.81 108:0.75 111:0.80 114:0.71 117:0.86 120:0.80 121:0.97 122:0.57 123:0.73 124:0.67 126:0.54 127:0.33 129:0.81 133:0.67 135:0.60 137:0.97 139:0.99 140:0.45 144:0.85 145:0.91 146:0.21 147:0.27 149:0.98 150:0.77 151:0.82 152:0.92 153:0.46 154:0.76 155:0.67 158:0.92 159:0.48 161:0.80 162:0.79 164:0.82 165:0.93 167:0.65 169:0.81 172:0.78 173:0.67 176:0.73 177:0.70 179:0.25 181:0.96 186:0.81 187:0.68 189:0.92 191:0.70 192:0.87 195:0.75 196:0.99 201:0.93 202:0.74 204:0.89 206:0.81 208:0.64 212:0.76 215:0.51 216:0.73 219:0.95 220:0.62 222:0.84 230:0.86 232:0.90 233:0.73 235:0.42 238:0.89 241:0.57 242:0.51 243:0.22 245:0.89 247:0.35 248:0.87 253:0.92 255:0.95 256:0.78 259:0.21 260:0.81 261:0.85 262:0.93 265:0.60 266:0.54 267:0.89 268:0.79 271:0.81 276:0.77 279:0.86 281:0.89 282:0.88 283:0.82 284:0.97 285:0.62 290:0.71 292:0.95 297:0.36 299:1.00 300:0.55 +1 1:0.74 11:0.88 12:0.06 17:0.34 18:0.85 21:0.54 27:0.45 28:0.87 29:0.86 30:0.90 34:0.79 36:0.25 37:0.50 39:0.90 43:0.81 64:0.77 66:0.51 69:0.70 70:0.29 71:0.71 74:0.69 81:0.47 83:0.91 85:0.91 86:0.92 91:0.08 98:0.41 101:0.87 108:0.53 114:0.59 122:0.57 123:0.51 124:0.72 126:0.54 127:0.46 129:0.81 133:0.74 135:0.78 139:0.90 144:0.85 145:0.50 146:0.64 147:0.69 149:0.89 150:0.71 154:0.77 155:0.67 159:0.66 161:0.80 165:0.93 167:0.59 172:0.57 173:0.60 176:0.73 177:0.70 178:0.55 179:0.58 181:0.96 187:0.68 192:0.87 201:0.93 208:0.64 212:0.28 215:0.39 216:0.77 222:0.84 235:0.74 241:0.43 242:0.45 243:0.61 245:0.64 247:0.55 253:0.45 259:0.21 265:0.43 266:0.71 267:0.28 271:0.81 276:0.53 290:0.59 297:0.36 300:0.43 +2 1:0.74 6:0.90 7:0.81 8:0.75 9:0.80 11:0.92 12:0.06 17:0.15 18:0.89 20:0.93 21:0.71 22:0.93 27:0.10 28:0.87 29:0.86 30:0.91 31:0.97 32:0.80 34:0.66 36:0.98 37:0.73 39:0.90 41:0.95 43:0.96 44:0.93 45:0.66 47:0.93 60:0.99 64:0.77 66:0.75 69:0.30 70:0.27 71:0.71 74:0.90 77:0.70 78:0.82 79:0.97 81:0.43 83:0.91 85:0.94 86:0.96 91:0.62 96:0.89 98:0.62 100:0.85 101:0.87 106:0.81 108:0.24 111:0.82 114:0.55 117:0.86 120:0.82 121:0.90 122:0.57 123:0.23 124:0.41 126:0.54 127:0.22 129:0.59 133:0.46 135:0.83 137:0.97 139:0.97 140:0.45 144:0.85 145:0.69 146:0.85 147:0.87 149:0.96 150:0.69 151:0.96 152:0.91 153:0.87 154:0.96 155:0.67 158:0.92 159:0.49 161:0.80 162:0.68 164:0.83 165:0.93 167:0.54 169:0.83 172:0.75 173:0.19 176:0.73 177:0.70 179:0.28 181:0.96 186:0.83 187:0.87 189:0.91 191:0.70 192:0.87 195:0.87 196:0.99 201:0.93 202:0.76 204:0.89 206:0.81 208:0.64 212:0.37 215:0.37 216:0.87 219:0.95 222:0.84 230:0.86 232:0.90 233:0.73 235:0.95 238:0.89 241:0.24 242:0.45 243:0.77 245:0.74 247:0.60 248:0.88 253:0.92 255:0.95 256:0.77 259:0.21 260:0.82 261:0.87 262:0.93 265:0.63 266:0.75 267:0.52 268:0.78 271:0.81 276:0.65 279:0.86 281:0.89 282:0.88 283:0.82 284:0.97 285:0.62 290:0.55 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 7:0.81 8:0.94 9:0.80 11:0.88 12:0.06 17:0.73 18:0.91 21:0.54 22:0.93 27:0.15 28:0.87 29:0.86 30:0.93 31:0.96 32:0.80 34:0.49 36:0.31 37:0.83 39:0.90 41:0.93 43:0.91 44:0.93 47:0.93 60:0.97 64:0.77 66:0.79 69:0.44 70:0.23 71:0.71 74:0.93 77:0.70 79:0.95 81:0.47 83:0.91 85:0.95 86:0.98 91:0.63 96:0.86 98:0.42 101:0.87 106:0.81 108:0.34 114:0.59 117:0.86 120:0.77 121:0.90 122:0.57 123:0.32 124:0.40 126:0.54 127:0.75 129:0.59 132:0.97 133:0.46 135:0.93 137:0.96 139:0.98 140:0.45 144:0.85 145:0.74 146:0.69 147:0.74 149:0.97 150:0.71 151:0.87 153:0.48 154:0.90 155:0.67 158:0.92 159:0.77 161:0.80 162:0.78 164:0.77 165:0.93 167:0.52 172:0.80 173:0.27 176:0.73 177:0.70 179:0.46 181:0.96 187:0.39 191:0.73 192:0.87 195:0.89 196:0.98 201:0.93 202:0.67 204:0.89 206:0.81 208:0.64 212:0.86 215:0.39 216:0.62 219:0.95 220:0.62 222:0.84 230:0.87 232:0.92 233:0.76 235:0.74 241:0.12 242:0.51 243:0.80 245:0.77 247:0.35 248:0.84 253:0.90 255:0.95 256:0.71 260:0.78 262:0.93 265:0.44 266:0.54 267:0.82 268:0.72 271:0.81 276:0.43 279:0.86 281:0.91 282:0.88 283:0.82 284:0.96 285:0.62 290:0.59 292:0.95 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.92 12:0.06 17:0.32 18:0.95 20:0.94 21:0.40 22:0.93 27:0.21 28:0.87 29:0.86 30:0.87 31:0.99 34:0.61 36:0.66 37:0.74 39:0.90 41:0.98 43:0.96 45:0.66 47:0.93 60:0.97 64:0.77 66:0.06 69:0.19 70:0.46 71:0.71 74:0.91 78:0.88 79:0.99 81:0.52 83:0.91 85:0.98 86:0.98 91:0.56 96:0.96 98:0.34 100:0.96 101:0.87 106:0.81 108:0.94 111:0.93 114:0.62 117:0.86 120:0.92 121:0.97 122:0.57 123:0.81 124:0.93 126:0.54 127:0.72 129:0.97 133:0.93 135:0.21 137:0.99 139:0.99 140:0.45 144:0.85 146:0.52 147:0.58 149:0.97 150:0.73 151:0.94 152:0.93 153:0.81 154:0.96 155:0.67 158:0.92 159:0.87 161:0.80 162:0.60 164:0.90 165:0.93 167:0.61 169:0.94 172:0.77 173:0.09 176:0.73 177:0.70 179:0.20 181:0.96 186:0.88 187:0.39 189:0.96 191:0.73 192:0.87 196:1.00 201:0.93 202:0.87 204:0.89 206:0.81 208:0.64 212:0.58 215:0.42 216:0.95 219:0.95 220:0.62 222:0.84 230:0.86 232:0.91 233:0.73 235:0.60 238:0.96 241:0.49 242:0.39 243:0.19 245:0.91 247:0.82 248:0.96 253:0.97 255:0.95 256:0.87 259:0.21 260:0.92 261:0.95 262:0.93 265:0.33 266:0.87 267:0.23 268:0.88 271:0.81 276:0.91 279:0.86 281:0.89 283:0.82 284:0.99 285:0.62 290:0.62 292:0.95 297:0.36 300:0.70 +3 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.92 12:0.06 17:0.59 18:0.95 20:0.84 21:0.40 22:0.93 27:0.21 28:0.87 29:0.86 30:0.87 31:0.98 34:0.49 36:0.67 37:0.74 39:0.90 41:0.93 43:0.96 45:0.66 47:0.93 60:0.87 64:0.77 66:0.06 69:0.19 70:0.48 71:0.71 74:0.92 78:0.86 79:0.97 81:0.52 83:0.91 85:0.98 86:0.99 91:0.18 96:0.91 98:0.33 100:0.95 101:0.87 106:0.81 108:0.94 111:0.90 114:0.62 117:0.86 120:0.84 121:0.97 122:0.57 123:0.82 124:0.93 126:0.54 127:0.72 129:0.97 133:0.93 135:0.20 137:0.98 139:0.99 140:0.45 144:0.85 146:0.54 147:0.60 149:0.98 150:0.73 151:0.98 152:0.93 153:0.91 154:0.96 155:0.67 158:0.92 159:0.87 161:0.80 162:0.76 164:0.79 165:0.93 167:0.54 169:0.94 172:0.77 173:0.09 176:0.73 177:0.70 179:0.20 181:0.96 186:0.86 187:0.39 189:0.95 192:0.87 196:0.99 201:0.93 202:0.70 204:0.89 206:0.81 208:0.64 212:0.59 215:0.42 216:0.95 219:0.95 222:0.84 230:0.86 232:0.88 233:0.73 235:0.60 238:0.96 241:0.21 242:0.39 243:0.23 245:0.91 247:0.82 248:0.90 253:0.94 255:0.95 256:0.71 259:0.21 260:0.85 261:0.95 262:0.93 265:0.33 266:0.87 267:0.59 268:0.74 271:0.81 276:0.90 279:0.86 281:0.89 283:0.82 284:0.96 285:0.62 290:0.62 292:0.95 297:0.36 300:0.84 +1 1:0.74 9:0.80 11:0.92 12:0.06 17:0.26 18:0.97 21:0.22 22:0.93 27:0.18 28:0.87 29:0.86 30:0.91 31:0.94 34:0.87 36:0.49 37:0.85 39:0.90 41:0.82 43:0.89 45:0.73 47:0.93 60:0.99 64:0.77 66:0.97 69:0.47 70:0.27 71:0.71 74:0.95 79:0.93 81:0.60 83:0.91 85:0.98 86:0.99 91:0.29 96:0.80 98:0.90 101:0.97 108:0.36 114:0.71 120:0.69 122:0.57 123:0.35 126:0.54 127:0.48 129:0.05 135:0.86 137:0.94 139:0.99 140:0.45 144:0.85 146:0.91 147:0.93 149:0.99 150:0.77 151:0.87 153:0.48 154:0.88 155:0.67 158:0.92 159:0.53 161:0.80 162:0.86 164:0.57 165:0.93 167:0.55 172:0.93 173:0.43 176:0.73 177:0.70 179:0.21 181:0.96 187:0.39 192:0.87 196:0.98 201:0.93 202:0.36 208:0.64 212:0.78 215:0.51 216:0.82 219:0.95 222:0.84 232:0.92 235:0.42 241:0.27 242:0.45 243:0.97 247:0.60 248:0.77 253:0.87 255:0.95 256:0.45 259:0.21 260:0.70 262:0.93 265:0.90 266:0.54 267:0.36 268:0.46 271:0.81 276:0.87 279:0.86 283:0.82 284:0.89 285:0.62 290:0.71 292:0.95 297:0.36 300:0.43 +3 1:0.74 6:0.94 8:0.87 9:0.80 11:0.88 12:0.06 17:0.15 18:0.88 20:0.96 21:0.68 28:0.87 29:0.86 30:0.91 31:0.99 32:0.80 34:0.47 36:0.23 37:0.97 39:0.90 41:0.99 43:0.80 44:0.93 60:1.00 64:0.77 66:0.48 69:0.74 70:0.30 71:0.71 74:0.87 77:0.70 78:0.88 79:0.99 81:0.44 83:0.91 85:0.96 86:0.96 91:0.88 96:0.97 98:0.28 100:0.94 101:0.87 108:0.81 111:0.90 114:0.56 120:0.94 122:0.57 123:0.80 124:0.75 126:0.54 127:0.30 129:0.89 133:0.78 135:0.51 137:0.99 139:0.97 140:0.45 144:0.85 145:0.67 146:0.36 147:0.42 149:0.95 150:0.70 151:0.98 152:0.89 153:0.91 154:0.74 155:0.67 158:0.92 159:0.86 161:0.80 162:0.56 164:0.93 165:0.93 167:0.58 169:0.92 172:0.73 173:0.39 176:0.73 177:0.70 179:0.29 181:0.96 186:0.88 187:0.21 189:0.95 191:0.86 192:0.87 195:0.98 196:1.00 201:0.93 202:0.92 208:0.64 212:0.47 215:0.37 216:0.98 219:0.95 222:0.84 232:0.84 235:0.88 238:0.94 241:0.39 242:0.50 243:0.33 245:0.81 247:0.47 248:0.97 253:0.98 255:0.95 256:0.92 259:0.21 260:0.94 261:0.93 265:0.30 266:0.84 267:0.94 268:0.92 271:0.81 276:0.60 279:0.86 282:0.88 283:0.82 284:0.99 285:0.62 290:0.56 292:0.95 297:0.36 299:0.88 300:0.70 +4 1:0.74 6:0.88 8:0.75 9:0.80 11:0.88 12:0.06 17:0.02 18:0.84 20:0.96 21:0.22 27:0.15 28:0.87 29:0.86 30:0.91 31:1.00 34:0.44 36:0.67 37:0.69 39:0.90 41:1.00 43:0.96 60:0.99 64:0.77 66:0.61 69:0.15 70:0.21 71:0.71 74:0.76 78:0.94 79:1.00 81:0.60 83:0.91 85:0.88 86:0.92 91:0.97 96:0.99 98:0.66 100:0.98 101:0.87 108:0.12 111:0.96 114:0.71 120:0.98 122:0.57 123:0.12 124:0.47 126:0.54 127:0.45 129:0.59 133:0.46 135:0.78 137:1.00 139:0.92 140:0.45 144:0.85 146:0.50 147:0.56 149:0.90 150:0.77 151:0.99 152:0.97 153:0.97 154:0.96 155:0.67 158:0.92 159:0.28 161:0.80 162:0.65 164:0.97 165:0.93 167:0.87 169:0.95 172:0.48 173:0.36 176:0.73 177:0.70 179:0.73 181:0.96 186:0.93 189:0.89 191:0.87 192:0.87 196:1.00 201:0.93 202:0.96 208:0.64 212:0.22 215:0.51 216:0.46 219:0.95 220:0.62 222:0.84 232:0.75 235:0.42 238:0.96 241:0.86 242:0.52 243:0.68 245:0.62 247:0.47 248:0.99 253:0.99 255:0.95 256:0.97 259:0.21 260:0.98 261:0.97 265:0.66 266:0.54 267:0.82 268:0.97 271:0.81 276:0.40 279:0.86 283:0.82 284:1.00 285:0.62 290:0.71 292:0.95 297:0.36 300:0.25 +1 1:0.74 11:0.88 12:0.06 17:0.30 18:0.76 21:0.40 27:0.45 28:0.87 29:0.86 30:0.94 32:0.80 34:0.75 36:0.20 37:0.50 39:0.90 43:0.81 44:0.93 64:0.77 66:0.80 69:0.70 70:0.18 71:0.71 74:0.68 77:0.70 81:0.52 83:0.91 85:0.85 86:0.87 91:0.12 98:0.39 101:0.87 108:0.53 114:0.62 122:0.57 123:0.51 126:0.54 127:0.13 129:0.05 135:0.65 139:0.88 144:0.85 145:0.47 146:0.50 147:0.56 149:0.82 150:0.73 154:0.77 155:0.67 159:0.18 161:0.80 165:0.93 167:0.51 172:0.45 173:0.68 176:0.73 177:0.70 178:0.55 179:0.30 181:0.96 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.55 215:0.42 216:0.77 222:0.84 235:0.60 241:0.10 242:0.58 243:0.82 247:0.35 253:0.48 259:0.21 265:0.41 266:0.27 267:0.36 271:0.81 276:0.20 282:0.88 290:0.62 297:0.36 299:0.88 300:0.18 +3 1:0.74 9:0.80 11:0.88 12:0.06 17:0.15 18:0.92 20:0.76 21:0.47 22:0.93 27:0.49 28:0.87 29:0.86 30:0.90 31:0.96 32:0.80 34:0.47 36:0.40 37:0.38 39:0.90 41:0.88 43:0.85 44:0.93 47:0.93 60:0.87 64:0.77 66:0.45 69:0.63 70:0.39 71:0.71 74:0.90 77:0.99 78:0.72 79:0.95 81:0.49 83:0.91 85:0.98 86:0.98 91:0.23 96:0.86 98:0.44 100:0.73 101:0.87 108:0.91 111:0.72 114:0.60 117:0.86 120:0.77 122:0.57 123:0.90 124:0.76 126:0.54 127:0.58 129:0.81 133:0.74 135:0.79 137:0.96 139:0.98 140:0.45 144:0.85 145:0.99 146:0.61 147:0.66 149:0.97 150:0.72 151:0.87 152:0.77 153:0.48 154:0.82 155:0.67 158:0.92 159:0.79 161:0.80 162:0.77 164:0.66 165:0.93 167:0.53 169:0.81 172:0.84 173:0.42 176:0.73 177:0.70 179:0.24 181:0.96 186:0.73 187:0.39 192:0.87 195:0.91 196:0.98 201:0.93 202:0.55 208:0.64 212:0.73 215:0.41 216:0.88 219:0.95 222:0.84 232:0.85 235:0.68 241:0.15 242:0.43 243:0.31 245:0.91 247:0.74 248:0.84 253:0.89 255:0.95 256:0.58 260:0.78 261:0.78 262:0.93 265:0.46 266:0.84 267:0.84 268:0.58 271:0.81 276:0.83 279:0.86 282:0.88 283:0.82 284:0.92 285:0.62 290:0.60 292:0.95 297:0.36 299:1.00 300:0.84 +3 1:0.74 6:0.91 7:0.81 8:0.80 9:0.80 11:0.88 12:0.06 17:0.12 18:0.87 20:0.82 21:0.30 22:0.93 27:0.21 28:0.87 29:0.86 30:0.93 31:0.96 34:0.66 36:0.31 37:0.71 39:0.90 41:0.93 43:0.76 47:0.93 60:0.99 64:0.77 66:0.93 69:0.81 70:0.24 71:0.71 74:0.87 78:0.82 79:0.95 81:0.55 83:0.91 85:0.95 86:0.95 91:0.49 96:0.87 97:0.89 98:0.76 100:0.88 101:0.87 106:0.81 108:0.65 111:0.83 114:0.65 117:0.86 120:0.78 121:0.90 122:0.57 123:0.62 126:0.54 127:0.25 129:0.05 135:0.92 137:0.96 139:0.97 140:0.45 144:0.85 146:0.81 147:0.84 149:0.95 150:0.75 151:0.87 152:0.81 153:0.48 154:0.68 155:0.67 158:0.92 159:0.37 161:0.80 162:0.86 164:0.78 165:0.93 167:0.59 169:0.84 172:0.82 173:0.83 176:0.73 177:0.70 179:0.27 181:0.96 186:0.82 187:0.21 189:0.94 191:0.70 192:0.87 196:0.98 201:0.93 202:0.65 204:0.89 206:0.81 208:0.64 212:0.60 215:0.44 216:0.79 219:0.95 220:0.62 222:0.84 230:0.86 232:0.94 233:0.73 235:0.52 238:0.91 241:0.44 242:0.51 243:0.94 247:0.35 248:0.85 253:0.90 255:0.95 256:0.73 259:0.21 260:0.79 261:0.83 262:0.93 265:0.76 266:0.38 267:0.65 268:0.74 271:0.81 276:0.70 279:0.86 281:0.89 283:0.82 284:0.96 285:0.62 290:0.65 292:0.95 297:0.36 300:0.43 +1 1:0.74 7:0.81 8:0.64 9:0.80 11:0.92 12:0.06 17:0.18 18:0.93 20:0.84 21:0.13 27:0.55 28:0.87 29:0.86 30:0.93 31:0.97 32:0.80 34:0.45 36:0.59 37:0.25 39:0.90 41:0.92 43:0.91 44:0.93 45:0.66 60:0.87 64:0.77 66:0.94 69:0.41 70:0.28 71:0.71 74:0.95 77:0.89 78:0.77 79:0.97 81:0.66 83:0.91 85:0.96 86:0.98 91:0.51 96:0.90 98:0.83 100:0.78 101:0.87 108:0.31 111:0.76 114:0.79 120:0.82 121:0.90 122:0.57 123:0.30 126:0.54 127:0.33 129:0.05 135:0.81 137:0.97 139:0.99 140:0.45 144:0.85 145:0.67 146:0.70 147:0.74 149:0.98 150:0.80 151:0.95 152:0.80 153:0.83 154:0.90 155:0.67 158:0.92 159:0.27 161:0.80 162:0.69 164:0.75 165:0.93 167:0.65 169:0.76 172:0.85 173:0.54 176:0.73 177:0.70 179:0.29 181:0.96 186:0.77 187:0.39 192:0.87 195:0.59 196:0.99 201:0.93 202:0.67 204:0.89 208:0.64 212:0.89 215:0.61 216:0.50 219:0.95 222:0.84 230:0.87 232:0.88 233:0.76 235:0.31 241:0.57 242:0.45 243:0.95 247:0.47 248:0.89 253:0.94 255:0.95 256:0.68 259:0.21 260:0.83 261:0.78 265:0.83 266:0.27 267:0.44 268:0.70 271:0.81 276:0.75 279:0.86 281:0.91 282:0.88 283:0.82 284:0.95 285:0.62 290:0.79 292:0.95 297:0.36 299:0.97 300:0.55 +0 8:0.97 10:0.82 12:0.69 17:0.70 18:0.64 21:0.78 26:0.94 27:0.69 29:0.62 30:0.67 34:0.40 36:0.42 37:0.62 39:0.85 43:0.10 46:0.88 55:0.92 58:0.93 66:0.20 69:0.93 70:0.52 71:0.72 74:0.27 86:0.57 89:0.98 91:0.07 98:0.32 107:0.92 108:0.86 110:0.91 122:0.90 123:0.85 124:0.96 125:0.88 127:0.78 129:0.05 131:0.61 133:0.95 135:0.26 139:0.56 141:0.91 145:0.56 146:0.71 147:0.05 149:0.16 154:0.09 157:0.61 159:0.87 160:0.88 163:0.96 166:0.61 170:0.79 172:0.37 173:0.83 174:0.79 175:0.91 177:0.05 178:0.55 179:0.65 182:0.61 187:0.39 197:0.80 199:0.94 202:0.56 212:0.19 216:0.17 222:0.76 223:0.91 224:0.93 225:0.97 227:0.61 229:0.61 231:0.63 234:0.91 235:0.98 239:0.81 240:0.95 241:0.43 242:0.63 243:0.11 244:0.93 245:0.52 246:0.61 247:0.74 253:0.24 254:0.95 257:0.97 259:0.21 264:0.90 265:0.34 266:0.87 267:0.96 271:0.24 274:0.91 275:0.95 276:0.59 277:0.87 287:0.61 289:0.89 294:0.94 300:0.55 +1 1:0.60 8:0.84 10:0.86 11:0.56 12:0.69 17:0.61 18:0.91 21:0.13 26:0.98 27:0.42 29:0.62 30:0.38 34:0.76 36:0.51 37:0.65 39:0.85 43:0.65 45:0.88 46:0.98 56:0.97 58:0.93 66:0.92 69:0.41 70:0.56 71:0.72 74:0.49 81:0.50 83:0.54 86:0.86 89:0.95 91:0.46 98:0.90 99:0.83 101:0.87 104:0.58 107:0.95 108:0.32 110:0.96 123:0.31 125:0.88 126:0.31 127:0.47 129:0.05 131:0.61 135:0.70 139:0.82 141:0.91 144:0.57 145:0.38 146:0.70 147:0.75 149:0.73 150:0.46 154:0.55 155:0.47 157:0.97 159:0.28 160:0.88 165:0.63 166:0.61 170:0.79 172:0.79 173:0.58 174:0.79 175:0.75 177:0.88 178:0.55 179:0.46 182:0.91 187:0.39 192:0.44 197:0.83 199:0.96 201:0.57 208:0.49 212:0.85 216:0.30 222:0.76 223:0.98 224:0.93 225:1.00 227:0.61 229:0.61 231:0.86 234:0.91 235:0.22 236:0.92 239:0.86 240:0.95 241:0.64 242:0.14 243:0.92 244:0.61 246:0.61 247:0.92 253:0.40 257:0.65 259:0.21 264:0.90 265:0.90 266:0.27 267:0.19 271:0.24 274:0.91 276:0.67 277:0.69 287:0.61 289:0.93 300:0.43 +2 1:0.60 11:0.53 12:0.69 17:0.59 18:0.94 21:0.13 26:0.98 27:0.72 29:0.62 30:0.86 32:0.67 34:0.59 36:0.88 39:0.85 43:0.62 44:0.88 45:0.95 46:1.00 56:0.97 58:0.93 66:0.94 69:0.41 70:0.41 71:0.72 74:0.68 77:0.70 81:0.50 83:0.72 86:0.87 89:0.96 91:0.35 97:0.94 98:0.86 99:0.83 101:0.69 104:0.57 107:0.96 108:0.32 110:0.96 122:0.91 123:0.31 125:0.88 126:0.31 127:0.37 129:0.05 131:0.61 135:0.70 139:0.88 141:0.91 144:0.57 145:0.39 146:0.76 147:0.80 149:0.85 150:0.46 154:0.52 155:0.47 157:0.99 158:0.77 159:0.32 160:0.88 165:0.63 166:0.61 170:0.79 172:0.83 173:0.51 175:0.75 177:0.88 178:0.55 179:0.35 182:0.95 187:0.39 192:0.44 195:0.59 199:0.95 201:0.57 208:0.64 212:0.82 216:0.09 219:0.91 222:0.76 223:0.98 224:0.93 225:1.00 227:0.61 229:0.61 231:0.86 234:0.91 235:0.22 236:0.92 240:0.95 241:0.58 242:0.33 243:0.94 244:0.83 246:0.61 247:0.67 253:0.50 257:0.65 259:0.21 264:0.90 265:0.86 266:0.27 267:0.28 271:0.24 274:0.91 275:0.91 276:0.71 277:0.69 279:0.77 282:0.75 283:0.82 287:0.61 289:0.94 292:0.95 294:0.93 300:0.55 +2 10:0.94 11:0.53 12:0.69 17:0.76 18:0.68 21:0.54 26:0.97 27:0.28 29:0.62 30:0.44 34:0.40 36:0.90 37:0.82 39:0.85 43:0.10 45:0.73 46:0.94 55:0.71 56:0.97 58:0.93 66:0.93 69:0.32 70:0.66 71:0.72 74:0.37 80:0.99 81:0.29 82:0.98 86:0.69 88:0.99 89:1.00 91:0.07 98:0.95 101:0.52 107:0.92 108:0.25 110:0.92 123:0.24 125:0.88 126:0.23 127:0.67 129:0.05 131:0.61 135:0.82 139:0.66 141:0.91 144:0.57 145:0.45 146:0.88 147:0.90 149:0.18 150:0.32 154:0.55 157:0.86 159:0.45 160:0.88 166:0.61 170:0.79 172:0.82 173:0.37 174:0.88 177:0.96 178:0.55 179:0.46 182:0.76 187:0.68 195:0.70 197:0.90 199:0.96 212:0.85 216:0.54 222:0.76 223:0.97 224:0.93 225:1.00 227:0.61 229:0.61 231:0.82 234:0.91 235:0.68 239:0.90 240:0.95 241:0.53 242:0.15 243:0.93 246:0.61 247:0.94 253:0.33 254:0.99 259:0.21 264:0.90 265:0.95 266:0.27 267:0.73 271:0.24 274:0.91 275:0.96 276:0.72 287:0.61 289:0.90 294:0.98 300:0.55 +1 1:0.60 9:0.73 10:0.85 11:0.56 12:0.69 17:0.06 18:0.63 21:0.13 23:0.99 26:0.99 27:0.42 29:0.62 30:0.68 34:0.40 36:0.23 37:0.65 39:0.85 43:0.57 45:0.77 46:0.95 55:0.59 56:0.97 58:1.00 62:0.97 66:0.86 69:0.63 70:0.29 71:0.72 74:0.35 75:0.96 81:0.50 83:0.54 86:0.66 89:0.98 91:0.95 97:0.89 98:0.85 99:0.83 101:0.69 104:0.58 107:0.89 108:0.48 110:0.90 120:0.92 123:0.46 125:0.88 126:0.31 127:0.36 128:0.99 129:0.05 131:0.86 135:0.42 139:0.65 140:0.87 141:0.99 143:1.00 144:0.57 146:0.62 147:0.67 149:0.37 150:0.46 151:0.96 153:0.95 154:0.47 155:0.54 157:0.87 159:0.23 160:0.88 162:0.84 164:0.86 165:0.63 166:0.61 168:0.99 170:0.79 172:0.61 173:0.82 174:0.79 177:0.96 179:0.64 182:0.77 190:0.95 192:0.48 197:0.83 199:0.98 201:0.57 202:0.85 205:0.99 208:0.49 212:0.81 216:0.30 222:0.76 223:0.99 224:0.99 225:1.00 226:0.96 227:0.86 229:0.61 231:0.80 234:0.98 235:0.22 236:0.92 239:0.87 240:1.00 241:0.96 242:0.18 243:0.87 244:0.83 246:0.61 247:0.79 248:0.94 253:0.31 255:0.72 256:0.88 259:0.21 260:0.91 264:0.90 265:0.85 266:0.27 267:0.19 268:0.90 271:0.24 274:0.99 275:0.93 276:0.51 279:0.75 285:0.55 287:0.61 289:0.89 294:0.96 295:0.98 300:0.25 +1 1:0.58 7:0.63 8:0.72 9:0.73 10:0.94 11:0.56 12:0.69 17:0.32 18:0.64 21:0.30 23:0.99 26:0.99 27:0.21 29:0.62 30:0.13 33:0.97 34:0.66 36:0.95 37:0.74 39:0.85 43:0.57 45:0.94 46:0.93 56:0.97 58:0.99 62:0.99 66:0.19 69:0.92 70:0.91 71:0.72 74:0.33 75:0.96 80:0.99 81:0.46 83:0.54 86:0.66 89:1.00 91:0.48 97:0.89 98:0.56 99:0.83 101:0.69 104:0.84 107:0.93 108:0.84 110:0.93 120:0.78 123:0.83 124:0.90 125:0.88 126:0.31 127:0.76 128:0.99 129:0.05 131:0.86 133:0.88 135:0.24 139:0.64 140:0.80 141:0.99 143:1.00 144:0.57 146:0.91 147:0.82 149:0.68 150:0.43 151:0.96 153:0.87 154:0.13 155:0.54 157:0.89 159:0.86 160:0.88 162:0.67 164:0.65 165:0.63 166:0.61 168:0.99 170:0.79 172:0.78 173:0.82 174:0.95 177:0.88 179:0.20 182:0.79 187:0.39 190:0.95 192:0.48 197:0.95 199:0.99 201:0.56 202:0.75 205:0.98 208:0.49 212:0.68 216:0.95 222:0.76 223:0.99 224:0.98 225:1.00 226:0.98 227:0.86 229:0.61 230:0.63 231:0.85 233:0.76 234:0.98 235:0.42 236:0.92 239:0.93 240:1.00 241:0.35 242:0.16 243:0.31 244:0.61 245:0.92 246:0.61 247:0.99 248:0.91 253:0.30 254:0.99 255:0.72 256:0.73 257:0.65 259:0.21 260:0.77 264:0.90 265:0.57 266:0.80 267:0.79 268:0.78 271:0.24 274:0.98 275:0.99 276:0.91 279:0.75 283:0.82 285:0.55 287:0.61 289:0.91 291:0.97 294:0.99 295:0.99 300:0.84 +0 1:0.57 8:0.61 9:0.71 10:0.81 11:0.45 12:0.69 17:0.67 18:0.92 21:0.54 26:0.94 27:0.52 29:0.62 30:0.72 31:0.90 34:0.37 36:0.51 37:0.43 39:0.85 43:0.39 44:0.85 45:0.93 46:0.98 58:0.93 64:0.77 66:0.63 69:0.93 70:0.56 71:0.72 74:0.39 77:0.89 79:0.90 81:0.38 83:0.54 86:0.68 89:0.98 91:0.23 96:0.75 97:0.89 98:0.52 99:0.83 101:0.46 107:0.96 108:0.50 110:0.96 114:0.66 122:0.91 123:0.48 124:0.64 125:0.99 126:0.31 127:0.52 129:0.05 131:0.86 133:0.73 135:0.73 137:0.90 139:0.69 140:0.45 141:0.91 145:0.74 146:0.49 147:0.81 149:0.74 150:0.36 151:0.70 153:0.69 154:0.29 155:0.53 157:0.61 158:0.72 159:0.72 160:0.96 162:0.86 165:0.63 166:0.80 170:0.79 172:0.75 173:0.91 174:0.79 175:0.91 176:0.59 177:0.38 179:0.43 182:0.61 187:0.87 190:0.95 192:0.56 195:0.88 196:0.89 197:0.81 199:0.94 201:0.54 202:0.46 204:0.83 208:0.49 212:0.49 216:0.79 219:0.87 220:0.74 222:0.76 223:0.91 224:0.93 225:0.96 227:0.86 229:0.61 231:0.71 234:0.91 235:0.68 239:0.81 240:0.95 241:0.10 242:0.55 243:0.68 244:0.93 245:0.73 246:0.61 247:0.67 248:0.65 253:0.60 255:0.70 256:0.45 257:0.93 259:0.21 264:0.90 265:0.53 266:0.84 267:0.17 268:0.55 271:0.24 274:0.91 275:0.94 276:0.69 277:0.95 279:0.75 281:0.86 282:0.72 284:0.89 285:0.49 287:0.61 289:0.95 290:0.66 292:0.88 294:0.93 300:0.70 +0 8:0.78 12:0.69 17:0.96 18:0.94 21:0.78 26:0.94 27:0.42 29:0.62 30:0.09 34:0.42 36:0.30 37:0.35 39:0.85 43:0.06 46:0.88 55:0.92 58:0.93 66:0.19 69:0.94 70:1.00 71:0.72 74:0.25 86:0.60 89:0.97 91:0.35 98:0.21 107:0.94 108:0.96 110:0.95 122:0.95 123:0.96 124:0.99 125:0.88 127:0.68 129:0.97 131:0.61 133:0.99 135:0.26 139:0.59 141:0.91 145:0.70 146:0.45 147:0.05 149:0.24 154:0.06 157:0.61 159:0.97 160:0.88 166:0.61 170:0.79 172:0.84 173:0.60 175:0.97 177:0.05 178:0.55 179:0.16 182:0.61 191:0.78 199:0.94 202:0.69 212:0.37 216:0.10 222:0.76 223:0.91 224:0.93 225:0.96 227:0.61 229:0.61 231:0.76 234:0.91 235:0.05 240:0.95 241:0.85 242:0.76 243:0.05 244:0.93 245:0.85 246:0.61 247:0.93 253:0.23 254:0.94 257:0.97 259:0.21 264:0.90 265:0.23 266:0.99 267:0.23 271:0.24 274:0.91 275:0.96 276:0.94 277:0.98 287:0.61 289:0.92 294:0.93 300:0.98 +1 7:0.63 10:0.93 11:0.56 12:0.69 17:0.53 18:0.64 21:0.30 26:0.98 27:0.50 29:0.62 30:0.13 33:0.97 34:0.42 36:0.90 37:0.60 39:0.85 43:0.55 45:0.93 46:0.93 55:0.59 58:0.98 66:0.36 69:0.93 70:0.93 71:0.72 74:0.31 75:0.96 81:0.31 82:0.98 83:0.54 86:0.64 88:0.99 89:1.00 91:0.10 97:0.94 98:0.64 101:0.87 102:0.95 104:0.84 107:0.93 108:0.86 110:0.93 120:0.58 123:0.86 124:0.85 125:0.88 126:0.23 127:0.84 129:0.05 131:0.86 133:0.85 135:0.84 139:0.62 140:0.45 141:0.97 144:0.57 145:0.70 146:0.95 147:0.35 149:0.63 150:0.35 151:0.52 153:0.48 154:0.13 157:0.81 159:0.87 160:0.88 162:0.86 164:0.55 166:0.81 170:0.79 172:0.88 173:0.84 174:0.95 177:0.70 179:0.20 182:0.74 187:0.39 190:0.98 195:0.95 197:0.94 199:0.99 202:0.36 208:0.49 212:0.60 215:0.72 216:0.86 220:0.87 222:0.76 223:0.98 224:0.98 225:0.99 226:0.98 227:0.86 229:0.61 230:0.63 231:0.85 233:0.76 234:0.98 235:0.31 239:0.93 240:0.95 241:0.03 242:0.22 243:0.08 244:0.61 245:0.93 246:0.61 247:0.99 248:0.51 253:0.28 254:0.99 256:0.45 257:0.84 259:0.21 260:0.58 264:0.90 265:0.65 266:0.87 267:0.85 268:0.46 271:0.24 274:0.97 275:0.98 276:0.92 279:0.77 283:0.82 285:0.45 287:0.61 289:0.91 291:0.97 294:0.99 295:0.99 297:0.36 300:0.84 +1 1:0.66 7:0.70 10:0.89 11:0.56 12:0.69 17:0.34 18:0.69 21:0.30 22:0.93 26:0.99 27:0.31 29:0.62 30:0.87 32:0.75 34:0.98 36:0.28 37:0.55 39:0.85 43:0.58 44:0.93 45:0.83 46:0.95 47:0.93 56:0.97 58:0.93 64:0.77 66:0.72 69:0.75 70:0.32 71:0.72 74:0.60 77:0.70 81:0.77 82:0.99 83:0.72 86:0.77 88:0.98 89:0.97 91:0.08 98:0.21 99:0.99 101:0.69 102:0.98 106:0.81 107:0.92 108:0.59 110:0.92 114:0.86 117:0.86 122:0.67 123:0.56 124:0.40 125:0.88 126:0.54 127:0.27 129:0.05 131:0.61 133:0.36 135:0.72 139:0.83 141:0.91 144:0.57 145:0.67 146:0.27 147:0.33 149:0.34 150:0.60 154:0.63 155:0.54 157:0.94 159:0.35 160:0.88 165:0.86 166:0.93 170:0.79 172:0.45 173:0.80 174:0.79 176:0.73 177:0.96 178:0.55 179:0.72 182:0.97 187:0.39 192:0.58 195:0.69 197:0.82 199:0.96 201:0.66 204:0.81 206:0.81 208:0.64 212:0.84 215:0.72 216:0.85 222:0.76 223:0.98 224:0.93 225:1.00 227:0.61 229:0.61 230:0.73 231:0.90 232:0.97 233:0.76 234:0.91 235:0.68 236:0.97 239:0.92 240:0.95 241:0.24 242:0.33 243:0.75 245:0.47 246:0.93 247:0.60 253:0.63 254:0.93 259:0.21 262:0.93 264:0.90 265:0.23 266:0.23 267:0.28 271:0.24 274:0.91 275:0.93 276:0.17 281:0.86 282:0.83 287:0.61 289:0.94 290:0.86 294:0.98 297:0.36 300:0.33 +1 7:0.63 10:0.90 11:0.56 12:0.69 17:0.61 18:0.91 21:0.13 26:0.98 27:0.68 29:0.62 30:0.27 32:0.62 34:0.50 36:0.73 37:0.40 39:0.85 43:0.56 45:0.97 46:0.97 58:0.93 66:0.42 69:0.93 70:0.88 71:0.72 74:0.54 81:0.34 86:0.83 89:0.97 91:0.20 98:0.61 101:0.87 104:0.58 107:0.96 108:0.86 110:0.97 122:0.91 123:0.86 124:0.86 125:0.88 126:0.23 127:0.98 129:0.05 131:0.61 133:0.86 135:0.37 139:0.79 141:0.91 144:0.57 145:1.00 146:0.92 147:0.49 149:0.76 150:0.37 154:0.13 157:0.99 159:0.86 160:0.88 166:0.81 170:0.79 172:0.91 173:0.86 174:0.94 177:0.05 178:0.55 179:0.18 182:0.95 187:0.39 195:0.94 197:0.90 199:0.99 212:0.73 215:0.84 216:0.12 222:0.76 223:0.98 224:0.93 225:0.96 227:0.61 229:0.61 230:0.63 231:0.89 233:0.66 234:0.91 235:0.12 239:0.88 240:0.95 241:0.68 242:0.14 243:0.12 244:0.61 245:0.95 246:0.61 247:0.99 253:0.43 254:0.99 257:0.97 259:0.21 264:0.90 265:0.62 266:0.80 267:0.73 271:0.24 274:0.91 275:0.93 276:0.93 287:0.61 289:0.94 294:0.93 297:0.36 300:0.84 +1 7:0.63 10:0.90 11:0.56 12:0.69 17:0.61 18:0.91 21:0.13 26:0.98 27:0.68 29:0.62 30:0.27 32:0.62 34:0.50 36:0.73 37:0.40 39:0.85 43:0.56 45:0.97 46:0.97 58:0.93 66:0.42 69:0.93 70:0.88 71:0.72 74:0.54 81:0.34 86:0.83 89:0.97 91:0.18 98:0.61 101:0.87 104:0.58 107:0.96 108:0.86 110:0.97 122:0.91 123:0.86 124:0.86 125:0.88 126:0.23 127:0.98 129:0.05 131:0.61 133:0.86 135:0.37 139:0.79 141:0.91 144:0.57 145:1.00 146:0.92 147:0.49 149:0.76 150:0.37 154:0.13 157:0.99 159:0.86 160:0.88 166:0.81 170:0.79 172:0.91 173:0.86 174:0.94 177:0.05 178:0.55 179:0.18 182:0.95 187:0.39 195:0.94 197:0.90 199:0.99 212:0.73 215:0.84 216:0.12 222:0.76 223:0.98 224:0.93 225:0.96 227:0.61 229:0.61 230:0.63 231:0.89 233:0.66 234:0.91 235:0.12 239:0.88 240:0.95 241:0.68 242:0.14 243:0.12 244:0.61 245:0.95 246:0.61 247:0.99 253:0.43 254:0.99 257:0.97 259:0.21 264:0.90 265:0.62 266:0.80 267:0.73 271:0.24 274:0.91 275:0.93 276:0.93 287:0.61 289:0.94 294:0.93 297:0.36 300:0.84 +1 7:0.63 8:0.76 10:0.90 11:0.64 12:0.69 17:0.67 18:0.70 21:0.22 26:0.98 27:0.72 29:0.62 30:0.44 32:0.62 34:0.67 36:0.22 37:0.40 39:0.85 43:0.43 44:0.85 45:0.85 46:0.94 55:0.71 58:0.93 66:0.85 69:0.15 70:0.75 71:0.72 74:0.38 80:0.99 81:0.32 82:0.98 83:0.54 86:0.66 88:0.99 89:0.98 91:0.74 98:0.89 101:0.87 102:0.95 104:0.54 107:0.91 108:0.12 110:0.92 123:0.12 124:0.37 125:0.88 126:0.23 127:0.95 129:0.05 131:0.61 133:0.36 135:0.18 139:0.64 141:0.91 144:0.57 145:1.00 146:0.83 147:0.86 149:0.57 150:0.36 154:0.33 155:0.49 157:0.86 159:0.48 160:0.88 166:0.81 170:0.79 172:0.76 173:0.25 174:0.88 177:0.96 178:0.55 179:0.58 182:0.76 192:0.45 193:0.96 195:0.65 197:0.90 199:0.97 204:0.79 208:0.49 212:0.84 215:0.79 216:0.04 222:0.76 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 230:0.63 231:0.84 233:0.66 234:0.91 235:0.22 239:0.91 240:0.95 241:0.89 242:0.41 243:0.86 244:0.83 245:0.60 246:0.61 247:0.86 253:0.33 259:0.21 264:0.90 265:0.89 266:0.47 267:0.36 271:0.24 274:0.91 275:0.94 276:0.65 283:0.82 287:0.61 289:0.90 294:0.97 297:0.36 300:0.70 +0 7:0.81 12:0.51 17:0.59 25:0.88 27:0.72 28:0.46 30:0.76 43:0.23 55:0.52 66:0.36 69:0.88 70:0.15 81:0.90 91:0.57 98:0.13 104:0.58 108:0.85 120:0.80 123:0.85 124:0.52 126:0.54 127:0.24 129:0.59 133:0.47 140:0.45 145:0.69 146:0.05 147:0.05 149:0.17 150:0.81 151:0.73 153:0.78 154:0.16 159:0.39 161:0.77 162:0.78 163:0.98 164:0.70 167:0.89 172:0.10 173:0.91 177:0.05 179:0.97 181:0.54 202:0.59 212:0.95 215:0.96 216:0.06 230:0.87 233:0.76 235:0.02 241:0.85 242:0.82 243:0.34 245:0.37 247:0.12 248:0.73 256:0.58 260:0.81 265:0.14 266:0.12 268:0.64 271:0.58 276:0.13 283:0.82 285:0.62 297:0.36 300:0.13 +2 7:0.81 12:0.51 17:0.40 21:0.05 27:0.53 28:0.46 30:0.58 34:0.88 36:0.46 37:0.49 43:0.52 55:0.84 66:0.80 69:0.05 70:0.60 81:0.89 91:0.15 98:0.82 104:0.86 106:0.81 108:0.05 120:0.69 123:0.05 126:0.54 127:0.30 129:0.05 135:0.21 140:0.45 146:0.76 147:0.80 149:0.50 150:0.78 151:0.66 153:0.48 154:0.41 159:0.32 161:0.77 162:0.86 163:0.81 164:0.57 167:0.57 172:0.45 173:0.17 177:0.05 179:0.78 181:0.54 187:0.39 202:0.36 206:0.81 212:0.37 215:0.91 216:0.87 230:0.87 233:0.76 235:0.05 241:0.24 242:0.82 243:0.82 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.82 266:0.38 267:0.91 268:0.46 271:0.58 276:0.37 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.90 8:0.97 12:0.51 17:0.45 20:0.93 21:0.54 27:0.10 28:0.46 30:0.76 32:0.80 34:0.58 36:0.56 37:0.90 43:0.34 55:0.84 66:0.36 69:0.65 70:0.15 78:0.76 81:0.76 91:0.93 98:0.37 100:0.80 104:0.77 108:0.50 111:0.76 120:0.74 123:0.48 124:0.52 126:0.54 127:0.42 129:0.59 133:0.47 135:0.54 140:0.45 145:0.49 146:0.31 147:0.38 149:0.25 150:0.57 151:0.60 152:0.86 153:0.93 154:0.25 159:0.28 161:0.77 162:0.67 163:0.96 164:0.90 167:0.91 169:0.78 172:0.10 173:0.81 177:0.05 179:0.99 181:0.54 186:0.77 189:0.91 191:0.96 195:0.60 202:0.85 212:0.95 215:0.58 216:0.38 220:0.62 235:0.60 238:0.93 241:0.93 242:0.82 243:0.51 245:0.37 247:0.12 248:0.67 256:0.90 259:0.21 260:0.75 261:0.81 265:0.39 266:0.12 267:0.36 268:0.87 271:0.58 276:0.16 283:0.60 285:0.62 297:0.36 300:0.13 +1 6:0.90 7:0.81 8:0.93 12:0.51 17:0.20 20:0.92 21:0.78 27:0.13 28:0.46 30:0.45 34:0.90 36:0.45 37:0.70 43:0.33 55:0.84 66:0.77 69:0.68 70:0.33 78:0.75 91:0.99 98:0.88 100:0.79 104:0.78 108:0.52 111:0.75 120:0.83 123:0.50 127:0.43 129:0.05 135:0.26 140:0.45 145:0.83 146:0.75 147:0.79 149:0.39 151:0.56 152:0.85 153:0.72 154:0.25 159:0.32 161:0.77 162:0.53 163:0.86 164:0.98 167:0.92 169:0.77 172:0.37 173:0.81 177:0.05 179:0.90 181:0.54 186:0.76 189:0.92 191:0.96 202:0.97 212:0.52 216:0.65 220:0.74 230:0.87 233:0.76 235:0.88 238:0.92 241:0.99 242:0.82 243:0.79 247:0.12 248:0.75 256:0.97 259:0.21 260:0.83 261:0.79 265:0.88 266:0.23 267:0.88 268:0.97 271:0.58 276:0.32 283:0.60 285:0.62 300:0.25 +1 12:0.51 17:0.38 21:0.68 27:0.45 28:0.46 30:0.30 32:0.80 34:0.81 36:0.23 37:0.50 43:0.32 55:0.84 66:0.51 69:0.70 70:0.80 81:0.67 91:0.35 98:0.41 108:0.54 123:0.51 124:0.65 126:0.54 127:0.40 129:0.81 133:0.67 135:0.83 145:0.47 146:0.57 147:0.63 149:0.49 150:0.49 154:0.24 159:0.56 161:0.77 163:0.90 167:0.57 172:0.41 173:0.65 177:0.05 178:0.55 179:0.74 181:0.54 187:0.68 195:0.79 212:0.23 215:0.49 216:0.77 235:0.80 241:0.27 242:0.82 243:0.61 245:0.55 247:0.12 259:0.21 265:0.43 266:0.71 267:0.23 271:0.58 276:0.41 297:0.36 300:0.55 +1 8:0.93 12:0.51 17:0.12 21:0.13 27:0.38 28:0.46 34:0.31 36:0.59 37:0.90 81:0.87 91:0.89 104:0.76 120:0.85 126:0.54 135:0.49 140:0.45 150:0.74 151:0.76 153:0.87 161:0.77 162:0.74 164:0.86 167:0.91 175:0.75 181:0.54 191:0.81 202:0.78 215:0.84 216:0.19 235:0.12 241:0.98 244:0.61 248:0.79 256:0.81 257:0.65 259:0.21 260:0.86 267:0.19 268:0.82 271:0.58 277:0.69 285:0.62 297:0.36 +0 12:0.51 17:0.34 21:0.22 25:0.88 27:0.72 28:0.46 43:0.44 66:0.36 69:0.46 81:0.85 91:0.85 98:0.16 104:0.58 108:0.35 120:0.80 123:0.34 126:0.54 127:0.09 129:0.05 140:0.80 146:0.05 147:0.05 149:0.15 150:0.68 151:0.66 153:0.48 154:0.33 159:0.05 161:0.77 162:0.58 164:0.83 167:0.90 172:0.05 173:0.94 177:0.05 178:0.42 181:0.54 202:0.79 215:0.79 216:0.21 220:0.82 235:0.22 241:0.88 243:0.51 248:0.72 256:0.79 260:0.80 265:0.17 268:0.79 271:0.58 283:0.82 285:0.62 297:0.36 +2 7:0.81 12:0.51 17:0.75 21:0.40 25:0.88 27:0.57 28:0.46 30:0.45 32:0.80 34:0.21 36:0.47 37:0.44 43:0.24 55:0.52 66:0.77 69:0.86 70:0.33 81:0.81 91:0.61 98:0.57 104:0.54 108:0.73 120:0.53 123:0.71 126:0.54 127:0.17 129:0.05 135:0.20 140:0.45 145:0.58 146:0.55 147:0.61 149:0.32 150:0.61 151:0.46 153:0.48 154:0.17 159:0.20 161:0.77 162:0.86 164:0.57 167:0.88 172:0.37 173:0.96 177:0.05 179:0.61 181:0.54 187:0.21 195:0.64 202:0.36 212:0.23 215:0.67 216:0.55 220:0.91 230:0.65 233:0.63 235:0.42 241:0.81 242:0.82 243:0.79 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.58 266:0.38 267:0.82 268:0.46 271:0.58 276:0.32 285:0.62 297:0.36 300:0.25 +1 8:0.69 12:0.51 17:0.15 20:0.77 21:0.78 27:0.33 28:0.46 30:0.38 32:0.80 34:0.66 36:0.92 37:0.46 43:0.51 55:0.96 66:0.20 69:0.27 70:0.63 78:0.75 91:0.92 98:0.32 100:0.73 104:0.73 108:0.21 111:0.74 120:0.76 123:0.20 124:0.85 127:0.94 129:0.93 133:0.84 135:0.61 140:0.45 145:0.80 146:0.41 147:0.48 149:0.49 151:0.60 152:0.75 153:0.92 154:0.40 159:0.60 161:0.77 162:0.57 163:0.81 164:0.94 167:0.91 169:0.72 172:0.21 173:0.26 177:0.05 179:0.86 181:0.54 186:0.76 191:0.82 195:0.77 202:0.92 212:0.28 216:0.38 220:0.82 235:0.80 241:0.92 242:0.82 243:0.40 245:0.50 247:0.12 248:0.68 256:0.91 259:0.21 260:0.77 261:0.74 265:0.35 266:0.63 267:0.96 268:0.92 271:0.58 276:0.42 283:0.60 285:0.62 300:0.43 +3 6:0.98 8:0.97 12:0.51 17:0.28 20:0.97 21:0.78 25:0.88 27:0.16 28:0.46 34:0.34 36:0.38 37:0.77 78:0.89 91:0.94 100:0.99 104:0.58 111:0.98 120:0.97 135:0.20 140:0.45 151:0.84 152:0.98 153:0.97 161:0.77 162:0.71 164:0.95 167:0.90 169:0.97 175:0.75 181:0.54 186:0.89 189:0.99 191:0.91 202:0.92 216:0.52 235:0.05 238:1.00 241:0.88 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.97 261:1.00 267:0.79 268:0.94 271:0.58 277:0.69 283:0.60 285:0.62 +2 6:0.83 8:0.71 12:0.51 17:0.84 20:0.81 21:0.13 25:0.88 27:0.72 28:0.46 30:0.91 34:0.40 36:0.88 43:0.52 55:0.71 66:0.77 69:0.07 70:0.19 78:0.75 81:0.87 91:0.86 98:0.98 100:0.79 104:0.58 108:0.06 111:0.75 120:0.77 123:0.06 126:0.54 127:0.82 129:0.05 135:0.49 140:0.45 146:0.59 147:0.65 149:0.44 150:0.74 151:0.73 152:0.78 153:0.78 154:0.41 159:0.22 161:0.77 162:0.85 164:0.82 167:0.90 169:0.77 172:0.37 173:0.40 177:0.05 179:0.93 181:0.54 186:0.76 189:0.85 202:0.70 212:0.73 215:0.84 216:0.15 220:0.74 235:0.12 238:0.90 241:0.89 242:0.82 243:0.79 247:0.12 248:0.69 256:0.77 259:0.21 260:0.78 261:0.77 265:0.98 266:0.23 267:0.92 268:0.77 271:0.58 276:0.32 283:0.82 285:0.62 297:0.36 300:0.18 +2 6:0.88 7:0.81 8:0.77 12:0.51 17:0.22 20:0.95 21:0.30 27:0.21 28:0.46 30:0.45 34:0.75 36:0.68 37:0.74 43:0.52 55:0.52 66:0.82 69:0.10 70:0.33 78:0.75 81:0.84 91:0.65 98:0.72 100:0.80 104:0.84 106:0.81 108:0.09 111:0.75 120:0.84 123:0.09 126:0.54 127:0.22 129:0.05 135:0.21 140:0.45 146:0.63 147:0.68 149:0.56 150:0.64 151:0.79 152:0.94 153:0.90 154:0.41 159:0.24 161:0.77 162:0.59 164:0.85 167:0.74 169:0.78 172:0.48 173:0.26 177:0.05 179:0.63 181:0.54 186:0.75 187:0.39 189:0.90 202:0.80 206:0.81 212:0.91 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.69 242:0.82 243:0.83 247:0.12 248:0.76 256:0.79 259:0.21 260:0.85 261:0.81 265:0.72 266:0.17 267:0.79 268:0.81 271:0.58 276:0.38 283:0.82 285:0.62 297:0.36 300:0.25 +2 7:0.81 12:0.51 17:0.40 20:0.75 21:0.74 27:0.56 28:0.46 30:0.91 32:0.80 34:0.68 36:0.32 37:0.60 43:0.39 55:0.45 66:0.52 69:0.59 70:0.27 78:0.72 81:0.78 91:0.38 98:0.22 100:0.73 104:0.82 106:0.81 108:0.81 111:0.72 120:0.53 123:0.80 124:0.64 126:0.54 127:0.48 129:0.81 133:0.67 135:0.76 140:0.45 145:0.82 146:0.05 147:0.05 149:0.48 150:0.58 151:0.46 152:0.74 153:0.72 154:0.29 159:0.50 161:0.77 162:0.86 164:0.69 167:0.60 169:0.72 172:0.48 173:0.58 177:0.05 179:0.70 181:0.54 186:0.73 187:0.39 195:0.74 202:0.53 206:0.81 212:0.75 215:0.46 216:0.76 220:0.93 230:0.87 233:0.76 235:0.95 241:0.37 242:0.82 243:0.13 245:0.60 247:0.12 248:0.46 256:0.58 259:0.21 260:0.53 261:0.73 265:0.24 266:0.38 267:0.65 268:0.62 271:0.58 276:0.38 283:0.82 285:0.62 297:0.36 300:0.33 +1 7:0.81 12:0.51 17:0.70 21:0.40 25:0.88 27:0.72 28:0.46 30:0.45 32:0.80 34:0.21 36:0.44 43:0.24 55:0.52 66:0.77 69:0.86 70:0.33 81:0.81 91:0.58 98:0.57 104:0.58 108:0.73 123:0.71 126:0.54 127:0.17 129:0.05 135:0.20 145:0.58 146:0.55 147:0.61 149:0.32 150:0.61 154:0.17 159:0.20 161:0.77 167:0.88 172:0.37 173:0.96 177:0.05 178:0.55 179:0.61 181:0.54 187:0.21 195:0.64 202:0.50 212:0.23 215:0.67 216:0.50 230:0.65 233:0.63 235:0.42 241:0.82 242:0.82 243:0.79 247:0.12 259:0.21 265:0.58 266:0.38 267:0.79 271:0.58 276:0.32 297:0.36 300:0.25 +1 8:0.89 12:0.51 17:0.05 20:0.80 21:0.47 27:0.72 28:0.46 32:0.80 34:0.89 36:0.60 37:0.77 78:0.74 81:0.79 91:0.85 100:0.73 104:0.57 111:0.73 120:0.60 126:0.54 135:0.70 140:0.45 145:0.87 150:0.59 151:0.54 152:0.77 153:0.48 161:0.77 162:0.69 164:0.73 167:0.90 169:0.72 175:0.75 181:0.54 186:0.75 195:0.54 202:0.64 215:0.63 216:0.04 220:0.93 235:0.52 241:0.91 244:0.61 248:0.54 256:0.64 257:0.65 259:0.21 260:0.61 261:0.74 267:0.28 268:0.66 271:0.58 277:0.69 285:0.62 297:0.36 +2 8:0.91 12:0.51 17:0.69 21:0.22 27:0.52 28:0.46 30:0.21 34:0.90 36:0.53 37:0.73 43:0.46 55:0.71 66:0.77 69:0.44 70:0.89 81:0.85 91:0.44 98:0.57 104:0.93 106:0.81 108:0.34 120:0.84 123:0.32 124:0.41 126:0.54 127:0.34 129:0.81 133:0.67 135:0.66 140:0.45 145:0.61 146:0.81 147:0.84 149:0.68 150:0.68 151:0.74 153:0.82 154:0.35 159:0.64 161:0.77 162:0.67 163:0.90 164:0.78 167:0.67 172:0.68 173:0.28 177:0.05 179:0.50 181:0.54 187:0.39 202:0.70 206:0.81 212:0.35 215:0.79 216:0.34 235:0.22 241:0.56 242:0.82 243:0.79 245:0.56 247:0.12 248:0.76 256:0.71 259:0.21 260:0.85 265:0.58 266:0.47 267:0.65 268:0.73 271:0.58 276:0.56 283:0.82 285:0.62 297:0.36 300:0.70 +0 12:0.51 17:0.45 21:0.78 25:0.88 27:0.72 28:0.46 30:0.76 43:0.23 55:0.45 66:0.72 69:0.88 70:0.22 78:0.78 91:0.58 98:0.44 104:0.73 108:0.77 111:0.78 120:0.78 123:0.75 127:0.14 129:0.05 140:0.96 145:0.45 146:0.46 147:0.53 149:0.25 151:0.66 153:0.48 154:0.16 159:0.17 161:0.77 162:0.48 163:0.75 164:0.72 167:0.89 169:0.81 172:0.27 173:0.97 177:0.05 178:0.54 179:0.59 181:0.54 202:0.78 212:0.42 216:0.12 235:0.31 241:0.86 242:0.82 243:0.75 247:0.12 248:0.71 256:0.64 260:0.79 265:0.46 266:0.23 268:0.65 271:0.58 276:0.17 285:0.62 300:0.18 +1 12:0.51 17:0.49 27:0.03 28:0.46 30:0.11 34:0.44 36:0.19 37:0.91 43:0.40 55:0.92 66:0.16 69:0.52 70:0.97 81:0.96 91:0.48 98:0.40 104:0.78 106:0.81 108:0.40 120:0.61 123:0.80 124:0.84 126:0.54 127:0.46 129:0.95 133:0.87 135:0.60 140:0.45 146:0.60 147:0.66 149:0.63 150:0.93 151:0.56 153:0.72 154:0.30 159:0.79 161:0.77 162:0.56 164:0.64 167:0.69 172:0.51 173:0.29 177:0.05 179:0.56 181:0.54 187:0.21 202:0.58 206:0.81 212:0.18 215:0.96 216:0.86 220:0.62 235:0.05 241:0.61 242:0.82 243:0.51 245:0.61 247:0.12 248:0.55 256:0.45 259:0.21 260:0.62 265:0.32 266:0.87 267:0.52 268:0.57 271:0.58 276:0.59 285:0.62 297:0.36 300:0.84 +2 6:0.98 8:0.97 12:0.51 17:0.28 20:0.97 21:0.78 25:0.88 27:0.16 28:0.46 34:0.34 36:0.38 37:0.77 78:0.89 91:0.94 100:0.99 104:0.58 111:0.98 120:0.97 135:0.20 140:0.45 151:0.84 152:0.98 153:0.97 161:0.77 162:0.71 164:0.95 167:0.90 169:0.97 175:0.75 181:0.54 186:0.89 189:0.99 191:0.91 202:0.92 216:0.52 235:0.05 238:1.00 241:0.88 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.97 261:1.00 267:0.79 268:0.94 271:0.58 277:0.69 283:0.60 285:0.62 +3 7:0.81 12:0.51 17:0.36 21:0.74 25:0.88 27:0.16 28:0.46 30:0.45 32:0.80 34:0.95 36:0.45 37:0.77 43:0.51 55:0.45 66:0.49 69:0.30 70:0.25 81:0.61 91:0.87 98:0.32 104:0.58 108:0.53 120:0.92 123:0.51 124:0.48 126:0.54 127:0.27 129:0.59 133:0.47 135:0.34 140:0.45 145:0.59 146:0.05 147:0.05 149:0.32 150:0.45 151:0.81 153:0.94 154:0.40 159:0.19 161:0.77 162:0.78 163:0.81 164:0.90 167:0.78 172:0.16 173:0.54 177:0.05 179:0.94 181:0.54 187:0.98 195:0.58 202:0.84 212:0.61 215:0.46 216:0.52 230:0.87 233:0.76 235:0.88 241:0.73 242:0.82 243:0.29 245:0.39 247:0.12 248:0.89 256:0.87 259:0.21 260:0.92 265:0.34 266:0.17 267:0.23 268:0.88 271:0.58 276:0.16 285:0.62 297:0.36 300:0.18 +1 7:0.81 8:0.95 12:0.51 17:0.26 20:0.86 21:0.47 25:0.88 27:0.24 28:0.46 34:0.62 36:0.44 37:0.88 43:0.28 66:0.36 69:0.78 78:0.74 81:0.79 91:0.98 98:0.08 100:0.78 104:0.76 108:0.62 111:0.74 120:0.84 123:0.60 126:0.54 127:0.06 129:0.05 135:0.34 140:0.45 145:0.52 146:0.05 147:0.05 149:0.12 150:0.59 151:0.63 152:0.81 153:0.99 154:0.20 159:0.05 161:0.77 162:0.59 164:0.97 167:0.90 169:0.75 172:0.05 173:0.87 177:0.05 181:0.54 186:0.75 191:0.86 202:0.95 215:0.63 216:0.50 220:0.62 230:0.87 233:0.76 235:0.52 241:0.91 243:0.51 248:0.76 256:0.96 259:0.21 260:0.85 261:0.76 265:0.09 267:0.73 268:0.96 271:0.58 283:0.60 285:0.62 297:0.36 +2 8:0.88 12:0.51 17:0.72 20:0.74 21:0.78 27:0.43 28:0.46 34:0.58 36:0.22 78:0.72 91:0.89 100:0.92 104:0.58 111:0.84 120:0.76 135:0.78 140:0.45 151:0.72 152:0.74 153:0.77 161:0.77 162:0.86 164:0.69 167:0.89 169:0.90 175:0.75 181:0.54 186:0.73 202:0.54 216:0.18 241:0.86 244:0.61 248:0.68 256:0.58 257:0.65 259:0.21 260:0.77 261:0.75 267:0.52 268:0.63 271:0.58 277:0.69 285:0.62 +1 7:0.81 8:0.95 12:0.51 17:0.45 21:0.54 27:0.67 28:0.46 32:0.80 34:0.40 36:0.87 37:0.96 43:0.52 66:0.36 69:0.17 81:0.76 91:0.84 98:0.14 104:0.58 108:0.14 120:0.77 123:0.13 126:0.54 127:0.09 129:0.05 135:0.58 140:0.45 145:0.56 146:0.05 147:0.05 149:0.16 150:0.57 151:0.71 153:0.72 154:0.41 159:0.05 161:0.77 162:0.81 164:0.83 167:0.89 172:0.05 173:0.66 177:0.05 181:0.54 191:0.76 195:0.54 202:0.73 215:0.58 216:0.13 220:0.62 230:0.87 233:0.76 235:0.60 241:0.85 243:0.51 248:0.69 256:0.78 259:0.21 260:0.78 265:0.15 267:0.23 268:0.78 271:0.58 285:0.62 297:0.36 +0 12:0.06 17:0.69 21:0.78 27:0.69 30:0.58 34:0.33 36:0.63 37:0.64 39:0.31 43:0.07 55:0.98 66:0.10 69:0.98 70:0.33 74:0.26 91:0.01 98:0.08 108:0.97 123:0.97 124:0.91 127:0.32 129:0.96 131:0.61 133:0.90 135:0.23 145:0.52 146:0.28 147:0.34 149:0.11 154:0.07 157:0.61 159:0.98 163:0.87 166:0.61 172:0.10 173:0.75 175:0.75 177:0.05 178:0.55 179:0.79 182:0.61 187:0.68 202:0.36 212:0.16 216:0.24 222:0.35 227:0.61 229:0.61 231:0.76 235:0.12 241:0.01 242:0.80 243:0.29 244:0.61 245:0.43 246:0.61 247:0.30 253:0.24 257:0.65 259:0.21 265:0.08 266:0.54 267:0.52 276:0.31 277:0.69 287:0.61 300:0.25 +0 12:0.06 17:0.43 21:0.78 27:0.06 30:0.95 34:0.74 36:0.43 37:0.85 39:0.31 43:0.36 55:0.99 66:0.20 69:0.49 74:0.41 98:0.05 108:0.38 123:0.36 124:0.61 127:0.12 129:0.59 131:0.61 133:0.47 135:0.88 146:0.05 147:0.05 149:0.16 154:0.27 157:0.61 159:0.76 166:0.61 172:0.05 173:0.07 175:0.75 177:0.05 178:0.55 179:0.84 182:0.61 187:0.68 216:0.84 222:0.35 227:0.61 229:0.61 231:0.62 243:0.40 244:0.61 245:0.36 246:0.61 253:0.36 257:0.65 259:0.21 265:0.05 267:0.36 277:0.69 287:0.61 300:0.08 +0 8:0.59 12:0.06 17:0.59 21:0.78 27:0.14 30:0.91 34:0.37 36:0.60 37:0.71 39:0.31 43:0.07 55:1.00 66:0.10 69:0.99 70:0.13 74:0.30 98:0.05 108:0.98 122:0.51 123:0.98 124:0.82 127:0.21 129:0.89 131:0.61 133:0.78 135:0.69 146:0.05 147:0.05 149:0.06 154:0.06 157:0.61 159:0.99 163:1.00 166:0.61 172:0.05 173:0.65 175:0.75 177:0.05 178:0.55 179:0.91 182:0.61 187:0.68 191:0.73 202:0.71 212:0.95 216:0.68 222:0.35 227:0.61 229:0.61 231:0.62 241:0.27 242:0.63 243:0.29 244:0.61 245:0.37 246:0.61 247:0.30 253:0.27 257:0.65 259:0.21 265:0.05 266:0.12 267:0.28 276:0.22 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.22 21:0.78 27:0.42 30:0.95 34:0.61 36:0.72 37:0.60 39:0.31 43:0.08 55:1.00 66:0.20 69:0.98 74:0.26 98:0.05 108:0.96 123:0.96 124:0.61 127:0.52 129:0.59 131:0.61 133:0.47 135:0.37 146:0.05 147:0.05 149:0.06 154:0.07 157:0.61 159:0.98 166:0.61 172:0.05 173:0.73 175:0.75 177:0.05 178:0.55 179:1.00 182:0.61 187:0.68 216:0.58 222:0.35 227:0.61 229:0.61 231:0.62 241:0.03 243:0.40 244:0.61 245:0.36 246:0.61 253:0.24 257:0.65 259:0.21 265:0.05 267:0.73 277:0.69 287:0.61 300:0.08 +0 8:0.64 12:0.06 17:0.43 21:0.78 27:0.16 30:0.76 34:0.34 36:0.60 37:0.84 39:0.31 43:0.05 55:0.42 66:0.36 69:1.00 70:0.15 74:0.27 91:0.06 98:0.05 108:1.00 122:0.51 123:1.00 124:0.52 127:0.18 129:0.59 131:0.61 133:0.47 135:0.54 146:0.05 147:0.05 149:0.05 154:0.05 157:0.61 159:0.96 163:1.00 166:0.61 172:0.10 173:0.92 175:0.75 177:0.05 178:0.55 179:0.93 182:0.61 187:0.87 202:0.46 212:0.95 216:0.62 222:0.35 227:0.61 229:0.61 231:0.63 235:0.05 241:0.05 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.25 257:0.65 259:0.21 265:0.05 266:0.12 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.28 21:0.78 27:0.69 30:0.95 34:0.56 36:0.58 37:0.27 39:0.31 43:0.10 55:1.00 66:0.20 69:0.97 74:0.28 91:0.11 98:0.05 108:0.94 123:0.93 124:0.61 127:0.17 129:0.59 131:0.61 133:0.47 135:0.19 146:0.05 147:0.05 149:0.07 154:0.09 157:0.61 159:0.91 166:0.61 172:0.05 173:0.67 175:0.75 177:0.05 178:0.55 179:0.97 182:0.61 187:0.39 202:0.62 216:0.51 222:0.35 227:0.61 229:0.61 231:0.63 235:0.22 241:0.02 243:0.40 244:0.61 245:0.36 246:0.61 253:0.26 257:0.65 259:0.21 265:0.05 267:0.65 277:0.69 287:0.61 300:0.08 +0 12:0.06 17:0.62 21:0.40 27:0.39 30:0.62 34:0.42 36:0.53 37:0.26 39:0.31 43:0.30 55:1.00 66:0.16 69:0.68 70:0.34 74:0.49 81:0.58 91:0.01 98:0.07 108:0.52 114:0.55 117:0.86 123:0.50 124:0.81 126:0.32 127:0.48 129:0.89 131:0.61 132:0.97 133:0.78 135:0.83 145:0.47 146:0.27 147:0.33 149:0.20 150:0.43 154:0.22 157:0.61 159:0.99 163:0.90 166:0.94 172:0.10 173:0.08 175:0.75 176:0.53 177:0.05 178:0.55 179:0.93 182:0.61 187:0.95 212:0.30 216:0.73 222:0.35 227:0.61 229:0.61 231:0.95 235:0.42 242:0.63 243:0.37 244:0.61 245:0.42 246:0.61 247:0.35 253:0.42 257:0.65 265:0.07 266:0.27 267:0.19 276:0.24 277:0.69 287:0.61 290:0.55 300:0.25 +0 12:0.06 17:0.83 21:0.78 27:0.66 30:0.58 34:0.89 36:0.44 37:0.32 39:0.31 43:0.10 55:0.52 66:0.18 69:0.97 70:0.44 74:0.30 91:0.03 98:0.09 108:0.95 123:0.93 124:0.68 127:0.12 129:0.81 131:0.61 133:0.67 135:0.66 146:0.30 147:0.36 149:0.18 154:0.09 157:0.61 159:0.68 166:0.61 172:0.27 173:0.69 175:0.75 177:0.05 178:0.55 179:0.19 182:0.61 187:0.21 212:0.37 216:0.40 222:0.35 227:0.61 229:0.61 231:0.82 235:0.97 241:0.05 242:0.80 243:0.39 244:0.61 245:0.50 246:0.61 247:0.30 253:0.27 257:0.65 259:0.21 265:0.09 266:0.47 267:0.23 276:0.30 277:0.69 287:0.61 300:0.33 +1 12:0.06 17:0.49 21:0.78 27:0.55 30:0.95 34:0.28 36:0.63 37:0.73 39:0.31 43:0.10 55:0.84 66:1.00 69:0.97 70:0.13 74:0.25 98:0.99 108:0.94 123:0.94 127:0.86 129:0.05 131:0.61 135:0.21 146:1.00 147:1.00 149:0.77 154:0.08 157:0.61 159:1.00 163:0.94 166:0.61 172:1.00 173:0.43 177:0.38 178:0.55 179:0.08 182:0.61 187:0.39 212:0.94 216:0.41 222:0.35 227:0.61 229:0.61 231:0.95 241:0.21 242:0.82 243:1.00 244:0.61 246:0.61 247:0.39 253:0.23 265:0.99 266:0.47 267:0.23 276:1.00 287:0.61 300:0.13 +0 12:0.06 17:0.59 21:0.78 27:0.06 30:0.76 34:0.74 36:0.44 37:0.85 39:0.31 43:0.24 55:0.52 66:0.36 69:0.84 70:0.15 74:0.37 98:0.06 108:0.92 122:0.55 123:0.91 124:0.52 127:0.11 129:0.59 131:0.61 133:0.47 135:0.88 146:0.05 147:0.05 149:0.15 154:0.17 157:0.61 159:0.72 163:0.99 166:0.61 172:0.10 173:0.25 175:0.75 177:0.05 178:0.55 179:0.53 182:0.61 187:0.68 212:0.95 216:0.84 222:0.35 227:0.61 229:0.61 231:0.64 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.33 257:0.65 259:0.21 265:0.06 266:0.12 267:0.36 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.47 21:0.78 27:0.06 30:0.76 34:0.74 36:0.44 37:0.85 39:0.31 43:0.21 55:0.52 66:0.36 69:0.91 70:0.15 74:0.34 98:0.06 108:0.92 122:0.55 123:0.92 124:0.52 127:0.11 129:0.59 131:0.61 133:0.47 135:0.84 146:0.05 147:0.05 149:0.13 154:0.14 157:0.61 159:0.64 163:1.00 166:0.61 172:0.10 173:0.39 175:0.75 177:0.05 178:0.55 179:0.44 182:0.61 187:0.68 212:0.95 216:0.84 222:0.35 227:0.61 229:0.61 231:0.64 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.31 257:0.65 259:0.21 265:0.06 266:0.12 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.64 21:0.78 27:0.55 30:0.87 34:0.97 36:0.45 37:0.53 39:0.31 43:0.22 55:0.59 66:0.20 69:0.89 70:0.27 74:0.32 91:0.14 98:0.20 108:0.89 123:0.76 124:0.70 127:0.30 129:0.81 131:0.61 133:0.67 135:0.69 145:0.91 146:0.34 147:0.41 149:0.27 154:0.15 157:0.61 159:0.62 166:0.61 172:0.21 173:0.84 175:0.75 177:0.05 178:0.55 179:0.81 182:0.61 187:0.95 212:0.19 216:0.30 222:0.35 227:0.61 229:0.61 231:0.95 235:0.88 241:0.07 242:0.79 243:0.40 244:0.61 245:0.48 246:0.61 247:0.30 253:0.29 257:0.65 265:0.20 266:0.47 267:0.23 276:0.28 277:0.69 287:0.61 300:0.25 +0 12:0.06 17:0.51 21:0.78 27:0.06 30:0.95 34:0.74 36:0.42 37:0.85 39:0.31 43:0.13 55:0.71 66:0.54 69:0.95 74:0.32 98:0.08 108:0.91 123:0.90 127:0.06 129:0.05 131:0.61 135:0.88 146:0.28 147:0.34 149:0.09 154:0.10 157:0.61 159:0.12 166:0.61 172:0.10 173:0.58 177:0.38 178:0.55 179:0.09 182:0.61 187:0.68 216:0.84 222:0.35 227:0.61 229:0.61 231:0.63 243:0.63 244:0.61 246:0.61 253:0.29 259:0.21 265:0.09 267:0.36 287:0.61 300:0.08 +0 12:0.06 17:0.81 21:0.78 27:0.17 30:0.95 34:0.63 36:0.52 37:0.95 39:0.31 43:0.29 55:1.00 66:0.20 69:0.71 74:0.40 91:0.01 98:0.05 108:0.54 123:0.52 124:0.61 127:0.13 129:0.59 131:0.61 133:0.47 135:0.70 146:0.05 147:0.05 149:0.14 154:0.21 157:0.61 159:0.87 166:0.61 172:0.05 173:0.11 175:0.75 177:0.05 178:0.55 179:0.89 182:0.61 187:0.39 202:0.53 216:0.42 222:0.35 227:0.61 229:0.61 231:0.62 235:0.05 241:0.56 243:0.40 244:0.61 245:0.36 246:0.61 253:0.35 257:0.65 259:0.21 265:0.05 267:0.36 277:0.69 287:0.61 300:0.08 +0 12:0.06 17:0.51 21:0.78 27:0.06 30:0.76 34:0.74 36:0.47 37:0.85 39:0.31 43:0.24 55:0.52 66:0.36 69:0.83 70:0.15 74:0.37 98:0.06 108:0.92 122:0.55 123:0.91 124:0.52 127:0.12 129:0.59 131:0.61 133:0.47 135:0.88 146:0.05 147:0.05 149:0.15 154:0.17 157:0.61 159:0.73 163:0.99 166:0.61 172:0.10 173:0.23 175:0.75 177:0.05 178:0.55 179:0.55 182:0.61 187:0.68 212:0.95 216:0.84 222:0.35 227:0.61 229:0.61 231:0.64 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 253:0.34 257:0.65 259:0.21 265:0.06 266:0.12 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 8:0.63 12:0.06 17:0.82 21:0.78 27:0.66 30:0.58 34:0.89 36:0.48 37:0.32 39:0.31 43:0.10 55:0.52 66:0.18 69:0.97 70:0.44 74:0.30 91:0.04 98:0.09 108:0.95 123:0.93 124:0.68 127:0.12 129:0.81 131:0.61 133:0.67 135:0.66 146:0.30 147:0.36 149:0.18 154:0.09 157:0.61 159:0.68 166:0.61 172:0.27 173:0.69 175:0.75 177:0.05 178:0.55 179:0.19 182:0.61 187:0.21 202:0.36 212:0.37 216:0.40 222:0.35 227:0.61 229:0.61 231:0.82 235:0.98 241:0.10 242:0.80 243:0.39 244:0.61 245:0.50 246:0.61 247:0.30 253:0.27 257:0.65 259:0.21 265:0.09 266:0.47 267:0.28 276:0.30 277:0.69 287:0.61 300:0.33 +0 12:0.06 17:0.53 21:0.78 27:0.06 30:0.91 34:0.74 36:0.46 37:0.85 39:0.31 43:0.12 55:0.52 66:0.49 69:0.96 70:0.13 74:0.29 98:0.06 108:0.92 123:0.91 124:0.48 127:0.15 129:0.59 131:0.61 133:0.47 135:0.84 146:0.22 147:0.28 149:0.12 154:0.10 157:0.61 159:0.90 163:0.81 166:0.61 172:0.16 173:0.58 175:0.75 177:0.05 178:0.55 179:0.73 182:0.61 187:0.68 212:0.61 216:0.84 222:0.35 227:0.61 229:0.61 231:0.67 242:0.63 243:0.60 244:0.61 245:0.39 246:0.61 247:0.30 253:0.26 257:0.65 259:0.21 265:0.07 266:0.17 267:0.28 276:0.12 277:0.69 287:0.61 300:0.13 +0 12:0.06 17:0.11 21:0.78 27:0.72 30:0.95 34:0.53 36:0.88 39:0.31 43:0.32 55:0.96 66:0.20 69:0.65 74:0.37 91:0.35 98:0.10 108:0.49 123:0.47 124:0.61 127:0.33 129:0.59 131:0.61 133:0.47 135:0.58 145:0.77 146:0.05 147:0.05 149:0.18 154:0.23 157:0.61 159:0.25 166:0.61 172:0.05 173:0.81 175:0.75 177:0.05 178:0.55 179:1.00 182:0.61 187:0.95 216:0.57 222:0.35 227:0.61 229:0.61 231:0.86 235:0.42 241:0.32 243:0.40 244:0.61 245:0.36 246:0.61 253:0.34 257:0.65 265:0.10 267:0.52 277:0.69 287:0.61 300:0.08 +3 1:0.74 8:0.67 9:0.80 11:0.88 12:0.51 17:0.02 27:0.33 28:0.91 30:0.61 34:0.33 36:0.32 37:0.41 39:0.75 41:0.92 43:0.86 66:0.09 69:0.57 70:0.75 74:0.89 81:0.94 83:0.83 85:0.98 91:0.49 96:0.93 98:0.21 101:0.83 104:0.84 106:0.81 108:0.78 114:0.98 117:0.86 120:0.87 122:0.43 123:0.94 124:0.94 126:0.54 127:0.75 129:0.97 133:0.94 135:0.89 138:0.96 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:0.98 153:0.91 154:0.84 155:0.67 159:0.86 161:0.77 162:0.73 164:0.79 165:0.92 167:0.64 172:0.61 173:0.30 176:0.73 177:0.38 179:0.33 181:0.80 187:0.39 192:0.59 201:0.74 202:0.71 206:0.81 208:0.64 212:0.52 215:0.96 216:0.88 222:0.25 232:0.83 235:0.05 241:0.61 242:0.51 243:0.15 245:0.80 247:0.67 248:0.93 253:0.95 255:0.79 256:0.71 259:0.21 260:0.88 265:0.19 266:0.92 267:0.59 268:0.74 271:0.32 276:0.80 285:0.62 290:0.98 297:0.36 300:0.84 +1 1:0.74 11:0.88 12:0.51 17:0.30 21:0.54 27:0.45 28:0.91 30:0.21 32:0.80 34:0.75 36:0.17 37:0.50 39:0.75 43:0.82 60:0.87 66:0.51 69:0.70 70:0.83 74:0.93 77:0.70 81:0.62 83:0.86 85:0.95 91:0.06 98:0.63 101:0.78 108:0.53 114:0.77 122:0.67 123:0.51 124:0.87 126:0.54 127:0.70 129:0.93 133:0.91 135:0.72 144:0.85 145:0.44 146:0.95 147:0.96 149:0.90 150:0.76 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 165:0.79 167:0.58 172:0.77 173:0.41 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 195:0.96 201:0.74 208:0.64 212:0.15 215:0.58 216:0.77 222:0.25 235:0.68 241:0.50 242:0.45 243:0.61 245:0.73 247:0.67 253:0.74 259:0.21 265:0.63 266:0.94 267:0.44 271:0.32 276:0.77 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.93 8:0.76 9:0.80 11:0.88 12:0.51 20:0.76 27:0.14 28:0.91 30:0.62 34:0.53 36:0.34 37:0.67 39:0.75 41:0.99 43:0.86 60:0.95 66:0.06 69:0.59 70:0.76 74:0.91 78:0.91 81:0.94 83:0.90 85:0.98 91:0.90 96:0.99 98:0.27 100:0.90 101:0.83 108:0.78 111:0.95 114:0.98 120:0.98 122:0.43 123:0.62 124:0.93 126:0.54 127:0.75 129:0.96 133:0.93 135:0.83 138:0.99 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:1.00 152:0.98 153:0.98 154:0.83 155:0.67 158:0.92 159:0.86 161:0.77 162:0.68 164:0.96 165:0.92 167:0.57 169:0.90 172:0.61 173:0.31 176:0.73 177:0.38 179:0.32 181:0.80 186:0.79 187:0.39 189:0.92 191:0.79 192:0.59 201:0.74 202:0.94 208:0.64 212:0.51 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.46 242:0.52 243:0.15 245:0.82 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.94 267:0.36 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +2 6:0.97 8:0.95 9:0.80 12:0.51 17:0.38 20:0.89 21:0.78 27:0.05 28:0.91 34:0.85 36:0.58 37:0.81 39:0.75 41:0.97 60:0.95 74:0.46 78:0.85 83:0.88 91:0.67 96:0.92 100:0.95 111:0.90 120:0.86 135:0.70 138:0.98 140:0.45 144:0.85 151:0.93 152:0.84 153:0.77 155:0.67 158:0.87 161:0.77 162:0.67 164:0.87 167:0.64 169:0.93 175:0.91 181:0.80 186:0.85 187:0.98 189:0.98 192:0.59 202:0.81 208:0.64 216:0.94 220:0.99 222:0.25 232:0.97 235:0.42 238:0.97 241:0.60 244:0.83 248:0.92 253:0.88 255:0.79 256:0.83 257:0.84 259:0.21 260:0.86 261:0.94 267:0.23 268:0.83 271:0.32 277:0.87 279:0.86 283:0.71 285:0.62 +1 1:0.74 8:0.66 9:0.80 11:0.88 12:0.51 17:0.04 21:0.22 27:0.14 28:0.91 30:0.71 32:0.80 34:0.63 36:0.30 37:0.67 39:0.75 41:0.98 43:0.86 60:0.99 66:0.52 69:0.58 70:0.66 74:0.86 77:0.70 78:0.80 81:0.81 83:0.88 85:0.96 91:0.77 96:0.95 98:0.53 101:0.83 108:0.86 111:0.79 114:0.90 120:0.91 122:0.43 123:0.85 124:0.73 126:0.54 127:0.47 129:0.89 133:0.78 135:0.98 140:0.45 144:0.85 145:0.94 146:0.13 147:0.18 149:0.89 150:0.87 151:0.91 153:0.72 154:0.84 155:0.67 158:0.92 159:0.71 161:0.77 162:0.72 164:0.91 165:0.86 167:0.58 169:0.95 172:0.67 173:0.42 176:0.73 177:0.38 179:0.46 181:0.80 187:0.39 191:0.75 192:0.59 195:0.88 201:0.74 202:0.85 208:0.64 212:0.36 215:0.79 216:0.93 220:0.74 222:0.25 235:0.31 238:0.86 241:0.47 242:0.61 243:0.14 245:0.74 247:0.39 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.91 265:0.54 266:0.71 267:0.87 268:0.88 271:0.32 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.88 12:0.51 17:0.26 20:0.97 21:0.30 27:0.25 28:0.91 30:0.75 32:0.80 34:0.31 36:0.90 37:0.43 39:0.75 41:0.97 43:0.83 60:0.87 66:0.60 69:0.65 70:0.59 74:0.90 77:0.89 78:0.78 81:0.76 83:0.89 85:0.97 91:0.40 96:0.94 98:0.32 100:0.83 101:0.83 104:0.93 106:0.81 108:0.90 111:0.79 114:0.86 117:0.86 120:0.90 121:0.99 122:0.43 123:0.90 124:0.58 126:0.54 127:0.44 129:0.81 133:0.67 135:0.93 140:0.45 144:0.85 145:0.72 146:0.54 147:0.60 149:0.90 150:0.84 151:0.96 152:0.90 153:0.85 154:0.80 155:0.67 158:0.92 159:0.75 161:0.77 162:0.85 164:0.89 165:0.84 167:0.55 169:0.81 172:0.74 173:0.46 176:0.73 177:0.38 179:0.40 181:0.80 186:0.79 187:0.39 189:0.94 192:0.59 195:0.92 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.73 215:0.72 216:0.80 220:0.74 222:0.25 230:0.87 232:0.95 233:0.76 235:0.42 238:0.92 241:0.39 242:0.59 243:0.31 245:0.79 247:0.55 248:0.94 253:0.96 255:0.79 256:0.83 259:0.21 260:0.90 261:0.87 265:0.35 266:0.71 267:0.65 268:0.86 271:0.32 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.84 +3 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.51 17:0.01 20:0.98 27:0.14 28:0.91 30:0.65 34:0.67 36:0.29 37:0.67 39:0.75 41:0.99 43:0.86 60:0.98 66:0.06 69:0.57 70:0.74 74:0.93 78:0.85 81:0.94 83:0.90 85:0.98 91:0.63 96:0.99 98:0.25 100:0.95 101:0.83 108:0.78 111:0.90 114:0.98 120:0.98 122:0.43 123:0.86 124:0.93 126:0.54 127:0.74 129:0.96 133:0.93 135:0.85 138:0.99 140:0.45 144:0.85 145:0.82 146:0.22 147:0.28 149:0.96 150:0.97 151:1.00 152:0.98 153:0.98 154:0.84 155:0.67 158:0.92 159:0.86 161:0.77 162:0.66 164:0.96 165:0.92 167:0.56 169:0.90 172:0.61 173:0.30 176:0.73 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.94 192:0.59 201:0.74 202:0.94 208:0.64 212:0.50 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.41 242:0.53 243:0.15 245:0.83 247:0.67 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.92 267:0.28 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +3 1:0.74 7:0.81 11:0.88 12:0.51 17:0.51 21:0.30 27:0.21 28:0.91 30:0.62 34:0.66 36:0.59 37:0.74 39:0.75 43:0.98 66:0.12 69:0.12 70:0.72 74:0.99 81:0.76 83:0.75 85:0.99 91:0.05 98:0.55 101:0.86 108:0.93 114:0.86 121:0.97 122:0.57 123:0.82 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.18 144:0.85 146:0.72 147:0.77 149:0.99 150:0.84 154:0.98 155:0.67 159:0.86 161:0.77 165:0.84 167:0.57 172:0.92 173:0.08 176:0.73 177:0.38 178:0.55 179:0.15 181:0.80 187:0.68 192:0.59 201:0.74 202:0.46 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 233:0.76 235:0.42 241:0.44 242:0.52 243:0.28 245:0.97 247:0.67 253:0.78 259:0.21 265:0.48 266:0.87 267:0.59 271:0.32 276:0.94 290:0.86 297:0.36 300:0.94 +1 1:0.74 11:0.88 12:0.51 17:0.26 21:0.40 27:0.45 28:0.91 30:0.31 34:0.68 36:0.17 37:0.50 39:0.75 43:0.82 55:0.71 60:0.87 66:0.10 69:0.69 70:0.81 74:0.91 81:0.71 83:0.84 85:0.93 91:0.04 98:0.32 101:0.76 108:0.82 114:0.82 122:0.67 123:0.63 124:0.93 126:0.54 127:0.71 129:0.96 133:0.93 135:0.78 144:0.85 145:0.49 146:0.13 147:0.18 149:0.83 150:0.81 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 163:0.81 165:0.83 167:0.52 172:0.54 173:0.40 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 201:0.74 208:0.64 212:0.18 215:0.67 216:0.77 222:0.25 235:0.52 241:0.27 242:0.61 243:0.11 245:0.76 247:0.60 253:0.75 259:0.21 265:0.34 266:0.94 267:0.65 271:0.32 276:0.77 279:0.86 283:0.71 290:0.82 297:0.36 300:0.70 +3 1:0.74 7:0.81 9:0.80 11:0.88 12:0.51 17:0.06 21:0.13 27:0.13 28:0.91 30:0.86 32:0.80 34:0.44 36:0.44 37:0.79 39:0.75 41:0.97 43:0.81 60:0.87 66:0.84 69:0.70 70:0.28 74:0.86 77:0.70 81:0.85 83:0.89 85:0.91 91:0.75 96:0.95 98:0.63 101:0.83 108:0.53 114:0.92 120:0.90 121:0.90 122:0.43 123:0.51 126:0.54 127:0.19 129:0.05 135:0.89 140:0.45 144:0.85 145:0.54 146:0.60 147:0.65 149:0.86 150:0.91 151:0.93 153:0.78 154:0.76 155:0.67 158:0.92 159:0.22 161:0.77 162:0.83 164:0.89 165:0.88 167:0.72 172:0.54 173:0.78 176:0.73 177:0.38 179:0.47 181:0.80 187:0.39 192:0.59 195:0.63 201:0.74 202:0.81 204:0.89 208:0.64 212:0.52 215:0.84 216:0.83 220:0.74 222:0.25 230:0.87 233:0.76 235:0.22 241:0.70 242:0.63 243:0.85 247:0.30 248:0.95 253:0.96 255:0.79 256:0.84 259:0.21 260:0.91 265:0.64 266:0.38 267:0.52 268:0.87 271:0.32 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.88 12:0.51 17:0.22 20:0.98 21:0.30 27:0.21 28:0.91 30:0.60 34:0.66 36:0.70 37:0.74 39:0.75 41:0.99 43:0.98 60:0.87 66:0.10 69:0.12 70:0.68 74:0.99 78:0.85 81:0.76 83:0.90 85:0.99 91:0.81 96:0.98 98:0.56 100:0.95 101:0.86 104:0.84 106:0.81 108:0.93 111:0.91 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.82 124:0.71 126:0.54 127:0.61 129:0.81 133:0.67 135:0.17 140:0.45 144:0.85 146:0.90 147:0.92 149:0.99 150:0.84 151:0.99 152:0.97 153:0.97 154:0.98 155:0.67 158:0.92 159:0.85 161:0.77 162:0.64 164:0.94 165:0.84 167:0.61 169:0.91 172:0.91 173:0.08 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.39 189:0.97 191:0.75 192:0.59 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.56 242:0.51 243:0.38 245:0.98 247:0.67 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.96 265:0.48 266:0.80 267:0.59 268:0.92 271:0.32 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +2 6:0.83 8:0.63 9:0.80 11:0.88 12:0.51 17:0.08 20:0.80 21:0.78 27:0.31 28:0.91 30:0.95 34:0.25 36:0.85 37:0.30 39:0.75 41:0.97 43:0.86 66:0.54 69:0.59 74:0.44 78:0.79 83:0.83 85:0.72 91:0.92 96:0.94 98:0.16 100:0.83 101:0.76 104:0.58 108:0.45 111:0.79 120:0.91 123:0.43 127:0.09 129:0.05 135:0.20 140:0.45 144:0.85 146:0.13 147:0.18 149:0.49 151:0.96 152:0.77 153:0.86 154:0.83 155:0.67 159:0.08 161:0.77 162:0.65 164:0.89 167:0.83 169:0.81 172:0.10 173:0.93 177:0.38 179:0.18 181:0.80 186:0.79 189:0.85 191:0.81 192:0.59 202:0.85 208:0.64 216:0.19 220:0.74 222:0.25 232:0.78 235:0.22 238:0.92 241:0.82 243:0.63 248:0.94 253:0.92 255:0.79 256:0.86 259:0.21 260:0.91 261:0.81 265:0.17 267:0.23 268:0.86 271:0.32 285:0.62 300:0.08 +1 1:0.74 6:0.95 8:0.86 9:0.80 11:0.88 12:0.51 17:0.45 20:0.74 21:0.59 27:0.06 28:0.91 30:0.38 32:0.80 34:0.75 36:0.75 37:0.82 39:0.75 41:0.95 43:0.76 55:0.71 60:0.97 66:0.13 69:0.81 70:0.80 74:0.94 77:0.96 78:0.99 81:0.58 83:0.86 85:0.94 91:0.48 96:0.87 98:0.38 100:0.96 101:0.79 108:0.90 111:0.98 114:0.74 120:0.78 122:0.67 123:0.77 124:0.88 126:0.54 127:0.38 129:0.93 133:0.87 135:0.77 140:0.45 144:0.85 145:0.90 146:0.36 147:0.43 149:0.86 150:0.73 151:0.87 152:0.74 153:0.48 154:0.67 155:0.67 158:0.87 159:0.79 161:0.77 162:0.81 164:0.85 165:0.78 167:0.57 169:0.92 172:0.61 173:0.61 176:0.73 177:0.38 179:0.26 181:0.80 186:0.99 187:0.39 189:0.96 192:0.59 195:0.94 201:0.74 202:0.78 208:0.64 212:0.37 215:0.55 216:0.92 220:0.91 222:0.25 235:0.74 238:0.80 241:0.44 242:0.55 243:0.21 245:0.85 247:0.47 248:0.86 253:0.91 255:0.79 256:0.81 259:0.21 260:0.79 261:0.78 265:0.38 266:0.80 267:0.52 268:0.83 271:0.32 276:0.81 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.79 8:0.59 12:0.51 17:0.83 20:0.87 27:0.64 28:0.59 30:0.94 34:0.90 36:0.47 37:0.33 39:0.35 43:0.41 55:0.59 66:0.72 69:0.29 70:0.13 74:0.69 78:0.98 81:0.92 83:0.80 91:0.48 98:0.62 100:0.73 104:0.95 106:0.81 108:0.22 111:0.95 114:0.98 122:0.51 123:0.22 126:0.54 127:0.18 129:0.05 135:0.34 146:0.31 147:0.38 149:0.22 150:0.96 152:0.82 154:0.67 155:0.67 159:0.13 161:0.96 165:0.92 167:0.45 169:0.72 172:0.27 173:0.67 176:0.73 177:0.38 178:0.55 179:0.79 181:0.46 186:0.98 187:0.68 189:0.82 192:0.59 201:0.74 206:0.81 212:0.95 215:0.96 216:0.33 222:0.76 235:0.05 238:0.81 241:0.12 242:0.45 243:0.75 247:0.35 253:0.76 254:0.97 259:0.21 261:0.87 265:0.63 266:0.12 267:0.19 276:0.26 283:0.82 290:0.98 297:0.36 300:0.13 +1 1:0.74 7:0.78 9:0.80 12:0.51 17:0.88 21:0.05 27:0.64 28:0.59 30:0.87 34:0.18 36:0.49 37:0.72 39:0.35 43:0.91 66:0.32 69:0.40 70:0.27 74:0.64 76:0.94 81:0.86 91:0.05 96:0.77 98:0.16 104:0.99 106:0.81 108:0.92 114:0.74 117:0.86 120:0.65 122:0.43 123:0.92 124:0.72 126:0.54 127:0.70 129:0.81 133:0.67 135:0.23 138:0.95 140:0.45 146:0.13 147:0.18 149:0.70 150:0.88 151:0.87 153:0.48 154:0.90 155:0.67 159:0.79 161:0.96 162:0.86 164:0.56 167:0.48 172:0.21 173:0.22 176:0.56 177:0.38 179:0.91 181:0.46 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.19 215:0.84 216:0.23 222:0.76 230:0.80 232:0.98 233:0.66 235:0.12 241:0.41 242:0.63 243:0.32 245:0.48 247:0.30 248:0.78 253:0.76 255:0.79 256:0.45 259:0.21 260:0.66 265:0.17 266:0.47 267:0.28 268:0.46 276:0.24 285:0.62 286:0.99 290:0.74 297:0.36 298:0.99 300:0.25 +2 1:0.74 7:0.81 8:0.59 9:0.80 11:0.57 12:0.51 17:0.43 21:0.05 27:0.58 28:0.59 30:0.73 34:0.55 36:0.18 37:0.29 39:0.35 41:0.82 43:0.81 55:0.42 60:0.87 66:0.61 69:0.61 70:0.51 74:0.98 76:0.85 77:0.70 81:0.87 83:0.81 85:0.88 91:0.62 96:0.80 98:0.73 101:0.79 104:0.94 106:0.81 108:0.71 114:0.95 117:0.86 120:0.69 122:0.43 123:0.69 124:0.51 126:0.54 127:0.56 129:0.59 132:0.97 133:0.47 135:0.85 140:0.45 145:0.41 146:0.60 147:0.65 149:0.93 150:0.89 151:0.87 153:0.48 154:0.80 155:0.67 158:0.92 159:0.50 161:0.96 162:0.86 164:0.57 167:0.45 172:0.88 173:0.61 176:0.73 177:0.38 179:0.24 181:0.46 187:0.68 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.91 216:0.33 222:0.76 230:0.84 232:0.88 233:0.69 235:0.12 241:0.07 242:0.37 243:0.37 245:0.95 247:0.67 248:0.78 253:0.88 255:0.79 256:0.45 259:0.21 260:0.70 265:0.74 266:0.27 267:0.23 268:0.46 276:0.85 279:0.86 282:0.84 283:0.82 285:0.62 290:0.95 297:0.36 300:0.70 +2 6:0.86 8:0.70 9:0.80 12:0.51 17:0.83 20:0.89 21:0.71 27:0.54 28:0.59 30:0.91 37:0.30 39:0.35 41:0.90 43:0.89 55:0.42 60:0.95 66:0.69 69:0.51 70:0.13 74:0.65 78:1.00 81:0.38 83:0.80 91:0.84 96:0.90 98:0.31 100:0.97 104:0.76 108:0.39 111:0.98 114:0.68 120:0.91 122:0.43 123:0.38 126:0.54 127:0.12 129:0.05 138:0.97 140:0.90 145:0.61 146:0.19 147:0.25 149:0.50 150:0.52 151:0.96 152:0.83 153:0.86 154:0.87 155:0.67 158:0.92 159:0.10 161:0.96 162:0.79 164:0.87 167:0.77 169:0.95 172:0.21 173:0.95 176:0.73 177:0.38 179:0.42 181:0.46 186:1.00 189:0.88 191:0.70 192:0.59 202:0.81 208:0.64 212:0.61 215:0.48 216:0.09 220:0.62 222:0.76 235:0.88 238:0.85 241:0.88 242:0.63 243:0.73 247:0.30 248:0.90 253:0.89 255:0.79 256:0.82 259:0.21 260:0.91 261:0.92 265:0.33 266:0.17 268:0.86 276:0.23 279:0.86 283:0.82 285:0.62 290:0.68 297:0.36 300:0.13 +1 1:0.74 7:0.81 8:0.69 9:0.80 11:0.57 12:0.51 17:0.78 21:0.54 27:0.57 28:0.59 30:0.68 32:0.80 34:0.69 36:0.87 37:0.48 39:0.35 41:0.88 43:0.96 55:0.59 60:0.87 66:0.35 69:0.30 70:0.66 74:0.93 76:0.94 77:0.89 81:0.67 83:0.80 85:0.85 91:0.70 96:0.88 98:0.60 101:0.78 104:0.87 106:0.81 108:0.68 114:0.64 120:0.88 121:0.90 123:0.66 124:0.67 126:0.54 127:0.75 129:0.59 132:0.97 133:0.64 135:0.42 140:0.90 145:0.86 146:0.44 147:0.50 149:0.81 150:0.77 151:0.92 153:0.72 154:0.96 155:0.67 158:0.92 159:0.49 161:0.96 162:0.86 164:0.80 165:0.70 167:0.45 172:0.51 173:0.34 176:0.56 177:0.38 179:0.62 181:0.46 187:0.39 192:0.59 195:0.72 201:0.74 202:0.67 204:0.89 206:0.81 208:0.64 212:0.81 215:0.55 216:0.35 220:0.62 222:0.76 230:0.87 233:0.76 235:0.88 241:0.07 242:0.24 243:0.49 245:0.73 247:0.67 248:0.87 253:0.91 255:0.79 256:0.73 259:0.21 260:0.88 265:0.61 266:0.54 267:0.28 268:0.75 276:0.59 279:0.86 282:0.88 283:0.82 285:0.62 290:0.64 297:0.36 299:0.88 300:0.70 +0 11:0.57 12:0.51 17:0.34 21:0.78 27:0.06 28:0.59 30:0.41 32:0.75 34:0.58 36:0.31 37:0.95 39:0.35 43:0.80 66:0.86 69:0.72 70:0.60 74:0.84 77:0.89 85:0.85 91:0.46 98:0.55 101:0.80 104:0.87 106:0.81 108:0.55 122:0.57 123:0.52 127:0.16 129:0.05 135:0.81 145:0.76 146:0.45 147:0.52 149:0.79 154:0.74 159:0.17 161:0.96 167:0.45 172:0.59 173:0.85 177:0.38 178:0.55 179:0.32 181:0.46 187:0.21 195:0.64 206:0.81 212:0.92 216:0.50 222:0.76 235:0.12 241:0.10 242:0.45 243:0.86 247:0.47 253:0.60 259:0.21 265:0.56 266:0.17 267:0.36 276:0.48 282:0.83 300:0.43 +2 7:0.81 8:0.70 12:0.51 17:0.79 21:0.68 27:0.53 28:0.59 30:0.87 34:0.83 36:0.31 37:0.55 39:0.35 43:0.41 55:0.42 66:0.20 69:0.39 70:0.19 74:1.00 76:0.98 81:0.34 91:0.77 96:0.78 98:0.63 104:0.93 108:0.72 114:0.62 117:0.86 122:0.67 123:0.29 124:0.52 126:0.32 127:0.60 129:0.05 132:0.97 133:0.39 135:0.51 140:0.45 146:0.59 147:0.65 149:0.87 150:0.31 151:0.63 153:0.69 154:0.71 155:0.67 158:0.84 159:0.41 161:0.96 162:0.86 167:0.46 172:0.86 173:0.46 176:0.56 177:0.38 179:0.27 181:0.46 187:0.39 190:0.77 192:0.59 202:0.46 204:0.89 208:0.64 212:0.92 216:0.27 220:0.62 222:0.76 230:0.83 232:0.96 233:0.66 235:0.80 241:0.27 242:0.82 243:0.40 245:0.95 247:0.39 248:0.61 253:0.85 254:0.84 256:0.45 265:0.47 266:0.54 267:0.19 268:0.55 276:0.81 285:0.50 290:0.62 300:0.33 +2 6:0.81 7:0.81 8:0.62 12:0.51 17:0.67 20:0.88 21:0.47 27:0.35 28:0.59 30:0.73 32:0.80 34:0.83 36:0.38 37:0.32 39:0.35 43:0.39 55:0.42 66:0.25 69:0.45 70:0.26 74:1.00 77:0.70 78:0.92 81:0.49 91:0.57 98:0.69 100:0.83 104:0.91 108:0.69 111:0.89 114:0.66 117:0.86 121:0.90 122:0.67 123:0.33 124:0.56 126:0.32 127:0.50 129:0.05 132:0.97 133:0.39 135:0.44 145:0.70 146:0.61 147:0.67 149:0.85 150:0.38 152:0.84 154:0.29 155:0.67 158:0.84 159:0.36 161:0.96 167:0.45 169:0.83 172:0.79 173:0.55 176:0.56 177:0.38 178:0.55 179:0.30 181:0.46 186:0.92 187:0.39 189:0.85 192:0.59 195:0.60 204:0.89 208:0.64 212:0.88 216:0.25 222:0.76 230:0.84 232:0.78 233:0.69 235:0.52 238:0.81 241:0.12 242:0.81 243:0.45 244:0.61 245:0.93 247:0.39 253:0.75 261:0.86 265:0.43 266:0.38 267:0.19 276:0.78 282:0.88 290:0.66 299:0.88 300:0.43 +1 1:0.74 7:0.78 12:0.51 17:0.85 21:0.05 27:0.72 28:0.59 30:0.44 34:0.62 36:0.39 39:0.35 43:0.76 55:0.42 66:0.27 69:0.07 70:0.71 74:0.96 76:0.85 77:0.96 81:0.87 91:0.53 98:0.30 106:0.81 108:0.63 114:0.95 117:0.86 122:0.67 123:0.61 124:0.79 126:0.54 127:0.72 129:0.81 132:0.97 133:0.76 135:0.49 145:0.47 146:0.27 147:0.33 149:0.86 150:0.89 154:0.87 155:0.67 159:0.55 161:0.96 167:0.45 172:0.54 173:0.14 176:0.73 177:0.38 178:0.55 179:0.51 181:0.46 187:0.68 192:0.59 195:0.73 201:0.74 206:0.81 212:0.84 215:0.91 216:0.21 222:0.76 230:0.81 232:0.75 233:0.76 235:0.12 241:0.18 242:0.57 243:0.39 245:0.77 247:0.60 253:0.80 259:0.21 265:0.33 266:0.54 267:0.18 276:0.63 282:0.84 283:0.71 290:0.95 297:0.36 300:0.70 +2 1:0.74 6:0.93 7:0.78 8:0.85 12:0.51 17:0.85 20:0.86 21:0.13 27:0.50 28:0.59 30:0.75 34:0.47 36:0.41 37:0.70 39:0.35 43:0.43 55:0.52 66:0.89 69:0.21 70:0.42 74:0.81 76:0.85 77:0.89 78:1.00 81:0.80 91:0.68 96:0.74 98:0.81 100:0.97 104:0.92 106:0.81 108:0.17 111:0.98 114:0.72 117:0.86 120:0.69 123:0.16 124:0.36 126:0.54 127:0.44 129:0.05 132:0.97 133:0.39 135:0.21 140:0.45 145:0.70 146:0.86 147:0.89 149:0.71 150:0.83 151:0.66 152:0.83 153:0.48 154:0.59 155:0.67 159:0.51 161:0.96 162:0.58 164:0.56 167:0.51 169:0.95 172:0.86 173:0.23 176:0.56 177:0.38 179:0.33 181:0.46 186:0.99 187:0.39 189:0.95 190:0.77 191:0.80 192:0.59 195:0.74 201:0.74 202:0.63 206:0.81 212:0.91 215:0.79 216:0.62 220:0.82 222:0.76 230:0.81 232:0.86 233:0.76 235:0.22 238:0.89 241:0.52 242:0.78 243:0.90 245:0.66 247:0.55 248:0.66 253:0.74 254:0.84 256:0.58 259:0.21 260:0.70 261:0.91 265:0.81 266:0.47 267:0.23 268:0.62 276:0.76 282:0.84 283:0.82 285:0.62 290:0.72 297:0.36 300:0.55 +2 8:0.64 12:0.51 17:0.90 21:0.13 27:0.61 28:0.59 30:0.45 34:0.18 36:0.59 37:0.39 39:0.35 43:0.42 55:0.52 66:0.79 69:0.78 70:0.56 74:0.88 81:0.77 87:0.98 91:0.89 98:0.77 108:0.61 114:0.74 122:0.67 123:0.59 124:0.38 126:0.32 127:0.73 129:0.05 133:0.39 135:0.23 146:0.68 147:0.05 149:0.51 150:0.57 154:0.55 159:0.40 161:0.96 167:0.76 172:0.61 173:0.88 176:0.56 177:0.05 178:0.55 179:0.73 181:0.46 187:0.21 212:0.55 216:0.29 222:0.76 232:0.72 235:0.12 241:0.82 242:0.72 243:0.12 245:0.53 247:0.39 253:0.71 254:0.94 257:0.65 259:0.21 265:0.77 266:0.47 267:0.18 276:0.48 290:0.74 300:0.43 +1 8:0.71 12:0.51 17:0.87 20:0.81 21:0.05 27:0.67 28:0.59 34:0.52 36:0.31 37:0.66 39:0.35 43:0.37 66:0.36 69:0.49 74:0.57 76:0.85 77:0.70 78:0.96 81:0.84 91:0.83 98:0.07 100:0.73 108:0.38 111:0.93 114:0.77 123:0.36 126:0.32 127:0.06 129:0.05 135:0.39 145:0.42 146:0.05 147:0.05 149:0.14 150:0.65 152:0.78 154:0.28 159:0.05 161:0.96 167:0.77 169:0.72 172:0.05 173:0.47 175:0.75 176:0.56 177:0.05 178:0.55 181:0.46 186:0.96 191:0.73 195:0.74 202:0.46 216:0.02 222:0.76 232:0.76 235:0.05 241:0.85 243:0.51 244:0.61 253:0.61 257:0.65 259:0.21 261:0.82 265:0.08 267:0.23 277:0.69 282:0.84 290:0.77 +2 7:0.78 11:0.57 12:0.51 17:0.94 21:0.78 27:0.72 28:0.59 30:0.60 32:0.78 34:0.64 36:0.65 39:0.35 43:0.95 55:0.52 66:0.86 69:0.05 70:0.56 74:0.93 76:0.85 77:0.70 85:0.80 91:0.60 98:0.79 101:0.79 104:0.82 106:0.81 108:0.05 122:0.67 123:0.05 127:0.27 129:0.05 132:0.97 135:0.49 145:0.47 146:0.55 147:0.61 149:0.77 154:0.95 155:0.67 158:0.87 159:0.20 161:0.96 167:0.45 172:0.59 173:0.25 177:0.38 178:0.55 179:0.59 181:0.46 187:0.39 192:0.59 195:0.56 206:0.81 208:0.64 212:0.84 216:0.14 222:0.76 230:0.81 232:0.70 233:0.76 241:0.05 242:0.72 243:0.86 247:0.55 253:0.66 259:0.21 265:0.79 266:0.27 267:0.23 276:0.47 279:0.86 282:0.86 283:0.82 300:0.43 +1 11:0.57 12:0.51 17:0.67 21:0.78 27:0.55 28:0.59 30:0.15 34:0.56 36:0.52 37:0.32 39:0.35 43:0.74 55:0.71 66:0.80 69:0.48 70:0.86 74:0.94 76:0.85 85:0.72 91:0.44 98:0.84 101:0.72 108:0.37 122:0.67 123:0.35 124:0.39 127:0.61 129:0.05 133:0.39 135:0.74 145:0.41 146:0.85 147:0.88 149:0.69 154:0.79 159:0.51 161:0.96 167:0.45 172:0.77 173:0.47 177:0.38 178:0.55 179:0.50 181:0.46 187:0.39 212:0.55 216:0.44 222:0.76 232:0.82 235:0.22 241:0.15 242:0.31 243:0.82 245:0.70 247:0.67 253:0.67 259:0.21 265:0.84 266:0.71 267:0.59 276:0.67 300:0.70 +1 12:0.51 17:0.51 21:0.78 27:0.45 28:0.59 30:0.91 34:0.85 36:0.17 37:0.50 39:0.35 43:0.26 55:0.98 66:0.27 69:0.81 70:0.13 74:0.45 91:0.40 98:0.19 108:0.65 122:0.51 123:0.62 124:0.72 127:0.38 129:0.05 133:0.62 135:0.82 145:0.50 146:0.30 147:0.36 149:0.22 154:0.18 159:0.59 161:0.96 163:0.97 167:0.46 172:0.10 173:0.75 177:0.38 178:0.55 179:0.97 181:0.46 187:0.68 212:0.61 216:0.77 222:0.76 235:0.97 241:0.21 242:0.63 243:0.46 244:0.61 245:0.38 247:0.30 253:0.39 259:0.21 265:0.20 266:0.17 267:0.59 276:0.21 300:0.13 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.95 21:0.54 27:0.59 28:0.59 30:0.67 32:0.80 34:0.50 36:0.35 37:0.23 39:0.35 43:0.73 55:0.52 66:0.80 69:0.70 70:0.46 74:1.00 76:0.99 77:0.99 81:0.75 85:0.72 91:0.42 98:0.84 101:0.70 104:0.94 106:0.81 108:0.53 114:0.77 121:0.90 122:0.67 123:0.51 124:0.42 126:0.54 127:0.86 129:0.05 132:0.97 133:0.74 135:0.30 145:0.74 146:0.96 147:0.23 149:0.88 150:0.78 154:0.63 155:0.67 159:0.77 161:0.96 167:0.45 172:0.94 173:0.57 176:0.73 177:0.05 178:0.55 179:0.22 181:0.46 187:0.39 192:0.59 195:0.88 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.58 216:0.22 222:0.76 230:0.87 232:0.77 233:0.76 235:0.88 241:0.02 242:0.81 243:0.07 245:0.86 247:0.39 253:0.77 257:0.65 259:0.21 265:0.84 266:0.54 267:0.17 276:0.90 282:0.88 290:0.77 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.78 12:0.51 17:0.32 21:0.13 27:0.55 28:0.59 30:0.72 32:0.78 34:0.80 36:0.70 37:0.32 39:0.35 43:0.37 55:0.71 66:0.67 69:0.48 70:0.48 74:0.82 77:0.70 81:0.82 91:0.77 98:0.73 108:0.37 114:0.92 123:0.35 124:0.43 126:0.54 127:0.49 129:0.05 133:0.39 135:0.70 145:0.41 146:0.65 147:0.70 149:0.45 150:0.84 154:0.81 155:0.67 159:0.36 161:0.96 167:0.45 172:0.48 173:0.57 176:0.73 177:0.38 178:0.55 179:0.77 181:0.46 187:0.68 192:0.59 195:0.60 201:0.74 212:0.39 215:0.84 216:0.44 222:0.76 230:0.81 232:0.82 233:0.69 235:0.22 241:0.10 242:0.42 243:0.72 245:0.56 247:0.55 253:0.76 254:0.80 259:0.21 265:0.73 266:0.54 267:0.76 276:0.43 282:0.86 290:0.92 297:0.36 300:0.43 +1 9:0.80 12:0.51 17:0.96 21:0.64 27:0.72 28:0.59 30:0.45 34:0.25 36:0.80 37:0.24 39:0.35 41:0.82 43:0.27 55:0.84 66:0.82 69:0.75 70:0.47 74:0.82 76:1.00 81:0.59 83:0.76 91:0.88 96:0.80 98:0.74 104:0.74 108:0.58 114:0.63 120:0.77 122:0.67 123:0.55 126:0.32 127:0.24 129:0.05 135:0.60 140:0.80 145:0.77 146:0.63 147:0.68 149:0.39 150:0.44 151:0.87 153:0.48 154:0.65 155:0.67 159:0.24 161:0.96 162:0.78 164:0.70 167:0.77 172:0.48 173:0.87 176:0.56 177:0.38 179:0.67 181:0.46 192:0.59 202:0.59 208:0.64 212:0.30 216:0.22 220:0.62 222:0.76 235:0.88 241:0.87 242:0.77 243:0.83 247:0.35 248:0.78 253:0.84 254:0.92 255:0.79 256:0.58 259:0.21 260:0.78 265:0.75 266:0.47 267:0.82 268:0.64 276:0.39 283:0.71 285:0.62 290:0.63 300:0.33 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.95 21:0.54 27:0.59 28:0.59 30:0.66 32:0.80 34:0.59 36:0.33 37:0.23 39:0.35 43:0.74 55:0.52 66:0.83 69:0.29 70:0.48 74:1.00 76:0.99 77:0.99 81:0.75 85:0.72 91:0.42 98:0.88 101:0.70 104:0.94 106:0.81 108:0.22 114:0.77 121:0.90 122:0.67 123:0.22 124:0.39 126:0.54 127:0.89 129:0.05 132:0.97 133:0.62 135:0.30 145:0.70 146:0.96 147:0.97 149:0.88 150:0.78 154:0.64 155:0.67 159:0.76 161:0.96 167:0.45 172:0.94 173:0.18 176:0.73 177:0.38 178:0.55 179:0.23 181:0.46 187:0.39 192:0.59 195:0.88 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.58 216:0.22 222:0.76 230:0.87 232:0.77 233:0.76 235:0.88 241:0.01 242:0.81 243:0.84 245:0.87 247:0.39 253:0.77 259:0.21 265:0.88 266:0.63 267:0.17 276:0.90 282:0.88 290:0.77 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.82 7:0.78 8:0.62 11:0.57 12:0.51 17:0.89 20:0.87 21:0.47 27:0.49 28:0.59 30:0.62 32:0.75 34:0.90 36:0.70 37:0.27 39:0.35 43:0.88 55:0.52 66:0.75 69:0.23 70:0.34 74:0.76 76:0.85 77:0.70 78:0.99 81:0.73 83:0.75 85:0.72 91:0.68 98:0.67 100:0.94 101:0.76 104:0.95 106:0.81 108:0.18 111:0.98 114:0.80 117:0.86 122:0.41 123:0.18 126:0.54 127:0.20 129:0.05 135:0.49 145:0.69 146:0.32 147:0.39 149:0.57 150:0.83 152:0.83 154:0.86 155:0.67 159:0.13 161:0.96 165:0.84 167:0.46 169:0.91 172:0.32 173:0.65 176:0.73 177:0.38 178:0.55 179:0.77 181:0.46 186:0.99 187:0.39 189:0.84 192:0.59 195:0.54 201:0.74 206:0.81 212:0.95 215:0.63 216:0.36 222:0.76 230:0.81 232:0.80 233:0.76 235:0.74 238:0.82 241:0.24 242:0.33 243:0.77 247:0.39 253:0.69 259:0.21 261:0.91 265:0.68 266:0.12 267:0.44 276:0.29 282:0.83 283:0.82 290:0.80 297:0.36 300:0.25 +0 1:0.56 7:0.70 10:0.81 11:0.84 12:0.06 17:0.75 18:0.64 21:0.77 27:0.12 29:0.62 30:0.45 32:0.67 34:0.98 36:0.58 37:0.85 43:0.25 45:0.81 66:0.16 69:0.94 70:0.61 71:0.95 74:0.53 77:0.70 81:0.39 83:0.56 86:0.61 91:0.12 98:0.31 99:0.83 101:0.47 108:0.83 114:0.63 122:0.65 123:0.82 124:0.82 126:0.32 127:0.28 129:0.05 131:0.61 133:0.77 135:0.96 139:0.59 144:0.85 145:0.70 146:0.38 147:0.25 149:0.32 150:0.34 154:0.46 155:0.50 157:0.61 159:0.58 163:0.89 165:0.65 166:0.61 170:0.87 172:0.21 173:0.95 174:0.86 176:0.63 177:0.70 178:0.55 179:0.62 182:0.61 187:0.87 192:0.50 195:0.87 197:0.85 201:0.53 204:0.80 208:0.50 212:0.22 215:0.44 216:0.69 222:0.35 227:0.61 229:0.61 230:0.73 231:0.62 233:0.76 235:0.98 239:0.80 241:0.46 242:0.22 243:0.31 244:0.61 245:0.57 246:0.61 247:0.74 253:0.50 254:0.84 257:0.65 259:0.21 265:0.33 266:0.75 267:0.94 271:0.27 276:0.44 277:0.69 282:0.75 287:0.61 290:0.63 297:0.36 300:0.43 +0 10:0.86 11:0.86 12:0.06 17:0.76 18:0.80 21:0.59 27:0.57 29:0.62 30:0.10 32:0.63 34:0.84 36:0.32 37:0.38 43:0.65 45:0.85 48:0.91 55:0.59 66:0.20 69:0.90 70:0.95 71:0.95 74:0.49 81:0.33 86:0.67 91:0.11 98:0.51 101:0.47 104:0.79 106:0.81 108:0.79 120:0.69 123:0.90 124:0.83 126:0.32 127:0.85 129:0.59 131:0.61 133:0.82 135:0.97 139:0.66 140:0.45 144:0.92 145:0.88 146:0.76 147:0.32 149:0.68 150:0.33 151:0.65 153:0.48 154:0.18 157:0.61 159:0.81 162:0.86 164:0.55 166:0.73 170:0.87 172:0.78 173:0.82 174:0.95 177:0.38 179:0.27 182:0.61 187:0.39 190:0.95 193:0.95 195:0.91 197:0.93 202:0.36 206:0.81 212:0.72 215:0.55 216:0.72 222:0.35 227:0.61 229:0.61 231:0.65 235:0.74 236:0.91 239:0.85 241:0.24 242:0.24 243:0.23 244:0.61 245:0.90 246:0.61 247:0.97 248:0.63 253:0.40 254:0.99 256:0.45 257:0.84 259:0.21 260:0.69 265:0.46 266:0.92 267:0.99 268:0.46 271:0.27 276:0.86 281:0.91 283:0.82 285:0.46 287:0.61 297:0.36 300:0.84 +2 1:0.62 7:0.70 8:0.75 10:0.90 11:0.88 12:0.06 17:0.55 18:0.75 21:0.13 27:0.55 29:0.62 30:0.66 32:0.67 34:0.95 36:0.48 37:0.84 43:0.66 45:0.89 66:0.61 69:0.84 70:0.70 71:0.95 74:0.62 77:0.70 81:0.52 83:0.56 86:0.75 91:0.38 97:0.89 98:0.52 99:0.83 101:0.65 104:0.98 106:0.81 108:0.81 114:0.88 117:0.86 122:0.65 123:0.80 124:0.56 126:0.32 127:0.56 129:0.59 131:0.61 132:0.97 133:0.64 135:1.00 139:0.72 144:0.92 145:0.72 146:0.29 147:0.18 149:0.59 150:0.46 154:0.25 155:0.53 157:0.88 158:0.76 159:0.62 165:0.65 166:0.61 170:0.87 172:0.65 173:0.82 174:0.89 176:0.63 177:0.38 178:0.55 179:0.56 182:0.77 187:0.39 192:0.55 195:0.79 197:0.88 201:0.58 204:0.82 206:0.81 208:0.50 212:0.42 215:0.84 216:0.49 222:0.35 227:0.61 229:0.61 230:0.73 231:0.63 233:0.76 235:0.22 239:0.89 241:0.01 242:0.16 243:0.16 244:0.61 245:0.70 246:0.61 247:0.86 253:0.64 254:0.86 257:0.84 265:0.54 266:0.63 267:0.44 271:0.27 276:0.57 279:0.75 282:0.75 283:0.67 287:0.61 290:0.88 297:0.36 300:0.70 +1 1:0.59 7:0.70 8:0.84 9:0.74 10:0.81 11:0.84 12:0.06 17:0.57 18:0.63 21:0.40 27:0.14 29:0.62 30:0.74 34:0.93 36:0.79 37:0.91 43:0.65 45:0.89 66:0.47 69:0.38 70:0.70 71:0.95 74:0.70 76:0.85 81:0.47 83:0.56 86:0.61 91:0.63 96:0.89 97:0.89 98:0.35 99:0.83 101:0.47 104:0.99 106:0.81 108:0.30 114:0.78 117:0.86 120:0.79 122:0.55 123:0.28 124:0.67 126:0.32 127:0.35 129:0.05 131:0.61 133:0.62 135:0.96 139:0.60 140:0.45 144:0.85 145:0.52 146:0.63 147:0.68 149:0.72 150:0.42 151:0.90 153:0.69 154:0.56 155:0.56 157:0.61 158:0.77 159:0.69 162:0.55 164:0.77 165:0.65 166:0.61 170:0.87 172:0.54 173:0.21 174:0.79 176:0.63 177:0.88 179:0.53 182:0.61 187:0.87 190:0.89 191:0.75 192:0.58 197:0.82 201:0.56 202:0.77 204:0.79 206:0.81 208:0.50 212:0.28 215:0.67 216:0.57 220:0.62 222:0.35 227:0.61 229:0.61 230:0.73 231:0.61 233:0.76 235:0.52 239:0.80 241:0.44 242:0.45 243:0.59 244:0.61 245:0.70 246:0.61 247:0.67 248:0.86 253:0.89 254:0.74 255:0.72 256:0.73 259:0.21 260:0.80 265:0.37 266:0.75 267:0.95 268:0.75 271:0.27 276:0.51 279:0.76 283:0.82 285:0.50 287:0.61 290:0.78 297:0.36 300:0.84 +1 1:0.62 7:0.70 8:0.71 10:0.82 11:0.84 12:0.06 17:0.70 18:0.76 21:0.13 27:0.24 29:0.62 30:0.45 32:0.67 34:1.00 36:0.38 37:0.60 43:0.25 45:0.81 55:0.71 66:0.61 69:0.71 70:0.65 71:0.95 74:0.62 77:0.70 81:0.52 83:0.56 86:0.65 91:0.42 97:0.89 98:0.45 99:0.83 101:0.47 104:0.95 108:0.54 114:0.88 122:0.65 123:0.52 124:0.55 126:0.32 127:0.49 129:0.05 131:0.61 133:0.60 135:0.74 139:0.63 144:0.85 145:0.87 146:0.62 147:0.67 149:0.24 150:0.46 154:0.52 155:0.53 157:0.61 158:0.76 159:0.60 165:0.65 166:0.61 170:0.87 172:0.54 173:0.66 174:0.88 176:0.63 177:0.88 178:0.55 179:0.68 182:0.61 187:0.68 192:0.55 195:0.80 197:0.89 201:0.58 204:0.83 208:0.50 212:0.55 215:0.84 216:0.55 222:0.35 227:0.61 229:0.61 230:0.73 231:0.63 233:0.76 235:0.22 239:0.82 241:0.27 242:0.28 243:0.68 244:0.61 245:0.60 246:0.61 247:0.74 253:0.64 254:0.88 259:0.21 265:0.47 266:0.54 267:0.52 271:0.27 276:0.43 279:0.75 282:0.75 283:0.67 287:0.61 290:0.88 297:0.36 300:0.55 +2 1:0.60 7:0.70 10:0.82 11:0.84 12:0.06 17:0.11 21:0.30 27:0.34 29:0.62 30:0.21 32:0.74 34:0.92 36:0.18 37:0.44 43:0.59 44:0.86 45:0.66 66:0.72 69:0.73 70:0.37 71:0.95 74:0.54 77:0.70 81:0.48 83:0.69 91:0.18 98:0.46 99:0.83 101:0.47 102:0.94 108:0.56 114:0.82 122:0.67 123:0.53 126:0.32 127:0.14 129:0.05 131:0.61 135:0.83 139:0.69 144:0.92 145:0.65 146:0.52 147:0.58 149:0.30 150:0.43 154:0.48 155:0.54 157:0.61 159:0.19 163:0.81 165:0.65 166:0.61 170:0.87 172:0.27 173:0.75 174:0.86 175:0.75 176:0.63 177:0.70 178:0.55 179:0.61 182:0.61 187:0.39 192:0.51 193:0.98 195:0.71 197:0.88 201:0.57 204:0.82 208:0.61 212:0.42 215:0.72 216:0.71 222:0.35 227:0.61 229:0.61 230:0.73 231:0.62 233:0.66 235:0.42 239:0.87 241:0.55 242:0.45 243:0.75 244:0.83 246:0.61 247:0.35 253:0.59 257:0.65 259:0.21 265:0.47 266:0.23 267:0.94 271:0.27 276:0.21 277:0.69 281:0.91 282:0.82 287:0.61 290:0.82 297:0.36 300:0.25 +2 1:0.57 8:0.71 10:0.88 11:0.84 12:0.06 17:0.85 18:0.67 21:0.59 27:0.57 29:0.62 30:0.40 32:0.67 34:0.84 36:0.30 37:0.38 43:0.65 45:0.92 48:0.91 66:0.95 69:0.43 70:0.88 71:0.95 74:0.77 77:0.98 81:0.50 83:0.56 86:0.71 91:0.10 97:0.89 98:0.93 99:0.83 101:0.47 104:0.79 106:0.81 108:0.33 114:0.73 117:0.86 122:0.55 123:0.32 126:0.51 127:0.58 129:0.05 131:0.61 135:0.94 139:0.67 144:0.85 145:0.74 146:0.92 147:0.94 149:0.81 150:0.40 154:0.55 155:0.46 157:0.61 158:0.77 159:0.55 165:0.65 166:0.73 170:0.87 172:0.88 173:0.39 174:0.91 176:0.70 177:0.88 178:0.55 179:0.34 182:0.61 187:0.21 192:0.45 193:0.95 195:0.75 197:0.93 201:0.55 206:0.81 208:0.50 212:0.71 215:0.55 216:0.72 222:0.35 227:0.61 229:0.61 231:0.63 235:0.80 236:0.91 239:0.87 241:0.27 242:0.19 243:0.95 246:0.61 247:0.92 253:0.62 254:0.99 259:0.21 265:0.93 266:0.54 267:0.99 271:0.27 276:0.79 279:0.76 281:0.91 282:0.75 283:0.82 287:0.61 290:0.73 297:0.36 300:0.70 +2 1:0.57 10:0.86 11:0.88 12:0.06 17:0.81 18:0.80 21:0.59 27:0.70 29:0.62 30:0.33 32:0.71 34:0.97 36:0.26 37:0.44 43:0.34 44:0.86 45:0.85 66:0.54 69:0.75 70:0.77 71:0.95 74:0.60 77:0.70 81:0.43 83:0.56 86:0.71 91:0.12 98:0.59 99:0.83 101:0.65 108:0.58 114:0.71 122:0.65 123:0.55 124:0.63 126:0.32 127:0.38 129:0.05 131:0.61 133:0.60 135:0.88 139:0.69 144:0.92 145:0.97 146:0.73 147:0.77 149:0.24 150:0.38 154:0.53 155:0.46 157:0.78 159:0.53 165:0.65 166:0.61 170:0.87 172:0.61 173:0.71 174:0.89 176:0.63 177:0.88 178:0.55 179:0.51 182:0.73 187:0.68 192:0.45 195:0.79 197:0.90 201:0.55 208:0.50 212:0.58 215:0.55 216:0.38 222:0.35 227:0.61 229:0.61 231:0.63 235:0.74 239:0.85 241:0.18 242:0.24 243:0.63 245:0.70 246:0.61 247:0.84 253:0.56 254:0.99 259:0.21 265:0.60 266:0.71 267:0.98 271:0.27 276:0.59 282:0.79 287:0.61 290:0.71 297:0.36 300:0.55 +1 1:0.59 10:0.86 11:0.88 12:0.06 17:0.67 18:0.79 21:0.68 27:0.45 29:0.62 30:0.33 34:0.94 36:0.25 37:0.50 43:0.60 45:0.73 64:0.77 66:0.12 69:0.71 70:0.49 71:0.95 74:0.33 81:0.40 83:0.56 86:0.74 91:0.14 98:0.12 99:0.83 101:0.65 108:0.54 122:0.65 123:0.81 124:0.81 126:0.32 127:0.24 129:0.59 131:0.61 133:0.76 135:0.83 139:0.71 144:0.92 145:0.50 146:0.19 147:0.24 149:0.39 150:0.35 154:0.49 155:0.47 157:0.77 159:0.47 163:0.75 165:0.65 166:0.76 170:0.87 172:0.16 173:0.63 174:0.79 175:0.91 177:0.38 178:0.55 179:0.75 182:0.86 187:0.68 192:0.44 197:0.84 201:0.56 208:0.50 212:0.19 216:0.77 222:0.35 227:0.61 229:0.61 231:0.65 235:0.88 236:0.94 239:0.85 241:0.15 242:0.19 243:0.40 244:0.83 245:0.46 246:0.85 247:0.55 253:0.29 257:0.84 259:0.21 265:0.11 266:0.47 267:0.88 271:0.27 276:0.32 277:0.95 287:0.61 300:0.33 +1 10:0.82 11:0.84 12:0.06 17:0.62 18:0.69 21:0.76 27:0.45 29:0.62 30:0.15 32:0.67 34:0.90 36:0.20 37:0.50 43:0.25 45:0.73 64:0.77 66:0.53 69:0.70 70:0.68 71:0.95 74:0.47 77:0.70 81:0.23 86:0.62 91:0.14 98:0.37 101:0.47 108:0.53 122:0.67 123:0.51 124:0.63 126:0.24 127:0.38 129:0.05 131:0.61 133:0.60 135:0.80 139:0.61 144:0.85 145:0.49 146:0.52 147:0.58 149:0.21 150:0.24 154:0.48 157:0.61 159:0.55 166:0.72 170:0.87 172:0.37 173:0.65 174:0.79 177:0.88 178:0.55 179:0.79 182:0.61 187:0.68 195:0.79 197:0.82 212:0.15 216:0.77 219:0.87 222:0.35 227:0.61 229:0.61 231:0.62 235:0.95 239:0.81 241:0.21 242:0.14 243:0.62 244:0.61 245:0.50 246:0.61 247:0.67 253:0.39 254:0.97 259:0.21 265:0.39 266:0.63 267:0.36 271:0.27 276:0.32 282:0.75 287:0.61 292:0.88 300:0.43 +1 1:0.56 7:0.70 10:0.82 11:0.84 12:0.06 17:0.76 18:0.66 21:0.77 27:0.72 29:0.62 30:0.70 34:1.00 36:0.49 43:0.39 45:0.87 48:0.91 66:0.72 69:0.82 70:0.56 71:0.95 74:0.61 81:0.39 83:0.56 86:0.63 91:0.18 97:0.94 98:0.51 99:0.83 101:0.47 108:0.66 114:0.63 122:0.55 123:0.64 124:0.41 126:0.32 127:0.23 129:0.05 131:0.61 132:0.97 133:0.39 135:0.92 139:0.62 144:0.85 146:0.54 147:0.60 149:0.41 150:0.34 154:0.58 155:0.52 157:0.61 158:0.77 159:0.31 165:0.65 166:0.61 170:0.87 172:0.61 173:0.87 174:0.79 176:0.63 177:0.88 178:0.55 179:0.45 182:0.61 187:0.68 192:0.54 197:0.82 201:0.53 204:0.82 208:0.50 212:0.73 215:0.44 216:0.15 222:0.35 227:0.61 229:0.61 230:0.73 231:0.61 233:0.66 235:0.98 239:0.81 241:0.07 242:0.24 243:0.75 245:0.62 246:0.61 247:0.67 253:0.52 254:0.93 259:0.21 265:0.52 266:0.54 267:0.44 271:0.27 276:0.47 279:0.78 281:0.91 283:0.82 287:0.61 290:0.63 297:0.36 300:0.55 +3 1:0.57 7:0.70 8:0.64 10:0.86 11:0.88 12:0.06 17:0.77 18:0.60 21:0.64 27:0.56 29:0.62 30:0.62 32:0.67 34:1.00 36:0.38 37:0.52 43:0.27 45:0.92 66:0.10 69:0.97 70:0.75 71:0.95 74:0.60 77:0.70 81:0.42 83:0.56 86:0.67 91:0.03 98:0.21 99:0.83 101:0.65 104:0.97 106:0.81 108:0.82 114:0.69 117:0.86 122:0.55 123:0.85 124:0.88 126:0.32 127:0.32 129:0.05 131:0.61 133:0.86 135:0.92 139:0.64 144:0.92 145:0.72 146:0.34 147:0.52 149:0.56 150:0.37 154:0.16 155:0.53 157:0.80 159:0.78 165:0.65 166:0.61 170:0.87 172:0.41 173:0.98 174:0.92 176:0.63 177:0.38 178:0.55 179:0.35 182:0.74 187:0.68 192:0.55 195:0.95 197:0.89 201:0.54 204:0.84 206:0.81 208:0.50 212:0.37 215:0.53 216:0.70 222:0.35 227:0.61 229:0.61 230:0.73 231:0.62 233:0.66 235:0.80 239:0.85 241:0.27 242:0.32 243:0.34 245:0.75 246:0.61 247:0.86 253:0.55 254:0.94 257:0.84 259:0.21 265:0.22 266:0.91 267:0.95 271:0.27 276:0.68 277:0.87 282:0.75 287:0.61 290:0.69 297:0.36 300:0.70 +1 1:0.58 10:0.87 11:0.64 12:0.79 17:0.67 18:0.79 21:0.30 26:0.96 27:0.67 28:0.70 29:0.71 30:0.76 34:0.95 36:0.24 37:0.83 39:0.38 43:0.60 45:0.83 58:0.93 66:0.83 69:0.64 70:0.28 71:0.79 74:0.38 81:0.48 83:0.54 86:0.69 91:0.49 98:0.43 99:0.83 101:0.69 104:0.58 108:0.49 114:0.80 122:0.57 123:0.47 126:0.31 127:0.14 129:0.05 131:0.61 135:0.63 139:0.67 141:0.91 144:0.76 146:0.40 147:0.46 149:0.37 150:0.45 154:0.63 155:0.47 157:0.97 159:0.15 161:0.79 165:0.63 166:0.78 167:0.72 170:0.82 172:0.51 173:0.74 174:0.83 176:0.62 177:0.96 178:0.55 179:0.28 181:0.50 182:0.96 187:0.21 192:0.46 197:0.87 199:0.94 201:0.56 208:0.49 212:0.91 215:0.72 216:0.29 222:0.60 223:0.95 224:0.93 227:0.61 229:0.61 231:0.76 232:0.80 234:0.91 235:0.42 239:0.86 241:0.51 242:0.13 243:0.84 246:0.96 247:0.74 253:0.57 254:0.97 259:0.21 264:0.90 265:0.45 266:0.17 267:0.82 271:0.56 274:0.91 275:0.91 276:0.40 287:0.61 290:0.80 294:0.95 297:0.36 300:0.25 +1 10:0.96 11:0.64 12:0.79 17:0.62 18:0.93 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.84 34:0.82 36:0.84 37:0.90 39:0.38 43:0.60 45:0.98 58:0.93 66:0.71 69:0.07 70:0.47 71:0.79 74:0.63 86:0.89 91:0.04 98:0.86 101:0.69 104:0.58 108:0.06 122:0.67 123:0.06 124:0.47 127:0.93 129:0.05 131:0.61 133:0.60 135:0.82 139:0.84 141:0.91 144:0.76 146:0.97 147:0.98 149:0.87 154:0.72 157:1.00 159:0.81 161:0.79 166:0.61 167:0.71 170:0.82 172:0.93 173:0.08 174:0.95 177:0.96 178:0.55 179:0.23 181:0.50 182:1.00 187:0.68 197:0.95 199:0.97 212:0.56 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.12 239:0.94 241:0.46 242:0.14 243:0.75 245:0.92 246:0.61 247:0.94 253:0.47 254:0.93 259:0.21 264:0.90 265:0.86 266:0.80 267:0.52 271:0.56 274:0.91 275:0.97 276:0.89 287:0.61 294:0.98 300:0.70 +1 1:0.57 10:0.92 11:0.64 12:0.79 17:0.91 18:0.90 21:0.54 26:0.97 27:0.67 28:0.70 29:0.71 30:0.30 34:0.63 36:0.49 37:0.83 39:0.38 43:0.34 45:0.95 58:0.93 66:0.84 69:0.73 70:0.57 71:0.79 74:0.42 81:0.42 83:0.54 86:0.81 91:0.44 98:0.82 99:0.83 101:0.87 104:0.58 108:0.56 114:0.72 122:0.99 123:0.53 124:0.38 126:0.31 127:0.48 129:0.05 131:0.61 133:0.38 135:0.30 139:0.77 141:0.91 144:0.76 146:0.85 147:0.88 149:0.81 150:0.40 154:0.25 155:0.46 157:1.00 159:0.50 161:0.79 165:0.63 166:0.78 167:0.72 170:0.82 172:0.95 173:0.73 174:0.97 176:0.62 177:0.96 178:0.55 179:0.17 181:0.50 182:1.00 187:0.68 192:0.45 197:0.98 199:0.96 201:0.54 208:0.49 212:0.94 215:0.58 216:0.29 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.82 232:0.80 234:0.91 235:0.68 239:0.91 241:0.50 242:0.45 243:0.85 244:0.83 245:0.93 246:0.96 247:0.98 253:0.53 259:0.21 264:0.90 265:0.82 266:0.38 267:0.28 271:0.56 274:0.91 275:0.99 276:0.91 287:0.61 290:0.72 294:0.97 297:0.36 300:0.55 +2 1:0.56 6:0.99 8:0.86 9:0.70 10:0.96 11:0.64 12:0.79 17:0.64 18:0.94 20:0.88 21:0.68 26:0.97 27:0.20 28:0.70 29:0.71 30:0.87 32:0.74 34:0.81 36:0.44 37:0.84 39:0.38 43:0.60 44:0.93 45:0.95 48:0.98 58:0.93 66:0.95 69:0.30 70:0.27 71:0.79 74:0.50 77:0.70 78:0.87 81:0.39 82:0.99 83:0.60 86:0.89 88:0.98 91:0.46 96:0.68 98:0.97 99:0.83 100:0.93 101:0.69 102:0.98 108:0.24 111:0.88 114:0.66 120:0.54 122:0.97 123:0.23 126:0.31 127:0.74 129:0.05 131:0.84 135:0.49 139:0.90 140:0.80 141:0.91 144:0.76 145:0.89 146:0.86 147:0.88 149:0.87 150:0.37 151:0.48 152:0.95 153:0.48 154:0.49 155:0.51 157:1.00 159:0.43 161:0.79 162:0.58 164:0.55 165:0.63 166:0.78 167:0.70 169:1.00 170:0.82 172:0.86 173:0.38 174:0.93 176:0.62 177:0.96 178:0.42 179:0.39 181:0.50 182:1.00 186:0.87 187:0.21 189:0.93 190:0.95 191:0.75 192:0.49 193:0.95 195:0.65 197:0.96 199:0.97 201:0.54 202:0.53 204:0.80 208:0.54 212:0.60 215:0.49 216:0.45 220:0.99 222:0.60 223:0.97 224:0.93 227:0.94 229:0.88 231:0.82 234:0.91 235:0.84 238:0.95 239:0.96 241:0.43 242:0.32 243:0.95 244:0.83 246:0.96 247:0.88 248:0.48 253:0.51 255:0.68 256:0.45 259:0.21 260:0.54 261:0.98 264:0.90 265:0.97 266:0.38 267:0.17 268:0.46 271:0.56 274:0.91 275:0.97 276:0.77 281:0.91 282:0.82 285:0.49 287:0.97 290:0.66 294:0.99 297:0.36 300:0.33 +2 1:0.65 10:0.95 11:0.64 12:0.79 17:0.62 18:0.89 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.73 32:0.78 33:0.97 34:0.77 36:0.23 37:0.60 39:0.38 43:0.36 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.88 69:0.83 70:0.56 71:0.79 74:0.77 77:0.89 81:0.75 82:0.99 83:0.72 86:0.86 88:0.98 91:0.06 98:0.89 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.68 114:0.82 117:0.86 122:0.57 123:0.66 124:0.37 126:0.54 127:0.75 129:0.05 131:0.61 133:0.38 135:0.56 139:0.86 141:0.91 144:0.76 145:0.72 146:0.96 147:0.47 149:0.78 150:0.57 154:0.58 155:0.50 157:1.00 159:0.73 161:0.79 165:0.86 166:0.88 167:0.61 170:0.82 172:0.96 173:0.75 174:0.97 176:0.73 177:0.38 178:0.55 179:0.17 181:0.50 182:1.00 187:0.39 192:0.51 195:0.85 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.69 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.95 241:0.07 242:0.15 243:0.11 245:0.93 246:1.00 247:0.96 253:0.66 254:0.84 257:0.93 259:0.21 262:0.93 264:0.90 265:0.89 266:0.75 267:0.44 271:0.56 274:0.91 275:0.98 276:0.93 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +1 1:0.65 10:0.98 11:0.64 12:0.79 17:0.55 18:0.92 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.84 32:0.78 33:0.97 34:0.93 36:0.26 37:0.60 39:0.38 43:0.38 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.79 69:0.81 70:0.52 71:0.79 74:0.85 77:0.89 81:0.75 82:0.99 83:0.72 86:0.94 88:0.98 91:0.05 98:0.86 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.65 114:0.82 117:0.86 122:0.57 123:0.63 124:0.40 126:0.54 127:0.64 129:0.05 131:0.61 133:0.45 135:0.17 139:0.92 141:0.91 144:0.76 145:0.72 146:0.94 147:0.46 149:0.75 150:0.57 154:0.60 155:0.50 157:1.00 159:0.67 161:0.79 165:0.86 166:0.88 167:0.65 170:0.82 172:0.96 173:0.75 174:0.97 176:0.73 177:0.88 178:0.55 179:0.17 181:0.50 182:1.00 187:0.39 192:0.51 195:0.81 197:0.98 199:0.98 201:0.66 206:0.81 208:0.64 212:0.78 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.98 241:0.24 242:0.14 243:0.19 245:0.96 246:1.00 247:0.94 253:0.69 254:0.90 257:0.65 259:0.21 262:0.93 264:0.90 265:0.86 266:0.75 267:0.44 271:0.56 274:0.91 275:0.97 276:0.93 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +2 1:0.60 8:0.72 9:0.72 10:0.95 11:0.64 12:0.79 17:0.81 18:0.89 20:0.82 21:0.47 23:0.97 26:0.98 27:0.67 28:0.70 29:0.71 30:0.91 31:0.90 32:0.74 34:0.64 36:0.78 37:0.42 39:0.38 43:0.65 44:0.93 45:0.93 56:0.97 58:0.98 62:0.97 64:0.77 66:0.91 69:0.33 70:0.23 71:0.79 74:0.51 77:0.70 78:0.72 79:0.89 80:1.00 81:0.54 82:0.98 83:0.72 86:0.92 88:0.98 91:0.72 96:0.74 98:0.84 99:0.95 100:0.73 101:0.69 102:0.94 108:0.26 111:0.72 120:0.62 122:0.97 123:0.25 126:0.43 127:0.34 128:0.96 129:0.05 131:0.90 135:0.61 137:0.90 139:0.91 140:0.80 141:0.99 144:0.76 145:0.72 146:0.54 147:0.60 149:0.73 150:0.42 151:0.61 152:0.81 153:0.48 154:0.65 155:0.63 157:1.00 159:0.19 161:0.79 162:0.54 164:0.70 165:0.70 166:0.76 167:0.98 168:0.96 169:0.72 170:0.82 172:0.74 173:0.60 174:0.92 177:0.96 178:0.42 179:0.47 181:0.50 182:1.00 186:0.73 190:0.95 192:0.87 193:0.98 195:0.55 196:0.93 197:0.95 199:0.98 201:0.59 202:0.72 204:0.84 205:0.97 208:0.64 212:0.95 216:0.22 220:0.87 222:0.60 223:0.98 224:0.98 227:0.95 229:0.89 231:0.83 234:0.97 235:0.68 236:0.94 239:0.94 241:0.88 242:0.32 243:0.91 246:0.95 247:0.74 248:0.69 253:0.56 254:0.94 255:0.70 256:0.68 259:0.21 260:0.62 261:0.73 264:0.90 265:0.84 266:0.12 267:0.18 268:0.69 271:0.56 274:0.98 275:0.96 276:0.61 281:0.86 282:0.82 284:0.91 285:0.61 287:0.97 294:0.98 300:0.25 +1 10:0.99 11:0.64 12:0.79 17:0.32 18:0.89 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.67 34:0.53 36:0.25 37:0.90 39:0.38 43:0.57 45:0.97 58:0.93 66:0.69 69:0.07 70:0.58 71:0.79 74:0.86 86:0.97 91:0.15 98:0.71 101:0.69 104:0.58 108:0.06 122:0.97 123:0.06 124:0.48 127:0.79 129:0.05 131:0.61 133:0.60 135:0.77 139:0.95 141:0.91 144:0.76 146:0.84 147:0.87 149:0.47 154:0.72 157:0.99 159:0.66 161:0.79 166:0.61 167:0.66 170:0.82 172:0.92 173:0.12 174:0.95 177:0.96 178:0.55 179:0.24 181:0.50 182:0.95 187:0.68 197:0.96 199:0.97 212:0.87 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.12 239:0.99 241:0.27 242:0.18 243:0.73 245:0.92 246:0.61 247:0.82 253:0.58 254:0.90 259:0.21 264:0.90 265:0.71 266:0.71 267:0.28 271:0.56 274:0.91 275:0.98 276:0.88 287:0.61 294:1.00 300:0.70 +2 1:0.61 6:0.99 8:0.93 9:0.74 10:0.91 11:0.64 12:0.79 17:0.59 18:0.88 20:0.96 21:0.64 23:0.99 26:0.98 27:0.20 28:0.70 29:0.71 30:0.87 31:0.92 32:0.74 34:0.44 36:0.37 37:0.84 39:0.38 43:0.57 44:0.93 45:0.88 48:0.98 56:0.97 58:0.99 62:0.97 64:0.77 66:0.36 69:0.73 70:0.41 71:0.79 74:0.53 75:0.95 77:0.70 78:0.94 79:0.92 81:0.70 82:0.99 83:0.72 86:0.86 88:0.98 91:0.85 96:0.93 97:0.89 98:0.36 99:0.99 100:1.00 101:0.69 102:0.98 108:0.56 111:0.99 114:0.72 120:0.88 122:0.97 123:0.54 124:0.70 126:0.54 127:0.76 128:0.97 129:0.59 131:0.93 133:0.62 135:0.54 137:0.92 139:0.86 140:0.80 141:0.99 143:0.98 144:0.76 145:0.89 146:0.31 147:0.23 149:0.58 150:0.50 151:0.54 152:0.95 153:0.82 154:0.63 155:0.64 157:0.99 159:0.34 161:0.79 162:0.64 164:0.87 165:0.86 166:0.88 167:0.98 168:0.97 169:1.00 170:0.82 172:0.51 173:0.87 174:0.89 176:0.73 177:0.38 178:0.42 179:0.63 181:0.50 182:1.00 186:0.94 189:0.99 190:0.95 191:0.92 192:0.99 193:0.95 195:0.60 196:0.94 197:0.92 199:0.97 201:0.62 202:0.84 204:0.80 205:0.98 208:0.64 212:0.80 215:0.53 216:0.45 219:0.87 220:0.93 222:0.60 223:0.98 224:0.98 226:0.96 227:0.96 229:1.00 231:0.83 234:0.97 235:0.95 236:0.97 238:0.99 239:0.93 241:0.89 242:0.13 243:0.23 245:0.73 246:1.00 247:0.88 248:0.55 253:0.91 254:0.95 255:0.73 256:0.85 257:0.93 259:0.21 260:0.88 261:0.98 264:0.90 265:0.38 266:0.54 267:0.65 268:0.86 271:0.56 274:0.99 275:0.95 276:0.53 279:0.75 281:0.91 282:0.82 284:0.97 285:0.62 287:1.00 290:0.72 292:0.88 294:0.98 297:0.36 300:0.55 +2 1:0.65 10:0.97 11:0.64 12:0.79 17:0.66 18:0.93 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.86 32:0.80 33:0.97 34:0.94 36:0.19 37:0.60 39:0.38 43:0.40 44:0.93 45:0.97 47:0.93 56:0.97 58:0.93 64:0.77 66:0.71 69:0.54 70:0.39 71:0.79 74:0.68 77:0.70 81:0.78 82:0.99 83:0.91 86:0.93 88:0.99 91:0.08 98:0.59 101:0.69 102:0.98 104:0.91 106:0.81 108:0.73 114:0.82 117:0.86 122:0.98 123:0.71 124:0.45 126:0.54 127:0.45 129:0.59 131:0.61 133:0.47 135:0.37 139:0.92 141:0.91 144:0.76 145:0.70 146:0.57 147:0.62 149:0.72 150:0.58 154:0.63 155:0.50 157:1.00 159:0.46 161:0.79 165:0.93 166:0.88 167:0.57 170:0.82 172:0.89 173:0.55 174:0.97 176:0.73 177:0.96 178:0.55 179:0.23 181:0.50 182:1.00 187:0.39 192:0.51 195:0.68 197:0.98 199:0.98 201:0.66 206:0.81 208:0.64 212:0.89 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.82 232:0.95 234:0.91 235:0.74 236:0.97 239:0.97 241:0.01 242:0.27 243:0.28 245:0.93 246:1.00 247:0.90 253:0.63 254:0.74 259:0.21 262:0.93 264:0.90 265:0.60 266:0.54 267:0.36 271:0.56 274:0.91 275:0.96 276:0.83 282:0.88 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 299:0.88 300:0.70 +1 1:0.67 7:0.69 8:0.58 10:0.92 11:0.64 12:0.79 17:0.66 18:0.67 20:0.85 21:0.22 26:0.98 27:0.67 28:0.70 29:0.71 30:0.76 32:0.69 34:0.89 36:0.24 37:0.44 39:0.38 43:0.32 44:0.86 45:0.87 56:0.97 58:0.93 64:0.77 66:0.77 69:0.74 70:0.42 71:0.79 74:0.58 77:0.70 78:0.79 81:0.80 83:0.91 86:0.80 91:0.29 98:0.56 100:0.73 101:0.69 102:0.94 108:0.58 111:0.78 114:0.90 122:0.67 123:0.55 124:0.38 126:0.54 127:0.27 129:0.05 131:0.61 133:0.36 135:0.78 139:0.76 141:0.91 144:0.76 145:0.70 146:0.54 147:0.05 149:0.24 150:0.62 152:0.81 154:0.64 155:0.55 157:0.99 159:0.30 161:0.79 165:0.93 166:0.89 167:0.63 169:0.72 170:0.82 172:0.59 173:0.83 174:0.89 176:0.73 177:0.05 178:0.55 179:0.56 181:0.50 182:1.00 186:0.80 187:0.39 192:0.58 193:0.95 195:0.64 197:0.92 199:0.95 201:0.67 204:0.82 208:0.64 212:0.75 215:0.79 216:0.56 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 230:0.71 231:0.82 233:0.76 234:0.91 235:0.60 236:0.97 239:0.90 241:0.15 242:0.18 243:0.13 245:0.52 246:1.00 247:0.74 253:0.64 254:0.92 257:0.97 259:0.21 261:0.79 264:0.90 265:0.58 266:0.47 267:0.36 271:0.56 274:0.91 275:0.93 276:0.44 281:0.91 282:0.77 287:0.61 290:0.90 294:0.97 297:0.36 300:0.43 +2 1:0.65 10:0.96 11:0.64 12:0.79 17:0.45 18:0.91 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.74 32:0.78 33:0.97 34:0.63 36:0.26 37:0.60 39:0.38 43:0.35 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.84 69:0.83 70:0.57 71:0.79 74:0.78 77:0.89 81:0.75 82:0.99 83:0.72 86:0.90 88:0.98 91:0.05 98:0.85 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.68 114:0.82 117:0.86 122:0.98 123:0.66 124:0.38 126:0.54 127:0.62 129:0.05 131:0.61 133:0.38 135:0.74 139:0.89 141:0.91 144:0.76 145:0.72 146:0.94 147:0.54 149:0.68 150:0.57 154:0.58 155:0.50 157:1.00 159:0.68 161:0.79 165:0.86 166:0.88 167:0.62 170:0.82 172:0.95 173:0.78 174:0.97 176:0.73 177:0.38 178:0.55 179:0.19 181:0.50 182:1.00 187:0.39 192:0.51 195:0.82 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.60 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.96 241:0.10 242:0.15 243:0.14 245:0.93 246:1.00 247:0.96 253:0.66 254:0.90 257:0.93 259:0.21 262:0.93 264:0.90 265:0.85 266:0.63 267:0.44 271:0.56 274:0.91 275:0.97 276:0.91 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +0 1:0.56 10:0.98 11:0.64 12:0.79 17:0.72 18:0.95 21:0.78 26:0.98 27:0.45 28:0.70 29:0.71 30:0.87 34:0.89 36:0.18 37:0.50 39:0.38 43:0.60 45:0.98 56:0.97 58:0.93 64:0.77 66:0.82 69:0.83 70:0.41 71:0.79 74:0.55 81:0.48 83:0.60 86:0.96 91:0.06 98:0.66 99:0.95 101:0.69 108:0.68 122:0.97 123:0.66 124:0.41 126:0.43 127:0.44 129:0.05 131:0.61 133:0.72 135:0.71 139:0.93 141:0.91 144:0.76 145:0.49 146:0.84 147:0.22 149:0.83 150:0.36 154:0.60 155:0.46 157:1.00 159:0.63 161:0.79 165:0.70 166:0.76 167:0.65 170:0.82 172:0.94 173:0.78 174:0.97 177:0.70 178:0.55 179:0.19 181:0.50 182:1.00 187:0.68 192:0.44 197:0.98 199:0.98 201:0.54 208:0.54 212:0.86 216:0.77 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.77 234:0.91 235:1.00 236:0.94 239:0.97 241:0.21 242:0.24 243:0.10 245:0.82 246:0.95 247:0.84 253:0.43 254:0.86 257:0.84 259:0.21 264:0.90 265:0.67 266:0.71 267:0.36 271:0.56 274:0.91 275:0.98 276:0.88 287:0.61 294:0.99 300:0.70 +1 1:0.65 10:0.97 11:0.64 12:0.79 17:0.47 18:0.91 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.76 32:0.78 33:0.97 34:0.84 36:0.19 37:0.60 39:0.38 43:0.36 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.75 69:0.83 70:0.50 71:0.79 74:0.81 77:0.89 81:0.75 82:0.99 83:0.72 86:0.91 88:0.98 91:0.04 98:0.84 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.67 114:0.82 117:0.86 122:0.98 123:0.65 124:0.42 126:0.54 127:0.59 129:0.05 131:0.61 133:0.45 135:0.47 139:0.89 141:0.91 144:0.76 145:0.72 146:0.94 147:0.53 149:0.69 150:0.57 154:0.58 155:0.50 157:1.00 159:0.68 161:0.79 165:0.86 166:0.88 167:0.67 170:0.82 172:0.95 173:0.77 174:0.97 176:0.73 177:0.88 178:0.55 179:0.19 181:0.50 182:1.00 187:0.39 192:0.51 195:0.82 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.60 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.97 241:0.30 242:0.15 243:0.23 245:0.96 246:1.00 247:0.93 253:0.67 254:0.88 257:0.65 259:0.21 262:0.93 264:0.90 265:0.84 266:0.75 267:0.36 271:0.56 274:0.91 275:0.97 276:0.91 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +1 1:0.63 10:0.97 11:0.64 12:0.79 17:0.69 18:0.93 21:0.54 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.87 32:0.80 33:0.97 34:0.91 36:0.18 37:0.60 39:0.38 43:0.60 44:0.93 45:0.97 47:0.93 56:0.97 58:0.93 64:0.77 66:0.16 69:0.48 70:0.42 71:0.79 74:0.66 77:0.70 81:0.75 82:0.99 83:0.91 86:0.91 88:0.99 91:0.10 98:0.62 101:0.69 102:0.98 104:0.97 106:0.81 108:0.73 114:0.77 117:0.86 122:0.98 123:0.35 124:0.50 126:0.54 127:0.50 129:0.59 131:0.61 133:0.47 135:0.68 139:0.90 141:0.91 144:0.76 145:0.74 146:0.67 147:0.72 149:0.84 150:0.54 154:0.61 155:0.49 157:1.00 159:0.48 161:0.79 165:0.93 166:0.88 167:0.65 170:0.82 172:0.87 173:0.48 174:0.97 176:0.73 177:0.96 178:0.55 179:0.24 181:0.50 182:1.00 187:0.21 192:0.50 195:0.68 197:0.98 199:0.98 201:0.64 206:0.81 208:0.64 212:0.87 215:0.58 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.82 232:0.99 234:0.91 235:0.84 236:0.97 239:0.97 241:0.24 242:0.24 243:0.37 245:0.95 246:1.00 247:0.88 253:0.60 254:0.74 259:0.21 262:0.93 264:0.90 265:0.61 266:0.75 267:0.84 271:0.56 274:0.91 275:0.96 276:0.84 282:0.88 287:0.61 290:0.77 291:0.97 294:0.99 297:0.36 299:0.88 300:0.84 +0 1:0.65 10:0.93 11:0.64 12:0.79 17:0.57 18:0.87 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.73 32:0.78 33:0.97 34:0.93 36:0.29 37:0.60 39:0.38 43:0.27 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.81 69:0.81 70:0.59 71:0.79 74:0.80 77:0.89 81:0.75 82:0.99 83:0.72 86:0.82 88:0.98 91:0.05 98:0.86 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.65 114:0.82 117:0.86 122:0.57 123:0.63 124:0.39 126:0.54 127:0.63 129:0.05 131:0.61 133:0.45 135:0.17 139:0.83 141:0.91 144:0.76 145:0.72 146:0.94 147:0.38 149:0.73 150:0.57 154:0.49 155:0.50 157:1.00 159:0.67 161:0.79 165:0.86 166:0.88 167:0.65 170:0.82 172:0.95 173:0.76 174:0.96 176:0.73 177:0.88 178:0.55 179:0.19 181:0.50 182:1.00 187:0.39 192:0.51 195:0.81 197:0.96 199:0.98 201:0.66 206:0.81 208:0.64 212:0.75 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.94 241:0.24 242:0.17 243:0.17 245:0.94 246:1.00 247:0.94 253:0.67 254:0.90 257:0.65 259:0.21 262:0.93 264:0.90 265:0.86 266:0.75 267:0.44 271:0.56 274:0.91 275:0.97 276:0.91 282:0.86 287:0.61 290:0.82 291:0.97 294:0.98 297:0.36 300:0.84 +2 1:0.66 10:0.99 11:0.83 12:0.79 17:0.94 18:0.93 21:0.30 26:0.98 27:0.63 28:0.70 29:0.71 30:0.87 32:0.78 34:0.31 36:0.21 37:0.61 39:0.38 43:0.63 44:0.93 45:0.99 56:0.97 58:0.93 64:0.77 66:0.77 69:0.54 70:0.44 71:0.79 74:0.91 77:0.70 81:0.76 82:0.99 83:0.72 85:0.72 86:0.97 88:0.98 91:0.05 98:0.84 99:0.99 101:0.97 102:0.98 104:0.94 106:0.81 108:0.41 114:0.86 117:0.86 122:0.97 123:0.40 124:0.43 126:0.54 127:0.79 129:0.05 131:0.61 133:0.62 135:0.96 139:0.97 141:0.91 144:0.76 145:0.67 146:0.98 147:0.98 149:0.84 150:0.59 154:0.77 155:0.51 157:1.00 159:0.83 161:0.79 165:0.86 166:0.89 167:0.61 170:0.82 172:0.97 173:0.31 174:0.97 176:0.73 177:0.96 178:0.55 179:0.15 181:0.50 182:1.00 187:0.39 192:0.55 195:0.92 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.68 215:0.72 216:0.55 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.97 234:0.91 235:0.68 236:0.97 239:0.99 241:0.07 242:0.18 243:0.79 245:0.96 246:1.00 247:0.91 253:0.72 259:0.21 264:0.90 265:0.84 266:0.75 267:0.87 271:0.56 274:0.91 275:0.99 276:0.95 282:0.86 287:0.61 290:0.86 294:1.00 297:0.36 300:0.84 +2 1:0.65 10:0.98 11:0.64 12:0.79 17:0.45 18:0.93 21:0.40 22:0.93 26:0.98 27:0.56 28:0.70 29:0.71 30:0.85 32:0.78 33:0.97 34:0.68 36:0.24 37:0.60 39:0.38 43:0.36 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.85 69:0.83 70:0.48 71:0.79 74:0.80 77:0.89 81:0.75 82:0.99 83:0.72 86:0.94 88:0.98 91:0.08 98:0.86 99:0.99 101:0.69 102:0.98 104:0.89 106:0.81 108:0.68 114:0.82 117:0.86 122:0.98 123:0.66 124:0.38 126:0.54 127:0.68 129:0.05 131:0.61 133:0.38 135:0.34 139:0.93 141:0.91 144:0.76 145:0.72 146:0.94 147:0.52 149:0.73 150:0.57 154:0.58 155:0.50 157:1.00 159:0.67 161:0.79 165:0.86 166:0.88 167:0.63 170:0.82 172:0.96 173:0.79 174:0.98 176:0.73 177:0.38 178:0.55 179:0.18 181:0.50 182:1.00 187:0.39 192:0.51 195:0.81 197:0.98 199:0.98 201:0.66 206:0.81 208:0.64 212:0.68 215:0.67 216:0.76 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 231:0.83 232:0.93 234:0.91 235:0.74 236:0.97 239:0.98 241:0.15 242:0.15 243:0.13 245:0.93 246:1.00 247:0.95 253:0.67 254:0.88 257:0.93 259:0.21 262:0.93 264:0.90 265:0.86 266:0.63 267:0.44 271:0.56 274:0.91 275:0.97 276:0.92 282:0.86 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.84 +2 1:0.65 8:0.67 9:0.75 10:0.98 11:0.64 12:0.79 17:0.91 18:0.97 20:0.90 21:0.59 23:0.99 26:0.98 27:0.62 28:0.70 29:0.71 30:0.87 31:0.95 34:0.29 36:0.48 37:0.27 39:0.38 43:0.62 44:0.88 45:0.98 58:1.00 62:0.99 64:0.77 66:0.67 69:0.56 70:0.42 71:0.79 74:0.60 75:0.97 78:0.84 79:0.95 80:0.98 81:0.44 82:0.99 83:0.91 86:0.95 88:0.99 91:0.73 96:0.85 97:0.97 98:0.85 99:0.99 100:0.82 101:0.69 102:0.96 108:0.43 111:0.82 120:0.76 122:0.97 123:0.41 124:0.47 126:0.31 127:0.66 128:0.98 129:0.59 131:0.93 133:0.47 135:0.28 137:0.95 139:0.94 140:0.97 141:0.99 143:0.99 144:0.76 145:0.82 146:0.93 147:0.94 149:0.87 150:0.41 151:0.66 152:0.84 153:0.78 154:0.70 155:0.65 157:1.00 159:0.66 161:0.79 162:0.75 164:0.81 165:0.63 166:0.73 167:0.97 168:0.98 169:0.80 170:0.82 172:0.93 173:0.46 174:0.98 177:0.96 178:0.48 179:0.20 181:0.50 182:1.00 186:0.84 190:0.77 192:1.00 193:0.95 195:0.83 196:0.97 197:0.99 199:0.99 201:0.61 202:0.81 204:0.81 205:0.99 208:0.64 212:0.77 216:0.23 219:0.91 220:0.74 222:0.60 223:0.98 224:0.98 226:0.96 227:0.96 229:1.00 231:0.83 234:0.98 235:0.95 236:0.91 239:0.98 241:0.85 242:0.28 243:0.72 245:0.97 246:0.61 247:0.74 248:0.63 253:0.82 254:0.86 255:0.78 256:0.85 259:0.21 260:0.77 261:0.85 264:0.90 265:0.85 266:0.38 267:0.23 268:0.85 271:0.56 274:0.99 275:0.97 276:0.90 279:0.79 281:0.91 284:0.97 285:0.62 287:1.00 292:0.88 294:0.99 295:0.98 300:0.70 +1 10:0.99 11:0.64 12:0.79 17:0.36 18:0.89 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.67 34:0.53 36:0.22 37:0.90 39:0.38 43:0.57 45:0.97 58:0.93 66:0.69 69:0.07 70:0.58 71:0.79 74:0.85 86:0.97 91:0.20 98:0.71 101:0.69 104:0.58 108:0.06 122:0.97 123:0.06 124:0.48 127:0.81 129:0.05 131:0.61 133:0.60 135:0.77 139:0.95 141:0.91 144:0.76 146:0.84 147:0.87 149:0.47 154:0.72 157:0.99 159:0.66 161:0.79 166:0.61 167:0.67 170:0.82 172:0.92 173:0.12 174:0.95 177:0.96 178:0.55 179:0.24 181:0.50 182:0.95 187:0.68 197:0.96 199:0.97 212:0.87 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.12 239:0.99 241:0.32 242:0.18 243:0.73 245:0.92 246:0.61 247:0.82 253:0.58 254:0.90 259:0.21 264:0.90 265:0.72 266:0.71 267:0.36 271:0.56 274:0.91 275:0.98 276:0.88 287:0.61 294:1.00 300:0.70 +1 10:0.97 11:0.64 12:0.79 17:0.47 18:0.92 21:0.78 26:0.97 27:0.06 28:0.70 29:0.71 30:0.85 34:0.77 36:0.66 37:0.90 39:0.38 43:0.59 45:0.98 58:0.93 66:0.71 69:0.08 70:0.46 71:0.79 74:0.66 86:0.94 91:0.12 98:0.80 101:0.69 104:0.58 108:0.07 122:0.97 123:0.07 124:0.47 127:0.87 129:0.05 131:0.61 133:0.60 135:0.83 139:0.87 141:0.91 144:0.76 146:0.93 147:0.95 149:0.85 154:0.71 157:1.00 159:0.74 161:0.79 166:0.61 167:0.71 170:0.82 172:0.93 173:0.10 174:0.94 177:0.96 178:0.55 179:0.23 181:0.50 182:1.00 187:0.68 197:0.96 199:0.97 212:0.73 216:0.70 222:0.60 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 232:0.80 234:0.91 235:0.22 239:0.96 241:0.46 242:0.17 243:0.75 245:0.92 246:0.61 247:0.88 253:0.49 254:0.93 259:0.21 264:0.90 265:0.80 266:0.75 267:0.44 271:0.56 274:0.91 275:0.98 276:0.89 287:0.61 294:0.99 300:0.70 +1 1:0.67 7:0.69 10:0.89 11:0.64 12:0.79 17:0.66 18:0.62 21:0.22 26:0.98 27:0.31 28:0.70 29:0.71 30:0.76 32:0.77 34:0.98 36:0.29 37:0.55 39:0.38 43:0.31 44:0.93 45:0.83 56:0.97 58:0.93 64:0.77 66:0.64 69:0.60 70:0.36 71:0.79 74:0.60 77:0.70 80:0.98 81:0.80 82:0.99 83:0.91 86:0.73 88:0.98 91:0.09 98:0.52 101:0.69 102:0.98 104:0.95 106:0.81 108:0.45 114:0.90 117:0.86 122:0.57 123:0.44 124:0.44 126:0.54 127:0.23 129:0.05 131:0.61 133:0.38 135:0.87 139:0.79 141:0.91 144:0.76 145:0.72 146:0.53 147:0.59 149:0.31 150:0.62 154:0.64 155:0.54 157:0.98 159:0.28 161:0.79 165:0.93 166:0.89 167:0.65 170:0.82 172:0.45 173:0.64 174:0.86 176:0.73 177:0.96 178:0.55 179:0.61 181:0.50 182:1.00 187:0.39 192:0.57 193:0.95 195:0.67 197:0.89 199:0.95 201:0.67 204:0.81 206:0.81 208:0.64 212:0.57 215:0.79 216:0.85 222:0.60 223:0.98 224:0.93 227:0.61 229:0.61 230:0.71 231:0.82 232:0.97 233:0.76 234:0.91 235:0.60 236:0.97 239:0.92 241:0.24 242:0.13 243:0.69 245:0.55 246:1.00 247:0.74 253:0.65 254:0.92 259:0.21 264:0.90 265:0.54 266:0.47 267:0.28 271:0.56 274:0.91 275:0.91 276:0.37 281:0.91 282:0.85 287:0.61 290:0.90 294:0.98 297:0.36 300:0.33 +1 6:0.84 7:0.81 8:0.70 12:0.06 17:0.03 20:0.85 21:0.30 27:0.50 28:0.53 30:0.45 34:0.56 36:0.80 37:0.25 43:0.52 55:0.59 66:0.94 69:0.10 70:0.49 78:0.76 81:0.91 91:0.87 98:0.95 100:0.79 104:0.79 106:0.81 108:0.09 111:0.76 120:0.98 123:0.09 126:0.54 127:0.64 129:0.05 135:0.37 140:0.45 146:0.91 147:0.92 149:0.76 150:0.83 151:0.89 152:0.90 153:0.99 154:0.41 159:0.51 161:0.68 162:0.79 164:0.98 167:0.78 169:0.77 172:0.83 173:0.18 177:0.05 179:0.43 181:0.64 186:0.76 187:0.21 189:0.87 191:0.82 202:0.96 206:0.81 212:0.75 215:0.72 216:0.75 230:0.87 233:0.76 235:0.31 238:0.92 241:0.73 242:0.82 243:0.94 247:0.12 248:0.98 256:0.97 259:0.21 260:0.98 261:0.80 265:0.95 266:0.47 267:0.94 268:0.97 271:0.52 276:0.73 283:0.82 285:0.62 297:0.36 300:0.33 +0 8:0.77 12:0.06 17:0.09 20:0.77 21:0.78 27:0.58 28:0.53 34:0.53 36:0.29 37:0.45 78:0.75 91:0.96 100:0.78 104:0.58 111:0.74 120:0.90 135:0.80 140:0.87 151:0.78 152:0.75 153:0.89 161:0.68 162:0.57 164:0.93 167:0.91 169:0.76 175:0.75 178:0.48 181:0.64 186:0.76 191:0.80 202:0.91 216:0.50 241:0.90 244:0.61 248:0.86 256:0.91 257:0.65 259:0.21 260:0.91 261:0.75 267:0.44 268:0.91 271:0.52 277:0.69 285:0.62 +1 6:0.79 8:0.58 12:0.06 17:0.06 20:0.98 21:0.78 27:0.07 28:0.53 34:0.80 36:0.28 37:0.24 78:0.82 91:0.99 100:0.88 104:0.58 111:0.83 120:0.99 135:0.81 140:0.45 151:0.90 152:0.93 153:0.99 161:0.68 162:0.74 164:0.99 167:0.91 169:0.85 175:0.75 181:0.64 186:0.82 189:0.82 191:0.81 202:0.98 216:0.76 238:0.93 241:0.89 244:0.61 248:0.98 256:0.98 257:0.65 259:0.21 260:0.99 261:0.89 267:0.87 268:0.99 271:0.52 277:0.69 285:0.62 +1 12:0.06 17:0.40 21:0.30 27:0.45 28:0.53 30:0.33 32:0.80 34:0.71 36:0.17 37:0.50 43:0.32 55:0.59 66:0.46 69:0.68 70:0.80 81:0.91 91:0.42 98:0.32 108:0.82 123:0.81 124:0.74 126:0.54 127:0.27 129:0.89 133:0.78 135:0.92 145:0.39 146:0.49 147:0.56 149:0.50 150:0.83 154:0.24 159:0.54 161:0.68 167:0.64 172:0.45 173:0.58 177:0.05 178:0.55 179:0.56 181:0.64 187:0.68 195:0.84 212:0.51 215:0.72 216:0.77 235:0.31 241:0.47 242:0.82 243:0.34 245:0.57 247:0.12 259:0.21 265:0.35 266:0.71 267:0.28 271:0.52 276:0.49 283:0.60 297:0.36 300:0.55 +1 6:0.82 8:0.64 12:0.06 17:0.06 20:0.83 21:0.05 27:0.63 28:0.53 30:0.33 34:0.92 36:0.39 37:0.31 43:0.51 55:0.52 66:0.86 69:0.10 70:0.72 78:0.74 81:0.95 91:0.80 98:0.90 100:0.78 104:0.78 106:0.81 108:0.09 111:0.74 120:0.95 123:0.09 126:0.54 127:0.46 129:0.05 135:0.69 140:0.87 146:0.70 147:0.75 149:0.63 150:0.91 151:0.80 152:0.79 153:0.93 154:0.40 159:0.28 161:0.68 162:0.59 164:0.95 167:0.74 169:0.76 172:0.61 173:0.32 177:0.05 178:0.48 179:0.69 181:0.64 186:0.75 187:0.21 189:0.84 191:0.76 202:0.94 206:0.81 212:0.69 215:0.91 216:0.58 235:0.05 238:0.89 241:0.67 242:0.82 243:0.87 247:0.12 248:0.92 256:0.94 259:0.21 260:0.95 261:0.76 265:0.90 266:0.27 267:0.59 268:0.94 271:0.52 276:0.48 283:0.60 285:0.62 297:0.36 300:0.55 +1 6:0.88 7:0.81 8:0.73 12:0.06 17:0.22 20:0.99 21:0.78 27:0.21 28:0.53 30:0.36 34:0.67 36:0.95 37:0.74 43:0.38 55:0.59 66:0.26 69:0.58 70:0.75 78:0.75 91:0.87 98:0.62 100:0.79 108:0.93 111:0.75 120:0.97 123:0.82 124:0.83 127:0.63 129:0.93 133:0.84 135:0.49 140:0.45 146:0.78 147:0.81 149:0.78 151:0.85 152:0.98 153:0.98 154:0.29 159:0.86 161:0.68 162:0.64 164:0.95 167:0.59 169:0.77 172:0.80 173:0.29 177:0.05 179:0.22 181:0.64 186:0.76 187:0.39 189:0.88 191:0.82 202:0.93 212:0.42 216:0.95 230:0.65 233:0.63 235:0.52 238:0.93 241:0.32 242:0.82 243:0.21 245:0.92 247:0.12 248:0.96 256:0.93 259:0.21 260:0.98 261:0.82 265:0.52 266:0.91 267:0.85 268:0.94 271:0.52 276:0.89 283:0.82 285:0.62 300:0.70 +1 6:0.82 7:0.81 8:0.74 12:0.06 17:0.01 20:0.92 21:0.78 27:0.48 28:0.53 30:0.33 34:0.49 36:0.85 37:0.80 43:0.46 55:0.52 66:0.72 69:0.40 70:0.64 78:0.74 91:0.91 98:0.68 100:0.77 104:0.74 108:0.68 111:0.73 120:0.93 123:0.66 124:0.42 127:0.34 129:0.59 133:0.47 135:0.71 140:0.96 146:0.50 147:0.56 149:0.62 151:0.74 152:0.86 153:0.84 154:0.35 159:0.53 161:0.68 162:0.51 164:0.95 167:0.89 169:0.75 172:0.68 173:0.31 177:0.05 178:0.54 179:0.48 181:0.64 186:0.75 189:0.84 191:0.82 202:0.96 212:0.58 216:0.62 220:0.62 230:0.87 233:0.76 235:0.02 238:0.88 241:0.82 242:0.82 243:0.22 245:0.71 247:0.12 248:0.90 256:0.93 259:0.21 260:0.93 261:0.76 265:0.68 266:0.75 267:0.89 268:0.94 271:0.52 276:0.60 283:0.60 285:0.62 300:0.55 +2 7:0.81 8:0.71 12:0.06 17:0.64 21:0.40 27:0.21 28:0.53 30:0.66 34:0.37 36:0.73 37:0.74 43:0.52 55:0.71 66:0.63 69:0.12 70:0.51 78:0.80 81:0.89 91:0.37 98:0.80 104:0.84 106:0.81 108:0.10 111:0.83 120:0.82 123:0.10 124:0.64 126:0.54 127:0.83 129:0.89 133:0.78 135:0.24 140:0.45 146:0.99 147:0.99 149:0.88 150:0.79 151:0.73 153:0.78 154:0.41 159:0.92 161:0.68 162:0.82 164:0.85 167:0.56 169:0.87 172:0.96 173:0.07 177:0.05 179:0.16 181:0.64 187:0.68 202:0.76 206:0.81 212:0.55 215:0.67 216:0.95 220:0.91 230:0.65 233:0.63 235:0.42 241:0.18 242:0.82 243:0.69 245:0.96 247:0.12 248:0.75 256:0.81 259:0.21 260:0.83 265:0.80 266:0.84 267:0.19 268:0.81 271:0.52 276:0.94 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.97 8:0.96 12:0.06 17:0.13 20:0.96 21:0.59 25:0.88 27:0.04 28:0.53 30:0.45 32:0.80 34:0.21 36:0.23 37:0.94 43:0.25 55:0.71 66:0.13 69:0.84 70:0.50 78:0.88 81:0.84 91:0.99 98:0.40 100:0.99 104:0.58 108:0.79 111:0.97 120:0.98 123:0.67 124:0.50 126:0.54 127:0.25 129:0.59 133:0.47 135:0.24 140:0.45 145:0.79 146:0.43 147:0.50 149:0.31 150:0.64 151:0.89 152:0.94 153:0.99 154:0.18 159:0.31 161:0.68 162:0.76 164:0.99 167:0.92 169:0.97 172:0.27 173:0.90 177:0.05 179:0.81 181:0.64 186:0.88 189:0.98 191:0.98 195:0.66 202:0.97 212:0.52 215:0.55 216:0.72 235:0.68 238:0.99 241:0.96 242:0.82 243:0.34 245:0.48 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:0.98 265:0.37 266:0.27 267:0.73 268:0.98 271:0.52 276:0.29 283:0.82 285:0.62 297:0.36 300:0.33 +1 7:0.81 8:0.60 12:0.06 17:0.30 21:0.30 25:0.88 27:0.72 28:0.53 30:0.45 34:0.53 36:0.80 43:0.52 55:0.59 66:0.94 69:0.10 70:0.49 81:0.91 91:0.54 98:0.95 104:0.54 108:0.09 120:0.93 123:0.09 126:0.54 127:0.67 129:0.05 135:0.32 140:0.45 146:0.91 147:0.92 149:0.76 150:0.83 151:0.80 153:0.92 154:0.41 159:0.51 161:0.68 162:0.85 164:0.88 167:0.90 172:0.83 173:0.19 177:0.05 179:0.44 181:0.64 202:0.78 212:0.75 215:0.72 216:0.34 230:0.87 233:0.76 235:0.31 241:0.87 242:0.82 243:0.94 247:0.12 248:0.89 256:0.84 259:0.21 260:0.93 265:0.95 266:0.47 267:0.88 268:0.85 271:0.52 276:0.73 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.06 17:0.34 21:0.30 27:0.45 28:0.53 30:0.55 32:0.80 34:0.71 36:0.24 37:0.50 43:0.32 55:0.84 66:0.52 69:0.68 70:0.51 81:0.91 91:0.14 98:0.70 108:0.78 123:0.77 124:0.64 126:0.54 127:0.51 129:0.81 133:0.67 135:0.95 145:0.47 146:0.71 147:0.75 149:0.63 150:0.83 154:0.24 159:0.73 161:0.68 163:0.81 167:0.62 172:0.65 173:0.54 177:0.05 178:0.55 179:0.50 181:0.64 187:0.68 195:0.88 212:0.19 215:0.72 216:0.77 235:0.31 241:0.43 242:0.82 243:0.39 245:0.75 247:0.12 259:0.21 265:0.70 266:0.84 267:0.65 271:0.52 276:0.66 283:0.60 297:0.36 300:0.43 +1 8:0.77 12:0.06 17:0.09 20:0.77 21:0.78 27:0.58 28:0.53 34:0.53 36:0.29 37:0.45 78:0.75 91:0.96 100:0.78 104:0.58 111:0.74 120:0.90 135:0.80 140:0.87 151:0.78 152:0.75 153:0.89 161:0.68 162:0.57 164:0.93 167:0.91 169:0.76 175:0.75 178:0.48 181:0.64 186:0.76 191:0.80 202:0.91 216:0.50 241:0.90 244:0.61 248:0.86 256:0.91 257:0.65 259:0.21 260:0.91 261:0.75 267:0.44 268:0.91 271:0.52 277:0.69 285:0.62 +1 12:0.06 17:0.26 20:0.81 21:0.13 25:0.88 27:0.72 28:0.53 34:0.26 36:0.61 37:0.24 43:0.52 66:0.36 69:0.07 78:0.74 81:0.93 91:0.89 98:0.09 100:0.73 104:0.54 108:0.06 111:0.73 120:0.90 123:0.06 126:0.54 127:0.06 129:0.05 135:0.37 140:0.45 146:0.05 147:0.05 149:0.16 150:0.89 151:0.73 152:0.78 153:0.80 154:0.41 159:0.05 161:0.68 162:0.83 164:0.93 167:0.90 169:0.72 172:0.05 173:0.21 177:0.05 181:0.64 186:0.75 202:0.86 215:0.84 216:0.62 220:0.74 235:0.12 241:0.85 243:0.51 248:0.85 256:0.90 259:0.21 260:0.90 261:0.74 265:0.09 267:0.69 268:0.91 271:0.52 283:0.60 285:0.62 297:0.36 +2 6:0.98 8:0.97 12:0.06 17:0.78 20:0.82 21:0.13 25:0.88 27:0.20 28:0.53 30:0.45 34:0.89 36:0.50 37:0.94 43:0.35 55:0.59 66:0.64 69:0.63 70:0.33 78:0.76 81:0.93 91:0.91 98:0.64 100:0.84 104:0.54 108:0.61 111:0.78 120:0.84 123:0.59 124:0.42 126:0.54 127:0.34 129:0.59 133:0.47 135:0.47 140:0.45 146:0.05 147:0.05 149:0.38 150:0.89 151:0.78 152:0.78 153:0.89 154:0.26 159:0.28 161:0.68 162:0.80 164:0.91 167:0.89 169:0.82 172:0.32 173:0.77 177:0.05 179:0.87 181:0.64 186:0.77 187:0.21 189:0.99 191:0.97 202:0.84 212:0.23 215:0.84 216:0.26 220:0.74 235:0.12 238:0.98 241:0.83 242:0.82 243:0.21 245:0.43 247:0.12 248:0.76 256:0.88 259:0.21 260:0.85 261:0.79 265:0.64 266:0.38 267:0.28 268:0.88 271:0.52 276:0.29 285:0.62 297:0.36 300:0.25 +2 1:0.65 8:0.64 10:0.96 11:0.77 12:0.69 17:0.95 18:0.86 21:0.47 26:0.97 27:0.64 29:0.77 30:0.85 34:0.75 36:0.61 37:0.34 39:0.72 43:0.40 45:0.99 56:0.97 58:0.93 64:0.77 66:0.61 69:0.29 70:0.56 71:0.88 74:0.44 77:0.70 80:0.99 81:0.77 83:0.73 86:0.73 89:0.98 91:0.09 97:0.94 98:0.66 99:1.00 101:0.87 108:0.22 114:0.80 122:0.98 123:0.22 124:0.72 126:0.54 127:0.90 129:0.05 131:0.61 133:0.81 135:0.32 139:0.72 141:0.91 144:0.76 145:0.54 146:0.89 147:0.91 149:0.83 150:0.54 154:0.37 155:0.53 157:0.87 158:0.73 159:0.79 165:0.88 166:0.81 170:0.77 172:0.93 173:0.17 174:0.96 176:0.73 177:0.99 178:0.55 179:0.20 182:0.80 187:0.68 192:0.58 193:0.96 195:0.89 197:0.98 199:0.98 201:0.65 204:0.80 208:0.64 212:0.71 215:0.63 216:0.15 219:0.88 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.71 234:0.91 235:0.95 236:0.97 239:0.95 240:0.95 241:0.18 242:0.23 243:0.68 244:0.83 245:0.93 246:0.91 247:0.97 253:0.58 254:0.93 259:0.21 264:0.96 265:0.66 266:0.91 267:0.23 271:0.44 274:0.91 275:0.98 276:0.92 279:0.75 282:0.71 283:0.67 287:0.61 290:0.80 292:0.88 294:0.99 297:0.36 300:0.84 +2 1:0.61 8:0.75 10:0.91 11:0.77 12:0.69 17:0.98 18:0.72 21:0.40 26:0.97 27:0.72 29:0.77 30:0.54 32:0.78 34:0.47 36:0.83 39:0.72 43:0.06 44:0.93 45:0.85 48:0.98 56:0.97 58:0.93 66:0.88 69:0.25 70:0.54 71:0.88 74:0.60 76:0.97 77:0.89 80:1.00 81:0.62 82:0.99 83:0.60 86:0.79 88:0.98 89:0.98 91:0.76 98:0.95 99:0.99 101:0.87 102:0.98 108:0.20 114:0.70 122:0.41 123:0.19 126:0.51 127:0.66 129:0.05 131:0.61 135:0.30 139:0.83 141:0.91 144:0.76 145:0.69 146:0.54 147:0.60 149:0.05 150:0.45 154:0.84 155:0.57 157:0.80 159:0.20 165:0.70 166:0.73 170:0.77 172:0.67 173:0.61 174:0.79 176:0.59 177:0.99 178:0.55 179:0.67 182:0.76 192:0.98 193:0.99 195:0.53 197:0.85 199:0.96 201:0.63 204:0.85 208:0.64 212:0.88 216:0.08 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.69 234:0.91 235:0.74 236:0.94 239:0.95 240:0.95 241:0.81 242:0.14 243:0.89 246:0.61 247:0.86 253:0.56 254:0.84 259:0.21 264:0.96 265:0.95 266:0.27 267:0.28 271:0.44 274:0.91 275:0.91 276:0.55 281:0.86 282:0.86 287:0.61 290:0.70 294:0.99 300:0.43 +1 10:0.92 11:0.77 12:0.69 17:0.32 18:0.70 21:0.78 26:0.95 27:0.28 29:0.77 30:0.91 34:0.77 36:0.65 37:0.93 39:0.72 43:0.28 45:0.92 58:0.93 66:0.64 69:0.87 70:0.24 71:0.88 74:0.43 86:0.77 89:0.97 91:0.23 98:0.40 101:0.71 104:0.58 108:0.74 122:0.98 123:0.73 124:0.50 127:0.79 129:0.05 131:0.61 133:0.59 135:0.34 139:0.72 141:0.91 144:0.76 146:0.40 147:0.22 149:0.25 154:0.53 157:0.80 159:0.44 166:0.61 170:0.77 172:0.61 173:0.97 174:0.86 177:0.70 178:0.55 179:0.67 182:0.74 187:0.39 197:0.88 199:0.95 212:0.85 216:0.25 222:0.84 223:0.94 224:0.93 225:0.96 227:0.61 229:0.61 231:0.64 232:0.79 234:0.91 235:0.05 239:0.89 240:0.95 241:0.21 242:0.21 243:0.24 245:0.63 246:0.61 247:0.79 253:0.37 254:0.90 257:0.97 259:0.21 264:0.96 265:0.42 266:0.38 267:0.36 271:0.44 274:0.91 275:0.94 276:0.46 287:0.61 294:0.97 300:0.33 +0 8:0.75 10:0.89 11:0.75 12:0.69 17:0.81 18:0.82 21:0.78 26:0.96 27:0.28 29:0.77 30:0.85 34:0.67 36:0.83 37:0.93 39:0.72 43:0.26 45:0.96 58:0.93 66:0.33 69:0.55 70:0.44 71:0.88 74:0.41 86:0.72 89:0.95 91:0.03 98:0.64 101:0.65 104:0.58 108:0.70 122:0.41 123:0.68 124:0.70 127:0.50 129:0.59 131:0.61 133:0.67 135:0.51 139:0.70 141:0.91 144:0.76 146:0.59 147:0.65 149:0.66 154:0.17 157:0.85 159:0.60 166:0.61 170:0.77 172:0.74 173:0.47 174:0.91 175:0.75 177:0.99 178:0.55 179:0.29 182:0.76 187:0.21 197:0.93 199:0.95 212:0.58 216:0.25 222:0.84 223:0.95 224:0.93 225:0.95 227:0.61 229:0.61 231:0.68 232:0.79 234:0.91 235:0.05 239:0.88 240:0.95 241:0.10 242:0.17 243:0.46 244:0.93 245:0.91 246:0.61 247:0.92 253:0.36 254:0.95 257:0.65 259:0.21 264:0.96 265:0.65 266:0.71 267:0.93 271:0.44 274:0.91 275:0.96 276:0.81 277:0.69 287:0.61 294:0.96 300:0.55 +1 1:0.58 10:0.94 11:0.77 12:0.69 17:0.79 18:0.68 21:0.13 26:0.97 27:0.72 29:0.77 30:0.85 34:0.75 36:0.99 37:0.26 39:0.72 43:0.28 45:0.97 56:0.97 58:0.93 66:0.54 69:0.82 70:0.51 71:0.88 74:0.48 80:0.99 81:0.38 82:0.98 83:0.56 86:0.80 88:0.99 89:0.98 91:0.27 98:0.75 99:0.83 101:0.87 102:0.95 108:0.67 122:0.98 123:0.65 124:0.52 126:0.26 127:0.62 129:0.05 131:0.61 133:0.43 135:0.26 139:0.75 141:0.91 144:0.76 145:0.45 146:0.75 147:0.64 149:0.57 150:0.37 154:0.51 155:0.53 157:0.82 159:0.48 165:0.63 166:0.61 170:0.77 172:0.83 173:0.87 174:0.93 177:0.88 178:0.55 179:0.31 182:0.75 187:0.21 192:0.55 193:0.96 195:0.68 197:0.95 199:0.97 201:0.55 204:0.83 208:0.50 212:0.82 216:0.21 222:0.84 223:0.97 224:0.93 225:0.97 227:0.61 229:0.61 231:0.65 234:0.91 235:0.22 236:0.91 239:0.94 240:0.95 241:0.35 242:0.27 243:0.40 245:0.93 246:0.61 247:0.93 253:0.39 254:0.88 257:0.93 259:0.21 264:0.96 265:0.75 266:0.47 267:0.19 271:0.44 274:0.91 275:0.97 276:0.81 287:0.61 294:0.98 300:0.70 +1 1:0.63 10:0.90 11:0.77 12:0.69 17:0.38 18:0.77 26:0.95 27:0.72 29:0.77 30:0.91 34:0.87 36:0.81 37:0.26 39:0.72 43:0.75 45:0.89 58:0.93 64:0.77 66:0.86 69:0.07 70:0.21 71:0.88 74:0.47 81:0.55 83:0.56 86:0.74 89:0.95 91:0.25 97:0.94 98:0.82 99:0.95 101:0.96 104:0.94 106:0.81 108:0.06 114:0.89 122:0.67 123:0.06 126:0.32 127:0.31 129:0.05 131:0.61 135:0.58 139:0.74 141:0.91 144:0.76 146:0.41 147:0.47 149:0.72 150:0.48 154:0.66 155:0.49 157:0.80 158:0.73 159:0.15 165:0.65 166:0.81 170:0.77 172:0.61 173:0.46 174:0.83 175:0.75 176:0.60 177:0.99 178:0.55 179:0.60 182:0.81 187:0.68 192:0.55 197:0.88 199:0.94 201:0.62 206:0.81 208:0.50 212:0.95 215:0.96 216:0.39 219:0.88 222:0.84 223:0.93 224:0.93 225:0.95 227:0.61 229:0.61 231:0.72 234:0.91 235:0.12 239:0.89 240:0.95 241:0.27 242:0.18 243:0.87 244:0.61 246:0.92 247:0.79 253:0.62 257:0.65 259:0.21 264:0.96 265:0.82 266:0.12 267:0.88 271:0.44 274:0.91 275:0.93 276:0.49 277:0.69 279:0.75 283:0.67 287:0.61 290:0.89 292:0.88 294:0.96 297:0.36 300:0.25 +1 1:0.65 10:0.96 11:0.77 12:0.69 17:0.55 18:0.92 21:0.40 22:0.93 26:0.97 27:0.56 29:0.77 30:0.93 32:0.80 33:0.97 34:0.92 36:0.23 37:0.60 39:0.72 43:0.36 44:0.93 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.97 69:0.58 70:0.28 71:0.88 74:0.72 77:0.70 81:0.79 82:0.99 83:0.91 86:0.90 88:0.99 89:0.98 91:0.03 97:0.94 98:0.82 101:0.87 102:0.98 104:0.91 106:0.81 108:0.44 114:0.82 117:0.86 122:0.67 123:0.43 126:0.54 127:0.31 129:0.05 131:0.61 135:0.75 139:0.90 141:0.91 144:0.76 145:0.74 146:0.83 147:0.86 149:0.63 150:0.55 154:0.58 155:0.50 157:0.88 158:0.74 159:0.39 165:0.93 166:0.81 170:0.77 172:0.92 173:0.59 174:0.91 176:0.73 177:0.99 178:0.55 179:0.20 182:0.80 187:0.39 192:0.57 195:0.68 197:0.93 199:0.96 201:0.67 206:0.81 208:0.64 212:0.82 215:0.67 216:0.76 219:0.88 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.72 232:0.95 234:0.91 235:0.84 236:0.97 239:0.96 240:0.95 241:0.02 242:0.15 243:0.97 246:0.91 247:0.90 253:0.64 254:0.74 259:0.21 262:0.93 264:0.96 265:0.82 266:0.27 267:0.79 271:0.44 274:0.91 275:0.96 276:0.83 279:0.76 282:0.88 283:0.82 287:0.61 290:0.82 291:0.97 292:0.95 294:0.99 297:0.36 299:0.88 300:0.70 +1 1:0.64 10:0.98 11:0.77 12:0.69 17:0.34 18:0.92 21:0.40 22:0.93 26:0.97 27:0.56 29:0.77 30:0.90 33:0.97 34:0.66 36:0.26 37:0.60 39:0.72 43:0.33 44:0.92 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.47 69:0.87 70:0.39 71:0.88 74:0.62 77:0.89 81:0.71 82:0.99 83:0.69 86:0.85 88:0.98 89:0.98 91:0.02 98:0.76 99:0.99 101:0.87 102:0.97 108:0.75 114:0.82 117:0.86 122:0.98 123:0.73 124:0.69 126:0.54 127:0.60 129:0.05 131:0.61 133:0.64 135:0.28 139:0.84 141:0.91 144:0.76 145:0.74 146:0.92 147:0.70 149:0.76 150:0.50 154:0.51 155:0.49 157:0.89 159:0.72 165:0.81 166:0.81 170:0.77 172:0.94 173:0.82 174:0.97 176:0.70 177:0.99 178:0.55 179:0.14 182:0.80 187:0.39 192:0.55 195:0.86 197:0.97 199:0.98 201:0.65 208:0.64 212:0.81 216:0.76 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.72 232:0.98 234:0.91 235:0.80 236:0.95 239:0.97 240:0.95 241:0.35 242:0.24 243:0.29 245:0.98 246:0.91 247:0.96 253:0.61 254:0.93 257:0.65 259:0.21 262:0.93 264:0.96 265:0.76 266:0.63 267:0.23 271:0.44 274:0.91 275:0.99 276:0.94 282:0.80 287:0.61 290:0.82 291:0.97 294:0.99 300:0.84 +1 1:0.60 10:0.92 11:0.77 12:0.69 17:0.70 18:0.92 21:0.22 22:0.93 26:0.95 27:0.72 29:0.77 30:0.88 34:0.80 36:0.54 39:0.72 43:0.27 45:0.96 47:0.93 58:0.93 64:0.77 66:0.45 69:0.12 70:0.41 71:0.88 74:0.50 81:0.50 83:0.56 86:0.82 89:0.97 91:0.27 98:0.76 99:0.95 101:0.87 104:0.80 106:0.81 108:0.73 114:0.80 117:0.86 122:0.97 123:0.72 124:0.59 126:0.32 127:0.73 129:0.59 131:0.61 133:0.47 135:0.97 139:0.77 141:0.91 144:0.76 146:0.68 147:0.73 149:0.55 150:0.43 154:0.56 155:0.48 157:0.87 159:0.60 165:0.65 166:0.81 170:0.77 172:0.78 173:0.17 174:0.89 176:0.60 177:0.99 178:0.55 179:0.34 182:0.80 187:0.39 192:0.50 197:0.90 199:0.95 201:0.59 206:0.81 208:0.50 212:0.60 215:0.79 216:0.18 222:0.84 223:0.95 224:0.93 225:0.96 227:0.61 229:0.61 231:0.72 232:0.87 234:0.91 235:0.42 239:0.90 240:0.95 241:0.05 242:0.13 243:0.48 244:0.61 245:0.94 246:0.92 247:0.94 253:0.58 254:0.92 259:0.21 262:0.93 264:0.96 265:0.76 266:0.75 267:0.73 271:0.44 274:0.91 275:0.96 276:0.80 287:0.61 290:0.80 294:0.97 297:0.36 300:0.70 +2 1:0.63 10:0.82 11:0.64 12:0.69 17:0.38 18:0.67 26:0.94 27:0.28 29:0.77 30:0.45 34:0.42 36:0.63 37:0.93 39:0.72 43:0.28 45:0.77 58:0.93 64:0.77 66:0.82 69:0.37 70:0.61 71:0.88 74:0.32 81:0.55 83:0.56 86:0.64 89:0.96 91:0.57 98:0.85 99:0.95 101:0.52 104:0.58 108:0.29 114:0.89 123:0.28 126:0.32 127:0.35 129:0.05 131:0.61 135:0.69 139:0.62 141:0.91 144:0.76 146:0.60 147:0.65 149:0.29 150:0.48 154:0.20 155:0.49 157:0.77 159:0.22 165:0.65 166:0.81 170:0.77 172:0.48 173:0.59 174:0.79 176:0.60 177:0.99 178:0.55 179:0.78 182:0.81 187:0.39 192:0.55 197:0.83 199:0.93 201:0.62 208:0.50 212:0.50 215:0.96 216:0.25 222:0.84 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.72 232:0.79 234:0.91 235:0.12 239:0.81 240:0.95 241:0.24 242:0.33 243:0.83 244:0.97 246:0.92 247:0.60 253:0.62 259:0.21 264:0.96 265:0.85 266:0.27 267:0.36 271:0.44 274:0.91 275:0.91 276:0.37 287:0.61 290:0.89 294:0.93 297:0.36 300:0.43 +1 1:0.65 10:0.97 11:0.77 12:0.69 17:0.53 18:0.86 21:0.40 22:0.93 26:0.97 27:0.56 29:0.77 30:0.93 32:0.80 33:0.97 34:0.93 36:0.28 37:0.60 39:0.72 43:0.36 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.20 69:0.58 70:0.28 71:0.88 74:0.65 77:0.70 81:0.79 82:0.99 83:0.91 86:0.83 88:0.99 89:0.97 91:0.05 97:0.94 98:0.60 101:0.87 102:0.98 104:0.91 106:0.81 108:0.73 114:0.82 117:0.86 122:0.98 123:0.43 124:0.52 126:0.54 127:0.45 129:0.59 131:0.61 133:0.47 135:0.74 139:0.86 141:0.91 144:0.76 145:0.74 146:0.65 147:0.70 149:0.78 150:0.55 154:0.58 155:0.50 157:0.86 158:0.74 159:0.46 165:0.93 166:0.81 170:0.77 172:0.89 173:0.59 174:0.96 176:0.73 177:0.99 178:0.55 179:0.20 182:0.80 187:0.21 192:0.57 195:0.68 197:0.97 199:0.98 201:0.67 206:0.81 208:0.64 212:0.86 215:0.67 216:0.76 219:0.88 222:0.84 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.71 232:0.95 234:0.91 235:0.84 236:0.97 239:0.97 240:0.95 241:0.18 242:0.30 243:0.40 245:0.97 246:0.91 247:0.93 253:0.63 254:0.74 259:0.21 262:0.93 264:0.96 265:0.61 266:0.27 267:0.52 271:0.44 274:0.91 275:0.98 276:0.87 279:0.76 282:0.88 283:0.82 287:0.61 290:0.82 291:0.97 292:0.95 294:0.99 297:0.36 299:0.88 300:0.70 +2 1:0.69 8:1.00 9:0.80 10:0.90 11:0.75 12:0.69 17:0.75 18:0.75 23:0.98 26:0.97 27:0.28 29:0.77 30:0.75 31:0.99 34:0.61 36:0.99 37:0.93 39:0.72 43:0.24 44:0.92 45:0.91 56:0.97 58:0.99 62:0.99 64:0.77 66:0.92 69:0.59 70:0.50 71:0.88 74:0.60 76:0.94 77:0.89 79:0.98 80:0.99 81:0.83 82:0.99 83:0.91 86:0.83 88:0.98 89:0.97 91:0.86 96:0.94 97:0.94 98:0.84 99:1.00 101:0.71 102:0.97 104:0.58 108:0.45 114:0.98 120:0.90 122:0.67 123:0.43 126:0.54 127:0.33 128:0.99 129:0.05 131:0.87 135:0.74 137:0.99 139:0.83 140:0.80 141:0.99 143:1.00 144:0.76 145:0.59 146:0.74 147:0.78 149:0.20 150:0.66 151:0.97 153:0.88 154:0.57 155:0.67 157:0.84 158:0.73 159:0.30 162:0.79 164:0.82 165:0.88 166:0.81 168:0.99 170:0.77 172:0.79 173:0.70 174:0.83 176:0.73 177:0.99 179:0.39 182:0.81 191:0.87 192:1.00 193:0.96 195:0.64 196:0.98 197:0.86 199:0.96 201:0.68 202:0.76 204:0.79 205:0.98 208:0.64 212:0.72 215:0.96 216:0.25 219:0.88 222:0.84 223:0.97 224:0.98 225:0.99 227:0.92 229:0.89 231:0.72 232:0.79 234:0.97 235:0.52 236:0.97 239:0.92 240:0.99 241:0.85 242:0.18 243:0.92 246:0.92 247:0.88 248:0.90 253:0.93 254:0.92 255:1.00 256:0.78 259:0.21 260:0.90 264:0.96 265:0.84 266:0.38 267:0.19 268:0.80 271:0.44 274:0.98 275:0.93 276:0.66 279:0.75 282:0.80 283:0.67 284:0.97 285:0.62 287:0.94 290:0.98 292:0.88 294:0.98 297:0.36 300:0.55 +0 1:0.59 8:0.87 9:0.76 10:0.91 11:0.75 12:0.69 17:0.79 18:0.63 21:0.40 23:0.97 26:0.97 27:0.44 29:0.77 30:0.42 31:0.94 34:0.75 36:0.96 37:0.69 39:0.72 43:0.23 45:0.90 56:0.97 58:0.98 62:0.97 66:0.93 69:0.41 70:0.62 71:0.88 74:0.32 79:0.93 80:0.99 81:0.53 82:0.98 83:0.73 86:0.62 88:0.99 89:0.98 91:0.73 96:0.80 98:0.94 99:0.95 101:0.65 102:0.95 104:0.78 108:0.31 120:0.76 123:0.30 126:0.43 127:0.59 128:0.98 129:0.05 131:0.87 135:0.58 137:0.94 139:0.61 140:0.94 141:0.99 143:0.99 144:0.76 145:0.70 146:0.79 147:0.82 149:0.21 150:0.42 151:0.86 153:0.48 154:0.45 155:0.66 157:0.77 159:0.34 162:0.79 164:0.81 165:0.65 166:0.61 168:0.98 170:0.77 172:0.82 173:0.53 174:0.88 177:0.99 179:0.43 182:0.80 191:0.80 192:1.00 193:0.96 195:0.62 196:0.88 197:0.90 199:0.96 201:0.61 202:0.72 204:0.79 205:0.97 208:0.64 212:0.89 215:0.67 216:0.26 220:0.82 222:0.84 223:0.97 224:0.98 225:0.98 227:0.92 229:0.89 231:0.72 234:0.97 235:0.68 236:0.92 239:0.91 240:0.99 241:0.81 242:0.15 243:0.94 244:0.83 246:0.61 247:0.88 248:0.77 253:0.74 254:0.93 255:1.00 256:0.77 259:0.21 260:0.77 264:0.96 265:0.94 266:0.27 267:0.36 268:0.77 271:0.44 274:0.98 275:0.95 276:0.72 283:0.67 284:0.89 285:0.62 287:0.93 294:0.97 297:0.36 300:0.55 +2 8:0.94 12:0.20 17:0.53 20:0.77 21:0.13 25:0.88 27:0.42 28:0.92 30:0.95 34:0.58 36:0.70 37:0.47 43:0.21 55:0.52 66:0.54 69:0.93 78:0.78 81:0.92 91:0.69 98:0.12 100:0.83 104:0.74 108:0.86 111:0.79 120:0.84 123:0.85 126:0.54 127:0.08 129:0.05 135:0.23 140:0.80 146:0.20 147:0.25 149:0.11 150:0.85 151:0.77 152:0.75 153:0.88 154:0.13 159:0.10 161:0.73 162:0.65 164:0.91 167:0.76 169:0.82 172:0.10 173:0.99 177:0.05 178:0.42 179:0.14 181:0.73 186:0.79 187:0.68 191:0.91 202:0.87 215:0.84 216:0.66 220:0.74 235:0.12 241:0.72 243:0.63 248:0.76 256:0.88 259:0.21 260:0.85 261:0.76 265:0.13 267:0.91 268:0.88 271:0.35 283:0.60 285:0.62 297:0.36 300:0.08 +2 6:0.91 7:0.81 8:0.79 12:0.20 17:0.38 20:0.95 21:0.30 27:0.21 28:0.92 30:0.16 34:0.61 36:0.90 37:0.74 43:0.52 55:0.59 66:0.23 69:0.10 70:0.85 78:0.80 81:0.89 91:0.90 98:0.51 100:0.90 104:0.84 106:0.81 108:0.98 111:0.84 120:0.95 123:0.98 124:0.95 126:0.54 127:0.88 129:0.99 133:0.95 135:0.32 140:0.45 146:0.83 147:0.86 149:0.90 150:0.79 151:0.83 152:0.97 153:0.96 154:0.41 159:0.96 161:0.73 162:0.68 164:0.95 167:0.69 169:0.86 172:0.95 173:0.06 177:0.05 179:0.11 181:0.73 186:0.81 187:0.39 189:0.93 191:0.78 202:0.93 206:0.81 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.96 241:0.62 242:0.82 243:0.19 245:0.98 247:0.12 248:0.93 256:0.93 259:0.21 260:0.96 261:0.88 265:0.53 266:0.89 267:0.99 268:0.94 271:0.35 276:0.98 283:0.82 285:0.62 297:0.36 300:0.94 +1 12:0.20 17:0.11 21:0.78 27:0.04 28:0.92 30:0.68 34:0.47 36:0.57 37:0.91 43:0.50 55:0.52 66:0.54 69:0.15 70:0.31 91:0.51 98:0.53 108:0.12 120:0.53 123:0.12 124:0.48 127:0.24 129:0.59 133:0.47 135:0.65 140:0.45 146:0.51 147:0.57 149:0.48 151:0.46 153:0.48 154:0.39 159:0.27 161:0.73 162:0.62 164:0.57 167:0.70 172:0.32 173:0.27 177:0.05 179:0.75 181:0.73 187:0.68 202:0.50 212:0.42 216:0.73 220:0.91 235:0.05 241:0.64 242:0.82 243:0.63 245:0.50 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.54 266:0.27 267:0.98 268:0.46 271:0.35 276:0.32 285:0.62 300:0.25 +3 7:0.81 12:0.20 17:0.53 21:0.30 27:0.21 28:0.92 30:0.21 34:0.55 36:0.86 37:0.74 43:0.52 55:0.71 66:0.12 69:0.10 70:0.85 81:0.89 91:0.44 98:0.50 104:0.87 106:0.81 108:0.85 123:0.84 124:0.95 126:0.54 127:0.88 129:0.99 133:0.95 135:0.42 146:0.83 147:0.86 149:0.90 150:0.79 154:0.41 159:0.96 161:0.73 167:0.61 172:0.90 173:0.06 177:0.05 178:0.55 179:0.11 181:0.73 187:0.39 206:0.81 212:0.52 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 241:0.44 242:0.82 243:0.19 245:0.98 247:0.12 259:0.21 265:0.52 266:0.92 267:0.97 271:0.35 276:0.98 297:0.36 300:0.94 +2 7:0.81 12:0.20 17:0.59 21:0.30 27:0.21 28:0.92 30:0.16 34:0.69 36:0.86 37:0.74 43:0.52 55:0.59 66:0.23 69:0.10 70:0.85 81:0.89 91:0.44 98:0.51 108:0.98 123:0.98 124:0.95 126:0.54 127:0.88 129:0.99 133:0.95 135:0.37 146:0.83 147:0.86 149:0.90 150:0.79 154:0.41 159:0.96 161:0.73 167:0.56 172:0.95 173:0.06 177:0.05 178:0.55 179:0.11 181:0.73 187:0.39 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 241:0.24 242:0.82 243:0.19 245:0.98 247:0.12 259:0.21 265:0.53 266:0.89 267:0.96 271:0.35 276:0.98 297:0.36 300:0.94 +1 8:0.79 12:0.20 17:0.08 21:0.13 25:0.88 27:0.42 28:0.92 30:0.45 34:0.22 36:0.59 37:0.47 43:0.44 55:0.52 66:0.69 69:0.45 70:0.25 81:0.92 91:0.86 98:0.89 104:0.74 108:0.35 120:0.81 123:0.34 126:0.54 127:0.43 129:0.05 135:0.61 140:0.45 146:0.56 147:0.62 149:0.31 150:0.85 151:0.78 153:0.89 154:0.33 159:0.20 161:0.73 162:0.73 163:0.93 164:0.86 167:0.89 172:0.21 173:0.73 177:0.05 179:0.97 181:0.73 191:0.86 202:0.79 212:0.61 215:0.84 216:0.66 235:0.12 241:0.90 242:0.82 243:0.73 247:0.12 248:0.73 256:0.81 259:0.21 260:0.82 265:0.89 266:0.17 267:0.36 268:0.83 271:0.35 276:0.20 283:0.82 285:0.62 297:0.36 300:0.18 +2 8:0.96 12:0.20 17:0.57 21:0.13 25:0.88 27:0.07 28:0.92 30:0.95 34:0.59 36:0.71 37:0.98 43:0.21 55:0.52 66:0.54 69:0.93 81:0.92 91:0.17 98:0.12 104:0.58 108:0.86 120:0.86 123:0.85 126:0.54 127:0.08 129:0.05 135:0.37 140:0.45 146:0.20 147:0.25 149:0.11 150:0.85 151:0.76 153:0.87 154:0.13 159:0.10 161:0.73 162:0.82 164:0.86 167:0.80 172:0.10 173:1.00 177:0.05 179:0.14 181:0.73 187:0.21 202:0.77 215:0.84 216:0.67 235:0.12 241:0.75 243:0.63 248:0.80 256:0.81 259:0.21 260:0.86 265:0.13 267:0.65 268:0.82 271:0.35 285:0.62 297:0.36 300:0.08 +1 6:0.95 8:0.95 12:0.20 17:0.11 20:0.77 21:0.78 27:0.04 28:0.92 30:0.68 34:0.53 36:0.60 37:0.91 43:0.50 55:0.52 66:0.54 69:0.15 70:0.31 78:0.91 91:0.38 98:0.53 100:0.99 104:0.90 106:0.81 108:0.12 111:0.98 123:0.12 124:0.48 127:0.24 129:0.59 133:0.47 135:0.63 146:0.51 147:0.57 149:0.48 152:0.79 154:0.39 159:0.27 161:0.73 167:0.60 169:0.94 172:0.32 173:0.27 177:0.05 178:0.55 179:0.75 181:0.73 186:0.91 187:0.95 189:0.99 206:0.81 212:0.42 216:0.73 235:0.05 238:0.99 241:0.41 242:0.82 243:0.63 245:0.50 247:0.12 259:0.21 261:0.88 265:0.54 266:0.27 267:0.97 271:0.35 276:0.32 300:0.25 +1 12:0.20 17:0.22 21:0.40 27:0.45 28:0.92 30:0.62 32:0.80 34:0.53 36:0.28 37:0.50 43:0.32 55:0.84 66:0.33 69:0.69 70:0.74 81:0.88 91:0.25 98:0.62 108:0.52 123:0.50 124:0.79 126:0.54 127:0.50 129:0.89 133:0.78 135:0.83 145:0.40 146:0.84 147:0.87 149:0.57 150:0.74 154:0.24 159:0.70 161:0.73 163:0.94 167:0.60 172:0.48 173:0.56 177:0.05 178:0.55 179:0.58 181:0.73 187:0.68 195:0.86 212:0.27 215:0.67 216:0.77 235:0.42 241:0.43 242:0.82 243:0.50 245:0.69 247:0.12 259:0.21 265:0.62 266:0.80 267:0.69 271:0.35 276:0.60 297:0.36 300:0.70 +2 8:0.74 12:0.20 17:0.08 21:0.78 27:0.72 28:0.92 34:0.70 36:0.77 37:0.84 91:0.84 104:0.58 120:0.92 135:0.28 140:0.80 151:0.79 153:0.91 161:0.73 162:0.54 164:0.93 167:0.90 175:0.75 178:0.42 181:0.73 191:0.75 202:0.93 216:0.36 235:0.52 241:0.94 244:0.61 248:0.88 256:0.91 257:0.65 259:0.21 260:0.92 267:0.19 268:0.91 271:0.35 277:0.69 283:0.82 285:0.62 +3 6:0.98 8:0.98 12:0.20 17:0.07 20:0.94 21:0.47 25:0.88 27:0.07 28:0.92 30:0.21 32:0.80 34:0.31 36:0.81 37:0.98 43:0.23 55:0.59 66:0.27 69:0.87 70:0.50 78:0.92 81:0.86 91:1.00 98:0.44 100:0.99 104:0.58 108:0.82 111:0.98 120:0.98 123:0.74 124:0.59 126:0.54 127:0.21 129:0.59 133:0.47 135:0.19 140:0.45 145:0.91 146:0.51 147:0.57 149:0.34 150:0.69 151:0.90 152:0.97 153:0.99 154:0.16 159:0.30 161:0.73 162:0.75 164:0.99 167:0.91 169:0.97 172:0.27 173:0.93 177:0.05 179:0.64 181:0.73 186:0.93 189:0.99 191:0.97 195:0.68 202:0.98 212:0.37 215:0.63 216:0.67 220:0.62 235:0.52 238:1.00 241:0.97 242:0.82 243:0.46 245:0.56 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.28 266:0.38 267:0.84 268:0.99 271:0.35 276:0.35 283:0.82 285:0.62 297:0.36 300:0.33 +1 8:0.60 12:0.20 17:0.34 21:0.13 25:0.88 27:0.69 28:0.92 34:0.26 36:0.33 37:0.29 81:0.92 91:0.85 104:0.58 120:0.86 126:0.54 135:0.30 140:0.80 150:0.85 151:0.75 153:0.86 161:0.73 162:0.58 164:0.88 167:0.90 175:0.75 178:0.42 181:0.73 202:0.85 215:0.84 216:0.51 235:0.12 241:0.92 244:0.61 248:0.81 256:0.86 257:0.65 259:0.21 260:0.87 267:0.65 268:0.85 271:0.35 277:0.69 283:0.82 285:0.62 297:0.36 +1 12:0.20 17:0.16 25:0.88 27:0.72 28:0.92 30:0.62 34:0.71 36:0.58 43:0.47 55:0.92 66:0.61 69:0.33 70:0.34 78:0.80 81:0.95 91:0.84 98:0.77 104:0.58 108:0.54 111:0.80 120:0.89 123:0.52 124:0.43 126:0.54 127:0.55 129:0.59 133:0.47 135:0.47 140:0.45 146:0.05 147:0.05 149:0.37 150:0.91 151:0.79 153:0.90 154:0.36 159:0.32 161:0.73 162:0.82 163:0.86 164:0.87 167:0.90 169:0.82 172:0.27 173:0.48 177:0.05 179:0.94 181:0.73 202:0.78 212:0.30 215:0.96 216:0.18 235:0.02 241:0.92 242:0.82 243:0.23 245:0.42 247:0.12 248:0.84 256:0.84 260:0.89 265:0.77 266:0.27 267:0.85 268:0.84 271:0.35 276:0.28 285:0.62 297:0.36 300:0.25 +2 6:0.87 8:0.81 12:0.20 17:0.26 20:0.81 21:0.05 25:0.88 27:0.72 28:0.92 30:0.66 32:0.80 34:0.82 36:0.55 43:0.51 55:0.59 66:0.46 69:0.32 70:0.42 78:0.77 81:0.99 91:0.66 98:0.73 100:0.81 104:0.76 108:0.49 111:0.77 120:0.82 123:0.47 124:0.74 126:0.54 127:0.65 129:0.89 133:0.78 135:0.70 140:0.45 145:0.96 146:0.05 147:0.05 149:0.62 150:0.99 151:0.75 152:0.78 153:0.86 154:0.40 159:0.41 161:0.73 162:0.69 164:0.85 167:0.80 169:0.79 172:0.45 173:0.40 177:0.05 179:0.72 181:0.73 186:0.78 187:0.21 189:0.88 191:0.96 195:0.61 202:0.78 212:0.36 215:0.91 216:0.34 220:0.93 235:0.52 238:0.91 241:0.76 242:0.82 243:0.13 245:0.57 247:0.12 248:0.74 256:0.80 259:0.21 260:0.83 261:0.78 265:0.74 266:0.54 267:0.99 268:0.81 271:0.35 276:0.46 285:0.62 297:1.00 300:0.33 +1 12:0.20 17:0.32 25:0.88 27:0.07 28:0.92 30:0.91 32:0.80 34:0.34 36:0.97 37:0.98 43:0.50 55:0.59 66:0.69 69:0.30 70:0.13 81:0.97 91:0.58 98:0.69 104:0.58 108:0.24 120:0.53 123:0.23 126:0.54 127:0.21 129:0.05 135:0.61 140:0.45 145:0.91 146:0.32 147:0.39 149:0.35 150:0.96 151:0.46 153:0.48 154:0.39 159:0.13 161:0.73 162:0.62 164:0.57 167:0.82 172:0.21 173:0.70 177:0.05 179:0.90 181:0.73 187:0.21 195:0.52 202:0.50 212:0.95 215:0.96 216:0.67 220:0.87 235:0.52 241:0.76 242:0.82 243:0.73 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.69 266:0.12 267:0.23 268:0.46 271:0.35 276:0.23 285:0.62 297:0.99 300:0.13 +1 6:0.84 8:0.74 12:0.20 17:0.24 20:0.94 25:0.88 27:0.32 28:0.92 34:0.53 36:0.48 37:0.60 78:0.80 81:0.95 91:0.96 100:0.83 104:0.58 111:0.80 120:0.98 126:0.54 135:0.61 140:0.45 150:0.91 151:0.84 152:0.87 153:0.98 161:0.73 162:0.56 164:0.98 167:0.90 169:0.82 175:0.75 181:0.73 186:0.80 189:0.86 191:0.85 202:0.97 215:0.96 216:0.50 235:0.02 238:0.91 241:0.95 244:0.61 248:0.97 256:0.97 257:0.65 260:0.98 261:0.84 267:0.82 268:0.97 271:0.35 277:0.69 283:0.82 285:0.62 297:0.36 +1 6:0.98 8:0.60 12:0.20 17:0.12 20:0.94 25:0.88 27:0.07 28:0.92 34:0.69 36:0.75 37:0.98 78:0.84 81:0.95 91:0.74 100:0.89 104:0.58 111:0.84 120:0.84 126:0.54 135:0.65 140:0.45 150:0.91 151:0.78 152:0.97 153:0.90 161:0.73 162:0.49 164:0.82 167:0.82 169:0.97 175:0.75 181:0.73 186:0.84 187:0.21 189:0.83 202:0.85 215:0.96 216:0.67 235:0.05 238:0.92 241:0.76 244:0.61 248:0.76 256:0.78 257:0.65 259:0.21 260:0.85 261:0.98 267:0.18 268:0.77 271:0.35 277:0.69 285:0.62 297:0.36 +2 6:0.84 7:0.81 8:0.73 12:0.20 17:0.55 20:0.83 21:0.30 27:0.50 28:0.92 30:0.21 32:0.80 34:0.67 36:0.88 37:0.60 43:0.40 55:0.71 66:0.19 69:0.53 70:0.81 78:0.77 81:0.89 91:0.37 98:0.50 100:0.79 104:0.84 106:0.81 108:0.99 111:0.76 120:0.80 123:0.99 124:0.97 126:0.54 127:0.97 129:0.99 133:0.97 135:0.74 140:0.45 145:0.70 146:0.97 147:0.98 149:0.91 150:0.79 151:0.73 152:0.79 153:0.78 154:0.31 159:0.97 161:0.73 162:0.72 164:0.79 167:0.55 169:0.76 172:0.97 173:0.09 177:0.05 179:0.10 181:0.73 186:0.77 187:0.39 189:0.86 195:1.00 202:0.71 206:0.81 212:0.54 215:0.72 216:0.86 220:0.87 230:0.87 233:0.76 235:0.31 238:0.85 241:0.21 242:0.82 243:0.24 245:0.99 247:0.12 248:0.73 256:0.71 259:0.21 260:0.81 261:0.77 265:0.52 266:0.94 267:0.96 268:0.75 271:0.35 276:0.99 283:0.82 285:0.62 297:0.36 300:0.94 +1 12:0.20 17:0.28 21:0.71 27:0.45 28:0.92 30:0.30 32:0.80 34:0.64 36:0.35 37:0.50 43:0.32 55:0.84 66:0.31 69:0.70 70:0.97 81:0.73 91:0.17 98:0.29 108:0.97 123:0.97 124:0.89 126:0.54 127:0.63 129:0.96 133:0.90 135:0.89 145:0.49 146:0.66 147:0.71 149:0.57 150:0.54 154:0.24 159:0.93 161:0.73 163:0.90 167:0.54 172:0.65 173:0.28 177:0.05 178:0.55 179:0.39 181:0.73 187:0.68 195:0.99 212:0.16 215:0.48 216:0.77 235:0.84 241:0.15 242:0.82 243:0.33 245:0.76 247:0.12 259:0.21 265:0.31 266:0.96 267:0.44 271:0.35 276:0.67 297:0.36 300:0.98 +2 7:0.81 8:0.92 12:0.20 17:0.92 21:0.05 27:0.72 28:0.92 30:0.62 34:0.45 36:0.77 37:0.84 43:0.49 55:0.84 66:0.83 69:0.21 70:0.34 81:0.93 91:0.81 98:0.95 104:0.54 108:0.17 120:0.80 123:0.16 126:0.54 127:0.64 129:0.05 135:0.51 140:0.45 146:0.83 147:0.86 149:0.52 150:0.88 151:0.70 153:0.71 154:0.38 159:0.40 161:0.73 162:0.66 164:0.74 167:0.89 172:0.51 173:0.33 177:0.05 179:0.83 181:0.73 191:0.83 202:0.66 212:0.28 215:0.91 216:0.29 220:0.62 230:0.87 233:0.76 235:0.05 241:0.91 242:0.82 243:0.84 247:0.12 248:0.72 256:0.64 259:0.21 260:0.81 265:0.95 266:0.47 267:0.69 268:0.68 271:0.35 276:0.43 285:0.62 297:0.36 300:0.25 +0 7:0.81 12:0.20 17:0.30 20:0.75 21:0.54 27:0.61 28:0.92 30:0.30 32:0.80 37:0.28 43:0.50 55:0.84 66:0.26 69:0.25 70:0.60 78:0.89 81:0.84 91:0.87 98:0.25 100:0.73 104:0.90 106:0.81 108:0.68 111:0.96 120:0.96 123:0.66 124:0.87 126:0.54 127:0.51 129:0.95 133:0.87 140:0.45 145:0.49 146:0.05 147:0.05 149:0.42 150:0.64 151:0.84 152:0.74 153:0.98 154:0.39 159:0.86 161:0.73 162:0.75 163:0.86 164:0.97 167:0.61 169:0.95 172:0.27 173:0.10 177:0.05 179:0.78 181:0.73 186:0.73 187:0.39 195:0.97 202:0.94 206:0.81 212:0.13 215:0.58 216:0.75 230:0.87 233:0.76 235:0.60 238:0.98 241:0.44 242:0.82 243:0.15 245:0.49 247:0.12 248:0.94 256:0.96 260:0.96 261:0.73 265:0.27 266:0.75 268:0.96 271:0.35 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 12:0.20 17:0.94 21:0.59 27:0.72 28:0.92 30:0.43 32:0.80 34:0.86 36:0.56 43:0.50 55:0.59 66:0.99 69:0.34 70:0.41 81:0.97 91:0.82 98:1.00 104:0.57 108:0.27 123:0.26 126:0.54 127:0.98 129:0.05 135:0.49 145:0.82 146:0.98 147:0.99 149:0.96 150:0.96 154:0.39 159:0.78 161:0.73 167:0.85 172:0.98 173:0.21 177:0.05 178:0.55 179:0.14 181:0.73 195:0.90 212:0.94 215:0.55 216:0.04 230:0.87 233:0.76 235:0.84 241:0.78 242:0.82 243:0.99 247:0.12 259:0.21 265:1.00 266:0.38 267:0.18 271:0.35 276:0.96 297:0.36 300:0.43 +1 6:0.80 8:0.59 12:0.20 17:0.34 20:0.94 27:0.35 28:0.92 30:0.53 34:0.67 36:0.76 37:0.24 43:0.52 55:0.84 66:0.54 69:0.05 70:0.64 78:0.83 81:0.95 91:0.79 98:0.67 100:0.87 104:0.89 106:0.81 108:0.05 111:0.83 120:0.87 123:0.05 124:0.69 126:0.54 127:0.75 129:0.89 133:0.78 135:0.79 140:0.45 146:0.91 147:0.93 149:0.79 150:0.91 151:0.79 152:0.88 153:0.91 154:0.41 159:0.80 161:0.73 162:0.77 164:0.92 167:0.64 169:0.84 172:0.80 173:0.07 177:0.05 179:0.36 181:0.73 186:0.83 187:0.39 189:0.82 202:0.86 206:0.81 212:0.37 215:0.96 216:0.51 220:0.74 235:0.02 238:0.90 241:0.53 242:0.82 243:0.63 245:0.84 247:0.12 248:0.81 256:0.87 259:0.21 260:0.87 261:0.87 265:0.68 266:0.89 267:0.52 268:0.89 271:0.35 276:0.79 283:0.82 285:0.62 297:0.36 300:0.70 +2 1:0.74 8:0.95 9:0.80 11:0.64 12:0.37 17:0.24 20:0.78 21:0.40 27:0.61 28:0.66 30:0.38 34:0.26 36:0.60 37:0.89 39:0.96 41:0.98 43:0.82 46:0.96 66:0.49 69:0.29 70:0.83 74:0.87 78:0.76 81:0.56 83:0.86 85:0.91 91:0.65 96:0.96 98:0.40 100:0.80 101:0.80 104:0.54 107:0.98 108:0.22 110:0.90 111:0.76 114:0.82 120:0.92 122:0.57 123:0.22 124:0.65 125:0.99 126:0.54 127:0.96 129:0.59 131:0.81 133:0.64 135:0.74 140:0.45 144:0.57 146:0.57 147:0.62 149:0.85 150:0.74 151:0.97 152:0.76 153:0.90 154:0.77 155:0.67 157:0.83 159:0.68 160:0.99 161:0.68 162:0.73 164:0.88 165:0.83 166:0.73 167:0.90 169:0.77 172:0.57 173:0.23 176:0.73 177:0.38 179:0.66 181:0.76 182:0.79 186:0.77 191:0.92 192:0.59 201:0.74 202:0.83 208:0.64 212:0.55 215:0.67 216:0.36 222:0.35 227:0.84 229:0.88 231:0.62 232:0.74 235:0.52 241:0.89 242:0.52 243:0.60 245:0.70 246:0.87 247:0.47 248:0.96 253:0.97 255:0.79 256:0.87 259:0.21 260:0.92 261:0.75 265:0.42 266:0.63 267:0.28 268:0.86 271:0.87 276:0.53 285:0.62 287:0.90 289:0.89 290:0.82 297:0.36 300:0.70 +3 1:0.74 6:0.98 7:0.81 8:0.99 9:0.80 11:0.64 12:0.37 17:0.09 20:0.85 21:0.47 27:0.09 28:0.66 30:0.33 32:0.80 34:0.44 36:0.83 37:0.83 39:0.96 41:0.99 43:0.91 46:0.95 60:0.99 66:0.18 69:0.43 70:0.94 74:0.91 76:0.85 77:0.89 78:0.77 81:0.54 83:0.90 85:0.93 91:0.80 96:0.99 98:0.26 100:0.86 101:0.77 104:0.79 107:0.98 108:0.94 110:0.90 111:0.79 114:0.80 120:0.98 121:0.90 122:0.67 123:0.94 124:0.88 125:0.99 126:0.54 127:0.92 129:0.93 131:0.81 133:0.87 135:0.39 138:0.99 140:0.45 144:0.57 145:0.59 146:0.54 147:0.60 149:0.77 150:0.72 151:0.99 152:0.90 153:0.97 154:0.91 155:0.67 157:0.82 158:0.92 159:0.86 160:1.00 161:0.68 162:0.75 164:0.96 165:0.80 166:0.73 167:0.84 169:0.85 172:0.41 173:0.20 176:0.73 177:0.38 179:0.57 181:0.76 182:0.79 186:0.78 187:0.21 189:1.00 191:0.93 192:0.59 195:0.94 201:0.74 202:0.94 204:0.89 208:0.64 212:0.19 215:0.63 216:0.84 220:0.62 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.84 233:0.69 235:0.68 238:0.97 241:0.77 242:0.45 243:0.32 245:0.70 246:0.87 247:0.47 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.98 261:0.85 265:0.28 266:0.91 267:0.28 268:0.96 271:0.87 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 287:0.90 289:0.89 290:0.80 297:0.36 299:0.88 300:0.94 +1 1:0.74 11:0.64 12:0.37 17:0.43 21:0.59 27:0.45 28:0.66 30:0.45 32:0.80 34:0.83 36:0.18 37:0.50 39:0.96 43:0.82 46:0.94 66:0.30 69:0.70 70:0.61 74:0.76 77:0.70 81:0.48 83:0.73 85:0.88 91:0.06 98:0.24 101:0.75 107:0.97 108:0.53 110:0.90 114:0.74 122:0.43 123:0.51 124:0.71 125:0.88 126:0.54 127:0.26 129:0.81 131:0.61 133:0.67 135:0.68 144:0.57 145:0.45 146:0.48 147:0.54 149:0.81 150:0.67 154:0.77 155:0.67 157:0.80 159:0.66 160:0.88 161:0.68 163:0.81 165:0.78 166:0.73 167:0.66 172:0.32 173:0.50 176:0.73 177:0.38 178:0.55 179:0.60 181:0.76 182:0.78 187:0.68 192:0.59 195:0.92 201:0.74 212:0.36 215:0.55 216:0.77 222:0.35 227:0.61 229:0.61 231:0.62 235:0.74 241:0.54 242:0.52 243:0.48 245:0.60 246:0.87 247:0.55 253:0.66 259:0.21 265:0.26 266:0.54 267:0.36 271:0.87 276:0.37 282:0.88 287:0.61 289:0.89 290:0.74 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.64 12:0.37 17:0.09 20:0.97 21:0.47 27:0.11 28:0.66 30:0.56 34:0.36 36:0.83 37:0.79 39:0.96 41:1.00 43:0.39 46:0.93 55:0.92 60:0.98 66:0.19 69:0.96 70:0.46 74:0.81 76:0.85 77:0.89 78:0.93 81:0.53 83:0.91 85:0.80 91:0.96 96:1.00 98:0.38 100:0.99 101:0.72 104:0.73 107:0.96 108:0.92 110:0.90 111:0.98 114:0.80 120:1.00 121:0.90 122:0.43 123:0.93 124:0.85 125:0.96 126:0.54 127:0.86 129:0.59 131:0.81 133:0.82 135:0.21 138:1.00 140:0.45 144:0.57 145:0.82 146:0.23 147:0.05 149:0.48 150:0.71 151:1.00 152:0.95 153:0.99 154:0.24 155:0.67 157:0.77 158:0.87 159:0.84 160:1.00 161:0.68 162:0.49 164:1.00 165:0.80 166:0.73 167:0.90 169:0.98 172:0.21 173:0.96 176:0.73 177:0.05 179:0.83 181:0.76 182:0.78 186:0.93 189:0.99 191:0.98 192:0.59 195:0.94 201:0.74 202:1.00 204:0.89 208:0.64 212:0.13 215:0.63 216:0.85 220:0.74 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.79 233:0.69 235:0.60 238:0.99 241:0.86 242:0.24 243:0.15 245:0.52 246:0.87 247:0.60 248:1.00 253:1.00 255:0.79 256:1.00 257:0.65 259:0.21 260:1.00 261:0.98 265:0.13 266:0.75 267:0.97 268:1.00 271:0.87 276:0.37 279:0.86 282:0.81 283:0.71 285:0.62 287:0.90 289:0.89 290:0.80 297:0.36 300:0.33 +3 1:0.74 6:0.98 7:0.81 8:0.81 9:0.80 11:0.64 12:0.37 17:0.01 20:0.88 21:0.64 27:0.11 28:0.66 30:0.15 34:0.29 36:0.97 37:0.79 39:0.96 41:1.00 43:0.73 46:0.93 55:0.71 60:0.95 66:0.33 69:0.86 70:0.84 74:0.83 76:0.85 77:0.70 78:0.77 81:0.48 83:0.90 85:0.72 91:0.82 96:0.99 98:0.48 100:0.81 101:0.71 104:0.73 107:0.95 108:0.86 110:0.89 111:0.77 114:0.72 117:0.86 120:0.97 121:0.90 122:0.51 123:0.85 124:0.71 125:0.96 126:0.54 127:0.71 129:0.59 131:0.81 133:0.64 135:0.84 138:1.00 140:0.45 144:0.57 145:0.86 146:0.13 147:0.41 149:0.54 150:0.66 151:0.99 152:0.95 153:0.97 154:0.63 155:0.67 157:0.76 158:0.87 159:0.70 160:1.00 161:0.68 162:0.48 163:0.75 164:0.98 165:0.70 166:0.73 167:0.82 169:0.98 172:0.27 173:0.82 176:0.73 177:0.05 179:0.86 181:0.76 182:0.78 186:0.78 187:0.39 189:0.97 191:0.91 192:0.59 195:0.83 201:0.74 202:0.99 204:0.89 208:0.64 212:0.15 215:0.53 216:0.85 220:0.74 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.79 233:0.69 235:0.88 238:0.91 241:0.76 242:0.33 243:0.37 245:0.53 246:0.87 247:0.39 248:0.99 253:0.99 255:0.79 256:0.99 257:0.65 259:0.21 260:0.97 261:0.98 265:0.50 266:0.63 267:0.94 268:0.98 271:0.87 276:0.36 279:0.86 282:0.81 283:0.71 285:0.62 287:0.90 289:0.89 290:0.72 297:0.36 300:0.55 +3 1:0.74 6:0.93 8:0.93 9:0.80 11:0.64 12:0.37 17:0.26 20:0.82 21:0.05 27:0.12 28:0.66 30:0.37 34:0.19 36:0.62 37:0.88 39:0.96 41:0.95 43:0.73 46:0.95 66:0.15 69:0.85 70:0.87 74:0.78 77:0.70 78:0.81 81:0.74 83:0.82 85:0.90 91:0.80 96:0.95 98:0.27 100:0.92 101:0.78 104:0.58 107:0.98 108:0.91 110:0.90 111:0.86 114:0.95 117:0.86 120:0.85 122:0.57 123:0.69 124:0.76 125:0.98 126:0.54 127:0.82 129:0.81 131:0.81 133:0.76 135:0.19 140:0.80 144:0.57 145:0.38 146:0.46 147:0.05 149:0.70 150:0.84 151:0.86 152:0.92 153:0.95 154:0.64 155:0.67 157:0.82 158:0.83 159:0.75 160:0.99 161:0.68 162:0.84 164:0.82 165:0.90 166:0.73 167:0.81 169:0.96 172:0.48 173:0.78 176:0.73 177:0.05 179:0.70 181:0.76 182:0.78 186:0.82 187:0.21 189:0.99 191:0.82 192:0.59 195:0.86 201:0.74 202:0.78 208:0.64 212:0.57 215:0.91 216:0.72 222:0.35 227:0.84 229:0.88 231:0.62 232:0.77 235:0.22 238:0.98 241:0.75 242:0.44 243:0.11 245:0.63 246:0.87 247:0.60 248:0.91 253:0.95 255:0.79 256:0.83 257:0.65 259:0.21 260:0.85 261:0.96 265:0.21 266:0.71 267:0.59 268:0.84 271:0.87 276:0.50 282:0.81 285:0.62 287:0.90 289:0.89 290:0.95 297:0.36 300:0.70 +3 1:0.74 6:0.83 7:0.76 8:0.93 9:0.80 11:0.64 12:0.37 17:0.16 20:0.89 27:0.13 28:0.66 30:0.68 32:0.80 34:0.25 36:0.92 37:0.86 39:0.96 41:0.98 43:0.93 46:0.96 60:0.97 66:0.32 69:0.33 70:0.24 74:0.79 77:0.70 78:0.77 81:0.82 83:0.90 85:0.85 91:0.89 96:0.97 98:0.17 100:0.80 101:0.78 104:0.58 107:0.96 108:0.26 110:0.90 111:0.77 114:0.98 120:0.93 122:0.43 123:0.25 124:0.72 125:0.98 126:0.54 127:0.64 129:0.59 131:0.81 133:0.64 135:0.24 138:0.98 140:0.45 144:0.57 145:0.86 146:0.24 147:0.30 149:0.77 150:0.89 151:0.98 152:0.84 153:0.93 154:0.93 155:0.67 157:0.80 158:0.92 159:0.52 160:1.00 161:0.68 162:0.80 164:0.92 165:0.93 166:0.73 167:0.92 169:0.78 172:0.21 173:0.33 176:0.73 177:0.38 179:0.90 181:0.76 182:0.79 186:0.78 189:0.85 191:0.91 192:0.59 195:0.73 201:0.74 202:0.88 208:0.64 212:0.42 215:0.96 216:0.35 220:0.74 222:0.35 227:0.84 229:0.88 230:0.78 231:0.62 232:0.77 233:0.69 235:0.05 238:0.89 241:0.96 242:0.45 243:0.49 245:0.48 246:0.87 247:0.39 248:0.97 253:0.97 255:0.79 256:0.91 259:0.21 260:0.94 261:0.80 265:0.19 266:0.27 267:0.44 268:0.92 271:0.87 276:0.28 279:0.86 282:0.88 283:0.82 285:0.62 287:0.90 289:0.89 290:0.98 297:0.99 299:0.88 300:0.18 +2 1:0.74 8:0.98 9:0.80 11:0.64 12:0.37 17:0.34 27:0.55 28:0.66 30:0.56 32:0.71 34:0.40 36:0.95 37:0.83 39:0.96 41:0.99 43:0.68 46:0.95 60:1.00 66:0.44 69:0.89 70:0.58 74:0.82 76:0.85 77:0.70 78:0.88 81:0.80 83:0.90 85:0.88 91:0.90 96:0.99 98:0.35 101:0.77 104:0.58 107:0.97 108:0.78 110:0.90 111:0.91 114:0.98 120:0.97 122:0.57 123:0.76 124:0.68 125:0.99 126:0.54 127:0.92 129:0.59 131:0.81 133:0.64 135:0.30 140:0.45 144:0.57 145:0.61 146:0.54 147:0.32 149:0.68 150:0.88 151:0.99 153:0.97 154:0.57 155:0.67 157:0.81 158:0.92 159:0.71 160:1.00 161:0.68 162:0.77 164:0.94 165:0.92 166:0.73 167:0.91 169:0.93 172:0.37 173:0.87 176:0.73 177:0.05 179:0.83 181:0.76 182:0.79 191:0.93 192:0.59 195:0.82 201:0.74 202:0.89 208:0.64 212:0.23 215:0.96 216:0.45 222:0.35 227:0.84 229:0.88 231:0.62 232:0.77 235:0.05 238:0.95 241:0.93 242:0.55 243:0.25 245:0.57 246:0.87 247:0.39 248:0.99 253:0.99 255:0.79 256:0.92 257:0.65 260:0.97 265:0.37 266:0.47 267:0.52 268:0.92 271:0.87 276:0.38 279:0.86 282:0.79 283:0.82 285:0.62 287:0.90 289:0.89 290:0.98 297:0.36 300:0.43 +1 1:0.74 8:0.95 9:0.80 11:0.64 12:0.37 17:0.24 20:0.78 21:0.40 27:0.61 28:0.66 30:0.38 34:0.26 36:0.60 37:0.89 39:0.96 41:0.98 43:0.82 46:0.96 66:0.49 69:0.29 70:0.83 74:0.87 78:0.76 81:0.56 83:0.86 85:0.91 91:0.65 96:0.96 98:0.40 100:0.80 101:0.80 104:0.54 107:0.98 108:0.22 110:0.90 111:0.76 114:0.82 120:0.92 122:0.57 123:0.22 124:0.65 125:0.99 126:0.54 127:0.96 129:0.59 131:0.81 133:0.64 135:0.74 140:0.45 144:0.57 146:0.57 147:0.62 149:0.85 150:0.74 151:0.97 152:0.76 153:0.90 154:0.77 155:0.67 157:0.83 159:0.68 160:0.99 161:0.68 162:0.73 164:0.88 165:0.83 166:0.73 167:0.90 169:0.77 172:0.57 173:0.23 176:0.73 177:0.38 179:0.66 181:0.76 182:0.79 186:0.77 191:0.92 192:0.59 201:0.74 202:0.83 208:0.64 212:0.55 215:0.67 216:0.36 222:0.35 227:0.84 229:0.88 231:0.62 232:0.74 235:0.52 241:0.89 242:0.52 243:0.60 245:0.70 246:0.87 247:0.47 248:0.96 253:0.97 255:0.79 256:0.87 259:0.21 260:0.92 261:0.75 265:0.42 266:0.63 267:0.28 268:0.86 271:0.87 276:0.53 285:0.62 287:0.90 289:0.89 290:0.82 297:0.36 300:0.70 +0 1:0.74 8:0.95 9:0.80 11:0.64 12:0.37 17:0.22 21:0.13 27:0.13 28:0.66 30:0.30 34:0.56 36:0.57 37:0.86 39:0.96 41:0.99 43:0.77 46:0.94 55:0.45 60:0.98 66:0.26 69:0.78 70:0.80 74:0.85 81:0.68 83:0.90 85:0.80 91:0.94 96:0.98 98:0.25 101:0.73 107:0.96 108:0.32 110:0.89 114:0.92 120:0.95 122:0.67 123:0.31 124:0.79 125:0.98 126:0.54 127:0.89 129:0.59 131:0.81 133:0.76 135:0.63 138:0.99 140:0.45 144:0.57 146:0.28 147:0.46 149:0.66 150:0.81 151:0.97 153:0.88 154:0.70 155:0.67 157:0.78 158:0.87 159:0.71 160:1.00 161:0.68 162:0.74 164:0.94 165:0.88 166:0.73 167:0.89 172:0.27 173:0.71 176:0.73 177:0.05 179:0.83 181:0.76 182:0.79 191:0.91 192:0.59 201:0.74 202:0.90 208:0.64 212:0.39 215:0.84 216:0.55 220:0.62 222:0.35 227:0.84 229:0.88 231:0.62 232:0.74 235:0.22 241:0.82 242:0.63 243:0.45 245:0.54 246:0.87 247:0.39 248:0.98 253:0.98 255:0.79 256:0.92 257:0.65 259:0.21 260:0.95 265:0.27 266:0.54 267:0.73 268:0.93 271:0.87 276:0.32 277:0.69 279:0.86 283:0.71 285:0.62 287:0.90 289:0.89 290:0.92 297:0.36 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.91 9:0.80 12:0.37 17:0.15 20:0.93 21:0.54 27:0.27 28:0.66 30:0.45 34:0.55 36:0.87 37:0.68 39:0.96 41:1.00 43:0.43 46:0.88 55:0.45 60:1.00 66:0.49 69:0.32 70:0.25 74:0.76 76:0.94 78:0.80 81:0.52 83:0.90 91:0.94 96:1.00 98:0.28 100:0.86 104:0.58 107:0.90 108:0.25 110:0.88 111:0.81 114:0.77 120:0.99 121:0.90 122:0.67 123:0.24 124:0.48 125:0.99 126:0.54 127:0.55 129:0.59 131:0.81 133:0.47 135:0.42 140:0.45 144:0.57 146:0.36 147:0.42 149:0.30 150:0.69 151:1.00 152:0.87 153:0.99 154:0.32 155:0.67 157:0.61 158:0.92 159:0.53 160:1.00 161:0.68 162:0.79 163:0.75 164:0.97 165:0.79 166:0.73 167:0.90 169:0.85 172:0.16 173:0.30 175:0.75 176:0.73 177:0.05 179:0.98 181:0.76 182:0.79 186:0.80 189:0.92 191:0.97 192:0.59 201:0.74 202:0.95 204:0.89 208:0.64 212:0.61 215:0.58 216:0.60 222:0.35 227:0.84 229:0.88 230:0.84 231:0.62 232:0.77 233:0.69 235:0.74 238:0.95 241:0.87 242:0.82 243:0.60 244:0.61 245:0.39 246:0.87 247:0.12 248:1.00 253:0.99 255:0.79 256:0.97 257:0.65 259:0.21 260:0.99 261:0.85 265:0.30 266:0.17 267:0.69 268:0.97 271:0.87 276:0.15 277:0.69 279:0.86 283:0.82 285:0.62 287:0.90 289:0.89 290:0.77 297:0.36 300:0.18 +3 1:0.74 6:0.93 8:0.92 9:0.80 11:0.64 12:0.37 17:0.16 20:0.98 21:0.13 27:0.12 28:0.66 30:0.28 34:0.22 36:0.84 37:0.88 39:0.96 41:1.00 43:0.67 46:0.95 60:1.00 66:0.07 69:0.89 70:0.97 74:0.75 78:0.91 81:0.68 83:0.91 85:0.90 91:0.99 96:1.00 98:0.21 100:0.98 101:0.76 104:0.58 107:0.98 108:0.94 110:0.90 111:0.95 114:0.92 120:0.99 122:0.57 123:0.83 124:0.88 125:0.98 126:0.54 127:0.89 129:0.89 131:0.81 133:0.89 135:0.20 138:1.00 140:0.45 144:0.57 146:0.13 147:0.05 149:0.65 150:0.81 151:1.00 152:0.92 153:0.98 154:0.57 155:0.67 157:0.81 158:0.92 159:0.83 160:1.00 161:0.68 162:0.73 164:0.98 165:0.88 166:0.73 167:0.90 169:0.96 172:0.48 173:0.79 176:0.73 177:0.05 179:0.67 181:0.76 182:0.79 186:0.91 189:0.94 191:0.96 192:0.59 201:0.74 202:0.97 208:0.64 212:0.51 215:0.84 216:0.72 220:0.74 222:0.35 227:0.84 229:0.88 231:0.62 232:0.77 235:0.22 238:0.98 241:0.89 242:0.39 243:0.11 245:0.59 246:0.87 247:0.67 248:1.00 253:1.00 255:0.79 256:0.98 257:0.65 259:0.21 260:0.99 261:0.96 265:0.18 266:0.80 267:0.76 268:0.98 271:0.87 276:0.55 279:0.86 283:0.82 285:0.62 287:0.90 289:0.89 290:0.92 297:0.36 300:0.84 +1 1:0.74 11:0.64 12:0.37 17:0.34 21:0.59 27:0.45 28:0.66 30:0.45 34:0.61 36:0.20 37:0.50 39:0.96 43:0.82 46:0.94 55:0.84 60:0.87 66:0.47 69:0.61 70:0.61 74:0.76 81:0.48 83:0.86 85:0.72 91:0.17 98:0.59 101:0.72 107:0.97 108:0.46 110:0.90 114:0.74 122:0.51 123:0.44 124:0.56 125:0.88 126:0.54 127:0.45 129:0.59 131:0.61 133:0.47 135:0.78 144:0.57 145:0.50 146:0.69 147:0.73 149:0.61 150:0.67 154:0.77 155:0.67 157:0.76 158:0.87 159:0.51 160:0.88 161:0.68 163:0.90 165:0.78 166:0.73 167:0.55 172:0.41 173:0.58 176:0.73 177:0.38 178:0.55 179:0.73 181:0.76 182:0.78 187:0.68 192:0.59 201:0.74 208:0.64 212:0.36 215:0.55 216:0.77 222:0.35 227:0.61 229:0.61 231:0.62 235:0.74 241:0.12 242:0.22 243:0.59 245:0.66 246:0.87 247:0.60 253:0.66 259:0.21 265:0.60 266:0.63 267:0.23 271:0.87 276:0.42 279:0.86 283:0.71 287:0.61 289:0.89 290:0.74 297:0.36 300:0.43 +1 11:0.88 12:0.01 17:0.43 18:0.63 21:0.78 27:0.45 29:0.77 30:0.95 34:0.84 36:0.18 37:0.50 39:0.78 43:0.77 66:0.64 69:0.80 71:0.75 74:0.41 85:0.72 86:0.73 91:0.18 98:0.10 101:0.87 108:0.63 122:0.57 123:0.61 127:0.07 129:0.05 135:0.87 139:0.70 144:0.85 145:0.49 146:0.17 147:0.22 149:0.52 154:0.69 159:0.09 163:0.97 172:0.16 173:0.76 177:0.70 178:0.55 179:0.08 187:0.68 212:0.95 216:0.77 222:0.60 235:0.60 241:0.10 242:0.82 243:0.69 247:0.12 253:0.32 259:0.21 265:0.10 266:0.12 267:0.23 271:0.66 276:0.19 300:0.08 +1 9:0.80 11:0.91 12:0.01 17:0.49 18:0.93 20:0.91 21:0.78 27:0.38 29:0.77 30:0.58 31:0.92 34:0.29 36:0.95 37:0.55 39:0.78 43:0.66 45:0.77 55:0.92 66:0.33 69:0.65 70:0.62 71:0.75 74:0.46 78:0.72 79:0.92 83:0.60 86:0.78 91:0.22 96:0.78 98:0.67 100:0.92 101:0.52 108:0.49 111:0.85 120:0.65 122:0.86 123:0.47 124:0.71 127:0.65 129:0.59 133:0.64 135:0.92 137:0.92 139:0.75 140:0.45 144:0.85 145:0.65 146:0.89 147:0.91 149:0.57 151:0.75 152:0.85 153:0.72 154:0.55 155:0.67 159:0.74 162:0.72 163:0.94 164:0.64 169:0.90 172:0.54 173:0.51 175:0.75 177:0.38 179:0.55 186:0.73 187:0.68 192:0.87 196:0.91 202:0.56 208:0.64 212:0.27 216:0.58 220:0.62 222:0.60 235:0.31 241:0.21 242:0.74 243:0.50 244:0.61 245:0.78 247:0.47 248:0.69 253:0.66 255:0.95 256:0.58 257:0.65 259:0.21 260:0.65 261:0.91 265:0.68 266:0.80 267:0.59 268:0.59 271:0.66 276:0.65 277:0.69 284:0.92 285:0.62 300:0.55 +1 11:0.88 12:0.01 17:0.16 18:0.60 21:0.78 27:0.45 29:0.77 30:0.95 34:0.66 36:0.25 37:0.50 39:0.78 43:0.64 66:0.64 69:0.91 71:0.75 74:0.37 85:0.72 86:0.69 91:0.07 98:0.07 101:0.87 108:0.81 122:0.57 123:0.80 127:0.06 129:0.05 135:0.88 139:0.67 144:0.85 145:0.45 146:0.17 147:0.22 149:0.40 154:0.54 159:0.09 163:0.99 172:0.16 173:0.78 177:0.70 178:0.55 179:0.08 187:0.68 212:0.95 216:0.77 222:0.60 235:0.52 241:0.27 242:0.82 243:0.69 247:0.12 253:0.30 259:0.21 265:0.08 266:0.12 267:0.36 271:0.66 276:0.19 300:0.08 +1 1:0.74 11:0.91 12:0.01 17:0.07 18:0.65 21:0.13 22:0.93 27:0.16 29:0.77 30:0.76 34:0.75 36:0.67 37:0.85 39:0.78 43:0.82 45:0.66 47:0.93 64:0.77 66:0.64 69:0.50 70:0.15 71:0.75 74:0.44 81:0.71 83:0.91 86:0.76 91:0.12 98:0.34 101:0.52 108:0.38 114:0.79 122:0.57 123:0.37 126:0.54 127:0.12 129:0.05 135:0.68 139:0.72 144:0.85 146:0.22 147:0.28 149:0.57 150:0.82 154:0.77 155:0.67 159:0.11 163:0.90 165:0.93 172:0.16 173:0.84 176:0.73 177:0.70 178:0.55 179:0.65 187:0.95 192:0.87 201:0.93 208:0.64 212:0.95 215:0.61 216:0.54 222:0.60 235:0.31 241:0.12 242:0.82 243:0.69 247:0.12 253:0.55 254:0.95 259:0.21 262:0.93 265:0.36 266:0.12 267:0.36 271:0.66 276:0.14 290:0.79 297:0.36 300:0.13 +0 1:0.74 11:0.91 12:0.01 17:0.01 18:0.82 21:0.13 27:0.47 29:0.77 30:0.76 34:0.62 36:0.45 37:0.58 39:0.78 43:0.68 45:0.66 48:0.91 55:0.71 64:0.77 66:0.72 69:0.42 70:0.22 71:0.75 74:0.46 81:0.82 83:0.60 86:0.70 91:0.18 98:0.52 99:0.83 101:0.52 108:0.32 114:0.79 122:0.82 123:0.31 126:0.54 127:0.16 129:0.05 135:0.42 139:0.74 144:0.85 145:0.63 146:0.47 147:0.53 149:0.37 150:0.87 154:0.57 155:0.67 159:0.17 165:0.70 172:0.27 173:0.52 175:0.75 176:0.73 177:0.38 178:0.55 179:0.69 187:0.68 192:0.87 201:0.93 204:0.85 208:0.64 212:0.78 215:0.61 216:0.62 219:0.87 222:0.60 235:0.68 241:0.27 242:0.45 243:0.75 244:0.61 247:0.35 253:0.55 257:0.65 259:0.21 265:0.53 266:0.17 267:0.23 271:0.66 276:0.24 277:0.69 281:0.89 290:0.79 292:0.91 297:0.36 300:0.18 +1 1:0.69 8:0.92 9:0.76 11:0.82 12:0.01 17:0.45 18:0.97 21:0.40 22:0.93 27:0.21 29:0.77 30:0.36 31:0.95 34:0.37 36:0.89 37:0.74 39:0.78 43:0.60 47:0.93 48:0.91 64:0.77 66:0.52 69:0.92 70:0.64 71:0.75 74:0.61 76:0.85 79:0.96 81:0.36 83:0.60 86:0.83 91:0.37 96:0.87 98:0.71 108:0.83 114:0.61 117:0.86 122:0.86 123:0.82 124:0.65 126:0.44 127:0.52 129:0.05 133:0.66 135:0.87 137:0.95 139:0.85 140:0.45 144:0.85 146:0.94 147:0.36 149:0.81 150:0.50 151:0.86 153:0.78 154:0.61 155:0.58 158:0.78 159:0.78 162:0.74 172:0.83 173:0.86 176:0.66 177:0.38 179:0.27 187:0.39 190:0.77 192:0.51 196:0.95 201:0.68 202:0.69 204:0.85 208:0.54 212:0.59 216:0.95 219:0.92 222:0.60 232:0.85 235:0.52 241:0.37 242:0.39 243:0.17 244:0.61 245:0.91 247:0.74 248:0.82 253:0.82 255:0.74 256:0.71 257:0.65 259:0.21 262:0.93 265:0.71 266:0.75 267:0.36 268:0.73 271:0.66 276:0.83 279:0.82 281:0.89 284:0.94 285:0.56 290:0.61 292:0.91 300:0.55 +1 1:0.74 11:0.92 12:0.01 17:0.61 18:0.86 21:0.47 22:0.93 27:0.62 29:0.77 30:0.87 32:0.80 34:0.66 36:0.28 37:0.40 39:0.78 43:0.82 44:0.93 45:0.66 47:0.93 60:0.87 64:0.77 66:0.88 69:0.69 70:0.37 71:0.75 74:0.77 77:0.70 81:0.54 83:0.91 85:0.85 86:0.92 91:0.25 97:0.89 98:0.79 101:0.97 106:0.81 108:0.53 114:0.60 117:0.86 122:0.57 123:0.50 126:0.54 127:0.28 129:0.05 135:0.98 139:0.94 144:0.85 145:0.87 146:0.66 147:0.71 149:0.85 150:0.74 154:0.77 155:0.67 158:0.91 159:0.25 165:0.93 172:0.65 173:0.83 176:0.73 177:0.70 178:0.55 179:0.52 187:1.00 192:0.87 195:0.58 201:0.93 206:0.81 208:0.64 212:0.59 215:0.41 216:0.14 219:0.95 222:0.60 235:0.80 241:0.12 242:0.24 243:0.88 247:0.67 253:0.47 259:0.21 262:0.93 265:0.79 266:0.47 267:0.23 271:0.66 276:0.49 279:0.86 282:0.88 283:0.78 290:0.60 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 7:0.81 11:0.88 12:0.01 17:0.64 18:0.87 21:0.40 22:0.93 27:0.65 29:0.77 30:0.94 32:0.80 34:0.82 36:0.54 37:0.52 39:0.78 43:0.90 44:0.93 47:0.93 48:0.91 60:0.95 64:0.77 66:0.88 69:0.47 70:0.19 71:0.75 74:0.89 76:0.85 77:0.70 81:0.54 83:0.91 85:0.91 86:0.95 91:0.38 98:0.78 101:0.87 108:0.36 114:0.62 117:0.86 121:0.90 122:0.57 123:0.34 126:0.54 127:0.27 129:0.05 135:0.30 139:0.97 144:0.85 145:0.72 146:0.54 147:0.60 149:0.94 150:0.74 154:0.89 155:0.67 158:0.92 159:0.19 165:0.93 172:0.67 173:0.68 176:0.73 177:0.70 178:0.55 179:0.48 187:0.87 192:0.87 195:0.57 201:0.93 204:0.89 208:0.64 212:0.93 215:0.42 216:0.47 219:0.95 222:0.60 230:0.87 233:0.76 235:0.60 241:0.43 242:0.53 243:0.89 247:0.35 253:0.51 259:0.21 262:0.93 265:0.78 266:0.17 267:0.23 271:0.66 276:0.53 279:0.86 281:0.91 282:0.88 283:0.82 290:0.62 292:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.69 11:0.93 12:0.01 17:0.55 18:0.88 22:0.93 27:0.72 29:0.77 30:0.45 34:0.89 36:0.83 39:0.78 43:0.64 44:0.90 45:0.73 47:0.93 64:0.77 66:0.87 69:0.45 70:0.61 71:0.75 74:0.74 77:0.70 81:0.76 83:0.60 86:0.81 91:0.57 97:0.89 98:0.83 99:0.83 101:0.87 108:0.35 114:0.95 117:0.86 122:0.83 123:0.33 126:0.44 127:0.33 129:0.05 135:0.37 139:0.84 144:0.85 145:0.38 146:0.70 147:0.75 149:0.62 150:0.73 154:0.85 155:0.58 158:0.78 159:0.28 165:0.70 172:0.63 173:0.58 176:0.66 177:0.70 178:0.55 179:0.60 187:0.68 192:0.51 195:0.57 201:0.68 208:0.54 212:0.48 216:0.39 219:0.92 222:0.60 232:0.97 235:0.05 241:0.47 242:0.28 243:0.88 247:0.67 253:0.64 254:0.74 259:0.21 262:0.93 265:0.83 266:0.38 267:0.19 271:0.66 276:0.49 279:0.82 282:0.77 290:0.95 292:0.91 300:0.43 +2 11:0.88 12:0.01 17:0.18 18:0.93 21:0.05 27:0.71 29:0.77 30:0.76 34:0.69 36:0.88 37:0.25 39:0.78 43:0.97 64:0.77 66:0.64 69:0.07 70:0.44 71:0.75 74:0.47 81:0.65 85:0.72 86:0.80 91:0.57 98:0.81 101:0.87 108:0.06 122:0.83 123:0.06 124:0.47 126:0.44 127:0.58 129:0.05 133:0.43 135:0.72 139:0.77 144:0.85 146:0.83 147:0.86 149:0.89 150:0.45 154:0.97 159:0.50 172:0.71 173:0.15 177:0.70 178:0.55 179:0.51 187:0.39 192:0.51 201:0.68 212:0.42 215:0.78 216:0.32 219:0.87 222:0.60 232:0.88 235:0.12 241:0.18 242:0.63 243:0.69 245:0.81 247:0.39 253:0.34 259:0.21 265:0.81 266:0.38 267:0.52 271:0.66 276:0.66 283:0.78 292:0.91 297:0.36 300:0.55 +1 1:0.74 7:0.81 11:0.92 12:0.01 17:0.22 18:0.90 21:0.74 22:0.93 27:0.62 29:0.77 30:0.89 32:0.80 34:0.96 36:0.70 37:0.59 39:0.78 43:0.89 44:0.93 45:0.66 47:0.93 48:0.91 60:0.87 64:0.77 66:0.52 69:0.52 70:0.31 71:0.75 74:0.83 77:0.70 81:0.40 83:0.91 85:0.85 86:0.88 91:0.31 97:0.89 98:0.63 99:0.83 101:0.97 106:0.81 108:0.40 114:0.54 117:0.86 122:0.83 123:0.39 124:0.55 126:0.54 127:0.55 129:0.05 133:0.43 135:0.75 139:0.94 144:0.85 145:0.72 146:0.59 147:0.64 149:0.90 150:0.65 154:0.88 155:0.67 158:0.91 159:0.40 165:0.70 172:0.61 173:0.60 176:0.73 177:0.70 178:0.55 179:0.56 187:0.95 192:0.87 195:0.66 201:0.93 204:0.89 206:0.81 208:0.64 212:0.37 215:0.36 216:0.67 219:0.95 222:0.60 230:0.86 233:0.73 235:0.97 241:0.55 242:0.40 243:0.61 245:0.80 247:0.60 253:0.45 259:0.21 262:0.93 265:0.64 266:0.63 267:0.28 271:0.66 276:0.59 279:0.86 281:0.89 282:0.88 283:0.78 290:0.54 292:0.91 297:0.36 300:0.43 +1 1:0.74 11:0.88 12:0.01 17:0.08 18:0.68 20:0.99 21:0.13 22:0.93 27:0.16 29:0.77 30:0.95 34:0.66 36:0.61 37:0.85 39:0.78 43:0.97 47:0.93 60:0.87 64:0.77 66:0.64 69:0.10 71:0.75 74:0.58 78:0.92 81:0.71 83:0.91 85:0.72 86:0.77 91:0.10 97:0.89 98:0.17 100:0.91 101:0.87 108:0.09 111:0.91 114:0.79 122:0.57 123:0.09 126:0.54 127:0.10 129:0.05 135:0.60 139:0.82 144:0.85 146:0.17 147:0.22 149:0.64 150:0.82 152:0.94 154:0.97 155:0.67 158:0.92 159:0.09 165:0.93 169:0.87 172:0.16 173:0.52 176:0.73 177:0.70 178:0.55 179:0.16 186:0.92 187:0.87 192:0.87 201:0.93 208:0.64 212:0.95 215:0.61 216:0.54 219:0.95 222:0.60 235:0.31 241:0.37 242:0.82 243:0.69 247:0.12 253:0.56 259:0.21 261:0.96 262:0.93 265:0.19 266:0.12 267:0.36 271:0.66 276:0.19 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.08 +1 1:0.74 7:0.81 11:0.88 12:0.01 17:0.13 18:0.67 21:0.54 22:0.93 27:0.29 29:0.77 30:0.95 32:0.80 34:0.59 36:0.75 37:0.88 39:0.78 43:0.89 44:0.93 47:0.93 64:0.77 66:0.64 69:0.52 71:0.75 74:0.70 77:0.70 81:0.48 83:0.91 85:0.72 86:0.76 91:0.33 97:0.89 98:0.18 101:0.87 108:0.40 114:0.59 121:0.90 122:0.57 123:0.38 126:0.54 127:0.10 129:0.05 132:0.97 135:0.37 139:0.89 144:0.85 145:0.49 146:0.17 147:0.22 149:0.62 150:0.71 154:0.87 155:0.67 158:0.79 159:0.09 163:0.90 165:0.93 172:0.16 173:0.86 176:0.73 177:0.70 178:0.55 179:0.17 187:0.39 192:0.87 195:0.53 201:0.93 204:0.89 208:0.64 212:0.95 215:0.39 216:0.61 219:0.92 222:0.60 230:0.87 233:0.76 235:0.74 241:0.32 242:0.82 243:0.69 247:0.12 253:0.45 262:0.93 265:0.19 266:0.12 267:0.23 271:0.66 276:0.19 279:0.82 281:0.91 282:0.88 290:0.59 292:0.95 297:0.36 299:0.88 300:0.08 +1 1:0.74 11:0.92 12:0.01 17:0.70 18:0.86 21:0.47 27:0.62 29:0.77 30:0.87 32:0.80 34:0.62 36:0.26 37:0.40 39:0.78 43:0.81 44:0.93 45:0.66 64:0.77 66:0.88 69:0.71 70:0.37 71:0.75 74:0.74 77:0.70 81:0.54 83:0.91 85:0.85 86:0.92 91:0.18 98:0.79 101:0.97 108:0.54 114:0.60 122:0.57 123:0.52 126:0.54 127:0.28 129:0.05 135:0.98 139:0.93 144:0.85 145:0.85 146:0.66 147:0.71 149:0.85 150:0.74 154:0.76 155:0.67 158:0.78 159:0.25 165:0.93 172:0.65 173:0.84 176:0.73 177:0.70 178:0.55 179:0.52 187:1.00 192:0.87 195:0.58 201:0.93 208:0.64 212:0.59 215:0.41 216:0.14 219:0.92 222:0.60 235:0.80 241:0.15 242:0.24 243:0.88 247:0.67 253:0.47 259:0.21 265:0.79 266:0.54 267:0.23 271:0.66 276:0.49 279:0.82 282:0.88 290:0.60 292:0.91 297:0.36 299:0.88 300:0.43 +2 1:0.62 7:0.66 9:0.74 10:0.96 11:0.64 12:0.51 17:0.73 18:0.92 21:0.30 22:0.93 27:0.21 29:0.94 30:0.14 31:0.93 34:0.47 36:0.63 37:0.74 39:0.89 43:0.61 45:0.98 47:0.93 55:0.42 64:0.77 66:0.25 69:0.17 70:0.98 71:0.59 74:0.67 79:0.93 81:0.56 83:0.54 86:0.87 91:0.57 96:0.80 97:0.94 98:0.54 99:0.95 101:1.00 104:0.84 106:0.81 108:0.90 114:0.76 117:0.86 120:0.69 123:0.90 124:0.96 126:0.43 127:0.96 129:0.89 131:0.95 133:0.96 135:0.23 137:0.93 139:0.84 140:0.45 144:0.57 146:0.52 147:0.58 149:0.77 150:0.44 151:0.85 153:0.48 154:0.54 155:0.58 157:0.99 158:0.74 159:0.94 162:0.86 164:0.55 165:0.70 166:0.92 170:0.94 172:0.94 173:0.07 174:0.99 176:0.60 177:0.96 179:0.12 182:0.98 187:0.39 190:0.98 192:0.98 193:0.95 196:0.94 197:0.98 201:0.60 202:0.36 204:0.81 206:0.81 208:0.54 212:0.61 215:0.72 216:0.95 219:0.88 222:0.48 227:0.91 229:0.99 230:0.68 231:0.88 232:0.91 233:0.76 235:0.60 239:0.94 241:0.02 242:0.16 243:0.19 244:0.61 245:0.97 246:0.92 247:1.00 248:0.76 253:0.81 255:0.73 256:0.45 259:0.21 260:0.70 262:0.93 264:0.93 265:0.56 266:0.96 267:0.65 268:0.46 271:0.26 275:0.98 276:0.97 279:0.77 281:0.91 283:0.82 284:0.88 285:0.55 287:0.94 290:0.76 292:0.95 294:0.97 297:0.36 300:0.94 +2 1:0.63 7:0.64 8:0.77 9:0.75 10:0.94 11:0.52 12:0.51 17:0.61 18:0.86 21:0.54 23:0.98 27:0.70 29:0.94 30:0.14 31:0.94 32:0.67 34:0.40 36:0.96 37:0.54 39:0.89 43:0.26 44:0.88 45:0.93 55:0.71 56:0.97 62:0.97 64:0.77 66:0.98 69:0.44 70:0.91 71:0.59 74:0.60 77:0.70 79:0.94 81:0.67 82:0.99 83:0.54 86:0.83 88:0.98 91:0.65 96:0.83 97:0.94 98:0.98 99:0.95 101:0.69 102:0.96 108:0.34 114:0.74 120:0.73 122:0.98 123:0.32 126:0.54 127:0.83 128:0.96 129:0.05 131:0.95 135:0.92 137:0.94 139:0.83 140:0.45 143:0.99 144:0.57 145:0.65 146:0.97 147:0.98 149:0.40 150:0.49 151:0.57 153:0.48 154:0.51 155:0.65 157:0.99 158:0.74 159:0.73 162:0.68 164:0.77 165:0.70 166:0.91 168:0.96 170:0.94 172:0.94 173:0.30 174:0.97 176:0.63 177:0.96 179:0.24 182:0.99 187:0.39 190:0.98 192:1.00 193:0.95 195:0.86 196:0.94 197:0.98 201:0.64 202:0.70 204:0.78 205:0.98 208:0.64 212:0.68 215:0.58 216:0.26 219:0.88 220:0.91 222:0.48 227:0.91 229:0.99 230:0.65 231:0.88 233:0.76 235:0.88 236:0.94 239:0.93 241:0.44 242:0.19 243:0.98 244:0.83 246:0.92 247:0.97 248:0.61 253:0.81 254:0.80 255:0.78 256:0.71 259:0.21 260:0.73 264:0.93 265:0.98 266:0.63 267:0.18 268:0.72 271:0.26 275:0.95 276:0.88 279:0.77 281:0.91 282:0.75 283:0.82 284:0.96 285:0.62 287:0.94 290:0.74 292:0.95 294:0.98 297:0.36 300:0.70 +1 1:0.57 7:0.64 9:0.79 10:0.88 11:0.46 12:0.51 17:0.12 21:0.47 23:0.97 27:0.72 29:0.94 30:0.10 31:0.94 34:0.77 36:0.62 39:0.89 41:0.82 43:0.17 45:0.77 55:0.52 60:0.87 62:0.98 64:0.77 66:0.23 69:0.85 70:0.88 71:0.59 74:0.47 75:0.99 79:0.93 81:0.39 83:0.60 91:0.62 96:0.80 97:0.97 98:0.52 101:0.46 102:0.94 104:0.84 106:0.81 108:0.80 120:0.71 122:0.97 123:0.68 124:0.65 126:0.43 127:0.36 128:0.98 129:0.59 131:0.95 133:0.61 135:0.58 137:0.94 139:0.74 140:0.87 143:0.99 144:0.57 145:0.56 146:0.60 147:0.05 149:0.16 150:0.40 151:0.86 153:0.48 154:0.10 155:0.66 157:0.61 158:0.92 159:0.45 162:0.62 164:0.66 166:0.82 168:0.98 170:0.94 172:0.57 173:0.88 174:0.95 175:0.91 177:0.05 178:0.42 179:0.50 182:0.98 187:0.39 192:1.00 193:0.95 195:0.74 196:0.92 197:0.95 201:0.59 202:0.60 205:0.97 206:0.81 208:0.64 212:0.58 215:0.63 216:0.54 219:0.95 220:0.93 222:0.48 226:0.98 227:0.91 229:0.98 230:0.64 231:0.88 233:0.66 235:0.68 236:0.91 239:0.92 241:0.64 242:0.24 243:0.10 244:0.97 245:0.72 246:0.61 247:0.79 248:0.77 253:0.75 254:0.86 255:0.95 256:0.58 257:0.97 259:0.21 260:0.72 264:0.93 265:0.43 266:0.63 267:0.82 268:0.59 271:0.26 275:0.94 276:0.60 277:0.87 279:0.81 283:0.82 284:0.92 285:0.62 287:0.94 292:0.95 294:0.97 295:0.99 297:0.36 300:0.55 +2 1:0.63 8:0.86 9:0.75 10:0.93 11:0.83 12:0.51 17:0.26 18:0.78 21:0.22 27:0.17 29:0.94 30:0.17 31:0.96 34:0.52 36:0.47 37:0.97 39:0.89 43:0.75 45:0.90 55:0.42 64:0.77 66:0.33 69:0.48 70:0.99 71:0.59 74:0.53 79:0.95 81:0.58 83:0.54 85:0.72 86:0.82 91:0.63 96:0.86 97:0.99 98:0.51 99:0.95 101:1.00 108:0.37 114:0.80 120:0.78 123:0.36 124:0.83 126:0.43 127:0.72 129:0.59 131:0.95 133:0.81 135:0.66 137:0.96 139:0.79 140:0.45 144:0.57 146:0.81 147:0.84 149:0.70 150:0.45 151:0.85 153:0.48 154:0.66 155:0.58 157:0.98 158:0.74 159:0.79 162:0.52 164:0.74 165:0.70 166:0.92 170:0.94 172:0.74 173:0.29 174:0.93 176:0.60 177:0.96 179:0.33 182:0.99 187:0.39 190:0.98 191:0.76 192:0.98 196:0.94 197:0.94 201:0.61 202:0.74 208:0.54 212:0.47 215:0.79 216:0.68 219:0.88 220:0.82 222:0.48 227:0.91 229:0.99 231:0.88 235:0.52 239:0.91 241:0.51 242:0.21 243:0.50 245:0.86 246:0.92 247:0.98 248:0.84 253:0.83 255:0.74 256:0.64 259:0.21 260:0.78 264:0.93 265:0.53 266:0.84 267:0.84 268:0.69 271:0.26 275:0.96 276:0.81 279:0.81 283:0.82 284:0.95 285:0.55 287:0.94 290:0.80 292:0.95 294:0.97 297:0.36 300:0.94 +1 1:0.61 2:0.99 7:0.66 8:0.80 9:0.76 10:0.83 11:0.52 12:0.51 17:0.13 18:0.69 21:0.40 23:0.97 27:0.04 29:0.94 30:0.33 31:0.97 34:0.64 36:0.53 37:0.98 39:0.89 43:0.22 45:0.85 55:0.92 62:0.97 64:0.77 66:0.25 69:0.95 70:0.82 71:0.59 74:0.48 75:0.98 79:0.97 81:0.55 83:0.54 86:0.67 91:0.77 96:0.91 97:0.99 98:0.37 99:0.95 101:0.52 108:0.89 114:0.74 120:0.85 122:0.67 123:0.89 124:0.88 126:0.43 127:0.42 128:0.98 129:0.05 131:0.95 133:0.85 135:0.81 137:0.97 139:0.75 140:0.98 143:0.99 144:0.57 146:0.65 147:0.23 149:0.26 150:0.43 151:0.61 153:0.81 154:0.14 155:0.65 157:0.96 158:0.81 159:0.72 162:0.52 164:0.88 165:0.70 166:0.92 168:0.98 170:0.94 172:0.48 173:0.95 174:0.86 176:0.60 177:0.70 178:0.42 179:0.47 182:0.99 187:0.68 190:0.89 191:0.80 192:1.00 196:0.94 197:0.83 201:0.60 202:0.88 204:0.82 205:0.97 208:0.64 212:0.15 215:0.67 216:0.74 219:0.94 220:0.91 222:0.48 226:0.95 227:0.91 229:0.99 230:0.68 231:0.88 233:0.76 235:0.68 239:0.88 241:0.66 242:0.14 243:0.19 244:0.83 245:0.69 246:0.92 247:0.95 248:0.59 253:0.87 254:0.90 255:0.78 256:0.84 257:0.84 259:0.21 260:0.85 264:0.93 265:0.39 266:0.84 267:0.52 268:0.85 271:0.26 275:0.94 276:0.65 279:0.81 281:0.91 283:0.82 284:0.98 285:0.62 287:0.94 290:0.74 292:0.95 294:0.96 295:0.98 297:0.36 300:0.70 +2 1:0.66 7:0.64 8:0.77 9:0.80 10:0.89 11:0.64 12:0.51 17:0.38 18:0.75 21:0.30 23:0.99 27:0.04 29:0.94 30:0.53 31:0.98 34:0.87 36:0.91 37:0.98 39:0.89 41:0.88 43:0.62 44:0.92 45:0.83 55:0.59 56:0.97 62:0.99 64:0.77 66:0.54 69:0.84 70:0.57 71:0.59 74:0.59 75:0.98 77:0.70 79:0.97 81:0.74 82:0.99 83:0.60 86:0.71 88:0.99 91:0.74 96:0.91 97:0.97 98:0.67 99:0.99 101:0.87 102:0.97 108:0.70 114:0.84 120:0.85 122:0.97 123:0.68 124:0.52 126:0.54 127:0.29 128:0.99 129:0.05 131:0.95 133:0.45 135:0.65 137:0.98 139:0.82 140:0.95 143:1.00 144:0.57 145:0.95 146:0.86 147:0.88 149:0.52 150:0.55 151:0.86 153:0.48 154:0.51 155:0.66 157:0.92 158:0.81 159:0.56 162:0.51 164:0.84 165:0.86 166:0.91 168:0.99 170:0.94 172:0.77 173:0.78 174:0.96 176:0.70 177:0.96 178:0.52 179:0.27 182:0.99 187:0.87 191:0.80 192:1.00 195:0.85 196:0.96 197:0.95 201:0.66 202:0.85 204:0.79 205:0.99 208:0.64 212:0.85 215:0.72 216:0.74 219:0.94 220:0.62 222:0.48 226:0.96 227:0.91 229:0.99 230:0.64 231:0.88 233:0.66 235:0.80 236:0.95 239:0.93 241:0.65 242:0.16 243:0.62 244:0.61 245:0.90 246:0.61 247:0.91 248:0.81 253:0.90 255:1.00 256:0.81 259:0.21 260:0.85 264:0.93 265:0.67 266:0.47 267:0.36 268:0.81 271:0.26 275:0.91 276:0.76 279:0.80 281:0.91 282:0.80 283:0.67 284:0.98 285:0.62 287:0.94 290:0.84 292:0.88 294:0.98 295:0.98 297:0.36 300:0.55 +2 1:0.62 7:0.66 8:0.78 9:0.79 10:0.96 11:0.64 12:0.51 17:0.61 18:0.90 21:0.30 22:0.93 23:0.97 27:0.21 29:0.94 30:0.13 31:0.99 34:0.55 36:0.68 37:0.74 39:0.89 43:0.61 45:0.98 47:0.93 55:0.42 62:0.99 64:0.77 66:0.25 69:0.17 70:0.98 71:0.59 74:0.68 75:0.97 79:0.99 81:0.56 83:0.60 86:0.86 91:0.87 96:0.97 97:0.99 98:0.54 99:0.95 101:1.00 104:0.84 106:0.81 108:0.90 114:0.76 117:0.86 120:0.95 123:0.90 124:0.96 126:0.43 127:0.95 128:0.99 129:0.89 131:0.95 133:0.96 135:0.19 137:0.99 139:0.85 140:0.93 144:0.57 146:0.51 147:0.57 149:0.75 150:0.44 151:0.95 153:0.94 154:0.55 155:0.65 157:0.99 158:0.77 159:0.94 162:0.60 164:0.92 165:0.70 166:0.92 168:0.99 170:0.94 172:0.94 173:0.07 174:0.99 176:0.60 177:0.96 179:0.12 182:0.99 187:0.39 190:0.89 191:0.75 192:0.99 193:0.95 196:0.99 197:0.98 201:0.60 202:0.90 204:0.81 205:0.97 206:0.81 208:0.64 212:0.61 215:0.72 216:0.95 219:0.91 222:0.48 226:0.98 227:0.91 229:1.00 230:0.68 231:0.88 232:0.91 233:0.76 235:0.60 239:0.95 241:0.21 242:0.16 243:0.17 244:0.61 245:0.97 246:0.92 247:1.00 248:0.84 253:0.97 255:0.94 256:0.90 259:0.21 260:0.95 262:0.93 264:0.93 265:0.56 266:0.96 267:0.69 268:0.91 271:0.26 275:0.98 276:0.97 279:0.81 281:0.91 283:0.82 284:0.99 285:0.61 287:0.94 290:0.76 292:0.95 294:0.97 297:0.36 300:0.94 +2 1:0.62 2:1.00 7:0.66 8:0.60 9:0.75 10:0.99 11:0.64 12:0.51 17:0.24 18:0.96 21:0.59 23:0.98 27:0.64 29:0.94 30:0.54 31:0.95 32:0.75 34:0.50 36:0.87 37:0.28 39:0.89 43:0.61 44:0.92 45:0.99 55:0.42 56:0.97 60:0.87 62:0.98 64:0.77 66:0.86 69:0.36 70:0.70 71:0.59 74:0.88 75:0.99 77:0.89 79:0.94 80:0.98 81:0.70 82:0.99 83:0.54 86:0.96 88:0.98 91:0.61 96:0.83 97:1.00 98:0.90 99:0.99 101:1.00 102:0.96 108:0.62 114:0.73 120:0.73 122:0.67 123:0.60 124:0.39 126:0.54 127:0.93 128:0.96 129:0.59 131:0.95 133:0.60 135:0.82 137:0.95 139:0.96 140:0.45 143:0.99 144:0.57 145:0.63 146:0.45 147:0.52 149:0.72 150:0.47 151:0.59 153:0.48 154:0.66 155:0.65 157:1.00 158:0.86 159:0.78 162:0.84 164:0.77 165:0.86 166:0.91 168:0.96 170:0.94 172:0.98 173:0.22 174:0.98 176:0.70 177:0.96 179:0.13 182:0.99 187:0.21 190:0.98 192:1.00 193:0.95 195:0.88 196:0.97 197:0.98 201:0.62 202:0.65 204:0.84 205:0.98 208:0.64 212:0.89 215:0.55 216:0.61 219:0.95 220:0.87 222:0.48 226:0.96 227:0.91 229:0.99 230:0.68 231:0.88 233:0.76 235:0.97 236:0.95 239:0.99 241:0.50 242:0.24 243:0.12 244:0.61 245:0.95 246:0.92 247:0.99 248:0.61 253:0.87 255:0.78 256:0.71 259:0.21 260:0.73 264:0.93 265:0.90 266:0.47 267:0.69 268:0.72 271:0.26 275:0.99 276:0.96 279:0.82 281:0.91 282:0.83 283:0.67 284:0.96 285:0.62 287:0.94 290:0.73 292:0.88 294:1.00 295:0.98 297:0.36 300:0.70 +2 1:0.62 7:0.66 8:0.77 9:0.79 10:0.96 11:0.83 12:0.51 17:0.67 18:0.91 21:0.30 23:0.97 27:0.21 29:0.94 30:0.15 31:0.98 34:0.70 36:0.67 37:0.74 39:0.89 43:0.82 45:0.98 55:0.42 62:0.98 64:0.77 66:0.23 69:0.17 70:0.98 71:0.59 74:0.71 75:0.97 79:0.98 81:0.56 83:0.60 85:0.72 86:0.88 91:0.72 96:0.93 97:0.99 98:0.53 99:0.95 101:1.00 108:0.90 114:0.76 120:0.88 123:0.90 124:0.97 126:0.43 127:0.97 128:0.98 129:0.93 131:0.95 133:0.97 135:0.21 137:0.98 139:0.88 140:0.95 144:0.57 146:0.55 147:0.61 149:0.88 150:0.44 151:0.85 153:0.83 154:0.78 155:0.65 157:0.99 158:0.77 159:0.95 162:0.55 164:0.84 165:0.70 166:0.92 168:0.98 170:0.94 172:0.95 173:0.06 174:0.99 176:0.60 177:0.96 178:0.42 179:0.12 182:0.99 187:0.39 190:0.89 192:0.99 193:0.95 196:0.98 197:0.98 201:0.60 202:0.83 204:0.81 205:0.97 208:0.64 212:0.59 215:0.72 216:0.95 219:0.91 220:0.87 222:0.48 226:0.98 227:0.91 229:1.00 230:0.68 231:0.88 233:0.76 235:0.60 239:0.96 241:0.44 242:0.15 243:0.17 245:0.97 246:0.92 247:1.00 248:0.76 253:0.93 255:0.79 256:0.81 259:0.21 260:0.88 264:0.93 265:0.54 266:0.97 267:0.65 268:0.81 271:0.26 275:0.98 276:0.98 279:0.81 281:0.91 283:0.82 284:0.97 285:0.61 287:0.94 290:0.76 292:0.95 294:0.98 297:0.36 300:0.94 +1 10:0.93 11:0.83 12:0.51 17:0.62 18:0.74 21:0.78 27:0.45 29:0.94 30:0.74 34:0.76 36:0.32 37:0.50 39:0.89 43:0.78 45:0.81 55:0.42 66:0.67 69:0.76 70:0.36 71:0.59 74:0.52 85:0.72 86:0.82 91:0.27 98:0.26 101:1.00 108:0.60 122:0.67 123:0.57 124:0.45 127:0.26 129:0.05 131:0.61 133:0.36 135:0.60 139:0.77 144:0.57 145:0.50 146:0.34 147:0.40 149:0.67 154:0.71 157:0.97 159:0.39 166:0.61 170:0.94 172:0.57 173:0.78 174:0.83 177:0.96 178:0.55 179:0.53 182:0.90 187:0.68 197:0.86 212:0.82 216:0.77 222:0.48 227:0.61 229:0.61 231:0.74 235:0.52 239:0.91 241:0.15 242:0.16 243:0.72 245:0.66 246:0.61 247:0.79 253:0.42 259:0.21 264:0.93 265:0.28 266:0.27 267:0.36 271:0.26 275:0.94 276:0.29 287:0.61 294:0.97 300:0.33 +1 1:0.62 7:0.66 8:0.69 9:0.75 10:0.97 11:0.64 12:0.51 17:0.64 18:0.95 21:0.30 22:0.93 23:0.95 27:0.50 29:0.94 30:0.26 31:0.95 32:0.64 34:0.68 36:0.91 37:0.60 39:0.89 43:0.60 44:0.86 45:0.99 47:0.93 55:0.42 62:0.98 64:0.77 66:0.43 69:0.21 70:0.95 71:0.59 74:0.79 75:0.97 77:0.70 79:0.94 81:0.56 83:0.54 86:0.93 91:0.37 96:0.84 97:0.99 98:0.58 99:0.95 101:1.00 104:0.84 106:0.81 108:0.17 114:0.76 117:0.86 120:0.78 122:0.67 123:0.16 124:0.87 126:0.43 127:0.95 128:0.98 129:0.59 131:0.95 133:0.88 135:0.60 137:0.95 139:0.92 140:0.87 144:0.57 145:0.70 146:0.98 147:0.98 149:0.81 150:0.44 151:0.85 153:0.48 154:0.64 155:0.65 157:1.00 158:0.77 159:0.94 162:0.84 164:0.76 165:0.70 166:0.92 168:0.98 170:0.94 172:0.98 173:0.07 174:0.99 176:0.60 177:0.96 179:0.11 182:0.99 187:0.39 190:0.89 192:0.99 195:0.99 196:0.97 197:0.98 201:0.60 202:0.65 204:0.82 205:0.95 206:0.81 208:0.64 212:0.73 215:0.72 216:0.86 219:0.91 220:0.87 222:0.48 226:0.98 227:0.91 229:0.99 230:0.68 231:0.88 232:0.91 233:0.76 235:0.60 239:0.96 242:0.14 243:0.57 245:0.99 246:0.92 247:1.00 248:0.76 253:0.86 254:0.84 255:0.78 256:0.71 259:0.21 260:0.78 262:0.93 264:0.93 265:0.60 266:0.94 267:0.36 268:0.72 271:0.26 275:0.98 276:0.98 279:0.81 281:0.91 282:0.73 283:0.82 284:0.94 285:0.61 287:0.94 290:0.76 292:0.95 294:0.98 297:0.36 300:0.94 +1 1:0.59 7:0.64 8:0.78 10:0.86 11:0.52 12:0.51 17:0.86 18:0.77 21:0.59 27:0.72 29:0.94 30:0.32 32:0.71 34:0.93 36:0.58 37:0.42 39:0.89 43:0.21 44:0.93 45:0.87 55:0.71 64:0.77 66:0.71 69:0.82 70:0.79 71:0.59 74:0.52 77:0.89 81:0.44 82:0.99 86:0.71 88:0.98 91:0.29 98:0.58 99:0.83 101:0.69 102:0.98 104:0.89 106:0.81 108:0.67 114:0.67 122:0.67 123:0.65 124:0.46 126:0.43 127:0.37 129:0.05 131:0.61 133:0.59 135:0.49 139:0.77 144:0.57 145:0.82 146:0.68 147:0.40 149:0.26 150:0.38 154:0.19 155:0.47 157:0.98 159:0.48 165:0.63 166:0.91 170:0.94 172:0.76 173:0.83 174:0.89 176:0.60 177:0.38 178:0.55 179:0.39 182:0.97 187:0.21 192:0.47 195:0.74 197:0.90 201:0.57 206:0.81 208:0.54 212:0.86 215:0.55 216:0.10 222:0.48 227:0.61 229:0.61 230:0.64 231:0.87 233:0.66 235:0.84 239:0.90 241:0.12 242:0.22 243:0.18 244:0.83 245:0.73 246:0.92 247:0.91 253:0.53 254:0.97 257:0.93 259:0.21 264:0.93 265:0.59 266:0.54 267:0.28 271:0.26 275:0.91 276:0.68 282:0.83 283:0.67 287:0.61 290:0.67 294:0.97 297:0.36 300:0.70 +1 10:0.92 11:0.83 12:0.51 17:0.36 18:0.84 21:0.78 27:0.45 29:0.94 30:0.38 34:0.88 36:0.21 37:0.50 39:0.89 43:0.66 45:0.93 55:0.42 66:0.48 69:0.77 70:0.90 71:0.59 74:0.61 85:0.72 86:0.87 91:0.33 98:0.42 101:1.00 108:0.60 122:0.67 123:0.57 124:0.76 127:0.44 129:0.59 131:0.61 133:0.74 135:0.85 139:0.83 144:0.57 145:0.42 146:0.67 147:0.71 149:0.61 154:0.69 157:0.99 159:0.68 166:0.61 170:0.94 172:0.80 173:0.66 174:0.92 177:0.96 178:0.55 179:0.27 182:0.95 187:0.68 197:0.92 212:0.78 216:0.77 222:0.48 227:0.61 229:0.61 231:0.81 235:0.84 239:0.90 241:0.43 242:0.16 243:0.59 245:0.88 246:0.61 247:0.97 253:0.47 259:0.21 264:0.93 265:0.44 266:0.71 267:0.59 271:0.26 275:0.96 276:0.80 287:0.61 294:0.97 300:0.94 +1 1:0.60 9:0.79 10:0.92 11:0.64 12:0.51 17:0.28 18:0.84 21:0.47 23:0.96 27:0.27 29:0.94 30:0.55 31:0.94 34:0.36 36:0.70 37:0.36 39:0.89 41:0.82 43:0.28 45:0.92 55:0.42 62:0.98 64:0.77 66:0.85 69:0.27 70:0.67 71:0.59 74:0.66 75:0.99 79:0.93 81:0.54 83:0.54 86:0.83 91:0.35 96:0.80 97:0.97 98:0.81 99:0.95 101:0.96 108:0.21 114:0.72 120:0.69 122:0.67 123:0.20 124:0.37 126:0.43 127:0.57 128:0.98 129:0.05 131:0.95 133:0.36 135:0.70 137:0.94 139:0.84 140:0.45 143:0.99 144:0.57 146:0.71 147:0.76 149:0.36 150:0.41 151:0.86 153:0.48 154:0.63 155:0.66 157:0.99 158:0.84 159:0.37 162:0.86 164:0.57 165:0.70 166:0.92 168:0.98 170:0.94 172:0.76 173:0.39 174:0.88 176:0.60 177:0.96 179:0.52 182:0.98 187:0.95 192:1.00 196:0.95 197:0.91 201:0.59 202:0.36 205:0.95 208:0.64 212:0.78 215:0.63 216:0.89 219:0.95 222:0.48 226:0.95 227:0.91 229:0.99 231:0.88 235:0.74 239:0.94 241:0.37 242:0.27 243:0.86 244:0.61 245:0.60 246:0.92 247:0.84 248:0.77 253:0.81 254:0.86 255:0.95 256:0.45 259:0.21 260:0.70 264:0.93 265:0.81 266:0.47 267:0.23 268:0.46 271:0.26 275:0.96 276:0.63 279:0.79 283:0.66 284:0.89 285:0.62 287:0.94 290:0.72 292:0.87 294:0.98 295:0.98 297:0.36 300:0.55 +0 12:0.20 17:1.00 18:0.57 21:0.78 27:0.72 29:0.86 30:0.45 34:0.31 36:0.51 39:0.94 43:0.05 46:0.88 55:0.52 66:0.27 69:0.98 70:0.25 71:0.63 74:0.25 86:0.57 91:0.01 98:0.06 107:0.89 108:0.99 110:0.89 122:0.82 123:0.99 124:0.72 125:0.88 127:0.33 129:0.81 131:0.61 133:0.67 135:0.49 139:0.55 146:0.05 147:0.05 149:0.05 154:0.05 157:0.61 159:0.97 160:0.88 163:1.00 166:0.61 172:0.10 173:0.78 175:0.91 177:0.05 178:0.55 179:0.96 182:0.61 187:0.21 202:0.60 212:0.61 216:0.13 222:0.21 227:0.61 229:0.61 231:0.61 241:0.41 242:0.63 243:0.29 244:0.83 245:0.38 246:0.61 247:0.30 253:0.22 257:0.84 265:0.06 266:0.17 267:0.97 276:0.13 277:0.87 287:0.61 289:0.88 300:0.18 +0 12:0.20 17:0.99 18:0.56 21:0.78 27:0.69 29:0.86 30:0.87 37:0.27 39:0.94 43:0.05 46:0.88 55:0.59 66:0.20 69:1.00 70:0.27 71:0.63 74:0.25 86:0.56 98:0.08 107:0.89 108:0.99 110:0.89 122:0.77 123:0.99 124:0.74 125:0.88 127:0.18 129:0.05 131:0.61 133:0.62 139:0.55 146:0.21 147:0.34 149:0.05 154:0.06 157:0.61 159:0.93 160:0.88 163:0.80 166:0.61 172:0.16 173:0.99 175:0.75 177:0.05 178:0.55 179:0.61 182:0.61 187:0.68 202:0.63 212:0.19 216:0.85 222:0.21 227:0.61 229:0.61 231:0.61 235:0.12 241:0.05 242:0.75 243:0.40 244:0.61 245:0.49 246:0.61 247:0.35 253:0.22 254:0.92 257:0.84 265:0.08 266:0.47 276:0.19 277:0.87 287:0.61 289:0.88 300:0.25 +0 12:0.20 17:0.99 18:0.57 21:0.78 27:0.72 29:0.86 30:0.76 34:0.84 36:0.32 39:0.94 43:0.05 46:0.88 55:0.45 66:0.36 69:0.99 70:0.15 71:0.63 74:0.25 86:0.56 91:0.20 98:0.06 107:0.89 108:0.98 110:0.88 122:0.57 123:0.98 124:0.52 125:0.88 127:0.21 129:0.05 131:0.61 133:0.39 135:0.30 139:0.55 146:0.18 147:0.23 149:0.05 154:0.08 157:0.61 159:0.93 160:0.88 163:1.00 166:0.61 172:0.10 173:0.89 175:0.75 177:0.38 178:0.55 179:0.95 182:0.61 187:0.21 212:0.95 222:0.21 227:0.61 229:0.61 231:0.61 235:0.52 241:0.39 242:0.82 243:0.51 244:0.61 245:0.37 246:0.61 247:0.12 253:0.22 254:0.80 257:0.65 265:0.06 266:0.12 267:0.36 276:0.12 277:0.69 287:0.61 289:0.88 300:0.13 +0 11:0.75 12:0.20 17:0.83 18:0.60 21:0.78 27:0.72 29:0.86 30:0.76 34:0.62 36:0.96 39:0.94 43:0.32 45:0.66 46:0.93 66:0.36 69:0.91 70:0.15 71:0.63 74:0.33 86:0.65 91:0.03 98:0.07 101:0.52 107:0.91 108:0.82 110:0.90 122:0.80 123:0.81 124:0.52 125:0.88 127:0.18 129:0.05 131:0.61 133:0.39 135:0.65 139:0.63 144:0.57 146:0.13 147:0.18 149:0.21 154:0.23 157:0.78 159:0.48 160:0.88 163:0.99 166:0.61 172:0.10 173:0.83 175:0.75 177:0.38 178:0.55 179:0.92 182:0.73 187:0.39 212:0.95 216:0.12 222:0.21 227:0.61 229:0.61 231:0.63 235:0.31 241:0.18 242:0.82 243:0.51 244:0.61 245:0.37 246:0.61 247:0.12 253:0.28 257:0.65 259:0.21 265:0.07 266:0.12 267:0.36 276:0.12 277:0.69 287:0.61 289:0.88 300:0.13 +0 12:0.20 17:1.00 18:0.73 21:0.78 27:0.72 29:0.86 30:0.62 34:0.30 36:0.47 39:0.94 43:0.15 46:0.88 55:0.52 66:0.16 69:0.50 70:0.23 71:0.63 74:0.24 86:0.59 91:0.02 98:0.06 107:0.89 108:0.38 110:0.89 122:0.41 123:0.37 124:0.81 125:0.88 127:0.49 129:0.89 131:0.61 133:0.78 135:0.71 139:0.58 146:0.20 147:0.26 149:0.10 154:0.11 157:0.61 159:0.98 160:0.88 163:0.90 166:0.61 172:0.10 173:0.07 175:0.91 177:0.05 178:0.55 179:0.93 182:0.61 187:0.21 202:0.63 212:0.30 216:0.09 222:0.21 227:0.61 229:0.61 231:0.62 241:0.43 242:0.33 243:0.37 244:0.83 245:0.42 246:0.61 247:0.39 253:0.20 257:0.84 265:0.06 266:0.27 267:0.99 276:0.26 277:0.87 287:0.61 289:0.88 300:0.18 +2 6:0.95 7:0.63 8:0.93 9:0.80 11:0.87 12:0.69 17:0.24 18:0.90 20:0.91 21:0.78 27:0.10 28:0.56 29:0.62 30:0.60 31:0.99 34:0.95 36:0.92 37:0.92 39:0.80 41:0.98 43:0.79 44:0.85 45:0.73 48:0.91 55:0.45 66:0.27 69:0.71 70:0.44 71:0.86 74:0.43 76:0.94 78:0.82 79:1.00 83:0.91 85:0.72 86:0.73 91:0.98 96:0.99 97:0.94 98:0.42 100:0.86 101:0.97 108:0.86 111:0.83 120:0.96 122:0.86 123:0.92 124:0.73 127:0.69 129:0.05 131:0.91 133:0.60 135:0.96 137:0.99 138:1.00 139:0.71 140:0.98 144:0.76 145:0.70 146:0.46 147:0.53 149:0.63 151:0.97 152:0.95 153:0.96 154:0.72 155:0.67 157:0.82 158:0.72 159:0.81 161:0.77 162:0.48 164:0.94 166:0.61 167:0.72 169:0.86 172:0.51 173:0.51 177:0.70 178:0.53 179:0.53 181:0.64 182:0.97 186:0.82 187:0.39 189:0.98 191:0.98 192:0.87 195:0.92 196:0.96 202:0.99 204:0.85 208:0.64 212:0.49 216:0.88 219:0.87 222:0.84 227:0.95 229:0.98 230:0.63 231:0.80 233:0.73 235:0.31 238:0.92 241:0.69 242:0.63 243:0.28 245:0.80 246:0.61 247:0.76 248:0.96 253:0.96 255:0.95 256:0.99 259:0.21 260:0.94 261:0.89 265:0.17 266:0.84 267:0.84 268:0.99 271:0.48 276:0.60 277:0.69 279:0.82 281:0.89 284:1.00 285:0.62 287:0.99 292:0.95 300:0.33 +2 1:0.69 6:0.86 8:0.66 9:0.80 11:0.92 12:0.69 17:0.30 18:0.99 20:0.86 21:0.30 22:0.93 27:0.21 28:0.56 29:0.62 30:0.67 31:0.98 34:0.40 36:0.84 37:0.74 39:0.80 41:0.82 43:0.80 45:0.99 47:0.93 64:0.77 66:0.34 69:0.12 70:0.70 71:0.86 74:0.84 78:0.81 79:1.00 81:0.40 83:0.91 85:0.80 86:0.97 91:0.82 96:0.99 97:0.99 98:0.71 99:0.83 100:0.84 101:0.97 108:0.83 111:0.80 114:0.60 117:0.86 120:0.85 122:0.85 123:0.82 124:0.82 126:0.44 127:0.72 129:0.89 131:0.91 133:0.84 135:0.95 137:0.98 138:0.97 139:0.96 140:0.99 144:0.76 146:0.86 147:0.88 149:0.97 150:0.55 151:0.73 152:0.83 153:0.96 154:0.82 155:0.67 157:0.88 158:0.72 159:0.88 161:0.77 162:0.76 164:0.74 165:0.70 166:0.78 167:0.54 169:0.81 172:0.94 173:0.08 176:0.55 177:0.70 179:0.13 181:0.64 182:0.97 186:0.81 187:0.39 189:0.88 191:0.84 192:0.87 196:0.99 201:0.68 202:0.92 204:0.85 208:0.64 212:0.78 216:0.95 219:0.87 222:0.84 227:0.95 229:0.98 231:0.80 232:0.85 235:0.42 238:0.86 241:0.24 242:0.37 243:0.40 245:0.98 246:0.61 247:0.93 248:0.68 253:0.95 255:0.95 256:0.94 259:0.21 260:0.69 261:0.82 262:0.93 265:0.71 266:0.87 267:0.96 268:0.94 271:0.48 276:0.96 279:0.86 281:0.91 283:0.82 284:0.97 285:0.62 287:0.99 290:0.60 292:0.95 300:0.84 +1 1:0.69 6:0.84 8:0.87 9:0.80 11:0.81 12:0.69 17:0.34 18:0.68 20:0.80 21:0.05 27:0.05 28:0.56 29:0.62 30:0.71 31:0.91 34:0.77 36:0.26 37:0.98 39:0.80 43:0.27 45:0.81 60:0.87 66:0.33 69:0.69 70:0.40 71:0.86 74:0.65 78:0.86 79:0.96 81:0.64 83:0.91 86:0.78 91:0.71 96:0.94 97:0.94 98:0.12 99:0.83 100:0.87 101:0.52 104:0.96 106:0.81 108:0.53 111:0.85 114:0.70 117:0.86 120:0.85 122:0.51 123:0.51 124:0.71 126:0.44 127:0.64 129:0.59 131:0.91 133:0.66 135:0.78 137:0.91 139:0.82 140:0.45 144:0.76 146:0.20 147:0.26 149:0.18 150:0.62 151:0.84 152:0.77 153:0.72 154:0.75 155:0.67 157:0.61 158:0.92 159:0.69 161:0.77 162:0.58 164:0.66 165:0.70 166:0.80 167:0.66 169:0.86 172:0.27 173:0.60 176:0.55 177:0.70 179:0.85 181:0.64 182:0.61 186:0.85 187:0.68 189:0.86 190:0.77 191:0.97 192:0.87 196:0.93 201:0.68 202:0.84 206:0.81 208:0.64 212:0.30 215:0.78 216:0.63 219:0.95 220:0.62 222:0.84 227:0.95 229:0.61 231:0.80 232:0.98 235:0.12 238:0.87 241:0.59 242:0.33 243:0.50 245:0.53 246:0.61 247:0.47 248:0.92 253:0.81 254:0.74 255:0.79 256:0.83 260:0.67 261:0.81 265:0.13 266:0.54 267:0.52 268:0.84 271:0.48 276:0.28 279:0.86 283:0.82 284:0.87 285:0.62 287:0.61 290:0.70 292:0.95 297:0.36 300:0.33 +2 1:0.69 6:0.91 7:0.63 8:0.82 9:0.80 12:0.69 17:0.18 20:0.96 27:0.06 28:0.56 29:0.62 31:1.00 34:0.59 36:0.30 37:0.81 39:0.80 41:0.98 44:0.85 64:0.77 71:0.86 74:0.48 77:0.70 78:0.96 79:1.00 81:0.70 83:0.91 91:0.99 96:1.00 97:0.99 99:0.83 100:0.98 111:0.97 114:0.78 120:0.95 126:0.44 131:0.91 135:0.78 137:1.00 138:1.00 139:0.75 140:0.94 144:0.76 145:0.65 150:0.66 151:0.99 152:0.92 153:0.99 155:0.67 157:0.61 158:0.87 161:0.77 162:0.61 164:0.94 165:0.70 166:0.79 167:0.88 169:0.97 175:0.97 176:0.55 181:0.64 182:0.97 186:0.96 189:0.92 191:0.96 192:0.87 195:0.75 196:0.98 201:0.68 202:0.99 204:0.85 208:0.64 216:0.80 219:0.95 222:0.84 227:0.95 229:0.98 230:0.63 231:0.80 233:0.76 235:0.05 238:0.96 241:0.90 244:0.93 246:0.61 248:0.97 253:0.98 255:0.95 256:0.99 257:0.93 259:0.21 260:0.94 261:0.97 267:0.95 268:0.99 271:0.48 277:0.95 279:0.86 281:0.91 282:0.71 283:0.71 284:1.00 285:0.62 287:0.99 290:0.78 292:0.95 +1 6:0.90 8:0.84 9:0.80 11:0.64 12:0.69 17:0.38 18:0.81 20:0.77 21:0.78 27:0.30 28:0.56 29:0.62 30:0.33 31:0.97 34:0.80 36:0.28 37:0.68 39:0.80 41:0.92 43:0.71 45:0.66 55:0.52 66:0.54 69:0.17 70:0.49 71:0.86 74:0.29 78:0.93 79:0.98 83:0.91 86:0.59 91:0.64 96:0.96 97:0.94 98:0.34 100:0.97 101:0.52 108:0.82 111:0.96 120:0.87 122:0.86 123:0.81 124:0.48 127:0.80 129:0.05 131:0.91 133:0.39 135:0.98 137:0.97 139:0.59 140:0.95 144:0.76 146:0.13 147:0.18 149:0.39 151:0.87 152:0.76 153:0.90 154:0.61 155:0.67 157:0.61 158:0.72 159:0.57 161:0.77 162:0.69 164:0.74 166:0.61 167:0.66 169:0.95 172:0.32 173:0.21 175:0.75 177:0.38 179:0.91 181:0.64 182:0.99 186:0.93 187:0.68 189:0.94 191:0.95 192:0.87 196:0.87 202:0.85 208:0.64 212:0.42 216:0.60 219:0.87 222:0.84 227:0.95 229:0.99 231:0.80 238:0.95 241:0.60 242:0.79 243:0.32 244:0.61 245:0.50 246:0.61 247:0.30 248:0.85 253:0.86 255:0.95 256:0.87 257:0.65 259:0.21 260:0.81 261:0.82 265:0.36 266:0.38 267:0.19 268:0.87 271:0.48 276:0.18 277:0.87 279:0.82 283:0.78 284:0.96 285:0.62 287:0.99 292:0.91 300:0.33 +2 1:0.69 6:0.96 8:0.92 9:0.80 11:0.85 12:0.69 17:0.11 18:0.77 20:0.96 21:0.54 27:0.04 28:0.56 29:0.62 30:0.45 31:0.99 34:0.90 36:0.69 37:0.96 39:0.80 41:0.98 43:0.30 45:0.77 66:0.53 69:0.92 70:0.78 71:0.86 74:0.48 76:0.85 78:0.87 79:1.00 81:0.34 83:0.91 86:0.58 91:0.99 96:1.00 97:0.98 98:0.40 99:0.83 100:0.92 101:0.87 108:0.83 111:0.88 114:0.57 120:0.95 122:0.41 123:0.82 124:0.63 126:0.44 127:0.58 129:0.05 131:0.91 133:0.59 135:0.83 137:0.99 138:1.00 139:0.75 140:0.99 144:0.76 145:0.52 146:0.54 147:0.05 149:0.32 150:0.52 151:0.95 152:0.97 153:0.97 154:0.47 155:0.67 157:0.61 158:0.91 159:0.59 161:0.77 162:0.49 163:0.81 164:0.93 165:0.70 166:0.80 167:0.87 169:0.93 172:0.37 173:0.96 176:0.55 177:0.05 178:0.52 179:0.84 181:0.64 182:0.96 186:0.88 189:0.94 191:0.99 192:0.87 196:0.97 201:0.68 202:1.00 204:0.85 208:0.64 212:0.30 215:0.39 216:0.92 219:0.95 220:0.62 222:0.84 227:0.95 229:0.98 231:0.80 235:0.68 238:0.92 241:0.89 242:0.14 243:0.17 245:0.50 246:0.61 247:0.67 248:0.94 253:0.96 254:0.96 255:0.95 256:0.99 257:0.84 259:0.21 260:0.91 261:0.95 265:0.42 266:0.54 267:0.82 268:0.99 271:0.48 276:0.33 279:0.86 281:0.91 283:0.78 284:1.00 285:0.62 287:0.99 290:0.57 292:0.95 297:0.36 300:0.55 +2 6:0.98 7:0.64 8:0.98 9:0.80 12:0.69 17:0.36 18:0.58 20:0.81 21:0.78 27:0.11 28:0.56 29:0.62 30:0.95 31:0.99 34:0.52 36:0.18 37:0.89 39:0.80 41:0.97 43:0.22 44:0.85 55:0.42 66:0.54 69:0.96 71:0.86 74:0.30 77:0.70 78:0.90 79:1.00 83:0.91 86:0.57 91:0.95 96:0.99 97:0.94 98:0.08 100:0.94 108:0.92 111:0.90 120:0.90 123:0.92 127:0.06 129:0.05 131:0.91 135:0.63 137:0.99 138:1.00 139:0.60 140:0.99 144:0.76 145:0.54 146:0.17 147:0.22 149:0.13 151:0.87 152:0.84 153:0.92 154:0.14 155:0.67 157:0.61 158:0.72 159:0.09 161:0.77 162:0.50 164:0.91 166:0.61 167:0.75 169:0.92 172:0.10 173:0.98 175:0.75 177:0.38 178:0.53 179:0.08 181:0.64 182:0.97 186:0.89 187:0.21 189:0.99 191:0.98 192:0.87 195:0.91 196:0.88 202:0.99 204:0.85 208:0.64 216:0.90 219:0.87 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.73 235:0.22 238:0.93 241:0.73 243:0.63 244:0.61 246:0.61 248:0.92 253:0.92 254:0.74 255:0.95 256:0.98 257:0.65 259:0.21 260:0.87 261:0.90 265:0.08 267:0.92 268:0.98 271:0.48 277:0.69 279:0.82 282:0.71 284:0.99 285:0.62 287:0.99 292:0.95 300:0.08 +2 6:0.96 7:0.64 8:0.97 9:0.80 11:0.92 12:0.69 17:0.13 18:0.70 20:0.85 21:0.78 27:0.04 28:0.56 29:0.62 30:0.76 31:1.00 34:0.93 36:0.93 37:0.96 39:0.80 41:0.98 43:0.52 45:0.73 66:0.36 69:0.95 70:0.28 71:0.86 74:0.36 78:0.91 79:1.00 83:0.91 85:0.72 86:0.66 91:0.98 96:1.00 97:0.89 98:0.26 100:0.96 101:0.97 108:0.90 111:0.93 120:0.96 122:0.41 123:0.90 124:0.68 127:0.66 129:0.59 131:0.91 133:0.61 135:0.56 137:1.00 138:1.00 139:0.66 140:0.99 144:0.76 145:0.52 146:0.17 147:0.05 149:0.37 151:0.97 152:0.97 153:0.98 154:0.41 155:0.67 157:0.83 158:0.72 159:0.76 161:0.77 162:0.48 163:0.75 164:0.93 166:0.61 167:0.76 169:0.93 172:0.27 173:0.96 177:0.05 178:0.53 179:0.88 181:0.64 182:0.96 186:0.91 187:0.21 189:0.98 191:0.98 192:0.87 196:0.93 202:1.00 204:0.89 208:0.64 212:0.37 216:0.92 219:0.87 220:0.62 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.76 235:0.22 238:0.96 241:0.74 242:0.16 243:0.18 245:0.50 246:0.61 247:0.60 248:0.96 253:0.97 255:0.95 256:0.99 257:0.84 259:0.21 260:0.93 261:0.95 265:0.28 266:0.38 267:0.76 268:0.99 271:0.48 276:0.21 279:0.82 281:0.89 284:1.00 285:0.62 287:0.98 292:0.95 300:0.25 +2 1:0.69 6:0.86 8:0.67 9:0.80 11:0.92 12:0.69 17:0.45 18:0.99 20:0.79 21:0.30 22:0.93 27:0.21 28:0.56 29:0.62 30:0.68 31:0.96 34:0.47 36:0.82 37:0.74 39:0.80 41:0.88 43:0.92 45:0.99 47:0.93 60:0.95 64:0.77 66:0.34 69:0.12 70:0.69 71:0.86 74:0.89 78:0.81 79:0.96 81:0.40 83:0.91 85:0.85 86:0.98 91:0.53 96:0.87 98:0.71 99:0.83 100:0.84 101:0.97 104:0.84 106:0.81 108:0.83 111:0.81 114:0.60 117:0.86 120:0.78 122:0.85 123:0.82 124:0.82 126:0.44 127:0.73 129:0.89 131:0.91 133:0.84 135:0.96 137:0.96 139:0.97 140:0.80 144:0.76 146:0.86 147:0.88 149:0.97 150:0.55 151:0.87 152:0.83 153:0.48 154:0.92 155:0.67 157:0.93 158:0.92 159:0.88 161:0.77 162:0.86 164:0.67 165:0.70 166:0.78 167:0.48 169:0.81 172:0.95 173:0.08 176:0.55 177:0.70 179:0.13 181:0.64 182:0.99 186:0.82 187:0.39 189:0.87 192:0.87 196:0.98 201:0.68 202:0.59 204:0.85 206:0.81 208:0.64 212:0.79 216:0.95 219:0.95 220:0.87 222:0.84 227:0.95 229:0.99 231:0.80 232:0.91 235:0.42 238:0.87 241:0.01 242:0.37 243:0.43 245:0.98 246:0.61 247:0.93 248:0.86 253:0.90 255:0.95 256:0.64 259:0.21 260:0.79 261:0.82 262:0.93 265:0.71 266:0.87 267:0.97 268:0.67 271:0.48 276:0.96 279:0.86 281:0.91 283:0.82 284:0.93 285:0.62 287:0.99 290:0.60 292:0.95 300:0.84 +1 1:0.69 6:0.95 7:0.63 8:0.92 9:0.80 11:0.88 12:0.69 17:0.11 18:0.72 20:0.92 21:0.54 27:0.10 28:0.56 29:0.62 30:0.33 31:0.99 32:0.80 34:0.76 36:0.82 37:0.92 39:0.80 41:0.98 43:0.61 44:0.93 45:0.85 66:0.68 69:0.67 70:0.54 71:0.86 74:0.58 76:0.85 77:0.70 78:0.83 79:1.00 81:0.34 83:0.91 86:0.70 91:0.97 96:0.99 97:0.89 98:0.67 99:0.83 100:0.90 101:0.87 108:0.71 111:0.85 114:0.57 120:0.96 122:0.75 123:0.69 124:0.44 126:0.44 127:0.39 129:0.59 131:0.91 133:0.46 135:0.79 137:0.99 138:1.00 139:0.78 140:0.98 144:0.76 145:0.61 146:0.41 147:0.48 149:0.58 150:0.52 151:0.97 152:0.95 153:0.97 154:0.55 155:0.67 157:0.61 158:0.72 159:0.45 161:0.77 162:0.50 164:0.95 165:0.70 166:0.80 167:0.87 169:0.86 172:0.67 173:0.69 176:0.55 177:0.70 178:0.52 179:0.52 181:0.64 182:0.97 186:0.84 189:0.95 191:0.97 192:0.87 195:0.70 196:0.98 201:0.68 202:0.99 204:0.85 208:0.64 212:0.70 215:0.39 216:0.88 222:0.84 227:0.95 229:0.98 230:0.63 231:0.80 233:0.73 235:0.68 238:0.93 241:0.86 242:0.42 243:0.27 245:0.74 246:0.61 247:0.76 248:0.96 253:0.97 254:0.84 255:0.95 256:0.99 259:0.21 260:0.93 261:0.89 265:0.68 266:0.75 267:0.95 268:0.99 271:0.48 276:0.59 279:0.82 281:0.88 282:0.88 283:0.78 284:1.00 285:0.62 287:0.99 290:0.57 297:0.36 299:0.88 300:0.43 +2 1:0.69 7:0.64 8:0.94 9:0.80 11:0.85 12:0.69 17:0.57 18:0.91 21:0.22 27:0.10 28:0.56 29:0.62 30:0.71 31:0.99 34:0.85 36:0.84 37:0.92 39:0.80 41:0.95 43:0.60 44:0.85 45:0.85 55:0.45 64:0.77 66:0.45 69:0.66 70:0.48 71:0.86 74:0.34 77:0.70 79:0.99 81:0.46 83:0.91 86:0.63 91:0.93 96:0.98 97:0.89 98:0.29 99:0.83 101:0.87 108:0.51 114:0.62 120:0.89 122:0.86 123:0.48 124:0.58 126:0.44 127:0.65 129:0.05 131:0.91 133:0.43 135:0.98 137:0.99 138:1.00 139:0.63 140:0.97 144:0.76 145:0.52 146:0.55 147:0.61 149:0.57 150:0.56 151:0.92 153:0.89 154:0.68 155:0.67 157:0.61 158:0.72 159:0.78 161:0.77 162:0.55 164:0.89 165:0.70 166:0.78 167:0.75 172:0.63 173:0.49 176:0.55 177:0.70 178:0.42 179:0.51 181:0.64 182:0.97 187:0.39 191:0.95 192:0.87 195:0.91 196:0.90 201:0.68 202:0.97 204:0.89 208:0.64 212:0.88 216:0.88 219:0.87 220:0.82 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.73 235:0.31 241:0.72 242:0.50 243:0.58 245:0.85 246:0.61 247:0.76 248:0.92 253:0.92 254:0.93 255:0.95 256:0.97 259:0.21 260:0.87 265:0.32 266:0.47 267:0.79 268:0.97 271:0.48 276:0.37 279:0.82 281:0.89 282:0.71 284:0.99 285:0.62 287:0.99 290:0.62 292:0.91 300:0.55 +2 6:0.98 7:0.64 8:0.97 9:0.80 12:0.69 17:0.11 20:0.80 21:0.78 27:0.11 28:0.56 29:0.62 31:0.99 34:0.53 36:0.26 37:0.89 39:0.80 41:0.92 44:0.85 71:0.86 74:0.30 77:0.70 78:0.87 79:1.00 83:0.91 91:0.96 96:0.99 97:0.94 100:0.93 111:0.89 120:0.89 131:0.91 135:0.65 137:0.99 139:0.59 140:0.99 144:0.76 145:0.54 151:0.93 152:0.84 153:0.94 155:0.67 157:0.61 158:0.72 161:0.77 162:0.49 164:0.81 166:0.61 167:0.75 169:0.91 175:0.97 178:0.52 181:0.64 182:0.96 186:0.87 187:0.39 189:0.98 191:0.96 192:0.87 195:0.91 196:0.88 202:0.98 204:0.85 208:0.64 216:0.90 219:0.87 222:0.84 227:0.95 229:0.98 230:0.64 231:0.80 233:0.73 235:0.22 238:0.94 241:0.72 244:0.93 246:0.61 248:0.88 253:0.91 255:0.95 256:0.96 257:0.93 259:0.21 260:0.84 261:0.90 267:0.65 268:0.97 271:0.48 277:0.95 279:0.82 282:0.71 284:0.99 285:0.62 287:0.99 292:0.95 +1 1:0.74 11:0.87 12:0.69 17:0.22 18:0.91 21:0.40 27:0.45 28:0.56 29:0.62 30:0.74 34:0.68 36:0.23 37:0.50 39:0.80 43:0.82 45:0.89 64:0.77 66:0.61 69:0.69 70:0.62 71:0.86 74:0.54 81:0.53 83:0.91 85:0.85 86:0.83 91:0.29 98:0.56 101:0.97 108:0.52 114:0.62 122:0.80 123:0.50 124:0.57 126:0.54 127:0.53 129:0.59 131:0.61 133:0.66 135:0.86 139:0.79 144:0.76 145:0.44 146:0.80 147:0.83 149:0.88 150:0.74 154:0.77 155:0.67 157:0.96 159:0.71 161:0.77 165:0.93 166:0.86 167:0.59 172:0.75 173:0.56 176:0.73 177:0.70 178:0.55 179:0.42 181:0.64 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.42 215:0.42 216:0.77 222:0.84 227:0.61 229:0.61 231:0.79 235:0.60 241:0.44 242:0.41 243:0.68 245:0.80 246:0.99 247:0.82 253:0.47 259:0.21 265:0.57 266:0.71 267:0.44 271:0.48 276:0.69 287:0.61 290:0.62 297:0.36 300:0.70 +0 12:0.95 17:0.83 21:0.78 27:0.35 29:0.53 34:0.99 36:0.17 37:0.59 39:0.94 43:0.06 66:0.36 69:0.94 71:0.86 91:0.12 98:0.06 108:0.89 123:0.88 127:0.05 129:0.05 135:0.99 145:0.70 146:0.05 147:0.05 149:0.05 154:0.06 159:0.05 170:0.79 172:0.05 173:0.93 175:0.99 177:0.05 178:0.55 187:0.68 202:0.36 216:0.39 222:0.25 235:0.68 241:0.32 243:0.51 244:0.97 253:0.20 257:0.97 259:0.21 264:0.93 265:0.06 267:0.28 271:0.13 277:0.98 +0 1:0.63 7:0.66 8:0.73 9:0.73 10:0.92 11:0.49 12:0.95 17:0.32 18:0.80 21:0.47 22:0.93 23:0.97 25:0.88 27:0.32 29:0.53 30:0.86 31:0.91 32:0.64 33:0.97 34:0.34 36:0.40 37:0.36 39:0.94 43:0.65 45:0.92 47:0.93 62:0.97 64:0.77 66:0.06 69:0.40 70:0.47 71:0.86 74:0.51 79:0.90 81:0.60 83:0.60 86:0.81 91:0.12 96:0.75 98:0.14 99:0.95 101:0.52 104:0.58 108:0.31 114:0.78 117:0.86 120:0.68 122:0.91 123:0.96 124:0.92 126:0.53 127:0.96 128:0.96 129:0.93 133:0.91 135:0.71 137:0.91 139:0.77 140:0.45 145:0.69 146:0.24 147:0.30 149:0.71 150:0.46 151:0.66 153:0.48 154:0.54 155:0.64 159:0.92 162:0.66 164:0.71 165:0.70 168:0.96 170:0.79 172:0.32 173:0.12 174:0.90 175:0.91 176:0.70 177:0.70 179:0.62 187:0.68 190:0.98 192:0.99 195:0.98 196:0.91 197:0.92 201:0.61 202:0.64 205:0.97 208:0.64 212:0.23 215:0.63 216:0.43 220:0.93 222:0.25 230:0.68 233:0.66 235:0.74 236:0.95 239:0.91 241:0.41 242:0.40 243:0.34 244:0.83 245:0.63 247:0.67 248:0.65 253:0.67 255:0.73 256:0.64 257:0.84 259:0.21 260:0.68 262:0.93 264:0.93 265:0.11 266:0.80 267:0.91 268:0.65 271:0.13 275:0.93 276:0.60 277:0.98 284:0.92 285:0.61 290:0.78 291:0.97 294:0.92 297:0.36 300:0.70 +1 7:0.66 8:0.91 10:0.99 11:0.57 12:0.95 17:0.89 18:0.87 21:0.22 27:0.65 29:0.53 30:0.88 32:0.64 34:0.78 36:0.18 37:0.69 39:0.94 43:0.76 66:0.48 69:0.71 70:0.48 71:0.86 74:0.79 81:0.34 85:0.94 86:0.95 91:0.40 98:0.33 101:0.87 104:0.96 106:0.81 108:0.54 123:0.52 124:0.82 126:0.23 127:0.70 129:0.59 133:0.85 135:0.58 139:0.94 145:0.42 146:0.62 147:0.67 149:0.98 150:0.38 154:0.68 159:0.79 170:0.79 172:0.85 173:0.54 174:0.93 177:0.96 178:0.55 179:0.26 187:0.87 195:0.92 197:0.94 202:0.36 206:0.81 212:0.86 215:0.79 216:0.34 222:0.25 230:0.68 233:0.76 235:0.22 239:0.98 241:0.15 242:0.22 243:0.59 245:0.86 247:0.79 253:0.55 259:0.21 264:0.93 265:0.36 266:0.80 267:0.36 271:0.13 275:0.98 276:0.84 283:0.82 294:1.00 297:0.36 300:0.94 +0 12:0.95 17:0.88 21:0.54 27:0.37 29:0.53 34:0.99 36:0.58 37:0.60 39:0.94 43:0.09 66:0.36 69:0.68 71:0.86 81:0.29 91:0.23 98:0.09 108:0.52 123:0.49 126:0.23 127:0.06 129:0.05 135:0.49 145:0.91 146:0.05 147:0.05 149:0.06 150:0.32 154:0.08 159:0.05 170:0.79 172:0.05 173:0.79 175:0.99 177:0.05 178:0.55 187:0.39 215:0.58 216:0.47 222:0.25 235:0.60 241:0.21 243:0.51 244:0.97 253:0.20 257:0.97 259:0.21 264:0.93 265:0.09 267:0.91 271:0.13 277:0.98 283:0.67 297:0.36 +0 7:0.66 8:0.72 12:0.95 17:0.62 21:0.30 27:0.72 29:0.53 30:0.62 32:0.64 34:0.92 36:0.47 37:0.73 39:0.94 43:0.11 55:0.71 66:0.75 69:0.10 70:0.34 71:0.86 81:0.33 91:0.90 98:0.96 104:0.58 108:0.09 120:0.85 123:0.09 126:0.23 127:0.69 129:0.05 135:0.19 140:0.45 145:0.47 146:0.63 147:0.68 149:0.14 150:0.36 151:0.65 153:0.48 154:0.09 159:0.23 162:0.80 163:0.75 164:0.82 170:0.79 172:0.32 173:0.42 175:0.97 177:0.38 179:0.95 190:0.98 195:0.56 202:0.73 212:0.61 215:0.72 216:0.15 220:0.62 222:0.25 230:0.68 233:0.76 235:0.31 241:0.86 242:0.63 243:0.77 244:0.97 247:0.35 248:0.77 253:0.20 256:0.78 257:0.93 259:0.21 260:0.85 264:0.93 265:0.96 266:0.23 267:0.23 268:0.79 271:0.13 275:0.91 276:0.29 277:0.95 285:0.45 294:0.94 297:0.36 300:0.25 +0 1:0.61 7:0.66 10:0.86 11:0.49 12:0.95 17:0.36 18:0.67 21:0.59 22:0.93 25:0.88 27:0.32 29:0.53 30:0.33 32:0.64 33:0.97 34:0.36 36:0.63 37:0.36 39:0.94 43:0.65 45:0.73 47:0.93 64:0.77 66:0.12 69:0.41 70:0.69 71:0.86 74:0.37 81:0.58 83:0.60 86:0.69 91:0.17 98:0.20 99:0.95 101:0.52 104:0.58 108:0.32 114:0.73 117:0.86 122:0.85 123:0.88 124:0.81 126:0.53 127:0.93 129:0.05 133:0.73 135:0.37 139:0.67 145:0.69 146:0.17 147:0.22 149:0.41 150:0.44 154:0.54 155:0.48 159:0.77 165:0.70 170:0.79 172:0.16 173:0.26 174:0.79 175:0.91 176:0.70 177:0.70 178:0.55 179:0.92 187:0.68 192:0.48 195:0.88 197:0.85 201:0.60 202:0.36 208:0.64 212:0.19 215:0.55 216:0.43 222:0.25 230:0.68 233:0.66 235:0.84 236:0.95 239:0.86 241:0.35 242:0.19 243:0.40 244:0.83 245:0.46 247:0.55 253:0.54 257:0.84 259:0.21 262:0.93 264:0.93 265:0.09 266:0.47 267:0.73 271:0.13 275:0.93 276:0.28 277:0.98 290:0.73 291:0.97 294:0.93 297:0.36 300:0.43 +0 7:0.66 12:0.95 17:0.28 21:0.64 27:0.34 29:0.53 30:0.76 32:0.64 34:0.80 36:0.32 37:0.46 39:0.94 43:0.10 55:0.84 66:0.72 69:0.52 70:0.22 71:0.86 81:0.28 91:0.27 98:0.60 104:0.86 106:0.81 108:0.40 123:0.38 126:0.23 127:0.18 129:0.05 135:0.58 145:0.63 146:0.63 147:0.68 149:0.12 150:0.30 154:0.09 159:0.24 163:0.86 170:0.79 172:0.27 173:0.54 175:0.97 177:0.38 178:0.55 179:0.78 187:0.87 195:0.68 206:0.81 212:0.42 215:0.53 216:0.53 222:0.25 230:0.68 233:0.66 235:0.74 241:0.24 242:0.75 243:0.75 244:0.97 247:0.30 253:0.20 257:0.93 259:0.21 264:0.93 265:0.61 266:0.23 267:0.36 271:0.13 275:0.91 276:0.26 277:0.95 283:0.67 294:0.94 297:0.36 300:0.18 +0 8:0.77 12:0.95 17:0.89 21:0.78 27:0.72 29:0.53 34:0.91 36:0.18 37:0.59 39:0.94 43:0.09 66:0.36 69:0.72 71:0.86 82:0.98 88:0.99 91:0.58 98:0.09 108:0.55 123:0.52 127:0.07 129:0.05 135:0.56 145:0.85 146:0.05 147:0.05 149:0.06 154:0.08 159:0.05 170:0.79 172:0.05 173:0.85 175:0.99 177:0.05 178:0.55 187:0.21 195:0.65 216:0.04 222:0.25 235:0.31 241:0.46 243:0.51 244:0.97 253:0.20 257:0.97 259:0.21 264:0.93 265:0.10 267:0.69 271:0.13 277:0.98 294:0.94 +2 10:0.93 11:0.54 12:0.95 17:0.75 18:0.70 21:0.54 27:0.18 29:0.53 30:0.54 34:0.81 36:0.66 37:0.84 39:0.94 43:0.10 45:0.85 66:0.88 69:0.49 70:0.39 71:0.86 74:0.51 81:0.29 86:0.81 91:0.51 98:0.78 101:0.69 104:0.95 106:0.81 108:0.38 123:0.36 126:0.23 127:0.27 129:0.05 135:0.80 139:0.78 145:0.59 146:0.69 147:0.74 149:0.12 150:0.32 154:0.71 159:0.27 170:0.79 172:0.67 173:0.59 174:0.83 177:0.96 178:0.55 179:0.49 187:0.39 197:0.87 206:0.81 212:0.75 215:0.58 216:0.62 222:0.25 235:0.60 239:0.91 241:0.21 242:0.14 243:0.89 247:0.76 253:0.42 254:0.74 259:0.21 264:0.93 265:0.78 266:0.38 267:0.36 271:0.13 275:0.96 276:0.51 294:0.98 297:0.36 300:0.33 +1 10:0.98 11:0.57 12:0.95 17:0.45 18:0.86 21:0.78 27:0.45 29:0.53 30:0.94 34:0.82 36:0.22 37:0.50 39:0.94 43:0.75 66:0.60 69:0.77 70:0.21 71:0.86 74:0.78 85:0.94 86:0.95 91:0.07 98:0.44 101:0.87 108:0.61 122:0.67 123:0.58 124:0.51 127:0.17 129:0.59 133:0.47 135:0.60 139:0.93 145:0.52 146:0.60 147:0.66 149:0.98 154:0.65 159:0.31 170:0.79 172:0.84 173:0.73 174:0.93 177:0.96 178:0.55 179:0.13 187:0.68 197:0.94 212:0.90 216:0.77 222:0.25 235:0.52 239:0.98 241:0.32 242:0.23 243:0.67 245:0.93 247:0.74 253:0.54 259:0.21 264:0.93 265:0.46 266:0.38 267:0.19 271:0.13 275:0.98 276:0.79 294:1.00 300:0.55 +2 7:0.66 8:0.89 10:0.96 11:0.57 12:0.95 17:0.16 18:0.78 21:0.13 27:0.02 29:0.53 30:0.87 34:0.45 36:0.21 37:0.92 39:0.94 43:0.57 66:0.42 69:0.93 70:0.32 71:0.86 74:0.65 81:0.35 85:0.95 86:0.89 91:0.15 98:0.58 101:0.87 108:0.89 120:0.75 123:0.89 124:0.65 126:0.23 127:0.58 129:0.59 133:0.60 135:0.87 139:0.86 140:0.45 145:0.77 146:0.60 147:0.05 149:0.93 150:0.39 151:0.63 153:0.72 154:0.46 159:0.79 162:0.81 164:0.73 170:0.79 172:0.85 173:0.89 174:0.93 177:0.05 179:0.22 187:0.39 190:0.98 191:0.73 197:0.91 202:0.62 212:0.86 215:0.84 216:0.99 220:0.74 222:0.25 230:0.68 233:0.76 235:0.12 239:0.95 241:0.56 242:0.28 243:0.06 245:0.94 247:0.79 248:0.67 253:0.48 256:0.64 257:0.97 259:0.21 260:0.76 264:0.93 265:0.59 266:0.47 267:0.59 268:0.68 271:0.13 275:0.98 276:0.84 283:0.67 285:0.45 294:0.99 297:0.36 300:0.55 +1 10:0.98 11:0.57 12:0.95 17:0.40 18:0.86 21:0.78 27:0.45 29:0.53 30:0.94 34:0.81 36:0.21 37:0.50 39:0.94 43:0.75 66:0.59 69:0.78 70:0.19 71:0.86 74:0.78 85:0.94 86:0.95 91:0.14 98:0.43 101:0.87 108:0.61 122:0.67 123:0.59 124:0.52 127:0.17 129:0.59 133:0.47 135:0.69 139:0.93 145:0.50 146:0.59 147:0.65 149:0.98 154:0.65 159:0.30 170:0.79 172:0.83 173:0.74 174:0.93 177:0.96 178:0.55 179:0.13 187:0.68 197:0.94 212:0.90 216:0.77 222:0.25 235:0.31 239:0.98 241:0.27 242:0.24 243:0.67 245:0.93 247:0.67 253:0.54 259:0.21 264:0.93 265:0.45 266:0.38 267:0.23 271:0.13 275:0.98 276:0.78 294:1.00 300:0.43 +0 10:0.89 11:0.57 12:0.95 17:0.51 18:0.64 21:0.78 27:0.03 29:0.53 30:0.95 32:0.80 34:0.20 36:0.35 37:0.95 39:0.94 43:0.81 44:0.93 66:0.72 69:0.71 71:0.86 74:0.56 77:0.70 82:0.99 85:0.72 86:0.74 88:0.99 91:0.33 98:0.12 101:0.87 102:0.98 108:0.54 122:0.67 123:0.52 127:0.08 129:0.05 135:0.80 139:0.80 145:0.77 146:0.19 147:0.24 149:0.70 154:0.75 159:0.10 170:0.79 172:0.27 173:0.71 174:0.79 177:0.96 178:0.55 179:0.08 187:0.21 195:0.75 197:0.84 212:0.78 216:0.65 222:0.25 235:0.12 239:0.93 241:0.35 242:0.45 243:0.75 247:0.35 253:0.44 259:0.21 264:0.93 265:0.13 266:0.17 267:0.84 271:0.13 275:0.91 276:0.27 282:0.88 294:0.98 299:0.88 300:0.08 +1 7:0.66 8:0.93 10:0.99 11:0.57 12:0.95 17:0.32 18:0.87 21:0.13 27:0.29 29:0.53 30:0.87 34:0.59 36:0.21 37:0.92 39:0.94 43:0.75 66:0.36 69:0.50 70:0.32 71:0.86 74:0.81 81:0.35 85:0.95 86:0.96 91:0.33 98:0.60 101:0.87 108:0.38 120:0.68 123:0.73 124:0.61 126:0.23 127:0.26 129:0.59 133:0.47 135:0.82 139:0.94 140:0.45 145:0.77 146:0.75 147:0.79 149:0.98 150:0.39 151:0.63 153:0.72 154:0.66 159:0.64 162:0.86 164:0.68 170:0.79 172:0.84 173:0.29 174:0.93 177:0.96 179:0.15 187:0.39 190:0.98 197:0.95 202:0.55 212:0.84 215:0.84 216:0.93 220:0.62 222:0.25 230:0.68 233:0.76 235:0.12 239:0.98 241:0.68 242:0.28 243:0.51 245:0.97 247:0.79 248:0.62 253:0.55 256:0.58 259:0.21 260:0.69 264:0.93 265:0.46 266:0.54 267:0.73 268:0.63 271:0.13 275:0.98 276:0.88 285:0.45 294:1.00 297:0.36 300:0.43 +0 12:0.95 17:0.92 21:0.78 27:0.72 29:0.53 30:0.76 34:0.63 36:0.55 37:0.49 39:0.94 43:0.08 55:0.52 66:0.64 69:0.79 70:0.15 71:0.86 91:0.40 98:0.39 108:0.63 122:0.85 123:0.60 127:0.13 129:0.05 135:0.74 145:0.80 146:0.34 147:0.40 149:0.08 154:0.08 159:0.14 163:0.98 170:0.79 172:0.16 173:0.92 175:0.97 177:0.38 178:0.55 179:0.75 187:0.68 212:0.95 216:0.08 222:0.25 235:0.95 241:0.39 242:0.82 243:0.69 244:0.97 247:0.12 253:0.20 257:0.93 259:0.21 264:0.93 265:0.41 266:0.12 267:0.28 271:0.13 275:0.93 276:0.15 277:0.95 294:0.94 300:0.13 +0 7:0.69 10:0.85 11:0.46 12:0.51 17:0.77 18:0.79 21:0.78 26:0.97 27:0.55 29:0.56 30:0.76 34:0.80 36:0.42 37:0.64 39:0.70 43:0.61 45:0.73 48:0.91 58:0.93 66:0.85 69:0.46 70:0.27 71:0.84 74:0.41 86:0.80 89:0.97 91:0.15 98:0.74 101:0.47 108:0.35 122:0.75 123:0.34 127:0.23 129:0.05 135:0.34 139:0.78 141:0.91 146:0.61 147:0.67 149:0.70 154:0.50 159:0.23 170:0.82 172:0.57 173:0.59 174:0.79 177:0.88 178:0.55 179:0.56 187:0.95 197:0.85 199:0.96 212:0.76 216:0.27 222:0.21 223:0.97 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.05 239:0.84 240:0.95 241:0.27 242:0.38 243:0.86 244:0.83 247:0.60 253:0.35 259:0.21 265:0.74 266:0.38 267:0.76 271:0.31 274:0.91 276:0.44 281:0.91 300:0.25 +1 7:0.69 8:0.85 10:0.88 12:0.51 17:0.47 18:0.88 21:0.40 26:0.98 27:0.55 29:0.56 30:0.13 32:0.65 34:0.64 36:0.31 37:0.64 39:0.70 43:0.62 48:0.91 58:0.93 64:0.77 66:0.06 69:0.49 70:0.97 71:0.84 74:0.29 81:0.42 86:0.88 89:0.96 91:0.04 98:0.19 108:0.98 123:0.98 124:0.99 126:0.32 127:0.95 129:0.59 133:0.99 135:0.95 139:0.88 141:0.91 145:0.76 146:0.67 147:0.72 149:0.67 150:0.43 154:0.65 159:0.97 170:0.82 172:0.57 173:0.09 174:0.79 175:0.75 177:0.70 178:0.55 179:0.15 187:0.95 192:0.45 195:1.00 197:0.85 199:1.00 201:0.56 202:0.50 212:0.22 215:0.67 216:0.27 219:0.91 222:0.21 223:0.98 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.52 239:0.87 240:0.95 241:0.10 242:0.38 243:0.17 244:0.61 245:0.89 247:0.86 253:0.26 254:0.74 257:0.65 259:0.21 265:0.20 266:0.99 267:0.36 271:0.31 274:0.91 276:0.95 277:0.87 281:0.91 283:0.67 292:0.88 297:0.36 300:0.99 +0 7:0.69 10:0.88 12:0.51 17:0.40 18:0.88 21:0.40 26:0.99 27:0.55 29:0.56 30:0.13 31:0.86 32:0.65 34:0.68 36:0.30 37:0.64 39:0.70 43:0.62 48:0.91 58:0.98 64:0.77 66:0.06 69:0.49 70:0.97 71:0.84 74:0.29 79:0.86 81:0.42 86:0.88 89:0.96 91:0.04 98:0.19 108:0.98 120:0.54 123:0.98 124:0.99 126:0.32 127:0.95 129:0.59 133:0.99 135:0.92 137:0.86 139:0.88 140:0.45 141:0.99 145:0.76 146:0.67 147:0.72 149:0.67 150:0.43 151:0.48 153:0.48 154:0.65 159:0.97 162:0.86 164:0.56 170:0.82 172:0.57 173:0.09 174:0.79 175:0.75 177:0.70 179:0.15 187:0.95 190:0.89 192:0.45 195:1.00 196:0.88 197:0.85 199:1.00 201:0.56 202:0.36 212:0.22 215:0.67 216:0.27 219:0.91 220:0.99 222:0.21 223:0.99 224:0.98 225:0.96 230:0.71 233:0.76 234:0.98 235:0.52 239:0.87 240:0.95 241:0.07 242:0.38 243:0.17 244:0.61 245:0.89 247:0.86 248:0.48 253:0.26 254:0.74 255:0.68 256:0.45 257:0.65 259:0.21 260:0.54 265:0.20 266:0.99 267:0.28 268:0.46 271:0.31 274:0.97 276:0.95 277:0.87 281:0.91 283:0.67 284:0.88 285:0.50 292:0.88 297:0.36 300:0.99 +1 7:0.69 10:0.85 11:0.46 12:0.51 17:0.75 18:0.70 21:0.78 26:0.98 27:0.55 29:0.56 30:0.74 34:0.62 36:0.47 37:0.64 39:0.70 43:0.71 45:0.83 58:0.93 66:0.88 69:0.46 70:0.42 71:0.84 74:0.55 86:0.76 89:0.98 91:0.23 98:0.81 101:0.47 108:0.35 122:0.55 123:0.34 127:0.30 129:0.05 135:0.37 139:0.72 141:0.91 146:0.79 147:0.82 149:0.64 154:0.61 159:0.34 170:0.82 172:0.67 173:0.50 174:0.79 177:0.88 178:0.55 179:0.52 187:0.95 197:0.85 199:0.97 212:0.41 216:0.27 222:0.21 223:0.97 224:0.93 225:0.97 230:0.71 233:0.76 234:0.91 235:0.05 239:0.84 240:0.95 241:0.18 242:0.33 243:0.89 244:0.61 247:0.60 253:0.44 259:0.21 265:0.81 266:0.38 267:0.52 271:0.31 274:0.91 276:0.53 300:0.43 +0 7:0.69 10:0.85 11:0.46 12:0.51 17:0.76 18:0.76 21:0.78 26:0.97 27:0.55 29:0.56 30:0.76 34:0.80 36:0.33 37:0.64 39:0.70 43:0.61 45:0.73 48:0.91 58:0.93 66:0.83 69:0.46 70:0.20 71:0.84 74:0.41 86:0.76 89:0.97 91:0.17 98:0.72 101:0.47 108:0.35 122:0.55 123:0.34 127:0.22 129:0.05 135:0.42 139:0.76 141:0.91 146:0.60 147:0.65 149:0.66 154:0.50 159:0.22 170:0.82 172:0.51 173:0.59 174:0.79 177:0.88 178:0.55 179:0.60 187:0.95 197:0.85 199:0.96 212:0.86 216:0.27 222:0.21 223:0.97 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.05 239:0.84 240:0.95 241:0.18 242:0.29 243:0.84 244:0.83 247:0.60 253:0.35 259:0.21 265:0.72 266:0.23 267:0.79 271:0.31 274:0.91 276:0.40 281:0.91 300:0.18 +1 1:0.64 10:0.85 12:0.51 17:0.81 18:0.67 21:0.22 26:0.98 27:0.43 29:0.56 30:0.76 34:0.50 36:0.33 37:0.73 39:0.70 43:0.77 58:0.93 64:0.77 66:0.54 69:0.12 70:0.22 71:0.84 74:0.40 81:0.55 86:0.69 89:0.97 91:0.18 98:0.47 108:0.10 114:0.82 122:0.75 123:0.10 124:0.45 126:0.51 127:0.32 129:0.05 133:0.36 135:0.49 139:0.72 141:0.91 146:0.33 147:0.40 149:0.54 150:0.55 154:0.69 155:0.49 159:0.21 170:0.82 172:0.21 173:0.39 174:0.79 176:0.63 177:0.88 178:0.55 179:0.93 187:0.39 192:0.49 197:0.85 199:0.95 201:0.62 208:0.61 212:0.42 215:0.72 216:0.23 219:0.91 222:0.21 223:0.98 224:0.93 225:0.98 234:0.91 235:0.42 239:0.84 240:0.95 241:0.32 242:0.45 243:0.63 244:0.61 245:0.40 247:0.35 253:0.58 259:0.21 265:0.49 266:0.23 267:0.23 271:0.31 274:0.91 276:0.20 283:0.67 290:0.82 292:0.88 297:0.36 300:0.18 +0 7:0.69 10:0.86 11:0.46 12:0.51 17:0.55 21:0.30 26:0.99 27:0.50 29:0.56 30:0.18 32:0.67 34:0.55 36:0.86 37:0.60 39:0.70 43:0.63 45:0.91 55:0.59 58:0.93 66:0.52 69:0.78 70:0.93 71:0.84 74:0.70 77:0.70 81:0.33 83:0.56 89:0.99 91:0.12 97:0.89 98:0.64 101:0.47 104:0.84 106:0.81 108:0.90 117:0.86 123:0.89 124:0.78 126:0.24 127:0.82 129:0.05 133:0.83 135:0.47 141:0.91 145:0.67 146:0.47 147:0.27 149:0.73 150:0.37 154:0.52 158:0.77 159:0.82 170:0.82 172:0.86 173:0.62 174:0.83 175:0.75 177:0.38 178:0.55 179:0.28 187:0.39 195:0.92 197:0.86 199:0.99 206:0.81 208:0.50 212:0.60 215:0.72 216:0.86 222:0.21 223:0.98 224:0.93 225:0.97 230:0.71 232:0.90 233:0.76 234:0.91 235:0.31 239:0.85 240:0.95 242:0.45 243:0.11 244:0.83 245:0.88 247:0.79 253:0.51 257:0.84 259:0.21 265:0.64 266:0.87 267:0.65 271:0.31 274:0.91 276:0.85 277:0.87 279:0.76 282:0.75 283:0.82 297:0.36 300:0.84 +1 1:0.63 10:0.87 11:0.46 12:0.51 17:0.66 18:0.63 21:0.40 26:0.98 27:0.69 29:0.56 30:0.41 32:0.65 34:0.74 36:0.66 37:0.25 39:0.70 43:0.64 45:0.66 58:0.93 66:0.86 69:0.30 70:0.77 71:0.84 74:0.43 81:0.55 83:0.56 86:0.66 89:0.97 91:0.10 98:0.91 99:0.83 101:0.47 104:0.93 106:0.81 108:0.24 114:0.78 117:0.86 123:0.23 126:0.32 127:0.49 129:0.05 135:0.79 139:0.64 141:0.91 145:0.67 146:0.76 147:0.80 149:0.46 150:0.50 154:0.67 155:0.49 159:0.32 165:0.65 170:0.82 172:0.59 173:0.44 174:0.79 176:0.63 177:0.88 178:0.55 179:0.72 187:0.95 192:0.47 195:0.58 197:0.85 199:0.98 201:0.59 206:0.81 208:0.50 212:0.54 215:0.67 216:0.62 222:0.21 223:0.98 224:0.93 225:0.98 232:0.88 234:0.91 235:0.60 239:0.86 240:0.95 241:0.10 242:0.45 243:0.86 244:0.61 247:0.67 253:0.57 254:0.74 259:0.21 265:0.91 266:0.47 267:0.36 271:0.31 274:0.91 276:0.47 290:0.78 297:0.36 300:0.55 +1 1:0.56 10:0.86 11:0.46 12:0.51 17:0.61 18:0.62 21:0.68 26:0.98 27:0.30 29:0.56 30:0.45 32:0.67 34:0.97 36:0.44 37:0.80 39:0.70 43:0.58 45:0.81 58:0.93 66:0.29 69:0.84 70:0.81 71:0.84 74:0.52 77:0.70 81:0.42 83:0.56 86:0.66 89:0.98 91:0.10 98:0.28 99:0.83 101:0.47 104:0.89 106:0.81 108:0.80 114:0.67 117:0.86 122:0.55 123:0.79 124:0.84 126:0.32 127:0.59 129:0.05 133:0.83 135:0.23 139:0.64 141:0.91 145:0.80 146:0.32 147:0.05 149:0.56 150:0.38 154:0.47 155:0.46 159:0.58 163:0.88 165:0.65 170:0.82 172:0.37 173:0.85 174:0.83 175:0.75 176:0.63 177:0.05 178:0.55 179:0.70 187:0.95 192:0.45 195:0.76 197:0.87 199:0.97 201:0.54 206:0.81 208:0.50 212:0.16 215:0.49 216:0.74 222:0.21 223:0.98 224:0.93 225:0.98 232:0.85 234:0.91 235:0.84 239:0.86 240:0.95 241:0.35 242:0.16 243:0.12 244:0.83 245:0.58 247:0.74 253:0.53 257:0.93 259:0.21 265:0.31 266:0.84 267:0.23 271:0.31 274:0.91 276:0.50 277:0.87 282:0.75 290:0.67 297:0.36 300:0.70 +0 7:0.69 10:0.85 11:0.46 12:0.51 17:0.62 18:0.66 21:0.78 26:0.96 27:0.55 29:0.56 30:0.62 34:0.80 36:0.71 37:0.64 39:0.70 43:0.61 45:0.66 48:0.91 58:0.93 66:0.30 69:0.47 70:0.34 71:0.84 74:0.35 86:0.68 89:0.96 91:0.14 98:0.32 101:0.47 108:0.36 122:0.41 123:0.35 124:0.61 127:0.19 129:0.05 133:0.43 135:0.68 139:0.70 141:0.91 145:0.40 146:0.33 147:0.40 149:0.42 154:0.51 159:0.23 163:0.81 170:0.82 172:0.16 173:0.54 174:0.79 175:0.75 177:0.70 178:0.55 179:0.76 187:0.95 197:0.85 199:0.95 202:0.46 212:0.30 216:0.27 222:0.21 223:0.96 224:0.93 225:0.96 230:0.71 233:0.76 234:0.91 235:0.22 239:0.84 240:0.95 241:0.27 242:0.33 243:0.48 244:0.83 245:0.47 247:0.39 253:0.31 257:0.65 259:0.21 265:0.34 266:0.27 267:0.23 271:0.31 274:0.91 276:0.23 277:0.69 281:0.91 300:0.25 +0 1:0.69 7:0.75 9:0.75 10:0.81 11:0.46 12:0.51 17:0.94 18:0.96 22:0.93 26:0.99 27:0.72 29:0.56 30:0.94 31:0.94 32:0.72 34:0.53 36:0.88 39:0.70 43:0.74 44:0.92 45:0.98 47:0.93 48:0.91 58:0.98 64:0.77 66:0.20 69:0.97 70:0.21 71:0.84 74:0.92 77:0.70 79:0.94 81:0.77 83:0.56 86:0.98 89:0.99 91:0.68 96:0.83 97:0.89 98:0.57 99:0.83 101:0.47 106:0.81 108:0.81 114:0.97 117:0.86 120:0.72 122:0.75 123:0.80 124:0.85 126:0.51 127:0.90 129:0.05 133:0.84 135:0.84 137:0.94 139:0.98 140:0.45 141:0.99 145:0.40 146:0.83 147:0.90 149:0.99 150:0.78 151:0.90 153:0.69 154:0.65 155:0.64 158:0.82 159:0.88 162:0.86 164:0.62 165:0.65 170:0.82 172:0.91 173:0.97 174:0.79 176:0.70 177:0.70 179:0.13 187:0.87 190:0.77 192:0.98 195:0.96 196:0.98 197:0.79 199:1.00 201:0.67 202:0.46 204:0.84 206:0.81 208:0.61 212:0.49 215:0.96 216:0.16 219:0.94 222:0.21 223:0.99 224:1.00 225:0.99 230:0.77 233:0.76 234:1.00 235:0.22 239:0.80 240:0.99 241:0.03 242:0.32 243:0.33 244:0.61 245:0.98 247:0.76 248:0.80 253:0.89 255:0.78 256:0.45 257:0.65 260:0.73 262:0.93 265:0.59 266:0.71 267:0.59 268:0.55 271:0.31 274:0.98 276:0.96 277:0.69 279:0.80 281:0.91 282:0.80 283:0.82 284:0.90 285:0.60 290:0.97 292:0.95 297:0.36 300:0.55 +0 12:0.51 17:0.59 18:0.87 21:0.78 26:0.94 27:0.15 29:0.56 30:0.45 34:0.56 36:0.44 37:0.90 39:0.70 43:0.16 44:0.88 48:0.91 55:0.59 58:0.93 66:0.63 69:0.78 70:0.61 71:0.84 74:0.40 76:0.85 86:0.83 89:0.96 91:0.11 98:0.70 108:0.62 122:0.92 123:0.59 124:0.45 127:0.48 129:0.05 133:0.37 135:0.23 139:0.83 141:0.91 145:0.42 146:0.57 147:0.28 149:0.18 154:0.46 159:0.32 170:0.82 172:0.41 173:0.91 175:0.75 177:0.05 178:0.55 179:0.82 187:0.39 193:0.97 195:0.59 199:0.94 212:0.61 216:0.35 222:0.21 223:0.92 224:0.93 225:0.96 234:0.91 235:0.52 239:0.84 240:0.95 241:0.43 242:0.63 243:0.28 244:0.61 245:0.54 247:0.47 253:0.35 254:0.93 257:0.93 259:0.21 265:0.70 266:0.38 267:0.69 271:0.31 274:0.91 276:0.37 277:0.69 281:0.91 300:0.43 +0 8:0.93 10:0.84 12:0.51 17:0.73 18:0.90 21:0.78 26:0.96 27:0.20 29:0.56 30:0.71 34:0.50 36:0.68 37:0.97 39:0.70 43:0.56 55:0.45 58:0.93 66:0.75 69:0.56 70:0.56 71:0.84 74:0.39 86:0.89 89:0.97 91:0.62 98:0.75 108:0.43 122:0.92 123:0.41 124:0.40 127:0.43 129:0.05 133:0.39 135:0.28 139:0.84 141:0.91 146:0.73 147:0.77 149:0.35 154:0.45 159:0.40 170:0.82 172:0.65 173:0.61 174:0.79 177:0.88 178:0.55 179:0.59 187:0.21 197:0.83 199:0.95 212:0.77 216:0.39 222:0.21 223:0.96 224:0.93 225:0.96 234:0.91 235:0.22 239:0.83 240:0.95 241:0.21 242:0.51 243:0.77 244:0.83 245:0.64 247:0.79 253:0.34 259:0.21 265:0.75 266:0.47 267:0.59 271:0.31 274:0.91 276:0.56 300:0.55 +0 1:0.64 10:0.87 11:0.46 12:0.51 17:0.45 18:0.62 21:0.30 26:0.98 27:0.69 29:0.56 30:0.14 32:0.65 34:0.94 36:0.18 37:0.25 39:0.70 43:0.64 45:0.66 55:0.71 58:0.93 66:0.62 69:0.29 70:0.90 71:0.84 74:0.43 81:0.57 83:0.56 86:0.66 89:0.97 91:0.23 98:0.75 99:0.83 101:0.47 104:0.93 106:0.81 108:0.22 114:0.82 117:0.86 123:0.22 124:0.48 126:0.32 127:0.79 129:0.05 133:0.36 135:0.96 139:0.64 141:0.91 145:0.52 146:0.78 147:0.82 149:0.50 150:0.52 154:0.67 155:0.49 159:0.53 165:0.65 170:0.82 172:0.67 173:0.30 174:0.79 176:0.63 177:0.88 178:0.55 179:0.59 187:0.87 192:0.48 195:0.68 197:0.85 199:0.99 201:0.60 206:0.81 208:0.50 212:0.54 215:0.72 216:0.62 222:0.21 223:0.98 224:0.93 225:0.98 232:0.88 234:0.91 235:0.52 239:0.86 240:0.95 241:0.37 242:0.74 243:0.68 244:0.61 245:0.79 247:0.67 253:0.58 254:0.74 259:0.21 265:0.75 266:0.75 267:0.28 271:0.31 274:0.91 276:0.63 290:0.82 297:0.36 300:0.70 +1 1:0.69 11:0.75 12:0.37 17:0.26 18:0.90 21:0.13 27:0.47 29:0.62 30:0.62 32:0.63 34:0.42 36:0.23 37:0.66 39:0.51 43:0.67 44:0.85 45:0.92 46:0.88 64:0.77 66:0.74 69:0.79 70:0.49 71:0.74 74:0.43 77:0.98 81:0.60 83:0.60 86:0.72 91:0.27 98:0.73 99:0.83 101:0.52 107:0.93 108:0.62 110:0.95 114:0.64 122:0.80 123:0.60 124:0.42 125:0.88 126:0.44 127:0.29 129:0.05 131:0.61 133:0.43 135:0.94 139:0.71 144:0.57 145:0.90 146:0.87 147:0.28 149:0.81 150:0.60 154:0.55 155:0.58 157:0.61 159:0.51 160:0.88 165:0.70 166:0.84 172:0.78 173:0.73 176:0.55 177:0.38 178:0.55 179:0.32 182:0.61 187:0.39 192:0.51 195:0.82 201:0.68 204:0.85 208:0.54 212:0.60 216:0.82 222:0.35 227:0.61 229:0.61 231:0.90 232:0.96 235:0.22 241:0.49 242:0.54 243:0.21 245:0.79 246:0.61 247:0.67 253:0.48 254:0.86 257:0.65 259:0.21 265:0.73 266:0.38 267:0.36 276:0.70 281:0.91 282:0.71 287:0.61 289:0.94 290:0.64 300:0.43 +1 11:0.64 12:0.37 17:0.45 18:0.68 21:0.68 27:0.56 29:0.62 30:0.15 32:0.62 34:0.95 36:0.18 37:0.60 39:0.51 43:0.13 45:0.66 46:0.88 66:0.53 69:0.84 70:0.52 71:0.74 74:0.27 81:0.24 86:0.56 91:0.20 98:0.50 101:0.52 104:0.91 106:0.81 107:0.94 108:0.69 110:0.96 123:0.67 124:0.51 125:0.88 126:0.26 127:0.28 129:0.05 131:0.61 133:0.37 135:0.69 139:0.55 145:0.72 146:0.53 147:0.53 149:0.14 150:0.24 154:0.56 157:0.61 159:0.36 160:0.88 166:0.86 172:0.37 173:0.90 177:0.05 178:0.55 179:0.73 182:0.61 187:0.21 195:0.66 206:0.81 212:0.30 215:0.37 216:0.76 222:0.35 227:0.61 229:0.61 231:0.90 232:0.97 235:0.80 241:0.01 242:0.14 243:0.37 245:0.57 246:0.61 247:0.67 253:0.24 254:0.95 257:0.84 259:0.21 265:0.51 266:0.47 267:0.36 276:0.38 283:0.78 287:0.61 289:0.95 297:0.36 300:0.33 +0 12:0.37 17:0.93 18:0.88 21:0.78 27:0.72 29:0.62 30:0.28 34:0.63 36:0.94 37:0.66 39:0.51 43:0.18 46:0.88 55:0.71 66:0.87 69:0.05 70:0.47 71:0.74 74:0.25 86:0.59 91:0.33 98:0.95 107:0.95 108:0.05 110:0.97 123:0.05 125:0.88 127:0.65 129:0.05 131:0.61 135:0.75 139:0.57 146:0.79 147:0.83 149:0.26 154:0.60 157:0.61 159:0.35 160:0.88 166:0.61 172:0.63 173:0.13 177:0.70 178:0.55 179:0.71 182:0.61 187:0.39 202:0.36 212:0.61 216:0.09 222:0.35 227:0.61 229:0.61 231:0.90 241:0.21 242:0.16 243:0.88 246:0.61 247:0.79 253:0.22 254:1.00 259:0.21 265:0.95 266:0.54 267:0.44 276:0.53 287:0.61 289:0.95 300:0.33 +1 8:0.84 11:0.64 12:0.37 17:0.67 18:0.83 21:0.54 27:0.52 29:0.62 30:0.36 34:0.33 36:0.94 37:0.47 39:0.51 43:0.13 45:0.66 46:0.88 55:0.84 66:0.48 69:0.54 70:0.64 71:0.74 74:0.26 81:0.25 86:0.58 91:0.38 98:0.75 101:0.52 107:0.96 108:0.78 110:0.98 114:0.55 122:0.41 123:0.77 124:0.67 125:0.88 126:0.26 127:0.67 129:0.59 131:0.61 133:0.66 135:0.89 139:0.56 146:0.36 147:0.43 149:0.25 150:0.25 154:0.40 157:0.61 159:0.67 160:0.88 163:0.94 166:0.76 172:0.61 173:0.43 176:0.54 177:0.70 178:0.55 179:0.55 182:0.61 187:0.39 212:0.21 216:0.39 222:0.35 227:0.61 229:0.61 231:0.93 232:0.93 235:0.60 241:0.24 242:0.15 243:0.25 245:0.76 246:0.61 247:0.84 253:0.36 254:0.92 259:0.21 265:0.75 266:0.80 267:0.87 276:0.65 287:0.61 289:0.96 290:0.55 300:0.55 +2 8:0.65 11:0.64 12:0.37 17:0.94 18:0.65 21:0.74 27:0.69 29:0.62 30:0.37 32:0.62 34:0.93 36:0.52 37:0.25 39:0.51 43:0.34 45:0.73 46:0.88 55:0.59 66:0.88 69:0.86 70:0.87 71:0.74 74:0.28 76:0.85 81:0.23 86:0.59 91:0.15 98:0.70 101:0.52 107:0.95 108:0.73 110:0.97 123:0.71 125:0.88 126:0.26 127:0.21 129:0.05 131:0.61 135:0.47 139:0.57 145:0.41 146:0.78 147:0.81 149:0.38 150:0.23 154:0.25 157:0.61 159:0.34 160:0.88 166:0.86 172:0.67 173:0.88 177:0.70 178:0.55 179:0.39 182:0.61 187:0.68 195:0.76 212:0.57 215:0.36 216:0.55 222:0.35 227:0.61 229:0.61 231:0.93 235:0.88 241:0.15 242:0.14 243:0.89 244:0.61 246:0.61 247:0.82 253:0.25 259:0.21 265:0.70 266:0.47 267:0.23 276:0.55 283:0.78 287:0.61 289:0.96 297:0.36 300:0.70 +1 8:0.62 11:0.64 12:0.37 17:0.34 18:0.63 21:0.30 27:0.58 29:0.62 30:0.30 34:0.47 36:0.69 37:0.42 39:0.51 43:0.59 45:0.66 46:0.88 55:0.84 66:0.45 69:0.43 70:0.76 71:0.74 74:0.28 81:0.32 86:0.59 91:0.23 96:0.77 98:0.67 101:0.52 107:0.92 108:0.33 110:0.93 114:0.57 117:0.86 122:0.77 123:0.32 124:0.81 125:0.88 126:0.26 127:0.79 129:0.05 131:0.83 133:0.81 135:0.24 139:0.57 140:0.45 146:0.89 147:0.91 149:0.47 150:0.29 151:0.55 153:0.48 154:0.48 157:0.61 158:0.71 159:0.77 160:0.88 162:0.55 166:0.76 172:0.68 173:0.26 176:0.54 177:0.70 179:0.46 182:0.61 187:0.68 190:0.89 202:0.70 212:0.18 216:0.88 220:0.87 222:0.35 227:0.61 229:0.61 231:0.78 232:0.88 235:0.31 241:0.30 242:0.79 243:0.58 244:0.61 245:0.77 246:0.61 247:0.60 248:0.56 253:0.42 256:0.64 259:0.21 265:0.67 266:0.80 267:0.73 268:0.68 276:0.73 285:0.47 287:0.61 289:0.91 290:0.57 300:0.70 +1 11:0.64 12:0.37 17:0.75 18:0.62 21:0.47 27:0.61 29:0.62 30:0.33 34:0.58 36:0.47 37:0.61 39:0.51 43:0.59 45:0.66 46:0.88 55:0.84 66:0.46 69:0.58 70:0.77 71:0.74 74:0.28 76:0.85 77:0.70 81:0.27 86:0.58 91:0.08 98:0.66 101:0.52 107:0.92 108:0.44 110:0.93 114:0.56 117:0.86 122:0.77 123:0.43 124:0.76 125:0.88 126:0.26 127:0.63 129:0.05 131:0.61 133:0.73 135:0.44 139:0.57 145:0.47 146:0.89 147:0.91 149:0.45 150:0.26 154:0.47 157:0.61 158:0.71 159:0.75 160:0.88 166:0.76 172:0.68 173:0.41 176:0.54 177:0.70 178:0.55 179:0.44 182:0.61 187:0.95 195:0.90 212:0.19 216:0.44 222:0.35 227:0.61 229:0.61 231:0.78 232:0.97 235:0.52 241:0.02 242:0.79 243:0.58 244:0.61 245:0.79 246:0.61 247:0.60 253:0.37 259:0.21 265:0.67 266:0.80 267:0.19 276:0.72 282:0.70 287:0.61 289:0.91 290:0.56 300:0.70 +0 12:0.37 17:0.61 18:0.67 21:0.78 27:0.32 29:0.62 30:0.76 32:0.62 34:0.68 36:0.32 37:0.89 39:0.51 43:0.18 46:0.88 55:0.71 66:0.64 69:0.08 70:0.15 71:0.74 74:0.24 76:0.85 86:0.57 91:0.31 98:0.82 104:0.58 106:0.81 107:0.89 108:0.07 110:0.91 122:0.65 123:0.07 125:0.88 127:0.31 129:0.05 131:0.61 135:0.94 139:0.55 145:0.89 146:0.41 147:0.47 149:0.12 154:0.12 157:0.61 159:0.15 160:0.88 163:0.75 166:0.61 172:0.16 173:0.50 175:0.75 177:0.38 178:0.55 179:0.98 182:0.61 187:0.87 195:0.53 206:0.81 212:0.95 216:0.32 222:0.35 227:0.61 229:0.61 231:0.74 235:0.22 241:0.46 242:0.82 243:0.69 244:0.83 246:0.61 247:0.12 253:0.20 257:0.65 259:0.21 265:0.82 266:0.12 267:0.44 276:0.17 277:0.69 287:0.61 289:0.90 300:0.13 +1 11:0.64 12:0.37 17:0.18 18:0.65 21:0.78 27:0.56 29:0.62 30:0.55 34:0.80 36:0.23 37:0.60 39:0.51 43:0.41 45:0.66 46:0.88 55:0.71 66:0.36 69:0.88 70:0.71 71:0.74 74:0.27 86:0.58 91:0.07 98:0.51 101:0.52 107:0.94 108:0.83 110:0.96 122:0.77 123:0.82 124:0.76 125:0.88 127:0.48 129:0.59 131:0.61 133:0.76 135:0.60 139:0.56 145:0.65 146:0.41 147:0.05 149:0.30 154:0.31 157:0.61 158:0.71 159:0.71 160:0.88 163:0.94 166:0.61 172:0.41 173:0.83 175:0.75 177:0.05 178:0.55 179:0.69 182:0.61 187:0.87 212:0.18 216:0.76 222:0.35 227:0.61 229:0.61 231:0.87 232:0.95 235:0.52 241:0.49 242:0.31 243:0.13 244:0.61 245:0.58 246:0.61 247:0.74 253:0.24 257:0.84 259:0.21 265:0.52 266:0.80 267:0.36 276:0.49 277:0.69 287:0.61 289:0.93 300:0.55 +1 11:0.86 12:0.37 17:0.55 18:0.69 21:0.78 27:0.47 29:0.62 30:0.76 32:0.62 34:0.20 36:0.19 37:0.66 39:0.51 43:0.10 44:0.85 45:0.73 46:0.88 66:0.54 69:0.85 70:0.22 71:0.74 74:0.49 77:0.70 86:0.71 91:0.42 98:0.13 101:0.87 107:0.91 108:0.71 110:0.92 122:0.55 123:0.70 124:0.45 125:0.88 127:0.14 129:0.05 131:0.61 133:0.37 135:0.74 139:0.69 144:0.57 145:0.82 146:0.20 147:0.05 149:0.06 154:0.62 155:0.58 157:0.61 159:0.20 160:0.88 166:0.61 172:0.21 173:0.89 177:0.05 178:0.55 179:0.62 182:0.61 187:0.39 192:0.51 195:0.74 202:0.50 204:0.85 208:0.54 212:0.42 216:0.82 222:0.35 227:0.61 229:0.61 231:0.83 235:0.12 241:0.65 242:0.45 243:0.26 245:0.40 246:0.61 247:0.35 253:0.35 254:0.84 257:0.84 259:0.21 265:0.14 266:0.23 267:0.52 276:0.14 281:0.91 282:0.71 287:0.61 289:0.92 300:0.18 +1 9:0.76 11:0.64 12:0.37 17:0.62 18:0.92 21:0.78 27:0.47 29:0.62 30:0.72 31:0.87 32:0.62 34:0.50 36:0.21 37:0.66 39:0.51 43:0.65 44:0.85 45:0.92 46:0.88 66:0.93 69:0.66 70:0.39 71:0.74 74:0.36 77:0.70 79:0.88 83:0.60 86:0.66 91:0.42 96:0.72 97:0.89 98:0.75 101:0.52 107:0.94 108:0.50 110:0.95 122:0.80 123:0.48 125:0.88 127:0.24 129:0.05 131:0.89 135:0.93 137:0.87 139:0.66 140:0.45 145:0.76 146:0.80 147:0.84 149:0.83 151:0.59 153:0.48 154:0.65 155:0.58 157:0.61 158:0.72 159:0.36 160:0.88 162:0.86 166:0.61 172:0.80 173:0.66 177:0.70 179:0.29 182:0.61 187:0.68 190:0.77 192:0.51 195:0.77 196:0.87 202:0.36 208:0.54 212:0.80 216:0.82 219:0.87 220:0.74 222:0.35 227:0.61 229:0.61 231:0.90 232:0.92 235:0.22 241:0.18 242:0.55 243:0.93 246:0.61 247:0.60 248:0.58 253:0.40 254:0.90 255:0.74 256:0.45 259:0.21 265:0.75 266:0.38 267:0.28 268:0.46 276:0.70 279:0.82 282:0.71 284:0.86 285:0.56 287:0.61 289:0.95 292:0.95 300:0.33 +1 11:0.64 12:0.37 17:0.88 18:0.66 21:0.47 27:0.69 29:0.62 30:0.38 34:0.37 36:0.70 37:0.27 39:0.51 43:0.59 45:0.66 46:0.88 55:0.71 66:0.49 69:0.86 70:0.77 71:0.74 74:0.28 81:0.27 86:0.59 91:0.14 98:0.77 101:0.52 107:0.92 108:0.73 110:0.94 114:0.56 117:0.86 122:0.77 123:0.72 124:0.67 125:0.88 126:0.26 127:0.66 129:0.05 131:0.61 133:0.60 135:0.60 139:0.57 146:0.95 147:0.78 149:0.47 150:0.26 154:0.49 157:0.61 158:0.71 159:0.79 160:0.88 166:0.76 172:0.74 173:0.76 176:0.54 177:0.38 178:0.55 179:0.40 182:0.61 187:0.39 212:0.28 216:0.21 222:0.35 227:0.61 229:0.61 231:0.79 232:0.92 235:0.52 241:0.27 242:0.77 243:0.33 244:0.61 245:0.86 246:0.61 247:0.60 253:0.37 257:0.65 259:0.21 265:0.77 266:0.80 267:0.36 276:0.76 287:0.61 289:0.91 290:0.56 300:0.84 +0 8:0.69 12:0.37 17:0.61 18:0.66 21:0.22 27:0.37 29:0.62 30:0.44 34:0.81 36:0.99 37:0.37 39:0.51 43:0.14 46:0.88 55:0.92 66:0.20 69:0.58 70:0.66 71:0.74 74:0.24 81:0.49 86:0.57 91:0.56 98:0.48 104:0.89 107:0.92 108:0.85 110:0.95 120:0.80 123:0.85 124:0.93 125:0.88 126:0.26 127:0.72 129:0.59 131:0.91 133:0.92 135:0.89 139:0.55 140:0.45 145:0.52 146:0.51 147:0.57 149:0.26 150:0.38 151:0.60 153:0.46 154:0.10 157:0.61 159:0.86 160:0.88 162:0.69 163:0.94 164:0.53 166:0.87 172:0.45 173:0.31 175:0.75 177:0.38 179:0.55 182:0.61 187:0.39 190:0.89 202:0.76 212:0.30 215:0.51 216:0.60 220:0.62 222:0.35 227:0.61 229:0.61 231:0.87 235:0.22 241:0.41 242:0.80 243:0.28 244:0.83 245:0.64 246:0.61 247:0.39 248:0.65 253:0.20 256:0.78 257:0.65 259:0.21 260:0.58 265:0.50 266:0.92 267:0.65 268:0.79 276:0.66 277:0.87 283:0.82 285:0.47 287:0.61 289:0.93 297:0.36 300:0.55 +1 9:0.76 11:0.75 12:0.37 17:0.55 18:0.91 21:0.78 27:0.47 29:0.62 30:0.76 31:0.88 34:0.59 36:0.18 37:0.66 39:0.51 43:0.65 44:0.85 45:0.92 46:0.88 66:0.91 69:0.65 70:0.30 71:0.74 74:0.43 77:0.70 79:0.90 83:0.60 86:0.74 91:0.31 96:0.75 97:0.89 98:0.75 101:0.52 107:0.93 108:0.50 110:0.95 122:0.80 123:0.48 125:0.88 127:0.24 129:0.05 131:0.89 135:0.97 137:0.88 139:0.72 140:0.45 144:0.57 145:0.79 146:0.82 147:0.85 149:0.83 151:0.65 153:0.48 154:0.59 155:0.58 157:0.61 158:0.72 159:0.38 160:0.88 162:0.86 166:0.61 172:0.76 173:0.64 177:0.70 179:0.33 182:0.61 187:0.68 190:0.77 192:0.51 195:0.79 196:0.89 202:0.36 208:0.54 212:0.71 216:0.82 219:0.87 220:0.62 222:0.35 227:0.61 229:0.61 231:0.86 232:0.87 235:0.12 241:0.02 242:0.59 243:0.92 246:0.61 247:0.47 248:0.63 253:0.44 254:0.86 255:0.74 256:0.45 259:0.21 265:0.75 266:0.38 267:0.28 268:0.46 276:0.64 279:0.82 282:0.71 284:0.86 285:0.56 287:0.61 289:0.93 292:0.95 300:0.33 +1 11:0.42 12:0.01 17:0.51 21:0.78 27:0.66 29:0.56 30:0.43 34:0.77 36:0.72 37:0.47 39:0.65 43:0.17 55:0.84 66:0.85 69:0.23 70:0.56 71:0.58 74:0.39 91:0.05 98:0.85 108:0.18 123:0.18 124:0.38 127:0.44 129:0.05 131:0.61 133:0.39 135:0.61 139:0.64 144:0.57 146:0.97 147:0.97 149:0.30 154:0.51 157:0.61 159:0.72 166:0.61 172:0.85 173:0.14 175:0.75 177:0.38 178:0.55 179:0.34 182:0.61 187:0.39 212:0.27 216:0.23 219:0.89 222:0.35 227:0.61 229:0.61 231:0.80 241:0.05 242:0.70 243:0.86 244:0.61 245:0.74 246:0.61 247:0.47 253:0.31 254:0.74 257:0.65 259:0.21 265:0.85 266:0.63 267:0.44 271:0.56 276:0.77 277:0.69 287:0.61 292:0.91 300:0.55 +1 7:0.63 8:0.68 11:0.75 12:0.01 17:0.24 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.14 34:0.44 36:0.91 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.45 69:0.07 70:0.92 71:0.58 74:0.38 81:0.41 86:0.63 91:0.25 98:0.48 101:0.52 108:0.06 123:0.06 124:0.87 126:0.26 127:0.89 129:0.05 131:0.61 133:0.88 135:0.34 139:0.61 144:0.57 145:0.42 146:0.82 147:0.85 149:0.61 150:0.35 154:0.59 157:0.77 159:0.84 166:0.79 172:0.82 173:0.08 177:0.70 178:0.55 179:0.30 182:0.72 187:0.98 212:0.57 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.73 233:0.76 235:0.12 241:0.32 242:0.81 243:0.58 244:0.61 245:0.84 246:0.61 247:0.60 253:0.30 259:0.21 265:0.49 266:0.84 267:0.28 271:0.56 276:0.84 287:0.61 297:0.36 300:0.84 +1 11:0.75 12:0.01 17:0.28 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.11 34:0.44 36:0.95 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.61 69:0.07 70:0.91 71:0.58 74:0.38 81:0.41 86:0.66 91:0.22 98:0.60 101:0.52 108:0.06 123:0.06 124:0.85 126:0.26 127:0.88 129:0.05 131:0.61 133:0.92 135:0.49 139:0.64 144:0.57 146:0.91 147:0.93 149:0.61 150:0.35 154:0.59 157:0.77 159:0.85 166:0.79 172:0.87 173:0.08 177:0.70 178:0.55 179:0.30 182:0.72 187:0.98 212:0.58 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 231:0.73 235:0.12 241:0.24 242:0.81 243:0.68 244:0.61 245:0.77 246:0.61 247:0.60 253:0.30 259:0.21 265:0.61 266:0.89 267:0.44 271:0.56 276:0.84 287:0.61 297:0.36 300:0.70 +0 11:0.86 12:0.01 17:0.72 18:0.98 21:0.13 27:0.53 29:0.56 30:0.32 34:0.44 36:0.26 37:0.38 39:0.65 43:0.09 45:0.73 55:0.59 66:0.42 69:0.96 70:0.75 71:0.58 74:0.35 81:0.38 86:0.96 91:0.11 98:0.64 101:0.87 108:0.95 114:0.63 117:0.86 122:0.86 123:0.95 124:0.92 126:0.26 127:0.84 129:0.89 131:0.61 133:0.93 135:0.75 139:0.95 144:0.57 145:0.70 146:0.97 147:0.05 149:0.46 150:0.34 154:0.22 157:0.75 158:0.71 159:0.96 166:0.77 172:0.97 173:0.74 176:0.55 177:0.05 178:0.55 179:0.11 182:0.72 187:0.68 212:0.52 216:0.80 222:0.35 227:0.61 229:0.61 231:0.79 232:0.87 235:0.12 241:0.24 242:0.74 243:0.05 245:0.98 246:0.61 247:0.96 253:0.47 254:0.80 257:0.84 259:0.21 265:0.65 266:0.95 267:0.91 271:0.56 276:0.98 287:0.61 290:0.63 300:0.94 +0 8:0.61 12:0.01 17:0.79 18:0.96 21:0.59 27:0.68 29:0.56 30:0.37 34:0.97 36:0.88 37:0.44 39:0.65 43:0.12 44:0.87 48:0.91 55:0.84 64:0.77 66:0.26 69:0.95 70:0.79 71:0.58 74:0.25 81:0.27 86:0.91 91:0.48 98:0.55 108:0.80 122:0.86 123:0.79 124:0.84 126:0.26 127:0.72 129:0.59 131:0.61 133:0.82 135:0.82 139:0.91 145:0.85 146:0.36 147:0.57 149:0.33 150:0.26 154:0.09 157:0.61 159:0.80 163:0.94 166:0.61 172:0.59 173:0.93 175:0.75 177:0.05 178:0.55 179:0.44 182:0.61 187:0.39 195:0.92 212:0.22 216:0.27 219:0.89 222:0.35 227:0.61 229:0.61 231:0.64 235:0.74 241:0.05 242:0.78 243:0.32 244:0.83 245:0.80 246:0.61 247:0.67 253:0.22 257:0.84 259:0.21 265:0.56 266:0.87 267:0.36 271:0.56 276:0.74 277:0.87 281:0.89 287:0.61 292:0.91 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.16 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.14 34:0.44 36:0.95 37:0.89 39:0.65 43:0.69 45:0.66 55:0.45 66:0.67 69:0.07 70:0.90 71:0.58 74:0.38 81:0.41 86:0.63 91:0.33 98:0.68 101:0.52 108:0.06 123:0.06 124:0.76 126:0.26 127:0.92 129:0.05 131:0.61 133:0.88 135:0.34 139:0.61 144:0.57 145:0.42 146:0.91 147:0.92 149:0.61 150:0.35 154:0.59 157:0.77 159:0.80 166:0.79 172:0.86 173:0.09 177:0.70 178:0.55 179:0.33 182:0.73 187:0.98 212:0.60 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.73 233:0.76 235:0.12 241:0.41 242:0.80 243:0.71 244:0.61 245:0.77 246:0.61 247:0.60 253:0.30 259:0.21 265:0.68 266:0.91 267:0.44 271:0.56 276:0.83 287:0.61 297:0.36 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.69 18:0.61 21:0.05 25:0.88 27:0.09 29:0.56 30:0.21 32:0.62 34:0.29 36:0.81 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.92 69:0.05 70:0.72 71:0.58 74:0.38 81:0.50 86:0.63 91:0.33 98:0.95 101:0.52 108:0.05 120:0.63 123:0.05 126:0.26 127:0.66 129:0.05 131:0.85 135:0.65 139:0.61 140:0.45 144:0.57 145:0.56 146:0.89 147:0.91 149:0.54 150:0.39 151:0.55 153:0.48 154:0.59 157:0.79 159:0.49 162:0.86 164:0.52 166:0.79 172:0.79 173:0.15 177:0.70 179:0.50 182:0.73 187:0.39 190:0.89 195:0.67 202:0.36 212:0.68 215:0.78 216:0.85 220:0.62 222:0.35 227:0.84 229:0.61 230:0.64 231:0.74 233:0.76 235:0.05 241:0.69 242:0.78 243:0.93 244:0.61 246:0.61 247:0.60 248:0.54 253:0.30 256:0.45 259:0.21 260:0.57 265:0.95 266:0.63 267:0.36 268:0.46 271:0.56 276:0.69 285:0.47 287:0.61 297:0.36 300:0.55 +0 11:0.42 12:0.01 21:0.05 27:0.69 29:0.56 30:0.76 34:0.68 36:0.90 37:0.25 39:0.65 43:0.69 55:0.45 64:0.77 66:0.64 69:0.38 70:0.15 71:0.58 74:0.38 81:0.58 91:0.33 98:0.35 108:0.30 122:0.51 123:0.28 126:0.26 127:0.12 129:0.05 131:0.61 135:0.94 144:0.57 146:0.23 147:0.29 149:0.34 150:0.43 154:0.58 157:0.61 159:0.11 166:0.61 172:0.16 173:0.74 175:0.75 177:0.38 178:0.55 179:0.67 182:0.61 187:0.87 212:0.95 216:0.52 222:0.35 227:0.61 229:0.61 231:0.67 235:0.05 241:0.32 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.30 254:0.80 257:0.65 259:0.21 265:0.37 266:0.12 267:0.28 271:0.56 276:0.17 277:0.69 287:0.61 300:0.13 +0 8:0.81 12:0.01 17:0.77 18:0.93 21:0.68 27:0.68 29:0.56 30:0.30 34:0.98 36:0.92 37:0.44 39:0.65 43:0.12 44:0.87 48:0.91 55:0.71 64:0.77 66:0.44 69:0.93 70:0.81 71:0.58 74:0.25 81:0.24 86:0.87 91:0.23 98:0.52 108:0.57 122:0.86 123:0.55 124:0.82 126:0.26 127:0.63 129:0.05 131:0.61 133:0.82 135:0.80 139:0.88 145:0.85 146:0.35 147:0.89 149:0.27 150:0.24 154:0.09 157:0.61 159:0.82 163:0.94 166:0.61 172:0.57 173:0.86 175:0.75 177:0.05 178:0.55 179:0.58 182:0.61 187:0.39 195:0.94 212:0.23 216:0.27 219:0.89 222:0.35 227:0.61 229:0.61 231:0.64 235:0.80 241:0.10 242:0.78 243:0.58 244:0.83 245:0.66 246:0.61 247:0.55 253:0.22 257:0.84 259:0.21 265:0.54 266:0.92 267:0.59 271:0.56 276:0.61 277:0.87 281:0.89 287:0.61 292:0.91 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.20 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.10 34:0.45 36:0.91 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.61 69:0.07 70:0.89 71:0.58 74:0.38 81:0.41 86:0.63 91:0.20 98:0.69 101:0.52 108:0.06 123:0.06 124:0.80 126:0.26 127:0.92 129:0.05 131:0.61 133:0.89 135:0.54 139:0.61 144:0.57 145:0.42 146:0.95 147:0.96 149:0.62 150:0.35 154:0.59 157:0.77 159:0.85 166:0.79 172:0.88 173:0.08 177:0.70 178:0.55 179:0.28 182:0.72 187:0.98 212:0.51 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.77 233:0.76 235:0.12 241:0.32 242:0.79 243:0.68 244:0.61 245:0.83 246:0.61 247:0.60 253:0.31 259:0.21 265:0.70 266:0.89 267:0.44 271:0.56 276:0.87 287:0.61 297:0.36 300:0.70 +1 7:0.63 12:0.01 17:0.62 18:0.66 21:0.78 27:0.16 29:0.56 30:0.21 34:0.42 36:0.95 37:0.70 39:0.65 43:0.12 55:0.92 66:0.16 69:0.94 70:0.70 71:0.58 74:0.30 86:0.67 91:0.14 98:0.38 108:0.89 122:0.77 123:0.88 124:0.94 127:0.70 129:0.59 131:0.61 133:0.93 135:0.87 139:0.65 145:0.56 146:0.79 147:0.32 149:0.23 154:0.13 157:0.61 159:0.87 163:0.94 166:0.61 172:0.37 173:0.87 177:0.05 178:0.55 179:0.56 182:0.61 187:0.87 202:0.36 212:0.27 216:0.66 222:0.35 227:0.61 229:0.61 230:0.64 231:0.76 232:0.72 233:0.76 235:0.88 241:0.32 242:0.28 243:0.20 244:0.61 245:0.62 246:0.61 247:0.86 253:0.26 254:0.90 257:0.84 259:0.21 265:0.41 266:0.87 267:0.19 271:0.56 276:0.65 287:0.61 300:0.55 +1 12:0.01 17:0.77 21:0.78 27:0.58 29:0.56 30:0.21 34:0.52 36:0.97 37:0.26 39:0.65 43:0.18 55:0.92 66:0.16 69:0.05 70:0.92 71:0.58 74:0.32 83:0.60 91:0.05 97:0.89 98:0.36 108:0.05 117:0.86 122:0.77 123:0.05 124:0.85 127:0.82 129:0.59 131:0.61 133:0.82 135:0.81 139:0.68 146:0.53 147:0.59 149:0.24 154:0.12 155:0.58 157:0.61 158:0.72 159:0.68 166:0.61 172:0.21 173:0.06 175:0.75 177:0.38 178:0.55 179:0.80 182:0.61 187:0.87 192:0.51 208:0.54 212:0.22 216:0.67 219:0.92 222:0.35 227:0.61 229:0.61 231:0.73 232:0.94 241:0.07 242:0.71 243:0.37 244:0.83 245:0.54 246:0.61 247:0.39 253:0.27 257:0.65 259:0.21 265:0.38 266:0.75 267:0.84 271:0.56 276:0.46 277:0.69 279:0.82 287:0.61 292:0.95 300:0.70 +1 12:0.01 17:0.70 18:0.62 21:0.47 27:0.63 29:0.56 30:0.90 34:0.91 36:0.28 37:0.41 39:0.65 43:0.14 55:0.59 66:0.89 69:0.63 70:0.21 71:0.58 74:0.28 76:0.85 77:0.70 81:0.26 86:0.67 91:0.60 98:0.86 106:0.81 108:0.48 114:0.57 123:0.46 126:0.26 127:0.38 129:0.05 131:0.61 135:0.30 139:0.65 145:0.70 146:0.72 147:0.77 149:0.32 150:0.25 154:0.45 157:0.61 159:0.29 166:0.77 172:0.70 173:0.77 176:0.55 177:0.70 178:0.55 179:0.55 182:0.61 187:0.39 195:0.61 206:0.81 212:0.93 216:0.18 222:0.35 227:0.61 229:0.61 231:0.83 235:0.52 241:0.41 242:0.80 243:0.90 244:0.61 246:0.61 247:0.35 253:0.39 254:0.84 259:0.21 265:0.86 266:0.17 267:0.23 271:0.56 276:0.59 282:0.71 287:0.61 290:0.57 300:0.25 +1 7:0.63 8:0.63 11:0.75 12:0.01 17:0.32 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.10 34:0.39 36:0.99 37:0.89 39:0.65 43:0.69 45:0.66 55:0.71 66:0.48 69:0.07 70:0.87 71:0.58 74:0.38 81:0.41 86:0.63 91:0.27 98:0.61 101:0.52 108:0.06 123:0.06 124:0.80 126:0.26 127:0.88 129:0.05 131:0.61 133:0.81 135:0.65 139:0.61 144:0.57 145:0.42 146:0.91 147:0.93 149:0.62 150:0.35 154:0.59 157:0.77 159:0.85 166:0.79 172:0.85 173:0.08 177:0.70 178:0.55 179:0.27 182:0.72 187:0.98 212:0.51 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.73 233:0.76 235:0.12 241:0.15 242:0.81 243:0.59 244:0.61 245:0.90 246:0.61 247:0.60 253:0.30 259:0.21 265:0.62 266:0.89 267:0.44 271:0.56 276:0.87 287:0.61 297:0.36 300:0.70 +1 7:0.63 11:0.75 12:0.01 17:0.28 18:0.61 21:0.13 25:0.88 27:0.09 29:0.56 30:0.10 34:0.44 36:0.93 37:0.89 39:0.65 43:0.69 45:0.66 55:0.52 66:0.49 69:0.07 70:0.84 71:0.58 74:0.38 81:0.41 86:0.66 91:0.18 98:0.64 101:0.52 108:0.06 123:0.06 124:0.84 126:0.26 127:0.91 129:0.05 131:0.61 133:0.89 135:0.37 139:0.64 144:0.57 145:0.42 146:0.94 147:0.95 149:0.62 150:0.35 154:0.59 157:0.77 159:0.86 166:0.79 172:0.87 173:0.07 177:0.70 178:0.55 179:0.26 182:0.72 187:0.98 212:0.55 215:0.61 216:0.85 222:0.35 227:0.61 229:0.61 230:0.64 231:0.78 233:0.76 235:0.12 241:0.27 242:0.78 243:0.60 244:0.61 245:0.86 246:0.61 247:0.60 253:0.31 259:0.21 265:0.65 266:0.80 267:0.36 271:0.56 276:0.87 283:0.78 287:0.61 297:0.36 300:0.70 +0 7:0.63 8:0.66 11:0.75 12:0.01 17:0.66 18:0.57 21:0.30 27:0.56 29:0.56 30:0.10 32:0.62 34:0.29 36:0.75 37:0.45 39:0.65 43:0.11 45:0.66 55:0.71 66:0.05 69:0.19 70:0.95 71:0.58 74:0.31 76:0.98 77:0.70 81:0.32 86:0.59 91:0.03 98:0.17 101:0.52 108:1.00 120:0.55 123:0.96 124:1.00 126:0.26 127:1.00 129:0.96 131:0.85 133:1.00 135:0.92 139:0.57 140:0.87 144:0.57 145:0.79 146:0.31 147:0.37 149:0.33 150:0.29 151:0.48 153:0.48 154:0.55 157:0.75 159:0.98 162:0.49 164:0.52 166:0.79 172:0.67 173:0.05 175:0.75 177:0.38 178:0.48 179:0.14 182:0.71 187:0.87 190:0.89 195:1.00 202:0.63 212:0.21 215:0.44 216:0.56 220:0.87 222:0.35 227:0.84 229:0.61 230:0.63 231:0.80 233:0.73 235:0.31 241:0.15 242:0.79 243:0.07 244:0.61 245:0.87 246:0.61 247:0.79 248:0.48 253:0.27 256:0.45 257:0.65 259:0.21 260:0.54 265:0.15 266:0.99 267:0.79 268:0.46 271:0.56 276:0.96 277:0.87 282:0.71 285:0.47 287:0.61 297:0.36 300:0.94 +2 7:0.81 12:0.69 17:0.72 20:0.80 27:0.58 28:0.46 30:0.94 32:0.80 34:0.96 36:0.49 37:0.30 43:0.52 55:0.59 66:0.72 69:0.05 70:0.13 78:0.75 81:0.99 91:0.33 98:0.58 100:0.79 104:0.89 106:0.81 108:0.05 111:0.75 120:0.69 123:0.05 126:0.54 127:0.17 129:0.05 135:0.49 140:0.45 145:0.38 146:0.52 147:0.58 149:0.37 150:0.99 151:0.66 152:0.77 153:0.48 154:0.41 159:0.19 161:0.94 162:0.86 164:0.57 167:0.52 169:0.77 172:0.27 173:0.17 177:0.05 179:0.76 181:0.52 186:0.76 187:0.39 195:0.60 202:0.36 206:0.81 212:0.42 215:0.96 216:0.33 230:0.87 233:0.76 235:0.02 241:0.02 242:0.82 243:0.75 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.76 265:0.59 266:0.23 267:0.36 268:0.46 276:0.22 283:0.82 285:0.62 297:0.36 300:0.13 +2 8:0.85 12:0.69 17:0.77 21:0.78 25:0.88 27:0.30 28:0.46 30:0.87 32:0.80 34:0.97 36:0.51 37:0.70 43:0.28 55:0.71 66:0.82 69:0.77 70:0.27 91:0.64 98:0.81 104:0.54 108:0.60 120:0.60 123:0.58 127:0.30 129:0.05 135:0.89 140:0.45 145:0.69 146:0.78 147:0.82 149:0.44 151:0.54 153:0.48 154:0.21 159:0.34 161:0.94 162:0.74 163:0.81 164:0.67 167:0.65 172:0.48 173:0.83 177:0.05 179:0.74 181:0.52 187:0.21 191:0.75 195:0.67 202:0.56 212:0.30 216:0.38 220:0.87 235:0.68 241:0.50 242:0.82 243:0.83 247:0.12 248:0.54 256:0.58 259:0.21 260:0.61 265:0.81 266:0.47 267:0.65 268:0.59 276:0.40 285:0.62 300:0.25 +2 6:0.97 8:0.95 12:0.69 17:0.70 20:0.86 21:0.47 25:0.88 27:0.27 28:0.46 34:0.50 36:0.83 37:0.88 43:0.44 66:0.36 69:0.47 78:0.76 81:0.95 91:0.33 98:0.16 100:0.83 104:0.58 108:0.36 111:0.77 120:0.60 123:0.35 126:0.54 127:0.09 129:0.05 135:0.37 140:0.45 146:0.05 147:0.05 149:0.15 150:0.92 151:0.54 152:1.00 153:0.48 154:0.33 159:0.05 161:0.94 162:0.86 164:0.57 167:0.73 169:0.88 172:0.05 173:0.95 177:0.05 181:0.52 186:0.77 187:0.21 189:0.99 202:0.36 215:0.63 216:0.28 220:0.62 235:0.52 238:0.96 241:0.66 243:0.51 248:0.54 256:0.45 259:0.21 260:0.61 261:0.92 265:0.17 267:0.44 268:0.46 285:0.62 297:0.36 +1 12:0.69 17:0.40 21:0.78 27:0.45 28:0.46 30:0.95 34:0.80 36:0.18 37:0.50 43:0.26 55:0.84 66:0.54 69:0.82 91:0.18 98:0.22 108:0.66 123:0.64 127:0.11 129:0.05 135:0.87 145:0.49 146:0.32 147:0.39 149:0.17 154:0.19 159:0.13 161:0.94 167:0.54 172:0.10 173:0.80 177:0.05 178:0.55 179:0.62 181:0.52 187:0.68 216:0.77 235:0.97 241:0.07 243:0.63 259:0.21 265:0.24 267:0.23 300:0.08 +3 8:0.95 12:0.69 17:0.66 21:0.76 25:0.88 27:0.30 28:0.46 30:0.76 32:0.80 34:0.79 36:0.62 37:0.70 43:0.36 55:0.71 66:0.72 69:0.64 70:0.22 81:0.83 91:0.90 98:0.80 104:0.54 108:0.49 120:0.85 123:0.46 126:0.54 127:0.29 129:0.05 135:0.47 140:0.45 145:0.72 146:0.50 147:0.56 149:0.35 150:0.63 151:0.72 153:0.48 154:0.27 159:0.18 161:0.94 162:0.81 164:0.85 167:0.91 172:0.27 173:0.88 177:0.05 179:0.92 181:0.52 187:0.21 191:0.87 195:0.56 202:0.77 212:0.42 215:0.46 216:0.38 235:0.95 241:0.90 242:0.82 243:0.75 247:0.12 248:0.78 256:0.82 259:0.21 260:0.85 265:0.80 266:0.23 267:0.36 268:0.82 276:0.25 283:0.60 285:0.62 297:0.36 300:0.18 +1 8:0.94 12:0.69 17:0.77 20:0.93 21:0.59 25:0.88 27:0.72 28:0.46 32:0.80 34:0.47 36:0.75 78:0.74 81:0.92 91:0.86 100:0.79 104:0.58 111:0.74 120:0.66 126:0.54 135:0.37 140:0.45 145:0.92 150:0.86 151:0.56 152:0.87 153:0.72 161:0.94 162:0.86 164:0.84 167:0.92 169:0.77 175:0.75 181:0.52 186:0.75 195:0.52 202:0.72 215:0.55 216:0.09 220:0.87 235:0.68 241:0.97 244:0.61 248:0.61 256:0.79 257:0.65 259:0.21 260:0.67 261:0.79 267:0.59 268:0.80 277:0.69 285:0.62 297:0.36 +2 8:0.80 12:0.69 17:0.88 21:0.40 25:0.88 27:0.27 28:0.46 30:0.76 34:0.90 36:0.87 37:0.88 43:0.52 55:0.71 66:0.36 69:0.12 70:0.15 81:0.96 91:0.53 98:0.21 104:0.58 108:0.10 120:0.53 123:0.10 124:0.52 126:0.54 127:0.16 129:0.59 133:0.47 135:0.71 140:0.45 146:0.27 147:0.33 149:0.28 150:0.94 151:0.46 153:0.48 154:0.41 159:0.20 161:0.94 162:0.74 163:0.75 164:0.67 167:0.77 172:0.10 173:0.22 177:0.05 179:0.88 181:0.52 187:0.21 202:0.56 212:0.95 215:0.67 216:0.28 220:0.91 235:0.42 241:0.72 242:0.82 243:0.51 245:0.37 247:0.12 248:0.46 256:0.58 259:0.21 260:0.53 265:0.23 266:0.12 267:0.65 268:0.59 276:0.16 285:0.62 297:0.36 300:0.13 +3 6:0.98 8:0.95 12:0.69 17:0.38 20:0.99 21:0.76 25:0.88 27:0.30 28:0.46 30:0.76 32:0.80 34:0.80 36:0.64 37:0.70 43:0.36 55:0.71 66:0.72 69:0.64 70:0.22 78:0.89 81:0.83 91:0.95 98:0.80 100:0.99 104:0.54 108:0.49 111:0.98 120:0.93 123:0.46 126:0.54 127:0.29 129:0.05 135:0.42 140:0.45 145:0.72 146:0.50 147:0.56 149:0.35 150:0.63 151:0.84 152:0.91 153:0.97 154:0.27 159:0.18 161:0.94 162:0.79 164:0.94 167:0.91 169:1.00 172:0.27 173:0.88 177:0.05 179:0.92 181:0.52 186:0.89 189:0.98 191:0.92 195:0.56 202:0.89 212:0.42 215:0.46 216:0.38 235:0.95 238:1.00 241:0.89 242:0.82 243:0.75 247:0.12 248:0.90 256:0.91 259:0.21 260:0.93 261:0.98 265:0.80 266:0.23 267:0.28 268:0.92 276:0.25 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.96 8:0.91 12:0.69 17:0.90 20:0.85 21:0.22 25:0.88 27:0.72 28:0.46 30:0.86 32:0.80 34:0.58 36:0.36 43:0.24 55:0.59 66:0.84 69:0.85 70:0.21 78:0.77 81:0.98 91:0.69 98:0.64 100:0.82 104:0.74 108:0.71 111:0.77 120:0.54 123:0.69 126:0.54 127:0.19 129:0.05 135:0.78 140:0.45 145:0.72 146:0.60 147:0.66 149:0.46 150:0.97 151:0.47 152:0.81 153:0.48 154:0.17 159:0.22 161:0.94 162:0.86 164:0.57 167:0.88 169:0.79 172:0.54 173:0.94 177:0.05 179:0.47 181:0.52 186:0.78 189:0.98 195:0.66 202:0.36 212:0.81 215:0.79 216:0.02 220:0.82 235:0.22 238:0.95 241:0.80 242:0.82 243:0.85 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 261:0.80 265:0.64 266:0.27 267:0.36 268:0.46 276:0.44 285:0.62 297:0.36 300:0.18 +1 12:0.69 17:0.49 21:0.78 27:0.72 28:0.46 34:0.82 36:0.23 91:0.90 104:0.58 120:0.63 135:0.81 140:0.45 151:0.54 153:0.48 161:0.94 162:0.84 164:0.77 167:0.90 175:0.75 181:0.52 191:0.70 202:0.65 216:0.11 220:0.93 241:0.86 244:0.61 248:0.57 256:0.68 257:0.65 259:0.21 260:0.64 267:0.44 268:0.71 277:0.69 285:0.62 +2 12:0.69 17:0.62 21:0.22 25:0.88 27:0.27 28:0.46 30:0.62 34:0.93 36:0.67 37:0.88 43:0.52 55:0.92 66:0.47 69:0.08 70:0.34 81:0.98 91:0.33 98:0.66 104:0.58 108:0.56 123:0.53 124:0.52 126:0.54 127:0.50 129:0.59 133:0.47 135:0.73 146:0.21 147:0.27 149:0.39 150:0.97 154:0.41 159:0.28 161:0.94 163:0.75 167:0.63 172:0.21 173:0.30 177:0.05 178:0.55 179:0.93 181:0.52 187:0.39 212:0.30 215:0.79 216:0.28 235:0.22 241:0.44 242:0.82 243:0.37 245:0.46 247:0.12 259:0.21 265:0.67 266:0.27 267:0.44 276:0.26 297:0.36 300:0.25 +1 12:0.69 17:0.05 20:0.76 21:0.78 27:0.72 28:0.46 34:0.33 36:0.54 43:0.30 66:0.36 69:0.74 78:0.72 91:0.82 98:0.12 100:0.73 104:0.58 108:0.58 111:0.72 120:0.74 123:0.55 127:0.08 129:0.05 132:0.97 135:0.60 140:0.45 146:0.05 147:0.05 149:0.12 151:0.66 152:0.74 153:0.48 154:0.22 159:0.05 161:0.94 162:0.66 164:0.79 167:0.91 169:0.72 172:0.05 173:1.00 177:0.05 181:0.52 186:0.73 202:0.72 216:0.12 235:0.05 241:0.89 243:0.51 248:0.67 256:0.75 260:0.75 261:0.73 265:0.13 267:0.28 268:0.74 283:0.82 285:0.62 +1 12:0.69 17:0.30 21:0.30 25:0.88 27:0.27 28:0.46 34:0.85 36:0.71 37:0.88 43:0.34 66:0.36 69:0.64 81:0.97 91:0.38 98:0.13 104:0.58 108:0.49 123:0.47 126:0.54 127:0.08 129:0.05 135:0.34 146:0.05 147:0.05 149:0.13 150:0.96 154:0.26 159:0.05 161:0.94 167:0.67 172:0.05 173:0.96 177:0.05 178:0.55 181:0.52 187:0.39 215:0.72 216:0.28 235:0.31 241:0.53 243:0.51 259:0.21 265:0.13 267:0.52 297:0.36 +2 8:0.88 12:0.69 17:0.75 21:0.47 25:0.88 27:0.30 28:0.46 30:0.76 34:0.89 36:0.47 37:0.70 43:0.25 55:0.71 66:0.64 69:0.84 70:0.15 81:0.95 91:0.68 98:0.49 104:0.54 108:0.69 120:0.53 123:0.67 126:0.54 127:0.15 129:0.05 135:0.81 140:0.45 145:0.45 146:0.44 147:0.50 149:0.21 150:0.92 151:0.46 153:0.69 154:0.18 159:0.16 161:0.94 162:0.86 163:0.98 164:0.62 167:0.62 172:0.16 173:0.95 177:0.05 179:0.85 181:0.52 187:0.21 191:0.70 202:0.46 212:0.95 215:0.63 216:0.38 220:0.93 235:0.52 241:0.41 242:0.82 243:0.69 247:0.12 248:0.46 256:0.45 259:0.21 260:0.53 265:0.50 266:0.12 267:0.44 268:0.55 276:0.18 285:0.62 297:0.36 300:0.13 +2 6:0.98 8:0.97 12:0.69 17:0.28 20:0.78 21:0.47 27:0.47 28:0.46 34:0.58 36:0.89 37:0.90 78:0.78 81:0.95 91:0.88 100:0.95 104:0.58 111:0.89 120:0.85 126:0.54 135:0.21 140:0.45 150:0.92 151:0.66 152:0.77 153:0.48 161:0.94 162:0.78 164:0.94 167:0.91 169:0.90 175:0.75 181:0.52 186:0.79 189:0.98 191:0.87 202:0.90 215:0.63 216:0.29 220:0.62 235:0.52 238:0.99 241:0.93 244:0.61 248:0.79 256:0.92 257:0.65 259:0.21 260:0.86 261:0.83 267:0.19 268:0.93 277:0.69 283:0.60 285:0.62 297:0.36 +2 8:0.94 12:0.69 17:0.77 20:0.93 21:0.59 25:0.88 27:0.72 28:0.46 32:0.80 34:0.47 36:0.75 78:0.74 81:0.92 91:0.86 100:0.79 104:0.58 111:0.74 120:0.66 126:0.54 135:0.37 140:0.45 145:0.92 150:0.86 151:0.56 152:0.87 153:0.72 161:0.94 162:0.86 164:0.84 167:0.92 169:0.77 175:0.75 181:0.52 186:0.75 195:0.52 202:0.72 215:0.55 216:0.09 220:0.87 235:0.68 241:0.97 244:0.61 248:0.61 256:0.79 257:0.65 259:0.21 260:0.67 261:0.79 267:0.59 268:0.80 277:0.69 285:0.62 297:0.36 +2 1:0.65 7:0.81 10:0.97 11:0.84 12:0.37 17:0.83 18:0.85 21:0.22 27:0.53 29:0.62 30:0.36 32:0.68 34:0.62 36:0.25 37:0.86 39:0.47 43:0.36 44:0.92 45:0.87 48:0.91 64:0.77 66:0.43 69:0.39 70:0.93 71:0.91 74:0.75 77:0.70 81:0.60 83:0.56 86:0.90 91:0.17 98:0.76 99:0.83 101:0.47 104:0.87 106:0.81 108:0.30 114:0.88 117:0.86 122:0.65 123:0.29 124:0.60 126:0.51 127:0.73 129:0.59 133:0.47 135:0.74 139:0.91 144:0.85 145:0.98 146:0.81 147:0.84 149:0.28 150:0.52 154:0.54 155:0.56 159:0.55 165:0.65 170:0.87 172:0.71 173:0.37 174:0.94 176:0.70 177:0.88 178:0.55 179:0.40 187:0.39 192:0.57 193:0.99 195:0.73 197:0.94 201:0.63 204:0.83 206:0.81 208:0.64 212:0.69 215:0.79 216:0.65 222:0.60 230:0.83 232:0.91 233:0.66 235:0.42 239:0.97 241:0.41 242:0.16 243:0.57 245:0.91 247:0.92 253:0.67 254:0.74 259:0.21 265:0.76 266:0.63 267:0.96 271:0.44 276:0.75 281:0.86 282:0.77 290:0.88 297:0.36 300:0.84 +0 8:0.62 10:0.83 11:0.84 12:0.37 17:0.22 18:0.69 21:0.78 27:0.45 29:0.62 30:0.76 34:0.84 36:0.18 37:0.50 39:0.47 43:0.32 45:0.66 55:0.96 66:0.36 69:0.87 70:0.22 71:0.91 74:0.33 86:0.67 91:0.23 98:0.21 101:0.47 108:0.74 122:0.65 123:0.72 124:0.67 127:0.18 129:0.05 133:0.59 135:0.91 139:0.65 144:0.85 145:0.47 146:0.38 147:0.05 149:0.24 154:0.24 159:0.46 163:0.93 170:0.87 172:0.16 173:0.77 174:0.79 175:0.75 177:0.05 178:0.55 179:0.80 187:0.68 197:0.82 212:0.42 216:0.77 222:0.60 235:0.22 239:0.82 241:0.24 242:0.45 243:0.26 244:0.83 245:0.40 247:0.35 253:0.29 257:0.93 259:0.21 265:0.23 266:0.23 267:0.44 271:0.44 276:0.24 277:0.69 300:0.18 +0 1:0.63 7:0.64 9:0.73 10:0.93 11:0.87 12:0.37 17:0.28 18:0.71 21:0.05 27:0.02 29:0.62 30:0.30 32:0.71 34:0.56 36:0.48 37:0.92 39:0.47 43:0.46 44:0.92 45:0.85 48:0.91 66:0.44 69:0.91 70:0.81 71:0.91 74:0.58 76:0.85 77:0.70 81:0.57 83:0.56 86:0.77 91:0.33 96:0.80 98:0.53 99:0.83 101:0.65 108:0.81 114:0.92 120:0.69 122:0.95 123:0.80 124:0.69 126:0.32 127:0.58 129:0.05 133:0.67 135:0.92 139:0.81 140:0.45 144:0.85 145:0.56 146:0.68 147:0.49 149:0.42 150:0.51 151:0.85 153:0.48 154:0.52 155:0.56 159:0.60 162:0.86 164:0.56 165:0.65 170:0.87 172:0.57 173:0.94 174:0.92 176:0.63 177:0.70 179:0.57 187:0.39 190:0.89 192:0.58 195:0.80 197:0.93 201:0.59 202:0.36 208:0.50 212:0.37 215:0.91 216:0.99 222:0.60 230:0.65 233:0.76 235:0.12 239:0.91 241:0.62 242:0.16 243:0.31 244:0.61 245:0.75 247:0.88 248:0.77 253:0.81 255:0.72 256:0.45 257:0.65 259:0.21 260:0.70 265:0.54 266:0.75 267:0.69 268:0.46 271:0.44 276:0.58 281:0.91 282:0.80 285:0.50 290:0.92 297:0.36 300:0.70 +1 10:0.96 11:0.84 12:0.37 17:0.82 18:0.65 27:0.27 29:0.62 30:0.38 34:0.25 36:0.41 37:0.94 39:0.47 43:0.65 45:0.81 66:0.90 69:0.05 70:0.73 71:0.91 74:0.56 76:0.85 77:0.70 81:0.59 86:0.71 91:0.38 98:0.87 101:0.47 102:0.96 108:0.05 114:0.84 117:0.86 122:0.95 123:0.05 126:0.32 127:0.38 129:0.05 135:0.75 139:0.68 144:0.85 145:0.38 146:0.73 147:0.77 149:0.71 150:0.58 154:0.63 159:0.29 170:0.87 172:0.71 173:0.21 174:0.93 176:0.59 177:0.88 178:0.55 179:0.53 187:0.39 192:0.45 193:0.97 195:0.58 197:0.96 201:0.60 212:0.80 216:0.59 222:0.60 232:0.82 235:0.05 236:0.94 239:0.96 241:0.21 242:0.27 243:0.90 244:0.61 247:0.84 253:0.61 254:0.74 259:0.21 265:0.87 266:0.27 267:0.44 271:0.44 276:0.58 282:0.72 290:0.84 300:0.55 +0 1:0.62 7:0.64 9:0.70 10:0.87 11:0.84 12:0.37 17:0.08 18:0.80 21:0.47 22:0.93 27:0.20 29:0.62 30:0.55 31:0.87 32:0.71 34:0.88 36:0.57 37:0.60 39:0.47 43:0.75 44:0.92 45:0.81 47:0.93 48:0.91 64:0.77 66:0.31 69:0.27 70:0.67 71:0.91 74:0.69 76:0.85 77:0.70 79:0.86 81:0.55 83:0.56 86:0.86 91:0.25 96:0.69 97:0.97 98:0.17 99:0.83 101:0.47 104:0.99 106:0.81 108:0.21 114:0.78 117:0.86 120:0.55 122:0.91 123:0.20 124:0.76 126:0.51 127:0.60 129:0.05 133:0.73 135:0.69 137:0.87 139:0.89 140:0.45 144:0.85 145:0.70 146:0.32 147:0.38 149:0.51 150:0.46 151:0.50 153:0.48 154:0.66 155:0.51 158:0.81 159:0.81 162:0.86 164:0.56 165:0.65 170:0.87 172:0.37 173:0.14 174:0.86 176:0.70 177:0.88 179:0.73 187:0.68 190:0.77 192:0.51 195:0.93 196:0.89 197:0.88 201:0.60 202:0.36 206:0.81 208:0.61 212:0.75 215:0.63 216:0.84 219:0.94 220:0.96 222:0.60 230:0.65 232:0.99 233:0.76 235:0.68 239:0.86 241:0.21 242:0.43 243:0.49 244:0.61 245:0.58 247:0.55 248:0.50 253:0.62 255:0.69 256:0.45 259:0.21 260:0.55 262:0.93 265:0.18 266:0.47 267:0.69 268:0.46 271:0.44 276:0.31 279:0.81 281:0.91 282:0.80 283:0.66 284:0.88 285:0.60 290:0.78 292:0.87 297:0.36 300:0.55 +2 1:0.62 10:0.91 11:0.87 12:0.37 17:0.70 18:0.73 21:0.05 27:0.20 29:0.62 30:0.57 34:0.92 36:0.65 37:0.60 39:0.47 43:0.67 45:0.90 66:0.77 69:0.17 70:0.49 71:0.91 74:0.70 81:0.71 83:0.56 86:0.79 91:0.12 98:0.71 99:0.83 101:0.65 102:0.95 104:0.84 106:0.81 108:0.14 114:0.95 117:0.86 122:0.55 123:0.13 124:0.40 126:0.51 127:0.36 129:0.05 133:0.45 135:0.60 139:0.76 144:0.85 145:0.59 146:0.77 147:0.81 149:0.73 150:0.64 154:0.62 155:0.48 159:0.45 165:0.65 170:0.87 172:0.76 173:0.21 174:0.86 176:0.70 177:0.88 178:0.55 179:0.41 187:0.39 192:0.49 193:0.97 195:0.73 197:0.89 201:0.64 206:0.81 208:0.50 212:0.72 215:0.91 216:0.84 222:0.60 232:0.89 235:0.22 236:0.95 239:0.92 241:0.21 242:0.24 243:0.79 244:0.61 245:0.74 247:0.79 253:0.69 259:0.21 265:0.71 266:0.38 267:0.69 271:0.44 276:0.67 290:0.95 297:0.36 300:0.43 +3 1:0.63 7:0.70 8:0.70 9:0.69 10:0.88 11:0.84 12:0.37 17:0.30 18:0.67 21:0.05 27:0.36 29:0.62 30:0.32 32:0.67 34:0.29 36:0.71 37:0.88 39:0.47 43:0.58 45:0.93 66:0.69 69:0.52 70:0.87 71:0.91 74:0.77 77:0.70 81:0.57 83:0.56 86:0.69 91:0.40 96:0.77 97:0.94 98:0.85 99:0.83 101:0.47 104:0.87 108:0.76 114:0.92 120:0.54 123:0.74 124:0.52 126:0.32 127:0.74 129:0.59 133:0.77 135:0.81 139:0.66 140:0.80 144:0.85 145:0.72 146:0.20 147:0.26 149:0.80 150:0.51 151:0.47 153:0.48 154:0.61 155:0.51 158:0.77 159:0.77 162:0.62 164:0.56 165:0.65 170:0.87 172:0.89 173:0.34 174:0.79 175:0.75 176:0.63 177:0.70 179:0.28 187:0.39 190:0.95 192:0.51 195:0.89 197:0.84 201:0.59 202:0.60 204:0.80 208:0.50 212:0.42 215:0.91 216:0.50 220:0.62 222:0.60 230:0.73 232:0.90 233:0.76 235:0.12 239:0.87 241:0.41 242:0.30 243:0.11 244:0.61 245:0.85 247:0.86 248:0.47 253:0.81 255:0.68 256:0.58 257:0.65 259:0.21 260:0.54 265:0.85 266:0.80 267:0.65 268:0.59 271:0.44 276:0.85 277:0.69 279:0.78 282:0.75 283:0.82 285:0.50 290:0.92 297:0.36 300:0.84 +1 8:0.59 10:0.96 11:0.84 12:0.37 17:0.75 18:0.65 27:0.27 29:0.62 30:0.38 34:0.24 36:0.42 37:0.94 39:0.47 43:0.65 45:0.81 66:0.90 69:0.05 70:0.73 71:0.91 74:0.56 76:0.85 77:0.70 81:0.59 86:0.71 91:0.44 96:0.80 98:0.87 101:0.47 102:0.96 108:0.05 114:0.84 117:0.86 122:0.95 123:0.05 126:0.32 127:0.38 129:0.05 135:0.75 139:0.68 140:0.45 144:0.85 145:0.38 146:0.73 147:0.77 149:0.71 150:0.58 151:0.65 153:0.48 154:0.63 158:0.73 159:0.29 162:0.86 170:0.87 172:0.71 173:0.21 174:0.93 176:0.59 177:0.88 179:0.53 187:0.39 190:0.95 192:0.45 193:0.97 195:0.58 197:0.96 201:0.60 202:0.36 212:0.80 216:0.59 222:0.60 232:0.82 235:0.05 236:0.94 239:0.96 241:0.32 242:0.27 243:0.90 244:0.61 247:0.84 248:0.63 253:0.80 254:0.74 256:0.45 259:0.21 265:0.87 266:0.27 267:0.44 268:0.46 271:0.44 276:0.58 282:0.72 285:0.46 290:0.84 300:0.55 +0 1:0.63 7:0.64 10:0.94 11:0.84 12:0.37 17:0.32 18:0.69 21:0.05 27:0.02 29:0.62 30:0.60 32:0.71 34:0.39 36:0.40 37:0.92 39:0.47 43:0.30 44:0.92 45:0.95 66:0.79 69:0.88 70:0.48 71:0.91 74:0.75 76:0.85 77:0.70 81:0.57 83:0.56 86:0.75 91:0.11 98:0.79 99:0.83 101:0.47 108:0.75 114:0.92 122:0.55 123:0.74 124:0.40 126:0.32 127:0.69 129:0.05 133:0.45 135:0.75 139:0.78 144:0.85 145:0.54 146:0.91 147:0.49 149:0.75 150:0.51 154:0.52 155:0.49 159:0.68 165:0.65 170:0.87 172:0.89 173:0.86 174:0.93 176:0.63 177:0.70 178:0.55 179:0.30 187:0.39 192:0.47 195:0.81 197:0.93 201:0.59 208:0.50 212:0.80 215:0.91 216:0.99 222:0.60 230:0.65 233:0.76 235:0.12 239:0.92 241:0.46 242:0.41 243:0.17 244:0.61 245:0.87 247:0.88 253:0.69 254:0.74 257:0.65 259:0.21 265:0.79 266:0.71 267:0.52 271:0.44 276:0.81 282:0.80 290:0.92 297:0.36 300:0.55 +1 1:0.59 7:0.70 8:0.74 9:0.75 10:0.89 11:0.84 12:0.37 17:0.09 18:0.82 21:0.40 27:0.20 29:0.62 30:0.66 31:0.90 32:0.71 34:0.44 36:0.73 37:0.57 39:0.47 43:0.62 44:0.92 45:0.95 66:0.96 69:0.57 70:0.52 71:0.91 74:0.86 77:0.70 79:0.90 81:0.49 83:0.56 86:0.89 91:0.71 96:0.96 97:0.94 98:0.83 99:0.83 101:0.47 108:0.44 114:0.78 120:0.93 122:0.55 123:0.42 126:0.32 127:0.33 129:0.05 135:0.42 137:0.90 138:0.98 139:0.86 140:0.97 144:0.85 145:0.92 146:0.80 147:0.83 149:0.80 150:0.44 151:0.66 153:0.91 154:0.58 155:0.64 158:0.76 159:0.36 162:0.81 164:0.92 165:0.65 170:0.87 172:0.90 173:0.62 174:0.79 176:0.63 177:0.88 179:0.23 187:0.39 190:0.89 191:0.76 192:0.87 195:0.69 196:0.93 197:0.82 201:0.56 202:0.86 204:0.80 208:0.61 212:0.91 215:0.67 216:0.73 222:0.60 230:0.73 233:0.76 235:0.52 239:0.86 241:0.68 242:0.38 243:0.96 247:0.90 248:0.63 253:0.97 254:0.97 255:0.73 256:0.89 259:0.21 260:0.93 265:0.83 266:0.38 267:0.76 268:0.90 271:0.44 276:0.82 279:0.78 282:0.79 283:0.67 284:0.88 285:0.60 290:0.78 297:0.36 300:0.43 +2 1:0.65 7:0.81 10:0.97 11:0.84 12:0.37 17:0.70 18:0.80 21:0.22 22:0.93 27:0.53 29:0.62 30:0.42 32:0.68 34:0.77 36:0.34 37:0.86 39:0.47 43:0.58 44:0.92 45:0.83 47:0.93 48:0.91 64:0.77 66:0.93 69:0.33 70:0.83 71:0.91 74:0.75 77:0.70 81:0.60 83:0.56 86:0.87 91:0.18 97:0.94 98:0.95 99:0.83 101:0.47 104:0.87 106:0.81 108:0.26 114:0.88 117:0.86 122:0.94 123:0.25 126:0.51 127:0.67 129:0.05 135:0.93 139:0.91 144:0.85 145:0.98 146:0.88 147:0.90 149:0.31 150:0.52 154:0.59 155:0.56 158:0.81 159:0.45 165:0.65 170:0.87 172:0.80 173:0.38 174:0.94 176:0.70 177:0.88 178:0.55 179:0.49 187:0.39 192:0.57 193:0.99 195:0.67 197:0.95 201:0.63 204:0.83 206:0.81 208:0.64 212:0.69 215:0.79 216:0.65 219:0.94 222:0.60 230:0.83 232:0.91 233:0.66 235:0.42 239:0.97 241:0.15 242:0.16 243:0.93 247:0.90 253:0.67 254:0.74 259:0.21 262:0.93 265:0.95 266:0.54 267:0.95 271:0.44 276:0.69 279:0.79 281:0.86 282:0.77 283:0.82 290:0.88 292:0.87 297:0.36 300:0.70 +2 1:0.62 10:0.97 11:0.84 12:0.37 17:0.55 18:0.75 21:0.47 22:0.93 27:0.04 29:0.62 30:0.13 32:0.67 34:0.94 36:0.33 37:0.94 39:0.47 43:0.56 45:0.81 47:0.93 64:0.77 66:0.54 69:0.35 70:0.93 71:0.91 74:0.62 77:0.70 81:0.55 83:0.56 86:0.85 91:0.31 98:0.74 99:0.83 101:0.47 104:0.99 106:0.81 108:0.60 114:0.78 117:0.86 122:0.95 123:0.58 124:0.52 126:0.51 127:0.50 129:0.59 133:0.47 135:0.60 139:0.80 144:0.85 145:0.49 146:0.34 147:0.41 149:0.27 150:0.46 154:0.84 155:0.48 159:0.39 165:0.65 170:0.87 172:0.65 173:0.42 174:0.94 176:0.70 177:0.88 178:0.55 179:0.51 187:0.87 192:0.48 193:0.96 195:0.64 197:0.96 201:0.60 206:0.81 208:0.61 212:0.81 215:0.63 216:0.74 222:0.60 232:0.99 235:0.68 239:0.96 241:0.32 242:0.21 243:0.39 245:0.82 247:0.86 253:0.59 254:0.84 259:0.21 262:0.93 265:0.74 266:0.47 267:0.92 271:0.44 276:0.63 282:0.75 290:0.78 297:0.36 300:0.70 +0 1:0.57 7:0.70 8:0.76 9:0.75 11:0.84 12:0.37 17:0.51 18:0.80 21:0.59 22:0.93 27:0.51 29:0.62 30:0.17 31:0.95 34:0.53 36:0.64 37:0.40 39:0.47 43:0.63 45:0.87 47:0.93 55:0.71 66:0.68 69:0.23 70:0.92 71:0.91 74:0.69 79:0.95 81:0.45 83:0.69 86:0.83 91:0.58 96:0.94 97:0.89 98:0.90 99:0.83 101:0.47 102:0.95 108:0.77 114:0.71 117:0.86 120:0.90 123:0.75 124:0.46 126:0.32 127:0.95 129:0.59 133:0.46 135:0.82 137:0.95 138:0.98 139:0.83 140:0.80 144:0.85 145:0.74 146:0.78 147:0.81 149:0.65 150:0.40 151:0.78 153:0.71 154:0.68 155:0.64 158:0.81 159:0.85 162:0.83 164:0.90 165:0.65 170:0.87 172:0.92 173:0.12 175:0.75 176:0.63 177:0.70 179:0.24 187:0.39 190:0.89 192:0.98 195:0.94 196:0.95 201:0.55 202:0.82 204:0.84 208:0.61 212:0.41 215:0.55 216:0.32 219:0.94 220:0.82 222:0.60 230:0.73 232:0.85 233:0.76 235:0.74 239:0.84 241:0.57 242:0.54 243:0.31 244:0.61 245:0.96 247:0.90 248:0.82 253:0.94 254:0.74 255:0.78 256:0.87 257:0.65 259:0.21 260:0.90 262:0.93 265:0.90 266:0.80 267:0.59 268:0.87 271:0.44 276:0.89 277:0.69 279:0.78 283:0.66 284:0.97 285:0.60 290:0.71 292:0.87 297:0.36 300:0.84 +0 10:0.82 11:0.84 12:0.37 17:0.45 18:0.82 21:0.78 27:0.45 29:0.62 30:0.38 34:0.71 36:0.19 37:0.50 39:0.47 43:0.25 45:0.66 55:0.84 66:0.48 69:0.89 70:0.45 71:0.91 74:0.31 86:0.77 91:0.12 98:0.42 101:0.47 108:0.78 122:0.92 123:0.77 124:0.78 127:0.27 129:0.05 133:0.80 135:0.86 139:0.73 144:0.85 145:0.47 146:0.79 147:0.05 149:0.25 154:0.18 159:0.71 163:0.94 170:0.87 172:0.37 173:0.77 174:0.79 177:0.05 178:0.55 179:0.68 187:0.68 197:0.81 212:0.28 216:0.77 222:0.60 235:1.00 239:0.82 241:0.21 242:0.60 243:0.16 244:0.83 245:0.47 247:0.55 253:0.28 257:0.93 259:0.21 265:0.44 266:0.63 267:0.73 271:0.44 276:0.42 300:0.33 +2 1:0.63 7:0.64 8:0.98 9:0.74 10:0.92 11:0.84 12:0.37 17:0.30 18:0.61 21:0.05 27:0.02 29:0.62 30:0.56 31:0.93 32:0.67 34:0.39 36:0.31 37:0.92 39:0.47 43:0.57 45:0.94 66:0.77 69:0.46 70:0.77 71:0.91 74:0.82 76:0.85 77:0.70 79:0.93 81:0.57 83:0.56 86:0.67 91:0.46 96:0.92 97:0.89 98:0.72 99:0.83 101:0.47 108:0.35 114:0.92 120:0.86 122:0.82 123:0.34 124:0.43 126:0.32 127:0.74 129:0.05 133:0.73 135:0.44 137:0.93 139:0.65 140:0.45 144:0.85 145:0.49 146:0.91 147:0.93 149:0.78 150:0.51 151:0.95 153:0.84 154:0.61 155:0.56 158:0.77 159:0.76 162:0.68 164:0.77 165:0.65 170:0.87 172:0.86 173:0.30 174:0.89 176:0.63 177:0.88 179:0.36 187:0.39 190:0.89 191:0.70 192:0.58 195:0.89 196:0.89 197:0.90 201:0.59 202:0.71 208:0.50 212:0.60 215:0.91 216:0.99 222:0.60 230:0.65 233:0.76 235:0.12 239:0.90 241:0.68 242:0.51 243:0.79 244:0.61 245:0.73 247:0.82 248:0.92 253:0.94 254:0.74 255:0.73 256:0.71 259:0.21 260:0.87 265:0.72 266:0.71 267:0.89 268:0.74 271:0.44 276:0.78 279:0.76 282:0.75 283:0.82 284:0.88 285:0.60 290:0.92 297:0.36 300:0.94 +2 6:0.99 8:0.97 9:0.80 11:0.83 12:0.20 17:0.73 18:0.81 20:0.85 21:0.05 27:0.01 28:0.93 29:0.71 30:0.44 31:0.95 34:0.89 36:0.61 37:0.98 39:0.47 41:0.90 43:0.59 45:0.88 46:0.97 64:0.77 66:0.60 69:0.90 70:0.85 71:0.89 74:0.55 78:0.83 79:0.96 81:0.41 83:0.91 85:0.72 86:0.85 91:0.72 96:0.88 98:0.50 100:0.95 101:0.87 104:0.58 107:0.96 108:0.85 110:0.94 111:0.90 120:0.81 122:0.51 123:0.84 124:0.58 125:0.99 126:0.26 127:0.45 129:0.59 133:0.61 135:0.39 137:0.95 139:0.82 140:0.87 146:0.34 147:0.23 149:0.66 150:0.35 151:0.72 152:0.96 153:0.48 154:0.49 155:0.67 159:0.60 160:0.99 161:0.80 162:0.86 164:0.79 167:0.93 169:0.98 172:0.74 173:0.91 177:0.05 179:0.41 181:0.69 186:0.83 187:0.21 189:0.99 190:0.77 191:0.91 192:0.87 196:0.95 202:0.72 208:0.64 212:0.71 216:0.82 219:0.89 220:0.62 222:0.48 232:0.76 235:0.05 238:0.98 241:0.78 242:0.24 243:0.13 245:0.79 247:0.79 248:0.74 253:0.83 255:0.95 256:0.79 257:0.84 259:0.21 260:0.79 261:0.99 265:0.52 266:0.71 267:0.52 268:0.80 271:0.94 276:0.66 284:0.96 285:0.62 289:0.93 292:0.91 300:0.94 +1 8:0.95 9:0.76 12:0.20 17:0.53 20:0.77 21:0.78 27:0.37 28:0.93 29:0.71 34:0.34 36:0.23 37:0.98 39:0.47 46:0.88 71:0.89 78:0.80 83:0.60 91:0.77 96:0.86 100:0.84 104:0.54 107:0.88 110:0.88 111:0.80 120:0.77 125:0.98 135:0.79 140:0.45 151:0.90 152:0.75 153:0.83 155:0.58 160:0.96 161:0.80 162:0.53 164:0.62 167:0.96 169:0.82 175:0.97 181:0.69 186:0.80 190:0.77 191:0.82 192:0.59 202:0.63 208:0.54 216:0.38 222:0.48 232:0.72 241:0.81 244:0.93 248:0.80 253:0.76 255:0.74 256:0.45 257:0.93 259:0.21 260:0.73 261:0.76 267:0.69 268:0.58 271:0.94 277:0.95 285:0.56 289:0.93 +1 1:0.69 6:0.94 8:0.86 9:0.76 11:0.64 12:0.20 17:0.51 18:0.82 20:0.76 21:0.13 27:0.08 28:0.93 29:0.71 30:0.65 31:0.89 34:0.91 36:0.59 37:0.95 39:0.47 43:0.69 45:0.94 46:0.99 66:0.74 69:0.48 70:0.82 71:0.89 74:0.75 78:0.72 79:0.89 81:0.52 83:0.60 86:0.85 91:0.64 96:0.74 98:0.79 99:0.83 100:0.83 101:0.52 104:0.75 107:0.97 108:0.74 110:0.95 111:0.76 114:0.75 120:0.61 122:0.51 123:0.72 124:0.42 125:0.98 126:0.44 127:0.72 129:0.59 133:0.46 135:0.39 137:0.89 139:0.81 140:0.45 146:0.62 147:0.68 149:0.82 150:0.58 151:0.62 152:0.94 153:0.80 154:0.54 155:0.58 159:0.63 160:0.96 161:0.80 162:0.79 164:0.62 165:0.70 167:0.89 169:0.82 172:0.85 173:0.41 176:0.66 177:0.70 179:0.37 181:0.69 186:0.73 187:0.21 190:0.77 192:0.59 196:0.90 201:0.68 202:0.60 208:0.54 212:0.57 215:0.61 216:0.47 220:0.74 222:0.48 232:0.80 235:0.22 241:0.75 242:0.38 243:0.23 245:0.87 247:0.79 248:0.60 253:0.64 254:0.74 255:0.74 256:0.58 259:0.21 260:0.60 261:0.85 265:0.79 266:0.63 267:0.69 268:0.65 271:0.94 276:0.78 284:0.86 285:0.62 289:0.93 290:0.75 297:0.36 300:0.84 +2 1:0.69 6:0.83 7:0.72 8:0.77 9:0.76 11:0.83 12:0.20 17:0.49 18:0.80 20:0.89 21:0.40 27:0.21 28:0.93 29:0.71 30:0.72 34:0.62 36:0.62 37:0.74 39:0.47 43:0.72 45:1.00 46:1.00 66:0.43 69:0.17 70:0.60 71:0.89 74:0.97 78:0.74 81:0.37 83:0.60 85:0.90 86:0.90 91:0.72 96:0.97 97:0.99 98:0.76 99:0.83 100:0.78 101:0.87 104:0.84 106:0.81 107:0.98 108:0.86 110:0.95 111:0.74 114:0.61 117:0.86 120:0.93 122:0.51 123:0.85 124:0.81 125:0.99 126:0.44 127:0.78 129:0.59 133:0.82 135:0.17 139:0.87 140:0.45 146:0.94 147:0.95 149:0.99 150:0.53 151:0.96 152:0.85 153:0.95 154:0.55 155:0.58 158:0.79 159:0.92 160:0.99 161:0.80 162:0.61 164:0.86 165:0.70 167:0.74 169:0.76 172:0.98 173:0.07 176:0.66 177:0.70 179:0.10 181:0.69 186:0.75 187:0.39 189:0.86 190:0.77 191:0.70 192:0.59 201:0.68 202:0.85 204:0.85 206:0.81 208:0.54 212:0.77 215:0.42 216:0.95 222:0.48 230:0.74 232:0.88 233:0.73 235:0.52 238:0.91 241:0.56 242:0.55 243:0.31 245:0.99 247:0.84 248:0.96 253:0.98 255:0.74 256:0.86 259:0.21 260:0.92 261:0.77 265:0.76 266:0.47 267:0.18 268:0.86 271:0.94 276:0.98 279:0.82 283:0.82 285:0.56 289:0.93 290:0.61 297:0.36 300:0.84 +2 6:0.99 8:0.97 9:0.80 11:0.83 12:0.20 17:0.73 18:0.81 20:0.85 21:0.05 27:0.01 28:0.93 29:0.71 30:0.44 31:0.95 34:0.89 36:0.60 37:0.98 39:0.47 41:0.90 43:0.59 45:0.88 46:0.97 64:0.77 66:0.60 69:0.90 70:0.85 71:0.89 74:0.55 78:0.83 79:0.96 81:0.41 83:0.91 85:0.72 86:0.85 91:0.72 96:0.88 98:0.50 100:0.95 101:0.87 104:0.58 107:0.96 108:0.85 110:0.94 111:0.90 120:0.81 122:0.51 123:0.84 124:0.58 125:0.99 126:0.26 127:0.45 129:0.59 133:0.61 135:0.39 137:0.95 139:0.82 140:0.87 146:0.34 147:0.23 149:0.66 150:0.35 151:0.72 152:0.96 153:0.48 154:0.49 155:0.67 159:0.60 160:0.99 161:0.80 162:0.86 164:0.79 167:0.93 169:0.98 172:0.74 173:0.91 177:0.05 179:0.41 181:0.69 186:0.83 187:0.21 189:0.99 190:0.77 191:0.91 192:0.87 196:0.95 202:0.72 208:0.64 212:0.71 216:0.82 219:0.89 220:0.62 222:0.48 232:0.76 235:0.05 238:0.98 241:0.78 242:0.24 243:0.13 245:0.79 247:0.79 248:0.74 253:0.83 255:0.95 256:0.79 257:0.84 259:0.21 260:0.79 261:0.99 265:0.52 266:0.71 267:0.52 268:0.80 271:0.94 276:0.66 284:0.96 285:0.62 289:0.93 292:0.91 300:0.94 +1 1:0.69 6:0.88 7:0.72 8:0.87 9:0.80 11:0.64 12:0.20 17:0.51 18:0.81 20:0.86 21:0.40 27:0.43 28:0.93 29:0.71 30:0.28 31:0.89 32:0.80 34:0.59 36:0.85 37:0.79 39:0.47 43:0.67 44:0.93 45:0.83 46:0.99 66:0.89 69:0.17 70:0.81 71:0.89 74:0.78 77:0.70 78:0.75 79:0.88 81:0.37 83:0.60 86:0.89 91:0.84 96:0.94 97:0.89 98:0.99 99:0.83 100:0.80 101:0.52 104:0.58 107:0.96 108:0.14 110:0.93 111:0.76 114:0.61 120:0.90 122:0.51 123:0.13 125:0.98 126:0.44 127:0.93 129:0.05 135:0.30 137:0.89 138:0.98 139:0.90 140:0.97 145:0.70 146:0.81 147:0.84 149:0.55 150:0.54 151:0.55 152:0.81 153:0.88 154:0.85 155:0.67 158:0.78 159:0.37 160:1.00 161:0.80 162:0.71 164:0.90 165:0.70 167:0.95 169:0.78 172:0.70 173:0.35 176:0.66 177:0.70 179:0.67 181:0.69 186:0.76 189:0.90 190:0.77 191:0.90 192:0.87 195:0.61 196:0.92 201:0.68 202:0.87 204:0.85 208:0.64 212:0.61 215:0.42 216:0.49 220:0.91 222:0.48 230:0.75 232:0.76 233:0.76 235:0.60 238:0.94 241:0.79 242:0.19 243:0.90 247:0.76 248:0.57 253:0.93 254:0.84 255:0.95 256:0.89 259:0.21 260:0.87 261:0.78 265:0.99 266:0.38 267:0.97 268:0.89 271:0.94 276:0.57 279:0.82 282:0.88 283:0.78 284:0.93 285:0.62 289:0.93 290:0.61 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.64 12:0.20 17:0.77 18:0.85 21:0.47 22:0.93 27:0.08 28:0.93 29:0.71 30:0.54 31:0.96 34:0.75 36:0.49 37:0.90 39:0.47 41:0.90 43:0.56 45:0.85 46:0.98 47:0.93 64:0.77 66:0.77 69:0.81 70:0.52 71:0.89 74:0.56 79:0.97 81:0.47 83:0.91 86:0.83 91:0.72 96:0.90 98:0.77 99:0.83 101:0.52 104:0.58 107:0.96 108:0.65 110:0.93 114:0.60 120:0.82 122:0.51 123:0.62 124:0.40 125:0.98 126:0.54 127:0.52 129:0.05 133:0.43 135:0.60 137:0.96 139:0.81 140:0.93 146:0.75 147:0.44 149:0.62 150:0.67 151:0.75 153:0.48 154:0.66 155:0.67 159:0.43 160:0.99 161:0.80 162:0.79 163:0.81 164:0.83 165:0.70 167:0.93 172:0.68 173:0.87 176:0.73 177:0.38 179:0.58 181:0.69 187:0.98 190:0.77 192:0.87 196:0.95 201:0.93 202:0.81 208:0.64 212:0.35 215:0.41 216:0.64 219:0.89 220:0.87 222:0.48 232:0.76 235:0.80 241:0.78 242:0.37 243:0.17 245:0.65 247:0.74 248:0.76 253:0.85 254:0.88 255:0.95 256:0.85 257:0.65 260:0.81 262:0.93 265:0.77 266:0.54 267:0.82 268:0.85 271:0.94 276:0.59 284:0.97 285:0.62 289:0.93 290:0.60 292:0.91 297:0.36 300:0.43 +1 1:0.69 7:0.72 8:0.91 9:0.76 11:0.64 12:0.20 17:0.26 18:0.78 20:0.77 21:0.54 27:0.18 28:0.93 29:0.71 30:0.67 34:0.49 36:0.98 37:0.97 39:0.47 43:0.72 45:0.95 46:0.98 48:0.91 66:0.19 69:0.67 70:0.57 71:0.89 74:0.82 76:0.85 78:0.78 81:0.34 83:0.60 86:0.81 91:0.75 96:0.83 97:0.94 98:0.53 99:0.83 100:0.73 101:0.52 104:0.57 107:0.97 108:0.83 110:0.95 111:0.76 114:0.58 120:0.73 122:0.51 123:0.82 124:0.85 125:0.98 126:0.44 127:0.86 129:0.81 133:0.84 135:0.86 139:0.80 140:0.45 146:0.78 147:0.18 149:0.85 150:0.52 151:0.81 152:0.76 153:0.48 154:0.67 155:0.67 158:0.78 159:0.76 160:0.99 161:0.80 162:0.73 163:0.81 164:0.78 165:0.70 167:0.97 169:0.72 172:0.61 173:0.54 176:0.66 177:0.38 179:0.36 181:0.69 186:0.78 190:0.77 192:0.87 201:0.68 202:0.73 204:0.89 208:0.64 212:0.36 215:0.39 216:0.10 220:0.96 222:0.48 230:0.74 232:0.75 233:0.73 235:0.68 241:0.83 242:0.52 243:0.10 245:0.85 247:0.76 248:0.75 253:0.82 254:0.84 255:0.74 256:0.80 257:0.65 259:0.21 260:0.69 261:0.75 265:0.55 266:0.75 267:0.17 268:0.77 271:0.94 276:0.80 277:0.69 279:0.82 281:0.86 283:0.78 285:0.56 289:0.93 290:0.58 297:0.36 300:0.70 +1 1:0.69 8:0.66 9:0.80 11:0.64 12:0.20 17:0.78 18:0.73 20:0.80 21:0.40 27:0.67 28:0.93 29:0.71 30:0.58 31:0.89 34:0.84 36:0.69 37:0.79 39:0.47 43:0.69 45:0.77 46:1.00 66:0.80 69:0.17 70:0.44 71:0.89 74:0.56 78:0.75 79:0.88 81:0.37 83:0.60 86:0.82 91:0.56 96:0.79 98:0.86 99:0.83 100:0.79 101:0.52 104:0.54 107:0.94 108:0.14 110:0.92 111:0.75 114:0.61 120:0.68 122:0.51 123:0.13 125:0.98 126:0.44 127:0.38 129:0.05 135:0.69 137:0.89 139:0.77 140:0.80 146:0.43 147:0.49 149:0.49 150:0.54 151:0.61 152:0.80 153:0.46 154:0.85 155:0.67 159:0.16 160:0.98 161:0.80 162:0.66 164:0.71 165:0.70 167:0.86 169:0.78 172:0.45 173:0.60 176:0.66 177:0.70 179:0.82 181:0.69 186:0.76 187:0.39 190:0.77 191:0.86 192:0.87 196:0.90 201:0.68 202:0.67 208:0.64 212:0.82 215:0.42 216:0.11 220:0.91 222:0.48 232:0.72 235:0.60 241:0.73 242:0.40 243:0.82 247:0.55 248:0.59 253:0.71 254:0.93 255:0.95 256:0.68 259:0.21 260:0.67 261:0.78 265:0.86 266:0.23 267:0.18 268:0.68 271:0.94 276:0.34 284:0.89 285:0.62 289:0.93 290:0.61 297:0.36 300:0.33 +2 1:0.69 6:0.97 7:0.72 8:0.88 9:0.76 11:0.83 12:0.20 17:0.81 18:0.82 20:0.79 27:0.17 28:0.93 29:0.71 30:0.45 32:0.69 34:0.50 36:0.47 37:0.80 39:0.47 43:0.94 45:0.83 46:0.98 66:0.60 69:0.19 70:0.65 71:0.89 74:0.69 77:0.70 78:0.79 81:0.69 83:0.60 85:0.72 86:0.86 91:0.73 96:0.83 98:0.72 99:0.83 100:0.88 101:0.87 104:0.87 107:0.96 108:0.70 110:0.93 111:0.81 114:0.95 120:0.72 122:0.51 123:0.68 124:0.50 125:1.00 126:0.44 127:0.75 129:0.59 133:0.46 135:0.72 139:0.82 140:0.45 145:0.54 146:0.64 147:0.69 149:0.77 150:0.65 151:0.83 152:0.77 153:0.69 154:0.94 155:0.58 159:0.50 160:0.96 161:0.80 162:0.86 164:0.55 165:0.70 167:0.91 169:0.85 172:0.63 173:0.26 176:0.66 177:0.70 179:0.61 181:0.69 186:0.79 187:0.21 189:0.98 190:0.77 192:0.59 195:0.73 201:0.68 202:0.46 204:0.85 208:0.54 212:0.30 215:0.96 216:0.34 222:0.48 230:0.75 232:0.89 233:0.76 235:0.05 238:0.96 241:0.77 242:0.17 243:0.36 245:0.78 247:0.82 248:0.75 253:0.81 255:0.74 256:0.45 259:0.21 260:0.69 261:0.79 265:0.73 266:0.54 267:0.28 268:0.55 271:0.94 276:0.60 282:0.77 285:0.56 289:0.93 290:0.95 297:0.36 300:0.55 +2 1:0.69 6:0.83 7:0.72 8:0.70 9:0.76 11:0.83 12:0.20 17:0.61 18:0.84 20:0.79 21:0.40 27:0.21 28:0.93 29:0.71 30:0.71 34:0.66 36:0.62 37:0.74 39:0.47 43:0.72 45:1.00 46:1.00 66:0.43 69:0.90 70:0.62 71:0.89 74:0.97 78:0.75 81:0.37 83:0.60 85:0.90 86:0.91 91:0.22 96:0.84 98:0.75 99:0.83 100:0.78 101:0.87 104:0.84 107:0.98 108:0.88 110:0.95 111:0.75 114:0.61 120:0.75 122:0.51 123:0.88 124:0.85 125:0.99 126:0.44 127:0.78 129:0.59 133:0.87 135:0.18 139:0.88 140:0.45 146:0.81 147:0.87 149:0.98 150:0.53 151:0.81 152:0.85 153:0.48 154:0.54 155:0.58 159:0.92 160:0.98 161:0.80 162:0.76 164:0.73 165:0.70 167:0.69 169:0.76 172:0.98 173:0.66 176:0.66 177:0.38 179:0.10 181:0.69 186:0.76 187:0.39 189:0.84 190:0.77 192:0.59 201:0.68 202:0.67 204:0.85 208:0.54 212:0.76 215:0.42 216:0.95 220:0.91 222:0.48 230:0.74 232:0.88 233:0.73 235:0.52 238:0.90 241:0.37 242:0.54 243:0.19 245:0.99 247:0.86 248:0.77 253:0.87 255:0.74 256:0.68 257:0.65 259:0.21 260:0.70 261:0.77 265:0.75 266:0.71 267:0.44 268:0.71 271:0.94 276:0.99 285:0.56 289:0.93 290:0.61 297:0.36 300:0.84 +1 1:0.74 6:0.84 8:0.63 9:0.80 11:0.83 12:0.20 17:0.24 18:0.74 20:0.87 21:0.59 27:0.45 28:0.93 29:0.71 30:0.33 31:0.86 34:0.66 36:0.20 37:0.50 39:0.47 41:0.82 43:0.82 45:0.81 46:0.96 60:0.87 64:0.77 66:0.46 69:0.60 70:0.95 71:0.89 74:0.71 78:0.74 79:0.86 81:0.46 83:0.91 85:0.72 86:0.85 91:0.25 96:0.68 98:0.43 100:0.77 101:0.87 107:0.96 108:0.46 110:0.93 111:0.74 114:0.58 120:0.54 122:0.41 123:0.44 124:0.76 125:0.98 126:0.54 127:0.40 129:0.59 133:0.73 135:0.83 137:0.86 139:0.87 140:0.45 145:0.52 146:0.63 147:0.68 149:0.71 150:0.70 151:0.48 152:0.82 153:0.48 154:0.77 155:0.67 158:0.91 159:0.60 160:0.96 161:0.80 162:0.86 163:0.90 164:0.57 165:0.93 167:0.73 169:0.76 172:0.57 173:0.50 176:0.73 177:0.70 179:0.52 181:0.69 186:0.75 187:0.68 189:0.84 192:0.87 196:0.88 201:0.93 202:0.36 208:0.64 212:0.30 215:0.39 216:0.77 219:0.95 220:0.93 222:0.48 235:0.80 238:0.90 241:0.54 242:0.17 243:0.59 245:0.68 247:0.74 248:0.48 253:0.44 255:0.95 256:0.45 259:0.21 260:0.54 261:0.77 265:0.45 266:0.71 267:0.69 268:0.46 271:0.94 276:0.59 279:0.86 283:0.78 284:0.89 285:0.62 289:0.93 290:0.58 292:0.91 297:0.36 300:0.84 +3 1:0.69 6:0.96 7:0.72 8:0.95 9:0.80 11:0.64 12:0.20 17:0.34 18:0.66 20:0.93 21:0.40 27:0.08 28:0.93 29:0.71 30:0.62 31:0.95 32:0.64 34:0.28 36:0.45 37:0.90 39:0.47 41:0.82 43:0.59 45:0.85 46:0.98 66:0.49 69:0.60 70:0.56 71:0.89 74:0.65 78:0.76 79:0.96 81:0.37 83:0.91 86:0.65 91:0.98 96:0.99 97:0.99 98:0.54 99:0.83 100:0.82 101:0.52 104:0.58 107:0.96 108:0.75 110:0.94 111:0.78 114:0.61 120:0.98 122:0.51 123:0.73 124:0.66 125:0.99 126:0.44 127:0.66 129:0.81 133:0.67 135:0.44 137:0.95 138:0.99 139:0.63 140:0.99 145:0.59 146:0.38 147:0.44 149:0.65 150:0.54 151:0.87 152:0.98 153:0.98 154:0.48 155:0.67 158:0.79 159:0.44 160:1.00 161:0.80 162:0.76 163:0.81 164:0.96 165:0.70 167:0.99 169:0.89 172:0.48 173:0.66 175:0.75 176:0.66 177:0.38 179:0.71 181:0.69 186:0.77 189:0.96 191:0.94 192:0.87 195:0.68 196:0.89 201:0.68 202:0.94 204:0.85 208:0.64 212:0.27 215:0.42 216:0.64 222:0.48 230:0.74 232:0.75 233:0.73 235:0.68 238:0.97 241:0.95 242:0.58 243:0.20 244:0.61 245:0.64 247:0.39 248:0.77 253:0.98 255:0.95 256:0.96 257:0.65 260:0.97 261:0.91 265:0.56 266:0.75 267:0.97 268:0.96 271:0.94 276:0.50 277:0.69 279:0.82 283:0.82 284:0.93 285:0.62 289:0.93 290:0.61 297:0.36 300:0.55 +2 8:0.87 9:0.76 11:0.83 12:0.20 17:0.85 18:0.81 21:0.05 27:0.01 28:0.93 29:0.71 30:0.44 31:0.91 34:0.91 36:0.59 37:0.98 39:0.47 43:0.59 45:0.88 46:0.97 64:0.77 66:0.60 69:0.90 70:0.85 71:0.89 74:0.55 79:0.92 81:0.41 83:0.60 85:0.72 86:0.85 91:0.51 96:0.68 98:0.50 101:0.87 104:0.58 107:0.96 108:0.85 110:0.94 120:0.54 122:0.51 123:0.84 124:0.58 125:0.96 126:0.26 127:0.45 129:0.59 133:0.61 135:0.34 137:0.91 139:0.82 140:0.87 146:0.34 147:0.23 149:0.66 150:0.35 151:0.48 153:0.83 154:0.49 155:0.58 159:0.60 160:0.96 161:0.80 162:0.56 164:0.54 167:0.89 172:0.74 173:0.91 177:0.05 179:0.41 181:0.69 187:0.21 190:0.89 191:0.93 192:0.59 196:0.92 202:0.66 208:0.54 212:0.71 216:0.82 219:0.89 220:0.62 222:0.48 232:0.76 235:0.05 241:0.75 242:0.24 243:0.13 245:0.79 247:0.79 248:0.48 253:0.38 255:0.74 256:0.58 257:0.84 259:0.21 260:0.54 265:0.52 266:0.71 267:0.69 268:0.64 271:0.94 276:0.66 284:0.87 285:0.62 289:0.93 292:0.91 300:0.94 +1 1:0.69 6:0.96 8:0.96 9:0.76 12:0.20 17:0.30 20:0.89 21:0.05 27:0.08 28:0.93 29:0.71 31:0.87 34:0.36 36:0.76 37:0.95 39:0.47 46:0.88 71:0.89 74:0.39 78:0.77 79:0.87 81:0.60 83:0.60 91:0.88 96:0.95 97:0.97 99:0.83 100:0.84 104:0.75 107:0.88 110:0.88 111:0.78 114:0.85 120:0.91 125:0.98 126:0.44 135:0.26 137:0.87 138:0.98 139:0.64 140:0.80 150:0.60 151:0.96 152:0.94 153:0.95 155:0.58 158:0.78 160:0.99 161:0.80 162:0.61 164:0.89 165:0.70 167:0.99 169:0.80 175:0.97 176:0.66 178:0.42 181:0.69 186:0.77 189:0.98 190:0.77 191:0.97 192:0.59 196:0.87 201:0.68 202:0.88 208:0.54 215:0.78 216:0.47 219:0.89 222:0.48 232:0.79 235:0.12 238:0.97 241:0.92 244:0.93 248:0.93 253:0.91 255:0.74 256:0.88 257:0.93 259:0.21 260:0.89 261:0.83 267:0.17 268:0.89 271:0.94 277:0.95 279:0.82 283:0.78 284:0.86 285:0.62 289:0.93 290:0.85 292:0.91 297:0.36 +1 1:0.69 6:0.96 7:0.72 8:0.95 9:0.80 11:0.64 12:0.20 17:0.34 18:0.66 20:0.93 21:0.40 27:0.08 28:0.93 29:0.71 30:0.62 31:0.95 32:0.64 34:0.28 36:0.45 37:0.90 39:0.47 41:0.82 43:0.59 45:0.85 46:0.98 66:0.49 69:0.60 70:0.56 71:0.89 74:0.65 78:0.76 79:0.96 81:0.37 83:0.91 86:0.65 91:0.98 96:0.99 97:0.99 98:0.54 99:0.83 100:0.82 101:0.52 104:0.58 107:0.96 108:0.75 110:0.94 111:0.78 114:0.61 120:0.98 122:0.51 123:0.73 124:0.66 125:0.99 126:0.44 127:0.66 129:0.81 133:0.67 135:0.44 137:0.95 138:0.99 139:0.63 140:0.99 145:0.59 146:0.38 147:0.44 149:0.65 150:0.54 151:0.87 152:0.98 153:0.98 154:0.48 155:0.67 158:0.79 159:0.44 160:1.00 161:0.80 162:0.76 163:0.81 164:0.96 165:0.70 167:0.99 169:0.89 172:0.48 173:0.66 175:0.75 176:0.66 177:0.38 179:0.71 181:0.69 186:0.77 189:0.96 191:0.94 192:0.87 195:0.68 196:0.89 201:0.68 202:0.94 204:0.85 208:0.64 212:0.27 215:0.42 216:0.64 222:0.48 230:0.74 232:0.75 233:0.73 235:0.68 238:0.97 241:0.95 242:0.58 243:0.20 244:0.61 245:0.64 247:0.39 248:0.77 253:0.98 255:0.95 256:0.96 257:0.65 260:0.97 261:0.91 265:0.56 266:0.75 267:0.97 268:0.96 271:0.94 276:0.50 277:0.69 279:0.82 283:0.82 284:0.93 285:0.62 289:0.93 290:0.61 297:0.36 300:0.55 +1 1:0.69 11:0.83 12:0.20 17:0.34 18:0.85 21:0.71 27:0.45 28:0.93 29:0.71 30:0.60 34:0.63 36:0.17 37:0.50 39:0.47 43:0.66 45:0.96 46:0.98 66:0.26 69:0.61 70:0.70 71:0.89 74:0.77 81:0.32 83:0.60 85:0.72 86:0.86 91:0.17 98:0.47 99:0.83 101:0.87 107:0.97 108:0.46 110:0.95 114:0.54 122:0.51 123:0.45 124:0.93 125:0.88 126:0.44 127:0.72 129:0.81 133:0.93 135:0.49 139:0.82 145:0.49 146:0.88 147:0.90 149:0.88 150:0.51 154:0.63 155:0.58 159:0.88 160:0.88 161:0.80 163:0.81 165:0.70 167:0.73 172:0.79 173:0.31 176:0.66 177:0.70 178:0.55 179:0.23 181:0.69 187:0.68 192:0.59 201:0.68 208:0.54 212:0.29 215:0.37 216:0.77 222:0.48 235:0.88 241:0.52 242:0.42 243:0.45 245:0.87 247:0.86 253:0.44 259:0.21 265:0.49 266:0.89 267:0.89 271:0.94 276:0.88 289:0.92 290:0.54 297:0.36 300:0.70 +1 1:0.69 9:0.76 11:0.83 12:0.20 17:0.38 18:0.85 21:0.54 27:0.08 28:0.93 29:0.71 30:0.76 34:0.76 36:0.47 37:0.90 39:0.47 43:0.75 45:0.92 46:0.99 66:0.18 69:0.81 70:0.52 71:0.89 74:0.83 81:0.34 83:0.60 85:0.90 86:0.93 91:0.38 96:0.82 98:0.57 99:0.83 101:0.87 104:0.58 107:0.97 108:0.65 110:0.95 114:0.58 120:0.71 122:0.51 123:0.79 124:0.64 125:0.99 126:0.44 127:0.64 129:0.05 133:0.66 135:0.82 139:0.90 140:0.80 146:0.67 147:0.44 149:0.93 150:0.52 151:0.81 153:0.48 154:0.66 155:0.58 159:0.56 160:0.97 161:0.80 162:0.67 164:0.64 165:0.70 167:0.83 172:0.79 173:0.81 176:0.66 177:0.38 178:0.42 179:0.34 181:0.69 187:0.98 190:0.77 192:0.59 201:0.68 202:0.59 208:0.54 212:0.60 215:0.39 216:0.64 222:0.48 232:0.76 235:0.80 241:0.71 242:0.31 243:0.10 245:0.87 247:0.76 248:0.74 253:0.81 255:0.74 256:0.58 257:0.65 260:0.68 265:0.58 266:0.54 267:0.99 268:0.59 271:0.94 276:0.79 277:0.69 285:0.56 289:0.93 290:0.58 297:0.36 300:0.70 +2 1:0.69 6:0.88 7:0.72 8:0.87 9:0.80 11:0.64 12:0.20 17:0.53 18:0.84 20:0.86 21:0.40 27:0.43 28:0.93 29:0.71 30:0.21 31:0.89 32:0.80 34:0.49 36:0.85 37:0.79 39:0.47 43:0.67 44:0.93 45:0.83 46:0.99 66:0.90 69:0.17 70:0.68 71:0.89 74:0.79 77:0.70 78:0.75 79:0.88 81:0.37 83:0.60 86:0.90 91:0.83 96:0.93 97:0.89 98:0.99 99:0.83 100:0.80 101:0.52 104:0.58 107:0.96 108:0.14 110:0.93 111:0.76 114:0.61 120:0.89 122:0.51 123:0.13 125:0.98 126:0.44 127:0.91 129:0.05 135:0.32 137:0.89 138:0.98 139:0.90 140:0.97 145:0.70 146:0.78 147:0.82 149:0.57 150:0.54 151:0.55 152:0.81 153:0.88 154:0.85 155:0.67 158:0.78 159:0.34 160:0.99 161:0.80 162:0.70 164:0.89 165:0.70 167:0.95 169:0.78 172:0.71 173:0.37 176:0.66 177:0.70 179:0.65 181:0.69 186:0.76 189:0.90 190:0.77 191:0.90 192:0.87 195:0.60 196:0.92 201:0.68 202:0.87 204:0.85 208:0.64 212:0.72 215:0.42 216:0.49 220:0.91 222:0.48 230:0.75 232:0.76 233:0.76 235:0.60 238:0.94 241:0.80 242:0.18 243:0.90 247:0.82 248:0.57 253:0.93 254:0.84 255:0.95 256:0.89 259:0.21 260:0.86 261:0.78 265:0.99 266:0.54 267:0.96 268:0.89 271:0.94 276:0.60 279:0.82 282:0.88 283:0.78 284:0.94 285:0.62 289:0.93 290:0.61 297:0.36 300:0.43 +4 6:0.99 7:0.72 8:0.97 9:0.80 11:0.64 12:0.20 17:0.06 18:0.64 20:0.99 21:0.78 27:0.01 28:0.93 29:0.71 30:0.45 31:0.99 32:0.69 34:0.53 36:0.64 37:0.98 39:0.47 41:0.97 43:0.25 45:0.87 46:0.97 66:0.54 69:0.93 70:0.89 71:0.89 74:0.63 77:0.70 78:0.90 79:0.99 83:0.91 86:0.67 91:0.99 96:1.00 97:0.99 98:0.42 100:0.99 101:0.52 104:0.58 107:0.96 108:0.86 110:0.94 111:0.99 120:1.00 122:0.51 123:0.86 124:0.69 125:0.98 127:0.51 129:0.05 133:0.73 135:0.32 137:0.99 138:0.99 139:0.65 140:0.99 145:0.74 146:0.60 147:0.23 149:0.56 151:0.92 152:0.96 153:1.00 154:0.21 155:0.67 158:0.79 159:0.62 160:1.00 161:0.80 162:0.68 164:0.99 167:1.00 169:0.98 172:0.61 173:0.98 177:0.05 179:0.56 181:0.69 186:0.90 189:0.99 191:0.95 192:0.87 195:0.84 196:0.91 202:0.99 204:0.85 208:0.64 212:0.52 216:0.82 222:0.48 230:0.75 232:0.75 233:0.76 235:0.88 238:0.99 241:1.00 242:0.42 243:0.16 244:0.61 245:0.66 247:0.60 248:0.94 253:1.00 255:0.95 256:0.99 257:0.84 259:0.21 260:0.99 261:0.99 265:0.44 266:0.80 267:0.95 268:0.99 271:0.94 276:0.56 279:0.82 282:0.77 283:0.82 284:0.99 285:0.62 289:0.93 300:0.84 +1 1:0.69 8:1.00 11:0.83 12:0.20 17:0.69 18:0.92 21:0.13 27:0.08 28:0.93 29:0.71 30:0.43 34:0.95 36:0.70 37:0.95 39:0.47 43:0.69 45:0.95 46:0.98 66:0.09 69:0.48 70:0.92 71:0.89 74:0.70 81:0.52 83:0.60 85:0.72 86:0.91 91:0.58 98:0.39 99:0.83 101:0.87 104:0.75 107:0.97 108:0.94 110:0.95 114:0.75 122:0.86 123:0.84 124:0.88 125:0.88 126:0.44 127:0.95 129:0.93 133:0.87 135:0.44 139:0.87 146:0.62 147:0.68 149:0.86 150:0.58 154:0.55 155:0.58 159:0.86 160:0.88 161:0.80 165:0.70 167:0.78 172:0.71 173:0.23 176:0.66 177:0.70 178:0.55 179:0.26 181:0.69 187:0.21 191:0.84 192:0.59 201:0.68 202:0.50 208:0.54 212:0.40 215:0.61 216:0.47 222:0.48 232:0.80 235:0.22 241:0.66 242:0.36 243:0.16 245:0.91 247:0.79 253:0.55 259:0.21 265:0.34 266:0.94 267:0.73 271:0.94 276:0.87 289:0.93 290:0.75 297:0.36 300:0.94 +1 7:0.81 8:0.79 9:0.80 11:0.83 12:0.20 17:0.96 18:0.97 21:0.13 22:0.93 27:0.60 29:0.53 30:0.15 31:0.96 32:0.69 34:0.67 36:0.39 37:0.80 39:0.99 41:0.82 43:0.88 44:0.90 45:0.73 47:0.93 48:1.00 55:0.42 64:0.77 66:0.96 69:0.12 70:0.84 71:0.82 74:0.68 78:0.97 79:0.97 81:0.67 83:0.91 85:0.72 86:0.99 89:0.99 91:0.58 96:0.78 98:0.98 101:0.87 108:0.10 111:0.94 120:0.73 121:0.90 122:0.83 123:0.10 126:0.44 127:0.80 129:0.05 135:0.66 137:0.96 139:0.99 140:0.80 141:0.97 145:0.61 146:0.90 147:0.92 149:0.78 150:0.46 151:0.78 153:0.77 154:0.87 155:0.67 159:0.50 162:0.85 164:0.71 169:0.90 172:0.90 173:0.22 177:0.70 179:0.33 187:0.39 190:0.89 191:0.81 192:0.87 195:0.68 196:0.98 201:0.68 202:0.68 204:0.89 208:0.64 212:0.85 215:0.61 216:0.21 219:0.92 220:0.62 222:0.21 223:0.94 225:0.99 230:0.86 233:0.73 234:1.00 235:0.42 240:0.99 241:0.44 242:0.19 243:0.96 247:0.84 248:0.71 253:0.72 255:0.95 256:0.71 259:0.21 260:0.72 262:0.93 265:0.98 266:0.27 267:0.85 268:0.75 271:0.86 274:0.99 276:0.82 281:0.89 283:0.78 284:0.95 285:0.62 292:0.95 297:0.36 300:0.70 +2 7:0.72 8:0.89 9:0.80 12:0.20 17:0.85 18:0.69 21:0.47 27:0.64 29:0.53 30:0.21 31:0.97 32:0.69 34:0.53 36:0.94 37:0.84 39:0.99 41:0.82 43:0.13 44:0.90 48:0.97 55:0.52 60:0.87 64:0.77 66:0.30 69:0.67 70:0.77 71:0.82 74:0.47 78:0.91 79:0.97 81:0.38 83:0.91 86:0.70 89:0.97 91:0.87 96:0.79 97:0.89 98:0.61 104:0.80 108:0.76 111:0.89 120:0.87 123:0.49 124:0.60 126:0.44 127:0.57 129:0.05 133:0.39 135:0.49 137:0.97 139:0.85 140:0.98 141:0.97 145:0.70 146:0.61 147:0.67 149:0.20 150:0.31 151:0.77 153:0.86 154:0.46 155:0.67 158:0.91 159:0.44 162:0.73 164:0.91 169:0.88 172:0.37 173:0.73 175:0.75 177:0.38 178:0.42 179:0.77 191:0.81 192:0.87 195:0.68 196:0.96 201:0.68 202:0.91 208:0.64 212:0.22 215:0.41 216:0.39 219:0.95 220:0.87 222:0.21 223:0.94 225:0.99 230:0.74 233:0.76 234:0.98 235:0.80 240:0.99 241:0.82 242:0.74 243:0.48 244:0.61 245:0.67 247:0.47 248:0.70 253:0.68 254:0.88 255:0.95 256:0.92 257:0.65 259:0.21 260:0.84 265:0.52 266:0.71 267:0.69 268:0.93 271:0.86 274:1.00 276:0.44 277:0.69 279:0.86 281:0.91 283:0.78 284:0.99 285:0.62 292:0.91 297:0.36 300:0.55 +1 1:0.74 6:0.91 7:0.81 8:0.92 9:0.80 11:0.83 12:0.20 17:0.84 18:0.84 20:0.80 21:0.47 27:0.41 29:0.53 30:0.62 31:0.91 32:0.80 34:0.71 36:0.50 37:0.80 39:0.99 41:0.90 43:0.76 44:0.93 45:0.95 48:0.97 60:0.87 64:0.77 66:0.89 69:0.78 70:0.48 71:0.82 74:0.92 77:0.70 78:0.91 79:0.91 81:0.53 83:0.91 85:0.72 86:0.90 89:0.98 91:0.62 96:0.76 98:0.84 100:0.91 101:0.87 104:0.74 108:0.61 111:0.90 114:0.60 120:0.73 121:0.97 122:0.51 123:0.59 124:0.37 126:0.54 127:0.78 129:0.05 133:0.37 135:0.56 137:0.91 139:0.95 140:0.80 141:0.97 145:0.92 146:0.83 147:0.25 149:0.92 150:0.74 151:0.72 152:0.90 153:0.88 154:0.68 155:0.67 158:0.91 159:0.50 162:0.77 164:0.77 165:0.93 169:0.87 172:0.92 173:0.83 176:0.73 177:0.05 179:0.28 186:0.91 187:0.39 191:0.70 192:0.87 195:0.71 196:0.95 201:0.93 202:0.70 204:0.89 208:0.64 212:0.90 215:0.41 216:0.28 219:0.95 220:0.62 222:0.21 223:0.94 225:0.99 230:0.87 233:0.76 234:0.98 235:0.84 240:0.99 241:0.74 242:0.44 243:0.09 245:0.80 247:0.82 248:0.68 253:0.79 255:0.95 256:0.71 257:0.84 259:0.21 260:0.69 261:0.90 265:0.84 266:0.54 267:0.19 268:0.74 271:0.86 274:0.98 276:0.84 279:0.86 281:0.91 282:0.88 283:0.78 284:0.95 285:0.62 290:0.60 292:0.91 297:0.36 299:0.88 300:0.70 +2 7:0.81 8:0.83 9:0.80 12:0.20 17:0.62 18:0.91 20:0.90 21:0.76 27:0.25 29:0.53 30:0.66 31:0.91 32:0.68 34:0.95 36:0.52 37:0.91 39:0.99 43:0.31 44:0.90 48:0.97 55:0.45 64:0.77 66:0.83 69:0.88 70:0.54 71:0.82 74:0.47 78:0.84 79:0.91 81:0.25 83:0.60 86:0.88 89:0.99 91:0.40 96:0.72 98:0.73 100:0.81 104:0.78 108:0.76 111:0.82 120:0.67 121:0.90 123:0.75 124:0.39 126:0.44 127:0.83 129:0.05 133:0.62 135:0.47 137:0.91 139:0.91 140:0.94 141:0.97 145:0.63 146:0.80 147:0.25 149:0.39 150:0.23 151:0.59 152:0.92 153:0.72 154:0.23 155:0.67 159:0.59 162:0.66 164:0.65 169:0.81 172:0.87 173:0.92 175:0.75 177:0.05 179:0.37 186:0.84 187:0.87 190:0.89 191:0.90 192:0.87 195:0.80 196:0.94 202:0.66 204:0.89 208:0.64 212:0.85 215:0.36 216:0.45 219:0.89 220:0.62 222:0.21 223:0.94 225:0.98 230:0.86 233:0.76 234:0.98 235:0.97 240:0.99 241:0.61 242:0.60 243:0.10 244:0.61 245:0.72 247:0.67 248:0.58 253:0.48 255:0.95 256:0.68 257:0.84 259:0.21 260:0.64 261:0.86 265:0.73 266:0.63 267:0.18 268:0.68 271:0.86 274:0.98 276:0.77 277:0.69 281:0.89 284:0.91 285:0.62 292:0.91 297:0.36 300:0.70 +1 7:0.72 8:0.95 9:0.80 12:0.20 17:0.89 18:0.88 20:0.86 21:0.54 22:0.93 27:0.12 29:0.53 30:0.11 31:0.97 32:0.68 34:0.66 36:0.62 37:0.91 39:0.99 41:0.82 43:0.64 44:0.90 47:0.93 48:0.99 55:0.45 64:0.77 66:0.36 69:0.67 70:0.84 71:0.82 78:0.88 79:0.97 81:0.33 83:0.91 86:0.88 89:0.99 91:0.73 96:0.79 98:0.48 100:0.90 108:0.70 111:0.87 120:0.68 122:0.86 123:0.68 124:0.60 126:0.44 127:0.39 129:0.59 133:0.47 135:0.66 137:0.97 139:0.90 140:0.96 141:0.98 145:0.61 146:0.23 147:0.29 149:0.34 150:0.27 151:0.63 152:0.81 153:0.86 154:0.54 155:0.67 159:0.32 162:0.86 164:0.79 169:0.87 172:0.32 173:0.79 175:0.75 177:0.38 179:0.77 186:0.88 187:0.21 191:0.79 192:0.87 195:0.61 196:0.97 201:0.68 202:0.78 208:0.64 212:0.28 215:0.39 216:0.60 219:0.89 222:0.21 223:0.94 225:0.99 230:0.74 233:0.73 234:0.99 235:0.74 240:0.99 241:0.75 242:0.45 243:0.48 244:0.61 245:0.62 247:0.39 248:0.74 253:0.68 255:0.95 256:0.89 257:0.65 259:0.21 260:0.69 261:0.86 262:0.93 265:0.49 266:0.54 267:0.36 268:0.85 271:0.86 274:1.00 276:0.32 277:0.69 281:0.89 284:0.98 285:0.62 292:0.95 297:0.36 300:0.55 +1 7:0.66 8:0.95 9:0.76 11:0.83 12:0.20 17:0.77 18:0.88 21:0.64 22:0.93 27:0.47 29:0.53 30:0.29 31:0.93 32:0.68 34:0.79 36:0.33 37:0.74 39:0.99 43:0.71 44:0.90 45:0.66 47:0.93 48:0.91 55:0.45 60:0.87 64:0.77 66:0.47 69:0.82 70:0.86 71:0.82 74:0.59 79:0.95 81:0.30 83:0.91 85:0.72 86:0.91 89:0.99 91:0.70 96:0.80 97:0.89 98:0.41 101:0.87 108:0.66 120:0.73 122:0.86 123:0.64 124:0.87 126:0.44 127:0.66 129:0.05 133:0.88 135:0.18 137:0.93 139:0.93 140:0.80 141:0.97 145:0.61 146:0.66 147:0.46 149:0.52 150:0.25 151:0.81 153:0.48 154:0.61 155:0.67 158:0.92 159:0.73 162:0.84 164:0.71 172:0.63 173:0.73 177:0.05 179:0.52 187:0.39 190:0.77 192:0.87 195:0.88 196:0.96 201:0.68 202:0.71 208:0.64 212:0.49 215:0.38 216:0.71 219:0.95 220:0.74 222:0.21 223:0.94 225:0.99 230:0.69 233:0.76 234:0.99 235:0.84 240:0.99 241:0.47 242:0.39 243:0.19 245:0.65 247:0.82 248:0.75 253:0.73 255:0.79 256:0.75 257:0.84 259:0.21 260:0.69 262:0.93 265:0.43 266:0.80 267:0.17 268:0.78 271:0.86 274:0.99 276:0.65 279:0.86 281:0.89 283:0.82 284:0.95 285:0.62 292:0.95 297:0.36 300:0.70 +3 7:0.81 8:0.97 12:0.20 17:0.49 18:0.74 21:0.76 27:0.49 29:0.53 30:0.91 31:0.91 34:0.44 36:0.67 37:0.91 39:0.99 43:0.11 48:0.99 55:0.92 64:0.77 66:0.13 69:0.84 70:0.19 71:0.82 74:0.47 79:0.92 81:0.23 86:0.70 89:0.98 91:0.79 98:0.34 108:0.60 120:0.64 121:0.90 122:0.43 123:0.68 124:0.82 126:0.26 127:0.40 129:0.05 133:0.74 135:0.18 137:0.91 139:0.78 140:0.94 141:0.97 146:0.39 147:0.29 149:0.15 150:0.23 151:0.65 153:0.48 154:0.09 155:0.67 159:0.42 162:0.53 163:0.94 164:0.63 172:0.10 173:0.90 175:0.75 177:0.05 178:0.50 179:0.89 190:0.77 191:0.87 192:0.87 196:0.92 202:0.76 204:0.89 208:0.64 212:0.52 216:0.21 220:0.97 222:0.21 223:0.94 225:0.99 230:0.86 233:0.73 234:0.97 235:0.95 240:0.99 241:0.96 242:0.24 243:0.34 244:0.83 245:0.43 247:0.47 248:0.63 253:0.34 255:0.74 256:0.71 257:0.84 259:0.21 260:0.63 265:0.19 266:0.27 267:0.44 268:0.72 271:0.86 274:0.98 276:0.32 277:0.69 281:0.91 284:0.93 285:0.56 300:0.18 +1 1:0.74 11:0.64 12:0.20 17:0.59 18:0.75 21:0.22 27:0.45 29:0.53 30:0.11 34:0.87 36:0.17 37:0.50 39:0.99 43:0.12 45:0.66 64:0.77 66:0.47 69:0.69 70:0.54 71:0.82 74:0.43 81:0.57 83:0.60 86:0.79 89:0.97 91:0.12 98:0.15 99:0.83 101:0.52 108:0.53 114:0.71 122:0.86 123:0.50 124:0.52 126:0.54 127:0.46 129:0.05 133:0.37 135:0.83 139:0.75 141:0.91 145:0.47 146:0.25 147:0.31 149:0.08 150:0.72 154:0.76 155:0.67 159:0.68 165:0.70 172:0.21 173:0.58 176:0.73 177:0.70 178:0.55 179:0.93 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.51 216:0.77 222:0.21 223:0.94 225:0.98 234:0.91 235:0.42 240:0.95 241:0.21 242:0.63 243:0.59 245:0.46 247:0.35 253:0.52 254:0.74 259:0.21 265:0.16 266:0.27 267:0.28 271:0.86 274:0.91 276:0.13 290:0.71 297:0.36 300:0.33 +1 1:0.74 7:0.72 8:0.83 12:0.20 17:0.64 18:0.73 20:0.78 21:0.74 27:0.72 29:0.53 30:0.89 31:0.89 32:0.79 34:0.55 36:0.90 37:0.95 39:0.99 43:0.18 44:0.93 48:0.91 55:0.71 64:0.77 66:0.75 69:0.35 70:0.20 71:0.82 74:0.53 77:0.70 78:0.72 79:0.89 81:0.40 83:0.60 86:0.70 89:0.97 91:0.83 98:0.92 99:0.83 100:0.73 104:0.74 108:0.27 111:0.72 114:0.54 120:0.72 122:0.43 123:0.26 126:0.54 127:0.52 129:0.05 135:0.37 137:0.89 139:0.81 140:0.80 141:0.97 145:0.56 146:0.47 147:0.53 149:0.19 150:0.65 151:0.60 152:0.76 153:0.85 154:0.12 155:0.67 159:0.17 162:0.65 164:0.72 165:0.70 169:0.72 172:0.32 173:0.73 175:0.75 176:0.73 177:0.38 179:0.93 186:0.73 190:0.89 192:0.87 195:0.53 196:0.91 201:0.93 202:0.71 204:0.85 208:0.64 212:0.95 215:0.36 216:0.23 220:0.62 222:0.21 223:0.94 225:0.98 230:0.74 233:0.73 234:0.97 235:0.97 240:0.99 241:0.95 242:0.33 243:0.77 244:0.83 247:0.39 248:0.59 253:0.38 255:0.74 256:0.71 257:0.65 259:0.21 260:0.67 261:0.73 265:0.92 266:0.12 267:0.84 268:0.72 271:0.86 274:0.98 276:0.29 277:0.69 281:0.86 282:0.87 284:0.91 285:0.56 290:0.54 297:0.36 300:0.18 +1 6:0.93 7:0.72 8:0.81 11:0.57 12:0.20 17:0.93 18:0.98 20:0.93 21:0.13 27:0.38 29:0.53 30:0.75 31:0.97 32:0.69 34:0.64 36:0.34 37:0.57 39:0.99 43:0.81 44:0.90 48:1.00 55:0.45 64:1.00 66:0.83 69:0.59 70:0.48 71:0.82 74:0.43 78:0.95 79:0.98 81:0.43 85:0.72 86:0.98 89:0.99 91:0.70 98:0.44 100:0.97 101:0.87 108:0.45 111:0.95 120:0.82 123:0.43 124:0.39 126:0.44 127:0.71 129:0.05 133:0.60 135:0.21 137:0.97 139:0.98 140:0.45 141:0.97 145:0.99 146:0.75 147:0.79 149:0.89 150:0.33 151:0.94 152:0.86 153:0.90 154:0.76 159:0.79 162:0.73 164:0.74 169:0.95 172:0.93 173:0.40 177:0.70 179:0.21 186:0.95 189:0.94 190:0.77 192:0.51 195:0.92 196:0.99 201:0.68 202:0.75 212:0.92 215:0.41 216:0.05 219:0.92 222:0.21 223:0.94 225:0.99 230:0.75 233:0.76 234:0.98 235:0.97 238:0.92 240:0.99 241:0.79 242:0.58 243:0.84 245:0.84 247:0.79 248:0.85 253:0.33 255:0.74 256:0.80 259:0.21 260:0.79 261:0.95 265:0.46 266:0.47 267:0.17 268:0.79 271:0.86 274:0.99 276:0.71 281:0.91 283:0.78 284:0.96 285:0.56 292:0.91 297:0.99 300:0.70 +1 7:0.81 8:0.74 12:0.20 17:0.78 18:0.98 21:0.22 22:0.93 27:0.21 29:0.53 30:0.88 32:0.80 34:0.28 36:0.28 37:0.89 39:0.99 43:0.12 44:0.93 47:0.93 55:0.71 64:0.77 66:0.97 69:0.44 70:0.21 71:0.82 74:0.58 77:0.70 81:0.46 86:0.98 89:0.99 91:0.23 98:0.90 104:0.83 108:0.34 121:0.90 122:0.83 123:0.32 126:0.44 127:0.49 129:0.05 135:0.69 139:0.98 141:0.91 145:0.63 146:0.97 147:0.98 149:0.20 150:0.34 154:0.57 155:0.67 159:0.70 172:0.92 173:0.28 175:0.75 177:0.38 178:0.55 179:0.23 187:0.87 191:0.73 192:0.87 195:0.88 201:0.68 204:0.89 208:0.64 212:0.93 215:0.51 216:0.56 219:0.89 222:0.21 223:0.94 225:0.98 230:0.86 233:0.73 234:0.91 235:0.31 240:0.95 241:0.30 242:0.51 243:0.97 244:0.61 247:0.60 253:0.38 254:0.74 257:0.65 259:0.21 262:0.93 265:0.90 266:0.27 267:0.28 271:0.86 274:0.91 276:0.86 277:0.69 281:0.89 282:0.88 292:0.95 297:0.36 299:0.88 300:0.43 +2 7:0.66 8:0.95 9:0.80 11:0.57 12:0.20 17:0.76 18:0.89 21:0.68 27:0.25 29:0.53 30:0.66 31:0.92 32:0.64 34:0.36 36:0.83 37:0.91 39:0.99 41:0.88 43:0.79 55:0.45 66:0.92 69:0.76 70:0.56 71:0.82 74:0.42 79:0.92 81:0.24 83:0.91 85:0.72 86:0.89 89:0.99 91:0.92 96:0.71 98:0.87 101:0.87 104:0.78 108:0.59 120:0.88 123:0.57 124:0.36 126:0.26 127:0.71 129:0.05 133:0.37 135:0.60 137:0.92 139:0.87 140:0.98 141:0.97 145:0.98 146:0.86 147:0.05 149:0.71 150:0.24 151:0.52 153:0.98 154:0.72 155:0.67 159:0.50 162:0.64 164:0.90 172:0.91 173:0.80 177:0.05 179:0.29 190:0.77 191:0.91 192:0.87 195:0.70 196:0.94 202:0.91 208:0.64 212:0.87 215:0.37 216:0.45 219:0.89 220:0.62 222:0.21 223:0.94 225:0.99 230:0.69 233:0.73 234:0.97 235:0.88 240:0.99 241:0.88 242:0.74 243:0.06 245:0.70 247:0.79 248:0.54 253:0.43 255:0.95 256:0.92 257:0.84 259:0.21 260:0.83 265:0.87 266:0.38 267:0.19 268:0.92 271:0.86 274:0.99 276:0.83 283:0.78 284:0.97 285:0.62 292:0.95 297:0.36 300:0.70 +1 8:0.59 11:0.64 12:0.20 17:0.76 18:0.96 21:0.77 27:0.45 29:0.53 30:0.43 34:0.87 36:0.28 37:0.50 39:0.99 43:0.13 44:0.87 45:0.77 55:0.59 64:0.77 66:0.86 69:0.71 70:0.83 71:0.82 74:0.53 81:0.22 86:0.97 89:0.99 91:0.04 98:0.75 101:0.52 108:0.54 122:0.86 123:0.52 124:0.37 126:0.26 127:0.53 129:0.05 133:0.37 135:0.83 139:0.96 141:0.91 145:0.58 146:0.93 147:0.95 149:0.19 150:0.22 154:0.65 159:0.74 172:0.86 173:0.56 177:0.70 178:0.55 179:0.34 187:0.68 195:0.89 212:0.77 216:0.77 222:0.21 223:0.94 225:0.98 234:0.91 235:0.98 240:0.95 241:0.18 242:0.33 243:0.86 245:0.75 247:0.67 253:0.36 254:0.74 259:0.21 265:0.75 266:0.80 267:0.36 271:0.86 274:0.91 276:0.75 300:0.70 +2 1:0.74 7:0.72 8:0.84 9:0.80 11:0.64 12:0.20 17:0.57 18:0.94 20:0.91 21:0.54 27:0.19 29:0.53 30:0.70 31:0.88 32:0.68 34:0.50 36:0.59 37:0.90 39:0.99 43:0.67 44:0.90 45:0.95 55:0.71 64:0.77 66:0.98 69:0.65 70:0.42 71:0.82 74:0.93 78:0.93 79:0.88 81:0.46 83:0.60 86:0.99 89:0.99 91:0.44 96:0.68 97:0.89 98:0.95 99:0.83 100:0.93 101:0.52 104:0.76 108:0.49 111:0.92 114:0.59 120:0.76 122:0.57 123:0.47 126:0.54 127:0.67 129:0.05 135:0.77 137:0.88 139:0.98 140:0.90 141:0.97 145:0.70 146:0.97 147:0.98 149:0.39 150:0.67 151:0.48 152:0.94 153:0.72 154:0.75 155:0.67 158:0.78 159:0.70 162:0.81 164:0.76 165:0.70 169:0.91 172:0.94 173:0.54 176:0.73 177:0.70 179:0.22 186:0.93 187:0.21 190:0.89 191:0.89 192:0.87 195:0.86 196:0.93 201:0.93 202:0.69 204:0.85 208:0.64 212:0.91 215:0.39 216:0.59 220:0.62 222:0.21 223:0.94 225:0.99 230:0.74 233:0.73 234:0.97 235:0.84 240:0.99 241:0.59 242:0.36 243:0.98 247:0.60 248:0.49 253:0.50 254:0.74 255:0.95 256:0.75 259:0.21 260:0.69 261:0.93 265:0.95 266:0.38 267:0.19 268:0.75 271:0.86 274:0.98 276:0.89 279:0.82 283:0.78 284:0.93 285:0.62 290:0.59 297:0.36 300:0.55 +1 1:0.74 7:0.66 8:0.84 9:0.80 11:0.64 12:0.20 17:0.78 18:0.91 20:0.79 21:0.13 22:0.93 27:0.35 29:0.53 30:0.70 31:0.90 32:0.74 34:0.40 36:0.76 37:0.79 39:0.99 43:0.26 44:0.90 45:0.89 47:0.93 48:0.91 55:0.52 64:0.77 66:0.99 69:0.33 70:0.55 71:0.82 74:0.74 77:0.70 78:0.83 79:0.90 81:0.63 83:0.60 86:0.95 89:0.99 91:0.46 96:0.74 98:0.98 99:0.83 100:0.89 101:0.52 104:0.80 106:0.81 108:0.26 111:0.84 114:0.79 120:0.68 123:0.25 126:0.54 127:0.83 129:0.05 135:0.49 137:0.90 139:0.94 140:0.90 141:0.97 145:0.76 146:0.98 147:0.98 149:0.71 150:0.76 151:0.63 152:0.77 153:0.48 154:0.91 155:0.67 159:0.74 162:0.85 164:0.74 165:0.70 169:0.86 172:0.98 173:0.21 176:0.73 177:0.70 179:0.15 186:0.83 187:0.39 190:0.89 191:0.78 192:0.87 195:0.86 196:0.94 201:0.93 202:0.71 206:0.81 208:0.64 212:0.88 215:0.61 216:0.70 220:0.74 222:0.21 223:0.94 225:0.99 230:0.69 233:0.73 234:0.98 235:0.31 240:0.99 241:0.62 242:0.74 243:0.99 247:0.90 248:0.62 253:0.67 254:0.74 255:0.95 256:0.75 259:0.21 260:0.66 261:0.80 262:0.93 265:0.98 266:0.54 267:0.17 268:0.78 271:0.86 274:0.99 276:0.95 281:0.89 282:0.77 283:0.78 284:0.95 285:0.62 290:0.79 297:0.36 300:0.70 +2 1:0.74 12:0.20 17:0.66 18:0.98 21:0.47 22:0.93 27:0.21 29:0.53 30:0.87 31:0.86 34:0.24 36:0.35 37:0.89 39:0.99 43:0.13 47:0.93 55:0.71 64:0.77 66:0.94 69:0.47 70:0.19 71:0.82 79:0.86 81:0.52 83:0.91 86:0.98 89:0.99 91:0.23 98:0.91 104:0.83 108:0.36 114:0.60 120:0.53 122:0.83 123:0.34 124:0.36 126:0.54 127:0.75 129:0.05 133:0.39 135:0.68 137:0.86 139:0.98 140:0.45 141:0.97 146:0.97 147:0.98 149:0.18 150:0.73 151:0.47 153:0.48 154:0.59 155:0.67 159:0.76 162:0.52 164:0.54 165:0.93 172:0.94 173:0.30 175:0.75 176:0.73 177:0.38 179:0.23 187:0.87 190:0.77 192:0.87 196:0.88 201:0.93 202:0.58 208:0.64 212:0.94 215:0.41 216:0.56 219:0.89 220:0.96 222:0.21 223:0.94 225:0.98 234:0.97 235:0.80 240:0.99 241:0.69 242:0.57 243:0.94 244:0.61 245:0.74 247:0.47 248:0.47 253:0.44 254:0.74 255:0.74 256:0.45 257:0.65 259:0.21 260:0.53 262:0.93 265:0.91 266:0.23 267:0.28 268:0.46 271:0.86 274:0.97 276:0.88 277:0.69 284:0.87 285:0.56 290:0.60 292:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.72 11:0.64 12:0.20 17:0.81 18:1.00 21:0.47 27:0.62 29:0.53 30:0.94 32:0.68 34:0.49 36:0.42 37:0.66 39:0.99 43:0.13 44:0.90 45:0.81 48:0.99 55:0.52 64:0.77 66:0.99 69:0.35 70:0.19 71:0.82 74:0.71 81:0.48 83:0.91 86:1.00 89:1.00 91:0.37 98:0.99 101:0.52 108:0.27 114:0.60 122:0.86 123:0.26 126:0.54 127:0.86 129:0.05 135:0.60 139:1.00 141:0.91 145:0.93 146:0.97 147:0.98 149:0.70 150:0.72 154:0.88 155:0.67 159:0.73 165:0.93 172:0.99 173:0.24 176:0.73 177:0.70 178:0.55 179:0.13 187:0.21 192:0.87 195:0.85 201:0.93 208:0.64 212:0.95 215:0.41 216:0.05 222:0.21 223:0.94 225:0.98 230:0.74 233:0.73 234:0.91 235:0.68 240:0.95 241:0.02 242:0.80 243:0.99 247:0.67 253:0.46 254:0.80 259:0.21 265:0.99 266:0.12 267:0.18 271:0.86 274:0.91 276:0.96 281:0.89 290:0.60 297:0.36 300:0.33 +2 1:0.74 6:0.91 7:0.81 8:0.91 9:0.80 11:0.83 12:0.20 17:0.77 18:0.84 20:0.93 21:0.47 27:0.41 29:0.53 30:0.62 31:0.96 32:0.80 34:0.31 36:0.45 37:0.80 39:0.99 41:0.92 43:0.76 44:0.93 45:0.95 48:0.97 64:0.77 66:0.89 69:0.78 70:0.48 71:0.82 74:0.90 77:0.70 78:0.90 79:0.96 81:0.53 83:0.91 85:0.72 86:0.90 89:0.98 91:0.86 96:0.85 98:0.86 100:0.88 101:0.87 104:0.74 108:0.61 111:0.88 114:0.60 120:0.87 121:0.97 122:0.51 123:0.59 124:0.37 126:0.54 127:0.92 129:0.05 133:0.37 135:0.39 137:0.96 139:0.93 140:0.45 141:0.97 145:0.92 146:0.83 147:0.25 149:0.92 150:0.74 151:0.90 152:0.90 153:0.96 154:0.68 155:0.67 159:0.50 162:0.79 164:0.86 165:0.93 169:0.86 172:0.92 173:0.83 176:0.73 177:0.05 179:0.30 186:0.90 189:0.92 191:0.75 192:0.87 195:0.70 196:0.97 201:0.93 202:0.85 204:0.89 208:0.64 212:0.90 215:0.41 216:0.28 219:0.89 220:0.62 222:0.21 223:0.94 225:0.99 230:0.87 233:0.76 234:0.98 235:0.84 238:0.85 240:0.99 241:0.95 242:0.44 243:0.09 245:0.80 247:0.82 248:0.83 253:0.88 255:0.95 256:0.87 257:0.84 259:0.21 260:0.83 261:0.90 265:0.86 266:0.54 267:0.19 268:0.89 271:0.86 274:0.99 276:0.84 281:0.91 282:0.88 283:0.78 284:0.97 285:0.62 290:0.60 292:0.91 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.66 8:0.85 9:0.80 11:0.64 12:0.20 17:0.85 18:0.92 21:0.13 22:0.93 27:0.35 29:0.53 30:0.70 31:0.89 32:0.74 34:0.39 36:0.75 37:0.79 39:0.99 43:0.26 44:0.90 45:0.92 47:0.93 48:0.91 55:0.52 64:0.77 66:0.35 69:0.33 70:0.56 71:0.82 74:0.81 77:0.70 79:0.89 81:0.63 83:0.60 86:0.97 89:0.99 91:0.49 96:0.72 98:0.73 99:0.83 101:0.52 104:0.80 106:0.81 108:0.26 114:0.79 120:0.64 123:0.82 124:0.60 126:0.54 127:0.81 129:0.59 133:0.46 135:0.39 137:0.89 139:0.96 140:0.92 141:0.97 145:0.76 146:0.90 147:0.92 149:0.71 150:0.76 151:0.59 153:0.90 154:0.91 155:0.67 159:0.75 162:0.85 164:0.74 165:0.70 172:0.94 173:0.21 176:0.73 177:0.70 179:0.15 187:0.39 191:0.70 192:0.87 195:0.87 196:0.94 201:0.93 202:0.69 206:0.81 208:0.64 212:0.88 215:0.61 216:0.70 219:0.89 220:0.74 222:0.21 223:0.94 225:0.99 230:0.69 233:0.73 234:0.98 235:0.31 240:0.99 241:0.61 242:0.73 243:0.57 245:0.99 247:0.90 248:0.58 253:0.65 254:0.74 255:0.95 256:0.73 259:0.21 260:0.63 262:0.93 265:0.72 266:0.47 267:0.17 268:0.76 271:0.86 274:0.99 276:0.95 281:0.89 282:0.77 283:0.78 284:0.95 285:0.62 290:0.79 292:0.91 297:0.36 300:0.70 +3 1:0.74 6:0.91 7:0.72 8:0.80 9:0.80 12:0.20 17:0.83 18:0.95 20:0.97 21:0.22 22:0.93 25:0.88 27:0.32 29:0.53 30:0.13 31:0.88 32:0.69 34:0.36 36:0.35 37:0.74 39:0.99 41:0.82 43:0.70 44:0.90 47:0.93 48:0.97 55:0.52 64:0.77 66:0.35 69:0.19 70:0.88 71:0.82 78:0.93 79:0.88 81:0.58 83:0.91 86:0.94 89:0.99 91:0.29 96:0.71 98:0.70 100:0.94 104:0.58 106:0.81 108:0.67 111:0.92 114:0.71 120:0.57 122:0.86 123:0.65 124:0.71 126:0.54 127:0.77 129:0.81 133:0.67 135:0.30 137:0.88 139:0.94 140:0.45 141:0.97 145:0.70 146:0.26 147:0.32 149:0.54 150:0.76 151:0.56 152:0.89 153:0.48 154:0.60 155:0.67 159:0.67 162:0.86 164:0.57 165:0.93 169:0.92 172:0.68 173:0.17 175:0.75 176:0.73 177:0.38 179:0.41 186:0.93 187:0.68 189:0.93 192:0.87 195:0.81 196:0.92 201:0.93 202:0.36 206:0.81 208:0.64 212:0.69 215:0.51 216:0.81 219:0.92 220:0.82 222:0.21 223:0.94 225:0.98 230:0.74 233:0.73 234:0.99 235:0.42 238:0.90 240:0.99 241:0.44 242:0.45 243:0.24 244:0.61 245:0.87 247:0.67 248:0.55 253:0.53 255:0.95 256:0.45 257:0.65 259:0.21 260:0.58 261:0.93 262:0.93 265:0.71 266:0.84 267:0.17 268:0.46 271:0.86 274:0.97 276:0.75 277:0.69 281:0.89 283:0.82 284:0.89 285:0.62 290:0.71 292:0.95 297:0.36 300:0.70 +2 6:0.93 7:0.66 8:0.89 11:0.57 12:0.20 17:0.81 18:0.99 20:0.98 21:0.40 22:0.93 27:0.53 29:0.53 30:0.53 31:0.93 32:0.68 34:0.21 36:0.70 37:0.70 39:0.99 43:0.95 44:0.90 47:0.93 55:0.45 60:0.98 64:0.77 66:0.79 69:0.25 70:0.52 71:0.82 74:0.71 78:0.92 79:0.95 81:0.36 83:0.91 85:0.85 86:1.00 89:1.00 91:0.17 98:0.82 100:0.93 101:0.87 104:0.74 108:0.75 111:0.92 120:0.64 123:0.74 124:0.41 126:0.44 127:0.66 129:0.05 133:0.43 135:0.32 137:0.93 139:1.00 140:0.87 141:0.97 145:0.44 146:0.65 147:0.70 149:0.91 150:0.29 151:0.65 152:0.90 153:0.48 154:0.95 155:0.67 158:0.91 159:0.75 162:0.86 164:0.68 169:0.91 172:0.99 173:0.16 177:0.70 179:0.11 186:0.92 187:0.39 189:0.94 190:0.89 192:0.87 195:0.87 196:0.97 201:0.68 202:0.63 208:0.64 212:0.94 215:0.42 216:0.59 219:0.95 222:0.21 223:0.94 225:0.99 230:0.69 233:0.76 234:0.99 235:0.52 238:0.89 240:0.99 241:0.59 242:0.50 243:0.19 245:0.99 247:0.82 248:0.63 253:0.42 255:0.74 256:0.71 259:0.21 260:0.63 261:0.93 262:0.93 265:0.82 266:0.27 267:0.17 268:0.71 271:0.86 274:0.99 276:0.97 277:0.69 279:0.86 283:0.78 284:0.94 285:0.56 292:0.95 297:0.36 300:0.55 +1 7:0.72 8:0.84 9:0.80 11:0.83 12:0.20 17:0.95 18:0.97 20:0.79 21:0.40 22:0.93 27:0.54 29:0.53 30:0.74 31:0.93 32:0.69 34:0.56 36:0.80 37:0.62 39:0.99 41:0.82 43:0.88 44:0.90 45:1.00 47:0.93 48:0.99 55:0.52 60:0.95 64:0.77 66:0.74 69:0.45 70:0.26 71:0.82 74:0.99 78:0.96 79:0.95 81:0.38 83:0.91 85:0.91 86:0.99 89:0.99 91:0.44 96:0.68 98:0.79 100:0.95 101:0.87 104:0.88 106:0.81 108:0.72 111:0.95 120:0.65 122:0.51 123:0.70 124:0.45 126:0.44 127:0.91 129:0.81 133:0.67 135:0.47 137:0.93 139:0.99 140:0.45 141:0.97 145:0.79 146:0.57 147:0.63 149:1.00 150:0.31 151:0.47 152:0.77 153:0.48 154:0.86 155:0.67 158:0.91 159:0.81 162:0.86 164:0.70 169:0.93 172:0.99 173:0.26 177:0.70 179:0.10 186:0.96 187:0.39 190:0.89 192:0.87 195:0.91 196:0.97 201:0.68 202:0.63 206:0.81 208:0.64 212:0.94 215:0.42 216:0.37 219:0.95 220:0.97 222:0.21 223:0.94 225:0.99 230:0.74 233:0.73 234:0.99 235:0.60 240:0.99 241:0.59 242:0.54 243:0.17 245:1.00 247:0.88 248:0.47 253:0.49 255:0.95 256:0.71 259:0.21 260:0.64 261:0.86 262:0.93 265:0.79 266:0.71 267:0.87 268:0.72 271:0.86 274:0.99 276:0.99 279:0.86 281:0.89 283:0.78 284:0.94 285:0.62 292:0.95 297:0.36 300:0.84 +1 10:0.90 11:0.88 12:0.69 17:0.77 18:0.56 21:0.78 27:0.72 29:0.94 30:0.15 34:0.21 36:0.48 39:0.54 43:0.09 55:0.98 66:0.05 69:1.00 70:0.99 74:0.29 85:0.72 86:0.59 91:0.17 98:0.12 101:0.87 108:0.99 122:0.95 123:0.98 124:0.98 127:0.58 129:0.05 133:0.98 135:0.39 139:0.58 144:0.85 145:0.47 146:0.18 147:0.25 149:0.17 154:0.08 159:0.98 163:0.79 170:0.79 172:0.21 173:0.97 174:0.98 177:0.38 178:0.55 179:0.39 187:0.21 197:0.90 212:0.12 216:0.15 222:0.84 235:0.74 239:0.89 241:0.12 242:0.61 243:0.11 245:0.61 247:0.82 253:0.26 257:0.84 259:0.21 265:0.06 266:0.98 267:0.23 271:0.27 276:0.75 277:0.95 300:0.98 +3 10:0.88 11:0.87 12:0.69 17:0.47 18:0.89 21:0.40 27:0.24 29:0.94 30:0.40 34:0.42 36:0.56 37:0.66 39:0.54 43:0.77 45:0.81 66:0.32 69:0.12 70:0.88 74:0.53 81:0.33 86:0.90 91:0.03 98:0.56 101:0.87 108:0.80 122:0.91 123:0.79 124:0.77 126:0.24 127:0.90 129:0.05 133:0.74 135:0.86 139:0.88 144:0.85 146:0.55 147:0.61 149:0.75 150:0.36 154:0.69 159:0.69 170:0.79 172:0.51 173:0.14 174:0.86 177:0.88 178:0.55 179:0.60 187:0.68 197:0.89 212:0.22 216:0.56 219:0.90 222:0.84 235:0.42 236:0.92 239:0.87 242:0.22 243:0.44 244:0.61 245:0.72 247:0.84 253:0.42 259:0.21 265:0.57 266:0.75 267:0.44 271:0.27 276:0.62 277:0.69 292:0.95 300:0.84 +2 11:0.84 12:0.69 17:0.40 18:0.59 21:0.78 27:0.43 29:0.94 30:0.95 34:0.63 36:0.44 37:0.53 39:0.54 43:0.33 45:0.66 66:0.64 69:0.93 74:0.33 86:0.65 91:0.04 98:0.08 101:0.47 108:0.86 122:0.91 123:0.85 127:0.06 129:0.05 135:0.19 139:0.63 144:0.85 145:0.69 146:0.17 147:0.22 149:0.26 154:0.25 159:0.09 163:0.99 170:0.79 172:0.16 173:0.89 175:0.75 177:0.70 178:0.55 179:0.08 187:0.87 193:0.97 212:0.95 216:0.81 222:0.84 235:0.68 239:0.84 241:0.07 242:0.82 243:0.69 244:0.61 247:0.12 253:0.30 257:0.65 259:0.21 265:0.08 266:0.12 267:0.28 271:0.27 276:0.19 277:0.69 300:0.08 +1 11:0.87 12:0.69 17:0.15 18:0.64 21:0.78 27:0.72 29:0.94 30:0.95 34:0.94 36:0.89 39:0.54 43:0.71 45:0.73 66:0.64 69:0.72 74:0.38 86:0.70 91:0.22 98:0.13 101:0.65 108:0.55 122:0.65 123:0.52 127:0.08 129:0.05 135:0.60 139:0.68 144:0.85 146:0.17 147:0.22 149:0.44 154:0.60 159:0.09 163:0.97 170:0.79 172:0.16 173:0.85 175:0.75 177:0.70 178:0.55 179:0.10 187:0.21 212:0.95 216:0.10 222:0.84 235:0.31 241:0.32 242:0.82 243:0.69 244:0.61 247:0.12 253:0.34 257:0.65 259:0.21 265:0.13 266:0.12 267:0.19 271:0.27 276:0.19 277:0.69 300:0.08 +1 10:0.93 11:0.87 12:0.69 17:0.45 18:0.76 21:0.22 27:0.64 29:0.94 30:0.09 34:0.58 36:0.93 37:0.34 39:0.54 43:0.75 45:0.73 66:0.51 69:0.35 70:0.85 74:0.40 81:0.35 86:0.81 91:0.06 98:0.62 101:0.65 108:0.27 122:0.94 123:0.26 124:0.64 126:0.24 127:0.69 129:0.05 133:0.60 135:0.86 139:0.76 144:0.85 146:0.59 147:0.64 149:0.55 150:0.39 154:0.66 159:0.43 170:0.79 172:0.41 173:0.42 174:0.91 177:0.88 178:0.55 179:0.81 187:0.39 197:0.94 212:0.39 216:0.45 222:0.84 235:0.22 236:0.92 239:0.91 241:0.18 242:0.24 243:0.61 244:0.61 245:0.55 247:0.67 253:0.35 259:0.21 265:0.63 266:0.63 267:0.52 271:0.27 276:0.43 300:0.55 +2 1:0.63 7:0.75 10:0.88 11:0.87 12:0.69 17:0.26 18:0.92 21:0.40 27:0.37 29:0.94 30:0.61 34:0.45 36:0.94 37:0.62 39:0.54 43:0.77 44:0.88 45:0.89 60:0.87 64:0.77 66:0.20 69:0.17 70:0.72 74:0.75 75:0.99 81:0.63 83:0.91 86:0.94 91:0.09 98:0.55 99:0.95 101:0.65 108:0.14 114:0.82 122:0.91 123:0.78 124:0.76 126:0.51 127:0.74 129:0.05 133:0.74 135:0.76 139:0.95 144:0.85 145:0.40 146:0.68 147:0.73 149:0.87 150:0.49 154:0.69 155:0.54 158:0.86 159:0.67 165:0.81 170:0.79 172:0.65 173:0.16 174:0.86 176:0.70 177:0.88 178:0.55 179:0.47 187:0.21 192:0.57 195:0.81 197:0.87 201:0.61 204:0.83 208:0.64 212:0.52 215:0.67 216:0.60 219:0.95 222:0.84 226:0.96 230:0.77 233:0.76 235:0.60 239:0.92 241:0.30 242:0.19 243:0.57 244:0.61 245:0.78 247:0.88 253:0.64 259:0.21 265:0.52 266:0.75 267:0.36 271:0.27 276:0.70 277:0.69 279:0.80 281:0.91 283:0.67 290:0.82 292:0.88 297:0.36 300:0.70 +1 10:0.95 11:0.87 12:0.69 17:0.32 18:0.60 21:0.78 27:0.52 29:0.94 30:0.28 34:0.22 36:0.68 37:0.49 39:0.54 43:0.22 45:0.73 55:0.92 66:0.13 69:0.97 70:0.98 74:0.30 86:0.66 91:0.25 98:0.25 101:0.65 108:0.94 122:0.95 123:0.94 124:0.96 127:0.82 129:0.05 133:0.96 135:0.56 139:0.64 144:0.85 146:0.70 147:0.65 149:0.31 154:0.15 159:0.92 163:0.98 170:0.79 172:0.45 173:0.91 174:0.98 177:0.05 178:0.55 179:0.45 187:0.21 197:0.96 202:0.36 212:0.15 216:0.49 222:0.84 235:0.31 239:0.93 241:0.15 242:0.78 243:0.23 244:0.61 245:0.67 247:0.74 253:0.27 257:0.93 259:0.21 265:0.28 266:0.96 267:0.79 271:0.27 276:0.74 300:0.98 +2 9:0.75 10:0.89 11:0.87 12:0.69 17:0.22 18:0.89 21:0.22 27:0.52 29:0.94 30:0.53 31:0.94 34:0.42 36:0.59 37:0.49 39:0.54 43:0.77 45:0.83 64:0.77 66:0.32 69:0.10 70:0.84 74:0.53 79:0.93 81:0.37 83:0.69 86:0.90 91:0.06 96:0.80 98:0.56 101:0.65 108:0.80 120:0.69 122:0.91 123:0.78 124:0.77 126:0.32 127:0.92 129:0.05 133:0.74 135:0.87 137:0.94 139:0.88 140:0.45 144:0.85 146:0.53 147:0.59 149:0.78 150:0.38 151:0.86 153:0.48 154:0.69 155:0.64 159:0.67 162:0.86 164:0.56 170:0.79 172:0.51 173:0.14 174:0.86 177:0.88 179:0.60 187:0.68 190:0.77 192:0.98 196:0.95 197:0.88 201:0.58 202:0.36 208:0.61 212:0.22 215:0.79 216:0.49 219:0.91 222:0.84 235:0.31 239:0.87 242:0.22 243:0.44 244:0.61 245:0.72 247:0.84 248:0.77 253:0.77 255:0.77 256:0.45 259:0.21 260:0.70 265:0.57 266:0.75 267:0.52 268:0.46 271:0.27 276:0.62 277:0.69 283:0.67 284:0.88 285:0.60 292:0.88 297:0.36 300:0.84 +1 10:0.91 11:0.87 12:0.69 17:0.15 18:0.61 21:0.05 27:0.68 29:0.94 30:0.58 33:0.97 34:0.71 36:0.44 37:0.46 39:0.54 43:0.60 45:0.73 55:0.96 66:0.27 69:0.85 70:0.33 74:0.35 81:0.38 86:0.67 91:0.04 98:0.25 101:0.65 108:0.70 122:0.95 123:0.69 124:0.84 126:0.24 127:0.63 129:0.05 132:0.97 133:0.80 135:0.34 139:0.66 144:0.85 146:0.35 147:0.05 149:0.41 150:0.43 154:0.49 159:0.59 163:0.79 170:0.79 172:0.21 173:0.85 174:0.90 177:0.05 178:0.55 179:0.88 187:0.68 197:0.93 212:0.16 216:0.20 222:0.84 235:0.05 236:0.92 239:0.90 241:0.21 242:0.40 243:0.18 244:0.61 245:0.45 247:0.55 253:0.32 257:0.93 259:0.21 265:0.27 266:0.54 267:0.23 271:0.27 276:0.36 291:0.97 300:0.25 +1 10:0.85 11:0.87 12:0.69 17:0.24 18:0.77 21:0.78 27:0.52 29:0.94 30:0.37 34:0.52 36:0.41 37:0.49 39:0.54 43:0.22 45:0.73 55:0.71 66:0.63 69:0.97 70:0.73 74:0.30 86:0.77 91:0.23 98:0.23 101:0.65 108:0.94 122:0.92 123:0.94 124:0.63 127:0.83 129:0.05 133:0.73 135:0.51 139:0.74 144:0.85 146:0.65 147:0.05 149:0.29 154:0.15 159:0.92 170:0.79 172:0.59 173:0.90 174:0.79 175:0.75 177:0.05 178:0.55 179:0.70 187:0.21 197:0.85 202:0.36 212:0.61 216:0.49 222:0.84 235:0.12 239:0.84 241:0.18 242:0.76 243:0.11 244:0.61 245:0.57 247:0.55 253:0.27 257:0.93 259:0.21 265:0.25 266:0.71 267:0.17 271:0.27 276:0.49 277:0.69 300:0.55 +2 1:0.66 7:0.70 8:0.72 10:0.95 11:0.56 12:0.37 17:0.77 18:0.92 21:0.40 27:0.58 29:0.97 30:0.42 32:0.67 34:0.97 36:0.54 37:0.49 39:0.86 43:0.17 44:0.86 45:0.66 48:0.91 55:0.45 64:0.77 66:0.27 69:0.90 70:0.88 71:0.61 74:0.38 81:0.56 86:0.75 91:0.54 98:0.30 101:0.47 104:0.81 108:0.95 114:0.76 122:0.75 123:0.95 124:0.91 126:0.51 127:0.93 129:0.59 131:0.61 133:0.91 135:0.75 139:0.74 144:0.76 145:0.86 146:0.67 147:0.62 149:0.22 150:0.55 154:0.30 155:0.51 157:0.61 159:0.88 166:0.87 170:0.82 172:0.86 173:0.74 174:0.94 176:0.63 177:0.70 178:0.55 179:0.19 182:0.61 187:0.21 192:0.55 195:0.96 197:0.92 201:0.65 208:0.61 212:0.75 215:0.63 216:0.49 219:0.87 222:0.25 227:0.61 229:0.61 230:0.73 231:0.80 233:0.66 235:0.74 239:0.92 241:0.32 242:0.41 243:0.17 245:0.93 246:0.61 247:0.98 253:0.55 254:0.88 257:0.65 259:0.21 265:0.32 266:0.84 267:0.44 271:0.82 276:0.92 281:0.86 283:0.67 287:0.61 290:0.76 292:0.88 297:0.36 300:0.94 +2 1:0.66 7:0.70 8:0.62 9:0.73 10:0.95 11:0.51 12:0.37 17:0.86 18:0.94 21:0.13 22:0.93 27:0.67 29:0.97 30:0.45 31:0.90 32:0.71 34:0.83 36:0.90 37:0.34 39:0.86 43:0.59 44:0.92 47:0.93 48:1.00 64:0.77 66:0.95 69:0.64 70:0.72 71:0.61 74:0.52 77:0.70 79:0.90 81:0.56 86:0.82 91:0.48 96:0.74 98:0.84 104:0.88 106:0.81 108:0.49 114:0.92 120:0.62 122:0.75 123:0.47 126:0.51 127:0.34 129:0.05 131:0.91 135:0.51 137:0.90 139:0.83 140:0.45 144:0.76 145:0.41 146:0.78 147:0.82 149:0.69 150:0.56 151:0.66 153:0.48 154:0.55 155:0.64 157:0.61 159:0.34 162:0.86 164:0.56 166:0.87 170:0.82 172:0.88 173:0.72 174:0.90 176:0.70 177:0.88 179:0.27 182:0.61 187:0.21 190:0.77 192:0.87 195:0.64 196:0.92 197:0.91 201:0.63 202:0.36 206:0.81 208:0.61 212:0.91 215:0.84 216:0.27 219:0.87 220:0.74 222:0.25 227:0.94 229:0.61 230:0.73 231:0.80 232:0.74 233:0.76 235:0.31 239:0.92 241:0.03 242:0.32 243:0.95 244:0.61 246:0.61 247:0.94 248:0.63 253:0.71 254:0.80 255:0.72 256:0.45 259:0.21 260:0.63 262:0.93 265:0.84 266:0.38 267:0.44 268:0.46 271:0.82 276:0.79 281:0.91 282:0.80 283:0.82 284:0.88 285:0.60 287:0.61 290:0.92 292:0.95 297:0.36 300:0.55 +2 1:0.59 7:0.75 10:1.00 11:0.56 12:0.37 17:0.69 18:0.99 21:0.64 22:0.93 27:0.51 29:0.97 30:0.54 32:0.71 34:0.45 36:0.46 37:0.33 39:0.86 43:0.12 44:0.92 45:0.97 47:0.93 48:0.91 55:0.45 64:0.77 66:1.00 69:0.63 70:0.64 71:0.61 74:0.96 77:0.70 81:0.51 83:0.56 86:1.00 91:0.20 98:0.95 99:0.83 101:0.47 104:0.99 106:0.81 108:0.48 114:0.70 117:0.86 122:0.65 123:0.46 126:0.51 127:0.65 129:0.05 131:0.61 135:0.96 139:1.00 144:0.76 145:0.72 146:0.99 147:0.99 149:0.30 150:0.44 154:0.59 155:0.54 157:0.61 158:0.76 159:0.82 165:0.65 166:0.86 170:0.82 172:0.99 173:0.41 174:0.99 176:0.70 177:0.88 178:0.55 179:0.10 182:0.61 187:0.21 192:0.56 195:0.93 197:0.99 201:0.58 204:0.83 206:0.81 208:0.61 212:0.93 215:0.53 216:0.85 219:0.91 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.66 235:0.84 239:1.00 242:0.13 243:1.00 246:0.61 247:0.99 253:0.70 254:0.88 259:0.21 262:0.93 265:0.95 266:0.47 267:0.98 271:0.82 276:0.99 279:0.75 281:0.86 282:0.80 283:0.82 287:0.61 290:0.70 292:0.88 297:0.36 300:0.70 +1 8:0.78 9:0.71 10:1.00 12:0.37 17:0.78 21:0.68 23:0.99 27:0.70 29:0.97 30:0.93 31:0.89 34:0.96 36:0.72 37:0.61 39:0.86 43:0.32 48:0.91 55:0.45 62:0.99 66:0.97 69:0.50 70:0.24 71:0.61 74:0.48 75:0.96 76:0.98 77:0.89 79:0.88 81:0.30 91:0.82 96:0.93 98:0.98 102:0.96 108:0.38 114:0.63 120:0.59 122:0.95 123:0.37 126:0.32 127:0.79 128:0.97 129:0.05 131:0.91 135:0.34 137:0.89 139:0.72 140:0.98 144:0.76 145:0.83 146:0.87 147:0.89 149:0.58 150:0.29 151:0.61 153:0.48 154:0.52 155:0.56 157:0.61 158:0.76 159:0.45 162:0.77 164:0.56 166:0.73 168:0.97 170:0.82 172:0.93 173:0.56 174:0.99 175:0.75 176:0.59 177:0.70 179:0.27 182:0.61 190:0.95 191:0.87 192:0.86 193:0.98 195:0.66 196:0.89 197:1.00 201:0.54 202:0.84 204:0.78 205:0.99 208:0.61 212:0.89 216:0.23 219:0.91 220:0.62 222:0.25 226:0.96 227:0.94 229:0.61 231:0.80 232:0.74 235:0.84 236:0.94 239:1.00 241:0.83 242:0.78 243:0.97 244:0.83 246:0.61 247:0.82 248:0.59 253:0.90 255:0.71 256:0.87 257:0.65 259:0.21 260:0.60 265:0.98 266:0.38 267:0.94 268:0.88 271:0.82 276:0.86 277:0.69 279:0.79 281:0.86 282:0.72 284:0.93 285:0.62 287:0.61 290:0.63 292:0.88 300:0.55 +0 1:0.59 7:0.75 10:0.94 11:0.56 12:0.37 17:0.77 18:0.88 21:0.68 22:0.93 27:0.51 29:0.97 30:0.43 32:0.71 34:0.44 36:0.35 37:0.33 39:0.86 43:0.11 44:0.92 45:0.89 47:0.93 48:0.91 55:0.45 64:0.77 66:0.98 69:0.69 70:0.76 71:0.61 74:0.80 76:0.85 77:0.70 81:0.39 86:0.92 91:0.05 98:0.90 101:0.47 104:1.00 106:0.81 108:0.52 114:0.69 117:0.86 122:0.65 123:0.50 126:0.51 127:0.47 129:0.05 131:0.61 135:0.94 139:0.92 144:0.76 145:0.74 146:0.95 147:0.96 149:0.28 150:0.41 154:0.61 155:0.54 157:0.61 159:0.63 166:0.86 170:0.82 172:0.96 173:0.61 174:0.89 176:0.70 177:0.88 178:0.55 179:0.17 182:0.61 187:0.21 192:0.56 195:0.82 197:0.88 201:0.57 204:0.83 206:0.81 208:0.61 212:0.91 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.65 235:0.88 239:0.92 241:0.07 242:0.13 243:0.98 246:0.61 247:0.97 253:0.61 254:0.88 259:0.21 262:0.93 265:0.90 266:0.27 267:0.69 271:0.82 276:0.92 281:0.86 282:0.80 287:0.61 290:0.69 297:0.36 300:0.70 +0 1:0.59 7:0.75 10:0.84 11:0.51 12:0.37 17:0.55 18:0.62 21:0.68 22:0.93 27:0.51 29:0.97 30:0.62 32:0.71 34:0.44 36:0.41 37:0.33 39:0.86 43:0.13 44:0.92 47:0.93 48:0.91 55:0.42 64:0.77 66:0.75 69:0.70 70:0.23 71:0.61 74:0.54 76:0.85 77:0.70 81:0.39 86:0.69 91:0.09 98:0.63 104:1.00 106:0.81 108:0.53 114:0.69 117:0.86 122:0.41 123:0.51 126:0.51 127:0.18 129:0.05 131:0.61 135:0.86 139:0.79 144:0.76 145:0.74 146:0.51 147:0.57 149:0.11 150:0.41 154:0.53 155:0.54 157:0.61 159:0.18 163:0.86 166:0.86 170:0.82 172:0.32 173:0.84 174:0.79 176:0.70 177:0.88 178:0.55 179:0.74 182:0.61 187:0.21 192:0.56 195:0.59 197:0.84 201:0.57 204:0.83 206:0.81 208:0.61 212:0.30 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.79 232:0.92 233:0.65 235:0.88 239:0.83 241:0.18 242:0.33 243:0.77 244:0.61 246:0.61 247:0.39 253:0.54 254:0.80 259:0.21 262:0.93 265:0.64 266:0.27 267:0.69 271:0.82 276:0.28 281:0.86 282:0.80 283:0.82 287:0.61 290:0.69 297:0.36 300:0.18 +0 1:0.61 10:0.93 11:0.56 12:0.37 17:0.12 18:0.77 21:0.64 27:0.08 29:0.97 30:0.37 32:0.65 34:0.99 36:0.27 37:0.94 39:0.86 43:0.38 45:0.77 55:0.59 64:0.77 66:0.53 69:0.77 70:0.70 71:0.61 74:0.53 81:0.59 83:0.56 86:0.84 91:0.44 98:0.59 99:0.83 101:0.47 108:0.70 114:0.72 122:0.86 123:0.68 124:0.64 126:0.54 127:0.43 129:0.05 131:0.61 133:0.60 135:0.39 139:0.78 144:0.76 145:0.41 146:0.24 147:0.05 149:0.25 150:0.48 154:0.66 155:0.48 157:0.84 159:0.43 165:0.65 166:0.86 170:0.82 172:0.54 173:0.81 174:0.88 176:0.73 177:0.05 178:0.55 179:0.61 182:0.84 187:0.68 192:0.47 195:0.66 197:0.90 201:0.61 208:0.64 212:0.75 215:0.53 216:0.67 222:0.25 227:0.61 229:0.61 231:0.79 235:0.88 236:0.97 239:0.91 241:0.54 242:0.33 243:0.11 245:0.65 246:0.93 247:0.82 253:0.55 254:0.88 257:0.93 259:0.21 265:0.60 266:0.47 267:0.59 271:0.82 276:0.52 277:0.69 283:0.67 287:0.61 290:0.72 297:0.36 300:0.55 +2 1:0.65 7:0.70 8:0.69 9:0.74 10:0.95 11:0.51 12:0.37 17:0.85 18:0.93 21:0.22 22:0.93 27:0.67 29:0.97 30:0.32 31:0.92 32:0.71 34:0.91 36:0.86 37:0.34 39:0.86 43:0.57 44:0.92 47:0.93 48:1.00 55:0.45 64:0.77 66:0.95 69:0.65 70:0.74 71:0.61 74:0.51 77:0.70 79:0.92 81:0.53 83:0.56 86:0.82 91:0.54 96:0.78 98:0.85 104:0.88 106:0.81 108:0.49 114:0.88 120:0.67 122:0.75 123:0.47 126:0.51 127:0.36 129:0.05 131:0.91 135:0.58 137:0.92 139:0.82 140:0.45 144:0.76 145:0.42 146:0.79 147:0.82 149:0.68 150:0.53 151:0.76 153:0.48 154:0.53 155:0.64 157:0.61 159:0.35 162:0.86 164:0.66 166:0.87 170:0.82 172:0.88 173:0.72 174:0.90 176:0.70 177:0.88 179:0.27 182:0.61 187:0.21 190:0.77 192:0.97 195:0.64 196:0.93 197:0.91 201:0.63 202:0.50 206:0.81 208:0.61 212:0.91 215:0.79 216:0.27 220:0.82 222:0.25 227:0.95 229:0.61 230:0.73 231:0.81 232:0.74 233:0.76 235:0.42 239:0.92 241:0.10 242:0.32 243:0.95 244:0.61 246:0.61 247:0.94 248:0.73 253:0.77 254:0.84 255:0.74 256:0.58 259:0.21 260:0.67 262:0.93 265:0.85 266:0.38 267:0.52 268:0.59 271:0.82 276:0.79 281:0.91 282:0.80 284:0.92 285:0.60 287:0.61 290:0.88 297:0.36 300:0.55 +0 1:0.58 8:0.78 11:0.56 12:0.37 17:0.61 18:0.93 21:0.47 22:0.93 27:0.12 29:0.97 30:0.52 33:0.97 34:0.58 36:0.75 37:0.82 39:0.86 43:0.62 44:0.88 45:0.95 47:0.93 64:0.77 66:0.96 69:0.59 70:0.47 71:0.61 74:0.80 77:0.70 81:0.46 83:0.56 86:0.95 91:0.23 98:0.90 99:0.83 101:0.47 108:0.45 114:0.76 117:0.86 122:0.86 123:0.43 126:0.32 127:0.49 129:0.05 131:0.61 135:0.49 139:0.94 144:0.76 145:0.41 146:0.93 147:0.94 149:0.68 150:0.41 154:0.51 155:0.52 157:0.93 159:0.55 165:0.65 166:0.87 170:0.82 172:0.91 173:0.54 175:0.75 176:0.63 177:0.70 178:0.55 179:0.26 182:0.85 187:0.98 192:0.50 195:0.77 201:0.55 204:0.82 208:0.50 212:0.72 216:0.79 222:0.25 227:0.61 229:0.61 231:0.80 232:0.98 235:0.60 241:0.35 242:0.24 243:0.97 244:0.61 246:0.93 247:0.60 253:0.64 254:0.88 257:0.65 259:0.21 262:0.93 265:0.90 266:0.27 267:0.23 271:0.82 276:0.84 277:0.69 281:0.91 282:0.75 287:0.61 290:0.76 291:0.97 300:0.33 +2 1:0.59 7:0.75 10:1.00 11:0.56 12:0.37 17:0.75 18:0.99 21:0.68 22:0.93 27:0.51 29:0.97 30:0.52 32:0.71 34:0.40 36:0.43 37:0.33 39:0.86 43:0.12 44:0.92 45:0.97 47:0.93 48:0.91 55:0.45 64:0.77 66:1.00 69:0.63 70:0.64 71:0.61 74:0.96 77:0.70 81:0.50 83:0.56 86:1.00 91:0.20 98:0.95 99:0.83 101:0.47 104:0.99 106:0.81 108:0.48 114:0.69 117:0.86 122:0.65 123:0.46 126:0.51 127:0.65 129:0.05 131:0.61 135:0.96 139:1.00 144:0.76 145:0.76 146:0.99 147:0.99 149:0.27 150:0.42 154:0.60 155:0.54 157:0.61 159:0.82 165:0.65 166:0.86 170:0.82 172:0.99 173:0.41 174:0.99 176:0.70 177:0.88 178:0.55 179:0.10 182:0.61 187:0.21 192:0.56 195:0.93 197:0.99 201:0.57 204:0.83 206:0.81 208:0.61 212:0.94 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.65 235:0.88 239:1.00 241:0.01 242:0.13 243:1.00 246:0.61 247:0.99 253:0.69 254:0.88 259:0.21 262:0.93 265:0.95 266:0.27 267:0.98 271:0.82 276:0.99 281:0.86 282:0.80 287:0.61 290:0.69 297:0.36 300:0.70 +0 1:0.56 10:0.84 11:0.56 12:0.37 17:0.45 18:0.80 21:0.77 27:0.45 29:0.97 30:0.68 34:0.80 36:0.28 37:0.50 39:0.86 43:0.29 44:0.88 45:0.85 64:0.77 66:0.36 69:0.72 70:0.41 71:0.61 74:0.62 77:0.89 81:0.46 83:0.56 86:0.84 91:0.23 98:0.22 99:0.83 101:0.47 108:0.79 114:0.63 122:0.86 123:0.78 124:0.60 126:0.51 127:0.32 129:0.05 131:0.61 133:0.39 135:0.65 139:0.82 144:0.76 145:0.47 146:0.29 147:0.35 149:0.44 150:0.39 154:0.58 155:0.46 157:0.89 159:0.42 165:0.65 166:0.86 170:0.82 172:0.41 173:0.73 174:0.79 176:0.70 177:0.88 178:0.55 179:0.61 182:0.84 187:0.68 192:0.45 195:0.71 197:0.83 201:0.55 208:0.61 212:0.81 215:0.43 216:0.77 222:0.25 227:0.61 229:0.61 231:0.79 235:1.00 239:0.83 241:0.10 242:0.43 243:0.49 244:0.61 245:0.71 246:0.92 247:0.67 253:0.52 254:0.84 259:0.21 265:0.24 266:0.27 267:0.36 271:0.82 276:0.26 277:0.87 282:0.75 287:0.61 290:0.63 297:0.36 300:0.33 +1 1:0.61 7:0.70 8:0.76 9:0.71 10:0.93 11:0.51 12:0.37 17:0.81 18:0.93 21:0.13 22:0.93 23:0.96 27:0.62 29:0.97 30:0.45 31:0.93 32:0.71 34:0.96 36:0.51 37:0.36 39:0.86 43:0.59 44:0.86 47:0.93 48:1.00 62:0.97 64:0.77 66:0.96 69:0.62 70:0.77 71:0.61 74:0.38 75:0.98 79:0.92 81:0.56 86:0.77 91:0.57 98:0.94 102:0.97 104:0.87 106:0.81 108:0.47 120:0.68 122:0.75 123:0.46 126:0.51 127:0.61 128:0.96 129:0.05 131:0.87 135:0.60 137:0.93 139:0.76 140:0.80 144:0.76 145:0.63 146:0.87 147:0.89 149:0.75 150:0.53 151:0.66 153:0.48 154:0.49 155:0.55 157:0.61 159:0.45 162:0.86 164:0.66 166:0.81 168:0.96 170:0.82 172:0.89 173:0.67 174:0.90 177:0.88 179:0.32 182:0.61 187:0.21 190:0.89 192:0.57 195:0.66 196:0.92 197:0.91 201:0.63 202:0.50 205:0.95 206:0.81 208:0.50 212:0.88 215:0.84 216:0.36 219:0.88 220:0.62 222:0.25 226:0.95 227:0.92 229:0.61 230:0.73 231:0.77 233:0.66 235:0.31 236:0.95 239:0.95 241:0.18 242:0.37 243:0.96 244:0.61 246:0.61 247:0.91 248:0.63 253:0.33 254:0.80 255:0.73 256:0.58 259:0.21 260:0.68 262:0.93 265:0.94 266:0.54 267:0.36 268:0.59 271:0.82 276:0.80 279:0.75 281:0.86 283:0.82 284:0.92 285:0.60 287:0.61 292:0.95 297:0.36 300:0.70 +0 8:0.97 10:0.84 11:0.56 12:0.37 17:0.88 18:0.70 21:0.78 23:0.95 27:0.53 29:0.97 30:0.62 34:0.64 36:0.34 37:0.64 39:0.86 43:0.64 45:0.73 62:0.95 66:0.30 69:0.45 70:0.23 71:0.61 74:0.40 86:0.71 91:0.81 98:0.44 101:0.47 108:0.35 122:0.65 123:0.33 124:0.71 127:0.76 128:0.96 129:0.05 131:0.61 133:0.60 135:0.30 139:0.69 140:0.98 144:0.76 145:0.45 146:0.47 147:0.53 149:0.40 151:0.52 153:0.45 154:0.54 157:0.82 159:0.48 162:0.45 163:0.87 166:0.61 168:0.96 170:0.82 172:0.16 173:0.48 174:0.79 175:0.75 177:0.70 178:0.54 179:0.95 182:0.74 190:0.95 191:0.87 197:0.84 202:0.85 205:0.98 212:0.30 216:0.28 220:0.62 222:0.25 227:0.61 229:0.61 231:0.67 235:0.74 239:0.84 241:0.87 242:0.33 243:0.48 244:0.83 245:0.43 246:0.61 247:0.39 248:0.52 253:0.35 256:0.68 257:0.65 259:0.21 265:0.46 266:0.27 267:0.28 268:0.45 271:0.82 276:0.27 277:0.69 285:0.46 287:0.61 300:0.18 +1 1:0.67 7:0.75 10:1.00 11:0.56 12:0.37 17:0.88 18:0.80 21:0.54 27:0.64 29:0.97 30:0.20 32:0.75 34:0.99 36:0.60 37:0.55 39:0.86 43:0.66 44:0.93 45:0.66 48:0.97 64:0.77 66:0.93 69:0.29 70:0.61 71:0.61 74:0.79 76:0.85 77:0.89 81:0.62 83:0.56 86:0.92 91:0.33 98:0.87 99:0.83 101:0.47 102:0.98 104:0.58 106:0.81 108:0.22 114:0.76 122:0.77 123:0.22 126:0.51 127:0.40 129:0.05 131:0.61 135:0.51 139:0.94 144:0.76 145:0.98 146:0.53 147:0.59 149:0.32 150:0.55 154:0.76 155:0.57 157:0.61 158:0.81 159:0.19 165:0.65 166:0.86 170:0.82 172:0.80 173:0.60 174:0.94 176:0.70 177:0.88 178:0.55 179:0.40 182:0.61 187:0.21 192:0.85 195:0.53 197:0.96 201:0.64 204:0.84 206:0.81 208:0.61 212:0.95 215:0.58 216:0.16 219:0.94 222:0.25 227:0.61 229:0.61 230:0.77 231:0.79 233:0.66 235:0.88 239:0.99 241:0.12 242:0.28 243:0.93 246:0.61 247:0.79 253:0.63 254:0.84 259:0.21 265:0.87 266:0.12 267:0.28 271:0.82 276:0.70 279:0.78 281:0.86 282:0.83 283:0.65 287:0.61 290:0.76 292:0.86 297:0.36 300:0.43 +1 1:0.62 7:0.70 8:0.72 10:0.93 11:0.75 12:0.37 17:0.83 18:0.82 21:0.59 27:0.70 29:0.97 30:0.20 32:0.67 34:0.26 36:0.83 37:0.70 39:0.86 43:0.13 45:0.73 55:0.52 64:0.77 66:0.43 69:0.62 70:0.99 71:0.61 74:0.35 76:0.85 77:0.70 81:0.55 83:0.56 86:0.67 91:0.31 98:0.40 101:0.65 102:0.95 104:0.92 106:0.81 108:0.95 114:0.66 123:0.94 124:0.89 126:0.54 127:0.86 129:0.59 131:0.61 133:0.90 135:0.49 139:0.64 144:0.76 145:0.87 146:0.62 147:0.67 149:0.25 150:0.50 154:0.57 155:0.52 157:0.61 159:0.88 166:0.86 170:0.82 172:0.78 173:0.33 174:0.93 176:0.59 177:0.88 178:0.55 179:0.33 182:0.61 187:0.39 192:0.54 193:0.98 195:0.96 197:0.89 201:0.63 204:0.80 206:0.81 208:0.64 212:0.54 215:0.55 216:0.21 222:0.25 227:0.61 229:0.61 230:0.73 231:0.79 232:0.72 233:0.76 235:0.88 236:0.97 239:0.93 241:0.32 242:0.33 243:0.27 244:0.61 245:0.81 246:0.61 247:0.96 253:0.51 254:0.84 259:0.21 265:0.42 266:0.91 267:0.23 271:0.82 276:0.81 282:0.72 283:0.82 287:0.61 290:0.66 297:0.36 300:0.98 +1 1:0.60 10:0.91 11:0.56 12:0.37 17:0.59 18:0.87 21:0.59 27:0.45 29:0.97 30:0.54 32:0.71 34:0.82 36:0.21 37:0.50 39:0.86 43:0.28 44:0.92 45:0.87 64:0.77 66:0.33 69:0.70 70:0.64 71:0.61 74:0.62 77:0.70 81:0.52 83:0.56 86:0.87 91:0.27 98:0.52 99:0.83 101:0.47 108:0.53 114:0.73 122:0.86 123:0.51 124:0.67 126:0.51 127:0.36 129:0.05 131:0.61 133:0.60 135:0.79 139:0.85 144:0.76 145:0.47 146:0.60 147:0.66 149:0.52 150:0.44 154:0.63 155:0.48 157:0.90 159:0.45 165:0.65 166:0.87 170:0.82 172:0.48 173:0.70 174:0.83 176:0.70 177:0.88 178:0.55 179:0.52 182:0.84 187:0.68 192:0.45 195:0.71 197:0.86 201:0.59 208:0.61 212:0.76 216:0.77 222:0.25 227:0.61 229:0.61 231:0.80 235:0.80 236:0.95 239:0.88 241:0.15 242:0.27 243:0.50 245:0.71 246:0.93 247:0.84 253:0.57 254:0.96 259:0.21 265:0.54 266:0.63 267:0.65 271:0.82 276:0.57 282:0.79 287:0.61 290:0.73 300:0.55 +2 1:0.68 10:0.98 11:0.56 12:0.37 17:0.91 18:0.86 21:0.13 27:0.51 29:0.97 30:0.31 32:0.77 34:0.33 36:0.92 37:0.37 39:0.86 43:0.20 44:0.93 45:0.66 55:0.45 64:0.77 66:0.98 69:0.17 70:0.63 71:0.61 74:0.61 77:0.70 81:0.71 83:0.56 86:0.85 91:0.12 98:0.98 99:0.83 101:0.47 102:0.98 104:0.91 108:0.14 114:0.92 123:0.13 126:0.54 127:0.82 129:0.05 131:0.61 135:0.90 139:0.85 144:0.76 145:0.42 146:0.95 147:0.96 149:0.38 150:0.61 154:0.58 155:0.52 157:0.61 159:0.64 165:0.65 166:0.87 170:0.82 172:0.95 173:0.18 174:0.97 176:0.73 177:0.88 178:0.55 179:0.21 182:0.61 187:0.68 192:0.54 195:0.76 197:0.98 201:0.67 208:0.64 212:0.91 215:0.84 216:0.42 222:0.25 227:0.61 229:0.61 231:0.80 232:0.85 235:0.42 236:0.97 239:0.98 241:0.03 242:0.44 243:0.98 246:0.61 247:0.93 253:0.67 254:0.80 259:0.21 265:0.98 266:0.54 267:0.59 271:0.82 276:0.91 282:0.85 283:0.82 287:0.61 290:0.92 297:0.36 300:0.70 +1 1:0.57 8:0.77 9:0.70 10:0.97 11:0.56 12:0.37 17:0.94 18:0.97 21:0.59 27:0.33 29:0.97 30:0.56 31:0.90 34:0.99 36:0.87 37:0.57 39:0.86 43:0.18 44:0.92 45:0.66 48:0.97 55:0.52 64:0.77 66:0.83 69:0.86 70:0.57 71:0.61 74:0.82 76:0.94 77:0.70 79:0.89 81:0.34 86:0.96 91:0.58 96:0.68 98:0.83 101:0.47 102:0.97 108:0.72 114:0.71 122:0.91 123:0.70 124:0.39 126:0.32 127:0.80 129:0.05 131:0.91 133:0.60 135:0.60 137:0.90 139:0.95 140:0.80 144:0.76 145:0.85 146:0.91 147:0.48 149:0.26 150:0.37 151:0.49 153:0.77 154:0.49 155:0.52 157:0.61 159:0.65 162:0.86 166:0.87 170:0.82 172:0.94 173:0.84 174:0.96 176:0.63 177:0.38 179:0.22 182:0.61 187:0.39 190:0.95 192:0.49 195:0.80 196:0.94 197:0.97 201:0.55 202:0.54 204:0.82 208:0.50 212:0.88 216:0.38 219:0.87 220:0.82 222:0.25 227:0.95 229:0.61 231:0.81 235:0.74 239:0.96 241:0.52 242:0.41 243:0.13 244:0.61 245:0.87 246:0.61 247:0.92 248:0.49 253:0.63 254:0.74 255:0.68 256:0.58 257:0.84 259:0.21 265:0.83 266:0.63 267:0.52 268:0.63 271:0.82 276:0.90 281:0.86 282:0.77 284:0.93 285:0.50 287:0.61 290:0.71 292:0.88 300:0.84 +2 1:0.58 7:0.69 8:0.82 10:0.97 12:0.37 17:0.73 18:0.93 21:0.47 22:0.93 27:0.55 29:0.97 30:0.52 32:0.65 34:0.98 36:0.50 37:0.63 39:0.86 43:0.19 44:0.85 47:0.93 48:0.91 55:0.45 64:0.77 66:0.98 69:0.39 70:0.72 71:0.61 74:0.34 77:0.70 81:0.46 86:0.77 91:0.54 98:0.98 104:0.91 106:0.81 108:0.30 122:0.75 123:0.29 126:0.51 127:0.81 129:0.05 131:0.61 135:0.76 139:0.74 145:0.96 146:0.95 147:0.96 149:0.26 150:0.44 154:0.45 155:0.47 157:0.61 159:0.64 166:0.81 170:0.82 172:0.96 173:0.32 174:0.95 177:0.88 178:0.55 179:0.20 182:0.61 187:0.39 192:0.46 195:0.77 197:0.95 201:0.60 206:0.81 208:0.50 212:0.92 215:0.63 216:0.42 219:0.87 222:0.25 227:0.61 229:0.61 230:0.71 231:0.78 233:0.66 235:0.68 236:0.95 239:0.96 241:0.46 242:0.28 243:0.98 244:0.61 246:0.61 247:0.96 253:0.30 254:0.80 259:0.21 262:0.93 265:0.98 266:0.54 267:0.44 271:0.82 276:0.91 281:0.86 282:0.72 283:0.67 287:0.61 292:0.88 297:0.36 300:0.70 +2 1:0.59 7:0.75 10:1.00 11:0.56 12:0.37 17:0.64 18:0.99 21:0.68 22:0.93 27:0.51 29:0.97 30:0.33 32:0.71 34:0.40 36:0.43 37:0.33 39:0.86 43:0.12 44:0.92 45:0.88 47:0.93 48:0.91 55:0.45 64:0.77 66:1.00 69:0.63 70:0.53 71:0.61 74:0.96 76:0.85 77:0.70 81:0.39 86:1.00 91:0.20 98:0.95 101:0.47 104:1.00 106:0.81 108:0.48 114:0.69 117:0.86 122:0.65 123:0.46 126:0.51 127:0.68 129:0.05 131:0.61 135:0.97 139:1.00 144:0.76 145:0.74 146:0.99 147:0.99 149:0.15 150:0.41 154:0.61 155:0.54 157:0.61 159:0.81 166:0.86 170:0.82 172:0.99 173:0.42 174:0.99 176:0.70 177:0.88 178:0.55 179:0.10 182:0.61 187:0.21 192:0.56 195:0.92 197:0.99 201:0.57 204:0.83 206:0.81 208:0.61 212:0.94 215:0.49 216:0.85 222:0.25 227:0.61 229:0.61 230:0.77 231:0.80 232:0.92 233:0.65 235:0.88 239:1.00 242:0.13 243:1.00 246:0.61 247:0.99 253:0.69 254:0.88 259:0.21 262:0.93 265:0.95 266:0.27 267:0.96 271:0.82 276:0.98 281:0.86 282:0.80 287:0.61 290:0.69 297:0.36 300:0.70 +0 10:0.84 11:0.56 12:0.37 17:0.72 18:0.78 21:0.78 22:0.93 27:0.62 29:0.97 30:0.32 33:0.97 34:0.42 36:0.21 37:0.66 39:0.86 43:0.58 45:0.83 47:0.93 66:0.89 69:0.61 70:0.86 71:0.61 74:0.56 86:0.84 91:0.42 98:0.85 101:0.47 104:0.99 106:0.81 108:0.47 122:0.86 123:0.45 127:0.35 129:0.05 131:0.61 135:0.74 139:0.78 144:0.76 146:0.79 147:0.82 149:0.39 154:0.50 157:0.88 159:0.35 166:0.61 170:0.82 172:0.68 173:0.68 174:0.79 177:0.88 178:0.55 179:0.55 182:0.78 187:0.87 197:0.84 206:0.81 212:0.73 216:0.13 222:0.25 227:0.61 229:0.61 231:0.74 239:0.84 241:0.15 242:0.21 243:0.89 244:0.61 246:0.61 247:0.84 253:0.44 254:0.92 262:0.93 265:0.85 266:0.47 267:0.36 271:0.82 276:0.55 287:0.61 291:0.97 300:0.70 +0 1:0.60 7:0.70 8:0.85 10:0.93 11:0.51 12:0.37 17:0.79 18:0.93 21:0.30 22:0.93 27:0.62 29:0.97 30:0.58 32:0.71 34:0.98 36:0.85 37:0.36 39:0.86 43:0.36 44:0.86 47:0.93 48:0.98 55:0.45 64:0.77 66:0.61 69:0.63 70:0.73 71:0.61 74:0.37 75:0.98 81:0.51 86:0.77 91:0.31 98:0.80 102:0.97 104:0.87 106:0.81 108:0.48 122:0.75 123:0.46 124:0.50 126:0.51 127:0.53 129:0.59 131:0.61 133:0.47 135:0.68 139:0.76 144:0.76 145:0.80 146:0.80 147:0.83 149:0.67 150:0.48 154:0.49 155:0.47 157:0.61 159:0.46 166:0.81 170:0.82 172:0.84 173:0.66 174:0.89 177:0.88 178:0.55 179:0.30 182:0.61 187:0.21 192:0.51 195:0.68 197:0.89 201:0.62 206:0.81 208:0.50 212:0.89 215:0.72 216:0.36 219:0.88 222:0.25 226:0.95 227:0.61 229:0.61 230:0.73 231:0.77 233:0.76 235:0.52 236:0.95 239:0.94 241:0.18 242:0.36 243:0.67 245:0.93 246:0.61 247:0.91 253:0.33 254:0.99 259:0.21 262:0.93 265:0.80 266:0.54 267:0.82 271:0.82 276:0.80 279:0.75 281:0.91 283:0.66 287:0.61 292:0.88 297:0.36 300:0.70 +1 1:0.74 11:0.42 12:0.69 17:0.47 21:0.59 26:0.95 27:0.45 28:0.68 30:0.68 34:0.66 36:0.17 37:0.50 39:0.35 43:0.29 55:0.71 58:0.93 66:0.54 69:0.69 70:0.43 74:0.62 81:0.57 91:0.11 98:0.47 108:0.53 114:0.74 122:0.51 123:0.51 124:0.48 126:0.54 127:0.31 129:0.05 131:0.61 133:0.39 135:0.75 141:0.91 144:0.57 145:0.52 146:0.60 147:0.65 149:0.32 150:0.65 154:0.75 155:0.67 157:0.61 159:0.47 161:0.95 166:0.95 167:0.49 172:0.32 173:0.67 176:0.73 177:0.38 178:0.55 179:0.82 181:0.96 182:0.61 187:0.68 192:0.59 199:0.95 201:0.74 212:0.42 215:0.55 216:0.77 222:0.76 223:0.94 224:0.93 227:0.61 229:0.61 231:0.92 234:0.91 235:0.74 241:0.15 242:0.45 243:0.63 245:0.50 246:0.61 247:0.47 253:0.61 254:0.74 259:0.21 265:0.49 266:0.38 267:0.36 271:0.84 274:0.91 276:0.27 287:0.61 290:0.74 297:0.36 300:0.33 +1 1:0.74 7:0.76 8:0.81 9:0.80 11:0.42 12:0.69 17:0.75 20:0.90 21:0.22 26:0.95 27:0.72 28:0.68 30:0.76 32:0.72 34:0.61 36:0.97 37:0.49 39:0.35 41:0.82 43:0.74 55:0.59 58:0.99 66:0.72 69:0.84 70:0.22 74:0.62 78:0.82 81:0.81 83:0.77 91:0.82 96:0.90 98:0.24 100:0.84 104:0.76 108:0.69 111:0.82 114:0.90 120:0.86 122:0.51 123:0.67 126:0.54 127:0.11 129:0.05 131:0.97 135:0.61 140:0.45 141:0.98 144:0.57 145:0.59 146:0.28 147:0.34 149:0.38 150:0.83 151:0.94 152:0.84 153:0.80 154:0.64 155:0.67 157:0.61 158:0.86 159:0.12 161:0.95 162:0.66 164:0.84 166:0.95 167:0.83 169:0.82 172:0.27 173:0.93 176:0.73 177:0.38 179:0.24 181:0.96 182:0.94 186:0.83 191:0.73 192:0.59 195:0.68 199:0.97 201:0.74 202:0.78 208:0.64 212:0.78 215:0.79 216:0.09 220:0.74 222:0.76 223:0.95 224:0.99 227:0.92 229:0.97 230:0.79 231:0.93 233:0.76 234:0.98 235:0.31 241:0.85 242:0.45 243:0.75 246:0.61 247:0.35 248:0.89 253:0.89 254:0.74 255:0.79 256:0.77 259:0.21 260:0.87 261:0.84 265:0.26 266:0.17 267:0.23 268:0.80 271:0.84 274:0.99 276:0.25 279:0.86 283:0.67 285:0.62 287:0.90 290:0.90 297:0.36 300:0.18 +2 1:0.74 7:0.76 8:0.93 9:0.80 11:0.42 12:0.69 17:0.55 21:0.22 26:0.95 27:0.31 28:0.68 30:0.72 32:0.80 34:0.53 36:0.55 37:0.55 39:0.35 43:0.25 55:0.92 58:0.98 66:0.35 69:0.74 70:0.54 74:0.78 76:0.85 77:0.98 81:0.81 91:0.14 96:0.82 98:0.62 104:0.82 106:0.81 108:0.90 114:0.90 117:0.86 120:0.55 123:0.89 124:0.86 126:0.54 127:0.80 129:0.59 131:0.97 133:0.86 135:0.88 140:0.45 141:0.98 144:0.57 145:0.92 146:0.61 147:0.66 149:0.56 150:0.83 151:0.51 153:0.48 154:0.70 155:0.67 157:0.61 158:0.83 159:0.88 161:0.95 162:0.86 163:0.94 164:0.64 166:0.95 167:0.53 172:0.70 173:0.48 176:0.73 177:0.38 179:0.39 181:0.96 182:0.75 187:0.39 190:0.77 192:0.59 195:0.96 199:0.99 201:0.74 202:0.53 206:0.81 208:0.64 212:0.40 215:0.79 216:0.85 220:0.96 222:0.76 223:0.95 224:0.98 227:0.92 229:0.61 230:0.79 231:0.93 232:0.91 233:0.76 234:0.98 235:0.31 241:0.35 242:0.74 243:0.26 245:0.80 246:0.61 247:0.60 248:0.51 253:0.84 254:0.80 255:0.79 256:0.58 259:0.21 260:0.56 265:0.63 266:0.71 267:0.19 268:0.62 271:0.84 274:0.97 276:0.77 282:0.88 285:0.62 287:0.61 290:0.90 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 8:0.81 9:0.80 11:0.64 12:0.69 17:0.88 20:0.79 26:0.95 27:0.06 28:0.68 30:0.36 32:0.80 34:0.42 36:0.44 37:0.87 39:0.35 41:0.82 43:0.98 58:0.98 66:0.91 69:0.05 70:0.70 74:0.91 76:0.85 77:0.70 78:0.85 81:0.95 83:0.81 85:0.90 91:0.17 96:0.80 98:0.89 100:0.91 101:0.83 104:0.79 106:0.81 108:0.05 111:0.86 114:0.98 117:0.86 120:0.69 121:0.90 123:0.05 126:0.54 127:0.44 129:0.05 131:0.97 135:0.23 140:0.45 141:0.98 144:0.57 145:0.42 146:0.75 147:0.79 149:0.92 150:0.98 151:0.87 152:0.85 153:0.48 154:0.98 155:0.67 157:0.96 159:0.31 161:0.95 162:0.86 164:0.57 165:0.92 166:0.95 167:0.47 169:0.86 172:0.76 173:0.21 176:0.73 177:0.38 179:0.49 181:0.96 182:0.95 186:0.86 187:0.68 192:0.59 195:0.59 199:0.98 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.03 242:0.28 243:0.92 246:0.88 247:0.60 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 261:0.88 265:0.89 266:0.27 267:0.36 268:0.46 271:0.84 274:0.97 276:0.65 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.92 7:0.76 8:0.84 9:0.80 11:0.64 12:0.69 17:0.57 20:0.99 21:0.40 26:0.95 27:0.47 28:0.68 30:0.20 32:0.80 34:0.36 36:0.54 37:0.57 39:0.35 41:0.90 43:0.98 55:0.45 58:0.99 60:0.87 66:0.27 69:0.15 70:0.92 74:0.89 76:0.94 77:0.70 78:0.96 81:0.73 83:0.83 85:0.80 91:0.85 96:0.87 98:0.74 100:0.98 101:0.77 104:0.79 108:0.12 111:0.97 114:0.82 120:0.78 123:0.60 124:0.58 126:0.54 127:0.80 129:0.59 131:0.97 133:0.47 135:0.32 140:0.45 141:0.98 144:0.57 145:0.50 146:0.38 147:0.44 149:0.81 150:0.82 151:0.92 152:0.93 153:0.72 154:0.98 155:0.67 157:0.89 158:0.87 159:0.40 161:0.95 162:0.76 164:0.77 165:0.83 166:0.95 167:0.83 169:0.97 172:0.57 173:0.30 176:0.73 177:0.38 179:0.62 181:0.96 182:0.95 186:0.96 189:0.93 192:0.59 195:0.63 199:0.98 201:0.74 202:0.69 208:0.64 212:0.70 215:0.67 216:0.23 220:0.74 222:0.76 223:0.95 224:0.98 227:0.92 229:0.97 230:0.79 231:0.93 232:0.72 233:0.76 234:0.98 235:0.52 238:0.95 241:0.86 242:0.63 243:0.59 245:0.80 246:0.88 247:0.60 248:0.85 253:0.90 255:0.79 256:0.68 259:0.21 260:0.79 261:0.97 265:0.43 266:0.71 267:0.69 268:0.73 271:0.84 274:0.98 276:0.59 279:0.86 282:0.88 283:0.71 285:0.62 287:0.90 290:0.82 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.69 17:0.99 21:0.05 26:0.95 27:0.72 28:0.68 30:0.40 32:0.78 34:0.34 36:0.49 39:0.35 41:0.82 43:0.98 55:0.59 58:0.98 60:0.87 66:0.91 69:0.07 70:0.67 74:0.93 76:0.94 77:0.89 81:0.91 83:0.85 85:0.85 91:0.83 96:0.78 98:0.99 101:0.81 104:0.58 108:0.06 114:0.95 120:0.66 121:0.90 122:0.41 123:0.06 126:0.54 127:0.90 129:0.05 131:0.97 135:0.77 140:0.45 141:0.98 144:0.57 145:0.69 146:0.73 147:0.77 149:0.81 150:0.95 151:0.79 153:0.69 154:0.98 155:0.67 157:0.93 158:0.87 159:0.30 161:0.95 162:0.86 164:0.62 165:0.90 166:0.95 167:0.80 172:0.75 173:0.30 176:0.73 177:0.38 179:0.60 181:0.96 182:0.95 192:0.59 195:0.57 199:0.98 201:0.74 202:0.46 204:0.89 208:0.64 212:0.90 215:0.91 216:0.03 220:0.62 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.87 231:0.93 233:0.76 234:1.00 235:0.12 241:0.79 242:0.22 243:0.91 246:0.88 247:0.67 248:0.72 253:0.85 255:0.79 256:0.45 259:0.21 260:0.67 265:0.99 266:0.27 267:0.17 268:0.55 271:0.84 274:0.97 276:0.64 279:0.86 282:0.86 283:0.71 285:0.62 287:0.90 290:0.95 297:0.36 300:0.55 +0 7:0.76 8:0.89 12:0.69 17:0.26 20:0.89 21:0.54 25:0.88 26:0.95 27:0.51 28:0.68 30:0.19 32:0.72 34:0.67 36:0.78 37:0.72 39:0.35 43:0.45 55:0.45 58:1.00 66:0.79 69:0.17 70:0.82 78:0.85 81:0.36 91:0.94 98:0.77 100:0.84 104:0.54 108:0.80 111:0.83 120:0.97 123:0.79 124:0.39 126:0.32 127:0.86 129:0.59 131:0.85 133:0.47 135:0.78 140:0.45 141:0.98 145:0.87 146:0.31 147:0.37 149:0.65 150:0.32 151:0.80 152:0.84 153:0.93 154:0.34 157:0.61 159:0.72 161:0.95 162:0.77 164:0.96 166:0.78 167:0.84 169:0.81 172:0.82 173:0.15 175:0.75 177:0.05 179:0.46 181:0.96 182:0.61 186:0.85 190:0.77 191:0.90 195:0.86 199:1.00 202:0.93 212:0.69 215:0.58 216:0.59 220:0.62 222:0.76 223:0.95 224:0.99 227:0.84 229:0.61 230:0.79 231:0.68 233:0.76 234:0.99 235:0.60 241:0.89 242:0.82 243:0.16 244:0.61 245:0.78 246:0.61 247:0.12 248:0.95 253:0.20 256:0.95 257:0.65 259:0.21 260:0.96 261:0.85 265:0.77 266:0.71 267:0.69 268:0.95 271:0.84 274:1.00 276:0.69 277:0.69 283:0.82 285:0.50 287:0.61 297:0.36 300:0.70 +1 7:0.78 11:0.64 12:0.69 17:0.90 21:0.76 26:0.95 27:0.72 28:0.68 30:0.08 32:0.77 34:0.70 36:0.52 39:0.35 43:0.98 58:0.98 66:0.45 69:0.29 70:0.89 74:0.73 76:0.94 77:0.70 81:0.27 85:0.72 91:0.67 98:0.69 101:0.74 104:0.58 108:0.60 120:0.54 122:0.43 123:0.58 124:0.56 126:0.32 127:0.65 129:0.59 131:0.84 133:0.47 135:0.24 140:0.45 141:0.98 144:0.57 145:0.59 146:0.24 147:0.30 149:0.61 150:0.26 151:0.46 153:0.69 154:0.98 155:0.67 157:0.81 159:0.33 161:0.95 162:0.86 164:0.62 166:0.78 167:0.82 172:0.27 173:0.45 177:0.38 179:0.90 181:0.96 182:0.74 190:0.77 192:0.59 195:0.64 199:0.97 202:0.46 204:0.89 208:0.64 212:0.19 215:0.46 216:0.01 220:1.00 222:0.76 223:0.95 224:0.98 227:0.84 229:0.61 230:0.81 231:0.88 233:0.76 234:0.98 235:0.95 241:0.83 242:0.45 243:0.40 245:0.53 246:0.61 247:0.47 248:0.46 253:0.54 256:0.45 259:0.21 260:0.54 265:0.69 266:0.47 267:0.19 268:0.55 271:0.84 274:0.97 276:0.31 282:0.85 285:0.50 287:0.61 297:0.36 300:0.55 +2 1:0.74 11:0.64 12:0.69 17:0.95 21:0.40 26:0.95 27:0.72 28:0.68 30:0.15 34:0.73 36:0.86 39:0.35 43:0.87 55:0.45 58:0.93 60:0.87 66:0.63 69:0.32 70:0.52 74:0.57 81:0.78 83:0.86 85:0.72 91:0.72 98:0.47 101:0.74 104:0.57 108:0.25 114:0.58 123:0.24 124:0.49 126:0.54 127:0.47 129:0.05 131:0.61 133:0.62 135:0.56 141:0.91 144:0.57 146:0.39 147:0.46 149:0.66 150:0.80 154:0.85 155:0.67 157:0.81 158:0.87 159:0.31 161:0.95 166:0.95 167:0.81 172:0.41 173:0.46 176:0.54 177:0.38 178:0.55 179:0.82 181:0.96 182:0.74 192:0.59 199:0.97 201:0.74 208:0.64 212:0.84 215:0.55 216:0.06 222:0.76 223:0.94 224:0.93 227:0.61 229:0.61 231:0.92 232:0.78 234:0.91 235:0.88 241:0.80 242:0.73 243:0.68 245:0.47 246:0.61 247:0.39 253:0.48 259:0.21 265:0.49 266:0.23 267:0.18 271:0.84 274:0.91 276:0.36 279:0.86 283:0.71 287:0.61 290:0.58 297:0.36 300:0.33 +2 1:0.74 6:0.94 7:0.81 8:0.77 9:0.80 11:0.64 12:0.69 17:0.89 20:0.80 26:0.95 27:0.06 28:0.68 30:0.86 32:0.80 34:0.26 36:0.47 37:0.87 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.96 69:0.05 70:0.42 74:0.90 76:0.85 77:0.70 78:0.90 81:0.95 83:0.81 85:0.90 91:0.11 96:0.80 98:0.96 100:0.92 101:0.79 104:0.79 106:0.81 108:0.05 111:0.90 114:0.98 117:0.86 120:0.69 121:0.90 123:0.05 126:0.54 127:0.72 129:0.05 131:0.97 135:0.24 140:0.45 141:0.98 144:0.57 145:0.42 146:0.96 147:0.97 149:0.93 150:0.98 151:0.87 152:0.84 153:0.48 154:0.98 155:0.67 157:0.95 159:0.67 161:0.95 162:0.86 164:0.57 165:0.92 166:0.95 167:0.46 169:0.90 172:0.89 173:0.10 176:0.73 177:0.38 179:0.33 181:0.96 182:0.95 186:0.90 187:0.68 192:0.59 195:0.80 199:0.99 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.72 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.02 242:0.63 243:0.96 246:0.88 247:0.60 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 261:0.90 265:0.96 266:0.38 267:0.36 268:0.46 271:0.84 274:0.97 276:0.82 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.86 7:0.81 8:0.73 9:0.80 11:0.42 12:0.69 17:0.93 20:0.92 21:0.54 26:0.95 27:0.31 28:0.68 30:0.85 32:0.78 34:0.66 36:0.66 37:0.62 39:0.35 41:0.90 43:0.71 55:0.59 58:0.99 66:0.91 69:0.30 70:0.41 74:0.84 77:0.70 78:0.94 81:0.68 83:0.77 91:0.08 96:0.86 98:0.90 100:0.92 106:0.81 108:0.24 111:0.92 114:0.77 117:0.86 120:0.77 121:0.90 123:0.23 126:0.54 127:0.46 129:0.05 131:0.97 135:0.68 138:0.96 140:0.45 141:0.98 144:0.57 145:0.44 146:0.87 147:0.89 149:0.73 150:0.74 151:0.87 152:0.86 153:0.48 154:0.83 155:0.67 157:0.61 159:0.44 161:0.95 162:0.86 164:0.75 166:0.95 167:0.59 169:0.90 172:0.75 173:0.33 176:0.73 177:0.38 179:0.52 181:0.96 182:0.95 186:0.93 187:0.98 189:0.88 192:0.59 195:0.67 199:0.98 201:0.74 202:0.61 204:0.89 206:0.81 208:0.64 212:0.73 215:0.58 216:0.38 220:0.96 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.87 231:0.93 232:0.85 233:0.76 234:0.99 235:0.74 238:0.86 241:0.54 242:0.63 243:0.91 246:0.61 247:0.55 248:0.86 253:0.88 255:0.79 256:0.68 259:0.21 260:0.77 261:0.92 265:0.90 266:0.27 267:0.59 268:0.70 271:0.84 274:0.99 276:0.64 282:0.86 283:0.82 285:0.62 287:0.90 290:0.77 297:0.36 300:0.43 +1 1:0.74 11:0.42 12:0.69 17:0.61 21:0.78 26:0.95 27:0.45 28:0.68 30:0.62 34:0.83 36:0.18 37:0.50 39:0.35 43:0.27 55:0.84 58:0.93 66:0.47 69:0.72 70:0.34 74:0.54 81:0.40 91:0.23 98:0.35 108:0.56 114:0.63 122:0.41 123:0.53 124:0.52 126:0.54 127:0.18 129:0.59 131:0.61 133:0.47 135:0.76 141:0.91 144:0.57 145:0.49 146:0.50 147:0.56 149:0.25 150:0.60 154:0.74 155:0.67 157:0.61 159:0.33 161:0.95 163:0.86 166:0.95 167:0.55 172:0.21 173:0.67 176:0.73 177:0.38 178:0.55 179:0.73 181:0.96 182:0.61 187:0.68 192:0.59 199:0.95 201:0.74 212:0.30 215:0.43 216:0.77 222:0.76 223:0.94 224:0.93 227:0.61 229:0.61 231:0.91 234:0.91 235:1.00 241:0.43 242:0.33 243:0.59 245:0.46 246:0.61 247:0.39 253:0.52 254:0.74 259:0.21 265:0.38 266:0.27 267:0.36 271:0.84 274:0.91 276:0.27 287:0.61 290:0.63 297:0.36 300:0.25 +2 1:0.74 6:0.85 7:0.81 8:0.69 9:0.80 11:0.42 12:0.69 17:0.57 20:0.95 21:0.22 26:0.95 27:0.72 28:0.68 30:0.68 32:0.78 34:0.50 36:0.25 37:0.37 39:0.35 41:0.82 43:0.32 55:0.42 58:0.99 60:0.87 66:0.45 69:0.58 70:0.31 74:0.79 76:0.94 77:0.89 78:0.88 81:0.87 83:0.83 91:0.84 96:0.90 98:0.34 100:0.90 104:0.54 108:0.64 111:0.88 114:0.69 120:0.83 122:0.43 123:0.61 124:0.56 126:0.54 127:0.43 129:0.59 131:0.97 133:0.47 135:0.32 138:0.96 140:0.45 141:0.98 144:0.57 145:0.42 146:0.27 147:0.33 149:0.13 150:0.89 151:0.95 152:0.88 153:0.84 154:0.82 155:0.67 157:0.61 158:0.92 159:0.24 161:0.95 162:0.83 164:0.82 166:0.95 167:0.82 169:0.87 172:0.27 173:0.78 176:0.56 177:0.38 179:0.87 181:0.96 182:0.95 186:0.88 189:0.87 192:0.59 195:0.56 199:0.98 201:0.74 202:0.71 204:0.89 208:0.64 212:0.42 215:0.72 216:0.15 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.81 233:0.69 234:0.99 235:0.60 238:0.87 241:0.83 242:0.63 243:0.40 245:0.53 246:0.61 247:0.39 248:0.92 253:0.91 254:0.74 255:0.79 256:0.73 259:0.21 260:0.84 261:0.90 265:0.37 266:0.38 267:0.23 268:0.77 271:0.84 274:0.99 276:0.25 279:0.86 282:0.86 283:0.82 285:0.62 287:0.90 290:0.69 297:0.36 300:0.25 +2 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.64 12:0.69 17:0.88 20:0.80 26:0.95 27:0.06 28:0.68 30:0.86 32:0.80 34:0.25 36:0.52 37:0.87 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.95 69:0.05 70:0.44 74:0.85 76:0.85 77:0.70 78:0.90 81:0.95 83:0.81 85:0.88 91:0.09 96:0.80 98:0.96 100:0.87 101:0.77 104:0.79 106:0.81 108:0.05 111:0.88 114:0.98 117:0.86 120:0.69 121:0.90 123:0.05 126:0.54 127:0.70 129:0.05 131:0.97 135:0.26 140:0.45 141:0.98 144:0.57 145:0.42 146:0.96 147:0.97 149:0.86 150:0.98 151:0.87 152:0.85 153:0.48 154:0.98 155:0.67 157:0.93 159:0.68 161:0.95 162:0.86 164:0.57 165:0.92 166:0.95 167:0.46 169:0.88 172:0.88 173:0.10 176:0.73 177:0.38 179:0.36 181:0.96 182:0.95 186:0.90 187:0.39 192:0.59 195:0.82 199:0.99 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.51 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.02 242:0.74 243:0.95 246:0.88 247:0.60 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 261:0.89 265:0.96 266:0.54 267:0.19 268:0.46 271:0.84 274:0.97 276:0.80 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.83 7:0.81 8:0.76 9:0.80 11:0.64 12:0.69 17:0.78 20:0.93 21:0.54 26:0.95 27:0.52 28:0.68 30:0.28 32:0.80 34:0.58 36:0.78 37:0.47 39:0.35 41:0.95 43:0.81 58:1.00 60:0.87 66:0.29 69:0.23 70:0.65 74:0.82 76:0.94 77:0.89 78:0.88 81:0.67 83:0.83 85:0.88 91:0.80 96:0.92 98:0.67 100:0.86 101:0.81 108:0.18 111:0.86 114:0.77 120:0.87 121:0.90 122:0.41 123:0.65 124:0.59 126:0.54 127:0.80 129:0.59 131:0.97 133:0.47 135:0.54 140:0.87 141:0.98 144:0.57 145:0.88 146:0.60 147:0.66 149:0.81 150:0.78 151:0.90 152:0.86 153:0.69 154:0.75 155:0.67 157:0.95 158:0.84 159:0.41 161:0.95 162:0.73 164:0.91 165:0.79 166:0.95 167:0.84 169:0.84 172:0.45 173:0.35 176:0.73 177:0.38 179:0.73 181:0.96 182:0.95 186:0.88 189:0.86 192:0.59 195:0.65 199:0.99 201:0.74 202:0.86 204:0.89 208:0.64 212:0.27 215:0.58 216:0.25 220:0.93 222:0.76 223:0.95 224:0.99 227:0.92 229:0.97 230:0.87 231:0.93 233:0.76 234:0.98 235:0.74 238:0.85 241:0.89 242:0.40 243:0.57 245:0.72 246:0.88 247:0.55 248:0.92 253:0.93 255:0.79 256:0.89 259:0.21 260:0.87 261:0.89 265:0.67 266:0.54 267:0.69 268:0.90 271:0.84 274:1.00 276:0.52 279:0.86 282:0.88 283:0.71 285:0.62 287:0.90 290:0.77 297:0.36 299:0.88 300:0.43 +2 1:0.74 7:0.81 8:0.84 9:0.80 11:0.64 12:0.69 17:0.85 20:0.80 26:0.95 27:0.06 28:0.68 30:0.88 32:0.80 34:0.29 36:0.46 37:0.87 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.95 69:0.05 70:0.36 74:0.84 76:0.85 77:0.70 78:0.91 81:0.95 83:0.81 85:0.88 91:0.33 96:0.83 98:0.96 100:0.89 101:0.78 104:0.79 106:0.81 108:0.05 111:0.90 114:0.98 117:0.86 120:0.72 121:0.90 123:0.05 126:0.54 127:0.69 129:0.05 131:0.97 135:0.32 140:0.45 141:0.98 144:0.57 145:0.42 146:0.96 147:0.97 149:0.87 150:0.98 151:0.90 152:0.85 153:0.69 154:0.98 155:0.67 157:0.93 159:0.66 161:0.95 162:0.86 164:0.62 165:0.92 166:0.95 167:0.46 169:0.86 172:0.87 173:0.10 176:0.73 177:0.38 179:0.37 181:0.96 182:0.95 186:0.91 187:0.68 192:0.59 195:0.80 199:0.99 201:0.74 202:0.46 204:0.89 206:0.81 208:0.64 212:0.61 215:0.96 216:0.54 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 230:0.84 231:0.93 232:0.86 233:0.69 234:1.00 235:0.05 241:0.02 242:0.77 243:0.95 246:0.88 247:0.60 248:0.80 253:0.87 255:0.79 256:0.45 259:0.21 260:0.73 261:0.88 265:0.96 266:0.54 267:0.28 268:0.55 271:0.84 274:0.97 276:0.79 282:0.83 285:0.62 287:0.90 290:0.98 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.84 7:0.81 8:0.70 9:0.80 11:0.64 12:0.69 17:0.88 20:0.93 21:0.22 26:0.95 27:0.72 28:0.68 30:0.68 32:0.80 34:0.81 36:0.48 37:0.41 39:0.35 41:0.94 43:0.85 58:0.99 60:0.95 66:0.71 69:0.29 70:0.43 74:0.89 76:0.85 77:0.70 78:0.90 81:0.82 83:0.87 85:0.91 91:0.88 96:0.89 98:0.76 100:0.90 101:0.84 104:0.58 108:0.22 111:0.89 114:0.90 120:0.80 121:0.90 122:0.43 123:0.22 124:0.42 126:0.54 127:0.84 129:0.59 131:0.97 133:0.47 135:0.26 140:0.45 141:0.98 144:0.57 145:0.47 146:0.63 147:0.68 149:0.88 150:0.89 151:0.95 152:0.87 153:0.84 154:0.87 155:0.67 157:0.96 158:0.92 159:0.37 161:0.95 162:0.84 164:0.83 165:0.86 166:0.95 167:0.83 169:0.88 172:0.57 173:0.43 176:0.73 177:0.38 179:0.76 181:0.96 182:0.95 186:0.90 189:0.86 191:0.80 192:0.59 195:0.59 199:0.98 201:0.74 202:0.75 204:0.89 208:0.64 212:0.75 215:0.79 216:0.23 220:0.87 222:0.76 223:0.95 224:0.99 227:0.92 229:0.97 230:0.87 231:0.93 232:0.79 233:0.76 234:0.98 235:0.31 238:0.87 241:0.86 242:0.54 243:0.75 245:0.60 246:0.88 247:0.47 248:0.87 253:0.92 255:0.79 256:0.78 259:0.21 260:0.81 261:0.90 265:0.76 266:0.47 267:0.23 268:0.81 271:0.84 274:0.99 276:0.45 279:0.86 282:0.88 283:0.82 285:0.62 287:0.90 290:0.90 297:0.36 299:0.88 300:0.43 +2 1:0.74 8:0.83 9:0.80 11:0.64 12:0.69 17:0.78 21:0.05 25:0.88 26:0.95 27:0.49 28:0.68 30:0.87 34:0.18 36:0.56 37:0.48 39:0.35 41:0.82 43:0.89 55:0.59 58:0.98 66:0.97 69:0.50 70:0.27 74:0.95 81:0.90 83:0.78 85:0.98 91:0.27 96:0.75 98:0.94 101:0.87 104:0.58 108:0.38 114:0.74 117:0.86 120:0.63 122:0.43 123:0.37 126:0.54 127:0.62 129:0.05 131:0.97 135:0.18 138:0.95 140:0.45 141:0.98 144:0.57 146:0.91 147:0.92 149:0.98 150:0.94 151:0.79 153:0.69 154:0.87 155:0.67 157:0.98 158:0.83 159:0.51 161:0.95 162:0.86 164:0.62 165:0.88 166:0.95 167:0.48 172:0.93 173:0.49 176:0.56 177:0.38 179:0.24 181:0.96 182:0.95 187:0.39 192:0.59 199:0.99 201:0.74 202:0.46 208:0.64 212:0.93 215:0.84 216:0.44 220:0.62 222:0.76 223:0.95 224:1.00 227:0.92 229:0.97 231:0.93 232:0.83 234:1.00 235:0.22 241:0.10 242:0.44 243:0.97 246:0.88 247:0.60 248:0.72 253:0.81 255:0.79 256:0.45 259:0.21 260:0.64 265:0.94 266:0.27 267:0.23 268:0.55 271:0.84 274:0.98 276:0.87 283:0.71 285:0.62 286:0.99 287:0.90 290:0.74 297:0.36 298:0.99 300:0.43 +1 10:0.85 11:0.64 12:0.51 17:0.57 18:0.70 21:0.78 27:0.43 29:0.52 30:0.28 34:0.90 36:0.19 37:0.61 39:0.52 43:0.23 45:0.81 66:0.96 69:0.47 70:0.71 71:0.93 74:0.34 85:0.80 86:0.66 91:0.46 98:0.98 101:0.96 108:0.36 122:0.98 123:0.35 127:0.81 129:0.05 135:0.66 139:0.64 146:0.98 147:0.98 149:0.54 154:0.16 159:0.76 170:0.77 172:0.91 173:0.31 174:0.97 177:1.00 178:0.55 179:0.31 187:0.39 197:0.98 212:0.55 216:0.72 222:0.48 235:0.60 239:0.84 241:0.02 242:0.21 243:0.97 244:0.93 247:0.94 253:0.30 259:0.21 264:0.93 265:0.98 266:0.63 267:0.73 271:0.88 275:0.94 276:0.84 294:0.94 300:0.55 +0 1:0.56 7:0.63 10:0.80 11:0.42 12:0.51 17:0.22 18:0.77 21:0.64 25:0.88 27:0.49 29:0.52 30:0.26 32:0.62 34:0.73 36:0.43 37:0.33 39:0.52 43:0.05 45:0.92 64:0.77 66:0.07 69:0.86 70:0.95 71:0.93 74:0.25 75:0.95 81:0.32 83:0.53 86:0.58 91:0.38 97:0.89 98:0.21 99:0.83 101:0.45 104:0.75 108:0.73 120:0.55 122:0.97 123:0.98 124:0.97 126:0.26 127:0.88 129:0.81 133:0.97 135:0.65 139:0.57 140:0.80 145:0.59 146:0.60 147:0.44 149:0.05 150:0.28 151:0.47 153:0.46 154:0.05 155:0.46 159:0.95 162:0.62 163:0.75 164:0.53 165:0.63 170:0.77 172:0.41 173:0.51 174:0.94 175:1.00 177:0.70 178:0.42 179:0.35 187:0.68 190:1.00 192:0.46 195:0.99 197:0.90 201:0.55 202:0.49 208:0.48 212:0.13 215:0.53 216:0.74 219:0.86 220:0.96 222:0.48 226:0.96 230:0.63 233:0.76 235:0.84 236:0.91 239:0.79 241:0.18 242:0.15 243:0.16 244:1.00 245:0.73 247:0.96 248:0.47 253:0.22 254:1.00 256:0.45 257:0.99 259:0.21 260:0.55 264:0.93 265:0.20 266:0.98 267:0.65 268:0.45 271:0.88 275:0.96 276:0.81 277:0.99 279:0.74 285:0.45 292:0.88 294:0.91 297:0.36 300:0.94 +2 10:0.84 11:0.64 12:0.51 17:0.59 18:0.78 21:0.78 27:0.71 29:0.52 30:0.21 32:0.62 34:0.47 36:0.33 37:0.49 39:0.52 43:0.31 44:0.85 45:0.85 66:0.08 69:0.71 70:0.90 71:0.93 74:0.33 77:0.70 82:0.98 85:0.72 86:0.65 88:0.99 91:0.29 98:0.29 101:0.96 102:0.94 108:0.54 122:0.98 123:0.95 124:0.97 127:0.85 129:0.05 133:0.97 135:0.79 139:0.64 145:0.83 146:0.36 147:0.42 149:0.49 154:0.23 159:0.91 170:0.77 172:0.51 173:0.37 174:0.96 177:1.00 178:0.55 179:0.34 187:0.68 195:0.98 197:0.94 202:0.63 212:0.23 216:0.29 222:0.48 235:0.88 239:0.84 241:0.12 242:0.12 243:0.32 244:0.93 245:0.74 247:0.98 253:0.30 259:0.21 264:0.93 265:0.16 266:0.96 267:0.73 271:0.88 275:0.96 276:0.81 277:0.99 282:0.71 294:0.94 300:0.84 +1 10:0.84 11:0.64 12:0.51 17:0.34 18:0.73 21:0.78 27:0.48 29:0.52 30:0.15 34:0.25 36:0.19 37:0.55 39:0.52 43:0.32 45:0.81 55:0.59 66:0.23 69:0.74 70:0.94 71:0.93 74:0.33 85:0.72 86:0.65 91:0.11 98:0.48 101:0.96 108:0.82 122:0.98 123:0.81 124:0.87 127:0.88 129:0.05 133:0.85 135:0.42 139:0.63 145:0.58 146:0.39 147:0.32 149:0.49 154:0.23 159:0.85 170:0.77 172:0.68 173:0.53 174:0.98 177:0.05 178:0.55 179:0.31 187:0.39 197:0.96 212:0.54 216:0.92 222:0.48 235:0.95 239:0.84 241:0.15 242:0.14 243:0.09 244:0.93 245:0.87 247:0.95 253:0.29 257:1.00 259:0.21 264:0.93 265:0.50 266:0.91 267:0.23 271:0.88 275:0.97 276:0.83 277:1.00 294:0.94 300:0.84 +0 10:0.79 11:0.42 12:0.51 17:0.45 18:0.66 21:0.78 27:0.52 29:0.52 30:0.21 34:0.68 36:0.39 37:0.43 39:0.52 43:0.05 45:0.81 55:0.71 66:0.54 69:0.90 70:0.89 71:0.93 74:0.25 76:0.85 86:0.57 91:0.18 98:0.57 101:0.45 108:0.81 122:0.55 123:0.79 124:0.64 127:0.28 129:0.05 133:0.59 135:0.65 139:0.56 145:0.69 146:0.92 147:0.93 149:0.05 154:0.05 159:0.75 170:0.77 172:0.73 173:0.78 174:0.88 175:0.75 177:1.00 178:0.55 179:0.30 187:0.68 197:0.85 212:0.51 216:0.79 222:0.48 235:0.80 239:0.78 241:0.10 242:0.12 243:0.62 244:1.00 245:0.81 247:0.97 253:0.23 254:0.80 257:0.65 259:0.21 264:0.93 265:0.58 266:0.84 267:0.44 271:0.88 275:0.97 276:0.72 277:0.69 294:0.91 300:0.70 +0 10:0.84 11:0.54 12:0.51 17:0.87 18:0.65 21:0.78 27:0.72 29:0.52 30:0.56 34:0.25 36:0.30 39:0.52 43:0.05 45:0.87 66:0.74 69:0.30 70:0.39 71:0.93 74:0.33 86:0.65 91:0.68 98:0.47 101:0.72 108:0.24 122:0.67 123:0.23 124:0.39 127:0.28 129:0.05 133:0.36 135:0.94 139:0.63 145:0.91 146:0.41 147:0.47 149:0.05 154:0.29 159:0.26 170:0.77 172:0.51 173:0.43 174:0.79 177:1.00 178:0.55 179:0.66 197:0.84 212:0.81 216:0.02 222:0.48 235:0.12 239:0.84 241:0.78 242:0.24 243:0.77 244:0.93 245:0.49 247:0.67 253:0.30 254:0.74 259:0.21 264:0.93 265:0.49 266:0.27 267:0.44 271:0.88 275:0.91 276:0.33 294:0.94 300:0.33 +0 1:0.57 8:0.63 9:0.71 10:0.79 11:0.42 12:0.51 17:0.89 21:0.13 27:0.72 29:0.52 30:0.53 34:0.50 36:0.39 39:0.52 43:0.05 45:0.97 66:0.11 69:0.94 70:0.82 71:0.93 74:0.28 81:0.34 83:0.53 91:0.78 96:0.77 98:0.32 99:0.83 101:0.46 108:0.43 114:0.66 120:0.65 122:0.86 123:0.42 124:0.94 126:0.24 127:0.85 129:0.59 133:0.94 135:0.96 140:0.45 146:0.70 147:0.80 149:0.08 150:0.34 151:0.68 153:0.72 154:0.05 155:0.50 159:0.90 162:0.86 163:0.81 164:0.70 165:0.63 170:0.77 172:0.68 173:0.83 174:0.79 175:0.99 176:0.55 177:0.88 179:0.20 190:1.00 192:0.57 197:0.80 201:0.55 202:0.58 208:0.48 212:0.26 215:0.84 216:0.02 220:0.74 222:0.48 232:0.84 235:0.22 239:0.78 241:0.79 242:0.30 243:0.31 244:1.00 245:0.91 247:0.98 248:0.68 253:0.62 254:0.92 255:0.70 256:0.64 257:0.99 259:0.21 260:0.65 264:0.93 265:0.34 266:0.84 267:0.52 268:0.66 271:0.88 275:0.94 276:0.91 277:0.99 285:0.46 290:0.66 294:0.91 297:0.36 300:0.84 +0 10:0.82 11:0.64 12:0.51 17:0.66 18:0.84 21:0.78 27:0.72 29:0.52 30:0.70 34:0.39 36:0.21 39:0.52 43:0.21 45:0.91 66:0.94 69:0.88 70:0.52 71:0.93 74:0.30 85:0.72 86:0.62 91:0.63 98:0.84 101:0.96 108:0.75 122:0.98 123:0.74 127:0.35 129:0.05 135:0.54 139:0.60 145:0.72 146:0.95 147:0.96 149:0.31 154:0.14 159:0.63 170:0.77 172:0.85 173:0.83 174:0.96 177:1.00 178:0.55 179:0.31 197:0.95 212:0.42 216:0.02 222:0.48 235:0.12 239:0.82 241:0.80 242:0.24 243:0.95 244:0.93 247:0.92 253:0.27 259:0.21 264:0.93 265:0.84 266:0.63 267:0.36 271:0.88 275:0.94 276:0.76 294:0.93 300:0.43 +0 1:0.56 10:0.80 11:0.42 12:0.51 17:0.32 18:0.87 21:0.74 27:0.45 29:0.52 30:0.26 34:0.91 36:0.18 37:0.50 39:0.52 43:0.05 44:0.85 45:0.89 64:0.77 66:0.18 69:0.71 70:0.78 71:0.93 75:0.95 81:0.29 83:0.53 86:0.59 91:0.29 97:0.89 98:0.62 99:0.83 101:0.45 102:0.94 108:0.54 122:0.97 123:0.84 124:0.76 126:0.24 127:0.44 129:0.59 133:0.74 135:0.89 139:0.58 145:0.50 146:0.66 147:0.71 149:0.05 150:0.27 154:0.05 155:0.46 159:0.71 165:0.63 170:0.77 172:0.70 173:0.56 174:0.93 175:1.00 177:0.88 178:0.55 179:0.36 187:0.68 192:0.44 195:0.88 197:0.94 201:0.53 208:0.48 212:0.54 216:0.77 219:0.86 222:0.48 226:0.96 235:0.95 236:0.91 239:0.80 241:0.41 242:0.14 243:0.58 244:1.00 245:0.81 247:0.93 253:0.20 254:0.80 257:0.99 259:0.21 264:0.93 265:0.41 266:0.84 267:0.65 271:0.88 275:0.97 276:0.74 277:0.99 279:0.74 292:0.88 294:0.91 300:0.70 +0 1:0.57 2:0.99 10:0.80 11:0.42 12:0.51 17:0.61 18:0.78 21:0.47 27:0.69 29:0.52 30:0.08 33:0.97 34:0.17 36:0.24 37:0.73 39:0.52 43:0.05 45:0.77 55:0.71 56:0.97 64:0.77 66:0.17 69:0.39 70:0.99 71:0.93 75:0.95 81:0.33 83:0.53 86:0.58 91:0.20 97:0.89 98:0.38 99:0.83 101:0.45 108:0.76 123:0.74 124:0.93 126:0.26 127:0.69 129:0.59 133:0.92 135:0.34 139:0.57 146:0.52 147:0.58 149:0.05 150:0.31 154:0.05 155:0.46 159:0.86 165:0.63 170:0.77 172:0.51 173:0.16 174:0.94 175:1.00 177:0.88 178:0.55 179:0.41 187:0.68 192:0.47 197:0.94 201:0.56 202:0.46 208:0.49 212:0.21 216:0.21 219:0.86 222:0.48 226:0.95 235:0.68 236:0.91 239:0.80 241:0.32 242:0.14 243:0.33 244:1.00 245:0.73 247:0.98 253:0.20 254:0.74 257:0.99 259:0.21 264:0.93 265:0.40 266:0.95 267:0.96 271:0.88 275:0.97 276:0.75 277:1.00 279:0.75 291:0.97 292:0.87 294:0.92 295:0.98 300:0.94 +1 1:0.57 7:0.63 10:0.84 11:0.64 12:0.51 17:0.89 18:0.64 21:0.13 27:0.72 29:0.52 30:0.86 32:0.62 34:0.99 36:0.19 39:0.52 43:0.33 45:0.66 66:0.84 69:0.58 70:0.27 71:0.93 74:0.33 77:0.70 81:0.34 83:0.53 85:0.72 86:0.65 91:0.53 98:0.67 99:0.83 101:0.87 106:0.81 108:0.45 114:0.66 117:0.86 122:0.67 123:0.43 126:0.24 127:0.20 129:0.05 135:0.73 139:0.63 145:0.94 146:0.45 147:0.51 149:0.50 150:0.34 154:0.25 155:0.48 159:0.16 165:0.63 170:0.77 172:0.54 173:0.79 174:0.79 176:0.55 177:1.00 178:0.55 179:0.51 187:0.39 192:0.51 195:0.55 197:0.84 201:0.55 204:0.78 206:0.81 208:0.48 212:0.81 215:0.84 216:0.12 222:0.48 230:0.63 232:0.87 233:0.66 235:0.22 239:0.83 241:0.35 242:0.24 243:0.85 244:0.93 247:0.67 253:0.50 259:0.21 264:0.93 265:0.67 266:0.27 267:0.73 271:0.88 275:0.91 276:0.30 282:0.71 290:0.66 294:0.94 297:0.36 300:0.25 +0 8:0.63 10:0.79 11:0.42 12:0.51 17:0.18 18:0.57 21:0.40 27:0.55 29:0.52 30:0.10 34:0.74 36:0.30 37:0.54 39:0.52 43:0.05 45:0.83 55:0.84 66:0.10 69:0.99 70:0.94 71:0.93 81:0.27 86:0.56 91:0.51 98:0.31 101:0.46 108:0.99 120:0.57 123:0.99 124:0.99 126:0.24 127:0.99 129:0.05 133:0.99 135:0.79 139:0.55 140:0.45 146:0.88 147:0.91 149:0.05 150:0.28 151:0.54 153:0.72 154:0.05 159:0.98 162:0.86 164:0.57 170:0.77 172:0.90 173:0.93 174:0.83 175:0.99 177:0.38 179:0.11 187:0.21 190:1.00 192:0.45 197:0.79 201:0.54 202:0.53 212:0.27 215:0.67 216:0.54 220:0.91 222:0.48 235:0.52 239:0.78 241:0.24 242:0.41 243:0.18 244:1.00 245:0.95 247:1.00 248:0.55 253:0.20 254:0.96 255:0.69 256:0.58 257:1.00 259:0.21 260:0.57 264:0.93 265:0.33 266:0.97 267:0.99 268:0.62 271:0.88 275:0.98 276:0.98 277:0.99 283:0.67 285:0.47 294:0.91 297:0.36 300:0.94 +0 8:0.87 10:0.80 11:0.42 12:0.51 17:0.64 18:0.90 21:0.78 27:0.51 29:0.52 30:0.72 34:0.83 36:0.25 37:0.80 39:0.52 43:0.05 45:0.98 66:0.09 69:0.98 70:0.66 71:0.93 86:0.59 91:0.65 98:0.25 101:0.45 108:0.96 122:0.97 123:0.96 124:0.98 127:0.81 129:0.05 133:0.98 135:0.91 139:0.57 145:0.79 146:0.86 147:0.41 149:0.06 154:0.05 159:0.96 170:0.77 172:0.63 173:0.88 174:0.99 175:1.00 177:0.38 178:0.55 179:0.20 187:0.21 191:0.70 197:0.96 212:0.18 216:0.10 222:0.48 235:0.88 239:0.80 241:0.02 242:0.52 243:0.10 244:1.00 245:0.83 247:0.82 253:0.20 257:1.00 259:0.21 264:0.93 265:0.28 266:0.97 267:0.18 271:0.88 275:0.95 276:0.91 277:0.99 294:0.91 300:0.84 +0 8:0.70 10:0.79 11:0.42 12:0.51 17:0.70 18:0.62 21:0.78 27:0.69 29:0.52 30:0.09 34:0.77 36:0.93 37:0.50 39:0.52 43:0.05 45:0.66 55:0.59 66:0.29 69:0.10 70:0.97 71:0.93 74:0.27 86:0.57 91:0.31 98:0.45 101:0.45 108:0.09 122:0.90 123:0.09 124:0.92 127:0.94 129:0.05 133:0.92 135:0.75 139:0.55 146:0.85 147:0.87 149:0.05 154:0.05 158:0.71 159:0.87 163:0.81 170:0.77 172:0.65 173:0.08 174:0.79 175:0.97 177:0.99 178:0.55 179:0.42 187:0.39 197:0.82 212:0.27 216:0.35 222:0.48 232:0.93 235:0.05 239:0.78 241:0.12 242:0.23 243:0.48 244:1.00 245:0.74 247:0.98 253:0.25 254:0.99 257:0.93 259:0.21 264:0.93 265:0.47 266:0.94 267:0.44 271:0.88 275:0.96 276:0.77 277:0.95 294:0.91 300:0.84 +0 7:0.63 8:0.92 10:0.79 11:0.42 12:0.51 17:0.13 18:0.57 21:0.40 27:0.35 29:0.52 30:0.10 32:0.62 34:0.80 36:0.50 37:0.67 39:0.52 43:0.05 45:0.66 55:0.71 66:0.13 69:0.66 70:0.96 71:0.93 74:0.25 81:0.28 86:0.56 91:0.60 98:0.34 101:0.45 104:0.95 106:0.81 108:0.50 120:0.79 123:0.48 124:0.96 126:0.24 127:0.83 129:0.05 133:0.96 135:0.56 139:0.55 140:0.45 145:0.47 146:0.84 147:0.86 149:0.05 150:0.28 151:0.70 153:0.81 154:0.05 159:0.92 162:0.70 164:0.84 170:0.77 172:0.68 173:0.30 174:0.79 175:0.99 177:0.96 179:0.22 187:0.21 190:1.00 191:0.70 192:0.51 195:0.98 197:0.79 201:0.54 202:0.80 206:0.81 212:0.26 215:0.67 216:0.75 220:0.87 222:0.48 230:0.63 233:0.65 235:0.52 239:0.78 241:0.39 242:0.50 243:0.33 244:1.00 245:0.86 247:0.98 248:0.75 253:0.22 254:1.00 255:0.70 256:0.81 257:0.97 259:0.21 260:0.77 264:0.93 265:0.36 266:0.95 267:0.99 268:0.83 271:0.88 275:0.96 276:0.90 277:0.98 283:0.82 285:0.46 294:0.91 297:0.36 300:0.84 +1 8:0.97 10:0.80 11:0.42 12:0.51 17:0.83 18:0.84 21:0.78 27:0.52 29:0.52 30:0.08 34:0.61 36:0.36 37:0.84 39:0.52 43:0.05 45:0.87 55:0.45 66:0.19 69:0.94 70:0.99 71:0.93 74:0.26 86:0.58 91:0.86 98:0.54 101:0.45 108:0.88 123:0.88 124:0.91 127:0.74 129:0.05 133:0.90 135:0.17 139:0.57 140:0.97 145:0.63 146:0.90 147:0.67 149:0.06 151:0.48 153:0.46 154:0.05 159:0.86 162:0.46 170:0.77 172:0.73 173:0.87 174:0.93 175:0.97 177:0.96 178:0.54 179:0.25 190:1.00 191:0.88 197:0.94 202:0.82 212:0.58 216:0.17 220:0.93 222:0.48 235:0.88 239:0.79 241:0.83 242:0.24 243:0.31 244:1.00 245:0.88 247:0.98 248:0.48 253:0.23 256:0.45 257:0.97 259:0.21 264:0.93 265:0.55 266:0.95 267:0.59 268:0.45 271:0.88 275:0.91 276:0.87 277:0.95 285:0.45 294:0.91 300:0.94 +0 1:0.57 8:0.77 9:0.71 10:0.79 11:0.42 12:0.51 17:0.38 18:0.57 21:0.05 27:0.19 29:0.52 30:0.14 34:0.34 36:0.68 37:0.62 39:0.52 43:0.05 45:0.83 55:0.71 66:0.05 69:0.88 70:0.97 71:0.93 74:0.27 81:0.35 83:0.53 86:0.56 91:0.15 96:0.82 97:0.89 98:0.17 99:0.83 101:0.46 108:0.77 114:0.67 117:0.86 120:0.72 123:0.75 124:1.00 126:0.24 127:0.98 129:0.59 133:1.00 135:0.78 139:0.55 140:0.45 146:0.90 147:0.75 149:0.05 150:0.35 151:0.79 153:0.80 154:0.05 155:0.50 158:0.71 159:1.00 162:0.86 164:0.71 165:0.63 170:0.77 172:0.67 173:0.24 174:0.88 175:0.99 176:0.55 177:0.70 179:0.09 187:0.39 190:1.00 191:0.82 192:0.58 197:0.79 201:0.55 202:0.59 208:0.48 212:0.16 215:0.91 216:0.73 220:0.62 222:0.48 232:0.95 235:0.12 239:0.78 241:0.01 242:0.39 243:0.07 244:1.00 245:0.95 247:1.00 248:0.75 253:0.73 254:0.88 255:0.70 256:0.64 257:0.99 259:0.21 260:0.69 264:0.93 265:0.18 266:0.99 267:0.99 268:0.67 271:0.88 275:1.00 276:0.99 277:0.98 279:0.75 283:0.67 285:0.46 290:0.67 294:0.92 297:0.36 300:0.98 +0 8:0.83 10:0.79 11:0.42 12:0.51 17:0.87 18:0.59 21:0.78 27:1.00 29:0.52 30:0.36 34:0.37 36:0.65 37:0.49 39:0.52 43:0.05 45:0.73 55:0.98 66:0.08 69:0.96 70:0.78 71:0.93 74:0.24 86:0.57 91:0.78 98:0.21 101:0.45 108:0.96 123:0.96 124:0.96 127:0.57 129:0.81 133:0.95 135:0.61 139:0.55 145:0.72 146:0.43 147:0.05 149:0.05 154:0.05 159:0.93 163:0.98 170:0.77 172:0.21 173:0.82 174:0.83 175:1.00 177:0.05 178:0.55 179:0.53 191:0.75 197:0.80 202:0.88 212:0.16 216:0.22 222:0.48 235:0.42 239:0.78 241:0.82 242:0.21 243:0.09 244:1.00 245:0.59 247:0.88 253:0.20 257:1.00 259:0.21 264:0.93 265:0.22 266:0.92 267:0.23 271:0.88 275:0.93 276:0.65 277:1.00 294:0.91 300:0.70 +0 1:0.57 8:0.77 9:0.71 10:0.79 11:0.42 12:0.51 17:0.38 18:0.57 21:0.05 27:0.19 29:0.52 30:0.14 34:0.34 36:0.68 37:0.62 39:0.52 43:0.05 45:0.83 55:0.71 66:0.05 69:0.88 70:0.97 71:0.93 74:0.27 81:0.35 83:0.53 86:0.56 91:0.15 96:0.82 97:0.89 98:0.17 99:0.83 101:0.46 108:0.77 114:0.67 117:0.86 120:0.72 123:0.75 124:1.00 126:0.24 127:0.98 129:0.59 133:1.00 135:0.78 139:0.55 140:0.45 146:0.90 147:0.75 149:0.05 150:0.35 151:0.79 153:0.80 154:0.05 155:0.50 158:0.71 159:1.00 162:0.86 164:0.71 165:0.63 170:0.77 172:0.67 173:0.24 174:0.88 175:0.99 176:0.55 177:0.70 179:0.09 187:0.39 190:1.00 191:0.82 192:0.58 197:0.79 201:0.55 202:0.59 208:0.48 212:0.16 215:0.91 216:0.73 220:0.62 222:0.48 232:0.95 235:0.12 239:0.78 241:0.01 242:0.39 243:0.07 244:1.00 245:0.95 247:1.00 248:0.75 253:0.73 254:0.88 255:0.70 256:0.64 257:0.99 259:0.21 260:0.69 264:0.93 265:0.18 266:0.99 267:0.99 268:0.67 271:0.88 275:1.00 276:0.99 277:0.98 279:0.75 283:0.67 285:0.46 290:0.67 294:0.92 297:0.36 300:0.98 +1 8:0.93 9:0.70 10:0.80 11:0.64 12:0.51 17:0.07 18:0.63 21:0.78 27:0.14 29:0.52 30:0.21 34:0.49 36:0.24 37:0.67 39:0.52 43:0.08 45:0.77 66:0.19 69:0.98 70:0.91 71:0.93 74:0.27 83:0.53 85:0.72 86:0.59 91:0.78 96:0.74 98:0.16 101:0.96 108:0.96 120:0.62 122:0.67 123:0.96 124:0.97 127:0.62 129:0.05 133:0.96 135:0.91 139:0.58 140:1.00 145:0.47 146:0.50 147:0.52 149:0.15 154:0.07 155:0.50 159:0.94 162:0.45 164:0.68 170:0.77 172:0.51 173:0.94 174:0.89 177:0.38 178:0.55 179:0.43 187:0.68 190:1.00 191:0.79 192:0.57 197:0.84 202:0.94 208:0.48 212:0.37 216:0.93 220:0.74 222:0.48 235:0.31 239:0.80 241:0.37 242:0.12 243:0.12 244:0.93 245:0.63 247:0.97 253:0.52 255:0.69 256:0.68 257:1.00 259:0.21 260:0.62 264:0.93 265:0.18 266:0.96 267:0.73 268:0.64 271:0.88 275:0.96 276:0.73 285:0.61 294:0.92 300:0.84 +0 8:0.75 10:0.79 11:0.42 12:0.51 17:0.51 18:0.61 21:0.78 27:0.28 29:0.52 30:0.12 34:0.62 36:0.92 37:0.72 39:0.52 43:0.05 45:0.83 55:0.71 66:0.10 69:0.82 70:0.99 71:0.93 74:0.27 76:0.99 86:0.57 91:0.49 96:0.77 98:0.25 101:0.46 108:0.96 120:0.57 122:0.90 123:0.65 124:0.95 127:0.84 129:0.81 132:0.97 133:0.94 135:0.88 139:0.55 140:0.45 145:0.54 146:0.64 147:0.42 149:0.05 151:0.53 153:0.48 154:0.05 159:0.91 162:0.53 164:0.65 170:0.77 172:0.51 173:0.55 174:0.88 175:1.00 177:0.38 179:0.34 187:0.39 190:1.00 191:0.79 192:0.44 197:0.81 202:0.70 212:0.30 216:0.67 220:0.93 222:0.48 235:0.84 239:0.78 241:0.21 242:0.17 243:0.18 244:1.00 245:0.79 247:0.98 248:0.53 253:0.59 254:0.96 255:0.69 256:0.58 257:1.00 259:0.21 260:0.57 264:0.93 265:0.26 266:0.95 267:1.00 268:0.66 271:0.88 275:0.95 276:0.81 277:0.99 283:0.67 285:0.47 294:0.91 300:0.98 +0 10:0.79 11:0.42 12:0.51 17:0.24 18:0.60 21:0.78 27:0.34 29:0.52 30:0.17 34:0.59 36:0.65 37:0.46 39:0.52 43:0.05 45:0.77 55:0.96 66:0.08 69:0.98 70:0.96 71:0.93 74:0.24 86:0.57 91:0.03 98:0.22 101:0.45 104:0.80 106:0.81 108:0.97 123:0.96 124:0.95 127:0.62 129:0.05 133:0.94 135:0.76 139:0.55 145:0.63 146:0.71 147:0.05 149:0.05 154:0.05 159:0.94 170:0.77 172:0.45 173:0.94 174:0.86 175:1.00 177:0.05 178:0.55 179:0.45 187:0.87 191:0.70 197:0.81 206:0.81 212:0.13 216:0.53 222:0.48 235:0.22 239:0.78 241:0.21 242:0.31 243:0.08 244:1.00 245:0.67 247:0.96 253:0.22 257:1.00 259:0.21 264:0.93 265:0.23 266:0.97 267:0.44 271:0.88 275:0.94 276:0.69 277:0.99 294:0.91 300:0.94 +0 10:0.79 11:0.42 12:0.51 17:0.28 18:0.61 21:0.74 27:0.45 29:0.52 30:0.37 34:0.89 36:0.17 37:0.50 39:0.52 43:0.05 45:0.73 55:0.71 66:0.48 69:0.70 70:0.83 71:0.93 74:0.25 81:0.23 86:0.57 91:0.31 98:0.64 101:0.46 108:0.53 123:0.51 124:0.66 126:0.22 127:0.44 129:0.05 133:0.59 135:0.93 139:0.55 145:0.49 146:0.84 147:0.87 149:0.05 150:0.24 154:0.05 159:0.67 170:0.77 172:0.71 173:0.59 174:0.79 175:0.75 177:1.00 178:0.55 179:0.37 187:0.68 197:0.82 212:0.49 215:0.46 216:0.77 222:0.48 235:0.95 239:0.78 241:0.44 242:0.22 243:0.60 244:1.00 245:0.83 247:0.96 253:0.22 257:0.65 259:0.21 264:0.93 265:0.64 266:0.63 267:0.44 271:0.88 275:0.97 276:0.74 277:0.69 294:0.91 297:0.36 300:0.70 +0 8:0.69 10:0.79 11:0.42 12:0.51 17:0.49 18:0.65 21:0.78 27:0.72 29:0.52 30:0.09 34:0.26 36:0.54 39:0.52 43:0.05 45:0.85 55:0.84 66:0.08 69:0.87 70:0.99 71:0.93 74:0.25 86:0.57 91:0.31 98:0.19 101:0.45 108:0.94 123:0.94 124:0.97 127:0.67 129:0.59 133:0.97 135:0.44 139:0.55 146:0.48 147:0.54 149:0.05 154:0.05 159:0.94 163:0.75 170:0.77 172:0.37 173:0.53 174:0.91 175:0.99 177:0.96 178:0.55 179:0.35 197:0.86 202:0.62 212:0.13 216:0.11 222:0.48 235:0.31 239:0.78 241:0.81 242:0.14 243:0.19 244:1.00 245:0.72 247:0.98 253:0.22 254:0.84 257:0.97 259:0.21 264:0.93 265:0.21 266:0.98 267:0.36 271:0.88 275:0.97 276:0.79 277:0.99 294:0.91 300:0.94 +0 10:0.84 11:0.64 12:0.51 17:0.49 18:0.63 21:0.78 27:0.31 29:0.52 30:0.09 34:0.53 36:0.88 37:0.60 39:0.52 43:0.28 45:0.66 55:0.71 66:0.08 69:0.75 70:0.99 71:0.93 74:0.32 85:0.72 86:0.64 91:0.04 98:0.27 101:0.96 108:0.96 123:0.96 124:0.97 127:0.86 129:0.05 133:0.97 135:0.87 139:0.62 146:0.47 147:0.53 149:0.46 154:0.20 159:0.95 170:0.77 172:0.59 173:0.31 174:0.79 177:1.00 178:0.55 179:0.19 187:0.68 197:0.83 202:0.36 212:0.26 216:0.71 222:0.48 235:0.80 239:0.83 241:0.32 242:0.24 243:0.23 244:0.93 245:0.88 247:0.99 253:0.29 259:0.21 264:0.93 265:0.29 266:0.99 267:0.96 271:0.88 275:0.97 276:0.92 277:1.00 294:0.94 300:0.94 +2 8:0.81 9:0.76 11:0.85 12:0.06 17:0.57 18:0.99 20:0.94 21:0.30 22:0.93 27:0.21 29:0.91 30:0.13 31:0.97 34:0.74 36:0.85 37:0.74 39:0.34 43:0.21 45:0.90 47:0.93 48:0.91 55:0.71 64:0.77 66:0.44 69:0.10 70:0.97 71:0.64 74:0.53 78:0.72 79:0.99 81:0.35 83:0.60 85:0.72 86:0.81 91:0.69 96:0.71 97:0.89 98:0.59 100:0.89 101:0.97 108:0.96 111:0.81 120:0.86 122:0.86 123:0.96 124:0.87 126:0.26 127:0.87 129:0.59 131:0.99 133:0.90 135:0.37 137:0.97 139:0.81 140:0.94 144:0.57 146:0.93 147:0.94 149:0.59 150:0.31 151:0.90 152:0.88 153:0.94 154:0.81 155:0.58 157:0.97 158:0.79 159:0.93 162:0.69 164:0.76 166:0.89 169:0.87 172:0.96 173:0.06 177:0.70 179:0.13 182:0.96 186:0.73 187:0.39 190:0.77 191:0.75 192:0.59 196:0.96 202:0.88 208:0.54 212:0.60 216:0.95 219:0.92 222:0.60 227:1.00 229:0.98 231:0.98 235:0.31 241:0.50 242:0.37 243:0.28 245:0.98 246:0.61 247:0.98 248:0.88 253:0.43 255:0.79 256:0.90 259:0.21 260:0.83 261:0.86 262:0.93 265:0.60 266:0.89 267:0.84 268:0.91 271:0.49 276:0.97 279:0.82 281:0.91 283:0.82 284:0.96 285:0.62 287:1.00 292:0.95 300:0.94 +2 6:0.95 8:0.95 9:0.80 12:0.06 17:0.01 20:0.86 21:0.13 27:0.10 29:0.91 30:0.76 31:0.98 34:0.93 36:0.44 37:0.97 39:0.34 41:0.82 43:0.12 44:0.85 48:0.99 55:0.59 66:0.64 69:0.76 70:0.15 71:0.64 74:0.30 76:0.94 77:0.70 78:0.88 79:0.99 81:0.49 83:0.91 87:0.98 91:0.97 96:1.00 98:0.44 100:0.98 108:0.59 111:0.96 114:0.63 120:0.91 122:0.77 123:0.56 126:0.26 127:0.14 129:0.05 131:0.99 135:0.44 137:0.98 138:0.99 139:0.59 140:0.99 144:0.57 145:0.65 146:0.32 147:0.38 149:0.10 150:0.38 151:0.87 152:0.95 153:0.99 154:0.09 155:0.67 157:0.61 158:0.72 159:0.13 162:0.74 163:0.97 164:0.86 166:0.89 169:0.97 172:0.16 173:0.95 175:0.91 176:0.55 177:0.05 179:0.81 182:0.96 186:0.88 187:0.39 191:0.97 192:0.87 195:0.58 196:0.87 202:0.96 208:0.64 212:0.95 216:0.82 219:0.87 222:0.60 227:1.00 229:0.98 231:0.98 232:0.72 235:0.12 241:0.85 242:0.82 243:0.69 244:0.83 246:0.61 247:0.12 248:0.87 253:0.95 255:0.95 256:0.97 257:0.84 259:0.21 260:0.89 261:0.97 265:0.46 266:0.12 267:0.79 268:0.97 271:0.49 276:0.19 277:0.87 279:0.82 281:0.91 282:0.71 284:0.98 285:0.62 287:1.00 290:0.63 292:0.95 300:0.13 +2 8:0.78 11:0.85 12:0.06 17:0.69 18:0.99 21:0.30 22:0.93 27:0.50 29:0.91 30:0.10 31:0.90 32:0.77 34:0.80 36:0.90 37:0.60 39:0.34 43:0.20 44:0.93 45:0.92 47:0.93 48:0.91 55:0.71 64:0.77 66:0.45 69:0.53 70:0.97 71:0.64 74:0.70 77:0.89 79:0.94 81:0.35 83:0.60 85:0.72 86:0.88 91:0.27 97:0.89 98:0.63 101:0.97 108:0.96 120:0.63 122:0.86 123:0.96 124:0.87 126:0.26 127:0.96 129:0.81 131:0.93 133:0.89 135:0.73 137:0.90 139:0.90 140:0.45 144:0.57 145:0.72 146:0.95 147:0.96 149:0.64 150:0.31 151:0.65 153:0.48 154:0.76 155:0.58 157:0.97 158:0.79 159:0.94 162:0.74 164:0.54 166:0.89 172:0.97 173:0.15 177:0.70 179:0.12 182:0.91 187:0.39 190:0.89 192:0.59 195:0.99 196:0.93 202:0.56 208:0.54 212:0.67 216:0.86 219:0.92 220:0.62 222:0.60 227:0.98 229:0.61 231:0.97 235:0.31 241:0.05 242:0.38 243:0.29 245:0.98 246:0.61 247:0.98 248:0.63 253:0.41 255:0.74 256:0.58 259:0.21 260:0.63 262:0.93 265:0.64 266:0.91 267:0.65 268:0.59 271:0.49 276:0.98 279:0.82 281:0.91 282:0.85 283:0.82 284:0.86 285:0.56 287:0.61 292:0.95 300:0.94 +1 1:0.69 9:0.80 11:0.42 12:0.06 17:0.36 18:0.91 21:0.22 22:0.93 25:0.88 27:0.54 29:0.91 30:0.21 31:0.97 37:0.67 39:0.34 41:0.82 43:0.13 44:0.85 47:0.93 48:1.00 55:0.59 64:0.77 66:0.10 69:0.56 70:0.77 71:0.64 74:0.47 77:0.70 79:0.98 81:0.59 83:0.91 86:0.71 91:0.54 96:0.91 97:0.89 98:0.40 104:0.74 108:0.43 114:0.67 120:0.87 122:0.86 123:0.90 124:0.85 126:0.44 127:0.72 129:0.05 131:0.99 133:0.82 137:0.97 138:0.98 139:0.75 140:0.97 144:0.57 145:0.58 146:0.68 147:0.73 149:0.23 150:0.58 151:0.87 153:0.78 154:0.55 155:0.67 157:0.61 158:0.78 159:0.82 162:0.72 164:0.74 166:0.99 172:0.45 173:0.34 175:0.75 176:0.66 177:0.38 179:0.56 182:0.96 187:0.39 192:0.87 195:0.93 196:0.94 201:0.68 202:0.80 208:0.64 212:0.21 216:0.74 219:0.92 222:0.60 227:1.00 229:0.98 231:0.98 235:0.31 241:0.64 242:0.73 243:0.43 244:0.61 245:0.71 246:0.61 247:0.60 248:0.77 253:0.85 254:0.84 255:0.95 256:0.83 257:0.65 259:0.21 260:0.85 262:0.93 265:0.38 266:0.91 268:0.83 271:0.49 276:0.64 277:0.87 279:0.82 281:0.91 282:0.71 284:0.96 285:0.62 287:1.00 290:0.67 292:0.91 300:0.55 +1 6:0.95 8:0.94 9:0.80 12:0.06 17:0.20 20:0.96 21:0.78 27:0.10 29:0.91 31:0.99 34:0.39 36:0.24 37:0.97 39:0.34 41:0.88 71:0.64 74:0.26 78:0.91 79:0.99 83:0.91 87:0.98 91:0.99 96:1.00 100:0.98 111:0.96 120:0.96 131:0.99 135:0.92 137:0.99 138:0.99 140:0.99 144:0.57 151:0.92 152:0.95 153:0.99 155:0.67 157:0.61 158:0.72 162:0.64 164:0.91 166:0.61 169:0.97 175:0.97 182:0.96 186:0.91 189:0.95 191:0.98 192:0.87 202:0.98 208:0.64 216:0.82 222:0.60 227:1.00 229:0.98 231:0.98 232:0.72 238:0.98 241:0.95 244:0.93 246:0.61 248:0.94 253:0.97 255:0.95 256:0.99 257:0.93 259:0.21 260:0.95 261:0.97 267:0.87 268:0.99 271:0.49 277:0.95 283:0.82 284:0.98 285:0.62 287:1.00 +1 1:0.74 11:0.42 12:0.06 17:0.43 18:0.72 21:0.47 27:0.45 29:0.91 30:0.21 32:0.64 34:0.78 36:0.21 37:0.50 39:0.34 43:0.82 64:0.77 66:0.27 69:0.69 70:0.50 71:0.64 74:0.37 81:0.45 86:0.69 91:0.27 98:0.27 108:0.53 114:0.58 122:0.82 123:0.78 124:0.70 126:0.54 127:0.35 129:0.05 131:0.61 133:0.60 135:0.49 139:0.67 144:0.57 145:0.52 146:0.24 147:0.30 149:0.59 150:0.64 154:0.77 155:0.67 157:0.61 159:0.45 166:0.98 172:0.21 173:0.70 176:0.66 177:0.70 178:0.55 179:0.81 182:0.61 187:0.68 192:0.87 195:0.72 201:0.93 208:0.64 212:0.37 215:0.39 216:0.77 222:0.60 227:0.61 229:0.61 231:0.97 235:0.68 241:0.03 242:0.40 243:0.46 245:0.50 246:0.61 247:0.55 253:0.41 259:0.21 265:0.18 266:0.47 267:0.73 271:0.49 276:0.29 277:0.69 287:0.61 290:0.58 297:0.36 300:0.33 +1 1:0.69 8:0.84 9:0.76 11:0.75 12:0.06 17:0.67 18:0.92 21:0.05 22:0.93 25:0.88 27:0.54 29:0.91 30:0.20 31:0.93 34:0.49 36:0.96 37:0.67 39:0.34 43:0.22 44:0.85 45:0.66 47:0.93 48:0.97 55:0.71 64:0.77 66:0.11 69:0.54 70:0.96 71:0.64 74:0.44 77:0.70 79:0.97 81:0.79 83:0.60 86:0.75 91:0.51 96:0.76 98:0.34 99:0.83 101:0.52 104:0.74 108:0.91 114:0.85 120:0.89 122:0.86 123:0.91 124:0.92 126:0.44 127:0.85 129:0.95 131:0.99 133:0.91 135:0.95 137:0.93 139:0.72 140:0.45 144:0.57 145:0.58 146:0.39 147:0.45 149:0.29 150:0.76 151:0.81 153:0.48 154:0.54 155:0.58 157:0.78 159:0.88 162:0.85 164:0.76 165:0.70 166:0.99 172:0.45 173:0.24 175:0.75 176:0.66 177:0.38 179:0.38 182:0.96 187:0.68 190:0.77 192:0.59 195:0.97 196:0.91 201:0.68 202:0.76 208:0.54 212:0.36 216:0.74 219:0.87 220:0.62 222:0.60 227:1.00 229:0.98 231:0.98 235:0.12 241:0.47 242:0.57 243:0.23 244:0.61 245:0.80 246:1.00 247:0.67 248:0.84 253:0.67 255:0.79 256:0.82 257:0.65 259:0.21 260:0.85 262:0.93 265:0.36 266:0.94 267:0.28 268:0.83 271:0.49 276:0.78 277:0.69 281:0.89 282:0.71 284:0.93 285:0.62 287:1.00 290:0.85 292:0.95 300:0.98 +1 9:0.80 11:0.42 12:0.06 17:0.05 18:0.62 21:0.30 25:0.88 27:0.64 29:0.91 30:0.30 31:0.92 34:0.80 36:0.31 37:0.82 39:0.34 43:0.16 55:0.52 66:0.74 69:0.76 70:0.48 71:0.64 74:0.37 79:0.97 81:0.35 83:0.91 86:0.67 91:0.90 96:0.98 98:0.66 104:0.75 108:0.60 114:0.59 117:0.86 120:0.94 122:0.77 123:0.57 124:0.39 126:0.26 127:0.47 129:0.05 131:0.99 133:0.37 135:0.34 137:0.92 138:0.99 139:0.65 140:0.94 144:0.57 145:0.38 146:0.54 147:0.05 149:0.16 150:0.31 151:0.90 153:0.90 154:0.46 155:0.67 157:0.61 158:0.71 159:0.32 162:0.71 164:0.90 166:0.89 172:0.51 173:0.89 176:0.55 177:0.05 179:0.77 182:0.95 187:0.95 190:0.77 192:0.87 196:0.88 202:0.93 208:0.64 212:0.87 216:0.66 220:0.87 222:0.60 227:1.00 229:0.97 231:0.98 232:0.99 235:0.31 241:0.68 242:0.55 243:0.15 244:0.61 245:0.49 246:0.61 247:0.60 248:0.95 253:0.86 254:0.80 255:0.95 256:0.95 257:0.84 259:0.21 260:0.92 265:0.66 266:0.23 267:0.88 268:0.95 271:0.49 276:0.39 284:0.94 285:0.62 287:1.00 290:0.59 300:0.33 +1 7:0.66 8:0.93 9:0.80 12:0.06 17:0.01 21:0.13 25:0.88 27:0.28 29:0.91 30:0.15 31:0.97 32:0.64 34:0.24 36:0.25 37:0.55 39:0.34 43:0.16 55:0.59 66:0.89 69:0.42 70:0.70 71:0.64 74:0.27 79:0.99 81:0.40 83:0.91 91:0.99 96:0.97 98:0.97 104:0.58 108:0.32 120:1.00 123:0.31 126:0.26 127:0.78 129:0.05 131:0.99 135:0.24 137:0.97 138:1.00 140:1.00 144:0.57 145:0.38 146:0.87 147:0.89 149:0.29 150:0.34 151:0.90 153:1.00 154:0.44 155:0.67 157:0.61 159:0.44 162:0.72 164:1.00 166:0.61 172:0.68 173:0.48 175:0.75 177:0.38 179:0.67 182:0.72 190:0.77 191:0.96 192:0.87 195:0.62 202:1.00 208:0.64 212:0.48 215:0.61 216:0.92 222:0.60 227:1.00 229:0.86 230:0.69 231:0.97 233:0.73 235:0.12 241:0.93 242:0.79 243:0.89 244:0.61 246:0.61 247:0.39 248:0.97 253:0.84 254:0.96 255:0.95 256:1.00 257:0.65 259:0.21 260:1.00 265:0.97 266:0.54 267:0.73 268:1.00 271:0.49 276:0.57 277:0.69 283:0.82 284:0.95 285:0.62 287:0.88 297:0.36 300:0.43 +1 1:0.69 8:0.88 9:0.80 11:0.75 12:0.06 17:0.67 18:0.92 20:0.92 21:0.05 22:0.93 25:0.88 27:0.54 29:0.91 30:0.20 31:0.98 34:0.53 36:0.96 37:0.67 39:0.34 41:0.88 43:0.22 44:0.85 45:0.66 47:0.93 48:0.97 55:0.71 64:0.77 66:0.11 69:0.54 70:0.96 71:0.64 74:0.44 77:0.70 78:0.89 79:0.99 81:0.79 83:0.91 86:0.75 91:0.57 96:0.92 98:0.34 99:0.83 100:0.95 101:0.52 104:0.74 108:0.91 111:0.92 114:0.85 120:0.92 122:0.86 123:0.91 124:0.92 126:0.44 127:0.85 129:0.95 131:0.99 133:0.91 135:0.95 137:0.98 138:0.98 139:0.72 140:0.95 144:0.57 145:0.58 146:0.39 147:0.45 149:0.29 150:0.76 151:0.87 152:0.94 153:0.82 154:0.54 155:0.67 157:0.78 159:0.88 162:0.79 164:0.82 165:0.70 166:0.99 169:0.92 172:0.45 173:0.24 175:0.75 176:0.66 177:0.38 179:0.38 182:0.96 186:0.89 187:0.39 191:0.80 192:0.87 195:0.97 196:0.94 201:0.68 202:0.84 208:0.64 212:0.36 216:0.74 219:0.87 220:0.62 222:0.60 227:1.00 229:0.98 231:0.98 235:0.12 241:0.54 242:0.57 243:0.23 244:0.61 245:0.80 246:1.00 247:0.67 248:0.89 253:0.88 255:0.95 256:0.88 257:0.65 259:0.21 260:0.89 261:0.93 262:0.93 265:0.36 266:0.94 267:0.36 268:0.88 271:0.49 276:0.78 277:0.69 281:0.89 282:0.71 284:0.97 285:0.62 287:1.00 290:0.85 292:0.95 300:0.98 +1 1:0.69 8:0.95 9:0.80 11:0.42 12:0.06 17:0.04 18:0.80 21:0.40 25:0.88 27:0.64 29:0.91 30:0.21 31:0.93 34:0.69 36:0.21 37:0.82 39:0.34 43:0.16 44:0.85 48:0.98 55:0.52 64:0.77 66:0.11 69:0.88 70:0.82 71:0.64 74:0.35 79:0.98 81:0.41 83:0.60 86:0.66 91:0.84 96:0.91 98:0.48 104:0.75 108:0.76 114:0.58 120:0.91 122:0.77 123:0.30 124:0.64 126:0.44 127:0.54 129:0.05 131:0.99 133:0.60 135:0.49 137:0.93 139:0.65 140:0.87 144:0.57 145:0.40 146:0.44 147:0.58 149:0.18 150:0.52 151:0.81 153:0.72 154:0.26 155:0.67 157:0.61 158:0.71 159:0.46 162:0.82 164:0.85 166:0.98 172:0.45 173:0.95 176:0.55 177:0.38 179:0.76 182:0.61 187:0.95 190:0.77 191:0.85 192:0.87 195:0.72 196:0.89 201:0.68 202:0.88 208:0.64 212:0.58 216:0.66 219:0.87 220:0.96 222:0.60 227:1.00 229:0.61 231:0.98 235:0.52 241:0.69 242:0.22 243:0.31 244:0.61 245:0.56 246:0.61 247:0.74 248:0.90 253:0.63 254:0.80 255:0.95 256:0.92 257:0.65 259:0.21 260:0.88 265:0.42 266:0.54 267:0.36 268:0.92 271:0.49 276:0.44 277:0.69 279:0.82 281:0.91 284:0.95 285:0.62 287:0.61 290:0.58 292:0.95 300:0.55 +1 1:0.69 8:0.95 9:0.80 11:0.42 12:0.06 17:0.04 18:0.80 21:0.40 25:0.88 27:0.64 29:0.91 30:0.21 31:0.92 34:0.69 36:0.21 37:0.82 39:0.34 43:0.16 44:0.85 48:0.98 55:0.52 64:0.77 66:0.11 69:0.88 70:0.82 71:0.64 74:0.34 79:0.97 81:0.41 83:0.60 86:0.66 91:0.84 96:0.86 98:0.48 104:0.75 108:0.76 114:0.58 120:0.84 122:0.77 123:0.30 124:0.64 126:0.44 127:0.54 129:0.05 131:0.99 133:0.60 135:0.49 137:0.92 138:0.96 139:0.65 140:0.80 144:0.57 145:0.40 146:0.44 147:0.58 149:0.18 150:0.52 151:0.84 153:0.72 154:0.26 155:0.67 157:0.61 159:0.46 162:0.76 164:0.81 166:0.98 172:0.45 173:0.95 176:0.55 177:0.38 179:0.76 182:0.96 187:0.87 190:0.77 191:0.77 192:0.87 195:0.72 196:0.89 201:0.68 202:0.85 208:0.64 212:0.58 216:0.66 219:0.87 220:0.62 222:0.60 227:1.00 229:0.98 231:0.98 235:0.52 241:0.73 242:0.22 243:0.31 244:0.61 245:0.56 246:0.61 247:0.74 248:0.79 253:0.56 254:0.80 255:0.95 256:0.89 257:0.65 259:0.21 260:0.78 265:0.42 266:0.54 267:0.23 268:0.89 271:0.49 276:0.44 277:0.69 281:0.91 284:0.94 285:0.62 287:1.00 290:0.58 292:0.95 300:0.55 +1 7:0.66 8:0.95 9:0.80 11:0.42 12:0.06 17:0.32 18:0.63 21:0.30 25:0.88 27:0.64 29:0.91 30:0.21 31:0.88 34:0.59 36:0.38 37:0.82 39:0.34 43:0.16 55:0.45 66:0.69 69:0.72 70:0.50 71:0.64 74:0.37 79:0.90 81:0.35 83:0.60 86:0.67 91:0.86 96:0.96 98:0.62 104:0.75 108:0.55 114:0.59 120:0.91 122:0.77 123:0.53 124:0.41 126:0.26 127:0.37 129:0.05 131:0.99 133:0.39 135:0.60 137:0.88 138:0.97 139:0.65 140:0.95 144:0.57 145:0.39 146:0.47 147:0.05 149:0.17 150:0.31 151:0.84 153:0.91 154:0.48 155:0.67 157:0.61 159:0.26 162:0.65 164:0.82 166:0.89 172:0.41 173:0.88 175:0.75 176:0.55 177:0.05 179:0.82 182:0.61 187:0.87 190:0.77 191:0.92 192:0.87 196:0.88 202:0.89 208:0.64 212:0.55 216:0.66 222:0.60 227:1.00 229:0.61 230:0.69 231:0.98 232:0.95 233:0.73 235:0.31 241:0.74 242:0.72 243:0.18 244:0.61 245:0.46 246:0.61 247:0.39 248:0.87 253:0.78 254:0.80 255:0.79 256:0.91 257:0.84 259:0.21 260:0.87 265:0.63 266:0.27 267:0.44 268:0.91 271:0.49 276:0.33 277:0.69 284:0.90 285:0.62 287:0.61 290:0.59 300:0.33 +2 6:0.95 8:0.93 9:0.80 12:0.06 17:0.01 20:0.83 21:0.13 27:0.10 29:0.91 30:0.76 31:0.98 34:0.93 36:0.44 37:0.97 39:0.34 41:0.82 43:0.12 44:0.85 48:0.99 55:0.59 66:0.64 69:0.76 70:0.15 71:0.64 74:0.30 76:0.94 77:0.70 78:0.91 79:0.99 81:0.49 83:0.91 87:0.98 91:0.93 96:1.00 98:0.44 100:0.98 108:0.59 111:0.96 114:0.63 120:0.91 122:0.77 123:0.56 126:0.26 127:0.14 129:0.05 131:0.99 135:0.44 137:0.98 138:0.99 139:0.59 140:0.99 144:0.57 145:0.65 146:0.32 147:0.38 149:0.10 150:0.38 151:0.87 152:0.95 153:0.99 154:0.09 155:0.67 157:0.61 158:0.72 159:0.13 162:0.74 163:0.97 164:0.86 166:0.89 169:0.97 172:0.16 173:0.95 175:0.91 176:0.55 177:0.05 179:0.81 182:0.96 186:0.91 187:0.39 191:0.90 192:0.87 195:0.58 196:0.87 202:0.96 208:0.64 212:0.95 216:0.82 219:0.87 222:0.60 227:1.00 229:0.98 231:0.98 232:0.72 235:0.12 241:0.85 242:0.82 243:0.69 244:0.83 246:0.61 247:0.12 248:0.87 253:0.95 255:0.95 256:0.97 257:0.84 259:0.21 260:0.89 261:0.97 265:0.46 266:0.12 267:0.65 268:0.97 271:0.49 276:0.19 277:0.87 279:0.82 281:0.91 282:0.71 284:0.98 285:0.62 287:1.00 290:0.63 292:0.95 300:0.13 +1 1:0.74 11:0.64 12:0.06 17:0.51 18:0.82 21:0.22 27:0.45 29:0.91 30:0.76 32:0.64 34:0.67 36:0.22 37:0.50 39:0.34 43:0.82 60:0.87 64:0.77 66:0.31 69:0.69 70:0.41 71:0.64 74:0.62 81:0.72 83:0.91 85:0.72 86:0.83 91:0.11 98:0.29 101:0.87 108:0.52 114:0.71 122:0.83 123:0.75 124:0.60 126:0.54 127:0.31 129:0.05 131:0.61 133:0.43 135:0.24 139:0.85 144:0.57 145:0.47 146:0.26 147:0.32 149:0.79 150:0.83 154:0.77 155:0.67 157:0.82 158:0.91 159:0.40 165:0.93 166:0.99 172:0.41 173:0.71 176:0.73 177:0.70 178:0.55 179:0.60 182:0.96 187:0.68 192:0.87 195:0.70 201:0.93 208:0.64 212:0.81 215:0.51 216:0.77 219:0.95 222:0.60 227:0.61 229:0.61 231:0.98 235:0.42 241:0.12 242:0.18 243:0.51 245:0.71 246:1.00 247:0.82 253:0.52 259:0.21 265:0.21 266:0.38 267:0.59 271:0.49 276:0.30 277:0.69 279:0.86 283:0.78 287:0.61 290:0.71 292:0.91 297:0.36 300:0.43 +1 1:0.69 7:0.72 8:0.82 9:0.80 12:0.06 17:0.34 18:0.63 20:0.85 21:0.22 25:0.88 27:0.28 29:0.91 30:0.42 31:0.97 32:0.69 34:0.67 36:0.94 37:0.55 39:0.34 41:0.82 43:0.13 55:0.59 66:0.93 69:0.44 70:0.59 71:0.64 74:0.36 76:0.85 77:0.70 78:0.85 79:0.99 81:0.55 83:0.91 86:0.58 91:0.66 96:0.94 97:0.89 98:0.90 100:0.91 104:0.58 106:0.81 108:0.34 111:0.86 114:0.62 120:0.95 122:0.51 123:0.32 126:0.44 127:0.47 129:0.05 131:0.99 135:0.47 137:0.97 139:0.58 140:0.99 144:0.57 145:0.40 146:0.79 147:0.83 149:0.20 150:0.57 151:0.82 152:0.81 153:0.90 154:0.59 155:0.67 157:0.61 158:0.72 159:0.35 162:0.80 164:0.91 166:0.89 169:0.88 172:0.82 173:0.53 176:0.55 177:0.70 179:0.40 182:0.95 186:0.85 187:0.87 191:0.75 192:0.87 195:0.60 196:0.87 201:0.68 202:0.89 204:0.85 206:0.81 208:0.64 212:0.86 215:0.51 216:0.92 219:0.87 222:0.60 227:1.00 229:0.98 230:0.74 231:0.98 233:0.73 235:0.31 241:0.63 242:0.30 243:0.94 244:0.61 246:0.61 247:0.79 248:0.74 253:0.80 254:0.74 255:0.95 256:0.92 259:0.21 260:0.94 261:0.86 265:0.90 266:0.27 267:0.44 268:0.93 271:0.49 276:0.73 279:0.82 282:0.71 283:0.82 284:0.96 285:0.62 287:1.00 290:0.62 292:0.95 297:0.36 300:0.43 +3 1:0.74 6:0.92 8:0.84 9:0.80 11:0.64 12:0.37 17:0.51 20:0.83 21:0.74 28:0.76 30:0.17 32:0.80 34:0.55 36:0.24 37:0.97 39:0.27 41:0.96 43:0.80 55:0.71 66:0.07 69:0.74 70:0.83 74:0.89 77:0.70 78:0.86 81:0.55 83:0.81 85:0.97 91:0.58 96:0.91 98:0.41 100:0.86 101:0.80 104:0.80 108:0.92 111:0.85 114:0.67 120:0.86 123:0.95 124:0.94 126:0.54 127:0.78 129:0.95 131:1.00 133:0.94 135:0.77 140:0.80 144:0.57 145:0.63 146:0.65 147:0.70 149:0.95 150:0.68 151:0.93 152:0.79 153:0.78 154:0.74 155:0.67 157:0.96 159:0.92 161:0.76 162:0.71 164:0.86 165:0.65 166:0.99 167:0.65 169:0.84 172:0.65 173:0.39 176:0.73 177:0.38 178:0.42 179:0.22 181:0.67 182:0.93 186:0.86 187:0.21 189:0.93 191:0.75 192:0.59 195:0.98 201:0.74 202:0.80 208:0.64 212:0.39 215:0.46 216:0.98 222:0.60 227:1.00 229:0.96 231:0.99 232:0.85 235:0.95 238:0.87 241:0.50 242:0.57 243:0.28 245:0.89 246:1.00 247:0.47 248:0.91 253:0.93 255:0.79 256:0.83 259:0.21 260:0.86 261:0.83 265:0.27 266:0.91 267:0.88 268:0.83 271:0.26 276:0.90 277:0.69 282:0.88 285:0.62 287:1.00 290:0.67 297:0.36 299:0.88 300:0.84 +2 1:0.74 8:0.77 11:0.64 12:0.37 17:0.26 27:0.13 28:0.76 30:0.76 34:0.87 36:0.36 37:0.68 39:0.27 43:0.77 66:0.64 69:0.79 70:0.15 74:0.41 81:0.99 83:0.80 85:0.72 91:0.51 98:0.28 101:0.75 104:0.58 108:0.62 114:0.98 120:0.65 122:0.41 123:0.60 126:0.54 127:0.11 129:0.05 131:0.95 135:0.23 140:0.80 144:0.57 146:0.24 147:0.30 149:0.44 150:1.00 151:0.61 153:0.48 154:0.69 155:0.67 157:0.80 159:0.11 161:0.76 162:0.50 163:0.97 164:0.55 165:0.92 166:0.99 167:0.75 172:0.16 173:0.98 176:0.73 177:0.38 178:0.42 179:0.52 181:0.67 182:0.92 187:0.21 190:0.77 191:0.70 192:0.59 201:0.74 202:0.63 212:0.95 215:0.96 216:0.39 220:0.62 222:0.60 227:1.00 229:0.61 231:0.99 232:0.77 235:0.05 241:0.69 242:0.82 243:0.69 246:1.00 247:0.12 248:0.59 253:0.73 256:0.45 259:0.21 260:0.65 265:0.30 266:0.12 267:0.89 268:0.46 271:0.26 276:0.14 285:0.50 287:0.61 290:0.98 297:0.36 300:0.13 +2 1:0.74 6:0.96 7:0.81 8:0.91 9:0.80 11:0.64 12:0.37 17:0.26 20:0.98 21:0.30 27:0.21 28:0.76 30:0.26 34:0.66 36:0.50 37:0.74 39:0.27 41:0.98 43:0.98 60:0.99 66:0.23 69:0.12 70:0.89 74:0.95 78:0.93 81:0.87 83:0.90 85:0.97 91:0.77 96:0.98 98:0.59 100:0.96 101:0.81 104:0.84 106:0.81 108:0.95 111:0.94 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.60 124:0.77 126:0.54 127:0.60 129:0.81 131:1.00 133:0.76 135:0.17 140:0.45 144:0.57 146:0.45 147:0.52 149:0.89 150:0.92 151:0.99 152:0.91 153:0.94 154:0.98 155:0.67 157:0.96 158:0.92 159:0.88 161:0.76 162:0.71 164:0.92 165:0.84 166:0.99 167:0.63 169:0.95 172:0.80 173:0.08 176:0.73 177:0.38 179:0.25 181:0.67 182:0.93 186:0.93 187:0.39 189:0.97 191:0.76 192:0.59 201:0.74 202:0.88 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.95 220:0.62 222:0.60 227:1.00 229:0.96 230:0.87 231:0.99 232:0.89 233:0.76 235:0.42 238:0.94 241:0.46 242:0.37 243:0.19 245:0.92 246:1.00 247:0.60 248:0.98 253:0.99 255:0.79 256:0.89 259:0.21 260:0.96 261:0.95 265:0.33 266:0.89 267:0.23 268:0.90 271:0.26 276:0.84 279:0.86 283:0.82 285:0.62 287:1.00 290:0.86 297:0.36 300:0.94 +3 1:0.74 6:0.93 8:0.73 9:0.80 11:0.64 12:0.37 17:0.26 20:0.85 27:0.13 28:0.76 30:0.54 34:0.52 36:0.23 37:0.68 39:0.27 41:0.82 43:0.79 66:0.97 69:0.76 70:0.48 74:0.91 78:0.84 81:0.99 83:0.81 85:0.98 91:0.46 96:0.84 98:0.91 100:0.83 101:0.86 104:0.58 108:0.59 111:0.82 114:0.98 120:0.75 122:0.43 123:0.56 126:0.54 127:0.50 129:0.05 131:0.99 135:0.51 140:0.45 144:0.57 146:0.94 147:0.95 149:0.96 150:1.00 151:0.93 152:0.97 153:0.77 154:0.72 155:0.67 157:0.97 159:0.58 161:0.76 162:0.68 164:0.64 165:0.92 166:0.99 167:0.68 169:0.96 172:0.92 173:0.72 176:0.73 177:0.38 179:0.24 181:0.67 182:0.93 186:0.84 187:0.21 189:0.86 192:0.59 201:0.74 202:0.54 208:0.64 212:0.85 215:0.96 216:0.39 222:0.60 227:1.00 229:0.96 231:0.99 232:0.77 235:0.05 238:0.84 241:0.57 242:0.51 243:0.97 246:1.00 247:0.47 248:0.82 253:0.89 255:0.79 256:0.45 259:0.21 260:0.75 261:0.97 265:0.91 266:0.38 267:0.52 268:0.57 271:0.26 276:0.86 285:0.62 287:1.00 290:0.98 297:0.36 300:0.55 +1 11:0.64 12:0.37 17:0.24 21:0.78 27:0.45 28:0.76 30:0.71 32:0.80 34:0.80 36:0.18 37:0.50 39:0.27 43:0.81 66:0.24 69:0.71 70:0.40 74:0.69 77:0.70 85:0.88 91:0.09 98:0.20 101:0.76 108:0.83 122:0.43 123:0.82 124:0.81 127:0.23 129:0.89 131:0.61 133:0.78 135:0.77 144:0.57 145:0.49 146:0.13 147:0.18 149:0.74 154:0.76 157:0.90 159:0.52 161:0.76 163:0.81 166:0.61 167:0.60 172:0.21 173:0.58 177:0.38 178:0.55 179:0.65 181:0.67 182:0.82 187:0.68 195:0.87 212:0.50 216:0.77 222:0.60 227:0.61 229:0.61 231:0.96 235:0.80 241:0.35 242:0.63 243:0.28 245:0.50 246:0.61 247:0.35 253:0.52 259:0.21 265:0.22 266:0.38 267:0.28 271:0.26 276:0.34 282:0.88 287:0.61 299:0.88 300:0.33 +2 1:0.74 6:0.93 8:0.64 9:0.80 12:0.37 17:0.09 20:0.88 27:0.13 28:0.76 30:0.95 34:0.68 36:0.23 37:0.68 39:0.27 41:0.88 43:0.35 55:0.71 66:0.54 69:0.54 78:0.85 81:0.99 83:0.81 91:0.80 96:0.87 98:0.55 100:0.84 104:0.58 108:0.42 111:0.84 114:0.98 120:0.78 123:0.40 126:0.54 127:0.16 129:0.05 131:1.00 135:0.39 140:0.80 144:0.57 146:0.28 147:0.35 149:0.21 150:1.00 151:0.87 152:0.97 153:0.48 154:0.26 155:0.67 157:0.61 159:0.12 161:0.76 162:0.56 164:0.67 165:0.92 166:0.99 167:0.77 169:0.96 172:0.10 173:0.88 175:0.75 176:0.73 177:0.05 178:0.42 179:0.97 181:0.67 182:0.93 186:0.86 187:0.21 189:0.85 192:0.59 201:0.74 202:0.63 208:0.64 215:0.96 216:0.39 222:0.60 227:1.00 229:0.96 231:0.99 232:0.77 235:0.05 238:0.84 241:0.71 243:0.63 244:0.61 246:1.00 248:0.86 253:0.85 255:0.79 256:0.58 257:0.65 259:0.21 260:0.79 261:0.97 265:0.56 267:0.44 268:0.59 271:0.26 277:0.69 285:0.62 287:1.00 290:0.98 297:0.36 300:0.08 +2 1:0.74 6:0.91 7:0.81 8:0.84 9:0.80 11:0.64 12:0.37 17:0.20 20:0.97 21:0.77 25:0.88 27:0.38 28:0.76 30:0.21 32:0.80 34:0.24 36:0.45 37:0.73 39:0.27 41:0.96 43:0.82 55:0.71 66:0.36 69:0.42 70:0.62 74:0.69 76:0.85 77:0.70 78:0.90 81:0.47 83:0.81 85:0.80 91:0.77 96:0.93 98:0.63 100:0.92 101:0.73 104:0.58 108:0.32 111:0.90 114:0.63 120:0.93 121:0.97 123:0.31 124:0.83 126:0.54 127:0.96 129:0.81 131:1.00 133:0.83 135:0.42 140:0.87 144:0.57 145:0.80 146:0.84 147:0.87 149:0.72 150:0.63 151:0.96 152:0.90 153:0.85 154:0.77 155:0.67 157:0.84 159:0.75 161:0.76 162:0.72 163:0.86 164:0.91 165:0.63 166:0.99 167:0.89 169:0.90 172:0.51 173:0.28 176:0.73 177:0.38 179:0.66 181:0.67 182:0.93 186:0.90 189:0.92 191:0.87 192:0.59 195:0.88 201:0.74 202:0.87 204:0.89 208:0.64 212:0.18 215:0.43 216:0.26 220:0.74 222:0.60 227:1.00 229:0.96 230:0.87 231:0.99 233:0.76 235:0.99 238:0.91 241:0.83 242:0.74 243:0.51 245:0.65 246:1.00 247:0.47 248:0.92 253:0.92 255:0.79 256:0.89 259:0.21 260:0.93 261:0.92 265:0.64 266:0.87 267:0.44 268:0.90 271:0.26 276:0.60 282:0.88 283:0.71 285:0.62 287:1.00 290:0.63 297:0.36 299:0.88 300:0.43 +1 11:0.64 12:0.37 17:0.18 21:0.78 27:0.45 28:0.76 30:0.84 34:0.92 36:0.18 37:0.50 39:0.27 43:0.73 66:0.48 69:0.87 70:0.39 74:0.89 85:0.98 91:0.09 98:0.33 101:0.85 108:0.74 122:0.43 123:0.72 124:0.67 127:0.24 129:0.81 131:0.61 133:0.67 135:0.84 144:0.57 145:0.50 146:0.57 147:0.63 149:0.95 154:0.62 157:0.97 159:0.54 161:0.76 166:0.61 167:0.56 172:0.75 173:0.80 177:0.38 178:0.55 179:0.22 181:0.67 182:0.90 187:0.68 212:0.87 216:0.77 222:0.60 227:0.61 229:0.61 231:0.98 235:0.80 241:0.18 242:0.63 243:0.60 245:0.86 246:0.61 247:0.30 253:0.63 259:0.21 265:0.36 266:0.47 267:0.19 271:0.26 276:0.74 287:0.61 300:0.43 +3 1:0.74 6:0.94 8:0.83 9:0.80 11:0.64 12:0.37 20:0.76 27:0.14 28:0.76 30:0.76 34:0.75 36:0.19 37:0.67 39:0.27 41:0.98 43:0.86 60:0.87 66:0.11 69:0.59 70:0.43 74:0.74 78:0.90 81:0.99 83:0.90 85:0.91 91:0.78 96:0.98 98:0.24 100:0.92 101:0.78 108:0.90 111:0.89 114:0.98 120:0.96 122:0.43 123:0.62 124:0.73 126:0.54 127:0.59 129:0.81 131:1.00 133:0.67 135:0.70 140:0.45 144:0.57 145:0.56 146:0.29 147:0.36 149:0.81 150:1.00 151:0.99 152:0.91 153:0.94 154:0.83 155:0.67 157:0.93 158:0.92 159:0.75 161:0.76 162:0.72 164:0.92 165:0.92 166:0.99 167:0.62 169:0.91 172:0.32 173:0.42 176:0.73 177:0.38 179:0.77 181:0.67 182:0.93 186:0.91 187:0.39 191:0.70 192:0.59 201:0.74 202:0.88 208:0.64 212:0.22 215:0.96 216:0.93 220:0.62 222:0.60 227:1.00 229:0.96 231:0.99 235:0.05 238:0.89 241:0.41 242:0.61 243:0.37 245:0.60 246:1.00 247:0.39 248:0.98 253:0.98 255:0.79 256:0.89 259:0.21 260:0.96 261:0.92 265:0.26 266:0.71 267:0.23 268:0.90 271:0.26 276:0.32 279:0.86 283:0.82 285:0.62 287:1.00 290:0.98 297:0.36 300:0.43 +2 1:0.74 6:0.94 8:0.88 9:0.80 11:0.64 12:0.37 17:0.01 20:0.97 27:0.14 28:0.76 30:0.86 34:0.83 36:0.18 37:0.67 39:0.27 41:0.98 43:0.86 60:0.97 66:0.33 69:0.57 70:0.32 74:0.75 78:0.90 81:0.99 83:0.90 85:0.91 91:0.54 96:0.98 98:0.25 100:0.93 101:0.78 108:0.90 111:0.90 114:0.98 120:0.96 122:0.43 123:0.90 124:0.71 126:0.54 127:0.57 129:0.81 131:1.00 133:0.67 135:0.65 140:0.45 144:0.57 145:0.56 146:0.22 147:0.28 149:0.81 150:1.00 151:0.99 152:0.91 153:0.94 154:0.84 155:0.67 157:0.93 158:0.92 159:0.74 161:0.76 162:0.70 164:0.93 165:0.92 166:0.99 167:0.63 169:0.91 172:0.32 173:0.40 176:0.73 177:0.38 179:0.79 181:0.67 182:0.93 186:0.90 187:0.39 189:0.95 192:0.59 201:0.74 202:0.88 208:0.64 212:0.23 215:0.96 216:0.93 220:0.62 222:0.60 227:1.00 229:0.96 231:0.99 235:0.05 238:0.91 241:0.44 242:0.63 243:0.39 245:0.58 246:1.00 247:0.30 248:0.98 253:0.98 255:0.79 256:0.90 259:0.21 260:0.96 261:0.92 265:0.27 266:0.54 267:0.19 268:0.91 271:0.26 276:0.26 279:0.86 283:0.82 285:0.62 287:1.00 290:0.98 297:0.36 300:0.33 +4 1:0.74 6:0.93 8:0.88 9:0.80 11:0.64 12:0.37 20:0.98 27:0.13 28:0.76 30:0.41 34:0.53 36:0.40 37:0.68 39:0.27 41:0.99 43:0.80 55:0.92 60:0.95 66:0.49 69:0.48 70:0.60 74:0.61 78:0.97 81:0.99 83:0.90 85:0.80 91:0.92 96:0.99 98:0.65 100:0.98 101:0.75 104:0.58 108:0.37 111:0.98 114:0.98 120:0.97 123:0.36 124:0.78 126:0.54 127:0.83 129:0.05 131:1.00 133:0.82 135:0.66 138:0.99 140:0.45 144:0.57 146:0.76 147:0.79 149:0.70 150:1.00 151:0.99 152:0.97 153:0.97 154:0.74 155:0.67 157:0.85 158:0.92 159:0.61 161:0.76 162:0.66 163:0.81 164:0.95 165:0.92 166:0.99 167:0.91 169:0.96 172:0.45 173:0.43 176:0.73 177:0.38 179:0.78 181:0.67 182:0.93 186:0.97 189:0.95 191:0.87 192:0.59 201:0.74 202:0.92 208:0.64 212:0.19 215:0.96 216:0.39 222:0.60 227:1.00 229:0.96 231:0.99 232:0.77 235:0.05 238:0.96 241:0.91 242:0.63 243:0.60 245:0.52 246:1.00 247:0.39 248:0.99 253:0.98 255:0.79 256:0.94 259:0.21 260:0.97 261:0.97 265:0.66 266:0.71 267:0.88 268:0.94 271:0.26 276:0.49 279:0.86 283:0.82 285:0.62 287:1.00 290:0.98 297:0.36 300:0.43 +2 1:0.74 6:0.91 7:0.81 8:0.81 9:0.80 11:0.64 12:0.37 17:0.26 20:0.90 21:0.13 27:0.13 28:0.76 30:0.76 32:0.80 34:0.86 36:0.36 37:0.79 39:0.27 41:0.93 43:0.81 60:0.87 66:0.86 69:0.70 70:0.29 74:0.88 77:0.70 78:0.80 81:0.94 83:0.87 85:0.93 91:0.42 96:0.91 98:0.69 100:0.83 101:0.84 108:0.53 111:0.79 114:0.92 120:0.84 121:0.90 122:0.57 123:0.51 126:0.54 127:0.21 129:0.05 131:1.00 135:0.76 140:0.45 144:0.57 145:0.54 146:0.62 147:0.67 149:0.88 150:0.97 151:0.93 152:0.85 153:0.78 154:0.76 155:0.67 157:0.95 158:0.92 159:0.23 161:0.76 162:0.84 164:0.78 165:0.88 166:0.99 167:0.63 169:0.80 172:0.61 173:0.80 176:0.73 177:0.38 179:0.45 181:0.67 182:0.93 186:0.80 187:0.68 189:0.93 192:0.59 195:0.61 201:0.74 202:0.66 204:0.89 208:0.64 212:0.69 215:0.84 216:0.83 222:0.60 227:1.00 229:0.96 230:0.87 231:0.99 233:0.76 235:0.22 238:0.87 241:0.44 242:0.61 243:0.87 246:1.00 247:0.39 248:0.90 253:0.93 255:0.79 256:0.71 259:0.21 260:0.85 261:0.83 265:0.70 266:0.38 267:0.59 268:0.73 271:0.26 276:0.49 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.92 297:0.36 299:0.88 300:0.25 +0 1:0.74 7:0.66 11:0.87 12:0.79 17:0.76 18:0.87 21:0.54 27:1.00 29:0.77 30:0.66 32:0.80 34:0.75 36:0.68 37:0.29 39:0.40 43:0.96 44:0.93 45:0.83 46:0.96 64:0.77 66:0.52 69:0.27 70:0.49 71:0.77 74:0.85 77:0.96 81:0.55 83:0.91 85:0.80 86:0.94 91:0.18 97:0.89 98:0.36 99:0.83 101:0.97 107:0.95 108:0.65 110:0.96 114:0.59 123:0.63 124:0.65 125:0.88 126:0.54 127:0.44 129:0.59 131:0.61 133:0.66 135:0.68 139:0.96 144:0.76 145:0.77 146:0.24 147:0.30 149:0.84 150:0.74 154:0.96 155:0.67 157:0.90 158:0.79 159:0.30 160:0.88 165:1.00 166:0.90 172:0.61 173:0.42 176:0.73 177:0.70 178:0.55 179:0.52 182:0.85 187:0.39 192:0.87 195:0.61 201:0.93 204:0.85 208:0.64 212:0.85 215:0.39 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.76 235:0.84 241:0.39 242:0.24 243:0.34 245:0.73 246:0.97 247:0.67 253:0.47 259:0.21 265:0.38 266:0.38 267:0.52 271:0.79 276:0.55 279:0.82 281:0.91 282:0.88 287:0.61 289:0.95 290:0.59 292:0.95 297:0.36 299:0.99 300:0.43 +1 1:0.74 11:0.81 12:0.79 17:0.49 18:0.94 21:0.54 27:0.26 29:0.77 30:0.38 32:0.79 34:0.30 36:0.62 37:0.49 39:0.40 43:0.66 44:0.93 45:0.87 46:0.88 48:0.91 55:0.52 64:0.77 66:0.93 69:0.25 70:0.52 71:0.77 74:0.86 77:0.70 81:0.47 83:0.60 86:0.96 91:0.42 97:0.89 98:0.88 99:0.83 101:0.52 107:0.96 108:0.20 110:0.96 114:0.59 122:0.85 123:0.19 125:0.88 126:0.54 127:0.42 129:0.05 131:0.61 135:0.88 139:0.97 144:0.76 145:0.86 146:0.73 147:0.78 149:0.51 150:0.67 154:0.85 155:0.67 157:0.90 158:0.78 159:0.30 160:0.88 165:0.70 166:0.90 172:0.81 173:0.41 176:0.73 177:0.70 178:0.55 179:0.40 182:0.84 187:0.68 192:0.87 195:0.58 201:0.93 204:0.85 208:0.64 212:0.93 215:0.39 216:0.35 219:0.92 222:0.90 227:0.61 228:0.99 229:0.61 231:0.84 235:0.74 241:0.35 242:0.16 243:0.93 246:0.97 247:0.88 253:0.48 254:0.80 259:0.21 265:0.88 266:0.23 267:0.23 271:0.79 276:0.71 279:0.82 281:0.89 282:0.87 287:0.61 289:0.95 290:0.59 292:0.91 297:0.36 300:0.43 +0 11:0.81 12:0.79 17:0.59 18:0.79 21:0.74 27:0.45 29:0.77 30:0.21 32:0.69 34:0.71 36:0.18 37:0.50 39:0.40 43:0.08 44:0.85 45:0.66 46:0.88 55:0.45 64:0.77 66:0.47 69:0.72 70:0.89 71:0.77 74:0.41 81:0.29 86:0.74 91:0.17 98:0.36 101:0.52 107:0.92 108:0.55 110:0.93 123:0.52 124:0.56 125:0.88 126:0.44 127:0.33 129:0.05 131:0.61 133:0.37 135:0.51 139:0.72 144:0.76 145:0.52 146:0.44 147:0.51 149:0.10 150:0.23 154:0.71 157:0.77 159:0.43 160:0.88 166:0.76 172:0.41 173:0.72 177:0.70 178:0.55 179:0.67 182:0.72 187:0.68 192:0.51 195:0.72 201:0.68 212:0.58 215:0.36 216:0.77 222:0.90 227:0.61 229:0.61 231:0.72 235:0.95 241:0.15 242:0.38 243:0.59 245:0.66 246:0.61 247:0.60 253:0.32 254:0.80 259:0.21 265:0.38 266:0.54 267:0.82 271:0.79 276:0.32 287:0.61 289:0.91 297:0.36 300:0.70 +0 11:0.81 12:0.79 17:0.62 18:0.62 21:0.78 27:0.45 29:0.77 30:0.76 34:0.80 36:0.26 37:0.50 39:0.40 43:0.74 45:0.66 46:0.88 66:0.64 69:0.78 70:0.15 71:0.77 74:0.40 86:0.73 91:0.06 98:0.16 101:0.52 107:0.89 108:0.62 110:0.90 122:0.57 123:0.59 125:0.88 127:0.09 129:0.05 131:0.61 135:0.83 139:0.69 144:0.76 145:0.44 146:0.24 147:0.30 149:0.47 154:0.64 157:0.77 159:0.11 160:0.88 163:0.97 166:0.61 172:0.16 173:0.78 177:0.70 178:0.55 179:0.18 182:0.72 187:0.68 212:0.95 216:0.77 222:0.90 227:0.61 229:0.61 231:0.64 235:0.99 241:0.43 242:0.82 243:0.69 246:0.61 247:0.12 253:0.31 254:0.97 259:0.21 265:0.18 266:0.12 267:0.28 271:0.79 276:0.14 287:0.61 289:0.88 300:0.13 +2 1:0.74 7:0.66 11:0.87 12:0.79 17:0.78 18:0.84 21:0.64 27:1.00 29:0.77 30:0.54 32:0.80 34:0.68 36:0.38 37:0.29 39:0.40 43:0.95 44:0.93 45:0.77 46:0.97 64:0.77 66:0.90 69:0.32 70:0.67 71:0.77 74:0.84 77:0.96 81:0.47 83:0.91 85:0.80 86:0.93 91:0.18 97:0.89 98:0.90 101:0.97 107:0.95 108:0.25 110:0.96 114:0.57 123:0.24 125:0.88 126:0.54 127:0.47 129:0.05 131:0.61 135:0.56 139:0.95 144:0.76 145:0.85 146:0.70 147:0.74 149:0.83 150:0.71 154:0.95 155:0.67 157:0.89 158:0.79 159:0.27 160:0.88 165:0.93 166:0.90 172:0.71 173:0.50 176:0.73 177:0.70 178:0.55 179:0.57 182:0.84 187:0.39 192:0.87 195:0.59 201:0.93 204:0.85 208:0.64 212:0.80 215:0.38 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.73 235:0.88 241:0.30 242:0.27 243:0.90 246:0.97 247:0.74 253:0.46 259:0.21 265:0.90 266:0.38 267:0.19 271:0.79 276:0.56 279:0.82 281:0.91 282:0.88 287:0.61 289:0.94 290:0.57 292:0.95 297:0.36 299:0.99 300:0.55 +2 1:0.74 7:0.66 11:0.87 12:0.79 17:0.79 18:0.87 21:0.54 27:1.00 29:0.77 30:0.60 32:0.80 34:0.64 36:0.55 37:0.29 39:0.40 43:0.96 44:0.93 45:0.81 46:0.97 64:0.77 66:0.23 69:0.27 70:0.53 71:0.77 74:0.83 77:0.96 81:0.55 83:0.91 85:0.80 86:0.95 91:0.15 97:0.89 98:0.56 99:0.83 101:0.97 107:0.95 108:0.61 110:0.96 114:0.59 123:0.20 124:0.56 125:0.88 126:0.54 127:0.47 129:0.59 131:0.61 133:0.46 135:0.61 139:0.96 144:0.76 145:0.77 146:0.42 147:0.48 149:0.85 150:0.74 154:0.96 155:0.67 157:0.90 158:0.79 159:0.28 160:0.88 165:1.00 166:0.90 172:0.59 173:0.46 176:0.73 177:0.70 178:0.55 179:0.55 182:0.85 187:0.39 192:0.87 195:0.59 201:0.93 204:0.85 208:0.64 212:0.84 215:0.39 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.76 235:0.84 241:0.30 242:0.24 243:0.43 245:0.79 246:0.97 247:0.76 253:0.47 259:0.21 265:0.47 266:0.38 267:0.52 271:0.79 276:0.55 279:0.82 281:0.91 282:0.88 287:0.61 289:0.95 290:0.59 292:0.95 297:0.36 299:0.99 300:0.55 +2 1:0.74 7:0.81 8:0.74 11:0.81 12:0.79 17:0.47 18:0.88 20:0.89 21:0.47 22:0.93 27:0.21 29:0.77 30:0.28 34:0.66 36:0.49 37:0.74 39:0.40 43:0.63 45:0.83 46:0.88 47:0.93 64:0.77 66:0.89 69:0.21 70:0.74 71:0.77 74:0.80 78:0.92 81:0.50 83:0.60 86:0.92 91:0.25 97:0.94 98:0.77 99:0.83 100:0.96 101:0.52 104:0.99 106:0.81 107:0.95 108:0.17 110:0.95 111:0.93 114:0.60 117:0.86 122:0.80 123:0.16 125:0.88 126:0.54 127:0.26 129:0.05 131:0.61 135:0.17 139:0.94 144:0.76 146:0.68 147:0.73 149:0.49 150:0.68 152:0.99 154:0.94 155:0.67 157:0.88 158:0.91 159:0.27 160:0.88 165:0.70 166:0.90 169:0.91 172:0.70 173:0.35 176:0.73 177:0.70 178:0.55 179:0.44 182:0.84 186:0.91 187:0.39 192:0.87 201:0.93 204:0.89 206:0.81 208:0.64 212:0.86 215:0.41 216:0.95 219:0.95 222:0.90 227:0.61 229:0.61 230:0.86 231:0.84 232:0.91 233:0.73 235:0.68 241:0.21 242:0.19 243:0.90 246:0.97 247:0.84 253:0.48 254:0.74 259:0.21 261:0.93 262:0.93 265:0.78 266:0.38 267:0.52 271:0.79 276:0.58 279:0.86 281:0.88 283:0.78 287:0.61 289:0.95 290:0.60 292:0.91 297:0.36 300:0.55 +2 1:0.74 8:0.59 11:0.81 12:0.79 17:0.26 18:0.69 21:0.64 22:0.93 27:0.42 29:0.77 30:0.45 32:0.79 34:0.50 36:0.46 37:0.63 39:0.40 43:0.09 44:0.93 45:0.66 46:0.88 47:0.93 64:0.77 66:0.49 69:0.76 70:0.25 71:0.77 74:0.55 77:0.70 81:0.42 83:0.60 86:0.73 91:0.22 98:0.13 99:0.83 101:0.52 104:0.98 106:0.81 107:0.90 108:0.59 110:0.90 114:0.57 117:0.86 122:0.80 123:0.57 124:0.48 125:0.88 126:0.54 127:0.18 129:0.05 131:0.61 133:0.37 135:0.23 139:0.80 144:0.76 145:0.61 146:0.19 147:0.24 149:0.06 150:0.66 154:0.71 155:0.67 157:0.77 159:0.23 160:0.88 163:0.79 165:0.70 166:0.90 172:0.16 173:0.83 176:0.73 177:0.70 178:0.55 179:0.86 182:0.84 187:0.87 192:0.87 195:0.64 201:0.93 206:0.81 208:0.64 212:0.61 215:0.38 216:0.68 222:0.90 227:0.61 229:0.61 231:0.84 232:0.89 235:0.84 241:0.50 242:0.63 243:0.60 245:0.39 246:0.97 247:0.30 253:0.40 254:0.80 259:0.21 262:0.93 265:0.14 266:0.17 267:0.36 271:0.79 276:0.13 282:0.87 287:0.61 289:0.94 290:0.57 297:0.36 300:0.18 +1 1:0.69 8:0.81 9:0.76 11:0.81 12:0.79 17:0.70 18:1.00 20:0.85 21:0.68 22:0.93 27:0.21 29:0.77 30:0.44 31:0.93 34:0.42 36:0.64 37:0.74 39:0.40 43:0.72 45:1.00 46:0.88 47:0.93 48:0.97 64:0.77 66:0.54 69:0.29 70:0.66 71:0.77 74:0.98 78:0.72 79:0.94 81:0.32 83:0.60 86:1.00 91:0.61 96:0.83 97:0.99 98:0.81 99:0.83 100:0.95 101:0.52 107:0.97 108:0.61 110:0.97 111:0.88 114:0.55 117:0.86 122:0.85 123:0.59 124:0.70 125:0.88 126:0.44 127:0.98 129:0.59 131:0.94 133:0.77 135:0.26 137:0.93 139:1.00 140:0.45 144:0.76 146:0.42 147:0.48 149:0.99 150:0.51 151:0.81 152:0.99 153:0.48 154:0.20 155:0.58 157:0.94 158:0.78 159:0.95 160:0.88 162:0.76 165:0.70 166:0.90 169:0.91 172:1.00 173:0.07 176:0.66 177:0.70 179:0.09 182:0.86 186:0.73 187:0.39 190:0.77 192:0.51 196:0.98 201:0.68 202:0.79 204:0.85 208:0.54 212:0.80 216:0.95 219:0.92 220:0.93 222:0.90 227:0.98 229:0.92 231:0.85 232:0.91 235:0.84 241:0.56 242:0.45 243:0.06 245:1.00 246:0.97 247:0.93 248:0.75 253:0.86 254:0.74 255:0.74 256:0.83 259:0.21 261:0.93 262:0.93 265:0.81 266:0.75 267:0.73 268:0.83 271:0.79 276:0.99 279:0.82 281:0.89 284:0.97 285:0.56 287:0.98 289:0.95 290:0.55 292:0.91 300:0.84 +1 7:0.66 8:0.77 11:0.81 12:0.79 17:0.51 18:0.97 20:0.90 22:0.93 27:0.69 29:0.77 30:0.32 31:0.90 34:0.28 36:0.91 37:0.47 39:0.40 43:0.69 45:0.77 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.09 69:0.97 70:0.87 71:0.77 74:0.35 76:0.85 78:0.87 79:0.94 81:0.77 86:0.72 91:0.20 98:0.31 100:0.89 101:0.52 104:0.83 106:0.81 107:0.95 108:0.96 110:0.95 111:0.86 120:0.72 122:0.86 123:0.96 124:0.96 125:0.88 126:0.44 127:0.89 129:0.59 131:0.84 133:0.95 135:0.39 137:0.90 139:0.70 140:0.45 144:0.76 146:0.75 147:0.44 149:0.59 150:0.55 151:0.81 152:0.84 153:0.48 154:0.59 157:0.80 159:0.91 160:0.88 162:0.86 163:0.81 164:0.64 166:0.77 169:0.87 172:0.54 173:0.95 177:0.38 179:0.27 182:0.74 186:0.87 187:0.21 190:0.77 192:0.51 196:0.89 201:0.68 202:0.50 206:0.81 212:0.14 215:0.96 216:0.30 220:0.74 222:0.90 227:0.92 229:0.61 230:0.69 231:0.78 233:0.76 235:0.05 241:0.21 242:0.70 243:0.17 244:0.61 245:0.84 246:0.61 247:0.82 248:0.75 253:0.29 255:0.74 256:0.58 257:0.65 259:0.21 260:0.69 261:0.87 262:0.93 265:0.33 266:0.95 267:0.23 268:0.59 271:0.79 276:0.87 281:0.91 283:0.82 284:0.86 285:0.56 287:0.61 289:0.93 297:0.36 300:0.94 +2 1:0.74 7:0.66 11:0.87 12:0.79 17:0.73 18:0.86 21:0.54 27:1.00 29:0.77 30:0.62 32:0.80 34:0.62 36:0.50 37:0.29 39:0.40 43:0.96 44:0.93 45:0.81 46:0.97 64:0.77 66:0.90 69:0.27 70:0.47 71:0.77 74:0.84 77:0.96 81:0.55 83:0.91 85:0.80 86:0.94 91:0.17 97:0.89 98:0.87 99:0.83 101:0.97 107:0.95 108:0.21 110:0.96 114:0.59 123:0.20 125:0.88 126:0.54 127:0.39 129:0.05 131:0.61 135:0.65 139:0.96 144:0.76 145:0.77 146:0.67 147:0.72 149:0.83 150:0.74 154:0.96 155:0.67 157:0.90 158:0.79 159:0.26 160:0.88 165:1.00 166:0.90 172:0.71 173:0.46 176:0.73 177:0.70 178:0.55 179:0.53 182:0.85 187:0.39 192:0.87 195:0.59 201:0.93 204:0.85 208:0.64 212:0.83 215:0.39 216:0.23 219:0.92 222:0.90 227:0.61 229:0.61 230:0.69 231:0.84 233:0.76 235:0.84 241:0.37 242:0.27 243:0.90 246:0.97 247:0.67 253:0.47 259:0.21 265:0.87 266:0.38 267:0.23 271:0.79 276:0.53 279:0.82 281:0.91 282:0.88 287:0.61 289:0.95 290:0.59 292:0.95 297:0.36 299:0.99 300:0.43 +2 6:1.00 7:0.66 8:0.98 9:0.80 12:0.79 17:0.22 20:0.99 21:0.78 27:0.13 29:0.77 31:0.98 34:0.49 36:0.78 37:0.97 39:0.40 43:0.13 44:0.85 46:0.88 48:0.91 66:0.36 69:0.64 71:0.77 78:0.94 79:0.99 83:0.91 91:0.96 96:0.94 98:0.09 100:0.99 107:0.88 108:0.49 110:0.88 111:0.99 120:0.63 123:0.47 125:0.88 127:0.07 129:0.05 131:0.94 135:0.42 137:0.98 139:0.59 140:0.95 144:0.76 145:0.89 146:0.05 147:0.05 149:0.07 151:0.52 152:0.90 153:0.72 154:0.10 155:0.67 157:0.61 159:0.05 160:0.88 162:0.52 164:0.85 166:0.61 169:1.00 172:0.05 173:0.78 175:0.91 177:0.05 178:0.42 182:0.86 186:0.94 189:1.00 190:0.77 191:0.84 192:0.87 195:0.61 196:0.87 202:0.93 208:0.64 216:0.53 219:0.87 220:0.74 222:0.90 227:0.98 229:0.92 230:0.69 231:0.85 233:0.76 235:0.12 238:0.99 241:0.87 243:0.51 244:0.83 246:0.61 248:0.54 253:0.88 255:0.95 256:0.91 257:0.84 259:0.21 260:0.62 261:0.98 265:0.10 267:0.44 268:0.92 271:0.79 277:0.87 281:0.89 284:0.99 285:0.62 287:0.98 289:0.95 292:0.91 +0 11:0.64 12:0.96 17:0.36 21:0.47 26:0.95 27:0.45 28:0.93 30:0.55 34:0.55 36:0.41 37:0.50 39:0.35 43:0.78 55:0.84 58:0.93 66:0.18 69:0.69 70:0.84 74:0.79 81:0.49 85:0.72 91:0.06 98:0.35 101:0.69 108:0.52 114:0.55 122:0.67 123:0.50 124:0.89 126:0.32 127:0.64 129:0.81 131:0.61 133:0.86 135:0.94 141:0.91 144:0.57 145:0.38 146:0.70 147:0.74 149:0.64 150:0.38 154:0.71 157:0.76 159:0.83 161:0.65 166:0.80 167:0.65 172:0.45 173:0.47 176:0.53 177:0.38 178:0.55 179:0.49 181:0.70 182:0.72 187:0.68 199:0.96 212:0.14 216:0.77 222:0.48 223:0.95 224:0.93 227:0.61 229:0.61 231:0.83 234:0.91 235:0.52 241:0.27 242:0.57 243:0.39 245:0.73 246:0.61 247:0.67 253:0.57 259:0.21 265:0.37 266:0.95 267:0.69 271:0.60 274:0.91 276:0.68 287:0.61 290:0.55 300:0.84 +2 1:0.74 7:0.81 11:0.64 12:0.96 17:0.64 21:0.30 26:0.97 27:0.04 28:0.93 30:0.14 32:0.80 34:0.75 36:0.27 37:0.99 39:0.35 43:0.59 58:0.93 66:0.19 69:0.93 70:0.97 74:0.72 77:0.70 81:0.79 83:0.75 85:0.85 91:0.20 98:0.21 101:0.72 108:0.86 114:0.86 121:0.99 122:0.67 123:0.85 124:0.85 126:0.54 127:0.33 129:0.81 131:0.61 133:0.83 135:0.79 141:0.91 144:0.57 145:0.49 146:0.49 147:0.55 149:0.50 150:0.86 154:0.48 155:0.67 157:0.81 159:0.80 161:0.65 163:0.81 165:0.84 166:0.91 167:0.60 172:0.27 173:0.83 176:0.73 177:0.38 178:0.55 179:0.62 181:0.70 182:0.84 187:0.87 192:0.59 195:0.96 199:0.97 201:0.74 204:0.89 208:0.64 212:0.18 215:0.72 216:0.57 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 230:0.87 231:0.85 233:0.76 234:0.91 235:0.42 241:0.07 242:0.31 243:0.40 245:0.58 246:0.97 247:0.60 253:0.70 259:0.21 265:0.23 266:0.84 267:0.44 271:0.60 274:0.91 276:0.47 282:0.88 287:0.61 290:0.86 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.81 8:0.78 9:0.80 11:0.64 12:0.96 17:0.49 21:0.47 26:0.97 27:0.21 28:0.93 30:0.21 34:0.61 36:0.75 37:0.74 39:0.35 41:0.88 43:0.97 55:0.84 58:0.98 60:0.95 66:0.52 69:0.19 70:0.94 74:0.96 81:0.69 83:0.82 85:0.96 91:0.17 96:0.85 98:0.69 101:0.77 104:0.89 106:0.81 108:0.94 114:0.80 117:0.86 120:0.76 121:1.00 122:0.67 123:0.94 124:0.87 126:0.54 127:0.87 129:0.96 131:0.94 133:0.91 135:0.17 140:0.45 141:0.99 144:0.57 146:0.68 147:0.73 149:0.90 150:0.80 151:0.94 153:0.80 154:0.97 155:0.67 157:0.89 158:0.92 159:0.93 161:0.65 162:0.79 164:0.71 165:0.80 166:0.91 167:0.72 172:0.92 173:0.07 176:0.73 177:0.38 179:0.19 181:0.70 182:0.85 187:0.39 192:0.59 199:0.99 201:0.74 202:0.60 204:0.89 206:0.81 208:0.64 212:0.30 215:0.63 216:0.95 222:0.48 223:0.96 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 232:0.95 233:0.76 234:0.99 235:0.60 241:0.55 242:0.38 243:0.19 245:0.91 246:0.97 247:0.67 248:0.83 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 265:0.69 266:0.95 267:0.76 268:0.65 271:0.60 274:0.98 276:0.92 279:0.86 283:0.82 285:0.62 287:0.98 290:0.80 297:0.36 300:0.94 +0 1:0.74 11:0.64 12:0.96 17:0.45 21:0.71 26:0.96 27:0.45 28:0.93 30:0.21 32:0.80 34:0.59 36:0.25 37:0.50 39:0.35 43:0.82 55:0.59 58:0.93 66:0.08 69:0.70 70:0.98 74:0.90 77:0.70 81:0.52 83:0.73 85:0.88 91:0.06 98:0.33 101:0.74 108:0.94 114:0.68 122:0.67 123:0.86 124:0.91 126:0.54 127:0.68 129:0.81 131:0.61 133:0.89 135:0.65 141:0.91 144:0.57 145:0.50 146:0.43 147:0.50 149:0.74 150:0.67 154:0.77 155:0.67 157:0.84 159:0.84 161:0.65 165:0.69 166:0.90 167:0.75 172:0.48 173:0.46 176:0.73 177:0.38 178:0.55 179:0.40 181:0.70 182:0.83 187:0.68 192:0.59 195:0.94 199:0.96 201:0.74 212:0.37 215:0.48 216:0.77 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 231:0.85 234:0.91 235:0.88 241:0.63 242:0.73 243:0.22 245:0.78 246:0.97 247:0.47 253:0.70 259:0.21 265:0.18 266:0.95 267:0.69 271:0.60 274:0.91 276:0.75 282:0.88 287:0.61 290:0.68 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.91 8:0.81 9:0.80 11:0.64 12:0.96 17:0.26 20:0.90 21:0.13 26:0.97 27:0.04 28:0.93 30:0.14 34:0.37 36:0.99 37:0.95 39:0.35 41:0.97 43:0.87 58:1.00 60:0.87 66:0.33 69:0.56 70:0.88 74:0.87 78:0.84 81:0.88 83:0.89 85:0.95 91:0.65 96:0.96 98:0.45 100:0.86 101:0.78 108:0.86 111:0.83 114:0.92 120:0.95 122:0.57 123:0.85 124:0.93 126:0.54 127:0.85 129:0.97 131:0.94 133:0.94 135:0.86 140:0.80 141:0.99 144:0.57 146:0.13 147:0.18 149:0.85 150:0.93 151:0.97 152:0.94 153:0.95 154:0.85 155:0.67 157:0.89 158:0.92 159:0.90 161:0.65 162:0.74 164:0.91 165:0.88 166:0.91 167:0.78 169:0.83 172:0.73 173:0.24 176:0.73 177:0.38 179:0.36 181:0.70 182:0.85 186:0.84 187:0.68 189:0.91 192:0.59 199:0.99 201:0.74 202:0.88 208:0.64 212:0.16 215:0.84 216:0.83 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 231:0.86 234:0.99 235:0.22 238:0.89 241:0.69 242:0.32 243:0.10 245:0.77 246:0.97 247:0.60 248:0.95 253:0.97 255:0.79 256:0.90 259:0.21 260:0.95 261:0.87 265:0.47 266:0.94 267:0.85 268:0.90 271:0.60 274:1.00 276:0.80 279:0.86 283:0.82 285:0.62 287:0.98 290:0.92 297:0.36 300:0.70 +0 1:0.74 6:0.98 8:0.92 9:0.80 11:0.64 12:0.96 17:0.24 20:0.90 21:0.40 26:0.97 27:0.08 28:0.93 30:0.86 34:0.20 36:0.62 37:0.91 39:0.35 41:0.92 43:0.62 55:0.45 58:0.99 66:0.49 69:0.91 70:0.41 74:0.95 78:0.88 81:0.74 83:0.80 85:0.88 87:0.98 91:0.75 96:0.95 98:0.43 100:0.95 101:0.79 108:0.41 111:0.91 114:0.82 120:0.84 122:0.67 123:0.40 124:0.56 126:0.54 127:0.65 129:0.05 131:0.94 133:0.39 135:0.44 140:0.87 141:0.99 144:0.57 146:0.55 147:0.62 149:0.81 150:0.83 151:0.95 152:0.99 153:0.86 154:0.51 155:0.67 157:0.86 158:0.82 159:0.59 161:0.65 162:0.64 164:0.79 165:0.83 166:0.91 167:0.92 169:0.98 172:0.67 173:0.96 176:0.73 177:0.05 179:0.49 181:0.70 182:0.85 186:0.88 187:0.39 189:0.95 191:0.84 192:0.59 199:0.97 201:0.74 202:0.84 208:0.64 212:0.81 215:0.67 216:0.63 220:0.62 222:0.48 223:0.96 224:0.98 227:0.98 229:0.92 231:0.86 232:0.72 234:0.97 235:0.52 238:0.96 241:0.78 242:0.72 243:0.60 245:0.86 246:0.97 247:0.60 248:0.88 253:0.96 255:0.79 256:0.85 257:0.65 259:0.21 260:0.84 261:0.98 265:0.45 266:0.71 267:0.44 268:0.86 271:0.60 274:0.99 276:0.57 277:0.69 285:0.62 287:0.98 290:0.82 297:0.36 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.82 9:0.80 11:0.64 12:0.96 17:0.34 20:0.99 21:0.54 26:0.97 27:0.11 28:0.93 30:0.33 32:0.80 34:0.49 36:0.98 37:0.80 39:0.35 41:0.98 43:0.89 55:0.71 58:1.00 60:0.98 66:0.31 69:0.50 70:0.66 74:0.93 77:0.70 78:0.83 81:0.65 83:0.89 85:0.93 91:0.86 96:0.98 98:0.61 100:0.83 101:0.79 108:0.67 111:0.82 114:0.77 120:0.96 121:0.99 122:0.67 123:0.65 124:0.83 126:0.54 127:0.77 129:0.93 131:0.94 133:0.84 135:0.37 140:0.45 141:0.99 144:0.57 145:0.54 146:0.13 147:0.18 149:0.85 150:0.77 151:0.99 152:0.91 153:0.97 154:0.88 155:0.67 157:0.88 158:0.92 159:0.80 161:0.65 162:0.79 164:0.93 165:0.79 166:0.90 167:0.81 169:0.81 172:0.68 173:0.30 176:0.73 177:0.38 179:0.37 181:0.70 182:0.85 186:0.83 187:0.68 189:0.92 191:0.91 192:0.59 195:0.91 199:1.00 201:0.74 202:0.90 204:0.89 208:0.64 212:0.27 215:0.58 216:0.86 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 233:0.76 234:0.98 235:0.68 238:0.86 241:0.71 242:0.38 243:0.10 245:0.83 246:0.97 247:0.67 248:0.97 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.86 265:0.62 266:0.84 267:0.28 268:0.93 271:0.60 274:1.00 276:0.79 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.77 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.95 7:0.81 8:0.83 9:0.80 11:0.64 12:0.96 17:0.55 20:0.99 21:0.30 26:0.97 27:0.21 28:0.93 30:0.10 34:0.63 36:0.83 37:0.74 39:0.35 41:0.98 43:0.98 58:1.00 60:0.98 66:0.07 69:0.12 70:0.94 74:0.99 78:0.78 81:0.79 83:0.89 85:0.99 91:0.78 96:0.98 98:0.36 100:0.86 101:0.81 104:0.84 106:0.81 108:0.98 111:0.80 114:0.86 117:0.86 120:0.95 121:0.90 122:0.67 123:0.88 124:0.98 126:0.54 127:0.89 129:0.99 131:0.94 133:0.98 135:0.19 140:0.45 141:0.99 144:0.57 146:0.61 147:0.67 149:0.96 150:0.86 151:0.99 152:0.91 153:0.97 154:0.98 155:0.67 157:0.92 158:0.92 159:0.96 161:0.65 162:0.73 164:0.92 165:0.84 166:0.91 167:0.65 169:0.84 172:0.84 173:0.06 176:0.73 177:0.38 179:0.12 181:0.70 182:0.85 186:0.78 187:0.39 189:0.96 191:0.73 192:0.59 199:1.00 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.36 215:0.72 216:0.95 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 232:0.93 233:0.76 234:0.98 235:0.42 238:0.97 241:0.27 242:0.45 243:0.11 245:0.96 246:0.97 247:0.67 248:0.97 253:0.99 255:0.79 256:0.91 259:0.21 260:0.95 261:0.85 265:0.28 266:0.96 267:0.28 268:0.92 271:0.60 274:1.00 276:0.97 279:0.86 283:0.82 285:0.62 287:0.98 290:0.86 297:0.36 300:0.94 +2 1:0.74 6:0.95 7:0.81 8:0.84 9:0.80 11:0.64 12:0.96 17:0.51 20:0.74 21:0.54 26:0.97 27:0.21 28:0.93 30:0.27 34:0.42 36:0.80 37:0.74 39:0.35 41:0.88 43:0.96 55:0.71 58:0.99 60:0.97 66:0.09 69:0.25 70:0.81 74:0.99 78:0.95 81:0.65 83:0.77 85:0.97 91:0.14 96:0.89 98:0.40 100:0.96 101:0.74 104:0.95 106:0.81 108:0.97 111:0.94 114:0.77 117:0.86 120:0.64 121:0.90 122:0.67 123:0.97 124:0.98 126:0.54 127:0.96 129:1.00 131:0.94 133:0.98 135:0.32 140:0.80 141:0.99 144:0.57 146:0.65 147:0.70 149:0.92 150:0.77 151:0.63 152:0.91 153:0.48 154:0.96 155:0.67 157:0.87 158:0.92 159:0.98 161:0.65 162:0.70 164:0.73 165:0.79 166:0.90 167:0.71 169:0.84 172:0.89 173:0.06 176:0.73 177:0.38 179:0.10 181:0.70 182:0.85 186:0.94 187:0.39 190:0.77 192:0.59 199:1.00 201:0.74 202:0.74 204:0.89 206:0.81 208:0.64 212:0.29 215:0.58 216:0.95 220:0.62 222:0.48 223:0.96 224:0.98 227:0.98 229:0.92 230:0.84 231:0.86 232:0.98 233:0.69 234:0.97 235:0.68 241:0.51 242:0.71 243:0.10 245:0.97 246:0.97 247:0.67 248:0.61 253:0.93 255:0.79 256:0.75 259:0.21 260:0.65 261:0.85 265:0.42 266:0.96 267:0.23 268:0.77 271:0.60 274:0.98 276:0.99 279:0.86 283:0.82 285:0.62 287:0.98 290:0.77 297:0.36 300:0.84 +4 1:0.74 6:0.98 7:0.76 8:0.95 9:0.80 12:0.96 17:0.01 20:0.99 21:0.40 26:0.97 27:0.08 28:0.93 30:0.62 32:0.80 34:0.21 36:0.53 37:0.91 39:0.35 41:1.00 43:0.35 55:0.45 58:1.00 60:0.99 66:0.67 69:0.56 70:0.45 74:0.97 76:1.00 77:1.00 78:0.93 81:0.74 83:0.91 87:0.98 91:0.99 96:1.00 98:0.77 100:0.99 108:0.43 111:0.99 114:0.82 120:0.99 122:0.67 123:0.41 124:0.46 126:0.54 127:0.63 129:0.59 131:0.94 133:0.47 135:0.34 138:0.99 140:0.45 141:0.99 144:0.57 145:0.76 146:0.86 147:0.89 149:0.76 150:0.83 151:1.00 152:0.99 153:0.99 154:0.26 155:0.67 157:0.61 158:0.92 159:0.60 161:0.65 162:0.73 164:0.98 165:0.83 166:0.91 167:0.98 169:0.98 172:0.78 173:0.49 176:0.73 177:0.38 179:0.43 181:0.70 182:0.85 186:0.93 189:0.99 191:0.96 192:0.59 195:0.80 199:1.00 201:0.74 202:0.97 208:0.64 212:0.59 215:0.67 216:0.63 222:0.48 223:0.97 224:0.98 227:0.98 229:0.92 230:0.79 231:0.86 232:0.72 233:0.76 234:0.98 235:0.52 238:0.99 241:0.96 242:0.77 243:0.72 244:0.61 245:0.85 246:0.97 247:0.47 248:1.00 253:1.00 255:0.79 256:0.98 259:0.21 260:0.99 261:0.98 265:0.77 266:0.63 267:0.82 268:0.98 271:0.60 274:1.00 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.82 297:0.36 299:0.88 300:0.43 +1 1:0.74 8:0.85 9:0.80 12:0.96 17:0.18 21:0.40 26:0.97 27:0.08 28:0.93 30:0.94 34:0.34 36:0.76 37:0.91 39:0.35 41:0.88 43:0.36 55:0.45 58:0.99 66:0.80 69:0.54 70:0.21 74:0.93 81:0.74 83:0.77 87:0.98 91:0.81 96:0.91 98:0.47 108:0.41 114:0.82 120:0.76 122:0.67 123:0.40 124:0.38 126:0.54 127:0.61 129:0.59 131:0.94 133:0.47 135:0.61 140:0.87 141:0.99 144:0.57 146:0.56 147:0.62 149:0.66 150:0.83 151:0.82 153:0.83 154:0.27 155:0.67 157:0.61 158:0.82 159:0.53 161:0.65 162:0.57 164:0.73 165:0.83 166:0.91 167:0.93 172:0.67 173:0.52 175:0.75 176:0.73 177:0.05 179:0.64 181:0.70 182:0.85 187:0.39 191:0.78 192:0.59 199:0.95 201:0.74 202:0.80 208:0.64 212:0.88 215:0.67 216:0.63 220:0.74 222:0.48 223:0.96 224:0.98 227:0.98 229:0.91 231:0.86 232:0.72 234:0.97 235:0.52 241:0.79 242:0.82 243:0.82 244:0.61 245:0.55 246:0.97 247:0.12 248:0.75 253:0.93 255:0.79 256:0.78 257:0.65 259:0.21 260:0.76 265:0.49 266:0.27 267:0.59 268:0.79 271:0.60 274:0.98 276:0.36 277:0.69 285:0.62 287:0.98 290:0.82 297:0.36 300:0.33 +2 1:0.74 6:0.90 8:0.77 9:0.80 11:0.64 12:0.96 17:0.47 20:0.99 21:0.47 26:0.97 27:0.12 28:0.93 30:0.36 32:0.80 34:0.96 36:0.30 37:0.78 39:0.35 41:0.97 43:0.76 58:1.00 60:0.99 66:0.61 69:0.81 70:0.89 74:0.94 77:0.70 78:0.78 81:0.69 83:0.88 85:0.96 91:0.72 96:0.97 98:0.63 100:0.81 101:0.82 108:0.65 111:0.77 114:0.80 120:0.95 122:0.57 123:0.62 124:0.65 126:0.54 127:0.44 129:0.81 131:0.94 133:0.76 135:0.23 140:0.45 141:0.99 144:0.57 145:0.54 146:0.90 147:0.91 149:0.91 150:0.80 151:0.99 152:0.90 153:0.97 154:0.68 155:0.67 157:0.91 158:0.92 159:0.75 161:0.65 162:0.76 164:0.91 165:0.80 166:0.91 167:0.75 169:0.79 172:0.82 173:0.66 176:0.73 177:0.38 179:0.29 181:0.70 182:0.85 186:0.78 187:0.68 189:0.92 191:0.89 192:0.59 195:0.91 199:0.99 201:0.74 202:0.88 208:0.64 212:0.68 215:0.63 216:0.81 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 231:0.86 234:0.98 235:0.60 238:0.89 241:0.63 242:0.38 243:0.68 245:0.83 246:0.97 247:0.60 248:0.96 253:0.98 255:0.79 256:0.90 259:0.21 260:0.95 261:0.82 265:0.64 266:0.89 267:0.23 268:0.91 271:0.60 274:1.00 276:0.79 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.80 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.84 9:0.80 11:0.64 12:0.96 17:0.55 20:0.88 21:0.64 26:0.97 27:0.05 28:0.93 30:0.45 32:0.72 34:0.45 36:0.86 37:0.78 39:0.35 41:0.97 43:0.52 55:0.59 58:1.00 60:0.98 66:0.30 69:0.59 70:0.82 74:0.91 78:0.79 81:0.58 83:0.84 85:0.88 91:0.82 96:0.97 98:0.59 100:0.81 101:0.76 108:0.90 111:0.79 114:0.72 117:0.86 120:0.92 121:0.90 122:0.67 123:0.43 124:0.59 126:0.54 127:0.60 129:0.59 131:0.94 133:0.47 135:0.51 140:0.80 141:0.99 144:0.57 145:0.77 146:0.84 147:0.87 149:0.67 150:0.72 151:0.98 152:0.90 153:0.97 154:0.66 155:0.67 157:0.85 158:0.92 159:0.75 161:0.65 162:0.76 164:0.90 165:0.70 166:0.90 167:0.81 169:0.79 172:0.68 173:0.42 176:0.73 177:0.38 179:0.42 181:0.70 182:0.85 186:0.80 187:0.87 189:0.95 191:0.76 192:0.59 195:0.89 199:0.99 201:0.74 202:0.87 204:0.89 208:0.64 212:0.73 215:0.53 216:0.83 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 230:0.87 231:0.86 232:0.97 233:0.76 234:0.98 235:0.80 238:0.88 241:0.71 242:0.44 243:0.48 245:0.89 246:0.97 247:0.47 248:0.95 253:0.98 255:0.79 256:0.89 259:0.21 260:0.92 261:0.84 265:0.37 266:0.80 267:0.19 268:0.90 271:0.60 274:0.99 276:0.65 279:0.86 283:0.82 285:0.62 287:0.98 290:0.72 297:0.36 300:0.70 +2 1:0.74 6:0.91 8:0.82 9:0.80 11:0.64 12:0.96 17:0.34 20:0.86 21:0.13 26:0.97 27:0.04 28:0.93 30:0.20 34:0.58 36:0.86 37:0.95 39:0.35 41:0.96 43:0.87 58:1.00 60:0.87 66:0.24 69:0.56 70:0.75 74:0.86 78:0.80 81:0.88 83:0.88 85:0.93 91:0.72 96:0.97 98:0.40 100:0.86 101:0.77 104:0.81 108:0.84 111:0.81 114:0.92 120:0.94 122:0.67 123:0.83 124:0.91 126:0.54 127:0.81 129:0.96 131:0.94 133:0.91 135:0.28 138:0.98 140:0.45 141:0.99 144:0.57 146:0.13 147:0.18 149:0.83 150:0.93 151:0.99 152:0.94 153:0.97 154:0.85 155:0.67 157:0.88 158:0.92 159:0.85 161:0.65 162:0.67 164:0.88 165:0.88 166:0.91 167:0.76 169:0.83 172:0.51 173:0.31 176:0.73 177:0.38 179:0.51 181:0.70 182:0.85 186:0.81 187:0.87 189:0.95 191:0.87 192:0.59 199:0.99 201:0.74 202:0.84 208:0.64 212:0.21 215:0.84 216:0.83 222:0.48 223:0.97 224:0.99 227:0.98 229:0.92 231:0.86 232:0.91 234:0.99 235:0.22 238:0.94 241:0.66 242:0.51 243:0.13 245:0.69 246:0.97 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 259:0.21 260:0.94 261:0.87 265:0.42 266:0.87 267:0.85 268:0.87 271:0.60 274:0.99 276:0.69 279:0.86 283:0.82 285:0.62 287:0.98 290:0.92 297:0.36 300:0.55 +2 1:0.74 7:0.81 8:0.73 9:0.80 11:0.64 12:0.96 17:0.69 21:0.30 26:0.97 27:0.21 28:0.93 30:0.18 34:0.40 36:0.74 37:0.74 39:0.35 41:0.82 43:0.98 55:0.71 58:0.98 66:0.07 69:0.12 70:0.87 74:0.99 81:0.79 83:0.79 85:0.99 91:0.06 96:0.91 98:0.37 101:0.76 104:0.84 106:0.81 108:0.97 114:0.86 117:0.86 120:0.75 121:0.90 122:0.67 123:0.97 124:0.99 126:0.54 127:0.99 129:1.00 131:0.94 133:0.99 135:0.32 140:0.45 141:0.99 144:0.57 146:0.64 147:0.69 149:0.96 150:0.86 151:0.93 153:0.77 154:0.98 155:0.67 157:0.90 158:0.84 159:0.98 161:0.65 162:0.61 164:0.64 165:0.84 166:0.91 167:0.73 172:0.89 173:0.05 176:0.73 177:0.38 179:0.10 181:0.70 182:0.85 187:0.39 192:0.59 199:1.00 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.30 215:0.72 216:0.95 222:0.48 223:0.96 224:0.98 227:0.98 229:0.91 230:0.87 231:0.86 232:0.93 233:0.76 234:0.97 235:0.42 241:0.57 242:0.70 243:0.10 245:0.98 246:0.97 247:0.67 248:0.82 253:0.94 255:0.79 256:0.68 259:0.21 260:0.75 265:0.39 266:0.96 267:0.69 268:0.69 271:0.60 274:0.97 276:0.99 279:0.86 283:0.71 285:0.62 287:0.98 290:0.86 297:0.36 300:0.94 +1 1:0.74 6:0.97 8:0.93 9:0.80 11:0.64 12:0.96 17:0.18 20:0.97 21:0.40 27:0.55 28:0.73 30:0.62 34:0.67 36:0.95 37:0.32 39:0.27 41:0.93 43:0.80 55:0.71 60:0.87 66:0.95 69:0.73 70:0.65 74:0.93 78:0.83 81:0.81 83:0.87 85:0.80 91:0.48 96:0.90 98:0.90 100:0.88 101:0.73 108:0.56 111:0.84 114:0.82 120:0.82 122:0.67 123:0.54 126:0.54 127:0.47 129:0.05 131:0.99 135:0.76 140:0.45 144:0.57 146:0.97 147:0.98 149:0.77 150:0.87 151:0.93 152:0.89 153:0.77 154:0.74 155:0.67 157:0.86 158:0.92 159:0.70 161:0.89 162:0.51 164:0.79 165:0.83 166:0.99 167:0.60 169:0.86 172:0.87 173:0.60 176:0.73 177:0.38 179:0.33 181:0.91 182:0.95 186:0.83 187:0.68 189:0.97 192:0.59 201:0.74 202:0.80 208:0.64 212:0.42 215:0.67 216:0.43 220:0.91 222:0.48 227:1.00 229:0.97 231:0.98 232:0.88 235:0.52 238:0.91 241:0.63 242:0.59 243:0.95 246:1.00 247:0.55 248:0.89 253:0.93 255:0.79 256:0.75 259:0.21 260:0.83 261:0.92 265:0.90 266:0.71 267:0.65 268:0.75 271:0.13 276:0.78 279:0.86 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 300:0.70 +1 9:0.80 11:0.64 12:0.96 17:0.18 21:0.78 27:0.33 28:0.73 30:0.76 34:0.39 36:0.61 37:0.55 39:0.27 41:0.95 43:0.64 60:0.97 66:0.77 69:0.91 70:0.29 74:0.62 83:0.88 85:0.85 91:0.49 96:0.95 98:0.28 101:0.75 108:0.81 120:0.91 122:0.57 123:0.80 127:0.12 129:0.05 131:0.99 135:0.73 140:0.45 144:0.57 145:0.67 146:0.47 147:0.53 149:0.57 151:0.97 153:0.90 154:0.54 155:0.67 157:0.90 158:0.92 159:0.17 161:0.89 162:0.53 164:0.85 166:0.61 167:0.48 172:0.37 173:0.88 177:0.38 179:0.22 181:0.91 182:0.95 187:0.68 192:0.59 202:0.85 208:0.64 212:0.23 216:0.81 222:0.48 227:1.00 229:0.97 231:0.98 235:0.60 241:0.24 242:0.55 243:0.79 246:0.61 247:0.39 248:0.95 253:0.93 255:0.79 256:0.81 259:0.21 260:0.91 265:0.30 266:0.38 267:0.76 268:0.82 271:0.13 276:0.27 279:0.86 283:0.82 285:0.62 287:1.00 300:0.25 +2 1:0.74 6:0.91 7:0.81 8:0.64 9:0.80 11:0.64 12:0.96 17:0.38 20:0.75 21:0.40 27:0.21 28:0.73 30:0.30 34:0.50 36:0.81 37:0.74 39:0.27 41:0.90 43:0.92 55:0.71 60:0.87 66:0.92 69:0.15 70:0.78 74:0.96 76:0.85 78:0.96 81:0.80 83:0.86 85:0.94 91:0.42 96:0.91 98:0.90 100:0.97 101:0.81 104:0.84 106:0.81 108:0.12 111:0.96 114:0.82 117:0.86 120:0.84 122:0.43 123:0.12 124:0.36 126:0.54 127:0.59 129:0.05 131:0.99 133:0.39 135:0.24 140:0.45 144:0.57 146:0.97 147:0.97 149:0.88 150:0.82 151:0.93 152:0.98 153:0.78 154:0.92 155:0.67 157:0.97 158:0.92 159:0.72 161:0.89 162:0.83 164:0.76 166:0.99 167:0.48 169:0.90 172:0.91 173:0.13 176:0.73 177:0.38 179:0.27 181:0.91 182:0.95 186:0.96 187:0.39 189:0.85 192:0.59 201:0.74 202:0.64 204:0.89 206:0.81 208:0.64 212:0.70 215:0.67 216:0.95 222:0.48 227:1.00 229:0.97 230:0.84 231:0.98 232:0.93 233:0.69 235:0.52 238:0.92 241:0.27 242:0.29 243:0.92 245:0.70 246:0.61 247:0.67 248:0.90 253:0.94 255:0.79 256:0.68 259:0.21 260:0.85 261:0.96 265:0.90 266:0.54 267:0.73 268:0.70 271:0.13 276:0.84 279:0.86 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 300:0.55 +1 1:0.74 6:0.91 7:0.81 8:0.80 9:0.80 11:0.64 12:0.96 17:0.22 20:0.85 21:0.40 27:0.21 28:0.73 30:0.29 34:0.44 36:0.83 37:0.74 39:0.27 41:0.82 43:0.75 55:0.71 60:0.87 66:0.92 69:0.15 70:0.78 74:0.96 76:0.94 78:0.84 81:0.80 83:0.79 85:0.94 91:0.49 96:0.88 98:0.89 100:0.91 101:0.81 104:0.92 106:0.81 108:0.12 111:0.86 114:0.82 117:0.86 120:0.78 122:0.43 123:0.12 124:0.36 126:0.54 127:0.55 129:0.05 131:0.99 133:0.39 135:0.17 140:0.45 144:0.57 146:0.97 147:0.97 149:0.85 150:0.82 151:0.90 152:0.98 153:0.69 154:0.92 155:0.67 157:0.97 158:0.92 159:0.71 161:0.89 162:0.58 164:0.75 166:0.99 167:0.46 169:0.90 172:0.90 173:0.13 176:0.73 177:0.38 179:0.28 181:0.91 182:0.95 186:0.84 187:0.39 189:0.92 192:0.59 201:0.74 202:0.71 204:0.89 206:0.81 208:0.64 212:0.68 215:0.67 216:0.95 220:0.62 222:0.48 227:1.00 229:0.97 230:0.84 231:0.98 232:0.85 233:0.69 235:0.52 238:0.94 241:0.12 242:0.39 243:0.92 245:0.70 246:0.61 247:0.67 248:0.86 253:0.92 255:0.79 256:0.68 259:0.21 260:0.79 261:0.96 265:0.89 266:0.54 267:0.97 268:0.70 271:0.13 276:0.83 279:0.86 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 300:0.55 +0 1:0.74 11:0.64 12:0.96 17:0.28 21:0.54 27:0.45 28:0.73 30:0.68 34:0.77 36:0.17 37:0.50 39:0.27 43:0.82 55:0.84 60:0.87 66:0.45 69:0.69 70:0.31 74:0.74 81:0.71 83:0.86 85:0.72 91:0.18 98:0.44 101:0.70 108:0.64 114:0.77 122:0.67 123:0.61 124:0.66 126:0.54 127:0.29 129:0.05 131:0.61 133:0.62 135:0.91 144:0.57 145:0.47 146:0.13 147:0.18 149:0.57 150:0.81 154:0.77 155:0.67 157:0.79 158:0.87 159:0.59 161:0.89 163:0.94 165:0.79 166:0.99 167:0.51 172:0.27 173:0.57 176:0.73 177:0.38 178:0.55 179:0.81 181:0.91 182:0.94 187:0.68 192:0.59 201:0.74 208:0.64 212:0.61 215:0.58 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.68 241:0.41 242:0.79 243:0.32 245:0.47 246:1.00 247:0.30 253:0.66 259:0.21 265:0.45 266:0.27 267:0.28 271:0.13 276:0.31 277:0.69 279:0.86 283:0.71 287:0.61 290:0.77 297:0.36 300:0.25 +1 1:0.74 6:0.94 8:0.90 9:0.80 11:0.64 12:0.96 17:0.20 20:0.95 21:0.54 27:0.06 28:0.73 30:0.21 32:0.78 34:0.78 36:0.98 37:0.82 39:0.27 41:0.92 43:0.75 55:0.96 60:0.97 66:0.09 69:0.82 70:0.91 74:0.83 77:0.70 78:0.78 81:0.70 83:0.84 85:0.80 91:0.57 96:0.91 98:0.22 100:0.81 101:0.72 108:0.67 111:0.78 114:0.77 120:0.86 122:0.67 123:0.84 124:0.88 126:0.54 127:0.28 129:0.59 131:0.99 133:0.86 135:0.74 140:0.45 144:0.57 145:0.69 146:0.32 147:0.39 149:0.63 150:0.75 151:0.92 152:0.97 153:0.76 154:0.66 155:0.67 157:0.85 158:0.92 159:0.68 161:0.89 162:0.60 164:0.88 166:0.99 167:0.54 169:0.80 172:0.32 173:0.68 176:0.73 177:0.38 179:0.53 181:0.91 182:0.95 186:0.78 187:0.39 189:0.96 192:0.59 195:0.92 201:0.74 202:0.85 208:0.64 212:0.35 215:0.58 216:0.92 220:0.96 222:0.48 227:1.00 229:0.97 231:0.98 235:0.68 238:0.89 241:0.51 242:0.55 243:0.40 245:0.58 246:0.61 247:0.60 248:0.89 253:0.92 255:0.79 256:0.86 259:0.21 260:0.86 261:0.90 265:0.19 266:0.75 267:0.44 268:0.86 271:0.13 276:0.53 277:0.69 279:0.86 282:0.86 283:0.82 285:0.62 287:1.00 290:0.77 297:0.36 300:0.70 +2 1:0.74 9:0.80 11:0.64 12:0.96 17:0.32 21:0.13 27:0.33 28:0.73 30:0.76 32:0.80 34:0.33 36:0.42 37:0.55 39:0.27 41:0.96 43:0.75 60:0.99 66:0.77 69:0.82 70:0.29 74:0.74 77:0.70 81:0.92 83:0.88 85:0.85 91:0.54 96:0.95 98:0.44 101:0.79 104:0.95 106:0.81 108:0.67 114:0.92 117:0.86 120:0.91 122:0.57 123:0.65 126:0.54 127:0.14 129:0.05 131:1.00 135:0.69 140:0.45 144:0.57 145:0.69 146:0.46 147:0.52 149:0.68 150:0.96 151:0.97 153:0.89 154:0.67 155:0.67 157:0.92 158:0.92 159:0.17 161:0.89 162:0.83 164:0.86 165:0.88 166:0.99 167:0.53 172:0.37 173:0.89 176:0.73 177:0.38 179:0.45 181:0.91 182:0.95 187:0.68 192:0.59 195:0.72 201:0.74 202:0.76 206:0.81 208:0.64 212:0.23 215:0.84 216:0.81 222:0.48 227:1.00 229:0.97 231:0.98 232:0.98 235:0.22 241:0.49 242:0.55 243:0.79 246:1.00 247:0.39 248:0.95 253:0.95 255:0.79 256:0.82 259:0.21 260:0.91 265:0.46 266:0.38 267:0.76 268:0.82 271:0.13 276:0.28 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.92 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.95 7:0.81 8:0.91 9:0.80 11:0.64 12:0.96 17:0.05 20:0.99 21:0.22 27:0.35 28:0.73 30:0.74 32:0.80 34:0.52 36:0.91 37:0.80 39:0.27 41:0.98 43:0.76 60:0.98 66:0.94 69:0.81 70:0.47 74:0.94 77:0.70 78:0.86 81:0.89 83:0.89 85:0.97 91:0.76 96:0.96 98:0.80 100:0.94 101:0.85 104:0.89 106:0.81 108:0.66 111:0.90 114:0.90 117:0.86 120:0.93 121:0.90 122:0.43 123:0.63 126:0.54 127:0.28 129:0.05 131:1.00 135:0.93 140:0.45 144:0.57 145:0.56 146:0.90 147:0.92 149:0.95 150:0.93 151:0.96 152:0.90 153:0.88 154:0.67 155:0.67 157:0.98 158:0.92 159:0.50 161:0.89 162:0.58 164:0.91 165:0.86 166:0.99 167:0.65 169:0.92 172:0.85 173:0.77 176:0.73 177:0.38 179:0.27 181:0.91 182:0.95 186:0.86 187:0.21 189:0.96 191:0.75 192:0.59 195:0.80 201:0.74 202:0.90 204:0.89 206:0.81 208:0.64 212:0.60 215:0.79 216:0.72 220:0.74 222:0.48 227:1.00 229:0.97 230:0.87 231:0.98 232:0.95 233:0.76 235:0.31 238:0.96 241:0.68 242:0.58 243:0.94 246:1.00 247:0.60 248:0.96 253:0.98 255:0.79 256:0.90 259:0.21 260:0.93 261:0.96 265:0.80 266:0.63 267:0.59 268:0.90 271:0.13 276:0.74 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.90 297:0.36 299:0.88 300:0.43 +1 6:0.91 8:0.92 9:0.80 11:0.64 12:0.96 17:0.43 20:0.90 21:0.78 27:0.21 28:0.73 30:0.21 34:0.62 36:0.75 37:0.74 39:0.27 41:0.82 43:0.92 60:0.95 66:0.27 69:0.41 70:1.00 74:0.90 78:0.78 83:0.88 85:0.97 91:0.05 96:0.85 98:0.19 100:0.83 101:0.73 104:0.84 106:0.81 108:0.96 111:0.78 117:0.86 120:0.76 122:0.57 123:0.96 124:0.91 127:0.62 129:0.95 131:0.99 133:0.93 135:0.30 140:0.45 144:0.57 146:0.51 147:0.57 149:0.88 151:0.94 152:0.98 153:0.80 154:0.91 155:0.67 157:0.93 158:0.92 159:0.98 161:0.89 162:0.56 164:0.65 166:0.61 167:0.46 169:0.90 172:0.76 173:0.07 177:0.38 179:0.25 181:0.91 182:0.95 186:0.78 187:0.39 189:0.97 192:0.59 202:0.60 206:0.81 208:0.64 212:0.54 216:0.95 222:0.48 227:1.00 229:0.97 231:0.98 232:0.93 235:0.68 238:0.93 241:0.12 242:0.28 243:0.17 245:0.84 246:0.61 247:0.60 248:0.83 253:0.88 255:0.79 256:0.45 259:0.21 260:0.77 261:0.96 265:0.21 266:0.95 267:0.84 268:0.58 271:0.13 276:0.83 279:0.86 283:0.82 285:0.62 287:1.00 300:1.00 +1 1:0.74 6:0.90 7:0.81 8:0.79 9:0.80 11:0.64 12:0.96 17:0.12 20:0.97 21:0.40 27:0.26 28:0.73 30:0.17 32:0.80 34:0.96 36:0.85 37:0.83 39:0.27 41:0.90 43:0.76 55:0.84 60:0.87 66:0.15 69:0.47 70:0.68 74:0.88 77:0.70 78:0.83 81:0.80 83:0.82 85:0.80 91:0.65 96:0.90 98:0.25 100:0.89 101:0.73 104:0.95 106:0.81 108:0.59 111:0.84 114:0.82 117:0.86 120:0.84 121:0.90 122:0.57 123:0.57 124:0.86 126:0.54 127:0.25 129:0.89 131:0.99 133:0.83 135:0.79 140:0.80 144:0.57 145:0.54 146:0.33 147:0.39 149:0.60 150:0.82 151:0.92 152:0.89 153:0.72 154:0.83 155:0.67 157:0.86 158:0.92 159:0.57 161:0.89 162:0.53 163:0.89 164:0.86 166:0.99 167:0.53 169:0.86 172:0.21 173:0.31 176:0.73 177:0.38 178:0.42 179:0.57 181:0.91 182:0.95 186:0.84 187:0.39 189:0.91 192:0.59 195:0.88 201:0.74 202:0.84 204:0.89 206:0.81 208:0.64 212:0.19 215:0.67 216:0.74 220:0.87 222:0.48 227:1.00 229:0.97 230:0.87 231:0.98 232:0.98 233:0.76 235:0.52 238:0.91 241:0.49 242:0.45 243:0.29 245:0.56 246:0.61 247:0.39 248:0.89 253:0.92 255:0.79 256:0.82 259:0.21 260:0.85 261:0.93 265:0.27 266:0.75 267:0.65 268:0.82 271:0.13 276:0.48 279:0.86 282:0.88 283:0.82 285:0.62 287:1.00 290:0.82 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.94 8:0.73 9:0.80 11:0.64 12:0.96 17:0.22 20:0.75 21:0.54 27:0.06 28:0.73 30:0.21 32:0.78 34:0.53 36:0.94 37:0.82 39:0.27 41:0.92 43:0.76 55:0.96 60:0.97 66:0.07 69:0.80 70:0.86 74:0.86 77:0.70 78:0.91 81:0.70 83:0.83 85:0.88 91:0.22 96:0.91 98:0.23 100:0.89 101:0.75 108:0.63 111:0.89 114:0.77 120:0.86 122:0.67 123:0.84 124:0.90 126:0.54 127:0.42 129:0.81 131:0.99 133:0.89 135:0.83 140:0.45 144:0.57 145:0.69 146:0.39 147:0.46 149:0.75 150:0.75 151:0.92 152:0.97 153:0.76 154:0.68 155:0.67 157:0.92 158:0.92 159:0.74 161:0.89 162:0.60 164:0.88 166:0.99 167:0.52 169:0.80 172:0.41 173:0.65 176:0.73 177:0.38 179:0.55 181:0.91 182:0.95 186:0.91 187:0.39 189:0.86 192:0.59 195:0.91 201:0.74 202:0.86 208:0.64 212:0.41 215:0.58 216:0.92 220:0.96 222:0.48 227:1.00 229:0.97 231:0.98 235:0.68 238:0.85 241:0.46 242:0.44 243:0.44 245:0.61 246:0.61 247:0.60 248:0.89 253:0.92 255:0.79 256:0.86 259:0.21 260:0.86 261:0.90 265:0.23 266:0.84 267:0.44 268:0.86 271:0.13 276:0.59 277:0.69 279:0.86 282:0.86 283:0.82 285:0.62 287:1.00 290:0.77 297:0.36 300:0.70 +2 1:0.74 11:0.64 12:0.96 17:0.92 27:0.72 28:0.73 30:0.68 32:0.80 34:0.47 36:0.85 39:0.27 43:0.93 66:0.79 69:0.07 70:0.21 74:0.79 77:0.70 81:0.98 83:0.80 85:0.85 91:0.82 98:0.97 101:0.83 104:0.54 108:0.06 114:0.98 122:0.57 123:0.06 126:0.54 127:0.77 129:0.05 131:0.61 135:0.44 144:0.57 145:0.44 146:0.48 147:0.54 149:0.80 150:0.99 154:0.92 155:0.67 157:0.93 159:0.17 161:0.89 165:0.92 166:0.99 167:0.76 172:0.41 173:0.50 176:0.73 177:0.38 178:0.55 179:0.91 181:0.91 182:0.95 192:0.59 195:0.52 201:0.74 212:0.89 215:0.96 222:0.48 227:0.61 229:0.61 231:0.98 232:0.80 235:0.05 241:0.77 242:0.63 243:0.80 246:1.00 247:0.35 253:0.77 265:0.97 266:0.17 267:0.36 271:0.13 276:0.29 282:0.88 287:0.61 290:0.98 297:0.36 299:0.88 300:0.18 +1 11:0.64 12:0.96 17:0.20 21:0.22 27:0.45 28:0.73 30:0.45 34:0.61 36:0.18 37:0.50 39:0.27 43:0.78 66:0.29 69:0.68 70:0.71 74:0.77 77:0.70 81:0.75 85:0.90 91:0.18 98:0.36 101:0.78 108:0.52 114:0.55 122:0.43 123:0.49 124:0.73 126:0.32 127:0.30 129:0.81 131:0.61 133:0.67 135:0.82 144:0.57 145:0.52 146:0.61 147:0.67 149:0.78 150:0.55 154:0.71 157:0.94 159:0.62 161:0.89 163:0.81 166:0.91 167:0.51 172:0.37 173:0.54 176:0.53 177:0.38 178:0.55 179:0.57 181:0.91 182:0.85 187:0.68 195:0.87 212:0.27 216:0.77 222:0.48 227:0.61 229:0.61 231:0.97 235:0.22 241:0.41 242:0.28 243:0.47 245:0.66 246:0.61 247:0.60 253:0.57 259:0.21 265:0.38 266:0.71 267:0.52 271:0.13 276:0.50 282:0.81 287:0.61 290:0.55 300:0.55 +1 6:0.91 7:0.76 8:0.81 9:0.80 11:0.64 12:0.96 17:0.36 20:0.97 21:0.30 27:0.21 28:0.73 30:0.27 34:0.61 36:0.78 37:0.74 39:0.27 41:0.82 43:0.58 55:0.84 66:0.06 69:0.91 70:0.96 74:0.65 78:0.84 81:0.67 83:0.73 85:0.94 91:0.73 96:0.79 98:0.29 100:0.94 101:0.75 104:0.84 106:0.81 108:0.90 111:0.89 120:0.90 123:0.96 124:0.97 126:0.32 127:0.85 129:0.98 131:0.99 133:0.96 135:0.17 140:0.94 144:0.57 146:0.55 147:0.73 149:0.81 150:0.48 151:0.70 152:0.98 153:0.93 154:0.50 155:0.67 157:0.94 159:0.94 161:0.89 162:0.59 164:0.86 166:0.90 167:0.48 169:0.90 172:0.54 173:0.65 177:0.05 179:0.18 181:0.91 182:0.94 186:0.84 187:0.39 189:0.92 191:0.77 192:0.59 202:0.83 206:0.81 208:0.64 212:0.22 215:0.72 216:0.95 222:0.48 227:1.00 229:0.97 230:0.79 231:0.98 233:0.76 235:0.31 238:0.97 241:0.24 242:0.70 243:0.10 245:0.90 246:0.61 247:0.67 248:0.74 253:0.76 255:0.79 256:0.84 257:0.65 259:0.21 260:0.90 261:0.96 265:0.28 266:0.95 267:0.91 268:0.84 271:0.13 276:0.93 283:0.82 285:0.62 287:1.00 297:0.36 300:0.94 +1 1:0.74 6:0.94 8:0.86 9:0.80 11:0.64 12:0.96 17:0.06 20:0.95 21:0.59 27:0.06 28:0.73 30:0.21 32:0.78 34:0.75 36:0.96 37:0.82 39:0.27 41:0.97 43:0.75 55:0.92 66:0.63 69:0.82 70:0.76 74:0.78 77:0.70 78:0.80 81:0.67 83:0.80 85:0.80 91:0.65 96:0.92 98:0.68 100:0.83 101:0.72 108:0.80 111:0.80 114:0.74 120:0.87 122:0.41 123:0.78 124:0.52 126:0.54 127:0.35 129:0.81 131:1.00 133:0.67 135:0.87 140:0.45 144:0.57 145:0.69 146:0.40 147:0.46 149:0.65 150:0.78 151:0.87 152:0.97 153:0.48 154:0.67 155:0.67 157:0.84 158:0.87 159:0.75 161:0.89 162:0.60 163:0.75 164:0.91 165:0.78 166:0.99 167:0.54 169:0.80 172:0.68 173:0.66 176:0.73 177:0.38 179:0.44 181:0.91 182:0.95 186:0.80 187:0.39 189:0.95 192:0.59 195:0.93 201:0.74 202:0.88 208:0.64 212:0.16 215:0.55 216:0.92 220:0.99 222:0.48 227:1.00 229:0.97 231:0.98 235:0.74 238:0.88 241:0.53 242:0.21 243:0.20 245:0.72 246:1.00 247:0.67 248:0.91 253:0.93 255:0.79 256:0.89 259:0.21 260:0.87 261:0.90 265:0.68 266:0.89 267:0.69 268:0.89 271:0.13 276:0.65 279:0.86 282:0.86 283:0.71 285:0.62 287:1.00 290:0.74 297:0.36 300:0.55 +1 1:0.68 7:0.66 9:0.75 12:0.51 17:0.98 18:0.65 21:0.59 27:0.72 28:0.89 29:0.71 30:0.72 31:0.96 32:0.67 34:0.45 36:0.34 39:0.51 43:0.21 44:0.88 48:0.91 55:0.45 64:0.77 66:0.44 69:0.92 70:0.41 71:0.94 74:0.78 76:0.98 77:0.99 79:0.96 81:0.64 83:0.91 86:0.68 91:0.42 96:0.79 98:0.75 104:0.91 106:0.81 108:0.84 114:0.71 120:0.88 123:0.84 124:0.60 126:0.54 127:0.89 129:0.05 133:0.43 135:0.51 137:0.96 139:0.74 140:0.97 145:0.89 146:0.93 147:0.87 149:0.59 150:0.62 151:0.82 153:0.46 154:0.14 155:0.65 159:0.78 161:0.96 162:0.85 164:0.86 167:0.59 172:0.94 173:0.90 176:0.63 177:0.38 179:0.15 181:0.48 187:0.21 190:0.89 192:0.87 195:0.89 196:0.93 201:0.67 202:0.78 206:0.81 208:0.64 212:0.92 215:0.55 216:0.18 220:0.62 222:0.35 230:0.68 233:0.66 235:0.97 241:0.53 242:0.70 243:0.50 244:0.83 245:0.99 247:0.86 248:0.74 253:0.82 255:0.78 256:0.85 257:0.65 259:0.21 260:0.88 265:0.75 266:0.71 267:0.44 268:0.84 271:0.54 276:0.95 281:0.91 282:0.75 283:0.67 284:0.96 285:0.62 290:0.71 297:0.36 300:0.84 +1 1:0.69 7:0.70 8:0.79 9:0.75 11:0.75 12:0.51 17:0.97 18:0.81 21:0.40 27:0.67 28:0.89 29:0.71 30:0.74 31:0.89 32:0.67 34:0.85 36:0.66 37:0.52 39:0.51 43:0.93 44:0.88 45:0.97 48:0.91 55:0.52 64:0.77 66:0.89 69:0.30 70:0.42 71:0.94 74:0.92 76:0.85 77:0.96 79:0.89 81:0.83 83:0.91 85:0.72 86:0.81 91:0.44 96:0.72 98:0.97 99:0.99 101:0.87 104:0.95 106:0.81 108:0.73 114:0.78 117:0.86 120:0.59 123:0.71 124:0.37 126:0.54 127:0.97 129:0.59 133:0.46 135:0.79 137:0.89 139:0.83 140:0.45 145:0.58 146:0.53 147:0.59 149:0.91 150:0.88 151:0.61 153:0.48 154:0.93 155:0.66 159:0.95 161:0.96 162:0.86 164:0.56 165:0.70 167:0.54 172:1.00 173:0.08 176:0.63 177:0.70 179:0.08 181:0.48 187:0.39 190:0.89 191:0.76 192:0.87 195:0.99 196:0.91 201:0.68 202:0.36 204:0.85 206:0.81 208:0.64 212:0.92 215:0.67 216:0.51 220:0.82 222:0.35 230:0.73 232:0.85 233:0.76 235:0.95 241:0.37 242:0.74 243:0.11 245:1.00 247:0.91 248:0.59 253:0.77 255:0.78 256:0.45 259:0.21 260:0.60 265:0.97 266:0.75 267:0.28 268:0.46 271:0.54 276:1.00 281:0.91 282:0.75 283:0.67 284:0.88 285:0.62 290:0.78 297:0.36 300:0.84 +1 1:0.67 8:0.85 9:0.75 12:0.51 17:0.86 18:0.60 21:0.47 27:0.72 28:0.89 29:0.71 30:0.91 31:0.96 34:0.18 36:0.43 39:0.51 43:0.10 44:0.86 48:0.91 55:0.42 64:0.77 66:0.17 69:0.82 70:0.30 71:0.94 74:0.57 76:0.98 77:0.99 79:0.96 81:0.45 83:0.60 86:0.65 91:0.68 96:0.87 97:0.89 98:0.64 108:0.40 114:0.66 123:0.64 124:0.51 126:0.44 127:0.55 129:0.05 133:0.37 135:0.54 137:0.96 138:0.97 139:0.76 140:0.87 145:0.82 146:0.57 147:0.55 149:0.37 150:0.48 151:0.91 153:0.72 154:0.18 155:0.58 158:0.81 159:0.37 161:0.96 162:0.69 167:0.84 172:0.77 173:0.92 176:0.59 177:0.05 179:0.38 181:0.48 190:0.77 192:0.51 195:0.64 196:0.93 201:0.62 202:0.73 208:0.54 212:0.86 216:0.09 219:0.94 220:0.62 222:0.35 232:0.75 235:0.74 241:0.85 242:0.76 243:0.67 244:0.83 245:0.89 247:0.74 248:0.81 253:0.84 254:0.96 255:0.73 256:0.71 257:0.84 259:0.21 265:0.56 266:0.38 267:0.36 268:0.75 271:0.54 276:0.73 279:0.82 281:0.86 282:0.73 284:0.96 285:0.56 286:0.99 290:0.66 292:0.88 300:0.55 +1 1:0.66 6:0.84 7:0.69 8:0.82 9:0.76 11:0.45 12:0.51 17:0.90 20:0.90 21:0.54 27:0.18 28:0.89 29:0.71 30:0.75 31:0.98 32:0.65 34:0.33 36:0.23 37:0.63 39:0.51 43:0.56 45:0.96 55:0.52 66:0.74 69:0.38 70:0.28 71:0.94 74:0.86 76:0.97 77:0.96 78:0.95 79:0.98 81:0.65 83:0.60 91:0.96 96:1.00 98:0.70 99:0.83 100:0.94 101:0.52 108:0.30 111:0.93 114:0.72 120:0.98 122:0.51 123:0.28 124:0.43 126:0.44 127:0.65 129:0.59 133:0.47 135:0.24 137:0.98 140:0.80 145:0.85 146:0.67 147:0.72 149:0.91 150:0.58 151:0.99 152:0.93 153:0.97 154:0.45 155:0.58 159:0.44 161:0.96 162:0.64 164:0.96 165:0.70 167:0.83 169:0.89 172:0.92 173:0.43 175:0.75 176:0.62 177:0.38 178:0.42 179:0.24 181:0.48 186:0.95 189:0.89 190:0.95 191:0.94 192:0.59 195:0.68 201:0.61 202:0.97 204:0.81 208:0.54 212:0.92 215:0.58 216:0.42 222:0.35 230:0.71 233:0.76 235:0.80 238:0.85 241:0.83 242:0.53 243:0.77 244:0.93 245:0.94 247:0.67 248:0.99 253:1.00 255:0.74 256:0.98 257:0.65 259:0.21 260:0.98 261:0.92 265:0.70 266:0.54 267:0.59 268:0.98 271:0.54 276:0.86 277:0.69 282:0.74 283:0.67 284:0.97 285:0.62 290:0.72 297:0.36 300:0.55 +1 7:0.69 8:0.94 9:0.79 11:0.45 12:0.51 17:0.88 21:0.78 27:0.72 28:0.89 29:0.71 30:0.55 31:0.95 32:0.74 34:0.61 36:0.68 37:0.93 39:0.51 43:0.56 44:0.88 45:0.73 55:0.45 66:0.52 69:0.19 70:0.71 71:0.94 74:0.62 76:0.85 77:0.96 79:0.94 83:0.60 91:0.88 96:0.84 98:0.56 101:0.52 108:0.60 120:0.74 123:0.58 124:0.52 127:0.46 129:0.59 133:0.47 135:0.47 137:0.95 139:0.67 140:0.45 145:0.85 146:0.32 147:0.39 149:0.38 151:0.91 153:0.72 154:0.45 155:0.66 159:0.30 161:0.96 162:0.65 164:0.64 167:0.85 172:0.48 173:0.37 175:0.75 177:0.38 179:0.69 181:0.48 190:0.89 191:0.89 192:0.87 195:0.61 196:0.90 202:0.55 204:0.81 208:0.64 212:0.69 216:0.04 222:0.35 230:0.71 233:0.66 241:0.89 242:0.31 243:0.40 244:0.93 245:0.69 247:0.60 248:0.81 253:0.81 255:0.79 256:0.45 257:0.65 259:0.21 260:0.74 265:0.58 266:0.54 267:0.28 268:0.57 271:0.54 276:0.45 277:0.69 282:0.86 284:0.91 285:0.62 300:0.55 +1 1:0.65 7:0.70 8:0.81 9:0.73 11:0.45 12:0.51 17:0.92 18:0.67 21:0.40 27:0.72 28:0.89 29:0.71 30:0.87 31:0.89 32:0.67 34:0.18 36:0.75 39:0.51 41:0.82 43:0.37 44:0.88 45:0.77 48:0.91 55:0.45 66:0.54 69:0.93 70:0.39 71:0.94 74:0.73 77:0.70 79:0.89 81:0.67 83:0.60 86:0.64 91:0.72 96:0.74 98:0.62 99:0.83 101:0.52 104:0.75 108:0.38 114:0.76 120:0.61 123:0.36 124:0.70 126:0.44 127:0.89 129:0.05 133:0.77 135:0.30 137:0.89 139:0.74 140:0.80 145:0.70 146:0.67 147:0.88 149:0.63 150:0.60 151:0.62 153:0.48 154:0.28 155:0.65 159:0.77 161:0.96 162:0.86 164:0.71 165:0.70 167:0.84 172:0.93 173:0.92 176:0.62 177:0.38 179:0.18 181:0.48 192:0.86 195:0.87 196:0.90 201:0.61 202:0.56 204:0.85 208:0.64 212:0.86 215:0.67 216:0.01 220:0.93 222:0.35 230:0.73 233:0.76 235:0.60 241:0.85 242:0.63 243:0.62 244:0.93 245:0.96 247:0.84 248:0.60 253:0.70 255:0.73 256:0.64 257:0.65 259:0.21 260:0.62 265:0.63 266:0.84 267:0.18 268:0.65 271:0.54 276:0.93 277:0.69 281:0.91 282:0.75 284:0.89 285:0.62 290:0.76 297:0.36 300:0.84 +1 1:0.57 8:0.84 12:0.51 17:0.85 21:0.74 27:0.72 28:0.89 29:0.71 30:0.45 34:0.25 36:0.57 37:0.70 39:0.51 43:0.11 44:0.88 48:0.91 55:0.59 64:0.77 66:0.69 69:0.48 70:0.25 71:0.94 74:0.48 76:0.98 77:0.70 81:0.33 91:0.88 96:0.72 98:0.82 108:0.37 114:0.63 122:0.77 123:0.35 126:0.44 127:0.31 129:0.05 135:0.37 139:0.66 140:0.90 145:0.70 146:0.47 147:0.53 149:0.11 150:0.37 151:0.51 153:0.71 154:0.09 155:0.46 159:0.17 161:0.96 162:0.56 163:0.75 167:0.86 172:0.21 173:0.78 175:0.91 176:0.62 177:0.05 178:0.50 179:0.96 181:0.48 190:0.98 191:0.87 192:0.44 195:0.55 201:0.54 202:0.73 208:0.54 212:0.61 216:0.02 220:0.93 222:0.35 235:0.95 241:0.94 242:0.82 243:0.73 244:0.97 247:0.12 248:0.52 253:0.53 256:0.68 257:0.84 259:0.21 265:0.82 266:0.17 267:0.65 268:0.72 271:0.54 276:0.21 277:0.87 281:0.86 282:0.73 285:0.47 290:0.63 300:0.18 +1 1:0.69 7:0.69 11:0.75 12:0.51 17:0.97 18:0.75 21:0.59 27:0.67 28:0.89 29:0.71 30:0.53 32:0.67 34:0.89 36:0.39 37:0.35 39:0.51 43:0.74 44:0.92 45:0.92 48:0.97 55:0.59 64:0.77 66:0.98 69:0.36 70:0.54 71:0.94 74:0.86 76:0.94 77:1.00 81:0.76 83:0.91 85:0.72 86:0.73 91:0.22 97:0.98 98:0.97 99:0.99 101:0.87 104:0.86 106:0.81 108:0.28 114:0.71 117:0.86 122:0.51 123:0.27 126:0.54 127:0.73 129:0.05 135:0.65 139:0.79 145:0.93 146:0.94 147:0.95 149:0.78 150:0.74 154:0.65 155:0.58 158:0.75 159:0.60 161:0.96 165:0.70 167:0.47 172:0.95 173:0.31 176:0.63 177:0.70 178:0.55 179:0.21 181:0.48 187:0.21 191:0.70 192:0.57 195:0.78 201:0.67 204:0.85 206:0.81 208:0.64 212:0.89 215:0.55 216:0.18 222:0.35 228:0.99 230:0.71 232:0.91 233:0.76 235:0.98 241:0.03 242:0.36 243:0.98 247:0.82 253:0.64 259:0.21 265:0.97 266:0.27 267:0.36 271:0.54 276:0.90 279:0.81 281:0.86 282:0.75 283:0.82 290:0.71 297:0.36 300:0.43 +1 1:0.64 6:0.84 7:0.69 8:0.75 9:0.76 11:0.45 12:0.51 17:0.91 20:0.89 21:0.47 27:0.18 28:0.89 29:0.71 30:0.75 31:0.99 32:0.65 34:0.31 36:0.30 37:0.63 39:0.51 43:0.56 45:0.99 55:0.52 66:0.61 69:0.36 70:0.29 71:0.94 74:0.95 76:0.85 77:0.89 78:0.93 79:0.99 81:0.65 83:0.60 91:0.92 96:0.99 97:0.89 98:0.85 99:0.83 100:0.73 101:0.52 104:0.82 106:0.81 108:0.72 111:0.89 114:0.74 117:0.86 120:0.99 122:0.51 123:0.70 124:0.51 126:0.44 127:0.76 129:0.59 133:0.47 135:0.47 137:0.99 140:0.45 145:0.79 146:0.67 147:0.72 149:0.97 150:0.57 151:0.98 152:0.93 153:0.48 154:0.45 155:0.58 158:0.75 159:0.72 161:0.96 162:0.69 164:0.97 165:0.70 167:0.70 169:0.89 172:0.98 173:0.24 175:0.75 176:0.62 177:0.38 179:0.12 181:0.48 186:0.92 187:0.21 189:0.81 190:0.95 191:0.90 192:0.59 195:0.86 201:0.60 202:0.96 204:0.84 206:0.81 208:0.54 212:0.93 215:0.63 216:0.42 220:0.62 222:0.35 230:0.71 232:0.88 233:0.76 235:0.68 238:0.81 241:0.69 242:0.53 243:0.38 244:0.93 245:1.00 247:0.67 248:0.99 253:1.00 255:0.74 256:0.97 257:0.65 259:0.21 260:0.99 261:0.92 265:0.85 266:0.54 267:0.69 268:0.97 271:0.54 276:0.97 277:0.69 279:0.77 282:0.74 283:0.67 284:0.99 285:0.62 290:0.74 297:0.36 300:0.84 +2 1:0.68 6:0.89 7:0.69 8:0.79 9:0.73 11:0.57 12:0.51 17:0.97 18:0.87 20:0.95 21:0.47 27:0.48 28:0.89 29:0.71 30:0.54 31:0.89 32:0.67 34:0.20 36:0.52 37:0.56 39:0.51 43:0.75 44:0.88 45:0.77 55:0.45 64:0.77 66:0.97 69:0.32 70:0.53 71:0.94 74:0.85 76:0.98 77:0.96 78:0.98 79:0.88 81:0.70 83:0.60 86:0.91 91:0.73 96:0.72 98:0.92 99:0.83 100:0.91 101:0.87 108:0.25 111:0.95 114:0.76 120:0.59 123:0.24 126:0.54 127:0.54 129:0.05 135:0.42 137:0.89 139:0.88 140:0.45 145:0.84 146:0.88 147:0.90 149:0.63 150:0.61 151:0.57 152:0.94 153:0.48 154:0.66 155:0.65 159:0.46 161:0.96 162:0.81 164:0.73 165:0.70 167:0.84 169:0.88 172:0.92 173:0.35 176:0.63 177:0.70 179:0.24 181:0.48 186:0.97 189:0.92 190:0.89 191:0.89 192:0.86 195:0.70 196:0.92 201:0.65 202:0.62 204:0.80 208:0.64 212:0.92 215:0.63 216:0.07 220:0.93 222:0.35 230:0.71 232:0.72 233:0.76 235:0.74 238:0.83 241:0.85 242:0.36 243:0.97 244:0.61 247:0.94 248:0.58 253:0.72 255:0.72 256:0.64 259:0.21 260:0.60 261:0.94 265:0.92 266:0.47 267:0.28 268:0.67 271:0.54 276:0.86 282:0.75 284:0.94 285:0.62 290:0.76 297:0.36 300:0.55 +2 1:0.62 6:0.85 7:0.70 8:0.78 9:0.70 11:0.45 12:0.51 17:0.88 18:0.78 20:0.85 21:0.71 27:0.42 28:0.89 29:0.71 30:0.76 31:0.86 32:0.64 34:0.22 36:0.69 37:0.62 39:0.51 43:0.63 44:0.88 45:0.73 48:0.97 55:0.52 64:0.77 66:0.54 69:0.48 70:0.45 71:0.94 74:0.73 76:0.85 77:0.89 78:0.94 79:0.86 81:0.63 83:0.60 86:0.78 87:0.98 91:0.91 96:0.78 98:0.59 99:0.83 100:0.89 101:0.52 108:0.37 111:0.92 114:0.66 120:0.54 123:0.35 124:0.64 126:0.54 127:0.79 129:0.05 133:0.60 135:0.26 137:0.86 139:0.80 140:0.45 145:0.76 146:0.78 147:0.81 149:0.63 150:0.52 151:0.48 152:0.82 153:0.48 154:0.53 155:0.57 159:0.70 161:0.96 162:0.56 164:0.74 165:0.70 167:0.86 169:0.85 172:0.90 173:0.36 176:0.63 177:0.70 179:0.22 181:0.48 186:0.94 190:0.98 191:0.90 192:0.56 195:0.85 196:0.88 201:0.61 202:0.74 204:0.84 208:0.64 212:0.91 215:0.48 216:0.10 220:1.00 222:0.35 230:0.73 232:0.79 233:0.76 235:0.97 241:0.95 242:0.76 243:0.62 244:0.83 245:0.95 247:0.84 248:0.49 253:0.78 255:0.69 256:0.73 259:0.21 260:0.55 261:0.87 265:0.60 266:0.71 267:0.19 268:0.73 271:0.54 276:0.88 281:0.91 282:0.73 284:0.95 285:0.62 290:0.66 297:0.36 300:0.94 +0 12:0.51 17:0.62 18:0.67 21:0.78 27:0.45 28:0.89 29:0.71 30:0.76 34:0.89 36:0.25 37:0.50 39:0.51 43:0.30 55:0.42 66:0.97 69:0.77 70:0.27 71:0.94 74:0.79 86:0.70 91:0.09 98:0.55 108:0.60 122:0.51 123:0.58 127:0.16 129:0.05 135:0.30 139:0.67 145:0.50 146:0.67 147:0.72 149:0.85 154:0.25 159:0.26 161:0.96 167:0.49 172:0.92 173:0.75 177:0.70 178:0.55 179:0.11 181:0.48 187:0.68 212:0.94 216:0.77 222:0.35 235:0.88 241:0.12 242:0.61 243:0.97 244:0.83 247:0.47 253:0.55 254:0.86 259:0.21 265:0.56 266:0.17 267:0.79 271:0.54 276:0.86 300:0.25 +0 1:0.64 7:0.70 8:0.78 9:0.71 11:0.45 12:0.51 17:0.88 18:0.91 20:0.81 21:0.76 27:0.63 28:0.89 29:0.71 30:0.61 31:0.87 34:0.71 36:0.41 37:0.70 39:0.51 43:0.16 45:0.66 48:0.97 55:0.45 64:0.77 66:0.59 69:0.39 70:0.66 71:0.94 74:0.79 76:0.97 78:0.92 79:0.86 81:0.61 83:0.91 86:0.87 91:0.27 96:0.69 98:0.46 99:0.83 100:0.73 101:0.52 104:0.86 106:0.81 108:0.30 111:0.89 114:0.63 117:0.86 120:0.55 123:0.29 124:0.69 126:0.54 127:0.90 129:0.05 133:0.74 135:0.39 137:0.87 139:0.85 140:0.45 145:0.40 146:0.74 147:0.78 149:0.41 150:0.51 151:0.50 152:0.78 153:0.48 154:0.50 155:0.58 159:0.78 161:0.96 162:0.62 164:0.56 165:0.70 167:0.52 169:0.72 172:0.94 173:0.23 176:0.63 177:0.70 179:0.18 181:0.48 186:0.92 187:0.39 190:0.89 192:0.56 196:0.89 201:0.62 202:0.50 204:0.83 206:0.81 208:0.64 212:0.91 215:0.46 216:0.28 220:0.96 222:0.35 230:0.73 232:0.91 233:0.76 235:1.00 241:0.30 242:0.55 243:0.67 244:0.83 245:0.95 247:0.93 248:0.50 253:0.59 254:0.80 255:0.70 256:0.45 259:0.21 260:0.55 261:0.84 265:0.48 266:0.54 267:0.69 268:0.46 271:0.54 276:0.90 281:0.91 284:0.88 285:0.62 290:0.63 297:0.36 300:0.84 +1 1:0.69 7:0.69 8:0.74 11:0.75 12:0.51 17:0.95 18:0.83 21:0.54 22:0.93 27:0.43 28:0.89 29:0.71 30:0.88 32:0.66 34:0.92 36:0.36 37:0.40 39:0.51 43:0.85 44:0.88 45:0.83 47:0.93 48:0.91 55:0.52 64:0.77 66:0.98 69:0.32 70:0.28 71:0.94 74:0.78 77:0.96 81:0.75 83:0.60 85:0.72 86:0.83 91:0.02 97:0.99 98:0.99 99:0.95 101:0.87 104:0.91 106:0.81 108:0.25 114:0.69 117:0.86 123:0.24 126:0.54 127:0.87 129:0.05 135:0.32 139:0.85 145:0.94 146:0.92 147:0.93 149:0.83 150:0.68 154:0.82 155:0.56 158:0.76 159:0.54 161:0.96 165:0.70 167:0.47 172:0.95 173:0.33 176:0.62 177:0.70 178:0.55 179:0.23 181:0.48 187:0.21 192:0.55 195:0.70 201:0.67 204:0.81 206:0.81 208:0.64 212:0.89 215:0.55 216:0.36 219:0.91 222:0.35 230:0.71 232:0.94 233:0.76 235:0.95 241:0.03 242:0.58 243:0.98 247:0.91 253:0.61 262:0.93 265:0.99 266:0.27 267:0.52 271:0.54 276:0.90 279:0.82 281:0.86 282:0.75 283:0.67 290:0.69 292:0.88 297:0.36 300:0.33 +1 1:0.59 6:0.82 7:0.66 8:0.75 9:0.72 11:0.45 12:0.51 17:0.98 18:0.73 20:0.84 21:0.77 27:0.52 28:0.89 29:0.71 30:0.27 31:0.87 32:0.64 34:0.28 36:0.36 39:0.51 43:0.57 45:0.73 48:0.91 55:0.52 64:0.77 66:0.77 69:0.73 70:0.83 71:0.94 74:0.67 76:0.85 77:0.98 78:0.95 79:0.87 81:0.59 83:0.91 86:0.70 91:0.63 96:0.70 98:0.91 99:0.83 100:0.88 101:0.52 104:0.79 108:0.56 111:0.92 114:0.63 120:0.56 123:0.54 124:0.41 126:0.54 127:0.94 129:0.05 133:0.43 135:0.28 137:0.87 138:0.95 139:0.69 140:0.45 145:0.67 146:0.88 147:0.31 149:0.47 150:0.47 151:0.55 152:0.84 153:0.72 154:0.15 155:0.64 159:0.53 161:0.96 162:0.86 164:0.63 165:0.70 167:0.82 169:0.82 172:0.85 173:0.76 176:0.63 177:0.38 179:0.39 181:0.48 186:0.95 190:0.89 191:0.76 192:0.86 195:0.69 196:0.88 201:0.58 202:0.49 208:0.64 212:0.82 215:0.44 216:0.40 220:0.91 222:0.35 228:0.99 230:0.68 233:0.76 235:1.00 241:0.80 242:0.38 243:0.20 244:0.83 245:0.85 247:0.88 248:0.54 253:0.55 254:0.97 255:0.71 256:0.45 257:0.65 259:0.21 260:0.56 261:0.89 265:0.91 266:0.27 267:0.52 268:0.57 271:0.54 276:0.78 281:0.91 282:0.73 284:0.91 285:0.62 286:0.99 290:0.63 297:0.36 298:0.99 300:0.70 +2 1:0.68 6:0.84 7:0.70 8:0.72 9:0.74 12:0.51 17:0.77 20:0.89 21:0.22 25:0.88 27:0.37 28:0.89 29:0.71 30:0.91 31:0.90 32:0.65 34:0.17 36:0.41 37:0.62 39:0.51 41:0.82 43:0.09 44:0.86 48:0.99 55:0.52 64:0.77 66:0.26 69:0.65 70:0.28 71:0.94 74:0.60 76:0.94 77:0.89 78:0.98 79:0.90 81:0.80 83:0.60 91:0.86 96:0.74 98:0.34 100:0.96 104:0.76 108:0.49 111:0.96 114:0.90 120:0.62 123:0.72 124:0.74 126:0.54 127:0.79 129:0.89 133:0.78 135:0.37 137:0.90 139:0.69 140:0.45 145:0.98 146:0.54 147:0.60 149:0.39 150:0.68 151:0.64 152:0.84 153:0.77 154:0.08 155:0.65 159:0.76 161:0.96 162:0.86 164:0.69 165:0.93 167:0.85 169:0.94 172:0.74 173:0.51 175:0.91 176:0.73 177:0.05 179:0.38 181:0.48 186:0.97 189:0.86 191:0.87 192:0.87 195:0.89 196:0.89 201:0.66 202:0.54 204:0.84 208:0.64 212:0.85 215:0.79 216:0.03 220:0.82 222:0.35 228:0.99 230:0.73 233:0.76 235:0.68 238:0.86 241:0.89 242:0.70 243:0.57 244:0.97 245:0.84 247:0.39 248:0.61 253:0.71 255:0.73 256:0.58 257:0.84 259:0.21 260:0.63 261:0.93 265:0.33 266:0.75 267:0.23 268:0.63 271:0.54 276:0.70 277:0.87 281:0.91 282:0.74 284:0.93 285:0.62 290:0.90 297:0.36 300:0.70 +0 1:0.61 11:0.45 12:0.51 17:0.57 18:0.62 21:0.68 27:0.45 28:0.89 29:0.71 30:0.45 34:0.84 36:0.19 37:0.50 39:0.51 43:0.35 45:0.77 55:0.52 66:0.61 69:0.70 70:0.59 71:0.94 74:0.59 77:0.70 81:0.56 83:0.60 86:0.64 91:0.10 98:0.45 99:0.83 101:0.52 108:0.53 114:0.66 122:0.77 123:0.51 124:0.50 126:0.44 127:0.39 129:0.05 133:0.43 135:0.70 139:0.62 145:0.50 146:0.62 147:0.67 149:0.33 150:0.46 154:0.27 155:0.48 159:0.56 161:0.96 165:0.70 167:0.60 172:0.59 173:0.64 176:0.62 177:0.70 178:0.55 179:0.57 181:0.48 187:0.68 192:0.46 195:0.79 201:0.57 208:0.54 212:0.73 215:0.49 216:0.77 222:0.35 235:0.88 241:0.55 242:0.41 243:0.67 244:0.93 245:0.74 247:0.67 253:0.53 259:0.21 265:0.47 266:0.54 267:0.36 271:0.54 276:0.41 282:0.73 290:0.66 297:0.36 300:0.55 +1 1:0.63 8:0.79 9:0.70 11:0.54 12:0.51 17:0.93 18:0.75 21:0.59 27:0.57 28:0.89 29:0.71 30:0.72 31:0.86 34:0.80 36:0.55 37:0.68 39:0.51 43:0.33 44:0.92 45:0.73 55:0.45 64:0.77 66:0.46 69:0.43 70:0.56 71:0.94 74:0.72 76:0.85 77:0.89 79:0.86 81:0.64 83:0.60 86:0.74 87:0.98 91:0.44 96:0.68 98:0.61 99:0.83 101:0.52 108:0.79 114:0.71 120:0.54 123:0.77 124:0.75 126:0.54 127:0.72 129:0.59 133:0.73 135:0.44 137:0.86 139:0.82 140:0.87 145:0.85 146:0.50 147:0.56 149:0.57 150:0.53 151:0.49 153:0.48 154:0.25 155:0.55 159:0.70 161:0.96 162:0.50 164:0.56 165:0.70 167:0.54 172:0.86 173:0.31 176:0.63 177:0.70 178:0.48 179:0.24 181:0.48 187:0.21 190:0.89 191:0.85 192:0.50 195:0.84 196:0.88 201:0.61 202:0.61 204:0.82 208:0.64 212:0.88 215:0.55 216:0.14 220:0.97 222:0.35 232:0.77 235:0.80 241:0.37 242:0.72 243:0.37 244:0.61 245:0.92 247:0.86 248:0.49 253:0.60 255:0.69 256:0.45 260:0.55 265:0.62 266:0.75 267:0.23 268:0.46 271:0.54 276:0.87 281:0.86 282:0.80 284:0.88 285:0.62 290:0.71 297:0.36 300:0.70 +1 1:0.65 8:0.84 11:0.54 12:0.51 17:0.96 18:0.79 21:0.47 27:0.57 28:0.89 29:0.71 30:0.62 34:0.70 36:0.60 37:0.68 39:0.51 43:0.11 44:0.88 45:0.73 48:0.98 55:0.45 64:0.77 66:0.59 69:0.41 70:0.51 71:0.94 74:0.72 76:0.98 77:0.98 81:0.67 83:0.60 86:0.73 87:0.98 91:0.23 98:0.74 99:0.83 101:0.52 108:0.79 114:0.76 123:0.77 124:0.64 126:0.54 127:0.77 129:0.59 133:0.61 135:0.47 139:0.76 145:0.72 146:0.50 147:0.56 149:0.48 150:0.57 154:0.17 155:0.56 159:0.67 161:0.96 165:0.70 167:0.50 172:0.92 173:0.32 176:0.63 177:0.70 178:0.55 179:0.20 181:0.48 187:0.21 192:0.50 195:0.81 201:0.62 202:0.50 204:0.84 208:0.64 212:0.90 215:0.63 216:0.14 222:0.35 232:0.77 235:0.68 241:0.18 242:0.60 243:0.32 244:0.93 245:0.96 247:0.86 253:0.61 254:0.74 265:0.74 266:0.75 267:0.19 271:0.54 276:0.91 281:0.86 282:0.74 290:0.76 297:0.36 300:0.70 +2 1:0.68 7:0.69 8:0.80 9:0.72 11:0.75 12:0.51 17:0.79 18:0.74 21:0.40 27:0.71 28:0.89 29:0.71 30:0.55 32:0.67 34:0.78 36:0.61 37:0.56 39:0.51 43:0.90 44:0.88 45:0.93 55:0.52 66:0.99 69:0.19 70:0.55 71:0.94 74:0.89 76:0.94 77:0.99 81:0.59 83:0.60 85:0.72 86:0.74 91:0.37 96:0.73 97:0.94 98:0.96 101:0.87 104:0.79 106:0.81 108:0.15 114:0.72 117:0.86 120:0.61 123:0.15 126:0.44 127:0.73 129:0.05 135:0.49 138:0.95 139:0.76 140:0.45 145:0.77 146:0.92 147:0.94 149:0.88 150:0.60 151:0.65 153:0.48 154:0.89 155:0.58 158:0.75 159:0.55 161:0.96 162:0.86 164:0.68 167:0.50 172:0.97 173:0.23 176:0.60 177:0.70 179:0.17 181:0.48 187:0.21 190:0.95 192:0.57 195:0.72 201:0.63 202:0.53 204:0.85 206:0.81 208:0.54 212:0.93 215:0.63 216:0.05 220:0.91 222:0.35 230:0.71 232:0.70 233:0.66 235:0.68 241:0.15 242:0.70 243:0.99 247:0.76 248:0.65 253:0.75 255:0.71 256:0.58 259:0.21 260:0.61 265:0.96 266:0.27 267:0.18 268:0.62 271:0.54 276:0.93 279:0.81 282:0.75 283:0.82 285:0.56 286:0.99 290:0.72 297:0.36 298:0.99 300:0.70 +0 1:0.69 7:0.69 8:0.72 11:0.45 12:0.51 17:0.90 18:0.74 21:0.13 27:0.32 28:0.89 29:0.71 30:0.76 32:0.67 34:0.28 36:0.34 37:0.64 39:0.51 43:0.40 44:0.88 45:0.83 55:0.52 64:0.77 66:0.93 69:0.15 70:0.42 71:0.94 74:0.77 76:0.94 77:0.99 81:0.83 83:0.60 86:0.75 91:0.37 98:0.93 99:0.83 101:0.52 108:0.12 114:0.88 117:0.86 122:0.77 123:0.12 126:0.54 127:0.59 129:0.05 135:0.51 139:0.76 145:0.97 146:0.73 147:0.77 149:0.42 150:0.86 154:0.54 155:0.56 159:0.29 161:0.96 165:0.70 167:0.52 172:0.82 173:0.37 176:0.63 177:0.70 178:0.55 179:0.43 181:0.48 187:0.21 191:0.70 192:0.55 195:0.60 201:0.68 204:0.80 208:0.64 212:0.88 215:0.84 216:0.16 222:0.35 230:0.71 232:0.86 233:0.76 235:0.52 241:0.27 242:0.70 243:0.94 244:0.83 247:0.79 253:0.68 254:0.74 259:0.21 265:0.93 266:0.27 267:0.23 271:0.54 276:0.72 282:0.75 290:0.88 297:0.36 300:0.55 +1 1:0.59 6:0.82 7:0.66 8:0.70 11:0.45 12:0.51 17:0.98 18:0.73 20:0.86 21:0.77 27:0.52 28:0.89 29:0.71 30:0.27 32:0.64 34:0.42 36:0.32 39:0.51 43:0.57 45:0.73 48:0.91 55:0.52 64:0.77 66:0.77 69:0.73 70:0.83 71:0.94 74:0.67 76:0.85 77:0.98 78:0.95 81:0.59 83:0.60 86:0.70 91:0.51 98:0.91 99:0.83 100:0.73 101:0.52 104:0.79 108:0.56 111:0.91 114:0.63 123:0.54 124:0.41 126:0.54 127:0.94 129:0.05 133:0.43 135:0.30 139:0.69 145:0.67 146:0.88 147:0.31 149:0.47 150:0.47 152:0.84 154:0.15 155:0.47 159:0.53 161:0.96 165:0.70 167:0.75 169:0.82 172:0.85 173:0.76 176:0.63 177:0.38 178:0.55 179:0.39 181:0.48 186:0.94 187:0.21 191:0.80 192:0.45 195:0.69 201:0.58 208:0.64 212:0.82 215:0.44 216:0.40 222:0.35 228:0.99 230:0.68 233:0.76 235:1.00 241:0.74 242:0.38 243:0.20 244:0.83 245:0.85 247:0.88 253:0.54 254:0.97 257:0.65 259:0.21 261:0.89 265:0.91 266:0.27 267:0.44 271:0.54 276:0.78 281:0.91 282:0.73 290:0.63 297:0.36 300:0.70 +1 1:0.58 6:0.82 7:0.69 8:0.71 9:0.71 11:0.45 12:0.51 17:0.88 18:0.64 20:0.89 21:0.64 27:0.51 28:0.89 29:0.71 30:0.16 32:0.65 34:0.78 36:0.56 37:0.33 39:0.51 43:0.39 45:0.92 55:0.45 66:0.88 69:0.83 70:0.88 71:0.94 74:0.85 76:0.85 77:0.70 78:0.87 81:0.38 86:0.67 91:0.18 96:0.73 98:0.90 100:0.90 101:0.52 104:0.98 106:0.81 108:0.67 111:0.87 114:0.67 117:0.86 120:0.61 122:0.51 123:0.65 124:0.38 126:0.44 127:0.63 129:0.05 133:0.43 135:0.97 139:0.65 140:0.80 145:0.70 146:0.98 147:0.37 149:0.65 150:0.43 151:0.61 152:0.90 153:0.48 154:0.25 155:0.57 158:0.75 159:0.80 161:0.96 162:0.86 164:0.76 167:0.48 169:0.90 172:0.98 173:0.69 176:0.62 177:0.38 179:0.13 181:0.48 186:0.87 187:0.21 190:0.95 192:0.56 195:0.91 201:0.55 202:0.63 204:0.82 206:0.81 208:0.54 212:0.88 215:0.53 216:0.85 220:0.97 222:0.35 230:0.71 232:0.92 233:0.66 235:0.80 241:0.10 242:0.39 243:0.11 244:0.83 245:0.97 247:0.79 248:0.60 253:0.72 254:0.84 255:0.70 256:0.68 257:0.65 259:0.21 260:0.61 261:0.88 265:0.90 266:0.54 267:1.00 268:0.71 271:0.54 276:0.96 279:0.82 282:0.74 283:0.82 285:0.56 290:0.67 297:0.36 300:0.84 +1 1:0.69 6:0.89 7:0.69 8:0.75 9:0.76 11:0.45 12:0.51 17:0.82 18:0.91 20:0.90 21:0.13 27:0.32 28:0.89 29:0.71 30:0.11 31:0.94 32:0.67 34:0.18 36:0.96 37:0.64 39:0.51 43:0.40 44:0.88 45:0.99 55:0.71 64:0.77 66:0.10 69:0.15 70:0.93 71:0.94 74:0.89 76:0.94 77:0.99 78:0.98 79:0.94 81:0.83 83:0.60 86:0.90 91:0.79 96:0.92 98:0.51 99:0.83 100:0.96 101:0.52 108:0.94 111:0.96 114:0.88 120:0.87 123:0.99 124:0.94 126:0.54 127:1.00 129:0.89 133:0.93 135:0.34 137:0.94 138:0.98 139:0.89 140:0.95 145:0.97 146:0.68 147:0.73 149:0.86 150:0.86 151:0.72 152:0.93 153:0.48 154:0.54 155:0.65 159:0.98 161:0.96 162:0.81 164:0.90 165:0.70 167:0.81 169:0.92 172:0.98 173:0.05 176:0.63 177:0.70 179:0.09 181:0.48 186:0.98 189:0.91 190:0.95 191:0.90 192:0.87 195:1.00 196:0.96 201:0.68 202:0.84 204:0.80 208:0.64 212:0.56 215:0.84 216:0.16 219:0.88 220:0.99 222:0.35 230:0.71 233:0.76 235:0.52 238:0.86 241:0.80 242:0.70 243:0.10 244:0.83 245:1.00 247:0.93 248:0.71 253:0.95 254:0.74 255:0.78 256:0.88 259:0.21 260:0.88 261:0.94 265:0.52 266:0.89 267:0.28 268:0.89 271:0.54 276:1.00 277:0.69 282:0.75 284:0.98 285:0.62 290:0.88 292:0.88 297:0.36 300:0.84 +2 1:0.69 7:0.70 11:0.75 12:0.51 17:0.82 18:0.85 21:0.54 27:0.59 28:0.89 29:0.71 30:0.88 32:0.67 34:0.37 36:0.22 37:0.47 39:0.51 43:0.91 44:0.88 45:0.87 48:0.91 55:0.52 64:0.77 66:0.98 69:0.44 70:0.29 71:0.94 74:0.85 76:0.85 77:0.99 81:0.76 83:0.91 85:0.72 86:0.85 91:0.04 97:0.89 98:0.95 99:0.95 101:0.87 108:0.34 114:0.69 123:0.33 126:0.54 127:0.64 129:0.05 135:0.26 139:0.88 145:1.00 146:0.93 147:0.94 149:0.87 150:0.73 154:0.91 155:0.64 158:0.77 159:0.56 161:0.96 165:0.70 167:0.57 172:0.96 173:0.40 176:0.62 177:0.70 178:0.55 179:0.18 181:0.48 187:0.39 192:0.58 195:0.73 201:0.68 204:0.85 208:0.64 212:0.93 215:0.55 216:0.36 219:0.91 222:0.35 230:0.73 233:0.66 235:0.97 241:0.47 242:0.53 243:0.99 247:0.88 253:0.64 265:0.95 266:0.27 267:0.19 271:0.54 276:0.92 279:0.80 281:0.86 282:0.75 283:0.82 290:0.69 292:0.95 297:0.36 300:0.70 +0 12:0.51 17:0.38 21:0.22 27:0.45 28:0.83 30:0.87 32:0.72 34:0.68 36:0.17 37:0.50 39:0.66 43:0.30 55:0.71 66:0.86 69:0.68 70:0.26 74:0.44 81:0.60 91:0.18 98:0.88 108:0.52 123:0.50 124:0.38 126:0.32 127:0.49 129:0.05 133:0.39 135:0.61 145:0.52 146:0.99 147:0.99 149:0.83 150:0.44 154:0.53 159:0.86 161:0.82 167:0.53 172:0.97 173:0.40 177:0.38 178:0.55 179:0.15 181:0.81 187:0.68 195:0.96 212:0.89 215:0.79 216:0.77 222:0.35 235:0.22 241:0.15 242:0.80 243:0.87 245:0.94 247:0.39 253:0.38 254:0.80 259:0.21 265:0.88 266:0.54 267:0.28 271:0.66 276:0.93 297:0.36 300:0.43 +3 1:0.74 6:0.95 7:0.81 8:0.92 9:0.80 12:0.51 17:0.64 20:0.96 27:0.25 28:0.83 32:0.80 34:0.21 36:0.38 37:0.92 39:0.66 41:0.94 74:0.59 76:0.94 77:0.70 78:0.95 81:0.92 83:0.84 91:0.88 96:0.93 100:0.97 104:0.58 111:0.96 114:0.98 120:0.88 121:0.90 126:0.54 135:0.32 140:0.45 144:0.85 145:0.40 150:0.96 151:0.96 152:0.89 153:0.85 155:0.67 161:0.82 162:0.61 164:0.80 165:0.92 167:0.88 169:0.96 175:0.91 176:0.73 181:0.81 186:0.95 189:0.96 191:0.90 192:0.59 195:0.52 201:0.74 202:0.74 204:0.89 208:0.64 215:0.96 216:0.09 222:0.35 230:0.87 232:0.78 233:0.76 235:0.05 238:0.95 241:0.87 244:0.83 248:0.93 253:0.92 255:0.79 256:0.75 257:0.84 259:0.21 260:0.88 261:0.96 267:0.44 268:0.75 271:0.66 277:0.87 282:0.88 285:0.62 290:0.98 297:0.36 299:0.88 +2 1:0.74 6:0.90 7:0.81 8:0.73 9:0.80 11:0.88 12:0.51 17:0.36 20:0.97 21:0.30 27:0.21 28:0.83 30:0.10 34:0.80 36:0.86 37:0.74 39:0.66 41:0.93 43:0.98 55:0.71 60:0.87 66:0.16 69:0.12 70:0.94 74:0.91 78:0.93 81:0.74 83:0.83 85:0.95 91:0.35 96:0.91 98:0.48 100:0.97 101:0.78 104:0.84 106:0.81 108:0.90 111:0.95 114:0.86 117:0.86 120:0.85 121:0.90 123:0.89 124:0.94 126:0.54 127:0.76 129:0.95 133:0.94 135:0.30 140:0.45 144:0.85 146:0.29 147:0.36 149:0.93 150:0.83 151:0.96 152:0.95 153:0.87 154:0.98 155:0.67 158:0.92 159:0.89 161:0.82 162:0.65 164:0.80 165:0.84 167:0.58 169:0.94 172:0.70 173:0.08 176:0.73 177:0.38 179:0.23 181:0.81 186:0.93 187:0.39 189:0.92 191:0.73 192:0.59 201:0.74 202:0.73 204:0.89 206:0.81 208:0.64 212:0.39 215:0.72 216:0.95 222:0.35 230:0.87 232:0.90 233:0.76 235:0.42 238:0.95 241:0.37 242:0.62 243:0.16 245:0.87 247:0.67 248:0.91 253:0.93 255:0.79 256:0.73 259:0.21 260:0.85 261:0.96 265:0.50 266:0.95 267:0.52 268:0.75 271:0.66 276:0.89 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +2 1:0.74 6:0.82 7:0.81 8:0.63 9:0.80 11:0.88 12:0.51 17:0.20 20:0.96 21:0.13 27:0.18 28:0.83 30:0.52 32:0.80 34:0.62 36:0.37 37:0.46 39:0.66 41:0.90 43:0.93 60:0.87 66:0.49 69:0.30 70:0.87 74:0.94 77:0.89 78:0.89 81:0.84 83:0.85 85:0.98 91:0.54 96:0.82 98:0.62 100:0.88 101:0.85 104:0.77 106:0.81 108:0.70 111:0.87 114:0.92 117:0.86 120:0.72 121:1.00 122:0.43 123:0.68 124:0.79 126:0.54 127:0.61 129:0.93 133:0.84 135:0.86 140:0.45 144:0.85 145:0.77 146:0.43 147:0.50 149:0.96 150:0.90 151:0.73 152:0.93 153:0.46 154:0.93 155:0.67 158:0.92 159:0.74 161:0.82 162:0.86 164:0.70 165:0.88 167:0.53 169:0.85 172:0.82 173:0.18 176:0.73 177:0.38 179:0.29 181:0.81 186:0.88 187:0.87 189:0.85 191:0.70 192:0.59 195:0.88 201:0.74 202:0.56 204:0.89 206:0.81 208:0.64 212:0.59 215:0.84 216:0.75 220:0.74 222:0.35 230:0.84 232:0.83 233:0.76 235:0.22 238:0.85 241:0.12 242:0.38 243:0.15 245:0.86 247:0.67 248:0.79 253:0.88 255:0.79 256:0.64 259:0.21 260:0.73 261:0.91 265:0.63 266:0.63 267:0.79 268:0.64 271:0.66 276:0.83 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.97 300:0.84 +0 12:0.51 17:0.38 21:0.74 27:0.45 28:0.83 30:0.72 32:0.72 34:0.69 36:0.17 37:0.50 39:0.66 43:0.30 55:0.71 66:0.86 69:0.71 70:0.36 74:0.38 81:0.28 91:0.12 98:0.84 108:0.54 123:0.51 124:0.38 126:0.32 127:0.47 129:0.05 133:0.39 135:0.84 145:0.50 146:0.98 147:0.98 149:0.83 150:0.27 154:0.48 159:0.78 161:0.82 167:0.54 172:0.94 173:0.51 177:0.38 178:0.55 179:0.19 181:0.81 187:0.68 195:0.93 212:0.90 215:0.46 216:0.77 222:0.35 235:0.88 241:0.18 242:0.81 243:0.87 245:0.89 247:0.39 253:0.34 254:0.80 259:0.21 265:0.84 266:0.71 267:0.28 271:0.66 276:0.89 297:0.36 300:0.84 +3 1:0.74 6:0.93 7:0.81 8:0.90 9:0.80 11:0.88 12:0.51 17:0.06 20:0.84 21:0.59 27:0.03 28:0.83 30:0.91 32:0.80 34:0.75 36:0.83 37:0.93 39:0.66 41:0.88 43:0.86 60:0.87 66:0.69 69:0.61 70:0.13 74:0.77 76:0.85 77:0.70 78:0.87 81:0.57 83:0.86 85:0.80 91:0.70 96:0.84 98:0.62 100:0.90 101:0.78 104:0.93 108:0.46 111:0.87 114:0.74 120:0.75 121:0.97 122:0.43 123:0.44 126:0.54 127:0.18 129:0.05 135:0.51 140:0.45 144:0.85 145:0.59 146:0.46 147:0.53 149:0.65 150:0.72 151:0.93 152:0.80 153:0.77 154:0.83 155:0.67 158:0.92 159:0.17 161:0.82 162:0.86 163:0.81 164:0.69 165:0.78 167:0.78 169:0.87 172:0.21 173:0.77 176:0.73 177:0.38 179:0.86 181:0.81 186:0.87 187:0.68 189:0.94 191:0.98 192:0.59 195:0.61 201:0.74 202:0.54 204:0.89 208:0.64 212:0.61 215:0.55 216:0.76 222:0.35 230:0.87 232:0.95 233:0.76 235:0.74 238:0.90 241:0.74 242:0.63 243:0.73 247:0.30 248:0.82 253:0.85 255:0.79 256:0.58 259:0.21 260:0.76 261:0.85 265:0.63 266:0.17 267:0.91 268:0.63 271:0.66 276:0.13 279:0.86 282:0.88 283:0.82 285:0.62 290:0.74 297:0.36 299:0.88 300:0.13 +2 1:0.74 6:0.80 8:0.61 9:0.80 11:0.88 12:0.51 17:0.57 20:0.88 21:0.22 27:0.09 28:0.83 30:0.33 32:0.80 34:0.61 36:0.95 37:0.36 39:0.66 41:0.92 43:0.79 55:0.59 60:0.87 66:0.98 69:0.74 70:0.66 74:0.89 77:0.70 78:0.83 81:0.79 83:0.87 85:0.95 91:0.60 96:0.86 98:0.90 100:0.80 101:0.84 108:0.58 111:0.81 114:0.90 120:0.77 123:0.55 126:0.54 127:0.47 129:0.05 135:0.42 140:0.80 144:0.85 145:0.82 146:0.93 147:0.94 149:0.95 150:0.86 151:0.87 152:0.83 153:0.48 154:0.73 155:0.67 158:0.92 159:0.57 161:0.82 162:0.68 164:0.75 165:0.86 167:0.63 169:0.78 172:0.96 173:0.71 176:0.73 177:0.38 178:0.42 179:0.17 181:0.81 186:0.83 187:0.21 189:0.82 192:0.59 195:0.80 201:0.74 202:0.67 208:0.64 212:0.93 215:0.79 216:0.46 222:0.35 232:0.82 235:0.31 238:0.82 241:0.52 242:0.76 243:0.98 247:0.67 248:0.84 253:0.89 255:0.79 256:0.68 259:0.21 260:0.78 261:0.83 265:0.90 266:0.38 267:0.28 268:0.69 271:0.66 276:0.92 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.90 7:0.81 8:0.68 9:0.80 11:0.88 12:0.51 17:0.51 20:0.89 21:0.54 27:0.21 28:0.83 30:0.45 34:0.62 36:0.67 37:0.74 39:0.66 41:0.88 43:0.96 55:0.71 60:0.87 66:0.62 69:0.23 70:0.79 74:0.83 78:0.86 81:0.60 83:0.81 85:0.93 91:0.08 96:0.71 98:0.85 100:0.85 101:0.75 104:0.91 106:0.81 108:0.18 111:0.85 114:0.77 117:0.86 120:0.58 121:0.90 123:0.18 124:0.50 126:0.54 127:0.79 129:0.59 133:0.47 135:0.21 140:0.45 144:0.85 146:0.99 147:0.99 149:0.95 150:0.75 151:0.54 152:0.95 153:0.48 154:0.96 155:0.67 158:0.87 159:0.91 161:0.82 162:0.86 164:0.67 165:0.79 167:0.55 169:0.94 172:0.98 173:0.09 176:0.73 177:0.38 179:0.13 181:0.81 186:0.86 187:0.39 189:0.85 191:0.70 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.73 215:0.58 216:0.95 220:0.96 222:0.35 230:0.84 232:0.94 233:0.69 235:0.68 238:0.84 241:0.21 242:0.79 243:0.68 245:0.99 247:0.47 248:0.55 253:0.72 255:0.79 256:0.58 259:0.21 260:0.58 261:0.96 265:0.85 266:0.80 267:0.85 268:0.59 271:0.66 276:0.97 279:0.86 283:0.71 285:0.62 290:0.77 297:0.36 300:0.84 +1 1:0.74 6:0.98 7:0.76 8:0.90 9:0.80 11:0.88 12:0.51 17:0.30 20:0.76 21:0.54 27:0.02 28:0.83 30:0.68 32:0.72 34:0.85 36:0.47 37:0.92 39:0.66 41:0.82 43:0.88 66:0.88 69:0.53 70:0.53 74:0.92 76:0.85 78:0.93 81:0.65 83:0.74 85:0.97 91:0.40 96:0.71 98:0.67 100:0.97 101:0.84 108:0.41 111:0.96 114:0.77 120:0.58 122:0.43 123:0.39 124:0.37 126:0.54 127:0.39 129:0.05 133:0.39 135:0.69 140:0.45 144:0.85 145:0.83 146:0.88 147:0.91 149:0.96 150:0.77 151:0.58 152:0.78 153:0.48 154:0.87 155:0.67 159:0.68 161:0.82 162:0.54 164:0.57 165:0.79 167:0.62 169:0.88 172:0.84 173:0.37 176:0.73 177:0.38 179:0.34 181:0.81 186:0.93 187:0.39 191:0.90 192:0.59 195:0.88 201:0.74 202:0.56 208:0.64 212:0.73 215:0.58 216:0.99 220:0.87 222:0.35 230:0.79 233:0.76 235:0.74 241:0.50 242:0.51 243:0.89 245:0.64 247:0.60 248:0.57 253:0.76 255:0.79 256:0.45 259:0.21 260:0.59 261:0.83 265:0.68 266:0.38 267:0.79 268:0.46 271:0.66 276:0.69 285:0.62 290:0.77 297:0.36 300:0.70 +3 1:0.74 8:0.59 9:0.80 11:0.88 12:0.51 17:0.04 21:0.05 27:0.33 28:0.83 30:0.53 32:0.80 34:0.59 36:0.26 37:0.41 39:0.66 41:0.82 43:0.86 60:0.97 66:0.71 69:0.57 70:0.90 74:0.90 77:0.89 81:0.88 83:0.87 85:0.94 91:0.38 96:0.80 98:0.68 101:0.81 108:0.67 114:0.95 120:0.69 122:0.57 123:0.65 124:0.46 126:0.54 127:0.42 129:0.81 133:0.67 135:0.98 140:0.45 144:0.85 145:0.96 146:0.13 147:0.18 149:0.89 150:0.93 151:0.87 153:0.48 154:0.84 155:0.67 158:0.92 159:0.68 161:0.82 162:0.86 164:0.57 165:0.90 167:0.53 172:0.76 173:0.42 176:0.73 177:0.38 179:0.42 181:0.81 187:0.39 192:0.59 195:0.87 201:0.74 202:0.36 208:0.64 212:0.27 215:0.91 216:0.88 222:0.35 235:0.12 241:0.15 242:0.33 243:0.13 245:0.73 247:0.67 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 265:0.68 266:0.75 267:0.82 268:0.46 271:0.66 276:0.68 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.80 8:0.58 9:0.80 11:0.88 12:0.51 17:0.11 20:0.96 21:0.05 27:0.14 28:0.83 30:0.28 32:0.80 34:0.50 36:0.18 37:0.67 39:0.66 41:0.90 43:0.86 60:0.97 66:0.60 69:0.57 70:0.95 74:0.91 77:0.89 78:0.91 81:0.88 83:0.86 85:0.93 91:0.49 96:0.88 98:0.69 100:0.90 101:0.80 108:0.68 111:0.90 114:0.95 120:0.79 122:0.67 123:0.66 124:0.63 126:0.54 127:0.46 129:0.81 133:0.67 135:0.98 140:0.45 144:0.85 145:0.96 146:0.34 147:0.41 149:0.88 150:0.93 151:0.87 152:0.92 153:0.48 154:0.84 155:0.67 158:0.92 159:0.69 161:0.82 162:0.54 164:0.73 165:0.90 167:0.55 169:0.88 172:0.73 173:0.42 176:0.73 177:0.38 179:0.42 181:0.81 186:0.91 187:0.39 189:0.82 191:0.70 192:0.59 195:0.87 201:0.74 202:0.70 208:0.64 212:0.26 215:0.91 216:0.93 220:0.62 222:0.35 235:0.12 238:0.86 241:0.21 242:0.44 243:0.21 245:0.79 247:0.67 248:0.86 253:0.91 255:0.79 256:0.64 259:0.21 260:0.80 261:0.92 265:0.69 266:0.80 267:0.88 268:0.66 271:0.66 276:0.70 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.79 7:0.81 8:0.58 9:0.80 11:0.88 12:0.51 17:0.28 20:0.85 21:0.13 27:0.39 28:0.83 30:0.41 32:0.80 34:0.28 36:0.91 37:0.30 39:0.66 41:0.82 43:0.80 60:0.87 66:0.86 69:0.73 70:0.45 74:0.81 76:0.94 77:0.70 78:0.84 81:0.84 83:0.84 85:0.88 91:0.62 96:0.84 98:0.73 100:0.78 101:0.78 104:0.95 106:0.81 108:0.56 111:0.82 114:0.92 117:0.86 120:0.74 121:0.90 122:0.43 123:0.53 126:0.54 127:0.23 129:0.05 135:1.00 140:0.45 144:0.85 145:0.61 146:0.83 147:0.85 149:0.80 150:0.90 151:0.92 152:0.81 153:0.72 154:0.74 155:0.67 158:0.92 159:0.39 161:0.82 162:0.67 163:0.75 164:0.64 165:0.88 167:0.56 169:0.77 172:0.59 173:0.70 176:0.73 177:0.38 179:0.52 181:0.81 186:0.84 187:0.39 189:0.81 192:0.59 195:0.77 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.19 215:0.84 216:0.65 222:0.35 230:0.87 232:0.96 233:0.76 235:0.22 238:0.81 241:0.30 242:0.45 243:0.86 247:0.47 248:0.81 253:0.86 255:0.79 256:0.45 259:0.21 260:0.75 261:0.82 265:0.73 266:0.63 267:0.73 268:0.57 271:0.66 276:0.49 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 6:0.81 8:0.61 9:0.80 11:0.88 12:0.51 17:0.07 20:0.97 21:0.30 27:0.11 28:0.83 30:0.60 34:0.24 36:0.47 37:0.83 39:0.66 41:0.88 43:0.95 60:0.95 66:0.86 69:0.23 70:0.53 74:0.97 78:0.87 81:0.74 83:0.84 85:0.99 91:0.37 96:0.73 98:0.95 100:0.85 101:0.86 104:0.93 108:0.61 111:0.85 114:0.86 120:0.61 122:0.43 123:0.59 124:0.38 126:0.54 127:0.89 129:0.59 133:0.47 135:0.98 140:0.45 144:0.85 146:0.40 147:0.46 149:0.99 150:0.83 151:0.60 152:0.89 153:0.72 154:0.95 155:0.67 158:0.92 159:0.83 161:0.82 162:0.76 164:0.69 165:0.84 167:0.55 169:0.82 172:0.96 173:0.13 176:0.73 177:0.38 179:0.19 181:0.81 186:0.86 187:0.87 189:0.84 191:0.73 192:0.59 201:0.74 202:0.58 208:0.64 212:0.57 215:0.72 216:0.83 220:0.87 222:0.35 232:0.95 235:0.42 238:0.83 241:0.24 242:0.45 243:0.13 245:0.93 247:0.67 248:0.61 253:0.81 255:0.79 256:0.58 259:0.21 260:0.61 261:0.88 265:0.95 266:0.38 267:0.59 268:0.62 271:0.66 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.55 +2 1:0.74 6:0.93 8:0.87 9:0.80 11:0.88 12:0.37 17:0.02 20:0.98 27:0.14 28:0.70 30:0.70 32:0.80 34:0.78 36:0.28 37:0.67 39:0.42 41:0.99 43:0.86 60:0.95 66:0.12 69:0.57 70:0.74 74:0.88 77:0.70 78:0.86 81:0.95 83:0.90 85:0.95 91:0.75 96:0.99 98:0.26 100:0.93 101:0.81 108:0.93 111:0.88 114:0.98 120:0.99 122:0.43 123:0.58 124:0.89 126:0.54 127:0.68 129:0.95 133:0.87 135:0.39 138:0.99 140:0.45 144:0.85 145:0.80 146:0.22 147:0.28 149:0.89 150:0.98 151:1.00 152:0.94 153:0.99 154:0.84 155:0.67 158:0.92 159:0.82 161:0.75 162:0.65 163:0.81 164:0.97 165:0.92 167:0.60 169:0.89 172:0.37 173:0.34 176:0.73 177:0.38 179:0.54 181:0.94 186:0.86 187:0.39 189:0.95 192:0.59 195:0.93 201:0.74 202:0.95 208:0.64 212:0.15 215:0.96 216:0.93 222:0.60 235:0.05 238:0.94 241:0.44 242:0.45 243:0.24 245:0.70 247:0.55 248:1.00 253:1.00 255:0.79 256:0.96 259:0.21 260:0.99 261:0.93 265:0.22 266:0.92 267:0.23 268:0.96 271:0.60 276:0.63 279:0.86 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.84 +3 1:0.74 6:0.85 7:0.81 8:0.68 9:0.80 11:0.88 12:0.37 17:0.18 20:0.99 21:0.40 27:0.09 28:0.70 30:0.55 34:0.19 36:0.52 37:0.32 39:0.42 41:0.99 43:0.92 55:0.71 60:0.95 66:0.92 69:0.37 70:0.60 74:0.77 78:0.88 81:0.73 83:0.90 85:0.88 91:0.90 96:0.99 98:0.98 100:0.90 101:0.80 104:0.54 108:0.29 111:0.87 114:0.82 120:0.98 121:0.90 123:0.28 126:0.54 127:0.84 129:0.05 135:0.49 138:0.98 140:0.45 144:0.85 146:0.91 147:0.93 149:0.85 150:0.83 151:0.98 152:0.90 153:0.93 154:0.92 155:0.67 158:0.92 159:0.53 161:0.75 162:0.73 164:0.96 165:0.83 167:0.88 169:0.88 172:0.77 173:0.38 176:0.73 177:0.38 179:0.56 181:0.94 186:0.88 189:0.87 191:0.77 192:0.59 201:0.74 202:0.93 204:0.89 208:0.64 212:0.30 215:0.67 216:0.23 222:0.60 230:0.87 232:0.74 233:0.76 235:0.52 238:0.88 241:0.85 242:0.75 243:0.92 247:0.47 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.92 265:0.98 266:0.63 267:0.28 268:0.95 271:0.60 276:0.66 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.55 +2 1:0.74 6:0.86 7:0.76 8:0.68 9:0.80 11:0.88 12:0.37 17:0.11 20:0.99 21:0.05 27:0.08 28:0.70 30:0.33 32:0.80 34:0.40 36:0.86 37:0.28 39:0.42 41:0.99 43:0.84 60:0.95 66:0.86 69:0.44 70:0.58 74:0.81 76:0.85 77:0.70 78:0.91 81:0.91 83:0.90 85:0.88 91:0.94 96:0.99 98:0.95 100:0.96 101:0.82 104:0.58 108:0.34 111:0.93 114:0.95 120:0.97 123:0.32 126:0.54 127:0.65 129:0.05 135:0.63 138:0.98 140:0.45 144:0.85 145:0.76 146:0.73 147:0.77 149:0.82 150:0.96 151:0.98 152:0.91 153:0.92 154:0.80 155:0.67 158:0.92 159:0.29 161:0.75 162:0.70 164:0.95 165:0.90 167:0.87 169:0.94 172:0.61 173:0.62 176:0.73 177:0.38 179:0.73 181:0.94 186:0.92 189:0.87 191:0.83 192:0.59 195:0.62 201:0.74 202:0.92 208:0.64 212:0.69 215:0.91 216:0.31 220:0.62 222:0.60 230:0.79 232:0.77 233:0.76 235:0.12 238:0.94 241:0.83 242:0.43 243:0.87 247:0.47 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.97 261:0.96 265:0.95 266:0.47 267:0.65 268:0.94 271:0.60 276:0.50 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.89 7:0.81 8:0.76 9:0.80 11:0.88 12:0.37 17:0.28 20:0.99 21:0.54 25:0.88 27:0.08 28:0.70 30:0.30 32:0.80 34:0.18 36:0.45 37:0.54 39:0.42 41:0.99 43:0.78 55:0.92 60:0.99 66:0.47 69:0.72 70:0.73 74:0.80 76:0.85 77:0.70 78:0.85 81:0.69 83:0.90 85:0.90 87:0.98 91:0.84 96:0.98 98:0.67 100:0.88 101:0.78 104:0.58 108:0.86 111:0.85 114:0.77 120:0.96 121:0.90 123:0.85 124:0.75 126:0.54 127:0.94 129:0.81 133:0.76 135:0.21 138:0.98 140:0.45 144:0.85 145:0.52 146:0.86 147:0.26 149:0.82 150:0.79 151:0.97 152:0.91 153:0.91 154:0.70 155:0.67 158:0.92 159:0.75 161:0.75 162:0.75 164:0.94 165:0.79 167:0.87 169:0.85 172:0.70 173:0.61 176:0.73 177:0.05 179:0.48 181:0.94 186:0.85 189:0.90 191:0.84 192:0.59 195:0.87 201:0.74 202:0.89 204:0.89 208:0.64 212:0.30 215:0.58 216:0.18 220:0.62 222:0.60 230:0.87 232:0.78 233:0.76 235:0.74 238:0.89 241:0.81 242:0.74 243:0.12 245:0.79 247:0.47 248:0.99 253:0.98 255:0.79 256:0.91 257:0.65 259:0.21 260:0.97 261:0.90 265:0.68 266:0.80 267:0.59 268:0.92 271:0.60 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 290:0.77 297:0.36 299:0.88 300:0.55 +3 1:0.74 6:0.93 8:0.78 9:0.80 11:0.88 12:0.37 17:0.02 20:0.75 27:0.14 28:0.70 30:0.70 32:0.80 34:0.66 36:0.34 37:0.67 39:0.42 41:0.99 43:0.86 60:0.87 66:0.08 69:0.57 70:0.66 74:0.86 77:0.70 78:0.85 81:0.95 83:0.90 85:0.95 91:0.92 96:0.99 98:0.22 100:0.95 101:0.80 108:0.93 111:0.87 114:0.98 120:0.98 122:0.43 123:0.91 124:0.89 126:0.54 127:0.67 129:0.95 133:0.87 135:0.51 138:0.98 140:0.45 144:0.85 145:0.80 146:0.29 147:0.36 149:0.89 150:0.98 151:1.00 152:0.94 153:0.99 154:0.84 155:0.67 158:0.92 159:0.83 161:0.75 162:0.65 164:0.96 165:0.92 167:0.60 169:0.89 172:0.41 173:0.33 176:0.73 177:0.38 179:0.54 181:0.94 186:0.88 187:0.39 189:0.94 191:0.83 192:0.59 195:0.93 201:0.74 202:0.95 208:0.64 212:0.19 215:0.96 216:0.93 222:0.60 235:0.05 238:0.94 241:0.43 242:0.53 243:0.24 245:0.70 247:0.60 248:1.00 253:1.00 255:0.79 256:0.95 259:0.21 260:0.99 261:0.93 265:0.24 266:0.84 267:0.65 268:0.96 271:0.60 276:0.63 279:0.86 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.88 12:0.37 17:0.18 20:0.99 21:0.30 27:0.21 28:0.70 30:0.21 34:0.49 36:0.54 37:0.74 39:0.42 41:0.99 43:0.98 55:0.71 60:0.98 66:0.08 69:0.12 70:0.89 74:0.98 78:0.82 81:0.78 83:0.90 85:0.99 91:0.84 96:0.99 98:0.34 100:0.93 101:0.80 104:0.84 106:0.81 108:0.97 111:0.86 114:0.86 117:0.86 120:0.97 121:0.90 122:0.67 123:0.96 124:0.97 126:0.54 127:0.75 129:0.99 133:0.97 135:0.28 140:0.45 144:0.85 146:0.90 147:0.92 149:0.94 150:0.86 151:0.99 152:0.91 153:0.97 154:0.98 155:0.67 158:0.92 159:0.96 161:0.75 162:0.68 164:0.95 165:0.84 167:0.59 169:0.90 172:0.80 173:0.06 176:0.73 177:0.38 179:0.12 181:0.94 186:0.83 187:0.39 189:0.96 191:0.82 192:0.59 201:0.74 202:0.92 204:0.89 206:0.81 208:0.64 212:0.35 215:0.72 216:0.95 222:0.60 230:0.87 232:0.89 233:0.76 235:0.42 238:0.96 241:0.41 242:0.45 243:0.19 245:0.96 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.97 261:0.92 265:0.29 266:0.95 267:0.28 268:0.94 271:0.60 276:0.97 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94 +3 1:0.74 7:0.81 11:0.88 12:0.37 17:0.43 21:0.30 27:0.21 28:0.70 30:0.26 34:0.55 36:0.47 37:0.74 39:0.42 43:0.98 55:0.71 66:0.08 69:0.12 70:0.88 74:0.98 81:0.78 83:0.75 85:0.99 91:0.04 98:0.35 101:0.80 108:0.96 114:0.86 121:0.90 122:0.67 123:0.96 124:0.97 126:0.54 127:0.86 129:0.99 133:0.97 135:0.37 144:0.85 146:0.90 147:0.92 149:0.93 150:0.86 154:0.98 155:0.67 159:0.96 161:0.75 165:0.84 167:0.66 172:0.79 173:0.06 176:0.73 177:0.38 178:0.55 179:0.12 181:0.94 187:0.68 192:0.59 201:0.74 202:0.36 204:0.89 208:0.64 212:0.35 215:0.72 216:0.95 222:0.60 230:0.87 233:0.76 235:0.42 241:0.57 242:0.52 243:0.19 245:0.96 247:0.67 253:0.78 259:0.21 265:0.37 266:0.95 267:0.44 271:0.60 276:0.97 290:0.86 297:0.36 300:0.94 +1 1:0.74 9:0.80 11:0.88 12:0.37 17:0.13 21:0.64 27:0.45 28:0.70 30:0.73 34:0.73 36:0.23 37:0.50 39:0.42 41:0.82 43:0.82 66:0.20 69:0.69 70:0.57 74:0.70 81:0.57 83:0.73 85:0.91 91:0.17 96:0.69 98:0.37 101:0.81 108:0.65 114:0.72 120:0.55 122:0.43 123:0.63 124:0.74 126:0.54 127:0.38 129:0.81 133:0.67 135:0.72 140:0.45 144:0.85 145:0.49 146:0.25 147:0.31 149:0.83 150:0.71 151:0.51 153:0.48 154:0.77 155:0.67 159:0.53 161:0.75 162:0.86 164:0.57 165:0.70 167:0.56 172:0.27 173:0.65 176:0.73 177:0.38 179:0.68 181:0.94 187:0.68 192:0.59 201:0.74 202:0.36 208:0.64 212:0.30 215:0.53 216:0.77 220:0.96 222:0.60 235:0.80 241:0.27 242:0.58 243:0.36 245:0.63 247:0.39 248:0.50 253:0.63 255:0.79 256:0.45 259:0.21 260:0.55 265:0.39 266:0.75 267:0.69 268:0.46 271:0.60 276:0.40 285:0.62 290:0.72 297:0.36 300:0.55 +3 1:0.74 6:0.85 7:0.81 8:0.63 9:0.80 11:0.88 12:0.37 17:0.79 20:0.98 27:0.05 28:0.70 30:0.44 32:0.80 34:0.17 36:0.48 37:0.28 39:0.42 41:0.97 43:0.56 55:0.71 66:0.36 69:0.94 70:0.65 74:0.67 77:0.70 78:0.90 81:0.95 83:0.88 85:0.85 91:0.83 96:0.96 98:0.69 100:0.91 101:0.75 104:0.58 108:0.45 111:0.89 114:0.98 120:0.92 121:0.90 123:0.44 124:0.69 126:0.54 127:0.79 129:0.05 133:0.62 135:0.19 140:0.45 144:0.85 145:0.70 146:0.78 147:0.90 149:0.72 150:0.98 151:0.98 152:0.90 153:0.92 154:0.45 155:0.67 159:0.73 161:0.75 162:0.56 163:0.75 164:0.87 165:0.92 167:0.90 169:0.89 172:0.61 173:0.96 176:0.73 177:0.05 179:0.52 181:0.94 186:0.90 189:0.86 191:0.84 192:0.59 195:0.86 201:0.74 202:0.84 204:0.89 208:0.64 212:0.18 215:0.96 216:0.15 222:0.60 230:0.87 232:0.77 233:0.76 235:0.05 238:0.89 241:0.95 242:0.77 243:0.51 245:0.80 247:0.55 248:0.96 253:0.96 255:0.79 256:0.83 257:0.65 259:0.21 260:0.93 261:0.94 265:0.69 266:0.75 267:0.28 268:0.84 271:0.60 276:0.69 277:0.69 282:0.88 285:0.62 290:0.98 297:0.36 299:0.88 300:0.70 +0 12:0.37 17:0.26 21:0.13 27:0.33 30:0.76 34:0.84 36:0.26 37:0.89 43:0.42 55:0.45 66:0.64 69:0.49 70:0.15 81:1.00 91:0.11 98:0.60 108:0.38 123:0.36 126:0.54 127:0.18 129:0.05 135:0.61 145:0.65 146:0.40 147:0.47 149:0.28 150:1.00 154:0.32 159:0.15 163:0.92 172:0.16 173:0.70 177:0.05 178:0.55 179:0.92 187:0.98 212:0.95 215:0.84 216:0.83 235:0.12 241:0.50 242:0.82 243:0.69 247:0.12 259:0.21 265:0.61 266:0.12 267:0.36 276:0.14 297:0.36 300:0.13 +1 8:0.79 12:0.37 17:0.28 21:0.78 27:0.45 30:0.95 34:0.76 36:0.26 37:0.50 43:0.25 55:0.84 66:0.54 69:0.84 91:0.17 98:0.19 108:0.70 123:0.68 127:0.10 129:0.05 135:0.86 145:0.45 146:0.35 147:0.41 149:0.17 154:0.18 159:0.14 172:0.10 173:0.74 177:0.05 178:0.55 179:0.51 187:0.68 216:0.77 235:0.60 241:0.18 243:0.63 259:0.21 265:0.21 267:0.69 300:0.08 +2 6:0.89 8:0.76 12:0.37 17:0.47 20:0.87 21:0.47 27:0.72 30:0.21 32:0.80 34:0.49 36:0.37 43:0.43 55:0.59 66:0.20 69:0.49 70:0.37 78:0.99 81:0.98 91:0.29 98:0.20 100:0.98 104:0.92 106:0.81 108:0.38 111:0.98 120:0.69 123:0.72 124:0.73 126:0.54 127:0.33 129:0.81 133:0.67 135:0.24 140:0.45 145:0.76 146:0.24 147:0.30 149:0.33 150:0.98 151:0.66 152:0.82 153:0.48 154:0.32 159:0.39 162:0.86 163:0.87 164:0.57 169:0.97 172:0.10 173:0.51 177:0.05 179:0.93 186:0.98 187:0.87 189:0.90 195:0.66 202:0.36 206:0.81 212:0.42 215:0.63 216:0.08 235:0.52 238:0.92 241:0.30 242:0.82 243:0.40 245:0.40 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.94 265:0.19 266:0.23 267:0.59 268:0.46 276:0.23 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.80 8:0.61 12:0.37 17:0.20 20:0.85 21:0.13 27:0.13 30:0.76 34:0.96 36:0.25 37:0.82 43:0.44 55:0.45 66:0.64 69:0.44 70:0.15 78:0.93 81:1.00 91:0.22 98:0.53 100:0.97 108:0.34 111:0.95 123:0.33 126:0.54 127:0.16 129:0.05 135:0.69 145:0.61 146:0.40 147:0.47 149:0.28 150:1.00 152:0.94 154:0.34 159:0.15 163:0.92 169:0.90 172:0.16 173:0.61 177:0.05 178:0.55 179:0.89 186:0.93 187:0.68 212:0.95 215:0.84 216:0.91 235:0.12 241:0.32 242:0.82 243:0.69 247:0.12 259:0.21 261:0.92 265:0.55 266:0.12 267:0.44 276:0.14 283:0.82 297:0.36 300:0.13 +2 6:0.90 8:0.83 12:0.37 17:0.49 20:0.91 21:0.78 27:0.72 34:0.30 36:0.83 37:0.56 78:0.88 91:0.86 100:0.93 111:0.89 120:0.65 135:0.32 140:0.98 145:0.70 151:0.60 152:0.85 153:0.46 162:0.45 164:0.53 169:0.91 175:0.75 178:0.55 186:0.88 189:0.92 191:0.75 202:0.87 216:0.26 235:0.12 238:0.93 241:0.86 244:0.61 248:0.58 256:0.45 257:0.65 259:0.21 260:0.65 261:0.89 267:0.28 268:0.45 277:0.69 285:0.62 +2 8:0.64 12:0.37 17:0.28 20:0.95 21:0.78 27:0.26 30:0.21 34:0.44 36:0.97 37:0.40 43:0.41 66:0.20 69:0.51 70:0.37 78:0.92 91:0.31 98:0.07 100:0.73 108:0.39 111:0.89 123:0.37 124:0.81 127:0.44 129:0.89 133:0.78 135:0.90 146:0.13 147:0.18 149:0.32 152:0.88 154:0.31 159:0.81 163:0.87 169:0.72 172:0.10 173:0.25 177:0.05 178:0.55 179:0.95 186:0.92 187:0.68 212:0.42 216:0.83 235:0.05 241:0.02 242:0.82 243:0.40 245:0.39 247:0.12 259:0.21 261:0.89 265:0.07 266:0.23 267:0.65 276:0.18 300:0.25 +1 12:0.37 17:0.16 21:0.05 27:0.13 30:0.76 34:0.95 36:0.29 37:0.82 43:0.40 55:0.45 66:0.64 69:0.52 70:0.15 81:1.00 91:0.20 98:0.53 108:0.40 123:0.38 126:0.54 127:0.16 129:0.05 135:0.58 145:0.61 146:0.40 147:0.47 149:0.27 150:1.00 154:0.30 159:0.15 163:0.92 172:0.16 173:0.68 177:0.05 178:0.55 179:0.88 187:0.98 212:0.95 215:0.91 216:0.91 235:0.05 241:0.49 242:0.82 243:0.69 247:0.12 259:0.21 265:0.55 266:0.12 267:0.52 276:0.14 297:0.36 300:0.13 +0 8:0.68 12:0.37 17:0.66 20:0.90 21:0.78 27:0.51 30:0.95 34:0.58 36:0.95 37:0.24 43:0.41 55:0.96 66:0.20 69:0.51 78:0.96 91:0.31 98:0.08 100:0.97 108:0.39 111:0.96 123:0.37 124:0.61 127:0.18 129:0.59 133:0.47 135:0.75 146:0.05 147:0.05 149:0.20 152:0.84 154:0.31 159:0.26 169:0.96 172:0.05 173:0.50 177:0.05 178:0.55 179:0.98 186:0.95 187:0.68 202:0.36 216:0.63 235:0.05 241:0.18 243:0.40 245:0.36 259:0.21 261:0.96 265:0.08 267:0.76 300:0.08 +1 8:0.64 12:0.37 20:0.83 21:0.78 27:0.66 34:0.73 36:0.27 37:0.25 78:0.92 91:0.96 100:0.90 111:0.90 120:0.73 135:0.77 140:0.99 151:0.66 152:0.79 153:0.48 162:0.45 164:0.67 169:0.88 175:0.75 178:0.55 186:0.92 191:0.85 202:0.94 216:0.62 220:0.62 241:0.94 244:0.61 248:0.66 256:0.58 257:0.65 259:0.21 260:0.74 261:0.85 267:0.69 268:0.59 277:0.69 285:0.62 +1 6:0.82 7:0.81 12:0.37 17:0.30 20:0.82 21:0.40 27:0.44 30:0.95 32:0.80 34:0.79 36:0.39 37:0.46 43:0.35 55:0.96 66:0.20 69:0.63 78:0.98 81:0.99 91:0.40 98:0.10 100:0.91 104:0.98 106:0.81 108:0.48 111:0.95 123:0.46 124:0.61 126:0.54 127:0.23 129:0.59 133:0.47 135:0.44 145:0.56 146:0.05 147:0.05 149:0.20 150:0.98 152:0.84 154:0.27 159:0.20 169:0.91 172:0.05 173:0.79 177:0.05 178:0.55 179:0.99 186:0.98 187:0.87 195:0.56 206:0.81 215:0.67 216:0.83 230:0.87 233:0.76 235:0.42 241:0.30 243:0.40 245:0.36 259:0.21 261:0.91 265:0.10 267:0.59 297:0.36 300:0.08 +1 6:0.80 8:0.67 12:0.37 20:0.91 21:0.78 27:0.13 34:0.98 36:0.32 37:0.82 78:0.89 91:0.86 100:0.87 111:0.87 120:0.87 135:0.56 140:0.98 145:0.47 151:0.75 152:0.93 153:0.85 162:0.45 164:0.82 169:0.90 175:0.75 178:0.55 186:0.89 187:0.39 191:0.92 202:0.99 216:0.91 235:0.88 241:0.72 244:0.61 248:0.81 256:0.78 257:0.65 259:0.21 260:0.87 261:0.92 267:0.88 268:0.78 277:0.69 285:0.62 +0 12:0.37 17:0.36 21:0.78 27:0.45 30:0.95 34:0.83 36:0.18 37:0.50 43:0.26 55:0.71 66:0.54 69:0.82 91:0.27 98:0.17 108:0.66 123:0.64 127:0.10 129:0.05 135:0.92 145:0.45 146:0.29 147:0.36 149:0.17 154:0.19 159:0.12 172:0.10 173:0.73 177:0.05 178:0.55 179:0.39 187:0.68 216:0.77 235:0.95 241:0.50 243:0.63 259:0.21 265:0.19 267:0.65 300:0.08 +0 1:0.74 11:0.64 12:0.95 17:0.57 21:0.30 27:0.55 30:0.15 34:0.84 36:0.49 37:0.57 39:0.37 43:0.92 55:0.59 66:0.54 69:0.12 70:0.89 74:0.99 81:0.73 83:0.75 85:0.85 91:0.40 98:0.68 101:0.71 106:0.81 108:0.75 114:0.86 117:0.86 122:0.67 123:0.73 124:0.91 126:0.54 127:0.98 129:0.89 131:0.61 133:0.95 135:0.71 144:0.57 146:0.67 147:0.72 149:0.91 150:0.83 154:0.91 155:0.67 157:0.91 159:0.93 165:0.84 166:1.00 172:0.97 173:0.07 176:0.73 177:0.38 178:0.55 179:0.14 182:0.97 187:0.68 192:0.59 201:0.74 206:0.81 212:0.68 215:0.72 216:0.23 222:0.21 227:0.61 229:0.61 231:1.00 235:0.42 241:0.05 242:0.50 243:0.25 245:0.94 246:0.61 247:0.67 253:0.78 259:0.21 265:0.68 266:0.95 267:0.17 276:0.96 287:0.61 290:0.86 297:0.36 300:0.84 +2 1:0.74 7:0.81 11:0.64 12:0.95 17:0.78 21:0.13 27:0.72 30:0.18 32:0.80 34:0.99 36:0.45 39:0.37 43:0.82 60:0.87 66:0.46 69:0.64 70:0.85 74:0.91 77:0.70 81:0.83 83:0.87 85:0.93 91:0.60 98:0.52 101:0.80 108:0.85 114:0.92 121:0.90 122:0.51 123:0.84 124:0.67 126:0.54 127:0.45 129:0.81 131:0.61 133:0.67 135:0.94 144:0.57 145:0.72 146:0.59 147:0.65 149:0.86 150:0.89 154:0.77 155:0.67 157:0.99 158:0.92 159:0.67 165:0.88 166:1.00 172:0.67 173:0.52 176:0.73 177:0.38 178:0.55 179:0.42 182:0.98 187:0.21 192:0.59 195:0.85 201:0.74 204:0.89 208:0.64 212:0.54 215:0.84 216:0.10 222:0.21 227:0.61 229:0.61 230:0.84 231:1.00 233:0.69 235:0.22 241:0.21 242:0.21 243:0.40 245:0.81 246:0.61 247:0.60 253:0.78 265:0.53 266:0.80 267:0.76 276:0.68 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.70 +1 1:0.74 9:0.80 11:0.64 12:0.95 17:0.76 21:0.22 27:0.72 30:0.53 34:0.80 36:0.77 39:0.37 41:0.82 43:0.92 55:0.71 66:0.32 69:0.10 70:0.62 74:0.99 81:0.78 83:0.77 85:0.85 91:0.40 96:0.74 98:0.71 101:0.71 106:0.81 108:0.95 114:0.90 117:0.86 120:0.61 122:0.67 123:0.94 124:0.83 126:0.54 127:0.97 129:0.89 131:1.00 133:0.83 135:0.88 140:0.45 144:0.57 146:0.91 147:0.93 149:0.90 150:0.86 151:0.64 153:0.46 154:0.92 155:0.67 157:0.91 159:0.92 162:0.62 164:0.54 165:0.86 166:1.00 172:0.94 173:0.07 176:0.73 177:0.38 179:0.14 182:0.98 187:0.87 192:0.59 201:0.74 202:0.49 206:0.81 208:0.64 212:0.67 215:0.79 216:0.17 220:0.74 222:0.21 227:0.61 229:0.99 231:1.00 235:0.31 241:0.03 242:0.70 243:0.38 245:0.98 246:0.61 247:0.67 248:0.62 253:0.82 255:0.79 256:0.45 259:0.21 260:0.62 265:0.71 266:0.89 267:0.44 268:0.45 276:0.96 285:0.62 287:0.61 290:0.90 297:0.36 300:0.84 +4 1:0.74 9:0.80 11:0.64 12:0.95 17:0.70 21:0.05 25:0.88 27:0.72 30:0.21 34:0.97 36:0.44 37:0.73 39:0.37 41:0.94 43:0.88 66:0.69 69:0.34 70:0.67 74:0.69 81:0.88 83:0.83 85:0.80 91:0.74 96:0.94 98:0.43 101:0.76 108:0.27 114:0.95 120:0.88 122:0.51 123:0.26 124:0.41 126:0.54 127:0.60 129:0.05 131:1.00 133:0.39 135:0.94 140:0.45 144:0.57 146:0.45 147:0.52 149:0.70 150:0.93 151:0.93 153:0.78 154:0.86 155:0.67 157:0.94 159:0.44 162:0.75 164:0.80 165:0.90 166:1.00 172:0.41 173:0.38 176:0.73 177:0.38 179:0.87 182:0.99 187:0.21 192:0.59 201:0.74 202:0.73 208:0.64 212:0.55 215:0.91 216:0.27 222:0.21 227:0.61 229:1.00 231:1.00 235:0.12 241:0.61 242:0.40 243:0.73 245:0.46 246:0.61 247:0.47 248:0.93 253:0.93 255:0.79 256:0.77 259:0.21 260:0.89 265:0.45 266:0.38 267:0.36 268:0.77 276:0.23 285:0.62 287:0.61 290:0.95 297:0.36 300:0.43 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.95 17:0.73 21:0.13 27:0.55 30:0.27 32:0.80 34:0.98 36:0.59 37:0.57 39:0.37 41:0.82 43:0.62 55:0.59 60:0.87 66:0.45 69:0.59 70:0.81 74:0.81 77:0.70 81:0.83 83:0.84 85:0.80 91:0.57 96:0.78 98:0.62 101:0.74 108:0.76 114:0.92 120:0.62 121:0.90 123:0.74 124:0.58 126:0.54 127:0.40 129:0.59 131:1.00 133:0.47 135:0.99 140:0.80 144:0.57 145:0.40 146:0.65 147:0.70 149:0.71 150:0.89 151:0.69 153:0.48 154:0.75 155:0.67 157:0.93 158:0.86 159:0.54 162:0.74 164:0.57 165:0.88 166:1.00 172:0.63 173:0.52 176:0.73 177:0.38 178:0.42 179:0.43 182:0.98 187:0.39 192:0.59 195:0.77 201:0.74 202:0.56 204:0.89 208:0.64 212:0.69 215:0.84 216:0.23 220:0.74 222:0.21 227:0.61 229:0.99 230:0.84 231:1.00 233:0.69 235:0.22 241:0.27 242:0.72 243:0.48 245:0.85 246:0.61 247:0.60 248:0.65 253:0.82 255:0.79 256:0.58 259:0.21 260:0.63 265:0.63 266:0.63 267:0.36 268:0.59 276:0.67 279:0.86 282:0.88 283:0.66 285:0.62 287:0.61 290:0.92 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:1.00 8:0.95 9:0.80 11:0.64 12:0.95 17:0.72 20:0.99 21:0.05 25:0.88 27:0.72 30:0.21 34:0.53 36:0.51 37:0.73 39:0.37 41:0.92 43:0.98 60:0.87 66:0.80 69:0.07 70:0.50 74:0.75 78:0.98 81:0.88 83:0.87 85:0.80 91:0.88 96:0.91 98:0.99 100:0.99 101:0.79 108:0.06 111:0.99 114:0.95 120:0.93 122:0.51 123:0.06 126:0.54 127:0.88 129:0.05 131:1.00 135:0.71 140:0.80 144:0.57 146:0.71 147:0.75 149:0.72 150:0.93 151:0.94 152:0.99 153:0.86 154:0.98 155:0.67 157:0.95 158:0.87 159:0.28 162:0.75 164:0.88 165:0.90 166:1.00 169:0.98 172:0.45 173:0.32 176:0.73 177:0.38 179:0.89 182:0.99 186:0.97 189:1.00 192:0.59 201:0.74 202:0.83 208:0.64 212:0.55 215:0.91 216:0.27 222:0.21 227:0.61 229:0.99 231:1.00 235:0.12 238:0.98 241:0.89 242:0.40 243:0.82 246:0.61 247:0.39 248:0.88 253:0.92 255:0.79 256:0.86 259:0.21 260:0.93 261:0.97 265:0.99 266:0.27 267:0.19 268:0.86 276:0.35 279:0.86 283:0.71 285:0.62 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.81 11:0.64 12:0.95 17:0.81 21:0.59 27:0.72 30:0.19 34:0.50 36:0.30 39:0.37 43:0.90 55:0.84 66:0.27 69:0.48 70:0.82 74:0.96 76:0.85 81:0.57 83:0.73 85:0.96 91:0.33 98:0.55 101:0.79 106:0.81 108:0.69 114:0.74 117:0.86 121:0.90 122:0.67 123:0.67 124:0.91 126:0.54 127:0.93 129:0.95 131:0.61 133:0.91 135:0.98 144:0.57 145:0.74 146:0.58 147:0.64 149:0.93 150:0.72 154:0.89 155:0.67 157:0.99 159:0.89 165:0.78 166:1.00 172:0.77 173:0.19 176:0.73 177:0.38 178:0.55 179:0.27 182:0.97 187:0.21 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.23 215:0.55 222:0.21 227:0.61 229:0.61 230:0.87 231:1.00 233:0.76 235:0.74 241:0.05 242:0.44 243:0.29 245:0.87 246:0.61 247:0.60 253:0.75 265:0.56 266:0.94 267:0.69 276:0.86 287:0.61 290:0.74 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.95 17:0.64 20:0.99 21:0.47 27:0.72 30:0.21 34:0.30 36:0.83 37:0.68 39:0.37 43:0.92 55:0.71 66:0.19 69:0.41 70:0.96 74:0.97 78:0.87 81:0.64 83:0.74 85:0.90 91:0.12 98:0.31 100:0.73 101:0.65 108:0.93 111:0.85 114:0.80 122:0.67 123:0.93 124:0.98 126:0.54 127:1.00 129:0.89 131:0.61 133:0.99 135:0.90 144:0.57 146:0.61 147:0.67 149:0.88 150:0.77 152:0.92 154:0.91 155:0.67 157:0.84 159:0.99 165:0.80 166:1.00 169:0.72 172:0.92 173:0.06 176:0.73 177:0.38 178:0.55 179:0.12 182:0.97 186:0.87 187:0.39 192:0.59 201:0.74 202:0.50 212:0.30 215:0.63 216:0.26 222:0.21 227:0.61 229:0.61 231:1.00 235:0.60 241:0.03 242:0.80 243:0.11 245:0.94 246:0.61 247:0.60 253:0.77 259:0.21 261:0.88 265:0.33 266:1.00 267:0.36 276:0.97 287:0.61 290:0.80 297:0.36 300:0.99 +2 7:0.78 12:0.06 17:0.72 27:0.70 30:0.56 32:0.78 34:0.33 36:0.87 37:0.32 39:0.96 43:0.32 55:0.84 66:0.35 69:0.07 70:0.76 74:0.89 76:0.85 77:0.70 81:0.87 91:0.54 98:0.73 104:0.89 106:0.81 108:0.93 117:0.86 123:0.93 124:0.88 126:0.32 127:0.92 129:0.59 133:0.89 135:0.71 145:0.50 146:0.80 147:0.83 149:0.79 150:0.73 154:0.86 159:0.91 172:0.89 173:0.06 177:0.38 178:0.55 179:0.19 187:0.39 195:0.98 206:0.81 212:0.27 215:0.96 216:0.38 222:0.97 230:0.81 232:0.82 233:0.76 235:0.02 241:0.21 242:0.70 243:0.36 245:0.93 247:0.67 253:0.63 254:0.74 259:0.21 265:0.74 266:0.92 267:0.76 271:0.22 276:0.92 282:0.86 283:0.82 297:0.36 300:0.70 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.06 17:0.47 21:0.13 27:0.72 30:0.28 34:0.93 36:0.48 37:0.55 39:0.96 43:0.83 60:0.87 66:0.79 69:0.65 70:0.83 74:0.90 81:0.78 83:0.85 85:0.88 91:0.61 96:0.75 98:0.75 101:0.79 104:0.80 106:0.81 108:0.49 114:0.92 117:0.86 120:0.63 121:0.90 122:0.51 123:0.47 124:0.38 126:0.54 127:0.37 129:0.05 133:0.39 135:0.54 140:0.45 145:0.97 146:0.78 147:0.82 149:0.81 150:0.86 151:0.70 153:0.69 154:0.80 155:0.67 158:0.92 159:0.43 162:0.86 164:0.62 165:0.88 172:0.61 173:0.66 176:0.73 177:0.38 179:0.62 187:0.21 192:0.59 201:0.74 202:0.46 204:0.89 206:0.81 208:0.64 212:0.27 215:0.84 216:0.16 220:0.74 222:0.97 230:0.87 232:0.84 233:0.76 235:0.22 241:0.24 242:0.40 243:0.80 245:0.53 247:0.47 248:0.66 253:0.82 255:0.79 256:0.45 259:0.21 260:0.64 265:0.75 266:0.47 267:0.52 268:0.55 271:0.22 276:0.51 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.55 +1 7:0.78 8:0.62 11:0.57 12:0.06 17:0.06 21:0.30 27:0.71 30:0.17 32:0.75 34:0.70 36:0.80 37:0.39 39:0.96 43:0.44 55:0.84 66:0.08 69:0.91 70:0.99 74:0.60 81:0.56 85:0.88 91:0.61 98:0.26 101:0.69 104:0.87 106:0.81 108:0.96 120:0.90 123:0.96 124:0.99 126:0.32 127:0.89 129:0.95 133:0.99 135:0.23 140:0.80 145:0.41 146:0.85 147:0.05 149:0.78 150:0.42 151:0.75 153:0.87 154:0.46 159:0.97 162:0.65 164:0.90 172:0.67 173:0.54 177:0.05 178:0.42 179:0.17 187:0.21 190:0.77 195:1.00 202:0.86 206:0.81 212:0.16 215:0.72 216:0.30 220:0.87 222:0.97 230:0.81 233:0.76 235:0.31 241:0.03 242:0.76 243:0.05 245:0.86 247:0.67 248:0.85 253:0.47 256:0.87 257:0.65 259:0.21 260:0.90 265:0.28 266:0.99 267:0.28 268:0.88 271:0.22 276:0.94 283:0.71 285:0.50 297:0.36 300:0.98 +1 1:0.74 7:0.78 8:0.71 9:0.80 12:0.06 17:0.53 21:0.05 27:0.64 30:0.20 34:0.53 36:0.74 37:0.32 39:0.96 41:0.82 43:0.44 55:0.92 66:0.07 69:0.97 70:0.95 74:0.59 81:0.83 83:0.79 91:0.54 96:0.77 98:0.24 108:0.10 114:0.95 120:0.65 123:0.94 124:0.98 126:0.54 127:0.92 129:0.81 133:0.97 135:0.78 140:0.45 146:0.38 147:0.44 149:0.72 150:0.90 151:0.77 153:0.48 154:0.17 155:0.67 159:0.95 162:0.86 163:0.94 164:0.57 165:0.90 172:0.45 173:0.83 176:0.73 177:0.05 179:0.30 187:0.21 192:0.59 201:0.74 202:0.36 208:0.64 212:0.16 215:0.91 216:0.13 220:0.62 222:0.97 230:0.81 233:0.69 235:0.12 241:0.21 242:0.73 243:0.24 245:0.77 247:0.60 248:0.71 253:0.80 254:0.74 255:0.79 256:0.45 257:0.65 259:0.21 260:0.66 265:0.14 266:0.98 267:0.84 268:0.46 271:0.22 276:0.85 283:0.71 285:0.62 290:0.95 297:0.36 300:0.94 +2 8:0.73 11:0.57 12:0.06 17:0.12 21:0.13 27:0.65 30:0.33 34:0.59 36:0.93 37:0.40 39:0.96 43:0.79 55:0.59 66:0.68 69:0.70 70:0.66 74:0.57 81:0.71 85:0.72 91:0.78 98:0.69 101:0.72 104:0.82 106:0.81 108:0.53 120:0.98 123:0.51 124:0.48 126:0.32 127:0.56 129:0.05 133:0.62 135:0.51 140:0.45 146:0.78 147:0.05 149:0.74 150:0.52 151:0.83 153:0.97 154:0.72 159:0.55 162:0.81 164:0.97 172:0.71 173:0.69 177:0.05 179:0.52 187:0.21 190:0.77 191:0.73 202:0.94 206:0.81 212:0.75 215:0.84 216:0.66 222:0.97 235:0.12 241:0.67 242:0.75 243:0.09 245:0.70 247:0.39 248:0.97 253:0.46 256:0.96 257:0.65 259:0.21 260:0.98 265:0.69 266:0.54 267:0.94 268:0.97 271:0.22 276:0.66 283:0.82 285:0.50 297:0.36 300:0.55 +1 7:0.78 8:0.65 11:0.57 12:0.06 17:0.16 21:0.13 27:0.71 30:0.42 32:0.75 34:0.71 36:0.83 37:0.27 39:0.96 43:0.48 55:0.71 66:0.16 69:0.08 70:0.85 74:0.60 81:0.71 85:0.72 91:0.70 98:0.47 101:0.65 104:0.97 106:0.81 108:0.07 120:0.74 123:0.07 124:0.94 126:0.32 127:0.95 129:0.93 133:0.93 135:0.24 140:0.45 145:0.52 146:0.91 147:0.93 149:0.81 150:0.52 151:0.66 153:0.48 154:0.60 159:0.91 162:0.71 164:0.69 172:0.75 173:0.07 177:0.38 179:0.21 187:0.87 190:0.77 195:0.97 202:0.60 206:0.81 212:0.40 215:0.84 216:0.37 220:0.74 222:0.97 230:0.81 233:0.69 235:0.12 241:0.03 242:0.75 243:0.37 245:0.90 247:0.60 248:0.67 253:0.48 256:0.58 259:0.21 260:0.75 265:0.49 266:0.94 267:0.85 268:0.63 271:0.22 276:0.91 283:0.82 285:0.50 297:0.36 300:0.84 +0 1:0.74 8:0.78 9:0.80 12:0.06 17:0.57 21:0.05 27:0.35 30:0.95 34:0.44 36:0.92 37:0.57 39:0.96 41:0.88 43:0.37 55:0.99 66:0.20 69:0.49 74:0.49 81:0.83 83:0.80 91:0.61 96:0.85 98:0.06 108:0.38 114:0.95 120:0.75 123:0.36 124:0.61 126:0.54 127:0.79 129:0.59 133:0.47 135:0.98 140:0.45 146:0.05 147:0.05 149:0.17 150:0.90 151:0.85 153:0.84 154:0.28 155:0.67 159:0.74 162:0.60 164:0.71 165:0.90 172:0.05 173:0.35 175:0.75 176:0.73 177:0.05 179:1.00 187:0.39 192:0.59 201:0.74 202:0.64 208:0.64 215:0.91 216:0.41 220:0.62 222:0.97 235:0.12 241:0.39 243:0.40 244:0.61 245:0.36 248:0.82 253:0.83 255:0.79 256:0.58 257:0.65 259:0.21 260:0.76 265:0.07 267:0.91 268:0.65 271:0.22 277:0.69 285:0.62 290:0.95 297:0.36 300:0.08 +0 1:0.74 8:0.96 9:0.80 12:0.06 17:0.30 21:0.30 27:0.27 30:0.76 34:0.71 36:0.89 37:0.86 39:0.96 43:0.38 55:0.45 66:0.64 69:0.15 70:0.15 74:0.65 81:0.65 91:0.70 96:0.80 98:0.44 108:0.12 114:0.86 117:0.86 120:0.61 122:0.57 123:0.12 126:0.54 127:0.14 129:0.05 135:0.69 140:0.80 146:0.28 147:0.35 149:0.14 150:0.70 151:0.60 153:0.84 154:0.88 155:0.67 158:0.84 159:0.12 162:0.52 164:0.71 172:0.16 173:0.48 176:0.73 177:0.38 179:0.80 187:0.39 190:0.77 191:0.96 192:0.59 201:0.74 202:0.78 208:0.64 212:0.95 215:0.72 216:0.45 220:0.74 222:0.97 232:0.85 235:0.42 241:0.65 242:0.82 243:0.69 247:0.12 248:0.62 253:0.81 254:0.80 255:0.79 256:0.77 259:0.21 260:0.62 265:0.46 266:0.12 267:0.73 268:0.74 271:0.22 276:0.15 285:0.62 290:0.86 297:0.36 300:0.13 +0 12:0.06 17:0.13 21:0.78 27:0.45 30:0.55 34:0.80 36:0.24 37:0.50 39:0.96 43:0.27 55:0.71 66:0.24 69:0.78 70:0.56 74:0.97 91:0.31 98:0.48 108:0.62 122:0.67 123:0.80 124:0.79 127:0.30 129:0.05 133:0.74 135:0.65 145:0.49 146:0.77 147:0.80 149:0.70 154:0.55 159:0.74 172:0.65 173:0.58 177:0.38 178:0.55 179:0.25 187:0.68 202:0.36 212:0.54 216:0.77 222:0.97 235:0.84 241:0.21 242:0.81 243:0.46 245:0.86 247:0.39 253:0.69 254:0.84 259:0.21 265:0.42 266:0.71 267:0.73 271:0.22 276:0.79 277:0.69 300:0.55 +1 1:0.74 11:0.57 12:0.06 17:0.34 21:0.59 27:0.41 30:0.26 32:0.78 34:0.91 36:0.86 37:0.72 39:0.96 43:0.89 55:0.71 66:0.15 69:0.53 70:0.87 74:0.88 77:0.70 81:0.53 83:0.73 85:0.80 91:0.38 98:0.45 101:0.72 108:0.85 114:0.74 123:0.85 124:0.86 126:0.54 127:0.64 129:0.93 133:0.84 135:0.65 145:0.87 146:0.68 147:0.73 149:0.78 150:0.70 154:0.87 155:0.67 159:0.82 165:0.78 172:0.59 173:0.30 176:0.73 177:0.38 178:0.55 179:0.30 187:0.39 192:0.59 195:0.93 201:0.74 212:0.37 215:0.55 216:0.94 222:0.97 235:0.74 241:0.27 242:0.63 243:0.31 245:0.88 247:0.67 253:0.71 259:0.21 265:0.47 266:0.94 267:0.95 271:0.22 276:0.83 282:0.86 290:0.74 297:0.36 300:0.70 +1 1:0.74 9:0.80 11:0.57 12:0.06 17:0.07 27:0.37 30:0.38 34:0.58 36:0.82 37:0.39 39:0.96 41:0.82 43:0.93 55:0.59 60:0.87 66:0.24 69:0.27 70:0.93 74:0.93 81:0.88 83:0.85 85:0.88 91:0.51 96:0.80 98:0.40 101:0.74 104:1.00 108:0.21 114:0.98 120:0.69 123:0.20 124:0.90 126:0.54 127:0.88 129:0.93 133:0.90 135:0.51 140:0.45 146:0.77 147:0.81 149:0.86 150:0.93 151:0.87 153:0.48 154:0.93 155:0.67 158:0.92 159:0.85 162:0.86 164:0.57 165:0.92 172:0.75 173:0.13 176:0.73 177:0.38 179:0.27 187:0.99 192:0.59 201:0.74 202:0.36 208:0.64 212:0.57 215:0.96 216:0.87 222:0.97 235:0.05 241:0.05 242:0.55 243:0.44 245:0.88 247:0.67 248:0.78 253:0.88 255:0.79 256:0.45 259:0.21 260:0.70 265:0.42 266:0.94 267:0.69 268:0.46 271:0.22 276:0.87 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +0 12:0.06 17:0.40 21:0.78 27:0.45 30:0.62 34:0.83 36:0.23 37:0.50 39:0.96 43:0.24 55:0.71 66:0.68 69:0.84 70:0.47 74:0.91 91:0.23 98:0.60 108:0.70 122:0.67 123:0.68 124:0.45 127:0.23 129:0.05 133:0.39 135:0.63 145:0.50 146:0.88 147:0.90 149:0.55 154:0.55 159:0.59 172:0.71 173:0.72 177:0.38 178:0.55 179:0.30 187:0.68 212:0.39 216:0.77 222:0.97 235:1.00 241:0.10 242:0.80 243:0.72 245:0.79 247:0.39 253:0.64 254:0.84 259:0.21 265:0.61 266:0.54 267:0.36 271:0.22 276:0.66 300:0.55 +0 1:0.74 8:0.89 9:0.80 12:0.06 17:0.01 21:0.40 27:0.43 30:0.45 32:0.78 34:0.47 36:0.40 37:0.72 39:0.96 43:0.21 55:0.42 66:0.49 69:0.75 70:0.25 74:0.71 76:0.85 77:0.70 81:0.60 91:0.51 96:0.80 98:0.14 108:0.58 114:0.82 120:0.68 122:0.51 123:0.56 124:0.48 126:0.54 127:0.31 129:0.05 133:0.39 135:0.28 140:0.45 145:0.44 146:0.21 147:0.27 149:0.09 150:0.67 151:0.79 153:0.69 154:0.71 155:0.67 159:0.42 162:0.84 163:0.79 164:0.72 172:0.16 173:0.76 176:0.73 177:0.38 179:0.96 187:0.87 192:0.59 195:0.72 201:0.74 202:0.60 208:0.64 212:0.61 215:0.67 216:0.66 220:0.62 222:0.97 235:0.52 241:0.65 242:0.63 243:0.60 245:0.39 247:0.30 248:0.76 253:0.83 254:0.74 255:0.79 256:0.64 259:0.21 260:0.70 265:0.15 266:0.17 267:0.93 268:0.66 271:0.22 276:0.13 282:0.86 285:0.62 290:0.82 297:0.36 300:0.18 +2 1:0.74 9:0.80 11:0.57 12:0.06 17:0.40 21:0.47 27:0.64 30:0.32 32:0.77 34:0.52 36:0.49 37:0.45 39:0.96 43:0.65 55:0.59 66:0.48 69:0.87 70:0.98 74:0.78 77:0.89 81:0.56 85:0.80 91:0.09 96:0.83 98:0.50 101:0.71 108:0.80 114:0.80 120:0.72 123:0.79 124:0.83 126:0.54 127:0.56 129:0.81 133:0.86 135:0.44 140:0.45 145:0.89 146:0.32 147:0.39 149:0.63 150:0.65 151:0.77 153:0.48 154:0.59 155:0.67 158:0.86 159:0.86 162:0.64 164:0.73 172:0.73 173:0.69 176:0.73 177:0.38 179:0.39 187:0.98 192:0.59 195:0.96 201:0.74 202:0.65 208:0.64 212:0.52 215:0.63 216:0.87 220:0.74 222:0.97 235:0.60 241:0.01 242:0.58 243:0.25 245:0.76 247:0.67 248:0.80 253:0.85 255:0.79 256:0.64 259:0.21 260:0.73 265:0.52 266:0.89 267:0.59 268:0.66 271:0.22 276:0.73 279:0.86 282:0.85 283:0.67 285:0.62 290:0.80 297:0.36 300:0.94 +2 1:0.74 8:0.82 11:0.57 12:0.06 17:0.15 21:0.30 27:0.58 30:0.21 34:0.33 36:0.64 37:0.45 39:0.96 43:0.96 55:0.71 60:0.95 66:0.09 69:0.15 70:0.75 74:0.89 81:0.75 83:0.87 85:0.88 91:0.51 98:0.50 101:0.78 108:0.12 114:0.86 122:0.67 123:0.83 124:0.80 126:0.54 127:0.89 129:0.81 133:0.76 135:0.51 146:0.35 147:0.41 149:0.84 150:0.84 154:0.96 155:0.67 158:0.86 159:0.65 165:0.84 172:0.45 173:0.17 176:0.73 177:0.38 178:0.55 179:0.65 187:0.68 192:0.59 201:0.74 202:0.46 208:0.64 212:0.55 215:0.72 216:0.50 222:0.97 235:0.52 241:0.10 242:0.44 243:0.47 245:0.70 247:0.60 253:0.75 259:0.21 265:0.26 266:0.75 267:0.84 271:0.22 276:0.58 277:0.69 279:0.86 283:0.66 290:0.86 297:0.36 300:0.55 +1 8:0.65 9:0.80 12:0.06 17:0.26 21:0.40 25:0.88 27:0.56 30:0.45 34:0.28 36:0.71 37:0.35 39:0.96 41:0.90 43:0.30 55:0.52 66:0.64 69:0.82 70:0.50 74:0.48 81:0.49 83:0.77 91:0.84 96:0.84 98:0.54 104:0.78 108:0.67 120:0.93 122:0.43 123:0.65 124:0.42 126:0.32 127:0.24 129:0.05 133:0.39 135:0.34 140:0.92 146:0.59 147:0.05 149:0.21 150:0.39 151:0.83 153:0.92 154:0.60 155:0.67 159:0.33 162:0.79 164:0.93 172:0.32 173:0.87 177:0.05 179:0.79 191:0.75 192:0.59 202:0.88 208:0.64 212:0.23 215:0.67 216:0.44 220:0.74 222:0.97 235:0.42 241:0.79 242:0.55 243:0.21 245:0.43 247:0.39 248:0.82 253:0.79 254:0.88 255:0.79 256:0.92 257:0.65 259:0.21 260:0.93 265:0.56 266:0.38 267:0.23 268:0.91 271:0.22 276:0.29 283:0.71 285:0.62 297:0.36 300:0.33 +2 6:0.87 7:0.81 8:0.86 12:0.06 17:0.43 20:0.91 21:0.40 25:0.88 27:0.05 28:0.46 34:0.86 36:0.66 37:0.78 78:0.79 81:0.84 91:0.91 100:0.83 104:0.72 111:0.79 120:0.92 126:0.54 135:0.51 140:0.90 150:0.64 151:0.74 152:0.85 153:0.82 161:0.54 162:0.53 164:0.93 167:0.79 169:0.83 175:0.75 178:0.50 181:0.62 186:0.79 187:0.39 189:0.89 191:0.99 202:0.93 215:0.67 216:0.67 220:0.62 230:0.65 233:0.63 235:0.42 238:0.93 241:0.71 244:0.61 248:0.88 256:0.91 257:0.65 259:0.21 260:0.92 261:0.82 267:0.91 268:0.91 271:0.94 277:0.69 283:0.60 285:0.62 297:0.36 +1 7:0.81 8:0.98 12:0.06 17:0.07 20:0.74 21:0.05 25:0.88 27:0.08 28:0.46 30:0.91 32:0.80 37:0.99 43:0.21 55:0.96 66:0.27 69:0.91 70:0.13 78:0.99 81:0.91 91:0.98 98:0.18 100:0.81 104:0.57 108:0.88 111:1.00 120:0.99 123:0.88 124:0.72 126:0.54 127:0.21 129:0.81 133:0.67 140:0.45 145:0.50 146:0.05 147:0.05 149:0.18 150:0.82 151:0.91 152:0.74 153:1.00 154:0.14 159:0.41 161:0.54 162:0.70 163:0.98 164:0.99 167:0.94 169:1.00 172:0.10 173:0.93 177:0.05 179:0.91 181:0.62 186:0.75 191:0.99 195:0.81 202:0.99 212:0.61 215:0.91 216:0.66 230:0.87 233:0.76 235:0.05 238:0.99 241:0.90 242:0.82 243:0.29 245:0.38 247:0.12 248:0.99 256:0.99 260:0.99 261:0.74 265:0.20 266:0.17 268:0.99 271:0.94 276:0.21 283:0.82 285:0.62 297:0.36 300:0.13 +1 6:0.91 8:0.74 12:0.06 17:0.20 20:0.75 21:0.78 25:0.88 27:0.32 28:0.46 34:0.71 36:0.51 37:0.73 43:0.51 66:0.36 69:0.15 78:0.77 91:0.94 98:0.10 100:0.78 104:0.74 108:0.12 111:0.76 120:0.98 123:0.12 127:0.07 129:0.05 135:0.56 140:0.45 146:0.05 147:0.05 149:0.16 151:0.85 152:0.77 153:0.98 154:0.40 159:0.05 161:0.54 162:0.72 164:0.99 167:0.81 169:0.76 172:0.05 173:0.41 177:0.05 181:0.62 186:0.76 187:0.39 189:0.83 191:0.93 202:0.98 216:0.88 235:0.22 238:0.83 241:0.72 243:0.51 248:0.98 256:0.98 259:0.21 260:0.98 261:0.75 265:0.11 267:0.98 268:0.99 271:0.94 283:0.82 285:0.62 +1 6:0.94 7:0.81 8:0.89 12:0.06 17:0.67 20:0.78 21:0.74 25:0.88 27:0.34 28:0.46 30:0.55 32:0.80 34:0.39 36:0.73 37:0.99 43:0.48 55:0.45 66:0.46 69:0.42 70:0.52 78:0.74 81:0.64 91:0.42 98:0.29 100:0.77 104:0.58 108:0.32 111:0.73 123:0.31 124:0.76 126:0.54 127:0.82 129:0.89 133:0.78 135:0.20 140:0.80 145:0.69 146:0.43 147:0.50 149:0.60 150:0.47 152:0.77 153:0.48 154:0.36 159:0.67 161:0.54 162:0.58 164:0.57 167:0.78 169:0.75 172:0.45 173:0.33 177:0.05 178:0.42 179:0.76 181:0.62 186:0.75 187:0.39 189:0.95 195:0.83 202:0.53 212:0.40 215:0.46 216:0.47 220:1.00 230:0.87 233:0.76 235:0.88 238:0.93 241:0.70 242:0.82 243:0.59 245:0.58 247:0.12 256:0.45 259:0.21 261:0.74 265:0.31 266:0.63 267:0.65 268:0.46 271:0.94 276:0.48 285:0.62 297:0.36 300:0.43 +2 12:0.06 17:0.43 21:0.78 27:0.07 28:0.46 34:0.21 36:0.36 37:0.87 43:0.51 66:0.36 69:0.10 91:0.92 98:0.13 108:0.09 120:0.94 123:0.09 127:0.08 129:0.05 135:0.23 140:0.87 145:0.50 146:0.05 147:0.05 149:0.16 151:0.80 153:0.93 154:0.40 159:0.05 161:0.54 162:0.52 164:0.94 167:0.89 172:0.05 173:0.52 177:0.05 178:0.48 181:0.62 187:0.39 202:0.94 216:0.88 235:0.05 241:0.77 243:0.51 248:0.91 256:0.92 259:0.21 260:0.94 265:0.13 267:0.44 268:0.92 271:0.94 283:0.82 285:0.62 +1 8:0.95 12:0.06 17:0.57 21:0.78 25:0.88 27:0.34 28:0.46 34:0.58 36:0.22 37:0.91 78:0.78 91:0.96 104:0.73 111:0.78 120:0.93 135:0.70 140:0.80 151:0.77 153:0.88 161:0.54 162:0.56 164:0.96 167:0.94 169:0.82 175:0.75 178:0.42 181:0.62 191:0.97 202:0.95 216:0.66 220:0.87 241:0.89 244:0.61 248:0.89 256:0.95 257:0.65 259:0.21 260:0.93 267:0.76 268:0.95 271:0.94 277:0.69 285:0.62 +4 8:0.90 12:0.06 17:0.38 21:0.13 25:0.88 27:0.64 28:0.46 30:0.21 32:0.80 34:0.75 36:0.63 37:0.85 43:0.42 55:0.52 66:0.24 69:0.48 70:0.87 81:0.89 91:0.82 98:0.45 104:0.54 108:0.87 120:0.84 123:0.87 124:0.84 126:0.54 127:0.87 129:0.93 133:0.84 135:0.18 140:0.45 145:0.85 146:0.54 147:0.60 149:0.59 150:0.79 151:0.78 153:0.89 154:0.32 159:0.71 161:0.54 162:0.68 163:0.92 164:0.84 167:0.94 172:0.41 173:0.36 177:0.05 179:0.65 181:0.62 191:0.98 195:0.81 202:0.77 212:0.18 215:0.84 216:0.18 235:0.12 241:0.88 242:0.82 243:0.37 245:0.66 247:0.12 248:0.76 256:0.78 259:0.21 260:0.84 265:0.47 266:0.87 267:0.18 268:0.80 271:0.94 276:0.59 283:0.60 285:0.62 297:0.36 300:0.70 +2 6:0.93 8:0.97 12:0.06 17:0.38 20:0.84 21:0.30 25:0.88 27:0.28 28:0.46 30:0.68 34:0.23 36:0.30 37:0.50 43:0.52 55:0.52 66:0.71 69:0.10 70:0.52 78:0.75 81:0.86 91:0.99 98:0.79 100:0.78 104:0.74 108:0.56 111:0.74 120:0.98 123:0.54 124:0.42 126:0.54 127:0.94 129:0.59 133:0.47 135:0.30 140:0.80 146:0.18 147:0.23 149:0.59 150:0.69 151:0.83 152:0.80 153:0.96 154:0.41 159:0.34 161:0.54 162:0.57 164:0.99 167:0.92 169:0.76 172:0.57 173:0.32 177:0.05 178:0.42 179:0.77 181:0.62 186:0.75 189:0.94 191:0.97 202:0.98 212:0.81 215:0.72 216:0.89 220:0.62 235:0.31 238:0.88 241:0.83 242:0.82 243:0.21 245:0.60 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.75 265:0.79 266:0.38 267:0.59 268:0.99 271:0.94 276:0.47 285:0.62 297:0.36 300:0.43 +1 6:0.93 7:0.81 8:0.97 12:0.06 17:0.07 20:0.81 21:0.74 25:0.88 27:0.15 28:0.46 30:0.15 32:0.80 34:0.25 36:0.52 37:0.93 43:0.48 55:0.45 66:0.53 69:0.42 70:0.68 78:0.75 81:0.64 91:0.88 98:0.34 100:0.78 104:0.57 108:0.32 111:0.75 120:0.93 123:0.31 124:0.68 126:0.54 127:0.93 129:0.89 133:0.78 135:0.19 140:0.45 145:0.69 146:0.51 147:0.57 149:0.50 150:0.47 151:0.80 152:0.78 153:0.93 154:0.36 159:0.69 161:0.54 162:0.65 164:0.95 167:0.95 169:0.77 172:0.37 173:0.32 177:0.05 179:0.88 181:0.62 186:0.75 189:0.94 191:0.99 195:0.84 202:0.93 212:0.15 215:0.46 216:0.49 220:0.62 230:0.87 233:0.76 235:0.88 238:0.94 241:0.91 242:0.82 243:0.62 245:0.46 247:0.12 248:0.90 256:0.94 259:0.21 260:0.93 261:0.75 265:0.37 266:0.63 267:0.97 268:0.94 271:0.94 276:0.36 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.91 7:0.81 8:0.81 12:0.06 17:0.02 20:0.87 21:0.05 25:0.88 27:0.30 28:0.46 30:0.30 32:0.80 34:0.20 36:0.58 37:0.32 43:0.47 55:0.71 66:0.51 69:0.37 70:0.60 78:0.75 81:0.91 91:0.98 98:0.76 100:0.80 104:0.79 108:0.67 111:0.75 120:0.98 123:0.65 124:0.52 126:0.54 127:0.75 129:0.59 133:0.47 135:0.37 140:0.45 145:0.38 146:0.49 147:0.56 149:0.51 150:0.82 151:0.83 152:0.82 153:0.96 154:0.35 159:0.48 161:0.54 162:0.52 163:0.81 164:0.99 167:0.94 169:0.77 172:0.41 173:0.41 177:0.05 179:0.82 181:0.62 186:0.76 189:0.92 191:0.95 195:0.64 202:0.99 212:0.23 215:0.91 216:0.65 220:0.62 230:0.65 233:0.63 235:0.05 238:0.94 241:0.90 242:0.82 243:0.39 245:0.63 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.78 265:0.76 266:0.71 267:0.96 268:0.99 271:0.94 276:0.44 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.93 8:0.88 12:0.06 17:0.20 20:0.83 21:0.05 25:0.88 27:0.42 28:0.46 30:0.62 32:0.80 34:0.47 36:0.55 37:0.62 43:0.44 55:0.45 66:0.30 69:0.44 70:0.34 78:0.75 81:0.91 91:0.99 98:0.22 100:0.79 104:0.73 108:0.73 111:0.75 120:0.97 123:0.71 124:0.71 126:0.54 127:0.63 129:0.81 133:0.67 135:0.30 140:0.45 145:0.76 146:0.05 147:0.05 149:0.34 150:0.82 151:0.81 152:0.79 153:0.94 154:0.34 159:0.37 161:0.54 162:0.69 163:0.81 164:0.99 167:0.95 169:0.76 172:0.16 173:0.54 177:0.05 179:0.94 181:0.62 186:0.76 189:0.95 191:0.94 195:0.60 202:0.97 212:0.30 215:0.91 216:0.82 220:0.62 235:0.05 238:0.91 241:0.92 242:0.82 243:0.23 245:0.43 247:0.12 248:0.96 256:0.98 259:0.21 260:0.97 261:0.76 265:0.24 266:0.27 267:0.79 268:0.98 271:0.94 276:0.23 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.97 8:0.91 12:0.06 17:0.55 20:0.75 21:0.05 25:0.88 27:0.64 28:0.46 30:0.62 34:0.23 36:0.36 37:0.77 43:0.37 55:0.92 66:0.30 69:0.59 70:0.34 78:0.77 81:0.91 91:0.87 98:0.27 100:0.79 104:0.57 108:0.45 111:0.77 120:0.87 123:0.61 124:0.61 126:0.54 127:0.15 129:0.59 133:0.47 135:0.30 140:0.45 146:0.38 147:0.44 149:0.37 150:0.82 151:0.74 152:0.74 153:0.84 154:0.28 159:0.28 161:0.54 162:0.66 163:0.93 164:0.90 167:0.95 169:0.79 172:0.16 173:0.47 177:0.05 179:0.59 181:0.62 186:0.76 189:0.98 191:0.98 202:0.85 212:0.61 215:0.91 216:0.38 220:0.62 235:0.05 238:0.95 241:0.96 242:0.82 243:0.48 245:0.47 247:0.12 248:0.81 256:0.86 259:0.21 260:0.87 261:0.74 265:0.26 266:0.23 267:0.28 268:0.87 271:0.94 276:0.28 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.94 8:0.96 12:0.06 17:0.05 20:0.80 21:0.78 28:0.46 34:0.53 36:0.25 37:0.96 78:0.82 91:0.99 100:0.82 111:0.84 120:0.98 135:0.78 140:0.95 151:0.83 152:0.77 153:0.95 161:0.54 162:0.46 164:0.99 167:0.95 169:0.87 175:0.75 178:0.53 181:0.62 186:0.77 189:0.95 191:1.00 202:1.00 216:0.88 220:0.62 238:0.97 241:0.95 244:0.61 248:0.97 256:0.99 257:0.65 259:0.21 260:0.98 261:0.77 267:0.91 268:0.99 271:0.94 277:0.69 285:0.62 +4 6:0.99 8:0.99 12:0.06 17:0.11 20:0.96 21:0.05 25:0.88 28:0.46 30:0.45 32:0.80 34:0.31 36:0.56 37:0.98 43:0.46 55:0.52 66:0.11 69:0.39 70:0.86 78:0.97 81:0.91 91:0.98 98:0.24 100:0.99 104:0.58 108:0.89 111:0.98 120:1.00 123:0.29 124:0.77 126:0.54 127:0.90 129:0.89 133:0.78 135:0.21 140:0.45 145:0.85 146:0.38 147:0.45 149:0.44 150:0.82 151:0.93 152:0.92 153:1.00 154:0.35 159:0.73 161:0.54 162:0.53 164:1.00 167:0.95 169:0.99 172:0.37 173:0.27 177:0.05 179:0.81 181:0.62 186:0.98 189:0.99 191:1.00 195:0.83 202:1.00 212:0.49 215:0.91 216:0.90 235:0.05 238:1.00 241:0.95 242:0.82 243:0.31 245:0.55 247:0.12 248:1.00 256:1.00 259:0.21 260:1.00 261:0.99 265:0.25 266:0.63 267:0.84 268:1.00 271:0.94 276:0.41 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.87 8:0.66 12:0.06 17:0.32 20:0.75 21:0.78 27:0.21 28:0.46 30:0.11 34:0.31 36:0.91 37:0.74 43:0.47 55:0.92 66:0.23 69:0.38 70:0.99 78:0.75 91:0.73 98:0.26 100:0.80 104:0.84 106:0.81 108:0.93 111:0.75 120:0.93 123:0.92 124:0.94 127:0.86 129:0.98 133:0.94 135:0.17 140:0.45 146:0.76 147:0.79 149:0.55 151:0.80 152:0.83 153:0.92 154:0.36 159:0.95 161:0.54 162:0.66 163:0.92 164:0.94 167:0.70 169:0.76 172:0.51 173:0.09 177:0.05 179:0.50 181:0.62 186:0.79 187:0.39 189:0.86 191:0.80 202:0.90 206:0.81 212:0.26 216:0.95 220:0.62 235:0.52 238:0.88 241:0.55 242:0.82 243:0.28 245:0.67 247:0.12 248:0.90 256:0.92 259:0.21 260:0.93 261:0.76 265:0.28 266:0.95 267:0.65 268:0.92 271:0.94 276:0.67 283:0.82 285:0.62 300:0.94 +1 6:0.98 7:0.81 8:0.98 12:0.06 17:0.05 20:0.75 21:0.74 25:0.88 27:0.11 28:0.46 30:0.55 32:0.80 34:0.21 36:0.96 37:0.98 43:0.43 55:0.98 66:0.10 69:0.51 70:0.71 78:0.77 81:0.64 91:0.93 98:0.23 100:0.91 104:0.57 108:0.94 111:0.78 120:0.98 123:0.92 124:0.92 126:0.54 127:0.96 129:0.97 132:0.97 133:0.92 135:0.34 140:0.45 145:0.52 146:0.05 147:0.05 149:0.37 150:0.47 151:0.90 152:0.74 153:0.99 154:0.33 159:0.87 161:0.54 162:0.64 163:0.99 164:0.99 167:0.93 169:0.81 172:0.27 173:0.24 177:0.05 179:0.77 181:0.62 186:0.81 189:0.99 191:1.00 195:0.95 202:0.98 212:0.29 215:0.46 216:0.55 230:0.65 233:0.63 235:0.88 238:0.96 241:0.86 242:0.82 243:0.13 245:0.51 247:0.12 248:0.97 256:0.98 260:0.98 261:0.75 265:0.17 266:0.75 267:0.76 268:0.98 271:0.94 276:0.51 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.99 8:0.93 12:0.06 17:0.43 20:0.74 21:0.05 25:0.88 28:0.46 30:0.38 34:0.58 36:0.44 37:0.98 43:0.46 55:0.42 66:0.54 69:0.41 70:0.58 78:0.84 81:0.91 91:0.81 98:0.63 100:0.88 104:0.58 108:0.32 111:0.84 120:0.89 123:0.31 124:0.50 126:0.54 127:0.64 129:0.59 133:0.47 135:0.20 140:0.45 146:0.52 147:0.58 149:0.54 150:0.82 151:0.75 152:0.92 153:0.85 154:0.35 159:0.34 161:0.54 162:0.79 164:0.92 167:0.84 169:0.99 172:0.41 173:0.54 177:0.05 179:0.83 181:0.62 186:0.84 187:0.21 189:0.90 191:0.96 202:0.86 212:0.42 215:0.91 216:0.90 220:0.62 235:0.05 238:0.91 241:0.75 242:0.82 243:0.63 245:0.59 247:0.12 248:0.85 256:0.89 259:0.21 260:0.90 261:0.99 265:0.64 266:0.54 267:0.79 268:0.90 271:0.94 276:0.38 283:0.60 285:0.62 297:0.36 300:0.43 +1 7:0.81 12:0.06 17:0.66 21:0.74 25:0.88 27:0.15 28:0.46 30:0.55 32:0.80 34:0.29 36:0.75 37:0.93 43:0.48 55:0.45 66:0.46 69:0.42 70:0.52 81:0.64 91:0.49 98:0.29 104:0.57 108:0.32 123:0.31 124:0.76 126:0.54 127:0.82 129:0.89 133:0.78 135:0.21 145:0.69 146:0.43 147:0.50 149:0.60 150:0.47 154:0.36 159:0.67 161:0.54 167:0.78 172:0.45 173:0.33 177:0.05 178:0.55 179:0.76 181:0.62 187:0.39 191:0.84 195:0.83 202:0.55 212:0.40 215:0.46 216:0.49 230:0.87 233:0.76 235:0.88 241:0.70 242:0.82 243:0.59 245:0.58 247:0.12 259:0.21 265:0.31 266:0.63 267:0.44 271:0.94 276:0.48 297:0.36 300:0.43 +2 6:0.87 7:0.81 8:0.73 12:0.06 17:0.28 20:0.88 21:0.30 27:0.21 28:0.46 30:0.43 34:0.75 36:0.96 37:0.74 43:0.52 55:0.59 66:0.93 69:0.10 70:0.59 78:0.74 81:0.86 91:0.77 98:0.93 100:0.78 104:0.84 106:0.81 108:0.09 111:0.74 120:0.96 123:0.09 124:0.37 126:0.54 127:0.75 129:0.81 133:0.67 135:0.17 140:0.45 146:1.00 147:1.00 149:0.91 150:0.69 151:0.84 152:0.83 153:0.97 154:0.41 159:0.91 161:0.54 162:0.71 164:0.94 167:0.73 169:0.76 172:0.98 173:0.07 177:0.05 179:0.13 181:0.62 186:0.75 187:0.39 189:0.88 191:0.73 202:0.90 206:0.81 212:0.75 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.91 241:0.62 242:0.82 243:0.93 245:0.89 247:0.12 248:0.94 256:0.92 259:0.21 260:0.96 261:0.76 265:0.93 266:0.75 267:0.82 268:0.92 271:0.94 276:0.96 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.97 8:0.94 12:0.06 17:0.30 20:0.91 21:0.54 25:0.88 27:0.16 28:0.46 30:0.38 32:0.80 34:0.66 36:0.30 37:0.85 43:0.52 55:0.92 66:0.29 69:0.17 70:0.78 78:0.74 81:0.79 91:0.96 98:0.31 100:0.79 104:0.78 108:0.14 111:0.74 120:0.95 123:0.13 124:0.83 126:0.54 127:0.79 129:0.93 133:0.84 135:0.88 140:0.80 145:0.82 146:0.49 147:0.55 149:0.49 150:0.59 151:0.80 152:0.91 153:0.92 154:0.41 159:0.71 161:0.54 162:0.58 164:0.99 167:0.77 169:0.77 172:0.27 173:0.15 177:0.05 178:0.42 179:0.84 181:0.62 186:0.75 187:0.39 189:0.98 191:0.98 195:0.84 202:0.98 212:0.28 215:0.58 216:0.81 220:0.62 235:0.60 238:0.97 241:0.69 242:0.82 243:0.48 245:0.49 247:0.12 248:0.93 256:0.98 259:0.21 260:0.95 261:0.79 265:0.33 266:0.63 267:1.00 268:0.98 271:0.94 276:0.39 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.93 8:0.90 12:0.06 17:0.11 20:0.81 21:0.78 27:0.07 28:0.46 34:0.19 36:0.33 37:0.87 43:0.51 66:0.36 69:0.10 78:0.86 91:0.99 98:0.13 100:0.79 108:0.09 111:0.85 120:0.99 123:0.09 127:0.08 129:0.05 135:0.24 140:0.95 145:0.50 146:0.05 147:0.05 149:0.16 151:0.86 152:0.85 153:0.99 154:0.40 159:0.05 161:0.54 162:0.51 164:1.00 167:0.94 169:0.82 172:0.05 173:0.54 177:0.05 178:0.53 181:0.62 186:0.76 189:0.96 191:1.00 202:1.00 216:0.88 220:0.62 235:0.05 238:0.88 241:0.91 243:0.51 248:0.99 256:0.99 259:0.21 260:0.99 261:0.79 265:0.14 267:0.44 268:1.00 271:0.94 285:0.62 +1 6:0.94 7:0.81 8:0.82 12:0.06 17:0.59 20:0.76 21:0.74 25:0.88 27:0.34 28:0.46 30:0.55 32:0.80 34:0.36 36:0.76 37:0.99 43:0.48 55:0.45 66:0.52 69:0.42 70:0.52 78:0.74 81:0.64 91:0.46 98:0.34 100:0.77 104:0.58 108:0.32 111:0.73 123:0.31 124:0.71 126:0.54 127:0.78 129:0.89 133:0.78 135:0.39 145:0.69 146:0.51 147:0.57 149:0.61 150:0.47 152:0.77 154:0.36 159:0.69 161:0.54 167:0.82 169:0.75 172:0.48 173:0.31 177:0.05 178:0.55 179:0.75 181:0.62 186:0.75 187:0.39 189:0.89 195:0.85 202:0.61 212:0.40 215:0.46 216:0.47 230:0.87 233:0.76 235:0.88 238:0.94 241:0.73 242:0.82 243:0.61 245:0.56 247:0.12 259:0.21 261:0.74 265:0.36 266:0.71 267:0.36 271:0.94 276:0.47 297:0.36 300:0.43 +1 7:0.81 12:0.06 17:0.49 21:0.22 27:0.45 28:0.46 30:0.14 32:0.80 34:0.77 36:0.28 37:0.50 43:0.36 55:0.52 66:0.43 69:0.60 70:0.96 81:0.88 91:0.06 98:0.44 108:0.83 123:0.82 124:0.76 126:0.54 127:0.62 129:0.89 133:0.78 135:0.66 145:0.52 146:0.58 147:0.63 149:0.67 150:0.74 154:0.27 159:0.80 161:0.54 167:0.59 172:0.70 173:0.39 177:0.05 178:0.55 179:0.39 181:0.62 187:0.68 195:0.92 212:0.54 215:0.79 216:0.77 230:0.87 233:0.76 235:0.22 241:0.15 242:0.82 243:0.22 245:0.82 247:0.12 259:0.21 265:0.46 266:0.87 267:0.69 271:0.94 276:0.74 283:0.60 297:0.36 300:0.94 +2 6:0.89 8:0.84 12:0.06 17:0.02 20:0.85 21:0.13 25:0.88 27:0.26 28:0.46 30:0.56 34:0.33 36:0.50 37:0.54 43:0.47 55:0.71 66:0.67 69:0.38 70:0.53 78:0.75 81:0.89 91:0.98 98:0.61 100:0.79 104:0.73 108:0.30 111:0.75 120:0.97 123:0.28 124:0.43 126:0.54 127:0.32 129:0.59 133:0.47 135:0.32 140:0.45 146:0.76 147:0.80 149:0.54 150:0.79 151:0.83 152:0.81 153:0.96 154:0.35 159:0.52 161:0.54 162:0.60 164:0.99 167:0.95 169:0.77 172:0.48 173:0.30 177:0.05 179:0.70 181:0.62 186:0.76 189:0.91 191:0.95 202:0.98 212:0.23 215:0.84 216:0.73 220:0.62 235:0.12 238:0.92 241:0.92 242:0.82 243:0.72 245:0.56 247:0.12 248:0.95 256:0.98 259:0.21 260:0.97 261:0.77 265:0.62 266:0.54 267:0.95 268:0.98 271:0.94 276:0.41 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.96 7:0.81 8:0.91 12:0.06 17:0.15 20:0.88 21:0.30 25:0.88 27:0.09 28:0.46 32:0.80 34:0.42 36:0.45 37:0.85 43:0.25 66:0.36 69:0.83 78:0.75 81:0.86 91:0.99 98:0.10 100:0.80 104:0.72 108:0.67 111:0.76 120:0.96 123:0.65 126:0.54 127:0.07 129:0.05 135:0.34 140:0.45 145:0.89 146:0.05 147:0.05 149:0.11 150:0.69 151:0.80 152:0.83 153:0.92 154:0.18 159:0.05 161:0.54 162:0.68 164:0.98 167:0.96 169:0.78 172:0.05 173:1.00 177:0.05 181:0.62 186:0.76 189:0.97 191:0.97 195:0.58 202:0.97 215:0.72 216:0.67 220:0.74 230:0.87 233:0.76 235:0.31 238:0.96 241:0.99 243:0.51 248:0.95 256:0.98 259:0.21 260:0.96 261:0.78 265:0.10 267:0.89 268:0.98 271:0.94 283:0.60 285:0.62 297:0.36 +1 1:0.74 11:0.57 12:0.69 17:0.49 21:0.68 27:0.45 28:0.73 30:0.45 32:0.75 34:0.83 36:0.17 37:0.50 43:0.81 55:0.92 60:0.87 66:0.36 69:0.71 70:0.50 74:0.56 81:0.48 83:0.85 85:0.72 91:0.25 98:0.30 101:0.71 108:0.54 114:0.70 123:0.52 124:0.76 126:0.54 127:0.41 129:0.05 133:0.74 135:0.85 145:0.50 146:0.46 147:0.52 149:0.53 150:0.66 154:0.76 155:0.67 158:0.87 159:0.61 161:0.57 163:0.75 165:0.69 167:0.47 172:0.21 173:0.63 176:0.73 177:0.38 178:0.55 179:0.89 181:0.51 187:0.68 192:0.59 195:0.82 201:0.74 208:0.64 212:0.23 215:0.49 216:0.77 222:0.25 235:0.88 241:0.18 242:0.73 243:0.51 245:0.42 247:0.35 253:0.57 259:0.21 265:0.32 266:0.38 267:0.59 271:0.97 276:0.30 279:0.86 283:0.71 290:0.70 297:0.36 300:0.33 +2 1:0.74 6:0.94 7:0.78 8:0.80 9:0.80 12:0.69 17:0.34 20:0.99 21:0.47 27:0.03 28:0.73 30:0.45 32:0.80 34:0.19 36:0.62 37:0.69 43:0.37 55:0.52 66:0.36 69:0.52 70:0.74 74:0.57 77:0.70 78:0.98 81:0.57 91:0.95 96:0.97 98:0.30 100:0.99 108:0.83 111:0.99 114:0.80 120:0.96 123:0.82 124:0.70 126:0.54 127:0.64 129:0.81 133:0.67 135:0.32 140:0.92 145:0.49 146:0.33 147:0.39 149:0.48 150:0.65 151:0.71 152:0.92 153:0.69 154:0.28 155:0.67 158:0.84 159:0.56 161:0.57 162:0.59 164:0.95 167:0.79 169:0.99 172:0.37 173:0.49 175:0.75 176:0.73 177:0.05 179:0.78 181:0.51 186:0.98 189:0.95 190:0.77 191:0.99 192:0.59 195:0.74 201:0.74 202:0.95 208:0.64 212:0.36 215:0.63 216:0.53 220:0.62 222:0.25 230:0.81 232:0.76 233:0.69 235:0.68 238:0.96 241:0.83 242:0.82 243:0.37 244:0.61 245:0.60 247:0.12 248:0.81 253:0.96 255:0.79 256:0.98 257:0.65 259:0.21 260:0.96 261:0.97 265:0.33 266:0.63 267:0.76 268:0.95 271:0.97 276:0.39 277:0.69 282:0.88 283:0.71 285:0.62 290:0.80 297:0.36 300:0.55 +3 6:0.89 7:0.78 8:0.70 9:0.80 11:0.57 12:0.69 17:0.89 20:0.87 21:0.54 27:0.68 28:0.73 30:0.60 32:0.75 34:0.19 36:0.68 37:0.64 41:0.82 43:0.60 55:0.45 66:0.99 69:0.43 70:0.50 74:0.36 78:0.95 81:0.68 83:0.75 85:0.72 91:0.83 96:0.79 98:0.99 100:0.95 101:0.71 104:0.58 108:0.33 111:0.94 120:0.94 123:0.32 126:0.32 127:0.90 129:0.05 135:0.37 140:0.98 145:0.70 146:0.95 147:0.96 149:0.92 150:0.49 151:0.82 152:0.82 153:0.46 154:0.49 155:0.67 159:0.62 161:0.57 162:0.80 164:0.92 167:0.78 169:0.93 172:0.97 173:0.37 177:0.38 179:0.18 181:0.51 186:0.94 189:0.91 191:0.86 192:0.59 195:0.77 202:0.86 208:0.64 212:0.93 215:0.58 216:0.19 220:0.62 222:0.25 230:0.81 233:0.69 235:0.88 238:0.90 241:0.79 242:0.82 243:0.99 247:0.39 248:0.74 253:0.68 255:0.79 256:0.91 259:0.21 260:0.94 261:0.90 265:0.99 266:0.47 267:1.00 268:0.90 271:0.97 276:0.93 283:0.71 285:0.62 297:0.36 300:0.70 +2 6:0.96 7:0.78 8:0.95 9:0.80 12:0.69 17:0.16 20:0.91 21:0.64 27:0.13 28:0.73 30:0.72 32:0.75 34:0.30 36:0.90 37:0.82 41:0.82 43:0.40 55:0.45 66:0.24 69:0.41 70:0.67 78:0.90 81:0.34 83:0.74 91:0.96 96:0.77 98:0.29 100:0.91 108:0.76 111:0.90 120:0.97 123:0.30 124:0.80 126:0.32 127:0.95 129:0.93 133:0.84 135:0.32 140:1.00 145:0.85 146:0.61 147:0.67 149:0.72 150:0.31 151:0.77 152:0.85 153:0.48 154:0.31 155:0.67 159:0.85 161:0.57 162:0.52 164:0.97 167:0.80 169:0.89 172:0.63 173:0.19 175:0.75 177:0.05 178:0.48 179:0.50 181:0.51 186:0.90 189:0.97 190:0.77 191:0.98 192:0.59 195:0.94 202:0.97 208:0.64 212:0.71 215:0.53 216:0.63 220:0.62 222:0.25 230:0.81 233:0.76 235:0.74 238:0.88 241:0.83 242:0.82 243:0.44 244:0.61 245:0.75 247:0.12 248:0.71 253:0.63 255:0.79 256:0.97 257:0.65 259:0.21 260:0.97 261:0.89 265:0.30 266:0.75 267:0.69 268:0.96 271:0.97 276:0.66 277:0.69 283:0.71 285:0.62 297:0.36 300:0.84 +1 6:0.83 7:0.78 8:0.71 12:0.69 17:0.75 20:0.85 21:0.59 27:0.18 28:0.73 30:0.95 34:0.31 36:0.39 37:0.37 43:0.37 55:0.84 66:0.54 69:0.53 74:0.44 78:0.88 81:0.28 91:0.94 96:0.72 98:0.35 100:0.86 108:0.41 111:0.86 114:0.64 120:0.76 123:0.39 126:0.32 127:0.12 129:0.05 135:0.24 140:0.45 145:0.47 146:0.32 147:0.38 149:0.21 150:0.27 151:0.62 152:0.80 153:0.45 154:0.28 159:0.13 161:0.57 162:0.50 164:0.72 167:0.78 169:0.84 172:0.10 173:0.66 176:0.56 177:0.38 179:0.87 181:0.51 186:0.88 189:0.85 190:0.77 191:0.97 202:0.86 216:0.35 220:0.97 222:0.25 230:0.81 233:0.69 235:0.74 238:0.84 241:0.80 243:0.63 244:0.61 248:0.69 253:0.55 256:0.91 259:0.21 260:0.77 261:0.83 265:0.37 267:0.88 268:0.79 271:0.97 283:0.71 285:0.62 290:0.64 300:0.08 +1 1:0.74 6:0.85 7:0.78 8:0.63 12:0.69 17:0.89 20:0.91 21:0.47 27:0.21 28:0.73 30:0.21 32:0.75 34:0.20 36:0.19 37:0.25 43:0.45 55:0.45 66:0.75 69:0.25 70:0.64 74:0.44 78:0.93 81:0.72 91:0.85 96:0.74 98:0.58 100:0.92 108:0.20 111:0.92 114:0.80 120:0.80 123:0.19 124:0.39 126:0.54 127:0.43 129:0.05 132:0.97 133:0.39 135:0.24 140:0.45 145:0.83 146:0.56 147:0.62 149:0.43 150:0.76 151:0.70 152:0.87 153:0.91 154:0.78 155:0.67 159:0.38 161:0.57 162:0.75 164:0.80 167:0.79 169:0.89 172:0.54 173:0.33 176:0.73 177:0.38 179:0.73 181:0.51 186:0.93 189:0.87 190:0.77 191:0.95 192:0.59 195:0.66 201:0.74 202:0.76 212:0.83 215:0.63 216:0.14 220:0.62 222:0.25 230:0.81 233:0.76 235:0.84 238:0.86 241:0.81 242:0.77 243:0.77 245:0.50 247:0.39 248:0.73 253:0.67 254:0.88 256:0.81 260:0.81 261:0.91 265:0.59 266:0.27 267:0.19 268:0.80 271:0.97 276:0.37 285:0.62 290:0.80 297:0.36 300:0.43 +2 8:0.62 12:0.69 17:0.67 21:0.64 27:0.45 28:0.73 30:0.76 34:0.83 36:0.18 37:0.50 43:0.74 55:0.42 66:0.77 69:0.70 70:0.53 74:0.49 81:0.34 91:0.17 98:0.59 108:0.53 123:0.51 124:0.41 126:0.32 127:0.44 129:0.05 133:0.39 135:0.76 145:0.49 146:0.77 147:0.81 149:0.88 150:0.31 154:0.65 159:0.62 161:0.57 167:0.48 172:0.89 173:0.62 177:0.38 178:0.55 179:0.24 181:0.51 187:0.68 212:0.86 215:0.53 216:0.77 222:0.25 235:0.74 241:0.21 242:0.81 243:0.79 245:0.90 247:0.47 253:0.41 259:0.21 265:0.60 266:0.75 267:0.52 271:0.97 276:0.80 297:0.36 300:0.70 +2 7:0.78 8:0.70 12:0.69 17:0.81 21:0.30 27:0.48 28:0.73 30:0.94 32:0.80 34:0.34 36:0.36 37:0.31 43:0.36 55:0.71 66:0.72 69:0.53 70:0.20 74:0.58 76:0.94 77:0.70 81:0.68 91:0.56 98:0.82 104:0.88 108:0.41 120:0.59 123:0.39 124:0.43 126:0.32 127:0.70 129:0.59 133:0.47 135:0.37 140:0.45 145:0.49 146:0.92 147:0.94 149:0.77 150:0.49 151:0.53 153:0.77 154:0.27 159:0.68 161:0.57 162:0.68 164:0.64 167:0.47 172:0.84 173:0.42 175:0.75 177:0.05 179:0.38 181:0.51 187:0.39 190:0.77 191:0.96 195:0.83 202:0.54 212:0.85 215:0.72 216:0.23 220:0.87 222:0.25 230:0.81 233:0.76 235:0.42 241:0.18 242:0.82 243:0.75 244:0.61 245:0.86 247:0.12 248:0.52 253:0.46 256:0.45 257:0.65 259:0.21 260:0.59 265:0.82 266:0.63 267:0.18 268:0.57 271:0.97 276:0.76 277:0.69 282:0.88 283:0.71 285:0.50 297:0.36 300:0.43 +2 6:0.93 7:0.78 8:0.88 12:0.69 17:0.13 20:0.87 21:0.30 27:0.09 28:0.73 30:0.31 32:0.75 34:0.18 36:0.49 37:0.54 43:0.45 55:0.59 66:0.20 69:0.17 70:0.82 78:0.95 81:0.77 91:0.97 98:0.62 100:0.95 108:0.92 111:0.94 120:0.98 123:0.92 124:0.91 126:0.32 127:0.99 129:0.97 133:0.92 135:0.34 140:0.45 145:0.91 146:0.33 147:0.39 149:0.93 150:0.57 151:0.78 152:0.82 153:0.78 154:0.34 159:0.97 161:0.57 162:0.57 164:0.98 167:0.77 169:0.93 172:0.98 173:0.06 175:0.75 177:0.05 179:0.09 181:0.51 186:0.95 189:0.94 190:0.77 191:0.98 195:1.00 202:0.97 212:0.69 215:0.72 216:0.57 220:0.62 222:0.25 230:0.81 233:0.69 235:0.60 238:0.90 241:0.79 242:0.82 243:0.05 244:0.61 245:1.00 247:0.12 248:0.97 253:0.20 256:0.99 257:0.65 259:0.21 260:0.98 261:0.90 265:0.63 266:0.91 267:0.98 268:0.97 271:0.97 276:0.99 277:0.69 283:0.71 285:0.50 297:0.36 300:0.94 +0 12:0.69 17:0.64 21:0.76 25:0.88 28:0.73 30:0.09 32:0.75 34:0.42 36:0.25 37:0.97 43:0.36 55:0.42 66:0.08 69:0.56 70:0.95 74:0.32 81:0.32 91:0.33 98:0.16 104:0.58 108:0.95 123:0.42 124:0.90 126:0.32 127:0.94 129:0.96 133:0.90 135:0.26 145:0.67 146:0.33 147:0.40 149:0.40 150:0.30 154:0.27 159:0.88 161:0.57 167:0.56 172:0.21 173:0.27 175:0.75 177:0.05 178:0.55 179:0.84 181:0.51 187:0.39 195:0.96 212:0.13 215:0.46 216:0.98 222:0.25 235:0.97 241:0.55 242:0.76 243:0.25 244:0.61 245:0.48 247:0.39 253:0.30 257:0.65 259:0.21 265:0.11 266:0.75 267:0.23 271:0.97 276:0.41 277:0.69 297:0.36 300:0.70 +2 6:0.82 7:0.78 8:0.69 9:0.80 12:0.69 17:0.83 20:0.80 21:0.30 27:0.48 28:0.73 30:0.95 32:0.80 34:0.44 36:0.28 37:0.31 43:0.36 55:0.71 66:0.72 69:0.53 70:0.19 74:0.58 76:0.94 77:0.70 78:0.90 81:0.68 91:0.65 96:0.72 98:0.82 100:0.91 104:0.88 108:0.41 111:0.89 120:0.74 123:0.39 124:0.43 126:0.32 127:0.61 129:0.59 133:0.47 135:0.70 140:0.94 145:0.49 146:0.91 147:0.92 149:0.78 150:0.49 151:0.60 152:0.78 153:0.48 154:0.27 155:0.67 159:0.63 161:0.57 162:0.83 164:0.81 167:0.51 169:0.89 172:0.84 173:0.44 175:0.75 177:0.05 179:0.36 181:0.51 186:0.90 187:0.39 189:0.85 190:0.77 191:0.97 192:0.59 195:0.81 202:0.71 208:0.64 212:0.92 215:0.72 216:0.23 220:0.87 222:0.25 230:0.81 233:0.76 235:0.42 238:0.90 241:0.39 242:0.82 243:0.75 244:0.61 245:0.86 247:0.12 248:0.58 253:0.53 255:0.79 256:0.82 257:0.65 259:0.21 260:0.75 261:0.83 265:0.82 266:0.38 267:0.18 268:0.77 271:0.97 276:0.77 277:0.69 282:0.88 283:0.71 285:0.62 297:0.36 300:0.33 +0 6:0.83 8:0.98 12:0.69 17:0.06 20:0.85 25:0.88 27:0.37 28:0.73 30:0.30 34:0.26 36:0.29 37:0.97 43:0.42 55:0.84 66:0.26 69:0.21 70:0.66 78:0.83 81:0.87 91:1.00 98:0.67 100:0.83 104:0.54 108:0.65 111:0.81 120:1.00 123:0.16 124:0.58 126:0.32 127:0.84 129:0.59 133:0.47 135:0.39 140:0.45 146:0.53 147:0.59 149:0.53 150:0.73 151:0.91 152:0.80 153:1.00 154:0.32 159:0.35 161:0.57 162:0.53 163:0.75 164:0.99 167:0.81 169:0.81 172:0.37 173:0.39 175:0.75 177:0.05 179:0.83 181:0.51 186:0.83 189:0.85 190:0.77 191:1.00 202:0.99 212:0.23 215:0.96 216:0.92 222:0.25 235:0.02 238:0.85 241:0.87 242:0.82 243:0.45 244:0.61 245:0.64 247:0.12 248:1.00 253:0.20 256:0.99 257:0.65 259:0.21 260:1.00 261:0.81 265:0.59 266:0.54 267:0.93 268:0.99 271:0.97 276:0.43 277:0.69 283:0.82 285:0.50 297:0.36 300:0.43 +2 1:0.74 6:0.85 7:0.78 8:0.63 12:0.69 17:0.89 20:0.91 21:0.47 27:0.21 28:0.73 30:0.21 32:0.75 34:0.20 36:0.19 37:0.25 43:0.45 55:0.45 66:0.75 69:0.25 70:0.64 74:0.44 78:0.93 81:0.72 91:0.85 96:0.74 98:0.58 100:0.92 108:0.20 111:0.92 114:0.80 120:0.80 123:0.19 124:0.39 126:0.54 127:0.43 129:0.05 132:0.97 133:0.39 135:0.24 140:0.45 145:0.83 146:0.56 147:0.62 149:0.43 150:0.76 151:0.70 152:0.87 153:0.91 154:0.78 155:0.67 159:0.38 161:0.57 162:0.75 164:0.80 167:0.79 169:0.89 172:0.54 173:0.33 176:0.73 177:0.38 179:0.73 181:0.51 186:0.93 189:0.87 190:0.77 191:0.95 192:0.59 195:0.66 201:0.74 202:0.76 212:0.83 215:0.63 216:0.14 220:0.62 222:0.25 230:0.81 233:0.76 235:0.84 238:0.86 241:0.81 242:0.77 243:0.77 245:0.50 247:0.39 248:0.73 253:0.67 254:0.88 256:0.81 260:0.81 261:0.91 265:0.59 266:0.27 267:0.19 268:0.80 271:0.97 276:0.37 285:0.62 290:0.80 297:0.36 300:0.43 +2 6:0.93 7:0.78 8:0.79 12:0.69 17:0.73 20:0.85 21:0.71 27:0.10 28:0.73 30:0.66 32:0.75 34:0.17 36:0.55 37:0.72 43:0.39 55:0.45 66:0.63 69:0.49 70:0.52 78:0.94 81:0.40 91:0.96 98:0.67 100:0.91 108:0.74 111:0.92 120:0.98 123:0.72 124:0.64 126:0.32 127:0.93 129:0.89 133:0.78 135:0.34 140:0.45 145:0.93 146:0.28 147:0.34 149:0.86 150:0.34 151:0.75 152:0.80 153:0.48 154:0.29 159:0.71 161:0.57 162:0.72 164:0.97 167:0.81 169:0.88 172:0.92 173:0.37 175:0.75 177:0.05 179:0.23 181:0.51 186:0.94 189:0.94 190:0.77 191:0.97 195:0.84 202:0.95 212:0.89 215:0.48 216:0.50 220:0.62 222:0.25 230:0.81 233:0.69 235:0.95 238:0.85 241:0.88 242:0.82 243:0.11 244:0.61 245:0.92 247:0.12 248:0.97 253:0.20 256:0.97 257:0.65 259:0.21 260:0.98 261:0.87 265:0.67 266:0.75 267:0.88 268:0.97 271:0.97 276:0.88 277:0.69 283:0.71 285:0.62 297:0.36 300:0.84 +3 6:0.79 7:0.78 8:0.58 12:0.69 17:0.89 20:0.79 21:0.78 27:0.57 28:0.73 30:0.38 32:0.75 34:0.33 36:0.33 37:0.31 43:0.42 55:0.59 66:0.64 69:0.29 70:0.45 74:0.46 78:0.93 91:0.84 98:0.48 100:0.84 104:0.58 108:0.22 111:0.90 123:0.22 124:0.44 127:0.94 129:0.59 133:0.47 135:0.39 145:0.52 146:0.56 147:0.62 149:0.53 152:0.77 154:0.32 158:0.85 159:0.57 161:0.57 167:0.81 169:0.82 172:0.45 173:0.29 175:0.75 177:0.05 178:0.55 179:0.86 181:0.51 186:0.93 189:0.81 191:0.79 195:0.71 202:0.60 212:0.57 216:0.04 222:0.25 230:0.81 233:0.69 235:0.42 238:0.82 241:0.89 242:0.82 243:0.69 244:0.61 245:0.55 247:0.12 253:0.39 257:0.65 259:0.21 261:0.82 265:0.50 266:0.47 267:0.19 271:0.97 276:0.27 277:0.69 283:0.71 300:0.33 +3 6:0.82 7:0.78 8:0.70 12:0.69 17:0.93 20:0.83 21:0.54 27:0.67 28:0.73 30:0.45 32:0.75 34:0.20 36:0.25 37:0.41 43:0.34 55:0.59 66:0.69 69:0.62 70:0.25 78:0.90 81:0.58 91:0.89 98:0.84 100:0.87 104:0.58 108:0.47 111:0.88 120:0.90 123:0.45 126:0.32 127:0.34 129:0.05 135:0.44 140:0.45 145:0.70 146:0.53 147:0.59 149:0.29 150:0.43 151:0.64 152:0.79 153:0.78 154:0.25 159:0.19 161:0.57 162:0.85 163:0.90 164:0.91 167:0.78 169:0.84 172:0.21 173:0.86 175:0.75 177:0.05 179:0.96 181:0.51 186:0.90 189:0.84 190:0.77 191:0.87 195:0.54 202:0.84 212:0.61 215:0.58 216:0.21 220:0.74 222:0.25 230:0.81 233:0.76 235:0.74 238:0.84 241:0.79 242:0.82 243:0.73 244:0.61 247:0.12 248:0.85 253:0.20 256:0.90 257:0.65 259:0.21 260:0.90 261:0.83 265:0.84 266:0.17 267:0.23 268:0.89 271:0.97 276:0.20 277:0.69 283:0.71 285:0.50 297:0.36 300:0.18 +1 1:0.74 8:0.58 9:0.80 12:0.69 17:0.90 21:0.30 27:0.72 28:0.73 30:0.76 32:0.74 34:0.37 36:0.80 37:0.24 43:0.34 55:0.59 66:0.77 69:0.55 70:0.29 74:0.61 77:0.70 81:0.73 91:0.85 96:0.84 98:0.84 108:0.42 114:0.86 120:0.77 123:0.41 126:0.54 127:0.34 129:0.05 135:0.49 140:0.90 145:0.52 146:0.51 147:0.57 149:0.26 150:0.77 151:0.58 153:0.72 154:0.74 155:0.67 159:0.18 161:0.57 162:0.84 163:0.90 164:0.76 167:0.79 172:0.37 173:0.83 176:0.73 177:0.38 179:0.87 181:0.51 190:0.77 192:0.59 195:0.53 201:0.74 202:0.67 208:0.64 212:0.52 215:0.72 216:0.05 220:0.74 222:0.25 235:0.60 241:0.82 242:0.55 243:0.79 247:0.39 248:0.57 253:0.83 254:0.88 255:0.79 256:0.71 259:0.21 260:0.78 265:0.84 266:0.27 267:0.23 268:0.74 271:0.97 276:0.31 282:0.83 283:0.71 285:0.62 290:0.86 297:0.36 300:0.25 +0 6:0.88 8:0.69 12:0.69 17:0.62 20:0.85 21:0.78 27:0.72 28:0.73 37:0.83 74:0.45 78:0.86 91:0.80 96:0.88 100:0.88 111:0.86 120:0.79 140:0.45 151:0.69 152:0.81 153:0.88 158:0.84 161:0.57 162:0.69 164:0.80 167:0.80 169:0.86 175:0.91 181:0.51 186:0.86 189:0.89 190:0.77 191:0.89 202:0.78 216:0.17 220:0.62 222:0.25 238:0.88 241:0.85 244:0.83 248:0.79 253:0.83 256:0.82 257:0.84 259:0.21 260:0.79 261:0.83 268:0.80 271:0.97 277:0.87 285:0.62 +3 7:0.78 8:0.79 12:0.69 17:0.40 21:0.59 27:0.04 28:0.73 30:0.30 32:0.75 34:0.20 36:0.60 37:0.62 43:0.39 55:0.45 66:0.12 69:0.45 70:0.75 81:0.53 91:0.89 98:0.43 104:0.58 108:0.69 120:0.94 123:0.77 124:0.64 126:0.32 127:0.88 129:0.81 133:0.67 135:0.44 140:0.45 145:0.82 146:0.05 147:0.05 149:0.46 150:0.40 151:0.74 154:0.30 159:0.51 161:0.57 162:0.60 164:0.93 167:0.82 172:0.41 173:0.47 175:0.75 177:0.05 179:0.83 181:0.51 190:0.77 191:0.95 195:0.68 202:0.91 212:0.39 215:0.55 216:0.34 220:0.62 222:0.25 230:0.81 233:0.69 235:0.80 241:0.93 242:0.82 243:0.15 244:0.61 245:0.55 247:0.12 248:0.91 253:0.20 256:0.93 257:0.65 259:0.21 260:0.94 265:0.41 266:0.63 267:0.79 268:0.91 271:0.97 276:0.40 277:0.69 283:0.71 285:0.50 297:0.36 300:0.55 +1 12:0.69 17:0.30 21:0.40 27:0.45 28:0.73 30:0.32 32:0.80 34:0.70 36:0.17 37:0.50 43:0.32 55:0.84 66:0.52 69:0.69 70:0.75 81:0.66 91:0.10 98:0.59 108:0.68 123:0.66 124:0.78 126:0.54 127:0.68 129:0.93 133:0.84 135:0.75 145:0.40 146:0.36 147:0.43 149:0.72 150:0.48 154:0.24 159:0.83 161:0.45 167:0.64 172:0.79 173:0.47 177:0.05 178:0.55 179:0.35 181:0.87 187:0.68 195:0.94 212:0.42 215:0.67 216:0.77 235:0.42 241:0.18 242:0.82 243:0.13 245:0.82 247:0.12 259:0.21 265:0.60 266:0.91 267:0.28 276:0.79 297:0.36 300:0.70 +0 6:0.84 8:0.67 12:0.69 17:0.12 20:0.85 21:0.74 27:0.08 28:0.73 30:0.71 32:0.80 34:0.26 36:0.28 37:0.59 43:0.46 55:0.71 66:0.82 69:0.48 70:0.52 78:0.75 81:0.47 91:0.99 98:0.89 100:0.77 108:0.37 111:0.74 120:0.98 123:0.35 126:0.54 127:0.44 129:0.05 135:0.54 140:0.45 145:0.63 146:0.71 147:0.75 149:0.53 150:0.38 151:0.84 152:0.87 153:0.97 154:0.35 159:0.28 161:0.45 162:0.61 164:0.98 167:0.97 169:0.75 172:0.48 173:0.64 177:0.05 179:0.81 181:0.87 186:0.76 189:0.84 191:0.97 195:0.59 202:0.98 212:0.61 215:0.46 216:0.52 235:0.88 238:0.87 241:0.86 242:0.82 243:0.83 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.77 265:0.89 266:0.38 267:0.88 268:0.98 276:0.39 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.87 8:0.81 12:0.69 17:0.18 20:0.75 21:0.05 25:0.88 27:0.19 28:0.73 34:0.36 36:0.21 37:0.64 78:0.80 81:0.77 91:0.94 100:0.82 104:0.74 111:0.79 120:0.94 126:0.54 135:0.37 140:0.45 150:0.57 151:0.83 152:0.74 153:0.95 161:0.45 162:0.76 164:0.94 167:0.95 169:0.79 175:0.75 181:0.87 186:0.81 189:0.89 191:0.93 202:0.90 215:0.91 216:0.27 235:0.05 238:0.87 241:0.81 244:0.61 248:0.92 256:0.92 257:0.65 259:0.21 260:0.95 261:0.74 267:0.85 268:0.93 277:0.69 283:0.82 285:0.62 297:0.36 +2 6:0.81 7:0.81 8:0.61 12:0.69 17:0.09 20:0.98 21:0.30 27:0.21 28:0.73 30:0.56 34:0.62 36:0.88 37:0.74 43:0.52 55:0.71 66:0.15 69:0.10 70:0.61 78:0.76 81:0.69 91:0.91 98:0.57 100:0.78 104:0.84 106:0.81 108:0.98 111:0.75 120:0.98 123:0.90 124:0.91 126:0.54 127:0.90 129:0.97 133:0.92 135:0.44 140:0.45 146:0.97 147:0.98 149:0.91 150:0.50 151:0.86 152:0.90 153:0.99 154:0.41 159:0.97 161:0.45 162:0.66 164:0.97 167:0.83 169:0.76 172:0.96 173:0.05 177:0.05 179:0.10 181:0.87 186:0.76 187:0.39 189:0.83 191:0.89 202:0.96 206:0.81 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.86 241:0.71 242:0.82 243:0.23 245:0.99 247:0.12 248:0.97 256:0.96 259:0.21 260:0.98 261:0.78 265:0.54 266:0.80 267:0.97 268:0.97 276:0.99 283:0.82 285:0.62 297:0.36 300:0.70 +3 7:0.81 8:0.74 12:0.69 17:0.28 21:0.54 25:0.88 28:0.73 30:0.21 32:0.80 34:0.29 36:0.98 37:0.97 43:0.48 55:0.42 66:0.18 69:0.36 70:0.50 81:0.61 91:0.94 98:0.32 104:0.58 108:0.83 120:0.87 123:0.27 124:0.68 126:0.54 127:0.43 129:0.81 133:0.67 135:0.94 140:0.80 145:0.97 146:0.44 147:0.51 149:0.41 150:0.45 151:0.73 153:0.81 154:0.37 159:0.55 161:0.45 162:0.56 164:0.93 167:0.87 172:0.27 173:0.30 177:0.05 178:0.42 179:0.84 181:0.87 187:0.39 191:0.94 195:0.84 202:0.92 212:0.37 215:0.58 216:0.98 220:0.96 230:0.65 233:0.63 235:0.60 241:0.74 242:0.82 243:0.39 245:0.50 247:0.12 248:0.81 256:0.91 259:0.21 260:0.87 265:0.21 266:0.47 267:0.76 268:0.92 276:0.32 285:0.62 297:0.36 300:0.33 +1 6:0.80 7:0.81 8:0.59 12:0.69 17:0.15 20:0.76 27:0.13 28:0.73 30:0.11 32:0.80 34:0.21 36:0.44 37:0.31 43:0.38 55:0.71 66:0.19 69:0.57 70:0.88 78:0.80 81:0.79 91:0.72 98:0.42 100:0.79 104:0.96 106:0.81 108:0.44 111:0.78 120:0.95 123:0.42 124:0.96 126:0.54 127:0.87 129:0.99 133:0.97 135:0.97 140:0.45 145:0.79 146:0.90 147:0.92 149:0.77 150:0.59 151:0.84 152:0.75 153:0.97 154:0.28 159:0.92 161:0.45 162:0.79 164:0.92 167:0.72 169:0.77 172:0.74 173:0.22 177:0.05 179:0.25 181:0.87 186:0.80 187:0.87 189:0.82 191:0.79 195:0.98 202:0.85 206:0.81 212:0.21 215:0.96 216:0.81 230:0.87 233:0.76 235:0.02 238:0.83 241:0.52 242:0.82 243:0.40 245:0.83 247:0.12 248:0.93 256:0.89 259:0.21 260:0.95 261:0.75 265:0.44 266:0.96 267:0.65 268:0.89 276:0.88 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.94 8:0.87 12:0.69 17:0.13 20:0.82 21:0.64 25:0.88 27:0.48 28:0.73 30:0.45 32:0.80 34:0.24 36:0.34 37:0.55 43:0.48 55:0.84 66:0.30 69:0.38 70:0.61 78:0.79 81:0.55 91:0.96 98:0.44 100:0.82 104:0.75 108:0.68 111:0.78 120:0.98 123:0.66 124:0.73 126:0.54 127:0.77 129:0.81 133:0.67 135:0.39 140:0.45 145:0.85 146:0.41 147:0.47 149:0.51 150:0.42 151:0.83 152:0.78 153:0.96 154:0.37 159:0.56 161:0.45 162:0.65 164:0.98 167:0.91 169:0.80 172:0.32 173:0.36 177:0.05 179:0.80 181:0.87 186:0.79 187:0.39 189:0.95 191:0.97 195:0.71 202:0.97 212:0.13 215:0.53 216:0.92 220:0.62 235:0.74 238:0.95 241:0.76 242:0.82 243:0.31 245:0.60 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:0.78 265:0.46 266:0.80 267:0.84 268:0.98 276:0.42 285:0.62 297:0.36 300:0.43 +0 6:0.99 8:0.91 12:0.69 17:0.40 20:0.75 21:0.64 25:0.88 28:0.73 30:0.21 32:0.80 34:0.25 36:0.24 37:0.97 43:0.40 55:0.42 66:0.27 69:0.55 70:0.67 78:0.87 81:0.70 91:0.95 98:0.23 100:0.73 104:0.58 108:0.42 111:0.84 120:0.97 123:0.41 124:0.70 126:0.54 127:0.94 129:0.81 133:0.67 135:0.37 140:0.80 145:0.61 146:0.57 147:0.62 149:0.35 150:0.52 151:0.81 152:0.97 153:0.94 154:0.31 159:0.89 161:0.45 162:0.59 164:0.96 167:0.78 169:1.00 172:0.21 173:0.25 177:0.05 178:0.42 179:0.90 181:0.87 186:0.87 187:0.21 191:0.79 195:0.97 202:0.94 212:0.16 215:0.53 216:0.98 235:0.80 241:0.67 242:0.82 243:0.46 245:0.50 247:0.12 248:0.95 256:0.95 259:0.21 260:0.97 261:1.00 265:0.25 266:0.54 267:0.95 268:0.95 276:0.27 283:0.82 285:0.62 297:0.36 300:0.43 +3 8:0.75 12:0.69 17:0.12 21:0.64 25:0.88 27:0.03 28:0.73 30:0.41 32:0.80 34:0.30 36:0.39 37:0.46 43:0.52 55:0.59 66:0.20 69:0.21 70:0.95 78:0.76 81:0.55 91:0.95 98:0.38 104:0.73 108:0.17 111:0.75 120:0.94 123:0.16 124:0.74 126:0.54 127:0.58 129:0.81 133:0.67 135:0.58 140:0.87 145:0.85 146:0.51 147:0.57 149:0.54 150:0.42 151:0.81 153:0.94 154:0.41 159:0.57 161:0.45 162:0.64 164:0.96 167:0.93 169:0.80 172:0.27 173:0.22 177:0.05 178:0.48 179:0.74 181:0.87 187:0.39 191:0.90 195:0.75 202:0.93 212:0.19 215:0.53 216:0.66 220:0.62 235:0.74 241:0.78 242:0.82 243:0.40 245:0.63 247:0.12 248:0.92 256:0.94 259:0.21 260:0.95 265:0.41 266:0.71 267:0.82 268:0.94 276:0.44 283:0.60 285:0.62 297:0.36 300:0.84 +3 6:1.00 8:0.86 12:0.69 17:0.13 20:0.79 21:0.64 25:0.88 28:0.73 30:0.45 32:0.80 34:0.24 36:0.36 37:0.97 43:0.48 55:0.84 66:0.30 69:0.38 70:0.61 78:0.78 81:0.55 91:0.96 98:0.44 100:0.81 104:0.58 108:0.68 111:0.78 120:0.98 123:0.66 124:0.73 126:0.54 127:0.77 129:0.81 133:0.67 135:0.32 140:0.45 145:0.85 146:0.41 147:0.47 149:0.51 150:0.42 151:0.84 152:0.95 153:0.97 154:0.37 159:0.56 161:0.45 162:0.64 164:0.99 167:0.90 169:0.98 172:0.32 173:0.36 177:0.05 179:0.80 181:0.87 186:0.79 187:0.39 189:0.92 191:0.94 195:0.71 202:0.98 212:0.13 215:0.53 216:0.98 220:0.62 235:0.74 238:0.93 241:0.76 242:0.82 243:0.31 245:0.60 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:1.00 265:0.46 266:0.80 267:0.84 268:0.98 276:0.42 285:0.62 297:0.36 300:0.43 +1 8:0.89 12:0.69 17:0.32 21:0.64 25:0.88 28:0.73 30:0.21 32:0.80 34:0.75 36:0.25 37:0.97 43:0.52 55:0.52 66:0.05 69:0.21 70:0.99 81:0.55 91:0.85 98:0.18 104:0.58 108:0.76 120:0.92 123:0.85 124:0.92 126:0.54 127:0.99 129:0.97 133:0.92 135:0.51 140:0.45 145:0.72 146:0.38 147:0.45 149:0.61 150:0.42 151:0.79 153:0.90 154:0.41 159:0.89 161:0.45 162:0.73 164:0.93 167:0.75 172:0.45 173:0.09 177:0.05 179:0.60 181:0.87 187:0.21 191:0.89 195:0.96 202:0.89 212:0.40 215:0.53 216:0.98 235:0.74 241:0.59 242:0.82 243:0.20 245:0.64 247:0.12 248:0.89 256:0.91 259:0.21 260:0.93 265:0.17 266:0.92 267:0.65 268:0.92 276:0.60 283:0.60 285:0.62 297:0.36 300:0.94 +1 12:0.69 17:0.20 21:0.59 25:0.88 27:0.19 28:0.73 30:0.15 32:0.80 34:0.53 36:0.23 37:0.38 43:0.52 55:0.59 66:0.24 69:0.19 70:0.84 81:0.58 91:0.76 98:0.35 104:0.72 108:0.84 120:0.61 123:0.15 124:0.69 126:0.54 127:0.58 129:0.81 133:0.67 135:0.87 140:0.92 145:0.79 146:0.48 147:0.54 149:0.45 150:0.43 151:0.54 153:0.48 154:0.41 159:0.60 161:0.45 162:0.46 164:0.69 167:0.89 172:0.27 173:0.19 177:0.05 178:0.51 179:0.84 181:0.87 187:0.39 195:0.79 202:0.85 212:0.30 215:0.55 216:0.87 220:0.82 235:0.68 241:0.75 242:0.82 243:0.44 245:0.53 247:0.12 248:0.54 256:0.58 259:0.21 260:0.61 265:0.22 266:0.54 267:0.92 268:0.62 276:0.30 285:0.62 297:0.36 300:0.55 +1 6:0.87 8:0.82 12:0.69 17:0.36 20:0.75 25:0.88 27:0.14 28:0.73 30:0.58 34:0.24 36:0.30 37:0.59 43:0.48 55:0.84 66:0.36 69:0.27 70:0.44 78:0.86 81:0.79 91:0.84 98:0.73 100:0.88 104:0.74 108:0.21 111:0.85 120:0.83 123:0.20 124:0.59 126:0.54 127:0.77 129:0.59 133:0.47 135:0.54 140:0.80 146:0.53 147:0.59 149:0.48 150:0.59 151:0.80 152:0.75 153:0.92 154:0.37 159:0.29 161:0.45 162:0.59 163:0.86 164:0.84 167:0.98 169:0.83 172:0.27 173:0.48 177:0.05 178:0.42 179:0.89 181:0.87 186:0.88 189:0.89 191:0.92 202:0.80 212:0.37 215:0.96 216:0.58 235:0.02 238:0.92 241:0.89 242:0.82 243:0.51 245:0.56 247:0.12 248:0.75 256:0.81 259:0.21 260:0.84 261:0.76 265:0.74 266:0.38 267:0.44 268:0.80 276:0.34 283:0.82 285:0.62 297:0.36 300:0.33 +4 6:1.00 8:0.93 12:0.69 17:0.09 20:0.76 25:0.88 28:0.73 30:0.41 34:0.24 36:0.35 37:0.97 43:0.52 55:0.59 66:0.86 69:0.05 70:0.42 78:0.85 81:0.79 91:0.98 98:0.85 100:0.90 104:0.58 108:0.05 111:0.86 120:0.97 123:0.05 126:0.54 127:0.36 129:0.05 135:0.34 140:0.87 146:0.76 147:0.80 149:0.60 150:0.59 151:0.83 152:0.95 153:0.96 154:0.41 159:0.32 161:0.45 162:0.52 164:0.97 167:0.93 169:0.99 172:0.59 173:0.17 177:0.05 178:0.48 179:0.67 181:0.87 186:0.85 187:0.39 189:0.99 191:0.99 202:0.98 212:0.42 215:0.96 216:0.98 220:0.62 235:0.02 238:0.96 241:0.78 242:0.82 243:0.86 247:0.12 248:0.95 256:0.97 259:0.21 260:0.97 261:1.00 265:0.85 266:0.54 267:0.76 268:0.97 276:0.49 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.91 8:0.82 12:0.69 17:0.49 20:0.74 25:0.88 27:0.03 28:0.73 30:0.62 34:0.22 36:0.28 37:0.46 43:0.48 55:0.42 66:0.16 69:0.27 70:0.34 78:0.79 81:0.79 91:0.73 98:0.20 100:0.81 104:0.73 108:0.89 111:0.78 120:0.88 123:0.20 124:0.52 126:0.54 127:0.84 129:0.59 133:0.47 135:0.37 140:0.45 146:0.31 147:0.37 149:0.30 150:0.59 151:0.79 152:0.84 153:0.91 154:0.37 159:0.70 161:0.45 162:0.72 164:0.83 167:0.80 169:0.78 172:0.21 173:0.20 177:0.05 179:0.95 181:0.87 186:0.80 187:0.39 189:0.89 191:0.95 202:0.75 212:0.30 215:0.96 216:0.66 235:0.02 238:0.95 241:0.69 242:0.82 243:0.37 245:0.46 247:0.12 248:0.83 256:0.77 259:0.21 260:0.89 261:0.80 265:0.14 266:0.27 267:0.79 268:0.79 276:0.14 285:0.62 297:0.36 300:0.25 +4 6:1.00 8:0.98 12:0.69 17:0.06 20:0.74 25:0.88 27:0.37 28:0.73 30:0.30 34:0.26 36:0.29 37:0.97 43:0.49 55:0.84 66:0.26 69:0.21 70:0.66 78:0.93 81:0.79 91:1.00 98:0.67 100:0.99 104:0.54 108:0.65 111:0.95 120:0.99 123:0.16 124:0.58 126:0.54 127:0.84 129:0.59 133:0.47 135:0.39 140:0.45 146:0.53 147:0.59 149:0.54 150:0.59 151:0.91 152:0.73 153:1.00 154:0.38 159:0.35 161:0.45 162:0.53 163:0.75 164:0.99 167:0.98 169:0.95 172:0.37 173:0.39 177:0.05 179:0.83 181:0.87 186:1.00 189:1.00 191:1.00 202:0.99 212:0.23 215:0.96 216:0.92 235:0.02 238:0.99 241:0.87 242:0.82 243:0.45 245:0.64 247:0.12 248:0.99 256:0.99 259:0.21 260:1.00 261:0.76 265:0.59 266:0.54 267:0.93 268:0.99 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +3 6:0.91 8:0.86 12:0.69 17:0.61 20:0.93 21:0.78 25:0.88 27:0.19 28:0.73 30:0.45 32:0.80 34:0.40 36:0.30 37:0.38 43:0.48 55:0.42 66:0.13 69:0.41 70:0.50 78:0.76 91:0.99 98:0.24 100:0.79 104:0.72 108:0.83 111:0.75 120:0.97 123:0.31 124:0.69 127:0.91 129:0.81 133:0.67 135:0.78 140:0.90 145:0.83 146:0.31 147:0.37 149:0.36 151:0.82 152:0.87 153:0.95 154:0.37 159:0.55 161:0.45 162:0.56 164:0.97 167:0.98 169:0.77 172:0.21 173:0.41 177:0.05 178:0.50 179:0.94 181:0.87 186:0.76 189:0.92 191:0.98 195:0.75 202:0.96 212:0.23 216:0.87 220:0.62 235:0.95 238:0.91 241:0.88 242:0.82 243:0.34 245:0.45 247:0.12 248:0.96 256:0.96 259:0.21 260:0.97 261:0.78 265:0.16 266:0.38 267:0.73 268:0.96 276:0.25 283:0.60 285:0.62 300:0.33 +1 6:0.81 7:0.81 8:0.91 12:0.69 17:0.43 20:0.73 21:0.68 25:0.88 27:0.68 28:0.73 30:0.21 32:0.80 34:0.19 36:0.27 43:0.52 55:0.42 66:0.36 69:0.23 70:0.37 78:0.80 81:0.52 91:0.99 98:0.12 100:0.81 104:0.73 108:0.18 111:0.79 120:0.88 123:0.18 124:0.67 126:0.54 127:0.99 129:0.81 133:0.67 135:0.51 140:0.94 145:0.85 146:0.22 147:0.27 149:0.32 150:0.40 151:0.74 152:0.73 153:0.84 154:0.41 159:0.82 161:0.45 162:0.48 164:0.92 167:0.98 169:0.78 172:0.16 173:0.13 177:0.05 178:0.53 179:0.97 181:0.87 186:0.81 189:0.84 191:0.95 195:0.92 202:0.95 212:0.42 215:0.49 216:0.55 220:0.62 230:0.65 233:0.63 235:0.80 238:0.80 241:0.89 242:0.82 243:0.51 245:0.40 247:0.12 248:0.83 256:0.89 259:0.21 260:0.89 261:0.74 265:0.12 266:0.23 267:0.85 268:0.89 276:0.16 283:0.60 285:0.62 297:0.36 300:0.25 +1 7:0.81 12:0.69 17:0.66 21:0.54 25:0.88 27:0.72 28:0.73 30:0.66 32:0.80 34:0.17 36:0.47 43:0.40 55:0.71 66:0.19 69:0.54 70:0.59 81:0.61 91:0.80 98:0.62 104:0.54 108:0.84 120:0.61 123:0.40 124:0.52 126:0.54 127:0.78 129:0.59 133:0.47 135:0.75 140:0.45 145:0.74 146:0.80 147:0.83 149:0.64 150:0.45 151:0.56 153:0.72 154:0.30 159:0.69 161:0.45 162:0.79 164:0.71 167:0.97 172:0.68 173:0.43 177:0.05 179:0.53 181:0.87 191:0.82 195:0.84 202:0.60 212:0.48 215:0.58 216:0.06 220:0.96 230:0.87 233:0.76 235:0.60 241:0.86 242:0.82 243:0.40 245:0.84 247:0.12 248:0.55 256:0.58 259:0.21 260:0.62 265:0.62 266:0.75 267:0.65 268:0.65 276:0.66 285:0.62 297:0.36 300:0.55 +1 7:0.81 12:0.69 17:0.36 21:0.30 27:0.45 28:0.73 30:0.66 34:0.83 36:0.18 37:0.50 43:0.36 55:0.71 66:0.54 69:0.61 70:0.41 81:0.69 91:0.14 98:0.67 108:0.46 123:0.44 124:0.52 126:0.54 127:0.51 129:0.59 133:0.47 135:0.92 145:0.39 146:0.86 147:0.88 149:0.69 150:0.50 154:0.27 159:0.67 161:0.45 167:0.67 172:0.70 173:0.48 177:0.05 178:0.55 179:0.45 181:0.87 187:0.68 212:0.74 215:0.72 216:0.77 230:0.87 233:0.76 235:0.31 241:0.30 242:0.82 243:0.62 245:0.86 247:0.12 259:0.21 265:0.68 266:0.75 267:0.23 276:0.67 297:0.36 300:0.43 +0 6:1.00 8:0.78 12:0.69 17:0.38 20:0.76 21:0.64 25:0.88 28:0.73 30:0.21 32:0.80 34:0.36 36:0.21 37:0.97 43:0.40 55:0.99 66:0.16 69:0.55 70:0.69 78:0.77 81:0.70 91:0.96 98:0.23 100:0.80 104:0.58 108:0.42 111:0.77 120:0.98 123:0.41 124:0.90 126:0.54 127:0.94 129:0.96 133:0.90 135:0.21 140:0.80 145:0.63 146:0.57 147:0.63 149:0.39 150:0.52 151:0.83 152:0.95 153:0.96 154:0.31 159:0.90 161:0.45 162:0.57 164:0.98 167:0.74 169:0.99 172:0.21 173:0.24 177:0.05 178:0.42 179:0.81 181:0.87 186:0.78 187:0.21 189:0.86 191:0.80 195:0.97 202:0.97 212:0.13 215:0.53 216:0.98 235:0.80 238:0.89 241:0.59 242:0.82 243:0.37 245:0.49 247:0.12 248:0.97 256:0.97 259:0.21 260:0.98 261:1.00 265:0.25 266:0.80 267:0.88 268:0.97 276:0.44 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.87 8:0.78 12:0.69 17:0.70 20:0.80 25:0.88 27:0.40 28:0.73 30:0.62 34:0.22 36:0.30 37:0.36 43:0.48 55:0.42 66:0.16 69:0.27 70:0.34 78:0.74 81:0.79 91:0.96 98:0.36 100:0.77 104:0.74 108:0.69 111:0.74 120:0.90 123:0.20 124:0.52 126:0.54 127:0.80 129:0.59 133:0.47 135:0.65 140:0.80 146:0.31 147:0.37 149:0.37 150:0.59 151:0.80 152:0.77 153:0.92 154:0.37 159:0.33 161:0.45 162:0.54 164:0.91 167:0.95 169:0.75 172:0.21 173:0.45 177:0.05 178:0.42 179:0.95 181:0.87 186:0.75 189:0.89 191:0.95 202:0.90 212:0.30 215:0.96 216:0.79 220:0.62 235:0.02 238:0.90 241:0.81 242:0.82 243:0.37 245:0.46 247:0.12 248:0.86 256:0.88 259:0.21 260:0.90 261:0.75 265:0.21 266:0.27 267:0.73 268:0.88 276:0.17 285:0.62 297:0.36 300:0.25 +1 7:0.81 12:0.69 17:0.24 21:0.71 25:0.88 27:0.19 28:0.73 30:0.55 32:0.80 34:0.69 36:0.41 37:0.38 43:0.52 55:0.84 66:0.46 69:0.27 70:0.71 81:0.65 91:0.27 98:0.54 104:0.72 108:0.21 120:0.58 123:0.20 124:0.66 126:0.54 127:0.69 129:0.81 133:0.67 135:0.83 140:0.45 145:0.85 146:0.77 147:0.81 149:0.55 150:0.47 151:0.50 153:0.72 154:0.41 159:0.72 161:0.45 162:0.76 163:0.94 164:0.69 167:0.82 172:0.45 173:0.18 177:0.05 179:0.74 181:0.87 187:0.39 195:0.87 202:0.58 212:0.29 215:0.48 216:0.87 220:0.74 230:0.87 233:0.76 235:0.88 241:0.70 242:0.82 243:0.59 245:0.62 247:0.12 248:0.51 256:0.58 259:0.21 260:0.58 265:0.56 266:0.75 267:0.95 268:0.62 276:0.46 283:0.60 285:0.62 297:0.36 300:0.55 +1 6:0.92 8:0.88 12:0.69 17:0.38 20:0.88 25:0.88 27:0.06 28:0.73 30:0.76 34:0.22 36:0.49 37:0.87 43:0.48 55:0.84 66:0.80 69:0.27 70:0.28 78:0.75 81:0.79 91:0.88 98:0.89 100:0.79 104:0.72 108:0.21 111:0.75 120:0.90 123:0.20 126:0.54 127:0.43 129:0.05 135:0.32 140:0.45 146:0.64 147:0.69 149:0.49 150:0.59 151:0.82 152:0.83 153:0.94 154:0.37 159:0.24 161:0.45 162:0.71 163:0.86 164:0.91 167:0.88 169:0.76 172:0.45 173:0.51 177:0.05 179:0.84 181:0.87 186:0.76 187:0.39 189:0.93 191:0.96 202:0.86 212:0.37 215:0.96 216:0.31 220:0.62 235:0.02 238:0.92 241:0.75 242:0.82 243:0.82 247:0.12 248:0.86 256:0.87 259:0.21 260:0.91 261:0.77 265:0.89 266:0.38 267:0.36 268:0.88 276:0.37 283:0.82 285:0.62 297:0.36 300:0.25 +0 1:0.74 11:0.83 12:0.37 17:0.40 18:0.75 21:0.40 27:0.45 28:0.91 29:0.86 30:0.85 32:0.64 34:0.75 36:0.19 37:0.50 39:0.95 43:0.82 45:0.73 60:0.87 64:0.77 66:0.49 69:0.68 70:0.40 71:0.56 74:0.71 81:0.58 83:0.91 85:0.85 86:0.86 91:0.06 98:0.40 101:0.87 108:0.52 114:0.62 122:0.57 123:0.50 124:0.66 126:0.54 127:0.36 129:0.05 133:0.66 135:0.61 139:0.88 145:0.50 146:0.51 147:0.57 149:0.84 150:0.76 154:0.77 155:0.67 158:0.91 159:0.48 161:0.78 165:0.93 167:0.50 172:0.48 173:0.67 176:0.73 177:0.70 178:0.55 179:0.62 181:0.97 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.48 215:0.42 216:0.77 219:0.95 222:0.25 235:0.60 241:0.18 242:0.28 243:0.60 245:0.64 247:0.67 253:0.48 259:0.21 265:0.42 266:0.63 267:0.28 271:0.34 276:0.46 279:0.86 283:0.78 290:0.62 292:0.91 297:0.36 300:0.43 +3 1:0.74 6:0.96 7:0.81 8:0.91 9:0.80 11:0.92 12:0.37 17:0.43 18:0.97 20:0.97 21:0.30 27:0.21 28:0.91 29:0.86 30:0.72 31:0.97 34:0.58 36:0.75 37:0.74 39:0.95 41:0.93 43:0.97 45:0.98 60:0.87 64:0.77 66:0.12 69:0.15 70:0.59 71:0.56 74:0.96 78:0.87 79:0.97 81:0.64 83:0.91 85:0.95 86:0.99 91:0.51 96:0.94 97:0.89 98:0.55 100:0.94 101:0.97 104:0.84 106:0.81 108:0.94 111:0.90 114:0.65 117:0.86 120:0.86 121:0.90 122:0.80 123:0.61 124:0.81 126:0.54 127:0.69 129:0.89 133:0.78 135:0.24 137:0.97 139:0.99 140:0.45 146:0.34 147:0.40 149:0.97 150:0.79 151:0.92 152:0.91 153:0.88 154:0.97 155:0.67 158:0.92 159:0.88 161:0.78 162:0.68 164:0.83 165:0.93 167:0.53 169:0.92 172:0.89 173:0.08 176:0.73 177:0.70 179:0.14 181:0.97 186:0.87 187:0.39 189:0.97 192:0.87 196:0.99 201:0.93 202:0.79 204:0.89 206:0.81 208:0.64 212:0.68 215:0.44 216:0.95 219:0.95 220:0.87 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 238:0.95 241:0.32 242:0.27 243:0.19 245:0.98 247:0.93 248:0.86 253:0.96 255:0.95 256:0.81 259:0.21 260:0.85 261:0.94 265:0.51 266:0.80 267:0.23 268:0.81 271:0.34 276:0.95 279:0.86 281:0.91 283:0.82 284:0.96 285:0.62 290:0.65 292:0.95 297:0.36 300:0.94 +3 1:0.74 6:0.93 7:0.81 8:0.81 9:0.80 11:0.92 12:0.37 17:0.85 18:0.94 20:0.88 21:0.68 27:0.12 28:0.91 29:0.86 30:0.73 31:0.89 32:0.80 34:0.83 36:0.99 37:0.80 39:0.95 41:0.90 43:0.77 44:0.93 45:0.96 64:0.77 66:0.71 69:0.80 70:0.59 71:0.56 74:0.90 77:0.89 78:0.81 79:0.89 81:0.45 83:0.91 85:0.90 86:0.97 91:0.27 96:0.73 98:0.63 100:0.82 101:0.97 108:0.77 111:0.80 114:0.56 120:0.60 121:0.90 122:0.80 123:0.75 124:0.47 126:0.54 127:0.58 129:0.59 133:0.66 135:0.78 137:0.89 139:0.97 140:0.45 145:0.92 146:0.56 147:0.62 149:0.93 150:0.70 151:0.61 152:0.89 153:0.48 154:0.70 155:0.67 159:0.89 161:0.78 162:0.86 164:0.73 165:0.93 167:0.51 169:0.79 172:0.92 173:0.52 176:0.73 177:0.70 179:0.22 181:0.97 186:0.81 187:0.68 189:0.95 192:0.87 195:0.97 196:0.94 201:0.93 202:0.58 204:0.89 208:0.64 212:0.51 215:0.37 216:0.75 220:0.96 222:0.25 230:0.87 233:0.76 235:0.88 238:0.86 241:0.21 242:0.42 243:0.23 245:0.91 247:0.88 248:0.60 253:0.63 255:0.95 256:0.64 259:0.21 260:0.60 261:0.85 265:0.64 266:0.80 267:0.28 268:0.66 271:0.34 276:0.84 281:0.91 282:0.88 284:0.94 285:0.62 290:0.56 297:0.36 299:0.97 300:0.70 +2 1:0.69 6:0.93 8:0.84 9:0.80 11:0.83 12:0.37 17:0.67 18:0.93 20:0.98 21:0.22 27:0.18 28:0.91 29:0.86 30:0.56 31:0.95 34:0.25 36:0.89 37:0.63 39:0.95 41:0.94 43:0.76 45:0.97 66:0.23 69:0.83 70:0.67 71:0.56 74:0.87 78:0.88 79:0.95 81:0.57 83:0.91 85:0.97 86:0.97 91:0.90 96:0.86 97:0.89 98:0.57 99:0.99 100:0.92 101:0.97 104:0.58 108:0.87 111:0.89 114:0.67 120:0.80 122:0.57 123:0.86 124:0.96 126:0.44 127:0.98 129:0.99 133:0.96 135:0.76 137:0.95 139:0.96 140:0.80 146:0.42 147:0.44 149:0.94 150:0.59 151:0.87 152:0.91 153:0.48 154:0.68 155:0.67 158:0.78 159:0.94 161:0.78 162:0.70 164:0.83 165:0.70 167:0.84 169:0.90 172:0.92 173:0.48 176:0.66 177:0.05 179:0.13 181:0.97 186:0.88 189:0.94 192:0.87 196:0.98 201:0.68 202:0.79 208:0.64 212:0.42 215:0.51 216:0.18 220:0.82 222:0.25 235:0.52 238:0.92 241:0.85 242:0.22 243:0.06 245:0.96 247:0.90 248:0.80 253:0.88 255:0.95 256:0.81 257:0.84 259:0.21 260:0.78 261:0.94 265:0.59 266:0.94 267:0.52 268:0.82 271:0.34 276:0.96 279:0.82 283:0.78 284:0.97 285:0.62 290:0.67 297:1.00 300:0.84 +2 1:0.74 9:0.80 11:0.83 12:0.37 17:0.57 18:0.93 21:0.30 22:0.93 27:0.06 28:0.91 29:0.86 30:0.90 31:0.95 34:0.47 36:0.54 37:0.84 39:0.95 41:0.88 43:0.71 45:0.66 47:0.93 60:0.87 64:0.77 66:0.86 69:0.87 70:0.27 71:0.56 74:0.93 79:0.94 81:0.64 83:0.91 85:0.99 86:0.98 91:0.15 96:0.84 98:0.81 101:0.87 104:0.84 106:0.81 108:0.74 114:0.65 117:0.86 120:0.74 122:0.57 123:0.72 124:0.38 126:0.54 127:0.43 129:0.05 133:0.37 135:0.34 137:0.95 139:0.98 140:0.45 145:0.92 146:0.95 147:0.96 149:0.98 150:0.79 151:0.92 153:0.72 154:0.61 155:0.67 158:0.92 159:0.72 161:0.78 162:0.86 164:0.69 165:0.93 167:0.58 172:0.95 173:0.79 176:0.73 177:0.70 179:0.17 181:0.97 187:0.39 192:0.87 196:0.98 201:0.93 202:0.53 206:0.81 208:0.64 212:0.54 215:0.44 216:0.70 219:0.95 222:0.25 232:0.89 235:0.52 241:0.52 242:0.44 243:0.86 245:0.91 247:0.67 248:0.81 253:0.88 255:0.95 256:0.58 259:0.21 260:0.75 262:0.93 265:0.81 266:0.54 267:0.28 268:0.62 271:0.34 276:0.90 279:0.86 283:0.82 284:0.93 285:0.62 290:0.65 292:0.95 297:0.36 300:0.43 +3 1:0.74 6:0.96 7:0.81 8:0.83 9:0.80 11:0.92 12:0.37 17:0.47 18:0.97 20:0.78 21:0.30 22:0.93 27:0.21 28:0.91 29:0.86 30:0.73 31:0.99 34:0.68 36:0.69 37:0.74 39:0.95 41:0.95 43:0.97 45:0.98 47:0.93 60:0.87 64:0.77 66:0.16 69:0.15 70:0.58 71:0.56 74:0.96 78:0.90 79:0.99 81:0.64 83:0.91 85:0.96 86:0.99 91:0.65 96:0.96 97:0.94 98:0.54 100:0.95 101:0.97 104:0.84 106:0.81 108:0.95 111:0.92 114:0.65 117:0.86 120:0.91 121:0.90 122:0.80 123:0.87 124:0.90 126:0.54 127:0.76 129:0.95 133:0.90 135:0.18 137:0.99 139:0.99 140:0.45 146:0.32 147:0.38 149:0.97 150:0.79 151:0.98 152:0.91 153:0.94 154:0.97 155:0.67 158:0.92 159:0.90 161:0.78 162:0.70 164:0.85 165:0.93 167:0.47 169:0.92 172:0.89 173:0.08 176:0.73 177:0.70 179:0.14 181:0.97 186:0.90 187:0.39 189:0.94 191:0.73 192:0.87 196:1.00 201:0.93 202:0.82 204:0.89 206:0.81 208:0.64 212:0.59 215:0.44 216:0.95 219:0.95 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 238:0.94 241:0.02 242:0.31 243:0.16 245:0.97 247:0.93 248:0.93 253:0.98 255:0.95 256:0.84 259:0.21 260:0.90 261:0.94 262:0.93 265:0.50 266:0.84 267:0.17 268:0.85 271:0.34 276:0.96 279:0.86 281:0.91 283:0.82 284:0.98 285:0.62 290:0.65 292:0.95 297:0.36 300:0.84 +1 1:0.69 11:0.92 12:0.37 17:0.38 18:0.92 21:0.47 27:0.45 28:0.91 29:0.86 30:0.72 32:0.69 34:0.79 36:0.43 37:0.50 39:0.95 43:0.77 45:0.95 66:0.63 69:0.69 70:0.71 71:0.56 74:0.88 77:0.70 81:0.36 83:0.60 85:0.88 86:0.95 91:0.02 97:0.89 98:0.67 99:0.83 101:0.97 108:0.74 114:0.59 122:0.80 123:0.72 124:0.65 126:0.44 127:0.59 129:0.59 133:0.77 135:0.81 139:0.93 145:0.42 146:0.62 147:0.67 149:0.92 150:0.53 154:0.70 155:0.58 158:0.78 159:0.78 161:0.78 165:0.70 167:0.56 172:0.88 173:0.51 176:0.66 177:0.70 178:0.55 179:0.25 181:0.97 187:0.68 192:0.59 195:0.91 201:0.68 208:0.54 212:0.54 215:0.41 216:0.77 222:0.25 235:0.60 241:0.44 242:0.36 243:0.25 245:0.88 247:0.90 253:0.49 259:0.21 265:0.68 266:0.63 267:0.36 271:0.34 276:0.85 279:0.82 282:0.77 283:0.78 290:0.59 297:0.36 300:0.94 +4 1:0.69 6:0.92 7:0.72 8:0.81 9:0.80 11:0.92 12:0.37 17:0.57 18:0.81 20:0.99 27:0.17 28:0.91 29:0.86 30:0.53 31:0.99 32:0.69 34:0.52 36:0.92 37:0.69 39:0.95 41:0.96 43:0.82 45:0.88 66:0.54 69:0.12 70:0.56 71:0.56 74:0.80 76:0.85 77:0.70 78:0.92 79:0.99 81:0.75 83:0.91 85:0.85 86:0.89 91:0.80 96:0.97 98:0.77 99:0.83 100:0.97 101:0.97 104:0.58 108:0.10 111:0.95 114:0.95 120:0.95 123:0.10 124:0.52 126:0.44 127:0.55 129:0.59 133:0.46 135:0.24 137:0.99 138:0.98 139:0.85 140:0.80 145:0.82 146:0.88 147:0.90 149:0.90 150:0.71 151:0.97 152:0.91 153:0.93 154:0.77 155:0.67 159:0.62 161:0.78 162:0.77 164:0.90 165:0.70 167:0.85 169:0.95 172:0.79 173:0.15 176:0.66 177:0.70 179:0.35 181:0.97 186:0.91 189:0.93 191:0.80 192:0.87 195:0.81 196:0.98 201:0.68 202:0.86 204:0.85 208:0.64 212:0.55 215:0.96 216:0.29 222:0.25 230:0.75 232:0.77 233:0.76 235:0.05 238:0.96 241:0.89 242:0.33 243:0.63 245:0.91 247:0.84 248:0.95 253:0.98 255:0.95 256:0.89 259:0.21 260:0.94 261:0.97 265:0.77 266:0.75 267:0.17 268:0.89 271:0.34 276:0.78 282:0.77 284:0.98 285:0.62 290:0.95 297:0.36 300:0.55 +2 1:0.74 7:0.81 9:0.80 11:0.92 12:0.37 17:0.51 18:0.98 21:0.30 22:0.93 27:0.50 28:0.91 29:0.86 30:0.75 31:0.87 32:0.80 34:0.55 36:0.74 37:0.60 39:0.95 41:0.82 43:0.88 44:0.93 45:0.98 47:0.93 60:0.87 64:0.77 66:0.12 69:0.54 70:0.55 71:0.56 74:0.98 77:0.70 79:0.87 81:0.64 83:0.91 85:0.98 86:0.99 91:0.08 96:0.69 97:0.94 98:0.53 101:0.97 104:0.84 106:0.81 108:0.95 114:0.65 117:0.86 120:0.55 121:0.90 122:0.80 123:0.91 124:0.90 126:0.54 127:0.83 129:0.95 133:0.90 135:0.32 137:0.87 139:1.00 140:0.45 145:0.74 146:0.53 147:0.59 149:0.99 150:0.79 151:0.52 153:0.48 154:0.86 155:0.67 158:0.92 159:0.91 161:0.78 162:0.86 164:0.57 165:0.93 167:0.45 172:0.94 173:0.21 176:0.73 177:0.70 179:0.12 181:0.97 187:0.39 192:0.87 195:0.98 196:0.91 201:0.93 202:0.36 204:0.89 206:0.81 208:0.64 212:0.71 215:0.44 216:0.86 219:0.95 220:0.87 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 242:0.27 243:0.16 245:0.98 247:0.94 248:0.51 253:0.56 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.51 266:0.80 267:0.19 268:0.46 271:0.34 276:0.97 279:0.86 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.65 292:0.95 297:0.36 299:0.88 300:0.84 +0 1:0.74 6:0.89 7:0.81 8:0.67 9:0.80 11:0.92 12:0.37 17:0.76 18:0.97 20:0.75 21:0.05 22:0.93 27:0.27 28:0.91 29:0.86 30:0.90 31:0.95 32:0.80 34:0.24 36:0.68 37:0.35 39:0.95 41:0.88 43:0.95 44:0.93 45:0.91 47:0.93 60:1.00 64:0.77 66:0.43 69:0.19 70:0.30 71:0.56 74:0.98 77:0.70 78:0.83 79:0.95 81:0.85 83:0.91 85:0.99 86:0.99 91:0.27 96:0.85 98:0.76 100:0.83 101:0.97 104:0.79 106:0.81 108:0.82 111:0.81 114:0.89 117:0.86 120:0.75 121:0.99 122:0.57 123:0.81 124:0.71 126:0.54 127:0.88 129:0.81 133:0.67 135:0.65 137:0.95 139:1.00 140:0.45 145:0.70 146:0.89 147:0.91 149:0.99 150:0.91 151:0.87 152:0.74 153:0.48 154:0.95 155:0.67 158:0.92 159:0.84 161:0.78 162:0.86 164:0.69 165:0.93 167:0.48 169:0.83 172:0.95 173:0.11 176:0.73 177:0.70 179:0.14 181:0.97 186:0.83 187:0.21 192:0.87 195:0.93 196:0.98 201:0.93 202:0.53 204:0.89 206:0.81 208:0.64 212:0.58 215:0.78 216:0.70 219:0.95 220:0.62 222:0.25 230:0.87 232:0.85 233:0.76 235:0.22 241:0.10 242:0.36 243:0.31 245:0.99 247:0.91 248:0.83 253:0.90 255:0.95 256:0.58 259:0.21 260:0.76 261:0.77 262:0.93 265:0.76 266:0.54 267:0.18 268:0.62 271:0.34 276:0.96 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 290:0.89 292:0.95 297:0.36 299:0.88 300:0.70 +3 1:0.74 7:0.81 9:0.80 11:0.92 12:0.37 17:0.64 18:0.97 21:0.30 22:0.93 27:0.21 28:0.91 29:0.86 30:0.73 31:0.94 34:0.58 36:0.68 37:0.74 39:0.95 41:0.82 43:0.97 45:0.98 47:0.93 64:0.77 66:0.16 69:0.15 70:0.57 71:0.56 74:0.96 79:0.93 81:0.64 83:0.91 85:0.96 86:0.99 91:0.18 96:0.87 98:0.54 101:0.97 104:0.84 106:0.81 108:0.95 114:0.65 117:0.86 120:0.78 121:0.90 122:0.80 123:0.87 124:0.90 126:0.54 127:0.76 129:0.95 133:0.90 135:0.18 137:0.94 139:0.99 140:0.80 146:0.34 147:0.40 149:0.98 150:0.79 151:0.87 153:0.48 154:0.97 155:0.67 159:0.90 161:0.78 162:0.86 164:0.65 165:0.93 167:0.46 172:0.89 173:0.08 176:0.73 177:0.70 179:0.14 181:0.97 187:0.39 192:0.87 196:0.98 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.60 215:0.44 216:0.95 222:0.25 230:0.87 232:0.89 233:0.76 235:0.52 241:0.01 242:0.30 243:0.19 245:0.97 247:0.93 248:0.77 253:0.91 255:0.95 256:0.58 259:0.21 260:0.78 262:0.93 265:0.50 266:0.84 267:0.18 268:0.59 271:0.34 276:0.96 281:0.91 284:0.89 285:0.62 290:0.65 297:0.36 300:0.84 +3 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.92 12:0.37 17:0.81 18:0.94 20:0.88 21:0.68 27:0.12 28:0.91 29:0.86 30:0.73 31:0.98 32:0.80 34:0.67 36:0.96 37:0.80 39:0.95 41:0.94 43:0.78 44:0.93 45:0.96 60:0.95 64:0.77 66:0.71 69:0.79 70:0.59 71:0.56 74:0.92 77:0.89 78:0.77 79:0.98 81:0.45 83:0.91 85:0.90 86:0.97 91:0.42 96:0.93 97:0.97 98:0.63 100:0.80 101:0.97 108:0.76 111:0.77 114:0.56 120:0.88 121:0.90 122:0.80 123:0.75 124:0.47 126:0.54 127:0.60 129:0.59 133:0.66 135:0.61 137:0.98 139:0.98 140:0.45 145:0.92 146:0.56 147:0.62 149:0.94 150:0.70 151:0.97 152:0.89 153:0.90 154:0.71 155:0.67 158:0.92 159:0.89 161:0.78 162:0.84 164:0.84 165:0.93 167:0.53 169:0.79 172:0.92 173:0.50 176:0.73 177:0.70 179:0.22 181:0.97 186:0.78 187:0.87 189:0.95 191:0.73 192:0.87 195:0.97 196:0.99 201:0.93 202:0.74 204:0.89 208:0.64 212:0.51 215:0.37 216:0.75 219:0.95 222:0.25 230:0.87 233:0.76 235:0.88 238:0.88 241:0.32 242:0.42 243:0.23 245:0.91 247:0.88 248:0.92 253:0.96 255:0.95 256:0.79 259:0.21 260:0.88 261:0.85 265:0.64 266:0.80 267:0.23 268:0.80 271:0.34 276:0.83 279:0.86 281:0.91 282:0.88 283:0.82 284:0.97 285:0.62 290:0.56 292:0.95 297:0.36 299:0.97 300:0.70 +0 6:0.98 8:0.98 12:0.06 17:0.04 20:0.90 21:0.78 27:0.06 28:0.64 30:0.45 34:0.40 36:0.85 37:0.82 43:0.32 55:0.98 66:0.13 69:0.71 70:0.33 78:0.80 91:0.98 98:0.13 100:0.86 104:0.73 108:0.94 111:0.81 120:0.98 123:0.94 124:0.89 127:0.48 129:0.95 133:0.87 135:0.24 140:0.94 145:0.76 146:0.05 147:0.05 149:0.24 151:0.84 152:0.84 153:0.98 154:0.23 159:0.86 161:0.50 162:0.46 163:0.98 164:0.99 167:0.94 169:0.84 172:0.10 173:0.42 177:0.05 178:0.53 179:0.91 181:0.82 186:0.81 189:0.98 191:0.99 202:1.00 212:0.23 216:0.92 220:0.62 235:0.88 238:0.96 241:0.82 242:0.82 243:0.21 245:0.40 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.84 265:0.14 266:0.38 267:0.59 268:0.99 271:0.67 276:0.32 285:0.62 300:0.25 +1 6:0.99 8:1.00 12:0.06 17:0.02 20:0.75 21:0.76 27:0.12 28:0.64 30:0.91 32:0.80 34:0.34 36:0.86 37:0.80 43:0.19 55:0.71 66:0.49 69:0.94 70:0.13 78:0.84 81:0.69 91:0.99 98:0.19 100:0.93 104:0.58 108:0.92 111:0.88 120:0.98 123:0.91 124:0.48 126:0.54 127:0.18 129:0.59 133:0.47 135:0.44 140:0.80 145:0.92 146:0.05 147:0.05 149:0.14 150:0.50 151:0.84 152:0.74 153:0.98 154:0.13 159:0.44 161:0.50 162:0.59 163:0.97 164:0.99 167:0.98 169:0.91 172:0.16 173:0.95 177:0.05 178:0.42 179:0.86 181:0.82 186:0.84 189:0.99 191:0.95 195:0.90 202:0.99 212:0.61 215:0.46 216:0.75 220:0.74 235:0.95 238:0.97 241:0.96 242:0.82 243:0.29 245:0.39 247:0.12 248:0.97 256:0.99 259:0.21 260:0.98 261:0.76 265:0.21 266:0.17 267:0.82 268:0.99 271:0.67 276:0.15 283:0.82 285:0.62 297:0.36 300:0.13 +2 7:0.81 12:0.06 17:0.22 21:0.74 25:0.88 27:0.03 28:0.64 30:0.21 32:0.80 34:0.52 36:0.96 37:0.95 43:0.52 55:0.84 66:0.43 69:0.27 70:0.82 81:0.72 91:0.75 98:0.52 104:0.58 108:0.88 120:0.94 123:0.87 124:0.87 126:0.54 127:0.95 129:0.96 133:0.90 135:0.81 140:0.45 145:0.83 146:0.05 147:0.05 149:0.63 150:0.53 151:0.84 153:0.97 154:0.41 159:0.87 161:0.50 162:0.70 163:0.88 164:0.96 167:0.80 172:0.70 173:0.12 177:0.05 179:0.45 181:0.82 187:0.21 195:0.95 202:0.93 212:0.14 215:0.46 216:0.86 220:0.62 230:0.87 233:0.76 235:0.88 241:0.70 242:0.82 243:0.08 245:0.75 247:0.12 248:0.92 256:0.94 259:0.21 260:0.95 265:0.54 266:0.92 267:0.36 268:0.95 271:0.67 276:0.74 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.96 7:0.81 8:0.93 12:0.06 17:0.01 20:0.98 21:0.74 25:0.88 27:0.03 28:0.64 30:0.17 32:0.80 34:0.28 36:0.90 37:0.95 43:0.52 55:0.71 66:0.34 69:0.27 70:0.98 78:0.94 81:0.72 91:0.99 98:0.37 100:1.00 104:0.58 108:0.78 111:0.99 120:1.00 123:0.77 124:0.77 126:0.54 127:0.99 129:0.89 133:0.78 135:0.82 140:0.45 145:0.83 146:0.05 147:0.05 149:0.45 150:0.53 151:0.91 152:0.99 153:1.00 154:0.41 159:0.79 161:0.50 162:0.68 163:0.88 164:1.00 167:0.98 169:0.99 172:0.37 173:0.16 177:0.05 179:0.80 181:0.82 186:0.94 189:0.97 191:0.98 195:0.90 202:0.99 212:0.19 215:0.46 216:0.86 230:0.87 233:0.76 235:0.88 238:0.99 241:0.94 242:0.82 243:0.13 245:0.57 247:0.12 248:0.99 256:0.99 259:0.21 260:1.00 261:1.00 265:0.39 266:0.80 267:0.91 268:1.00 271:0.67 276:0.42 283:0.82 285:0.62 297:0.36 300:0.84 +2 6:0.96 7:0.81 8:0.81 12:0.06 17:0.07 20:0.74 21:0.74 25:0.88 27:0.03 28:0.64 30:0.09 32:0.80 34:0.77 36:0.74 37:0.95 43:0.52 55:0.52 66:0.25 69:0.27 70:0.94 78:0.83 81:0.72 91:0.94 98:0.33 100:0.89 104:0.58 108:0.76 111:0.86 120:0.97 123:0.74 124:0.88 126:0.54 127:0.95 129:0.95 133:0.87 135:0.83 140:0.45 145:0.83 146:0.05 147:0.05 149:0.51 150:0.53 151:0.84 152:0.99 153:0.97 154:0.41 159:0.79 161:0.50 162:0.58 163:0.88 164:0.98 167:0.86 169:0.99 172:0.37 173:0.16 177:0.05 179:0.71 181:0.82 186:0.80 187:0.21 191:0.94 195:0.90 202:0.98 212:0.22 215:0.46 216:0.86 220:0.62 230:0.87 233:0.76 235:0.88 238:0.94 241:0.75 242:0.82 243:0.11 245:0.60 247:0.12 248:0.95 256:0.98 259:0.21 260:0.97 261:1.00 265:0.35 266:0.84 267:0.82 268:0.98 271:0.67 276:0.54 283:0.60 285:0.62 297:0.36 300:0.70 +1 8:0.72 12:0.06 17:0.36 21:0.76 27:0.45 28:0.64 30:0.62 34:0.66 36:0.46 37:0.50 43:0.32 55:0.84 66:0.52 69:0.71 70:0.64 81:0.69 91:0.04 98:0.61 108:0.54 123:0.52 124:0.65 126:0.54 127:0.38 129:0.81 133:0.67 135:0.87 145:0.45 146:0.88 147:0.90 149:0.65 150:0.50 154:0.23 159:0.72 161:0.50 167:0.69 172:0.65 173:0.54 177:0.05 178:0.55 179:0.45 181:0.82 187:0.68 212:0.19 215:0.46 216:0.77 235:0.95 241:0.46 242:0.82 243:0.62 245:0.76 247:0.12 259:0.21 265:0.62 266:0.84 267:0.28 271:0.67 276:0.66 283:0.60 297:0.36 300:0.55 +1 6:0.96 7:0.81 8:0.96 12:0.06 20:0.81 21:0.77 25:0.88 27:0.03 28:0.64 30:0.30 32:0.80 34:0.49 36:0.72 37:0.95 43:0.52 55:0.92 66:0.08 69:0.30 70:0.80 78:0.84 81:0.65 91:0.97 98:0.20 100:0.93 104:0.58 108:0.84 111:0.87 120:0.93 123:0.90 124:0.90 126:0.54 127:0.93 129:0.96 133:0.90 135:0.37 140:0.94 145:0.80 146:0.05 147:0.05 149:0.42 150:0.48 151:0.80 152:0.99 153:0.92 154:0.41 159:0.76 161:0.50 162:0.50 164:0.98 167:0.95 169:0.99 172:0.21 173:0.19 177:0.05 178:0.52 179:0.83 181:0.82 186:0.84 187:0.21 189:0.95 191:0.94 195:0.87 202:0.98 212:0.13 215:0.44 216:0.86 220:0.74 230:0.87 233:0.76 235:0.97 238:0.97 241:0.85 242:0.82 243:0.15 245:0.48 247:0.12 248:0.90 256:0.97 259:0.21 260:0.94 261:1.00 265:0.19 266:0.75 267:0.82 268:0.97 271:0.67 276:0.43 283:0.82 285:0.62 297:0.36 300:0.55 +0 6:0.92 7:0.81 8:0.95 12:0.06 17:0.28 20:0.95 21:0.74 27:0.13 28:0.64 30:0.62 32:0.80 34:0.22 36:0.96 37:0.67 43:0.23 55:0.99 66:0.30 69:0.88 70:0.34 78:0.76 81:0.72 91:0.95 98:0.14 100:0.81 104:0.58 108:0.93 111:0.76 120:0.90 123:0.93 124:0.71 126:0.54 127:0.44 129:0.81 133:0.67 135:0.63 140:0.94 145:0.82 146:0.19 147:0.24 149:0.22 150:0.53 151:0.76 152:0.88 153:0.87 154:0.16 159:0.77 161:0.50 162:0.51 163:0.90 164:0.94 167:0.96 169:0.79 172:0.16 173:0.78 177:0.05 178:0.53 179:0.93 181:0.82 186:0.77 189:0.93 191:0.93 195:0.93 202:0.95 212:0.30 215:0.46 216:0.61 230:0.87 233:0.76 235:0.88 238:0.94 241:0.86 242:0.82 243:0.37 245:0.43 247:0.12 248:0.85 256:0.92 259:0.21 260:0.90 261:0.81 265:0.15 266:0.27 267:0.98 268:0.92 271:0.67 276:0.24 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.92 7:0.81 8:0.81 12:0.06 17:0.32 20:0.99 21:0.30 27:0.21 28:0.64 30:0.20 34:0.33 36:0.86 37:0.74 43:0.52 55:0.71 66:0.19 69:0.10 70:0.83 78:0.77 81:0.90 91:0.88 98:0.46 100:0.82 104:0.84 106:0.81 108:0.98 111:0.77 120:0.98 123:0.98 124:0.97 126:0.54 127:0.95 129:0.99 133:0.97 135:0.24 140:0.45 146:0.87 147:0.89 149:0.89 150:0.82 151:0.85 152:0.92 153:0.99 154:0.41 159:0.97 161:0.50 162:0.77 164:0.96 167:0.76 169:0.80 172:0.96 173:0.05 177:0.05 179:0.10 181:0.82 186:0.77 187:0.39 189:0.93 191:0.82 202:0.92 206:0.81 212:0.39 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.95 241:0.65 242:0.82 243:0.15 245:0.98 247:0.12 248:0.97 256:0.94 259:0.21 260:0.98 261:0.81 265:0.48 266:0.96 267:0.44 268:0.94 271:0.67 276:0.99 283:0.82 285:0.62 297:0.36 300:0.84 +1 12:0.06 17:0.28 21:0.71 27:0.72 28:0.64 30:0.21 32:0.80 34:0.37 36:0.77 43:0.52 55:0.45 66:0.36 69:0.25 70:0.37 81:0.75 91:0.75 98:0.16 104:0.58 108:0.84 120:0.80 123:0.83 124:0.67 126:0.54 127:0.94 129:0.81 133:0.67 135:0.19 140:0.95 145:0.77 146:0.05 147:0.05 149:0.29 150:0.56 151:0.71 153:0.72 154:0.41 159:0.59 161:0.50 162:0.48 163:0.88 164:0.90 167:0.98 172:0.16 173:0.26 177:0.05 178:0.53 179:0.97 181:0.82 195:0.74 202:0.94 212:0.42 215:0.48 216:0.08 220:0.62 235:0.84 241:0.95 242:0.82 243:0.26 245:0.40 247:0.12 248:0.72 256:0.87 259:0.21 260:0.81 265:0.18 266:0.23 267:0.18 268:0.87 271:0.67 276:0.17 283:0.60 285:0.62 297:0.36 300:0.25 +1 12:0.06 17:0.08 21:0.74 27:0.72 28:0.64 30:0.21 32:0.80 34:0.25 36:0.79 43:0.52 55:0.45 66:0.36 69:0.27 70:0.37 81:0.72 91:0.76 98:0.16 104:0.58 108:0.85 120:0.93 123:0.84 124:0.67 126:0.54 127:0.94 129:0.81 133:0.67 135:0.34 140:0.92 145:0.77 146:0.05 147:0.05 149:0.29 150:0.53 151:0.80 153:0.93 154:0.41 159:0.59 161:0.50 162:0.49 163:0.88 164:0.97 167:0.98 172:0.16 173:0.27 177:0.05 178:0.51 179:0.97 181:0.82 195:0.75 202:0.98 212:0.42 215:0.46 216:0.16 235:0.88 241:0.97 242:0.82 243:0.26 245:0.40 247:0.12 248:0.90 256:0.96 259:0.21 260:0.93 265:0.18 266:0.23 267:0.28 268:0.96 271:0.67 276:0.17 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.96 8:0.92 12:0.06 17:0.06 20:0.84 21:0.76 25:0.88 27:0.03 28:0.64 30:0.21 32:0.80 34:0.56 36:0.58 37:0.95 43:0.52 55:0.71 66:0.24 69:0.29 70:0.92 78:0.77 81:0.69 91:0.94 98:0.33 100:0.82 104:0.58 108:0.78 111:0.77 120:0.94 123:0.77 124:0.84 126:0.54 127:0.96 129:0.93 133:0.84 135:0.66 140:0.80 145:0.91 146:0.05 147:0.05 149:0.45 150:0.50 151:0.79 152:0.99 153:0.91 154:0.41 159:0.65 161:0.50 162:0.52 163:0.88 164:0.96 167:0.93 169:0.99 172:0.27 173:0.25 177:0.05 178:0.42 179:0.81 181:0.82 186:0.77 187:0.21 189:0.93 191:0.94 195:0.80 202:0.96 212:0.22 215:0.46 216:0.86 235:0.95 238:0.95 241:0.79 242:0.82 243:0.14 245:0.53 247:0.12 248:0.92 256:0.95 259:0.21 260:0.94 261:1.00 265:0.36 266:0.71 267:0.76 268:0.95 271:0.67 276:0.45 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.92 7:0.81 8:0.96 12:0.06 17:0.78 20:0.77 21:0.78 27:0.10 28:0.64 34:0.55 36:0.42 37:0.74 78:0.76 91:0.98 100:0.80 104:0.75 111:0.76 120:0.89 135:0.23 140:0.80 145:0.87 151:0.71 152:0.75 153:0.72 161:0.50 162:0.51 164:0.95 167:0.98 169:0.78 175:0.75 178:0.42 181:0.82 186:0.77 189:0.93 191:0.97 202:0.95 216:0.55 220:0.62 230:0.65 233:0.63 235:0.74 238:0.91 241:0.97 244:0.61 248:0.85 256:0.93 257:0.65 259:0.21 260:0.90 261:0.75 267:0.52 268:0.93 271:0.67 277:0.69 285:0.62 +2 8:0.73 12:0.06 17:0.34 21:0.74 25:0.88 27:0.03 28:0.64 30:0.10 34:0.40 36:0.37 37:0.95 43:0.52 55:0.71 66:0.45 69:0.27 70:0.94 81:0.72 91:0.82 98:0.48 104:0.58 108:0.76 120:0.94 123:0.74 124:0.80 126:0.54 127:0.87 129:0.93 133:0.84 135:0.60 140:0.80 146:0.05 147:0.05 149:0.52 150:0.53 151:0.81 153:0.94 154:0.41 159:0.79 161:0.50 162:0.64 163:0.81 164:0.95 167:0.87 172:0.48 173:0.16 177:0.05 178:0.42 179:0.72 181:0.82 187:0.21 191:0.89 202:0.93 212:0.23 215:0.46 216:0.86 235:0.88 241:0.75 242:0.82 243:0.12 245:0.58 247:0.12 248:0.91 256:0.94 259:0.21 260:0.94 265:0.49 266:0.84 267:0.52 268:0.94 271:0.67 276:0.52 283:0.82 285:0.62 297:0.36 300:0.70 +2 6:0.96 7:0.81 8:0.85 12:0.06 17:0.36 20:0.78 21:0.74 25:0.88 27:0.03 28:0.64 30:0.17 32:0.80 34:0.31 36:0.30 37:0.95 43:0.52 55:0.71 66:0.34 69:0.27 70:0.84 78:0.83 81:0.72 91:0.70 98:0.50 100:0.92 104:0.58 108:0.76 111:0.87 120:0.90 123:0.74 124:0.77 126:0.54 127:0.77 129:0.89 133:0.78 135:0.34 140:0.45 145:0.83 146:0.05 147:0.05 149:0.49 150:0.53 151:0.77 152:0.99 153:0.88 154:0.41 159:0.58 161:0.50 162:0.75 163:0.81 164:0.95 167:0.90 169:0.99 172:0.37 173:0.26 177:0.05 179:0.77 181:0.82 186:0.84 187:0.21 189:0.93 191:0.76 195:0.75 202:0.91 212:0.30 215:0.46 216:0.86 220:0.62 230:0.87 233:0.76 235:0.88 238:0.96 241:0.77 242:0.82 243:0.13 245:0.57 247:0.12 248:0.85 256:0.93 259:0.21 260:0.90 261:1.00 265:0.52 266:0.71 267:0.18 268:0.93 271:0.67 276:0.47 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.06 17:0.24 21:0.30 27:0.45 28:0.64 30:0.68 34:0.68 36:0.17 37:0.50 43:0.32 55:0.59 66:0.31 69:0.68 70:0.37 81:0.90 91:0.17 98:0.25 108:0.68 123:0.65 124:0.84 126:0.54 127:0.34 129:0.93 133:0.84 135:0.86 145:0.45 146:0.23 147:0.29 149:0.51 150:0.82 154:0.24 159:0.66 161:0.50 167:0.65 172:0.37 173:0.54 177:0.05 178:0.55 179:0.63 181:0.82 187:0.68 212:0.40 215:0.72 216:0.77 235:0.31 241:0.27 242:0.82 243:0.21 245:0.56 247:0.12 259:0.21 265:0.28 266:0.54 267:0.52 271:0.67 276:0.49 283:0.60 297:0.36 300:0.33 +2 12:0.06 17:0.73 18:0.80 21:0.64 22:0.93 27:0.69 29:0.71 30:0.66 34:1.00 36:0.42 37:0.53 43:0.18 44:0.87 47:0.93 55:0.71 64:0.77 66:0.94 69:0.23 70:0.41 71:0.77 74:0.33 81:0.25 86:0.78 91:0.27 98:0.90 108:0.18 123:0.18 126:0.26 127:0.46 129:0.05 135:0.74 139:0.79 145:0.77 146:0.95 147:0.96 149:0.41 150:0.25 154:0.47 159:0.63 172:0.85 173:0.19 177:0.70 178:0.55 179:0.36 187:0.68 195:0.81 212:0.73 216:0.33 219:0.89 222:0.76 235:0.80 241:0.39 242:0.77 243:0.94 244:0.61 247:0.67 253:0.28 254:0.97 259:0.21 262:0.93 265:0.90 266:0.54 267:0.73 271:0.22 276:0.76 292:0.91 300:0.43 +0 12:0.06 17:0.72 18:0.85 21:0.13 22:0.93 27:0.72 29:0.71 30:0.58 37:0.65 43:0.14 44:0.87 47:0.93 48:0.91 55:0.52 64:0.77 66:0.80 69:0.60 70:0.60 71:0.77 74:0.51 81:0.68 86:0.89 91:0.64 98:0.86 104:0.97 106:0.81 108:0.46 122:0.67 123:0.44 126:0.44 127:0.37 129:0.05 139:0.88 145:0.70 146:0.61 147:0.66 149:0.09 150:0.47 154:0.70 159:0.23 172:0.45 173:0.81 177:0.70 178:0.55 179:0.82 187:0.68 192:0.51 195:0.60 201:0.68 206:0.81 212:0.71 215:0.61 216:0.29 222:0.76 235:0.22 241:0.07 242:0.16 243:0.82 247:0.60 253:0.36 254:0.96 262:0.93 265:0.86 266:0.27 271:0.22 276:0.32 281:0.91 283:0.82 297:0.36 300:0.43 +1 1:0.69 7:0.66 12:0.06 17:0.66 18:0.76 21:0.74 27:0.63 29:0.71 30:0.56 32:0.68 34:0.96 36:0.24 37:0.27 43:0.09 55:0.59 66:0.17 69:0.70 70:0.66 71:0.77 74:0.60 76:0.85 77:0.70 81:0.29 86:0.71 91:0.08 98:0.25 104:0.87 106:0.81 108:0.75 114:0.54 117:0.86 122:0.86 123:0.74 124:0.85 126:0.44 127:0.69 129:0.81 133:0.83 135:0.24 139:0.69 145:0.74 146:0.19 147:0.25 149:0.13 150:0.47 154:0.51 155:0.58 158:0.78 159:0.87 172:0.37 173:0.43 175:0.75 176:0.66 177:0.38 178:0.55 179:0.59 187:0.39 192:0.59 195:0.96 201:0.68 206:0.81 208:0.54 212:0.55 215:0.36 216:0.84 222:0.76 230:0.69 233:0.73 235:0.95 241:0.03 242:0.62 243:0.16 244:0.61 245:0.69 247:0.74 253:0.39 254:0.84 257:0.65 259:0.21 265:0.27 266:0.71 267:0.88 271:0.22 276:0.56 277:0.69 279:0.82 282:0.77 283:0.67 290:0.54 297:0.36 300:0.70 +1 12:0.06 17:0.95 18:0.98 21:0.54 22:0.93 27:0.51 29:0.71 30:0.38 34:0.90 36:0.34 37:0.60 43:0.14 44:0.87 47:0.93 48:0.91 55:0.52 64:0.77 66:0.49 69:0.58 70:0.75 71:0.77 74:0.30 81:0.25 86:0.96 91:0.33 98:0.91 108:0.44 122:0.86 123:0.43 124:0.58 126:0.26 127:0.90 129:0.05 133:0.37 135:0.68 139:0.96 145:0.56 146:0.99 147:0.99 149:0.40 150:0.25 154:0.50 159:0.88 172:0.93 173:0.30 177:0.70 178:0.55 179:0.18 187:0.39 195:0.96 212:0.52 216:0.53 219:0.89 222:0.76 235:0.60 241:0.27 242:0.59 243:0.60 244:0.61 245:0.99 247:0.76 253:0.26 254:0.74 259:0.21 262:0.93 265:0.91 266:0.63 267:0.82 271:0.22 276:0.93 281:0.89 292:0.91 300:0.55 +1 1:0.69 12:0.06 17:0.34 18:0.80 21:0.77 27:0.66 29:0.71 30:0.58 34:0.99 36:0.45 37:0.26 43:0.08 44:0.90 48:0.97 55:0.45 64:0.77 66:0.20 69:0.93 70:0.88 71:0.77 74:0.54 76:0.85 77:0.70 81:0.28 86:0.82 91:0.11 98:0.26 108:0.86 114:0.53 122:0.85 123:0.85 124:0.81 126:0.44 127:0.73 129:0.05 132:0.97 133:0.77 135:0.91 139:0.83 145:0.72 146:0.57 147:0.28 149:0.10 150:0.47 154:0.51 155:0.58 159:0.85 172:0.32 173:0.85 176:0.66 177:0.38 178:0.55 179:0.71 187:0.68 192:0.51 195:0.95 201:0.68 208:0.54 212:0.16 216:0.73 222:0.76 235:0.99 241:0.10 242:0.24 243:0.26 244:0.61 245:0.64 247:0.76 253:0.37 254:0.84 257:0.65 265:0.28 266:0.87 267:0.99 271:0.22 276:0.46 281:0.91 282:0.77 290:0.53 300:0.84 +1 12:0.06 17:0.84 18:0.98 21:0.54 27:0.34 29:0.71 30:0.65 34:0.68 36:0.44 37:0.41 43:0.15 44:0.87 55:0.52 64:0.77 66:0.94 69:0.77 70:0.48 71:0.77 74:0.33 81:0.25 86:0.98 91:0.18 98:0.83 108:0.60 122:0.86 123:0.58 124:0.36 126:0.26 127:0.52 129:0.05 133:0.37 135:0.63 139:0.98 145:0.45 146:0.86 147:0.05 149:0.28 150:0.25 154:0.52 159:0.51 172:0.94 173:0.78 177:0.05 178:0.55 179:0.21 187:0.95 195:0.70 212:0.94 216:0.28 219:0.89 222:0.76 235:0.60 241:0.18 242:0.51 243:0.06 245:0.73 247:0.76 253:0.27 254:0.88 257:0.84 259:0.21 265:0.83 266:0.23 267:0.52 271:0.22 276:0.88 292:0.95 300:0.55 +2 7:0.72 12:0.06 17:0.66 18:0.86 21:0.40 27:0.51 29:0.71 30:0.57 32:0.77 34:1.00 36:0.53 37:0.60 43:0.12 44:0.93 48:0.91 55:0.59 64:0.77 66:0.89 69:0.58 70:0.52 71:0.77 74:0.51 77:0.70 81:0.41 86:0.84 91:0.33 98:0.84 104:0.99 108:0.44 123:0.42 126:0.44 127:0.34 129:0.05 135:0.54 139:0.90 145:0.50 146:0.85 147:0.88 149:0.24 150:0.32 154:0.56 159:0.42 172:0.70 173:0.58 177:0.70 178:0.55 179:0.52 187:0.39 192:0.51 195:0.69 201:0.68 212:0.57 215:0.42 216:0.53 219:0.92 222:0.76 230:0.74 233:0.73 235:0.52 241:0.27 242:0.45 243:0.90 244:0.61 247:0.76 253:0.36 254:0.74 259:0.21 265:0.84 266:0.47 267:0.52 271:0.22 276:0.59 281:0.88 282:0.85 283:0.77 292:0.91 297:0.36 300:0.43 +1 12:0.06 17:0.98 18:0.87 21:0.22 22:0.93 27:0.57 29:0.71 30:0.42 34:0.94 36:0.24 37:0.32 43:0.17 47:0.93 48:0.91 55:0.52 64:0.77 66:0.83 69:0.87 70:0.52 71:0.77 74:0.35 81:0.36 86:0.89 91:0.37 98:0.70 108:0.75 122:0.65 123:0.74 124:0.39 126:0.26 127:0.62 129:0.05 133:0.59 135:0.66 139:0.86 145:0.50 146:0.80 147:0.05 149:0.12 150:0.32 154:0.48 159:0.59 172:0.81 173:0.89 177:0.05 178:0.55 179:0.44 187:0.87 212:0.91 216:0.29 222:0.76 235:0.22 241:0.01 242:0.41 243:0.08 245:0.63 247:0.74 253:0.29 254:0.93 257:0.84 259:0.21 262:0.93 265:0.70 266:0.38 267:0.85 271:0.22 276:0.70 281:0.91 300:0.43 +1 7:0.72 12:0.06 17:0.57 18:0.86 21:0.40 27:0.51 29:0.71 30:0.43 32:0.78 34:1.00 36:0.55 37:0.60 43:0.11 44:0.93 48:0.91 55:0.71 64:0.77 66:0.89 69:0.58 70:0.59 71:0.77 74:0.51 77:0.70 81:0.41 86:0.85 91:0.35 98:0.83 104:0.98 108:0.44 123:0.42 126:0.44 127:0.33 129:0.05 135:0.68 139:0.90 145:0.45 146:0.84 147:0.87 149:0.23 150:0.32 154:0.72 159:0.41 172:0.70 173:0.58 177:0.70 178:0.55 179:0.51 187:0.39 192:0.51 195:0.69 201:0.68 212:0.51 215:0.42 216:0.53 219:0.92 222:0.76 230:0.74 233:0.73 235:0.52 241:0.30 242:0.54 243:0.90 247:0.76 253:0.36 254:0.98 259:0.21 265:0.83 266:0.63 267:0.59 271:0.22 276:0.59 281:0.88 282:0.86 283:0.77 292:0.91 297:0.36 300:0.55 +1 1:0.69 7:0.66 11:0.64 12:0.06 17:0.91 18:0.75 21:0.74 27:0.64 29:0.71 30:0.14 32:0.69 34:1.00 36:0.33 37:0.33 43:0.66 45:0.77 66:0.25 69:0.57 70:0.91 71:0.77 74:0.64 76:0.85 77:0.70 81:0.32 83:0.60 86:0.71 91:0.08 98:0.20 99:0.83 101:0.52 104:0.92 106:0.81 108:0.44 114:0.54 117:0.86 122:0.86 123:0.42 124:0.84 126:0.44 127:0.47 129:0.59 133:0.82 135:0.95 139:0.68 145:0.50 146:0.39 147:0.45 149:0.53 150:0.51 154:0.56 155:0.58 158:0.78 159:0.78 165:0.70 172:0.32 173:0.35 175:0.75 176:0.66 177:0.38 178:0.55 179:0.69 187:0.87 192:0.59 195:0.93 201:0.68 206:0.81 208:0.54 212:0.18 215:0.36 216:0.39 222:0.76 230:0.69 233:0.76 235:0.97 241:0.02 242:0.54 243:0.45 244:0.61 245:0.57 247:0.60 253:0.40 257:0.65 259:0.21 265:0.21 266:0.84 267:0.36 271:0.22 276:0.46 277:0.69 282:0.77 283:0.66 290:0.54 297:0.36 300:0.70 +0 1:0.58 8:0.72 9:0.73 10:0.79 11:0.45 12:0.20 17:0.98 18:0.72 21:0.40 22:0.93 26:0.98 27:0.66 29:0.53 30:0.89 31:0.87 34:0.30 36:0.93 37:0.57 39:0.95 43:0.20 44:0.86 45:1.00 46:0.88 47:0.93 48:0.99 58:0.98 64:0.77 66:0.72 69:0.34 70:0.31 71:0.83 74:0.33 79:0.87 81:0.49 83:0.56 86:0.64 89:0.98 91:0.11 98:0.85 99:0.95 101:0.47 107:0.98 108:0.27 110:0.89 122:0.67 123:0.26 124:0.51 125:0.88 126:0.32 127:0.83 129:0.05 131:0.61 133:0.74 135:0.70 137:0.87 139:0.66 140:0.45 141:0.99 145:0.63 146:0.99 147:0.99 149:0.67 150:0.39 151:0.53 153:0.48 154:0.09 155:0.55 157:0.61 159:0.88 160:0.88 162:0.86 165:0.65 166:0.61 170:0.79 172:0.99 173:0.13 174:0.79 177:1.00 179:0.12 182:0.61 187:0.68 190:0.99 192:0.56 195:0.96 196:0.88 197:0.80 199:1.00 201:0.60 202:0.50 204:0.82 208:0.50 212:0.82 216:0.61 220:0.91 222:0.35 223:0.98 224:0.99 225:0.99 227:0.61 229:0.61 231:0.62 234:0.99 235:0.68 239:0.79 240:0.99 241:0.03 242:0.39 243:0.75 244:0.97 245:0.98 246:0.61 247:0.96 248:0.53 253:0.30 254:0.90 255:0.72 256:0.58 259:0.21 262:0.93 264:0.93 265:0.85 266:0.75 267:0.99 268:0.59 271:0.60 274:0.98 275:0.91 276:0.97 281:0.86 284:0.87 285:0.50 287:0.61 289:0.88 294:0.91 300:0.70 +0 10:0.82 11:0.53 12:0.20 17:0.30 18:0.77 21:0.47 26:0.94 27:0.40 29:0.53 30:0.15 34:0.76 36:0.51 37:0.72 39:0.95 43:0.09 45:0.85 46:0.94 58:0.93 66:0.92 69:0.67 70:0.78 71:0.83 74:0.29 81:0.26 86:0.64 89:0.97 91:0.33 98:0.92 101:0.64 107:0.94 108:0.51 110:0.89 122:0.97 123:0.49 125:0.88 126:0.22 127:0.55 129:0.05 131:0.61 135:0.74 139:0.61 141:0.91 144:0.57 146:0.91 147:0.93 149:0.11 150:0.28 154:0.09 157:0.79 159:0.52 160:0.88 166:0.80 170:0.79 172:0.79 173:0.68 174:0.93 177:1.00 178:0.55 179:0.49 182:0.73 187:0.68 197:0.93 199:0.94 212:0.69 216:0.66 222:0.35 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.82 234:0.91 235:0.52 236:0.91 239:0.81 240:0.95 241:0.51 242:0.14 243:0.92 244:0.98 246:0.61 247:0.96 253:0.26 254:0.97 259:0.21 264:0.93 265:0.92 266:0.54 267:0.79 271:0.60 274:0.91 275:0.96 276:0.68 287:0.61 289:0.88 294:0.93 300:0.55 +0 1:0.56 10:0.79 11:0.45 12:0.20 17:0.69 18:0.93 21:0.47 26:0.98 27:0.72 29:0.53 30:0.14 31:0.88 34:0.42 36:0.77 37:0.44 39:0.95 43:0.06 44:0.85 45:0.94 46:0.88 48:0.97 55:0.92 58:0.93 64:0.77 66:0.13 69:0.99 70:0.96 71:0.83 74:0.26 79:0.88 81:0.37 83:0.54 86:0.67 89:0.98 91:0.09 98:0.31 99:0.83 101:0.47 107:0.91 108:0.98 110:0.88 122:0.95 123:0.98 124:0.99 125:0.88 126:0.31 127:0.99 129:0.05 131:0.61 133:0.99 135:0.65 137:0.88 139:0.66 140:0.80 141:0.91 145:0.99 146:0.94 147:0.05 149:0.13 150:0.34 151:0.51 153:0.46 154:0.05 155:0.47 157:0.61 159:0.97 160:0.88 162:0.54 165:0.63 166:0.61 170:0.79 172:0.79 173:0.94 174:0.91 175:0.75 177:0.05 178:0.42 179:0.16 182:0.61 187:0.39 190:1.00 192:0.46 195:1.00 196:0.88 197:0.86 199:0.98 201:0.57 202:0.49 204:0.78 208:0.48 212:0.14 216:0.13 220:0.82 222:0.35 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.70 234:0.91 235:0.68 239:0.79 240:0.98 241:0.01 242:0.36 243:0.05 244:0.98 245:0.87 246:0.61 247:0.99 248:0.51 253:0.24 254:0.90 256:0.45 257:1.00 259:0.21 264:0.93 265:0.33 266:0.98 267:0.19 268:0.45 271:0.60 274:0.91 275:0.96 276:0.95 277:0.69 281:0.91 284:0.86 285:0.45 287:0.61 289:0.88 294:0.91 300:0.94 +0 8:0.77 10:0.82 11:0.51 12:0.20 17:0.59 18:0.98 21:0.59 22:0.93 26:0.96 27:0.67 29:0.53 30:0.66 31:0.86 34:0.77 36:0.25 37:0.35 39:0.95 43:0.09 44:0.85 45:0.91 46:0.88 47:0.93 48:0.97 58:0.93 64:0.77 66:0.78 69:0.85 70:0.39 71:0.83 74:0.28 79:0.86 81:0.24 86:0.83 89:0.99 91:0.53 98:0.75 101:0.52 107:0.95 108:0.71 110:0.89 122:0.95 123:0.69 124:0.41 125:0.88 126:0.22 127:0.53 129:0.05 131:0.61 133:0.39 135:0.63 137:0.86 139:0.79 140:0.45 141:0.91 144:0.57 145:0.41 146:0.80 147:0.64 149:0.12 150:0.26 151:0.47 153:0.48 154:0.12 157:0.88 159:0.51 160:0.88 162:0.62 166:0.61 170:0.79 172:0.95 173:0.88 174:0.86 177:0.88 179:0.18 182:0.78 187:0.21 190:1.00 195:0.71 196:0.88 197:0.88 199:0.95 202:0.50 212:0.93 216:0.34 219:0.87 220:0.97 222:0.35 223:0.96 224:0.93 225:0.97 227:0.61 229:0.61 231:0.78 234:0.91 235:0.68 239:0.82 240:0.98 241:0.60 242:0.23 243:0.19 244:0.97 245:0.95 246:0.61 247:0.94 248:0.47 253:0.25 254:0.84 256:0.45 257:0.97 259:0.21 262:0.93 264:0.93 265:0.75 266:0.38 267:0.28 268:0.46 271:0.60 274:0.91 275:0.99 276:0.91 281:0.91 284:0.87 285:0.45 287:0.61 289:0.88 292:0.88 294:0.94 300:0.55 +0 1:0.57 10:0.81 11:0.49 12:0.20 17:0.24 18:0.69 21:0.30 26:0.95 27:0.45 29:0.53 30:0.21 34:0.80 36:0.17 37:0.50 39:0.95 43:0.05 45:0.73 46:0.88 55:0.52 58:0.93 66:0.36 69:0.79 70:0.91 71:0.83 74:0.30 81:0.27 86:0.60 89:0.96 91:0.14 98:0.27 101:0.47 107:0.94 108:0.63 110:0.89 114:0.71 122:0.41 123:0.61 124:0.82 125:0.88 126:0.24 127:0.78 129:0.05 131:0.61 133:0.80 135:0.66 139:0.58 141:0.91 144:0.57 145:0.47 146:0.42 147:0.49 149:0.06 150:0.29 154:0.09 155:0.46 157:0.78 159:0.69 160:0.88 166:0.61 170:0.79 172:0.45 173:0.73 174:0.79 175:0.91 176:0.59 177:0.38 178:0.55 179:0.71 182:0.73 187:0.68 192:0.46 197:0.82 199:0.94 201:0.54 208:0.48 212:0.52 215:0.72 216:0.77 222:0.35 223:0.93 224:0.93 225:0.96 227:0.61 229:0.61 231:0.65 234:0.91 235:0.42 239:0.80 240:0.95 241:0.21 242:0.12 243:0.26 244:0.99 245:0.59 246:0.61 247:0.88 253:0.53 254:0.86 257:0.99 259:0.21 264:0.93 265:0.30 266:0.71 267:0.23 271:0.60 274:0.91 275:0.93 276:0.50 277:0.87 287:0.61 289:0.89 290:0.71 294:0.92 297:0.36 300:0.70 +0 1:0.56 10:0.79 11:0.45 12:0.20 17:0.40 18:0.80 21:0.71 26:0.98 29:0.53 30:0.54 34:0.37 36:0.28 37:0.97 39:0.95 43:0.13 45:0.91 46:0.88 58:0.93 66:0.13 69:0.94 70:0.70 71:0.83 74:0.25 81:0.38 83:0.56 86:0.63 89:0.98 91:0.31 98:0.36 99:0.95 101:0.47 107:0.89 108:0.82 110:0.88 122:0.67 123:0.87 124:0.88 125:0.88 126:0.31 127:0.31 129:0.05 131:0.61 133:0.86 135:0.60 139:0.61 141:0.91 145:0.65 146:0.82 147:0.22 149:0.21 150:0.33 154:0.08 155:0.46 157:0.61 159:0.84 160:0.88 165:0.65 166:0.61 170:0.79 172:0.45 173:0.81 174:0.79 175:0.91 177:0.70 178:0.55 179:0.33 182:0.61 187:0.21 192:0.44 197:0.80 199:0.96 201:0.54 202:0.67 208:0.50 212:0.30 216:0.98 222:0.35 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.62 234:0.91 235:0.95 239:0.78 240:0.95 241:0.61 242:0.24 243:0.17 244:0.98 245:0.75 246:0.61 247:0.86 253:0.23 254:0.93 257:0.99 259:0.21 264:0.93 265:0.23 266:0.94 267:0.76 271:0.60 274:0.91 275:0.91 276:0.70 277:0.87 287:0.61 289:0.88 294:0.91 300:0.55 +0 8:0.83 10:0.79 11:0.45 12:0.20 17:0.45 18:1.00 21:0.40 26:0.97 27:0.58 29:0.53 30:0.84 31:0.87 32:0.63 34:0.30 36:0.62 37:0.30 39:0.95 43:0.12 44:0.86 45:0.97 46:0.88 48:0.91 55:0.71 58:0.93 64:0.77 66:1.00 69:0.78 70:0.27 71:0.83 74:0.30 77:0.70 79:0.87 81:0.25 86:0.86 89:0.99 91:0.02 98:1.00 101:0.47 107:0.88 108:0.62 110:0.88 122:0.95 123:0.60 124:0.36 125:0.88 126:0.22 127:1.00 129:0.05 131:0.61 133:0.36 135:0.44 137:0.87 139:0.83 140:0.45 141:0.91 145:0.90 146:1.00 147:0.05 149:0.33 150:0.27 151:0.50 153:0.48 154:0.09 157:0.61 159:0.97 160:0.88 162:0.86 166:0.61 170:0.79 172:1.00 173:0.27 174:0.79 175:0.91 177:0.05 179:0.09 182:0.61 187:0.39 190:1.00 195:1.00 196:0.90 197:0.79 199:0.97 202:0.36 212:0.94 216:0.36 219:0.87 220:0.91 222:0.35 223:0.97 224:0.93 225:0.97 227:0.61 229:0.61 231:0.61 234:0.91 235:0.42 239:0.78 240:0.98 241:0.07 242:0.77 243:0.05 244:0.98 245:0.94 246:0.61 247:0.92 248:0.50 253:0.27 254:0.97 256:0.45 257:1.00 259:0.21 264:0.93 265:1.00 266:0.27 267:0.36 268:0.46 271:0.60 274:0.91 276:1.00 277:0.87 281:0.86 282:0.72 284:0.87 285:0.45 287:0.61 289:0.88 292:0.95 300:0.70 +1 10:0.81 11:0.53 12:0.20 17:0.15 18:0.70 21:0.59 26:0.97 27:0.09 29:0.53 30:0.45 34:0.79 36:0.83 37:0.95 39:0.95 43:0.09 45:0.93 46:0.88 48:0.91 56:0.97 58:0.93 66:0.11 69:0.92 70:0.81 71:0.83 74:0.27 81:0.25 83:0.54 86:0.61 89:0.97 91:0.12 98:0.40 101:0.64 107:0.92 108:0.84 110:0.88 122:0.86 123:0.49 124:0.92 125:0.88 126:0.22 127:0.76 129:0.05 131:0.61 133:0.91 135:0.92 139:0.60 141:0.91 144:0.57 145:0.45 146:0.59 147:0.76 149:0.18 150:0.26 154:0.07 155:0.47 157:0.82 159:0.80 160:0.88 166:0.77 170:0.79 172:0.59 173:0.87 174:0.88 175:0.91 177:0.88 178:0.55 179:0.37 182:0.75 187:0.87 192:0.45 195:0.91 197:0.86 199:0.97 202:0.36 204:0.78 208:0.48 212:0.42 216:0.74 222:0.35 223:0.97 224:0.93 225:0.96 227:0.61 229:0.61 231:0.79 234:0.91 235:0.68 239:0.81 240:0.95 241:0.21 242:0.17 243:0.31 244:0.99 245:0.79 246:0.61 247:0.98 253:0.24 257:0.97 259:0.21 264:0.93 265:0.33 266:0.87 267:0.28 271:0.60 274:0.91 275:0.97 276:0.78 277:0.98 281:0.91 287:0.61 289:0.88 294:0.93 300:0.84 +0 8:0.85 10:0.79 11:0.45 12:0.20 17:0.55 18:0.67 21:0.78 26:0.94 27:0.52 29:0.53 30:0.14 34:0.26 36:0.48 37:0.43 39:0.95 43:0.05 45:0.73 46:0.93 55:0.96 58:0.93 66:0.05 69:0.99 70:0.99 71:0.83 74:0.28 86:0.59 89:0.96 91:0.01 98:0.14 101:0.47 107:0.97 108:0.99 110:0.89 122:0.41 123:0.91 124:0.98 125:0.88 127:0.96 129:0.05 131:0.61 133:0.98 135:0.79 139:0.58 141:0.91 145:0.54 146:0.35 147:0.05 149:0.06 154:0.05 157:0.61 159:0.98 160:0.88 163:0.99 166:0.61 170:0.79 172:0.32 173:0.99 174:0.79 175:1.00 177:0.05 178:0.55 179:0.44 182:0.61 187:0.68 197:0.79 199:0.94 202:0.56 212:0.14 216:0.79 222:0.35 223:0.92 224:0.93 225:0.95 227:0.61 229:0.61 231:0.61 234:0.91 235:0.52 239:0.78 240:0.95 241:0.12 242:0.19 243:0.08 244:1.00 245:0.61 246:0.61 247:0.92 253:0.25 257:1.00 259:0.21 264:0.93 265:0.12 266:0.98 267:0.52 271:0.60 274:0.91 275:0.93 276:0.76 277:1.00 287:0.61 289:0.88 294:0.91 300:0.94 +0 8:0.63 10:0.79 11:0.45 12:0.20 17:0.30 18:0.90 21:0.78 26:0.94 27:0.37 29:0.53 30:0.17 34:0.37 36:0.82 37:0.94 39:0.95 43:0.05 45:0.77 46:0.88 55:0.71 58:0.93 66:0.09 69:0.85 70:0.90 71:0.83 74:0.30 86:0.67 89:0.97 91:0.08 98:0.33 101:0.47 107:0.96 108:0.99 110:0.89 122:0.99 123:0.99 124:0.99 125:0.88 127:0.97 129:0.59 131:0.83 133:0.99 135:0.56 139:0.64 140:0.45 141:0.91 143:0.99 146:0.91 147:0.38 149:0.13 151:0.60 153:0.48 154:0.05 157:0.61 159:0.98 160:0.88 162:0.86 166:0.61 170:0.79 172:0.93 173:0.31 174:0.90 175:0.97 177:0.70 179:0.10 182:0.61 187:0.39 190:1.00 197:0.86 199:0.94 202:0.36 212:0.48 216:0.28 220:0.62 222:0.35 223:0.92 224:0.93 225:0.96 227:0.86 229:0.61 231:0.75 234:0.91 235:0.12 239:0.79 240:0.95 241:0.05 242:0.51 243:0.06 244:0.97 245:0.98 246:0.61 247:1.00 248:0.58 253:0.27 254:0.86 256:0.45 257:0.99 259:0.21 264:0.93 265:0.35 266:0.98 267:0.87 268:0.46 271:0.60 274:0.91 275:1.00 276:0.99 277:0.95 285:0.45 287:0.61 289:0.88 294:0.94 300:0.98 +0 1:0.60 10:0.80 11:0.51 12:0.20 17:0.78 18:0.92 21:0.22 26:0.98 27:0.72 29:0.53 30:0.68 34:0.77 36:0.72 39:0.95 43:0.20 45:0.87 46:0.88 55:0.84 58:0.93 66:0.92 69:0.19 70:0.37 71:0.83 74:0.26 81:0.45 83:0.56 86:0.67 89:0.98 91:0.25 97:0.94 98:0.94 99:0.95 101:0.52 107:0.91 108:0.15 110:0.88 122:0.95 123:0.15 125:0.88 126:0.31 127:0.59 129:0.05 131:0.61 135:0.97 139:0.65 141:0.91 144:0.57 146:0.94 147:0.95 149:0.21 150:0.39 154:0.13 155:0.47 157:0.79 159:0.58 160:0.88 163:0.75 165:0.65 166:0.61 170:0.79 172:0.79 173:0.20 174:0.79 177:1.00 178:0.55 179:0.48 182:0.73 187:0.39 192:0.45 197:0.81 199:0.96 201:0.59 208:0.50 212:0.22 216:0.32 222:0.35 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.64 234:0.91 235:0.42 239:0.79 240:0.95 241:0.07 242:0.36 243:0.93 244:0.98 246:0.61 247:0.82 253:0.24 254:0.98 259:0.21 264:0.93 265:0.94 266:0.63 267:0.28 271:0.60 274:0.91 275:0.91 276:0.69 279:0.75 287:0.61 289:0.88 294:0.92 300:0.33 +0 1:0.58 10:0.79 11:0.48 12:0.20 17:0.98 18:0.62 21:0.47 26:0.98 27:0.72 29:0.53 30:0.89 34:0.39 36:0.18 39:0.95 43:0.12 45:0.99 46:0.88 48:0.91 58:0.93 66:0.80 69:0.40 70:0.24 71:0.83 74:0.31 81:0.41 83:0.56 86:0.61 89:0.98 91:0.72 98:0.90 99:0.95 101:0.52 107:0.94 108:0.31 110:0.89 122:0.67 123:0.30 124:0.40 125:0.88 126:0.31 127:0.88 129:0.05 131:0.61 133:0.36 135:0.21 139:0.60 141:0.91 145:0.85 146:0.97 147:0.97 149:0.53 150:0.36 154:0.09 155:0.48 157:0.61 159:0.76 160:0.88 165:0.65 166:0.61 170:0.79 172:0.95 173:0.25 174:0.79 177:1.00 178:0.55 179:0.20 182:0.61 192:0.46 195:0.88 197:0.80 199:0.99 201:0.57 204:0.78 208:0.50 212:0.88 222:0.35 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.63 234:0.91 235:0.68 239:0.79 240:0.95 241:0.77 242:0.36 243:0.82 244:0.97 245:0.95 246:0.61 247:0.92 253:0.28 254:0.74 259:0.21 264:0.93 265:0.90 266:0.54 267:0.85 271:0.60 274:0.91 275:0.91 276:0.91 281:0.91 287:0.61 289:0.88 294:0.91 300:0.70 +1 10:0.83 11:0.53 12:0.20 17:0.43 18:0.78 21:0.47 26:0.94 27:0.40 29:0.53 30:0.20 34:0.77 36:0.49 37:0.72 39:0.95 43:0.09 45:0.87 46:0.94 58:0.93 66:0.93 69:0.68 70:0.80 71:0.83 74:0.29 81:0.26 86:0.64 89:0.97 91:0.29 98:0.92 101:0.64 107:0.93 108:0.52 110:0.89 122:0.97 123:0.49 125:0.88 126:0.22 127:0.53 129:0.05 131:0.61 135:0.69 139:0.61 141:0.91 144:0.57 146:0.89 147:0.91 149:0.12 150:0.28 154:0.09 157:0.84 159:0.49 160:0.88 166:0.80 170:0.79 172:0.80 173:0.70 174:0.94 177:1.00 178:0.55 179:0.45 182:0.76 187:0.68 197:0.94 199:0.94 212:0.71 216:0.66 222:0.35 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.83 234:0.91 235:0.52 236:0.91 239:0.82 240:0.95 241:0.32 242:0.16 243:0.93 244:0.98 246:0.61 247:0.93 253:0.26 254:0.97 259:0.21 264:0.93 265:0.92 266:0.54 267:0.82 271:0.60 274:0.91 275:0.96 276:0.70 287:0.61 289:0.88 294:0.93 300:0.55 +0 8:0.64 10:0.79 11:0.42 12:0.20 17:0.73 18:0.99 21:0.64 22:0.93 26:0.97 27:0.38 29:0.53 30:0.36 31:0.94 34:0.40 36:0.99 37:0.41 39:0.95 43:0.06 45:0.91 46:0.88 47:0.93 48:0.91 55:0.71 58:0.93 64:0.77 66:0.26 69:0.98 70:0.81 71:0.83 74:0.25 79:0.94 81:0.25 86:0.75 89:0.97 91:0.12 98:0.66 101:0.45 107:0.90 108:0.90 110:0.88 122:0.95 123:0.89 124:0.90 125:0.88 126:0.22 127:0.99 129:0.05 131:0.61 133:0.89 135:0.84 137:0.94 139:0.73 140:0.45 141:0.91 145:0.45 146:0.93 147:0.91 149:0.18 150:0.27 151:0.70 153:0.72 154:0.05 157:0.61 159:0.94 160:0.88 162:0.76 166:0.61 170:0.79 172:0.92 173:0.93 174:0.79 175:0.99 177:0.70 179:0.14 182:0.61 187:0.68 190:1.00 196:0.92 197:0.80 199:0.97 202:0.58 212:0.41 216:0.50 219:0.87 222:0.35 223:0.97 224:0.93 225:0.96 227:0.61 229:0.61 231:0.62 234:0.91 235:0.80 239:0.78 240:0.98 241:0.35 242:0.74 243:0.18 244:0.99 245:0.98 246:0.61 247:0.97 248:0.65 253:0.22 254:0.88 256:0.58 257:0.99 259:0.21 262:0.93 264:0.93 265:0.67 266:0.89 267:0.69 268:0.62 271:0.60 274:0.91 275:0.91 276:0.96 277:0.99 281:0.91 284:0.92 285:0.45 287:0.61 289:0.88 292:0.95 294:0.91 300:0.94 +0 8:0.72 10:0.79 11:0.45 12:0.20 17:0.66 18:0.79 21:0.78 26:0.94 27:0.51 29:0.53 30:0.40 34:0.39 36:0.99 37:0.49 39:0.95 43:0.14 45:0.73 46:0.88 55:0.71 58:0.93 66:0.60 69:0.71 70:0.74 71:0.83 74:0.27 86:0.62 89:0.97 91:0.40 98:0.79 101:0.47 107:0.95 108:0.79 110:0.89 122:0.99 123:0.78 124:0.58 125:0.88 127:0.56 129:0.05 131:0.61 133:0.60 135:0.88 139:0.61 141:0.91 146:0.67 147:0.72 149:0.14 154:0.10 157:0.61 159:0.72 160:0.88 166:0.61 170:0.79 172:0.75 173:0.58 174:0.83 175:0.91 177:0.99 178:0.55 179:0.42 182:0.61 187:0.21 197:0.83 199:0.94 212:0.22 216:0.38 219:0.87 222:0.35 223:0.92 224:0.93 225:0.96 227:0.61 229:0.61 231:0.74 234:0.91 235:0.05 239:0.78 240:0.95 241:0.03 242:0.14 243:0.36 244:0.99 245:0.81 246:0.61 247:0.96 253:0.25 257:0.84 259:0.21 264:0.93 265:0.79 266:0.71 267:0.88 271:0.60 274:0.91 275:0.98 276:0.73 277:0.95 287:0.61 289:0.88 292:0.88 294:0.92 300:0.70 +0 10:0.80 11:0.51 12:0.20 17:0.69 18:0.92 21:0.78 26:0.96 27:1.00 29:0.53 30:0.70 34:0.79 36:0.91 37:0.53 39:0.95 43:0.09 44:0.86 45:0.83 46:0.88 48:0.97 55:0.84 58:0.93 66:0.91 69:0.30 70:0.39 71:0.83 74:0.26 83:0.54 86:0.66 89:0.97 91:0.20 98:0.96 101:0.52 107:0.91 108:0.24 110:0.88 122:0.95 123:0.23 125:0.88 127:0.70 129:0.05 131:0.61 135:0.96 139:0.66 141:0.91 144:0.57 145:0.85 146:0.92 147:0.94 149:0.16 154:0.07 155:0.47 157:0.79 159:0.55 160:0.88 163:0.90 166:0.61 170:0.79 172:0.75 173:0.29 174:0.79 177:1.00 178:0.55 179:0.57 182:0.73 187:0.87 192:0.45 195:0.76 197:0.83 199:0.95 204:0.78 208:0.48 212:0.29 216:0.28 222:0.35 223:0.96 224:0.93 225:0.96 227:0.61 229:0.61 231:0.64 234:0.91 235:0.02 239:0.80 240:0.95 241:0.03 242:0.38 243:0.91 244:0.99 246:0.61 247:0.86 253:0.24 259:0.21 264:0.93 265:0.96 266:0.54 267:0.73 271:0.60 274:0.91 275:0.91 276:0.64 281:0.91 287:0.61 289:0.88 294:0.92 300:0.33 +0 11:0.88 12:0.51 17:0.18 21:0.78 27:0.55 30:0.95 34:0.62 36:0.88 37:0.32 39:0.31 43:0.61 66:0.54 69:0.92 74:0.36 85:0.72 91:0.01 98:0.08 101:0.71 108:0.83 123:0.82 127:0.06 129:0.05 135:0.61 144:0.85 146:0.13 147:0.18 149:0.29 154:0.51 159:0.08 172:0.10 173:0.94 177:0.38 178:0.55 179:0.08 187:0.87 216:0.58 222:0.48 241:0.03 243:0.63 253:0.32 259:0.21 265:0.09 267:0.36 271:0.69 300:0.08 +1 11:0.88 12:0.51 17:0.45 21:0.78 27:0.17 30:0.91 34:0.63 36:0.70 37:0.84 39:0.31 43:0.78 66:0.69 69:0.78 70:0.13 74:0.51 85:0.80 91:0.02 98:0.15 101:0.75 108:0.61 122:0.43 123:0.59 127:0.09 129:0.05 135:0.37 144:0.85 146:0.22 147:0.28 149:0.61 154:0.71 159:0.10 172:0.21 173:0.79 177:0.38 178:0.55 179:0.12 187:0.95 212:0.61 216:0.45 222:0.48 235:0.22 241:0.21 242:0.63 243:0.73 247:0.30 253:0.42 259:0.21 265:0.16 266:0.17 267:0.36 271:0.69 276:0.17 300:0.13 +1 11:0.88 12:0.51 17:0.62 21:0.78 27:0.49 30:0.95 34:0.42 36:0.41 37:0.42 39:0.31 43:0.71 66:0.54 69:0.87 74:0.38 85:0.72 91:0.03 98:0.11 101:0.73 108:0.74 123:0.72 127:0.07 129:0.05 135:0.63 144:0.85 146:0.13 147:0.18 149:0.36 154:0.61 159:0.08 172:0.10 173:0.99 177:0.38 178:0.55 179:0.09 187:0.39 202:0.36 216:0.94 222:0.48 235:0.05 241:0.32 243:0.63 253:0.35 259:0.21 265:0.11 267:0.17 271:0.69 300:0.08 +0 1:0.74 7:0.81 11:0.88 12:0.51 17:0.94 21:0.71 27:0.72 30:0.95 39:0.31 43:0.95 66:0.54 69:0.34 74:0.58 81:0.53 83:0.73 85:0.72 91:0.72 98:0.39 101:0.78 108:0.27 114:0.68 121:0.90 123:0.26 126:0.54 127:0.13 129:0.05 144:0.85 145:0.58 146:0.13 147:0.18 149:0.53 150:0.67 154:0.95 155:0.67 159:0.08 165:0.69 172:0.10 173:1.00 176:0.73 177:0.38 178:0.55 179:0.76 192:0.59 201:0.74 204:0.89 208:0.64 215:0.48 216:0.04 222:0.48 230:0.87 233:0.76 235:0.88 241:0.78 243:0.63 253:0.57 265:0.41 271:0.69 290:0.68 297:0.36 300:0.08 +0 12:0.51 17:0.49 21:0.78 27:0.45 30:0.76 34:0.59 36:0.17 37:0.50 39:0.31 43:0.24 55:0.92 66:0.13 69:0.84 70:0.15 74:0.37 91:0.03 98:0.06 108:0.69 122:0.55 123:0.67 124:0.75 127:0.16 129:0.81 133:0.67 135:0.75 145:0.49 146:0.05 147:0.05 149:0.18 154:0.17 159:0.54 163:0.98 172:0.05 173:0.61 175:0.75 177:0.05 178:0.55 179:0.88 187:0.68 212:0.95 216:0.77 222:0.48 235:0.60 241:0.24 242:0.82 243:0.34 244:0.61 245:0.37 247:0.12 253:0.33 257:0.65 259:0.21 265:0.06 266:0.12 267:0.28 271:0.69 276:0.16 277:0.69 300:0.13 +0 11:0.82 12:0.51 17:0.94 21:0.78 30:0.62 34:0.74 36:0.30 37:0.97 39:0.31 43:0.59 55:0.52 66:0.33 69:0.93 70:0.54 74:0.93 91:0.02 98:0.25 108:0.97 122:0.67 123:0.97 124:0.96 127:0.66 129:0.81 133:0.96 135:0.47 144:0.85 145:0.94 146:0.30 147:0.05 149:0.70 154:0.48 159:0.95 172:0.92 173:0.63 177:0.05 178:0.55 179:0.14 187:0.68 212:0.84 216:0.94 222:0.48 235:0.52 241:0.10 242:0.80 243:0.05 245:0.92 247:0.55 253:0.66 257:0.65 259:0.21 265:0.27 266:0.97 267:0.36 271:0.69 276:0.95 300:0.94 +1 11:0.88 12:0.51 17:0.12 21:0.78 27:0.28 30:0.62 34:0.44 36:0.90 37:0.50 39:0.31 43:0.88 55:0.71 66:0.75 69:0.52 70:0.23 74:0.68 85:0.72 91:0.02 98:0.71 101:0.74 108:0.40 122:0.67 123:0.38 127:0.22 129:0.05 135:0.71 144:0.85 146:0.59 147:0.64 149:0.59 154:0.86 159:0.21 163:0.75 172:0.32 173:0.64 177:0.38 178:0.55 179:0.81 187:0.87 212:0.30 216:0.67 222:0.48 235:0.12 241:0.18 242:0.77 243:0.77 247:0.30 253:0.51 259:0.21 265:0.71 266:0.27 267:0.65 271:0.69 276:0.29 300:0.18 +1 11:0.88 12:0.51 17:0.72 21:0.78 27:0.72 30:0.68 39:0.31 43:0.92 66:0.32 69:0.34 70:0.43 74:0.65 85:0.80 91:0.31 98:0.20 101:0.77 108:0.27 122:0.43 123:0.69 124:0.61 127:0.47 129:0.59 133:0.47 144:0.85 145:0.83 146:0.19 147:0.24 149:0.71 154:0.91 159:0.35 172:0.21 173:0.44 177:0.38 178:0.55 179:0.88 187:0.21 212:0.61 216:0.05 222:0.48 235:0.22 241:0.43 242:0.45 243:0.49 245:0.54 247:0.39 253:0.50 265:0.15 266:0.27 271:0.69 276:0.15 300:0.33 +0 11:0.88 12:0.51 17:0.61 21:0.78 27:0.43 30:0.62 34:0.93 36:0.74 37:0.71 39:0.31 43:0.68 55:0.71 66:0.61 69:0.88 70:0.34 74:0.54 85:0.72 91:0.20 98:0.26 101:0.72 108:0.76 122:0.57 123:0.75 124:0.43 127:0.21 129:0.05 133:0.39 135:0.44 144:0.85 145:0.84 146:0.34 147:0.05 149:0.41 154:0.57 159:0.33 172:0.27 173:0.92 177:0.05 178:0.55 179:0.79 187:0.68 212:0.61 216:0.61 222:0.48 235:0.88 241:0.01 242:0.63 243:0.23 245:0.42 247:0.35 253:0.44 257:0.65 259:0.21 265:0.28 266:0.23 267:0.36 271:0.69 276:0.18 300:0.25 +0 12:0.06 17:0.43 21:0.78 27:0.45 30:0.95 34:0.81 36:0.20 37:0.50 43:0.27 55:0.84 66:0.54 69:0.79 91:0.18 98:0.24 108:0.63 123:0.61 127:0.11 129:0.05 135:0.81 145:0.50 146:0.34 147:0.40 149:0.18 154:0.20 159:0.13 172:0.10 173:0.78 177:0.05 178:0.55 179:0.69 187:0.68 216:0.77 235:0.95 241:0.12 243:0.63 259:0.21 265:0.26 267:0.44 300:0.08 +0 7:0.81 12:0.06 17:0.49 21:0.30 27:0.50 30:0.43 32:0.80 34:0.83 36:0.97 37:0.60 43:0.40 55:0.71 66:0.52 69:0.53 70:0.64 81:0.69 91:0.54 98:0.78 104:0.84 106:0.81 108:0.82 120:0.53 123:0.81 124:0.65 126:0.54 127:0.78 129:0.81 133:0.67 135:0.92 140:0.80 145:0.84 146:0.87 147:0.89 149:0.76 150:0.50 151:0.46 153:0.48 154:0.31 159:0.83 162:0.80 163:0.92 164:0.72 172:0.82 173:0.30 177:0.05 178:0.42 179:0.32 187:0.39 195:0.93 202:0.60 206:0.81 212:0.22 215:0.72 216:0.86 220:1.00 230:0.87 233:0.76 235:0.31 241:0.07 242:0.82 243:0.39 245:0.90 247:0.12 248:0.46 256:0.64 259:0.21 260:0.53 265:0.78 266:0.87 267:0.59 268:0.65 276:0.83 283:0.82 285:0.62 297:0.36 300:0.55 +1 8:0.77 12:0.06 17:0.02 20:0.89 21:0.40 25:0.88 27:0.68 30:0.21 34:0.70 36:0.50 37:0.36 43:0.44 55:0.84 66:0.30 69:0.47 70:0.69 78:0.74 81:0.66 91:0.71 98:0.52 100:0.78 104:0.58 108:0.69 111:0.74 120:0.84 123:0.35 124:0.60 126:0.54 127:0.53 129:0.59 133:0.47 135:0.18 140:0.80 145:0.85 146:0.48 147:0.54 149:0.55 150:0.48 151:0.71 152:0.85 153:0.72 154:0.33 159:0.38 162:0.51 164:0.88 169:0.76 172:0.37 173:0.56 177:0.05 178:0.42 179:0.76 186:0.75 202:0.88 212:0.36 215:0.67 216:0.49 220:0.91 235:0.42 241:0.89 242:0.82 243:0.48 245:0.67 247:0.12 248:0.76 256:0.84 259:0.21 260:0.84 261:0.77 265:0.52 266:0.63 267:0.18 268:0.85 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 7:0.81 8:0.92 12:0.06 17:0.43 21:0.59 27:0.72 30:0.95 32:0.80 34:0.61 36:0.55 37:0.68 43:0.49 55:0.92 66:0.54 69:0.34 81:0.58 91:0.80 98:0.89 104:0.58 108:0.27 120:0.73 123:0.26 126:0.54 127:0.45 129:0.05 135:0.28 140:0.80 145:0.58 146:0.39 147:0.45 149:0.22 150:0.43 151:0.66 153:0.48 154:0.38 159:0.15 162:0.59 164:0.80 172:0.10 173:0.78 177:0.05 178:0.42 179:1.00 191:0.75 195:0.53 202:0.75 215:0.55 216:0.34 220:0.74 230:0.87 233:0.76 235:0.68 241:0.90 243:0.63 248:0.66 256:0.75 259:0.21 260:0.74 265:0.89 267:0.94 268:0.75 285:0.62 297:0.36 300:0.08 +0 12:0.06 17:0.49 21:0.78 27:0.45 30:0.76 34:0.50 36:0.46 37:0.50 43:0.26 55:0.71 66:0.52 69:0.82 70:0.29 91:0.20 98:0.40 108:0.67 123:0.65 124:0.50 127:0.18 129:0.59 133:0.47 135:0.85 145:0.47 146:0.58 147:0.64 149:0.33 154:0.18 159:0.34 163:0.86 172:0.27 173:0.78 177:0.05 178:0.55 179:0.67 187:0.68 212:0.23 216:0.77 235:0.74 241:0.27 242:0.82 243:0.61 245:0.48 247:0.12 259:0.21 265:0.42 266:0.38 267:0.65 276:0.31 300:0.25 +0 12:0.06 17:0.49 25:0.88 27:0.72 30:0.76 34:0.40 36:0.39 43:0.35 55:0.84 66:0.36 69:0.62 70:0.15 81:0.79 91:0.72 98:0.32 104:0.54 108:0.47 120:0.69 123:0.45 124:0.52 126:0.54 127:0.46 129:0.59 133:0.47 135:0.24 140:0.45 145:0.41 146:0.30 147:0.36 149:0.25 150:0.59 151:0.66 153:0.48 154:0.26 159:0.30 162:0.86 163:0.96 164:0.57 172:0.10 173:0.76 177:0.05 179:0.99 187:0.21 202:0.36 212:0.95 215:0.96 216:0.14 235:0.02 241:0.84 242:0.82 243:0.51 245:0.37 247:0.12 248:0.63 256:0.45 260:0.70 265:0.35 266:0.12 267:0.89 268:0.46 276:0.15 285:0.62 297:0.36 300:0.13 +0 6:0.84 7:0.81 8:0.79 12:0.06 17:0.62 20:0.80 21:0.30 25:0.88 27:0.72 30:0.45 32:0.80 34:0.44 36:0.41 37:0.82 43:0.34 55:0.52 66:0.27 69:0.65 70:0.25 78:0.81 81:0.82 91:0.78 98:0.26 100:0.87 104:0.54 108:0.50 111:0.82 120:0.61 123:0.48 124:0.61 126:0.54 127:0.31 129:0.59 133:0.47 135:0.34 140:0.45 145:0.74 146:0.23 147:0.29 149:0.30 150:0.61 151:0.54 152:0.79 153:0.48 154:0.25 159:0.21 162:0.76 163:0.81 164:0.80 169:0.85 172:0.10 173:0.87 177:0.05 179:0.96 186:0.81 189:0.85 191:0.85 195:0.57 202:0.71 212:0.61 215:0.72 216:0.30 220:0.87 230:0.87 233:0.76 235:0.42 238:0.96 241:0.85 242:0.82 243:0.46 245:0.40 247:0.12 248:0.55 256:0.75 259:0.21 260:0.62 261:0.81 265:0.28 266:0.17 267:0.52 268:0.76 276:0.16 285:0.62 297:0.36 300:0.18 +4 6:0.97 8:0.93 12:0.06 17:0.02 20:0.97 27:0.11 30:0.45 34:0.18 36:0.25 37:0.87 43:0.49 55:0.84 66:0.27 69:0.21 70:0.25 78:0.92 81:0.79 91:0.95 98:0.36 100:1.00 108:0.77 111:0.99 120:0.95 123:0.75 124:0.72 126:0.54 127:0.85 129:0.81 133:0.67 135:0.17 140:0.45 146:0.05 147:0.05 149:0.28 150:0.59 151:0.82 152:0.91 153:0.48 154:0.38 159:0.47 162:0.58 163:0.89 164:0.96 169:1.00 172:0.10 173:0.30 177:0.05 179:0.98 186:0.92 189:0.97 191:0.86 202:0.94 212:0.61 215:0.96 216:0.49 235:0.02 238:0.99 241:0.88 242:0.82 243:0.29 245:0.38 247:0.12 248:0.93 256:0.95 259:0.21 260:0.95 261:0.99 265:0.38 266:0.17 267:0.69 268:0.95 276:0.21 283:0.82 285:0.62 297:0.36 300:0.18 +1 12:0.06 17:0.84 20:0.82 21:0.05 25:0.88 27:0.72 30:0.68 34:0.45 36:0.46 43:0.49 55:0.71 66:0.79 69:0.23 70:0.31 78:0.72 81:0.77 91:0.81 98:0.84 100:0.73 104:0.58 108:0.18 111:0.72 120:0.61 123:0.18 126:0.54 127:0.35 129:0.05 135:0.70 140:0.45 146:0.62 147:0.68 149:0.47 150:0.57 151:0.56 152:0.78 153:0.77 154:0.38 159:0.23 162:0.68 164:0.64 169:0.72 172:0.41 173:0.46 177:0.05 179:0.84 186:0.73 202:0.54 212:0.42 215:0.91 216:0.10 220:0.62 235:0.05 241:0.84 242:0.82 243:0.80 247:0.12 248:0.55 256:0.45 259:0.21 260:0.62 261:0.73 265:0.84 266:0.27 267:0.73 268:0.57 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +0 6:0.79 8:0.63 12:0.06 17:0.02 20:0.89 21:0.78 25:0.88 27:0.72 34:0.56 36:0.23 78:0.77 91:0.93 100:0.79 104:0.58 111:0.76 120:0.75 135:0.77 140:0.87 151:0.70 152:0.84 153:0.69 162:0.50 164:0.79 169:0.77 175:0.75 178:0.48 186:0.78 189:0.81 202:0.81 216:0.25 220:0.62 238:0.87 241:0.94 244:0.61 248:0.68 256:0.73 257:0.65 259:0.21 260:0.76 261:0.80 267:0.65 268:0.74 277:0.69 285:0.62 +0 6:0.84 7:0.81 8:0.66 12:0.06 17:0.09 20:0.96 21:0.30 27:0.21 30:0.38 34:0.64 36:0.87 37:0.74 43:0.52 55:0.59 66:0.63 69:0.10 70:0.55 78:0.86 81:0.69 91:0.71 98:0.77 100:0.94 104:0.84 106:0.81 108:0.67 111:0.89 120:0.88 123:0.65 124:0.48 126:0.54 127:0.45 129:0.59 133:0.47 135:0.19 140:0.45 146:0.54 147:0.60 149:0.63 150:0.50 151:0.80 152:0.95 153:0.93 154:0.41 159:0.61 162:0.58 164:0.83 169:0.90 172:0.63 173:0.13 177:0.05 179:0.56 186:0.87 187:0.39 189:0.86 202:0.79 206:0.81 212:0.18 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.95 241:0.62 242:0.82 243:0.32 245:0.75 247:0.12 248:0.82 256:0.77 259:0.21 260:0.88 261:0.92 265:0.77 266:0.75 267:0.88 268:0.79 276:0.60 283:0.82 285:0.62 297:0.36 300:0.43 +0 7:0.81 8:0.78 12:0.06 17:0.72 20:0.83 21:0.22 27:0.11 30:0.62 32:0.80 34:0.61 36:0.75 37:0.86 43:0.40 55:0.52 66:0.25 69:0.53 70:0.65 78:0.76 81:0.72 91:0.53 98:0.69 100:0.81 104:0.93 106:0.81 108:0.41 111:0.76 120:0.74 123:0.76 124:0.79 126:0.54 127:0.71 129:0.93 133:0.84 135:0.61 140:0.45 145:0.63 146:0.64 147:0.69 149:0.86 150:0.53 151:0.71 152:0.79 153:0.72 154:0.30 159:0.74 162:0.83 164:0.77 169:0.79 172:0.79 173:0.37 177:0.05 179:0.29 186:0.77 187:0.39 191:0.83 195:0.88 202:0.66 206:0.81 212:0.69 215:0.79 216:0.84 230:0.87 233:0.76 235:0.22 241:0.52 242:0.82 243:0.57 245:0.87 247:0.12 248:0.67 256:0.71 259:0.21 260:0.75 261:0.77 265:0.41 266:0.80 267:0.82 268:0.72 276:0.84 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.96 7:0.81 8:0.86 12:0.06 17:0.38 20:0.83 21:0.54 27:0.40 30:0.62 34:0.19 36:0.77 37:0.81 43:0.28 55:0.96 66:0.20 69:0.77 70:0.54 78:0.81 81:0.61 91:0.73 98:0.27 100:0.89 104:0.58 108:0.88 111:0.83 120:0.81 123:0.87 124:0.85 126:0.54 127:0.37 129:0.93 133:0.84 135:0.42 140:0.87 145:0.44 146:0.43 147:0.50 149:0.40 150:0.45 151:0.71 152:0.79 153:0.48 154:0.21 159:0.65 162:0.51 163:0.98 164:0.84 169:0.86 172:0.21 173:0.66 177:0.05 178:0.48 179:0.76 186:0.82 189:0.97 191:0.73 202:0.85 212:0.28 215:0.58 216:0.33 220:0.96 230:0.87 233:0.76 235:0.60 238:0.96 241:0.79 242:0.82 243:0.34 245:0.49 247:0.12 248:0.73 256:0.80 259:0.21 260:0.81 261:0.82 265:0.30 266:0.63 267:1.00 268:0.80 276:0.42 285:0.62 297:0.36 300:0.43 +1 8:0.95 12:0.06 17:0.34 20:0.86 21:0.78 25:0.88 27:0.60 30:0.76 34:0.34 36:0.42 37:0.78 43:0.47 55:0.59 66:0.36 69:0.37 70:0.15 78:0.74 91:0.90 98:0.25 100:0.78 104:0.58 108:0.29 111:0.74 120:0.59 123:0.28 124:0.52 127:0.56 129:0.59 133:0.47 135:0.17 140:0.96 146:0.24 147:0.30 149:0.27 151:0.53 152:0.82 153:0.46 154:0.36 159:0.28 162:0.48 163:0.79 164:0.72 169:0.76 172:0.10 173:0.57 177:0.05 178:0.54 179:0.99 186:0.75 202:0.80 212:0.95 216:0.16 220:0.87 235:0.22 241:0.96 242:0.82 243:0.51 245:0.37 247:0.12 248:0.52 256:0.68 259:0.21 260:0.60 261:0.76 265:0.27 266:0.12 267:0.69 268:0.66 276:0.14 285:0.62 300:0.13 +1 8:0.70 12:0.06 17:0.03 20:0.95 21:0.78 25:0.88 27:0.63 30:0.95 34:0.53 36:1.00 37:0.32 43:0.46 55:0.96 66:0.13 69:0.44 78:0.74 91:0.81 98:0.08 100:0.78 104:0.58 108:0.34 111:0.74 120:0.94 123:0.32 124:0.75 127:0.34 129:0.81 133:0.67 135:0.37 140:0.87 146:0.05 147:0.05 149:0.24 151:0.80 152:0.90 153:0.92 154:0.35 159:0.38 162:0.55 163:0.97 164:0.94 169:0.76 172:0.05 173:0.46 177:0.05 178:0.48 179:0.98 186:0.75 202:0.93 212:0.95 216:0.62 235:0.52 238:0.89 241:0.88 242:0.82 243:0.34 245:0.37 247:0.12 248:0.91 256:0.92 259:0.21 260:0.94 261:0.77 265:0.08 266:0.12 267:0.65 268:0.92 276:0.19 283:0.82 285:0.62 300:0.08 +0 6:0.81 8:0.80 12:0.06 17:0.83 20:0.87 21:0.30 25:0.88 27:0.72 30:0.21 34:0.45 36:0.62 43:0.46 55:0.59 66:0.80 69:0.41 70:0.67 78:0.75 81:0.69 91:0.73 98:0.94 100:0.78 104:0.58 108:0.32 111:0.75 120:0.71 123:0.31 126:0.54 127:0.61 129:0.05 135:0.28 140:0.45 146:0.67 147:0.72 149:0.49 150:0.50 151:0.66 152:0.84 153:0.48 154:0.35 159:0.26 162:0.86 164:0.67 169:0.76 172:0.45 173:0.64 177:0.05 179:0.87 186:0.76 187:0.21 202:0.50 212:0.55 215:0.72 216:0.08 220:0.87 235:0.31 241:0.86 242:0.82 243:0.82 247:0.12 248:0.64 256:0.58 259:0.21 260:0.72 261:0.77 265:0.94 266:0.27 267:0.44 268:0.59 276:0.36 285:0.62 297:0.36 300:0.43 +2 6:0.87 8:0.76 12:0.06 17:0.28 20:0.79 21:0.78 27:0.21 30:0.62 34:0.80 36:0.92 37:0.74 43:0.39 55:0.92 66:0.23 69:0.56 70:0.65 78:0.87 91:0.46 98:0.27 100:0.90 108:0.91 111:0.87 120:0.76 123:0.90 124:0.88 127:0.51 129:0.95 133:0.87 135:0.58 140:0.80 146:0.45 147:0.52 149:0.47 151:0.66 152:0.92 153:0.48 154:0.30 159:0.77 162:0.48 163:0.94 164:0.70 169:0.90 172:0.32 173:0.35 177:0.05 178:0.42 179:0.68 186:0.87 187:0.39 189:1.00 202:0.77 212:0.27 216:0.95 235:0.52 238:0.90 241:0.32 242:0.82 243:0.27 245:0.56 247:0.12 248:0.69 256:0.64 259:0.21 260:0.77 261:0.91 265:0.29 266:0.84 267:0.44 268:0.63 276:0.52 283:0.82 285:0.62 300:0.55 +1 11:0.88 12:0.51 17:0.26 21:0.78 27:0.45 30:0.91 34:0.80 36:0.19 37:0.50 43:0.76 66:0.27 69:0.82 70:0.13 74:0.49 85:0.80 91:0.05 98:0.08 101:0.73 108:0.66 122:0.43 123:0.64 124:0.61 127:0.13 129:0.59 133:0.47 135:0.60 144:0.85 145:0.49 146:0.13 147:0.18 149:0.55 154:0.68 159:0.25 163:0.88 172:0.10 173:0.73 177:0.38 178:0.55 179:0.66 187:0.68 212:0.61 216:0.77 222:0.99 235:0.80 241:0.52 242:0.63 243:0.46 245:0.40 247:0.30 253:0.41 259:0.21 265:0.08 266:0.17 267:0.28 271:0.19 276:0.12 300:0.13 +1 8:0.61 11:0.88 12:0.51 17:0.03 20:0.95 21:0.54 27:0.55 30:0.45 32:0.72 34:0.86 36:0.27 37:0.32 43:0.80 55:0.84 66:0.67 69:0.41 70:0.57 74:0.43 78:0.97 81:0.49 85:0.72 91:0.35 98:0.75 100:0.73 101:0.72 104:0.93 106:0.81 108:0.31 111:0.94 123:0.30 124:0.47 126:0.32 127:0.49 129:0.05 133:0.62 135:0.81 144:0.85 145:0.69 146:0.82 147:0.85 149:0.67 150:0.39 152:0.88 154:0.74 159:0.53 163:0.75 169:0.72 172:0.57 173:0.37 177:0.38 178:0.55 179:0.67 186:0.97 187:0.87 195:0.72 206:0.81 212:0.16 215:0.58 216:0.62 222:0.99 235:0.60 241:0.01 242:0.78 243:0.72 245:0.57 247:0.39 253:0.37 259:0.21 261:0.92 265:0.75 266:0.63 267:0.52 271:0.19 276:0.52 283:0.71 297:0.36 300:0.43 +1 11:0.88 12:0.51 17:0.77 21:0.78 27:0.26 30:0.71 34:0.90 36:0.88 37:0.64 43:0.67 55:0.92 66:0.82 69:0.75 70:0.24 74:0.44 85:0.80 91:0.64 98:0.79 101:0.75 106:0.81 108:0.59 123:0.56 127:0.28 129:0.05 135:0.80 144:0.85 145:0.42 146:0.85 147:0.88 149:0.59 154:0.56 159:0.42 163:0.94 172:0.48 173:0.75 177:0.38 178:0.55 179:0.72 187:0.68 206:0.81 212:0.61 216:0.40 222:0.99 235:0.42 241:0.03 242:0.73 243:0.83 247:0.39 253:0.38 259:0.21 265:0.79 266:0.38 267:0.44 271:0.19 276:0.40 300:0.18 +1 11:0.88 12:0.51 17:0.84 21:0.78 27:0.43 30:0.85 34:0.58 36:0.88 37:0.42 43:0.77 66:0.89 69:0.79 70:0.27 74:0.77 85:0.95 91:0.60 98:0.80 101:0.84 104:0.99 106:0.81 108:0.63 122:0.43 123:0.60 127:0.28 129:0.05 135:0.65 144:0.85 145:0.42 146:0.88 147:0.90 149:0.88 154:0.70 159:0.47 163:0.81 172:0.70 173:0.77 177:0.38 178:0.55 179:0.47 187:0.68 206:0.81 212:0.57 216:0.41 222:0.99 235:0.42 241:0.03 242:0.63 243:0.90 247:0.30 253:0.56 259:0.21 265:0.80 266:0.47 267:0.36 271:0.19 276:0.58 300:0.25 +1 8:0.59 12:0.51 17:0.47 21:0.74 27:0.12 30:0.76 34:0.73 36:0.38 37:0.78 43:0.25 55:0.96 66:0.13 69:0.83 70:0.29 81:0.33 91:0.12 98:0.16 104:0.95 106:0.81 108:0.93 117:0.86 120:0.55 123:0.92 124:0.89 126:0.32 127:0.37 129:0.95 133:0.87 135:0.20 140:0.45 145:0.49 146:0.05 147:0.05 149:0.23 150:0.30 151:0.48 153:0.48 154:0.18 159:0.82 162:0.86 163:0.97 164:0.55 172:0.10 173:0.61 175:0.75 177:0.05 179:0.88 187:0.95 190:0.77 202:0.36 206:0.81 212:0.23 215:0.46 216:0.81 220:0.96 222:0.99 232:0.97 235:0.88 241:0.30 242:0.82 243:0.21 244:0.61 245:0.40 247:0.12 248:0.48 253:0.20 256:0.45 257:0.65 259:0.21 260:0.55 265:0.18 266:0.38 267:0.44 268:0.46 271:0.19 276:0.31 277:0.69 285:0.50 297:0.36 300:0.25 +1 1:0.74 11:0.88 12:0.51 17:0.51 21:0.22 27:0.57 30:0.95 34:0.47 36:0.35 37:0.31 43:0.96 60:0.87 66:0.54 69:0.15 74:0.58 81:0.82 83:0.85 85:0.72 91:0.20 98:0.15 101:0.76 104:0.98 108:0.12 114:0.90 123:0.12 126:0.54 127:0.09 129:0.05 135:0.58 144:0.85 145:0.38 146:0.13 147:0.18 149:0.53 150:0.89 154:0.96 155:0.67 158:0.87 159:0.08 165:0.86 172:0.10 173:0.57 176:0.73 177:0.38 178:0.55 179:0.16 187:0.95 192:0.59 201:0.74 208:0.64 215:0.79 216:0.90 222:0.99 235:0.31 241:0.12 243:0.63 253:0.69 259:0.21 265:0.16 267:0.23 271:0.19 279:0.86 283:0.71 290:0.90 297:0.36 300:0.08 +1 11:0.88 12:0.51 17:0.06 21:0.13 27:0.33 30:0.62 32:0.72 34:0.82 36:0.36 37:0.41 43:0.76 55:0.84 66:0.47 69:0.79 70:0.34 74:0.40 81:0.83 85:0.72 91:0.44 98:0.37 101:0.73 108:0.63 123:0.61 124:0.52 126:0.32 127:0.21 129:0.05 133:0.39 135:0.95 144:0.85 145:0.47 146:0.39 147:0.34 149:0.52 150:0.62 154:0.68 159:0.27 163:0.89 172:0.21 173:0.86 177:0.05 178:0.55 179:0.81 187:0.68 195:0.65 212:0.30 215:0.84 216:0.88 222:0.99 235:0.12 241:0.05 242:0.63 243:0.37 245:0.46 247:0.35 253:0.36 257:0.65 259:0.21 265:0.39 266:0.27 267:0.91 271:0.19 276:0.26 297:0.36 300:0.25 +2 11:0.88 12:0.51 17:0.22 21:0.78 27:0.33 30:0.76 34:1.00 36:0.43 37:0.69 43:0.85 66:0.64 69:0.61 70:0.29 74:0.61 85:0.85 91:0.15 98:0.56 101:0.79 108:0.47 122:0.43 123:0.45 124:0.42 127:0.29 129:0.05 133:0.39 135:0.51 144:0.85 145:0.61 146:0.54 147:0.60 149:0.75 154:0.82 159:0.31 163:0.81 172:0.32 173:0.68 177:0.38 178:0.55 179:0.84 187:0.68 212:0.52 216:0.76 222:0.99 235:0.52 241:0.27 242:0.73 243:0.69 245:0.43 247:0.30 253:0.48 259:0.21 265:0.58 266:0.23 267:0.65 271:0.19 276:0.28 300:0.25 +1 11:0.88 12:0.51 17:0.08 21:0.13 27:0.69 30:0.62 32:0.72 34:0.82 36:0.37 37:0.44 43:0.76 55:0.84 66:0.47 69:0.79 70:0.34 74:0.40 81:0.83 85:0.72 91:0.48 98:0.37 101:0.73 108:0.63 123:0.61 124:0.52 126:0.32 127:0.21 129:0.05 133:0.39 135:0.94 144:0.85 145:0.47 146:0.39 147:0.34 149:0.52 150:0.62 154:0.68 159:0.27 163:0.89 172:0.21 173:0.86 177:0.05 178:0.55 179:0.81 187:0.68 195:0.65 212:0.30 215:0.84 216:0.25 222:0.99 235:0.12 241:0.05 242:0.63 243:0.37 245:0.46 247:0.35 253:0.36 257:0.65 259:0.21 265:0.39 266:0.27 267:0.88 271:0.19 276:0.26 297:0.36 300:0.25 +2 11:0.88 12:0.51 17:0.82 21:0.78 27:1.00 30:0.76 34:0.50 36:0.60 37:0.40 43:0.82 60:0.87 66:0.72 69:0.68 70:0.22 74:0.63 83:0.83 85:0.80 91:0.31 98:0.17 101:0.75 108:0.52 122:0.41 123:0.50 127:0.10 129:0.05 135:0.20 144:0.85 145:0.82 146:0.24 147:0.30 149:0.65 154:0.77 155:0.67 158:0.87 159:0.11 172:0.27 173:0.71 177:0.38 178:0.55 179:0.13 187:0.39 192:0.59 208:0.64 212:0.78 216:0.10 222:0.99 235:0.52 241:0.61 242:0.45 243:0.75 247:0.35 253:0.49 259:0.21 265:0.19 266:0.17 267:0.23 271:0.19 276:0.23 279:0.86 283:0.71 300:0.18 +1 8:0.60 11:0.88 12:0.51 17:0.32 20:0.99 21:0.78 27:0.14 30:0.95 34:0.28 36:0.35 37:0.67 43:0.82 55:0.59 66:0.99 69:0.60 70:0.15 74:0.43 78:0.86 85:0.72 91:0.42 98:0.97 100:0.73 101:0.69 104:0.82 106:0.81 108:0.46 111:0.83 120:0.58 123:0.44 127:0.75 129:0.05 135:0.78 140:0.45 144:0.85 145:0.52 146:0.99 147:0.99 149:0.94 151:0.52 152:0.90 153:0.48 154:0.77 159:0.82 162:0.86 164:0.55 169:0.72 172:0.98 173:0.38 177:0.38 179:0.13 186:0.86 187:0.39 190:0.77 202:0.36 206:0.81 212:0.94 216:0.93 220:0.87 222:0.99 235:0.12 241:0.53 242:0.82 243:0.99 247:0.39 248:0.52 253:0.38 256:0.45 259:0.21 260:0.58 261:0.84 265:0.97 266:0.23 267:0.97 268:0.46 271:0.19 276:0.96 285:0.50 300:0.43 +1 11:0.88 12:0.51 17:0.40 21:0.78 27:0.43 30:0.76 34:0.88 36:0.19 37:0.35 43:0.21 66:0.20 69:0.98 70:0.22 74:0.32 85:0.80 91:0.01 98:0.05 101:0.64 108:0.97 122:0.41 123:0.97 124:0.73 127:0.11 129:0.59 133:0.64 135:0.76 144:0.85 145:0.44 146:0.13 147:0.05 149:0.19 154:0.14 159:0.46 163:0.88 172:0.10 173:0.86 177:0.05 178:0.55 179:0.23 187:0.95 212:0.42 216:0.82 222:0.99 235:0.42 242:0.45 243:0.26 245:0.40 247:0.35 253:0.29 257:0.65 259:0.21 265:0.05 266:0.23 267:0.36 271:0.19 276:0.18 300:0.18 +1 8:0.82 11:0.88 12:0.51 17:0.93 21:0.78 27:0.44 30:0.86 34:0.78 36:0.67 37:0.45 43:0.58 55:0.52 66:0.97 69:0.84 70:0.23 74:0.42 85:0.80 91:0.46 98:0.50 101:0.69 108:0.69 123:0.67 124:0.36 127:0.26 129:0.05 133:0.39 135:0.68 144:0.85 145:0.56 146:0.95 147:0.96 149:0.91 154:0.47 159:0.86 172:0.97 173:0.52 177:0.38 178:0.55 179:0.12 187:0.21 212:0.94 216:0.41 222:0.99 235:0.95 242:0.82 243:0.97 245:0.80 247:0.47 253:0.37 259:0.21 265:0.51 266:0.23 267:0.69 271:0.19 276:0.89 300:0.55 +2 11:0.88 12:0.51 17:0.05 21:0.13 27:0.33 30:0.62 32:0.72 34:0.76 36:0.36 37:0.41 43:0.76 55:0.84 66:0.47 69:0.79 70:0.34 74:0.40 81:0.83 85:0.72 91:0.44 98:0.37 101:0.73 108:0.63 123:0.61 124:0.52 126:0.32 127:0.21 129:0.05 133:0.39 135:0.95 144:0.85 145:0.47 146:0.40 147:0.34 149:0.52 150:0.62 154:0.67 159:0.27 163:0.89 172:0.21 173:0.86 177:0.05 178:0.55 179:0.81 187:0.68 195:0.65 212:0.30 215:0.84 216:0.88 222:0.99 235:0.12 241:0.12 242:0.63 243:0.37 245:0.46 247:0.35 253:0.36 257:0.65 259:0.21 265:0.39 266:0.27 267:0.91 271:0.19 276:0.26 297:0.36 300:0.25 +2 6:0.83 7:0.76 8:0.85 11:0.88 12:0.51 17:0.36 20:0.99 21:0.40 27:0.21 30:0.33 34:0.59 36:0.79 37:0.74 43:0.90 66:0.12 69:0.12 70:0.69 74:0.53 78:1.00 81:0.61 85:0.80 91:0.37 98:0.21 100:0.98 101:0.71 104:0.84 106:0.81 108:0.10 111:0.99 120:0.81 122:0.43 123:0.92 124:0.72 126:0.32 127:0.40 129:0.59 133:0.64 135:0.24 140:0.45 144:0.85 146:0.29 147:0.35 149:0.67 150:0.45 151:0.70 152:0.90 153:0.91 154:0.89 159:0.81 162:0.57 164:0.79 169:0.98 172:0.21 173:0.09 177:0.38 179:0.86 186:1.00 187:0.39 189:0.85 190:0.77 202:0.76 206:0.81 212:0.42 215:0.67 216:0.95 220:0.62 222:0.99 230:0.78 233:0.69 235:0.42 238:0.89 241:0.47 242:0.45 243:0.49 245:0.48 247:0.47 248:0.73 253:0.43 256:0.73 259:0.21 260:0.81 261:0.96 265:0.15 266:0.38 267:0.59 268:0.75 271:0.19 276:0.24 277:0.69 283:0.71 285:0.50 297:0.36 300:0.43 +2 6:0.96 8:0.85 9:0.80 11:0.57 12:0.69 17:0.03 20:0.99 21:0.78 27:0.59 28:0.66 30:0.89 32:0.80 34:0.53 36:0.57 37:0.31 39:0.79 41:0.98 43:0.76 55:0.92 60:0.95 66:0.61 69:0.76 70:0.15 74:0.77 77:0.70 78:0.88 83:0.89 85:0.80 91:0.81 96:0.97 98:0.73 100:0.92 101:0.78 108:0.60 111:0.88 120:0.93 122:0.57 123:0.57 124:0.43 127:0.48 129:0.05 133:0.39 135:0.61 140:0.45 145:0.91 146:0.58 147:0.05 149:0.61 151:0.98 152:0.92 153:0.93 154:0.67 155:0.67 158:0.87 159:0.30 161:0.82 162:0.52 163:0.81 164:0.92 167:0.99 169:0.89 172:0.27 173:0.91 177:0.05 179:0.93 181:0.56 186:0.88 189:0.97 191:0.85 192:0.59 195:0.58 202:0.92 208:0.64 212:0.30 216:0.52 222:0.48 235:0.60 238:0.93 241:0.94 242:0.63 243:0.23 245:0.42 247:0.35 248:0.97 253:0.97 255:0.79 256:0.90 257:0.65 259:0.21 260:0.94 261:0.90 265:0.73 266:0.27 267:0.79 268:0.90 276:0.27 279:0.86 282:0.88 283:0.71 285:0.62 299:0.88 300:0.13 +2 1:0.74 9:0.80 11:0.57 12:0.69 17:0.08 21:0.40 27:0.28 28:0.66 30:0.62 34:0.29 36:0.86 37:0.55 39:0.79 41:0.90 43:0.98 66:0.61 69:0.15 70:0.34 74:0.58 81:0.60 83:0.78 85:0.80 91:0.53 96:0.85 98:0.26 101:0.76 108:0.12 114:0.82 120:0.76 122:0.43 123:0.12 124:0.43 126:0.54 127:0.39 129:0.05 133:0.39 135:0.39 140:0.45 146:0.30 147:0.36 149:0.72 150:0.76 151:0.92 153:0.72 154:0.98 155:0.67 159:0.39 161:0.82 162:0.59 164:0.73 165:0.83 167:0.87 172:0.27 173:0.25 176:0.73 177:0.38 179:0.92 181:0.56 187:0.21 192:0.59 201:0.74 202:0.68 208:0.64 212:0.84 215:0.67 216:0.67 222:0.48 235:0.52 241:0.74 242:0.63 243:0.68 245:0.42 247:0.35 248:0.83 253:0.83 255:0.79 256:0.64 259:0.21 260:0.76 265:0.28 266:0.17 267:0.28 268:0.68 276:0.15 285:0.62 290:0.82 297:0.36 300:0.25 +2 1:0.74 6:0.96 8:0.92 9:0.80 11:0.57 12:0.69 17:0.02 20:0.80 21:0.13 27:0.28 28:0.66 30:0.76 34:0.28 36:0.41 37:0.55 39:0.79 41:0.90 43:0.83 66:0.36 69:0.65 70:0.22 74:0.50 78:0.81 81:0.75 83:0.81 85:0.80 91:0.42 96:0.89 98:0.29 100:0.87 101:0.75 108:0.80 111:0.82 114:0.92 120:0.89 122:0.41 123:0.79 124:0.57 126:0.54 127:0.50 129:0.59 133:0.47 135:0.56 140:0.80 146:0.13 147:0.18 149:0.59 150:0.84 151:0.93 152:0.98 153:0.78 154:0.80 155:0.67 159:0.48 161:0.82 162:0.59 163:0.89 164:0.87 165:0.88 167:0.76 169:0.99 172:0.16 173:0.66 176:0.73 177:0.38 179:0.96 181:0.56 186:0.82 187:0.39 192:0.59 201:0.74 202:0.84 208:0.64 212:0.42 215:0.84 216:0.67 220:0.74 222:0.48 235:0.22 241:0.63 242:0.45 243:0.40 245:0.43 247:0.35 248:0.88 253:0.87 255:0.79 256:0.84 259:0.21 260:0.89 261:0.98 265:0.31 266:0.23 267:0.44 268:0.84 276:0.15 283:0.82 285:0.62 290:0.92 297:0.36 300:0.18 +1 8:0.65 11:0.57 12:0.69 17:0.24 21:0.78 27:0.45 28:0.66 30:0.20 34:0.66 36:0.25 37:0.50 39:0.79 43:0.76 55:0.71 66:0.24 69:0.76 70:0.97 74:0.61 85:0.72 91:0.08 98:0.31 101:0.69 108:0.60 123:0.57 124:0.89 127:0.38 129:0.05 133:0.89 135:0.78 145:0.42 146:0.61 147:0.67 149:0.69 154:0.68 159:0.76 161:0.82 167:0.68 172:0.51 173:0.58 177:0.38 178:0.55 179:0.40 181:0.56 187:0.68 212:0.48 216:0.77 222:0.48 235:0.60 241:0.35 242:0.78 243:0.44 245:0.70 247:0.55 253:0.48 259:0.21 265:0.33 266:0.87 267:0.44 276:0.68 300:0.94 +2 1:0.74 6:0.96 7:0.81 8:0.86 9:0.80 11:0.57 12:0.69 17:0.32 20:0.99 21:0.30 27:0.21 28:0.66 30:0.17 34:0.34 36:0.84 37:0.74 39:0.79 41:0.92 43:0.98 55:0.71 60:0.95 66:0.35 69:0.12 70:0.90 74:0.88 78:0.87 81:0.65 83:0.82 85:0.91 91:0.62 96:0.92 98:0.62 100:0.89 101:0.77 104:0.84 106:0.81 108:0.10 111:0.86 114:0.86 117:0.86 120:0.85 121:0.90 123:0.10 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.30 140:0.45 146:0.91 147:0.92 149:0.85 150:0.79 151:0.96 152:1.00 153:0.86 154:0.98 155:0.67 158:0.92 159:0.81 161:0.82 162:0.73 164:0.80 165:0.84 167:0.63 169:0.84 172:0.75 173:0.09 176:0.73 177:0.38 179:0.32 181:0.56 186:0.87 187:0.39 189:0.99 192:0.59 201:0.74 202:0.72 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 222:0.48 230:0.87 232:0.91 233:0.76 235:0.42 238:0.91 241:0.12 242:0.57 243:0.51 245:0.85 247:0.60 248:0.91 253:0.93 255:0.79 256:0.73 259:0.21 260:0.86 261:0.89 265:0.63 266:0.84 267:0.87 268:0.76 276:0.81 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +1 1:0.74 6:0.96 7:0.81 8:0.62 9:0.80 11:0.57 12:0.69 17:0.38 20:0.99 21:0.47 27:0.21 28:0.66 30:0.13 34:0.53 36:0.87 37:0.74 39:0.79 43:0.79 55:0.42 66:0.20 69:0.17 70:0.83 74:0.91 76:0.85 78:0.80 81:0.54 85:0.80 91:0.22 96:0.76 98:0.50 100:0.82 101:0.73 104:0.92 106:0.81 108:0.14 111:0.79 114:0.80 117:0.86 120:0.65 122:0.43 123:0.83 124:0.79 126:0.54 127:0.57 129:0.89 133:0.78 135:0.20 140:0.45 146:0.66 147:0.71 149:0.72 150:0.64 151:0.69 152:1.00 153:0.48 154:0.92 155:0.67 158:0.86 159:0.67 161:0.82 162:0.65 164:0.69 167:0.65 169:0.84 172:0.61 173:0.15 176:0.73 177:0.38 179:0.41 181:0.56 186:0.81 187:0.39 192:0.59 201:0.74 202:0.61 204:0.89 206:0.81 208:0.64 212:0.40 215:0.63 216:0.95 220:0.82 222:0.48 230:0.83 232:0.85 233:0.66 235:0.60 241:0.21 242:0.43 243:0.48 245:0.82 247:0.55 248:0.69 253:0.82 255:0.79 256:0.58 259:0.21 260:0.65 261:0.89 265:0.46 266:0.63 267:0.52 268:0.62 276:0.74 279:0.86 283:0.71 285:0.62 290:0.80 297:0.36 300:0.55 +3 1:0.74 6:0.96 8:0.93 9:0.80 11:0.57 12:0.69 20:0.98 21:0.05 27:0.28 28:0.66 30:0.58 34:0.44 36:0.60 37:0.55 39:0.79 41:1.00 43:0.83 60:0.95 66:0.18 69:0.65 70:0.60 74:0.64 78:0.97 81:0.81 83:0.90 85:0.85 91:0.97 96:0.99 98:0.17 100:1.00 101:0.72 108:0.95 111:1.00 114:0.95 120:0.98 122:0.41 123:0.95 124:0.82 126:0.54 127:0.83 129:0.81 133:0.76 135:0.42 138:0.99 140:0.45 146:0.13 147:0.18 149:0.58 150:0.88 151:1.00 152:0.98 153:0.99 154:0.80 155:0.67 158:0.92 159:0.87 161:0.82 162:0.65 163:0.90 164:0.98 165:0.90 167:0.98 169:0.99 172:0.16 173:0.38 176:0.73 177:0.38 179:0.89 181:0.56 186:0.96 189:0.97 191:0.89 192:0.59 201:0.74 202:0.96 208:0.64 212:0.37 215:0.91 216:0.67 222:0.48 235:0.12 238:0.98 241:0.90 242:0.16 243:0.29 245:0.48 247:0.60 248:0.99 253:0.99 255:0.79 256:0.97 259:0.21 260:0.98 261:0.98 265:0.18 266:0.47 267:0.92 268:0.97 276:0.32 279:0.86 283:0.82 285:0.62 290:0.95 297:0.36 300:0.43 +1 1:0.68 7:0.70 8:0.75 9:0.73 10:0.88 11:0.86 12:0.20 17:0.85 18:0.98 21:0.54 22:0.93 27:0.51 29:0.91 30:0.75 31:0.93 32:0.72 34:0.42 36:0.66 37:0.46 39:0.55 43:0.32 44:0.92 45:0.98 47:0.93 64:0.77 66:0.98 69:0.75 70:0.32 71:0.63 74:0.84 77:0.89 79:0.93 81:0.63 83:0.69 86:0.96 91:0.37 96:0.80 98:0.91 99:0.95 101:0.65 108:0.59 114:0.74 117:0.86 122:0.86 123:0.56 126:0.51 127:0.51 129:0.05 135:0.28 137:0.93 139:0.96 140:0.45 144:0.85 145:0.83 146:0.96 147:0.97 149:0.88 150:0.57 151:0.85 153:0.48 154:0.18 155:0.58 159:0.67 162:0.86 165:0.65 170:0.82 172:0.96 173:0.66 174:0.88 176:0.63 177:0.88 179:0.17 187:0.39 190:0.95 192:0.86 195:0.85 196:0.97 197:0.86 201:0.65 202:0.36 204:0.85 208:0.61 212:0.92 215:0.58 216:0.39 222:0.60 230:0.73 232:0.88 233:0.76 235:0.88 239:0.87 241:0.50 242:0.57 243:0.99 244:0.83 247:0.74 248:0.76 253:0.85 255:0.72 256:0.45 259:0.21 262:0.93 265:0.91 266:0.47 267:0.28 268:0.46 271:0.78 276:0.92 281:0.91 282:0.80 284:0.88 285:0.50 290:0.74 297:0.36 300:0.43 +2 1:0.66 10:0.84 11:0.84 12:0.20 17:0.15 18:0.69 21:0.30 22:0.93 27:0.38 29:0.91 30:0.33 32:0.67 34:0.64 36:0.17 37:0.66 39:0.55 43:0.30 44:0.88 45:0.73 47:0.93 64:0.77 66:0.45 69:0.85 70:0.36 71:0.63 74:0.44 77:0.89 81:0.63 83:0.56 86:0.69 91:0.54 98:0.16 99:0.83 101:0.47 108:0.71 114:0.82 117:0.86 122:0.85 123:0.69 124:0.66 126:0.51 127:0.24 129:0.05 133:0.60 135:0.96 139:0.72 144:0.85 145:0.47 146:0.23 147:0.18 149:0.20 150:0.56 154:0.23 155:0.51 158:0.72 159:0.37 165:0.65 170:0.82 172:0.27 173:0.87 174:0.79 175:0.75 176:0.63 177:0.38 178:0.55 179:0.75 187:0.87 192:0.51 195:0.72 197:0.82 201:0.64 208:0.61 212:0.42 215:0.72 216:0.66 222:0.60 232:0.86 235:0.60 239:0.83 241:0.18 242:0.19 243:0.32 244:0.83 245:0.47 247:0.55 253:0.58 254:0.88 257:0.84 259:0.21 262:0.93 265:0.17 266:0.38 267:0.28 271:0.78 276:0.28 277:0.69 282:0.75 290:0.82 297:0.36 300:0.25 +0 1:0.59 10:0.82 11:0.84 12:0.20 17:0.08 18:0.90 21:0.40 22:0.93 27:0.42 29:0.91 30:0.14 34:0.83 36:0.55 37:0.72 39:0.55 43:0.56 45:0.83 47:0.93 64:0.77 66:0.53 69:0.44 70:0.96 71:0.63 74:0.52 81:0.50 83:0.56 86:0.80 91:0.23 97:0.94 98:0.71 99:0.83 101:0.47 108:0.34 114:0.76 117:0.86 123:0.32 124:0.52 126:0.32 127:0.78 129:0.05 133:0.36 135:0.93 139:0.79 144:0.85 146:0.77 147:0.81 149:0.60 150:0.44 154:0.45 155:0.47 158:0.75 159:0.56 165:0.65 170:0.82 172:0.63 173:0.41 174:0.79 176:0.62 177:0.88 178:0.55 179:0.59 187:0.87 192:0.45 197:0.82 201:0.56 208:0.50 212:0.80 216:0.55 219:0.90 222:0.60 232:0.85 235:0.52 239:0.82 241:0.02 242:0.38 243:0.62 244:0.93 245:0.81 247:0.79 253:0.57 259:0.21 262:0.93 265:0.72 266:0.63 267:0.28 271:0.78 276:0.57 279:0.78 290:0.76 292:0.95 300:0.84 +1 1:0.66 10:0.84 11:0.84 12:0.20 17:0.20 18:0.72 21:0.30 22:0.93 27:0.38 29:0.91 30:0.58 32:0.67 34:0.75 36:0.17 37:0.66 39:0.55 43:0.31 44:0.88 45:0.77 47:0.93 64:0.77 66:0.61 69:0.85 70:0.44 71:0.63 74:0.48 77:0.89 81:0.63 83:0.56 86:0.71 91:0.37 98:0.26 99:0.83 101:0.47 108:0.71 114:0.82 117:0.86 122:0.86 123:0.69 124:0.47 126:0.51 127:0.16 129:0.05 133:0.43 135:0.93 139:0.73 144:0.85 145:0.47 146:0.38 147:0.18 149:0.28 150:0.56 154:0.23 155:0.51 158:0.72 159:0.26 165:0.65 170:0.82 172:0.37 173:0.83 174:0.79 175:0.75 176:0.63 177:0.38 178:0.55 179:0.44 187:0.87 192:0.51 195:0.78 197:0.82 201:0.64 208:0.61 212:0.37 215:0.72 216:0.66 222:0.60 232:0.86 235:0.60 239:0.83 241:0.02 242:0.40 243:0.29 244:0.83 245:0.52 247:0.55 253:0.59 254:0.88 257:0.84 259:0.21 262:0.93 265:0.28 266:0.47 267:0.44 271:0.78 276:0.27 277:0.69 282:0.75 290:0.82 297:0.36 300:0.33 +0 1:0.64 8:0.60 9:0.73 10:0.82 11:0.84 12:0.20 17:0.67 18:0.94 27:0.72 29:0.91 30:0.54 31:0.93 34:0.36 36:1.00 37:0.42 39:0.55 43:0.57 45:0.83 55:0.71 64:0.77 66:0.90 69:0.05 70:0.43 71:0.63 74:0.52 79:0.93 81:0.60 83:0.56 86:0.84 91:0.54 96:0.80 97:0.89 98:0.94 99:0.83 101:0.47 108:0.05 114:0.92 122:0.92 123:0.05 126:0.32 127:0.63 129:0.05 135:0.94 137:0.93 139:0.81 140:0.45 144:0.85 146:0.82 147:0.85 149:0.49 150:0.55 151:0.85 153:0.48 154:0.51 155:0.56 158:0.75 159:0.38 162:0.86 165:0.65 170:0.82 172:0.71 173:0.19 174:0.79 176:0.62 177:0.88 179:0.61 187:0.39 190:0.95 192:0.55 196:0.94 197:0.82 201:0.60 202:0.36 208:0.50 212:0.72 216:0.04 219:0.90 222:0.60 232:0.92 235:0.05 239:0.82 241:0.15 242:0.44 243:0.90 244:0.83 247:0.79 248:0.76 253:0.80 254:0.80 255:0.72 256:0.45 259:0.21 265:0.94 266:0.47 267:0.52 268:0.46 271:0.78 276:0.60 279:0.76 284:0.88 285:0.50 290:0.92 292:0.95 300:0.33 +0 1:0.60 8:0.66 9:0.72 10:0.84 11:0.84 12:0.20 17:0.47 18:0.98 21:0.59 22:0.93 27:0.38 29:0.91 30:0.70 31:0.90 32:0.66 34:0.75 36:0.28 37:0.66 39:0.55 43:0.31 44:0.88 45:0.98 47:0.93 64:0.77 66:0.68 69:0.77 70:0.72 71:0.63 74:0.75 77:0.70 79:0.90 81:0.53 83:0.56 86:0.92 91:0.33 96:0.74 97:0.89 98:0.76 99:0.83 101:0.47 108:0.60 114:0.71 117:0.86 122:0.91 123:0.57 124:0.46 126:0.51 127:0.58 129:0.05 133:0.45 135:0.98 137:0.90 139:0.91 140:0.45 144:0.85 145:0.52 146:0.96 147:0.96 149:0.84 150:0.45 151:0.65 153:0.48 154:0.16 155:0.56 158:0.75 159:0.80 162:0.86 165:0.65 170:0.82 172:0.94 173:0.59 174:0.86 176:0.63 177:0.88 179:0.18 187:0.39 190:0.95 192:0.56 195:0.92 196:0.94 197:0.86 201:0.59 202:0.36 208:0.61 212:0.71 215:0.55 216:0.66 219:0.90 220:0.74 222:0.60 232:0.86 235:0.80 239:0.83 241:0.02 242:0.54 243:0.72 244:0.83 245:0.97 247:0.88 248:0.63 253:0.72 255:0.71 256:0.45 259:0.21 262:0.93 265:0.76 266:0.80 267:0.59 268:0.46 271:0.78 276:0.92 279:0.76 282:0.75 284:0.88 285:0.50 290:0.71 292:0.95 297:0.36 300:0.84 +0 1:0.57 7:0.70 10:0.81 11:0.84 12:0.20 17:0.61 18:0.86 21:0.59 22:0.93 27:0.34 29:0.91 30:0.76 34:0.66 36:0.79 37:0.46 39:0.55 43:0.40 44:0.88 45:0.90 47:0.93 64:0.77 66:0.09 69:0.65 70:0.46 71:0.63 74:0.62 77:0.70 81:0.45 83:0.56 86:0.81 91:0.20 97:0.97 98:0.20 99:0.83 101:0.47 108:0.49 114:0.69 117:0.86 122:0.86 123:0.85 124:0.91 126:0.32 127:0.45 129:0.95 133:0.90 135:0.32 139:0.85 144:0.85 145:0.59 146:0.33 147:0.39 149:0.61 150:0.40 154:0.30 155:0.56 158:0.77 159:0.81 163:0.81 165:0.65 170:0.82 172:0.32 173:0.40 174:0.79 175:0.91 176:0.62 177:0.38 178:0.55 179:0.56 187:0.87 192:0.57 195:0.95 197:0.80 201:0.55 204:0.85 208:0.61 212:0.27 216:0.53 219:0.91 222:0.60 230:0.72 232:0.86 233:0.65 235:0.74 239:0.80 241:0.24 242:0.58 243:0.37 244:0.93 245:0.62 247:0.47 253:0.56 257:0.84 259:0.21 262:0.93 265:0.18 266:0.80 267:0.65 271:0.78 276:0.58 277:0.87 279:0.81 281:0.86 282:0.74 283:0.82 290:0.69 292:0.95 300:0.55 +0 1:0.63 8:0.77 9:0.73 10:0.83 11:0.84 12:0.20 17:0.83 18:0.93 21:0.05 27:0.55 29:0.91 30:0.72 31:0.95 34:0.20 36:0.94 37:0.44 39:0.55 43:0.48 44:0.88 45:0.94 64:0.77 66:0.45 69:0.54 70:0.39 71:0.63 74:0.65 77:0.70 79:0.95 81:0.57 83:0.56 86:0.87 91:0.25 96:0.85 98:0.68 99:0.83 101:0.47 108:0.41 114:0.89 122:0.91 123:0.40 124:0.84 126:0.32 127:0.69 129:0.59 133:0.87 135:0.95 137:0.95 139:0.86 140:0.45 144:0.85 145:0.65 146:0.92 147:0.93 149:0.74 150:0.53 151:0.85 153:0.48 154:0.37 155:0.56 159:0.80 162:0.86 163:0.81 165:0.65 170:0.82 172:0.73 173:0.33 174:0.83 175:0.75 176:0.62 177:0.70 179:0.40 187:0.39 190:0.95 191:0.80 192:0.56 195:0.91 196:0.96 197:0.84 201:0.59 202:0.50 204:0.83 208:0.50 212:0.30 216:0.37 222:0.60 232:0.87 235:0.12 239:0.82 241:0.12 242:0.54 243:0.58 244:0.93 245:0.77 247:0.76 248:0.82 253:0.84 255:0.72 256:0.58 257:0.65 259:0.21 265:0.68 266:0.84 267:0.44 268:0.59 271:0.78 276:0.76 277:0.69 281:0.91 282:0.74 284:0.92 285:0.50 290:0.89 300:0.33 +1 1:0.61 10:0.87 11:0.84 12:0.20 17:0.24 18:0.70 21:0.22 27:0.13 29:0.91 30:0.62 34:0.50 36:0.22 37:0.79 39:0.55 43:0.37 44:0.88 45:0.73 64:0.77 66:0.75 69:0.67 70:0.34 71:0.63 74:0.49 77:0.70 81:0.53 83:0.56 86:0.73 91:0.35 98:0.53 99:0.83 101:0.47 108:0.51 114:0.82 122:0.86 123:0.49 126:0.32 127:0.16 129:0.05 135:0.87 139:0.76 144:0.85 145:0.54 146:0.40 147:0.47 149:0.22 150:0.48 154:0.50 155:0.51 159:0.15 165:0.65 170:0.82 172:0.32 173:0.84 174:0.79 176:0.62 177:0.88 178:0.55 179:0.63 187:0.95 192:0.49 195:0.57 197:0.82 201:0.58 204:0.81 208:0.50 212:0.30 216:0.83 222:0.60 235:0.31 239:0.85 241:0.12 242:0.33 243:0.77 244:0.61 247:0.39 253:0.59 254:0.93 259:0.21 265:0.55 266:0.27 267:0.44 271:0.78 276:0.18 281:0.91 282:0.74 290:0.82 300:0.25 +1 8:0.66 12:0.20 17:0.51 18:0.62 21:0.78 27:0.25 29:0.98 30:0.72 34:0.47 36:0.97 37:0.56 39:0.86 43:0.12 55:0.52 66:0.61 69:0.84 70:0.56 74:0.41 86:0.67 91:0.10 98:0.31 108:0.84 123:0.83 124:0.55 127:0.21 129:0.59 133:0.64 135:0.91 139:0.65 146:0.23 147:0.05 149:0.21 154:0.46 159:0.59 172:0.65 173:0.69 175:0.75 177:0.05 178:0.55 179:0.31 187:0.95 212:0.75 216:0.87 222:0.84 235:0.05 241:0.07 242:0.72 243:0.10 244:0.61 245:0.70 247:0.60 253:0.32 254:0.74 257:0.84 259:0.21 265:0.33 266:0.63 267:0.52 276:0.53 277:0.69 300:0.70 +0 12:0.20 17:0.53 18:0.87 21:0.74 27:0.40 29:0.98 30:0.21 34:0.93 36:0.72 37:0.83 39:0.86 43:0.14 55:0.52 66:0.25 69:0.60 70:0.88 74:0.59 81:0.23 86:0.81 91:0.10 98:0.55 108:0.80 114:0.54 122:0.77 123:0.79 124:0.83 126:0.26 127:0.52 129:0.59 133:0.82 135:0.88 139:0.77 146:0.29 147:0.35 149:0.32 150:0.23 154:0.14 159:0.76 172:0.57 173:0.41 175:0.75 176:0.61 177:0.38 178:0.55 179:0.41 187:0.68 212:0.41 216:0.30 222:0.84 235:0.88 241:0.10 242:0.72 243:0.16 244:0.61 245:0.78 247:0.79 253:0.39 254:0.94 257:0.65 259:0.21 265:0.56 266:0.84 267:0.36 276:0.72 277:0.87 290:0.54 300:0.70 +2 8:0.79 12:0.20 17:0.30 18:0.66 21:0.78 27:0.48 29:0.98 30:0.11 34:0.76 36:0.74 37:0.47 39:0.86 43:0.12 55:0.59 66:0.09 69:1.00 70:0.98 74:0.48 86:0.67 91:0.02 98:0.18 108:0.96 123:0.96 124:0.99 127:0.86 129:0.05 133:0.99 135:0.37 139:0.65 146:0.63 147:0.63 149:0.35 154:0.10 159:0.99 172:0.75 173:0.96 177:0.05 178:0.55 179:0.15 187:0.95 212:0.26 216:0.74 222:0.84 235:0.68 241:0.03 242:0.55 243:0.16 244:0.61 245:0.87 247:0.94 253:0.34 254:0.92 257:0.84 259:0.21 265:0.20 266:1.00 267:0.23 276:0.95 277:0.87 300:0.94 +0 12:0.20 17:0.78 18:0.87 21:0.78 27:0.72 29:0.98 30:0.32 34:0.69 36:0.52 37:0.70 39:0.86 43:0.07 55:0.59 66:0.12 69:0.93 70:0.94 74:0.40 86:0.86 91:0.11 98:0.20 108:0.93 122:0.86 123:0.86 124:0.92 127:0.19 129:0.59 133:0.91 135:0.30 139:0.81 146:0.72 147:0.76 149:0.15 154:0.20 159:0.87 172:0.57 173:0.64 177:0.70 178:0.55 179:0.16 187:0.39 212:0.27 216:0.11 222:0.84 235:0.12 241:0.03 242:0.32 243:0.32 245:0.79 247:0.84 253:0.31 254:0.97 259:0.21 265:0.22 266:0.91 267:0.23 276:0.78 300:0.94 +0 12:0.20 17:0.49 18:0.89 21:0.78 27:0.40 29:0.98 30:0.33 34:0.94 36:0.61 37:0.83 39:0.86 43:0.14 55:0.52 66:0.13 69:0.60 70:0.86 74:0.33 86:0.82 91:0.14 98:0.25 108:0.66 122:0.86 123:0.89 124:0.85 127:0.50 129:0.05 133:0.82 135:0.92 139:0.79 146:0.29 147:0.35 149:0.24 154:0.10 159:0.71 172:0.27 173:0.44 175:0.75 177:0.38 178:0.55 179:0.70 187:0.68 212:0.18 216:0.30 222:0.84 235:0.84 241:0.10 242:0.54 243:0.28 244:0.83 245:0.58 247:0.74 253:0.28 257:0.65 259:0.21 265:0.17 266:0.75 267:0.44 276:0.47 277:0.87 300:0.70 +0 12:0.20 17:0.38 18:0.75 21:0.78 27:0.43 29:0.98 30:0.17 34:0.55 36:0.73 37:0.53 39:0.86 43:0.12 55:0.92 66:0.08 69:0.73 70:0.71 74:0.70 86:0.71 91:0.03 98:0.34 108:0.93 117:0.86 122:0.77 123:0.85 124:0.96 127:0.80 129:0.93 133:0.96 135:0.88 139:0.69 146:0.62 147:0.67 149:0.35 154:0.09 159:0.92 172:0.61 173:0.39 175:0.75 177:0.38 178:0.55 179:0.27 187:0.68 212:0.23 216:0.81 222:0.84 232:0.85 241:0.07 242:0.77 243:0.17 244:0.61 245:0.82 247:0.74 253:0.41 254:0.97 257:0.65 259:0.21 265:0.30 266:0.96 267:0.44 276:0.86 277:0.69 300:0.43 +0 12:0.20 17:0.26 18:0.65 21:0.78 27:0.67 29:0.98 30:0.76 34:1.00 36:0.43 37:0.23 39:0.86 43:0.13 55:0.84 66:0.64 69:0.68 70:0.15 74:0.32 86:0.65 91:0.23 98:0.57 104:0.87 106:0.81 108:0.52 122:0.83 123:0.50 127:0.17 129:0.05 135:0.51 139:0.63 146:0.54 147:0.60 149:0.10 154:0.10 159:0.19 163:0.94 172:0.16 173:0.77 177:0.70 178:0.55 179:0.91 187:0.39 206:0.81 212:0.95 216:0.42 222:0.84 235:0.68 241:0.41 242:0.82 243:0.69 244:0.83 247:0.12 253:0.27 259:0.21 265:0.58 266:0.12 267:0.44 276:0.18 283:0.78 300:0.13 +0 11:0.64 12:0.20 17:0.70 18:0.65 21:0.78 27:0.72 29:0.98 30:0.45 34:0.74 36:0.55 39:0.86 43:0.59 45:0.66 55:0.45 66:0.82 69:0.69 70:0.61 74:0.46 86:0.65 91:0.23 98:0.72 101:0.52 108:0.52 122:0.41 123:0.50 127:0.22 129:0.05 135:0.34 139:0.63 146:0.55 147:0.61 149:0.29 154:0.50 159:0.20 172:0.48 173:0.85 177:0.70 178:0.55 179:0.64 187:0.21 212:0.50 216:0.08 222:0.84 235:0.02 241:0.50 242:0.33 243:0.83 244:0.61 247:0.55 253:0.34 259:0.21 265:0.72 266:0.38 267:0.44 276:0.38 300:0.43 +1 12:0.20 17:0.75 18:0.85 21:0.78 27:0.72 29:0.98 30:0.28 34:0.31 36:0.74 39:0.86 43:0.16 55:0.52 66:0.87 69:0.36 70:0.54 74:0.60 86:0.90 91:0.76 98:0.91 108:0.28 122:0.80 123:0.27 127:0.51 129:0.05 135:0.18 139:0.85 146:0.80 147:0.83 149:0.15 154:0.61 159:0.36 172:0.63 173:0.46 177:0.70 178:0.55 179:0.68 187:0.21 212:0.55 216:0.01 222:0.84 235:0.05 241:0.77 242:0.40 243:0.88 247:0.67 253:0.38 254:0.99 259:0.21 265:0.91 266:0.54 267:0.36 276:0.52 300:0.43 +0 12:0.20 17:0.30 18:0.98 21:0.78 22:0.93 27:0.60 29:0.98 30:0.21 34:0.61 36:0.82 37:0.33 39:0.86 43:0.13 47:0.93 55:0.71 66:0.19 69:0.97 70:0.80 74:0.43 86:0.96 91:0.03 98:0.52 108:0.96 122:0.86 123:0.96 124:0.95 127:0.94 129:0.05 133:0.95 135:0.88 139:0.94 146:0.93 147:0.05 149:0.41 154:0.16 159:0.94 172:0.88 173:0.88 177:0.05 178:0.55 179:0.15 187:0.87 212:0.30 216:0.29 222:0.84 241:0.12 242:0.78 243:0.05 245:0.95 247:0.79 253:0.33 254:0.97 257:0.84 259:0.21 262:0.93 265:0.54 266:0.94 267:0.79 276:0.95 277:0.87 300:0.70 +0 12:0.20 17:0.49 18:0.87 21:0.74 27:0.40 29:0.98 30:0.21 34:0.92 36:0.73 37:0.83 39:0.86 43:0.14 55:0.52 66:0.25 69:0.60 70:0.88 74:0.59 81:0.23 86:0.81 91:0.09 98:0.55 108:0.80 114:0.54 122:0.77 123:0.79 124:0.83 126:0.26 127:0.52 129:0.59 133:0.82 135:0.88 139:0.77 146:0.29 147:0.35 149:0.32 150:0.23 154:0.14 159:0.76 172:0.57 173:0.41 175:0.75 176:0.61 177:0.38 178:0.55 179:0.41 187:0.68 212:0.41 216:0.30 222:0.84 235:0.88 241:0.10 242:0.72 243:0.16 244:0.61 245:0.78 247:0.79 253:0.39 254:0.94 257:0.65 259:0.21 265:0.56 266:0.84 267:0.36 276:0.72 277:0.87 290:0.54 300:0.70 +0 8:0.64 11:0.64 12:0.20 17:0.94 18:0.92 21:0.78 27:0.70 29:0.98 30:0.11 34:0.34 36:0.47 37:0.54 39:0.86 43:0.16 44:0.87 45:0.66 48:0.97 55:0.52 66:0.35 69:0.41 70:0.99 74:0.63 86:0.87 91:0.15 98:0.27 101:0.52 108:0.97 122:0.86 123:0.97 124:0.97 127:0.97 129:0.81 133:0.97 135:0.72 139:0.86 145:0.94 146:0.58 147:0.64 149:0.39 154:0.09 159:0.95 172:0.90 173:0.09 175:0.75 177:0.38 178:0.55 179:0.18 187:0.39 195:0.99 212:0.59 216:0.14 222:0.84 235:0.12 241:0.03 242:0.74 243:0.12 244:0.61 245:0.87 247:0.79 253:0.39 257:0.65 259:0.21 265:0.30 266:0.98 267:0.36 276:0.93 277:0.69 281:0.89 300:0.98 +0 7:0.66 12:0.20 17:0.36 18:0.69 21:0.64 27:0.42 29:0.98 30:0.76 32:0.64 34:0.95 36:0.81 37:0.47 39:0.86 43:0.17 55:0.92 66:0.25 69:0.39 70:0.21 74:0.34 81:0.26 86:0.71 91:0.15 98:0.45 104:0.99 106:0.81 108:0.30 123:0.58 124:0.71 126:0.26 127:0.29 129:0.05 133:0.59 135:0.71 139:0.68 145:0.49 146:0.39 147:0.45 149:0.14 150:0.25 154:0.49 159:0.39 163:0.94 172:0.16 173:0.39 177:0.70 178:0.55 179:0.85 187:0.99 195:0.70 206:0.81 212:0.52 215:0.38 216:0.66 222:0.84 230:0.69 233:0.73 235:0.74 241:0.01 242:0.24 243:0.45 244:0.61 245:0.45 247:0.47 253:0.28 254:0.96 259:0.21 265:0.34 266:0.27 267:0.59 276:0.32 277:0.87 297:0.36 300:0.18 +0 8:0.64 12:0.20 17:0.26 18:0.99 21:0.71 27:0.52 29:0.98 30:0.15 31:0.86 34:0.64 36:0.55 37:0.40 39:0.86 43:0.15 55:0.84 64:0.77 66:0.16 69:0.87 70:0.90 74:0.36 79:0.86 81:0.24 86:0.97 91:0.18 98:0.44 108:0.75 122:0.86 123:0.73 124:0.96 126:0.26 127:0.93 129:0.05 133:0.96 135:0.20 137:0.86 139:0.96 140:0.80 146:0.96 147:0.76 149:0.46 150:0.24 151:0.46 153:0.46 154:0.20 159:0.95 162:0.69 172:0.83 173:0.52 177:0.05 178:0.42 179:0.16 187:0.87 190:0.89 196:0.87 202:0.55 212:0.18 216:0.82 219:0.89 220:0.99 222:0.84 235:0.84 241:0.12 242:0.81 243:0.12 244:0.61 245:0.92 247:0.67 248:0.46 253:0.29 254:0.84 256:0.58 257:0.84 259:0.21 265:0.46 266:0.97 267:0.23 268:0.58 276:0.94 284:0.87 285:0.47 292:0.91 300:0.84 +1 12:0.20 17:0.72 18:0.81 21:0.78 27:0.72 29:0.98 30:0.45 34:0.50 36:0.84 39:0.86 43:0.16 55:0.52 66:0.85 69:0.36 70:0.57 74:0.58 86:0.89 91:0.77 98:0.89 108:0.28 122:0.80 123:0.27 127:0.44 129:0.05 135:0.26 139:0.84 146:0.73 147:0.77 149:0.13 154:0.65 159:0.29 172:0.57 173:0.51 177:0.70 178:0.55 179:0.73 202:0.36 212:0.69 216:0.01 222:0.84 235:0.05 241:0.83 242:0.22 243:0.86 247:0.74 253:0.38 254:0.99 259:0.21 265:0.89 266:0.38 267:0.44 276:0.46 300:0.43 +1 1:0.74 7:0.66 11:0.57 12:0.37 17:0.36 18:0.91 21:0.68 27:0.02 28:0.87 29:0.56 30:0.68 32:0.80 34:0.92 36:0.54 37:0.92 39:0.79 43:0.88 44:0.93 48:0.97 64:0.77 66:0.60 69:0.55 70:0.56 71:0.62 74:0.82 76:0.85 77:0.70 81:0.44 83:0.91 85:0.93 86:0.96 91:0.31 98:0.59 101:0.87 108:0.42 114:0.56 122:0.57 123:0.41 124:0.67 126:0.54 127:0.66 129:0.59 133:0.73 135:0.69 139:0.96 145:0.82 146:0.86 147:0.88 149:0.93 150:0.70 154:0.86 155:0.67 159:0.78 161:0.85 163:0.90 165:0.93 167:0.58 172:0.75 173:0.36 176:0.73 177:0.70 178:0.55 179:0.44 181:0.60 187:0.39 192:0.87 195:0.92 201:0.93 208:0.64 212:0.30 215:0.37 216:0.99 222:0.35 230:0.69 233:0.76 235:0.88 241:0.24 242:0.29 243:0.67 245:0.76 247:0.82 253:0.45 259:0.21 265:0.60 266:0.71 267:0.89 271:0.31 276:0.70 281:0.91 282:0.88 290:0.56 297:0.36 299:0.88 300:0.70 +4 6:0.84 8:0.84 9:0.80 12:0.37 17:0.51 18:0.96 20:0.88 21:0.30 22:0.93 27:0.72 28:0.87 29:0.56 30:0.11 31:0.99 34:0.92 36:0.49 39:0.79 41:0.95 43:0.17 47:0.93 55:0.52 64:0.77 66:0.61 69:0.30 70:0.76 71:0.62 78:0.91 79:1.00 81:0.33 83:0.91 86:0.92 91:0.79 96:0.92 98:0.77 100:0.95 108:0.60 111:0.91 120:0.86 122:0.86 123:0.57 124:0.66 126:0.26 127:0.74 129:0.89 133:0.78 135:0.30 137:0.99 139:0.89 140:0.97 146:0.24 147:0.30 149:0.33 150:0.30 151:0.91 152:0.95 153:0.97 154:0.12 155:0.67 159:0.68 161:0.85 162:0.74 164:0.78 167:0.75 169:0.94 172:0.68 173:0.22 175:0.91 177:0.05 179:0.55 181:0.60 186:0.93 187:0.21 189:0.95 191:0.84 192:0.87 196:0.99 202:0.92 208:0.64 212:0.30 216:0.32 222:0.35 235:0.31 238:0.91 241:0.69 242:0.82 243:0.19 244:0.83 245:0.69 247:0.12 248:0.92 253:0.89 255:0.95 256:0.96 257:0.84 260:0.87 261:0.94 262:0.93 265:0.77 266:0.75 267:0.91 268:0.95 271:0.31 276:0.66 277:0.87 284:0.99 285:0.62 300:0.55 +4 9:0.80 12:0.37 17:0.13 18:0.97 21:0.40 22:0.93 27:0.72 28:0.87 29:0.56 30:0.26 31:0.98 39:0.79 41:0.82 43:0.17 47:0.93 55:0.71 64:0.77 66:0.63 69:0.33 70:0.73 71:0.62 78:0.95 79:0.99 81:0.30 83:0.91 86:0.93 91:0.78 96:0.76 98:0.68 108:0.79 111:0.97 120:0.65 122:0.86 123:0.78 124:0.52 126:0.26 127:0.38 129:0.81 133:0.67 137:0.98 139:0.91 140:0.94 146:0.65 147:0.70 149:0.34 150:0.28 151:0.75 153:0.95 154:0.12 155:0.67 159:0.72 161:0.85 162:0.70 164:0.64 167:0.68 169:0.93 172:0.75 173:0.18 175:0.91 177:0.05 179:0.37 181:0.60 187:0.39 190:0.89 191:0.86 192:0.87 196:0.98 202:0.85 208:0.64 212:0.23 216:0.32 219:0.89 222:0.35 235:0.42 238:0.96 241:0.55 242:0.82 243:0.31 244:0.83 245:0.78 247:0.12 248:0.69 253:0.61 255:0.95 256:0.91 257:0.84 260:0.65 262:0.93 265:0.69 266:0.80 268:0.88 271:0.31 276:0.71 277:0.87 284:0.98 285:0.62 292:0.91 300:0.55 +2 1:0.74 6:0.80 7:0.81 8:0.60 11:0.57 12:0.37 17:0.51 18:0.90 20:0.88 21:0.59 22:0.93 27:0.72 28:0.87 29:0.56 30:0.76 32:0.80 34:0.92 36:0.25 39:0.79 43:0.89 44:0.93 47:0.93 60:0.99 64:0.77 66:0.79 69:0.52 70:0.37 71:0.62 74:0.83 77:0.70 78:0.87 81:0.47 83:0.91 85:0.88 86:0.93 91:0.11 98:0.68 100:0.88 101:0.87 104:0.89 106:0.81 108:0.40 111:0.85 114:0.58 117:0.86 121:0.90 122:0.57 123:0.38 124:0.38 126:0.54 127:0.32 129:0.05 133:0.37 135:0.39 139:0.96 145:0.85 146:0.70 147:0.74 149:0.89 150:0.71 152:0.93 154:0.87 155:0.67 158:0.91 159:0.38 161:0.85 165:0.93 167:0.58 169:0.80 172:0.63 173:0.54 176:0.73 177:0.70 178:0.55 179:0.57 181:0.60 186:0.90 187:0.87 192:0.87 195:0.68 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.59 215:0.39 216:0.28 219:0.95 222:0.35 230:0.87 232:0.92 233:0.76 235:0.80 238:0.83 241:0.21 242:0.37 243:0.80 245:0.54 247:0.55 253:0.46 261:0.89 262:0.93 265:0.68 266:0.38 267:0.73 271:0.31 276:0.51 279:0.86 281:0.91 282:0.88 283:0.78 290:0.58 292:0.91 297:0.36 299:0.88 300:0.43 +2 1:0.74 11:0.64 12:0.37 17:0.30 18:0.89 21:0.54 27:0.54 28:0.87 29:0.56 30:0.21 31:0.92 34:0.44 36:0.23 37:0.58 39:0.79 43:0.90 45:0.77 64:0.77 66:0.49 69:0.47 70:0.80 71:0.62 74:0.65 79:0.93 81:0.46 83:0.60 86:0.92 91:0.46 98:0.42 99:0.83 101:0.52 108:0.36 114:0.59 122:0.82 123:0.34 124:0.79 126:0.54 127:0.62 129:0.05 133:0.81 135:0.84 137:0.92 139:0.89 140:0.45 146:0.68 147:0.73 149:0.79 150:0.67 151:0.60 153:0.48 154:0.89 155:0.67 159:0.74 161:0.85 162:0.62 165:0.70 167:0.56 172:0.57 173:0.30 176:0.73 177:0.70 179:0.61 181:0.60 187:0.68 190:0.89 192:0.87 196:0.94 201:0.93 202:0.50 208:0.64 212:0.35 215:0.39 216:0.41 222:0.35 235:0.74 241:0.15 242:0.18 243:0.60 245:0.63 247:0.82 248:0.59 253:0.44 256:0.45 259:0.21 265:0.44 266:0.75 267:0.73 268:0.46 271:0.31 276:0.58 284:0.86 285:0.47 290:0.59 297:0.36 300:0.70 +1 1:0.74 7:0.66 11:0.83 12:0.37 17:0.24 18:0.91 21:0.13 27:0.02 28:0.87 29:0.56 30:0.53 34:0.83 36:0.22 37:0.92 39:0.79 43:0.89 45:0.66 48:0.91 60:0.95 64:0.77 66:0.72 69:0.50 70:0.66 71:0.62 74:0.85 76:0.85 81:0.75 83:0.91 85:0.94 86:0.96 91:0.11 98:0.68 101:0.87 108:0.56 114:0.79 122:0.57 123:0.53 124:0.49 126:0.54 127:0.68 129:0.81 133:0.78 135:0.89 139:0.96 145:0.77 146:0.17 147:0.22 149:0.92 150:0.84 154:0.87 155:0.67 158:0.91 159:0.82 161:0.85 165:0.93 167:0.67 172:0.87 173:0.27 176:0.73 177:0.70 178:0.55 179:0.32 181:0.60 187:0.39 192:0.87 201:0.93 202:0.50 208:0.64 212:0.54 215:0.61 216:0.99 219:0.95 222:0.35 230:0.69 233:0.76 235:0.31 241:0.53 242:0.23 243:0.12 245:0.80 247:0.90 253:0.57 259:0.21 265:0.69 266:0.87 267:0.91 271:0.31 276:0.80 279:0.86 281:0.91 283:0.78 290:0.79 292:0.91 297:0.36 300:0.94 +2 1:0.74 6:0.93 7:0.81 8:0.88 9:0.80 11:0.57 12:0.37 17:0.75 18:0.96 20:0.92 21:0.13 27:0.56 28:0.87 29:0.56 30:0.58 31:0.98 34:0.37 36:0.54 39:0.79 41:0.90 43:0.93 44:0.87 48:0.97 55:0.71 64:0.77 66:0.88 69:0.29 70:0.40 71:0.62 74:0.58 78:0.95 79:0.98 81:0.75 83:0.91 85:0.72 86:0.94 91:0.54 96:0.91 98:0.96 100:0.99 101:0.87 108:0.22 111:0.96 114:0.79 120:0.84 121:0.90 122:0.86 123:0.22 126:0.54 127:0.72 129:0.05 135:0.39 137:0.98 139:0.95 140:0.87 145:0.42 146:0.66 147:0.71 149:0.68 150:0.84 151:0.90 152:0.94 153:0.78 154:0.93 155:0.67 159:0.25 161:0.85 162:0.58 164:0.73 165:0.93 167:0.89 169:0.95 172:0.65 173:0.55 176:0.73 177:0.70 179:0.70 181:0.60 186:0.98 189:0.95 192:0.87 195:0.56 196:0.99 201:0.93 202:0.78 204:0.89 208:0.64 212:0.87 215:0.61 216:0.10 219:0.89 222:0.35 228:0.99 230:0.87 233:0.76 235:0.31 238:0.95 241:0.82 242:0.80 243:0.88 247:0.35 248:0.90 253:0.89 255:0.95 256:0.80 259:0.21 260:0.85 261:0.97 265:0.96 266:0.27 267:0.69 268:0.78 271:0.31 276:0.55 281:0.91 284:0.96 285:0.62 290:0.79 292:0.95 297:0.36 300:0.33 +2 1:0.74 9:0.80 11:0.57 12:0.37 17:0.62 18:0.67 20:0.83 21:0.68 22:0.93 27:0.36 28:0.87 29:0.56 30:0.95 31:0.89 34:0.98 36:0.32 37:0.64 39:0.79 41:0.82 43:0.89 47:0.93 64:0.77 66:0.64 69:0.51 71:0.62 74:0.45 78:0.85 79:0.89 81:0.44 83:0.91 85:0.72 86:0.76 91:0.33 96:0.73 98:0.12 100:0.87 101:0.87 104:0.97 106:0.81 108:0.39 111:0.84 114:0.56 117:0.86 120:0.60 122:0.57 123:0.38 126:0.54 127:0.08 129:0.05 135:0.58 137:0.89 139:0.73 140:0.45 145:0.67 146:0.17 147:0.22 149:0.62 150:0.70 151:0.63 152:0.79 153:0.69 154:0.88 155:0.67 159:0.09 161:0.85 162:0.86 163:0.90 164:0.62 165:0.93 167:0.68 169:0.85 172:0.16 173:0.61 176:0.73 177:0.70 179:0.09 181:0.60 186:0.85 187:0.87 192:0.87 196:0.90 201:0.93 202:0.46 206:0.81 208:0.64 212:0.95 215:0.37 216:0.74 220:0.74 222:0.35 232:0.98 235:0.88 241:0.56 242:0.82 243:0.69 247:0.12 248:0.61 253:0.51 255:0.95 256:0.45 259:0.21 260:0.61 261:0.81 262:0.93 265:0.13 266:0.12 267:0.52 268:0.55 271:0.31 276:0.19 284:0.90 285:0.62 290:0.56 297:0.36 300:0.08 +2 1:0.74 6:0.93 7:0.81 8:0.94 9:0.80 11:0.57 12:0.37 17:0.75 18:0.96 20:0.84 21:0.13 22:0.93 27:0.56 28:0.87 29:0.56 30:0.58 31:0.99 34:0.56 36:0.55 39:0.79 41:0.94 43:0.93 44:0.87 47:0.93 48:0.97 55:0.71 64:0.77 66:0.88 69:0.29 70:0.40 71:0.62 74:0.58 78:0.96 79:0.99 81:0.75 83:0.91 85:0.72 86:0.94 91:0.61 96:0.92 98:0.92 100:0.98 101:0.87 108:0.22 111:0.96 114:0.79 120:0.87 121:0.90 122:0.86 123:0.22 126:0.54 127:0.53 129:0.05 135:0.39 137:0.99 139:0.95 140:0.45 145:0.42 146:0.66 147:0.71 149:0.68 150:0.84 151:0.94 152:0.94 153:0.82 154:0.93 155:0.67 159:0.25 161:0.85 162:0.76 164:0.82 165:0.93 167:0.75 169:0.96 172:0.65 173:0.52 176:0.73 177:0.70 179:0.66 181:0.60 186:0.98 187:0.21 189:0.97 191:0.77 192:0.87 195:0.57 196:0.99 201:0.93 202:0.83 204:0.89 208:0.64 212:0.87 215:0.61 216:0.10 219:0.89 222:0.35 228:0.99 230:0.87 233:0.76 235:0.31 238:0.93 241:0.68 242:0.80 243:0.88 247:0.35 248:0.91 253:0.90 255:0.95 256:0.87 259:0.21 260:0.87 261:0.97 262:0.93 265:0.92 266:0.27 267:0.76 268:0.86 271:0.31 276:0.55 281:0.91 284:0.98 285:0.62 290:0.79 292:0.91 297:0.36 300:0.33 +4 6:0.84 8:0.67 9:0.80 12:0.37 17:0.38 18:0.96 20:0.87 21:0.30 22:0.93 27:0.72 28:0.87 29:0.56 30:0.11 31:1.00 34:0.93 36:0.49 39:0.79 41:0.96 43:0.17 47:0.93 55:0.52 64:0.77 66:0.61 69:0.30 70:0.76 71:0.62 78:0.95 79:1.00 81:0.33 83:0.91 86:0.92 91:0.69 96:0.95 98:0.77 100:0.98 108:0.60 111:0.96 120:0.90 122:0.86 123:0.57 124:0.66 126:0.26 127:0.74 129:0.89 133:0.78 135:0.28 137:1.00 139:0.90 140:0.95 146:0.24 147:0.30 149:0.33 150:0.30 151:0.94 152:0.95 153:0.97 154:0.12 155:0.67 159:0.68 161:0.85 162:0.74 164:0.84 167:0.69 169:0.94 172:0.68 173:0.22 175:0.91 177:0.05 179:0.55 181:0.60 186:0.97 187:0.39 189:0.83 191:0.82 192:0.87 196:1.00 202:0.93 208:0.64 212:0.30 216:0.32 219:0.89 222:0.35 235:0.31 238:0.96 241:0.58 242:0.82 243:0.19 244:0.83 245:0.69 247:0.12 248:0.95 253:0.92 255:0.95 256:0.97 257:0.84 260:0.91 261:0.94 262:0.93 265:0.77 266:0.75 267:0.44 268:0.95 271:0.31 276:0.66 277:0.87 284:0.99 285:0.62 292:0.95 300:0.55 +3 1:0.74 7:0.66 9:0.80 11:0.57 12:0.37 17:0.36 18:0.95 21:0.76 22:0.93 27:0.72 28:0.87 29:0.56 30:0.15 31:0.92 39:0.79 41:0.88 43:0.88 47:0.93 48:0.97 53:1.00 60:0.95 64:0.77 66:0.17 69:0.56 70:0.94 71:0.62 74:0.67 76:0.85 79:0.91 81:0.43 83:0.91 85:0.88 86:0.94 91:0.37 96:0.77 98:0.34 101:0.87 108:0.90 114:0.54 120:0.65 122:0.86 123:0.90 124:0.90 126:0.54 127:0.73 129:0.81 133:0.89 137:0.92 139:0.94 140:0.45 145:0.40 146:0.70 147:0.74 149:0.80 150:0.69 151:0.75 153:0.72 154:0.86 155:0.67 158:0.91 159:0.88 161:0.85 162:0.86 164:0.69 165:0.93 167:0.60 172:0.51 173:0.27 176:0.73 177:0.70 179:0.42 181:0.60 187:0.21 192:0.87 196:0.95 201:0.93 202:0.58 208:0.64 212:0.35 215:0.36 216:0.10 219:0.95 220:0.62 222:0.35 230:0.69 233:0.76 235:0.98 241:0.32 242:0.71 243:0.36 245:0.77 247:0.67 248:0.70 253:0.70 255:0.95 256:0.64 260:0.66 262:0.93 265:0.36 266:0.94 268:0.66 271:0.31 276:0.74 279:0.86 281:0.91 283:0.78 284:0.94 285:0.62 290:0.54 292:0.91 297:0.36 300:0.84 +1 1:0.74 11:0.57 12:0.37 17:0.90 18:0.88 21:0.22 22:0.93 27:0.72 28:0.87 29:0.56 30:0.88 34:0.79 36:0.28 39:0.79 43:0.93 47:0.93 60:0.95 64:0.77 66:0.85 69:0.33 70:0.29 71:0.62 74:0.70 81:0.67 83:0.91 85:0.85 86:0.93 91:0.37 98:0.63 101:0.87 104:0.95 106:0.81 108:0.26 114:0.71 117:0.86 122:0.57 123:0.25 126:0.54 127:0.18 129:0.05 135:0.34 139:0.93 146:0.63 147:0.68 149:0.86 150:0.80 154:0.93 155:0.67 158:0.92 159:0.23 161:0.85 165:0.93 167:0.53 172:0.57 173:0.37 176:0.73 177:0.70 178:0.55 179:0.43 181:0.60 187:0.87 192:0.87 201:0.93 206:0.81 208:0.64 212:0.69 215:0.51 216:0.05 219:0.95 222:0.35 232:0.96 235:0.42 241:0.03 242:0.38 243:0.86 247:0.47 253:0.53 262:0.93 265:0.64 266:0.38 267:0.44 271:0.31 276:0.43 279:0.86 283:0.82 290:0.71 292:0.95 297:0.36 300:0.33 +1 1:0.74 11:0.83 12:0.37 17:0.36 18:0.65 21:0.30 27:0.45 28:0.87 29:0.56 30:0.89 34:0.82 36:0.18 37:0.50 39:0.79 43:0.82 45:0.66 64:0.77 66:0.30 69:0.69 70:0.20 71:0.62 74:0.49 81:0.61 83:0.91 85:0.72 86:0.74 91:0.22 98:0.12 101:0.87 108:0.52 114:0.65 122:0.55 123:0.81 124:0.61 126:0.54 127:0.33 129:0.05 133:0.43 135:0.80 139:0.72 145:0.52 146:0.17 147:0.22 149:0.61 150:0.77 154:0.77 155:0.67 159:0.48 161:0.85 163:0.80 165:0.93 167:0.54 172:0.16 173:0.66 176:0.73 177:0.70 178:0.55 179:0.90 181:0.60 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.44 216:0.77 222:0.35 235:0.52 241:0.07 242:0.33 243:0.48 245:0.47 247:0.39 253:0.49 259:0.21 265:0.10 266:0.27 267:0.36 271:0.31 276:0.13 277:0.69 290:0.65 297:0.36 300:0.18 +2 1:0.74 6:0.93 8:0.58 9:0.80 11:0.57 12:0.37 17:0.06 18:0.79 20:0.84 21:0.22 27:0.56 28:0.87 29:0.56 30:0.76 31:0.86 34:0.21 36:0.51 39:0.79 43:0.94 64:0.77 66:0.72 69:0.25 70:0.22 71:0.62 74:0.47 78:0.92 79:0.86 81:0.67 83:0.91 85:0.72 86:0.87 91:0.56 96:0.68 98:0.67 100:0.86 101:0.87 108:0.20 111:0.90 114:0.71 120:0.55 122:0.57 123:0.19 126:0.54 127:0.20 129:0.05 135:0.42 137:0.86 139:0.83 140:0.45 146:0.39 147:0.45 149:0.64 150:0.80 151:0.50 152:0.94 153:0.48 154:0.94 155:0.67 159:0.15 161:0.85 162:0.86 164:0.57 165:0.93 167:0.60 169:0.96 172:0.27 173:0.56 176:0.73 177:0.70 179:0.83 181:0.60 186:0.94 187:0.21 192:0.87 196:0.89 201:0.93 202:0.36 208:0.64 212:0.78 215:0.51 216:0.10 220:0.91 222:0.35 228:0.99 235:0.42 241:0.30 242:0.45 243:0.75 247:0.35 248:0.49 253:0.52 255:0.95 256:0.45 259:0.21 260:0.55 261:0.97 265:0.67 266:0.17 267:0.23 268:0.46 271:0.31 276:0.17 284:0.89 285:0.62 290:0.71 297:0.36 300:0.18 +1 11:0.78 12:0.79 17:0.12 20:0.95 21:0.30 27:0.31 30:0.13 34:0.31 36:0.90 37:0.46 39:0.51 43:0.77 55:0.71 66:0.12 69:0.19 70:0.98 74:0.83 78:0.86 81:0.60 85:0.80 91:0.11 98:0.24 100:0.73 101:0.52 108:0.95 111:0.83 120:0.58 123:0.95 124:0.99 126:0.32 127:0.95 129:0.99 131:0.91 133:0.99 135:0.65 140:0.45 144:0.76 146:0.83 147:0.86 149:0.74 150:0.44 151:0.52 152:0.88 153:0.48 154:0.70 157:0.76 159:0.98 162:0.86 164:0.55 166:0.87 169:0.72 172:0.76 173:0.05 177:0.38 179:0.17 182:0.72 186:0.86 187:0.39 190:0.77 202:0.36 212:0.22 215:0.72 216:0.60 220:0.87 222:0.76 227:0.96 229:0.61 231:0.94 235:0.31 241:0.07 242:0.27 243:0.25 245:0.84 246:0.61 247:0.67 248:0.52 253:0.59 256:0.45 259:0.21 260:0.58 261:0.84 265:0.26 266:1.00 267:0.52 268:0.46 271:0.17 276:0.94 285:0.50 287:0.61 297:0.36 300:0.98 +2 6:0.85 8:0.75 9:0.80 12:0.79 17:0.15 20:0.91 21:0.78 27:0.32 30:0.56 34:0.24 36:0.91 37:0.42 39:0.51 41:0.82 43:0.27 55:0.96 66:0.19 69:0.78 70:0.42 74:0.63 78:0.94 83:0.76 91:0.72 96:0.80 98:0.23 100:0.92 108:0.83 111:0.92 120:0.69 122:0.67 123:0.82 124:0.90 127:0.37 129:0.05 131:0.98 133:0.89 135:0.79 140:0.92 144:0.76 146:0.36 147:0.43 149:0.40 151:0.87 152:0.85 153:0.48 154:0.19 155:0.67 157:0.61 159:0.73 162:0.49 163:0.93 164:0.57 166:0.61 169:0.90 172:0.21 173:0.62 177:0.38 178:0.51 179:0.73 182:0.96 186:0.93 187:0.39 189:0.86 192:0.59 202:0.66 208:0.64 212:0.23 216:0.55 222:0.76 227:0.99 229:0.98 231:0.95 235:0.22 238:0.86 241:0.37 242:0.63 243:0.33 244:0.61 245:0.48 246:0.61 247:0.39 248:0.78 253:0.79 255:0.79 256:0.45 259:0.21 260:0.70 261:0.90 265:0.25 266:0.71 267:0.88 268:0.46 271:0.17 276:0.44 277:0.69 285:0.62 287:0.99 300:0.33 +2 8:0.76 11:0.78 12:0.79 17:0.24 20:0.86 21:0.78 27:0.56 30:0.37 34:0.49 36:0.83 37:0.53 39:0.51 43:0.50 55:0.71 66:0.27 69:0.80 70:0.96 74:0.92 78:0.93 85:0.72 91:0.56 98:0.36 100:0.73 101:0.64 108:0.64 111:0.90 122:0.67 123:0.61 124:0.94 127:0.75 129:0.59 131:0.61 133:0.94 135:0.23 144:0.76 146:0.90 147:0.92 149:0.68 152:0.81 154:0.54 157:0.77 159:0.93 166:0.61 169:0.72 172:0.71 173:0.43 177:0.38 178:0.55 179:0.32 182:0.72 186:0.93 187:0.68 191:0.70 202:0.63 212:0.19 216:0.87 222:0.76 227:0.61 229:0.61 231:0.86 235:0.74 241:0.15 242:0.79 243:0.46 245:0.79 246:0.61 247:0.39 253:0.65 259:0.21 261:0.87 265:0.38 266:0.95 267:0.94 271:0.17 276:0.81 287:0.61 300:0.99 +1 11:0.78 12:0.79 17:0.26 21:0.78 27:0.45 30:0.33 34:0.76 36:0.67 37:0.50 39:0.51 43:0.70 66:0.79 69:0.84 70:0.36 74:0.63 85:0.80 91:0.22 98:0.47 101:0.73 108:0.69 122:0.57 123:0.67 127:0.14 129:0.05 131:0.61 135:0.70 144:0.76 145:0.41 146:0.69 147:0.74 149:0.58 154:0.60 157:0.89 159:0.27 166:0.61 172:0.41 173:0.78 177:0.38 178:0.55 179:0.43 182:0.78 187:0.68 212:0.42 216:0.77 222:0.76 227:0.61 229:0.61 231:0.84 235:0.52 241:0.12 242:0.45 243:0.80 246:0.61 247:0.47 253:0.49 259:0.21 265:0.48 266:0.38 267:0.36 271:0.17 276:0.32 287:0.61 300:0.25 +1 11:0.78 12:0.79 17:0.22 21:0.78 27:0.66 30:0.12 34:0.79 36:0.91 37:0.36 39:0.51 43:0.90 55:0.71 66:0.06 69:0.44 70:0.97 74:0.86 85:0.98 91:0.11 98:0.22 101:0.75 108:0.72 123:0.96 124:0.99 127:0.97 129:0.99 131:0.61 133:0.99 135:0.51 144:0.76 146:0.22 147:0.27 149:0.95 154:0.89 157:0.97 159:0.98 166:0.61 172:0.63 173:0.07 177:0.38 178:0.55 179:0.18 182:0.90 187:0.68 212:0.21 216:0.32 222:0.76 227:0.61 229:0.61 231:0.93 235:0.02 241:0.05 242:0.60 243:0.09 245:0.85 246:0.61 247:0.60 253:0.61 259:0.21 265:0.19 266:1.00 267:0.36 271:0.17 276:0.93 287:0.61 300:0.99 +2 1:0.74 8:0.66 11:0.78 12:0.79 17:0.06 27:0.63 30:0.11 34:0.77 36:0.53 37:0.32 39:0.51 43:0.98 66:0.23 69:0.05 70:0.68 74:0.86 81:0.95 83:0.80 85:0.85 91:0.25 98:0.70 101:0.80 108:0.05 114:0.98 122:0.67 123:0.57 124:0.56 126:0.54 127:0.61 129:0.59 131:0.61 133:0.47 135:0.77 144:0.76 146:0.40 147:0.47 149:0.81 150:0.98 154:0.98 155:0.67 157:0.95 159:0.34 165:0.92 166:0.97 172:0.48 173:0.21 176:0.73 177:0.38 178:0.55 179:0.70 182:0.96 187:0.39 192:0.59 201:0.74 212:0.71 215:0.96 216:0.46 222:0.76 227:0.61 229:0.61 231:0.95 235:0.05 241:0.12 242:0.51 243:0.60 245:0.71 246:0.99 247:0.60 253:0.79 259:0.21 265:0.50 266:0.27 267:0.65 271:0.17 276:0.51 283:0.82 287:0.61 290:0.98 297:0.36 300:0.43 +1 8:0.72 11:0.78 12:0.79 17:0.26 21:0.47 27:0.21 30:0.19 34:0.55 36:0.87 37:0.74 39:0.51 43:0.69 55:0.71 66:0.23 69:0.19 70:0.91 74:0.95 76:0.94 81:0.42 85:0.93 91:0.72 98:0.49 101:0.79 108:0.15 114:0.55 122:0.67 123:0.15 124:0.90 126:0.32 127:0.59 129:0.89 131:0.61 133:0.89 135:0.34 144:0.76 146:0.79 147:0.82 149:0.87 150:0.36 154:0.59 157:0.97 159:0.77 166:0.83 172:0.59 173:0.13 176:0.53 177:0.38 178:0.55 179:0.38 182:0.91 187:0.39 202:0.75 212:0.48 216:0.95 222:0.76 227:0.61 229:0.61 231:0.93 235:0.52 241:0.50 242:0.63 243:0.43 245:0.78 246:0.61 247:0.47 253:0.68 259:0.21 265:0.51 266:0.87 267:0.94 271:0.17 276:0.76 287:0.61 290:0.55 300:0.70 +1 11:0.78 12:0.79 17:0.47 21:0.30 27:0.28 30:0.17 34:0.94 36:0.74 37:0.72 39:0.51 43:0.58 55:0.71 66:0.68 69:0.91 70:0.97 74:0.93 77:0.70 81:0.55 85:0.93 91:0.42 98:0.55 101:0.77 108:0.83 114:0.55 117:0.86 122:0.67 123:0.82 124:0.64 126:0.32 127:0.50 129:0.05 131:0.61 133:0.82 135:0.71 144:0.76 145:0.70 146:0.89 147:0.58 149:0.81 150:0.41 154:0.47 157:0.97 158:0.82 159:0.82 166:0.83 172:0.84 173:0.82 176:0.53 177:0.05 178:0.55 179:0.32 182:0.90 187:0.68 195:0.94 212:0.55 216:0.54 222:0.76 227:0.61 229:0.61 231:0.93 232:0.99 235:0.31 241:0.30 242:0.72 243:0.13 245:0.77 246:0.61 247:0.60 253:0.66 257:0.65 259:0.21 265:0.57 266:0.84 267:0.65 271:0.17 276:0.78 282:0.81 287:0.61 290:0.55 300:0.94 +1 1:0.74 11:0.51 12:0.79 17:0.30 21:0.22 27:0.72 30:0.76 34:0.94 36:0.66 37:0.44 39:0.51 43:0.92 55:0.42 66:0.85 69:0.15 70:0.43 74:0.84 81:0.82 91:0.29 98:0.82 108:0.12 114:0.90 122:0.43 123:0.12 126:0.54 127:0.31 129:0.05 131:0.61 135:0.32 144:0.76 146:0.57 147:0.63 149:0.90 150:0.84 154:0.91 155:0.67 157:0.61 158:0.87 159:0.21 166:0.97 172:0.57 173:0.41 176:0.73 177:0.38 178:0.55 179:0.65 182:0.61 187:0.39 192:0.59 201:0.74 208:0.64 212:0.69 215:0.79 216:0.15 222:0.76 227:0.61 229:0.61 231:0.95 235:0.31 241:0.46 242:0.61 243:0.86 246:0.61 247:0.39 253:0.75 254:0.74 259:0.21 265:0.82 266:0.27 267:0.36 271:0.17 276:0.44 279:0.86 283:0.71 287:0.61 290:0.90 297:0.36 300:0.43 +2 6:0.83 8:0.67 11:0.78 12:0.79 17:0.20 20:0.85 27:0.63 30:0.60 32:0.72 34:0.62 36:0.62 37:0.32 39:0.51 43:0.84 55:0.71 66:0.20 69:0.05 70:0.56 74:0.81 78:0.99 81:0.90 85:0.80 91:0.18 98:0.65 100:0.98 101:0.78 108:0.05 111:0.98 122:0.67 123:0.59 124:0.55 126:0.32 127:0.60 129:0.59 131:0.61 133:0.47 135:0.51 144:0.76 145:0.49 146:0.46 147:0.53 149:0.72 150:0.80 152:0.83 154:0.83 157:0.91 159:0.34 166:0.87 169:0.96 172:0.45 173:0.19 177:0.38 178:0.55 179:0.75 182:0.81 186:0.99 187:0.68 189:0.85 195:0.57 212:0.72 215:0.96 216:0.46 222:0.76 227:0.61 229:0.61 231:0.94 235:0.02 238:0.87 241:0.60 242:0.45 243:0.60 245:0.68 246:0.61 247:0.60 253:0.58 259:0.21 261:0.94 265:0.57 266:0.38 267:0.52 271:0.17 276:0.47 283:0.82 287:0.61 297:0.36 300:0.43 +2 1:0.74 7:0.81 11:0.78 12:0.79 17:0.12 21:0.22 27:0.63 30:0.43 34:0.83 36:0.60 37:0.49 39:0.51 43:0.91 60:0.87 66:0.31 69:0.42 70:0.87 74:0.95 81:0.83 83:0.85 85:0.97 91:0.07 98:0.59 101:0.84 108:0.71 114:0.90 121:0.90 122:0.43 123:0.69 124:0.79 126:0.54 127:0.63 129:0.89 131:0.61 133:0.78 135:0.54 144:0.76 145:0.70 146:0.66 147:0.71 149:0.95 150:0.89 154:0.90 155:0.67 157:0.99 158:0.87 159:0.73 165:0.86 166:0.97 172:0.70 173:0.27 176:0.73 177:0.38 178:0.55 179:0.34 182:0.96 187:0.95 192:0.59 201:0.74 204:0.89 208:0.64 212:0.40 215:0.79 216:0.64 222:0.76 227:0.61 229:0.61 230:0.84 231:0.95 233:0.69 235:0.31 241:0.21 242:0.62 243:0.39 245:0.87 246:0.99 247:0.55 253:0.78 265:0.60 266:0.63 267:0.23 271:0.17 276:0.79 279:0.86 283:0.71 287:0.61 290:0.90 297:0.36 300:0.84 +2 1:0.74 6:0.83 7:0.81 11:0.51 12:0.79 17:0.36 20:0.89 21:0.47 27:0.21 30:0.62 34:0.53 36:0.51 37:0.74 39:0.51 43:0.30 55:0.42 66:0.75 69:0.19 70:0.34 74:0.79 76:0.85 78:0.88 81:0.67 91:0.22 98:0.70 100:0.73 104:0.91 106:0.81 108:0.15 111:0.86 114:0.80 117:0.86 122:0.43 123:0.15 126:0.54 127:0.21 129:0.05 131:0.61 135:0.21 144:0.76 146:0.53 147:0.59 149:0.19 150:0.72 152:0.92 154:0.92 155:0.67 157:0.61 158:0.86 159:0.19 166:0.97 169:0.92 172:0.32 173:0.40 176:0.73 177:0.38 178:0.55 179:0.81 182:0.61 186:0.88 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.61 215:0.63 216:0.95 222:0.76 227:0.61 229:0.61 230:0.83 231:0.95 232:0.96 233:0.66 235:0.60 241:0.35 242:0.33 243:0.77 246:0.61 247:0.39 253:0.69 254:0.74 259:0.21 261:0.92 265:0.71 266:0.23 267:0.44 271:0.17 276:0.22 279:0.86 283:0.67 287:0.61 290:0.80 297:0.36 300:0.25 +0 8:0.78 11:0.78 12:0.79 17:0.20 21:0.78 27:0.56 30:0.18 34:0.61 36:0.96 37:0.43 39:0.51 43:0.86 55:0.96 66:0.06 69:0.12 70:0.96 74:0.65 85:0.93 91:0.06 98:0.19 101:0.71 108:0.10 123:0.10 124:0.99 127:0.97 129:0.95 131:0.61 133:0.99 135:0.79 144:0.76 146:0.72 147:0.77 149:0.84 154:0.84 157:0.91 159:0.97 163:0.81 166:0.61 172:0.37 173:0.05 177:0.38 178:0.55 179:0.26 182:0.81 187:0.87 202:0.36 212:0.12 216:0.89 222:0.76 227:0.61 229:0.61 231:0.91 235:0.31 241:0.01 242:0.74 243:0.18 245:0.75 246:0.61 247:0.47 253:0.50 259:0.21 265:0.20 266:0.99 267:0.59 271:0.17 276:0.88 283:0.71 287:0.61 300:0.94 +2 8:0.69 11:0.78 12:0.79 17:0.30 21:0.47 27:0.21 30:0.17 34:0.42 36:0.94 37:0.74 39:0.51 43:0.69 55:0.71 66:0.25 69:0.19 70:0.91 74:0.96 76:0.85 81:0.42 85:0.93 91:0.70 96:0.75 98:0.50 101:0.79 108:0.15 114:0.55 117:0.86 122:0.67 123:0.15 124:0.90 126:0.32 127:0.59 129:0.89 131:0.88 133:0.89 135:0.26 140:0.45 144:0.76 146:0.79 147:0.82 149:0.87 150:0.36 151:0.59 153:0.69 154:0.59 157:0.97 158:0.82 159:0.77 162:0.86 166:0.83 172:0.61 173:0.13 176:0.53 177:0.38 179:0.37 182:0.91 187:0.39 190:0.77 202:0.46 212:0.47 216:0.95 220:0.74 222:0.76 227:0.95 229:0.61 231:0.93 232:0.91 235:0.52 241:0.12 242:0.63 243:0.44 245:0.78 246:0.61 247:0.47 248:0.57 253:0.79 256:0.45 259:0.21 265:0.51 266:0.87 267:0.92 268:0.55 271:0.17 276:0.77 285:0.50 287:0.61 290:0.55 300:0.70 +1 8:0.68 11:0.78 12:0.79 17:0.24 21:0.78 27:0.45 30:0.28 34:0.64 36:0.27 37:0.50 39:0.51 43:0.78 55:0.52 66:0.30 69:0.75 70:0.75 74:0.89 85:0.72 91:0.20 98:0.45 101:0.71 108:0.59 122:0.67 123:0.71 124:0.60 127:0.19 129:0.05 131:0.61 133:0.39 135:0.89 144:0.76 145:0.42 146:0.51 147:0.57 149:0.65 154:0.71 157:0.81 159:0.41 166:0.61 172:0.51 173:0.66 177:0.38 178:0.55 179:0.31 182:0.74 187:0.68 212:0.70 216:0.77 222:0.76 227:0.61 229:0.61 231:0.83 235:0.60 241:0.44 242:0.79 243:0.57 245:0.78 246:0.61 247:0.39 253:0.63 259:0.21 265:0.34 266:0.54 267:0.59 271:0.17 276:0.58 277:0.69 287:0.61 300:0.70 +1 8:0.73 11:0.78 12:0.79 17:0.28 21:0.47 27:0.21 30:0.19 34:0.44 36:0.88 37:0.74 39:0.51 43:0.69 55:0.71 66:0.23 69:0.19 70:0.91 74:0.96 76:0.85 81:0.42 85:0.93 91:0.58 98:0.49 101:0.79 108:0.15 114:0.55 117:0.86 122:0.67 123:0.15 124:0.90 126:0.32 127:0.58 129:0.89 131:0.61 133:0.89 135:0.30 144:0.76 146:0.78 147:0.82 149:0.87 150:0.36 154:0.59 157:0.97 158:0.82 159:0.77 166:0.83 172:0.59 173:0.13 176:0.53 177:0.38 178:0.55 179:0.38 182:0.91 187:0.39 212:0.48 216:0.95 222:0.76 227:0.61 229:0.61 231:0.93 232:0.91 235:0.52 241:0.07 242:0.63 243:0.43 245:0.78 246:0.61 247:0.47 253:0.68 259:0.21 265:0.51 266:0.87 267:0.94 271:0.17 276:0.76 287:0.61 290:0.55 300:0.70 +1 11:0.88 12:0.79 17:0.20 21:0.64 27:0.45 28:0.70 30:0.11 34:0.89 36:0.18 37:0.50 39:0.55 43:0.78 55:0.84 66:0.29 69:0.70 70:0.85 74:0.75 81:0.37 85:0.80 91:0.06 98:0.27 101:0.72 108:0.53 122:0.67 123:0.51 124:0.84 126:0.32 127:0.34 129:0.05 133:0.82 135:0.83 144:0.85 145:0.47 146:0.53 147:0.59 149:0.70 150:0.33 154:0.70 159:0.71 161:0.79 167:0.60 172:0.37 173:0.52 177:0.38 178:0.55 179:0.60 181:0.98 187:0.68 212:0.27 215:0.53 216:0.77 222:0.35 235:0.74 241:0.61 242:0.72 243:0.47 245:0.58 247:0.47 253:0.55 259:0.21 265:0.30 266:0.80 267:0.28 271:0.34 276:0.51 283:0.71 297:0.36 300:0.55 +2 1:0.74 6:0.93 7:0.81 8:0.87 9:0.80 11:0.88 12:0.79 17:0.12 20:0.87 21:0.13 27:0.13 28:0.70 30:0.67 32:0.80 34:0.78 36:0.43 37:0.79 39:0.55 41:0.94 43:0.81 60:0.97 66:0.89 69:0.70 70:0.43 74:0.91 77:0.70 78:0.79 81:0.88 83:0.89 85:0.94 91:0.48 96:0.94 98:0.75 100:0.85 101:0.85 108:0.53 111:0.80 114:0.92 120:0.89 121:0.90 122:0.43 123:0.51 126:0.54 127:0.24 129:0.05 135:0.80 140:0.45 144:0.85 145:0.54 146:0.70 147:0.75 149:0.91 150:0.93 151:0.96 152:0.90 153:0.87 154:0.76 155:0.67 158:0.92 159:0.28 161:0.79 162:0.81 164:0.83 165:0.88 167:0.56 169:0.82 172:0.70 173:0.78 176:0.73 177:0.38 179:0.41 181:0.98 186:0.79 187:0.68 189:0.95 191:0.76 192:0.59 195:0.63 201:0.74 202:0.74 204:0.89 208:0.64 212:0.70 215:0.84 216:0.83 222:0.35 230:0.87 233:0.76 235:0.22 238:0.94 241:0.51 242:0.54 243:0.90 247:0.55 248:0.94 253:0.96 255:0.79 256:0.77 259:0.21 260:0.89 261:0.88 265:0.75 266:0.38 267:0.59 268:0.79 271:0.34 276:0.58 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.33 +3 1:0.74 6:0.93 8:0.82 9:0.80 11:0.88 12:0.79 17:0.05 20:0.76 27:0.14 28:0.70 30:0.75 34:0.70 36:0.28 37:0.67 39:0.55 41:0.99 43:0.86 60:0.97 66:0.24 69:0.59 70:0.69 74:0.90 78:0.88 81:0.96 83:0.91 85:0.98 91:0.88 96:1.00 98:0.23 100:0.96 101:0.80 108:0.64 111:0.89 114:0.98 120:0.99 122:0.43 123:0.62 124:0.93 126:0.54 127:0.83 129:0.97 133:0.93 135:0.60 138:0.99 140:0.45 144:0.85 145:0.49 146:0.29 147:0.36 149:0.95 150:0.98 151:0.99 152:0.96 153:0.97 154:0.83 155:0.67 158:0.92 159:0.92 161:0.79 162:0.76 164:0.97 165:0.92 167:0.56 169:0.91 172:0.61 173:0.23 176:0.73 177:0.38 179:0.40 181:0.98 186:0.84 187:0.39 189:0.94 191:0.83 192:0.59 201:0.74 202:0.95 208:0.64 212:0.35 215:0.96 216:0.93 222:0.35 235:0.05 238:0.93 241:0.52 242:0.59 243:0.18 245:0.76 247:0.47 248:1.00 253:1.00 255:0.79 256:0.96 259:0.21 260:0.99 261:0.96 265:0.26 266:0.94 267:0.95 268:0.96 271:0.34 276:0.75 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +3 1:0.74 7:0.81 11:0.88 12:0.79 17:0.66 21:0.30 27:0.21 28:0.70 30:0.44 34:0.63 36:0.92 37:0.74 39:0.55 43:0.98 66:0.16 69:0.12 70:0.75 74:0.95 81:0.80 83:0.75 85:0.97 91:0.10 98:0.44 101:0.81 108:0.95 114:0.86 121:0.90 122:0.57 123:0.78 124:0.84 126:0.54 127:0.68 129:0.81 133:0.83 135:0.30 144:0.85 146:0.63 147:0.68 149:0.95 150:0.87 154:0.98 155:0.67 159:0.89 161:0.79 165:0.84 167:0.47 172:0.67 173:0.08 176:0.73 177:0.38 178:0.55 179:0.31 181:0.98 187:0.68 192:0.59 201:0.74 202:0.36 204:0.89 208:0.64 212:0.35 215:0.72 216:0.95 222:0.35 230:0.87 233:0.76 235:0.42 241:0.10 242:0.45 243:0.34 245:0.87 247:0.47 253:0.77 259:0.21 265:0.34 266:0.87 267:0.52 271:0.34 276:0.78 290:0.86 297:0.36 300:0.84 +0 1:0.74 6:0.83 8:0.61 9:0.80 11:0.88 12:0.79 17:0.92 20:0.79 21:0.13 25:0.88 27:0.18 28:0.70 30:0.56 34:0.92 36:0.43 37:0.26 39:0.55 41:0.99 43:0.94 60:0.95 66:0.64 69:0.23 70:0.64 74:0.92 78:0.90 81:0.88 83:0.90 85:0.94 87:0.98 91:0.87 96:0.99 98:0.68 100:0.93 101:0.84 104:0.58 108:0.18 111:0.89 114:0.92 120:0.98 122:0.57 123:0.18 124:0.47 126:0.54 127:0.30 129:0.59 133:0.47 135:0.54 138:0.98 140:0.45 144:0.85 146:0.67 147:0.72 149:0.93 150:0.93 151:0.99 152:0.78 153:0.95 154:0.94 155:0.67 158:0.92 159:0.35 161:0.79 162:0.79 164:0.96 165:0.88 167:0.81 169:0.89 172:0.67 173:0.30 176:0.73 177:0.38 179:0.43 181:0.98 186:0.90 189:0.83 191:0.78 192:0.59 201:0.74 202:0.92 208:0.64 212:0.82 215:0.84 216:0.22 222:0.35 232:0.81 235:0.22 238:0.90 241:0.85 242:0.40 243:0.69 245:0.77 247:0.60 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.89 265:0.68 266:0.27 267:0.73 268:0.95 271:0.34 276:0.61 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.55 +3 1:0.74 6:0.87 7:0.81 8:0.73 9:0.80 11:0.88 12:0.79 17:0.51 20:0.75 21:0.68 25:0.88 27:0.72 28:0.70 30:0.33 32:0.80 34:0.19 36:0.93 39:0.55 41:0.99 43:0.75 60:0.97 66:0.12 69:0.83 70:0.62 74:0.82 76:0.94 77:0.89 78:0.86 81:0.65 83:0.90 85:0.91 87:0.98 91:0.77 96:0.99 98:0.34 100:0.91 101:0.78 104:0.58 108:0.86 111:0.85 114:0.70 120:0.97 121:1.00 122:0.43 123:0.86 124:0.91 126:0.54 127:0.98 129:0.95 133:0.90 135:0.58 138:0.99 140:0.45 144:0.85 145:0.79 146:0.13 147:0.59 149:0.77 150:0.75 151:0.99 152:0.74 153:0.96 154:0.66 155:0.67 158:0.92 159:0.83 161:0.79 162:0.83 163:0.86 164:0.94 165:0.69 167:0.80 169:0.84 172:0.27 173:0.70 176:0.73 177:0.05 179:0.64 181:0.98 186:0.89 189:0.88 191:0.82 192:0.59 195:0.93 201:0.74 202:0.89 204:0.89 208:0.64 212:0.13 215:0.49 216:0.24 222:0.35 230:0.87 232:0.81 233:0.76 235:0.95 238:0.88 241:0.82 242:0.24 243:0.22 245:0.64 247:0.60 248:0.99 253:0.99 255:0.79 256:0.92 257:0.65 259:0.21 260:0.97 261:0.77 265:0.36 266:0.89 267:0.84 268:0.93 271:0.34 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.97 300:0.43 +0 1:0.74 7:0.81 8:0.72 9:0.80 11:0.88 12:0.79 17:0.62 20:0.75 21:0.30 27:0.52 28:0.70 30:0.70 37:0.58 39:0.55 41:0.97 43:0.98 60:0.87 66:0.15 69:0.12 70:0.84 74:0.93 78:0.82 81:0.80 83:0.87 85:0.98 91:0.74 96:0.96 98:0.25 100:0.82 101:0.80 108:0.10 111:0.85 114:0.86 117:0.86 120:0.93 121:0.90 122:0.43 123:0.10 124:0.94 126:0.54 127:0.77 129:0.97 133:0.94 138:0.98 140:0.45 144:0.85 146:0.70 147:0.74 149:0.95 150:0.87 151:0.99 152:0.74 153:0.96 154:0.98 155:0.67 158:0.92 159:0.91 161:0.79 162:0.69 164:0.87 165:0.84 167:0.54 169:0.89 172:0.54 173:0.07 176:0.73 177:0.38 179:0.36 181:0.98 186:0.81 187:0.39 192:0.59 201:0.74 202:0.83 204:0.89 208:0.64 212:0.22 215:0.72 216:0.90 222:0.35 230:0.87 232:0.92 233:0.76 235:0.42 238:0.95 241:0.47 242:0.53 243:0.36 245:0.78 247:0.60 248:0.96 253:0.97 255:0.79 256:0.86 259:0.21 260:0.93 261:0.75 265:0.28 266:0.95 268:0.85 271:0.34 276:0.77 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.98 +2 1:0.74 7:0.81 8:0.70 9:0.80 11:0.88 12:0.79 17:0.18 21:0.13 27:0.03 28:0.70 30:0.62 32:0.80 34:0.96 36:0.62 37:0.92 39:0.55 41:0.82 43:0.90 60:0.87 66:0.30 69:0.44 70:0.59 74:0.98 77:0.70 81:0.88 83:0.86 85:0.98 91:0.22 96:0.74 98:0.69 101:0.86 104:0.84 106:0.81 108:0.34 114:0.92 117:0.86 120:0.62 121:0.90 122:0.43 123:0.75 124:0.58 126:0.54 127:0.43 129:0.59 133:0.47 135:0.92 140:0.45 144:0.85 145:0.59 146:0.78 147:0.81 149:0.99 150:0.93 151:0.69 153:0.48 154:0.89 155:0.67 158:0.92 159:0.65 161:0.79 162:0.62 164:0.57 165:0.88 167:0.49 172:0.82 173:0.30 176:0.73 177:0.38 179:0.24 181:0.98 187:0.68 192:0.59 195:0.85 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.57 215:0.84 216:0.79 220:0.74 222:0.35 230:0.87 232:0.91 233:0.76 235:0.22 241:0.21 242:0.51 243:0.58 245:0.95 247:0.60 248:0.65 253:0.83 255:0.79 256:0.45 259:0.21 260:0.63 265:0.57 266:0.38 267:0.36 268:0.46 271:0.34 276:0.84 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.88 12:0.79 17:0.45 21:0.71 27:0.45 28:0.70 30:0.45 32:0.80 34:0.80 36:0.18 37:0.50 39:0.55 43:0.81 55:0.84 66:0.47 69:0.71 70:0.86 74:0.56 77:0.70 81:0.52 83:0.73 85:0.72 91:0.04 98:0.36 101:0.70 108:0.64 114:0.68 123:0.61 124:0.79 126:0.54 127:0.58 129:0.05 133:0.82 135:0.70 144:0.85 145:0.50 146:0.13 147:0.18 149:0.63 150:0.67 154:0.77 155:0.67 159:0.76 161:0.79 163:0.75 165:0.69 167:0.56 172:0.41 173:0.55 176:0.73 177:0.38 178:0.55 179:0.77 181:0.98 187:0.68 192:0.59 195:0.90 201:0.74 212:0.13 215:0.48 216:0.77 222:0.35 235:0.88 241:0.53 242:0.79 243:0.23 245:0.51 247:0.35 253:0.56 259:0.21 265:0.38 266:0.80 267:0.59 271:0.34 276:0.40 277:0.69 282:0.88 290:0.68 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.79 17:0.04 20:0.96 27:0.14 28:0.70 30:0.75 34:0.75 36:0.28 37:0.67 39:0.55 41:1.00 43:0.85 60:0.99 66:0.23 69:0.57 70:0.60 74:0.87 78:0.84 81:0.96 83:0.91 85:0.97 91:0.67 96:1.00 98:0.22 100:0.95 101:0.79 108:0.61 111:0.89 114:0.98 120:0.99 122:0.43 123:0.58 124:0.93 126:0.54 127:0.79 129:0.97 133:0.93 135:0.61 138:0.99 140:0.45 144:0.85 145:0.47 146:0.22 147:0.28 149:0.93 150:0.98 151:0.99 152:0.96 153:0.97 154:0.82 155:0.67 158:0.92 159:0.92 161:0.79 162:0.75 164:0.97 165:0.92 167:0.53 169:0.91 172:0.57 173:0.22 176:0.73 177:0.38 179:0.44 181:0.98 186:0.84 187:0.39 189:0.94 191:0.75 192:0.59 201:0.74 202:0.95 208:0.64 212:0.30 215:0.96 216:0.93 222:0.35 235:0.05 238:0.97 241:0.44 242:0.60 243:0.16 245:0.73 247:0.47 248:1.00 253:1.00 255:0.79 256:0.96 259:0.21 260:0.99 261:0.96 265:0.24 266:0.92 267:0.87 268:0.97 271:0.34 276:0.71 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.94 +2 1:0.74 6:0.93 7:0.81 8:0.85 9:0.80 11:0.88 12:0.79 17:0.28 20:0.99 21:0.30 27:0.21 28:0.70 30:0.42 34:0.80 36:0.94 37:0.74 39:0.55 41:0.99 43:0.98 60:0.98 66:0.20 69:0.12 70:0.81 74:0.97 78:0.82 81:0.80 83:0.90 85:0.97 91:0.78 96:0.98 98:0.58 100:0.93 101:0.82 108:0.78 111:0.87 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.77 124:0.75 126:0.54 127:0.69 129:0.81 133:0.67 135:0.26 138:0.99 140:0.45 144:0.85 146:0.83 147:0.85 149:0.95 150:0.87 151:0.98 152:0.91 153:0.94 154:0.98 155:0.67 158:0.92 159:0.86 161:0.79 162:0.79 164:0.93 165:0.84 167:0.50 169:0.91 172:0.68 173:0.08 176:0.73 177:0.38 179:0.29 181:0.98 186:0.82 187:0.39 189:0.94 192:0.59 201:0.74 202:0.89 204:0.89 208:0.64 212:0.51 215:0.72 216:0.95 222:0.35 230:0.87 232:0.92 233:0.76 235:0.42 238:0.97 241:0.30 242:0.44 243:0.40 245:0.93 247:0.47 248:0.99 253:0.99 255:0.79 256:0.92 259:0.21 260:0.97 261:0.95 265:0.59 266:0.80 267:0.36 268:0.92 271:0.34 276:0.81 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +2 1:0.74 6:0.94 7:0.81 8:0.88 9:0.80 11:0.88 12:0.79 17:0.08 20:0.97 21:0.13 27:0.03 28:0.70 30:0.62 32:0.80 34:0.95 36:0.63 37:0.92 39:0.55 41:0.95 43:0.90 60:0.98 66:0.30 69:0.44 70:0.59 74:0.98 77:0.70 78:0.81 81:0.88 83:0.87 85:0.98 91:0.54 96:0.92 98:0.69 100:0.88 101:0.86 104:0.84 106:0.81 108:0.34 111:0.83 114:0.92 117:0.86 120:0.87 121:0.90 122:0.43 123:0.75 124:0.58 126:0.54 127:0.43 129:0.59 133:0.47 135:0.94 140:0.45 144:0.85 145:0.59 146:0.78 147:0.81 149:0.99 150:0.93 151:0.94 152:0.93 153:0.81 154:0.89 155:0.67 158:0.92 159:0.65 161:0.79 162:0.86 164:0.83 165:0.88 167:0.52 169:0.85 172:0.82 173:0.30 176:0.73 177:0.38 179:0.24 181:0.98 186:0.81 187:0.39 189:0.95 192:0.59 195:0.85 201:0.74 202:0.72 204:0.89 206:0.81 208:0.64 212:0.57 215:0.84 216:0.79 220:0.74 222:0.35 230:0.87 232:0.91 233:0.76 235:0.22 238:0.94 241:0.39 242:0.51 243:0.58 245:0.95 247:0.60 248:0.92 253:0.95 255:0.79 256:0.78 259:0.21 260:0.88 261:0.92 265:0.57 266:0.38 267:0.36 268:0.79 271:0.34 276:0.84 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +2 1:0.74 8:0.61 9:0.80 11:0.88 12:0.79 17:0.07 20:0.77 21:0.22 27:0.17 28:0.70 30:0.76 34:0.71 36:0.52 37:0.97 39:0.55 41:0.97 43:0.76 60:0.98 66:0.36 69:0.81 70:0.37 74:0.71 78:0.72 81:0.89 83:0.89 85:0.88 91:0.31 96:0.95 98:0.24 100:0.73 101:0.78 104:0.90 106:0.81 108:0.65 111:0.72 114:0.90 117:0.86 120:0.91 122:0.57 123:0.63 124:0.70 126:0.54 127:0.19 129:0.81 133:0.67 135:0.94 140:0.45 144:0.85 146:0.36 147:0.42 149:0.73 150:0.94 151:0.96 152:0.82 153:0.86 154:0.68 155:0.67 158:0.92 159:0.35 161:0.79 162:0.86 163:0.81 164:0.84 165:0.90 167:0.68 169:0.79 172:0.27 173:0.78 176:0.73 177:0.38 179:0.60 181:0.98 186:0.73 187:0.87 192:0.59 201:0.74 202:0.72 206:0.81 208:0.64 212:0.37 215:0.79 216:0.71 222:0.35 232:0.94 235:0.52 241:0.69 242:0.58 243:0.51 245:0.50 247:0.39 248:0.95 253:0.95 255:0.79 256:0.84 259:0.21 260:0.92 261:0.82 265:0.26 266:0.38 267:0.36 268:0.80 271:0.34 276:0.32 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.33 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.79 17:0.40 21:0.13 27:0.16 28:0.70 30:0.76 32:0.80 34:0.52 36:0.70 37:0.69 39:0.55 41:0.98 43:0.80 60:0.98 66:0.93 69:0.73 70:0.46 74:1.00 77:0.89 81:0.88 83:0.90 85:1.00 91:0.37 96:0.97 98:0.77 101:0.87 104:0.96 106:0.81 108:0.70 114:0.92 117:0.86 120:0.94 121:0.90 122:0.43 123:0.68 124:0.37 126:0.54 127:0.34 129:0.81 133:0.76 135:0.87 140:0.45 144:0.85 145:0.74 146:0.32 147:0.38 149:1.00 150:0.93 151:0.98 153:0.92 154:0.74 155:0.67 158:0.92 159:0.85 161:0.79 162:0.81 164:0.93 165:0.88 167:0.46 172:0.99 173:0.43 176:0.73 177:0.38 179:0.10 181:0.98 187:0.87 192:0.59 195:0.97 201:0.74 202:0.87 204:0.89 206:0.81 208:0.64 212:0.86 215:0.84 216:0.74 220:0.74 222:0.35 230:0.87 232:0.98 233:0.76 235:0.22 241:0.05 242:0.63 243:0.06 245:0.93 247:0.47 248:0.97 253:0.99 255:0.79 256:0.90 259:0.21 260:0.95 265:0.77 266:0.80 267:0.69 268:0.91 271:0.34 276:0.98 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.97 300:0.84 +2 1:0.74 7:0.81 11:0.88 12:0.79 17:0.53 21:0.30 27:0.21 28:0.70 30:0.43 34:0.76 36:0.92 37:0.74 39:0.55 43:0.98 66:0.20 69:0.12 70:0.80 74:0.97 81:0.80 83:0.75 85:0.98 91:0.08 98:0.57 101:0.82 104:0.91 106:0.81 108:0.10 114:0.86 117:0.86 121:0.90 122:0.57 123:0.77 124:0.75 126:0.54 127:0.65 129:0.81 133:0.67 135:0.34 144:0.85 146:0.83 147:0.86 149:0.96 150:0.87 154:0.98 155:0.67 159:0.86 161:0.79 165:0.84 167:0.49 172:0.70 173:0.08 176:0.73 177:0.38 178:0.55 179:0.27 181:0.98 187:0.39 192:0.59 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.54 215:0.72 216:0.95 222:0.35 230:0.87 232:0.95 233:0.76 235:0.42 241:0.24 242:0.45 243:0.43 245:0.93 247:0.47 253:0.78 259:0.21 265:0.44 266:0.80 267:0.36 271:0.34 276:0.82 290:0.86 297:0.36 300:0.84 +2 1:0.74 6:0.93 8:0.75 9:0.80 11:0.88 12:0.79 17:0.07 20:0.87 21:0.05 27:0.14 28:0.70 30:0.20 32:0.80 34:0.45 36:0.74 37:0.67 39:0.55 41:0.90 43:0.85 55:0.92 66:0.18 69:0.57 70:0.88 74:0.86 77:0.70 78:0.77 81:0.92 83:0.81 85:0.88 91:0.44 96:0.89 98:0.50 100:0.81 101:0.75 108:0.73 111:0.77 114:0.95 120:0.81 122:0.57 123:0.71 124:0.90 126:0.54 127:0.63 129:0.93 133:0.90 135:0.91 140:0.45 144:0.85 145:0.47 146:0.22 147:0.28 149:0.83 150:0.96 151:0.92 152:0.96 153:0.72 154:0.82 155:0.67 159:0.82 161:0.79 162:0.58 163:0.92 164:0.75 165:0.90 167:0.50 169:0.91 172:0.41 173:0.34 176:0.73 177:0.38 179:0.53 181:0.98 186:0.78 187:0.68 189:0.90 191:0.70 192:0.59 195:0.93 201:0.74 202:0.72 208:0.64 212:0.19 215:0.91 216:0.93 220:0.62 222:0.35 235:0.12 238:0.90 241:0.30 242:0.41 243:0.19 245:0.68 247:0.55 248:0.88 253:0.92 255:0.79 256:0.68 259:0.21 260:0.82 261:0.96 265:0.52 266:0.91 267:0.65 268:0.71 271:0.34 276:0.66 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 8:0.88 9:0.80 11:0.88 12:0.79 17:0.62 21:0.05 27:0.15 28:0.70 30:0.76 32:0.80 34:0.73 36:0.88 37:0.88 39:0.55 41:0.90 43:0.74 60:0.97 66:0.43 69:0.85 70:0.41 74:0.97 77:0.70 81:0.92 83:0.85 85:1.00 91:0.11 96:0.89 98:0.65 101:0.85 108:0.70 114:0.95 120:0.82 121:0.90 122:0.57 123:0.68 124:0.85 126:0.54 127:0.59 129:0.95 133:0.87 135:0.83 140:0.45 144:0.85 145:0.40 146:0.98 147:0.98 149:0.98 150:0.96 151:0.87 153:0.48 154:0.64 155:0.67 158:0.92 159:0.91 161:0.79 162:0.81 163:0.81 164:0.73 165:0.90 167:0.49 172:0.91 173:0.56 176:0.73 177:0.38 179:0.16 181:0.98 187:0.68 192:0.59 195:0.98 201:0.74 202:0.62 204:0.89 208:0.64 212:0.48 215:0.91 216:0.80 220:0.62 222:0.35 230:0.84 232:0.88 233:0.69 235:0.12 241:0.21 242:0.63 243:0.57 245:0.95 247:0.39 248:0.88 253:0.93 255:0.79 256:0.64 259:0.21 260:0.82 265:0.66 266:0.71 267:0.44 268:0.67 271:0.34 276:0.93 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.57 8:0.62 10:0.93 11:0.75 12:0.37 17:0.55 18:0.72 21:0.64 27:0.68 29:0.86 30:0.45 33:0.97 34:0.78 36:0.46 37:0.60 39:0.25 43:0.61 44:0.88 45:0.77 48:0.91 64:0.77 66:0.63 69:0.53 70:0.78 71:0.95 74:0.43 75:0.95 81:0.33 86:0.79 91:0.31 98:0.46 101:0.65 102:0.96 108:0.41 122:0.94 123:0.39 124:0.45 126:0.32 127:0.39 129:0.05 131:0.61 133:0.36 135:0.23 139:0.79 144:0.76 145:0.86 146:0.42 147:0.48 149:0.33 150:0.37 154:0.75 155:0.46 157:0.80 159:0.32 166:0.85 170:0.82 172:0.41 173:0.63 174:0.90 177:0.88 178:0.55 179:0.80 182:0.75 187:0.39 192:0.44 195:0.68 197:0.93 201:0.54 208:0.50 212:0.61 216:0.31 222:0.84 226:0.98 227:0.61 229:0.61 231:0.78 235:0.80 236:0.91 239:0.93 241:0.32 242:0.33 243:0.68 245:0.54 246:0.61 247:0.60 253:0.37 254:0.74 259:0.21 265:0.48 266:0.38 267:0.94 271:0.27 276:0.35 281:0.91 287:0.61 291:0.97 300:0.55 +1 10:0.95 11:0.75 12:0.37 17:0.78 18:0.82 21:0.74 27:0.65 29:0.86 30:0.76 34:0.68 36:0.80 37:0.57 39:0.25 43:0.61 44:0.85 45:0.81 48:0.97 64:0.77 66:0.80 69:0.36 70:0.28 71:0.95 74:0.45 75:0.95 81:0.25 86:0.88 91:0.17 98:0.81 101:0.65 108:0.28 122:0.65 123:0.27 126:0.24 127:0.30 129:0.05 131:0.61 135:0.24 139:0.84 144:0.76 145:0.52 146:0.39 147:0.45 149:0.29 150:0.26 154:0.84 157:0.83 159:0.15 166:0.74 170:0.82 172:0.45 173:0.75 174:0.86 177:0.88 178:0.55 179:0.77 182:0.75 187:0.21 193:0.95 195:0.55 197:0.90 212:0.82 216:0.29 222:0.84 226:0.98 227:0.61 229:0.61 231:0.73 235:0.88 239:0.94 241:0.41 242:0.16 243:0.82 246:0.61 247:0.60 253:0.38 254:0.80 259:0.21 265:0.81 266:0.23 267:0.52 271:0.27 276:0.27 281:0.86 287:0.61 300:0.25 +1 8:0.89 10:0.92 11:0.75 12:0.37 17:0.97 18:0.65 21:0.78 27:0.65 29:0.86 30:0.33 34:0.50 36:0.57 37:0.39 39:0.25 43:0.17 45:0.73 55:0.59 66:0.86 69:0.49 70:0.80 71:0.95 74:0.43 83:0.56 86:0.75 91:0.14 98:0.82 101:0.65 102:0.94 108:0.38 122:0.95 123:0.36 127:0.31 129:0.05 131:0.61 132:0.97 135:0.49 139:0.76 144:0.76 145:0.69 146:0.76 147:0.80 149:0.22 154:0.76 155:0.50 157:0.77 159:0.32 166:0.61 170:0.82 172:0.61 173:0.56 174:0.94 177:0.88 178:0.55 179:0.60 182:0.72 187:0.21 192:0.45 193:0.98 195:0.66 197:0.97 204:0.80 208:0.50 212:0.51 216:0.21 222:0.84 227:0.61 229:0.61 231:0.70 235:0.60 239:0.92 241:0.01 242:0.73 243:0.87 246:0.61 247:0.55 253:0.37 254:0.74 259:0.21 265:0.82 266:0.47 267:0.23 271:0.27 276:0.50 281:0.91 287:0.61 300:0.55 +2 10:0.90 11:0.75 12:0.37 17:0.64 18:0.65 21:0.78 27:0.72 29:0.86 30:0.76 34:0.85 36:0.95 39:0.25 43:0.83 45:0.73 48:0.97 66:0.72 69:0.60 70:0.22 71:0.95 74:0.44 86:0.75 91:0.17 98:0.22 101:0.65 108:0.46 122:0.65 123:0.44 127:0.11 129:0.05 131:0.61 135:0.54 139:0.73 144:0.76 145:0.72 146:0.20 147:0.26 149:0.66 154:0.79 157:0.77 159:0.10 166:0.61 170:0.82 172:0.27 173:0.86 174:0.79 177:0.88 178:0.55 179:0.18 182:0.72 187:0.21 197:0.84 212:0.78 216:0.09 222:0.84 227:0.61 229:0.61 231:0.63 235:0.68 239:0.89 241:0.57 242:0.45 243:0.75 246:0.61 247:0.35 253:0.37 254:0.80 259:0.21 265:0.24 266:0.17 267:0.87 271:0.27 276:0.26 281:0.91 287:0.61 300:0.18 +1 10:0.94 11:0.75 12:0.37 17:0.69 18:0.91 21:0.22 27:0.39 29:0.86 30:0.41 34:0.87 36:0.79 37:0.83 39:0.25 43:0.37 45:0.81 64:0.77 66:0.69 69:0.12 70:0.88 71:0.95 74:0.43 81:0.29 86:0.86 91:0.35 98:0.61 101:0.65 108:0.10 122:0.92 123:0.10 124:0.42 126:0.24 127:0.58 129:0.05 131:0.61 133:0.39 135:0.61 139:0.82 144:0.76 146:0.54 147:0.60 149:0.31 150:0.32 154:0.76 157:0.83 159:0.37 166:0.74 170:0.82 172:0.54 173:0.28 174:0.86 177:0.88 178:0.55 179:0.74 182:0.75 187:0.21 197:0.89 212:0.72 216:0.29 222:0.84 227:0.61 229:0.61 231:0.72 235:0.22 239:0.93 241:0.18 242:0.45 243:0.73 245:0.59 246:0.61 247:0.67 253:0.37 254:0.74 259:0.21 265:0.62 266:0.47 267:0.36 271:0.27 276:0.38 287:0.61 300:0.70 +4 1:0.61 8:0.82 9:0.75 10:0.96 11:0.75 12:0.37 17:0.38 18:0.80 21:0.13 23:0.99 27:0.53 29:0.86 30:0.66 31:0.95 34:0.74 36:0.58 37:0.85 39:0.25 43:0.63 45:0.83 48:0.91 62:0.99 64:0.77 66:0.85 69:0.15 70:0.53 71:0.95 74:0.55 75:0.99 79:0.95 81:0.66 83:0.69 86:0.88 91:0.82 96:0.82 97:0.97 98:0.93 99:0.83 101:0.65 102:0.94 104:0.87 108:0.12 120:0.78 122:0.76 123:0.12 126:0.51 127:0.58 128:0.98 129:0.05 131:0.90 135:0.28 137:0.95 139:0.88 140:0.90 144:0.76 145:0.38 146:0.47 147:0.53 149:0.32 150:0.58 151:0.68 153:0.46 154:0.74 155:0.65 157:0.85 158:0.85 159:0.17 162:0.70 164:0.78 165:0.65 166:0.86 168:0.98 170:0.82 172:0.57 173:0.58 174:0.88 177:0.88 179:0.77 182:0.82 190:0.89 192:0.98 193:0.95 195:0.53 196:0.96 197:0.91 201:0.63 202:0.71 205:0.98 208:0.64 212:0.83 215:0.84 216:0.16 219:0.95 220:0.74 222:0.84 226:0.98 227:0.94 229:0.90 231:0.79 235:0.31 236:0.95 239:0.96 241:0.84 242:0.22 243:0.86 246:0.92 247:0.60 248:0.66 253:0.78 254:0.84 255:0.78 256:0.73 259:0.21 260:0.78 265:0.93 266:0.27 267:0.23 268:0.74 271:0.27 276:0.45 279:0.81 281:0.91 283:0.66 284:0.95 285:0.62 287:0.94 292:0.95 297:0.36 300:0.43 +2 10:0.92 11:0.75 12:0.37 17:0.85 18:0.75 21:0.78 27:0.52 29:0.86 30:0.58 34:0.45 36:0.65 37:0.94 39:0.25 43:0.60 45:0.77 66:0.69 69:0.67 70:0.33 71:0.95 74:0.42 86:0.79 91:0.05 98:0.36 101:0.65 108:0.51 122:0.41 123:0.49 124:0.41 127:0.21 129:0.05 131:0.61 133:0.36 135:0.30 139:0.75 144:0.76 146:0.32 147:0.39 149:0.30 154:0.71 157:0.80 159:0.21 166:0.61 170:0.82 172:0.41 173:0.81 174:0.86 177:0.88 178:0.55 179:0.65 182:0.74 187:0.21 197:0.90 212:0.82 216:0.27 222:0.84 227:0.61 229:0.61 231:0.69 235:0.02 239:0.90 241:0.39 242:0.16 243:0.73 245:0.46 246:0.61 247:0.60 253:0.36 254:0.80 259:0.21 265:0.38 266:0.23 267:0.59 271:0.27 276:0.28 287:0.61 300:0.25 +1 10:0.94 11:0.75 12:0.37 17:0.64 18:0.88 21:0.78 27:0.39 29:0.86 30:0.45 34:0.88 36:0.71 37:0.83 39:0.25 43:0.33 45:0.81 66:0.68 69:0.62 70:0.69 71:0.95 74:0.42 86:0.85 91:0.20 98:0.58 101:0.65 108:0.47 122:0.92 123:0.45 124:0.43 127:0.47 129:0.05 131:0.61 133:0.39 135:0.47 139:0.80 144:0.76 146:0.54 147:0.60 149:0.28 154:0.72 157:0.83 159:0.38 166:0.61 170:0.82 172:0.51 173:0.70 174:0.86 177:0.88 178:0.55 179:0.74 182:0.75 187:0.21 197:0.88 212:0.58 216:0.29 222:0.84 227:0.61 229:0.61 231:0.71 235:1.00 239:0.92 241:0.18 242:0.38 243:0.72 245:0.58 246:0.61 247:0.67 253:0.36 254:0.74 259:0.21 265:0.59 266:0.54 267:0.36 271:0.27 276:0.36 287:0.61 300:0.55 +2 1:0.74 6:0.92 8:0.68 9:0.80 11:0.57 12:0.95 17:0.64 20:0.89 21:0.05 27:0.09 30:0.10 34:0.84 36:0.84 37:0.79 39:0.24 41:0.82 43:0.86 55:0.59 60:0.87 66:0.13 69:0.07 70:0.96 74:1.00 78:0.98 81:0.95 83:0.87 85:0.99 91:0.44 96:0.78 98:0.34 100:0.96 101:0.80 108:0.06 111:0.97 114:0.95 120:0.66 122:0.67 123:0.06 124:0.99 126:0.54 127:0.97 129:1.00 133:0.99 135:0.72 140:0.45 146:0.96 147:0.97 149:0.97 150:0.98 151:0.79 152:0.94 153:0.69 154:0.83 155:0.67 158:0.87 159:0.97 162:0.86 164:0.62 165:0.90 169:0.94 172:0.90 173:0.05 176:0.73 177:0.38 179:0.12 186:0.98 187:0.39 189:0.93 192:0.59 201:0.74 202:0.46 208:0.64 212:0.39 215:0.91 216:0.75 220:0.62 222:0.21 235:0.12 238:0.90 241:0.27 242:0.54 243:0.33 245:0.95 247:0.67 248:0.72 253:0.87 255:0.79 256:0.45 259:0.21 260:0.67 261:0.95 265:0.36 266:0.98 267:0.96 268:0.55 271:0.53 276:0.98 279:0.86 283:0.71 285:0.62 290:0.95 297:0.36 300:0.94 +2 1:0.74 11:0.57 12:0.95 17:0.32 21:0.30 27:0.45 30:0.08 34:0.68 36:0.21 37:0.50 39:0.24 43:0.71 55:0.42 66:0.10 69:0.69 70:0.99 74:0.82 81:0.82 85:0.72 91:0.40 98:0.34 101:0.71 108:0.71 114:0.86 122:0.67 123:0.79 124:0.67 126:0.54 127:0.26 129:0.59 133:0.64 135:0.85 145:0.50 146:0.24 147:0.30 149:0.45 150:0.84 154:0.75 155:0.67 158:0.86 159:0.51 172:0.45 173:0.59 176:0.73 177:0.38 178:0.55 179:0.54 187:0.68 192:0.59 201:0.74 208:0.64 212:0.40 215:0.72 216:0.77 222:0.21 235:0.42 241:0.24 242:0.43 243:0.28 245:0.63 247:0.60 253:0.73 259:0.21 265:0.32 266:0.63 267:0.44 271:0.53 276:0.48 277:0.69 279:0.86 283:0.67 290:0.86 297:0.36 300:0.84 +2 1:0.74 8:0.68 11:0.57 12:0.95 17:0.57 21:0.22 27:0.49 30:0.26 32:0.80 34:0.52 36:0.90 37:0.42 39:0.24 43:0.97 66:0.44 69:0.15 70:0.88 74:0.99 76:0.94 77:0.70 81:0.85 83:0.76 85:0.99 91:0.25 98:0.77 101:0.84 108:0.80 114:0.90 122:0.57 123:0.79 124:0.69 126:0.54 127:0.89 129:0.81 133:0.67 135:0.61 145:0.41 146:0.84 147:0.87 149:0.98 150:0.91 154:0.97 155:0.67 159:0.87 165:0.86 172:0.91 173:0.09 176:0.73 177:0.38 178:0.55 179:0.19 187:0.39 192:0.59 195:0.95 201:0.74 212:0.50 215:0.79 216:0.94 222:0.21 235:0.42 241:0.01 242:0.36 243:0.40 245:0.97 247:0.60 253:0.79 259:0.21 265:0.77 266:0.75 267:0.93 271:0.53 276:0.92 282:0.88 290:0.90 297:0.36 299:0.88 300:0.84 +1 8:0.87 11:0.57 12:0.95 17:0.13 20:0.87 21:0.71 27:0.11 30:0.32 34:0.40 36:0.88 37:0.80 39:0.24 43:0.34 55:0.84 66:0.09 69:0.96 70:0.78 74:1.00 76:1.00 78:0.90 81:0.35 85:0.72 91:0.79 96:0.96 98:0.38 100:0.73 101:0.52 108:0.93 111:0.87 114:0.61 117:0.86 122:0.67 123:0.97 124:0.98 126:0.32 127:0.93 129:0.89 133:0.98 135:0.39 140:0.45 146:0.97 147:0.31 149:0.89 150:0.31 151:0.80 152:0.82 153:0.93 154:0.16 158:0.85 159:0.98 162:0.71 169:0.72 172:0.90 173:0.69 176:0.56 177:0.05 179:0.11 186:0.90 187:0.68 190:0.77 191:0.80 202:0.87 212:0.29 216:0.81 222:0.21 235:0.88 241:0.05 242:0.81 243:0.06 245:0.97 247:0.67 248:0.88 253:0.97 256:0.88 257:0.65 259:0.21 261:0.85 265:0.36 266:0.97 267:0.95 268:0.89 271:0.53 276:0.98 285:0.50 290:0.61 300:0.94 +0 8:0.80 9:0.80 11:0.57 12:0.95 17:0.18 20:0.86 21:0.05 27:0.18 30:0.29 34:0.53 36:0.45 37:0.71 39:0.24 43:0.59 55:0.59 66:0.75 69:0.90 70:0.68 74:1.00 76:0.85 77:0.70 78:0.94 81:0.87 85:0.72 91:0.81 96:0.98 98:0.83 100:0.92 101:0.70 108:0.79 111:0.92 114:0.77 120:0.61 122:0.67 123:0.78 124:0.43 126:0.32 127:0.66 129:0.05 133:0.62 135:0.19 140:0.99 145:0.38 146:0.96 147:0.05 149:0.85 150:0.73 151:0.64 152:0.81 153:0.96 154:0.48 155:0.67 158:0.85 159:0.78 162:0.67 164:0.54 169:0.90 172:0.95 173:0.83 176:0.56 177:0.05 179:0.18 186:0.93 187:0.39 190:0.77 191:0.84 192:0.59 195:0.89 202:0.91 208:0.64 212:0.86 216:0.90 222:0.21 235:0.12 241:0.54 242:0.81 243:0.06 245:0.93 247:0.39 248:0.62 253:0.99 255:0.79 256:0.92 257:0.65 259:0.21 260:0.62 261:0.88 265:0.83 266:0.54 267:0.73 268:0.93 271:0.53 276:0.92 282:0.84 285:0.62 290:0.77 300:0.70 +1 7:0.81 8:0.89 11:0.57 12:0.95 17:0.36 21:0.05 27:0.29 30:0.13 32:0.77 34:0.44 36:0.93 37:0.70 39:0.24 43:0.73 55:0.71 66:0.79 69:0.48 70:0.92 74:0.98 76:0.99 77:0.96 81:0.87 85:0.85 91:0.57 96:0.86 98:0.81 101:0.76 108:0.37 114:0.77 121:0.90 122:0.67 123:0.35 124:0.39 126:0.32 127:0.54 129:0.05 133:0.39 135:0.95 140:0.45 145:0.89 146:0.90 147:0.92 149:0.81 150:0.73 151:0.70 153:0.69 154:0.75 155:0.67 158:0.85 159:0.61 162:0.81 172:0.82 173:0.39 176:0.56 177:0.38 179:0.40 187:0.39 190:0.77 192:0.59 195:0.80 202:0.58 204:0.89 208:0.64 212:0.60 216:0.41 220:0.62 222:0.21 230:0.84 233:0.69 235:0.12 241:0.39 242:0.73 243:0.80 245:0.78 247:0.55 248:0.69 253:0.91 256:0.58 259:0.21 265:0.81 266:0.80 267:0.79 268:0.64 271:0.53 276:0.73 282:0.85 285:0.50 290:0.77 300:0.70 +2 7:0.78 8:0.90 11:0.57 12:0.95 17:0.30 21:0.30 27:0.09 30:0.19 34:0.49 36:0.97 37:0.79 39:0.24 43:0.66 55:0.52 60:0.87 66:0.96 69:0.59 70:0.87 74:0.98 77:0.70 81:0.81 83:0.75 85:0.85 91:0.72 96:0.94 98:0.94 101:0.76 108:0.45 114:0.69 117:0.86 120:0.65 122:0.67 123:0.43 126:0.32 127:0.61 129:0.05 135:0.87 140:0.45 145:0.72 146:0.96 147:0.96 149:0.83 150:0.60 151:0.76 153:0.88 154:0.55 155:0.67 158:0.87 159:0.65 162:0.84 164:0.56 172:0.89 173:0.50 176:0.56 177:0.38 179:0.32 187:0.68 190:0.77 191:0.78 192:0.59 195:0.81 202:0.77 208:0.64 212:0.58 216:0.75 220:0.74 222:0.21 230:0.81 233:0.69 235:0.52 241:0.66 242:0.75 243:0.96 247:0.47 248:0.84 253:0.96 256:0.81 259:0.21 260:0.66 265:0.94 266:0.54 267:0.91 268:0.83 271:0.53 276:0.81 279:0.86 282:0.84 283:0.71 285:0.62 290:0.69 300:0.70 +2 6:0.99 8:0.82 9:0.80 11:0.57 12:0.95 17:0.18 20:0.89 21:0.22 27:0.06 30:0.42 34:0.64 36:0.62 37:0.85 39:0.24 41:0.82 43:0.66 55:0.52 66:0.61 69:0.76 70:0.84 74:0.99 76:0.85 77:0.70 78:0.95 81:0.75 83:0.74 85:0.85 91:0.57 96:0.78 98:0.74 100:0.94 101:0.73 108:0.59 111:0.93 114:0.72 117:0.86 120:0.65 122:0.67 123:0.57 124:0.57 126:0.32 127:0.49 129:0.05 133:0.62 135:0.39 140:0.80 145:0.45 146:0.96 147:0.96 149:0.87 150:0.55 151:0.77 152:0.83 153:0.48 154:0.55 155:0.67 158:0.84 159:0.80 162:0.86 164:0.57 169:0.92 172:0.93 173:0.57 176:0.56 177:0.38 179:0.17 186:0.95 187:0.68 189:1.00 191:0.81 192:0.59 195:0.93 202:0.50 208:0.64 212:0.73 216:0.87 220:0.82 222:0.21 235:0.31 238:0.89 241:0.64 242:0.79 243:0.68 245:0.96 247:0.60 248:0.71 253:0.86 255:0.79 256:0.58 259:0.21 260:0.66 261:0.91 265:0.74 266:0.75 267:0.93 268:0.59 271:0.53 276:0.91 282:0.84 285:0.62 290:0.72 300:0.84 +1 8:0.82 11:0.57 12:0.95 17:0.32 21:0.30 27:0.50 30:0.26 34:0.52 36:0.80 37:0.60 39:0.24 43:0.58 55:0.71 66:0.18 69:0.53 70:0.81 74:1.00 76:0.85 77:0.89 81:0.68 85:0.95 91:0.35 96:0.89 98:0.53 101:0.71 108:0.41 114:0.69 117:0.86 122:0.67 123:0.39 124:0.98 126:0.32 127:0.97 129:0.98 133:0.98 135:0.30 140:0.45 145:0.59 146:1.00 147:1.00 149:0.91 150:0.49 151:0.74 153:0.83 154:0.50 158:0.85 159:0.98 162:0.82 172:0.97 173:0.08 176:0.56 177:0.38 179:0.10 187:0.39 190:0.77 191:0.70 195:1.00 202:0.63 212:0.30 216:0.86 222:0.21 235:0.42 241:0.02 242:0.78 243:0.38 245:0.98 247:0.67 248:0.73 253:0.93 256:0.64 259:0.21 265:0.54 266:0.96 267:0.95 268:0.69 271:0.53 276:0.99 282:0.84 285:0.50 290:0.69 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.95 17:0.43 21:0.40 27:0.09 30:0.38 32:0.80 34:0.77 36:0.92 37:0.79 39:0.24 41:0.82 43:0.79 60:0.87 66:0.95 69:0.60 70:0.85 74:0.97 77:0.89 81:0.85 83:0.83 85:0.96 91:0.51 96:0.73 98:0.94 101:0.85 106:0.81 108:0.45 114:0.82 117:0.86 120:0.60 121:0.99 122:0.43 123:0.44 126:0.54 127:0.61 129:0.05 135:0.73 140:0.45 145:0.76 146:0.95 147:0.96 149:0.94 150:0.90 151:0.63 153:0.48 154:0.73 155:0.67 158:0.87 159:0.63 162:0.86 164:0.57 165:0.81 172:0.88 173:0.52 176:0.73 177:0.38 179:0.34 187:0.87 192:0.59 195:0.79 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.74 215:0.67 216:0.75 220:0.82 222:0.21 230:0.87 233:0.76 235:0.60 241:0.46 242:0.27 243:0.96 247:0.67 248:0.60 253:0.80 255:0.79 256:0.45 259:0.21 260:0.60 265:0.94 266:0.54 267:0.69 268:0.46 271:0.53 276:0.80 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.90 7:0.81 8:0.88 9:0.80 11:0.57 12:0.95 17:0.22 20:0.91 21:0.13 27:0.21 30:0.17 32:0.80 34:0.67 36:0.93 37:0.60 39:0.24 41:0.90 43:0.97 60:0.99 66:0.91 69:0.17 70:0.76 74:0.95 76:0.94 77:0.89 78:0.93 81:0.91 83:0.83 85:0.90 91:0.49 96:0.80 98:0.96 100:0.92 101:0.83 104:0.93 108:0.14 111:0.92 114:0.92 120:0.69 121:0.90 122:0.57 123:0.13 126:0.54 127:0.71 129:0.05 132:0.97 135:0.73 140:0.80 145:0.44 146:0.79 147:0.83 149:0.86 150:0.95 151:0.80 152:0.96 153:0.71 154:0.97 155:0.67 158:0.87 159:0.35 162:0.74 164:0.71 165:0.86 169:0.92 172:0.74 173:0.35 176:0.73 177:0.38 178:0.42 179:0.59 186:0.93 187:0.21 189:0.89 192:0.59 195:0.58 201:0.74 202:0.61 204:0.89 208:0.64 212:0.75 215:0.84 216:0.46 220:0.62 222:0.21 230:0.84 233:0.69 235:0.52 238:0.87 241:0.39 242:0.45 243:0.91 247:0.47 248:0.77 253:0.88 255:0.79 256:0.64 259:0.21 260:0.70 261:0.95 265:0.96 266:0.63 267:0.85 268:0.65 271:0.53 276:0.61 279:0.86 282:0.88 283:0.71 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +1 8:0.79 11:0.57 12:0.95 17:0.53 20:0.87 27:0.59 30:0.21 34:0.40 36:0.92 37:0.52 39:0.24 43:0.79 55:0.84 66:0.06 69:0.05 70:0.95 74:1.00 78:0.93 81:0.92 85:0.98 91:0.42 96:0.74 98:0.26 100:0.94 101:0.74 108:0.05 111:0.92 114:0.80 122:0.67 123:0.96 124:0.99 126:0.32 127:1.00 129:0.99 133:0.99 135:0.44 140:0.45 146:0.96 147:0.97 149:0.94 150:0.85 151:0.58 152:0.82 153:0.48 154:0.72 159:0.99 162:0.62 169:0.92 172:0.89 173:0.05 176:0.56 177:0.38 179:0.10 186:0.92 187:0.87 190:0.77 202:0.50 212:0.19 216:0.59 220:0.74 222:0.21 235:0.05 241:0.05 242:0.76 243:0.24 245:0.97 247:0.67 248:0.56 253:0.82 256:0.45 259:0.21 261:0.89 265:0.28 266:0.98 267:0.76 268:0.46 271:0.53 276:0.99 277:0.69 285:0.50 290:0.80 300:0.94 +1 6:0.98 8:0.95 9:0.80 11:0.57 12:0.95 17:0.15 20:0.96 21:0.30 27:0.21 30:0.56 34:0.30 36:0.78 37:0.74 39:0.24 41:0.82 43:0.72 55:0.71 66:0.11 69:0.12 70:0.66 74:1.00 76:0.85 78:0.93 81:0.68 83:0.74 85:0.91 91:0.93 96:0.99 98:0.45 100:0.97 101:0.70 108:0.98 111:0.95 114:0.69 117:0.86 120:0.65 122:0.67 123:0.98 124:0.97 126:0.32 127:1.00 129:0.98 133:0.97 135:0.20 140:0.98 146:0.82 147:0.85 149:0.88 150:0.49 151:0.77 152:0.92 153:0.98 154:0.62 155:0.67 158:0.85 159:0.97 162:0.66 164:0.57 169:0.95 172:0.92 173:0.05 176:0.56 177:0.38 179:0.10 186:0.93 187:0.39 189:0.99 190:0.77 191:0.87 192:0.59 202:0.92 208:0.64 212:0.36 216:0.95 222:0.21 235:0.42 238:0.95 241:0.24 242:0.78 243:0.11 245:0.98 247:0.67 248:0.71 253:0.99 255:0.79 256:0.94 259:0.21 260:0.66 261:0.94 265:0.47 266:0.96 267:0.95 268:0.94 271:0.53 276:0.99 285:0.62 290:0.69 300:0.94 +1 8:0.83 11:0.57 12:0.95 17:0.32 21:0.47 27:0.21 30:0.43 34:0.44 36:0.79 37:0.74 39:0.24 43:0.82 55:0.84 66:0.17 69:0.21 70:0.83 74:0.99 76:0.97 81:0.56 85:0.96 91:0.48 96:0.79 98:0.57 101:0.74 108:0.98 114:0.66 117:0.86 122:0.67 123:0.98 124:0.97 126:0.32 127:0.98 129:0.95 133:0.97 135:0.24 140:0.45 146:0.82 147:0.85 149:0.93 150:0.42 151:0.61 153:0.86 154:0.77 158:0.84 159:0.96 162:0.58 172:0.91 173:0.06 176:0.56 177:0.38 179:0.12 187:0.39 190:0.77 191:0.70 202:0.72 212:0.21 216:0.95 220:0.74 222:0.21 235:0.60 241:0.43 242:0.76 243:0.21 245:0.96 247:0.67 248:0.62 253:0.86 256:0.68 259:0.21 265:0.58 266:0.98 267:0.84 268:0.72 271:0.53 276:0.97 285:0.50 290:0.66 300:0.94 +2 1:0.74 6:0.90 7:0.81 8:0.74 9:0.80 11:0.57 12:0.95 17:0.15 20:0.90 21:0.13 27:0.21 30:0.27 32:0.80 34:0.68 36:0.74 37:0.60 39:0.24 41:0.88 43:0.97 55:0.52 60:0.95 66:0.91 69:0.17 70:0.83 74:0.95 76:0.94 77:0.89 78:0.96 81:0.91 83:0.84 85:0.90 91:0.46 96:0.83 98:0.92 100:0.95 101:0.84 104:0.91 108:0.14 111:0.95 114:0.92 120:0.73 121:0.90 122:0.43 123:0.13 126:0.54 127:0.52 129:0.05 132:0.97 135:0.65 140:0.45 145:0.44 146:0.72 147:0.76 149:0.88 150:0.95 151:0.81 152:0.96 153:0.72 154:0.97 155:0.67 158:0.87 159:0.29 162:0.76 164:0.69 165:0.86 169:0.92 172:0.75 173:0.38 176:0.73 177:0.38 179:0.53 186:0.96 187:0.21 189:0.93 191:0.70 192:0.59 195:0.56 201:0.74 202:0.58 204:0.89 208:0.64 212:0.88 215:0.84 216:0.46 220:0.62 222:0.21 230:0.84 233:0.69 235:0.52 238:0.89 241:0.44 242:0.30 243:0.91 247:0.60 248:0.80 253:0.89 255:0.79 256:0.58 259:0.21 260:0.74 261:0.95 265:0.92 266:0.38 267:0.36 268:0.62 271:0.53 276:0.62 279:0.86 282:0.88 283:0.71 285:0.62 290:0.92 297:0.36 299:0.88 300:0.70 +0 8:0.86 11:0.57 12:0.95 17:0.59 21:0.74 27:0.45 30:0.26 34:0.66 36:0.39 37:0.50 39:0.24 43:0.65 55:0.59 66:0.61 69:0.71 70:0.86 74:0.98 77:0.70 81:0.33 85:0.80 91:0.29 98:0.63 101:0.71 108:0.54 114:0.61 122:0.67 123:0.52 124:0.71 126:0.32 127:0.37 129:0.05 133:0.82 135:0.85 145:0.52 146:0.92 147:0.94 149:0.76 150:0.30 154:0.54 158:0.84 159:0.77 172:0.82 173:0.49 176:0.56 177:0.38 178:0.55 179:0.28 187:0.68 195:0.94 212:0.30 216:0.77 222:0.21 235:0.95 241:0.30 242:0.78 243:0.68 245:0.79 247:0.39 253:0.71 259:0.21 265:0.64 266:0.75 267:0.89 271:0.53 276:0.79 282:0.84 290:0.61 300:0.84 +2 1:0.74 7:0.81 11:0.57 12:0.95 17:0.32 21:0.22 27:0.09 30:0.21 32:0.80 34:0.59 36:0.92 37:0.79 39:0.24 43:0.79 55:0.71 66:0.53 69:0.59 70:0.94 74:0.95 76:0.94 77:0.99 81:0.87 85:0.85 91:0.57 98:0.72 101:0.75 106:0.81 108:0.45 114:0.90 117:0.86 120:0.67 121:0.97 122:0.65 123:0.43 124:0.64 126:0.54 127:0.69 129:0.59 133:0.64 135:0.82 140:0.45 145:0.76 146:0.90 147:0.92 149:0.82 150:0.88 151:0.63 153:0.72 154:0.73 155:0.67 159:0.74 162:0.67 164:0.64 172:0.76 173:0.44 176:0.73 177:0.38 179:0.40 187:0.87 190:0.77 192:0.59 195:0.88 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.49 215:0.79 216:0.75 220:0.62 222:0.21 230:0.84 233:0.76 235:0.31 241:0.50 242:0.24 243:0.62 245:0.84 247:0.67 248:0.61 253:0.78 256:0.45 259:0.21 260:0.68 265:0.72 266:0.75 267:0.87 268:0.57 271:0.53 276:0.75 282:0.88 283:0.71 285:0.50 290:0.90 297:0.36 299:0.97 300:0.84 +0 8:0.78 10:0.79 12:0.69 17:0.99 18:0.57 21:0.78 27:0.72 29:0.62 30:0.28 34:0.37 36:0.44 39:0.58 43:0.05 55:0.45 66:0.07 69:0.98 70:0.75 71:0.67 74:0.25 86:0.57 91:0.14 98:0.13 108:0.98 122:0.98 123:0.98 124:0.91 127:0.43 129:0.59 131:0.61 133:0.91 135:0.32 139:0.56 145:0.40 146:0.17 147:0.05 149:0.06 154:0.06 157:0.61 159:0.96 166:0.61 170:0.90 172:0.37 173:0.86 174:0.93 175:0.99 177:0.05 178:0.55 179:0.65 182:0.61 187:0.95 197:0.82 202:0.61 212:0.37 216:0.18 222:0.21 227:0.61 229:0.61 231:0.68 235:0.22 239:0.79 241:0.41 242:0.51 243:0.12 244:0.97 245:0.52 246:0.61 247:0.76 253:0.23 254:0.74 257:0.99 264:0.96 265:0.10 266:0.80 267:0.99 271:0.17 275:0.94 276:0.51 277:0.98 287:0.61 294:0.91 300:0.55 +0 10:0.80 12:0.69 17:0.99 18:0.57 21:0.78 27:0.72 29:0.62 30:0.62 34:0.67 36:0.25 37:0.24 39:0.58 43:0.07 55:0.92 66:0.16 69:0.97 70:0.23 71:0.67 74:0.26 86:0.58 91:0.01 98:0.17 108:0.94 122:0.67 123:0.93 124:0.81 127:0.71 129:0.59 131:0.61 133:0.76 135:0.58 139:0.56 146:0.44 147:0.05 149:0.06 154:0.07 157:0.61 159:0.91 163:0.97 166:0.61 170:0.90 172:0.10 173:0.90 174:0.79 175:0.99 177:0.05 178:0.55 179:0.95 182:0.61 187:0.39 197:0.84 212:0.30 216:0.09 222:0.21 227:0.61 229:0.61 231:0.62 239:0.79 241:0.12 242:0.33 243:0.23 244:0.98 245:0.42 246:0.61 247:0.39 253:0.24 257:0.99 259:0.21 264:0.96 265:0.18 266:0.27 267:0.23 271:0.17 275:0.91 276:0.25 277:0.98 287:0.61 294:0.91 300:0.18 +0 8:0.59 10:0.80 11:0.54 12:0.69 17:0.96 18:0.58 21:0.78 27:1.00 29:0.62 30:0.38 34:0.86 36:0.48 39:0.58 43:0.05 45:0.66 55:0.99 66:0.13 69:0.98 70:0.63 71:0.67 74:0.24 86:0.58 98:0.05 101:0.45 108:0.97 122:0.95 123:0.97 124:0.93 127:0.68 129:0.05 131:0.61 133:0.92 135:0.44 139:0.57 144:0.76 146:0.19 147:0.25 149:0.05 154:0.07 157:0.75 159:1.00 163:1.00 166:0.61 170:0.90 172:0.16 173:0.60 174:0.79 175:0.97 177:0.70 178:0.55 179:0.83 182:0.71 187:0.21 197:0.79 212:0.28 216:0.01 222:0.21 227:0.61 229:0.61 231:0.61 239:0.80 241:0.12 242:0.60 243:0.34 244:0.93 245:0.45 246:0.61 247:0.55 253:0.22 254:0.84 257:0.93 259:0.21 264:0.96 265:0.05 266:0.47 267:0.84 271:0.17 275:0.91 276:0.41 277:0.95 287:0.61 294:0.92 300:0.43 +0 10:0.82 11:0.54 12:0.69 17:0.97 18:0.63 21:0.78 27:0.65 29:0.62 30:0.45 34:0.53 36:0.43 37:0.31 39:0.58 43:0.21 45:0.66 55:0.92 66:0.52 69:0.82 70:0.50 71:0.67 74:0.30 86:0.61 91:0.01 98:0.22 101:0.45 108:0.66 123:0.64 124:0.63 127:0.18 129:0.05 131:0.61 133:0.59 135:0.94 139:0.59 144:0.76 146:0.62 147:0.05 149:0.16 154:0.14 157:0.76 159:0.72 163:0.75 166:0.61 170:0.90 172:0.27 173:0.51 174:0.79 175:0.75 177:0.05 178:0.55 179:0.65 182:0.72 187:0.95 197:0.82 212:0.23 216:0.63 222:0.21 227:0.61 229:0.61 231:0.62 235:0.22 239:0.82 242:0.24 243:0.21 244:0.97 245:0.43 246:0.61 247:0.47 253:0.27 257:0.99 259:0.21 264:0.96 265:0.24 266:0.38 267:0.44 271:0.17 275:0.91 276:0.23 277:0.69 287:0.61 294:0.93 300:0.33 +0 10:0.81 11:0.54 12:0.69 17:0.96 18:0.58 21:0.78 27:0.72 29:0.62 30:0.11 34:0.94 36:0.38 39:0.58 43:0.05 45:0.66 66:0.30 69:0.97 70:0.54 71:0.67 74:0.26 86:0.59 91:0.14 98:0.09 101:0.45 108:0.95 122:0.76 123:0.95 124:0.78 127:0.19 129:0.05 131:0.61 133:0.73 135:0.37 139:0.57 144:0.76 146:0.20 147:0.05 149:0.05 154:0.08 157:0.76 159:0.67 163:0.90 166:0.61 170:0.90 172:0.16 173:0.97 174:0.79 175:0.97 177:0.05 178:0.55 179:0.75 182:0.72 187:0.21 197:0.79 212:0.30 216:0.06 222:0.21 227:0.61 229:0.61 231:0.63 235:0.60 239:0.80 241:0.07 242:0.33 243:0.23 244:0.93 245:0.40 246:0.61 247:0.39 253:0.24 254:0.88 257:0.99 264:0.96 265:0.10 266:0.27 267:0.17 271:0.17 275:0.91 276:0.26 277:0.95 287:0.61 294:0.92 300:0.33 +0 10:0.84 11:0.64 12:0.69 17:0.64 18:0.57 21:0.78 27:0.67 29:0.62 30:0.18 34:0.30 36:0.20 37:0.48 39:0.58 43:0.24 45:0.77 55:0.59 66:0.07 69:0.98 70:0.93 71:0.67 74:0.27 80:0.98 86:0.59 91:0.40 98:0.16 101:0.64 102:0.94 108:0.27 122:0.55 123:0.26 124:0.98 127:0.95 129:0.05 131:0.61 133:0.98 135:0.69 139:0.57 144:0.76 145:0.45 146:0.32 147:0.55 149:0.25 154:0.07 157:0.75 159:0.95 166:0.61 170:0.90 172:0.32 173:0.96 174:0.92 175:0.91 177:0.38 178:0.55 179:0.39 182:0.72 187:0.68 193:0.94 195:0.99 197:0.94 212:0.13 216:0.24 222:0.21 227:0.61 229:0.61 231:0.81 235:0.42 239:0.84 242:0.13 243:0.22 244:0.93 245:0.68 246:0.61 247:0.95 253:0.24 257:0.97 259:0.21 264:0.96 265:0.17 266:0.98 267:0.28 271:0.17 275:0.98 276:0.79 277:0.98 287:0.61 294:0.94 300:0.84 +0 10:0.79 12:0.69 17:0.99 18:0.60 21:0.78 27:0.71 29:0.62 30:0.68 34:0.73 36:0.28 39:0.58 43:0.06 55:0.96 66:0.12 69:0.94 70:0.43 71:0.67 74:0.27 86:0.60 91:0.01 98:0.17 108:0.89 122:0.85 123:0.93 124:0.86 127:0.19 129:0.59 131:0.61 133:0.82 135:0.71 139:0.58 146:0.17 147:0.05 149:0.06 154:0.09 157:0.61 159:0.66 163:0.98 166:0.61 170:0.90 172:0.10 173:0.86 174:0.83 175:0.99 177:0.05 178:0.55 179:0.65 182:0.61 187:0.39 197:0.82 212:0.42 216:0.14 222:0.21 227:0.61 229:0.61 231:0.64 239:0.79 241:0.05 242:0.19 243:0.19 244:0.97 245:0.44 246:0.61 247:0.55 253:0.24 254:0.74 257:0.99 259:0.21 264:0.96 265:0.07 266:0.38 267:0.36 271:0.17 275:0.91 276:0.33 277:0.98 287:0.61 294:0.91 300:0.33 +0 10:0.84 11:0.64 12:0.69 17:0.62 18:0.59 21:0.78 27:0.67 29:0.62 30:0.33 34:0.26 36:0.21 37:0.48 39:0.58 43:0.24 45:0.77 55:0.96 66:0.09 69:0.82 70:0.88 71:0.67 74:0.29 80:0.98 86:0.61 91:0.38 98:0.19 101:0.64 102:0.94 108:0.27 122:0.98 123:0.26 124:0.95 127:0.90 129:0.59 131:0.61 133:0.95 135:0.54 139:0.60 144:0.76 145:0.44 146:0.40 147:0.55 149:0.30 154:0.13 157:0.76 159:0.91 166:0.61 170:0.90 172:0.32 173:0.55 174:0.94 175:0.97 177:0.38 178:0.55 179:0.48 182:0.72 187:0.68 193:0.94 195:0.98 197:0.94 212:0.29 216:0.24 222:0.21 227:0.61 229:0.61 231:0.83 235:0.31 239:0.84 241:0.01 242:0.15 243:0.27 244:0.93 245:0.67 246:0.61 247:0.96 253:0.27 257:0.97 259:0.21 264:0.96 265:0.21 266:0.96 267:0.36 271:0.17 275:0.97 276:0.69 277:0.98 287:0.61 294:0.94 300:0.84 +1 1:0.57 11:0.48 12:0.69 17:0.67 18:0.64 21:0.30 27:0.30 29:0.62 30:0.90 34:0.42 36:0.35 37:0.71 39:0.58 43:0.32 45:0.73 55:0.59 56:0.97 66:0.69 69:0.66 70:0.19 71:0.67 74:0.33 81:0.48 86:0.65 91:0.48 98:0.33 101:0.52 108:0.50 123:0.48 124:0.41 126:0.32 127:0.19 129:0.05 131:0.61 133:0.37 135:0.69 139:0.63 144:0.76 146:0.40 147:0.46 149:0.29 150:0.47 154:0.23 155:0.46 157:0.61 159:0.27 166:0.93 170:0.90 172:0.41 173:0.68 175:0.97 177:0.70 178:0.55 179:0.59 182:0.61 187:0.39 192:0.47 201:0.59 208:0.49 212:0.82 215:0.72 216:0.35 222:0.21 227:0.61 229:0.61 231:0.88 235:0.52 236:0.92 241:0.07 242:0.72 243:0.73 244:0.93 245:0.46 246:0.61 247:0.39 253:0.29 257:0.93 259:0.21 264:0.96 265:0.35 266:0.23 267:0.52 271:0.17 276:0.29 277:0.95 287:0.61 297:0.36 300:0.18 +0 10:0.85 11:0.64 12:0.69 17:0.67 18:0.61 21:0.76 27:1.00 29:0.62 30:0.30 34:0.34 36:0.24 37:0.48 39:0.58 43:0.24 45:0.87 55:0.92 56:0.97 66:0.13 69:0.94 70:0.97 71:0.67 74:0.31 80:0.98 81:0.25 82:0.98 86:0.63 88:0.98 91:0.53 98:0.39 101:0.64 102:0.94 108:0.96 123:0.96 124:0.98 126:0.22 127:0.95 129:0.59 131:0.61 133:0.98 135:0.69 139:0.61 144:0.76 145:0.59 146:0.61 147:0.56 149:0.33 150:0.26 154:0.13 157:0.75 159:0.95 166:0.81 170:0.90 172:0.63 173:0.72 174:0.90 175:0.91 177:0.38 178:0.55 179:0.28 182:0.72 187:0.68 193:0.94 195:0.99 197:0.93 212:0.23 216:0.11 222:0.21 227:0.61 229:0.61 231:0.84 235:0.95 239:0.86 241:0.03 242:0.17 243:0.19 244:0.93 245:0.77 246:0.61 247:0.93 253:0.28 257:0.97 259:0.21 264:0.96 265:0.41 266:0.98 267:0.28 271:0.17 275:0.99 276:0.86 277:0.87 287:0.61 294:0.95 300:0.94 +0 8:0.71 10:0.79 12:0.69 17:0.96 18:0.57 21:0.78 27:0.65 29:0.62 30:0.45 34:0.74 36:0.78 37:0.49 39:0.58 43:0.05 55:1.00 66:0.09 69:1.00 70:0.47 71:0.67 74:0.26 86:0.57 91:0.03 98:0.05 108:0.99 122:0.43 123:0.99 124:0.92 127:0.30 129:0.59 131:0.61 133:0.91 135:0.54 139:0.56 146:0.13 147:0.05 149:0.05 154:0.05 157:0.61 159:0.99 163:1.00 166:0.61 170:0.90 172:0.10 173:0.86 174:0.79 175:0.99 177:0.05 178:0.55 179:0.75 182:0.61 187:0.39 197:0.79 202:0.46 212:0.30 216:0.02 222:0.21 227:0.61 229:0.61 231:0.61 235:0.42 239:0.78 241:0.05 242:0.14 243:0.17 244:0.97 245:0.44 246:0.61 247:0.67 253:0.23 257:0.99 259:0.21 264:0.96 265:0.05 266:0.54 267:0.69 271:0.17 275:0.93 276:0.39 277:0.98 287:0.61 294:0.91 300:0.33 +0 8:0.69 10:0.79 12:0.69 17:0.94 18:0.69 21:0.78 27:0.65 29:0.62 30:0.38 34:0.68 36:0.72 37:0.49 39:0.58 43:0.07 55:1.00 66:0.09 69:1.00 70:0.63 71:0.67 74:0.25 86:0.63 91:0.09 98:0.05 108:1.00 122:0.75 123:1.00 124:0.93 127:0.70 129:0.59 131:0.61 133:0.92 135:0.58 139:0.61 146:0.13 147:0.05 149:0.07 154:0.05 157:0.61 159:1.00 163:1.00 166:0.61 170:0.90 172:0.10 173:0.90 174:0.79 175:0.99 177:0.05 178:0.55 179:0.84 182:0.61 187:0.39 197:0.79 202:0.50 212:0.28 216:0.02 222:0.21 227:0.61 229:0.61 231:0.61 235:0.31 239:0.78 241:0.07 242:0.13 243:0.16 244:0.97 245:0.45 246:0.61 247:0.74 253:0.22 257:0.99 259:0.21 264:0.96 265:0.05 266:0.63 267:0.36 271:0.17 275:0.94 276:0.41 277:0.98 287:0.61 294:0.91 300:0.43 +1 7:0.81 12:0.37 17:0.89 20:0.97 21:0.13 27:0.06 32:0.80 34:0.89 36:0.36 37:0.60 43:0.28 66:0.36 69:0.77 78:0.83 81:0.97 91:0.25 98:0.06 100:0.85 104:0.79 106:0.81 108:0.60 111:0.83 123:0.57 126:0.54 127:0.05 129:0.05 135:0.94 145:0.38 146:0.05 147:0.05 149:0.12 150:0.96 152:0.89 154:0.21 159:0.05 169:0.83 172:0.05 173:0.58 177:0.05 178:0.55 186:0.84 187:0.68 195:0.93 206:0.81 215:0.84 216:0.85 230:0.65 233:0.63 235:0.12 241:0.32 243:0.51 259:0.21 261:0.94 265:0.06 267:0.84 297:0.36 +2 7:0.81 12:0.37 17:0.16 20:0.83 21:0.05 27:0.65 30:0.71 37:0.44 43:0.52 55:0.71 66:0.82 69:0.05 70:0.31 78:0.84 81:0.98 91:0.25 98:0.78 100:0.92 104:0.84 106:0.81 108:0.05 111:0.91 120:0.76 123:0.05 126:0.54 127:0.27 129:0.05 140:0.45 146:0.66 147:0.71 149:0.54 150:0.97 151:0.74 152:0.79 153:0.82 154:0.41 159:0.25 162:0.55 164:0.70 169:0.93 172:0.48 173:0.21 177:0.05 179:0.71 186:0.73 187:0.39 202:0.67 206:0.81 212:0.61 215:0.91 216:0.94 230:0.87 233:0.76 235:0.05 241:0.67 242:0.82 243:0.83 247:0.12 248:0.69 256:0.58 260:0.77 261:0.89 265:0.79 266:0.27 268:0.64 276:0.40 283:0.82 285:0.62 297:0.36 300:0.25 +0 12:0.37 17:0.72 20:0.84 21:0.78 25:0.88 27:0.72 30:0.33 43:0.52 55:0.59 66:0.61 69:0.35 70:0.49 78:0.83 91:0.72 98:0.77 100:0.73 104:0.73 108:0.59 111:0.81 120:0.79 123:0.57 124:0.51 127:0.92 129:0.81 133:0.67 140:0.45 146:0.05 147:0.05 149:0.48 151:0.66 152:0.80 153:0.48 154:0.41 159:0.34 162:0.70 163:0.75 164:0.72 169:0.72 172:0.37 173:0.52 177:0.05 179:0.91 186:0.86 202:0.63 212:0.19 216:0.06 235:0.12 241:0.97 242:0.82 243:0.18 245:0.45 247:0.12 248:0.72 256:0.64 260:0.80 261:0.89 265:0.77 266:0.47 268:0.65 276:0.33 285:0.62 300:0.33 +2 7:0.81 12:0.37 17:0.79 20:0.80 21:0.22 27:0.11 30:0.76 32:0.80 34:0.33 36:0.55 37:0.86 43:0.39 55:0.59 66:0.64 69:0.56 70:0.15 78:0.86 81:0.97 91:0.40 98:0.44 100:0.92 104:0.93 106:0.81 108:0.43 111:0.88 123:0.41 126:0.54 127:0.14 129:0.05 135:0.78 145:0.63 146:0.37 147:0.44 149:0.27 150:0.95 152:0.77 154:0.29 159:0.14 163:0.93 169:0.90 172:0.16 173:0.69 177:0.05 178:0.55 179:0.81 186:0.87 187:0.39 195:0.63 202:0.36 206:0.81 212:0.95 215:0.79 216:0.84 230:0.87 233:0.76 235:0.22 241:0.55 242:0.82 243:0.69 247:0.12 259:0.21 261:0.89 265:0.46 266:0.12 267:0.69 276:0.17 297:0.36 300:0.13 +1 12:0.37 17:0.02 21:0.05 27:0.72 30:0.95 34:0.86 36:0.77 37:0.32 43:0.50 55:0.92 66:0.54 69:0.12 81:0.98 91:0.44 98:0.77 104:0.96 108:0.10 123:0.10 126:0.54 127:0.25 129:0.05 135:0.89 146:0.46 147:0.52 149:0.22 150:0.97 154:0.39 159:0.17 172:0.10 173:0.45 177:0.05 178:0.55 179:0.99 187:0.68 215:0.91 216:0.14 235:0.05 241:0.15 243:0.63 259:0.21 265:0.77 267:0.89 297:0.36 300:0.08 +1 7:0.81 8:0.58 12:0.37 17:0.70 20:0.89 25:0.88 27:0.72 34:0.52 36:0.49 78:0.85 81:0.99 91:0.86 100:0.93 104:0.58 111:0.88 120:0.75 126:0.54 135:0.49 140:0.90 150:0.98 151:0.64 152:0.83 153:0.46 162:0.53 164:0.65 169:0.91 175:0.75 178:0.50 186:0.87 202:0.62 215:0.96 216:0.04 230:0.87 233:0.76 235:0.02 238:0.96 241:0.87 244:0.61 248:0.68 256:0.58 257:0.65 259:0.21 260:0.76 261:0.96 267:0.23 268:0.58 277:0.69 285:0.62 297:0.36 +2 12:0.37 17:0.32 21:0.47 27:0.43 30:0.76 34:0.95 36:0.39 37:0.67 43:0.43 55:0.92 66:0.64 69:0.49 70:0.29 81:0.93 91:0.17 98:0.69 108:0.66 123:0.64 124:0.42 126:0.54 127:0.37 129:0.59 133:0.47 135:0.47 145:0.67 146:0.05 147:0.05 149:0.37 150:0.87 154:0.32 159:0.46 163:0.75 172:0.32 173:0.47 177:0.05 178:0.55 179:0.88 187:0.95 212:0.23 215:0.63 216:0.60 235:0.52 241:0.41 242:0.82 243:0.21 245:0.43 247:0.12 259:0.21 265:0.69 266:0.38 267:0.28 276:0.31 283:0.60 297:0.36 300:0.25 +2 12:0.37 17:0.81 21:0.54 27:0.54 30:0.61 32:0.80 34:0.30 36:0.90 37:0.39 43:0.46 55:0.71 66:0.67 69:0.44 70:0.62 81:0.98 91:0.10 98:0.79 104:0.97 106:0.81 108:0.73 123:0.71 124:0.47 126:0.54 127:0.47 129:0.59 133:0.47 135:0.65 145:0.63 146:0.68 147:0.73 149:0.74 150:0.97 154:0.35 159:0.72 172:0.79 173:0.27 177:0.05 178:0.55 179:0.36 187:0.39 195:0.88 206:0.81 212:0.47 215:0.58 216:0.79 235:0.68 241:0.03 242:0.82 243:0.31 245:0.87 247:0.12 259:0.21 265:0.79 266:0.80 267:0.44 276:0.75 297:0.36 300:0.70 +1 7:0.81 12:0.37 17:0.18 20:0.83 21:0.13 27:0.63 30:0.38 37:0.58 43:0.52 55:0.84 66:0.93 69:0.07 70:0.57 78:0.72 81:0.97 91:0.33 98:0.91 100:0.73 108:0.06 111:0.72 120:0.85 123:0.06 126:0.54 127:0.51 129:0.05 140:0.45 146:0.94 147:0.95 149:0.73 150:0.96 151:0.71 152:0.79 153:0.72 154:0.41 159:0.60 162:0.86 163:0.75 164:0.80 169:0.72 172:0.81 173:0.12 177:0.05 179:0.44 186:0.73 187:0.39 202:0.66 212:0.30 215:0.84 216:0.91 220:0.62 230:0.65 233:0.63 235:0.12 241:0.37 242:0.82 243:0.93 247:0.12 248:0.77 256:0.73 260:0.85 261:0.73 265:0.91 266:0.54 268:0.75 276:0.71 283:0.82 285:0.62 297:0.36 300:0.43 +1 12:0.37 17:0.64 21:0.64 30:0.21 32:0.80 34:0.31 36:0.36 37:0.97 43:0.39 55:0.96 66:0.33 69:0.58 70:0.87 81:0.88 91:0.09 98:0.33 104:0.83 108:0.58 120:0.69 123:0.55 124:0.92 126:0.54 127:0.81 129:0.98 133:0.93 135:0.47 140:0.45 145:0.72 146:0.05 147:0.05 149:0.58 150:0.75 151:0.66 153:0.48 154:0.29 159:0.86 162:0.86 163:0.75 164:0.57 172:0.48 173:0.31 177:0.05 179:0.64 187:0.21 195:0.95 202:0.36 212:0.18 215:0.53 216:0.98 235:0.74 242:0.82 243:0.10 245:0.57 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.36 266:0.87 267:0.65 268:0.46 276:0.59 283:0.60 285:0.62 297:0.36 300:0.70 +1 12:0.37 17:0.77 25:0.88 27:0.72 78:0.87 81:0.99 91:0.85 104:0.86 111:0.90 120:0.80 126:0.54 140:0.45 150:0.98 151:0.66 153:0.48 162:0.81 164:0.79 169:0.93 175:0.75 202:0.69 215:0.96 216:0.16 220:0.82 235:0.02 241:0.89 244:0.61 248:0.72 256:0.73 257:0.65 260:0.81 268:0.74 277:0.69 285:0.62 297:0.36 +2 7:0.81 12:0.37 17:0.75 21:0.47 27:0.39 30:0.76 32:0.80 34:0.90 36:0.44 37:0.63 43:0.52 55:0.45 66:0.64 69:0.15 70:0.15 81:0.93 91:0.44 98:0.39 104:0.86 106:0.81 108:0.12 123:0.12 126:0.54 127:0.13 129:0.05 135:0.32 145:0.39 146:0.27 147:0.33 149:0.30 150:0.87 154:0.41 159:0.12 172:0.16 173:0.48 177:0.05 178:0.55 179:0.74 187:0.21 195:0.55 206:0.81 212:0.95 215:0.63 216:0.75 230:0.87 233:0.76 235:0.52 241:0.39 242:0.82 243:0.69 247:0.12 259:0.21 265:0.41 266:0.12 267:0.44 276:0.16 283:0.82 297:0.36 300:0.13 +2 12:0.37 17:0.01 20:0.80 27:0.43 34:0.42 36:0.29 37:0.41 43:0.52 66:0.36 69:0.05 78:0.72 81:0.99 91:0.20 98:0.12 100:0.73 104:0.96 106:0.81 108:0.05 111:0.72 123:0.05 126:0.54 127:0.08 129:0.05 135:0.74 146:0.05 147:0.05 149:0.16 150:0.98 152:0.77 154:0.41 159:0.05 169:0.72 172:0.05 173:0.39 177:0.05 178:0.55 186:0.73 187:0.68 206:0.81 215:0.96 216:0.55 235:0.02 241:0.35 243:0.51 259:0.21 261:0.73 265:0.13 267:0.52 283:0.82 297:0.36 +1 12:0.37 17:0.26 21:0.76 27:0.48 30:0.36 32:0.80 34:0.68 36:0.19 37:0.55 43:0.30 55:0.52 66:0.48 69:0.74 70:0.88 81:0.79 91:0.31 98:0.35 108:0.57 123:0.54 124:0.83 126:0.54 127:0.58 129:0.95 133:0.87 135:0.51 145:0.63 146:0.76 147:0.79 149:0.65 150:0.59 154:0.22 159:0.86 172:0.61 173:0.48 177:0.05 178:0.55 179:0.54 187:0.39 195:0.96 212:0.60 215:0.46 216:0.92 235:0.95 241:0.30 242:0.82 243:0.59 245:0.65 247:0.12 259:0.21 265:0.38 266:0.75 267:0.28 276:0.62 283:0.60 297:0.36 300:0.84 +0 12:0.37 17:0.18 21:0.30 27:0.32 30:0.87 34:0.96 36:0.39 37:0.54 43:0.44 55:0.84 66:0.45 69:0.47 70:0.15 81:0.95 91:0.06 98:0.64 108:0.36 123:0.34 124:0.67 126:0.54 127:0.36 129:0.81 133:0.67 135:0.21 146:0.77 147:0.81 149:0.45 150:0.93 154:0.33 159:0.52 163:0.94 172:0.32 173:0.39 177:0.05 178:0.55 179:0.78 187:0.68 212:0.95 215:0.72 216:0.85 235:0.31 241:0.24 242:0.82 243:0.58 245:0.52 247:0.12 259:0.21 265:0.65 266:0.12 267:0.28 276:0.41 283:0.60 297:0.36 300:0.13 +0 8:0.80 12:0.37 17:0.26 20:0.81 21:0.78 27:0.05 34:0.44 36:0.44 37:0.97 78:0.72 91:0.68 100:0.73 111:0.72 120:0.60 135:0.34 140:0.95 151:0.54 152:0.78 153:0.48 162:0.47 164:0.57 169:0.72 175:0.75 178:0.53 186:0.73 187:0.39 191:0.76 202:0.75 216:0.58 220:0.62 235:0.31 241:0.68 244:0.61 248:0.54 256:0.45 257:0.65 259:0.21 260:0.61 261:0.73 267:0.52 268:0.46 277:0.69 285:0.62 +2 7:0.81 12:0.37 17:0.45 20:0.80 21:0.30 27:0.50 30:0.95 32:0.80 34:0.37 36:0.91 37:0.60 43:0.50 55:0.92 66:0.36 69:0.21 78:0.72 81:0.95 91:0.44 98:0.43 100:0.73 104:0.84 106:0.81 108:0.17 111:0.72 120:0.69 123:0.16 124:0.52 126:0.54 127:0.22 129:0.59 133:0.47 135:0.78 140:0.45 145:0.69 146:0.39 147:0.46 149:0.27 150:0.93 151:0.66 152:0.77 153:0.48 154:0.39 159:0.24 162:0.86 163:0.86 164:0.57 169:0.72 172:0.10 173:0.34 177:0.05 179:0.96 186:0.73 187:0.39 195:0.60 202:0.36 206:0.81 212:0.95 215:0.72 216:0.86 230:0.87 233:0.76 235:0.31 241:0.27 242:0.82 243:0.51 245:0.37 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.73 265:0.45 266:0.12 267:0.52 268:0.46 276:0.18 283:0.82 285:0.62 297:0.36 300:0.08 +2 7:0.81 12:0.37 17:0.76 21:0.54 27:0.62 30:0.68 32:0.80 34:0.91 36:0.81 37:0.55 43:0.47 55:0.71 66:0.89 69:0.41 70:0.45 81:0.98 91:0.14 98:0.90 106:0.81 108:0.31 123:0.30 124:0.36 126:0.54 127:0.64 129:0.59 133:0.47 135:0.61 145:0.76 146:0.94 147:0.95 149:0.80 150:0.97 154:0.36 159:0.63 172:0.86 173:0.32 177:0.05 178:0.55 179:0.37 187:0.98 195:0.80 206:0.81 212:0.81 215:0.58 216:0.70 230:0.65 233:0.63 235:0.68 242:0.82 243:0.90 245:0.66 247:0.12 259:0.21 265:0.90 266:0.27 267:0.94 276:0.78 283:0.60 297:0.36 300:0.43 +2 7:0.81 8:0.98 12:0.37 17:0.95 21:0.77 27:0.72 28:0.66 32:0.80 34:0.36 36:0.55 37:0.99 81:0.80 91:0.92 104:0.54 126:0.54 135:0.30 145:1.00 150:0.60 161:0.61 167:0.82 175:0.75 178:0.55 181:0.91 191:0.86 195:0.61 202:0.36 215:0.43 216:0.06 230:0.65 233:0.63 235:0.98 241:0.87 244:0.61 257:0.65 259:0.21 267:0.65 271:0.55 277:0.69 297:0.99 +1 8:0.62 12:0.37 17:0.40 20:0.88 21:0.71 28:0.66 30:0.37 32:0.80 34:0.40 36:0.28 37:0.97 43:0.30 55:0.71 66:0.62 69:0.73 70:0.89 78:0.74 81:0.76 91:0.54 98:0.47 100:0.77 104:0.80 108:0.70 111:0.73 120:0.93 123:0.68 124:0.75 126:0.54 127:0.66 129:0.95 133:0.87 135:0.69 140:0.45 145:0.63 146:0.40 147:0.46 149:0.71 150:0.57 151:0.80 152:0.83 153:0.93 154:0.22 159:0.89 161:0.61 162:0.72 164:0.92 167:0.55 169:0.75 172:0.77 173:0.44 177:0.05 179:0.43 181:0.91 186:0.75 187:0.21 195:0.97 202:0.87 212:0.42 215:0.48 216:0.98 235:0.84 241:0.49 242:0.82 243:0.23 245:0.70 247:0.12 248:0.90 256:0.89 259:0.21 260:0.93 261:0.75 265:0.49 266:0.75 267:0.44 268:0.90 271:0.55 276:0.70 283:0.60 285:0.62 297:0.36 300:0.70 +3 8:0.88 12:0.37 17:0.57 20:0.76 21:0.22 27:0.72 28:0.66 30:0.21 34:0.19 36:0.25 37:0.86 43:0.44 55:0.45 66:0.54 69:0.45 70:0.37 78:0.76 81:0.92 91:0.87 98:0.33 100:0.80 104:0.54 108:0.35 111:0.76 120:0.77 123:0.33 124:0.45 126:0.54 127:0.83 129:0.59 133:0.47 135:0.54 140:0.45 145:0.70 146:0.41 147:0.48 149:0.35 150:0.86 151:0.72 152:0.75 153:0.78 154:0.34 159:0.57 161:0.61 162:0.72 163:0.75 164:0.79 167:0.85 169:0.78 172:0.21 173:0.42 177:0.05 179:0.97 181:0.91 186:0.77 202:0.71 212:0.42 215:0.79 216:0.21 220:0.62 235:0.22 241:0.97 242:0.82 243:0.63 245:0.40 247:0.12 248:0.69 256:0.71 259:0.21 260:0.78 261:0.75 265:0.36 266:0.23 267:0.44 268:0.74 271:0.55 276:0.15 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.81 7:0.81 8:0.68 12:0.37 17:0.36 20:0.98 21:0.78 27:0.66 28:0.66 30:0.38 32:0.80 34:0.87 36:0.61 37:0.29 43:0.50 55:0.59 66:0.72 69:0.25 70:0.45 78:0.76 91:0.82 98:0.84 100:0.83 104:0.58 108:0.57 111:0.77 120:0.85 123:0.55 124:0.40 127:0.89 129:0.59 133:0.47 135:0.30 140:0.45 145:0.98 146:0.05 147:0.05 149:0.50 151:0.77 152:0.90 153:0.88 154:0.39 159:0.40 161:0.61 162:0.78 164:0.89 167:0.83 169:0.81 172:0.48 173:0.38 177:0.05 179:0.85 181:0.91 186:0.77 189:0.84 191:0.77 195:0.64 202:0.82 212:0.28 216:0.20 220:0.62 230:0.65 233:0.63 235:0.60 238:0.97 241:0.90 242:0.82 243:0.16 245:0.48 247:0.12 248:0.79 256:0.85 259:0.21 260:0.86 261:0.82 265:0.84 266:0.54 267:0.36 268:0.86 271:0.55 276:0.41 283:0.60 285:0.62 300:0.33 +1 12:0.37 17:0.36 21:0.30 27:0.45 28:0.66 30:0.68 34:0.71 36:0.30 37:0.50 43:0.33 55:0.71 66:0.68 69:0.68 70:0.43 81:0.91 91:0.22 98:0.53 108:0.52 123:0.49 124:0.41 126:0.54 127:0.37 129:0.59 133:0.47 135:0.69 145:0.52 146:0.64 147:0.69 149:0.42 150:0.83 154:0.24 159:0.48 161:0.61 163:0.75 167:0.49 172:0.37 173:0.67 177:0.05 178:0.55 179:0.85 181:0.91 187:0.68 212:0.42 215:0.72 216:0.77 235:0.31 241:0.21 242:0.82 243:0.72 245:0.45 247:0.12 259:0.21 265:0.55 266:0.27 267:0.28 271:0.55 276:0.28 297:0.36 300:0.33 +3 6:0.85 7:0.81 8:0.78 12:0.37 17:0.73 20:0.76 21:0.74 27:0.43 28:0.66 30:0.21 32:0.80 34:0.59 36:0.62 37:0.69 43:0.49 55:0.45 66:0.54 69:0.38 70:0.37 78:0.89 81:0.73 91:0.76 98:0.49 100:0.95 104:0.58 108:0.59 111:0.91 120:0.85 123:0.56 124:0.45 126:0.54 127:0.75 129:0.59 133:0.47 135:0.42 140:0.45 145:0.98 146:0.05 147:0.05 149:0.35 150:0.54 151:0.75 152:0.74 153:0.86 154:0.38 159:0.24 161:0.61 162:0.72 164:0.91 167:0.83 169:0.92 172:0.21 173:0.64 177:0.05 179:0.97 181:0.91 186:0.89 189:0.87 191:0.83 195:0.55 202:0.85 212:0.42 215:0.46 216:0.26 220:0.62 230:0.65 233:0.63 235:0.88 238:0.80 241:0.90 242:0.82 243:0.26 245:0.40 247:0.12 248:0.79 256:0.86 259:0.21 260:0.86 261:0.77 265:0.51 266:0.23 267:0.19 268:0.88 271:0.55 276:0.18 283:0.60 285:0.62 297:0.36 300:0.25 +3 6:0.98 8:0.96 12:0.37 17:0.15 20:0.96 21:0.13 27:0.05 28:0.66 30:0.76 32:0.80 34:0.37 36:0.86 37:0.96 43:0.32 55:0.59 66:0.36 69:0.68 70:0.15 78:0.90 81:0.94 91:0.97 98:0.21 100:0.99 108:0.64 111:0.98 120:0.95 123:0.62 124:0.52 126:0.54 127:0.28 129:0.59 133:0.47 135:0.78 140:0.45 145:0.70 146:0.05 147:0.05 149:0.24 150:0.89 151:0.83 152:0.89 153:0.95 154:0.24 159:0.20 161:0.61 162:0.58 163:0.93 164:0.99 167:0.84 169:1.00 172:0.10 173:0.88 177:0.05 179:0.98 181:0.91 186:0.90 189:0.99 191:0.96 195:0.56 202:0.98 212:0.95 215:0.84 216:0.55 220:0.62 235:0.12 238:1.00 241:0.97 242:0.82 243:0.34 245:0.37 247:0.12 248:0.92 256:0.98 259:0.21 260:0.95 261:0.99 265:0.23 266:0.12 267:0.69 268:0.98 271:0.55 276:0.14 283:0.60 285:0.62 297:0.36 300:0.13 +3 8:1.00 12:0.37 17:0.77 20:0.80 21:0.13 27:0.54 28:0.66 30:0.76 34:0.45 36:0.97 37:0.85 43:0.32 55:0.92 66:0.72 69:0.70 70:0.22 78:0.72 81:0.94 91:0.85 98:0.78 100:0.78 104:0.72 108:0.53 111:0.73 120:0.85 123:0.51 126:0.54 127:0.26 129:0.05 135:0.74 140:0.45 146:0.66 147:0.71 149:0.31 150:0.89 151:0.78 152:0.78 153:0.89 154:0.23 159:0.25 161:0.61 162:0.79 163:0.90 164:0.88 167:0.83 169:0.75 172:0.27 173:0.82 177:0.05 179:0.91 181:0.91 186:0.73 191:0.90 202:0.81 212:0.42 215:0.84 216:0.24 220:0.74 235:0.12 241:0.89 242:0.82 243:0.75 247:0.12 248:0.79 256:0.83 259:0.21 260:0.86 261:0.75 265:0.78 266:0.23 267:0.28 268:0.85 271:0.55 276:0.24 283:0.60 285:0.62 297:0.36 300:0.18 +1 7:0.81 8:0.85 12:0.37 17:0.57 20:0.76 27:0.20 28:0.66 30:0.94 32:0.80 34:0.24 36:0.41 37:0.73 43:0.43 55:0.71 66:0.64 69:0.45 70:0.20 78:0.72 81:0.96 91:0.29 98:0.81 100:0.73 104:0.88 106:0.81 108:0.68 111:0.72 123:0.66 124:0.47 126:0.54 127:0.52 129:0.59 133:0.47 135:0.71 145:0.39 146:0.46 147:0.53 149:0.76 150:0.94 152:0.75 154:0.33 159:0.65 161:0.61 167:0.54 169:0.72 172:0.80 173:0.34 177:0.05 178:0.55 179:0.36 181:0.91 186:0.73 187:0.68 195:0.82 206:0.81 212:0.86 215:0.96 216:0.72 230:0.87 233:0.76 235:0.02 241:0.44 242:0.82 243:0.32 245:0.88 247:0.12 259:0.21 261:0.73 265:0.81 266:0.63 267:0.23 271:0.55 276:0.77 283:0.82 297:0.36 300:0.43 +2 7:0.81 8:0.93 12:0.37 17:0.13 21:0.54 27:0.54 28:0.66 30:0.33 32:0.80 34:0.55 36:0.56 37:0.95 43:0.50 55:0.52 66:0.86 69:0.27 70:0.69 81:0.94 91:0.67 98:0.92 104:0.58 108:0.21 120:0.75 123:0.20 126:0.54 127:0.54 129:0.05 135:0.60 140:0.45 145:0.47 146:0.57 147:0.63 149:0.65 150:0.90 151:0.73 153:0.78 154:0.39 159:0.21 161:0.61 162:0.60 164:0.73 167:0.73 172:0.61 173:0.58 177:0.05 179:0.71 181:0.91 187:0.39 191:0.82 195:0.56 202:0.67 212:0.85 215:0.58 216:0.24 230:0.87 233:0.76 235:0.68 241:0.74 242:0.82 243:0.87 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 265:0.92 266:0.27 267:0.28 268:0.67 271:0.55 276:0.51 283:0.60 285:0.62 297:0.36 300:0.55 +2 7:0.81 12:0.37 17:0.49 20:0.81 21:0.22 27:0.64 28:0.66 30:0.54 32:0.80 34:0.92 36:0.90 37:0.55 43:0.49 55:0.71 66:0.88 69:0.27 70:0.42 78:0.74 81:0.92 91:0.74 98:0.98 100:0.79 104:0.58 108:0.21 111:0.74 120:0.73 123:0.20 126:0.54 127:0.80 129:0.05 135:0.77 140:0.45 145:0.84 146:0.85 147:0.88 149:0.62 150:0.86 151:0.66 152:0.82 153:0.48 154:0.38 159:0.42 161:0.61 162:0.74 164:0.67 167:0.83 169:0.75 172:0.67 173:0.37 177:0.05 179:0.69 181:0.91 186:0.75 187:0.21 195:0.67 202:0.56 212:0.61 215:0.79 216:0.21 230:0.87 233:0.76 235:0.22 241:0.91 242:0.82 243:0.89 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 261:0.76 265:0.98 266:0.54 267:0.59 268:0.59 271:0.55 276:0.56 285:0.62 297:0.36 300:0.33 +2 7:0.81 8:0.80 12:0.37 17:0.36 20:0.83 21:0.05 25:0.88 27:0.37 28:0.66 30:0.72 32:0.80 34:0.42 36:0.78 37:0.67 43:0.49 55:0.71 66:0.84 69:0.25 70:0.29 78:0.75 81:0.95 91:0.83 98:0.98 100:0.79 104:0.58 108:0.20 111:0.75 120:0.64 123:0.19 126:0.54 127:0.85 129:0.05 135:0.39 140:0.45 145:0.76 146:0.82 147:0.85 149:0.54 150:0.92 151:0.59 152:0.79 153:0.91 154:0.37 159:0.38 161:0.61 162:0.69 163:0.75 164:0.82 167:0.83 169:0.77 172:0.54 173:0.39 177:0.05 179:0.83 181:0.91 186:0.76 191:0.73 195:0.63 202:0.74 212:0.39 215:0.91 216:0.24 220:0.62 230:0.65 233:0.63 235:0.05 241:0.90 242:0.82 243:0.85 247:0.12 248:0.58 256:0.75 259:0.21 260:0.65 261:0.76 265:0.98 266:0.47 267:0.28 268:0.77 271:0.55 276:0.45 283:0.60 285:0.62 297:0.36 300:0.25 +3 6:0.94 7:0.81 8:0.94 12:0.37 17:0.20 20:0.99 21:0.05 27:0.16 28:0.66 30:0.91 32:0.80 34:0.64 36:0.73 37:0.90 43:0.42 55:0.59 66:0.69 69:0.47 70:0.13 78:0.78 81:0.95 91:0.98 98:0.44 100:0.90 104:0.58 108:0.36 111:0.83 120:0.97 123:0.35 126:0.54 127:0.14 129:0.05 135:0.34 140:0.45 145:0.47 146:0.32 147:0.39 149:0.33 150:0.92 151:0.83 152:0.91 153:0.96 154:0.32 159:0.13 161:0.61 162:0.80 164:0.96 167:0.84 169:0.87 172:0.21 173:0.68 177:0.05 179:0.69 181:0.91 186:0.78 189:0.95 191:0.88 195:0.65 202:0.93 212:0.61 215:0.91 216:0.48 220:0.62 230:0.65 233:0.63 235:0.05 238:0.98 241:0.93 242:0.82 243:0.73 247:0.12 248:0.95 256:0.95 259:0.21 260:0.97 261:0.88 265:0.46 266:0.17 267:0.79 268:0.95 271:0.55 276:0.23 283:0.82 285:0.62 297:0.36 300:0.13 +2 7:0.81 8:0.92 12:0.37 17:0.32 20:0.84 21:0.22 27:0.64 28:0.66 30:0.54 32:0.80 34:0.91 36:0.90 37:0.55 43:0.49 55:0.71 66:0.88 69:0.27 70:0.42 78:0.74 81:0.92 91:0.76 98:0.98 100:0.73 104:0.58 108:0.21 111:0.73 120:0.78 123:0.20 126:0.54 127:0.80 129:0.05 135:0.78 140:0.45 145:0.84 146:0.85 147:0.88 149:0.62 150:0.86 151:0.73 152:0.82 153:0.78 154:0.38 159:0.42 161:0.61 162:0.73 164:0.85 167:0.84 169:0.75 172:0.67 173:0.37 177:0.05 179:0.69 181:0.91 186:0.75 195:0.67 202:0.77 212:0.61 215:0.79 216:0.21 220:0.82 230:0.87 233:0.76 235:0.22 241:0.93 242:0.82 243:0.89 247:0.12 248:0.70 256:0.80 259:0.21 260:0.79 261:0.76 265:0.98 266:0.54 267:0.59 268:0.81 271:0.55 276:0.56 285:0.62 297:0.36 300:0.33 +2 8:0.85 12:0.37 17:0.84 21:0.05 27:1.00 28:0.66 30:0.76 32:0.80 34:0.59 36:0.57 37:0.31 43:0.46 55:0.59 66:0.64 69:0.41 70:0.15 81:0.95 91:0.93 98:0.74 104:0.58 108:0.31 120:0.79 123:0.30 126:0.54 127:0.24 129:0.05 135:0.49 140:0.45 145:0.63 146:0.35 147:0.42 149:0.28 150:0.92 151:0.74 153:0.82 154:0.35 159:0.14 161:0.61 162:0.80 163:0.75 164:0.82 167:0.84 172:0.16 173:0.78 177:0.05 179:0.97 181:0.91 195:0.53 202:0.73 212:0.95 215:0.91 216:0.18 220:0.62 235:0.05 241:0.95 242:0.82 243:0.69 247:0.12 248:0.72 256:0.75 259:0.21 260:0.80 265:0.75 266:0.12 267:0.44 268:0.78 271:0.55 276:0.17 285:0.62 297:0.36 300:0.13 +3 8:0.89 12:0.37 17:0.47 20:0.82 21:0.64 27:0.51 28:0.66 30:0.72 32:0.80 34:0.55 36:0.44 37:0.96 43:0.39 55:0.45 66:0.90 69:0.57 70:0.37 78:0.88 81:0.81 91:0.91 98:0.91 100:0.80 104:0.58 108:0.44 111:0.95 120:0.91 123:0.42 126:0.54 127:0.51 129:0.05 135:0.32 140:0.45 145:0.74 146:0.70 147:0.74 149:0.70 150:0.61 151:0.77 152:0.78 153:0.88 154:0.30 159:0.27 161:0.61 162:0.69 164:0.95 167:0.84 169:0.96 172:0.71 173:0.75 177:0.05 179:0.58 181:0.91 186:0.75 191:0.80 195:0.56 202:0.93 212:0.83 215:0.53 216:0.49 220:0.74 235:0.74 238:0.99 241:0.97 242:0.82 243:0.90 247:0.12 248:0.87 256:0.94 260:0.92 261:0.76 265:0.91 266:0.38 267:0.18 268:0.94 271:0.55 276:0.60 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.37 17:0.16 21:0.40 27:0.45 28:0.66 30:0.45 32:0.80 34:0.75 36:0.18 37:0.50 43:0.32 55:0.92 66:0.13 69:0.69 70:0.33 81:0.89 91:0.14 98:0.21 108:0.52 123:0.82 124:0.82 126:0.54 127:0.21 129:0.89 133:0.78 135:0.78 145:0.40 146:0.34 147:0.40 149:0.35 150:0.79 154:0.24 159:0.53 161:0.61 163:0.89 167:0.53 172:0.10 173:0.52 177:0.05 178:0.55 179:0.75 181:0.91 187:0.68 195:0.90 212:0.23 215:0.67 216:0.77 235:0.42 241:0.41 242:0.82 243:0.34 245:0.43 247:0.12 259:0.21 265:0.20 266:0.38 267:0.36 271:0.55 276:0.32 283:0.60 297:0.36 300:0.25 +3 6:0.83 7:0.81 8:0.89 12:0.37 17:0.66 20:0.84 21:0.05 27:0.64 28:0.66 30:0.95 32:0.80 34:0.75 36:0.45 37:0.90 43:0.46 55:0.45 66:0.54 69:0.39 78:0.76 81:0.95 91:0.91 98:0.19 100:0.80 104:0.58 108:0.30 111:0.76 120:0.74 123:0.29 126:0.54 127:0.10 129:0.05 135:0.39 140:0.45 145:0.70 146:0.19 147:0.24 149:0.23 150:0.92 151:0.64 152:0.80 153:0.46 154:0.35 159:0.10 161:0.61 162:0.86 164:0.78 167:0.84 169:0.78 172:0.10 173:0.71 177:0.05 179:0.43 181:0.91 186:0.77 189:0.85 191:0.77 195:0.80 202:0.65 215:0.91 216:0.08 220:0.62 230:0.87 233:0.76 235:0.05 238:0.93 241:0.95 243:0.63 248:0.67 256:0.71 259:0.21 260:0.75 261:0.78 265:0.21 267:0.36 268:0.73 271:0.55 285:0.62 297:0.36 300:0.08 +2 6:0.96 7:0.81 8:0.93 12:0.37 17:0.43 20:0.99 21:0.64 27:0.54 28:0.66 30:0.61 32:0.80 34:0.49 36:0.48 37:0.95 43:0.50 55:0.59 66:0.89 69:0.32 70:0.48 78:0.77 81:0.81 91:0.87 98:0.96 100:0.83 104:0.58 108:0.25 111:0.82 120:0.82 123:0.24 126:0.54 127:0.71 129:0.05 135:0.66 140:0.45 145:0.52 146:0.61 147:0.66 149:0.71 150:0.61 151:0.75 152:0.90 153:0.85 154:0.39 159:0.22 161:0.61 162:0.76 164:0.91 167:0.83 169:0.87 172:0.68 173:0.61 177:0.05 179:0.66 181:0.91 186:0.76 189:0.97 191:0.85 195:0.56 202:0.86 212:0.95 215:0.53 216:0.24 220:0.82 230:0.87 233:0.76 235:0.74 238:0.99 241:0.91 242:0.82 243:0.89 247:0.12 248:0.74 256:0.89 259:0.21 260:0.82 261:0.82 265:0.96 266:0.12 267:0.17 268:0.89 271:0.55 276:0.58 283:0.60 285:0.62 297:0.36 300:0.43 +1 7:0.81 8:0.93 12:0.37 17:0.11 21:0.54 27:0.54 28:0.66 30:0.41 32:0.80 34:0.66 36:0.45 37:0.95 43:0.50 55:0.52 66:0.86 69:0.27 70:0.72 81:0.94 91:0.73 98:0.89 104:0.58 108:0.21 120:0.75 123:0.20 126:0.54 127:0.46 129:0.05 135:0.60 140:0.45 145:0.47 146:0.55 147:0.61 149:0.64 150:0.90 151:0.73 153:0.78 154:0.39 159:0.20 161:0.61 162:0.60 164:0.73 167:0.74 172:0.59 173:0.58 177:0.05 179:0.71 181:0.91 187:0.39 191:0.82 195:0.56 202:0.67 212:0.84 215:0.58 216:0.24 230:0.87 233:0.76 235:0.68 241:0.74 242:0.82 243:0.86 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 265:0.89 266:0.27 267:0.28 268:0.67 271:0.55 276:0.49 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.85 8:0.75 12:0.37 17:0.51 20:0.85 21:0.68 25:0.88 27:0.28 28:0.66 30:0.62 34:0.63 36:0.46 37:0.92 43:0.47 55:0.71 66:0.75 69:0.43 70:0.34 78:0.74 81:0.79 91:0.76 98:0.94 100:0.77 104:0.58 108:0.33 111:0.74 123:0.32 126:0.54 127:0.60 129:0.05 135:0.32 146:0.64 147:0.69 149:0.40 150:0.59 152:0.89 154:0.36 159:0.24 161:0.61 163:0.81 167:0.84 169:0.75 172:0.32 173:0.67 177:0.05 178:0.55 179:0.94 181:0.91 186:0.75 187:0.21 212:0.61 215:0.49 216:0.38 235:0.80 241:0.93 242:0.82 243:0.77 247:0.12 259:0.21 261:0.77 265:0.94 266:0.17 267:0.36 271:0.55 276:0.29 297:0.36 300:0.25 +2 7:0.81 12:0.37 17:0.49 21:0.22 27:0.19 28:0.66 30:0.72 32:0.80 34:0.37 36:0.25 37:0.70 43:0.25 55:0.84 66:0.33 69:0.82 70:0.26 81:0.92 91:0.25 98:0.66 108:0.74 120:0.72 123:0.78 124:0.67 126:0.54 127:0.29 129:0.81 133:0.67 135:0.73 140:0.45 145:0.52 146:0.05 147:0.05 149:0.57 150:0.86 151:0.70 153:0.69 154:0.18 159:0.55 161:0.61 162:0.86 163:0.94 164:0.62 167:0.53 172:0.48 173:0.77 177:0.05 179:0.46 181:0.91 187:0.87 195:0.84 202:0.46 212:0.55 215:0.79 216:0.63 230:0.87 233:0.76 235:0.22 241:0.39 242:0.82 243:0.10 245:0.71 247:0.12 248:0.66 256:0.45 259:0.21 260:0.73 265:0.23 266:0.47 267:0.93 268:0.55 271:0.55 276:0.60 285:0.62 297:0.36 300:0.25 +1 8:0.94 12:0.37 17:0.55 21:0.78 27:0.21 28:0.66 30:0.33 34:0.39 36:0.90 37:0.74 43:0.43 55:0.71 66:0.13 69:0.48 70:0.74 78:0.72 91:0.53 98:0.39 108:0.94 111:0.72 120:0.78 123:0.94 124:0.96 127:0.79 129:0.99 133:0.96 135:0.28 140:0.45 146:0.50 147:0.57 149:0.84 151:0.59 153:0.45 154:0.33 159:0.95 161:0.61 162:0.68 164:0.87 167:0.51 169:0.82 172:0.84 173:0.11 177:0.05 179:0.14 181:0.91 187:0.39 191:0.80 202:0.81 212:0.35 216:0.95 220:0.62 235:0.42 241:0.30 242:0.82 243:0.11 245:0.94 247:0.12 248:0.71 256:0.84 259:0.21 260:0.79 265:0.41 266:0.97 267:0.82 268:0.84 271:0.55 276:0.96 283:0.82 285:0.62 300:0.84 +0 8:0.85 11:0.81 12:0.20 17:0.62 18:0.56 21:0.78 27:0.72 28:0.68 29:0.62 30:0.45 34:0.24 36:0.61 39:0.66 43:0.06 45:0.66 66:0.27 69:1.00 70:0.25 71:0.92 74:0.26 86:0.58 91:0.27 98:0.05 101:0.52 108:0.99 122:0.80 123:0.99 124:0.72 127:0.12 129:0.05 131:0.61 133:0.62 135:0.92 139:0.57 144:0.76 145:0.91 146:0.13 147:0.18 149:0.06 154:0.06 157:0.75 159:0.85 161:0.96 163:0.90 166:0.61 167:0.82 172:0.10 173:0.93 175:0.75 177:0.38 178:0.55 179:0.51 181:0.73 182:0.71 202:0.64 212:0.61 216:0.02 222:0.48 227:0.61 229:0.61 231:0.61 235:0.42 241:0.78 242:0.63 243:0.46 244:0.61 245:0.38 246:0.61 247:0.30 253:0.23 257:0.65 259:0.21 265:0.05 266:0.17 267:0.28 271:0.37 276:0.17 277:0.69 287:0.61 300:0.18 +0 11:0.81 12:0.20 17:0.34 18:0.59 21:0.78 27:0.34 28:0.68 29:0.62 30:0.21 34:0.66 36:0.77 37:0.46 39:0.66 43:0.26 45:0.66 66:0.20 69:0.86 70:0.37 71:0.92 74:0.32 86:0.63 91:0.07 98:0.19 101:0.52 108:0.72 122:0.41 123:0.70 124:0.73 127:0.21 129:0.59 131:0.61 133:0.64 135:0.71 139:0.62 144:0.76 145:0.63 146:0.33 147:0.40 149:0.20 154:0.18 157:0.76 159:0.50 161:0.96 163:0.97 166:0.61 167:0.54 172:0.10 173:0.77 175:0.75 177:0.38 178:0.55 179:0.85 181:0.73 182:0.72 187:0.87 212:0.42 216:0.53 222:0.48 227:0.61 229:0.61 231:0.62 235:0.22 241:0.30 242:0.45 243:0.40 244:0.61 245:0.40 246:0.61 247:0.35 253:0.27 257:0.65 259:0.21 265:0.20 266:0.23 267:0.28 271:0.37 276:0.23 277:0.69 287:0.61 300:0.25 +0 9:0.80 12:0.20 17:0.59 18:0.74 20:0.92 21:0.78 27:0.49 28:0.68 29:0.62 30:0.76 31:0.87 34:0.75 36:0.43 37:0.42 39:0.66 41:0.82 43:0.15 55:0.45 66:0.64 69:0.35 70:0.15 71:0.92 78:0.72 79:0.87 83:0.91 86:0.60 91:0.56 96:0.69 98:0.66 100:0.73 108:0.27 111:0.72 120:0.57 122:0.83 123:0.26 127:0.20 129:0.05 131:0.86 135:0.77 137:0.87 139:0.59 140:0.80 144:0.76 146:0.32 147:0.38 149:0.07 151:0.52 152:0.86 153:0.48 154:0.59 155:0.67 157:0.61 159:0.13 161:0.96 162:0.62 163:0.75 164:0.65 166:0.61 167:0.59 169:0.72 172:0.16 173:0.74 175:0.75 177:0.38 179:0.94 181:0.73 182:0.80 186:0.73 187:0.68 192:0.87 196:0.87 202:0.60 208:0.64 212:0.95 216:0.94 220:0.87 222:0.48 227:0.91 229:0.89 231:0.71 235:0.88 241:0.49 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 248:0.51 253:0.39 254:0.80 255:0.95 256:0.58 257:0.65 259:0.21 260:0.57 261:0.73 265:0.67 266:0.12 267:0.44 268:0.59 271:0.37 276:0.14 277:0.69 284:0.89 285:0.62 287:0.93 300:0.13 +0 11:0.81 12:0.20 17:0.40 18:0.85 20:0.90 21:0.78 27:0.04 28:0.68 29:0.62 30:0.71 34:0.66 36:0.44 37:0.96 39:0.66 43:0.65 45:0.77 55:0.59 66:0.33 69:0.67 70:0.29 71:0.92 74:0.49 78:0.82 86:0.79 91:0.37 98:0.35 100:0.88 101:0.52 108:0.51 111:0.83 120:0.69 122:0.86 123:0.49 124:0.61 127:0.21 129:0.59 131:0.61 133:0.47 135:0.79 139:0.76 140:0.80 144:0.76 146:0.37 147:0.44 149:0.57 151:0.60 152:0.96 153:0.48 154:0.54 157:0.82 159:0.27 161:0.96 162:0.62 164:0.53 166:0.61 167:0.62 169:0.86 172:0.27 173:0.73 175:0.75 177:0.38 178:0.42 179:0.62 181:0.73 182:0.75 186:0.82 187:0.95 190:0.89 202:0.50 212:0.50 216:0.65 222:0.48 227:0.61 229:0.61 231:0.66 235:0.22 241:0.54 242:0.53 243:0.50 244:0.61 245:0.60 246:0.61 247:0.47 248:0.59 253:0.34 256:0.45 257:0.65 259:0.21 260:0.66 261:0.90 265:0.37 266:0.47 267:0.59 268:0.46 271:0.37 276:0.34 277:0.69 285:0.47 287:0.61 300:0.25 +1 1:0.74 11:0.81 12:0.20 17:0.55 18:0.86 21:0.64 27:0.45 28:0.68 29:0.62 30:0.15 34:0.86 36:0.18 37:0.50 39:0.66 43:0.11 44:0.85 45:0.66 55:0.52 64:0.77 66:0.33 69:0.70 70:0.84 71:0.92 74:0.43 81:0.42 83:0.60 86:0.75 91:0.25 98:0.39 99:0.83 101:0.52 108:0.54 114:0.57 122:0.86 123:0.51 124:0.61 126:0.54 127:0.21 129:0.05 131:0.61 133:0.37 135:0.66 139:0.73 144:0.76 145:0.49 146:0.48 147:0.54 149:0.14 150:0.66 154:0.76 155:0.67 157:0.76 159:0.32 161:0.96 165:0.70 166:0.80 167:0.51 172:0.27 173:0.71 176:0.73 177:0.70 178:0.55 179:0.62 181:0.73 182:0.80 187:0.68 192:0.87 195:0.72 201:0.93 208:0.64 212:0.15 215:0.38 216:0.77 222:0.48 227:0.61 229:0.61 231:0.71 235:0.88 241:0.12 242:0.77 243:0.50 245:0.60 246:0.91 247:0.35 253:0.39 254:0.74 259:0.21 265:0.41 266:0.63 267:0.28 271:0.37 276:0.37 287:0.61 290:0.57 297:0.36 300:0.55 +2 6:0.90 8:0.80 9:0.76 11:0.81 12:0.20 17:0.55 18:0.89 20:0.96 21:0.13 27:0.53 28:0.68 29:0.62 30:0.37 31:0.92 34:0.26 36:0.98 37:0.39 39:0.66 43:0.59 45:0.66 55:0.92 66:0.43 69:0.45 70:0.90 71:0.92 74:0.44 78:0.96 79:0.92 81:0.49 83:0.60 86:0.76 91:0.15 96:0.79 98:0.51 100:0.98 101:0.52 108:0.64 111:0.98 120:0.57 122:0.86 123:0.62 124:0.82 126:0.26 127:0.67 129:0.59 131:0.86 133:0.82 135:0.96 137:0.92 139:0.73 140:0.45 144:0.76 146:0.43 147:0.49 149:0.37 150:0.38 151:0.70 152:0.89 153:0.72 154:0.49 155:0.58 157:0.76 159:0.73 161:0.96 162:0.64 163:0.81 164:0.53 166:0.61 167:0.51 169:0.98 172:0.48 173:0.30 175:0.75 177:0.38 179:0.67 181:0.73 182:0.81 186:0.96 187:0.87 189:0.91 190:0.77 192:0.51 196:0.91 202:0.65 208:0.54 212:0.22 215:0.61 216:0.68 220:0.62 222:0.48 227:0.92 229:0.89 231:0.72 235:0.22 238:0.96 241:0.12 242:0.53 243:0.25 244:0.61 245:0.60 246:0.61 247:0.60 248:0.72 253:0.65 255:0.74 256:0.64 257:0.65 259:0.21 260:0.56 261:0.96 265:0.53 266:0.80 267:0.52 268:0.66 271:0.37 276:0.55 277:0.69 284:0.92 285:0.62 287:0.93 297:0.36 300:0.70 +2 8:0.89 11:0.81 12:0.20 17:0.57 18:0.69 20:0.88 27:0.41 28:0.68 29:0.62 30:0.41 34:0.26 36:0.99 37:0.39 39:0.66 43:0.21 45:0.66 55:0.59 66:0.49 69:0.97 70:0.54 71:0.92 74:0.30 78:0.89 81:0.69 86:0.62 91:0.38 98:0.26 100:0.92 101:0.52 108:0.94 111:0.89 120:0.81 122:0.86 123:0.94 124:0.66 126:0.26 127:0.92 129:0.05 131:0.61 133:0.62 135:0.89 139:0.60 140:0.45 144:0.76 146:0.61 147:0.44 149:0.19 150:0.50 151:0.66 152:0.83 153:0.89 154:0.13 157:0.76 159:0.88 161:0.96 162:0.59 164:0.71 166:0.61 167:0.52 169:0.90 172:0.45 173:0.97 175:0.75 177:0.05 179:0.79 181:0.73 182:0.72 186:0.89 187:0.68 190:0.89 202:0.73 212:0.30 215:0.96 216:0.63 222:0.48 227:0.61 229:0.61 231:0.63 235:0.05 241:0.21 242:0.63 243:0.22 244:0.61 245:0.60 246:0.61 247:0.60 248:0.66 253:0.26 256:0.68 257:0.84 259:0.21 260:0.74 261:0.88 265:0.29 266:0.75 267:0.52 268:0.73 271:0.37 276:0.30 277:0.69 283:0.82 285:0.47 287:0.61 297:0.36 300:0.43 +1 1:0.74 8:0.66 11:0.78 12:0.20 17:0.51 18:0.75 21:0.59 27:0.45 28:0.68 29:0.62 30:0.21 34:0.75 36:0.19 37:0.50 39:0.66 43:0.81 44:0.85 64:0.77 66:0.54 69:0.70 70:0.37 71:0.92 74:0.43 81:0.47 83:0.91 85:0.72 86:0.75 91:0.27 98:0.14 101:0.87 108:0.54 114:0.58 122:0.57 123:0.51 124:0.45 126:0.54 127:0.18 129:0.05 131:0.61 133:0.37 135:0.85 139:0.72 144:0.76 145:0.47 146:0.21 147:0.27 149:0.57 150:0.71 154:0.76 155:0.67 157:0.76 159:0.29 161:0.96 165:0.93 166:0.80 167:0.52 172:0.21 173:0.69 176:0.73 177:0.70 178:0.55 179:0.80 181:0.73 182:0.80 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.42 215:0.39 216:0.77 222:0.48 227:0.61 229:0.61 231:0.71 235:0.84 241:0.21 242:0.45 243:0.63 245:0.40 246:0.91 247:0.35 253:0.40 259:0.21 265:0.15 266:0.23 267:0.36 271:0.37 276:0.14 287:0.61 290:0.58 297:0.36 300:0.25 +2 6:0.97 7:0.66 8:0.83 11:0.81 12:0.20 17:0.32 18:0.83 20:0.96 21:0.47 27:0.21 28:0.68 29:0.62 30:0.10 34:0.49 36:0.82 37:0.74 39:0.66 43:0.62 45:0.87 55:0.84 66:0.29 69:0.19 70:0.99 71:0.92 74:0.46 78:0.90 81:0.28 86:0.78 91:0.51 98:0.38 100:0.97 101:0.52 106:0.81 108:0.96 111:0.95 120:0.79 123:0.96 124:0.96 126:0.26 127:0.91 129:0.81 131:0.61 133:0.96 135:0.78 139:0.74 140:0.80 144:0.76 146:0.57 147:0.63 149:0.58 150:0.27 151:0.63 152:0.91 153:0.72 154:0.15 157:0.81 159:0.95 161:0.96 162:0.49 163:0.81 164:0.77 166:0.61 167:0.57 169:0.96 172:0.79 173:0.06 177:0.70 178:0.42 179:0.26 181:0.73 182:0.74 186:0.90 187:0.39 189:0.97 190:0.89 202:0.86 206:0.81 212:0.18 215:0.41 216:0.95 220:0.74 222:0.48 227:0.61 229:0.61 230:0.69 231:0.68 233:0.73 235:0.60 238:0.97 241:0.41 242:0.30 243:0.22 245:0.81 246:0.61 247:0.91 248:0.65 253:0.34 254:0.98 256:0.78 259:0.21 260:0.72 261:0.95 265:0.40 266:0.99 267:0.52 268:0.79 271:0.37 276:0.87 277:0.69 283:0.82 285:0.47 287:0.61 297:0.36 300:0.98 +0 7:0.66 8:0.86 11:0.81 12:0.20 17:0.89 18:0.73 20:0.90 21:0.47 27:0.64 28:0.68 29:0.62 30:0.45 32:0.79 34:0.91 36:0.57 37:0.35 39:0.66 43:0.66 44:0.93 45:0.66 48:0.91 55:0.45 66:0.77 69:0.52 70:0.33 71:0.92 74:0.57 77:0.70 78:0.82 81:0.28 86:0.76 91:0.23 98:0.58 100:0.73 101:0.52 106:0.81 108:0.40 111:0.80 122:0.43 123:0.38 126:0.26 127:0.17 129:0.05 131:0.61 135:0.98 139:0.82 144:0.76 145:0.89 146:0.34 147:0.41 149:0.32 150:0.27 152:0.84 154:0.84 157:0.77 159:0.14 161:0.96 166:0.61 167:0.51 169:0.72 172:0.37 173:0.79 177:0.70 178:0.55 179:0.62 181:0.73 182:0.74 186:0.82 187:0.39 195:0.54 206:0.81 212:0.87 215:0.41 216:0.08 222:0.48 227:0.61 229:0.61 230:0.69 231:0.64 233:0.76 235:0.60 241:0.12 242:0.24 243:0.79 246:0.61 247:0.47 253:0.38 254:0.80 259:0.21 261:0.82 265:0.59 266:0.17 267:0.59 271:0.37 276:0.31 281:0.91 282:0.87 283:0.78 287:0.61 297:0.36 300:0.25 +1 6:0.93 8:0.86 12:0.06 17:0.16 20:0.93 21:0.78 27:0.03 28:0.68 30:0.21 34:0.17 36:0.78 37:0.45 43:0.44 55:0.96 66:0.27 69:0.45 70:0.67 78:0.90 91:0.97 98:0.23 100:0.91 108:0.80 111:0.89 120:0.75 123:0.79 124:0.87 127:0.83 129:0.95 133:0.87 135:0.75 140:0.98 146:0.05 147:0.05 149:0.34 151:0.69 152:0.87 153:0.69 154:0.33 159:0.84 161:0.61 162:0.46 163:0.81 164:0.83 167:0.81 169:0.89 172:0.21 173:0.22 177:0.05 178:0.54 179:0.89 181:0.76 186:0.89 189:0.94 191:0.94 202:0.95 212:0.16 216:0.52 220:0.91 235:0.12 238:0.91 241:0.82 242:0.82 243:0.18 245:0.43 247:0.12 248:0.68 256:0.82 259:0.21 260:0.76 261:0.91 265:0.25 266:0.54 267:0.95 268:0.79 271:0.97 276:0.36 285:0.62 300:0.43 +1 8:0.74 12:0.06 17:0.06 27:0.69 28:0.68 30:0.60 34:0.30 36:0.99 37:0.26 43:0.52 55:0.71 66:0.72 69:0.05 70:0.62 78:1.00 81:0.87 91:0.85 98:0.87 104:0.94 108:0.05 111:0.99 120:0.75 123:0.05 124:0.43 126:0.54 127:0.92 129:0.59 133:0.47 135:0.85 140:0.45 146:0.95 147:0.96 149:0.75 150:0.74 151:0.71 153:0.72 154:0.41 159:0.72 161:0.61 162:0.55 164:0.78 167:0.48 169:0.91 172:0.82 173:0.08 177:0.05 179:0.43 181:0.76 187:0.68 191:0.70 202:0.75 212:0.37 215:0.96 216:0.42 235:0.02 238:0.80 241:0.12 242:0.82 243:0.75 245:0.85 247:0.12 248:0.68 256:0.75 259:0.21 260:0.76 265:0.87 266:0.63 267:0.91 268:0.73 271:0.97 276:0.76 283:0.60 285:0.62 297:0.36 300:0.55 +1 6:0.79 7:0.81 8:0.60 12:0.06 17:0.24 20:0.80 21:0.13 27:0.08 28:0.68 30:0.38 32:0.80 34:0.76 36:0.76 37:0.24 43:0.52 55:0.71 66:0.36 69:0.07 70:0.50 78:0.92 81:0.84 91:0.92 98:0.49 100:0.87 104:0.58 108:0.06 111:0.89 120:0.85 123:0.06 124:0.70 126:0.54 127:0.95 129:0.81 133:0.67 135:0.32 140:0.45 145:0.88 146:0.49 147:0.55 149:0.53 150:0.64 151:0.74 152:0.78 153:0.82 154:0.41 159:0.47 161:0.61 162:0.69 164:0.95 167:0.84 169:0.85 172:0.32 173:0.18 177:0.05 179:0.86 181:0.76 186:0.91 189:0.81 191:0.90 195:0.68 202:0.93 212:0.14 215:0.84 216:0.53 220:0.74 230:0.87 233:0.76 235:0.12 238:0.84 241:0.92 242:0.82 243:0.51 245:0.55 247:0.12 248:0.77 256:0.93 259:0.21 260:0.85 261:0.84 265:0.51 266:0.71 267:0.59 268:0.94 271:0.97 276:0.40 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.82 7:0.81 8:0.61 12:0.06 17:0.04 20:0.95 21:0.30 27:0.21 28:0.68 30:0.11 34:0.74 36:0.85 37:0.74 43:0.52 55:0.71 66:0.32 69:0.10 70:0.88 78:0.89 81:0.80 91:0.94 98:0.47 100:0.90 104:0.84 106:0.81 108:0.94 111:0.88 120:0.96 123:0.93 124:0.93 126:0.54 127:0.77 129:0.98 133:0.94 135:0.18 140:0.45 146:0.72 147:0.76 149:0.73 150:0.59 151:0.85 152:0.92 153:0.98 154:0.41 159:0.91 161:0.61 162:0.58 164:0.96 167:0.63 169:0.87 172:0.82 173:0.07 177:0.05 179:0.24 181:0.76 186:0.89 187:0.39 189:0.84 191:0.94 202:0.94 206:0.81 212:0.40 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.87 241:0.62 242:0.82 243:0.19 245:0.86 247:0.12 248:0.95 256:0.94 259:0.21 260:0.97 261:0.91 265:0.48 266:0.95 267:0.94 268:0.95 271:0.97 276:0.88 283:0.82 285:0.62 297:0.36 300:0.84 +1 6:0.82 8:0.62 12:0.06 17:0.03 20:0.88 21:0.47 27:0.05 28:0.68 30:0.89 32:0.80 34:0.58 36:0.45 37:0.28 43:0.52 55:0.84 66:0.83 69:0.17 70:0.20 78:0.93 81:0.86 91:0.97 98:0.93 100:0.93 104:0.58 108:0.14 111:0.92 120:0.90 123:0.13 126:0.54 127:0.59 129:0.05 135:0.49 140:0.45 145:0.74 146:0.81 147:0.84 149:0.52 150:0.71 151:0.66 152:0.83 153:0.48 154:0.41 159:0.37 161:0.61 162:0.72 163:0.86 164:0.96 167:0.85 169:0.91 172:0.51 173:0.32 177:0.05 179:0.82 181:0.76 186:0.93 189:0.84 191:0.94 195:0.59 202:0.93 212:0.28 215:0.63 216:0.51 220:0.62 235:0.60 238:0.88 241:0.96 242:0.82 243:0.84 247:0.12 248:0.86 256:0.95 259:0.21 260:0.90 261:0.90 265:0.93 266:0.47 267:0.87 268:0.95 271:0.97 276:0.43 283:0.60 285:0.62 297:0.36 300:0.18 +1 8:0.58 12:0.06 17:0.16 21:0.13 27:0.45 28:0.68 30:0.28 32:0.80 34:0.80 36:0.17 37:0.50 43:0.32 55:0.59 66:0.72 69:0.68 70:0.75 81:0.84 91:0.54 98:0.61 108:0.51 123:0.49 124:0.41 126:0.54 127:0.31 129:0.59 133:0.47 135:0.84 145:0.47 146:0.85 147:0.87 149:0.56 150:0.64 154:0.24 159:0.63 161:0.61 163:0.75 167:0.50 172:0.59 173:0.54 177:0.05 178:0.55 179:0.58 181:0.76 187:0.68 195:0.88 212:0.27 215:0.84 216:0.77 235:0.12 241:0.27 242:0.82 243:0.75 245:0.61 247:0.12 259:0.21 265:0.62 266:0.63 267:0.73 271:0.97 276:0.50 297:0.36 300:0.55 +1 6:0.84 8:0.68 12:0.06 17:0.24 20:0.78 21:0.30 27:0.04 28:0.68 30:0.55 34:0.56 36:0.47 37:0.40 43:0.33 55:0.92 66:0.19 69:0.67 70:0.36 78:0.94 81:0.93 91:0.82 98:0.44 100:0.96 104:0.83 106:0.81 108:0.73 111:0.93 120:0.90 123:0.71 124:0.82 126:0.54 127:0.42 129:0.89 133:0.78 135:0.85 140:0.45 146:0.57 147:0.62 149:0.51 150:0.87 151:0.80 152:0.88 153:0.93 154:0.25 159:0.61 161:0.61 162:0.77 163:0.94 164:0.92 167:0.66 169:0.94 172:0.27 173:0.59 177:0.05 179:0.67 181:0.76 186:0.95 187:0.39 189:0.86 191:0.93 202:0.86 206:0.81 212:0.40 215:0.72 216:0.67 235:0.52 238:0.89 241:0.66 242:0.82 243:0.34 245:0.61 247:0.12 248:0.86 256:0.88 259:0.21 260:0.91 261:0.94 265:0.46 266:0.47 267:0.23 268:0.89 271:0.97 276:0.51 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.81 7:0.81 8:0.60 12:0.06 17:0.03 20:0.95 21:0.78 27:0.06 28:0.68 30:0.11 34:0.52 36:0.95 37:0.29 43:0.46 66:0.75 69:0.42 70:0.54 78:0.89 91:0.95 98:0.78 100:0.88 104:0.58 108:0.32 111:0.87 120:0.84 123:0.31 127:0.27 129:0.05 135:0.44 140:0.95 145:0.50 146:0.46 147:0.52 149:0.42 151:0.71 152:0.88 153:0.77 154:0.35 159:0.17 161:0.61 162:0.47 164:0.86 167:0.82 169:0.85 172:0.32 173:0.71 177:0.05 178:0.53 179:0.87 181:0.76 186:0.89 189:0.83 191:0.92 202:0.95 212:0.61 216:0.56 220:0.62 230:0.87 233:0.76 235:0.22 238:0.85 241:0.84 242:0.82 243:0.77 247:0.12 248:0.76 256:0.86 259:0.21 260:0.85 261:0.90 265:0.78 266:0.23 267:0.95 268:0.83 271:0.97 276:0.26 285:0.62 300:0.33 +1 6:0.84 8:0.66 12:0.06 17:0.32 20:0.94 21:0.30 27:0.04 28:0.68 30:0.41 34:0.58 36:0.42 37:0.40 43:0.33 55:0.84 66:0.20 69:0.67 70:0.54 78:0.94 81:0.93 91:0.80 98:0.44 100:0.96 104:0.83 106:0.81 108:0.51 111:0.94 120:0.90 123:0.72 124:0.80 126:0.54 127:0.41 129:0.89 133:0.78 135:0.84 140:0.45 146:0.61 147:0.66 149:0.51 150:0.87 151:0.80 152:0.88 153:0.93 154:0.25 159:0.60 161:0.61 162:0.79 163:0.94 164:0.93 167:0.63 169:0.94 172:0.27 173:0.59 177:0.05 179:0.69 181:0.76 186:0.94 187:0.39 189:0.86 191:0.92 202:0.87 206:0.81 212:0.30 215:0.72 216:0.67 235:0.52 238:0.92 241:0.63 242:0.82 243:0.40 245:0.58 247:0.12 248:0.86 256:0.90 259:0.21 260:0.91 261:0.94 265:0.43 266:0.63 267:0.23 268:0.91 271:0.97 276:0.49 283:0.82 285:0.62 297:0.36 300:0.43 +1 7:0.81 8:0.66 12:0.06 17:0.26 21:0.78 27:0.06 28:0.68 30:0.21 34:0.88 36:0.92 37:0.85 43:0.27 55:0.71 66:0.35 69:0.80 70:0.87 78:0.98 91:0.79 98:0.51 104:0.79 106:0.81 108:0.90 111:0.96 120:0.61 123:0.90 124:0.87 127:0.55 129:0.96 133:0.90 135:0.92 140:0.90 145:0.47 146:0.05 147:0.05 149:0.67 151:0.56 153:0.71 154:0.20 159:0.81 161:0.61 162:0.47 164:0.63 167:0.71 169:0.84 172:0.71 173:0.62 177:0.05 178:0.50 179:0.35 181:0.76 187:0.68 191:0.86 202:0.73 206:0.81 212:0.50 216:0.87 220:0.62 230:0.87 233:0.76 235:0.88 241:0.71 242:0.82 243:0.07 245:0.78 247:0.12 248:0.55 256:0.45 259:0.21 260:0.62 265:0.53 266:0.87 267:0.89 268:0.57 271:0.97 276:0.78 285:0.62 300:0.70 +2 6:0.82 8:0.72 12:0.06 17:0.20 20:0.80 21:0.78 27:0.21 28:0.68 30:0.10 34:0.71 36:0.88 37:0.74 43:0.39 55:0.59 66:0.12 69:0.57 70:0.98 78:0.90 91:0.90 98:0.40 100:0.88 104:0.84 108:0.96 111:0.88 120:0.92 123:0.96 124:0.95 127:0.74 129:0.99 133:0.95 135:0.28 140:0.80 146:0.05 147:0.05 149:0.69 151:0.82 152:0.92 153:0.95 154:0.29 159:0.93 161:0.61 162:0.48 164:0.89 167:0.53 169:0.88 172:0.65 173:0.19 177:0.05 178:0.42 179:0.22 181:0.76 186:0.89 187:0.39 189:0.88 191:0.92 202:0.94 212:0.29 216:0.95 235:0.42 238:0.86 241:0.41 242:0.82 243:0.06 245:0.87 247:0.12 248:0.89 256:0.87 259:0.21 260:0.93 261:0.91 265:0.42 266:0.97 267:0.59 268:0.86 271:0.97 276:0.89 283:0.82 285:0.62 300:0.94 +1 8:0.59 12:0.06 17:0.16 21:0.30 27:0.45 28:0.68 30:0.76 34:0.80 36:0.19 37:0.50 43:0.32 55:0.71 66:0.75 69:0.68 70:0.57 81:0.80 91:0.42 98:0.57 108:0.52 123:0.50 124:0.41 126:0.54 127:0.36 129:0.81 133:0.67 135:0.87 145:0.50 146:0.81 147:0.84 149:0.60 150:0.59 154:0.24 159:0.64 161:0.61 167:0.51 172:0.65 173:0.56 177:0.05 178:0.55 179:0.55 181:0.76 187:0.68 212:0.77 215:0.72 216:0.77 235:0.31 241:0.32 242:0.82 243:0.77 245:0.55 247:0.12 259:0.21 265:0.58 266:0.54 267:0.44 271:0.97 276:0.54 283:0.60 297:0.36 300:0.70 +0 12:0.01 17:0.73 21:0.78 27:0.72 37:0.77 39:0.27 91:0.27 104:1.00 106:0.81 117:0.86 145:0.56 175:0.91 178:0.55 187:0.95 206:0.81 216:0.52 222:0.35 232:1.00 235:0.84 241:0.41 244:0.93 253:0.20 257:0.84 271:0.14 277:0.87 +0 8:0.82 12:0.01 17:0.85 21:0.78 27:0.31 30:0.37 37:0.77 39:0.27 43:0.15 55:0.92 66:0.24 69:0.74 70:0.46 74:0.71 91:0.38 98:0.48 108:0.57 122:0.67 123:0.55 124:0.93 127:0.49 129:0.59 133:0.92 145:0.74 146:0.85 147:0.05 149:0.32 154:0.15 158:0.74 159:0.83 163:0.94 172:0.57 173:0.51 177:0.05 178:0.55 179:0.39 187:0.87 191:0.76 202:0.36 212:0.27 216:0.43 222:0.35 232:0.71 235:0.80 241:0.18 242:0.79 243:0.08 244:0.61 245:0.72 247:0.39 253:0.42 254:0.96 257:0.65 259:0.21 265:0.49 266:0.80 271:0.14 276:0.74 300:0.33 +0 7:0.66 12:0.01 17:0.90 21:0.22 27:0.64 30:0.93 32:0.64 34:0.29 36:0.29 39:0.27 43:0.14 55:0.59 66:0.88 69:0.56 70:0.19 74:0.35 81:0.56 91:0.57 98:0.70 104:0.80 106:0.81 108:0.43 123:0.42 126:0.32 127:0.21 129:0.05 135:0.56 145:0.63 146:0.78 147:0.82 149:0.26 150:0.42 154:0.40 159:0.34 172:0.65 173:0.53 177:0.38 178:0.55 179:0.41 187:0.39 195:0.77 206:0.81 212:0.73 215:0.51 216:0.30 222:0.35 230:0.69 233:0.76 235:0.22 241:0.49 242:0.78 243:0.88 244:0.61 247:0.39 253:0.29 254:0.94 259:0.21 265:0.70 266:0.47 267:0.52 271:0.14 276:0.55 283:0.82 297:0.36 300:0.18 +0 7:0.66 8:0.93 12:0.01 17:0.78 21:0.78 27:0.18 34:0.53 36:0.43 37:0.84 39:0.27 74:0.34 77:0.89 91:0.64 135:0.54 145:0.90 175:0.91 178:0.55 187:0.21 191:0.73 195:0.57 202:0.64 216:0.35 222:0.35 230:0.69 233:0.73 235:0.12 241:0.78 244:0.93 253:0.28 257:0.84 259:0.21 267:0.28 271:0.14 277:0.87 282:0.73 +1 12:0.01 17:0.32 21:0.78 27:0.72 37:0.25 39:0.27 74:0.34 91:0.89 96:0.93 120:0.68 140:0.45 151:0.60 153:0.95 158:0.74 162:0.57 164:0.62 175:0.91 190:0.89 202:0.88 216:0.36 220:0.62 222:0.35 232:0.74 235:0.74 241:0.88 244:0.93 248:0.74 253:0.84 256:0.87 257:0.84 260:0.65 268:0.88 271:0.14 277:0.87 285:0.62 +0 12:0.01 17:0.34 21:0.78 27:0.72 30:0.95 34:0.36 36:0.90 39:0.27 43:0.13 55:0.92 66:0.54 69:0.67 74:0.33 91:0.06 98:0.13 108:0.51 123:0.49 127:0.08 129:0.05 135:0.54 146:0.43 147:0.50 149:0.09 154:0.10 159:0.16 172:0.10 173:0.16 177:0.38 178:0.55 179:0.17 187:0.39 216:0.11 222:0.35 232:0.79 235:0.88 241:0.07 243:0.63 244:0.83 253:0.28 259:0.21 265:0.14 267:0.23 271:0.14 300:0.08 +0 7:0.66 8:0.68 12:0.01 17:0.97 21:0.78 27:0.70 30:0.45 34:0.39 36:0.75 37:0.39 39:0.27 43:0.13 55:0.59 66:0.64 69:0.68 70:0.50 74:0.51 91:0.29 98:0.43 108:0.52 122:0.67 123:0.49 124:0.42 127:0.20 129:0.59 133:0.47 135:0.58 145:0.77 146:0.52 147:0.58 149:0.18 154:0.10 159:0.31 172:0.32 173:0.68 175:0.75 177:0.05 178:0.55 179:0.73 187:0.21 212:0.52 216:0.15 222:0.35 230:0.69 232:0.92 233:0.76 235:0.80 241:0.01 242:0.82 243:0.69 244:0.83 245:0.43 247:0.12 253:0.35 257:0.65 259:0.21 265:0.45 266:0.27 267:0.52 271:0.14 276:0.28 277:0.69 300:0.33 +1 12:0.01 17:0.92 21:0.78 27:0.68 34:0.45 36:0.34 39:0.27 91:0.04 104:0.99 106:0.81 117:0.86 135:0.39 145:0.70 175:0.91 178:0.55 187:0.39 206:0.81 216:0.31 222:0.35 232:0.96 235:0.68 241:0.39 244:0.93 253:0.20 257:0.84 259:0.21 267:0.52 271:0.14 277:0.87 +0 7:0.66 12:0.01 17:0.36 21:0.22 27:0.44 34:0.62 36:0.50 37:0.35 39:0.27 74:0.34 77:0.70 81:0.53 91:0.57 96:0.71 114:0.66 126:0.32 135:0.28 140:0.45 145:0.67 150:0.40 151:0.49 153:0.48 162:0.62 175:0.91 176:0.61 187:0.39 190:0.89 195:0.57 202:0.50 216:0.85 220:0.82 222:0.35 230:0.69 233:0.73 235:0.22 241:0.63 244:0.93 248:0.49 253:0.50 256:0.45 257:0.84 259:0.21 267:0.97 268:0.46 271:0.14 277:0.87 282:0.73 285:0.50 290:0.66 +0 12:0.01 17:0.18 21:0.68 27:0.48 30:0.42 34:0.31 36:0.21 37:0.55 39:0.27 43:0.14 55:0.92 66:0.45 69:0.59 70:0.75 74:0.63 77:0.96 81:0.24 91:0.23 98:0.47 108:0.58 114:0.55 120:0.63 122:0.67 123:0.55 124:0.75 126:0.32 127:0.40 129:0.89 133:0.78 135:0.68 140:0.45 145:0.77 146:0.05 147:0.05 149:0.25 150:0.24 151:0.55 153:0.48 154:0.10 159:0.72 162:0.86 163:0.81 164:0.53 172:0.48 173:0.40 175:0.75 176:0.61 177:0.05 179:0.62 187:0.39 190:0.89 195:0.90 202:0.36 212:0.23 216:0.92 220:0.62 222:0.35 235:0.80 241:0.55 242:0.82 243:0.12 244:0.83 245:0.60 247:0.12 248:0.54 253:0.41 256:0.45 257:0.65 259:0.21 260:0.61 265:0.48 266:0.75 267:0.69 268:0.46 271:0.14 276:0.53 277:0.69 282:0.73 285:0.50 290:0.55 300:0.55 +0 12:0.01 17:0.77 21:0.78 27:0.68 39:0.27 91:0.05 104:0.99 106:0.81 117:0.86 145:0.67 175:0.91 178:0.55 187:0.39 202:0.36 206:0.81 216:0.31 222:0.35 232:0.96 235:0.52 241:0.46 244:0.93 253:0.20 257:0.84 259:0.21 271:0.14 277:0.87 +1 12:0.01 17:0.85 21:0.78 27:0.68 39:0.27 91:0.05 104:0.99 106:0.81 117:0.86 145:0.67 175:0.91 178:0.55 187:0.39 206:0.81 216:0.31 222:0.35 232:0.96 235:0.52 241:0.35 244:0.93 253:0.20 257:0.84 259:0.21 271:0.14 277:0.87 +0 8:0.60 12:0.20 17:0.81 18:0.56 21:0.78 26:0.93 27:0.50 29:0.77 30:0.56 34:0.29 36:0.56 37:0.42 39:0.21 43:0.06 55:0.99 58:0.93 66:0.05 69:0.98 70:0.58 71:0.89 74:0.25 86:0.57 89:0.95 98:0.05 108:0.96 123:0.96 124:0.96 127:0.47 129:0.99 133:0.95 135:0.83 139:0.56 141:0.91 146:0.05 147:0.05 149:0.06 154:0.06 159:1.00 163:0.97 170:0.77 172:0.05 173:0.55 175:0.97 177:0.05 178:0.55 179:0.77 187:0.39 199:0.94 202:0.36 212:0.13 216:0.92 222:0.48 223:0.91 224:0.93 225:0.95 234:0.91 240:0.95 241:0.03 242:0.73 243:0.15 244:0.93 245:0.43 247:0.47 253:0.23 257:0.93 259:0.21 265:0.05 266:0.75 267:0.19 271:0.50 274:0.91 276:0.43 277:0.95 300:0.43 +0 8:0.80 10:0.83 12:0.20 17:0.83 18:0.57 21:0.78 26:0.94 27:0.69 29:0.77 30:0.45 34:0.45 36:0.30 37:0.63 39:0.21 43:0.07 55:0.99 58:0.93 66:0.07 69:0.95 70:0.61 71:0.89 74:0.25 86:0.58 89:0.96 91:0.01 98:0.12 108:0.90 123:0.90 124:0.94 127:0.60 129:0.05 133:0.94 135:0.78 139:0.56 141:0.91 145:0.52 146:0.43 147:0.50 149:0.08 154:0.06 159:0.98 163:0.99 170:0.77 172:0.10 173:0.56 174:0.90 175:0.91 177:0.38 178:0.55 179:0.77 187:0.21 197:0.81 199:0.94 212:0.22 216:0.32 222:0.48 223:0.91 224:0.93 225:0.96 234:0.91 235:0.42 239:0.82 240:0.95 241:0.01 242:0.22 243:0.23 244:0.93 245:0.47 247:0.76 253:0.23 257:0.84 259:0.21 265:0.12 266:0.75 267:0.28 271:0.50 274:0.91 276:0.46 277:0.87 300:0.43 +0 10:0.91 11:0.46 12:0.20 17:1.00 18:0.62 21:0.78 26:0.94 27:0.72 29:0.77 30:0.36 34:0.88 36:0.54 39:0.21 43:0.19 45:0.83 55:0.98 58:0.93 66:0.05 69:0.98 70:0.88 71:0.89 74:0.25 86:0.65 89:0.98 98:0.11 101:0.47 108:0.98 122:0.95 123:0.98 124:1.00 127:0.94 129:0.95 133:1.00 135:0.28 139:0.63 141:0.91 146:0.13 147:0.05 149:0.23 154:0.09 159:1.00 163:0.99 170:0.77 172:0.16 173:0.60 174:0.99 175:0.91 177:0.05 178:0.55 179:0.29 187:0.21 197:0.93 199:0.97 212:0.18 222:0.48 223:0.92 224:0.93 225:0.97 234:0.91 235:0.02 239:0.90 240:0.95 242:0.44 243:0.06 244:0.83 245:0.64 247:0.86 253:0.22 257:0.93 265:0.11 266:0.99 267:0.98 271:0.50 274:0.91 276:0.85 277:0.95 300:0.84 +0 10:0.83 12:0.20 17:0.84 18:0.61 21:0.78 26:0.94 27:0.58 29:0.77 30:0.76 34:0.61 36:0.41 37:0.32 39:0.21 43:0.31 55:0.92 58:0.93 66:0.18 69:0.88 70:0.37 71:0.89 74:0.26 86:0.63 89:0.96 91:0.10 98:0.29 108:0.81 123:0.89 124:0.73 127:0.22 129:0.59 133:0.64 135:0.58 139:0.61 141:0.91 145:0.69 146:0.13 147:0.18 149:0.25 154:0.23 159:0.63 163:0.81 170:0.77 172:0.21 173:0.77 174:0.79 175:0.91 177:0.38 178:0.55 179:0.68 187:0.21 197:0.81 199:0.94 202:0.36 212:0.37 216:0.69 222:0.48 223:0.91 224:0.93 225:0.95 234:0.91 235:0.31 239:0.82 240:0.95 242:0.40 243:0.29 244:0.83 245:0.51 247:0.55 253:0.24 257:0.84 259:0.21 265:0.23 266:0.47 267:0.98 271:0.50 274:0.91 276:0.32 277:0.87 300:0.33 +1 1:0.63 7:0.70 9:0.79 12:0.51 17:0.66 18:0.91 21:0.22 22:0.93 26:0.98 29:0.99 30:0.58 31:0.94 34:0.80 36:0.92 37:0.98 39:0.79 43:0.14 44:0.88 47:0.93 48:0.97 55:0.45 58:0.98 64:0.77 66:0.33 69:0.80 70:0.48 71:0.59 74:0.42 76:0.85 77:0.70 79:0.93 81:0.60 83:0.60 86:0.90 91:0.10 96:0.80 98:0.31 99:0.83 108:0.79 114:0.77 117:0.86 120:0.69 122:0.86 123:0.78 124:0.76 126:0.44 127:0.45 129:0.05 131:0.85 133:0.73 135:0.32 137:0.94 139:0.88 140:0.45 141:0.99 144:0.57 145:0.59 146:0.40 147:0.30 149:0.26 150:0.49 151:0.86 153:0.48 154:0.34 155:0.66 157:0.61 159:0.52 162:0.86 164:0.56 165:0.70 166:0.73 172:0.54 173:0.81 176:0.59 177:0.05 179:0.49 182:0.82 187:0.68 190:0.77 192:0.87 195:0.74 196:0.95 199:0.98 201:0.59 202:0.36 204:0.81 208:0.64 212:0.81 216:0.95 222:0.25 223:0.97 224:1.00 227:0.85 229:0.90 230:0.73 231:0.69 232:0.72 233:0.76 234:1.00 235:0.42 241:0.66 242:0.59 243:0.15 244:0.61 245:0.72 246:0.61 247:0.74 248:0.77 253:0.78 254:0.99 255:0.78 256:0.45 257:0.84 259:0.21 260:0.70 262:0.93 265:0.33 266:0.63 267:0.44 268:0.46 271:0.79 274:0.97 276:0.61 277:0.69 281:0.91 282:0.72 284:0.88 285:0.62 287:0.88 290:0.77 300:0.43 +2 1:0.69 11:0.82 12:0.51 17:0.85 18:0.89 21:0.30 22:0.93 26:0.97 27:0.54 29:0.99 30:0.45 34:0.85 36:0.30 37:0.73 39:0.79 43:0.89 44:0.92 45:0.66 47:0.93 48:0.91 55:0.52 58:0.93 64:0.77 66:0.61 69:0.50 70:0.80 71:0.59 74:0.59 76:0.97 77:0.89 81:0.79 83:0.60 85:0.80 86:0.95 91:0.20 97:0.89 98:0.60 99:0.95 101:0.97 108:0.67 114:0.86 117:0.86 122:0.82 123:0.65 124:0.50 126:0.54 127:0.57 129:0.59 131:0.61 133:0.46 135:0.37 139:0.95 141:0.91 144:0.57 145:0.79 146:0.24 147:0.30 149:0.77 150:0.66 154:0.88 155:0.57 157:0.81 158:0.73 159:0.37 165:0.93 166:0.78 172:0.59 173:0.60 176:0.73 177:0.70 178:0.55 179:0.63 182:0.81 187:0.87 192:0.54 195:0.61 199:0.98 201:0.67 204:0.83 208:0.64 212:0.81 215:0.72 216:0.67 219:0.94 222:0.25 223:0.97 224:0.93 227:0.61 229:0.61 231:0.68 232:0.99 234:0.91 235:0.74 241:0.39 242:0.21 243:0.34 245:0.74 246:0.86 247:0.74 253:0.63 259:0.21 262:0.93 265:0.61 266:0.47 267:0.28 271:0.79 274:0.91 276:0.47 279:0.77 281:0.86 282:0.73 287:0.61 290:0.86 292:0.88 297:0.36 300:0.70 +1 1:0.63 7:0.70 9:0.73 12:0.51 17:0.64 18:0.94 21:0.22 22:0.93 26:0.98 29:0.99 30:0.53 31:0.92 34:0.80 36:0.80 37:0.98 39:0.79 43:0.14 44:0.88 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.64 69:0.78 70:0.57 71:0.59 74:0.41 76:0.85 77:0.70 79:0.91 81:0.60 83:0.60 86:0.91 91:0.09 96:0.77 98:0.74 99:0.83 108:0.72 114:0.77 117:0.86 122:0.86 123:0.70 124:0.52 126:0.44 127:0.45 129:0.59 131:0.81 133:0.64 135:0.54 137:0.92 139:0.90 140:0.45 141:0.99 144:0.57 145:0.59 146:0.33 147:0.30 149:0.30 150:0.49 151:0.76 153:0.48 154:0.21 155:0.64 157:0.61 159:0.54 162:0.86 165:0.70 166:0.73 172:0.74 173:0.77 175:0.75 176:0.59 177:0.05 179:0.43 182:0.61 187:0.68 190:0.77 192:0.56 195:0.76 196:0.94 199:0.99 201:0.59 202:0.36 204:0.81 208:0.64 212:0.71 216:0.95 220:0.62 222:0.25 223:0.97 224:1.00 227:0.83 229:0.61 230:0.73 231:0.64 232:0.72 233:0.76 234:0.99 235:0.42 241:0.64 242:0.72 243:0.13 244:0.83 245:0.76 246:0.61 247:0.74 248:0.70 253:0.70 254:0.90 255:0.72 256:0.45 257:0.84 259:0.21 262:0.93 265:0.74 266:0.54 267:0.79 268:0.46 271:0.79 274:0.97 276:0.69 277:0.69 281:0.91 282:0.72 284:0.88 285:0.56 287:0.61 290:0.77 300:0.43 +0 12:0.51 17:0.36 18:0.74 21:0.78 26:0.97 27:0.45 29:0.99 30:0.33 34:0.70 36:0.20 37:0.50 39:0.79 43:0.13 55:0.84 58:0.93 64:0.77 66:0.12 69:0.88 70:0.69 71:0.59 74:0.26 81:0.25 86:0.71 91:0.04 98:0.16 108:0.76 122:0.41 123:0.52 124:0.81 126:0.26 127:0.41 129:0.05 131:0.61 133:0.74 135:0.85 139:0.68 141:0.91 145:0.50 146:0.26 147:0.40 149:0.12 150:0.27 154:0.18 157:0.61 159:0.80 163:0.80 166:0.61 172:0.16 173:0.74 175:0.75 177:0.05 178:0.55 179:0.86 182:0.61 187:0.68 199:0.95 212:0.19 216:0.77 222:0.25 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.99 241:0.49 242:0.19 243:0.32 244:0.83 245:0.46 246:0.61 247:0.55 253:0.24 254:0.92 257:0.84 259:0.21 265:0.14 266:0.47 267:0.73 271:0.79 274:0.91 276:0.29 277:0.87 287:0.61 300:0.43 +1 1:0.63 7:0.70 9:0.71 12:0.51 17:0.69 18:0.90 21:0.22 22:0.93 26:0.98 29:0.99 30:0.53 31:0.88 34:0.80 36:0.82 37:0.98 39:0.79 41:0.82 43:0.14 44:0.88 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.43 69:0.78 70:0.65 71:0.59 74:0.41 76:0.85 77:0.70 79:0.88 81:0.60 83:0.60 86:0.88 91:0.06 96:0.71 98:0.36 99:0.83 108:0.82 114:0.77 117:0.86 120:0.58 122:0.86 123:0.81 124:0.77 126:0.44 127:0.46 129:0.59 131:0.85 133:0.76 135:0.49 137:0.88 139:0.86 140:0.45 141:0.99 144:0.57 145:0.59 146:0.33 147:0.30 149:0.24 150:0.49 151:0.58 153:0.48 154:0.21 155:0.64 157:0.61 159:0.54 162:0.86 164:0.57 165:0.70 166:0.73 172:0.57 173:0.77 175:0.75 176:0.59 177:0.05 179:0.51 182:0.82 187:0.68 192:0.59 195:0.77 196:0.91 199:0.98 201:0.59 202:0.36 204:0.81 208:0.64 212:0.73 216:0.95 220:0.87 222:0.25 223:0.97 224:0.99 227:0.85 229:0.90 230:0.73 231:0.69 232:0.72 233:0.76 234:0.98 235:0.42 241:0.67 242:0.61 243:0.15 244:0.83 245:0.72 246:0.61 247:0.74 248:0.56 253:0.59 254:0.90 255:0.71 256:0.45 257:0.84 259:0.21 260:0.58 262:0.93 265:0.38 266:0.54 267:0.76 268:0.46 271:0.79 274:0.97 276:0.60 277:0.69 281:0.91 282:0.72 284:0.89 285:0.62 287:0.88 290:0.77 300:0.55 +0 12:0.51 17:0.83 18:0.93 21:0.68 26:0.97 27:0.52 29:0.99 30:0.89 34:0.55 36:0.26 37:0.43 39:0.79 43:0.14 44:0.88 48:1.00 55:0.59 58:0.93 64:0.77 66:0.60 69:0.67 70:0.33 71:0.59 74:0.30 77:0.70 81:0.35 86:0.90 91:0.14 98:0.54 108:0.51 122:0.86 123:0.49 124:0.50 126:0.26 127:0.21 129:0.59 131:0.61 133:0.47 135:0.70 139:0.90 141:0.91 145:0.82 146:0.71 147:0.76 149:0.26 150:0.39 154:0.17 157:0.61 159:0.40 166:0.61 172:0.57 173:0.60 175:0.75 177:0.38 178:0.55 179:0.39 182:0.61 187:0.39 195:0.81 199:0.99 212:0.80 216:0.79 219:0.90 222:0.25 223:0.97 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.88 241:0.07 242:0.76 243:0.67 244:0.83 245:0.73 246:0.61 247:0.55 253:0.27 254:0.97 257:0.65 259:0.21 265:0.55 266:0.47 267:0.17 271:0.79 274:0.91 276:0.54 277:0.69 281:0.86 282:0.72 287:0.61 292:0.88 300:0.43 +2 1:0.69 6:0.94 8:0.81 9:0.73 11:0.64 12:0.51 17:0.73 18:0.98 20:0.99 21:0.54 22:0.93 26:0.98 27:0.63 29:0.99 30:0.33 31:0.93 32:0.66 34:0.83 36:0.80 37:0.70 39:0.79 43:0.65 44:0.92 45:0.83 47:0.93 48:0.91 58:0.99 64:0.77 66:0.97 69:0.27 70:0.72 71:0.59 74:0.65 77:0.96 78:0.97 79:0.92 81:0.67 83:0.91 86:0.98 91:0.61 96:0.87 98:0.93 99:0.83 100:0.91 101:0.87 108:0.21 111:0.95 114:0.67 120:0.57 122:0.80 123:0.20 126:0.54 127:0.57 129:0.05 131:0.85 135:0.54 137:0.93 138:0.96 139:0.98 140:0.80 141:0.99 144:0.57 145:0.59 146:0.86 147:0.89 149:0.83 150:0.58 151:0.61 152:0.95 153:0.48 154:0.66 155:0.65 157:0.81 158:0.72 159:0.43 162:0.82 164:0.55 165:0.70 166:0.78 169:0.87 172:0.93 173:0.33 176:0.62 177:0.70 179:0.24 182:0.81 186:0.97 187:0.39 190:0.95 191:0.83 192:0.86 195:0.66 196:0.97 199:0.99 201:0.66 202:0.71 208:0.64 212:0.87 215:0.53 216:0.64 219:0.90 220:0.74 222:0.25 223:0.98 224:0.99 227:0.85 229:0.61 231:0.69 232:0.83 234:0.99 235:0.88 241:0.12 242:0.22 243:0.97 244:0.61 246:0.86 247:0.79 248:0.60 253:0.85 255:0.72 256:0.78 260:0.57 261:0.93 262:0.93 265:0.93 266:0.47 267:0.36 268:0.77 271:0.79 274:0.99 276:0.86 279:0.77 281:0.91 282:0.75 284:0.95 285:0.62 287:0.61 290:0.67 292:0.95 297:0.36 300:0.55 +2 7:0.70 12:0.51 17:0.70 18:0.84 21:0.78 26:0.97 27:0.59 29:0.99 30:0.66 34:0.92 36:0.41 37:1.00 39:0.79 43:0.31 55:0.59 58:0.93 66:0.97 69:0.58 70:0.57 71:0.59 74:0.38 76:0.94 83:0.60 86:0.82 91:0.18 98:0.98 108:0.44 117:0.86 123:0.43 127:0.80 129:0.05 131:0.61 135:0.39 139:0.78 141:0.91 144:0.57 145:0.83 146:0.96 147:0.97 149:0.50 154:0.47 155:0.51 157:0.61 159:0.67 166:0.61 172:0.93 173:0.49 177:0.70 178:0.55 179:0.26 182:0.61 187:0.68 192:0.48 199:0.96 204:0.81 208:0.54 212:0.77 216:0.48 222:0.25 223:0.96 224:0.93 227:0.61 229:0.61 230:0.73 231:0.65 232:0.78 233:0.76 234:0.91 235:0.80 241:0.10 242:0.75 243:0.97 244:0.83 246:0.61 247:0.76 253:0.33 265:0.98 266:0.63 267:0.23 271:0.79 274:0.91 276:0.87 287:0.61 300:0.70 +1 1:0.69 7:0.81 8:0.73 9:0.74 11:0.82 12:0.51 17:0.95 18:0.98 21:0.13 26:0.98 27:0.53 29:0.99 30:0.15 31:0.92 32:0.80 34:0.90 36:0.98 37:0.39 39:0.79 43:0.85 44:0.93 45:0.87 48:1.00 55:0.59 58:0.98 64:0.77 66:0.96 69:0.17 70:0.82 71:0.59 74:0.77 76:0.85 77:0.89 79:0.91 81:0.82 83:0.91 85:0.80 86:0.99 91:0.35 96:0.77 97:0.89 98:0.96 99:0.95 101:0.97 108:0.14 114:0.92 121:0.90 122:0.86 123:0.13 126:0.54 127:0.72 129:0.05 131:0.81 135:0.26 137:0.92 139:0.99 140:0.45 141:0.99 144:0.57 145:0.72 146:0.86 147:0.88 149:0.92 150:0.78 151:0.76 153:0.48 154:0.82 155:0.65 157:0.81 158:0.73 159:0.43 162:0.86 165:0.93 166:0.79 172:0.90 173:0.29 176:0.73 177:0.70 179:0.32 182:0.82 187:0.39 190:0.77 192:0.58 195:0.65 196:0.96 199:0.99 201:0.68 202:0.36 204:0.84 208:0.64 212:0.92 215:0.84 216:0.29 219:0.94 220:0.62 222:0.25 223:0.97 224:1.00 227:0.83 229:0.61 230:0.87 231:0.69 232:0.75 233:0.76 234:1.00 235:0.60 241:0.07 242:0.33 243:0.96 246:0.86 247:0.82 248:0.70 253:0.81 255:0.73 256:0.45 259:0.21 265:0.96 266:0.27 267:0.98 268:0.46 271:0.79 274:0.98 276:0.82 279:0.77 281:0.91 282:0.88 284:0.88 285:0.56 287:0.61 290:0.92 292:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.66 6:0.82 7:0.64 8:0.65 9:0.71 11:0.50 12:0.51 17:0.47 18:0.77 20:0.93 21:0.40 26:0.98 27:0.49 29:0.99 30:0.44 31:0.88 32:0.71 34:0.89 36:0.62 37:0.27 39:0.79 43:0.63 44:0.92 45:0.92 48:0.91 55:0.71 58:0.98 64:0.77 66:0.72 69:0.23 70:0.63 71:0.59 74:0.82 76:0.85 77:0.70 78:1.00 79:0.87 81:0.65 83:0.60 86:0.83 91:0.66 96:0.70 97:0.89 98:0.92 99:0.83 100:0.94 101:0.52 104:0.97 106:0.81 108:0.73 111:0.98 114:0.82 117:0.86 120:0.57 122:0.77 123:0.71 124:0.44 126:0.54 127:0.95 129:0.59 131:0.85 133:0.46 135:0.70 137:0.88 139:0.87 140:0.45 141:0.99 144:0.57 145:0.76 146:0.73 147:0.77 149:0.74 150:0.56 151:0.55 152:0.86 153:0.69 154:0.66 155:0.58 157:0.89 158:0.81 159:0.85 162:0.86 164:0.62 165:0.70 166:0.79 169:0.92 172:0.97 173:0.12 176:0.70 177:0.70 179:0.16 182:0.82 186:0.99 187:0.39 189:0.84 190:0.77 192:0.57 195:0.94 196:0.90 199:0.97 201:0.63 202:0.46 206:0.81 208:0.64 212:0.81 215:0.67 216:0.33 219:0.94 220:0.91 222:0.25 223:0.97 224:0.99 227:0.85 229:0.90 230:0.65 231:0.69 232:0.72 233:0.76 234:0.98 235:0.60 238:0.84 241:0.37 242:0.70 243:0.27 244:0.61 245:0.98 246:0.86 247:0.92 248:0.54 253:0.69 254:0.80 255:0.70 256:0.45 259:0.21 260:0.57 261:0.95 265:0.92 266:0.54 267:0.28 268:0.55 271:0.79 274:0.97 276:0.95 279:0.79 281:0.91 282:0.79 283:0.66 284:0.90 285:0.62 287:0.88 290:0.82 292:0.87 297:0.36 300:0.70 +1 1:0.63 7:0.70 9:0.79 12:0.51 17:0.69 18:0.91 21:0.22 22:0.93 26:0.98 29:0.99 30:0.53 31:0.94 34:0.77 36:0.78 37:0.98 39:0.79 43:0.14 44:0.88 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.43 69:0.78 70:0.65 71:0.59 74:0.41 76:0.85 77:0.70 79:0.93 81:0.60 83:0.60 86:0.88 91:0.06 96:0.80 98:0.38 99:0.83 108:0.82 114:0.77 117:0.86 120:0.69 122:0.86 123:0.81 124:0.77 126:0.44 127:0.53 129:0.59 131:0.85 133:0.76 135:0.51 137:0.94 139:0.86 140:0.45 141:0.99 144:0.57 145:0.59 146:0.33 147:0.30 149:0.25 150:0.49 151:0.86 153:0.48 154:0.21 155:0.66 157:0.61 159:0.54 162:0.86 164:0.56 165:0.70 166:0.73 172:0.57 173:0.78 175:0.75 176:0.59 177:0.05 179:0.54 182:0.82 187:0.68 190:0.77 192:0.87 195:0.74 196:0.95 199:0.98 201:0.59 202:0.36 204:0.81 208:0.64 212:0.73 216:0.95 222:0.25 223:0.97 224:1.00 227:0.85 229:0.90 230:0.73 231:0.69 232:0.72 233:0.76 234:1.00 235:0.42 241:0.63 242:0.61 243:0.15 244:0.83 245:0.72 246:0.61 247:0.74 248:0.77 253:0.78 254:0.90 255:0.78 256:0.45 257:0.84 259:0.21 260:0.70 262:0.93 265:0.40 266:0.54 267:0.73 268:0.46 271:0.79 274:0.97 276:0.60 277:0.69 281:0.91 282:0.72 284:0.88 285:0.62 287:0.88 290:0.77 300:0.55 +0 11:0.42 12:0.51 17:0.49 18:0.60 21:0.78 26:0.94 27:0.45 29:0.99 30:0.11 34:0.86 36:0.19 37:0.50 39:0.79 43:0.08 55:0.45 58:0.93 66:0.16 69:0.86 70:0.54 71:0.59 74:0.33 86:0.62 91:0.17 98:0.18 108:0.73 122:0.51 123:0.77 124:0.81 127:0.22 129:0.59 131:0.61 133:0.76 135:0.95 139:0.61 141:0.91 144:0.57 145:0.49 146:0.19 147:0.24 149:0.08 154:0.23 157:0.61 159:0.64 163:0.89 166:0.61 172:0.10 173:0.71 175:0.75 177:0.38 178:0.55 179:0.81 182:0.61 187:0.68 199:0.94 212:0.30 216:0.77 222:0.25 223:0.92 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.84 241:0.21 242:0.33 243:0.37 244:0.83 245:0.42 246:0.61 247:0.39 253:0.29 254:0.84 257:0.65 259:0.21 265:0.09 266:0.27 267:0.65 271:0.79 274:0.91 276:0.25 277:0.69 287:0.61 300:0.33 +2 1:0.69 8:0.84 11:0.52 12:0.51 17:0.91 18:0.96 22:0.93 26:0.98 27:0.63 29:0.99 30:0.53 31:0.92 34:0.22 36:0.85 37:0.78 39:0.79 43:0.77 44:0.92 45:0.91 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.77 69:0.10 70:0.68 71:0.59 74:0.68 76:0.97 77:0.96 79:0.91 81:0.74 86:0.95 91:0.07 96:0.80 97:0.89 98:0.70 99:0.83 101:0.52 108:0.65 114:0.81 122:0.77 123:0.62 124:0.58 126:0.44 127:0.88 129:0.89 131:0.81 133:0.89 135:0.63 137:0.92 139:0.95 140:0.45 141:0.99 145:0.47 146:0.22 147:0.28 149:0.89 150:0.69 151:0.65 153:0.48 154:0.69 155:0.56 157:0.61 158:0.73 159:0.80 162:0.86 165:0.70 166:0.73 172:0.98 173:0.10 175:0.75 176:0.59 177:0.38 179:0.14 182:0.61 187:0.39 190:0.95 191:0.75 192:0.50 195:0.90 196:0.96 199:0.99 201:0.66 202:0.50 204:0.84 208:0.54 212:0.91 216:0.46 219:0.94 222:0.25 223:0.97 224:1.00 227:0.83 229:0.61 231:0.64 232:0.83 234:1.00 235:0.31 241:0.10 242:0.73 243:0.09 244:0.61 245:0.91 246:0.61 247:0.79 248:0.68 253:0.82 256:0.58 257:0.65 262:0.93 265:0.70 266:0.75 267:0.17 268:0.59 271:0.79 274:0.97 276:0.95 277:0.69 279:0.77 281:0.91 282:0.73 284:0.88 285:0.56 287:0.61 290:0.81 292:0.95 300:0.84 +2 1:0.60 8:0.69 9:0.70 11:0.64 12:0.51 17:0.76 18:0.84 20:0.93 21:0.71 26:0.98 27:0.08 29:0.99 30:0.38 31:0.87 34:1.00 36:0.57 37:0.32 39:0.79 43:0.73 44:0.92 45:0.85 55:0.45 58:0.98 64:0.77 66:0.93 69:0.42 70:0.74 71:0.59 74:0.74 76:0.94 77:0.99 78:0.96 79:0.86 81:0.51 86:0.91 91:0.20 96:0.69 97:0.89 98:0.93 99:0.83 100:0.73 101:0.87 108:0.32 111:0.92 114:0.63 117:0.86 122:0.75 123:0.31 126:0.44 127:0.59 129:0.05 131:0.81 135:0.72 137:0.87 139:0.93 140:0.45 141:0.99 144:0.57 145:0.95 146:0.84 147:0.87 149:0.43 150:0.40 151:0.50 152:0.96 153:0.48 154:0.64 155:0.56 157:0.85 158:0.73 159:0.40 162:0.86 165:0.70 166:0.73 169:0.72 172:0.81 173:0.49 176:0.59 177:0.70 179:0.46 182:0.76 186:0.95 187:0.39 190:0.77 192:0.49 195:0.63 196:0.90 199:0.97 201:0.57 202:0.36 204:0.84 208:0.54 212:0.70 216:0.66 219:0.94 220:0.96 222:0.25 223:0.97 224:0.98 227:0.83 229:0.61 231:0.67 232:0.72 234:0.98 235:0.97 241:0.65 242:0.16 243:0.93 246:0.61 247:0.84 248:0.50 253:0.57 254:0.88 255:0.69 256:0.45 259:0.21 261:0.92 265:0.93 266:0.54 267:0.59 268:0.46 271:0.79 274:0.97 276:0.70 279:0.77 281:0.86 282:0.73 284:0.88 285:0.56 287:0.61 290:0.63 292:0.88 300:0.55 +2 6:0.96 8:0.91 12:0.51 17:0.16 20:0.86 21:0.40 27:0.04 28:0.73 30:0.76 32:0.80 34:0.97 36:0.21 37:0.88 43:0.33 55:0.71 66:0.52 69:0.67 70:0.29 78:0.95 81:0.92 91:0.54 98:0.45 100:0.99 104:0.95 106:0.81 108:0.63 111:0.99 120:0.55 123:0.61 124:0.63 126:0.54 127:0.31 129:0.81 133:0.67 135:0.99 140:0.45 145:0.76 146:0.05 147:0.05 149:0.37 150:0.86 151:0.47 152:1.00 153:0.69 154:0.25 159:0.42 161:0.65 162:0.57 164:0.68 167:0.57 169:0.94 172:0.27 173:0.68 177:0.05 179:0.86 181:0.77 186:0.95 187:0.39 189:0.98 191:0.90 195:0.70 202:0.63 206:0.81 212:0.23 215:0.67 216:0.55 220:0.82 235:0.42 238:0.99 241:0.60 242:0.82 243:0.21 245:0.43 247:0.12 248:0.48 256:0.58 259:0.21 260:0.55 261:0.96 265:0.47 266:0.38 267:0.69 268:0.61 276:0.30 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.79 8:0.58 12:0.51 17:0.13 20:0.86 21:0.47 27:0.62 28:0.73 30:0.21 34:0.56 36:0.87 37:0.26 43:0.52 55:0.71 66:0.20 69:0.15 70:0.70 78:0.80 81:0.91 91:0.40 98:0.32 100:0.78 104:0.88 106:0.81 108:0.80 111:0.78 120:0.89 123:0.79 124:0.90 126:0.54 127:0.83 129:0.96 133:0.90 135:0.24 140:0.45 146:0.05 147:0.05 149:0.54 150:0.83 151:0.76 152:0.81 153:0.87 154:0.41 159:0.78 161:0.65 162:0.85 163:0.90 164:0.85 167:0.52 169:0.76 172:0.32 173:0.12 177:0.05 179:0.72 181:0.77 186:0.80 187:0.39 189:0.81 202:0.75 206:0.81 212:0.16 215:0.63 216:0.75 235:0.52 238:0.82 241:0.50 242:0.82 243:0.12 245:0.56 247:0.12 248:0.85 256:0.81 259:0.21 260:0.90 261:0.80 265:0.34 266:0.89 267:0.88 268:0.81 276:0.54 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.93 7:0.81 8:0.81 12:0.51 17:0.13 20:0.80 21:0.30 27:0.16 28:0.73 30:0.76 32:0.80 34:0.93 36:0.22 37:0.68 43:0.46 55:0.71 66:0.36 69:0.44 70:0.29 78:0.92 81:0.94 91:0.72 98:0.29 100:0.91 104:0.86 106:0.81 108:0.87 111:0.91 120:0.65 123:0.87 124:0.58 126:0.54 127:0.61 129:0.59 133:0.47 135:1.00 140:0.45 145:0.98 146:0.45 147:0.52 149:0.34 150:0.89 151:0.54 152:0.77 153:0.48 154:0.35 159:0.68 161:0.65 162:0.74 163:0.86 164:0.75 167:0.56 169:0.90 172:0.21 173:0.32 177:0.05 179:0.92 181:0.77 186:0.91 187:0.39 189:0.94 191:0.89 195:0.84 202:0.65 206:0.81 212:0.23 215:0.72 216:0.52 220:0.62 230:0.65 233:0.63 235:0.31 238:0.89 241:0.59 242:0.82 243:0.45 245:0.50 247:0.12 248:0.58 256:0.68 259:0.21 260:0.65 261:0.84 265:0.31 266:0.38 267:0.65 268:0.69 276:0.20 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.94 7:0.81 8:0.85 12:0.51 17:0.84 20:0.80 21:0.22 27:0.11 28:0.73 30:0.33 32:0.80 34:0.83 36:0.66 37:0.86 43:0.40 55:0.84 66:0.51 69:0.53 70:0.73 78:0.82 81:0.95 91:0.58 98:0.81 100:0.90 104:0.93 106:0.81 108:0.76 111:0.84 120:0.76 123:0.75 124:0.64 126:0.54 127:0.79 129:0.81 133:0.67 135:0.56 140:0.45 145:0.63 146:0.67 147:0.71 149:0.72 150:0.92 151:0.73 152:0.77 153:0.80 154:0.30 159:0.69 161:0.65 162:0.78 164:0.71 167:0.52 169:0.87 172:0.71 173:0.42 177:0.05 179:0.47 181:0.77 186:0.82 187:0.39 189:0.95 191:0.86 195:0.84 202:0.61 206:0.81 212:0.35 215:0.79 216:0.84 230:0.87 233:0.76 235:0.22 238:0.94 241:0.49 242:0.82 243:0.40 245:0.81 247:0.12 248:0.68 256:0.58 259:0.21 260:0.77 261:0.80 265:0.81 266:0.63 267:0.79 268:0.65 276:0.73 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.51 17:0.55 21:0.68 27:0.45 28:0.73 30:0.76 34:0.97 36:0.17 37:0.50 43:0.32 55:0.71 66:0.64 69:0.70 70:0.15 81:0.83 91:0.42 98:0.36 108:0.54 123:0.51 126:0.54 127:0.13 129:0.05 135:0.89 145:0.45 146:0.51 147:0.57 149:0.25 150:0.64 154:0.24 159:0.18 161:0.65 163:0.98 167:0.50 172:0.16 173:0.65 177:0.05 178:0.55 179:0.70 181:0.77 187:0.68 212:0.95 215:0.49 216:0.77 235:0.80 241:0.39 242:0.82 243:0.69 247:0.12 259:0.21 265:0.38 266:0.12 267:0.44 276:0.16 297:0.36 300:0.13 +2 6:0.94 8:0.83 12:0.51 17:0.78 20:0.85 21:0.68 27:0.18 28:0.73 30:0.76 34:0.86 36:0.55 37:0.42 43:0.51 55:0.84 66:0.85 69:0.29 70:0.27 78:0.96 81:0.83 91:0.80 98:0.99 100:0.97 104:0.58 108:0.22 111:0.96 120:0.79 123:0.22 126:0.54 127:0.90 129:0.05 135:0.75 140:0.45 145:0.45 146:0.86 147:0.88 149:0.56 150:0.64 151:0.66 152:0.81 153:0.48 154:0.40 159:0.42 161:0.65 162:0.73 163:0.81 164:0.80 167:0.80 169:0.96 172:0.57 173:0.39 177:0.05 179:0.81 181:0.77 186:0.96 189:0.95 191:0.91 202:0.72 212:0.22 215:0.49 216:0.09 220:0.62 235:0.80 238:0.95 241:0.95 242:0.82 243:0.86 247:0.12 248:0.72 256:0.75 259:0.21 260:0.80 261:0.92 265:0.99 266:0.54 267:0.69 268:0.76 276:0.47 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.79 7:0.81 8:0.58 12:0.51 17:0.61 20:0.89 25:0.88 27:0.12 28:0.73 32:0.80 34:0.61 36:0.38 37:0.23 78:0.97 81:0.98 91:0.91 100:0.97 104:0.58 111:0.96 120:0.75 126:0.54 135:0.37 140:0.45 145:0.98 150:0.97 151:0.71 152:0.84 153:0.72 161:0.65 162:0.81 164:0.73 167:0.80 169:0.95 175:0.75 181:0.77 186:0.97 189:0.82 191:0.90 195:0.52 202:0.61 215:0.96 216:0.13 230:0.87 233:0.76 235:0.02 238:0.91 241:0.90 244:0.61 248:0.68 256:0.64 257:0.65 259:0.21 260:0.76 261:0.94 267:0.28 268:0.66 277:0.69 285:0.62 297:0.36 +1 6:0.89 7:0.81 8:0.74 12:0.51 17:0.77 20:0.75 21:0.30 27:0.43 28:0.73 30:0.27 32:0.80 34:0.97 36:0.33 37:0.47 43:0.44 55:0.71 66:0.35 69:0.45 70:0.78 78:0.99 81:0.94 91:0.27 98:0.44 100:0.97 104:0.91 106:0.81 108:0.64 111:0.97 123:0.62 124:0.78 126:0.54 127:0.44 129:0.89 133:0.78 135:0.97 145:0.67 146:0.40 147:0.47 149:0.66 150:0.89 152:0.74 154:0.34 159:0.79 161:0.65 163:0.81 167:0.46 169:0.94 172:0.54 173:0.23 177:0.05 178:0.55 179:0.51 181:0.77 186:1.00 187:0.39 189:0.91 191:0.73 195:0.93 206:0.81 212:0.16 215:0.72 216:0.24 230:0.87 233:0.76 235:0.31 238:0.94 241:0.12 242:0.82 243:0.33 245:0.72 247:0.12 259:0.21 261:0.79 265:0.46 266:0.89 267:0.65 276:0.61 283:0.82 297:0.36 300:0.55 +1 12:0.51 17:0.22 21:0.30 27:0.45 28:0.73 30:0.62 32:0.80 34:0.74 36:0.18 37:0.50 43:0.32 55:0.59 66:0.47 69:0.69 70:0.23 81:0.94 91:0.12 98:0.38 108:0.52 123:0.50 124:0.52 126:0.54 127:0.20 129:0.59 133:0.47 135:0.92 145:0.39 146:0.49 147:0.55 149:0.35 150:0.89 154:0.24 159:0.31 161:0.65 163:0.87 167:0.47 172:0.21 173:0.67 177:0.05 178:0.55 179:0.77 181:0.77 187:0.68 195:0.73 212:0.30 215:0.72 216:0.77 235:0.31 241:0.18 242:0.82 243:0.59 245:0.46 247:0.12 259:0.21 265:0.41 266:0.27 267:0.36 276:0.26 283:0.60 297:0.36 300:0.18 +2 6:0.85 7:0.81 8:0.71 12:0.51 17:0.26 20:0.88 21:0.54 27:0.04 28:0.73 30:0.33 32:0.80 34:0.81 36:0.57 37:0.68 43:0.26 55:0.59 66:0.20 69:0.81 70:0.36 78:0.82 81:0.89 91:0.29 98:0.39 100:0.88 104:0.94 106:0.81 108:0.76 111:0.83 120:0.81 123:0.64 124:0.56 126:0.54 127:0.23 129:0.59 133:0.47 135:0.66 140:0.45 145:0.67 146:0.43 147:0.50 149:0.36 150:0.79 151:0.74 152:0.84 153:0.82 154:0.19 159:0.31 161:0.65 162:0.73 163:0.75 164:0.79 167:0.62 169:0.85 172:0.27 173:0.87 177:0.05 179:0.74 181:0.77 186:0.83 187:0.39 189:0.87 191:0.81 195:0.71 202:0.70 206:0.81 212:0.19 215:0.58 216:0.80 230:0.87 233:0.76 235:0.60 238:0.91 241:0.66 242:0.82 243:0.40 245:0.53 247:0.12 248:0.73 256:0.71 259:0.21 260:0.82 261:0.85 265:0.40 266:0.47 267:0.69 268:0.74 276:0.34 283:0.82 285:0.62 297:0.36 300:0.25 +1 7:0.81 8:0.81 12:0.51 17:0.87 20:0.75 21:0.30 25:0.88 27:0.56 28:0.73 30:0.21 32:0.80 34:0.23 36:0.47 43:0.29 55:0.59 66:0.72 69:0.75 70:0.78 78:0.94 81:0.94 91:0.62 98:0.58 100:0.86 104:0.54 108:0.59 111:0.91 120:0.77 123:0.56 124:0.44 126:0.54 127:0.43 129:0.81 133:0.67 135:0.86 140:0.45 145:0.88 146:0.79 147:0.83 149:0.62 150:0.89 151:0.73 152:0.74 153:0.78 154:0.21 159:0.64 161:0.65 162:0.86 164:0.74 167:0.77 169:0.84 172:0.67 173:0.67 177:0.05 179:0.55 181:0.77 186:0.94 191:0.73 195:0.84 202:0.60 212:0.49 215:0.72 216:0.19 230:0.87 233:0.76 235:0.31 241:0.79 242:0.82 243:0.75 245:0.61 247:0.12 248:0.69 256:0.64 259:0.21 260:0.77 261:0.76 265:0.59 266:0.75 267:0.96 268:0.68 276:0.56 283:0.82 285:0.62 297:0.36 300:0.55 +1 6:0.98 7:0.81 8:0.96 12:0.51 17:0.76 20:0.87 21:0.64 27:0.17 28:0.73 30:0.60 32:0.80 34:0.68 36:0.31 37:0.97 43:0.36 55:0.71 66:0.77 69:0.63 70:0.66 78:0.94 81:0.85 91:0.82 98:0.78 100:0.96 104:0.58 108:0.48 111:0.94 123:0.46 124:0.40 126:0.54 127:0.64 129:0.81 133:0.67 135:0.54 140:0.45 145:0.96 146:0.85 147:0.88 149:0.66 150:0.68 152:0.82 153:0.48 154:0.27 159:0.57 161:0.65 162:0.86 164:0.67 167:0.79 169:0.94 172:0.70 173:0.59 177:0.05 179:0.60 181:0.77 186:0.93 189:0.99 191:0.87 195:0.79 202:0.50 212:0.48 215:0.53 216:0.07 220:0.99 230:0.87 233:0.76 235:0.74 238:0.94 241:0.88 242:0.82 243:0.79 245:0.57 247:0.12 256:0.58 259:0.21 261:0.92 265:0.78 266:0.47 267:0.59 268:0.59 276:0.61 283:0.60 285:0.62 297:0.36 300:0.70 +0 7:0.66 12:0.01 17:0.98 18:0.66 21:0.68 27:0.72 29:0.62 30:0.90 32:0.64 34:0.56 36:0.30 39:0.82 43:0.17 48:0.91 55:0.71 64:0.77 66:0.86 69:0.39 70:0.24 71:0.56 74:0.33 81:0.30 86:0.70 89:1.00 91:0.67 98:0.91 104:0.58 108:0.30 123:0.29 126:0.44 127:0.49 129:0.05 135:0.32 139:0.71 141:0.91 145:0.52 146:0.73 147:0.77 149:0.24 150:0.25 154:0.54 159:0.30 172:0.59 173:0.54 177:0.70 178:0.55 179:0.72 192:0.51 195:0.60 201:0.68 212:0.84 215:0.37 216:0.01 222:0.76 223:0.95 225:1.00 230:0.69 233:0.76 234:0.91 235:0.88 240:0.95 241:0.77 242:0.72 243:0.86 244:0.61 247:0.55 253:0.28 254:0.90 259:0.21 265:0.91 266:0.27 267:0.23 271:0.57 274:0.91 276:0.48 281:0.89 297:0.36 300:0.25 +0 12:0.01 17:0.66 21:0.78 25:0.88 27:0.34 29:0.62 30:0.15 34:0.58 36:0.66 37:0.74 39:0.82 43:0.18 55:0.59 66:0.27 69:0.19 70:0.97 71:0.56 74:0.44 89:0.99 91:0.01 98:0.15 104:0.58 108:0.83 123:0.82 124:0.91 127:0.97 129:0.81 133:0.91 135:0.58 141:0.91 146:0.47 147:0.53 149:0.19 154:0.46 159:0.95 172:0.41 173:0.06 175:0.75 177:0.38 178:0.55 179:0.70 187:0.95 212:0.48 216:0.72 222:0.76 223:0.93 225:0.97 234:0.91 235:0.52 240:0.95 241:0.05 242:0.51 243:0.29 244:0.61 245:0.56 247:0.60 253:0.33 254:0.80 257:0.65 259:0.21 265:0.17 266:0.75 267:0.23 271:0.57 274:0.91 276:0.55 277:0.69 300:0.84 +2 7:0.72 11:0.64 12:0.01 17:0.90 21:0.71 27:0.38 29:0.62 30:0.38 32:0.69 34:0.80 36:0.86 37:0.72 39:0.82 43:0.67 45:0.77 55:0.59 66:0.36 69:0.51 70:0.83 71:0.56 74:0.76 76:0.99 77:0.70 81:0.23 89:0.99 91:0.04 98:0.62 101:0.52 108:0.39 114:0.54 122:0.55 123:0.37 124:0.60 126:0.26 127:0.50 129:0.59 133:0.47 135:0.42 141:0.91 145:0.79 146:0.61 147:0.67 149:0.56 150:0.23 154:0.56 155:0.58 159:0.42 172:0.51 173:0.55 175:0.75 176:0.61 177:0.38 178:0.55 179:0.58 187:0.39 192:0.59 195:0.70 204:0.85 208:0.54 212:0.68 216:0.46 222:0.76 223:0.94 225:0.98 230:0.74 232:0.74 233:0.73 234:0.91 235:0.88 240:0.95 241:0.32 242:0.27 243:0.51 244:0.61 245:0.79 247:0.60 253:0.43 257:0.65 259:0.21 265:0.63 266:0.54 267:0.28 271:0.57 274:0.91 276:0.59 277:0.69 282:0.77 290:0.54 300:0.70 +0 8:0.85 12:0.01 17:0.78 21:0.30 27:0.51 29:0.62 30:0.33 34:0.47 36:0.53 37:0.55 39:0.82 43:0.18 55:0.92 66:0.54 69:0.15 70:0.69 71:0.56 74:0.56 76:0.98 77:0.96 81:0.33 89:0.97 91:0.89 96:0.76 98:0.61 108:0.12 114:0.63 122:0.77 123:0.12 124:0.48 126:0.26 127:0.89 129:0.05 133:0.39 135:0.68 140:0.45 141:0.91 145:0.72 146:0.44 147:0.51 149:0.22 150:0.30 151:0.57 153:0.72 154:0.12 159:0.31 162:0.67 172:0.32 173:0.38 175:0.75 176:0.61 177:0.38 179:0.92 190:0.89 195:0.59 202:0.53 212:0.42 216:0.08 220:0.62 222:0.76 223:0.92 225:0.96 232:0.79 234:0.91 235:0.42 240:0.95 241:0.95 242:0.45 243:0.63 244:0.83 245:0.50 247:0.47 248:0.55 253:0.59 256:0.45 257:0.65 259:0.21 265:0.62 266:0.38 267:0.44 268:0.57 271:0.57 274:0.91 276:0.33 277:0.69 282:0.73 285:0.47 290:0.63 300:0.43 +0 8:0.75 11:0.64 12:0.01 17:0.64 21:0.71 27:0.54 29:0.62 30:0.75 32:0.64 34:0.92 36:0.48 37:0.62 39:0.82 43:0.21 45:0.66 55:0.59 66:0.35 69:0.97 70:0.64 71:0.56 74:0.37 81:0.23 89:1.00 91:0.10 98:0.46 101:0.52 104:0.91 106:0.81 108:0.97 120:0.68 123:0.97 124:0.95 126:0.26 127:0.93 129:0.05 133:0.96 135:0.42 140:0.80 141:0.98 145:0.40 146:0.71 147:0.72 149:0.51 150:0.23 151:0.57 153:0.72 154:0.14 159:0.94 162:0.75 164:0.70 172:0.93 173:0.85 175:0.75 177:0.05 178:0.42 179:0.15 187:0.39 190:0.89 195:0.99 202:0.68 206:0.81 212:0.78 215:0.37 216:0.80 220:0.62 222:0.76 223:0.96 225:1.00 234:0.99 235:0.88 240:1.00 241:0.41 242:0.81 243:0.15 244:0.61 245:0.93 247:0.60 248:0.58 253:0.30 256:0.71 257:0.84 259:0.21 260:0.65 265:0.48 266:0.97 267:0.28 268:0.72 271:0.57 274:0.99 276:0.95 277:0.87 283:0.78 285:0.47 297:0.36 300:0.94 +2 11:0.64 12:0.01 17:0.45 18:0.65 21:0.05 27:0.39 29:0.62 30:0.91 34:0.71 36:0.99 37:0.86 39:0.82 43:0.64 45:0.66 55:0.84 66:0.69 69:0.36 70:0.13 71:0.56 74:0.40 81:0.39 86:0.68 89:0.96 91:0.25 98:0.80 101:0.52 108:0.28 114:0.81 122:0.55 123:0.27 126:0.26 127:0.29 129:0.05 135:0.23 139:0.66 141:0.91 146:0.49 147:0.55 149:0.34 150:0.34 154:0.53 159:0.18 163:0.86 172:0.21 173:0.65 176:0.61 177:0.70 178:0.55 179:0.95 187:0.39 212:0.61 216:0.19 222:0.76 223:0.92 225:0.96 232:0.76 234:0.91 235:0.05 240:0.95 241:0.59 242:0.63 243:0.73 244:0.61 247:0.30 253:0.56 259:0.21 265:0.80 266:0.17 267:0.79 271:0.57 274:0.91 276:0.20 290:0.81 300:0.13 +0 8:0.95 12:0.01 17:0.02 21:0.40 25:0.88 27:0.44 29:0.62 30:0.95 34:0.53 36:0.55 37:0.65 39:0.82 43:0.17 55:0.84 66:0.54 69:0.39 71:0.56 74:0.34 81:0.28 89:0.96 91:0.38 98:0.79 108:0.30 123:0.29 126:0.26 127:0.27 129:0.05 135:0.37 141:0.91 145:0.38 146:0.38 147:0.45 149:0.10 150:0.27 154:0.11 159:0.15 172:0.10 173:0.76 175:0.75 177:0.38 178:0.55 179:0.99 187:0.39 215:0.42 216:0.45 222:0.76 223:0.95 225:1.00 234:0.91 235:0.42 240:0.95 241:0.68 243:0.63 244:0.83 253:0.28 257:0.65 259:0.21 265:0.79 267:0.28 271:0.57 274:0.91 277:0.69 297:0.36 300:0.08 +0 11:0.64 12:0.01 17:0.09 18:0.80 21:0.78 27:0.27 29:0.62 30:0.27 34:0.26 36:0.96 37:0.74 39:0.82 43:0.69 45:0.81 55:0.71 66:0.13 69:0.46 70:0.79 71:0.56 74:0.74 86:0.88 89:0.99 91:0.07 98:0.53 101:0.52 108:0.35 122:0.80 123:0.70 124:0.64 127:0.53 129:0.59 133:0.61 135:0.66 139:0.84 141:0.91 146:0.63 147:0.68 149:0.49 154:0.78 159:0.57 172:0.63 173:0.40 177:0.70 178:0.55 179:0.54 187:0.95 202:0.36 212:0.73 216:0.63 222:0.76 223:0.93 225:0.97 234:0.91 235:0.12 240:0.95 241:0.55 242:0.16 243:0.62 245:0.74 247:0.88 253:0.42 254:0.84 259:0.21 265:0.52 266:0.71 267:0.59 271:0.57 274:0.91 276:0.62 300:0.55 +1 7:0.72 11:0.64 12:0.01 17:0.73 21:0.22 27:0.38 29:0.62 30:0.37 32:0.69 34:0.34 36:0.54 37:0.72 39:0.82 43:0.67 45:0.83 55:0.71 66:0.10 69:0.53 70:0.82 71:0.56 74:0.80 76:0.99 77:0.70 81:0.31 89:0.99 91:0.04 98:0.37 101:0.52 108:0.94 114:0.66 122:0.77 123:0.93 124:0.94 126:0.26 127:0.91 129:0.89 133:0.93 135:0.42 141:0.91 145:0.79 146:0.44 147:0.50 149:0.60 150:0.29 154:0.57 155:0.58 159:0.89 172:0.51 173:0.24 175:0.75 176:0.61 177:0.38 178:0.55 179:0.30 187:0.39 192:0.59 195:0.97 204:0.85 208:0.54 212:0.29 216:0.46 222:0.76 223:0.93 225:0.97 230:0.74 232:0.74 233:0.73 234:0.91 235:0.22 240:0.95 241:0.47 242:0.76 243:0.16 244:0.61 245:0.84 247:0.60 253:0.52 257:0.65 259:0.21 265:0.39 266:0.94 267:0.36 271:0.57 274:0.91 276:0.85 277:0.87 282:0.77 290:0.66 300:0.84 +2 11:0.64 12:0.01 17:0.09 18:0.79 21:0.78 27:0.27 29:0.62 30:0.27 34:0.24 36:0.95 37:0.74 39:0.82 43:0.69 45:0.81 55:0.71 66:0.13 69:0.52 70:0.79 71:0.56 74:0.74 86:0.87 89:0.99 91:0.07 98:0.53 101:0.52 108:0.40 122:0.80 123:0.70 124:0.64 127:0.54 129:0.59 133:0.61 135:0.51 139:0.83 141:0.91 146:0.63 147:0.68 149:0.48 154:0.76 159:0.57 172:0.63 173:0.46 177:0.70 178:0.55 179:0.54 187:0.39 212:0.73 216:0.63 222:0.76 223:0.93 225:0.97 234:0.91 235:0.12 240:0.95 241:0.65 242:0.16 243:0.62 245:0.74 247:0.88 253:0.42 254:0.84 259:0.21 265:0.52 266:0.71 267:0.59 271:0.57 274:0.91 276:0.62 300:0.55 +0 11:0.64 12:0.01 17:0.34 18:0.60 21:0.13 27:0.61 29:0.62 30:0.56 34:0.85 36:0.60 39:0.82 43:0.14 45:0.66 55:0.84 66:0.33 69:0.84 70:0.32 71:0.56 74:0.58 81:0.35 86:0.70 89:0.96 91:0.51 98:0.58 101:0.52 108:0.44 114:0.72 117:0.86 122:0.77 123:0.68 124:0.61 126:0.26 127:0.29 129:0.05 133:0.37 135:0.24 139:0.67 141:0.91 146:0.70 147:0.67 149:0.20 150:0.31 154:0.54 159:0.46 163:0.94 172:0.32 173:0.85 176:0.61 177:0.05 178:0.55 179:0.67 187:0.87 212:0.39 216:0.54 222:0.76 223:0.91 225:0.96 232:0.88 234:0.91 235:0.12 240:0.95 241:0.56 242:0.73 243:0.50 245:0.64 247:0.47 253:0.53 254:0.95 257:0.84 259:0.21 265:0.50 266:0.38 267:0.76 271:0.57 274:0.91 276:0.45 290:0.72 300:0.25 +1 8:0.96 9:0.76 12:0.01 17:0.32 21:0.13 27:0.26 29:0.62 30:0.45 34:0.55 36:0.74 37:0.85 39:0.82 43:0.16 55:0.59 66:0.69 69:0.45 70:0.25 71:0.56 74:0.49 81:0.35 89:0.95 91:0.99 96:0.94 98:0.72 108:0.35 114:0.72 120:0.68 122:0.77 123:0.34 126:0.26 127:0.22 129:0.05 135:0.24 140:0.97 141:0.98 146:0.41 147:0.48 149:0.14 150:0.31 151:0.61 153:0.80 154:0.11 155:0.58 158:0.74 159:0.15 162:0.74 163:0.81 164:0.78 172:0.21 173:0.74 175:0.91 176:0.61 177:0.05 179:0.92 190:0.89 191:0.94 192:0.59 202:0.92 208:0.54 212:0.61 216:0.53 220:0.74 222:0.76 223:0.96 225:1.00 234:0.97 235:0.12 240:1.00 241:0.98 242:0.82 243:0.73 244:0.83 247:0.12 248:0.72 253:0.87 255:0.74 256:0.94 257:0.84 259:0.21 260:0.67 265:0.72 266:0.17 267:0.52 268:0.95 271:0.57 274:0.99 276:0.21 277:0.87 285:0.56 290:0.72 300:0.18 +0 7:0.66 12:0.01 17:0.98 18:0.66 21:0.68 27:0.72 29:0.62 30:0.90 32:0.64 34:0.49 36:0.28 39:0.82 43:0.17 48:0.91 55:0.71 64:0.77 66:0.86 69:0.39 70:0.24 71:0.56 74:0.33 81:0.30 86:0.70 89:1.00 91:0.67 98:0.91 104:0.58 108:0.30 123:0.29 126:0.44 127:0.49 129:0.05 135:0.37 139:0.71 141:0.91 145:0.52 146:0.73 147:0.77 149:0.24 150:0.25 154:0.54 159:0.30 172:0.59 173:0.54 177:0.70 178:0.55 179:0.72 192:0.51 195:0.60 201:0.68 212:0.84 215:0.37 216:0.01 222:0.76 223:0.95 225:1.00 230:0.69 233:0.76 234:0.91 235:0.88 240:0.95 241:0.77 242:0.72 243:0.86 244:0.61 247:0.55 253:0.28 254:0.90 259:0.21 265:0.91 266:0.27 267:0.23 271:0.57 274:0.91 276:0.48 281:0.89 297:0.36 300:0.25 +0 8:0.78 12:0.01 17:0.06 21:0.40 25:0.88 27:0.44 29:0.62 30:0.76 34:0.34 36:0.68 37:0.65 39:0.82 43:0.17 55:0.92 66:0.36 69:0.39 70:0.22 71:0.56 74:0.44 81:0.28 89:0.96 91:0.60 98:0.27 108:0.53 122:0.77 123:0.51 124:0.67 126:0.26 127:0.66 129:0.81 133:0.67 135:0.34 141:0.91 145:0.38 146:0.05 147:0.05 149:0.16 150:0.27 154:0.11 159:0.26 163:0.75 172:0.16 173:0.61 175:0.91 177:0.05 178:0.55 179:0.96 187:0.39 202:0.46 212:0.42 215:0.42 216:0.45 222:0.76 223:0.95 225:1.00 234:0.91 235:0.42 240:0.95 241:0.73 242:0.75 243:0.26 244:0.83 245:0.40 247:0.30 253:0.33 257:0.84 259:0.21 265:0.29 266:0.23 267:0.23 271:0.57 274:0.91 276:0.23 277:0.87 297:0.36 300:0.18 +1 7:0.72 11:0.64 12:0.01 17:0.78 21:0.54 27:0.38 29:0.62 30:0.54 32:0.69 34:0.39 36:0.68 37:0.72 39:0.82 43:0.22 45:0.73 55:0.71 66:0.51 69:0.92 70:0.78 71:0.56 74:0.70 76:0.99 77:0.70 81:0.24 89:0.98 91:0.18 98:0.63 101:0.52 108:0.84 114:0.58 122:0.77 123:0.83 124:0.70 126:0.26 127:0.82 129:0.59 133:0.76 135:0.44 141:0.91 145:0.79 146:0.88 147:0.05 149:0.34 150:0.24 154:0.16 155:0.58 159:0.79 172:0.70 173:0.88 175:0.75 176:0.61 177:0.05 178:0.55 179:0.49 187:0.39 192:0.59 195:0.91 204:0.85 208:0.54 212:0.35 216:0.46 222:0.76 223:0.93 225:0.97 230:0.74 232:0.74 233:0.73 234:0.91 235:0.60 240:0.95 241:0.32 242:0.76 243:0.08 244:0.61 245:0.76 247:0.60 253:0.44 257:0.84 259:0.21 265:0.64 266:0.80 267:0.59 271:0.57 274:0.91 276:0.69 277:0.69 282:0.77 290:0.58 300:0.84 +0 1:0.58 9:0.71 10:0.88 12:0.79 17:0.62 18:0.66 21:0.47 23:0.95 27:0.57 29:0.77 30:0.33 31:0.89 34:0.82 36:0.31 37:0.54 39:0.46 43:0.08 46:0.88 55:0.84 62:0.96 64:0.77 66:0.54 69:0.32 70:0.49 71:0.84 74:0.29 79:0.89 81:0.47 83:0.56 86:0.66 91:0.22 98:0.53 107:0.89 108:0.25 110:0.90 122:0.95 123:0.24 124:0.55 125:0.88 126:0.51 127:0.53 128:0.96 129:0.05 133:0.59 135:0.65 137:0.89 139:0.64 140:0.45 146:0.59 147:0.64 149:0.09 150:0.45 151:0.61 153:0.48 154:0.35 155:0.54 159:0.49 160:0.88 162:0.86 168:0.96 170:0.77 172:0.32 173:0.33 174:0.89 175:0.75 177:0.70 179:0.89 187:0.39 190:0.98 192:0.49 196:0.88 197:0.93 201:0.60 202:0.36 205:0.95 208:0.50 212:0.19 215:0.63 216:0.44 220:0.82 222:0.35 235:0.74 236:0.92 239:0.87 241:0.61 242:0.45 243:0.63 244:0.93 245:0.45 247:0.47 248:0.59 253:0.26 254:0.84 255:0.70 256:0.45 257:0.65 259:0.21 265:0.54 266:0.47 267:0.69 268:0.46 271:0.50 276:0.33 277:0.69 284:0.88 285:0.50 289:0.92 297:0.36 300:0.33 +0 8:0.69 10:0.81 12:0.79 17:0.66 18:0.60 21:0.64 25:0.88 27:0.70 29:0.77 30:0.45 34:0.33 36:0.70 37:0.37 39:0.46 43:0.06 46:0.88 55:0.52 66:0.27 69:0.86 70:0.25 71:0.84 74:0.27 81:0.26 86:0.60 91:0.20 98:0.11 104:0.58 107:0.89 108:0.93 110:0.89 122:0.86 123:0.92 124:0.72 125:0.88 126:0.24 127:0.42 129:0.81 133:0.67 135:0.89 139:0.58 145:0.93 146:0.05 147:0.05 149:0.06 150:0.28 154:0.06 159:0.75 160:0.88 163:0.99 170:0.77 172:0.10 173:0.75 174:0.79 175:0.97 177:0.05 178:0.55 179:0.97 187:0.68 197:0.82 202:0.36 212:0.61 215:0.53 216:0.38 222:0.35 235:0.74 239:0.81 241:0.07 242:0.63 243:0.29 244:0.98 245:0.38 247:0.30 253:0.25 257:0.93 259:0.21 265:0.12 266:0.17 267:0.44 271:0.50 276:0.17 277:0.95 289:0.90 297:0.36 300:0.18 +2 1:0.68 7:0.65 10:0.85 11:0.51 12:0.79 17:0.61 18:0.85 21:0.30 27:0.60 29:0.77 30:0.38 32:0.64 34:0.85 36:0.50 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 64:0.77 66:0.77 69:0.40 70:0.78 71:0.84 74:0.46 81:0.71 86:0.77 91:0.44 98:0.87 99:0.83 101:0.47 102:0.94 107:0.95 108:0.31 110:0.96 114:0.82 120:0.69 122:0.75 123:0.30 124:0.40 125:0.88 126:0.54 127:0.71 129:0.59 132:0.97 133:0.46 135:0.28 139:0.73 140:0.45 145:0.41 146:0.89 147:0.91 149:0.09 150:0.61 151:0.65 153:0.48 154:0.30 155:0.52 159:0.55 160:0.88 162:0.86 163:0.75 164:0.55 165:0.65 170:0.77 172:0.76 173:0.38 174:0.79 175:0.75 176:0.70 177:0.70 179:0.52 187:0.39 190:0.99 192:0.51 193:0.95 195:0.75 197:0.81 201:0.67 202:0.36 208:0.64 212:0.29 215:0.67 216:0.20 222:0.35 230:0.68 233:0.76 235:0.88 236:0.95 239:0.87 241:0.65 242:0.38 243:0.79 244:0.61 245:0.74 247:0.67 248:0.62 253:0.58 254:0.74 256:0.45 257:0.65 260:0.69 265:0.87 266:0.54 267:0.28 268:0.46 271:0.50 276:0.67 277:0.69 283:0.82 285:0.46 289:0.95 290:0.82 297:0.36 300:0.84 +0 7:0.65 10:0.87 11:0.54 12:0.79 17:0.82 18:0.94 21:0.74 27:0.61 29:0.77 30:0.09 32:0.64 34:0.85 36:0.42 37:0.45 39:0.46 43:0.09 45:0.81 46:0.88 55:0.92 66:0.05 69:0.97 70:1.00 71:0.84 74:0.34 81:0.25 86:0.80 91:0.01 98:0.16 101:0.65 107:0.95 108:1.00 110:0.96 122:0.92 123:1.00 124:1.00 125:0.88 126:0.24 127:0.99 129:0.93 133:1.00 135:1.00 139:0.77 145:0.56 146:0.79 147:0.66 149:0.24 150:0.27 154:0.06 159:0.99 160:0.88 170:0.77 172:0.57 173:0.58 174:0.96 177:0.38 178:0.55 179:0.11 187:0.21 195:1.00 197:0.90 212:0.15 215:0.46 216:0.17 222:0.35 230:0.67 233:0.66 235:0.88 239:0.85 241:0.10 242:0.52 243:0.10 244:0.61 245:0.92 247:0.99 253:0.31 254:0.88 257:0.84 259:0.21 265:0.14 266:1.00 267:0.73 271:0.50 276:0.98 277:0.87 289:0.93 297:0.36 300:0.98 +2 1:0.63 7:0.65 10:0.85 11:0.51 12:0.79 17:0.75 18:0.73 21:0.30 23:0.95 27:0.60 29:0.77 30:0.09 32:0.64 34:0.85 36:0.95 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 62:0.95 64:0.77 66:0.19 69:0.91 70:0.95 71:0.84 74:0.35 81:0.59 86:0.70 91:0.46 98:0.39 99:0.83 101:0.47 102:0.94 107:0.93 108:0.74 110:0.94 114:0.84 123:0.72 124:0.82 125:0.88 126:0.51 127:0.70 128:0.95 129:0.05 132:0.97 133:0.73 135:0.61 139:0.67 140:0.45 145:0.41 146:0.19 147:0.38 149:0.11 150:0.51 151:0.50 153:0.48 154:0.35 155:0.49 159:0.52 160:0.88 162:0.86 163:0.81 165:0.65 168:0.95 170:0.77 172:0.21 173:0.99 174:0.79 175:0.75 176:0.70 177:0.05 179:0.81 187:0.39 190:0.99 192:0.46 193:0.95 195:0.73 197:0.81 201:0.62 202:0.36 205:0.95 208:0.61 212:0.23 216:0.20 220:0.91 222:0.35 230:0.68 233:0.76 235:0.74 236:0.95 239:0.87 241:0.67 242:0.42 243:0.33 244:0.61 245:0.55 247:0.67 248:0.50 253:0.59 254:0.74 256:0.45 257:0.93 265:0.41 266:0.54 267:0.52 268:0.46 271:0.50 276:0.42 277:0.87 285:0.46 289:0.94 290:0.84 300:0.70 +1 10:0.85 12:0.79 17:0.67 18:0.89 21:0.59 27:0.43 29:0.77 30:0.09 34:0.98 36:0.58 37:0.78 39:0.46 43:0.08 46:0.88 55:0.71 66:0.26 69:0.74 70:0.94 71:0.84 74:0.41 76:0.94 77:0.70 81:0.34 86:0.77 91:0.20 98:0.47 102:0.94 107:0.94 108:0.58 110:0.95 114:0.67 122:0.65 123:0.55 124:0.83 125:0.88 126:0.32 127:0.56 129:0.59 133:0.81 135:0.68 139:0.73 145:0.61 146:0.71 147:0.05 149:0.11 150:0.34 154:0.11 159:0.70 160:0.88 170:0.77 172:0.51 173:0.64 174:0.89 176:0.60 177:0.05 178:0.55 179:0.49 187:0.98 192:0.44 193:0.95 195:0.86 197:0.88 201:0.55 212:0.48 216:0.60 222:0.35 235:0.80 236:0.92 239:0.87 241:0.15 242:0.14 243:0.09 244:0.93 245:0.73 247:0.94 253:0.52 254:0.95 257:0.93 259:0.21 265:0.49 266:0.84 267:0.76 271:0.50 276:0.68 282:0.73 289:0.93 290:0.67 300:0.70 +0 1:0.59 10:0.88 12:0.79 17:0.57 18:0.67 21:0.30 23:0.95 27:0.57 29:0.77 30:0.33 34:0.83 36:0.33 37:0.54 39:0.46 43:0.56 46:0.88 62:0.96 64:0.77 66:0.12 69:0.30 70:0.36 71:0.84 74:0.29 81:0.51 83:0.56 86:0.66 91:0.20 98:0.28 107:0.89 108:0.24 110:0.90 120:0.62 122:0.95 123:0.54 124:0.78 125:0.88 126:0.51 127:0.61 128:0.96 129:0.05 133:0.73 135:0.66 139:0.64 140:0.45 146:0.31 147:0.37 149:0.33 150:0.49 151:0.65 153:0.48 154:0.45 155:0.47 159:0.50 160:0.88 162:0.86 164:0.55 168:0.96 170:0.77 172:0.21 173:0.31 174:0.89 175:0.75 177:0.70 179:0.90 187:0.39 190:0.98 192:0.50 197:0.93 201:0.62 202:0.36 205:0.95 208:0.50 212:0.19 215:0.72 216:0.44 220:0.74 222:0.35 235:0.60 236:0.92 239:0.87 241:0.60 242:0.45 243:0.49 244:0.93 245:0.45 247:0.47 248:0.63 253:0.26 255:0.70 256:0.45 257:0.65 259:0.21 260:0.62 265:0.26 266:0.47 267:0.36 268:0.46 271:0.50 276:0.33 277:0.95 285:0.50 289:0.92 297:0.36 300:0.25 +2 1:0.65 7:0.65 8:0.85 9:0.75 10:0.87 11:0.51 12:0.79 17:0.62 18:0.74 21:0.13 23:0.98 27:0.58 29:0.77 30:0.30 31:0.98 32:0.64 34:0.31 36:0.71 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 62:0.97 64:0.77 66:0.51 69:0.35 70:0.92 71:0.84 74:0.41 79:0.98 81:0.64 86:0.72 91:0.82 96:0.91 98:0.61 99:0.83 101:0.47 102:0.94 107:0.93 108:0.27 110:0.94 114:0.92 120:0.64 123:0.26 124:0.52 125:0.88 126:0.51 127:0.79 128:0.98 129:0.59 133:0.46 135:0.47 137:0.98 139:0.71 140:0.97 145:0.41 146:0.49 147:0.55 149:0.09 150:0.56 151:0.76 153:0.86 154:0.49 155:0.64 158:0.73 159:0.35 160:0.88 162:0.57 164:0.65 165:0.65 168:0.98 170:0.77 172:0.41 173:0.50 174:0.83 175:0.75 176:0.70 177:0.70 178:0.42 179:0.82 190:0.77 191:0.70 192:0.97 193:0.95 195:0.61 196:0.93 197:0.86 201:0.63 202:0.82 205:0.98 208:0.61 212:0.39 216:0.27 219:0.88 220:0.62 222:0.35 230:0.68 233:0.76 235:0.60 236:0.95 239:0.88 241:0.85 242:0.13 243:0.61 244:0.61 245:0.63 247:0.76 248:0.75 253:0.87 254:0.74 255:0.78 256:0.81 257:0.65 260:0.65 265:0.62 266:0.54 267:0.19 268:0.82 271:0.50 276:0.41 277:0.69 279:0.75 284:0.98 285:0.62 289:0.95 290:0.92 292:0.88 300:0.70 +0 8:0.72 10:0.81 12:0.79 17:0.73 18:0.78 21:0.78 22:0.93 27:0.43 29:0.77 30:0.68 34:0.82 36:0.29 37:0.84 39:0.46 43:0.08 46:0.88 47:0.93 55:0.71 66:0.79 69:0.56 70:0.43 71:0.84 74:0.30 86:0.70 91:0.44 98:0.77 107:0.91 108:0.43 110:0.92 122:0.75 123:0.41 125:0.88 127:0.26 129:0.05 135:0.47 139:0.68 146:0.68 147:0.73 149:0.09 154:0.15 159:0.26 160:0.88 163:0.81 170:0.77 172:0.41 173:0.66 174:0.79 177:0.88 178:0.55 179:0.77 187:0.39 197:0.82 212:0.42 216:0.29 219:0.88 222:0.35 235:0.22 239:0.81 241:0.18 242:0.19 243:0.80 244:0.93 247:0.55 253:0.27 254:0.99 259:0.21 262:0.93 265:0.77 266:0.38 267:0.19 271:0.50 276:0.34 289:0.90 292:0.88 300:0.33 +0 1:0.57 8:0.72 9:0.71 10:0.82 11:0.42 12:0.79 17:0.47 18:0.96 21:0.64 27:0.43 29:0.77 30:0.55 31:0.90 34:0.25 36:0.49 37:0.52 39:0.46 43:0.21 44:0.86 45:0.66 46:0.88 48:0.91 55:0.52 64:0.77 66:0.20 69:0.25 70:0.52 71:0.84 74:0.35 76:0.85 79:0.89 81:0.33 83:0.56 86:0.83 91:0.89 96:0.79 98:0.69 101:0.47 107:0.92 108:0.73 110:0.93 114:0.66 122:0.92 123:0.19 124:0.55 125:0.88 126:0.32 127:0.95 129:0.59 133:0.46 135:0.49 137:0.90 139:0.81 140:0.94 145:0.80 146:0.65 147:0.70 149:0.24 150:0.37 151:0.56 153:0.48 154:0.14 155:0.54 159:0.46 160:0.88 162:0.48 170:0.77 172:0.65 173:0.34 174:0.79 175:0.75 176:0.60 177:0.70 178:0.53 179:0.58 190:0.99 191:0.86 192:0.51 195:0.68 196:0.91 197:0.81 201:0.54 202:0.90 208:0.50 212:0.87 216:0.28 220:0.91 222:0.35 235:0.80 239:0.81 241:0.94 242:0.75 243:0.40 244:0.97 245:0.83 247:0.74 248:0.61 253:0.70 255:0.70 256:0.78 257:0.65 259:0.21 265:0.58 266:0.47 267:0.23 268:0.79 271:0.50 276:0.64 277:0.69 281:0.86 284:0.97 285:0.50 289:0.93 290:0.66 300:0.55 +1 1:0.59 7:0.66 10:0.84 12:0.79 17:0.69 18:0.96 21:0.64 22:0.93 27:0.39 29:0.77 30:0.21 31:0.87 32:0.64 33:0.97 34:0.52 36:0.35 37:0.67 39:0.46 43:0.06 44:0.88 46:0.88 47:0.93 48:0.97 64:0.77 66:0.12 69:0.90 70:0.97 71:0.84 74:0.57 76:0.85 77:0.89 79:0.87 81:0.42 86:0.87 91:0.07 98:0.27 102:0.95 104:1.00 106:0.81 107:0.96 108:0.98 110:0.97 114:0.64 117:0.86 122:0.82 123:0.84 124:0.97 125:0.88 126:0.51 127:0.86 129:0.81 133:0.97 135:0.99 137:0.87 139:0.85 140:0.45 145:0.84 146:0.49 147:0.23 149:0.10 150:0.44 151:0.50 153:0.48 154:0.16 155:0.53 159:0.95 160:0.88 162:0.86 170:0.77 172:0.90 173:0.57 174:0.92 176:0.60 177:0.70 179:0.14 187:0.21 190:0.99 192:0.56 193:0.95 195:0.99 196:0.90 197:0.85 201:0.57 202:0.36 204:0.82 206:0.81 208:0.61 212:0.68 215:0.49 216:0.89 219:0.88 220:0.91 222:0.35 230:0.68 233:0.65 235:0.88 239:0.87 241:0.02 242:0.33 243:0.07 244:0.83 245:0.93 247:0.96 248:0.50 253:0.52 254:0.86 256:0.45 257:0.65 259:0.21 262:0.93 265:0.26 266:0.98 267:0.36 268:0.46 271:0.50 276:0.95 281:0.86 282:0.73 284:0.87 285:0.46 289:0.95 290:0.64 291:0.97 292:0.88 297:0.36 300:0.98 +0 10:0.81 12:0.79 17:0.89 18:0.57 21:0.78 27:0.72 29:0.77 30:0.45 34:0.52 36:0.34 39:0.46 43:0.06 46:0.88 55:0.96 66:0.10 69:0.90 70:0.25 71:0.84 74:0.26 86:0.58 91:0.27 98:0.05 107:0.88 108:0.80 110:0.89 122:0.82 123:0.78 124:0.82 125:0.88 127:0.31 129:0.89 132:0.97 133:0.78 135:0.30 139:0.56 145:0.58 146:0.05 147:0.05 149:0.05 154:0.06 159:0.91 160:0.88 163:1.00 170:0.77 172:0.05 173:0.59 174:0.79 175:0.97 177:0.05 178:0.55 179:0.96 187:0.39 197:0.81 212:0.61 216:0.17 222:0.35 235:0.05 239:0.80 241:0.15 242:0.63 243:0.29 244:0.98 245:0.37 247:0.30 253:0.24 257:0.93 265:0.05 266:0.17 267:0.73 271:0.50 276:0.17 277:0.95 289:0.88 300:0.18 +0 8:0.93 9:0.70 12:0.79 17:0.49 21:0.59 27:0.53 29:0.77 31:0.87 34:0.90 36:0.37 37:0.88 39:0.46 46:0.88 71:0.84 79:0.87 81:0.27 91:0.76 96:0.73 107:0.88 110:0.88 120:0.57 125:0.88 126:0.24 135:0.34 137:0.87 138:0.97 140:0.92 145:0.40 150:0.30 151:0.57 153:0.48 155:0.54 160:0.88 162:0.49 164:0.55 170:0.77 175:0.99 178:0.51 187:0.21 190:0.99 191:0.77 192:0.57 193:0.95 202:0.72 208:0.61 216:0.18 220:0.87 222:0.35 235:0.74 236:0.91 239:0.82 241:0.78 244:0.99 248:0.56 253:0.50 255:0.69 256:0.58 257:0.97 259:0.21 260:0.57 267:0.23 268:0.59 271:0.50 277:0.98 284:0.88 285:0.60 286:0.99 289:0.95 298:0.99 +0 10:0.89 11:0.51 12:0.79 17:0.84 18:0.90 21:0.22 27:0.25 29:0.77 30:0.21 34:0.52 36:0.95 37:0.36 39:0.46 43:0.63 45:0.66 46:0.88 55:0.52 64:0.77 66:0.51 69:0.72 70:0.73 71:0.84 74:0.30 81:0.31 86:0.82 91:0.12 98:0.75 101:0.47 107:0.91 108:0.55 110:0.92 122:0.92 123:0.53 124:0.66 125:0.88 126:0.24 127:0.61 129:0.05 133:0.66 135:0.54 139:0.77 146:0.78 147:0.28 149:0.33 150:0.35 154:0.52 159:0.51 160:0.88 170:0.77 172:0.51 173:0.74 174:0.88 175:0.75 177:0.38 178:0.55 179:0.68 187:0.39 197:0.90 212:0.35 216:0.68 222:0.35 235:0.22 239:0.88 241:0.07 242:0.24 243:0.26 244:0.83 245:0.65 247:0.76 253:0.27 257:0.84 259:0.21 265:0.75 266:0.63 267:0.28 271:0.50 276:0.54 277:0.69 289:0.90 300:0.55 +2 1:0.65 7:0.66 9:0.74 10:0.84 12:0.79 17:0.67 18:0.96 21:0.47 22:0.93 27:0.39 29:0.77 30:0.28 31:0.91 32:0.64 33:0.97 34:0.59 36:0.30 37:0.67 39:0.46 43:0.07 44:0.86 46:0.88 47:0.93 48:0.97 55:0.42 64:0.77 66:0.27 69:0.92 70:0.90 71:0.84 74:0.59 75:0.96 76:0.85 77:0.89 79:0.91 81:0.59 86:0.87 91:0.22 96:0.77 98:0.37 104:1.00 106:0.81 107:0.97 108:0.95 110:0.97 114:0.72 117:0.86 120:0.65 122:0.82 123:0.95 124:0.94 125:0.88 126:0.54 127:0.74 129:0.81 133:0.94 135:0.98 137:0.91 138:0.96 139:0.85 140:0.80 145:0.90 146:0.65 147:0.25 149:0.10 150:0.55 151:0.72 153:0.46 154:0.15 155:0.64 158:0.74 159:0.89 160:0.88 162:0.80 164:0.67 170:0.77 172:0.89 173:0.77 174:0.90 176:0.62 177:0.70 178:0.42 179:0.16 187:0.21 190:0.95 191:0.73 192:0.97 193:0.95 195:0.97 196:0.93 197:0.85 201:0.64 202:0.56 204:0.82 206:0.81 208:0.64 212:0.77 215:0.58 216:0.89 219:0.90 220:0.62 222:0.35 226:0.95 230:0.68 233:0.65 235:0.84 236:0.92 239:0.87 241:0.12 242:0.36 243:0.09 244:0.83 245:0.93 247:0.94 248:0.76 253:0.72 254:0.96 255:0.73 256:0.64 257:0.65 259:0.21 260:0.65 262:0.93 265:0.39 266:0.91 267:0.69 268:0.62 271:0.50 276:0.94 279:0.82 281:0.91 282:0.73 283:0.66 284:0.92 285:0.60 286:0.99 289:0.95 290:0.72 291:0.97 292:0.87 297:0.36 298:0.99 300:0.94 +1 1:0.64 10:0.88 12:0.79 17:0.66 18:0.66 21:0.40 27:0.57 29:0.77 30:0.21 34:0.87 36:0.39 37:0.54 39:0.46 43:0.08 46:0.88 55:0.42 64:0.77 66:0.10 69:0.32 70:0.50 71:0.84 74:0.35 81:0.59 86:0.66 91:0.15 98:0.27 107:0.90 108:0.25 110:0.91 114:0.74 122:0.95 123:0.58 124:0.77 125:0.88 126:0.54 127:0.57 129:0.05 133:0.72 135:0.63 139:0.64 146:0.34 147:0.41 149:0.08 150:0.54 154:0.53 155:0.50 159:0.52 160:0.88 170:0.77 172:0.27 173:0.31 174:0.89 176:0.62 177:0.88 178:0.55 179:0.87 187:0.39 192:0.49 197:0.92 201:0.64 208:0.64 212:0.37 215:0.63 216:0.44 222:0.35 235:0.74 236:0.92 239:0.87 241:0.50 242:0.40 243:0.51 244:0.83 245:0.47 247:0.55 253:0.54 254:0.80 259:0.21 265:0.29 266:0.47 267:0.69 271:0.50 276:0.35 277:0.95 289:0.95 290:0.74 297:0.36 300:0.33 +2 1:0.68 7:0.65 9:0.71 10:0.85 11:0.51 12:0.79 17:0.61 18:0.83 21:0.30 27:0.60 29:0.77 30:0.11 31:0.88 32:0.64 34:0.76 36:0.86 37:0.83 39:0.46 43:0.08 45:0.66 46:0.88 55:0.42 64:0.77 66:0.49 69:0.84 70:0.93 71:0.84 74:0.37 79:0.88 81:0.71 86:0.75 91:0.44 96:0.71 98:0.66 99:0.83 101:0.47 102:0.94 107:0.94 108:0.31 110:0.95 114:0.82 120:0.58 122:0.92 123:0.30 124:0.56 125:0.88 126:0.54 127:0.70 129:0.05 132:0.97 133:0.45 135:0.81 137:0.88 138:0.96 139:0.72 140:0.80 145:0.41 146:0.59 147:0.67 149:0.11 150:0.61 151:0.61 153:0.48 154:0.46 155:0.58 159:0.42 160:0.88 162:0.54 164:0.55 165:0.65 170:0.77 172:0.48 173:0.93 174:0.79 176:0.70 177:0.70 178:0.42 179:0.72 187:0.39 190:0.95 192:0.86 193:0.95 195:0.67 196:0.89 197:0.81 201:0.67 202:0.56 208:0.64 212:0.37 215:0.67 216:0.20 220:0.82 222:0.35 230:0.68 233:0.76 235:0.88 236:0.95 239:0.87 241:0.66 242:0.16 243:0.60 244:0.61 245:0.71 247:0.84 248:0.59 253:0.60 254:0.74 255:0.70 256:0.45 257:0.65 260:0.58 265:0.66 266:0.54 267:0.28 268:0.46 271:0.50 276:0.51 277:0.69 284:0.88 285:0.60 286:0.99 289:0.95 290:0.82 297:0.36 298:0.99 300:0.70 +1 1:0.65 8:0.78 10:0.85 11:0.42 12:0.79 17:0.86 18:0.90 21:0.13 27:0.15 29:0.77 30:0.21 32:0.64 34:0.92 36:0.95 37:0.94 39:0.46 43:0.07 44:0.86 45:0.66 46:0.88 64:0.77 66:0.49 69:0.49 70:0.91 71:0.84 74:0.53 77:0.70 81:0.55 86:0.84 91:0.23 98:0.55 101:0.47 107:0.96 108:0.38 110:0.96 114:0.80 122:0.82 123:0.36 124:0.75 125:0.88 126:0.51 127:0.55 129:0.05 133:0.76 135:0.93 139:0.80 145:0.41 146:0.67 147:0.72 149:0.08 150:0.55 154:0.48 155:0.50 158:0.73 159:0.57 160:0.88 170:0.77 172:0.70 173:0.43 174:0.83 176:0.60 177:0.88 178:0.55 179:0.43 187:0.39 191:0.82 192:0.50 195:0.78 197:0.86 201:0.63 208:0.61 212:0.58 215:0.79 216:0.26 219:0.88 222:0.35 235:0.31 239:0.84 241:0.30 242:0.30 243:0.60 244:0.83 245:0.78 247:0.92 253:0.59 254:0.90 259:0.21 265:0.56 266:0.63 267:0.36 271:0.50 276:0.70 279:0.78 282:0.73 283:0.66 289:0.95 290:0.80 292:0.87 297:0.36 300:0.84 +1 1:0.59 7:0.66 10:0.84 12:0.79 17:0.62 18:0.95 21:0.64 22:0.93 27:0.39 29:0.77 30:0.21 32:0.64 33:0.97 34:0.58 36:0.25 37:0.67 39:0.46 43:0.06 44:0.88 46:0.88 47:0.93 48:0.91 64:0.77 66:0.11 69:0.91 70:0.98 71:0.84 74:0.56 76:0.85 77:0.89 81:0.40 86:0.86 91:0.10 98:0.26 102:0.95 104:1.00 106:0.81 107:0.96 108:0.98 110:0.97 114:0.64 117:0.86 122:0.82 123:0.84 124:0.97 125:0.88 126:0.51 127:0.92 129:0.81 133:0.97 135:0.99 139:0.84 145:0.80 146:0.26 147:0.23 149:0.09 150:0.42 154:0.16 155:0.54 159:0.96 160:0.88 170:0.77 172:0.90 173:0.58 174:0.92 176:0.60 177:0.70 178:0.55 179:0.15 187:0.21 192:0.56 193:0.95 195:0.99 197:0.85 201:0.58 202:0.36 204:0.82 206:0.81 208:0.61 212:0.68 215:0.49 216:0.89 222:0.35 230:0.68 233:0.65 235:0.84 239:0.87 241:0.10 242:0.33 243:0.07 244:0.83 245:0.92 247:0.97 253:0.52 254:0.86 257:0.65 259:0.21 262:0.93 265:0.27 266:0.98 267:0.28 271:0.50 276:0.95 281:0.86 282:0.73 289:0.95 290:0.64 291:0.97 297:0.36 300:0.98 +1 1:0.59 9:0.70 10:0.86 11:0.51 12:0.79 17:0.18 18:0.81 21:0.64 27:0.45 29:0.77 30:0.11 31:0.86 34:0.75 36:0.17 37:0.50 39:0.46 43:0.57 45:0.66 46:0.88 55:0.71 64:0.77 66:0.29 69:0.70 70:0.99 71:0.84 74:0.35 79:0.86 81:0.40 86:0.72 91:0.29 96:0.68 98:0.31 101:0.47 107:0.90 108:0.53 110:0.92 114:0.64 120:0.54 122:0.92 123:0.51 124:0.69 125:0.88 126:0.51 127:0.43 129:0.05 133:0.60 135:0.89 137:0.86 138:0.95 139:0.72 140:0.45 145:0.54 146:0.45 147:0.52 149:0.26 150:0.42 151:0.48 153:0.48 154:0.45 155:0.48 158:0.73 159:0.57 160:0.88 162:0.86 164:0.55 170:0.77 172:0.27 173:0.65 174:0.83 176:0.60 177:0.88 179:0.78 187:0.68 190:0.95 192:0.47 196:0.87 197:0.85 201:0.58 202:0.36 208:0.61 212:0.14 215:0.49 216:0.77 219:0.88 220:0.99 222:0.35 235:0.84 239:0.85 241:0.37 242:0.29 243:0.48 244:0.83 245:0.55 247:0.60 248:0.48 253:0.50 255:0.68 256:0.45 259:0.21 260:0.54 265:0.34 266:0.71 267:0.28 268:0.46 271:0.50 276:0.37 279:0.78 283:0.66 284:0.88 285:0.60 286:0.99 289:0.95 290:0.64 292:0.87 297:0.36 298:0.99 300:0.84 +2 1:0.67 7:0.66 10:0.87 12:0.79 17:0.81 18:0.96 21:0.47 27:0.33 29:0.77 30:0.27 32:0.64 34:0.77 36:0.25 37:0.79 39:0.46 43:0.07 44:0.86 46:0.88 48:0.98 55:0.42 64:1.00 66:0.26 69:0.93 70:0.92 71:0.84 74:0.56 76:0.85 77:0.89 81:0.66 86:0.88 91:0.14 98:0.36 107:0.97 108:0.95 110:0.97 114:0.72 122:0.82 123:0.95 124:0.94 125:0.88 126:0.54 127:0.73 129:0.81 133:0.95 135:0.95 139:0.85 145:0.97 146:0.65 147:0.30 149:0.10 150:0.61 154:0.15 155:0.54 159:0.89 160:0.88 170:0.77 172:0.89 173:0.79 174:0.92 176:0.62 177:0.38 178:0.55 179:0.15 187:0.39 192:0.56 193:0.95 195:0.97 197:0.88 201:0.66 204:0.80 208:0.64 212:0.75 215:0.58 216:0.27 222:0.35 230:0.68 233:0.65 235:0.84 236:0.92 239:0.86 241:0.01 242:0.36 243:0.06 244:0.83 245:0.93 247:0.95 253:0.55 254:0.96 257:0.84 259:0.21 265:0.38 266:0.91 267:0.79 271:0.50 276:0.94 281:0.91 282:0.73 289:0.95 290:0.72 297:0.99 300:0.94 +2 1:0.67 7:0.66 10:0.84 12:0.79 17:0.66 18:0.95 21:0.22 22:0.93 27:0.39 29:0.77 30:0.30 32:0.64 33:0.97 34:0.63 36:0.47 37:0.67 39:0.46 43:0.10 44:0.86 46:0.88 47:0.93 48:0.97 64:0.77 66:0.29 69:0.92 70:0.89 71:0.84 74:0.59 76:0.85 77:0.89 81:0.71 86:0.86 91:0.07 98:0.39 99:0.83 104:1.00 106:0.81 107:0.97 108:0.95 110:0.97 114:0.84 117:0.86 122:0.82 123:0.95 124:0.93 125:0.88 126:0.54 127:0.73 129:0.81 133:0.93 135:0.97 139:0.85 145:0.89 146:0.65 147:0.23 149:0.14 150:0.61 154:0.15 155:0.55 158:0.73 159:0.89 160:0.88 165:0.65 170:0.77 172:0.90 173:0.77 174:0.90 176:0.70 177:0.70 178:0.55 179:0.15 187:0.21 192:0.57 193:0.95 195:0.97 197:0.85 201:0.66 202:0.36 204:0.82 206:0.81 208:0.64 212:0.73 215:0.72 216:0.89 219:0.88 222:0.35 230:0.68 233:0.65 235:0.80 236:0.95 239:0.85 241:0.10 242:0.37 243:0.07 244:0.83 245:0.94 247:0.96 253:0.62 254:0.96 257:0.65 259:0.21 262:0.93 265:0.41 266:0.89 267:0.23 271:0.50 276:0.94 279:0.82 281:0.91 282:0.73 283:0.66 289:0.95 290:0.84 291:0.97 292:0.87 297:0.36 300:0.94 +1 1:0.65 9:0.72 10:0.85 11:0.42 12:0.79 17:0.85 18:0.90 21:0.13 27:0.15 29:0.77 30:0.21 31:0.89 32:0.64 34:0.83 36:0.96 37:0.94 39:0.46 43:0.07 44:0.86 45:0.66 46:0.88 64:0.77 66:0.49 69:0.49 70:0.91 71:0.84 74:0.53 77:0.70 79:0.89 81:0.55 86:0.84 91:0.20 96:0.72 98:0.55 101:0.47 107:0.96 108:0.38 110:0.96 114:0.80 120:0.59 122:0.82 123:0.36 124:0.75 125:0.88 126:0.51 127:0.55 129:0.05 133:0.76 135:0.93 137:0.89 138:0.95 139:0.80 140:0.45 145:0.41 146:0.67 147:0.72 149:0.08 150:0.55 151:0.65 153:0.48 154:0.48 155:0.58 158:0.73 159:0.57 160:0.88 162:0.86 164:0.55 170:0.77 172:0.70 173:0.43 174:0.83 176:0.60 177:0.88 179:0.43 187:0.39 190:0.95 192:0.87 195:0.78 196:0.91 197:0.86 201:0.63 202:0.36 208:0.61 212:0.58 215:0.79 216:0.26 219:0.88 220:0.74 222:0.35 235:0.31 239:0.84 241:0.27 242:0.30 243:0.60 244:0.83 245:0.78 247:0.92 248:0.63 253:0.63 254:0.90 255:0.71 256:0.45 259:0.21 260:0.60 265:0.56 266:0.63 267:0.52 268:0.46 271:0.50 276:0.70 279:0.78 282:0.73 283:0.66 284:0.88 285:0.60 286:0.99 289:0.95 290:0.80 292:0.87 297:0.36 298:0.99 300:0.84 +1 1:0.62 10:0.87 12:0.79 17:0.38 18:0.80 21:0.40 27:0.45 29:0.77 30:0.45 34:0.80 36:0.18 37:0.50 39:0.46 43:0.36 46:0.88 55:0.71 64:0.77 66:0.49 69:0.61 70:0.85 71:0.84 74:0.35 81:0.48 86:0.69 91:0.22 98:0.57 107:0.90 108:0.78 110:0.91 114:0.72 122:0.94 123:0.77 124:0.65 125:0.88 126:0.51 127:0.42 129:0.05 133:0.60 135:0.87 139:0.69 145:0.47 146:0.39 147:0.46 149:0.31 150:0.48 154:0.27 155:0.49 158:0.73 159:0.55 160:0.88 170:0.77 172:0.48 173:0.55 174:0.92 176:0.60 177:0.88 178:0.55 179:0.65 187:0.68 192:0.48 193:0.95 197:0.93 201:0.61 208:0.61 212:0.48 215:0.63 216:0.77 219:0.88 222:0.35 235:0.60 239:0.87 241:0.15 242:0.28 243:0.38 244:0.93 245:0.63 247:0.74 253:0.53 259:0.21 265:0.58 266:0.63 267:0.23 271:0.50 276:0.50 277:0.87 279:0.78 283:0.66 289:0.95 290:0.72 292:0.87 297:0.36 300:0.70 +0 10:0.96 12:0.79 17:0.85 18:0.92 21:0.47 27:0.43 29:0.77 30:0.40 34:0.82 36:0.25 37:0.78 39:0.46 43:0.07 46:0.88 55:0.71 66:0.60 69:0.73 70:0.71 71:0.84 74:0.42 75:0.95 81:0.33 86:0.82 91:0.15 98:0.81 102:0.94 107:0.96 108:0.56 110:0.96 122:0.94 123:0.54 124:0.51 125:0.88 126:0.24 127:0.72 129:0.05 133:0.39 135:0.71 139:0.77 145:0.74 146:0.98 147:0.99 149:0.20 150:0.36 154:0.16 159:0.86 160:0.88 170:0.77 172:0.94 173:0.49 174:0.99 177:0.88 178:0.55 179:0.17 187:1.00 193:0.95 195:0.95 197:0.98 212:0.57 216:0.60 222:0.35 226:0.96 235:0.74 236:0.91 239:0.95 241:0.05 242:0.13 243:0.67 244:0.83 245:0.98 247:0.96 253:0.36 254:0.84 259:0.21 265:0.81 266:0.80 267:0.28 271:0.50 276:0.93 289:0.94 300:0.84 +0 10:0.87 12:0.79 17:0.72 18:0.84 21:0.78 27:0.43 29:0.77 30:0.38 34:0.89 36:0.63 37:0.84 39:0.46 43:0.08 46:0.88 55:0.71 66:0.77 69:0.56 70:0.83 71:0.84 74:0.30 86:0.74 91:0.22 98:0.62 104:0.94 106:0.81 107:0.92 108:0.43 110:0.93 122:0.51 123:0.41 124:0.41 125:0.88 127:0.63 129:0.05 133:0.59 135:0.69 139:0.70 146:0.86 147:0.88 149:0.11 154:0.16 159:0.74 160:0.88 170:0.77 172:0.68 173:0.39 174:0.88 177:0.88 178:0.55 179:0.61 187:0.39 197:0.87 206:0.81 212:0.55 216:0.29 222:0.35 235:0.22 239:0.85 241:0.10 242:0.18 243:0.79 244:0.93 245:0.56 247:0.86 253:0.27 254:0.90 259:0.21 265:0.63 266:0.71 267:0.73 271:0.50 276:0.55 283:0.67 289:0.91 300:0.70 +0 10:0.93 12:0.79 17:0.73 18:0.93 21:0.54 27:0.57 29:0.77 30:0.30 33:0.97 34:0.71 36:0.92 37:0.54 39:0.46 43:0.08 46:0.88 55:0.84 66:0.36 69:0.73 70:0.80 71:0.84 74:0.27 75:0.95 81:0.28 86:0.82 91:0.05 98:0.61 107:0.91 108:0.56 110:0.93 122:0.92 123:0.54 124:0.85 125:0.88 126:0.24 127:0.88 129:0.81 133:0.86 135:0.87 139:0.77 146:0.91 147:0.37 149:0.18 150:0.30 154:0.17 159:0.84 160:0.88 170:0.77 172:0.76 173:0.52 174:0.97 175:0.75 177:0.38 178:0.55 179:0.35 187:0.68 197:0.97 212:0.30 216:0.44 222:0.35 226:0.98 235:0.68 236:0.91 239:0.92 241:0.21 242:0.15 243:0.10 244:0.93 245:0.83 247:0.84 253:0.24 254:0.92 257:0.84 259:0.21 265:0.62 266:0.80 267:0.28 271:0.50 276:0.81 277:0.69 289:0.90 291:0.97 300:0.70 +0 7:0.76 11:0.42 12:0.37 17:0.34 21:0.40 27:0.21 28:0.62 30:0.30 34:0.47 36:0.99 37:0.74 39:0.68 43:0.45 55:0.84 66:0.54 69:0.90 70:0.75 74:0.37 81:0.54 91:0.48 98:0.72 104:0.84 106:0.81 108:0.80 120:0.84 123:0.78 124:0.67 126:0.32 127:0.65 129:0.05 131:0.89 133:0.74 135:0.28 140:0.45 144:0.57 146:0.93 147:0.31 149:0.68 150:0.41 151:0.70 153:0.90 154:0.53 157:0.61 159:0.78 161:0.67 162:0.63 163:0.81 164:0.81 166:0.84 167:0.59 172:0.77 173:0.83 177:0.05 179:0.40 181:0.97 182:0.61 187:0.39 190:0.77 202:0.77 206:0.81 212:0.19 215:0.67 216:0.95 220:0.62 222:0.35 227:0.91 229:0.61 230:0.78 231:0.82 233:0.69 235:0.42 241:0.32 242:0.81 243:0.11 245:0.80 246:0.61 247:0.39 248:0.76 253:0.33 254:0.80 256:0.78 257:0.65 259:0.21 260:0.83 265:0.73 266:0.89 267:0.87 268:0.78 271:0.33 276:0.76 283:0.71 285:0.50 287:0.61 297:0.36 300:0.55 +0 1:0.74 9:0.80 12:0.37 17:0.40 21:0.47 27:0.68 28:0.62 30:0.45 32:0.72 34:0.26 36:0.71 37:0.35 39:0.68 43:0.31 55:0.59 66:0.27 69:0.67 70:0.25 81:0.65 91:0.12 96:0.69 98:0.18 108:0.51 114:0.80 120:0.55 123:0.66 124:0.61 126:0.54 127:0.14 129:0.59 131:0.96 133:0.47 135:0.54 140:0.45 144:0.57 145:0.56 146:0.24 147:0.30 149:0.27 150:0.71 151:0.51 153:0.48 154:0.23 155:0.67 157:0.61 159:0.27 161:0.67 162:0.86 163:0.89 164:0.55 166:0.94 167:0.63 172:0.10 173:0.52 175:0.75 176:0.73 177:0.05 179:0.67 181:0.97 182:0.61 187:0.39 192:0.59 195:0.91 201:0.74 202:0.36 208:0.64 212:0.61 215:0.63 216:0.63 220:0.96 222:0.35 227:0.96 229:0.61 231:0.90 232:0.98 235:0.60 241:0.47 242:0.82 243:0.46 244:0.61 245:0.40 246:0.61 247:0.12 248:0.50 253:0.61 255:0.79 256:0.45 257:0.65 259:0.21 260:0.55 265:0.15 266:0.17 267:0.28 268:0.46 271:0.33 276:0.18 277:0.69 285:0.62 287:0.61 290:0.80 297:0.36 300:0.18 +0 1:0.74 7:0.76 12:0.37 17:0.05 21:0.59 27:0.12 28:0.62 30:0.42 32:0.72 34:0.75 36:0.96 37:0.88 39:0.68 43:0.21 55:0.45 66:0.86 69:0.93 70:0.53 74:0.51 76:0.85 81:0.57 91:0.10 98:0.64 108:0.86 114:0.54 123:0.85 124:0.37 126:0.54 127:0.23 129:0.05 131:0.61 133:0.39 135:0.54 145:0.89 146:0.91 147:0.05 149:0.51 150:0.65 154:0.14 155:0.67 157:0.61 159:0.61 161:0.67 163:0.81 166:0.93 167:0.56 172:0.79 173:0.89 176:0.53 177:0.05 178:0.55 179:0.27 181:0.97 182:0.61 187:0.68 192:0.59 195:0.92 201:0.74 212:0.30 215:0.55 216:0.82 222:0.35 227:0.61 229:0.61 230:0.78 231:0.90 233:0.69 235:0.74 241:0.18 242:0.79 243:0.08 244:0.61 245:0.62 246:0.61 247:0.47 253:0.43 257:0.65 259:0.21 265:0.65 266:0.71 267:0.85 271:0.33 276:0.69 287:0.61 290:0.54 297:0.36 300:0.55 +0 7:0.76 12:0.37 21:0.13 27:0.38 28:0.62 30:0.67 32:0.72 34:0.56 36:0.99 37:0.59 39:0.68 43:0.35 55:0.84 66:0.51 69:0.55 70:0.39 74:0.42 81:0.76 91:0.65 98:0.76 108:0.81 120:0.92 123:0.80 124:0.56 126:0.32 127:0.64 129:0.05 131:0.89 133:0.39 135:0.96 140:0.45 145:0.79 146:0.82 147:0.85 149:0.62 150:0.56 151:0.70 153:0.69 154:0.26 157:0.61 159:0.74 161:0.67 162:0.79 163:0.92 164:0.88 166:0.84 167:0.54 172:0.65 173:0.39 177:0.38 179:0.52 181:0.97 182:0.61 187:0.87 190:0.77 195:0.89 202:0.82 212:0.23 215:0.84 216:0.93 220:0.62 222:0.35 227:0.91 229:0.61 230:0.79 231:0.84 233:0.76 235:0.12 241:0.12 242:0.79 243:0.44 244:0.61 245:0.84 246:0.61 247:0.47 248:0.88 253:0.37 256:0.86 259:0.21 260:0.92 265:0.76 266:0.75 267:0.96 268:0.86 271:0.33 276:0.68 277:0.69 283:0.82 285:0.50 287:0.61 297:0.36 300:0.33 +4 6:0.99 8:0.96 9:0.80 12:0.37 17:0.34 20:0.82 21:0.78 27:0.10 28:0.62 34:0.45 36:0.22 37:0.97 39:0.68 41:0.82 78:0.99 83:0.74 91:0.88 96:0.77 100:0.99 104:0.54 111:1.00 120:0.65 131:0.96 135:0.78 140:0.87 144:0.57 151:0.77 152:0.86 153:0.48 155:0.67 157:0.61 161:0.67 162:0.49 164:0.57 166:0.61 167:0.91 169:1.00 175:0.91 178:0.48 181:0.97 182:0.93 186:0.98 189:0.99 191:0.87 192:0.59 202:0.64 208:0.64 216:0.29 220:0.62 222:0.35 227:0.96 229:0.97 231:0.90 232:0.74 238:1.00 241:0.90 244:0.83 246:0.61 248:0.71 253:0.63 255:0.79 256:0.45 257:0.84 259:0.21 260:0.66 261:0.98 267:0.36 268:0.46 271:0.33 277:0.87 285:0.62 287:0.95 +1 1:0.74 7:0.81 11:0.64 12:0.37 17:0.28 21:0.71 27:0.35 28:0.62 30:0.76 32:0.80 34:0.97 36:0.30 37:0.62 39:0.68 43:0.83 60:0.87 66:0.36 69:0.66 70:0.29 74:0.80 77:0.70 81:0.50 83:0.85 85:0.85 91:0.40 98:0.12 101:0.74 108:0.50 114:0.68 121:0.90 122:0.43 123:0.48 124:0.69 126:0.54 127:0.15 129:0.59 131:0.61 133:0.64 135:0.47 144:0.57 145:0.49 146:0.22 147:0.28 149:0.75 150:0.66 154:0.80 155:0.67 157:0.88 158:0.87 159:0.34 161:0.67 165:0.69 166:0.93 167:0.62 172:0.21 173:0.48 176:0.73 177:0.38 178:0.55 179:0.49 181:0.97 182:0.93 187:0.87 192:0.59 195:0.91 201:0.74 204:0.89 208:0.64 212:0.23 215:0.48 216:0.62 222:0.35 227:0.61 229:0.61 230:0.87 231:0.89 233:0.76 235:0.88 241:0.43 242:0.73 243:0.51 245:0.45 246:0.93 247:0.30 253:0.65 259:0.21 265:0.13 266:0.38 267:0.95 271:0.33 276:0.25 279:0.86 282:0.88 283:0.71 287:0.61 290:0.68 297:0.36 299:0.88 300:0.25 +1 1:0.74 11:0.64 12:0.37 17:0.38 21:0.78 27:0.45 28:0.62 30:0.95 32:0.80 34:0.80 36:0.18 37:0.50 39:0.68 43:0.82 66:0.54 69:0.72 74:0.56 77:0.70 81:0.40 83:0.71 85:0.72 91:0.17 98:0.08 101:0.71 108:0.55 114:0.63 123:0.53 126:0.54 127:0.06 129:0.05 131:0.61 135:0.91 144:0.57 145:0.70 146:0.13 147:0.18 149:0.46 150:0.61 154:0.77 155:0.67 157:0.79 159:0.08 161:0.67 165:0.63 166:0.93 167:0.62 172:0.10 173:0.65 176:0.73 177:0.38 178:0.55 179:0.08 181:0.97 182:0.92 187:0.68 192:0.59 195:0.80 201:0.74 215:0.43 216:0.77 222:0.35 227:0.61 229:0.61 231:0.89 235:1.00 241:0.44 243:0.63 246:0.93 253:0.53 259:0.21 265:0.09 267:0.28 271:0.33 282:0.88 287:0.61 290:0.63 297:0.36 299:0.88 300:0.08 +0 12:0.37 17:0.49 21:0.78 27:0.45 28:0.62 30:0.91 34:0.79 36:0.17 37:0.50 39:0.68 43:0.26 55:0.59 66:0.69 69:0.79 70:0.13 74:0.52 91:0.20 98:0.21 108:0.63 122:0.51 123:0.61 127:0.10 129:0.05 131:0.61 135:0.89 145:0.47 146:0.32 147:0.39 149:0.26 154:0.19 157:0.61 159:0.13 161:0.67 166:0.61 167:0.56 172:0.21 173:0.75 177:0.38 178:0.55 179:0.24 181:0.97 182:0.61 187:0.68 212:0.61 216:0.77 222:0.35 227:0.61 229:0.61 231:0.69 235:0.74 241:0.21 242:0.63 243:0.73 244:0.61 246:0.61 247:0.30 253:0.43 259:0.21 265:0.23 266:0.17 267:0.52 271:0.33 276:0.23 287:0.61 300:0.13 +0 7:0.76 8:0.61 12:0.37 17:0.75 21:0.13 27:0.63 28:0.62 30:0.54 32:0.72 34:1.00 36:0.38 37:0.69 39:0.68 43:0.21 55:0.59 66:0.88 69:0.91 70:0.42 74:0.49 81:0.76 91:0.35 98:0.62 104:0.88 106:0.81 108:0.81 123:0.80 126:0.32 127:0.18 129:0.05 131:0.61 135:0.94 145:0.44 146:0.86 147:0.88 149:0.39 150:0.56 154:0.30 157:0.61 159:0.43 161:0.67 163:0.81 166:0.84 167:0.59 172:0.67 173:0.87 177:0.38 178:0.55 179:0.32 181:0.97 182:0.61 187:0.39 195:0.87 206:0.81 212:0.30 215:0.84 216:0.24 222:0.35 227:0.61 229:0.61 230:0.78 231:0.87 233:0.69 235:0.12 241:0.32 242:0.59 243:0.89 246:0.61 247:0.55 253:0.41 254:0.93 259:0.21 265:0.63 266:0.63 267:0.65 271:0.33 276:0.56 287:0.61 297:0.36 300:0.33 +0 12:0.37 17:0.84 21:0.59 27:0.72 28:0.62 30:0.68 39:0.68 43:0.24 55:0.96 66:0.32 69:0.93 70:0.31 74:0.46 81:0.33 91:0.65 96:0.77 98:0.33 108:0.85 114:0.54 120:0.69 123:0.84 124:0.83 126:0.32 127:0.41 129:0.05 131:0.96 133:0.82 140:0.45 146:0.63 147:0.05 149:0.26 150:0.30 151:0.66 153:0.48 154:0.17 157:0.61 158:0.82 159:0.74 161:0.67 162:0.82 163:0.94 164:0.55 166:0.77 167:0.86 172:0.21 173:0.88 176:0.53 177:0.05 179:0.86 181:0.97 182:0.61 190:0.77 202:0.58 212:0.19 216:0.04 220:0.74 222:0.35 227:0.96 229:0.61 231:0.90 232:0.74 235:0.68 241:0.78 242:0.63 243:0.19 244:0.61 245:0.43 246:0.61 247:0.39 248:0.68 253:0.61 256:0.64 257:0.65 259:0.21 260:0.69 265:0.36 266:0.47 268:0.64 271:0.33 276:0.34 285:0.62 287:0.61 290:0.54 300:0.25 +0 7:0.76 12:0.37 17:0.59 21:0.22 27:0.34 28:0.62 30:0.45 32:0.72 34:0.86 36:0.65 37:0.46 39:0.68 43:0.32 55:0.59 66:0.75 69:0.64 70:0.43 74:0.37 81:0.68 91:0.07 98:0.59 104:0.86 106:0.81 108:0.49 123:0.47 124:0.39 126:0.32 127:0.22 129:0.05 131:0.61 133:0.39 135:0.91 145:0.59 146:0.71 147:0.75 149:0.48 150:0.49 154:0.62 157:0.61 159:0.38 161:0.67 163:0.90 166:0.84 167:0.58 172:0.54 173:0.60 177:0.38 178:0.55 179:0.54 181:0.97 182:0.61 187:0.87 195:0.75 206:0.81 212:0.36 215:0.79 216:0.53 222:0.35 227:0.61 229:0.61 230:0.78 231:0.82 233:0.69 235:0.22 241:0.30 242:0.77 243:0.77 245:0.50 246:0.61 247:0.39 253:0.34 254:0.96 259:0.21 265:0.60 266:0.54 267:0.23 271:0.33 276:0.46 287:0.61 297:0.36 300:0.33 +1 1:0.74 6:0.79 9:0.80 11:0.64 12:0.37 17:0.51 20:0.81 21:0.05 27:0.38 28:0.62 30:0.30 32:0.72 34:0.29 36:0.73 37:0.63 39:0.68 41:0.82 43:0.83 55:0.92 66:0.62 69:0.65 70:0.82 74:0.48 78:0.80 81:0.90 83:0.79 85:0.72 91:0.11 96:0.77 98:0.81 100:0.80 101:0.69 104:0.88 106:0.81 108:0.75 111:0.79 114:0.95 117:0.86 120:0.65 123:0.73 124:0.55 126:0.54 127:0.61 129:0.59 131:0.96 133:0.64 135:0.61 140:0.45 144:0.57 145:0.41 146:0.70 147:0.74 149:0.76 150:0.95 151:0.77 152:0.82 153:0.48 154:0.78 155:0.67 157:0.78 159:0.82 161:0.67 162:0.86 164:0.57 165:0.90 166:0.94 167:0.49 169:0.78 172:0.84 173:0.42 176:0.73 177:0.38 179:0.31 181:0.97 182:0.94 186:0.81 187:0.68 192:0.59 195:0.93 201:0.74 202:0.36 206:0.81 208:0.64 212:0.47 215:0.91 216:0.72 220:0.62 222:0.35 227:0.96 229:0.97 231:0.91 232:0.82 235:0.12 242:0.80 243:0.32 245:0.87 246:0.94 247:0.55 248:0.71 253:0.79 255:0.79 256:0.45 259:0.21 260:0.66 261:0.81 265:0.81 266:0.75 267:0.95 268:0.46 271:0.33 276:0.82 285:0.62 287:0.95 290:0.95 297:0.36 300:0.84 +0 7:0.76 12:0.37 17:0.95 20:0.89 21:0.76 27:0.63 28:0.62 30:0.62 34:0.99 36:0.43 37:0.28 39:0.68 43:0.18 55:0.96 66:0.30 69:0.94 70:0.34 74:0.50 77:0.96 78:0.72 81:0.29 91:0.25 98:0.22 100:0.73 104:0.93 106:0.81 108:0.91 111:0.72 120:0.55 122:0.41 123:0.90 124:0.71 126:0.32 127:0.18 129:0.81 131:0.89 133:0.67 135:0.82 140:0.45 145:1.00 146:0.23 147:0.29 149:0.19 150:0.28 151:0.48 152:0.83 153:0.48 154:0.12 157:0.61 159:0.47 161:0.67 162:0.86 163:0.87 164:0.55 166:0.84 167:0.52 169:0.72 172:0.16 173:0.93 175:0.75 177:0.05 179:0.73 181:0.97 182:0.61 186:0.73 187:0.21 190:0.77 195:0.91 202:0.36 206:0.81 212:0.30 215:0.46 216:0.28 220:0.96 222:0.35 227:0.91 229:0.61 230:0.79 231:0.88 233:0.76 235:0.95 241:0.05 242:0.33 243:0.37 244:0.61 245:0.43 246:0.61 247:0.39 248:0.48 253:0.42 256:0.45 257:0.65 259:0.21 260:0.55 261:0.73 265:0.24 266:0.27 267:0.95 268:0.46 271:0.33 276:0.27 277:0.69 282:0.81 283:0.71 285:0.50 287:0.61 297:0.36 300:0.25 +1 12:0.37 17:0.53 21:0.05 27:0.58 28:0.62 30:0.54 34:0.18 36:0.46 37:0.35 39:0.68 43:0.34 55:0.59 66:0.95 69:0.59 70:0.57 74:0.66 81:0.84 91:0.12 98:0.95 104:0.80 106:0.81 108:0.45 123:0.43 126:0.32 127:0.64 129:0.05 131:0.61 135:0.66 146:0.95 147:0.96 149:0.75 150:0.64 154:0.61 157:0.61 158:0.82 159:0.63 161:0.67 166:0.84 167:0.57 172:0.88 173:0.51 177:0.38 178:0.55 179:0.35 181:0.97 182:0.61 187:0.21 206:0.81 212:0.70 215:0.91 216:0.86 222:0.35 227:0.61 229:0.61 231:0.87 235:0.05 241:0.24 242:0.80 243:0.95 246:0.61 247:0.47 253:0.50 254:0.96 259:0.21 265:0.95 266:0.38 267:0.36 271:0.33 276:0.80 283:0.82 287:0.61 297:0.36 300:0.55 +0 11:0.42 12:0.37 17:0.75 21:0.30 27:0.58 28:0.62 30:0.67 32:0.77 34:0.19 36:0.98 37:0.35 39:0.68 43:0.32 55:0.71 66:0.51 69:0.39 70:0.47 74:0.60 77:0.70 81:0.60 91:0.06 98:0.70 108:0.30 120:0.58 123:0.29 124:0.55 126:0.32 127:0.67 129:0.59 131:0.89 133:0.47 135:0.32 140:0.45 144:0.57 145:0.77 146:0.72 147:0.76 149:0.58 150:0.45 151:0.52 153:0.48 154:0.81 157:0.61 159:0.49 161:0.67 162:0.86 164:0.55 166:0.84 167:0.54 172:0.57 173:0.41 177:0.38 179:0.64 181:0.97 182:0.61 187:0.95 190:0.77 195:0.66 202:0.36 212:0.61 215:0.72 216:0.85 220:0.87 222:0.35 227:0.91 229:0.61 231:0.87 232:0.80 235:0.31 241:0.12 242:0.79 243:0.61 245:0.77 246:0.61 247:0.39 248:0.52 253:0.47 254:0.84 256:0.45 259:0.21 260:0.58 265:0.71 266:0.38 267:0.91 268:0.46 271:0.33 276:0.59 282:0.85 285:0.50 287:0.61 297:0.36 300:0.43 +0 12:0.37 17:0.20 20:0.77 21:0.30 27:0.15 28:0.62 30:0.76 34:0.39 36:0.70 37:0.56 39:0.68 43:0.39 55:0.92 66:0.64 69:0.43 70:0.21 74:0.42 78:0.72 81:0.60 91:0.64 98:0.81 100:0.73 104:0.91 106:0.81 108:0.54 111:0.72 120:0.63 123:0.52 124:0.42 126:0.32 127:0.53 129:0.59 131:0.89 133:0.47 135:0.90 140:0.45 146:0.05 147:0.05 149:0.38 150:0.45 151:0.59 152:0.75 153:0.72 154:0.29 157:0.61 159:0.36 161:0.67 162:0.67 163:0.94 164:0.63 166:0.84 167:0.68 169:0.72 172:0.32 173:0.52 175:0.75 177:0.05 179:0.91 181:0.97 182:0.61 186:0.73 187:0.68 190:0.77 202:0.53 206:0.81 212:0.52 215:0.72 216:0.86 220:0.74 222:0.35 227:0.91 229:0.61 231:0.83 235:0.31 241:0.57 242:0.78 243:0.21 244:0.61 245:0.43 246:0.61 247:0.30 248:0.57 253:0.37 256:0.45 257:0.65 259:0.21 260:0.64 261:0.73 265:0.81 266:0.27 267:0.52 268:0.57 271:0.33 276:0.32 277:0.69 285:0.50 287:0.61 297:0.36 300:0.18 +2 1:0.74 7:0.72 8:0.73 9:0.80 11:0.57 12:0.69 17:0.70 21:0.68 27:0.40 28:0.78 30:0.45 32:0.69 34:0.88 36:0.53 37:0.54 39:0.50 43:0.63 60:0.99 66:0.07 69:0.73 70:0.81 74:0.77 76:0.85 77:0.89 81:0.42 83:0.91 85:0.96 91:0.37 96:0.79 98:0.25 101:0.87 104:0.94 106:0.81 108:0.89 114:0.55 117:0.86 120:0.78 121:0.90 122:0.57 123:0.86 124:0.93 126:0.54 127:0.60 129:0.96 133:0.93 135:0.88 140:0.80 145:0.76 146:0.13 147:0.18 149:0.76 150:0.71 151:0.70 153:0.86 154:0.53 155:0.67 158:0.78 159:0.78 161:0.90 162:0.86 164:0.82 165:0.93 167:0.67 172:0.54 173:0.55 176:0.66 177:0.38 179:0.36 181:0.48 187:0.21 190:0.77 192:0.59 195:0.92 201:0.74 202:0.75 204:0.89 206:0.81 208:0.64 212:0.56 215:0.37 216:0.80 220:0.62 222:0.60 230:0.74 232:0.96 233:0.73 235:0.95 241:0.47 242:0.43 243:0.11 244:0.61 245:0.76 247:0.67 248:0.70 253:0.76 255:0.79 256:0.81 259:0.21 260:0.72 265:0.24 266:0.84 267:0.96 268:0.83 271:0.18 276:0.77 279:0.86 282:0.77 283:0.78 285:0.62 290:0.55 297:0.36 299:0.97 300:0.70 +1 9:0.80 11:0.57 12:0.69 17:0.47 21:0.78 27:0.27 28:0.78 30:0.41 34:0.89 36:0.91 37:0.83 39:0.50 41:0.82 43:0.63 55:0.92 60:0.87 66:0.86 69:0.71 70:0.57 74:0.60 83:0.91 85:0.85 91:0.18 96:0.70 98:0.84 101:0.87 108:0.54 120:0.57 123:0.52 127:0.34 129:0.05 135:0.68 140:0.45 146:0.85 147:0.88 149:0.58 151:0.53 153:0.46 154:0.53 155:0.67 158:0.78 159:0.42 161:0.90 162:0.62 164:0.53 167:0.67 172:0.59 173:0.73 177:0.38 179:0.65 181:0.48 187:0.95 190:0.77 191:0.73 192:0.59 202:0.49 208:0.64 212:0.30 216:0.66 220:0.82 222:0.60 235:0.31 241:0.46 242:0.45 243:0.86 244:0.61 247:0.55 248:0.52 253:0.43 255:0.79 256:0.45 259:0.21 260:0.57 265:0.84 266:0.63 267:0.36 268:0.45 271:0.18 276:0.48 279:0.86 283:0.78 285:0.62 300:0.43 +3 1:0.74 6:0.97 8:0.89 9:0.80 12:0.69 17:0.64 20:0.87 21:0.68 27:0.09 28:0.78 30:0.89 32:0.64 34:0.88 36:0.67 37:0.89 39:0.50 41:0.88 43:0.18 55:0.59 66:0.47 69:0.27 70:0.20 78:0.80 81:0.35 83:0.91 91:0.58 96:0.68 98:0.31 100:0.86 104:0.58 108:0.74 111:0.81 114:0.55 120:0.78 123:0.72 124:0.65 126:0.54 127:0.64 129:0.81 133:0.67 135:0.58 140:0.96 145:0.61 146:0.05 147:0.05 149:0.15 150:0.62 151:0.48 152:0.98 153:0.72 154:0.12 155:0.67 159:0.41 161:0.90 162:0.54 164:0.70 167:0.82 169:0.97 172:0.21 173:0.36 175:0.75 176:0.66 177:0.05 178:0.42 179:0.94 181:0.48 186:0.81 187:0.21 190:0.89 191:0.83 192:0.59 195:0.63 201:0.74 202:0.74 208:0.64 212:0.30 215:0.37 216:0.52 222:0.60 235:0.84 241:0.74 242:0.82 243:0.23 244:0.83 245:0.42 247:0.12 248:0.48 253:0.38 255:0.79 256:0.71 257:0.65 259:0.21 260:0.70 261:0.98 265:0.33 266:0.27 267:0.23 268:0.71 271:0.18 276:0.24 277:0.69 285:0.62 290:0.55 297:0.36 300:0.18 +2 1:0.74 8:0.86 9:0.80 11:0.57 12:0.69 17:0.83 20:0.88 21:0.22 27:0.07 28:0.78 30:0.17 34:0.70 36:0.57 37:0.88 39:0.50 41:0.88 43:0.62 55:0.96 60:0.95 66:0.13 69:0.74 70:0.95 74:0.45 78:0.80 81:0.70 83:0.91 85:0.80 91:0.14 96:0.72 98:0.18 100:0.73 101:0.87 108:0.72 111:0.78 114:0.67 120:0.81 123:0.70 124:0.97 126:0.54 127:0.42 129:0.59 133:0.96 135:0.98 140:0.80 146:0.13 147:0.18 149:0.39 150:0.83 151:0.59 152:0.83 153:0.69 154:0.52 155:0.67 158:0.78 159:0.95 161:0.90 162:0.86 163:0.81 164:0.71 165:0.93 167:0.66 169:0.72 172:0.37 173:0.25 176:0.66 177:0.38 179:0.46 181:0.48 186:0.80 187:0.68 190:0.89 191:0.70 192:0.59 201:0.74 202:0.62 208:0.64 212:0.12 215:0.51 216:0.81 222:0.60 235:0.31 241:0.44 242:0.78 243:0.14 244:0.61 245:0.58 247:0.39 248:0.58 253:0.54 255:0.79 256:0.81 259:0.21 260:0.75 261:0.81 265:0.19 266:0.96 267:0.36 268:0.70 271:0.18 276:0.66 277:0.69 279:0.86 283:0.82 285:0.62 290:0.67 297:0.36 300:0.84 +0 7:0.66 9:0.80 12:0.69 17:0.64 21:0.68 27:0.26 28:0.78 30:0.54 32:0.68 34:0.96 36:0.97 37:0.87 39:0.50 43:0.14 55:0.71 66:0.63 69:0.25 70:0.71 74:0.70 77:0.89 81:0.24 91:0.38 96:0.71 98:0.81 104:0.96 106:0.81 108:0.61 117:0.86 120:0.65 122:0.67 123:0.59 124:0.47 126:0.32 127:0.64 129:0.59 133:0.47 135:0.81 140:0.92 145:0.95 146:0.36 147:0.43 149:0.21 150:0.24 151:0.48 153:0.71 154:0.60 155:0.67 158:0.74 159:0.48 161:0.90 162:0.60 164:0.66 167:0.82 172:0.59 173:0.30 177:0.38 179:0.67 181:0.48 187:0.39 190:0.89 192:0.59 195:0.68 202:0.70 206:0.81 208:0.64 212:0.41 215:0.37 216:0.53 220:0.82 222:0.60 230:0.69 232:0.76 233:0.73 235:0.80 241:0.74 242:0.44 243:0.31 244:0.61 245:0.71 247:0.60 248:0.48 253:0.46 254:0.80 255:0.79 256:0.68 259:0.21 260:0.62 265:0.81 266:0.54 267:0.36 268:0.70 271:0.18 276:0.55 282:0.77 283:0.78 285:0.62 297:0.36 300:0.55 +1 1:0.74 6:0.98 7:0.72 8:0.78 11:0.57 12:0.69 17:0.49 20:0.87 21:0.68 27:0.27 28:0.78 30:0.13 32:0.64 34:0.74 36:0.96 37:0.56 39:0.50 43:0.65 55:0.84 60:0.97 66:0.69 69:0.59 70:0.89 74:0.74 78:0.92 81:0.37 83:0.91 85:0.91 91:0.22 98:0.78 100:0.91 101:0.87 104:0.98 108:0.45 111:0.90 114:0.55 121:0.90 122:0.43 123:0.43 124:0.46 126:0.54 127:0.63 129:0.59 133:0.64 135:0.85 145:0.86 146:0.89 147:0.91 149:0.69 150:0.63 152:0.82 154:0.56 155:0.67 158:0.78 159:0.65 161:0.90 163:0.81 167:0.52 169:0.89 172:0.79 173:0.50 176:0.66 177:0.38 178:0.55 179:0.43 181:0.48 186:0.91 187:0.87 189:0.99 192:0.59 195:0.80 201:0.74 202:0.36 204:0.89 208:0.64 212:0.37 215:0.37 216:0.44 222:0.60 230:0.74 233:0.73 235:0.95 238:0.91 242:0.33 243:0.73 244:0.61 245:0.76 247:0.60 253:0.43 259:0.21 261:0.90 265:0.78 266:0.63 267:0.36 271:0.18 276:0.72 279:0.86 283:0.78 290:0.55 297:0.36 300:0.70 +1 12:0.69 17:0.47 21:0.47 27:0.45 28:0.78 30:0.60 34:0.78 36:0.21 37:0.50 39:0.50 43:0.13 55:0.92 66:0.44 69:0.80 70:0.44 74:0.35 81:0.30 91:0.17 98:0.42 108:0.64 123:0.62 124:0.67 126:0.32 127:0.24 129:0.05 133:0.62 135:0.88 145:0.52 146:0.66 147:0.05 149:0.20 150:0.28 154:0.46 159:0.52 161:0.90 163:0.75 167:0.65 172:0.41 173:0.72 177:0.05 178:0.55 179:0.55 181:0.48 187:0.68 212:0.19 215:0.41 216:0.77 222:0.60 235:0.52 241:0.39 242:0.77 243:0.13 244:0.61 245:0.60 247:0.39 253:0.29 254:0.86 257:0.65 259:0.21 265:0.44 266:0.63 267:0.28 271:0.18 276:0.49 297:0.36 300:0.33 +2 1:0.74 8:0.92 9:0.80 12:0.69 17:0.67 21:0.68 27:0.09 28:0.78 30:0.62 32:0.64 34:0.82 36:0.94 37:0.89 39:0.50 43:0.18 55:0.59 66:0.61 69:0.27 70:0.23 81:0.35 91:0.62 96:0.67 98:0.36 104:0.58 108:0.77 114:0.55 120:0.53 123:0.75 124:0.43 126:0.54 127:0.61 129:0.59 133:0.47 135:0.54 140:0.93 145:0.61 146:0.05 147:0.05 149:0.14 150:0.62 151:0.46 153:0.48 154:0.12 155:0.67 159:0.47 161:0.90 162:0.47 164:0.54 167:0.83 172:0.27 173:0.31 175:0.75 176:0.66 177:0.05 178:0.52 179:0.94 181:0.48 187:0.21 190:0.77 191:0.75 192:0.59 195:0.67 201:0.74 202:0.76 208:0.64 212:0.61 215:0.37 216:0.52 220:0.99 222:0.60 235:0.84 241:0.74 242:0.82 243:0.23 244:0.83 245:0.42 247:0.12 248:0.46 253:0.37 255:0.79 256:0.45 257:0.65 259:0.21 260:0.53 265:0.38 266:0.23 267:0.87 268:0.46 271:0.18 276:0.17 277:0.69 285:0.62 290:0.55 297:0.36 300:0.18 +2 7:0.72 8:0.73 11:0.57 12:0.69 17:0.90 20:0.90 21:0.64 27:0.30 28:0.78 30:0.36 32:0.69 34:0.97 36:0.90 37:0.84 39:0.50 43:0.23 55:0.71 66:0.30 69:0.84 70:0.73 74:0.58 77:0.70 78:0.83 81:0.25 85:0.72 91:0.17 98:0.52 100:0.86 101:0.87 104:0.86 106:0.81 108:0.70 111:0.82 117:0.86 121:0.97 123:0.68 124:0.89 126:0.32 127:0.72 129:0.81 133:0.89 135:0.65 145:0.98 146:0.82 147:0.65 149:0.37 150:0.25 152:0.85 154:0.45 155:0.67 159:0.79 161:0.90 167:0.60 169:0.83 172:0.59 173:0.73 177:0.05 178:0.55 179:0.47 181:0.48 186:0.83 187:0.39 192:0.59 195:0.91 204:0.89 206:0.81 208:0.64 212:0.19 215:0.38 216:0.32 222:0.60 230:0.75 232:0.91 233:0.76 235:0.74 241:0.21 242:0.75 243:0.28 244:0.61 245:0.72 247:0.60 253:0.38 257:0.65 259:0.21 261:0.86 265:0.53 266:0.84 267:0.28 271:0.18 276:0.71 282:0.77 297:0.36 300:0.70 +2 9:0.80 11:0.57 12:0.69 17:0.69 21:0.30 27:0.08 28:0.78 30:0.45 34:0.80 36:0.97 37:0.85 39:0.50 43:0.58 55:0.96 66:0.30 69:0.83 70:0.61 74:0.55 81:0.36 85:0.80 91:0.63 96:0.78 98:0.36 101:0.87 108:0.68 114:0.63 120:0.67 123:0.66 124:0.87 126:0.32 127:0.74 129:0.05 133:0.87 135:0.77 140:0.87 145:0.77 146:0.53 147:0.44 149:0.43 150:0.32 151:0.60 153:0.46 154:0.47 155:0.67 159:0.67 161:0.90 162:0.63 163:0.93 164:0.78 167:0.80 172:0.32 173:0.80 176:0.61 177:0.05 178:0.42 179:0.79 181:0.48 187:0.39 190:0.77 192:0.59 202:0.78 208:0.64 212:0.22 216:0.67 220:0.82 222:0.60 235:0.31 241:0.72 242:0.22 243:0.31 244:0.61 245:0.50 247:0.60 248:0.68 253:0.64 255:0.79 256:0.81 257:0.65 259:0.21 260:0.65 265:0.38 266:0.75 267:0.73 268:0.79 271:0.18 276:0.46 285:0.62 290:0.63 300:0.43 +2 7:0.66 8:0.83 9:0.80 12:0.69 17:0.49 20:0.96 21:0.40 27:0.21 28:0.78 30:0.37 34:0.52 36:0.97 37:0.74 39:0.50 43:0.18 55:0.84 60:0.95 66:0.51 69:0.15 70:0.70 74:0.60 78:0.83 81:0.34 83:0.91 91:0.62 96:0.78 98:0.73 100:0.85 104:0.89 106:0.81 108:0.12 111:0.83 120:0.87 123:0.12 124:0.56 126:0.32 127:0.47 129:0.05 133:0.39 135:0.24 140:0.96 146:0.92 147:0.94 149:0.32 150:0.31 151:0.64 152:0.92 153:0.82 154:0.46 155:0.67 158:0.78 159:0.73 161:0.90 162:0.78 164:0.85 167:0.61 169:0.83 172:0.73 173:0.12 177:0.38 179:0.38 181:0.48 186:0.83 187:0.39 190:0.89 191:0.73 192:0.59 202:0.83 206:0.81 208:0.64 212:0.22 215:0.42 216:0.95 220:0.62 222:0.60 230:0.69 233:0.73 235:0.42 241:0.24 242:0.70 243:0.61 244:0.61 245:0.89 247:0.60 248:0.68 253:0.64 254:0.74 255:0.79 256:0.85 259:0.21 260:0.81 261:0.87 265:0.73 266:0.80 267:0.84 268:0.87 271:0.18 276:0.74 279:0.86 283:0.82 285:0.62 297:0.36 300:0.70 +3 1:0.74 8:0.84 9:0.80 12:0.69 17:0.93 21:0.68 27:0.09 28:0.78 30:0.58 32:0.64 34:0.87 36:0.68 37:0.89 39:0.50 43:0.18 55:0.71 66:0.80 69:0.27 70:0.33 81:0.35 91:0.66 96:0.72 98:0.91 104:0.58 108:0.21 114:0.55 120:0.75 123:0.20 126:0.54 127:0.51 129:0.05 135:0.58 140:0.45 145:0.80 146:0.76 147:0.80 149:0.21 150:0.62 151:0.56 153:0.48 154:0.12 155:0.67 159:0.32 161:0.90 162:0.84 163:0.81 164:0.81 167:0.81 172:0.45 173:0.42 175:0.75 176:0.66 177:0.05 179:0.86 181:0.48 187:0.21 190:0.89 191:0.70 192:0.59 195:0.59 201:0.74 202:0.75 208:0.64 212:0.55 215:0.37 216:0.52 220:0.99 222:0.60 235:0.84 241:0.73 242:0.82 243:0.82 244:0.83 247:0.12 248:0.58 253:0.47 255:0.79 256:0.79 257:0.65 259:0.21 260:0.68 265:0.91 266:0.27 267:0.96 268:0.82 271:0.18 276:0.37 277:0.69 285:0.62 290:0.55 297:0.36 300:0.25 +2 7:0.72 8:0.83 11:0.57 12:0.69 17:0.62 20:0.88 21:0.30 27:0.12 28:0.78 30:0.32 32:0.64 34:0.69 36:0.97 37:0.85 39:0.50 43:0.65 55:0.84 66:0.45 69:0.59 70:0.88 74:0.45 78:0.81 81:0.39 85:0.72 91:0.48 98:0.63 100:0.88 101:0.87 104:0.84 106:0.81 108:0.67 111:0.83 117:0.86 120:0.77 121:0.90 123:0.64 124:0.66 126:0.32 127:0.52 129:0.05 133:0.62 135:0.95 140:0.45 145:0.59 146:0.44 147:0.51 149:0.43 150:0.34 151:0.64 152:0.83 153:0.82 154:0.54 155:0.67 159:0.60 161:0.90 162:0.82 164:0.66 167:0.68 169:0.86 172:0.51 173:0.51 177:0.38 179:0.62 181:0.48 186:0.82 187:0.39 190:0.89 191:0.70 192:0.59 195:0.79 202:0.63 204:0.89 206:0.81 208:0.64 212:0.30 215:0.44 216:0.66 222:0.60 230:0.75 232:0.82 233:0.76 235:0.31 241:0.50 242:0.79 243:0.43 244:0.61 245:0.68 247:0.39 248:0.62 253:0.33 256:0.64 259:0.21 260:0.69 261:0.85 265:0.64 266:0.71 267:0.59 268:0.69 271:0.18 276:0.55 277:0.69 283:0.82 285:0.50 297:0.36 300:0.70 +0 7:0.66 12:0.69 17:0.67 21:0.68 27:0.26 28:0.78 30:0.30 32:0.68 34:0.93 36:0.80 37:0.87 39:0.50 43:0.11 55:0.59 66:0.77 69:0.85 70:0.65 74:0.62 77:0.96 81:0.24 91:0.37 96:0.67 98:0.79 104:0.86 106:0.81 108:0.71 122:0.43 123:0.69 124:0.39 126:0.32 127:0.37 129:0.05 133:0.39 135:0.79 140:0.45 145:0.97 146:0.86 147:0.30 149:0.18 150:0.24 151:0.46 153:0.48 154:0.29 158:0.74 159:0.50 161:0.90 162:0.86 167:0.74 172:0.71 173:0.86 177:0.05 179:0.49 181:0.48 187:0.39 190:0.89 195:0.75 202:0.36 206:0.81 212:0.42 215:0.37 216:0.53 220:0.99 222:0.60 230:0.69 232:0.76 233:0.73 235:0.80 241:0.64 242:0.32 243:0.16 244:0.61 245:0.67 247:0.67 248:0.46 253:0.39 254:0.80 256:0.45 257:0.65 259:0.21 265:0.79 266:0.63 267:0.52 268:0.46 271:0.18 276:0.63 282:0.77 285:0.50 297:0.36 300:0.43 +1 11:0.57 12:0.69 17:0.64 21:0.78 27:0.45 28:0.78 30:0.45 34:0.85 36:0.27 37:0.50 39:0.50 43:0.61 66:0.27 69:0.77 70:0.25 74:0.37 85:0.72 91:0.04 98:0.07 101:0.87 108:0.61 122:0.51 123:0.58 124:0.72 127:0.35 129:0.05 133:0.62 135:0.78 145:0.50 146:0.13 147:0.18 149:0.30 154:0.50 159:0.74 161:0.90 163:0.90 167:0.72 172:0.10 173:0.60 177:0.38 178:0.55 179:0.96 181:0.48 187:0.68 212:0.61 216:0.77 222:0.60 235:0.68 241:0.61 242:0.63 243:0.46 244:0.61 245:0.38 247:0.30 253:0.30 259:0.21 265:0.07 266:0.17 267:0.28 271:0.18 276:0.19 300:0.18 +3 1:0.74 8:0.75 9:0.80 12:0.69 17:0.70 21:0.68 27:0.09 28:0.78 30:0.76 32:0.64 34:0.92 36:0.79 37:0.89 39:0.50 43:0.18 55:0.59 66:0.54 69:0.27 70:0.22 81:0.35 91:0.67 96:0.67 98:0.56 104:0.58 108:0.65 114:0.55 120:0.53 123:0.63 124:0.45 126:0.54 127:0.51 129:0.59 133:0.47 135:0.44 140:0.87 145:0.61 146:0.05 147:0.05 149:0.14 150:0.62 151:0.46 153:0.48 154:0.12 155:0.67 159:0.36 161:0.90 162:0.50 163:0.89 164:0.54 167:0.78 172:0.21 173:0.39 175:0.75 176:0.66 177:0.05 178:0.48 179:0.96 181:0.48 187:0.21 190:0.77 191:0.70 192:0.59 195:0.61 201:0.74 202:0.63 208:0.64 212:0.42 215:0.37 216:0.52 220:0.97 222:0.60 235:0.84 241:0.71 242:0.82 243:0.26 244:0.83 245:0.40 247:0.12 248:0.46 253:0.37 255:0.79 256:0.45 257:0.65 259:0.21 260:0.53 265:0.57 266:0.23 267:0.23 268:0.46 271:0.18 276:0.21 277:0.69 285:0.62 290:0.55 297:0.36 300:0.18 +1 6:0.84 8:0.61 9:0.80 12:0.69 17:0.69 20:0.99 21:0.47 27:0.20 28:0.78 30:0.21 34:0.64 36:1.00 37:0.48 39:0.50 41:0.88 43:0.18 55:0.92 66:0.85 69:0.27 70:0.69 74:0.45 78:0.87 81:0.30 83:0.91 91:0.79 96:0.79 98:0.87 100:0.85 104:0.84 106:0.81 108:0.21 111:0.85 120:0.92 123:0.20 126:0.32 127:0.39 129:0.05 135:0.66 140:0.93 146:0.83 147:0.86 149:0.20 150:0.28 151:0.64 152:0.90 153:0.90 154:0.53 155:0.67 159:0.39 161:0.90 162:0.85 163:0.81 164:0.89 167:0.81 169:0.83 172:0.57 173:0.33 177:0.38 179:0.71 181:0.48 186:0.87 187:0.21 189:0.86 190:0.89 191:0.84 192:0.59 202:0.86 206:0.81 208:0.64 212:0.36 215:0.41 216:0.44 222:0.60 235:0.52 238:0.87 241:0.73 242:0.52 243:0.86 244:0.61 247:0.47 248:0.72 253:0.65 254:0.74 255:0.79 256:0.90 259:0.21 260:0.88 261:0.89 265:0.87 266:0.47 267:0.88 268:0.91 271:0.18 276:0.46 283:0.78 285:0.62 297:0.36 300:0.43 +4 1:0.74 6:0.97 7:0.66 8:0.97 9:0.80 12:0.69 17:0.73 20:0.99 21:0.40 27:0.09 28:0.78 30:0.91 32:0.68 34:0.67 36:0.53 37:0.89 39:0.50 41:0.97 43:0.18 55:0.59 60:0.87 66:0.69 69:0.17 70:0.13 74:0.46 77:0.70 78:0.95 81:0.51 83:0.91 91:0.95 96:0.94 98:0.86 100:0.99 104:0.58 108:0.14 111:0.99 114:0.61 120:0.96 123:0.13 126:0.54 127:0.37 129:0.05 135:0.44 140:0.90 145:0.77 146:0.38 147:0.45 149:0.15 150:0.68 151:0.91 152:0.98 153:0.96 154:0.12 155:0.67 158:0.78 159:0.15 161:0.90 162:0.75 163:0.81 164:0.94 167:0.95 169:0.97 172:0.21 173:0.64 175:0.75 176:0.66 177:0.05 179:0.96 181:0.48 186:0.94 189:0.98 190:0.77 191:0.93 192:0.59 195:0.53 201:0.74 202:0.93 208:0.64 212:0.61 215:0.42 216:0.52 222:0.60 230:0.69 233:0.73 235:0.52 238:0.99 241:0.96 242:0.82 243:0.73 244:0.83 247:0.12 248:0.91 253:0.88 255:0.79 256:0.95 257:0.65 259:0.21 260:0.94 261:0.98 265:0.86 266:0.17 267:0.84 268:0.95 271:0.18 276:0.22 277:0.69 279:0.86 282:0.77 283:0.82 285:0.62 290:0.61 297:0.36 300:0.13 +0 8:0.79 9:0.80 12:0.69 17:0.03 20:0.80 21:0.22 27:0.26 28:0.78 30:0.76 34:0.75 36:0.61 37:0.87 39:0.50 43:0.10 55:0.59 66:0.77 69:0.81 70:0.21 74:0.42 78:0.72 81:0.42 91:0.61 96:0.68 98:0.70 100:0.89 104:0.86 106:0.81 108:0.65 111:0.81 114:0.66 120:0.55 122:0.51 123:0.63 126:0.32 127:0.21 129:0.05 135:0.65 140:0.45 146:0.65 147:0.70 149:0.11 150:0.35 151:0.48 152:0.85 153:0.48 154:0.25 155:0.67 159:0.25 161:0.90 162:0.86 164:0.54 167:0.74 169:0.80 172:0.37 173:0.90 176:0.61 177:0.38 179:0.75 181:0.48 186:0.73 187:0.39 190:0.77 192:0.59 202:0.36 206:0.81 208:0.64 212:0.23 216:0.53 220:0.91 222:0.60 232:0.76 235:0.22 241:0.64 242:0.55 243:0.79 244:0.61 247:0.39 248:0.48 253:0.49 254:0.95 255:0.79 256:0.45 259:0.21 260:0.54 261:0.80 265:0.70 266:0.38 267:0.44 268:0.46 271:0.18 276:0.31 285:0.62 290:0.66 300:0.18 +2 7:0.66 8:0.83 9:0.80 12:0.69 17:0.53 20:0.96 21:0.40 27:0.21 28:0.78 30:0.17 34:0.63 36:0.96 37:0.74 39:0.50 43:0.18 55:0.59 60:0.95 66:0.45 69:0.71 70:0.80 74:0.60 78:0.83 81:0.34 83:0.91 91:0.64 96:0.78 98:0.68 100:0.85 104:0.89 106:0.81 108:0.54 111:0.83 120:0.87 123:0.52 124:0.67 126:0.32 127:0.47 129:0.05 133:0.62 135:0.19 140:0.96 146:0.89 147:0.34 149:0.30 150:0.31 151:0.64 152:0.92 153:0.82 154:0.46 155:0.67 158:0.78 159:0.71 161:0.90 162:0.78 164:0.85 167:0.69 169:0.83 172:0.67 173:0.58 177:0.05 179:0.41 181:0.48 186:0.83 187:0.39 190:0.89 191:0.73 192:0.59 202:0.83 206:0.81 208:0.64 212:0.23 215:0.42 216:0.95 220:0.62 222:0.60 230:0.69 233:0.73 235:0.42 241:0.53 242:0.62 243:0.17 244:0.61 245:0.82 247:0.60 248:0.68 253:0.65 254:0.74 255:0.79 256:0.85 257:0.65 259:0.21 260:0.81 261:0.87 265:0.69 266:0.75 267:0.79 268:0.87 271:0.18 276:0.72 279:0.86 283:0.82 285:0.62 297:0.36 300:0.70 +1 1:0.57 7:0.70 9:0.70 11:0.50 12:0.51 17:0.47 18:0.72 21:0.68 27:0.72 29:0.62 30:0.73 34:0.49 36:0.37 39:0.63 43:0.64 44:0.88 45:0.85 66:0.86 69:0.51 70:0.47 71:0.70 74:0.67 81:0.53 83:0.60 86:0.74 91:0.12 96:0.69 97:0.89 98:0.46 99:0.83 101:0.52 104:0.99 106:0.81 108:0.39 114:0.67 117:0.86 120:0.55 122:0.51 123:0.37 126:0.44 127:0.14 129:0.05 131:1.00 135:0.47 139:0.74 140:0.87 144:0.57 145:0.79 146:0.52 147:0.58 149:0.67 150:0.44 151:0.50 153:0.46 154:0.59 155:0.55 157:0.99 158:0.76 159:0.19 162:0.48 164:0.53 165:0.70 166:0.99 172:0.59 173:0.51 176:0.63 177:0.70 178:0.48 179:0.24 182:0.98 187:0.95 190:0.89 192:0.55 195:0.71 201:0.55 202:0.60 204:0.84 206:0.81 208:0.54 212:0.78 215:0.49 216:0.10 220:0.93 222:0.25 227:0.61 229:0.98 230:0.73 231:0.99 232:1.00 233:0.66 235:0.84 241:0.18 242:0.45 243:0.86 244:0.61 246:0.61 247:0.55 248:0.50 253:0.57 254:0.84 255:0.69 256:0.45 259:0.21 260:0.55 265:0.48 266:0.27 267:0.28 268:0.45 276:0.47 279:0.77 283:0.67 285:0.56 287:0.61 290:0.67 297:0.36 300:0.43 +1 1:0.57 9:0.72 11:0.50 12:0.51 17:0.22 18:0.68 21:0.68 27:0.49 29:0.62 30:0.75 32:0.67 34:0.58 36:0.23 37:0.38 39:0.63 43:0.62 45:0.93 66:0.35 69:0.62 70:0.54 71:0.70 74:0.73 77:0.96 81:0.53 83:0.60 86:0.67 91:0.64 96:0.76 97:0.99 98:0.51 99:0.83 101:0.52 104:0.84 106:0.81 108:0.47 114:0.67 117:0.86 120:0.65 122:0.51 123:0.45 124:0.77 126:0.44 127:0.43 129:0.59 131:1.00 133:0.77 135:0.81 139:0.65 140:0.45 144:0.57 145:1.00 146:0.75 147:0.79 149:0.81 150:0.44 151:0.66 153:0.48 154:0.51 155:0.57 157:0.99 158:0.76 159:0.66 162:0.75 164:0.80 165:0.70 166:0.99 172:0.61 173:0.48 176:0.63 177:0.70 179:0.42 182:0.99 187:0.87 190:0.89 192:0.57 195:0.85 201:0.55 202:0.72 206:0.81 208:0.54 212:0.52 215:0.49 216:0.88 220:0.91 222:0.25 227:0.61 229:1.00 231:0.99 232:0.91 235:0.84 241:0.52 242:0.55 243:0.51 244:0.83 245:0.77 246:0.61 247:0.47 248:0.69 253:0.77 255:0.71 256:0.77 260:0.65 265:0.53 266:0.84 267:0.76 268:0.76 276:0.68 279:0.82 282:0.75 283:0.67 285:0.56 287:0.61 290:0.67 297:0.36 300:0.55 +0 11:0.42 12:0.51 17:0.43 18:0.62 21:0.78 27:0.45 29:0.62 30:0.76 34:0.89 36:0.17 37:0.50 39:0.63 43:0.63 55:0.42 66:0.64 69:0.79 70:0.15 71:0.70 74:0.37 86:0.69 91:0.06 98:0.13 108:0.63 122:0.57 123:0.61 127:0.08 129:0.05 131:0.61 135:0.51 139:0.66 144:0.57 145:0.45 146:0.19 147:0.25 149:0.39 154:0.53 157:0.61 159:0.10 163:0.97 166:0.61 172:0.16 173:0.81 177:0.70 178:0.55 179:0.10 182:0.61 187:0.68 212:0.95 216:0.77 222:0.25 227:0.61 229:0.61 231:0.78 235:1.00 241:0.01 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.33 254:0.84 259:0.21 265:0.13 266:0.12 267:0.23 276:0.19 287:0.61 300:0.13 +1 1:0.58 11:0.64 12:0.51 17:0.79 18:0.71 21:0.77 22:0.93 27:0.66 29:0.62 30:0.76 34:0.79 36:0.63 37:0.49 39:0.63 43:0.67 47:0.93 64:0.77 66:0.54 69:0.73 70:0.15 71:0.70 74:0.38 81:0.67 83:0.91 85:0.72 86:0.72 91:0.17 98:0.37 101:0.87 104:1.00 106:0.81 108:0.56 114:0.63 117:0.86 122:0.57 123:0.53 124:0.45 126:0.54 127:0.18 129:0.05 131:0.61 133:0.37 135:0.63 139:0.70 144:0.57 146:0.41 147:0.48 149:0.43 150:0.46 154:0.56 155:0.47 157:0.85 159:0.24 163:0.75 165:0.93 166:0.99 172:0.21 173:0.77 176:0.70 177:0.70 178:0.55 179:0.79 182:0.97 187:0.87 192:0.45 201:0.57 206:0.81 208:0.64 212:0.42 215:0.44 216:0.35 222:0.25 227:0.61 229:0.61 231:0.99 232:1.00 235:0.99 241:0.30 242:0.45 243:0.63 244:0.61 245:0.40 246:0.61 247:0.35 253:0.48 259:0.21 262:0.93 265:0.39 266:0.23 267:0.65 276:0.22 287:0.61 290:0.63 297:0.36 300:0.13 +1 1:0.60 7:0.70 11:0.50 12:0.51 17:0.47 18:0.65 21:0.54 27:0.65 29:0.62 30:0.76 32:0.67 34:0.85 36:0.92 37:0.39 39:0.63 43:0.63 45:0.89 66:0.13 69:0.53 70:0.47 71:0.70 74:0.66 77:0.70 81:0.57 83:0.60 86:0.65 91:0.25 97:0.94 98:0.32 99:0.83 101:0.52 104:0.87 106:0.81 108:0.89 114:0.74 117:0.86 122:0.51 123:0.39 124:0.77 126:0.44 127:0.70 129:0.05 131:0.61 132:0.97 133:0.77 135:0.99 139:0.63 144:0.57 145:0.56 146:0.50 147:0.56 149:0.67 150:0.48 154:0.53 155:0.53 157:0.99 158:0.76 159:0.70 165:0.70 166:0.99 172:0.48 173:0.41 176:0.63 177:0.70 178:0.55 179:0.66 182:0.97 187:0.39 192:0.50 195:0.83 201:0.57 202:0.36 204:0.82 206:0.81 208:0.54 212:0.54 215:0.58 216:0.54 222:0.25 227:0.61 229:0.61 230:0.73 231:0.99 232:0.92 233:0.66 235:0.68 241:0.12 242:0.57 243:0.34 244:0.83 245:0.65 246:0.61 247:0.47 253:0.59 259:0.21 265:0.21 266:0.80 267:0.44 276:0.48 279:0.79 282:0.75 283:0.67 287:0.61 290:0.74 297:0.36 300:0.55 +1 1:0.67 9:0.72 11:0.50 12:0.51 17:0.47 18:0.69 21:0.40 22:0.93 27:0.20 29:0.62 30:0.62 34:0.52 36:0.51 37:0.47 39:0.63 43:0.62 45:0.81 47:0.93 64:0.77 66:0.36 69:0.27 70:0.69 71:0.70 74:0.59 81:0.74 83:0.60 86:0.74 91:0.04 96:0.74 97:0.89 98:0.24 99:0.83 101:0.52 104:0.97 106:0.81 108:0.21 114:0.82 117:0.86 120:0.62 122:0.51 123:0.20 124:0.68 126:0.54 127:0.37 129:0.05 131:1.00 133:0.60 135:0.78 139:0.77 140:0.45 144:0.57 146:0.34 147:0.41 149:0.49 150:0.67 151:0.66 153:0.48 154:0.64 155:0.58 157:0.98 158:0.81 159:0.53 162:0.86 164:0.56 165:0.70 166:0.99 172:0.32 173:0.23 176:0.70 177:0.70 179:0.76 182:0.98 187:0.68 190:0.89 192:0.57 201:0.66 202:0.36 206:0.81 208:0.64 212:0.42 215:0.67 216:0.73 219:0.94 220:0.74 222:0.25 227:0.61 229:0.99 231:0.99 232:0.98 235:0.68 241:0.07 242:0.45 243:0.51 244:0.61 245:0.54 246:0.61 247:0.55 248:0.63 253:0.69 254:0.86 255:0.71 256:0.45 259:0.21 260:0.63 262:0.93 265:0.26 266:0.54 267:0.28 268:0.46 276:0.34 279:0.81 283:0.65 285:0.56 287:0.61 290:0.82 292:0.86 297:0.36 300:0.55 +1 1:0.66 7:0.64 11:0.50 12:0.51 17:0.36 18:0.70 21:0.40 27:0.19 29:0.62 30:0.87 32:0.71 34:0.62 36:0.26 37:0.38 39:0.63 43:0.10 44:0.92 45:0.66 48:0.91 55:0.71 64:0.77 66:0.64 69:0.71 70:0.29 71:0.70 74:0.54 76:0.94 77:0.70 81:0.68 83:0.60 86:0.74 91:0.15 98:0.59 99:0.83 101:0.52 108:0.66 114:0.82 122:0.77 123:0.64 124:0.45 126:0.54 127:0.23 129:0.59 131:0.61 133:0.46 135:0.91 139:0.79 144:0.57 145:0.50 146:0.18 147:0.23 149:0.17 150:0.59 154:0.59 155:0.50 157:0.61 159:0.36 165:0.70 166:0.99 172:0.54 173:0.70 176:0.70 177:0.70 178:0.55 179:0.49 182:0.61 187:0.39 192:0.48 195:0.76 201:0.63 208:0.64 212:0.59 215:0.67 216:0.87 222:0.25 227:0.61 229:0.61 230:0.65 231:0.99 233:0.76 235:0.60 241:0.54 242:0.54 243:0.28 244:0.61 245:0.64 246:0.61 247:0.74 253:0.59 254:0.74 259:0.21 265:0.60 266:0.38 267:0.36 276:0.49 281:0.91 282:0.80 287:0.61 290:0.82 297:0.36 300:0.33 +1 11:0.50 12:0.51 17:0.57 18:0.74 21:0.13 27:0.32 29:0.62 30:0.33 34:0.58 36:0.86 37:0.48 39:0.63 43:0.61 45:0.73 64:0.77 66:0.45 69:0.79 70:0.49 71:0.70 74:0.48 81:0.30 83:0.60 86:0.71 91:0.18 97:0.89 98:0.27 101:0.52 104:0.90 106:0.81 108:0.63 117:0.86 122:0.86 123:0.60 124:0.66 126:0.26 127:0.71 129:0.05 131:0.61 133:0.60 135:0.80 139:0.68 144:0.57 146:0.52 147:0.18 149:0.43 150:0.33 154:0.50 157:0.91 158:0.76 159:0.79 163:0.92 166:0.61 172:0.27 173:0.65 177:0.38 178:0.55 179:0.91 182:0.81 187:0.39 206:0.81 208:0.54 212:0.42 216:0.58 222:0.25 227:0.61 229:0.61 231:0.87 232:0.94 235:0.12 241:0.01 242:0.45 243:0.32 244:0.83 245:0.47 246:0.61 247:0.47 253:0.39 257:0.65 259:0.21 265:0.30 266:0.38 267:0.52 276:0.21 279:0.77 283:0.67 287:0.61 300:0.33 +1 1:0.64 8:0.78 9:0.76 11:0.50 12:0.51 17:0.08 21:0.13 27:0.41 29:0.62 30:0.91 31:0.92 32:0.67 34:0.50 36:0.80 37:0.34 39:0.63 43:0.66 45:0.73 66:0.27 69:0.19 70:0.13 71:0.70 74:0.53 77:0.70 79:0.91 81:0.67 83:0.60 91:0.94 96:0.98 97:0.97 98:0.12 99:0.83 101:0.52 104:0.58 108:0.15 114:0.88 120:0.96 122:0.51 123:0.15 124:0.61 126:0.44 127:0.80 129:0.59 131:1.00 133:0.47 135:0.63 137:0.92 140:0.45 144:0.57 145:0.74 146:0.13 147:0.18 149:0.43 150:0.60 151:0.92 153:0.48 154:0.55 155:0.58 157:0.95 158:0.76 159:0.28 162:0.65 163:0.79 164:0.96 165:0.70 166:0.99 172:0.10 173:0.45 175:0.75 176:0.63 177:0.38 179:0.98 182:1.00 190:0.89 192:0.58 195:0.56 201:0.60 202:0.94 208:0.54 212:0.61 215:0.84 216:0.30 220:0.74 222:0.25 227:0.61 229:1.00 231:0.99 232:0.79 235:0.22 241:0.86 242:0.63 243:0.46 244:0.83 245:0.40 246:0.61 247:0.30 248:0.98 253:0.97 255:0.74 256:0.94 257:0.65 260:0.96 265:0.13 266:0.17 267:0.73 268:0.95 276:0.12 277:0.69 279:0.81 282:0.75 283:0.67 284:0.88 285:0.62 287:0.61 290:0.88 297:0.36 300:0.13 +2 1:0.69 11:0.64 12:0.51 17:0.81 18:0.74 21:0.05 27:0.09 29:0.62 30:0.95 34:0.78 36:0.53 37:0.73 39:0.63 43:0.76 64:0.77 66:0.75 69:0.45 70:0.13 71:0.70 74:0.50 81:0.81 83:0.91 85:0.80 86:0.79 91:0.06 98:0.34 101:0.87 108:0.35 114:0.95 122:0.57 123:0.33 126:0.54 127:0.12 129:0.05 131:0.61 135:0.77 139:0.76 144:0.57 146:0.25 147:0.31 149:0.68 150:0.76 154:0.67 155:0.53 157:0.95 159:0.11 165:0.93 166:0.99 172:0.32 173:0.73 176:0.70 177:0.70 178:0.55 179:0.35 182:0.98 187:0.39 192:0.50 201:0.66 208:0.64 212:0.84 215:0.91 216:0.92 222:0.25 227:0.61 229:0.61 231:0.99 235:0.22 241:0.07 242:0.63 243:0.77 244:0.61 246:0.61 247:0.35 253:0.65 259:0.21 265:0.37 266:0.17 267:0.97 276:0.20 287:0.61 290:0.95 297:0.36 300:0.13 +1 1:0.56 8:0.89 9:0.75 11:0.50 12:0.51 17:0.22 18:0.58 21:0.76 29:0.62 30:0.75 32:0.67 34:0.53 36:0.23 37:0.97 39:0.63 43:0.58 45:0.93 66:0.33 69:0.75 70:0.61 71:0.70 74:0.69 77:0.96 81:0.50 83:0.60 86:0.60 91:0.93 96:0.97 97:0.99 98:0.25 99:0.83 101:0.52 104:0.82 108:0.80 114:0.63 120:0.94 122:0.75 123:0.79 124:0.87 126:0.44 127:0.48 129:0.93 131:1.00 133:0.87 135:0.23 139:0.59 140:0.45 144:0.57 145:0.91 146:0.37 147:0.44 149:0.76 150:0.40 151:0.90 153:0.89 154:0.47 155:0.58 157:0.99 158:0.76 159:0.82 162:0.59 164:0.95 165:0.70 166:0.99 172:0.59 173:0.53 175:0.75 176:0.63 177:0.38 179:0.44 182:1.00 187:0.21 190:0.89 191:0.76 192:0.58 195:0.94 201:0.54 202:0.94 208:0.54 212:0.37 215:0.46 216:0.98 220:0.74 222:0.25 227:0.61 229:1.00 231:0.99 232:0.88 235:0.97 241:0.21 242:0.58 243:0.29 244:0.83 245:0.72 246:0.61 247:0.60 248:0.97 253:0.97 255:0.74 256:0.94 257:0.65 259:0.21 260:0.94 265:0.28 266:0.87 267:0.59 268:0.94 276:0.66 277:0.69 279:0.82 282:0.75 283:0.67 285:0.56 287:0.61 290:0.63 297:0.36 300:0.70 +0 12:0.51 17:0.38 18:0.69 21:0.54 27:0.45 29:0.62 30:0.76 34:0.63 36:0.17 37:0.50 39:0.63 43:0.13 55:0.99 64:0.77 66:0.13 69:0.70 70:0.15 71:0.70 81:0.26 86:0.67 91:0.06 98:0.06 108:0.53 122:0.86 123:0.51 124:0.75 126:0.26 127:0.60 129:0.81 131:0.61 133:0.67 135:0.89 139:0.65 145:0.52 146:0.05 147:0.05 149:0.08 150:0.29 154:0.10 157:0.61 159:0.85 163:0.97 166:0.61 172:0.05 173:0.45 175:0.91 177:0.05 178:0.55 179:0.99 182:0.61 187:0.68 212:0.95 216:0.77 222:0.25 227:0.61 229:0.61 231:0.61 235:0.60 241:0.21 242:0.82 243:0.34 244:0.93 245:0.37 246:0.61 247:0.12 253:0.20 257:0.84 259:0.21 265:0.06 266:0.12 267:0.59 276:0.17 277:0.87 287:0.61 300:0.13 +1 1:0.56 9:0.74 11:0.50 12:0.51 17:0.28 18:0.58 21:0.76 27:0.48 29:0.62 30:0.75 32:0.67 34:0.29 36:0.28 37:0.55 39:0.63 43:0.58 45:0.93 66:0.33 69:0.75 70:0.61 71:0.70 74:0.69 77:0.96 81:0.50 83:0.60 86:0.60 91:0.42 96:0.83 97:1.00 98:0.25 99:0.83 101:0.52 108:0.80 114:0.63 120:0.73 122:0.75 123:0.79 124:0.87 126:0.44 127:0.48 129:0.93 131:1.00 133:0.87 135:0.30 139:0.59 140:0.45 144:0.57 145:0.91 146:0.37 147:0.44 149:0.76 150:0.40 151:0.74 153:0.45 154:0.47 155:0.58 157:0.99 158:0.76 159:0.82 162:0.57 164:0.75 165:0.70 166:0.99 172:0.59 173:0.53 175:0.75 176:0.63 177:0.38 179:0.44 182:0.99 187:0.39 190:0.89 192:0.58 195:0.94 201:0.54 202:0.70 208:0.54 212:0.37 215:0.46 216:0.92 220:0.74 222:0.25 227:0.61 229:0.99 231:0.99 235:0.97 241:0.46 242:0.58 243:0.29 244:0.83 245:0.72 246:0.61 247:0.60 248:0.80 253:0.83 255:0.73 256:0.71 257:0.65 259:0.21 260:0.73 265:0.28 266:0.87 267:0.28 268:0.69 276:0.66 277:0.69 279:0.82 282:0.75 283:0.67 285:0.56 287:0.61 290:0.63 297:0.36 300:0.70 +1 1:0.60 8:0.73 9:0.73 10:0.82 11:0.45 12:0.51 17:0.61 18:0.86 21:0.13 27:0.13 29:0.56 30:0.21 31:0.98 34:0.24 36:0.89 37:0.69 39:0.42 43:0.48 44:0.88 45:0.92 55:0.71 64:0.77 66:0.35 69:0.95 70:0.86 71:0.94 74:0.82 76:0.85 77:0.70 79:0.98 81:0.46 83:0.54 86:0.81 89:0.96 91:0.62 96:0.93 97:0.98 98:0.67 99:0.83 101:0.46 108:0.90 114:0.85 122:0.90 123:0.90 124:0.88 126:0.31 127:0.91 129:0.59 133:0.89 135:0.63 137:0.98 139:0.81 140:0.45 141:0.91 145:0.56 146:0.98 147:0.35 149:0.63 150:0.43 151:0.91 153:0.72 154:0.16 155:0.54 158:0.75 159:0.91 162:0.78 165:0.63 170:0.77 172:0.92 173:0.85 174:0.88 175:0.75 176:0.62 177:0.38 179:0.17 187:0.21 190:0.95 191:0.82 192:0.56 195:0.98 196:0.97 197:0.81 201:0.57 202:0.79 208:0.49 212:0.52 216:0.37 219:0.90 220:0.74 222:0.84 223:0.92 225:0.96 232:0.72 234:0.91 235:0.22 239:0.81 240:0.95 241:0.53 242:0.58 243:0.15 244:0.61 245:0.95 247:0.98 248:0.92 253:0.94 254:0.92 255:0.72 256:0.82 257:0.93 259:0.21 264:0.95 265:0.68 266:0.80 267:0.59 268:0.84 271:0.80 274:0.91 275:0.96 276:0.94 277:0.69 279:0.79 282:0.74 284:0.98 285:0.49 290:0.85 292:0.88 294:0.95 300:0.70 +1 1:0.56 8:0.88 9:0.70 10:0.89 11:0.45 12:0.51 17:0.32 18:0.93 21:0.74 27:0.38 29:0.56 30:0.15 31:0.90 34:0.19 36:0.60 37:0.57 39:0.42 43:0.46 45:0.88 55:0.71 64:0.77 66:0.27 69:0.61 70:0.97 71:0.94 74:0.67 77:0.70 79:0.89 81:0.36 82:0.98 83:0.54 86:0.87 88:0.99 89:0.97 91:0.31 96:0.72 97:0.89 98:0.53 99:0.83 101:0.46 102:0.95 108:0.47 114:0.63 122:0.90 123:0.45 124:0.94 126:0.31 127:0.75 129:0.05 133:0.94 135:0.83 137:0.90 139:0.84 140:0.45 141:0.91 145:0.91 146:0.95 147:0.96 149:0.49 150:0.33 151:0.57 153:0.48 154:0.21 155:0.51 158:0.75 159:0.91 162:0.61 165:0.63 170:0.77 172:0.84 173:0.26 174:0.91 176:0.62 177:0.96 179:0.20 187:0.87 190:0.98 192:0.50 195:0.98 196:0.92 197:0.90 201:0.53 202:0.74 208:0.49 212:0.23 216:0.80 219:0.90 220:0.99 222:0.84 223:0.92 225:0.96 234:0.91 235:0.95 239:0.89 240:0.95 241:0.35 242:0.18 243:0.46 244:0.83 245:0.89 247:0.99 248:0.57 253:0.58 254:0.84 255:0.69 256:0.73 259:0.21 264:0.95 265:0.55 266:0.96 267:0.69 268:0.75 271:0.80 274:0.91 275:0.97 276:0.91 279:0.75 282:0.74 284:0.96 285:0.49 290:0.63 292:0.88 294:0.96 300:0.84 +1 1:0.56 10:0.85 11:0.45 12:0.51 17:0.47 18:0.75 21:0.77 27:0.45 29:0.56 30:0.17 34:0.95 36:0.19 37:0.50 39:0.42 43:0.09 45:0.66 55:0.92 64:0.77 66:0.24 69:0.71 70:0.91 71:0.94 74:0.55 81:0.40 83:0.54 86:0.73 89:0.96 91:0.15 98:0.40 99:0.83 101:0.46 108:0.54 114:0.62 122:0.90 123:0.51 124:0.88 126:0.43 127:0.50 129:0.05 133:0.85 135:0.87 139:0.70 141:0.91 145:0.49 146:0.68 147:0.73 149:0.16 150:0.36 154:0.46 155:0.46 159:0.74 163:0.81 165:0.63 170:0.77 172:0.48 173:0.55 174:0.79 176:0.63 177:0.96 178:0.55 179:0.49 187:0.68 192:0.45 197:0.82 201:0.54 208:0.54 212:0.19 215:0.43 216:0.77 222:0.84 223:0.91 225:0.95 234:0.91 235:1.00 239:0.83 240:0.95 241:0.55 242:0.39 243:0.44 244:0.83 245:0.70 247:0.79 253:0.50 254:0.86 259:0.21 264:0.95 265:0.42 266:0.89 267:0.76 271:0.80 274:0.91 275:0.91 276:0.67 290:0.62 294:0.94 297:0.36 300:0.70 +1 1:0.56 10:0.83 12:0.51 17:0.99 18:0.97 21:0.74 27:0.72 29:0.56 30:0.42 34:0.24 36:0.67 39:0.42 43:0.11 44:0.86 48:1.00 55:0.71 64:0.77 66:0.67 69:0.34 70:0.74 71:0.94 80:0.99 81:0.42 83:0.54 86:0.91 89:0.96 91:0.72 98:0.81 99:0.83 104:0.58 108:0.27 114:0.64 122:0.95 123:0.26 124:0.45 126:0.43 127:0.96 129:0.59 133:0.47 135:0.63 139:0.89 141:0.91 145:0.67 146:0.87 147:0.89 149:0.30 150:0.38 154:0.19 155:0.46 159:0.61 165:0.63 170:0.77 172:0.75 173:0.31 174:0.83 175:0.97 176:0.63 177:0.38 178:0.55 179:0.53 192:0.44 195:0.78 197:0.83 201:0.53 208:0.54 212:0.71 215:0.46 222:0.84 223:0.91 225:0.96 232:0.78 234:0.91 235:0.98 239:0.82 240:0.95 241:0.77 242:0.75 243:0.72 244:0.93 245:0.82 247:0.67 253:0.49 254:0.88 257:0.93 259:0.21 264:0.95 265:0.81 266:0.75 267:0.65 271:0.80 274:0.91 276:0.70 277:0.95 281:0.86 290:0.64 294:0.94 297:0.36 300:0.55 +1 1:0.63 2:0.99 9:0.70 10:0.91 11:0.45 12:0.51 17:0.89 18:0.94 21:0.47 23:0.98 27:0.60 29:0.56 30:0.32 31:0.87 32:0.64 33:0.97 34:0.86 36:0.68 37:0.55 39:0.42 43:0.26 45:0.83 55:0.45 56:0.97 62:0.96 64:0.77 66:0.52 69:0.29 70:0.95 71:0.94 74:0.70 75:0.98 79:0.87 81:0.57 83:0.54 86:0.91 89:0.96 91:0.44 96:0.72 97:0.89 98:0.72 99:0.83 101:0.46 108:0.22 114:0.78 117:0.86 122:0.95 123:0.22 124:0.72 126:0.53 127:0.79 128:0.95 129:0.05 133:0.73 135:0.90 137:0.87 139:0.90 140:0.90 141:0.97 143:0.99 145:0.76 146:0.87 147:0.89 149:0.35 150:0.48 151:0.52 153:0.48 154:0.67 155:0.54 158:0.81 159:0.69 162:0.86 165:0.63 168:0.95 170:0.77 172:0.79 173:0.22 174:0.86 176:0.70 177:0.96 179:0.37 187:0.21 190:0.95 192:0.56 195:0.82 196:0.90 197:0.89 201:0.62 202:0.60 204:0.79 205:0.98 208:0.64 212:0.61 216:0.32 219:0.94 220:0.99 222:0.84 223:0.93 225:0.97 226:0.95 232:0.87 234:0.97 235:0.74 236:0.95 239:0.92 240:0.99 241:0.03 242:0.44 243:0.61 244:0.61 245:0.84 247:0.97 248:0.51 253:0.66 254:0.80 255:0.70 256:0.68 259:0.21 264:0.95 265:0.73 266:0.84 267:0.28 268:0.69 271:0.80 274:0.98 275:0.93 276:0.78 279:0.79 281:0.91 284:0.88 285:0.61 290:0.78 291:0.97 292:0.87 294:0.98 295:0.98 300:0.84 +0 7:0.66 8:0.88 10:0.88 12:0.51 17:0.20 18:0.90 21:0.74 27:0.11 29:0.56 30:0.10 31:0.87 34:0.82 36:1.00 37:0.67 39:0.42 43:0.10 44:0.88 55:0.45 64:0.77 66:0.46 69:0.62 70:0.73 71:0.94 74:0.55 76:0.94 77:0.70 79:0.87 81:0.24 86:0.82 89:0.97 91:0.22 98:0.77 102:0.95 108:0.71 122:0.91 123:0.69 124:0.67 126:0.23 127:0.65 129:0.59 133:0.67 135:0.97 137:0.87 139:0.81 140:0.87 141:0.91 145:0.86 146:0.57 147:0.63 149:0.22 150:0.25 151:0.48 153:0.46 154:0.19 158:0.73 159:0.72 162:0.59 163:0.75 170:0.77 172:0.68 173:0.48 174:0.88 175:0.75 177:0.88 178:0.48 179:0.45 187:0.68 190:0.98 195:0.87 196:0.89 197:0.87 202:0.68 212:0.23 216:0.92 219:0.88 220:0.99 222:0.84 223:0.92 225:0.96 230:0.68 233:0.76 234:0.91 235:0.88 239:0.87 240:0.95 241:0.37 242:0.13 243:0.40 244:0.83 245:0.83 247:0.96 248:0.48 253:0.44 254:0.97 256:0.68 257:0.65 259:0.21 264:0.95 265:0.78 266:0.80 267:0.95 268:0.68 271:0.80 274:0.91 275:0.93 276:0.72 277:0.69 282:0.73 284:0.94 285:0.45 292:0.88 294:0.95 300:0.55 +0 8:0.83 9:0.72 12:0.51 17:0.24 18:0.80 21:0.78 23:0.97 27:0.45 29:0.56 30:0.17 31:0.96 34:0.17 36:0.99 37:0.39 39:0.42 43:0.07 55:0.71 66:0.13 69:0.94 70:0.97 71:0.94 74:0.26 79:0.96 83:0.54 86:0.72 89:0.95 91:0.80 96:0.83 98:0.21 108:0.96 120:0.72 122:0.95 123:0.95 124:0.93 127:0.53 128:0.96 129:0.81 133:0.92 135:0.58 137:0.96 139:0.69 140:0.97 141:0.97 145:0.87 146:0.48 147:0.25 149:0.10 151:0.85 153:0.48 154:0.10 155:0.53 159:0.93 162:0.47 163:0.88 164:0.77 168:0.96 170:0.77 172:0.32 173:0.74 175:0.97 177:0.05 178:0.54 179:0.55 187:0.21 190:0.95 191:0.83 192:0.86 196:0.91 202:0.94 205:0.97 208:0.49 212:0.13 216:0.84 220:0.99 222:0.84 223:0.92 225:0.96 234:0.97 235:0.12 240:0.98 241:0.57 242:0.40 243:0.16 244:0.93 245:0.61 247:0.79 248:0.80 253:0.76 254:0.74 255:0.71 256:0.89 257:0.97 259:0.21 260:0.70 264:0.95 265:0.23 266:0.95 267:0.96 268:0.85 271:0.80 274:0.98 275:0.91 276:0.61 277:0.95 284:0.98 285:0.61 294:0.92 300:0.84 +0 10:0.88 11:0.45 12:0.51 17:0.30 18:0.89 21:0.78 27:0.12 29:0.56 30:0.32 32:0.69 34:0.87 36:0.83 37:0.81 39:0.42 43:0.26 44:0.92 45:0.73 55:0.71 66:0.89 69:0.72 70:0.77 71:0.94 74:0.48 77:0.70 86:0.87 89:0.96 91:0.23 98:0.69 101:0.46 102:0.97 108:0.55 122:0.91 123:0.53 127:0.21 129:0.05 135:0.75 139:0.85 141:0.91 145:0.74 146:0.75 147:0.79 149:0.24 154:0.52 159:0.31 170:0.77 172:0.68 173:0.73 174:0.83 175:0.75 177:0.88 178:0.55 179:0.36 187:0.98 195:0.71 197:0.86 202:0.46 212:0.59 216:0.90 222:0.84 223:0.92 225:0.96 234:0.91 235:0.84 239:0.90 240:0.95 241:0.30 242:0.14 243:0.89 244:0.61 247:0.84 253:0.39 254:0.93 257:0.65 259:0.21 264:0.95 265:0.69 266:0.38 267:0.79 271:0.80 274:0.91 276:0.57 277:0.69 282:0.77 300:0.55 +0 8:0.81 10:0.97 11:0.45 12:0.51 17:0.05 18:0.78 21:0.78 23:0.97 27:0.53 29:0.56 30:0.42 34:0.40 36:1.00 37:0.47 39:0.42 43:0.49 44:0.86 45:0.66 48:0.91 55:0.71 62:0.96 66:0.98 69:0.36 70:0.73 71:0.94 74:0.82 75:0.96 86:0.76 89:0.98 91:0.42 96:0.82 98:0.96 101:0.46 108:0.28 117:0.86 122:0.90 123:0.27 127:0.72 128:0.95 129:0.05 135:0.75 139:0.76 140:0.45 141:0.97 145:0.54 146:0.99 147:0.99 149:0.60 151:0.51 153:0.48 154:0.47 158:0.75 159:0.82 162:0.60 168:0.95 170:0.77 172:0.96 173:0.18 174:0.97 175:0.75 177:0.88 179:0.18 187:0.68 190:0.98 192:0.46 195:0.93 197:0.98 202:0.68 205:0.95 212:0.72 216:0.42 220:0.96 222:0.84 223:0.93 225:0.97 226:0.96 232:0.87 234:0.97 235:0.12 239:0.96 240:0.98 241:0.50 242:0.38 243:0.99 244:0.83 247:0.94 248:0.51 253:0.84 254:0.93 255:0.69 256:0.64 257:0.65 259:0.21 264:0.95 265:0.96 266:0.54 267:0.52 268:0.68 271:0.80 274:0.97 276:0.92 277:0.69 281:0.86 285:0.49 300:0.70 +1 7:0.66 8:0.74 10:0.87 11:0.45 12:0.51 17:0.83 18:0.95 21:0.78 22:0.93 27:0.24 29:0.56 30:0.41 31:0.87 34:0.95 36:1.00 37:0.72 39:0.42 43:0.11 44:0.88 45:0.66 47:0.93 48:0.97 55:0.71 66:0.27 69:0.93 70:0.67 71:0.94 74:0.57 76:0.94 77:0.70 79:0.86 86:0.87 89:0.97 91:0.38 96:0.74 98:0.62 101:0.46 108:0.35 122:0.95 123:0.33 124:0.78 127:0.93 129:0.59 133:0.74 135:0.95 137:0.87 139:0.85 140:0.45 141:0.91 145:0.91 146:0.79 147:0.93 149:0.31 151:0.57 153:0.48 154:0.17 159:0.85 162:0.74 170:0.77 172:0.76 173:0.86 174:0.93 175:0.75 177:0.70 179:0.28 187:0.39 190:0.98 191:0.85 195:0.94 196:0.89 197:0.89 202:0.56 212:0.49 216:0.45 220:0.74 222:0.84 223:0.92 225:0.96 230:0.68 233:0.66 234:0.91 235:0.80 239:0.86 240:0.95 241:0.24 242:0.22 243:0.46 244:0.83 245:0.92 247:0.99 248:0.56 253:0.58 254:0.99 256:0.58 257:0.84 259:0.21 262:0.93 264:0.95 265:0.63 266:0.84 267:0.73 268:0.59 271:0.80 274:0.91 275:0.93 276:0.86 277:0.87 281:0.86 282:0.73 284:0.88 285:0.49 294:0.93 300:0.70 +1 1:0.61 7:0.66 9:0.70 10:0.87 11:0.45 12:0.51 17:0.79 18:0.78 21:0.64 27:0.13 29:0.56 30:0.21 32:0.64 34:0.49 36:0.97 37:0.69 39:0.42 43:0.48 45:0.83 55:0.45 64:0.77 66:0.25 69:0.78 70:0.87 71:0.94 74:0.74 76:0.85 77:0.70 81:0.48 83:0.54 86:0.79 89:0.96 91:0.27 96:0.69 98:0.67 99:0.83 101:0.46 104:1.00 106:0.81 108:0.76 114:0.69 117:0.86 120:0.55 122:0.90 123:0.75 124:0.74 126:0.43 127:0.55 129:0.05 133:0.65 135:0.21 139:0.73 140:0.45 141:0.91 145:0.63 146:0.50 147:0.35 149:0.39 150:0.43 151:0.50 153:0.48 154:0.30 155:0.49 158:0.74 159:0.57 162:0.86 164:0.55 165:0.63 170:0.77 172:0.57 173:0.77 174:0.83 175:0.75 176:0.63 177:0.38 179:0.41 187:0.87 190:0.95 192:0.49 193:0.96 195:0.79 197:0.84 201:0.58 202:0.36 206:0.81 208:0.54 212:0.71 215:0.53 216:0.37 220:0.96 222:0.84 223:0.91 225:0.96 230:0.68 232:0.72 233:0.76 234:0.91 235:0.88 239:0.87 240:0.95 241:0.03 242:0.39 243:0.36 244:0.61 245:0.85 247:0.86 248:0.50 253:0.59 254:1.00 255:0.68 256:0.45 257:0.93 259:0.21 260:0.55 264:0.95 265:0.68 266:0.63 267:0.36 268:0.46 271:0.80 274:0.91 276:0.72 277:0.87 279:0.75 282:0.73 283:0.65 285:0.49 290:0.69 297:0.36 300:0.70 +1 1:0.56 10:0.95 11:0.45 12:0.51 17:0.66 18:0.75 21:0.68 27:0.53 29:0.56 30:0.18 34:0.39 36:1.00 37:0.45 39:0.42 43:0.35 45:0.73 55:0.52 64:0.77 66:0.68 69:0.52 70:0.99 71:0.94 74:0.73 75:0.96 76:0.85 81:0.35 86:0.69 89:0.97 91:0.20 98:0.62 101:0.46 108:0.40 114:0.67 122:0.90 123:0.38 124:0.64 126:0.43 127:0.78 129:0.05 133:0.80 135:0.81 139:0.67 141:0.91 145:0.41 146:0.89 147:0.91 149:0.46 150:0.37 154:0.53 155:0.46 158:0.74 159:0.80 170:0.77 172:0.84 173:0.32 174:0.93 176:0.63 177:0.96 178:0.55 179:0.37 187:0.68 192:0.45 197:0.95 201:0.55 202:0.36 208:0.54 212:0.68 216:0.59 222:0.84 223:0.92 225:0.97 226:0.95 234:0.91 235:0.88 236:0.94 239:0.94 240:0.95 241:0.10 242:0.19 243:0.72 244:0.61 245:0.76 247:0.96 253:0.58 254:0.94 259:0.21 264:0.95 265:0.63 266:0.80 267:0.59 271:0.80 274:0.91 275:0.91 276:0.78 290:0.67 294:0.95 300:0.94 +0 1:0.58 2:0.99 9:0.70 10:0.85 11:0.45 12:0.51 17:0.62 18:0.93 21:0.30 22:0.93 27:0.72 29:0.56 30:0.37 31:0.88 34:0.36 36:0.84 39:0.42 43:0.23 45:0.77 47:0.93 55:0.71 56:0.97 64:0.77 66:0.48 69:0.88 70:0.87 71:0.94 74:0.60 79:0.88 81:0.53 83:0.54 86:0.87 89:0.96 91:0.27 96:0.71 97:0.89 98:0.79 99:0.83 101:0.46 108:0.76 114:0.82 117:0.86 122:0.95 123:0.75 124:0.58 126:0.43 127:0.77 129:0.05 133:0.45 135:0.85 137:0.88 139:0.85 140:0.45 141:0.91 143:0.98 146:0.91 147:0.83 149:0.34 150:0.45 151:0.57 153:0.48 154:0.51 155:0.51 158:0.76 159:0.69 162:0.54 165:0.63 170:0.77 172:0.76 173:0.86 174:0.83 176:0.63 177:0.88 179:0.38 187:0.87 190:0.89 192:0.55 196:0.91 197:0.85 201:0.61 202:0.56 208:0.49 212:0.50 216:0.10 219:0.91 220:0.87 222:0.84 223:0.91 225:0.96 232:0.97 234:0.91 235:0.52 239:0.84 240:0.95 241:0.10 242:0.21 243:0.46 244:0.83 245:0.92 247:0.96 248:0.56 253:0.63 254:0.84 255:0.70 256:0.45 257:0.65 259:0.21 262:0.93 264:0.95 265:0.79 266:0.63 267:0.52 268:0.46 271:0.80 274:0.91 275:0.95 276:0.78 279:0.75 284:0.88 285:0.55 290:0.82 292:0.86 294:0.97 295:0.98 300:0.70 +1 1:0.62 8:0.69 9:0.72 10:0.84 11:0.45 12:0.51 17:0.88 18:0.95 22:0.93 27:0.60 29:0.56 30:0.32 31:0.93 34:0.85 36:0.98 37:0.31 39:0.42 43:0.32 45:0.92 47:0.93 64:0.77 66:0.96 69:0.52 70:0.68 71:0.94 74:0.71 79:0.93 81:0.50 83:0.54 86:0.93 89:0.96 91:0.57 96:0.80 97:0.89 98:0.93 99:0.83 101:0.46 108:0.40 114:0.92 117:0.86 122:0.91 123:0.39 126:0.31 127:0.56 129:0.05 135:0.83 137:0.93 139:0.90 140:0.45 141:0.91 146:0.90 147:0.92 149:0.62 150:0.47 151:0.85 153:0.48 154:0.20 155:0.54 158:0.75 159:0.50 162:0.86 165:0.63 170:0.77 172:0.89 173:0.52 174:0.79 176:0.62 177:0.96 179:0.31 187:0.21 190:0.95 192:0.56 196:0.96 197:0.80 201:0.59 202:0.36 208:0.49 212:0.85 216:0.14 219:0.90 222:0.84 223:0.91 225:0.95 232:0.83 234:0.91 235:0.05 239:0.82 240:0.95 241:0.07 242:0.24 243:0.96 244:0.61 247:0.92 248:0.76 253:0.83 254:0.99 255:0.71 256:0.45 259:0.21 262:0.93 264:0.95 265:0.93 266:0.47 267:0.28 268:0.46 271:0.80 274:0.91 275:0.93 276:0.81 279:0.75 284:0.88 285:0.49 290:0.92 292:0.88 294:0.95 300:0.55 +2 1:0.59 10:0.90 11:0.49 12:0.51 17:0.67 18:0.93 21:0.54 27:0.69 29:0.56 30:0.18 32:0.66 34:0.73 36:0.90 37:0.44 39:0.42 43:0.10 45:0.77 55:0.45 64:0.77 66:0.68 69:0.50 70:0.87 71:0.94 74:0.60 77:0.89 81:0.47 83:0.54 86:0.87 89:0.96 91:0.20 98:0.77 99:0.83 101:0.52 102:0.96 108:0.39 114:0.74 122:0.95 123:0.37 124:0.45 126:0.43 127:0.51 129:0.05 133:0.36 135:0.83 139:0.83 141:0.91 145:0.96 146:0.77 147:0.81 149:0.20 150:0.42 154:0.59 155:0.47 159:0.45 165:0.63 170:0.77 172:0.76 173:0.52 174:0.83 175:0.75 176:0.63 177:0.88 178:0.55 179:0.44 187:0.39 192:0.47 195:0.68 197:0.87 201:0.59 208:0.54 212:0.60 215:0.58 216:0.32 222:0.84 223:0.92 225:0.96 234:0.91 235:0.74 239:0.90 240:0.95 241:0.05 242:0.27 243:0.72 244:0.61 245:0.83 247:0.90 253:0.57 254:0.84 257:0.65 259:0.21 264:0.95 265:0.77 266:0.63 267:0.44 271:0.80 274:0.91 276:0.69 277:0.69 282:0.75 290:0.74 297:0.36 300:0.70 +1 7:0.66 10:0.91 12:0.51 17:0.49 18:0.69 21:0.68 27:0.08 29:0.56 30:0.32 32:0.65 34:0.80 36:0.92 37:0.70 39:0.42 43:0.26 55:0.59 66:0.81 69:0.80 70:0.79 71:0.94 74:0.43 81:0.35 82:0.98 86:0.66 88:0.98 89:0.97 91:0.06 98:0.78 108:0.64 114:0.67 122:0.97 123:0.62 124:0.38 126:0.43 127:0.42 129:0.05 133:0.38 135:0.73 139:0.64 141:0.91 145:0.49 146:0.88 147:0.44 149:0.21 150:0.35 154:0.26 159:0.57 170:0.77 172:0.79 173:0.77 174:0.90 176:0.63 177:0.38 178:0.55 179:0.42 187:0.39 192:0.44 195:0.79 197:0.90 201:0.54 208:0.49 212:0.52 215:0.49 216:0.58 222:0.84 223:0.92 225:0.97 230:0.68 233:0.76 234:0.91 235:0.88 236:0.94 239:0.89 240:0.95 241:0.07 242:0.13 243:0.13 244:0.83 245:0.71 247:0.95 253:0.51 254:0.92 257:0.93 259:0.21 264:0.95 265:0.78 266:0.75 267:0.95 271:0.80 274:0.91 275:0.94 276:0.69 290:0.67 294:0.96 297:0.36 300:0.70 +1 8:0.85 9:0.72 10:0.86 11:0.45 12:0.51 17:0.81 18:0.95 21:0.13 27:0.13 29:0.56 30:0.21 31:0.93 34:0.58 36:0.98 37:0.69 39:0.42 43:0.56 45:0.87 55:0.45 66:0.98 69:0.36 70:0.80 71:0.94 74:0.88 76:0.85 77:0.70 79:0.92 80:0.99 81:0.32 82:0.98 83:0.54 86:0.92 88:0.99 89:0.96 91:0.65 96:0.85 98:0.97 101:0.46 108:0.28 114:0.82 122:0.90 123:0.27 126:0.23 127:0.74 129:0.05 135:0.63 137:0.93 139:0.88 140:0.87 141:0.91 145:0.70 146:0.96 147:0.97 149:0.61 150:0.35 151:0.75 153:0.48 154:0.17 155:0.54 158:0.73 159:0.65 162:0.84 170:0.77 172:0.96 173:0.28 174:0.86 176:0.60 177:0.96 179:0.19 187:0.21 190:0.95 191:0.75 192:0.56 195:0.81 196:0.95 197:0.86 202:0.67 208:0.49 212:0.91 216:0.37 220:0.74 222:0.84 223:0.92 225:0.96 232:0.72 234:0.91 235:0.12 239:0.85 240:0.95 241:0.39 242:0.45 243:0.98 244:0.83 247:0.94 248:0.72 253:0.88 254:0.97 255:0.71 256:0.71 259:0.21 264:0.95 265:0.97 266:0.47 267:0.52 268:0.73 271:0.80 274:0.91 275:0.93 276:0.91 282:0.73 284:0.94 285:0.49 290:0.82 294:0.96 300:0.70 +1 1:0.62 8:0.76 10:0.85 11:0.45 12:0.51 17:0.91 18:0.95 22:0.93 27:0.47 29:0.56 30:0.12 34:0.52 36:0.92 37:0.66 39:0.42 43:0.57 44:0.88 45:0.83 47:0.93 48:0.99 55:0.52 64:0.77 66:0.75 69:0.07 70:0.90 71:0.94 74:0.67 77:0.70 80:0.99 81:0.50 83:0.54 86:0.91 89:0.96 91:0.35 97:0.89 98:0.83 99:0.83 101:0.46 108:0.06 114:0.92 117:0.86 122:0.94 123:0.06 124:0.41 126:0.31 127:0.63 129:0.05 133:0.43 135:0.89 139:0.90 141:0.91 145:0.49 146:0.90 147:0.92 149:0.65 150:0.47 154:0.46 155:0.49 158:0.74 159:0.61 165:0.63 170:0.77 172:0.84 173:0.12 174:0.83 176:0.62 177:0.96 178:0.55 179:0.38 187:0.21 192:0.48 195:0.79 197:0.84 201:0.59 204:0.78 208:0.49 212:0.68 216:0.26 219:0.90 222:0.84 223:0.91 225:0.96 232:0.85 234:0.91 235:0.05 239:0.84 240:0.95 241:0.10 242:0.13 243:0.77 244:0.83 245:0.84 247:0.96 253:0.67 259:0.21 262:0.93 264:0.95 265:0.83 266:0.75 267:0.99 271:0.80 274:0.91 275:0.94 276:0.77 279:0.75 281:0.91 282:0.74 290:0.92 292:0.86 294:0.96 300:0.84 +0 8:0.93 10:0.88 11:0.45 12:0.51 17:0.94 18:0.95 21:0.78 27:0.13 29:0.56 30:0.38 34:0.44 36:0.94 37:0.69 39:0.42 43:0.56 44:0.88 45:0.85 48:0.91 55:0.45 66:0.63 69:0.36 70:0.92 71:0.94 74:0.83 76:0.85 77:0.70 86:0.92 89:0.97 91:0.65 98:0.78 101:0.46 108:0.28 122:0.90 123:0.27 124:0.52 127:0.80 129:0.05 133:0.60 135:0.76 139:0.90 141:0.91 145:0.67 146:0.93 147:0.95 149:0.57 154:0.17 155:0.49 159:0.75 170:0.77 172:0.92 173:0.23 174:0.91 177:0.96 178:0.55 179:0.22 187:0.21 191:0.70 192:0.48 193:0.95 195:0.88 197:0.89 202:0.64 204:0.79 208:0.49 212:0.82 216:0.37 222:0.84 223:0.92 225:0.96 232:0.72 234:0.91 235:0.12 239:0.88 240:0.95 241:0.21 242:0.21 243:0.69 244:0.83 245:0.94 247:0.98 253:0.57 254:0.90 259:0.21 264:0.95 265:0.78 266:0.71 267:0.36 271:0.80 274:0.91 275:0.94 276:0.89 281:0.86 282:0.74 294:0.94 300:0.84 +1 1:0.74 8:0.81 11:0.57 12:0.37 17:0.51 20:0.78 21:0.05 27:0.36 28:0.78 30:0.45 34:0.86 36:0.81 37:0.93 39:0.31 43:0.95 60:0.87 66:0.68 69:0.15 70:0.57 74:0.85 78:0.96 81:0.92 83:0.88 85:0.88 91:0.31 98:0.73 100:0.91 101:0.82 108:0.12 111:0.93 114:0.95 117:0.86 122:0.43 123:0.12 124:0.43 126:0.54 127:0.58 129:0.59 133:0.47 135:0.63 146:0.60 147:0.66 149:0.86 150:0.96 152:0.76 154:0.95 155:0.67 158:0.92 159:0.33 161:0.82 165:0.90 167:0.55 169:0.89 172:0.51 173:0.33 176:0.73 177:0.38 178:0.55 179:0.77 181:0.53 186:0.96 187:0.21 192:0.59 201:0.74 208:0.64 212:0.83 215:0.91 216:0.18 222:0.48 232:0.91 235:0.12 241:0.44 242:0.38 243:0.72 245:0.58 247:0.60 253:0.77 259:0.21 261:0.81 265:0.73 266:0.27 267:0.23 271:0.42 276:0.42 279:0.86 283:0.82 290:0.95 297:0.36 300:0.43 +1 7:0.81 11:0.57 12:0.37 17:0.64 21:0.78 27:0.33 28:0.78 30:0.38 34:0.63 36:0.93 37:0.81 39:0.31 43:0.78 55:0.71 66:0.45 69:0.79 70:0.59 74:0.92 85:0.80 91:0.33 98:0.61 101:0.72 108:0.84 121:0.90 122:0.67 123:0.83 124:0.76 127:0.36 129:0.05 133:0.74 135:0.34 145:0.54 146:0.54 147:0.60 149:0.74 154:0.70 155:0.67 159:0.76 161:0.82 163:0.81 167:0.47 172:0.63 173:0.60 177:0.38 178:0.55 179:0.41 181:0.53 187:0.68 192:0.59 204:0.89 208:0.64 212:0.14 216:0.84 222:0.48 230:0.87 233:0.76 235:0.74 241:0.07 242:0.80 243:0.31 245:0.75 247:0.39 253:0.65 259:0.21 265:0.62 266:0.87 267:0.52 271:0.42 276:0.68 277:0.69 300:0.55 +2 1:0.74 6:0.87 7:0.81 8:0.68 11:0.57 12:0.37 17:0.53 20:0.87 21:0.22 27:0.42 28:0.78 30:0.75 32:0.80 34:0.42 36:0.18 37:0.39 39:0.31 43:0.86 60:0.95 66:0.48 69:0.59 70:0.52 74:0.96 77:0.89 78:0.94 81:0.90 83:0.89 85:0.98 91:0.10 98:0.68 100:0.89 101:0.87 104:0.98 106:0.81 108:0.45 111:0.91 114:0.90 117:0.86 121:1.00 122:0.43 123:0.43 124:0.57 126:0.54 127:0.47 129:0.59 133:0.47 135:0.96 145:0.47 146:0.73 147:0.77 149:0.98 150:0.94 152:0.82 154:0.83 155:0.67 158:0.92 159:0.47 161:0.82 165:0.90 167:0.47 169:0.87 172:0.75 173:0.60 176:0.73 177:0.38 178:0.55 179:0.34 181:0.53 186:0.93 187:0.87 189:0.89 192:0.59 195:0.73 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.79 216:0.43 222:0.48 230:0.87 232:0.99 233:0.76 235:0.52 238:0.83 241:0.03 242:0.62 243:0.60 245:0.91 247:0.47 253:0.78 259:0.21 261:0.88 265:0.69 266:0.54 267:0.19 271:0.42 276:0.75 279:0.86 282:0.88 283:0.82 290:0.90 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.89 7:0.81 8:0.74 9:0.80 11:0.57 12:0.37 17:0.16 20:0.88 21:0.71 27:0.03 28:0.78 30:0.62 34:0.97 36:0.70 37:0.92 39:0.31 41:0.88 43:0.77 60:0.87 66:0.75 69:0.80 70:0.34 74:0.76 78:0.93 81:0.61 83:0.83 85:0.80 91:0.38 96:0.72 98:0.39 100:0.91 101:0.76 108:0.64 111:0.91 114:0.68 120:0.59 121:0.90 122:0.43 123:0.61 126:0.54 127:0.13 129:0.05 135:0.65 140:0.45 145:0.54 146:0.43 147:0.49 149:0.62 150:0.74 151:0.58 152:0.93 153:0.48 154:0.70 155:0.67 158:0.87 159:0.16 161:0.82 162:0.64 164:0.71 165:0.78 167:0.51 169:0.88 172:0.32 173:0.85 176:0.73 177:0.38 179:0.43 181:0.53 186:0.93 187:0.68 189:0.90 191:0.75 192:0.59 201:0.74 202:0.63 204:0.89 208:0.64 212:0.30 215:0.48 216:0.79 220:0.93 222:0.48 230:0.87 233:0.76 235:0.97 238:0.84 241:0.27 242:0.33 243:0.77 247:0.39 248:0.58 253:0.68 255:0.79 256:0.58 259:0.21 260:0.60 261:0.91 265:0.41 266:0.27 267:0.76 268:0.64 271:0.42 276:0.27 279:0.86 283:0.71 285:0.62 290:0.68 297:0.36 300:0.25 +2 1:0.74 6:0.83 8:0.71 11:0.57 12:0.37 20:0.91 27:0.14 28:0.78 30:0.86 34:0.90 36:0.18 37:0.67 39:0.31 43:0.86 60:0.87 66:0.15 69:0.57 70:0.32 74:0.81 78:0.97 81:0.96 83:0.86 85:0.93 91:0.33 98:0.38 100:0.98 101:0.83 108:0.74 111:0.98 114:0.98 122:0.43 123:0.57 124:0.74 126:0.54 127:0.30 129:0.81 133:0.67 135:0.88 145:0.58 146:0.22 147:0.28 149:0.88 150:0.98 152:0.98 154:0.84 155:0.67 158:0.92 159:0.37 161:0.82 165:0.92 167:0.49 169:0.96 172:0.32 173:0.59 176:0.73 177:0.38 178:0.55 179:0.62 181:0.53 186:0.97 187:0.39 189:0.88 192:0.59 201:0.74 202:0.36 208:0.64 212:0.42 215:0.96 216:0.93 222:0.48 235:0.05 238:0.94 241:0.15 242:0.63 243:0.36 245:0.63 247:0.30 253:0.78 259:0.21 261:0.97 265:0.20 266:0.63 267:0.44 271:0.42 276:0.43 279:0.86 283:0.82 290:0.98 297:0.36 300:0.33 +1 1:0.74 7:0.81 9:0.80 11:0.57 12:0.37 17:0.79 21:0.74 27:0.39 28:0.78 30:0.76 32:0.80 34:0.66 36:0.32 37:0.49 39:0.31 41:0.88 43:0.90 60:0.87 66:0.77 69:0.49 70:0.40 74:0.95 77:0.70 81:0.50 83:0.83 85:0.98 91:0.23 96:0.70 98:0.76 101:0.85 104:0.90 106:0.81 108:0.38 114:0.67 120:0.56 121:0.97 122:0.43 123:0.36 124:0.41 126:0.54 127:0.41 129:0.59 133:0.47 135:0.37 140:0.45 145:0.88 146:0.92 147:0.94 149:0.96 150:0.66 151:0.51 153:0.72 154:0.89 155:0.67 158:0.87 159:0.67 161:0.82 162:0.86 163:0.81 164:0.70 165:0.65 167:0.48 172:0.82 173:0.33 176:0.73 177:0.38 179:0.34 181:0.53 187:0.39 192:0.59 195:0.87 201:0.74 202:0.55 204:0.89 206:0.81 208:0.64 212:0.47 215:0.46 216:0.29 220:0.96 222:0.48 230:0.87 233:0.76 235:0.95 241:0.10 242:0.63 243:0.79 245:0.81 247:0.39 248:0.52 253:0.73 255:0.79 256:0.58 259:0.21 260:0.57 265:0.77 266:0.63 267:0.94 268:0.63 271:0.42 276:0.74 279:0.86 282:0.88 283:0.71 285:0.62 290:0.67 297:0.36 299:0.88 300:0.70 +1 11:0.57 12:0.37 17:0.36 21:0.78 27:0.45 28:0.78 30:0.21 34:0.74 36:0.18 37:0.50 39:0.31 43:0.78 66:0.54 69:0.80 70:0.37 74:0.55 85:0.72 91:0.23 98:0.28 101:0.71 108:0.63 122:0.51 123:0.61 124:0.45 127:0.16 129:0.05 133:0.39 135:0.58 145:0.52 146:0.39 147:0.45 149:0.46 154:0.70 159:0.27 161:0.82 163:0.75 167:0.50 172:0.21 173:0.77 177:0.38 178:0.55 179:0.73 181:0.53 187:0.68 212:0.42 216:0.77 222:0.48 235:0.95 241:0.21 242:0.45 243:0.63 245:0.40 247:0.35 253:0.45 259:0.21 265:0.30 266:0.23 267:0.28 271:0.42 276:0.21 300:0.25 +1 1:0.74 7:0.81 11:0.57 12:0.37 17:0.02 20:0.80 21:0.30 27:0.11 28:0.78 30:0.89 34:0.82 36:0.62 37:0.79 39:0.31 43:0.87 66:0.75 69:0.56 70:0.20 74:0.70 78:0.88 81:0.80 83:0.75 85:0.85 91:0.31 98:0.51 100:0.85 101:0.79 104:0.89 106:0.81 108:0.43 111:0.86 114:0.86 117:0.86 121:0.90 122:0.43 123:0.41 126:0.54 127:0.15 129:0.05 135:0.21 145:0.52 146:0.46 147:0.52 149:0.76 150:0.87 152:0.77 154:0.85 155:0.67 159:0.17 161:0.82 165:0.84 167:0.49 169:0.83 172:0.32 173:0.66 176:0.73 177:0.38 178:0.55 179:0.60 181:0.53 186:0.88 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.30 215:0.72 216:0.85 222:0.48 230:0.87 232:0.92 233:0.76 235:0.42 241:0.18 242:0.63 243:0.77 247:0.30 253:0.70 259:0.21 261:0.80 265:0.53 266:0.27 267:0.65 271:0.42 276:0.21 290:0.86 297:0.36 300:0.18 +0 1:0.74 7:0.78 11:0.57 12:0.37 17:0.67 27:0.55 28:0.78 30:0.76 34:0.21 36:0.94 37:0.23 39:0.31 43:0.92 66:0.64 69:0.45 70:0.15 74:0.46 81:0.96 83:0.80 85:0.72 91:0.44 98:0.63 101:0.76 104:0.76 108:0.35 114:0.98 120:0.65 122:0.43 123:0.33 126:0.54 127:0.18 129:0.05 135:0.83 140:0.45 145:0.88 146:0.30 147:0.37 149:0.55 150:0.98 151:0.61 153:0.48 154:0.91 155:0.67 159:0.13 161:0.82 162:0.86 163:0.92 164:0.56 165:0.92 167:0.54 172:0.16 173:0.82 176:0.73 177:0.38 179:0.93 181:0.53 187:0.21 190:0.77 192:0.59 201:0.74 202:0.36 212:0.95 215:0.96 216:0.49 220:0.62 222:0.48 230:0.81 232:0.82 233:0.76 235:0.60 241:0.39 242:0.82 243:0.69 247:0.12 248:0.59 253:0.73 256:0.45 259:0.21 260:0.66 265:0.64 266:0.12 267:0.17 268:0.46 271:0.42 276:0.13 285:0.50 290:0.98 297:0.36 300:0.13 +1 1:0.74 11:0.57 12:0.37 17:0.62 21:0.71 27:0.45 28:0.78 30:0.45 32:0.80 34:0.87 36:0.20 37:0.50 39:0.31 43:0.81 66:0.69 69:0.71 70:0.25 74:0.56 77:0.70 81:0.52 83:0.73 85:0.72 91:0.12 98:0.46 101:0.72 108:0.54 114:0.68 122:0.43 123:0.52 126:0.54 127:0.14 129:0.05 135:0.75 145:0.58 146:0.55 147:0.61 149:0.51 150:0.67 154:0.77 155:0.67 159:0.20 161:0.82 163:0.86 165:0.69 167:0.48 172:0.21 173:0.71 176:0.73 177:0.38 178:0.55 179:0.72 181:0.53 187:0.68 192:0.59 195:0.72 201:0.74 212:0.61 215:0.48 216:0.77 222:0.48 235:0.88 241:0.12 242:0.63 243:0.73 247:0.30 253:0.56 259:0.21 265:0.48 266:0.17 267:0.28 271:0.42 276:0.20 282:0.88 290:0.68 297:0.36 299:0.88 300:0.18 +0 1:0.74 6:0.79 7:0.78 8:0.57 9:0.80 11:0.57 12:0.37 17:0.40 20:0.96 27:0.55 28:0.78 30:0.76 34:0.22 36:0.95 37:0.23 39:0.31 41:0.92 43:0.92 60:0.87 66:0.64 69:0.45 70:0.15 74:0.58 78:0.94 81:0.96 83:0.88 85:0.72 91:0.56 96:0.78 98:0.63 100:0.85 101:0.76 104:0.76 108:0.35 111:0.91 114:0.98 120:0.67 122:0.43 123:0.33 126:0.54 127:0.18 129:0.05 135:0.84 140:0.45 145:0.88 146:0.30 147:0.37 149:0.55 150:0.98 151:0.69 152:0.89 153:0.45 154:0.91 155:0.67 158:0.92 159:0.13 161:0.82 162:0.66 163:0.92 164:0.69 165:0.92 167:0.64 169:0.82 172:0.16 173:0.82 176:0.73 177:0.38 179:0.93 181:0.53 186:0.94 187:0.21 189:0.81 192:0.59 201:0.74 202:0.61 208:0.64 212:0.95 215:0.96 216:0.49 220:0.82 222:0.48 230:0.81 232:0.82 233:0.76 235:0.60 238:0.81 241:0.64 242:0.82 243:0.69 247:0.12 248:0.72 253:0.81 255:0.79 256:0.73 259:0.21 260:0.68 261:0.91 265:0.64 266:0.12 267:0.17 268:0.63 271:0.42 276:0.13 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.13 +1 1:0.74 6:0.79 8:0.58 9:0.80 11:0.57 12:0.37 17:0.30 20:0.93 21:0.22 27:0.32 28:0.78 30:0.87 34:0.55 36:0.41 37:0.26 39:0.31 41:0.82 43:0.85 60:0.97 66:0.63 69:0.60 70:0.27 74:0.77 78:0.97 81:0.84 83:0.87 85:0.90 91:0.62 96:0.80 98:0.67 100:0.86 101:0.82 104:0.83 108:0.46 111:0.94 114:0.90 120:0.69 122:0.43 123:0.44 124:0.45 126:0.54 127:0.33 129:0.59 133:0.47 135:0.91 140:0.45 145:0.54 146:0.63 147:0.68 149:0.84 150:0.90 151:0.87 152:0.86 153:0.48 154:0.83 155:0.67 158:0.92 159:0.33 161:0.82 162:0.86 163:0.81 164:0.57 165:0.86 167:0.49 169:0.83 172:0.41 173:0.67 176:0.73 177:0.38 179:0.76 181:0.53 186:0.97 187:0.39 189:0.81 192:0.59 201:0.74 202:0.36 208:0.64 212:0.30 215:0.79 216:0.25 222:0.48 232:0.88 235:0.31 238:0.81 241:0.18 242:0.63 243:0.68 245:0.54 247:0.30 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 261:0.92 265:0.68 266:0.54 267:0.69 268:0.46 271:0.42 276:0.36 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.25 +2 1:0.74 6:0.83 8:0.61 9:0.80 11:0.57 12:0.37 17:0.04 20:0.98 21:0.05 27:0.14 28:0.78 30:0.76 32:0.80 34:0.80 36:0.30 37:0.67 39:0.31 41:0.82 43:0.86 66:0.36 69:0.57 70:0.28 74:0.74 77:0.70 78:0.99 81:0.92 83:0.80 85:0.88 91:0.42 96:0.80 98:0.51 100:0.97 101:0.80 108:0.60 111:0.97 114:0.95 120:0.69 122:0.43 123:0.58 124:0.59 126:0.54 127:0.23 129:0.59 133:0.47 135:0.87 140:0.45 145:0.47 146:0.22 147:0.28 149:0.81 150:0.96 151:0.87 152:0.98 153:0.48 154:0.84 155:0.67 159:0.29 161:0.82 162:0.86 163:0.81 164:0.57 165:0.90 167:0.49 169:0.96 172:0.27 173:0.62 176:0.73 177:0.38 179:0.69 181:0.53 186:0.98 187:0.68 189:0.84 192:0.59 195:0.65 201:0.74 202:0.36 208:0.64 212:0.37 215:0.91 216:0.93 222:0.48 235:0.12 238:0.87 241:0.15 242:0.72 243:0.46 245:0.56 247:0.30 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 261:0.97 265:0.52 266:0.38 267:0.52 268:0.46 271:0.42 276:0.33 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.25 +2 6:1.00 8:0.98 12:0.06 17:0.01 20:0.99 21:0.78 27:0.01 28:0.49 34:0.28 36:0.57 37:0.96 43:0.21 66:0.36 69:0.93 78:0.91 91:0.99 98:0.07 100:0.99 108:0.85 111:0.98 120:0.92 123:0.84 127:0.06 129:0.05 135:0.28 140:0.98 145:0.74 146:0.05 147:0.05 149:0.08 151:0.83 152:0.93 153:0.95 154:0.14 159:0.05 161:0.71 162:0.45 164:0.92 167:0.92 169:0.98 172:0.05 173:0.99 177:0.05 178:0.54 181:0.63 186:0.91 189:1.00 191:0.99 202:1.00 216:0.94 235:0.68 238:1.00 241:0.96 243:0.51 248:0.89 256:0.95 259:0.21 260:0.93 261:0.97 265:0.07 267:0.84 268:0.90 271:0.28 285:0.62 +2 8:0.67 12:0.06 17:0.30 20:0.82 21:0.78 25:0.88 27:0.53 28:0.49 34:0.59 36:0.50 37:0.33 78:0.74 91:0.88 100:0.78 104:0.58 111:0.74 120:0.74 135:0.28 140:0.94 151:0.66 152:0.79 153:0.48 161:0.71 162:0.47 164:0.75 167:0.92 169:0.76 175:0.75 178:0.53 181:0.63 186:0.75 191:0.95 202:0.85 216:0.23 220:0.91 235:0.02 241:0.95 244:0.61 248:0.67 256:0.68 257:0.65 259:0.21 260:0.75 261:0.75 267:0.23 268:0.69 271:0.28 277:0.69 285:0.62 +3 6:0.88 8:0.93 12:0.06 17:0.07 20:0.99 21:0.54 25:0.88 27:0.40 28:0.49 30:0.15 34:0.47 36:0.77 37:0.74 43:0.47 66:0.16 69:0.39 70:0.68 78:0.74 81:0.91 91:0.90 98:0.63 100:0.79 104:0.58 108:0.63 111:0.74 120:0.80 123:0.29 124:0.51 126:0.54 127:0.78 129:0.59 133:0.47 135:0.26 140:0.93 146:0.47 147:0.53 149:0.49 150:0.83 151:0.74 152:0.94 153:0.83 154:0.36 159:0.31 161:0.71 162:0.48 164:0.89 167:0.92 169:0.77 172:0.37 173:0.57 177:0.05 178:0.52 179:0.87 181:0.63 186:0.75 189:0.94 191:0.84 202:0.94 212:0.30 215:0.58 216:0.59 220:0.96 235:0.60 238:0.97 241:0.96 242:0.82 243:0.37 245:0.57 247:0.12 248:0.72 256:0.86 259:0.21 260:0.81 261:0.78 265:0.60 266:0.54 267:0.28 268:0.86 271:0.28 276:0.37 283:0.60 285:0.62 297:0.36 300:0.43 +1 6:0.99 8:0.98 12:0.06 17:0.28 20:0.78 21:0.78 27:0.32 28:0.49 34:0.68 36:0.29 37:0.95 78:0.76 91:0.92 100:0.86 104:0.58 111:0.79 120:0.69 135:0.80 140:0.97 151:0.66 152:0.76 153:0.48 161:0.71 162:0.46 164:0.57 167:0.92 169:0.83 175:0.75 178:0.54 181:0.63 186:0.77 189:0.99 191:0.94 202:0.79 216:0.23 238:0.99 241:0.94 244:0.61 248:0.63 256:0.45 257:0.65 259:0.21 260:0.70 261:0.77 267:0.44 268:0.46 271:0.28 277:0.69 283:0.60 285:0.62 +2 6:1.00 8:0.91 12:0.06 17:0.30 20:0.99 21:0.78 25:0.88 27:0.10 28:0.49 34:0.26 36:0.72 37:0.97 43:0.33 66:0.36 69:0.67 78:0.78 91:0.73 98:0.09 100:0.86 104:0.58 108:0.51 111:0.80 120:0.75 123:0.49 127:0.07 129:0.05 135:0.18 140:0.94 145:0.45 146:0.05 147:0.05 149:0.13 151:0.71 152:1.00 153:0.72 154:0.25 159:0.05 161:0.71 162:0.47 164:0.76 167:0.73 169:0.95 172:0.05 173:0.80 177:0.05 178:0.53 181:0.63 186:0.79 187:0.68 189:0.98 191:0.93 202:0.85 216:0.67 235:0.42 238:0.97 241:0.65 243:0.51 248:0.68 256:0.68 259:0.21 260:0.76 261:0.96 265:0.10 267:0.44 268:0.70 271:0.28 285:0.62 +1 12:0.06 17:0.59 21:0.54 27:0.45 28:0.49 30:0.62 34:0.94 36:0.19 37:0.50 43:0.32 55:0.71 66:0.54 69:0.69 70:0.54 81:0.91 91:0.09 98:0.52 108:0.53 123:0.51 124:0.50 126:0.54 127:0.22 129:0.59 133:0.47 135:0.80 145:0.50 146:0.72 147:0.76 149:0.47 150:0.83 154:0.24 159:0.43 161:0.71 167:0.56 172:0.41 173:0.61 177:0.05 178:0.55 179:0.59 181:0.63 187:0.68 212:0.14 215:0.58 216:0.77 235:0.60 241:0.15 242:0.82 243:0.63 245:0.59 247:0.12 259:0.21 265:0.54 266:0.71 267:0.28 271:0.28 276:0.41 297:0.36 300:0.43 +1 6:1.00 8:0.99 12:0.06 17:0.02 20:0.99 21:0.78 25:0.88 27:0.10 28:0.49 34:0.24 36:0.37 37:0.97 43:0.52 66:0.36 69:0.12 78:0.90 91:0.97 98:0.10 100:0.98 104:0.58 108:0.10 111:0.97 120:0.82 123:0.10 127:0.07 129:0.05 135:0.37 140:0.93 146:0.05 147:0.05 149:0.16 151:0.79 152:1.00 153:0.91 154:0.41 159:0.05 161:0.71 162:0.48 164:0.91 167:0.92 169:0.95 172:0.05 173:0.35 177:0.05 178:0.52 181:0.63 186:0.90 189:1.00 191:0.96 202:0.95 216:0.67 220:0.82 235:0.42 238:1.00 241:0.93 243:0.51 248:0.74 256:0.89 259:0.21 260:0.83 261:0.96 265:0.10 267:0.73 268:0.89 271:0.28 283:0.82 285:0.62 +1 6:0.81 8:0.61 12:0.06 17:0.11 20:0.92 21:0.54 27:0.11 28:0.49 30:0.30 32:0.80 34:0.90 36:0.39 37:0.89 43:0.26 55:0.96 66:0.33 69:0.82 70:0.66 78:0.74 81:0.91 91:0.40 98:0.33 100:0.79 104:0.84 106:0.81 108:0.83 111:0.74 120:0.87 123:0.83 124:0.83 126:0.54 127:0.32 129:0.93 133:0.84 135:0.86 140:0.45 145:0.77 146:0.20 147:0.25 149:0.41 150:0.83 151:0.79 152:0.89 153:0.90 154:0.19 159:0.61 161:0.71 162:0.61 163:0.81 164:0.81 167:0.65 169:0.77 172:0.32 173:0.74 177:0.05 179:0.70 181:0.63 186:0.75 187:0.39 189:0.83 191:0.80 195:0.87 202:0.75 206:0.81 212:0.23 215:0.58 216:0.90 235:0.60 238:0.94 241:0.50 242:0.82 243:0.25 245:0.51 247:0.12 248:0.81 256:0.75 259:0.21 260:0.87 261:0.78 265:0.35 266:0.63 267:0.69 268:0.76 271:0.28 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.99 8:0.95 12:0.06 17:0.28 20:0.83 21:0.78 27:0.40 28:0.49 30:0.15 34:0.22 36:0.98 37:0.79 43:0.47 55:0.52 66:0.33 69:0.35 70:0.68 78:0.75 91:0.87 98:0.30 100:0.84 104:0.58 108:0.58 111:0.78 120:0.77 123:0.56 124:0.77 127:0.27 129:0.89 133:0.78 135:0.84 140:0.95 146:0.05 147:0.05 149:0.45 151:0.72 152:0.80 153:0.78 154:0.36 159:0.49 161:0.71 162:0.46 163:0.97 164:0.75 167:0.91 169:0.81 172:0.27 173:0.27 177:0.05 178:0.53 179:0.71 181:0.63 186:0.76 189:1.00 191:0.91 202:0.91 212:0.30 216:0.45 235:0.22 238:0.99 241:0.89 242:0.82 243:0.17 245:0.49 247:0.12 248:0.69 256:0.73 259:0.21 260:0.78 261:0.79 265:0.32 266:0.54 267:0.65 268:0.69 271:0.28 276:0.38 285:0.62 300:0.43 +1 7:0.81 8:0.79 12:0.06 17:0.30 20:0.85 21:0.64 27:0.16 28:0.49 30:0.60 32:0.80 34:0.80 36:0.35 37:0.85 43:0.28 55:0.84 66:0.77 69:0.78 70:0.53 78:0.74 81:0.88 91:0.14 98:0.76 100:0.77 104:0.98 106:0.81 108:0.71 111:0.73 120:0.69 123:0.69 124:0.39 126:0.54 127:0.31 129:0.59 133:0.47 135:0.72 140:0.80 145:0.91 146:0.05 147:0.05 149:0.46 150:0.75 151:0.66 152:0.81 153:0.48 154:0.20 159:0.55 161:0.71 162:0.62 163:0.86 164:0.57 167:0.62 169:0.75 172:0.57 173:0.72 177:0.05 178:0.42 179:0.63 181:0.63 186:0.75 187:0.68 195:0.84 202:0.50 206:0.81 212:0.30 215:0.53 216:0.81 230:0.87 233:0.76 235:0.74 241:0.39 242:0.82 243:0.13 245:0.51 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.75 265:0.76 266:0.63 267:0.28 268:0.46 271:0.28 276:0.49 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.88 12:0.06 17:0.18 20:0.75 21:0.59 25:0.88 27:0.40 28:0.49 30:0.45 34:0.80 36:0.87 37:0.74 43:0.47 55:0.59 66:0.24 69:0.40 70:0.61 78:0.72 81:0.89 91:0.22 98:0.45 100:0.73 104:0.58 108:0.65 111:0.72 120:0.69 123:0.30 124:0.57 126:0.54 127:0.58 129:0.59 133:0.47 135:0.80 140:0.80 146:0.35 147:0.41 149:0.51 150:0.79 151:0.66 152:0.94 153:0.48 154:0.36 159:0.29 161:0.71 162:0.62 164:0.57 167:0.65 169:0.77 172:0.32 173:0.57 177:0.05 178:0.42 179:0.84 181:0.63 186:0.73 187:0.39 202:0.50 212:0.61 215:0.55 216:0.59 235:0.68 241:0.47 242:0.82 243:0.44 245:0.59 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.78 265:0.42 266:0.38 267:0.28 268:0.46 271:0.28 276:0.32 285:0.62 297:0.36 300:0.43 +1 12:0.06 17:0.67 21:0.78 27:0.45 28:0.49 30:0.45 32:0.80 34:0.95 36:0.20 37:0.50 43:0.32 55:0.59 66:0.36 69:0.72 70:0.33 81:0.72 91:0.29 98:0.33 108:0.69 123:0.67 124:0.58 126:0.54 127:0.17 129:0.59 133:0.47 135:0.60 145:0.54 146:0.29 147:0.35 149:0.38 150:0.53 154:0.24 159:0.30 161:0.71 163:0.79 167:0.56 172:0.21 173:0.68 177:0.05 178:0.55 179:0.64 181:0.63 187:0.68 195:0.77 212:0.23 215:0.43 216:0.77 235:1.00 241:0.15 242:0.82 243:0.45 245:0.50 247:0.12 259:0.21 265:0.36 266:0.38 267:0.52 271:0.28 276:0.29 297:0.36 300:0.25 +1 6:0.81 8:0.65 12:0.06 17:0.13 20:0.83 21:0.54 27:0.11 28:0.49 30:0.30 32:0.80 34:0.86 36:0.36 37:0.89 43:0.26 55:0.96 66:0.33 69:0.82 70:0.66 78:0.74 81:0.91 91:0.37 98:0.33 100:0.80 104:0.84 106:0.81 108:0.83 111:0.75 120:0.81 123:0.83 124:0.83 126:0.54 127:0.32 129:0.93 133:0.84 135:0.85 140:0.45 145:0.77 146:0.20 147:0.25 149:0.41 150:0.83 151:0.72 152:0.89 153:0.77 154:0.19 159:0.61 161:0.71 162:0.86 163:0.81 164:0.72 167:0.61 169:0.77 172:0.32 173:0.74 177:0.05 179:0.70 181:0.63 186:0.75 187:0.39 189:0.83 191:0.87 195:0.87 202:0.57 206:0.81 212:0.23 215:0.58 216:0.90 235:0.60 238:0.97 241:0.37 242:0.82 243:0.25 245:0.51 247:0.12 248:0.73 256:0.64 259:0.21 260:0.81 261:0.78 265:0.35 266:0.63 267:0.65 268:0.66 271:0.28 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 8:0.99 12:0.06 17:0.69 20:0.82 21:0.59 27:0.49 28:0.49 30:0.61 32:0.80 34:0.52 36:0.56 37:0.94 43:0.44 55:0.59 66:0.89 69:0.48 70:0.42 78:0.74 81:0.89 91:0.74 98:0.95 100:0.78 104:0.58 108:0.37 111:0.74 120:0.74 123:0.35 126:0.54 127:0.64 129:0.05 135:0.30 140:0.45 145:0.98 146:0.71 147:0.75 149:0.68 150:0.79 151:0.66 152:0.79 153:0.48 154:0.34 159:0.28 161:0.71 162:0.75 164:0.84 167:0.92 169:0.75 172:0.68 173:0.67 177:0.05 179:0.65 181:0.63 186:0.75 195:0.57 202:0.76 212:0.85 215:0.55 216:0.24 220:0.62 235:0.68 241:0.92 242:0.82 243:0.89 247:0.12 248:0.67 256:0.79 259:0.21 260:0.75 261:0.75 265:0.95 266:0.27 267:0.52 268:0.80 271:0.28 276:0.58 285:0.62 297:0.36 300:0.33 +1 12:0.06 17:0.36 21:0.05 25:0.88 27:0.40 28:0.49 30:0.21 34:0.58 36:0.45 37:0.74 43:0.47 66:0.36 69:0.33 70:0.67 81:0.98 91:0.31 98:0.42 104:0.58 108:0.26 123:0.25 124:0.59 126:0.54 127:0.54 129:0.59 133:0.47 135:0.26 146:0.31 147:0.37 149:0.51 150:0.97 154:0.36 159:0.25 161:0.71 167:0.73 172:0.27 173:0.56 177:0.05 178:0.55 179:0.86 181:0.63 187:0.21 212:0.71 215:0.91 216:0.59 235:0.05 241:0.66 242:0.82 243:0.51 245:0.56 247:0.12 259:0.21 265:0.44 266:0.27 267:0.28 271:0.28 276:0.28 297:0.36 300:0.43 +2 12:0.06 17:0.28 21:0.22 25:0.88 27:0.40 28:0.49 30:0.21 34:0.50 36:0.81 37:0.74 43:0.47 55:0.59 66:0.16 69:0.35 70:0.92 81:0.96 91:0.15 98:0.25 104:0.58 108:0.85 123:0.26 124:0.78 126:0.54 127:0.89 129:0.89 133:0.78 135:0.28 146:0.35 147:0.41 149:0.49 150:0.94 154:0.36 159:0.62 161:0.71 167:0.72 172:0.32 173:0.30 177:0.05 178:0.55 179:0.81 181:0.63 187:0.21 202:0.62 212:0.49 215:0.79 216:0.59 235:0.22 241:0.64 242:0.82 243:0.37 245:0.56 247:0.12 259:0.21 265:0.25 266:0.47 267:0.23 271:0.28 276:0.40 297:0.36 300:0.70 +1 7:0.81 8:0.74 12:0.06 17:0.34 21:0.78 25:0.88 27:0.10 28:0.49 34:0.40 36:0.96 37:0.97 43:0.22 66:0.36 69:0.90 78:0.79 91:0.33 98:0.08 104:0.58 108:0.79 111:0.77 120:0.69 123:0.78 127:0.06 129:0.05 135:0.20 140:0.96 146:0.05 147:0.05 149:0.09 151:0.66 153:0.48 154:0.15 159:0.05 161:0.71 162:0.46 164:0.57 167:0.59 169:0.84 172:0.05 173:0.96 177:0.05 178:0.53 181:0.63 187:0.39 191:0.88 202:0.77 216:0.67 230:0.87 233:0.76 235:0.31 241:0.30 243:0.51 248:0.63 256:0.45 259:0.21 260:0.70 265:0.08 267:0.82 268:0.46 271:0.28 285:0.62 +2 7:0.78 8:0.70 11:0.57 12:0.69 17:0.34 21:0.13 27:0.48 28:0.70 30:0.11 34:0.23 36:0.93 37:0.47 39:0.76 43:0.28 55:0.71 66:0.11 69:0.33 70:0.94 74:0.98 76:1.00 77:0.89 81:0.70 85:0.72 91:0.56 98:0.36 101:0.64 104:0.95 106:0.81 108:0.95 117:0.86 122:0.67 123:0.95 124:0.96 126:0.32 127:0.95 129:0.97 133:0.96 135:0.74 145:0.61 146:0.63 147:0.68 149:0.72 150:0.50 154:0.88 159:0.93 161:0.88 167:0.45 172:0.73 173:0.10 177:0.38 178:0.55 179:0.18 181:0.45 187:0.87 195:0.98 206:0.81 212:0.42 215:0.84 216:0.74 222:0.84 230:0.81 232:0.86 233:0.76 235:0.12 241:0.05 242:0.58 243:0.19 245:0.91 247:0.67 253:0.70 259:0.21 265:0.38 266:0.96 267:0.59 271:0.34 276:0.93 282:0.84 297:0.36 300:0.94 +2 1:0.74 7:0.81 12:0.69 17:0.28 21:0.40 27:0.21 28:0.70 30:0.15 34:0.55 36:0.58 37:0.74 39:0.76 43:0.37 55:0.59 66:0.64 69:0.15 70:0.84 74:0.97 76:0.94 81:0.65 91:0.17 98:0.86 108:0.12 114:0.82 117:0.86 123:0.12 124:0.64 126:0.54 127:0.81 129:0.89 133:0.78 135:0.18 146:0.99 147:0.99 149:0.70 150:0.71 154:0.92 155:0.67 159:0.91 161:0.88 167:0.47 172:0.96 173:0.07 176:0.73 177:0.38 178:0.55 179:0.15 181:0.45 187:0.39 192:0.59 201:0.74 204:0.89 208:0.64 212:0.54 215:0.67 216:0.95 222:0.84 230:0.84 232:0.85 233:0.69 235:0.52 241:0.21 242:0.22 243:0.69 245:0.96 247:0.67 253:0.77 254:0.74 259:0.21 265:0.86 266:0.80 267:0.52 271:0.34 276:0.95 290:0.82 297:0.36 300:0.70 +1 1:0.74 7:0.81 11:0.57 12:0.69 17:0.72 21:0.71 27:0.72 28:0.70 30:0.38 32:0.80 34:0.49 36:0.98 39:0.76 43:0.78 55:0.84 60:0.87 66:0.80 69:0.80 70:0.64 74:0.89 77:0.70 81:0.48 83:0.82 85:0.72 91:0.18 98:0.85 101:0.70 108:0.63 114:0.68 121:0.90 123:0.61 124:0.42 126:0.54 127:0.70 129:0.05 133:0.74 135:0.93 145:0.41 146:0.97 147:0.05 149:0.72 150:0.65 154:0.70 155:0.67 158:0.87 159:0.79 161:0.88 165:0.69 167:0.45 172:0.87 173:0.66 176:0.73 177:0.05 178:0.55 179:0.35 181:0.45 187:0.95 192:0.59 195:0.92 201:0.74 202:0.36 204:0.89 208:0.64 212:0.26 215:0.48 216:0.16 222:0.84 230:0.87 233:0.76 235:0.88 241:0.10 242:0.43 243:0.07 245:0.71 247:0.67 253:0.69 257:0.65 259:0.21 265:0.85 266:0.75 267:0.89 271:0.34 276:0.80 279:0.86 282:0.88 283:0.71 290:0.68 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.99 7:0.81 8:0.96 9:0.80 12:0.69 17:0.20 20:0.98 21:0.40 27:0.21 28:0.70 30:0.15 34:0.34 36:0.57 37:0.74 39:0.76 43:0.38 55:0.59 66:0.33 69:0.15 70:0.86 74:0.97 76:0.94 78:0.98 81:0.65 91:0.44 96:0.87 98:0.68 100:0.99 104:0.94 106:0.81 108:0.12 111:0.99 114:0.82 117:0.86 120:0.87 123:0.12 124:0.82 126:0.54 127:0.83 129:0.93 133:0.84 135:0.17 140:0.45 146:0.98 147:0.98 149:0.70 150:0.71 151:0.83 152:0.98 153:0.72 154:0.92 155:0.67 158:0.87 159:0.91 161:0.88 162:0.61 164:0.80 167:0.47 169:0.98 172:0.91 173:0.07 176:0.73 177:0.38 179:0.16 181:0.45 186:0.98 187:0.39 189:0.99 190:0.77 192:0.59 201:0.74 202:0.76 204:0.89 206:0.81 208:0.64 212:0.54 215:0.67 216:0.95 220:0.62 222:0.84 230:0.84 232:0.85 233:0.69 235:0.52 238:0.96 241:0.21 242:0.27 243:0.50 245:0.97 247:0.67 248:0.79 253:0.91 254:0.74 255:0.79 256:0.78 259:0.21 260:0.87 261:0.97 265:0.68 266:0.87 267:0.65 268:0.77 271:0.34 276:0.95 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.70 +0 12:0.69 17:0.30 21:0.30 27:0.45 28:0.70 30:0.42 34:0.63 36:0.36 37:0.50 39:0.76 43:0.30 55:0.59 66:0.78 69:0.68 70:0.46 74:0.98 81:0.46 91:0.11 98:0.81 108:0.52 114:0.69 122:0.67 123:0.50 124:0.40 126:0.32 127:0.59 129:0.05 133:0.39 135:0.83 145:0.50 146:0.96 147:0.97 149:0.73 150:0.37 154:0.45 158:0.84 159:0.78 161:0.88 167:0.47 172:0.88 173:0.51 176:0.56 177:0.38 178:0.55 179:0.31 181:0.45 187:0.68 212:0.55 216:0.77 222:0.84 235:0.31 241:0.27 242:0.80 243:0.80 245:0.86 247:0.39 253:0.74 254:0.94 259:0.21 265:0.81 266:0.80 267:0.28 271:0.34 276:0.80 290:0.69 300:0.43 +2 1:0.74 7:0.78 11:0.57 12:0.69 17:0.62 21:0.30 27:0.49 28:0.70 30:0.61 32:0.80 34:0.92 36:0.91 37:0.53 39:0.76 43:0.92 55:0.71 66:0.89 69:0.37 70:0.51 74:0.92 76:0.85 77:0.70 81:0.73 83:0.75 85:0.85 91:0.12 98:0.95 101:0.80 104:0.90 106:0.81 108:0.29 114:0.86 117:0.86 122:0.67 123:0.28 126:0.54 127:0.64 129:0.05 135:0.42 145:0.42 146:0.78 147:0.82 149:0.81 150:0.83 154:0.92 155:0.67 158:0.85 159:0.34 161:0.88 165:0.84 167:0.46 172:0.68 173:0.50 176:0.73 177:0.38 178:0.55 179:0.65 181:0.45 187:0.68 192:0.59 195:0.59 201:0.74 206:0.81 212:0.77 215:0.72 216:0.71 222:0.84 230:0.81 232:0.97 233:0.76 235:0.42 241:0.15 242:0.51 243:0.89 247:0.67 253:0.76 259:0.21 265:0.95 266:0.38 267:0.59 271:0.34 276:0.58 282:0.88 283:0.82 290:0.86 297:0.36 299:0.88 300:0.43 +0 7:0.78 11:0.57 12:0.69 17:0.24 21:0.54 27:0.21 28:0.70 30:0.21 34:0.45 36:0.50 37:0.74 39:0.76 43:0.48 55:0.59 66:0.43 69:0.23 70:0.86 74:0.95 81:0.38 85:0.72 91:0.37 98:0.66 101:0.68 104:0.97 106:0.81 108:0.18 120:0.59 123:0.18 124:0.85 126:0.32 127:0.78 129:0.93 133:0.87 135:0.17 140:0.45 146:0.96 147:0.96 149:0.71 150:0.33 151:0.52 153:0.48 154:0.86 159:0.87 161:0.88 162:0.86 164:0.66 167:0.46 172:0.89 173:0.10 177:0.38 179:0.19 181:0.45 187:0.39 190:0.77 202:0.50 206:0.81 212:0.49 215:0.58 216:0.95 220:0.96 222:0.84 230:0.81 233:0.69 235:0.60 241:0.15 242:0.23 243:0.57 245:0.93 247:0.67 248:0.52 253:0.67 256:0.58 259:0.21 260:0.59 265:0.66 266:0.75 267:0.69 268:0.59 271:0.34 276:0.92 285:0.50 297:0.36 300:0.84 +0 6:0.92 7:0.78 8:0.70 12:0.69 17:0.45 20:0.94 21:0.05 27:0.48 28:0.70 30:0.15 34:0.24 36:0.92 37:0.47 39:0.76 43:0.33 55:0.84 66:0.13 69:0.32 70:0.89 74:0.84 76:1.00 77:0.89 78:0.89 81:0.79 91:0.23 98:0.40 100:0.84 104:0.95 106:0.81 108:0.25 111:0.86 117:0.86 123:0.89 124:0.90 126:0.32 127:0.79 129:0.81 133:0.89 135:0.76 145:0.61 146:0.43 147:0.50 149:0.45 150:0.58 152:0.97 154:0.88 159:0.81 161:0.88 163:0.81 167:0.45 169:0.86 172:0.37 173:0.17 177:0.38 178:0.55 179:0.53 181:0.45 186:0.89 187:0.87 195:0.92 206:0.81 212:0.18 215:0.91 216:0.74 222:0.84 230:0.81 232:0.86 233:0.76 235:0.05 241:0.07 242:0.71 243:0.33 245:0.70 247:0.60 253:0.60 254:0.74 259:0.21 261:0.92 265:0.24 266:0.94 267:0.59 271:0.34 276:0.68 282:0.84 297:0.36 300:0.70 +0 12:0.69 17:0.32 21:0.47 27:0.45 28:0.70 30:0.26 32:0.75 34:0.89 36:0.20 37:0.50 39:0.76 43:0.30 55:0.52 66:0.33 69:0.84 70:0.91 74:0.37 81:0.42 91:0.12 98:0.41 108:0.69 123:0.67 124:0.84 126:0.32 127:0.52 129:0.05 133:0.82 135:0.72 145:0.41 146:0.68 147:0.43 149:0.64 150:0.35 154:0.52 159:0.73 161:0.88 167:0.50 172:0.61 173:0.74 177:0.05 178:0.55 179:0.43 181:0.45 187:0.68 195:0.88 212:0.52 215:0.63 216:0.77 222:0.84 235:0.52 241:0.44 242:0.81 243:0.17 245:0.77 247:0.39 253:0.33 254:0.92 257:0.65 259:0.21 265:0.43 266:0.89 267:0.28 271:0.34 276:0.71 283:0.71 297:0.36 300:0.84 +0 1:0.74 6:0.80 7:0.78 8:0.60 11:0.57 12:0.69 17:0.45 20:0.93 21:0.22 27:0.71 28:0.70 30:0.17 32:0.80 34:0.52 36:0.64 37:0.54 39:0.76 43:0.72 55:0.59 60:0.87 66:0.60 69:0.87 70:0.88 74:0.94 76:0.85 77:0.89 78:0.92 81:0.81 83:0.81 85:0.80 87:0.98 91:0.08 98:0.66 100:0.87 101:0.71 104:0.83 106:0.81 108:0.74 111:0.90 114:0.90 122:0.57 123:0.72 124:0.77 126:0.54 127:0.72 129:0.59 133:0.86 135:0.78 145:0.65 146:0.95 147:0.41 149:0.72 150:0.87 152:0.86 154:0.62 155:0.67 158:0.86 159:0.86 161:0.88 165:0.86 167:0.46 169:0.84 172:0.85 173:0.70 176:0.73 177:0.05 178:0.55 179:0.31 181:0.45 186:0.92 187:0.21 189:0.82 192:0.59 195:0.95 201:0.74 206:0.81 208:0.64 212:0.35 215:0.79 216:0.15 222:0.84 230:0.81 232:0.70 233:0.76 235:0.42 238:0.82 241:0.15 242:0.21 243:0.09 245:0.81 247:0.67 253:0.78 257:0.65 259:0.21 261:0.89 265:0.67 266:0.91 267:0.73 271:0.34 276:0.83 279:0.86 282:0.88 283:0.82 290:0.90 297:0.36 299:0.88 300:0.84 +0 1:0.74 6:0.91 7:0.78 8:0.80 11:0.57 12:0.69 17:0.83 20:0.93 21:0.13 27:0.59 28:0.70 30:0.21 32:0.80 34:0.78 36:0.29 37:0.77 39:0.76 43:0.94 55:0.59 66:0.27 69:0.25 70:0.67 74:0.86 76:0.85 77:0.70 78:0.97 81:0.83 83:0.77 85:0.72 91:0.63 98:0.42 100:0.93 101:0.73 108:0.20 111:0.94 114:0.92 120:0.85 122:0.67 123:0.70 124:0.59 126:0.54 127:0.59 129:0.59 133:0.47 135:0.37 140:0.45 145:0.39 146:0.35 147:0.41 149:0.65 150:0.89 151:0.71 152:0.86 153:0.77 154:0.94 155:0.67 159:0.41 161:0.88 162:0.86 164:0.74 165:0.88 167:0.47 169:0.91 172:0.27 173:0.34 176:0.73 177:0.38 179:0.87 181:0.45 186:0.96 187:0.39 189:0.92 190:0.77 192:0.59 195:0.63 201:0.74 202:0.60 212:0.55 215:0.84 216:0.25 222:0.84 230:0.81 233:0.76 235:0.22 238:0.86 241:0.24 242:0.58 243:0.51 245:0.56 247:0.39 248:0.76 253:0.76 256:0.68 259:0.21 260:0.85 261:0.92 265:0.36 266:0.38 267:0.36 268:0.68 271:0.34 276:0.28 282:0.88 283:0.82 285:0.50 290:0.92 297:0.36 299:0.88 300:0.43 +0 7:0.78 11:0.57 12:0.69 17:0.36 21:0.78 27:0.48 28:0.70 30:0.45 34:0.36 36:0.96 37:0.47 39:0.76 43:0.60 55:0.71 66:0.25 69:0.37 70:0.77 74:0.99 85:0.96 91:0.01 98:0.59 101:0.73 104:0.96 106:0.81 108:0.92 122:0.67 123:0.92 124:0.96 127:0.97 129:0.98 133:0.96 135:0.89 145:0.61 146:0.39 147:0.45 149:0.87 154:0.85 159:0.98 161:0.88 167:0.45 172:0.99 173:0.07 177:0.38 178:0.55 179:0.09 181:0.45 187:0.95 206:0.81 212:0.60 216:0.74 222:0.84 230:0.81 233:0.76 235:0.22 241:0.02 242:0.19 243:0.05 245:0.99 247:0.67 253:0.71 259:0.21 265:0.60 266:0.95 267:0.36 271:0.34 276:0.99 300:0.94 +1 1:0.74 6:0.92 7:0.78 8:0.73 12:0.69 17:0.11 20:0.82 21:0.05 27:0.48 28:0.70 30:0.55 34:0.19 36:0.98 37:0.47 39:0.76 43:0.33 55:0.71 66:0.97 69:0.37 70:0.56 74:0.80 76:1.00 77:0.89 78:0.98 81:0.88 83:0.79 91:0.27 96:0.70 98:0.98 100:0.94 104:0.95 106:0.81 108:0.29 111:0.95 114:0.95 117:0.86 123:0.28 126:0.54 127:0.79 129:0.05 135:0.47 140:0.45 145:0.61 146:0.98 147:0.98 149:0.76 150:0.93 151:0.50 152:0.97 153:0.48 154:0.88 155:0.67 159:0.76 161:0.88 162:0.86 165:0.90 167:0.47 169:0.86 172:0.92 173:0.23 176:0.73 177:0.38 179:0.29 181:0.45 186:0.97 187:0.87 190:0.77 192:0.59 195:0.89 201:0.74 202:0.36 206:0.81 212:0.39 215:0.91 216:0.74 220:0.91 222:0.84 230:0.81 232:0.86 233:0.76 235:0.12 241:0.21 242:0.79 243:0.97 247:0.60 248:0.50 253:0.77 254:0.74 256:0.45 259:0.21 261:0.92 265:0.98 266:0.54 267:0.92 268:0.46 271:0.34 276:0.85 282:0.84 285:0.50 290:0.95 297:0.36 300:0.70 +2 8:0.88 11:0.57 12:0.69 17:0.16 20:0.92 21:0.22 27:0.57 28:0.70 30:0.38 32:0.77 34:0.33 36:0.84 37:0.54 39:0.76 43:0.80 55:0.59 60:0.87 66:0.45 69:0.70 70:0.65 74:0.89 77:0.89 78:0.90 81:0.52 83:0.80 85:0.72 91:0.27 98:0.72 100:0.84 101:0.71 104:0.93 106:0.81 108:0.70 111:0.87 114:0.72 117:0.86 120:0.69 123:0.68 124:0.76 126:0.32 127:0.72 129:0.81 133:0.76 135:0.66 140:0.45 145:0.56 146:0.26 147:0.05 149:0.67 150:0.40 151:0.66 152:0.86 153:0.48 154:0.75 155:0.67 158:0.87 159:0.71 161:0.88 162:0.86 164:0.56 167:0.45 169:0.82 172:0.63 173:0.60 176:0.56 177:0.05 179:0.52 181:0.45 186:0.90 187:0.68 190:0.77 192:0.59 195:0.84 202:0.36 206:0.81 208:0.64 212:0.51 216:0.70 222:0.84 232:0.97 235:0.22 241:0.01 242:0.38 243:0.09 245:0.75 247:0.67 248:0.63 253:0.70 256:0.45 257:0.65 259:0.21 260:0.70 261:0.88 265:0.73 266:0.80 267:0.28 268:0.46 271:0.34 276:0.68 279:0.86 282:0.85 283:0.82 285:0.50 290:0.72 300:0.55 +2 1:0.74 6:0.92 8:0.89 9:0.80 11:0.57 12:0.69 17:0.38 20:0.93 21:0.05 27:0.48 28:0.70 30:0.76 34:0.26 36:0.62 37:0.47 39:0.76 41:0.88 43:0.85 55:0.45 60:0.95 66:0.72 69:0.33 70:0.22 74:0.84 76:1.00 77:0.89 78:0.93 81:0.88 83:0.82 85:0.72 91:0.69 96:0.80 98:0.53 100:0.90 101:0.75 104:0.95 106:0.81 108:0.26 111:0.91 114:0.95 117:0.86 120:0.82 122:0.57 123:0.25 126:0.54 127:0.16 129:0.05 135:0.60 140:0.45 145:0.61 146:0.36 147:0.43 149:0.55 150:0.93 151:0.83 152:0.97 153:0.48 154:0.82 155:0.67 158:0.92 159:0.14 161:0.88 162:0.83 163:0.75 164:0.76 165:0.90 167:0.51 169:0.86 172:0.27 173:0.55 176:0.73 177:0.38 179:0.70 181:0.45 186:0.92 187:0.87 189:0.97 190:0.77 192:0.59 195:0.58 201:0.74 202:0.64 206:0.81 208:0.64 212:0.42 215:0.91 216:0.74 220:0.62 222:0.84 232:0.86 235:0.12 238:0.85 241:0.47 242:0.45 243:0.75 247:0.35 248:0.76 253:0.85 255:0.79 256:0.68 259:0.21 260:0.83 261:0.92 265:0.54 266:0.23 267:0.76 268:0.70 271:0.34 276:0.17 279:0.86 282:0.84 283:0.82 285:0.62 290:0.95 297:0.36 300:0.18 +0 1:0.74 11:0.57 12:0.69 17:0.22 20:0.95 21:0.68 27:0.18 28:0.70 30:0.27 34:0.36 36:0.73 37:0.45 39:0.76 43:0.80 55:0.71 66:0.08 69:0.34 70:0.87 74:0.99 78:0.78 81:0.48 85:0.97 91:0.10 98:0.36 100:0.73 101:0.72 108:0.99 111:0.77 114:0.70 120:0.69 122:0.57 123:0.99 124:0.98 126:0.54 127:1.00 129:1.00 133:0.98 135:0.34 140:0.45 146:0.96 147:0.96 149:0.93 150:0.62 151:0.66 152:0.91 153:0.48 154:0.87 155:0.67 159:0.99 161:0.88 162:0.86 164:0.56 167:0.45 169:0.72 172:0.95 173:0.06 176:0.73 177:0.38 179:0.09 181:0.45 186:0.79 187:0.87 190:0.77 192:0.59 201:0.74 202:0.36 212:0.47 215:0.49 216:0.83 222:0.84 235:0.84 241:0.05 242:0.22 243:0.19 245:0.99 247:0.67 248:0.63 253:0.76 256:0.45 259:0.21 260:0.70 261:0.80 265:0.39 266:0.98 267:0.65 268:0.46 271:0.34 276:1.00 285:0.50 290:0.70 297:0.36 300:0.94 +0 6:0.99 7:0.78 8:0.91 9:0.80 11:0.57 12:0.69 17:0.03 20:0.98 21:0.30 27:0.21 28:0.70 30:0.16 34:0.45 36:0.67 37:0.74 39:0.76 41:0.82 43:0.45 55:0.71 66:0.10 69:0.85 70:0.87 74:0.56 78:0.93 81:0.54 83:0.76 85:0.72 91:0.93 96:0.80 98:0.33 100:0.89 101:0.52 104:0.84 106:0.81 108:0.97 111:0.91 120:0.99 123:0.97 124:0.99 126:0.32 127:1.00 129:0.81 133:0.99 135:0.44 140:1.00 146:0.89 147:0.48 149:0.80 150:0.41 151:0.87 152:0.98 153:0.99 154:0.47 155:0.67 159:0.97 161:0.88 162:0.60 164:0.97 167:0.55 169:0.98 172:0.84 173:0.35 177:0.05 179:0.13 181:0.45 186:0.93 187:0.39 189:0.94 191:0.85 192:0.59 202:0.96 206:0.81 208:0.64 212:0.16 215:0.72 216:0.95 222:0.84 230:0.81 233:0.76 235:0.31 238:0.85 241:0.57 242:0.79 243:0.08 245:0.93 247:0.60 248:0.78 253:0.77 255:0.79 256:0.96 257:0.65 259:0.21 260:0.99 261:0.97 265:0.35 266:0.98 267:0.85 268:0.97 271:0.34 276:0.97 283:0.82 285:0.62 297:0.36 300:0.94 +2 8:0.75 12:0.51 17:0.81 21:0.40 27:0.72 28:0.47 30:0.89 32:0.80 34:0.56 36:0.54 37:0.32 43:0.31 66:0.47 69:0.72 70:0.20 81:0.92 91:0.88 98:0.22 104:0.58 108:0.71 120:0.89 123:0.69 124:0.52 126:0.54 127:0.28 129:0.59 133:0.47 135:0.58 140:0.45 145:0.77 146:0.13 147:0.18 149:0.34 150:0.85 151:0.80 153:0.93 154:0.22 159:0.26 161:0.89 162:0.86 164:0.84 167:0.83 172:0.21 173:0.85 177:0.05 179:0.87 181:0.98 195:0.59 202:0.72 212:0.30 215:0.67 216:0.26 235:0.52 241:0.91 242:0.82 243:0.37 245:0.46 247:0.12 248:0.85 256:0.78 259:0.21 260:0.90 265:0.24 266:0.27 267:0.52 268:0.80 271:0.64 276:0.15 285:0.62 297:0.36 300:0.18 +3 8:0.84 12:0.51 17:0.26 21:0.22 25:0.88 27:0.26 28:0.47 34:0.21 36:0.45 37:0.79 43:0.52 66:0.36 69:0.10 78:0.88 81:0.87 91:0.98 98:0.22 104:0.58 108:0.09 111:0.93 120:0.98 123:0.09 126:0.54 127:0.11 129:0.05 135:0.30 140:0.45 146:0.05 147:0.05 149:0.16 150:0.73 151:0.85 153:0.98 154:0.41 159:0.05 161:0.89 162:0.78 164:0.98 167:0.85 169:0.94 172:0.05 173:0.87 177:0.05 181:0.98 191:0.93 202:0.96 215:0.79 216:0.69 235:0.22 238:0.97 241:1.00 243:0.51 248:0.98 256:0.97 259:0.21 260:0.98 265:0.24 267:0.73 268:0.98 271:0.64 283:0.82 285:0.62 297:0.36 +1 7:0.81 8:0.76 12:0.51 17:0.51 21:0.40 27:0.50 28:0.47 30:0.26 32:0.80 34:0.31 36:0.79 37:0.60 43:0.40 55:0.84 66:0.05 69:0.54 70:0.64 81:0.83 91:0.06 98:0.26 104:0.91 106:0.81 108:0.98 120:0.80 123:0.96 124:0.99 126:0.54 127:0.99 129:1.00 133:0.99 135:0.60 140:0.45 145:0.70 146:0.85 147:0.88 149:0.85 150:0.63 151:0.73 153:0.80 154:0.30 159:0.99 161:0.89 162:0.77 164:0.84 167:0.46 172:0.86 173:0.08 177:0.05 179:0.10 181:0.98 187:0.39 191:0.73 195:1.00 202:0.76 206:0.81 212:0.23 215:0.67 216:0.86 220:0.62 230:0.65 233:0.63 235:0.42 241:0.03 242:0.82 243:0.09 245:0.97 247:0.12 248:0.73 256:0.80 259:0.21 260:0.81 265:0.25 266:0.98 267:0.23 268:0.81 271:0.64 276:0.99 283:0.82 285:0.62 297:0.36 300:0.84 +2 8:0.83 12:0.51 17:0.18 20:0.86 21:0.47 25:0.88 27:0.38 28:0.47 30:0.68 32:0.80 34:0.55 36:0.85 37:0.64 43:0.41 55:0.71 66:0.79 69:0.53 70:0.31 78:0.76 81:0.81 91:0.85 98:0.85 100:0.79 104:0.58 108:0.41 111:0.76 120:0.88 123:0.39 126:0.54 127:0.36 129:0.05 135:0.21 140:0.45 145:0.41 146:0.50 147:0.56 149:0.48 150:0.61 151:0.83 152:0.81 153:0.96 154:0.31 159:0.18 161:0.89 162:0.83 164:0.93 167:0.83 169:0.77 172:0.41 173:0.82 177:0.05 179:0.85 181:0.98 186:0.77 191:0.90 195:0.58 202:0.87 212:0.89 215:0.63 216:0.35 235:0.52 241:0.91 242:0.82 243:0.80 247:0.12 248:0.83 256:0.90 259:0.21 260:0.89 261:0.78 265:0.85 266:0.17 267:0.23 268:0.91 271:0.64 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +2 8:0.86 12:0.51 17:0.07 21:0.30 27:0.50 28:0.47 30:0.76 34:0.17 36:0.92 37:0.31 43:0.36 55:0.52 66:0.72 69:0.61 70:0.15 78:0.79 81:0.85 91:0.97 98:0.79 104:0.73 108:0.46 111:0.79 120:0.92 123:0.45 126:0.54 127:0.27 129:0.05 135:0.37 140:0.80 146:0.45 147:0.52 149:0.36 150:0.68 151:0.78 153:0.90 154:0.27 159:0.16 161:0.89 162:0.77 163:0.86 164:0.98 167:0.85 169:0.82 172:0.27 173:0.88 177:0.05 178:0.42 179:0.91 181:0.98 191:0.87 202:0.95 212:0.42 215:0.72 216:0.61 220:0.74 235:0.31 241:0.98 242:0.82 243:0.75 247:0.12 248:0.88 256:0.96 259:0.21 260:0.92 265:0.79 266:0.23 267:0.44 268:0.97 271:0.64 276:0.24 283:0.60 285:0.62 297:0.36 300:0.13 +1 12:0.51 17:0.45 21:0.54 27:0.45 28:0.47 30:0.66 34:0.50 36:0.59 37:0.50 43:0.32 55:0.71 66:0.54 69:0.69 70:0.31 81:0.79 91:0.05 98:0.65 108:0.86 123:0.85 124:0.64 126:0.54 127:0.39 129:0.81 133:0.67 135:0.73 145:0.44 146:0.56 147:0.62 149:0.64 150:0.58 154:0.24 159:0.78 161:0.89 163:0.93 167:0.50 172:0.75 173:0.48 177:0.05 178:0.55 179:0.35 181:0.98 187:0.68 212:0.22 215:0.58 216:0.77 235:0.60 241:0.27 242:0.82 243:0.19 245:0.82 247:0.12 259:0.21 265:0.66 266:0.80 267:0.28 271:0.64 276:0.74 297:0.36 300:0.33 +0 6:0.90 8:0.72 12:0.51 17:0.59 20:0.81 21:0.22 25:0.88 27:0.26 28:0.47 34:0.55 36:0.66 37:0.79 78:0.82 81:0.87 91:0.82 100:0.88 104:0.58 111:0.83 120:0.88 126:0.54 135:0.47 140:0.45 150:0.73 151:0.74 152:0.84 153:0.84 161:0.89 162:0.86 164:0.85 167:0.80 169:0.89 175:0.75 181:0.98 186:0.82 187:0.21 189:0.88 202:0.74 215:0.79 216:0.69 235:0.22 238:0.94 241:0.82 244:0.61 248:0.84 256:0.80 257:0.65 259:0.21 260:0.89 261:0.89 267:0.18 268:0.81 271:0.64 277:0.69 285:0.62 297:0.36 +0 6:0.90 8:0.92 12:0.51 17:0.32 20:0.87 21:0.22 25:0.88 27:0.26 28:0.47 34:0.68 36:0.68 37:0.79 78:0.84 81:0.87 91:0.93 100:0.91 104:0.58 111:0.86 120:0.96 126:0.54 135:0.56 140:0.45 150:0.73 151:0.82 152:0.84 153:0.95 161:0.89 162:0.77 164:0.95 167:0.78 169:0.94 175:0.75 181:0.98 186:0.85 187:0.87 189:0.92 191:0.83 202:0.92 215:0.79 216:0.69 235:0.22 238:0.95 241:0.77 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.96 261:0.89 267:0.52 268:0.94 271:0.64 277:0.69 283:0.60 285:0.62 297:0.36 +2 6:0.95 8:0.88 12:0.51 17:0.20 20:0.95 21:0.78 27:0.16 28:0.47 34:0.58 36:0.26 37:0.85 78:0.91 91:0.97 100:0.99 104:0.58 111:0.98 120:0.97 135:0.77 140:0.45 151:0.84 152:0.93 153:0.97 161:0.89 162:0.80 164:0.97 167:0.82 169:0.98 175:0.75 181:0.98 186:0.91 189:0.96 191:0.84 202:0.94 216:0.55 238:0.99 241:0.86 244:0.61 248:0.96 256:0.95 257:0.65 259:0.21 260:0.98 261:0.98 267:0.87 268:0.96 271:0.64 277:0.69 285:0.62 +2 6:0.96 8:0.92 12:0.51 17:0.34 20:0.92 21:0.78 27:0.18 28:0.47 34:0.56 36:0.43 37:0.87 78:0.85 91:0.99 100:0.99 104:0.58 111:0.97 120:0.88 135:0.28 140:0.80 145:0.42 151:0.66 152:0.86 153:0.48 161:0.89 162:0.50 164:0.90 167:0.84 169:0.99 175:0.75 178:0.42 181:0.98 186:0.85 189:0.97 191:0.91 202:0.91 216:0.43 220:0.62 235:0.05 238:1.00 241:0.97 244:0.61 248:0.82 256:0.88 257:0.65 259:0.21 260:0.88 261:0.98 267:0.36 268:0.88 271:0.64 277:0.69 285:0.62 +1 6:0.90 8:0.91 12:0.51 17:0.38 20:0.76 21:0.22 25:0.88 27:0.26 28:0.47 34:0.44 36:0.49 37:0.79 43:0.52 66:0.36 69:0.10 78:0.89 81:0.87 91:0.90 98:0.15 100:0.98 104:0.58 106:0.81 108:0.09 111:0.96 120:0.98 123:0.09 126:0.54 127:0.09 129:0.05 135:0.42 140:0.45 146:0.05 147:0.05 149:0.16 150:0.73 151:0.84 152:0.84 153:0.98 154:0.41 159:0.05 161:0.89 162:0.79 164:0.97 167:0.82 169:0.89 172:0.05 173:0.68 177:0.05 181:0.98 186:0.89 187:0.68 189:0.92 191:0.91 202:0.95 206:0.81 215:0.79 216:0.69 235:0.22 238:0.99 241:0.87 243:0.51 248:0.97 256:0.96 259:0.21 260:0.98 261:0.89 265:0.16 267:0.85 268:0.97 271:0.64 283:0.82 285:0.62 297:0.36 +1 12:0.51 17:0.53 21:0.22 27:0.45 28:0.47 30:0.45 34:0.39 36:0.67 37:0.50 43:0.32 55:0.71 66:0.34 69:0.68 70:0.31 81:0.87 91:0.11 98:0.57 108:0.52 123:0.49 124:0.71 126:0.54 127:0.31 129:0.81 133:0.67 135:0.88 145:0.45 146:0.81 147:0.84 149:0.52 150:0.73 154:0.24 159:0.62 161:0.89 163:0.94 167:0.49 172:0.41 173:0.55 177:0.05 178:0.55 179:0.58 181:0.98 187:0.68 212:0.37 215:0.79 216:0.77 235:0.22 241:0.21 242:0.82 243:0.51 245:0.66 247:0.12 259:0.21 265:0.59 266:0.71 267:0.28 271:0.64 276:0.53 297:0.36 300:0.25 +2 7:0.81 8:0.78 12:0.51 17:0.38 21:0.47 27:0.21 28:0.47 30:0.10 34:0.67 36:0.86 37:0.74 43:0.52 55:0.52 66:0.30 69:0.15 70:0.87 78:0.76 81:0.81 91:0.23 98:0.60 104:0.84 106:0.81 108:0.87 111:0.75 120:0.84 123:0.86 124:0.89 126:0.54 127:0.78 129:0.96 133:0.90 135:0.17 140:0.45 146:0.45 147:0.52 149:0.76 150:0.61 151:0.80 153:0.92 154:0.41 159:0.88 161:0.89 162:0.70 164:0.83 167:0.52 169:0.84 172:0.78 173:0.08 177:0.05 179:0.27 181:0.98 187:0.39 202:0.76 206:0.81 212:0.30 215:0.63 216:0.95 230:0.87 233:0.76 235:0.52 241:0.39 242:0.82 243:0.15 245:0.87 247:0.12 248:0.77 256:0.77 259:0.21 260:0.85 265:0.61 266:0.91 267:0.88 268:0.79 271:0.64 276:0.86 283:0.82 285:0.62 297:0.36 300:0.84 +0 8:0.74 12:0.51 17:0.18 21:0.40 25:0.88 27:0.13 28:0.47 34:0.40 36:0.55 37:0.65 81:0.83 91:0.85 104:0.73 120:0.80 126:0.54 135:0.54 140:0.80 150:0.63 151:0.66 153:0.48 161:0.89 162:0.57 164:0.88 167:0.82 175:0.75 178:0.42 181:0.98 191:0.88 202:0.85 215:0.67 216:0.14 220:0.62 235:0.42 241:0.85 244:0.61 248:0.72 256:0.85 257:0.65 259:0.21 260:0.80 267:0.91 268:0.85 271:0.64 277:0.69 285:0.62 297:0.36 +2 6:0.91 7:0.81 8:0.88 12:0.51 17:0.30 20:0.96 21:0.40 27:0.21 28:0.47 30:0.45 34:0.34 36:0.63 37:0.74 43:0.52 55:0.71 66:0.07 69:0.15 70:0.66 78:0.77 81:0.83 91:0.89 98:0.33 100:0.88 104:0.91 106:0.81 108:0.99 111:0.80 120:0.93 123:0.95 124:0.98 126:0.54 127:0.98 129:1.00 133:0.98 135:0.47 140:0.45 146:0.25 147:0.31 149:0.85 150:0.63 151:0.79 152:0.96 153:0.90 154:0.41 159:0.98 161:0.89 162:0.64 164:0.96 167:0.51 169:0.83 172:0.94 173:0.05 177:0.05 179:0.10 181:0.98 186:0.78 187:0.39 189:0.93 191:0.84 202:0.94 206:0.81 212:0.41 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.98 241:0.35 242:0.82 243:0.05 245:0.98 247:0.12 248:0.89 256:0.94 259:0.21 260:0.93 261:0.88 265:0.32 266:0.97 267:0.69 268:0.95 271:0.64 276:0.99 283:0.82 285:0.62 297:0.36 300:0.94 +2 6:0.88 7:0.81 8:0.84 12:0.51 17:0.07 20:0.95 21:0.30 27:0.28 28:0.47 30:0.41 32:0.80 34:0.21 36:0.33 37:0.62 43:0.47 55:0.84 66:0.10 69:0.39 70:0.36 78:0.75 81:0.85 91:0.84 98:0.38 100:0.82 104:0.58 108:0.71 111:0.76 120:0.91 123:0.81 124:0.86 126:0.54 127:0.88 129:0.93 133:0.84 135:0.18 140:0.45 145:0.77 146:0.30 147:0.36 149:0.53 150:0.68 151:0.83 152:0.88 153:0.96 154:0.36 159:0.57 161:0.89 162:0.79 163:0.94 164:0.94 167:0.83 169:0.79 172:0.27 173:0.37 177:0.05 179:0.79 181:0.98 186:0.76 189:0.89 191:0.91 195:0.74 202:0.89 212:0.54 215:0.72 216:0.40 230:0.87 233:0.76 235:0.31 238:0.97 241:0.89 242:0.82 243:0.22 245:0.56 247:0.12 248:0.87 256:0.91 259:0.21 260:0.91 261:0.82 265:0.31 266:0.47 267:0.73 268:0.92 271:0.64 276:0.49 283:0.82 285:0.62 297:0.36 300:0.25 +1 1:0.66 10:0.96 11:0.64 12:0.69 17:0.32 18:0.89 21:0.22 22:0.93 26:0.98 27:0.56 29:0.77 30:0.76 33:0.97 34:0.87 36:0.24 37:0.60 39:0.78 43:0.62 44:0.92 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.53 69:0.83 70:0.53 71:0.81 74:0.61 77:0.89 81:0.73 82:0.99 83:0.69 86:0.83 88:0.99 89:0.99 91:0.10 97:0.89 98:0.79 99:0.99 101:0.97 102:0.97 108:0.68 114:0.88 117:0.86 122:0.98 123:0.66 124:0.66 126:0.54 127:0.58 129:0.05 131:0.61 133:0.62 135:0.66 139:0.83 141:0.91 144:0.57 145:0.70 146:0.92 147:0.60 149:0.66 150:0.54 154:0.24 155:0.50 157:0.94 158:0.73 159:0.69 165:0.88 166:0.88 170:0.79 172:0.92 173:0.77 174:0.97 176:0.70 177:0.96 178:0.55 179:0.18 182:0.89 187:0.95 192:0.51 195:0.84 197:0.97 199:0.98 201:0.66 208:0.64 212:0.69 216:0.76 219:0.88 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.82 234:0.91 235:0.68 236:0.95 239:0.95 240:0.95 241:0.15 242:0.14 243:0.23 244:0.61 245:0.96 246:0.92 247:0.98 253:0.63 254:0.90 257:0.65 259:0.21 262:0.93 264:0.98 265:0.79 266:0.38 267:0.36 274:0.91 275:0.99 276:0.92 279:0.74 282:0.80 287:0.61 290:0.88 291:0.97 292:0.95 294:0.99 300:0.70 +1 1:0.68 10:0.98 11:0.55 12:0.69 17:0.75 18:0.90 21:0.40 22:0.93 26:0.98 27:0.40 29:0.77 30:0.89 32:0.80 34:0.74 36:0.43 37:0.54 39:0.78 43:0.59 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.99 69:0.68 70:0.33 71:0.81 74:0.66 77:0.89 81:0.76 82:0.99 83:0.72 86:0.87 88:0.99 89:0.99 91:0.07 98:0.91 99:1.00 101:0.70 102:0.98 104:0.94 106:0.81 108:0.52 114:0.78 117:0.86 122:0.99 123:0.50 126:0.54 127:0.50 129:0.05 131:0.61 135:0.73 139:0.87 141:0.91 144:0.57 145:0.76 146:0.97 147:0.97 149:0.81 150:0.63 154:0.50 155:0.52 157:0.94 159:0.68 165:0.79 166:0.88 170:0.79 172:0.99 173:0.57 174:0.99 176:0.63 177:0.99 178:0.55 179:0.12 182:0.90 187:0.21 192:0.58 195:0.85 197:0.99 199:0.99 201:0.67 206:0.81 208:0.64 212:0.91 215:0.67 216:0.80 222:0.90 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.81 232:0.96 234:0.91 235:0.88 236:0.94 239:0.98 240:0.95 241:0.07 242:0.27 243:0.99 244:0.61 246:0.93 247:0.96 253:0.61 259:0.21 262:0.93 264:0.98 265:0.91 266:0.27 267:0.52 274:0.91 275:1.00 276:0.96 282:0.88 287:0.61 290:0.78 294:1.00 297:0.36 299:0.88 300:0.70 +1 1:0.58 10:0.95 11:0.55 12:0.69 17:0.36 18:0.69 21:0.22 26:0.98 27:0.42 29:0.77 30:0.89 33:0.97 34:0.30 36:0.32 37:0.66 39:0.78 43:0.27 45:0.96 56:0.97 58:0.93 66:0.77 69:0.10 70:0.36 71:0.81 74:0.37 80:0.98 81:0.38 82:0.98 83:0.54 86:0.70 88:0.99 89:0.98 91:0.31 98:0.75 99:0.83 101:0.70 102:0.94 108:0.09 122:0.98 123:0.09 124:0.40 126:0.26 127:0.41 129:0.05 131:0.61 133:0.37 135:0.80 139:0.67 141:0.91 144:0.57 145:0.63 146:0.76 147:0.80 149:0.57 150:0.37 154:0.53 155:0.50 157:0.79 159:0.43 165:0.63 166:0.61 170:0.79 172:0.83 173:0.20 174:0.95 177:0.99 178:0.55 179:0.34 182:0.73 187:0.95 192:0.47 193:0.95 195:0.69 197:0.97 199:0.98 201:0.55 204:0.80 208:0.49 212:0.82 216:0.52 222:0.90 223:0.98 224:0.93 225:0.97 227:0.61 229:0.61 231:0.66 234:0.91 235:0.31 236:0.91 239:0.94 240:0.95 241:0.18 242:0.24 243:0.79 244:0.61 245:0.82 246:0.61 247:0.84 253:0.33 254:0.80 259:0.21 264:0.98 265:0.75 266:0.63 267:0.44 274:0.91 275:0.98 276:0.73 287:0.61 291:0.97 294:0.98 300:0.55 +0 1:0.61 10:0.96 11:0.64 12:0.69 17:0.91 18:0.99 21:0.22 22:0.93 26:0.98 27:0.69 29:0.77 30:0.26 34:0.99 36:0.86 37:0.85 39:0.78 43:0.25 44:0.85 45:0.97 47:0.93 55:0.52 56:0.97 58:0.93 64:1.00 66:0.27 69:0.79 70:0.78 71:0.81 74:0.34 80:0.99 81:0.64 82:0.98 83:0.60 86:0.72 88:0.98 89:0.98 91:0.06 98:0.64 99:0.99 101:0.87 102:0.95 104:0.97 106:0.81 108:0.63 122:0.95 123:0.86 124:0.72 126:0.54 127:0.58 129:0.81 131:0.61 133:0.67 135:0.93 139:0.70 141:0.91 144:0.57 145:0.89 146:0.91 147:0.93 149:0.55 150:0.53 154:0.17 155:0.53 157:0.77 159:0.82 165:0.69 166:0.80 170:0.79 172:0.93 173:0.60 174:0.98 175:0.75 177:0.96 178:0.55 179:0.12 182:0.72 187:0.39 192:0.51 193:0.95 195:0.94 197:0.98 199:0.99 201:0.61 204:0.82 206:0.81 208:0.61 212:0.87 215:0.63 216:0.47 219:0.87 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.80 234:0.91 235:0.88 236:0.92 239:0.95 240:0.95 241:0.62 242:0.39 243:0.48 244:0.93 245:0.99 246:0.61 247:0.99 253:0.30 257:0.65 259:0.21 262:0.93 264:0.98 265:0.61 266:0.71 267:0.36 274:0.91 275:0.99 276:0.96 277:0.69 283:0.66 287:0.61 292:0.87 294:0.98 297:0.36 300:0.70 +0 8:0.91 9:0.71 10:0.95 11:0.46 12:0.69 17:0.61 18:0.84 21:0.30 23:0.98 26:0.98 27:0.66 29:0.77 30:0.75 31:0.94 34:0.73 36:0.97 37:0.96 39:0.78 43:0.17 45:0.93 58:0.99 62:0.97 64:0.77 66:0.95 69:0.15 70:0.48 71:0.81 74:0.26 79:0.94 81:0.28 83:0.60 86:0.65 89:0.99 91:0.82 98:0.97 101:0.52 108:0.12 122:0.98 123:0.12 126:0.22 127:0.74 128:0.96 129:0.05 131:0.86 135:0.76 137:0.94 139:0.63 140:0.45 141:0.99 143:0.99 146:0.81 147:0.84 149:0.14 150:0.31 151:0.57 153:0.48 154:0.28 155:0.55 157:0.61 159:0.37 162:0.69 166:0.80 168:0.96 170:0.79 172:0.86 173:0.31 174:0.94 177:0.99 179:0.39 182:0.61 190:0.99 191:0.87 192:0.56 196:0.88 197:0.96 199:0.97 202:0.72 205:0.98 208:0.54 212:0.80 216:0.24 220:0.91 222:0.90 223:0.98 224:0.98 225:0.97 227:0.89 229:0.61 231:0.75 234:0.97 235:0.31 239:0.92 240:0.95 241:0.80 242:0.18 243:0.95 244:0.93 246:0.61 247:0.91 248:0.60 253:0.23 254:0.80 255:0.70 256:0.73 259:0.21 264:0.98 265:0.97 266:0.27 267:0.19 268:0.75 274:0.98 275:0.97 276:0.77 284:0.96 285:0.50 287:0.61 294:0.98 300:0.55 +0 1:0.60 8:0.98 9:0.70 10:0.98 11:0.64 12:0.69 17:0.93 18:0.98 21:0.40 23:0.95 26:0.98 27:0.69 29:0.77 30:0.42 33:0.97 34:0.31 36:0.67 37:0.85 39:0.78 43:0.27 45:0.99 55:0.52 56:0.97 58:0.98 62:0.97 66:0.19 69:0.41 70:0.65 71:0.81 74:0.33 75:0.95 81:0.50 82:0.98 83:0.60 86:0.72 88:0.99 89:0.99 91:0.03 97:0.89 98:0.69 99:0.95 101:0.87 102:0.94 108:0.76 122:0.95 123:0.89 124:0.74 126:0.32 127:0.91 128:0.95 129:0.81 131:0.61 133:0.67 135:0.19 139:0.69 140:0.45 141:0.99 143:0.98 144:0.57 145:0.82 146:0.69 147:0.74 149:0.72 150:0.41 151:0.53 153:0.48 154:0.27 155:0.52 157:0.77 159:0.83 162:0.86 165:0.69 166:0.61 168:0.95 170:0.79 172:0.93 173:0.21 174:0.99 175:0.75 177:0.96 179:0.13 182:0.72 187:0.21 190:0.98 191:0.86 192:0.49 195:0.92 197:0.99 199:0.99 201:0.59 202:0.36 205:0.95 208:0.54 212:0.90 216:0.47 220:0.91 222:0.90 223:0.98 224:0.99 225:0.99 226:0.96 227:0.61 229:0.61 231:0.80 234:0.98 235:0.60 236:0.92 239:0.97 240:0.99 241:0.47 242:0.29 243:0.40 244:0.83 245:0.99 246:0.61 247:0.99 248:0.53 253:0.29 254:0.92 255:0.69 256:0.45 257:0.65 259:0.21 264:0.98 265:0.64 266:0.75 267:0.59 268:0.46 274:0.97 275:0.99 276:0.97 277:0.69 279:0.74 285:0.50 287:0.61 291:0.97 294:0.99 295:0.98 300:0.84 +0 10:0.92 11:0.55 12:0.69 17:0.90 18:0.99 21:0.22 22:0.93 26:0.98 27:0.69 29:0.77 30:0.33 34:0.97 36:0.87 37:0.85 39:0.78 43:0.22 45:0.94 47:0.93 55:0.52 58:0.93 64:0.77 66:0.32 69:0.79 70:0.79 71:0.81 74:0.34 81:0.51 86:0.72 89:0.98 91:0.07 98:0.66 101:0.70 104:0.97 106:0.81 108:0.63 122:0.95 123:0.86 124:0.67 126:0.32 127:0.56 129:0.59 131:0.61 133:0.66 135:0.69 139:0.69 141:0.91 144:0.57 145:0.69 146:0.91 147:0.93 149:0.53 150:0.48 154:0.14 157:0.77 159:0.81 166:0.79 170:0.79 172:0.93 173:0.61 174:0.97 175:0.75 177:0.96 178:0.55 179:0.13 182:0.72 187:0.39 192:0.47 195:0.93 197:0.97 199:0.97 201:0.60 202:0.36 206:0.81 212:0.87 215:0.79 216:0.47 219:0.87 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.80 234:0.91 235:0.52 239:0.91 240:0.95 241:0.55 242:0.44 243:0.51 244:0.93 245:0.98 246:0.61 247:0.99 253:0.30 257:0.65 259:0.21 262:0.93 264:0.98 265:0.63 266:0.71 267:0.28 274:0.91 275:0.98 276:0.95 277:0.69 283:0.67 287:0.61 292:0.88 294:0.96 297:0.36 300:0.70 +0 1:0.60 8:0.79 9:0.71 10:0.93 11:0.64 12:0.69 17:0.86 18:0.99 21:0.22 23:0.95 26:0.98 27:0.69 29:0.77 30:0.43 33:0.97 34:0.97 36:0.77 37:0.85 39:0.78 43:0.25 45:0.95 55:0.52 56:0.97 58:0.98 62:0.96 66:0.30 69:0.79 70:0.77 71:0.81 74:0.34 75:0.95 81:0.53 82:0.98 83:0.60 86:0.71 88:0.99 89:0.98 91:0.17 97:0.89 98:0.63 99:0.95 101:0.87 102:0.94 108:0.63 122:0.95 123:0.86 124:0.74 126:0.32 127:0.54 128:0.95 129:0.59 131:0.61 133:0.74 135:0.95 139:0.68 140:0.80 141:0.99 143:0.98 144:0.57 145:0.70 146:0.91 147:0.92 149:0.53 150:0.44 151:0.57 153:0.48 154:0.14 155:0.54 157:0.77 159:0.82 162:0.51 165:0.69 166:0.61 168:0.95 170:0.79 172:0.93 173:0.60 174:0.97 175:0.75 177:0.96 178:0.42 179:0.13 182:0.72 187:0.39 190:0.98 192:0.50 195:0.94 197:0.98 199:0.98 201:0.59 202:0.60 205:0.95 208:0.54 212:0.86 216:0.47 220:0.87 222:0.90 223:0.98 224:0.98 225:0.99 226:0.96 227:0.61 229:0.61 231:0.80 234:0.97 235:0.52 236:0.92 239:0.92 240:0.99 241:0.57 242:0.44 243:0.57 244:0.93 245:0.97 246:0.61 247:0.99 248:0.56 253:0.30 255:0.70 256:0.45 257:0.65 259:0.21 264:0.98 265:0.60 266:0.80 267:0.59 268:0.46 274:0.97 275:0.98 276:0.95 277:0.69 279:0.74 285:0.50 287:0.61 291:0.97 294:0.97 295:0.98 300:0.84 +0 10:0.92 11:0.55 12:0.69 17:0.89 18:0.99 21:0.22 26:0.98 27:0.69 29:0.77 30:0.43 32:0.63 34:0.97 36:0.79 37:0.85 39:0.78 43:0.20 45:0.93 55:0.52 58:0.93 66:0.29 69:0.79 70:0.78 71:0.81 74:0.34 81:0.41 86:0.71 89:0.98 91:0.03 98:0.63 101:0.70 104:0.99 106:0.81 108:0.63 122:0.95 123:0.86 124:0.69 126:0.26 127:0.62 129:0.59 131:0.61 133:0.66 135:0.89 139:0.68 141:0.91 144:0.57 145:0.97 146:0.91 147:0.92 149:0.42 150:0.43 154:0.14 157:0.77 159:0.83 166:0.61 170:0.79 172:0.93 173:0.60 174:0.97 175:0.75 177:0.96 178:0.55 179:0.14 182:0.72 187:0.39 192:0.44 195:0.94 197:0.97 199:0.97 201:0.54 206:0.81 212:0.86 215:0.67 216:0.47 222:0.90 223:0.97 224:0.93 225:0.98 227:0.61 229:0.61 231:0.80 234:0.91 235:0.68 239:0.90 240:0.95 241:0.37 242:0.43 243:0.51 244:0.93 245:0.98 246:0.61 247:0.99 253:0.30 257:0.65 259:0.21 264:0.98 265:0.60 266:0.71 267:0.59 274:0.91 275:0.98 276:0.95 277:0.69 283:0.67 287:0.61 294:0.96 297:0.99 300:0.70 +1 1:0.67 10:0.98 11:0.64 12:0.69 17:0.61 18:0.91 21:0.22 22:0.93 26:0.98 27:0.56 29:0.77 30:0.87 32:0.80 33:0.97 34:0.64 36:0.35 37:0.60 39:0.78 43:0.74 44:0.93 45:0.99 47:0.93 56:0.97 58:0.93 64:0.77 66:0.75 69:0.81 70:0.41 71:0.81 74:0.70 77:0.89 81:0.82 82:0.99 83:0.72 86:0.89 88:0.99 89:0.99 91:0.06 97:0.94 98:0.82 101:0.97 102:0.98 104:0.90 106:0.81 108:0.66 114:0.90 117:0.86 122:0.99 123:0.64 124:0.42 126:0.54 127:0.51 129:0.05 131:0.61 133:0.45 135:0.18 139:0.89 141:0.91 144:0.57 145:0.74 146:0.89 147:0.42 149:0.77 150:0.62 154:0.65 155:0.51 157:0.95 158:0.73 159:0.58 165:0.93 166:0.88 170:0.79 172:0.96 173:0.80 174:0.98 176:0.73 177:0.96 178:0.55 179:0.15 182:0.89 187:0.39 192:0.57 195:0.76 197:0.98 199:0.99 201:0.67 206:0.81 208:0.64 212:0.92 215:0.79 216:0.76 219:0.88 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.81 232:0.94 234:0.91 235:0.74 236:0.97 239:0.98 240:0.95 241:0.30 242:0.18 243:0.23 244:0.61 245:0.97 246:0.92 247:0.97 253:0.67 257:0.65 259:0.21 262:0.93 264:0.98 265:0.82 266:0.54 267:0.73 274:0.91 275:0.99 276:0.93 279:0.77 282:0.88 283:0.67 287:0.61 290:0.90 291:0.97 292:0.88 294:0.99 297:0.36 299:0.88 300:0.84 +1 1:0.65 10:0.97 11:0.82 12:0.69 17:0.53 18:0.91 21:0.40 22:0.93 26:0.98 27:0.56 29:0.77 30:0.87 32:0.72 33:0.97 34:0.95 36:0.20 37:0.60 39:0.78 43:0.70 44:0.92 45:0.98 47:0.93 56:0.97 58:0.93 64:0.77 66:0.98 69:0.58 70:0.37 71:0.81 74:0.69 77:0.70 81:0.78 82:0.99 83:0.91 85:0.72 86:0.91 88:0.99 89:0.98 91:0.15 98:0.87 99:0.99 101:1.00 102:0.97 104:0.91 106:0.81 108:0.44 114:0.82 117:0.86 122:0.99 123:0.42 126:0.54 127:0.40 129:0.05 131:0.61 135:0.68 139:0.89 141:0.91 144:0.57 145:0.72 146:0.87 147:0.89 149:0.77 150:0.59 154:0.60 155:0.50 157:0.95 159:0.45 165:0.88 166:0.88 170:0.79 172:0.94 173:0.58 174:0.96 176:0.70 177:0.99 178:0.55 179:0.19 182:0.89 187:0.21 192:0.55 195:0.68 197:0.97 199:0.98 201:0.66 206:0.81 208:0.64 212:0.88 215:0.67 216:0.76 222:0.90 223:0.98 224:0.93 225:0.98 227:0.61 229:0.61 231:0.81 232:0.95 234:0.91 235:0.80 236:0.95 239:0.96 240:0.95 241:0.18 242:0.16 243:0.98 246:0.92 247:0.94 253:0.63 259:0.21 262:0.93 264:0.98 265:0.87 266:0.27 267:0.36 274:0.91 275:0.98 276:0.88 282:0.80 287:0.61 290:0.82 291:0.97 294:0.99 297:0.36 300:0.70 +0 1:0.60 8:0.92 9:0.71 10:0.92 11:0.55 12:0.69 17:0.92 18:0.98 21:0.54 23:0.98 26:0.98 27:0.69 29:0.77 30:0.37 31:0.90 34:0.24 36:0.99 37:0.85 39:0.78 43:0.22 45:0.95 55:0.52 56:0.97 58:0.99 62:0.96 66:0.26 69:0.45 70:0.87 71:0.81 74:0.34 79:0.89 81:0.50 83:0.60 86:0.71 89:0.99 91:0.70 96:0.73 98:0.60 99:0.95 101:0.70 108:0.72 122:0.95 123:0.88 124:0.82 126:0.32 127:0.95 128:0.96 129:0.81 131:0.92 133:0.83 135:0.61 137:0.90 139:0.68 140:0.80 141:0.99 143:0.99 144:0.57 145:0.79 146:0.54 147:0.60 149:0.49 150:0.41 151:0.64 153:0.72 154:0.13 155:0.55 157:0.76 159:0.87 162:0.73 165:0.69 166:0.61 168:0.96 170:0.79 172:0.93 173:0.20 174:0.98 175:0.75 177:0.96 179:0.14 182:0.72 190:0.99 191:0.85 192:0.51 195:0.95 196:0.89 197:0.97 199:0.98 201:0.59 202:0.83 205:0.98 208:0.54 212:0.83 216:0.47 220:0.82 222:0.90 223:0.98 224:0.98 225:0.99 227:0.93 229:0.61 231:0.82 234:0.97 235:0.80 236:0.92 239:0.90 240:0.99 241:0.82 242:0.39 243:0.12 244:0.93 245:0.98 246:0.61 247:1.00 248:0.62 253:0.48 255:0.70 256:0.87 257:0.65 259:0.21 264:0.98 265:0.57 266:0.92 267:0.73 268:0.86 274:0.98 275:0.98 276:0.96 277:0.69 284:0.93 285:0.61 287:0.61 294:0.96 300:0.84 +2 6:0.96 7:0.81 8:0.85 11:0.57 12:0.51 17:0.79 20:0.78 21:0.47 27:0.06 28:0.99 30:0.33 34:0.39 36:0.49 37:0.86 39:0.39 43:0.80 55:0.45 66:0.06 69:0.62 70:0.94 74:0.92 76:0.99 77:0.70 78:0.96 81:0.47 85:0.80 91:0.46 96:0.78 98:0.26 100:0.93 101:0.72 108:0.47 111:0.94 114:0.66 117:0.86 121:0.90 122:0.67 123:0.66 124:0.85 126:0.32 127:0.50 129:0.81 133:0.83 135:0.94 140:0.45 145:0.59 146:0.45 147:0.52 149:0.67 150:0.38 151:0.61 152:0.76 153:0.48 154:0.74 155:0.67 158:0.85 159:0.78 161:0.88 162:0.86 167:0.68 169:0.91 172:0.45 173:0.41 176:0.56 177:0.38 179:0.50 181:0.47 186:0.95 187:0.87 189:0.97 190:0.77 191:0.82 192:0.59 195:0.93 202:0.54 204:0.89 208:0.64 212:0.50 216:0.76 220:0.93 222:0.48 230:0.87 232:0.91 233:0.76 235:0.52 238:0.86 241:0.68 242:0.41 243:0.40 245:0.72 247:0.60 248:0.60 253:0.83 256:0.58 259:0.21 261:0.82 265:0.25 266:0.92 267:0.82 268:0.63 276:0.62 277:0.69 282:0.84 285:0.50 290:0.66 300:0.84 +1 1:0.74 7:0.78 8:0.58 11:0.57 12:0.51 17:0.49 21:0.40 27:0.37 28:0.99 30:0.28 32:0.80 34:0.99 36:0.26 37:0.28 39:0.39 43:0.98 60:0.87 66:0.67 69:0.15 70:0.74 74:0.92 76:0.94 77:0.70 81:0.82 83:0.83 85:0.88 91:0.46 98:0.76 101:0.81 104:0.58 108:0.12 114:0.82 123:0.12 124:0.45 126:0.54 127:0.59 129:0.05 133:0.39 135:0.58 145:0.76 146:0.67 147:0.72 149:0.89 150:0.88 154:0.98 155:0.67 158:0.87 159:0.37 161:0.88 165:0.83 167:0.56 172:0.63 173:0.30 176:0.73 177:0.38 178:0.55 179:0.62 181:0.47 187:0.21 192:0.59 195:0.62 201:0.74 208:0.64 212:0.75 215:0.67 216:0.22 222:0.48 230:0.81 232:0.79 233:0.76 235:0.52 241:0.47 242:0.45 243:0.72 245:0.72 247:0.60 253:0.75 259:0.21 265:0.76 266:0.47 267:0.52 276:0.56 279:0.86 282:0.88 283:0.71 290:0.82 297:0.36 299:0.88 300:0.55 +1 7:0.81 8:0.59 9:0.80 11:0.57 12:0.51 17:0.79 21:0.78 27:0.37 28:0.99 30:0.33 34:0.75 36:0.27 37:0.36 39:0.39 41:0.82 43:0.89 66:0.46 69:0.49 70:0.55 74:0.85 83:0.76 85:0.88 91:0.46 96:0.80 98:0.62 101:0.78 104:0.58 108:0.72 120:0.69 121:0.90 122:0.67 123:0.70 124:0.57 127:0.37 129:0.59 133:0.47 135:0.42 140:0.45 145:0.72 146:0.24 147:0.30 149:0.79 151:0.87 153:0.48 154:0.87 155:0.67 159:0.51 161:0.88 162:0.86 164:0.57 167:0.54 172:0.45 173:0.44 177:0.38 179:0.65 181:0.47 187:0.21 192:0.59 202:0.36 204:0.89 208:0.64 212:0.40 216:0.15 222:0.48 230:0.83 232:0.79 233:0.66 235:0.31 241:0.39 242:0.31 243:0.45 245:0.70 247:0.60 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 265:0.63 266:0.63 267:0.36 268:0.46 276:0.44 285:0.62 300:0.43 +1 11:0.57 12:0.51 17:0.20 21:0.64 27:0.45 28:0.99 30:0.58 32:0.75 34:0.79 36:0.17 37:0.50 39:0.39 43:0.77 55:0.84 66:0.36 69:0.80 70:0.60 74:0.49 81:0.45 85:0.72 91:0.09 98:0.27 101:0.70 108:0.64 123:0.61 124:0.77 126:0.32 127:0.55 129:0.05 133:0.74 135:0.88 145:0.45 146:0.48 147:0.05 149:0.54 150:0.37 154:0.69 159:0.73 161:0.88 163:0.92 167:0.63 172:0.27 173:0.69 177:0.05 178:0.55 179:0.87 181:0.47 187:0.68 195:0.87 212:0.37 215:0.53 216:0.77 222:0.48 235:0.74 241:0.61 242:0.40 243:0.18 245:0.47 247:0.47 253:0.41 257:0.65 259:0.21 265:0.30 266:0.47 267:0.44 276:0.32 283:0.71 297:0.36 300:0.43 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.55 21:0.30 27:0.37 28:0.99 30:0.30 32:0.80 34:0.95 36:0.29 37:0.28 39:0.39 43:0.98 60:0.95 66:0.17 69:0.12 70:0.87 74:0.94 77:0.70 81:0.86 83:0.84 85:0.90 91:0.38 98:0.49 101:0.81 104:0.58 106:0.81 108:0.10 114:0.86 117:0.86 121:0.90 123:0.64 124:0.70 126:0.54 127:0.74 129:0.59 133:0.64 135:0.61 145:0.72 146:0.44 147:0.51 149:0.90 150:0.91 154:0.98 155:0.67 158:0.92 159:0.50 161:0.88 165:0.84 167:0.54 172:0.54 173:0.22 176:0.73 177:0.38 178:0.55 179:0.60 181:0.47 187:0.21 192:0.59 195:0.69 201:0.74 204:0.89 206:0.81 208:0.64 212:0.51 215:0.72 216:0.22 222:0.48 230:0.87 232:0.79 233:0.76 235:0.42 241:0.39 242:0.24 243:0.51 245:0.75 247:0.67 253:0.77 259:0.21 265:0.42 266:0.71 267:0.36 276:0.60 279:0.86 282:0.88 283:0.82 290:0.86 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.88 7:0.81 8:0.74 9:0.80 11:0.57 12:0.51 17:0.85 20:0.83 21:0.30 27:0.33 28:0.99 30:0.55 32:0.75 34:0.83 36:0.19 37:0.57 39:0.39 41:0.88 43:0.77 55:0.59 60:0.87 66:0.35 69:0.54 70:0.78 74:0.95 78:0.96 81:0.86 83:0.83 85:0.90 91:0.57 96:0.82 98:0.33 100:0.90 101:0.77 104:0.96 106:0.81 108:0.42 111:0.93 114:0.86 120:0.72 121:0.90 122:0.67 123:0.40 124:0.81 126:0.54 127:0.42 129:0.81 133:0.83 135:0.89 140:0.45 145:0.58 146:0.61 147:0.66 149:0.80 150:0.91 151:0.87 152:0.79 153:0.48 154:0.70 155:0.67 158:0.92 159:0.74 161:0.88 162:0.86 164:0.67 165:0.84 167:0.58 169:0.87 172:0.57 173:0.35 176:0.73 177:0.38 179:0.47 181:0.47 186:0.96 187:0.39 189:0.90 192:0.59 195:0.90 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.69 215:0.72 216:0.31 220:0.87 222:0.48 230:0.87 233:0.76 235:0.42 238:0.83 241:0.52 242:0.53 243:0.51 245:0.70 247:0.60 248:0.79 253:0.88 255:0.79 256:0.58 259:0.21 260:0.73 261:0.85 265:0.35 266:0.75 267:0.36 268:0.59 276:0.62 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +1 1:0.74 6:0.91 7:0.81 8:0.84 9:0.80 11:0.57 12:0.51 17:0.93 20:0.82 21:0.22 27:0.63 28:0.99 30:0.27 32:0.80 34:0.64 36:0.24 37:0.88 39:0.39 41:0.92 43:0.85 55:0.52 60:0.97 66:0.69 69:0.63 70:0.89 74:0.96 77:0.89 78:0.97 81:0.94 83:0.89 85:0.94 91:0.75 96:0.73 98:0.64 100:0.84 101:0.83 104:0.58 108:0.79 111:0.94 114:0.69 120:0.61 121:0.90 122:0.67 123:0.77 124:0.47 126:0.54 127:0.76 129:0.81 133:0.67 135:0.54 138:0.96 140:0.80 145:0.74 146:0.13 147:0.18 149:0.88 150:0.97 151:0.64 152:0.79 153:0.72 154:0.81 155:0.67 158:0.87 159:0.66 161:0.88 162:0.63 164:0.68 165:0.88 167:0.81 169:0.82 172:0.84 173:0.55 176:0.56 177:0.38 178:0.42 179:0.37 181:0.47 186:0.97 189:0.92 192:0.59 195:0.81 201:0.74 202:0.62 204:0.89 208:0.64 212:0.85 215:0.72 216:0.13 220:0.82 222:0.48 230:0.84 232:0.79 233:0.69 235:0.68 238:0.81 241:0.81 242:0.60 243:0.10 245:0.82 247:0.60 248:0.66 253:0.79 255:0.79 256:0.58 259:0.21 260:0.61 261:0.86 265:0.65 266:0.71 267:0.89 268:0.62 276:0.74 279:0.86 282:0.88 283:0.71 285:0.62 286:0.99 290:0.69 297:0.36 298:0.99 299:0.88 300:0.84 +2 1:0.74 7:0.81 8:0.88 11:0.57 12:0.51 17:0.85 21:0.22 27:0.63 28:0.99 30:0.21 32:0.80 34:0.96 36:0.34 37:0.88 39:0.39 43:0.33 66:0.45 69:0.91 70:0.88 74:0.93 77:1.00 81:0.96 83:0.79 85:0.96 91:0.10 98:0.40 101:0.80 104:0.58 106:0.81 108:0.92 114:0.90 117:0.86 121:0.99 122:0.57 123:0.92 124:0.80 126:0.54 127:0.70 129:0.59 133:0.82 135:0.89 145:0.67 146:0.56 147:0.05 149:0.84 150:0.98 154:0.24 155:0.67 159:0.87 161:0.88 165:0.91 167:0.46 172:0.82 173:0.76 176:0.73 177:0.05 178:0.55 179:0.28 181:0.47 187:0.39 192:0.59 195:0.96 201:0.74 204:0.89 206:0.81 208:0.64 212:0.71 215:0.79 216:0.13 222:0.48 230:0.87 232:0.79 233:0.76 235:0.80 241:0.02 242:0.29 243:0.06 245:0.87 247:0.67 253:0.77 257:0.65 259:0.21 265:0.42 266:0.87 267:0.23 276:0.82 282:0.88 290:0.90 297:0.36 299:1.00 300:0.84 +2 1:0.74 6:0.86 7:0.81 8:0.63 9:0.80 11:0.57 12:0.51 17:0.90 20:0.84 21:0.54 27:0.55 28:0.99 30:0.31 32:0.80 34:0.80 36:0.28 39:0.39 41:0.90 43:0.92 66:0.47 69:0.19 70:0.84 74:0.92 76:0.85 77:0.96 78:0.96 81:0.73 83:0.78 85:0.95 91:0.78 96:0.85 98:0.72 100:0.92 101:0.84 104:0.58 108:0.79 111:0.93 114:0.77 120:0.82 121:0.90 123:0.77 124:0.67 126:0.54 127:0.97 129:0.81 133:0.67 135:0.34 140:0.80 145:0.83 146:0.27 147:0.33 149:0.92 150:0.81 151:0.87 152:0.80 153:0.48 154:0.91 155:0.67 159:0.64 161:0.88 162:0.80 164:0.80 165:0.79 167:0.84 169:0.89 172:0.75 173:0.20 176:0.73 177:0.38 179:0.41 181:0.47 186:0.95 189:0.88 192:0.59 195:0.79 201:0.74 202:0.71 204:0.89 208:0.64 212:0.49 215:0.58 216:0.09 220:0.74 222:0.48 230:0.87 232:0.79 233:0.76 235:0.68 238:0.84 241:0.88 242:0.27 243:0.21 245:0.87 247:0.67 248:0.83 253:0.89 255:0.79 256:0.73 259:0.21 260:0.82 261:0.87 265:0.72 266:0.54 267:0.52 268:0.76 276:0.77 282:0.88 283:0.71 285:0.62 290:0.77 297:0.36 299:0.97 300:0.70 +2 1:0.74 11:0.57 12:0.51 17:0.93 21:0.54 27:0.72 28:0.99 30:0.36 32:0.80 34:0.29 36:0.22 39:0.39 43:0.90 55:0.59 60:0.87 66:0.67 69:0.47 70:0.78 74:0.88 77:0.70 81:0.73 83:0.82 85:0.80 87:0.98 91:0.06 98:0.71 101:0.75 104:0.94 106:0.81 108:0.65 114:0.77 117:0.86 123:0.63 124:0.45 126:0.54 127:0.38 129:0.59 133:0.47 135:0.30 145:0.52 146:0.26 147:0.32 149:0.79 150:0.81 154:0.89 155:0.67 158:0.92 159:0.46 161:0.88 165:0.79 167:0.55 172:0.70 173:0.44 176:0.73 177:0.38 178:0.55 179:0.46 181:0.47 187:0.21 192:0.59 195:0.70 201:0.74 206:0.81 208:0.64 212:0.71 215:0.58 216:0.17 222:0.48 232:0.77 235:0.68 241:0.43 242:0.71 243:0.29 245:0.78 247:0.60 253:0.72 259:0.21 265:0.71 266:0.54 267:0.28 276:0.63 279:0.86 282:0.88 283:0.82 290:0.77 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.82 27:0.09 28:0.99 30:0.42 32:0.80 34:0.90 36:0.75 37:0.84 39:0.39 43:0.95 55:0.84 60:0.87 66:0.62 69:0.12 70:0.56 74:0.84 77:0.89 81:0.99 83:0.88 85:0.88 91:0.58 98:0.62 101:0.78 104:0.88 106:0.81 108:0.10 114:0.98 117:0.86 121:0.90 123:0.10 124:0.52 126:0.54 127:0.57 129:0.59 133:0.64 135:0.71 145:0.72 146:0.75 147:0.79 149:0.83 150:1.00 154:0.95 155:0.67 158:0.92 159:0.60 161:0.88 163:0.75 165:0.92 167:0.57 172:0.57 173:0.16 176:0.73 177:0.38 178:0.55 179:0.68 181:0.47 187:0.21 192:0.59 195:0.76 201:0.74 204:0.89 206:0.81 208:0.64 212:0.23 215:0.96 216:0.45 222:0.48 230:0.87 232:0.92 233:0.76 235:0.05 241:0.49 242:0.45 243:0.68 245:0.61 247:0.67 253:0.78 259:0.21 265:0.63 266:0.71 267:0.82 276:0.50 279:0.86 282:0.88 283:0.82 290:0.98 297:0.36 299:0.97 300:0.43 +3 1:0.74 6:0.87 7:0.81 8:0.77 9:0.80 11:0.57 12:0.51 17:0.79 20:0.98 21:0.64 27:0.37 28:0.99 30:0.43 32:0.80 34:0.83 36:0.29 37:0.36 39:0.39 41:0.90 43:0.89 60:0.87 66:0.23 69:0.52 70:0.73 74:0.98 77:0.70 78:0.98 81:0.70 83:0.81 85:0.96 91:0.85 96:0.88 98:0.71 100:0.97 101:0.85 104:0.58 108:0.40 111:0.97 114:0.72 120:0.79 121:0.90 122:0.57 123:0.69 124:0.55 126:0.54 127:0.47 129:0.59 133:0.47 135:0.61 140:0.45 145:0.72 146:0.61 147:0.66 149:0.96 150:0.75 151:0.97 152:0.96 153:0.88 154:0.87 155:0.67 158:0.92 159:0.52 161:0.88 162:0.73 164:0.81 167:0.83 169:0.94 172:0.76 173:0.49 176:0.73 177:0.38 179:0.35 181:0.47 186:0.98 189:0.90 191:0.76 192:0.59 195:0.72 201:0.74 202:0.74 204:0.89 208:0.64 212:0.77 215:0.53 216:0.15 222:0.48 230:0.83 232:0.79 233:0.66 235:0.84 238:0.89 241:0.85 242:0.28 243:0.61 245:0.90 247:0.60 248:0.86 253:0.92 255:0.79 256:0.75 259:0.21 260:0.80 261:0.95 265:0.52 266:0.54 267:0.65 268:0.78 276:0.75 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.88 300:0.55 +0 12:0.51 17:0.18 21:0.78 27:0.45 28:0.99 30:0.09 34:0.87 36:0.17 37:0.50 39:0.39 43:0.30 55:0.45 66:0.44 69:0.83 70:0.99 74:0.80 77:0.70 91:0.37 98:0.21 108:0.68 122:0.57 123:0.66 124:0.76 127:0.51 129:0.05 133:0.74 135:0.90 145:0.52 146:0.38 147:0.40 149:0.25 154:0.64 159:0.74 161:0.88 167:0.52 172:0.37 173:0.72 177:0.05 178:0.55 179:0.78 181:0.47 187:0.68 195:0.89 212:0.52 216:0.77 222:0.48 235:0.52 241:0.30 242:0.42 243:0.25 245:0.52 247:0.47 253:0.57 254:0.74 257:0.65 259:0.21 265:0.23 266:0.54 267:0.52 276:0.39 282:0.84 300:0.84 +3 1:0.74 6:0.86 7:0.81 8:0.71 9:0.80 11:0.57 12:0.51 17:0.95 20:0.87 21:0.30 27:0.34 28:0.99 30:0.28 32:0.80 34:0.75 36:0.33 37:0.38 39:0.39 41:0.90 43:0.78 60:0.87 66:0.36 69:0.77 70:0.88 74:0.85 77:0.98 78:0.98 81:0.89 83:0.86 85:0.94 91:0.33 96:0.85 98:0.32 100:0.95 101:0.77 104:0.87 108:0.89 111:0.96 114:0.68 120:0.76 121:0.90 123:0.88 124:0.92 126:0.54 127:0.69 129:0.89 133:0.93 135:0.58 138:0.96 140:0.45 145:0.98 146:0.13 147:0.18 149:0.82 150:0.93 151:0.94 152:0.82 153:0.80 154:0.71 155:0.67 158:0.92 159:0.88 161:0.88 162:0.86 164:0.74 165:0.83 167:0.55 169:0.93 172:0.70 173:0.51 176:0.56 177:0.38 179:0.40 181:0.47 186:0.98 187:0.21 189:0.88 191:0.73 192:0.59 195:0.97 201:0.74 202:0.60 204:0.89 208:0.64 212:0.51 215:0.67 216:0.40 222:0.48 230:0.87 232:0.91 233:0.76 235:0.52 238:0.84 241:0.43 242:0.57 243:0.11 245:0.71 247:0.60 248:0.84 253:0.87 255:0.79 256:0.64 259:0.21 260:0.77 261:0.91 265:0.35 266:0.95 267:0.76 268:0.68 276:0.75 279:0.86 282:0.88 283:0.82 285:0.62 290:0.68 297:0.36 299:1.00 300:0.84 +1 1:0.74 6:0.86 7:0.81 8:0.69 9:0.80 11:0.57 12:0.51 17:0.93 20:0.98 21:0.40 27:0.37 28:0.99 30:0.19 32:0.80 34:0.86 36:0.40 37:0.28 39:0.39 41:0.93 43:0.98 60:0.87 66:0.35 69:0.17 70:0.85 74:0.99 76:0.85 77:0.70 78:0.97 81:0.84 83:0.87 85:0.95 91:0.80 96:0.91 98:0.81 100:0.93 101:0.84 104:0.58 108:0.14 111:0.95 114:0.82 120:0.85 121:0.99 122:0.67 123:0.70 124:0.61 126:0.54 127:0.79 129:0.59 133:0.47 135:0.68 140:0.45 145:0.54 146:0.69 147:0.74 149:0.95 150:0.89 151:0.97 152:0.94 153:0.90 154:0.98 155:0.67 158:0.92 159:0.65 161:0.88 162:0.82 164:0.81 165:0.81 167:0.83 169:0.90 172:0.75 173:0.18 176:0.73 177:0.38 179:0.34 181:0.47 186:0.97 189:0.89 192:0.59 195:0.78 201:0.74 202:0.71 204:0.89 208:0.64 212:0.61 215:0.67 216:0.22 222:0.48 230:0.87 232:0.79 233:0.76 235:0.60 238:0.83 241:0.84 242:0.55 243:0.51 245:0.94 247:0.47 248:0.91 253:0.95 255:0.79 256:0.73 259:0.21 260:0.86 261:0.93 265:0.55 266:0.54 267:0.87 268:0.76 276:0.81 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.55 +2 6:0.85 8:0.75 12:0.06 17:0.15 20:0.78 21:0.13 28:0.87 30:0.68 34:0.37 36:0.34 37:0.99 43:0.31 55:0.59 66:0.32 69:0.71 70:0.31 78:0.77 81:0.96 91:0.87 98:0.49 100:0.78 104:0.86 106:0.81 108:0.54 111:0.76 120:0.94 123:0.68 124:0.61 126:0.54 127:0.35 129:0.59 132:0.97 133:0.47 135:0.24 140:0.45 145:0.44 146:0.40 147:0.47 149:0.41 150:0.95 151:0.75 152:0.84 153:0.86 154:0.23 159:0.32 161:0.50 162:0.72 163:0.75 164:0.96 167:0.92 169:0.76 172:0.21 173:0.81 177:0.05 179:0.85 181:0.66 186:0.78 187:0.39 189:0.90 191:0.91 202:0.92 206:0.81 212:0.42 215:0.84 216:1.00 220:0.62 235:0.12 238:0.86 241:0.77 242:0.82 243:0.49 245:0.54 247:0.12 248:0.91 256:0.93 259:0.21 260:0.94 261:0.77 265:0.46 266:0.27 267:0.36 268:0.95 271:0.59 276:0.33 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.85 8:0.72 12:0.06 17:0.16 20:0.82 21:0.13 28:0.87 30:0.68 34:0.37 36:0.35 37:0.99 43:0.31 55:0.59 66:0.32 69:0.71 70:0.31 78:0.75 81:0.96 91:0.93 98:0.49 100:0.78 104:0.86 106:0.81 108:0.54 111:0.75 120:0.94 123:0.68 124:0.61 126:0.54 127:0.35 129:0.59 132:0.97 133:0.47 135:0.26 140:0.45 145:0.44 146:0.34 147:0.41 149:0.41 150:0.95 151:0.75 152:0.84 153:0.86 154:0.23 159:0.32 161:0.50 162:0.72 163:0.97 164:0.96 167:0.92 169:0.76 172:0.21 173:0.81 177:0.05 179:0.85 181:0.66 186:0.76 187:0.39 189:0.89 191:0.87 202:0.92 206:0.81 212:0.42 215:0.84 216:1.00 220:0.62 235:0.12 238:0.87 241:0.77 242:0.82 243:0.49 245:0.54 247:0.12 248:0.91 256:0.93 259:0.21 260:0.94 261:0.77 265:0.38 266:0.38 267:0.69 268:0.95 271:0.59 276:0.32 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.88 7:0.81 8:0.72 12:0.06 17:0.79 20:0.85 21:0.47 27:0.04 28:0.87 30:0.58 32:0.80 34:0.52 36:0.98 37:0.42 43:0.51 55:0.71 66:0.93 69:0.21 70:0.46 78:0.79 81:0.91 91:0.89 98:1.00 100:0.81 104:0.58 108:0.17 111:0.78 120:0.95 123:0.16 126:0.54 127:0.95 129:0.05 135:0.39 140:0.45 145:0.74 146:0.93 147:0.95 149:0.76 150:0.84 151:0.83 152:0.80 153:0.96 154:0.40 159:0.57 161:0.50 162:0.77 164:0.97 167:0.97 169:0.79 172:0.82 173:0.24 177:0.05 179:0.49 181:0.66 186:0.79 189:0.89 191:0.93 195:0.73 202:0.95 212:0.58 215:0.63 216:0.29 230:0.87 233:0.76 235:0.52 238:0.89 241:0.83 242:0.82 243:0.94 247:0.12 248:0.93 256:0.95 259:0.21 260:0.95 261:0.79 265:1.00 266:0.54 267:0.82 268:0.97 271:0.59 276:0.73 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.97 8:0.96 12:0.06 17:0.03 20:0.78 21:0.13 25:0.88 27:0.30 28:0.87 30:0.21 34:0.28 36:0.72 37:0.85 43:0.37 55:0.52 66:0.69 69:0.60 70:0.50 78:0.90 81:0.96 91:0.95 98:0.39 100:0.88 104:0.57 108:0.91 111:0.94 120:0.98 123:0.90 124:0.41 126:0.54 127:0.87 129:0.59 132:0.97 133:0.47 135:0.42 140:0.45 145:0.47 146:0.05 147:0.05 149:0.28 150:0.95 151:0.85 152:0.76 153:0.98 154:0.28 159:0.78 161:0.50 162:0.68 163:0.81 164:0.98 167:0.99 169:0.95 172:0.41 173:0.43 177:0.05 179:0.89 181:0.66 186:0.81 189:0.98 191:0.98 202:0.96 212:0.16 215:0.84 216:0.88 235:0.12 238:0.99 241:0.92 242:0.82 243:0.18 245:0.46 247:0.12 248:0.97 256:0.97 259:0.21 260:0.98 261:0.78 265:0.41 266:0.54 267:0.89 268:0.97 271:0.59 276:0.21 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.81 7:0.81 8:0.67 12:0.06 17:0.38 20:0.77 21:0.47 27:0.21 28:0.87 30:0.52 34:0.37 36:0.81 37:0.74 43:0.51 55:0.71 66:0.53 69:0.19 70:0.59 78:0.74 81:0.91 91:0.38 98:0.78 100:0.77 104:0.93 106:0.81 108:0.15 111:0.74 120:0.80 123:0.15 124:0.65 126:0.54 127:0.76 129:0.81 133:0.67 135:0.34 140:0.45 146:0.98 147:0.98 149:0.83 150:0.84 151:0.72 152:0.92 153:0.77 154:0.40 159:0.87 161:0.50 162:0.78 164:0.86 167:0.64 169:0.77 172:0.90 173:0.10 177:0.05 179:0.22 181:0.66 186:0.75 187:0.39 189:0.84 191:0.70 202:0.78 206:0.81 212:0.42 215:0.63 216:0.95 220:0.93 230:0.65 233:0.63 235:0.52 238:0.85 241:0.15 242:0.82 243:0.62 245:0.95 247:0.12 248:0.73 256:0.81 259:0.21 260:0.81 261:0.81 265:0.78 266:0.87 267:0.94 268:0.83 271:0.59 276:0.90 283:0.60 285:0.62 297:0.36 300:0.84 +2 7:0.81 12:0.06 17:0.55 21:0.05 27:0.18 28:0.87 30:0.38 32:0.80 34:0.40 36:0.68 37:0.49 43:0.47 55:0.59 66:0.98 69:0.34 70:0.62 81:0.97 91:0.37 98:0.97 104:0.79 106:0.81 108:0.27 120:0.79 123:0.26 126:0.54 127:0.74 129:0.05 135:0.19 140:0.45 145:0.77 146:0.97 147:0.98 149:0.89 150:0.96 151:0.74 153:0.83 154:0.36 159:0.72 161:0.50 162:0.83 164:0.76 167:0.70 172:0.96 173:0.23 177:0.05 179:0.19 181:0.66 187:0.21 195:0.84 202:0.64 206:0.81 212:0.84 215:0.91 216:0.63 230:0.87 233:0.76 235:0.05 241:0.41 242:0.82 243:0.98 247:0.12 248:0.71 256:0.68 259:0.21 260:0.80 265:0.97 266:0.54 267:0.73 268:0.70 271:0.59 276:0.92 283:0.82 285:0.62 297:0.36 300:0.55 +1 7:0.81 12:0.06 17:0.40 21:0.13 27:0.45 28:0.87 30:0.33 32:0.80 34:0.83 36:0.22 37:0.50 43:0.36 55:0.59 66:0.20 69:0.60 70:0.88 81:0.96 91:0.12 98:0.51 108:0.46 123:0.44 124:0.84 126:0.54 127:0.52 129:0.93 133:0.84 135:0.82 145:0.50 146:0.80 147:0.83 149:0.64 150:0.95 154:0.27 159:0.75 161:0.50 167:0.62 172:0.48 173:0.41 177:0.05 178:0.55 179:0.46 181:0.66 187:0.68 195:0.90 212:0.35 215:0.84 216:0.77 230:0.87 233:0.76 235:0.12 241:0.07 242:0.82 243:0.40 245:0.75 247:0.12 259:0.21 265:0.52 266:0.89 267:0.59 271:0.59 276:0.68 297:0.36 300:0.70 +1 7:0.81 12:0.06 17:0.45 21:0.22 27:0.45 28:0.87 30:0.38 32:0.80 34:0.79 36:0.21 37:0.50 43:0.36 55:0.59 66:0.46 69:0.60 70:0.62 81:0.95 91:0.11 98:0.76 108:0.46 123:0.44 124:0.58 126:0.54 127:0.45 129:0.59 133:0.47 135:0.84 145:0.47 146:0.91 147:0.93 149:0.68 150:0.93 154:0.27 159:0.67 161:0.50 167:0.65 172:0.67 173:0.46 177:0.05 178:0.55 179:0.42 181:0.66 187:0.68 195:0.86 212:0.26 215:0.79 216:0.77 230:0.87 233:0.76 235:0.22 241:0.18 242:0.82 243:0.59 245:0.87 247:0.12 259:0.21 265:0.76 266:0.80 267:0.59 271:0.59 276:0.71 297:0.36 300:0.55 +2 6:0.80 8:0.58 12:0.06 17:0.13 20:0.92 21:0.13 25:0.88 27:0.01 28:0.87 34:0.37 36:0.36 37:0.23 43:0.51 66:0.36 69:0.12 78:0.83 81:0.96 91:0.95 98:0.16 100:0.86 104:0.57 108:0.10 111:0.82 120:0.99 123:0.10 126:0.54 127:0.09 129:0.05 135:0.39 140:0.45 146:0.05 147:0.05 149:0.16 150:0.95 151:0.84 152:0.93 153:0.97 154:0.40 159:0.05 161:0.50 162:0.75 164:0.99 167:0.99 169:0.81 172:0.05 173:0.71 177:0.05 181:0.66 186:0.85 189:0.82 191:0.95 202:0.97 215:0.84 216:0.68 220:0.74 235:0.12 238:0.89 241:0.92 243:0.51 248:0.98 256:0.98 259:0.21 260:0.99 261:0.87 265:0.17 267:0.79 268:0.98 271:0.59 283:0.82 285:0.62 297:0.36 +4 6:0.99 8:0.99 12:0.06 17:0.05 20:0.97 21:0.13 25:0.88 27:0.02 28:0.87 30:0.33 34:0.40 36:0.34 37:0.92 43:0.37 55:0.52 66:0.68 69:0.60 70:0.69 78:0.97 81:0.96 91:1.00 98:0.44 100:0.99 104:0.58 108:0.87 111:0.98 120:1.00 123:0.86 124:0.41 126:0.54 127:0.81 129:0.59 133:0.47 135:0.47 140:0.45 145:0.47 146:0.05 147:0.05 149:0.29 150:0.95 151:0.93 152:0.96 153:1.00 154:0.28 159:0.68 161:0.50 162:0.57 163:0.75 164:1.00 167:0.99 169:0.97 172:0.37 173:0.50 177:0.05 179:0.91 181:0.66 186:0.98 189:1.00 191:1.00 202:1.00 212:0.19 215:0.84 216:0.99 235:0.12 238:0.99 241:0.91 242:0.82 243:0.19 245:0.45 247:0.12 248:1.00 256:1.00 259:0.21 260:1.00 261:0.99 265:0.46 266:0.47 267:0.91 268:1.00 271:0.59 276:0.21 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.80 8:0.61 12:0.06 17:0.51 20:0.81 21:0.13 25:0.88 27:0.01 28:0.87 34:0.33 36:0.22 37:0.23 78:0.78 81:0.96 91:0.78 100:0.81 104:0.57 111:0.78 120:0.59 126:0.54 135:0.51 140:0.45 150:0.95 151:0.51 152:0.93 153:0.83 161:0.50 162:0.85 164:0.79 167:0.85 169:0.81 175:0.75 181:0.66 186:0.79 187:0.39 189:0.84 191:0.82 202:0.67 215:0.84 216:0.68 220:0.87 235:0.12 238:0.92 241:0.73 244:0.61 248:0.52 256:0.71 257:0.65 259:0.21 260:0.59 261:0.87 267:0.88 268:0.74 271:0.59 277:0.69 285:0.62 297:0.36 +3 6:0.83 7:0.81 8:0.65 12:0.06 17:0.45 20:0.81 21:0.54 27:0.50 28:0.87 30:0.21 32:0.80 34:0.42 36:0.77 37:0.60 43:0.51 55:0.71 66:0.18 69:0.21 70:0.70 78:0.74 81:0.90 91:0.14 98:0.56 100:0.77 104:0.84 106:0.81 108:0.84 111:0.74 120:0.88 123:0.84 124:0.95 126:0.54 127:0.98 129:0.99 133:0.95 135:0.51 140:0.45 145:0.80 146:0.61 147:0.67 149:0.90 150:0.80 151:0.79 152:0.78 153:0.90 154:0.40 159:0.97 161:0.50 162:0.86 164:0.88 167:0.63 169:0.75 172:0.95 173:0.06 177:0.05 179:0.10 181:0.66 186:0.75 187:0.39 189:0.85 195:1.00 202:0.79 206:0.81 212:0.42 215:0.58 216:0.86 220:0.87 230:0.87 233:0.76 235:0.60 238:0.86 241:0.12 242:0.82 243:0.07 245:0.99 247:0.12 248:0.83 256:0.85 259:0.21 260:0.89 261:0.75 265:0.57 266:0.92 267:0.28 268:0.85 271:0.59 276:0.99 283:0.82 285:0.62 297:0.36 300:0.70 +3 6:0.99 8:0.95 12:0.06 17:0.43 20:0.81 21:0.13 25:0.88 27:0.02 28:0.87 30:0.15 34:0.25 36:0.29 37:0.92 43:0.36 55:0.59 66:0.45 69:0.60 70:0.84 78:0.85 81:0.96 91:0.96 98:0.27 100:0.93 104:0.58 108:0.91 111:0.88 120:0.98 123:0.91 124:0.75 126:0.54 127:0.80 129:0.89 133:0.78 135:0.63 140:0.80 145:0.47 146:0.05 147:0.05 149:0.33 150:0.95 151:0.82 152:0.96 153:0.95 154:0.27 159:0.77 161:0.50 162:0.60 163:0.75 164:0.98 167:0.83 169:0.99 172:0.32 173:0.43 177:0.05 178:0.42 179:0.87 181:0.66 186:0.86 187:0.39 189:0.98 191:0.99 202:0.97 212:0.15 215:0.84 216:0.99 235:0.12 238:0.96 241:0.71 242:0.82 243:0.17 245:0.48 247:0.12 248:0.98 256:0.97 259:0.21 260:0.98 261:0.99 265:0.29 266:0.63 267:0.97 268:0.97 271:0.59 276:0.33 285:0.62 297:0.36 300:0.55 +3 6:0.81 7:0.81 8:0.60 12:0.06 17:0.11 20:0.93 21:0.30 27:0.21 28:0.87 30:0.55 34:0.45 36:0.82 37:0.74 43:0.52 55:0.71 66:0.52 69:0.10 70:0.62 78:0.78 81:0.94 91:0.88 98:0.75 100:0.80 104:0.84 106:0.81 108:0.96 111:0.77 120:0.98 123:0.96 124:0.84 126:0.54 127:0.87 129:0.96 133:0.90 135:0.17 140:0.45 146:0.65 147:0.70 149:0.88 150:0.90 151:0.86 152:0.92 153:0.99 154:0.41 159:0.96 161:0.50 162:0.66 164:0.97 167:0.82 169:0.77 172:0.98 173:0.05 177:0.05 179:0.11 181:0.66 186:0.79 187:0.39 189:0.83 191:0.91 202:0.96 206:0.81 212:0.51 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.87 241:0.70 242:0.82 243:0.08 245:0.99 247:0.12 248:0.97 256:0.96 259:0.21 260:0.98 261:0.81 265:0.75 266:0.94 267:0.44 268:0.97 271:0.59 276:0.98 283:0.82 285:0.62 297:0.36 300:0.84 +1 7:0.81 8:0.90 12:0.95 17:0.90 21:0.78 27:0.72 34:0.23 36:0.66 91:0.85 135:0.20 145:1.00 175:0.75 178:0.55 202:0.36 216:0.02 230:0.87 233:0.76 235:0.12 241:0.86 244:0.61 257:0.65 259:0.21 267:0.23 277:0.69 +1 12:0.95 17:0.87 21:0.76 27:0.72 30:0.95 34:0.87 36:0.46 37:0.47 43:0.43 55:0.92 66:0.20 69:0.52 81:0.44 91:0.20 98:0.06 106:0.81 108:0.40 123:0.39 124:0.61 126:0.54 127:0.11 129:0.59 133:0.47 135:0.78 146:0.05 147:0.05 149:0.21 150:0.36 154:0.32 159:0.17 172:0.05 173:0.34 177:0.05 178:0.55 179:0.75 187:0.39 206:0.81 215:0.46 216:0.10 235:0.95 241:0.21 243:0.40 245:0.36 259:0.21 265:0.06 267:0.65 297:0.36 300:0.08 +0 7:0.81 12:0.95 17:0.85 21:0.78 27:0.72 34:0.36 36:0.76 91:0.85 135:0.30 145:1.00 175:0.75 178:0.55 187:0.21 216:0.02 230:0.87 233:0.76 235:0.12 241:0.79 244:0.61 257:0.65 259:0.21 267:0.23 277:0.69 +2 12:0.95 17:0.91 21:0.78 27:0.60 34:0.20 36:0.30 37:0.81 43:0.34 66:0.36 69:0.64 91:0.17 98:0.08 106:0.81 108:0.49 123:0.47 127:0.06 129:0.05 135:0.81 146:0.05 147:0.05 149:0.13 154:0.26 159:0.05 172:0.05 173:0.72 177:0.05 178:0.55 187:0.39 206:0.81 216:0.20 235:0.12 241:0.01 243:0.51 259:0.21 265:0.09 267:0.19 283:0.82 +1 12:0.95 17:0.49 21:0.78 27:0.50 30:0.94 34:0.91 36:0.55 37:0.60 43:0.39 55:0.59 66:0.72 69:0.56 70:0.13 91:0.29 98:0.39 108:0.43 123:0.41 127:0.13 129:0.05 135:0.75 145:0.67 146:0.34 147:0.41 149:0.37 154:0.29 159:0.14 172:0.27 173:0.69 177:0.05 178:0.55 179:0.52 187:0.39 212:0.78 216:0.86 235:0.31 241:0.01 242:0.82 243:0.75 247:0.12 259:0.21 265:0.41 266:0.17 267:0.59 276:0.26 300:0.13 +1 8:0.73 12:0.95 17:0.55 21:0.78 27:0.72 34:0.26 36:0.62 37:0.47 43:0.27 66:0.36 69:0.80 91:0.66 98:0.06 108:0.64 123:0.61 127:0.05 129:0.05 135:0.44 146:0.05 147:0.05 149:0.11 154:0.19 159:0.05 172:0.05 173:0.69 177:0.05 178:0.55 187:0.21 191:0.84 216:0.10 235:0.22 241:0.77 243:0.51 259:0.21 265:0.06 267:0.79 +2 12:0.95 17:0.36 21:0.77 27:0.45 32:0.80 34:0.83 36:0.19 37:0.50 43:0.32 66:0.36 69:0.71 81:0.41 91:0.07 98:0.06 108:0.54 123:0.52 126:0.54 127:0.05 129:0.05 135:0.82 145:0.54 146:0.05 147:0.05 149:0.13 150:0.35 154:0.24 159:0.05 172:0.05 173:0.50 177:0.05 178:0.55 187:0.68 195:0.93 215:0.44 216:0.77 235:0.97 241:0.10 243:0.51 259:0.21 265:0.06 267:0.98 297:0.36 +1 12:0.95 17:0.32 21:0.78 27:0.70 30:0.71 34:0.59 36:0.21 37:0.38 43:0.24 55:0.52 66:0.82 69:0.85 70:0.29 91:0.14 98:0.46 108:0.71 123:0.69 127:0.14 129:0.05 135:0.32 146:0.53 147:0.59 149:0.41 154:0.17 159:0.19 172:0.48 173:0.90 177:0.05 178:0.55 179:0.34 187:0.39 212:0.84 216:0.45 235:0.42 241:0.32 242:0.82 243:0.83 247:0.12 259:0.21 265:0.48 266:0.23 267:0.23 276:0.40 300:0.25 +1 12:0.95 17:0.62 21:0.78 27:0.72 30:0.76 34:0.53 36:0.29 37:0.47 43:0.29 55:0.59 66:0.54 69:0.75 70:0.22 91:0.31 98:0.30 108:0.58 123:0.56 124:0.45 127:0.35 129:0.59 133:0.47 135:0.26 146:0.44 147:0.50 149:0.30 154:0.21 159:0.53 163:0.81 172:0.21 173:0.71 177:0.05 178:0.55 179:0.94 187:0.39 212:0.42 216:0.10 235:0.42 241:0.27 242:0.82 243:0.63 245:0.40 247:0.12 259:0.21 265:0.32 266:0.23 267:0.52 276:0.16 300:0.18 +1 12:0.95 17:0.90 21:0.13 27:0.72 30:0.62 34:0.40 36:0.17 37:0.90 43:0.35 55:0.96 66:0.16 69:0.64 70:0.34 81:0.74 91:0.12 98:0.17 106:0.81 108:0.81 123:0.80 124:0.81 126:0.54 127:0.22 129:0.89 133:0.78 135:0.65 146:0.05 147:0.05 149:0.29 150:0.55 154:0.26 159:0.61 163:0.96 172:0.10 173:0.41 177:0.05 178:0.55 179:0.81 187:0.68 206:0.81 212:0.30 215:0.84 216:0.06 235:0.12 241:0.01 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.18 266:0.27 267:0.28 276:0.28 283:0.60 297:0.36 300:0.25 +1 7:0.81 12:0.95 17:0.81 21:0.40 27:1.00 30:0.89 32:0.80 37:0.26 43:0.25 55:0.71 66:0.75 69:0.83 70:0.20 81:0.66 91:0.17 98:0.41 106:0.81 108:0.67 123:0.65 126:0.54 127:0.13 129:0.05 145:0.84 146:0.56 147:0.62 149:0.31 150:0.48 154:0.18 159:0.20 172:0.32 173:0.81 177:0.05 178:0.55 179:0.47 187:0.39 195:0.79 206:0.81 212:0.61 215:0.67 216:0.41 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.77 247:0.12 265:0.43 266:0.23 276:0.28 283:0.82 297:0.36 300:0.18 +1 12:0.95 17:0.57 21:0.40 27:0.72 30:0.95 34:0.95 36:0.35 37:0.47 43:0.46 55:0.84 66:0.54 69:0.43 81:0.66 91:0.20 98:0.42 108:0.33 123:0.32 126:0.54 127:0.14 129:0.05 135:0.26 146:0.36 147:0.43 149:0.22 150:0.48 154:0.35 159:0.14 172:0.10 173:0.56 177:0.05 178:0.55 179:0.93 187:0.39 215:0.67 216:0.10 235:0.42 241:0.35 243:0.63 259:0.21 265:0.44 267:0.44 297:0.36 300:0.08 +0 12:0.95 17:0.64 21:0.78 27:0.72 30:0.89 34:0.18 36:0.31 43:0.34 55:0.84 66:0.47 69:0.66 70:0.20 91:0.60 98:0.20 108:0.96 123:0.96 124:0.65 127:0.87 129:0.81 133:0.67 135:0.77 146:0.05 147:0.05 149:0.18 154:0.25 159:0.92 163:0.94 172:0.21 173:0.30 177:0.05 178:0.55 179:0.95 202:0.50 212:0.30 216:0.01 235:0.22 241:0.78 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.22 266:0.27 267:0.36 276:0.16 300:0.18 +1 8:0.75 12:0.95 17:0.22 21:0.78 27:0.49 30:0.62 34:0.49 36:0.37 37:0.38 43:0.24 55:0.84 66:0.75 69:0.85 70:0.34 91:0.35 98:0.40 108:0.72 123:0.70 127:0.13 129:0.05 135:0.61 145:0.98 146:0.66 147:0.71 149:0.29 154:0.17 159:0.25 163:0.75 172:0.32 173:0.78 177:0.05 178:0.55 179:0.46 187:0.39 191:0.75 202:0.58 212:0.30 216:0.88 235:0.52 241:0.24 242:0.82 243:0.77 247:0.12 265:0.42 266:0.27 267:0.85 276:0.29 300:0.25 +3 6:0.97 8:0.97 12:0.06 17:0.70 20:0.81 21:0.47 27:0.43 28:0.48 30:0.60 34:0.34 36:0.32 37:0.96 43:0.52 55:0.59 66:0.49 69:0.17 70:0.56 78:0.74 81:0.94 91:0.88 98:0.54 100:0.78 104:0.73 108:0.84 111:0.74 120:0.80 123:0.84 124:0.55 126:0.54 127:0.98 129:0.59 133:0.47 135:0.42 140:0.45 146:0.54 147:0.60 149:0.51 150:0.89 151:0.77 152:0.78 153:0.88 154:0.41 159:0.67 161:0.74 162:0.83 164:0.89 167:0.98 169:0.76 172:0.45 173:0.17 177:0.05 179:0.80 181:0.64 186:0.75 189:0.97 191:0.78 202:0.81 212:0.42 215:0.63 216:0.23 220:0.93 235:0.60 238:0.92 241:0.97 242:0.82 243:0.40 245:0.68 247:0.12 248:0.72 256:0.85 259:0.21 260:0.81 261:0.76 265:0.55 266:0.63 267:0.18 268:0.86 271:0.61 276:0.39 283:0.60 285:0.62 297:0.36 300:0.43 +2 6:0.98 8:0.94 12:0.06 17:0.15 20:0.83 21:0.13 25:0.88 27:0.04 28:0.48 30:0.91 34:0.55 36:0.70 37:0.98 43:0.48 55:0.52 66:0.69 69:0.33 70:0.13 78:0.81 81:0.92 91:0.95 98:0.59 100:0.91 104:0.58 108:0.26 111:0.85 120:0.95 123:0.25 126:0.54 127:0.17 129:0.05 135:0.49 140:0.45 146:0.30 147:0.36 149:0.35 150:0.85 151:0.84 152:0.93 153:0.98 154:0.36 159:0.13 161:0.74 162:0.76 164:0.96 167:0.86 169:0.98 172:0.21 173:0.70 177:0.05 179:0.84 181:0.64 186:0.81 187:0.68 189:0.97 191:0.91 202:0.92 212:0.95 215:0.84 216:0.81 235:0.12 238:0.97 241:0.75 242:0.82 243:0.73 247:0.12 248:0.93 256:0.94 259:0.21 260:0.95 261:0.98 265:0.60 266:0.12 267:0.52 268:0.94 271:0.61 276:0.21 283:0.60 285:0.62 297:0.36 300:0.13 +3 6:0.97 8:0.97 12:0.06 17:0.47 20:0.92 21:0.13 27:0.33 28:0.48 30:0.38 32:0.80 34:0.61 36:0.40 37:0.84 43:0.39 55:0.52 66:0.83 69:0.55 70:0.45 78:0.76 81:0.92 91:0.95 98:0.90 100:0.81 104:0.74 108:0.43 111:0.76 120:0.90 123:0.41 126:0.54 127:0.48 129:0.05 135:0.58 140:0.45 145:0.61 146:0.74 147:0.78 149:0.52 150:0.85 151:0.80 152:0.86 153:0.92 154:0.29 159:0.30 161:0.74 162:0.82 163:0.90 164:0.94 167:0.97 169:0.79 172:0.51 173:0.70 177:0.05 179:0.80 181:0.64 186:0.77 189:0.98 191:0.90 195:0.56 202:0.89 212:0.28 215:0.84 216:0.42 220:0.74 235:0.12 238:0.95 241:0.93 242:0.82 243:0.84 247:0.12 248:0.86 256:0.92 259:0.21 260:0.90 261:0.82 265:0.90 266:0.54 267:0.17 268:0.92 271:0.61 276:0.42 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.92 8:0.91 12:0.06 20:0.90 21:0.22 27:0.42 28:0.48 30:0.45 32:0.80 34:0.34 36:0.72 37:0.53 43:0.47 55:0.84 66:0.85 69:0.35 70:0.46 78:0.74 81:0.91 91:0.98 98:0.94 100:0.78 104:0.58 108:0.27 111:0.74 120:0.94 123:0.26 126:0.54 127:0.63 129:0.05 135:0.20 140:0.80 145:0.40 146:0.85 147:0.88 149:0.56 150:0.82 151:0.82 152:0.84 153:0.95 154:0.36 159:0.42 161:0.74 162:0.52 163:0.94 164:0.96 167:0.97 169:0.77 172:0.57 173:0.42 177:0.05 178:0.42 179:0.78 181:0.64 186:0.75 189:0.93 191:0.88 195:0.62 202:0.96 212:0.58 215:0.79 216:0.66 220:0.82 235:0.22 238:0.95 241:0.93 242:0.82 243:0.86 247:0.12 248:0.92 256:0.95 259:0.21 260:0.95 261:0.78 265:0.94 266:0.47 267:0.44 268:0.95 271:0.61 276:0.47 283:0.82 285:0.62 297:0.36 300:0.33 +0 12:0.06 17:0.12 21:0.22 25:0.88 27:0.12 28:0.48 30:0.45 34:0.79 36:0.68 37:0.98 43:0.44 55:0.42 66:0.69 69:0.45 70:0.25 81:0.91 91:0.73 98:0.84 104:0.58 108:0.35 120:0.83 123:0.34 126:0.54 127:0.33 129:0.05 135:0.75 140:0.87 146:0.42 147:0.48 149:0.33 150:0.82 151:0.71 153:0.72 154:0.33 159:0.16 161:0.74 162:0.53 163:0.75 164:0.81 167:0.85 172:0.21 173:0.81 177:0.05 178:0.48 179:0.96 181:0.64 187:0.39 191:0.93 202:0.79 212:0.61 215:0.79 216:0.58 235:0.22 241:0.74 242:0.82 243:0.73 247:0.12 248:0.75 256:0.77 259:0.21 260:0.84 265:0.84 266:0.17 267:0.87 268:0.76 271:0.61 276:0.20 285:0.62 297:0.36 300:0.18 +4 6:0.97 8:0.94 12:0.06 17:0.49 20:0.78 21:0.13 27:0.33 28:0.48 30:0.56 32:0.80 34:0.49 36:0.42 37:0.84 43:0.34 55:0.52 66:0.44 69:0.65 70:0.42 78:0.76 81:0.92 91:0.86 98:0.46 100:0.80 104:0.74 108:0.49 111:0.76 120:0.84 123:0.47 124:0.58 126:0.54 127:0.53 129:0.59 133:0.47 135:0.54 140:0.45 145:0.61 146:0.48 147:0.55 149:0.51 150:0.85 151:0.73 152:0.86 153:0.81 154:0.26 159:0.44 161:0.74 162:0.86 164:0.84 167:0.97 169:0.79 172:0.37 173:0.69 177:0.05 179:0.78 181:0.64 186:0.77 187:0.21 189:0.96 191:0.92 195:0.64 202:0.72 212:0.23 215:0.84 216:0.42 220:0.74 235:0.12 238:0.93 241:0.95 242:0.82 243:0.57 245:0.64 247:0.12 248:0.77 256:0.80 259:0.21 260:0.85 261:0.82 265:0.47 266:0.63 267:0.19 268:0.80 271:0.61 276:0.38 283:0.60 285:0.62 297:0.36 300:0.33 +3 6:0.96 8:0.97 12:0.06 17:0.07 20:0.99 21:0.22 25:0.88 27:0.12 28:0.48 30:0.45 34:0.66 36:0.66 37:0.98 43:0.44 55:0.42 66:0.69 69:0.45 70:0.25 78:0.76 81:0.91 91:0.97 98:0.85 100:0.82 104:0.58 108:0.35 111:0.77 120:0.94 123:0.34 126:0.54 127:0.36 129:0.05 135:0.63 140:0.87 146:0.42 147:0.48 149:0.33 150:0.82 151:0.82 152:0.90 153:0.95 154:0.33 159:0.16 161:0.74 162:0.56 163:0.75 164:0.96 167:0.96 169:0.79 172:0.21 173:0.82 177:0.05 178:0.48 179:0.96 181:0.64 186:0.77 189:0.97 191:0.89 202:0.95 212:0.61 215:0.79 216:0.58 235:0.22 238:0.95 241:0.90 242:0.82 243:0.73 247:0.12 248:0.91 256:0.95 259:0.21 260:0.94 261:0.83 265:0.85 266:0.17 267:0.94 268:0.95 271:0.61 276:0.20 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.94 8:0.95 12:0.06 17:0.02 20:0.85 21:0.68 27:0.12 28:0.48 30:0.91 34:0.67 36:0.76 37:0.88 43:0.41 55:0.84 66:0.69 69:0.54 70:0.13 78:0.74 81:0.76 91:0.98 98:0.80 100:0.79 108:0.41 111:0.74 120:0.94 123:0.40 126:0.54 127:0.29 129:0.05 135:0.73 140:0.80 146:0.44 147:0.50 149:0.32 150:0.56 151:0.81 152:0.81 153:0.94 154:0.31 159:0.16 161:0.74 162:0.51 163:0.93 164:0.96 167:0.97 169:0.77 172:0.21 173:0.84 177:0.05 178:0.42 179:0.95 181:0.64 186:0.75 189:0.95 191:0.91 202:0.96 212:0.61 215:0.49 216:0.74 220:0.82 235:0.80 238:0.96 241:0.95 242:0.82 243:0.73 247:0.12 248:0.91 256:0.95 259:0.21 260:0.94 261:0.77 265:0.80 266:0.17 267:0.84 268:0.95 271:0.61 276:0.21 283:0.82 285:0.62 297:0.36 300:0.13 +1 12:0.06 17:0.32 21:0.59 27:0.45 28:0.48 30:0.21 34:0.93 36:0.21 37:0.50 43:0.32 55:0.59 66:0.68 69:0.69 70:0.73 81:0.81 91:0.29 98:0.59 108:0.53 123:0.50 124:0.51 126:0.54 127:0.49 129:0.89 133:0.78 135:0.65 145:0.50 146:0.81 147:0.84 149:0.56 150:0.61 154:0.24 159:0.68 161:0.74 167:0.59 172:0.59 173:0.58 177:0.05 178:0.55 179:0.65 181:0.64 187:0.68 212:0.23 215:0.55 216:0.77 235:0.68 241:0.07 242:0.82 243:0.72 245:0.53 247:0.12 259:0.21 265:0.60 266:0.71 267:0.89 271:0.61 276:0.53 297:0.36 300:0.55 +4 6:0.98 7:0.81 8:0.96 12:0.06 17:0.26 20:0.98 21:0.40 25:0.88 27:0.04 28:0.48 30:0.58 32:0.80 34:0.40 36:0.83 37:0.98 43:0.52 55:0.59 66:0.88 69:0.12 70:0.42 78:0.87 81:0.87 91:0.99 98:0.98 100:0.99 104:0.58 108:0.10 111:0.96 120:0.99 123:0.10 126:0.54 127:0.85 129:0.05 135:0.30 140:0.45 145:0.80 146:0.82 147:0.85 149:0.64 150:0.74 151:0.90 152:0.93 153:0.99 154:0.41 159:0.38 161:0.74 162:0.69 164:0.99 167:0.95 169:0.97 172:0.65 173:0.30 177:0.05 179:0.72 181:0.64 186:0.89 189:0.99 191:0.97 195:0.60 202:0.99 212:0.35 215:0.67 216:0.81 230:0.87 233:0.76 235:0.42 238:0.99 241:0.86 242:0.82 243:0.88 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.98 266:0.38 267:0.97 268:0.99 271:0.61 276:0.54 283:0.82 285:0.62 297:0.36 300:0.33 +3 6:0.95 7:0.81 8:0.96 12:0.06 17:0.32 20:0.96 21:0.78 27:0.11 28:0.48 30:0.91 32:0.80 34:0.75 36:0.61 37:0.98 43:0.48 55:0.84 66:0.69 69:0.41 70:0.13 78:0.80 91:0.96 98:0.87 100:0.88 108:0.31 111:0.82 120:0.93 123:0.30 127:0.40 129:0.05 135:0.39 140:0.80 145:0.76 146:0.43 147:0.49 149:0.34 151:0.82 152:0.90 153:0.95 154:0.37 159:0.16 161:0.74 162:0.66 163:0.75 164:0.96 167:0.98 169:0.86 172:0.21 173:0.78 177:0.05 178:0.42 179:0.97 181:0.64 186:0.80 189:0.96 191:0.93 195:0.53 202:0.94 212:0.61 216:0.57 230:0.87 233:0.76 235:0.84 238:0.96 241:0.98 242:0.82 243:0.73 247:0.12 248:0.90 256:0.95 259:0.21 260:0.93 261:0.88 265:0.87 266:0.17 267:0.79 268:0.96 271:0.61 276:0.22 283:0.82 285:0.62 300:0.13 +0 8:0.94 12:0.06 17:0.38 21:0.78 27:0.37 28:0.48 34:0.50 36:0.21 37:0.57 78:0.91 91:0.91 111:0.98 120:0.93 135:0.83 140:0.45 151:0.78 153:0.88 161:0.74 162:0.67 164:0.92 167:0.97 169:0.97 175:0.75 181:0.64 191:0.88 202:0.88 216:0.17 238:0.99 241:0.96 244:0.61 248:0.90 256:0.91 257:0.65 259:0.21 260:0.93 267:0.65 268:0.90 271:0.61 277:0.69 285:0.62 +1 12:0.06 17:0.24 21:0.78 27:0.45 28:0.48 30:0.40 34:0.63 36:0.21 37:0.50 43:0.29 55:0.52 66:0.35 69:0.75 70:0.70 91:0.14 98:0.31 108:0.70 123:0.68 124:0.77 127:0.19 129:0.89 133:0.78 135:0.78 145:0.42 146:0.27 147:0.33 149:0.64 154:0.21 159:0.55 161:0.74 167:0.67 172:0.54 173:0.56 177:0.05 178:0.55 179:0.26 181:0.64 187:0.68 212:0.42 216:0.77 235:0.60 241:0.39 242:0.82 243:0.29 245:0.72 247:0.12 259:0.21 265:0.33 266:0.80 267:0.69 271:0.61 276:0.63 300:0.55 +2 6:0.96 8:0.95 12:0.06 17:0.22 20:0.88 21:0.78 27:0.20 28:0.48 34:0.47 36:0.27 37:0.85 78:0.75 91:0.92 100:0.80 104:0.58 111:0.76 120:0.85 135:0.88 140:0.45 151:0.80 152:0.86 153:0.92 161:0.74 162:0.79 164:0.95 167:0.97 169:0.78 175:0.75 181:0.64 186:0.76 189:0.96 191:0.89 202:0.90 216:0.53 220:0.99 238:0.95 241:0.93 244:0.61 248:0.77 256:0.93 257:0.65 259:0.21 260:0.85 261:0.81 267:0.65 268:0.94 271:0.61 277:0.69 283:0.60 285:0.62 +2 6:0.90 8:0.92 12:0.06 17:0.13 20:0.78 21:0.78 27:0.36 28:0.48 30:0.56 34:0.30 36:0.99 37:0.41 43:0.52 55:0.84 66:0.44 69:0.08 70:0.53 78:0.75 91:0.82 98:0.72 100:0.79 104:0.54 108:0.77 111:0.75 120:0.94 123:0.75 124:0.58 127:0.98 129:0.59 133:0.47 135:0.32 140:0.80 145:0.47 146:0.49 147:0.55 149:0.50 151:0.80 152:0.76 153:0.93 154:0.41 159:0.61 161:0.74 162:0.56 163:0.92 164:0.94 167:0.96 169:0.77 172:0.37 173:0.15 177:0.05 178:0.42 179:0.84 181:0.64 186:0.76 189:0.91 191:0.87 202:0.92 212:0.23 216:0.56 220:0.82 235:0.22 238:0.88 241:0.88 242:0.82 243:0.45 245:0.64 247:0.12 248:0.91 256:0.92 259:0.21 260:0.94 261:0.75 265:0.72 266:0.71 267:0.44 268:0.92 271:0.61 276:0.42 283:0.82 285:0.62 300:0.43 +4 6:0.98 7:0.81 8:0.95 12:0.06 17:0.36 20:0.76 21:0.40 25:0.88 27:0.04 28:0.48 30:0.58 32:0.80 34:0.67 36:0.86 37:0.98 43:0.52 55:0.59 66:0.88 69:0.12 70:0.42 78:0.81 81:0.87 91:0.90 98:0.94 100:0.91 104:0.58 108:0.10 111:0.84 120:0.95 123:0.10 126:0.54 127:0.63 129:0.05 135:0.42 140:0.45 145:0.80 146:0.82 147:0.85 149:0.64 150:0.74 151:0.84 152:0.93 153:0.97 154:0.41 159:0.38 161:0.74 162:0.73 164:0.96 167:0.83 169:0.98 172:0.65 173:0.28 177:0.05 179:0.69 181:0.64 186:0.82 187:0.95 189:0.99 191:0.92 195:0.62 202:0.93 212:0.35 215:0.67 216:0.81 230:0.87 233:0.76 235:0.42 238:0.96 241:0.73 242:0.82 243:0.88 247:0.12 248:0.93 256:0.94 259:0.21 260:0.95 261:0.98 265:0.94 266:0.38 267:0.95 268:0.95 271:0.61 276:0.54 285:0.62 297:0.36 300:0.33 +4 6:0.89 8:0.92 12:0.06 17:0.83 20:0.81 21:0.22 25:0.88 27:0.51 28:0.48 30:0.66 34:0.36 36:0.44 37:1.00 43:0.49 55:0.59 66:0.85 69:0.23 70:0.29 78:0.75 81:0.91 91:0.82 98:0.98 100:0.79 104:0.58 108:0.18 111:0.75 120:0.86 123:0.18 126:0.54 127:0.83 129:0.05 135:0.69 140:0.45 146:0.72 147:0.76 149:0.58 150:0.82 151:0.71 152:0.78 153:0.72 154:0.38 159:0.29 161:0.74 162:0.83 164:0.88 167:0.97 169:0.76 172:0.57 173:0.47 177:0.05 179:0.80 181:0.64 186:0.76 189:0.90 202:0.79 212:0.58 215:0.79 216:0.23 220:0.82 235:0.22 238:0.93 241:0.93 242:0.82 243:0.86 247:0.12 248:0.80 256:0.83 259:0.21 260:0.86 261:0.76 265:0.98 266:0.27 267:0.28 268:0.84 271:0.61 276:0.47 285:0.62 297:0.36 300:0.25 +1 8:0.62 12:0.06 17:0.07 20:0.74 21:0.78 27:0.44 28:0.48 34:0.55 36:0.62 37:0.32 78:0.75 91:0.92 100:0.73 104:0.82 111:0.75 120:0.97 135:0.47 140:0.45 151:0.85 152:0.74 153:0.98 161:0.74 162:0.59 164:0.99 167:0.71 169:0.76 175:0.75 181:0.64 186:0.73 187:0.39 191:0.78 202:0.99 216:0.94 220:0.62 235:0.74 241:0.53 244:0.61 248:0.96 256:0.99 257:0.65 259:0.21 260:0.98 261:0.73 267:0.73 268:0.99 271:0.61 277:0.69 283:0.82 285:0.62 +2 6:0.96 7:0.81 8:0.96 12:0.06 17:0.55 20:0.85 21:0.64 27:0.20 28:0.48 30:0.57 34:0.80 36:0.59 37:0.85 43:0.52 55:0.52 66:0.26 69:0.21 70:0.53 78:0.75 81:0.79 91:0.77 98:0.67 100:0.80 104:0.58 108:0.83 111:0.76 120:0.90 123:0.16 124:0.71 126:0.54 127:0.77 129:0.81 133:0.67 135:0.51 140:0.45 145:0.72 146:0.81 147:0.84 149:0.68 150:0.58 151:0.80 152:0.86 153:0.93 154:0.41 159:0.65 161:0.74 162:0.79 164:0.93 167:0.82 169:0.78 172:0.57 173:0.19 177:0.05 179:0.53 181:0.64 186:0.76 187:0.21 189:0.97 191:0.93 202:0.87 212:0.29 215:0.53 216:0.53 220:0.82 230:0.87 233:0.76 235:0.74 238:0.95 241:0.72 242:0.82 243:0.45 245:0.80 247:0.12 248:0.85 256:0.90 259:0.21 260:0.90 261:0.81 265:0.56 266:0.75 267:0.94 268:0.91 271:0.61 276:0.68 285:0.62 297:0.36 300:0.43 +3 6:0.98 8:0.95 12:0.20 17:0.34 20:0.81 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.52 36:0.93 37:0.82 43:0.52 55:0.52 66:0.27 69:0.27 70:0.73 78:0.81 81:0.77 91:0.78 98:0.75 100:0.90 104:0.58 108:0.81 111:0.83 120:0.79 123:0.20 124:0.58 126:0.54 127:0.78 129:0.59 133:0.47 135:0.81 140:0.45 145:0.97 146:0.85 147:0.88 149:0.76 150:0.57 151:0.75 152:1.00 153:0.86 154:0.41 159:0.63 161:0.69 162:0.84 164:0.81 167:0.82 169:0.96 172:0.73 173:0.24 177:0.05 179:0.43 181:0.72 186:0.81 187:0.21 189:0.98 195:0.80 202:0.69 212:0.58 215:0.46 216:0.87 235:0.88 238:0.97 241:0.75 242:0.82 243:0.46 245:0.90 247:0.12 248:0.71 256:0.73 259:0.21 260:0.79 261:0.98 265:0.62 266:0.80 267:0.65 268:0.76 271:0.50 276:0.75 285:0.62 297:0.36 300:0.55 +1 8:0.82 12:0.20 17:0.66 21:0.40 27:0.26 28:0.78 30:0.33 32:0.80 34:0.71 36:0.62 37:0.91 43:0.52 55:0.52 66:0.36 69:0.12 70:0.61 81:0.92 91:0.65 98:0.66 104:0.73 108:0.63 120:0.73 123:0.61 124:0.60 126:0.54 127:0.58 129:0.59 133:0.47 135:0.54 140:0.87 145:0.52 146:0.47 147:0.53 149:0.62 150:0.85 151:0.66 153:0.48 154:0.41 159:0.37 161:0.69 162:0.61 164:0.75 167:0.78 172:0.41 173:0.28 177:0.05 178:0.48 179:0.72 181:0.72 187:0.21 191:0.75 195:0.64 202:0.68 212:0.59 215:0.67 216:0.45 220:0.82 235:0.42 241:0.72 242:0.82 243:0.49 245:0.71 247:0.12 248:0.66 256:0.68 259:0.21 260:0.74 265:0.66 266:0.63 267:0.65 268:0.69 271:0.50 276:0.48 285:0.62 297:0.36 300:0.43 +3 6:0.96 7:0.81 8:0.92 12:0.20 17:0.47 20:0.99 21:0.30 27:0.21 28:0.78 30:0.27 34:0.66 36:0.87 37:0.74 43:0.52 55:0.71 66:0.44 69:0.10 70:0.80 78:0.79 81:0.93 91:0.80 98:0.75 100:0.85 104:0.84 106:0.81 108:0.87 111:0.80 120:0.92 123:0.86 124:0.67 126:0.54 127:0.66 129:0.81 133:0.67 135:0.20 140:0.45 146:0.92 147:0.93 149:0.85 150:0.88 151:0.82 152:0.90 153:0.95 154:0.41 159:0.84 161:0.69 162:0.61 164:0.90 167:0.72 169:0.82 172:0.89 173:0.08 177:0.05 179:0.19 181:0.72 186:0.79 187:0.39 189:0.97 191:0.70 202:0.86 206:0.81 212:0.60 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.94 241:0.62 242:0.82 243:0.45 245:0.96 247:0.12 248:0.88 256:0.89 259:0.21 260:0.92 261:0.85 265:0.75 266:0.63 267:0.44 268:0.88 271:0.50 276:0.91 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.98 8:0.97 12:0.20 17:0.06 20:0.99 21:0.74 25:0.88 27:0.13 28:0.78 30:0.21 32:0.80 34:0.50 36:0.74 37:0.82 43:0.47 55:0.59 66:0.33 69:0.46 70:0.81 78:0.92 81:0.77 91:1.00 98:0.56 100:0.99 104:0.58 108:0.81 111:0.98 120:0.99 123:0.80 124:0.78 126:0.54 127:0.90 129:0.89 133:0.78 135:0.32 140:0.45 145:0.97 146:0.25 147:0.31 149:0.67 150:0.57 151:0.91 152:1.00 153:1.00 154:0.35 159:0.59 161:0.69 162:0.69 164:0.99 167:0.94 169:0.96 172:0.54 173:0.42 177:0.05 179:0.59 181:0.72 186:0.91 189:0.99 191:0.97 195:0.76 202:0.99 212:0.40 215:0.46 216:0.87 235:0.88 238:0.99 241:0.98 242:0.82 243:0.29 245:0.73 247:0.12 248:0.98 256:0.99 259:0.21 260:0.99 261:0.98 265:0.57 266:0.63 267:0.79 268:0.99 271:0.50 276:0.64 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.98 8:0.93 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.94 98:0.71 100:0.85 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.86 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.79 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +0 12:0.20 17:0.53 21:0.59 27:0.45 28:0.78 30:0.11 32:0.80 34:0.91 36:0.18 37:0.50 43:0.32 55:0.59 66:0.30 69:0.70 70:0.54 81:0.87 91:0.27 98:0.30 108:0.53 123:0.51 124:0.61 126:0.54 127:0.21 129:0.59 133:0.47 135:0.75 145:0.44 146:0.48 147:0.54 149:0.33 150:0.72 154:0.24 159:0.44 161:0.69 163:0.88 167:0.68 172:0.16 173:0.60 177:0.05 178:0.55 179:0.80 181:0.72 187:0.68 195:0.83 212:0.30 215:0.55 216:0.77 235:0.68 241:0.53 242:0.82 243:0.48 245:0.47 247:0.12 259:0.21 265:0.32 266:0.27 267:0.28 271:0.50 276:0.25 283:0.60 297:0.36 300:0.33 +2 6:1.00 8:1.00 12:0.20 17:0.59 20:0.82 21:0.74 25:0.88 27:0.05 28:0.78 30:0.45 32:0.80 34:0.21 36:0.55 43:0.52 55:0.45 66:0.36 69:0.27 70:0.53 78:0.79 81:0.77 91:0.89 98:0.75 100:0.89 104:0.73 108:0.67 111:0.82 120:0.84 123:0.64 124:0.60 126:0.54 127:0.95 129:0.59 133:0.47 135:0.54 140:0.90 145:0.92 146:0.51 147:0.57 149:0.68 150:0.57 151:0.76 152:0.78 153:0.87 154:0.41 159:0.44 161:0.69 162:0.59 164:0.92 167:0.94 169:0.87 172:0.48 173:0.36 177:0.05 178:0.50 179:0.69 181:0.72 186:0.80 189:1.00 191:0.95 195:0.65 202:0.89 212:0.68 215:0.46 216:0.16 220:0.62 235:0.88 238:0.98 241:0.99 242:0.82 243:0.50 245:0.77 247:0.12 248:0.76 256:0.88 259:0.21 260:0.84 261:0.82 265:0.75 266:0.63 267:0.92 268:0.90 271:0.50 276:0.55 283:0.60 285:0.62 297:0.36 300:0.43 +3 6:0.96 7:0.81 8:0.97 12:0.20 17:0.12 20:0.85 21:0.78 25:0.88 27:0.44 28:0.78 30:0.45 32:0.80 34:0.55 36:0.63 43:0.47 55:0.45 66:0.43 69:0.46 70:0.57 78:0.77 91:0.88 98:0.39 100:0.82 104:0.76 108:0.86 111:0.77 120:0.94 123:0.85 124:0.66 127:0.93 129:0.81 133:0.67 135:0.65 140:0.45 145:0.85 146:0.27 147:0.33 149:0.56 151:0.83 152:0.81 153:0.96 154:0.35 159:0.67 161:0.69 162:0.55 164:0.99 167:0.92 169:0.80 172:0.45 173:0.37 177:0.05 179:0.75 181:0.72 186:0.77 189:0.97 191:0.92 195:0.81 202:0.98 212:0.55 216:0.49 220:0.62 230:0.65 233:0.63 235:0.88 238:0.96 241:0.92 242:0.82 243:0.43 245:0.64 247:0.12 248:0.91 256:0.98 259:0.21 260:0.94 261:0.80 265:0.41 266:0.63 267:0.44 268:0.98 271:0.50 276:0.39 283:0.60 285:0.62 300:0.55 +2 6:0.98 8:0.97 12:0.20 17:0.11 20:0.75 21:0.68 25:0.88 27:0.13 28:0.78 30:0.45 34:0.47 36:0.73 37:0.82 43:0.52 55:0.52 66:0.61 69:0.23 70:0.58 78:0.72 81:0.82 91:0.87 98:0.71 100:0.82 104:0.58 108:0.66 111:0.76 120:0.91 123:0.63 124:0.50 126:0.54 127:0.70 129:0.59 133:0.47 135:0.60 140:0.45 145:0.84 146:0.24 147:0.30 149:0.65 150:0.62 151:0.83 152:1.00 153:0.96 154:0.41 159:0.42 161:0.69 162:0.74 164:0.97 167:0.84 169:0.96 172:0.59 173:0.33 177:0.05 179:0.66 181:0.72 186:0.73 187:0.39 191:0.75 202:0.94 212:0.54 215:0.49 216:0.87 235:0.80 241:0.76 242:0.82 243:0.34 245:0.74 247:0.12 248:0.87 256:0.95 259:0.21 260:0.91 261:0.98 265:0.71 266:0.71 267:0.28 268:0.96 271:0.50 276:0.51 283:0.60 285:0.62 297:0.36 300:0.43 +1 12:0.20 17:0.64 21:0.54 27:0.45 28:0.78 30:0.76 34:0.80 36:0.17 37:0.50 43:0.32 55:0.92 66:0.36 69:0.69 70:0.15 81:0.89 91:0.35 98:0.34 108:0.53 123:0.50 124:0.52 126:0.54 127:0.19 129:0.59 133:0.47 135:0.56 145:0.52 146:0.46 147:0.52 149:0.23 150:0.77 154:0.24 159:0.32 161:0.69 163:0.96 167:0.56 172:0.10 173:0.66 177:0.05 178:0.55 179:0.94 181:0.72 187:0.68 212:0.95 215:0.58 216:0.77 235:0.60 241:0.12 242:0.82 243:0.51 245:0.37 247:0.12 259:0.21 265:0.36 266:0.12 267:0.28 271:0.50 276:0.18 297:0.36 300:0.13 +3 6:1.00 7:0.81 8:1.00 12:0.20 17:0.11 20:0.99 21:0.13 27:0.07 28:0.78 30:0.45 32:0.80 34:0.67 36:0.82 37:0.84 43:0.47 55:0.45 66:0.33 69:0.37 70:0.42 78:0.83 81:0.96 91:0.96 98:0.55 100:0.93 104:0.58 108:0.68 111:0.88 120:0.94 123:0.66 124:0.71 126:0.54 127:0.88 129:0.81 133:0.67 135:0.30 140:0.45 145:0.86 146:0.19 147:0.25 149:0.47 150:0.93 151:0.81 152:0.95 153:0.94 154:0.36 159:0.51 161:0.69 162:0.58 164:0.97 167:0.92 169:0.89 172:0.27 173:0.39 177:0.05 179:0.87 181:0.72 186:0.84 189:1.00 191:0.94 195:0.70 202:0.96 212:0.15 215:0.84 216:0.63 220:0.74 230:0.87 233:0.76 235:0.12 238:0.98 241:0.91 242:0.82 243:0.37 245:0.53 247:0.12 248:0.92 256:0.95 259:0.21 260:0.95 261:0.92 265:0.56 266:0.63 267:0.99 268:0.96 271:0.50 276:0.36 283:0.60 285:0.62 297:0.36 300:0.33 +1 12:0.20 17:0.18 21:0.78 25:0.88 27:0.13 28:0.78 30:0.21 34:0.37 36:0.92 37:0.82 43:0.52 55:0.52 66:0.63 69:0.05 70:0.80 91:0.58 98:0.80 104:0.58 108:0.05 120:0.80 123:0.05 124:0.52 127:0.86 129:0.81 133:0.67 135:0.34 140:0.45 146:0.87 147:0.90 149:0.75 151:0.74 153:0.83 154:0.41 159:0.62 161:0.69 162:0.85 164:0.82 167:0.86 172:0.73 173:0.10 177:0.05 179:0.53 181:0.72 187:0.21 202:0.70 212:0.54 216:0.87 235:0.02 241:0.77 242:0.82 243:0.69 245:0.75 247:0.12 248:0.72 256:0.77 259:0.21 260:0.81 265:0.80 266:0.80 267:0.44 268:0.78 271:0.50 276:0.68 283:0.60 285:0.62 300:0.55 +1 12:0.06 17:0.38 21:0.54 25:0.88 27:0.28 30:0.41 32:0.75 34:0.93 36:0.88 37:0.85 39:0.71 43:0.41 55:0.59 66:0.63 69:0.37 70:0.47 74:0.59 81:0.56 89:0.97 91:0.17 98:0.82 104:0.58 108:0.29 120:0.62 123:0.28 124:0.46 126:0.32 127:0.84 129:0.05 133:0.39 135:0.68 140:0.45 141:0.91 145:0.59 146:0.76 147:0.80 149:0.45 150:0.42 151:0.58 153:0.48 154:0.69 159:0.44 162:0.86 164:0.56 172:0.51 173:0.44 177:0.38 179:0.78 187:0.68 190:0.77 195:0.61 202:0.36 212:0.42 215:0.58 216:0.39 220:0.74 222:0.21 223:0.92 225:0.96 234:0.91 235:0.68 240:0.95 241:0.37 242:0.58 243:0.69 245:0.63 247:0.47 248:0.56 253:0.47 254:0.95 256:0.45 259:0.21 260:0.63 265:0.82 266:0.54 267:0.36 268:0.46 271:0.26 274:0.91 276:0.48 283:0.71 285:0.50 297:0.36 300:0.33 +2 1:0.74 12:0.06 17:0.91 21:0.30 27:0.72 30:0.45 34:0.36 36:0.79 39:0.71 43:0.36 55:0.45 66:0.53 69:0.29 70:0.61 74:0.74 81:0.75 89:0.98 91:0.72 98:0.58 104:0.58 108:0.64 114:0.86 122:0.51 123:0.61 124:0.51 126:0.54 127:0.84 129:0.59 133:0.47 135:0.47 141:0.91 146:0.30 147:0.37 149:0.19 150:0.78 154:0.87 155:0.67 159:0.32 172:0.37 173:0.47 176:0.73 177:0.38 178:0.55 179:0.87 192:0.59 201:0.74 202:0.36 212:0.50 215:0.72 216:0.08 222:0.21 223:0.94 225:0.98 234:0.91 235:0.42 240:0.95 241:0.86 242:0.33 243:0.37 245:0.57 247:0.55 253:0.71 254:0.80 259:0.21 265:0.60 266:0.47 267:0.28 271:0.26 274:0.91 276:0.34 290:0.86 297:0.36 300:0.43 +1 1:0.74 8:0.70 12:0.06 17:0.57 21:0.54 27:0.59 30:0.76 32:0.75 34:0.99 36:0.33 37:0.42 39:0.71 43:0.79 55:0.45 66:0.64 69:0.27 70:0.15 74:0.55 77:0.70 81:0.60 89:0.96 91:0.31 98:0.23 104:0.99 106:0.81 108:0.21 114:0.77 122:0.41 123:0.20 126:0.54 127:0.11 129:0.05 135:0.51 141:0.91 145:0.82 146:0.24 147:0.30 149:0.43 150:0.67 154:0.72 155:0.67 159:0.11 172:0.16 173:0.46 176:0.73 177:0.38 178:0.55 179:0.41 187:0.39 192:0.59 195:0.60 201:0.74 206:0.81 212:0.95 215:0.58 216:0.18 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.41 242:0.82 243:0.69 247:0.12 253:0.61 254:0.95 259:0.21 265:0.26 266:0.12 267:0.59 271:0.26 274:0.91 276:0.17 282:0.83 283:0.71 290:0.77 297:0.36 300:0.13 +1 8:0.97 12:0.06 17:0.55 21:0.40 25:0.88 27:0.72 30:0.66 32:0.75 34:0.49 36:0.81 37:1.00 39:0.71 43:0.40 55:0.59 66:0.67 69:0.38 70:0.57 74:0.39 81:0.67 89:0.96 91:0.66 98:0.78 104:0.76 108:0.30 120:0.76 123:0.28 124:0.48 126:0.32 127:0.92 129:0.05 133:0.62 135:0.76 140:0.45 141:0.91 145:0.42 146:0.82 147:0.85 149:0.68 150:0.49 151:0.64 153:0.46 154:0.62 159:0.57 162:0.78 164:0.77 172:0.70 173:0.37 177:0.38 179:0.59 190:0.77 191:0.87 195:0.69 202:0.67 212:0.48 215:0.67 216:0.17 220:0.91 222:0.21 223:0.91 225:0.96 234:0.91 235:0.52 240:0.95 241:0.96 242:0.80 243:0.72 245:0.70 247:0.39 248:0.68 253:0.35 254:0.80 256:0.71 260:0.77 265:0.78 266:0.71 267:0.65 268:0.72 271:0.26 274:0.91 276:0.64 283:0.82 285:0.50 297:0.36 300:0.55 +1 12:0.06 17:0.32 21:0.78 27:0.45 30:0.95 34:0.85 36:0.17 37:0.50 39:0.71 43:0.28 55:0.84 66:0.54 69:0.74 74:0.42 89:0.96 91:0.37 98:0.19 108:0.57 123:0.55 127:0.10 129:0.05 135:0.82 141:0.91 145:0.50 146:0.32 147:0.38 149:0.18 154:0.21 159:0.13 172:0.10 173:0.65 177:0.38 178:0.55 179:0.50 187:0.68 216:0.77 222:0.21 223:0.91 225:0.96 234:0.91 235:0.74 240:0.95 241:0.21 243:0.63 244:0.61 253:0.37 259:0.21 265:0.21 267:0.65 271:0.26 274:0.91 300:0.08 +0 7:0.78 9:0.80 12:0.06 17:0.67 21:0.74 27:0.25 30:0.33 34:0.99 36:0.39 37:0.74 39:0.71 43:0.35 55:0.59 66:0.54 69:0.37 70:0.36 74:0.71 81:0.30 89:0.98 91:0.40 96:0.69 98:0.39 104:0.89 106:0.81 108:0.29 120:0.55 122:0.41 123:0.28 124:0.55 126:0.32 127:0.56 129:0.05 133:0.62 135:0.56 140:0.45 141:0.97 145:0.45 146:0.43 147:0.49 149:0.37 150:0.28 151:0.51 153:0.48 154:0.70 155:0.67 158:0.84 159:0.46 162:0.86 164:0.57 172:0.32 173:0.39 177:0.38 179:0.89 187:0.68 192:0.59 202:0.36 206:0.81 208:0.64 212:0.78 215:0.46 216:0.66 220:0.96 222:0.21 223:0.94 225:0.98 230:0.81 233:0.76 234:0.98 235:0.88 240:0.99 241:0.24 242:0.19 243:0.63 245:0.45 247:0.55 248:0.50 253:0.54 254:0.96 255:0.79 256:0.45 259:0.21 260:0.55 265:0.41 266:0.23 267:0.59 268:0.46 271:0.26 274:0.97 276:0.31 279:0.86 283:0.71 285:0.62 297:0.36 300:0.25 +2 8:0.77 12:0.06 17:0.95 21:0.54 27:0.60 30:0.40 34:0.92 36:0.71 37:0.55 39:0.71 43:0.34 55:0.45 66:0.27 69:0.17 70:0.59 74:0.95 81:0.35 89:0.99 91:0.51 96:0.76 98:0.66 104:0.58 108:0.14 114:0.65 117:0.86 122:0.67 123:0.66 124:0.58 126:0.32 127:0.72 129:0.59 133:0.47 135:0.39 140:0.45 141:0.97 146:0.56 147:0.62 149:0.50 150:0.31 151:0.59 153:0.72 154:0.86 158:0.84 159:0.43 162:0.79 172:0.59 173:0.29 176:0.56 177:0.38 179:0.58 187:0.21 190:0.77 202:0.64 212:0.86 216:0.26 220:0.96 222:0.21 223:0.94 225:0.99 232:0.83 234:0.98 235:0.60 240:0.99 241:0.18 242:0.52 243:0.58 245:0.82 247:0.39 248:0.58 253:0.81 254:0.80 256:0.64 259:0.21 265:0.61 266:0.47 267:0.76 268:0.69 271:0.26 274:0.98 276:0.62 285:0.50 290:0.65 300:0.55 +0 12:0.06 17:0.64 21:0.68 27:0.64 30:0.38 34:0.96 36:0.17 37:0.97 39:0.71 43:0.31 55:0.59 66:0.83 69:0.68 70:0.50 74:0.79 77:0.70 81:0.30 89:0.98 91:0.48 98:0.71 104:0.99 106:0.81 108:0.51 114:0.62 117:0.86 122:0.57 123:0.49 126:0.32 127:0.22 129:0.05 135:0.98 141:0.91 145:0.47 146:0.81 147:0.84 149:0.31 150:0.28 154:0.59 159:0.36 163:0.81 172:0.51 173:0.65 176:0.56 177:0.38 178:0.55 179:0.59 187:0.95 195:0.75 206:0.81 212:0.42 216:0.57 222:0.21 223:0.94 225:0.98 234:0.91 235:0.80 240:0.95 241:0.44 242:0.60 243:0.84 247:0.39 253:0.61 254:0.92 259:0.21 265:0.71 266:0.38 267:0.76 271:0.26 274:0.91 276:0.41 282:0.84 290:0.62 300:0.33 +0 12:0.06 17:0.75 21:0.54 27:0.72 30:0.45 34:0.42 36:0.57 39:0.71 43:0.43 55:0.71 66:0.82 69:0.29 70:0.61 74:0.57 76:0.85 81:0.55 89:0.96 91:0.75 98:0.96 104:0.58 108:0.22 114:0.77 123:0.22 126:0.54 127:0.69 129:0.05 135:0.51 141:0.91 145:0.88 146:0.65 147:0.70 149:0.52 150:0.58 154:0.33 159:0.25 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.86 202:0.36 212:0.61 215:0.58 216:0.03 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.87 242:0.80 243:0.83 244:0.61 247:0.30 253:0.61 259:0.21 265:0.96 266:0.38 267:0.36 271:0.26 274:0.91 276:0.39 290:0.77 297:0.36 300:0.43 +1 1:0.74 7:0.78 12:0.06 17:0.49 21:0.22 27:0.43 30:0.68 32:0.74 34:0.45 36:0.24 37:0.71 39:0.71 43:0.27 55:0.45 66:0.79 69:0.77 70:0.31 74:0.75 77:0.70 81:0.80 89:0.98 91:0.42 98:0.55 104:0.83 106:0.81 108:0.60 114:0.90 117:0.86 122:0.41 123:0.58 126:0.54 127:0.16 129:0.05 135:0.20 141:0.91 145:0.50 146:0.49 147:0.56 149:0.11 150:0.83 154:0.66 155:0.67 158:0.86 159:0.18 172:0.41 173:0.87 176:0.73 177:0.38 178:0.55 179:0.53 187:0.39 192:0.59 195:0.60 201:0.74 206:0.81 208:0.64 212:0.78 215:0.79 216:0.51 222:0.21 223:0.94 225:0.98 230:0.81 233:0.76 234:0.91 235:0.31 240:0.95 241:0.32 242:0.19 243:0.80 247:0.55 253:0.72 254:0.74 259:0.21 265:0.57 266:0.23 267:0.84 271:0.26 274:0.91 276:0.32 279:0.86 282:0.83 283:0.71 290:0.90 297:0.36 300:0.25 +2 8:0.84 12:0.06 17:0.11 21:0.13 27:0.37 30:0.68 34:0.92 36:0.83 37:0.58 39:0.71 43:0.42 55:0.59 66:0.79 69:0.21 70:0.31 74:0.39 81:0.76 89:0.96 91:0.38 98:0.90 104:0.78 106:0.81 108:0.17 120:0.59 123:0.16 126:0.32 127:0.48 129:0.05 135:0.26 140:0.80 141:0.91 146:0.65 147:0.70 149:0.39 150:0.56 151:0.51 153:0.46 154:0.56 159:0.24 162:0.66 164:0.65 172:0.41 173:0.47 177:0.38 178:0.42 179:0.88 187:0.21 190:0.77 202:0.56 206:0.81 212:0.42 215:0.84 216:0.45 220:0.87 222:0.21 223:0.91 225:0.96 234:0.91 235:0.12 240:0.95 241:0.65 242:0.75 243:0.80 247:0.35 248:0.52 253:0.35 254:0.97 256:0.58 259:0.21 260:0.59 265:0.90 266:0.27 267:0.69 268:0.58 271:0.26 274:0.91 276:0.34 285:0.50 297:0.36 300:0.25 +2 12:0.06 17:0.90 21:0.54 27:0.72 30:0.45 34:0.49 36:0.55 39:0.71 43:0.43 55:0.71 66:0.82 69:0.29 70:0.61 74:0.57 76:0.85 81:0.55 89:0.96 91:0.71 98:0.96 104:0.54 108:0.22 114:0.77 123:0.22 126:0.54 127:0.69 129:0.05 135:0.51 141:0.91 145:0.88 146:0.65 147:0.70 149:0.52 150:0.58 154:0.33 159:0.25 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.86 187:0.21 212:0.61 215:0.58 216:0.04 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.78 242:0.80 243:0.83 244:0.61 247:0.30 253:0.61 259:0.21 265:0.96 266:0.38 267:0.23 271:0.26 274:0.91 276:0.39 290:0.77 297:0.36 300:0.43 +1 12:0.06 17:0.77 21:0.13 25:0.88 27:0.72 30:0.74 34:0.83 36:0.59 37:0.23 39:0.71 43:0.45 55:0.52 66:0.67 69:0.07 70:0.59 74:0.54 81:0.76 89:0.97 91:0.62 98:0.53 104:0.58 108:0.82 123:0.81 124:0.49 126:0.32 127:0.57 129:0.05 133:0.62 135:0.19 141:0.91 146:0.40 147:0.47 149:0.54 150:0.56 154:0.34 159:0.65 172:0.63 173:0.11 177:0.38 178:0.55 179:0.62 202:0.55 212:0.28 215:0.84 216:0.20 222:0.21 223:0.92 225:0.96 234:0.91 235:0.12 240:0.95 241:0.80 242:0.79 243:0.23 244:0.61 245:0.64 247:0.39 253:0.44 259:0.21 265:0.54 266:0.63 267:0.76 271:0.26 274:0.91 276:0.54 277:0.69 297:0.36 300:0.70 +0 8:0.70 12:0.06 17:0.43 21:0.78 27:0.25 30:0.62 34:0.90 36:0.37 37:0.74 39:0.71 43:0.27 55:0.71 66:0.16 69:0.90 70:0.23 74:0.57 89:0.98 91:0.46 98:0.20 104:0.89 106:0.81 108:0.68 122:0.67 123:0.66 124:0.81 127:0.29 129:0.05 133:0.74 135:0.42 141:0.91 145:0.54 146:0.27 147:0.05 149:0.30 154:0.20 159:0.41 163:0.98 172:0.10 173:0.96 177:0.05 178:0.55 179:0.88 187:0.68 191:0.86 202:0.36 206:0.81 212:0.61 216:0.66 222:0.21 223:0.92 225:0.96 234:0.91 235:0.88 240:0.95 241:0.18 242:0.63 243:0.23 244:0.61 245:0.42 247:0.35 253:0.46 257:0.65 259:0.21 265:0.22 266:0.23 267:0.65 271:0.26 274:0.91 276:0.29 277:0.69 283:0.82 300:0.18 +2 1:0.74 12:0.06 17:0.95 21:0.30 27:0.72 30:0.45 34:0.40 36:0.80 39:0.71 43:0.36 55:0.45 66:0.53 69:0.29 70:0.61 74:0.74 81:0.75 89:0.98 91:0.71 98:0.58 104:0.58 108:0.64 114:0.86 122:0.51 123:0.61 124:0.51 126:0.54 127:0.84 129:0.59 133:0.47 135:0.47 141:0.91 146:0.30 147:0.37 149:0.19 150:0.78 154:0.87 155:0.67 159:0.32 172:0.37 173:0.47 176:0.73 177:0.38 178:0.55 179:0.87 187:0.21 192:0.59 201:0.74 212:0.50 215:0.72 216:0.08 222:0.21 223:0.94 225:0.98 234:0.91 235:0.42 240:0.95 241:0.77 242:0.33 243:0.37 245:0.57 247:0.55 253:0.71 254:0.80 259:0.21 265:0.60 266:0.47 267:0.36 271:0.26 274:0.91 276:0.34 290:0.86 297:0.36 300:0.43 +1 12:0.06 17:0.53 21:0.59 25:0.88 27:0.28 30:0.21 32:0.77 34:0.78 36:0.81 37:0.85 39:0.71 43:0.41 55:0.71 66:0.11 69:0.39 70:0.64 74:0.70 77:0.70 81:0.58 89:0.97 91:0.11 98:0.57 104:0.58 108:0.30 114:0.74 123:0.61 124:0.73 126:0.54 127:0.68 129:0.05 133:0.62 135:0.82 141:0.91 145:0.87 146:0.40 147:0.47 149:0.42 150:0.59 154:0.75 158:0.84 159:0.45 172:0.32 173:0.43 176:0.73 177:0.38 178:0.55 179:0.78 187:0.68 195:0.64 202:0.36 208:0.64 212:0.58 215:0.55 216:0.39 222:0.21 223:0.94 225:0.98 234:0.91 235:0.80 240:0.95 241:0.21 242:0.74 243:0.48 245:0.60 247:0.47 253:0.64 254:0.84 259:0.21 265:0.41 266:0.54 267:0.65 271:0.26 274:0.91 276:0.45 277:0.69 282:0.85 283:0.65 290:0.74 297:0.36 300:0.43 +2 1:0.74 12:0.06 17:0.30 21:0.40 25:0.88 27:0.27 30:0.30 32:0.74 34:0.94 36:0.62 37:0.90 39:0.71 43:0.88 55:0.71 66:0.60 69:0.41 70:0.80 74:0.80 77:0.89 81:0.70 89:0.98 91:0.23 98:0.65 104:0.58 108:0.31 114:0.82 122:0.43 123:0.30 124:0.55 126:0.54 127:0.70 129:0.05 132:0.97 133:0.62 135:0.91 141:0.91 145:0.59 146:0.57 147:0.63 149:0.66 150:0.75 154:0.86 155:0.67 158:0.84 159:0.39 172:0.45 173:0.50 176:0.73 177:0.38 178:0.55 179:0.81 187:0.68 192:0.59 195:0.59 201:0.74 212:0.52 215:0.67 216:0.75 222:0.21 223:0.94 225:0.98 234:0.91 235:0.52 240:0.95 241:0.35 242:0.24 243:0.67 245:0.53 247:0.67 253:0.71 265:0.66 266:0.47 267:0.59 271:0.26 274:0.91 276:0.42 282:0.82 290:0.82 297:0.36 300:0.55 +2 12:0.06 17:0.75 21:0.54 27:0.72 30:0.45 34:0.42 36:0.57 39:0.71 43:0.43 55:0.71 66:0.82 69:0.29 70:0.61 74:0.57 76:0.85 81:0.55 89:0.96 91:0.75 98:0.96 104:0.58 108:0.22 114:0.77 123:0.22 126:0.54 127:0.69 129:0.05 135:0.51 141:0.91 145:0.88 146:0.65 147:0.70 149:0.52 150:0.58 154:0.33 159:0.25 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.86 202:0.36 212:0.61 215:0.58 216:0.03 222:0.21 223:0.94 225:0.98 234:0.91 235:0.68 240:0.95 241:0.87 242:0.80 243:0.83 244:0.61 247:0.30 253:0.61 259:0.21 265:0.96 266:0.38 267:0.36 271:0.26 274:0.91 276:0.39 290:0.77 297:0.36 300:0.43 +1 8:0.96 12:0.06 17:0.70 21:0.05 25:0.88 27:0.71 30:0.41 32:0.75 34:0.93 36:0.77 37:0.89 39:0.71 43:0.41 55:0.59 66:0.68 69:0.29 70:0.65 74:0.38 81:0.84 89:0.96 91:0.67 98:0.85 104:0.58 108:0.22 120:0.85 123:0.22 124:0.45 126:0.32 127:0.91 129:0.05 133:0.39 135:0.60 140:0.45 141:0.91 145:0.92 146:0.87 147:0.90 149:0.69 150:0.64 151:0.70 153:0.71 154:0.57 159:0.57 162:0.82 164:0.84 172:0.73 173:0.29 177:0.38 179:0.56 190:0.77 191:0.84 195:0.70 202:0.75 212:0.71 215:0.91 216:0.25 220:0.62 222:0.21 223:0.91 225:0.96 234:0.91 235:0.05 240:0.95 241:0.89 242:0.80 243:0.72 245:0.79 247:0.39 248:0.79 253:0.34 254:0.80 256:0.80 260:0.86 265:0.85 266:0.63 267:0.23 268:0.80 271:0.26 274:0.91 276:0.66 283:0.82 285:0.50 297:0.36 300:0.55 +2 8:0.91 12:0.06 17:0.57 21:0.30 25:0.88 27:0.28 30:0.55 32:0.75 34:0.45 36:0.45 37:0.85 39:0.71 43:0.41 55:0.71 66:0.52 69:0.33 70:0.45 74:0.41 81:0.61 89:0.96 91:0.77 98:0.66 104:0.58 108:0.69 120:0.91 123:0.67 124:0.52 126:0.32 127:0.61 129:0.05 133:0.39 135:0.78 140:0.45 141:0.91 145:0.47 146:0.63 147:0.68 149:0.56 150:0.45 151:0.75 153:0.86 154:0.31 159:0.45 162:0.79 164:0.90 172:0.48 173:0.38 177:0.38 179:0.73 187:0.21 190:0.77 191:0.86 195:0.64 202:0.83 212:0.59 215:0.72 216:0.39 220:0.87 222:0.21 223:0.91 225:0.96 234:0.91 235:0.31 240:0.95 241:0.75 242:0.78 243:0.40 244:0.61 245:0.69 247:0.39 248:0.86 253:0.36 256:0.87 259:0.21 260:0.91 265:0.67 266:0.47 267:0.59 268:0.87 271:0.26 274:0.91 276:0.51 277:0.69 283:0.71 285:0.50 297:0.36 300:0.33 +1 12:0.06 17:0.36 21:0.78 27:0.45 30:0.95 34:0.75 36:0.17 37:0.50 39:0.71 43:0.24 55:0.71 66:0.54 69:0.84 74:0.39 89:0.96 91:0.27 98:0.17 108:0.69 123:0.67 127:0.10 129:0.05 135:0.83 141:0.91 145:0.47 146:0.27 147:0.33 149:0.16 154:0.17 159:0.12 172:0.10 173:0.79 177:0.38 178:0.55 179:0.36 187:0.68 216:0.77 222:0.21 223:0.91 225:0.96 234:0.91 235:0.31 240:0.95 241:0.44 243:0.63 244:0.61 253:0.35 259:0.21 265:0.18 267:0.44 271:0.26 274:0.91 300:0.08 +1 7:0.78 12:0.06 17:0.97 21:0.54 27:0.72 30:0.87 32:0.75 34:0.75 36:0.42 39:0.71 43:0.36 55:0.71 66:0.86 69:0.96 70:0.33 74:0.74 81:0.56 89:0.99 91:0.56 98:0.69 104:0.80 106:0.81 108:0.93 120:0.69 122:0.57 123:0.93 124:0.38 126:0.32 127:0.74 129:0.05 133:0.62 135:0.84 140:0.45 141:0.91 145:0.52 146:0.98 147:0.05 149:0.43 150:0.42 151:0.66 153:0.48 154:0.17 159:0.90 162:0.86 164:0.56 172:0.91 173:0.91 177:0.05 179:0.28 187:0.21 190:0.77 195:0.97 202:0.36 206:0.81 212:0.76 215:0.58 216:0.40 222:0.21 223:0.94 225:0.98 230:0.81 233:0.76 234:0.91 235:0.68 240:0.95 241:0.12 242:0.30 243:0.06 245:0.76 247:0.67 248:0.63 253:0.55 254:0.92 256:0.45 257:0.65 259:0.21 260:0.70 265:0.69 266:0.80 267:0.28 268:0.46 271:0.26 274:0.91 276:0.80 283:0.82 285:0.50 297:0.36 300:0.70 +2 8:0.77 12:0.06 17:0.85 21:0.40 27:0.60 30:0.44 34:0.66 36:0.91 37:0.55 39:0.71 43:0.45 55:0.71 66:0.92 69:0.15 70:0.57 74:0.95 81:0.54 89:0.99 91:0.78 96:0.85 98:1.00 104:0.58 108:0.12 114:0.68 120:0.69 122:0.67 123:0.12 126:0.32 127:0.96 129:0.05 135:0.51 140:0.80 141:0.97 146:0.89 147:0.91 149:0.65 150:0.41 151:0.66 153:0.48 154:0.61 159:0.48 162:0.56 164:0.56 172:0.77 173:0.25 176:0.56 177:0.38 178:0.42 179:0.58 190:0.77 191:0.82 202:0.83 212:0.69 216:0.26 220:0.91 222:0.21 223:0.94 225:0.99 234:0.98 235:0.52 240:0.99 241:0.86 242:0.72 243:0.92 247:0.47 248:0.73 253:0.89 254:0.80 256:0.81 259:0.21 260:0.70 265:1.00 266:0.54 267:0.23 268:0.83 271:0.26 274:0.99 276:0.66 285:0.62 290:0.68 300:0.43 +3 1:0.74 9:0.80 11:0.64 12:0.69 17:0.79 21:0.30 27:0.17 28:0.78 30:0.56 32:0.80 34:0.67 36:0.67 37:0.80 39:0.86 41:0.82 43:0.78 46:0.98 66:0.33 69:0.23 70:0.66 74:0.78 77:0.70 81:0.70 83:0.76 85:0.94 91:0.58 96:0.70 98:0.66 101:0.83 104:0.58 107:0.96 108:0.78 110:0.96 114:0.86 120:0.57 122:0.43 123:0.18 124:0.60 125:0.98 126:0.54 127:0.67 129:0.59 131:0.87 133:0.47 135:0.24 140:0.45 144:0.57 145:0.52 146:0.75 147:0.79 149:0.88 150:0.81 151:0.54 153:0.48 154:0.71 155:0.67 157:0.89 159:0.57 160:0.96 161:0.45 162:0.86 163:0.81 164:0.57 165:0.84 166:0.81 167:0.76 172:0.54 173:0.24 176:0.73 177:0.38 179:0.59 181:0.94 182:0.83 187:0.39 192:0.59 195:0.74 201:0.74 202:0.36 208:0.64 212:0.30 215:0.72 216:0.22 220:0.91 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.42 241:0.59 242:0.54 243:0.50 245:0.81 246:0.88 247:0.47 248:0.54 253:0.73 255:0.79 256:0.45 259:0.21 260:0.57 265:0.60 266:0.75 267:0.96 268:0.46 271:0.72 276:0.61 282:0.88 285:0.62 287:0.90 289:0.94 290:0.86 297:0.36 299:0.88 300:0.55 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.26 21:0.30 25:0.88 27:0.06 28:0.78 30:0.73 32:0.80 34:0.83 36:0.70 37:0.95 39:0.86 41:0.82 43:0.85 46:0.99 66:0.86 69:0.60 70:0.29 74:0.81 77:0.89 81:0.70 83:0.76 85:0.91 91:0.53 96:0.72 98:0.83 101:0.84 104:0.58 107:0.95 108:0.46 110:0.95 114:0.86 120:0.72 122:0.43 123:0.44 125:0.98 126:0.54 127:0.32 129:0.05 131:0.87 135:0.60 140:0.45 144:0.57 145:0.52 146:0.69 147:0.74 149:0.88 150:0.81 151:0.60 153:0.48 154:0.83 155:0.67 157:0.88 159:0.27 160:0.96 161:0.45 162:0.86 163:0.81 164:0.68 165:0.84 166:0.81 167:0.87 172:0.59 173:0.73 176:0.73 177:0.38 179:0.64 181:0.94 182:0.83 187:0.39 190:0.77 192:0.59 195:0.57 201:0.74 202:0.53 208:0.64 212:0.54 215:0.72 216:0.58 220:0.87 222:0.60 227:0.88 229:0.91 231:0.72 235:0.42 241:0.73 242:0.63 243:0.86 246:0.88 247:0.35 248:0.58 253:0.76 255:0.79 256:0.58 259:0.21 260:0.70 265:0.83 266:0.27 267:0.44 268:0.62 271:0.72 276:0.48 282:0.88 285:0.62 287:0.90 289:0.95 290:0.86 297:0.36 299:0.88 300:0.25 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.67 21:0.22 27:0.17 28:0.78 30:0.43 32:0.80 34:0.45 36:0.72 37:0.80 39:0.86 41:0.82 43:0.79 46:0.98 66:0.89 69:0.46 70:0.71 74:0.78 77:0.70 81:0.75 83:0.77 85:0.91 91:0.57 96:0.73 98:0.90 101:0.83 104:0.58 107:0.96 108:0.35 110:0.96 114:0.90 120:0.61 122:0.43 123:0.34 125:0.99 126:0.54 127:0.46 129:0.05 131:0.87 135:0.54 140:0.45 144:0.57 145:0.84 146:0.82 147:0.85 149:0.88 150:0.84 151:0.63 153:0.69 154:0.73 155:0.67 157:0.88 159:0.38 160:0.96 161:0.45 162:0.86 163:0.81 164:0.62 165:0.86 166:0.81 167:0.76 172:0.70 173:0.53 176:0.73 177:0.38 179:0.58 181:0.94 182:0.84 187:0.39 192:0.59 195:0.65 201:0.74 202:0.46 208:0.64 212:0.42 215:0.79 216:0.22 220:0.82 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.31 241:0.61 242:0.45 243:0.90 246:0.88 247:0.47 248:0.61 253:0.77 255:0.79 256:0.45 259:0.21 260:0.61 265:0.90 266:0.38 267:0.73 268:0.55 271:0.72 276:0.58 282:0.88 285:0.62 287:0.90 289:0.94 290:0.90 297:0.36 299:0.88 300:0.55 +3 9:0.80 11:0.64 12:0.69 17:0.72 21:0.78 25:0.88 27:0.06 28:0.78 30:0.76 32:0.80 34:0.58 36:0.49 37:0.95 39:0.86 41:0.93 43:0.86 46:0.98 55:0.71 66:0.77 69:0.47 70:0.21 74:0.65 77:0.70 83:0.80 85:0.80 91:0.72 96:0.90 98:0.73 101:0.79 104:0.58 107:0.93 108:0.36 110:0.94 120:0.86 122:0.43 123:0.35 125:0.97 127:0.23 129:0.05 131:0.87 135:0.26 140:0.80 144:0.57 145:0.63 146:0.43 147:0.49 149:0.72 151:0.94 153:0.82 154:0.83 155:0.67 157:0.82 159:0.16 160:0.98 161:0.45 162:0.67 164:0.81 166:0.61 167:0.90 172:0.37 173:0.75 177:0.38 178:0.42 179:0.78 181:0.94 182:0.84 187:0.39 192:0.59 195:0.54 202:0.74 208:0.64 212:0.87 216:0.58 222:0.60 227:0.88 229:0.91 231:0.72 235:0.52 241:0.75 242:0.55 243:0.79 246:0.61 247:0.39 248:0.89 253:0.88 255:0.79 256:0.77 259:0.21 260:0.86 265:0.73 266:0.17 267:0.23 268:0.76 271:0.72 276:0.31 282:0.88 285:0.62 287:0.90 289:0.95 299:0.88 300:0.18 +0 1:0.74 7:0.81 8:0.61 9:0.80 11:0.64 12:0.69 17:0.08 20:0.75 21:0.13 25:0.88 27:0.72 28:0.78 30:0.91 34:0.40 36:0.71 37:0.23 39:0.86 41:0.94 43:0.98 46:1.00 60:0.87 66:0.69 69:0.08 70:0.13 74:0.73 78:0.89 81:0.80 83:0.88 85:0.80 87:0.98 91:0.78 96:0.91 98:0.88 100:0.85 101:0.81 104:0.58 107:0.92 108:0.07 110:0.92 111:0.89 114:0.92 120:0.85 121:0.90 122:0.43 123:0.07 125:0.99 126:0.54 127:0.41 129:0.05 131:0.87 132:0.97 135:0.58 140:0.45 144:0.57 145:0.49 146:0.32 147:0.38 149:0.70 150:0.87 151:0.96 152:0.74 153:0.86 154:0.98 155:0.67 157:0.82 158:0.92 159:0.13 160:0.98 161:0.45 162:0.83 163:0.75 164:0.82 165:0.88 166:0.81 167:1.00 169:0.89 172:0.21 173:0.66 176:0.73 177:0.38 179:0.97 181:0.94 182:0.84 186:0.80 192:0.59 201:0.74 202:0.71 204:0.89 208:0.64 212:0.61 215:0.84 216:0.27 220:0.82 222:0.60 227:0.88 229:0.91 230:0.84 231:0.72 232:0.80 233:0.69 235:0.22 238:0.88 241:0.92 242:0.63 243:0.73 246:0.88 247:0.30 248:0.91 253:0.92 255:0.79 256:0.73 260:0.86 261:0.74 265:0.88 266:0.17 267:0.91 268:0.77 271:0.72 276:0.14 279:0.86 283:0.82 285:0.62 287:0.90 289:0.95 290:0.92 297:0.36 300:0.13 +3 1:0.74 12:0.69 17:0.89 21:0.22 27:0.17 28:0.78 30:0.45 34:0.25 36:0.62 37:0.80 39:0.86 43:0.43 46:0.88 55:0.45 66:0.69 69:0.23 70:0.25 74:0.75 76:0.85 77:0.70 81:0.73 91:0.33 98:0.36 104:0.58 107:0.89 108:0.18 110:0.89 114:0.90 117:0.86 122:0.67 123:0.18 125:0.88 126:0.54 127:0.13 129:0.05 131:0.61 135:0.34 144:0.57 145:0.58 146:0.38 147:0.45 149:0.33 150:0.77 154:0.32 155:0.67 157:0.61 159:0.15 160:0.88 161:0.45 163:0.75 166:0.81 167:0.70 172:0.21 173:0.31 175:0.75 176:0.73 177:0.05 178:0.55 179:0.56 181:0.94 182:0.61 187:0.39 192:0.59 195:0.67 201:0.74 212:0.61 215:0.79 216:0.22 222:0.60 227:0.61 229:0.61 231:0.72 232:0.79 235:0.31 241:0.35 242:0.82 243:0.73 244:0.61 246:0.61 247:0.12 253:0.72 257:0.65 259:0.21 265:0.38 266:0.17 267:0.59 271:0.72 276:0.21 277:0.69 282:0.81 287:0.61 289:0.94 290:0.90 297:0.36 300:0.18 +3 1:0.74 9:0.80 11:0.64 12:0.69 17:0.82 21:0.13 27:0.17 28:0.78 30:0.56 32:0.80 34:0.70 36:0.93 37:0.80 39:0.86 41:0.90 43:0.80 46:0.98 55:0.71 66:0.91 69:0.45 70:0.70 74:0.79 77:0.70 81:0.80 83:0.79 85:0.90 91:0.61 96:0.79 98:0.92 101:0.82 104:0.58 107:0.96 108:0.35 110:0.96 114:0.92 120:0.68 123:0.34 125:0.99 126:0.54 127:0.53 129:0.05 131:0.87 135:0.76 140:0.45 144:0.57 145:0.44 146:0.86 147:0.88 149:0.85 150:0.87 151:0.73 153:0.78 154:0.73 155:0.67 157:0.87 159:0.43 160:0.98 161:0.45 162:0.86 164:0.73 165:0.88 166:0.81 167:0.78 172:0.74 173:0.49 176:0.73 177:0.38 179:0.55 181:0.94 182:0.84 187:0.39 192:0.59 195:0.65 201:0.74 202:0.59 208:0.64 212:0.71 215:0.84 216:0.22 220:0.74 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.22 241:0.65 242:0.40 243:0.91 246:0.88 247:0.60 248:0.75 253:0.84 255:0.79 256:0.64 259:0.21 260:0.69 265:0.92 266:0.38 267:0.69 268:0.67 271:0.72 276:0.62 282:0.88 285:0.62 287:0.90 289:0.95 290:0.92 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.82 7:0.81 8:0.68 9:0.80 11:0.64 12:0.69 17:0.59 20:0.88 21:0.47 27:0.21 28:0.78 30:0.30 34:0.62 36:0.80 37:0.74 39:0.86 41:0.82 43:0.98 46:0.97 55:0.71 60:0.87 66:0.94 69:0.17 70:0.74 74:0.89 78:0.75 81:0.61 83:0.83 85:0.93 91:0.48 96:0.70 98:0.88 100:0.78 101:0.81 104:0.84 106:0.81 107:0.96 108:0.14 110:0.96 111:0.74 114:0.80 117:0.86 120:0.56 121:0.90 122:0.43 123:0.13 125:0.98 126:0.54 127:0.41 129:0.05 131:0.87 135:0.20 140:0.45 144:0.57 146:0.93 147:0.94 149:0.91 150:0.76 151:0.53 152:0.83 153:0.77 154:0.98 155:0.67 157:0.88 158:0.87 159:0.57 160:0.96 161:0.45 162:0.68 164:0.64 165:0.80 166:0.81 167:0.64 169:0.75 172:0.83 173:0.17 176:0.73 177:0.38 179:0.37 181:0.94 182:0.83 186:0.76 187:0.39 189:0.84 192:0.59 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.54 215:0.63 216:0.95 220:0.93 222:0.60 227:0.88 229:0.91 230:0.84 231:0.72 232:0.91 233:0.69 235:0.60 238:0.87 241:0.10 242:0.39 243:0.94 246:0.88 247:0.60 248:0.52 253:0.74 255:0.79 256:0.45 259:0.21 260:0.57 261:0.76 265:0.88 266:0.63 267:0.88 268:0.57 271:0.72 276:0.73 279:0.86 283:0.71 285:0.62 287:0.90 289:0.94 290:0.80 297:0.36 300:0.70 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.09 21:0.30 25:0.88 27:0.06 28:0.78 30:0.45 32:0.80 34:0.96 36:0.48 37:0.95 39:0.86 41:0.88 43:0.90 46:0.97 66:0.36 69:0.23 70:0.33 74:0.64 77:0.89 81:0.70 83:0.78 85:0.80 91:0.54 96:0.82 98:0.37 101:0.79 104:0.58 107:0.93 108:0.57 110:0.94 114:0.86 120:0.72 122:0.43 123:0.55 124:0.58 125:0.99 126:0.54 127:0.40 129:0.59 131:0.87 133:0.47 135:0.30 140:0.45 144:0.57 145:0.52 146:0.26 147:0.32 149:0.71 150:0.81 151:0.87 153:0.48 154:0.89 155:0.67 157:0.82 159:0.23 160:0.97 161:0.45 162:0.86 164:0.67 165:0.84 166:0.81 167:0.83 172:0.21 173:0.48 176:0.73 177:0.38 179:0.89 181:0.94 182:0.84 187:0.39 192:0.59 195:0.54 201:0.74 202:0.50 208:0.64 212:0.52 215:0.72 216:0.58 222:0.60 227:0.88 229:0.91 231:0.72 235:0.42 241:0.70 242:0.55 243:0.45 245:0.50 246:0.88 247:0.39 248:0.79 253:0.82 255:0.79 256:0.58 259:0.21 260:0.73 265:0.39 266:0.27 267:0.44 268:0.59 271:0.72 276:0.25 282:0.88 285:0.62 287:0.90 289:0.95 290:0.86 297:0.36 299:0.88 300:0.25 +1 11:0.64 12:0.69 17:0.36 21:0.78 27:0.45 28:0.78 30:0.76 34:0.77 36:0.19 37:0.50 39:0.86 43:0.79 46:0.97 55:0.59 66:0.72 69:0.76 70:0.28 74:0.76 85:0.93 91:0.27 98:0.41 101:0.81 107:0.95 108:0.59 110:0.96 122:0.43 123:0.56 124:0.42 125:0.88 127:0.39 129:0.05 131:0.61 133:0.39 135:0.51 144:0.57 145:0.38 146:0.60 147:0.66 149:0.88 154:0.72 157:0.88 159:0.60 160:0.88 161:0.45 166:0.61 167:0.70 172:0.67 173:0.69 177:0.38 178:0.55 179:0.53 181:0.94 182:0.78 187:0.68 212:0.87 216:0.77 222:0.60 227:0.61 229:0.61 231:0.69 235:0.52 241:0.35 242:0.52 243:0.75 245:0.70 246:0.61 247:0.47 253:0.56 259:0.21 265:0.43 266:0.38 267:0.19 271:0.72 276:0.39 287:0.61 289:0.93 300:0.33 +0 1:0.74 11:0.42 12:0.69 17:0.67 21:0.13 27:0.72 28:0.78 30:0.40 34:0.59 36:0.68 39:0.86 43:0.43 46:0.88 55:0.45 66:0.25 69:0.21 70:0.83 74:0.93 81:0.82 83:0.77 87:0.98 91:0.25 98:0.30 107:0.96 108:0.17 110:0.96 114:0.92 122:0.67 123:0.16 124:0.84 125:0.88 126:0.54 127:0.78 129:0.89 131:0.61 133:0.83 135:0.79 144:0.57 146:0.55 147:0.61 149:0.58 150:0.88 154:0.84 155:0.67 157:0.61 159:0.78 160:0.88 161:0.45 165:0.88 166:0.81 167:0.77 172:0.59 173:0.14 176:0.73 177:0.38 178:0.55 179:0.43 181:0.94 182:0.83 187:0.39 192:0.59 201:0.74 212:0.76 215:0.84 222:0.60 227:0.61 229:0.61 231:0.72 232:0.70 235:0.31 241:0.63 242:0.36 243:0.44 245:0.81 246:0.88 247:0.67 253:0.78 254:0.74 265:0.33 266:0.71 267:0.79 271:0.72 276:0.71 287:0.61 289:0.94 290:0.92 297:0.36 300:0.84 +2 1:0.74 11:0.64 12:0.69 17:0.90 21:0.40 27:0.17 28:0.78 30:0.68 32:0.80 34:0.75 36:0.58 37:0.80 39:0.86 43:0.84 46:0.99 55:0.59 66:0.86 69:0.35 70:0.31 74:0.73 77:0.70 81:0.65 83:0.75 85:0.88 91:0.33 98:0.91 101:0.83 104:0.58 107:0.95 108:0.27 110:0.95 114:0.82 123:0.26 125:0.88 126:0.54 127:0.50 129:0.05 131:0.61 135:0.30 144:0.57 145:0.44 146:0.65 147:0.70 149:0.84 150:0.78 154:0.81 155:0.67 157:0.86 159:0.25 160:0.88 161:0.45 165:0.83 166:0.81 167:0.71 172:0.61 173:0.58 176:0.73 177:0.38 178:0.55 179:0.70 181:0.94 182:0.83 187:0.39 192:0.59 195:0.54 201:0.74 212:0.69 215:0.67 216:0.22 222:0.60 227:0.61 229:0.61 231:0.72 232:0.79 235:0.52 241:0.39 242:0.54 243:0.87 246:0.88 247:0.47 253:0.69 259:0.21 265:0.91 266:0.27 267:0.82 271:0.72 276:0.51 282:0.88 283:0.71 287:0.61 289:0.94 290:0.82 297:0.36 299:0.88 300:0.25 +1 1:0.74 6:0.84 8:0.70 9:0.80 11:0.64 12:0.69 17:0.73 20:0.87 21:0.05 27:0.72 28:0.78 30:0.70 34:0.28 36:0.81 37:0.23 39:0.86 41:0.88 43:0.94 46:0.99 66:0.62 69:0.21 70:0.48 74:0.84 76:0.85 78:0.74 81:0.85 83:0.80 85:0.93 91:0.80 96:0.80 98:0.66 100:0.77 101:0.84 104:0.58 107:0.96 108:0.68 110:0.95 111:0.73 114:0.95 120:0.69 122:0.57 123:0.66 124:0.52 125:1.00 126:0.54 127:0.79 129:0.81 131:0.87 133:0.67 135:0.49 140:0.45 144:0.57 146:0.13 147:0.18 149:0.87 150:0.91 151:0.82 152:0.82 153:0.77 154:0.94 155:0.67 157:0.89 159:0.43 160:0.97 161:0.45 162:0.86 164:0.69 165:0.90 166:0.81 167:0.98 169:0.75 172:0.57 173:0.32 176:0.73 177:0.38 179:0.71 181:0.94 182:0.84 186:0.75 189:0.86 192:0.59 201:0.74 202:0.54 208:0.64 212:0.35 215:0.91 216:0.10 220:0.62 222:0.60 227:0.88 229:0.91 231:0.72 232:0.79 235:0.12 238:0.83 241:0.83 242:0.55 243:0.19 245:0.61 246:0.88 247:0.39 248:0.77 253:0.86 255:0.79 256:0.58 259:0.21 260:0.70 261:0.75 265:0.67 266:0.63 267:0.69 268:0.63 271:0.72 276:0.51 285:0.62 287:0.90 289:0.95 290:0.95 297:0.36 300:0.43 +4 1:0.74 6:0.99 7:0.81 8:0.98 9:0.80 12:0.69 17:0.13 20:0.99 21:0.30 25:0.88 27:0.06 28:0.78 30:0.11 32:0.80 34:0.33 36:0.47 37:0.95 39:0.86 41:0.99 43:0.43 46:0.88 55:0.45 60:0.87 66:0.47 69:0.23 70:0.54 74:0.67 76:0.85 77:0.89 78:0.92 81:0.70 83:0.90 91:0.98 96:0.98 98:0.52 100:1.00 104:0.58 107:0.90 108:0.18 110:0.91 111:1.00 114:0.86 120:0.98 121:0.90 123:0.18 124:0.52 125:0.98 126:0.54 127:0.65 129:0.59 131:0.87 133:0.47 135:0.54 138:0.98 140:0.45 144:0.57 145:0.88 146:0.42 147:0.49 149:0.38 150:0.81 151:0.99 152:0.99 153:0.97 154:0.33 155:0.67 157:0.61 158:0.87 159:0.33 160:1.00 161:0.45 162:0.78 163:0.75 164:0.96 165:0.84 166:0.81 167:1.00 169:0.99 172:0.21 173:0.40 175:0.75 176:0.73 177:0.05 179:0.94 181:0.94 182:0.84 186:0.92 189:0.99 191:0.97 192:0.59 195:0.63 201:0.74 202:0.94 204:0.89 208:0.64 212:0.30 215:0.72 216:0.58 220:0.74 222:0.60 227:0.88 229:0.91 230:0.87 231:0.72 233:0.76 235:0.42 238:1.00 241:0.93 242:0.82 243:0.59 244:0.61 245:0.46 246:0.88 247:0.12 248:0.99 253:0.98 255:0.79 256:0.95 257:0.65 259:0.21 260:0.98 261:0.99 265:0.53 266:0.27 267:0.82 268:0.96 271:0.72 276:0.22 277:0.69 279:0.86 282:0.88 283:0.71 285:0.62 287:0.90 289:0.95 290:0.86 297:0.36 299:0.88 300:0.33 +3 1:0.74 12:0.69 17:0.79 21:0.30 27:0.17 28:0.78 30:0.95 32:0.80 34:0.52 36:0.59 37:0.80 39:0.86 43:0.42 46:0.88 55:0.84 66:0.54 69:0.27 74:0.47 77:0.70 81:0.70 83:0.75 91:0.42 98:0.55 104:0.58 107:0.89 108:0.21 110:0.90 114:0.86 123:0.20 125:0.88 126:0.54 127:0.16 129:0.05 131:0.61 135:0.19 144:0.57 145:0.52 146:0.33 147:0.40 149:0.22 150:0.81 154:0.32 155:0.67 157:0.61 159:0.13 160:0.88 161:0.45 165:0.84 166:0.81 167:0.71 172:0.10 173:0.57 175:0.75 176:0.73 177:0.05 178:0.55 179:0.97 181:0.94 182:0.83 187:0.39 192:0.59 195:0.54 201:0.74 215:0.72 216:0.22 222:0.60 227:0.61 229:0.61 231:0.71 232:0.79 235:0.42 241:0.43 243:0.63 244:0.61 246:0.88 253:0.65 257:0.65 259:0.21 265:0.57 267:0.65 271:0.72 277:0.69 282:0.88 287:0.61 289:0.94 290:0.86 297:0.36 299:0.88 300:0.08 +1 11:0.64 12:0.69 17:0.47 21:0.78 27:0.45 28:0.78 30:0.19 34:0.90 36:0.19 37:0.50 39:0.86 43:0.78 46:0.95 66:0.53 69:0.78 70:0.86 74:0.83 85:0.88 91:0.11 98:0.41 101:0.78 107:0.95 108:0.61 110:0.95 122:0.67 123:0.59 124:0.64 125:0.88 127:0.38 129:0.05 131:0.61 133:0.62 135:0.68 144:0.57 145:0.56 146:0.59 147:0.05 149:0.79 154:0.70 157:0.85 159:0.57 160:0.88 161:0.45 166:0.61 167:0.65 172:0.54 173:0.73 177:0.05 178:0.55 179:0.59 181:0.94 182:0.76 187:0.68 212:0.50 216:0.77 222:0.60 227:0.61 229:0.61 231:0.67 235:0.98 241:0.12 242:0.44 243:0.11 245:0.65 246:0.61 247:0.47 253:0.59 257:0.65 259:0.21 265:0.44 266:0.71 267:0.44 271:0.72 276:0.49 287:0.61 289:0.92 300:0.70 +2 1:0.74 11:0.64 12:0.69 17:0.40 21:0.30 27:0.17 28:0.78 30:0.45 32:0.80 34:0.96 36:0.47 37:0.80 39:0.86 43:0.90 46:0.97 66:0.36 69:0.23 70:0.33 74:0.64 77:0.89 81:0.70 83:0.75 85:0.80 91:0.38 98:0.37 101:0.79 104:0.58 107:0.93 108:0.57 110:0.94 114:0.86 122:0.43 123:0.54 124:0.58 125:0.88 126:0.54 127:0.38 129:0.59 131:0.61 133:0.47 135:0.23 144:0.57 145:0.52 146:0.26 147:0.32 149:0.71 150:0.81 154:0.89 155:0.67 157:0.82 159:0.23 160:0.88 161:0.45 165:0.84 166:0.81 167:0.74 172:0.21 173:0.48 176:0.73 177:0.38 178:0.55 179:0.89 181:0.94 182:0.83 187:0.39 192:0.59 195:0.54 201:0.74 212:0.52 215:0.72 216:0.22 222:0.60 227:0.61 229:0.61 231:0.72 232:0.79 235:0.42 241:0.55 242:0.55 243:0.45 245:0.50 246:0.88 247:0.39 253:0.68 259:0.21 265:0.39 266:0.27 267:0.28 271:0.72 276:0.25 282:0.88 287:0.61 289:0.94 290:0.86 297:0.36 299:0.88 300:0.25 +2 12:0.69 17:0.51 21:0.13 27:0.17 28:0.78 30:0.33 32:0.80 34:0.34 36:0.34 37:0.80 39:0.86 43:0.43 46:0.88 55:0.59 66:0.54 69:0.17 70:0.69 74:0.47 77:0.70 81:0.67 91:0.35 98:0.67 104:0.58 107:0.90 108:0.14 110:0.91 120:0.62 123:0.13 124:0.48 125:0.88 126:0.32 127:0.49 129:0.59 131:0.82 133:0.47 135:0.24 140:0.45 144:0.57 145:0.54 146:0.60 147:0.65 149:0.44 150:0.48 151:0.58 153:0.48 154:0.33 157:0.61 159:0.36 160:0.88 161:0.45 162:0.86 163:0.81 164:0.55 166:0.75 167:0.78 172:0.32 173:0.31 175:0.75 177:0.05 179:0.88 181:0.94 182:0.73 187:0.39 190:0.77 195:0.60 202:0.36 212:0.42 215:0.84 216:0.22 220:0.74 222:0.60 227:0.85 229:0.61 231:0.67 232:0.79 235:0.12 241:0.65 242:0.82 243:0.63 244:0.61 245:0.50 246:0.61 247:0.12 248:0.56 253:0.40 256:0.45 257:0.65 259:0.21 260:0.62 265:0.68 266:0.38 267:0.36 268:0.46 271:0.72 276:0.31 277:0.69 282:0.88 285:0.50 287:0.61 289:0.92 297:0.36 299:0.88 300:0.43 +1 12:0.51 17:0.26 18:0.78 21:0.78 27:0.72 29:0.62 30:0.19 34:0.66 36:0.70 37:0.36 39:0.71 43:0.09 55:0.92 66:0.15 69:0.37 70:0.90 71:0.56 74:0.28 86:0.73 91:0.18 98:0.21 108:0.29 123:0.28 124:0.93 127:0.81 129:0.59 133:0.92 135:0.81 139:0.70 146:0.55 147:0.61 149:0.11 154:0.29 159:0.91 163:0.75 170:0.99 172:0.27 173:0.12 175:0.91 177:0.38 178:0.55 179:0.70 187:0.68 202:0.36 212:0.15 216:0.30 222:0.35 235:0.12 241:0.10 242:0.33 243:0.36 244:0.93 245:0.55 247:0.76 253:0.25 254:0.80 257:0.84 259:0.21 265:0.22 266:0.91 267:0.23 271:0.61 276:0.55 277:0.87 300:0.70 +0 10:0.92 12:0.51 17:0.20 18:0.68 21:0.78 27:0.68 29:0.62 30:0.30 34:0.61 36:0.69 37:0.63 39:0.71 43:0.09 55:0.92 66:0.07 69:0.56 70:0.63 71:0.56 74:0.30 86:0.64 91:0.48 98:0.21 108:0.43 122:0.95 123:0.94 124:0.94 127:0.71 129:0.93 133:0.93 135:0.58 139:0.63 146:0.49 147:0.55 149:0.17 154:0.17 159:0.89 163:0.94 170:0.99 172:0.27 173:0.24 174:0.95 175:0.91 177:0.38 178:0.55 179:0.59 187:0.98 197:0.95 212:0.23 216:0.68 222:0.35 235:0.12 239:0.90 241:0.07 242:0.40 243:0.31 244:0.93 245:0.60 247:0.88 253:0.27 254:0.96 257:0.84 259:0.21 265:0.21 266:0.94 267:0.65 271:0.61 276:0.59 277:0.87 300:0.43 +0 10:0.89 12:0.51 17:0.22 18:0.69 21:0.78 27:0.36 29:0.62 30:0.16 34:0.62 36:0.93 37:0.31 39:0.71 43:0.07 55:0.92 66:0.07 69:0.99 70:0.99 71:0.56 86:0.66 91:0.06 98:0.21 108:0.99 122:0.95 123:0.99 124:0.99 127:0.72 129:0.93 133:0.99 135:0.70 139:0.64 146:0.62 147:0.05 149:0.13 154:0.06 159:0.98 170:0.99 172:0.54 173:0.91 174:0.99 175:0.91 177:0.05 178:0.55 179:0.20 187:0.68 197:0.92 212:0.12 216:0.70 222:0.35 239:0.88 241:0.01 242:0.55 243:0.06 244:0.93 245:0.81 247:0.74 253:0.20 254:0.74 257:0.93 259:0.21 265:0.23 266:1.00 267:0.97 271:0.61 276:0.91 277:0.87 300:0.94 +0 8:0.61 10:0.80 12:0.51 17:0.38 18:0.57 21:0.78 27:0.27 29:0.62 30:0.20 34:0.80 36:0.61 37:0.64 39:0.71 43:0.09 55:0.99 66:0.07 69:0.73 70:0.90 71:0.56 74:0.28 86:0.58 91:0.15 98:0.13 108:1.00 123:0.54 124:0.95 127:0.63 129:0.99 133:0.95 135:0.76 139:0.57 146:0.55 147:0.61 149:0.11 154:0.08 159:0.98 170:0.99 172:0.32 173:0.15 174:0.90 175:0.97 177:0.05 178:0.55 179:0.59 187:0.68 197:0.80 202:0.50 212:0.18 216:0.75 222:0.35 235:0.12 239:0.80 241:0.18 242:0.51 243:0.22 244:0.97 245:0.56 247:0.76 253:0.25 257:0.93 259:0.21 265:0.11 266:0.95 267:0.44 271:0.61 276:0.60 277:0.95 300:0.70 +0 10:0.84 12:0.51 17:0.59 18:0.57 21:0.78 27:0.55 29:0.62 30:0.28 34:0.28 36:0.44 37:0.31 39:0.71 43:0.17 55:0.71 66:0.32 69:0.95 70:0.99 71:0.56 74:0.32 86:0.57 91:0.02 98:0.25 108:0.91 123:0.90 124:0.98 127:0.66 129:0.59 133:0.98 135:0.26 139:0.56 146:0.91 147:0.05 149:0.31 154:0.11 159:0.97 170:0.99 172:0.83 173:0.64 174:0.89 175:0.91 177:0.05 178:0.55 179:0.22 187:0.68 197:0.84 212:0.37 216:0.60 222:0.35 235:0.02 239:0.84 241:0.46 242:0.77 243:0.06 244:0.93 245:0.77 247:0.88 253:0.29 257:0.93 259:0.21 265:0.27 266:0.98 267:0.23 271:0.61 276:0.89 277:0.87 300:0.98 +0 10:0.82 12:0.51 17:0.32 18:0.82 21:0.47 27:0.69 29:0.62 30:0.68 34:0.87 36:0.20 37:0.34 39:0.71 43:0.11 55:0.84 64:0.77 66:0.45 69:0.86 70:0.21 71:0.56 74:0.29 81:0.31 86:0.72 91:0.33 98:0.62 108:0.18 122:0.92 123:0.18 124:0.56 126:0.24 127:0.51 129:0.05 133:0.43 135:0.60 139:0.70 146:0.55 147:0.70 149:0.16 150:0.35 154:0.09 159:0.46 163:0.94 170:0.99 172:0.27 173:0.92 174:0.79 175:0.75 177:0.38 178:0.55 179:0.89 187:0.39 197:0.82 212:0.42 216:0.49 222:0.35 235:0.52 239:0.81 241:0.01 242:0.63 243:0.58 244:0.97 245:0.53 247:0.39 253:0.27 257:0.84 259:0.21 265:0.63 266:0.38 267:0.28 271:0.61 276:0.35 277:0.87 300:0.18 +1 12:0.51 17:0.26 18:0.83 21:0.78 27:0.72 29:0.62 30:0.40 34:0.55 36:0.81 37:0.36 39:0.71 43:0.11 55:0.96 66:0.13 69:0.74 70:0.76 71:0.56 74:0.28 86:0.75 91:0.33 98:0.26 108:0.91 122:0.92 123:0.91 124:0.95 127:0.61 129:0.81 133:0.95 135:0.83 139:0.72 146:0.28 147:0.05 149:0.16 154:0.20 159:0.88 163:0.88 170:0.99 172:0.32 173:0.46 175:0.91 177:0.05 178:0.55 179:0.56 187:0.68 202:0.46 212:0.16 216:0.30 222:0.35 235:0.22 241:0.12 242:0.45 243:0.10 244:0.93 245:0.58 247:0.79 253:0.25 254:0.80 257:0.93 259:0.21 265:0.28 266:0.94 267:0.36 271:0.61 276:0.64 277:0.87 300:0.70 +0 10:0.90 12:0.51 17:0.24 18:0.70 21:0.78 27:0.36 29:0.62 30:0.09 34:0.64 36:0.95 37:0.31 39:0.71 43:0.07 55:0.84 66:0.06 69:0.99 70:0.97 71:0.56 86:0.67 91:0.03 98:0.18 108:1.00 122:0.95 123:0.98 124:0.99 127:0.79 129:0.99 133:0.99 135:0.63 139:0.65 146:0.39 147:0.05 149:0.15 154:0.06 159:0.98 163:0.75 170:0.99 172:0.51 173:0.87 174:0.99 175:0.91 177:0.05 178:0.55 179:0.16 187:0.68 197:0.94 212:0.12 216:0.70 222:0.35 239:0.89 241:0.01 242:0.57 243:0.05 244:0.93 245:0.87 247:0.82 253:0.20 254:0.84 257:0.93 259:0.21 265:0.17 266:1.00 267:0.97 271:0.61 276:0.94 277:0.87 300:0.94 +1 12:0.51 17:0.22 18:0.56 21:0.78 25:0.88 27:0.38 29:0.62 30:0.17 34:0.33 36:0.79 37:0.66 39:0.71 43:0.08 55:0.92 66:0.05 69:0.81 70:0.98 71:0.56 74:0.26 86:0.57 98:0.16 104:0.58 108:0.98 123:0.98 124:1.00 127:0.98 129:1.00 133:1.00 135:0.71 139:0.55 146:0.05 147:0.05 149:0.21 154:0.07 159:1.00 163:0.92 170:0.99 172:0.54 173:0.13 175:0.97 177:0.05 178:0.55 179:0.14 187:0.95 202:0.56 212:0.12 216:0.88 222:0.35 235:0.88 241:0.07 242:0.82 243:0.05 244:0.97 245:0.85 247:0.67 253:0.23 257:0.93 259:0.21 265:0.17 266:1.00 267:0.59 271:0.61 276:0.96 277:0.95 300:0.99 +1 12:0.51 17:0.09 18:0.78 21:0.78 27:0.56 29:0.62 30:0.09 34:0.53 36:0.76 37:0.39 39:0.71 43:0.09 55:0.71 66:0.15 69:0.37 70:0.94 71:0.56 74:0.28 86:0.73 91:0.17 98:0.21 108:0.29 123:0.28 124:0.93 127:0.83 129:0.59 133:0.92 135:0.86 139:0.70 146:0.55 147:0.61 149:0.11 154:0.29 159:0.91 163:0.75 170:0.99 172:0.27 173:0.12 175:0.91 177:0.38 178:0.55 179:0.70 187:0.87 212:0.15 216:0.61 222:0.35 235:0.12 241:0.05 242:0.33 243:0.36 244:0.93 245:0.55 247:0.76 253:0.25 254:0.80 257:0.84 259:0.21 265:0.22 266:0.87 267:0.52 271:0.61 276:0.55 277:0.87 300:0.70 +1 8:0.74 10:0.88 12:0.51 17:0.15 18:0.57 21:0.54 27:0.60 29:0.62 30:0.21 34:0.75 36:0.75 37:0.42 39:0.71 43:0.22 55:0.71 66:0.11 69:0.91 70:0.83 71:0.56 74:0.31 81:0.26 86:0.58 91:0.04 98:0.33 104:0.99 106:0.81 108:0.93 123:0.93 124:0.99 126:0.24 127:0.97 129:0.96 133:0.99 135:0.65 139:0.57 146:0.59 147:0.40 149:0.40 150:0.28 154:0.15 159:0.98 163:0.94 170:0.99 172:0.85 173:0.41 174:0.93 175:0.91 177:0.05 178:0.55 179:0.13 187:0.95 197:0.89 206:0.81 212:0.15 215:0.58 216:0.59 222:0.35 235:0.60 239:0.86 241:0.07 242:0.79 243:0.06 244:0.93 245:0.91 247:0.91 253:0.28 257:0.93 259:0.21 265:0.35 266:0.99 267:0.36 271:0.61 276:0.97 277:0.87 297:0.36 300:0.98 +0 12:0.51 17:0.18 18:0.84 21:0.78 27:0.50 29:0.62 30:0.14 34:0.62 36:0.75 37:0.66 39:0.71 43:0.11 55:0.71 66:0.13 69:0.34 70:0.84 71:0.56 74:0.28 86:0.75 91:0.27 98:0.26 108:0.27 122:0.92 123:0.91 124:0.95 127:0.59 129:0.81 133:0.94 135:0.87 139:0.72 146:0.61 147:0.66 149:0.17 154:0.20 159:0.88 170:0.99 172:0.32 173:0.12 175:0.91 177:0.38 178:0.55 179:0.55 187:0.87 212:0.13 216:0.51 222:0.35 235:0.22 241:0.07 242:0.45 243:0.33 244:0.93 245:0.59 247:0.79 253:0.25 254:0.80 257:0.84 259:0.21 265:0.27 266:0.92 267:0.52 271:0.61 276:0.64 277:0.87 300:0.70 +1 10:0.80 12:0.51 17:0.43 18:0.56 21:0.13 27:0.66 29:0.62 30:0.16 34:0.37 36:0.72 37:0.55 39:0.71 43:0.11 55:0.84 66:0.08 69:1.00 70:0.91 71:0.56 74:0.26 81:0.28 86:0.57 91:0.04 98:0.25 108:0.96 120:0.65 123:0.96 124:1.00 126:0.24 127:1.00 129:0.93 133:1.00 135:0.44 139:0.56 140:0.45 146:0.94 147:0.51 149:0.47 150:0.31 151:0.58 153:0.69 154:0.05 159:0.99 162:0.86 164:0.66 170:0.99 172:0.93 173:0.91 174:0.88 175:0.91 177:0.05 179:0.09 187:0.99 190:0.98 197:0.79 202:0.50 212:0.27 215:0.84 216:0.68 220:0.74 222:0.35 235:0.12 239:0.79 241:0.21 242:0.81 243:0.05 244:0.93 245:0.97 247:0.82 248:0.58 253:0.24 254:0.80 256:0.58 257:0.93 259:0.21 260:0.65 265:0.27 266:0.99 267:0.84 268:0.59 271:0.61 276:0.99 277:0.95 285:0.46 297:0.36 300:0.98 +1 12:0.51 17:0.12 18:0.83 21:0.78 27:0.25 29:0.62 30:0.40 34:0.49 36:0.87 37:0.65 39:0.71 43:0.11 55:0.96 66:0.13 69:0.74 70:0.76 71:0.56 74:0.28 86:0.75 91:0.25 98:0.26 108:0.91 122:0.92 123:0.91 124:0.95 127:0.61 129:0.81 133:0.95 135:0.88 139:0.72 146:0.28 147:0.05 149:0.16 154:0.20 159:0.88 163:0.88 170:0.99 172:0.32 173:0.46 175:0.91 177:0.05 178:0.55 179:0.56 187:0.68 212:0.16 216:0.66 222:0.35 235:0.22 241:0.05 242:0.45 243:0.10 244:0.93 245:0.58 247:0.79 253:0.25 254:0.80 257:0.93 259:0.21 265:0.28 266:0.94 267:0.52 271:0.61 276:0.64 277:0.87 300:0.70 +1 10:0.79 12:0.51 17:0.49 18:0.57 21:0.78 25:0.88 27:0.67 29:0.62 30:0.11 34:0.19 36:0.64 37:0.27 39:0.71 43:0.11 55:0.42 66:0.16 69:0.41 70:0.54 71:0.56 74:0.25 86:0.58 91:0.01 98:0.05 104:0.75 108:0.31 123:0.30 124:0.86 127:0.99 129:0.93 133:0.84 135:0.51 139:0.57 146:0.17 147:0.22 149:0.09 154:0.09 159:1.00 163:0.80 170:0.99 172:0.10 173:0.05 174:0.79 175:0.97 177:0.05 178:0.55 179:0.96 187:0.39 197:0.79 202:0.56 212:0.30 216:0.75 222:0.35 235:0.22 239:0.79 241:0.07 242:0.33 243:0.37 244:0.97 245:0.40 247:0.39 253:0.22 257:0.93 259:0.21 265:0.05 266:0.27 267:0.19 271:0.61 276:0.24 277:0.95 300:0.33 +1 10:0.95 12:0.51 17:0.11 18:0.61 21:0.78 27:0.62 29:0.62 30:0.17 34:0.87 36:0.66 37:0.35 39:0.71 43:0.41 55:0.59 66:0.08 69:0.36 70:0.93 71:0.56 74:0.25 86:0.62 91:0.08 98:0.29 108:0.94 123:0.94 124:0.98 127:0.99 129:0.05 133:0.99 135:0.68 139:0.60 146:0.95 147:0.96 149:0.67 154:0.31 159:0.98 170:0.99 172:0.90 173:0.06 174:0.99 175:0.75 177:0.70 178:0.55 179:0.10 187:0.95 197:0.97 212:0.55 216:0.66 222:0.35 235:0.31 239:0.94 241:0.07 242:0.70 243:0.23 244:0.93 245:0.98 247:0.86 253:0.23 257:0.65 259:0.21 265:0.31 266:0.98 267:0.44 271:0.61 276:0.99 277:0.87 300:0.98 +1 12:1.00 17:0.59 21:0.78 27:0.45 28:0.49 34:0.73 36:0.18 37:0.50 43:0.29 66:0.36 69:0.77 91:0.23 98:0.07 108:0.60 123:0.57 127:0.06 129:0.05 135:0.82 145:0.52 146:0.05 147:0.05 149:0.12 154:0.21 159:0.05 161:0.59 167:0.61 172:0.05 173:0.72 177:0.05 178:0.55 181:0.52 187:0.68 216:0.77 235:0.95 241:0.32 243:0.51 259:0.21 265:0.07 267:0.23 +2 6:0.81 8:0.74 12:1.00 17:0.62 20:0.75 21:0.78 27:0.28 28:0.49 30:0.21 32:0.80 34:0.52 36:0.69 37:0.41 43:0.39 55:0.71 66:0.20 69:0.56 70:0.37 78:0.83 91:0.22 98:0.18 100:0.89 108:0.43 111:0.84 123:0.41 124:0.81 127:0.50 129:0.89 133:0.78 135:0.94 145:0.82 146:0.27 147:0.33 149:0.32 152:0.93 154:0.29 159:0.56 161:0.59 163:0.89 167:0.50 169:0.80 172:0.10 173:0.51 177:0.05 178:0.55 179:0.96 181:0.52 186:0.83 187:0.39 195:0.74 212:0.42 216:0.67 235:0.12 242:0.82 243:0.40 245:0.39 247:0.12 259:0.21 261:0.85 265:0.20 266:0.23 267:0.79 276:0.24 300:0.25 +1 6:0.83 8:0.63 12:1.00 17:0.70 20:0.87 21:0.22 27:0.66 28:0.49 30:0.18 34:0.58 36:0.41 37:0.56 43:0.48 55:0.96 66:0.08 69:0.33 70:0.81 78:0.82 81:0.84 91:0.18 98:0.21 100:0.80 106:0.81 108:0.89 111:0.81 120:0.54 123:0.88 124:0.97 126:0.54 127:0.95 129:0.99 133:0.97 135:0.69 140:0.45 146:0.29 147:0.35 149:0.50 150:0.64 151:0.47 152:0.85 153:0.48 154:0.37 159:0.93 161:0.59 162:0.86 163:0.99 164:0.57 167:0.56 169:0.78 172:0.27 173:0.09 177:0.05 179:0.51 181:0.52 186:0.83 187:0.68 189:0.84 202:0.36 206:0.81 212:0.16 215:0.79 216:0.07 220:0.82 235:0.31 238:0.84 241:0.12 242:0.82 243:0.13 245:0.61 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 261:0.82 265:0.23 266:0.95 267:0.17 268:0.46 276:0.71 283:0.60 285:0.62 297:0.36 300:0.55 +0 6:0.80 8:0.59 12:1.00 17:0.99 20:0.86 21:0.13 25:0.88 27:0.20 28:0.49 34:0.44 36:0.59 37:0.23 78:0.92 81:0.74 91:0.87 100:0.92 111:0.91 126:0.54 135:0.23 150:0.55 152:0.81 161:0.59 167:0.91 169:0.90 175:0.75 178:0.55 181:0.52 186:0.92 189:0.82 215:0.84 235:0.12 238:0.88 241:0.83 244:0.61 257:0.65 259:0.21 261:0.88 267:0.28 277:0.69 297:0.36 +1 6:0.81 8:0.62 12:1.00 17:0.73 20:0.93 21:0.13 27:0.28 28:0.49 30:0.45 32:0.80 34:0.22 36:0.18 37:0.41 43:0.44 55:0.99 66:0.25 69:0.45 70:0.50 78:0.84 81:0.74 91:0.03 98:0.13 100:0.83 108:0.35 111:0.83 123:0.34 124:0.84 126:0.54 127:0.89 129:0.93 133:0.84 135:0.91 145:0.80 146:0.31 147:0.37 149:0.34 150:0.55 152:0.93 154:0.33 159:0.93 161:0.59 163:0.79 167:0.53 169:0.80 172:0.16 173:0.13 177:0.05 178:0.55 179:0.94 181:0.52 186:0.84 187:0.39 189:0.83 191:0.84 195:0.98 202:0.36 212:0.23 215:0.84 216:0.67 235:0.12 238:0.85 241:0.02 242:0.82 243:0.45 245:0.42 247:0.12 259:0.21 261:0.85 265:0.14 266:0.38 267:0.73 276:0.29 297:0.36 300:0.33 +3 6:0.96 8:0.93 12:1.00 17:0.78 20:0.99 21:0.78 25:0.88 27:0.07 28:0.49 34:0.36 36:0.39 37:0.95 78:0.91 91:0.92 100:0.98 111:0.97 120:0.62 135:0.44 140:0.90 151:0.56 152:0.99 153:0.78 161:0.59 162:0.46 164:0.65 167:0.93 169:0.99 175:0.75 178:0.50 181:0.52 186:0.91 189:0.96 191:0.91 202:0.84 216:0.26 220:0.62 235:0.12 238:0.98 241:0.95 244:0.61 248:0.55 256:0.45 257:0.65 259:0.21 260:0.62 261:0.98 267:0.44 268:0.58 277:0.69 285:0.62 +1 6:0.83 12:1.00 17:0.66 20:0.80 21:0.30 27:0.66 28:0.49 30:0.76 34:0.95 36:0.75 37:0.56 43:0.51 55:0.45 66:0.36 69:0.17 70:0.15 78:0.75 81:0.82 91:0.29 98:0.15 100:0.73 106:0.81 108:0.14 111:0.74 123:0.13 124:0.52 126:0.54 127:0.27 129:0.59 133:0.47 135:0.24 146:0.18 147:0.23 149:0.29 150:0.61 152:0.85 154:0.40 159:0.19 161:0.59 163:0.75 167:0.53 169:0.78 172:0.10 173:0.43 177:0.05 178:0.55 179:0.97 181:0.52 186:0.76 187:0.68 206:0.81 212:0.95 215:0.72 216:0.07 235:0.42 241:0.03 242:0.82 243:0.51 245:0.37 247:0.12 259:0.21 261:0.82 265:0.16 266:0.12 267:0.82 276:0.13 297:0.36 300:0.13 +1 8:0.64 12:1.00 17:0.75 20:0.83 21:0.30 27:0.34 28:0.49 30:0.68 32:0.80 34:0.69 36:0.68 37:0.28 43:0.40 55:0.92 66:0.32 69:0.54 70:0.43 78:0.76 81:0.69 91:0.42 98:0.38 100:0.73 106:0.81 108:0.41 111:0.75 123:0.39 124:0.78 126:0.54 127:0.24 129:0.89 133:0.78 135:0.70 145:0.90 146:0.59 147:0.64 149:0.41 150:0.50 152:0.79 154:0.30 159:0.48 161:0.59 163:0.86 167:0.55 169:0.72 172:0.21 173:0.41 177:0.05 178:0.55 179:0.75 181:0.52 186:0.77 187:0.21 191:0.73 195:0.82 206:0.81 212:0.19 215:0.72 216:0.94 235:0.31 241:0.07 242:0.82 243:0.49 245:0.45 247:0.12 259:0.21 261:0.76 265:0.41 266:0.47 267:0.69 276:0.35 297:0.36 300:0.33 +1 6:0.81 12:1.00 17:0.84 20:0.86 21:0.13 27:0.28 28:0.49 30:0.91 34:0.34 36:0.51 37:0.41 43:0.43 55:0.59 66:0.77 69:0.46 70:0.19 78:0.74 81:0.74 91:0.15 98:0.77 100:0.73 106:0.81 108:0.35 111:0.74 123:0.34 126:0.54 127:0.25 129:0.05 135:0.86 146:0.53 147:0.59 149:0.44 150:0.55 152:0.93 154:0.33 159:0.19 161:0.59 163:0.90 167:0.51 169:0.80 172:0.37 173:0.68 177:0.05 178:0.55 179:0.81 181:0.52 186:0.75 187:0.68 206:0.81 212:0.52 215:0.84 216:0.67 235:0.12 241:0.01 242:0.82 243:0.79 247:0.12 259:0.21 261:0.85 265:0.77 266:0.27 267:0.76 276:0.32 283:0.82 297:0.36 300:0.18 +2 7:0.81 12:0.20 17:0.36 21:0.54 27:0.20 28:0.92 30:0.45 34:0.75 36:0.37 37:0.81 43:0.37 55:0.52 66:0.25 69:0.61 70:0.50 81:0.89 91:0.48 98:0.18 108:0.62 120:0.79 123:0.87 124:0.71 126:0.54 127:0.47 129:0.81 133:0.67 135:0.54 140:0.80 145:0.61 146:0.05 147:0.05 149:0.34 150:0.78 151:0.76 153:0.87 154:0.28 159:0.66 161:0.57 162:0.48 163:0.80 164:0.83 167:0.82 172:0.16 173:0.49 177:0.05 178:0.42 179:0.90 181:0.57 187:0.68 202:0.88 212:0.23 215:0.58 216:0.84 230:0.87 233:0.76 235:0.68 241:0.72 242:0.82 243:0.21 245:0.45 247:0.12 248:0.72 256:0.77 259:0.21 260:0.80 265:0.15 266:0.38 267:0.23 268:0.78 271:0.32 276:0.21 283:0.60 285:0.62 297:0.36 300:0.33 +1 12:0.20 17:0.26 21:0.71 27:0.45 28:0.92 30:0.62 34:0.85 36:0.30 37:0.50 43:0.32 55:0.92 66:0.16 69:0.70 70:0.34 81:0.80 91:0.14 98:0.14 108:0.87 123:0.86 124:0.86 126:0.54 127:0.45 129:0.93 133:0.84 135:0.77 145:0.52 146:0.05 147:0.05 149:0.30 150:0.60 154:0.24 159:0.60 161:0.57 163:0.94 167:0.62 172:0.10 173:0.63 177:0.05 178:0.55 179:0.93 181:0.57 187:0.68 212:0.30 215:0.48 216:0.77 235:0.84 241:0.18 242:0.82 243:0.23 245:0.40 247:0.12 259:0.21 265:0.15 266:0.27 267:0.59 271:0.32 276:0.28 283:0.60 297:0.36 300:0.25 +2 6:0.83 8:0.76 12:0.20 17:0.81 20:0.82 21:0.13 25:0.88 27:0.68 28:0.92 30:0.45 34:0.31 36:0.52 37:0.41 43:0.46 55:0.45 66:0.35 69:0.42 70:0.49 78:0.74 81:0.96 91:0.86 98:0.69 100:0.77 104:0.76 108:0.32 111:0.74 120:0.85 123:0.62 124:0.61 126:0.54 127:0.73 129:0.59 133:0.47 135:0.23 140:0.80 146:0.45 147:0.51 149:0.71 150:0.94 151:0.79 152:0.79 153:0.90 154:0.35 159:0.36 161:0.57 162:0.64 164:0.88 167:0.94 169:0.75 172:0.51 173:0.54 177:0.05 178:0.42 179:0.61 181:0.57 186:0.75 189:0.85 191:0.89 202:0.84 212:0.81 215:0.84 216:0.56 220:0.74 235:0.12 238:0.87 241:0.83 242:0.82 243:0.51 245:0.80 247:0.12 248:0.79 256:0.83 259:0.21 260:0.86 261:0.75 265:0.55 266:0.27 267:0.23 268:0.85 271:0.32 276:0.59 283:0.60 285:0.62 297:0.36 300:0.43 +1 7:0.81 12:0.20 17:0.40 21:0.54 27:0.20 28:0.92 30:0.45 34:0.89 36:0.36 37:0.81 43:0.38 55:0.59 66:0.25 69:0.60 70:0.50 81:0.89 91:0.12 98:0.18 108:0.66 123:0.88 124:0.71 126:0.54 127:0.51 129:0.81 133:0.67 135:0.49 145:0.61 146:0.05 147:0.05 149:0.33 150:0.78 154:0.28 159:0.68 161:0.57 163:0.80 167:0.78 172:0.16 173:0.47 177:0.05 178:0.55 179:0.91 181:0.57 187:0.68 212:0.23 215:0.58 216:0.84 230:0.87 233:0.76 235:0.68 241:0.69 242:0.82 243:0.21 245:0.45 247:0.12 259:0.21 265:0.15 266:0.38 267:0.28 271:0.32 276:0.22 283:0.60 297:0.36 300:0.33 +2 6:0.99 8:0.84 12:0.20 17:0.08 20:0.75 21:0.54 25:0.88 27:0.03 28:0.92 30:0.66 32:0.80 34:0.92 36:0.49 37:0.96 43:0.40 55:0.71 66:0.75 69:0.54 70:0.64 78:0.85 81:0.89 91:0.61 98:0.75 100:0.92 104:0.58 108:0.68 111:0.87 120:0.96 123:0.66 124:0.39 126:0.54 127:0.62 129:0.59 133:0.47 135:0.60 140:0.45 145:0.54 146:0.05 147:0.05 149:0.48 150:0.78 151:0.79 152:0.99 153:0.91 154:0.30 159:0.48 161:0.57 162:0.53 164:0.95 167:0.74 169:0.99 172:0.54 173:0.57 177:0.05 179:0.78 181:0.57 186:0.88 187:0.21 189:0.93 191:0.87 195:0.68 202:0.95 212:0.22 215:0.58 216:0.89 235:0.60 238:0.95 241:0.62 242:0.82 243:0.14 245:0.50 247:0.12 248:0.94 256:0.95 259:0.21 260:0.96 261:1.00 265:0.76 266:0.63 267:0.19 268:0.94 271:0.32 276:0.43 283:0.82 285:0.62 297:0.36 300:0.55 +2 8:0.85 12:0.20 17:0.61 21:0.54 25:0.88 27:0.03 28:0.92 30:0.15 32:0.80 34:0.92 36:0.41 37:0.96 43:0.22 55:0.42 66:0.33 69:0.90 70:0.68 81:0.89 91:0.78 98:0.24 104:0.58 108:0.88 120:0.87 123:0.88 124:0.71 126:0.54 127:0.23 129:0.81 133:0.67 135:0.42 140:0.45 145:0.54 146:0.19 147:0.24 149:0.31 150:0.78 151:0.79 153:0.90 154:0.15 159:0.47 161:0.57 162:0.77 164:0.89 167:0.76 172:0.27 173:0.90 177:0.05 179:0.65 181:0.57 187:0.21 191:0.83 195:0.83 202:0.82 212:0.30 215:0.58 216:0.89 220:0.87 235:0.60 241:0.65 242:0.82 243:0.28 245:0.53 247:0.12 248:0.82 256:0.86 259:0.21 260:0.88 265:0.26 266:0.54 267:0.84 268:0.86 271:0.32 276:0.36 283:0.60 285:0.62 297:0.36 300:0.43 +2 12:0.20 17:0.08 21:0.40 25:0.88 27:0.03 28:0.92 30:0.15 32:0.80 34:0.19 36:0.67 37:0.96 43:0.50 55:0.42 66:0.33 69:0.23 70:0.84 81:0.92 91:0.63 98:0.21 104:0.58 106:0.81 108:0.94 120:0.87 123:0.94 124:0.87 126:0.54 127:0.93 129:0.95 133:0.87 135:0.49 140:0.45 145:0.54 146:0.05 147:0.05 149:0.29 150:0.86 151:0.77 153:0.88 154:0.39 159:0.85 161:0.57 162:0.48 164:0.89 167:0.78 172:0.27 173:0.12 177:0.05 179:0.88 181:0.57 187:0.39 195:0.94 202:0.92 206:0.81 212:0.15 215:0.67 216:0.89 220:0.91 235:0.42 241:0.69 242:0.82 243:0.17 245:0.45 247:0.12 248:0.82 256:0.90 259:0.21 260:0.88 265:0.23 266:0.63 267:0.36 268:0.86 271:0.32 276:0.37 283:0.60 285:0.62 297:0.36 300:0.55 +3 6:0.80 8:0.59 12:0.20 17:0.47 20:0.83 21:0.13 25:0.88 27:0.39 28:0.92 34:0.50 36:0.61 78:0.76 81:0.96 91:0.88 100:0.78 104:0.73 111:0.75 120:0.89 126:0.54 135:0.30 140:0.45 150:0.94 151:0.80 152:0.79 153:0.93 161:0.57 162:0.69 164:0.89 167:0.82 169:0.76 175:0.75 181:0.57 186:0.76 187:0.68 189:0.82 191:0.76 202:0.84 215:0.84 216:0.16 235:0.12 238:0.87 241:0.72 244:0.61 248:0.85 256:0.86 257:0.65 259:0.21 260:0.90 261:0.76 267:0.28 268:0.87 271:0.32 277:0.69 283:0.82 285:0.62 297:0.36 +2 6:0.80 8:0.76 12:0.20 17:0.30 20:0.75 21:0.78 25:0.88 27:0.72 28:0.92 30:0.62 34:0.52 36:0.56 37:0.65 43:0.34 55:0.45 66:0.75 69:0.65 70:0.23 78:0.75 91:0.84 98:0.82 100:0.73 104:0.77 108:0.49 111:0.74 120:0.84 123:0.47 127:0.31 129:0.05 135:0.21 140:0.87 146:0.60 147:0.66 149:0.37 151:0.73 152:0.81 153:0.78 154:0.25 159:0.22 161:0.57 162:0.52 163:0.86 164:0.79 167:0.95 169:0.75 172:0.32 173:0.84 177:0.05 178:0.48 179:0.89 181:0.57 186:0.73 191:0.89 202:0.79 212:0.30 216:0.53 235:0.05 241:0.84 242:0.82 243:0.77 247:0.12 248:0.76 256:0.73 259:0.21 260:0.85 261:0.75 265:0.82 266:0.27 267:0.36 268:0.74 271:0.32 276:0.26 283:0.60 285:0.62 300:0.18 +3 6:0.99 8:0.97 12:0.20 17:0.02 20:0.98 21:0.54 25:0.88 27:0.03 28:0.92 30:0.44 32:0.80 34:0.39 36:0.53 37:0.96 43:0.22 55:0.71 66:0.54 69:0.90 70:0.90 78:0.97 81:0.89 91:0.98 98:0.59 100:1.00 104:0.58 108:0.91 111:1.00 120:0.99 123:0.91 124:0.68 126:0.54 127:0.38 129:0.89 133:0.78 135:0.32 140:0.45 145:0.65 146:0.19 147:0.24 149:0.45 150:0.78 151:0.91 152:0.99 153:1.00 154:0.15 159:0.75 161:0.57 162:0.66 164:0.99 167:0.97 169:0.99 172:0.67 173:0.82 177:0.05 179:0.45 181:0.57 186:0.97 189:0.99 191:0.98 195:0.93 202:0.99 212:0.30 215:0.58 216:0.89 235:0.60 238:1.00 241:0.96 242:0.82 243:0.15 245:0.70 247:0.12 248:0.99 256:0.99 259:0.21 260:0.99 261:1.00 265:0.60 266:0.87 267:0.91 268:0.99 271:0.32 276:0.64 283:0.82 285:0.62 297:0.36 300:0.84 +1 6:0.99 8:0.75 12:0.20 17:0.02 20:0.86 21:0.30 25:0.88 27:0.03 28:0.92 30:0.75 32:0.80 34:0.79 36:0.88 37:0.96 43:0.25 55:0.71 66:0.68 69:0.84 70:0.47 78:0.82 81:0.93 91:0.71 98:0.66 100:0.82 104:0.58 108:0.86 111:0.85 120:0.92 123:0.85 124:0.44 126:0.54 127:0.43 129:0.59 133:0.47 135:0.47 140:0.90 145:0.69 146:0.52 147:0.58 149:0.49 150:0.89 151:0.81 152:0.99 153:0.94 154:0.18 159:0.69 161:0.57 162:0.47 164:0.94 167:0.90 169:0.99 172:0.67 173:0.75 177:0.05 178:0.50 179:0.54 181:0.57 186:0.79 187:0.39 189:0.86 191:0.88 195:0.89 202:0.98 212:0.39 215:0.72 216:0.89 220:0.87 235:0.31 238:0.97 241:0.77 242:0.82 243:0.27 245:0.74 247:0.12 248:0.89 256:0.91 259:0.21 260:0.93 261:1.00 265:0.67 266:0.75 267:0.44 268:0.92 271:0.32 276:0.58 283:0.82 285:0.62 297:0.36 300:0.55 +3 6:0.83 7:0.81 8:0.65 12:0.20 17:0.18 20:0.97 21:0.30 27:0.21 28:0.92 30:0.45 34:0.73 36:0.77 37:0.74 43:0.52 55:0.71 66:0.42 69:0.10 70:0.62 78:0.75 81:0.93 91:0.37 98:0.60 100:0.77 104:0.84 106:0.81 108:0.09 111:0.74 120:0.81 123:0.09 124:0.86 126:0.54 127:0.68 129:0.95 133:0.87 135:0.44 140:0.45 146:0.91 147:0.93 149:0.79 150:0.89 151:0.76 152:0.90 153:0.87 154:0.41 159:0.84 161:0.57 162:0.64 164:0.78 167:0.63 169:0.75 172:0.78 173:0.09 177:0.05 179:0.31 181:0.57 186:0.75 187:0.39 189:0.85 202:0.71 206:0.81 212:0.35 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.86 241:0.21 242:0.82 243:0.57 245:0.85 247:0.12 248:0.73 256:0.68 259:0.21 260:0.82 261:0.77 265:0.61 266:0.89 267:0.85 268:0.72 271:0.32 276:0.83 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.20 17:0.61 21:0.05 25:0.88 27:0.68 28:0.92 30:0.66 32:0.80 34:0.30 36:0.75 37:0.41 43:0.52 55:0.71 66:0.93 69:0.07 70:0.32 81:0.97 91:0.17 98:0.98 104:0.76 106:0.81 108:0.06 123:0.06 126:0.54 127:0.81 129:0.05 135:0.30 145:0.65 146:0.87 147:0.89 149:0.76 150:0.95 154:0.41 159:0.44 161:0.57 167:0.68 172:0.80 173:0.19 177:0.05 178:0.55 179:0.51 181:0.57 187:0.39 191:0.89 195:0.65 202:0.36 206:0.81 212:0.94 215:0.91 216:0.56 235:0.05 241:0.43 242:0.82 243:0.93 247:0.12 259:0.21 265:0.98 266:0.17 267:0.44 271:0.32 276:0.70 297:0.36 300:0.33 +2 6:0.90 8:0.73 12:0.20 17:0.13 20:0.93 21:0.54 25:0.88 27:0.42 28:0.92 30:0.33 32:0.80 34:0.30 36:0.90 37:0.53 43:0.36 55:0.71 66:0.45 69:0.62 70:0.69 78:0.74 81:0.89 91:0.67 98:0.14 100:0.77 104:0.79 108:0.97 111:0.74 120:0.87 123:0.97 124:0.66 126:0.54 127:0.94 129:0.81 133:0.67 135:0.60 140:0.45 145:0.84 146:0.05 147:0.05 149:0.24 150:0.78 151:0.75 152:0.86 153:0.86 154:0.27 159:0.93 161:0.57 162:0.51 164:0.92 167:0.94 169:0.75 172:0.27 173:0.25 177:0.05 179:0.92 181:0.57 186:0.75 189:0.91 191:0.79 195:0.98 202:0.93 212:0.42 215:0.58 216:0.53 220:0.96 235:0.60 238:0.87 241:0.82 242:0.82 243:0.19 245:0.47 247:0.12 248:0.81 256:0.92 259:0.21 260:0.87 261:0.76 265:0.15 266:0.38 267:0.96 268:0.90 271:0.32 276:0.21 285:0.62 297:0.36 300:0.43 +1 12:0.20 17:0.43 21:0.78 27:0.45 28:0.92 30:0.45 34:0.83 36:0.47 37:0.50 43:0.29 55:0.92 66:0.53 69:0.77 70:0.47 91:0.10 98:0.55 108:0.68 123:0.66 124:0.63 127:0.36 129:0.81 133:0.67 135:0.30 145:0.49 146:0.05 147:0.05 149:0.40 154:0.21 159:0.56 161:0.57 163:0.75 167:0.64 172:0.37 173:0.71 177:0.05 178:0.55 179:0.79 181:0.57 187:0.68 212:0.15 216:0.77 235:0.84 241:0.27 242:0.82 243:0.17 245:0.50 247:0.12 259:0.21 265:0.57 266:0.63 267:0.91 271:0.32 276:0.37 300:0.33 +1 7:0.81 12:0.20 17:0.36 21:0.54 27:0.20 28:0.92 30:0.45 34:0.89 36:0.39 37:0.81 43:0.36 55:0.59 66:0.25 69:0.63 70:0.50 81:0.89 91:0.10 98:0.18 108:0.66 123:0.88 124:0.71 126:0.54 127:0.50 129:0.81 133:0.67 135:0.49 145:0.61 146:0.05 147:0.05 149:0.33 150:0.78 154:0.27 159:0.67 161:0.57 163:0.80 167:0.78 172:0.16 173:0.51 177:0.05 178:0.55 179:0.91 181:0.57 187:0.68 212:0.23 215:0.58 216:0.84 230:0.87 233:0.76 235:0.68 241:0.69 242:0.82 243:0.21 245:0.45 247:0.12 259:0.21 265:0.15 266:0.38 267:0.28 271:0.32 276:0.22 297:0.36 300:0.33 +1 8:0.74 12:0.20 17:0.34 21:0.30 25:0.88 27:0.03 28:0.92 30:0.76 32:0.80 34:0.67 36:0.86 37:0.96 43:0.25 55:0.71 66:0.69 69:0.83 70:0.44 78:0.96 81:0.93 91:0.40 98:0.69 104:0.58 108:0.86 111:1.00 120:0.82 123:0.85 124:0.44 126:0.54 127:0.47 129:0.59 133:0.47 135:0.60 140:0.45 145:0.69 146:0.52 147:0.58 149:0.50 150:0.89 151:0.74 153:0.84 154:0.18 159:0.71 161:0.57 162:0.66 164:0.80 167:0.86 169:0.99 172:0.68 173:0.74 177:0.05 179:0.53 181:0.57 187:0.21 195:0.89 202:0.74 212:0.30 215:0.72 216:0.89 220:0.87 235:0.31 238:0.99 241:0.75 242:0.82 243:0.26 245:0.75 247:0.12 248:0.74 256:0.73 259:0.21 260:0.83 265:0.69 266:0.80 267:0.76 268:0.76 271:0.32 276:0.60 285:0.62 297:0.36 300:0.55 +1 12:0.20 17:0.55 21:0.13 25:0.88 27:0.72 28:0.92 30:0.40 32:0.80 34:0.68 36:0.84 43:0.21 55:0.71 66:0.25 69:0.92 70:0.88 81:0.96 91:0.22 98:0.36 104:0.72 108:0.84 123:0.84 124:0.84 126:0.54 127:0.39 129:0.93 133:0.84 135:0.61 145:0.61 146:0.78 147:0.81 149:0.51 150:0.94 154:0.14 159:0.83 161:0.57 167:0.67 172:0.57 173:0.81 177:0.05 178:0.55 179:0.36 181:0.57 187:0.21 191:0.70 195:0.96 212:0.22 215:0.84 216:0.48 235:0.12 241:0.37 242:0.82 243:0.44 245:0.79 247:0.12 259:0.21 265:0.39 266:0.94 267:0.65 271:0.32 276:0.72 297:0.36 300:0.84 +2 12:0.20 17:0.36 21:0.64 25:0.88 27:0.03 28:0.92 30:0.72 32:0.80 34:0.56 36:0.63 37:0.96 43:0.40 55:0.92 66:0.74 69:0.55 70:0.37 81:0.85 91:0.84 98:0.81 104:0.58 106:0.81 108:0.68 120:0.96 123:0.66 124:0.39 126:0.54 127:0.64 129:0.59 133:0.47 135:0.56 140:0.80 145:0.65 146:0.05 147:0.05 149:0.44 150:0.67 151:0.73 153:0.78 154:0.30 159:0.56 161:0.57 162:0.72 163:0.94 164:0.94 167:0.77 172:0.51 173:0.52 177:0.05 178:0.42 179:0.80 181:0.57 187:0.39 195:0.75 202:0.90 206:0.81 212:0.39 215:0.53 216:0.89 220:0.96 235:0.74 241:0.67 242:0.82 243:0.15 245:0.49 247:0.12 248:0.94 256:0.92 259:0.21 260:0.96 265:0.81 266:0.63 267:0.28 268:0.93 271:0.32 276:0.43 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.20 17:0.47 21:0.78 25:0.88 27:0.72 28:0.92 30:0.62 34:0.70 36:0.57 37:0.65 43:0.34 55:0.45 66:0.75 69:0.65 70:0.23 91:0.68 98:0.78 104:0.77 108:0.49 120:0.84 123:0.47 127:0.27 129:0.05 135:0.24 140:0.45 146:0.60 147:0.66 149:0.37 151:0.73 153:0.78 154:0.25 159:0.22 161:0.57 162:0.53 163:0.86 164:0.79 167:0.78 172:0.32 173:0.82 177:0.05 179:0.87 181:0.57 187:0.39 202:0.78 212:0.30 216:0.53 220:0.97 235:0.05 241:0.68 242:0.82 243:0.77 247:0.12 248:0.76 256:0.73 259:0.21 260:0.84 265:0.78 266:0.27 267:0.65 268:0.74 271:0.32 276:0.26 285:0.62 300:0.18 +2 7:0.81 12:0.20 17:0.36 21:0.40 27:0.20 28:0.92 30:0.21 34:0.92 36:0.39 37:0.81 43:0.35 55:0.59 66:0.20 69:0.63 70:0.37 81:0.92 91:0.29 98:0.13 108:0.48 123:0.46 124:0.81 126:0.54 127:0.31 129:0.89 133:0.78 135:0.65 146:0.22 147:0.27 149:0.31 150:0.86 154:0.26 159:0.53 161:0.57 163:0.87 167:0.77 172:0.10 173:0.55 177:0.05 178:0.55 179:0.93 181:0.57 187:0.68 191:0.81 212:0.42 215:0.67 216:0.84 230:0.87 233:0.76 235:0.42 241:0.67 242:0.82 243:0.40 245:0.39 247:0.12 259:0.21 265:0.14 266:0.23 267:0.23 271:0.32 276:0.24 297:0.36 300:0.25 +1 6:0.93 7:0.81 8:0.88 12:0.20 17:0.26 20:0.83 21:0.59 25:0.88 27:0.71 28:0.92 30:0.11 32:0.80 34:0.31 36:0.34 37:0.42 43:0.36 55:0.42 66:0.61 69:0.62 70:0.54 78:0.74 81:0.87 91:0.80 98:0.36 100:0.77 104:0.73 108:0.87 111:0.74 120:0.72 123:0.86 124:0.43 126:0.54 127:0.74 129:0.59 133:0.47 135:0.51 140:0.80 145:0.61 146:0.05 147:0.05 149:0.26 150:0.73 151:0.66 152:0.79 153:0.48 154:0.27 159:0.66 161:0.57 162:0.73 164:0.86 167:0.96 169:0.75 172:0.27 173:0.54 177:0.05 178:0.42 179:0.95 181:0.57 186:0.75 189:0.94 191:0.78 195:0.80 202:0.80 212:0.30 215:0.55 216:0.54 220:0.87 230:0.87 233:0.76 235:0.68 238:0.89 241:0.89 242:0.82 243:0.23 245:0.42 247:0.12 248:0.65 256:0.86 259:0.21 260:0.73 261:0.75 265:0.38 266:0.27 267:0.28 268:0.83 271:0.32 276:0.17 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.89 8:0.75 12:0.20 17:0.45 20:0.81 21:0.13 27:0.29 28:0.92 30:0.38 34:0.59 36:0.60 37:0.92 43:0.45 55:0.59 66:0.48 69:0.44 70:0.50 78:0.74 81:0.96 91:0.61 98:0.59 100:0.77 104:0.78 106:0.81 108:0.34 111:0.74 120:0.83 123:0.32 124:0.56 126:0.54 127:0.29 129:0.59 133:0.47 135:0.47 140:0.45 146:0.62 147:0.67 149:0.52 150:0.94 151:0.72 152:0.78 153:0.77 154:0.34 159:0.37 161:0.57 162:0.86 163:0.75 164:0.72 167:0.77 169:0.75 172:0.37 173:0.45 177:0.05 179:0.70 181:0.57 186:0.75 187:0.21 189:0.90 202:0.58 206:0.81 212:0.28 215:0.84 216:0.93 235:0.12 238:0.91 241:0.68 242:0.82 243:0.60 245:0.61 247:0.12 248:0.75 256:0.64 259:0.21 260:0.84 261:0.75 265:0.60 266:0.54 267:0.82 268:0.66 271:0.32 276:0.42 285:0.62 297:0.36 300:0.33 +1 1:0.68 8:0.77 9:0.76 10:0.99 11:0.83 12:0.86 17:0.69 18:0.94 23:0.98 27:0.60 29:0.91 30:0.75 31:0.96 34:0.30 36:0.95 37:0.49 39:0.82 43:0.62 45:0.99 56:0.97 62:0.99 64:0.77 66:0.63 69:0.54 70:0.58 71:0.59 74:0.76 75:0.95 79:0.95 81:0.73 83:0.72 85:0.72 86:0.94 91:0.57 96:0.86 97:0.89 98:0.79 99:0.99 101:1.00 108:0.42 114:0.95 117:0.86 122:0.91 123:0.40 124:0.55 126:0.53 127:0.84 128:0.98 129:0.05 131:0.89 133:0.67 135:0.61 137:0.96 139:0.91 140:0.45 143:0.99 144:0.57 146:0.97 147:0.98 149:0.95 150:0.58 151:0.93 153:0.78 154:0.51 155:0.65 157:0.98 159:0.84 162:0.84 165:0.86 166:0.84 168:0.98 170:0.82 172:0.97 173:0.31 174:0.99 176:0.63 177:0.96 179:0.14 182:0.94 187:0.68 190:0.89 191:0.73 192:0.87 196:0.97 197:0.99 201:0.67 202:0.62 205:0.97 208:0.64 212:0.77 216:0.60 220:0.82 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 232:0.92 235:0.22 236:0.94 239:0.98 241:0.30 242:0.18 243:0.69 245:0.98 246:0.89 247:0.99 248:0.83 253:0.88 255:0.79 256:0.64 259:0.21 264:0.95 265:0.79 266:0.63 267:0.82 268:0.68 271:0.29 275:0.99 276:0.96 279:0.75 284:0.95 285:0.61 287:0.91 290:0.95 294:0.99 295:0.99 300:0.70 +1 1:0.68 8:0.73 9:0.75 10:0.95 11:0.54 12:0.86 17:0.91 18:0.81 23:0.96 27:0.33 29:0.91 30:0.84 31:0.94 33:0.97 34:0.94 36:1.00 37:0.45 39:0.82 43:0.63 45:0.96 56:0.97 62:0.98 64:0.77 66:0.96 69:0.51 70:0.36 71:0.59 74:0.57 75:0.97 76:0.85 79:0.94 80:0.99 81:0.73 82:0.98 83:0.72 86:0.80 88:0.99 91:0.58 96:0.83 97:0.97 98:0.91 99:0.99 101:0.87 108:0.39 114:0.95 122:0.99 123:0.37 126:0.53 127:0.52 128:0.98 129:0.05 131:0.88 135:0.73 137:0.94 139:0.81 140:0.45 143:0.99 144:0.57 145:0.47 146:0.91 147:0.92 149:0.82 150:0.58 151:0.90 153:0.69 154:0.53 155:0.65 157:0.97 158:0.77 159:0.51 162:0.86 165:0.86 166:0.84 168:0.98 170:0.82 172:0.90 173:0.48 174:0.96 175:0.75 176:0.63 177:0.88 179:0.29 182:0.94 187:0.21 190:0.89 192:0.87 193:0.96 195:0.72 196:0.94 197:0.97 201:0.67 202:0.46 204:0.81 205:0.95 208:0.64 212:0.80 216:0.35 219:0.91 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 235:0.22 236:0.94 239:0.95 241:0.12 242:0.14 243:0.96 244:0.83 246:0.89 247:0.88 248:0.79 253:0.82 255:0.78 256:0.45 257:0.65 259:0.21 264:0.95 265:0.91 266:0.54 267:0.69 268:0.55 271:0.29 275:0.99 276:0.82 277:0.69 279:0.79 281:0.91 284:0.90 285:0.61 287:0.91 290:0.95 291:0.97 292:0.95 294:0.99 295:0.99 300:0.43 +1 1:0.66 9:0.72 10:0.94 11:0.54 12:0.86 17:0.78 18:0.82 21:0.22 22:0.93 23:0.95 27:0.55 29:0.91 30:0.84 31:0.89 33:0.97 34:0.97 36:0.71 37:0.58 39:0.82 43:0.58 44:0.88 45:0.97 47:0.93 56:0.97 62:0.97 64:0.77 66:0.16 69:0.75 70:0.48 71:0.59 74:0.65 75:0.97 77:0.70 79:0.89 80:0.98 81:0.69 82:0.99 83:0.72 86:0.84 88:0.99 91:0.27 96:0.72 97:0.97 98:0.67 99:0.99 101:0.69 102:0.96 108:0.82 114:0.85 117:0.86 122:0.99 123:0.56 124:0.51 126:0.53 127:0.52 128:0.96 129:0.05 131:0.88 133:0.45 135:0.90 137:0.89 139:0.86 140:0.45 143:0.99 144:0.57 145:0.44 146:0.82 147:0.85 149:0.78 150:0.52 151:0.61 153:0.48 154:0.47 155:0.64 157:0.97 158:0.76 159:0.63 162:0.86 165:0.86 166:0.83 168:0.96 170:0.82 172:0.87 173:0.70 174:0.94 176:0.63 177:0.96 179:0.25 182:0.94 187:0.39 190:0.89 192:0.86 195:0.80 196:0.92 197:0.93 201:0.65 202:0.36 204:0.79 205:0.95 208:0.64 212:0.73 216:0.70 219:0.91 220:0.82 222:0.35 226:0.96 227:0.88 229:0.97 231:0.76 232:0.88 235:0.52 236:0.94 239:0.95 241:0.37 242:0.27 243:0.37 244:0.61 245:0.95 246:0.89 247:0.84 248:0.59 253:0.68 254:0.94 255:0.72 256:0.45 259:0.21 262:0.93 264:0.95 265:0.65 266:0.63 267:0.73 268:0.46 271:0.29 275:0.99 276:0.85 279:0.79 281:0.91 282:0.75 284:0.88 285:0.61 287:0.91 290:0.85 291:0.97 292:0.88 294:0.99 295:0.98 300:0.55 +1 10:0.94 11:0.54 12:0.86 17:0.89 18:0.81 21:0.78 27:0.72 29:0.91 30:0.61 34:0.97 36:0.89 37:0.23 39:0.82 43:0.66 44:0.88 45:0.92 48:0.91 66:0.53 69:0.10 70:0.79 71:0.59 74:0.62 76:0.85 77:0.70 80:1.00 82:0.99 83:0.72 86:0.82 88:0.99 91:0.17 98:0.46 101:0.87 102:0.96 108:0.09 122:0.91 123:0.09 124:0.65 127:0.78 129:0.05 131:0.61 133:0.65 135:0.74 139:0.84 144:0.57 145:0.59 146:0.60 147:0.66 149:0.75 154:0.55 155:0.54 157:0.92 159:0.63 166:0.61 170:0.82 172:0.71 173:0.15 174:0.94 175:0.75 177:0.88 178:0.55 179:0.48 182:0.85 187:0.21 192:0.56 193:0.98 195:0.78 197:0.96 204:0.84 208:0.64 212:0.76 216:0.26 222:0.35 227:0.61 229:0.61 231:0.72 232:0.72 235:0.31 239:0.95 241:0.35 242:0.15 243:0.62 244:0.83 245:0.81 246:0.61 247:0.91 253:0.47 257:0.65 259:0.21 264:0.95 265:0.48 266:0.71 267:0.65 271:0.29 275:0.97 276:0.68 277:0.69 281:0.91 282:0.75 287:0.61 294:0.99 300:0.84 +1 10:0.94 11:0.54 12:0.86 17:0.53 18:0.84 21:0.78 27:0.72 29:0.91 30:0.91 34:0.89 36:0.56 37:0.23 39:0.82 43:0.66 44:0.88 45:0.93 48:0.91 66:0.19 69:0.12 70:0.28 71:0.59 74:0.66 75:0.97 76:0.85 77:0.70 80:1.00 82:0.99 83:0.72 86:0.83 88:0.99 91:0.22 97:0.97 98:0.56 101:0.87 102:0.96 108:0.10 122:0.91 123:0.56 124:0.52 127:0.48 129:0.59 131:0.61 133:0.47 135:0.44 139:0.87 144:0.57 145:0.59 146:0.38 147:0.45 149:0.82 154:0.55 155:0.54 157:0.95 158:0.76 159:0.28 166:0.61 170:0.82 172:0.63 173:0.35 174:0.89 175:0.75 177:0.88 178:0.55 179:0.52 182:0.87 187:0.21 192:0.56 193:0.98 195:0.58 197:0.94 204:0.84 208:0.64 212:0.88 216:0.26 219:0.91 222:0.35 226:0.96 227:0.61 229:0.61 231:0.72 232:0.72 235:0.42 239:0.96 241:0.39 242:0.22 243:0.62 244:0.83 245:0.81 246:0.61 247:0.79 253:0.49 257:0.65 259:0.21 264:0.95 265:0.52 266:0.38 267:0.59 271:0.29 275:0.97 276:0.59 277:0.69 279:0.79 281:0.91 282:0.75 287:0.61 292:0.88 294:0.99 295:0.98 300:0.43 +1 1:0.65 10:0.92 11:0.54 12:0.86 17:0.84 18:0.73 21:0.05 27:0.72 29:0.91 30:0.76 34:0.90 36:0.63 37:0.23 39:0.82 43:0.66 44:0.88 45:0.85 48:0.91 56:0.97 64:0.77 66:0.85 69:0.12 70:0.28 71:0.59 74:0.54 76:0.85 77:0.70 80:1.00 81:0.69 82:0.99 83:0.72 86:0.75 88:0.99 91:0.40 98:0.82 99:0.95 101:0.87 102:0.96 108:0.10 114:0.95 122:0.91 123:0.10 126:0.53 127:0.32 129:0.05 131:0.61 135:0.44 139:0.79 144:0.57 145:0.59 146:0.50 147:0.56 149:0.49 150:0.55 154:0.55 155:0.56 157:0.92 159:0.18 165:0.70 166:0.76 170:0.82 172:0.57 173:0.46 174:0.86 175:0.75 176:0.70 177:0.88 178:0.55 179:0.66 182:0.84 187:0.21 192:0.86 193:0.98 195:0.55 197:0.91 201:0.66 204:0.84 208:0.64 212:0.92 215:0.91 216:0.26 222:0.35 227:0.61 229:0.61 231:0.73 232:0.72 235:0.42 239:0.93 241:0.37 242:0.22 243:0.86 244:0.83 246:0.61 247:0.67 253:0.66 257:0.65 259:0.21 264:0.95 265:0.82 266:0.17 267:0.28 271:0.29 275:0.96 276:0.42 277:0.69 281:0.91 282:0.75 287:0.61 290:0.95 294:0.99 297:0.36 300:0.25 +1 1:0.57 7:0.69 8:0.76 9:0.70 10:0.95 11:0.64 12:0.86 17:0.75 18:0.64 21:0.68 23:0.96 27:0.48 29:0.91 30:0.28 34:0.89 36:0.94 37:0.45 39:0.82 43:0.21 45:0.99 56:0.97 62:0.96 66:0.47 69:0.83 70:0.94 71:0.59 74:0.69 75:0.98 77:0.70 80:0.99 81:0.57 82:0.98 83:0.54 86:0.67 88:0.98 91:0.07 96:0.69 97:1.00 98:0.73 99:0.95 101:0.96 102:0.95 108:0.91 114:0.69 120:0.56 122:0.93 123:0.90 124:0.81 126:0.53 127:0.58 128:0.95 129:0.81 131:0.88 133:0.84 135:0.91 139:0.65 140:0.45 143:0.98 144:0.57 145:0.96 146:0.78 147:0.82 149:0.44 150:0.39 151:0.52 153:0.48 154:0.52 155:0.54 157:0.98 158:0.81 159:0.92 162:0.86 164:0.56 165:0.70 166:0.83 168:0.95 170:0.82 172:0.97 173:0.50 174:0.99 175:0.75 176:0.70 177:0.88 179:0.12 182:0.94 187:0.68 190:0.77 192:0.57 193:0.95 195:0.99 197:0.99 201:0.59 202:0.36 204:0.83 205:0.95 208:0.54 212:0.69 215:0.49 216:0.74 220:0.93 222:0.35 226:0.95 227:0.88 229:0.97 230:0.71 231:0.76 233:0.66 235:0.97 236:0.95 239:0.96 241:0.12 242:0.14 243:0.19 244:0.61 245:0.98 246:0.89 247:0.99 248:0.51 253:0.58 254:0.74 255:0.69 256:0.45 257:0.65 259:0.21 260:0.56 264:0.95 265:0.73 266:0.89 267:0.73 268:0.46 271:0.29 275:0.99 276:0.97 277:0.69 279:0.82 282:0.74 283:0.66 285:0.61 287:0.91 290:0.69 294:0.99 295:0.98 297:0.36 300:0.94 +1 1:0.66 9:0.75 10:0.98 11:0.54 12:0.86 17:0.92 18:0.97 21:0.22 22:0.93 23:0.95 27:0.42 29:0.91 30:0.89 31:0.93 33:0.97 34:0.96 36:0.67 37:0.47 39:0.82 43:0.65 44:0.88 45:1.00 47:0.93 56:0.97 62:0.99 64:0.77 66:0.45 69:0.82 70:0.40 71:0.59 74:0.88 75:0.97 77:0.70 79:0.93 80:1.00 81:0.69 82:0.99 83:0.72 86:0.97 88:0.99 91:0.22 96:0.80 97:0.97 98:0.71 99:0.99 101:0.87 102:0.96 108:0.66 114:0.85 117:0.86 122:0.91 123:0.64 124:0.77 126:0.53 127:0.82 128:0.98 129:0.59 131:0.88 133:0.78 135:0.91 137:0.93 139:0.97 140:0.45 143:0.99 144:0.57 145:0.42 146:0.92 147:0.64 149:0.95 150:0.52 151:0.85 153:0.48 154:0.37 155:0.65 157:0.98 158:0.77 159:0.79 162:0.86 165:0.86 166:0.83 168:0.98 170:0.82 172:0.96 173:0.69 174:0.99 176:0.63 177:0.88 179:0.13 182:0.94 187:0.95 190:0.89 192:0.87 193:0.98 195:0.90 196:0.97 197:0.99 201:0.65 202:0.36 204:0.85 205:0.95 208:0.64 212:0.87 216:0.72 219:0.91 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 235:0.52 236:0.94 239:0.99 241:0.03 242:0.21 243:0.21 244:0.61 245:0.98 246:0.89 247:0.98 248:0.76 253:0.87 254:0.80 255:0.78 256:0.45 257:0.65 259:0.21 262:0.93 264:0.95 265:0.71 266:0.47 267:0.82 268:0.46 271:0.29 275:0.99 276:0.96 279:0.79 281:0.91 282:0.75 284:0.88 285:0.61 287:0.91 290:0.85 291:0.97 292:0.95 294:1.00 295:0.99 300:0.70 +1 8:0.77 10:0.94 11:0.54 12:0.86 17:0.78 18:0.83 21:0.78 27:0.50 29:0.91 30:0.85 34:0.79 36:0.90 37:0.30 39:0.82 43:0.64 44:0.88 45:0.96 66:0.24 69:0.76 70:0.49 71:0.59 74:0.64 75:0.97 77:0.70 82:0.99 83:0.72 86:0.85 88:0.99 91:0.15 97:0.97 98:0.27 101:0.69 102:0.96 108:0.79 122:0.91 123:0.77 124:0.91 127:0.66 129:0.05 131:0.61 133:0.91 135:0.42 139:0.85 144:0.57 145:0.50 146:0.27 147:0.26 149:0.73 154:0.51 157:0.94 158:0.77 159:0.80 166:0.61 170:0.82 172:0.63 173:0.59 174:0.95 177:0.88 178:0.55 179:0.36 182:0.86 187:0.39 195:0.91 197:0.95 208:0.64 212:0.60 216:0.36 219:0.91 222:0.35 226:0.98 227:0.61 229:0.61 231:0.73 235:0.42 239:0.95 241:0.07 242:0.24 243:0.20 244:0.61 245:0.78 246:0.61 247:0.90 253:0.48 254:0.92 257:0.65 259:0.21 264:0.95 265:0.29 266:0.80 267:0.65 271:0.29 275:0.98 276:0.77 277:0.69 279:0.79 282:0.75 287:0.61 292:0.95 294:0.99 295:0.99 300:0.70 +1 1:0.65 8:0.58 10:0.98 11:0.54 12:0.86 17:0.40 18:0.93 21:0.05 27:0.72 29:0.91 30:0.90 34:0.67 36:0.89 37:0.23 39:0.82 43:0.66 44:0.88 45:0.98 48:0.97 56:0.97 64:0.77 66:0.64 69:0.51 70:0.28 71:0.59 74:0.75 76:0.94 77:0.96 80:1.00 81:0.69 82:0.99 83:0.72 86:0.92 88:0.99 91:0.72 98:0.77 99:0.95 101:0.87 102:0.96 108:0.39 114:0.95 122:0.91 123:0.38 124:0.48 126:0.53 127:0.56 129:0.59 131:0.61 133:0.47 135:0.24 139:0.92 144:0.57 145:0.98 146:0.69 147:0.74 149:0.92 150:0.55 154:0.55 155:0.55 157:0.97 159:0.38 165:0.70 166:0.76 170:0.82 172:0.87 173:0.60 174:0.95 175:0.75 176:0.70 177:0.88 178:0.55 179:0.25 182:0.92 192:0.85 193:0.98 195:0.70 197:0.98 201:0.66 202:0.74 204:0.83 208:0.64 212:0.93 215:0.91 216:0.26 222:0.35 227:0.61 229:0.61 231:0.74 232:0.72 235:0.95 239:0.98 241:0.86 242:0.24 243:0.69 244:0.83 245:0.93 246:0.61 247:0.82 253:0.70 257:0.65 259:0.21 264:0.95 265:0.77 266:0.27 267:0.19 271:0.29 275:0.99 276:0.83 277:0.69 281:0.91 282:0.75 287:0.61 290:0.95 294:1.00 297:0.36 300:0.43 +1 1:0.60 9:0.73 10:0.97 11:0.54 12:0.86 17:0.81 18:0.96 21:0.64 22:0.93 23:0.95 27:0.52 29:0.91 30:0.88 31:0.90 33:0.97 34:0.99 36:0.76 37:0.60 39:0.82 43:0.64 44:0.88 45:0.99 47:0.93 56:0.97 62:0.98 64:0.77 66:0.54 69:0.48 70:0.45 71:0.59 74:0.85 75:0.97 77:0.96 79:0.90 81:0.63 82:0.99 83:0.72 86:0.96 88:0.98 91:0.25 96:0.74 97:0.97 98:0.69 99:0.99 101:0.87 102:0.96 108:0.37 114:0.69 117:0.86 122:0.91 123:0.35 124:0.70 126:0.53 127:0.77 128:0.96 129:0.05 131:0.88 133:0.74 135:0.95 137:0.90 139:0.95 140:0.45 143:0.99 144:0.57 145:0.87 146:0.86 147:0.88 149:0.91 150:0.45 151:0.66 153:0.48 154:0.57 155:0.64 157:0.97 158:0.77 159:0.70 162:0.62 165:0.86 166:0.83 168:0.96 170:0.82 172:0.94 173:0.35 174:0.98 176:0.63 177:0.96 179:0.17 182:0.94 187:0.21 190:0.89 192:0.87 195:0.82 196:0.94 197:0.99 201:0.60 202:0.50 205:0.95 208:0.64 212:0.84 216:0.48 219:0.91 220:0.74 222:0.35 226:0.98 227:0.88 229:0.97 231:0.76 232:0.81 235:0.95 236:0.94 239:0.98 241:0.02 242:0.21 243:0.63 244:0.61 245:0.96 246:0.89 247:0.98 248:0.63 251:1.00 253:0.77 254:0.80 255:0.73 256:0.45 259:0.21 262:0.93 264:0.95 265:0.69 266:0.71 267:0.92 268:0.46 271:0.29 275:0.99 276:0.93 279:0.79 282:0.75 284:0.88 285:0.61 287:0.91 290:0.69 291:0.97 292:0.95 294:0.99 295:0.99 300:0.84 +1 6:0.89 7:0.81 8:0.84 12:0.37 17:0.67 20:0.90 21:0.13 27:0.43 28:0.62 30:0.66 32:0.80 34:0.58 36:0.27 37:0.98 43:0.35 55:0.99 66:0.07 69:0.63 70:0.36 78:0.81 81:0.99 91:0.29 98:0.06 100:0.81 106:0.81 108:0.48 111:0.79 120:0.76 123:0.46 124:0.95 126:0.54 127:0.99 129:0.99 133:0.95 135:0.77 140:0.45 145:0.59 146:0.22 147:0.28 149:0.27 150:0.99 151:0.73 152:0.91 153:0.80 154:0.26 159:0.99 161:0.60 162:0.79 163:0.90 164:0.71 167:0.64 169:0.78 172:0.10 173:0.09 177:0.05 179:0.82 181:0.84 186:0.81 187:0.87 189:0.91 191:0.84 195:1.00 202:0.60 206:0.81 212:0.13 215:0.84 216:0.64 230:0.87 233:0.76 235:0.22 238:0.89 241:0.43 242:0.82 243:0.23 245:0.46 247:0.12 248:0.68 256:0.58 259:0.21 260:0.77 261:0.82 265:0.06 266:0.80 267:0.69 268:0.65 271:0.23 276:0.47 283:0.82 285:0.62 297:0.36 300:0.33 +1 6:0.79 8:0.58 12:0.37 17:0.73 20:0.92 25:0.88 27:0.12 28:0.62 30:0.94 34:0.39 36:0.36 37:0.23 43:0.48 55:0.59 66:0.69 69:0.30 70:0.18 78:0.85 81:1.00 91:0.84 98:0.70 100:0.84 104:0.58 108:0.62 111:0.84 120:0.83 123:0.59 124:0.41 126:0.54 127:0.80 129:0.59 133:0.47 135:0.49 140:0.45 146:0.05 147:0.05 149:0.44 150:1.00 151:0.79 152:0.86 153:0.90 154:0.37 159:0.35 161:0.60 162:0.79 164:0.85 167:0.93 169:0.83 172:0.41 173:0.46 177:0.05 179:0.89 181:0.84 186:0.84 189:0.81 191:0.80 202:0.77 212:0.71 215:0.96 216:0.34 235:0.05 238:0.88 241:0.97 242:0.82 243:0.18 245:0.46 247:0.12 248:0.75 256:0.79 259:0.21 260:0.84 261:0.86 265:0.71 266:0.27 267:0.23 268:0.81 271:0.23 276:0.32 283:0.60 285:0.62 297:0.36 300:0.18 +2 8:0.93 12:0.37 17:0.92 20:0.75 21:0.30 27:0.56 28:0.62 30:0.76 34:0.45 36:0.86 37:0.97 43:0.41 55:0.92 66:0.36 69:0.53 70:0.15 78:0.79 81:0.98 91:0.85 98:0.58 100:0.73 108:0.41 111:0.77 120:0.62 123:0.39 124:0.52 126:0.54 127:0.52 129:0.59 133:0.47 135:0.37 140:0.45 146:0.40 147:0.46 149:0.26 150:0.97 151:0.56 152:0.74 153:0.78 154:0.31 159:0.26 161:0.60 162:0.81 163:0.96 164:0.73 167:0.90 169:0.72 172:0.10 173:0.73 177:0.05 179:0.99 181:0.84 186:0.79 191:0.79 202:0.62 212:0.95 215:0.72 216:0.07 220:0.62 235:0.42 241:0.83 242:0.82 243:0.51 245:0.37 247:0.12 248:0.56 256:0.64 259:0.21 260:0.62 261:0.74 265:0.59 266:0.12 267:0.65 268:0.67 271:0.23 276:0.18 285:0.62 297:0.36 300:0.13 +3 6:0.84 7:0.81 8:0.68 12:0.37 17:0.28 20:0.80 21:0.30 27:0.21 28:0.62 30:0.14 34:0.69 36:0.91 37:0.74 43:0.52 55:0.96 66:0.11 69:0.10 70:0.97 78:0.80 81:0.98 91:0.51 98:0.22 100:0.82 104:0.84 106:0.81 108:0.99 111:0.79 120:0.81 123:0.98 124:0.97 126:0.54 127:0.92 129:1.00 133:0.97 135:0.39 140:0.45 146:0.05 147:0.05 149:0.48 150:0.97 151:0.78 152:0.93 153:0.89 154:0.41 159:0.96 161:0.60 162:0.56 164:0.81 167:0.56 169:0.78 172:0.51 173:0.05 177:0.05 179:0.39 181:0.84 186:0.80 187:0.39 189:0.84 191:0.76 202:0.78 206:0.81 212:0.23 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.89 241:0.12 242:0.82 243:0.07 245:0.68 247:0.12 248:0.73 256:0.73 259:0.21 260:0.82 261:0.82 265:0.16 266:0.98 267:0.84 268:0.76 271:0.23 276:0.76 283:0.82 285:0.62 297:0.36 300:0.94 +2 6:0.89 7:0.81 8:0.63 12:0.37 17:0.30 20:0.86 21:0.22 27:0.43 28:0.62 30:0.21 32:0.80 34:0.20 36:0.62 37:0.98 43:0.35 55:0.45 66:0.36 69:0.63 70:0.37 78:0.75 81:0.98 91:0.62 98:0.30 100:0.78 106:0.81 108:0.82 111:0.74 120:0.71 123:0.81 124:0.57 126:0.54 127:0.43 129:0.59 133:0.47 135:0.99 140:0.45 145:0.65 146:0.22 147:0.28 149:0.29 150:0.98 151:0.66 152:0.91 153:0.48 154:0.26 159:0.53 161:0.60 162:0.86 163:0.90 164:0.73 167:0.58 169:0.78 172:0.16 173:0.59 177:0.05 179:0.95 181:0.84 186:0.76 187:0.87 189:0.85 195:0.78 202:0.58 206:0.81 212:0.42 215:0.79 216:0.64 220:0.82 230:0.87 233:0.76 235:0.31 238:0.84 241:0.21 242:0.82 243:0.40 245:0.43 247:0.12 248:0.64 256:0.64 259:0.21 260:0.72 261:0.83 265:0.32 266:0.23 267:0.76 268:0.66 271:0.23 276:0.16 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.94 8:0.89 12:0.37 17:0.84 20:0.96 27:0.08 28:0.62 34:0.63 36:0.51 37:0.85 78:0.91 81:1.00 91:0.84 100:0.95 111:0.92 120:0.95 126:0.54 135:0.47 140:0.45 150:1.00 151:0.78 152:0.96 153:0.89 161:0.60 162:0.59 164:0.91 167:0.76 169:0.90 175:0.75 181:0.84 186:0.91 187:0.39 189:0.96 191:0.95 202:0.88 215:0.96 216:0.27 235:0.02 238:0.97 241:0.69 244:0.61 248:0.92 256:0.89 257:0.65 259:0.21 260:0.95 261:0.93 267:0.18 268:0.89 271:0.23 277:0.69 283:0.82 285:0.62 297:0.36 +2 6:0.97 8:0.82 12:0.37 17:0.30 20:0.93 21:0.78 27:0.05 28:0.62 30:0.76 34:0.77 36:0.85 37:0.94 43:0.30 55:0.59 66:0.64 69:0.72 70:0.15 78:0.77 91:0.68 98:0.53 100:0.80 108:0.56 111:0.77 120:0.83 123:0.53 127:0.16 129:0.05 135:0.49 140:0.87 146:0.44 147:0.51 149:0.24 151:0.71 152:0.96 153:0.72 154:0.22 159:0.16 161:0.60 162:0.56 163:0.98 164:0.73 167:0.64 169:0.98 172:0.16 173:0.85 177:0.05 178:0.48 179:0.88 181:0.84 186:0.78 187:0.21 189:0.93 191:0.92 202:0.69 212:0.95 216:0.64 235:0.02 238:0.93 241:0.44 242:0.82 243:0.69 247:0.12 248:0.75 256:0.64 259:0.21 260:0.84 261:0.98 265:0.55 266:0.12 267:0.91 268:0.66 271:0.23 276:0.16 285:0.62 300:0.13 +3 6:0.89 8:0.78 12:0.37 17:0.30 20:0.83 27:0.09 28:0.62 30:0.28 34:0.78 36:0.40 37:0.82 43:0.35 55:0.84 66:0.23 69:0.62 70:0.47 78:0.80 81:1.00 91:0.38 98:0.53 100:0.83 104:0.91 106:0.81 108:0.47 111:0.80 120:0.80 123:0.45 124:0.85 126:0.54 127:0.54 129:0.93 133:0.84 135:0.98 140:0.45 145:0.61 146:0.76 147:0.79 149:0.51 150:1.00 151:0.71 152:0.79 153:0.72 154:0.26 159:0.68 161:0.60 162:0.86 163:0.96 164:0.73 167:0.67 169:0.81 172:0.32 173:0.50 177:0.05 179:0.69 181:0.84 186:0.80 187:0.39 189:0.91 191:0.76 202:0.58 206:0.81 212:0.16 215:0.96 216:0.94 235:0.02 238:0.93 241:0.51 242:0.82 243:0.43 245:0.59 247:0.12 248:0.72 256:0.64 259:0.21 260:0.81 261:0.79 265:0.55 266:0.80 267:0.52 268:0.66 271:0.23 276:0.53 283:0.82 285:0.62 297:0.36 300:0.33 +2 6:0.97 8:0.92 12:0.37 17:0.30 20:0.79 27:0.05 28:0.62 30:0.95 34:0.73 36:0.87 37:0.94 43:0.30 55:0.92 66:0.20 69:0.72 78:0.85 81:1.00 91:0.56 98:0.09 100:0.92 108:0.56 111:0.87 123:0.53 124:0.61 126:0.54 127:0.17 129:0.59 133:0.47 135:0.60 146:0.05 147:0.05 149:0.19 150:1.00 152:0.98 154:0.22 159:0.17 161:0.60 167:0.58 169:0.98 172:0.05 173:0.85 177:0.05 178:0.55 179:0.97 181:0.84 186:0.85 187:0.21 189:0.96 191:0.98 202:0.36 215:0.96 216:0.64 235:0.02 238:0.97 241:0.21 243:0.40 245:0.36 259:0.21 261:0.98 265:0.10 267:0.84 271:0.23 283:0.82 297:0.36 300:0.08 +3 6:0.95 8:0.89 12:0.37 17:0.82 20:0.82 27:0.08 28:0.62 34:0.63 36:0.51 37:0.85 78:0.84 81:1.00 91:0.76 100:0.92 111:0.86 120:0.88 126:0.54 135:0.44 140:0.45 150:1.00 151:0.74 152:0.97 153:0.84 161:0.60 162:0.53 164:0.84 167:0.74 169:0.88 175:0.75 181:0.84 186:0.85 187:0.68 189:0.96 191:0.98 202:0.83 215:0.96 216:0.27 235:0.02 238:0.97 241:0.67 244:0.61 248:0.83 256:0.79 257:0.65 259:0.21 260:0.89 261:0.92 267:0.19 268:0.80 271:0.23 277:0.69 285:0.62 297:0.36 +1 12:0.37 17:0.55 21:0.64 27:0.45 28:0.62 30:0.45 34:0.90 36:0.23 37:0.50 43:0.32 55:0.52 66:0.49 69:0.70 70:0.25 81:0.98 91:0.11 98:0.34 108:0.53 123:0.51 124:0.48 126:0.54 127:0.32 129:0.59 133:0.47 135:0.60 145:0.50 146:0.44 147:0.50 149:0.28 150:0.97 154:0.24 159:0.45 161:0.60 163:0.87 167:0.55 172:0.16 173:0.69 177:0.05 178:0.55 179:0.96 181:0.84 187:0.68 212:0.61 215:0.53 216:0.77 235:0.88 241:0.10 242:0.82 243:0.60 245:0.39 247:0.12 259:0.21 265:0.36 266:0.17 267:0.36 271:0.23 276:0.16 297:0.36 300:0.18 +1 6:0.84 7:0.81 8:0.65 12:0.37 17:0.18 20:0.95 21:0.30 27:0.21 28:0.62 30:0.91 34:0.66 36:0.61 37:0.74 43:0.52 55:0.59 66:0.69 69:0.12 70:0.13 78:0.78 81:0.98 91:0.51 98:0.39 100:0.81 106:0.81 108:0.10 111:0.78 120:0.75 123:0.10 126:0.54 127:0.13 129:0.05 135:0.28 140:0.45 146:0.32 147:0.38 149:0.35 150:0.97 151:0.73 152:0.93 153:0.78 154:0.41 159:0.13 161:0.60 162:0.60 164:0.74 167:0.67 169:0.78 172:0.21 173:0.35 177:0.05 179:0.62 181:0.84 186:0.79 187:0.39 189:0.86 191:0.76 202:0.68 206:0.81 212:0.95 215:0.72 216:0.95 230:0.87 233:0.76 235:0.42 238:0.89 241:0.52 242:0.82 243:0.73 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 261:0.82 265:0.41 266:0.12 267:0.97 268:0.68 271:0.23 276:0.23 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.82 7:0.81 8:0.64 12:0.37 17:0.30 20:0.89 27:0.34 28:0.62 30:0.40 32:0.80 34:0.70 36:0.90 37:0.36 43:0.39 55:0.71 66:0.59 69:0.54 70:0.76 78:0.78 81:1.00 91:0.42 98:0.35 100:0.79 104:0.80 106:0.81 108:0.94 111:0.77 120:0.78 123:0.94 124:0.67 126:0.54 127:0.51 129:0.89 133:0.78 135:0.98 140:0.45 145:1.00 146:0.38 147:0.45 149:0.43 150:1.00 151:0.74 152:0.84 153:0.83 154:0.30 159:0.87 161:0.60 162:0.82 164:0.74 167:0.55 169:0.77 172:0.65 173:0.24 177:0.05 179:0.53 181:0.84 186:0.79 187:0.21 189:0.84 191:0.70 195:0.97 202:0.63 206:0.81 212:0.49 215:0.96 216:0.84 230:0.87 233:0.76 235:0.05 238:0.86 241:0.07 242:0.82 243:0.15 245:0.67 247:0.12 248:0.70 256:0.64 259:0.21 260:0.79 261:0.80 265:0.37 266:0.80 267:0.95 268:0.68 271:0.23 276:0.53 283:0.82 285:0.62 297:0.36 300:0.70 +4 6:0.97 8:0.95 12:0.37 17:0.03 20:0.97 27:0.05 28:0.62 30:0.76 34:0.23 36:0.94 37:0.94 43:0.30 55:0.71 66:0.20 69:0.73 70:0.22 78:0.99 81:1.00 91:0.97 98:0.20 100:0.99 108:0.80 111:0.99 120:0.99 123:0.79 124:0.73 126:0.54 127:0.54 129:0.81 133:0.67 135:0.37 140:0.45 146:0.05 147:0.05 149:0.25 150:1.00 151:0.86 152:0.96 153:0.99 154:0.22 159:0.57 161:0.60 162:0.53 163:0.88 164:0.98 167:0.91 169:0.98 172:0.10 173:0.70 177:0.05 179:0.96 181:0.84 186:0.98 189:0.98 191:0.99 202:0.98 212:0.42 215:0.96 216:0.64 235:0.02 238:0.99 241:0.84 242:0.82 243:0.26 245:0.40 247:0.12 248:0.98 256:0.98 259:0.21 260:0.99 261:0.98 265:0.22 266:0.23 267:0.97 268:0.97 271:0.23 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +1 8:0.73 12:0.37 17:0.47 27:0.05 28:0.62 34:0.52 36:0.86 37:0.94 43:0.31 66:0.36 69:0.71 81:1.00 91:0.58 98:0.09 108:0.54 120:0.78 123:0.52 126:0.54 127:0.06 129:0.05 135:0.42 140:0.45 146:0.05 147:0.05 149:0.13 150:1.00 151:0.66 153:0.48 154:0.23 159:0.05 161:0.60 162:0.74 164:0.67 167:0.61 172:0.05 173:0.81 177:0.05 181:0.84 187:0.21 191:0.99 202:0.56 215:0.96 216:0.64 235:0.02 241:0.35 243:0.51 248:0.70 256:0.58 259:0.21 260:0.79 265:0.09 267:0.89 268:0.59 271:0.23 285:0.62 297:0.36 +1 1:0.74 11:0.85 12:0.06 17:0.57 18:0.96 21:0.54 27:0.45 28:0.70 29:0.94 30:0.15 34:0.89 36:0.21 37:0.50 39:0.86 43:0.82 45:0.85 46:0.93 48:0.91 64:0.77 66:0.48 69:0.61 70:0.94 71:0.93 74:0.44 81:0.49 83:0.91 85:0.72 86:0.75 91:0.18 98:0.47 101:0.97 107:0.91 108:0.85 110:0.91 114:0.59 122:0.86 123:0.84 124:0.76 125:0.88 126:0.54 127:0.49 129:0.05 131:0.61 133:0.73 135:0.90 139:0.73 144:0.57 145:0.50 146:0.58 147:0.63 149:0.75 150:0.72 154:0.77 155:0.67 157:0.83 159:0.65 160:0.88 161:0.74 165:0.93 166:0.91 167:0.66 172:0.73 173:0.50 176:0.73 177:0.70 178:0.55 179:0.37 181:0.84 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.67 215:0.39 216:0.77 222:0.35 227:0.61 229:0.61 231:0.86 235:0.74 241:0.37 242:0.70 243:0.28 245:0.81 246:0.96 247:0.60 253:0.42 259:0.21 265:0.49 266:0.87 267:0.28 271:0.39 276:0.75 277:0.69 281:0.91 287:0.61 289:0.93 290:0.59 297:0.36 300:0.70 +1 6:0.91 8:0.93 9:0.80 11:0.75 12:0.06 17:0.75 18:0.86 20:0.98 21:0.78 27:0.19 28:0.70 29:0.94 30:0.41 31:0.97 34:0.74 36:0.65 37:0.95 39:0.86 41:0.93 43:0.12 45:0.66 46:0.88 55:0.42 66:0.86 69:0.58 70:0.60 71:0.93 74:0.52 78:0.82 79:0.98 83:0.91 86:0.82 91:0.87 96:0.94 98:0.86 100:0.86 101:0.52 104:0.58 107:0.92 108:0.45 110:0.93 111:0.82 120:0.93 122:0.57 123:0.43 125:0.97 127:0.37 129:0.05 131:0.95 135:0.23 137:0.97 138:0.97 139:0.78 140:0.95 144:0.57 146:0.62 147:0.67 149:0.06 151:0.87 152:0.93 153:0.92 154:0.78 155:0.67 157:0.61 159:0.23 160:0.98 161:0.74 162:0.77 164:0.82 166:0.61 167:0.96 169:0.83 172:0.59 173:0.79 177:0.70 179:0.67 181:0.84 182:0.99 186:0.82 189:0.92 191:0.87 192:0.87 196:0.95 202:0.84 208:0.64 212:0.54 216:0.38 220:0.62 222:0.35 227:0.94 229:0.99 231:0.87 232:0.76 235:0.05 238:0.91 241:0.94 242:0.19 243:0.86 246:0.61 247:0.74 248:0.89 253:0.87 254:0.80 255:0.95 256:0.86 259:0.21 260:0.85 261:0.88 265:0.86 266:0.27 267:0.65 268:0.87 271:0.39 276:0.47 284:0.97 285:0.62 287:0.97 289:0.93 300:0.43 +1 6:0.96 8:0.91 9:0.80 12:0.06 17:0.32 20:0.92 21:0.78 27:0.15 28:0.70 29:0.94 31:0.98 34:0.64 36:0.27 37:0.94 39:0.86 41:0.95 46:0.88 60:0.87 71:0.93 74:0.47 78:0.86 79:0.99 83:0.91 91:0.93 96:0.93 100:0.93 104:0.58 107:0.88 110:0.88 111:0.88 120:0.93 125:0.96 131:0.95 135:0.80 137:0.98 138:0.98 139:0.74 140:0.94 144:0.57 151:0.94 152:0.94 153:0.89 155:0.67 157:0.61 158:0.92 160:0.99 161:0.74 162:0.78 164:0.86 166:0.61 167:0.96 169:0.92 175:0.97 181:0.84 182:0.99 186:0.86 189:0.96 191:0.95 192:0.87 196:0.94 202:0.87 208:0.64 216:0.47 219:0.95 220:0.87 222:0.35 227:0.94 229:0.99 231:0.87 238:0.94 241:0.94 244:0.93 246:0.61 248:0.93 253:0.89 255:0.95 256:0.90 257:0.93 259:0.21 260:0.88 261:0.95 267:0.36 268:0.91 271:0.39 277:0.95 279:0.86 283:0.82 284:0.98 285:0.62 287:0.97 289:0.93 292:0.95 +1 9:0.80 12:0.06 17:0.64 21:0.78 25:0.88 27:0.72 28:0.70 29:0.94 31:0.91 34:0.53 36:0.25 37:0.73 39:0.86 41:0.82 46:0.88 71:0.93 74:0.28 79:0.94 83:0.91 87:0.98 91:0.80 96:0.82 97:0.89 104:0.79 107:0.88 110:0.88 120:0.59 125:0.98 131:0.95 135:0.82 137:0.91 139:0.58 140:0.80 144:0.57 151:0.61 153:0.48 155:0.67 157:0.61 158:0.72 160:0.96 161:0.74 162:0.86 164:0.57 166:0.61 167:0.94 175:0.97 181:0.84 182:0.98 190:0.77 191:0.76 192:0.87 196:0.87 202:0.50 208:0.64 216:0.20 219:0.87 222:0.35 227:0.94 228:0.99 229:0.99 231:0.87 232:0.70 241:0.85 244:0.93 246:0.61 248:0.59 253:0.56 255:0.95 256:0.58 257:0.93 259:0.21 260:0.60 267:0.59 268:0.59 271:0.39 277:0.95 279:0.82 284:0.91 285:0.62 287:0.97 289:0.93 292:0.95 +1 1:0.74 6:1.00 7:0.81 8:0.89 9:0.80 11:0.85 12:0.06 17:0.77 18:0.98 20:0.80 21:0.22 22:0.93 27:0.49 28:0.70 29:0.94 30:0.14 31:0.98 32:0.80 34:0.28 36:0.33 37:0.68 39:0.86 41:0.94 43:0.86 44:0.93 45:0.85 46:0.93 47:0.93 55:0.71 60:0.87 64:0.77 66:0.09 69:0.56 70:0.97 71:0.93 74:0.81 77:0.70 78:0.78 79:0.98 81:0.66 83:0.91 85:0.90 86:0.92 91:0.18 96:0.91 98:0.36 100:0.84 101:0.97 104:0.94 106:0.81 107:0.94 108:0.85 110:0.95 111:0.79 114:0.71 117:0.86 120:0.84 121:0.90 123:0.84 124:0.98 125:0.98 126:0.54 127:0.85 129:0.99 131:0.95 133:0.98 135:0.93 137:0.98 138:0.97 139:0.96 140:0.45 144:0.57 145:0.42 146:0.60 147:0.65 149:0.88 150:0.80 151:0.94 152:0.77 153:0.82 154:0.83 155:0.67 157:0.90 158:0.92 159:0.97 160:0.98 161:0.74 162:0.85 164:0.83 165:0.93 166:0.91 167:0.56 169:0.82 172:0.87 173:0.10 176:0.73 177:0.70 179:0.11 181:0.84 182:0.99 186:0.79 187:0.39 189:1.00 192:0.87 195:1.00 196:0.99 201:0.93 202:0.74 204:0.89 206:0.81 208:0.64 212:0.30 215:0.51 216:0.67 219:0.95 220:0.82 222:0.35 227:0.94 229:1.00 230:0.87 231:0.87 232:0.95 233:0.76 235:0.42 238:0.96 241:0.02 242:0.33 243:0.19 245:0.97 246:0.96 247:0.94 248:0.91 253:0.92 255:0.95 256:0.80 259:0.21 260:0.85 261:0.79 262:0.93 265:0.38 266:0.96 267:0.88 268:0.81 271:0.39 276:0.98 279:0.86 281:0.91 282:0.88 283:0.82 284:0.97 285:0.62 287:0.97 289:0.93 290:0.71 292:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 9:0.80 11:0.64 12:0.06 17:0.67 18:0.79 21:0.05 22:0.93 25:0.88 27:0.72 28:0.70 29:0.94 30:0.45 31:0.91 34:0.90 36:0.81 39:0.86 41:0.82 43:0.72 45:0.66 46:0.88 47:0.93 64:0.77 66:0.69 69:0.10 70:0.25 71:0.93 74:0.28 79:0.90 81:0.81 83:0.91 86:0.59 91:0.25 96:0.75 98:0.79 101:0.52 104:0.58 107:0.89 108:0.09 110:0.89 114:0.89 120:0.74 122:0.82 123:0.09 125:0.99 126:0.54 127:0.28 129:0.05 131:0.95 135:0.79 137:0.91 139:0.58 140:0.80 144:0.57 146:0.48 147:0.54 149:0.38 150:0.88 151:0.72 153:0.48 154:0.62 155:0.67 157:0.61 159:0.17 160:0.96 161:0.74 162:0.86 164:0.62 165:0.93 166:0.91 167:0.78 172:0.21 173:0.43 175:0.75 176:0.73 177:0.38 179:0.95 181:0.84 182:0.98 187:0.87 190:0.89 192:0.87 196:0.87 201:0.93 202:0.50 208:0.64 212:0.61 215:0.78 216:0.39 222:0.35 227:0.94 229:0.99 231:0.87 235:0.22 241:0.69 242:0.63 243:0.73 244:0.61 246:0.96 247:0.30 248:0.67 253:0.69 255:0.95 256:0.58 257:0.65 259:0.21 260:0.65 262:0.93 265:0.79 266:0.17 267:0.28 268:0.59 271:0.39 276:0.16 277:0.69 284:0.89 285:0.62 287:0.97 289:0.93 290:0.89 297:0.36 300:0.18 +1 1:0.69 6:0.93 8:0.84 9:0.80 11:0.75 12:0.06 17:0.84 18:0.99 20:0.75 21:0.59 22:0.93 25:0.88 27:0.11 28:0.70 29:0.94 30:0.45 31:0.98 32:0.80 34:0.87 36:0.60 37:0.86 39:0.86 41:0.94 43:0.65 44:0.93 45:0.90 46:0.88 47:0.93 48:0.98 55:0.45 64:0.77 66:0.97 69:0.27 70:0.78 71:0.93 74:0.55 77:0.96 78:0.88 79:0.98 81:0.42 83:0.91 86:0.82 91:0.62 96:0.94 97:0.89 98:0.98 99:0.83 100:0.73 101:0.52 104:0.58 107:0.94 108:0.21 110:0.95 111:0.88 114:0.56 117:0.86 120:0.90 122:0.86 123:0.20 125:0.97 126:0.54 127:0.79 129:0.05 131:0.95 135:0.24 137:0.98 138:0.97 139:0.85 140:0.90 144:0.57 145:0.77 146:0.92 147:0.93 149:0.79 150:0.59 151:0.96 152:0.91 153:0.88 154:0.77 155:0.67 157:0.61 158:0.72 159:0.53 160:0.99 161:0.74 162:0.86 164:0.83 165:0.70 166:0.91 167:0.76 169:0.96 172:0.92 173:0.29 176:0.55 177:0.70 179:0.28 181:0.84 182:0.99 186:0.83 187:0.21 191:0.95 192:0.87 195:0.69 196:0.97 201:0.74 202:0.83 204:0.85 208:0.64 212:0.83 215:0.39 216:0.75 219:0.87 220:0.62 222:0.35 227:0.94 229:0.99 231:0.87 232:0.80 235:0.88 241:0.67 242:0.30 243:0.97 246:0.61 247:0.90 248:0.92 253:0.90 254:0.86 255:0.95 256:0.87 259:0.21 260:0.86 261:0.97 262:0.93 265:0.98 266:0.27 267:0.73 268:0.89 271:0.39 276:0.85 279:0.82 281:0.88 282:0.88 284:0.98 285:0.62 287:0.97 289:0.93 290:0.56 292:0.95 297:0.36 300:0.70 +1 1:0.69 6:0.93 8:0.90 9:0.80 11:0.75 12:0.06 17:0.15 18:0.94 20:0.98 21:0.59 25:0.88 27:0.11 28:0.70 29:0.94 30:0.17 31:0.99 32:0.63 34:0.73 36:0.49 37:0.86 39:0.86 41:0.94 43:0.64 44:0.85 45:0.81 46:0.88 48:0.98 55:0.71 64:0.77 66:0.45 69:0.27 70:0.93 71:0.93 74:0.39 77:0.70 78:0.91 79:1.00 81:0.42 83:0.91 86:0.72 91:0.98 96:0.95 98:0.73 99:0.83 100:0.98 101:0.52 104:0.58 107:0.94 108:0.21 110:0.95 111:0.96 114:0.56 120:0.99 122:0.86 123:0.20 124:0.59 125:0.96 126:0.54 127:0.97 129:0.59 131:0.95 133:0.46 135:0.37 137:0.99 138:1.00 139:0.70 140:0.94 144:0.57 145:0.83 146:0.73 147:0.77 149:0.57 150:0.59 151:0.93 152:0.91 153:0.99 154:0.78 155:0.67 157:0.61 159:0.50 160:0.98 161:0.74 162:0.73 164:0.93 165:0.70 166:0.91 167:0.96 169:0.96 172:0.67 173:0.32 176:0.55 177:0.70 179:0.50 181:0.84 182:0.96 186:0.91 189:0.94 191:0.95 192:0.87 195:0.66 196:0.93 201:0.74 202:0.98 204:0.85 208:0.64 212:0.71 215:0.39 216:0.75 219:0.86 222:0.35 227:0.94 229:0.98 231:0.87 235:0.88 238:0.97 241:0.93 242:0.24 243:0.58 245:0.88 246:0.61 247:0.88 248:0.92 253:0.89 254:0.86 255:0.95 256:0.99 259:0.21 260:0.92 261:0.97 265:0.73 266:0.63 267:0.82 268:0.99 271:0.39 276:0.71 281:0.89 282:0.70 283:0.82 284:0.99 285:0.62 287:0.96 289:0.93 290:0.56 292:0.95 297:0.36 300:0.70 +1 6:0.99 8:0.96 9:0.80 12:0.06 17:0.73 20:0.99 21:0.78 27:0.47 28:0.83 30:0.11 34:0.21 36:0.49 37:0.91 39:0.69 41:0.90 43:0.20 66:0.29 69:0.39 70:0.84 74:0.49 78:0.82 83:0.80 91:0.84 96:0.91 98:0.24 100:0.93 104:0.58 108:0.86 111:0.86 120:0.94 123:0.86 124:0.72 127:0.63 129:0.59 133:0.64 135:0.18 140:0.96 145:0.79 146:0.23 147:0.29 149:0.19 151:0.93 152:0.91 153:0.78 154:0.82 155:0.67 159:0.69 161:0.71 162:0.56 164:0.91 167:0.97 169:0.91 172:0.27 173:0.27 177:0.38 178:0.52 179:0.83 181:0.71 186:0.82 189:0.99 191:0.91 192:0.59 202:0.89 208:0.64 212:0.57 216:0.36 220:0.62 222:0.95 235:0.97 238:0.98 241:0.91 242:0.71 243:0.40 245:0.56 247:0.39 248:0.90 253:0.87 254:0.86 255:0.79 256:0.89 259:0.21 260:0.94 261:0.90 265:0.26 266:0.47 267:0.73 268:0.89 271:0.40 276:0.25 283:0.82 285:0.62 300:0.55 +2 6:0.99 8:0.98 9:0.80 12:0.06 17:0.01 20:0.97 21:0.59 25:0.88 27:0.08 28:0.83 30:0.09 32:0.75 34:0.33 36:0.86 37:0.87 39:0.69 41:0.82 43:0.38 55:0.52 66:0.24 69:0.33 70:0.98 74:0.48 78:0.84 81:0.44 83:0.73 91:0.99 96:0.75 98:0.34 100:0.93 104:0.58 108:0.94 111:0.87 120:1.00 123:0.94 124:0.90 126:0.32 127:0.99 129:0.59 133:0.89 135:0.54 140:1.00 145:0.69 146:0.49 147:0.55 149:0.44 150:0.36 151:0.63 152:0.91 153:1.00 154:0.82 155:0.67 159:0.86 161:0.71 162:0.60 164:0.99 167:0.96 169:0.91 172:0.45 173:0.15 177:0.38 179:0.61 181:0.71 186:0.84 189:0.99 191:0.98 192:0.59 195:0.94 202:0.99 208:0.64 212:0.29 215:0.55 216:0.83 222:0.95 235:0.68 238:0.97 241:0.89 242:0.79 243:0.33 245:0.65 247:0.39 248:0.65 253:0.58 254:0.86 255:0.79 256:0.99 259:0.21 260:1.00 261:0.91 265:0.36 266:0.91 267:1.00 268:0.99 271:0.40 276:0.62 283:0.82 285:0.62 297:0.36 300:0.84 +3 6:0.98 8:0.96 9:0.80 12:0.06 17:0.82 20:0.97 21:0.59 27:0.11 28:0.83 30:0.62 34:0.42 36:0.35 37:0.94 39:0.69 41:0.95 43:0.28 55:0.52 66:0.30 69:0.33 70:0.34 74:0.66 78:0.93 81:0.33 83:0.85 91:0.92 96:0.97 98:0.14 100:0.99 104:0.58 108:0.85 111:0.98 114:0.64 120:0.94 122:0.51 123:0.85 124:0.71 126:0.32 127:0.89 129:0.59 133:0.64 135:0.42 140:0.80 146:0.20 147:0.25 149:0.12 150:0.30 151:0.99 152:0.98 153:0.98 154:0.80 155:0.67 158:0.85 159:0.61 161:0.71 162:0.64 163:0.79 164:0.87 167:0.94 169:0.97 172:0.16 173:0.29 176:0.56 177:0.38 179:0.95 181:0.71 186:0.93 189:0.99 191:0.92 192:0.59 202:0.88 208:0.64 212:0.30 216:0.37 222:0.95 232:0.75 235:0.68 238:0.99 241:0.82 242:0.33 243:0.37 245:0.43 247:0.39 248:0.95 253:0.97 254:0.80 255:0.79 256:0.88 259:0.21 260:0.94 261:0.98 265:0.15 266:0.27 267:0.92 268:0.90 271:0.40 276:0.20 285:0.62 290:0.64 300:0.25 +1 12:0.06 17:0.45 21:0.78 27:0.45 28:0.83 30:0.76 34:0.83 36:0.18 37:0.50 39:0.69 43:0.76 66:0.64 69:0.82 70:0.15 74:0.50 91:0.14 98:0.10 108:0.66 122:0.57 123:0.64 127:0.07 129:0.05 135:0.90 145:0.47 146:0.18 147:0.23 149:0.41 154:0.68 159:0.09 161:0.71 163:0.97 167:0.68 172:0.16 173:0.75 177:0.38 178:0.55 179:0.08 181:0.71 187:0.68 212:0.95 216:0.77 222:0.95 235:0.98 241:0.43 242:0.82 243:0.69 247:0.12 253:0.41 259:0.21 265:0.10 266:0.12 267:0.28 271:0.40 276:0.18 300:0.13 +2 1:0.74 6:0.98 8:0.95 9:0.80 12:0.06 17:0.85 20:0.82 21:0.05 27:0.11 28:0.83 30:0.68 34:0.74 36:0.29 37:0.94 39:0.69 41:0.88 43:0.35 55:0.45 66:0.32 69:0.15 70:0.31 74:0.81 76:0.85 77:0.70 78:0.86 81:0.93 83:0.81 91:0.49 96:0.87 98:0.34 100:0.95 104:0.58 108:0.12 111:0.91 114:0.95 120:0.78 122:0.67 123:0.12 124:0.70 126:0.54 127:0.59 129:0.59 133:0.64 135:0.39 140:0.45 145:0.41 146:0.35 147:0.42 149:0.25 150:0.96 151:0.90 152:0.99 153:0.69 154:0.88 155:0.67 159:0.41 161:0.71 162:0.74 163:0.75 164:0.71 165:0.90 167:0.73 169:0.97 172:0.21 173:0.27 176:0.73 177:0.38 179:0.90 181:0.71 186:0.86 187:0.21 189:0.98 192:0.59 195:0.63 201:0.74 202:0.61 208:0.64 212:0.19 215:0.91 216:0.37 220:0.62 222:0.95 232:0.75 235:0.12 238:0.98 241:0.58 242:0.45 243:0.49 245:0.48 247:0.47 248:0.85 253:0.89 254:0.80 255:0.79 256:0.58 259:0.21 260:0.79 261:0.98 265:0.37 266:0.47 267:0.98 268:0.64 271:0.40 276:0.30 282:0.84 285:0.62 290:0.95 297:0.36 300:0.25 +2 1:0.74 12:0.06 17:0.87 25:0.88 27:0.61 28:0.83 30:0.76 34:0.17 36:0.91 37:0.31 39:0.69 43:0.30 55:0.52 66:0.36 69:0.21 70:0.22 74:0.48 81:0.96 83:0.80 87:0.98 91:0.20 98:0.12 104:0.78 106:0.81 108:0.92 114:0.98 117:0.86 122:0.43 123:0.92 124:0.57 126:0.54 127:0.90 129:0.59 133:0.47 135:0.34 146:0.20 147:0.25 149:0.19 150:0.98 154:0.80 155:0.67 159:0.80 161:0.71 163:0.80 165:0.92 167:0.57 172:0.16 173:0.13 176:0.73 177:0.38 178:0.55 179:0.97 181:0.71 187:0.68 192:0.59 201:0.74 206:0.81 212:0.42 215:0.96 216:0.17 222:0.95 232:0.70 235:0.05 241:0.02 242:0.45 243:0.40 245:0.43 247:0.35 253:0.73 254:0.80 259:0.21 265:0.13 266:0.23 267:0.28 271:0.40 276:0.12 290:0.98 297:0.36 300:0.18 +1 12:0.06 17:0.43 21:0.40 25:0.88 27:0.08 28:0.83 30:0.45 34:0.66 36:0.34 37:0.87 39:0.69 43:0.23 55:0.71 66:0.53 69:0.21 70:0.74 74:0.56 81:0.42 91:0.17 98:0.60 104:0.58 108:0.74 114:0.68 117:0.86 123:0.72 124:0.52 126:0.32 127:0.75 129:0.59 133:0.47 135:0.60 146:0.22 147:0.27 149:0.30 150:0.36 154:0.88 159:0.50 161:0.71 167:0.66 172:0.45 173:0.27 176:0.56 177:0.38 178:0.55 179:0.79 181:0.71 187:0.95 191:0.76 212:0.58 216:0.83 222:0.95 232:0.84 235:0.42 241:0.32 242:0.52 243:0.37 245:0.64 247:0.39 253:0.56 254:0.80 259:0.21 265:0.61 266:0.54 267:0.98 271:0.40 276:0.36 290:0.68 300:0.55 +1 12:0.06 17:0.59 21:0.78 27:0.45 28:0.83 30:0.76 34:0.89 36:0.20 37:0.50 39:0.69 43:0.77 66:0.64 69:0.80 70:0.15 74:0.41 91:0.15 98:0.11 108:0.63 122:0.55 123:0.61 127:0.08 129:0.05 135:0.61 145:0.50 146:0.18 147:0.23 149:0.27 154:0.69 159:0.09 161:0.71 163:0.97 167:0.60 172:0.16 173:0.82 177:0.38 178:0.55 179:0.09 181:0.71 187:0.68 212:0.95 216:0.77 222:0.95 235:0.99 241:0.10 242:0.82 243:0.69 247:0.12 253:0.36 259:0.21 265:0.12 266:0.12 267:0.23 271:0.40 276:0.18 300:0.13 +1 6:0.98 8:0.97 9:0.80 12:0.06 17:0.24 20:0.97 21:0.78 27:0.10 28:0.83 34:0.56 36:0.26 37:0.92 39:0.69 41:0.90 78:0.85 83:0.80 91:0.95 96:0.86 100:0.96 104:0.58 111:0.92 120:0.94 135:0.80 140:0.97 151:0.86 152:0.93 153:0.84 155:0.67 161:0.71 162:0.55 164:0.90 167:0.97 169:0.94 175:0.91 178:0.48 181:0.71 186:0.86 189:0.99 191:0.95 192:0.59 202:0.88 208:0.64 216:0.41 220:0.62 222:0.95 238:0.98 241:0.93 244:0.83 248:0.84 253:0.80 255:0.79 256:0.86 257:0.84 259:0.21 260:0.94 261:0.94 267:0.85 268:0.87 271:0.40 277:0.87 285:0.62 +1 1:0.74 6:0.98 8:0.86 9:0.80 12:0.06 17:0.61 20:0.92 21:0.13 27:0.11 28:0.83 30:0.21 34:0.36 36:0.56 37:0.94 39:0.69 41:0.82 43:0.30 66:0.61 69:0.17 70:0.50 74:0.70 76:0.85 77:0.70 78:0.85 81:0.89 83:0.78 91:0.44 96:0.75 98:0.67 100:0.93 104:0.58 108:0.53 111:0.88 114:0.92 120:0.63 123:0.51 124:0.47 126:0.54 127:0.42 129:0.59 133:0.47 135:0.39 140:0.45 145:0.42 146:0.18 147:0.23 149:0.29 150:0.94 151:0.72 152:0.99 153:0.72 154:0.88 155:0.67 159:0.27 161:0.71 162:0.65 163:0.81 164:0.64 165:0.88 167:0.72 169:0.97 172:0.37 173:0.38 176:0.73 177:0.38 179:0.84 181:0.71 186:0.85 187:0.21 189:0.94 192:0.59 195:0.57 201:0.74 202:0.55 208:0.64 212:0.55 215:0.84 216:0.37 220:0.74 222:0.95 232:0.75 235:0.22 238:0.96 241:0.54 242:0.58 243:0.29 245:0.52 247:0.39 248:0.67 253:0.79 254:0.80 255:0.79 256:0.45 259:0.21 260:0.64 261:0.98 265:0.67 266:0.27 267:0.82 268:0.57 271:0.40 276:0.33 282:0.84 285:0.62 290:0.92 297:0.36 300:0.33 +0 11:0.57 12:0.51 17:0.34 21:0.78 27:0.45 28:0.99 30:0.45 34:0.84 36:0.17 37:0.50 39:0.79 43:0.78 66:0.49 69:0.78 70:0.25 74:0.46 85:0.72 91:0.27 98:0.19 101:0.71 108:0.62 122:0.43 123:0.59 124:0.48 127:0.40 129:0.05 133:0.39 135:0.73 145:0.52 146:0.29 147:0.35 149:0.46 154:0.70 159:0.59 161:0.84 163:0.80 167:0.55 172:0.16 173:0.73 177:0.38 178:0.55 179:0.97 181:0.56 187:0.68 212:0.61 216:0.77 222:0.60 235:0.31 241:0.37 242:0.63 243:0.60 245:0.39 247:0.30 253:0.39 259:0.21 265:0.20 266:0.17 267:0.44 271:0.20 276:0.13 300:0.18 +1 1:0.74 6:0.90 7:0.78 8:0.68 9:0.80 11:0.57 12:0.51 17:0.84 20:0.86 25:0.88 27:0.51 28:0.99 30:0.30 32:0.75 34:0.49 36:0.99 37:0.69 39:0.79 41:0.82 43:0.98 66:0.91 69:0.05 70:0.76 74:0.76 78:0.88 81:0.96 83:0.81 85:0.91 91:0.73 96:0.83 98:0.96 100:0.88 101:0.83 104:0.58 106:0.81 108:0.05 111:0.86 114:0.98 120:0.81 122:0.43 123:0.05 126:0.54 127:0.72 129:0.05 135:0.89 140:0.80 145:0.47 146:0.83 147:0.86 149:0.89 150:0.98 151:0.90 152:0.93 153:0.77 154:0.98 155:0.67 159:0.39 161:0.84 162:0.86 164:0.70 165:0.92 167:0.51 169:0.87 172:0.74 173:0.19 176:0.73 177:0.38 179:0.59 181:0.56 186:0.88 187:0.68 189:0.90 192:0.59 195:0.63 201:0.74 202:0.55 206:0.81 208:0.64 212:0.60 215:0.96 216:0.28 222:0.60 230:0.81 233:0.76 235:0.05 238:0.86 241:0.18 242:0.40 243:0.91 247:0.39 248:0.80 253:0.86 255:0.79 256:0.58 259:0.21 260:0.81 261:0.90 265:0.96 266:0.38 267:0.59 268:0.64 271:0.20 276:0.62 285:0.62 290:0.98 297:0.36 300:0.55 +1 1:0.74 6:0.97 7:0.78 8:0.86 9:0.80 12:0.51 17:0.16 20:0.92 21:0.30 27:0.28 28:0.99 30:0.29 32:0.75 34:0.56 36:0.92 37:0.62 39:0.79 43:0.39 55:0.59 66:0.95 69:0.15 70:0.66 74:0.52 78:0.91 81:0.86 83:0.75 91:0.62 96:0.74 98:0.94 100:0.93 104:0.95 106:0.81 108:0.12 111:0.91 114:0.86 120:0.85 123:0.12 126:0.54 127:0.60 129:0.05 135:0.51 140:0.94 145:0.61 146:0.92 147:0.93 149:0.77 150:0.91 151:0.69 152:0.97 153:0.72 154:0.88 155:0.67 159:0.53 161:0.84 162:0.81 164:0.80 165:0.84 167:0.57 169:0.96 172:0.87 173:0.20 176:0.73 177:0.38 179:0.36 181:0.56 186:0.91 187:0.68 189:0.96 190:0.77 192:0.59 195:0.73 201:0.74 202:0.70 206:0.81 208:0.64 212:0.84 215:0.72 216:0.50 220:0.62 222:0.60 230:0.81 233:0.76 235:0.52 238:0.91 241:0.46 242:0.80 243:0.95 247:0.39 248:0.65 253:0.72 254:0.74 255:0.79 256:0.75 259:0.21 260:0.85 261:0.97 265:0.94 266:0.54 267:0.52 268:0.76 271:0.20 276:0.78 283:0.71 285:0.62 290:0.86 297:0.36 300:0.55 +3 1:0.74 6:0.94 7:0.81 8:0.87 9:0.80 11:0.57 12:0.51 17:0.55 20:0.91 21:0.30 27:0.36 28:0.99 30:0.09 32:0.80 34:0.82 36:0.87 37:0.71 39:0.79 41:0.82 43:0.98 55:0.59 66:0.52 69:0.12 70:0.92 74:0.86 76:0.85 77:0.70 78:0.89 81:0.80 83:0.76 85:0.91 91:0.51 96:0.82 98:0.77 100:0.88 101:0.78 104:0.84 106:0.81 108:0.60 111:0.88 114:0.86 117:0.86 120:0.79 121:0.90 123:0.58 124:0.70 126:0.54 127:0.60 129:0.59 133:0.76 135:0.60 140:0.87 145:0.40 146:0.44 147:0.50 149:0.90 150:0.87 151:0.58 152:0.85 153:0.48 154:0.98 155:0.67 158:0.86 159:0.76 161:0.84 162:0.86 164:0.71 165:0.84 167:0.49 169:0.87 172:0.76 173:0.11 176:0.73 177:0.38 179:0.38 181:0.56 186:0.89 187:0.21 189:0.95 190:0.77 191:0.70 192:0.59 195:0.89 201:0.74 202:0.60 204:0.89 206:0.81 208:0.64 212:0.54 215:0.72 216:0.44 222:0.60 230:0.87 232:0.90 233:0.76 235:0.42 238:0.88 241:0.10 242:0.74 243:0.37 245:0.81 247:0.47 248:0.57 253:0.86 255:0.79 256:0.68 259:0.21 260:0.79 261:0.88 265:0.77 266:0.75 267:0.89 268:0.69 271:0.20 276:0.76 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.95 7:0.81 8:0.81 9:0.80 11:0.57 12:0.51 17:0.95 20:0.88 21:0.68 27:0.55 28:0.99 30:0.21 32:0.80 34:0.61 36:0.98 37:0.66 39:0.79 41:0.90 43:0.98 60:0.87 66:0.99 69:0.25 70:0.87 74:0.98 77:0.70 78:0.90 81:0.55 83:0.86 85:0.99 91:0.46 96:0.86 98:0.99 100:0.91 101:0.86 104:0.58 106:0.81 108:0.20 111:0.90 114:0.70 117:0.86 120:0.82 121:0.90 123:0.19 126:0.54 127:0.90 129:0.05 135:0.96 140:0.45 145:0.96 146:0.99 147:0.99 149:0.99 150:0.69 151:0.95 152:0.84 153:0.84 154:0.98 155:0.67 158:0.92 159:0.83 161:0.84 162:0.86 164:0.78 165:0.70 167:0.51 169:0.89 172:0.98 173:0.13 176:0.73 177:0.38 179:0.15 181:0.56 186:0.90 187:0.21 189:0.96 192:0.59 195:0.93 201:0.74 202:0.65 204:0.89 206:0.81 208:0.64 212:0.74 215:0.49 216:0.36 222:0.60 230:0.87 232:0.88 233:0.76 235:0.84 238:0.88 241:0.18 242:0.50 243:0.99 247:0.60 248:0.84 253:0.90 255:0.79 256:0.71 259:0.21 260:0.82 261:0.89 265:0.99 266:0.63 267:0.73 268:0.73 271:0.20 276:0.95 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.90 7:0.81 8:0.77 9:0.80 11:0.57 12:0.51 17:0.73 20:0.92 21:0.22 25:0.88 27:0.51 28:0.99 30:0.26 32:0.75 34:0.39 36:0.97 37:0.69 39:0.79 41:0.94 43:0.84 55:0.45 66:0.45 69:0.15 70:0.89 74:0.72 78:0.88 81:0.91 83:0.82 85:0.85 91:0.82 96:0.92 98:0.70 100:0.90 101:0.79 104:0.58 108:0.12 111:0.88 114:0.90 120:0.93 121:0.90 123:0.12 124:0.59 126:0.54 127:0.91 129:0.59 133:0.47 135:0.79 138:0.98 140:0.87 145:0.49 146:0.65 147:0.70 149:0.83 150:0.95 151:0.97 152:0.93 153:0.96 154:0.81 155:0.67 159:0.45 161:0.84 162:0.86 164:0.92 165:0.84 167:0.82 169:0.87 172:0.67 173:0.27 176:0.73 177:0.38 179:0.50 181:0.56 186:0.89 189:0.92 192:0.59 195:0.63 201:0.74 202:0.84 204:0.89 208:0.64 212:0.71 215:0.79 216:0.28 222:0.60 230:0.87 233:0.76 235:0.52 238:0.88 241:0.79 242:0.74 243:0.58 245:0.88 247:0.39 248:0.93 253:0.92 255:0.79 256:0.89 259:0.21 260:0.94 261:0.90 265:0.70 266:0.47 267:0.69 268:0.90 271:0.20 276:0.71 283:0.71 285:0.62 290:0.90 297:0.36 300:0.70 +2 1:0.74 7:0.81 11:0.57 12:0.51 17:0.98 21:0.30 25:0.88 27:0.62 28:0.99 30:0.38 32:0.80 34:0.36 36:0.94 37:0.78 39:0.79 43:0.79 55:0.59 66:0.92 69:0.53 70:0.86 74:0.83 76:0.85 77:0.70 81:0.80 83:0.75 85:0.85 91:0.72 98:0.95 101:0.78 104:0.73 108:0.41 114:0.86 121:0.90 123:0.39 126:0.54 127:0.66 129:0.05 135:0.51 145:0.88 146:0.89 147:0.91 149:0.80 150:0.87 154:0.73 155:0.67 159:0.48 161:0.84 165:0.84 167:0.79 172:0.79 173:0.55 176:0.73 177:0.38 178:0.55 179:0.51 181:0.56 187:0.21 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 208:0.64 212:0.61 215:0.72 216:0.11 222:0.60 230:0.87 233:0.76 235:0.42 241:0.77 242:0.71 243:0.92 247:0.60 253:0.73 259:0.21 265:0.95 266:0.54 267:0.52 271:0.20 276:0.68 282:0.88 290:0.86 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.97 7:0.81 8:0.95 9:0.80 11:0.57 12:0.51 17:0.16 20:0.85 27:0.28 28:0.99 30:0.57 32:0.80 34:0.37 36:0.96 37:0.62 39:0.79 41:0.88 43:0.98 55:0.52 66:0.97 69:0.05 70:0.66 74:0.85 76:0.94 77:0.89 78:0.96 81:0.96 83:0.82 85:0.93 91:0.76 96:0.91 98:0.94 100:0.98 101:0.83 104:0.82 106:0.81 108:0.05 111:0.97 114:0.98 120:0.93 121:0.90 123:0.05 126:0.54 127:0.60 129:0.05 135:0.60 140:0.80 145:0.89 146:0.90 147:0.92 149:0.94 150:0.98 151:0.95 152:0.97 153:0.93 154:0.98 155:0.67 159:0.50 161:0.84 162:0.80 164:0.89 165:0.92 167:0.71 169:0.96 172:0.93 173:0.14 176:0.73 177:0.38 179:0.24 181:0.56 186:0.96 187:0.21 189:0.99 192:0.59 195:0.72 201:0.74 202:0.82 204:0.89 206:0.81 208:0.64 212:0.90 215:0.96 216:0.50 222:0.60 230:0.87 233:0.76 235:0.05 238:0.96 241:0.69 242:0.75 243:0.97 247:0.39 248:0.90 253:0.93 255:0.79 256:0.85 259:0.21 260:0.93 261:0.97 265:0.94 266:0.27 267:0.36 268:0.86 271:0.20 276:0.87 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.55 +2 6:0.94 7:0.78 8:0.81 9:0.80 11:0.57 12:0.51 17:0.26 20:0.99 21:0.30 27:0.21 28:0.99 30:0.33 34:0.34 36:0.84 37:0.74 39:0.79 41:0.82 43:0.75 55:0.52 66:0.92 69:0.10 70:0.76 74:0.69 76:0.97 78:0.88 81:0.70 83:0.76 85:0.80 91:0.63 96:0.80 98:0.86 100:0.92 101:0.76 104:0.84 106:0.81 108:0.09 111:0.88 120:0.89 123:0.09 126:0.32 127:0.37 129:0.05 135:0.19 140:0.93 146:0.82 147:0.85 149:0.75 150:0.50 151:0.87 152:0.90 153:0.90 154:0.65 155:0.67 159:0.38 161:0.84 162:0.82 164:0.82 167:0.62 169:0.89 172:0.77 173:0.21 177:0.38 179:0.44 181:0.56 186:0.88 187:0.39 189:0.95 192:0.59 202:0.73 206:0.81 208:0.64 212:0.61 215:0.72 216:0.95 222:0.60 230:0.81 233:0.76 235:0.31 238:0.91 241:0.57 242:0.72 243:0.92 247:0.39 248:0.78 253:0.80 255:0.79 256:0.77 259:0.21 260:0.89 261:0.90 265:0.86 266:0.38 267:0.79 268:0.78 271:0.20 276:0.66 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:0.91 8:0.80 9:0.80 12:0.51 17:0.47 20:0.97 21:0.78 27:0.54 28:0.99 30:0.76 34:0.75 36:0.84 37:0.58 39:0.79 41:0.92 43:0.27 55:0.42 60:0.87 66:0.64 69:0.75 70:0.15 74:0.55 78:0.93 83:0.79 91:0.74 96:0.85 98:0.50 100:0.95 104:0.58 108:0.59 111:0.93 120:0.79 122:0.55 123:0.56 127:0.15 129:0.05 135:0.63 140:0.87 146:0.33 147:0.40 149:0.23 151:0.87 152:0.93 153:0.48 154:0.20 155:0.67 158:0.92 159:0.13 161:0.84 162:0.69 163:0.98 164:0.78 167:0.86 169:0.92 172:0.16 173:0.96 177:0.38 178:0.42 179:0.86 181:0.56 186:0.93 189:0.92 192:0.59 202:0.71 208:0.64 212:0.95 216:0.17 220:0.82 222:0.60 232:0.78 235:0.31 238:0.92 241:0.89 242:0.82 243:0.69 244:0.61 247:0.12 248:0.83 253:0.81 255:0.79 256:0.73 259:0.21 260:0.80 261:0.93 265:0.51 266:0.12 267:0.88 268:0.73 271:0.20 276:0.13 279:0.86 283:0.82 285:0.62 300:0.13 +1 1:0.74 7:0.81 12:0.69 17:0.47 21:0.59 27:0.70 28:0.66 32:0.75 34:0.50 36:0.99 37:0.43 39:0.50 43:0.22 66:0.36 69:0.89 74:0.47 81:0.65 83:0.73 91:0.11 98:0.08 104:0.58 108:0.78 114:0.74 121:0.90 123:0.76 126:0.54 127:0.06 129:0.05 135:0.73 145:0.59 146:0.05 147:0.05 149:0.09 150:0.76 154:0.15 155:0.67 159:0.05 161:0.86 165:0.70 167:0.65 172:0.05 173:0.98 175:0.75 176:0.73 177:0.05 178:0.55 181:0.59 187:0.39 192:0.59 195:0.67 201:0.74 204:0.89 208:0.64 215:0.55 216:0.27 222:0.76 230:0.87 233:0.76 235:0.84 241:0.44 243:0.51 244:0.61 253:0.58 257:0.65 259:0.21 265:0.09 267:0.44 271:0.66 277:0.69 290:0.74 297:0.36 +3 1:0.74 7:0.81 8:0.89 12:0.69 17:0.55 20:0.79 21:0.54 27:0.21 28:0.66 30:0.10 34:0.85 36:0.51 37:0.74 39:0.50 43:0.35 55:0.42 66:0.23 69:0.23 70:0.93 74:0.97 76:0.94 78:0.83 81:0.59 91:0.05 98:0.38 100:0.89 104:0.97 106:0.81 108:0.93 111:0.84 114:0.77 117:0.86 122:0.67 123:0.93 124:0.91 126:0.54 127:0.72 129:0.89 133:0.91 135:0.24 146:0.52 147:0.58 149:0.59 150:0.66 152:0.93 154:0.91 155:0.67 158:0.86 159:0.86 161:0.86 167:0.52 169:0.82 172:0.61 173:0.11 176:0.73 177:0.38 178:0.55 179:0.37 181:0.59 186:0.84 187:0.39 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.49 215:0.58 216:0.95 222:0.76 230:0.83 232:0.91 233:0.76 235:0.68 241:0.01 242:0.55 243:0.34 245:0.78 247:0.67 253:0.76 254:0.74 259:0.21 261:0.86 265:0.40 266:0.87 267:0.23 271:0.66 276:0.78 279:0.86 283:0.82 290:0.77 297:0.36 300:0.84 +1 1:0.74 6:0.92 8:0.86 9:0.80 11:0.57 12:0.69 17:0.82 20:0.94 21:0.30 27:0.52 28:0.66 30:0.54 32:0.80 34:0.56 36:0.75 37:0.69 39:0.50 41:0.88 43:0.98 55:0.52 60:0.87 66:0.90 69:0.12 70:0.72 74:0.93 77:0.70 78:0.93 81:0.75 83:0.81 85:0.90 91:0.38 96:0.91 98:0.94 100:0.99 101:0.84 104:0.95 106:0.81 108:0.10 111:0.99 114:0.86 117:0.86 120:0.84 122:0.43 123:0.10 126:0.54 127:0.60 129:0.05 135:0.18 140:0.45 145:0.47 146:0.73 147:0.77 149:0.87 150:0.84 151:0.96 152:0.87 153:0.87 154:0.98 155:0.67 158:0.92 159:0.30 161:0.86 162:0.85 164:0.77 165:0.84 167:0.58 169:1.00 172:0.71 173:0.35 176:0.73 177:0.38 179:0.60 181:0.59 186:0.93 187:0.68 189:0.94 192:0.59 195:0.56 201:0.74 202:0.69 206:0.81 208:0.64 212:0.80 215:0.72 216:0.38 222:0.76 232:0.97 235:0.42 238:0.98 241:0.18 242:0.37 243:0.90 247:0.60 248:0.86 253:0.93 255:0.79 256:0.73 259:0.21 260:0.85 261:0.97 265:0.94 266:0.54 267:0.59 268:0.76 271:0.66 276:0.58 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.95 7:0.81 8:0.79 12:0.69 17:0.55 20:0.83 21:0.13 27:0.51 28:0.66 30:0.45 32:0.78 34:0.87 36:0.26 37:0.68 39:0.50 43:0.21 55:0.42 66:0.27 69:0.48 70:0.25 74:0.75 76:0.85 77:0.70 78:0.85 81:0.84 91:0.37 98:0.10 100:0.90 104:0.87 106:0.81 108:0.37 111:0.86 114:0.92 117:0.86 121:0.90 122:0.41 123:0.35 124:0.72 126:0.54 127:0.49 129:0.05 133:0.62 135:0.42 145:0.86 146:0.17 147:0.22 149:0.12 150:0.85 152:0.82 154:0.85 155:0.67 158:0.87 159:0.60 161:0.86 163:0.90 167:0.67 169:0.90 172:0.10 173:0.39 176:0.73 177:0.38 178:0.55 179:0.97 181:0.59 186:0.85 187:0.68 192:0.59 195:0.78 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.84 216:0.40 222:0.76 230:0.87 232:0.82 233:0.76 235:0.22 241:0.50 242:0.63 243:0.46 245:0.38 247:0.30 253:0.74 254:0.74 259:0.21 261:0.86 265:0.10 266:0.17 267:0.19 271:0.66 276:0.18 279:0.86 282:0.86 283:0.71 290:0.92 297:0.36 300:0.18 +0 12:0.69 17:0.32 21:0.71 27:0.45 28:0.66 30:0.38 34:0.77 36:0.22 37:0.50 39:0.50 43:0.27 55:0.59 66:0.20 69:0.71 70:0.78 74:0.60 77:0.70 81:0.29 91:0.10 98:0.35 108:0.86 123:0.52 124:0.72 126:0.32 127:0.42 129:0.05 133:0.62 135:0.81 145:0.44 146:0.52 147:0.58 149:0.34 150:0.27 154:0.65 159:0.59 161:0.86 167:0.59 172:0.27 173:0.64 177:0.38 178:0.55 179:0.78 181:0.59 187:0.68 195:0.80 212:0.28 215:0.48 216:0.77 222:0.76 235:0.84 241:0.21 242:0.75 243:0.40 245:0.56 247:0.39 253:0.48 254:0.94 259:0.21 265:0.24 266:0.63 267:0.89 271:0.66 276:0.38 282:0.84 283:0.71 297:0.36 300:0.55 +2 1:0.74 7:0.81 8:0.73 11:0.57 12:0.69 17:0.88 20:0.81 21:0.30 27:0.32 28:0.66 30:0.14 32:0.80 34:0.89 36:0.63 37:0.86 39:0.50 43:0.98 55:0.84 60:0.95 66:0.60 69:0.19 70:0.94 74:0.84 76:0.97 77:0.89 78:0.81 81:0.83 83:0.84 85:0.80 91:0.20 98:0.61 100:0.73 101:0.75 104:0.90 106:0.81 108:0.71 111:0.79 114:0.86 117:0.86 121:0.90 123:0.69 124:0.65 126:0.54 127:0.82 129:0.59 133:0.76 135:0.49 145:0.47 146:0.13 147:0.18 149:0.70 150:0.89 152:0.78 154:0.98 155:0.67 158:0.92 159:0.54 161:0.86 165:0.83 167:0.52 169:0.72 172:0.51 173:0.24 176:0.73 177:0.38 178:0.55 179:0.76 181:0.59 186:0.81 187:0.39 192:0.59 195:0.73 201:0.74 204:0.89 206:0.81 208:0.64 212:0.51 215:0.72 216:0.29 222:0.76 230:0.87 232:0.84 233:0.76 235:0.68 241:0.01 242:0.31 243:0.21 245:0.54 247:0.67 253:0.74 259:0.21 261:0.79 265:0.62 266:0.54 267:0.23 271:0.66 276:0.48 279:0.86 282:0.86 283:0.82 290:0.86 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.81 12:0.69 17:0.73 20:0.80 21:0.59 27:0.72 28:0.66 30:0.33 32:0.75 34:0.26 36:0.95 39:0.50 43:0.39 55:0.71 66:0.54 69:0.90 70:0.69 74:0.64 78:0.72 81:0.65 83:0.73 91:0.65 98:0.57 100:0.73 104:0.74 108:0.79 111:0.72 114:0.74 121:0.90 122:0.41 123:0.78 124:0.55 126:0.54 127:0.80 129:0.05 133:0.62 135:0.23 145:0.59 146:0.65 147:0.05 149:0.34 150:0.76 152:0.77 154:0.29 155:0.67 159:0.56 161:0.86 165:0.70 167:0.92 169:0.72 172:0.32 173:0.95 176:0.73 177:0.05 178:0.55 179:0.91 181:0.59 186:0.73 192:0.59 195:0.73 201:0.74 204:0.89 208:0.64 212:0.19 215:0.55 216:0.02 222:0.76 230:0.87 233:0.76 235:0.84 241:0.87 242:0.19 243:0.19 244:0.61 245:0.45 247:0.55 253:0.62 257:0.65 259:0.21 261:0.73 265:0.58 266:0.47 267:0.69 271:0.66 276:0.31 290:0.74 297:0.36 300:0.43 +1 1:0.74 7:0.81 12:0.69 17:0.61 21:0.59 27:0.70 28:0.66 32:0.75 34:0.26 36:0.98 37:0.43 39:0.50 43:0.21 66:0.36 69:0.91 74:0.47 81:0.65 83:0.73 91:0.37 98:0.08 104:0.58 108:0.81 114:0.74 121:0.90 123:0.80 126:0.54 127:0.06 129:0.05 135:0.75 145:0.59 146:0.05 147:0.05 149:0.08 150:0.76 154:0.14 155:0.67 159:0.05 161:0.86 165:0.70 167:0.70 172:0.05 173:0.99 175:0.75 176:0.73 177:0.05 178:0.55 181:0.59 187:0.39 192:0.59 195:0.71 201:0.74 204:0.89 208:0.64 215:0.55 216:0.27 222:0.76 230:0.87 233:0.76 235:0.84 241:0.58 243:0.51 244:0.61 253:0.58 257:0.65 259:0.21 265:0.08 267:0.36 271:0.66 277:0.69 290:0.74 297:0.36 +2 1:0.74 6:0.93 8:0.85 12:0.69 17:0.51 20:0.84 21:0.05 27:0.62 28:0.66 30:0.33 34:0.70 36:0.49 37:0.50 39:0.50 43:0.79 55:0.42 66:0.79 69:0.74 70:0.49 74:0.74 77:0.89 78:0.87 81:0.88 91:0.44 98:0.30 100:0.95 108:0.57 111:0.91 114:0.95 122:0.57 123:0.55 126:0.54 127:0.12 129:0.05 135:0.44 145:0.85 146:0.35 147:0.42 149:0.61 150:0.90 152:0.80 154:0.72 155:0.67 159:0.14 161:0.86 167:0.62 169:0.93 172:0.41 173:0.79 176:0.73 177:0.38 178:0.55 179:0.22 181:0.59 186:0.87 187:0.39 189:0.94 192:0.59 195:0.69 201:0.74 212:0.89 215:0.91 216:0.19 222:0.76 235:0.12 238:0.96 241:0.35 242:0.63 243:0.80 247:0.39 253:0.75 254:0.74 259:0.21 261:0.87 265:0.32 266:0.17 267:0.23 271:0.66 276:0.34 282:0.84 283:0.82 290:0.95 297:0.36 300:0.33 +1 1:0.74 6:0.87 7:0.81 8:0.73 9:0.80 12:0.69 17:0.51 20:0.91 21:0.47 27:0.21 28:0.66 30:0.72 34:0.77 36:0.77 37:0.74 39:0.50 43:0.33 55:0.42 66:0.33 69:0.19 70:0.56 74:0.87 76:0.85 78:0.79 81:0.63 91:0.40 96:0.78 98:0.68 100:0.82 104:0.93 106:0.81 108:0.15 111:0.79 114:0.80 117:0.86 120:0.66 122:0.43 123:0.15 124:0.73 126:0.54 127:0.49 129:0.81 133:0.67 135:0.26 140:0.45 146:0.78 147:0.82 149:0.24 150:0.69 151:0.73 152:0.94 153:0.78 154:0.92 155:0.67 158:0.86 159:0.55 161:0.86 162:0.57 164:0.74 167:0.62 169:0.82 172:0.51 173:0.20 176:0.73 177:0.38 179:0.54 181:0.59 186:0.80 187:0.39 192:0.59 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.37 215:0.63 216:0.95 220:0.74 222:0.76 230:0.83 232:0.98 233:0.76 235:0.60 241:0.32 242:0.40 243:0.50 245:0.76 247:0.60 248:0.72 253:0.83 254:0.74 255:0.79 256:0.64 259:0.21 260:0.67 261:0.86 265:0.68 266:0.75 267:0.44 268:0.68 271:0.66 276:0.61 279:0.86 283:0.67 285:0.62 290:0.80 297:0.36 300:0.55 +2 1:0.74 6:0.87 7:0.81 8:0.82 11:0.57 12:0.69 17:0.66 20:0.92 21:0.22 25:0.88 27:0.62 28:0.66 30:0.30 32:0.80 34:0.64 36:0.45 37:0.55 39:0.50 43:0.98 66:0.33 69:0.15 70:0.60 74:0.86 76:0.85 77:0.89 78:0.86 81:0.86 83:0.75 85:0.85 91:0.20 98:0.62 100:0.91 101:0.80 104:0.58 108:0.12 111:0.86 114:0.90 117:0.86 121:0.90 122:0.41 123:0.62 124:0.61 126:0.54 127:0.75 129:0.59 133:0.47 135:0.60 145:0.50 146:0.26 147:0.32 149:0.74 150:0.91 152:0.86 154:0.98 155:0.67 158:0.85 159:0.36 161:0.86 165:0.84 167:0.65 169:0.88 172:0.32 173:0.33 176:0.73 177:0.38 178:0.55 179:0.82 181:0.59 186:0.86 187:0.39 189:0.88 192:0.59 195:0.58 201:0.74 204:0.89 208:0.64 212:0.61 215:0.79 216:0.18 222:0.76 230:0.87 232:0.95 233:0.76 235:0.52 238:0.92 241:0.44 242:0.24 243:0.50 245:0.64 247:0.60 253:0.75 259:0.21 261:0.88 265:0.28 266:0.47 267:0.59 271:0.66 276:0.35 282:0.88 283:0.82 290:0.90 297:0.36 299:0.88 300:0.43 +3 6:0.86 8:0.80 9:0.80 12:0.69 17:0.53 20:0.96 21:0.13 27:0.34 28:0.66 30:0.58 34:0.53 36:0.64 37:0.62 39:0.50 41:0.82 43:0.19 55:0.42 66:0.27 69:0.21 70:0.44 74:0.62 78:0.83 81:0.71 83:0.74 91:0.11 96:0.89 98:0.30 100:0.89 108:0.17 111:0.84 114:0.74 117:0.86 120:0.80 122:0.43 123:0.16 124:0.80 126:0.32 127:0.75 129:0.81 133:0.76 135:0.69 138:0.97 140:0.87 146:0.45 147:0.51 149:0.07 150:0.52 151:0.87 152:0.89 153:0.48 154:0.77 155:0.67 159:0.67 161:0.86 162:0.68 163:0.88 164:0.78 167:0.62 169:0.86 172:0.21 173:0.18 176:0.56 177:0.38 178:0.48 179:0.89 181:0.59 186:0.83 187:0.68 189:0.88 192:0.59 202:0.73 208:0.64 212:0.37 216:0.79 220:0.62 222:0.76 232:0.82 235:0.12 238:0.91 241:0.35 242:0.58 243:0.46 245:0.48 247:0.39 248:0.87 253:0.87 254:0.74 255:0.79 256:0.75 259:0.21 260:0.81 261:0.87 265:0.32 266:0.38 267:0.17 268:0.75 271:0.66 276:0.35 283:0.82 285:0.62 290:0.74 300:0.33 +0 11:0.57 12:0.69 17:0.32 21:0.13 27:0.45 28:0.66 30:0.30 32:0.75 34:0.52 36:0.17 37:0.50 39:0.50 43:0.76 55:0.84 66:0.25 69:0.68 70:0.88 74:0.50 81:0.65 85:0.72 91:0.09 98:0.43 101:0.69 108:0.83 123:0.82 124:0.84 126:0.32 127:0.61 129:0.05 133:0.82 135:0.76 145:0.49 146:0.54 147:0.60 149:0.64 150:0.47 154:0.68 159:0.80 161:0.86 163:0.93 167:0.60 172:0.45 173:0.49 177:0.38 178:0.55 179:0.57 181:0.59 187:0.68 195:0.92 212:0.16 215:0.84 216:0.77 222:0.76 235:0.12 241:0.24 242:0.79 243:0.34 245:0.69 247:0.47 253:0.41 259:0.21 265:0.45 266:0.91 267:0.36 271:0.66 276:0.61 277:0.69 283:0.82 297:0.36 300:0.70 +1 7:0.81 8:0.92 9:0.80 12:0.69 17:0.43 20:0.91 21:0.78 27:0.29 28:0.66 34:0.52 36:0.51 37:0.89 39:0.50 41:0.82 74:0.47 78:0.82 83:0.73 91:0.94 96:0.89 100:0.87 111:0.82 120:0.76 121:0.90 135:0.23 138:0.99 140:0.98 145:0.99 151:0.69 152:0.85 153:0.72 155:0.67 161:0.86 162:0.47 164:0.81 167:0.92 169:0.85 175:0.91 178:0.54 181:0.59 186:0.82 190:0.77 191:0.82 192:0.59 202:0.93 204:0.89 208:0.64 216:0.39 222:0.76 230:0.87 233:0.76 235:0.12 241:0.86 244:0.83 248:0.79 253:0.85 255:0.79 256:0.81 257:0.84 259:0.21 260:0.77 261:0.85 267:0.44 268:0.80 271:0.66 277:0.87 285:0.62 +2 1:0.74 6:0.87 7:0.81 8:0.77 11:0.57 12:0.69 17:0.55 20:0.88 21:0.47 27:0.21 28:0.66 30:0.16 34:0.85 36:0.53 37:0.74 39:0.50 43:0.86 55:0.42 66:0.95 69:0.19 70:0.81 74:0.96 76:0.85 78:0.82 81:0.63 85:0.80 91:0.14 98:0.90 100:0.83 101:0.75 104:0.97 106:0.81 108:0.15 111:0.81 114:0.80 117:0.86 122:0.57 123:0.15 126:0.54 127:0.47 129:0.05 135:0.17 146:0.90 147:0.92 149:0.89 150:0.69 152:0.94 154:0.92 155:0.67 158:0.87 159:0.50 161:0.86 167:0.56 169:0.82 172:0.87 173:0.23 176:0.73 177:0.38 178:0.55 179:0.33 181:0.59 186:0.82 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.71 215:0.63 216:0.95 222:0.76 230:0.83 232:0.91 233:0.66 235:0.60 241:0.10 242:0.36 243:0.95 247:0.60 253:0.76 259:0.21 261:0.86 265:0.90 266:0.38 267:0.76 271:0.66 276:0.77 279:0.86 283:0.71 290:0.80 297:0.36 300:0.70 +3 1:0.74 6:0.95 7:0.81 8:0.92 12:0.69 17:0.69 20:0.84 21:0.13 27:0.51 28:0.66 30:0.45 32:0.78 34:0.85 36:0.25 37:0.68 39:0.50 43:0.21 55:0.42 66:0.27 69:0.48 70:0.25 74:0.75 76:0.85 77:0.70 78:0.81 81:0.84 91:0.37 98:0.10 100:0.94 104:0.87 106:0.81 108:0.37 111:0.87 114:0.92 117:0.86 121:0.90 122:0.41 123:0.35 124:0.72 126:0.54 127:0.49 129:0.05 133:0.62 135:0.47 145:0.86 146:0.17 147:0.22 149:0.12 150:0.85 152:0.82 154:0.85 155:0.67 158:0.87 159:0.60 161:0.86 163:0.90 167:0.67 169:0.90 172:0.10 173:0.39 176:0.73 177:0.38 178:0.55 179:0.97 181:0.59 186:0.82 187:0.39 192:0.59 195:0.78 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.84 216:0.40 222:0.76 230:0.87 232:0.82 233:0.76 235:0.22 241:0.51 242:0.63 243:0.46 245:0.38 247:0.30 253:0.74 254:0.74 259:0.21 261:0.87 265:0.10 266:0.17 267:0.19 271:0.66 276:0.18 279:0.86 282:0.86 283:0.71 290:0.92 297:0.36 300:0.18 +2 1:0.74 6:0.97 7:0.81 8:0.84 9:0.80 11:0.87 12:0.51 17:0.15 18:0.98 20:0.99 21:0.30 22:0.93 27:0.21 28:0.73 29:0.62 30:0.42 31:0.99 34:0.44 36:0.84 37:0.74 39:0.67 41:0.95 43:0.97 45:0.93 47:0.93 60:0.98 64:0.77 66:0.43 69:0.15 70:0.82 71:0.73 74:0.85 78:0.85 79:0.99 81:0.59 83:0.91 85:0.90 86:0.96 91:0.73 96:0.96 97:0.89 98:0.58 100:0.91 101:0.97 104:0.84 106:0.81 108:0.12 111:0.86 114:0.65 117:0.86 120:0.92 121:0.90 122:0.85 123:0.12 124:0.79 126:0.54 127:0.81 129:0.59 131:0.93 133:0.81 135:0.44 137:0.99 139:0.97 140:0.45 144:0.76 146:0.88 147:0.90 149:0.95 150:0.76 151:0.99 152:0.99 153:0.95 154:0.97 155:0.67 157:0.98 158:0.92 159:0.82 161:0.71 162:0.61 164:0.86 165:0.93 166:0.89 167:0.73 169:0.86 172:0.87 173:0.10 176:0.73 177:0.70 179:0.22 181:0.73 182:0.99 186:0.85 187:0.39 189:0.98 192:0.87 196:0.99 201:0.93 202:0.88 204:0.89 206:0.81 208:0.64 212:0.61 215:0.44 216:0.95 219:0.95 222:0.90 227:0.96 229:0.99 230:0.87 231:0.85 232:0.90 233:0.76 235:0.52 238:0.94 241:0.51 242:0.19 243:0.57 245:0.93 246:0.99 247:0.90 248:0.94 253:0.96 255:0.95 256:0.88 259:0.21 260:0.90 261:0.89 262:0.93 265:0.59 266:0.87 267:0.52 268:0.89 271:0.69 276:0.90 279:0.86 281:0.91 283:0.82 284:0.98 285:0.62 287:0.99 290:0.65 292:0.95 297:0.36 300:0.84 +3 1:0.74 6:0.91 9:0.80 11:0.87 12:0.51 17:0.02 18:0.96 20:0.84 21:0.05 27:0.52 28:0.73 29:0.62 30:0.56 31:0.96 34:0.66 36:0.76 37:0.45 39:0.67 41:0.90 43:0.96 45:0.77 64:0.77 66:0.94 69:0.10 70:0.62 71:0.73 74:0.77 78:0.80 79:0.95 81:0.79 83:0.91 85:0.88 86:0.97 91:0.54 96:0.86 98:0.98 100:0.84 101:0.97 108:0.09 111:0.80 114:0.89 120:0.78 122:0.57 123:0.09 126:0.54 127:0.85 129:0.05 131:0.93 135:0.42 137:0.96 139:0.96 140:0.45 144:0.76 146:0.81 147:0.84 149:0.90 150:0.87 151:0.93 152:0.96 153:0.78 154:0.96 155:0.67 157:0.98 159:0.37 161:0.71 162:0.72 164:0.73 165:0.93 166:0.90 167:0.74 169:0.92 172:0.83 173:0.28 176:0.73 177:0.70 179:0.47 181:0.73 182:0.99 186:0.81 187:0.87 192:0.87 196:0.98 201:0.93 202:0.64 208:0.64 212:0.73 215:0.78 216:0.46 222:0.90 227:0.96 229:0.99 231:0.84 235:0.22 241:0.56 242:0.18 243:0.94 246:0.99 247:0.86 248:0.85 253:0.88 255:0.95 256:0.64 259:0.21 260:0.78 261:0.94 265:0.98 266:0.47 267:0.44 268:0.67 271:0.69 276:0.72 284:0.94 285:0.62 287:0.99 290:0.89 297:0.36 300:0.55 +4 1:0.74 6:0.91 8:0.77 9:0.80 11:0.87 12:0.51 17:0.04 18:0.95 20:0.99 21:0.05 27:0.52 28:0.73 29:0.62 30:0.66 31:0.99 34:0.36 36:0.55 37:0.45 39:0.67 41:0.98 43:0.81 45:0.73 60:0.95 64:0.77 66:0.93 69:0.23 70:0.49 71:0.73 74:0.75 78:0.92 79:0.99 81:0.79 83:0.91 85:0.90 86:0.91 91:0.90 96:0.97 98:0.92 100:0.96 101:0.97 108:0.18 111:0.93 114:0.89 120:0.95 122:0.57 123:0.18 126:0.54 127:0.53 129:0.05 131:0.93 135:0.28 137:0.99 139:0.92 140:0.45 144:0.76 146:0.85 147:0.88 149:0.91 150:0.87 151:0.99 152:0.96 153:0.95 154:0.76 155:0.67 157:0.99 158:0.92 159:0.42 161:0.71 162:0.80 164:0.92 165:0.93 166:0.90 167:1.00 169:0.92 172:0.80 173:0.32 176:0.73 177:0.70 179:0.45 181:0.73 182:0.99 186:0.92 189:0.93 191:0.89 192:0.87 196:0.99 201:0.93 202:0.90 208:0.64 212:0.77 215:0.78 216:0.46 219:0.95 220:0.62 222:0.90 227:0.96 229:1.00 231:0.85 235:0.22 238:0.94 241:0.95 242:0.22 243:0.93 246:0.99 247:0.76 248:0.97 253:0.97 255:0.95 256:0.92 259:0.21 260:0.94 261:0.94 265:0.92 266:0.27 267:0.52 268:0.93 271:0.69 276:0.69 279:0.86 283:0.82 284:0.99 285:0.62 287:0.99 290:0.89 292:0.95 297:0.36 300:0.43 +2 1:0.74 6:0.99 8:0.88 9:0.80 11:0.78 12:0.51 17:0.16 18:0.64 20:0.80 21:0.30 27:0.17 28:0.73 29:0.62 30:0.62 31:0.87 34:0.55 36:0.77 37:1.00 39:0.67 41:0.82 43:0.80 64:0.77 66:0.75 69:0.41 70:0.34 71:0.73 74:0.42 78:0.85 79:0.87 81:0.59 83:0.91 85:0.72 86:0.74 91:0.70 96:0.70 98:0.81 100:0.91 101:0.87 108:0.31 111:0.86 114:0.65 120:0.56 123:0.30 126:0.54 127:0.30 129:0.05 131:0.93 135:0.51 137:0.87 139:0.71 140:0.45 144:0.76 146:0.56 147:0.62 149:0.57 150:0.76 151:0.53 152:0.92 153:0.78 154:0.74 155:0.67 157:0.87 159:0.20 161:0.71 162:0.53 164:0.65 165:0.93 166:0.89 167:0.79 169:1.00 172:0.32 173:0.63 176:0.73 177:0.70 179:0.89 181:0.73 182:0.98 186:0.85 187:0.21 191:0.80 192:0.87 196:0.88 201:0.93 202:0.62 208:0.64 212:0.30 215:0.44 216:0.48 220:0.87 222:0.90 227:0.96 229:0.98 231:0.84 235:0.52 241:0.67 242:0.33 243:0.77 246:0.99 247:0.39 248:0.52 253:0.50 255:0.95 256:0.45 259:0.21 260:0.57 261:0.98 265:0.81 266:0.27 267:0.36 268:0.58 271:0.69 276:0.25 284:0.91 285:0.62 287:0.99 290:0.65 297:0.36 300:0.25 +2 1:0.74 6:0.97 7:0.81 8:0.86 9:0.80 11:0.87 12:0.51 17:0.12 18:0.88 20:0.98 21:0.40 22:0.93 27:0.21 28:0.73 29:0.62 30:0.72 31:0.96 34:0.50 36:0.61 37:0.74 39:0.67 41:0.92 43:0.97 45:0.66 47:0.93 48:0.91 60:0.87 64:0.77 66:0.77 69:0.17 70:0.50 71:0.73 74:0.82 78:0.79 79:0.96 81:0.54 83:0.91 85:0.91 86:0.93 91:0.44 96:0.87 98:0.80 100:0.81 101:0.97 104:0.84 106:0.81 108:0.61 111:0.78 114:0.62 117:0.86 120:0.77 121:0.99 122:0.57 123:0.59 124:0.40 126:0.54 127:0.56 129:0.59 131:0.93 133:0.46 135:0.17 137:0.96 139:0.95 140:0.45 144:0.76 146:0.17 147:0.22 149:0.92 150:0.74 151:0.82 152:0.99 153:0.46 154:0.97 155:0.67 157:0.99 158:0.91 159:0.49 161:0.71 162:0.68 164:0.76 165:0.93 166:0.89 167:0.72 169:0.86 172:0.78 173:0.23 176:0.73 177:0.70 179:0.46 181:0.73 182:0.99 186:0.80 187:0.39 192:0.87 196:0.98 201:0.93 202:0.76 204:0.89 206:0.81 208:0.64 212:0.82 215:0.42 216:0.95 219:0.95 220:0.62 222:0.90 227:0.96 229:0.99 230:0.87 231:0.84 232:0.90 233:0.76 235:0.60 241:0.46 242:0.33 243:0.18 245:0.76 246:0.99 247:0.74 248:0.84 253:0.88 255:0.95 256:0.79 259:0.21 260:0.78 261:0.89 262:0.93 265:0.80 266:0.63 267:0.85 268:0.79 271:0.69 276:0.67 279:0.86 281:0.91 283:0.78 284:0.96 285:0.62 287:0.99 290:0.62 292:0.91 297:0.36 300:0.55 +2 1:0.74 8:0.84 9:0.80 11:0.78 12:0.51 17:0.15 18:0.64 20:0.86 21:0.30 25:0.88 27:0.16 28:0.73 29:0.62 30:0.76 31:0.87 34:0.49 36:0.39 37:1.00 39:0.67 41:0.82 43:0.79 64:0.77 66:0.54 69:0.75 70:0.22 71:0.73 74:0.42 78:0.81 79:0.87 81:0.59 83:0.91 85:0.72 86:0.73 91:0.69 96:0.69 98:0.23 100:0.87 101:0.87 104:0.75 108:0.58 111:0.82 114:0.65 120:0.55 122:0.41 123:0.56 124:0.45 126:0.54 127:0.32 129:0.05 131:0.93 133:0.37 135:0.34 137:0.87 139:0.71 140:0.45 144:0.76 146:0.22 147:0.05 149:0.56 150:0.76 151:0.52 152:0.81 153:0.48 154:0.73 155:0.67 157:0.87 159:0.21 161:0.71 162:0.86 164:0.57 165:0.93 166:0.89 167:0.72 169:0.85 172:0.21 173:0.94 176:0.73 177:0.05 179:0.93 181:0.73 182:0.98 186:0.81 187:0.39 191:0.86 192:0.87 196:0.88 201:0.93 202:0.36 208:0.64 212:0.42 215:0.44 216:0.22 220:0.87 222:0.90 227:0.96 229:0.99 231:0.84 235:0.52 241:0.46 242:0.45 243:0.26 245:0.40 246:0.99 247:0.35 248:0.51 253:0.50 255:0.95 256:0.45 257:0.84 259:0.21 260:0.56 261:0.82 265:0.25 266:0.23 267:0.59 268:0.46 271:0.69 276:0.15 284:0.89 285:0.62 287:0.99 290:0.65 297:0.36 300:0.18 +2 1:0.74 8:0.79 9:0.80 11:0.92 12:0.51 17:0.47 18:0.98 20:0.96 21:0.71 27:0.68 28:0.73 29:0.62 30:0.42 31:0.95 34:0.70 36:0.95 37:0.47 39:0.67 41:0.94 43:0.91 45:0.88 60:0.87 64:0.77 66:0.20 69:0.45 70:0.86 71:0.73 74:0.84 78:0.76 79:0.95 81:0.44 83:0.91 85:0.98 86:0.96 91:0.40 96:0.84 98:0.43 100:0.73 101:0.97 104:0.88 106:0.81 108:0.80 111:0.75 114:0.55 120:0.81 122:0.57 123:0.78 124:0.95 126:0.54 127:0.98 129:0.98 131:0.93 133:0.95 135:0.66 137:0.95 139:0.96 140:0.80 144:0.76 146:0.17 147:0.22 149:0.96 150:0.69 151:0.92 152:0.89 153:0.72 154:0.91 155:0.67 157:0.99 158:0.91 159:0.95 161:0.71 162:0.60 164:0.76 165:0.93 166:0.89 167:0.67 169:0.72 172:0.90 173:0.11 176:0.73 177:0.70 178:0.42 179:0.14 181:0.73 182:0.99 186:0.77 187:0.39 192:0.87 196:0.97 201:0.93 202:0.74 206:0.81 208:0.64 212:0.48 215:0.37 216:0.29 219:0.95 222:0.90 227:0.96 229:0.99 231:0.84 235:0.95 241:0.24 242:0.16 243:0.07 245:0.96 246:0.99 247:0.93 248:0.82 253:0.87 255:0.95 256:0.79 259:0.21 260:0.76 261:0.78 265:0.45 266:0.96 267:0.52 268:0.75 271:0.69 276:0.96 279:0.86 283:0.78 284:0.95 285:0.62 287:0.99 290:0.55 292:0.91 297:0.36 300:0.94 +1 1:0.74 11:0.78 12:0.51 17:0.30 18:0.65 21:0.54 27:0.45 28:0.73 29:0.62 30:0.91 34:0.89 36:0.18 37:0.50 39:0.67 43:0.82 64:0.77 66:0.69 69:0.70 70:0.13 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.74 91:0.20 98:0.34 101:0.87 108:0.53 114:0.59 122:0.57 123:0.51 126:0.54 127:0.12 129:0.05 131:0.61 135:0.26 139:0.72 144:0.76 145:0.52 146:0.42 147:0.49 149:0.58 150:0.72 154:0.77 155:0.67 157:0.85 159:0.16 161:0.71 163:0.75 165:0.93 166:0.89 167:0.65 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.54 181:0.73 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.61 215:0.39 216:0.77 222:0.90 227:0.61 229:0.61 231:0.83 235:0.74 241:0.12 242:0.63 243:0.73 246:0.99 247:0.30 253:0.42 259:0.21 265:0.36 266:0.17 267:0.23 271:0.69 276:0.13 287:0.61 290:0.59 297:0.36 300:0.13 +1 1:0.74 11:0.78 12:0.51 17:0.24 18:0.83 21:0.54 27:0.45 28:0.73 29:0.62 30:0.44 34:0.86 36:0.18 37:0.50 39:0.67 43:0.82 55:0.71 64:0.77 66:0.10 69:0.69 70:0.82 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.75 91:0.18 98:0.25 101:0.87 108:0.53 114:0.59 123:0.88 124:0.90 126:0.54 127:0.38 129:0.05 131:0.61 133:0.89 135:0.85 139:0.72 144:0.76 145:0.49 146:0.39 147:0.46 149:0.73 150:0.72 154:0.77 155:0.67 157:0.82 159:0.73 161:0.71 165:0.93 166:0.89 167:0.71 172:0.48 173:0.51 176:0.73 177:0.70 178:0.55 179:0.45 181:0.73 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.50 215:0.39 216:0.77 222:0.90 227:0.61 229:0.61 231:0.83 235:0.74 241:0.44 242:0.45 243:0.44 245:0.67 246:0.99 247:0.74 253:0.42 259:0.21 265:0.23 266:0.87 267:0.52 271:0.69 276:0.65 277:0.69 287:0.61 290:0.59 297:0.36 300:0.70 +2 9:0.80 11:0.87 12:0.51 17:0.30 18:0.94 20:0.87 21:0.30 27:0.55 28:0.73 29:0.62 30:0.17 31:0.93 32:0.80 34:0.36 36:0.93 37:0.34 39:0.67 41:0.82 43:0.74 44:0.93 45:0.66 55:0.71 60:0.87 64:0.77 66:0.30 69:0.83 70:0.92 71:0.73 74:0.73 77:0.70 78:0.76 79:0.94 81:0.30 83:0.91 85:0.90 86:0.89 91:0.15 96:0.79 98:0.45 100:0.73 101:0.97 108:0.89 111:0.75 120:0.67 122:0.85 123:0.74 124:0.76 126:0.26 127:0.29 129:0.81 131:0.93 133:0.74 135:0.92 137:0.93 139:0.92 140:0.80 144:0.76 145:0.91 146:0.31 147:0.38 149:0.81 150:0.28 151:0.82 152:0.82 153:0.46 154:0.65 155:0.67 157:0.97 158:0.92 159:0.81 161:0.71 162:0.77 164:0.54 166:0.74 167:0.67 169:0.72 172:0.75 173:0.59 177:0.70 179:0.20 181:0.73 182:0.98 186:0.77 187:0.68 192:0.87 195:0.97 196:0.96 202:0.55 208:0.64 212:0.58 216:0.72 219:0.95 220:0.62 222:0.90 227:0.96 229:0.99 231:0.84 235:0.31 241:0.24 242:0.21 243:0.17 245:0.89 246:0.61 247:0.90 248:0.74 253:0.79 255:0.95 256:0.58 259:0.21 260:0.68 261:0.77 265:0.38 266:0.63 267:0.36 268:0.58 271:0.69 276:0.82 279:0.86 282:0.88 283:0.82 284:0.87 285:0.62 287:0.99 292:0.95 299:0.88 300:0.94 +3 1:0.74 6:0.99 7:0.63 8:0.99 9:0.80 12:0.51 17:0.13 20:0.95 21:0.30 27:0.17 28:0.73 29:0.62 30:0.11 31:0.99 32:0.80 34:0.62 36:0.73 37:1.00 39:0.67 41:0.98 43:0.14 44:0.93 48:0.91 55:0.45 60:0.87 64:0.77 66:0.61 69:0.63 70:0.54 71:0.73 74:0.59 76:0.85 77:0.70 78:0.96 79:0.99 81:0.59 83:0.91 91:0.92 96:0.97 97:0.99 98:0.54 100:1.00 108:0.48 111:0.99 114:0.65 120:0.92 123:0.46 124:0.43 126:0.54 127:0.48 129:0.59 131:0.93 133:0.47 135:0.42 137:0.99 139:0.82 140:0.87 144:0.76 145:0.72 146:0.48 147:0.54 149:0.17 150:0.76 151:0.96 152:0.92 153:0.92 154:0.10 155:0.67 157:0.61 158:0.91 159:0.35 161:0.71 162:0.72 164:0.91 165:0.93 166:0.89 167:1.00 169:1.00 172:0.27 173:0.73 175:0.91 176:0.73 177:0.05 179:0.93 181:0.73 182:0.99 186:0.95 189:0.99 191:0.91 192:0.87 195:0.65 196:0.98 201:0.93 202:0.91 204:0.85 208:0.64 212:0.30 215:0.44 216:0.48 219:0.95 220:0.62 222:0.90 227:0.96 229:1.00 230:0.63 231:0.85 233:0.73 235:0.52 238:0.99 241:0.95 242:0.82 243:0.68 244:0.83 245:0.42 246:0.99 247:0.12 248:0.95 253:0.94 255:0.95 256:0.92 257:0.84 259:0.21 260:0.91 261:0.98 265:0.55 266:0.27 267:0.44 268:0.93 271:0.69 276:0.23 277:0.87 279:0.86 281:0.91 282:0.88 283:0.78 284:0.99 285:0.62 287:0.99 290:0.65 292:0.95 297:0.36 299:0.88 300:0.33 +1 12:0.92 17:0.43 21:0.40 27:0.45 28:0.56 30:0.45 32:0.80 34:0.87 36:0.19 37:0.50 43:0.32 55:0.59 66:0.69 69:0.69 70:0.25 81:0.95 91:0.11 98:0.39 108:0.53 123:0.50 126:0.54 127:0.13 129:0.05 135:0.75 145:0.47 146:0.48 147:0.54 149:0.30 150:0.91 154:0.24 159:0.17 161:0.65 163:0.81 167:0.64 172:0.21 173:0.70 177:0.05 178:0.55 179:0.62 181:0.53 187:0.68 195:0.72 212:0.61 215:0.67 216:0.77 235:0.42 241:0.18 242:0.82 243:0.73 247:0.12 259:0.21 265:0.41 266:0.17 267:0.23 271:0.43 276:0.20 297:0.36 300:0.18 +1 12:0.92 17:0.38 21:0.30 27:0.45 28:0.56 30:0.33 32:0.80 34:0.86 36:0.17 37:0.50 43:0.32 55:0.71 66:0.68 69:0.69 70:0.69 81:0.96 91:0.18 98:0.48 108:0.52 123:0.50 124:0.41 126:0.54 127:0.19 129:0.59 133:0.47 135:0.72 145:0.49 146:0.68 147:0.73 149:0.41 150:0.93 154:0.24 159:0.37 161:0.65 167:0.62 172:0.37 173:0.60 177:0.05 178:0.55 179:0.64 181:0.53 187:0.68 195:0.82 212:0.19 215:0.72 216:0.77 235:0.31 241:0.12 242:0.82 243:0.72 245:0.45 247:0.12 259:0.21 265:0.50 266:0.47 267:0.36 271:0.43 276:0.33 283:0.60 297:0.36 300:0.43 +1 6:0.93 7:0.81 8:0.89 12:0.92 17:0.06 20:0.93 25:0.88 27:0.12 28:0.56 30:0.76 32:0.80 34:0.61 36:0.29 37:0.82 43:0.40 55:0.71 66:0.72 69:0.53 70:0.22 78:0.87 81:0.99 91:0.72 98:0.83 100:0.94 104:0.72 108:0.41 111:0.90 120:0.81 123:0.39 126:0.54 127:0.32 129:0.05 135:0.23 140:0.45 145:0.65 146:0.51 147:0.57 149:0.36 150:0.98 151:0.73 152:0.92 153:0.80 154:0.30 159:0.18 161:0.65 162:0.78 163:0.81 164:0.70 167:0.91 169:0.92 172:0.27 173:0.79 177:0.05 179:0.93 181:0.53 186:0.87 187:0.39 189:0.95 191:0.77 195:0.56 202:0.59 212:0.42 215:0.96 216:0.45 230:0.87 233:0.76 235:0.02 238:0.96 241:0.77 242:0.82 243:0.75 247:0.12 248:0.73 256:0.58 259:0.21 260:0.81 261:0.92 265:0.83 266:0.23 267:0.18 268:0.64 271:0.43 276:0.26 285:0.62 297:0.36 300:0.18 +0 12:0.92 17:0.81 25:0.88 27:0.07 28:0.56 34:0.83 36:0.39 37:0.91 81:0.99 91:0.37 104:0.58 120:0.75 126:0.54 135:0.72 140:0.45 150:0.98 151:0.71 153:0.72 161:0.65 162:0.86 164:0.69 167:0.77 175:0.75 181:0.53 187:0.39 202:0.53 215:0.96 216:0.69 235:0.02 241:0.66 244:0.61 248:0.68 256:0.58 257:0.65 259:0.21 260:0.76 267:0.44 268:0.62 271:0.43 277:0.69 285:0.62 297:0.36 +0 6:0.88 7:0.81 8:0.73 12:0.92 17:0.24 20:0.93 21:0.30 27:0.21 28:0.56 30:0.33 34:0.61 36:0.69 37:0.74 43:0.52 55:0.52 66:0.79 69:0.10 70:0.36 78:0.77 81:0.96 91:0.63 98:0.74 100:0.81 104:0.84 106:0.81 108:0.09 111:0.77 120:0.85 123:0.09 126:0.54 127:0.23 129:0.05 135:0.26 140:0.45 146:0.56 147:0.62 149:0.50 150:0.93 151:0.80 152:0.99 153:0.92 154:0.41 159:0.20 161:0.65 162:0.56 164:0.80 167:0.80 169:0.78 172:0.41 173:0.32 177:0.05 179:0.74 181:0.53 186:0.78 187:0.39 189:0.89 202:0.77 206:0.81 212:0.89 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.90 241:0.70 242:0.82 243:0.80 247:0.12 248:0.79 256:0.73 259:0.21 260:0.86 261:0.82 265:0.74 266:0.17 267:0.28 268:0.76 271:0.43 276:0.33 283:0.82 285:0.62 297:0.36 300:0.25 +2 7:0.81 12:0.92 17:0.59 21:0.40 27:0.21 28:0.56 30:0.52 34:0.67 36:0.77 37:0.74 43:0.52 55:0.71 66:0.45 69:0.15 70:0.74 81:0.95 91:0.11 98:0.67 104:0.89 106:0.81 108:0.74 120:0.71 123:0.73 124:0.79 126:0.54 127:0.67 129:0.93 133:0.84 135:0.18 140:0.45 146:0.52 147:0.58 149:0.79 150:0.91 151:0.66 153:0.48 154:0.41 159:0.88 161:0.65 162:0.78 163:0.81 164:0.70 167:0.63 172:0.85 173:0.08 177:0.05 179:0.24 181:0.53 187:0.39 202:0.59 206:0.81 212:0.30 215:0.67 216:0.95 220:0.74 230:0.65 233:0.63 235:0.42 241:0.15 242:0.82 243:0.14 245:0.90 247:0.12 248:0.65 256:0.58 259:0.21 260:0.72 265:0.67 266:0.75 267:0.52 268:0.64 271:0.43 276:0.88 283:0.82 285:0.62 297:0.36 300:0.84 +1 6:0.90 7:0.81 8:0.80 12:0.92 17:0.45 20:0.99 21:0.05 27:0.19 28:0.56 30:0.21 32:0.80 34:0.80 36:0.74 37:0.82 43:0.37 55:0.84 66:0.36 69:0.59 70:0.67 78:0.76 81:0.98 91:0.68 98:0.47 100:0.80 104:0.97 106:0.81 108:0.75 111:0.76 120:0.79 123:0.73 124:0.70 126:0.54 127:0.39 129:0.81 133:0.67 135:0.69 140:0.45 145:0.88 146:0.46 147:0.52 149:0.41 150:0.98 151:0.74 152:0.90 153:0.82 154:0.28 159:0.45 161:0.65 162:0.80 163:0.75 164:0.78 167:0.74 169:0.78 172:0.27 173:0.59 177:0.05 179:0.83 181:0.53 186:0.77 187:0.87 189:0.92 191:0.70 195:0.69 202:0.68 206:0.81 212:0.16 215:0.91 216:0.59 230:0.87 233:0.76 235:0.05 238:0.91 241:0.59 242:0.82 243:0.29 245:0.50 247:0.12 248:0.71 256:0.71 259:0.21 260:0.80 261:0.80 265:0.48 266:0.54 267:0.52 268:0.73 271:0.43 276:0.37 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 8:0.85 12:0.92 17:0.53 21:0.05 25:0.88 27:0.07 28:0.56 30:0.62 32:0.80 34:0.68 36:0.81 37:0.91 43:0.49 55:0.71 66:0.79 69:0.25 70:0.41 81:0.98 91:0.72 98:0.83 104:0.58 108:0.20 120:0.73 123:0.19 124:0.38 126:0.54 127:0.51 129:0.59 133:0.47 135:0.72 140:0.45 145:0.65 146:0.80 147:0.83 149:0.64 150:0.98 151:0.66 153:0.48 154:0.37 159:0.43 161:0.65 162:0.64 164:0.76 167:0.73 172:0.61 173:0.32 177:0.05 179:0.68 181:0.53 187:0.39 191:0.85 195:0.69 202:0.69 212:0.55 215:0.91 216:0.69 220:0.87 230:0.87 233:0.76 235:0.05 241:0.56 242:0.82 243:0.80 245:0.53 247:0.12 248:0.66 256:0.68 259:0.21 260:0.74 265:0.83 266:0.47 267:0.59 268:0.70 271:0.43 276:0.53 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.93 8:0.90 12:0.92 17:0.32 20:0.99 21:0.05 27:0.31 28:0.56 34:0.24 36:0.65 37:0.85 78:0.79 81:0.98 91:0.92 100:0.84 104:0.58 111:0.79 120:0.85 126:0.54 135:0.44 140:0.45 150:0.98 151:0.79 152:0.92 153:0.90 161:0.65 162:0.60 164:0.82 167:0.99 169:0.81 175:0.75 181:0.53 186:0.80 189:0.95 191:0.95 202:0.77 215:0.91 216:0.25 235:0.05 238:0.92 241:0.97 244:0.61 248:0.77 256:0.88 257:0.65 259:0.21 260:0.85 261:0.84 267:0.84 268:0.78 271:0.43 277:0.69 285:0.62 297:0.36 +1 6:0.96 7:0.81 8:0.91 12:0.92 17:0.45 20:0.93 21:0.05 25:0.88 27:0.07 28:0.56 30:0.68 32:0.80 34:0.69 36:0.83 37:0.91 43:0.48 55:0.71 66:0.77 69:0.29 70:0.37 78:0.84 81:0.98 91:0.80 98:0.83 100:0.93 104:0.58 108:0.22 111:0.87 120:0.82 123:0.22 124:0.38 126:0.54 127:0.49 129:0.59 133:0.47 135:0.65 140:0.45 145:0.65 146:0.80 147:0.83 149:0.62 150:0.98 151:0.78 152:0.97 153:0.89 154:0.37 159:0.42 161:0.65 162:0.54 164:0.83 167:0.77 169:0.98 172:0.59 173:0.34 177:0.05 179:0.70 181:0.53 186:0.85 187:0.21 189:0.96 191:0.92 195:0.69 202:0.81 212:0.51 215:0.91 216:0.69 230:0.87 233:0.76 235:0.05 238:0.96 241:0.65 242:0.82 243:0.79 245:0.52 247:0.12 248:0.74 256:0.80 259:0.21 260:0.82 261:0.98 265:0.83 266:0.47 267:0.52 268:0.78 271:0.43 276:0.51 285:0.62 297:0.36 300:0.33 +2 6:0.88 7:0.81 8:0.78 12:0.92 17:0.62 20:0.81 21:0.47 27:0.21 28:0.56 30:0.52 34:0.66 36:0.77 37:0.74 43:0.52 55:0.71 66:0.43 69:0.17 70:0.70 78:0.76 81:0.93 91:0.06 98:0.62 100:0.78 104:0.89 106:0.81 108:0.74 111:0.75 120:0.77 123:0.73 124:0.84 126:0.54 127:0.65 129:0.95 133:0.87 135:0.17 140:0.45 146:0.52 147:0.58 149:0.78 150:0.88 151:0.74 152:0.99 153:0.83 154:0.41 159:0.88 161:0.65 162:0.61 163:0.81 164:0.71 167:0.59 169:0.78 172:0.84 173:0.09 177:0.05 179:0.24 181:0.53 186:0.77 187:0.39 189:0.93 202:0.64 206:0.81 212:0.30 215:0.63 216:0.95 230:0.65 233:0.63 235:0.52 238:0.89 241:0.03 242:0.82 243:0.15 245:0.88 247:0.12 248:0.69 256:0.58 259:0.21 260:0.77 261:0.82 265:0.63 266:0.75 267:0.28 268:0.64 271:0.43 276:0.87 283:0.82 285:0.62 297:0.36 300:0.84 +1 8:0.87 12:0.92 17:0.04 21:0.05 25:0.88 27:0.07 28:0.56 30:0.95 34:0.90 36:0.71 37:0.91 43:0.47 55:0.96 66:0.20 69:0.38 81:0.98 91:0.73 98:0.10 104:0.58 108:0.30 120:0.73 123:0.28 124:0.61 126:0.54 127:0.33 129:0.59 133:0.47 135:0.58 140:0.45 146:0.05 147:0.05 149:0.21 150:0.98 151:0.66 153:0.48 154:0.35 159:0.25 161:0.65 162:0.82 164:0.74 167:0.82 172:0.05 173:0.55 177:0.05 179:1.00 181:0.53 187:0.68 191:0.89 202:0.62 215:0.91 216:0.69 220:0.93 235:0.05 241:0.71 243:0.40 245:0.36 248:0.66 256:0.64 259:0.21 260:0.74 265:0.10 267:0.69 268:0.68 271:0.43 285:0.62 297:0.36 300:0.08 +2 7:0.81 8:0.79 12:0.92 17:0.18 25:0.88 27:0.12 28:0.56 30:0.76 32:0.80 34:0.59 36:0.28 37:0.82 43:0.33 55:0.71 66:0.36 69:0.67 70:0.22 81:0.99 91:0.58 98:0.36 104:0.72 108:0.68 120:0.69 123:0.66 124:0.57 126:0.54 127:0.31 129:0.59 133:0.47 135:0.54 140:0.45 145:0.65 146:0.26 147:0.32 149:0.32 150:0.98 151:0.66 153:0.48 154:0.24 159:0.27 161:0.65 162:0.62 163:0.88 164:0.57 167:0.77 172:0.16 173:0.80 177:0.05 179:0.93 181:0.53 187:0.68 191:0.73 195:0.62 202:0.50 212:0.42 215:0.96 216:0.45 230:0.87 233:0.76 235:0.02 241:0.66 242:0.82 243:0.40 245:0.43 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 265:0.38 266:0.23 267:0.59 268:0.46 271:0.43 276:0.21 285:0.62 297:0.36 300:0.18 +0 8:0.84 12:0.92 17:0.61 21:0.05 27:0.40 28:0.56 34:0.45 36:0.30 37:0.85 81:0.98 91:0.67 104:0.54 120:0.73 126:0.54 135:0.61 140:0.45 150:0.98 151:0.69 153:0.69 161:0.65 162:0.64 164:0.68 167:0.85 175:0.75 181:0.53 187:0.39 191:0.78 202:0.61 215:0.91 216:0.19 235:0.05 241:0.73 244:0.61 248:0.66 256:0.68 257:0.65 259:0.21 260:0.74 267:0.59 268:0.62 271:0.43 277:0.69 285:0.62 297:0.36 +4 6:0.96 8:0.92 12:0.92 17:0.11 20:0.98 21:0.78 25:0.88 27:0.07 28:0.56 34:0.64 36:0.27 37:0.91 78:0.93 91:0.98 100:0.99 104:0.58 111:0.99 120:0.97 135:0.75 140:0.45 151:0.86 152:0.97 153:0.99 161:0.65 162:0.56 164:0.97 167:0.98 169:0.98 175:0.75 181:0.53 186:0.93 189:0.97 191:0.93 202:0.96 216:0.69 238:0.99 241:0.94 244:0.61 248:0.96 256:0.96 257:0.65 259:0.21 260:0.97 261:0.98 267:0.84 268:0.96 271:0.43 277:0.69 283:0.82 285:0.62 +1 1:0.74 6:0.82 8:0.64 9:0.80 11:0.91 12:0.51 17:0.26 18:0.90 20:0.79 21:0.40 22:0.93 27:0.25 28:0.76 29:0.56 30:0.17 31:0.88 34:0.42 36:0.38 37:0.34 39:0.25 43:0.67 45:0.81 47:0.93 55:0.84 64:0.77 66:0.11 69:0.55 70:0.91 71:0.97 74:0.60 78:0.88 79:0.88 81:0.57 83:0.60 86:0.89 91:0.25 96:0.71 98:0.30 99:0.83 100:0.89 101:0.52 104:0.99 106:0.81 108:0.42 111:0.87 114:0.62 117:0.86 120:0.57 122:0.86 123:0.89 124:0.77 126:0.54 127:0.46 129:0.59 133:0.74 135:0.90 137:0.88 139:0.86 140:0.80 144:0.85 146:0.60 147:0.65 149:0.53 150:0.73 151:0.56 152:0.78 153:0.48 154:0.83 155:0.67 159:0.78 161:0.78 162:0.54 164:0.57 165:0.70 167:0.45 169:0.88 172:0.54 173:0.33 176:0.73 177:0.70 178:0.42 179:0.53 181:0.96 186:0.88 187:0.95 189:0.86 192:0.87 196:0.91 201:0.93 202:0.56 206:0.81 208:0.64 212:0.42 215:0.42 216:0.40 220:0.82 222:0.60 232:1.00 235:0.60 238:0.88 241:0.05 242:0.54 243:0.51 245:0.71 247:0.67 248:0.55 253:0.51 254:0.74 255:0.95 256:0.45 259:0.21 260:0.58 261:0.86 262:0.93 265:0.32 266:0.87 267:0.73 268:0.46 271:0.52 276:0.59 277:0.69 284:0.89 285:0.62 290:0.62 297:0.36 300:0.84 +2 1:0.69 7:0.72 8:0.63 9:0.76 11:0.91 12:0.51 17:0.84 21:0.64 27:0.72 28:0.76 29:0.56 30:0.21 32:0.69 34:0.96 36:0.89 37:0.80 39:0.25 43:0.70 45:0.81 48:0.91 66:0.12 69:0.43 70:0.91 71:0.97 74:0.66 76:0.85 77:0.70 81:0.34 83:0.60 91:0.51 96:0.72 98:0.41 99:0.83 101:0.52 104:0.83 106:0.81 108:0.33 114:0.56 117:0.86 120:0.59 122:0.51 123:0.81 124:0.71 126:0.44 127:0.61 129:0.05 133:0.74 135:0.70 139:0.64 140:0.45 144:0.85 145:0.50 146:0.50 147:0.56 149:0.70 150:0.52 151:0.53 153:0.48 154:0.60 155:0.58 159:0.58 161:0.78 162:0.86 164:0.68 165:0.70 167:0.47 172:0.51 173:0.37 175:0.75 176:0.66 177:0.38 179:0.68 181:0.96 187:0.39 190:0.77 192:0.59 195:0.80 201:0.68 202:0.56 204:0.85 206:0.81 208:0.54 212:0.42 215:0.38 216:0.09 220:0.99 222:0.60 230:0.75 232:0.89 233:0.76 235:0.80 241:0.18 242:0.37 243:0.61 244:0.61 245:0.60 247:0.60 248:0.56 253:0.50 255:0.74 256:0.64 257:0.65 259:0.21 260:0.58 265:0.40 266:0.75 267:0.52 268:0.65 271:0.52 276:0.52 277:0.87 281:0.91 282:0.77 285:0.56 290:0.56 297:0.36 300:0.70 +2 1:0.74 9:0.80 11:0.91 12:0.51 17:0.64 18:0.69 21:0.05 22:0.93 27:0.21 28:0.76 29:0.56 30:0.60 31:0.91 32:0.69 34:0.50 36:0.71 37:0.60 39:0.25 43:0.10 45:0.77 47:0.93 55:0.71 64:0.77 66:0.86 69:0.78 70:0.44 71:0.97 74:0.69 77:0.70 79:0.90 81:0.86 83:0.60 86:0.79 91:0.37 96:0.75 97:0.94 98:0.64 99:0.83 101:0.52 104:0.98 106:0.81 108:0.62 114:0.89 117:0.86 120:0.63 122:0.82 123:0.59 126:0.54 127:0.19 129:0.05 135:0.94 137:0.91 139:0.83 140:0.45 144:0.85 145:0.83 146:0.69 147:0.73 149:0.14 150:0.90 151:0.72 153:0.48 154:0.70 155:0.67 158:0.91 159:0.27 161:0.78 162:0.86 164:0.57 165:0.70 167:0.46 172:0.59 173:0.81 176:0.73 177:0.70 179:0.41 181:0.96 187:0.68 192:0.87 195:0.70 196:0.93 201:0.93 202:0.36 206:0.81 208:0.64 212:0.54 215:0.78 216:0.62 219:0.95 220:0.62 222:0.60 232:0.99 235:0.22 241:0.12 242:0.19 243:0.86 247:0.79 248:0.67 253:0.75 254:0.74 255:0.95 256:0.45 259:0.21 260:0.64 262:0.93 265:0.64 266:0.38 267:0.93 268:0.46 271:0.52 276:0.49 279:0.86 282:0.77 283:0.78 284:0.89 285:0.62 290:0.89 292:0.91 297:0.36 300:0.33 +0 11:0.91 12:0.51 17:0.43 18:0.74 21:0.40 27:0.45 28:0.76 29:0.56 30:0.21 32:0.69 34:0.58 36:0.17 37:0.50 39:0.25 43:0.11 45:0.66 64:0.77 66:0.54 69:0.69 70:0.37 71:0.97 74:0.50 77:0.70 81:0.26 86:0.79 91:0.18 98:0.32 101:0.52 108:0.52 122:0.57 123:0.50 124:0.45 126:0.26 127:0.19 129:0.05 133:0.37 135:0.83 139:0.75 144:0.85 145:0.49 146:0.43 147:0.50 149:0.08 150:0.25 154:0.71 159:0.32 161:0.78 163:0.75 167:0.51 172:0.21 173:0.66 177:0.70 178:0.55 179:0.82 181:0.96 187:0.68 195:0.75 212:0.42 216:0.77 222:0.60 235:0.42 241:0.41 242:0.45 243:0.63 245:0.40 247:0.35 253:0.35 254:0.94 259:0.21 265:0.34 266:0.23 267:0.36 271:0.52 276:0.20 282:0.77 300:0.25 +2 7:0.72 8:0.70 9:0.76 11:0.91 12:0.51 17:0.59 20:0.75 21:0.78 27:0.12 28:0.76 29:0.56 30:0.21 32:0.69 34:0.25 36:0.26 37:0.29 39:0.25 43:0.40 45:0.73 55:0.59 66:0.85 69:0.81 70:0.64 71:0.97 74:0.53 76:0.85 77:0.70 78:0.82 83:0.60 91:0.77 96:0.98 98:0.78 100:0.91 101:0.52 104:0.58 108:0.65 111:0.85 120:0.95 123:0.63 127:0.27 129:0.05 135:0.20 138:0.99 140:0.45 144:0.85 145:0.74 146:0.76 147:0.80 149:0.39 151:0.90 152:0.74 153:0.83 154:0.30 155:0.58 159:0.32 161:0.78 162:0.71 164:0.90 167:0.79 169:0.89 172:0.57 173:0.87 175:0.75 177:0.38 179:0.61 181:0.96 186:0.83 190:0.77 191:0.77 192:0.59 195:0.67 202:0.88 204:0.85 208:0.54 212:0.36 216:0.15 220:0.62 222:0.60 230:0.75 232:0.78 233:0.76 235:0.05 241:0.85 242:0.71 243:0.86 244:0.61 247:0.47 248:0.97 253:0.95 255:0.74 256:0.90 257:0.65 259:0.21 260:0.94 261:0.76 265:0.78 266:0.38 267:0.44 268:0.90 271:0.52 276:0.46 277:0.69 282:0.77 285:0.56 300:0.43 +2 6:0.87 11:0.91 12:0.51 17:0.18 18:0.87 20:0.76 21:0.78 27:0.19 28:0.76 29:0.56 30:0.40 31:0.90 34:0.61 36:0.29 37:0.73 39:0.25 43:0.56 45:0.77 55:0.71 66:0.91 69:0.73 70:0.57 71:0.97 74:0.55 78:0.95 79:0.90 83:0.60 86:0.85 91:0.08 97:0.89 98:0.85 100:0.93 101:0.52 108:0.56 111:0.93 120:0.62 122:0.86 123:0.53 127:0.35 129:0.05 135:0.84 137:0.90 139:0.84 140:0.45 144:0.85 146:0.93 147:0.95 149:0.45 151:0.59 152:0.75 153:0.48 154:0.66 155:0.58 158:0.79 159:0.58 161:0.78 162:0.86 163:0.90 164:0.53 167:0.49 169:0.91 172:0.75 173:0.65 177:0.70 179:0.46 181:0.96 186:0.94 187:0.68 189:0.88 190:0.77 192:0.59 196:0.92 202:0.50 208:0.54 212:0.29 216:0.63 219:0.92 220:0.74 222:0.60 235:0.05 238:0.90 241:0.35 242:0.38 243:0.91 247:0.84 248:0.61 253:0.37 254:0.80 255:0.74 256:0.58 259:0.21 260:0.58 261:0.80 265:0.85 266:0.71 267:0.84 268:0.59 271:0.52 276:0.64 279:0.82 283:0.82 284:0.91 285:0.56 292:0.91 300:0.43 +2 1:0.69 6:0.86 7:0.72 8:0.83 9:0.76 11:0.91 12:0.51 17:0.49 18:0.61 20:0.98 21:0.30 27:0.21 28:0.76 29:0.56 30:0.43 34:0.69 36:0.72 37:0.74 39:0.25 43:0.72 45:0.94 66:0.15 69:0.85 70:0.81 71:0.97 74:0.80 78:0.78 81:0.54 83:0.60 86:0.67 91:0.73 96:0.96 97:0.97 98:0.65 99:0.83 100:0.81 101:0.52 104:0.84 106:0.81 108:0.91 111:0.77 114:0.63 117:0.86 120:0.94 122:0.77 123:0.69 124:0.72 126:0.44 127:0.56 129:0.59 133:0.66 135:0.18 139:0.65 140:0.45 144:0.85 146:0.92 147:0.74 149:0.85 150:0.58 151:0.96 152:0.90 153:0.94 154:0.22 155:0.58 158:0.79 159:0.80 161:0.78 162:0.60 164:0.87 165:0.70 167:0.49 169:0.79 172:0.79 173:0.71 176:0.66 177:0.38 179:0.26 181:0.96 186:0.79 187:0.39 189:0.87 190:0.77 191:0.81 192:0.59 201:0.68 202:0.86 204:0.85 206:0.81 208:0.54 212:0.60 215:0.44 216:0.95 222:0.60 230:0.75 232:0.90 233:0.76 235:0.42 238:0.87 241:0.35 242:0.40 243:0.33 245:0.93 247:0.74 248:0.95 253:0.96 254:0.96 255:0.74 256:0.86 257:0.65 259:0.21 260:0.91 261:0.83 265:0.41 266:0.80 267:0.23 268:0.87 271:0.52 276:0.84 279:0.82 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.74 6:0.84 8:0.66 9:0.80 11:0.91 12:0.51 17:0.67 18:0.71 20:0.99 21:0.22 22:0.93 27:0.21 28:0.76 29:0.56 30:0.66 31:0.90 32:0.69 34:0.61 36:0.36 37:0.60 39:0.25 43:0.56 45:0.83 47:0.93 60:0.87 64:0.77 66:0.47 69:0.78 70:0.64 71:0.97 74:0.72 77:0.70 78:0.92 79:0.89 81:0.71 83:0.91 86:0.83 91:0.57 96:0.73 97:0.94 98:0.45 99:0.83 100:0.97 101:0.52 104:1.00 106:0.81 108:0.73 111:0.95 114:0.71 120:0.61 122:0.51 123:0.71 124:0.56 126:0.54 127:0.21 129:0.59 133:0.46 135:0.96 137:0.90 139:0.85 140:0.45 144:0.85 145:0.93 146:0.24 147:0.30 149:0.36 150:0.80 151:0.58 152:0.99 153:0.78 154:0.69 155:0.67 158:0.91 159:0.33 161:0.78 162:0.86 164:0.73 165:0.70 167:0.46 169:0.94 172:0.41 173:0.78 176:0.73 177:0.70 179:0.50 181:0.96 186:0.92 187:0.68 189:0.86 192:0.87 195:0.74 196:0.92 201:0.93 202:0.59 206:0.81 208:0.64 212:0.36 215:0.51 216:0.62 219:0.95 220:0.82 222:0.60 235:0.42 238:0.96 241:0.07 242:0.38 243:0.44 245:0.66 247:0.55 248:0.61 253:0.63 254:0.84 255:0.95 256:0.64 259:0.21 260:0.61 261:0.97 262:0.93 265:0.46 266:0.63 267:0.84 268:0.67 271:0.52 276:0.40 279:0.86 282:0.77 283:0.78 284:0.94 285:0.62 290:0.71 292:0.91 297:0.36 300:0.55 +1 7:0.72 8:0.70 9:0.76 11:0.91 12:0.51 17:0.66 20:0.75 21:0.78 27:0.12 28:0.76 29:0.56 30:0.38 32:0.69 34:0.21 36:0.41 37:0.29 39:0.25 43:0.62 45:0.83 55:0.92 66:0.24 69:0.70 70:0.70 71:0.97 74:0.60 76:0.85 77:0.70 78:0.82 83:0.60 91:0.73 96:0.98 98:0.67 100:0.91 101:0.52 104:0.58 108:0.84 111:0.85 120:0.95 123:0.64 124:0.78 127:0.81 129:0.81 133:0.76 135:0.28 138:0.99 140:0.45 144:0.85 145:0.74 146:0.29 147:0.05 149:0.61 151:0.90 152:0.74 153:0.83 154:0.52 155:0.58 159:0.62 161:0.78 162:0.72 164:0.90 167:0.79 169:0.89 172:0.45 173:0.67 175:0.75 177:0.05 179:0.64 181:0.96 186:0.83 190:0.77 191:0.77 192:0.59 195:0.78 202:0.87 204:0.85 208:0.54 212:0.18 216:0.15 220:0.62 222:0.60 230:0.75 232:0.78 233:0.76 235:0.05 241:0.84 242:0.18 243:0.10 244:0.61 245:0.68 247:0.67 248:0.97 253:0.96 255:0.74 256:0.90 257:0.84 259:0.21 260:0.94 261:0.76 265:0.44 266:0.80 267:0.65 268:0.90 271:0.52 276:0.60 277:0.69 282:0.77 285:0.56 300:0.55 +2 1:0.69 6:0.87 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.49 20:0.78 21:0.05 27:0.18 28:0.76 29:0.56 30:0.75 32:0.69 34:0.61 36:0.81 37:0.96 39:0.25 43:0.70 44:0.87 45:0.97 48:0.91 66:0.05 69:0.44 70:0.43 71:0.97 74:0.89 77:0.70 78:0.86 81:0.82 83:0.60 91:0.51 96:0.90 97:0.89 98:0.46 99:0.83 100:0.86 101:0.52 104:0.82 106:0.81 108:0.80 111:0.85 114:0.85 117:0.86 120:0.83 122:0.51 123:0.64 124:0.49 126:0.44 127:0.70 129:0.81 133:0.67 135:0.42 139:0.68 140:0.45 144:0.85 145:0.93 146:0.43 147:0.50 149:0.93 150:0.79 151:0.90 152:0.76 153:0.83 154:0.59 155:0.58 158:0.79 159:0.55 161:0.78 162:0.84 164:0.76 165:0.70 167:0.52 169:0.84 172:0.89 173:0.42 175:0.75 176:0.66 177:0.38 179:0.27 181:0.96 186:0.86 187:0.21 189:0.91 190:0.77 191:0.70 192:0.59 195:0.76 201:0.68 202:0.68 204:0.85 206:0.81 208:0.54 212:0.87 215:0.78 216:0.50 222:0.60 230:0.75 232:0.88 233:0.76 235:0.12 238:0.84 241:0.47 242:0.62 243:0.22 244:0.61 245:0.89 247:0.47 248:0.87 253:0.91 255:0.74 256:0.71 257:0.65 259:0.21 260:0.80 261:0.81 265:0.46 266:0.84 267:0.44 268:0.74 271:0.52 276:0.83 277:0.69 279:0.82 281:0.89 282:0.77 283:0.82 285:0.56 290:0.85 297:0.36 300:0.70 +0 1:0.74 11:0.91 12:0.51 17:0.36 18:0.76 21:0.59 27:0.45 28:0.76 29:0.56 30:0.33 32:0.80 34:0.81 36:0.17 37:0.50 39:0.25 43:0.32 44:0.93 45:0.73 64:0.77 66:0.45 69:0.69 70:0.69 71:0.97 74:0.67 77:0.70 81:0.45 83:0.60 86:0.79 91:0.17 97:0.89 98:0.30 99:0.83 101:0.52 108:0.53 114:0.58 122:0.41 123:0.51 124:0.66 126:0.54 127:0.34 129:0.05 133:0.60 135:0.86 139:0.88 144:0.85 145:0.47 146:0.39 147:0.46 149:0.24 150:0.67 154:0.76 155:0.67 158:0.91 159:0.47 161:0.78 163:0.75 165:0.70 167:0.46 172:0.27 173:0.68 176:0.73 177:0.70 178:0.55 179:0.84 181:0.96 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.19 215:0.39 216:0.77 219:0.95 222:0.60 235:0.80 241:0.12 242:0.19 243:0.58 245:0.47 247:0.55 253:0.43 254:0.74 259:0.21 265:0.32 266:0.47 267:0.28 271:0.52 276:0.30 279:0.86 282:0.88 283:0.77 290:0.58 292:0.91 297:0.36 300:0.43 +2 1:0.69 6:0.84 8:0.77 9:0.76 11:0.91 12:0.51 17:0.03 18:0.60 20:0.94 21:0.05 27:0.14 28:0.76 29:0.56 30:0.41 32:0.69 34:0.37 36:0.25 37:0.67 39:0.25 43:0.67 45:0.81 66:0.49 69:0.57 70:0.45 71:0.97 74:0.58 77:0.70 78:0.81 81:0.82 83:0.60 86:0.62 91:0.44 96:0.79 98:0.36 99:0.83 100:0.84 101:0.52 108:0.61 111:0.81 114:0.85 120:0.68 122:0.51 123:0.59 124:0.66 126:0.44 127:0.18 129:0.81 133:0.67 135:0.95 139:0.60 140:0.45 144:0.85 145:0.47 146:0.22 147:0.28 149:0.64 150:0.79 151:0.70 152:0.96 153:0.72 154:0.57 155:0.58 159:0.48 161:0.78 162:0.55 164:0.65 165:0.70 167:0.47 169:0.82 172:0.45 173:0.37 175:0.75 176:0.66 177:0.38 179:0.39 181:0.96 186:0.82 187:0.68 189:0.86 190:0.77 192:0.59 195:0.91 201:0.68 202:0.65 208:0.54 212:0.19 215:0.78 216:0.93 220:0.62 222:0.60 235:0.12 238:0.88 241:0.18 242:0.33 243:0.29 244:0.61 245:0.60 247:0.55 248:0.72 253:0.75 255:0.74 256:0.58 257:0.65 259:0.21 260:0.67 261:0.90 265:0.38 266:0.54 267:0.36 268:0.62 271:0.52 276:0.48 277:0.69 282:0.77 285:0.56 290:0.85 297:0.36 300:0.33 +1 1:0.69 6:0.89 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.91 18:0.70 20:0.94 21:0.30 27:0.35 28:0.76 29:0.56 30:0.75 32:0.69 34:0.39 36:0.47 37:0.55 39:0.25 43:0.72 45:0.98 66:0.77 69:0.80 70:0.41 71:0.97 74:0.92 77:0.70 78:0.85 81:0.54 83:0.60 86:0.79 91:0.67 96:0.76 97:0.89 98:0.76 99:0.83 100:0.87 101:0.52 104:0.91 108:0.77 111:0.85 114:0.63 120:0.64 122:0.51 123:0.75 124:0.42 126:0.44 127:0.81 129:0.59 133:0.66 135:0.37 138:0.96 139:0.75 140:0.45 144:0.85 145:0.41 146:0.37 147:0.18 149:0.94 150:0.58 151:0.81 152:0.88 153:0.48 154:0.65 155:0.58 158:0.79 159:0.66 161:0.78 162:0.81 164:0.68 165:0.70 167:0.47 169:0.85 172:0.93 173:0.77 176:0.66 177:0.38 179:0.23 181:0.96 186:0.85 187:0.39 189:0.90 190:0.77 192:0.59 195:0.79 201:0.68 202:0.61 204:0.85 208:0.54 212:0.92 215:0.44 216:0.52 220:0.87 222:0.60 230:0.74 232:0.93 233:0.73 235:0.42 238:0.88 241:0.15 242:0.60 243:0.08 245:0.89 247:0.60 248:0.74 253:0.71 254:0.80 255:0.74 256:0.64 257:0.65 259:0.21 260:0.62 261:0.90 265:0.76 266:0.54 267:0.36 268:0.66 271:0.52 276:0.88 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.70 +1 1:0.74 7:0.72 8:0.77 9:0.76 11:0.91 12:0.51 17:0.85 18:0.76 21:0.71 27:0.27 28:0.76 29:0.56 30:0.74 32:0.79 34:0.75 36:0.47 37:0.40 39:0.25 43:0.59 44:0.93 45:0.93 48:0.97 64:0.77 66:0.93 69:0.73 70:0.63 71:0.97 74:0.84 77:0.70 81:0.41 83:0.60 86:0.83 91:0.64 96:0.72 97:0.89 98:0.65 99:0.83 101:0.52 104:0.86 106:0.81 108:0.56 114:0.55 117:0.86 120:0.59 122:0.51 123:0.54 126:0.54 127:0.19 129:0.05 135:0.86 139:0.87 140:0.45 144:0.85 145:0.93 146:0.82 147:0.85 149:0.85 150:0.65 151:0.59 153:0.48 154:0.73 155:0.67 158:0.79 159:0.38 161:0.78 162:0.86 164:0.54 165:0.70 167:0.45 172:0.82 173:0.66 176:0.73 177:0.70 179:0.20 181:0.96 187:0.68 190:0.77 192:0.87 195:0.84 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.72 215:0.37 216:0.40 220:0.74 222:0.60 230:0.74 232:0.91 233:0.73 235:0.97 241:0.01 242:0.58 243:0.94 247:0.60 248:0.58 253:0.56 254:0.80 255:0.74 256:0.45 259:0.21 260:0.59 265:0.66 266:0.38 267:0.28 268:0.46 271:0.52 276:0.71 279:0.82 281:0.89 282:0.87 283:0.82 285:0.56 290:0.55 297:0.36 300:0.70 +1 1:0.69 6:0.87 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.57 20:0.78 21:0.05 27:0.18 28:0.76 29:0.56 30:0.74 32:0.69 34:0.49 36:0.87 37:0.96 39:0.25 43:0.70 44:0.87 45:0.98 48:0.91 66:0.72 69:0.44 70:0.41 71:0.97 74:0.91 77:0.70 78:0.86 81:0.82 83:0.60 91:0.54 96:0.90 97:0.89 98:0.63 99:0.83 100:0.86 101:0.52 104:0.82 106:0.81 108:0.78 111:0.85 114:0.85 117:0.86 120:0.83 122:0.51 123:0.77 124:0.45 126:0.44 127:0.73 129:0.81 133:0.67 135:0.61 139:0.68 140:0.45 144:0.85 145:0.93 146:0.43 147:0.50 149:0.95 150:0.79 151:0.90 152:0.76 153:0.83 154:0.59 155:0.58 158:0.79 159:0.58 161:0.78 162:0.84 164:0.76 165:0.70 167:0.50 169:0.84 172:0.93 173:0.40 175:0.75 176:0.66 177:0.38 179:0.22 181:0.96 186:0.86 187:0.21 189:0.91 190:0.77 191:0.70 192:0.59 195:0.77 201:0.68 202:0.68 204:0.85 206:0.81 208:0.54 212:0.88 215:0.78 216:0.50 222:0.60 230:0.75 232:0.88 233:0.76 235:0.12 238:0.84 241:0.39 242:0.62 243:0.19 244:0.61 245:0.91 247:0.47 248:0.87 253:0.91 255:0.74 256:0.71 257:0.65 259:0.21 260:0.80 261:0.81 265:0.64 266:0.80 267:0.52 268:0.74 271:0.52 276:0.88 277:0.69 279:0.82 281:0.89 282:0.77 283:0.82 285:0.56 290:0.85 297:0.36 300:0.70 +2 1:0.69 6:0.84 8:0.84 9:0.76 11:0.91 12:0.51 17:0.02 20:0.75 27:0.14 28:0.76 29:0.56 30:0.76 32:0.69 34:0.85 36:0.23 37:0.67 39:0.25 43:0.67 45:0.88 66:0.32 69:0.57 70:0.58 71:0.97 74:0.66 77:0.70 78:0.72 81:0.88 83:0.60 91:0.65 96:0.99 97:0.98 98:0.22 99:0.83 100:0.73 101:0.52 108:0.89 111:0.72 114:0.95 120:0.97 122:0.51 123:0.89 124:0.79 126:0.44 127:0.53 129:0.89 133:0.78 135:0.85 138:0.99 140:0.45 144:0.85 145:0.80 146:0.22 147:0.28 149:0.70 150:0.87 151:0.97 152:0.96 153:0.97 154:0.57 155:0.58 158:0.79 159:0.71 161:0.78 162:0.66 164:0.94 165:0.70 167:0.47 169:0.82 172:0.41 173:0.41 175:0.75 176:0.66 177:0.38 179:0.67 181:0.96 186:0.73 187:0.39 190:0.77 192:0.59 195:0.87 201:0.68 202:0.93 208:0.54 212:0.42 215:0.96 216:0.93 222:0.60 235:0.05 241:0.15 242:0.62 243:0.26 244:0.61 245:0.63 247:0.35 248:0.99 253:0.98 255:0.74 256:0.93 257:0.65 259:0.21 260:0.97 261:0.90 265:0.24 266:0.75 267:0.19 268:0.94 271:0.52 276:0.42 277:0.69 279:0.82 282:0.77 283:0.82 285:0.56 290:0.95 297:0.36 300:0.70 +1 7:0.72 11:0.91 12:0.51 17:0.81 18:0.60 21:0.78 27:0.72 28:0.76 29:0.56 30:0.33 32:0.69 34:0.33 36:0.79 37:0.29 39:0.25 43:0.68 45:0.85 66:0.43 69:0.57 70:0.77 71:0.97 74:0.63 76:0.85 77:0.70 86:0.62 91:0.22 98:0.51 101:0.52 104:0.54 108:0.87 122:0.51 123:0.86 124:0.76 127:0.67 129:0.89 133:0.78 135:0.44 139:0.60 144:0.85 145:0.74 146:0.13 147:0.18 149:0.66 154:0.57 155:0.58 159:0.71 161:0.78 167:0.45 172:0.54 173:0.44 175:0.75 177:0.38 178:0.55 179:0.60 181:0.96 187:0.39 192:0.59 195:0.86 202:0.36 204:0.85 208:0.54 212:0.18 216:0.13 222:0.60 230:0.75 232:0.74 233:0.76 235:0.22 241:0.05 242:0.24 243:0.16 244:0.61 245:0.69 247:0.76 253:0.40 257:0.65 259:0.21 265:0.53 266:0.84 267:0.91 271:0.52 276:0.59 277:0.69 282:0.77 300:0.55 +1 1:0.74 7:0.72 11:0.91 12:0.51 17:0.88 18:0.67 21:0.71 27:0.16 28:0.76 29:0.56 30:0.76 32:0.77 34:0.80 36:0.63 37:0.27 39:0.25 43:0.68 44:0.93 45:0.81 48:0.91 60:0.87 64:0.77 66:0.80 69:0.29 70:0.28 71:0.97 74:0.80 77:0.89 81:0.41 83:0.91 86:0.77 91:0.66 97:0.89 98:0.55 99:0.83 101:0.52 104:0.97 106:0.81 108:0.22 114:0.55 117:0.86 122:0.51 123:0.22 126:0.54 127:0.16 129:0.05 135:0.60 139:0.88 144:0.85 145:0.74 146:0.45 147:0.51 149:0.49 150:0.65 154:0.94 155:0.67 158:0.91 159:0.16 161:0.78 165:0.70 167:0.46 172:0.45 173:0.44 176:0.73 177:0.70 178:0.55 179:0.48 181:0.96 187:0.87 192:0.87 195:0.63 201:0.93 204:0.85 206:0.81 208:0.64 212:0.55 215:0.37 216:0.30 219:0.95 222:0.60 230:0.75 232:0.98 233:0.76 235:0.95 241:0.10 242:0.40 243:0.82 247:0.47 253:0.44 254:0.74 259:0.21 265:0.56 266:0.27 267:0.59 271:0.52 276:0.33 279:0.86 281:0.91 282:0.85 283:0.78 290:0.55 292:0.91 297:0.36 300:0.25 +2 1:0.69 6:0.86 7:0.81 8:0.62 9:0.76 11:0.92 12:0.51 17:0.82 18:0.61 20:0.77 27:0.34 28:0.76 29:0.56 30:0.70 32:0.69 34:0.29 36:0.73 37:0.36 39:0.25 43:0.69 45:0.99 66:0.99 69:0.45 70:0.67 71:0.97 74:0.96 76:0.85 77:0.89 78:0.88 81:0.88 83:0.60 85:0.80 86:0.70 91:0.38 96:0.80 97:0.89 98:0.90 99:0.83 100:0.91 101:0.87 104:0.80 106:0.81 108:0.35 111:0.88 114:0.95 117:0.86 120:0.69 121:0.90 122:0.51 123:0.34 126:0.44 127:0.47 129:0.05 135:0.97 139:0.79 140:0.45 144:0.85 145:0.91 146:0.98 147:0.98 149:0.97 150:0.87 151:0.81 152:0.75 153:0.48 154:0.45 155:0.67 158:0.79 159:0.76 161:0.78 162:0.86 164:0.54 165:0.70 167:0.45 169:0.88 172:0.98 173:0.26 176:0.66 177:0.70 179:0.13 181:0.96 186:0.88 187:0.21 189:0.88 190:0.77 192:0.87 195:0.92 201:0.68 202:0.36 204:0.89 206:0.81 208:0.64 212:0.83 215:0.96 216:0.84 222:0.60 230:0.87 232:0.86 233:0.76 235:0.05 238:0.87 241:0.01 242:0.53 243:0.99 247:0.76 248:0.73 253:0.86 255:0.74 256:0.45 259:0.21 260:0.68 261:0.80 265:0.90 266:0.63 267:0.23 268:0.46 271:0.52 276:0.95 279:0.82 281:0.91 282:0.77 283:0.82 285:0.56 290:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.82 8:0.61 9:0.76 11:0.91 12:0.51 17:0.32 18:0.96 20:0.79 21:0.40 22:0.93 27:0.25 28:0.76 29:0.56 30:0.12 34:0.52 36:0.40 37:0.34 39:0.25 43:0.67 45:0.81 47:0.93 55:0.84 64:0.77 66:0.13 69:0.55 70:0.94 71:0.97 74:0.64 78:0.92 81:0.57 83:0.60 86:0.93 91:0.25 96:0.80 97:0.89 98:0.39 99:0.83 100:0.91 101:0.52 104:0.99 106:0.81 108:0.42 111:0.90 114:0.62 117:0.86 120:0.69 122:0.86 123:0.88 124:0.85 126:0.54 127:0.45 129:0.81 133:0.81 135:0.79 139:0.93 140:0.45 144:0.85 146:0.72 147:0.77 149:0.52 150:0.73 151:0.81 152:0.78 153:0.48 154:0.83 155:0.67 158:0.87 159:0.79 161:0.78 162:0.86 164:0.54 165:0.70 167:0.45 169:0.88 172:0.59 173:0.32 176:0.73 177:0.70 179:0.33 181:0.96 186:0.92 187:0.95 189:0.83 190:0.77 192:0.87 201:0.93 202:0.36 206:0.81 208:0.64 212:0.18 215:0.42 216:0.40 219:0.95 222:0.60 232:1.00 235:0.60 238:0.86 241:0.05 242:0.74 243:0.43 245:0.83 247:0.76 248:0.73 253:0.76 254:0.80 255:0.74 256:0.45 259:0.21 260:0.68 261:0.86 262:0.93 265:0.40 266:0.89 267:0.52 268:0.46 271:0.52 276:0.77 279:0.86 283:0.71 285:0.56 290:0.62 292:0.91 297:0.36 300:0.84 +2 1:0.69 7:0.72 8:0.67 9:0.76 11:0.91 12:0.51 17:0.67 18:0.61 21:0.30 27:0.50 28:0.76 29:0.56 30:0.60 32:0.69 34:0.69 36:0.88 37:0.60 39:0.25 43:0.72 45:0.97 66:0.17 69:0.94 70:0.75 71:0.97 74:0.87 77:0.70 81:0.54 83:0.60 86:0.66 91:0.14 96:0.69 97:0.94 98:0.57 99:0.83 101:0.52 104:0.84 106:0.81 108:0.92 114:0.63 117:0.86 120:0.55 122:0.75 123:0.13 124:0.76 126:0.44 127:0.64 129:0.59 133:0.74 135:0.85 139:0.64 140:0.45 144:0.85 145:0.70 146:0.68 147:0.92 149:0.90 150:0.58 151:0.51 153:0.48 154:0.20 155:0.58 158:0.79 159:0.84 161:0.78 162:0.62 164:0.54 165:0.70 167:0.45 172:0.90 173:0.88 176:0.66 177:0.38 179:0.19 181:0.96 187:0.39 190:0.77 192:0.59 195:0.94 201:0.68 202:0.50 204:0.85 206:0.81 208:0.54 212:0.71 215:0.44 216:0.86 220:0.87 222:0.60 230:0.75 232:0.90 233:0.76 235:0.42 241:0.01 242:0.45 243:0.38 245:0.95 247:0.79 248:0.50 253:0.53 254:0.97 255:0.74 256:0.45 257:0.65 259:0.21 260:0.55 265:0.50 266:0.80 267:0.69 268:0.46 271:0.52 276:0.91 277:0.69 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +1 1:0.69 8:0.71 9:0.76 11:0.91 12:0.51 17:0.85 18:0.68 20:0.76 21:0.05 27:0.11 28:0.76 29:0.56 30:0.76 34:0.18 36:0.33 37:0.68 39:0.25 43:0.59 45:0.91 66:0.20 69:0.80 70:0.39 71:0.97 74:0.67 78:0.89 81:0.82 83:0.60 86:0.66 91:0.60 96:0.93 97:0.89 98:0.27 99:0.83 100:0.92 101:0.52 108:0.84 111:0.89 114:0.85 120:0.88 122:0.51 123:0.62 124:0.65 126:0.44 127:0.53 129:0.59 133:0.64 135:0.63 139:0.64 140:0.45 144:0.85 146:0.39 147:0.05 149:0.77 150:0.79 151:0.94 152:0.74 153:0.89 154:0.49 155:0.58 158:0.79 159:0.59 161:0.78 162:0.84 164:0.80 165:0.70 167:0.59 169:0.90 172:0.59 173:0.78 175:0.75 176:0.66 177:0.05 179:0.55 181:0.96 186:0.89 187:0.39 190:0.77 191:0.77 192:0.59 201:0.68 202:0.73 208:0.54 212:0.78 215:0.78 216:0.47 222:0.60 235:0.12 241:0.62 242:0.62 243:0.10 244:0.61 245:0.73 247:0.35 248:0.91 253:0.91 255:0.74 256:0.77 257:0.84 259:0.21 260:0.86 261:0.77 265:0.21 266:0.47 267:0.44 268:0.79 271:0.52 276:0.51 277:0.69 279:0.82 283:0.82 285:0.56 290:0.85 297:0.36 300:0.55 +1 1:0.74 7:0.81 11:0.78 12:0.86 17:0.87 27:0.13 28:0.89 30:0.70 32:0.80 34:0.36 36:0.79 37:0.85 39:0.86 43:0.96 66:0.99 69:0.08 70:0.36 74:0.98 77:0.96 81:0.96 83:0.80 85:0.99 91:0.09 98:1.00 101:0.87 104:0.57 108:0.07 114:0.98 121:1.00 122:0.43 123:0.07 126:0.54 127:0.96 129:0.05 131:0.61 135:0.44 144:0.76 145:0.98 146:0.99 147:0.99 149:0.99 150:0.98 154:0.96 155:0.67 157:0.95 159:0.84 161:0.63 163:0.81 165:0.92 166:0.93 167:0.57 172:0.97 173:0.08 176:0.73 177:0.38 178:0.55 179:0.19 181:0.50 182:0.86 187:0.39 192:0.59 195:0.93 201:0.74 204:0.89 208:0.64 212:0.48 215:0.96 216:0.31 222:0.25 227:0.61 229:0.61 230:0.87 231:0.89 232:0.80 233:0.76 235:0.05 241:0.02 242:0.54 243:0.99 246:0.94 247:0.60 253:0.81 259:0.21 265:1.00 266:0.38 267:0.23 271:0.44 276:0.93 282:0.88 287:0.61 290:0.98 297:0.36 299:0.97 300:0.43 +1 1:0.74 6:0.92 7:0.81 8:0.84 9:0.80 11:0.78 12:0.86 17:0.43 20:0.99 27:0.13 28:0.89 30:0.71 32:0.80 34:0.36 36:0.40 37:0.85 39:0.86 41:0.82 43:0.96 55:0.59 66:0.69 69:0.08 70:0.64 74:0.76 77:0.96 78:0.83 81:0.96 83:0.82 85:0.85 91:0.83 96:0.89 98:0.81 100:0.91 101:0.81 104:0.57 108:0.07 111:0.85 114:0.98 120:0.80 121:1.00 123:0.07 124:0.43 126:0.54 127:0.89 129:0.59 131:0.96 133:0.47 135:0.20 140:0.80 144:0.76 145:0.98 146:0.63 147:0.68 149:0.82 150:0.98 151:0.97 152:0.95 153:0.90 154:0.96 155:0.67 157:0.86 159:0.33 161:0.63 162:0.52 164:0.72 165:0.92 166:0.93 167:0.94 169:0.87 172:0.63 173:0.30 176:0.73 177:0.38 178:0.42 179:0.69 181:0.50 182:0.87 186:0.83 189:0.94 191:0.84 192:0.59 195:0.59 201:0.74 202:0.75 204:0.89 208:0.64 212:0.77 215:0.96 216:0.31 222:0.25 227:0.97 229:0.92 230:0.87 231:0.89 232:0.80 233:0.76 235:0.05 238:0.96 241:0.83 242:0.74 243:0.73 245:0.68 246:0.94 247:0.39 248:0.86 253:0.90 255:0.79 256:0.64 259:0.21 260:0.81 261:0.90 265:0.81 266:0.27 267:0.76 268:0.70 271:0.44 276:0.56 282:0.88 283:0.71 285:0.62 287:0.95 290:0.98 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.80 7:0.81 8:0.59 9:0.80 11:0.78 12:0.86 17:0.75 20:0.93 21:0.05 27:0.40 28:0.89 30:0.41 32:0.80 34:0.37 36:0.72 37:0.23 39:0.86 41:0.82 43:0.84 60:0.95 66:0.69 69:0.33 70:0.57 74:0.84 77:0.89 78:0.76 81:0.93 83:0.87 85:0.88 91:0.71 96:0.79 98:0.72 100:0.78 101:0.80 104:0.74 108:0.26 111:0.75 114:0.95 120:0.68 121:0.99 122:0.43 123:0.25 124:0.42 126:0.54 127:0.54 129:0.59 131:0.95 133:0.47 135:0.54 140:0.45 144:0.76 145:0.97 146:0.72 147:0.76 149:0.83 150:0.96 151:0.83 152:0.90 153:0.78 154:0.81 155:0.67 157:0.87 158:0.92 159:0.45 161:0.63 162:0.57 164:0.65 165:0.90 166:0.93 167:0.96 169:0.76 172:0.54 173:0.36 176:0.73 177:0.38 179:0.73 181:0.50 182:0.87 186:0.77 189:0.83 192:0.59 195:0.67 201:0.74 202:0.59 204:0.89 208:0.64 212:0.30 215:0.91 216:0.11 220:0.62 222:0.25 227:0.97 229:0.92 230:0.84 231:0.89 233:0.69 235:0.12 238:0.85 241:0.86 242:0.45 243:0.73 245:0.59 246:0.94 247:0.47 248:0.75 253:0.85 255:0.79 256:0.45 259:0.21 260:0.69 261:0.80 265:0.72 266:0.54 267:0.18 268:0.58 271:0.44 276:0.46 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.95 297:0.36 299:0.97 300:0.43 +2 1:0.74 11:0.78 12:0.86 17:0.79 21:0.05 27:0.28 28:0.89 30:0.62 34:0.86 36:0.42 37:0.67 39:0.86 43:0.85 66:0.83 69:0.61 70:0.40 74:0.66 81:0.93 83:0.79 85:0.88 91:0.58 98:0.77 101:0.83 104:0.77 108:0.47 114:0.95 122:0.43 123:0.45 126:0.54 127:0.26 129:0.05 131:0.61 135:0.21 144:0.76 146:0.53 147:0.59 149:0.81 150:0.96 154:0.82 155:0.67 157:0.88 159:0.19 161:0.63 165:0.90 166:0.93 167:0.72 172:0.51 173:0.82 176:0.73 177:0.38 178:0.55 179:0.66 181:0.50 182:0.86 187:0.21 192:0.59 201:0.74 202:0.36 212:0.78 215:0.91 216:0.06 222:0.25 227:0.61 229:0.61 231:0.89 232:0.84 235:0.12 241:0.54 242:0.45 243:0.84 246:0.94 247:0.47 253:0.74 259:0.21 265:0.77 266:0.27 267:0.19 271:0.44 276:0.34 283:0.71 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.81 11:0.78 12:0.86 17:0.83 21:0.30 27:0.50 28:0.89 30:0.16 32:0.80 34:0.75 36:0.79 37:0.60 39:0.86 43:0.86 60:0.95 66:0.35 69:0.57 70:0.95 74:0.97 77:0.89 81:0.80 83:0.81 85:0.99 91:0.06 98:0.55 101:0.84 104:0.84 106:0.81 108:0.94 114:0.86 117:0.86 121:0.90 122:0.67 123:0.94 124:0.87 126:0.54 127:0.86 129:0.95 131:0.61 133:0.87 135:0.21 144:0.76 145:0.70 146:0.85 147:0.88 149:0.97 150:0.87 154:0.84 155:0.67 157:0.94 158:0.92 159:0.91 161:0.63 165:0.84 166:0.93 167:0.54 172:0.95 173:0.24 176:0.73 177:0.38 178:0.55 179:0.13 181:0.50 182:0.86 187:0.39 192:0.59 195:0.97 201:0.74 204:0.89 206:0.81 208:0.64 212:0.68 215:0.72 216:0.86 222:0.25 227:0.61 229:0.61 230:0.87 231:0.89 232:0.92 233:0.76 235:0.42 242:0.43 243:0.22 245:0.98 246:0.94 247:0.67 253:0.78 259:0.21 265:0.57 266:0.80 267:0.28 271:0.44 276:0.96 279:0.86 282:0.88 283:0.82 287:0.61 290:0.86 297:0.36 299:0.97 300:0.84 +1 1:0.74 11:0.78 12:0.86 17:0.61 21:0.13 27:0.45 28:0.89 30:0.21 32:0.80 34:0.75 36:0.20 37:0.50 39:0.86 43:0.82 55:0.92 60:0.87 66:0.11 69:0.60 70:0.69 74:0.86 76:0.85 77:0.70 81:0.89 83:0.87 85:0.80 91:0.08 98:0.40 101:0.74 108:0.45 114:0.92 122:0.67 123:0.78 124:0.84 126:0.54 127:0.43 129:0.05 131:0.61 133:0.82 135:0.89 144:0.76 145:0.50 146:0.35 147:0.42 149:0.72 150:0.94 154:0.78 155:0.67 157:0.81 158:0.92 159:0.58 161:0.63 163:0.90 165:0.88 166:0.93 167:0.63 172:0.27 173:0.52 176:0.73 177:0.38 178:0.55 179:0.73 181:0.50 182:0.86 187:0.68 192:0.59 195:0.79 201:0.74 208:0.64 212:0.22 215:0.84 216:0.77 222:0.25 227:0.61 229:0.61 231:0.89 235:0.22 241:0.21 242:0.61 243:0.44 245:0.53 246:0.94 247:0.39 253:0.76 259:0.21 265:0.26 266:0.71 267:0.28 271:0.44 276:0.46 277:0.69 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.43 +1 1:0.74 6:0.96 8:0.91 9:0.80 11:0.78 12:0.86 17:0.88 20:0.76 21:0.54 27:0.67 28:0.89 30:0.20 34:0.20 36:0.22 37:0.72 39:0.86 41:0.90 43:0.92 55:0.84 60:0.95 66:0.06 69:0.41 70:0.86 74:0.84 78:0.82 81:0.66 83:0.83 85:0.99 91:0.05 96:0.74 98:0.23 100:0.82 101:0.75 104:0.97 106:0.81 108:0.98 111:0.81 114:0.77 117:0.86 120:0.68 123:0.92 124:1.00 126:0.54 127:0.97 129:1.00 131:0.96 133:1.00 135:0.69 140:0.45 144:0.76 146:0.37 147:0.44 149:0.92 150:0.78 151:0.60 152:0.75 153:0.46 154:0.91 155:0.67 157:0.89 158:0.87 159:0.99 161:0.63 162:0.86 163:0.94 164:0.74 165:0.79 166:0.93 167:0.59 169:0.80 172:0.71 173:0.06 176:0.73 177:0.38 179:0.12 181:0.50 182:0.87 186:0.83 187:0.68 189:0.97 190:0.77 192:0.59 201:0.74 202:0.59 206:0.81 208:0.64 212:0.13 215:0.58 216:0.14 220:0.96 222:0.25 227:0.97 229:0.93 231:0.89 232:0.98 235:0.68 238:1.00 241:0.07 242:0.63 243:0.08 245:0.91 246:0.94 247:0.67 248:0.62 253:0.77 255:0.79 256:0.68 259:0.21 260:0.69 261:0.75 265:0.20 266:1.00 267:0.82 268:0.68 271:0.44 276:0.97 279:0.86 283:0.82 285:0.62 287:0.96 290:0.77 297:0.36 300:0.84 +1 1:0.74 11:0.78 12:0.86 17:0.55 20:0.74 21:0.22 27:0.45 28:0.89 30:0.66 32:0.72 34:0.80 36:0.20 37:0.50 39:0.86 43:0.82 55:0.92 60:0.87 66:0.53 69:0.60 70:0.42 74:0.81 76:0.85 78:0.84 81:0.85 83:0.87 85:0.80 91:0.10 98:0.58 100:0.73 101:0.74 108:0.46 111:0.82 114:0.90 122:0.43 123:0.44 124:0.52 126:0.54 127:0.35 129:0.05 131:0.61 133:0.39 135:0.89 144:0.76 145:0.47 146:0.70 147:0.75 149:0.68 150:0.90 152:0.74 154:0.78 155:0.67 157:0.82 158:0.87 159:0.50 161:0.63 163:0.75 165:0.86 166:0.93 167:0.61 169:0.72 172:0.45 173:0.55 176:0.73 177:0.38 178:0.55 179:0.69 181:0.50 182:0.86 186:0.85 187:0.68 192:0.59 195:0.76 201:0.74 208:0.64 212:0.22 215:0.79 216:0.77 222:0.25 227:0.61 229:0.61 231:0.89 235:0.31 241:0.12 242:0.38 243:0.62 245:0.64 246:0.94 247:0.55 253:0.74 259:0.21 261:0.75 265:0.59 266:0.54 267:0.28 271:0.44 276:0.47 279:0.86 283:0.71 287:0.61 290:0.90 297:0.36 300:0.33 +1 1:0.74 7:0.81 11:0.78 12:0.86 17:0.88 27:0.13 28:0.89 30:0.66 32:0.80 34:0.31 36:0.89 37:0.85 39:0.86 43:0.96 66:0.31 69:0.08 70:0.52 74:0.97 77:0.96 81:0.96 83:0.80 85:0.99 91:0.14 98:0.69 101:0.86 104:0.57 108:0.58 114:0.98 121:1.00 122:0.57 123:0.88 124:0.67 126:0.54 127:0.90 129:0.81 131:0.61 133:0.67 135:0.56 144:0.76 145:0.98 146:0.25 147:0.31 149:0.98 150:0.98 154:0.96 155:0.67 157:0.94 159:0.83 161:0.63 163:0.81 165:0.92 166:0.93 167:0.58 172:0.86 173:0.09 176:0.73 177:0.38 178:0.55 179:0.23 181:0.50 182:0.86 187:0.39 192:0.59 195:0.93 201:0.74 204:0.89 208:0.64 212:0.39 215:0.96 216:0.31 222:0.25 227:0.61 229:0.61 230:0.87 231:0.89 232:0.80 233:0.76 235:0.05 241:0.03 242:0.50 243:0.11 245:0.95 246:0.94 247:0.60 253:0.80 259:0.21 265:0.69 266:0.71 267:0.59 271:0.44 276:0.90 282:0.88 287:0.61 290:0.98 297:0.36 299:0.97 300:0.55 +1 1:0.74 6:0.80 7:0.81 8:0.59 9:0.80 11:0.78 12:0.86 17:0.99 20:0.81 27:0.72 28:0.89 30:0.71 32:0.80 34:0.23 36:0.74 39:0.86 41:0.88 43:0.78 60:0.87 66:0.82 69:0.78 70:0.31 74:0.83 77:0.70 78:0.78 81:0.94 83:0.88 85:0.88 91:0.79 96:0.87 98:0.67 100:0.79 101:0.80 104:0.58 108:0.61 111:0.77 114:0.98 120:0.78 121:0.90 122:0.43 123:0.59 126:0.54 127:0.20 129:0.05 131:0.96 135:0.30 140:0.45 144:0.76 145:0.38 146:0.67 147:0.72 149:0.78 150:0.97 151:0.93 152:0.78 153:0.77 154:0.71 155:0.67 157:0.87 158:0.87 159:0.26 161:0.63 162:0.86 164:0.69 165:0.91 166:0.93 167:0.91 169:0.77 172:0.48 173:0.84 176:0.73 177:0.38 179:0.58 181:0.50 182:0.87 186:0.79 189:0.82 192:0.59 195:0.64 201:0.74 202:0.54 204:0.89 208:0.64 212:0.61 215:0.96 216:0.06 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.81 233:0.76 235:0.22 238:0.84 241:0.78 242:0.33 243:0.83 246:0.94 247:0.55 248:0.85 253:0.89 255:0.79 256:0.58 259:0.21 260:0.79 261:0.78 265:0.68 266:0.38 267:0.18 268:0.63 271:0.44 276:0.35 279:0.86 282:0.88 283:0.71 285:0.62 287:0.96 290:0.98 297:0.99 299:0.88 300:0.25 +1 1:0.74 6:0.92 8:0.65 11:0.78 12:0.86 17:0.93 20:0.81 21:0.64 27:0.13 28:0.89 30:0.38 34:0.71 36:0.68 37:0.85 39:0.86 43:0.92 66:0.45 69:0.44 70:0.65 74:0.86 78:0.77 81:0.67 83:0.73 85:0.94 91:0.02 98:0.72 100:0.79 101:0.83 104:0.57 108:0.76 111:0.76 114:0.72 122:0.43 123:0.75 124:0.67 126:0.54 127:0.75 129:0.81 131:0.61 133:0.67 135:0.60 144:0.76 146:0.62 147:0.67 149:0.91 150:0.77 152:0.95 154:0.91 155:0.67 157:0.91 159:0.60 161:0.63 163:0.81 165:0.70 166:0.92 167:0.60 169:0.86 172:0.63 173:0.38 176:0.73 177:0.38 178:0.55 179:0.53 181:0.50 182:0.85 186:0.78 187:0.39 189:0.85 192:0.59 201:0.74 212:0.40 215:0.53 216:0.31 222:0.25 227:0.61 229:0.61 231:0.88 232:0.80 235:0.88 238:0.88 241:0.10 242:0.24 243:0.40 245:0.79 246:0.94 247:0.67 253:0.69 259:0.21 261:0.90 265:0.72 266:0.54 267:0.36 271:0.44 276:0.68 287:0.61 290:0.72 297:0.36 300:0.55 +1 1:0.74 6:0.98 7:0.81 8:0.92 9:0.80 11:0.78 12:0.86 17:0.61 20:0.93 21:0.54 27:0.05 28:0.89 30:0.68 32:0.80 34:0.74 36:0.36 37:0.96 39:0.86 41:0.93 43:0.96 66:0.17 69:0.23 70:0.65 74:0.98 77:0.89 78:0.88 81:0.66 83:0.81 85:1.00 91:0.56 96:0.93 98:0.39 100:0.96 101:0.85 104:0.58 108:0.64 111:0.92 114:0.77 120:0.87 121:1.00 122:0.43 123:0.62 124:0.97 126:0.54 127:1.00 129:0.99 131:0.96 133:0.97 135:0.86 140:0.45 144:0.76 145:0.98 146:0.49 147:0.55 149:0.99 150:0.78 151:0.98 152:0.99 153:0.92 154:0.96 155:0.67 157:0.94 159:0.96 161:0.63 162:0.65 164:0.83 165:0.79 166:0.93 167:0.72 169:0.97 172:0.90 173:0.06 176:0.73 177:0.38 179:0.13 181:0.50 182:0.87 186:0.88 187:0.21 189:0.97 191:0.87 192:0.59 195:0.99 201:0.74 202:0.80 204:0.89 208:0.64 212:0.39 215:0.58 216:0.58 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.81 233:0.76 235:0.68 238:0.97 241:0.54 242:0.57 243:0.10 245:0.96 246:0.94 247:0.60 248:0.91 253:0.96 255:0.79 256:0.80 259:0.21 260:0.88 261:0.99 265:0.42 266:0.95 267:0.36 268:0.82 271:0.44 276:0.97 282:0.88 285:0.62 287:0.96 290:0.77 297:0.36 299:0.97 300:0.84 +2 1:0.74 6:0.90 7:0.81 8:0.78 9:0.80 11:0.78 12:0.86 17:0.67 20:0.99 21:0.30 27:0.21 28:0.89 30:0.14 34:0.80 36:0.76 37:0.74 39:0.86 41:0.95 43:0.98 60:0.97 66:0.13 69:0.12 70:0.96 74:0.96 76:0.85 78:0.78 81:0.80 83:0.88 85:0.99 91:0.37 96:0.94 98:0.63 100:0.81 101:0.83 104:0.84 106:0.81 108:0.94 111:0.77 114:0.86 117:0.86 120:0.88 121:0.90 122:0.67 123:0.77 124:0.77 126:0.54 127:0.76 129:0.89 131:0.96 133:0.78 135:0.17 140:0.45 144:0.76 146:0.82 147:0.85 149:0.97 150:0.87 151:0.98 152:0.92 153:0.92 154:0.98 155:0.67 157:0.93 158:0.92 159:0.89 161:0.63 162:0.70 164:0.84 165:0.84 166:0.93 167:0.65 169:0.79 172:0.93 173:0.08 176:0.73 177:0.38 179:0.15 181:0.50 182:0.87 186:0.78 187:0.39 189:0.91 191:0.78 192:0.59 201:0.74 202:0.78 204:0.89 206:0.81 208:0.64 212:0.60 215:0.72 216:0.95 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.92 233:0.76 235:0.42 238:0.90 241:0.30 242:0.44 243:0.23 245:0.98 246:0.94 247:0.67 248:0.93 253:0.96 255:0.79 256:0.78 259:0.21 260:0.89 261:0.82 265:0.56 266:0.80 267:0.36 268:0.81 271:0.44 276:0.95 279:0.86 283:0.82 285:0.62 287:0.96 290:0.86 297:0.36 300:0.84 +4 1:0.74 6:0.98 8:0.98 9:0.80 11:0.78 12:0.86 17:0.69 20:0.99 21:0.64 27:0.05 28:0.89 30:0.45 32:0.80 34:0.71 36:0.59 37:0.96 39:0.86 41:0.99 43:0.96 60:0.98 66:0.07 69:0.27 70:0.69 74:0.80 77:0.96 78:0.94 81:0.59 83:0.90 85:0.88 91:0.98 96:0.99 98:0.38 100:0.99 101:0.79 104:0.58 108:0.72 111:0.98 114:0.72 120:0.98 122:0.43 123:0.76 124:0.81 126:0.54 127:0.96 129:0.89 131:0.96 133:0.78 135:0.49 138:0.99 140:0.45 144:0.76 145:0.98 146:0.19 147:0.24 149:0.77 150:0.72 151:1.00 152:0.99 153:0.99 154:0.96 155:0.67 157:0.87 158:0.92 159:0.57 161:0.63 162:0.65 164:0.96 165:0.70 166:0.92 167:0.97 169:0.97 172:0.27 173:0.28 176:0.73 177:0.38 179:0.82 181:0.50 182:0.87 186:0.94 189:0.99 191:0.97 192:0.59 195:0.72 201:0.74 202:0.95 208:0.64 212:0.36 215:0.53 216:0.58 222:0.25 227:0.97 229:0.93 231:0.89 232:0.81 235:0.80 238:0.99 241:0.93 242:0.38 243:0.31 245:0.57 246:0.94 247:0.60 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.98 261:0.99 265:0.19 266:0.71 267:0.98 268:0.96 271:0.44 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 287:0.96 290:0.72 297:0.36 299:0.97 300:0.55 +3 1:0.74 6:0.98 8:0.97 9:0.80 11:0.78 12:0.86 17:0.59 20:0.81 27:0.05 28:0.89 30:0.66 34:0.90 36:0.27 37:0.96 39:0.86 41:0.90 43:0.96 66:0.30 69:0.08 70:0.76 74:0.63 78:0.89 81:0.96 83:0.82 85:0.90 91:0.44 96:0.89 98:0.32 100:0.97 101:0.80 104:0.58 108:0.83 111:0.94 114:0.98 120:0.82 122:0.43 123:0.82 124:0.73 126:0.54 127:0.85 129:0.81 131:0.96 133:0.67 135:0.37 140:0.80 144:0.76 146:0.19 147:0.24 149:0.78 150:0.98 151:0.92 152:0.99 153:0.72 154:0.96 155:0.67 157:0.88 159:0.60 161:0.63 162:0.60 164:0.75 165:0.92 166:0.93 167:0.79 169:0.97 172:0.32 173:0.15 176:0.73 177:0.38 178:0.42 179:0.81 181:0.50 182:0.87 186:0.89 187:0.68 189:0.99 191:0.91 192:0.59 201:0.74 202:0.70 208:0.64 212:0.36 215:0.96 216:0.58 222:0.25 227:0.97 229:0.93 231:0.89 232:0.81 235:0.05 238:0.98 241:0.69 242:0.52 243:0.31 245:0.60 246:0.94 247:0.47 248:0.88 253:0.89 255:0.79 256:0.68 259:0.21 260:0.83 261:0.99 265:0.34 266:0.71 267:0.18 268:0.70 271:0.44 276:0.34 285:0.62 287:0.96 290:0.98 297:0.36 300:0.70 +0 7:0.81 8:0.97 9:0.80 11:0.78 12:0.86 17:0.70 21:0.78 27:0.05 28:0.89 30:0.33 32:0.72 34:0.94 36:0.51 37:0.96 39:0.86 41:0.88 43:0.96 66:0.45 69:0.12 70:0.49 74:0.66 78:0.94 83:0.77 85:0.80 91:0.57 96:0.84 98:0.55 101:0.77 104:0.58 108:0.71 111:0.96 120:0.74 121:0.90 122:0.41 123:0.69 124:0.56 127:0.76 129:0.59 131:0.96 133:0.47 135:0.49 140:0.45 144:0.76 145:1.00 146:0.19 147:0.24 149:0.66 151:0.87 153:0.48 154:0.96 155:0.67 157:0.82 159:0.43 161:0.63 162:0.74 164:0.67 166:0.61 167:0.77 169:0.99 172:0.27 173:0.25 177:0.38 179:0.91 181:0.50 182:0.87 187:0.21 192:0.59 195:0.66 202:0.56 204:0.89 208:0.64 212:0.42 216:0.58 222:0.25 227:0.97 229:0.93 230:0.87 231:0.89 232:0.81 233:0.76 235:0.22 241:0.67 242:0.45 243:0.40 245:0.53 246:0.61 247:0.35 248:0.81 253:0.81 255:0.79 256:0.58 259:0.21 260:0.74 265:0.56 266:0.38 267:0.28 268:0.59 271:0.44 276:0.25 285:0.62 287:0.96 300:0.33 +0 17:0.53 21:0.78 27:0.72 28:0.83 91:0.93 120:0.75 140:0.99 151:0.73 153:0.80 161:0.99 162:0.45 164:0.65 167:0.67 175:0.75 178:0.55 181:0.98 187:0.39 202:0.98 216:0.09 235:0.05 241:0.64 244:0.61 248:0.68 256:0.58 257:0.65 260:0.76 268:0.58 277:0.69 285:0.62 +0 17:0.92 20:0.80 21:0.78 27:0.62 28:0.83 34:0.99 36:0.37 37:0.27 43:0.32 66:0.36 69:0.70 78:0.72 91:0.10 98:0.08 100:0.73 108:0.53 111:0.72 123:0.51 127:0.06 129:0.05 135:0.21 145:0.44 146:0.05 147:0.05 149:0.13 152:0.77 154:0.23 159:0.05 161:0.99 167:0.53 169:0.72 172:0.05 173:0.75 177:0.05 178:0.55 181:0.98 186:0.73 187:0.21 216:0.60 235:0.05 241:0.24 243:0.51 259:0.21 261:0.73 265:0.08 267:0.59 +1 17:0.24 20:0.76 21:0.22 27:0.17 28:0.83 30:0.45 34:0.86 36:0.96 37:0.58 43:0.51 55:0.59 66:0.27 69:0.15 70:0.25 78:0.84 81:0.72 91:0.04 98:0.12 100:0.73 108:0.12 111:0.81 123:0.12 124:0.72 126:0.54 127:0.85 129:0.81 133:0.67 135:1.00 146:0.24 147:0.30 149:0.28 150:0.53 152:0.76 154:0.40 159:0.84 161:0.99 163:0.79 167:0.52 169:0.85 172:0.10 173:0.10 177:0.05 178:0.55 179:0.98 181:0.98 186:0.84 187:0.68 202:0.36 212:0.61 215:0.79 216:0.90 235:0.31 241:0.18 242:0.82 243:0.46 245:0.38 247:0.12 259:0.21 261:0.80 265:0.13 266:0.17 267:0.52 276:0.16 297:0.36 300:0.18 +0 8:0.79 17:0.26 20:0.78 21:0.78 27:0.40 28:0.83 32:0.80 34:0.40 36:0.18 37:0.90 43:0.33 66:0.36 69:0.67 78:0.72 91:0.14 98:0.07 100:0.73 108:0.51 111:0.72 123:0.49 127:0.06 129:0.05 135:0.56 145:0.88 146:0.05 147:0.05 149:0.13 152:0.76 154:0.24 159:0.05 161:0.99 167:0.52 169:0.72 172:0.05 173:0.64 177:0.05 178:0.55 181:0.98 186:0.73 187:0.68 195:0.80 216:0.65 235:0.05 241:0.18 243:0.51 259:0.21 261:0.73 265:0.07 267:0.28 +0 17:0.57 21:0.78 27:0.25 28:0.83 34:0.70 36:0.29 91:0.78 120:0.73 135:0.63 140:1.00 151:0.70 153:0.71 161:0.99 162:0.45 164:0.63 167:0.67 175:0.75 178:0.55 181:0.98 187:0.68 202:0.99 216:0.14 241:0.63 244:0.61 248:0.66 256:0.58 257:0.65 259:0.21 260:0.74 267:0.94 268:0.57 277:0.69 285:0.62 +0 6:0.78 7:0.81 8:0.59 17:0.24 20:0.81 21:0.30 27:0.24 28:0.83 30:0.95 32:0.80 34:0.95 36:0.70 37:0.90 43:0.42 55:0.92 66:0.20 69:0.50 78:0.85 81:0.69 91:0.48 98:0.10 100:0.85 108:0.39 111:0.83 120:0.63 123:0.37 124:0.61 126:0.54 127:0.23 129:0.59 133:0.47 135:0.79 140:0.45 145:0.42 146:0.05 147:0.05 149:0.21 150:0.50 151:0.54 152:0.78 153:0.48 154:0.32 159:0.18 161:0.99 162:0.61 164:0.76 167:0.62 169:0.83 172:0.05 173:0.72 177:0.05 179:0.99 181:0.98 186:0.85 187:0.21 189:0.81 191:0.77 195:0.57 202:0.70 215:0.72 216:0.74 220:0.87 230:0.65 233:0.63 235:0.42 238:0.89 241:0.56 243:0.40 245:0.36 248:0.57 256:0.68 259:0.21 260:0.64 261:0.83 265:0.11 267:0.44 268:0.70 283:0.60 285:0.62 297:0.36 300:0.08 +0 17:0.85 21:0.78 27:0.64 28:0.83 34:0.68 36:0.42 37:0.35 91:0.14 120:0.86 135:0.49 140:0.45 151:0.74 153:0.83 161:0.99 162:0.86 164:0.76 167:0.54 175:0.75 181:0.98 187:0.68 202:0.62 216:0.12 241:0.30 244:0.61 248:0.80 256:0.68 257:0.65 259:0.21 260:0.87 267:0.69 268:0.71 277:0.69 285:0.62 +0 17:0.24 21:0.78 27:0.72 28:0.83 34:0.82 36:0.37 37:0.33 91:0.92 120:0.72 135:0.34 140:1.00 151:0.69 153:0.69 161:0.99 162:0.45 164:0.62 167:0.54 175:0.75 178:0.55 181:0.98 187:0.68 202:0.98 216:0.19 235:0.68 241:0.30 244:0.61 248:0.65 256:0.58 257:0.65 259:0.21 260:0.73 267:0.87 268:0.55 277:0.69 285:0.62 +2 17:0.62 21:0.30 27:0.72 28:0.83 30:0.73 34:0.56 36:0.62 37:0.57 43:0.46 55:0.92 66:0.69 69:0.41 70:0.22 81:0.69 91:0.18 98:0.77 106:0.81 108:0.32 123:0.31 124:0.42 126:0.54 127:0.38 129:0.59 133:0.47 135:0.74 146:0.89 147:0.91 149:0.55 150:0.50 154:0.35 159:0.57 161:0.99 163:0.94 167:0.49 172:0.54 173:0.32 177:0.05 178:0.55 179:0.68 181:0.98 187:0.39 206:0.81 212:0.72 215:0.72 216:0.32 235:0.31 241:0.05 242:0.82 243:0.73 245:0.59 247:0.12 259:0.21 265:0.77 266:0.38 267:0.36 276:0.50 297:0.36 300:0.18 +0 6:0.79 8:0.61 17:0.88 20:0.76 21:0.22 27:0.63 28:0.83 30:0.95 34:0.78 36:0.37 37:0.54 43:0.48 55:0.99 66:0.20 69:0.35 78:0.93 81:0.72 91:0.37 98:0.07 100:0.94 108:0.27 111:0.92 123:0.26 124:0.61 126:0.54 127:0.24 129:0.59 133:0.47 135:0.74 146:0.05 147:0.05 149:0.19 150:0.53 152:0.76 154:0.36 159:0.49 161:0.99 167:0.56 169:0.91 172:0.05 173:0.25 177:0.05 178:0.55 179:0.99 181:0.98 186:0.92 187:0.21 202:0.36 215:0.79 216:0.13 235:0.31 241:0.39 243:0.40 245:0.36 259:0.21 261:0.83 265:0.07 267:0.97 283:0.60 297:0.36 300:0.08 +2 17:0.51 21:0.64 27:0.45 28:0.83 30:0.76 34:0.89 36:0.32 37:0.50 43:0.32 55:0.96 66:0.20 69:0.70 70:0.22 81:0.55 91:0.03 98:0.25 108:0.65 123:0.63 124:0.73 126:0.54 127:0.22 129:0.81 133:0.67 135:0.69 145:0.52 146:0.05 147:0.05 149:0.30 150:0.42 154:0.24 159:0.39 161:0.99 163:0.96 167:0.52 172:0.10 173:0.66 177:0.05 178:0.55 179:0.87 181:0.98 187:0.68 212:0.42 215:0.53 216:0.77 235:0.80 241:0.18 242:0.82 243:0.26 245:0.40 247:0.12 259:0.21 265:0.27 266:0.23 267:0.76 276:0.24 297:0.36 300:0.18 +1 6:0.79 7:0.81 8:0.58 17:0.97 20:0.83 21:0.22 27:0.58 28:0.83 32:0.80 34:0.36 36:0.33 37:0.23 78:0.99 81:0.72 91:0.85 100:0.99 111:0.99 126:0.54 135:0.39 145:0.91 150:0.53 152:0.79 161:0.99 167:0.85 169:0.99 175:0.75 178:0.55 181:0.98 186:0.99 189:0.81 195:0.73 202:0.36 215:0.79 216:0.01 230:0.87 233:0.76 235:0.31 238:0.97 241:0.83 244:0.61 257:0.65 259:0.21 261:0.94 267:0.23 277:0.69 297:0.36 +0 17:0.15 20:0.78 21:0.22 27:0.17 28:0.83 30:0.45 34:0.52 36:0.99 37:0.58 43:0.51 55:0.59 66:0.27 69:0.15 70:0.25 78:0.86 81:0.72 91:0.04 98:0.13 100:0.87 108:0.12 111:0.85 120:0.54 123:0.12 124:0.72 126:0.54 127:0.88 129:0.81 133:0.67 135:0.99 140:0.45 146:0.24 147:0.30 149:0.28 150:0.53 151:0.47 152:0.76 153:0.48 154:0.40 159:0.83 161:0.99 162:0.52 163:0.79 164:0.57 167:0.57 169:0.83 172:0.10 173:0.10 177:0.05 179:0.98 181:0.98 186:0.87 187:0.68 202:0.58 212:0.61 215:0.79 216:0.90 220:0.82 235:0.31 241:0.41 242:0.82 243:0.46 245:0.38 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 261:0.81 265:0.13 266:0.17 267:0.84 268:0.46 276:0.16 285:0.62 297:0.36 300:0.18 +0 6:0.79 8:0.65 17:0.76 20:0.78 21:0.40 27:0.63 28:0.83 30:0.62 34:0.75 36:0.48 37:0.54 43:0.47 55:0.98 66:0.16 69:0.39 70:0.34 78:0.81 81:0.66 91:0.48 98:0.11 100:0.93 108:0.88 111:0.86 120:0.53 123:0.87 124:0.86 126:0.54 127:0.24 129:0.93 133:0.84 135:0.66 140:0.45 146:0.05 147:0.05 149:0.27 150:0.48 151:0.46 152:0.76 153:0.48 154:0.36 159:0.77 161:0.99 162:0.65 163:0.89 164:0.69 167:0.59 169:0.91 172:0.10 173:0.14 177:0.05 179:0.85 181:0.98 186:0.81 187:0.21 202:0.61 212:0.30 215:0.67 216:0.13 220:0.91 235:0.52 241:0.49 242:0.82 243:0.23 245:0.40 247:0.12 248:0.46 256:0.58 259:0.21 260:0.53 261:0.83 265:0.12 266:0.27 267:0.76 268:0.62 276:0.28 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.78 17:0.88 20:0.87 21:0.13 27:0.44 28:0.83 30:0.45 32:0.80 34:0.24 36:0.21 37:0.28 43:0.52 55:0.59 66:0.82 69:0.07 70:0.42 78:0.80 81:0.74 91:0.61 98:0.83 100:0.79 106:0.81 108:0.06 111:0.79 120:0.57 123:0.06 126:0.54 127:0.32 129:0.05 135:0.66 140:0.45 145:0.84 146:0.73 147:0.77 149:0.50 150:0.55 151:0.50 152:0.86 153:0.77 154:0.41 159:0.30 161:0.99 162:0.86 163:0.81 164:0.72 167:0.53 169:0.79 172:0.48 173:0.21 177:0.05 179:0.76 181:0.98 186:0.81 187:0.39 189:0.81 195:0.59 202:0.57 206:0.81 212:0.30 215:0.84 216:0.91 220:0.74 235:0.12 238:0.82 241:0.24 242:0.82 243:0.83 247:0.12 248:0.51 256:0.64 259:0.21 260:0.58 261:0.85 265:0.83 266:0.47 267:0.44 268:0.66 276:0.40 283:0.60 285:0.62 297:0.36 300:0.33 +0 17:0.38 20:0.76 21:0.47 27:0.48 28:0.83 30:0.66 34:0.23 36:0.25 37:0.39 43:0.41 55:0.92 66:0.13 69:0.53 70:0.33 78:0.72 81:0.63 91:0.25 98:0.28 100:0.94 108:0.87 111:0.87 120:0.53 123:0.86 124:0.95 126:0.54 127:0.79 129:0.99 133:0.95 135:0.88 140:0.45 146:0.50 147:0.56 149:0.54 150:0.46 151:0.45 152:0.75 153:0.48 154:0.31 159:0.88 161:0.99 162:0.86 163:0.92 164:0.57 167:0.52 169:0.92 172:0.32 173:0.25 177:0.05 179:0.61 181:0.98 186:0.73 187:0.95 202:0.36 212:0.23 215:0.63 216:0.51 220:0.93 235:0.60 241:0.18 242:0.82 243:0.21 245:0.57 247:0.12 248:0.45 256:0.45 259:0.21 260:0.53 261:0.78 265:0.30 266:0.75 267:0.65 268:0.46 276:0.63 285:0.62 297:0.36 300:0.33 +1 17:0.08 20:0.76 21:0.30 27:0.52 28:0.83 30:0.91 34:0.47 36:0.58 37:0.34 43:0.32 55:0.92 66:0.49 69:0.70 70:0.13 78:0.87 81:0.69 91:0.33 98:0.58 100:0.92 108:0.53 111:0.86 120:0.73 123:0.51 124:0.48 126:0.54 127:0.28 129:0.59 133:0.47 135:0.20 140:0.45 146:0.48 147:0.54 149:0.29 150:0.50 151:0.66 152:0.75 153:0.48 154:0.23 159:0.25 161:0.99 162:0.86 163:0.86 164:0.73 167:0.64 169:0.87 172:0.16 173:0.83 177:0.05 179:0.95 181:0.98 186:0.73 187:0.39 202:0.59 212:0.61 215:0.72 216:0.81 220:0.62 235:0.31 241:0.59 242:0.82 243:0.60 245:0.39 247:0.12 248:0.66 256:0.64 259:0.21 260:0.74 261:0.76 265:0.59 266:0.17 267:0.79 268:0.68 276:0.21 283:0.60 285:0.62 297:0.36 300:0.13 +1 7:0.81 12:0.51 17:0.38 20:0.77 21:0.40 27:0.72 28:0.96 30:0.58 32:0.80 34:0.85 36:0.81 43:0.52 55:0.52 66:0.69 69:0.12 70:0.44 78:0.74 81:0.78 91:0.79 98:0.74 100:0.73 104:0.58 108:0.10 111:0.73 120:0.62 123:0.10 124:0.41 126:0.54 127:0.59 129:0.59 133:0.47 135:0.32 140:0.45 145:0.93 146:0.56 147:0.62 149:0.52 150:0.58 151:0.56 152:0.75 153:0.78 154:0.41 159:0.30 161:0.55 162:0.86 164:0.84 167:0.93 169:0.72 172:0.41 173:0.34 177:0.05 179:0.87 181:0.57 186:0.75 195:0.61 202:0.73 212:0.55 215:0.67 216:0.17 220:0.74 230:0.65 233:0.63 235:0.42 241:0.87 242:0.82 243:0.73 245:0.46 247:0.12 248:0.56 256:0.81 259:0.21 260:0.63 261:0.74 265:0.74 266:0.27 267:0.19 268:0.80 271:0.82 276:0.35 283:0.60 285:0.62 297:0.36 300:0.33 +1 6:0.98 7:0.81 8:0.97 12:0.51 17:0.03 20:0.92 21:0.68 25:0.88 27:0.13 28:0.96 30:0.21 34:0.44 36:0.33 37:0.46 43:0.48 55:0.59 66:0.30 69:0.41 70:0.64 78:0.83 81:0.63 91:1.00 98:0.19 100:0.84 104:0.58 108:0.74 111:0.90 120:0.99 123:0.72 124:0.78 126:0.54 127:0.97 129:0.89 132:0.97 133:0.78 135:0.42 140:0.45 145:0.77 146:0.27 147:0.33 149:0.45 150:0.46 151:0.91 152:0.86 153:1.00 154:0.36 159:0.82 161:0.55 162:0.71 164:1.00 167:0.94 169:0.93 172:0.32 173:0.22 177:0.05 179:0.82 181:0.57 186:0.77 189:0.99 191:0.98 202:0.99 212:0.49 215:0.49 216:0.93 230:0.87 233:0.76 235:0.80 238:0.99 241:0.95 242:0.82 243:0.23 245:0.56 247:0.12 248:0.99 256:1.00 260:0.99 261:0.82 265:0.21 266:0.54 267:0.97 268:1.00 271:0.82 276:0.40 283:0.60 285:0.62 297:0.36 300:0.43 +1 6:0.87 7:0.81 8:0.74 12:0.51 17:0.12 20:0.97 21:0.40 27:0.21 28:0.96 30:0.30 34:0.34 36:0.76 37:0.74 43:0.52 55:0.84 66:0.10 69:0.12 70:0.85 78:0.75 81:0.78 91:0.74 98:0.42 100:0.78 104:0.84 106:0.81 108:0.81 111:0.74 120:0.90 123:0.80 124:0.95 126:0.54 127:0.83 129:0.99 133:0.95 135:0.17 140:0.45 146:0.75 147:0.79 149:0.82 150:0.58 151:0.80 152:0.98 153:0.93 154:0.41 159:0.94 161:0.55 162:0.59 164:0.93 167:0.71 169:0.76 172:0.71 173:0.06 177:0.05 179:0.17 181:0.57 186:0.75 187:0.39 189:0.88 191:0.80 202:0.91 206:0.81 212:0.29 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.92 241:0.58 242:0.82 243:0.22 245:0.92 247:0.12 248:0.86 256:0.91 259:0.21 260:0.91 261:0.79 265:0.44 266:0.96 267:0.28 268:0.92 271:0.82 276:0.93 283:0.82 285:0.62 297:0.36 300:0.84 +1 7:0.81 12:0.51 17:0.36 21:0.77 25:0.88 27:0.37 28:0.96 30:0.21 32:0.80 34:0.66 36:0.46 37:0.31 43:0.41 55:0.59 66:0.49 69:0.55 70:0.67 81:0.52 91:0.64 98:0.21 104:0.58 108:0.92 120:0.72 123:0.92 124:0.64 126:0.54 127:0.81 129:0.81 133:0.67 135:0.54 140:0.80 145:0.67 146:0.05 147:0.05 149:0.33 150:0.40 151:0.58 153:0.86 154:0.31 159:0.80 161:0.55 162:0.72 164:0.89 167:0.76 172:0.32 173:0.36 177:0.05 178:0.42 179:0.89 181:0.57 187:0.68 195:0.90 202:0.83 212:0.55 215:0.44 216:0.86 220:1.00 230:0.65 233:0.63 235:0.97 241:0.69 242:0.82 243:0.18 245:0.48 247:0.12 248:0.65 256:0.86 259:0.21 260:0.73 265:0.23 266:0.38 267:0.65 268:0.87 271:0.82 276:0.21 285:0.62 297:0.36 300:0.43 +1 12:0.51 17:0.57 21:0.22 27:0.45 28:0.96 30:0.62 32:0.80 34:0.70 36:0.31 37:0.50 43:0.32 55:0.52 66:0.75 69:0.68 70:0.34 81:0.83 91:0.14 98:0.51 108:0.52 123:0.50 126:0.54 127:0.15 129:0.05 135:0.69 145:0.41 146:0.62 147:0.67 149:0.37 150:0.62 154:0.24 159:0.23 161:0.55 163:0.75 167:0.58 172:0.32 173:0.67 177:0.05 178:0.55 179:0.60 181:0.57 187:0.68 195:0.73 212:0.30 215:0.79 216:0.77 235:0.22 241:0.15 242:0.82 243:0.77 247:0.12 259:0.21 265:0.52 266:0.27 267:0.23 271:0.82 276:0.27 283:0.60 297:0.36 300:0.25 +1 8:0.94 12:0.51 17:0.88 21:0.71 27:0.59 28:0.96 30:0.62 32:0.80 34:0.81 36:0.66 37:0.48 43:0.34 55:0.84 66:0.75 69:0.66 70:0.34 81:0.60 91:0.78 98:0.74 104:0.58 108:0.50 120:0.64 123:0.48 126:0.54 127:0.24 129:0.05 135:0.47 140:0.45 145:0.97 146:0.55 147:0.61 149:0.39 150:0.45 151:0.56 153:0.78 154:0.25 159:0.20 161:0.55 162:0.79 163:0.75 164:0.89 167:0.94 172:0.32 173:0.84 177:0.05 179:0.84 181:0.57 191:0.75 195:0.56 202:0.81 212:0.30 215:0.48 216:0.23 220:0.82 235:0.84 241:0.93 242:0.82 243:0.77 247:0.12 248:0.58 256:0.85 259:0.21 260:0.65 265:0.74 266:0.27 267:0.23 268:0.86 271:0.82 276:0.29 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.87 7:0.81 8:0.79 12:0.51 17:0.40 20:0.98 21:0.40 27:0.21 28:0.96 30:0.36 34:0.89 36:0.85 37:0.74 43:0.52 55:0.71 66:0.11 69:0.12 70:0.78 78:0.74 81:0.78 91:0.76 98:0.42 100:0.78 104:0.84 106:0.81 108:0.96 111:0.74 120:0.93 123:0.96 124:0.96 126:0.54 127:0.76 129:0.99 133:0.96 135:0.21 140:0.45 146:0.30 147:0.37 149:0.81 150:0.58 151:0.81 152:0.98 153:0.94 154:0.41 159:0.94 161:0.55 162:0.69 164:0.95 167:0.71 169:0.76 172:0.73 173:0.06 177:0.05 179:0.17 181:0.57 186:0.75 187:0.39 189:0.89 191:0.85 202:0.91 206:0.81 212:0.27 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.92 241:0.57 242:0.82 243:0.08 245:0.92 247:0.12 248:0.90 256:0.93 259:0.21 260:0.93 261:0.79 265:0.44 266:0.95 267:0.69 268:0.93 271:0.82 276:0.93 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.87 8:0.81 12:0.51 17:0.34 20:0.73 21:0.78 27:0.21 28:0.96 30:0.33 34:0.56 36:0.86 37:0.74 43:0.46 55:0.84 66:0.36 69:0.41 70:0.91 78:0.74 91:0.72 98:0.36 100:0.86 104:0.84 108:0.96 111:0.73 120:0.92 123:0.96 124:0.94 127:0.55 129:0.99 133:0.95 135:0.21 140:0.80 146:0.19 147:0.24 149:0.51 151:0.82 152:0.98 153:0.95 154:0.35 159:0.92 161:0.55 162:0.49 163:0.86 164:0.88 167:0.58 169:0.76 172:0.61 173:0.11 177:0.05 178:0.42 179:0.47 181:0.57 186:0.85 187:0.39 191:0.70 202:0.91 212:0.14 216:0.95 235:0.31 238:0.88 241:0.15 242:0.82 243:0.13 245:0.61 247:0.12 248:0.89 256:0.85 259:0.21 260:0.92 261:0.79 265:0.39 266:0.92 267:0.73 268:0.85 271:0.82 276:0.69 283:0.82 285:0.62 300:0.84 +1 6:0.89 7:0.81 8:0.91 12:0.51 17:0.24 20:0.91 21:0.74 25:0.88 27:0.37 28:0.96 30:0.45 32:0.80 34:0.50 36:0.63 37:0.31 43:0.46 55:0.84 66:0.36 69:0.48 70:0.50 78:0.75 81:0.57 91:1.00 98:0.19 100:0.79 104:0.58 108:0.93 111:0.76 120:0.97 123:0.92 124:0.69 126:0.54 127:0.96 129:0.81 133:0.67 135:0.44 140:0.45 145:0.61 146:0.31 147:0.38 149:0.29 150:0.43 151:0.85 152:0.87 153:0.98 154:0.35 159:0.81 161:0.55 162:0.74 163:0.90 164:0.99 167:0.93 169:0.78 172:0.21 173:0.28 177:0.05 179:0.94 181:0.57 186:0.76 189:0.90 191:0.95 195:0.90 202:0.98 212:0.23 215:0.46 216:0.86 220:0.62 230:0.65 233:0.63 235:0.88 238:0.96 241:0.90 242:0.82 243:0.34 245:0.45 247:0.12 248:0.96 256:0.99 259:0.21 260:0.97 261:0.78 265:0.20 266:0.38 267:0.89 268:0.99 271:0.82 276:0.22 283:0.60 285:0.62 297:0.36 300:0.33 +2 6:0.92 8:0.89 12:0.51 17:0.12 20:0.87 21:0.78 25:0.88 27:0.47 28:0.96 34:0.62 36:0.24 37:0.90 78:0.91 91:0.97 100:0.99 104:0.58 111:0.98 120:0.97 135:0.87 140:0.45 151:0.84 152:0.82 153:0.97 161:0.55 162:0.69 164:0.99 167:0.93 169:1.00 175:0.75 181:0.57 186:0.91 189:0.93 191:0.89 202:0.98 216:0.68 220:0.74 238:0.98 241:0.90 244:0.61 248:0.96 256:0.99 257:0.65 259:0.21 260:0.97 261:0.95 267:0.91 268:0.99 271:0.82 277:0.69 283:0.60 285:0.62 +1 6:0.83 7:0.81 8:0.91 12:0.51 17:0.16 20:0.78 21:0.68 25:0.88 27:0.41 28:0.96 30:0.66 34:0.89 36:0.84 37:0.84 43:0.32 55:0.71 66:0.47 69:0.71 70:0.40 78:0.75 81:0.63 91:0.92 98:0.61 100:0.79 104:0.81 108:0.73 111:0.74 120:0.96 123:0.71 124:0.56 126:0.54 127:0.43 129:0.59 133:0.47 135:0.65 140:0.45 145:0.77 146:0.55 147:0.61 149:0.51 150:0.46 151:0.83 152:0.80 153:0.97 154:0.23 159:0.40 161:0.55 162:0.79 164:0.98 167:0.68 169:0.76 172:0.41 173:0.77 177:0.05 179:0.73 181:0.57 186:0.75 187:0.68 189:0.89 191:0.91 202:0.96 212:0.49 215:0.49 216:0.68 220:0.62 230:0.65 233:0.63 235:0.80 238:0.92 241:0.50 242:0.82 243:0.44 245:0.66 247:0.12 248:0.94 256:0.97 259:0.21 260:0.96 261:0.76 265:0.62 266:0.63 267:0.91 268:0.98 271:0.82 276:0.46 283:0.82 285:0.62 297:0.36 300:0.33 +1 12:0.51 17:0.45 21:0.77 25:0.88 27:0.07 28:0.96 30:0.66 34:0.87 36:0.67 37:0.63 43:0.46 55:0.84 66:0.30 69:0.50 70:0.53 81:0.52 91:0.97 98:0.31 104:0.58 108:0.76 120:0.97 123:0.75 124:0.73 126:0.54 127:0.42 129:0.81 133:0.67 135:0.71 140:0.45 145:0.72 146:0.50 147:0.56 149:0.48 150:0.40 151:0.84 153:0.97 154:0.35 159:0.79 161:0.55 162:0.65 163:0.75 164:0.99 167:0.75 172:0.32 173:0.26 177:0.05 179:0.73 181:0.57 187:0.68 202:0.98 212:0.13 215:0.44 216:0.95 220:0.62 235:0.97 241:0.67 242:0.82 243:0.37 245:0.60 247:0.12 248:0.96 256:0.99 259:0.21 260:0.98 265:0.34 266:0.80 267:0.87 268:0.99 271:0.82 276:0.39 283:0.82 285:0.62 297:0.36 300:0.43 +0 6:0.89 7:0.81 8:0.96 12:0.51 17:0.16 20:0.75 21:0.74 25:0.88 27:0.25 28:0.96 30:0.37 37:0.30 43:0.43 55:0.71 66:0.19 69:0.52 70:0.70 78:0.97 81:0.57 91:0.98 98:0.26 100:0.82 104:0.58 108:0.73 111:1.00 120:0.99 123:0.92 124:0.68 126:0.54 127:0.94 129:0.81 133:0.67 140:0.45 145:0.89 146:0.26 147:0.32 149:0.53 150:0.43 151:0.91 152:0.74 153:1.00 154:0.33 159:0.82 161:0.55 162:0.66 164:1.00 167:0.93 169:0.99 172:0.48 173:0.31 177:0.05 179:0.71 181:0.57 186:0.78 189:0.91 191:0.96 202:0.99 212:0.71 215:0.46 216:0.92 220:0.74 230:0.87 233:0.76 235:0.88 238:1.00 241:0.87 242:0.82 243:0.19 245:0.68 247:0.12 248:0.99 256:1.00 260:0.99 261:0.74 265:0.25 266:0.63 268:0.99 271:0.82 276:0.41 283:0.82 285:0.62 297:0.36 300:0.55 +1 12:0.51 17:0.59 21:0.74 25:0.88 27:0.07 28:0.96 30:0.71 34:0.96 36:0.40 37:0.63 43:0.46 55:0.71 66:0.24 69:0.47 70:0.40 81:0.57 91:0.81 98:0.25 104:0.58 108:0.90 120:0.81 123:0.89 124:0.85 126:0.54 127:0.91 129:0.93 133:0.84 135:0.87 140:0.80 145:0.77 146:0.26 147:0.32 149:0.37 150:0.43 151:0.75 153:0.84 154:0.35 159:0.73 161:0.55 162:0.60 163:0.81 164:0.91 167:0.75 172:0.21 173:0.34 177:0.05 178:0.42 179:0.88 181:0.57 187:0.68 202:0.88 212:0.15 215:0.46 216:0.95 220:0.74 235:0.88 241:0.66 242:0.82 243:0.28 245:0.48 247:0.12 248:0.73 256:0.89 259:0.21 260:0.82 265:0.27 266:0.63 267:0.44 268:0.89 271:0.82 276:0.37 283:0.82 285:0.62 297:0.36 300:0.33 +1 7:0.81 12:0.51 17:0.43 21:0.78 25:0.88 27:0.47 28:0.96 30:0.17 32:0.80 34:0.68 36:0.88 37:0.90 43:0.43 55:0.45 66:0.23 69:0.46 70:0.70 91:0.72 98:0.72 104:0.58 108:0.91 120:0.76 123:0.77 124:0.74 127:0.89 129:0.81 133:0.67 135:0.92 140:0.80 145:0.93 146:0.80 147:0.83 149:0.84 151:0.57 153:0.83 154:0.33 159:0.82 161:0.55 162:0.57 164:0.92 167:0.75 172:0.81 173:0.25 177:0.05 178:0.42 179:0.24 181:0.57 187:0.21 195:0.92 202:0.90 212:0.58 216:0.68 220:0.82 230:0.65 233:0.63 235:0.12 241:0.66 242:0.82 243:0.34 245:0.96 247:0.12 248:0.68 256:0.89 259:0.21 260:0.76 265:0.51 266:0.84 267:0.73 268:0.90 271:0.82 276:0.89 283:0.60 285:0.62 300:0.55 +1 12:0.51 17:0.57 21:0.22 25:0.88 27:0.37 28:0.96 30:0.62 34:0.45 36:0.54 37:0.31 43:0.41 55:0.71 66:0.61 69:0.52 70:0.34 81:0.83 91:0.22 98:0.22 104:0.58 108:0.89 120:0.54 123:0.89 124:0.43 126:0.54 127:0.75 129:0.59 133:0.47 135:0.88 140:0.80 146:0.05 147:0.05 149:0.25 150:0.62 151:0.47 153:0.48 154:0.31 159:0.72 161:0.55 162:0.53 164:0.57 167:0.67 172:0.27 173:0.38 177:0.05 178:0.42 179:0.95 181:0.57 187:0.68 202:0.57 212:0.61 215:0.79 216:0.86 220:0.82 235:0.22 241:0.49 242:0.82 243:0.23 245:0.42 247:0.12 248:0.47 256:0.45 259:0.21 260:0.54 265:0.24 266:0.23 267:0.79 268:0.46 271:0.82 276:0.14 285:0.62 297:0.36 300:0.25 +1 6:0.97 7:0.81 8:0.97 12:0.51 17:0.24 20:0.75 21:0.76 25:0.88 27:0.18 28:0.96 30:0.21 32:0.80 37:0.37 43:0.47 55:0.71 66:0.20 69:0.45 70:0.91 78:0.78 81:0.54 91:1.00 98:0.30 100:0.86 104:0.58 108:0.75 111:0.81 120:0.99 123:0.73 124:0.85 126:0.54 127:0.95 129:0.93 133:0.84 140:0.45 145:0.76 146:0.27 147:0.33 149:0.49 150:0.41 151:0.91 152:0.74 153:1.00 154:0.36 159:0.76 161:0.55 162:0.69 164:1.00 167:0.94 169:0.86 172:0.32 173:0.30 177:0.05 179:0.73 181:0.57 186:0.77 189:0.98 191:0.96 195:0.86 202:0.99 212:0.23 215:0.46 216:0.94 230:0.65 233:0.63 235:0.95 238:1.00 241:0.91 242:0.82 243:0.19 245:0.60 247:0.12 248:0.99 256:1.00 260:0.99 261:0.74 265:0.32 266:0.71 268:1.00 271:0.82 276:0.50 283:0.82 285:0.62 297:0.36 300:0.70 +2 12:0.51 17:0.24 21:0.74 25:0.88 27:0.07 28:0.96 30:0.53 34:0.90 36:0.67 37:0.63 43:0.46 55:0.71 66:0.34 69:0.49 70:0.63 81:0.57 91:0.95 98:0.37 104:0.58 108:0.71 120:0.96 123:0.69 124:0.89 126:0.54 127:0.89 129:0.96 133:0.90 135:0.82 140:0.80 145:0.59 146:0.54 147:0.60 149:0.65 150:0.43 151:0.84 153:0.97 154:0.35 159:0.86 161:0.55 162:0.63 164:0.98 167:0.79 172:0.57 173:0.24 177:0.05 178:0.42 179:0.56 181:0.57 187:0.68 202:0.97 212:0.19 215:0.46 216:0.95 220:0.62 235:0.88 241:0.72 242:0.82 243:0.28 245:0.67 247:0.12 248:0.95 256:1.00 259:0.21 260:0.96 265:0.39 266:0.92 267:0.94 268:0.98 271:0.82 276:0.65 283:0.60 285:0.62 297:0.36 300:0.55 +4 6:0.99 7:0.81 8:0.98 12:0.51 17:0.05 20:0.98 21:0.68 25:0.88 27:0.07 28:0.96 30:0.38 34:0.53 36:0.35 37:0.63 43:0.46 55:0.71 66:0.13 69:0.48 70:0.83 78:0.90 81:0.63 91:1.00 98:0.44 100:1.00 104:0.58 108:0.78 111:0.97 120:1.00 123:0.89 124:0.82 126:0.54 127:0.94 129:0.93 133:0.84 135:0.56 140:0.45 145:0.89 146:0.35 147:0.42 149:0.64 150:0.46 151:0.91 152:0.99 153:1.00 154:0.35 159:0.77 161:0.55 162:0.64 164:1.00 167:0.94 169:0.96 172:0.61 173:0.32 177:0.05 179:0.55 181:0.57 186:0.96 189:0.99 191:0.99 202:1.00 212:0.48 215:0.49 216:0.95 220:0.62 230:0.87 233:0.76 235:0.80 238:1.00 241:0.94 242:0.82 243:0.19 245:0.72 247:0.12 248:1.00 256:1.00 259:0.21 260:1.00 261:1.00 265:0.40 266:0.89 267:0.82 268:1.00 271:0.82 276:0.66 283:0.82 285:0.62 297:0.36 300:0.70 +1 6:0.83 8:0.93 12:0.51 17:0.28 20:0.76 21:0.22 25:0.88 27:0.41 28:0.96 30:0.36 34:0.79 36:0.62 37:0.84 43:0.34 55:0.71 66:0.91 69:0.65 70:0.44 78:0.75 81:0.83 91:0.84 98:0.95 100:0.77 104:0.81 108:0.50 111:0.74 120:0.96 123:0.48 126:0.54 127:0.64 129:0.05 135:0.65 140:0.45 146:0.92 147:0.93 149:0.67 150:0.62 151:0.81 152:0.80 153:0.94 154:0.25 159:0.53 161:0.55 162:0.77 164:0.97 167:0.91 169:0.76 172:0.76 173:0.66 177:0.05 179:0.55 181:0.57 186:0.75 191:0.91 202:0.94 212:0.48 215:0.79 216:0.68 220:0.62 235:0.22 238:0.84 241:0.82 242:0.82 243:0.92 247:0.12 248:0.94 256:0.96 259:0.21 260:0.96 261:0.76 265:0.95 266:0.47 267:0.65 268:0.96 271:0.82 276:0.65 283:0.60 285:0.62 297:0.36 300:0.33 +1 12:0.51 17:0.76 21:0.40 25:0.88 27:0.37 28:0.96 30:0.62 34:0.52 36:0.48 37:0.31 43:0.41 55:0.71 66:0.61 69:0.52 70:0.34 81:0.78 91:0.09 98:0.24 104:0.58 108:0.87 123:0.86 124:0.43 126:0.54 127:0.77 129:0.59 133:0.47 135:0.81 146:0.05 147:0.05 149:0.26 150:0.58 154:0.31 159:0.65 161:0.55 167:0.56 172:0.27 173:0.43 177:0.05 178:0.55 179:0.95 181:0.57 187:0.68 212:0.61 215:0.67 216:0.86 235:0.42 241:0.07 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.26 266:0.23 267:0.69 271:0.82 276:0.15 297:0.36 300:0.25 +1 12:0.51 17:0.72 21:0.54 27:0.45 28:0.96 30:0.66 32:0.80 34:0.90 36:0.18 37:0.50 43:0.32 55:0.59 66:0.91 69:0.69 70:0.41 81:0.72 91:0.15 98:0.78 108:0.53 123:0.51 126:0.54 127:0.27 129:0.05 135:0.87 145:0.50 146:0.90 147:0.92 149:0.66 150:0.54 154:0.24 159:0.51 161:0.55 167:0.59 172:0.76 173:0.61 177:0.05 178:0.55 179:0.37 181:0.57 187:0.68 195:0.82 212:0.52 215:0.58 216:0.77 235:0.60 241:0.18 242:0.82 243:0.92 247:0.12 259:0.21 265:0.78 266:0.54 267:0.79 271:0.82 276:0.65 297:0.36 300:0.43 +1 6:0.84 7:0.81 8:0.65 12:0.51 17:0.69 20:0.79 21:0.74 27:0.55 28:0.96 34:0.50 36:0.79 37:0.86 43:0.26 66:0.36 69:0.82 78:0.74 81:0.57 91:0.81 98:0.09 100:0.77 104:0.76 106:0.81 108:0.67 111:0.73 120:0.71 123:0.65 126:0.54 127:0.07 129:0.05 135:0.68 140:0.45 145:0.91 146:0.05 147:0.05 149:0.11 150:0.43 151:0.66 152:0.77 153:0.48 154:0.18 159:0.05 161:0.55 162:0.86 164:0.76 167:0.77 169:0.75 172:0.05 173:0.96 177:0.05 181:0.57 186:0.75 187:0.21 189:0.86 202:0.62 206:0.81 215:0.46 216:0.41 220:0.99 230:0.87 233:0.76 235:0.88 238:0.90 241:0.70 243:0.51 248:0.64 256:0.68 259:0.21 260:0.72 261:0.74 265:0.10 267:0.18 268:0.70 271:0.82 283:0.82 285:0.62 297:0.36 +1 1:0.74 11:0.57 12:0.79 17:0.55 21:0.13 27:0.45 28:0.80 30:0.86 34:0.75 36:0.27 37:0.50 39:0.79 43:0.82 46:0.96 66:0.33 69:0.68 70:0.32 74:0.71 81:0.75 83:0.77 85:0.91 91:0.11 98:0.34 101:0.80 107:0.94 108:0.51 110:0.92 114:0.92 122:0.43 123:0.49 124:0.61 125:0.88 126:0.54 127:0.18 129:0.59 133:0.47 135:0.90 145:0.50 146:0.52 147:0.58 149:0.84 150:0.85 154:0.78 155:0.67 159:0.34 160:0.88 161:0.66 163:0.81 165:0.88 167:0.65 172:0.32 173:0.59 176:0.73 177:0.38 178:0.55 179:0.43 181:0.52 187:0.68 192:0.59 201:0.74 212:0.39 215:0.84 216:0.77 222:0.76 235:0.22 241:0.18 242:0.63 243:0.50 245:0.64 247:0.30 253:0.73 259:0.21 265:0.36 266:0.54 267:0.44 271:0.95 276:0.41 289:0.92 290:0.92 297:0.36 300:0.33 +2 8:0.90 9:0.80 11:0.57 12:0.79 17:0.02 21:0.76 27:0.05 28:0.80 30:0.85 32:0.80 34:0.93 36:0.80 37:0.98 39:0.79 41:0.96 43:0.91 46:0.99 66:0.60 69:0.48 70:0.57 74:0.87 77:0.70 78:0.72 81:0.25 83:0.80 85:0.95 91:0.78 96:0.91 98:0.61 101:0.86 104:0.72 107:0.95 108:0.70 110:0.94 111:0.77 120:0.84 122:0.43 123:0.68 124:0.50 125:0.97 126:0.32 127:0.67 129:0.59 133:0.47 135:0.34 140:0.87 145:0.76 146:0.52 147:0.58 149:0.93 150:0.25 151:0.95 153:0.83 154:0.90 155:0.67 159:0.42 160:0.99 161:0.66 162:0.60 164:0.85 167:0.89 169:0.90 172:0.63 173:0.55 177:0.38 178:0.48 179:0.60 181:0.52 187:0.39 192:0.59 195:0.63 202:0.81 208:0.64 212:0.58 215:0.46 216:0.70 222:0.76 232:0.81 235:0.95 241:0.75 242:0.63 243:0.36 245:0.78 247:0.30 248:0.91 253:0.93 255:0.79 256:0.81 259:0.21 260:0.85 265:0.62 266:0.54 267:0.87 268:0.82 271:0.95 276:0.58 282:0.88 285:0.62 289:0.92 297:0.36 299:0.88 300:0.84 +3 6:0.98 8:0.95 9:0.80 11:0.57 12:0.79 17:0.01 20:0.81 21:0.76 27:0.05 28:0.80 30:0.86 32:0.80 34:0.77 36:0.77 37:0.98 39:0.79 41:0.94 43:0.87 46:1.00 66:0.86 69:0.57 70:0.46 74:0.84 77:0.70 78:0.77 81:0.25 83:0.80 85:0.93 91:0.68 96:0.90 98:0.76 100:0.83 101:0.86 104:0.72 107:0.95 108:0.44 110:0.93 111:0.78 120:0.84 122:0.43 123:0.42 125:0.99 126:0.32 127:0.25 129:0.05 135:0.68 140:0.45 145:0.76 146:0.52 147:0.58 149:0.91 150:0.25 151:0.95 152:0.93 153:0.84 154:0.86 155:0.67 159:0.18 160:0.99 161:0.66 162:0.82 164:0.81 167:0.88 169:0.94 172:0.59 173:0.79 177:0.38 179:0.56 181:0.52 186:0.78 187:0.68 189:0.98 192:0.59 195:0.55 202:0.70 208:0.64 212:0.84 215:0.46 216:0.70 222:0.76 232:0.81 235:0.95 238:0.96 241:0.74 242:0.63 243:0.86 247:0.30 248:0.90 253:0.92 255:0.79 256:0.75 259:0.21 260:0.84 261:0.94 265:0.76 266:0.27 267:0.85 268:0.76 271:0.95 276:0.41 282:0.88 285:0.62 289:0.92 297:0.36 299:0.88 300:0.55 +2 1:0.74 9:0.80 11:0.57 12:0.79 17:0.16 21:0.13 27:0.50 28:0.80 30:0.76 34:0.94 36:0.94 37:0.69 39:0.79 41:0.97 43:0.90 46:1.00 60:0.97 66:0.71 69:0.44 70:0.46 74:0.93 81:0.75 83:0.88 85:0.97 91:0.60 96:0.94 98:0.81 101:0.87 104:0.72 107:0.96 108:0.61 110:0.94 114:0.92 120:0.90 122:0.43 123:0.59 124:0.44 125:0.99 126:0.54 127:0.58 129:0.59 133:0.47 135:0.34 140:0.45 146:0.32 147:0.38 149:0.97 150:0.85 151:0.97 153:0.89 154:0.89 155:0.67 158:0.87 159:0.47 160:0.99 161:0.66 162:0.73 164:0.87 165:0.88 167:0.86 172:0.79 173:0.46 176:0.73 177:0.38 179:0.41 181:0.52 187:0.39 192:0.59 201:0.74 202:0.81 208:0.64 212:0.69 215:0.84 216:0.52 222:0.76 232:0.81 235:0.22 241:0.73 242:0.63 243:0.26 245:0.84 247:0.39 248:0.94 253:0.96 255:0.79 256:0.83 259:0.21 260:0.90 265:0.81 266:0.63 267:0.84 268:0.84 271:0.95 276:0.72 279:0.86 283:0.71 285:0.62 289:0.92 290:0.92 297:0.36 300:0.70 +1 1:0.74 6:0.98 8:0.88 9:0.80 11:0.57 12:0.79 17:0.08 20:0.82 21:0.22 27:0.02 28:0.80 30:0.85 34:0.63 36:0.74 37:0.96 39:0.79 41:1.00 43:0.89 46:0.99 66:0.72 69:0.49 70:0.54 74:0.78 78:0.76 81:0.70 83:0.90 85:0.94 91:0.97 96:0.98 98:0.55 100:0.80 101:0.85 104:0.58 107:0.95 108:0.67 110:0.93 111:0.76 114:0.90 120:0.96 122:0.43 123:0.65 124:0.41 125:0.96 126:0.54 127:0.48 129:0.59 133:0.47 135:0.30 140:0.80 146:0.13 147:0.18 149:0.89 150:0.82 151:0.99 152:1.00 153:0.97 154:0.88 155:0.67 159:0.35 160:1.00 161:0.66 162:0.58 164:0.97 165:0.86 167:0.87 169:0.96 172:0.59 173:0.58 176:0.73 177:0.38 178:0.42 179:0.67 181:0.52 186:0.76 187:0.39 189:0.93 191:0.83 192:0.59 201:0.74 202:0.96 208:0.64 212:0.77 215:0.79 216:0.87 222:0.76 232:0.80 235:0.31 238:0.94 241:0.73 242:0.63 243:0.20 245:0.61 247:0.30 248:0.99 253:0.99 255:0.79 256:0.96 259:0.21 260:0.97 261:0.98 265:0.56 266:0.47 267:0.88 268:0.97 271:0.95 276:0.39 285:0.62 289:0.92 290:0.90 297:0.36 300:0.70 +1 1:0.74 6:0.94 8:0.94 9:0.80 11:0.57 12:0.79 17:0.05 20:0.81 21:0.13 27:0.50 28:0.80 30:0.76 34:0.88 36:0.93 37:0.69 39:0.79 41:0.97 43:0.90 46:1.00 60:0.87 66:0.71 69:0.44 70:0.46 74:0.93 78:0.74 81:0.75 83:0.87 85:0.97 91:0.77 96:0.92 98:0.84 100:0.78 101:0.87 104:0.72 107:0.96 108:0.61 110:0.94 111:0.74 114:0.92 120:0.86 122:0.43 123:0.59 124:0.44 125:0.98 126:0.54 127:0.81 129:0.59 133:0.47 135:0.26 140:0.45 146:0.32 147:0.38 149:0.97 150:0.85 151:0.87 152:0.83 153:0.48 154:0.89 155:0.67 158:0.87 159:0.47 160:0.99 161:0.66 162:0.80 164:0.88 165:0.88 167:1.00 169:0.76 172:0.79 173:0.48 176:0.73 177:0.38 179:0.45 181:0.52 186:0.75 189:0.96 192:0.59 201:0.74 202:0.81 208:0.64 212:0.69 215:0.84 216:0.52 220:0.74 222:0.76 232:0.81 235:0.22 238:0.94 241:0.95 242:0.63 243:0.26 245:0.84 247:0.39 248:0.92 253:0.95 255:0.79 256:0.84 259:0.21 260:0.87 261:0.77 265:0.84 266:0.63 267:0.85 268:0.86 271:0.95 276:0.72 279:0.86 283:0.71 285:0.62 289:0.92 290:0.92 297:0.36 300:0.70 +2 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.57 12:0.79 17:0.13 20:0.76 21:0.47 27:0.02 28:0.80 30:0.86 34:0.42 36:0.45 37:0.96 39:0.79 41:0.97 43:0.98 46:1.00 66:0.86 69:0.17 70:0.46 74:0.86 76:0.85 78:0.88 81:0.57 83:0.87 85:0.93 91:0.79 96:0.96 98:0.83 100:0.98 101:0.86 104:0.58 107:0.95 108:0.14 110:0.93 111:0.96 114:0.80 120:0.92 121:0.90 122:0.43 123:0.13 125:0.99 126:0.54 127:0.32 129:0.05 135:0.37 140:0.45 146:0.50 147:0.56 149:0.92 150:0.74 151:0.98 152:1.00 153:0.91 154:0.98 155:0.67 159:0.18 160:0.99 161:0.66 162:0.81 164:0.88 165:0.80 167:0.97 169:0.96 172:0.59 173:0.50 176:0.73 177:0.38 179:0.64 181:0.52 186:0.88 187:0.21 189:0.99 191:0.82 192:0.59 201:0.74 202:0.80 204:0.89 208:0.64 212:0.84 215:0.63 216:0.87 222:0.76 230:0.84 232:0.80 233:0.69 235:0.60 238:0.99 241:0.82 242:0.63 243:0.86 247:0.30 248:0.96 253:0.97 255:0.79 256:0.85 259:0.21 260:0.93 261:0.98 265:0.83 266:0.27 267:0.95 268:0.85 271:0.95 276:0.42 285:0.62 289:0.92 290:0.80 297:0.36 300:0.55 +2 1:0.74 8:0.97 9:0.80 11:0.57 12:0.79 17:0.03 21:0.68 27:0.02 28:0.80 30:0.85 34:0.73 36:0.86 37:0.96 39:0.79 41:0.90 43:0.87 46:0.99 66:0.72 69:0.58 70:0.54 74:0.77 81:0.47 83:0.78 85:0.94 91:0.74 96:0.84 98:0.55 101:0.85 104:0.58 107:0.95 108:0.68 110:0.93 114:0.70 120:0.75 122:0.43 123:0.66 124:0.41 125:0.98 126:0.54 127:0.47 129:0.59 133:0.47 135:0.54 140:0.45 146:0.13 147:0.18 149:0.88 150:0.65 151:0.93 153:0.77 154:0.85 155:0.67 159:0.35 160:0.98 161:0.66 162:0.86 164:0.74 165:0.70 167:0.83 172:0.59 173:0.69 176:0.73 177:0.38 179:0.67 181:0.52 187:0.21 191:0.91 192:0.59 201:0.74 202:0.60 208:0.64 212:0.77 215:0.49 216:0.87 220:0.99 222:0.76 232:0.80 235:0.84 241:0.71 242:0.63 243:0.20 245:0.61 247:0.30 248:0.82 253:0.85 255:0.79 256:0.64 259:0.21 260:0.76 265:0.56 266:0.47 267:0.84 268:0.69 271:0.95 276:0.39 285:0.62 289:0.92 290:0.70 297:0.36 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.57 12:0.79 17:0.55 20:0.77 21:0.30 27:0.21 28:0.80 30:0.74 34:0.53 36:0.82 37:0.74 39:0.79 41:0.90 43:0.98 46:0.99 60:0.95 66:0.16 69:0.12 70:0.55 74:0.99 78:0.74 81:0.65 83:0.88 85:1.00 91:0.04 96:0.85 98:0.54 100:0.77 101:0.87 104:0.84 106:0.81 107:0.97 108:0.96 110:0.95 111:0.73 114:0.86 117:0.86 120:0.75 121:0.90 122:0.57 123:0.87 124:0.95 125:0.99 126:0.54 127:0.88 129:0.99 133:0.96 135:0.44 140:0.45 146:0.53 147:0.59 149:1.00 150:0.79 151:0.82 152:0.91 153:0.46 154:0.98 155:0.67 158:0.92 159:0.95 160:0.98 161:0.66 162:0.81 164:0.73 165:0.84 167:0.64 169:0.77 172:0.96 173:0.06 176:0.73 177:0.38 179:0.10 181:0.52 186:0.75 187:0.39 192:0.59 201:0.74 202:0.61 204:0.89 206:0.81 208:0.64 212:0.56 215:0.72 216:0.95 220:0.87 222:0.76 230:0.87 232:0.91 233:0.76 235:0.42 241:0.15 242:0.60 243:0.09 245:0.99 247:0.67 248:0.83 253:0.90 255:0.79 256:0.64 259:0.21 260:0.76 261:0.79 265:0.51 266:0.87 267:0.52 268:0.67 271:0.95 276:0.99 279:0.86 283:0.82 285:0.62 289:0.92 290:0.86 297:0.36 300:0.84 +1 8:0.97 9:0.80 12:0.79 17:0.08 21:0.78 27:0.28 28:0.80 34:0.56 36:0.22 37:0.94 39:0.79 41:0.92 46:0.88 78:0.79 83:0.78 91:0.81 96:0.87 104:0.54 107:0.88 110:0.88 111:0.86 120:0.80 125:0.98 132:0.97 135:0.75 140:0.45 151:0.93 153:0.78 155:0.67 160:0.98 161:0.66 162:0.81 164:0.78 167:0.98 169:0.91 175:0.91 181:0.52 191:0.86 192:0.59 202:0.67 208:0.64 216:0.18 222:0.76 232:0.76 238:0.98 241:0.87 244:0.83 248:0.85 253:0.81 255:0.79 256:0.71 257:0.84 259:0.21 260:0.81 267:0.65 268:0.73 271:0.95 277:0.87 285:0.62 289:0.92 +1 1:0.74 8:0.88 9:0.80 11:0.57 12:0.79 17:0.04 27:0.02 28:0.80 30:0.86 34:0.63 36:0.66 37:0.96 39:0.79 41:0.94 43:0.76 46:0.99 66:0.84 69:0.82 70:0.40 74:0.71 78:0.80 81:0.86 83:0.83 85:0.91 91:0.77 96:0.92 98:0.39 101:0.84 104:0.58 107:0.94 108:0.66 110:0.93 111:0.79 114:0.98 120:0.86 122:0.43 123:0.64 125:1.00 126:0.54 127:0.13 129:0.05 135:0.60 140:0.45 146:0.34 147:0.41 149:0.84 150:0.92 151:0.93 153:0.78 154:0.67 155:0.67 159:0.14 160:0.99 161:0.66 162:0.85 164:0.81 165:0.92 167:0.85 169:0.92 172:0.54 173:0.95 176:0.73 177:0.38 179:0.22 181:0.52 187:0.21 192:0.59 201:0.74 202:0.69 208:0.64 212:0.92 215:0.96 216:0.87 222:0.76 232:0.80 235:0.05 241:0.73 242:0.63 243:0.85 247:0.30 248:0.91 253:0.92 255:0.79 256:0.75 259:0.21 260:0.86 265:0.41 266:0.17 267:0.59 268:0.76 271:0.95 276:0.42 285:0.62 289:0.92 290:0.98 297:0.36 300:0.43 +3 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.57 12:0.79 17:0.36 20:0.98 21:0.30 27:0.21 28:0.80 30:0.74 34:0.58 36:0.84 37:0.74 39:0.79 41:0.99 43:0.98 46:0.99 60:1.00 66:0.16 69:0.12 70:0.55 74:0.99 78:0.75 81:0.65 83:0.90 85:1.00 91:0.73 96:0.98 98:0.54 100:0.79 101:0.87 104:0.84 106:0.81 107:0.97 108:0.96 110:0.95 111:0.75 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.87 124:0.95 125:0.99 126:0.54 127:0.87 129:0.99 133:0.96 135:0.39 140:0.45 146:0.51 147:0.57 149:1.00 150:0.79 151:0.99 152:0.91 153:0.97 154:0.98 155:0.67 158:0.92 159:0.95 160:1.00 161:0.66 162:0.68 164:0.93 165:0.84 167:0.71 169:0.77 172:0.96 173:0.06 176:0.73 177:0.38 179:0.10 181:0.52 186:0.76 187:0.39 189:0.93 191:0.75 192:0.59 201:0.74 202:0.90 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.95 222:0.76 230:0.87 232:0.91 233:0.76 235:0.42 238:0.94 241:0.46 242:0.60 243:0.08 245:0.99 247:0.67 248:0.98 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.79 265:0.51 266:0.87 267:0.36 268:0.92 271:0.95 276:0.99 279:0.86 283:0.82 285:0.62 289:0.92 290:0.86 297:0.36 300:0.84 +2 6:0.97 8:0.98 9:0.80 12:0.79 17:0.01 20:0.79 21:0.78 27:0.14 28:0.80 34:0.68 36:0.28 37:1.00 39:0.79 41:0.95 46:0.88 78:0.75 83:0.80 91:0.85 96:0.90 100:0.80 104:0.72 107:0.88 110:0.88 111:0.75 120:0.83 125:0.97 135:0.81 140:0.80 151:0.96 152:0.76 153:0.85 155:0.67 160:0.99 161:0.66 162:0.55 164:0.81 167:0.98 169:0.78 175:0.91 178:0.42 181:0.52 186:0.76 189:0.98 191:0.92 192:0.59 202:0.78 208:0.64 216:0.41 222:0.76 232:0.81 238:0.95 241:0.85 244:0.83 248:0.89 253:0.85 255:0.79 256:0.77 257:0.84 259:0.21 260:0.83 261:0.76 267:0.52 268:0.77 271:0.95 277:0.87 285:0.62 289:0.92 +2 1:0.74 8:0.95 9:0.80 11:0.57 12:0.79 17:0.78 27:0.72 28:0.80 30:0.91 34:0.36 36:0.40 37:0.87 39:0.79 41:0.82 43:0.89 46:0.99 66:0.69 69:0.58 70:0.13 74:0.56 81:0.83 83:0.81 85:0.80 91:0.88 96:0.84 98:0.52 101:0.81 107:0.92 108:0.44 110:0.91 114:0.98 120:0.75 122:0.43 123:0.43 125:0.99 126:0.54 127:0.16 129:0.05 135:0.51 140:0.45 145:0.96 146:0.26 147:0.32 149:0.69 150:0.90 151:0.93 153:0.77 154:0.88 155:0.67 159:0.11 160:0.96 161:0.66 162:0.68 164:0.64 165:0.92 167:1.00 172:0.21 173:0.95 176:0.73 177:0.38 179:0.77 181:0.52 192:0.59 201:0.74 202:0.54 208:0.64 212:0.61 215:0.96 216:0.07 222:0.76 235:0.22 241:0.99 242:0.63 243:0.73 247:0.30 248:0.82 253:0.84 255:0.79 256:0.45 259:0.21 260:0.75 265:0.54 266:0.17 267:0.59 268:0.57 271:0.95 276:0.15 285:0.62 289:0.92 290:0.98 297:1.00 300:0.13 +3 1:0.74 6:0.98 7:0.81 8:0.97 9:0.80 11:0.57 12:0.79 17:0.15 20:0.97 21:0.13 27:0.02 28:0.80 30:0.86 34:0.25 36:0.49 37:0.96 39:0.79 41:0.98 43:0.98 46:1.00 66:0.86 69:0.08 70:0.46 74:0.86 78:0.87 81:0.75 83:0.89 85:0.93 91:0.87 96:0.97 98:0.82 100:0.97 101:0.86 104:0.58 107:0.95 108:0.07 110:0.93 111:0.94 114:0.92 120:0.93 121:0.90 122:0.43 123:0.07 125:0.99 126:0.54 127:0.31 129:0.05 135:0.30 140:0.45 146:0.50 147:0.56 149:0.92 150:0.85 151:0.98 152:1.00 153:0.92 154:0.98 155:0.67 159:0.18 160:0.99 161:0.66 162:0.75 164:0.91 165:0.88 167:0.96 169:0.96 172:0.59 173:0.41 176:0.73 177:0.38 179:0.63 181:0.52 186:0.87 187:0.21 189:0.99 191:0.81 192:0.59 201:0.74 202:0.85 204:0.89 208:0.64 212:0.84 215:0.84 216:0.87 222:0.76 230:0.84 232:0.80 233:0.69 235:0.22 238:0.99 241:0.81 242:0.63 243:0.86 247:0.30 248:0.97 253:0.97 255:0.79 256:0.87 259:0.21 260:0.94 261:0.98 265:0.82 266:0.27 267:0.44 268:0.88 271:0.95 276:0.42 285:0.62 289:0.92 290:0.92 297:0.36 300:0.55 +1 1:0.74 11:0.57 12:0.79 17:0.47 21:0.13 27:0.45 28:0.80 30:0.58 32:0.80 34:0.77 36:0.19 37:0.50 39:0.79 43:0.82 46:0.97 66:0.67 69:0.68 70:0.67 74:0.89 77:0.70 81:0.75 83:0.77 85:0.94 91:0.18 98:0.48 101:0.82 107:0.95 108:0.51 110:0.94 114:0.92 122:0.43 123:0.49 124:0.48 125:0.88 126:0.54 127:0.46 129:0.59 133:0.64 135:0.86 145:0.50 146:0.66 147:0.71 149:0.89 150:0.85 154:0.78 155:0.67 159:0.61 160:0.88 161:0.66 165:0.88 167:0.65 172:0.70 173:0.61 176:0.73 177:0.38 178:0.55 179:0.50 181:0.52 187:0.68 192:0.59 195:0.80 201:0.74 212:0.60 215:0.84 216:0.77 222:0.76 235:0.22 241:0.18 242:0.70 243:0.72 245:0.70 247:0.30 253:0.77 259:0.21 265:0.50 266:0.63 267:0.19 271:0.95 276:0.59 282:0.88 289:0.92 290:0.92 297:0.36 299:0.88 300:0.55 +0 1:0.66 8:0.77 9:0.75 11:0.45 12:0.79 17:0.62 18:0.96 27:0.71 29:0.77 30:0.57 31:0.99 34:0.28 36:0.97 37:0.40 39:0.95 43:0.56 44:0.88 45:0.90 46:0.88 48:0.98 55:0.71 64:0.77 66:0.46 69:0.88 70:0.62 71:0.66 74:0.66 77:0.70 79:0.99 81:0.62 83:0.60 86:0.92 91:0.87 96:0.95 97:0.97 98:0.66 99:0.83 101:0.52 107:0.91 108:0.75 110:0.92 114:0.92 122:0.85 123:0.74 124:0.65 125:0.88 126:0.44 127:0.89 129:0.05 133:0.60 135:0.30 137:0.99 139:0.91 140:0.80 145:0.65 146:0.77 147:0.75 149:0.67 150:0.54 151:0.98 153:0.94 154:0.16 155:0.58 158:0.75 159:0.62 160:0.88 162:0.63 165:0.70 172:0.75 173:0.90 176:0.62 177:0.38 178:0.42 179:0.40 190:0.95 191:0.76 192:0.51 195:0.76 196:0.99 201:0.62 202:0.87 204:0.81 208:0.54 212:0.72 216:0.39 219:0.90 222:0.25 232:0.72 235:0.05 241:0.80 242:0.33 243:0.46 244:0.83 245:0.87 247:0.86 248:0.95 253:0.95 254:0.96 255:0.74 256:0.90 257:0.65 259:0.21 265:0.67 266:0.75 267:0.87 268:0.88 271:0.95 276:0.78 279:0.81 281:0.91 282:0.74 284:0.99 285:0.56 289:0.90 290:0.92 292:0.95 300:0.55 +1 8:0.89 11:0.45 12:0.79 17:0.24 18:1.00 21:0.71 22:0.93 27:0.21 29:0.77 30:0.28 31:0.98 34:0.29 36:0.75 37:0.74 39:0.95 43:0.11 45:0.66 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.10 69:0.27 70:0.82 71:0.66 74:0.49 79:0.98 81:0.27 86:0.98 91:0.60 98:0.39 101:0.52 107:0.92 108:0.99 110:0.92 120:0.77 122:0.86 123:0.99 124:0.98 125:0.88 126:0.26 127:0.99 129:0.99 133:0.98 135:0.26 137:0.98 139:0.97 140:0.96 146:0.87 147:0.89 149:0.52 150:0.29 151:0.80 153:0.48 154:0.08 159:0.98 160:0.88 162:0.60 164:0.72 172:0.95 173:0.06 177:0.70 178:0.48 179:0.09 187:0.39 190:0.98 191:0.70 192:0.50 196:0.99 202:0.90 212:0.52 216:0.95 219:0.90 220:0.62 222:0.25 235:0.84 241:0.57 242:0.71 243:0.11 244:0.83 245:0.99 247:0.97 248:0.73 253:0.40 254:0.97 255:0.71 256:0.91 259:0.21 260:0.77 262:0.93 265:0.41 266:0.96 267:0.18 268:0.91 271:0.95 276:0.99 281:0.86 283:0.67 284:0.99 285:0.56 289:0.90 292:0.88 300:0.94 +0 7:0.69 8:0.65 11:0.45 12:0.79 17:0.45 18:0.99 21:0.22 22:0.93 27:0.66 29:0.77 30:0.13 31:0.91 34:0.40 36:0.93 37:0.54 39:0.95 43:0.22 45:0.95 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.42 69:0.74 70:0.93 71:0.66 74:0.58 79:0.90 81:0.43 86:0.96 91:0.20 98:0.72 101:0.52 104:0.83 106:0.81 107:0.92 108:0.57 110:0.93 120:0.63 122:0.85 123:0.54 124:0.86 125:0.88 126:0.44 127:0.90 129:0.93 133:0.87 135:0.60 137:0.91 139:0.95 140:0.45 146:0.99 147:0.37 149:0.66 150:0.42 151:0.70 153:0.72 154:0.14 159:0.93 160:0.88 162:0.65 164:0.63 172:0.96 173:0.37 177:0.38 179:0.13 187:0.21 190:0.95 192:0.51 196:0.95 201:0.60 202:0.55 206:0.81 212:0.47 215:0.79 216:0.45 219:0.90 220:0.74 222:0.25 230:0.69 233:0.66 235:0.31 242:0.18 243:0.07 244:0.83 245:0.98 247:0.95 248:0.66 253:0.45 255:0.71 256:0.45 257:0.65 259:0.21 260:0.64 262:0.93 265:0.72 266:0.80 267:0.44 268:0.57 271:0.95 276:0.97 281:0.86 283:0.66 284:0.91 285:0.56 289:0.90 292:0.88 297:0.36 300:0.84 +0 1:0.67 12:0.79 17:0.57 18:0.86 21:0.22 22:0.93 27:0.47 29:0.77 30:0.65 31:0.92 32:0.66 34:0.64 36:0.28 37:0.66 39:0.95 43:0.60 44:0.88 46:0.88 47:0.93 64:0.77 66:0.29 69:0.61 70:0.66 71:0.66 74:0.73 76:0.85 77:0.70 79:0.91 81:0.55 83:0.60 86:0.85 91:0.18 98:0.75 104:0.94 107:0.92 108:0.47 110:0.92 114:0.85 120:0.65 122:0.51 123:0.80 124:0.67 125:0.88 126:0.54 127:0.73 129:0.81 133:0.67 135:0.90 137:0.92 139:0.83 140:0.45 145:0.79 146:0.66 147:0.71 149:0.70 150:0.56 151:0.75 153:0.48 154:0.50 155:0.52 158:0.75 159:0.70 160:0.88 162:0.86 164:0.55 172:0.73 173:0.50 176:0.63 177:0.70 179:0.37 187:0.68 190:0.95 192:0.55 195:0.86 196:0.93 201:0.65 202:0.36 208:0.64 212:0.59 215:0.79 216:0.82 220:0.62 222:0.25 232:0.91 235:0.42 241:0.12 242:0.21 243:0.51 244:0.83 245:0.88 247:0.79 248:0.70 253:0.65 255:0.72 256:0.45 259:0.21 260:0.66 262:0.93 265:0.46 266:0.80 267:0.44 268:0.46 271:0.95 276:0.78 279:0.77 282:0.75 283:0.82 284:0.88 285:0.56 289:0.90 290:0.85 297:0.36 300:0.70 +0 1:0.63 8:0.87 9:0.75 12:0.79 17:0.01 18:0.67 21:0.22 27:0.56 29:0.77 30:0.60 34:0.75 36:0.66 37:0.58 39:0.95 43:0.57 46:0.88 66:0.10 69:0.12 70:0.79 71:0.66 74:0.44 81:0.35 86:0.64 91:0.93 96:0.98 98:0.20 107:0.89 108:0.10 110:0.89 114:0.76 120:0.96 122:0.75 123:0.60 124:0.85 125:0.88 126:0.44 127:0.94 129:0.05 133:0.83 135:0.20 138:0.95 139:0.62 140:0.45 146:0.30 147:0.36 149:0.48 150:0.39 151:0.96 153:0.84 154:0.45 155:0.58 158:0.73 159:0.86 160:0.88 162:0.61 164:0.97 172:0.27 173:0.09 176:0.60 177:0.70 179:0.79 187:0.68 190:0.95 191:0.92 192:0.58 201:0.59 202:0.96 208:0.54 212:0.30 215:0.72 216:0.79 220:0.62 222:0.25 235:0.31 241:0.79 242:0.33 243:0.40 244:0.93 245:0.56 247:0.67 248:0.99 253:0.96 255:0.73 256:0.97 259:0.21 260:0.95 265:0.16 266:0.71 267:0.44 268:0.97 271:0.95 276:0.38 277:0.69 279:0.79 283:0.66 285:0.56 286:0.99 289:0.88 290:0.76 297:0.36 300:0.70 +0 7:0.66 8:0.88 9:0.75 11:0.45 12:0.79 17:0.32 18:0.91 21:0.78 27:0.67 29:0.77 30:0.44 31:0.94 32:0.67 34:0.77 36:0.64 37:0.54 39:0.95 43:0.21 44:0.88 45:0.66 46:0.88 48:0.91 55:0.92 66:0.32 69:0.96 70:0.77 71:0.66 74:0.49 76:0.85 77:0.70 79:0.94 86:0.82 91:0.66 96:0.86 98:0.61 101:0.52 107:0.90 108:0.72 110:0.91 120:0.79 122:0.86 123:0.71 124:0.87 125:0.88 127:0.96 129:0.05 133:0.86 135:0.18 137:0.94 139:0.82 140:0.96 145:0.52 146:0.48 147:0.46 149:0.25 151:0.76 153:0.48 154:0.13 155:0.65 159:0.80 160:0.88 162:0.77 163:0.94 164:0.85 172:0.54 173:0.99 177:0.38 179:0.58 190:0.77 191:0.73 192:0.87 195:0.90 196:0.94 202:0.79 208:0.64 212:0.19 216:0.39 220:0.82 222:0.25 230:0.68 233:0.66 235:0.22 241:0.85 242:0.58 243:0.15 244:0.83 245:0.69 247:0.74 248:0.71 253:0.81 254:0.80 255:0.78 256:0.84 257:0.65 259:0.21 260:0.79 265:0.62 266:0.84 267:0.23 268:0.84 271:0.95 276:0.66 277:0.69 281:0.91 282:0.75 284:0.97 285:0.62 289:0.90 300:0.70 +0 1:0.61 7:0.66 11:0.75 12:0.79 17:0.38 18:0.96 21:0.64 27:0.26 29:0.77 30:0.32 32:0.66 34:0.99 36:0.32 37:0.68 39:0.95 43:0.75 44:0.88 45:0.77 46:0.94 48:0.91 55:0.45 64:0.77 66:0.93 69:0.61 70:0.64 71:0.66 74:0.60 77:0.70 81:0.61 83:0.60 85:0.72 86:0.95 91:0.29 97:0.89 98:0.85 99:0.83 101:0.97 107:0.91 108:0.47 110:0.92 114:0.69 122:0.86 123:0.45 125:0.88 126:0.54 127:0.35 129:0.05 135:0.93 139:0.94 145:0.54 146:0.78 147:0.82 149:0.62 150:0.49 154:0.66 155:0.54 158:0.74 159:0.34 160:0.88 165:0.70 172:0.80 173:0.69 176:0.63 177:0.70 178:0.55 179:0.38 187:0.68 192:0.49 195:0.63 201:0.60 204:0.84 208:0.64 212:0.88 215:0.53 216:0.69 219:0.90 222:0.25 230:0.68 233:0.66 235:0.88 241:0.07 242:0.28 243:0.93 247:0.79 253:0.55 259:0.21 265:0.85 266:0.27 267:0.65 271:0.95 276:0.69 279:0.79 281:0.86 282:0.75 289:0.90 290:0.69 292:0.87 297:0.36 300:0.55 +2 1:0.66 8:0.96 9:0.75 12:0.79 17:0.69 18:0.89 21:0.30 27:0.72 29:0.77 30:0.76 31:0.92 34:0.29 36:0.96 39:0.95 43:0.10 46:0.88 55:0.71 64:0.77 66:0.47 69:0.89 70:0.43 71:0.66 74:0.34 79:0.91 81:0.53 86:0.80 91:0.71 96:0.78 98:0.65 107:0.89 108:0.62 110:0.90 114:0.82 120:0.68 122:0.86 123:0.60 124:0.67 125:0.88 126:0.54 127:0.48 129:0.05 133:0.60 135:0.69 137:0.92 139:0.76 140:0.90 146:0.19 147:0.28 149:0.17 150:0.52 151:0.69 153:0.46 154:0.20 155:0.65 159:0.43 160:0.88 162:0.85 164:0.75 172:0.41 173:0.98 176:0.63 177:0.05 179:0.74 190:0.77 192:0.87 196:0.92 201:0.64 202:0.63 208:0.64 212:0.22 215:0.72 216:0.04 220:0.82 222:0.25 235:0.52 241:0.86 242:0.61 243:0.23 244:0.83 245:0.58 247:0.60 248:0.64 253:0.73 254:0.80 255:0.74 256:0.71 257:0.84 259:0.21 260:0.68 265:0.65 266:0.75 267:0.19 268:0.70 271:0.95 276:0.46 277:0.69 284:0.95 285:0.62 289:0.90 290:0.82 297:0.36 300:0.43 +2 1:0.66 9:0.74 12:0.79 17:0.75 18:0.89 21:0.30 27:0.72 29:0.77 30:0.76 31:0.90 34:0.31 36:0.96 39:0.95 43:0.10 46:0.88 55:0.71 64:0.77 66:0.47 69:0.89 70:0.43 71:0.66 74:0.34 79:0.89 81:0.53 86:0.80 91:0.69 96:0.76 98:0.65 107:0.89 108:0.62 110:0.90 114:0.82 120:0.65 122:0.86 123:0.60 124:0.67 125:0.88 126:0.54 127:0.48 129:0.05 133:0.60 135:0.69 137:0.90 139:0.76 140:0.80 146:0.19 147:0.28 149:0.17 150:0.52 151:0.69 153:0.46 154:0.20 155:0.65 159:0.43 160:0.88 162:0.82 164:0.67 172:0.41 173:0.98 176:0.63 177:0.05 179:0.74 187:0.21 190:0.77 192:0.86 196:0.90 201:0.64 202:0.56 208:0.64 212:0.22 215:0.72 216:0.04 220:0.82 222:0.25 235:0.52 241:0.83 242:0.61 243:0.23 244:0.83 245:0.58 247:0.60 248:0.64 253:0.70 254:0.80 255:0.73 256:0.64 257:0.84 259:0.21 260:0.65 265:0.65 266:0.75 267:0.19 268:0.61 271:0.95 276:0.46 277:0.69 284:0.91 285:0.62 289:0.90 290:0.82 297:0.36 300:0.43 +0 1:0.59 7:0.66 8:0.65 9:0.71 11:0.75 12:0.79 17:0.40 18:0.99 21:0.59 27:0.31 29:0.77 30:0.28 31:0.88 34:0.59 36:0.87 37:0.42 39:0.95 43:0.56 44:0.88 45:0.95 46:0.93 55:0.71 64:0.77 66:0.48 69:0.33 70:0.85 71:0.66 74:0.69 77:0.70 79:0.88 81:0.51 83:0.60 85:0.72 86:0.96 91:0.23 96:0.71 98:0.77 99:0.83 101:0.97 107:0.92 108:0.94 110:0.93 114:0.69 122:0.86 123:0.94 124:0.86 125:0.88 126:0.44 127:0.96 129:0.89 133:0.90 135:0.69 137:0.88 139:0.95 140:0.45 145:0.45 146:0.88 147:0.90 149:0.71 150:0.41 151:0.57 153:0.48 154:0.14 155:0.56 159:0.93 160:0.88 162:0.86 165:0.70 172:0.96 173:0.09 176:0.62 177:0.70 179:0.14 187:0.39 190:0.95 192:0.49 195:0.98 196:0.92 201:0.56 202:0.36 204:0.81 208:0.54 212:0.57 216:0.87 220:0.87 222:0.25 230:0.68 233:0.66 235:0.74 241:0.07 242:0.19 243:0.32 245:0.97 247:0.93 248:0.56 253:0.61 255:0.69 256:0.45 259:0.21 265:0.77 266:0.92 267:0.36 268:0.46 271:0.95 276:0.96 281:0.86 282:0.74 284:0.88 285:0.56 289:0.90 290:0.69 300:0.84 +0 11:0.54 12:0.79 17:0.30 18:0.69 21:0.59 27:0.45 29:0.77 30:0.45 34:0.71 36:0.51 37:0.50 39:0.95 43:0.60 45:0.66 46:0.88 55:0.84 66:0.64 69:0.47 70:0.59 71:0.66 74:0.59 77:0.70 81:0.24 86:0.65 91:0.04 98:0.72 101:0.52 107:0.90 108:0.36 110:0.90 114:0.67 122:0.77 123:0.34 124:0.46 125:0.88 126:0.26 127:0.61 129:0.05 133:0.37 135:0.60 139:0.63 145:0.44 146:0.84 147:0.87 149:0.36 150:0.26 154:0.49 158:0.73 159:0.63 160:0.88 163:0.75 172:0.61 173:0.38 176:0.60 177:0.70 178:0.55 179:0.64 187:0.68 195:0.79 212:0.21 216:0.77 222:0.25 235:0.68 241:0.24 242:0.70 243:0.69 244:0.61 245:0.71 247:0.67 253:0.54 259:0.21 265:0.72 266:0.75 267:0.23 271:0.95 276:0.55 282:0.73 289:0.88 290:0.67 300:0.55 +0 1:0.56 11:0.54 12:0.79 17:0.38 18:0.63 21:0.76 27:0.45 29:0.77 30:0.09 32:0.64 34:0.92 36:0.18 37:0.50 39:0.95 43:0.70 45:0.73 46:0.88 55:0.59 66:0.11 69:0.85 70:0.93 71:0.66 74:0.60 81:0.46 86:0.67 91:0.14 98:0.25 99:0.83 101:0.52 107:0.90 108:0.56 110:0.90 114:0.64 122:0.77 123:0.69 124:0.94 125:0.88 126:0.44 127:0.78 129:0.05 133:0.93 135:0.77 139:0.64 145:0.47 146:0.49 147:0.05 149:0.54 150:0.33 154:0.59 155:0.46 159:0.82 160:0.88 165:0.70 172:0.32 173:0.72 176:0.70 177:0.05 178:0.55 179:0.53 187:0.68 192:0.44 195:0.92 201:0.54 208:0.54 212:0.23 215:0.46 216:0.77 222:0.25 235:0.99 241:0.53 242:0.58 243:0.09 244:0.61 245:0.65 247:0.74 253:0.53 257:0.84 259:0.21 265:0.21 266:0.91 267:0.36 271:0.95 276:0.68 289:0.89 290:0.64 297:0.36 300:0.70 +0 1:0.66 7:0.66 11:0.75 12:0.79 17:0.13 18:0.85 21:0.30 27:0.31 29:0.77 30:0.21 32:0.80 34:0.82 36:0.71 37:0.42 39:0.95 43:0.94 44:0.93 45:0.73 46:0.95 48:0.91 55:0.52 64:0.77 66:0.90 69:0.23 70:0.70 71:0.66 74:0.68 76:0.85 77:0.70 81:0.72 83:0.60 85:0.72 86:0.86 91:0.27 97:0.89 98:0.97 101:0.97 104:0.82 107:0.91 108:0.18 110:0.91 114:0.86 120:0.56 123:0.18 125:0.88 126:0.54 127:0.77 129:0.05 135:0.24 139:0.89 140:0.45 145:0.44 146:0.75 147:0.79 149:0.68 150:0.53 151:0.49 153:0.48 154:0.94 155:0.51 158:0.75 159:0.31 160:0.88 162:0.86 164:0.55 165:0.93 172:0.71 173:0.43 176:0.73 177:0.70 179:0.63 187:0.39 190:0.98 192:0.48 195:0.57 201:0.64 202:0.36 208:0.64 212:0.80 215:0.72 216:0.87 219:0.90 220:0.93 222:0.25 230:0.68 233:0.76 235:0.68 241:0.41 242:0.37 243:0.90 247:0.82 248:0.48 253:0.65 256:0.45 259:0.21 260:0.56 265:0.97 266:0.38 267:0.36 268:0.46 271:0.95 276:0.59 279:0.77 281:0.91 282:0.88 285:0.47 289:0.90 290:0.86 292:0.88 297:0.36 299:0.88 300:0.43 +0 8:0.97 11:0.54 12:0.79 17:0.55 18:0.92 21:0.54 27:0.72 29:0.77 30:0.21 31:0.87 34:0.91 36:0.30 37:0.92 39:0.95 43:0.11 44:0.86 45:0.66 46:0.88 48:0.91 55:0.59 64:0.77 66:0.68 69:0.38 70:0.64 71:0.66 74:0.40 79:0.86 81:0.30 86:0.86 91:0.81 98:0.71 101:0.52 107:0.89 108:0.30 110:0.90 122:0.86 123:0.28 124:0.43 125:0.88 126:0.26 127:0.65 129:0.05 133:0.37 135:0.42 137:0.87 139:0.84 140:0.45 145:0.45 146:0.69 147:0.74 149:0.19 150:0.33 151:0.48 153:0.48 154:0.66 159:0.45 160:0.88 162:0.54 172:0.51 173:0.42 177:0.70 179:0.78 190:0.98 191:0.80 195:0.66 196:0.89 202:0.56 212:0.49 216:0.10 220:0.96 222:0.25 235:0.60 241:0.86 242:0.74 243:0.72 245:0.58 247:0.47 248:0.48 253:0.35 254:0.84 256:0.45 259:0.21 265:0.71 266:0.54 267:0.28 268:0.46 271:0.95 276:0.42 281:0.91 284:0.88 285:0.47 289:0.89 300:0.43 +1 8:0.62 12:0.79 17:0.67 18:0.87 21:0.22 27:0.72 29:0.77 30:0.68 31:0.95 34:0.66 36:0.91 37:0.28 39:0.95 43:0.44 44:0.86 46:0.88 48:1.00 64:0.77 66:0.86 69:0.56 70:0.48 71:0.66 74:0.34 79:0.94 81:0.52 86:0.86 91:0.84 98:0.87 104:0.57 107:0.90 108:0.43 110:0.91 120:0.74 122:0.83 123:0.42 125:0.88 126:0.44 127:0.40 129:0.05 135:0.63 137:0.95 139:0.83 140:0.80 145:0.58 146:0.56 147:0.62 149:0.41 150:0.49 151:0.80 153:0.72 154:0.45 159:0.20 160:0.88 162:0.59 164:0.71 172:0.61 173:0.81 177:0.70 178:0.42 179:0.66 190:0.95 192:0.51 195:0.55 196:0.95 201:0.62 202:0.66 212:0.81 215:0.79 216:0.15 220:0.62 222:0.25 235:0.42 241:0.89 242:0.54 243:0.87 244:0.83 247:0.60 248:0.81 253:0.31 254:0.84 255:0.73 256:0.64 259:0.21 260:0.75 265:0.87 266:0.27 267:0.23 268:0.66 271:0.95 276:0.47 281:0.91 284:0.94 285:0.56 289:0.90 297:0.36 300:0.43 +0 1:0.65 8:0.66 9:0.75 11:0.45 12:0.79 17:0.32 18:0.98 21:0.05 27:0.17 29:0.77 30:0.26 31:0.97 34:0.42 36:0.93 37:0.59 39:0.95 43:0.28 45:0.96 46:0.88 64:0.77 66:0.68 69:0.93 70:0.75 71:0.66 74:0.65 79:0.96 81:0.60 83:0.60 86:0.94 91:0.58 96:0.89 97:0.94 98:0.75 99:0.83 101:0.52 107:0.92 108:0.86 110:0.92 114:0.89 122:0.85 123:0.85 124:0.50 125:0.88 126:0.44 127:0.75 129:0.05 133:0.60 135:0.69 137:0.97 139:0.93 140:0.45 146:0.94 147:0.05 149:0.72 150:0.51 151:0.92 153:0.77 154:0.20 155:0.58 158:0.75 159:0.80 160:0.88 162:0.86 165:0.70 172:0.95 173:0.90 176:0.62 177:0.05 179:0.18 187:0.21 190:0.95 192:0.51 196:0.98 201:0.61 202:0.60 208:0.54 212:0.84 216:0.79 219:0.90 220:0.62 222:0.25 232:0.80 235:0.12 241:0.71 242:0.39 243:0.05 244:0.93 245:0.95 247:0.90 248:0.88 253:0.88 255:0.73 256:0.64 257:0.84 259:0.21 265:0.75 266:0.75 267:0.84 268:0.69 271:0.95 276:0.92 279:0.79 284:0.95 285:0.56 289:0.90 290:0.89 292:0.88 300:0.70 +0 11:0.45 12:0.79 17:0.32 18:0.88 21:0.22 22:0.93 27:0.38 29:0.77 30:0.11 34:0.67 36:0.68 37:0.50 39:0.95 43:0.21 45:0.73 46:0.88 47:0.93 48:0.91 55:0.71 64:0.77 66:0.26 69:0.51 70:0.96 71:0.66 74:0.49 81:0.43 86:0.80 91:0.07 98:0.31 101:0.52 104:0.73 107:0.91 108:0.76 110:0.91 122:0.85 123:0.74 124:0.92 125:0.88 126:0.44 127:0.50 129:0.93 133:0.93 135:0.60 139:0.79 146:0.22 147:0.27 149:0.27 150:0.42 154:0.20 159:0.85 160:0.88 163:0.81 172:0.59 173:0.23 175:0.75 177:0.38 178:0.55 179:0.38 187:0.39 192:0.45 201:0.60 212:0.18 215:0.79 216:0.30 219:0.88 222:0.25 232:0.84 235:0.31 241:0.21 242:0.21 243:0.12 244:0.93 245:0.72 247:0.91 253:0.40 257:0.65 259:0.21 262:0.93 265:0.34 266:0.94 267:0.36 271:0.95 276:0.74 277:0.69 281:0.86 289:0.90 292:0.88 297:0.36 300:0.84 +1 1:0.60 7:0.75 9:0.71 11:0.54 12:0.79 17:0.22 18:0.87 21:0.68 22:0.93 27:0.07 29:0.77 30:0.30 31:0.88 32:0.74 34:0.98 36:0.23 37:0.63 39:0.95 43:0.11 44:0.93 45:0.73 46:0.88 47:0.93 48:0.97 55:0.84 64:0.77 66:0.18 69:0.38 70:0.92 71:0.66 74:0.75 77:0.70 79:0.87 81:0.57 86:0.85 91:0.22 96:0.70 97:0.89 98:0.45 99:0.83 101:0.52 104:0.79 106:0.81 107:0.92 108:0.92 110:0.92 114:0.70 120:0.63 122:0.86 123:0.92 124:0.88 125:0.88 126:0.54 127:0.82 129:0.81 133:0.87 135:1.00 137:0.88 139:0.89 140:0.45 145:0.61 146:0.69 147:0.74 149:0.24 150:0.43 151:0.52 153:0.48 154:0.62 155:0.58 158:0.83 159:0.85 160:0.88 162:0.70 163:0.90 164:0.71 165:0.70 172:0.59 173:0.17 176:0.73 177:0.70 179:0.37 187:0.68 190:0.98 192:0.56 195:0.94 196:0.91 201:0.57 202:0.63 204:0.81 206:0.81 208:0.64 212:0.16 215:0.49 216:0.95 219:0.95 220:0.93 222:0.25 230:0.77 233:0.66 235:0.97 241:0.01 242:0.27 243:0.27 245:0.83 247:0.90 248:0.53 253:0.62 254:0.90 255:0.69 256:0.64 259:0.21 260:0.63 262:0.93 265:0.47 266:0.94 267:0.69 268:0.65 271:0.95 276:0.80 277:0.69 279:0.79 281:0.86 282:0.82 283:0.67 284:0.92 285:0.62 289:0.90 290:0.70 292:0.86 297:0.36 300:0.84 +1 1:0.62 8:0.61 9:0.74 11:0.54 12:0.79 17:0.02 18:0.86 21:0.30 27:0.05 29:0.77 30:0.45 31:0.87 32:0.72 34:0.64 36:0.82 37:0.79 39:0.95 43:0.74 45:0.66 46:0.88 55:0.92 66:0.29 69:0.61 70:0.57 71:0.66 74:0.58 77:0.70 79:0.87 81:0.51 86:0.77 91:0.22 96:0.82 97:0.89 98:0.55 99:0.83 101:0.52 107:0.90 108:0.77 110:0.91 114:0.84 120:0.76 122:0.75 123:0.76 124:0.84 125:0.88 126:0.44 127:0.35 129:0.59 133:0.81 135:0.72 137:0.87 139:0.73 140:0.45 145:0.42 146:0.34 147:0.41 149:0.32 150:0.39 151:0.76 153:0.48 154:0.64 155:0.58 158:0.81 159:0.65 160:0.88 162:0.60 163:0.92 164:0.77 165:0.70 172:0.37 173:0.45 176:0.70 177:0.70 179:0.61 187:0.87 190:0.77 192:0.58 195:0.89 196:0.88 201:0.59 202:0.75 208:0.54 212:0.37 215:0.72 216:0.90 220:0.74 222:0.25 232:0.71 235:0.60 241:0.32 242:0.28 243:0.33 245:0.58 247:0.74 248:0.79 253:0.81 254:0.97 255:0.72 256:0.75 259:0.21 260:0.77 265:0.56 266:0.63 267:0.59 268:0.75 271:0.95 276:0.53 279:0.77 282:0.80 283:0.67 284:0.88 285:0.62 289:0.90 290:0.84 297:0.36 300:0.43 +0 1:0.66 11:0.75 12:0.79 17:0.55 18:0.93 21:0.40 27:0.25 29:0.77 30:0.17 34:0.76 36:0.97 37:0.54 39:0.95 43:0.56 45:0.85 46:0.93 55:0.84 64:0.77 66:0.49 69:0.65 70:0.92 71:0.66 74:0.52 81:0.51 85:0.72 86:0.90 91:0.03 98:0.73 101:0.97 107:0.92 108:0.87 110:0.92 114:0.78 122:0.82 123:0.86 124:0.64 125:0.88 126:0.54 127:0.75 129:0.59 133:0.61 135:0.54 139:0.85 145:0.45 146:0.93 147:0.94 149:0.54 150:0.50 154:0.45 155:0.50 159:0.83 160:0.88 172:0.86 173:0.43 176:0.63 177:0.70 178:0.55 179:0.25 187:0.68 192:0.48 201:0.63 208:0.64 212:0.29 215:0.67 216:0.86 222:0.25 232:0.82 235:0.60 241:0.12 242:0.14 243:0.45 245:0.93 247:0.95 253:0.58 259:0.21 265:0.73 266:0.80 267:0.44 271:0.95 276:0.87 289:0.90 290:0.78 297:0.36 300:0.84 +0 1:0.63 8:0.89 9:0.75 12:0.79 17:0.02 18:0.67 21:0.22 27:0.56 29:0.77 30:0.60 34:0.75 36:0.66 37:0.58 39:0.95 43:0.57 46:0.88 66:0.10 69:0.12 70:0.79 71:0.66 74:0.44 81:0.35 86:0.64 91:0.89 96:0.96 98:0.20 107:0.89 108:0.10 110:0.89 114:0.76 120:0.93 122:0.75 123:0.60 124:0.85 125:0.88 126:0.44 127:0.94 129:0.05 133:0.83 135:0.26 138:0.95 139:0.62 140:0.45 146:0.30 147:0.36 149:0.48 150:0.39 151:0.94 153:0.78 154:0.45 155:0.58 158:0.73 159:0.86 160:0.88 162:0.63 164:0.96 172:0.27 173:0.09 176:0.60 177:0.70 179:0.79 187:0.39 190:0.95 191:0.95 192:0.58 201:0.59 202:0.94 208:0.54 212:0.30 215:0.72 216:0.79 220:0.74 222:0.25 235:0.31 241:0.78 242:0.33 243:0.40 244:0.93 245:0.56 247:0.67 248:0.98 253:0.94 255:0.73 256:0.95 259:0.21 260:0.93 265:0.16 266:0.71 267:0.36 268:0.95 271:0.95 276:0.38 277:0.69 279:0.79 283:0.66 285:0.56 286:0.99 289:0.88 290:0.76 297:0.36 298:0.99 300:0.70 +0 8:0.84 12:0.79 17:0.94 18:0.71 21:0.78 27:0.47 29:0.77 30:0.15 31:0.92 34:0.24 36:0.75 37:0.77 39:0.95 43:0.11 46:0.88 48:0.91 55:0.84 66:0.16 69:0.90 70:0.94 71:0.66 74:0.64 79:0.91 86:0.69 91:0.87 98:0.54 107:0.90 108:0.29 110:0.90 122:0.77 123:0.84 124:0.77 125:0.88 127:0.90 129:0.05 133:0.74 135:0.79 137:0.92 139:0.69 140:0.96 145:0.74 146:0.54 147:0.72 149:0.24 151:0.60 153:0.48 154:0.21 159:0.67 160:0.88 162:0.46 172:0.59 173:0.91 177:0.38 178:0.54 179:0.55 190:0.98 191:0.88 196:0.90 202:0.78 212:0.51 216:0.10 220:0.62 222:0.25 235:0.68 241:0.82 242:0.72 243:0.51 244:0.93 245:0.76 247:0.67 248:0.59 253:0.48 254:0.74 256:0.45 257:0.65 259:0.21 265:0.52 266:0.75 267:0.23 268:0.46 271:0.95 276:0.68 277:0.69 281:0.86 284:0.88 285:0.47 289:0.89 300:0.70 +3 6:0.95 8:0.89 9:0.80 12:0.79 17:0.01 20:0.96 21:0.40 27:0.13 28:0.53 30:0.21 34:0.42 36:0.70 37:0.64 39:0.42 41:0.82 43:0.36 55:0.45 66:0.61 69:0.54 70:0.67 74:0.84 78:0.91 81:0.52 83:0.77 91:0.86 96:0.97 98:0.39 100:0.94 108:0.42 111:0.91 114:0.68 117:0.86 120:0.74 122:0.67 123:0.40 124:0.51 126:0.32 127:0.64 129:0.05 133:0.62 135:0.34 140:0.96 146:0.47 147:0.54 149:0.39 150:0.40 151:0.92 152:0.98 153:0.90 154:0.75 155:0.67 158:0.84 159:0.54 161:0.71 162:0.61 163:0.90 164:0.64 167:0.85 169:0.96 172:0.37 173:0.52 176:0.56 177:0.38 179:0.88 181:0.81 186:0.91 187:0.68 189:0.97 191:0.81 192:0.59 202:0.91 208:0.64 212:0.37 216:0.92 220:0.91 222:0.95 232:0.88 235:0.42 238:0.91 241:0.76 242:0.72 243:0.67 245:0.45 247:0.39 248:0.81 253:0.97 254:0.84 255:0.79 256:0.92 259:0.21 260:0.75 261:0.97 265:0.41 266:0.47 267:0.19 268:0.92 276:0.32 285:0.62 290:0.68 300:0.43 +1 1:0.74 12:0.79 17:0.47 21:0.59 27:0.45 28:0.53 30:0.45 32:0.78 34:0.85 36:0.17 37:0.50 39:0.42 43:0.27 55:0.59 66:0.33 69:0.69 70:0.61 74:0.67 77:0.70 81:0.53 91:0.07 98:0.30 108:0.53 114:0.74 123:0.50 124:0.61 126:0.54 127:0.55 129:0.05 133:0.39 135:0.58 145:0.45 146:0.55 147:0.61 149:0.29 150:0.63 154:0.75 155:0.67 159:0.76 161:0.71 167:0.66 172:0.27 173:0.53 176:0.73 177:0.38 178:0.55 179:0.84 181:0.81 187:0.68 192:0.59 195:0.90 201:0.74 212:0.30 215:0.55 216:0.77 222:0.95 235:0.74 241:0.49 242:0.63 243:0.50 245:0.60 247:0.47 253:0.63 254:0.74 259:0.21 265:0.32 266:0.54 267:0.28 276:0.21 282:0.86 290:0.74 297:0.36 300:0.43 +2 1:0.74 6:0.82 8:0.73 9:0.80 12:0.79 20:0.76 21:0.22 27:0.19 28:0.53 30:0.95 34:0.42 36:0.91 37:0.56 39:0.42 41:0.95 43:0.61 55:0.59 66:0.54 69:0.85 74:0.51 78:0.83 81:0.73 83:0.81 91:0.81 96:0.98 98:0.19 100:0.73 108:0.72 111:0.81 114:0.69 117:0.86 120:0.93 123:0.70 126:0.54 127:0.10 129:0.05 135:0.63 138:0.98 140:0.45 145:0.96 146:0.23 147:0.29 149:0.29 150:0.77 151:0.95 152:0.98 153:0.84 154:0.50 155:0.67 158:0.86 159:0.11 161:0.71 162:0.83 164:0.94 167:0.77 169:0.80 172:0.10 173:0.97 176:0.56 177:0.38 179:0.47 181:0.81 186:0.83 187:0.87 191:0.80 192:0.59 201:0.74 202:0.91 208:0.64 215:0.72 216:0.92 220:0.74 222:0.95 232:0.85 235:0.31 241:0.71 243:0.63 248:0.96 253:0.97 254:0.97 255:0.79 256:0.94 259:0.21 260:0.94 261:0.89 265:0.21 267:0.23 268:0.94 279:0.86 283:0.66 285:0.62 290:0.69 297:0.36 300:0.08 +2 6:0.95 8:0.88 9:0.80 12:0.79 20:0.98 21:0.40 27:0.13 28:0.53 30:0.58 34:0.75 36:0.62 37:0.64 39:0.42 41:0.97 43:0.36 55:0.45 60:0.87 66:0.69 69:0.54 70:0.60 74:0.88 77:0.70 78:0.97 81:0.52 83:0.83 91:0.91 96:0.99 98:0.56 100:0.98 108:0.42 111:0.98 114:0.68 117:0.86 120:0.91 122:0.67 123:0.40 124:0.41 126:0.32 127:0.41 129:0.05 133:0.39 135:0.37 140:0.96 145:0.63 146:0.50 147:0.56 149:0.41 150:0.40 151:0.98 152:0.98 153:0.97 154:0.75 155:0.67 158:0.87 159:0.33 161:0.71 162:0.65 164:0.88 167:0.85 169:0.96 172:0.41 173:0.64 176:0.56 177:0.38 179:0.84 181:0.81 186:0.96 187:0.39 189:0.96 191:0.88 192:0.59 195:0.64 202:0.98 208:0.64 212:0.37 216:0.92 220:0.91 222:0.95 232:0.88 235:0.42 238:0.95 241:0.76 242:0.72 243:0.73 245:0.46 247:0.39 248:0.94 253:1.00 254:0.84 255:0.79 256:0.99 259:0.21 260:0.92 261:0.97 265:0.57 266:0.38 267:0.59 268:0.99 276:0.30 279:0.86 282:0.84 283:0.71 285:0.62 290:0.68 300:0.43 +2 1:0.74 11:0.57 12:0.79 17:0.51 21:0.40 27:0.45 28:0.53 30:0.68 34:0.80 36:0.18 37:0.50 39:0.42 43:0.82 55:0.59 66:0.77 69:0.69 70:0.43 74:0.81 77:0.70 81:0.67 83:0.75 85:0.88 91:0.07 98:0.47 101:0.79 108:0.52 114:0.82 122:0.43 123:0.50 124:0.38 126:0.54 127:0.33 129:0.05 133:0.39 135:0.51 145:0.49 146:0.56 147:0.62 149:0.81 150:0.79 154:0.77 155:0.67 159:0.43 161:0.71 165:0.83 167:0.57 172:0.59 173:0.69 176:0.73 177:0.38 178:0.55 179:0.62 181:0.81 187:0.68 192:0.59 195:0.72 201:0.74 212:0.75 215:0.67 216:0.77 222:0.95 235:0.52 241:0.15 242:0.43 243:0.79 245:0.52 247:0.60 253:0.71 259:0.21 265:0.49 266:0.38 267:0.28 276:0.37 282:0.84 290:0.82 297:0.36 300:0.43 +2 9:0.80 11:0.57 12:0.79 17:0.45 21:0.68 27:0.21 28:0.53 30:0.45 34:0.45 36:0.50 37:0.74 39:0.42 41:0.82 43:0.91 55:0.71 60:0.87 66:0.16 69:0.27 70:0.66 74:0.92 76:0.94 78:0.95 81:0.33 83:0.83 85:0.72 91:0.09 96:0.80 98:0.22 101:0.68 104:0.84 106:0.81 108:0.21 111:0.99 114:0.62 117:0.86 120:0.69 122:0.67 123:0.20 124:0.91 126:0.32 127:0.66 129:0.59 133:0.89 135:0.30 140:0.45 146:0.49 147:0.56 149:0.70 150:0.30 151:0.87 153:0.48 154:0.91 155:0.67 158:0.92 159:0.85 161:0.71 162:0.86 164:0.57 167:0.63 169:0.97 172:0.37 173:0.12 176:0.56 177:0.38 179:0.55 181:0.81 187:0.39 192:0.59 202:0.36 206:0.81 208:0.64 212:0.27 216:0.95 222:0.95 232:0.91 235:0.80 238:0.98 241:0.39 242:0.74 243:0.36 245:0.67 247:0.39 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 265:0.24 266:0.80 267:0.19 268:0.46 276:0.56 279:0.86 283:0.82 285:0.62 290:0.62 300:0.55 +2 1:0.74 7:0.81 8:0.95 9:0.80 12:0.79 17:0.11 21:0.68 27:0.10 28:0.53 30:0.94 34:0.70 36:0.68 37:0.81 39:0.42 41:0.93 43:0.34 55:0.92 60:0.87 66:0.36 69:0.61 70:0.13 74:0.78 76:0.97 77:0.89 81:0.56 83:0.82 91:0.75 96:0.93 98:0.62 108:0.46 114:0.70 117:0.86 120:0.80 121:0.90 122:0.51 123:0.45 124:0.67 126:0.54 127:0.44 129:0.05 133:0.62 135:0.24 140:0.80 145:0.76 146:0.62 147:0.67 149:0.31 150:0.69 151:0.96 153:0.86 154:0.25 155:0.67 158:0.92 159:0.40 161:0.71 162:0.82 163:0.94 164:0.79 165:0.69 167:0.89 172:0.16 173:0.66 176:0.73 177:0.38 179:0.95 181:0.81 187:0.21 191:0.76 192:0.59 195:0.67 201:0.74 202:0.77 204:0.89 208:0.64 212:0.78 215:0.49 216:0.76 220:0.62 222:0.95 230:0.87 232:0.83 233:0.76 235:0.88 241:0.79 242:0.45 243:0.51 244:0.61 245:0.40 247:0.35 248:0.87 253:0.94 255:0.79 256:0.81 259:0.21 260:0.81 265:0.63 266:0.17 267:0.36 268:0.83 276:0.27 279:0.86 282:0.84 283:0.82 285:0.62 290:0.70 297:0.36 300:0.13 +3 1:0.74 7:0.78 8:0.78 9:0.80 12:0.79 17:0.45 21:0.40 27:0.38 28:0.53 30:0.95 32:0.74 34:0.74 36:0.90 39:0.42 41:0.94 43:0.60 55:0.59 66:0.54 69:0.85 74:0.68 76:0.94 77:0.70 78:0.82 81:0.64 83:0.80 91:0.79 96:0.94 98:0.19 104:0.89 108:0.71 111:0.80 114:0.82 120:0.89 123:0.69 126:0.54 127:0.10 129:0.05 135:0.23 140:0.80 145:0.69 146:0.24 147:0.30 149:0.28 150:0.70 151:0.96 153:0.86 154:0.49 155:0.67 158:0.84 159:0.11 161:0.71 162:0.57 164:0.90 167:0.77 169:0.80 172:0.10 173:0.95 176:0.73 177:0.38 179:0.48 181:0.81 187:0.39 191:0.86 192:0.59 195:0.64 201:0.74 202:0.89 208:0.64 215:0.67 216:0.50 220:0.91 222:0.95 230:0.81 233:0.69 235:0.52 241:0.71 243:0.63 248:0.91 253:0.93 254:0.98 255:0.79 256:0.88 259:0.21 260:0.90 265:0.21 267:0.18 268:0.89 282:0.83 283:0.71 285:0.62 290:0.82 297:0.36 300:0.08 +2 1:0.74 6:0.82 8:0.63 9:0.80 11:0.57 12:0.79 17:0.22 20:0.80 21:0.13 27:0.35 28:0.53 30:0.72 34:0.75 36:0.89 37:0.31 39:0.42 41:0.90 43:0.81 66:0.44 69:0.70 70:0.40 74:0.72 78:0.84 81:0.82 83:0.81 85:0.90 91:0.29 96:0.91 98:0.51 100:0.86 101:0.83 104:0.58 108:0.53 111:0.83 114:0.92 120:0.81 122:0.43 123:0.51 124:0.58 126:0.54 127:0.34 129:0.59 133:0.47 135:0.39 140:0.45 146:0.45 147:0.52 149:0.82 150:0.89 151:0.97 152:0.98 153:0.88 154:0.76 155:0.67 159:0.31 161:0.71 162:0.85 163:0.75 164:0.75 165:0.88 167:0.74 169:0.92 172:0.37 173:0.80 176:0.73 177:0.38 179:0.71 181:0.81 186:0.84 187:0.21 189:0.85 192:0.59 201:0.74 202:0.69 208:0.64 212:0.39 215:0.84 216:0.57 222:0.95 232:0.80 235:0.22 238:0.86 241:0.67 242:0.42 243:0.57 245:0.64 247:0.55 248:0.88 253:0.91 255:0.79 256:0.73 259:0.21 260:0.82 261:0.95 265:0.52 266:0.27 267:0.28 268:0.76 276:0.41 285:0.62 290:0.92 297:0.36 300:0.33 +3 6:0.92 8:0.77 9:0.80 12:0.79 17:0.01 20:0.98 21:0.78 27:0.31 28:0.53 34:0.61 36:0.28 37:0.58 39:0.42 41:0.96 78:0.92 83:0.82 91:0.96 96:0.96 100:0.93 104:0.58 111:0.91 120:0.95 135:0.89 140:0.90 151:0.99 152:0.90 153:0.95 155:0.67 161:0.71 162:0.70 164:0.95 167:0.92 169:0.90 175:0.91 181:0.81 186:0.92 189:0.93 191:0.84 192:0.59 202:0.93 208:0.64 216:0.54 220:0.62 222:0.95 238:0.89 241:0.88 244:0.83 248:0.94 253:0.93 255:0.79 256:0.94 257:0.84 259:0.21 260:0.95 261:0.92 267:0.52 268:0.95 277:0.87 285:0.62 +2 6:0.96 8:0.95 9:0.80 11:0.57 12:0.79 17:0.32 20:0.80 21:0.68 27:0.21 28:0.53 30:0.45 34:0.44 36:0.48 37:0.74 39:0.42 41:0.82 43:0.91 55:0.71 60:0.87 66:0.16 69:0.27 70:0.66 74:0.92 76:0.94 78:0.92 81:0.33 83:0.82 85:0.72 91:0.33 96:0.86 98:0.22 100:0.95 101:0.68 104:0.84 106:0.81 108:0.21 111:0.95 114:0.62 117:0.86 120:0.69 122:0.67 123:0.20 124:0.91 126:0.32 127:0.66 129:0.59 133:0.89 135:0.24 140:0.80 146:0.49 147:0.56 149:0.70 150:0.30 151:0.87 152:0.84 153:0.48 154:0.91 155:0.67 158:0.92 159:0.85 161:0.71 162:0.72 164:0.57 167:0.71 169:0.93 172:0.37 173:0.12 176:0.56 177:0.38 178:0.42 179:0.55 181:0.81 186:0.91 187:0.39 189:1.00 191:0.70 192:0.59 202:0.68 206:0.81 208:0.64 212:0.27 216:0.95 222:0.95 232:0.91 235:0.80 238:0.95 241:0.61 242:0.74 243:0.36 245:0.67 247:0.39 248:0.78 253:0.89 255:0.79 256:0.71 259:0.21 260:0.70 261:0.89 265:0.24 266:0.80 267:0.59 268:0.71 276:0.56 279:0.86 283:0.82 285:0.62 290:0.62 300:0.55 +2 1:0.74 6:0.82 8:0.63 9:0.80 11:0.57 12:0.79 20:0.98 21:0.13 27:0.35 28:0.53 30:0.72 34:0.53 36:0.86 37:0.31 39:0.42 41:1.00 43:0.81 60:1.00 66:0.90 69:0.70 70:0.41 74:0.87 78:0.96 81:0.82 83:0.90 85:0.94 91:0.94 96:0.99 98:0.85 100:0.96 101:0.85 104:0.58 108:0.53 111:0.96 114:0.92 120:0.98 122:0.43 123:0.51 126:0.54 127:0.35 129:0.05 135:0.28 138:0.99 140:0.45 146:0.75 147:0.79 149:0.92 150:0.89 151:1.00 152:0.98 153:0.98 154:0.76 155:0.67 158:0.92 159:0.32 161:0.71 162:0.79 164:0.98 165:0.88 167:0.92 169:0.92 172:0.71 173:0.80 176:0.73 177:0.38 179:0.51 181:0.81 186:0.96 189:0.84 191:0.88 192:0.59 201:0.74 202:0.96 208:0.64 212:0.83 215:0.84 216:0.57 220:0.74 222:0.95 232:0.80 235:0.22 238:0.91 241:0.91 242:0.62 243:0.90 247:0.39 248:0.99 253:1.00 255:0.79 256:0.97 259:0.21 260:0.98 261:0.95 265:0.85 266:0.27 267:0.79 268:0.98 276:0.59 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.43 +2 1:0.74 8:0.64 9:0.80 11:0.57 12:0.79 17:0.04 21:0.13 27:0.35 28:0.53 30:0.87 34:0.47 36:0.97 37:0.31 39:0.42 41:0.97 43:0.81 66:0.54 69:0.70 70:0.20 74:0.66 81:0.82 83:0.83 85:0.88 91:0.81 96:0.95 98:0.19 101:0.79 104:0.58 108:0.53 114:0.92 120:0.88 122:0.43 123:0.51 124:0.48 126:0.54 127:0.15 129:0.59 133:0.47 135:0.58 140:0.45 146:0.27 147:0.33 149:0.80 150:0.89 151:0.94 153:0.82 154:0.76 155:0.67 159:0.22 161:0.71 162:0.59 164:0.88 165:0.88 167:0.74 172:0.32 173:0.68 176:0.73 177:0.38 179:0.45 181:0.81 187:0.21 191:0.82 192:0.59 201:0.74 202:0.87 208:0.64 212:0.61 215:0.84 216:0.57 220:0.74 222:0.95 232:0.80 235:0.22 241:0.67 242:0.63 243:0.63 245:0.50 247:0.30 248:0.93 253:0.94 255:0.79 256:0.86 259:0.21 260:0.89 265:0.21 266:0.27 267:0.65 268:0.87 276:0.21 285:0.62 290:0.92 297:0.36 300:0.18 +1 1:0.74 6:0.82 8:0.61 9:0.80 11:0.57 12:0.79 17:0.18 20:0.93 21:0.13 27:0.35 28:0.53 30:0.73 34:0.71 36:0.59 37:0.31 39:0.42 41:0.97 43:0.81 60:0.87 66:0.92 69:0.70 70:0.39 74:0.92 78:0.86 81:0.82 83:0.88 85:0.96 91:0.65 96:0.96 98:0.85 100:0.84 101:0.86 104:0.58 108:0.53 111:0.84 114:0.92 120:0.91 122:0.57 123:0.51 126:0.54 127:0.35 129:0.05 135:0.30 138:0.98 140:0.45 146:0.75 147:0.79 149:0.95 150:0.89 151:0.96 152:0.98 153:0.87 154:0.76 155:0.67 158:0.92 159:0.31 161:0.71 162:0.76 164:0.90 165:0.88 167:0.77 169:0.92 172:0.78 173:0.80 176:0.73 177:0.38 179:0.41 181:0.81 186:0.86 187:0.21 189:0.83 192:0.59 201:0.74 202:0.85 208:0.64 212:0.82 215:0.84 216:0.57 220:0.74 222:0.95 232:0.80 235:0.22 238:0.83 241:0.71 242:0.60 243:0.92 247:0.47 248:0.95 253:0.97 255:0.79 256:0.88 259:0.21 260:0.91 261:0.95 265:0.85 266:0.27 267:0.19 268:0.89 276:0.67 279:0.86 283:0.82 285:0.62 290:0.92 297:0.36 300:0.43 +3 1:0.74 6:0.87 7:0.81 8:0.85 9:0.80 12:0.79 17:0.08 20:0.79 21:0.71 27:0.10 28:0.53 30:0.95 34:0.70 36:0.75 37:0.81 39:0.42 41:0.96 43:0.30 55:0.92 60:0.98 66:0.27 69:0.69 74:0.72 76:0.97 77:0.89 78:0.87 81:0.53 83:0.82 91:0.75 96:0.94 98:0.45 100:0.83 108:0.53 111:0.85 114:0.68 120:0.85 121:0.90 123:0.50 124:0.72 126:0.54 127:0.35 129:0.05 133:0.62 135:0.47 140:0.45 145:0.76 146:0.44 147:0.50 149:0.26 150:0.67 151:0.96 152:0.77 153:0.86 154:0.22 155:0.67 158:0.92 159:0.34 161:0.71 162:0.81 163:0.94 164:0.86 165:0.69 167:0.88 169:0.80 172:0.10 173:0.78 176:0.73 177:0.38 179:0.96 181:0.81 186:0.88 187:0.39 189:0.89 191:0.85 192:0.59 195:0.65 201:0.74 202:0.82 204:0.89 208:0.64 212:0.95 215:0.48 216:0.76 222:0.95 230:0.87 233:0.76 235:0.95 238:0.82 241:0.78 242:0.63 243:0.46 244:0.61 245:0.38 247:0.30 248:0.90 253:0.94 255:0.79 256:0.86 259:0.21 260:0.86 261:0.81 265:0.47 266:0.12 267:0.52 268:0.87 276:0.23 279:0.86 282:0.84 283:0.82 285:0.62 290:0.68 297:0.36 300:0.08 +2 6:0.90 7:0.78 8:0.77 9:0.80 11:0.57 12:0.79 17:0.08 20:0.80 21:0.30 27:0.27 28:0.53 30:0.76 34:0.47 36:0.64 37:0.32 39:0.42 41:0.96 43:0.75 55:0.59 66:0.64 69:0.83 70:0.37 74:0.92 76:0.85 77:0.70 78:0.82 81:0.58 83:0.83 85:0.80 91:0.80 96:0.99 98:0.51 100:0.83 101:0.77 108:0.68 111:0.81 114:0.69 120:0.91 122:0.67 123:0.66 124:0.45 126:0.32 127:0.60 129:0.05 133:0.39 135:0.30 140:0.95 145:0.45 146:0.45 147:0.30 149:0.67 150:0.43 151:0.97 152:0.86 153:0.97 154:0.66 155:0.67 158:0.85 159:0.38 161:0.71 162:0.70 164:0.89 167:0.75 169:0.77 172:0.54 173:0.94 176:0.56 177:0.05 179:0.73 181:0.81 186:0.83 187:0.39 189:0.93 192:0.59 195:0.61 202:0.95 208:0.64 212:0.85 216:0.70 222:0.95 230:0.81 233:0.69 235:0.31 238:0.86 241:0.68 242:0.61 243:0.28 245:0.64 247:0.39 248:0.95 253:0.99 255:0.79 256:0.96 257:0.65 259:0.21 260:0.92 261:0.82 265:0.52 266:0.27 267:0.23 268:0.96 276:0.37 282:0.84 285:0.62 290:0.69 300:0.33 +3 8:0.93 9:0.80 11:0.57 12:0.79 17:0.12 21:0.68 27:0.21 28:0.53 30:0.44 34:0.30 36:0.55 37:0.74 39:0.42 41:0.90 43:0.44 55:0.52 66:0.92 69:0.80 70:0.41 74:0.99 76:0.98 81:0.33 83:0.77 85:1.00 91:0.74 96:0.96 98:0.80 101:0.85 108:0.94 114:0.62 117:0.86 120:0.76 122:0.57 123:0.94 124:0.37 126:0.32 127:0.98 129:0.59 133:0.76 135:0.17 140:0.98 146:0.25 147:0.34 149:0.93 150:0.30 151:0.87 153:0.85 154:0.19 155:0.67 158:0.85 159:0.93 161:0.71 162:0.56 164:0.72 167:0.84 172:0.99 173:0.45 176:0.56 177:0.05 178:0.42 179:0.11 181:0.81 187:0.39 191:0.88 192:0.59 202:0.92 208:0.64 212:0.94 216:0.95 220:0.93 222:0.95 232:0.91 235:0.80 241:0.76 242:0.45 243:0.06 245:0.93 247:0.60 248:0.81 253:0.98 255:0.79 256:0.92 257:0.65 259:0.21 260:0.77 265:0.81 266:0.71 267:0.98 268:0.92 276:0.98 285:0.62 290:0.62 300:0.70 +1 1:0.67 7:0.75 11:0.55 12:0.37 17:0.79 18:0.65 21:0.54 27:0.61 29:0.94 30:0.58 32:0.74 34:0.97 36:0.63 37:0.30 39:0.47 43:0.76 44:0.93 45:0.83 55:0.84 66:0.26 69:0.44 70:0.54 71:0.61 74:0.72 77:0.89 81:0.59 86:0.65 91:0.07 97:0.89 98:0.22 99:0.95 101:0.52 108:0.34 114:0.76 122:0.51 123:0.33 124:0.84 126:0.44 127:0.45 129:0.59 131:0.61 133:0.82 135:0.56 139:0.74 144:0.57 145:0.98 146:0.63 147:0.68 149:0.65 150:0.49 154:0.67 155:0.56 157:0.96 158:0.82 159:0.90 163:0.86 165:0.70 166:0.95 172:0.37 173:0.13 176:0.70 177:0.70 178:0.55 179:0.64 182:0.98 187:0.21 192:0.56 195:0.98 201:0.62 204:0.85 208:0.54 212:0.16 215:0.58 216:0.37 222:0.35 227:0.61 229:0.61 230:0.77 231:0.93 233:0.76 235:0.88 241:0.21 242:0.24 243:0.46 244:0.61 245:0.60 246:0.85 247:0.60 253:0.61 259:0.21 265:0.24 266:0.84 267:0.19 271:0.24 276:0.46 279:0.77 282:0.82 283:0.82 287:0.61 290:0.76 297:0.36 300:0.55 +0 1:0.61 9:0.71 11:0.46 12:0.37 17:0.72 18:0.75 21:0.40 22:0.93 27:0.72 29:0.94 30:0.33 31:0.88 34:0.37 36:0.63 39:0.47 43:0.66 45:0.73 47:0.93 64:0.77 66:0.12 69:0.17 70:0.69 71:0.61 74:0.35 79:0.88 81:0.59 83:0.60 86:0.75 91:0.27 96:0.71 97:0.89 98:0.11 99:0.83 101:0.52 108:0.14 114:0.70 117:0.86 122:0.80 123:0.98 124:0.78 126:0.44 127:0.79 129:0.59 131:0.92 133:0.76 135:0.32 137:0.88 139:0.80 140:0.45 146:0.25 147:0.31 149:0.48 150:0.51 151:0.57 153:0.48 154:0.56 155:0.56 157:0.61 158:0.73 159:0.96 162:0.86 165:0.70 166:0.87 172:0.21 173:0.06 175:0.75 176:0.59 177:0.38 179:0.91 182:0.61 187:0.39 190:0.89 192:0.49 196:0.90 201:0.58 202:0.36 204:0.82 208:0.54 212:0.42 216:0.06 219:0.91 220:0.87 222:0.35 227:0.83 229:0.61 231:0.86 232:0.85 235:0.52 241:0.01 242:0.45 243:0.49 244:0.83 245:0.45 246:0.61 247:0.47 248:0.56 253:0.55 255:0.69 256:0.45 257:0.65 259:0.21 262:0.93 265:0.09 266:0.38 267:0.59 268:0.46 271:0.24 276:0.28 277:0.69 279:0.77 281:0.86 284:0.88 285:0.56 287:0.61 290:0.70 292:0.88 300:0.43 +1 8:0.60 11:0.46 12:0.37 17:0.57 18:0.80 21:0.22 27:0.45 29:0.94 30:0.45 34:0.55 36:0.38 37:0.50 39:0.47 43:0.57 45:0.66 55:0.92 64:0.77 66:0.36 69:0.68 70:0.33 71:0.61 74:0.28 81:0.37 86:0.77 91:0.09 98:0.28 101:0.52 108:0.52 122:0.75 123:0.50 124:0.76 126:0.26 127:0.21 129:0.05 131:0.61 133:0.74 135:0.61 139:0.73 145:0.50 146:0.46 147:0.52 149:0.30 150:0.41 154:0.46 157:0.61 159:0.46 163:0.81 166:0.61 172:0.21 173:0.58 175:0.75 177:0.38 178:0.55 179:0.76 182:0.61 187:0.68 212:0.23 216:0.77 222:0.35 227:0.61 229:0.61 231:0.66 235:0.22 241:0.24 242:0.24 243:0.51 244:0.83 245:0.42 246:0.61 247:0.47 253:0.26 257:0.65 259:0.21 265:0.30 266:0.38 267:0.65 271:0.24 276:0.31 277:0.69 287:0.61 300:0.25 +0 1:0.57 8:0.58 12:0.37 17:0.36 18:0.74 21:0.71 22:0.93 27:0.13 29:0.94 30:0.76 34:0.91 36:0.90 37:0.98 39:0.47 43:0.14 47:0.93 55:0.42 64:0.77 66:0.72 69:0.32 70:0.15 71:0.61 74:0.33 81:0.35 86:0.75 91:0.10 98:0.52 108:0.25 114:0.63 122:0.80 123:0.24 126:0.44 127:0.16 129:0.05 131:0.61 135:0.76 139:0.76 145:0.83 146:0.43 147:0.49 149:0.07 150:0.40 154:0.54 155:0.46 157:0.61 158:0.73 159:0.16 163:0.75 166:0.87 172:0.27 173:0.46 176:0.59 177:0.70 178:0.55 179:0.69 182:0.61 187:0.39 192:0.44 201:0.55 202:0.36 208:0.54 212:0.42 216:0.38 219:0.91 222:0.35 227:0.61 229:0.61 231:0.86 235:0.88 241:0.12 242:0.45 243:0.75 244:0.83 246:0.61 247:0.35 253:0.47 254:0.74 259:0.21 262:0.93 265:0.54 266:0.23 267:0.36 271:0.24 276:0.23 279:0.77 287:0.61 290:0.63 292:0.87 300:0.13 +1 8:0.74 12:0.37 17:0.59 18:0.85 21:0.78 22:0.93 27:0.31 29:0.94 30:0.17 34:0.53 36:0.96 37:0.64 39:0.47 43:0.12 44:0.88 47:0.93 48:0.91 55:0.45 66:0.34 69:0.78 70:0.68 71:0.61 74:0.32 76:0.85 77:0.70 86:0.81 91:0.37 98:0.37 108:0.61 122:0.82 123:0.59 124:0.77 127:0.40 129:0.81 131:0.61 133:0.76 135:0.96 139:0.81 145:0.99 146:0.63 147:0.68 149:0.18 154:0.26 157:0.61 159:0.69 163:0.75 166:0.61 172:0.37 173:0.65 175:0.75 177:0.38 178:0.55 179:0.69 182:0.61 187:0.68 195:0.90 202:0.65 212:0.19 216:0.43 222:0.35 227:0.61 229:0.61 231:0.80 235:0.84 241:0.15 242:0.19 243:0.50 244:0.83 245:0.57 246:0.61 247:0.67 253:0.29 254:0.84 257:0.65 259:0.21 262:0.93 265:0.39 266:0.47 267:0.84 271:0.24 276:0.46 277:0.69 281:0.86 282:0.72 287:0.61 300:0.43 +1 7:0.64 11:0.55 12:0.37 17:0.69 18:0.65 21:0.68 27:0.61 29:0.94 30:0.41 32:0.63 34:0.95 36:0.61 37:0.30 39:0.47 43:0.75 45:0.81 55:0.42 66:0.15 69:0.78 70:0.88 71:0.61 74:0.51 81:0.28 86:0.65 91:0.03 98:0.14 101:0.52 108:0.61 122:0.41 123:0.59 124:0.86 126:0.26 127:0.84 129:0.59 131:0.61 133:0.82 135:0.58 139:0.63 144:0.57 145:0.92 146:0.30 147:0.30 149:0.61 150:0.30 154:0.66 157:0.96 159:0.89 166:0.78 172:0.21 173:0.52 177:0.38 178:0.55 179:0.78 182:0.88 187:0.21 195:0.96 212:0.19 215:0.49 216:0.37 222:0.35 227:0.61 229:0.61 230:0.64 231:0.85 233:0.66 235:0.84 241:0.07 242:0.33 243:0.29 244:0.61 245:0.56 246:0.61 247:0.67 253:0.41 257:0.65 259:0.21 265:0.15 266:0.80 267:0.28 271:0.24 276:0.36 283:0.67 287:0.61 297:0.36 300:0.70 +0 8:0.66 12:0.37 17:0.28 18:0.87 21:0.40 22:0.93 27:0.21 29:0.94 30:0.15 31:0.98 34:0.59 36:0.83 37:0.74 39:0.47 43:0.20 47:0.93 48:0.91 55:0.71 64:0.77 66:0.10 69:0.98 70:0.81 71:0.61 74:0.25 79:0.98 81:0.34 86:0.82 91:0.65 98:0.19 108:0.62 122:0.86 123:0.59 124:0.95 126:0.26 127:0.80 129:0.05 131:0.61 133:0.94 135:0.17 137:0.98 139:0.82 140:0.45 146:0.30 147:0.05 149:0.22 150:0.38 151:0.70 153:0.46 154:0.13 157:0.61 159:0.89 162:0.61 163:0.79 166:0.61 172:0.21 173:0.99 175:0.75 177:0.05 179:0.68 182:0.61 187:0.39 190:0.95 191:0.76 196:0.97 202:0.84 212:0.12 216:0.95 219:0.90 220:0.62 222:0.35 227:0.61 229:0.61 231:0.68 235:0.42 241:0.15 242:0.70 243:0.11 244:0.93 245:0.54 246:0.61 247:0.74 248:0.84 253:0.23 256:0.85 257:0.84 259:0.21 262:0.93 265:0.20 266:0.94 267:0.87 268:0.85 271:0.24 276:0.57 277:0.87 281:0.86 284:0.98 285:0.47 287:0.61 292:0.95 300:0.55 +0 8:0.71 12:0.37 17:0.43 18:0.88 21:0.13 27:0.61 29:0.94 30:0.38 32:0.63 34:0.95 36:0.52 37:0.35 39:0.47 43:0.15 48:0.98 55:0.71 64:0.77 66:0.36 69:0.61 70:0.63 71:0.61 74:0.29 81:0.38 86:0.83 91:0.57 98:0.36 108:0.46 122:0.86 123:0.45 124:0.77 126:0.26 127:0.29 129:0.05 131:0.61 133:0.73 135:0.34 139:0.81 145:0.69 146:0.50 147:0.56 149:0.20 150:0.43 154:0.47 157:0.61 159:0.47 166:0.61 172:0.32 173:0.55 177:0.70 178:0.55 179:0.70 182:0.61 187:0.39 195:0.80 212:0.28 216:0.10 222:0.35 227:0.61 228:0.99 229:0.61 231:0.71 235:0.12 241:0.15 242:0.71 243:0.51 244:0.83 245:0.51 246:0.61 247:0.47 253:0.26 254:0.84 259:0.21 265:0.38 266:0.63 267:0.65 271:0.24 276:0.41 281:0.91 287:0.61 300:0.43 +0 8:0.63 11:0.55 12:0.37 17:0.28 18:0.80 21:0.59 22:0.93 27:0.41 29:0.94 30:0.21 31:0.86 34:0.80 36:0.53 37:0.44 39:0.47 43:0.59 45:0.66 47:0.93 48:0.91 55:0.84 64:0.77 66:0.10 69:0.86 70:0.67 71:0.61 74:0.35 79:0.86 81:0.30 86:0.75 91:0.49 98:0.25 101:0.52 108:0.72 122:0.86 123:0.85 124:0.86 126:0.26 127:0.76 129:0.05 131:0.61 133:0.81 135:0.80 137:0.86 139:0.78 140:0.45 144:0.57 145:0.59 146:0.35 147:0.05 149:0.32 150:0.33 151:0.47 153:0.48 154:0.48 157:0.82 159:0.79 162:0.86 163:0.96 166:0.61 172:0.16 173:0.76 177:0.05 179:0.89 182:0.75 187:0.68 190:0.95 196:0.88 202:0.36 212:0.37 216:0.75 219:0.90 220:0.97 222:0.35 227:0.61 229:0.61 231:0.66 235:0.68 241:0.01 242:0.58 243:0.18 244:0.61 245:0.46 246:0.61 247:0.47 248:0.47 253:0.32 256:0.45 257:0.84 259:0.21 262:0.93 265:0.21 266:0.47 267:0.69 268:0.46 271:0.24 276:0.36 277:0.87 281:0.91 284:0.88 285:0.47 287:0.61 292:0.88 300:0.43 +1 1:0.57 11:0.46 12:0.37 17:0.86 18:0.73 21:0.68 27:0.61 29:0.94 30:0.15 34:0.55 36:0.99 37:0.53 39:0.47 43:0.21 45:0.66 55:0.71 64:0.77 66:0.08 69:0.90 70:0.76 71:0.61 74:0.32 81:0.36 86:0.73 91:0.49 98:0.19 101:0.52 108:0.80 114:0.63 122:0.77 123:0.86 124:0.93 126:0.44 127:0.30 129:0.05 131:0.61 133:0.92 135:0.93 139:0.74 145:0.61 146:0.49 147:0.55 149:0.19 150:0.41 154:0.17 155:0.47 157:0.61 158:0.73 159:0.85 163:0.87 166:0.87 172:0.21 173:0.69 176:0.59 177:0.70 178:0.55 179:0.51 182:0.61 187:0.21 192:0.44 201:0.55 208:0.54 212:0.14 216:0.26 219:0.91 222:0.35 227:0.61 229:0.61 231:0.84 235:0.84 241:0.10 242:0.21 243:0.29 244:0.83 245:0.56 246:0.61 247:0.79 253:0.48 259:0.21 265:0.20 266:0.89 267:0.44 271:0.24 276:0.57 277:0.87 279:0.77 287:0.61 290:0.63 292:0.86 300:0.55 +1 11:0.46 12:0.37 17:0.91 18:0.85 21:0.54 27:0.72 29:0.94 30:0.09 31:0.89 32:0.63 34:0.31 36:0.96 39:0.47 43:0.58 45:0.66 48:0.91 55:0.59 64:0.77 66:0.67 69:0.32 70:0.95 71:0.61 74:0.34 77:0.70 79:0.88 81:0.31 83:0.60 86:0.83 91:0.18 98:0.53 101:0.52 108:0.25 122:0.83 123:0.24 124:0.46 126:0.26 127:0.46 129:0.05 131:0.61 133:0.59 135:0.56 137:0.89 139:0.84 140:0.45 145:0.72 146:0.61 147:0.67 149:0.32 150:0.35 151:0.52 153:0.69 154:0.47 155:0.51 157:0.61 159:0.49 162:0.86 166:0.61 172:0.48 173:0.31 177:0.70 179:0.76 182:0.61 187:0.87 190:0.95 192:0.46 195:0.74 196:0.91 202:0.46 204:0.81 208:0.54 212:0.52 216:0.09 219:0.90 220:0.87 222:0.35 227:0.61 229:0.61 231:0.85 235:0.60 242:0.24 243:0.72 244:0.83 245:0.49 246:0.61 247:0.60 248:0.52 253:0.31 256:0.45 259:0.21 265:0.54 266:0.47 267:0.69 268:0.55 271:0.24 276:0.39 281:0.91 282:0.71 284:0.90 285:0.47 287:0.61 292:0.88 300:0.70 +0 8:0.66 12:0.37 17:0.55 18:0.96 21:0.30 22:0.93 27:0.50 29:0.94 30:0.26 31:0.97 34:0.42 36:0.77 37:0.60 39:0.47 43:0.20 44:0.88 47:0.93 48:0.91 55:0.84 64:0.77 66:0.07 69:0.95 70:0.89 71:0.61 74:0.26 79:0.96 81:0.35 86:0.92 91:0.29 98:0.28 108:0.97 122:0.86 123:0.95 124:0.97 126:0.26 127:0.86 129:0.59 131:0.61 133:0.97 135:0.69 137:0.97 139:0.92 140:0.80 145:0.70 146:0.45 147:0.77 149:0.34 150:0.39 151:0.71 153:0.78 154:0.08 157:0.61 159:0.93 162:0.69 166:0.61 172:0.67 173:0.80 175:0.75 177:0.05 178:0.42 179:0.27 182:0.61 187:0.39 190:0.95 195:0.99 196:0.98 202:0.66 212:0.21 216:0.86 219:0.90 222:0.35 227:0.61 229:0.61 231:0.65 235:0.31 241:0.07 242:0.79 243:0.17 244:0.83 245:0.79 246:0.61 247:0.60 248:0.72 253:0.23 254:0.88 256:0.64 257:0.84 259:0.21 262:0.93 265:0.28 266:0.96 267:0.44 268:0.68 271:0.24 276:0.87 277:0.87 281:0.91 284:0.95 285:0.47 287:0.61 292:0.95 300:0.70 +0 12:0.37 17:0.55 18:0.57 21:0.78 27:0.71 29:0.94 30:0.21 34:0.61 36:0.73 37:0.29 39:0.47 43:0.06 55:0.96 66:0.07 69:0.96 70:0.64 71:0.61 74:0.26 86:0.59 91:0.02 98:0.09 108:0.98 123:0.98 124:0.96 127:0.25 129:0.99 131:0.61 133:0.95 135:0.60 139:0.57 145:0.76 146:0.05 147:0.05 149:0.07 154:0.06 157:0.61 159:0.97 163:0.98 166:0.61 172:0.10 173:0.61 175:0.91 177:0.05 178:0.55 179:0.59 182:0.61 187:0.21 202:0.50 212:0.22 216:0.31 222:0.35 227:0.61 229:0.61 231:0.65 235:0.80 241:0.27 242:0.38 243:0.14 244:0.93 245:0.45 246:0.61 247:0.67 253:0.23 257:0.84 259:0.21 265:0.10 266:0.71 267:0.44 271:0.24 276:0.45 277:0.87 287:0.61 300:0.43 +0 8:0.66 12:0.37 17:0.40 18:0.83 21:0.13 27:0.61 29:0.94 30:0.76 34:0.95 36:0.54 37:0.35 39:0.47 43:0.15 44:0.88 48:0.99 55:0.92 64:0.77 66:0.36 69:0.61 70:0.21 71:0.61 74:0.29 81:0.38 86:0.79 91:0.54 98:0.43 108:0.46 122:0.43 123:0.45 124:0.68 126:0.26 127:0.28 129:0.05 131:0.61 133:0.60 135:0.75 139:0.80 145:0.80 146:0.56 147:0.62 149:0.17 150:0.43 154:0.47 157:0.61 159:0.44 166:0.61 172:0.27 173:0.56 177:0.70 178:0.55 179:0.76 182:0.61 187:0.39 195:0.77 212:0.16 216:0.10 222:0.35 227:0.61 228:0.99 229:0.61 231:0.70 235:0.12 241:0.27 242:0.16 243:0.51 244:0.83 245:0.50 246:0.61 247:0.60 253:0.26 254:0.84 259:0.21 265:0.45 266:0.54 267:0.65 271:0.24 276:0.37 281:0.91 287:0.61 300:0.18 +1 7:0.76 11:0.42 12:0.86 17:0.51 21:0.54 27:0.19 28:0.96 30:0.42 32:0.72 34:0.55 36:0.64 37:0.67 39:0.70 43:0.34 55:0.84 66:0.51 69:0.60 70:0.70 74:0.74 77:0.70 81:0.47 91:0.44 98:0.43 104:0.96 106:0.81 108:0.46 120:0.84 122:0.41 123:0.44 124:0.66 126:0.32 127:0.60 129:0.05 131:0.97 133:0.62 135:0.95 140:0.45 144:0.57 145:0.69 146:0.66 147:0.71 149:0.41 150:0.37 151:0.66 153:0.48 154:0.68 157:0.61 159:0.71 161:0.53 162:0.81 164:0.72 166:0.94 167:0.50 172:0.51 173:0.46 177:0.38 179:0.68 181:0.86 182:0.61 187:0.68 190:0.77 195:0.86 202:0.62 206:0.81 212:0.23 215:0.58 216:0.63 222:0.48 227:0.61 229:0.61 230:0.78 231:0.97 233:0.69 235:0.60 241:0.21 242:0.15 243:0.61 245:0.65 246:0.61 247:0.67 248:0.76 253:0.54 254:0.74 256:0.68 259:0.21 260:0.83 265:0.45 266:0.75 267:0.73 268:0.67 271:0.13 276:0.47 282:0.81 285:0.50 287:0.61 297:0.36 300:0.55 +1 6:0.92 7:0.76 8:0.94 11:0.42 12:0.86 17:0.53 20:0.91 21:0.54 27:0.19 28:0.96 30:0.42 32:0.72 34:0.59 36:0.62 37:0.67 39:0.70 43:0.34 55:0.84 66:0.51 69:0.60 70:0.70 74:0.74 77:0.70 78:0.82 81:0.47 91:0.72 98:0.43 100:0.73 104:0.96 106:0.81 108:0.46 111:0.80 120:0.95 122:0.41 123:0.44 124:0.66 126:0.32 127:0.60 129:0.05 131:0.97 133:0.62 135:0.95 140:0.45 144:0.57 145:0.69 146:0.66 147:0.71 149:0.41 150:0.37 151:0.78 152:0.93 153:0.89 154:0.68 157:0.61 159:0.71 161:0.53 162:0.67 164:0.91 166:0.94 167:0.54 169:0.84 172:0.51 173:0.46 177:0.38 179:0.68 181:0.86 182:0.61 186:0.82 187:0.68 190:0.77 195:0.86 202:0.87 206:0.81 212:0.23 215:0.58 216:0.63 220:0.62 222:0.48 227:0.61 229:0.61 230:0.78 231:0.97 233:0.69 235:0.60 241:0.43 242:0.15 243:0.61 245:0.65 246:0.61 247:0.67 248:0.92 253:0.54 254:0.74 256:0.88 259:0.21 260:0.94 261:0.90 265:0.45 266:0.75 267:0.65 268:0.89 271:0.13 276:0.47 282:0.81 283:0.82 285:0.50 287:0.61 297:0.36 300:0.55 +2 6:0.94 8:0.89 11:0.64 12:0.86 17:0.34 20:0.92 21:0.68 27:0.33 28:0.96 30:0.33 34:0.20 36:0.29 37:0.64 39:0.70 43:0.38 55:0.98 66:0.08 69:0.98 70:0.97 74:0.51 78:0.95 81:0.36 85:0.72 91:0.53 98:0.14 100:0.90 101:0.47 104:0.93 106:0.81 108:0.38 111:0.92 120:0.86 122:0.67 123:0.37 124:0.98 126:0.32 127:0.81 129:0.05 131:0.97 133:0.98 135:0.97 140:0.45 144:0.57 145:0.88 146:0.24 147:0.67 149:0.56 150:0.32 151:0.74 152:0.86 153:0.83 154:0.14 157:0.75 159:0.98 161:0.53 162:0.84 163:0.99 164:0.78 166:0.94 167:0.48 169:0.88 172:0.32 173:0.82 177:0.05 179:0.40 181:0.86 182:0.72 186:0.94 187:0.87 189:0.95 190:0.77 202:0.67 206:0.81 212:0.12 215:0.49 216:0.55 222:0.48 227:0.61 229:0.61 231:0.97 235:0.80 238:0.87 241:0.12 242:0.62 243:0.24 245:0.63 246:0.61 247:0.55 248:0.80 253:0.42 256:0.73 257:0.65 259:0.21 260:0.86 261:0.91 265:0.15 266:0.99 267:0.99 268:0.74 271:0.13 276:0.77 277:0.69 283:0.82 285:0.50 287:0.61 297:0.36 300:0.98 +1 1:0.74 11:0.42 12:0.86 17:0.43 21:0.59 27:0.45 28:0.96 30:0.76 32:0.72 34:0.91 36:0.25 37:0.50 39:0.70 43:0.80 55:0.42 66:0.64 69:0.70 70:0.15 74:0.43 81:0.59 91:0.31 98:0.15 108:0.53 114:0.74 122:0.55 123:0.51 126:0.54 127:0.09 129:0.05 131:0.61 135:0.19 144:0.57 145:0.45 146:0.22 147:0.27 149:0.45 150:0.66 154:0.75 155:0.67 157:0.61 159:0.10 161:0.53 163:0.97 166:0.98 167:0.49 172:0.16 173:0.71 176:0.73 177:0.38 178:0.55 179:0.14 181:0.86 182:0.61 187:0.68 192:0.59 195:0.72 201:0.74 212:0.95 215:0.55 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.74 241:0.15 242:0.82 243:0.69 246:0.61 247:0.12 253:0.58 254:0.74 259:0.21 265:0.16 266:0.12 267:0.69 271:0.13 276:0.16 287:0.61 290:0.74 297:0.36 300:0.13 +2 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.64 12:0.86 17:0.38 20:0.96 21:0.13 27:0.68 28:0.96 30:0.10 32:0.80 34:0.23 36:0.31 37:0.82 39:0.70 41:0.88 43:0.87 60:0.95 66:0.23 69:0.54 70:1.00 74:0.97 76:0.94 77:0.96 78:0.99 81:0.88 83:0.88 85:0.97 91:0.42 96:0.84 98:0.26 100:0.99 101:0.76 104:0.77 106:0.81 108:0.41 111:1.00 114:0.92 117:0.86 120:0.80 121:0.90 122:0.43 123:0.39 124:0.97 126:0.54 127:0.77 129:0.98 131:0.99 133:0.98 135:0.65 140:0.45 144:0.57 145:0.63 146:0.85 147:0.87 149:0.96 150:0.93 151:0.87 152:0.98 153:0.48 154:0.86 155:0.67 157:0.99 158:0.92 159:0.95 161:0.53 162:0.83 164:0.72 165:0.88 166:0.99 167:0.55 169:0.98 172:0.81 173:0.12 176:0.73 177:0.38 179:0.20 181:0.86 182:0.99 186:0.99 187:0.21 189:0.97 192:0.59 195:0.99 201:0.74 202:0.63 204:0.89 206:0.81 208:0.64 212:0.29 215:0.84 216:0.50 220:0.74 222:0.48 227:0.61 229:0.99 230:0.87 231:0.98 232:0.85 233:0.76 235:0.22 238:0.97 241:0.44 242:0.22 243:0.43 245:0.85 246:0.61 247:0.67 248:0.82 253:0.90 255:0.79 256:0.68 259:0.21 260:0.80 261:0.98 265:0.28 266:0.99 267:0.65 268:0.70 271:0.13 276:0.91 279:0.86 282:0.88 283:0.82 285:0.62 287:0.61 290:0.92 297:0.36 299:0.99 300:0.98 +2 6:0.96 7:0.76 8:0.92 11:0.64 12:0.86 17:0.66 20:0.92 21:0.22 27:0.68 28:0.96 30:0.40 32:0.80 34:0.26 36:0.33 37:0.82 39:0.70 43:0.80 55:0.71 66:0.49 69:0.54 70:0.92 74:0.83 77:0.70 78:0.98 81:0.73 85:0.93 91:0.44 98:0.57 100:0.95 101:0.74 104:0.77 106:0.81 108:0.41 111:0.96 120:0.69 123:0.39 124:0.92 126:0.32 127:0.64 129:0.93 131:0.97 133:0.94 135:0.85 140:0.45 144:0.57 145:0.72 146:0.97 147:0.97 149:0.90 150:0.54 151:0.63 152:0.98 153:0.72 154:0.74 157:0.98 159:0.91 161:0.53 162:0.86 164:0.72 166:0.94 167:0.46 169:0.98 172:0.89 173:0.19 177:0.38 179:0.20 181:0.86 182:0.94 186:0.98 187:0.21 189:0.94 190:0.77 195:0.98 202:0.59 206:0.81 212:0.56 215:0.79 216:0.50 220:0.62 222:0.48 227:0.61 229:0.61 230:0.79 231:0.98 233:0.76 235:0.22 238:0.89 241:0.03 242:0.71 243:0.60 245:0.85 246:0.61 247:0.67 248:0.63 253:0.59 256:0.64 259:0.21 260:0.69 261:0.98 265:0.58 266:0.95 267:0.52 268:0.68 271:0.13 276:0.90 282:0.88 283:0.71 285:0.50 287:0.61 297:0.36 299:0.88 300:0.84 +1 1:0.74 9:0.80 11:0.42 12:0.86 17:0.34 21:0.30 27:0.05 28:0.96 30:0.62 34:0.81 36:0.51 37:0.91 39:0.70 43:0.28 55:0.92 66:0.23 69:0.69 70:0.53 74:0.78 81:0.78 91:0.49 96:0.71 98:0.48 104:0.97 106:0.81 108:0.83 114:0.86 117:0.86 120:0.58 122:0.67 123:0.82 124:0.80 126:0.54 127:0.67 129:0.59 131:0.99 133:0.76 135:0.74 140:0.45 144:0.57 146:0.32 147:0.39 149:0.34 150:0.80 151:0.58 153:0.48 154:0.75 155:0.67 157:0.61 158:0.87 159:0.75 161:0.53 162:0.62 163:0.92 164:0.57 166:0.99 167:0.46 172:0.32 173:0.55 176:0.73 177:0.38 179:0.72 181:0.86 182:0.61 187:0.87 192:0.59 201:0.74 202:0.50 206:0.81 208:0.64 212:0.37 215:0.72 216:0.62 220:0.87 222:0.48 227:0.61 229:0.61 231:0.98 232:0.99 235:0.42 241:0.02 242:0.78 243:0.27 245:0.62 246:0.61 247:0.39 248:0.57 253:0.74 254:0.74 255:0.79 256:0.45 259:0.21 260:0.59 265:0.50 266:0.63 267:0.82 268:0.46 271:0.13 276:0.49 277:0.69 279:0.86 283:0.71 285:0.62 287:0.61 290:0.86 297:0.36 300:0.43 +1 6:0.96 7:0.76 8:0.70 11:0.64 12:0.86 17:0.55 20:0.84 21:0.40 27:0.68 28:0.96 30:0.21 32:0.72 34:0.24 36:0.61 37:0.82 39:0.70 43:0.73 55:0.52 66:0.53 69:0.54 70:0.96 74:0.66 78:0.88 81:0.59 85:0.85 91:0.44 98:0.50 100:0.73 101:0.73 104:0.77 106:0.81 108:0.42 111:0.85 120:0.65 123:0.40 124:0.84 126:0.32 127:0.46 129:0.89 131:0.97 133:0.89 135:0.80 140:0.45 144:0.57 145:0.77 146:0.86 147:0.88 149:0.78 150:0.44 151:0.61 152:0.98 153:0.48 154:0.67 157:0.95 159:0.81 161:0.53 162:0.86 164:0.55 166:0.94 167:0.47 169:0.97 172:0.80 173:0.29 177:0.38 179:0.30 181:0.86 182:0.85 186:0.88 187:0.21 190:0.77 195:0.95 202:0.36 206:0.81 212:0.69 215:0.67 216:0.50 220:0.62 222:0.48 227:0.61 229:0.61 230:0.79 231:0.97 233:0.76 235:0.42 241:0.05 242:0.73 243:0.62 245:0.78 246:0.61 247:0.55 248:0.59 253:0.51 256:0.45 259:0.21 260:0.65 261:0.98 265:0.51 266:0.84 267:0.59 268:0.46 271:0.13 276:0.80 283:0.82 285:0.50 287:0.61 297:0.36 300:0.94 +1 1:0.74 6:0.95 7:0.81 8:0.88 9:0.80 11:0.64 12:0.86 17:0.09 20:0.82 21:0.71 27:0.19 28:0.96 30:0.42 32:0.80 34:0.69 36:0.28 37:0.67 39:0.70 41:0.82 43:0.86 55:0.52 60:0.87 66:0.36 69:0.61 70:0.62 74:0.82 77:0.70 78:0.98 81:0.52 83:0.84 85:0.80 91:0.53 96:0.70 98:0.32 100:0.92 101:0.72 108:0.61 111:0.95 114:0.68 120:0.56 121:0.90 123:0.58 124:0.86 126:0.54 127:0.52 129:0.81 131:0.99 133:0.86 135:0.18 140:0.45 144:0.57 145:0.76 146:0.13 147:0.18 149:0.73 150:0.67 151:0.52 152:0.94 153:0.48 154:0.83 155:0.67 157:0.91 158:0.87 159:0.75 161:0.53 162:0.62 164:0.57 165:0.69 166:0.98 167:0.48 169:0.86 172:0.45 173:0.43 176:0.73 177:0.38 179:0.66 181:0.86 182:0.98 186:0.98 187:0.87 192:0.59 195:0.91 201:0.74 202:0.50 204:0.89 208:0.64 212:0.35 215:0.48 216:0.63 220:0.93 222:0.48 227:0.61 229:0.98 230:0.87 231:0.98 233:0.76 235:0.88 241:0.12 242:0.37 243:0.19 245:0.56 246:0.61 247:0.55 248:0.52 253:0.67 255:0.79 256:0.45 259:0.21 260:0.56 261:0.91 265:0.34 266:0.84 267:0.97 268:0.46 271:0.13 276:0.52 279:0.86 282:0.88 283:0.71 285:0.62 287:0.61 290:0.68 297:0.36 299:0.88 300:0.43 +0 12:0.86 17:0.51 21:0.78 27:0.45 28:0.96 30:0.95 34:0.75 36:0.36 37:0.50 39:0.70 43:0.71 55:0.59 66:0.54 69:0.71 74:0.37 91:0.09 98:0.16 108:0.54 123:0.52 127:0.09 129:0.05 131:0.61 135:0.24 145:0.49 146:0.23 147:0.29 149:0.35 154:0.61 157:0.61 159:0.11 161:0.53 166:0.61 167:0.62 172:0.10 173:0.73 177:0.38 178:0.55 179:0.33 181:0.86 182:0.61 187:0.68 216:0.77 222:0.48 227:0.61 229:0.61 231:0.74 235:0.31 241:0.61 243:0.63 246:0.61 253:0.34 254:0.97 259:0.21 265:0.18 267:0.89 271:0.13 287:0.61 300:0.08 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.86 17:0.08 21:0.30 27:0.19 28:0.96 30:0.72 32:0.80 34:0.61 36:0.30 37:0.67 39:0.70 41:0.90 43:0.86 55:0.84 60:0.95 66:0.26 69:0.59 70:0.48 74:0.84 77:0.70 81:0.79 83:0.87 85:0.72 91:0.49 96:0.72 98:0.38 101:0.71 108:0.45 114:0.86 120:0.59 121:0.90 122:0.67 123:0.43 124:0.80 126:0.54 127:0.46 129:0.81 131:0.99 133:0.76 135:0.42 140:0.45 144:0.57 145:0.59 146:0.58 147:0.64 149:0.63 150:0.86 151:0.58 153:0.48 154:0.83 155:0.67 157:0.83 158:0.87 159:0.64 161:0.53 162:0.86 163:0.94 164:0.72 165:0.84 166:0.99 167:0.47 172:0.27 173:0.48 176:0.73 177:0.38 179:0.76 181:0.86 182:0.99 187:0.87 192:0.59 195:0.84 201:0.74 202:0.56 204:0.89 208:0.64 212:0.39 215:0.72 216:0.63 220:0.87 222:0.48 227:0.61 229:0.99 230:0.84 231:0.98 233:0.69 235:0.42 241:0.05 242:0.42 243:0.45 245:0.54 246:0.61 247:0.60 248:0.57 253:0.76 255:0.79 256:0.64 259:0.21 260:0.59 265:0.40 266:0.47 267:0.59 268:0.65 271:0.13 276:0.43 279:0.86 282:0.88 283:0.71 285:0.62 287:0.61 290:0.86 297:0.36 299:0.88 300:0.43 +1 1:0.74 11:0.42 12:0.86 17:0.45 21:0.30 27:0.05 28:0.96 30:0.62 34:0.88 36:0.49 37:0.91 39:0.70 43:0.28 55:0.92 66:0.23 69:0.69 70:0.53 74:0.73 81:0.78 91:0.35 98:0.48 104:0.97 106:0.81 108:0.83 114:0.86 117:0.86 122:0.67 123:0.82 124:0.80 126:0.54 127:0.67 129:0.59 131:0.61 133:0.76 135:0.74 144:0.57 146:0.32 147:0.39 149:0.34 150:0.80 154:0.75 155:0.67 157:0.61 159:0.75 161:0.53 163:0.92 166:0.99 167:0.46 172:0.32 173:0.55 176:0.73 177:0.38 178:0.55 179:0.72 181:0.86 182:0.61 187:0.98 192:0.59 201:0.74 206:0.81 212:0.37 215:0.72 216:0.62 222:0.48 227:0.61 229:0.61 231:0.98 232:0.99 235:0.42 241:0.03 242:0.78 243:0.27 245:0.62 246:0.61 247:0.39 253:0.70 254:0.74 259:0.21 265:0.50 266:0.63 267:0.73 271:0.13 276:0.49 277:0.69 287:0.61 290:0.86 297:0.36 300:0.43 +0 1:0.69 7:0.72 11:0.91 12:0.06 17:0.96 18:0.67 21:0.68 27:0.72 29:0.53 30:0.40 32:0.69 34:0.84 36:0.39 39:0.44 43:0.72 44:0.90 45:0.66 55:0.45 66:0.36 69:0.30 70:0.55 71:0.83 74:0.53 77:0.70 81:0.34 83:0.60 86:0.70 91:0.08 98:0.35 99:0.83 101:0.52 104:0.94 106:0.81 108:0.24 114:0.55 123:0.23 124:0.78 126:0.44 127:0.85 129:0.05 133:0.73 135:0.24 139:0.74 144:0.85 145:0.88 146:0.45 147:0.51 149:0.67 150:0.52 154:0.62 155:0.58 159:0.59 165:0.70 172:0.75 173:0.28 176:0.66 177:0.70 178:0.55 179:0.36 187:0.87 192:0.59 195:0.74 201:0.68 204:0.85 206:0.81 208:0.54 212:0.87 215:0.37 216:0.05 222:0.76 230:0.74 233:0.73 235:0.97 241:0.10 242:0.80 243:0.51 244:0.61 245:0.87 247:0.55 253:0.39 259:0.21 265:0.37 266:0.27 267:0.19 271:0.62 276:0.76 282:0.77 283:0.78 290:0.55 297:0.36 300:0.55 +2 8:0.87 11:0.93 12:0.06 17:0.47 18:1.00 21:0.78 27:0.62 29:0.53 30:0.95 34:0.29 36:0.37 37:0.59 39:0.44 43:0.62 45:0.85 55:0.52 66:0.98 69:0.66 70:0.13 71:0.83 74:0.44 86:0.99 91:0.08 98:0.85 101:0.87 108:0.50 122:0.86 123:0.48 127:0.36 129:0.05 135:0.30 139:0.99 144:0.85 145:0.40 146:0.84 147:0.87 149:0.72 154:0.73 159:0.40 172:0.95 173:0.70 177:0.70 178:0.55 179:0.17 187:0.39 212:0.94 216:0.67 222:0.76 235:0.22 241:0.39 242:0.78 243:0.98 247:0.76 253:0.33 254:0.80 259:0.21 265:0.85 266:0.17 267:0.59 271:0.62 276:0.90 300:0.25 +1 11:0.91 12:0.06 17:0.77 18:0.80 21:0.78 27:0.42 29:0.53 30:0.30 34:0.94 36:0.51 37:0.78 39:0.44 43:0.41 45:0.73 55:0.71 66:0.84 69:0.89 70:0.48 71:0.83 74:0.39 86:0.78 91:0.03 98:0.71 101:0.52 108:0.77 122:0.86 123:0.76 127:0.22 129:0.05 135:0.58 139:0.74 144:0.85 146:0.81 147:0.84 149:0.27 154:0.47 159:0.37 163:0.75 172:0.54 173:0.91 177:0.70 178:0.55 179:0.56 187:0.99 212:0.23 216:0.30 222:0.76 235:0.84 241:0.12 242:0.55 243:0.85 247:0.47 253:0.31 254:0.93 259:0.21 265:0.71 266:0.63 267:0.69 271:0.62 276:0.45 300:0.33 +0 12:0.06 17:1.00 18:0.59 21:0.78 27:0.72 29:0.53 30:0.21 34:0.94 36:0.47 37:0.67 39:0.44 43:0.07 55:0.84 66:0.20 69:0.94 70:0.37 71:0.83 74:0.25 86:0.61 91:0.23 98:0.12 108:0.88 122:0.75 123:0.87 124:0.81 127:0.26 129:0.05 133:0.74 135:0.42 139:0.59 145:0.77 146:0.37 147:0.44 149:0.07 154:0.07 159:0.94 163:0.97 172:0.10 173:0.60 175:0.75 177:0.38 178:0.55 179:0.91 187:0.68 212:0.42 216:0.16 222:0.76 235:0.52 242:0.45 243:0.40 244:0.83 245:0.39 247:0.35 253:0.22 257:0.65 259:0.21 265:0.12 266:0.23 267:0.28 271:0.62 276:0.24 277:0.69 300:0.25 +2 11:0.88 12:0.06 17:0.34 18:0.76 21:0.78 27:0.71 29:0.53 30:0.71 34:0.50 36:0.93 37:0.59 39:0.44 43:0.24 55:0.45 66:0.89 69:0.97 70:0.41 71:0.83 74:0.31 85:0.72 86:0.81 91:0.42 98:0.25 101:0.87 108:0.95 122:0.86 123:0.95 127:0.11 129:0.05 135:0.60 139:0.77 144:0.85 146:0.57 147:0.63 149:0.23 154:0.17 159:0.21 172:0.68 173:0.99 177:0.70 178:0.55 179:0.10 187:0.39 212:0.85 216:0.42 222:0.76 235:0.22 241:0.24 242:0.78 243:0.89 247:0.47 253:0.27 259:0.21 265:0.27 266:0.38 267:0.28 271:0.62 276:0.54 300:0.43 +1 11:0.91 12:0.06 17:0.84 21:0.71 27:0.58 29:0.53 30:0.40 34:0.47 36:0.42 37:0.69 39:0.44 43:0.52 44:0.87 45:0.66 55:0.71 64:0.77 66:0.35 69:0.79 70:0.58 71:0.83 74:0.35 81:0.23 91:0.06 98:0.60 101:0.52 104:0.91 106:0.81 108:0.73 117:0.86 123:0.71 124:0.76 126:0.26 127:0.59 129:0.59 133:0.76 135:0.44 139:0.64 144:0.85 145:0.63 146:0.32 147:0.38 149:0.44 150:0.23 154:0.45 159:0.88 172:0.84 173:0.52 175:0.75 177:0.38 178:0.55 179:0.21 187:0.68 195:0.97 206:0.81 212:0.23 216:0.18 222:0.76 232:0.95 235:0.84 241:0.10 242:0.76 243:0.10 244:0.61 245:0.93 247:0.47 253:0.29 257:0.65 259:0.21 265:0.61 266:0.91 267:0.17 271:0.62 276:0.89 277:0.69 300:0.84 +1 11:0.91 12:0.06 17:0.28 18:0.62 21:0.13 27:0.58 29:0.53 30:0.91 34:0.86 36:0.49 37:0.43 39:0.44 43:0.72 45:0.73 66:0.36 69:0.84 70:0.13 71:0.83 74:0.49 81:0.41 86:0.72 91:0.08 98:0.19 101:0.52 108:0.69 122:0.82 123:0.67 124:0.57 126:0.26 127:0.58 129:0.05 133:0.43 135:0.63 139:0.69 144:0.85 146:0.22 147:0.18 149:0.36 150:0.35 154:0.65 159:0.33 163:0.97 172:0.16 173:0.97 177:0.38 178:0.55 179:0.19 187:0.87 212:0.95 215:0.61 216:0.50 222:0.76 235:0.22 241:0.47 242:0.82 243:0.40 245:0.43 247:0.12 253:0.35 254:0.93 257:0.65 259:0.21 265:0.21 266:0.12 267:0.44 271:0.62 276:0.17 297:0.36 300:0.13 +2 1:0.74 7:0.72 11:0.92 12:0.06 17:0.20 18:0.73 21:0.68 22:0.93 27:0.62 29:0.53 30:0.72 32:0.69 34:0.97 36:0.52 37:0.59 39:0.44 43:0.89 45:0.66 47:0.93 48:0.91 60:0.87 64:0.77 66:0.67 69:0.52 70:0.60 71:0.83 74:0.76 77:0.70 81:0.44 83:0.91 85:0.80 86:0.84 91:0.42 98:0.58 101:0.87 106:0.81 108:0.40 114:0.56 117:0.86 122:0.57 123:0.38 124:0.43 126:0.54 127:0.35 129:0.05 133:0.37 135:0.49 139:0.87 144:0.85 145:0.80 146:0.48 147:0.54 149:0.77 150:0.70 154:0.88 155:0.67 158:0.91 159:0.28 165:0.93 172:0.48 173:0.65 176:0.73 177:0.70 178:0.55 179:0.71 187:0.95 192:0.87 195:0.62 201:0.93 204:0.85 206:0.81 208:0.64 212:0.52 215:0.37 216:0.67 219:0.95 222:0.76 230:0.74 233:0.73 235:0.88 241:0.63 242:0.24 243:0.72 245:0.56 247:0.74 253:0.44 259:0.21 262:0.93 265:0.59 266:0.47 267:0.28 271:0.62 276:0.37 279:0.86 281:0.91 282:0.77 283:0.78 290:0.56 292:0.91 297:0.36 300:0.55 +1 1:0.69 7:0.72 11:0.91 12:0.06 17:0.32 21:0.71 22:0.93 27:0.62 29:0.53 30:0.56 32:0.69 34:0.95 36:0.73 37:0.59 39:0.44 43:0.69 45:0.81 47:0.93 66:0.12 69:0.57 70:0.53 71:0.83 74:0.68 77:0.70 81:0.32 83:0.60 91:0.15 97:0.89 98:0.27 99:0.83 101:0.52 106:0.81 108:0.44 114:0.54 117:0.86 122:0.75 123:0.79 124:0.74 126:0.44 127:0.55 129:0.59 133:0.64 135:0.32 144:0.85 145:0.67 146:0.27 147:0.33 149:0.66 150:0.51 154:0.58 155:0.58 158:0.78 159:0.49 165:0.70 172:0.27 173:0.57 175:0.75 176:0.66 177:0.38 178:0.55 179:0.79 187:0.95 192:0.59 195:0.73 201:0.68 204:0.85 206:0.81 208:0.54 212:0.23 215:0.37 216:0.67 222:0.76 230:0.74 233:0.73 235:0.88 241:0.27 242:0.42 243:0.45 244:0.61 245:0.59 247:0.47 253:0.42 257:0.65 259:0.21 262:0.93 265:0.22 266:0.63 267:0.91 271:0.62 276:0.36 277:0.87 279:0.82 282:0.77 283:0.78 290:0.54 297:0.36 300:0.43 +1 11:0.91 12:0.06 17:0.49 18:0.62 21:0.13 27:0.58 29:0.53 30:0.91 34:0.53 36:0.56 37:0.43 39:0.44 43:0.72 45:0.73 66:0.36 69:0.86 70:0.13 71:0.83 74:0.48 81:0.54 86:0.72 91:0.04 98:0.18 101:0.52 108:0.72 122:0.82 123:0.70 124:0.57 126:0.26 127:0.59 129:0.05 133:0.43 135:0.58 139:0.69 144:0.85 146:0.22 147:0.18 149:0.36 150:0.41 154:0.63 159:0.37 163:0.97 172:0.16 173:0.98 177:0.38 178:0.55 179:0.17 187:0.68 212:0.95 215:0.61 216:0.50 222:0.76 232:0.85 235:0.12 241:0.46 242:0.82 243:0.40 245:0.43 247:0.12 253:0.34 254:0.93 257:0.65 259:0.21 265:0.19 266:0.12 267:0.23 271:0.62 276:0.17 297:0.99 300:0.13 +1 11:0.91 12:0.06 17:0.01 18:0.75 21:0.78 27:0.67 29:0.53 30:0.45 34:0.34 36:0.57 37:0.41 39:0.44 43:0.64 45:0.73 66:0.52 69:0.44 70:0.50 71:0.83 74:0.43 86:0.71 91:0.38 98:0.59 101:0.52 108:0.34 122:0.51 123:0.32 124:0.50 127:0.41 129:0.05 133:0.37 135:0.54 139:0.69 144:0.85 145:0.84 146:0.49 147:0.55 149:0.48 154:0.54 159:0.30 172:0.27 173:0.57 177:0.70 178:0.55 179:0.89 187:0.68 202:0.46 212:0.23 216:0.44 222:0.76 235:0.31 241:0.41 242:0.24 243:0.61 244:0.61 245:0.48 247:0.47 253:0.33 259:0.21 265:0.60 266:0.38 267:0.44 271:0.62 276:0.28 300:0.33 +0 11:0.91 12:0.06 17:0.76 18:0.63 21:0.13 27:0.60 29:0.53 30:0.17 34:0.37 36:0.64 37:0.49 39:0.44 43:0.18 45:0.73 55:0.59 66:0.07 69:0.98 70:0.95 71:0.83 74:0.48 81:0.44 83:0.60 86:0.65 91:0.11 96:0.68 97:0.89 98:0.26 101:0.52 108:0.85 114:0.63 117:0.86 122:0.77 123:0.84 124:0.97 126:0.26 127:0.91 129:0.05 133:0.97 135:0.24 139:0.63 140:0.45 144:0.85 146:0.61 147:0.38 149:0.41 150:0.36 151:0.47 153:0.48 154:0.16 155:0.58 158:0.78 159:0.94 162:0.86 172:0.54 173:0.95 176:0.55 177:0.05 179:0.20 187:0.68 190:0.89 192:0.59 202:0.36 208:0.54 212:0.39 216:0.21 220:0.91 222:0.76 232:0.91 235:0.12 241:0.01 242:0.71 243:0.08 244:0.61 245:0.88 247:0.92 248:0.47 253:0.47 256:0.45 257:0.84 259:0.21 265:0.28 266:0.96 267:0.52 268:0.46 271:0.62 276:0.92 277:0.69 279:0.82 283:0.78 285:0.47 290:0.63 300:0.94 +0 8:0.78 12:0.06 17:0.96 18:0.61 21:0.78 27:0.42 29:0.53 30:0.45 34:0.42 36:0.30 37:0.35 39:0.44 43:0.05 55:0.52 66:0.09 69:0.98 70:0.61 71:0.83 74:0.25 86:0.63 91:0.35 98:0.07 108:0.96 122:0.86 123:0.97 124:0.89 127:0.21 129:0.95 133:0.87 135:0.26 139:0.61 145:0.70 146:0.17 147:0.22 149:0.07 154:0.05 159:0.92 163:0.81 172:0.10 173:0.85 175:0.91 177:0.05 178:0.55 179:0.62 191:0.78 202:0.69 212:0.15 216:0.10 222:0.76 235:0.05 241:0.85 242:0.63 243:0.28 244:0.83 245:0.47 247:0.47 253:0.22 257:0.84 259:0.21 265:0.06 266:0.63 267:0.23 271:0.62 276:0.36 277:0.87 300:0.43 +1 11:0.91 12:0.06 17:0.02 18:0.65 21:0.13 27:0.58 29:0.53 30:0.91 34:0.52 36:0.57 37:0.43 39:0.44 43:0.72 45:0.73 66:0.36 69:0.66 70:0.13 71:0.83 74:0.51 81:0.54 86:0.76 91:0.05 98:0.33 101:0.52 108:0.50 122:0.82 123:0.48 124:0.57 126:0.26 127:0.77 129:0.05 133:0.43 135:0.73 139:0.72 144:0.85 146:0.22 147:0.18 149:0.36 150:0.41 154:0.77 159:0.17 163:0.90 172:0.16 173:0.99 177:0.38 178:0.55 179:0.52 187:0.39 212:0.95 215:0.61 216:0.50 222:0.76 232:0.85 235:0.12 241:0.57 242:0.82 243:0.40 245:0.43 247:0.12 253:0.35 254:0.93 257:0.65 259:0.21 265:0.36 266:0.12 267:0.28 271:0.62 276:0.17 283:0.78 297:0.99 300:0.13 +1 8:0.69 12:0.06 17:0.75 20:0.91 21:0.78 27:0.41 29:0.86 34:0.42 36:0.45 37:0.66 39:0.70 71:0.59 74:0.41 78:0.97 83:0.69 91:0.44 97:0.99 100:0.73 104:0.86 111:0.94 135:0.77 139:0.70 144:0.85 152:0.86 158:0.82 169:0.72 170:0.87 175:0.99 178:0.55 186:0.97 187:0.21 208:0.61 216:0.34 219:0.94 222:0.48 232:0.88 235:0.22 241:0.32 244:0.97 253:0.36 257:0.97 259:0.21 261:0.89 267:0.36 271:0.52 277:0.98 279:0.81 283:0.82 292:0.95 +3 1:0.67 8:0.59 10:0.89 11:0.87 12:0.06 17:0.05 18:0.79 27:0.14 29:0.86 30:0.91 34:0.78 36:0.18 37:0.67 39:0.70 43:0.74 45:0.91 64:0.77 66:0.35 69:0.76 70:0.24 71:0.59 74:0.65 81:0.76 83:0.69 86:0.86 91:0.40 97:0.94 98:0.18 99:0.95 101:0.65 108:0.77 114:0.97 122:0.65 123:0.76 124:0.72 126:0.51 127:0.34 129:0.05 133:0.67 135:0.94 139:0.86 144:0.85 145:0.58 146:0.20 147:0.28 149:0.73 150:0.68 154:0.67 155:0.52 158:0.82 159:0.42 165:0.81 170:0.87 172:0.45 173:0.79 174:0.79 176:0.70 177:0.70 178:0.55 179:0.56 187:0.39 192:0.51 197:0.83 201:0.65 208:0.61 212:0.71 215:0.96 216:0.93 219:0.94 222:0.48 235:0.12 239:0.87 241:0.10 242:0.44 243:0.36 245:0.69 247:0.67 253:0.70 254:0.86 257:0.65 259:0.21 265:0.20 266:0.54 267:0.36 271:0.52 276:0.41 277:0.69 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.33 +0 11:0.87 12:0.06 17:0.38 18:0.80 21:0.54 27:0.27 29:0.86 30:0.94 34:0.90 36:0.57 37:0.46 39:0.70 43:0.77 45:0.88 66:0.60 69:0.27 70:0.19 71:0.59 74:0.64 81:0.26 83:0.69 86:0.86 91:0.18 97:0.94 98:0.35 101:0.65 108:0.57 122:0.65 123:0.55 124:0.48 126:0.24 127:0.26 129:0.59 133:0.46 135:0.65 139:0.86 144:0.85 146:0.17 147:0.22 149:0.81 150:0.28 154:0.69 158:0.82 159:0.24 170:0.87 172:0.45 173:0.42 175:0.75 177:0.70 178:0.55 179:0.63 187:0.39 208:0.61 212:0.81 216:0.70 219:0.94 222:0.48 235:0.60 236:0.92 241:0.30 242:0.55 243:0.33 244:0.61 245:0.60 247:0.35 253:0.48 257:0.65 259:0.21 265:0.37 266:0.27 267:0.76 271:0.52 276:0.29 277:0.69 279:0.78 283:0.82 292:0.95 300:0.18 +0 1:0.67 8:0.58 10:0.89 11:0.87 12:0.06 17:0.06 18:0.79 27:0.33 29:0.86 30:0.91 34:0.83 36:0.18 37:0.41 39:0.70 43:0.74 45:0.91 64:0.77 66:0.35 69:0.76 70:0.24 71:0.59 74:0.65 81:0.76 83:0.69 86:0.86 91:0.42 97:0.94 98:0.18 99:0.95 101:0.65 108:0.77 114:0.97 122:0.65 123:0.76 124:0.72 126:0.51 127:0.34 129:0.05 133:0.67 135:0.96 139:0.86 144:0.85 145:0.58 146:0.20 147:0.28 149:0.73 150:0.68 154:0.67 155:0.52 158:0.82 159:0.42 165:0.81 170:0.87 172:0.45 173:0.79 174:0.79 176:0.70 177:0.70 178:0.55 179:0.56 187:0.39 192:0.51 197:0.83 201:0.65 208:0.61 212:0.71 215:0.96 216:0.88 219:0.94 222:0.48 235:0.12 239:0.87 241:0.10 242:0.44 243:0.36 245:0.69 247:0.67 253:0.70 254:0.86 257:0.65 259:0.21 265:0.20 266:0.54 267:0.52 271:0.52 276:0.41 277:0.69 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.33 +0 11:0.87 12:0.06 17:0.82 18:0.94 21:0.78 22:0.93 27:0.27 29:0.86 30:0.93 34:0.25 36:0.44 37:0.46 39:0.70 43:0.42 45:0.99 47:0.93 66:0.99 69:0.92 70:0.19 71:0.59 74:0.84 86:0.97 91:0.03 98:0.84 101:0.65 102:0.95 104:1.00 106:0.81 108:0.83 117:0.86 122:0.65 123:0.82 127:0.33 129:0.05 135:0.58 139:0.95 144:0.85 145:0.83 146:0.98 147:0.99 149:0.96 154:0.32 159:0.77 170:0.87 172:0.98 173:0.82 175:0.75 177:0.70 178:0.55 179:0.12 187:0.39 195:0.95 206:0.81 212:0.71 216:0.70 222:0.48 232:1.00 235:0.05 239:0.84 241:0.15 242:0.50 243:0.99 244:0.61 247:0.35 253:0.57 257:0.65 259:0.21 262:0.93 265:0.84 266:0.38 267:0.28 271:0.52 276:0.94 277:0.69 300:0.18 +0 7:0.75 8:0.61 10:0.82 11:0.87 12:0.06 17:0.40 18:0.70 21:0.78 27:0.21 29:0.86 30:0.90 34:0.58 36:0.88 37:0.74 39:0.70 43:0.75 45:0.83 66:0.36 69:0.56 70:0.19 71:0.59 74:0.56 83:0.69 86:0.76 91:0.35 98:0.34 101:0.65 108:0.73 122:0.65 123:0.71 124:0.68 127:0.29 129:0.59 133:0.61 135:0.19 139:0.79 144:0.85 146:0.17 147:0.22 149:0.65 154:0.65 155:0.54 159:0.48 163:0.75 170:0.87 172:0.27 173:0.49 174:0.79 175:0.75 177:0.70 178:0.55 179:0.77 187:0.39 192:0.56 197:0.81 202:0.58 204:0.83 208:0.61 212:0.16 216:0.95 222:0.48 230:0.77 233:0.76 235:0.42 239:0.82 241:0.44 242:0.40 243:0.39 244:0.61 245:0.50 247:0.39 253:0.44 257:0.65 259:0.21 265:0.36 266:0.54 267:0.76 271:0.52 276:0.31 277:0.69 281:0.91 300:0.18 +0 1:0.67 6:0.79 8:0.59 10:0.89 11:0.87 12:0.06 17:0.01 18:0.79 20:0.90 27:0.15 29:0.86 30:0.91 34:0.83 36:0.18 37:0.58 39:0.70 43:0.74 45:0.91 64:0.77 66:0.35 69:0.76 70:0.24 71:0.59 74:0.65 78:1.00 81:0.76 83:0.69 86:0.86 91:0.42 97:0.94 98:0.18 99:0.95 100:0.97 101:0.65 108:0.77 111:0.98 114:0.97 122:0.65 123:0.76 124:0.72 126:0.51 127:0.34 129:0.05 132:0.97 133:0.67 135:0.95 139:0.86 144:0.85 145:0.58 146:0.20 147:0.28 149:0.73 150:0.68 152:0.84 154:0.67 155:0.52 158:0.82 159:0.42 165:0.81 169:0.95 170:0.87 172:0.45 173:0.79 174:0.79 176:0.70 177:0.70 178:0.55 179:0.56 186:0.99 187:0.39 189:0.81 192:0.51 197:0.83 201:0.65 208:0.61 212:0.71 215:0.96 216:0.68 219:0.94 222:0.48 235:0.12 238:0.84 239:0.87 241:0.10 242:0.44 243:0.36 245:0.69 247:0.67 253:0.70 254:0.86 257:0.65 259:0.21 261:0.93 265:0.20 266:0.54 267:0.44 271:0.52 276:0.41 277:0.69 279:0.78 283:0.82 290:0.97 292:0.95 297:0.36 300:0.33 +1 1:0.61 10:0.92 11:0.87 12:0.06 17:0.38 18:0.84 21:0.54 27:0.02 29:0.86 30:0.66 32:0.72 34:0.75 36:0.43 37:0.92 39:0.70 43:0.75 44:0.92 45:0.92 64:0.77 66:0.36 69:0.44 70:0.72 71:0.59 74:0.71 77:0.70 81:0.63 83:0.69 86:0.89 91:0.06 98:0.53 99:0.95 101:0.65 108:0.34 114:0.76 122:0.95 123:0.32 124:0.70 126:0.51 127:0.42 129:0.05 133:0.60 135:0.61 139:0.89 144:0.85 145:0.49 146:0.75 147:0.79 149:0.88 150:0.50 154:0.66 155:0.48 159:0.64 165:0.81 170:0.87 172:0.65 173:0.30 174:0.94 176:0.70 177:0.88 178:0.55 179:0.38 187:0.39 192:0.47 195:0.84 197:0.94 201:0.60 208:0.61 212:0.48 215:0.58 216:0.99 222:0.48 235:0.74 239:0.91 241:0.32 242:0.41 243:0.51 244:0.61 245:0.84 247:0.79 253:0.61 259:0.21 265:0.55 266:0.71 267:0.44 271:0.52 276:0.70 282:0.80 290:0.76 297:0.36 300:0.84 +1 1:0.65 10:0.89 11:0.87 12:0.06 17:0.02 18:0.76 21:0.22 27:0.33 29:0.86 30:0.91 32:0.72 34:0.52 36:0.30 37:0.41 39:0.70 43:0.74 44:0.92 45:0.85 64:0.77 66:0.45 69:0.79 70:0.21 71:0.59 74:0.61 77:0.70 81:0.70 83:0.69 86:0.84 91:0.31 98:0.22 99:0.95 101:0.65 108:0.44 114:0.88 122:0.65 123:0.43 124:0.57 126:0.51 127:0.15 129:0.05 133:0.45 135:0.94 139:0.84 144:0.85 145:0.49 146:0.22 147:0.37 149:0.66 150:0.58 154:0.59 155:0.50 159:0.23 163:0.90 165:0.81 170:0.87 172:0.32 173:0.78 174:0.79 176:0.70 177:0.70 178:0.55 179:0.36 187:0.68 192:0.49 195:0.76 197:0.82 201:0.63 208:0.61 212:0.50 215:0.79 216:0.88 222:0.48 235:0.42 239:0.86 241:0.05 242:0.53 243:0.58 245:0.59 247:0.55 253:0.63 254:0.94 257:0.65 259:0.21 265:0.24 266:0.27 267:0.36 271:0.52 276:0.29 277:0.69 282:0.80 290:0.88 297:0.36 300:0.25 +0 1:0.65 8:0.58 10:0.89 11:0.87 12:0.06 18:0.70 21:0.22 27:0.14 29:0.86 30:0.90 32:0.72 34:0.75 36:0.19 37:0.67 39:0.70 43:0.74 44:0.92 45:0.83 64:0.77 66:0.36 69:0.77 70:0.19 71:0.59 74:0.56 77:0.70 81:0.70 83:0.69 86:0.80 91:0.27 98:0.17 99:0.95 101:0.65 108:0.44 114:0.88 122:0.65 123:0.43 124:0.59 126:0.51 127:0.16 129:0.05 133:0.45 135:0.90 139:0.80 144:0.85 145:0.49 146:0.22 147:0.28 149:0.48 150:0.58 154:0.61 155:0.50 159:0.20 165:0.81 170:0.87 172:0.27 173:0.83 174:0.79 176:0.70 177:0.70 178:0.55 179:0.47 187:0.68 192:0.49 195:0.65 197:0.82 201:0.63 208:0.61 212:0.55 215:0.79 216:0.93 222:0.48 235:0.42 239:0.87 241:0.02 242:0.40 243:0.51 245:0.56 247:0.55 253:0.63 254:0.94 257:0.65 259:0.21 265:0.18 266:0.27 267:0.36 271:0.52 276:0.23 277:0.69 282:0.80 290:0.88 297:0.36 300:0.18 +1 11:0.87 12:0.06 17:0.32 18:0.77 21:0.54 27:0.27 29:0.86 30:0.91 34:0.96 36:0.43 37:0.46 39:0.70 43:0.77 45:0.85 66:0.82 69:0.25 70:0.13 71:0.59 74:0.55 81:0.26 86:0.83 91:0.22 98:0.72 101:0.65 108:0.20 122:0.65 123:0.19 126:0.24 127:0.22 129:0.05 135:0.47 139:0.79 144:0.85 146:0.59 147:0.65 149:0.76 150:0.28 154:0.69 159:0.22 163:0.75 170:0.87 172:0.48 173:0.41 175:0.75 177:0.70 178:0.55 179:0.64 187:0.39 212:0.30 216:0.70 222:0.48 235:0.60 236:0.92 241:0.32 242:0.53 243:0.83 244:0.61 247:0.55 253:0.43 257:0.65 259:0.21 265:0.72 266:0.47 267:0.94 271:0.52 276:0.36 277:0.69 300:0.13 +0 10:0.83 11:0.87 12:0.06 17:0.02 18:0.61 21:0.78 27:0.14 29:0.86 30:0.76 34:0.78 36:0.20 37:0.67 39:0.70 43:0.60 45:0.73 66:0.54 69:0.86 70:0.22 71:0.59 74:0.36 86:0.67 91:0.37 98:0.35 101:0.65 108:0.73 122:0.65 123:0.71 124:0.45 127:0.18 129:0.05 133:0.37 135:0.97 139:0.66 144:0.85 145:0.74 146:0.41 147:0.05 149:0.37 154:0.49 159:0.25 163:0.75 170:0.87 172:0.21 173:0.91 174:0.79 175:0.75 177:0.05 178:0.55 179:0.79 187:0.39 197:0.83 212:0.42 216:0.93 222:0.48 235:0.60 239:0.83 241:0.05 242:0.45 243:0.26 244:0.61 245:0.40 247:0.35 253:0.32 257:0.93 259:0.21 265:0.37 266:0.23 267:0.44 271:0.52 276:0.21 277:0.69 300:0.18 +0 11:0.87 12:0.06 17:0.59 18:0.88 21:0.54 27:0.27 29:0.86 30:0.90 34:0.75 36:0.49 37:0.46 39:0.70 43:0.76 45:0.96 66:0.71 69:0.43 70:0.21 71:0.59 74:0.72 81:0.26 86:0.92 91:0.18 98:0.43 101:0.65 108:0.75 122:0.65 123:0.74 124:0.46 126:0.24 127:0.44 129:0.59 133:0.61 135:0.69 139:0.90 144:0.85 146:0.42 147:0.49 149:0.91 150:0.28 154:0.67 159:0.46 170:0.87 172:0.80 173:0.42 175:0.75 177:0.70 178:0.55 179:0.36 187:0.39 212:0.88 216:0.70 222:0.48 235:0.60 236:0.92 241:0.30 242:0.45 243:0.23 244:0.61 245:0.77 247:0.67 253:0.51 257:0.65 259:0.21 265:0.45 266:0.54 267:0.79 271:0.52 276:0.70 277:0.69 300:0.33 +0 11:0.87 12:0.06 17:0.55 18:0.89 21:0.54 27:0.27 29:0.86 30:0.91 34:0.75 36:0.49 37:0.46 39:0.70 43:0.76 45:0.96 66:0.77 69:0.43 70:0.19 71:0.59 74:0.78 81:0.26 83:0.69 86:0.93 91:0.15 97:0.94 98:0.49 101:0.65 108:0.73 122:0.65 123:0.71 124:0.41 126:0.24 127:0.45 129:0.59 133:0.46 135:0.61 139:0.93 144:0.85 146:0.21 147:0.27 149:0.92 150:0.28 154:0.67 158:0.82 159:0.44 170:0.87 172:0.82 173:0.45 175:0.75 177:0.70 178:0.55 179:0.36 187:0.39 208:0.61 212:0.91 216:0.70 219:0.94 222:0.48 235:0.60 236:0.92 241:0.27 242:0.50 243:0.19 244:0.61 245:0.81 247:0.39 253:0.54 257:0.65 259:0.21 265:0.50 266:0.38 267:0.59 271:0.52 276:0.58 277:0.69 279:0.78 283:0.82 292:0.95 300:0.25 +0 1:0.65 7:0.75 8:0.58 9:0.75 11:0.87 12:0.06 17:0.64 18:0.72 21:0.22 22:0.93 27:0.09 29:0.86 30:0.94 31:0.94 32:0.72 34:0.92 36:0.54 37:0.44 39:0.70 43:0.64 44:0.92 45:0.87 47:0.93 64:0.77 66:0.83 69:0.81 70:0.19 71:0.59 74:0.64 77:0.89 79:0.93 81:0.70 83:0.69 86:0.78 91:0.37 96:0.80 98:0.62 99:0.95 101:0.65 104:0.99 106:0.81 108:0.65 114:0.88 117:0.86 120:0.69 122:0.65 123:0.63 126:0.51 127:0.18 129:0.05 135:0.97 137:0.94 139:0.84 140:0.45 144:0.85 145:0.69 146:0.62 147:0.67 149:0.66 150:0.58 151:0.86 153:0.48 154:0.54 155:0.64 159:0.23 162:0.86 164:0.56 165:0.81 170:0.87 172:0.51 173:0.88 175:0.75 176:0.70 177:0.70 179:0.49 187:0.39 190:0.77 192:0.98 195:0.66 196:0.95 201:0.63 202:0.36 204:0.83 206:0.81 208:0.61 212:0.28 215:0.79 216:0.40 222:0.48 230:0.77 232:0.99 233:0.66 235:0.42 241:0.37 242:0.45 243:0.84 244:0.61 247:0.35 248:0.77 253:0.82 255:0.77 256:0.45 257:0.65 259:0.21 260:0.70 262:0.93 265:0.63 266:0.38 267:0.79 268:0.46 271:0.52 276:0.34 277:0.69 281:0.86 282:0.80 284:0.88 285:0.60 290:0.88 297:0.36 300:0.18 +0 11:0.87 12:0.06 17:0.43 18:0.72 21:0.54 27:0.27 29:0.86 30:0.95 34:0.83 36:0.57 37:0.46 39:0.70 43:0.76 45:0.81 66:0.30 69:0.39 70:0.13 71:0.59 74:0.48 81:0.26 86:0.78 91:0.27 98:0.12 101:0.65 108:0.30 122:0.65 123:0.29 124:0.61 126:0.24 127:0.31 129:0.59 133:0.46 135:0.63 139:0.74 144:0.85 146:0.17 147:0.22 149:0.64 150:0.28 154:0.68 159:0.29 170:0.87 172:0.16 173:0.49 175:0.75 177:0.70 178:0.55 179:0.89 187:0.39 212:0.30 216:0.70 222:0.48 235:0.60 236:0.92 241:0.44 242:0.63 243:0.48 244:0.61 245:0.47 247:0.35 253:0.39 257:0.65 259:0.21 265:0.13 266:0.27 267:0.59 271:0.52 276:0.13 277:0.69 300:0.13 +1 1:0.65 7:0.75 8:0.58 9:0.75 11:0.87 12:0.06 17:0.45 18:0.73 21:0.22 22:0.93 27:0.09 29:0.86 30:0.94 31:0.94 32:0.72 34:0.94 36:0.57 37:0.44 39:0.70 43:0.65 44:0.92 45:0.87 47:0.93 64:0.77 66:0.83 69:0.81 70:0.19 71:0.59 74:0.64 77:0.89 79:0.93 81:0.70 83:0.69 86:0.79 91:0.42 96:0.80 98:0.62 99:0.95 101:0.65 104:0.99 106:0.81 108:0.65 114:0.88 117:0.86 120:0.69 122:0.65 123:0.62 126:0.51 127:0.18 129:0.05 135:0.96 137:0.94 139:0.84 140:0.45 144:0.85 145:0.69 146:0.62 147:0.67 149:0.66 150:0.58 151:0.86 153:0.48 154:0.55 155:0.64 159:0.23 162:0.86 164:0.56 165:0.81 170:0.87 172:0.51 173:0.87 175:0.75 176:0.70 177:0.70 179:0.49 187:0.39 190:0.77 192:0.98 195:0.66 196:0.95 201:0.63 202:0.36 204:0.83 206:0.81 208:0.61 212:0.28 215:0.79 216:0.40 222:0.48 230:0.77 232:0.99 233:0.66 235:0.42 241:0.35 242:0.45 243:0.84 244:0.61 247:0.35 248:0.77 253:0.82 255:0.77 256:0.45 257:0.65 259:0.21 260:0.70 262:0.93 265:0.63 266:0.38 267:0.85 268:0.46 271:0.52 276:0.34 277:0.69 281:0.86 282:0.80 284:0.88 285:0.60 290:0.88 297:0.36 300:0.18 +0 1:0.57 12:0.06 17:0.95 21:0.76 22:0.93 27:0.71 29:0.86 34:0.47 36:0.81 37:0.23 39:0.70 47:0.93 64:0.77 71:0.59 81:0.58 83:0.69 91:0.15 99:0.95 104:0.96 106:0.81 114:0.64 117:0.86 126:0.51 135:0.39 144:0.85 145:0.79 150:0.42 155:0.46 165:0.81 170:0.87 175:0.99 176:0.70 178:0.55 187:0.39 192:0.45 193:0.97 201:0.56 206:0.81 208:0.61 215:0.46 216:0.25 222:0.48 232:0.96 235:0.98 239:0.84 241:0.41 244:0.97 253:0.49 257:0.97 259:0.21 262:0.93 267:0.44 271:0.52 277:0.98 290:0.64 297:0.36 +1 1:0.67 10:0.89 11:0.87 12:0.06 17:0.03 18:0.76 21:0.05 27:0.14 29:0.86 30:0.91 32:0.72 34:0.33 36:0.34 37:0.67 39:0.70 43:0.74 44:0.92 45:0.85 64:0.77 66:0.45 69:0.79 70:0.21 71:0.59 74:0.61 77:0.70 81:0.74 83:0.69 86:0.84 91:0.25 98:0.20 99:0.95 101:0.65 108:0.44 114:0.95 122:0.65 123:0.42 124:0.57 126:0.51 127:0.14 129:0.05 133:0.45 135:0.78 139:0.84 144:0.85 145:0.49 146:0.22 147:0.37 149:0.66 150:0.64 154:0.62 155:0.51 159:0.22 165:0.81 170:0.87 172:0.32 173:0.74 174:0.79 176:0.70 177:0.70 178:0.55 179:0.30 187:0.68 192:0.51 195:0.81 197:0.82 201:0.64 208:0.61 212:0.61 215:0.91 216:0.93 222:0.48 235:0.22 239:0.87 241:0.01 242:0.53 243:0.58 245:0.59 247:0.55 253:0.67 254:0.92 257:0.65 259:0.21 265:0.21 266:0.27 267:0.87 271:0.52 276:0.28 277:0.69 282:0.80 290:0.95 297:0.36 300:0.25 +0 11:0.87 12:0.06 17:0.40 18:0.82 21:0.54 27:0.27 29:0.86 30:0.94 34:0.87 36:0.55 37:0.46 39:0.70 43:0.77 45:0.90 66:0.64 69:0.27 70:0.19 71:0.59 74:0.67 81:0.26 83:0.69 86:0.87 91:0.25 97:0.94 98:0.40 101:0.65 108:0.62 122:0.65 123:0.60 124:0.45 126:0.24 127:0.31 129:0.59 133:0.46 135:0.68 139:0.87 144:0.85 146:0.17 147:0.22 149:0.83 150:0.28 154:0.69 158:0.82 159:0.29 170:0.87 172:0.54 173:0.39 175:0.75 177:0.70 178:0.55 179:0.60 187:0.39 208:0.61 212:0.85 216:0.70 219:0.94 222:0.48 235:0.60 236:0.92 241:0.24 242:0.54 243:0.28 244:0.61 245:0.64 247:0.35 253:0.49 257:0.65 259:0.21 265:0.42 266:0.27 267:0.59 271:0.52 276:0.34 277:0.69 279:0.78 283:0.82 292:0.95 300:0.25 +2 1:0.74 7:0.81 11:0.88 12:0.20 17:0.57 21:0.22 27:0.72 30:0.45 34:0.58 36:0.97 39:0.53 43:0.97 46:0.96 66:0.36 69:0.12 70:0.33 74:0.75 76:0.85 81:0.80 83:0.76 85:0.80 91:0.75 98:0.59 101:0.78 104:0.72 107:0.96 108:0.10 110:0.96 114:0.90 121:0.90 122:0.51 123:0.10 124:0.69 125:0.88 126:0.54 127:0.84 129:0.59 133:0.64 135:0.89 144:0.85 145:0.74 146:0.49 147:0.55 149:0.70 150:0.87 154:0.97 155:0.67 159:0.37 160:0.88 163:0.75 165:0.86 172:0.21 173:0.30 176:0.73 177:0.38 178:0.55 179:0.93 187:0.21 192:0.59 201:0.74 204:0.89 208:0.64 212:0.23 215:0.79 216:0.04 222:0.84 230:0.84 232:0.77 233:0.69 235:0.31 241:0.77 242:0.55 243:0.51 245:0.45 247:0.39 253:0.72 259:0.21 265:0.60 266:0.38 267:0.23 271:0.62 276:0.30 289:0.97 290:0.90 297:0.36 300:0.25 +3 9:0.80 12:0.20 17:0.53 21:0.78 27:0.72 34:0.37 36:0.67 39:0.53 41:0.82 46:0.88 60:0.87 74:0.47 83:0.86 91:0.88 96:0.86 104:0.58 107:0.88 110:0.88 120:0.77 125:0.97 135:0.47 140:0.45 144:0.85 151:0.95 153:0.83 155:0.67 158:0.92 160:0.96 162:0.55 164:0.65 175:0.91 192:0.59 202:0.62 208:0.64 216:0.13 222:0.84 232:0.76 235:0.05 241:0.91 244:0.83 248:0.84 253:0.81 255:0.79 256:0.45 257:0.84 259:0.21 260:0.77 267:0.36 268:0.58 271:0.62 277:0.87 279:0.86 283:0.82 285:0.62 289:0.97 +1 11:0.88 12:0.20 17:0.32 21:0.78 27:0.45 30:0.76 34:0.83 36:0.18 37:0.50 39:0.53 43:0.75 46:0.94 66:0.64 69:0.83 70:0.15 74:0.47 85:0.72 91:0.07 98:0.25 101:0.71 107:0.92 108:0.68 110:0.92 122:0.51 123:0.66 125:0.88 127:0.11 129:0.05 135:0.72 144:0.85 145:0.49 146:0.34 147:0.41 149:0.41 154:0.66 159:0.14 160:0.88 163:0.98 172:0.16 173:0.84 177:0.38 178:0.55 179:0.46 187:0.68 212:0.95 216:0.77 222:0.84 235:0.84 241:0.21 242:0.82 243:0.69 247:0.12 253:0.40 259:0.21 265:0.27 266:0.12 267:0.36 271:0.62 276:0.13 289:0.90 300:0.13 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.20 17:0.16 21:0.30 27:0.21 30:0.68 34:0.52 36:0.81 37:0.74 39:0.53 41:0.82 43:0.98 46:0.94 60:0.87 66:0.54 69:0.12 70:0.43 74:0.79 81:0.75 83:0.84 85:0.85 91:0.22 96:0.77 98:0.20 101:0.73 104:0.84 106:0.81 107:0.95 108:0.10 110:0.95 114:0.86 117:0.86 120:0.65 121:0.90 122:0.43 123:0.10 124:0.55 125:0.98 126:0.54 127:0.50 129:0.05 133:0.62 135:0.42 140:0.87 144:0.85 146:0.41 147:0.47 149:0.80 150:0.84 151:0.77 153:0.48 154:0.98 155:0.67 158:0.92 159:0.82 160:0.96 162:0.54 164:0.57 165:0.84 172:0.32 173:0.09 176:0.73 177:0.38 178:0.48 179:0.88 187:0.39 192:0.59 201:0.74 202:0.56 204:0.89 206:0.81 208:0.64 212:0.42 215:0.72 216:0.95 220:0.62 222:0.84 230:0.87 232:0.88 233:0.76 235:0.42 241:0.21 242:0.63 243:0.63 245:0.45 247:0.30 248:0.71 253:0.81 255:0.79 256:0.45 259:0.21 260:0.66 265:0.21 266:0.38 267:0.44 268:0.46 271:0.62 276:0.20 279:0.86 283:0.82 285:0.62 289:0.97 290:0.86 297:0.36 300:0.33 +2 1:0.74 11:0.88 12:0.20 17:0.82 21:0.05 27:0.72 30:0.76 34:0.67 36:0.33 39:0.53 43:0.98 46:0.97 66:0.64 69:0.07 70:0.15 74:0.55 81:0.89 83:0.79 85:0.72 91:0.76 98:0.44 101:0.76 107:0.93 108:0.06 110:0.93 114:0.95 120:0.65 122:0.51 123:0.06 125:0.88 126:0.54 127:0.14 129:0.05 135:0.51 140:0.80 144:0.85 146:0.26 147:0.32 149:0.56 150:0.94 151:0.61 153:0.48 154:0.98 155:0.67 159:0.11 160:0.88 162:0.62 164:0.55 165:0.90 172:0.16 173:0.46 176:0.73 177:0.38 178:0.42 179:0.80 190:0.77 192:0.59 201:0.74 202:0.50 212:0.95 215:0.91 216:0.03 220:0.62 222:0.84 235:0.12 241:0.86 242:0.82 243:0.69 247:0.12 248:0.59 253:0.72 256:0.45 259:0.21 260:0.65 265:0.46 266:0.12 267:0.17 268:0.46 271:0.62 276:0.14 285:0.50 289:0.97 290:0.95 297:0.36 300:0.13 +1 1:0.74 7:0.81 9:0.80 11:0.88 12:0.20 17:0.34 20:0.99 21:0.54 27:0.30 30:0.91 32:0.80 34:0.92 36:0.96 37:0.29 39:0.53 41:0.92 43:0.87 46:0.95 66:0.27 69:0.55 70:0.13 74:0.70 77:0.70 78:0.90 81:0.61 83:0.82 85:0.80 91:0.61 96:0.93 98:0.09 100:0.94 101:0.75 107:0.93 108:0.43 110:0.94 111:0.91 114:0.77 117:0.86 120:0.88 121:0.90 122:0.43 123:0.41 124:0.61 125:0.99 126:0.54 127:0.25 129:0.59 133:0.47 135:0.93 140:0.45 144:0.85 145:0.50 146:0.13 147:0.18 149:0.62 150:0.75 151:0.98 152:0.91 153:0.92 154:0.86 155:0.67 159:0.37 160:0.98 162:0.69 163:0.88 164:0.79 165:0.79 169:0.92 172:0.10 173:0.54 176:0.73 177:0.38 179:0.93 186:0.90 187:0.68 192:0.59 195:0.72 201:0.74 202:0.71 204:0.89 208:0.64 212:0.61 215:0.58 216:0.63 222:0.84 230:0.87 232:0.88 233:0.76 235:0.68 241:0.57 242:0.63 243:0.46 245:0.40 247:0.30 248:0.93 253:0.93 255:0.79 256:0.68 259:0.21 260:0.89 261:0.94 265:0.10 266:0.17 267:0.44 268:0.74 271:0.62 276:0.12 282:0.88 285:0.62 289:0.97 290:0.77 297:0.36 299:0.88 300:0.13 +1 1:0.74 11:0.88 12:0.20 17:0.13 21:0.22 27:0.72 30:0.76 34:0.94 36:0.86 39:0.53 43:0.90 46:0.95 66:0.36 69:0.45 70:0.15 74:0.52 81:0.80 83:0.76 85:0.72 91:0.54 98:0.12 101:0.74 104:0.72 107:0.92 108:0.35 110:0.93 114:0.90 122:0.51 123:0.34 124:0.52 125:0.88 126:0.54 127:0.28 129:0.05 133:0.39 135:0.37 144:0.85 146:0.13 147:0.18 149:0.54 150:0.87 154:0.89 155:0.67 159:0.23 160:0.88 163:0.93 165:0.86 172:0.10 173:0.63 176:0.73 177:0.38 178:0.55 179:0.98 187:0.21 192:0.59 201:0.74 212:0.95 215:0.79 216:0.04 222:0.84 232:0.77 235:0.31 241:0.50 242:0.82 243:0.51 245:0.37 247:0.12 253:0.68 259:0.21 265:0.12 266:0.12 267:0.23 271:0.62 276:0.12 289:0.97 290:0.90 297:0.36 300:0.13 +2 1:0.74 7:0.81 8:0.70 9:0.80 11:0.88 12:0.20 17:0.43 20:0.91 21:0.22 27:0.71 30:0.45 34:0.45 36:0.98 37:0.32 39:0.53 41:0.96 43:0.97 46:0.96 66:0.36 69:0.12 70:0.33 74:0.75 76:0.85 78:0.91 81:0.80 83:0.82 85:0.80 91:0.82 96:0.95 98:0.59 100:0.73 101:0.78 104:0.76 107:0.96 108:0.10 110:0.96 111:0.88 114:0.90 120:0.88 121:0.90 122:0.51 123:0.10 124:0.69 125:0.98 126:0.54 127:0.84 129:0.59 133:0.64 135:0.90 140:0.45 144:0.85 145:0.74 146:0.49 147:0.55 149:0.70 150:0.87 151:0.91 152:0.95 153:0.48 154:0.97 155:0.67 159:0.37 160:0.99 162:0.81 163:0.75 164:0.86 165:0.86 169:0.72 172:0.21 173:0.30 176:0.73 177:0.38 179:0.93 186:0.91 190:0.77 192:0.59 201:0.74 202:0.80 204:0.89 208:0.64 212:0.23 215:0.79 216:0.14 220:0.62 222:0.84 230:0.84 232:0.81 233:0.69 235:0.31 241:0.96 242:0.55 243:0.51 245:0.45 247:0.39 248:0.93 253:0.95 255:0.79 256:0.85 259:0.21 260:0.89 261:0.92 265:0.60 266:0.38 267:0.36 268:0.85 271:0.62 276:0.30 285:0.62 289:0.97 290:0.90 297:0.36 300:0.25 +0 9:0.80 12:0.20 17:0.73 21:0.05 27:0.72 34:0.30 36:0.64 39:0.53 41:0.82 46:0.88 81:0.78 83:0.76 91:0.80 96:0.80 107:0.88 110:0.88 114:0.56 120:0.69 125:1.00 126:0.32 135:0.24 140:0.45 144:0.85 150:0.58 151:0.87 153:0.48 155:0.67 160:0.96 162:0.86 164:0.57 175:0.91 176:0.53 192:0.59 202:0.36 208:0.64 216:0.02 222:0.84 232:0.74 235:0.05 241:1.00 244:0.83 248:0.78 253:0.74 255:0.79 256:0.45 257:0.84 259:0.21 260:0.70 267:0.28 268:0.46 271:0.62 277:0.87 285:0.62 289:0.97 290:0.56 +2 1:0.74 9:0.80 11:0.88 12:0.20 17:0.81 20:0.88 21:0.30 27:0.40 30:0.45 34:0.34 36:0.90 37:0.69 39:0.53 41:0.82 43:0.79 46:0.95 55:0.84 60:0.87 66:0.61 69:0.75 70:0.72 74:0.85 78:0.72 81:0.75 83:0.86 85:0.85 91:0.31 96:0.80 98:0.81 100:0.95 101:0.76 104:0.98 107:0.97 108:0.71 110:0.97 111:0.88 114:0.86 117:0.86 120:0.69 122:0.67 123:0.69 124:0.50 125:1.00 126:0.54 127:0.45 129:0.59 133:0.47 135:0.66 140:0.45 144:0.85 145:0.79 146:0.26 147:0.32 149:0.74 150:0.84 151:0.87 152:0.83 153:0.48 154:0.73 155:0.67 158:0.92 159:0.58 160:0.96 162:0.86 163:0.92 164:0.57 165:0.84 169:0.93 172:0.59 173:0.70 176:0.73 177:0.38 179:0.60 186:0.73 187:0.87 192:0.59 201:0.74 202:0.36 208:0.64 212:0.30 215:0.72 216:0.30 222:0.84 232:0.96 235:0.42 241:0.07 242:0.57 243:0.34 245:0.74 247:0.39 248:0.78 253:0.85 255:0.79 256:0.45 259:0.21 260:0.70 261:0.90 265:0.81 266:0.75 267:0.84 268:0.46 271:0.62 276:0.57 279:0.86 283:0.82 285:0.62 289:0.97 290:0.86 297:0.36 300:0.55 +1 1:0.74 11:0.88 12:0.20 17:0.72 21:0.13 27:0.72 30:0.91 34:0.61 36:0.57 39:0.53 43:0.84 46:0.97 55:0.71 66:0.69 69:0.63 70:0.13 74:0.44 81:0.84 83:0.77 85:0.72 91:0.78 98:0.59 101:0.76 107:0.94 108:0.48 110:0.93 114:0.92 122:0.43 123:0.46 125:0.88 126:0.54 127:0.17 129:0.05 135:0.28 144:0.85 146:0.32 147:0.38 149:0.54 150:0.90 154:0.81 155:0.67 159:0.13 160:0.88 165:0.88 172:0.21 173:0.91 176:0.73 177:0.38 178:0.55 179:0.84 187:0.21 192:0.59 201:0.74 212:0.61 215:0.84 222:0.84 232:0.77 235:0.22 241:0.77 242:0.63 243:0.73 247:0.30 253:0.69 259:0.21 265:0.60 266:0.17 267:0.23 271:0.62 276:0.21 289:0.97 290:0.92 297:0.36 300:0.13 +0 1:0.74 11:0.88 12:0.20 17:0.70 21:0.71 30:0.56 32:0.80 34:0.61 36:0.25 37:0.97 39:0.53 43:0.80 46:0.97 60:0.87 66:0.35 69:0.74 70:0.71 74:0.88 77:0.70 81:0.49 83:0.86 85:0.96 91:0.25 98:0.49 101:0.82 104:0.82 107:0.99 108:0.78 110:0.98 114:0.68 122:0.43 123:0.76 124:0.77 125:0.88 126:0.54 127:0.55 129:0.81 133:0.76 135:0.61 144:0.85 145:0.65 146:0.58 147:0.64 149:0.93 150:0.66 154:0.74 155:0.67 158:0.87 159:0.75 160:0.88 165:0.69 172:0.65 173:0.59 176:0.73 177:0.38 178:0.55 179:0.41 187:0.21 192:0.59 195:0.89 201:0.74 208:0.64 212:0.27 215:0.48 216:0.98 222:0.84 232:0.87 235:0.88 241:0.07 242:0.53 243:0.40 245:0.80 247:0.47 253:0.69 259:0.21 265:0.50 266:0.92 267:0.28 271:0.62 276:0.72 279:0.86 282:0.88 283:0.71 289:0.97 290:0.68 297:0.36 299:0.88 300:0.70 +1 1:0.74 11:0.88 12:0.20 17:0.03 21:0.22 27:0.72 30:0.76 34:0.97 36:0.88 39:0.53 43:0.85 46:0.95 66:0.36 69:0.62 70:0.15 74:0.51 81:0.80 83:0.76 85:0.72 91:0.54 98:0.12 101:0.74 104:0.72 107:0.92 108:0.47 110:0.93 114:0.90 122:0.51 123:0.46 124:0.52 125:0.88 126:0.54 127:0.24 129:0.05 133:0.39 135:0.42 144:0.85 146:0.13 147:0.18 149:0.50 150:0.87 154:0.81 155:0.67 159:0.21 160:0.88 163:0.93 165:0.86 172:0.10 173:0.79 176:0.73 177:0.38 178:0.55 179:0.97 187:0.21 192:0.59 201:0.74 212:0.95 215:0.79 216:0.04 222:0.84 232:0.77 235:0.31 241:0.50 242:0.82 243:0.51 245:0.37 247:0.12 253:0.67 259:0.21 265:0.12 266:0.12 267:0.28 271:0.62 276:0.12 289:0.97 290:0.90 297:0.36 300:0.13 +1 11:0.88 12:0.20 17:0.32 21:0.78 27:0.45 30:0.95 34:0.62 36:0.17 37:0.50 39:0.53 43:0.78 46:0.93 66:0.54 69:0.78 74:0.41 85:0.72 91:0.06 98:0.07 101:0.70 107:0.90 108:0.61 110:0.90 123:0.59 125:0.88 127:0.06 129:0.05 135:0.85 144:0.85 145:0.50 146:0.13 147:0.18 149:0.42 154:0.71 159:0.08 160:0.88 172:0.10 173:0.65 177:0.38 178:0.55 179:0.08 187:0.68 216:0.77 222:0.84 235:0.22 241:0.27 243:0.63 253:0.37 259:0.21 265:0.08 267:0.36 271:0.62 289:0.89 300:0.08 +1 1:0.74 11:0.88 12:0.20 17:0.06 21:0.22 27:0.72 30:0.45 34:0.96 36:0.55 39:0.53 43:0.97 46:0.97 66:0.52 69:0.12 70:0.33 74:0.69 81:0.80 83:0.76 85:0.80 91:0.67 98:0.57 101:0.79 104:0.72 107:0.96 108:0.52 110:0.96 114:0.90 122:0.67 123:0.50 124:0.50 125:0.88 126:0.54 127:0.40 129:0.59 133:0.47 135:0.30 144:0.85 146:0.13 147:0.18 149:0.71 150:0.87 154:0.97 155:0.67 159:0.23 160:0.88 163:0.75 165:0.86 172:0.27 173:0.39 176:0.73 177:0.38 178:0.55 179:0.89 187:0.21 192:0.59 201:0.74 212:0.52 215:0.79 216:0.04 222:0.84 232:0.77 235:0.31 241:0.47 242:0.55 243:0.34 245:0.48 247:0.39 253:0.71 259:0.21 265:0.58 266:0.27 267:0.82 271:0.62 276:0.27 289:0.97 290:0.90 297:0.36 300:0.25 +2 1:0.74 7:0.81 11:0.64 12:0.79 17:0.51 21:0.40 26:0.99 27:0.21 28:0.80 30:0.38 34:0.40 36:0.87 37:0.74 39:0.85 43:0.97 58:0.93 66:0.63 69:0.17 70:0.65 74:0.89 81:0.73 83:0.75 85:0.91 91:0.14 98:0.70 101:0.82 108:0.14 114:0.82 117:0.86 121:0.90 122:0.43 123:0.13 124:0.48 126:0.54 127:0.33 129:0.59 131:0.61 133:0.47 135:0.18 141:0.91 144:0.57 146:0.71 147:0.76 149:0.90 150:0.83 154:0.97 155:0.67 157:0.92 159:0.39 161:0.61 165:0.83 166:0.93 167:0.67 172:0.63 173:0.24 176:0.73 177:0.38 178:0.55 179:0.50 181:0.60 182:0.89 187:0.39 192:0.59 199:0.97 201:0.74 202:0.36 204:0.89 208:0.64 212:0.55 215:0.67 216:0.95 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 230:0.84 231:0.89 232:0.91 233:0.76 234:0.91 235:0.52 241:0.21 242:0.37 243:0.68 245:0.75 246:0.94 247:0.67 253:0.74 259:0.21 265:0.70 266:0.47 267:0.28 271:0.27 274:0.91 276:0.59 287:0.61 290:0.82 297:0.36 300:0.55 +3 1:0.74 6:0.98 8:0.85 9:0.80 11:0.64 12:0.79 17:0.67 20:0.82 21:0.13 26:0.99 27:0.16 28:0.80 30:0.09 34:0.23 36:0.94 37:0.84 39:0.85 41:0.93 43:0.31 55:0.52 58:0.99 66:0.12 69:0.99 70:0.85 74:0.60 78:0.83 81:0.87 83:0.81 85:0.72 91:0.58 96:0.90 98:0.09 100:0.90 101:0.64 104:0.58 108:0.50 111:0.85 114:0.92 120:0.83 122:0.57 123:0.48 124:0.93 126:0.54 127:0.88 129:0.05 131:0.96 133:0.93 135:0.63 140:0.80 141:1.00 144:0.57 145:0.74 146:0.13 147:0.29 149:0.33 150:0.92 151:0.96 152:0.97 153:0.85 154:0.11 155:0.67 157:0.75 159:0.95 161:0.61 162:0.64 163:0.80 164:0.79 165:0.88 166:0.93 167:0.78 169:0.99 172:0.16 173:1.00 176:0.73 177:0.05 178:0.42 179:0.83 181:0.60 182:0.90 186:0.84 187:0.21 189:0.94 192:0.59 199:0.96 201:0.74 202:0.72 208:0.64 212:0.13 215:0.84 216:0.53 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.22 238:0.94 241:0.64 242:0.42 243:0.33 245:0.46 246:0.94 247:0.47 248:0.90 253:0.89 255:0.79 256:0.71 257:0.65 259:0.21 260:0.84 261:0.99 265:0.09 266:0.75 267:0.59 268:0.74 271:0.27 274:0.99 276:0.43 277:0.69 285:0.62 287:0.95 290:0.92 297:0.36 300:0.55 +2 1:0.74 9:0.80 11:0.64 12:0.79 17:0.24 21:0.13 26:0.99 27:0.20 28:0.80 30:0.33 32:0.80 34:0.36 36:0.46 37:0.77 39:0.85 41:0.82 43:0.62 58:0.98 60:0.95 66:0.20 69:0.91 70:0.49 74:0.74 77:0.70 81:0.87 83:0.85 85:0.80 91:0.42 96:0.80 98:0.21 101:0.74 104:0.79 108:0.67 114:0.92 120:0.69 122:0.41 123:0.65 124:0.81 126:0.54 127:0.33 129:0.59 131:0.95 133:0.76 135:0.58 140:0.80 141:1.00 144:0.57 145:0.52 146:0.13 147:0.36 149:0.52 150:0.92 151:0.87 153:0.48 154:0.52 155:0.67 157:0.83 158:0.87 159:0.48 161:0.61 162:0.53 163:0.79 164:0.57 165:0.88 166:0.93 167:0.74 172:0.16 173:0.96 176:0.73 177:0.05 178:0.42 179:0.83 181:0.60 182:0.90 187:0.39 192:0.59 195:0.74 199:0.96 201:0.74 202:0.57 208:0.64 212:0.19 215:0.84 216:0.83 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 232:0.86 234:0.99 235:0.22 241:0.53 242:0.19 243:0.40 245:0.46 246:0.94 247:0.55 248:0.78 253:0.84 255:0.79 256:0.45 257:0.65 259:0.21 260:0.70 265:0.23 266:0.47 267:0.76 268:0.46 271:0.27 274:0.97 276:0.32 277:0.69 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 6:0.96 8:0.93 9:0.80 11:0.64 12:0.79 17:0.91 20:0.98 21:0.05 26:0.99 27:0.54 28:0.80 30:0.45 34:0.55 36:0.54 37:0.88 39:0.85 41:0.82 43:0.58 58:0.98 66:0.49 69:0.94 70:0.25 74:0.50 78:0.84 81:0.91 83:0.79 85:0.72 91:0.66 96:0.84 98:0.23 100:0.92 101:0.71 104:0.54 108:0.87 111:0.86 114:0.95 120:0.66 122:0.51 123:0.86 124:0.48 126:0.54 127:0.88 129:0.05 131:0.95 133:0.39 135:0.32 140:0.80 141:1.00 144:0.57 146:0.34 147:0.05 149:0.34 150:0.96 151:0.79 152:0.92 153:0.77 154:0.47 155:0.67 157:0.78 159:0.67 161:0.61 162:0.86 163:0.90 164:0.62 165:0.90 166:0.93 167:0.99 169:0.89 172:0.16 173:0.99 176:0.73 177:0.05 179:0.98 181:0.60 182:0.90 186:0.84 189:0.97 192:0.59 199:0.95 201:0.74 202:0.55 208:0.64 212:0.61 215:0.91 216:0.12 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.76 234:0.99 235:0.12 238:0.97 241:0.89 242:0.63 243:0.29 245:0.39 246:0.94 247:0.30 248:0.72 253:0.83 255:0.79 256:0.58 257:0.65 259:0.21 260:0.67 261:0.89 265:0.25 266:0.17 267:0.73 268:0.64 271:0.27 274:0.97 276:0.14 285:0.62 287:0.95 290:0.95 297:0.36 300:0.18 +3 1:0.74 6:0.81 7:0.81 8:0.59 9:0.80 11:0.64 12:0.79 17:0.24 20:0.85 21:0.40 26:0.99 27:0.21 28:0.80 30:0.45 34:0.62 36:0.92 37:0.74 39:0.85 41:0.88 43:0.97 58:0.98 60:0.95 66:0.90 69:0.17 70:0.53 74:0.92 78:0.77 81:0.73 83:0.85 85:0.93 91:0.27 96:0.82 98:0.85 100:0.79 101:0.84 104:0.84 106:0.81 108:0.14 111:0.76 114:0.82 117:0.86 120:0.72 121:0.90 122:0.43 123:0.13 126:0.54 127:0.36 129:0.05 131:0.96 135:0.18 140:0.45 141:1.00 144:0.57 146:0.82 147:0.85 149:0.93 150:0.83 151:0.87 152:0.90 153:0.48 154:0.97 155:0.67 157:0.93 158:0.92 159:0.38 161:0.61 162:0.86 164:0.69 165:0.83 166:0.93 167:0.68 169:0.76 172:0.73 173:0.26 176:0.73 177:0.38 179:0.50 181:0.60 182:0.90 186:0.78 187:0.39 192:0.59 199:0.98 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.58 215:0.67 216:0.95 220:0.91 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 230:0.84 231:0.89 232:0.91 233:0.76 234:0.99 235:0.52 241:0.30 242:0.51 243:0.91 246:0.94 247:0.67 248:0.79 253:0.87 255:0.79 256:0.58 259:0.21 260:0.72 261:0.79 265:0.85 266:0.47 267:0.69 268:0.62 271:0.27 274:0.98 276:0.61 279:0.86 283:0.82 285:0.62 287:0.95 290:0.82 297:0.36 300:0.43 +2 1:0.74 9:0.80 11:0.64 12:0.79 17:0.30 21:0.13 26:0.99 27:0.20 28:0.80 30:0.33 32:0.80 34:0.37 36:0.44 37:0.77 39:0.85 41:0.82 43:0.62 58:0.98 60:0.87 66:0.20 69:0.91 70:0.49 74:0.74 77:0.70 81:0.87 83:0.86 85:0.80 91:0.53 96:0.75 98:0.21 101:0.74 104:0.79 108:0.67 114:0.92 120:0.63 122:0.41 123:0.65 124:0.81 126:0.54 127:0.33 129:0.59 131:0.95 133:0.76 135:0.54 140:0.45 141:1.00 144:0.57 145:0.52 146:0.13 147:0.36 149:0.52 150:0.92 151:0.70 153:0.69 154:0.52 155:0.67 157:0.83 158:0.87 159:0.48 161:0.61 162:0.86 163:0.79 164:0.62 165:0.88 166:0.93 167:0.74 172:0.16 173:0.96 176:0.73 177:0.05 179:0.83 181:0.60 182:0.90 187:0.21 192:0.59 195:0.74 199:0.96 201:0.74 202:0.46 208:0.64 212:0.19 215:0.84 216:0.83 220:0.74 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 232:0.86 234:0.99 235:0.22 241:0.53 242:0.19 243:0.40 245:0.46 246:0.94 247:0.55 248:0.66 253:0.79 255:0.79 256:0.45 257:0.65 259:0.21 260:0.64 265:0.23 266:0.47 267:0.76 268:0.55 271:0.27 274:0.97 276:0.32 277:0.69 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.64 12:0.79 17:0.30 21:0.13 26:0.99 27:0.20 28:0.80 30:0.38 32:0.80 34:0.42 36:0.46 37:0.77 39:0.85 43:0.62 55:0.52 58:0.93 60:0.95 66:0.20 69:0.92 70:0.45 74:0.74 77:0.70 81:0.87 83:0.84 85:0.80 91:0.48 98:0.19 101:0.74 104:0.79 108:0.78 114:0.92 123:0.77 124:0.81 126:0.54 127:0.34 129:0.59 131:0.61 133:0.76 135:0.44 141:0.91 144:0.57 145:0.52 146:0.13 147:0.33 149:0.56 150:0.92 154:0.52 155:0.67 157:0.83 158:0.87 159:0.49 161:0.61 163:0.88 165:0.88 166:0.93 167:0.70 172:0.21 173:0.96 176:0.73 177:0.05 178:0.55 179:0.74 181:0.60 182:0.90 187:0.39 192:0.59 195:0.74 199:0.97 201:0.74 202:0.50 208:0.64 212:0.42 215:0.84 216:0.83 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.89 232:0.86 234:0.91 235:0.22 241:0.39 242:0.45 243:0.34 245:0.53 246:0.94 247:0.60 253:0.73 257:0.65 259:0.21 265:0.21 266:0.38 267:0.76 271:0.27 274:0.91 276:0.38 277:0.69 279:0.86 282:0.88 283:0.71 287:0.61 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 11:0.64 12:0.79 17:0.61 21:0.22 26:0.99 27:0.72 28:0.80 30:0.71 34:0.23 36:0.46 39:0.85 43:0.69 58:0.93 66:0.16 69:0.88 70:0.29 74:0.54 81:0.83 83:0.76 85:0.88 91:0.09 98:0.18 101:0.76 104:0.54 108:0.84 114:0.90 122:0.43 123:0.83 124:0.86 126:0.54 127:0.23 129:0.89 131:0.61 133:0.83 135:0.21 141:0.91 144:0.57 145:0.61 146:0.13 147:0.18 149:0.64 150:0.89 154:0.58 155:0.67 157:0.88 159:0.53 161:0.61 163:0.92 165:0.86 166:0.93 167:0.64 172:0.16 173:0.83 176:0.73 177:0.38 178:0.55 179:0.65 181:0.60 182:0.89 187:0.39 192:0.59 199:0.96 201:0.74 212:0.30 215:0.79 216:0.06 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.89 232:0.76 234:0.91 235:0.31 241:0.10 242:0.53 243:0.28 245:0.48 246:0.94 247:0.47 253:0.68 259:0.21 265:0.20 266:0.54 267:0.69 271:0.27 274:0.91 276:0.37 287:0.61 290:0.90 297:0.36 300:0.25 +1 1:0.74 11:0.64 12:0.79 17:0.59 21:0.22 26:0.99 27:0.72 28:0.80 30:0.62 34:0.23 36:0.51 39:0.85 43:0.69 55:0.96 58:0.93 66:0.16 69:0.88 70:0.34 74:0.38 81:0.83 83:0.76 85:0.72 91:0.08 98:0.17 101:0.70 104:0.54 108:0.77 114:0.90 123:0.84 124:0.81 126:0.54 127:0.28 129:0.05 131:0.61 133:0.74 135:0.17 141:0.91 144:0.57 145:0.61 146:0.13 147:0.18 149:0.40 150:0.89 154:0.58 155:0.67 157:0.77 159:0.60 161:0.61 163:0.98 165:0.86 166:0.93 167:0.64 172:0.10 173:0.83 176:0.73 177:0.38 178:0.55 179:0.88 181:0.60 182:0.89 187:0.39 192:0.59 199:0.96 201:0.74 212:0.61 215:0.79 216:0.06 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.89 232:0.76 234:0.91 235:0.31 241:0.10 242:0.63 243:0.37 245:0.42 246:0.94 247:0.35 253:0.67 259:0.21 265:0.08 266:0.23 267:0.84 271:0.27 274:0.91 276:0.27 277:0.69 287:0.61 290:0.90 297:0.36 300:0.25 +1 1:0.74 9:0.80 11:0.64 12:0.79 17:0.26 21:0.22 26:0.99 27:0.70 28:0.80 30:0.11 34:0.45 36:0.84 37:0.28 39:0.85 41:0.82 43:0.71 58:0.98 66:0.30 69:0.40 70:0.54 74:0.50 81:0.83 83:0.78 85:0.72 91:0.25 96:0.80 98:0.26 101:0.73 108:0.31 114:0.90 120:0.69 123:0.30 124:0.61 126:0.54 127:0.48 129:0.59 131:0.95 133:0.47 135:0.56 140:0.45 141:1.00 144:0.57 146:0.29 147:0.35 149:0.47 150:0.89 151:0.87 153:0.48 154:0.61 155:0.67 157:0.79 159:0.39 161:0.61 162:0.86 164:0.57 165:0.86 166:0.93 167:0.70 172:0.16 173:0.47 176:0.73 177:0.38 179:0.93 181:0.60 182:0.90 187:0.21 192:0.59 199:0.96 201:0.74 202:0.36 208:0.64 212:0.30 215:0.79 216:0.41 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 234:1.00 235:0.31 241:0.37 242:0.33 243:0.48 245:0.47 246:0.94 247:0.39 248:0.78 253:0.81 255:0.79 256:0.45 259:0.21 260:0.70 265:0.28 266:0.27 267:0.91 268:0.46 271:0.27 274:0.97 276:0.16 285:0.62 287:0.95 290:0.90 297:0.36 300:0.33 +2 1:0.74 6:0.98 8:0.89 9:0.80 11:0.64 12:0.79 17:0.20 20:0.91 21:0.05 26:0.99 27:0.16 28:0.80 30:0.14 34:0.25 36:0.93 37:0.84 39:0.85 41:0.92 43:0.90 55:0.92 58:0.99 66:0.59 69:0.45 70:0.94 74:0.81 78:0.89 81:0.91 83:0.81 85:0.88 91:0.35 96:0.88 98:0.54 100:0.95 101:0.75 104:0.58 108:0.35 111:0.92 114:0.95 120:0.80 122:0.51 123:0.34 124:0.67 126:0.54 127:0.76 129:0.81 131:0.96 133:0.76 135:0.90 140:0.80 141:1.00 144:0.57 145:0.74 146:0.86 147:0.89 149:0.79 150:0.96 151:0.87 152:0.97 153:0.48 154:0.89 155:0.67 157:0.87 159:0.83 161:0.61 162:0.83 163:0.81 164:0.77 165:0.90 166:0.93 167:0.74 169:0.99 172:0.65 173:0.24 176:0.73 177:0.38 178:0.42 179:0.58 181:0.60 182:0.90 186:0.89 187:0.68 189:0.96 192:0.59 199:0.98 201:0.74 202:0.66 208:0.64 212:0.22 215:0.91 216:0.53 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.12 238:0.96 241:0.55 242:0.30 243:0.67 245:0.68 246:0.94 247:0.60 248:0.87 253:0.90 255:0.79 256:0.71 259:0.21 260:0.81 261:0.99 265:0.56 266:0.80 267:0.69 268:0.72 271:0.27 274:0.99 276:0.56 285:0.62 287:0.95 290:0.95 297:0.36 300:0.84 +0 12:0.79 17:0.34 21:0.78 26:0.98 27:0.45 28:0.80 30:0.21 32:0.72 34:0.82 36:0.23 37:0.50 39:0.85 43:0.30 55:0.52 58:0.93 66:0.36 69:0.71 70:0.50 74:0.59 91:0.10 98:0.41 108:0.54 122:0.57 123:0.52 124:0.59 127:0.24 129:0.59 131:0.61 133:0.47 135:0.47 141:0.91 145:0.45 146:0.57 147:0.63 149:0.32 154:0.50 157:0.61 159:0.43 161:0.61 163:0.98 166:0.61 167:0.73 172:0.27 173:0.65 177:0.38 178:0.55 179:0.71 181:0.60 182:0.61 187:0.68 195:0.79 199:0.95 202:0.36 212:0.37 216:0.77 222:0.35 223:0.97 224:0.93 227:0.61 229:0.61 231:0.80 234:0.91 235:0.68 241:0.50 242:0.58 243:0.51 245:0.56 246:0.61 247:0.39 253:0.47 254:0.92 259:0.21 265:0.43 266:0.47 267:0.28 271:0.27 274:0.91 276:0.35 287:0.61 300:0.33 +1 1:0.74 6:0.86 8:0.74 9:0.80 11:0.64 12:0.79 17:0.22 20:0.88 21:0.47 26:0.99 27:0.06 28:0.80 30:0.21 32:0.80 34:0.45 36:0.71 37:0.82 39:0.85 41:0.90 43:0.75 58:0.99 60:0.97 66:0.54 69:0.83 70:0.37 74:0.66 77:0.70 78:0.83 81:0.77 83:0.82 85:0.72 91:0.42 96:0.78 98:0.24 100:0.83 101:0.72 108:0.67 111:0.82 114:0.80 120:0.67 123:0.65 124:0.45 126:0.54 127:0.17 129:0.05 131:0.96 133:0.39 135:0.68 140:0.45 141:1.00 144:0.57 145:0.44 146:0.31 147:0.05 149:0.45 150:0.84 151:0.79 152:0.89 153:0.69 154:0.65 155:0.67 157:0.79 158:0.92 159:0.24 161:0.61 162:0.86 164:0.75 165:0.80 166:0.93 167:0.73 169:0.80 172:0.21 173:0.87 176:0.73 177:0.05 179:0.76 181:0.60 182:0.90 186:0.83 187:0.39 189:0.89 191:0.83 192:0.59 195:0.68 199:0.98 201:0.74 202:0.61 208:0.64 212:0.42 215:0.63 216:0.92 220:0.93 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 234:0.99 235:0.68 238:0.87 241:0.50 242:0.45 243:0.26 245:0.40 246:0.94 247:0.35 248:0.73 253:0.79 255:0.79 256:0.64 257:0.65 259:0.21 260:0.68 261:0.85 265:0.26 266:0.23 267:0.44 268:0.69 271:0.27 274:0.99 276:0.18 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.80 297:0.36 299:0.88 300:0.25 +4 1:0.74 6:0.98 8:0.97 9:0.80 11:0.64 12:0.79 17:0.13 20:0.99 21:0.05 26:0.99 27:0.16 28:0.80 30:0.21 34:0.20 36:0.76 37:0.84 39:0.85 41:0.99 43:0.43 58:1.00 60:0.99 66:0.16 69:0.15 70:0.64 74:0.71 78:0.97 81:0.91 83:0.90 85:0.85 91:0.94 96:0.99 98:0.21 100:1.00 101:0.73 104:0.58 108:0.12 111:1.00 114:0.95 120:0.97 122:0.51 123:0.12 124:0.85 126:0.54 127:0.93 129:0.81 131:0.96 133:0.83 135:0.28 138:0.99 140:0.45 141:1.00 144:0.57 145:0.74 146:0.41 147:0.47 149:0.58 150:0.96 151:0.99 152:0.97 153:0.97 154:0.27 155:0.67 157:0.85 158:0.92 159:0.83 161:0.61 162:0.80 163:0.79 164:0.96 165:0.90 166:0.93 167:1.00 169:0.99 172:0.21 173:0.10 176:0.73 177:0.38 179:0.81 181:0.60 182:0.90 186:0.97 189:0.99 191:0.93 192:0.59 199:1.00 201:0.74 202:0.92 208:0.64 212:0.22 215:0.91 216:0.53 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.12 238:0.99 241:0.93 242:0.22 243:0.37 245:0.54 246:0.94 247:0.67 248:0.99 253:0.99 255:0.79 256:0.95 259:0.21 260:0.97 261:0.99 265:0.23 266:0.75 267:0.93 268:0.95 271:0.27 274:1.00 276:0.38 279:0.86 283:0.82 285:0.62 287:0.95 290:0.95 297:0.36 300:0.43 +0 1:0.74 6:0.94 8:0.88 9:0.80 11:0.64 12:0.79 17:0.20 20:0.88 21:0.05 26:0.99 27:0.16 28:0.80 30:0.21 34:0.76 36:0.59 37:0.80 39:0.85 41:0.82 43:0.75 58:0.99 66:0.54 69:0.73 70:0.37 74:0.49 78:0.77 81:0.94 83:0.79 85:0.72 91:0.57 96:0.79 98:0.65 100:0.83 101:0.74 104:0.92 106:0.81 108:0.57 111:0.78 114:0.95 120:0.77 122:0.41 123:0.54 124:0.45 126:0.54 127:0.40 129:0.05 131:0.96 133:0.39 135:0.54 140:0.45 141:1.00 144:0.57 146:0.54 147:0.05 149:0.49 150:0.97 151:0.81 152:0.94 153:0.48 154:0.66 155:0.67 157:0.79 159:0.30 161:0.61 162:0.86 163:0.89 164:0.68 165:0.90 166:0.93 167:0.68 169:0.79 172:0.21 173:0.87 176:0.73 177:0.05 179:0.94 181:0.60 182:0.90 186:0.78 187:0.39 189:0.97 190:0.77 192:0.59 199:0.97 201:0.74 202:0.53 206:0.81 208:0.64 212:0.42 215:0.91 216:0.45 220:0.62 222:0.35 223:0.99 224:1.00 227:0.96 229:0.95 231:0.89 234:1.00 235:0.22 238:0.95 241:0.27 242:0.45 243:0.26 245:0.40 246:0.94 247:0.35 248:0.73 253:0.80 255:0.79 256:0.58 257:0.65 259:0.21 260:0.77 261:0.81 265:0.66 266:0.23 267:0.19 268:0.62 271:0.27 274:0.98 276:0.24 283:0.82 285:0.62 287:0.95 290:0.95 297:0.36 300:0.25 +0 1:0.74 11:0.64 12:0.79 17:0.64 21:0.78 26:0.99 27:0.45 28:0.80 30:0.95 34:0.74 36:0.18 37:0.50 39:0.85 43:0.82 58:0.93 66:0.54 69:0.71 74:0.43 81:0.41 83:0.71 85:0.72 91:0.09 98:0.09 101:0.72 108:0.54 114:0.63 123:0.52 126:0.54 127:0.07 129:0.05 131:0.61 135:0.82 141:0.91 144:0.57 145:0.50 146:0.13 147:0.18 149:0.46 150:0.61 154:0.77 155:0.67 157:0.78 159:0.08 161:0.61 165:0.63 166:0.92 167:0.67 172:0.10 173:0.70 176:0.73 177:0.38 178:0.55 179:0.08 181:0.60 182:0.88 187:0.68 192:0.59 199:0.94 201:0.74 215:0.43 216:0.77 222:0.35 223:0.99 224:0.93 227:0.61 229:0.61 231:0.87 234:0.91 235:1.00 241:0.21 243:0.63 246:0.93 253:0.50 259:0.21 265:0.09 267:0.28 271:0.27 274:0.91 287:0.61 290:0.63 297:0.36 300:0.08 +2 1:0.74 8:0.94 9:0.80 11:0.64 12:0.79 17:0.12 21:0.13 26:0.99 27:0.16 28:0.80 30:0.33 34:0.23 36:0.75 37:0.84 39:0.85 41:0.90 43:0.78 58:0.99 66:0.32 69:0.79 70:0.49 74:0.71 81:0.87 83:0.80 85:0.80 91:0.51 96:0.86 98:0.22 101:0.75 104:0.58 108:0.42 114:0.92 120:0.78 122:0.57 123:0.40 124:0.72 126:0.54 127:0.43 129:0.59 131:0.96 133:0.64 135:0.18 140:0.80 141:1.00 144:0.57 145:0.74 146:0.13 147:0.36 149:0.63 150:0.92 151:0.87 153:0.48 154:0.70 155:0.67 157:0.83 159:0.48 161:0.61 162:0.60 163:0.88 164:0.73 165:0.88 166:0.93 167:0.79 172:0.21 173:0.80 176:0.73 177:0.05 178:0.42 179:0.87 181:0.60 182:0.90 187:0.21 192:0.59 199:0.96 201:0.74 202:0.68 208:0.64 212:0.42 215:0.84 216:0.53 220:0.62 222:0.35 223:0.99 224:0.99 227:0.96 229:0.95 231:0.89 232:0.80 234:0.99 235:0.22 241:0.67 242:0.45 243:0.49 245:0.48 246:0.94 247:0.39 248:0.85 253:0.87 255:0.79 256:0.64 257:0.65 259:0.21 260:0.78 265:0.24 266:0.38 267:0.79 268:0.68 271:0.27 274:0.98 276:0.26 277:0.69 285:0.62 287:0.95 290:0.92 297:0.36 300:0.33 +0 6:0.95 8:0.88 9:0.80 12:0.06 17:0.11 20:0.98 21:0.78 25:0.88 27:0.25 28:0.68 30:0.76 34:0.22 36:0.57 37:0.74 39:0.33 41:0.88 43:0.24 55:0.52 66:0.72 69:0.85 70:0.22 74:0.39 78:0.95 83:0.77 91:0.95 96:0.88 98:0.34 100:0.98 104:0.80 108:0.71 111:0.97 120:0.98 123:0.69 127:0.12 129:0.05 135:0.20 140:1.00 145:0.38 146:0.31 147:0.38 149:0.27 151:0.90 152:0.92 153:0.91 154:0.16 155:0.67 159:0.13 161:0.60 162:0.46 164:0.98 167:0.88 169:0.97 172:0.27 173:0.98 177:0.38 178:0.51 179:0.42 181:0.93 186:0.95 189:0.96 191:0.92 192:0.59 202:1.00 208:0.64 212:0.78 216:0.50 220:0.74 222:0.35 235:0.12 238:0.97 241:0.86 242:0.75 243:0.75 244:0.61 247:0.30 248:0.86 253:0.82 255:0.79 256:0.97 259:0.21 260:0.98 261:0.97 265:0.36 266:0.17 267:0.65 268:0.97 271:0.90 276:0.24 285:0.62 300:0.18 +1 1:0.74 6:0.90 8:0.81 9:0.80 11:0.57 12:0.06 17:0.57 20:0.88 21:0.40 27:0.34 28:0.68 30:0.17 32:0.80 34:0.44 36:0.94 37:0.57 39:0.33 41:0.88 43:0.86 60:0.87 66:0.72 69:0.58 70:0.78 74:0.91 77:0.96 78:0.83 81:0.71 83:0.85 85:0.91 91:0.51 96:0.74 98:0.82 100:0.88 101:0.81 104:0.91 106:0.81 108:0.60 111:0.83 114:0.82 117:0.86 120:0.62 122:0.41 123:0.57 124:0.43 126:0.54 127:0.44 129:0.59 133:0.47 135:0.94 140:0.45 145:0.92 146:0.27 147:0.33 149:0.86 150:0.81 151:0.64 152:0.83 153:0.77 154:0.83 155:0.67 158:0.87 159:0.51 161:0.60 162:0.86 164:0.70 165:0.81 167:0.59 169:0.86 172:0.74 173:0.54 176:0.73 177:0.38 179:0.46 181:0.93 186:0.83 187:0.21 189:0.91 192:0.59 195:0.72 201:0.74 202:0.55 206:0.81 208:0.64 212:0.57 215:0.67 216:0.76 220:0.82 222:0.35 235:0.60 238:0.92 241:0.39 242:0.19 243:0.23 245:0.77 247:0.60 248:0.63 253:0.80 255:0.79 256:0.58 259:0.21 260:0.62 261:0.85 265:0.82 266:0.54 267:0.98 268:0.64 271:0.90 276:0.67 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.97 300:0.55 +2 1:0.74 7:0.81 11:0.57 12:0.06 17:0.89 21:0.22 27:0.28 28:0.68 30:0.27 34:0.85 36:0.93 39:0.33 43:0.98 55:0.52 60:0.87 66:0.51 69:0.12 70:0.89 74:0.94 76:0.85 81:0.80 83:0.81 85:0.94 91:0.48 98:0.64 101:0.81 104:0.93 106:0.81 108:0.10 114:0.90 117:0.86 121:0.90 123:0.10 124:0.65 126:0.54 127:0.86 129:0.81 133:0.67 135:0.24 146:0.85 147:0.88 149:0.92 150:0.87 154:0.98 155:0.67 158:0.92 159:0.74 161:0.60 165:0.86 167:0.48 172:0.79 173:0.12 176:0.73 177:0.38 178:0.55 179:0.36 181:0.93 187:0.21 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.60 215:0.79 216:0.24 222:0.35 230:0.87 233:0.76 235:0.42 242:0.29 243:0.61 245:0.89 247:0.67 253:0.78 259:0.21 265:0.65 266:0.87 267:0.52 271:0.90 276:0.79 279:0.86 283:0.82 290:0.90 297:0.36 300:0.70 +0 6:0.97 8:0.95 9:0.80 12:0.06 17:0.28 20:0.98 21:0.78 27:0.16 28:0.68 34:0.21 36:0.40 37:0.54 39:0.33 41:0.88 43:0.35 66:0.36 69:0.54 74:0.56 78:0.94 83:0.78 91:0.90 96:0.94 98:0.07 100:0.97 108:0.42 111:0.96 120:0.88 123:0.40 127:0.05 129:0.05 135:0.21 140:0.97 145:0.49 146:0.05 147:0.05 149:0.14 151:0.90 152:0.90 153:0.86 154:0.26 155:0.67 158:0.85 159:0.05 161:0.60 162:0.52 164:0.85 167:0.89 169:0.96 172:0.05 173:0.45 175:0.75 177:0.05 178:0.50 181:0.93 186:0.94 189:0.98 191:0.97 192:0.59 202:0.89 208:0.64 216:0.41 220:0.62 222:0.35 235:0.05 238:0.97 241:0.88 243:0.51 244:0.61 248:0.87 253:0.92 255:0.79 256:0.86 257:0.65 259:0.21 260:0.89 261:0.95 265:0.07 267:0.94 268:0.86 271:0.90 277:0.69 285:0.62 +1 11:0.57 12:0.06 17:0.13 21:0.22 25:0.88 27:0.36 28:0.68 30:0.38 34:0.98 36:0.80 37:0.78 39:0.33 43:0.82 55:0.71 66:0.93 69:0.67 70:0.61 74:0.71 81:0.55 85:0.72 91:0.38 98:0.92 101:0.72 104:0.72 108:0.51 123:0.49 126:0.32 127:0.53 129:0.05 135:0.84 146:0.91 147:0.93 149:0.70 150:0.42 154:0.78 159:0.52 161:0.60 167:0.56 172:0.81 173:0.68 177:0.38 178:0.55 179:0.44 181:0.93 187:0.87 212:0.73 215:0.79 216:0.66 222:0.35 235:0.22 241:0.27 242:0.73 243:0.93 247:0.60 253:0.53 259:0.21 265:0.92 266:0.38 267:0.73 271:0.90 276:0.71 297:0.36 300:0.55 +0 6:0.82 8:0.62 9:0.80 12:0.06 17:0.13 20:0.91 21:0.78 27:0.43 28:0.68 30:0.13 34:0.42 36:0.67 37:0.45 39:0.33 41:0.82 43:0.38 55:0.71 66:0.10 69:0.45 70:0.94 74:0.42 78:0.76 83:0.76 91:0.60 96:0.80 98:0.27 100:0.78 104:0.58 108:0.96 111:0.75 120:0.69 123:0.96 124:0.94 127:0.89 129:0.05 133:0.94 135:0.84 140:0.94 146:0.35 147:0.42 149:0.55 151:0.87 152:0.92 153:0.48 154:0.69 155:0.67 159:0.90 161:0.60 162:0.48 164:0.57 167:0.57 169:0.77 172:0.32 173:0.16 177:0.38 178:0.52 179:0.52 181:0.93 186:0.77 187:0.21 189:0.83 192:0.59 202:0.68 208:0.64 212:0.12 216:0.37 222:0.35 235:0.05 238:0.85 241:0.32 242:0.81 243:0.25 245:0.67 247:0.39 248:0.78 253:0.74 254:0.94 255:0.79 256:0.45 259:0.21 260:0.70 261:0.80 265:0.29 266:0.96 267:0.91 268:0.46 271:0.90 276:0.69 277:0.69 285:0.62 300:0.84 +2 1:0.74 6:0.82 8:0.63 9:0.80 12:0.06 17:0.13 20:0.96 21:0.22 25:0.88 27:0.36 28:0.68 30:0.41 34:0.56 36:0.47 37:0.78 39:0.33 43:0.41 55:0.52 66:0.86 69:0.35 70:0.57 74:0.55 78:0.80 81:0.79 91:0.68 96:0.75 98:0.92 100:0.81 104:0.72 108:0.27 111:0.79 114:0.90 117:0.86 120:0.63 123:0.26 126:0.54 127:0.54 129:0.05 135:0.61 140:0.45 146:0.72 147:0.76 149:0.58 150:0.81 151:0.64 152:0.92 153:0.77 154:0.31 155:0.67 158:0.85 159:0.29 161:0.60 162:0.86 164:0.70 167:0.71 169:0.78 172:0.59 173:0.53 176:0.73 177:0.38 179:0.73 181:0.93 186:0.80 187:0.87 189:0.84 192:0.59 201:0.74 202:0.55 208:0.64 212:0.78 215:0.79 216:0.66 220:0.82 222:0.35 235:0.42 238:0.84 241:0.65 242:0.77 243:0.86 244:0.61 247:0.39 248:0.67 253:0.76 255:0.79 256:0.58 259:0.21 260:0.64 261:0.83 265:0.92 266:0.38 267:0.36 268:0.64 271:0.90 276:0.48 285:0.62 290:0.90 297:0.36 300:0.43 +2 11:0.57 12:0.06 17:0.30 21:0.22 25:0.88 27:0.36 28:0.68 30:0.42 34:0.96 36:0.75 37:0.78 39:0.33 43:0.83 55:0.71 66:0.93 69:0.43 70:0.64 74:0.71 81:0.55 85:0.72 91:0.56 98:0.96 101:0.72 104:0.72 108:0.33 120:0.61 123:0.32 126:0.32 127:0.71 129:0.05 135:0.86 140:0.45 146:0.92 147:0.94 149:0.73 150:0.42 151:0.55 153:0.69 154:0.79 159:0.55 161:0.60 162:0.86 164:0.62 167:0.68 172:0.82 173:0.40 177:0.38 179:0.46 181:0.93 187:0.68 190:0.77 202:0.46 212:0.70 215:0.79 216:0.66 220:0.82 222:0.35 235:0.22 241:0.59 242:0.73 243:0.94 247:0.60 248:0.54 253:0.53 256:0.45 259:0.21 260:0.61 265:0.96 266:0.38 267:0.85 268:0.55 271:0.90 276:0.72 285:0.50 297:0.36 300:0.55 +1 1:0.74 6:0.84 7:0.81 8:0.63 9:0.80 11:0.57 12:0.06 17:0.51 20:0.82 21:0.40 27:0.59 28:0.68 30:0.09 32:0.80 34:0.67 36:0.21 37:0.56 39:0.33 41:0.92 43:0.95 60:0.98 66:0.18 69:0.32 70:1.00 74:0.88 77:0.89 78:0.78 81:0.78 83:0.87 85:0.90 91:0.37 96:0.74 98:0.18 100:0.82 101:0.72 108:0.25 111:0.78 114:0.82 120:0.62 121:0.90 123:0.24 124:0.95 126:0.54 127:0.75 129:0.89 133:0.94 135:0.90 140:0.45 145:0.49 146:0.53 147:0.59 149:0.84 150:0.86 151:0.64 152:0.78 153:0.77 154:0.95 155:0.67 158:0.92 159:0.93 161:0.60 162:0.86 164:0.69 165:0.86 167:0.54 169:0.80 172:0.45 173:0.09 176:0.73 177:0.38 179:0.51 181:0.93 186:0.79 187:0.39 189:0.86 192:0.59 195:0.98 201:0.74 202:0.54 204:0.89 208:0.64 212:0.22 215:0.67 216:0.37 220:0.82 222:0.35 230:0.87 233:0.76 235:0.80 238:0.86 241:0.18 242:0.42 243:0.39 245:0.64 247:0.60 248:0.62 253:0.79 255:0.79 256:0.58 259:0.21 260:0.62 261:0.78 265:0.20 266:0.91 267:0.23 268:0.63 271:0.90 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.97 300:0.99 +0 11:0.57 12:0.06 17:0.43 21:0.59 27:0.45 28:0.68 30:0.58 32:0.75 34:0.86 36:0.18 37:0.50 39:0.33 43:0.75 55:0.59 66:0.80 69:0.70 70:0.44 74:0.40 81:0.33 85:0.72 91:0.15 98:0.49 101:0.72 108:0.53 123:0.51 126:0.32 127:0.15 129:0.05 135:0.76 145:0.52 146:0.60 147:0.66 149:0.56 150:0.30 154:0.67 159:0.22 161:0.60 163:0.81 167:0.54 172:0.45 173:0.68 177:0.38 178:0.55 179:0.42 181:0.93 187:0.68 195:0.73 212:0.37 215:0.55 216:0.77 222:0.35 235:0.68 241:0.15 242:0.76 243:0.82 247:0.35 253:0.36 259:0.21 265:0.51 266:0.38 267:0.36 271:0.90 276:0.36 297:0.36 300:0.33 +1 1:0.74 6:0.83 8:0.65 9:0.80 12:0.06 17:0.06 20:0.90 21:0.13 25:0.88 27:0.31 28:0.68 30:0.41 34:0.77 36:0.65 37:0.81 39:0.33 43:0.27 55:0.59 66:0.86 69:0.53 70:0.60 74:0.62 78:0.83 81:0.83 91:0.71 96:0.82 98:0.90 100:0.84 104:0.54 108:0.41 111:0.82 114:0.92 117:0.86 120:0.66 123:0.39 126:0.54 127:0.46 129:0.05 135:0.42 140:0.45 146:0.75 147:0.79 149:0.44 150:0.85 151:0.74 152:0.85 153:0.48 154:0.76 155:0.67 158:0.84 159:0.31 161:0.60 162:0.83 164:0.72 167:0.71 169:0.82 172:0.59 173:0.66 176:0.73 177:0.38 179:0.71 181:0.93 186:0.83 187:0.68 189:0.85 190:0.77 191:0.70 192:0.59 201:0.74 202:0.63 208:0.64 212:0.30 215:0.84 216:0.68 220:0.87 222:0.35 232:0.93 235:0.31 238:0.86 241:0.65 242:0.77 243:0.86 247:0.39 248:0.71 253:0.83 254:0.86 255:0.79 256:0.64 259:0.21 260:0.67 261:0.84 265:0.90 266:0.38 267:0.65 268:0.69 271:0.90 276:0.48 285:0.62 290:0.92 297:0.36 300:0.43 +0 1:0.74 11:0.57 12:0.06 17:0.61 21:0.78 27:0.45 28:0.68 30:0.33 34:0.76 36:0.18 37:0.50 39:0.33 43:0.81 55:0.71 66:0.45 69:0.72 70:0.69 74:0.64 81:0.41 83:0.71 85:0.72 91:0.14 98:0.35 101:0.71 108:0.55 114:0.63 122:0.51 123:0.53 124:0.66 126:0.54 127:0.41 129:0.05 133:0.62 135:0.73 145:0.49 146:0.52 147:0.58 149:0.56 150:0.61 154:0.76 155:0.67 159:0.59 161:0.60 163:0.79 165:0.63 167:0.54 172:0.27 173:0.66 176:0.73 177:0.38 178:0.55 179:0.87 181:0.93 187:0.68 192:0.59 201:0.74 212:0.19 215:0.43 216:0.77 222:0.35 235:1.00 241:0.18 242:0.45 243:0.58 245:0.47 247:0.47 253:0.55 259:0.21 265:0.37 266:0.47 267:0.87 271:0.90 276:0.31 290:0.63 297:0.36 300:0.43 +3 6:0.95 8:0.71 9:0.80 12:0.06 17:0.45 20:0.85 21:0.78 25:0.88 27:0.25 28:0.68 30:0.76 34:0.28 36:0.55 37:0.74 39:0.33 41:0.82 43:0.24 55:0.52 66:0.72 69:0.85 70:0.22 74:0.39 78:0.95 83:0.76 91:0.92 96:0.80 98:0.31 100:0.98 104:0.80 108:0.71 111:0.96 120:0.91 123:0.69 127:0.12 129:0.05 135:0.30 140:0.98 145:0.38 146:0.31 147:0.38 149:0.27 151:0.87 152:0.82 153:0.72 154:0.16 155:0.67 159:0.13 161:0.60 162:0.66 164:0.91 167:0.82 169:0.97 172:0.27 173:0.96 177:0.38 178:0.52 179:0.38 181:0.93 186:0.94 187:0.21 191:0.93 192:0.59 202:0.87 208:0.64 212:0.78 216:0.50 220:0.74 222:0.35 235:0.12 241:0.77 242:0.75 243:0.75 244:0.61 247:0.30 248:0.78 253:0.74 255:0.79 256:0.89 259:0.21 260:0.91 261:0.95 265:0.34 266:0.17 267:0.69 268:0.89 271:0.90 276:0.24 283:0.71 285:0.62 300:0.18 +1 6:0.86 7:0.78 8:0.82 9:0.80 12:0.06 17:0.45 20:0.96 21:0.78 25:0.88 27:0.08 28:0.68 30:0.62 34:0.40 36:0.51 37:0.68 39:0.33 43:0.43 55:0.52 66:0.87 69:0.17 70:0.53 74:0.54 76:0.94 78:0.84 91:0.57 96:0.77 98:0.92 100:0.86 104:0.72 108:0.14 111:0.83 120:0.76 123:0.13 127:0.54 129:0.05 135:0.51 140:0.90 146:0.72 147:0.76 149:0.64 151:0.77 152:0.95 153:0.48 154:0.33 155:0.67 159:0.29 161:0.60 162:0.53 164:0.67 167:0.68 169:0.83 172:0.63 173:0.38 177:0.38 178:0.48 179:0.69 181:0.93 186:0.84 187:0.39 189:0.88 190:0.77 191:0.70 192:0.59 202:0.66 208:0.64 212:0.82 216:0.48 222:0.35 230:0.81 233:0.69 235:0.22 238:0.87 241:0.60 242:0.78 243:0.88 244:0.61 247:0.39 248:0.71 253:0.67 255:0.79 256:0.58 259:0.21 260:0.76 261:0.88 265:0.92 266:0.38 267:0.52 268:0.59 271:0.90 276:0.52 285:0.62 300:0.43 +2 6:0.95 8:0.84 12:0.06 17:0.61 20:0.79 21:0.30 25:0.88 27:0.25 28:0.68 30:0.62 34:0.64 36:0.49 37:0.74 39:0.33 43:0.25 55:0.59 66:0.75 69:0.82 70:0.34 74:0.48 78:0.87 81:0.60 91:0.82 98:0.56 100:0.92 104:0.80 108:0.66 111:0.88 120:0.65 123:0.64 126:0.32 127:0.17 129:0.05 135:0.49 140:0.45 146:0.50 147:0.56 149:0.24 150:0.44 151:0.54 152:0.93 153:0.88 154:0.53 159:0.18 161:0.60 162:0.85 164:0.80 167:0.69 169:0.97 172:0.32 173:0.92 177:0.38 179:0.66 181:0.93 186:0.87 187:0.21 189:0.92 190:0.77 191:0.83 202:0.68 212:0.61 215:0.72 216:0.50 220:0.87 222:0.35 235:0.42 238:0.92 241:0.63 242:0.63 243:0.77 247:0.35 248:0.59 253:0.40 254:0.94 256:0.71 259:0.21 260:0.66 261:0.97 265:0.57 266:0.23 267:0.73 268:0.75 271:0.90 276:0.24 283:0.66 285:0.50 297:0.36 300:0.25 +1 1:0.74 6:0.91 7:0.81 8:0.86 9:0.80 11:0.57 12:0.86 17:0.75 18:0.94 20:0.96 21:0.54 27:0.52 28:0.56 29:0.62 30:0.58 31:0.97 32:0.80 34:0.62 36:0.93 37:0.56 39:0.99 41:0.96 43:0.92 44:0.93 48:0.91 60:0.98 64:0.77 66:0.96 69:0.42 70:0.71 71:0.89 74:0.90 77:0.70 78:0.89 79:0.97 81:0.50 83:0.91 85:0.94 86:0.98 91:0.76 96:0.91 98:0.99 100:0.89 101:0.87 108:0.32 111:0.87 114:0.59 120:0.87 121:0.90 122:0.57 123:0.31 126:0.54 127:0.86 129:0.05 135:0.24 137:0.97 139:0.99 140:0.87 145:0.79 146:0.91 147:0.92 149:0.96 150:0.72 151:0.98 152:0.96 153:0.92 154:0.91 155:0.67 158:0.92 159:0.51 161:0.69 162:0.52 164:0.89 165:0.93 167:0.93 169:0.84 172:0.90 173:0.44 176:0.73 177:0.70 178:0.48 179:0.33 181:0.48 186:0.88 189:0.93 191:0.94 192:0.87 195:0.69 196:0.99 201:0.93 202:0.90 204:0.89 208:0.64 212:0.60 215:0.39 216:0.50 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 238:0.86 241:0.81 242:0.22 243:0.96 247:0.79 248:0.90 253:0.94 255:0.95 256:0.85 259:0.21 260:0.86 261:0.91 265:0.99 266:0.38 267:0.88 268:0.87 271:0.66 276:0.82 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 290:0.59 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.92 7:0.81 8:0.92 9:0.80 11:0.57 12:0.86 17:0.61 18:0.77 20:0.98 21:0.64 27:0.44 28:0.56 29:0.62 30:0.71 31:1.00 32:0.69 34:0.67 36:0.63 37:0.78 39:0.99 41:0.99 43:0.95 44:0.90 48:0.99 55:0.45 60:0.87 64:0.77 66:0.72 69:0.30 70:0.33 71:0.89 74:0.67 78:0.89 79:1.00 81:0.44 83:0.91 85:0.72 86:0.83 91:0.94 96:0.98 98:0.64 100:0.88 101:0.87 108:0.76 111:0.88 114:0.57 120:0.96 121:0.90 123:0.75 124:0.45 126:0.54 127:0.63 129:0.05 133:0.59 135:0.19 137:1.00 139:0.91 140:0.80 145:0.80 146:0.50 147:0.57 149:0.74 150:0.70 151:1.00 152:0.90 153:0.98 154:0.95 155:0.67 158:0.92 159:0.57 161:0.69 162:0.64 164:0.96 165:0.93 167:0.96 169:0.86 172:0.91 173:0.28 176:0.73 177:0.70 178:0.42 179:0.25 181:0.48 186:0.89 189:0.93 191:0.97 192:0.87 195:0.77 196:1.00 201:0.93 202:0.96 204:0.89 208:0.64 212:0.93 215:0.38 216:0.56 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 238:0.85 241:0.91 242:0.80 243:0.23 245:0.88 247:0.55 248:0.98 253:0.97 255:0.95 256:0.96 259:0.21 260:0.96 261:0.91 265:0.65 266:0.47 267:0.89 268:0.96 271:0.66 276:0.85 277:0.87 279:0.86 281:0.91 283:0.82 284:1.00 285:0.62 290:0.57 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.81 7:0.81 8:0.60 9:0.80 11:0.57 12:0.86 17:0.69 18:0.76 20:0.80 21:0.13 27:0.44 28:0.56 29:0.62 30:0.62 31:0.96 32:0.80 34:0.40 36:0.76 37:0.25 39:0.99 41:0.90 43:0.80 44:0.93 48:0.91 60:0.97 64:0.77 66:0.83 69:0.19 70:0.43 71:0.89 74:0.76 77:0.70 78:0.82 79:0.96 81:0.73 83:0.91 85:0.80 86:0.84 91:0.76 96:0.87 98:0.98 100:0.79 101:0.87 108:0.15 111:0.81 114:0.79 120:0.79 121:0.90 122:0.57 123:0.15 126:0.54 127:0.85 129:0.05 135:0.60 137:0.96 139:0.93 140:0.45 145:0.67 146:0.71 147:0.76 149:0.73 150:0.83 151:0.95 152:0.77 153:0.85 154:0.75 155:0.67 158:0.91 159:0.28 161:0.69 162:0.79 163:0.81 164:0.77 165:0.93 167:0.94 169:0.79 172:0.51 173:0.44 176:0.73 177:0.70 179:0.85 181:0.48 186:0.82 189:0.83 192:0.87 195:0.59 196:0.98 201:0.93 202:0.67 204:0.89 208:0.64 212:0.28 215:0.61 216:0.22 219:0.95 222:0.25 230:0.86 233:0.73 235:0.42 238:0.82 241:0.84 242:0.29 243:0.84 247:0.67 248:0.85 253:0.88 255:0.95 256:0.68 259:0.21 260:0.79 261:0.79 265:0.98 266:0.54 267:0.36 268:0.72 271:0.66 276:0.41 279:0.86 281:0.89 282:0.88 283:0.78 284:0.96 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.91 7:0.66 8:0.90 9:0.80 11:0.57 12:0.86 17:0.86 18:0.69 20:0.94 21:0.13 27:0.10 28:0.56 29:0.62 30:0.95 31:0.99 32:0.80 34:0.53 36:0.50 37:0.88 39:0.99 41:0.97 43:0.83 44:0.93 48:0.91 55:0.45 60:0.87 64:0.77 66:0.90 69:0.40 70:0.13 71:0.89 74:0.65 77:0.70 78:0.87 79:0.99 81:0.67 83:0.91 85:0.72 86:0.77 91:0.97 96:0.96 98:0.79 100:0.86 101:0.87 108:0.73 111:0.86 114:0.79 120:0.94 123:0.71 124:0.37 126:0.54 127:0.77 129:0.05 133:0.43 135:0.20 137:0.99 139:0.88 140:0.87 145:0.59 146:0.41 147:0.47 149:0.80 150:0.80 151:0.99 152:0.87 153:0.99 154:0.79 155:0.67 158:0.91 159:0.61 161:0.69 162:0.51 164:0.91 165:0.93 167:0.97 169:0.84 172:0.98 173:0.34 176:0.73 177:0.70 179:0.15 181:0.48 186:0.87 189:0.92 191:0.96 192:0.87 195:0.80 196:0.99 201:0.93 202:0.92 208:0.64 212:0.93 215:0.61 216:0.47 219:0.95 222:0.25 230:0.69 233:0.76 235:0.31 238:0.84 241:0.95 242:0.82 243:0.09 245:0.94 247:0.60 248:0.95 253:0.95 255:0.95 256:0.88 259:0.21 260:0.94 261:0.89 265:0.79 266:0.54 267:0.52 268:0.90 271:0.66 276:0.94 277:0.69 279:0.86 281:0.89 282:0.88 283:0.82 284:0.99 285:0.62 290:0.79 292:0.91 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.57 12:0.86 17:0.47 18:0.76 21:0.59 27:0.45 28:0.56 29:0.62 30:0.62 32:0.80 34:0.84 36:0.27 37:0.50 39:0.99 43:0.81 44:0.93 60:0.87 64:0.77 66:0.13 69:0.70 70:0.62 71:0.89 74:0.71 77:0.70 81:0.45 83:0.91 85:0.85 86:0.84 91:0.07 98:0.35 101:0.87 108:0.82 114:0.58 123:0.51 124:0.48 126:0.54 127:0.35 129:0.59 133:0.46 135:0.83 139:0.90 145:0.47 146:0.47 147:0.54 149:0.75 150:0.70 154:0.77 155:0.67 158:0.91 159:0.49 161:0.69 165:0.93 167:0.64 172:0.54 173:0.67 176:0.73 177:0.70 178:0.55 179:0.61 181:0.48 187:0.68 192:0.87 195:0.75 201:0.93 208:0.64 212:0.77 215:0.39 216:0.77 219:0.95 222:0.25 235:0.80 241:0.27 242:0.51 243:0.33 245:0.68 247:0.67 253:0.44 259:0.21 265:0.26 266:0.38 267:0.52 271:0.66 276:0.35 279:0.86 282:0.88 283:0.78 290:0.58 292:0.91 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.96 7:0.66 8:0.93 9:0.80 12:0.86 17:0.47 18:0.70 20:0.98 21:0.64 25:0.88 27:0.15 28:0.56 29:0.62 30:0.95 31:0.98 32:0.80 34:0.61 36:0.60 37:0.87 39:0.99 41:0.97 43:0.14 44:0.93 48:0.98 55:0.45 64:0.77 66:0.98 69:0.48 70:0.13 71:0.89 74:0.47 77:0.70 78:0.94 79:0.98 81:0.45 83:0.91 86:0.72 91:0.99 96:0.91 98:0.94 100:0.98 104:0.58 108:0.37 111:0.96 114:0.57 120:0.98 123:0.35 126:0.54 127:0.63 129:0.05 135:0.23 137:0.98 139:0.84 140:0.95 145:0.76 146:0.83 147:0.86 149:0.68 150:0.70 151:0.98 152:0.93 153:0.98 154:0.57 155:0.67 159:0.39 161:0.69 162:0.68 164:0.97 165:0.93 167:0.98 169:0.95 172:0.95 173:0.56 175:0.75 176:0.73 177:0.38 179:0.19 181:0.48 186:0.94 189:0.97 191:0.97 192:0.87 195:0.65 196:0.98 201:0.93 202:0.97 208:0.64 212:0.93 215:0.38 216:0.73 219:0.92 220:0.62 222:0.25 230:0.69 233:0.76 235:0.88 238:0.95 241:0.99 242:0.82 243:0.98 244:0.61 247:0.39 248:0.91 253:0.88 254:0.84 255:0.95 256:0.97 257:0.65 259:0.21 260:0.97 261:0.96 265:0.94 266:0.47 267:0.82 268:0.98 271:0.66 276:0.91 277:0.69 281:0.91 282:0.88 283:0.78 284:0.99 285:0.62 290:0.57 292:0.87 297:0.36 299:0.88 300:0.18 +1 1:0.74 6:0.96 7:0.81 8:0.65 9:0.80 12:0.86 17:0.87 20:0.82 21:0.59 25:0.88 27:0.15 28:0.56 29:0.62 31:0.87 32:0.64 34:0.58 36:0.70 37:0.87 39:0.99 41:0.82 64:0.77 71:0.89 74:0.47 78:0.78 79:0.86 81:0.48 83:0.91 91:0.68 96:0.69 100:0.79 104:0.58 111:0.77 114:0.58 120:0.84 121:0.90 126:0.54 135:0.32 137:0.87 139:0.74 140:0.90 145:0.50 150:0.71 151:0.50 152:0.93 153:0.87 155:0.67 161:0.69 162:0.70 164:0.73 165:0.93 167:0.87 169:0.95 175:0.97 176:0.73 181:0.48 186:0.79 187:0.21 189:0.84 190:0.89 191:0.82 192:0.87 195:0.53 196:0.88 201:0.93 202:0.71 204:0.89 208:0.64 215:0.39 216:0.73 222:0.25 230:0.87 233:0.76 235:0.88 238:0.84 241:0.75 244:0.93 248:0.50 253:0.42 255:0.95 256:0.71 257:0.93 259:0.21 260:0.77 261:0.96 267:0.65 268:0.74 271:0.66 277:0.95 281:0.91 283:0.82 284:0.91 285:0.62 290:0.58 297:0.36 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.86 17:0.90 18:0.77 21:0.64 27:0.44 28:0.56 29:0.62 30:0.71 31:0.97 32:0.69 34:0.68 36:0.64 37:0.78 39:0.99 41:0.88 43:0.95 44:0.90 48:0.99 55:0.45 60:0.87 64:0.77 66:0.72 69:0.30 70:0.33 71:0.89 74:0.67 79:0.97 81:0.44 83:0.91 85:0.72 86:0.83 91:0.38 96:0.86 98:0.63 101:0.87 108:0.76 114:0.57 120:0.87 121:0.90 123:0.75 124:0.45 126:0.54 127:0.56 129:0.05 133:0.59 135:0.34 137:0.97 139:0.91 140:0.90 145:0.80 146:0.50 147:0.57 149:0.74 150:0.70 151:0.94 153:0.90 154:0.95 155:0.67 158:0.92 159:0.57 161:0.69 162:0.64 164:0.77 165:0.93 167:0.79 172:0.91 173:0.27 176:0.73 177:0.70 179:0.24 181:0.48 187:0.39 192:0.87 195:0.78 196:0.98 201:0.93 202:0.75 204:0.89 208:0.64 212:0.93 215:0.38 216:0.56 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 241:0.70 242:0.80 243:0.23 245:0.88 247:0.55 248:0.85 253:0.85 255:0.95 256:0.75 259:0.21 260:0.85 265:0.64 266:0.47 267:0.69 268:0.77 271:0.66 276:0.85 277:0.87 279:0.86 281:0.91 283:0.82 284:0.95 285:0.62 290:0.57 292:0.95 297:0.36 300:0.70 +2 1:0.74 11:0.57 12:0.86 17:0.45 18:0.83 21:0.13 27:0.45 28:0.56 29:0.62 30:0.19 34:0.85 36:0.17 37:0.50 39:0.99 43:0.82 44:0.87 60:0.87 64:0.77 66:0.30 69:0.68 70:0.90 71:0.89 74:0.60 81:0.67 83:0.91 85:0.80 86:0.90 91:0.10 98:0.20 101:0.87 108:0.84 114:0.79 123:0.83 124:0.83 126:0.54 127:0.39 129:0.59 133:0.81 135:0.60 139:0.91 145:0.50 146:0.24 147:0.30 149:0.68 150:0.80 154:0.77 155:0.67 158:0.92 159:0.54 161:0.69 165:0.93 167:0.64 172:0.41 173:0.64 176:0.73 177:0.70 178:0.55 179:0.59 181:0.48 187:0.68 192:0.87 195:0.77 201:0.93 208:0.64 212:0.57 215:0.61 216:0.77 219:0.95 222:0.25 235:0.31 241:0.30 242:0.23 243:0.36 245:0.61 247:0.76 253:0.56 259:0.21 265:0.22 266:0.71 267:0.28 271:0.66 276:0.53 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.70 +1 1:0.74 7:0.81 8:0.82 9:0.80 12:0.86 17:0.88 21:0.64 25:0.88 27:0.15 28:0.56 29:0.62 31:0.86 32:0.64 34:0.52 36:0.54 37:0.87 39:0.99 41:0.88 64:0.77 71:0.89 74:0.47 79:0.86 81:0.45 83:0.91 91:0.78 96:0.68 104:0.58 114:0.57 120:0.84 121:0.90 126:0.54 135:0.34 137:0.86 139:0.74 140:0.87 145:0.54 150:0.70 151:0.47 153:0.90 155:0.67 161:0.69 162:0.64 164:0.75 165:0.93 167:0.86 175:0.97 176:0.73 181:0.48 187:0.21 190:0.89 191:0.80 192:0.87 195:0.53 196:0.87 201:0.93 202:0.72 204:0.89 208:0.64 215:0.38 216:0.73 222:0.25 230:0.87 233:0.76 235:0.88 241:0.75 244:0.93 248:0.47 253:0.40 255:0.95 256:0.71 257:0.93 259:0.21 260:0.77 267:0.23 268:0.74 271:0.66 277:0.95 281:0.91 284:0.93 285:0.62 290:0.57 297:0.36 +1 1:0.74 6:0.95 7:0.81 8:0.90 9:0.80 11:0.57 12:0.86 17:0.64 18:0.91 20:0.92 21:0.64 22:0.93 27:0.30 28:0.56 29:0.62 30:0.45 31:0.98 32:0.80 34:0.77 36:0.42 37:0.80 39:0.99 41:0.96 43:0.83 44:0.93 47:0.93 48:0.98 55:0.71 60:0.95 64:0.77 66:0.54 69:0.67 70:0.64 71:0.89 74:0.75 77:0.70 78:0.90 79:0.97 81:0.44 83:0.91 85:0.80 86:0.93 91:0.87 96:0.91 98:0.57 100:0.94 101:0.87 104:0.83 106:0.81 108:0.78 111:0.91 114:0.57 117:0.86 120:0.84 121:0.97 122:0.65 123:0.76 124:0.52 126:0.54 127:0.38 129:0.59 133:0.46 135:0.49 137:0.98 139:0.96 140:0.80 145:0.85 146:0.60 147:0.66 149:0.72 150:0.70 151:1.00 152:0.86 153:0.98 154:0.79 155:0.67 158:0.92 159:0.53 161:0.69 162:0.52 164:0.87 165:0.93 167:0.76 169:0.92 172:0.74 173:0.63 176:0.73 177:0.70 178:0.42 179:0.36 181:0.48 186:0.90 187:0.21 189:0.96 191:0.88 192:0.87 195:0.79 196:0.99 201:0.93 202:0.87 204:0.89 206:0.81 208:0.64 212:0.78 215:0.38 216:0.74 219:0.95 222:0.25 230:0.87 233:0.76 235:0.84 238:0.92 241:0.65 242:0.29 243:0.40 245:0.88 247:0.76 248:0.90 253:0.91 255:0.95 256:0.81 259:0.21 260:0.85 261:0.92 262:0.93 265:0.59 266:0.71 267:0.36 268:0.84 271:0.66 276:0.71 279:0.86 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 290:0.57 292:0.95 297:0.36 299:0.88 300:0.55 +0 1:0.74 6:0.96 7:0.81 8:0.92 9:0.80 12:0.86 17:0.85 20:0.74 21:0.64 25:0.88 27:0.15 28:0.56 29:0.62 31:0.91 32:0.64 34:0.55 36:0.81 37:0.87 39:0.99 41:0.88 64:0.77 71:0.89 74:0.47 78:0.87 79:0.91 81:0.45 83:0.91 91:0.61 96:0.76 100:0.89 104:0.58 111:0.86 114:0.57 120:0.79 121:0.90 126:0.54 135:0.30 137:0.91 139:0.74 140:0.87 145:0.54 150:0.70 151:0.72 152:0.93 153:0.82 155:0.67 161:0.69 162:0.76 164:0.74 165:0.93 167:0.91 169:0.95 175:0.97 176:0.73 181:0.48 186:0.87 187:0.21 190:0.89 192:0.87 195:0.53 196:0.91 201:0.93 202:0.68 204:0.89 208:0.64 215:0.38 216:0.73 222:0.25 230:0.87 233:0.76 235:0.88 241:0.78 244:0.93 248:0.68 253:0.60 255:0.95 256:0.68 257:0.93 259:0.21 260:0.74 261:0.96 267:0.19 268:0.72 271:0.66 277:0.95 281:0.91 284:0.93 285:0.62 290:0.57 297:0.36 +2 1:0.74 6:0.95 7:0.81 8:0.93 9:0.80 11:0.57 12:0.86 17:0.55 18:0.60 20:0.98 27:0.19 28:0.56 29:0.62 30:0.95 31:0.99 34:0.64 36:0.58 37:0.84 39:0.99 41:0.98 43:0.66 48:0.91 55:0.45 60:0.95 64:0.77 66:0.92 69:0.81 70:0.13 71:0.89 74:0.62 78:0.92 79:0.99 81:0.81 83:0.91 85:0.72 86:0.69 91:0.97 96:0.95 98:0.80 100:0.94 101:0.87 108:0.65 111:0.92 114:0.98 120:0.95 121:0.90 123:0.63 124:0.36 126:0.54 127:0.82 129:0.05 133:0.37 135:0.18 137:0.99 139:0.85 140:0.90 145:0.67 146:0.78 147:0.50 149:0.70 150:0.88 151:0.98 152:0.90 153:0.93 154:0.55 155:0.67 158:0.92 159:0.48 161:0.69 162:0.51 164:0.95 165:0.93 167:0.96 169:0.91 172:0.95 173:0.88 176:0.73 177:0.05 178:0.50 179:0.22 181:0.48 186:0.92 189:0.96 191:0.97 192:0.87 196:0.98 201:0.93 202:0.96 204:0.89 208:0.64 212:0.93 215:0.96 216:0.59 219:0.95 220:0.82 222:0.25 230:0.87 233:0.76 235:0.12 238:0.90 241:0.91 242:0.82 243:0.08 245:0.83 247:0.35 248:0.95 253:0.95 255:0.95 256:0.95 257:0.84 259:0.21 260:0.94 261:0.93 265:0.80 266:0.54 267:0.52 268:0.95 271:0.66 276:0.88 279:0.86 281:0.91 283:0.82 284:0.99 285:0.62 290:0.98 292:0.95 297:0.36 300:0.55 +2 1:0.74 6:0.90 7:0.81 8:0.88 9:0.80 11:0.57 12:0.86 17:0.28 18:0.86 20:0.98 21:0.22 27:0.29 28:0.56 29:0.62 30:0.41 31:1.00 32:0.68 34:0.80 36:0.73 37:0.63 39:0.99 41:0.99 43:0.76 44:0.90 48:0.98 55:0.59 60:0.87 64:0.77 66:0.54 69:0.77 70:0.51 71:0.89 74:0.64 78:0.92 79:1.00 81:0.65 83:0.91 85:0.72 86:0.90 91:0.98 96:0.96 98:0.69 100:0.94 101:0.87 108:0.61 111:0.92 114:0.71 120:0.95 121:0.90 122:0.43 123:0.58 124:0.50 126:0.54 127:0.54 129:0.05 133:0.43 135:0.20 137:1.00 139:0.94 140:0.45 145:0.79 146:0.56 147:0.37 149:0.54 150:0.79 151:1.00 152:0.90 153:0.99 154:0.68 155:0.67 158:0.87 159:0.32 161:0.69 162:0.56 164:0.93 165:0.93 167:0.95 169:0.92 172:0.48 173:0.91 176:0.73 177:0.38 179:0.73 181:0.48 186:0.92 189:0.92 191:0.97 192:0.87 195:0.59 196:1.00 201:0.93 202:0.97 204:0.89 208:0.64 212:0.72 215:0.51 216:0.64 219:0.95 222:0.25 228:0.99 230:0.87 233:0.76 235:0.52 238:0.90 241:0.89 242:0.19 243:0.36 245:0.66 247:0.74 248:0.96 253:0.96 255:0.95 256:1.00 257:0.65 259:0.21 260:0.94 261:0.94 265:0.70 266:0.27 267:0.28 268:0.97 271:0.66 276:0.45 279:0.86 281:0.91 283:0.82 284:1.00 285:0.62 290:0.71 292:0.95 297:0.36 300:0.33 +1 1:0.74 6:0.81 7:0.81 8:0.94 9:0.80 12:0.86 17:0.49 20:0.76 21:0.05 27:0.56 28:0.56 29:0.62 30:0.95 31:0.95 32:0.80 34:0.84 36:0.73 37:0.84 39:0.99 41:0.88 43:0.10 44:0.93 48:0.98 55:0.45 64:0.77 66:0.54 69:0.81 71:0.89 74:0.59 77:0.70 78:0.85 79:0.94 81:0.80 83:0.91 91:0.88 96:0.83 98:0.15 100:0.81 104:0.76 108:0.66 111:0.83 114:0.89 120:0.82 121:0.90 123:0.63 126:0.54 127:0.09 129:0.05 135:0.28 137:0.95 139:0.82 140:0.45 145:0.65 146:0.19 147:0.24 149:0.08 150:0.87 151:0.82 152:0.75 153:0.72 154:0.09 155:0.67 159:0.10 161:0.69 162:0.72 164:0.77 165:0.93 167:0.97 169:0.79 172:0.10 173:0.96 175:0.91 176:0.73 177:0.05 179:0.22 181:0.48 186:0.85 189:0.83 190:0.89 191:0.86 192:0.87 195:0.58 196:0.94 201:0.93 202:0.72 204:0.89 208:0.64 215:0.78 216:0.13 220:0.62 222:0.25 230:0.87 233:0.76 235:0.31 238:0.82 241:0.95 243:0.63 244:0.83 248:0.80 253:0.82 255:0.95 256:0.73 257:0.84 259:0.21 260:0.80 261:0.76 265:0.16 267:0.36 268:0.75 271:0.66 277:0.87 281:0.91 282:0.88 284:0.94 285:0.62 290:0.89 297:0.36 299:0.88 300:0.08 +2 1:0.60 6:0.80 7:0.75 8:0.60 9:0.80 10:0.95 11:0.46 12:0.86 17:0.82 18:0.97 20:0.92 21:0.54 23:1.00 25:0.88 26:0.95 27:0.43 28:0.76 29:0.86 30:0.62 31:1.00 32:0.67 34:0.78 36:0.81 37:0.26 39:0.99 41:0.90 43:0.66 44:0.88 45:0.83 48:1.00 58:1.00 62:1.00 64:0.77 66:0.61 69:0.27 70:0.66 71:0.92 74:0.41 75:0.98 78:0.96 79:1.00 81:0.60 83:0.91 86:0.97 89:0.98 91:0.85 96:0.96 97:0.99 98:0.80 99:0.83 100:0.92 101:0.47 108:0.21 111:0.93 120:0.97 122:0.91 123:0.20 124:0.50 126:0.51 127:0.94 128:1.00 129:0.59 133:0.46 135:0.19 137:1.00 139:0.98 140:0.96 141:0.98 145:0.94 146:0.72 147:0.76 149:0.82 150:0.49 151:0.96 152:0.92 153:0.93 154:0.65 155:0.67 159:0.43 161:0.90 162:0.86 164:0.96 165:0.65 167:0.81 168:1.00 169:0.88 170:0.77 172:0.80 173:0.37 174:0.89 175:0.75 177:0.70 179:0.41 181:0.50 186:0.96 189:0.82 191:0.73 192:0.99 193:0.99 195:0.64 196:1.00 197:0.92 199:1.00 201:0.64 202:0.91 204:0.82 205:0.99 208:0.64 212:0.84 215:0.58 216:0.29 219:0.94 220:0.62 222:0.21 223:0.95 224:0.99 225:0.99 226:0.98 228:0.99 230:0.77 233:0.76 234:0.99 235:0.88 236:0.95 238:0.84 239:0.96 240:0.99 241:0.82 242:0.27 243:0.68 244:0.61 245:0.90 247:0.84 248:0.91 253:0.93 255:1.00 256:0.93 257:0.65 259:0.21 260:0.97 261:0.93 265:0.80 266:0.47 267:0.17 268:0.95 271:0.66 274:1.00 276:0.76 277:0.69 279:0.81 281:0.91 283:0.82 284:1.00 285:0.62 292:0.95 297:0.36 300:0.70 +2 1:0.66 6:0.84 8:0.67 10:0.97 11:0.75 12:0.86 17:0.64 18:0.92 20:0.89 21:0.47 22:0.93 26:0.95 27:0.42 28:0.76 29:0.86 30:0.74 32:0.80 33:0.97 34:0.31 36:0.34 37:0.63 39:0.99 43:0.77 44:0.93 45:0.91 47:0.93 58:0.93 64:0.77 66:0.82 69:0.80 70:0.33 71:0.92 74:0.54 77:0.70 78:0.82 81:0.74 83:0.91 85:0.72 86:0.95 89:0.99 91:0.12 98:0.69 100:0.80 101:0.96 102:0.98 104:0.90 106:0.81 108:0.63 111:0.80 114:0.80 122:0.93 123:0.61 124:0.38 126:0.54 127:0.26 129:0.05 133:0.39 135:0.20 139:0.94 141:0.91 145:0.59 146:0.79 147:0.18 149:0.84 150:0.54 152:0.84 154:0.69 155:0.51 159:0.43 161:0.90 165:0.93 167:0.53 169:0.77 170:0.77 172:0.79 173:0.79 174:0.94 176:0.73 177:0.38 178:0.55 179:0.30 181:0.50 186:0.82 187:0.87 189:0.86 192:0.50 195:0.74 197:0.96 199:0.98 201:0.66 206:0.81 208:0.64 212:0.83 215:0.63 216:0.68 219:0.91 222:0.21 223:0.94 224:0.93 225:0.98 234:0.91 235:0.80 236:0.97 238:0.82 239:0.97 240:0.95 241:0.37 242:0.44 243:0.13 245:0.71 247:0.76 253:0.59 257:0.84 259:0.21 261:0.82 262:0.93 265:0.69 266:0.38 267:0.23 271:0.66 274:0.91 276:0.70 282:0.88 283:0.82 290:0.80 291:0.97 292:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.64 7:0.69 9:0.71 10:0.99 11:0.46 12:0.86 17:0.53 18:0.97 21:0.30 26:0.95 27:0.51 28:0.76 29:0.86 30:0.73 31:0.88 32:0.80 34:0.36 36:0.35 37:0.72 39:0.99 43:0.63 44:0.93 45:0.95 48:0.97 55:0.59 58:0.98 64:0.77 66:0.74 69:0.15 70:0.45 71:0.92 74:0.47 77:0.70 79:0.88 81:0.62 83:0.69 86:0.98 89:0.99 91:0.22 96:0.71 98:0.80 99:0.95 101:0.47 102:0.98 108:0.12 114:0.84 120:0.58 122:0.76 123:0.12 124:0.43 126:0.51 127:0.63 129:0.05 133:0.37 135:0.82 137:0.88 139:0.97 140:0.45 141:0.98 145:0.61 146:0.91 147:0.93 149:0.46 150:0.47 151:0.58 153:0.48 154:0.48 155:0.57 159:0.67 161:0.90 162:0.86 164:0.56 165:0.81 167:0.58 170:0.77 172:0.93 173:0.15 174:0.96 175:0.75 176:0.70 177:0.70 179:0.21 181:0.50 187:0.39 190:0.77 192:0.86 195:0.83 196:0.93 197:0.97 199:0.99 201:0.62 202:0.36 208:0.61 212:0.89 215:0.72 216:0.59 220:0.87 222:0.21 223:0.95 224:0.99 225:0.98 228:0.99 230:0.71 233:0.76 234:0.98 235:0.52 239:0.99 240:0.95 241:0.54 242:0.31 243:0.77 244:0.61 245:0.95 247:0.86 248:0.56 253:0.62 254:0.92 255:0.70 256:0.45 257:0.65 259:0.21 260:0.58 265:0.80 266:0.54 267:0.44 268:0.46 271:0.66 274:0.97 276:0.89 277:0.69 281:0.91 282:0.88 284:0.88 285:0.60 290:0.84 297:0.36 299:0.88 300:0.70 +2 6:0.97 7:0.75 8:0.90 9:0.72 10:0.96 11:0.57 12:0.86 17:0.75 18:0.89 20:0.82 22:0.93 26:0.95 27:0.19 28:0.76 29:0.86 30:0.73 31:0.97 32:0.67 33:0.97 34:0.59 36:0.47 37:0.84 39:0.99 43:0.73 44:0.88 45:0.93 47:0.93 48:0.98 58:0.99 64:0.77 66:0.61 69:0.40 70:0.40 71:0.92 74:0.56 75:0.97 78:0.90 79:0.97 81:0.46 83:0.91 86:0.91 89:0.99 91:0.42 96:0.73 97:0.97 98:0.76 100:0.93 101:0.87 104:0.86 106:0.81 108:0.74 111:0.90 120:0.81 122:0.93 123:0.72 124:0.58 126:0.32 127:0.80 129:0.05 133:0.65 135:0.68 137:0.97 139:0.93 140:0.95 141:0.98 145:0.54 146:0.37 147:0.44 149:0.76 150:0.46 151:0.63 152:0.78 153:0.83 154:0.66 155:0.64 158:0.81 159:0.76 161:0.90 162:0.75 163:0.81 164:0.74 167:0.54 169:0.91 170:0.77 172:0.77 173:0.25 174:0.95 177:0.88 179:0.44 181:0.50 186:0.90 187:0.68 189:0.98 190:0.77 191:0.73 192:0.87 193:0.99 195:0.88 196:0.98 197:0.95 199:0.99 201:0.60 202:0.65 204:0.83 206:0.81 208:0.64 212:0.48 215:0.96 216:0.59 219:0.94 222:0.21 223:0.95 224:1.00 225:0.97 226:0.98 230:0.77 233:0.76 234:1.00 235:0.05 238:0.93 239:0.97 240:0.95 241:0.43 242:0.41 243:0.29 244:0.61 245:0.82 247:0.82 248:0.61 253:0.54 255:0.73 256:0.80 259:0.21 260:0.82 261:0.84 262:0.93 265:0.76 266:0.71 267:0.52 268:0.69 271:0.66 274:0.99 276:0.73 277:0.69 279:0.80 281:0.91 283:0.82 284:0.95 285:0.60 291:0.97 292:0.95 297:0.36 300:0.55 +2 1:0.59 7:0.75 8:0.89 9:0.70 10:0.95 11:0.52 12:0.86 17:0.85 18:0.89 21:0.40 22:0.93 23:0.96 26:0.95 27:0.35 28:0.76 29:0.86 30:0.75 31:0.88 32:0.72 33:0.97 34:0.23 36:0.29 37:0.79 39:0.99 43:0.32 44:0.92 45:0.92 47:0.93 48:0.98 55:0.45 58:0.98 62:0.96 64:0.77 66:0.91 69:0.88 70:0.41 71:0.92 74:0.54 77:0.70 79:0.87 81:0.54 83:0.69 86:0.89 89:0.99 91:0.38 98:0.77 101:0.65 104:0.78 106:0.81 108:0.77 120:0.57 123:0.75 124:0.37 126:0.51 127:0.38 128:0.95 129:0.05 133:0.39 135:0.51 137:0.88 139:0.92 140:0.45 141:0.98 145:0.77 146:0.88 147:0.23 149:0.74 150:0.50 151:0.54 153:0.48 154:0.25 155:0.55 159:0.55 161:0.90 162:0.86 164:0.56 167:0.52 168:0.95 170:0.77 172:0.94 173:0.89 174:0.94 177:0.38 179:0.19 181:0.50 187:0.39 190:0.77 192:0.58 193:0.96 195:0.80 196:0.91 197:0.94 199:0.99 201:0.65 202:0.36 204:0.81 205:0.95 206:0.81 208:0.64 212:0.91 215:0.67 216:0.70 219:0.91 220:0.91 222:0.21 223:0.95 224:0.99 225:0.99 230:0.77 233:0.66 234:0.99 235:0.74 236:0.95 239:0.94 240:0.99 241:0.32 242:0.63 243:0.08 244:0.61 245:0.82 247:0.91 248:0.53 253:0.43 254:0.80 255:0.70 256:0.45 257:0.84 259:0.21 260:0.57 262:0.93 265:0.77 266:0.54 267:0.79 268:0.46 271:0.66 274:0.98 276:0.87 281:0.86 282:0.80 283:0.67 284:0.88 285:0.60 291:0.97 292:0.88 297:0.36 300:0.70 +2 1:0.69 6:0.83 7:0.81 8:0.69 9:0.80 11:0.52 12:0.86 17:0.89 18:0.96 20:0.81 22:0.93 23:0.96 26:0.95 27:0.61 28:0.76 29:0.86 30:0.85 31:0.94 32:0.72 34:0.64 36:0.55 37:0.43 39:0.99 41:0.82 43:0.73 44:0.92 45:0.95 47:0.93 48:0.98 58:0.99 62:0.97 64:0.77 66:0.97 69:0.21 70:0.41 71:0.92 74:0.82 77:0.70 78:0.91 79:0.93 81:0.86 83:0.91 86:0.98 89:0.95 91:0.46 96:0.80 97:0.94 98:0.97 99:0.95 100:0.89 101:0.65 104:0.91 106:0.81 108:0.17 111:0.89 114:0.98 120:0.69 121:0.90 122:0.65 123:0.16 126:0.54 127:0.75 128:0.98 129:0.05 135:0.42 137:0.94 139:0.98 140:0.45 141:0.98 145:0.89 146:0.91 147:0.93 149:0.94 150:0.83 151:0.86 152:0.79 153:0.48 154:0.63 155:0.67 158:0.82 159:0.53 161:0.90 162:0.86 164:0.57 165:0.93 167:0.56 168:0.98 169:0.86 170:0.77 172:0.92 173:0.26 175:0.75 176:0.73 177:0.70 179:0.28 181:0.50 186:0.91 187:0.39 189:0.84 192:0.99 193:1.00 195:0.71 196:0.97 199:0.99 201:0.74 202:0.36 204:0.85 205:0.95 206:0.81 208:0.64 212:0.78 215:0.96 216:0.30 219:0.94 222:0.21 223:0.95 224:1.00 225:0.98 230:0.87 233:0.76 234:1.00 235:0.68 236:0.97 238:0.84 239:0.90 240:0.99 241:0.49 242:0.18 243:0.97 244:0.61 247:0.84 248:0.77 253:0.86 255:1.00 256:0.45 257:0.65 259:0.21 260:0.70 261:0.84 262:0.93 265:0.97 266:0.38 267:0.28 268:0.46 271:0.66 274:0.98 276:0.85 277:0.69 279:0.78 281:0.91 282:0.80 283:0.82 284:0.89 285:0.62 290:0.98 292:0.95 297:0.36 300:0.43 +1 1:0.63 7:0.81 8:0.83 9:0.73 10:0.90 11:0.46 12:0.86 17:0.90 18:0.92 23:0.96 26:0.95 27:0.68 28:0.76 29:0.86 30:0.11 31:0.94 32:0.71 34:0.45 36:0.77 37:0.87 39:0.99 43:0.59 44:0.92 45:0.66 48:0.99 55:0.84 58:0.98 62:0.98 64:0.77 66:0.16 69:0.73 70:0.97 71:0.92 74:0.47 79:0.94 81:0.66 83:0.91 86:0.92 89:0.97 91:0.23 98:0.48 99:0.83 101:0.47 102:0.97 104:0.81 106:0.81 108:0.56 120:0.72 121:0.90 122:0.92 123:0.72 124:0.89 126:0.51 127:0.53 128:0.98 129:0.05 133:0.89 135:0.85 137:0.94 139:0.94 140:0.45 141:0.98 145:0.94 146:0.50 147:0.05 149:0.38 150:0.57 151:0.90 153:0.69 154:0.49 155:0.63 159:0.78 161:0.90 162:0.86 164:0.62 165:0.65 167:0.50 168:0.98 170:0.77 172:0.48 173:0.55 174:0.86 175:0.75 177:0.05 179:0.44 181:0.50 187:0.21 190:0.77 192:0.87 193:1.00 195:0.92 196:0.97 197:0.88 199:0.99 201:0.65 202:0.46 204:0.84 205:0.95 206:0.81 208:0.64 212:0.26 215:0.96 216:0.04 219:0.91 222:0.21 223:0.95 224:1.00 225:0.99 228:0.99 230:0.87 233:0.76 234:1.00 235:0.12 236:0.95 239:0.94 240:0.99 241:0.24 242:0.32 243:0.08 244:0.61 245:0.71 247:0.82 248:0.80 251:1.00 253:0.39 254:0.95 255:0.78 256:0.45 257:0.93 259:0.21 260:0.73 265:0.28 266:0.94 267:0.28 268:0.55 271:0.66 274:0.97 276:0.71 277:0.87 281:0.91 283:0.82 284:0.90 285:0.60 292:0.95 297:0.36 300:0.84 +1 7:0.70 8:0.80 9:0.72 10:0.96 11:0.52 12:0.86 17:0.79 18:0.85 21:0.59 23:0.99 26:0.95 27:0.66 28:0.76 29:0.86 30:0.19 31:0.98 32:0.67 34:0.40 36:0.62 37:0.45 39:0.99 43:0.34 44:0.88 45:0.81 58:1.00 62:0.98 66:0.35 69:0.86 70:0.97 71:0.92 74:0.44 75:0.97 79:0.98 81:0.33 83:0.56 86:0.88 89:0.99 91:0.84 96:0.80 98:0.66 101:0.65 108:0.85 120:0.92 122:0.75 123:0.84 124:0.76 126:0.32 127:0.96 128:0.98 129:0.59 133:0.74 135:0.42 137:0.98 139:0.87 140:0.99 141:0.98 145:0.99 146:0.74 147:0.47 149:0.47 150:0.33 151:0.53 153:0.82 154:0.18 155:0.56 159:0.73 161:0.90 162:0.70 164:0.93 167:0.83 168:0.98 170:0.77 172:0.77 173:0.81 174:0.95 177:0.70 179:0.34 181:0.50 190:0.89 191:0.88 192:0.58 193:0.97 195:0.87 196:0.98 197:0.96 199:0.99 201:0.55 202:0.90 204:0.79 205:0.99 208:0.61 212:0.55 215:0.55 216:0.21 219:0.91 220:0.62 222:0.21 223:0.95 224:0.98 225:0.99 226:0.96 230:0.73 233:0.66 234:0.97 235:0.74 236:0.94 239:0.96 240:0.99 241:0.88 242:0.13 243:0.20 245:0.88 247:0.97 248:0.52 253:0.74 254:0.99 255:0.73 256:0.91 257:0.65 259:0.21 260:0.92 265:0.66 266:0.75 267:0.93 268:0.92 271:0.66 274:0.99 276:0.82 283:0.67 284:0.99 285:0.62 292:0.88 297:0.36 300:0.84 +3 1:0.69 6:0.94 7:0.70 8:0.80 9:0.71 10:0.99 11:0.75 12:0.86 17:0.81 18:0.96 20:0.84 21:0.22 22:0.93 23:0.96 26:0.95 27:0.52 28:0.76 29:0.86 30:0.54 31:0.89 32:0.80 33:0.97 34:0.53 36:0.64 37:0.56 39:0.99 43:0.93 44:0.93 45:0.97 47:0.93 48:0.97 58:0.98 62:0.98 64:0.77 66:0.15 69:0.35 70:0.75 71:0.92 74:0.58 77:0.70 78:0.97 79:0.89 81:0.84 83:0.91 85:0.72 86:0.97 89:0.99 91:0.18 98:0.58 99:0.83 100:0.96 101:0.96 102:0.98 106:0.81 108:0.94 111:0.96 114:0.90 120:0.59 122:0.93 123:0.85 124:0.90 126:0.54 127:0.96 128:0.96 129:0.05 133:0.90 135:0.32 137:0.89 139:0.97 140:0.45 141:0.98 145:0.80 146:0.22 147:0.28 149:0.88 150:0.67 151:0.61 152:0.87 153:0.48 154:0.93 155:0.57 159:0.88 161:0.90 162:0.86 163:0.81 164:0.56 165:1.00 167:0.47 168:0.96 169:0.95 170:0.77 172:0.82 173:0.14 174:0.98 176:0.73 177:0.88 179:0.20 181:0.50 186:0.97 187:0.21 189:0.93 190:0.77 192:0.86 195:0.96 196:0.94 197:0.97 199:0.99 201:0.68 202:0.36 205:0.95 206:0.81 208:0.64 212:0.30 215:0.79 216:0.50 219:0.91 220:0.82 222:0.21 223:0.95 224:0.99 225:0.99 230:0.73 233:0.76 234:0.99 235:0.74 236:0.97 238:0.88 239:0.99 240:0.99 241:0.07 242:0.29 243:0.15 245:0.93 247:0.94 248:0.59 253:0.64 255:0.71 256:0.45 259:0.21 260:0.60 261:0.95 262:0.93 265:0.56 266:0.87 267:0.23 268:0.46 271:0.66 274:0.97 276:0.92 277:0.69 281:0.91 282:0.88 283:0.67 284:0.88 285:0.60 290:0.90 291:0.97 292:0.88 297:0.36 299:0.88 300:0.84 +2 7:0.75 8:0.95 10:0.96 11:0.57 12:0.86 17:0.85 18:0.91 21:0.54 23:0.95 26:0.95 27:0.54 28:0.76 29:0.86 30:0.54 31:0.98 32:0.71 34:0.34 36:0.49 37:0.95 39:0.99 43:0.71 44:0.92 45:0.94 48:0.91 58:0.99 62:0.98 64:0.77 66:0.43 69:0.46 70:0.77 71:0.92 74:0.75 79:0.98 81:0.51 83:0.91 86:0.95 89:0.99 91:0.62 98:0.72 101:0.87 102:0.97 108:0.35 120:0.88 123:0.34 124:0.60 126:0.51 127:0.78 128:0.96 129:0.59 133:0.47 135:0.26 137:0.98 139:0.95 140:0.45 141:0.98 145:0.82 146:0.77 147:0.81 149:0.85 150:0.47 151:0.95 153:0.85 154:0.50 155:0.55 159:0.55 161:0.90 162:0.70 164:0.85 167:0.79 168:0.96 170:0.77 172:0.86 173:0.44 174:0.93 177:0.88 179:0.23 181:0.50 190:0.89 191:0.86 192:0.86 193:0.99 195:0.72 196:0.99 197:0.93 199:0.98 201:0.62 202:0.80 204:0.84 205:0.95 208:0.64 212:0.87 215:0.58 216:0.24 220:0.62 222:0.21 223:0.95 224:0.98 225:0.98 230:0.77 233:0.76 234:0.98 235:0.88 236:0.95 239:0.96 240:0.99 241:0.79 242:0.16 243:0.57 245:0.97 247:0.97 248:0.91 253:0.53 254:0.84 255:0.73 256:0.81 259:0.21 260:0.88 265:0.72 266:0.54 267:0.18 268:0.83 271:0.66 274:0.99 276:0.89 281:0.91 284:0.97 285:0.60 297:0.36 300:0.70 +0 1:0.64 6:0.80 7:0.75 8:0.60 9:0.80 10:0.95 11:0.82 12:0.86 17:0.86 18:0.97 20:0.88 21:0.40 23:1.00 26:0.95 27:0.55 28:0.76 29:0.86 30:0.74 31:1.00 32:0.72 34:0.59 36:0.88 37:0.25 39:0.99 41:0.94 43:0.68 44:0.92 45:0.89 48:0.97 58:1.00 60:0.87 62:1.00 64:0.77 66:0.48 69:0.45 70:0.53 71:0.92 74:0.66 75:0.99 78:0.93 79:1.00 81:0.68 83:0.91 85:0.72 86:0.98 89:0.98 91:0.88 96:0.96 97:0.99 98:0.75 99:0.83 100:0.89 101:0.96 102:0.97 108:0.35 111:0.91 120:0.97 122:0.91 123:0.34 124:0.58 126:0.51 127:0.78 128:1.00 129:0.59 133:0.47 135:0.30 137:1.00 139:0.98 140:0.80 141:0.98 145:0.77 146:0.78 147:0.81 149:0.92 150:0.58 151:0.99 152:0.82 153:0.96 154:0.56 155:0.67 158:0.92 159:0.53 161:0.90 162:0.86 164:0.96 165:0.65 167:0.81 168:1.00 169:0.87 170:0.77 172:0.83 173:0.45 174:0.89 177:0.88 179:0.29 181:0.50 186:0.93 189:0.83 191:0.70 192:0.99 193:0.99 195:0.70 196:1.00 197:0.90 199:1.00 201:0.67 202:0.91 204:0.84 205:1.00 208:0.64 212:0.89 215:0.67 216:0.21 219:0.95 222:0.21 223:0.95 224:0.99 225:0.99 226:0.98 230:0.77 233:0.76 234:0.99 235:0.88 236:0.95 238:0.83 239:0.98 240:0.99 241:0.81 242:0.22 243:0.59 245:0.95 247:0.90 248:0.95 253:0.96 255:1.00 256:0.93 259:0.21 260:0.98 261:0.89 265:0.75 266:0.54 267:0.96 268:0.95 271:0.66 274:1.00 276:0.84 279:0.82 281:0.91 283:0.82 284:1.00 285:0.62 292:0.95 297:0.36 300:0.84 +3 1:0.57 6:0.94 7:0.70 8:0.87 9:0.73 10:0.98 11:0.52 12:0.86 17:0.88 18:0.96 20:0.81 21:0.59 22:0.93 23:0.96 26:0.95 27:0.44 28:0.76 29:0.86 30:0.19 31:0.96 32:0.71 34:0.44 36:0.52 37:0.78 39:0.99 43:0.25 44:0.92 45:0.94 47:0.93 48:0.99 58:0.99 62:0.99 64:0.77 66:0.11 69:0.33 70:0.97 71:0.92 74:0.51 75:0.98 78:0.89 79:0.96 81:0.47 83:0.56 86:0.97 89:0.99 91:0.42 97:0.89 98:0.50 100:0.91 101:0.65 102:0.97 104:0.86 106:0.81 108:0.62 111:0.89 120:0.71 122:0.93 123:0.90 124:0.89 126:0.51 127:0.84 128:0.98 129:0.59 133:0.89 135:0.78 137:0.96 139:0.97 140:0.90 141:0.98 145:0.91 146:0.45 147:0.52 149:0.62 150:0.44 151:0.86 152:0.83 153:0.48 154:0.86 155:0.55 159:0.86 161:0.90 162:0.86 164:0.73 167:0.57 168:0.98 169:0.87 170:0.77 172:0.80 173:0.14 174:0.96 177:0.88 179:0.25 181:0.50 186:0.89 187:0.68 189:0.97 190:0.77 191:0.80 192:0.58 193:0.98 195:0.95 196:0.98 197:0.95 199:0.99 201:0.62 202:0.62 204:0.79 205:0.95 206:0.81 208:0.50 212:0.49 215:0.55 216:0.56 219:0.94 220:0.96 222:0.21 223:0.95 224:0.99 225:0.98 226:0.98 230:0.72 233:0.66 234:0.99 235:0.88 236:0.95 238:0.90 239:0.98 240:0.99 241:0.51 242:0.19 243:0.16 245:0.89 247:0.95 248:0.77 253:0.41 254:0.80 255:0.74 256:0.68 259:0.21 260:0.72 261:0.87 262:0.93 265:0.38 266:0.80 267:0.65 268:0.70 271:0.66 274:0.98 276:0.87 277:0.69 279:0.76 281:0.86 283:0.82 284:0.95 285:0.60 292:0.95 297:0.36 300:0.94 +0 1:0.68 10:0.94 11:0.75 12:0.86 17:0.40 18:0.88 21:0.13 26:0.95 27:0.45 28:0.76 29:0.86 30:0.60 34:0.75 36:0.20 37:0.50 39:0.99 43:0.82 45:0.83 48:0.91 58:0.93 60:0.87 64:0.77 66:0.60 69:0.61 70:0.71 71:0.92 74:0.56 75:0.99 81:0.77 83:0.91 85:0.72 86:0.90 89:0.98 91:0.09 98:0.60 101:0.96 108:0.78 114:0.92 122:0.93 123:0.77 124:0.50 126:0.54 127:0.46 129:0.05 133:0.45 135:0.66 139:0.91 141:0.91 145:0.50 146:0.37 147:0.44 149:0.75 150:0.59 154:0.77 155:0.52 158:0.92 159:0.57 161:0.90 165:0.93 167:0.49 170:0.77 172:0.63 173:0.55 174:0.91 176:0.73 177:0.88 178:0.55 179:0.55 181:0.50 187:0.68 192:0.54 197:0.91 199:0.97 201:0.67 208:0.64 212:0.58 215:0.84 216:0.77 219:0.95 222:0.21 223:0.95 224:0.93 225:0.98 226:0.98 234:0.91 235:0.42 236:0.97 239:0.95 240:0.95 241:0.15 242:0.17 243:0.36 245:0.78 247:0.84 253:0.66 259:0.21 265:0.61 266:0.75 267:0.23 271:0.66 274:0.91 276:0.54 277:0.69 279:0.80 281:0.91 283:0.82 290:0.92 292:0.95 297:0.36 300:0.70 +2 1:0.67 6:0.88 8:0.74 9:0.73 10:0.97 11:0.57 12:0.86 17:0.34 18:0.92 20:0.93 21:0.22 22:0.93 23:0.97 26:0.95 27:0.56 28:0.76 29:0.86 30:0.28 31:0.94 32:0.80 33:0.97 34:0.80 36:0.22 37:0.60 39:0.99 41:0.82 43:0.75 44:0.93 45:0.94 47:0.93 58:0.98 62:0.97 64:0.77 66:0.54 69:0.57 70:0.89 71:0.92 74:0.56 77:0.70 78:0.90 79:0.94 81:0.76 83:0.91 86:0.94 89:0.99 91:0.14 96:0.73 98:0.70 100:0.91 101:0.87 102:0.98 104:0.96 106:0.81 108:0.86 111:0.90 114:0.90 120:0.73 122:0.93 123:0.85 124:0.71 126:0.54 127:0.72 128:0.96 129:0.59 133:0.74 135:0.60 137:0.94 139:0.93 140:0.45 141:0.98 145:0.65 146:0.44 147:0.50 149:0.67 150:0.57 151:0.63 152:0.87 153:0.48 154:0.19 155:0.65 159:0.81 161:0.90 162:0.86 164:0.68 165:0.93 167:0.57 168:0.96 169:0.89 170:0.77 172:0.87 173:0.35 174:0.96 176:0.73 177:0.88 179:0.25 181:0.50 186:0.90 187:0.87 189:0.89 190:0.89 191:0.70 192:0.98 195:0.92 196:0.97 197:0.94 199:0.99 201:0.66 202:0.53 205:0.95 206:0.81 208:0.64 212:0.42 215:0.79 216:0.76 220:0.82 222:0.21 223:0.95 224:0.99 225:0.99 234:0.99 235:0.52 236:0.97 238:0.87 239:0.97 240:0.99 241:0.52 242:0.19 243:0.23 245:0.91 247:0.95 248:0.61 253:0.70 254:0.97 255:0.74 256:0.58 259:0.21 260:0.73 261:0.90 262:0.93 265:0.71 266:0.80 267:0.44 268:0.62 271:0.66 274:0.98 276:0.86 282:0.88 283:0.82 284:0.93 285:0.62 290:0.90 291:0.97 297:0.36 299:0.88 300:0.84 +2 1:0.64 6:0.96 7:0.75 8:0.90 9:0.73 10:0.98 11:0.52 12:0.86 17:0.66 18:0.97 20:0.85 21:0.22 22:0.93 23:0.97 26:0.95 27:0.35 28:0.76 29:0.86 30:0.21 31:0.91 32:0.75 33:0.97 34:0.22 36:0.63 37:0.79 39:0.99 43:0.62 44:0.93 45:0.98 47:0.93 48:0.98 58:0.99 62:0.98 64:0.77 66:0.48 69:0.98 70:0.83 71:0.92 74:0.55 77:0.70 78:0.94 79:0.91 81:0.67 83:0.91 86:0.98 89:0.99 91:0.27 96:0.72 98:0.79 99:0.83 100:0.91 101:0.65 102:0.98 104:0.78 106:0.81 108:0.90 111:0.92 120:0.65 123:0.89 124:0.83 126:0.51 127:0.72 128:0.96 129:0.05 133:0.87 135:0.61 137:0.91 139:0.98 140:0.45 141:0.98 145:0.67 146:0.71 147:0.33 149:0.81 150:0.57 151:0.63 152:0.81 153:0.72 154:0.50 155:0.65 159:0.92 161:0.90 162:0.86 164:0.72 165:0.65 167:0.46 168:0.96 169:0.89 170:0.77 172:0.97 173:0.98 174:0.99 177:0.70 179:0.12 181:0.50 186:0.94 187:0.39 189:0.97 190:0.77 192:0.98 193:0.99 195:0.98 196:0.96 197:0.97 199:1.00 201:0.67 202:0.58 204:0.83 205:0.97 206:0.81 208:0.64 212:0.48 215:0.79 216:0.70 219:0.91 220:0.82 222:0.21 223:0.95 224:0.99 225:0.99 230:0.77 233:0.76 234:0.99 235:0.60 236:0.95 238:0.85 239:0.98 240:0.99 241:0.02 242:0.21 243:0.08 244:0.61 245:0.98 247:0.95 248:0.69 253:0.53 254:0.74 255:0.77 256:0.64 257:0.65 259:0.21 260:0.65 261:0.88 262:0.93 265:0.79 266:0.87 267:0.18 268:0.66 271:0.66 274:0.99 276:0.97 277:0.69 281:0.91 282:0.83 283:0.82 284:0.94 285:0.62 291:0.97 292:0.95 297:0.36 300:0.84 +1 1:0.69 7:0.75 8:0.92 9:0.76 10:0.94 11:0.57 12:0.86 17:0.92 18:0.96 20:0.80 21:0.13 22:0.93 26:0.95 27:0.54 28:0.76 29:0.86 30:0.72 31:0.98 32:0.80 34:0.88 36:0.60 37:0.95 39:0.99 43:0.74 44:0.93 45:0.89 47:0.93 48:1.00 58:0.99 64:0.77 66:0.97 69:0.27 70:0.56 71:0.92 74:0.70 77:0.70 78:0.83 79:0.97 81:0.86 83:0.91 86:0.98 89:0.98 91:0.25 96:0.79 98:0.95 99:0.99 100:0.87 101:0.87 102:0.98 104:0.98 106:0.81 108:0.21 111:0.83 114:0.92 120:0.84 122:0.75 123:0.20 126:0.54 127:0.67 129:0.05 135:0.51 137:0.98 139:0.98 140:0.80 141:0.98 145:0.86 146:0.89 147:0.91 149:0.87 150:0.81 151:0.82 152:0.77 153:0.78 154:0.55 155:0.65 159:0.48 161:0.90 162:0.77 164:0.78 165:0.93 167:0.52 169:0.85 170:0.77 172:0.92 173:0.31 174:0.89 176:0.73 177:0.88 179:0.26 181:0.50 186:0.83 187:0.39 190:0.89 192:0.98 193:0.99 195:0.69 196:0.99 197:0.88 199:0.99 201:0.68 202:0.69 204:0.83 206:0.81 208:0.64 212:0.87 215:0.84 216:0.24 219:0.91 220:0.87 222:0.21 223:0.95 224:1.00 225:0.98 230:0.77 233:0.76 234:1.00 235:0.88 236:0.97 239:0.96 240:0.95 241:0.32 242:0.23 243:0.97 247:0.93 248:0.74 253:0.82 254:0.84 255:0.78 256:0.71 259:0.21 260:0.85 261:0.80 262:0.93 265:0.95 266:0.27 267:0.93 268:0.73 271:0.66 274:0.99 276:0.85 281:0.91 282:0.88 283:0.82 284:0.96 285:0.60 290:0.92 292:0.95 297:0.36 300:0.70 +1 1:0.57 7:0.69 9:0.70 12:0.86 17:0.77 21:0.64 22:0.93 23:0.96 25:0.88 26:0.95 27:0.15 28:0.76 29:0.86 31:0.87 32:0.71 33:0.97 34:0.58 36:0.85 37:0.87 39:0.99 44:0.92 47:0.93 48:0.91 58:0.98 62:0.95 64:0.77 71:0.92 79:0.87 81:0.42 89:0.95 91:0.37 102:0.97 104:0.58 120:0.77 126:0.51 128:0.95 135:0.34 137:0.87 139:0.73 140:0.87 141:0.98 145:0.77 150:0.40 151:0.52 153:0.48 155:0.50 161:0.90 162:0.86 164:0.72 167:0.73 168:0.95 170:0.77 175:0.99 181:0.50 187:0.39 190:0.95 192:0.50 193:0.96 195:0.53 196:0.88 199:0.96 201:0.59 202:0.58 205:0.95 208:0.50 215:0.53 216:0.73 220:0.62 222:0.21 223:0.95 224:0.98 225:0.98 230:0.71 233:0.66 234:0.98 235:0.88 236:0.95 239:0.89 240:0.99 241:0.73 244:0.97 248:0.51 253:0.20 255:0.69 256:0.64 257:0.97 259:0.21 260:0.77 262:0.93 267:0.19 268:0.66 271:0.66 274:0.97 277:0.98 281:0.86 284:0.88 285:0.60 291:0.97 297:0.36 +1 1:0.68 8:0.60 10:0.93 11:0.82 12:0.86 17:0.34 18:0.90 21:0.13 26:0.95 27:0.45 28:0.76 29:0.86 30:0.61 32:0.67 34:0.75 36:0.19 37:0.50 39:0.99 43:0.82 45:0.85 48:0.91 58:0.93 60:0.87 64:0.77 66:0.51 69:0.61 70:0.65 71:0.92 74:0.74 75:0.99 77:0.70 81:0.77 83:0.91 85:0.80 86:0.93 89:0.98 91:0.06 98:0.67 101:0.96 108:0.78 114:0.92 122:0.65 123:0.77 124:0.66 126:0.54 127:0.48 129:0.59 133:0.64 135:0.85 139:0.93 141:0.91 145:0.52 146:0.40 147:0.46 149:0.81 150:0.59 154:0.77 155:0.52 158:0.92 159:0.61 161:0.90 165:0.93 167:0.49 170:0.77 172:0.74 173:0.52 174:0.89 176:0.73 177:0.88 178:0.55 179:0.37 181:0.50 187:0.68 192:0.54 195:0.81 197:0.89 199:0.98 201:0.67 208:0.64 212:0.60 215:0.84 216:0.77 219:0.95 222:0.21 223:0.95 224:0.93 225:0.98 226:0.98 234:0.91 235:0.42 236:0.97 239:0.95 240:0.95 241:0.18 242:0.14 243:0.36 245:0.85 247:0.93 253:0.70 259:0.21 265:0.68 266:0.80 267:0.19 271:0.66 274:0.91 276:0.74 279:0.80 281:0.91 282:0.75 283:0.82 290:0.92 292:0.95 297:0.36 300:0.70 +1 1:0.66 6:0.91 7:0.69 8:0.82 9:0.74 10:0.92 11:0.46 12:0.86 17:0.51 18:0.93 20:0.93 21:0.13 22:0.93 23:0.97 26:0.95 27:0.31 28:0.76 29:0.86 30:0.45 31:0.91 32:0.80 33:0.97 34:0.56 36:0.50 37:0.55 39:0.99 41:0.88 43:0.22 44:0.93 45:0.95 47:0.93 48:0.91 58:0.98 62:0.97 64:0.77 66:0.42 69:0.95 70:0.79 71:0.92 74:0.47 77:0.96 78:0.89 79:0.90 81:0.64 83:0.91 86:0.91 89:0.99 91:0.06 96:0.75 98:0.59 99:0.95 100:0.91 101:0.47 102:0.98 104:0.92 106:0.81 108:0.94 111:0.89 114:0.92 120:0.74 122:0.93 123:0.94 124:0.86 126:0.51 127:0.81 128:0.96 129:0.81 133:0.86 135:0.92 137:0.91 139:0.93 140:0.80 141:0.98 145:0.92 146:0.69 147:0.63 149:0.60 150:0.50 151:0.69 152:0.86 153:0.48 154:0.16 155:0.65 159:0.90 161:0.90 162:0.86 163:0.81 164:0.71 165:0.81 167:0.51 168:0.96 169:0.89 170:0.77 172:0.79 173:0.84 174:0.96 175:0.75 176:0.70 177:0.38 179:0.30 181:0.50 186:0.89 187:0.39 189:0.92 190:0.95 192:0.98 195:0.97 196:0.94 197:0.91 199:0.99 201:0.63 202:0.56 205:0.97 206:0.81 208:0.64 212:0.39 215:0.84 216:0.85 219:0.91 222:0.21 223:0.95 224:0.99 225:0.99 230:0.71 233:0.76 234:0.98 235:0.31 238:0.88 239:0.94 240:0.99 241:0.27 242:0.42 243:0.19 244:0.61 245:0.86 247:0.90 248:0.66 253:0.71 254:0.90 255:0.74 256:0.64 257:0.84 259:0.21 260:0.74 261:0.90 262:0.93 265:0.60 266:0.94 267:0.28 268:0.65 271:0.66 274:0.98 276:0.84 277:0.69 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 290:0.92 291:0.97 292:0.95 297:0.36 299:0.88 300:0.94 +3 1:0.69 7:0.75 9:0.79 10:0.99 11:0.75 12:0.86 17:0.82 18:0.96 22:0.93 23:0.96 25:0.88 26:0.95 27:0.69 28:0.76 29:0.86 30:0.84 31:0.95 32:0.67 34:0.19 36:0.42 37:0.58 39:0.99 41:0.82 43:0.78 44:0.88 45:0.96 47:0.93 48:0.97 58:0.98 60:0.87 62:0.99 64:0.77 66:0.97 69:0.73 70:0.41 71:0.92 74:0.61 75:0.99 79:0.94 81:0.80 83:0.91 85:0.72 86:0.97 89:0.99 91:0.37 96:0.83 98:0.87 101:0.96 104:0.54 108:0.57 114:0.98 120:0.72 122:0.93 123:0.54 126:0.54 127:0.39 128:0.98 129:0.05 135:0.37 137:0.95 139:0.97 140:0.45 141:0.98 145:0.56 146:0.88 147:0.90 149:0.93 150:0.63 151:0.90 153:0.69 154:0.70 155:0.66 158:0.92 159:0.47 161:0.90 162:0.86 164:0.62 165:0.93 167:0.52 168:0.98 170:0.77 172:0.91 173:0.74 174:0.97 176:0.73 177:0.88 179:0.23 181:0.50 187:0.39 192:0.99 195:0.72 196:0.97 197:0.99 199:0.99 201:0.68 202:0.46 204:0.83 205:0.95 208:0.64 212:0.69 215:0.96 216:0.03 219:0.95 222:0.21 223:0.95 224:1.00 225:0.99 226:0.98 230:0.77 233:0.76 234:0.99 235:0.22 236:0.97 239:0.99 240:0.99 241:0.35 242:0.60 243:0.97 247:0.60 248:0.80 253:0.83 255:0.94 256:0.45 259:0.21 260:0.73 262:0.93 265:0.87 266:0.38 267:0.28 268:0.55 271:0.66 274:0.97 276:0.84 279:0.80 281:0.91 283:0.82 284:0.90 285:0.62 290:0.98 292:0.95 297:0.36 300:0.43 +3 1:0.57 6:0.95 7:0.75 8:0.82 9:0.70 10:0.99 11:0.75 12:0.86 17:0.82 18:0.97 20:0.79 21:0.59 22:0.93 23:0.97 26:0.95 27:0.44 28:0.76 29:0.86 30:0.66 31:0.87 32:0.71 34:0.40 36:0.50 37:0.78 39:0.99 43:0.92 44:0.92 45:0.97 47:0.93 48:0.98 58:0.98 62:0.97 64:0.77 66:0.46 69:0.33 70:0.62 71:0.92 74:0.52 78:0.87 79:0.87 81:0.56 83:0.69 85:0.72 86:0.99 89:0.99 91:0.20 98:0.86 99:0.83 100:0.91 101:0.96 102:0.97 104:0.86 106:0.81 108:0.80 111:0.87 120:0.56 122:0.93 123:0.79 124:0.67 126:0.51 127:0.80 128:0.95 129:0.05 133:0.67 135:0.87 137:0.87 139:0.98 140:0.45 141:0.98 145:0.86 146:0.46 147:0.52 149:0.89 150:0.45 151:0.53 152:0.81 153:0.72 154:0.92 155:0.54 159:0.85 161:0.90 162:0.67 164:0.64 165:0.65 167:0.56 168:0.95 169:0.87 170:0.77 172:0.88 173:0.15 174:0.98 177:0.88 179:0.22 181:0.50 186:0.87 187:0.95 190:0.77 191:0.79 192:0.57 193:0.99 195:0.94 196:0.91 197:0.97 199:0.99 201:0.61 202:0.53 204:0.82 205:0.95 206:0.81 208:0.61 212:0.37 215:0.55 216:0.56 219:0.90 220:0.93 222:0.21 223:0.95 224:0.99 225:0.99 230:0.77 233:0.76 234:0.99 235:0.88 236:0.95 239:0.99 240:0.99 241:0.50 242:0.33 243:0.18 245:0.96 247:0.93 248:0.52 253:0.42 255:0.70 256:0.45 259:0.21 260:0.56 261:0.86 262:0.93 265:0.86 266:0.54 267:0.44 268:0.57 271:0.66 274:0.98 276:0.90 277:0.69 281:0.91 284:0.91 285:0.60 292:0.95 297:0.36 300:0.70 +2 1:0.69 7:0.70 8:0.81 9:0.72 10:0.99 11:0.82 12:0.86 17:0.75 18:0.99 21:0.30 22:0.93 23:0.98 26:0.95 27:0.52 28:0.76 29:0.86 30:0.44 31:0.95 32:0.80 33:0.97 34:0.91 36:0.76 37:0.56 39:0.99 43:0.78 44:0.93 45:0.97 47:0.93 48:0.99 58:0.99 62:0.98 64:0.77 66:0.19 69:0.40 70:0.74 71:0.92 74:0.79 75:0.98 77:0.70 79:0.95 81:0.81 83:0.91 85:0.80 86:0.99 89:0.99 91:0.20 97:0.98 98:0.65 99:0.99 101:0.96 102:0.98 104:0.93 106:0.81 108:0.94 114:0.86 120:0.75 123:0.86 124:0.79 126:0.54 127:0.97 128:0.97 129:0.81 133:0.78 135:0.30 137:0.95 139:0.99 140:0.45 141:0.98 145:0.70 146:0.80 147:0.83 149:0.95 150:0.65 151:0.74 153:0.48 154:0.71 155:0.58 158:0.82 159:0.87 161:0.90 162:0.82 164:0.73 165:0.93 167:0.49 168:0.97 170:0.77 172:0.92 173:0.17 174:0.97 176:0.73 177:0.88 179:0.13 181:0.50 187:0.39 190:0.89 192:0.86 195:0.95 196:0.98 197:0.97 199:1.00 201:0.68 202:0.62 205:0.97 206:0.81 208:0.64 212:0.85 215:0.72 216:0.50 219:0.94 220:0.74 222:0.21 223:0.95 224:0.99 225:0.99 226:0.98 230:0.73 233:0.76 234:0.99 235:0.84 236:0.97 239:0.99 240:0.99 241:0.18 242:0.23 243:0.38 245:0.99 247:0.98 248:0.69 253:0.68 255:0.74 256:0.64 259:0.21 260:0.76 262:0.93 265:0.43 266:0.87 267:0.76 268:0.68 271:0.66 274:0.98 276:0.96 279:0.81 281:0.91 282:0.88 283:0.82 284:0.95 285:0.60 290:0.86 291:0.97 292:0.95 297:0.36 299:0.88 300:0.94 +1 1:0.74 11:0.64 12:0.20 17:0.95 21:0.22 27:0.72 30:0.94 34:0.34 36:0.32 39:0.55 43:0.96 55:0.45 60:0.87 66:0.79 69:0.17 70:0.24 74:0.99 81:0.90 83:0.87 85:0.88 91:0.20 98:0.64 101:0.79 104:0.97 106:0.81 108:0.66 114:0.90 117:0.86 122:0.67 123:0.64 124:0.41 126:0.54 127:0.66 129:0.59 131:0.61 133:0.64 135:0.63 144:0.57 146:0.39 147:0.45 149:0.94 150:0.94 154:0.96 155:0.67 157:0.98 158:0.92 159:0.57 165:0.86 166:1.00 172:0.94 173:0.20 176:0.73 177:0.38 178:0.55 179:0.21 182:0.98 187:0.39 192:0.59 201:0.74 206:0.81 208:0.64 212:0.92 215:0.79 216:0.07 222:0.84 227:0.61 229:0.61 231:1.00 232:0.87 235:0.31 241:0.03 242:0.80 243:0.17 245:0.89 246:0.61 247:0.47 253:0.79 259:0.21 265:0.65 266:0.54 267:0.19 276:0.87 279:0.86 283:0.82 287:0.61 290:0.90 297:0.36 300:0.70 +1 11:0.64 12:0.20 17:0.22 21:0.78 27:0.25 30:0.28 34:0.64 36:0.91 37:0.44 39:0.55 43:0.32 55:0.71 66:0.72 69:0.94 70:0.89 74:0.50 85:0.85 91:0.02 98:0.40 101:0.72 108:0.92 122:0.43 123:0.92 124:0.41 127:0.21 129:0.59 131:0.61 133:0.47 135:0.80 144:0.57 146:0.18 147:0.23 149:0.45 154:0.45 157:0.94 159:0.65 166:0.61 172:0.59 173:0.87 177:0.38 178:0.55 179:0.43 182:0.85 187:0.39 202:0.50 212:0.55 216:0.81 222:0.84 227:0.61 229:0.61 231:0.99 235:0.05 241:0.07 242:0.40 243:0.20 245:0.61 246:0.61 247:0.60 253:0.42 259:0.21 265:0.42 266:0.63 267:0.28 276:0.42 287:0.61 300:0.70 +1 7:0.76 11:0.64 12:0.20 17:0.76 21:0.22 27:0.67 30:0.15 32:0.72 34:0.88 36:0.53 37:0.32 39:0.55 43:0.75 55:0.71 66:0.89 69:0.59 70:0.80 74:0.54 81:0.84 85:0.80 91:0.23 98:0.84 101:0.76 104:0.93 106:0.81 108:0.45 123:0.43 126:0.32 127:0.34 129:0.05 131:0.61 132:0.97 135:0.49 144:0.57 145:0.40 146:0.81 147:0.84 149:0.73 150:0.65 154:0.66 157:0.94 159:0.36 166:0.99 172:0.68 173:0.64 177:0.38 178:0.55 179:0.53 182:0.85 187:0.39 195:0.66 206:0.81 212:0.54 215:0.79 216:0.44 222:0.84 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.22 242:0.72 243:0.89 246:0.61 247:0.47 253:0.44 259:0.21 265:0.84 266:0.38 267:0.59 276:0.57 287:0.61 297:0.36 300:0.55 +1 12:0.20 17:0.84 21:0.78 27:0.72 30:0.95 34:0.21 36:0.22 39:0.55 43:0.24 55:0.99 66:0.20 69:0.84 74:0.39 91:0.20 98:0.06 104:0.58 108:0.70 120:0.69 123:0.68 124:0.61 127:0.53 129:0.59 131:0.99 133:0.47 135:0.60 140:0.45 146:0.05 147:0.05 149:0.12 151:0.66 153:0.48 154:0.17 157:0.61 159:0.84 162:0.86 164:0.55 166:0.61 172:0.05 173:0.65 175:0.75 177:0.05 179:1.00 182:0.61 190:0.77 202:0.36 216:0.03 222:0.84 227:0.61 229:0.61 231:0.99 235:0.12 241:0.79 243:0.40 244:0.61 245:0.36 246:0.61 248:0.63 253:0.35 256:0.45 257:0.65 259:0.21 260:0.69 265:0.06 267:0.52 268:0.46 277:0.69 283:0.82 285:0.50 287:0.61 300:0.08 +1 11:0.64 12:0.20 17:0.40 21:0.78 27:0.72 30:0.17 34:0.53 36:0.87 37:0.41 39:0.55 43:0.76 55:0.71 66:0.13 69:0.72 70:0.78 74:0.85 85:0.72 91:0.25 98:0.34 101:0.70 108:0.55 122:0.67 123:0.52 124:0.89 127:0.61 129:0.05 131:0.61 133:0.87 135:0.51 144:0.57 146:0.59 147:0.65 149:0.66 154:0.67 157:0.83 159:0.75 163:0.93 166:0.61 172:0.32 173:0.57 177:0.38 178:0.55 179:0.57 182:0.75 187:0.39 212:0.16 216:0.33 222:0.84 227:0.61 229:0.61 231:0.93 232:0.93 235:0.68 241:0.30 242:0.80 243:0.34 245:0.67 246:0.61 247:0.39 253:0.61 259:0.21 265:0.36 266:0.89 267:0.44 276:0.62 287:0.61 300:0.55 +1 1:0.74 7:0.81 11:0.64 12:0.20 17:0.59 21:0.13 27:0.68 30:0.62 34:0.58 36:0.59 37:0.81 39:0.55 43:0.78 55:0.59 60:0.87 66:0.75 69:0.77 70:0.34 74:0.71 81:0.93 83:0.87 85:0.72 91:0.54 98:0.44 101:0.74 108:0.61 114:0.92 121:0.90 122:0.43 123:0.58 126:0.54 127:0.14 129:0.05 131:0.61 135:0.80 144:0.57 145:0.83 146:0.34 147:0.41 149:0.48 150:0.97 154:0.71 155:0.67 157:0.87 158:0.92 159:0.14 165:0.88 166:1.00 172:0.32 173:0.93 176:0.73 177:0.38 178:0.55 179:0.50 182:0.98 187:0.68 192:0.59 201:0.74 204:0.89 208:0.64 212:0.84 215:0.84 216:0.21 222:0.84 227:0.61 229:0.61 230:0.87 231:1.00 233:0.76 235:0.22 241:0.51 242:0.33 243:0.77 246:0.61 247:0.39 253:0.73 259:0.21 265:0.46 266:0.17 267:0.76 276:0.28 279:0.86 283:0.82 287:0.61 290:0.92 297:0.36 300:0.25 +1 8:0.79 11:0.64 12:0.20 17:0.32 21:0.78 27:0.70 30:0.53 34:0.49 36:0.97 37:0.79 39:0.55 43:0.31 55:0.71 66:0.33 69:0.98 70:0.73 74:0.98 85:0.72 91:0.51 98:0.73 101:0.65 108:0.91 122:0.67 123:0.90 124:0.83 127:0.83 129:0.05 131:0.61 133:0.82 135:0.21 144:0.57 145:0.88 146:0.66 147:0.93 149:0.83 154:0.16 157:0.79 159:0.90 166:0.61 172:0.89 173:0.98 177:0.05 178:0.55 179:0.18 182:0.74 187:0.21 202:0.53 212:0.56 216:0.27 222:0.84 227:0.61 229:0.61 231:0.98 232:0.85 235:0.22 241:0.59 242:0.81 243:0.36 245:0.96 246:0.61 247:0.47 253:0.70 257:0.65 259:0.21 265:0.73 266:0.80 267:0.73 276:0.93 277:0.69 287:0.61 300:0.84 +1 11:0.64 12:0.20 17:0.08 21:0.78 27:0.70 30:0.41 34:0.30 36:0.50 37:0.79 39:0.55 43:0.98 55:0.71 66:0.86 69:0.05 70:0.60 74:0.85 85:0.72 91:0.15 98:0.88 101:0.74 108:0.05 122:0.67 123:0.05 127:0.42 129:0.05 131:0.61 135:0.78 144:0.57 146:0.69 147:0.74 149:0.70 154:0.98 157:0.87 159:0.27 166:0.61 172:0.59 173:0.24 177:0.38 178:0.55 179:0.70 182:0.77 187:0.87 212:0.72 216:0.27 222:0.84 227:0.61 229:0.61 231:0.98 235:0.05 241:0.24 242:0.63 243:0.86 246:0.61 247:0.47 253:0.60 259:0.21 265:0.88 266:0.27 267:0.36 276:0.49 287:0.61 300:0.43 +2 11:0.64 12:0.20 17:0.73 21:0.40 27:0.62 30:0.11 34:0.55 36:0.66 37:0.73 39:0.55 43:0.88 66:0.83 69:0.12 70:0.69 74:0.56 81:0.72 85:0.72 91:0.38 98:0.51 101:0.72 104:0.80 108:0.10 123:0.10 126:0.32 127:0.15 129:0.05 131:0.61 135:0.77 144:0.57 145:0.98 146:0.55 147:0.61 149:0.63 150:0.52 154:0.86 157:0.85 159:0.20 166:0.99 172:0.51 173:0.22 177:0.38 178:0.55 179:0.36 182:0.76 187:0.21 212:0.86 215:0.67 216:0.10 222:0.84 227:0.61 229:0.61 231:0.99 235:0.42 241:0.12 242:0.45 243:0.84 246:0.61 247:0.39 253:0.46 259:0.21 265:0.52 266:0.23 267:0.44 276:0.41 283:0.71 287:0.61 297:0.36 300:0.43 +3 1:0.69 7:0.72 8:0.67 11:0.64 12:0.86 17:0.97 18:0.71 27:0.72 29:0.71 30:0.17 32:0.69 34:0.80 36:0.61 37:0.81 39:0.84 43:0.72 45:0.73 55:0.71 66:0.91 69:0.05 70:0.79 71:0.89 74:0.82 76:0.85 77:0.89 81:0.89 83:0.91 86:0.78 91:0.62 97:0.89 98:0.97 99:0.83 101:0.52 104:0.58 108:0.05 114:0.95 122:0.77 123:0.05 126:0.44 127:0.79 129:0.05 135:0.51 139:0.77 145:0.65 146:0.80 147:0.83 149:0.48 150:0.88 154:0.65 155:0.67 158:0.74 159:0.36 165:0.70 172:0.74 173:0.21 176:0.66 177:0.70 178:0.55 179:0.60 187:0.21 192:0.87 195:0.58 201:0.68 204:0.85 208:0.64 212:0.55 215:0.96 216:0.05 219:0.89 222:0.48 230:0.75 232:0.81 233:0.76 235:0.05 241:0.07 242:0.73 243:0.91 247:0.60 253:0.65 254:0.88 259:0.21 265:0.97 266:0.27 267:0.87 276:0.62 279:0.82 282:0.77 290:0.95 292:0.91 297:0.36 300:0.55 +1 1:0.69 8:0.69 9:0.76 11:0.64 12:0.86 17:0.26 18:0.64 21:0.22 27:0.20 29:0.71 30:0.11 34:0.67 36:0.24 37:0.65 39:0.84 43:0.80 45:0.66 66:0.43 69:0.67 70:0.99 71:0.89 74:0.70 81:0.54 86:0.74 91:0.53 96:0.69 98:0.29 101:0.52 104:0.95 106:0.81 108:0.51 114:0.63 117:0.86 120:0.55 122:0.55 123:0.49 124:0.89 126:0.44 127:0.66 129:0.05 133:0.91 135:0.99 138:0.96 139:0.71 140:0.80 145:0.59 146:0.63 147:0.68 149:0.67 150:0.56 151:0.53 153:0.48 154:0.75 155:0.58 158:0.74 159:0.84 162:0.62 164:0.53 172:0.59 173:0.43 176:0.61 177:0.70 178:0.42 179:0.54 187:0.68 190:0.77 191:0.73 192:0.59 201:0.68 202:0.50 206:0.81 208:0.54 212:0.39 215:0.44 216:0.70 220:0.82 222:0.48 232:0.92 235:0.31 241:0.32 242:0.19 243:0.57 245:0.63 247:0.79 248:0.53 253:0.49 255:0.74 256:0.45 259:0.21 260:0.55 265:0.31 266:0.91 267:0.79 268:0.46 276:0.65 279:0.82 283:0.77 285:0.56 286:0.99 290:0.63 297:0.36 298:0.99 300:0.94 +1 8:0.83 12:0.86 17:0.43 18:0.66 21:0.05 25:0.88 27:0.36 29:0.71 30:0.38 34:0.22 36:0.50 37:0.57 39:0.84 43:0.15 55:0.71 66:0.54 69:0.78 70:0.78 71:0.89 74:0.41 76:0.85 77:0.70 81:0.77 86:0.65 91:0.80 98:0.61 104:0.54 108:0.62 120:0.75 123:0.59 124:0.58 126:0.26 127:0.50 129:0.05 133:0.62 135:0.68 139:0.64 140:0.45 145:0.74 146:0.60 147:0.05 149:0.21 150:0.56 151:0.59 153:0.85 154:0.11 159:0.42 162:0.71 164:0.71 172:0.41 173:0.85 175:0.75 177:0.05 179:0.80 190:0.89 191:0.83 195:0.65 202:0.70 212:0.28 215:0.78 216:0.18 220:0.62 222:0.48 235:0.05 241:0.82 242:0.60 243:0.16 244:0.83 245:0.51 247:0.55 248:0.61 253:0.32 256:0.68 257:0.84 259:0.21 260:0.68 265:0.62 266:0.47 267:0.44 268:0.73 276:0.40 277:0.69 282:0.73 283:0.78 285:0.47 297:0.36 300:0.55 +1 1:0.69 11:0.64 12:0.86 17:0.36 18:0.83 21:0.13 27:0.45 29:0.71 30:0.28 34:0.79 36:0.18 37:0.50 39:0.84 43:0.65 44:0.90 45:0.66 55:0.59 64:0.77 66:0.30 69:0.68 70:0.96 71:0.89 74:0.70 77:0.70 81:0.52 83:0.60 86:0.83 91:0.09 97:0.89 98:0.31 99:0.83 101:0.52 108:0.51 114:0.75 122:0.77 123:0.49 124:0.79 126:0.44 127:0.45 129:0.81 133:0.76 135:0.75 139:0.85 145:0.42 146:0.50 147:0.56 149:0.38 150:0.58 154:0.54 155:0.58 158:0.79 159:0.64 165:0.70 172:0.45 173:0.58 175:0.75 176:0.66 177:0.38 178:0.55 179:0.58 187:0.68 192:0.51 195:0.83 201:0.68 208:0.54 212:0.42 216:0.77 219:0.92 222:0.48 235:0.22 241:0.18 242:0.45 243:0.48 244:0.61 245:0.68 247:0.76 253:0.55 257:0.65 259:0.21 265:0.34 266:0.80 267:0.92 276:0.55 277:0.69 279:0.82 282:0.77 290:0.75 292:0.95 300:0.84 +1 1:0.74 7:0.66 8:0.81 9:0.80 11:0.64 12:0.86 17:0.72 18:0.93 21:0.22 27:0.52 29:0.71 30:0.76 31:0.87 32:0.69 34:0.86 36:0.41 37:0.55 39:0.84 43:0.76 44:0.90 45:0.66 48:0.91 64:0.77 66:0.62 69:0.81 70:0.58 71:0.89 74:0.87 76:0.85 77:0.89 79:0.87 81:0.63 86:0.95 91:0.25 96:0.69 98:0.68 101:0.52 108:0.65 114:0.63 120:0.55 122:0.76 123:0.63 124:0.56 126:0.54 127:0.34 129:0.81 133:0.67 135:0.90 137:0.87 138:0.95 139:0.95 140:0.45 145:0.77 146:0.92 147:0.94 149:0.96 150:0.73 151:0.56 153:0.48 154:0.68 155:0.67 158:0.78 159:0.72 162:0.86 164:0.54 172:0.90 173:0.66 176:0.66 177:0.70 179:0.18 187:0.21 192:0.87 195:0.91 196:0.90 201:0.93 202:0.36 204:0.85 208:0.64 212:0.52 215:0.44 216:0.46 219:0.92 220:0.82 222:0.48 230:0.68 233:0.73 235:0.42 241:0.01 242:0.32 243:0.68 245:0.93 247:0.90 248:0.55 253:0.53 255:0.95 256:0.45 259:0.21 260:0.55 265:0.69 266:0.38 267:0.44 268:0.46 276:0.87 279:0.86 281:0.89 282:0.86 283:0.77 284:0.87 285:0.62 286:0.99 290:0.63 292:0.91 297:0.36 298:0.99 300:0.70 +0 12:0.86 17:0.64 21:0.78 27:0.72 29:0.71 30:0.76 34:0.17 36:0.32 37:0.26 39:0.84 43:0.13 44:0.87 55:0.99 66:0.13 69:0.65 70:0.15 71:0.89 74:0.34 91:0.65 98:0.05 108:0.50 122:0.77 123:0.48 124:0.75 127:0.53 129:0.81 133:0.67 135:0.76 139:0.64 145:0.84 146:0.05 147:0.05 149:0.08 154:0.10 159:0.93 163:0.97 172:0.05 173:0.25 175:0.91 177:0.05 178:0.55 179:0.99 195:0.99 202:0.66 212:0.95 216:0.07 222:0.48 235:0.12 241:0.85 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.28 257:0.84 259:0.21 265:0.05 266:0.12 267:0.98 276:0.18 277:0.87 300:0.13 +2 11:0.85 12:0.86 17:0.18 18:0.91 21:0.47 22:0.93 27:0.41 29:0.71 30:0.15 32:0.68 34:0.53 36:0.30 37:0.44 39:0.84 43:0.60 44:0.90 45:0.81 47:0.93 48:0.91 55:0.92 64:0.77 66:0.35 69:0.53 70:0.97 71:0.89 74:0.59 76:0.85 81:0.25 86:0.90 91:0.38 98:0.58 101:0.87 104:0.99 108:0.72 122:0.86 123:0.70 124:0.83 126:0.26 127:0.70 129:0.59 133:0.84 135:0.82 139:0.90 145:0.89 146:0.26 147:0.32 149:0.52 150:0.25 154:0.74 159:0.78 172:0.59 173:0.34 177:0.70 178:0.55 179:0.52 187:0.68 195:0.90 212:0.40 216:0.75 219:0.89 222:0.48 235:0.52 241:0.05 242:0.24 243:0.23 245:0.73 247:0.82 253:0.38 254:0.96 259:0.21 262:0.93 265:0.59 266:0.89 267:0.92 276:0.67 277:0.69 281:0.91 292:0.91 300:0.84 +0 11:0.64 12:0.86 17:0.75 18:0.80 21:0.13 27:0.63 29:0.71 30:0.10 32:0.78 34:0.71 36:0.31 37:0.32 39:0.84 43:0.13 44:0.93 45:0.66 55:0.52 66:0.16 69:0.69 70:0.97 71:0.89 74:0.70 77:0.89 81:0.66 86:0.82 91:0.23 98:0.41 101:0.52 104:0.83 108:0.52 122:0.86 123:0.85 124:0.87 126:0.26 127:0.62 129:0.59 133:0.86 135:0.88 139:0.84 145:0.80 146:0.65 147:0.70 149:0.22 150:0.48 154:0.67 159:0.74 172:0.51 173:0.55 177:0.70 178:0.55 179:0.45 187:0.39 195:0.87 212:0.30 215:0.61 216:0.56 222:0.48 235:0.12 241:0.18 242:0.24 243:0.40 245:0.75 247:0.84 253:0.41 254:0.95 259:0.21 265:0.41 266:0.84 267:0.59 276:0.71 282:0.86 283:0.78 297:0.36 300:0.84 +1 1:0.74 8:0.69 9:0.80 12:0.86 17:0.62 18:0.75 21:0.59 22:0.93 27:0.28 29:0.71 30:0.56 31:0.86 34:0.95 36:0.26 37:0.41 39:0.84 43:0.14 47:0.93 55:0.96 64:0.77 66:0.08 69:0.51 70:0.71 71:0.89 74:0.60 79:0.86 81:0.42 86:0.80 91:0.40 96:0.68 98:0.23 104:0.94 106:0.81 108:0.39 114:0.58 117:0.86 120:0.54 122:0.77 123:0.65 124:0.85 126:0.54 127:0.58 129:0.05 133:0.81 135:0.99 137:0.86 139:0.83 140:0.45 145:0.85 146:0.27 147:0.33 149:0.16 150:0.63 151:0.47 153:0.48 154:0.83 155:0.67 158:0.86 159:0.67 162:0.78 164:0.70 172:0.21 173:0.39 176:0.73 177:0.70 179:0.79 187:0.39 192:0.87 196:0.88 201:0.93 202:0.59 206:0.81 208:0.64 212:0.13 215:0.39 216:0.67 219:0.95 220:0.97 222:0.48 232:0.98 235:0.80 241:0.01 242:0.24 243:0.39 245:0.52 247:0.74 248:0.47 253:0.42 254:0.86 255:0.95 256:0.58 259:0.21 260:0.54 262:0.93 265:0.18 266:0.75 267:0.59 268:0.64 276:0.43 277:0.87 279:0.86 283:0.77 284:0.93 285:0.62 290:0.58 292:0.87 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.64 12:0.86 17:0.49 18:0.69 21:0.13 22:0.93 27:0.20 29:0.71 30:0.62 31:0.91 34:0.56 36:0.41 37:0.65 39:0.84 43:0.79 45:0.66 47:0.93 64:0.77 66:0.47 69:0.66 70:0.34 71:0.89 74:0.57 79:0.91 81:0.80 83:0.60 86:0.77 91:0.68 96:0.76 97:0.94 98:0.33 99:0.83 101:0.52 104:0.93 106:0.81 108:0.50 114:0.79 117:0.86 120:0.64 122:0.80 123:0.48 124:0.52 126:0.54 127:0.23 129:0.05 133:0.43 135:0.75 137:0.91 139:0.82 140:0.45 145:0.47 146:0.39 147:0.46 149:0.56 150:0.86 151:0.74 153:0.69 154:0.73 155:0.67 158:0.91 159:0.33 162:0.86 163:0.79 164:0.62 165:0.70 172:0.21 173:0.68 176:0.73 177:0.70 179:0.83 187:0.68 192:0.87 196:0.93 201:0.93 202:0.46 206:0.81 208:0.64 212:0.30 215:0.61 216:0.70 219:0.95 220:0.62 222:0.48 232:0.96 235:0.31 241:0.47 242:0.63 243:0.59 245:0.46 247:0.35 248:0.69 253:0.71 255:0.95 256:0.45 259:0.21 260:0.65 262:0.93 265:0.35 266:0.27 267:0.65 268:0.55 276:0.21 279:0.86 283:0.82 284:0.90 285:0.62 290:0.79 292:0.91 297:0.36 300:0.25 +2 7:0.66 11:0.64 12:0.86 17:0.96 18:0.83 21:0.22 27:0.53 29:0.71 30:0.62 32:0.68 34:0.81 36:0.52 37:0.25 39:0.84 43:0.58 45:0.73 48:0.91 55:0.59 64:0.77 66:0.92 69:0.12 70:0.58 71:0.89 74:0.69 76:0.99 77:0.70 81:0.63 86:0.92 91:0.56 98:0.98 101:0.52 104:0.89 106:0.81 108:0.10 123:0.10 126:0.44 127:0.81 129:0.05 135:0.47 139:0.90 145:0.49 146:0.76 147:0.80 149:0.37 150:0.44 154:0.83 155:0.58 159:0.32 172:0.77 173:0.35 177:0.70 178:0.55 179:0.56 187:0.39 192:0.59 195:0.60 201:0.68 204:0.85 206:0.81 208:0.54 212:0.91 215:0.51 216:0.18 222:0.48 230:0.69 232:0.74 233:0.76 235:0.31 241:0.05 242:0.58 243:0.92 247:0.79 253:0.41 254:0.95 259:0.21 265:0.98 266:0.27 267:0.52 276:0.66 281:0.91 282:0.77 283:0.82 297:0.36 300:0.55 +1 1:0.74 7:0.81 8:0.58 11:0.64 12:0.86 17:0.73 18:0.90 21:0.22 22:0.93 27:0.51 29:0.71 30:0.53 32:0.80 34:0.75 36:0.62 37:0.35 39:0.84 43:0.40 44:0.93 45:0.89 47:0.93 64:0.77 66:0.33 69:0.55 70:0.83 71:0.89 74:0.91 77:0.70 81:0.72 83:0.60 86:0.97 91:0.37 97:0.94 98:0.65 99:0.83 101:0.52 104:0.92 106:0.81 108:0.42 114:0.71 117:0.86 122:0.80 123:0.68 124:0.60 126:0.54 127:0.37 129:0.59 133:0.46 135:0.94 139:0.98 145:0.42 146:0.51 147:0.57 149:0.35 150:0.81 154:0.84 155:0.67 158:0.91 159:0.48 165:0.70 172:0.68 173:0.52 176:0.73 177:0.70 178:0.55 179:0.33 187:0.21 192:0.87 195:0.74 201:0.93 204:0.89 206:0.81 208:0.64 212:0.77 215:0.51 216:0.39 219:0.95 222:0.48 230:0.86 232:0.97 233:0.73 235:0.42 241:0.01 242:0.27 243:0.57 245:0.90 247:0.90 253:0.55 254:0.74 259:0.21 262:0.93 265:0.42 266:0.75 267:0.94 276:0.72 279:0.86 281:0.89 282:0.88 283:0.77 290:0.71 292:0.91 297:0.36 300:0.84 +1 7:0.66 12:0.86 17:0.66 18:0.79 21:0.05 27:0.72 29:0.71 30:0.27 31:0.88 32:0.75 34:0.97 36:0.65 39:0.84 43:0.14 44:0.93 55:0.42 64:0.77 66:0.43 69:0.08 70:0.73 71:0.89 74:0.72 76:0.97 77:0.70 79:0.88 81:0.82 86:0.86 87:0.98 91:0.65 98:0.39 104:0.88 106:0.81 108:0.07 120:0.57 123:0.07 124:0.90 126:0.44 127:0.91 129:0.59 133:0.91 135:0.74 137:0.88 139:0.87 140:0.45 145:0.59 146:0.71 147:0.75 149:0.20 150:0.60 151:0.53 153:0.48 154:0.78 159:0.82 162:0.86 164:0.54 172:0.61 173:0.09 177:0.70 179:0.55 187:0.21 190:0.77 192:0.51 195:0.92 196:0.90 201:0.68 202:0.36 206:0.81 212:0.55 215:0.78 216:0.07 220:0.82 222:0.48 228:0.99 230:0.69 232:0.77 233:0.73 235:0.12 241:0.07 242:0.14 243:0.57 245:0.65 247:0.86 248:0.53 253:0.42 254:0.80 255:0.74 256:0.45 259:0.21 260:0.57 265:0.41 266:0.84 267:0.88 268:0.46 276:0.67 282:0.84 283:0.78 284:0.87 285:0.56 297:0.36 300:0.55 +2 7:0.72 11:0.64 12:0.86 17:0.97 18:0.63 21:0.05 27:0.72 29:0.71 30:0.41 32:0.69 34:0.44 36:0.84 37:0.81 39:0.84 43:0.18 45:0.66 55:0.52 66:0.86 69:0.05 70:0.65 71:0.89 74:0.69 76:0.85 77:0.89 81:0.77 86:0.68 91:0.85 98:0.99 101:0.52 104:0.58 108:0.05 122:0.55 123:0.05 126:0.26 127:0.87 129:0.05 135:0.44 139:0.66 145:0.65 146:0.64 147:0.69 149:0.19 150:0.56 154:0.70 155:0.58 159:0.24 172:0.59 173:0.34 177:0.70 178:0.55 179:0.78 187:0.21 192:0.59 195:0.53 204:0.85 208:0.54 212:0.61 215:0.78 216:0.05 222:0.48 230:0.74 232:0.81 233:0.73 235:0.05 241:0.75 242:0.19 243:0.86 247:0.74 253:0.41 254:0.88 259:0.21 265:0.99 266:0.27 267:0.93 276:0.48 282:0.77 297:0.36 300:0.43 +1 1:0.74 9:0.80 11:0.64 12:0.86 17:0.47 18:0.68 21:0.13 22:0.93 27:0.20 29:0.71 30:0.62 31:0.91 34:0.71 36:0.33 37:0.65 39:0.84 43:0.78 45:0.66 47:0.93 64:0.77 66:0.47 69:0.71 70:0.34 71:0.89 74:0.57 79:0.91 81:0.80 83:0.60 86:0.76 91:0.66 96:0.76 97:0.94 98:0.33 99:0.83 101:0.52 104:0.93 106:0.81 108:0.54 114:0.79 117:0.86 120:0.64 122:0.80 123:0.52 124:0.52 126:0.54 127:0.23 129:0.05 133:0.43 135:0.74 137:0.91 139:0.81 140:0.45 145:0.47 146:0.39 147:0.46 149:0.54 150:0.86 151:0.74 153:0.69 154:0.71 155:0.67 158:0.91 159:0.33 162:0.86 163:0.79 164:0.62 165:0.70 172:0.21 173:0.73 176:0.73 177:0.70 179:0.83 187:0.68 192:0.87 196:0.92 201:0.93 202:0.46 206:0.81 208:0.64 212:0.30 215:0.61 216:0.70 219:0.95 220:0.62 222:0.48 232:0.96 235:0.31 241:0.50 242:0.63 243:0.59 245:0.46 247:0.35 248:0.69 253:0.70 255:0.95 256:0.45 259:0.21 260:0.65 262:0.93 265:0.35 266:0.27 267:0.84 268:0.55 276:0.21 279:0.86 283:0.82 284:0.90 285:0.62 290:0.79 292:0.91 297:0.36 300:0.25 +2 1:0.69 7:0.66 8:0.73 9:0.76 11:0.64 12:0.86 17:0.53 18:0.80 22:0.93 27:0.17 29:0.71 30:0.28 31:0.90 34:0.71 36:0.67 37:0.55 39:0.84 43:0.58 45:0.81 47:0.93 64:0.77 66:0.61 69:0.84 70:0.83 71:0.89 74:0.68 77:0.70 79:0.90 81:0.69 83:0.60 86:0.84 91:0.40 96:0.78 97:0.89 98:0.31 99:0.83 101:0.52 108:0.69 114:0.95 117:0.86 122:0.80 123:0.67 124:0.55 126:0.44 127:0.24 129:0.05 133:0.60 135:0.98 137:0.90 139:0.83 140:0.80 145:0.72 146:0.53 147:0.59 149:0.46 150:0.65 151:0.65 153:0.48 154:0.60 155:0.58 158:0.79 159:0.54 162:0.86 165:0.70 172:0.54 173:0.76 176:0.66 177:0.70 179:0.50 187:0.68 190:0.77 191:0.70 192:0.51 195:0.88 196:0.92 201:0.68 202:0.53 208:0.54 212:0.61 216:0.71 219:0.92 220:0.74 222:0.48 230:0.69 232:0.92 233:0.73 235:0.05 241:0.56 242:0.28 243:0.68 245:0.60 247:0.76 248:0.63 253:0.75 254:0.93 255:0.74 256:0.58 259:0.21 262:0.93 265:0.33 266:0.63 267:0.69 268:0.62 276:0.45 279:0.82 282:0.73 284:0.87 285:0.56 290:0.95 292:0.95 300:0.70 +1 7:0.72 8:0.83 11:0.64 12:0.86 17:0.92 18:0.72 21:0.05 27:0.72 29:0.71 30:0.21 34:0.36 36:0.43 39:0.84 43:0.59 45:0.73 48:0.97 55:0.84 66:0.93 69:0.08 70:0.88 71:0.89 74:0.68 81:0.77 86:0.83 91:0.80 98:0.92 101:0.52 104:0.58 108:0.07 120:0.74 123:0.07 126:0.26 127:0.55 129:0.05 135:0.54 139:0.83 140:0.45 146:0.92 147:0.93 149:0.45 150:0.56 151:0.60 153:0.48 154:0.92 155:0.58 159:0.53 162:0.86 164:0.56 172:0.82 173:0.15 177:0.70 179:0.42 190:0.89 192:0.59 202:0.50 204:0.85 208:0.54 212:0.60 215:0.78 216:0.02 220:0.62 222:0.48 230:0.75 233:0.76 235:0.05 241:0.79 242:0.72 243:0.94 247:0.79 248:0.61 253:0.41 254:0.74 256:0.58 259:0.21 260:0.68 265:0.92 266:0.63 267:0.84 268:0.59 276:0.72 281:0.91 283:0.78 285:0.47 297:0.36 300:0.70 +3 7:0.72 8:0.73 9:0.76 11:0.64 12:0.86 17:0.91 18:0.63 21:0.05 27:0.72 29:0.71 30:0.41 32:0.69 34:0.50 36:0.83 37:0.81 39:0.84 43:0.18 45:0.66 55:0.52 66:0.86 69:0.05 70:0.65 71:0.89 74:0.73 76:0.85 77:0.89 81:0.77 83:0.60 86:0.68 91:0.90 96:0.84 97:0.89 98:0.99 101:0.52 104:0.58 108:0.05 120:0.78 122:0.55 123:0.05 126:0.26 127:0.87 129:0.05 135:0.42 139:0.66 140:0.80 145:0.65 146:0.64 147:0.69 149:0.19 150:0.56 151:0.85 153:0.77 154:0.70 155:0.58 158:0.79 159:0.24 162:0.70 164:0.65 172:0.59 173:0.34 177:0.70 179:0.78 190:0.77 192:0.59 195:0.53 202:0.62 204:0.85 208:0.54 212:0.61 215:0.78 216:0.05 220:0.62 222:0.48 230:0.74 232:0.81 233:0.73 235:0.05 241:0.80 242:0.19 243:0.86 247:0.74 248:0.76 253:0.81 254:0.88 255:0.74 256:0.58 259:0.21 260:0.74 265:0.99 266:0.27 267:0.96 268:0.65 276:0.48 279:0.82 282:0.77 283:0.82 285:0.56 297:0.36 300:0.43 +1 1:0.69 7:0.72 8:0.68 9:0.76 11:0.64 12:0.86 17:0.88 18:0.73 21:0.22 27:0.49 29:0.71 30:0.73 32:0.69 34:0.63 36:0.73 37:0.38 39:0.84 43:0.65 45:0.81 55:0.52 66:0.91 69:0.10 70:0.39 71:0.89 74:0.87 76:0.97 77:0.99 81:0.62 86:0.79 91:0.46 96:0.71 98:0.85 101:0.52 108:0.09 114:0.67 117:0.86 120:0.57 122:0.75 123:0.09 126:0.44 127:0.36 129:0.05 135:0.49 139:0.79 140:0.45 145:0.92 146:0.66 147:0.71 149:0.66 150:0.59 151:0.53 153:0.48 154:0.77 155:0.67 158:0.78 159:0.25 162:0.86 164:0.54 172:0.76 173:0.32 176:0.66 177:0.70 179:0.45 187:0.21 190:0.77 192:0.87 195:0.57 201:0.68 202:0.36 204:0.85 208:0.64 212:0.95 215:0.51 216:0.30 219:0.92 220:0.82 222:0.48 230:0.74 232:0.84 233:0.73 235:0.31 241:0.01 242:0.28 243:0.92 247:0.84 248:0.53 253:0.57 254:0.93 255:0.74 256:0.45 259:0.21 260:0.57 265:0.85 266:0.12 267:0.28 268:0.46 276:0.65 279:0.82 282:0.77 285:0.56 290:0.67 292:0.91 297:0.36 300:0.33 +2 7:0.72 8:0.83 11:0.64 12:0.86 17:0.92 18:0.72 21:0.05 27:0.72 29:0.71 30:0.21 34:0.36 36:0.43 39:0.84 43:0.59 45:0.73 48:0.97 55:0.84 66:0.93 69:0.08 70:0.88 71:0.89 74:0.68 81:0.77 86:0.83 91:0.80 98:0.92 101:0.52 104:0.58 108:0.07 120:0.74 123:0.07 126:0.26 127:0.55 129:0.05 135:0.54 139:0.83 140:0.45 146:0.92 147:0.93 149:0.45 150:0.56 151:0.60 153:0.48 154:0.92 155:0.58 159:0.53 162:0.86 164:0.56 172:0.82 173:0.15 177:0.70 179:0.42 190:0.89 192:0.59 202:0.50 204:0.85 208:0.54 212:0.60 215:0.78 216:0.02 220:0.62 222:0.48 230:0.75 233:0.76 235:0.05 241:0.79 242:0.72 243:0.94 247:0.79 248:0.61 253:0.41 254:0.74 256:0.58 259:0.21 260:0.68 265:0.92 266:0.63 267:0.84 268:0.59 276:0.72 281:0.91 285:0.47 297:0.36 300:0.70 +2 1:0.74 11:0.64 12:0.86 17:0.18 18:0.76 21:0.30 27:0.45 29:0.71 30:0.30 34:0.91 36:0.21 37:0.50 39:0.84 43:0.20 45:0.73 55:0.59 64:0.77 66:0.33 69:0.68 70:0.92 71:0.89 74:0.52 81:0.64 83:0.60 86:0.83 91:0.11 98:0.15 99:0.83 101:0.52 108:0.52 114:0.65 122:0.55 123:0.50 124:0.77 126:0.54 127:0.69 129:0.59 133:0.73 135:0.87 139:0.79 145:0.49 146:0.31 147:0.38 149:0.11 150:0.77 154:0.76 155:0.67 159:0.86 165:0.70 172:0.32 173:0.43 176:0.73 177:0.70 178:0.55 179:0.81 187:0.68 192:0.87 201:0.93 208:0.64 212:0.52 215:0.44 216:0.77 222:0.48 235:0.52 241:0.27 242:0.13 243:0.50 245:0.53 247:0.76 253:0.49 254:0.80 259:0.21 265:0.16 266:0.54 267:0.84 276:0.34 290:0.65 297:0.36 300:0.70 +2 1:0.74 6:0.95 8:0.90 9:0.80 11:0.51 12:0.37 17:0.16 20:0.83 21:0.22 25:0.88 27:0.07 28:0.85 30:0.17 34:0.95 36:0.88 37:0.90 39:0.27 41:0.96 43:0.35 55:0.71 66:0.43 69:0.10 70:0.81 74:0.94 77:0.70 78:0.89 81:0.84 83:0.81 91:0.77 96:0.98 98:0.68 100:0.95 104:0.73 108:0.09 111:0.92 114:0.90 117:0.86 120:0.97 122:0.67 123:0.09 124:0.71 126:0.54 127:0.86 129:0.59 131:0.98 133:0.64 135:0.81 138:1.00 140:0.80 144:0.76 145:0.38 146:0.83 147:0.86 149:0.65 150:0.86 151:0.92 152:0.91 153:0.91 154:0.77 155:0.67 157:0.61 159:0.68 161:0.68 162:0.66 164:0.97 166:0.97 167:0.79 169:0.96 172:0.68 173:0.13 176:0.73 177:0.38 178:0.42 179:0.45 181:0.76 182:0.94 186:0.89 187:0.98 189:0.97 191:0.83 192:0.59 195:0.80 201:0.74 202:0.96 208:0.64 212:0.51 215:0.79 216:0.92 220:0.82 222:0.60 227:0.98 229:0.97 231:0.96 232:0.94 235:0.31 238:0.95 241:0.73 242:0.73 243:0.57 245:0.85 246:0.61 247:0.67 248:0.95 253:0.99 254:0.74 255:0.79 256:0.96 259:0.21 260:0.97 261:0.96 265:0.68 266:0.75 267:0.36 268:0.97 271:0.77 276:0.74 282:0.81 285:0.62 287:0.97 290:0.90 297:0.36 300:0.70 +2 1:0.74 6:0.94 7:0.81 8:0.89 9:0.80 11:0.78 12:0.37 17:0.22 20:0.97 21:0.59 27:0.08 28:0.85 30:0.31 32:0.80 34:0.55 36:0.96 37:0.90 39:0.27 41:0.99 43:0.83 55:0.59 60:0.95 66:0.34 69:0.67 70:0.76 74:1.00 76:0.98 77:0.98 78:0.83 81:0.77 83:0.89 85:0.99 91:0.89 96:0.99 98:0.72 100:0.87 101:0.84 104:0.78 106:0.81 108:0.94 111:0.83 114:0.74 117:0.86 120:0.95 121:0.90 122:0.67 123:0.94 124:0.72 126:0.54 127:0.83 129:0.81 131:0.98 133:0.67 135:1.00 140:0.87 144:0.76 145:0.85 146:0.92 147:0.93 149:0.98 150:0.83 151:0.99 152:0.89 153:0.98 154:0.79 155:0.67 157:0.98 158:0.92 159:0.91 161:0.68 162:0.80 164:0.93 165:0.70 166:0.97 167:0.73 169:0.85 172:0.97 173:0.33 176:0.73 177:0.38 179:0.11 181:0.76 182:0.95 186:0.83 187:0.21 189:0.95 191:0.87 192:0.59 195:0.98 201:0.74 202:0.93 204:0.89 206:0.81 208:0.64 212:0.79 215:0.55 216:0.85 220:0.62 222:0.60 227:0.98 229:0.97 230:0.87 231:0.96 232:0.84 233:0.76 235:0.88 238:0.91 241:0.65 242:0.43 243:0.28 245:1.00 246:0.96 247:0.60 248:0.97 253:1.00 255:0.79 256:0.96 259:0.21 260:0.95 261:0.88 265:0.72 266:0.54 267:0.85 268:0.96 271:0.77 276:0.98 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 290:0.74 297:0.36 299:0.88 300:0.70 +4 1:0.74 6:0.97 8:0.93 9:0.80 11:0.51 12:0.37 17:0.01 20:0.82 25:0.88 27:0.07 28:0.85 30:0.68 34:0.18 36:0.68 37:0.88 39:0.27 41:1.00 43:0.39 55:0.59 66:0.67 69:0.05 70:0.54 74:0.96 76:0.85 77:0.89 78:0.93 81:0.96 83:0.91 91:0.98 96:1.00 98:0.80 100:0.99 104:0.58 108:0.63 111:0.97 114:0.98 117:0.86 120:1.00 122:0.67 123:0.61 124:0.46 126:0.54 127:0.84 129:0.59 131:0.98 133:0.47 135:0.47 138:0.99 140:0.80 144:0.76 145:0.96 146:0.53 147:0.59 149:0.67 150:0.99 151:1.00 152:0.94 153:1.00 154:0.75 155:0.67 157:0.61 158:0.86 159:0.48 161:0.68 162:0.65 164:1.00 165:0.92 166:0.97 167:0.84 169:0.96 172:0.78 173:0.16 176:0.73 177:0.38 179:0.47 181:0.76 182:0.95 186:0.92 187:0.21 189:0.99 191:0.97 192:0.59 195:0.72 201:0.74 202:1.00 208:0.64 212:0.84 215:0.96 216:0.99 222:0.60 227:0.98 229:0.97 231:0.96 232:0.82 235:0.05 238:0.98 241:0.76 242:0.72 243:0.29 245:0.85 246:0.97 247:0.55 248:1.00 253:1.00 254:0.84 255:0.79 256:1.00 259:0.21 260:1.00 261:0.97 265:0.80 266:0.38 267:0.76 268:1.00 271:0.77 276:0.73 279:0.86 282:0.81 283:0.67 285:0.62 287:0.98 290:0.98 297:0.36 300:0.55 +1 1:0.74 8:0.64 11:0.78 12:0.37 17:0.53 20:0.74 21:0.13 27:0.45 28:0.85 30:0.17 32:0.80 34:0.71 36:0.27 37:0.50 39:0.27 43:0.82 55:0.84 60:0.87 66:0.20 69:0.68 70:0.90 74:0.90 77:0.70 78:0.85 81:0.89 83:0.85 85:0.80 91:0.11 98:0.40 100:0.88 101:0.72 108:0.52 111:0.85 114:0.92 122:0.67 123:0.50 124:0.85 126:0.54 127:0.59 129:0.93 131:0.61 133:0.84 135:0.90 144:0.76 145:0.49 146:0.68 147:0.73 149:0.77 150:0.94 152:0.74 154:0.77 155:0.67 157:0.85 158:0.92 159:0.76 161:0.68 165:0.88 166:0.97 167:0.66 169:0.85 172:0.51 173:0.53 176:0.73 177:0.38 178:0.55 179:0.45 181:0.76 182:0.94 186:0.86 187:0.68 192:0.59 195:0.89 201:0.74 208:0.64 212:0.30 215:0.84 216:0.77 222:0.60 227:0.61 229:0.61 231:0.95 235:0.22 241:0.47 242:0.55 243:0.40 245:0.78 246:0.97 247:0.67 253:0.78 259:0.21 261:0.75 265:0.42 266:0.87 267:0.36 271:0.77 276:0.70 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.84 +2 6:0.90 8:0.85 9:0.80 11:0.51 12:0.37 17:0.08 20:0.78 21:0.05 27:0.37 28:0.85 30:0.20 34:0.18 36:0.78 37:0.48 39:0.27 41:0.88 43:0.23 55:0.84 60:0.87 66:0.29 69:0.85 70:0.90 74:0.93 78:0.82 81:0.88 83:0.78 91:0.65 96:0.99 98:0.57 100:0.84 108:0.94 111:0.81 114:0.56 117:0.86 120:0.76 122:0.67 123:0.94 124:0.91 126:0.32 127:0.57 129:0.96 131:0.98 133:0.91 135:0.99 140:0.99 144:0.76 146:0.77 147:0.81 149:0.59 150:0.76 151:0.82 152:0.87 153:0.97 154:0.54 155:0.67 157:0.61 158:0.92 159:0.88 161:0.68 162:0.71 164:0.70 166:0.91 167:0.68 169:0.84 172:0.80 173:0.63 176:0.53 177:0.38 179:0.22 181:0.76 182:0.94 186:0.83 187:0.39 189:0.91 191:0.70 192:0.59 202:0.91 208:0.64 212:0.30 216:0.82 222:0.60 227:0.98 229:0.97 231:0.95 232:0.83 235:0.05 238:0.88 241:0.54 242:0.50 243:0.31 245:0.89 246:0.61 247:0.60 248:0.83 253:0.99 255:0.79 256:0.93 259:0.21 260:0.77 261:0.88 265:0.58 266:0.92 267:0.65 268:0.93 271:0.77 276:0.88 279:0.86 283:0.82 285:0.62 287:0.97 290:0.56 300:0.84 +2 6:0.95 7:0.76 8:0.87 9:0.80 11:0.78 12:0.37 17:0.09 20:0.96 21:0.30 27:0.21 28:0.85 30:0.27 34:0.37 36:0.84 37:0.74 39:0.27 41:0.98 43:0.45 55:0.59 60:0.95 66:0.35 69:0.10 70:0.81 74:0.99 76:0.85 78:0.79 81:0.67 83:0.89 85:0.97 91:0.92 96:1.00 98:0.59 100:0.84 101:0.74 108:0.98 111:0.79 114:0.55 117:0.86 120:0.94 122:0.67 123:0.98 124:0.92 126:0.32 127:0.99 129:0.97 131:0.98 133:0.94 135:0.23 138:1.00 140:0.90 144:0.76 146:0.45 147:0.51 149:0.84 150:0.49 151:0.98 152:0.89 153:0.99 154:0.77 155:0.67 157:0.95 158:0.92 159:0.97 161:0.68 162:0.66 164:0.90 166:0.90 167:0.82 169:0.81 172:0.99 173:0.05 176:0.53 177:0.38 179:0.09 181:0.76 182:0.95 186:0.80 187:0.39 189:0.96 191:0.87 192:0.59 202:0.96 208:0.64 212:0.73 216:0.95 222:0.60 227:0.98 229:0.97 230:0.78 231:0.96 232:0.85 233:0.69 235:0.31 238:0.91 241:0.75 242:0.33 243:0.06 245:1.00 246:0.61 247:0.67 248:0.97 253:1.00 255:0.79 256:0.97 259:0.21 260:0.95 261:0.85 265:0.60 266:0.94 267:0.69 268:0.97 271:0.77 276:0.99 279:0.86 283:0.82 285:0.62 287:0.98 290:0.55 300:0.84 +1 11:0.78 12:0.37 17:0.34 21:0.54 27:0.45 28:0.85 30:0.20 34:0.89 36:0.18 37:0.50 39:0.27 43:0.79 55:0.59 66:0.35 69:0.69 70:0.88 74:0.90 77:0.70 81:0.48 85:0.85 91:0.25 96:0.69 98:0.52 101:0.76 108:0.52 114:0.54 122:0.67 123:0.50 124:0.78 126:0.32 127:0.42 129:0.89 131:0.94 133:0.78 135:0.79 140:0.45 144:0.76 145:0.42 146:0.69 147:0.73 149:0.77 150:0.38 151:0.48 153:0.48 154:0.73 157:0.91 158:0.82 159:0.57 161:0.68 162:0.86 166:0.90 167:0.58 172:0.57 173:0.63 176:0.53 177:0.38 179:0.47 181:0.76 182:0.80 187:0.68 190:0.77 195:0.79 202:0.36 212:0.61 216:0.77 220:0.96 222:0.60 227:0.96 229:0.61 231:0.94 235:0.60 241:0.18 242:0.41 243:0.51 245:0.74 246:0.61 247:0.60 248:0.48 253:0.65 256:0.45 259:0.21 265:0.54 266:0.54 267:0.28 268:0.46 271:0.77 276:0.65 282:0.81 285:0.50 287:0.61 290:0.54 300:0.70 +2 1:0.74 6:0.94 7:0.76 8:0.92 9:0.80 12:0.37 17:0.03 20:0.84 21:0.68 27:0.06 28:0.85 30:0.58 32:0.77 34:0.93 36:0.92 37:0.92 39:0.27 41:0.96 43:0.29 55:0.71 66:0.88 69:0.72 70:0.39 74:0.90 76:0.94 77:0.70 78:0.86 81:0.53 83:0.80 91:0.93 96:0.99 98:0.90 100:0.91 108:0.55 111:0.87 114:0.54 117:0.86 120:0.88 122:0.67 123:0.52 126:0.54 127:0.46 129:0.05 131:0.98 135:0.81 140:0.95 144:0.76 145:0.63 146:0.92 147:0.94 149:0.53 150:0.63 151:0.94 152:0.88 153:0.95 154:0.22 155:0.67 157:0.61 158:0.83 159:0.54 161:0.68 162:0.57 163:0.92 164:0.93 166:0.97 167:0.79 169:0.88 172:0.65 173:0.69 175:0.75 176:0.53 177:0.05 178:0.42 179:0.64 181:0.76 182:0.94 186:0.86 187:0.68 189:0.97 191:0.95 192:0.59 195:0.77 201:0.74 202:0.97 208:0.64 212:0.23 215:0.49 216:0.75 222:0.60 227:0.98 229:0.97 230:0.79 231:0.96 232:0.98 233:0.76 235:0.84 238:0.93 241:0.72 242:0.82 243:0.88 244:0.61 246:0.61 247:0.12 248:0.91 253:0.99 255:0.79 256:0.97 257:0.65 259:0.21 260:0.88 261:0.90 265:0.90 266:0.63 267:0.28 268:0.97 271:0.77 276:0.55 277:0.69 282:0.85 283:0.82 285:0.62 287:0.97 290:0.54 297:0.36 300:0.33 +3 1:0.74 6:0.97 8:0.86 9:0.80 12:0.37 20:0.77 21:0.05 25:0.88 27:0.07 28:0.85 30:0.45 34:0.28 36:0.92 37:0.88 39:0.27 41:1.00 43:0.39 55:0.59 60:0.98 66:0.29 69:0.07 70:0.59 74:0.95 76:0.85 77:0.89 78:0.88 81:0.93 83:0.90 91:0.92 96:1.00 98:0.73 100:0.89 104:0.58 108:0.78 111:0.87 114:0.95 117:0.86 120:0.99 122:0.67 123:0.06 124:0.58 126:0.54 127:0.82 129:0.59 131:0.98 133:0.47 135:0.49 138:1.00 140:0.80 144:0.76 145:0.96 146:0.79 147:0.82 149:0.69 150:0.96 151:0.98 152:0.94 153:0.98 154:0.75 155:0.67 157:0.61 158:0.87 159:0.57 161:0.68 162:0.56 164:0.98 165:0.90 166:0.97 167:0.76 169:0.96 172:0.67 173:0.14 176:0.73 177:0.38 179:0.50 181:0.76 182:0.95 186:0.83 187:0.68 189:0.96 191:0.89 192:0.59 195:0.76 201:0.74 202:0.99 208:0.64 212:0.83 215:0.91 216:0.99 222:0.60 227:0.98 229:0.97 231:0.96 232:0.82 235:0.12 238:0.90 241:0.69 242:0.74 243:0.47 245:0.87 246:0.97 247:0.55 248:1.00 253:1.00 254:0.93 255:0.79 256:0.99 259:0.21 260:0.99 261:0.97 265:0.62 266:0.63 267:0.52 268:0.99 271:0.77 276:0.70 279:0.86 282:0.81 283:0.82 285:0.62 287:0.98 290:0.95 297:0.36 300:0.55 +1 1:0.74 8:0.64 11:0.57 12:0.51 17:0.36 20:0.81 21:0.59 27:0.45 28:0.73 30:0.28 34:0.83 36:0.17 37:0.50 39:0.38 43:0.82 66:0.51 69:0.70 70:0.79 74:0.79 78:0.90 81:0.67 83:0.73 85:0.90 91:0.25 98:0.45 100:0.73 101:0.79 108:0.53 111:0.87 114:0.74 122:0.43 123:0.51 124:0.65 126:0.54 127:0.38 129:0.59 133:0.64 135:0.87 145:0.49 146:0.63 147:0.68 149:0.81 150:0.78 152:0.78 154:0.77 155:0.67 159:0.57 161:0.78 165:0.78 167:0.54 169:0.72 172:0.57 173:0.63 176:0.73 177:0.38 178:0.55 179:0.55 181:0.97 186:0.90 187:0.68 192:0.59 201:0.74 212:0.61 215:0.55 216:0.77 222:0.48 235:0.74 241:0.39 242:0.39 243:0.61 245:0.69 247:0.67 253:0.67 259:0.21 261:0.84 265:0.47 266:0.75 267:0.28 271:0.11 276:0.56 290:0.74 297:0.36 300:0.55 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.57 20:0.79 21:0.71 28:0.73 30:0.85 32:0.80 34:0.28 36:0.28 37:0.97 39:0.38 41:0.82 43:0.80 60:0.87 66:0.69 69:0.73 70:0.44 74:0.90 77:0.70 78:0.72 81:0.57 83:0.85 85:0.96 91:0.37 96:0.80 98:0.39 100:0.73 101:0.81 104:0.80 108:0.57 111:0.72 114:0.68 120:0.69 122:0.43 123:0.54 124:0.46 126:0.54 127:0.25 129:0.81 133:0.67 135:0.66 140:0.80 145:0.63 146:0.73 147:0.77 149:0.93 150:0.70 151:0.87 152:0.82 153:0.48 154:0.74 155:0.67 158:0.87 159:0.65 161:0.78 162:0.51 164:0.57 165:0.69 167:0.57 169:0.79 172:0.70 173:0.54 176:0.73 177:0.38 178:0.42 179:0.35 181:0.97 186:0.73 187:0.21 192:0.59 195:0.93 201:0.74 202:0.59 208:0.64 212:0.42 215:0.48 216:0.98 222:0.48 232:0.85 235:0.88 241:0.50 242:0.63 243:0.73 245:0.67 247:0.30 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 261:0.81 265:0.42 266:0.63 267:0.73 268:0.46 271:0.11 276:0.53 279:0.86 282:0.88 283:0.71 285:0.62 290:0.68 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.93 7:0.81 8:0.85 9:0.80 11:0.57 12:0.51 17:0.55 20:0.98 21:0.30 27:0.21 28:0.73 30:0.45 34:0.42 36:0.95 37:0.74 39:0.38 41:0.96 43:0.98 55:0.71 60:0.97 66:0.63 69:0.12 70:0.64 74:0.98 76:0.85 78:0.93 81:0.85 83:0.88 85:0.97 91:0.70 96:0.96 98:0.82 100:0.99 101:0.82 104:0.84 106:0.81 108:0.84 111:0.98 114:0.86 117:0.86 120:0.92 121:0.90 122:0.57 123:0.83 124:0.49 126:0.54 127:0.68 129:0.59 133:0.47 135:0.23 140:0.45 146:0.91 147:0.93 149:0.93 150:0.91 151:0.98 152:0.92 153:0.92 154:0.98 155:0.67 158:0.92 159:0.85 161:0.78 162:0.71 164:0.86 165:0.84 167:0.54 169:0.98 172:0.94 173:0.09 176:0.73 177:0.38 179:0.18 181:0.97 186:0.93 187:0.39 189:0.94 192:0.59 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.58 215:0.72 216:0.95 222:0.48 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.39 242:0.36 243:0.34 245:0.98 247:0.67 248:0.96 253:0.97 255:0.79 256:0.83 259:0.21 260:0.93 261:0.97 265:0.82 266:0.63 267:0.88 268:0.83 271:0.11 276:0.92 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.51 17:0.83 21:0.64 27:0.72 28:0.73 30:0.13 32:0.80 34:0.22 36:0.68 39:0.38 41:0.93 43:0.98 55:0.59 60:0.99 66:0.97 69:0.27 70:0.86 74:0.90 77:0.89 81:0.74 83:0.82 85:0.95 91:0.72 96:0.82 98:1.00 101:0.83 104:0.54 108:0.21 114:0.72 120:0.72 121:0.99 123:0.20 126:0.54 127:0.95 129:0.05 135:0.30 140:0.45 145:0.65 146:0.98 147:0.98 149:0.93 150:0.82 151:0.77 153:0.48 154:0.98 155:0.67 158:0.87 159:0.75 161:0.78 162:0.78 164:0.77 165:0.80 167:0.81 172:0.93 173:0.18 176:0.73 177:0.38 179:0.27 181:0.97 192:0.59 195:0.86 201:0.74 202:0.67 204:0.89 208:0.64 212:0.60 215:0.53 216:0.08 220:0.62 222:0.48 230:0.87 232:0.74 233:0.76 235:0.88 241:0.81 242:0.70 243:0.97 247:0.47 248:0.80 253:0.86 255:0.79 256:0.71 259:0.21 260:0.73 265:1.00 266:0.54 267:0.94 268:0.71 271:0.11 276:0.87 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.97 300:0.70 +1 1:0.74 6:0.83 8:0.64 9:0.80 11:0.57 12:0.51 17:0.62 20:0.96 27:0.44 28:0.73 30:0.85 34:0.39 36:0.84 37:0.41 39:0.38 41:0.88 43:0.98 55:0.71 66:0.87 69:0.05 70:0.28 74:0.75 78:0.92 81:0.99 83:0.81 85:0.80 91:0.68 96:0.88 98:0.92 100:0.94 101:0.78 104:0.92 106:0.81 108:0.05 111:0.91 114:0.98 120:0.78 122:0.43 123:0.05 126:0.54 127:0.55 129:0.05 135:0.69 140:0.45 146:0.77 147:0.81 149:0.75 150:0.99 151:0.91 152:0.89 153:0.71 154:0.98 155:0.67 159:0.33 161:0.78 162:0.86 164:0.66 165:0.92 167:0.55 169:0.92 172:0.63 173:0.21 176:0.73 177:0.38 179:0.69 181:0.97 186:0.92 187:0.68 189:0.85 192:0.59 201:0.74 202:0.56 206:0.81 208:0.64 212:0.71 215:0.96 216:0.93 222:0.48 235:0.05 238:0.90 241:0.43 242:0.40 243:0.88 247:0.60 248:0.85 253:0.90 255:0.79 256:0.64 259:0.21 260:0.79 261:0.93 265:0.92 266:0.27 267:0.44 268:0.65 271:0.11 276:0.53 285:0.62 290:0.98 297:0.36 300:0.25 +1 1:0.74 6:0.87 8:0.60 9:0.80 11:0.57 12:0.51 17:0.01 20:0.89 21:0.05 27:0.14 28:0.73 30:0.76 32:0.80 34:0.82 36:0.35 37:0.67 39:0.38 41:0.88 43:0.86 60:0.95 66:0.64 69:0.59 70:0.21 74:0.76 77:0.70 78:0.87 81:0.96 83:0.88 85:0.85 91:0.54 96:0.88 98:0.55 100:0.83 101:0.79 108:0.45 111:0.85 114:0.95 120:0.80 122:0.43 123:0.43 124:0.42 126:0.54 127:0.25 129:0.05 133:0.39 135:0.98 140:0.45 145:0.49 146:0.53 147:0.59 149:0.75 150:0.98 151:0.92 152:0.95 153:0.72 154:0.83 155:0.67 158:0.92 159:0.28 161:0.78 162:0.86 163:0.92 164:0.69 165:0.90 167:0.51 169:0.88 172:0.32 173:0.66 176:0.73 177:0.38 179:0.81 181:0.97 186:0.88 187:0.39 189:0.82 192:0.59 195:0.62 201:0.74 202:0.53 208:0.64 212:0.73 215:0.91 216:0.93 222:0.48 235:0.12 238:0.83 241:0.24 242:0.73 243:0.69 245:0.43 247:0.30 248:0.87 253:0.89 255:0.79 256:0.58 259:0.21 260:0.80 261:0.93 265:0.56 266:0.17 267:0.44 268:0.62 271:0.11 276:0.28 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.18 +1 11:0.57 12:0.51 17:0.53 21:0.77 27:0.45 28:0.73 30:0.30 32:0.75 34:0.77 36:0.26 37:0.50 39:0.38 43:0.79 66:0.15 69:0.71 70:0.94 74:0.72 81:0.37 85:0.94 91:0.06 98:0.32 101:0.78 108:0.90 123:0.83 124:0.90 126:0.32 127:0.62 129:0.93 133:0.90 135:0.78 145:0.58 146:0.49 147:0.55 149:0.83 150:0.33 154:0.72 159:0.84 161:0.78 163:0.81 167:0.60 172:0.48 173:0.47 177:0.38 178:0.55 179:0.43 181:0.97 187:0.68 195:0.95 212:0.18 215:0.44 216:0.77 222:0.48 235:0.98 241:0.57 242:0.33 243:0.29 245:0.75 247:0.67 253:0.53 259:0.21 265:0.30 266:0.87 267:0.65 271:0.11 276:0.72 297:0.36 300:0.84 +1 1:0.74 6:0.97 7:0.78 8:0.93 9:0.80 11:0.57 12:0.51 17:0.11 20:0.95 21:0.13 27:0.02 28:0.73 30:0.65 32:0.80 34:0.28 36:0.30 37:0.92 39:0.38 41:0.90 43:0.82 60:0.87 66:0.67 69:0.67 70:0.44 74:0.88 76:0.85 77:0.70 78:0.84 81:0.93 83:0.86 85:0.97 91:0.44 96:0.88 98:0.60 100:0.88 101:0.84 108:0.86 111:0.84 114:0.92 120:0.80 122:0.43 123:0.86 124:0.50 126:0.54 127:0.49 129:0.59 133:0.64 135:0.85 140:0.80 145:0.67 146:0.13 147:0.18 149:0.89 150:0.96 151:0.87 152:0.89 153:0.48 154:0.78 155:0.67 158:0.87 159:0.74 161:0.78 162:0.52 164:0.74 165:0.88 167:0.68 169:0.86 172:0.80 173:0.52 176:0.73 177:0.38 178:0.42 179:0.36 181:0.97 186:0.84 187:0.39 189:0.97 192:0.59 195:0.90 201:0.74 202:0.73 208:0.64 212:0.54 215:0.84 216:0.99 220:0.62 222:0.48 230:0.81 233:0.76 235:0.22 238:0.90 241:0.68 242:0.52 243:0.11 245:0.81 247:0.60 248:0.87 253:0.91 255:0.79 256:0.64 259:0.21 260:0.81 261:0.88 265:0.61 266:0.87 267:0.82 268:0.68 271:0.11 276:0.74 279:0.86 282:0.88 283:0.71 285:0.62 290:0.92 297:0.36 299:0.88 300:0.55 +2 6:0.88 8:0.89 9:0.80 11:0.57 12:0.51 17:0.05 20:0.97 21:0.78 27:0.14 28:0.73 30:0.87 34:0.75 36:0.31 37:0.67 39:0.38 41:0.92 43:0.59 60:0.87 66:0.54 69:0.93 70:0.20 74:0.60 78:0.88 83:0.82 85:0.88 91:0.40 96:0.91 98:0.14 100:0.92 101:0.74 108:0.87 111:0.89 120:0.89 122:0.43 123:0.86 124:0.48 127:0.13 129:0.59 133:0.47 135:0.78 140:0.87 145:0.77 146:0.28 147:0.35 149:0.57 151:0.96 152:0.95 153:0.87 154:0.47 155:0.67 158:0.92 159:0.28 161:0.78 162:0.48 164:0.80 167:0.55 169:0.88 172:0.32 173:0.87 177:0.38 178:0.48 179:0.28 181:0.97 186:0.88 187:0.39 189:0.93 192:0.59 202:0.87 208:0.64 212:0.61 216:0.93 222:0.48 235:0.05 238:0.91 241:0.46 242:0.63 243:0.63 245:0.50 247:0.30 248:0.91 253:0.89 255:0.79 256:0.75 259:0.21 260:0.89 261:0.93 265:0.16 266:0.27 267:0.28 268:0.76 271:0.11 276:0.18 279:0.86 283:0.82 285:0.62 300:0.18 +1 1:0.74 7:0.81 11:0.57 12:0.51 17:0.75 21:0.59 27:0.57 28:0.73 30:0.84 32:0.80 34:0.55 36:0.42 37:0.61 39:0.38 43:0.86 66:0.97 69:0.59 70:0.26 74:0.99 77:0.70 81:0.67 83:0.73 85:1.00 91:0.08 98:0.88 101:0.87 104:0.80 106:0.81 108:0.73 114:0.74 117:0.86 121:0.90 122:0.43 123:0.72 124:0.36 126:0.54 127:0.62 129:0.59 133:0.47 135:0.74 145:0.74 146:0.13 147:0.18 149:1.00 150:0.78 154:0.84 155:0.67 159:0.85 161:0.78 165:0.78 167:0.51 172:0.99 173:0.32 176:0.73 177:0.38 178:0.55 179:0.12 181:0.97 187:0.21 192:0.59 195:0.95 201:0.74 204:0.89 206:0.81 208:0.64 212:0.94 215:0.55 216:0.86 222:0.48 230:0.87 232:0.86 233:0.76 235:0.74 241:0.24 242:0.63 243:0.06 245:0.91 247:0.30 253:0.76 265:0.88 266:0.54 267:0.44 271:0.11 276:0.97 282:0.88 290:0.74 297:0.36 299:0.88 300:0.33 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.73 20:0.82 21:0.74 28:0.73 30:0.76 32:0.80 34:0.66 36:0.32 37:0.97 39:0.38 41:0.88 43:0.80 60:0.87 66:0.36 69:0.74 70:0.62 74:0.89 77:0.70 78:0.82 81:0.54 83:0.85 85:0.96 91:0.37 96:0.88 98:0.35 100:0.73 101:0.81 104:0.86 108:0.57 111:0.80 114:0.67 120:0.80 122:0.43 123:0.54 124:0.82 126:0.54 127:0.59 129:0.89 133:0.83 135:0.42 140:0.45 145:0.69 146:0.73 147:0.77 149:0.92 150:0.67 151:0.93 152:0.82 153:0.78 154:0.74 155:0.67 158:0.87 159:0.85 161:0.78 162:0.78 163:0.81 164:0.70 165:0.65 167:0.52 169:0.79 172:0.59 173:0.49 176:0.73 177:0.38 179:0.51 181:0.97 186:0.82 187:0.21 192:0.59 195:0.95 201:0.74 202:0.59 208:0.64 212:0.23 215:0.46 216:0.98 222:0.48 232:0.90 235:0.95 241:0.32 242:0.63 243:0.51 245:0.71 247:0.39 248:0.87 253:0.91 255:0.79 256:0.58 259:0.21 260:0.81 261:0.81 265:0.37 266:0.84 267:0.36 268:0.64 271:0.11 276:0.62 279:0.86 282:0.88 283:0.71 285:0.62 290:0.67 297:0.36 299:0.88 300:0.84 +2 1:0.74 11:0.57 12:0.51 17:0.47 21:0.64 28:0.73 30:0.75 32:0.80 34:0.50 36:0.25 37:0.97 39:0.38 43:0.80 60:0.87 66:0.35 69:0.75 70:0.59 74:0.91 77:0.96 81:0.63 83:0.86 85:0.96 91:0.22 98:0.27 101:0.82 104:0.80 108:0.58 114:0.72 122:0.43 123:0.55 124:0.86 126:0.54 127:0.68 129:0.89 133:0.87 135:0.54 145:0.74 146:0.52 147:0.05 149:0.94 150:0.75 154:0.73 155:0.67 158:0.87 159:0.79 161:0.78 165:0.70 167:0.47 172:0.59 173:0.59 176:0.73 177:0.05 178:0.55 179:0.51 181:0.97 187:0.21 192:0.59 195:0.90 201:0.74 208:0.64 212:0.59 215:0.53 216:0.98 222:0.48 232:0.85 235:0.80 241:0.03 242:0.63 243:0.09 245:0.70 247:0.35 253:0.71 257:0.65 259:0.21 265:0.29 266:0.71 267:0.73 271:0.11 276:0.62 279:0.86 282:0.88 283:0.71 290:0.72 297:0.36 299:0.99 300:0.70 +2 1:0.74 9:0.80 11:0.57 12:0.51 17:0.28 21:0.64 27:0.48 28:0.73 30:0.60 32:0.80 34:0.53 36:0.30 37:0.55 39:0.38 41:0.88 43:0.80 60:0.98 66:0.71 69:0.75 70:0.74 74:0.91 77:0.96 81:0.63 83:0.84 85:0.97 91:0.20 96:0.87 98:0.52 101:0.82 108:0.58 114:0.72 120:0.78 122:0.43 123:0.55 124:0.51 126:0.54 127:0.35 129:0.81 133:0.76 135:0.42 140:0.45 145:0.79 146:0.88 147:0.05 149:0.95 150:0.75 151:0.87 153:0.48 154:0.73 155:0.67 158:0.92 159:0.78 161:0.78 162:0.86 164:0.67 165:0.70 167:0.60 172:0.82 173:0.53 176:0.73 177:0.05 179:0.30 181:0.97 187:0.39 192:0.59 195:0.94 201:0.74 202:0.50 208:0.64 212:0.47 215:0.53 216:0.92 222:0.48 235:0.80 241:0.57 242:0.55 243:0.07 245:0.76 247:0.47 248:0.86 253:0.90 255:0.79 256:0.58 257:0.65 259:0.21 260:0.79 265:0.53 266:0.87 267:0.92 268:0.59 271:0.11 276:0.74 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.99 300:0.84 +1 6:0.79 8:0.58 9:0.80 12:0.51 17:0.53 20:0.91 21:0.78 27:0.72 28:0.73 34:0.49 36:0.24 39:0.38 41:0.82 78:0.87 83:0.77 91:0.89 96:0.84 100:0.82 104:0.58 111:0.84 120:0.74 135:0.61 140:0.45 151:0.92 152:0.85 153:0.72 155:0.67 161:0.78 162:0.67 164:0.64 167:0.84 169:0.79 175:0.91 181:0.97 186:0.86 189:0.81 192:0.59 202:0.53 208:0.64 216:0.05 222:0.48 232:0.78 238:0.82 241:0.91 244:0.83 248:0.81 253:0.77 255:0.79 256:0.45 257:0.84 259:0.21 260:0.75 261:0.87 267:0.52 268:0.57 271:0.11 277:0.87 285:0.62 +2 1:0.74 7:0.81 8:0.82 9:0.80 11:0.57 12:0.51 17:0.49 20:0.81 21:0.40 27:0.72 28:0.73 30:0.45 32:0.80 34:0.96 36:0.73 37:0.55 39:0.38 41:0.82 43:0.83 55:0.71 60:0.87 66:0.75 69:0.66 70:0.79 74:0.97 77:0.70 78:0.90 81:0.81 83:0.83 85:0.94 91:0.33 96:0.80 98:0.81 100:0.88 101:0.80 104:0.83 106:0.81 108:0.50 111:0.89 114:0.82 117:0.86 120:0.69 121:0.90 122:0.67 123:0.48 124:0.43 126:0.54 127:0.59 129:0.81 133:0.67 135:0.78 140:0.45 145:0.79 146:0.96 147:0.97 149:0.89 150:0.87 151:0.87 152:0.78 153:0.48 154:0.79 155:0.67 158:0.92 159:0.77 161:0.78 162:0.86 164:0.57 165:0.83 167:0.51 169:0.86 172:0.86 173:0.49 176:0.73 177:0.38 179:0.33 181:0.97 186:0.90 187:0.39 192:0.59 195:0.91 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.39 215:0.67 216:0.09 222:0.48 230:0.87 232:0.89 233:0.76 235:0.52 241:0.27 242:0.54 243:0.77 245:0.81 247:0.60 248:0.78 253:0.88 255:0.79 256:0.45 259:0.21 260:0.70 261:0.84 265:0.81 266:0.80 267:0.85 268:0.46 271:0.11 276:0.80 279:0.86 282:0.88 283:0.82 285:0.62 290:0.82 297:0.36 299:0.88 300:0.84 +2 9:0.80 11:0.57 12:0.51 17:0.05 21:0.78 27:0.14 28:0.73 30:0.87 34:0.66 36:0.41 37:0.67 39:0.38 41:0.92 43:0.58 60:0.87 66:0.45 69:0.94 70:0.27 74:0.61 78:0.86 83:0.82 85:0.90 91:0.73 96:0.91 98:0.14 101:0.75 108:0.88 111:0.89 120:0.89 122:0.43 123:0.87 124:0.67 127:0.22 129:0.81 133:0.67 135:0.70 140:0.80 145:0.77 146:0.28 147:0.35 149:0.60 151:0.96 153:0.87 154:0.46 155:0.67 158:0.92 159:0.66 161:0.78 162:0.48 164:0.80 167:0.53 169:0.91 172:0.32 173:0.87 177:0.38 178:0.42 179:0.63 181:0.97 187:0.39 191:0.75 192:0.59 202:0.87 208:0.64 212:0.50 216:0.93 222:0.48 235:0.05 241:0.35 242:0.63 243:0.58 245:0.52 247:0.30 248:0.91 253:0.89 255:0.79 256:0.75 259:0.21 260:0.89 265:0.15 266:0.47 267:0.28 268:0.76 271:0.11 276:0.27 279:0.86 283:0.82 285:0.62 300:0.25 +1 1:0.74 9:0.80 11:0.57 12:0.51 17:0.01 20:0.95 21:0.05 27:0.63 28:0.73 30:0.72 34:0.68 36:0.50 37:0.28 39:0.38 41:0.90 43:0.87 66:0.84 69:0.55 70:0.48 74:0.72 78:0.79 81:0.96 83:0.81 85:0.90 91:0.44 96:0.88 98:0.82 100:0.80 101:0.83 108:0.42 111:0.78 114:0.95 120:0.81 122:0.43 123:0.41 126:0.54 127:0.31 129:0.05 135:0.58 140:0.45 145:0.49 146:0.69 147:0.74 149:0.85 150:0.98 151:0.87 152:0.88 153:0.48 154:0.85 155:0.67 159:0.27 161:0.78 162:0.86 164:0.72 165:0.90 167:0.52 169:0.77 172:0.54 173:0.68 176:0.73 177:0.38 179:0.69 181:0.97 186:0.80 187:0.87 192:0.59 201:0.74 202:0.56 208:0.64 212:0.39 215:0.91 216:0.59 222:0.48 235:0.12 241:0.30 242:0.55 243:0.85 247:0.47 248:0.88 253:0.89 255:0.79 256:0.64 259:0.21 260:0.81 261:0.83 265:0.82 266:0.27 267:0.44 268:0.65 271:0.11 276:0.39 285:0.62 290:0.95 297:0.36 300:0.43 +2 8:0.98 9:0.80 12:0.20 17:0.08 21:0.78 27:0.42 34:0.45 36:0.26 37:0.86 39:0.96 41:0.98 60:0.87 74:0.46 83:0.88 91:0.92 96:0.94 104:0.76 120:0.90 135:0.71 140:0.45 151:0.94 153:0.96 155:0.67 158:0.87 162:0.79 164:0.90 175:0.91 192:0.59 202:0.83 208:0.64 216:0.38 220:0.62 222:0.35 232:0.81 241:0.90 244:0.83 248:0.94 253:0.91 255:0.79 256:0.87 257:0.84 260:0.90 267:0.28 268:0.88 271:0.36 277:0.87 279:0.86 283:0.71 285:0.62 +1 1:0.74 7:0.81 8:0.60 9:0.80 12:0.20 17:0.26 21:0.47 27:0.21 30:0.38 34:0.44 36:0.89 37:0.74 39:0.96 43:0.32 55:0.71 66:0.63 69:0.19 70:0.73 74:0.92 76:0.85 81:0.56 91:0.42 96:0.77 98:0.80 104:0.97 106:0.81 108:0.15 114:0.80 117:0.86 120:0.69 123:0.15 124:0.48 126:0.54 127:0.58 129:0.59 133:0.47 135:0.23 140:0.45 146:0.92 147:0.93 149:0.59 150:0.64 151:0.75 153:0.48 154:0.92 155:0.67 158:0.86 159:0.67 162:0.59 164:0.71 172:0.83 173:0.16 176:0.73 177:0.38 179:0.33 187:0.39 190:0.77 192:0.59 201:0.74 202:0.65 204:0.89 206:0.81 208:0.64 212:0.77 215:0.63 216:0.95 220:0.74 222:0.35 230:0.83 232:0.91 233:0.66 235:0.60 241:0.10 242:0.36 243:0.69 245:0.91 247:0.60 248:0.69 253:0.83 254:0.74 255:0.79 256:0.58 259:0.21 260:0.70 265:0.80 266:0.71 267:0.82 268:0.65 271:0.36 276:0.80 279:0.86 283:0.71 285:0.62 290:0.80 297:0.36 300:0.70 +2 1:0.74 7:0.81 11:0.57 12:0.20 17:0.61 21:0.40 27:0.34 30:0.53 34:0.67 36:0.93 37:0.42 39:0.96 43:0.59 66:0.74 69:0.94 70:0.50 74:0.89 76:0.97 77:0.96 81:0.46 83:0.72 85:0.94 91:0.46 98:0.58 101:0.80 104:0.58 108:0.88 114:0.68 121:0.90 122:0.57 123:0.87 124:0.42 126:0.54 127:0.77 129:0.05 133:0.62 135:0.72 145:0.86 146:0.82 147:0.05 149:0.78 150:0.63 154:0.47 155:0.67 159:0.75 165:0.63 172:0.73 173:0.95 176:0.73 177:0.05 178:0.55 179:0.56 187:0.39 192:0.59 195:0.90 201:0.74 204:0.89 208:0.64 212:0.54 215:0.46 216:0.38 222:0.35 230:0.84 232:0.82 233:0.69 235:0.99 241:0.41 242:0.52 243:0.09 245:0.64 247:0.47 253:0.69 257:0.65 259:0.21 265:0.59 266:0.63 267:0.79 271:0.36 276:0.52 282:0.84 290:0.68 297:0.36 300:0.43 +1 1:0.74 9:0.80 12:0.20 17:0.38 21:0.05 27:0.35 30:0.52 34:0.18 36:0.79 37:0.24 39:0.96 43:0.45 55:0.71 66:0.34 69:0.07 70:0.64 74:0.58 81:0.82 91:0.11 96:0.85 98:0.44 104:0.58 108:0.96 114:0.95 117:0.86 120:0.65 123:0.96 124:0.92 126:0.54 127:0.96 129:0.59 133:0.93 135:0.39 140:0.45 146:0.25 147:0.31 149:0.58 150:0.84 151:0.77 153:0.48 154:0.15 155:0.67 158:0.84 159:0.94 162:0.80 164:0.57 172:0.82 173:0.06 176:0.73 177:0.38 179:0.26 187:0.21 192:0.59 201:0.74 202:0.60 208:0.64 212:0.49 215:0.91 216:0.55 220:0.62 222:0.35 232:0.83 235:0.12 241:0.41 242:0.81 243:0.08 245:0.85 247:0.39 248:0.71 253:0.84 254:0.80 255:0.79 256:0.64 259:0.21 260:0.66 265:0.46 266:0.96 267:0.52 268:0.65 271:0.36 276:0.87 277:0.69 285:0.62 290:0.95 297:0.36 300:0.84 +2 1:0.74 7:0.78 9:0.80 12:0.20 17:0.28 21:0.22 27:0.64 30:0.73 32:0.75 34:0.66 36:0.67 37:0.37 39:0.96 43:0.41 55:0.71 66:0.97 69:0.33 70:0.39 74:0.88 81:0.70 87:0.98 91:0.20 96:0.78 98:0.99 104:0.72 108:0.26 114:0.90 120:0.60 123:0.25 126:0.54 127:0.92 129:0.05 135:0.21 140:0.45 145:0.93 146:0.99 147:0.99 149:0.83 150:0.75 151:0.62 153:0.48 154:0.31 155:0.67 158:0.85 159:0.82 162:0.86 164:0.57 172:0.93 173:0.17 176:0.73 177:0.38 179:0.26 187:0.21 190:0.77 192:0.59 195:0.92 201:0.74 202:0.50 208:0.64 212:0.48 215:0.79 216:0.21 220:0.82 222:0.35 230:0.81 232:0.70 233:0.69 235:0.31 241:0.37 242:0.80 243:0.97 244:0.61 247:0.39 248:0.60 253:0.84 255:0.79 256:0.58 259:0.21 260:0.60 265:0.99 266:0.63 267:0.85 268:0.59 271:0.36 276:0.88 285:0.62 290:0.90 297:0.36 300:0.55 +2 8:0.63 9:0.80 12:0.20 17:0.02 21:0.78 25:0.88 27:0.72 30:0.76 34:0.24 36:0.96 37:0.28 39:0.96 41:0.88 43:0.45 55:0.71 66:0.83 69:0.10 70:0.29 83:0.78 91:0.86 96:0.87 98:0.97 104:0.58 108:0.09 120:0.92 123:0.09 127:0.76 129:0.05 132:0.97 135:0.54 140:0.98 145:0.70 146:0.68 147:0.73 149:0.54 151:0.87 153:0.92 154:0.34 155:0.67 159:0.27 162:0.60 164:0.90 172:0.51 173:0.38 175:0.75 177:0.05 178:0.48 179:0.84 192:0.59 202:0.86 208:0.64 212:0.57 216:0.41 222:0.35 235:0.31 241:0.90 242:0.82 243:0.84 244:0.61 247:0.12 248:0.86 253:0.82 255:0.79 256:0.87 257:0.65 260:0.93 265:0.97 266:0.38 267:0.96 268:0.87 271:0.36 276:0.43 277:0.69 285:0.62 300:0.25 +2 8:0.76 9:0.80 12:0.20 17:0.47 21:0.78 27:1.00 30:0.45 34:0.20 36:0.94 37:0.33 39:0.96 41:0.95 43:0.44 55:0.45 66:0.27 69:0.05 70:0.25 74:0.77 76:0.94 77:0.70 83:0.80 91:0.88 96:0.93 98:0.26 104:0.58 108:0.05 120:0.85 122:0.67 123:0.05 124:0.61 127:0.33 129:0.59 133:0.47 135:0.76 140:0.87 145:0.61 146:0.32 147:0.38 149:0.28 151:0.91 153:0.90 154:0.34 155:0.67 159:0.40 162:0.49 163:0.87 164:0.82 172:0.10 173:0.14 175:0.75 177:0.05 178:0.48 179:0.96 191:0.81 192:0.59 195:0.70 202:0.90 208:0.64 212:0.61 216:0.43 220:0.62 222:0.35 232:0.76 235:0.02 241:0.80 242:0.82 243:0.46 244:0.61 245:0.40 247:0.12 248:0.91 253:0.94 255:0.79 256:0.84 257:0.65 259:0.21 260:0.85 265:0.28 266:0.17 267:0.87 268:0.83 271:0.36 276:0.15 277:0.69 282:0.84 285:0.62 300:0.18 +0 12:0.20 17:0.38 21:0.77 27:0.45 30:0.56 34:0.78 36:0.18 37:0.50 39:0.96 43:0.34 55:0.84 66:0.33 69:0.62 70:0.42 74:0.83 77:0.70 81:0.24 91:0.10 98:0.43 108:0.47 114:0.60 122:0.67 123:0.45 124:0.78 126:0.32 127:0.47 129:0.05 133:0.74 135:0.76 145:0.61 146:0.67 147:0.72 149:0.39 150:0.24 154:0.60 159:0.69 163:0.94 172:0.32 173:0.47 176:0.56 177:0.38 178:0.55 179:0.77 187:0.68 195:0.87 212:0.39 216:0.77 222:0.35 235:0.97 241:0.53 242:0.76 243:0.50 245:0.53 247:0.39 253:0.62 254:0.93 259:0.21 265:0.44 266:0.63 267:0.44 271:0.36 276:0.44 282:0.84 290:0.60 300:0.33 +1 8:0.86 9:0.80 12:0.20 17:0.20 21:0.30 27:0.37 30:0.76 34:0.20 36:1.00 37:0.44 39:0.96 41:0.82 43:0.44 55:0.84 66:0.25 69:0.15 70:0.29 74:0.78 81:0.31 83:0.76 91:0.44 96:0.96 98:0.28 104:0.58 108:0.12 114:0.69 117:0.86 120:0.69 122:0.67 123:0.12 124:0.71 126:0.32 127:0.65 129:0.81 133:0.67 135:0.90 140:0.98 145:0.44 146:0.41 147:0.47 149:0.35 150:0.29 151:0.87 153:0.89 154:0.33 155:0.67 158:0.85 159:0.63 162:0.67 163:0.86 164:0.57 172:0.16 173:0.16 175:0.75 176:0.56 177:0.05 179:0.92 187:0.68 191:0.82 192:0.59 202:0.87 208:0.64 212:0.23 216:0.67 222:0.35 232:0.88 235:0.31 241:0.56 242:0.82 243:0.45 244:0.61 245:0.45 247:0.12 248:0.78 253:0.96 255:0.79 256:0.89 257:0.65 259:0.21 260:0.70 265:0.30 266:0.38 267:0.23 268:0.89 271:0.36 276:0.24 277:0.69 285:0.62 290:0.69 300:0.25 +2 1:0.74 7:0.81 11:0.57 12:0.20 17:0.49 21:0.74 27:0.64 30:0.67 34:0.39 36:0.73 37:0.64 39:0.96 43:0.83 66:0.92 69:0.37 70:0.29 74:0.93 76:0.94 81:0.44 83:0.72 85:0.95 91:0.23 96:0.84 98:0.96 101:0.86 104:0.95 108:0.29 114:0.67 117:0.86 121:0.90 122:0.43 123:0.28 126:0.54 127:0.70 129:0.05 135:0.68 140:0.45 146:0.88 147:0.90 149:0.94 150:0.63 151:0.71 153:0.77 154:0.79 155:0.67 158:0.85 159:0.45 162:0.68 163:0.81 165:0.65 172:0.78 173:0.42 176:0.73 177:0.38 179:0.53 187:0.68 190:0.77 192:0.59 201:0.74 202:0.54 204:0.89 208:0.64 212:0.42 215:0.46 216:0.62 222:0.35 230:0.84 232:0.79 233:0.69 235:0.97 241:0.50 242:0.55 243:0.92 247:0.55 248:0.67 253:0.88 256:0.45 259:0.21 265:0.96 266:0.54 267:0.23 268:0.57 271:0.36 276:0.67 285:0.50 290:0.67 297:0.36 300:0.33 +1 1:0.74 7:0.81 11:0.57 12:0.20 17:0.69 21:0.76 27:0.64 30:0.66 34:0.80 36:0.90 37:0.64 39:0.96 43:0.85 66:0.91 69:0.34 70:0.36 74:0.94 76:0.94 81:0.43 83:0.72 85:0.95 91:0.33 98:0.91 101:0.85 108:0.27 114:0.66 121:0.90 122:0.57 123:0.26 126:0.54 127:0.51 129:0.05 135:0.73 146:0.85 147:0.88 149:0.93 150:0.63 154:0.82 155:0.67 159:0.42 163:0.81 165:0.65 172:0.76 173:0.39 176:0.73 177:0.38 178:0.55 179:0.52 187:0.87 192:0.59 201:0.74 204:0.89 208:0.64 212:0.56 215:0.46 216:0.62 222:0.35 230:0.84 232:0.79 233:0.69 235:0.98 241:0.37 242:0.59 243:0.92 247:0.39 253:0.71 259:0.21 265:0.91 266:0.27 267:0.96 271:0.36 276:0.65 290:0.66 297:0.36 300:0.43 +2 9:0.80 11:0.57 12:0.20 17:0.01 21:0.78 27:0.37 30:0.86 34:0.93 36:0.86 37:0.42 39:0.96 41:0.88 43:0.98 66:0.84 69:0.05 70:0.28 74:0.79 83:0.77 85:0.91 91:0.20 96:0.85 98:0.86 101:0.84 108:0.05 120:0.76 122:0.43 123:0.05 127:0.38 129:0.05 135:0.34 140:0.45 146:0.68 147:0.72 149:0.89 151:0.93 153:0.77 154:0.98 155:0.67 159:0.26 162:0.86 164:0.69 172:0.54 173:0.21 177:0.38 179:0.73 187:0.21 192:0.59 202:0.54 208:0.64 212:0.39 216:0.35 222:0.35 232:0.82 235:0.02 241:0.39 242:0.63 243:0.85 247:0.30 248:0.83 253:0.85 255:0.79 256:0.58 259:0.21 260:0.76 265:0.86 266:0.38 267:0.65 268:0.63 271:0.36 276:0.42 285:0.62 300:0.25 +2 8:0.86 9:0.80 12:0.20 17:0.64 21:0.05 27:0.34 30:0.45 34:0.18 36:0.63 37:0.42 39:0.96 41:0.82 43:0.41 55:0.98 66:0.13 69:0.30 70:0.50 74:0.84 76:0.85 77:0.89 81:0.42 83:0.76 91:0.74 96:0.97 98:0.18 104:0.58 108:0.24 114:0.77 120:0.69 122:0.67 123:0.23 124:0.86 126:0.32 127:0.90 129:0.93 133:0.84 135:0.21 140:0.98 145:0.70 146:0.32 147:0.38 149:0.33 150:0.35 151:0.87 153:0.48 154:0.31 155:0.67 158:0.84 159:0.78 162:0.76 163:0.87 164:0.57 172:0.10 173:0.18 175:0.75 176:0.56 177:0.05 179:0.94 191:0.89 192:0.59 195:0.88 202:0.85 208:0.64 212:0.23 216:0.38 220:0.62 222:0.35 232:0.82 235:0.05 241:0.77 242:0.82 243:0.34 244:0.61 245:0.42 247:0.12 248:0.78 253:0.97 255:0.79 256:0.89 257:0.65 259:0.21 260:0.70 265:0.20 266:0.38 267:0.65 268:0.89 271:0.36 276:0.30 277:0.69 282:0.84 285:0.62 290:0.77 300:0.33 +2 1:0.74 7:0.81 8:0.97 9:0.80 11:0.57 12:0.20 17:0.16 21:0.74 27:0.64 30:0.58 34:0.62 36:0.89 37:0.64 39:0.96 41:0.98 43:0.59 60:0.95 66:0.68 69:0.93 70:0.52 74:0.82 76:0.94 81:0.44 83:0.88 85:0.91 91:0.57 96:0.94 98:0.54 101:0.79 104:0.95 108:0.87 114:0.67 120:0.90 121:0.90 122:0.43 123:0.86 124:0.47 126:0.54 127:0.66 129:0.05 133:0.62 135:0.95 140:0.45 146:0.77 147:0.41 149:0.72 150:0.63 151:0.94 153:0.96 154:0.47 155:0.67 158:0.87 159:0.72 162:0.75 164:0.90 165:0.65 172:0.59 173:0.95 176:0.73 177:0.05 179:0.69 187:0.39 191:0.81 192:0.59 201:0.74 202:0.84 204:0.89 208:0.64 212:0.23 215:0.46 216:0.62 220:0.62 222:0.35 230:0.84 232:0.79 233:0.69 235:0.97 241:0.74 242:0.45 243:0.19 245:0.58 247:0.60 248:0.94 253:0.95 255:0.79 256:0.87 257:0.65 259:0.21 260:0.90 265:0.55 266:0.71 267:0.52 268:0.88 271:0.36 276:0.45 279:0.86 283:0.71 285:0.62 290:0.67 297:0.36 300:0.43 +0 12:0.20 17:0.11 21:0.78 27:0.45 30:0.41 34:0.70 36:0.18 37:0.50 39:0.96 43:0.34 55:0.71 66:0.34 69:0.80 70:0.77 74:0.80 91:0.15 98:0.32 108:0.64 122:0.67 123:0.62 124:0.83 127:0.63 129:0.05 133:0.82 135:0.68 145:0.45 146:0.69 147:0.05 149:0.45 154:0.25 159:0.85 163:0.90 172:0.37 173:0.59 177:0.05 178:0.55 179:0.76 187:0.68 212:0.19 216:0.77 222:0.35 235:0.68 241:0.68 242:0.79 243:0.13 244:0.61 245:0.54 247:0.35 253:0.58 257:0.65 259:0.21 265:0.35 266:0.80 267:0.28 271:0.36 276:0.44 300:0.55 +1 8:0.74 9:0.80 12:0.20 17:0.04 21:0.74 27:0.38 30:0.21 34:0.30 36:0.92 37:0.54 39:0.96 41:0.82 43:0.40 55:0.71 66:0.36 69:0.78 70:0.67 74:0.89 76:0.85 77:0.70 81:0.24 83:0.76 91:0.92 96:0.89 98:0.31 104:0.58 108:0.62 114:0.61 120:0.83 122:0.67 123:0.59 124:0.68 126:0.32 127:0.22 129:0.05 133:0.62 135:0.18 140:0.99 145:0.84 146:0.45 147:0.47 149:0.45 150:0.24 151:0.87 153:0.48 154:0.30 155:0.67 158:0.84 159:0.40 162:0.46 164:0.77 172:0.27 173:0.74 176:0.56 177:0.05 178:0.54 179:0.67 191:0.87 192:0.59 195:0.80 202:0.96 208:0.64 212:0.16 216:0.50 220:0.91 222:0.35 235:0.88 241:0.83 242:0.76 243:0.39 244:0.61 245:0.50 247:0.35 248:0.78 253:0.91 255:0.79 256:0.82 257:0.65 259:0.21 260:0.84 265:0.33 266:0.54 267:0.79 268:0.82 271:0.36 276:0.36 282:0.84 285:0.62 290:0.61 300:0.43 +1 1:0.74 6:0.79 8:0.58 9:0.80 11:0.87 12:0.98 17:0.70 18:0.82 20:0.83 22:0.93 27:0.53 28:0.91 29:0.77 30:0.70 31:0.97 34:0.88 36:0.35 37:0.27 39:0.53 41:0.92 43:0.83 45:0.94 47:0.93 60:0.95 64:0.77 66:0.93 69:0.41 70:0.54 71:0.73 74:0.84 78:0.96 79:0.97 81:0.83 83:0.91 85:0.90 86:0.92 91:0.65 96:0.91 97:0.94 98:0.91 100:0.84 101:0.87 104:0.94 106:0.81 108:0.31 111:0.93 114:0.98 117:0.86 120:0.84 122:0.75 123:0.30 124:0.36 126:0.54 127:0.76 129:0.05 131:0.89 133:0.37 135:0.32 137:0.97 139:0.92 140:0.45 144:0.76 146:0.93 147:0.94 149:0.96 150:0.89 151:0.95 152:0.79 153:0.83 154:0.79 155:0.67 157:0.89 158:0.92 159:0.61 161:0.73 162:0.86 164:0.77 165:0.93 166:0.84 167:0.47 169:0.82 172:0.92 173:0.35 176:0.73 177:0.70 179:0.27 181:0.49 182:0.88 186:0.96 187:0.95 189:0.82 192:0.87 196:0.98 201:0.93 202:0.66 206:0.81 208:0.64 212:0.86 215:0.96 216:0.29 219:0.95 222:0.48 227:0.95 229:0.93 231:0.77 232:0.77 235:0.12 238:0.81 241:0.24 242:0.36 243:0.93 245:0.71 246:0.96 247:0.82 248:0.89 253:0.93 255:0.95 256:0.73 259:0.21 260:0.84 261:0.85 262:0.93 265:0.91 266:0.63 267:0.79 268:0.74 276:0.85 279:0.86 283:0.82 284:0.95 285:0.62 287:0.97 290:0.98 292:0.95 297:0.36 300:0.55 +1 1:0.69 6:0.79 7:0.72 8:0.59 9:0.80 11:0.64 12:0.98 17:0.85 18:0.69 20:0.77 21:0.22 27:0.35 28:0.91 29:0.77 30:0.13 31:0.98 32:0.69 34:0.97 36:0.61 37:0.27 39:0.53 41:0.90 43:0.72 45:0.83 55:0.52 66:0.35 69:0.10 70:0.97 71:0.73 74:0.37 76:0.85 77:0.70 78:1.00 79:0.97 81:0.48 83:0.91 86:0.59 91:0.81 96:0.96 97:0.94 98:0.64 99:0.83 100:0.94 101:0.52 104:0.83 106:0.81 108:0.09 111:0.98 114:0.62 117:0.86 120:0.93 122:0.77 123:0.09 124:0.67 126:0.44 127:0.62 129:0.05 131:0.89 133:0.60 135:0.63 137:0.98 138:0.97 139:0.57 140:0.80 144:0.76 145:0.56 146:0.72 147:0.76 149:0.75 150:0.57 151:0.97 152:0.75 153:0.94 154:0.62 155:0.67 157:0.61 158:0.72 159:0.53 161:0.73 162:0.80 164:0.85 165:0.70 166:0.77 167:0.52 169:0.91 172:0.61 173:0.17 176:0.55 177:0.70 179:0.47 181:0.49 182:0.88 186:0.99 187:0.21 189:0.82 191:0.73 192:0.87 195:0.72 196:0.87 201:0.68 202:0.79 204:0.85 206:0.81 208:0.64 212:0.74 215:0.51 216:0.46 222:0.48 227:0.95 229:0.93 230:0.75 231:0.77 232:0.93 233:0.76 235:0.31 238:0.81 241:0.47 242:0.62 243:0.51 244:0.61 245:0.81 246:0.61 247:0.79 248:0.91 253:0.89 255:0.95 256:0.83 259:0.21 260:0.92 261:0.80 265:0.65 266:0.75 267:0.69 268:0.84 276:0.66 279:0.82 282:0.71 283:0.82 284:0.95 285:0.62 287:0.97 290:0.62 297:0.36 300:0.84 +3 8:0.61 9:0.80 11:0.87 12:0.98 17:0.26 18:0.83 27:0.18 28:0.91 29:0.77 30:0.68 31:0.98 34:0.42 36:0.79 37:0.64 39:0.53 41:0.93 43:0.97 45:0.66 64:0.77 66:0.79 69:0.05 70:0.31 71:0.73 74:0.58 79:0.98 81:0.63 83:0.91 85:0.72 86:0.86 91:0.64 96:0.91 98:0.61 101:0.87 108:0.05 120:0.88 122:0.57 123:0.05 126:0.44 127:0.18 129:0.05 131:0.89 135:0.39 137:0.98 139:0.82 140:0.45 144:0.76 146:0.41 147:0.48 149:0.65 150:0.44 151:0.98 153:0.92 154:0.97 155:0.67 157:0.79 159:0.15 161:0.73 162:0.76 164:0.82 166:0.73 167:0.52 172:0.41 173:0.28 177:0.70 179:0.60 181:0.49 182:0.88 187:0.39 191:0.76 192:0.87 196:0.96 201:0.68 202:0.79 208:0.64 212:0.78 215:0.96 216:0.53 222:0.48 227:0.95 229:0.93 231:0.77 235:0.05 241:0.50 242:0.45 243:0.80 246:0.61 247:0.39 248:0.89 253:0.88 255:0.95 256:0.81 259:0.21 260:0.87 265:0.62 266:0.23 267:0.44 268:0.83 276:0.24 284:0.97 285:0.62 287:0.97 297:0.36 300:0.25 +2 1:0.74 8:0.67 9:0.80 11:0.92 12:0.98 17:0.94 18:0.96 21:0.40 25:0.88 27:0.72 28:0.91 29:0.77 30:0.73 31:0.96 34:0.28 36:0.34 39:0.53 41:0.93 43:0.97 45:0.98 60:0.97 64:0.77 66:0.16 69:0.17 70:0.63 71:0.73 74:0.94 79:0.96 81:0.53 83:0.91 85:0.99 86:0.99 87:0.98 91:0.62 96:0.94 98:0.54 101:0.97 104:0.54 108:0.80 114:0.62 120:0.89 122:0.57 123:0.79 124:0.89 126:0.54 127:0.98 129:0.95 131:0.89 133:0.87 135:0.30 137:0.96 139:0.99 140:0.80 144:0.76 146:0.60 147:0.65 149:0.99 150:0.74 151:0.87 153:0.86 154:0.97 155:0.67 157:0.94 158:0.91 159:0.90 161:0.73 162:0.86 164:0.82 165:0.93 166:0.84 167:0.75 172:0.90 173:0.08 176:0.73 177:0.70 179:0.12 181:0.49 182:0.88 192:0.87 196:0.99 201:0.93 202:0.72 208:0.64 212:0.75 215:0.42 216:0.06 219:0.95 222:0.48 227:0.95 228:0.99 229:0.93 231:0.77 232:0.82 235:0.60 241:0.77 242:0.18 243:0.18 245:0.99 246:0.96 247:0.95 248:0.86 253:0.93 255:0.95 256:0.79 259:0.21 260:0.88 265:0.55 266:0.80 267:0.96 268:0.79 276:0.97 279:0.86 283:0.78 284:0.96 285:0.62 287:0.97 290:0.62 292:0.91 297:0.36 300:0.84 +3 1:0.74 6:0.86 7:0.81 8:0.74 9:0.80 11:0.92 12:0.98 17:0.92 18:0.98 20:0.83 21:0.22 22:0.93 27:0.33 28:0.91 29:0.77 30:0.73 31:0.95 32:0.80 34:0.63 36:0.88 37:0.57 39:0.53 41:0.92 43:0.85 44:0.93 45:0.95 47:0.93 48:0.91 60:0.87 64:0.77 66:0.90 69:0.23 70:0.47 71:0.73 74:0.96 76:0.97 77:0.89 78:0.99 79:0.95 81:0.70 83:0.91 85:0.90 86:0.99 91:0.78 96:0.85 98:0.69 100:0.96 101:0.97 104:0.87 106:0.81 108:0.18 111:0.98 114:0.71 117:0.86 120:0.75 121:0.90 122:0.80 123:0.18 124:0.37 126:0.54 127:0.84 129:0.05 131:0.89 133:0.43 135:0.69 137:0.95 139:0.99 140:0.45 144:0.76 145:0.67 146:0.86 147:0.88 149:0.97 150:0.82 151:0.87 152:0.79 153:0.48 154:0.82 155:0.67 157:0.94 158:0.92 159:0.71 161:0.73 162:0.85 164:0.76 165:0.93 166:0.84 167:0.50 169:0.94 172:0.93 173:0.18 176:0.73 177:0.70 179:0.26 181:0.49 182:0.88 186:0.99 187:0.21 189:0.88 191:0.78 192:0.87 195:0.84 196:0.98 201:0.93 202:0.67 204:0.89 206:0.81 208:0.64 212:0.89 215:0.51 216:0.17 219:0.95 220:0.82 222:0.48 227:0.95 229:0.93 230:0.87 231:0.77 232:0.76 233:0.76 235:0.60 238:0.85 241:0.39 242:0.37 243:0.91 245:0.81 246:0.96 247:0.86 248:0.83 253:0.90 255:0.95 256:0.73 259:0.21 260:0.76 261:0.88 262:0.93 265:0.69 266:0.54 267:0.99 268:0.74 276:0.79 279:0.86 281:0.91 282:0.88 283:0.82 284:0.95 285:0.62 287:0.97 290:0.71 292:0.95 297:0.36 299:0.88 300:0.55 +1 1:0.74 11:0.81 12:0.98 17:0.28 18:0.77 21:0.22 27:0.45 28:0.91 29:0.77 30:0.21 34:0.80 36:0.23 37:0.50 39:0.53 43:0.60 45:0.73 64:0.77 66:0.36 69:0.68 70:0.86 71:0.73 74:0.62 81:0.60 83:0.60 86:0.79 91:0.20 97:0.89 98:0.26 99:0.83 101:0.52 108:0.64 114:0.71 122:0.57 123:0.61 124:0.68 126:0.54 127:0.24 129:0.05 131:0.61 133:0.60 135:0.65 139:0.83 144:0.76 145:0.50 146:0.18 147:0.23 149:0.29 150:0.75 154:0.76 155:0.67 157:0.82 158:0.91 159:0.45 161:0.73 163:0.75 165:0.70 166:0.84 167:0.46 172:0.27 173:0.61 176:0.73 177:0.70 178:0.55 179:0.71 181:0.49 182:0.87 187:0.68 192:0.87 201:0.93 208:0.64 212:0.37 215:0.51 216:0.77 219:0.95 222:0.48 227:0.61 229:0.61 231:0.76 235:0.42 241:0.15 242:0.16 243:0.39 245:0.50 246:0.96 247:0.60 253:0.52 254:0.74 259:0.21 265:0.28 266:0.38 267:0.36 276:0.30 277:0.69 279:0.86 283:0.78 287:0.61 290:0.71 292:0.91 297:0.36 300:0.55 +0 6:0.80 8:0.59 9:0.80 11:0.87 12:0.98 17:0.79 18:0.68 20:0.95 21:0.47 27:0.40 28:0.91 29:0.77 30:0.17 31:0.97 34:0.98 36:0.69 37:0.38 39:0.53 41:0.90 43:0.70 45:0.91 66:0.45 69:0.43 70:0.91 71:0.73 74:0.52 78:0.94 79:0.97 81:0.24 83:0.91 85:0.72 86:0.78 91:0.60 96:0.94 97:0.94 98:0.72 100:0.82 101:0.87 108:0.33 111:0.91 120:0.89 122:0.77 123:0.32 124:0.77 126:0.26 127:0.86 129:0.05 131:0.89 133:0.74 135:0.92 137:0.97 139:0.74 140:0.87 144:0.76 146:0.91 147:0.93 149:0.84 150:0.24 151:0.92 152:0.89 153:0.78 154:0.63 155:0.67 157:0.77 158:0.72 159:0.77 161:0.73 162:0.86 164:0.82 166:0.61 167:0.45 169:0.80 172:0.85 173:0.27 177:0.70 179:0.26 181:0.49 182:0.88 186:0.94 187:0.68 189:0.82 192:0.87 196:0.94 202:0.76 208:0.64 212:0.57 215:0.41 216:0.31 220:0.62 222:0.48 227:0.95 229:0.93 231:0.77 235:0.52 238:0.81 241:0.03 242:0.43 243:0.58 245:0.92 246:0.61 247:0.84 248:0.89 253:0.87 255:0.95 256:0.83 259:0.21 260:0.88 261:0.89 265:0.72 266:0.63 267:0.96 268:0.83 276:0.87 279:0.82 283:0.82 284:0.95 285:0.62 287:0.97 297:0.36 300:0.70 +2 8:0.75 9:0.76 11:0.81 12:0.98 17:0.43 18:0.81 22:0.93 27:0.50 28:0.91 29:0.77 30:0.15 31:0.92 34:0.55 36:0.99 37:0.45 39:0.53 43:0.68 45:0.66 47:0.93 55:0.59 64:0.77 66:0.33 69:0.30 70:0.96 71:0.73 74:0.48 76:0.85 79:0.96 81:0.46 83:0.60 86:0.72 91:0.72 96:0.86 97:0.89 98:0.20 101:0.52 108:0.93 122:0.85 123:0.93 124:0.69 126:0.26 127:0.90 129:0.59 131:0.89 133:0.64 135:0.78 137:0.92 139:0.75 140:0.80 144:0.76 145:0.63 146:0.32 147:0.39 149:0.36 150:0.37 151:0.70 153:0.78 154:0.57 155:0.58 157:0.76 158:0.79 159:0.83 161:0.73 162:0.79 166:0.73 167:0.48 172:0.27 173:0.15 175:0.75 177:0.38 179:0.88 181:0.49 182:0.87 187:0.39 190:0.89 191:0.89 192:0.51 196:0.92 202:0.72 208:0.54 212:0.30 216:0.29 219:0.92 222:0.48 227:0.95 229:0.93 231:0.77 235:0.02 241:0.30 242:0.53 243:0.44 244:0.61 245:0.53 246:0.61 247:0.47 248:0.69 253:0.64 255:0.74 256:0.77 257:0.65 259:0.21 262:0.93 265:0.22 266:0.54 267:0.76 268:0.77 276:0.32 277:0.69 279:0.82 284:0.93 285:0.56 287:0.97 292:0.95 300:0.70 +2 1:0.74 6:0.80 8:0.59 9:0.80 11:0.87 12:0.98 17:0.67 18:0.88 20:0.93 21:0.05 22:0.93 27:0.40 28:0.91 29:0.77 30:0.15 31:0.93 34:0.81 36:0.55 37:0.28 39:0.53 43:0.78 45:0.73 47:0.93 64:0.77 66:0.27 69:0.49 70:0.97 71:0.73 74:0.72 78:0.98 79:0.95 81:0.75 83:0.60 85:0.72 86:0.91 87:0.98 91:0.53 96:0.85 97:0.94 98:0.64 99:0.83 100:0.84 101:0.97 104:0.93 106:0.81 108:0.38 111:0.95 114:0.89 117:0.86 120:0.76 122:0.77 123:0.78 124:0.58 126:0.54 127:0.70 129:0.59 131:0.89 133:0.46 135:0.78 137:0.93 139:0.92 140:0.80 144:0.76 146:0.66 147:0.71 149:0.59 150:0.83 151:0.79 152:0.96 153:0.82 154:0.74 155:0.67 157:0.85 158:0.91 159:0.60 161:0.73 162:0.86 164:0.81 165:0.70 166:0.84 167:0.45 169:0.85 172:0.73 173:0.43 176:0.73 177:0.70 179:0.41 181:0.49 182:0.88 186:0.98 187:0.95 189:0.82 192:0.87 196:0.96 201:0.93 202:0.74 206:0.81 208:0.64 212:0.67 215:0.78 216:0.29 219:0.95 220:0.74 222:0.48 227:0.95 229:0.93 231:0.77 232:0.75 235:0.22 238:0.81 241:0.07 242:0.40 243:0.59 245:0.90 246:0.96 247:0.88 248:0.74 253:0.82 255:0.95 256:0.81 259:0.21 260:0.75 261:0.94 262:0.93 265:0.55 266:0.63 267:0.82 268:0.82 276:0.72 279:0.86 283:0.78 284:0.95 285:0.62 287:0.97 290:0.89 292:0.91 297:0.36 300:0.84 +3 6:0.82 8:0.61 9:0.80 11:0.87 12:0.98 17:0.20 18:0.83 20:0.96 27:0.18 28:0.91 29:0.77 30:0.68 31:0.98 34:0.47 36:0.83 37:0.64 39:0.53 41:0.94 43:0.97 45:0.66 60:0.87 64:0.77 66:0.79 69:0.05 70:0.31 71:0.73 74:0.67 78:1.00 79:0.99 81:0.63 83:0.91 85:0.72 86:0.86 91:0.67 96:0.94 98:0.61 100:0.98 101:0.87 108:0.05 111:0.99 120:0.91 122:0.57 123:0.05 126:0.44 127:0.18 129:0.05 131:0.89 135:0.39 137:0.98 139:0.87 140:0.45 144:0.76 146:0.41 147:0.48 149:0.65 150:0.44 151:0.98 152:0.89 153:0.92 154:0.97 155:0.67 157:0.79 158:0.92 159:0.15 161:0.73 162:0.78 164:0.85 166:0.73 167:0.56 169:0.97 172:0.41 173:0.28 177:0.70 179:0.60 181:0.49 182:0.88 186:1.00 187:0.39 189:0.84 191:0.86 192:0.87 196:0.98 201:0.68 202:0.82 208:0.64 212:0.78 215:0.96 216:0.53 219:0.95 222:0.48 227:0.95 229:0.93 231:0.77 235:0.05 238:0.86 241:0.58 242:0.45 243:0.80 246:0.61 247:0.39 248:0.90 253:0.92 255:0.95 256:0.86 259:0.21 260:0.90 261:0.95 265:0.62 266:0.23 267:0.59 268:0.87 276:0.24 279:0.86 283:0.82 284:0.97 285:0.62 287:0.97 292:0.95 297:0.36 300:0.25 +1 1:0.74 7:0.81 11:0.92 12:0.98 17:0.85 18:0.92 21:0.05 22:0.93 27:0.35 28:0.91 29:0.77 30:0.54 32:0.69 34:0.97 36:0.52 37:0.27 39:0.53 43:0.97 45:0.88 47:0.93 64:0.77 66:0.43 69:0.08 70:0.89 71:0.73 74:0.90 76:0.85 77:0.70 81:0.76 83:0.91 85:0.90 86:0.97 91:0.62 98:0.62 101:0.97 104:0.83 106:0.81 108:0.07 114:0.89 117:0.86 121:0.90 122:0.77 123:0.07 124:0.70 126:0.54 127:0.87 129:0.81 131:0.61 133:0.67 135:0.65 139:0.97 144:0.76 145:0.69 146:0.77 147:0.80 149:0.94 150:0.85 154:0.97 155:0.67 157:0.93 159:0.66 161:0.73 165:0.93 166:0.84 167:0.48 172:0.79 173:0.13 176:0.73 177:0.70 178:0.55 179:0.31 181:0.49 182:0.87 187:0.21 192:0.87 195:0.79 201:0.93 204:0.89 206:0.81 208:0.64 212:0.86 215:0.78 216:0.46 222:0.48 227:0.61 229:0.61 230:0.87 231:0.76 232:0.96 233:0.76 235:0.22 241:0.27 242:0.30 243:0.57 245:0.92 246:0.96 247:0.86 253:0.62 259:0.21 262:0.93 265:0.62 266:0.71 267:0.82 276:0.82 281:0.91 282:0.71 287:0.61 290:0.89 297:0.36 300:0.94 +3 1:0.69 6:0.87 7:0.72 8:0.69 9:0.76 11:0.92 12:0.98 17:0.72 18:0.85 20:0.91 21:0.05 27:0.54 28:0.91 29:0.77 30:0.28 32:0.80 34:0.99 36:0.47 37:0.86 39:0.53 43:0.79 44:0.93 45:0.91 60:0.87 66:0.16 69:0.30 70:0.92 71:0.73 74:0.88 76:0.94 77:0.70 78:0.99 81:0.64 83:0.91 85:0.88 86:0.94 91:0.61 96:0.77 97:0.94 98:0.49 99:0.83 100:0.90 101:0.97 104:0.86 106:0.81 108:0.93 111:0.96 114:0.70 117:0.86 120:0.65 122:0.77 123:0.23 124:0.88 126:0.44 127:0.97 129:0.81 131:0.84 133:0.86 135:0.95 139:0.95 140:0.45 144:0.76 145:0.41 146:0.83 147:0.86 149:0.91 150:0.62 151:0.65 152:0.85 153:0.48 154:0.73 155:0.67 157:0.88 158:0.92 159:0.84 161:0.73 162:0.86 164:0.64 165:0.70 166:0.77 167:0.47 169:0.87 172:0.79 173:0.14 176:0.55 177:0.70 179:0.21 181:0.49 182:0.79 186:0.99 187:0.21 189:0.89 190:0.77 192:0.87 195:0.93 201:0.68 202:0.50 204:0.85 206:0.81 208:0.64 212:0.80 215:0.78 216:0.12 219:0.95 220:0.74 222:0.48 227:0.91 229:0.61 230:0.75 231:0.75 232:0.94 233:0.76 235:0.12 238:0.81 241:0.21 242:0.71 243:0.37 245:0.94 246:0.61 247:0.88 248:0.67 253:0.61 255:0.74 256:0.58 259:0.21 260:0.64 261:0.91 265:0.29 266:0.80 267:0.85 268:0.59 276:0.90 279:0.86 282:0.88 283:0.82 285:0.56 287:0.61 290:0.70 292:0.95 297:0.36 299:0.88 300:0.94 +2 1:0.74 6:0.80 8:0.59 9:0.80 11:0.87 12:0.98 17:0.73 18:0.88 20:0.95 22:0.93 27:0.40 28:0.91 29:0.77 30:0.53 31:0.96 34:0.88 36:0.55 37:0.28 39:0.53 41:0.88 43:0.80 45:0.83 47:0.93 60:0.95 64:0.77 66:0.23 69:0.49 70:0.77 71:0.73 74:0.73 78:0.99 79:0.96 81:0.83 83:0.91 85:0.80 86:0.89 87:0.98 91:0.57 96:0.93 97:0.89 98:0.65 100:0.89 101:0.87 104:0.93 106:0.81 108:0.38 111:0.97 114:0.98 117:0.86 120:0.86 122:0.77 123:0.76 124:0.67 126:0.54 127:0.66 129:0.81 131:0.89 133:0.67 135:0.81 137:0.96 139:0.90 140:0.80 144:0.76 146:0.63 147:0.68 149:0.82 150:0.89 151:0.93 152:0.96 153:0.78 154:0.75 155:0.67 157:0.82 158:0.92 159:0.57 161:0.73 162:0.85 164:0.82 165:0.93 166:0.84 167:0.46 169:0.85 172:0.73 173:0.44 176:0.73 177:0.70 179:0.39 181:0.49 182:0.88 186:0.99 187:0.95 189:0.83 192:0.87 196:0.97 201:0.93 202:0.76 206:0.81 208:0.64 212:0.73 215:0.96 216:0.29 219:0.95 220:0.62 222:0.48 227:0.95 229:0.93 231:0.77 232:0.75 235:0.12 238:0.81 241:0.12 242:0.32 243:0.58 245:0.86 246:0.96 247:0.86 248:0.83 253:0.89 255:0.95 256:0.83 259:0.21 260:0.84 261:0.94 262:0.93 265:0.53 266:0.71 267:0.93 268:0.83 276:0.75 279:0.86 283:0.82 284:0.94 285:0.62 287:0.97 290:0.98 292:0.95 297:0.36 300:0.70 +2 1:0.74 11:0.81 12:0.98 17:0.32 18:0.80 21:0.30 27:0.45 28:0.91 29:0.77 30:0.11 32:0.69 34:0.84 36:0.17 37:0.50 39:0.53 43:0.12 45:0.73 64:0.77 66:0.48 69:0.69 70:0.95 71:0.73 74:0.54 77:0.70 81:0.55 83:0.60 86:0.83 91:0.07 98:0.20 99:0.83 101:0.52 108:0.52 114:0.65 122:0.57 123:0.50 124:0.74 126:0.54 127:0.25 129:0.05 131:0.61 133:0.73 135:0.77 139:0.79 144:0.76 145:0.49 146:0.35 147:0.42 149:0.08 150:0.71 154:0.76 155:0.67 157:0.61 159:0.57 161:0.73 165:0.70 166:0.84 167:0.47 172:0.37 173:0.55 176:0.73 177:0.70 178:0.55 179:0.66 181:0.49 182:0.61 187:0.68 192:0.87 195:0.87 201:0.93 208:0.64 212:0.42 215:0.44 216:0.77 222:0.48 227:0.61 229:0.61 231:0.76 235:0.52 241:0.24 242:0.29 243:0.60 245:0.49 246:0.61 247:0.67 253:0.49 254:0.74 259:0.21 265:0.22 266:0.54 267:0.69 276:0.37 282:0.71 287:0.61 290:0.65 297:0.36 300:0.70 +2 1:0.74 6:0.82 8:0.64 9:0.80 11:0.92 12:0.98 17:0.98 18:0.83 20:0.80 21:0.54 27:0.53 28:0.91 29:0.77 30:0.36 31:0.86 32:0.78 34:0.98 36:0.51 37:0.36 39:0.53 43:0.77 44:0.93 45:0.98 60:0.87 64:0.77 66:0.99 69:0.32 70:0.70 71:0.73 74:0.88 77:0.96 78:0.97 79:0.86 81:0.44 83:0.91 85:0.80 86:0.88 91:0.51 96:0.68 97:1.00 98:0.98 99:0.83 100:0.90 101:0.97 104:0.94 106:0.81 108:0.25 111:0.94 114:0.59 117:0.86 120:0.55 122:0.77 123:0.24 126:0.54 127:0.82 129:0.05 131:0.89 135:0.88 137:0.86 139:0.92 140:0.45 144:0.76 145:0.67 146:0.96 147:0.97 149:0.96 150:0.66 151:0.50 152:0.77 153:0.48 154:0.80 155:0.67 157:0.87 158:0.92 159:0.68 161:0.73 162:0.86 164:0.57 165:0.70 166:0.84 167:0.46 169:0.88 172:0.98 173:0.24 176:0.73 177:0.70 179:0.15 181:0.49 182:0.77 186:0.97 187:0.39 189:0.84 191:0.70 192:0.87 195:0.80 196:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.91 215:0.39 216:0.09 219:0.95 220:0.91 222:0.48 227:0.95 229:0.61 231:0.77 232:0.79 235:0.74 238:0.81 241:0.10 242:0.38 243:0.99 246:0.61 247:0.92 248:0.49 253:0.49 255:0.95 256:0.45 259:0.21 260:0.55 261:0.83 265:0.98 266:0.27 267:0.89 268:0.46 276:0.95 279:0.86 282:0.86 283:0.82 284:0.89 285:0.62 287:0.61 290:0.59 292:0.95 297:0.36 300:0.70 +1 12:0.20 17:0.22 21:0.64 27:0.45 28:0.48 30:0.33 34:0.86 36:0.17 37:0.50 39:0.39 43:0.30 55:0.96 66:0.32 69:0.80 70:0.69 74:0.59 81:0.32 91:0.18 98:0.20 108:0.63 122:0.43 123:0.61 124:0.83 126:0.32 127:0.36 129:0.05 133:0.82 135:0.86 145:0.52 146:0.32 147:0.05 149:0.26 150:0.30 154:0.65 159:0.58 161:0.54 167:0.60 172:0.21 173:0.74 177:0.05 178:0.55 179:0.85 181:0.73 187:0.68 212:0.19 215:0.53 216:0.77 222:0.25 235:0.74 241:0.12 242:0.45 243:0.19 245:0.43 247:0.39 253:0.47 254:0.84 257:0.65 259:0.21 265:0.22 266:0.47 267:0.73 271:0.74 276:0.33 283:0.71 297:0.36 300:0.43 +3 6:0.92 8:0.88 9:0.80 12:0.20 17:0.49 20:0.99 25:0.88 27:0.26 28:0.48 30:0.76 34:0.34 36:0.53 37:0.60 39:0.39 41:0.88 43:0.42 55:0.52 66:0.80 69:0.21 70:0.21 74:0.87 78:0.89 81:0.80 83:0.76 91:0.72 96:0.97 98:0.92 100:0.93 104:0.58 108:0.17 111:0.90 114:0.80 117:0.86 120:0.87 122:0.67 123:0.16 126:0.32 127:0.54 129:0.05 135:0.61 138:0.98 140:0.95 146:0.48 147:0.54 149:0.51 150:0.60 151:0.77 152:0.99 153:0.95 154:0.32 155:0.67 159:0.17 161:0.54 162:0.84 164:0.87 167:0.87 169:0.88 172:0.45 173:0.62 175:0.75 176:0.56 177:0.05 179:0.86 181:0.73 186:0.89 187:0.21 189:0.96 190:0.77 191:0.93 192:0.59 202:0.85 208:0.64 212:0.95 216:0.83 222:0.25 232:0.81 235:0.12 238:0.94 241:0.76 242:0.82 243:0.82 244:0.61 247:0.12 248:0.85 253:0.98 255:0.79 256:0.89 257:0.65 259:0.21 260:0.87 261:0.92 265:0.92 266:0.12 267:0.44 268:0.90 271:0.74 276:0.37 277:0.69 283:0.71 285:0.62 290:0.80 300:0.18 +1 12:0.20 17:0.34 21:0.47 27:0.45 28:0.48 30:0.20 34:0.77 36:0.21 37:0.50 39:0.39 43:0.30 55:0.52 66:0.83 69:0.68 70:0.84 74:0.69 81:0.39 91:0.11 98:0.64 108:0.52 123:0.50 124:0.37 126:0.32 127:0.34 129:0.05 133:0.39 135:0.72 145:0.47 146:0.82 147:0.85 149:0.54 150:0.34 154:0.63 159:0.57 161:0.54 167:0.59 172:0.71 173:0.60 177:0.38 178:0.55 179:0.48 181:0.73 187:0.68 212:0.77 215:0.63 216:0.77 222:0.25 235:0.52 241:0.10 242:0.63 243:0.84 245:0.57 247:0.55 253:0.52 254:0.88 259:0.21 265:0.65 266:0.54 267:0.19 271:0.74 276:0.56 283:0.71 297:0.36 300:0.70 +2 7:0.78 12:0.20 17:0.34 21:0.30 25:0.88 27:0.26 28:0.48 30:0.36 32:0.75 34:0.47 36:0.80 37:0.60 39:0.39 43:0.45 55:0.71 66:0.71 69:0.77 70:0.48 74:0.51 76:0.85 81:0.51 91:0.74 98:0.84 104:0.58 108:0.60 123:0.57 124:0.43 126:0.32 127:0.65 129:0.05 133:0.39 135:0.49 145:0.58 146:0.87 147:0.49 149:0.68 150:0.40 154:0.34 159:0.54 161:0.54 167:0.92 172:0.71 173:0.78 177:0.05 178:0.55 179:0.55 181:0.73 187:0.21 195:0.75 212:0.27 215:0.72 216:0.83 222:0.25 230:0.81 233:0.76 235:0.31 241:0.80 242:0.82 243:0.25 244:0.61 245:0.76 247:0.30 253:0.42 257:0.65 259:0.21 265:0.84 266:0.54 267:0.65 271:0.74 276:0.65 297:0.36 300:0.33 +0 8:0.98 12:0.20 17:0.08 20:0.85 21:0.54 25:0.88 27:0.07 28:0.48 30:0.62 34:0.52 36:0.49 37:0.95 39:0.39 43:0.38 55:0.71 66:0.83 69:0.47 70:0.43 74:0.50 78:0.76 81:0.36 91:0.93 98:0.87 100:0.77 104:0.76 108:0.36 111:0.75 120:0.95 123:0.35 126:0.32 127:0.40 129:0.05 135:0.37 140:0.45 146:0.65 147:0.70 149:0.49 150:0.32 151:0.75 152:0.80 153:0.86 154:0.63 159:0.24 161:0.54 162:0.68 164:0.96 167:0.97 169:0.75 172:0.51 173:0.67 177:0.38 179:0.77 181:0.73 186:0.77 190:0.77 191:0.97 202:0.94 212:0.70 215:0.58 216:0.54 220:0.82 222:0.25 235:0.60 241:0.96 242:0.78 243:0.84 247:0.35 248:0.93 253:0.41 254:0.92 256:0.95 259:0.21 260:0.95 261:0.76 265:0.87 266:0.27 267:0.69 268:0.96 271:0.74 276:0.42 283:0.71 285:0.50 297:0.36 300:0.33 +1 12:0.20 17:0.34 25:0.88 27:0.25 28:0.48 30:0.45 34:0.62 36:0.95 37:0.88 39:0.39 43:0.40 55:0.59 66:0.87 69:0.05 70:0.40 74:0.46 81:0.90 91:0.20 96:0.80 98:0.82 104:0.76 108:0.05 114:0.80 123:0.05 126:0.32 127:0.32 129:0.05 135:0.39 140:0.45 146:0.80 147:0.83 149:0.54 150:0.80 151:0.66 153:0.48 154:0.92 159:0.36 161:0.54 162:0.86 167:0.70 172:0.63 173:0.14 176:0.56 177:0.38 179:0.58 181:0.73 187:0.39 190:0.77 202:0.36 212:0.37 216:0.50 222:0.25 235:0.02 241:0.51 242:0.81 243:0.88 247:0.30 248:0.63 253:0.79 254:0.74 256:0.45 259:0.21 265:0.82 266:0.38 267:0.44 268:0.46 271:0.74 276:0.52 285:0.50 290:0.80 300:0.33 +0 8:0.92 12:0.20 17:0.40 21:0.78 25:0.88 27:0.72 28:0.48 30:0.89 34:0.21 36:0.63 37:0.38 39:0.39 43:0.43 55:0.71 66:0.47 69:0.15 70:0.20 74:0.75 91:0.70 96:0.74 98:0.42 104:0.75 108:0.59 120:0.60 122:0.67 123:0.57 124:0.52 127:0.27 129:0.59 133:0.47 135:0.56 140:0.80 146:0.27 147:0.33 149:0.38 151:0.58 153:0.48 154:0.33 159:0.28 161:0.54 162:0.59 163:0.79 164:0.56 167:0.96 172:0.21 173:0.28 175:0.75 177:0.05 178:0.42 179:0.87 181:0.73 190:0.77 191:0.85 202:0.62 212:0.30 216:0.56 220:0.74 222:0.25 235:0.12 241:0.94 242:0.82 243:0.37 244:0.61 245:0.46 247:0.12 248:0.58 253:0.69 256:0.58 257:0.65 259:0.21 260:0.60 265:0.44 266:0.27 267:0.59 268:0.59 271:0.74 276:0.26 277:0.69 285:0.62 300:0.18 +0 11:0.57 12:0.20 17:0.02 21:0.68 25:0.88 27:0.47 28:0.48 30:0.58 34:0.55 36:0.55 37:0.55 39:0.39 43:0.77 66:0.61 69:0.79 70:0.44 74:0.62 81:0.31 85:0.72 91:0.48 98:0.48 101:0.74 104:0.74 108:0.63 120:0.71 123:0.60 124:0.47 126:0.32 127:0.52 129:0.05 133:0.39 135:0.68 140:0.45 146:0.37 147:0.18 149:0.61 150:0.29 151:0.66 153:0.48 154:0.69 159:0.28 161:0.54 162:0.59 164:0.70 167:0.80 172:0.37 173:0.95 177:0.05 179:0.86 181:0.73 187:0.39 190:0.77 202:0.64 212:0.55 215:0.49 216:0.50 220:0.99 222:0.25 235:0.80 241:0.71 242:0.40 243:0.29 245:0.52 247:0.47 248:0.63 253:0.48 256:0.58 257:0.65 259:0.21 260:0.70 265:0.50 266:0.38 267:0.36 268:0.64 271:0.74 276:0.25 283:0.71 285:0.50 297:0.36 300:0.33 +0 6:0.88 8:0.75 9:0.80 12:0.20 17:0.26 20:0.88 21:0.78 25:0.88 27:0.03 28:0.48 34:0.58 36:0.28 37:0.70 39:0.39 41:0.95 78:1.00 83:0.82 91:0.94 96:0.99 100:0.99 104:0.74 111:1.00 120:0.96 135:0.72 138:0.98 140:0.95 151:0.95 152:0.92 153:0.97 155:0.67 161:0.54 162:0.80 164:0.96 167:0.96 169:0.98 175:0.91 181:0.73 186:1.00 189:0.90 191:0.98 192:0.59 202:0.95 208:0.64 216:0.67 222:0.25 238:0.99 241:0.94 244:0.83 248:0.96 253:0.98 255:0.79 256:0.97 257:0.84 259:0.21 260:0.97 261:0.99 267:0.91 268:0.97 271:0.74 277:0.87 285:0.62 +2 11:0.57 12:0.20 17:0.34 21:0.13 25:0.88 27:0.26 28:0.48 30:0.26 34:0.39 36:0.98 37:0.60 39:0.39 43:0.64 55:0.59 66:0.61 69:0.07 70:0.87 74:0.97 81:0.66 85:0.72 91:0.44 98:0.82 101:0.71 104:0.58 108:0.06 117:0.86 120:0.63 122:0.65 123:0.06 124:0.50 126:0.32 127:0.85 129:0.59 133:0.47 135:0.76 140:0.45 146:0.92 147:0.94 149:0.64 150:0.48 151:0.59 153:0.69 154:0.82 159:0.70 161:0.54 162:0.86 164:0.62 167:0.64 172:0.88 173:0.11 177:0.38 179:0.27 181:0.73 187:0.87 190:0.77 191:0.75 202:0.46 212:0.77 215:0.84 216:0.83 220:0.74 222:0.25 232:0.81 235:0.12 241:0.30 242:0.22 243:0.68 245:0.95 247:0.67 248:0.57 253:0.69 256:0.45 259:0.21 260:0.64 265:0.82 266:0.75 267:0.73 268:0.55 271:0.74 276:0.86 285:0.50 297:0.36 300:0.84 +2 6:0.92 7:0.78 8:0.85 9:0.80 12:0.20 20:0.91 21:0.30 25:0.88 27:0.26 28:0.48 30:0.33 32:0.75 34:0.39 36:0.79 37:0.60 39:0.39 43:0.42 55:0.59 66:0.36 69:0.25 70:0.58 74:0.67 76:0.85 78:0.83 81:0.51 91:0.97 96:0.80 98:0.73 100:0.83 104:0.58 108:0.72 111:0.82 120:1.00 123:0.70 124:0.60 126:0.32 127:0.94 129:0.05 133:0.39 135:0.18 140:1.00 145:0.58 146:0.65 147:0.70 149:0.73 150:0.40 151:0.49 152:0.99 153:0.99 154:0.32 155:0.67 159:0.53 161:0.54 162:0.74 164:1.00 167:0.97 169:0.89 172:0.61 173:0.29 177:0.38 179:0.54 181:0.73 186:0.83 189:0.91 190:0.77 191:0.97 192:0.59 195:0.69 202:0.99 208:0.64 212:0.60 215:0.72 216:0.83 222:0.25 230:0.81 233:0.69 235:0.31 238:0.87 241:0.98 242:0.79 243:0.50 244:0.61 245:0.86 247:0.55 248:0.49 253:0.79 255:0.79 256:0.99 259:0.21 260:1.00 261:0.92 265:0.73 266:0.54 267:0.92 268:1.00 271:0.74 276:0.68 277:0.69 283:0.71 285:0.62 297:0.36 300:0.43 +2 7:0.78 8:0.74 11:0.57 12:0.20 17:0.16 21:0.40 25:0.88 27:0.61 28:0.48 30:0.45 32:0.75 34:0.20 36:0.25 37:0.79 39:0.39 43:0.98 55:0.84 66:0.72 69:0.12 70:0.61 74:0.70 81:0.45 85:0.72 91:0.80 98:0.79 101:0.74 104:0.78 108:0.10 120:0.89 122:0.55 123:0.10 124:0.40 126:0.32 127:0.78 129:0.05 133:0.39 135:0.23 140:0.80 145:0.45 146:0.59 147:0.65 149:0.67 150:0.37 151:0.69 153:0.69 154:0.98 159:0.30 161:0.54 162:0.68 164:0.92 167:0.96 172:0.45 173:0.36 177:0.38 178:0.42 179:0.87 181:0.73 190:0.77 191:0.80 195:0.56 202:0.89 212:0.30 215:0.67 216:0.37 220:0.82 222:0.25 230:0.81 233:0.69 235:0.42 241:0.93 242:0.33 243:0.75 245:0.47 247:0.60 248:0.85 253:0.53 256:0.90 259:0.21 260:0.90 265:0.79 266:0.47 267:0.19 268:0.91 271:0.74 276:0.38 283:0.71 285:0.50 297:0.36 300:0.43 +1 8:0.64 12:0.20 17:0.32 25:0.88 27:0.25 28:0.48 30:0.58 34:0.75 36:0.81 37:0.88 39:0.39 43:0.40 55:0.45 66:0.80 69:0.05 70:0.33 74:0.55 81:0.90 91:0.48 98:0.67 104:0.76 108:0.05 114:0.80 123:0.05 126:0.32 127:0.20 129:0.05 135:0.44 146:0.55 147:0.61 149:0.36 150:0.80 154:0.92 159:0.20 161:0.54 167:0.68 172:0.45 173:0.19 176:0.56 177:0.38 178:0.55 179:0.62 181:0.73 187:0.39 212:0.71 216:0.50 222:0.25 235:0.02 241:0.46 242:0.76 243:0.82 247:0.35 253:0.62 254:0.74 259:0.21 265:0.68 266:0.27 267:0.44 271:0.74 276:0.34 290:0.80 300:0.25 +1 1:0.66 10:0.91 11:0.64 12:0.20 17:0.45 18:0.76 21:0.30 27:0.45 28:0.92 29:0.91 30:0.56 34:0.56 36:0.21 37:0.50 39:0.44 43:0.58 45:0.81 64:0.77 66:0.44 69:0.69 70:0.71 71:0.74 74:0.48 81:0.73 83:0.69 86:0.78 91:0.18 98:0.44 99:0.95 101:0.65 108:0.52 114:0.86 122:0.65 123:0.50 124:0.67 126:0.54 127:0.36 129:0.05 131:0.61 133:0.60 135:0.81 139:0.74 144:0.76 145:0.50 146:0.55 147:0.61 149:0.28 150:0.59 154:0.75 155:0.51 157:0.98 159:0.48 161:0.57 165:0.81 166:0.89 167:0.68 170:0.77 172:0.37 173:0.68 174:0.83 176:0.73 177:0.88 178:0.55 179:0.72 181:0.62 182:1.00 187:0.68 192:0.50 197:0.85 201:0.65 208:0.64 212:0.23 215:0.72 216:0.77 222:0.35 227:0.61 229:0.61 231:0.83 235:0.60 236:0.97 239:0.89 241:0.39 242:0.24 243:0.57 245:0.56 246:0.97 247:0.74 253:0.61 254:0.74 259:0.21 265:0.46 266:0.71 267:0.28 271:0.74 276:0.40 287:0.61 290:0.86 297:0.36 300:0.55 +2 7:0.70 10:0.97 11:0.75 12:0.20 17:0.57 18:0.92 21:0.78 27:0.72 28:0.92 29:0.91 30:0.45 32:0.77 34:0.86 36:0.54 37:0.71 39:0.44 43:0.63 44:0.93 45:1.00 66:0.07 69:0.25 70:0.90 71:0.74 74:0.96 75:0.95 77:0.89 83:0.56 86:0.93 91:0.02 97:0.94 98:0.34 101:0.87 102:0.98 108:0.99 122:0.55 123:0.87 124:0.98 127:0.98 129:0.98 131:0.61 133:0.98 135:0.87 139:0.91 144:0.76 145:0.74 146:0.78 147:0.81 149:0.89 154:0.79 155:0.51 157:1.00 158:0.81 159:0.97 161:0.57 166:0.61 167:0.67 170:0.77 172:0.92 173:0.06 174:0.97 177:0.88 178:0.55 179:0.09 181:0.62 182:1.00 187:0.21 192:0.51 195:1.00 197:0.93 204:0.81 208:0.61 212:0.55 216:0.06 219:0.88 222:0.35 226:0.96 227:0.61 229:0.61 230:0.73 231:0.82 233:0.76 235:0.74 239:0.97 241:0.32 242:0.21 243:0.20 245:0.99 246:0.61 247:1.00 253:0.64 254:0.86 259:0.21 265:0.36 266:0.97 267:0.69 271:0.74 276:0.99 277:0.69 279:0.79 282:0.85 283:0.82 287:0.61 292:0.86 300:0.94 +1 1:0.62 6:0.85 8:0.69 9:0.71 10:0.87 11:0.64 12:0.20 17:0.22 18:0.77 20:0.88 21:0.59 23:0.95 27:0.19 28:0.92 29:0.91 30:0.09 32:0.63 33:0.97 34:0.80 36:0.97 37:0.78 39:0.44 43:0.57 45:0.85 62:0.96 64:0.77 66:0.43 69:0.29 70:0.98 71:0.74 74:0.56 78:0.79 81:0.62 83:0.56 86:0.69 91:0.11 96:0.74 98:0.57 99:0.83 100:0.81 101:0.65 102:0.94 104:0.58 108:0.22 111:0.78 114:0.74 120:0.62 123:0.22 124:0.77 126:0.54 127:0.88 128:0.96 129:0.59 131:0.89 133:0.73 135:0.79 139:0.66 140:0.45 144:0.76 145:0.52 146:0.71 147:0.75 149:0.42 150:0.52 151:0.66 152:0.83 153:0.48 154:0.71 155:0.56 157:0.98 159:0.64 161:0.57 162:0.86 164:0.56 165:0.65 166:0.89 167:0.67 168:0.96 169:0.79 170:0.77 172:0.67 173:0.25 174:0.89 176:0.73 177:0.88 179:0.47 181:0.62 182:0.96 186:0.80 187:0.68 189:0.87 190:0.77 192:0.86 193:0.95 195:0.77 197:0.92 201:0.61 202:0.36 205:0.95 208:0.64 212:0.59 215:0.55 216:0.65 220:0.74 222:0.35 227:0.87 229:0.96 231:0.84 232:0.98 235:0.88 236:0.91 238:0.86 239:0.87 241:0.32 242:0.29 243:0.57 245:0.80 246:0.87 247:0.93 248:0.63 253:0.66 254:0.86 255:0.72 256:0.45 259:0.21 260:0.63 261:0.81 265:0.58 266:0.75 267:0.87 268:0.46 271:0.74 276:0.72 285:0.60 287:0.89 290:0.74 291:0.97 297:0.36 300:0.84 +3 1:0.64 6:0.95 7:0.70 8:0.85 9:0.74 10:0.89 11:0.50 12:0.20 17:0.61 18:0.83 20:0.88 21:0.54 23:0.98 27:0.08 28:0.92 29:0.91 30:0.61 32:0.67 34:0.53 36:0.36 37:0.81 39:0.44 43:0.74 45:0.98 62:0.97 66:0.99 69:0.38 70:0.64 71:0.74 74:0.93 75:0.95 77:0.96 78:0.87 81:0.51 83:0.56 86:0.68 91:0.63 96:0.91 97:1.00 98:0.99 99:0.95 100:0.90 101:0.47 104:0.87 106:0.81 108:0.30 111:0.87 114:0.69 117:0.86 120:0.84 122:0.55 123:0.28 126:0.32 127:0.93 128:0.98 129:0.05 131:0.89 135:0.86 138:0.96 139:0.66 140:0.80 144:0.57 145:0.65 146:0.99 147:0.99 149:0.94 150:0.46 151:0.58 152:0.86 153:0.93 154:0.66 155:0.56 157:0.98 158:0.81 159:0.80 161:0.57 162:0.82 164:0.82 165:0.65 166:0.80 167:0.62 168:0.98 169:0.86 170:0.77 172:0.97 173:0.21 174:0.95 176:0.62 177:0.88 179:0.17 181:0.62 182:0.93 186:0.87 187:0.68 189:0.96 190:0.89 191:0.79 192:0.86 195:0.90 197:0.95 201:0.60 202:0.74 204:0.81 205:0.98 206:0.81 208:0.50 212:0.77 215:0.55 216:0.79 222:0.35 226:0.96 227:0.87 229:0.97 230:0.73 231:0.83 232:0.90 233:0.76 235:0.80 238:0.90 239:0.88 241:0.12 242:0.28 243:0.99 244:0.61 246:0.87 247:0.97 248:0.59 253:0.94 255:0.73 256:0.78 259:0.21 260:0.84 261:0.88 265:0.99 266:0.54 267:0.98 268:0.80 271:0.74 276:0.94 279:0.82 282:0.75 283:0.82 285:0.60 287:0.89 290:0.69 297:0.36 300:0.70 +2 1:0.60 6:0.95 7:0.75 8:0.81 9:0.70 10:0.90 11:0.50 12:0.20 17:0.51 18:0.90 20:0.85 21:0.30 23:0.95 27:0.08 28:0.92 29:0.91 30:0.62 32:0.72 34:0.31 36:0.72 37:0.81 39:0.44 43:0.75 45:0.99 62:0.96 66:0.99 69:0.34 70:0.63 71:0.74 74:0.94 77:0.70 78:0.83 81:0.59 83:0.56 86:0.73 91:0.06 96:0.71 98:0.99 99:0.83 100:0.84 101:0.47 102:0.94 104:0.87 106:0.81 108:0.27 111:0.82 114:0.84 117:0.86 120:0.58 122:0.55 123:0.26 126:0.51 127:0.92 128:0.95 129:0.05 131:0.89 135:0.89 139:0.70 140:0.45 144:0.57 145:0.45 146:0.99 147:1.00 149:0.94 150:0.48 151:0.58 152:0.86 153:0.48 154:0.20 155:0.53 157:0.98 159:0.88 161:0.57 162:0.86 164:0.56 165:0.65 166:0.84 167:0.62 168:0.95 169:0.86 170:0.77 172:0.99 173:0.14 174:0.97 176:0.70 177:0.88 179:0.14 181:0.62 182:0.93 186:0.83 187:0.87 189:0.94 190:0.77 192:0.58 193:0.95 195:0.95 197:0.96 201:0.62 202:0.36 204:0.81 205:0.95 206:0.81 208:0.50 212:0.67 215:0.72 216:0.79 220:0.87 222:0.35 227:0.87 229:0.96 230:0.77 231:0.83 232:0.90 233:0.66 235:0.52 236:0.91 238:0.88 239:0.90 241:0.12 242:0.18 243:0.99 246:0.87 247:0.97 248:0.56 253:0.76 254:1.00 255:0.70 256:0.45 259:0.21 260:0.58 261:0.88 265:0.99 266:0.54 267:0.44 268:0.46 271:0.74 276:0.96 282:0.80 285:0.60 287:0.89 290:0.84 297:0.36 300:0.70 +3 1:0.60 7:0.70 8:0.86 9:0.75 10:0.92 11:0.64 12:0.20 17:0.20 18:0.80 20:0.80 21:0.30 27:0.21 28:0.92 29:0.91 30:0.61 34:0.66 36:0.89 37:0.74 39:0.44 43:0.66 45:0.96 66:0.46 69:0.12 70:0.81 71:0.74 74:0.82 78:0.79 81:0.50 83:0.56 86:0.80 91:0.70 96:0.95 97:0.89 98:0.71 99:0.83 100:0.81 101:0.65 104:0.84 106:0.81 108:0.10 111:0.78 114:0.82 117:0.86 120:0.91 122:0.55 123:0.10 124:0.67 126:0.32 127:0.64 129:0.59 131:0.86 133:0.64 135:0.21 139:0.75 140:0.45 144:0.76 146:0.90 147:0.92 149:0.83 150:0.45 151:0.97 152:0.77 153:0.90 154:0.63 155:0.56 157:1.00 158:0.77 159:0.73 161:0.57 162:0.72 164:0.87 165:0.65 166:0.80 167:0.73 169:0.79 170:0.77 172:0.83 173:0.12 174:0.89 176:0.63 177:0.88 179:0.26 181:0.62 182:0.98 186:0.79 187:0.39 190:0.89 191:0.79 192:0.59 197:0.90 201:0.57 202:0.80 204:0.80 206:0.81 208:0.50 212:0.78 215:0.72 216:0.95 222:0.35 227:0.85 229:0.97 230:0.73 231:0.81 232:0.88 233:0.76 235:0.42 239:0.90 241:0.56 242:0.39 243:0.58 245:0.93 246:0.87 247:0.96 248:0.95 253:0.96 254:0.93 255:0.73 256:0.82 259:0.21 260:0.91 261:0.77 265:0.72 266:0.75 267:0.82 268:0.83 271:0.74 276:0.84 279:0.76 283:0.82 285:0.50 287:0.89 290:0.82 297:0.36 300:0.84 +2 1:0.67 6:0.93 7:0.75 8:0.76 9:0.74 10:0.88 11:0.50 12:0.20 17:0.20 20:0.88 21:0.30 23:0.97 27:0.13 28:0.92 29:0.91 30:0.27 31:0.89 32:0.72 34:0.81 36:0.28 37:0.62 39:0.44 43:0.73 45:0.85 62:0.97 66:0.26 69:0.59 70:0.92 71:0.74 74:0.75 75:0.95 77:0.89 78:0.82 79:0.89 81:0.68 83:0.56 91:0.35 96:0.88 97:0.97 98:0.24 99:0.95 100:0.85 101:0.47 102:0.94 104:0.78 106:0.81 108:0.45 111:0.82 114:0.84 117:0.86 120:0.79 122:0.95 123:0.64 124:0.78 126:0.51 127:0.93 128:0.97 129:0.59 131:0.93 133:0.73 135:0.92 137:0.89 139:0.58 140:0.80 144:0.57 145:0.77 146:0.34 147:0.41 149:0.60 150:0.58 151:0.76 152:0.85 153:0.82 154:0.63 155:0.56 157:0.90 158:0.81 159:0.93 161:0.57 162:0.83 164:0.73 165:0.65 166:0.84 167:0.71 168:0.97 169:0.83 170:0.77 172:0.51 173:0.22 174:0.94 175:0.75 176:0.70 177:0.70 179:0.55 181:0.62 182:0.93 186:0.82 187:0.68 189:0.95 190:0.89 191:0.90 192:0.87 193:0.95 195:0.98 196:0.87 197:0.94 201:0.67 202:0.64 204:0.84 205:0.97 206:0.81 208:0.50 212:0.69 215:0.72 216:0.70 219:0.87 222:0.35 226:0.96 227:0.91 229:0.97 230:0.77 231:0.84 232:0.83 233:0.66 235:0.74 236:0.91 238:0.89 239:0.89 241:0.47 242:0.24 243:0.45 244:0.61 245:0.76 246:0.87 247:0.82 248:0.72 251:1.00 253:0.89 255:0.77 256:0.68 257:0.65 259:0.21 260:0.80 261:0.85 265:0.15 266:0.75 267:0.69 268:0.70 271:0.74 276:0.38 277:0.69 279:0.79 282:0.80 283:0.82 284:0.87 285:0.62 287:0.89 290:0.84 292:0.88 297:0.36 300:0.84 +1 1:0.61 6:0.86 8:0.72 9:0.74 10:0.90 11:0.83 12:0.20 17:0.73 18:0.79 20:0.88 21:0.13 23:0.95 27:0.25 28:0.92 29:0.91 30:0.66 34:0.22 36:0.38 37:0.87 39:0.44 43:0.61 45:0.98 62:0.96 66:0.30 69:0.12 70:0.70 71:0.74 74:0.86 78:0.79 81:0.62 83:0.56 85:0.72 86:0.74 91:0.53 96:0.87 97:0.97 98:0.57 99:0.83 100:0.80 101:0.96 104:0.80 106:0.81 108:0.81 111:0.78 114:0.92 117:0.86 120:0.78 122:0.55 123:0.80 124:0.78 126:0.51 127:0.78 128:0.96 129:0.81 131:0.89 133:0.74 135:0.98 139:0.71 140:0.80 144:0.76 146:0.87 147:0.89 149:0.91 150:0.53 151:0.68 152:0.84 153:0.78 154:0.51 155:0.56 157:1.00 158:0.77 159:0.83 161:0.57 162:0.86 164:0.73 165:0.65 166:0.84 167:0.66 168:0.96 169:0.78 170:0.77 172:0.86 173:0.09 174:0.94 176:0.70 177:0.88 179:0.19 181:0.62 182:0.97 186:0.80 187:0.21 189:0.88 190:0.89 191:0.70 192:0.86 197:0.95 201:0.63 202:0.59 205:0.95 206:0.81 208:0.50 212:0.59 215:0.84 216:0.44 222:0.35 227:0.87 229:0.97 231:0.83 232:0.85 235:0.31 236:0.91 238:0.84 239:0.88 241:0.30 242:0.32 243:0.39 245:0.96 246:0.87 247:0.97 248:0.64 253:0.90 255:0.73 256:0.64 259:0.21 260:0.79 261:0.81 265:0.58 266:0.75 267:0.91 268:0.67 271:0.74 276:0.92 279:0.81 283:0.82 285:0.60 287:0.89 290:0.92 297:0.36 300:0.84 +2 1:0.62 6:0.93 7:0.75 8:0.86 9:0.74 10:0.86 11:0.50 12:0.20 17:0.24 18:0.69 20:0.83 21:0.05 23:0.99 27:0.18 28:0.92 29:0.91 30:0.73 32:0.72 34:0.64 36:0.23 37:0.46 39:0.44 43:0.76 45:0.96 62:0.97 66:0.18 69:0.19 70:0.68 71:0.74 74:0.85 77:0.89 78:0.84 81:0.65 83:0.56 86:0.62 91:0.64 96:0.93 97:0.89 98:0.54 99:0.83 100:0.87 101:0.47 102:0.94 104:0.77 106:0.81 108:0.88 111:0.83 114:0.95 117:0.86 120:0.88 122:0.82 123:0.69 124:0.83 126:0.51 127:0.84 128:0.99 129:0.59 131:0.89 133:0.81 135:0.96 139:0.60 140:0.80 144:0.57 145:0.77 146:0.65 147:0.70 149:0.89 150:0.56 151:0.84 152:0.79 153:0.72 154:0.18 155:0.56 157:0.97 158:0.77 159:0.76 161:0.57 162:0.86 164:0.82 165:0.65 166:0.84 167:0.68 168:0.99 169:0.84 170:0.77 172:0.75 173:0.14 174:0.90 176:0.70 177:0.88 179:0.29 181:0.62 182:0.93 186:0.84 187:0.87 189:0.94 190:0.89 191:0.70 192:0.87 193:0.95 195:0.87 197:0.91 201:0.64 202:0.76 204:0.84 205:0.99 206:0.81 208:0.50 212:0.61 215:0.91 216:0.75 220:0.62 222:0.35 227:0.87 229:0.97 230:0.77 231:0.81 232:0.83 233:0.66 235:0.22 236:0.91 238:0.88 239:0.87 241:0.37 242:0.41 243:0.31 245:0.90 246:0.87 247:0.91 248:0.85 251:1.00 253:0.95 254:0.99 255:0.78 256:0.87 259:0.21 260:0.89 261:0.82 265:0.49 266:0.80 267:0.59 268:0.84 271:0.74 276:0.85 277:0.87 279:0.76 282:0.80 283:0.82 285:0.60 287:0.89 290:0.95 297:0.36 300:0.84 +1 1:0.65 10:0.80 11:0.50 12:0.20 17:0.20 18:0.71 21:0.22 27:0.45 28:0.92 29:0.91 30:0.60 32:0.71 34:0.75 36:0.20 37:0.50 39:0.44 43:0.57 44:0.86 45:0.83 64:0.77 66:0.54 69:0.68 70:0.67 71:0.74 74:0.67 77:0.70 81:0.62 83:0.56 86:0.64 91:0.14 97:0.89 98:0.33 99:0.83 101:0.47 108:0.52 114:0.88 122:0.55 123:0.50 124:0.67 126:0.51 127:0.43 129:0.05 131:0.61 133:0.73 135:0.68 139:0.67 144:0.57 145:0.47 146:0.50 147:0.57 149:0.45 150:0.55 154:0.61 155:0.50 157:0.93 158:0.81 159:0.62 161:0.57 165:0.65 166:0.86 167:0.69 170:0.77 172:0.48 173:0.60 174:0.79 175:0.75 176:0.70 177:0.70 178:0.55 179:0.70 181:0.62 182:0.93 187:0.68 192:0.49 195:0.82 197:0.82 201:0.63 208:0.61 212:0.42 215:0.79 216:0.77 219:0.88 222:0.35 227:0.61 229:0.61 231:0.80 235:0.42 239:0.80 241:0.41 242:0.19 243:0.63 244:0.61 245:0.53 246:0.87 247:0.67 253:0.65 254:0.74 257:0.65 259:0.21 265:0.35 266:0.63 267:0.23 271:0.74 276:0.43 277:0.69 279:0.78 282:0.80 283:0.67 287:0.61 290:0.88 292:0.88 297:0.36 300:0.55 +4 1:0.62 6:0.97 7:0.70 8:0.95 9:0.80 10:0.86 11:0.50 12:0.20 17:0.06 18:0.87 20:0.95 21:0.13 23:0.99 27:0.07 28:0.92 29:0.91 30:0.44 31:1.00 33:0.97 34:0.44 36:0.43 37:0.93 39:0.44 41:0.82 43:0.57 45:0.94 62:0.97 66:0.17 69:0.21 70:0.92 71:0.74 74:0.68 78:0.96 79:1.00 81:0.54 83:0.91 86:0.67 91:0.80 96:0.99 97:1.00 98:0.35 99:0.83 100:1.00 101:0.47 104:0.92 106:0.81 108:0.89 111:0.99 114:0.88 117:0.86 120:0.97 122:0.55 123:0.16 124:0.87 126:0.32 127:0.98 128:1.00 129:0.59 131:0.93 133:0.87 135:0.58 137:1.00 138:0.99 139:0.67 140:0.97 144:0.76 145:0.98 146:0.78 147:0.81 149:0.72 150:0.48 151:0.91 152:0.97 153:0.97 154:0.64 155:0.67 157:0.96 158:0.82 159:0.89 161:0.57 162:0.78 164:0.95 165:0.65 166:0.80 167:0.79 168:1.00 169:0.99 170:0.77 172:0.70 173:0.09 174:0.92 176:0.63 177:0.88 179:0.33 181:0.62 182:1.00 186:0.95 187:0.68 189:0.98 191:0.94 192:0.99 196:0.93 197:0.96 201:0.58 202:0.92 204:0.80 205:0.99 206:0.81 208:0.64 212:0.57 215:0.84 216:0.61 219:0.88 222:0.35 227:0.91 229:1.00 230:0.73 231:0.84 232:0.94 233:0.76 235:0.22 238:0.99 239:0.85 241:0.68 242:0.28 243:0.38 245:0.86 246:0.87 247:0.92 248:0.94 253:0.99 254:0.97 255:1.00 256:0.95 259:0.21 260:0.97 261:0.98 265:0.25 266:0.92 267:0.28 268:0.95 271:0.74 276:0.81 279:0.82 283:0.82 284:0.99 285:0.62 287:0.98 290:0.88 291:0.97 292:0.95 297:0.36 300:0.94 +1 1:0.58 7:0.75 10:0.84 11:0.50 12:0.20 17:0.43 18:0.78 21:0.47 27:0.34 28:0.92 29:0.91 30:0.73 32:0.67 33:0.97 34:0.63 36:0.41 37:0.46 39:0.44 43:0.61 45:1.00 66:0.95 69:0.19 70:0.42 71:0.74 74:0.98 76:0.85 77:0.70 81:0.47 83:0.56 86:0.68 91:0.03 98:0.92 99:0.83 101:0.47 104:0.82 106:0.81 108:0.15 114:0.76 117:0.86 122:0.55 123:0.15 124:0.37 126:0.32 127:0.94 129:0.05 131:0.61 133:0.72 135:0.24 139:0.65 144:0.57 145:0.54 146:0.99 147:0.99 149:0.98 150:0.42 154:0.22 155:0.52 157:0.98 159:0.88 161:0.57 165:0.65 166:0.80 167:0.63 170:0.77 172:0.99 173:0.09 174:0.92 176:0.63 177:0.88 178:0.55 179:0.11 181:0.62 182:0.93 187:0.98 192:0.56 193:0.95 195:0.96 197:0.92 201:0.55 204:0.82 206:0.81 208:0.50 212:0.92 215:0.63 216:0.53 222:0.35 227:0.61 229:0.61 230:0.77 231:0.81 232:0.87 233:0.66 235:0.60 239:0.85 241:0.15 242:0.57 243:0.95 244:0.61 245:0.91 246:0.87 247:0.82 253:0.72 254:0.74 259:0.21 265:0.92 266:0.71 267:0.88 271:0.74 276:0.98 282:0.75 287:0.61 290:0.76 291:0.97 297:0.36 300:0.84 +2 6:0.97 7:0.70 8:0.86 9:0.73 10:0.86 11:0.50 12:0.20 17:0.20 18:0.87 20:0.90 21:0.30 23:0.95 27:0.07 28:0.92 29:0.91 30:0.13 31:0.93 33:0.97 34:0.59 36:0.78 37:0.93 39:0.44 43:0.57 45:0.81 55:0.84 62:0.97 66:0.43 69:0.23 70:0.97 71:0.74 74:0.56 78:0.87 79:0.93 81:0.28 83:0.69 86:0.67 91:0.23 97:0.94 98:0.52 100:0.92 101:0.47 104:0.92 106:0.81 108:0.18 111:0.88 117:0.86 122:0.92 123:0.18 124:0.77 126:0.24 127:0.85 128:0.98 129:0.59 131:0.82 133:0.74 135:0.86 137:0.93 139:0.71 140:0.45 144:0.76 146:0.78 147:0.81 149:0.48 150:0.30 151:0.85 152:0.97 153:0.48 154:0.64 155:0.57 157:0.91 158:0.77 159:0.77 161:0.57 162:0.86 166:0.72 167:0.70 168:0.98 169:0.99 170:0.77 172:0.63 173:0.15 174:0.93 177:0.88 179:0.51 181:0.62 182:1.00 186:0.87 187:0.68 189:0.94 190:0.89 191:0.86 192:0.56 193:0.98 196:0.91 197:0.96 202:0.36 204:0.83 205:0.95 206:0.81 208:0.61 212:0.48 216:0.61 222:0.35 227:0.85 229:0.92 230:0.73 231:0.77 232:0.94 233:0.76 235:0.31 236:0.91 238:0.93 239:0.88 241:0.46 242:0.33 243:0.57 245:0.77 246:0.61 247:0.90 248:0.77 253:0.44 254:0.97 255:0.72 256:0.45 259:0.21 261:0.98 265:0.54 266:0.84 267:0.69 268:0.46 271:0.74 276:0.67 279:0.78 281:0.91 283:0.82 284:0.88 285:0.50 287:0.92 291:0.97 300:0.84 +1 8:0.72 11:0.75 12:0.37 17:0.34 18:0.92 21:0.30 27:0.20 29:0.56 30:0.32 34:0.45 36:0.93 37:0.58 39:0.39 43:0.16 45:0.66 55:0.52 66:0.35 69:0.78 70:0.84 71:0.65 74:0.42 77:0.89 81:0.34 86:0.73 91:0.56 96:0.70 98:0.59 101:0.52 108:0.61 114:0.59 122:0.86 123:0.59 124:0.68 126:0.26 127:0.46 129:0.59 131:0.88 133:0.61 135:0.89 139:0.70 140:0.45 144:0.57 145:0.74 146:0.78 147:0.38 149:0.29 150:0.30 151:0.48 153:0.71 154:0.62 157:0.61 158:0.71 159:0.63 162:0.67 166:0.82 172:0.67 173:0.72 176:0.55 177:0.05 179:0.36 182:0.61 187:0.68 190:0.89 195:0.81 202:0.60 212:0.76 216:0.68 220:0.87 222:0.76 227:0.92 229:0.61 231:0.90 235:0.31 241:0.35 242:0.44 243:0.15 245:0.85 246:0.61 247:0.86 248:0.48 253:0.43 254:0.90 256:0.58 257:0.84 259:0.21 265:0.60 266:0.87 267:0.79 268:0.62 271:0.37 276:0.75 282:0.71 285:0.47 287:0.61 290:0.59 300:0.84 +1 11:0.42 12:0.37 17:0.28 18:0.62 21:0.78 27:0.45 29:0.56 30:0.76 34:0.75 36:0.18 37:0.50 39:0.39 43:0.10 55:0.84 66:0.54 69:0.81 70:0.22 71:0.65 74:0.35 86:0.67 91:0.22 98:0.25 108:0.65 122:0.75 123:0.63 124:0.45 127:0.15 129:0.05 131:0.61 133:0.37 135:0.90 139:0.65 144:0.57 145:0.49 146:0.43 147:0.49 149:0.09 154:0.46 157:0.61 159:0.29 163:0.87 166:0.61 172:0.21 173:0.72 177:0.70 178:0.55 179:0.63 182:0.61 187:0.68 212:0.42 216:0.77 222:0.76 227:0.61 229:0.61 231:0.71 235:0.31 241:0.43 242:0.45 243:0.63 244:0.61 245:0.40 246:0.61 247:0.35 253:0.29 254:0.80 259:0.21 265:0.27 266:0.23 267:0.89 271:0.37 276:0.21 287:0.61 300:0.18 +0 8:0.74 11:0.75 12:0.37 17:0.92 18:0.97 21:0.22 22:0.93 27:0.50 29:0.56 30:0.28 31:0.89 34:0.94 36:0.25 37:0.37 39:0.39 43:0.49 44:0.90 45:0.88 47:0.93 48:0.99 55:0.45 64:0.77 66:0.67 69:0.97 70:0.93 71:0.65 74:0.71 77:0.89 79:0.93 81:0.36 86:0.91 91:0.40 98:0.47 101:0.52 108:0.74 122:0.86 123:0.72 124:0.71 126:0.26 127:0.80 129:0.05 131:0.86 133:0.86 135:0.69 137:0.89 139:0.91 140:0.45 144:0.57 145:0.61 146:0.28 147:0.33 149:0.70 150:0.32 151:0.60 153:0.48 154:0.38 155:0.58 157:0.93 159:0.83 162:0.86 166:0.80 172:0.87 173:0.99 177:0.38 179:0.31 182:0.86 187:0.21 190:0.89 192:0.51 195:0.93 196:0.92 202:0.53 204:0.85 208:0.54 212:0.82 216:0.40 219:0.87 220:0.96 222:0.76 227:0.90 229:0.61 231:0.90 235:0.22 241:0.64 242:0.28 243:0.09 244:0.61 245:0.79 246:0.61 247:0.82 248:0.59 253:0.41 256:0.58 257:0.65 259:0.21 262:0.93 265:0.49 266:0.89 267:0.23 268:0.62 271:0.37 276:0.80 277:0.69 281:0.91 282:0.77 284:0.86 285:0.47 287:0.61 292:0.95 300:0.94 +0 8:0.65 11:0.64 12:0.37 17:0.55 18:0.95 21:0.78 27:0.57 29:0.56 30:0.53 34:0.80 36:0.63 37:0.60 39:0.39 43:0.56 45:0.66 55:0.59 66:0.74 69:0.75 70:0.81 71:0.65 74:0.30 86:0.65 91:0.48 96:0.85 98:0.73 101:0.52 108:0.59 122:0.86 123:0.56 124:0.44 127:0.48 129:0.05 131:0.88 133:0.59 135:0.81 139:0.63 140:0.45 146:0.89 147:0.91 149:0.40 151:0.60 153:0.48 154:0.52 157:0.61 159:0.65 162:0.52 166:0.61 172:0.81 173:0.67 177:0.70 179:0.38 182:0.61 187:0.68 190:0.89 191:0.81 202:0.78 212:0.71 216:0.58 220:0.87 222:0.76 227:0.92 229:0.61 231:0.90 232:0.92 235:0.05 241:0.37 242:0.45 243:0.77 245:0.75 246:0.61 247:0.88 248:0.62 253:0.51 254:0.94 256:0.71 259:0.21 265:0.73 266:0.63 267:0.92 268:0.74 271:0.37 276:0.74 285:0.47 287:0.61 300:0.84 +2 11:0.75 12:0.37 17:0.77 18:0.84 21:0.22 27:0.34 29:0.56 30:0.60 34:0.74 36:0.44 37:0.64 39:0.39 43:0.59 45:0.91 64:0.77 66:0.74 69:0.78 70:0.68 71:0.65 74:0.43 81:0.36 86:0.72 91:0.27 98:0.49 101:0.52 108:0.62 122:0.75 123:0.60 124:0.43 126:0.26 127:0.41 129:0.05 131:0.61 133:0.59 135:0.66 139:0.70 144:0.57 146:0.59 147:0.05 149:0.76 150:0.32 154:0.65 157:0.61 159:0.50 166:0.80 172:0.77 173:0.78 177:0.05 178:0.55 179:0.41 182:0.61 187:0.68 212:0.88 216:0.32 219:0.87 222:0.76 227:0.61 229:0.61 231:0.89 235:0.22 241:0.39 242:0.45 243:0.08 245:0.70 246:0.61 247:0.76 253:0.33 254:0.74 257:0.84 259:0.21 265:0.50 266:0.47 267:0.76 271:0.37 276:0.67 287:0.61 292:0.91 300:0.84 +1 12:0.37 17:0.43 18:0.68 21:0.54 27:0.45 29:0.56 30:0.21 34:0.93 36:0.27 37:0.50 39:0.39 43:0.13 55:0.52 66:0.18 69:0.70 70:0.67 71:0.65 74:0.29 81:0.26 86:0.58 91:0.15 98:0.35 108:0.53 114:0.56 122:0.77 123:0.51 124:0.75 126:0.26 127:0.37 129:0.59 131:0.61 133:0.64 135:0.65 139:0.57 145:0.40 146:0.48 147:0.55 149:0.18 150:0.25 154:0.10 157:0.61 158:0.71 159:0.53 163:0.92 166:0.82 172:0.16 173:0.65 175:0.75 176:0.55 177:0.38 178:0.55 179:0.82 182:0.61 187:0.68 212:0.37 216:0.77 222:0.76 227:0.61 229:0.61 231:0.83 235:0.60 241:0.39 242:0.40 243:0.39 244:0.83 245:0.52 246:0.61 247:0.55 253:0.38 257:0.65 259:0.21 265:0.37 266:0.47 267:0.52 271:0.37 276:0.32 277:0.69 287:0.61 290:0.56 300:0.43 +2 1:0.74 11:0.75 12:0.37 17:0.85 18:0.82 21:0.76 27:0.58 29:0.56 30:0.38 34:1.00 36:0.40 37:0.58 39:0.39 43:0.68 45:0.77 64:0.77 66:0.48 69:0.54 70:0.92 71:0.65 74:0.39 81:0.40 83:0.60 86:0.71 91:0.18 98:0.43 99:0.83 101:0.52 108:0.41 114:0.54 122:0.82 123:0.40 124:0.65 126:0.54 127:0.53 129:0.59 131:0.61 133:0.61 135:0.80 139:0.69 144:0.57 146:0.41 147:0.48 149:0.36 150:0.65 154:0.74 155:0.67 157:0.61 159:0.39 163:0.75 165:0.70 166:0.94 172:0.37 173:0.61 176:0.73 177:0.70 178:0.55 179:0.81 182:0.61 187:0.39 192:0.87 201:0.93 208:0.64 212:0.28 215:0.36 216:0.68 222:0.76 227:0.61 229:0.61 231:0.90 235:0.98 241:0.12 242:0.29 243:0.60 245:0.53 246:0.61 247:0.60 253:0.34 254:0.80 259:0.21 265:0.45 266:0.54 267:0.87 271:0.37 276:0.36 287:0.61 290:0.54 297:0.36 300:0.70 +0 1:0.69 8:0.65 9:0.76 11:0.75 12:0.37 17:0.93 18:0.97 21:0.22 22:0.93 27:0.50 29:0.56 30:0.40 31:0.95 34:0.71 36:0.19 37:0.37 39:0.39 43:0.56 44:0.90 45:0.96 47:0.93 64:0.77 66:0.77 69:0.97 70:0.77 71:0.65 74:0.87 77:0.70 79:0.96 81:0.56 83:0.60 86:0.97 91:0.29 96:0.87 97:0.89 98:0.65 99:0.83 101:0.52 108:0.74 114:0.67 117:0.86 122:0.80 123:0.72 124:0.51 126:0.44 127:0.85 129:0.05 131:0.96 133:0.86 135:0.72 137:0.95 139:0.97 140:0.45 144:0.57 145:0.67 146:0.28 147:0.33 149:0.88 150:0.59 151:0.81 153:0.48 154:0.44 155:0.58 157:0.97 158:0.79 159:0.87 162:0.86 165:0.70 166:0.94 172:0.95 173:0.99 176:0.66 177:0.38 179:0.19 182:0.92 187:0.21 190:0.77 192:0.51 195:0.95 196:0.98 201:0.68 202:0.50 204:0.85 208:0.54 212:0.88 216:0.40 219:0.92 222:0.76 227:0.97 229:0.96 231:0.91 232:0.83 235:0.31 241:0.65 242:0.45 243:0.07 244:0.61 245:0.86 246:0.95 247:0.84 248:0.82 253:0.87 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.66 266:0.89 267:0.23 268:0.59 271:0.37 276:0.91 277:0.69 279:0.82 281:0.91 282:0.77 284:0.91 285:0.56 287:0.96 290:0.67 292:0.95 300:0.94 +2 1:0.74 11:0.75 12:0.37 17:0.77 18:0.84 21:0.54 27:0.34 29:0.56 30:0.11 34:0.99 36:0.27 37:0.64 39:0.39 43:0.15 45:0.73 64:0.77 66:0.13 69:0.79 70:0.69 71:0.65 74:0.48 81:0.47 83:0.60 86:0.79 91:0.11 98:0.48 99:0.83 101:0.52 108:0.62 114:0.59 122:0.57 123:0.68 124:0.80 126:0.54 127:0.62 129:0.59 131:0.61 133:0.73 135:0.44 139:0.75 144:0.57 146:0.22 147:0.05 149:0.11 150:0.67 154:0.69 155:0.67 157:0.61 159:0.48 163:0.96 165:0.70 166:0.94 172:0.21 173:0.83 176:0.73 177:0.05 178:0.55 179:0.83 182:0.61 187:0.68 192:0.87 201:0.93 208:0.64 212:0.28 215:0.39 216:0.32 222:0.76 227:0.61 229:0.61 231:0.90 235:0.74 241:0.39 242:0.29 243:0.16 245:0.52 246:0.61 247:0.47 253:0.42 254:0.74 257:0.84 259:0.21 265:0.18 266:0.54 267:0.73 271:0.37 276:0.40 277:0.87 287:0.61 290:0.59 297:0.36 300:0.43 +2 11:0.75 12:0.37 17:0.78 18:0.84 21:0.22 27:0.34 29:0.56 30:0.53 34:0.73 36:0.41 37:0.64 39:0.39 43:0.58 45:0.90 64:0.77 66:0.74 69:0.78 70:0.62 71:0.65 74:0.43 81:0.36 86:0.72 91:0.29 98:0.51 101:0.52 108:0.62 122:0.75 123:0.59 124:0.43 126:0.26 127:0.41 129:0.05 131:0.61 133:0.59 135:0.66 139:0.70 144:0.57 146:0.62 147:0.05 149:0.74 150:0.32 154:0.65 157:0.61 159:0.50 166:0.80 172:0.77 173:0.78 177:0.05 178:0.55 179:0.41 182:0.61 187:0.68 212:0.88 216:0.32 219:0.87 222:0.76 227:0.61 229:0.61 231:0.89 235:0.22 241:0.39 242:0.40 243:0.08 245:0.70 246:0.61 247:0.84 253:0.32 254:0.74 257:0.84 259:0.21 265:0.53 266:0.47 267:0.76 271:0.37 276:0.68 287:0.61 292:0.91 300:0.84 +1 8:0.67 11:0.64 12:0.37 17:0.47 18:0.95 21:0.78 27:0.57 29:0.56 30:0.53 34:0.80 36:0.61 37:0.60 39:0.39 43:0.56 45:0.66 55:0.59 66:0.74 69:0.75 70:0.81 71:0.65 74:0.30 86:0.65 91:0.58 96:0.78 98:0.73 101:0.52 108:0.59 122:0.86 123:0.56 124:0.44 127:0.48 129:0.05 131:0.88 133:0.59 135:0.77 139:0.63 140:0.45 146:0.89 147:0.91 149:0.40 151:0.55 153:0.48 154:0.52 157:0.61 159:0.65 162:0.61 166:0.61 172:0.81 173:0.67 177:0.70 179:0.38 182:0.61 187:0.68 190:0.89 191:0.84 202:0.78 212:0.71 216:0.58 220:0.91 222:0.76 227:0.92 229:0.61 231:0.90 232:0.92 235:0.05 241:0.46 242:0.45 243:0.77 245:0.75 246:0.61 247:0.88 248:0.57 253:0.45 254:0.94 256:0.77 259:0.21 265:0.73 266:0.63 267:0.92 268:0.79 271:0.37 276:0.74 285:0.47 287:0.61 300:0.84 +0 1:0.69 8:0.72 9:0.76 11:0.75 12:0.37 17:0.88 18:0.97 21:0.22 22:0.93 27:0.50 29:0.56 30:0.40 31:0.96 34:0.66 36:0.20 37:0.37 39:0.39 43:0.56 44:0.90 45:0.96 47:0.93 64:0.77 66:0.77 69:0.97 70:0.77 71:0.65 74:0.87 77:0.70 79:0.96 81:0.56 83:0.60 86:0.97 91:0.38 96:0.89 97:0.97 98:0.65 99:0.83 101:0.52 108:0.74 114:0.67 117:0.86 122:0.80 123:0.72 124:0.51 126:0.44 127:0.85 129:0.05 131:0.97 133:0.86 135:0.73 137:0.96 139:0.97 140:0.45 144:0.57 145:0.67 146:0.28 147:0.33 149:0.88 150:0.59 151:0.89 153:0.78 154:0.44 155:0.58 157:0.97 158:0.79 159:0.87 162:0.83 165:0.70 166:0.94 172:0.95 173:0.99 176:0.66 177:0.38 179:0.19 182:0.92 187:0.21 190:0.77 192:0.51 195:0.95 196:0.98 201:0.68 202:0.65 204:0.85 208:0.54 212:0.88 216:0.40 219:0.92 222:0.76 227:0.97 229:0.96 231:0.91 232:0.83 235:0.31 241:0.67 242:0.45 243:0.07 244:0.61 245:0.86 246:0.95 247:0.84 248:0.85 253:0.89 255:0.74 256:0.68 257:0.65 259:0.21 262:0.93 265:0.66 266:0.89 267:0.23 268:0.71 271:0.37 276:0.91 277:0.69 279:0.82 281:0.91 282:0.77 284:0.94 285:0.56 287:0.96 290:0.67 292:0.95 300:0.94 +0 1:0.69 11:0.75 12:0.37 17:0.96 18:0.92 21:0.59 22:0.93 27:0.50 29:0.56 30:0.15 34:0.90 36:0.20 37:0.37 39:0.39 43:0.38 44:0.90 45:0.77 47:0.93 55:0.42 64:0.77 66:0.59 69:0.97 70:0.91 71:0.65 74:0.71 77:0.70 81:0.34 83:0.60 86:0.89 91:0.29 98:0.43 99:0.83 101:0.52 108:0.82 114:0.57 117:0.86 122:0.85 123:0.81 124:0.78 126:0.44 127:0.87 129:0.05 131:0.61 133:0.87 135:0.51 139:0.89 144:0.57 145:0.47 146:0.28 147:0.35 149:0.39 150:0.52 154:0.28 155:0.58 157:0.85 159:0.85 165:0.70 166:0.94 172:0.82 173:1.00 176:0.66 177:0.38 178:0.55 179:0.37 182:0.91 187:0.39 192:0.51 195:0.94 201:0.68 204:0.85 208:0.54 212:0.78 216:0.40 222:0.76 227:0.61 229:0.61 231:0.90 232:0.94 235:0.74 241:0.57 242:0.45 243:0.13 244:0.61 245:0.79 246:0.94 247:0.82 253:0.44 257:0.65 259:0.21 262:0.93 265:0.45 266:0.91 267:0.52 271:0.37 276:0.77 277:0.69 281:0.91 282:0.77 287:0.61 290:0.57 300:0.94 +1 8:0.77 11:0.75 12:0.37 17:0.73 18:0.80 21:0.78 27:0.53 29:0.56 30:0.37 34:0.69 36:0.19 37:0.31 39:0.39 43:0.59 45:0.66 55:0.45 66:0.68 69:0.73 70:0.76 71:0.65 74:0.59 86:0.81 91:0.71 98:0.52 101:0.52 108:0.56 120:0.69 122:0.77 123:0.54 124:0.43 127:0.32 129:0.59 131:0.61 133:0.47 135:0.71 139:0.77 140:0.80 144:0.57 145:0.67 146:0.53 147:0.59 149:0.30 151:0.60 153:0.48 154:0.51 157:0.79 159:0.36 162:0.51 164:0.53 166:0.61 172:0.61 173:0.79 175:0.75 177:0.38 178:0.42 179:0.54 182:0.74 187:0.39 190:0.89 191:0.75 202:0.60 212:0.75 216:0.35 222:0.76 227:0.61 229:0.61 231:0.86 235:0.31 241:0.59 242:0.53 243:0.72 244:0.61 245:0.68 246:0.61 247:0.47 248:0.59 253:0.38 256:0.45 257:0.65 259:0.21 260:0.66 265:0.54 266:0.54 267:0.23 268:0.46 271:0.37 276:0.53 277:0.69 285:0.47 287:0.61 300:0.55 +1 1:0.69 8:0.66 9:0.76 11:0.75 12:0.37 17:0.95 18:0.89 22:0.93 27:0.57 29:0.56 30:0.21 31:0.93 34:0.90 36:0.22 37:0.43 39:0.39 43:0.26 44:0.90 45:0.88 47:0.93 48:0.97 55:0.59 64:0.77 66:0.86 69:0.93 70:0.63 71:0.65 74:0.74 77:0.70 79:0.93 81:0.83 83:0.60 86:0.89 91:0.27 96:0.80 97:0.94 98:0.67 99:0.83 101:0.52 108:0.86 114:0.95 117:0.86 122:0.80 123:0.86 124:0.37 126:0.44 127:0.61 129:0.05 131:0.96 133:0.62 135:0.37 137:0.93 139:0.91 140:0.45 144:0.57 145:0.54 146:0.89 147:0.05 149:0.62 150:0.80 151:0.81 153:0.48 154:0.19 155:0.58 157:0.94 158:0.79 159:0.75 162:0.62 165:0.70 166:0.94 172:0.88 173:0.92 175:0.75 176:0.66 177:0.05 179:0.33 182:0.92 187:0.39 190:0.77 192:0.51 195:0.89 196:0.95 201:0.68 202:0.50 204:0.85 208:0.54 212:0.87 216:0.15 219:0.92 222:0.76 227:0.97 229:0.96 231:0.91 232:0.85 235:0.05 241:0.30 242:0.52 243:0.07 244:0.61 245:0.67 246:0.95 247:0.67 248:0.73 253:0.81 255:0.74 256:0.45 257:0.84 259:0.21 262:0.93 265:0.67 266:0.54 267:0.79 268:0.46 271:0.37 276:0.77 277:0.69 279:0.82 281:0.91 282:0.77 284:0.87 285:0.56 287:0.96 290:0.95 292:0.95 300:0.55 +4 1:0.74 7:0.81 11:0.64 12:0.79 17:0.88 20:0.88 27:0.72 28:0.56 30:0.95 32:0.80 34:0.56 36:0.32 39:0.80 43:0.87 46:0.97 66:0.54 69:0.56 74:0.66 76:0.94 77:0.89 78:0.79 81:0.97 83:0.80 85:0.72 91:0.79 98:0.14 100:0.81 101:0.76 104:0.58 107:0.91 108:0.43 110:0.94 111:0.78 114:0.98 121:0.99 123:0.41 125:0.88 126:0.54 127:0.09 129:0.05 131:0.61 135:0.20 144:0.57 145:0.92 146:0.13 147:0.18 149:0.49 150:0.99 152:0.82 154:0.85 155:0.67 157:0.81 159:0.08 160:0.88 161:0.68 165:0.92 166:0.97 167:0.94 169:0.79 172:0.10 173:0.85 176:0.73 177:0.38 178:0.55 179:0.14 181:0.60 182:0.94 186:0.80 192:0.59 195:0.55 201:0.74 204:0.89 208:0.64 215:0.96 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 232:0.80 233:0.76 235:0.05 241:0.80 243:0.63 246:0.94 253:0.75 261:0.80 265:0.15 267:0.69 282:0.88 287:0.61 289:0.99 290:0.98 297:0.36 299:0.88 300:0.08 +0 12:0.79 17:0.51 21:0.78 27:0.45 28:0.56 30:0.95 34:0.89 36:0.21 37:0.50 39:0.80 43:0.26 46:0.88 55:0.71 66:0.54 69:0.80 74:0.38 91:0.15 98:0.19 107:0.90 108:0.64 110:0.93 123:0.61 125:0.88 127:0.10 129:0.05 131:0.61 135:0.66 145:0.42 146:0.27 147:0.33 149:0.17 154:0.18 157:0.61 159:0.12 160:0.88 161:0.68 166:0.61 167:0.60 172:0.10 173:0.80 177:0.38 178:0.55 179:0.46 181:0.60 182:0.61 187:0.68 216:0.77 222:0.35 227:0.61 229:0.61 231:0.69 235:0.60 241:0.05 243:0.63 244:0.61 246:0.61 253:0.34 259:0.21 265:0.20 267:0.79 287:0.61 289:0.90 300:0.08 +1 1:0.74 11:0.64 12:0.79 17:0.79 27:0.72 28:0.56 30:0.76 34:0.49 36:0.49 37:0.36 39:0.80 43:0.93 46:1.00 60:0.95 66:0.77 69:0.25 70:0.21 74:0.77 81:0.97 83:0.89 85:0.85 91:0.48 98:0.39 101:0.83 107:0.96 108:0.20 110:0.98 114:0.98 122:0.43 123:0.19 125:0.88 126:0.54 127:0.13 129:0.05 131:0.61 135:0.56 144:0.57 146:0.24 147:0.30 149:0.80 150:0.99 154:0.93 155:0.67 157:0.92 158:0.92 159:0.11 160:0.88 161:0.68 165:0.92 166:0.97 167:0.65 172:0.37 173:0.65 176:0.73 177:0.38 178:0.55 179:0.36 181:0.60 182:0.94 187:0.68 192:0.59 201:0.74 208:0.64 212:0.95 215:0.96 216:0.14 222:0.35 227:0.61 229:0.61 231:0.95 235:0.05 241:0.24 242:0.55 243:0.79 246:0.94 247:0.35 253:0.77 259:0.21 265:0.41 266:0.12 267:0.28 276:0.31 279:0.86 283:0.82 287:0.61 289:0.99 290:0.98 297:0.36 300:0.18 +2 7:0.81 11:0.64 12:0.79 17:0.85 20:0.87 21:0.05 27:0.72 28:0.56 30:0.38 32:0.80 34:0.62 36:0.74 39:0.80 43:0.82 46:0.99 66:0.67 69:0.51 70:0.71 74:0.87 76:0.94 77:0.89 78:0.79 81:0.91 85:0.91 91:0.73 98:0.74 100:0.81 101:0.84 104:0.58 107:0.99 108:0.64 110:1.00 111:0.78 121:0.99 122:0.43 123:0.61 124:0.45 125:0.88 126:0.32 127:0.66 129:0.59 131:0.61 133:0.47 135:0.17 144:0.57 145:0.92 146:0.39 147:0.45 149:0.84 150:0.82 152:0.82 154:0.78 155:0.67 157:0.96 159:0.38 160:0.88 161:0.68 166:0.93 167:0.93 169:0.79 172:0.65 173:0.62 177:0.38 178:0.55 179:0.62 181:0.60 182:0.88 186:0.80 192:0.59 195:0.63 204:0.89 208:0.64 212:0.80 215:0.91 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 233:0.76 235:0.05 241:0.79 242:0.27 243:0.28 245:0.73 246:0.61 247:0.67 253:0.62 259:0.21 261:0.80 265:0.74 266:0.38 267:0.91 276:0.58 282:0.88 287:0.61 289:0.99 297:0.36 299:0.88 300:0.55 +1 7:0.81 11:0.64 12:0.79 17:0.85 20:0.87 21:0.05 27:0.72 28:0.56 30:0.38 32:0.80 34:0.62 36:0.74 39:0.80 43:0.82 46:0.99 66:0.67 69:0.51 70:0.71 74:0.87 76:0.94 77:0.89 78:0.79 81:0.91 85:0.91 91:0.73 98:0.74 100:0.81 101:0.84 104:0.58 107:0.99 108:0.64 110:1.00 111:0.78 121:0.99 122:0.43 123:0.61 124:0.45 125:0.88 126:0.32 127:0.66 129:0.59 131:0.61 133:0.47 135:0.17 144:0.57 145:0.92 146:0.39 147:0.45 149:0.84 150:0.82 152:0.82 154:0.78 155:0.67 157:0.96 159:0.38 160:0.88 161:0.68 166:0.93 167:0.93 169:0.79 172:0.65 173:0.62 177:0.38 178:0.55 179:0.62 181:0.60 182:0.88 186:0.80 192:0.59 195:0.63 204:0.89 208:0.64 212:0.80 215:0.91 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 233:0.76 235:0.05 241:0.79 242:0.27 243:0.28 245:0.73 246:0.61 247:0.67 253:0.62 259:0.21 261:0.80 265:0.74 266:0.38 267:0.91 276:0.58 282:0.88 287:0.61 289:0.99 297:0.36 299:0.88 300:0.55 +1 1:0.74 9:0.80 11:0.64 12:0.79 17:0.08 20:0.90 21:0.71 27:0.14 28:0.56 30:0.62 34:0.33 36:0.99 37:0.68 39:0.80 41:0.82 43:0.84 46:0.96 66:0.83 69:0.65 70:0.32 74:0.76 78:0.72 81:0.53 83:0.76 85:0.88 91:0.04 96:0.79 98:0.61 100:0.73 101:0.80 107:0.97 108:0.50 110:0.99 111:0.72 114:0.68 120:0.67 122:0.57 123:0.48 125:0.99 126:0.54 127:0.18 129:0.05 131:0.98 135:0.58 140:0.45 144:0.57 146:0.67 147:0.72 149:0.81 150:0.68 151:0.82 152:0.85 153:0.46 154:0.80 155:0.67 157:0.93 159:0.25 160:0.96 161:0.68 162:0.62 164:0.54 165:0.69 166:0.97 167:0.64 169:0.72 172:0.51 173:0.67 176:0.73 177:0.38 179:0.48 181:0.60 182:0.94 186:0.73 187:0.95 192:0.59 201:0.74 202:0.49 208:0.64 212:0.28 215:0.48 216:0.86 222:0.35 227:0.98 229:0.97 231:0.95 235:0.88 241:0.21 242:0.45 243:0.84 246:0.94 247:0.47 248:0.74 253:0.81 255:0.79 256:0.45 259:0.21 260:0.68 261:0.73 265:0.62 266:0.47 267:0.59 268:0.45 276:0.39 285:0.62 287:0.96 289:0.99 290:0.68 297:0.36 300:0.25 +1 1:0.74 7:0.81 11:0.64 12:0.79 17:0.43 20:0.85 21:0.40 27:0.31 28:0.56 30:0.91 32:0.80 34:0.93 36:0.31 37:0.55 39:0.80 43:0.79 46:0.95 60:0.87 66:0.69 69:0.75 70:0.13 74:0.76 77:0.70 78:0.78 81:0.76 83:0.87 85:0.80 91:0.14 98:0.17 100:0.82 101:0.76 104:0.95 106:0.81 107:0.94 108:0.58 110:0.97 111:0.78 114:0.82 117:0.86 121:0.90 122:0.43 123:0.56 125:0.88 126:0.54 127:0.10 129:0.05 131:0.61 135:0.87 144:0.57 145:0.63 146:0.22 147:0.28 149:0.62 150:0.84 152:0.83 154:0.73 155:0.67 157:0.87 158:0.92 159:0.10 160:0.88 161:0.68 165:0.83 166:0.97 167:0.66 169:0.79 172:0.21 173:0.82 176:0.73 177:0.38 178:0.55 179:0.14 181:0.60 182:0.93 186:0.79 187:0.39 192:0.59 195:0.67 201:0.74 204:0.89 206:0.81 208:0.64 212:0.61 215:0.67 216:0.85 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 232:0.97 233:0.76 235:0.52 241:0.27 242:0.63 243:0.73 246:0.94 247:0.30 253:0.70 259:0.21 261:0.80 265:0.18 266:0.17 267:0.28 276:0.17 279:0.86 282:0.88 283:0.82 287:0.61 289:0.99 290:0.82 297:0.36 299:0.88 300:0.13 +1 1:0.74 11:0.64 12:0.79 17:0.75 20:0.89 21:0.13 27:0.71 28:0.56 30:0.62 34:0.92 36:0.62 37:0.57 39:0.80 43:0.92 46:0.98 66:0.83 69:0.38 70:0.43 74:0.71 78:0.75 81:0.89 83:0.77 85:0.88 91:0.33 98:0.85 100:0.73 101:0.82 104:0.82 106:0.81 107:0.97 108:0.30 110:0.99 111:0.74 114:0.92 117:0.86 122:0.43 123:0.28 125:0.88 126:0.54 127:0.35 129:0.05 131:0.61 135:0.74 144:0.57 145:0.58 146:0.71 147:0.76 149:0.85 150:0.94 152:0.84 154:0.91 155:0.67 157:0.93 159:0.28 160:0.88 161:0.68 165:0.88 166:0.97 167:0.69 169:0.72 172:0.51 173:0.51 176:0.73 177:0.38 178:0.55 179:0.75 181:0.60 182:0.93 186:0.76 187:0.21 192:0.59 201:0.74 206:0.81 212:0.57 215:0.84 216:0.22 222:0.35 227:0.61 229:0.61 231:0.95 232:0.90 235:0.22 241:0.41 242:0.60 243:0.84 246:0.94 247:0.39 253:0.73 259:0.21 261:0.77 265:0.85 266:0.47 267:0.36 276:0.40 287:0.61 289:0.99 290:0.92 297:0.36 300:0.33 +0 12:0.79 17:0.38 21:0.78 27:0.45 28:0.56 30:0.44 34:0.78 36:0.18 37:0.50 39:0.80 43:0.28 46:0.88 55:0.71 66:0.51 69:0.75 70:0.73 74:0.88 91:0.06 98:0.60 107:0.94 108:0.87 110:0.95 122:0.67 123:0.86 124:0.74 125:0.88 127:0.42 129:0.05 131:0.61 133:0.74 135:0.82 145:0.44 146:0.50 147:0.57 149:0.58 154:0.64 157:0.61 159:0.78 160:0.88 161:0.68 163:0.75 166:0.61 167:0.73 172:0.70 173:0.56 177:0.38 178:0.55 179:0.39 181:0.60 182:0.61 187:0.68 212:0.23 216:0.77 222:0.35 227:0.61 229:0.61 231:0.75 235:0.68 241:0.56 242:0.81 243:0.31 245:0.77 246:0.61 247:0.39 253:0.62 254:0.88 259:0.21 265:0.61 266:0.87 267:0.84 276:0.71 277:0.69 287:0.61 289:0.92 300:0.70 +1 7:0.76 8:0.85 11:0.64 12:0.79 17:0.93 21:0.30 27:0.58 28:0.56 30:0.44 34:0.24 36:0.98 37:0.71 39:0.80 43:0.84 46:0.96 55:0.71 66:0.94 69:0.54 70:0.55 74:0.98 76:0.94 77:0.89 81:0.44 85:0.85 91:0.49 96:0.72 98:0.89 101:0.79 107:0.97 108:0.41 110:0.99 114:0.55 122:0.67 123:0.39 125:0.88 126:0.32 127:0.43 129:0.05 131:0.82 135:0.84 140:0.45 144:0.57 145:0.84 146:0.84 147:0.87 149:0.86 150:0.36 151:0.53 153:0.77 154:0.80 157:0.91 158:0.82 159:0.40 160:0.88 161:0.68 162:0.68 166:0.75 167:0.67 172:0.84 173:0.58 176:0.53 177:0.38 179:0.37 181:0.60 182:0.80 187:0.39 190:0.77 195:0.68 202:0.54 212:0.91 216:0.28 220:0.87 222:0.35 227:0.86 229:0.61 230:0.79 231:0.88 232:0.92 233:0.76 235:0.31 241:0.32 242:0.77 243:0.94 246:0.61 247:0.39 248:0.52 253:0.75 256:0.45 259:0.21 265:0.89 266:0.27 267:0.52 268:0.57 276:0.74 282:0.81 285:0.50 287:0.61 289:0.97 290:0.55 300:0.43 +4 1:0.74 7:0.81 8:0.83 11:0.64 12:0.79 17:0.89 20:0.85 27:0.72 28:0.56 30:0.95 32:0.80 34:0.56 36:0.32 39:0.80 43:0.87 46:0.97 66:0.54 69:0.56 74:0.66 76:0.94 77:0.89 78:0.82 81:0.97 83:0.80 85:0.72 91:0.79 98:0.14 100:0.87 101:0.76 104:0.58 107:0.91 108:0.43 110:0.94 111:0.82 114:0.98 121:0.99 123:0.41 125:0.88 126:0.54 127:0.09 129:0.05 131:0.61 135:0.20 144:0.57 145:0.92 146:0.13 147:0.18 149:0.49 150:0.99 152:0.81 154:0.85 155:0.67 157:0.81 159:0.08 160:0.88 161:0.68 165:0.92 166:0.97 167:0.96 169:0.85 172:0.10 173:0.85 176:0.73 177:0.38 178:0.55 179:0.14 181:0.60 182:0.94 186:0.82 192:0.59 195:0.55 201:0.74 204:0.89 208:0.64 215:0.96 222:0.35 227:0.61 229:0.61 230:0.87 231:0.95 232:0.80 233:0.76 235:0.05 241:0.85 243:0.63 246:0.94 253:0.75 261:0.82 265:0.15 267:0.69 282:0.88 287:0.61 289:0.99 290:0.98 297:0.36 299:0.88 300:0.08 +1 1:0.74 11:0.64 12:0.79 17:0.43 21:0.54 27:0.56 28:0.56 30:0.73 32:0.80 34:0.93 36:0.24 37:0.60 39:0.80 43:0.86 46:0.97 60:0.87 66:0.63 69:0.60 70:0.47 74:0.87 77:0.70 81:0.67 83:0.86 85:0.91 91:0.12 98:0.61 101:0.82 104:0.91 106:0.81 107:0.98 108:0.46 110:0.99 114:0.77 117:0.86 122:0.57 123:0.44 124:0.46 125:0.88 126:0.54 127:0.30 129:0.59 131:0.61 133:0.47 135:0.66 144:0.57 145:0.69 146:0.67 147:0.72 149:0.88 150:0.78 154:0.83 155:0.67 157:0.95 158:0.92 159:0.39 160:0.88 161:0.68 165:0.79 166:0.97 167:0.63 172:0.51 173:0.60 176:0.73 177:0.38 178:0.55 179:0.62 181:0.60 182:0.93 187:0.21 192:0.59 195:0.68 201:0.74 206:0.81 208:0.64 212:0.30 215:0.58 216:0.76 222:0.35 227:0.61 229:0.61 231:0.95 232:0.95 235:0.68 241:0.15 242:0.58 243:0.69 245:0.63 246:0.94 247:0.39 253:0.71 259:0.21 265:0.62 266:0.75 267:0.28 276:0.45 279:0.86 282:0.88 283:0.82 287:0.61 289:0.99 290:0.77 297:0.36 299:0.88 300:0.43 +1 1:0.74 11:0.64 12:0.79 17:0.57 21:0.30 27:0.20 28:0.56 30:0.86 34:0.83 36:0.40 37:0.94 39:0.80 43:0.77 46:0.95 66:0.33 69:0.79 70:0.32 74:0.62 81:0.81 83:0.75 85:0.91 91:0.27 98:0.19 101:0.77 104:0.58 107:0.97 108:0.83 110:0.99 114:0.86 122:0.43 123:0.82 124:0.78 125:0.88 126:0.54 127:0.22 129:0.89 131:0.61 133:0.78 135:0.73 144:0.57 146:0.13 147:0.18 149:0.75 150:0.87 154:0.69 155:0.67 157:0.94 159:0.59 160:0.88 161:0.68 163:0.81 165:0.84 166:0.97 167:0.57 172:0.32 173:0.64 176:0.73 177:0.38 178:0.55 179:0.56 181:0.60 182:0.93 187:0.39 192:0.59 201:0.74 212:0.39 215:0.72 216:0.29 222:0.35 227:0.61 229:0.61 231:0.95 232:0.80 235:0.42 241:0.01 242:0.63 243:0.25 245:0.54 246:0.94 247:0.30 253:0.67 259:0.21 265:0.21 266:0.54 267:0.52 276:0.36 287:0.61 289:0.99 290:0.86 297:0.36 300:0.33 +4 1:0.74 6:0.97 7:0.81 8:0.96 9:0.80 11:0.64 12:0.79 17:0.45 20:0.99 27:0.20 28:0.56 30:0.95 32:0.80 34:0.59 36:0.33 37:0.94 39:0.80 41:0.96 43:0.87 46:0.97 66:0.54 69:0.54 74:0.66 76:0.94 77:0.89 78:0.97 81:0.97 83:0.89 85:0.72 91:0.91 96:0.97 98:0.14 100:1.00 101:0.76 104:0.58 107:0.91 108:0.41 110:0.94 111:1.00 114:0.98 120:0.93 121:0.99 123:0.40 125:0.99 126:0.54 127:0.09 129:0.05 131:0.98 135:0.20 140:0.45 144:0.57 145:0.92 146:0.13 147:0.18 149:0.49 150:0.99 151:0.99 152:0.91 153:0.95 154:0.85 155:0.67 157:0.81 159:0.08 160:0.99 161:0.68 162:0.78 164:0.87 165:0.92 166:0.97 167:0.98 169:1.00 172:0.10 173:0.84 176:0.73 177:0.38 179:0.14 181:0.60 182:0.94 186:0.96 189:0.98 191:0.93 192:0.59 195:0.55 201:0.74 202:0.79 204:0.89 208:0.64 215:0.96 216:0.29 222:0.35 227:0.98 229:0.97 230:0.87 231:0.95 232:0.80 233:0.76 235:0.05 238:1.00 241:0.94 243:0.63 246:0.94 248:0.97 253:0.96 255:0.79 256:0.81 259:0.21 260:0.94 261:1.00 265:0.15 267:0.59 268:0.84 282:0.88 285:0.62 287:0.96 289:0.99 290:0.98 297:0.36 299:0.88 300:0.08 +2 1:0.74 6:0.89 8:0.76 11:0.75 12:0.79 17:0.22 18:0.65 20:0.84 21:0.54 27:0.45 28:0.60 29:0.77 30:0.72 32:0.80 34:0.91 36:0.17 37:0.50 39:0.60 43:0.14 44:0.93 45:0.66 46:0.93 55:0.84 64:0.77 66:0.26 69:0.62 70:0.48 71:0.99 74:0.66 76:0.85 77:0.70 78:0.84 81:0.46 83:0.60 86:0.74 91:0.17 97:0.89 98:0.53 99:0.83 100:0.86 101:0.52 107:0.97 108:0.47 110:0.99 111:0.83 114:0.59 122:0.55 123:0.79 124:0.58 125:0.88 126:0.54 127:0.44 129:0.05 131:0.61 133:0.43 135:0.94 139:0.86 144:0.57 145:0.45 146:0.65 147:0.70 149:0.18 150:0.67 152:0.81 154:0.76 155:0.67 157:0.81 158:0.91 159:0.58 160:0.88 161:0.68 163:0.94 165:0.70 166:0.98 167:0.66 169:0.83 172:0.37 173:0.54 176:0.73 177:0.70 178:0.55 179:0.76 181:0.80 182:0.95 186:0.84 187:0.68 189:0.93 192:0.87 195:0.79 201:0.93 208:0.64 212:0.23 215:0.39 216:0.77 219:0.95 222:0.95 227:0.61 229:0.61 231:0.96 235:0.74 238:0.89 241:0.41 242:0.24 243:0.57 245:0.64 246:0.85 247:0.67 253:0.44 254:0.74 259:0.21 261:0.84 265:0.51 266:0.71 267:0.52 271:0.24 276:0.42 277:0.69 279:0.86 282:0.88 283:0.77 287:0.61 289:0.99 290:0.59 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.64 9:0.80 11:0.85 12:0.79 17:0.98 18:0.88 21:0.22 27:0.70 28:0.60 29:0.77 30:0.38 31:0.88 32:0.80 34:0.59 36:0.99 37:0.65 39:0.60 41:0.82 43:0.68 44:0.93 45:0.87 46:0.97 48:0.91 60:0.87 64:0.77 66:0.64 69:0.25 70:0.94 71:0.99 74:0.86 76:0.85 77:0.89 79:0.88 81:0.67 83:0.91 85:0.72 86:0.92 91:0.42 96:0.71 98:0.68 101:0.87 104:0.90 106:0.81 107:0.99 108:0.20 110:0.99 114:0.71 120:0.57 122:0.57 123:0.19 124:0.51 125:0.99 126:0.54 127:0.53 129:0.59 131:0.99 133:0.61 135:0.78 137:0.88 139:0.95 140:0.45 144:0.57 145:0.96 146:0.86 147:0.89 149:0.72 150:0.80 151:0.56 153:0.48 154:0.89 155:0.67 157:0.97 158:0.92 159:0.68 160:0.96 161:0.68 162:0.86 164:0.57 165:0.93 166:0.98 167:0.56 172:0.75 173:0.18 176:0.73 177:0.70 179:0.44 181:0.80 182:0.96 187:0.21 192:0.87 195:0.84 196:0.92 201:0.93 202:0.36 206:0.81 208:0.64 212:0.70 215:0.51 216:0.18 219:0.95 220:0.82 222:0.95 227:0.88 229:0.98 230:0.64 231:0.96 233:0.73 235:0.42 241:0.05 242:0.16 243:0.69 245:0.77 246:0.85 247:0.88 248:0.55 253:0.59 255:0.95 256:0.45 259:0.21 260:0.58 265:0.68 266:0.80 267:0.79 268:0.46 271:0.24 276:0.67 279:0.86 281:0.88 282:0.88 283:0.82 284:0.89 285:0.62 287:0.88 289:0.99 290:0.71 292:0.95 297:0.36 300:0.94 +0 9:0.76 11:0.64 12:0.79 17:0.88 18:0.93 21:0.59 22:0.93 27:0.40 28:0.60 29:0.77 30:0.71 34:0.61 36:0.49 37:0.57 39:0.60 43:0.60 44:0.87 45:0.73 46:0.88 47:0.93 48:0.99 55:0.45 64:0.77 66:0.98 69:0.41 70:0.37 71:0.99 74:0.53 77:0.70 81:0.23 83:0.60 86:0.95 91:0.25 96:0.83 97:0.89 98:0.95 101:0.52 104:0.95 107:0.91 108:0.32 110:0.93 120:0.72 122:0.77 123:0.31 125:1.00 126:0.26 127:0.64 129:0.05 131:0.99 135:0.32 139:0.93 140:0.45 144:0.57 145:0.88 146:0.91 147:0.92 149:0.60 150:0.23 151:0.83 153:0.69 154:0.57 155:0.58 157:0.61 158:0.79 159:0.51 160:0.96 161:0.68 162:0.86 164:0.55 166:0.61 167:0.62 172:0.95 173:0.41 175:0.75 177:0.38 179:0.21 181:0.80 182:0.96 187:0.95 190:0.77 191:0.75 192:0.59 195:0.71 202:0.46 208:0.54 212:0.93 216:0.55 222:0.95 227:0.88 229:0.98 231:0.96 232:0.97 235:0.68 241:0.27 242:0.74 243:0.98 244:0.61 246:0.61 247:0.67 248:0.75 253:0.73 255:0.74 256:0.45 257:0.65 259:0.21 260:0.69 262:0.93 265:0.95 266:0.38 267:0.28 268:0.55 271:0.24 276:0.90 277:0.69 279:0.82 281:0.89 282:0.71 283:0.82 285:0.56 287:0.88 289:0.99 300:0.55 +1 11:0.75 12:0.79 17:0.57 18:0.85 21:0.74 28:0.60 29:0.77 30:0.17 32:0.62 34:0.36 36:0.18 37:0.97 39:0.60 43:0.59 45:0.85 46:0.94 48:0.91 55:0.71 66:0.69 69:0.59 70:0.94 71:0.99 74:0.49 81:0.23 86:0.84 91:0.08 98:0.74 101:0.52 104:0.80 106:0.81 107:0.99 108:0.45 110:0.99 123:0.43 124:0.76 125:0.88 126:0.26 127:0.82 129:0.59 131:0.61 133:0.91 135:0.32 139:0.82 144:0.57 145:0.63 146:0.98 147:0.98 149:0.66 150:0.23 154:0.48 157:0.95 159:0.89 160:0.88 161:0.68 166:0.90 167:0.49 172:0.92 173:0.29 177:0.70 178:0.55 179:0.24 181:0.80 182:0.86 187:0.39 195:0.96 206:0.81 212:0.54 215:0.36 216:0.98 222:0.95 227:0.61 229:0.61 231:0.94 232:0.87 235:0.88 242:0.50 243:0.72 244:0.61 245:0.81 246:0.61 247:0.86 253:0.34 259:0.21 265:0.74 266:0.95 267:0.36 271:0.24 276:0.88 281:0.89 287:0.61 289:0.98 297:0.36 300:0.84 +1 1:0.69 7:0.72 8:0.64 9:0.76 11:0.75 12:0.79 17:0.95 18:0.80 20:0.89 27:0.57 28:0.60 29:0.77 30:0.70 32:0.69 34:0.64 36:0.79 37:0.60 39:0.60 43:0.71 45:0.95 46:0.99 48:0.91 66:0.84 69:0.32 70:0.58 71:0.99 74:0.92 77:0.96 78:0.78 81:0.84 83:0.60 86:0.88 91:0.37 96:0.84 97:0.89 98:0.71 99:0.83 100:0.79 101:0.52 104:0.95 106:0.81 107:0.99 108:0.25 110:1.00 111:0.77 114:0.95 117:0.86 120:0.74 122:0.75 123:0.24 124:0.38 125:0.99 126:0.44 127:0.80 129:0.59 131:0.99 133:0.46 135:0.75 139:0.85 140:0.45 144:0.57 145:0.86 146:0.90 147:0.92 149:0.91 150:0.82 151:0.84 152:0.84 153:0.72 154:0.70 155:0.58 157:0.99 158:0.79 159:0.75 160:0.96 161:0.68 162:0.67 164:0.55 165:0.70 166:0.98 167:0.55 169:0.77 172:0.88 173:0.20 176:0.66 177:0.70 179:0.34 181:0.80 182:0.96 186:0.79 187:0.39 190:0.77 191:0.75 192:0.59 195:0.87 201:0.68 202:0.53 204:0.85 206:0.81 208:0.54 212:0.73 215:0.96 216:0.56 222:0.95 227:0.88 229:0.98 230:0.75 231:0.96 232:0.97 233:0.76 235:0.05 241:0.03 242:0.50 243:0.85 245:0.82 246:0.85 247:0.74 248:0.76 253:0.86 254:0.88 255:0.74 256:0.45 259:0.21 260:0.69 261:0.80 265:0.71 266:0.54 267:0.69 268:0.57 271:0.24 276:0.75 279:0.82 281:0.91 282:0.77 283:0.82 285:0.56 287:0.88 289:0.99 290:0.95 297:0.36 300:0.70 +1 1:0.69 7:0.72 9:0.76 11:0.85 12:0.79 17:0.36 18:0.70 21:0.47 27:0.19 28:0.60 29:0.77 30:0.58 32:0.69 34:0.52 36:0.28 37:0.38 39:0.60 43:0.71 45:0.98 46:0.99 48:0.91 66:0.99 69:0.35 70:0.58 71:0.99 74:0.90 76:0.94 77:0.89 81:0.38 83:0.60 85:0.72 86:0.81 91:0.27 96:0.86 97:0.89 98:0.96 99:0.83 101:0.87 107:1.00 108:0.27 110:1.00 114:0.59 120:0.77 122:0.51 123:0.26 125:0.97 126:0.44 127:0.68 129:0.05 131:0.99 135:0.95 139:0.79 140:0.45 144:0.57 145:0.59 146:0.99 147:0.99 149:0.93 150:0.54 151:0.90 153:0.83 154:0.70 155:0.58 157:0.99 158:0.78 159:0.80 160:0.97 161:0.68 162:0.82 164:0.71 165:0.70 166:0.98 167:0.81 172:0.96 173:0.18 176:0.66 177:0.70 179:0.18 181:0.80 182:0.96 187:0.39 190:0.77 192:0.59 195:0.93 201:0.68 202:0.63 204:0.85 208:0.54 212:0.54 215:0.41 216:0.87 222:0.95 227:0.88 229:0.98 230:0.74 231:0.96 233:0.76 235:0.60 241:0.73 242:0.45 243:0.99 246:0.85 247:0.84 248:0.81 253:0.87 255:0.74 256:0.64 259:0.21 260:0.74 265:0.96 266:0.54 267:0.59 268:0.69 271:0.24 276:0.92 279:0.82 281:0.91 282:0.77 283:0.78 285:0.56 287:0.88 289:0.99 290:0.59 297:0.36 300:0.70 +1 1:0.74 11:0.64 12:0.79 17:0.51 18:0.73 21:0.64 27:0.45 28:0.60 29:0.77 30:0.62 34:0.83 36:0.25 37:0.50 39:0.60 43:0.81 46:0.94 60:0.87 64:0.77 66:0.16 69:0.70 70:0.23 71:0.99 74:0.56 81:0.45 83:0.91 85:0.72 86:0.78 91:0.15 98:0.34 101:0.87 107:0.92 108:0.54 110:0.94 114:0.57 122:0.57 123:0.77 124:0.71 125:0.88 126:0.54 127:0.31 129:0.05 131:0.61 133:0.60 135:0.34 139:0.82 144:0.57 145:0.47 146:0.17 147:0.22 149:0.58 150:0.70 154:0.77 155:0.67 157:0.81 158:0.91 159:0.44 160:0.88 161:0.68 163:0.79 165:0.93 166:0.97 167:0.66 172:0.16 173:0.69 176:0.73 177:0.70 178:0.55 179:0.89 181:0.80 182:0.95 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.38 216:0.77 219:0.95 222:0.95 227:0.61 229:0.61 231:0.96 235:0.84 241:0.41 242:0.33 243:0.48 245:0.43 246:0.85 247:0.39 253:0.41 259:0.21 265:0.11 266:0.27 267:0.28 271:0.24 276:0.25 277:0.69 279:0.86 283:0.78 287:0.61 289:0.99 290:0.57 292:0.91 297:0.36 300:0.18 +2 6:0.87 7:0.64 8:0.77 11:0.75 12:0.79 17:0.89 18:0.80 20:0.87 21:0.30 22:0.93 27:0.56 28:0.60 29:0.77 30:0.33 34:0.91 36:0.55 37:0.69 39:0.60 43:0.58 45:0.81 46:0.96 47:0.93 48:0.91 60:0.95 64:0.77 66:0.60 69:0.83 70:0.89 71:0.99 74:0.61 78:0.80 81:0.42 83:0.91 86:0.79 91:0.42 97:0.89 98:0.52 100:0.81 101:0.52 104:0.89 106:0.81 107:0.97 108:0.68 110:0.99 111:0.79 120:0.64 122:0.51 123:0.66 124:0.65 125:0.88 126:0.44 127:0.76 129:0.05 131:0.94 133:0.73 135:0.54 139:0.86 140:0.45 144:0.57 146:0.65 147:0.05 149:0.50 150:0.33 151:0.56 152:0.82 153:0.69 154:0.57 155:0.67 157:0.95 158:0.92 159:0.61 160:0.88 161:0.68 162:0.86 164:0.52 166:0.91 167:0.65 169:0.79 172:0.51 173:0.83 177:0.05 179:0.75 181:0.80 182:0.86 186:0.81 187:0.39 189:0.89 190:0.89 191:0.70 192:0.87 201:0.68 202:0.46 206:0.81 208:0.64 212:0.18 215:0.44 216:0.48 219:0.95 220:0.62 222:0.95 227:0.85 229:0.61 230:0.64 231:0.94 233:0.73 235:0.42 238:0.85 241:0.39 242:0.18 243:0.13 245:0.54 246:0.61 247:0.74 248:0.55 253:0.39 254:0.95 256:0.45 257:0.84 259:0.21 260:0.58 261:0.81 262:0.93 265:0.54 266:0.71 267:0.52 268:0.55 271:0.24 276:0.48 279:0.86 281:0.88 283:0.82 285:0.47 287:0.61 289:0.98 292:0.95 297:0.36 300:0.70 +1 1:0.74 7:0.81 9:0.76 11:0.85 12:0.79 17:0.98 18:0.79 21:0.47 22:0.93 27:0.64 28:0.60 29:0.77 30:0.75 32:0.69 34:0.39 36:0.56 37:0.65 39:0.60 43:0.86 45:0.92 46:0.99 47:0.93 48:0.91 64:0.77 66:0.20 69:0.60 70:0.56 71:0.99 74:0.83 76:0.85 77:0.70 81:0.52 83:0.91 85:0.72 86:0.87 91:0.35 96:0.80 98:0.49 101:0.87 104:0.98 106:0.81 107:0.99 108:0.80 110:1.00 114:0.60 117:0.86 120:0.69 122:0.51 123:0.44 124:0.69 125:1.00 126:0.54 127:0.62 129:0.59 131:0.99 133:0.66 135:0.39 139:0.88 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.88 150:0.74 151:0.81 153:0.48 154:0.83 155:0.67 157:0.98 159:0.48 160:0.96 161:0.68 162:0.86 164:0.54 165:0.93 166:0.98 167:0.67 172:0.65 173:0.62 176:0.73 177:0.70 179:0.45 181:0.80 182:0.96 187:0.87 190:0.77 192:0.87 195:0.67 201:0.93 202:0.36 204:0.89 206:0.81 208:0.64 212:0.71 215:0.41 216:0.44 222:0.95 227:0.88 229:0.98 230:0.86 231:0.96 232:0.99 233:0.73 235:0.74 241:0.46 242:0.55 243:0.40 245:0.83 246:0.85 247:0.60 248:0.73 253:0.81 255:0.74 256:0.45 259:0.21 260:0.68 262:0.93 265:0.30 266:0.63 267:0.59 268:0.46 271:0.24 276:0.68 281:0.89 282:0.77 285:0.56 287:0.88 289:0.99 290:0.60 297:0.36 300:0.70 +1 1:0.69 7:0.72 8:0.95 9:0.76 11:0.75 12:0.79 17:0.49 20:0.76 21:0.30 27:0.47 28:0.60 29:0.77 30:0.66 31:0.90 32:0.69 34:0.36 36:0.31 37:0.26 39:0.60 43:0.71 45:0.83 46:0.99 66:0.85 69:0.32 70:0.40 71:0.99 74:0.71 77:0.89 78:0.72 79:0.90 81:0.50 83:0.60 91:0.58 96:0.72 97:0.89 98:0.90 99:0.83 100:0.73 101:0.52 104:0.84 107:0.99 108:0.25 110:0.99 111:0.72 114:0.63 120:0.67 122:0.75 123:0.24 125:0.97 126:0.44 127:0.48 129:0.05 131:0.99 135:0.47 137:0.90 140:0.87 144:0.57 145:0.74 146:0.71 147:0.75 149:0.72 150:0.57 151:0.64 152:0.75 153:0.48 154:0.61 155:0.58 157:0.97 158:0.79 159:0.28 160:0.96 161:0.68 162:0.65 164:0.54 165:0.70 166:0.98 167:0.72 169:0.72 172:0.57 173:0.50 175:0.75 176:0.66 177:0.38 178:0.42 179:0.74 181:0.80 182:0.96 186:0.73 187:0.21 190:0.77 191:0.78 192:0.59 195:0.58 201:0.68 202:0.62 204:0.85 208:0.54 212:0.58 215:0.44 216:0.93 220:0.62 222:0.95 227:0.88 229:0.98 230:0.74 231:0.96 233:0.73 235:0.42 241:0.61 242:0.52 243:0.86 244:0.61 246:0.85 247:0.39 248:0.63 253:0.54 255:0.79 256:0.64 257:0.65 259:0.21 260:0.61 261:0.73 265:0.90 266:0.27 267:0.76 268:0.63 271:0.24 276:0.44 277:0.69 279:0.82 282:0.77 283:0.82 284:0.86 285:0.62 287:0.88 289:0.99 290:0.63 297:0.36 300:0.33 +4 1:0.69 6:0.96 7:0.72 8:0.93 9:0.80 11:0.75 12:0.79 17:0.36 20:0.88 21:0.71 27:0.12 28:0.60 29:0.77 30:0.76 31:0.95 32:0.69 34:0.69 36:0.55 37:0.95 39:0.60 41:0.82 43:0.68 45:0.95 46:1.00 66:0.67 69:0.58 70:0.29 71:0.99 74:0.86 77:0.70 78:0.94 79:0.94 81:0.32 83:0.91 91:0.78 96:0.93 97:0.94 98:0.57 99:0.83 100:0.99 101:0.52 104:0.58 107:0.99 108:0.69 110:1.00 111:0.98 114:0.54 120:0.91 122:0.51 123:0.67 124:0.50 125:0.96 126:0.44 127:0.63 129:0.81 131:0.99 133:0.67 135:0.34 137:0.95 140:0.99 144:0.57 145:0.82 146:0.23 147:0.29 149:0.91 150:0.51 151:0.87 152:0.93 153:0.94 154:0.58 155:0.67 157:0.99 158:0.78 159:0.36 160:0.99 161:0.68 162:0.50 164:0.86 165:0.70 166:0.97 167:0.94 169:0.97 172:0.79 173:0.70 175:0.75 176:0.66 177:0.38 178:0.50 179:0.40 181:0.80 182:0.96 186:0.94 189:0.97 191:0.95 192:0.87 195:0.62 201:0.68 202:0.92 204:0.85 208:0.64 212:0.89 215:0.37 216:0.24 222:0.95 227:0.88 229:0.98 230:0.75 231:0.96 232:0.80 233:0.76 235:0.88 238:0.98 241:0.89 242:0.63 243:0.25 244:0.61 245:0.80 246:0.85 247:0.39 248:0.77 253:0.93 255:0.95 256:0.87 257:0.65 259:0.21 260:0.86 261:0.97 265:0.58 266:0.38 267:0.28 268:0.88 271:0.24 276:0.70 277:0.69 279:0.82 282:0.77 283:0.82 284:0.93 285:0.62 287:0.88 289:0.99 290:0.54 297:0.36 300:0.33 +2 7:0.72 12:0.79 17:0.73 18:0.82 21:0.22 27:0.54 28:0.60 29:0.77 30:0.58 32:0.69 34:0.89 36:0.54 37:0.27 39:0.60 43:0.15 46:0.88 55:0.59 66:0.80 69:0.52 70:0.33 71:0.99 74:0.48 77:0.89 81:0.57 86:0.76 91:0.53 98:0.87 104:0.58 107:0.93 108:0.40 110:0.95 122:0.86 123:0.38 125:0.88 126:0.26 127:0.39 129:0.05 131:0.61 135:0.61 139:0.73 144:0.57 145:0.95 146:0.77 147:0.80 149:0.21 150:0.42 154:0.11 155:0.58 157:0.61 159:0.33 160:0.88 161:0.68 163:0.81 166:0.91 167:0.66 172:0.45 173:0.61 175:0.75 177:0.38 178:0.55 179:0.83 181:0.80 182:0.81 187:0.39 192:0.59 195:0.63 204:0.85 208:0.54 212:0.37 215:0.51 216:0.13 222:0.95 227:0.61 229:0.61 230:0.75 231:0.93 233:0.76 235:0.31 241:0.43 242:0.58 243:0.82 244:0.83 246:0.61 247:0.47 253:0.34 257:0.65 259:0.21 265:0.87 266:0.38 267:0.28 271:0.24 276:0.37 277:0.69 282:0.77 287:0.61 289:0.98 297:0.36 300:0.25 +4 6:0.79 7:0.63 8:0.58 12:0.79 17:0.47 18:0.80 20:0.89 21:0.74 27:0.54 28:0.60 29:0.77 30:0.21 32:0.62 34:0.58 36:0.31 37:0.27 39:0.60 43:0.16 46:0.88 55:0.45 66:0.80 69:0.52 70:0.50 71:0.99 78:0.92 81:0.23 86:0.81 91:0.88 98:0.91 100:0.92 104:0.58 107:0.93 108:0.40 110:0.96 111:0.91 120:0.66 122:0.65 123:0.38 125:0.88 126:0.26 127:0.51 129:0.05 131:0.94 135:0.51 139:0.77 140:0.45 145:0.82 146:0.69 147:0.74 149:0.11 150:0.23 151:0.57 152:0.84 153:0.78 154:0.56 157:0.61 159:0.27 160:0.88 161:0.68 162:0.51 163:0.75 164:0.53 166:0.90 167:0.95 169:0.89 172:0.45 173:0.70 175:0.75 177:0.38 179:0.86 181:0.80 182:0.61 186:0.92 189:0.81 190:0.89 191:0.81 195:0.59 202:0.72 212:0.37 215:0.36 216:0.13 220:0.62 222:0.95 227:0.85 229:0.61 230:0.63 231:0.87 233:0.73 235:0.88 238:0.88 241:0.93 242:0.40 243:0.82 244:0.61 246:0.61 247:0.47 248:0.57 253:0.20 254:0.74 256:0.64 257:0.65 259:0.21 260:0.58 261:0.91 265:0.91 266:0.38 267:0.28 268:0.65 271:0.24 276:0.35 277:0.69 283:0.78 285:0.47 287:0.61 289:0.95 297:0.36 300:0.33 +1 1:0.74 6:0.84 8:0.67 11:0.75 12:0.79 17:0.89 18:0.89 20:0.99 21:0.13 22:0.93 27:0.54 28:0.60 29:0.77 30:0.45 32:0.69 34:0.81 36:0.56 37:0.65 39:0.60 43:0.67 44:0.87 45:0.87 46:0.99 47:0.93 64:0.77 66:0.90 69:0.29 70:0.62 71:0.99 74:0.82 77:0.89 78:0.88 81:0.74 83:0.91 86:0.94 91:0.37 97:0.89 98:0.91 100:0.90 101:0.52 104:0.99 106:0.81 107:0.99 108:0.22 110:0.99 111:0.87 114:0.79 117:0.86 122:0.57 123:0.22 125:0.88 126:0.54 127:0.49 129:0.05 131:0.61 135:0.65 139:0.91 144:0.57 145:0.82 146:0.76 147:0.80 149:0.50 150:0.84 152:0.91 154:0.85 155:0.67 157:0.98 158:0.79 159:0.32 160:0.88 161:0.68 165:0.93 166:0.98 167:0.58 169:0.87 172:0.73 173:0.43 176:0.73 177:0.70 178:0.55 179:0.56 181:0.80 182:0.95 186:0.88 187:0.68 189:0.86 192:0.87 195:0.57 201:0.93 206:0.81 208:0.64 212:0.84 215:0.61 216:0.62 222:0.95 227:0.61 229:0.61 231:0.96 235:0.31 238:0.88 241:0.12 242:0.24 243:0.91 246:0.85 247:0.67 253:0.57 254:0.74 259:0.21 261:0.90 262:0.93 265:0.91 266:0.27 267:0.44 271:0.24 276:0.60 279:0.82 282:0.77 283:0.82 287:0.61 289:0.99 290:0.79 297:0.36 300:0.55 +2 1:0.74 9:0.76 11:0.85 12:0.79 17:0.90 18:0.77 21:0.13 27:0.72 28:0.60 29:0.77 30:0.89 34:0.85 36:0.57 39:0.60 43:0.92 45:0.87 46:1.00 64:0.77 66:0.91 69:0.38 70:0.22 71:0.99 74:0.79 81:0.74 83:0.91 85:0.85 86:0.88 91:0.89 96:0.75 98:0.95 101:0.87 104:0.75 107:0.99 108:0.30 110:0.99 114:0.79 120:0.63 122:0.51 123:0.28 125:1.00 126:0.54 127:0.68 129:0.05 131:0.99 135:0.47 139:0.85 140:0.45 144:0.57 146:0.72 147:0.76 149:0.93 150:0.84 151:0.65 153:0.48 154:0.91 155:0.67 157:0.98 159:0.29 160:0.96 161:0.68 162:0.86 164:0.54 165:0.93 166:0.98 167:0.91 172:0.74 173:0.58 176:0.73 177:0.70 179:0.59 181:0.80 182:0.96 190:0.77 192:0.87 201:0.93 202:0.36 208:0.64 212:0.82 215:0.61 220:0.62 222:0.95 227:0.88 229:0.98 231:0.96 235:0.31 241:0.80 242:0.45 243:0.91 246:0.85 247:0.47 248:0.63 253:0.72 255:0.74 256:0.45 259:0.21 260:0.63 265:0.95 266:0.27 267:0.23 268:0.46 271:0.24 276:0.62 285:0.56 287:0.88 289:0.99 290:0.79 297:0.36 300:0.25 +2 9:0.76 11:0.75 12:0.79 17:0.30 18:0.84 21:0.74 27:0.48 28:0.60 29:0.77 30:0.14 32:0.62 34:0.21 36:0.20 37:0.55 39:0.60 43:0.59 45:0.85 46:0.94 48:0.91 55:0.71 66:0.68 69:0.59 70:0.94 71:0.99 74:0.54 81:0.23 83:0.60 86:0.84 91:0.35 96:0.86 97:0.97 98:0.74 101:0.52 107:0.99 108:0.45 110:0.99 120:0.79 123:0.43 124:0.76 125:0.97 126:0.26 127:0.81 129:0.59 131:0.99 133:0.91 135:0.34 139:0.81 140:0.45 144:0.57 145:0.63 146:0.98 147:0.98 149:0.66 150:0.23 151:0.90 153:0.83 154:0.48 155:0.58 157:0.95 158:0.78 159:0.89 160:0.98 161:0.68 162:0.76 164:0.73 166:0.90 167:0.69 172:0.91 173:0.29 177:0.70 179:0.25 181:0.80 182:0.96 187:0.39 190:0.77 192:0.59 195:0.96 202:0.68 208:0.54 212:0.54 215:0.36 216:0.92 222:0.95 227:0.88 229:0.98 231:0.96 235:0.88 241:0.52 242:0.54 243:0.72 244:0.61 245:0.81 246:0.61 247:0.86 248:0.81 253:0.79 255:0.74 256:0.71 259:0.21 260:0.74 265:0.74 266:0.95 267:0.76 268:0.73 271:0.24 276:0.88 279:0.82 281:0.89 283:0.82 285:0.56 287:0.88 289:0.99 297:0.36 300:0.84 +2 7:0.63 12:0.79 17:0.70 18:0.66 21:0.30 27:0.54 28:0.60 29:0.77 30:0.76 32:0.69 34:0.79 36:0.44 37:0.27 39:0.60 43:0.16 46:0.88 55:0.71 66:0.36 69:0.41 70:0.15 71:0.99 74:0.39 77:0.89 81:0.36 86:0.65 91:0.46 98:0.27 104:0.58 107:0.91 108:0.32 110:0.93 122:0.65 123:0.31 124:0.52 125:0.88 126:0.26 127:0.42 129:0.05 131:0.61 133:0.39 135:0.42 139:0.64 144:0.57 145:0.76 146:0.27 147:0.33 149:0.11 150:0.32 154:0.11 157:0.61 159:0.30 160:0.88 161:0.68 163:0.79 166:0.91 167:0.72 172:0.10 173:0.54 175:0.75 177:0.38 178:0.55 179:0.99 181:0.80 182:0.81 187:0.39 195:0.61 212:0.95 215:0.44 216:0.13 222:0.95 227:0.61 229:0.61 230:0.63 231:0.93 233:0.73 235:0.31 241:0.59 242:0.82 243:0.51 244:0.83 245:0.37 246:0.61 247:0.12 253:0.31 257:0.65 259:0.21 265:0.29 266:0.12 267:0.36 271:0.24 276:0.14 277:0.69 282:0.77 287:0.61 289:0.98 297:0.36 300:0.13 +2 7:0.81 8:0.68 12:0.79 17:0.94 18:0.80 20:0.80 21:0.54 22:0.93 27:0.64 28:0.60 29:0.77 30:0.66 32:0.62 34:0.58 36:0.54 37:0.65 39:0.60 43:0.13 46:0.88 47:0.93 48:0.91 55:0.45 60:0.95 64:0.77 66:0.25 69:0.60 70:0.61 71:0.99 74:0.58 76:0.85 78:0.81 81:0.33 83:0.91 86:0.80 91:0.35 96:0.75 97:0.94 98:0.49 100:0.84 104:0.98 106:0.81 107:0.95 108:0.80 110:0.97 111:0.81 117:0.86 123:0.44 124:0.69 125:0.88 126:0.44 127:0.63 129:0.59 131:0.86 133:0.64 135:0.39 139:0.88 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.24 150:0.27 151:0.55 152:0.78 153:0.48 154:0.57 155:0.67 157:0.61 158:0.91 159:0.48 160:0.88 161:0.68 162:0.86 166:0.91 167:0.61 169:0.83 172:0.45 173:0.62 175:0.75 177:0.38 179:0.67 181:0.80 182:0.61 186:0.82 187:0.68 190:0.89 192:0.87 195:0.67 201:0.68 202:0.36 204:0.89 206:0.81 208:0.64 212:0.57 215:0.39 216:0.44 219:0.95 220:0.62 222:0.95 227:0.83 229:0.61 230:0.86 231:0.96 232:0.99 233:0.73 235:0.74 241:0.24 242:0.76 243:0.44 244:0.61 245:0.68 246:0.61 247:0.55 248:0.54 253:0.45 254:0.74 256:0.45 257:0.65 259:0.21 261:0.80 262:0.93 265:0.30 266:0.75 267:0.59 268:0.46 271:0.24 276:0.53 277:0.69 279:0.86 281:0.88 283:0.82 285:0.47 287:0.61 289:0.99 292:0.91 297:0.36 300:0.55 +1 8:0.94 12:0.37 17:0.96 18:0.65 21:0.54 22:0.93 27:0.04 29:0.71 30:0.62 31:0.96 34:0.42 36:0.60 37:0.96 39:0.58 43:0.17 47:0.93 55:0.84 66:0.72 69:0.30 70:0.47 71:0.80 74:0.51 79:0.96 81:0.25 86:0.76 91:0.78 98:0.84 104:0.80 106:0.81 108:0.24 114:0.58 120:0.81 123:0.23 124:0.41 126:0.26 127:0.76 129:0.05 133:0.37 135:0.81 137:0.96 139:0.77 140:0.45 146:0.83 147:0.86 149:0.24 150:0.25 151:0.90 153:0.83 154:0.78 159:0.50 162:0.64 163:0.86 164:0.66 172:0.59 173:0.33 176:0.61 177:0.70 179:0.73 187:0.39 190:0.77 192:0.51 196:0.94 202:0.63 206:0.81 212:0.27 216:0.31 219:0.92 222:0.35 235:0.60 241:0.37 242:0.72 243:0.75 245:0.61 247:0.60 248:0.85 253:0.41 254:0.95 255:0.74 256:0.58 259:0.21 260:0.78 262:0.93 265:0.84 266:0.63 267:0.44 268:0.64 271:0.79 276:0.52 283:0.82 284:0.92 285:0.56 290:0.58 292:0.95 300:0.43 +1 7:0.72 8:0.90 12:0.37 17:0.49 21:0.30 27:0.01 29:0.71 31:0.88 32:0.69 34:0.64 36:0.79 37:0.93 39:0.58 43:0.12 44:0.90 48:0.91 64:0.77 66:0.36 69:0.75 71:0.80 74:0.42 76:0.85 77:0.70 79:0.88 81:0.39 91:0.93 98:0.10 108:0.59 120:0.59 123:0.56 126:0.44 127:0.07 129:0.05 135:0.86 137:0.88 139:0.79 140:0.45 145:0.70 146:0.05 147:0.05 149:0.06 150:0.31 151:0.56 153:0.78 154:0.09 159:0.05 162:0.78 164:0.65 172:0.05 173:0.95 175:0.91 177:0.05 187:0.21 190:0.77 191:0.93 192:0.51 195:0.61 196:0.90 201:0.68 202:0.59 215:0.44 216:0.51 219:0.92 220:0.82 222:0.35 230:0.74 233:0.73 235:0.42 241:0.76 243:0.51 244:0.83 248:0.56 253:0.32 255:0.74 256:0.58 257:0.84 259:0.21 260:0.58 265:0.11 267:0.65 268:0.64 271:0.79 277:0.87 281:0.89 282:0.73 283:0.78 284:0.92 285:0.56 292:0.91 297:0.36 +1 7:0.72 12:0.37 17:0.59 21:0.30 22:0.93 27:0.33 29:0.71 32:0.64 37:0.60 39:0.58 47:0.93 48:0.91 71:0.80 74:0.39 81:0.35 91:0.58 104:0.77 126:0.26 139:0.68 145:0.45 150:0.32 155:0.58 175:0.97 178:0.55 187:0.39 192:0.59 195:0.77 204:0.85 208:0.54 215:0.44 216:0.24 222:0.35 230:0.75 233:0.76 235:0.42 241:0.65 244:0.93 253:0.31 257:0.93 262:0.93 271:0.79 277:0.95 281:0.91 297:0.36 +1 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.97 21:0.59 27:0.72 29:0.71 30:0.59 32:0.69 34:0.17 36:0.37 39:0.58 43:0.38 45:0.77 55:0.45 66:0.98 69:0.32 70:0.42 71:0.80 74:0.91 76:0.94 77:0.98 81:0.36 83:0.60 91:0.92 96:0.77 98:0.99 99:0.83 101:0.52 104:0.58 108:0.25 114:0.56 120:0.65 123:0.24 126:0.44 127:0.93 129:0.05 135:0.23 138:0.96 140:0.45 145:0.84 146:0.95 147:0.96 149:0.48 150:0.53 151:0.71 153:0.77 154:0.58 155:0.58 158:0.78 159:0.61 162:0.86 164:0.65 165:0.70 172:0.96 173:0.29 175:0.75 176:0.61 177:0.38 179:0.21 190:0.77 192:0.59 195:0.76 201:0.68 202:0.54 204:0.85 208:0.54 212:0.89 215:0.38 216:0.02 220:0.62 222:0.35 230:0.75 232:0.79 233:0.76 235:0.97 241:0.79 242:0.54 243:0.98 244:0.61 247:0.55 248:0.67 253:0.76 255:0.74 256:0.58 257:0.65 259:0.21 260:0.64 265:0.99 266:0.54 267:0.18 268:0.63 271:0.79 276:0.91 277:0.69 279:0.82 282:0.77 283:0.78 285:0.56 290:0.56 297:0.36 300:0.55 +1 7:0.72 12:0.37 17:0.51 21:0.30 27:0.33 29:0.71 30:0.76 32:0.64 34:0.21 36:0.43 37:0.60 39:0.58 43:0.16 48:0.91 55:0.84 66:0.36 69:0.47 70:0.15 71:0.80 74:0.39 81:0.35 91:0.81 96:0.73 98:0.24 104:0.77 108:0.36 120:0.68 123:0.34 124:0.52 126:0.26 127:0.23 129:0.59 133:0.47 135:0.30 139:0.68 140:0.80 145:0.45 146:0.32 147:0.38 149:0.11 150:0.32 151:0.53 153:0.46 154:0.11 155:0.58 159:0.34 162:0.77 163:0.96 164:0.71 172:0.10 173:0.44 175:0.91 177:0.05 178:0.42 179:0.96 187:0.39 190:0.89 192:0.59 195:0.76 202:0.70 204:0.85 208:0.54 212:0.95 215:0.44 216:0.24 220:0.74 222:0.35 230:0.75 233:0.76 235:0.42 241:0.70 242:0.82 243:0.51 244:0.83 245:0.37 247:0.12 248:0.59 253:0.45 256:0.81 257:0.84 260:0.65 265:0.26 266:0.12 267:0.69 268:0.74 271:0.79 276:0.15 277:0.87 281:0.91 285:0.56 297:0.36 300:0.13 +2 1:0.69 7:0.72 8:0.58 9:0.76 11:0.64 12:0.37 17:0.89 18:0.79 21:0.30 27:0.39 29:0.71 30:0.56 32:0.69 34:0.40 36:0.64 37:0.23 39:0.58 43:0.80 45:0.66 66:0.84 69:0.25 70:0.71 71:0.80 74:0.64 77:0.89 81:0.63 83:0.60 86:0.86 91:0.96 96:0.75 98:0.86 99:0.95 101:0.52 104:0.58 108:0.20 114:0.63 120:0.63 123:0.19 126:0.44 127:0.37 129:0.05 135:0.23 139:0.82 140:0.45 145:0.65 146:0.65 147:0.70 149:0.70 150:0.61 151:0.65 153:0.48 154:0.75 155:0.58 159:0.25 162:0.86 164:0.54 165:0.70 172:0.54 173:0.46 176:0.66 177:0.70 179:0.73 190:0.77 192:0.59 195:0.59 201:0.68 202:0.36 204:0.85 208:0.54 212:0.61 215:0.44 216:0.02 220:0.62 222:0.35 230:0.75 232:0.77 233:0.76 235:0.80 241:0.98 242:0.24 243:0.85 247:0.74 248:0.63 253:0.62 255:0.74 256:0.45 259:0.21 260:0.63 265:0.86 266:0.27 267:0.93 268:0.46 271:0.79 276:0.40 282:0.77 285:0.56 290:0.63 297:0.36 300:0.55 +2 7:0.66 8:0.90 9:0.76 11:0.64 12:0.37 17:0.95 21:0.30 27:0.52 29:0.71 30:0.36 32:0.64 34:0.19 36:0.62 37:0.88 39:0.58 43:0.24 45:0.77 55:0.52 66:0.78 69:0.86 70:0.85 71:0.80 74:0.55 81:0.30 83:0.60 91:0.92 96:0.76 97:0.89 98:0.48 101:0.52 104:0.58 108:0.89 120:0.83 122:0.51 123:0.89 124:0.41 126:0.26 127:0.43 129:0.59 133:0.64 135:0.30 140:0.87 145:0.56 146:0.40 147:0.46 149:0.35 150:0.28 151:0.65 153:0.78 154:0.17 155:0.58 158:0.78 159:0.69 162:0.73 164:0.84 172:0.79 173:0.78 175:0.75 177:0.38 178:0.42 179:0.40 190:0.77 191:0.93 192:0.59 195:0.88 202:0.83 208:0.54 212:0.87 215:0.44 216:0.09 220:0.93 222:0.35 230:0.69 232:0.80 233:0.73 235:0.31 241:0.85 242:0.37 243:0.13 244:0.61 245:0.67 247:0.60 248:0.65 253:0.58 255:0.74 256:0.84 257:0.65 259:0.21 260:0.77 265:0.49 266:0.54 267:0.59 268:0.86 271:0.79 276:0.64 277:0.69 279:0.82 283:0.78 285:0.56 297:0.36 300:0.84 +1 1:0.69 7:0.66 8:0.97 11:0.64 12:0.37 17:0.83 18:0.79 21:0.74 25:0.88 27:0.71 29:0.71 30:0.21 32:0.64 34:0.42 36:0.39 37:0.71 39:0.58 43:0.23 45:0.81 55:0.59 66:0.49 69:0.82 70:0.95 71:0.80 74:0.57 81:0.32 83:0.60 86:0.73 87:0.98 91:0.90 98:0.49 99:0.83 101:0.52 104:0.76 108:0.90 114:0.54 123:0.89 124:0.78 126:0.44 127:0.93 129:0.05 133:0.83 135:0.49 139:0.71 145:0.94 146:0.71 147:0.31 149:0.46 150:0.51 154:0.16 155:0.58 159:0.84 165:0.70 172:0.85 173:0.66 176:0.66 177:0.38 178:0.55 179:0.29 187:0.21 192:0.59 195:0.93 201:0.68 208:0.54 212:0.69 215:0.36 216:0.07 222:0.35 230:0.69 232:0.82 233:0.73 235:0.95 241:0.75 242:0.38 243:0.08 244:0.61 245:0.88 247:0.90 253:0.39 257:0.65 259:0.21 265:0.51 266:0.89 267:0.79 271:0.79 276:0.85 277:0.69 290:0.54 297:0.36 300:0.94 +2 7:0.72 8:0.72 12:0.37 17:0.64 21:0.54 27:0.34 29:0.71 30:0.76 32:0.64 34:0.26 36:0.51 37:0.36 39:0.58 43:0.17 48:0.91 55:0.92 66:0.36 69:0.47 70:0.22 71:0.80 81:0.25 91:0.95 98:0.51 108:0.67 120:0.64 123:0.65 124:0.57 126:0.26 127:0.37 129:0.59 133:0.47 135:0.47 139:0.68 140:0.80 145:0.69 146:0.40 147:0.46 149:0.15 150:0.25 151:0.55 153:0.48 154:0.11 159:0.36 162:0.53 163:0.96 164:0.56 172:0.16 173:0.53 175:0.91 177:0.05 178:0.42 179:0.94 190:0.89 191:0.84 192:0.51 195:0.67 202:0.66 212:0.42 215:0.39 216:0.21 220:0.99 222:0.35 230:0.74 233:0.73 235:0.74 241:0.97 242:0.82 243:0.40 244:0.83 245:0.43 247:0.12 248:0.54 253:0.20 256:0.58 257:0.84 259:0.21 260:0.61 265:0.53 266:0.23 267:0.59 268:0.59 271:0.79 276:0.25 277:0.87 281:0.89 285:0.47 297:0.99 300:0.18 +1 7:0.66 9:0.76 12:0.37 17:0.94 21:0.40 27:0.72 29:0.71 30:0.94 32:0.68 34:0.90 36:0.58 37:0.23 39:0.58 43:0.18 44:0.90 48:0.97 55:0.71 66:0.79 69:0.21 70:0.19 71:0.80 74:0.46 76:0.94 77:0.70 81:0.28 83:0.60 91:0.77 96:0.80 98:0.85 104:0.58 108:0.17 120:0.69 123:0.16 126:0.26 127:0.36 129:0.05 135:0.18 139:0.71 140:0.45 145:0.80 146:0.52 147:0.58 149:0.22 150:0.27 151:0.81 153:0.48 154:0.12 155:0.58 159:0.19 162:0.86 164:0.54 172:0.41 173:0.53 175:0.75 177:0.38 179:0.84 187:0.21 190:0.77 191:0.70 192:0.59 195:0.55 202:0.36 208:0.54 212:0.95 215:0.42 216:0.08 222:0.35 230:0.69 232:0.77 233:0.76 235:0.42 241:0.47 242:0.75 243:0.80 244:0.83 247:0.35 248:0.73 253:0.69 255:0.74 256:0.45 257:0.65 259:0.21 260:0.68 265:0.85 266:0.12 267:0.23 268:0.46 271:0.79 276:0.36 277:0.69 281:0.89 282:0.77 285:0.56 297:0.36 300:0.18 +1 1:0.69 7:0.66 8:0.80 9:0.76 11:0.64 12:0.37 17:0.59 21:0.40 27:0.02 29:0.71 30:0.62 34:0.50 36:0.37 37:0.99 39:0.58 43:0.65 45:0.66 55:0.45 66:0.77 69:0.17 70:0.56 71:0.80 74:0.93 76:0.98 77:0.70 81:0.34 83:0.60 91:0.85 96:0.87 97:0.89 98:0.77 101:0.52 104:0.86 106:0.81 108:0.14 114:0.59 120:0.81 122:0.77 123:0.13 124:0.42 126:0.44 127:0.94 129:0.05 133:0.62 135:0.65 138:0.96 140:0.45 145:0.54 146:0.86 147:0.89 149:0.63 150:0.49 151:0.91 153:0.84 154:0.55 155:0.58 158:0.79 159:0.65 162:0.84 164:0.73 172:0.91 173:0.18 175:0.75 176:0.61 177:0.38 179:0.29 187:0.39 190:0.77 191:0.91 192:0.59 195:0.79 201:0.68 202:0.67 206:0.81 208:0.54 212:0.87 215:0.41 216:0.47 222:0.35 230:0.69 233:0.76 235:0.52 241:0.12 242:0.77 243:0.79 244:0.61 245:0.85 247:0.60 248:0.82 253:0.89 255:0.74 256:0.71 257:0.65 260:0.77 265:0.77 266:0.63 267:0.65 268:0.74 271:0.79 276:0.85 277:0.69 279:0.82 282:0.73 283:0.82 285:0.56 290:0.59 297:0.36 300:0.84 +1 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.95 18:0.59 21:0.59 27:0.72 29:0.71 30:0.37 32:0.69 34:0.25 36:0.87 39:0.58 43:0.26 45:0.88 55:0.52 66:0.99 69:0.32 70:0.75 71:0.80 74:0.74 76:0.97 77:0.96 81:0.35 83:0.60 86:0.64 91:0.92 96:0.86 97:0.97 98:1.00 99:0.83 101:0.52 104:0.72 108:0.25 114:0.57 120:0.77 123:0.24 126:0.44 127:0.98 129:0.05 135:0.23 139:0.62 140:0.45 145:0.63 146:0.99 147:0.99 149:0.73 150:0.53 151:0.85 153:0.77 154:0.45 155:0.58 158:0.79 159:0.81 162:0.86 164:0.66 165:0.70 172:0.98 173:0.17 176:0.66 177:0.70 179:0.15 190:0.77 192:0.59 195:0.91 201:0.68 202:0.55 204:0.85 208:0.54 212:0.86 215:0.39 216:0.08 222:0.35 230:0.75 232:0.78 233:0.76 235:0.84 241:0.77 242:0.74 243:0.99 244:0.61 247:0.79 248:0.81 253:0.83 255:0.74 256:0.58 259:0.21 260:0.74 265:1.00 266:0.54 267:0.85 268:0.64 271:0.79 276:0.95 279:0.82 282:0.77 283:0.82 285:0.56 290:0.57 297:0.36 300:0.70 +0 11:0.64 12:0.37 17:0.20 18:0.87 21:0.78 27:0.45 29:0.71 30:0.10 34:0.87 36:0.21 37:0.50 39:0.58 43:0.61 45:0.73 55:0.71 66:0.54 69:0.84 70:0.98 71:0.80 74:0.72 86:0.91 91:0.15 98:0.52 101:0.52 108:0.69 123:0.67 124:0.85 127:0.63 129:0.05 133:0.91 135:0.54 139:0.88 145:0.52 146:0.89 147:0.38 149:0.47 154:0.62 159:0.85 172:0.84 173:0.65 177:0.38 178:0.55 179:0.29 187:0.68 212:0.56 216:0.77 222:0.35 235:0.68 241:0.32 242:0.18 243:0.12 245:0.79 247:0.95 253:0.42 254:0.74 257:0.65 259:0.21 265:0.54 266:0.95 267:0.52 271:0.79 276:0.83 300:0.84 +2 1:0.69 7:0.72 8:0.58 9:0.76 11:0.64 12:0.37 17:0.85 18:0.69 21:0.30 27:0.54 29:0.71 30:0.45 32:0.69 34:0.44 36:0.63 37:0.30 39:0.58 43:0.72 45:0.73 66:0.33 69:0.76 70:0.61 71:0.80 74:0.65 77:0.89 81:0.63 83:0.60 86:0.75 91:0.94 96:0.75 98:0.52 99:0.95 101:0.52 104:0.58 108:0.18 114:0.63 120:0.63 122:0.51 123:0.18 124:0.61 126:0.44 127:0.41 129:0.05 133:0.43 135:0.18 139:0.71 140:0.45 145:0.65 146:0.37 147:0.47 149:0.59 150:0.61 151:0.65 153:0.48 154:0.65 155:0.58 159:0.27 162:0.86 163:0.81 164:0.54 165:0.70 172:0.27 173:0.92 176:0.66 177:0.38 179:0.80 190:0.77 191:0.84 192:0.59 195:0.60 201:0.68 202:0.36 204:0.85 208:0.54 212:0.50 215:0.44 216:0.02 220:0.62 222:0.35 230:0.75 232:0.77 233:0.76 235:0.80 241:0.91 242:0.33 243:0.50 245:0.60 247:0.60 248:0.63 253:0.63 254:0.97 255:0.74 256:0.45 257:0.65 259:0.21 260:0.63 265:0.54 266:0.38 267:0.94 268:0.46 271:0.79 276:0.36 277:0.69 282:0.77 285:0.56 290:0.63 297:0.36 300:0.43 +2 1:0.69 7:0.72 8:0.84 9:0.76 11:0.64 12:0.37 17:0.96 21:0.59 27:0.72 29:0.71 30:0.62 32:0.68 34:0.21 36:0.34 39:0.58 43:0.67 45:0.83 48:0.91 55:0.52 66:0.96 69:0.41 70:0.51 71:0.80 74:0.69 77:0.70 81:0.31 91:0.94 96:0.75 98:0.94 101:0.52 104:0.75 108:0.31 114:0.56 120:0.63 123:0.30 126:0.44 127:0.62 129:0.05 135:0.24 138:0.95 139:0.68 140:0.45 145:0.54 146:0.85 147:0.87 149:0.76 150:0.48 151:0.73 153:0.80 154:0.56 155:0.58 158:0.74 159:0.41 162:0.78 164:0.66 172:0.90 173:0.47 175:0.75 176:0.61 177:0.38 179:0.30 190:0.77 191:0.80 192:0.59 195:0.67 201:0.68 202:0.63 204:0.85 208:0.54 212:0.91 215:0.38 216:0.03 220:0.62 222:0.35 230:0.75 232:0.72 233:0.76 235:0.80 241:0.84 242:0.75 243:0.96 244:0.61 247:0.67 248:0.73 253:0.57 255:0.74 256:0.64 257:0.65 260:0.61 265:0.94 266:0.27 267:0.44 268:0.68 271:0.79 276:0.83 277:0.69 279:0.82 281:0.91 282:0.77 283:0.78 285:0.56 286:0.99 290:0.56 297:0.36 298:0.99 300:0.55 +0 8:0.84 9:0.80 12:0.37 17:0.69 18:0.62 21:0.13 27:0.06 29:0.71 30:0.38 31:0.93 34:0.23 36:0.72 37:0.72 39:0.58 43:0.18 55:0.59 66:0.26 69:0.83 70:0.90 71:0.80 74:0.76 76:0.97 77:0.89 79:0.94 81:0.40 83:0.60 86:0.67 91:0.97 96:1.00 98:0.41 108:0.67 114:0.72 120:0.87 122:0.77 123:0.65 124:0.90 126:0.26 127:0.98 129:0.05 133:0.89 135:0.32 137:0.93 138:0.99 139:0.65 140:0.94 145:0.72 146:0.73 147:0.67 149:0.36 150:0.34 151:0.94 153:0.99 154:0.47 155:0.67 158:0.74 159:0.83 162:0.78 164:0.81 172:0.63 173:0.68 176:0.61 177:0.38 179:0.42 190:0.77 191:0.96 192:0.87 195:0.93 196:0.89 202:0.98 208:0.64 212:0.60 216:0.54 222:0.35 235:0.12 241:0.94 242:0.72 243:0.21 244:0.61 245:0.79 247:0.76 248:0.92 253:0.99 254:0.74 255:0.79 256:0.98 257:0.65 259:0.21 260:0.84 265:0.43 266:0.92 267:0.85 268:0.99 271:0.79 276:0.77 282:0.73 284:0.98 285:0.62 290:0.72 300:0.84 +1 7:0.72 8:0.60 11:0.64 12:0.37 17:0.97 18:0.72 21:0.78 22:0.93 27:0.49 29:0.71 30:0.62 32:0.80 34:0.78 36:0.53 37:0.23 39:0.58 43:0.61 44:0.93 45:0.73 47:0.93 66:0.54 69:0.05 70:0.54 71:0.80 74:0.75 77:0.70 86:0.81 91:0.83 98:0.37 101:0.52 104:0.58 106:0.81 108:0.05 120:0.53 122:0.51 123:0.05 124:0.50 127:0.40 129:0.05 133:0.43 135:0.51 139:0.84 140:0.45 145:0.47 146:0.29 147:0.35 149:0.29 151:0.46 153:0.48 154:0.88 155:0.58 159:0.24 162:0.86 164:0.53 172:0.41 173:0.17 177:0.70 179:0.77 187:0.39 190:0.89 192:0.59 195:0.58 202:0.36 204:0.85 206:0.81 208:0.54 212:0.78 216:0.18 220:0.99 222:0.35 230:0.75 232:0.77 233:0.76 241:0.49 242:0.29 243:0.63 245:0.59 247:0.67 248:0.46 253:0.43 254:0.86 256:0.45 259:0.21 260:0.53 262:0.93 265:0.40 266:0.27 267:0.65 268:0.46 271:0.79 276:0.26 282:0.88 285:0.47 300:0.43 +2 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.99 18:0.70 21:0.05 27:0.72 29:0.71 30:0.21 32:0.80 34:0.23 36:0.69 37:0.55 39:0.58 43:0.60 44:0.93 45:0.95 55:0.45 66:0.99 69:0.08 70:0.64 71:0.80 74:0.96 77:0.89 81:0.68 83:0.60 86:0.80 91:0.92 96:0.89 98:1.00 99:0.83 101:0.52 104:0.54 108:0.07 114:0.85 120:0.82 123:0.07 126:0.44 127:0.96 129:0.05 135:0.18 139:0.84 140:0.45 145:0.74 146:0.92 147:0.93 149:0.87 150:0.64 151:0.90 153:0.83 154:0.74 155:0.58 159:0.54 162:0.79 164:0.69 165:0.70 172:0.98 173:0.17 176:0.66 177:0.70 179:0.16 190:0.77 191:0.76 192:0.59 195:0.68 201:0.68 202:0.63 204:0.85 208:0.54 212:0.94 215:0.78 216:0.04 222:0.35 230:0.75 232:0.74 233:0.76 235:0.12 241:0.93 242:0.41 243:0.99 247:0.79 248:0.85 253:0.91 254:0.74 255:0.74 256:0.64 259:0.21 260:0.79 265:1.00 266:0.38 267:0.76 268:0.68 271:0.79 276:0.95 282:0.88 285:0.56 290:0.85 297:0.36 300:0.55 +0 8:0.67 12:0.37 17:0.26 21:0.30 27:0.01 29:0.71 31:0.89 34:0.67 36:0.84 37:1.00 39:0.58 71:0.80 79:0.88 81:0.31 91:0.57 114:0.63 120:0.59 126:0.26 135:0.51 137:0.89 140:0.45 150:0.29 151:0.59 153:0.48 162:0.62 164:0.54 175:0.97 176:0.61 187:0.39 190:0.77 192:0.51 202:0.50 216:0.53 220:0.74 222:0.35 232:0.75 235:0.31 241:0.69 244:0.93 248:0.58 253:0.46 255:0.74 256:0.45 257:0.93 259:0.21 260:0.59 267:0.52 268:0.46 271:0.79 277:0.95 284:0.87 285:0.56 290:0.63 +0 9:0.80 11:0.64 12:0.37 17:0.72 18:0.63 21:0.71 27:0.51 29:0.71 30:0.61 31:0.93 34:0.71 36:0.32 37:0.46 39:0.58 43:0.63 45:0.90 55:0.42 66:0.94 69:0.60 70:0.42 71:0.80 74:0.89 79:0.92 81:0.23 83:0.60 86:0.73 91:0.89 96:0.92 98:0.72 101:0.52 108:0.46 120:0.85 122:0.51 123:0.44 126:0.26 127:0.23 129:0.05 135:0.30 137:0.93 139:0.70 140:0.97 145:0.80 146:0.64 147:0.69 149:0.72 150:0.23 151:0.65 153:0.69 154:0.71 155:0.67 159:0.24 162:0.81 164:0.86 172:0.85 173:0.70 177:0.70 179:0.22 187:0.68 192:0.87 196:0.91 202:0.80 208:0.64 212:0.93 215:0.37 216:0.42 220:0.62 222:0.35 235:0.84 241:0.41 242:0.58 243:0.94 247:0.67 248:0.75 253:0.93 254:0.74 255:0.95 256:0.89 259:0.21 260:0.83 265:0.73 266:0.23 267:0.73 268:0.85 271:0.79 276:0.73 283:0.78 284:0.93 285:0.62 297:0.36 300:0.55 +1 1:0.69 7:0.72 8:0.89 9:0.76 11:0.64 12:0.37 17:0.90 21:0.40 27:0.72 29:0.71 30:0.90 32:0.64 34:0.19 36:0.39 37:0.93 39:0.58 43:0.55 45:0.73 55:0.45 66:0.74 69:0.41 70:0.31 71:0.80 74:0.65 81:0.40 91:0.92 96:0.69 98:0.75 101:0.52 104:0.54 108:0.32 114:0.61 120:0.64 123:0.31 124:0.45 126:0.44 127:0.81 129:0.59 133:0.64 135:0.47 140:0.45 145:0.94 146:0.95 147:0.96 149:0.79 150:0.52 151:0.50 153:0.48 154:0.44 155:0.58 159:0.81 162:0.78 164:0.65 172:0.98 173:0.22 175:0.75 176:0.66 177:0.38 179:0.13 190:0.89 191:0.82 192:0.59 195:0.92 201:0.68 202:0.59 204:0.85 208:0.54 212:0.91 215:0.42 216:0.02 220:0.91 222:0.35 230:0.75 233:0.76 235:0.60 241:0.94 242:0.78 243:0.77 244:0.61 245:0.98 247:0.67 248:0.49 253:0.47 255:0.74 256:0.58 257:0.65 259:0.21 260:0.62 265:0.75 266:0.80 267:0.69 268:0.64 271:0.79 276:0.96 277:0.69 283:0.78 285:0.56 290:0.61 297:0.36 300:0.94 +0 11:0.64 12:0.79 17:0.55 18:0.73 21:0.78 27:0.45 29:0.52 30:0.28 34:0.77 36:0.18 37:0.50 39:0.51 43:0.62 45:0.73 55:0.59 66:0.54 69:0.72 70:0.87 71:0.86 74:0.42 81:0.25 86:0.83 91:0.17 98:0.51 101:0.87 108:0.55 114:0.60 122:0.80 123:0.53 124:0.63 126:0.26 127:0.36 129:0.05 131:0.61 133:0.60 135:0.72 139:0.76 144:0.57 145:0.45 146:0.66 147:0.71 149:0.23 150:0.26 154:0.51 157:0.84 159:0.52 166:0.86 172:0.59 173:0.68 176:0.59 177:0.70 178:0.55 179:0.54 182:0.76 187:0.68 212:0.61 216:0.77 222:0.76 227:0.61 229:0.61 231:0.94 235:1.00 241:0.24 242:0.19 243:0.63 245:0.68 246:0.61 247:0.79 253:0.44 254:0.98 259:0.21 265:0.53 266:0.54 267:0.28 271:0.36 276:0.54 287:0.61 290:0.60 300:0.70 +0 12:0.79 17:0.89 18:0.60 21:0.78 27:0.72 29:0.52 30:0.21 34:0.34 36:0.99 39:0.51 43:0.08 55:0.59 66:0.54 69:0.90 70:0.37 71:0.86 86:0.62 91:0.74 98:0.24 108:0.84 123:0.83 124:0.45 127:0.15 129:0.59 131:0.61 133:0.47 135:0.88 139:0.61 146:0.05 147:0.05 149:0.10 154:0.08 157:0.61 159:0.28 163:0.90 166:0.61 172:0.21 173:0.89 175:0.91 177:0.05 178:0.55 179:0.66 182:0.61 212:0.42 216:0.07 222:0.76 227:0.61 229:0.61 231:0.67 235:0.12 241:0.79 242:0.75 243:0.26 244:0.93 245:0.40 246:0.61 247:0.30 253:0.20 257:0.84 259:0.21 265:0.26 266:0.23 267:0.44 271:0.36 276:0.21 277:0.87 287:0.61 300:0.25 +0 8:0.62 11:0.64 12:0.79 17:0.03 18:1.00 21:0.30 27:0.66 29:0.52 30:0.20 31:0.88 34:0.64 36:0.59 37:0.33 39:0.51 43:0.66 45:0.83 64:0.77 66:0.05 69:0.88 70:0.97 71:0.86 74:0.40 79:0.88 81:0.38 86:1.00 91:0.23 98:0.23 101:0.87 108:1.00 120:0.58 122:0.86 123:0.96 124:0.99 126:0.44 127:1.00 129:0.99 131:0.88 133:0.99 135:0.54 137:0.88 139:1.00 140:0.45 144:0.57 146:0.94 147:0.84 149:0.70 150:0.37 151:0.57 153:0.48 154:0.45 157:0.75 159:0.99 162:0.86 164:0.55 166:0.83 172:0.92 173:0.29 177:0.38 179:0.09 182:0.71 187:0.87 190:0.89 192:0.49 196:0.93 201:0.59 202:0.36 212:0.54 215:0.72 216:0.52 219:0.91 220:0.87 222:0.76 227:0.61 229:0.61 231:0.93 235:0.42 241:0.56 242:0.62 243:0.10 245:0.99 246:0.61 247:0.97 248:0.56 253:0.35 254:0.84 255:0.69 256:0.45 257:0.65 259:0.21 260:0.58 265:0.25 266:0.98 267:0.59 268:0.46 271:0.36 276:1.00 277:0.69 283:0.67 284:0.88 285:0.56 287:0.61 292:0.88 297:0.36 300:0.98 +0 1:0.66 11:0.42 12:0.79 17:0.34 18:0.82 21:0.30 27:0.64 29:0.52 30:0.10 34:0.73 36:0.56 37:0.37 39:0.51 43:0.14 55:0.42 64:0.77 66:0.88 69:0.38 70:0.81 71:0.86 74:0.50 81:0.56 86:0.84 91:0.18 98:0.78 108:0.30 114:0.84 123:0.28 126:0.54 127:0.27 129:0.05 131:0.61 135:0.73 139:0.83 144:0.57 146:0.78 147:0.82 149:0.17 150:0.54 154:0.65 155:0.51 157:0.61 158:0.81 159:0.34 166:0.97 172:0.65 173:0.40 176:0.70 177:0.70 178:0.55 179:0.51 182:0.61 187:0.39 192:0.48 201:0.64 202:0.36 208:0.64 212:0.59 215:0.72 216:0.61 219:0.94 222:0.76 227:0.61 229:0.61 231:0.95 235:0.52 241:0.66 242:0.37 243:0.88 244:0.61 246:0.61 247:0.67 253:0.60 254:0.80 259:0.21 265:0.78 266:0.38 267:0.76 271:0.36 276:0.53 279:0.80 283:0.66 287:0.61 290:0.84 292:0.87 297:0.36 300:0.55 +0 11:0.64 12:0.79 17:0.18 18:0.89 21:0.78 27:0.72 29:0.52 30:0.33 34:0.97 36:0.97 39:0.51 43:0.26 45:0.87 55:0.71 66:0.72 69:0.84 70:0.91 71:0.86 74:0.48 86:0.91 91:0.27 98:0.75 101:0.87 108:0.84 123:0.83 124:0.50 127:0.48 129:0.59 131:0.61 133:0.74 135:0.88 139:0.87 144:0.57 146:0.65 147:0.70 149:0.43 154:0.46 157:0.97 159:0.80 166:0.61 172:0.93 173:0.68 177:0.70 178:0.55 179:0.19 182:0.90 187:0.68 212:0.71 216:0.29 222:0.76 227:0.61 229:0.61 231:0.93 235:0.88 241:0.01 242:0.27 243:0.19 245:0.89 246:0.61 247:0.94 253:0.39 254:0.86 259:0.21 265:0.75 266:0.71 267:0.73 271:0.36 276:0.89 287:0.61 300:0.70 +0 1:0.64 11:0.55 12:0.79 17:0.28 18:0.73 21:0.47 27:0.45 29:0.52 30:0.08 34:0.91 36:0.20 37:0.50 39:0.51 43:0.13 45:0.66 55:0.42 64:0.77 66:0.16 69:0.69 70:0.95 71:0.86 74:0.43 81:0.62 86:0.79 91:0.09 98:0.16 99:0.83 101:0.52 108:0.53 114:0.80 122:0.82 123:0.51 124:0.91 126:0.54 127:0.60 129:0.05 131:0.61 133:0.89 135:0.92 139:0.76 144:0.57 145:0.47 146:0.30 147:0.36 149:0.12 150:0.49 154:0.75 155:0.50 157:0.61 159:0.79 165:0.70 166:0.97 172:0.21 173:0.52 176:0.73 177:0.70 178:0.55 179:0.77 182:0.61 187:0.68 192:0.47 201:0.62 208:0.64 212:0.13 215:0.63 216:0.77 222:0.76 227:0.61 229:0.61 231:0.95 235:0.74 241:0.15 242:0.22 243:0.37 245:0.50 246:0.61 247:0.76 253:0.58 254:0.74 259:0.21 265:0.18 266:0.80 267:0.52 271:0.36 276:0.44 287:0.61 290:0.80 297:0.36 300:0.70 +0 12:0.79 17:0.51 18:0.96 21:0.77 27:0.52 29:0.52 30:0.61 34:0.40 36:0.42 37:0.56 39:0.51 43:0.13 48:0.91 55:0.71 64:0.77 66:0.95 69:0.72 70:0.47 71:0.86 74:0.29 81:0.23 86:0.94 91:0.15 98:0.85 108:0.56 122:0.86 123:0.53 126:0.26 127:0.35 129:0.05 131:0.61 135:0.66 139:0.93 146:0.98 147:0.99 149:0.31 150:0.24 154:0.25 157:0.61 159:0.77 166:0.61 172:0.87 173:0.50 177:0.70 178:0.55 179:0.28 182:0.61 187:0.21 212:0.21 216:0.48 219:0.90 222:0.76 227:0.61 229:0.61 231:0.80 235:0.97 241:0.24 242:0.72 243:0.95 244:0.83 246:0.61 247:0.74 253:0.27 254:0.80 259:0.21 265:0.85 266:0.71 267:0.52 271:0.36 276:0.79 281:0.86 287:0.61 292:0.88 300:0.43 +0 1:0.62 7:0.64 8:0.62 9:0.71 11:0.42 12:0.79 17:0.57 18:0.61 21:0.30 27:0.70 29:0.52 30:0.84 32:0.67 34:0.31 36:0.86 37:0.45 39:0.51 43:0.16 55:0.52 66:0.54 69:0.32 70:0.52 71:0.86 74:0.53 77:0.70 81:0.44 86:0.63 91:0.62 96:0.71 98:0.50 108:0.66 114:0.82 117:0.86 120:0.58 122:0.77 123:0.64 124:0.69 126:0.44 127:0.52 129:0.59 131:0.98 133:0.76 135:0.78 139:0.61 140:0.80 144:0.57 145:0.44 146:0.53 147:0.59 149:0.42 150:0.48 151:0.57 153:0.48 154:0.51 155:0.55 157:0.61 159:0.72 162:0.58 164:0.56 166:0.97 172:0.78 173:0.19 175:0.75 176:0.63 177:0.38 178:0.42 179:0.35 182:0.61 187:0.87 190:0.89 192:0.55 195:0.88 201:0.59 202:0.53 208:0.54 212:0.78 215:0.72 216:0.36 220:0.87 222:0.76 227:0.61 229:0.61 230:0.64 231:0.95 232:0.93 233:0.66 235:0.42 241:0.03 242:0.76 243:0.29 244:0.83 245:0.81 246:0.61 247:0.74 248:0.56 253:0.62 254:0.80 255:0.69 256:0.45 257:0.65 259:0.21 260:0.58 265:0.52 266:0.80 267:0.73 268:0.46 271:0.36 276:0.74 277:0.87 282:0.75 285:0.56 287:0.61 290:0.82 297:0.36 300:0.84 +0 7:0.64 8:0.80 12:0.79 17:0.92 18:0.91 21:0.22 27:0.72 29:0.52 30:0.55 31:0.89 32:0.63 34:0.34 36:0.46 37:0.93 39:0.51 43:0.20 44:0.88 48:0.97 55:0.59 64:0.77 66:0.52 69:0.08 70:0.60 71:0.86 74:0.31 79:0.89 81:0.26 86:0.87 91:0.60 98:0.53 108:0.07 122:0.86 123:0.07 124:0.65 126:0.26 127:0.74 129:0.05 131:0.61 133:0.62 135:0.44 137:0.89 139:0.88 140:0.45 145:0.54 146:0.66 147:0.71 149:0.26 150:0.29 151:0.53 153:0.48 154:0.29 157:0.61 159:0.61 162:0.86 166:0.61 172:0.48 173:0.14 175:0.75 177:0.38 179:0.75 182:0.61 187:0.39 190:0.95 191:0.82 195:0.79 196:0.92 202:0.36 212:0.29 216:0.15 219:0.90 220:0.82 222:0.76 227:0.61 228:0.99 229:0.61 230:0.65 231:0.81 233:0.76 235:0.22 241:0.37 242:0.54 243:0.61 244:0.83 245:0.61 246:0.61 247:0.60 248:0.53 253:0.28 254:0.95 256:0.45 257:0.65 259:0.21 265:0.54 266:0.71 267:0.28 268:0.46 271:0.36 276:0.45 277:0.69 281:0.91 284:0.88 285:0.47 287:0.61 292:0.88 300:0.55 +0 8:0.63 11:0.55 12:0.79 17:0.05 18:0.88 21:0.54 27:0.66 29:0.52 30:0.19 34:0.89 36:0.79 37:0.33 39:0.51 43:0.33 45:0.66 55:0.59 66:0.27 69:0.95 70:0.91 71:0.86 74:0.35 81:0.28 86:0.86 91:0.31 98:0.45 101:0.52 108:0.84 120:0.55 122:0.85 123:0.83 124:0.89 126:0.26 127:0.66 129:0.05 131:0.88 133:0.89 135:0.51 139:0.81 140:0.45 144:0.57 146:0.58 147:0.50 149:0.38 150:0.31 151:0.48 153:0.48 154:0.25 157:0.61 159:0.80 162:0.86 164:0.55 166:0.83 172:0.63 173:0.94 177:0.05 179:0.39 182:0.61 187:0.87 190:0.95 202:0.36 212:0.42 215:0.58 216:0.52 220:0.96 222:0.76 227:0.61 229:0.61 231:0.94 235:0.60 241:0.15 242:0.13 243:0.18 244:0.61 245:0.77 246:0.61 247:0.95 248:0.48 253:0.31 256:0.45 257:0.84 259:0.21 260:0.55 265:0.47 266:0.87 267:0.52 268:0.46 271:0.36 276:0.76 277:0.69 283:0.67 285:0.47 287:0.61 297:0.36 300:0.84 +0 7:0.64 8:0.62 12:0.79 17:0.09 18:0.97 21:0.71 22:0.93 27:0.56 29:0.52 30:0.26 34:0.90 36:0.70 37:0.53 39:0.51 43:0.11 47:0.93 48:0.91 55:0.52 64:0.77 66:0.67 69:0.32 70:0.87 71:0.86 81:0.32 86:0.97 91:0.44 98:0.70 104:0.99 106:0.81 108:0.60 122:0.43 123:0.58 124:0.63 126:0.44 127:0.78 129:0.81 131:0.61 133:0.76 135:0.47 139:0.96 146:0.40 147:0.46 149:0.31 150:0.29 154:0.54 157:0.61 159:0.89 166:0.82 172:0.94 173:0.11 175:0.75 177:0.38 178:0.55 179:0.19 182:0.61 187:0.68 192:0.44 201:0.55 206:0.81 212:0.74 215:0.48 216:0.87 219:0.91 222:0.76 227:0.61 229:0.61 230:0.64 231:0.74 233:0.66 235:0.88 241:0.07 242:0.17 243:0.19 244:0.83 245:0.93 246:0.61 247:0.67 253:0.20 254:0.74 257:0.65 259:0.21 262:0.93 265:0.71 266:0.89 267:0.79 271:0.36 276:0.91 277:0.69 281:0.86 283:0.66 287:0.61 292:0.87 297:0.36 300:0.84 +0 1:0.58 11:0.50 12:0.79 17:0.36 18:0.85 21:0.64 27:0.69 29:0.52 30:0.33 34:0.79 36:0.23 37:0.38 39:0.51 43:0.33 45:0.66 55:0.42 66:0.36 69:0.63 70:0.69 71:0.86 74:0.36 77:0.89 81:0.36 86:0.83 91:0.07 98:0.50 101:0.52 108:0.76 114:0.63 117:0.86 122:0.86 123:0.74 124:0.60 126:0.44 127:0.44 129:0.59 131:0.61 133:0.47 135:0.63 139:0.79 144:0.57 145:0.65 146:0.48 147:0.55 149:0.29 150:0.40 154:0.48 155:0.47 157:0.85 158:0.72 159:0.46 166:0.97 172:0.41 173:0.64 175:0.75 176:0.59 177:0.38 178:0.55 179:0.68 182:0.76 187:0.98 192:0.45 195:0.68 201:0.55 208:0.54 212:0.29 215:0.53 216:0.68 222:0.76 227:0.61 229:0.61 231:0.95 232:0.98 235:0.80 241:0.02 242:0.43 243:0.49 244:0.83 245:0.71 246:0.61 247:0.74 253:0.48 257:0.65 259:0.21 265:0.51 266:0.54 267:0.76 271:0.36 276:0.44 277:0.69 282:0.72 287:0.61 290:0.63 297:0.36 300:0.55 +2 1:0.64 9:0.71 11:0.42 12:0.79 17:0.38 18:0.84 21:0.13 27:0.72 29:0.52 30:0.13 34:0.45 36:0.97 37:0.25 39:0.51 43:0.59 55:0.71 66:0.45 69:0.66 70:0.97 71:0.86 74:0.48 81:0.47 86:0.84 91:0.73 96:0.72 98:0.47 104:0.95 106:0.81 108:0.50 114:0.82 117:0.86 120:0.59 123:0.48 124:0.89 126:0.44 127:0.65 129:0.81 131:0.98 133:0.91 135:0.94 138:0.95 139:0.79 140:0.45 144:0.57 146:0.83 147:0.86 149:0.51 150:0.50 151:0.66 153:0.48 154:0.60 155:0.57 157:0.61 159:0.83 162:0.86 164:0.55 166:0.97 172:0.70 173:0.44 176:0.62 177:0.70 179:0.43 182:0.61 187:0.21 190:0.89 192:0.56 201:0.60 202:0.36 206:0.81 208:0.54 212:0.40 215:0.79 216:0.11 220:0.74 222:0.76 227:0.61 229:0.61 231:0.95 232:0.91 235:0.22 241:0.21 242:0.14 243:0.58 244:0.61 245:0.70 246:0.61 247:0.92 248:0.63 253:0.63 254:0.80 255:0.70 256:0.45 259:0.21 260:0.60 265:0.49 266:0.94 267:0.76 268:0.46 271:0.36 276:0.73 285:0.56 286:0.99 287:0.61 290:0.82 297:0.36 298:0.99 300:0.84 +0 1:0.65 11:0.42 12:0.79 17:0.82 18:0.78 21:0.47 27:0.54 29:0.52 30:0.87 34:0.93 36:0.49 37:0.60 39:0.51 43:0.75 55:0.42 64:0.77 66:0.32 69:0.32 70:0.37 71:0.86 74:0.55 81:0.51 86:0.85 91:0.01 98:0.13 108:0.25 114:0.78 122:0.76 123:0.99 124:0.60 126:0.54 127:0.94 129:0.59 131:0.61 133:0.46 135:0.85 139:0.80 144:0.57 146:0.39 147:0.45 149:0.69 150:0.50 154:0.66 155:0.50 157:0.61 159:0.97 166:0.97 172:0.45 173:0.06 176:0.70 177:0.70 178:0.55 179:0.73 182:0.61 187:0.68 192:0.47 201:0.62 208:0.64 212:0.52 215:0.63 216:0.64 222:0.76 227:0.61 229:0.61 231:0.95 235:0.68 241:0.01 242:0.45 243:0.51 244:0.61 245:0.74 246:0.61 247:0.47 253:0.58 254:0.80 259:0.21 265:0.13 266:0.54 267:0.44 271:0.36 276:0.14 287:0.61 290:0.78 297:0.36 300:0.43 +0 1:0.69 7:0.72 8:0.71 9:0.80 11:0.64 12:0.37 17:0.30 18:0.84 20:0.90 21:0.40 27:0.21 29:0.98 30:0.68 31:0.90 34:0.71 36:0.96 37:0.74 39:0.42 43:0.72 45:0.95 66:0.25 69:0.15 70:0.75 71:0.65 74:0.85 78:0.91 79:0.89 81:0.42 83:0.60 86:0.78 91:0.65 96:0.95 97:0.98 98:0.39 99:0.83 100:0.73 101:0.52 104:0.84 106:0.81 108:0.85 111:0.88 114:0.61 117:0.86 120:0.91 122:0.51 123:0.85 124:0.87 126:0.44 127:0.63 129:0.59 133:0.87 135:0.24 137:0.90 139:0.75 140:0.98 146:0.55 147:0.61 149:0.91 150:0.55 151:0.63 152:0.84 153:0.69 154:0.62 155:0.67 158:0.79 159:0.75 162:0.71 164:0.89 165:0.70 169:0.72 172:0.68 173:0.12 176:0.66 177:0.70 179:0.31 186:0.91 187:0.39 190:0.77 192:0.87 196:0.90 201:0.68 202:0.86 204:0.85 206:0.81 208:0.64 212:0.60 215:0.42 216:0.95 220:0.62 222:0.48 230:0.75 232:0.89 233:0.76 235:0.52 241:0.52 242:0.53 243:0.38 244:0.61 245:0.85 247:0.76 248:0.61 253:0.95 255:0.95 256:0.89 259:0.21 260:0.89 261:0.87 265:0.42 266:0.75 267:0.36 268:0.89 271:0.38 276:0.81 277:0.69 279:0.82 283:0.82 284:0.86 285:0.62 290:0.61 297:0.36 300:0.84 +1 1:0.69 7:0.72 8:0.87 9:0.76 11:0.64 12:0.37 17:0.67 18:0.69 21:0.64 27:0.32 29:0.98 30:0.68 32:0.69 34:0.62 36:0.88 37:0.62 39:0.42 43:0.67 45:0.92 66:0.36 69:0.63 70:0.56 71:0.65 74:0.80 77:0.89 81:0.35 83:0.60 86:0.67 91:0.57 96:0.92 97:0.94 98:0.61 99:0.95 101:0.52 104:0.92 106:0.81 108:0.69 114:0.56 117:0.86 120:0.86 122:0.51 123:0.67 124:0.70 126:0.44 127:0.49 129:0.59 133:0.66 135:0.89 139:0.65 140:0.45 145:0.86 146:0.49 147:0.56 149:0.84 150:0.53 151:0.94 153:0.90 154:0.57 155:0.58 158:0.79 159:0.53 162:0.86 164:0.74 165:0.70 172:0.61 173:0.60 176:0.66 177:0.70 179:0.45 187:0.87 190:0.77 191:0.70 192:0.59 195:0.75 201:0.68 202:0.64 204:0.85 206:0.81 208:0.54 212:0.42 215:0.38 216:0.69 222:0.48 230:0.75 232:0.94 233:0.76 235:0.88 241:0.59 242:0.57 243:0.40 244:0.61 245:0.81 247:0.60 248:0.89 253:0.91 255:0.74 256:0.68 259:0.21 260:0.83 265:0.62 266:0.71 267:0.73 268:0.72 271:0.38 276:0.68 279:0.82 282:0.77 283:0.82 285:0.56 290:0.56 297:0.36 300:0.55 +1 1:0.69 11:0.64 12:0.37 17:0.16 18:0.78 22:0.93 27:0.72 29:0.98 30:0.71 34:0.84 36:0.67 39:0.42 43:0.70 45:0.73 47:0.93 55:0.71 66:0.82 69:0.38 70:0.24 71:0.65 74:0.50 81:0.84 83:0.60 86:0.81 91:0.54 98:0.89 99:0.83 101:0.52 104:0.80 106:0.81 108:0.30 114:0.95 117:0.86 123:0.28 126:0.44 127:0.44 129:0.05 135:0.37 139:0.78 146:0.56 147:0.62 149:0.40 150:0.81 154:0.72 155:0.58 159:0.20 165:0.70 172:0.48 173:0.66 176:0.66 177:0.70 178:0.55 179:0.81 187:0.39 192:0.59 201:0.68 206:0.81 208:0.54 212:0.95 215:0.96 216:0.17 219:0.89 222:0.48 232:0.85 235:0.05 241:0.24 242:0.14 243:0.83 247:0.67 253:0.63 254:0.97 259:0.21 262:0.93 265:0.89 266:0.12 267:0.28 271:0.38 276:0.40 290:0.95 292:0.95 297:0.36 300:0.18 +1 1:0.69 6:0.87 8:0.75 9:0.80 11:0.64 12:0.37 17:0.18 20:0.89 21:0.22 27:0.09 29:0.98 30:0.89 31:0.88 34:0.74 36:0.84 37:0.91 39:0.42 43:0.67 45:0.77 66:0.75 69:0.61 70:0.20 71:0.65 74:0.55 78:0.98 79:0.88 81:0.58 83:0.60 91:0.78 96:0.91 97:0.89 98:0.75 99:0.83 100:0.89 101:0.52 104:0.58 108:0.46 111:0.95 114:0.67 120:0.86 122:0.51 123:0.44 126:0.44 127:0.24 129:0.05 132:0.97 135:0.58 137:0.88 140:0.97 145:0.38 146:0.44 147:0.51 149:0.59 150:0.60 151:0.56 152:0.94 153:0.69 154:0.57 155:0.67 158:0.78 159:0.16 162:0.59 164:0.82 165:0.70 169:0.93 172:0.32 173:0.86 175:0.75 176:0.66 177:0.38 179:0.84 186:0.98 187:0.68 189:0.83 190:0.77 191:0.73 192:0.87 201:0.68 202:0.81 208:0.64 212:0.30 215:0.51 216:0.66 220:0.62 222:0.48 232:0.77 235:0.31 238:0.82 241:0.76 242:0.63 243:0.77 244:0.61 247:0.30 248:0.55 253:0.86 255:0.95 256:0.82 257:0.65 260:0.83 261:0.94 265:0.75 266:0.27 267:0.19 268:0.81 271:0.38 276:0.20 277:0.69 279:0.82 283:0.78 284:0.86 285:0.62 290:0.67 297:0.36 300:0.18 +2 1:0.69 11:0.64 12:0.37 17:0.82 18:0.87 21:0.22 27:0.53 29:0.98 30:0.74 32:0.69 34:0.21 36:0.49 37:0.58 39:0.42 43:0.72 45:0.99 66:0.99 69:0.10 70:0.39 71:0.65 74:0.99 77:0.89 81:0.58 83:0.60 86:0.95 91:0.12 97:0.89 98:0.96 99:0.83 101:0.52 108:0.09 114:0.67 122:0.51 123:0.09 126:0.44 127:0.72 129:0.05 135:0.56 139:0.93 145:0.69 146:0.97 147:0.98 149:0.99 150:0.60 154:0.65 155:0.58 158:0.79 159:0.71 165:0.70 172:0.99 173:0.12 176:0.66 177:0.70 178:0.55 179:0.12 187:0.39 192:0.59 195:0.84 201:0.68 208:0.54 212:0.94 215:0.51 216:0.17 222:0.48 235:0.31 241:0.56 242:0.54 243:0.99 247:0.47 253:0.55 254:0.80 259:0.21 265:0.96 266:0.27 267:0.17 271:0.38 276:0.97 279:0.82 282:0.77 283:0.82 290:0.67 297:0.36 300:0.43 +0 11:0.64 12:0.37 17:0.51 18:0.63 21:0.78 27:0.45 29:0.98 30:0.73 34:0.82 36:0.25 37:0.50 39:0.42 43:0.60 45:0.85 66:0.20 69:0.74 70:0.44 71:0.65 74:0.56 86:0.74 91:0.09 98:0.32 101:0.52 108:0.82 122:0.51 123:0.55 124:0.55 127:0.35 129:0.05 133:0.43 135:0.81 139:0.70 145:0.47 146:0.41 147:0.48 149:0.56 154:0.70 159:0.47 172:0.45 173:0.73 177:0.70 178:0.55 179:0.66 187:0.68 212:0.61 216:0.77 222:0.48 235:0.95 241:0.44 242:0.58 243:0.40 245:0.68 247:0.47 253:0.37 254:0.94 259:0.21 265:0.26 266:0.47 267:0.95 271:0.38 276:0.31 300:0.43 +0 1:0.69 11:0.64 12:0.37 17:0.43 18:0.70 21:0.71 27:0.45 29:0.98 30:0.74 32:0.69 34:0.83 36:0.20 37:0.50 39:0.42 43:0.64 45:0.87 66:0.49 69:0.70 70:0.52 71:0.65 74:0.65 77:0.70 81:0.32 83:0.60 86:0.75 91:0.18 98:0.39 99:0.83 101:0.52 108:0.53 114:0.54 122:0.51 123:0.51 124:0.66 126:0.44 127:0.40 129:0.05 133:0.66 135:0.78 139:0.72 145:0.47 146:0.57 147:0.63 149:0.67 150:0.51 154:0.66 155:0.58 159:0.58 163:0.81 165:0.70 172:0.48 173:0.63 176:0.66 177:0.70 178:0.55 179:0.64 187:0.68 192:0.59 195:0.80 201:0.68 208:0.54 212:0.27 215:0.37 216:0.77 222:0.48 235:0.88 241:0.21 242:0.40 243:0.60 245:0.64 247:0.47 253:0.41 254:0.84 259:0.21 265:0.42 266:0.75 267:0.19 271:0.38 276:0.46 282:0.77 290:0.54 297:0.36 300:0.55 +1 1:0.74 6:0.79 7:0.66 8:0.58 9:0.80 11:0.64 12:0.37 17:0.34 18:0.88 20:0.88 21:0.22 22:0.93 27:0.40 29:0.98 30:0.21 31:0.88 32:0.80 34:0.85 36:0.68 37:0.36 39:0.42 43:0.13 44:0.93 45:0.66 47:0.93 48:0.91 55:0.71 64:0.77 66:0.80 69:0.17 70:0.86 71:0.65 74:0.61 76:0.85 77:0.70 78:0.97 79:0.88 81:0.79 83:0.60 86:0.90 91:0.33 96:0.71 98:0.89 99:0.95 100:0.94 101:0.52 104:0.58 108:0.14 111:0.95 114:0.71 120:0.58 122:0.86 123:0.13 126:0.54 127:0.44 129:0.05 132:0.97 135:0.75 137:0.88 139:0.92 140:0.45 145:0.65 146:0.63 147:0.68 149:0.15 150:0.85 151:0.54 152:0.93 153:0.46 154:0.94 155:0.67 159:0.24 162:0.86 164:0.65 165:0.70 169:0.93 172:0.45 173:0.44 176:0.73 177:0.70 179:0.84 186:0.97 187:0.39 192:0.87 195:0.57 196:0.92 201:0.93 202:0.49 208:0.64 212:0.37 215:0.51 216:0.37 219:0.89 220:0.82 222:0.48 230:0.69 232:0.77 233:0.76 235:0.60 241:0.44 242:0.76 243:0.82 247:0.35 248:0.56 253:0.56 254:0.74 255:0.95 256:0.58 260:0.58 261:0.95 262:0.93 265:0.89 266:0.27 267:0.44 268:0.58 271:0.38 276:0.35 281:0.91 282:0.88 284:0.91 285:0.62 290:0.71 292:0.91 297:0.36 300:0.55 +1 1:0.69 7:0.72 8:0.83 9:0.76 11:0.64 12:0.37 17:0.61 18:0.69 21:0.30 27:0.19 29:0.98 30:0.75 32:0.78 34:0.24 36:1.00 37:0.73 39:0.42 43:0.69 44:0.93 45:0.98 48:0.91 66:0.23 69:0.50 70:0.43 71:0.65 74:0.93 77:0.70 81:0.50 83:0.60 86:0.81 91:0.33 96:0.76 97:0.94 98:0.65 99:0.83 101:0.52 104:0.80 108:0.82 114:0.63 120:0.64 122:0.51 123:0.37 124:0.55 126:0.44 127:0.69 129:0.05 133:0.43 135:0.99 139:0.85 140:0.45 145:0.63 146:0.80 147:0.83 149:0.94 150:0.57 151:0.64 153:0.46 154:0.73 155:0.58 158:0.78 159:0.64 162:0.73 164:0.68 165:0.70 172:0.89 173:0.42 176:0.66 177:0.70 179:0.23 187:0.68 190:0.77 192:0.59 195:0.79 201:0.68 202:0.63 204:0.85 208:0.54 212:0.89 215:0.44 216:0.81 220:0.87 222:0.48 230:0.74 232:0.85 233:0.73 235:0.42 241:0.55 242:0.61 243:0.43 245:0.97 247:0.60 248:0.66 253:0.76 254:0.86 255:0.74 256:0.64 259:0.21 260:0.64 265:0.62 266:0.54 267:0.87 268:0.66 271:0.38 276:0.87 279:0.82 281:0.91 282:0.86 283:0.78 285:0.56 290:0.63 297:0.36 300:0.70 +1 1:0.74 6:0.79 7:0.66 8:0.57 11:0.64 12:0.37 17:0.04 18:0.86 20:0.87 21:0.22 22:0.93 27:0.40 29:0.98 30:0.45 32:0.79 34:0.50 36:0.66 37:0.36 39:0.42 43:0.12 44:0.93 45:0.73 47:0.93 48:0.91 55:0.52 64:0.77 66:0.63 69:0.17 70:0.61 71:0.65 74:0.68 76:0.85 77:0.70 78:0.98 81:0.79 83:0.60 86:0.94 91:0.35 98:0.37 99:0.95 100:0.94 101:0.52 104:0.58 108:0.14 111:0.96 114:0.71 122:0.57 123:0.13 124:0.49 126:0.54 127:0.51 129:0.05 132:0.97 133:0.59 135:0.76 139:0.94 145:0.65 146:0.35 147:0.42 149:0.08 150:0.85 152:0.93 154:0.94 155:0.67 159:0.36 165:0.70 169:0.93 172:0.41 173:0.31 176:0.73 177:0.70 178:0.55 179:0.83 186:0.98 187:0.39 192:0.87 195:0.63 201:0.93 208:0.64 212:0.84 215:0.51 216:0.37 219:0.89 222:0.48 230:0.69 232:0.77 233:0.76 235:0.60 241:0.21 242:0.33 243:0.68 245:0.47 247:0.47 253:0.53 254:0.74 261:0.95 262:0.93 265:0.39 266:0.23 267:0.65 271:0.38 276:0.33 281:0.91 282:0.87 290:0.71 292:0.91 297:0.36 300:0.43 +1 1:0.74 7:0.81 11:0.83 12:0.37 17:0.88 18:0.89 20:0.85 21:0.30 22:0.93 27:0.62 29:0.98 30:0.75 32:0.80 34:0.97 36:0.90 37:0.59 39:0.42 43:0.95 44:0.93 45:0.94 47:0.93 64:0.77 66:0.96 69:0.27 70:0.52 71:0.65 74:0.93 77:0.89 78:0.96 81:0.72 83:0.91 85:0.88 86:0.95 91:0.20 97:0.89 98:0.92 99:0.83 100:0.92 101:0.87 104:0.83 106:0.81 108:0.21 111:0.94 114:0.65 117:0.86 121:0.97 122:0.51 123:0.20 126:0.54 127:0.54 129:0.05 135:0.93 139:0.96 145:0.88 146:0.93 147:0.94 149:0.96 150:0.83 152:0.81 154:0.95 155:0.67 158:0.79 159:0.57 165:0.93 169:0.89 172:0.91 173:0.25 176:0.73 177:0.70 178:0.55 179:0.28 186:0.96 187:0.95 192:0.87 195:0.76 201:0.93 204:0.89 206:0.81 208:0.64 212:0.74 215:0.44 216:0.31 222:0.48 230:0.87 232:0.88 233:0.76 235:0.68 241:0.30 242:0.41 243:0.96 247:0.82 253:0.53 259:0.21 261:0.88 262:0.93 265:0.92 266:0.38 267:0.44 271:0.38 276:0.83 279:0.82 281:0.91 282:0.88 283:0.82 290:0.65 297:0.36 299:0.88 300:0.55 +1 1:0.74 7:0.81 11:0.83 12:0.37 17:0.30 18:0.89 21:0.13 27:0.32 29:0.98 30:0.54 32:0.80 34:0.44 36:0.53 37:0.80 39:0.42 43:0.94 44:0.93 45:0.81 48:0.97 60:0.87 64:0.77 66:0.88 69:0.27 70:0.48 71:0.65 74:0.85 77:0.70 81:0.80 83:0.91 85:0.72 86:0.97 91:0.05 97:0.89 98:0.55 101:0.87 108:0.21 114:0.79 121:0.90 122:0.57 123:0.20 126:0.54 127:0.16 129:0.05 135:0.98 139:0.98 145:0.76 146:0.59 147:0.64 149:0.70 150:0.87 154:0.94 155:0.67 158:0.92 159:0.21 165:0.93 172:0.67 173:0.31 176:0.73 177:0.70 178:0.55 179:0.26 187:0.39 192:0.87 195:0.67 201:0.93 204:0.89 208:0.64 212:0.80 215:0.61 216:0.86 219:0.95 222:0.48 230:0.87 233:0.76 235:0.31 241:0.05 242:0.33 243:0.89 247:0.55 253:0.57 259:0.21 265:0.57 266:0.38 267:0.94 271:0.38 276:0.54 279:0.86 281:0.91 282:0.88 283:0.82 290:0.79 292:0.95 297:0.36 299:0.88 300:0.33 +0 6:0.78 11:0.83 12:0.37 17:0.89 18:0.85 20:0.94 21:0.78 22:0.93 27:0.30 29:0.98 30:0.68 34:0.99 36:0.32 37:0.65 39:0.42 43:0.86 45:0.73 47:0.93 60:0.87 66:0.52 69:0.54 70:0.52 71:0.65 74:0.71 78:0.97 83:0.91 85:0.80 86:0.90 91:0.12 98:0.44 100:0.90 101:0.87 104:0.81 106:0.81 108:0.41 111:0.94 117:0.86 122:0.86 123:0.40 124:0.52 127:0.42 129:0.59 133:0.46 135:0.99 139:0.91 146:0.51 147:0.57 149:0.78 152:0.91 154:0.83 155:0.67 158:0.91 159:0.46 169:0.87 172:0.48 173:0.54 177:0.70 178:0.55 179:0.67 186:0.97 187:0.39 189:0.81 192:0.87 206:0.81 208:0.64 212:0.40 216:0.61 219:0.95 222:0.48 232:0.87 238:0.82 241:0.59 242:0.31 243:0.61 245:0.69 247:0.60 253:0.42 259:0.21 261:0.93 262:0.93 265:0.46 266:0.63 267:0.59 271:0.38 276:0.42 279:0.86 283:0.78 292:0.91 300:0.43 +2 1:0.74 6:0.91 7:0.81 8:0.79 11:0.83 12:0.37 17:0.75 18:0.87 20:0.86 21:0.59 27:0.32 29:0.98 30:0.76 32:0.80 34:0.91 36:0.90 37:0.62 39:0.42 43:0.82 44:0.93 45:0.93 64:0.77 66:0.96 69:0.63 70:0.37 71:0.65 74:0.94 77:0.89 78:0.97 81:0.51 83:0.91 85:0.90 86:0.94 91:0.25 97:0.89 98:0.90 99:0.83 100:0.96 101:0.87 104:0.92 106:0.81 108:0.48 111:0.96 114:0.58 117:0.86 121:0.97 122:0.51 123:0.46 126:0.54 127:0.46 129:0.05 135:0.94 139:0.96 145:0.85 146:0.87 147:0.89 149:0.95 150:0.73 152:0.87 154:0.78 155:0.67 158:0.79 159:0.44 165:0.93 169:0.92 172:0.89 173:0.66 176:0.73 177:0.70 178:0.55 179:0.28 186:0.96 187:0.95 192:0.87 195:0.69 201:0.93 204:0.89 206:0.81 208:0.64 212:0.78 215:0.39 216:0.69 219:0.89 222:0.48 230:0.87 232:0.94 233:0.76 235:0.88 241:0.46 242:0.36 243:0.96 247:0.60 253:0.49 259:0.21 261:0.92 265:0.90 266:0.27 267:0.65 271:0.38 276:0.81 279:0.82 281:0.91 282:0.88 283:0.82 290:0.58 292:0.95 297:0.36 299:0.97 300:0.33 +2 1:0.69 7:0.72 9:0.76 11:0.64 12:0.37 17:0.28 18:0.80 21:0.47 27:0.52 29:0.98 30:0.74 34:0.31 36:0.57 37:0.56 39:0.42 43:0.64 45:0.99 66:0.99 69:0.70 70:0.46 71:0.65 74:0.95 81:0.38 83:0.60 86:0.91 91:0.14 96:0.88 98:0.83 99:0.83 101:0.52 104:0.98 108:0.54 114:0.59 120:0.80 122:0.51 123:0.51 126:0.44 127:0.32 129:0.05 135:0.34 139:0.88 140:0.45 146:0.97 147:0.98 149:0.93 150:0.54 151:0.85 153:0.77 154:0.65 155:0.58 159:0.72 162:0.86 164:0.65 165:0.70 172:0.97 173:0.51 176:0.66 177:0.70 179:0.13 187:0.21 190:0.77 192:0.59 201:0.68 202:0.54 204:0.85 208:0.54 212:0.58 215:0.41 216:0.48 222:0.48 230:0.75 232:0.99 233:0.76 235:0.60 241:0.32 242:0.54 243:0.99 247:0.60 248:0.84 253:0.90 254:0.74 255:0.74 256:0.58 259:0.21 260:0.77 265:0.83 266:0.47 267:0.79 268:0.63 271:0.38 276:0.93 285:0.56 290:0.59 297:0.36 300:0.55 +2 1:0.74 7:0.81 11:0.64 12:0.37 17:0.47 21:0.54 22:0.93 27:0.02 29:0.98 30:0.89 32:0.69 34:0.84 36:0.49 37:0.99 39:0.42 43:0.64 45:0.77 47:0.93 48:0.97 64:0.77 66:0.75 69:0.70 70:0.20 71:0.65 74:0.69 77:0.70 81:0.51 83:0.91 91:0.64 97:0.89 98:0.30 101:0.52 104:0.95 106:0.81 108:0.53 114:0.59 117:0.86 121:0.97 122:0.51 123:0.51 126:0.54 127:0.12 129:0.05 135:0.86 139:0.74 145:0.54 146:0.26 147:0.32 149:0.57 150:0.73 154:0.54 155:0.67 158:0.79 159:0.11 165:0.93 172:0.32 173:0.90 175:0.75 176:0.73 177:0.38 178:0.55 179:0.29 187:0.68 192:0.87 195:0.55 201:0.93 204:0.89 206:0.81 208:0.64 212:0.84 215:0.39 216:0.85 222:0.48 230:0.86 232:0.96 233:0.73 235:0.74 241:0.47 242:0.63 243:0.77 244:0.61 247:0.30 253:0.45 257:0.65 259:0.21 262:0.93 265:0.33 266:0.17 267:0.52 271:0.38 276:0.25 277:0.69 279:0.82 281:0.89 282:0.77 283:0.82 290:0.59 297:0.36 300:0.18 +1 1:0.69 7:0.72 9:0.80 11:0.64 12:0.37 17:0.59 20:0.89 21:0.47 27:0.31 29:0.98 30:0.85 31:0.94 32:0.69 34:0.75 36:0.88 37:0.55 39:0.42 41:0.82 43:0.70 45:0.91 66:0.51 69:0.47 70:0.41 71:0.65 74:0.73 77:0.89 78:0.92 79:0.93 81:0.38 83:0.91 91:0.22 96:0.80 98:0.35 99:0.83 100:0.89 101:0.52 104:0.97 106:0.81 108:0.83 111:0.90 114:0.59 117:0.86 120:0.69 122:0.51 123:0.82 124:0.65 126:0.44 127:0.53 129:0.81 133:0.67 135:0.99 137:0.94 140:0.45 145:0.95 146:0.33 147:0.39 149:0.80 150:0.54 151:0.87 152:0.93 153:0.48 154:0.59 155:0.67 159:0.59 162:0.86 164:0.57 165:0.70 169:0.83 172:0.59 173:0.39 175:0.75 176:0.66 177:0.38 179:0.57 186:0.92 187:0.87 192:0.87 195:0.77 201:0.68 202:0.36 204:0.85 206:0.81 208:0.64 212:0.61 215:0.41 216:0.64 222:0.48 230:0.74 232:0.98 233:0.73 235:0.60 241:0.27 242:0.63 243:0.36 244:0.61 245:0.72 247:0.30 248:0.77 253:0.82 255:0.95 256:0.45 257:0.65 259:0.21 260:0.70 261:0.90 265:0.37 266:0.63 267:0.52 268:0.46 271:0.38 276:0.53 277:0.69 282:0.77 284:0.89 285:0.62 290:0.59 297:0.36 300:0.43 +1 1:0.74 6:0.81 7:0.81 8:0.64 12:0.06 17:0.93 20:0.81 27:0.72 28:0.64 29:0.71 32:0.80 34:0.40 36:0.78 37:0.56 39:0.94 44:0.93 64:1.00 71:0.91 74:0.59 77:0.89 78:0.84 81:0.91 83:0.91 91:0.86 100:0.85 104:0.54 111:0.83 114:0.98 121:0.97 126:0.54 135:0.32 139:0.82 145:0.45 150:0.95 152:0.78 155:0.67 161:0.73 165:0.93 167:0.86 169:0.83 175:0.97 176:0.73 178:0.55 181:0.78 186:0.84 189:0.83 191:0.88 192:0.87 195:0.52 201:0.93 204:0.89 208:0.64 215:0.96 222:0.21 230:0.87 233:0.76 235:0.12 238:0.87 241:0.80 244:0.93 253:0.66 257:0.93 259:0.21 261:0.81 267:0.19 277:0.95 281:0.91 282:0.88 290:0.98 297:0.99 299:0.97 +4 6:0.98 7:0.66 8:0.95 9:0.76 12:0.06 17:0.20 18:0.85 20:0.79 21:0.54 22:0.93 27:0.06 28:0.64 29:0.71 30:0.41 31:0.94 32:0.68 34:0.76 36:0.36 37:0.92 39:0.94 43:0.17 44:0.90 47:0.93 48:0.91 55:0.71 64:0.77 66:0.86 69:0.37 70:0.60 71:0.91 74:0.32 78:0.90 79:0.95 81:0.32 83:0.60 86:0.79 91:0.81 96:0.84 98:0.93 100:0.98 108:0.29 111:0.96 117:0.86 120:0.76 123:0.28 126:0.44 127:0.58 129:0.05 135:0.37 137:0.94 139:0.81 140:0.80 145:0.72 146:0.81 147:0.84 149:0.26 150:0.26 151:0.86 152:0.97 153:0.78 154:0.11 155:0.58 159:0.37 161:0.73 162:0.69 164:0.77 167:0.84 169:0.97 172:0.59 173:0.47 177:0.70 178:0.42 179:0.74 181:0.78 186:0.90 187:0.68 189:1.00 190:0.77 191:0.94 192:0.59 195:0.62 196:0.94 201:0.68 202:0.83 208:0.54 212:0.54 215:0.39 216:0.75 220:0.96 222:0.21 230:0.69 233:0.73 235:0.68 238:0.98 241:0.78 242:0.33 243:0.86 244:0.83 247:0.67 248:0.77 253:0.73 255:0.79 256:0.85 259:0.21 260:0.68 261:0.98 262:0.93 265:0.93 266:0.47 267:0.23 268:0.85 276:0.49 281:0.89 284:0.97 285:0.62 297:0.36 300:0.43 +1 1:0.74 7:0.66 9:0.76 12:0.06 17:0.32 18:0.89 21:0.71 22:0.93 27:0.06 28:0.64 29:0.71 30:0.21 31:0.93 32:0.79 34:1.00 36:0.26 37:0.92 39:0.94 43:0.17 44:0.93 47:0.93 48:0.91 55:0.71 64:0.77 66:0.45 69:0.41 70:0.87 71:0.91 74:0.60 76:0.85 77:0.70 79:0.93 81:0.38 83:0.60 86:0.88 91:0.20 96:0.80 98:0.63 108:0.76 114:0.55 117:0.86 123:0.75 124:0.58 126:0.54 127:0.83 129:0.05 133:0.43 135:0.70 137:0.93 139:0.90 140:0.45 145:0.59 146:0.65 147:0.70 149:0.22 150:0.62 151:0.81 153:0.48 154:0.48 155:0.67 159:0.53 161:0.73 162:0.86 167:0.70 172:0.54 173:0.41 176:0.73 177:0.70 179:0.64 181:0.78 187:0.68 190:0.77 192:0.87 195:0.72 196:0.95 201:0.93 202:0.36 204:0.85 208:0.64 212:0.49 215:0.37 216:0.75 222:0.21 230:0.69 233:0.76 235:0.95 241:0.65 242:0.18 243:0.47 244:0.61 245:0.79 247:0.82 248:0.73 253:0.73 254:0.80 255:0.74 256:0.45 259:0.21 262:0.93 265:0.64 266:0.71 267:0.52 268:0.46 276:0.58 277:0.69 281:0.88 282:0.87 284:0.87 285:0.56 290:0.55 297:0.36 300:0.70 +1 6:0.95 7:0.66 8:0.97 12:0.06 17:0.16 18:0.79 20:0.84 21:0.54 27:0.05 28:0.64 29:0.71 30:0.21 31:0.94 34:0.45 36:0.65 37:0.94 39:0.94 43:0.16 44:0.87 48:0.91 55:0.52 64:0.77 66:0.16 69:0.46 70:0.82 71:0.91 78:0.83 79:0.95 81:0.32 86:0.74 91:0.93 96:0.68 98:0.42 100:0.88 108:0.75 111:0.84 120:0.96 123:0.34 124:0.70 126:0.44 127:0.79 129:0.05 133:0.62 135:0.18 137:0.94 139:0.77 140:0.97 145:0.59 146:0.40 147:0.46 149:0.25 150:0.26 151:0.81 152:0.80 153:0.48 154:0.11 159:0.41 161:0.73 162:0.56 164:0.92 167:0.89 169:0.86 172:0.37 173:0.55 175:0.75 177:0.38 179:0.80 181:0.78 186:0.83 189:0.96 190:0.77 191:0.98 192:0.51 195:0.65 196:0.93 201:0.68 202:0.94 212:0.49 215:0.39 216:0.55 222:0.21 230:0.69 233:0.76 235:0.68 238:0.93 241:0.96 242:0.61 243:0.37 244:0.83 245:0.60 247:0.55 248:0.79 253:0.32 255:0.74 256:0.93 257:0.65 259:0.21 260:0.94 261:0.83 265:0.30 266:0.63 267:0.73 268:0.94 276:0.43 277:0.69 281:0.89 284:0.94 285:0.62 297:0.36 300:0.55 +2 7:0.66 8:0.97 12:0.06 17:0.24 18:0.92 21:0.64 27:0.04 28:0.64 29:0.71 30:0.45 31:0.90 34:0.83 36:0.42 37:0.94 39:0.94 43:0.13 48:0.91 55:0.71 64:0.77 66:0.93 69:0.55 70:0.63 71:0.91 74:0.45 79:0.91 81:0.31 86:0.92 91:0.27 98:0.94 108:0.42 120:0.64 123:0.40 126:0.44 127:0.63 129:0.05 135:0.39 137:0.90 139:0.89 140:0.45 146:0.87 147:0.89 149:0.25 150:0.25 151:0.48 153:0.48 154:0.51 158:0.74 159:0.45 161:0.73 162:0.78 164:0.71 167:0.79 172:0.81 173:0.59 177:0.70 179:0.47 181:0.78 187:0.68 190:0.89 191:0.94 192:0.51 196:0.93 201:0.68 202:0.68 212:0.80 215:0.38 216:0.87 220:0.93 222:0.21 230:0.69 233:0.73 235:0.84 241:0.75 242:0.21 243:0.93 247:0.84 248:0.48 253:0.34 254:0.99 255:0.74 256:0.71 259:0.21 260:0.62 265:0.94 266:0.38 267:0.36 268:0.73 276:0.71 281:0.89 283:0.78 284:0.92 285:0.56 297:0.36 300:0.55 +1 1:0.74 11:0.64 12:0.06 17:0.36 18:0.89 21:0.40 27:0.45 28:0.64 29:0.71 30:0.09 34:0.89 36:0.17 37:0.50 39:0.94 43:0.63 45:0.81 64:0.77 66:0.53 69:0.69 70:0.99 71:0.91 74:0.68 81:0.51 83:0.60 86:0.89 91:0.29 97:0.89 98:0.52 99:0.83 101:0.52 108:0.52 114:0.62 122:0.86 123:0.50 124:0.64 126:0.54 127:0.51 129:0.05 133:0.60 135:0.89 139:0.90 145:0.47 146:0.73 147:0.77 149:0.56 150:0.69 154:0.76 155:0.67 158:0.91 159:0.66 161:0.73 165:0.70 167:0.55 172:0.63 173:0.59 176:0.73 177:0.70 178:0.55 179:0.53 181:0.78 187:0.68 192:0.87 201:0.93 208:0.64 212:0.29 215:0.42 216:0.77 219:0.95 222:0.21 235:0.60 241:0.24 242:0.30 243:0.62 245:0.74 247:0.88 253:0.48 254:0.74 259:0.21 265:0.53 266:0.80 267:0.44 276:0.61 279:0.86 283:0.77 290:0.62 292:0.91 297:0.36 300:0.94 +3 6:0.96 8:0.93 12:0.06 17:0.38 18:0.70 20:0.82 21:0.68 22:0.93 27:0.06 28:0.64 29:0.71 30:0.62 31:0.91 32:0.64 34:0.71 36:0.63 37:0.92 39:0.94 43:0.15 47:0.93 48:0.97 55:0.52 64:0.77 66:0.64 69:0.57 70:0.40 71:0.91 78:0.86 79:0.92 81:0.29 86:0.68 91:0.82 96:0.72 98:0.51 100:0.91 108:0.68 111:0.86 120:0.83 123:0.66 124:0.44 126:0.44 127:0.46 129:0.59 133:0.47 135:0.65 137:0.91 139:0.73 140:0.80 145:0.69 146:0.28 147:0.34 149:0.21 150:0.24 151:0.53 152:0.97 153:0.48 154:0.11 159:0.34 161:0.73 162:0.59 164:0.79 167:0.83 169:0.96 172:0.45 173:0.68 175:0.75 177:0.38 178:0.42 179:0.79 181:0.78 186:0.86 187:0.68 189:0.98 190:0.89 191:0.95 192:0.51 195:0.62 196:0.90 201:0.68 202:0.85 212:0.78 215:0.37 216:0.75 219:0.89 220:0.87 222:0.21 235:0.84 238:0.94 241:0.77 242:0.60 243:0.26 244:0.83 245:0.55 247:0.39 248:0.55 253:0.45 255:0.74 256:0.84 257:0.65 259:0.21 260:0.77 261:0.98 262:0.93 265:0.53 266:0.27 267:0.19 268:0.85 276:0.34 277:0.69 281:0.89 284:0.97 285:0.62 292:0.91 297:0.36 300:0.33 +4 6:0.98 7:0.66 8:0.93 9:0.80 12:0.06 17:0.24 18:0.85 20:0.94 21:0.54 22:0.93 27:0.06 28:0.64 29:0.71 30:0.41 31:0.98 32:0.68 34:0.78 36:0.35 37:0.92 39:0.94 41:0.88 43:0.17 44:0.90 47:0.93 48:0.91 55:0.71 64:0.77 66:0.86 69:0.37 70:0.60 71:0.91 74:0.38 78:0.94 79:0.98 81:0.32 83:0.91 86:0.79 91:0.84 96:0.94 98:0.93 100:0.99 108:0.29 111:0.98 117:0.86 120:0.87 123:0.28 126:0.44 127:0.58 129:0.05 135:0.37 137:0.98 139:0.81 140:0.96 145:0.72 146:0.81 147:0.84 149:0.26 150:0.26 151:0.87 152:0.95 153:0.83 154:0.11 155:0.67 158:0.75 159:0.37 161:0.73 162:0.59 164:0.86 167:0.80 169:0.97 172:0.59 173:0.47 177:0.70 178:0.42 179:0.75 181:0.78 186:0.94 187:0.39 189:0.98 191:0.93 192:0.87 195:0.62 196:0.97 201:0.68 202:0.89 208:0.64 212:0.54 215:0.39 216:0.75 220:0.74 222:0.21 230:0.69 233:0.73 235:0.68 238:0.98 241:0.76 242:0.33 243:0.86 244:0.83 247:0.67 248:0.88 253:0.89 255:0.95 256:0.89 259:0.21 260:0.86 261:0.98 262:0.93 265:0.93 266:0.47 267:0.18 268:0.90 276:0.49 281:0.89 284:0.98 285:0.62 297:0.36 300:0.43 +2 1:0.74 6:0.84 7:0.81 8:0.68 9:0.80 11:0.83 12:0.06 17:0.82 18:0.96 20:0.92 21:0.22 27:0.29 28:0.64 29:0.71 30:0.15 31:0.90 32:0.80 34:0.67 36:0.57 37:0.69 39:0.94 41:0.90 43:0.89 44:0.93 45:0.66 48:0.91 64:0.77 66:0.29 69:0.51 70:0.92 71:0.91 74:0.85 76:0.85 77:0.70 78:0.88 79:0.89 81:0.67 83:0.91 85:0.90 86:0.99 91:0.70 96:0.73 98:0.62 100:0.91 101:0.97 108:0.78 111:0.88 114:0.71 120:0.61 121:0.90 122:0.57 123:0.56 124:0.70 126:0.54 127:0.66 129:0.81 133:0.67 135:0.89 137:0.90 139:0.99 140:0.45 145:0.80 146:0.29 147:0.35 149:0.90 150:0.80 151:0.59 152:0.87 153:0.83 154:0.87 155:0.67 159:0.54 161:0.73 162:0.86 164:0.74 165:0.93 167:0.56 169:0.88 172:0.75 173:0.48 176:0.73 177:0.70 179:0.31 181:0.78 186:0.88 187:0.87 189:0.86 191:0.73 192:0.87 195:0.75 196:0.95 201:0.93 202:0.62 204:0.89 208:0.64 212:0.85 215:0.51 216:0.61 220:0.82 222:0.21 230:0.87 233:0.76 235:0.52 238:0.91 241:0.30 242:0.16 243:0.20 245:0.91 247:0.88 248:0.61 253:0.68 255:0.95 256:0.68 259:0.21 260:0.61 261:0.91 265:0.60 266:0.54 267:0.73 268:0.71 276:0.82 281:0.91 282:0.88 283:0.78 284:0.95 285:0.62 290:0.71 297:0.36 299:0.88 300:0.70 +1 6:0.82 7:0.66 8:0.73 12:0.06 17:0.43 18:0.83 20:0.85 21:0.47 27:0.15 28:0.64 29:0.71 30:0.62 31:0.93 32:0.69 34:0.44 36:0.64 37:0.79 39:0.94 43:0.17 44:0.90 48:0.97 55:0.59 64:0.77 66:0.61 69:0.37 70:0.47 71:0.91 78:0.85 79:0.94 81:0.37 86:0.83 91:0.90 98:0.79 100:0.87 108:0.29 111:0.84 120:0.85 123:0.28 124:0.48 126:0.44 127:0.76 129:0.05 133:0.39 135:0.49 137:0.93 139:0.84 140:0.80 145:0.74 146:0.61 147:0.66 149:0.24 150:0.30 151:0.65 152:0.81 153:0.48 154:0.51 159:0.31 161:0.73 162:0.59 164:0.86 167:0.89 169:0.85 172:0.54 173:0.55 175:0.75 177:0.38 178:0.42 179:0.73 181:0.78 186:0.85 189:0.84 190:0.89 191:0.91 192:0.51 195:0.58 196:0.94 201:0.68 202:0.86 212:0.71 215:0.41 216:0.33 220:0.93 222:0.21 230:0.69 233:0.76 235:0.68 238:0.89 241:0.94 242:0.40 243:0.68 244:0.61 245:0.68 247:0.60 248:0.75 253:0.20 254:0.80 255:0.74 256:0.86 257:0.65 259:0.21 260:0.80 261:0.85 265:0.79 266:0.27 267:0.88 268:0.86 276:0.51 277:0.69 281:0.91 284:0.97 285:0.56 297:0.36 300:0.43 +1 6:0.98 7:0.66 8:0.88 12:0.06 17:0.82 18:0.85 20:0.79 21:0.54 22:0.93 27:0.06 28:0.64 29:0.71 30:0.45 31:0.87 32:0.68 34:0.83 36:0.61 37:0.92 39:0.94 43:0.15 44:0.90 47:0.93 48:0.97 55:0.92 64:0.77 66:0.97 69:0.55 70:0.43 71:0.91 74:0.67 78:0.91 79:0.87 81:0.32 86:0.85 91:0.78 96:0.71 98:0.99 100:0.98 108:0.42 111:0.96 120:0.55 122:0.77 123:0.41 126:0.44 127:0.91 129:0.05 135:0.66 137:0.87 139:0.85 140:0.45 145:0.54 146:0.99 147:1.00 149:0.30 150:0.26 151:0.50 152:0.95 153:0.46 154:0.29 158:0.74 159:0.87 161:0.73 162:0.55 163:0.94 164:0.70 167:0.74 169:0.97 172:0.91 173:0.28 177:0.70 179:0.32 181:0.78 186:0.91 187:0.68 189:0.97 190:0.89 191:0.92 192:0.51 195:0.96 196:0.89 201:0.68 202:0.77 212:0.35 215:0.39 216:0.75 220:0.91 222:0.21 230:0.69 233:0.76 235:0.68 238:0.98 241:0.70 242:0.30 243:0.97 244:0.61 247:0.92 248:0.50 253:0.45 254:0.93 255:0.74 256:0.73 259:0.21 260:0.55 261:0.98 262:0.93 265:0.99 266:0.54 267:0.69 268:0.74 276:0.84 281:0.89 284:0.93 285:0.62 297:0.36 300:0.33 +1 1:0.74 11:0.57 12:0.06 17:0.45 18:0.83 21:0.13 27:0.45 28:0.64 29:0.71 30:0.58 34:0.95 36:0.20 37:0.50 39:0.94 43:0.82 60:0.87 64:0.77 66:0.20 69:0.68 70:0.79 71:0.91 74:0.72 81:0.70 83:0.91 85:0.85 86:0.88 91:0.29 98:0.40 101:0.87 108:0.52 114:0.79 122:0.57 123:0.85 124:0.79 126:0.54 127:0.44 129:0.59 133:0.74 135:0.87 139:0.89 145:0.41 146:0.40 147:0.46 149:0.81 150:0.82 154:0.77 155:0.67 158:0.92 159:0.65 161:0.73 163:0.90 165:0.93 167:0.60 172:0.37 173:0.58 176:0.73 177:0.70 178:0.55 179:0.64 181:0.78 187:0.68 192:0.87 201:0.93 208:0.64 212:0.23 215:0.61 216:0.77 219:0.95 222:0.21 235:0.31 241:0.46 242:0.24 243:0.46 245:0.63 247:0.79 253:0.56 259:0.21 265:0.26 266:0.80 267:0.59 276:0.50 277:0.69 279:0.86 283:0.82 290:0.79 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.89 8:0.78 9:0.80 11:0.83 12:0.06 17:0.81 18:0.98 20:0.91 21:0.40 27:0.49 28:0.64 29:0.71 30:0.15 31:0.96 32:0.68 34:0.18 36:0.26 37:0.72 39:0.94 41:0.94 43:0.78 44:0.90 45:0.95 55:0.71 60:0.87 64:0.77 66:0.13 69:0.77 70:0.92 71:0.91 74:0.87 78:0.83 79:0.96 81:0.53 83:0.91 85:0.95 86:0.99 91:0.61 96:0.87 97:0.94 98:0.45 100:0.84 101:0.97 104:0.86 108:0.60 111:0.82 114:0.62 120:0.81 122:0.86 123:0.58 124:0.97 126:0.54 127:0.97 129:0.99 133:0.97 135:1.00 137:0.96 139:0.99 140:0.45 145:0.69 146:0.99 147:0.05 149:0.90 150:0.74 151:0.94 152:0.85 153:0.82 154:0.71 155:0.67 158:0.92 159:0.97 161:0.73 162:0.73 164:0.81 165:0.93 167:0.57 169:0.82 172:0.95 173:0.25 176:0.73 177:0.05 179:0.10 181:0.78 186:0.83 187:0.21 189:0.90 191:0.70 192:0.87 195:1.00 196:0.99 201:0.93 202:0.77 208:0.64 212:0.48 215:0.42 216:0.21 219:0.95 220:0.91 222:0.21 235:0.60 238:0.88 241:0.32 242:0.18 243:0.05 245:0.99 247:0.97 248:0.85 253:0.90 255:0.95 256:0.80 257:0.84 259:0.21 260:0.80 261:0.86 265:0.47 266:0.95 267:0.85 268:0.81 276:0.99 279:0.86 283:0.82 284:0.97 285:0.62 290:0.62 292:0.95 297:0.36 300:0.94 +1 6:0.89 8:0.96 9:0.76 12:0.06 17:0.22 20:0.90 21:0.78 27:0.06 28:0.64 29:0.71 31:0.93 34:0.59 36:0.26 37:0.93 39:0.94 71:0.91 74:0.38 78:0.78 79:0.94 91:0.91 96:0.76 100:0.82 111:0.78 120:0.92 135:0.78 137:0.93 140:0.80 151:0.76 152:0.88 153:0.48 155:0.58 158:0.78 161:0.73 162:0.59 164:0.90 167:0.88 169:0.79 175:0.97 181:0.78 186:0.78 189:0.92 190:0.77 191:0.94 192:0.59 202:0.91 208:0.54 216:0.59 222:0.21 238:0.92 241:0.87 244:0.93 248:0.75 253:0.51 255:0.79 256:0.92 257:0.93 259:0.21 260:0.88 261:0.83 267:0.88 268:0.91 277:0.95 279:0.82 283:0.78 284:0.97 285:0.62 +1 6:0.98 8:0.98 12:0.06 17:0.16 18:0.68 20:0.76 21:0.64 22:0.93 27:0.06 28:0.64 29:0.71 30:0.21 31:0.91 32:0.68 34:0.89 36:0.38 37:0.92 39:0.94 43:0.15 44:0.90 47:0.93 48:0.97 55:0.52 64:0.77 66:0.69 69:0.82 70:0.67 71:0.91 78:0.90 79:0.92 81:0.30 86:0.70 91:0.90 98:0.54 100:0.95 108:0.67 111:0.92 120:0.95 123:0.65 124:0.41 126:0.44 127:0.43 129:0.05 133:0.39 135:0.32 137:0.91 139:0.76 140:0.94 145:0.76 146:0.48 147:0.05 149:0.16 150:0.24 151:0.59 152:0.97 153:0.48 154:0.30 159:0.34 161:0.73 162:0.55 164:0.91 167:0.84 169:0.97 172:0.41 173:0.93 175:0.75 177:0.05 178:0.48 179:0.84 181:0.78 186:0.90 187:0.68 190:0.89 191:0.97 192:0.51 195:0.61 196:0.91 201:0.68 202:0.95 212:0.37 215:0.38 216:0.75 222:0.21 235:0.80 241:0.78 242:0.58 243:0.18 244:0.61 245:0.46 247:0.39 248:0.58 253:0.20 254:0.97 255:0.74 256:0.94 257:0.84 259:0.21 260:0.91 261:0.98 262:0.93 265:0.55 266:0.47 267:0.23 268:0.94 276:0.29 277:0.69 281:0.89 283:0.78 284:0.98 285:0.56 297:0.36 300:0.43 +1 7:0.70 9:0.74 12:0.06 17:0.36 18:0.77 21:0.05 23:0.96 27:0.28 29:0.71 30:0.61 31:0.90 32:0.65 34:0.80 36:0.48 37:0.91 39:0.95 43:0.65 48:0.91 55:0.52 66:0.78 69:0.25 70:0.66 79:0.90 81:0.34 86:0.78 91:0.49 96:0.74 98:0.83 108:0.52 120:0.68 123:0.50 124:0.39 126:0.24 127:0.61 128:0.96 129:0.59 133:0.47 135:0.30 137:0.90 138:0.96 139:0.78 140:0.80 145:0.83 146:0.13 147:0.18 149:0.61 150:0.38 151:0.77 153:0.48 154:0.55 155:0.65 159:0.38 162:0.86 164:0.66 168:0.96 170:0.99 172:0.73 173:0.37 175:0.91 177:0.38 179:0.56 187:0.39 192:0.98 195:0.63 196:0.91 202:0.50 205:0.95 208:0.64 212:0.69 215:0.91 216:0.41 220:0.62 222:0.60 230:0.73 233:0.66 235:0.05 241:0.70 242:0.76 243:0.15 244:0.83 245:0.67 247:0.47 248:0.70 253:0.54 255:0.74 256:0.58 257:0.84 259:0.21 260:0.68 265:0.83 266:0.47 267:0.96 268:0.59 276:0.63 277:0.87 281:0.86 284:0.88 285:0.62 297:0.36 298:0.99 300:0.55 +1 1:0.69 8:0.60 9:0.75 10:0.95 12:0.06 17:0.09 18:0.74 23:0.96 27:0.42 29:0.71 30:0.27 31:0.92 34:0.69 36:0.27 37:0.60 39:0.95 43:0.78 62:0.98 64:0.77 66:0.32 69:0.77 70:0.76 74:0.48 79:0.91 81:0.69 86:0.80 91:0.23 96:0.77 98:0.27 108:0.60 114:0.95 120:0.65 122:0.95 123:0.58 124:0.91 126:0.54 127:0.45 128:0.97 129:0.05 133:0.90 135:0.88 137:0.92 138:0.95 139:0.77 140:0.45 146:0.52 147:0.58 149:0.66 150:0.64 151:0.86 153:0.48 154:0.71 155:0.65 159:0.75 162:0.86 164:0.56 168:0.97 170:0.99 172:0.51 173:0.62 174:0.94 176:0.70 177:0.88 179:0.51 187:0.39 192:0.98 196:0.92 197:0.94 201:0.67 202:0.36 205:0.95 208:0.64 212:0.16 215:0.91 216:0.56 222:0.60 235:0.22 236:0.95 239:0.94 241:0.01 242:0.22 243:0.49 245:0.62 247:0.86 248:0.77 253:0.76 255:0.78 256:0.45 259:0.21 260:0.66 265:0.30 266:0.87 267:0.89 268:0.46 276:0.63 284:0.88 285:0.62 286:0.99 290:0.95 297:0.36 298:0.99 300:0.55 +1 1:0.67 8:0.77 10:0.95 11:0.46 12:0.06 17:0.53 18:0.96 21:0.47 27:0.45 29:0.71 30:0.43 32:0.80 34:0.87 36:0.24 37:0.50 39:0.95 43:0.82 44:0.93 45:0.66 48:0.91 64:0.77 66:0.77 69:0.23 70:0.64 74:0.70 75:0.99 77:0.70 81:0.65 83:0.56 86:0.96 91:0.10 97:0.89 98:0.90 99:0.83 101:0.47 102:0.98 108:0.18 114:0.80 122:0.92 123:0.18 124:0.41 126:0.54 127:0.80 129:0.05 133:0.36 135:0.54 139:0.97 145:0.47 146:0.92 147:0.94 149:0.82 150:0.55 154:0.77 155:0.52 158:0.86 159:0.61 165:0.65 170:0.99 172:0.82 173:0.23 174:0.89 176:0.73 177:0.88 178:0.55 179:0.44 187:0.68 192:0.50 195:0.75 197:0.91 201:0.66 208:0.64 212:0.71 215:0.63 216:0.77 219:0.95 222:0.60 226:0.96 228:0.99 235:0.80 236:0.97 239:0.97 241:0.27 242:0.31 243:0.79 245:0.81 247:0.94 253:0.63 259:0.21 265:0.90 266:0.63 267:0.44 276:0.74 279:0.80 281:0.91 282:0.88 283:0.67 290:0.80 292:0.88 297:0.36 300:0.55 +0 10:0.82 12:0.06 17:0.94 18:0.64 21:0.78 27:0.72 29:0.71 30:0.58 34:0.55 36:0.67 39:0.95 43:0.09 55:0.92 66:0.49 69:0.55 70:0.44 74:0.48 86:0.72 91:0.60 98:0.58 108:0.75 122:0.83 123:0.73 124:0.52 127:0.32 129:0.05 133:0.43 135:0.85 139:0.68 145:0.50 146:0.21 147:0.27 149:0.12 154:0.58 159:0.54 163:0.79 170:0.99 172:0.32 173:0.46 174:0.79 175:0.75 177:0.70 178:0.55 179:0.79 197:0.81 202:0.36 212:0.16 216:0.07 222:0.60 235:0.80 239:0.82 241:0.79 242:0.58 243:0.39 244:0.61 245:0.55 247:0.47 253:0.40 254:0.92 257:0.65 259:0.21 265:0.59 266:0.54 267:0.82 276:0.33 277:0.87 300:0.33 +1 1:0.65 7:0.75 8:0.63 9:0.73 10:0.92 12:0.06 17:0.40 18:0.83 21:0.40 22:0.93 23:0.99 27:0.48 29:0.71 30:0.62 31:0.91 32:0.71 33:0.97 34:0.63 36:0.67 37:0.57 39:0.95 43:0.86 44:0.92 47:0.93 48:0.97 62:0.98 64:0.77 66:0.52 69:0.58 70:0.62 74:0.69 75:0.98 76:0.94 77:0.70 79:0.91 81:0.57 86:0.86 91:0.58 96:0.79 98:0.48 102:0.97 106:0.81 108:0.44 114:0.78 117:0.86 120:0.56 123:0.43 124:0.65 126:0.54 127:0.52 128:0.97 129:0.05 133:0.65 135:0.93 137:0.91 138:0.95 139:0.90 140:0.45 145:0.47 146:0.69 147:0.74 149:0.85 150:0.52 151:0.56 153:0.48 154:0.83 155:0.65 158:0.81 159:0.66 162:0.86 164:0.65 168:0.97 170:0.99 172:0.65 173:0.46 174:0.86 176:0.70 177:0.88 179:0.50 187:0.39 190:0.77 192:0.98 193:0.99 195:0.83 196:0.94 197:0.89 201:0.64 202:0.69 204:0.85 205:0.99 206:0.81 208:0.64 212:0.26 215:0.63 216:0.76 219:0.94 220:0.91 222:0.60 226:0.95 230:0.77 232:0.99 233:0.65 235:0.68 236:0.95 239:0.96 241:0.10 242:0.19 243:0.62 245:0.76 247:0.90 248:0.55 253:0.81 255:0.73 256:0.78 259:0.21 260:0.57 262:0.93 265:0.50 266:0.87 267:0.73 268:0.77 276:0.63 279:0.82 281:0.86 282:0.80 283:0.66 284:0.96 285:0.62 286:0.99 290:0.78 291:0.97 292:0.87 297:0.36 298:0.99 300:0.70 +1 1:0.68 8:0.98 9:0.75 10:0.97 12:0.06 17:0.36 18:0.92 21:0.22 23:0.99 27:0.27 29:0.71 30:0.53 31:0.95 34:0.86 36:0.48 37:0.98 39:0.95 43:0.75 62:0.99 64:0.77 66:0.93 69:0.15 70:0.55 74:0.69 75:0.97 79:0.95 81:0.52 86:0.94 91:0.64 96:0.93 98:0.97 108:0.12 114:0.82 122:0.92 123:0.12 126:0.51 127:0.78 128:0.99 129:0.05 135:0.56 137:0.95 138:0.95 139:0.90 140:0.45 146:0.86 147:0.89 149:0.79 150:0.51 151:0.91 153:0.85 154:0.70 155:0.64 158:0.76 159:0.43 162:0.84 168:0.99 170:0.99 172:0.82 173:0.27 174:0.92 176:0.63 177:0.88 179:0.47 187:0.21 190:0.77 191:0.91 192:0.58 196:0.96 197:0.94 201:0.64 202:0.74 205:0.99 208:0.61 212:0.72 216:0.29 220:0.82 222:0.60 226:0.96 228:0.99 232:0.70 235:0.52 236:0.94 239:0.95 241:0.74 242:0.24 243:0.94 247:0.88 248:0.84 253:0.93 254:0.97 255:0.78 256:0.79 259:0.21 265:0.97 266:0.47 267:0.59 268:0.80 276:0.72 284:0.96 285:0.60 286:0.99 290:0.82 298:0.99 300:0.43 +3 8:0.86 10:0.99 12:0.06 17:0.18 18:0.94 21:0.30 23:0.95 27:0.15 29:0.71 30:0.87 34:0.75 36:0.60 37:0.78 39:0.95 43:0.88 62:0.97 66:0.25 69:0.12 70:0.44 74:0.83 75:0.97 81:0.38 86:0.96 91:0.48 96:0.71 98:0.58 108:0.71 114:0.82 122:0.94 123:0.69 124:0.74 126:0.32 127:0.76 128:0.95 129:0.81 133:0.67 135:0.42 139:0.94 140:0.45 146:0.54 147:0.60 149:0.96 150:0.40 151:0.57 153:0.48 154:0.87 158:0.76 159:0.63 162:0.86 168:0.95 170:0.99 172:0.77 173:0.16 174:0.96 176:0.63 177:0.88 179:0.24 187:0.87 190:0.89 191:0.80 192:0.47 197:0.97 201:0.57 202:0.36 205:0.95 212:0.80 216:0.91 220:0.87 222:0.60 226:0.96 228:0.99 235:0.42 236:0.94 239:0.99 241:0.52 242:0.19 243:0.43 245:0.95 247:0.86 248:0.56 253:0.71 255:0.69 256:0.45 259:0.21 265:0.59 266:0.63 267:0.59 268:0.46 276:0.87 285:0.50 290:0.82 300:0.84 +1 1:0.65 10:0.98 11:0.57 12:0.06 17:0.67 18:0.94 21:0.54 27:0.45 29:0.71 30:0.58 32:0.80 34:0.88 36:0.20 37:0.50 39:0.95 43:0.82 44:0.93 48:0.91 60:0.87 64:0.77 66:0.75 69:0.25 70:0.63 74:0.79 75:0.99 77:0.70 81:0.74 83:0.91 85:0.88 86:0.96 91:0.15 98:0.90 101:0.87 102:0.98 108:0.20 114:0.77 122:0.92 123:0.19 124:0.41 126:0.54 127:0.81 129:0.05 133:0.36 135:0.83 139:0.97 145:0.44 146:0.90 147:0.92 149:0.92 150:0.55 154:0.77 155:0.50 158:0.86 159:0.56 165:0.93 170:0.99 172:0.79 173:0.27 174:0.92 176:0.73 177:0.88 178:0.55 179:0.47 187:0.68 192:0.49 195:0.71 197:0.94 201:0.64 208:0.64 212:0.58 215:0.58 216:0.77 219:0.95 222:0.60 226:0.96 228:0.99 235:0.84 236:0.97 239:0.99 241:0.10 242:0.19 243:0.77 245:0.80 247:0.93 253:0.64 259:0.21 265:0.90 266:0.63 267:0.28 276:0.72 279:0.80 281:0.91 282:0.88 283:0.67 290:0.77 292:0.88 297:0.36 299:0.88 300:0.55 +1 7:0.70 9:0.74 12:0.06 17:0.40 18:0.77 21:0.05 23:0.96 27:0.28 29:0.71 30:0.71 31:0.90 32:0.67 34:0.78 36:0.37 37:0.91 39:0.95 43:0.65 44:0.88 48:0.91 55:0.52 66:0.75 69:0.25 70:0.47 79:0.90 81:0.34 86:0.78 91:0.49 96:0.74 98:0.83 108:0.53 120:0.68 123:0.50 124:0.40 126:0.24 127:0.67 128:0.96 129:0.59 133:0.47 135:0.21 137:0.90 138:0.96 139:0.81 140:0.80 145:0.84 146:0.13 147:0.18 149:0.59 150:0.38 151:0.77 153:0.72 154:0.55 155:0.65 159:0.35 162:0.76 164:0.68 168:0.96 170:0.99 172:0.65 173:0.40 175:0.91 177:0.38 179:0.65 187:0.39 192:0.98 195:0.61 196:0.92 202:0.58 205:0.95 208:0.64 212:0.54 215:0.91 216:0.41 220:0.62 222:0.60 230:0.73 233:0.66 235:0.05 241:0.62 242:0.72 243:0.18 244:0.83 245:0.64 247:0.47 248:0.70 253:0.54 255:0.74 256:0.58 257:0.84 259:0.21 260:0.69 265:0.83 266:0.47 267:0.52 268:0.62 276:0.55 277:0.87 281:0.86 284:0.88 285:0.62 297:0.36 298:0.99 300:0.43 +0 7:0.69 8:0.97 9:0.70 12:0.06 17:0.08 21:0.59 27:0.04 29:0.71 30:0.21 31:1.00 34:0.39 36:0.68 37:0.94 39:0.95 43:0.18 55:0.52 66:0.16 69:0.46 70:0.77 74:0.42 79:1.00 81:0.27 83:0.56 91:0.98 96:0.93 98:0.67 108:0.71 120:1.00 123:0.34 124:0.52 126:0.24 127:0.80 129:0.59 133:0.47 135:0.18 137:1.00 138:1.00 139:0.64 140:1.00 145:0.59 146:0.60 147:0.65 149:0.25 150:0.30 151:0.56 153:0.48 154:0.12 155:0.53 159:0.41 162:0.55 164:0.99 170:0.99 172:0.45 173:0.55 175:0.91 177:0.38 179:0.80 190:0.89 191:0.99 192:0.57 196:0.92 202:1.00 208:0.61 212:0.49 215:0.55 216:0.87 219:0.90 222:0.60 230:0.71 233:0.76 235:0.68 241:0.93 242:0.61 243:0.37 244:0.93 245:0.64 247:0.55 248:0.55 253:0.89 255:0.70 256:0.99 257:0.84 259:0.21 260:1.00 265:0.58 266:0.63 267:0.79 268:0.99 276:0.44 277:0.87 283:0.82 284:1.00 285:0.60 292:0.88 297:0.36 298:0.99 300:0.55 +2 1:0.68 8:0.70 9:0.76 10:0.97 12:0.06 17:0.57 18:0.92 21:0.05 23:0.97 27:0.15 29:0.71 30:0.76 31:0.93 34:0.53 36:0.28 37:0.78 39:0.95 43:0.88 62:0.98 64:0.77 66:0.92 69:0.10 70:0.41 74:0.65 79:0.92 81:0.68 86:0.94 91:0.51 96:0.79 98:0.92 108:0.09 114:0.95 120:0.67 122:0.75 123:0.09 126:0.54 127:0.55 128:0.97 129:0.05 135:0.42 137:0.93 139:0.92 140:0.45 146:0.67 147:0.71 149:0.90 150:0.63 151:0.82 153:0.77 154:0.87 155:0.66 159:0.25 162:0.68 164:0.64 168:0.97 170:0.99 172:0.79 173:0.37 174:0.89 176:0.73 177:0.88 179:0.47 187:0.95 191:0.88 192:0.99 196:0.95 197:0.93 201:0.67 202:0.54 205:0.95 208:0.64 212:0.86 215:0.91 216:0.91 220:0.62 222:0.60 228:0.99 235:0.31 236:0.97 239:0.95 241:0.59 242:0.13 243:0.93 247:0.90 248:0.74 253:0.81 255:0.79 256:0.45 259:0.21 260:0.68 265:0.92 266:0.27 267:0.97 268:0.57 276:0.67 284:0.91 285:0.62 290:0.95 297:0.36 300:0.43 +0 8:0.95 9:0.71 12:0.06 17:0.20 21:0.78 23:0.97 27:0.18 29:0.71 31:0.96 34:0.61 36:0.18 37:0.86 39:0.95 79:0.96 91:0.84 96:0.72 120:0.76 128:0.96 135:0.86 137:0.96 138:0.97 139:0.67 140:0.94 151:0.64 153:0.78 155:0.57 162:0.85 164:0.73 168:0.96 170:0.99 175:0.99 190:0.89 191:0.96 192:0.87 196:0.90 202:0.68 205:0.97 208:0.61 216:0.20 219:0.91 220:0.62 222:0.60 228:0.99 241:0.86 244:0.97 248:0.62 253:0.48 255:0.73 256:0.75 257:0.97 259:0.21 260:0.76 267:0.59 268:0.75 277:0.98 283:0.67 284:0.96 285:0.62 292:0.88 298:0.99 +4 1:0.66 8:0.68 9:0.70 10:0.88 12:0.06 17:0.55 18:0.79 21:0.22 22:0.93 23:0.98 27:0.28 29:0.71 30:0.62 31:0.87 33:0.97 34:0.28 36:0.70 37:0.91 39:0.95 43:0.91 47:0.93 62:0.95 64:0.77 66:0.83 69:0.15 70:0.43 74:0.40 79:0.87 81:0.61 86:0.82 91:0.62 96:0.69 98:0.74 108:0.12 114:0.84 117:0.86 120:0.55 122:0.75 123:0.12 126:0.54 127:0.24 128:0.95 129:0.05 135:0.54 137:0.87 138:0.95 139:0.78 140:0.45 146:0.36 147:0.42 149:0.75 150:0.57 151:0.50 153:0.48 154:0.91 155:0.54 159:0.14 162:0.86 164:0.70 168:0.95 170:0.99 172:0.51 173:0.57 174:0.79 176:0.70 177:0.88 179:0.62 187:0.87 192:0.56 196:0.88 197:0.85 201:0.66 202:0.55 205:0.98 208:0.64 212:0.95 215:0.72 216:0.41 220:0.96 222:0.60 232:0.93 235:0.52 236:0.95 239:0.87 241:0.55 242:0.29 243:0.84 247:0.67 248:0.52 253:0.60 255:0.69 256:0.64 259:0.21 260:0.55 262:0.93 265:0.74 266:0.12 267:0.59 268:0.63 276:0.40 284:0.93 285:0.62 286:0.99 290:0.84 291:0.97 297:0.36 298:0.99 300:0.33 +4 1:0.68 10:0.97 12:0.06 17:0.51 18:0.92 21:0.05 27:0.15 29:0.71 30:0.76 34:0.73 36:0.28 37:0.78 39:0.95 43:0.88 64:0.77 66:0.92 69:0.10 70:0.41 74:0.70 75:0.98 81:0.68 86:0.94 91:0.29 98:0.92 108:0.09 114:0.95 122:0.75 123:0.09 126:0.54 127:0.55 129:0.05 135:0.42 139:0.93 146:0.67 147:0.71 149:0.90 150:0.63 154:0.87 155:0.53 158:0.81 159:0.25 170:0.99 172:0.79 173:0.37 174:0.89 176:0.73 177:0.88 178:0.55 179:0.47 187:0.87 191:0.77 192:0.55 197:0.93 201:0.67 208:0.64 212:0.86 215:0.91 216:0.91 219:0.94 222:0.60 226:0.96 228:0.99 235:0.31 236:0.97 239:0.96 241:0.30 242:0.13 243:0.93 247:0.90 253:0.70 259:0.21 265:0.92 266:0.27 267:0.44 276:0.67 279:0.81 283:0.67 290:0.95 292:0.88 297:0.36 300:0.43 +0 1:0.74 11:0.64 12:0.79 17:0.81 18:0.83 21:0.05 26:0.98 27:0.72 28:0.47 29:0.91 30:0.87 32:0.80 34:0.62 36:0.42 39:0.88 43:0.92 44:0.93 58:0.93 64:0.77 66:0.79 69:0.36 70:0.27 71:0.56 74:0.66 77:0.89 81:0.82 83:0.91 85:0.80 86:0.91 91:0.82 98:0.62 101:0.87 104:0.54 108:0.28 114:0.89 122:0.57 123:0.27 126:0.54 127:0.18 129:0.05 131:0.61 135:0.34 139:0.91 141:0.91 144:0.57 145:0.56 146:0.32 147:0.39 149:0.79 150:0.88 154:0.91 155:0.67 157:0.80 159:0.13 161:0.68 165:0.93 166:0.78 167:0.89 172:0.41 173:0.71 176:0.73 177:0.70 178:0.55 179:0.61 181:0.60 182:0.79 187:0.21 192:0.87 195:0.53 199:0.95 201:0.93 208:0.64 212:0.95 215:0.78 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.76 234:0.91 235:0.22 241:0.75 242:0.45 243:0.80 246:0.89 247:0.39 253:0.60 259:0.21 265:0.63 266:0.12 267:0.18 271:0.63 274:0.91 276:0.30 282:0.88 287:0.61 290:0.89 297:0.36 299:0.88 300:0.25 +4 1:0.74 6:0.99 8:0.99 9:0.80 11:0.64 12:0.79 17:0.38 18:0.92 20:0.93 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.91 31:0.98 34:0.31 36:0.40 37:0.96 39:0.88 41:0.95 43:0.94 58:0.99 60:0.87 64:0.77 66:0.71 69:0.25 70:0.27 71:0.56 74:0.88 78:0.92 79:0.98 81:0.75 83:0.91 85:0.95 86:0.97 91:0.88 96:0.93 98:0.84 100:1.00 101:0.87 104:0.58 108:0.54 111:1.00 114:0.79 120:0.87 122:0.57 123:0.52 124:0.46 126:0.54 127:0.93 129:0.81 131:0.85 133:0.67 135:0.32 137:0.98 139:0.97 140:0.45 141:0.99 144:0.57 146:0.17 147:0.22 149:0.97 150:0.84 151:0.98 152:0.99 153:0.93 154:0.94 155:0.67 157:0.87 158:0.92 159:0.52 161:0.68 162:0.81 164:0.84 165:0.93 166:0.78 167:1.00 169:1.00 172:0.79 173:0.30 176:0.73 177:0.70 179:0.47 181:0.60 182:0.80 186:0.92 189:1.00 191:0.90 192:0.87 196:0.99 199:0.98 201:0.93 202:0.78 208:0.64 212:0.72 215:0.61 216:0.34 219:0.95 222:0.35 223:0.98 224:0.99 227:0.88 229:0.89 231:0.68 232:0.80 234:0.99 235:0.31 238:1.00 241:0.94 242:0.45 243:0.16 245:0.77 246:0.89 247:0.55 248:0.92 253:0.95 255:0.95 256:0.81 259:0.21 260:0.88 261:1.00 265:0.84 266:0.54 267:0.19 268:0.83 271:0.63 274:0.99 276:0.72 279:0.86 283:0.82 284:0.98 285:0.62 287:0.91 290:0.79 292:0.95 297:0.36 300:0.33 +0 1:0.74 11:0.64 12:0.79 17:0.84 18:0.83 20:0.90 21:0.05 26:0.98 27:0.72 28:0.47 29:0.91 30:0.87 32:0.80 34:0.62 36:0.42 39:0.88 43:0.92 44:0.93 58:0.93 64:0.77 66:0.79 69:0.36 70:0.27 71:0.56 74:0.66 77:0.89 78:0.76 81:0.82 83:0.91 85:0.80 86:0.91 91:0.82 98:0.62 100:0.73 101:0.87 104:0.54 108:0.28 111:0.75 114:0.89 122:0.57 123:0.27 126:0.54 127:0.18 129:0.05 131:0.61 135:0.34 139:0.91 141:0.91 144:0.57 145:0.56 146:0.32 147:0.39 149:0.79 150:0.88 152:0.84 154:0.91 155:0.67 157:0.80 159:0.13 161:0.68 165:0.93 166:0.78 167:0.94 169:0.72 172:0.41 173:0.71 176:0.73 177:0.70 178:0.55 179:0.61 181:0.60 182:0.79 186:0.77 192:0.87 195:0.53 199:0.95 201:0.93 208:0.64 212:0.95 215:0.78 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.76 234:0.91 235:0.22 241:0.78 242:0.45 243:0.80 246:0.89 247:0.39 253:0.60 259:0.21 261:0.78 265:0.63 266:0.12 267:0.18 271:0.63 274:0.91 276:0.30 282:0.88 287:0.61 290:0.89 297:0.36 299:0.88 300:0.25 +2 1:0.74 7:0.81 8:0.65 11:0.85 12:0.79 17:0.61 18:0.86 20:0.92 21:0.30 22:0.93 26:0.98 27:0.31 28:0.47 29:0.91 30:0.45 32:0.80 34:0.97 36:0.27 37:0.55 39:0.88 43:0.79 44:0.93 45:0.66 47:0.93 48:0.99 58:0.93 60:0.87 64:0.77 66:0.87 69:0.60 70:0.61 71:0.56 74:0.80 77:0.96 78:0.77 81:0.66 83:0.91 85:0.80 86:0.87 91:0.08 98:0.75 99:0.83 100:0.78 101:0.87 104:0.95 106:0.81 108:0.46 111:0.76 114:0.65 117:0.86 121:0.90 122:0.86 123:0.44 126:0.54 127:0.24 129:0.05 131:0.61 135:0.85 139:0.94 141:0.91 144:0.57 145:0.70 146:0.76 147:0.79 149:0.74 150:0.80 152:0.87 154:0.73 155:0.67 157:0.81 158:0.92 159:0.32 161:0.68 165:0.93 166:0.78 167:0.67 169:0.75 172:0.63 173:0.62 176:0.73 177:0.70 178:0.55 179:0.49 181:0.60 182:0.80 186:0.78 187:0.39 192:0.87 195:0.70 199:0.97 201:0.93 204:0.89 206:0.81 208:0.64 212:0.55 215:0.44 216:0.85 219:0.95 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 230:0.87 231:0.68 232:0.97 233:0.76 234:0.91 235:0.68 241:0.24 242:0.40 243:0.88 246:0.89 247:0.60 253:0.51 259:0.21 261:0.80 262:0.93 265:0.75 266:0.47 267:0.28 271:0.63 274:0.91 276:0.50 279:0.86 281:0.91 282:0.88 283:0.82 287:0.61 290:0.65 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 8:0.96 9:0.80 11:0.64 12:0.79 17:0.55 18:0.71 20:0.85 21:0.22 26:0.98 27:0.72 28:0.47 29:0.91 30:0.95 31:0.91 34:0.56 36:0.30 37:0.86 39:0.88 41:0.95 43:0.83 58:0.99 64:0.77 66:0.75 69:0.66 70:0.13 71:0.56 74:0.53 78:0.76 79:0.91 81:0.67 83:0.91 85:0.80 86:0.82 91:0.91 96:0.76 98:0.71 100:0.79 101:0.87 108:0.50 111:0.75 114:0.71 120:0.65 122:0.57 123:0.48 126:0.54 127:0.22 129:0.05 131:0.85 135:0.37 137:0.91 139:0.79 140:0.45 141:0.99 144:0.57 146:0.41 147:0.48 149:0.74 150:0.80 151:0.61 152:0.80 153:0.48 154:0.79 155:0.67 157:0.80 159:0.15 161:0.68 162:0.82 163:0.90 164:0.80 165:0.93 166:0.78 167:1.00 169:0.77 172:0.32 173:0.91 176:0.73 177:0.70 179:0.81 181:0.60 182:0.80 186:0.77 192:0.87 196:0.92 199:0.97 201:0.93 202:0.70 208:0.64 212:0.61 215:0.51 216:0.14 220:0.91 222:0.35 223:0.98 224:0.99 227:0.88 229:0.89 231:0.68 234:0.98 235:0.42 241:0.94 242:0.63 243:0.77 246:0.89 247:0.35 248:0.69 253:0.68 255:0.95 256:0.77 259:0.21 260:0.65 261:0.77 265:0.71 266:0.23 267:0.44 268:0.76 271:0.63 274:0.99 276:0.14 284:0.97 285:0.62 287:0.91 290:0.71 297:0.36 300:0.13 +1 6:0.78 7:0.64 8:0.58 9:0.80 12:0.79 17:0.88 18:0.84 20:0.96 21:0.78 26:0.98 27:0.52 28:0.47 29:0.91 30:0.87 31:0.97 32:0.63 34:0.68 36:0.54 37:0.23 39:0.88 41:0.82 43:0.72 44:0.90 48:0.91 58:0.98 66:0.82 69:0.25 70:0.27 71:0.56 78:0.81 79:0.97 83:0.91 86:0.86 91:0.70 96:0.75 98:0.92 100:0.81 104:0.58 108:0.20 111:0.79 120:0.85 122:0.65 123:0.19 127:0.55 129:0.05 131:0.85 135:0.42 137:0.97 139:0.88 140:0.93 141:0.99 144:0.57 145:0.74 146:0.65 147:0.70 149:0.73 151:0.72 152:0.89 153:0.81 154:0.62 155:0.67 157:0.61 159:0.24 161:0.68 162:0.79 163:0.81 164:0.70 166:0.61 167:0.98 169:0.79 172:0.48 173:0.51 175:0.75 177:0.38 179:0.84 181:0.60 182:0.79 186:0.81 189:0.81 190:0.77 191:0.78 192:0.87 195:0.56 196:0.97 199:0.94 202:0.74 208:0.64 212:0.50 216:0.23 220:0.74 222:0.35 223:0.98 224:0.98 227:0.88 229:0.88 230:0.64 231:0.68 233:0.73 234:0.97 235:0.84 238:0.86 241:0.86 242:0.63 243:0.83 244:0.61 246:0.61 247:0.30 248:0.67 253:0.58 255:0.95 256:0.77 257:0.65 259:0.21 260:0.70 261:0.83 265:0.92 266:0.38 267:0.89 268:0.79 271:0.63 274:0.97 276:0.37 277:0.69 281:0.89 284:0.97 285:0.62 287:0.91 300:0.25 +2 1:0.74 7:0.72 11:0.92 12:0.79 17:0.75 18:0.83 20:0.78 21:0.30 22:0.93 26:0.98 27:0.31 28:0.47 29:0.91 30:0.43 32:0.80 34:0.56 36:0.47 37:0.55 39:0.88 43:0.60 44:0.93 45:0.95 47:0.93 48:0.91 58:0.93 64:0.77 66:0.34 69:0.61 70:0.88 71:0.56 74:0.82 77:0.96 78:0.72 81:0.59 83:0.60 85:0.72 86:0.89 91:0.03 98:0.55 99:0.83 100:0.73 101:0.97 104:0.82 106:0.81 108:0.94 111:0.72 114:0.65 117:0.86 122:0.57 123:0.94 124:0.83 126:0.54 127:0.87 129:0.89 131:0.61 133:0.83 135:0.96 139:0.91 141:0.91 144:0.57 145:0.96 146:0.56 147:0.62 149:0.62 150:0.74 152:0.87 154:0.72 155:0.67 157:0.87 159:0.88 161:0.68 165:0.70 166:0.78 167:0.65 169:0.75 172:0.82 173:0.31 176:0.73 177:0.70 178:0.55 179:0.26 181:0.60 182:0.79 186:0.73 187:0.39 192:0.87 195:0.96 199:0.98 201:0.93 204:0.85 206:0.81 208:0.64 212:0.23 215:0.44 216:0.85 219:0.89 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 230:0.75 231:0.68 232:0.90 233:0.76 234:0.91 235:0.52 241:0.15 242:0.21 243:0.27 245:0.91 246:0.89 247:0.91 253:0.51 259:0.21 261:0.80 262:0.93 265:0.56 266:0.92 267:0.19 271:0.63 274:0.91 276:0.86 281:0.89 282:0.88 287:0.61 290:0.65 292:0.91 297:0.36 299:0.88 300:0.94 +1 1:0.74 8:0.80 9:0.80 11:0.64 12:0.79 17:0.20 18:0.88 21:0.40 26:0.98 27:0.19 28:0.47 29:0.91 30:0.94 31:0.94 34:0.87 36:0.75 37:0.96 39:0.88 41:0.88 43:0.94 58:0.98 64:0.77 66:0.69 69:0.32 70:0.19 71:0.56 74:0.78 79:0.94 81:0.56 83:0.91 85:0.91 86:0.96 91:0.57 96:0.82 98:0.68 101:0.87 104:0.58 108:0.25 114:0.62 120:0.73 122:0.57 123:0.24 124:0.43 126:0.54 127:0.51 129:0.59 131:0.85 133:0.46 135:0.49 137:0.94 139:0.95 140:0.45 141:0.99 144:0.57 146:0.55 147:0.61 149:0.95 150:0.75 151:0.87 153:0.48 154:0.94 155:0.67 157:0.85 159:0.32 161:0.68 162:0.86 164:0.68 165:0.93 166:0.78 167:0.75 172:0.63 173:0.46 176:0.73 177:0.70 179:0.62 181:0.60 182:0.80 187:0.68 192:0.87 196:0.97 199:0.96 201:0.93 202:0.58 208:0.64 212:0.59 215:0.42 216:0.34 220:0.82 222:0.35 223:0.98 224:0.98 227:0.88 229:0.89 231:0.68 232:0.80 234:0.98 235:0.60 241:0.59 242:0.51 243:0.73 245:0.68 246:0.89 247:0.47 248:0.79 253:0.83 255:0.95 256:0.68 259:0.21 260:0.73 265:0.68 266:0.54 267:0.19 268:0.67 271:0.63 274:0.98 276:0.50 284:0.94 285:0.62 287:0.91 290:0.62 297:0.36 300:0.25 +0 1:0.74 11:0.75 12:0.79 17:0.34 18:0.62 20:0.92 21:0.13 26:0.98 27:0.13 28:0.47 29:0.91 30:0.21 34:0.74 36:0.69 37:0.26 39:0.88 43:0.47 45:0.66 55:0.59 58:0.93 64:0.77 66:0.88 69:0.45 70:0.73 71:0.56 74:0.41 78:0.74 81:0.77 83:0.91 86:0.67 91:0.35 98:0.93 100:0.78 101:0.52 104:0.76 108:0.35 111:0.74 114:0.79 122:0.77 123:0.34 126:0.54 127:0.57 129:0.05 131:0.61 135:0.30 139:0.65 141:0.91 144:0.57 146:0.84 147:0.87 149:0.35 150:0.85 152:0.86 154:0.46 155:0.67 157:0.76 159:0.40 161:0.68 165:0.93 166:0.78 167:0.69 169:0.76 172:0.65 173:0.52 176:0.73 177:0.70 178:0.55 179:0.67 181:0.60 182:0.79 186:0.75 187:0.21 192:0.87 199:0.94 201:0.93 208:0.64 212:0.35 215:0.61 216:0.28 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.72 234:0.91 235:0.42 241:0.37 242:0.75 243:0.88 244:0.61 246:0.89 247:0.55 253:0.55 259:0.21 261:0.76 265:0.93 266:0.54 267:0.28 271:0.63 274:0.91 276:0.54 287:0.61 290:0.79 297:0.36 300:0.55 +1 1:0.74 8:0.92 11:0.64 12:0.79 17:0.34 18:0.89 21:0.22 26:0.98 27:0.72 28:0.47 29:0.91 30:0.93 34:0.82 36:0.73 37:0.86 39:0.88 43:0.88 58:0.93 64:0.77 66:0.20 69:0.51 70:0.28 71:0.56 74:0.83 81:0.67 83:0.91 85:0.96 86:0.96 91:0.40 98:0.36 101:0.87 108:0.39 114:0.71 122:0.57 123:0.38 124:0.85 126:0.54 127:0.79 129:0.93 131:0.61 133:0.84 135:0.34 139:0.95 141:0.91 144:0.57 146:0.56 147:0.62 149:0.96 150:0.80 154:0.87 155:0.67 157:0.87 159:0.71 161:0.68 165:0.93 166:0.78 167:0.70 172:0.54 173:0.39 176:0.73 177:0.70 178:0.55 179:0.44 181:0.60 182:0.79 187:0.21 192:0.87 199:0.97 201:0.93 208:0.64 212:0.35 215:0.51 216:0.14 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 234:0.91 235:0.42 241:0.41 242:0.51 243:0.40 245:0.80 246:0.89 247:0.35 253:0.54 259:0.21 265:0.38 266:0.54 267:0.23 271:0.63 274:0.91 276:0.72 287:0.61 290:0.71 297:0.36 300:0.55 +4 1:0.74 6:0.99 8:1.00 9:0.80 11:0.64 12:0.79 17:0.20 18:0.92 20:0.80 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.91 31:0.98 34:0.37 36:0.42 37:0.96 39:0.88 41:0.95 43:0.94 58:0.99 60:0.87 64:0.77 66:0.71 69:0.25 70:0.27 71:0.56 74:0.88 78:0.93 79:0.98 81:0.75 83:0.91 85:0.95 86:0.97 91:0.83 96:0.93 98:0.81 100:1.00 101:0.87 104:0.58 108:0.54 111:1.00 114:0.79 120:0.89 122:0.57 123:0.52 124:0.46 126:0.54 127:0.67 129:0.81 131:0.85 133:0.67 135:0.44 137:0.98 139:0.97 140:0.45 141:0.99 144:0.57 146:0.17 147:0.22 149:0.97 150:0.84 151:0.98 152:0.99 153:0.92 154:0.94 155:0.67 157:0.87 158:0.92 159:0.52 161:0.68 162:0.83 164:0.83 165:0.93 166:0.78 167:0.88 169:1.00 172:0.79 173:0.28 176:0.73 177:0.70 179:0.43 181:0.60 182:0.80 186:0.93 187:0.21 189:1.00 191:0.89 192:0.87 196:0.99 199:0.98 201:0.93 202:0.74 208:0.64 212:0.72 215:0.61 216:0.34 219:0.95 222:0.35 223:0.98 224:0.99 227:0.88 229:0.89 231:0.68 232:0.80 234:0.99 235:0.31 238:1.00 241:0.74 242:0.45 243:0.16 245:0.77 246:0.89 247:0.55 248:0.93 253:0.95 255:0.95 256:0.80 259:0.21 260:0.89 261:1.00 265:0.81 266:0.54 267:0.19 268:0.80 271:0.63 274:0.99 276:0.72 279:0.86 283:0.82 284:0.97 285:0.62 287:0.91 290:0.79 292:0.95 297:0.36 300:0.33 +0 11:0.75 12:0.79 17:0.34 18:0.63 21:0.78 26:0.94 27:0.45 28:0.47 29:0.91 30:0.76 34:0.86 36:0.21 37:0.50 39:0.88 43:0.62 45:0.66 58:0.93 66:0.64 69:0.76 70:0.15 71:0.56 74:0.36 86:0.64 91:0.20 98:0.22 101:0.52 108:0.59 122:0.57 123:0.57 127:0.11 129:0.05 131:0.61 135:0.70 139:0.62 141:0.91 144:0.57 145:0.44 146:0.42 147:0.48 149:0.30 154:0.51 157:0.76 159:0.16 161:0.68 163:0.98 166:0.61 167:0.65 172:0.16 173:0.61 177:0.70 178:0.55 179:0.38 181:0.60 182:0.72 187:0.68 199:0.94 212:0.95 216:0.77 222:0.35 223:0.92 224:0.93 227:0.61 229:0.61 231:0.61 234:0.91 235:0.68 241:0.18 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.29 259:0.21 265:0.24 266:0.12 267:0.59 271:0.63 274:0.91 276:0.12 287:0.61 300:0.13 +0 6:0.93 8:0.60 9:0.80 12:0.79 17:0.53 20:0.78 21:0.78 26:0.98 27:0.51 28:0.47 29:0.91 31:0.96 34:0.40 36:0.39 37:0.94 39:0.88 41:0.90 58:0.99 71:0.56 78:0.78 79:0.96 83:0.91 91:0.67 96:0.85 100:0.73 111:0.77 120:0.75 131:0.85 135:0.47 137:0.96 140:0.97 141:0.99 144:0.57 151:0.87 152:0.89 153:0.48 155:0.67 157:0.61 161:0.68 162:0.50 164:0.72 166:0.61 167:0.85 169:0.81 175:0.97 178:0.53 181:0.60 182:0.80 186:0.79 187:0.39 191:0.92 192:0.87 199:0.95 202:0.81 208:0.64 216:0.43 220:0.62 222:0.35 223:0.98 224:0.98 227:0.88 229:0.89 231:0.68 234:0.97 241:0.72 244:0.93 246:0.61 248:0.83 253:0.79 255:0.95 256:0.73 257:0.93 259:0.21 260:0.76 261:0.82 267:0.44 268:0.73 271:0.63 274:0.98 277:0.95 284:0.95 285:0.62 287:0.91 +2 1:0.74 6:0.99 8:0.94 9:0.80 11:0.85 12:0.79 17:0.06 18:0.82 20:0.81 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.91 31:0.95 34:0.74 36:0.56 37:0.96 39:0.88 41:0.82 43:0.96 45:0.66 58:0.98 64:0.77 66:0.19 69:0.12 70:0.24 71:0.56 74:0.75 78:0.83 79:0.94 81:0.75 83:0.91 85:0.90 86:0.92 91:0.72 96:0.80 98:0.25 100:0.95 101:0.87 104:0.58 108:0.54 111:0.90 114:0.79 120:0.69 122:0.57 123:0.63 124:0.81 126:0.54 127:0.69 129:0.89 131:0.84 133:0.78 135:0.47 137:0.95 139:0.89 140:0.45 141:0.99 144:0.57 146:0.17 147:0.22 149:0.90 150:0.84 151:0.87 152:0.99 153:0.48 154:0.96 155:0.67 157:0.85 159:0.39 161:0.68 162:0.62 164:0.57 165:0.93 166:0.78 167:0.76 169:1.00 172:0.32 173:0.28 176:0.73 177:0.70 179:0.68 181:0.60 182:0.80 186:0.83 187:0.21 189:0.99 191:0.75 192:0.87 196:0.96 199:0.96 201:0.93 202:0.60 208:0.64 212:0.57 215:0.61 216:0.34 222:0.35 223:0.98 224:0.99 227:0.88 229:0.88 231:0.68 232:0.80 234:0.98 235:0.31 238:0.98 241:0.63 242:0.44 243:0.25 245:0.66 246:0.89 247:0.55 248:0.77 253:0.83 255:0.95 256:0.58 259:0.21 260:0.70 261:1.00 265:0.24 266:0.27 267:0.28 268:0.59 271:0.63 274:0.97 276:0.51 284:0.91 285:0.62 287:0.91 290:0.79 297:0.36 300:0.33 +1 1:0.74 8:0.59 11:0.64 12:0.79 17:0.11 18:0.79 21:0.13 26:0.98 27:0.19 28:0.47 29:0.91 30:0.86 34:0.42 36:0.56 37:0.96 39:0.88 43:0.96 58:0.93 64:0.77 66:0.26 69:0.12 70:0.19 71:0.56 74:0.55 81:0.75 83:0.91 85:0.85 86:0.86 91:0.58 98:0.50 101:0.87 104:0.58 108:0.10 114:0.79 122:0.57 123:0.10 124:0.74 126:0.54 127:0.91 129:0.81 131:0.61 133:0.67 135:0.70 139:0.82 141:0.91 144:0.57 146:0.63 147:0.68 149:0.77 150:0.84 154:0.96 155:0.67 157:0.81 159:0.63 161:0.68 163:0.75 165:0.93 166:0.78 167:0.72 172:0.27 173:0.17 176:0.73 177:0.70 178:0.55 179:0.83 181:0.60 182:0.79 187:0.21 192:0.87 199:0.95 201:0.93 202:0.36 208:0.64 212:0.23 215:0.61 216:0.34 222:0.35 223:0.98 224:0.93 227:0.61 229:0.61 231:0.68 232:0.80 234:0.91 235:0.31 241:0.47 242:0.42 243:0.45 245:0.59 246:0.89 247:0.60 253:0.56 259:0.21 265:0.52 266:0.63 267:0.91 271:0.63 274:0.91 276:0.38 287:0.61 290:0.79 297:0.36 300:0.18 +0 1:0.74 7:0.81 9:0.80 11:0.64 12:0.79 17:0.59 18:0.91 21:0.40 22:0.93 26:0.98 27:0.68 28:0.47 29:0.91 30:0.68 31:0.86 32:0.80 34:0.76 36:0.35 37:0.37 39:0.88 41:0.82 43:0.91 44:0.93 47:0.93 58:0.98 60:0.87 64:0.77 66:0.86 69:0.44 70:0.41 71:0.56 74:0.81 77:0.89 79:0.86 81:0.56 83:0.91 85:0.85 86:0.92 91:0.38 96:0.68 98:0.80 101:0.87 104:0.95 106:0.81 108:0.34 114:0.62 117:0.86 120:0.55 121:0.97 122:0.86 123:0.33 126:0.54 127:0.29 129:0.05 131:0.84 135:0.74 137:0.86 139:0.96 140:0.45 141:0.99 144:0.57 145:0.70 146:0.61 147:0.67 149:0.85 150:0.75 151:0.50 153:0.48 154:0.90 155:0.67 157:0.82 158:0.91 159:0.23 161:0.68 162:0.86 164:0.57 165:0.93 166:0.78 167:0.65 172:0.61 173:0.61 176:0.73 177:0.70 179:0.58 181:0.60 182:0.80 187:0.39 192:0.87 195:0.58 196:0.90 199:0.97 201:0.93 202:0.36 204:0.89 206:0.81 208:0.64 212:0.59 215:0.42 216:0.40 219:0.95 220:0.91 222:0.35 223:0.98 224:0.99 227:0.88 229:0.88 230:0.86 231:0.68 232:0.97 233:0.73 234:0.98 235:0.60 241:0.15 242:0.54 243:0.87 246:0.89 247:0.55 248:0.49 253:0.50 255:0.95 256:0.45 259:0.21 260:0.55 262:0.93 265:0.80 266:0.27 267:0.23 268:0.46 271:0.63 274:0.97 276:0.48 279:0.86 281:0.89 282:0.88 283:0.78 284:0.89 285:0.62 287:0.91 290:0.62 292:0.91 297:0.36 299:0.97 300:0.33 +2 1:0.74 6:0.82 8:0.67 9:0.80 11:0.78 12:0.37 17:0.64 20:0.86 21:0.05 27:0.71 28:0.56 30:0.89 32:0.80 34:0.37 36:0.60 37:0.65 39:0.79 41:0.90 43:0.79 46:0.97 60:0.87 66:0.47 69:0.75 70:0.20 74:0.72 77:0.70 78:0.75 81:0.91 83:0.87 85:0.85 91:0.87 96:0.83 98:0.49 100:0.78 101:0.80 104:0.58 107:0.97 108:0.70 110:0.97 111:0.74 114:0.95 120:0.73 122:0.43 123:0.68 124:0.52 125:0.98 126:0.54 127:0.30 129:0.59 131:0.95 133:0.47 135:0.89 140:0.45 144:0.76 145:0.69 146:0.13 147:0.18 149:0.68 150:0.96 151:0.85 152:0.87 153:0.84 154:0.72 155:0.67 157:0.86 158:0.87 159:0.28 160:0.98 161:0.45 162:0.60 163:0.81 164:0.73 165:0.90 166:0.91 167:0.95 169:0.75 172:0.21 173:0.87 176:0.73 177:0.38 179:0.89 181:0.96 182:0.87 186:0.76 189:0.84 191:0.80 192:0.59 195:0.58 201:0.74 202:0.67 208:0.64 212:0.30 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 238:0.86 241:0.83 242:0.63 243:0.37 245:0.46 246:0.97 247:0.30 248:0.80 253:0.85 255:0.79 256:0.64 259:0.21 260:0.74 261:0.77 265:0.50 266:0.27 267:0.59 268:0.67 271:0.35 276:0.23 279:0.86 282:0.88 283:0.71 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 299:0.88 300:0.18 +4 1:0.74 6:0.98 8:0.97 9:0.80 11:0.78 12:0.37 17:0.12 20:0.99 27:0.07 28:0.56 30:0.70 34:0.52 36:0.46 37:0.99 39:0.79 41:0.99 43:0.95 46:0.99 60:0.95 66:0.88 69:0.15 70:0.39 74:0.87 78:0.89 81:0.95 83:0.90 85:0.93 91:0.98 96:0.99 98:0.98 100:0.99 101:0.85 104:0.58 107:0.99 108:0.12 110:0.99 111:0.98 114:0.98 120:0.97 122:0.57 123:0.12 125:0.98 126:0.54 127:0.86 129:0.05 131:0.95 135:0.47 138:0.99 140:0.45 144:0.76 146:0.76 147:0.80 149:0.91 150:0.98 151:1.00 152:0.99 153:0.98 154:0.95 155:0.67 157:0.91 158:0.92 159:0.32 160:1.00 161:0.45 162:0.73 164:0.96 165:0.92 166:0.91 167:0.97 169:0.99 172:0.65 173:0.37 176:0.73 177:0.38 179:0.72 181:0.96 182:0.88 186:0.89 189:0.99 191:0.96 192:0.59 201:0.74 202:0.93 208:0.64 212:0.35 215:0.96 216:0.59 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.05 238:0.99 241:0.90 242:0.62 243:0.88 246:0.97 247:0.39 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.97 261:1.00 265:0.98 266:0.38 267:0.79 268:0.95 271:0.35 276:0.52 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.98 297:0.36 300:0.33 +2 1:0.74 6:0.84 8:0.88 9:0.80 11:0.78 12:0.37 17:0.45 20:0.85 21:0.05 27:0.50 28:0.56 30:0.61 34:0.39 36:0.62 37:0.37 39:0.79 41:0.90 43:0.84 46:1.00 60:0.87 66:0.89 69:0.55 70:0.63 74:0.88 78:0.75 81:0.91 83:0.86 85:0.93 91:0.83 96:0.85 98:0.87 100:0.78 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.74 114:0.95 120:0.76 122:0.43 123:0.40 125:0.99 126:0.54 127:0.40 129:0.05 131:0.95 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.89 152:0.95 153:0.87 154:0.81 155:0.67 157:0.91 158:0.87 159:0.23 160:0.98 161:0.45 162:0.68 164:0.75 165:0.90 166:0.91 167:0.95 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.76 192:0.59 201:0.74 202:0.67 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 241:0.84 242:0.57 243:0.89 246:0.97 247:0.47 248:0.83 253:0.89 255:0.79 256:0.64 259:0.21 260:0.77 261:0.81 265:0.87 266:0.23 267:0.28 268:0.69 271:0.35 276:0.56 279:0.86 283:0.71 285:0.62 287:0.98 289:0.99 290:0.95 297:0.36 300:0.55 +2 1:0.74 6:0.84 8:0.70 9:0.80 11:0.78 12:0.37 17:0.66 20:0.90 21:0.05 27:0.50 28:0.56 30:0.61 34:0.44 36:0.62 37:0.37 39:0.79 41:0.82 43:0.84 46:1.00 66:0.89 69:0.55 70:0.63 74:0.86 78:0.76 81:0.91 83:0.79 85:0.93 91:0.80 96:0.78 98:0.87 100:0.79 101:0.85 104:0.58 107:1.00 108:0.42 110:0.99 111:0.76 114:0.95 120:0.66 122:0.43 123:0.40 125:1.00 126:0.54 127:0.40 129:0.05 131:0.94 135:0.37 140:0.45 144:0.76 146:0.63 147:0.68 149:0.91 150:0.96 151:0.79 152:0.95 153:0.69 154:0.81 155:0.67 157:0.91 159:0.23 160:0.96 161:0.45 162:0.86 164:0.62 165:0.90 166:0.91 167:0.93 169:0.77 172:0.68 173:0.76 176:0.73 177:0.38 179:0.58 181:0.96 182:0.87 186:0.77 187:0.21 189:0.88 192:0.59 201:0.74 202:0.46 208:0.64 212:0.91 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 238:0.89 241:0.80 242:0.57 243:0.89 246:0.97 247:0.47 248:0.72 253:0.84 255:0.79 256:0.45 259:0.21 260:0.67 261:0.81 265:0.87 266:0.23 267:0.44 268:0.55 271:0.35 276:0.56 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 300:0.55 +0 1:0.74 9:0.80 11:0.78 12:0.37 17:0.40 21:0.64 27:0.45 28:0.56 30:0.58 32:0.80 34:0.68 36:0.19 37:0.50 39:0.79 41:0.82 43:0.82 46:0.95 60:0.87 66:0.18 69:0.70 70:0.60 74:0.78 77:0.70 81:0.57 83:0.83 85:0.85 91:0.23 96:0.68 98:0.19 101:0.77 107:0.98 108:0.53 110:0.98 114:0.72 120:0.54 122:0.43 123:0.81 124:0.73 125:0.97 126:0.54 127:0.36 129:0.81 131:0.94 133:0.67 135:0.63 140:0.45 144:0.76 145:0.50 146:0.23 147:0.29 149:0.75 150:0.72 151:0.49 153:0.48 154:0.77 155:0.67 157:0.85 158:0.87 159:0.48 160:0.96 161:0.45 162:0.86 163:0.88 164:0.57 165:0.70 166:0.91 167:0.67 172:0.21 173:0.68 176:0.73 177:0.38 179:0.82 181:0.96 182:0.87 187:0.68 192:0.59 195:0.73 201:0.74 202:0.36 208:0.64 212:0.37 215:0.53 216:0.77 220:0.97 222:0.60 227:0.98 229:0.93 231:0.87 235:0.80 241:0.35 242:0.58 243:0.46 245:0.51 246:0.97 247:0.35 248:0.49 253:0.66 255:0.79 256:0.45 259:0.21 260:0.55 265:0.17 266:0.47 267:0.36 268:0.46 271:0.35 276:0.28 279:0.86 282:0.88 283:0.71 285:0.62 287:0.98 289:0.98 290:0.72 297:0.36 299:0.88 300:0.43 +2 1:0.74 7:0.81 8:0.84 9:0.80 11:0.78 12:0.37 17:0.62 20:0.75 21:0.30 27:0.50 28:0.56 30:0.45 32:0.80 34:0.64 36:0.68 37:0.60 39:0.79 41:0.88 43:0.88 46:0.98 60:0.95 66:0.36 69:0.53 70:0.72 74:0.97 77:0.89 78:0.83 81:0.78 83:0.84 85:0.99 91:0.20 96:0.76 98:0.66 100:0.86 101:0.84 104:0.84 106:0.81 107:1.00 108:0.95 110:0.99 111:0.83 114:0.86 117:0.86 120:0.64 121:0.90 122:0.43 123:0.94 124:0.90 125:0.99 126:0.54 127:0.87 129:0.95 131:0.94 133:0.91 135:0.42 140:0.45 144:0.76 145:0.65 146:0.85 147:0.88 149:0.98 150:0.86 151:0.72 152:0.74 153:0.72 154:0.86 155:0.67 157:0.94 158:0.92 159:0.92 160:0.97 161:0.45 162:0.86 164:0.69 165:0.84 166:0.91 167:0.55 169:0.84 172:0.93 173:0.18 176:0.73 177:0.38 179:0.16 181:0.96 182:0.87 186:0.83 187:0.39 192:0.59 195:0.98 201:0.74 202:0.53 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.86 220:0.74 222:0.60 227:0.98 229:0.93 230:0.87 231:0.87 232:0.89 233:0.76 235:0.42 241:0.01 242:0.51 243:0.32 245:0.95 246:0.97 247:0.67 248:0.69 253:0.84 255:0.79 256:0.58 259:0.21 260:0.65 261:0.75 265:0.66 266:0.91 267:0.44 268:0.62 271:0.35 276:0.95 279:0.86 282:0.88 283:0.82 285:0.62 287:0.98 289:0.98 290:0.86 297:0.36 299:0.97 300:0.70 +0 1:0.74 9:0.80 11:0.78 12:0.37 17:0.22 27:0.32 28:0.56 30:0.95 32:0.80 34:0.66 36:0.52 37:0.67 39:0.79 41:0.88 43:0.90 46:0.99 66:0.54 69:0.44 74:0.58 77:0.70 81:0.95 83:0.81 85:0.72 91:0.63 96:0.82 98:0.19 101:0.77 104:0.80 106:0.81 107:0.93 108:0.34 110:0.93 114:0.98 120:0.71 123:0.33 125:0.99 126:0.54 127:0.10 129:0.05 131:0.94 135:0.37 140:0.45 144:0.76 145:0.39 146:0.13 147:0.18 149:0.51 150:0.98 151:0.87 153:0.48 154:0.89 155:0.67 157:0.79 159:0.08 160:0.97 161:0.45 162:0.86 164:0.67 165:0.92 166:0.91 167:0.75 172:0.10 173:0.91 176:0.73 177:0.38 179:0.28 181:0.96 182:0.87 187:0.39 192:0.59 195:0.53 201:0.74 202:0.50 206:0.81 208:0.64 215:0.96 216:0.47 222:0.60 227:0.98 229:0.93 231:0.87 235:0.05 241:0.63 243:0.63 246:0.97 248:0.79 253:0.83 255:0.79 256:0.58 259:0.21 260:0.72 265:0.21 267:0.91 268:0.59 271:0.35 282:0.88 285:0.62 287:0.98 289:0.98 290:0.98 297:0.36 299:0.88 300:0.08 +3 1:0.74 6:0.98 8:0.98 9:0.80 11:0.78 12:0.37 17:0.67 20:0.81 27:0.07 28:0.56 30:0.66 34:0.74 36:0.40 37:0.99 39:0.79 41:0.98 43:0.87 46:0.98 60:0.87 66:0.36 69:0.44 70:0.53 74:0.83 78:0.88 81:0.95 83:0.89 85:0.90 91:0.90 96:0.96 98:0.62 100:0.98 101:0.83 104:0.58 107:0.99 108:0.65 110:0.98 111:0.96 114:0.98 120:0.94 122:0.57 123:0.63 124:0.60 125:0.99 126:0.54 127:0.66 129:0.59 131:0.95 133:0.47 135:0.47 140:0.45 144:0.76 146:0.45 147:0.51 149:0.84 150:0.98 151:0.99 152:0.99 153:0.97 154:0.85 155:0.67 157:0.90 158:0.92 159:0.35 160:0.99 161:0.45 162:0.80 164:0.91 165:0.92 166:0.91 167:0.90 169:0.99 172:0.37 173:0.56 176:0.73 177:0.38 179:0.78 181:0.96 182:0.88 186:0.88 187:0.39 189:0.99 191:0.94 192:0.59 201:0.74 202:0.85 208:0.64 212:0.22 215:0.96 216:0.59 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.05 238:0.99 241:0.77 242:0.61 243:0.48 245:0.67 246:0.97 247:0.39 248:0.96 253:0.97 255:0.79 256:0.88 259:0.21 260:0.94 261:1.00 265:0.63 266:0.54 267:0.19 268:0.89 271:0.35 276:0.43 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.98 297:0.36 300:0.43 +2 6:0.78 8:0.58 9:0.80 12:0.37 17:0.09 20:0.86 21:0.05 25:0.88 27:0.51 28:0.56 30:0.95 34:0.80 36:0.92 37:0.23 39:0.79 41:0.82 43:0.45 46:0.88 55:0.71 66:0.77 69:0.05 70:0.13 78:0.75 81:0.88 83:0.77 91:0.83 96:0.84 98:0.96 100:0.77 104:0.58 107:0.95 108:0.05 110:0.96 111:0.74 120:0.89 123:0.05 125:0.96 126:0.32 127:0.69 129:0.05 131:0.94 135:0.34 140:0.93 144:0.76 146:0.61 147:0.66 149:0.43 150:0.74 151:0.92 152:0.81 153:0.85 154:0.34 155:0.67 157:0.61 159:0.22 160:0.96 161:0.45 162:0.73 164:0.85 166:0.84 167:0.97 169:0.75 172:0.37 173:0.35 175:0.75 177:0.05 179:0.93 181:0.96 182:0.87 186:0.76 189:0.81 191:0.92 192:0.59 202:0.78 208:0.64 212:0.52 215:0.91 216:0.23 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 235:0.05 238:0.83 241:0.92 242:0.82 243:0.79 244:0.61 246:0.61 247:0.12 248:0.81 253:0.77 255:0.79 256:0.79 257:0.65 259:0.21 260:0.89 261:0.77 265:0.96 266:0.27 267:0.82 268:0.82 271:0.35 276:0.33 277:0.69 283:0.71 285:0.62 287:0.98 289:0.98 297:0.36 300:0.13 +2 1:0.74 6:0.82 8:0.66 9:0.80 11:0.78 12:0.37 17:0.88 20:0.88 21:0.05 27:0.71 28:0.56 30:0.89 32:0.80 34:0.47 36:0.51 37:0.65 39:0.79 41:0.82 43:0.79 46:0.97 66:0.47 69:0.76 70:0.20 74:0.65 77:0.70 78:0.74 81:0.91 83:0.79 85:0.85 91:0.83 96:0.77 98:0.48 100:0.78 101:0.80 104:0.58 107:0.97 108:0.70 110:0.97 111:0.74 114:0.95 120:0.65 122:0.43 123:0.69 124:0.52 125:1.00 126:0.54 127:0.29 129:0.59 131:0.94 133:0.47 135:0.90 140:0.45 144:0.76 145:0.69 146:0.13 147:0.18 149:0.68 150:0.96 151:0.77 152:0.87 153:0.48 154:0.72 155:0.67 157:0.86 159:0.28 160:0.96 161:0.45 162:0.86 163:0.81 164:0.57 165:0.90 166:0.91 167:0.92 169:0.75 172:0.21 173:0.87 176:0.73 177:0.38 179:0.88 181:0.96 182:0.87 186:0.75 187:0.21 192:0.59 195:0.58 201:0.74 202:0.36 208:0.64 212:0.30 215:0.91 216:0.21 220:0.62 222:0.60 227:0.98 229:0.93 231:0.87 232:0.77 235:0.12 241:0.79 242:0.63 243:0.37 245:0.46 246:0.97 247:0.30 248:0.71 253:0.80 255:0.79 256:0.45 259:0.21 260:0.66 261:0.77 265:0.50 266:0.27 267:0.65 268:0.46 271:0.35 276:0.23 282:0.88 285:0.62 287:0.98 289:0.98 290:0.95 297:0.36 299:0.88 300:0.18 +1 1:0.74 6:0.87 7:0.81 8:0.78 9:0.80 11:0.78 12:0.37 17:0.45 20:0.83 21:0.30 27:0.12 28:0.56 30:0.33 34:0.82 36:0.39 37:0.81 39:0.79 41:0.93 43:0.59 46:0.95 55:0.59 60:0.87 66:0.44 69:0.93 70:0.64 74:0.86 78:0.75 81:0.78 83:0.88 85:0.91 91:0.33 96:0.92 98:0.43 100:0.78 101:0.77 107:0.99 108:0.89 110:0.98 111:0.75 114:0.86 120:0.86 121:0.90 122:0.67 123:0.89 124:0.81 125:1.00 126:0.54 127:0.51 129:0.81 131:0.95 133:0.83 135:0.70 140:0.45 144:0.76 146:0.27 147:0.05 149:0.71 150:0.86 151:0.92 152:0.96 153:0.72 154:0.48 155:0.67 157:0.88 158:0.92 159:0.79 160:0.98 161:0.45 162:0.86 164:0.78 165:0.84 166:0.91 167:0.66 169:0.75 172:0.63 173:0.88 176:0.73 177:0.05 179:0.46 181:0.96 182:0.87 186:0.76 187:0.98 192:0.59 201:0.74 202:0.64 204:0.89 208:0.64 212:0.60 215:0.72 216:0.90 220:0.87 222:0.60 227:0.98 229:0.93 230:0.87 231:0.87 233:0.76 235:0.42 241:0.32 242:0.63 243:0.09 245:0.73 246:0.97 247:0.47 248:0.92 253:0.93 255:0.79 256:0.71 257:0.65 259:0.21 260:0.86 261:0.78 265:0.45 266:0.75 267:0.91 268:0.73 271:0.35 276:0.65 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.86 297:0.36 300:0.55 +2 1:0.74 6:0.84 7:0.81 8:0.68 9:0.80 11:0.78 12:0.37 17:0.36 20:0.99 21:0.30 27:0.21 28:0.56 30:0.73 34:0.66 36:0.89 37:0.74 39:0.79 41:0.92 43:0.98 46:0.99 60:0.95 66:0.94 69:0.12 70:0.42 74:0.94 78:0.77 81:0.78 83:0.88 85:0.97 91:0.58 96:0.91 98:0.88 100:0.80 101:0.85 104:0.84 106:0.81 107:1.00 108:0.10 110:0.99 111:0.76 114:0.86 117:0.86 120:0.84 121:0.90 122:0.43 123:0.10 125:0.99 126:0.54 127:0.41 129:0.05 131:0.95 135:0.17 140:0.45 144:0.76 146:0.93 147:0.95 149:0.97 150:0.86 151:0.96 152:0.90 153:0.86 154:0.98 155:0.67 157:0.94 158:0.92 159:0.57 160:0.98 161:0.45 162:0.71 164:0.78 165:0.84 166:0.91 167:0.66 169:0.78 172:0.84 173:0.15 176:0.73 177:0.38 179:0.35 181:0.96 182:0.87 186:0.77 187:0.39 189:0.86 192:0.59 201:0.74 202:0.69 204:0.89 206:0.81 208:0.64 212:0.48 215:0.72 216:0.95 222:0.60 227:0.98 229:0.93 230:0.87 231:0.87 232:0.89 233:0.76 235:0.42 238:0.89 241:0.32 242:0.60 243:0.94 246:0.97 247:0.47 248:0.90 253:0.93 255:0.79 256:0.68 259:0.21 260:0.84 261:0.81 265:0.88 266:0.54 267:0.88 268:0.73 271:0.35 276:0.74 279:0.86 283:0.82 285:0.62 287:0.98 289:0.99 290:0.86 297:0.36 300:0.43 +1 11:0.57 12:0.20 17:0.59 21:0.78 27:0.45 28:0.98 30:0.08 34:0.80 36:0.17 37:0.50 39:0.61 43:0.75 55:0.42 66:0.36 69:0.83 70:0.74 74:0.46 85:0.72 91:0.33 98:0.22 101:0.71 108:0.86 123:0.85 124:0.69 127:0.29 129:0.05 133:0.62 135:0.94 145:0.52 146:0.17 147:0.22 149:0.46 154:0.66 159:0.51 161:0.90 163:0.89 167:0.62 172:0.21 173:0.80 177:0.38 178:0.55 179:0.85 181:0.67 187:0.68 212:0.23 216:0.77 222:0.60 235:1.00 241:0.41 242:0.55 243:0.34 245:0.45 247:0.39 253:0.40 259:0.21 265:0.24 266:0.38 267:0.28 271:0.16 276:0.27 277:0.69 300:0.43 +1 1:0.74 6:0.80 7:0.81 8:0.85 9:0.80 11:0.57 12:0.20 17:0.94 20:0.89 21:0.54 27:0.72 28:0.98 30:0.76 32:0.80 34:0.58 36:0.68 37:0.85 39:0.61 41:0.92 43:0.94 55:0.42 66:0.53 69:0.32 70:0.43 74:0.87 77:0.89 78:0.89 81:0.75 83:0.77 85:0.91 91:0.87 96:0.79 98:0.71 100:0.83 101:0.84 104:0.75 108:0.54 111:0.86 114:0.77 120:0.74 121:0.97 122:0.57 123:0.52 124:0.52 126:0.54 127:0.40 129:0.59 133:0.47 135:0.28 140:0.45 145:0.54 146:0.22 147:0.28 149:0.88 150:0.83 151:0.77 152:0.83 153:0.48 154:0.94 155:0.67 159:0.30 161:0.90 162:0.86 164:0.75 165:0.81 167:0.90 169:0.80 172:0.45 173:0.45 176:0.73 177:0.38 179:0.71 181:0.67 186:0.89 189:0.83 192:0.59 195:0.62 201:0.74 202:0.60 204:0.89 208:0.64 212:0.49 215:0.58 216:0.02 220:0.97 222:0.60 230:0.87 232:0.83 233:0.76 235:0.88 238:0.81 241:0.85 242:0.61 243:0.37 245:0.64 247:0.39 248:0.75 253:0.84 255:0.79 256:0.68 259:0.21 260:0.75 261:0.85 265:0.71 266:0.38 267:0.44 268:0.69 271:0.16 276:0.43 282:0.88 285:0.62 290:0.77 297:0.36 299:0.97 300:0.43 +1 6:0.86 7:0.81 8:0.91 9:0.80 12:0.20 17:0.16 20:0.88 21:0.78 27:0.31 28:0.98 32:0.75 34:0.42 36:0.43 37:0.68 39:0.61 74:0.47 78:0.96 91:0.90 96:0.83 100:0.89 104:0.58 111:0.94 120:0.89 121:0.90 135:0.30 140:0.98 145:0.86 151:0.77 152:0.82 153:0.95 155:0.67 161:0.90 162:0.66 164:0.91 167:0.91 169:0.87 175:0.91 181:0.67 186:0.96 189:0.88 191:0.83 192:0.59 195:0.53 202:0.87 204:0.89 208:0.64 216:0.30 220:0.62 222:0.60 230:0.87 233:0.76 235:0.22 238:0.82 241:0.88 244:0.83 248:0.71 253:0.77 255:0.79 256:0.88 257:0.84 259:0.21 260:0.89 261:0.89 267:0.23 268:0.89 271:0.16 277:0.87 283:0.71 285:0.62 +1 1:0.74 7:0.78 8:0.80 12:0.20 17:0.38 20:0.84 21:0.74 27:0.38 28:0.98 30:0.76 32:0.75 34:0.40 36:0.36 37:0.47 39:0.61 43:0.30 55:0.59 60:0.87 66:0.47 69:0.87 70:0.28 74:0.79 76:0.85 77:0.70 78:0.88 81:0.47 83:0.83 91:0.17 96:0.68 98:0.52 100:0.82 108:0.75 111:0.85 114:0.61 117:0.86 123:0.73 124:0.74 126:0.54 127:0.34 129:0.05 133:0.74 135:0.68 140:0.45 145:0.88 146:0.73 147:0.05 149:0.43 150:0.61 151:0.47 152:0.80 153:0.48 154:0.22 155:0.67 158:0.92 159:0.58 161:0.90 162:0.86 167:0.52 169:0.80 172:0.41 173:0.84 176:0.56 177:0.05 179:0.68 181:0.67 186:0.88 187:0.21 190:0.77 192:0.59 195:0.84 201:0.74 202:0.36 208:0.64 212:0.22 215:0.46 216:0.90 220:0.99 222:0.60 230:0.81 232:0.96 233:0.69 235:0.97 241:0.02 242:0.74 243:0.14 244:0.61 245:0.53 247:0.39 248:0.47 253:0.60 256:0.45 257:0.65 259:0.21 261:0.82 265:0.54 266:0.75 267:0.96 268:0.46 271:0.16 276:0.46 279:0.86 282:0.83 283:0.82 285:0.50 290:0.61 297:0.36 300:0.25 +3 1:0.74 6:0.91 7:0.78 8:0.93 9:0.80 12:0.20 17:0.85 20:0.98 21:0.30 27:0.55 28:0.98 30:0.45 32:0.77 34:0.62 36:0.76 37:0.52 39:0.61 41:0.90 43:0.31 55:0.71 66:0.49 69:0.66 70:0.25 74:0.63 77:0.70 78:0.97 81:0.84 83:0.75 91:0.94 96:0.85 98:0.37 100:0.95 104:0.58 108:0.50 111:0.95 114:0.86 120:0.91 122:0.55 123:0.48 124:0.48 126:0.54 127:0.51 129:0.05 133:0.39 135:0.37 140:0.93 145:0.54 146:0.37 147:0.44 149:0.28 150:0.89 151:0.77 152:0.95 153:0.48 154:0.23 155:0.67 159:0.39 161:0.90 162:0.74 163:0.87 164:0.90 165:0.83 167:0.91 169:0.92 172:0.16 173:0.75 176:0.73 177:0.38 179:0.97 181:0.67 186:0.96 189:0.94 190:0.77 191:0.88 192:0.59 195:0.65 201:0.74 202:0.85 208:0.64 212:0.61 215:0.72 216:0.27 220:0.62 222:0.60 230:0.81 233:0.69 235:0.68 238:0.87 241:0.89 242:0.63 243:0.60 244:0.61 245:0.39 247:0.30 248:0.81 253:0.84 255:0.79 256:0.87 259:0.21 260:0.92 261:0.94 265:0.39 266:0.17 267:0.82 268:0.88 271:0.16 276:0.16 282:0.85 283:0.71 285:0.62 290:0.86 297:0.36 300:0.18 +3 1:0.74 7:0.81 11:0.57 12:0.20 17:0.88 21:0.40 27:0.70 28:0.98 30:0.45 32:0.75 34:0.25 36:0.41 37:0.58 39:0.61 43:0.62 55:0.42 66:0.27 69:0.91 70:0.25 74:0.52 76:0.85 81:0.77 83:0.75 85:0.72 91:0.75 98:0.22 101:0.72 104:0.73 108:0.55 114:0.82 121:0.90 123:0.53 124:0.61 126:0.54 127:0.51 129:0.05 133:0.39 135:0.54 145:0.54 146:0.17 147:0.35 149:0.38 150:0.85 154:0.51 155:0.67 159:0.48 161:0.90 163:0.88 165:0.83 167:0.84 172:0.10 173:1.00 176:0.73 177:0.05 178:0.55 179:0.97 181:0.67 187:0.21 192:0.59 195:0.72 201:0.74 204:0.89 208:0.64 212:0.61 215:0.67 216:0.10 222:0.60 230:0.87 232:0.82 233:0.76 235:0.52 241:0.76 242:0.63 243:0.46 245:0.40 247:0.30 253:0.64 257:0.65 259:0.21 265:0.25 266:0.17 267:0.23 271:0.16 276:0.14 277:0.69 290:0.82 297:0.36 300:0.18 +2 1:0.74 6:0.87 8:0.75 11:0.57 12:0.20 17:0.77 20:0.85 21:0.22 27:0.36 28:0.98 30:0.15 34:0.63 36:0.39 37:0.56 39:0.61 43:0.73 55:0.52 66:0.24 69:0.85 70:0.96 74:0.62 76:0.85 78:0.96 81:0.84 83:0.76 85:0.72 91:0.29 98:0.14 100:0.89 101:0.69 108:0.72 111:0.94 114:0.90 122:0.43 123:0.94 124:0.78 126:0.54 127:0.70 129:0.59 133:0.76 135:0.78 145:0.42 146:0.22 147:0.05 149:0.47 150:0.89 152:0.97 154:0.63 155:0.67 159:0.83 161:0.90 165:0.86 167:0.54 169:0.95 172:0.21 173:0.71 176:0.73 177:0.05 178:0.55 179:0.86 181:0.67 186:0.96 187:0.39 189:0.87 191:0.70 192:0.59 201:0.74 212:0.30 215:0.79 216:0.37 222:0.60 232:0.80 235:0.42 238:0.82 241:0.07 242:0.33 243:0.17 245:0.49 247:0.55 253:0.69 257:0.65 259:0.21 261:0.97 265:0.12 266:0.54 267:0.18 271:0.16 276:0.24 290:0.90 297:0.36 300:0.70 +2 1:0.74 6:0.87 8:0.75 11:0.57 12:0.20 17:0.81 20:0.85 21:0.22 27:0.36 28:0.98 30:0.15 34:0.63 36:0.44 37:0.56 39:0.61 43:0.73 55:0.52 66:0.24 69:0.85 70:0.96 74:0.62 76:0.85 78:0.96 81:0.84 83:0.76 85:0.72 91:0.31 98:0.14 100:0.89 101:0.69 108:0.72 111:0.94 114:0.90 122:0.43 123:0.94 124:0.78 126:0.54 127:0.69 129:0.59 133:0.76 135:0.82 145:0.42 146:0.22 147:0.05 149:0.46 150:0.89 152:0.97 154:0.63 155:0.67 159:0.84 161:0.90 165:0.86 167:0.54 169:0.95 172:0.21 173:0.70 176:0.73 177:0.05 178:0.55 179:0.86 181:0.67 186:0.96 187:0.39 189:0.87 191:0.70 192:0.59 201:0.74 212:0.30 215:0.79 216:0.37 222:0.60 232:0.80 235:0.42 238:0.82 241:0.07 242:0.33 243:0.17 245:0.49 247:0.55 253:0.69 257:0.65 259:0.21 261:0.97 265:0.12 266:0.54 267:0.18 271:0.16 276:0.24 290:0.90 297:0.36 300:0.70 +3 1:0.74 6:0.89 7:0.81 8:0.81 9:0.80 11:0.57 12:0.20 17:0.83 20:0.90 21:0.40 27:0.70 28:0.98 30:0.45 32:0.75 34:0.20 36:0.44 37:0.58 39:0.61 41:0.82 43:0.62 55:0.42 66:0.27 69:0.91 70:0.25 74:0.52 76:0.85 78:0.97 81:0.77 83:0.77 85:0.72 91:0.76 96:0.77 98:0.23 100:0.90 101:0.72 104:0.73 108:0.55 111:0.94 114:0.82 120:0.65 121:0.90 123:0.53 124:0.61 126:0.54 127:0.51 129:0.05 133:0.39 135:0.51 140:0.45 145:0.54 146:0.17 147:0.35 149:0.38 150:0.85 151:0.77 152:0.84 153:0.48 154:0.52 155:0.67 159:0.47 161:0.90 162:0.86 163:0.88 164:0.57 165:0.83 167:0.90 169:0.88 172:0.10 173:1.00 176:0.73 177:0.05 179:0.97 181:0.67 186:0.96 189:0.91 192:0.59 195:0.72 201:0.74 202:0.36 204:0.89 208:0.64 212:0.61 215:0.67 216:0.10 220:0.62 222:0.60 230:0.87 232:0.82 233:0.76 235:0.52 238:0.82 241:0.84 242:0.63 243:0.46 245:0.40 247:0.30 248:0.71 253:0.76 255:0.79 256:0.45 257:0.65 259:0.21 260:0.66 261:0.91 265:0.25 266:0.17 267:0.23 268:0.46 271:0.16 276:0.14 277:0.69 283:0.71 285:0.62 290:0.82 297:0.36 300:0.18 +3 1:0.74 6:0.83 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.97 21:0.05 27:0.63 28:0.98 30:0.88 32:0.80 34:0.36 36:0.49 37:0.45 39:0.61 41:0.97 43:0.61 55:0.92 60:0.87 66:0.24 69:0.75 70:0.29 74:0.87 76:0.85 77:0.89 78:0.98 81:0.91 83:0.88 85:0.72 91:0.91 96:0.96 98:0.67 100:0.95 101:0.72 108:0.80 111:0.96 114:0.95 120:0.93 121:0.90 122:0.67 123:0.56 124:0.67 126:0.54 127:0.80 129:0.59 133:0.64 135:0.66 140:0.80 145:0.72 146:0.73 147:0.05 149:0.51 150:0.95 151:0.98 152:0.95 153:0.91 154:0.54 155:0.67 158:0.87 159:0.55 161:0.90 162:0.64 163:0.81 164:0.89 165:0.90 167:0.91 169:0.91 172:0.37 173:0.77 176:0.73 177:0.05 178:0.42 179:0.80 181:0.67 186:0.98 189:0.85 191:0.83 192:0.59 195:0.71 201:0.74 202:0.84 204:0.89 208:0.64 212:0.22 215:0.91 216:0.28 220:0.62 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.85 241:0.89 242:0.38 243:0.14 245:0.59 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.95 265:0.56 266:0.54 267:0.65 268:0.86 271:0.16 276:0.47 279:0.86 282:0.88 283:0.71 285:0.62 290:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.87 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.95 21:0.05 27:0.36 28:0.98 30:0.33 32:0.80 34:0.87 36:0.64 37:0.56 39:0.61 41:0.96 43:0.77 55:0.42 60:0.95 66:0.60 69:0.75 70:0.58 74:0.85 77:0.89 78:0.99 81:0.91 83:0.89 85:0.85 91:0.86 96:0.95 98:0.55 100:0.97 101:0.77 104:0.58 108:0.58 111:0.97 114:0.95 120:0.91 121:0.97 122:0.41 123:0.56 124:0.50 126:0.54 127:0.50 129:0.59 133:0.47 135:0.49 140:0.45 145:0.52 146:0.66 147:0.71 149:0.69 150:0.95 151:0.98 152:0.95 153:0.92 154:0.69 155:0.67 158:0.92 159:0.54 161:0.90 162:0.64 164:0.87 165:0.90 167:0.90 169:0.93 172:0.51 173:0.74 176:0.73 177:0.38 179:0.70 181:0.67 186:0.99 189:0.89 191:0.78 192:0.59 195:0.76 201:0.74 202:0.82 204:0.89 208:0.64 212:0.69 215:0.91 216:0.37 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.87 241:0.86 242:0.31 243:0.67 245:0.67 247:0.60 248:0.95 253:0.96 255:0.79 256:0.83 259:0.21 260:0.91 261:0.95 265:0.56 266:0.54 267:0.69 268:0.84 271:0.16 276:0.40 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.79 8:0.62 9:0.80 11:0.57 12:0.20 17:0.38 20:0.86 21:0.13 27:0.72 28:0.98 30:0.89 34:0.61 36:0.81 37:0.56 39:0.61 41:0.90 43:0.94 55:0.42 66:0.75 69:0.12 70:0.15 74:0.64 78:0.95 81:0.88 83:0.79 85:0.85 91:0.88 96:0.82 98:0.85 100:0.83 101:0.82 108:0.10 111:0.91 114:0.92 120:0.72 122:0.43 123:0.10 126:0.54 127:0.36 129:0.05 135:0.60 140:0.45 146:0.49 147:0.55 149:0.78 150:0.93 151:0.77 152:0.81 153:0.48 154:0.94 155:0.67 159:0.18 161:0.90 162:0.86 163:0.81 164:0.72 165:0.88 167:0.92 169:0.81 172:0.32 173:0.49 176:0.73 177:0.38 179:0.91 181:0.67 186:0.94 189:0.81 192:0.59 201:0.74 202:0.56 208:0.64 212:0.61 215:0.84 216:0.07 220:0.62 222:0.60 235:0.31 238:0.81 241:0.93 242:0.63 243:0.77 247:0.30 248:0.79 253:0.83 255:0.79 256:0.64 259:0.21 260:0.73 261:0.87 265:0.85 266:0.23 267:0.19 268:0.65 271:0.16 276:0.25 285:0.62 290:0.92 297:0.36 300:0.13 +2 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.90 21:0.78 27:0.54 28:0.98 30:0.76 34:0.91 36:0.54 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.16 69:0.21 70:0.36 74:0.80 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.17 147:0.22 149:0.64 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.64 172:0.21 173:0.13 177:0.38 179:0.62 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.36 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.47 242:0.61 243:0.23 245:0.57 247:0.47 248:0.64 253:0.74 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.47 267:0.89 268:0.71 271:0.16 276:0.44 279:0.86 283:0.82 285:0.62 300:0.33 +2 6:0.87 7:0.78 8:0.66 11:0.57 12:0.20 17:0.97 20:0.81 21:0.59 27:0.36 28:0.98 30:0.15 32:0.80 34:0.90 36:0.55 37:0.56 39:0.61 43:0.28 55:0.92 66:0.16 69:0.99 70:0.96 74:0.55 77:0.70 78:0.85 81:0.43 85:0.72 91:0.23 98:0.19 100:0.85 101:0.64 104:0.58 108:0.98 111:0.84 122:0.41 123:0.56 124:0.84 126:0.32 127:0.89 129:0.05 133:0.82 135:0.49 145:0.59 146:0.28 147:0.63 149:0.30 150:0.36 152:0.97 154:0.13 159:0.94 161:0.90 167:0.52 169:0.94 172:0.21 173:1.00 177:0.05 178:0.55 179:0.87 181:0.67 186:0.85 187:0.39 195:0.99 212:0.15 215:0.55 216:0.37 222:0.60 230:0.81 232:0.80 233:0.69 235:0.74 241:0.02 242:0.33 243:0.37 245:0.47 247:0.55 253:0.44 257:0.65 259:0.21 261:0.96 265:0.12 266:0.63 267:0.91 271:0.16 276:0.31 277:0.69 282:0.88 297:0.36 299:0.88 300:0.70 +2 11:0.57 12:0.20 17:0.89 21:0.22 27:0.63 28:0.98 30:0.38 32:0.80 34:0.93 36:0.36 37:0.45 39:0.61 43:0.63 55:0.42 66:0.09 69:0.91 70:0.63 74:0.76 77:0.89 81:0.73 85:0.85 91:0.67 98:0.25 101:0.78 104:0.58 106:0.81 108:0.81 122:0.43 123:0.54 124:0.74 126:0.32 127:0.62 129:0.05 133:0.74 135:0.58 145:0.69 146:0.31 147:0.05 149:0.65 150:0.54 154:0.53 159:0.51 161:0.90 167:0.57 172:0.37 173:0.98 177:0.05 178:0.55 179:0.82 181:0.67 187:0.21 195:0.69 202:0.36 206:0.81 212:0.57 215:0.79 216:0.28 222:0.60 232:0.80 235:0.31 241:0.21 242:0.29 243:0.16 245:0.49 247:0.60 253:0.55 257:0.65 259:0.21 265:0.27 266:0.47 267:0.52 271:0.16 276:0.39 277:0.69 282:0.88 283:0.71 297:0.36 299:0.88 300:0.43 +2 1:0.74 6:0.83 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.97 21:0.05 27:0.63 28:0.98 30:0.88 32:0.80 34:0.36 36:0.49 37:0.45 39:0.61 41:0.97 43:0.61 55:0.92 60:0.87 66:0.24 69:0.75 70:0.29 74:0.87 76:0.85 77:0.89 78:0.98 81:0.91 83:0.88 85:0.72 91:0.91 96:0.96 98:0.67 100:0.95 101:0.72 108:0.80 111:0.96 114:0.95 120:0.93 121:0.90 122:0.67 123:0.56 124:0.67 126:0.54 127:0.80 129:0.59 133:0.64 135:0.66 140:0.80 145:0.72 146:0.73 147:0.05 149:0.51 150:0.95 151:0.98 152:0.95 153:0.91 154:0.54 155:0.67 158:0.87 159:0.55 161:0.90 162:0.64 163:0.81 164:0.89 165:0.90 167:0.91 169:0.91 172:0.37 173:0.77 176:0.73 177:0.05 178:0.42 179:0.80 181:0.67 186:0.98 189:0.85 191:0.83 192:0.59 195:0.71 201:0.74 202:0.84 204:0.89 208:0.64 212:0.22 215:0.91 216:0.28 220:0.62 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.85 241:0.89 242:0.38 243:0.14 245:0.59 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.95 265:0.56 266:0.54 267:0.65 268:0.86 271:0.16 276:0.47 279:0.86 282:0.88 283:0.71 285:0.62 290:0.95 297:0.36 299:0.88 300:0.33 +3 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.88 21:0.78 27:0.54 28:0.98 30:0.73 34:0.91 36:0.59 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.15 69:0.21 70:0.37 74:0.84 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.31 147:0.38 149:0.66 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.67 172:0.21 173:0.13 177:0.38 179:0.58 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.30 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.54 242:0.63 243:0.29 245:0.59 247:0.47 248:0.64 253:0.76 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.63 267:0.91 268:0.71 271:0.16 276:0.47 279:0.86 283:0.82 285:0.62 300:0.33 +2 6:0.82 7:0.81 8:0.61 12:0.20 17:0.43 20:0.87 21:0.30 27:0.21 28:0.78 30:0.30 34:0.58 36:0.98 37:0.74 43:0.52 55:0.52 66:0.44 69:0.12 70:0.60 78:0.74 81:0.95 91:0.57 98:0.44 100:0.77 106:0.81 108:0.60 111:0.74 120:0.77 123:0.57 124:0.58 126:0.54 127:0.27 129:0.59 133:0.47 135:0.20 140:0.45 146:0.42 147:0.48 149:0.57 150:0.92 151:0.74 152:0.82 153:0.84 154:0.41 159:0.29 161:0.54 162:0.69 164:0.79 167:0.72 169:0.75 172:0.37 173:0.25 177:0.05 179:0.64 181:0.52 186:0.75 187:0.39 189:0.84 202:0.71 206:0.81 212:0.73 215:0.72 216:0.95 220:0.87 230:0.87 233:0.76 235:0.42 238:0.85 241:0.52 242:0.82 243:0.45 245:0.64 247:0.12 248:0.69 256:0.68 259:0.21 260:0.78 261:0.76 265:0.46 266:0.38 267:0.85 268:0.74 271:0.61 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43 +1 6:0.90 7:0.81 8:0.89 12:0.20 17:0.75 20:0.94 21:0.71 25:0.88 27:0.06 28:0.78 30:0.95 32:0.80 34:0.53 36:0.80 37:0.95 43:0.37 55:0.84 66:0.54 69:0.61 78:0.82 81:0.83 91:0.93 98:0.54 100:0.86 104:0.58 108:0.46 111:0.85 120:0.96 123:0.45 126:0.54 127:0.16 129:0.05 135:0.26 140:0.45 145:0.89 146:0.33 147:0.39 149:0.21 150:0.63 151:0.83 152:0.88 153:0.95 154:0.28 159:0.13 161:0.54 162:0.60 164:0.95 167:0.97 169:0.90 172:0.10 173:0.86 177:0.05 179:0.97 181:0.52 186:0.81 189:0.91 191:0.95 195:0.58 202:0.93 215:0.48 216:0.52 220:0.62 230:0.87 233:0.76 235:0.84 238:0.97 241:0.86 243:0.63 248:0.94 256:0.93 260:0.96 261:0.84 265:0.55 267:0.76 268:0.94 271:0.61 283:0.60 285:0.62 297:0.36 300:0.08 +1 12:0.20 17:0.64 21:0.30 25:0.88 27:0.05 28:0.78 34:0.67 36:0.41 37:0.96 81:0.95 91:0.62 104:0.58 126:0.54 132:0.97 135:0.49 150:0.92 161:0.54 167:0.74 175:0.75 178:0.55 181:0.52 187:0.39 215:0.72 216:0.64 235:0.42 241:0.61 244:0.61 257:0.65 267:0.93 271:0.61 277:0.69 297:0.36 +2 6:0.81 8:0.82 12:0.20 17:0.55 20:0.86 21:0.22 25:0.88 27:0.16 28:0.78 30:0.76 34:0.56 36:0.74 37:0.95 43:0.48 55:0.52 66:0.64 69:0.34 70:0.15 78:0.77 81:0.96 91:0.88 98:0.83 100:0.80 104:0.75 108:0.27 111:0.77 120:0.62 123:0.26 126:0.54 127:0.33 129:0.05 135:0.51 140:0.45 146:0.30 147:0.36 149:0.29 150:0.94 151:0.56 152:0.81 153:0.72 154:0.36 159:0.13 161:0.54 162:0.85 164:0.81 167:0.98 169:0.78 172:0.16 173:0.86 177:0.05 179:0.98 181:0.52 186:0.78 189:0.83 191:0.89 202:0.70 212:0.95 215:0.79 216:0.32 220:0.87 235:0.22 238:0.90 241:0.92 242:0.82 243:0.69 247:0.12 248:0.56 256:0.73 259:0.21 260:0.63 261:0.79 265:0.83 266:0.12 267:0.36 268:0.77 271:0.61 276:0.16 283:0.60 285:0.62 297:0.36 300:0.13 +1 6:0.84 7:0.81 8:0.89 12:0.20 17:0.96 20:0.80 21:0.76 27:0.24 28:0.78 30:0.42 32:0.80 34:0.50 36:0.83 37:0.68 43:0.47 55:0.84 66:0.88 69:0.47 70:0.48 78:0.76 81:0.78 91:0.66 98:0.95 100:0.78 104:0.58 108:0.36 111:0.75 120:0.73 123:0.34 126:0.54 127:0.67 129:0.05 135:0.47 140:0.80 145:0.63 146:0.83 147:0.86 149:0.62 150:0.58 151:0.66 152:0.80 153:0.48 154:0.35 159:0.39 161:0.54 162:0.49 163:0.93 164:0.67 167:0.97 169:0.77 172:0.65 173:0.56 177:0.05 178:0.42 179:0.69 181:0.52 186:0.77 189:0.88 191:0.90 195:0.64 202:0.73 212:0.35 215:0.46 216:0.10 230:0.87 233:0.76 235:0.97 238:0.87 241:0.89 242:0.82 243:0.88 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 261:0.77 265:0.95 266:0.47 267:0.84 268:0.59 271:0.61 276:0.55 285:0.62 297:0.36 300:0.33 +4 8:0.85 12:0.20 17:0.83 21:0.22 25:0.88 27:0.63 28:0.78 30:0.68 32:0.80 34:0.40 36:0.53 37:0.83 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 81:0.96 91:0.66 98:0.94 104:0.76 108:0.39 120:0.73 123:0.38 126:0.54 127:0.60 129:0.05 135:0.32 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.66 153:0.48 154:0.31 159:0.34 161:0.54 162:0.80 163:0.94 164:0.79 167:0.97 172:0.41 173:0.64 177:0.05 179:0.90 181:0.52 191:0.80 195:0.60 202:0.69 212:0.61 215:0.79 216:0.21 220:0.99 235:0.22 241:0.88 242:0.82 243:0.80 247:0.12 248:0.66 256:0.73 259:0.21 260:0.74 265:0.94 266:0.27 267:0.73 268:0.74 271:0.61 276:0.35 285:0.62 297:0.36 300:0.25 +3 6:0.99 8:0.97 12:0.20 17:0.53 20:0.89 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.49 36:0.64 37:0.98 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 78:0.89 81:0.96 91:0.68 98:0.88 100:0.96 104:0.58 108:0.39 111:0.93 120:0.89 123:0.38 126:0.54 127:0.43 129:0.05 135:0.58 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.71 152:1.00 153:0.72 154:0.31 159:0.34 161:0.54 162:0.81 163:0.94 164:0.88 167:0.84 169:0.98 172:0.41 173:0.61 177:0.05 179:0.87 181:0.52 186:0.89 187:0.68 189:0.99 191:0.96 195:0.63 202:0.81 212:0.61 215:0.79 216:0.73 220:0.99 235:0.22 238:0.98 241:0.72 242:0.82 243:0.80 247:0.12 248:0.84 256:0.85 259:0.21 260:0.89 261:0.99 265:0.88 266:0.27 267:0.69 268:0.85 271:0.61 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.96 8:0.95 12:0.20 17:0.24 20:0.98 21:0.68 25:0.88 27:0.05 28:0.78 30:0.11 34:0.52 36:0.92 37:0.96 43:0.52 55:0.45 66:0.49 69:0.25 70:0.82 78:0.92 81:0.85 91:0.97 98:0.60 100:0.91 104:0.58 108:0.80 111:0.96 120:0.98 123:0.79 124:0.74 126:0.54 127:0.86 129:0.89 132:0.97 133:0.78 135:0.82 140:0.45 145:0.86 146:0.45 147:0.51 149:0.54 150:0.68 151:0.86 152:0.93 153:0.99 154:0.41 159:0.60 161:0.54 162:0.73 164:0.98 167:0.99 169:0.96 172:0.48 173:0.25 177:0.05 179:0.74 181:0.52 186:0.85 189:0.97 191:0.98 202:0.97 212:0.48 215:0.49 216:0.64 235:0.84 238:0.99 241:0.98 242:0.82 243:0.27 245:0.59 247:0.12 248:0.98 256:0.98 260:0.98 261:0.89 265:0.61 266:0.71 267:0.97 268:0.98 271:0.61 276:0.52 283:0.82 285:0.62 297:0.36 300:0.55 +1 6:0.87 7:0.81 8:0.86 12:0.20 17:0.40 20:0.94 21:0.77 25:0.88 27:0.12 28:0.78 30:0.76 32:0.80 34:0.31 36:0.94 37:0.87 43:0.26 55:0.52 66:0.72 69:0.82 70:0.22 78:0.79 81:0.75 91:0.87 98:0.66 100:0.82 104:0.58 108:0.66 111:0.78 120:0.93 123:0.64 126:0.54 127:0.20 129:0.05 135:0.51 140:0.45 145:0.86 146:0.56 147:0.62 149:0.29 150:0.56 151:0.80 152:0.92 153:0.92 154:0.19 159:0.20 161:0.54 162:0.81 163:0.75 164:0.94 167:0.98 169:0.79 172:0.27 173:0.94 177:0.05 179:0.83 181:0.52 186:0.79 189:0.89 191:0.96 195:0.65 202:0.88 212:0.42 215:0.44 216:0.24 230:0.87 233:0.76 235:0.98 238:0.90 241:0.91 242:0.82 243:0.75 247:0.12 248:0.90 256:0.91 260:0.93 261:0.82 265:0.67 266:0.23 267:0.98 268:0.92 271:0.61 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +2 6:0.97 8:0.95 12:0.20 17:0.59 20:0.83 21:0.68 27:0.34 28:0.78 30:0.45 34:0.29 36:0.29 43:0.21 55:0.45 66:0.49 69:0.93 70:0.25 78:0.78 81:0.85 91:0.82 98:0.16 100:0.81 104:0.58 108:0.90 111:0.78 120:0.83 123:0.89 124:0.48 126:0.54 127:0.17 129:0.59 133:0.47 135:0.42 140:0.94 145:0.65 146:0.05 147:0.05 149:0.16 150:0.68 151:0.66 152:0.80 153:0.48 154:0.13 159:0.35 161:0.54 162:0.47 163:0.97 164:0.90 167:0.97 169:0.79 172:0.16 173:0.95 177:0.05 178:0.53 179:0.84 181:0.52 186:0.79 189:0.98 191:0.89 202:0.95 212:0.61 215:0.49 216:0.35 220:0.99 235:0.80 238:0.92 241:0.86 242:0.82 243:0.29 245:0.39 247:0.12 248:0.75 256:0.86 259:0.21 260:0.83 261:0.78 265:0.17 266:0.17 267:0.59 268:0.87 271:0.61 276:0.14 283:0.60 285:0.62 297:0.36 300:0.18 +1 6:0.93 7:0.81 8:0.97 12:0.20 17:0.59 20:0.82 21:0.68 25:0.88 27:0.28 28:0.78 30:0.68 32:0.80 37:0.80 43:0.22 55:0.71 66:0.68 69:0.90 70:0.31 78:0.76 81:0.85 91:0.95 98:0.44 100:0.78 104:0.58 108:0.86 111:0.76 120:0.97 123:0.86 124:0.41 126:0.54 127:0.25 129:0.59 133:0.47 140:0.45 145:0.79 146:0.05 147:0.05 149:0.27 150:0.68 151:0.85 152:0.78 153:0.98 154:0.15 159:0.47 161:0.54 162:0.74 164:0.97 167:0.98 169:0.79 172:0.37 173:0.90 177:0.05 179:0.77 181:0.52 186:0.75 189:0.94 191:0.93 195:0.84 202:0.94 212:0.19 215:0.49 216:0.52 230:0.87 233:0.76 235:0.84 238:0.93 241:0.93 242:0.82 243:0.19 245:0.45 247:0.12 248:0.96 256:0.96 260:0.97 261:0.75 265:0.46 266:0.47 268:0.96 271:0.61 276:0.28 283:0.82 285:0.62 297:0.36 300:0.25 +3 6:0.99 8:0.97 12:0.20 17:0.34 20:0.75 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.52 36:0.63 37:0.98 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 78:0.80 81:0.96 91:0.70 98:0.88 100:0.92 104:0.58 108:0.39 111:0.79 120:0.81 123:0.38 126:0.54 127:0.43 129:0.05 135:0.44 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.71 152:1.00 153:0.72 154:0.31 159:0.34 161:0.54 162:0.64 163:0.94 164:0.86 167:0.83 169:0.97 172:0.41 173:0.61 177:0.05 179:0.87 181:0.52 186:0.84 187:0.39 189:0.99 191:0.94 195:0.63 202:0.81 212:0.61 215:0.79 216:0.73 220:0.99 235:0.22 238:0.90 241:0.72 242:0.82 243:0.80 247:0.12 248:0.73 256:0.81 259:0.21 260:0.82 261:0.99 265:0.88 266:0.27 267:0.44 268:0.83 271:0.61 276:0.35 283:0.60 285:0.62 297:0.36 300:0.25 +4 6:0.99 8:0.98 12:0.20 17:0.16 20:0.96 21:0.22 25:0.88 27:0.02 28:0.78 30:0.45 32:0.80 34:0.50 36:0.71 37:0.98 43:0.41 55:0.84 66:0.77 69:0.50 70:0.33 78:0.95 81:0.96 91:0.98 98:0.92 100:0.99 104:0.58 108:0.39 111:0.98 120:0.99 123:0.37 126:0.54 127:0.55 129:0.05 135:0.30 140:0.45 145:0.93 146:0.72 147:0.77 149:0.41 150:0.94 151:0.89 152:1.00 153:0.99 154:0.31 159:0.29 161:0.54 162:0.53 163:0.94 164:0.99 167:0.98 169:0.97 172:0.37 173:0.67 177:0.05 179:0.92 181:0.52 186:0.96 189:1.00 191:0.99 195:0.58 202:0.99 212:0.52 215:0.79 216:0.73 220:0.62 235:0.22 238:0.99 241:0.92 242:0.82 243:0.79 247:0.12 248:0.98 256:0.98 259:0.21 260:0.99 261:0.99 265:0.92 266:0.27 267:0.91 268:0.98 271:0.61 276:0.31 283:0.82 285:0.62 297:0.36 300:0.25 +1 12:0.20 17:0.24 21:0.54 27:0.45 28:0.78 30:0.91 32:0.80 34:0.73 36:0.17 37:0.50 43:0.32 55:0.84 66:0.69 69:0.70 70:0.13 81:0.91 91:0.17 98:0.39 108:0.53 123:0.51 126:0.54 127:0.13 129:0.05 135:0.97 145:0.42 146:0.51 147:0.57 149:0.29 150:0.83 154:0.24 159:0.18 161:0.54 163:0.92 167:0.68 172:0.21 173:0.67 177:0.05 178:0.55 179:0.61 181:0.52 187:0.68 195:0.75 212:0.61 215:0.58 216:0.77 235:0.60 241:0.39 242:0.82 243:0.73 247:0.12 259:0.21 265:0.41 266:0.17 267:0.95 271:0.61 276:0.23 283:0.60 297:0.36 300:0.13 +1 6:0.89 7:0.81 8:0.94 12:0.20 17:0.55 20:0.93 21:0.71 25:0.88 27:0.18 28:0.78 30:0.76 32:0.80 34:0.80 36:0.88 37:0.74 43:0.32 55:0.84 66:0.36 69:0.71 70:0.22 78:0.76 81:0.83 91:0.94 98:0.37 100:0.79 104:0.58 108:0.69 111:0.76 120:0.93 123:0.67 124:0.57 126:0.54 127:0.30 129:0.59 133:0.47 135:0.26 140:0.45 145:0.76 146:0.32 147:0.38 149:0.31 150:0.63 151:0.79 152:0.91 153:0.91 154:0.23 159:0.26 161:0.54 162:0.81 163:0.96 164:0.93 167:0.98 169:0.78 172:0.16 173:0.84 177:0.05 179:0.92 181:0.52 186:0.76 189:0.94 191:0.94 195:0.59 202:0.87 212:0.42 215:0.48 216:0.36 220:0.62 230:0.65 233:0.63 235:0.84 238:0.94 241:0.92 242:0.82 243:0.40 245:0.43 247:0.12 248:0.90 256:0.90 260:0.93 261:0.78 265:0.39 266:0.23 267:0.97 268:0.91 271:0.61 276:0.23 283:0.82 285:0.62 297:0.36 300:0.18 +1 7:0.81 12:0.20 17:0.85 21:0.68 27:0.31 28:0.78 30:0.76 32:0.80 34:0.50 36:0.82 37:0.83 43:0.25 55:0.59 66:0.36 69:0.83 70:0.15 81:0.85 91:0.78 98:0.19 104:0.89 106:0.81 108:0.76 120:0.83 123:0.74 124:0.52 126:0.54 127:0.18 129:0.59 133:0.47 135:0.93 140:0.45 145:0.93 146:0.05 147:0.05 149:0.20 150:0.68 151:0.70 153:0.71 154:0.18 159:0.23 161:0.54 162:0.73 163:0.98 164:0.84 167:0.91 172:0.10 173:0.90 177:0.05 179:0.93 181:0.52 187:0.39 195:0.65 202:0.76 206:0.81 212:0.95 215:0.49 216:0.59 220:0.97 230:0.65 233:0.63 235:0.84 241:0.77 242:0.82 243:0.34 245:0.37 247:0.12 248:0.75 256:0.80 260:0.84 265:0.21 266:0.12 267:0.87 268:0.80 271:0.61 276:0.14 285:0.62 297:0.36 300:0.13 +1 6:0.84 7:0.81 8:0.79 12:0.20 17:0.79 20:0.80 21:0.77 27:0.24 28:0.78 32:0.80 34:0.31 36:0.65 37:0.68 43:0.17 66:0.36 69:0.95 78:0.74 81:0.75 91:0.38 98:0.07 100:0.77 104:0.58 108:0.90 111:0.74 120:0.60 123:0.89 126:0.54 127:0.05 129:0.05 135:0.34 140:0.92 145:0.65 146:0.05 147:0.05 149:0.07 150:0.56 151:0.54 152:0.80 153:0.48 154:0.11 159:0.05 161:0.54 162:0.48 164:0.57 167:0.83 169:0.77 172:0.05 173:1.00 177:0.05 178:0.51 181:0.52 186:0.75 187:0.68 189:0.89 191:0.86 195:0.86 202:0.69 215:0.44 216:0.10 220:0.62 230:0.65 233:0.63 235:0.98 238:0.87 241:0.72 243:0.51 248:0.54 256:0.45 259:0.21 260:0.61 261:0.77 265:0.07 267:0.52 268:0.46 271:0.61 285:0.62 297:0.36 +2 6:0.99 8:0.86 12:0.20 17:0.57 20:0.73 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.42 36:0.64 37:0.98 43:0.41 55:0.84 66:0.79 69:0.51 70:0.31 78:0.84 81:0.96 91:0.31 98:0.88 100:0.73 104:0.58 108:0.39 111:0.82 120:0.69 123:0.38 126:0.54 127:0.43 129:0.05 135:0.61 140:0.45 145:0.93 146:0.78 147:0.82 149:0.42 150:0.94 151:0.66 152:1.00 153:0.48 154:0.31 159:0.34 161:0.54 162:0.54 163:0.94 164:0.57 167:0.73 169:0.98 172:0.41 173:0.61 177:0.05 179:0.87 181:0.52 186:0.84 187:0.87 195:0.63 202:0.56 212:0.61 215:0.79 216:0.73 235:0.22 241:0.57 242:0.82 243:0.80 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.99 265:0.88 266:0.27 267:0.82 268:0.46 271:0.61 276:0.35 285:0.62 297:0.36 300:0.25 +3 6:0.99 8:0.99 12:0.20 17:0.38 20:0.75 21:0.22 25:0.88 27:0.02 28:0.78 30:0.68 32:0.80 34:0.55 36:0.76 37:0.98 43:0.37 55:0.84 66:0.79 69:0.60 70:0.31 78:0.90 81:0.96 91:0.29 98:0.88 100:0.97 104:0.58 108:0.46 111:0.94 120:0.72 123:0.44 126:0.54 127:0.43 129:0.05 135:0.78 140:0.45 145:0.93 146:0.78 147:0.82 149:0.41 150:0.94 151:0.70 152:1.00 153:0.69 154:0.28 159:0.34 161:0.54 162:0.86 163:0.94 164:0.62 167:0.78 169:0.98 172:0.41 173:0.70 177:0.05 179:0.87 181:0.52 186:0.90 187:0.68 189:0.99 191:0.86 195:0.63 202:0.46 212:0.61 215:0.79 216:0.73 235:0.22 238:0.98 241:0.67 242:0.82 243:0.80 247:0.12 248:0.66 256:0.45 259:0.21 260:0.73 261:0.99 265:0.88 266:0.27 267:0.97 268:0.55 271:0.61 276:0.35 285:0.62 297:0.36 300:0.25 +1 1:0.69 11:0.92 12:0.37 17:0.70 18:0.87 21:0.47 27:0.54 29:0.62 30:0.57 32:0.80 34:0.66 36:0.75 37:0.61 39:0.37 43:0.81 44:0.93 45:0.96 66:0.98 69:0.53 70:0.77 71:0.70 74:0.85 77:0.89 81:0.38 83:0.60 85:0.80 86:0.95 91:0.38 97:0.89 98:0.91 99:0.83 101:0.97 104:0.96 106:0.81 108:0.41 114:0.57 117:0.86 122:0.75 123:0.39 126:0.44 127:0.50 129:0.05 131:0.61 135:0.69 139:0.95 144:0.76 145:0.89 146:0.97 147:0.98 149:0.91 150:0.54 154:0.76 155:0.58 157:0.95 158:0.72 159:0.70 165:0.70 166:0.82 172:0.95 173:0.38 176:0.55 177:0.70 178:0.55 179:0.20 182:0.86 187:0.39 192:0.59 195:0.86 201:0.68 206:0.81 208:0.54 212:0.60 215:0.41 216:0.65 222:0.48 227:0.61 229:0.61 231:0.88 232:0.97 235:0.60 241:0.12 242:0.28 243:0.98 246:0.61 247:0.88 253:0.47 259:0.21 265:0.91 266:0.54 267:0.99 271:0.14 276:0.89 279:0.82 282:0.88 283:0.78 287:0.61 290:0.57 297:0.36 299:0.88 300:0.70 +2 1:0.69 8:0.77 11:0.87 12:0.37 17:0.28 18:0.85 21:0.13 27:0.45 29:0.62 30:0.73 32:0.80 34:0.75 36:0.17 37:0.50 39:0.37 43:0.81 44:0.93 45:0.83 64:0.77 66:0.08 69:0.69 70:0.62 71:0.70 74:0.78 77:0.70 81:0.65 83:0.60 85:0.85 86:0.88 91:0.29 97:0.89 98:0.35 99:0.83 101:0.97 108:0.52 114:0.75 122:0.80 123:0.80 124:0.85 126:0.44 127:0.49 129:0.59 131:0.61 133:0.84 135:0.90 139:0.91 144:0.76 145:0.47 146:0.54 147:0.60 149:0.82 150:0.62 154:0.76 155:0.58 157:0.93 158:0.79 159:0.73 165:0.70 166:0.94 172:0.45 173:0.54 176:0.66 177:0.70 178:0.55 179:0.51 182:0.90 187:0.68 192:0.51 195:0.88 201:0.68 208:0.54 212:0.27 216:0.77 219:0.92 222:0.48 227:0.61 229:0.61 231:0.90 235:0.22 241:0.21 242:0.28 243:0.43 245:0.71 246:0.95 247:0.76 253:0.55 259:0.21 265:0.33 266:0.91 267:0.52 271:0.14 276:0.63 277:0.69 279:0.82 282:0.88 287:0.61 290:0.75 292:0.95 299:0.88 300:0.70 +2 1:0.74 11:0.87 12:0.37 17:0.26 18:0.81 21:0.40 27:0.45 29:0.62 30:0.86 32:0.80 34:0.75 36:0.18 37:0.50 39:0.37 43:0.82 44:0.93 45:0.77 60:0.87 64:0.77 66:0.54 69:0.69 70:0.46 71:0.70 74:0.76 77:0.70 81:0.58 83:0.91 85:0.80 86:0.88 91:0.22 98:0.36 101:0.97 108:0.52 114:0.62 122:0.80 123:0.50 124:0.50 126:0.54 127:0.17 129:0.05 131:0.61 133:0.43 135:0.93 139:0.92 144:0.76 145:0.50 146:0.55 147:0.61 149:0.82 150:0.76 154:0.77 155:0.67 157:0.91 158:0.91 159:0.33 165:0.93 166:0.93 172:0.48 173:0.61 176:0.73 177:0.70 178:0.55 179:0.36 182:0.90 187:0.68 192:0.87 195:0.82 201:0.93 208:0.64 212:0.42 215:0.42 216:0.77 219:0.95 222:0.48 227:0.61 229:0.61 231:0.90 235:0.60 241:0.41 242:0.33 243:0.63 245:0.66 246:0.95 247:0.67 253:0.49 259:0.21 265:0.38 266:0.63 267:0.82 271:0.14 276:0.43 279:0.86 282:0.88 283:0.78 287:0.61 290:0.62 292:0.91 297:0.36 299:0.88 300:0.55 +1 8:0.83 9:0.76 11:0.81 12:0.37 17:0.72 18:0.80 21:0.78 22:0.93 27:0.21 29:0.62 30:0.15 31:0.95 34:0.56 36:0.81 37:0.74 39:0.37 43:0.71 45:0.90 47:0.93 66:0.13 69:0.89 70:0.98 71:0.70 74:0.64 79:0.96 83:0.60 86:0.84 91:0.60 96:0.87 97:0.89 98:0.27 101:0.52 108:0.77 117:0.86 122:0.77 123:0.76 124:0.95 127:0.72 129:0.05 131:0.96 133:0.94 135:0.68 137:0.95 139:0.84 140:0.45 144:0.76 146:0.69 147:0.54 149:0.66 151:0.90 153:0.83 154:0.55 155:0.58 157:0.93 158:0.79 159:0.90 162:0.82 166:0.61 172:0.51 173:0.68 177:0.38 179:0.35 182:0.91 187:0.39 190:0.77 192:0.51 196:0.95 202:0.62 208:0.54 212:0.26 216:0.95 219:0.92 222:0.48 227:0.97 229:0.95 231:0.90 232:0.85 235:0.31 241:0.50 242:0.59 243:0.24 245:0.77 246:0.61 247:0.67 248:0.82 253:0.82 254:0.80 255:0.74 256:0.64 257:0.65 259:0.21 262:0.93 265:0.29 266:0.95 267:0.99 268:0.68 271:0.14 276:0.80 279:0.82 284:0.93 285:0.56 287:0.96 292:0.95 300:0.84 +2 1:0.74 7:0.81 9:0.76 11:0.81 12:0.37 17:0.93 18:0.88 21:0.59 22:0.93 27:0.72 29:0.62 30:0.60 31:0.93 32:0.75 34:0.90 36:0.32 37:0.37 39:0.37 43:0.65 44:0.93 45:0.88 47:0.93 64:0.77 66:0.90 69:0.23 70:0.74 71:0.70 74:0.86 77:0.98 79:0.93 81:0.45 83:0.60 86:0.94 91:0.61 96:0.80 97:0.89 98:0.98 99:0.83 101:0.52 108:0.18 114:0.58 117:0.86 121:0.90 122:0.80 123:0.18 126:0.54 127:0.81 129:0.05 131:0.96 135:0.70 137:0.93 139:0.96 140:0.45 144:0.76 145:0.76 146:0.86 147:0.89 149:0.64 150:0.67 151:0.81 153:0.48 154:0.94 155:0.67 157:0.94 158:0.79 159:0.44 162:0.86 165:0.70 166:0.93 172:0.73 173:0.33 176:0.73 177:0.70 179:0.62 182:0.91 187:0.21 190:0.77 192:0.87 195:0.63 196:0.96 201:0.93 202:0.36 204:0.89 208:0.64 212:0.52 215:0.39 216:0.10 219:0.92 222:0.48 227:0.97 229:0.95 230:0.87 231:0.90 232:0.92 233:0.76 235:0.80 241:0.21 242:0.42 243:0.91 246:0.95 247:0.76 248:0.73 253:0.82 254:0.74 255:0.74 256:0.45 259:0.21 262:0.93 265:0.98 266:0.63 267:0.76 268:0.46 271:0.14 276:0.60 279:0.82 281:0.91 282:0.84 284:0.87 285:0.56 287:0.96 290:0.58 292:0.95 297:0.36 300:0.70 +1 1:0.74 7:0.66 8:0.63 11:0.87 12:0.37 17:0.95 18:0.87 21:0.59 22:0.93 27:0.68 29:0.62 30:0.29 32:0.80 34:0.58 36:0.87 37:0.28 39:0.37 43:0.75 44:0.93 45:0.89 47:0.93 60:0.95 64:0.77 66:0.90 69:0.29 70:0.81 71:0.70 74:0.83 76:0.94 77:0.96 81:0.49 83:0.91 85:0.72 86:0.91 87:0.98 91:0.44 98:0.91 101:0.97 104:0.97 106:0.81 108:0.22 114:0.56 117:0.86 122:0.77 123:0.22 124:0.36 126:0.54 127:0.79 129:0.05 131:0.61 133:0.37 135:0.79 139:0.95 144:0.76 145:0.49 146:0.92 147:0.93 149:0.82 150:0.72 154:0.66 155:0.67 157:0.95 158:0.91 159:0.58 165:0.93 166:0.93 172:0.87 173:0.27 176:0.66 177:0.70 178:0.55 179:0.38 182:0.90 187:0.39 192:0.87 195:0.74 201:0.93 204:0.85 206:0.81 208:0.64 212:0.61 215:0.38 216:0.10 219:0.95 222:0.48 227:0.61 229:0.61 230:0.69 231:0.90 232:0.72 233:0.76 235:0.95 241:0.02 242:0.51 243:0.90 245:0.66 246:0.95 247:0.67 253:0.46 259:0.21 262:0.93 265:0.91 266:0.38 267:0.92 271:0.14 276:0.78 279:0.86 281:0.91 282:0.88 283:0.78 287:0.61 290:0.56 292:0.91 297:0.36 299:0.88 300:0.55 +1 1:0.74 8:0.77 11:0.87 12:0.37 17:0.77 18:0.93 21:0.22 22:0.93 27:0.37 29:0.62 30:0.84 32:0.80 34:0.84 36:0.95 37:0.70 39:0.37 43:0.95 44:0.93 45:0.83 47:0.93 60:0.87 64:0.77 66:0.86 69:0.19 70:0.40 71:0.70 74:0.92 77:0.70 81:0.71 83:0.91 85:0.94 86:0.97 91:0.54 98:0.86 101:0.97 104:0.89 106:0.81 108:0.15 114:0.71 117:0.86 122:0.57 123:0.15 124:0.37 126:0.54 127:0.57 129:0.05 131:0.61 133:0.43 135:0.89 139:0.98 144:0.76 145:0.49 146:0.88 147:0.90 149:0.96 150:0.82 154:0.95 155:0.67 157:0.96 158:0.91 159:0.52 165:0.93 166:0.94 172:0.86 173:0.23 176:0.73 177:0.70 178:0.55 179:0.35 182:0.90 187:0.21 192:0.87 195:0.72 201:0.93 204:0.85 206:0.81 208:0.64 212:0.81 215:0.51 216:0.65 219:0.95 222:0.48 227:0.61 229:0.61 231:0.90 232:0.92 235:0.42 241:0.10 242:0.24 243:0.86 245:0.75 246:0.95 247:0.79 253:0.55 259:0.21 262:0.93 265:0.86 266:0.47 267:0.95 271:0.14 276:0.77 279:0.86 281:0.91 282:0.88 283:0.78 287:0.61 290:0.71 292:0.91 297:0.36 299:0.88 300:0.43 +1 8:0.61 9:0.80 12:0.37 17:0.90 18:0.57 21:0.78 27:0.55 29:0.62 30:0.21 31:0.96 32:0.64 34:0.96 36:0.85 37:0.46 39:0.37 41:0.82 43:0.17 55:0.92 66:0.18 69:0.97 70:0.86 71:0.70 74:0.27 79:0.96 83:0.91 86:0.57 91:0.71 96:0.89 98:0.18 108:0.95 120:0.73 123:0.94 124:0.88 127:0.98 129:0.59 131:0.96 133:0.86 135:0.90 137:0.96 139:0.55 140:0.95 144:0.76 145:0.80 146:0.47 147:0.05 149:0.12 151:0.91 153:0.72 154:0.10 155:0.67 157:0.61 159:0.92 162:0.80 163:0.87 164:0.64 166:0.61 172:0.16 173:0.93 175:0.75 177:0.05 178:0.48 179:0.90 182:0.91 187:0.21 192:0.87 195:0.98 196:0.87 202:0.75 208:0.64 212:0.37 216:0.14 220:0.74 222:0.48 227:0.97 229:0.95 231:0.90 235:0.52 241:0.32 242:0.16 243:0.18 244:0.61 245:0.45 246:0.61 247:0.60 248:0.81 253:0.82 254:0.90 255:0.95 256:0.77 257:0.84 259:0.21 260:0.74 265:0.19 266:0.47 267:0.69 268:0.80 271:0.14 276:0.33 277:0.69 284:0.95 285:0.62 287:0.96 300:0.55 +2 1:0.74 8:0.75 11:0.87 12:0.37 17:0.49 18:0.87 21:0.47 27:0.34 29:0.62 30:0.62 32:0.80 34:0.40 36:0.36 37:0.57 39:0.37 43:0.86 44:0.93 45:0.88 64:0.77 66:0.09 69:0.60 70:0.57 71:0.70 74:0.79 77:0.70 81:0.53 83:0.91 85:0.88 86:0.93 91:0.44 98:0.37 101:0.97 108:0.62 114:0.60 122:0.80 123:0.91 124:0.93 126:0.54 127:0.69 129:0.81 131:0.61 133:0.93 135:0.70 139:0.93 144:0.76 145:0.87 146:0.23 147:0.29 149:0.91 150:0.74 154:0.83 155:0.67 157:0.95 159:0.81 165:0.93 166:0.93 172:0.41 173:0.38 176:0.73 177:0.70 178:0.55 179:0.38 182:0.90 187:0.21 192:0.87 195:0.92 201:0.93 202:0.36 208:0.64 212:0.21 215:0.41 216:0.76 222:0.48 227:0.61 229:0.61 231:0.90 235:0.68 241:0.37 242:0.31 243:0.23 245:0.77 246:0.95 247:0.76 253:0.48 259:0.21 265:0.32 266:0.91 267:0.59 271:0.14 276:0.77 277:0.69 282:0.88 287:0.61 290:0.60 297:0.36 299:0.88 300:0.55 +2 1:0.69 8:0.80 9:0.76 11:0.81 12:0.37 17:0.95 18:0.95 21:0.05 27:0.72 29:0.62 30:0.70 31:0.95 34:0.20 36:0.45 37:0.73 39:0.37 43:0.71 45:0.95 64:0.77 66:0.95 69:0.33 70:0.57 71:0.70 74:0.87 79:0.95 81:0.75 83:0.60 86:0.97 87:0.98 91:0.86 96:0.86 97:0.94 98:0.94 99:0.83 101:0.52 108:0.26 114:0.85 122:0.80 123:0.25 126:0.44 127:0.62 129:0.05 131:0.96 135:0.76 137:0.95 139:0.96 140:0.45 144:0.76 146:0.92 147:0.94 149:0.90 150:0.70 151:0.90 153:0.80 154:0.54 155:0.58 157:0.96 158:0.79 159:0.54 162:0.78 165:0.70 166:0.94 172:0.88 173:0.31 176:0.66 177:0.70 179:0.34 182:0.91 190:0.77 192:0.51 196:0.97 201:0.68 202:0.59 208:0.54 212:0.55 216:0.04 219:0.92 222:0.48 227:0.97 228:0.99 229:0.95 231:0.90 232:0.72 235:0.12 241:0.84 242:0.55 243:0.96 246:0.95 247:0.67 248:0.80 253:0.87 254:0.84 255:0.74 256:0.58 259:0.21 265:0.94 266:0.38 267:0.76 268:0.64 271:0.14 276:0.80 279:0.82 284:0.92 285:0.56 287:0.96 290:0.85 292:0.95 300:0.55 +2 1:0.69 7:0.81 8:0.71 9:0.76 11:0.87 12:0.37 17:0.87 18:0.97 21:0.40 22:0.93 27:0.31 29:0.62 30:0.67 31:0.86 34:0.59 36:0.79 37:0.77 39:0.37 43:0.70 44:0.90 45:0.96 47:0.93 48:0.91 64:0.77 66:0.67 69:0.80 70:0.64 71:0.70 74:0.91 76:0.85 77:0.70 79:0.86 81:0.42 83:0.60 85:0.72 86:0.97 91:0.75 96:0.68 97:0.97 98:0.88 99:0.83 101:0.97 108:0.64 114:0.61 117:0.86 121:0.90 122:0.80 123:0.62 124:0.47 126:0.44 127:0.71 129:0.05 131:0.96 133:0.43 135:0.89 137:0.86 139:0.98 140:0.80 144:0.76 145:0.49 146:0.94 147:0.55 149:0.94 150:0.55 151:0.49 153:0.48 154:0.53 155:0.67 157:0.96 158:0.79 159:0.68 162:0.62 165:0.70 166:0.93 172:0.89 173:0.75 176:0.66 177:0.38 178:0.42 179:0.26 182:0.91 187:0.21 190:0.77 191:0.70 192:0.87 195:0.84 196:0.90 201:0.68 202:0.50 204:0.89 208:0.64 212:0.60 216:0.31 219:0.92 220:0.91 222:0.48 227:0.97 229:0.95 230:0.87 231:0.90 232:0.93 233:0.76 235:0.52 241:0.03 242:0.50 243:0.31 245:0.94 246:0.95 247:0.84 248:0.48 253:0.51 255:0.74 256:0.45 257:0.65 259:0.21 262:0.93 265:0.88 266:0.54 267:0.79 268:0.46 271:0.14 276:0.86 279:0.82 281:0.91 282:0.77 284:0.87 285:0.56 287:0.96 290:0.61 292:0.95 300:0.55 +2 1:0.74 8:0.61 9:0.80 11:0.87 12:0.37 17:0.40 18:0.81 21:0.30 22:0.93 27:0.34 29:0.62 30:0.74 31:0.95 32:0.80 34:0.64 36:0.71 37:0.57 39:0.37 41:0.90 43:0.86 44:0.93 45:0.77 47:0.93 60:0.95 64:0.77 66:0.26 69:0.59 70:0.41 71:0.70 74:0.77 77:0.70 79:0.94 81:0.64 83:0.91 85:0.85 86:0.89 91:0.53 96:0.83 98:0.42 101:0.97 104:0.84 106:0.81 108:0.74 114:0.65 117:0.86 120:0.73 122:0.80 123:0.73 124:0.81 126:0.54 127:0.40 129:0.81 131:0.96 133:0.78 135:0.99 137:0.95 139:0.93 140:0.45 144:0.76 145:0.89 146:0.17 147:0.22 149:0.84 150:0.79 151:0.87 153:0.48 154:0.83 155:0.67 157:0.92 158:0.92 159:0.60 162:0.81 163:0.75 164:0.73 165:0.93 166:0.93 172:0.41 173:0.48 176:0.73 177:0.70 179:0.56 182:0.91 187:0.21 192:0.87 195:0.81 196:0.97 201:0.93 202:0.61 206:0.81 208:0.64 212:0.28 215:0.44 216:0.76 219:0.95 220:0.74 222:0.48 227:0.97 229:0.95 231:0.90 232:0.89 235:0.52 241:0.32 242:0.29 243:0.23 245:0.68 246:0.95 247:0.67 248:0.80 253:0.84 255:0.95 256:0.64 259:0.21 260:0.74 262:0.93 265:0.44 266:0.80 267:0.69 268:0.66 271:0.14 276:0.57 277:0.69 279:0.86 282:0.88 283:0.82 284:0.94 285:0.62 287:0.96 290:0.65 292:0.95 297:0.36 299:0.88 300:0.43 +1 1:0.74 11:0.87 12:0.37 17:0.59 18:0.84 21:0.47 27:0.34 29:0.62 30:0.75 32:0.80 34:0.63 36:0.62 37:0.57 39:0.37 43:0.86 44:0.93 45:0.73 64:0.77 66:0.31 69:0.60 70:0.47 71:0.70 74:0.76 77:0.70 81:0.53 83:0.91 85:0.88 86:0.92 91:0.38 98:0.44 101:0.97 108:0.62 114:0.60 122:0.57 123:0.60 124:0.73 126:0.54 127:0.53 129:0.81 131:0.61 133:0.67 135:0.87 139:0.92 144:0.76 145:0.87 146:0.23 147:0.29 149:0.89 150:0.74 154:0.83 155:0.67 157:0.92 159:0.63 165:0.93 166:0.93 172:0.48 173:0.51 176:0.73 177:0.70 178:0.55 179:0.57 182:0.90 187:0.21 192:0.87 195:0.79 201:0.93 208:0.64 212:0.23 215:0.41 216:0.76 222:0.48 227:0.61 229:0.61 231:0.90 235:0.68 241:0.27 242:0.33 243:0.36 245:0.75 246:0.95 247:0.76 253:0.47 259:0.21 265:0.46 266:0.75 267:0.85 271:0.14 276:0.55 282:0.88 287:0.61 290:0.60 297:0.36 299:0.88 300:0.55 +2 1:0.74 7:0.81 8:0.59 9:0.80 11:0.87 12:0.37 17:0.89 18:0.96 21:0.47 22:0.93 27:0.58 29:0.62 30:0.76 31:0.86 34:0.99 36:0.41 37:0.42 39:0.37 41:0.82 43:0.64 44:0.90 45:0.96 47:0.93 64:0.77 66:0.97 69:0.78 70:0.53 71:0.70 74:0.93 77:0.99 79:0.86 81:0.60 83:0.91 85:0.85 86:0.98 91:0.37 96:0.68 97:0.89 98:0.75 99:0.83 101:0.97 108:0.61 114:0.60 117:0.86 120:0.54 121:0.90 122:0.80 123:0.59 126:0.54 127:0.24 129:0.05 131:0.96 135:0.86 137:0.86 139:0.98 140:0.45 144:0.76 145:0.80 146:0.87 147:0.89 149:0.89 150:0.76 151:0.48 153:0.48 154:0.54 155:0.67 157:0.97 158:0.79 159:0.44 162:0.86 164:0.57 165:1.00 166:0.93 172:0.92 173:0.74 176:0.73 177:0.70 179:0.16 182:0.91 187:0.21 192:0.87 195:0.79 196:0.89 201:0.93 202:0.36 204:0.89 208:0.64 212:0.87 215:0.41 216:0.23 219:0.92 220:0.93 222:0.48 227:0.97 229:0.95 230:0.87 231:0.90 232:0.91 233:0.76 235:0.80 241:0.03 242:0.50 243:0.97 246:0.95 247:0.76 248:0.48 253:0.50 255:0.95 256:0.45 259:0.21 260:0.54 262:0.93 265:0.75 266:0.47 267:0.69 268:0.46 271:0.14 276:0.85 279:0.82 281:0.91 282:0.77 284:0.89 285:0.62 287:0.96 290:0.60 292:0.95 297:0.36 300:0.70 +1 1:0.74 11:0.87 12:0.37 17:0.36 18:0.89 21:0.47 27:0.34 29:0.62 30:0.60 32:0.80 34:0.80 36:0.31 37:0.57 39:0.37 43:0.86 44:0.93 45:0.87 64:0.77 66:0.10 69:0.60 70:0.70 71:0.70 74:0.80 77:0.70 81:0.53 83:0.91 85:0.90 86:0.94 91:0.35 98:0.43 101:0.97 108:0.92 114:0.60 122:0.80 123:0.88 124:0.91 126:0.54 127:0.71 129:0.89 131:0.61 133:0.90 135:0.91 139:0.94 144:0.76 145:0.87 146:0.23 147:0.29 149:0.91 150:0.74 154:0.83 155:0.67 157:0.95 159:0.81 165:0.93 166:0.93 172:0.51 173:0.38 176:0.73 177:0.70 178:0.55 179:0.35 182:0.90 187:0.21 192:0.87 195:0.92 201:0.93 208:0.64 212:0.47 215:0.41 216:0.76 222:0.48 227:0.61 229:0.61 231:0.90 235:0.68 241:0.27 242:0.22 243:0.22 245:0.82 246:0.95 247:0.84 253:0.48 259:0.21 265:0.30 266:0.92 267:0.82 271:0.14 276:0.80 282:0.88 287:0.61 290:0.60 297:0.36 299:0.88 300:0.70 +2 1:0.74 8:0.60 11:0.87 12:0.37 17:0.66 18:0.85 21:0.40 22:0.93 27:0.34 29:0.62 30:0.55 32:0.80 34:0.49 36:0.87 37:0.57 39:0.37 43:0.83 44:0.93 45:0.83 47:0.93 64:0.77 66:0.61 69:0.59 70:0.80 71:0.70 74:0.84 77:0.89 81:0.56 83:0.60 85:0.80 86:0.94 91:0.23 97:0.89 98:0.74 99:0.83 101:0.97 104:0.88 106:0.81 108:0.45 114:0.62 117:0.86 122:0.57 123:0.43 124:0.50 126:0.54 127:0.43 129:0.59 131:0.61 133:0.46 135:0.99 139:0.95 144:0.76 145:0.86 146:0.77 147:0.81 149:0.74 150:0.72 154:0.82 155:0.67 157:0.94 158:0.91 159:0.46 165:0.70 166:0.93 172:0.68 173:0.60 176:0.73 177:0.70 178:0.55 179:0.47 182:0.90 187:0.21 192:0.87 195:0.68 201:0.93 206:0.81 208:0.64 212:0.50 215:0.42 216:0.76 219:0.95 222:0.48 227:0.61 229:0.61 231:0.90 232:0.95 235:0.60 241:0.27 242:0.27 243:0.68 245:0.81 246:0.95 247:0.82 253:0.50 259:0.21 262:0.93 265:0.74 266:0.47 267:0.98 271:0.14 276:0.65 279:0.86 282:0.88 283:0.77 287:0.61 290:0.62 292:0.91 297:0.36 300:0.70 +0 7:0.81 12:0.20 17:0.34 18:0.62 21:0.22 26:0.96 27:0.67 29:0.62 30:0.91 32:0.75 34:0.40 36:1.00 37:0.84 39:0.37 43:0.15 44:0.93 48:0.97 55:0.84 58:0.93 66:0.60 69:0.79 70:0.28 71:0.97 74:0.57 77:0.70 81:0.48 86:0.63 91:0.68 98:0.64 104:0.58 108:0.63 120:0.59 121:0.90 123:0.60 124:0.55 126:0.26 127:0.54 129:0.05 133:0.62 135:0.34 139:0.82 140:0.45 141:0.91 145:0.70 146:0.69 147:0.44 149:0.23 150:0.38 151:0.51 153:0.48 154:0.11 155:0.67 159:0.50 162:0.86 163:0.94 164:0.53 172:0.45 173:0.82 175:0.75 177:0.05 179:0.79 187:0.21 190:0.89 192:0.87 195:0.73 199:0.94 202:0.36 204:0.89 208:0.64 212:0.39 215:0.51 216:0.15 220:0.74 222:0.76 223:0.95 224:0.93 230:0.87 233:0.76 234:0.91 235:0.22 241:0.83 242:0.76 243:0.25 244:0.83 245:0.53 247:0.39 248:0.51 253:0.38 256:0.45 257:0.84 259:0.21 260:0.58 265:0.65 266:0.38 267:0.82 268:0.46 271:0.21 274:0.91 276:0.44 277:0.69 281:0.91 282:0.83 285:0.47 297:0.36 300:0.33 +1 1:0.74 7:0.66 11:0.64 12:0.20 17:0.67 18:0.79 21:0.22 26:0.99 27:0.49 29:0.62 30:0.38 32:0.75 34:0.95 36:0.21 37:0.72 39:0.37 43:0.59 44:0.93 45:0.77 58:0.93 64:0.77 66:0.83 69:0.77 70:0.50 71:0.97 74:0.66 77:0.70 81:0.73 83:0.91 86:0.86 91:0.42 98:0.43 101:0.52 108:0.60 114:0.71 122:0.57 123:0.58 126:0.54 127:0.14 129:0.05 135:0.87 139:0.89 141:0.91 145:0.67 146:0.54 147:0.60 149:0.29 150:0.83 154:0.66 155:0.67 159:0.19 165:0.93 172:0.51 173:0.77 176:0.73 177:0.70 178:0.55 179:0.28 187:0.39 192:0.87 195:0.74 199:0.96 201:0.93 204:0.85 208:0.64 212:0.70 215:0.51 216:0.62 222:0.76 223:0.99 224:0.93 230:0.69 233:0.76 234:0.91 235:0.42 241:0.35 242:0.29 243:0.84 247:0.67 253:0.53 254:0.92 259:0.21 265:0.45 266:0.38 267:0.52 271:0.21 274:0.91 276:0.41 281:0.91 282:0.84 290:0.71 297:0.36 300:0.33 +2 1:0.69 7:0.72 11:0.64 12:0.20 17:0.15 18:0.75 21:0.22 26:0.99 27:0.39 29:0.62 30:0.28 32:0.69 34:0.29 36:0.26 37:0.89 39:0.37 43:0.79 45:0.66 55:0.45 58:0.93 66:0.87 69:0.60 70:0.70 71:0.97 74:0.60 76:0.85 77:0.70 81:0.62 86:0.84 91:0.49 98:0.69 101:0.52 104:0.79 108:0.45 114:0.67 123:0.44 126:0.44 127:0.21 129:0.05 135:0.44 139:0.80 141:0.91 145:0.56 146:0.52 147:0.58 149:0.57 150:0.59 154:0.73 155:0.58 159:0.19 172:0.63 173:0.76 176:0.66 177:0.70 178:0.55 179:0.42 187:0.39 192:0.59 195:0.58 199:0.94 201:0.68 204:0.85 208:0.54 212:0.93 215:0.51 216:0.82 222:0.76 223:0.99 224:0.93 230:0.74 233:0.73 234:0.91 235:0.31 241:0.44 242:0.28 243:0.88 247:0.74 253:0.51 259:0.21 265:0.69 266:0.17 267:0.44 271:0.21 274:0.91 276:0.51 282:0.77 290:0.67 297:0.36 300:0.43 +3 7:0.81 8:0.89 12:0.20 17:0.11 18:0.62 21:0.22 26:0.96 27:0.67 29:0.62 30:0.91 32:0.75 34:0.33 36:1.00 37:0.84 39:0.37 43:0.16 44:0.93 48:0.97 55:0.84 58:0.93 66:0.60 69:0.77 70:0.28 71:0.97 74:0.57 77:0.70 81:0.48 86:0.63 91:0.74 98:0.65 104:0.58 108:0.61 120:0.64 121:0.90 123:0.58 124:0.55 126:0.26 127:0.57 129:0.05 133:0.62 135:0.37 139:0.82 140:0.45 141:0.91 145:0.70 146:0.69 147:0.44 149:0.23 150:0.38 151:0.53 153:0.82 154:0.11 155:0.67 159:0.49 162:0.54 163:0.86 164:0.71 172:0.45 173:0.80 175:0.75 177:0.05 179:0.79 190:0.89 192:0.87 195:0.72 199:0.94 202:0.76 204:0.89 208:0.64 212:0.39 215:0.51 216:0.15 220:0.82 222:0.76 223:0.95 224:0.93 230:0.87 233:0.76 234:0.91 235:0.22 241:0.89 242:0.76 243:0.25 244:0.83 245:0.53 247:0.39 248:0.55 253:0.38 256:0.71 257:0.84 259:0.21 260:0.62 265:0.65 266:0.38 267:0.79 268:0.74 271:0.21 274:0.91 276:0.44 277:0.69 281:0.91 282:0.83 285:0.47 297:0.36 300:0.33 +2 1:0.74 11:0.64 12:0.20 17:0.34 18:0.79 21:0.30 26:0.99 27:0.47 29:0.62 30:0.76 32:0.79 34:0.74 36:0.41 37:0.29 39:0.37 43:0.76 44:0.93 45:0.83 58:0.93 64:0.77 66:0.30 69:0.52 70:0.53 71:0.97 74:0.71 77:0.70 81:0.64 83:0.60 86:0.89 91:0.25 98:0.23 99:0.83 101:0.52 104:0.91 108:0.62 114:0.65 122:0.57 123:0.60 124:0.73 126:0.54 127:0.62 129:0.59 133:0.66 135:0.37 139:0.89 141:0.91 145:0.44 146:0.19 147:0.24 149:0.61 150:0.76 154:0.84 155:0.67 159:0.53 165:0.70 172:0.32 173:0.50 176:0.73 177:0.70 178:0.55 179:0.78 187:0.68 192:0.87 195:0.71 199:0.96 201:0.93 208:0.64 212:0.49 215:0.44 216:0.73 222:0.76 223:0.99 224:0.93 234:0.91 235:0.52 241:0.01 242:0.38 243:0.31 245:0.60 247:0.47 253:0.50 259:0.21 265:0.25 266:0.38 267:0.36 271:0.21 274:0.91 276:0.34 282:0.87 290:0.65 297:0.36 300:0.55 +4 1:0.74 6:1.00 8:0.96 9:0.80 11:0.64 12:0.20 17:0.30 18:0.73 20:0.99 21:0.59 26:1.00 27:1.00 29:0.62 30:0.33 31:0.97 34:0.49 36:0.88 37:0.73 39:0.37 43:0.74 45:0.66 58:1.00 64:0.77 66:0.32 69:0.85 70:0.49 71:0.97 74:0.39 78:0.96 79:0.97 81:0.45 83:0.60 86:0.80 91:0.82 96:0.91 98:0.55 99:0.83 100:1.00 101:0.52 104:0.58 108:0.41 111:1.00 114:0.58 120:0.78 123:0.39 124:0.61 126:0.54 127:0.36 129:0.05 133:0.43 135:0.58 137:0.97 139:0.77 140:0.45 141:1.00 146:0.22 147:0.57 149:0.51 150:0.67 151:0.94 152:0.99 153:0.82 154:0.64 155:0.67 159:0.33 162:0.81 164:0.86 165:0.70 169:1.00 172:0.21 173:0.96 176:0.73 177:0.38 179:0.85 186:0.96 189:1.00 192:0.87 196:0.94 199:0.98 201:0.93 202:0.80 208:0.64 212:0.19 215:0.39 216:0.16 222:0.76 223:1.00 224:0.99 234:0.98 235:0.80 238:1.00 241:0.89 242:0.19 243:0.49 245:0.54 247:0.55 248:0.85 253:0.86 255:0.95 256:0.85 257:0.65 259:0.21 260:0.79 261:1.00 265:0.57 266:0.47 267:0.91 268:0.85 271:0.21 274:0.99 276:0.29 277:0.69 284:0.98 285:0.62 290:0.58 297:0.36 300:0.33 +0 8:0.99 11:0.64 12:0.20 17:0.04 18:0.70 21:0.13 26:0.95 27:0.47 29:0.62 30:0.76 34:0.36 36:0.83 37:0.64 39:0.37 43:0.60 45:0.66 55:0.71 58:0.93 66:0.77 69:0.63 70:0.29 71:0.97 74:0.36 81:0.58 86:0.73 91:0.88 98:0.76 101:0.52 104:0.76 108:0.48 120:0.64 123:0.46 126:0.26 127:0.25 129:0.05 135:0.47 139:0.70 140:0.45 141:0.91 146:0.57 147:0.63 149:0.33 150:0.43 151:0.53 153:0.83 154:0.49 159:0.21 162:0.80 164:0.82 172:0.37 173:0.80 177:0.70 179:0.81 190:0.89 199:0.94 202:0.79 212:0.73 215:0.61 216:0.31 220:0.91 222:0.76 223:0.94 224:0.93 234:0.91 235:0.12 241:0.90 242:0.24 243:0.79 244:0.61 247:0.47 248:0.55 253:0.29 256:0.84 259:0.21 260:0.62 265:0.76 266:0.23 267:0.28 268:0.84 271:0.21 274:0.91 276:0.29 283:0.78 285:0.47 297:0.36 300:0.25 +0 11:0.64 12:0.20 17:0.64 18:0.96 21:0.78 26:0.99 27:0.67 29:0.62 30:0.13 34:0.47 36:0.47 37:0.84 39:0.37 43:0.06 45:0.93 58:0.93 66:0.12 69:0.08 70:0.88 71:0.97 74:0.81 86:0.99 91:0.18 98:0.39 101:0.52 104:0.58 108:0.90 122:0.57 123:0.90 124:0.94 127:0.85 129:0.98 133:0.94 135:0.19 139:0.98 141:0.91 146:0.22 147:0.27 149:0.11 154:0.91 159:0.85 172:0.61 173:0.08 177:0.70 178:0.55 179:0.26 187:0.21 199:0.97 212:0.47 216:0.15 222:0.76 223:0.99 224:0.93 234:0.91 235:0.05 241:0.21 242:0.23 243:0.12 245:0.86 247:0.76 253:0.44 254:0.80 259:0.21 265:0.41 266:0.71 267:0.23 271:0.21 274:0.91 276:0.87 300:0.70 +1 6:1.00 9:0.80 11:0.64 12:0.20 17:0.32 18:0.70 20:0.91 21:0.40 26:1.00 27:1.00 29:0.62 30:0.71 31:0.89 34:0.83 36:0.81 37:0.73 39:0.37 43:0.80 45:0.73 58:0.98 66:0.82 69:0.74 70:0.24 71:0.97 74:0.52 78:0.72 79:0.88 81:0.34 83:0.60 86:0.81 91:0.44 96:0.72 98:0.65 100:0.96 101:0.52 104:0.58 108:0.57 111:0.90 120:0.59 123:0.54 126:0.26 127:0.19 129:0.05 135:0.47 137:0.89 139:0.78 140:0.45 141:1.00 146:0.65 147:0.70 149:0.73 150:0.31 151:0.56 152:0.99 153:0.48 154:0.74 155:0.67 159:0.24 162:0.86 164:0.67 169:1.00 172:0.48 173:0.80 177:0.70 179:0.56 186:0.73 187:0.21 192:0.87 196:0.90 199:0.95 202:0.50 208:0.64 212:0.50 215:0.42 216:0.16 220:0.82 222:0.76 223:1.00 224:0.99 234:0.99 235:0.42 241:0.39 242:0.53 243:0.83 247:0.47 248:0.58 253:0.48 255:0.95 256:0.58 259:0.21 260:0.59 261:1.00 265:0.66 266:0.38 267:0.59 268:0.59 271:0.21 274:0.98 276:0.38 284:0.92 285:0.62 297:0.36 300:0.18 +1 1:0.74 7:0.66 11:0.64 12:0.20 17:0.66 18:0.69 20:0.91 21:0.40 26:0.99 27:0.59 29:0.62 30:0.21 32:0.80 34:0.77 36:0.34 37:0.62 39:0.37 43:0.11 44:0.93 45:0.73 58:0.93 64:0.77 66:0.80 69:0.78 70:0.50 71:0.97 74:0.65 77:0.70 78:0.72 81:0.57 83:0.60 86:0.80 91:0.37 98:0.38 99:0.83 100:0.73 101:0.52 108:0.61 111:0.72 114:0.62 123:0.59 126:0.54 127:0.13 129:0.05 135:0.89 139:0.86 141:0.91 145:0.52 146:0.38 147:0.45 149:0.08 150:0.73 152:0.85 154:0.67 155:0.67 159:0.15 165:0.70 169:0.72 172:0.45 173:0.86 176:0.73 177:0.70 178:0.55 179:0.28 186:0.73 187:0.39 192:0.87 195:0.64 199:0.95 201:0.93 204:0.85 208:0.64 212:0.90 215:0.42 216:0.50 222:0.76 223:0.99 224:0.93 230:0.69 233:0.73 234:0.91 235:0.60 241:0.60 242:0.40 243:0.82 247:0.47 253:0.47 254:0.90 259:0.21 261:0.73 265:0.41 266:0.17 267:0.44 271:0.21 274:0.91 276:0.37 281:0.91 282:0.88 290:0.62 297:0.36 300:0.33 +3 11:0.64 12:0.20 17:0.47 18:0.82 21:0.22 26:0.98 27:1.00 29:0.62 30:0.72 34:0.73 36:0.81 37:0.73 39:0.37 43:0.93 45:0.83 58:0.93 66:0.49 69:0.27 70:0.53 71:0.97 74:0.71 81:0.48 86:0.92 91:0.35 98:0.58 101:0.52 104:0.58 108:0.21 122:0.80 123:0.20 124:0.56 126:0.26 127:0.33 129:0.59 133:0.46 135:0.65 139:0.89 141:0.91 146:0.68 147:0.73 149:0.89 150:0.38 154:0.93 159:0.46 172:0.57 173:0.26 177:0.70 178:0.55 179:0.49 187:0.21 199:0.95 212:0.60 215:0.51 216:0.16 222:0.76 223:0.98 224:0.93 234:0.91 235:0.22 241:0.27 242:0.37 243:0.60 245:0.79 247:0.55 253:0.41 259:0.21 265:0.59 266:0.54 267:0.69 271:0.21 274:0.91 276:0.56 297:0.36 300:0.55 +1 1:0.69 7:0.63 11:0.75 12:0.20 17:0.36 18:0.64 21:0.13 27:0.45 28:0.96 29:0.71 30:0.10 34:0.74 36:0.17 37:0.50 39:0.90 43:0.64 45:0.73 55:0.71 66:0.43 69:0.60 70:0.92 71:0.77 74:0.52 81:0.51 83:0.60 86:0.74 91:0.18 98:0.45 99:0.83 101:0.52 108:0.86 114:0.75 123:0.85 124:0.80 126:0.44 127:0.50 129:0.05 131:0.61 133:0.81 135:0.86 139:0.71 144:0.57 145:0.49 146:0.53 147:0.59 149:0.32 150:0.57 154:0.74 155:0.58 157:0.82 159:0.67 161:0.86 165:0.70 166:0.79 167:0.53 172:0.54 173:0.47 176:0.66 177:0.70 178:0.55 179:0.56 181:0.80 182:0.88 187:0.68 192:0.59 201:0.68 208:0.54 212:0.39 215:0.61 216:0.77 222:0.97 227:0.61 229:0.61 230:0.63 231:0.69 233:0.73 235:0.22 241:0.18 242:0.76 243:0.39 245:0.64 246:0.86 247:0.60 253:0.54 254:0.84 259:0.21 265:0.47 266:0.80 267:0.52 271:0.94 276:0.60 277:0.87 287:0.61 290:0.75 297:0.36 300:0.70 +2 1:0.74 11:0.75 12:0.20 17:0.66 18:0.65 21:0.47 27:0.07 28:0.96 29:0.71 30:0.15 32:0.68 34:0.56 36:0.32 37:0.88 39:0.90 43:0.28 44:0.90 45:0.66 55:0.59 64:0.77 66:0.20 69:0.69 70:0.95 71:0.77 74:0.45 77:0.70 81:0.47 83:0.60 86:0.66 91:0.46 96:0.80 98:0.53 99:0.83 101:0.52 104:0.92 106:0.81 108:0.84 114:0.60 117:0.86 123:0.50 124:0.84 126:0.54 127:0.63 129:0.59 131:0.81 133:0.81 135:0.73 139:0.72 140:0.45 144:0.57 145:0.58 146:0.81 147:0.84 149:0.36 150:0.67 151:0.60 153:0.48 154:0.47 155:0.67 157:0.77 158:0.72 159:0.76 161:0.86 162:0.86 165:0.70 166:0.79 167:0.56 172:0.61 173:0.54 176:0.73 177:0.70 179:0.36 181:0.80 182:0.88 187:0.68 190:0.89 192:0.87 195:0.89 201:0.93 202:0.36 206:0.81 208:0.64 212:0.29 215:0.41 216:0.58 222:0.97 227:0.82 229:0.61 231:0.69 232:0.94 235:0.68 241:0.32 242:0.61 243:0.40 244:0.61 245:0.83 246:0.86 247:0.76 248:0.59 253:0.51 256:0.45 259:0.21 265:0.45 266:0.91 267:0.36 268:0.46 271:0.94 276:0.78 282:0.77 285:0.47 287:0.61 290:0.60 297:0.36 300:0.84 +1 1:0.69 7:0.63 11:0.64 12:0.20 17:0.43 18:0.74 21:0.64 27:0.45 28:0.96 29:0.71 30:0.53 34:0.59 36:0.18 37:0.50 39:0.90 43:0.64 45:0.66 55:0.59 64:0.77 66:0.46 69:0.62 70:0.72 71:0.77 74:0.30 77:0.70 81:0.32 83:0.60 86:0.81 91:0.17 98:0.56 99:0.83 101:0.52 108:0.77 114:0.55 123:0.75 124:0.58 126:0.44 127:0.39 129:0.05 131:0.61 133:0.37 135:0.88 139:0.76 145:0.45 146:0.68 147:0.73 149:0.41 150:0.51 154:0.59 155:0.58 157:0.61 159:0.51 161:0.86 165:0.70 166:0.73 167:0.54 172:0.59 173:0.57 176:0.55 177:0.70 178:0.55 179:0.48 181:0.80 182:0.61 187:0.68 192:0.51 195:0.75 201:0.68 208:0.54 212:0.58 216:0.77 222:0.97 227:0.61 229:0.61 230:0.63 231:0.68 233:0.73 235:0.80 241:0.21 242:0.72 243:0.46 245:0.82 246:0.61 247:0.76 253:0.36 254:0.99 259:0.21 265:0.57 266:0.71 267:0.28 271:0.94 276:0.63 277:0.87 282:0.71 287:0.61 290:0.55 300:0.70 +1 8:0.92 9:0.76 12:0.20 17:0.22 21:0.78 27:0.32 28:0.96 29:0.71 31:0.86 34:0.63 36:0.27 37:0.71 39:0.90 71:0.77 79:0.86 91:0.95 96:0.76 104:0.78 120:0.84 131:0.85 135:0.76 137:0.86 140:0.98 144:0.57 151:0.51 153:0.72 155:0.58 157:0.61 161:0.86 162:0.59 164:0.82 166:0.61 167:0.89 175:0.97 181:0.80 182:0.61 190:0.89 191:0.90 192:0.59 202:0.92 208:0.54 216:0.46 220:0.74 222:0.97 227:0.85 229:0.61 231:0.69 241:0.93 244:0.93 246:0.61 248:0.52 253:0.44 255:0.79 256:0.93 257:0.93 259:0.21 260:0.65 267:0.89 268:0.93 271:0.94 277:0.95 283:0.78 284:0.87 285:0.62 287:0.61 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.20 17:0.79 18:0.87 21:0.40 22:0.93 27:0.72 28:0.96 29:0.71 30:0.28 31:0.95 32:0.80 34:0.68 36:0.67 39:0.90 41:0.82 43:0.86 44:0.93 47:0.93 60:0.87 64:0.77 66:0.96 69:0.59 70:0.62 71:0.77 74:0.88 77:0.89 79:0.95 81:0.58 83:0.91 85:0.91 86:0.95 91:0.82 96:0.85 98:0.92 101:0.87 104:0.75 106:0.81 108:0.45 114:0.62 117:0.86 120:0.76 121:0.90 123:0.43 126:0.54 127:0.54 129:0.05 131:0.85 135:0.86 137:0.95 139:0.97 140:0.45 144:0.57 145:0.98 146:0.89 147:0.91 149:0.93 150:0.76 151:0.94 153:0.80 154:0.83 155:0.67 157:0.91 158:0.92 159:0.49 161:0.86 162:0.70 164:0.65 165:0.93 166:0.79 167:0.54 172:0.88 173:0.60 176:0.73 177:0.70 179:0.32 181:0.80 182:0.89 187:0.39 192:0.87 195:0.72 196:0.98 201:0.93 202:0.55 204:0.89 206:0.81 208:0.64 212:0.91 215:0.42 216:0.09 219:0.95 222:0.97 227:0.84 229:0.94 230:0.87 231:0.69 232:0.85 233:0.76 235:0.80 241:0.24 242:0.38 243:0.96 246:0.86 247:0.90 248:0.83 253:0.88 255:0.95 256:0.45 259:0.21 260:0.77 262:0.93 265:0.92 266:0.38 267:0.92 268:0.58 271:0.94 276:0.80 279:0.86 281:0.91 282:0.88 283:0.82 284:0.91 285:0.62 287:0.88 290:0.62 292:0.95 297:0.36 299:0.88 300:0.55 +2 6:0.86 8:0.73 12:0.20 17:0.70 18:0.77 20:0.86 21:0.05 27:0.33 28:0.96 29:0.71 30:0.21 31:0.93 32:0.62 34:0.98 36:0.41 37:0.48 39:0.90 43:0.13 55:0.45 64:0.77 66:0.85 69:0.55 70:0.59 71:0.77 78:0.91 79:0.94 81:0.64 86:0.78 91:0.84 98:0.82 100:0.91 104:0.91 108:0.43 111:0.90 120:0.71 123:0.41 126:0.44 127:0.32 129:0.05 131:0.81 135:0.37 137:0.93 139:0.79 140:0.80 145:0.44 146:0.64 147:0.69 149:0.17 150:0.44 151:0.70 152:0.81 153:0.72 154:0.56 157:0.61 159:0.24 161:0.86 162:0.77 164:0.54 166:0.74 167:0.72 169:0.89 172:0.57 173:0.72 175:0.75 177:0.38 179:0.66 181:0.80 182:0.61 186:0.91 187:0.39 189:0.88 190:0.77 191:0.84 192:0.51 195:0.56 196:0.93 201:0.68 202:0.72 212:0.49 215:0.78 216:0.29 219:0.92 220:0.74 222:0.97 227:0.83 229:0.61 231:0.63 235:0.12 238:0.86 241:0.68 242:0.71 243:0.86 244:0.61 246:0.61 247:0.47 248:0.72 253:0.20 254:0.80 255:0.74 256:0.77 257:0.65 259:0.21 260:0.61 261:0.87 265:0.82 266:0.27 267:0.82 268:0.77 271:0.94 276:0.43 277:0.69 283:0.78 284:0.94 285:0.56 287:0.61 292:0.91 297:0.36 300:0.43 +2 8:0.89 9:0.80 11:0.75 12:0.20 17:0.30 18:0.63 21:0.13 27:0.16 28:0.96 29:0.71 30:0.14 31:0.91 32:0.62 34:0.75 36:0.52 37:0.80 39:0.90 43:0.26 45:0.66 55:0.45 66:0.61 69:0.67 70:0.82 71:0.77 74:0.49 76:1.00 78:0.92 79:0.91 81:0.33 83:0.91 86:0.73 91:0.88 96:0.92 98:0.75 101:0.52 104:0.58 108:0.72 111:0.89 114:0.63 117:0.86 120:0.79 123:0.70 124:0.50 126:0.26 127:0.38 129:0.05 131:0.85 133:0.43 135:0.87 137:0.91 139:0.70 140:0.98 144:0.57 145:0.56 146:0.54 147:0.60 149:0.39 150:0.30 151:0.72 153:0.69 154:0.64 155:0.67 157:0.77 158:0.78 159:0.61 161:0.86 162:0.68 164:0.83 166:0.73 167:0.74 169:0.87 172:0.81 173:0.58 176:0.55 177:0.70 179:0.29 181:0.80 182:0.88 187:0.21 190:0.89 191:0.90 192:0.87 195:0.84 196:0.90 202:0.90 208:0.64 212:0.75 216:0.72 220:0.62 222:0.97 227:0.85 229:0.93 231:0.70 232:0.93 235:0.12 241:0.71 242:0.76 243:0.37 245:0.91 246:0.61 247:0.60 248:0.67 253:0.78 254:0.98 255:0.95 256:0.91 259:0.21 260:0.69 265:0.76 266:0.54 267:0.44 268:0.92 271:0.94 276:0.79 277:0.69 279:0.82 283:0.78 284:0.91 285:0.62 287:0.88 290:0.63 300:0.70 +2 1:0.69 8:0.66 9:0.76 11:0.86 12:0.20 17:0.62 18:0.93 20:0.83 21:0.64 22:0.93 27:0.68 28:0.96 29:0.71 30:0.41 31:0.86 34:0.87 36:0.47 37:0.56 39:0.90 43:0.52 45:0.83 47:0.93 55:0.59 64:0.77 66:0.95 69:0.30 70:0.73 71:0.77 74:0.37 78:0.90 79:0.86 81:0.30 83:0.60 86:0.97 91:0.65 96:0.67 97:0.89 98:0.88 100:0.89 101:0.87 104:0.58 108:0.24 111:0.88 114:0.55 117:0.86 122:0.82 123:0.23 126:0.44 127:0.42 129:0.05 131:0.81 135:0.92 137:0.86 139:0.96 140:0.45 144:0.57 146:0.88 147:0.90 149:0.35 150:0.47 151:0.46 152:0.79 153:0.48 154:0.74 155:0.58 157:0.78 158:0.72 159:0.46 161:0.86 162:0.86 166:0.73 167:0.54 169:0.86 172:0.86 173:0.31 176:0.55 177:0.70 179:0.32 181:0.80 182:0.73 186:0.90 187:0.87 190:0.77 192:0.51 196:0.88 201:0.68 202:0.36 208:0.54 212:0.85 216:0.13 219:0.92 220:0.97 222:0.97 227:0.82 229:0.61 231:0.69 232:0.98 235:0.80 241:0.24 242:0.15 243:0.95 246:0.61 247:0.92 248:0.46 253:0.36 254:0.92 255:0.74 256:0.45 259:0.21 261:0.84 262:0.93 265:0.88 266:0.27 267:0.79 268:0.46 271:0.94 276:0.77 279:0.82 284:0.87 285:0.56 287:0.61 290:0.55 292:0.91 300:0.55 +2 8:0.77 9:0.76 11:0.75 12:0.20 17:0.09 18:0.68 21:0.47 22:0.93 27:0.16 28:0.96 29:0.71 30:0.10 31:0.95 34:0.79 36:0.28 37:0.80 39:0.90 43:0.21 44:0.87 45:0.77 47:0.93 55:0.45 64:0.77 66:0.15 69:0.54 70:0.96 71:0.77 74:0.37 76:0.85 79:0.97 81:0.26 83:0.60 86:0.77 91:0.74 96:0.77 98:0.29 101:0.52 104:0.58 108:0.94 120:0.57 123:0.40 124:0.88 126:0.26 127:0.65 129:0.05 131:0.85 133:0.86 135:0.95 137:0.95 139:0.78 140:0.98 144:0.57 145:0.42 146:0.64 147:0.69 149:0.43 150:0.26 151:0.58 153:0.77 154:0.64 155:0.58 157:0.83 159:0.85 161:0.86 162:0.54 164:0.53 166:0.61 167:0.62 172:0.63 173:0.27 177:0.70 178:0.42 179:0.32 181:0.80 182:0.75 187:0.21 190:0.89 191:0.86 192:0.51 195:0.95 196:0.94 202:0.88 208:0.54 212:0.72 216:0.72 219:0.89 220:0.91 222:0.97 227:0.84 229:0.61 231:0.69 235:0.52 241:0.51 242:0.73 243:0.36 245:0.85 246:0.61 247:0.76 248:0.57 253:0.44 254:0.98 255:0.74 256:0.86 259:0.21 260:0.54 262:0.93 265:0.23 266:0.87 267:0.99 268:0.86 271:0.94 276:0.78 283:0.78 284:0.97 285:0.62 287:0.61 292:0.91 300:0.94 +2 1:0.74 7:0.63 8:0.60 11:0.75 12:0.20 17:0.94 18:0.68 20:0.86 21:0.22 27:0.57 28:0.96 29:0.71 30:0.09 32:0.78 34:0.91 36:0.73 37:0.64 39:0.90 43:0.63 44:0.93 45:0.66 48:0.91 55:0.45 64:0.77 66:0.26 69:0.54 70:0.91 71:0.77 74:0.57 76:0.85 77:0.70 78:0.91 81:0.58 86:0.71 91:0.69 98:0.69 100:0.84 101:0.52 104:0.82 108:0.41 111:0.88 114:0.71 123:0.64 124:0.71 126:0.54 127:0.63 129:0.59 131:0.61 133:0.64 135:0.65 139:0.81 144:0.57 145:0.52 146:0.45 147:0.52 149:0.48 150:0.70 152:0.81 154:0.53 155:0.67 157:0.78 158:0.78 159:0.51 161:0.86 166:0.79 167:0.49 169:0.82 172:0.57 173:0.54 175:0.75 176:0.73 177:0.38 178:0.55 179:0.50 181:0.80 182:0.73 186:0.90 187:0.39 192:0.87 195:0.73 201:0.93 208:0.64 212:0.61 215:0.51 216:0.33 222:0.97 227:0.61 229:0.61 230:0.64 231:0.69 233:0.76 235:0.42 241:0.03 242:0.77 243:0.50 244:0.61 245:0.80 246:0.61 247:0.60 253:0.52 257:0.65 259:0.21 261:0.85 265:0.41 266:0.54 267:0.79 271:0.94 276:0.67 277:0.69 279:0.82 281:0.91 282:0.86 283:0.71 287:0.61 290:0.71 297:0.36 300:0.70 +3 1:0.74 6:0.93 8:0.82 9:0.80 11:0.75 12:0.20 17:0.16 20:0.94 21:0.40 22:0.93 27:0.42 28:0.96 29:0.71 30:0.70 31:0.87 34:0.78 36:0.65 37:0.60 39:0.90 41:0.82 43:0.66 45:0.77 47:0.93 55:0.52 64:0.77 66:0.93 69:0.33 70:0.46 71:0.77 74:0.53 78:0.96 79:0.86 81:0.52 83:0.91 91:0.63 96:0.82 97:0.99 98:0.93 100:0.95 101:0.52 104:0.58 106:0.81 108:0.26 111:0.95 114:0.62 120:0.74 123:0.25 126:0.54 127:0.58 129:0.05 131:0.85 132:0.97 135:0.96 137:0.87 139:0.68 140:0.45 144:0.57 146:0.89 147:0.91 149:0.64 150:0.74 151:0.50 152:0.88 153:0.48 154:0.55 155:0.67 157:0.86 158:0.79 159:0.47 161:0.86 162:0.81 164:0.69 165:0.93 166:0.79 167:0.64 169:0.93 172:0.82 173:0.35 175:0.75 176:0.73 177:0.38 179:0.43 181:0.80 182:0.89 186:0.95 187:0.87 189:0.94 190:0.77 192:0.87 196:0.87 201:0.93 202:0.62 206:0.81 208:0.64 212:0.60 215:0.42 216:0.60 219:0.92 220:0.91 222:0.97 227:0.85 229:0.94 231:0.69 235:0.60 238:0.88 241:0.57 242:0.73 243:0.94 244:0.61 246:0.86 247:0.39 248:0.50 253:0.72 255:0.95 256:0.64 257:0.65 260:0.68 261:0.93 262:0.93 265:0.93 266:0.54 267:0.96 268:0.67 271:0.94 276:0.72 277:0.69 279:0.86 283:0.82 284:0.91 285:0.62 287:0.88 290:0.62 292:0.95 297:0.36 300:0.43 +2 1:0.74 6:0.86 7:0.63 8:0.67 11:0.92 12:0.20 17:0.78 18:0.97 20:0.91 22:0.93 27:0.33 28:0.96 29:0.71 30:0.84 32:0.80 34:0.66 36:0.91 37:0.61 39:0.90 43:0.88 44:0.93 45:0.87 47:0.93 48:0.91 60:0.87 64:0.77 66:0.62 69:0.19 70:0.45 71:0.77 74:0.96 76:0.85 77:0.96 78:0.96 81:0.83 83:0.91 85:0.98 86:0.99 91:0.46 97:0.89 98:0.71 100:0.90 101:0.97 104:0.58 106:0.81 108:0.78 111:0.94 114:0.98 117:0.86 122:0.57 123:0.77 124:0.50 126:0.54 127:0.74 129:0.59 131:0.61 132:0.97 133:0.46 135:0.30 139:0.99 144:0.57 145:0.90 146:0.64 147:0.69 149:0.99 150:0.89 152:0.85 154:0.87 155:0.67 157:0.95 158:0.92 159:0.65 161:0.86 165:0.93 166:0.79 167:0.49 169:0.92 172:0.95 173:0.18 176:0.73 177:0.70 178:0.55 179:0.17 181:0.80 182:0.88 186:0.91 187:0.21 189:0.88 191:0.70 192:0.87 195:0.81 201:0.93 206:0.81 208:0.64 212:0.91 215:0.96 216:0.46 219:0.95 222:0.97 227:0.61 229:0.61 230:0.64 231:0.69 232:0.88 233:0.76 235:0.12 238:0.87 241:0.02 242:0.29 243:0.37 245:0.98 246:0.86 247:0.92 253:0.69 261:0.89 262:0.93 265:0.72 266:0.71 267:0.44 271:0.94 276:0.92 279:0.86 281:0.91 282:0.88 283:0.82 287:0.61 290:0.98 292:0.95 297:0.36 299:0.99 300:0.70 +1 7:0.63 8:0.82 11:0.42 12:0.20 17:0.75 18:0.65 21:0.59 27:0.37 28:0.96 29:0.71 30:0.71 32:0.62 34:0.37 36:0.53 37:0.60 39:0.90 43:0.18 55:0.52 66:0.98 69:0.29 70:0.48 71:0.77 74:0.37 81:0.26 86:0.70 91:0.80 98:0.98 104:0.78 108:0.22 120:0.76 123:0.22 126:0.26 127:0.82 129:0.05 131:0.81 135:0.98 139:0.67 140:0.45 144:0.57 145:0.72 146:0.96 147:0.97 149:0.52 150:0.25 151:0.57 153:0.72 154:0.54 157:0.61 159:0.66 161:0.86 162:0.86 164:0.62 166:0.74 167:0.61 172:0.94 173:0.23 177:0.70 179:0.24 181:0.80 182:0.61 187:0.21 190:0.89 191:0.73 195:0.79 202:0.70 212:0.83 215:0.39 216:0.42 220:0.97 222:0.97 227:0.83 229:0.61 230:0.63 231:0.65 233:0.73 235:0.80 241:0.49 242:0.81 243:0.98 244:0.61 246:0.61 247:0.55 248:0.62 253:0.30 254:0.74 256:0.77 259:0.21 260:0.61 265:0.98 266:0.47 267:0.79 268:0.78 271:0.94 276:0.89 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +2 1:0.69 8:0.68 11:0.42 12:0.20 17:0.82 18:0.65 21:0.54 27:0.37 28:0.96 29:0.71 30:0.38 32:0.68 34:0.66 36:0.85 37:0.60 39:0.90 43:0.18 55:0.59 66:0.53 69:0.25 70:0.74 71:0.77 74:0.51 76:0.97 77:0.89 81:0.31 86:0.69 91:0.71 96:0.76 98:0.71 104:0.78 108:0.20 114:0.56 117:0.86 123:0.19 124:0.73 126:0.44 127:0.82 129:0.05 131:0.81 133:0.73 135:0.94 139:0.67 140:0.45 144:0.57 145:0.74 146:0.90 147:0.92 149:0.40 150:0.48 151:0.55 153:0.48 154:0.58 155:0.58 157:0.61 158:0.71 159:0.75 161:0.86 162:0.74 166:0.79 167:0.52 172:0.82 173:0.17 176:0.55 177:0.70 179:0.33 181:0.80 182:0.61 187:0.68 190:0.89 191:0.73 192:0.59 195:0.87 201:0.68 202:0.56 208:0.54 212:0.48 215:0.39 216:0.42 220:0.62 222:0.97 227:0.82 229:0.61 231:0.69 232:0.98 235:0.68 241:0.12 242:0.78 243:0.62 244:0.61 245:0.87 246:0.61 247:0.74 248:0.54 253:0.45 254:0.74 256:0.58 259:0.21 265:0.72 266:0.75 267:0.89 268:0.59 271:0.94 276:0.82 282:0.77 283:0.78 285:0.47 287:0.61 290:0.56 297:0.36 300:0.70 +2 6:0.97 8:0.93 9:0.76 11:0.75 12:0.20 17:0.15 18:0.81 20:0.93 21:0.78 27:0.16 28:0.96 29:0.71 30:0.21 31:0.93 32:0.79 34:0.82 36:0.45 37:0.80 39:0.90 43:0.56 44:0.93 45:0.73 55:0.52 66:0.31 69:0.70 70:0.86 71:0.77 74:0.56 76:0.94 77:0.70 78:0.97 79:0.95 83:0.60 86:0.81 91:0.68 96:0.82 98:0.56 100:0.99 101:0.52 104:0.58 108:0.87 111:0.98 120:0.71 123:0.87 124:0.78 127:0.48 129:0.59 131:0.85 133:0.74 135:0.98 137:0.93 139:0.84 140:0.95 144:0.57 145:0.49 146:0.54 147:0.60 149:0.47 151:0.81 152:0.98 153:0.48 154:0.64 155:0.58 157:0.81 158:0.71 159:0.74 161:0.86 162:0.53 164:0.54 166:0.61 167:0.60 169:0.96 172:0.75 173:0.53 177:0.70 178:0.50 179:0.26 181:0.80 182:0.76 186:0.96 187:0.21 189:0.98 190:0.77 191:0.83 192:0.51 195:0.90 196:0.94 202:0.81 208:0.54 212:0.68 216:0.72 220:0.62 222:0.97 227:0.85 229:0.61 231:0.69 235:0.84 238:0.96 241:0.47 242:0.52 243:0.31 245:0.90 246:0.61 247:0.88 248:0.74 253:0.53 254:0.98 255:0.74 256:0.79 259:0.21 260:0.60 261:0.97 265:0.57 266:0.80 267:0.99 268:0.78 271:0.94 276:0.83 277:0.69 282:0.87 283:0.78 284:0.92 285:0.62 287:0.61 300:0.84 +1 7:0.72 8:0.91 9:0.76 11:0.75 12:0.20 17:0.34 18:0.81 21:0.78 27:0.16 28:0.96 29:0.71 30:0.45 34:0.56 36:0.29 37:0.80 39:0.90 43:0.25 45:0.77 55:0.52 66:0.74 69:0.66 70:0.56 71:0.77 74:0.62 76:0.94 83:0.60 86:0.85 91:0.74 96:0.94 97:0.98 98:0.85 101:0.52 104:0.58 106:0.81 108:0.79 120:0.86 123:0.77 124:0.45 127:0.60 129:0.81 131:0.85 133:0.67 135:0.86 139:0.81 140:0.80 144:0.57 145:0.38 146:0.54 147:0.60 149:0.58 151:0.84 153:0.87 154:0.65 155:0.58 157:0.83 158:0.79 159:0.84 161:0.86 162:0.60 164:0.79 166:0.61 167:0.67 172:0.97 173:0.42 177:0.70 179:0.14 181:0.80 182:0.89 187:0.21 190:0.77 191:0.76 192:0.59 202:0.84 204:0.85 206:0.81 208:0.54 212:0.87 216:0.72 222:0.97 227:0.85 229:0.94 230:0.74 231:0.70 233:0.73 235:0.05 241:0.62 242:0.54 243:0.17 245:0.97 246:0.61 247:0.95 248:0.88 253:0.87 254:0.93 255:0.74 256:0.84 259:0.21 260:0.82 265:0.85 266:0.63 267:0.98 268:0.84 271:0.94 276:0.95 279:0.82 283:0.82 285:0.56 287:0.88 300:0.70 +2 6:0.98 8:0.88 11:0.75 12:0.20 17:0.09 18:0.82 20:0.85 21:0.47 22:0.93 27:0.16 28:0.96 29:0.71 30:0.20 31:0.92 32:0.62 34:0.91 36:0.49 37:0.80 39:0.90 43:0.22 45:0.77 47:0.93 55:0.52 64:0.77 66:0.36 69:0.69 70:0.93 71:0.77 74:0.48 76:0.85 78:0.94 79:0.94 81:0.26 86:0.86 91:0.65 96:0.76 98:0.60 100:0.98 101:0.52 104:0.58 108:0.89 111:0.97 120:0.66 123:0.88 124:0.78 126:0.26 127:0.52 129:0.89 131:0.85 133:0.78 135:0.98 137:0.92 139:0.83 140:0.96 144:0.57 145:0.56 146:0.54 147:0.60 149:0.35 150:0.26 151:0.65 152:0.98 153:0.46 154:0.64 157:0.84 159:0.79 161:0.86 162:0.60 164:0.53 166:0.61 167:0.55 169:0.97 172:0.77 173:0.50 177:0.70 178:0.48 179:0.28 181:0.80 182:0.76 186:0.94 187:0.21 189:0.97 190:0.89 191:0.87 192:0.51 195:0.93 196:0.93 202:0.78 212:0.73 216:0.72 219:0.89 220:0.91 222:0.97 227:0.84 229:0.61 231:0.69 235:0.52 238:0.95 241:0.30 242:0.57 243:0.33 245:0.89 246:0.61 247:0.91 248:0.67 253:0.44 254:0.88 255:0.74 256:0.80 259:0.21 260:0.59 261:0.97 262:0.93 265:0.61 266:0.87 267:1.00 268:0.78 271:0.94 276:0.81 284:0.95 285:0.62 287:0.61 292:0.91 300:0.94 +2 1:0.69 7:0.63 8:0.79 9:0.76 11:0.75 12:0.20 17:0.78 18:0.62 20:0.83 21:0.47 27:0.39 28:0.96 29:0.71 30:0.21 32:0.69 34:0.61 36:0.61 37:0.48 39:0.90 43:0.66 45:0.66 48:0.91 55:0.45 66:0.63 69:0.51 70:0.79 71:0.77 74:0.55 76:0.85 77:0.70 78:0.93 81:0.34 83:0.60 86:0.72 91:0.72 96:0.76 98:0.71 99:0.83 100:0.86 101:0.52 104:0.75 108:0.39 111:0.91 114:0.58 117:0.86 120:0.54 123:0.37 124:0.48 126:0.44 127:0.55 129:0.05 131:0.85 133:0.43 135:0.73 138:0.95 139:0.72 140:0.45 144:0.57 145:0.50 146:0.72 147:0.77 149:0.41 150:0.52 151:0.48 152:0.79 153:0.48 154:0.65 155:0.58 157:0.78 158:0.71 159:0.46 161:0.86 162:0.86 164:0.53 165:0.70 166:0.79 167:0.58 169:0.86 172:0.73 173:0.53 176:0.61 177:0.70 179:0.47 181:0.80 182:0.89 186:0.93 187:0.21 190:0.89 191:0.70 192:0.59 195:0.69 201:0.68 202:0.58 208:0.54 212:0.73 215:0.39 216:0.35 220:0.93 222:0.97 227:0.84 229:0.94 230:0.64 231:0.69 232:0.83 233:0.76 235:0.68 241:0.41 242:0.45 243:0.69 245:0.83 246:0.86 247:0.76 248:0.48 253:0.47 254:0.74 255:0.74 256:0.64 259:0.21 260:0.53 261:0.85 265:0.71 266:0.63 267:0.79 268:0.66 271:0.94 276:0.66 281:0.91 282:0.77 285:0.56 286:0.99 287:0.88 290:0.58 297:0.36 298:0.99 300:0.70 +2 6:0.93 7:0.81 12:0.20 17:0.34 20:0.98 21:0.40 27:0.21 28:0.76 30:0.20 34:0.42 36:0.88 37:0.74 43:0.52 55:0.59 66:0.25 69:0.15 70:0.82 78:0.76 81:0.95 91:0.54 98:0.62 100:0.78 104:0.89 106:0.81 108:0.72 111:0.75 120:0.74 123:0.70 124:0.89 126:0.54 127:0.74 129:0.96 133:0.90 135:0.34 140:0.45 146:0.71 147:0.76 149:0.78 150:0.92 151:0.59 152:0.99 153:0.91 154:0.41 159:0.88 161:0.64 162:0.65 164:0.85 167:0.68 169:0.77 172:0.78 173:0.08 177:0.05 179:0.23 181:0.84 186:0.76 187:0.39 202:0.80 206:0.81 212:0.27 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 241:0.43 242:0.82 243:0.18 245:0.90 247:0.12 248:0.67 256:0.81 259:0.21 260:0.75 261:0.82 265:0.63 266:0.89 267:0.44 268:0.81 271:0.18 276:0.88 283:0.60 285:0.62 297:0.36 300:0.70 +2 6:0.93 7:0.81 12:0.20 17:0.43 20:0.84 21:0.40 27:0.21 28:0.76 30:0.43 34:0.68 36:0.94 37:0.74 43:0.52 55:0.71 66:0.24 69:0.12 70:0.68 78:0.79 81:0.95 91:0.20 98:0.58 100:0.81 104:0.84 106:0.81 108:0.88 111:0.78 120:0.78 123:0.87 124:0.90 126:0.54 127:0.83 129:0.96 133:0.90 135:0.32 140:0.45 146:0.81 147:0.84 149:0.87 150:0.92 151:0.74 152:0.99 153:0.83 154:0.41 159:0.92 161:0.64 162:0.77 164:0.76 167:0.68 169:0.78 172:0.87 173:0.07 177:0.05 179:0.16 181:0.84 186:0.80 187:0.39 202:0.66 206:0.81 212:0.56 215:0.67 216:0.95 230:0.65 233:0.63 235:0.42 241:0.43 242:0.82 243:0.26 245:0.96 247:0.12 248:0.70 256:0.68 259:0.21 260:0.79 261:0.82 265:0.59 266:0.92 267:0.82 268:0.70 271:0.18 276:0.94 283:0.82 285:0.62 297:0.36 300:0.84 +3 6:1.00 8:1.00 12:0.20 17:0.24 20:0.99 21:0.78 27:0.16 28:0.76 30:0.33 34:0.39 36:0.50 37:0.98 43:0.42 55:0.84 66:0.45 69:0.52 70:0.36 78:0.94 91:0.95 98:0.62 100:0.98 108:0.62 111:0.98 120:0.80 123:0.60 124:0.66 127:0.43 129:0.81 133:0.67 135:0.26 140:0.92 145:0.87 146:0.05 147:0.05 149:0.39 151:0.76 152:0.91 153:0.87 154:0.32 159:0.50 161:0.64 162:0.48 163:0.94 164:0.86 167:0.95 169:0.98 172:0.27 173:0.49 177:0.05 178:0.51 179:0.87 181:0.84 186:0.94 189:1.00 191:0.92 202:0.92 212:0.42 216:0.60 235:0.88 238:1.00 241:0.85 242:0.82 243:0.19 245:0.47 247:0.12 248:0.72 256:0.83 259:0.21 260:0.81 261:0.97 265:0.63 266:0.38 267:0.73 268:0.83 271:0.18 276:0.35 283:0.82 285:0.62 300:0.25 +3 6:0.98 7:0.81 8:0.97 12:0.20 17:0.12 20:0.99 21:0.47 25:0.88 27:0.28 28:0.76 30:0.91 32:0.80 34:0.21 36:0.64 37:0.79 43:0.36 55:0.71 66:0.69 69:0.61 70:0.13 78:0.92 81:0.94 91:0.98 98:0.60 100:0.98 104:0.58 108:0.47 111:0.98 120:0.97 123:0.45 126:0.54 127:0.18 129:0.05 135:0.32 140:0.45 145:0.49 146:0.38 147:0.45 149:0.32 150:0.90 151:0.84 152:0.99 153:0.98 154:0.27 159:0.15 161:0.64 162:0.79 164:0.98 167:0.96 169:0.97 172:0.21 173:0.84 177:0.05 179:0.85 181:0.84 186:0.91 189:0.99 191:0.92 195:0.57 202:0.96 212:0.61 215:0.63 216:0.60 220:0.62 230:0.87 233:0.76 235:0.52 238:0.99 241:0.92 242:0.82 243:0.73 247:0.12 248:0.96 256:0.97 259:0.21 260:0.97 261:0.97 265:0.61 266:0.17 267:0.82 268:0.97 271:0.18 276:0.22 283:0.82 285:0.62 297:0.36 300:0.13 +2 6:0.93 7:0.81 8:0.77 12:0.20 17:0.32 20:0.91 21:0.40 27:0.21 28:0.76 30:0.45 34:0.68 36:0.97 37:0.74 43:0.52 55:0.71 66:0.24 69:0.12 70:0.68 78:0.78 81:0.95 91:0.37 98:0.58 100:0.80 104:0.84 106:0.81 108:0.88 111:0.77 120:0.83 123:0.87 124:0.90 126:0.54 127:0.83 129:0.96 133:0.90 135:0.32 140:0.45 146:0.80 147:0.84 149:0.87 150:0.92 151:0.73 152:0.99 153:0.78 154:0.41 159:0.92 161:0.64 162:0.69 164:0.83 167:0.64 169:0.77 172:0.87 173:0.07 177:0.05 179:0.16 181:0.84 186:0.78 187:0.39 202:0.76 206:0.81 212:0.56 215:0.67 216:0.95 220:0.62 230:0.65 233:0.63 235:0.42 238:0.90 241:0.30 242:0.82 243:0.25 245:0.96 247:0.12 248:0.75 256:0.77 259:0.21 260:0.83 261:0.82 265:0.59 266:0.92 267:0.73 268:0.79 271:0.18 276:0.94 283:0.82 285:0.62 297:0.36 300:0.84 +1 12:0.20 17:0.45 21:0.78 27:0.45 28:0.76 30:0.45 34:0.81 36:0.20 37:0.50 43:0.27 55:0.59 66:0.49 69:0.79 70:0.25 91:0.35 98:0.30 108:0.63 123:0.61 124:0.48 127:0.23 129:0.59 133:0.47 135:0.78 145:0.47 146:0.46 147:0.53 149:0.25 154:0.20 159:0.45 161:0.64 163:0.87 167:0.61 172:0.16 173:0.73 177:0.05 178:0.55 179:0.92 181:0.84 187:0.68 212:0.61 216:0.77 235:0.80 241:0.15 242:0.82 243:0.60 245:0.39 247:0.12 259:0.21 265:0.32 266:0.17 267:0.44 271:0.18 276:0.17 300:0.18 +0 8:0.89 12:0.20 17:0.53 20:0.99 21:0.13 25:0.88 27:0.47 28:0.76 34:0.61 36:0.19 37:0.65 43:0.51 66:0.36 69:0.10 78:0.77 81:0.98 91:0.89 98:0.13 100:0.79 104:0.58 108:0.09 111:0.77 120:0.85 123:0.09 126:0.54 127:0.08 129:0.05 135:0.18 140:0.45 146:0.05 147:0.05 149:0.16 150:0.97 151:0.71 152:0.91 153:0.76 154:0.40 159:0.05 161:0.64 162:0.72 164:0.91 167:0.95 169:0.79 172:0.05 173:0.56 177:0.05 181:0.84 186:0.77 191:0.73 202:0.85 215:0.84 216:0.27 220:0.62 235:0.12 238:0.95 241:0.88 243:0.51 248:0.77 256:0.88 259:0.21 260:0.85 261:0.80 265:0.14 267:0.76 268:0.88 271:0.18 283:0.82 285:0.62 297:0.36 +2 6:0.98 7:0.81 8:0.77 12:0.20 17:0.07 20:0.81 21:0.13 25:0.88 27:0.28 28:0.76 30:0.28 34:0.33 36:0.80 37:0.79 43:0.52 55:0.98 66:0.09 69:0.15 70:0.75 78:0.89 81:0.98 91:0.93 98:0.18 100:0.97 104:0.58 108:0.50 111:0.94 120:0.95 123:0.78 124:0.93 126:0.54 127:0.94 129:0.98 133:0.93 135:0.90 140:0.45 145:0.72 146:0.05 147:0.05 149:0.50 150:0.97 151:0.80 152:0.99 153:0.93 154:0.41 159:0.90 161:0.64 162:0.68 163:0.79 164:0.98 167:0.78 169:0.97 172:0.16 173:0.08 177:0.05 179:0.74 181:0.84 186:0.89 187:0.21 189:0.99 191:0.91 202:0.97 212:0.12 215:0.84 216:0.60 220:0.74 230:0.65 233:0.63 235:0.22 238:0.98 241:0.69 242:0.82 243:0.12 245:0.53 247:0.12 248:0.93 256:0.97 259:0.21 260:0.95 261:0.97 265:0.14 266:0.89 267:0.92 268:0.98 271:0.18 276:0.51 283:0.82 285:0.62 297:0.36 300:0.55 +2 12:0.20 17:0.81 21:0.78 27:0.70 28:0.76 30:0.45 34:0.45 36:0.45 37:0.54 43:0.28 55:0.71 66:0.25 69:0.77 70:0.50 91:0.69 98:0.27 108:0.60 123:0.58 124:0.74 127:0.27 129:0.81 133:0.67 135:0.47 145:0.87 146:0.36 147:0.43 149:0.35 154:0.20 159:0.42 161:0.64 163:0.86 167:0.95 172:0.16 173:0.76 177:0.05 178:0.55 179:0.83 181:0.84 202:0.56 212:0.23 216:0.21 241:0.87 242:0.82 243:0.45 245:0.46 247:0.12 259:0.21 265:0.29 266:0.38 267:0.28 271:0.18 276:0.29 283:0.60 300:0.33 +1 6:0.99 8:0.97 12:0.20 17:0.49 20:0.99 21:0.78 27:0.67 28:0.76 30:0.71 34:0.52 36:0.92 37:0.38 43:0.52 55:0.84 66:0.72 69:0.05 70:0.40 78:0.77 91:0.82 98:0.33 100:0.84 108:0.05 111:0.78 120:0.88 123:0.05 124:0.40 127:0.97 129:0.59 133:0.47 135:0.60 140:0.80 145:0.41 146:0.65 147:0.70 149:0.53 151:0.73 152:0.91 153:0.80 154:0.41 159:0.84 161:0.64 162:0.56 164:0.93 167:0.94 169:0.81 172:0.45 173:0.07 177:0.05 178:0.42 179:0.88 181:0.84 186:0.77 189:1.00 191:0.87 202:0.91 212:0.30 216:0.47 220:0.62 235:0.05 238:0.95 241:0.83 242:0.82 243:0.75 245:0.47 247:0.12 248:0.82 256:0.91 259:0.21 260:0.88 261:0.82 265:0.35 266:0.47 267:0.28 268:0.91 271:0.18 276:0.18 283:0.60 285:0.62 300:0.33 +0 12:0.20 17:0.32 21:0.78 27:0.45 28:0.76 30:0.95 34:0.71 36:0.19 37:0.50 43:0.25 55:0.84 66:0.54 69:0.83 91:0.12 98:0.20 108:0.68 123:0.66 127:0.10 129:0.05 135:0.76 145:0.49 146:0.31 147:0.37 149:0.17 154:0.18 159:0.13 161:0.64 167:0.60 172:0.10 173:0.78 177:0.05 178:0.55 179:0.51 181:0.84 187:0.68 216:0.77 235:0.74 241:0.12 243:0.63 259:0.21 265:0.21 267:0.44 271:0.18 300:0.08 +1 12:0.51 17:0.69 18:0.88 21:0.30 27:0.58 29:0.62 30:0.14 32:0.63 34:0.83 36:0.24 37:0.34 39:0.55 43:0.12 44:0.90 55:0.92 64:0.77 66:0.43 69:0.12 70:0.90 71:0.92 81:0.42 86:0.87 91:0.20 98:0.53 108:0.10 123:0.10 124:0.75 126:0.44 127:0.69 129:0.05 131:0.61 133:0.74 135:0.96 139:0.86 145:0.58 146:0.78 147:0.82 149:0.25 150:0.33 154:0.59 157:0.61 159:0.75 166:0.86 172:0.57 173:0.11 175:0.75 177:0.38 178:0.55 179:0.57 182:0.61 187:0.68 192:0.51 195:0.88 201:0.68 212:0.29 215:0.44 216:0.46 222:0.35 227:0.61 229:0.61 231:0.78 235:0.42 242:0.52 243:0.57 244:0.61 245:0.70 246:0.61 247:0.60 253:0.20 254:0.84 257:0.65 259:0.21 265:0.54 266:0.80 267:0.92 271:0.63 276:0.63 277:0.69 287:0.61 297:0.36 300:0.70 +0 7:0.63 8:0.59 12:0.51 17:0.47 18:0.66 21:0.22 27:0.31 29:0.62 30:0.45 31:0.90 32:0.63 34:0.49 36:0.74 37:0.54 39:0.55 43:0.16 44:0.90 48:0.91 55:0.59 66:0.64 69:0.10 70:0.33 71:0.92 74:0.27 79:0.90 81:0.37 86:0.70 91:0.70 98:0.74 104:0.95 108:0.09 120:0.65 122:0.41 123:0.09 124:0.42 126:0.26 127:0.73 129:0.05 131:0.90 133:0.37 135:0.63 137:0.90 139:0.76 140:0.80 145:0.54 146:0.57 147:0.62 149:0.16 150:0.32 151:0.65 153:0.48 154:0.54 157:0.61 159:0.31 162:0.64 164:0.53 166:0.86 172:0.32 173:0.32 177:0.70 179:0.93 182:0.61 187:0.39 190:0.77 191:0.88 192:0.51 195:0.56 196:0.91 202:0.69 212:0.52 215:0.51 216:0.60 220:0.82 222:0.35 227:0.82 229:0.61 230:0.63 231:0.90 233:0.73 235:0.22 241:0.62 242:0.24 243:0.69 244:0.61 245:0.43 246:0.61 247:0.47 248:0.63 253:0.24 254:0.95 255:0.74 256:0.68 259:0.21 260:0.59 265:0.74 266:0.27 267:0.36 268:0.70 271:0.63 276:0.29 281:0.89 283:0.82 284:0.87 285:0.56 287:0.61 297:0.36 300:0.25 +0 7:0.72 8:0.65 11:0.75 12:0.51 17:0.22 21:0.40 27:0.48 29:0.62 30:0.67 32:0.69 34:0.34 36:0.84 37:0.35 39:0.55 43:0.68 45:0.73 55:0.52 66:0.95 69:0.32 70:0.44 71:0.92 74:0.57 77:0.70 81:0.29 91:0.53 98:0.97 101:0.52 108:0.25 120:0.61 123:0.24 126:0.26 127:0.74 129:0.05 131:0.90 135:0.47 140:0.45 144:0.57 145:0.97 146:0.88 147:0.90 149:0.65 150:0.28 151:0.51 153:0.48 154:0.57 155:0.58 157:0.92 159:0.46 162:0.81 164:0.53 166:0.86 172:0.86 173:0.37 175:0.75 177:0.38 179:0.40 182:0.87 187:0.68 190:0.89 192:0.59 195:0.68 202:0.61 204:0.85 208:0.54 212:0.81 215:0.42 216:0.76 220:0.91 222:0.35 227:0.82 229:0.61 230:0.75 231:0.92 233:0.76 235:0.42 241:0.58 242:0.79 243:0.95 244:0.61 246:0.61 247:0.39 248:0.52 253:0.38 256:0.64 257:0.65 259:0.21 260:0.56 265:0.97 266:0.38 267:0.44 268:0.66 271:0.63 276:0.76 277:0.69 282:0.77 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +0 8:0.88 9:0.76 12:0.51 17:0.43 18:0.61 21:0.78 27:0.60 29:0.62 30:0.76 34:0.17 36:0.42 37:0.77 39:0.55 43:0.10 55:0.96 66:0.13 69:0.49 70:0.21 71:0.92 74:0.35 86:0.63 91:0.80 96:0.82 98:0.26 104:0.75 108:0.38 120:0.68 122:0.55 123:0.80 124:0.82 127:0.35 129:0.81 131:0.98 133:0.76 135:0.18 138:0.95 139:0.61 140:0.45 144:0.57 146:0.20 147:0.26 149:0.11 151:0.94 153:0.91 154:0.54 155:0.58 157:0.61 158:0.74 159:0.53 162:0.55 163:0.89 164:0.65 166:0.61 172:0.10 173:0.41 175:0.75 177:0.38 179:0.88 182:0.61 190:0.77 191:0.89 192:0.59 202:0.72 208:0.54 212:0.23 216:0.22 222:0.35 227:0.83 229:0.61 231:0.95 232:0.72 235:0.52 241:0.91 242:0.24 243:0.34 244:0.61 245:0.43 246:0.61 247:0.47 248:0.84 253:0.60 254:0.90 255:0.74 256:0.64 257:0.65 259:0.21 260:0.65 265:0.13 266:0.38 267:0.65 268:0.69 271:0.63 276:0.31 277:0.69 279:0.82 283:0.78 285:0.56 286:0.99 287:0.61 298:0.99 300:0.18 +0 12:0.51 17:0.45 18:0.58 21:0.78 27:0.34 29:0.62 30:0.45 34:0.62 36:0.82 37:0.46 39:0.55 43:0.11 55:0.84 66:0.27 69:0.96 70:0.25 71:0.92 86:0.60 91:0.03 98:0.15 108:0.92 122:0.65 123:0.92 124:0.72 127:0.25 129:0.05 131:0.61 133:0.62 135:0.44 139:0.59 145:0.63 146:0.37 147:0.05 149:0.08 154:0.09 157:0.61 159:0.82 163:0.99 166:0.61 172:0.10 173:0.87 175:0.75 177:0.05 178:0.55 179:0.93 182:0.61 187:0.87 212:0.61 216:0.53 222:0.35 227:0.61 229:0.61 231:0.65 235:0.22 241:0.24 242:0.63 243:0.29 244:0.83 245:0.38 246:0.61 247:0.30 253:0.20 257:0.84 259:0.21 265:0.17 266:0.17 267:0.52 271:0.63 276:0.17 277:0.69 287:0.61 300:0.18 +1 12:0.51 17:0.09 18:0.64 21:0.13 27:0.14 29:0.62 30:0.76 34:0.82 36:0.33 37:0.85 39:0.55 43:0.62 64:0.77 66:0.64 69:0.74 70:0.15 71:0.92 81:0.59 86:0.68 91:0.27 98:0.32 108:0.57 122:0.65 123:0.54 126:0.44 127:0.12 129:0.05 131:0.61 135:0.73 139:0.73 145:0.38 146:0.32 147:0.38 149:0.31 150:0.42 154:0.52 157:0.61 159:0.13 163:0.98 166:0.86 172:0.16 173:0.84 175:0.75 177:0.38 178:0.55 179:0.64 182:0.61 187:0.68 192:0.51 201:0.68 212:0.95 215:0.61 216:0.71 219:0.92 222:0.35 227:0.61 229:0.61 231:0.78 235:0.22 241:0.24 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.20 257:0.65 259:0.21 265:0.34 266:0.12 267:0.36 271:0.63 276:0.13 277:0.69 283:0.82 287:0.61 292:0.95 297:0.36 300:0.13 +1 1:0.74 7:0.64 12:0.51 17:0.87 18:0.69 21:0.40 27:0.36 29:0.62 30:0.62 34:0.81 36:0.84 37:0.36 39:0.55 43:0.09 48:0.91 55:0.42 64:0.77 66:0.47 69:0.83 70:0.34 71:0.92 74:0.28 81:0.51 86:0.72 91:0.62 98:0.19 108:0.68 114:0.62 122:0.65 123:0.66 124:0.52 126:0.54 127:0.16 129:0.05 131:0.61 133:0.39 135:0.70 139:0.79 144:0.57 145:0.69 146:0.25 147:0.31 149:0.07 150:0.66 154:0.46 155:0.67 157:0.61 158:0.71 159:0.20 166:0.97 172:0.21 173:0.89 175:0.75 176:0.73 177:0.38 178:0.55 179:0.62 182:0.61 187:0.21 192:0.87 201:0.93 208:0.64 212:0.30 215:0.42 216:0.39 219:0.92 222:0.35 227:0.61 229:0.61 230:0.64 231:0.95 232:0.79 233:0.73 235:0.60 241:0.02 242:0.33 243:0.59 244:0.61 245:0.46 246:0.61 247:0.39 253:0.46 254:0.74 257:0.65 259:0.21 265:0.20 266:0.27 267:0.36 271:0.63 276:0.18 277:0.69 281:0.88 283:0.77 287:0.61 290:0.62 292:0.91 297:0.36 300:0.25 +1 11:0.64 12:0.51 17:0.30 18:0.69 21:0.78 27:0.45 29:0.62 30:0.21 32:0.62 34:0.75 36:0.17 37:0.50 39:0.55 43:0.61 45:0.66 66:0.36 69:0.70 70:0.37 71:0.92 74:0.27 86:0.70 91:0.22 98:0.25 101:0.52 108:0.53 122:0.57 123:0.51 124:0.57 127:0.16 129:0.05 131:0.61 133:0.43 135:0.92 139:0.68 145:0.41 146:0.39 147:0.46 149:0.31 154:0.50 157:0.61 159:0.30 163:0.88 166:0.61 172:0.16 173:0.62 177:0.70 178:0.55 179:0.71 182:0.61 187:0.68 195:0.82 212:0.42 216:0.77 222:0.35 227:0.61 229:0.61 231:0.77 235:0.22 241:0.43 242:0.45 243:0.51 244:0.61 245:0.43 246:0.61 247:0.35 253:0.24 259:0.21 265:0.28 266:0.23 267:0.36 271:0.63 276:0.22 287:0.61 300:0.25 +1 7:0.64 8:0.91 11:0.42 12:0.51 17:0.07 18:0.82 21:0.59 27:0.29 29:0.62 30:0.76 31:0.86 32:0.63 34:0.80 36:0.38 37:0.41 39:0.55 43:0.68 44:0.90 48:0.91 55:0.42 64:0.77 66:0.30 69:0.80 70:0.53 71:0.92 74:0.41 79:0.86 81:0.31 86:0.86 91:0.57 98:0.37 108:0.44 120:0.53 122:0.65 123:0.43 124:0.71 126:0.44 127:0.43 129:0.05 131:0.90 133:0.66 135:0.93 137:0.86 139:0.90 140:0.45 144:0.57 145:0.80 146:0.24 147:0.48 149:0.63 150:0.25 151:0.46 153:0.48 154:0.67 157:0.61 159:0.43 162:0.86 164:0.53 166:0.85 172:0.32 173:0.85 177:0.38 179:0.73 182:0.61 187:0.87 190:0.77 192:0.51 195:0.67 196:0.87 201:0.68 202:0.36 212:0.36 215:0.39 216:0.63 219:0.92 220:0.97 222:0.35 227:0.82 229:0.61 230:0.64 231:0.87 233:0.73 235:0.74 241:0.03 242:0.38 243:0.48 245:0.60 246:0.61 247:0.67 248:0.46 253:0.32 254:0.84 255:0.74 256:0.45 257:0.65 259:0.21 260:0.53 265:0.39 266:0.54 267:0.59 268:0.46 271:0.63 276:0.38 277:0.69 281:0.88 283:0.77 284:0.87 285:0.56 287:0.61 292:0.91 297:0.36 300:0.55 +1 12:0.51 17:0.34 18:0.79 21:0.40 27:0.45 29:0.62 30:0.45 34:0.71 36:0.17 37:0.50 39:0.55 43:0.08 55:0.59 64:0.77 66:0.52 69:0.69 70:0.50 71:0.92 74:0.28 77:0.70 81:0.27 86:0.81 91:0.27 98:0.23 108:0.52 122:0.75 123:0.50 124:0.63 126:0.26 127:0.26 129:0.05 131:0.61 133:0.59 135:0.82 139:0.77 145:0.52 146:0.35 147:0.42 149:0.07 150:0.26 154:0.51 157:0.61 159:0.49 166:0.61 172:0.27 173:0.61 177:0.70 178:0.55 179:0.82 182:0.61 187:0.68 195:0.82 212:0.52 216:0.77 222:0.35 227:0.61 229:0.61 231:0.73 235:0.42 241:0.47 242:0.24 243:0.61 244:0.61 245:0.43 246:0.61 247:0.47 253:0.24 254:0.74 259:0.21 265:0.25 266:0.27 267:0.52 271:0.63 276:0.26 282:0.71 287:0.61 300:0.33 +0 9:0.76 12:0.51 17:0.81 18:0.63 21:0.78 27:0.60 29:0.62 30:0.91 34:0.39 36:0.55 37:0.77 39:0.55 43:0.10 55:0.96 66:0.27 69:0.47 70:0.13 71:0.92 74:0.26 86:0.64 91:0.40 96:0.75 98:0.14 104:0.75 108:0.36 120:0.63 122:0.57 123:0.35 124:0.72 127:0.25 129:0.05 131:0.98 133:0.62 135:0.19 138:0.95 139:0.62 140:0.45 144:0.57 146:0.20 147:0.26 149:0.08 151:0.81 153:0.48 154:0.54 155:0.58 157:0.61 159:0.35 162:0.86 163:0.89 164:0.53 166:0.61 172:0.10 173:0.47 175:0.75 177:0.38 179:0.94 182:0.61 187:0.21 190:0.77 191:0.73 192:0.59 202:0.36 208:0.54 212:0.61 216:0.22 222:0.35 227:0.83 229:0.61 231:0.95 232:0.72 235:0.12 241:0.52 242:0.63 243:0.46 244:0.61 245:0.38 246:0.61 247:0.30 248:0.73 253:0.51 254:0.90 255:0.74 256:0.45 257:0.65 259:0.21 260:0.61 265:0.15 266:0.17 267:0.36 268:0.46 271:0.63 276:0.21 277:0.69 285:0.56 286:0.99 287:0.61 298:0.99 300:0.13 +1 7:0.64 12:0.51 17:0.73 18:0.69 21:0.22 22:0.93 27:0.39 29:0.62 30:0.76 34:0.61 36:0.25 37:0.27 39:0.55 43:0.60 44:0.87 47:0.93 48:0.91 64:0.77 66:0.64 69:0.80 70:0.15 71:0.92 81:0.50 86:0.73 91:0.79 98:0.17 104:0.89 106:0.81 108:0.64 122:0.75 123:0.61 126:0.44 127:0.10 129:0.05 131:0.61 135:0.94 139:0.78 145:0.67 146:0.21 147:0.26 149:0.27 150:0.37 154:0.49 157:0.61 159:0.10 163:0.97 166:0.86 172:0.16 173:0.93 175:0.75 177:0.38 178:0.55 179:0.20 182:0.61 187:0.39 192:0.51 195:0.60 201:0.68 202:0.50 206:0.81 212:0.95 215:0.51 216:0.30 222:0.35 227:0.61 229:0.61 230:0.64 231:0.78 233:0.73 235:0.31 241:0.41 242:0.82 243:0.69 244:0.61 246:0.61 247:0.12 253:0.20 257:0.65 259:0.21 262:0.93 265:0.19 266:0.12 267:0.44 271:0.63 276:0.15 277:0.69 281:0.89 287:0.61 297:0.36 300:0.13 +1 1:0.74 7:0.64 8:0.61 9:0.80 12:0.51 17:0.04 18:0.81 21:0.47 27:0.29 29:0.62 30:0.38 31:0.89 34:0.82 36:0.53 37:0.41 39:0.55 43:0.10 48:0.97 55:0.42 64:0.77 66:0.64 69:0.78 70:0.63 71:0.92 74:0.48 76:0.85 79:0.89 81:0.47 86:0.84 91:0.64 96:0.68 98:0.36 108:0.61 114:0.60 120:0.59 122:0.65 123:0.59 124:0.44 126:0.54 127:0.18 129:0.05 131:0.98 133:0.43 135:0.39 137:0.89 139:0.88 140:0.45 144:0.57 145:0.54 146:0.42 147:0.22 149:0.10 150:0.64 151:0.48 153:0.48 154:0.67 155:0.67 157:0.61 158:0.86 159:0.25 162:0.86 164:0.63 166:0.97 172:0.45 173:0.81 176:0.73 177:0.38 179:0.48 182:0.61 187:0.39 190:0.77 192:0.87 196:0.92 201:0.93 202:0.50 208:0.64 212:0.57 215:0.41 216:0.63 219:0.95 220:0.93 222:0.35 227:0.83 229:0.61 230:0.64 231:0.95 232:0.95 233:0.66 235:0.68 241:0.24 242:0.29 243:0.26 245:0.55 246:0.61 247:0.60 248:0.48 253:0.44 254:0.88 255:0.95 256:0.58 257:0.65 259:0.21 260:0.56 265:0.38 266:0.38 267:0.59 268:0.59 271:0.63 276:0.35 279:0.86 281:0.89 283:0.67 284:0.92 285:0.62 287:0.61 290:0.60 292:0.91 297:0.36 300:0.43 +3 1:0.58 6:0.96 8:0.91 9:0.76 11:0.42 12:0.51 17:0.12 20:0.94 21:0.64 27:0.41 28:0.78 29:0.94 30:0.89 32:0.67 34:0.44 36:0.72 37:0.39 39:0.33 43:0.28 66:0.16 69:0.90 70:0.20 71:0.65 74:0.50 77:0.70 78:0.76 81:0.37 83:0.60 91:0.91 96:0.99 98:0.08 100:0.83 104:0.73 108:0.81 111:0.78 114:0.69 120:0.97 122:0.51 123:0.79 124:0.75 126:0.44 127:0.22 129:0.81 131:0.99 133:0.67 135:0.78 140:0.45 144:0.57 145:0.79 146:0.13 147:0.18 149:0.32 150:0.44 151:0.97 152:0.88 153:0.94 154:0.20 155:0.58 157:0.61 158:0.76 159:0.43 161:0.45 162:0.71 163:0.88 164:0.95 166:0.99 167:1.00 169:0.81 172:0.10 173:0.91 175:0.75 176:0.63 177:0.38 179:0.81 181:0.95 182:0.99 186:0.77 189:0.97 190:0.77 191:0.81 192:0.59 195:0.84 201:0.55 202:0.93 208:0.54 212:0.61 215:0.53 216:0.69 220:0.62 222:0.48 227:0.99 229:0.99 231:0.98 232:0.82 235:0.80 238:0.96 241:0.93 242:0.63 243:0.37 244:0.83 245:0.43 246:0.61 247:0.30 248:0.95 253:0.98 255:0.74 256:0.94 257:0.65 259:0.21 260:0.97 261:0.83 265:0.08 266:0.23 267:0.52 268:0.95 271:0.75 276:0.23 277:0.69 279:0.77 282:0.75 283:0.67 285:0.56 287:0.98 290:0.69 297:0.36 300:0.18 +1 1:0.56 11:0.55 12:0.51 17:0.67 18:0.76 21:0.76 27:0.45 28:0.78 29:0.94 30:0.60 32:0.72 34:0.86 36:0.28 37:0.50 39:0.33 43:0.71 45:0.73 66:0.53 69:0.87 70:0.64 71:0.65 74:0.78 77:0.70 81:0.49 86:0.82 91:0.08 98:0.26 99:0.83 101:0.52 108:0.75 114:0.64 122:0.51 123:0.73 124:0.77 126:0.44 127:0.29 129:0.05 131:0.61 133:0.84 135:0.73 139:0.78 144:0.57 145:0.56 146:0.43 147:0.22 149:0.74 150:0.38 154:0.61 155:0.46 157:0.92 159:0.57 161:0.45 165:0.70 166:0.99 167:0.65 172:0.74 173:0.82 176:0.70 177:0.38 178:0.55 179:0.29 181:0.95 182:0.97 187:0.68 192:0.44 195:0.86 201:0.54 208:0.54 212:0.81 215:0.46 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.98 241:0.15 242:0.43 243:0.12 244:0.61 245:0.76 246:0.98 247:0.60 253:0.58 257:0.65 259:0.21 265:0.28 266:0.63 267:0.23 271:0.75 276:0.70 282:0.80 287:0.61 290:0.64 297:0.36 300:0.55 +4 1:0.58 6:0.97 8:0.95 9:0.79 11:0.42 12:0.51 17:0.02 18:0.61 20:0.96 21:0.64 27:0.03 28:0.78 29:0.94 30:0.76 31:1.00 34:0.82 36:0.91 37:0.94 39:0.33 41:0.97 43:0.25 44:0.88 66:0.20 69:0.92 70:0.22 71:0.65 74:0.45 78:0.90 79:1.00 81:0.37 83:0.91 86:0.63 91:1.00 96:1.00 97:0.89 98:0.09 100:0.98 104:0.58 108:0.84 111:0.97 114:0.69 120:1.00 122:0.51 123:0.83 124:0.73 126:0.44 127:0.25 129:0.59 131:0.99 133:0.64 135:0.65 137:1.00 138:1.00 139:0.67 140:0.98 144:0.57 145:0.82 146:0.13 147:0.05 149:0.26 150:0.44 151:0.99 152:1.00 153:1.00 154:0.18 155:0.66 157:0.61 158:0.82 159:0.44 161:0.45 162:0.57 163:0.88 164:1.00 166:0.99 167:1.00 169:0.96 172:0.10 173:0.96 175:0.75 176:0.63 177:0.05 179:0.90 181:0.95 182:0.99 186:0.90 189:0.98 191:0.98 192:0.87 195:0.81 196:0.95 201:0.55 202:1.00 208:0.64 212:0.42 215:0.53 216:0.84 222:0.48 227:0.99 229:0.99 231:0.98 232:0.81 235:0.80 238:0.99 241:0.98 242:0.45 243:0.26 244:0.83 245:0.40 246:0.61 247:0.35 248:0.97 253:1.00 255:0.78 256:1.00 257:0.84 259:0.21 260:1.00 261:0.98 265:0.09 266:0.23 267:0.59 268:1.00 271:0.75 276:0.21 277:0.69 279:0.82 283:0.82 284:1.00 285:0.62 287:0.98 290:0.69 297:0.36 300:0.18 +4 1:0.61 6:0.96 8:0.88 9:0.80 11:0.42 12:0.51 17:0.51 20:0.81 21:0.68 27:0.63 28:0.78 29:0.94 30:0.91 31:0.96 34:0.66 36:0.84 37:0.48 39:0.33 43:0.60 64:0.77 66:0.69 69:0.68 70:0.13 71:0.65 74:0.41 78:0.75 79:0.95 81:0.49 83:0.91 91:0.79 96:0.96 98:0.21 100:0.80 104:0.72 108:0.52 111:0.75 114:0.69 120:0.93 122:0.51 123:0.49 126:0.54 127:0.11 129:0.05 131:0.99 135:0.51 137:0.96 140:0.97 144:0.57 146:0.29 147:0.35 149:0.42 150:0.50 151:0.66 152:0.79 153:0.85 154:0.50 155:0.66 157:0.61 159:0.12 161:0.45 162:0.83 164:0.90 166:0.99 167:1.00 169:0.78 172:0.21 173:0.68 175:0.75 176:0.70 177:0.38 179:0.24 181:0.95 182:0.99 186:0.76 189:0.97 190:0.77 191:0.75 192:0.87 201:0.60 202:0.82 208:0.64 212:0.61 215:0.49 216:0.54 222:0.48 227:0.99 229:0.99 231:0.98 232:0.82 235:0.88 238:0.95 241:0.94 242:0.63 243:0.73 244:0.83 246:0.61 247:0.30 248:0.66 253:0.94 255:0.79 256:0.86 257:0.65 259:0.21 260:0.93 261:0.77 265:0.23 266:0.17 267:0.73 268:0.87 271:0.75 276:0.14 277:0.69 284:0.97 285:0.62 287:0.99 290:0.69 297:0.36 300:0.13 +3 1:0.63 6:0.98 8:0.95 9:0.79 11:0.42 12:0.51 17:0.47 20:0.95 21:0.22 27:0.07 28:0.78 29:0.94 30:0.95 31:0.95 34:0.52 36:0.54 37:0.95 39:0.33 43:0.63 66:0.54 69:0.54 71:0.65 74:0.43 78:0.85 79:0.94 81:0.52 83:0.91 91:0.88 96:0.97 98:0.17 100:0.95 104:0.78 108:0.42 111:0.90 114:0.85 120:0.94 123:0.40 126:0.44 127:0.10 129:0.05 131:0.99 135:0.39 137:0.95 140:0.94 144:0.57 146:0.13 147:0.18 149:0.30 150:0.56 151:0.95 152:0.92 153:0.92 154:0.52 155:0.66 157:0.61 158:0.76 159:0.08 161:0.45 162:0.64 164:0.92 166:0.99 167:0.99 169:0.93 172:0.10 173:0.93 175:0.75 176:0.63 177:0.38 178:0.42 179:0.21 181:0.95 182:0.99 186:0.85 189:0.98 190:0.77 191:0.87 192:0.87 201:0.60 202:0.89 208:0.64 215:0.79 216:0.63 222:0.48 227:0.99 229:0.99 231:0.98 232:0.85 235:0.31 238:0.97 241:0.85 243:0.63 244:0.83 246:0.61 248:0.92 253:0.95 255:0.78 256:0.89 257:0.65 259:0.21 260:0.94 261:0.93 265:0.18 267:0.69 268:0.90 271:0.75 277:0.69 279:0.77 283:0.67 284:0.93 285:0.62 287:0.99 290:0.85 297:0.36 300:0.08 +4 1:0.58 9:0.79 11:0.42 12:0.51 17:0.45 18:0.61 21:0.64 27:0.03 28:0.78 29:0.94 30:0.76 31:0.95 34:0.89 36:0.58 37:0.94 39:0.33 41:0.82 43:0.25 44:0.88 66:0.20 69:0.93 70:0.22 71:0.65 74:0.34 79:0.94 81:0.37 83:0.91 86:0.63 91:0.88 96:0.98 98:0.08 104:0.58 108:0.85 114:0.69 120:0.96 122:0.51 123:0.84 124:0.73 126:0.44 127:0.27 129:0.59 131:0.99 133:0.64 135:0.65 137:0.95 139:0.67 140:0.98 144:0.57 145:0.82 146:0.13 147:0.05 149:0.25 150:0.44 151:0.63 153:0.95 154:0.17 155:0.66 157:0.61 159:0.49 161:0.45 162:0.78 163:0.88 164:0.92 166:0.99 167:0.96 172:0.10 173:0.96 175:0.75 176:0.63 177:0.05 179:0.91 181:0.95 182:0.99 187:0.39 192:0.87 195:0.83 196:0.90 201:0.55 202:0.87 208:0.64 212:0.42 215:0.53 216:0.84 222:0.48 227:0.99 229:0.99 231:0.98 232:0.81 235:0.80 241:0.79 242:0.45 243:0.26 244:0.83 245:0.40 246:0.61 247:0.35 248:0.61 253:0.96 255:0.78 256:0.90 257:0.84 259:0.21 260:0.96 265:0.09 266:0.23 267:0.79 268:0.90 271:0.75 276:0.20 277:0.69 284:0.91 285:0.62 287:0.99 290:0.69 297:0.36 300:0.18 +1 1:0.56 8:0.69 11:0.55 12:0.51 17:0.40 21:0.76 27:0.45 28:0.78 29:0.94 30:0.26 32:0.67 34:0.75 36:0.17 37:0.50 39:0.33 43:0.68 45:0.90 55:0.42 66:0.27 69:0.70 70:0.91 71:0.65 74:0.75 77:0.70 81:0.34 83:0.60 91:0.07 98:0.51 101:0.52 108:0.54 114:0.63 122:0.51 123:0.51 124:0.87 126:0.44 127:0.66 129:0.95 131:0.61 133:0.87 135:0.86 144:0.57 145:0.56 146:0.86 147:0.88 149:0.85 150:0.40 154:0.58 155:0.46 157:0.99 159:0.84 161:0.45 163:0.81 166:0.99 167:0.73 172:0.63 173:0.48 175:0.75 176:0.63 177:0.38 178:0.55 179:0.38 181:0.95 182:0.96 187:0.68 192:0.44 195:0.94 201:0.54 208:0.54 212:0.30 215:0.46 216:0.77 222:0.48 227:0.61 229:0.61 231:0.98 235:0.97 241:0.51 242:0.60 243:0.46 244:0.61 245:0.80 246:0.61 247:0.47 253:0.57 257:0.65 259:0.21 265:0.52 266:0.84 267:0.28 271:0.75 276:0.76 277:0.69 282:0.75 287:0.61 290:0.63 297:0.36 300:0.84 +3 1:0.62 7:0.70 9:0.75 11:0.50 12:0.51 17:0.43 18:0.83 21:0.30 27:0.21 28:0.78 29:0.94 30:0.73 34:0.58 36:0.83 37:0.74 39:0.33 43:0.66 45:0.77 66:0.35 69:0.12 70:0.64 71:0.65 74:0.89 76:0.85 81:0.49 83:0.60 86:0.88 91:0.27 96:0.88 98:0.60 101:0.52 104:0.84 106:0.81 108:0.91 114:0.82 120:0.80 122:0.51 123:0.91 124:0.78 126:0.44 127:0.69 129:0.81 131:0.99 133:0.78 135:0.47 139:0.82 140:0.80 144:0.57 146:0.69 147:0.73 149:0.92 150:0.54 151:0.90 153:0.69 154:0.49 155:0.58 157:0.61 159:0.85 161:0.45 162:0.86 164:0.74 166:0.99 167:0.69 172:0.92 173:0.09 176:0.63 177:0.70 179:0.15 181:0.95 182:0.98 187:0.39 190:0.77 192:0.58 201:0.59 202:0.60 204:0.82 206:0.81 208:0.54 212:0.69 215:0.72 216:0.95 222:0.48 227:0.99 229:0.98 230:0.73 231:0.98 233:0.76 235:0.42 241:0.32 242:0.53 243:0.21 244:0.61 245:0.98 246:0.61 247:0.84 248:0.79 253:0.91 255:0.73 256:0.68 259:0.21 260:0.81 265:0.61 266:0.71 267:0.59 268:0.68 271:0.75 276:0.95 285:0.56 287:0.98 290:0.82 297:0.36 300:0.84 +3 1:0.63 9:0.80 11:0.42 12:0.51 17:0.40 21:0.22 27:0.07 28:0.78 29:0.94 30:0.95 31:0.98 34:0.40 36:0.55 37:0.95 39:0.33 41:0.82 43:0.63 66:0.54 69:0.54 71:0.65 74:0.36 79:0.98 81:0.52 83:0.91 91:0.58 96:0.98 98:0.15 104:0.78 108:0.42 114:0.85 120:0.96 123:0.40 126:0.44 127:0.09 129:0.05 131:0.99 135:0.49 137:0.98 140:0.96 144:0.57 146:0.13 147:0.18 149:0.30 150:0.56 151:0.91 153:0.94 154:0.52 155:0.67 157:0.61 159:0.08 161:0.45 162:0.69 164:0.94 166:0.99 167:0.87 172:0.10 173:0.88 175:0.75 176:0.63 177:0.38 179:0.16 181:0.95 182:0.99 187:0.39 192:0.87 201:0.60 202:0.91 208:0.64 215:0.79 216:0.63 222:0.48 227:0.99 229:0.99 231:0.98 232:0.85 235:0.31 241:0.73 243:0.63 244:0.83 246:0.61 248:0.81 253:0.97 255:0.94 256:0.92 257:0.65 259:0.21 260:0.96 265:0.16 267:0.19 268:0.93 271:0.75 277:0.69 284:0.96 285:0.62 287:0.99 290:0.85 297:0.36 300:0.08 +1 1:0.63 6:0.98 8:0.88 9:0.79 11:0.42 12:0.51 17:0.06 20:0.80 21:0.22 27:0.07 28:0.78 29:0.94 30:0.87 31:0.95 34:0.95 36:0.21 37:0.95 39:0.33 41:0.82 43:0.65 66:0.32 69:0.41 70:0.27 71:0.65 74:0.51 78:0.78 79:0.94 81:0.52 83:0.91 91:0.53 96:0.90 98:0.54 100:0.84 104:0.78 108:0.31 111:0.79 114:0.85 120:0.83 122:0.51 123:0.30 124:0.61 126:0.44 127:0.63 129:0.59 131:0.99 133:0.47 135:0.63 137:0.95 140:0.87 144:0.57 146:0.37 147:0.43 149:0.60 150:0.56 151:0.91 152:0.92 153:0.72 154:0.54 155:0.66 157:0.61 159:0.26 161:0.45 162:0.72 163:0.81 164:0.79 166:0.99 167:0.81 169:0.92 172:0.21 173:0.63 175:0.75 176:0.63 177:0.38 178:0.42 179:0.90 181:0.95 182:0.99 186:0.79 187:0.39 189:0.94 192:0.87 201:0.60 202:0.71 208:0.64 212:0.42 215:0.79 216:0.63 222:0.48 227:0.99 229:0.99 231:0.98 232:0.85 235:0.31 238:0.94 241:0.68 242:0.63 243:0.49 244:0.83 245:0.54 246:0.61 247:0.30 248:0.81 253:0.87 255:0.79 256:0.73 257:0.65 259:0.21 260:0.84 261:0.93 265:0.56 266:0.38 267:0.36 268:0.74 271:0.75 276:0.29 277:0.69 284:0.91 285:0.62 287:0.99 290:0.85 297:0.36 300:0.25 +3 1:0.62 6:0.87 7:0.70 8:0.74 9:0.79 11:0.64 12:0.51 17:0.34 18:0.83 20:0.83 21:0.30 27:0.21 28:0.78 29:0.94 30:0.72 31:0.94 34:0.69 36:0.87 37:0.74 39:0.33 43:0.76 45:0.81 66:0.35 69:0.12 70:0.66 71:0.65 74:0.91 76:0.85 78:0.74 79:0.93 81:0.49 83:0.91 86:0.88 91:0.64 96:0.95 97:0.94 98:0.60 100:0.78 101:0.87 104:0.84 106:0.81 108:0.91 111:0.74 114:0.82 117:0.86 120:0.91 122:0.51 123:0.91 124:0.79 126:0.44 127:0.69 129:0.81 131:0.99 133:0.78 135:0.32 137:0.94 139:0.82 140:0.45 144:0.57 146:0.69 147:0.73 149:0.93 150:0.54 151:0.98 152:0.79 153:0.93 154:0.68 155:0.66 157:0.81 158:0.82 159:0.85 161:0.45 162:0.85 164:0.86 166:0.99 167:0.71 169:0.76 172:0.92 173:0.09 176:0.63 177:0.70 179:0.15 181:0.95 182:0.99 186:0.75 187:0.39 189:0.88 190:0.77 191:0.70 192:0.87 196:0.94 201:0.59 202:0.76 204:0.82 206:0.81 208:0.64 212:0.69 215:0.72 216:0.95 222:0.48 227:0.99 229:1.00 230:0.73 231:0.98 232:0.92 233:0.76 235:0.42 238:0.91 241:0.41 242:0.54 243:0.21 244:0.61 245:0.98 246:0.61 247:0.82 248:0.94 253:0.97 255:0.79 256:0.82 259:0.21 260:0.92 261:0.76 265:0.61 266:0.71 267:0.52 268:0.83 271:0.75 276:0.95 279:0.81 283:0.82 284:0.88 285:0.62 287:0.99 290:0.82 297:0.36 300:0.84 +3 1:0.59 6:0.97 8:0.92 9:0.79 11:0.42 12:0.51 17:0.32 20:0.78 21:0.59 22:0.93 27:0.03 28:0.78 29:0.94 30:0.95 31:0.95 32:0.67 34:0.81 36:0.83 37:0.94 39:0.33 41:0.82 43:0.21 47:0.93 48:0.91 60:0.87 66:0.54 69:0.95 71:0.65 74:0.53 77:0.70 78:0.86 79:0.95 81:0.39 83:0.91 91:0.84 96:0.97 98:0.08 100:0.95 104:0.58 108:0.91 111:0.91 114:0.71 120:0.94 123:0.90 126:0.44 127:0.06 129:0.05 131:0.99 135:0.32 137:0.95 139:0.77 140:0.98 144:0.57 145:0.80 146:0.13 147:0.18 149:0.13 150:0.45 151:0.86 152:1.00 153:0.91 154:0.14 155:0.66 157:0.61 158:0.92 159:0.08 161:0.45 162:0.76 164:0.92 166:0.99 167:0.88 169:0.96 172:0.10 173:0.99 175:0.75 176:0.63 177:0.38 178:0.42 179:0.08 181:0.95 182:0.99 186:0.86 187:0.21 189:0.97 191:0.95 192:0.87 195:0.89 196:0.93 201:0.56 202:0.86 208:0.64 215:0.55 216:0.84 219:0.95 220:0.62 222:0.48 227:0.99 229:0.99 231:0.98 232:0.81 235:0.74 238:0.97 241:0.74 243:0.63 244:0.83 246:0.61 248:0.77 253:0.95 255:0.79 256:0.88 257:0.65 259:0.21 260:0.94 261:0.98 262:0.93 265:0.08 267:0.69 268:0.90 271:0.75 277:0.69 279:0.80 281:0.91 282:0.75 283:0.82 284:0.92 285:0.62 287:0.99 290:0.71 292:0.95 297:0.36 300:0.08 +3 1:0.69 6:0.97 8:0.93 9:0.76 12:0.51 17:0.04 20:0.95 21:0.13 27:0.03 28:0.78 29:0.94 32:0.67 34:0.59 36:1.00 37:0.94 39:0.33 71:0.65 74:0.44 77:0.70 78:0.78 81:0.69 83:0.60 91:0.80 96:0.98 100:0.85 104:0.58 111:0.79 114:0.88 120:0.96 126:0.44 131:0.99 135:0.98 140:0.94 144:0.57 145:0.40 150:0.77 151:0.96 152:1.00 153:0.89 155:0.58 157:0.61 158:0.76 161:0.45 162:0.65 164:0.94 166:0.99 167:0.85 169:0.95 175:0.97 176:0.63 178:0.50 181:0.95 182:0.99 186:0.78 187:0.39 189:0.97 190:0.77 191:0.80 192:0.59 195:0.72 201:0.66 202:0.92 208:0.54 215:0.84 216:0.84 220:0.62 222:0.48 227:0.99 229:1.00 231:0.98 232:0.81 235:0.42 238:0.95 241:0.72 244:0.97 246:0.61 248:0.95 253:0.97 255:0.74 256:0.92 257:0.93 259:0.21 260:0.96 261:0.98 267:0.84 268:0.93 271:0.75 277:0.95 279:0.79 282:0.75 283:0.67 285:0.56 287:0.99 290:0.88 297:0.36 +3 1:0.62 6:0.97 8:0.93 9:0.76 11:0.42 12:0.51 17:0.07 20:0.94 21:0.30 27:0.03 28:0.78 29:0.94 30:0.95 32:0.67 34:0.61 36:0.61 37:0.94 39:0.33 43:0.23 66:0.54 69:0.94 71:0.65 74:0.39 77:0.89 78:0.84 81:0.49 83:0.60 91:0.86 96:0.97 98:0.08 100:0.95 104:0.58 108:0.88 111:0.89 114:0.82 120:0.95 123:0.88 126:0.44 127:0.06 129:0.05 131:0.99 135:0.61 140:0.87 144:0.57 145:0.70 146:0.13 147:0.18 149:0.15 150:0.54 151:0.97 152:1.00 153:0.92 154:0.16 155:0.58 157:0.61 159:0.08 161:0.45 162:0.77 164:0.94 166:0.99 167:0.87 169:0.95 172:0.10 173:0.99 175:0.75 176:0.63 177:0.38 178:0.42 179:0.08 181:0.95 182:0.99 186:0.85 187:0.39 189:0.98 190:0.77 191:0.89 192:0.58 195:0.84 201:0.59 202:0.89 208:0.54 215:0.72 216:0.84 222:0.48 227:0.99 229:1.00 231:0.98 232:0.81 235:0.42 238:0.97 241:0.73 243:0.63 244:0.83 246:0.61 248:0.94 253:0.96 255:0.74 256:0.92 257:0.65 259:0.21 260:0.95 261:0.98 265:0.08 267:0.89 268:0.92 271:0.75 277:0.69 282:0.75 285:0.56 287:0.99 290:0.82 297:0.36 300:0.08 +2 1:0.74 11:0.91 12:0.37 17:0.49 18:0.89 21:0.47 22:0.93 26:0.99 27:0.56 29:0.86 30:0.33 32:0.80 34:0.55 36:0.19 37:0.60 39:0.75 43:0.65 44:0.93 45:0.95 46:0.99 47:0.93 58:0.93 64:0.77 66:0.67 69:0.57 70:0.86 71:0.62 74:0.90 77:0.70 81:0.54 83:0.91 86:0.93 91:0.11 97:0.89 98:0.84 101:0.52 104:0.91 106:0.81 107:0.99 108:0.44 110:0.99 114:0.60 117:0.86 122:0.75 123:0.42 124:0.47 125:0.88 126:0.54 127:0.58 129:0.59 133:0.46 135:0.89 139:0.93 141:0.91 144:0.85 145:0.69 146:0.92 147:0.94 149:0.85 150:0.74 154:0.77 155:0.67 158:0.79 159:0.64 160:0.88 165:0.93 172:0.90 173:0.48 176:0.73 177:0.70 178:0.55 179:0.24 187:0.21 192:0.87 195:0.79 199:0.98 201:0.93 206:0.81 208:0.64 212:0.82 215:0.41 216:0.76 222:0.76 223:0.99 224:0.93 232:0.94 234:0.91 235:0.68 241:0.10 242:0.36 243:0.71 245:0.95 247:0.88 253:0.49 254:0.74 259:0.21 262:0.93 265:0.84 266:0.38 267:0.28 271:0.16 274:0.91 276:0.87 279:0.82 282:0.88 283:0.82 289:0.98 290:0.60 297:0.36 299:0.88 300:0.70 +1 1:0.69 11:0.91 12:0.37 17:0.15 18:0.94 21:0.40 22:0.93 26:0.99 27:0.56 29:0.86 30:0.19 34:0.88 36:0.25 37:0.60 39:0.75 43:0.56 44:0.90 45:0.85 46:0.96 47:0.93 55:0.52 58:0.93 64:0.77 66:0.54 69:0.83 70:0.81 71:0.62 74:0.64 77:0.89 81:0.39 86:0.96 91:0.10 98:0.78 101:0.52 107:0.98 108:0.68 110:0.98 114:0.58 117:0.86 122:0.77 123:0.66 124:0.65 125:0.88 126:0.44 127:0.54 129:0.05 133:0.60 135:0.42 139:0.95 141:0.91 144:0.85 145:0.65 146:0.92 147:0.55 149:0.39 150:0.51 154:0.61 155:0.58 158:0.72 159:0.68 160:0.88 172:0.87 173:0.77 176:0.55 177:0.38 178:0.55 179:0.23 187:0.95 192:0.51 195:0.84 199:0.99 201:0.68 208:0.54 212:0.71 216:0.76 222:0.76 223:0.99 224:0.93 232:0.98 234:0.91 235:0.52 241:0.46 242:0.21 243:0.28 245:0.93 247:0.88 253:0.44 254:0.84 257:0.65 259:0.21 262:0.93 265:0.78 266:0.80 267:0.36 271:0.16 274:0.91 276:0.87 282:0.71 289:0.96 290:0.58 300:0.84 +2 1:0.74 7:0.72 11:0.91 12:0.37 17:0.53 18:0.68 21:0.40 26:0.99 27:0.49 29:0.86 30:0.41 32:0.80 34:0.67 36:0.25 37:0.65 39:0.75 43:0.60 44:0.93 45:0.81 46:0.96 48:0.91 58:0.93 64:0.77 66:0.86 69:0.72 70:0.36 71:0.62 74:0.68 77:0.70 81:0.57 83:0.60 86:0.74 91:0.40 98:0.66 99:0.83 101:0.52 107:0.97 108:0.55 110:0.98 114:0.62 122:0.75 123:0.53 125:0.88 126:0.54 127:0.19 129:0.05 135:0.95 139:0.82 141:0.91 144:0.85 145:0.52 146:0.75 147:0.79 149:0.53 150:0.72 154:0.67 155:0.67 159:0.31 160:0.88 165:0.70 172:0.59 173:0.71 176:0.73 177:0.70 178:0.55 179:0.44 187:0.39 192:0.87 195:0.74 199:0.95 201:0.93 204:0.85 208:0.64 212:0.42 215:0.42 216:0.61 222:0.76 223:0.99 224:0.93 230:0.74 233:0.73 234:0.91 235:0.60 241:0.62 242:0.33 243:0.86 247:0.67 253:0.48 254:0.92 259:0.21 265:0.67 266:0.38 267:0.44 271:0.16 274:0.91 276:0.48 281:0.91 282:0.88 289:0.98 290:0.62 297:0.36 300:0.25 +4 1:0.74 8:0.74 9:0.80 11:0.91 12:0.37 17:0.90 18:0.86 21:0.30 26:0.99 27:0.72 29:0.86 30:0.21 31:0.87 32:0.78 34:0.67 36:0.63 37:0.92 39:0.75 43:0.67 44:0.93 45:0.83 46:1.00 48:0.91 58:0.98 64:0.77 66:0.90 69:0.15 70:0.62 71:0.62 74:0.78 76:1.00 77:0.96 79:0.87 81:0.63 83:0.60 86:0.94 91:0.86 96:0.82 98:0.95 99:0.83 101:0.52 104:0.58 107:0.98 108:0.12 110:0.98 114:0.65 120:0.71 122:0.57 123:0.12 125:0.99 126:0.54 127:0.64 129:0.05 135:0.39 137:0.87 139:0.94 140:0.45 141:0.99 144:0.85 145:0.86 146:0.61 147:0.67 149:0.33 150:0.76 151:0.53 153:0.48 154:0.87 155:0.67 159:0.23 160:0.97 162:0.78 164:0.68 165:0.70 172:0.71 173:0.47 176:0.73 177:0.70 179:0.61 190:0.77 192:0.87 195:0.56 196:0.91 199:0.96 201:0.93 202:0.59 204:0.85 208:0.64 212:0.89 215:0.44 216:0.03 220:0.87 222:0.76 223:0.99 224:0.98 232:0.77 234:0.98 235:0.52 241:0.88 242:0.27 243:0.90 247:0.76 248:0.52 253:0.81 254:0.88 255:0.95 256:0.58 259:0.21 260:0.68 265:0.95 266:0.27 267:0.36 268:0.64 271:0.16 274:0.97 276:0.60 281:0.89 282:0.86 284:0.91 285:0.62 289:0.98 290:0.65 297:0.36 300:0.43 +1 1:0.74 7:0.72 11:0.91 12:0.37 17:0.55 18:0.63 21:0.30 26:0.99 27:0.31 29:0.86 30:0.33 32:0.79 34:0.97 36:0.32 37:0.55 39:0.75 43:0.62 44:0.93 45:0.73 46:0.95 58:0.93 64:0.77 66:0.20 69:0.75 70:0.49 71:0.62 74:0.68 76:0.94 77:0.70 81:0.65 83:0.91 86:0.73 91:0.10 97:0.89 98:0.35 101:0.52 104:0.95 106:0.81 107:0.95 108:0.58 110:0.96 114:0.65 117:0.86 122:0.51 123:0.66 124:0.56 125:0.88 126:0.54 127:0.18 129:0.05 133:0.37 135:0.86 139:0.80 141:0.91 144:0.85 145:0.67 146:0.25 147:0.31 149:0.32 150:0.79 154:0.68 155:0.67 158:0.79 159:0.24 160:0.88 165:0.93 172:0.27 173:0.80 176:0.73 177:0.70 178:0.55 179:0.60 187:0.39 192:0.87 195:0.69 199:0.95 201:0.93 204:0.85 206:0.81 208:0.64 212:0.42 215:0.44 216:0.85 222:0.76 223:0.99 224:0.93 230:0.75 232:0.96 233:0.76 234:0.91 235:0.52 241:0.30 242:0.19 243:0.58 245:0.53 247:0.55 253:0.50 254:0.92 259:0.21 265:0.21 266:0.38 267:0.28 271:0.16 274:0.91 276:0.30 277:0.87 279:0.82 282:0.87 283:0.82 289:0.98 290:0.65 297:0.36 300:0.33 +2 1:0.74 7:0.72 9:0.80 11:0.93 12:0.37 17:0.72 18:0.98 21:0.54 26:0.99 27:0.53 29:0.86 30:0.87 31:0.86 34:0.88 36:0.31 37:0.85 39:0.75 43:0.66 44:0.90 45:0.94 46:0.94 48:0.99 55:0.59 58:0.98 64:0.77 66:0.69 69:0.57 70:0.37 71:0.62 74:0.64 76:0.99 77:1.00 79:0.86 81:0.52 83:0.60 85:0.80 86:0.99 91:0.15 96:0.68 98:0.76 99:0.83 101:0.97 107:0.97 108:0.79 110:0.97 114:0.59 120:0.55 122:0.85 123:0.78 124:0.49 125:0.97 126:0.54 127:0.59 129:0.59 133:0.61 135:0.88 137:0.86 139:0.98 140:0.45 141:0.99 144:0.85 145:0.97 146:0.51 147:0.57 149:0.85 150:0.70 151:0.50 153:0.48 154:0.70 155:0.67 159:0.71 160:0.96 162:0.86 164:0.57 165:0.70 172:0.95 173:0.43 176:0.73 177:0.70 179:0.16 187:0.68 192:0.87 195:0.86 196:0.90 199:1.00 201:0.93 202:0.36 204:0.89 208:0.64 212:0.93 215:0.39 216:0.41 220:0.91 222:0.76 223:0.99 224:0.98 230:0.75 232:0.79 233:0.76 234:0.98 235:0.88 241:0.18 242:0.30 243:0.16 245:0.96 247:0.84 248:0.49 253:0.45 255:0.95 256:0.45 259:0.21 260:0.55 265:0.76 266:0.38 267:0.87 268:0.46 271:0.16 274:0.97 276:0.93 281:0.88 282:0.71 284:0.89 285:0.62 289:0.98 290:0.59 297:0.36 300:0.70 +1 1:0.74 11:0.91 12:0.37 17:0.49 18:0.90 21:0.47 22:0.93 26:0.99 27:0.56 29:0.86 30:0.17 32:0.80 34:0.61 36:0.18 37:0.60 39:0.75 43:0.65 44:0.93 45:0.90 46:0.98 47:0.93 55:0.45 58:0.93 64:0.77 66:0.48 69:0.67 70:0.90 71:0.62 74:0.83 77:0.70 81:0.54 83:0.91 86:0.93 91:0.07 97:0.89 98:0.75 101:0.52 104:0.91 106:0.81 107:0.99 108:0.51 110:0.99 114:0.60 117:0.86 122:0.86 123:0.49 124:0.58 125:0.88 126:0.54 127:0.42 129:0.59 133:0.46 135:0.86 139:0.93 141:0.91 144:0.85 145:0.69 146:0.84 147:0.87 149:0.66 150:0.74 154:0.70 155:0.67 158:0.79 159:0.53 160:0.88 165:0.93 172:0.79 173:0.64 176:0.73 177:0.70 178:0.55 179:0.27 187:0.39 192:0.87 195:0.74 199:0.98 201:0.93 206:0.81 208:0.64 212:0.80 215:0.41 216:0.76 222:0.76 223:0.99 224:0.93 232:0.94 234:0.91 235:0.68 241:0.01 242:0.21 243:0.59 245:0.94 247:0.88 253:0.48 254:0.74 259:0.21 262:0.93 265:0.76 266:0.38 267:0.28 271:0.16 274:0.91 276:0.81 279:0.82 282:0.88 283:0.82 289:0.98 290:0.60 297:0.36 299:0.88 300:0.70 +1 1:0.74 11:0.91 12:0.37 17:0.55 18:0.92 21:0.47 22:0.93 26:0.99 27:0.56 29:0.86 30:0.43 32:0.80 34:0.93 36:0.17 37:0.60 39:0.75 43:0.67 44:0.93 45:0.83 46:0.97 47:0.93 55:0.45 58:0.93 64:0.77 66:0.95 69:0.64 70:0.81 71:0.62 74:0.76 77:0.70 81:0.54 83:0.91 86:0.94 91:0.08 97:0.89 98:0.86 101:0.52 104:0.91 106:0.81 107:0.98 108:0.49 110:0.98 114:0.60 117:0.86 122:0.77 123:0.47 125:0.88 126:0.54 127:0.38 129:0.05 135:0.60 139:0.94 141:0.91 144:0.85 145:0.69 146:0.88 147:0.90 149:0.55 150:0.74 154:0.71 155:0.67 158:0.79 159:0.46 160:0.88 165:0.93 172:0.88 173:0.64 176:0.73 177:0.70 178:0.55 179:0.28 187:0.39 192:0.87 195:0.70 199:0.98 201:0.93 206:0.81 208:0.64 212:0.77 215:0.41 216:0.76 222:0.76 223:0.99 224:0.93 232:0.94 234:0.91 235:0.68 242:0.27 243:0.96 247:0.90 253:0.47 254:0.74 259:0.21 262:0.93 265:0.86 266:0.38 267:0.28 271:0.16 274:0.91 276:0.79 279:0.82 282:0.88 283:0.82 289:0.98 290:0.60 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.72 11:0.91 12:0.37 17:0.61 18:0.67 21:0.22 22:0.93 26:0.99 27:0.67 29:0.86 30:0.53 32:0.80 34:0.75 36:0.25 37:0.44 39:0.75 43:0.61 44:0.93 45:0.90 46:0.98 47:0.93 48:0.91 58:0.93 64:0.77 66:0.93 69:0.73 70:0.59 71:0.62 74:0.77 77:0.96 81:0.72 83:0.91 86:0.77 91:0.27 98:0.81 101:0.52 104:0.99 106:0.81 107:0.99 108:0.56 110:0.99 114:0.71 117:0.86 122:0.75 123:0.54 125:0.88 126:0.54 127:0.30 129:0.05 135:0.96 139:0.83 141:0.91 144:0.85 145:0.76 146:0.87 147:0.89 149:0.73 150:0.83 154:0.64 155:0.67 159:0.44 160:0.88 165:0.93 172:0.80 173:0.72 176:0.73 177:0.70 178:0.55 179:0.34 187:0.95 192:0.87 195:0.74 199:0.96 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.69 215:0.51 216:0.56 222:0.76 223:0.99 224:0.93 230:0.75 232:0.99 233:0.76 234:0.91 235:0.42 241:0.18 242:0.45 243:0.93 247:0.67 253:0.53 254:0.90 259:0.21 262:0.93 265:0.81 266:0.47 267:0.36 271:0.16 274:0.91 276:0.69 281:0.91 282:0.88 289:0.98 290:0.71 297:0.36 300:0.43 +2 6:0.93 7:0.66 8:0.75 12:0.20 17:0.72 18:0.99 20:0.81 27:0.34 28:0.56 29:0.71 30:0.17 31:0.96 32:0.69 34:0.49 36:0.51 37:0.71 39:0.30 43:0.72 44:0.90 48:0.91 55:0.52 64:0.77 66:0.97 69:0.07 70:0.81 71:0.75 74:0.50 78:0.96 79:0.97 81:0.74 86:0.99 91:0.44 98:0.96 100:0.91 106:0.81 108:0.06 111:0.94 120:0.85 123:0.06 126:0.44 127:0.72 129:0.05 135:0.32 137:0.96 139:0.99 140:0.45 145:0.40 146:0.90 147:0.92 149:0.82 150:0.52 151:0.85 152:0.98 153:0.77 154:0.64 159:0.49 161:0.76 162:0.86 164:0.70 167:0.56 169:0.97 172:0.93 173:0.16 177:0.70 179:0.26 181:0.76 186:0.95 187:0.39 189:0.92 190:0.77 191:0.70 192:0.51 195:0.66 196:0.98 201:0.68 202:0.60 206:0.81 212:0.90 215:0.96 216:0.55 219:0.92 222:0.35 230:0.69 233:0.76 235:0.05 238:0.88 241:0.07 242:0.30 243:0.97 247:0.84 248:0.85 253:0.35 254:0.74 255:0.74 256:0.68 259:0.21 260:0.81 261:0.97 265:0.96 266:0.27 267:0.36 268:0.69 276:0.86 281:0.91 283:0.82 284:0.92 285:0.56 292:0.95 297:0.36 300:0.70 +2 1:0.74 7:0.81 9:0.76 11:0.83 12:0.20 17:0.91 18:0.99 22:0.93 27:0.19 28:0.56 29:0.71 30:0.33 31:0.95 32:0.80 34:0.87 36:0.34 37:0.93 39:0.30 43:0.79 44:0.93 45:0.81 47:0.93 48:0.98 55:0.45 64:0.77 66:0.83 69:0.73 70:0.64 71:0.75 74:0.91 76:0.85 77:0.96 79:0.96 81:0.89 83:0.91 85:0.80 86:0.99 91:0.56 96:0.87 97:0.94 98:0.90 101:0.97 106:0.81 108:0.56 114:0.98 117:0.86 121:0.90 122:0.86 123:0.54 124:0.39 126:0.54 127:0.74 129:0.05 133:0.43 135:0.88 137:0.95 139:0.99 140:0.45 145:0.76 146:0.93 147:0.39 149:0.84 150:0.94 151:0.81 153:0.48 154:0.72 155:0.67 158:0.79 159:0.63 161:0.76 162:0.86 165:0.93 167:0.53 172:0.95 173:0.69 176:0.73 177:0.38 179:0.19 181:0.76 187:0.21 190:0.77 192:0.87 195:0.79 196:0.98 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.93 215:0.96 216:0.61 219:0.92 222:0.35 230:0.87 232:0.84 233:0.76 235:0.12 241:0.01 242:0.70 243:0.15 245:0.94 247:0.91 248:0.82 253:0.89 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.90 266:0.27 267:0.85 268:0.59 276:0.91 279:0.82 281:0.91 282:0.88 284:0.91 285:0.56 290:0.98 292:0.95 297:0.36 299:0.88 300:0.55 +2 6:0.82 7:0.72 8:0.70 12:0.20 17:0.90 18:0.99 20:0.92 22:0.93 27:0.19 28:0.56 29:0.71 30:0.68 31:0.98 32:0.69 34:0.78 36:0.29 37:0.93 39:0.30 43:0.72 44:0.90 47:0.93 48:0.91 55:0.45 64:0.77 66:0.77 69:0.75 70:0.52 71:0.75 74:0.39 78:0.96 79:0.99 81:0.74 86:0.99 91:0.68 98:0.82 100:0.93 106:0.81 108:0.58 111:0.94 120:0.91 122:0.86 123:0.56 124:0.41 126:0.44 127:0.74 129:0.05 133:0.43 135:0.95 137:0.98 139:0.99 140:0.45 145:0.40 146:0.76 147:0.39 149:0.72 150:0.52 151:0.91 152:0.88 153:0.84 154:0.61 159:0.43 161:0.76 162:0.86 164:0.79 167:0.61 169:0.90 172:0.91 173:0.84 177:0.38 179:0.28 181:0.76 186:0.96 187:0.21 189:0.84 190:0.77 192:0.51 195:0.62 196:0.99 201:0.68 202:0.70 206:0.81 212:0.94 215:0.96 216:0.61 219:0.92 222:0.35 230:0.75 233:0.76 235:0.05 238:0.86 241:0.30 242:0.75 243:0.20 245:0.91 247:0.76 248:0.93 253:0.31 254:0.88 255:0.74 256:0.80 257:0.65 259:0.21 260:0.89 261:0.92 262:0.93 265:0.82 266:0.27 267:0.59 268:0.79 276:0.85 281:0.91 283:0.82 284:0.96 285:0.56 292:0.95 297:0.36 300:0.55 +1 6:0.80 7:0.72 8:0.74 12:0.20 17:0.75 18:0.99 20:0.85 22:0.93 27:0.29 28:0.56 29:0.71 30:0.66 31:0.94 32:0.69 34:0.49 36:0.22 37:0.70 39:0.30 43:0.72 44:0.90 47:0.93 48:0.98 55:0.52 64:0.77 66:0.72 69:0.05 70:0.62 71:0.75 78:0.92 79:0.95 81:0.74 86:0.98 91:0.33 98:0.80 100:0.83 106:0.81 108:0.05 111:0.90 120:0.77 122:0.86 123:0.05 124:0.43 126:0.44 127:0.91 129:0.59 133:0.47 135:0.89 137:0.94 139:0.98 140:0.80 145:0.70 146:0.77 147:0.81 149:0.81 150:0.52 151:0.80 152:0.81 153:0.48 154:0.62 159:0.49 161:0.76 162:0.78 164:0.65 167:0.67 169:0.80 172:0.87 173:0.16 175:0.75 177:0.38 179:0.35 181:0.76 186:0.92 187:0.68 189:0.82 190:0.77 191:0.70 192:0.51 195:0.65 196:0.98 201:0.68 202:0.59 206:0.81 212:0.88 215:0.96 216:0.42 219:0.92 220:0.74 222:0.35 230:0.75 233:0.76 235:0.05 238:0.82 241:0.49 242:0.73 243:0.75 244:0.61 245:0.90 247:0.67 248:0.80 253:0.20 255:0.74 256:0.64 257:0.65 259:0.21 260:0.74 261:0.86 262:0.93 265:0.80 266:0.47 267:0.36 268:0.64 276:0.81 277:0.69 281:0.91 283:0.82 284:0.90 285:0.56 292:0.95 297:0.36 300:0.70 +0 1:0.69 7:0.72 9:0.76 11:0.64 12:0.20 17:0.45 21:0.47 27:0.18 28:0.56 29:0.71 30:0.11 32:0.79 34:0.76 36:0.34 37:0.85 39:0.30 43:0.62 44:0.93 45:0.66 48:0.91 55:0.59 66:0.54 69:0.69 70:0.95 71:0.75 74:0.66 76:0.85 77:0.89 81:0.37 83:0.60 91:0.35 96:0.88 97:0.98 98:0.45 99:0.83 101:0.52 106:0.81 108:0.84 114:0.59 117:0.86 120:0.81 122:0.51 123:0.83 124:0.50 126:0.44 127:0.46 129:0.59 133:0.47 135:0.87 138:0.95 139:0.77 140:0.45 145:0.74 146:0.35 147:0.42 149:0.32 150:0.53 151:0.91 153:0.84 154:0.51 155:0.58 158:0.79 159:0.61 161:0.76 162:0.86 163:0.81 164:0.73 165:0.70 167:0.68 172:0.41 173:0.62 175:0.75 176:0.66 177:0.38 179:0.79 181:0.76 187:0.87 190:0.77 192:0.59 195:0.82 201:0.68 202:0.67 204:0.85 206:0.81 208:0.54 212:0.28 215:0.41 216:0.82 222:0.35 230:0.75 232:0.98 233:0.76 235:0.60 241:0.52 242:0.29 243:0.34 244:0.61 245:0.59 247:0.60 248:0.92 253:0.82 255:0.74 256:0.73 257:0.65 259:0.21 260:0.74 265:0.46 266:0.54 267:0.99 268:0.75 276:0.31 277:0.69 279:0.82 281:0.91 282:0.87 283:0.82 285:0.56 286:0.99 290:0.59 297:0.36 298:0.99 300:0.70 +2 7:0.72 8:0.83 11:0.64 12:0.20 17:0.98 18:1.00 20:0.83 22:0.93 27:0.29 28:0.56 29:0.71 30:0.73 31:0.97 32:0.69 34:0.23 36:0.41 37:0.88 39:0.30 43:0.72 44:0.90 45:0.77 47:0.93 48:0.91 55:0.52 64:0.77 66:0.99 69:0.05 70:0.33 71:0.75 74:0.52 78:0.91 79:0.97 81:0.74 86:1.00 91:0.49 98:0.96 100:0.73 101:0.52 106:0.81 108:0.05 111:0.88 120:0.86 122:0.86 123:0.05 126:0.44 127:0.69 129:0.05 135:0.42 137:0.97 139:1.00 140:0.45 145:0.38 146:0.90 147:0.92 149:0.84 150:0.52 151:0.84 152:0.79 153:0.72 154:0.59 159:0.49 161:0.76 162:0.86 164:0.71 167:0.57 169:0.72 172:0.97 173:0.14 177:0.70 179:0.17 181:0.76 186:0.91 187:0.39 190:0.77 191:0.82 192:0.51 195:0.67 196:0.99 201:0.68 202:0.61 206:0.81 212:0.94 215:0.96 216:0.61 219:0.92 222:0.35 230:0.75 233:0.76 235:0.05 241:0.12 242:0.75 243:0.99 247:0.79 248:0.89 253:0.36 254:0.99 255:0.74 256:0.68 259:0.21 260:0.83 261:0.84 262:0.93 265:0.96 266:0.27 267:0.28 268:0.70 276:0.93 281:0.91 283:0.82 284:0.94 285:0.56 292:0.95 297:0.36 300:0.55 +2 6:0.92 8:0.91 9:0.76 11:0.64 12:0.20 17:0.20 20:0.79 21:0.13 27:0.02 28:0.56 29:0.71 30:0.85 34:0.30 36:0.32 37:0.92 39:0.30 43:0.25 45:0.77 55:0.71 66:0.63 69:0.93 70:0.39 71:0.75 74:0.65 76:0.85 77:0.70 78:0.92 81:0.51 83:0.60 91:0.76 96:0.96 98:0.55 100:0.91 101:0.52 108:0.86 111:0.90 114:0.72 120:0.79 122:0.77 123:0.85 124:0.51 126:0.26 127:0.51 129:0.05 133:0.62 135:0.76 140:0.97 145:0.38 146:0.80 147:0.05 149:0.41 150:0.39 151:0.81 152:0.93 153:0.87 154:0.18 155:0.58 158:0.74 159:0.70 161:0.76 162:0.60 164:0.72 167:0.82 169:0.91 172:0.70 173:0.93 175:0.75 176:0.61 177:0.05 179:0.50 181:0.76 186:0.92 187:0.39 190:0.77 191:0.82 192:0.59 195:0.86 202:0.86 208:0.54 212:0.72 216:0.99 222:0.35 235:0.12 241:0.74 242:0.61 243:0.09 244:0.61 245:0.72 247:0.60 248:0.82 253:0.92 255:0.74 256:0.87 257:0.84 259:0.21 260:0.76 261:0.93 265:0.57 266:0.80 267:0.52 268:0.87 276:0.62 277:0.69 282:0.73 285:0.56 290:0.72 300:0.43 +2 1:0.74 6:0.92 7:0.66 8:0.74 9:0.76 11:0.92 12:0.20 17:0.61 18:0.99 20:0.83 21:0.13 22:0.93 27:0.26 28:0.56 29:0.71 30:0.60 31:0.93 32:0.80 37:0.86 39:0.30 43:0.97 44:0.93 45:0.81 47:0.93 48:0.97 55:0.45 64:0.77 66:0.36 69:0.10 70:0.64 71:0.75 74:0.90 76:0.85 77:0.70 78:0.99 79:0.93 81:0.77 83:0.91 85:0.80 86:0.99 91:0.38 96:0.80 97:0.89 98:0.79 100:0.98 101:0.97 106:0.81 108:0.78 111:0.98 114:0.79 117:0.86 122:0.86 123:0.76 124:0.61 126:0.54 127:0.86 129:0.59 133:0.46 137:0.93 139:0.99 140:0.45 145:0.69 146:0.77 147:0.81 149:0.84 150:0.85 151:0.81 152:0.87 153:0.48 154:0.97 155:0.67 158:0.79 159:0.70 161:0.76 162:0.86 165:0.93 167:0.59 169:0.94 172:0.88 173:0.13 176:0.73 177:0.70 179:0.20 181:0.76 186:0.98 187:0.87 189:0.92 190:0.77 192:0.87 195:0.83 196:0.97 201:0.93 202:0.36 206:0.81 208:0.64 212:0.91 215:0.61 216:0.57 219:0.92 222:0.35 230:0.69 232:0.95 233:0.76 235:0.31 238:0.94 241:0.18 242:0.61 243:0.51 245:0.98 247:0.91 248:0.73 253:0.84 255:0.74 256:0.45 261:0.93 262:0.93 265:0.79 266:0.54 268:0.46 276:0.91 279:0.82 281:0.91 282:0.88 284:0.87 285:0.56 290:0.79 292:0.95 297:0.36 299:0.88 300:0.84 +2 7:0.72 11:0.64 12:0.20 17:0.75 18:1.00 22:0.93 27:0.31 28:0.56 29:0.71 30:0.76 31:0.93 32:0.77 34:0.66 36:0.23 37:0.81 39:0.30 43:0.72 44:0.93 45:0.66 47:0.93 48:0.97 55:0.52 64:0.77 66:0.92 69:0.67 70:0.41 71:0.75 74:0.55 77:0.70 79:0.94 81:0.74 86:1.00 91:0.69 98:0.90 101:0.52 106:0.81 108:0.51 120:0.72 122:0.86 123:0.49 124:0.36 126:0.44 127:0.69 129:0.05 133:0.43 135:0.89 137:0.93 139:1.00 140:0.45 145:0.61 146:0.91 147:0.25 149:0.83 150:0.52 151:0.83 153:0.69 154:0.54 159:0.57 161:0.76 162:0.86 164:0.55 167:0.56 172:0.97 173:0.66 177:0.38 179:0.16 181:0.76 187:0.68 190:0.77 192:0.51 195:0.74 196:0.97 201:0.68 202:0.46 206:0.81 212:0.94 215:0.96 216:0.59 219:0.92 222:0.35 230:0.75 233:0.76 235:0.05 241:0.07 242:0.73 243:0.08 245:0.90 247:0.86 248:0.75 253:0.37 254:0.80 255:0.74 256:0.45 257:0.65 259:0.21 260:0.69 262:0.93 265:0.90 266:0.27 267:0.36 268:0.55 276:0.94 281:0.91 282:0.85 283:0.82 284:0.87 285:0.56 292:0.95 297:0.36 300:0.55 +2 1:0.74 11:0.64 12:0.20 17:0.96 18:0.96 21:0.64 22:0.93 27:0.70 28:0.56 29:0.71 30:0.55 34:0.80 36:0.27 37:0.49 39:0.30 43:0.41 45:0.77 47:0.93 55:0.52 60:0.97 64:0.77 66:0.97 69:0.36 70:0.62 71:0.75 74:0.86 81:0.39 83:0.91 86:0.97 91:0.53 97:0.94 98:0.87 101:0.52 106:0.81 108:0.28 114:0.55 117:0.86 122:0.77 123:0.27 126:0.54 127:0.39 129:0.05 135:0.88 139:0.97 146:0.93 147:0.95 149:0.43 150:0.62 154:0.62 155:0.67 158:0.92 159:0.58 161:0.76 167:0.52 172:0.92 173:0.27 176:0.66 177:0.70 178:0.55 179:0.23 181:0.76 187:0.39 192:0.87 201:0.93 206:0.81 208:0.64 212:0.89 215:0.37 216:0.13 219:0.95 222:0.35 232:0.93 235:0.84 241:0.01 242:0.41 243:0.97 247:0.93 253:0.46 254:0.84 259:0.21 262:0.93 265:0.87 266:0.27 267:0.28 276:0.85 279:0.86 283:0.82 290:0.55 292:0.95 297:0.36 300:0.70 +2 6:0.93 7:0.66 8:0.86 11:0.64 12:0.20 17:0.53 18:0.99 20:0.98 21:0.59 27:0.34 28:0.56 29:0.71 30:0.38 31:0.90 32:0.80 34:0.85 36:0.36 37:0.71 39:0.30 43:0.69 44:0.93 45:0.73 48:0.91 55:0.59 64:0.77 66:0.67 69:0.25 70:0.74 71:0.75 74:0.96 76:0.85 77:0.70 78:0.98 79:0.91 81:0.36 86:1.00 91:0.40 96:0.67 98:0.86 100:0.99 101:0.52 106:0.81 108:0.58 111:0.98 114:0.58 120:0.53 122:0.67 123:0.55 124:0.52 126:0.54 127:0.89 129:0.81 133:0.67 135:0.47 137:0.90 139:1.00 140:0.45 145:0.61 146:0.32 147:0.38 149:0.68 150:0.46 151:0.47 152:0.97 153:0.48 154:0.84 158:0.85 159:0.80 161:0.76 162:0.86 164:0.57 167:0.59 169:0.97 172:0.97 173:0.15 176:0.73 177:0.70 179:0.14 181:0.76 186:0.97 187:0.68 189:0.95 190:0.89 191:0.73 192:0.51 195:0.91 196:0.95 201:0.68 202:0.50 206:0.81 208:0.64 212:0.92 215:0.39 216:0.55 219:0.95 220:0.97 222:0.35 230:0.69 233:0.76 235:0.80 238:0.96 241:0.18 242:0.18 243:0.17 245:0.98 247:0.93 248:0.46 253:0.49 254:0.84 255:0.74 256:0.58 259:0.21 260:0.53 261:0.97 265:0.86 266:0.54 267:0.19 268:0.59 276:0.96 281:0.91 282:0.88 283:0.66 284:0.91 285:0.62 290:0.58 292:0.87 297:0.36 300:0.84 +2 6:0.92 7:0.66 8:0.81 11:0.64 12:0.20 17:0.86 18:0.99 20:0.89 21:0.13 22:0.93 27:0.26 28:0.56 29:0.71 30:0.62 31:0.96 32:0.69 34:0.29 36:0.35 37:0.86 39:0.30 43:0.72 44:0.90 45:0.66 47:0.93 48:0.97 55:0.45 64:0.77 66:0.97 69:0.08 70:0.45 71:0.75 74:0.47 78:0.94 79:0.97 81:0.56 86:0.99 91:0.62 98:0.94 100:0.94 101:0.52 106:0.81 108:0.07 111:0.93 120:0.83 122:0.86 123:0.07 126:0.44 127:0.62 129:0.05 135:0.65 137:0.96 139:0.99 140:0.45 145:0.45 146:0.78 147:0.82 149:0.88 150:0.40 151:0.85 152:0.87 153:0.77 154:0.68 159:0.34 161:0.76 162:0.86 164:0.71 167:0.59 169:0.94 172:0.92 173:0.26 177:0.70 179:0.27 181:0.76 186:0.93 187:0.68 189:0.94 190:0.77 192:0.51 195:0.61 196:0.99 201:0.68 202:0.60 206:0.81 212:0.92 215:0.61 216:0.57 219:0.92 222:0.35 230:0.69 233:0.76 235:0.22 238:0.89 241:0.18 242:0.50 243:0.97 247:0.79 248:0.86 253:0.34 254:0.84 255:0.74 256:0.68 260:0.80 261:0.93 262:0.93 265:0.94 266:0.27 267:0.59 268:0.68 276:0.84 281:0.91 283:0.82 284:0.94 285:0.56 292:0.95 297:0.36 300:0.55 +3 6:0.81 8:0.64 9:0.76 12:0.20 17:0.45 18:0.85 20:0.95 21:0.30 25:0.88 27:0.59 28:0.56 29:0.71 30:0.62 34:0.17 36:0.30 39:0.30 43:0.18 55:0.71 66:0.87 69:0.23 70:0.47 71:0.75 74:0.44 78:0.93 81:0.27 83:0.60 86:0.81 91:0.65 96:0.95 98:0.98 100:0.93 108:0.18 111:0.92 120:0.94 123:0.18 126:0.26 127:0.85 129:0.05 135:0.65 139:0.78 140:0.99 146:0.89 147:0.91 149:0.25 150:0.26 151:0.81 152:0.93 153:0.86 154:0.56 155:0.58 159:0.48 161:0.76 162:0.50 164:0.92 167:0.91 169:0.90 172:0.63 173:0.31 177:0.70 178:0.50 179:0.74 181:0.76 186:0.93 189:0.83 190:0.77 191:0.77 192:0.59 202:0.97 208:0.54 212:0.27 215:0.44 216:0.12 220:0.74 222:0.35 235:0.31 238:0.88 241:0.82 242:0.16 243:0.88 244:0.61 247:0.79 248:0.74 253:0.89 254:0.80 255:0.74 256:0.95 259:0.21 260:0.91 261:0.92 265:0.98 266:0.63 267:0.97 268:0.95 276:0.53 285:0.56 297:0.36 300:0.43 +2 1:0.74 12:0.20 17:0.26 18:0.88 21:0.54 27:0.45 28:0.56 29:0.71 30:0.45 34:0.89 36:0.21 37:0.50 39:0.30 43:0.14 48:0.91 55:0.71 64:0.77 66:0.46 69:0.62 70:0.51 71:0.75 74:0.69 81:0.44 86:0.87 91:0.25 98:0.62 108:0.47 114:0.59 122:0.77 123:0.45 124:0.58 126:0.54 127:0.39 129:0.05 133:0.43 135:0.74 139:0.89 145:0.38 146:0.73 147:0.77 149:0.26 150:0.63 154:0.74 155:0.67 158:0.87 159:0.51 161:0.76 167:0.55 172:0.57 173:0.57 176:0.73 177:0.70 178:0.55 179:0.51 181:0.76 187:0.68 192:0.87 201:0.93 208:0.64 212:0.48 215:0.39 216:0.77 219:0.95 222:0.35 235:0.74 241:0.05 242:0.42 243:0.59 245:0.80 247:0.74 253:0.45 254:0.80 259:0.21 265:0.62 266:0.71 267:0.79 276:0.61 279:0.86 281:0.91 283:0.71 290:0.59 292:0.91 297:0.36 300:0.43 +2 1:0.74 7:0.72 8:0.84 11:0.92 12:0.20 17:0.91 18:1.00 22:0.93 27:0.31 28:0.56 29:0.71 30:0.57 32:0.80 34:0.66 36:0.37 37:0.81 39:0.30 43:0.97 44:0.93 45:0.89 47:0.93 48:0.91 55:0.52 64:0.77 66:0.92 69:0.07 70:0.64 71:0.75 74:0.92 77:0.89 81:0.89 83:0.60 85:0.85 86:1.00 91:0.49 97:0.89 98:0.94 99:0.83 101:0.97 106:0.81 108:0.51 114:0.98 117:0.86 122:0.86 123:0.49 124:0.37 126:0.54 127:0.85 129:0.59 133:0.46 135:0.89 139:1.00 145:0.79 146:0.20 147:0.26 149:0.93 150:0.93 154:0.97 155:0.67 158:0.79 159:0.70 161:0.76 165:0.70 167:0.56 172:0.98 173:0.11 176:0.73 177:0.70 178:0.55 179:0.14 181:0.76 187:0.87 192:0.87 195:0.81 201:0.93 204:0.85 206:0.81 208:0.64 212:0.92 215:0.96 216:0.59 219:0.92 222:0.35 230:0.75 232:0.96 233:0.76 235:0.12 241:0.07 242:0.70 243:0.08 245:0.93 247:0.98 253:0.68 259:0.21 262:0.93 265:0.94 266:0.27 267:0.36 276:0.96 279:0.82 281:0.91 282:0.88 290:0.98 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.74 9:0.80 11:0.57 12:0.20 17:0.34 18:0.89 21:0.22 27:0.45 28:0.56 29:0.71 30:0.72 31:0.88 32:0.79 34:0.89 36:0.20 37:0.50 39:0.30 41:0.82 43:0.82 44:0.93 48:0.91 55:0.71 60:0.87 64:0.77 66:0.35 69:0.61 70:0.54 71:0.75 74:0.85 77:0.70 79:0.88 81:0.69 83:0.91 85:0.72 86:0.90 91:0.17 96:0.71 98:0.62 101:0.87 108:0.79 114:0.71 120:0.57 122:0.77 123:0.77 124:0.72 126:0.54 127:0.59 129:0.05 133:0.66 135:0.81 137:0.88 139:0.93 140:0.45 145:0.50 146:0.74 147:0.78 149:0.67 150:0.81 151:0.56 153:0.48 154:0.77 155:0.67 158:0.91 159:0.72 161:0.76 162:0.86 164:0.57 165:0.93 167:0.59 172:0.73 173:0.46 176:0.73 177:0.70 179:0.33 181:0.76 187:0.68 192:0.87 195:0.87 196:0.91 201:0.93 202:0.36 208:0.64 212:0.76 215:0.51 216:0.77 219:0.95 220:0.82 222:0.35 235:0.42 241:0.21 242:0.70 243:0.39 245:0.90 247:0.84 248:0.55 253:0.58 255:0.95 256:0.45 259:0.21 260:0.58 265:0.63 266:0.71 267:0.73 268:0.46 276:0.80 277:0.69 279:0.86 281:0.91 282:0.87 283:0.78 284:0.89 285:0.62 290:0.71 292:0.91 297:0.36 300:0.84 +1 1:0.66 9:0.79 11:0.82 12:0.06 17:0.20 18:0.78 21:0.40 27:0.11 28:1.00 29:0.77 30:0.88 31:0.95 32:0.72 34:0.93 36:0.44 37:0.89 39:0.63 43:0.76 44:0.92 45:0.83 64:0.77 66:0.16 69:0.82 70:0.33 71:0.85 74:0.64 77:0.70 79:0.95 81:0.65 83:0.91 85:0.72 86:0.85 91:0.58 96:0.85 98:0.18 99:0.83 101:0.87 108:0.80 114:0.82 120:0.76 122:0.57 123:0.79 124:0.86 126:0.54 127:0.31 129:0.93 131:0.85 133:0.84 135:0.85 137:0.95 139:0.85 140:0.87 144:0.76 145:0.77 146:0.18 147:0.23 149:0.79 150:0.56 151:0.86 153:0.48 154:0.67 155:0.66 157:0.82 159:0.59 161:0.57 162:0.50 164:0.66 165:0.70 166:0.79 167:0.67 172:0.32 173:0.74 176:0.70 177:0.70 178:0.48 179:0.49 181:0.55 182:0.78 187:0.39 190:0.77 192:0.87 195:0.87 196:0.95 201:0.63 202:0.70 208:0.64 212:0.37 215:0.67 216:0.90 220:0.62 222:0.35 227:0.92 229:0.87 231:0.69 235:0.60 241:0.44 242:0.45 243:0.23 245:0.66 246:0.91 247:0.35 248:0.83 253:0.84 255:0.79 256:0.58 259:0.21 260:0.76 265:0.19 266:0.54 267:0.73 268:0.59 271:0.78 276:0.55 282:0.80 284:0.92 285:0.62 287:0.93 290:0.82 297:0.36 300:0.43 +2 1:0.66 7:0.64 8:0.58 9:0.75 11:0.78 12:0.06 17:0.16 18:0.83 20:0.74 21:0.30 22:0.93 27:0.18 28:1.00 29:0.77 30:0.45 31:0.92 32:0.80 34:0.63 36:0.29 37:0.46 39:0.63 41:0.88 43:0.92 44:0.93 47:0.93 60:0.87 64:0.77 66:0.10 69:0.37 70:0.48 71:0.85 74:0.81 77:0.70 78:0.75 79:0.92 81:0.74 83:0.60 85:0.91 86:0.92 91:0.35 96:0.78 98:0.18 100:0.73 101:0.87 104:0.89 106:0.81 108:0.83 111:0.74 114:0.86 117:0.86 120:0.66 122:0.57 123:0.68 124:0.86 126:0.54 127:0.56 129:0.93 131:0.85 133:0.84 135:0.98 137:0.92 139:0.95 140:0.45 144:0.76 145:0.76 146:0.18 147:0.23 149:0.89 150:0.56 151:0.77 152:0.74 153:0.48 154:0.92 155:0.65 157:0.82 158:0.92 159:0.65 161:0.57 162:0.86 164:0.67 165:0.93 166:0.79 167:0.55 169:0.77 172:0.32 173:0.27 176:0.73 177:0.70 179:0.63 181:0.55 182:0.78 186:0.76 187:0.95 192:0.87 195:0.82 196:0.96 201:0.64 202:0.50 204:0.82 206:0.81 208:0.64 212:0.39 215:0.72 216:0.75 219:0.95 220:0.62 222:0.35 227:0.92 229:0.88 230:0.64 231:0.69 232:0.91 233:0.66 235:0.60 241:0.03 242:0.51 243:0.24 245:0.64 246:0.91 247:0.47 248:0.71 253:0.82 255:0.77 256:0.58 259:0.21 260:0.67 261:0.74 262:0.93 265:0.20 266:0.54 267:0.59 268:0.59 271:0.78 276:0.53 279:0.80 281:0.86 282:0.88 283:0.82 284:0.92 285:0.62 287:0.93 290:0.86 292:0.95 297:0.36 299:0.88 300:0.43 +0 11:0.82 12:0.06 17:0.30 18:0.75 21:0.30 27:0.45 28:1.00 29:0.77 30:0.27 34:0.81 36:0.21 37:0.50 39:0.63 43:0.75 45:0.73 66:0.62 69:0.68 70:0.91 71:0.85 74:0.64 81:0.30 85:0.88 86:0.86 91:0.20 98:0.58 101:0.87 108:0.52 123:0.50 124:0.64 126:0.26 127:0.46 129:0.05 131:0.61 133:0.73 135:0.93 139:0.83 144:0.76 145:0.47 146:0.78 147:0.82 149:0.85 150:0.33 154:0.66 157:0.82 159:0.64 161:0.57 166:0.73 167:0.70 172:0.67 173:0.59 177:0.70 178:0.55 179:0.51 181:0.55 182:0.75 187:0.68 212:0.42 215:0.72 216:0.77 222:0.35 227:0.61 229:0.61 231:0.67 235:0.31 241:0.55 242:0.30 243:0.68 245:0.66 246:0.61 247:0.67 253:0.48 259:0.21 265:0.59 266:0.80 267:0.36 271:0.78 276:0.62 283:0.67 287:0.61 297:0.36 300:0.70 +1 1:0.69 7:0.64 9:0.79 11:0.56 12:0.06 17:0.97 18:0.67 21:0.05 27:0.08 28:1.00 29:0.77 30:0.55 31:0.94 32:0.80 34:0.66 36:0.40 37:0.69 39:0.63 43:0.23 44:0.93 45:0.83 64:0.77 66:0.18 69:0.96 70:0.72 71:0.85 74:0.57 76:0.85 77:0.70 79:0.93 81:0.72 83:0.91 86:0.72 91:0.31 96:0.80 98:0.34 99:0.83 101:0.52 104:0.58 108:0.65 114:0.95 120:0.69 122:0.77 123:0.62 124:0.88 126:0.54 127:0.67 129:0.81 131:0.85 133:0.86 135:0.73 137:0.94 139:0.84 140:0.45 144:0.76 145:0.40 146:0.28 147:0.74 149:0.53 150:0.64 151:0.86 153:0.48 154:0.15 155:0.66 157:0.81 159:0.84 161:0.57 162:0.86 164:0.56 165:0.70 166:0.79 167:0.76 172:0.45 173:0.97 176:0.70 177:0.38 179:0.50 181:0.55 182:0.78 187:0.39 190:0.77 192:0.87 195:0.94 196:0.94 201:0.66 202:0.36 204:0.81 208:0.64 212:0.39 215:0.91 216:0.08 222:0.35 227:0.92 229:0.87 230:0.65 231:0.69 232:0.75 233:0.76 235:0.22 241:0.67 242:0.53 243:0.39 244:0.61 245:0.73 246:0.92 247:0.74 248:0.77 253:0.81 255:0.78 256:0.45 257:0.65 259:0.21 260:0.70 265:0.36 266:0.71 267:0.44 268:0.46 271:0.78 276:0.63 277:0.69 281:0.91 282:0.88 284:0.88 285:0.62 287:0.93 290:0.95 297:0.36 299:0.88 300:0.70 +3 1:0.66 7:0.75 9:0.80 11:0.82 12:0.06 17:0.36 18:0.92 21:0.30 22:0.93 27:0.21 28:1.00 29:0.77 30:0.87 31:0.98 34:0.49 36:0.86 37:0.74 39:0.63 41:0.90 43:0.83 45:0.87 47:0.93 48:0.91 64:0.77 66:0.45 69:0.15 70:0.46 71:0.85 74:0.81 79:0.98 81:0.66 83:0.91 85:0.72 86:0.95 91:0.56 96:0.94 97:0.98 98:0.47 99:0.83 101:0.87 104:0.84 106:0.81 108:0.64 114:0.84 117:0.86 120:0.89 122:0.57 123:0.62 124:0.58 126:0.54 127:0.24 129:0.59 131:0.85 133:0.46 135:0.18 137:0.98 139:0.95 140:0.96 144:0.76 146:0.49 147:0.55 149:0.92 150:0.58 151:0.86 153:0.92 154:0.79 155:0.67 157:0.84 158:0.82 159:0.38 161:0.57 162:0.64 164:0.84 165:0.70 166:0.79 167:0.71 172:0.63 173:0.19 176:0.70 177:0.70 179:0.30 181:0.55 182:0.78 187:0.39 192:0.87 196:0.99 201:0.64 202:0.80 204:0.84 206:0.81 208:0.64 212:0.69 215:0.72 216:0.95 219:0.94 222:0.35 227:0.92 229:0.88 230:0.77 231:0.69 232:0.88 233:0.76 235:0.52 241:0.57 242:0.43 243:0.48 245:0.85 246:0.92 247:0.55 248:0.86 253:0.95 255:0.95 256:0.81 259:0.21 260:0.89 262:0.93 265:0.49 266:0.27 267:0.65 268:0.81 271:0.78 276:0.65 279:0.82 281:0.91 283:0.82 284:0.97 285:0.62 287:0.94 290:0.84 292:0.95 297:0.36 300:0.70 +2 1:0.69 6:0.84 7:0.64 8:0.72 9:0.80 11:0.56 12:0.06 17:0.97 18:0.59 20:0.94 21:0.05 27:0.08 28:1.00 29:0.77 30:0.21 31:0.98 32:0.80 34:0.22 36:0.32 37:0.69 39:0.63 41:0.82 43:0.22 44:0.93 45:0.66 55:0.59 64:0.77 66:0.36 69:0.96 70:0.88 71:0.85 74:0.53 76:0.85 77:0.70 78:0.76 79:0.98 81:0.72 83:0.91 86:0.65 91:0.85 96:0.92 98:0.37 99:0.83 100:0.79 101:0.52 104:0.58 108:0.65 111:0.75 114:0.95 120:0.86 122:0.77 123:0.62 124:0.77 126:0.54 127:0.66 129:0.05 131:0.85 133:0.73 135:0.56 137:0.98 139:0.81 140:0.45 144:0.76 145:0.40 146:0.43 147:0.74 149:0.29 150:0.64 151:0.93 152:0.94 153:0.78 154:0.15 155:0.67 157:0.75 159:0.81 161:0.57 162:0.78 164:0.81 165:0.70 166:0.79 167:0.96 169:0.83 172:0.45 173:0.99 176:0.70 177:0.38 179:0.69 181:0.55 182:0.78 186:0.76 189:0.87 191:0.81 192:0.87 195:0.93 196:0.96 201:0.66 202:0.71 204:0.81 208:0.64 212:0.35 215:0.91 216:0.08 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.22 238:0.91 241:0.98 242:0.75 243:0.51 244:0.61 245:0.62 246:0.92 247:0.55 248:0.83 253:0.90 255:0.94 256:0.75 257:0.65 259:0.21 260:0.86 261:0.86 265:0.39 266:0.80 267:0.44 268:0.76 271:0.78 276:0.47 277:0.69 281:0.91 282:0.88 284:0.97 285:0.62 287:0.94 290:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.66 11:0.78 12:0.06 17:0.40 18:0.71 21:0.30 27:0.45 28:1.00 29:0.77 30:0.62 34:0.85 36:0.20 37:0.50 39:0.63 43:0.82 60:0.87 64:0.77 66:0.75 69:0.69 70:0.23 71:0.85 74:0.63 81:0.74 83:0.60 85:0.80 86:0.82 91:0.14 98:0.19 101:0.87 108:0.52 114:0.86 122:0.57 123:0.50 126:0.54 127:0.10 129:0.05 131:0.61 135:0.80 139:0.85 144:0.76 145:0.49 146:0.27 147:0.33 149:0.75 150:0.56 154:0.77 155:0.51 157:0.78 158:0.86 159:0.12 161:0.57 165:0.93 166:0.79 167:0.58 172:0.32 173:0.69 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 182:0.77 187:0.68 192:0.48 201:0.64 208:0.64 212:0.84 215:0.72 216:0.77 219:0.95 222:0.35 227:0.61 229:0.61 231:0.68 235:0.60 241:0.12 242:0.63 243:0.77 246:0.91 247:0.35 253:0.64 259:0.21 265:0.21 266:0.17 267:0.28 271:0.78 276:0.22 279:0.80 283:0.67 287:0.61 290:0.86 292:0.88 297:0.36 300:0.18 +2 1:0.66 6:0.96 8:0.97 9:0.79 12:0.06 17:0.64 20:0.77 21:0.30 27:0.16 28:1.00 29:0.77 31:0.94 34:0.85 36:0.51 37:0.91 39:0.63 64:0.77 71:0.85 74:0.41 78:0.74 79:0.93 81:0.74 83:0.91 91:0.37 96:0.80 97:0.89 100:0.78 104:0.98 111:0.74 114:0.86 120:0.69 126:0.54 131:0.85 135:0.60 137:0.94 139:0.70 140:0.45 144:0.76 150:0.56 151:0.86 152:0.75 153:0.48 155:0.66 157:0.61 158:0.82 161:0.57 162:0.86 164:0.56 165:0.93 166:0.79 167:0.74 169:0.75 175:0.97 176:0.73 181:0.55 182:0.78 186:0.75 187:0.95 189:0.97 190:0.77 192:0.87 196:0.91 201:0.64 202:0.36 208:0.64 215:0.72 216:0.44 219:0.94 222:0.35 227:0.92 229:0.87 231:0.69 232:0.98 235:0.60 238:0.91 241:0.64 244:0.97 246:0.91 248:0.77 253:0.79 255:0.78 256:0.45 257:0.93 259:0.21 260:0.70 261:0.74 267:0.36 268:0.46 271:0.78 277:0.95 279:0.80 283:0.82 284:0.88 285:0.62 287:0.93 290:0.86 292:0.95 297:0.36 +1 1:0.64 6:0.89 7:0.75 8:0.76 9:0.70 11:0.82 12:0.06 17:0.38 18:0.73 20:0.75 21:0.54 27:0.16 28:1.00 29:0.77 30:0.91 31:0.87 32:0.72 34:0.75 36:0.33 37:0.85 39:0.63 43:0.62 44:0.92 45:0.77 48:0.99 64:0.77 66:0.33 69:0.85 70:0.22 71:0.85 74:0.63 77:0.89 78:0.76 79:0.86 81:0.62 83:0.91 85:0.72 86:0.80 91:0.27 96:0.69 98:0.17 99:0.83 100:0.73 101:0.87 108:0.79 111:0.75 114:0.76 120:0.55 122:0.57 123:0.78 124:0.72 126:0.54 127:0.20 129:0.81 131:0.85 133:0.67 135:0.88 137:0.87 139:0.85 140:0.90 144:0.76 145:0.91 146:0.17 147:0.22 149:0.70 150:0.52 151:0.50 152:0.81 153:0.48 154:0.50 155:0.64 157:0.81 159:0.40 161:0.57 162:0.49 164:0.56 165:0.70 166:0.79 167:0.70 169:0.75 172:0.32 173:0.81 176:0.70 177:0.70 178:0.50 179:0.52 181:0.55 182:0.78 186:0.77 187:0.68 190:0.77 191:0.70 192:0.58 195:0.84 196:0.89 201:0.62 202:0.65 204:0.85 208:0.64 212:0.39 215:0.58 216:0.81 220:0.96 222:0.35 227:0.92 229:0.87 230:0.77 231:0.69 233:0.66 235:0.74 241:0.53 242:0.55 243:0.33 245:0.58 246:0.91 247:0.35 248:0.50 253:0.59 255:0.69 256:0.45 259:0.21 260:0.55 261:0.75 265:0.18 266:0.27 267:0.36 268:0.46 271:0.78 276:0.35 281:0.86 282:0.80 284:0.88 285:0.62 287:0.93 290:0.76 297:0.36 300:0.25 +1 1:0.68 7:0.81 11:0.56 12:0.06 17:0.47 18:0.97 21:0.13 27:0.03 28:1.00 29:0.77 30:0.93 32:0.72 34:0.96 36:0.93 37:0.98 39:0.63 43:0.74 44:0.92 45:0.97 64:0.77 66:0.51 69:0.61 70:0.29 71:0.85 74:0.94 77:0.70 81:0.70 83:0.91 86:0.98 91:0.38 97:0.94 98:0.74 99:0.83 101:0.52 104:0.93 108:0.73 114:0.92 121:1.00 122:0.57 123:0.72 124:0.56 126:0.54 127:0.54 129:0.59 131:0.61 133:0.46 135:0.73 139:0.99 144:0.76 145:0.45 146:0.62 147:0.67 149:0.98 150:0.61 154:0.64 155:0.65 157:0.87 158:0.82 159:0.57 161:0.57 165:0.70 166:0.79 167:0.65 172:0.89 173:0.57 176:0.70 177:0.70 178:0.55 179:0.20 181:0.55 182:0.78 187:0.95 192:0.86 195:0.77 201:0.66 204:0.85 208:0.64 212:0.82 215:0.84 216:0.80 219:0.94 222:0.35 227:0.61 229:0.61 230:0.87 231:0.69 232:0.93 233:0.76 235:0.31 241:0.39 242:0.50 243:0.45 244:0.61 245:0.97 246:0.92 247:0.35 253:0.74 259:0.21 265:0.75 266:0.54 267:0.52 271:0.78 276:0.89 279:0.81 281:0.91 282:0.80 283:0.82 287:0.61 290:0.92 292:0.95 297:0.36 300:0.70 +0 1:0.60 11:0.82 12:0.06 17:0.84 18:0.97 21:0.71 22:0.93 27:0.57 28:1.00 29:0.77 30:0.08 32:0.80 34:0.68 36:0.57 37:0.38 39:0.63 43:0.90 44:0.93 45:0.83 47:0.93 60:0.95 64:0.77 66:0.62 69:0.44 70:0.97 71:0.85 74:0.95 77:0.96 81:0.67 83:0.60 85:0.93 86:0.98 91:0.11 98:0.90 101:0.87 104:0.79 106:0.81 108:0.73 114:0.68 117:0.86 122:0.77 123:0.71 124:0.50 126:0.54 127:0.73 129:0.59 131:0.61 133:0.46 135:0.89 139:0.99 144:0.76 145:0.97 146:0.68 147:0.73 149:0.96 150:0.45 154:0.89 155:0.48 157:0.84 158:0.92 159:0.86 161:0.57 165:0.93 166:0.78 167:0.54 172:0.96 173:0.19 176:0.73 177:0.70 178:0.55 179:0.15 181:0.55 182:0.77 187:0.39 191:0.85 192:0.45 195:0.95 201:0.59 206:0.81 208:0.64 212:0.73 215:0.48 216:0.72 219:0.95 222:0.35 227:0.61 229:0.61 231:0.69 232:0.84 235:0.97 241:0.01 242:0.17 243:0.37 245:0.99 246:0.91 247:0.97 253:0.68 259:0.21 262:0.93 265:0.90 266:0.75 267:0.82 271:0.78 276:0.95 279:0.81 282:0.88 283:0.82 287:0.61 290:0.68 292:0.95 297:0.36 299:0.88 300:0.84 +4 1:0.68 6:0.97 7:0.64 8:0.97 9:0.80 11:0.56 12:0.06 17:0.75 18:0.61 20:0.89 21:0.13 27:0.01 28:1.00 29:0.77 30:0.10 31:1.00 32:0.80 34:0.28 36:0.39 37:0.96 39:0.63 41:0.99 43:0.21 44:0.93 45:0.66 55:0.59 60:0.97 64:0.77 66:0.27 69:0.98 70:0.99 71:0.85 74:0.63 76:0.85 77:0.70 78:0.92 79:1.00 81:0.70 83:0.91 86:0.65 91:1.00 96:1.00 97:0.99 98:0.29 99:0.83 100:0.99 101:0.52 104:0.58 108:0.76 111:0.99 114:0.92 120:0.99 122:0.77 123:0.75 124:0.92 126:0.54 127:0.99 129:0.05 131:0.85 133:0.92 135:0.49 137:1.00 138:0.99 139:0.87 140:0.87 144:0.76 145:0.40 146:0.25 147:0.05 149:0.31 150:0.61 151:1.00 152:0.96 153:0.99 154:0.13 155:0.67 157:0.75 158:0.92 159:0.89 161:0.57 162:0.71 164:0.98 165:0.70 166:0.79 167:0.96 169:0.98 172:0.51 173:1.00 176:0.70 177:0.05 178:0.42 179:0.57 181:0.55 182:0.78 186:0.92 189:0.99 191:0.98 192:0.87 195:0.96 196:1.00 201:0.66 202:0.97 204:0.81 208:0.64 212:0.42 215:0.84 216:0.68 219:0.95 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.31 238:0.99 241:0.99 242:0.75 243:0.09 244:0.61 245:0.64 246:0.92 247:0.74 248:1.00 253:1.00 255:0.95 256:0.98 257:0.84 259:0.21 260:0.99 261:0.98 265:0.31 266:0.92 267:0.69 268:0.98 271:0.78 276:0.64 277:0.87 279:0.86 281:0.91 282:0.88 283:0.82 284:1.00 285:0.62 287:0.94 290:0.92 292:0.95 297:0.36 299:0.88 300:0.94 +3 1:0.66 6:0.97 7:0.64 8:0.93 9:0.80 11:0.56 12:0.06 17:0.28 18:0.80 20:0.94 21:0.30 27:0.01 28:1.00 29:0.77 30:0.62 31:1.00 32:0.80 34:0.78 36:0.39 37:0.96 39:0.63 41:0.98 43:0.70 44:0.93 45:0.66 64:0.77 66:0.61 69:0.70 70:0.34 71:0.85 74:0.54 76:0.85 77:0.70 78:0.92 79:1.00 81:0.66 83:0.91 86:0.82 91:0.98 96:0.99 98:0.35 99:0.83 100:0.98 101:0.52 104:0.58 108:0.54 111:0.97 114:0.84 120:0.97 122:0.57 123:0.51 124:0.43 126:0.54 127:0.41 129:0.05 131:0.85 133:0.37 135:0.54 137:1.00 138:0.99 139:0.87 140:0.87 144:0.76 145:0.40 146:0.25 147:0.05 149:0.46 150:0.58 151:0.99 152:0.96 153:0.97 154:0.60 155:0.67 157:0.76 159:0.19 161:0.57 162:0.67 164:0.95 165:0.70 166:0.79 167:0.89 169:0.98 172:0.27 173:0.95 176:0.70 177:0.05 178:0.42 179:0.92 181:0.55 182:0.78 186:0.92 187:0.39 189:0.97 191:0.92 192:0.87 195:0.55 196:1.00 201:0.64 202:0.92 204:0.81 208:0.64 212:0.61 215:0.72 216:0.68 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.52 238:0.98 241:0.77 242:0.33 243:0.23 244:0.61 245:0.42 246:0.92 247:0.39 248:0.97 253:0.98 255:0.95 256:0.94 257:0.84 259:0.21 260:0.97 261:0.98 265:0.37 266:0.23 267:0.52 268:0.94 271:0.78 276:0.17 281:0.91 282:0.88 284:1.00 285:0.62 287:0.94 290:0.84 297:0.36 299:0.88 300:0.25 +1 1:0.69 6:0.84 7:0.64 8:0.65 9:0.80 11:0.56 12:0.06 17:0.43 18:0.71 20:0.84 27:0.08 28:1.00 29:0.77 30:0.91 31:0.97 32:0.80 34:0.59 36:0.49 37:0.69 39:0.63 41:0.82 43:0.74 44:0.93 45:0.66 64:0.77 66:0.69 69:0.56 70:0.13 71:0.85 74:0.55 76:0.85 77:0.70 78:0.88 79:0.96 81:0.74 83:0.91 86:0.79 91:0.80 96:0.89 98:0.35 99:0.83 100:0.92 101:0.52 104:0.58 108:0.43 111:0.89 114:0.97 120:0.81 122:0.57 123:0.41 126:0.54 127:0.13 129:0.05 131:0.85 135:0.73 137:0.97 139:0.86 140:0.80 144:0.76 145:0.40 146:0.23 147:0.29 149:0.48 150:0.68 151:0.90 152:0.94 153:0.77 154:0.65 155:0.66 157:0.76 159:0.11 161:0.57 162:0.86 164:0.70 165:0.70 166:0.79 167:0.81 169:0.83 172:0.21 173:0.89 176:0.70 177:0.70 179:0.53 181:0.55 182:0.78 186:0.88 187:0.21 189:0.86 191:0.76 192:0.87 195:0.54 196:0.97 201:0.67 202:0.55 204:0.81 208:0.64 212:0.61 215:0.96 216:0.08 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.12 238:0.93 241:0.73 242:0.63 243:0.73 244:0.61 246:0.92 247:0.30 248:0.80 253:0.87 255:0.94 256:0.58 259:0.21 260:0.81 261:0.86 265:0.38 266:0.17 267:0.28 268:0.64 271:0.78 276:0.16 281:0.91 282:0.88 284:0.93 285:0.62 287:0.93 290:0.97 297:0.36 299:0.88 300:0.13 +2 1:0.66 7:0.64 9:0.80 11:0.56 12:0.06 17:0.34 18:0.83 21:0.30 27:0.01 28:1.00 29:0.77 30:0.76 31:0.99 32:0.80 34:0.83 36:0.40 37:0.96 39:0.63 41:0.95 43:0.73 44:0.93 45:0.73 60:0.97 64:0.77 66:0.80 69:0.60 70:0.28 71:0.85 74:0.68 76:0.85 77:0.70 79:0.99 81:0.66 83:0.91 86:0.85 91:0.86 96:0.96 97:0.94 98:0.74 99:0.83 101:0.52 104:0.58 108:0.46 114:0.84 120:0.93 122:0.57 123:0.44 126:0.54 127:0.23 129:0.05 131:0.85 135:0.51 137:0.99 139:0.92 140:0.87 144:0.76 145:0.40 146:0.50 147:0.56 149:0.64 150:0.58 151:0.97 153:0.90 154:0.62 155:0.67 157:0.79 158:0.86 159:0.18 161:0.57 162:0.69 163:0.81 164:0.88 165:0.70 166:0.79 167:0.89 172:0.45 173:0.81 176:0.70 177:0.70 178:0.42 179:0.70 181:0.55 182:0.78 187:0.39 192:0.87 195:0.57 196:0.99 201:0.64 202:0.82 204:0.81 208:0.64 212:0.37 215:0.72 216:0.68 219:0.95 222:0.35 227:0.92 229:0.88 230:0.65 231:0.69 232:0.75 233:0.76 235:0.52 241:0.77 242:0.40 243:0.82 244:0.61 246:0.92 247:0.39 248:0.93 253:0.96 255:0.95 256:0.85 259:0.21 260:0.93 265:0.74 266:0.38 267:0.23 268:0.85 271:0.78 276:0.33 279:0.82 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 287:0.94 290:0.84 292:0.95 297:0.36 299:0.88 300:0.25 +1 12:0.79 17:0.99 21:0.05 25:0.88 27:0.72 28:0.85 30:0.58 34:0.37 36:0.48 43:0.52 55:0.45 66:0.91 69:0.29 70:0.46 81:1.00 91:0.60 98:0.90 104:0.58 108:0.22 123:0.22 126:0.54 127:0.48 129:0.05 135:0.23 146:0.61 147:0.67 149:0.78 150:1.00 154:0.41 159:0.23 161:0.49 167:0.87 172:0.76 173:0.55 177:0.05 178:0.55 179:0.50 181:0.84 212:0.91 215:0.91 216:0.02 235:0.68 241:0.83 242:0.82 243:0.92 247:0.12 259:0.21 265:0.90 266:0.27 267:0.36 271:0.18 276:0.65 297:0.99 300:0.43 +1 6:0.87 7:0.81 8:0.79 12:0.79 17:0.53 20:0.76 21:0.78 27:0.27 28:0.85 32:0.80 34:0.50 36:0.91 37:0.90 78:0.85 91:0.90 100:0.86 111:0.84 120:0.97 135:0.30 140:0.45 145:0.80 151:0.83 152:0.75 153:0.96 161:0.49 162:0.65 164:0.95 167:0.84 169:0.83 175:0.75 181:0.84 186:0.86 187:0.21 189:0.88 191:0.90 195:0.54 202:0.92 216:0.29 230:0.87 233:0.76 235:0.74 238:0.87 241:0.78 244:0.61 248:0.95 256:0.93 257:0.65 259:0.21 260:0.97 261:0.76 267:0.52 268:0.94 271:0.18 277:0.69 285:0.62 +1 12:0.79 17:0.47 21:0.78 27:0.45 28:0.85 30:0.62 32:0.80 34:0.90 36:0.39 37:0.50 43:0.32 55:0.92 66:0.47 69:0.71 70:0.34 81:0.82 91:0.10 98:0.59 108:0.54 123:0.52 124:0.52 126:0.54 127:0.35 129:0.59 133:0.47 135:0.42 145:0.63 146:0.69 147:0.74 149:0.32 150:0.61 154:0.24 159:0.47 161:0.49 163:0.94 167:0.55 172:0.21 173:0.70 177:0.05 178:0.55 179:0.91 181:0.84 187:0.68 195:0.73 212:0.30 215:0.43 216:0.77 235:1.00 241:0.21 242:0.82 243:0.59 245:0.46 247:0.12 259:0.21 265:0.60 266:0.27 267:0.23 271:0.18 276:0.27 297:0.36 300:0.25 +2 7:0.81 8:0.62 12:0.79 17:0.26 20:0.79 21:0.30 27:0.21 28:0.85 30:0.53 34:0.52 36:0.99 37:0.74 43:0.52 55:0.59 66:0.32 69:0.10 70:0.60 78:0.84 81:0.99 91:0.44 98:0.55 100:0.73 104:0.84 106:0.81 108:0.58 111:0.82 120:0.83 123:0.55 124:0.79 126:0.54 127:0.51 129:0.89 133:0.78 135:0.56 140:0.45 146:0.36 147:0.42 149:0.66 150:0.98 151:0.71 152:0.79 153:0.72 154:0.41 159:0.67 161:0.49 162:0.86 164:0.75 167:0.56 169:0.72 172:0.51 173:0.12 177:0.05 179:0.53 181:0.84 186:0.84 187:0.39 202:0.61 206:0.81 212:0.16 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 241:0.27 242:0.82 243:0.25 245:0.73 247:0.12 248:0.75 256:0.68 259:0.21 260:0.84 261:0.81 265:0.57 266:0.89 267:0.88 268:0.70 271:0.18 276:0.63 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.84 7:0.81 8:0.65 12:0.79 17:0.12 20:0.87 21:0.30 27:0.21 28:0.85 30:0.30 34:0.55 36:0.99 37:0.74 43:0.52 55:0.59 66:0.33 69:0.10 70:0.65 78:0.80 81:0.99 91:0.71 98:0.55 100:0.81 104:0.84 106:0.81 108:0.57 111:0.79 120:0.95 123:0.55 124:0.79 126:0.54 127:0.50 129:0.89 133:0.78 135:0.42 140:0.45 146:0.34 147:0.41 149:0.65 150:0.98 151:0.83 152:0.82 153:0.96 154:0.41 159:0.67 161:0.49 162:0.71 163:0.75 164:0.92 167:0.58 169:0.79 172:0.51 173:0.12 177:0.05 179:0.54 181:0.84 186:0.80 187:0.39 189:0.86 191:0.70 202:0.87 206:0.81 212:0.16 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.85 241:0.37 242:0.82 243:0.21 245:0.71 247:0.12 248:0.93 256:0.88 259:0.21 260:0.95 261:0.80 265:0.57 266:0.89 267:0.84 268:0.90 271:0.18 276:0.62 283:0.82 285:0.62 297:0.36 300:0.43 +2 7:0.81 12:0.79 17:0.08 21:0.78 27:0.08 28:0.85 32:0.80 34:0.89 36:0.64 37:0.87 91:0.63 120:0.97 135:0.51 140:0.45 145:0.80 151:0.83 153:0.96 161:0.49 162:0.66 164:0.95 167:0.83 175:0.75 181:0.84 187:0.21 195:0.57 202:0.93 216:0.67 230:0.87 233:0.76 235:0.74 241:0.77 244:0.61 248:0.95 256:0.94 257:0.65 259:0.21 260:0.97 267:0.23 268:0.94 271:0.18 277:0.69 283:0.60 285:0.62 +1 12:0.79 17:0.34 21:0.47 27:0.45 28:0.85 30:0.11 32:0.80 34:0.86 36:0.32 37:0.50 43:0.32 55:0.45 66:0.13 69:0.69 70:0.84 81:0.97 91:0.09 98:0.26 108:0.83 123:0.63 124:0.79 126:0.54 127:0.40 129:0.89 133:0.78 135:0.70 145:0.47 146:0.22 147:0.27 149:0.44 150:0.96 154:0.24 159:0.52 161:0.49 167:0.54 172:0.27 173:0.66 177:0.05 178:0.55 179:0.77 181:0.84 187:0.68 195:0.75 212:0.28 215:0.63 216:0.77 235:0.52 241:0.18 242:0.82 243:0.26 245:0.52 247:0.12 259:0.21 265:0.20 266:0.63 267:0.36 271:0.18 276:0.38 283:0.60 297:0.36 300:0.55 +0 6:0.97 8:0.94 12:0.79 17:0.28 20:0.94 21:0.30 27:0.04 28:0.85 30:0.95 32:0.80 34:0.64 36:0.63 37:0.91 43:0.50 55:0.52 66:0.54 69:0.21 78:0.89 81:0.99 91:0.56 98:0.33 100:0.95 108:0.17 111:0.92 120:0.63 123:0.16 126:0.54 127:0.12 129:0.05 135:0.39 140:0.45 145:0.74 146:0.19 147:0.25 149:0.24 150:0.98 151:0.54 152:0.95 153:0.48 154:0.39 159:0.10 161:0.49 162:0.69 164:0.75 167:0.88 169:0.97 172:0.10 173:0.77 177:0.05 179:0.82 181:0.84 186:0.89 187:0.39 189:0.97 191:0.99 195:0.52 202:0.67 215:0.72 216:0.73 220:0.87 235:0.31 238:0.97 241:0.86 243:0.63 248:0.57 256:0.68 259:0.21 260:0.64 261:0.97 265:0.35 267:0.69 268:0.70 271:0.18 285:0.62 297:0.36 300:0.08 +3 6:0.89 7:0.81 8:0.84 12:0.79 17:0.15 20:0.87 21:0.47 27:0.09 28:0.85 30:0.89 32:0.80 34:0.55 36:0.78 37:0.85 43:0.47 55:0.59 66:0.75 69:0.40 70:0.20 78:0.85 81:0.97 91:0.82 98:0.42 100:0.87 104:0.80 108:0.31 111:0.84 120:0.88 123:0.30 126:0.54 127:0.14 129:0.05 135:0.54 140:0.45 145:0.88 146:0.39 147:0.45 149:0.43 150:0.96 151:0.75 152:0.86 153:0.85 154:0.36 159:0.15 161:0.49 162:0.68 164:0.90 167:0.81 169:0.90 172:0.32 173:0.50 177:0.05 179:0.48 181:0.84 186:0.85 187:0.68 189:0.90 191:0.96 195:0.63 202:0.85 212:0.95 215:0.63 216:0.67 220:0.93 230:0.87 233:0.76 235:0.52 238:0.90 241:0.76 242:0.82 243:0.77 247:0.12 248:0.83 256:0.88 259:0.21 260:0.88 261:0.90 265:0.44 266:0.12 267:0.98 268:0.87 271:0.18 276:0.29 283:0.60 285:0.62 297:0.36 300:0.18 +2 6:0.89 7:0.81 8:0.77 12:0.79 17:0.40 20:0.75 21:0.40 27:0.09 28:0.85 30:0.45 32:0.80 34:0.61 36:0.87 37:0.85 43:0.48 55:0.52 66:0.69 69:0.35 70:0.25 78:0.89 81:0.98 91:0.65 98:0.36 100:0.92 104:0.80 106:0.81 108:0.27 111:0.89 120:0.95 123:0.26 126:0.54 127:0.13 129:0.05 135:0.51 140:0.45 145:0.79 146:0.32 147:0.39 149:0.34 150:0.97 151:0.80 152:0.86 153:0.93 154:0.37 159:0.13 161:0.49 162:0.69 164:0.91 167:0.83 169:0.90 172:0.21 173:0.49 177:0.05 179:0.56 181:0.84 186:0.89 187:0.39 189:0.93 191:0.70 195:0.61 202:0.87 206:0.81 212:0.95 215:0.67 216:0.67 230:0.87 233:0.76 235:0.42 238:0.92 241:0.77 242:0.82 243:0.73 247:0.12 248:0.92 256:0.89 259:0.21 260:0.95 261:0.90 265:0.38 266:0.12 267:0.94 268:0.89 271:0.18 276:0.21 283:0.82 285:0.62 297:0.36 300:0.18 +1 8:0.76 12:0.79 17:0.40 21:0.78 27:0.04 28:0.85 30:0.76 34:0.64 36:0.39 37:0.91 43:0.31 55:0.71 66:0.36 69:0.71 70:0.15 91:0.78 98:0.16 108:0.80 123:0.78 124:0.52 127:0.19 129:0.59 133:0.47 135:0.94 145:0.69 146:0.05 147:0.05 149:0.20 154:0.23 159:0.43 161:0.49 163:0.98 167:0.77 172:0.10 173:0.60 177:0.05 178:0.55 179:0.94 181:0.84 187:0.21 191:0.97 202:0.80 212:0.95 216:0.73 235:0.31 241:0.73 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.17 266:0.12 267:0.94 271:0.18 276:0.14 300:0.13 +2 6:0.89 7:0.81 8:0.76 12:0.79 17:0.32 20:0.87 21:0.40 27:0.09 28:0.85 30:0.45 32:0.80 34:0.63 36:0.86 37:0.85 43:0.48 55:0.52 66:0.69 69:0.34 70:0.25 78:0.91 81:0.98 91:0.88 98:0.36 100:0.94 104:0.78 106:0.81 108:0.27 111:0.92 120:0.96 123:0.26 126:0.54 127:0.13 129:0.05 135:0.32 140:0.45 145:0.79 146:0.32 147:0.39 149:0.34 150:0.97 151:0.83 152:0.86 153:0.97 154:0.37 159:0.13 161:0.49 162:0.61 164:0.94 167:0.83 169:0.90 172:0.21 173:0.48 177:0.05 179:0.56 181:0.84 186:0.91 187:0.21 189:0.90 195:0.60 202:0.92 206:0.81 212:0.95 215:0.67 216:0.67 230:0.87 233:0.76 235:0.42 238:0.92 241:0.77 242:0.82 243:0.73 247:0.12 248:0.94 256:0.93 259:0.21 260:0.96 261:0.90 265:0.38 266:0.12 267:0.94 268:0.93 271:0.18 276:0.21 283:0.82 285:0.62 297:0.36 300:0.18 +2 6:0.97 8:0.94 12:0.79 17:0.03 20:0.85 21:0.30 27:0.04 28:0.85 30:0.33 32:0.80 34:0.94 36:0.24 37:0.88 43:0.33 55:0.71 66:0.54 69:0.67 70:0.36 78:0.94 81:0.99 91:0.79 98:0.63 100:0.99 104:0.95 108:0.66 111:0.98 120:0.89 123:0.63 124:0.55 126:0.54 127:0.45 129:0.81 133:0.67 135:0.99 140:0.45 145:0.72 146:0.05 147:0.05 149:0.39 150:0.98 151:0.75 152:1.00 153:0.86 154:0.25 159:0.44 161:0.49 162:0.66 163:0.86 164:0.89 167:0.77 169:0.91 172:0.32 173:0.71 177:0.05 179:0.87 181:0.84 186:0.94 187:0.39 189:0.99 191:0.97 195:0.66 202:0.84 212:0.42 215:0.72 216:0.55 220:0.87 235:0.31 238:0.98 241:0.74 242:0.82 243:0.19 245:0.45 247:0.12 248:0.84 256:0.85 259:0.21 260:0.89 261:0.94 265:0.64 266:0.38 267:0.97 268:0.86 271:0.18 276:0.34 283:0.82 285:0.62 297:0.36 300:0.25 +2 6:0.79 7:0.81 8:0.71 12:0.79 17:0.75 20:0.74 21:0.30 27:0.38 28:0.85 30:0.15 32:0.80 34:0.98 36:0.54 37:0.48 43:0.41 55:0.59 66:0.24 69:0.52 70:0.68 78:0.81 81:0.99 91:0.75 98:0.43 100:0.84 104:0.86 106:0.81 108:0.65 111:0.80 120:0.95 123:0.63 124:0.73 126:0.54 127:0.35 129:0.81 132:0.97 133:0.67 135:0.85 140:0.45 145:0.58 146:0.33 147:0.40 149:0.45 150:0.98 151:0.80 152:0.74 153:0.93 154:0.31 159:0.43 161:0.49 162:0.73 163:0.90 164:0.90 167:0.62 169:0.80 172:0.21 173:0.51 177:0.05 179:0.78 181:0.84 186:0.88 187:0.39 191:0.77 195:0.73 202:0.84 206:0.81 212:0.30 215:0.72 216:0.53 230:0.87 233:0.76 235:0.31 238:0.84 241:0.49 242:0.82 243:0.28 245:0.54 247:0.12 248:0.92 256:0.86 260:0.95 261:0.75 265:0.45 266:0.54 267:0.97 268:0.88 271:0.18 276:0.39 283:0.82 285:0.62 297:0.36 300:0.43 +2 6:0.97 8:0.94 12:0.79 17:0.26 20:0.94 21:0.30 27:0.04 28:0.85 30:0.95 32:0.80 34:0.44 36:0.62 37:0.91 43:0.50 55:0.52 66:0.54 69:0.21 78:0.89 81:0.99 91:0.57 98:0.33 100:0.95 108:0.17 111:0.92 120:0.63 123:0.16 126:0.54 127:0.12 129:0.05 135:0.39 140:0.45 145:0.74 146:0.19 147:0.25 149:0.24 150:0.98 151:0.54 152:0.95 153:0.48 154:0.39 159:0.10 161:0.49 162:0.69 164:0.75 167:0.88 169:0.97 172:0.10 173:0.77 177:0.05 179:0.82 181:0.84 186:0.89 187:0.39 189:0.97 191:0.99 195:0.52 202:0.67 215:0.72 216:0.73 220:0.87 235:0.31 238:0.97 241:0.86 243:0.63 248:0.57 256:0.68 259:0.21 260:0.64 261:0.97 265:0.35 267:0.73 268:0.70 271:0.18 285:0.62 297:0.36 300:0.08 +2 6:0.96 8:0.92 12:0.79 17:0.05 20:0.85 21:0.78 27:0.08 28:0.85 34:0.55 36:0.24 37:0.87 78:0.88 91:0.99 100:0.91 111:0.88 120:0.95 135:0.91 140:0.93 151:0.83 152:0.86 153:0.96 161:0.49 162:0.46 164:0.92 167:0.90 169:0.94 175:0.75 178:0.52 181:0.84 186:0.88 189:0.91 191:0.98 202:0.98 216:0.67 238:0.91 241:0.97 244:0.61 248:0.93 256:0.90 257:0.65 259:0.21 260:0.95 261:0.94 267:0.89 268:0.90 271:0.18 277:0.69 285:0.62 +1 10:0.92 11:0.52 12:0.37 17:0.75 18:0.94 21:0.78 27:0.37 29:0.53 30:0.33 37:0.83 39:0.94 43:0.56 45:0.90 66:0.68 69:0.72 70:0.92 71:0.92 74:0.61 86:0.95 91:0.54 98:0.78 101:0.65 104:0.80 108:0.56 122:0.91 123:0.53 124:0.45 127:0.52 129:0.05 133:0.45 139:0.92 146:0.84 147:0.87 149:0.48 154:0.51 159:0.54 170:0.90 172:0.79 173:0.71 174:0.86 177:0.88 178:0.55 179:0.39 187:0.21 197:0.86 202:0.36 212:0.73 216:0.46 222:0.21 235:0.05 239:0.89 241:0.41 242:0.21 243:0.72 245:0.86 247:0.90 253:0.46 254:0.99 259:0.21 265:0.78 266:0.80 271:0.41 276:0.73 300:0.84 +1 8:0.96 10:0.81 11:0.57 12:0.37 17:0.93 18:1.00 21:0.78 27:0.37 29:0.53 30:0.09 37:0.33 39:0.94 43:0.60 45:0.99 55:0.59 66:0.06 69:0.99 70:0.99 71:0.92 74:0.60 86:0.99 91:0.31 98:0.22 101:0.96 108:1.00 122:0.92 123:0.93 124:1.00 127:0.97 129:0.98 133:1.00 139:0.99 145:0.45 146:0.32 147:0.46 149:0.81 154:0.06 159:1.00 170:0.90 172:0.92 173:0.76 174:0.88 175:0.75 177:0.38 178:0.55 179:0.09 187:0.21 191:0.90 197:0.79 202:0.77 212:0.39 216:0.69 222:0.21 235:0.22 239:0.80 241:0.62 242:0.63 243:0.05 244:0.61 245:0.99 247:0.96 253:0.46 257:0.84 259:0.21 265:0.24 266:0.99 271:0.41 276:1.00 277:0.87 300:0.98 +0 8:0.62 10:0.84 11:0.46 12:0.37 17:0.32 18:0.92 21:0.78 27:0.21 29:0.53 30:0.09 34:0.63 36:0.67 37:0.74 39:0.94 43:0.43 45:0.81 55:0.45 66:0.09 69:0.81 70:0.98 71:0.92 74:0.41 86:0.90 91:0.31 98:0.31 101:0.47 108:0.85 122:0.92 123:0.89 124:0.87 127:0.31 129:0.59 133:0.86 135:0.24 139:0.86 146:0.46 147:0.05 149:0.46 154:0.32 159:0.72 170:0.90 172:0.54 173:0.65 174:0.79 175:0.75 177:0.05 178:0.55 179:0.36 187:0.39 197:0.83 202:0.66 212:0.50 216:0.95 222:0.21 235:0.74 239:0.83 241:0.12 242:0.60 243:0.09 244:0.83 245:0.72 247:0.74 253:0.35 257:0.93 259:0.21 265:0.26 266:0.75 267:0.87 271:0.41 276:0.68 277:0.87 300:0.84 +0 8:0.86 9:0.72 10:0.89 11:0.46 12:0.37 17:0.76 18:0.84 21:0.78 27:0.72 29:0.53 30:0.41 31:0.91 34:0.94 36:0.99 39:0.94 43:0.66 45:0.81 66:0.07 69:0.81 70:0.95 71:0.92 74:0.57 79:0.90 83:0.56 86:0.86 91:0.38 96:0.75 97:0.97 98:0.22 101:0.47 108:0.15 122:0.91 123:0.71 124:0.83 127:0.77 129:0.05 133:0.81 135:0.63 137:0.91 139:0.84 140:0.80 145:0.40 146:0.22 147:0.34 149:0.59 151:0.62 153:0.46 154:0.46 155:0.55 158:0.76 159:0.61 162:0.57 170:0.90 172:0.37 173:0.80 174:0.88 175:0.75 177:0.38 178:0.42 179:0.77 187:0.39 190:0.89 192:0.55 196:0.92 197:0.88 202:0.56 208:0.50 212:0.54 216:0.23 219:0.91 220:0.74 222:0.21 235:0.12 239:0.88 241:0.32 242:0.19 243:0.50 244:0.61 245:0.54 247:0.79 248:0.65 253:0.60 254:0.95 255:0.71 256:0.58 257:0.84 259:0.21 265:0.21 266:0.63 267:0.19 268:0.55 271:0.41 276:0.46 277:0.87 279:0.79 284:0.90 285:0.50 292:0.88 300:0.84 +1 7:0.69 8:0.90 12:0.37 17:0.13 21:0.78 23:0.98 27:0.26 29:0.53 30:0.76 31:0.90 32:0.65 34:0.30 36:0.61 37:0.92 39:0.94 43:0.16 55:0.52 66:0.36 69:0.53 70:0.15 71:0.92 79:0.90 91:0.92 98:0.20 104:0.58 108:0.62 120:0.85 123:0.59 124:0.52 127:0.44 128:0.98 129:0.59 133:0.47 135:0.42 137:0.90 140:0.93 145:0.87 146:0.05 147:0.05 149:0.11 151:0.76 153:0.48 154:0.11 159:0.22 162:0.55 163:0.96 164:0.80 168:0.98 170:0.90 172:0.10 173:0.77 175:0.97 177:0.05 178:0.42 179:0.99 190:0.95 191:0.95 192:0.49 195:0.60 202:0.79 205:0.98 212:0.95 216:0.25 220:0.62 222:0.21 230:0.71 233:0.76 235:0.02 241:0.94 242:0.82 243:0.34 244:0.93 245:0.37 247:0.12 248:0.83 253:0.20 255:0.72 256:0.80 257:0.93 259:0.21 260:0.86 265:0.22 266:0.12 267:0.52 268:0.77 271:0.41 276:0.13 277:0.95 284:0.88 285:0.60 300:0.13 +1 7:0.69 10:0.90 12:0.37 17:0.32 18:0.66 21:0.30 27:0.20 29:0.53 30:0.45 32:0.65 34:0.79 36:0.19 37:0.60 39:0.94 43:0.08 55:0.71 66:0.33 69:0.48 70:0.72 71:0.92 75:0.97 81:0.39 86:0.69 91:0.20 98:0.42 108:0.81 123:0.80 124:0.78 126:0.32 127:0.60 129:0.81 133:0.76 135:0.85 139:0.66 145:0.65 146:0.45 147:0.52 149:0.12 150:0.41 154:0.52 159:0.74 170:0.90 172:0.45 173:0.32 174:0.86 175:0.91 177:0.38 178:0.55 179:0.64 187:0.39 192:0.44 195:0.87 197:0.89 201:0.57 212:0.21 215:0.72 216:0.84 222:0.21 226:0.95 230:0.71 233:0.76 235:0.42 236:0.94 239:0.91 241:0.44 242:0.41 243:0.29 244:0.83 245:0.66 247:0.76 253:0.20 254:0.74 257:0.84 259:0.21 265:0.44 266:0.75 267:0.36 271:0.41 276:0.54 277:0.87 283:0.66 297:0.36 300:0.55 +0 8:0.77 10:0.99 11:0.52 12:0.37 17:0.75 18:0.90 21:0.13 23:0.97 27:0.20 29:0.53 30:0.67 33:0.97 34:0.58 36:0.30 37:0.60 39:0.94 43:0.73 45:0.92 55:0.71 62:0.99 66:0.96 69:0.45 70:0.41 71:0.92 74:0.67 75:0.96 81:0.33 86:0.94 91:0.38 98:0.90 101:0.87 108:0.35 122:0.95 123:0.34 126:0.24 127:0.47 128:0.98 129:0.05 135:0.78 139:0.91 140:0.45 145:0.58 146:0.94 147:0.95 149:0.88 150:0.36 151:0.69 153:0.69 154:0.63 159:0.59 162:0.86 168:0.98 170:0.90 172:0.90 173:0.37 174:0.98 177:0.88 179:0.27 187:0.39 190:0.95 197:0.99 202:0.50 205:0.97 212:0.86 216:0.84 222:0.21 226:0.98 235:0.12 236:0.92 239:0.99 241:0.21 242:0.19 243:0.96 244:0.61 247:0.91 248:0.69 253:0.49 256:0.58 259:0.21 265:0.90 266:0.38 267:0.69 268:0.59 271:0.41 276:0.83 285:0.46 291:0.97 300:0.43 +0 8:0.77 10:0.99 11:0.52 12:0.37 17:0.72 18:0.91 21:0.13 23:0.97 27:0.20 29:0.53 30:0.73 33:0.97 34:0.68 36:0.32 37:0.60 39:0.94 43:0.73 45:0.92 55:0.71 62:0.99 66:0.96 69:0.45 70:0.33 71:0.92 74:0.69 75:0.96 81:0.33 86:0.94 91:0.46 98:0.88 101:0.87 108:0.35 122:0.95 123:0.34 126:0.24 127:0.43 128:0.98 129:0.05 135:0.70 139:0.92 140:0.45 145:0.59 146:0.94 147:0.95 149:0.89 150:0.36 151:0.69 153:0.69 154:0.63 159:0.60 162:0.86 168:0.98 170:0.90 172:0.91 173:0.35 174:0.98 175:0.75 177:0.70 179:0.25 187:0.39 190:0.95 197:0.99 202:0.50 205:0.97 212:0.83 216:0.84 222:0.21 226:0.98 235:0.12 236:0.92 239:0.99 241:0.21 242:0.31 243:0.96 244:0.61 247:0.86 248:0.69 253:0.50 256:0.58 257:0.65 259:0.21 265:0.88 266:0.38 267:0.69 268:0.59 271:0.41 276:0.83 277:0.69 285:0.46 291:0.97 300:0.43 +0 10:0.90 11:0.46 12:0.37 17:0.66 18:0.93 21:0.78 27:0.72 29:0.53 30:0.32 34:0.47 36:0.90 39:0.94 43:0.64 45:0.83 66:0.89 69:0.43 70:0.63 71:0.92 74:0.56 86:0.93 91:0.80 98:0.88 101:0.47 108:0.33 122:0.92 123:0.32 127:0.42 129:0.05 135:0.39 139:0.90 146:0.65 147:0.70 149:0.63 154:0.59 159:0.25 170:0.90 172:0.68 173:0.63 174:0.79 177:0.88 178:0.55 179:0.58 191:0.80 197:0.82 202:0.50 212:0.77 216:0.05 222:0.21 235:0.12 239:0.87 241:0.85 242:0.31 243:0.89 247:0.76 253:0.44 254:0.99 259:0.21 265:0.88 266:0.27 267:0.28 271:0.41 276:0.57 300:0.43 +1 10:0.89 11:0.52 12:0.37 17:0.66 18:0.92 21:0.47 27:0.72 29:0.53 30:0.32 39:0.94 43:0.63 45:0.83 66:0.89 69:0.21 70:0.51 71:0.92 74:0.47 81:0.29 86:0.92 91:0.67 98:0.98 101:0.87 104:0.58 108:0.17 120:0.64 122:0.91 123:0.16 126:0.24 127:0.82 129:0.05 139:0.88 140:0.45 146:0.79 147:0.82 149:0.66 150:0.32 151:0.59 153:0.72 154:0.52 159:0.35 162:0.58 164:0.68 170:0.90 172:0.68 173:0.39 174:0.83 177:0.88 179:0.68 190:0.95 197:0.86 202:0.64 212:0.59 215:0.63 216:0.07 220:0.74 222:0.21 235:0.52 239:0.88 241:0.93 242:0.21 243:0.89 244:0.61 247:0.82 248:0.58 253:0.39 256:0.58 260:0.65 265:0.98 266:0.54 268:0.62 271:0.41 276:0.57 285:0.46 297:0.36 300:0.33 +1 1:0.61 10:0.88 11:0.46 12:0.37 17:0.24 18:0.91 21:0.54 27:0.02 29:0.53 30:0.21 34:0.49 36:0.36 37:0.92 39:0.94 43:0.25 44:0.88 45:0.88 64:0.77 66:0.34 69:0.68 70:0.87 71:0.92 74:0.58 77:0.70 81:0.51 83:0.56 86:0.92 91:0.17 98:0.35 99:0.83 101:0.47 108:0.84 114:0.76 122:0.92 123:0.83 124:0.78 126:0.51 127:0.45 129:0.81 133:0.78 135:0.92 139:0.90 145:0.45 146:0.20 147:0.26 149:0.42 150:0.42 154:0.56 155:0.48 159:0.66 165:0.65 170:0.90 172:0.63 173:0.57 174:0.83 175:0.75 176:0.70 177:0.70 178:0.55 179:0.40 187:0.39 192:0.46 193:0.97 195:0.85 197:0.85 201:0.60 202:0.36 208:0.61 212:0.60 216:0.99 222:0.21 235:0.74 236:0.95 239:0.89 241:0.53 242:0.30 243:0.17 244:0.61 245:0.80 247:0.79 253:0.57 254:0.80 257:0.65 259:0.21 265:0.37 266:0.54 267:0.79 271:0.41 276:0.67 277:0.87 282:0.75 290:0.76 300:0.70 +0 10:0.94 11:0.52 12:0.37 17:0.78 18:0.93 21:0.22 27:0.70 29:0.53 30:0.15 34:0.52 36:0.17 37:0.27 39:0.94 43:0.77 45:0.81 66:0.75 69:0.10 70:0.89 71:0.92 74:0.51 81:0.32 86:0.95 91:0.17 98:0.80 101:0.65 104:0.93 106:0.81 108:0.09 120:0.66 122:0.92 123:0.09 124:0.40 126:0.24 127:0.72 129:0.05 133:0.36 135:0.49 139:0.91 140:0.45 146:0.82 147:0.85 149:0.64 150:0.35 151:0.62 153:0.69 154:0.70 159:0.52 162:0.86 164:0.62 170:0.90 172:0.65 173:0.18 174:0.83 177:0.88 179:0.66 187:0.68 190:0.95 197:0.88 202:0.46 206:0.81 212:0.59 215:0.79 216:0.11 220:0.62 222:0.21 235:0.22 239:0.91 241:0.32 242:0.70 243:0.77 245:0.64 247:0.67 248:0.60 253:0.41 254:0.97 256:0.45 259:0.21 260:0.67 265:0.80 266:0.54 267:0.28 268:0.55 271:0.41 276:0.55 283:0.67 285:0.46 297:0.36 300:0.70 +0 1:0.69 9:0.75 10:0.90 11:0.52 12:0.37 17:0.76 18:0.95 21:0.05 22:0.93 23:0.97 27:0.61 29:0.53 30:0.62 31:0.93 33:0.97 37:0.44 39:0.94 43:0.74 45:0.89 47:0.93 62:0.97 64:0.77 66:0.63 69:0.35 70:0.64 71:0.92 74:0.63 75:0.98 79:0.92 81:0.68 83:0.69 86:0.94 91:0.44 96:0.79 97:0.98 98:0.67 99:0.95 101:0.65 108:0.27 114:0.95 117:0.86 122:0.91 123:0.26 124:0.64 126:0.51 127:0.47 128:0.97 129:0.05 133:0.74 137:0.93 139:0.94 140:0.45 145:0.74 146:0.87 147:0.89 149:0.80 150:0.56 151:0.83 153:0.78 154:0.64 155:0.64 158:0.81 159:0.68 162:0.57 163:0.81 165:0.81 168:0.97 170:0.90 172:0.73 173:0.23 174:0.86 176:0.70 177:0.88 179:0.45 187:0.68 190:0.77 192:0.58 196:0.96 197:0.87 201:0.66 202:0.59 205:0.95 208:0.61 212:0.28 216:0.60 219:0.94 220:0.62 222:0.21 226:0.96 235:0.31 236:0.95 239:0.92 241:0.03 242:0.23 243:0.69 244:0.61 245:0.71 247:0.86 248:0.74 253:0.81 255:0.78 256:0.45 259:0.21 262:0.93 265:0.67 266:0.80 268:0.58 271:0.41 276:0.68 279:0.82 284:0.91 285:0.60 290:0.95 291:0.97 292:0.88 300:0.70 +0 1:0.61 9:0.79 12:0.37 17:0.94 21:0.64 23:0.96 27:0.67 29:0.53 31:0.95 34:0.50 36:0.60 37:0.53 39:0.94 41:0.82 60:0.95 62:0.97 64:0.77 71:0.92 74:0.47 75:0.99 79:0.94 81:0.70 83:0.91 91:0.51 96:0.83 114:0.72 120:0.72 126:0.54 128:0.98 135:0.51 137:0.95 139:0.74 140:0.45 145:0.56 150:0.48 151:0.90 153:0.69 155:0.66 158:0.92 162:0.86 164:0.62 165:0.93 168:0.98 170:0.90 175:0.99 176:0.73 187:0.39 192:0.99 196:0.92 201:0.61 202:0.46 205:0.95 208:0.64 215:0.53 216:0.29 219:0.95 222:0.21 226:0.98 235:0.88 236:0.97 239:0.90 241:0.46 244:0.97 248:0.80 253:0.79 255:0.94 256:0.45 257:0.97 260:0.73 267:0.28 268:0.55 271:0.41 277:0.98 279:0.82 283:0.82 284:0.90 285:0.62 290:0.72 292:0.95 297:0.36 +0 10:0.92 11:0.46 12:0.37 17:0.51 18:0.74 21:0.78 27:0.45 29:0.53 30:0.60 34:0.89 36:0.18 37:0.50 39:0.94 43:0.36 45:0.83 66:0.10 69:0.74 70:0.41 71:0.92 86:0.79 91:0.17 98:0.16 101:0.47 108:0.57 122:0.76 123:0.85 124:0.86 127:0.31 129:0.89 133:0.84 135:0.95 139:0.75 145:0.50 146:0.22 147:0.28 149:0.35 154:0.50 159:0.65 163:0.79 170:0.90 172:0.16 173:0.60 174:0.89 175:0.75 177:0.70 178:0.55 179:0.63 187:0.68 197:0.89 212:0.42 216:0.77 222:0.21 235:0.84 239:0.90 241:0.05 242:0.45 243:0.29 244:0.61 245:0.56 247:0.47 253:0.20 254:0.94 257:0.65 259:0.21 265:0.13 266:0.23 267:0.59 271:0.41 276:0.46 277:0.69 300:0.33 +1 1:0.67 10:0.99 11:0.52 12:0.37 17:0.86 18:1.00 21:0.40 22:0.93 27:0.72 29:0.53 30:0.57 33:0.97 37:0.83 39:0.94 43:0.70 44:0.92 45:0.99 47:0.93 64:0.77 66:0.99 69:0.52 70:0.73 71:0.92 74:0.90 75:0.98 77:0.70 81:0.62 83:0.69 86:1.00 91:0.37 97:0.98 98:0.87 99:0.95 101:0.65 102:0.97 108:0.40 114:0.82 117:0.86 122:0.91 123:0.38 126:0.51 127:0.39 129:0.05 139:1.00 145:0.74 146:0.98 147:0.98 149:0.96 150:0.48 154:0.74 155:0.51 158:0.81 159:0.75 165:0.81 170:0.90 172:0.98 173:0.30 174:0.97 176:0.70 177:0.88 178:0.55 179:0.12 187:0.39 192:0.48 195:0.93 197:0.97 201:0.63 208:0.61 212:0.87 216:0.30 219:0.94 222:0.21 226:0.96 235:0.68 236:0.95 239:0.99 241:0.03 242:0.29 243:0.99 247:0.94 253:0.70 254:0.90 262:0.93 265:0.87 266:0.54 271:0.41 276:0.95 279:0.82 282:0.80 290:0.82 291:0.97 292:0.88 300:0.70 +1 7:0.69 8:0.83 9:0.73 12:0.37 17:0.15 20:0.94 21:0.22 23:0.96 27:0.02 29:0.53 30:0.76 31:0.92 32:0.65 34:0.34 36:0.36 37:0.92 39:0.94 43:0.13 55:0.59 66:0.36 69:0.68 70:0.15 71:0.92 78:0.84 79:0.91 81:0.32 83:0.69 91:0.71 96:0.77 98:0.15 100:0.89 108:0.86 111:0.85 120:0.89 123:0.86 124:0.67 126:0.24 127:0.26 128:0.97 129:0.81 133:0.67 135:0.66 137:0.92 140:0.97 145:0.39 146:0.05 147:0.05 149:0.11 150:0.35 151:0.76 152:0.94 153:0.78 154:0.10 155:0.64 159:0.60 162:0.66 163:0.97 164:0.86 168:0.97 169:0.85 170:0.90 172:0.16 173:0.53 175:0.97 177:0.05 178:0.42 179:0.90 186:0.84 187:0.39 190:0.95 191:0.84 192:0.57 195:0.89 202:0.81 205:0.95 208:0.61 212:0.42 215:0.79 216:0.99 220:0.74 222:0.21 230:0.71 233:0.76 235:0.22 241:0.75 242:0.82 243:0.26 244:0.93 245:0.40 247:0.12 248:0.70 253:0.62 255:0.73 256:0.82 257:0.93 259:0.21 260:0.89 261:0.88 265:0.16 266:0.23 267:0.36 268:0.83 271:0.41 276:0.22 277:0.95 283:0.67 284:0.88 285:0.62 297:0.36 300:0.13 +0 10:0.91 11:0.52 12:0.37 17:0.57 18:0.90 21:0.78 27:0.28 29:0.53 30:0.56 34:0.36 36:0.17 37:0.50 39:0.94 43:0.60 45:0.88 66:0.91 69:0.61 70:0.82 71:0.92 74:0.60 86:0.93 91:0.12 98:0.84 101:0.65 108:0.46 122:0.91 123:0.44 127:0.34 129:0.05 135:0.47 139:0.89 146:0.81 147:0.84 149:0.66 154:0.61 159:0.37 170:0.90 172:0.74 173:0.65 174:0.86 177:0.88 178:0.55 179:0.47 187:0.39 197:0.88 212:0.75 216:0.89 222:0.21 235:0.31 239:0.89 241:0.10 242:0.24 243:0.91 247:0.82 253:0.46 254:0.99 259:0.21 265:0.84 266:0.54 267:0.44 271:0.41 276:0.61 300:0.70 +0 1:0.63 10:0.92 11:0.52 12:0.37 17:0.79 18:0.88 21:0.40 27:0.69 29:0.53 30:0.76 32:0.72 34:0.94 36:0.83 37:0.29 39:0.94 43:0.76 44:0.92 45:0.83 64:0.77 66:0.46 69:0.37 70:0.42 71:0.92 74:0.67 77:0.70 81:0.61 83:0.69 86:0.92 91:0.25 98:0.56 99:0.95 101:0.65 108:0.58 114:0.82 122:0.89 123:0.56 124:0.57 126:0.51 127:0.31 129:0.59 133:0.46 135:0.44 139:0.92 145:0.86 146:0.24 147:0.30 149:0.79 150:0.46 154:0.68 155:0.54 159:0.29 165:0.81 170:0.90 172:0.45 173:0.47 174:0.83 175:0.75 176:0.70 177:0.70 178:0.55 179:0.60 187:0.39 192:0.51 193:0.99 195:0.63 197:0.89 201:0.61 204:0.83 208:0.61 212:0.81 216:0.59 222:0.21 235:0.60 236:0.95 239:0.93 241:0.18 242:0.18 243:0.45 244:0.61 245:0.70 247:0.67 253:0.62 257:0.65 259:0.21 265:0.57 266:0.38 267:0.44 271:0.41 276:0.43 277:0.69 281:0.86 282:0.80 290:0.82 300:0.43 +0 1:0.68 9:0.79 10:0.89 11:0.57 12:0.37 17:0.75 18:0.65 21:0.13 23:0.96 27:0.67 29:0.53 30:0.95 31:0.94 34:0.59 36:0.58 37:0.53 39:0.94 41:0.82 43:0.81 60:0.95 62:0.98 64:0.77 66:0.69 69:0.71 71:0.92 74:0.56 75:0.99 79:0.93 81:0.78 83:0.91 85:0.72 86:0.74 91:0.54 96:0.80 98:0.14 101:0.87 108:0.54 114:0.92 120:0.69 122:0.65 123:0.51 126:0.54 127:0.09 128:0.98 129:0.05 135:0.94 137:0.94 139:0.81 140:0.45 145:0.70 146:0.18 147:0.23 149:0.64 150:0.60 151:0.86 153:0.48 154:0.76 155:0.66 158:0.92 159:0.09 162:0.86 164:0.57 165:0.93 168:0.98 170:0.90 172:0.21 173:0.84 174:0.79 176:0.73 177:0.88 179:0.10 187:0.39 192:0.99 196:0.94 197:0.84 201:0.67 202:0.36 205:0.95 208:0.64 212:0.61 215:0.84 216:0.29 219:0.95 222:0.21 226:0.98 235:0.42 236:0.97 239:0.93 241:0.62 242:0.63 243:0.73 247:0.30 248:0.77 253:0.81 255:0.94 256:0.45 260:0.70 265:0.14 266:0.17 267:0.52 268:0.46 271:0.41 276:0.23 279:0.82 283:0.82 284:0.89 285:0.62 290:0.92 292:0.95 297:0.36 300:0.08 +0 10:0.86 11:0.52 12:0.37 17:0.45 18:0.74 21:0.78 27:0.45 29:0.53 30:0.45 34:0.75 36:0.18 37:0.50 39:0.94 43:0.60 45:0.73 66:0.77 69:0.81 70:0.33 71:0.92 74:0.36 86:0.81 91:0.25 98:0.25 101:0.65 108:0.66 122:0.75 123:0.63 127:0.11 129:0.05 135:0.83 139:0.76 145:0.47 146:0.35 147:0.42 149:0.42 154:0.49 159:0.14 170:0.90 172:0.37 173:0.81 174:0.79 177:0.88 178:0.55 179:0.19 187:0.68 197:0.82 212:0.87 216:0.77 222:0.21 235:0.42 239:0.85 241:0.21 242:0.24 243:0.79 244:0.61 247:0.47 253:0.32 254:0.84 259:0.21 265:0.27 266:0.17 267:0.28 271:0.41 276:0.29 300:0.25 +0 1:0.63 9:0.71 10:0.94 11:0.52 12:0.37 17:0.87 18:0.98 21:0.40 22:0.93 23:0.97 27:0.70 29:0.53 30:0.40 31:0.88 32:0.71 33:0.97 37:0.32 39:0.94 43:0.73 44:0.92 45:0.93 47:0.93 62:0.96 64:0.77 66:0.59 69:0.63 70:0.89 71:0.92 74:0.69 75:0.98 79:0.88 81:0.61 83:0.69 86:0.98 91:0.38 96:0.71 97:0.99 98:0.70 99:0.95 101:0.87 102:0.97 108:0.48 114:0.82 117:0.86 122:0.92 123:0.46 124:0.67 126:0.51 127:0.56 128:0.95 129:0.05 133:0.74 137:0.88 139:0.97 140:0.45 145:0.77 146:0.91 147:0.93 149:0.82 150:0.46 151:0.56 153:0.77 154:0.33 155:0.56 158:0.81 159:0.75 162:0.68 165:0.81 168:0.95 170:0.90 172:0.85 173:0.45 174:0.89 176:0.70 177:0.88 179:0.28 187:0.68 190:0.77 192:0.55 195:0.89 196:0.92 197:0.90 201:0.61 202:0.54 205:0.95 208:0.61 212:0.48 216:0.45 219:0.94 220:0.91 222:0.21 226:0.96 235:0.60 236:0.95 239:0.95 241:0.03 242:0.43 243:0.67 245:0.87 247:0.84 248:0.55 253:0.65 254:0.98 255:0.70 256:0.45 259:0.21 262:0.93 265:0.70 266:0.80 268:0.57 271:0.41 276:0.83 279:0.81 284:0.91 285:0.60 290:0.82 291:0.97 292:0.88 300:0.84 +0 1:0.58 7:0.70 8:0.99 9:0.70 10:0.88 11:0.52 12:0.37 17:0.73 18:0.90 21:0.71 27:0.72 29:0.53 30:0.61 31:0.86 32:0.72 39:0.94 43:0.59 44:0.92 45:0.87 48:0.91 64:0.77 66:0.89 69:0.76 70:0.60 71:0.92 74:0.60 76:0.85 77:0.70 79:0.86 81:0.47 83:0.56 86:0.89 91:0.76 96:0.68 98:0.82 99:0.83 101:0.65 108:0.59 114:0.67 120:0.54 122:0.91 123:0.57 126:0.51 127:0.31 129:0.05 137:0.86 139:0.90 140:0.45 145:0.83 146:0.80 147:0.83 149:0.67 150:0.39 151:0.49 153:0.48 154:0.48 155:0.49 159:0.36 162:0.57 164:0.56 165:0.65 170:0.90 172:0.68 173:0.82 174:0.83 175:0.75 176:0.70 177:0.70 179:0.51 190:0.77 191:0.77 192:0.50 195:0.66 196:0.89 197:0.85 201:0.57 202:0.55 208:0.61 212:0.39 215:0.48 216:0.03 220:0.97 222:0.21 230:0.73 233:0.66 235:0.95 239:0.88 241:0.85 242:0.21 243:0.89 244:0.61 247:0.60 248:0.49 253:0.54 255:0.69 256:0.45 257:0.65 259:0.21 260:0.55 265:0.82 266:0.47 268:0.46 271:0.41 276:0.56 277:0.69 281:0.86 282:0.80 284:0.88 285:0.60 290:0.67 297:0.36 300:0.55 +1 6:0.96 7:0.69 8:0.88 9:0.75 10:0.89 12:0.37 17:0.61 20:0.91 21:0.05 23:0.99 27:0.26 29:0.53 30:0.76 31:0.95 32:0.67 37:0.82 39:0.94 41:0.82 43:0.64 55:0.42 62:0.99 66:0.72 69:0.41 70:0.22 71:0.92 75:0.97 78:0.98 79:0.94 81:0.48 83:0.91 91:0.88 96:0.84 98:0.65 100:0.99 102:0.96 104:0.58 108:0.31 111:0.99 120:0.89 122:0.51 123:0.30 126:0.32 127:0.19 128:0.99 129:0.05 137:0.95 140:0.93 145:0.98 146:0.41 147:0.48 149:0.45 150:0.47 151:0.91 152:0.85 153:0.72 154:0.54 155:0.66 159:0.16 162:0.59 164:0.86 168:0.99 169:1.00 170:0.90 172:0.27 173:0.65 174:0.83 175:0.91 177:0.38 179:0.82 186:0.98 189:0.97 191:0.89 192:0.98 195:0.66 197:0.88 201:0.59 202:0.84 205:1.00 208:0.64 212:0.42 215:0.91 216:0.16 220:0.62 222:0.21 226:0.96 230:0.71 233:0.76 235:0.12 236:0.94 238:0.99 239:0.92 241:0.93 242:0.45 243:0.75 244:0.83 247:0.35 248:0.81 253:0.77 254:0.74 255:0.79 256:0.88 257:0.84 260:0.89 261:0.98 265:0.66 266:0.23 268:0.85 271:0.41 276:0.17 277:0.87 283:0.67 284:0.91 285:0.62 297:0.36 300:0.18 +0 1:0.69 7:0.81 9:0.79 10:0.98 11:0.81 12:0.37 17:0.98 18:0.97 21:0.30 23:0.96 27:0.72 29:0.53 30:0.84 31:0.94 32:0.80 34:0.49 36:0.74 39:0.94 41:0.82 43:0.73 44:0.93 45:0.92 48:0.91 62:0.99 64:0.77 66:0.96 69:0.27 70:0.40 71:0.92 74:0.84 75:0.98 77:0.70 79:0.93 81:0.88 83:0.91 85:0.88 86:0.98 91:0.54 96:0.80 97:0.94 98:0.95 99:0.99 101:1.00 102:0.98 108:0.21 114:0.86 120:0.69 121:0.90 122:0.89 123:0.20 126:0.54 127:0.68 128:0.98 129:0.05 135:0.80 137:0.94 139:0.98 140:0.80 145:0.91 146:0.87 147:0.90 149:0.95 150:0.67 151:0.86 153:0.48 154:0.63 155:0.66 158:0.81 159:0.45 162:0.62 164:0.57 165:1.00 168:0.98 170:0.90 172:0.89 173:0.33 174:0.94 176:0.73 177:0.88 178:0.42 179:0.33 187:0.21 192:0.99 193:1.00 195:0.66 196:0.97 197:0.97 201:0.68 202:0.50 204:0.85 205:0.95 208:0.64 212:0.82 215:0.72 216:0.19 219:0.94 222:0.21 226:0.96 230:0.87 233:0.76 235:0.88 236:0.97 239:0.99 241:0.27 242:0.21 243:0.96 247:0.84 248:0.77 253:0.86 255:0.94 256:0.45 260:0.70 265:0.95 266:0.38 267:0.69 268:0.46 271:0.41 276:0.81 279:0.78 281:0.91 282:0.88 284:0.89 285:0.62 290:0.86 292:0.88 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.91 7:0.81 8:0.83 9:0.80 11:0.88 12:0.06 17:0.57 20:0.98 21:0.30 27:0.05 28:0.66 30:0.56 32:0.80 34:0.77 36:0.94 37:0.94 39:0.47 41:0.92 43:0.87 60:0.87 66:0.09 69:0.55 70:0.85 74:0.88 77:0.70 78:0.95 81:0.74 83:0.87 85:0.93 91:0.11 96:0.87 98:0.19 100:0.97 101:0.76 108:0.94 111:0.96 114:0.86 120:0.79 121:1.00 122:0.57 123:0.71 124:0.92 126:0.54 127:0.44 129:0.93 133:0.91 135:0.72 140:0.45 144:0.85 145:0.79 146:0.25 147:0.31 149:0.85 150:0.84 151:0.95 152:0.97 153:0.84 154:0.85 155:0.67 158:0.92 159:0.87 161:0.84 162:0.71 164:0.76 165:0.84 167:0.53 169:0.94 172:0.27 173:0.24 176:0.73 177:0.38 179:0.52 181:0.64 186:0.95 187:0.68 189:0.93 192:0.59 195:0.97 201:0.74 202:0.67 204:0.89 208:0.64 212:0.16 215:0.72 216:0.82 222:0.60 230:0.87 233:0.76 235:0.42 238:0.94 241:0.55 242:0.40 243:0.26 245:0.63 247:0.47 248:0.86 253:0.90 255:0.79 256:0.68 259:0.21 260:0.80 261:0.95 265:0.15 266:0.92 267:0.52 268:0.70 271:0.62 276:0.60 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.84 +1 8:0.84 11:0.88 12:0.06 17:0.30 21:0.78 27:0.45 28:0.66 30:0.33 34:0.90 36:0.18 37:0.50 39:0.47 43:0.78 66:0.20 69:0.79 70:0.36 74:0.62 85:0.80 91:0.33 98:0.25 101:0.72 108:0.63 122:0.51 123:0.83 124:0.74 127:0.22 129:0.59 133:0.64 135:0.86 144:0.85 145:0.49 146:0.28 147:0.34 149:0.62 154:0.70 159:0.52 161:0.84 163:0.75 167:0.50 172:0.16 173:0.68 177:0.38 178:0.55 179:0.71 181:0.64 187:0.68 212:0.19 216:0.77 222:0.60 235:0.95 241:0.44 242:0.45 243:0.40 245:0.49 247:0.39 253:0.49 259:0.21 265:0.17 266:0.47 267:0.44 271:0.62 276:0.28 277:0.69 300:0.25 +2 1:0.74 6:0.91 7:0.81 9:0.80 11:0.88 12:0.06 17:0.34 20:0.84 21:0.22 27:0.05 28:0.66 30:0.94 32:0.80 34:0.78 36:0.87 37:0.94 39:0.47 41:0.90 43:0.87 60:0.87 66:0.36 69:0.55 70:0.13 74:0.75 77:0.70 78:0.80 81:0.80 83:0.87 85:0.80 91:0.05 96:0.90 98:0.08 100:0.83 101:0.72 108:0.90 111:0.79 114:0.90 120:0.82 121:1.00 122:0.43 123:0.89 124:0.57 126:0.54 127:0.31 129:0.59 133:0.47 135:0.58 140:0.45 144:0.85 145:0.79 146:0.13 147:0.18 149:0.59 150:0.87 151:0.87 152:0.97 153:0.48 154:0.85 155:0.67 158:0.92 159:0.72 161:0.84 162:0.86 163:0.90 164:0.72 165:0.86 167:0.53 169:0.94 172:0.16 173:0.32 176:0.73 177:0.38 179:0.93 181:0.64 186:0.80 187:0.99 192:0.59 195:0.93 201:0.74 202:0.56 204:0.89 208:0.64 212:0.42 215:0.79 216:0.82 222:0.60 230:0.87 233:0.76 235:0.31 241:0.54 242:0.75 243:0.40 245:0.43 247:0.30 248:0.89 253:0.90 255:0.79 256:0.64 259:0.21 260:0.83 261:0.95 265:0.08 266:0.23 267:0.23 268:0.65 271:0.62 276:0.12 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.13 +2 1:0.74 6:0.88 7:0.81 8:0.77 9:0.80 11:0.88 12:0.06 17:0.22 20:0.99 21:0.13 27:0.11 28:0.66 30:0.68 32:0.80 34:0.44 36:0.85 37:0.80 39:0.47 41:0.94 43:0.90 60:0.97 66:0.32 69:0.46 70:0.43 74:0.81 77:0.70 78:0.92 81:0.84 83:0.83 85:0.85 91:0.31 96:0.92 98:0.22 100:0.95 101:0.75 108:0.69 111:0.93 114:0.92 120:0.86 121:0.99 122:0.43 123:0.67 124:0.72 126:0.54 127:0.21 129:0.81 133:0.67 135:0.26 138:0.96 140:0.45 144:0.85 145:0.50 146:0.13 147:0.18 149:0.71 150:0.90 151:0.97 152:0.92 153:0.89 154:0.89 155:0.67 158:0.92 159:0.45 161:0.84 162:0.83 163:0.88 164:0.82 165:0.88 167:0.52 169:0.93 172:0.21 173:0.33 176:0.73 177:0.38 179:0.70 181:0.64 186:0.92 187:0.87 189:0.89 191:0.73 192:0.59 195:0.85 201:0.74 202:0.72 204:0.89 208:0.64 212:0.42 215:0.84 216:0.86 222:0.60 230:0.87 233:0.76 235:0.22 238:0.92 241:0.52 242:0.63 243:0.32 245:0.48 247:0.30 248:0.92 253:0.93 255:0.79 256:0.78 259:0.21 260:0.87 261:0.93 265:0.24 266:0.27 267:0.91 268:0.78 271:0.62 276:0.29 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.33 +1 1:0.74 7:0.81 9:0.80 11:0.88 12:0.06 17:0.47 27:0.14 28:0.66 30:0.56 32:0.80 34:0.26 36:0.82 37:0.86 39:0.47 41:0.88 43:0.63 60:0.87 66:0.88 69:0.91 70:0.48 74:0.90 76:0.85 77:0.70 81:0.93 83:0.88 85:0.96 91:0.09 96:0.84 98:0.76 101:0.83 108:0.82 114:0.98 120:0.74 121:0.97 122:0.57 123:0.81 124:0.36 126:0.54 127:0.42 129:0.05 133:0.39 135:0.68 140:0.45 144:0.85 145:0.50 146:0.94 147:0.05 149:0.87 150:0.97 151:0.87 153:0.48 154:0.52 155:0.67 158:0.92 159:0.70 161:0.84 162:0.86 164:0.67 165:0.92 167:0.51 172:0.84 173:0.88 176:0.73 177:0.05 179:0.35 181:0.64 187:0.87 192:0.59 195:0.89 201:0.74 202:0.50 204:0.89 208:0.64 212:0.47 215:0.96 216:0.54 220:0.74 222:0.60 230:0.87 233:0.76 235:0.05 241:0.47 242:0.50 243:0.08 245:0.64 247:0.55 248:0.81 253:0.88 255:0.79 256:0.58 257:0.65 259:0.21 260:0.74 265:0.77 266:0.63 267:0.44 268:0.59 271:0.62 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 290:0.98 297:0.36 299:0.88 300:0.43 +3 6:0.79 8:0.58 9:0.80 12:0.06 17:0.49 20:0.87 21:0.78 27:0.72 28:0.66 34:0.19 36:0.54 37:0.23 39:0.47 41:0.96 43:0.41 60:0.87 66:0.36 69:0.27 74:0.55 78:0.89 83:0.88 91:0.72 96:0.96 98:0.06 100:0.87 104:0.58 108:0.21 111:0.87 120:0.92 123:0.20 127:0.05 129:0.05 135:0.28 140:0.45 144:0.85 146:0.05 147:0.05 149:0.15 151:0.98 152:0.82 153:0.93 154:0.31 155:0.67 158:0.92 159:0.05 161:0.84 162:0.69 164:0.87 167:0.77 169:0.85 172:0.05 173:0.17 175:0.75 177:0.05 181:0.64 186:0.89 189:0.81 191:0.73 192:0.59 202:0.83 208:0.64 216:0.31 222:0.60 232:0.75 235:0.05 238:0.87 241:0.85 243:0.51 244:0.61 248:0.96 253:0.95 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.86 265:0.06 267:0.79 268:0.86 271:0.62 277:0.69 279:0.86 283:0.82 285:0.62 +0 1:0.74 11:0.88 12:0.06 17:0.53 21:0.78 27:0.45 28:0.66 30:0.45 32:0.80 34:0.79 36:0.20 37:0.50 39:0.47 43:0.81 66:0.27 69:0.72 70:0.25 74:0.61 77:0.70 81:0.41 83:0.71 85:0.72 91:0.10 98:0.15 101:0.71 108:0.55 114:0.63 122:0.51 123:0.83 124:0.61 126:0.54 127:0.36 129:0.05 133:0.39 135:0.47 144:0.85 145:0.63 146:0.13 147:0.18 149:0.48 150:0.61 154:0.76 155:0.67 159:0.52 161:0.84 163:0.90 165:0.63 167:0.50 172:0.10 173:0.68 176:0.73 177:0.38 178:0.55 179:0.96 181:0.64 187:0.68 192:0.59 195:0.77 201:0.74 212:0.61 215:0.43 216:0.77 222:0.60 235:1.00 241:0.46 242:0.63 243:0.46 245:0.40 247:0.30 253:0.54 259:0.21 265:0.09 266:0.17 267:0.19 271:0.62 276:0.13 277:0.69 282:0.88 290:0.63 297:0.36 299:0.88 300:0.18 +1 1:0.74 7:0.81 9:0.80 11:0.88 12:0.06 17:0.28 21:0.22 27:0.05 28:0.66 30:0.94 32:0.80 34:0.73 36:0.83 37:0.94 39:0.47 41:0.82 43:0.87 60:0.87 66:0.36 69:0.55 70:0.13 74:0.75 77:0.70 81:0.80 83:0.85 85:0.80 91:0.03 96:0.80 98:0.08 101:0.72 108:0.90 114:0.90 120:0.69 121:1.00 122:0.43 123:0.89 124:0.57 126:0.54 127:0.31 129:0.59 133:0.47 135:0.56 140:0.45 144:0.85 145:0.79 146:0.13 147:0.18 149:0.59 150:0.87 151:0.87 153:0.48 154:0.85 155:0.67 158:0.92 159:0.72 161:0.84 162:0.86 163:0.90 164:0.57 165:0.86 167:0.51 172:0.16 173:0.32 176:0.73 177:0.38 179:0.93 181:0.64 187:0.99 192:0.59 195:0.93 201:0.74 202:0.36 204:0.89 208:0.64 212:0.42 215:0.79 216:0.82 222:0.60 230:0.87 233:0.76 235:0.31 241:0.49 242:0.75 243:0.40 245:0.43 247:0.30 248:0.78 253:0.84 255:0.79 256:0.45 259:0.21 260:0.70 265:0.08 266:0.23 267:0.23 268:0.46 271:0.62 276:0.12 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.13 +2 1:0.74 7:0.76 9:0.80 12:0.06 17:0.49 20:0.96 21:0.13 27:0.72 28:0.66 32:0.80 34:0.26 36:0.71 39:0.47 41:0.94 74:0.55 76:0.85 77:0.70 78:0.80 81:0.84 83:0.83 91:0.91 96:0.94 100:0.83 111:0.80 114:0.92 120:0.89 126:0.54 135:0.37 140:0.45 144:0.85 145:0.50 150:0.90 151:0.96 152:0.89 153:0.87 155:0.67 161:0.84 162:0.82 164:0.81 165:0.88 167:0.79 169:0.81 175:0.91 176:0.73 181:0.64 186:0.81 192:0.59 195:0.53 201:0.74 202:0.71 208:0.64 215:0.84 216:0.14 222:0.60 230:0.79 232:0.74 233:0.76 235:0.22 241:0.94 244:0.83 248:0.94 253:0.92 255:0.79 256:0.75 257:0.84 259:0.21 260:0.89 261:0.83 267:0.28 268:0.77 271:0.62 277:0.87 282:0.88 285:0.62 290:0.92 297:0.36 299:0.88 +1 1:0.74 11:0.88 12:0.51 17:0.26 21:0.40 27:0.45 28:0.91 30:0.31 34:0.68 36:0.17 37:0.50 39:0.75 43:0.82 55:0.71 60:0.87 66:0.10 69:0.69 70:0.81 74:0.91 81:0.71 83:0.84 85:0.93 91:0.04 98:0.32 101:0.76 108:0.82 114:0.82 122:0.67 123:0.63 124:0.93 126:0.54 127:0.71 129:0.96 133:0.93 135:0.78 144:0.85 145:0.49 146:0.13 147:0.18 149:0.83 150:0.81 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 163:0.81 165:0.83 167:0.52 172:0.54 173:0.40 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 201:0.74 208:0.64 212:0.18 215:0.67 216:0.77 222:0.25 235:0.52 241:0.27 242:0.61 243:0.11 245:0.76 247:0.60 253:0.75 259:0.21 265:0.34 266:0.94 267:0.65 271:0.32 276:0.77 279:0.86 283:0.71 290:0.82 297:0.36 300:0.70 +3 1:0.74 7:0.81 11:0.88 12:0.51 17:0.51 21:0.30 27:0.21 28:0.91 30:0.62 34:0.66 36:0.59 37:0.74 39:0.75 43:0.98 66:0.12 69:0.12 70:0.72 74:0.99 81:0.76 83:0.75 85:0.99 91:0.05 98:0.55 101:0.86 108:0.93 114:0.86 121:0.97 122:0.57 123:0.82 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.18 144:0.85 146:0.72 147:0.77 149:0.99 150:0.84 154:0.98 155:0.67 159:0.86 161:0.77 165:0.84 167:0.57 172:0.92 173:0.08 176:0.73 177:0.38 178:0.55 179:0.15 181:0.80 187:0.68 192:0.59 201:0.74 202:0.46 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 233:0.76 235:0.42 241:0.44 242:0.52 243:0.28 245:0.97 247:0.67 253:0.78 259:0.21 265:0.48 266:0.87 267:0.59 271:0.32 276:0.94 290:0.86 297:0.36 300:0.94 +3 1:0.74 8:0.67 9:0.80 11:0.88 12:0.51 17:0.02 27:0.33 28:0.91 30:0.61 34:0.33 36:0.32 37:0.41 39:0.75 41:0.92 43:0.86 66:0.09 69:0.57 70:0.75 74:0.89 81:0.94 83:0.83 85:0.98 91:0.49 96:0.93 98:0.21 101:0.83 104:0.84 106:0.81 108:0.78 114:0.98 117:0.86 120:0.87 122:0.43 123:0.94 124:0.94 126:0.54 127:0.75 129:0.97 133:0.94 135:0.89 138:0.96 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:0.98 153:0.91 154:0.84 155:0.67 159:0.86 161:0.77 162:0.73 164:0.79 165:0.92 167:0.64 172:0.61 173:0.30 176:0.73 177:0.38 179:0.33 181:0.80 187:0.39 192:0.59 201:0.74 202:0.71 206:0.81 208:0.64 212:0.52 215:0.96 216:0.88 222:0.25 232:0.83 235:0.05 241:0.61 242:0.51 243:0.15 245:0.80 247:0.67 248:0.93 253:0.95 255:0.79 256:0.71 259:0.21 260:0.88 265:0.19 266:0.92 267:0.59 268:0.74 271:0.32 276:0.80 285:0.62 290:0.98 297:0.36 300:0.84 +2 6:0.97 8:0.95 9:0.80 12:0.51 17:0.38 20:0.89 21:0.78 27:0.05 28:0.91 34:0.85 36:0.58 37:0.81 39:0.75 41:0.97 60:0.95 74:0.46 78:0.85 83:0.88 91:0.67 96:0.92 100:0.95 111:0.90 120:0.86 135:0.70 138:0.98 140:0.45 144:0.85 151:0.93 152:0.84 153:0.77 155:0.67 158:0.87 161:0.77 162:0.67 164:0.87 167:0.64 169:0.93 175:0.91 181:0.80 186:0.85 187:0.98 189:0.98 192:0.59 202:0.81 208:0.64 216:0.94 220:0.99 222:0.25 232:0.97 235:0.42 238:0.97 241:0.60 244:0.83 248:0.92 253:0.88 255:0.79 256:0.83 257:0.84 259:0.21 260:0.86 261:0.94 267:0.23 268:0.83 271:0.32 277:0.87 279:0.86 283:0.71 285:0.62 +2 6:0.83 8:0.63 9:0.80 11:0.88 12:0.51 17:0.08 20:0.80 21:0.78 27:0.31 28:0.91 30:0.95 34:0.25 36:0.85 37:0.30 39:0.75 41:0.97 43:0.86 66:0.54 69:0.59 74:0.44 78:0.79 83:0.83 85:0.72 91:0.92 96:0.94 98:0.16 100:0.83 101:0.76 104:0.58 108:0.45 111:0.79 120:0.91 123:0.43 127:0.09 129:0.05 135:0.20 140:0.45 144:0.85 146:0.13 147:0.18 149:0.49 151:0.96 152:0.77 153:0.86 154:0.83 155:0.67 159:0.08 161:0.77 162:0.65 164:0.89 167:0.83 169:0.81 172:0.10 173:0.93 177:0.38 179:0.18 181:0.80 186:0.79 189:0.85 191:0.81 192:0.59 202:0.85 208:0.64 216:0.19 220:0.74 222:0.25 232:0.78 235:0.22 238:0.92 241:0.82 243:0.63 248:0.94 253:0.92 255:0.79 256:0.86 259:0.21 260:0.91 261:0.81 265:0.17 267:0.23 268:0.86 271:0.32 285:0.62 300:0.08 +1 1:0.74 6:0.95 8:0.86 9:0.80 11:0.88 12:0.51 17:0.45 20:0.74 21:0.59 27:0.06 28:0.91 30:0.38 32:0.80 34:0.75 36:0.75 37:0.82 39:0.75 41:0.95 43:0.76 55:0.71 60:0.97 66:0.13 69:0.81 70:0.80 74:0.94 77:0.96 78:0.99 81:0.58 83:0.86 85:0.94 91:0.48 96:0.87 98:0.38 100:0.96 101:0.79 108:0.90 111:0.98 114:0.74 120:0.78 122:0.67 123:0.77 124:0.88 126:0.54 127:0.38 129:0.93 133:0.87 135:0.77 140:0.45 144:0.85 145:0.90 146:0.36 147:0.43 149:0.86 150:0.73 151:0.87 152:0.74 153:0.48 154:0.67 155:0.67 158:0.87 159:0.79 161:0.77 162:0.81 164:0.85 165:0.78 167:0.57 169:0.92 172:0.61 173:0.61 176:0.73 177:0.38 179:0.26 181:0.80 186:0.99 187:0.39 189:0.96 192:0.59 195:0.94 201:0.74 202:0.78 208:0.64 212:0.37 215:0.55 216:0.92 220:0.91 222:0.25 235:0.74 238:0.80 241:0.44 242:0.55 243:0.21 245:0.85 247:0.47 248:0.86 253:0.91 255:0.79 256:0.81 259:0.21 260:0.79 261:0.78 265:0.38 266:0.80 267:0.52 268:0.83 271:0.32 276:0.81 279:0.86 282:0.88 283:0.71 285:0.62 290:0.74 297:0.36 299:0.88 300:0.70 +3 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.88 12:0.51 17:0.22 20:0.98 21:0.30 27:0.21 28:0.91 30:0.60 34:0.66 36:0.70 37:0.74 39:0.75 41:0.99 43:0.98 60:0.87 66:0.10 69:0.12 70:0.68 74:0.99 78:0.85 81:0.76 83:0.90 85:0.99 91:0.81 96:0.98 98:0.56 100:0.95 101:0.86 104:0.84 106:0.81 108:0.93 111:0.91 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.82 124:0.71 126:0.54 127:0.61 129:0.81 133:0.67 135:0.17 140:0.45 144:0.85 146:0.90 147:0.92 149:0.99 150:0.84 151:0.99 152:0.97 153:0.97 154:0.98 155:0.67 158:0.92 159:0.85 161:0.77 162:0.64 164:0.94 165:0.84 167:0.61 169:0.91 172:0.91 173:0.08 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.39 189:0.97 191:0.75 192:0.59 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.56 242:0.51 243:0.38 245:0.98 247:0.67 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.96 265:0.48 266:0.80 267:0.59 268:0.92 271:0.32 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.51 17:0.01 20:0.98 27:0.14 28:0.91 30:0.65 34:0.67 36:0.29 37:0.67 39:0.75 41:0.99 43:0.86 60:0.98 66:0.06 69:0.57 70:0.74 74:0.93 78:0.85 81:0.94 83:0.90 85:0.98 91:0.63 96:0.99 98:0.25 100:0.95 101:0.83 108:0.78 111:0.90 114:0.98 120:0.98 122:0.43 123:0.86 124:0.93 126:0.54 127:0.74 129:0.96 133:0.93 135:0.85 138:0.99 140:0.45 144:0.85 145:0.82 146:0.22 147:0.28 149:0.96 150:0.97 151:1.00 152:0.98 153:0.98 154:0.84 155:0.67 158:0.92 159:0.86 161:0.77 162:0.66 164:0.96 165:0.92 167:0.56 169:0.90 172:0.61 173:0.30 176:0.73 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.94 192:0.59 201:0.74 202:0.94 208:0.64 212:0.50 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.41 242:0.53 243:0.15 245:0.83 247:0.67 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.92 267:0.28 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +3 1:0.74 6:0.93 8:0.76 9:0.80 11:0.88 12:0.51 20:0.76 27:0.14 28:0.91 30:0.62 34:0.53 36:0.34 37:0.67 39:0.75 41:0.99 43:0.86 60:0.95 66:0.06 69:0.59 70:0.76 74:0.91 78:0.91 81:0.94 83:0.90 85:0.98 91:0.90 96:0.99 98:0.27 100:0.90 101:0.83 108:0.78 111:0.95 114:0.98 120:0.98 122:0.43 123:0.62 124:0.93 126:0.54 127:0.75 129:0.96 133:0.93 135:0.83 138:0.99 140:0.45 144:0.85 145:0.82 146:0.29 147:0.36 149:0.95 150:0.97 151:1.00 152:0.98 153:0.98 154:0.83 155:0.67 158:0.92 159:0.86 161:0.77 162:0.68 164:0.96 165:0.92 167:0.57 169:0.90 172:0.61 173:0.31 176:0.73 177:0.38 179:0.32 181:0.80 186:0.79 187:0.39 189:0.92 191:0.79 192:0.59 201:0.74 202:0.94 208:0.64 212:0.51 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.46 242:0.52 243:0.15 245:0.82 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.94 267:0.36 268:0.95 271:0.32 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +1 1:0.74 11:0.88 12:0.51 17:0.30 21:0.54 27:0.45 28:0.91 30:0.21 32:0.80 34:0.75 36:0.17 37:0.50 39:0.75 43:0.82 60:0.87 66:0.51 69:0.70 70:0.83 74:0.93 77:0.70 81:0.62 83:0.86 85:0.95 91:0.06 98:0.63 101:0.78 108:0.53 114:0.77 122:0.67 123:0.51 124:0.87 126:0.54 127:0.70 129:0.93 133:0.91 135:0.72 144:0.85 145:0.44 146:0.95 147:0.96 149:0.90 150:0.76 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 165:0.79 167:0.58 172:0.77 173:0.41 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 195:0.96 201:0.74 208:0.64 212:0.15 215:0.58 216:0.77 222:0.25 235:0.68 241:0.50 242:0.45 243:0.61 245:0.73 247:0.67 253:0.74 259:0.21 265:0.63 266:0.94 267:0.44 271:0.32 276:0.77 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.70 +1 1:0.74 8:0.66 9:0.80 11:0.88 12:0.51 17:0.04 21:0.22 27:0.14 28:0.91 30:0.71 32:0.80 34:0.63 36:0.30 37:0.67 39:0.75 41:0.98 43:0.86 60:0.99 66:0.52 69:0.58 70:0.66 74:0.86 77:0.70 78:0.80 81:0.81 83:0.88 85:0.96 91:0.77 96:0.95 98:0.53 101:0.83 108:0.86 111:0.79 114:0.90 120:0.91 122:0.43 123:0.85 124:0.73 126:0.54 127:0.47 129:0.89 133:0.78 135:0.98 140:0.45 144:0.85 145:0.94 146:0.13 147:0.18 149:0.89 150:0.87 151:0.91 153:0.72 154:0.84 155:0.67 158:0.92 159:0.71 161:0.77 162:0.72 164:0.91 165:0.86 167:0.58 169:0.95 172:0.67 173:0.42 176:0.73 177:0.38 179:0.46 181:0.80 187:0.39 191:0.75 192:0.59 195:0.88 201:0.74 202:0.85 208:0.64 212:0.36 215:0.79 216:0.93 220:0.74 222:0.25 235:0.31 238:0.86 241:0.47 242:0.61 243:0.14 245:0.74 247:0.39 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.91 265:0.54 266:0.71 267:0.87 268:0.88 271:0.32 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.88 12:0.51 17:0.26 20:0.97 21:0.30 27:0.25 28:0.91 30:0.75 32:0.80 34:0.31 36:0.90 37:0.43 39:0.75 41:0.97 43:0.83 60:0.87 66:0.60 69:0.65 70:0.59 74:0.90 77:0.89 78:0.78 81:0.76 83:0.89 85:0.97 91:0.40 96:0.94 98:0.32 100:0.83 101:0.83 104:0.93 106:0.81 108:0.90 111:0.79 114:0.86 117:0.86 120:0.90 121:0.99 122:0.43 123:0.90 124:0.58 126:0.54 127:0.44 129:0.81 133:0.67 135:0.93 140:0.45 144:0.85 145:0.72 146:0.54 147:0.60 149:0.90 150:0.84 151:0.96 152:0.90 153:0.85 154:0.80 155:0.67 158:0.92 159:0.75 161:0.77 162:0.85 164:0.89 165:0.84 167:0.55 169:0.81 172:0.74 173:0.46 176:0.73 177:0.38 179:0.40 181:0.80 186:0.79 187:0.39 189:0.94 192:0.59 195:0.92 201:0.74 202:0.80 204:0.89 206:0.81 208:0.64 212:0.73 215:0.72 216:0.80 220:0.74 222:0.25 230:0.87 232:0.95 233:0.76 235:0.42 238:0.92 241:0.39 242:0.59 243:0.31 245:0.79 247:0.55 248:0.94 253:0.96 255:0.79 256:0.83 259:0.21 260:0.90 261:0.87 265:0.35 266:0.71 267:0.65 268:0.86 271:0.32 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.84 +2 1:0.74 7:0.81 9:0.80 11:0.88 12:0.51 17:0.06 21:0.13 27:0.13 28:0.91 30:0.86 32:0.80 34:0.44 36:0.44 37:0.79 39:0.75 41:0.97 43:0.81 60:0.87 66:0.84 69:0.70 70:0.28 74:0.86 77:0.70 81:0.85 83:0.89 85:0.91 91:0.75 96:0.95 98:0.63 101:0.83 108:0.53 114:0.92 120:0.90 121:0.90 122:0.43 123:0.51 126:0.54 127:0.19 129:0.05 135:0.89 140:0.45 144:0.85 145:0.54 146:0.60 147:0.65 149:0.86 150:0.91 151:0.93 153:0.78 154:0.76 155:0.67 158:0.92 159:0.22 161:0.77 162:0.83 164:0.89 165:0.88 167:0.72 172:0.54 173:0.78 176:0.73 177:0.38 179:0.47 181:0.80 187:0.39 192:0.59 195:0.63 201:0.74 202:0.81 204:0.89 208:0.64 212:0.52 215:0.84 216:0.83 220:0.74 222:0.25 230:0.87 233:0.76 235:0.22 241:0.70 242:0.63 243:0.85 247:0.30 248:0.95 253:0.96 255:0.79 256:0.84 259:0.21 260:0.91 265:0.64 266:0.38 267:0.52 268:0.87 271:0.32 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.25 +2 1:0.74 6:0.93 7:0.72 8:0.77 11:0.93 12:0.51 17:0.32 18:0.86 20:0.80 21:0.59 22:0.93 27:0.41 28:0.73 29:0.71 30:0.76 31:0.86 32:0.80 34:0.97 36:0.29 37:0.44 39:0.75 43:0.88 44:0.93 45:0.93 47:0.93 48:0.91 60:0.95 64:0.77 66:0.80 69:0.54 70:0.58 71:0.65 74:0.91 77:0.89 78:0.84 79:0.86 81:0.47 83:0.91 85:0.91 86:0.94 91:0.42 98:0.83 100:0.83 101:0.97 104:0.96 106:0.81 108:0.42 111:0.83 114:0.58 120:0.53 122:0.75 123:0.40 124:0.40 126:0.54 127:0.65 129:0.05 133:0.43 135:0.78 137:0.86 139:0.96 140:0.80 144:0.85 145:0.82 146:0.93 147:0.94 149:0.95 150:0.71 151:0.47 152:0.85 153:0.48 154:0.86 155:0.67 158:0.91 159:0.69 161:0.63 162:0.58 164:0.53 165:0.93 167:0.45 169:0.86 172:0.90 173:0.42 176:0.73 177:0.70 178:0.42 179:0.29 181:0.82 186:0.84 187:0.68 189:0.91 190:0.77 192:0.87 195:0.83 196:0.88 201:0.93 202:0.53 204:0.85 206:0.81 208:0.64 212:0.71 215:0.39 216:0.75 219:0.95 220:0.96 222:0.25 230:0.75 232:0.98 233:0.76 235:0.80 238:0.83 242:0.30 243:0.82 245:0.88 247:0.86 248:0.47 253:0.48 255:0.74 256:0.45 259:0.21 260:0.53 261:0.88 262:0.93 265:0.83 266:0.80 267:0.95 268:0.46 271:0.32 276:0.82 279:0.86 281:0.91 282:0.88 283:0.78 284:0.87 285:0.56 290:0.58 292:0.91 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.94 7:0.72 8:0.92 9:0.80 11:0.92 12:0.51 17:0.40 18:0.78 20:0.78 21:0.30 22:0.93 27:0.48 28:0.73 29:0.71 30:0.60 31:0.87 32:0.69 34:0.80 36:0.81 37:0.72 39:0.75 41:0.82 43:0.85 45:0.81 47:0.93 48:0.91 60:0.87 64:0.77 66:0.72 69:0.62 70:0.48 71:0.65 74:0.81 77:0.70 78:0.96 79:0.87 81:0.61 83:0.91 85:0.85 86:0.87 91:0.27 96:0.69 98:0.75 100:0.98 101:0.87 104:0.89 106:0.81 108:0.59 111:0.98 114:0.65 117:0.86 120:0.55 122:0.77 123:0.57 124:0.44 126:0.54 127:0.54 129:0.59 133:0.61 135:0.60 137:0.87 139:0.90 140:0.45 144:0.85 145:0.90 146:0.17 147:0.22 149:0.88 150:0.78 151:0.52 152:0.87 153:0.48 154:0.82 155:0.67 158:0.91 159:0.52 161:0.63 162:0.86 164:0.57 165:0.93 167:0.53 169:0.94 172:0.74 173:0.61 176:0.73 177:0.70 179:0.49 181:0.82 186:0.96 187:0.39 189:0.96 192:0.87 195:0.71 196:0.90 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.60 215:0.44 216:0.69 219:0.95 220:0.87 222:0.25 230:0.75 232:0.93 233:0.76 235:0.52 238:0.96 241:0.37 242:0.32 243:0.19 245:0.68 247:0.67 248:0.51 253:0.53 255:0.95 256:0.45 259:0.21 260:0.56 261:0.94 262:0.93 265:0.75 266:0.47 267:0.36 268:0.46 271:0.32 276:0.65 279:0.86 281:0.89 282:0.77 283:0.78 284:0.89 285:0.62 290:0.65 292:0.91 297:0.36 300:0.43 +0 1:0.69 6:0.93 8:0.84 9:0.80 11:0.91 12:0.51 17:0.16 18:0.71 20:0.77 21:0.68 27:0.05 28:0.73 29:0.71 30:0.33 31:0.93 32:0.69 34:0.42 36:0.71 37:0.93 39:0.75 41:0.82 43:0.26 45:0.88 55:0.84 66:0.90 69:0.93 70:0.86 71:0.65 74:0.61 77:0.70 78:0.84 79:0.92 81:0.32 83:0.91 86:0.74 91:0.66 96:0.94 97:0.97 98:0.78 99:0.83 100:0.84 101:0.52 108:0.87 111:0.83 114:0.55 120:0.90 122:0.77 123:0.86 124:0.36 126:0.44 127:0.50 129:0.05 133:0.37 135:0.76 137:0.93 139:0.71 140:0.98 144:0.85 145:0.50 146:0.97 147:0.05 149:0.55 150:0.51 151:0.82 152:0.76 153:0.86 154:0.44 155:0.67 158:0.78 159:0.80 161:0.63 162:0.59 164:0.89 165:0.70 167:0.59 169:0.82 172:0.87 173:0.88 176:0.66 177:0.05 178:0.42 179:0.33 181:0.82 186:0.84 187:0.68 189:0.94 192:0.87 195:0.93 196:0.91 201:0.68 202:0.89 208:0.64 212:0.49 215:0.37 216:0.84 220:0.99 222:0.25 232:0.96 235:0.84 238:0.86 241:0.53 242:0.38 243:0.07 245:0.67 247:0.86 248:0.75 253:0.91 254:0.92 255:0.95 256:0.90 257:0.84 259:0.21 260:0.87 261:0.78 265:0.79 266:0.75 267:0.44 268:0.90 271:0.32 276:0.77 279:0.82 282:0.77 283:0.78 284:0.92 285:0.62 290:0.55 297:0.36 300:0.84 +2 1:0.74 6:0.93 7:0.72 8:0.87 11:0.92 12:0.51 17:0.22 18:0.94 20:0.76 21:0.54 22:0.93 27:0.41 28:0.73 29:0.71 30:0.66 32:0.74 34:0.62 36:0.30 37:0.44 39:0.75 43:0.88 44:0.93 45:0.95 47:0.93 64:0.77 66:0.33 69:0.54 70:0.72 71:0.65 74:0.95 77:0.70 78:0.82 81:0.49 83:0.91 85:0.95 86:0.98 91:0.37 98:0.52 100:0.73 101:0.87 104:0.96 106:0.81 108:0.41 111:0.80 114:0.59 122:0.51 123:0.40 124:0.87 126:0.54 127:0.74 129:0.89 133:0.87 135:0.72 139:0.98 144:0.85 145:0.83 146:0.88 147:0.90 149:0.98 150:0.72 152:0.85 154:0.86 155:0.67 159:0.85 161:0.63 165:0.93 167:0.48 169:0.86 172:0.89 173:0.28 176:0.73 177:0.70 178:0.55 179:0.18 181:0.82 186:0.83 187:0.87 192:0.87 195:0.95 201:0.93 202:0.50 204:0.85 206:0.81 208:0.64 212:0.68 215:0.39 216:0.75 222:0.25 230:0.75 232:0.98 233:0.76 235:0.74 241:0.07 242:0.19 243:0.50 245:0.95 247:0.92 253:0.50 259:0.21 261:0.88 262:0.93 265:0.53 266:0.80 267:0.44 271:0.32 276:0.92 282:0.83 290:0.59 297:0.36 300:0.84 +2 1:0.74 6:0.98 7:0.72 8:0.98 9:0.80 11:0.92 12:0.51 17:0.20 18:0.68 20:0.83 21:0.40 27:0.03 28:0.73 29:0.71 30:0.45 31:0.96 34:0.93 36:0.61 37:0.92 39:0.75 41:0.92 43:0.78 45:0.77 64:0.77 66:0.43 69:0.78 70:0.56 71:0.65 74:0.59 78:0.95 79:0.95 81:0.62 83:0.91 85:0.72 86:0.76 91:0.25 96:0.93 98:0.43 99:0.83 100:0.98 101:0.87 108:0.62 111:0.97 114:0.62 120:0.87 122:0.77 123:0.59 124:0.69 126:0.54 127:0.29 129:0.05 133:0.66 135:0.79 137:0.96 139:0.73 140:0.45 144:0.85 145:0.56 146:0.64 147:0.69 149:0.70 150:0.78 151:0.87 152:0.87 153:0.72 154:0.71 155:0.67 159:0.54 161:0.63 162:0.68 164:0.83 165:0.93 167:0.62 169:0.97 172:0.45 173:0.71 176:0.73 177:0.70 179:0.56 181:0.82 186:0.95 187:0.87 189:0.99 192:0.87 196:0.92 201:0.93 202:0.78 204:0.85 208:0.64 212:0.37 215:0.42 216:0.79 220:0.74 222:0.25 230:0.75 233:0.76 235:0.74 238:0.95 241:0.59 242:0.28 243:0.57 245:0.65 247:0.79 248:0.84 253:0.90 255:0.95 256:0.80 259:0.21 260:0.86 261:0.96 265:0.45 266:0.71 267:0.69 268:0.81 271:0.32 276:0.51 284:0.96 285:0.62 290:0.62 297:0.36 300:0.43 +1 1:0.74 6:0.94 7:0.72 8:0.89 9:0.80 11:0.92 12:0.51 17:0.28 18:0.77 20:0.88 21:0.30 22:0.93 27:0.48 28:0.73 29:0.71 30:0.60 31:0.90 32:0.69 34:0.82 36:0.84 37:0.72 39:0.75 41:0.88 43:0.83 45:0.81 47:0.93 48:0.91 60:0.87 64:0.77 66:0.72 69:0.67 70:0.48 71:0.65 74:0.80 77:0.70 78:0.91 79:0.89 81:0.61 83:0.91 85:0.85 86:0.87 91:0.51 96:0.74 98:0.75 100:0.97 101:0.87 104:0.84 106:0.81 108:0.62 111:0.94 114:0.65 117:0.86 120:0.61 122:0.77 123:0.59 124:0.44 126:0.54 127:0.54 129:0.59 133:0.61 135:0.60 137:0.90 139:0.89 140:0.45 144:0.85 145:0.90 146:0.17 147:0.22 149:0.87 150:0.78 151:0.63 152:0.87 153:0.72 154:0.79 155:0.67 158:0.91 159:0.52 161:0.63 162:0.60 164:0.71 165:0.93 167:0.62 169:0.94 172:0.74 173:0.67 176:0.73 177:0.70 179:0.49 181:0.82 186:0.91 187:0.39 189:0.95 192:0.87 195:0.71 196:0.93 201:0.93 202:0.65 204:0.85 206:0.81 208:0.64 212:0.60 215:0.44 216:0.69 219:0.95 220:0.87 222:0.25 230:0.75 232:0.90 233:0.76 235:0.52 238:0.96 241:0.59 242:0.32 243:0.19 245:0.68 247:0.67 248:0.62 253:0.66 255:0.95 256:0.58 259:0.21 260:0.62 261:0.94 262:0.93 265:0.75 266:0.47 267:0.44 268:0.65 271:0.32 276:0.65 279:0.86 281:0.89 282:0.77 283:0.78 284:0.94 285:0.62 290:0.65 292:0.91 297:0.36 300:0.43 +2 1:0.74 6:0.98 7:0.72 8:0.98 9:0.80 11:0.92 12:0.51 17:0.06 18:0.68 20:0.85 21:0.40 27:0.03 28:0.73 29:0.71 30:0.45 31:0.97 34:0.93 36:0.71 37:0.92 39:0.75 41:0.94 43:0.78 45:0.77 60:0.95 64:0.77 66:0.43 69:0.79 70:0.56 71:0.65 74:0.67 78:0.96 79:0.97 81:0.62 83:0.91 85:0.72 86:0.76 91:0.69 96:0.92 97:0.89 98:0.43 99:0.83 100:0.98 101:0.87 108:0.63 111:0.98 114:0.62 120:0.86 122:0.77 123:0.60 124:0.69 126:0.54 127:0.29 129:0.05 133:0.66 135:0.69 137:0.97 139:0.81 140:0.80 144:0.85 145:0.56 146:0.64 147:0.69 149:0.69 150:0.78 151:0.94 152:0.87 153:0.81 154:0.70 155:0.67 158:0.91 159:0.54 161:0.63 162:0.53 164:0.86 165:0.93 167:0.57 169:0.97 172:0.45 173:0.72 176:0.73 177:0.70 178:0.42 179:0.56 181:0.82 186:0.96 187:0.68 189:0.99 192:0.87 196:0.96 201:0.93 202:0.87 204:0.85 208:0.64 212:0.37 215:0.42 216:0.79 219:0.95 220:0.74 222:0.25 230:0.75 233:0.76 235:0.74 238:0.96 241:0.50 242:0.28 243:0.57 245:0.65 247:0.79 248:0.89 253:0.91 255:0.95 256:0.84 259:0.21 260:0.86 261:0.96 265:0.45 266:0.71 267:0.82 268:0.85 271:0.32 276:0.51 279:0.86 283:0.78 284:0.97 285:0.62 290:0.62 292:0.91 297:0.36 300:0.43 +1 1:0.74 11:0.92 12:0.51 17:0.45 18:0.65 21:0.22 27:0.45 28:0.73 29:0.71 30:0.71 34:0.80 36:0.17 37:0.50 39:0.75 43:0.82 45:0.73 60:0.87 64:0.77 66:0.45 69:0.68 70:0.29 71:0.65 74:0.61 81:0.68 83:0.91 85:0.72 86:0.74 91:0.11 98:0.33 101:0.87 108:0.52 114:0.71 122:0.51 123:0.50 124:0.57 126:0.54 127:0.43 129:0.05 133:0.43 135:0.71 139:0.81 144:0.85 145:0.40 146:0.48 147:0.55 149:0.68 150:0.81 154:0.77 155:0.67 158:0.91 159:0.58 161:0.63 163:0.81 165:0.93 167:0.54 172:0.32 173:0.63 176:0.73 177:0.70 178:0.55 179:0.81 181:0.82 187:0.68 192:0.87 201:0.93 208:0.64 212:0.30 215:0.51 216:0.77 219:0.95 222:0.25 235:0.42 241:0.41 242:0.14 243:0.58 245:0.59 247:0.67 253:0.52 259:0.21 265:0.36 266:0.54 267:0.28 271:0.32 276:0.25 279:0.86 283:0.78 290:0.71 292:0.91 297:0.36 300:0.25 +2 1:0.74 9:0.80 11:0.92 12:0.51 17:0.13 18:0.77 21:0.22 22:0.93 27:0.16 28:0.73 29:0.71 30:0.62 31:0.90 32:0.80 34:0.92 36:0.76 37:0.53 39:0.75 41:0.82 43:0.84 44:0.93 45:0.73 47:0.93 64:0.77 66:0.90 69:0.64 70:0.53 71:0.65 74:0.75 76:0.85 77:0.70 79:0.90 81:0.68 83:0.91 85:0.85 86:0.88 91:0.07 96:0.74 98:0.74 101:0.87 104:0.98 106:0.81 108:0.49 114:0.71 117:0.86 120:0.62 122:0.57 123:0.47 126:0.54 127:0.23 129:0.05 135:0.71 137:0.90 139:0.89 140:0.45 144:0.85 145:0.63 146:0.82 147:0.85 149:0.87 150:0.81 151:0.69 153:0.46 154:0.80 155:0.67 159:0.37 161:0.63 162:0.62 164:0.54 165:0.93 167:0.49 172:0.71 173:0.62 176:0.73 177:0.70 179:0.38 181:0.82 187:0.99 192:0.87 195:0.75 196:0.93 201:0.93 202:0.49 206:0.81 208:0.64 212:0.49 215:0.51 216:0.90 220:0.62 222:0.25 232:0.99 235:0.42 241:0.15 242:0.18 243:0.90 247:0.76 248:0.64 253:0.69 255:0.95 256:0.45 259:0.21 260:0.63 262:0.93 265:0.74 266:0.54 267:0.44 268:0.45 271:0.32 276:0.59 282:0.88 284:0.87 285:0.62 290:0.71 297:0.36 299:0.88 300:0.43 +3 1:0.74 6:0.91 7:0.81 8:0.82 11:0.93 12:0.51 17:0.05 18:0.75 20:0.80 21:0.30 22:0.93 27:0.08 28:0.73 29:0.71 30:0.45 32:0.78 34:0.49 36:0.94 37:0.82 39:0.75 43:0.78 44:0.93 45:0.92 47:0.93 64:0.77 66:0.95 69:0.46 70:0.63 71:0.65 74:0.88 77:0.70 78:0.89 81:0.60 83:0.60 85:0.72 86:0.85 91:0.04 97:0.89 98:0.70 99:0.95 100:0.90 101:0.97 104:0.82 106:0.81 108:0.35 111:0.88 114:0.61 117:0.86 121:0.90 122:0.51 123:0.34 126:0.54 127:0.21 129:0.05 135:0.70 139:0.93 144:0.85 145:0.72 146:0.91 147:0.92 149:0.86 150:0.75 152:0.77 154:0.87 155:0.67 158:0.91 159:0.51 161:0.63 165:0.70 167:0.51 169:0.88 172:0.88 173:0.29 176:0.66 177:0.70 178:0.55 179:0.18 181:0.82 186:0.89 187:0.21 189:0.92 192:0.87 195:0.89 201:0.93 202:0.46 204:0.89 206:0.81 208:0.64 212:0.75 215:0.42 216:0.75 219:0.95 222:0.25 230:0.87 232:0.88 233:0.76 235:0.68 238:0.89 241:0.24 242:0.28 243:0.95 247:0.86 253:0.50 259:0.21 261:0.83 262:0.93 265:0.71 266:0.54 267:0.28 271:0.32 276:0.79 279:0.86 281:0.91 282:0.86 283:0.77 290:0.61 292:0.91 297:0.36 300:0.55 +2 1:0.69 6:0.95 7:0.63 8:0.96 9:0.76 11:0.92 12:0.51 17:0.40 18:0.67 20:0.74 21:0.30 27:0.02 28:0.73 29:0.71 30:0.73 32:0.69 34:0.58 36:0.32 37:0.92 39:0.75 43:0.69 45:0.98 66:0.34 69:0.91 70:0.49 71:0.65 74:0.89 76:0.85 77:0.96 78:0.87 81:0.46 83:0.60 85:0.80 86:0.77 91:0.35 96:0.72 98:0.48 99:0.83 100:0.98 101:0.87 108:0.87 111:0.96 114:0.63 120:0.59 122:0.75 123:0.86 124:0.82 126:0.44 127:0.71 129:0.81 133:0.82 135:0.76 139:0.74 140:0.45 144:0.85 145:0.56 146:0.17 147:0.86 149:0.94 150:0.56 151:0.55 152:0.78 153:0.72 154:0.52 155:0.58 159:0.85 161:0.63 162:0.73 164:0.70 165:0.70 167:0.54 169:0.83 172:0.86 173:0.81 176:0.66 177:0.38 179:0.20 181:0.82 186:0.87 187:0.39 190:0.77 192:0.59 195:0.95 201:0.68 202:0.64 208:0.54 212:0.76 215:0.44 216:0.99 220:0.82 222:0.25 230:0.64 233:0.76 235:0.42 241:0.39 242:0.54 243:0.44 245:0.93 247:0.67 248:0.58 253:0.61 255:0.74 256:0.64 257:0.65 259:0.21 260:0.59 261:0.82 265:0.50 266:0.80 267:0.36 268:0.68 271:0.32 276:0.89 282:0.77 285:0.56 290:0.63 297:0.36 300:0.94 +2 1:0.69 6:0.98 7:0.72 8:0.92 9:0.76 11:0.93 12:0.51 17:0.24 18:0.60 20:0.84 21:0.54 27:0.03 28:0.73 29:0.71 30:0.45 34:0.95 36:0.61 37:0.92 39:0.75 43:0.60 45:0.83 55:0.71 66:0.91 69:0.79 70:0.47 71:0.65 74:0.62 78:0.94 81:0.38 83:0.60 86:0.65 91:0.69 96:0.90 97:0.99 98:0.78 99:0.95 100:0.97 101:0.87 108:0.63 111:0.96 114:0.58 120:0.83 122:0.77 123:0.61 126:0.44 127:0.27 129:0.05 135:0.86 139:0.67 140:0.90 144:0.85 145:0.61 146:0.92 147:0.93 149:0.66 150:0.54 151:0.81 152:0.87 153:0.48 154:0.49 155:0.58 158:0.78 159:0.54 161:0.63 162:0.52 164:0.82 165:0.70 167:0.49 169:0.97 172:0.76 173:0.71 176:0.66 177:0.70 178:0.50 179:0.37 181:0.82 186:0.94 187:0.68 189:0.97 190:0.77 192:0.59 201:0.68 202:0.85 204:0.85 208:0.54 212:0.27 215:0.39 216:0.79 219:0.89 220:0.87 222:0.25 230:0.75 233:0.76 235:0.80 238:0.95 241:0.15 242:0.70 243:0.92 244:0.61 247:0.67 248:0.87 253:0.86 255:0.74 256:0.80 259:0.21 260:0.81 261:0.96 265:0.78 266:0.71 267:0.59 268:0.81 271:0.32 276:0.65 279:0.82 283:0.78 285:0.56 290:0.58 292:0.91 297:0.36 300:0.43 +2 1:0.74 7:0.72 8:0.95 11:0.92 12:0.51 17:0.69 18:0.88 21:0.47 22:0.93 27:0.44 28:0.73 29:0.71 30:0.41 32:0.69 34:0.95 36:0.66 37:0.54 39:0.75 43:0.92 45:0.87 47:0.93 48:0.91 60:0.87 64:0.77 66:0.07 69:0.41 70:0.98 71:0.65 74:0.77 77:0.89 81:0.52 83:0.91 85:0.85 86:0.92 91:0.14 98:0.19 101:0.87 104:0.97 106:0.81 108:0.32 114:0.60 117:0.86 122:0.86 123:0.73 124:0.96 126:0.54 127:0.95 129:0.89 133:0.96 135:0.80 139:0.92 144:0.85 145:0.96 146:0.70 147:0.75 149:0.82 150:0.74 154:0.91 155:0.67 158:0.91 159:0.96 161:0.63 165:0.93 167:0.48 172:0.63 173:0.08 176:0.73 177:0.70 178:0.55 179:0.35 181:0.82 187:0.68 192:0.87 195:0.99 201:0.93 204:0.85 206:0.81 208:0.64 212:0.26 215:0.41 216:0.33 219:0.95 222:0.25 230:0.75 232:0.98 233:0.76 235:0.68 241:0.07 242:0.19 243:0.40 245:0.75 247:0.86 253:0.47 259:0.21 262:0.93 265:0.21 266:0.99 267:0.69 271:0.32 276:0.81 277:0.69 279:0.86 281:0.89 282:0.77 283:0.78 290:0.60 292:0.91 297:0.36 300:0.99 +1 1:0.74 6:0.88 7:0.81 8:0.75 11:0.92 12:0.51 17:0.67 18:0.80 20:0.90 21:0.47 22:0.93 27:0.51 28:0.73 29:0.71 30:0.45 31:0.86 32:0.69 34:0.84 36:0.26 37:0.71 39:0.75 43:0.79 45:0.88 47:0.93 60:0.95 64:0.77 66:0.15 69:0.75 70:0.88 71:0.65 74:0.84 77:0.70 78:0.92 79:0.86 81:0.52 83:0.91 85:0.80 86:0.90 91:0.29 98:0.51 100:0.92 101:0.87 104:0.82 106:0.81 108:0.59 111:0.91 114:0.60 117:0.86 121:0.97 122:0.77 123:0.86 124:0.73 126:0.54 127:0.56 129:0.05 133:0.74 135:0.34 137:0.86 139:0.93 140:0.45 144:0.85 145:0.50 146:0.74 147:0.78 149:0.82 150:0.74 151:0.47 152:0.84 153:0.72 154:0.73 155:0.67 158:0.91 159:0.74 161:0.63 162:0.56 165:0.93 167:0.50 169:0.90 172:0.74 173:0.62 176:0.73 177:0.70 179:0.39 181:0.82 186:0.92 187:0.21 189:0.90 190:0.89 192:0.87 195:0.89 196:0.88 201:0.93 202:0.58 204:0.89 206:0.81 208:0.64 212:0.60 215:0.41 216:0.20 219:0.95 220:0.93 222:0.25 230:0.87 232:0.88 233:0.76 235:0.68 238:0.88 241:0.18 242:0.21 243:0.61 245:0.80 247:0.82 248:0.47 253:0.48 256:0.45 259:0.21 261:0.91 262:0.93 265:0.48 266:0.89 267:0.28 268:0.57 271:0.32 276:0.74 277:0.69 279:0.86 281:0.91 282:0.77 283:0.78 284:0.86 285:0.47 290:0.60 292:0.91 297:0.36 300:0.94 +1 1:0.74 7:0.72 9:0.80 11:0.93 12:0.51 17:0.76 18:0.85 21:0.30 22:0.93 27:0.43 28:0.73 29:0.71 30:0.84 31:0.95 32:0.80 34:0.75 36:0.87 39:0.75 41:0.88 43:0.88 44:0.93 45:0.88 47:0.93 48:0.91 60:0.95 64:0.77 66:0.68 69:0.54 70:0.56 71:0.65 74:0.90 77:0.89 78:0.88 79:0.95 81:0.61 83:0.91 85:0.88 86:0.95 91:0.42 96:0.85 98:0.56 101:0.97 104:0.93 106:0.81 108:0.41 111:0.86 114:0.65 117:0.86 120:0.76 122:0.57 123:0.40 124:0.48 126:0.54 127:0.57 129:0.81 133:0.67 135:0.61 137:0.95 139:0.96 140:0.45 144:0.85 145:0.77 146:0.69 147:0.74 149:0.89 150:0.78 151:0.94 153:0.80 154:0.86 155:0.67 158:0.92 159:0.57 161:0.63 162:0.78 164:0.70 165:0.93 167:0.45 169:0.72 172:0.77 173:0.49 176:0.73 177:0.70 179:0.44 181:0.82 187:0.87 192:0.87 195:0.77 196:0.98 201:0.93 202:0.59 204:0.85 206:0.81 208:0.64 212:0.71 215:0.44 216:0.21 219:0.95 222:0.25 230:0.75 232:0.95 233:0.76 235:0.52 242:0.24 243:0.72 245:0.76 247:0.74 248:0.83 253:0.89 255:0.95 256:0.58 259:0.21 260:0.77 262:0.93 265:0.58 266:0.54 267:0.44 268:0.64 271:0.32 276:0.66 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 290:0.65 292:0.95 297:0.36 299:0.88 300:0.84 +3 1:0.69 6:0.95 8:0.96 9:0.80 11:0.92 12:0.51 17:0.04 18:0.66 20:0.82 21:0.22 27:0.06 28:0.73 29:0.71 30:0.41 31:0.91 32:0.80 34:0.59 36:0.98 37:0.85 39:0.75 41:0.88 43:0.73 44:0.93 45:0.92 66:0.54 69:0.66 70:0.85 71:0.65 74:0.82 77:0.89 78:0.91 79:0.92 81:0.58 83:0.91 85:0.72 86:0.72 91:0.75 96:0.95 97:0.99 98:0.42 99:0.95 100:0.93 101:0.87 104:0.96 106:0.81 108:0.50 111:0.90 114:0.63 117:0.86 120:0.92 122:0.51 123:0.48 124:0.76 126:0.44 127:0.60 129:0.59 133:0.81 135:0.90 137:0.91 138:0.96 139:0.79 140:0.98 144:0.85 145:0.56 146:0.81 147:0.84 149:0.87 150:0.60 151:0.52 152:0.80 153:0.89 154:0.62 155:0.67 158:0.79 159:0.85 161:0.63 162:0.58 164:0.90 165:0.70 167:0.60 169:0.93 172:0.83 173:0.41 176:0.61 177:0.70 179:0.30 181:0.82 186:0.90 187:0.39 189:0.97 190:0.77 192:0.87 195:0.95 196:0.91 201:0.68 202:0.91 206:0.81 208:0.64 212:0.68 215:0.44 216:0.88 220:0.82 222:0.25 232:0.98 235:0.52 238:0.90 241:0.55 242:0.33 243:0.63 245:0.83 247:0.79 248:0.53 253:0.95 255:0.95 256:0.91 259:0.21 260:0.89 261:0.90 265:0.44 266:0.84 267:0.69 268:0.91 271:0.32 276:0.80 279:0.82 282:0.88 283:0.82 284:0.94 285:0.62 290:0.63 297:0.36 300:0.84 +2 1:0.69 6:0.96 7:0.72 8:0.93 9:0.76 11:0.91 12:0.51 17:0.22 18:0.75 20:0.87 21:0.30 27:0.21 28:0.73 29:0.71 30:0.55 31:0.88 34:0.66 36:0.70 37:0.74 39:0.75 43:0.72 45:0.98 66:0.10 69:0.12 70:0.70 71:0.65 74:0.89 78:0.85 79:0.88 81:0.46 83:0.60 86:0.72 91:0.81 96:0.98 97:0.89 98:0.59 99:0.83 100:0.88 101:0.52 104:0.84 106:0.81 108:0.93 111:0.85 114:0.63 117:0.86 120:0.96 122:0.75 123:0.82 124:0.71 126:0.44 127:0.65 129:0.81 133:0.67 135:0.17 137:0.88 139:0.70 140:0.45 144:0.85 146:0.90 147:0.92 149:0.94 150:0.56 151:0.97 152:0.82 153:0.97 154:0.62 155:0.58 158:0.79 159:0.86 161:0.63 162:0.64 164:0.92 165:0.70 167:0.60 169:0.86 172:0.92 173:0.08 176:0.66 177:0.70 179:0.16 181:0.82 186:0.85 187:0.39 189:0.97 190:0.77 191:0.75 192:0.59 196:0.88 201:0.68 202:0.91 204:0.85 206:0.81 208:0.54 212:0.75 215:0.44 216:0.95 222:0.25 230:0.75 232:0.90 233:0.76 235:0.42 238:0.89 241:0.56 242:0.45 243:0.38 244:0.61 245:0.98 247:0.90 248:0.98 253:0.98 255:0.74 256:0.93 259:0.21 260:0.95 261:0.86 265:0.50 266:0.80 267:0.59 268:0.92 271:0.32 276:0.94 279:0.82 283:0.82 284:0.86 285:0.62 290:0.63 297:0.36 300:0.84 +2 1:0.69 7:0.72 11:0.91 12:0.51 17:0.51 18:0.74 21:0.30 27:0.21 28:0.73 29:0.71 30:0.58 34:0.66 36:0.59 37:0.74 39:0.75 43:0.72 45:0.98 66:0.12 69:0.12 70:0.72 71:0.65 74:0.89 81:0.46 83:0.60 86:0.71 91:0.05 98:0.58 99:0.83 101:0.52 108:0.93 114:0.63 122:0.75 123:0.82 124:0.77 126:0.44 127:0.68 129:0.81 133:0.78 135:0.18 139:0.69 144:0.85 146:0.91 147:0.92 149:0.95 150:0.56 154:0.62 155:0.58 159:0.86 161:0.63 165:0.70 167:0.55 172:0.92 173:0.08 176:0.66 177:0.70 178:0.55 179:0.15 181:0.82 187:0.68 192:0.59 201:0.68 202:0.46 204:0.85 208:0.54 212:0.75 215:0.44 216:0.95 222:0.25 230:0.75 233:0.76 235:0.42 241:0.44 242:0.50 243:0.34 244:0.61 245:0.97 247:0.92 253:0.52 259:0.21 265:0.51 266:0.84 267:0.59 271:0.32 276:0.95 290:0.63 297:0.36 300:0.84 +1 1:0.74 7:0.63 9:0.80 11:0.92 12:0.51 17:0.38 18:0.82 21:0.30 27:0.02 28:0.73 29:0.71 30:0.85 31:0.87 32:0.79 34:0.40 36:0.39 37:0.92 39:0.75 41:0.82 43:0.88 44:0.93 45:0.95 48:0.91 64:0.77 66:0.54 69:0.52 70:0.33 71:0.65 74:0.86 76:0.85 77:0.70 79:0.87 81:0.61 83:0.91 85:0.85 86:0.92 91:0.29 96:0.74 98:0.48 101:0.87 108:0.86 114:0.65 120:0.61 122:0.51 123:0.85 124:0.70 126:0.54 127:0.64 129:0.59 133:0.77 135:0.76 137:0.87 139:0.93 140:0.80 144:0.85 145:0.65 146:0.54 147:0.60 149:0.91 150:0.78 151:0.52 153:0.72 154:0.86 155:0.67 159:0.85 161:0.63 162:0.86 164:0.66 165:0.93 167:0.52 172:0.84 173:0.27 176:0.73 177:0.70 179:0.30 181:0.82 187:0.39 190:0.77 192:0.87 195:0.95 196:0.90 201:0.93 202:0.53 208:0.64 212:0.71 215:0.44 216:0.99 220:0.74 222:0.25 230:0.64 233:0.76 235:0.52 241:0.32 242:0.53 243:0.32 245:0.87 247:0.60 248:0.51 253:0.64 255:0.95 256:0.58 259:0.21 260:0.60 265:0.50 266:0.80 267:0.97 268:0.62 271:0.32 276:0.80 281:0.91 282:0.87 284:0.89 285:0.62 290:0.65 297:0.36 300:0.55 +1 1:0.69 8:0.87 11:0.93 12:0.51 17:0.34 18:0.85 21:0.64 27:0.60 28:0.73 29:0.71 30:0.57 32:0.69 34:0.33 36:0.21 37:0.78 39:0.75 43:0.67 45:0.99 66:0.12 69:0.64 70:0.75 71:0.65 74:0.89 77:0.70 81:0.33 83:0.60 86:0.89 91:0.08 97:0.89 98:0.34 99:0.83 101:0.87 108:0.49 114:0.56 122:0.75 123:0.47 124:0.96 126:0.44 127:0.78 129:0.96 133:0.96 135:0.81 139:0.85 144:0.85 145:0.50 146:0.85 147:0.87 149:0.91 150:0.52 154:0.66 155:0.58 158:0.78 159:0.92 161:0.63 165:0.70 167:0.47 172:0.79 173:0.28 176:0.66 177:0.70 178:0.55 179:0.15 181:0.82 187:0.68 192:0.59 195:0.98 201:0.68 208:0.54 212:0.42 215:0.38 216:0.30 222:0.25 235:0.80 241:0.03 242:0.31 243:0.31 245:0.94 247:0.88 253:0.47 254:0.74 259:0.21 265:0.36 266:0.87 267:0.52 271:0.32 276:0.95 279:0.82 282:0.77 283:0.78 290:0.56 297:0.36 300:0.84 +1 1:0.69 11:0.93 12:0.51 17:0.32 18:0.79 21:0.30 27:0.45 28:0.73 29:0.71 30:0.61 32:0.69 34:0.66 36:0.17 37:0.50 39:0.75 43:0.75 45:0.93 66:0.23 69:0.68 70:0.70 71:0.65 74:0.78 77:0.70 81:0.46 83:0.60 85:0.72 86:0.85 91:0.10 97:0.89 98:0.34 99:0.83 101:0.97 108:0.52 114:0.63 122:0.51 123:0.50 124:0.89 126:0.44 127:0.34 129:0.81 133:0.89 135:0.71 139:0.81 144:0.85 145:0.49 146:0.70 147:0.74 149:0.86 150:0.56 154:0.66 155:0.58 158:0.78 159:0.77 161:0.63 165:0.70 167:0.56 172:0.63 173:0.45 176:0.66 177:0.70 178:0.55 179:0.26 181:0.82 187:0.68 192:0.59 195:0.94 201:0.68 208:0.54 212:0.42 215:0.44 216:0.77 222:0.25 235:0.42 241:0.46 242:0.31 243:0.43 245:0.81 247:0.90 253:0.50 259:0.21 265:0.36 266:0.89 267:0.36 271:0.32 276:0.79 279:0.82 282:0.77 283:0.78 290:0.63 297:0.36 300:0.55 +2 1:0.69 7:0.72 8:0.89 9:0.76 11:0.91 12:0.51 17:0.40 18:0.75 21:0.30 27:0.21 28:0.73 29:0.71 30:0.55 34:0.70 36:0.63 37:0.74 39:0.75 43:0.72 45:0.98 66:0.10 69:0.12 70:0.70 71:0.65 74:0.88 81:0.46 83:0.60 86:0.72 91:0.07 96:0.69 98:0.59 99:0.83 101:0.52 108:0.93 114:0.63 120:0.55 122:0.75 123:0.82 124:0.71 126:0.44 127:0.66 129:0.81 133:0.67 135:0.18 139:0.70 140:0.45 144:0.85 146:0.90 147:0.92 149:0.94 150:0.56 151:0.51 153:0.48 154:0.62 155:0.58 159:0.86 161:0.63 162:0.62 164:0.54 165:0.70 167:0.54 172:0.92 173:0.08 176:0.66 177:0.70 179:0.16 181:0.82 187:0.68 190:0.77 192:0.59 201:0.68 202:0.50 204:0.85 208:0.54 212:0.75 215:0.44 216:0.95 220:0.87 222:0.25 230:0.75 233:0.76 235:0.42 241:0.41 242:0.45 243:0.38 244:0.61 245:0.98 247:0.90 248:0.50 253:0.53 255:0.74 256:0.45 259:0.21 260:0.55 265:0.50 266:0.80 267:0.69 268:0.46 271:0.32 276:0.94 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.74 6:0.96 7:0.81 8:0.93 9:0.80 11:0.57 12:0.51 17:0.22 20:0.98 21:0.30 27:0.21 28:0.91 30:0.60 34:0.66 36:0.70 37:0.74 39:0.75 41:0.99 43:0.98 60:0.87 66:0.10 69:0.12 70:0.68 74:0.99 78:0.85 81:0.65 83:0.90 85:0.99 91:0.81 96:0.98 98:0.56 100:0.95 101:0.86 104:0.84 106:0.81 108:0.93 111:0.91 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.82 124:0.71 126:0.54 127:0.61 129:0.81 133:0.67 135:0.17 140:0.45 146:0.90 147:0.92 149:0.99 150:0.79 151:0.99 152:0.97 153:0.97 154:0.98 155:0.67 158:0.92 159:0.85 161:0.77 162:0.64 164:0.94 165:0.84 167:0.61 169:0.91 172:0.91 173:0.08 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.39 189:0.97 191:0.75 192:0.59 201:0.74 202:0.91 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.97 241:0.56 242:0.51 243:0.38 245:0.98 247:0.67 248:0.99 253:0.99 255:0.79 256:0.93 259:0.21 260:0.96 261:0.96 265:0.48 266:0.80 267:0.59 268:0.92 276:0.93 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.84 +3 1:0.74 6:0.93 8:0.76 9:0.80 11:0.57 12:0.51 20:0.76 27:0.14 28:0.91 30:0.62 34:0.53 36:0.34 37:0.67 39:0.75 41:0.99 43:0.86 60:0.95 66:0.06 69:0.59 70:0.76 74:0.92 78:0.91 81:0.87 83:0.90 85:0.98 91:0.90 96:0.99 98:0.27 100:0.90 101:0.83 108:0.78 111:0.95 114:0.98 120:0.98 122:0.43 123:0.62 124:0.93 126:0.54 127:0.75 129:0.96 133:0.93 135:0.83 138:0.99 140:0.45 145:0.82 146:0.29 147:0.36 149:0.95 150:0.92 151:1.00 152:0.98 153:0.98 154:0.83 155:0.67 158:0.92 159:0.86 161:0.77 162:0.68 164:0.96 165:0.92 167:0.57 169:0.90 172:0.61 173:0.31 176:0.73 177:0.38 179:0.32 181:0.80 186:0.79 187:0.39 189:0.92 191:0.79 192:0.59 201:0.74 202:0.94 208:0.64 212:0.51 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.46 242:0.52 243:0.15 245:0.82 247:0.67 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.94 267:0.36 268:0.95 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +0 6:0.84 8:0.92 9:0.80 12:0.51 17:0.01 20:0.80 21:0.78 27:0.05 28:0.91 34:0.19 36:0.67 37:0.94 39:0.75 41:0.98 78:0.75 83:0.84 91:0.99 96:0.95 100:0.80 111:0.75 120:0.91 135:0.30 140:0.99 151:0.97 152:0.77 153:0.90 155:0.67 161:0.77 162:0.45 164:0.89 167:0.86 169:0.81 175:0.91 178:0.55 181:0.80 186:0.75 189:0.90 191:0.98 192:0.59 202:0.99 208:0.64 216:0.82 222:0.25 235:0.12 238:0.95 241:0.92 244:0.83 248:0.95 253:0.93 255:0.79 256:0.88 257:0.84 259:0.21 260:0.92 261:0.80 267:0.79 268:0.86 277:0.87 285:0.62 +1 1:0.74 11:0.57 12:0.51 17:0.30 21:0.54 27:0.45 28:0.91 30:0.21 32:0.80 34:0.75 36:0.17 37:0.50 39:0.75 43:0.82 60:0.87 66:0.51 69:0.70 70:0.83 74:0.94 77:0.70 81:0.54 83:0.86 85:0.95 91:0.06 98:0.63 101:0.78 108:0.53 114:0.77 122:0.67 123:0.51 124:0.87 126:0.54 127:0.70 129:0.93 133:0.91 135:0.72 145:0.44 146:0.95 147:0.96 149:0.90 150:0.71 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 165:0.79 167:0.58 172:0.77 173:0.41 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 195:0.96 201:0.74 208:0.64 212:0.15 215:0.58 216:0.77 222:0.25 235:0.68 241:0.50 242:0.45 243:0.61 245:0.73 247:0.67 253:0.75 259:0.21 265:0.63 266:0.94 267:0.44 276:0.77 279:0.86 282:0.88 283:0.71 290:0.77 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.97 7:0.78 8:0.94 9:0.80 11:0.57 12:0.51 17:0.15 20:0.75 21:0.30 27:0.02 28:0.91 30:0.72 32:0.80 34:0.47 36:0.19 37:0.92 39:0.75 41:0.97 43:0.89 60:0.87 66:0.67 69:0.51 70:0.47 74:0.94 76:0.85 77:0.70 78:0.89 81:0.65 83:0.87 85:0.97 91:0.86 96:0.94 98:0.60 100:0.96 101:0.83 108:0.39 111:0.91 114:0.86 120:0.88 122:0.43 123:0.37 124:0.47 126:0.54 127:0.46 129:0.59 133:0.47 135:0.66 140:0.45 145:0.42 146:0.86 147:0.88 149:0.95 150:0.79 151:0.96 152:0.75 153:0.86 154:0.87 155:0.67 158:0.87 159:0.73 161:0.77 162:0.60 164:0.90 165:0.84 167:0.74 169:0.90 172:0.76 173:0.33 176:0.73 177:0.38 179:0.41 181:0.80 186:0.90 187:0.39 189:1.00 191:0.87 192:0.59 195:0.89 201:0.74 202:0.87 208:0.64 212:0.49 215:0.72 216:0.99 220:0.87 222:0.25 230:0.81 233:0.76 235:0.42 238:0.96 241:0.72 242:0.55 243:0.71 245:0.84 247:0.60 248:0.93 253:0.96 255:0.79 256:0.87 259:0.21 260:0.88 261:0.79 265:0.61 266:0.80 267:0.28 268:0.88 276:0.66 279:0.86 282:0.88 283:0.71 285:0.62 290:0.86 297:0.36 299:0.88 300:0.55 +3 1:0.74 7:0.81 11:0.57 12:0.51 17:0.51 21:0.30 27:0.21 28:0.91 30:0.62 34:0.66 36:0.59 37:0.74 39:0.75 43:0.98 66:0.12 69:0.12 70:0.72 74:0.99 81:0.65 83:0.75 85:0.99 91:0.05 98:0.55 101:0.86 108:0.93 114:0.86 121:0.97 122:0.57 123:0.82 124:0.82 126:0.54 127:0.64 129:0.89 133:0.83 135:0.18 146:0.72 147:0.77 149:0.99 150:0.79 154:0.98 155:0.67 159:0.86 161:0.77 165:0.84 167:0.57 172:0.92 173:0.08 176:0.73 177:0.38 178:0.55 179:0.15 181:0.80 187:0.68 192:0.59 201:0.74 202:0.46 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 222:0.25 230:0.87 233:0.76 235:0.42 241:0.44 242:0.52 243:0.28 245:0.97 247:0.67 253:0.78 259:0.21 265:0.48 266:0.87 267:0.59 276:0.94 290:0.86 297:0.36 300:0.94 +3 1:0.74 8:0.66 9:0.80 11:0.57 12:0.51 17:0.04 21:0.22 27:0.14 28:0.91 30:0.71 32:0.80 34:0.63 36:0.30 37:0.67 39:0.75 41:0.98 43:0.86 60:0.99 66:0.52 69:0.58 70:0.66 74:0.86 77:0.70 78:0.80 81:0.70 83:0.88 85:0.96 91:0.77 96:0.95 98:0.53 101:0.83 108:0.86 111:0.79 114:0.90 120:0.91 122:0.43 123:0.85 124:0.73 126:0.54 127:0.47 129:0.89 133:0.78 135:0.98 140:0.45 145:0.94 146:0.13 147:0.18 149:0.89 150:0.82 151:0.91 153:0.72 154:0.84 155:0.67 158:0.92 159:0.71 161:0.77 162:0.72 164:0.91 165:0.86 167:0.58 169:0.95 172:0.67 173:0.42 176:0.73 177:0.38 179:0.46 181:0.80 187:0.39 191:0.75 192:0.59 195:0.88 201:0.74 202:0.85 208:0.64 212:0.36 215:0.79 216:0.93 220:0.74 222:0.25 235:0.31 238:0.86 241:0.47 242:0.61 243:0.14 245:0.74 247:0.39 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.91 265:0.54 266:0.71 267:0.87 268:0.88 276:0.66 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.84 +0 1:0.74 6:0.98 8:0.84 9:0.80 11:0.57 12:0.51 17:0.16 20:0.74 21:0.68 27:0.05 28:0.91 30:0.54 32:0.80 34:0.42 36:0.71 37:0.93 39:0.75 41:0.98 43:0.59 55:0.84 60:0.97 66:0.94 69:0.93 70:0.66 74:0.88 77:0.70 78:0.78 81:0.47 83:0.89 85:0.94 91:0.66 96:0.95 98:0.76 100:0.87 101:0.79 108:0.87 111:0.79 114:0.70 120:0.91 122:0.67 123:0.86 126:0.54 127:0.25 129:0.05 135:0.76 140:0.80 145:0.50 146:0.96 147:0.97 149:0.77 150:0.65 151:0.96 152:0.73 153:0.86 154:0.48 155:0.67 158:0.87 159:0.67 161:0.77 162:0.59 164:0.92 165:0.70 167:0.60 169:0.89 172:0.85 173:0.88 176:0.73 177:0.38 178:0.42 179:0.24 181:0.80 186:0.82 187:0.68 192:0.59 195:0.93 201:0.74 202:0.89 208:0.64 212:0.37 215:0.49 216:0.84 220:0.99 222:0.25 232:0.96 235:0.84 241:0.53 242:0.45 243:0.95 247:0.67 248:0.95 253:0.96 255:0.79 256:0.89 259:0.21 260:0.92 261:0.75 265:0.76 266:0.71 267:0.44 268:0.90 276:0.76 279:0.86 282:0.88 283:0.71 285:0.62 290:0.70 297:0.36 299:0.88 300:0.70 +2 1:0.74 7:0.81 9:0.80 11:0.57 12:0.51 17:0.06 21:0.13 27:0.13 28:0.91 30:0.86 32:0.80 34:0.44 36:0.44 37:0.79 39:0.75 41:0.97 43:0.81 60:0.87 66:0.84 69:0.70 70:0.28 74:0.86 77:0.70 81:0.76 83:0.89 85:0.91 91:0.75 96:0.95 98:0.63 101:0.83 108:0.53 114:0.92 120:0.90 121:0.90 122:0.43 123:0.51 126:0.54 127:0.19 129:0.05 135:0.89 140:0.45 145:0.54 146:0.60 147:0.65 149:0.86 150:0.85 151:0.93 153:0.78 154:0.76 155:0.67 158:0.92 159:0.22 161:0.77 162:0.83 164:0.89 165:0.88 167:0.72 172:0.54 173:0.78 176:0.73 177:0.38 179:0.47 181:0.80 187:0.39 192:0.59 195:0.63 201:0.74 202:0.81 204:0.89 208:0.64 212:0.52 215:0.84 216:0.83 220:0.74 222:0.25 230:0.87 233:0.76 235:0.22 241:0.70 242:0.63 243:0.85 247:0.30 248:0.95 253:0.96 255:0.79 256:0.84 259:0.21 260:0.91 265:0.64 266:0.38 267:0.52 268:0.87 276:0.41 279:0.86 282:0.88 283:0.82 285:0.62 290:0.92 297:0.36 299:0.88 300:0.25 +2 6:0.83 8:0.63 9:0.80 11:0.57 12:0.51 17:0.08 20:0.80 21:0.78 27:0.31 28:0.91 30:0.95 34:0.25 36:0.85 37:0.30 39:0.75 41:0.97 43:0.86 66:0.54 69:0.59 74:0.44 78:0.79 83:0.83 85:0.72 91:0.92 96:0.94 98:0.16 100:0.83 101:0.76 104:0.58 108:0.45 111:0.79 120:0.91 123:0.43 127:0.09 129:0.05 135:0.20 140:0.45 146:0.13 147:0.18 149:0.49 151:0.96 152:0.77 153:0.86 154:0.83 155:0.67 159:0.08 161:0.77 162:0.65 164:0.89 167:0.83 169:0.81 172:0.10 173:0.93 177:0.38 179:0.18 181:0.80 186:0.79 189:0.85 191:0.81 192:0.59 202:0.85 208:0.64 216:0.19 220:0.74 222:0.25 232:0.78 235:0.22 238:0.92 241:0.82 243:0.63 248:0.94 253:0.92 255:0.79 256:0.86 259:0.21 260:0.91 261:0.81 265:0.17 267:0.23 268:0.86 285:0.62 300:0.08 +2 1:0.74 6:0.93 8:0.83 9:0.80 11:0.57 12:0.51 17:0.12 20:0.78 21:0.05 27:0.14 28:0.91 30:0.53 32:0.80 34:0.26 36:0.54 37:0.67 39:0.75 41:0.88 43:0.86 66:0.15 69:0.59 70:0.59 74:0.97 77:0.70 78:0.85 81:0.81 83:0.81 85:0.99 91:0.08 96:0.86 98:0.45 100:0.92 101:0.84 108:0.96 111:0.87 114:0.95 120:0.77 122:0.57 123:0.95 124:0.96 126:0.54 127:0.83 129:0.99 133:0.97 135:0.83 140:0.45 145:0.47 146:0.22 147:0.28 149:0.97 150:0.88 151:0.92 152:0.98 153:0.72 154:0.83 155:0.67 159:0.93 161:0.77 162:0.76 164:0.69 165:0.90 167:0.47 169:0.90 172:0.82 173:0.20 176:0.73 177:0.38 179:0.16 181:0.80 186:0.85 187:0.68 189:0.91 192:0.59 195:0.99 201:0.74 202:0.58 208:0.64 212:0.41 215:0.91 216:0.93 222:0.25 235:0.12 238:0.95 241:0.01 242:0.44 243:0.08 245:0.92 247:0.60 248:0.85 253:0.91 255:0.79 256:0.58 259:0.21 260:0.78 261:0.96 265:0.47 266:0.92 267:0.28 268:0.62 276:0.95 282:0.88 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.74 6:0.99 8:0.96 9:0.80 11:0.57 12:0.51 17:0.04 20:0.79 21:0.22 27:0.06 28:0.91 30:0.40 32:0.80 34:0.59 36:0.98 37:0.85 39:0.75 41:0.99 43:0.83 60:0.99 66:0.60 69:0.66 70:0.80 74:0.97 77:0.89 78:0.85 81:0.75 83:0.89 85:0.97 91:0.75 96:0.96 98:0.42 100:0.92 101:0.81 104:0.96 106:0.81 108:0.50 111:0.87 114:0.69 117:0.86 120:0.93 122:0.57 123:0.48 124:0.67 126:0.54 127:0.56 129:0.81 133:0.76 135:0.90 138:0.96 140:0.45 145:0.56 146:0.81 147:0.84 149:0.96 150:0.84 151:0.97 152:0.77 153:0.89 154:0.79 155:0.67 158:0.92 159:0.84 161:0.77 162:0.57 164:0.93 165:0.88 167:0.61 169:0.90 172:0.82 173:0.41 176:0.56 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.99 192:0.59 195:0.95 201:0.74 202:0.91 206:0.81 208:0.64 212:0.68 215:0.72 216:0.88 220:0.82 222:0.25 232:0.98 235:0.52 238:0.94 241:0.55 242:0.44 243:0.67 245:0.84 247:0.60 248:0.96 253:0.98 255:0.79 256:0.91 259:0.21 260:0.93 261:0.85 265:0.44 266:0.75 267:0.69 268:0.91 276:0.74 279:0.86 282:0.88 283:0.82 285:0.62 290:0.69 297:0.36 299:0.97 300:0.70 +2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.57 12:0.51 17:0.01 20:0.98 27:0.14 28:0.91 30:0.65 34:0.67 36:0.29 37:0.67 39:0.75 41:0.99 43:0.86 60:0.98 66:0.06 69:0.57 70:0.74 74:0.93 78:0.85 81:0.87 83:0.90 85:0.98 91:0.63 96:0.99 98:0.25 100:0.95 101:0.83 108:0.78 111:0.90 114:0.98 120:0.98 122:0.43 123:0.86 124:0.93 126:0.54 127:0.74 129:0.96 133:0.93 135:0.85 138:0.99 140:0.45 145:0.82 146:0.22 147:0.28 149:0.96 150:0.92 151:1.00 152:0.98 153:0.98 154:0.84 155:0.67 158:0.92 159:0.86 161:0.77 162:0.66 164:0.96 165:0.92 167:0.56 169:0.90 172:0.61 173:0.30 176:0.73 177:0.38 179:0.31 181:0.80 186:0.85 187:0.39 189:0.94 192:0.59 201:0.74 202:0.94 208:0.64 212:0.50 215:0.96 216:0.93 222:0.25 235:0.05 238:0.97 241:0.41 242:0.53 243:0.15 245:0.83 247:0.67 248:0.99 253:1.00 255:0.79 256:0.94 259:0.21 260:0.98 261:0.96 265:0.19 266:0.92 267:0.28 268:0.95 276:0.81 279:0.86 283:0.82 285:0.62 290:0.98 297:0.36 300:0.84 +2 1:0.74 6:0.92 7:0.81 8:0.80 9:0.80 11:0.57 12:0.51 17:0.47 20:0.91 21:0.30 27:0.50 28:0.91 30:0.52 32:0.80 34:0.44 36:0.66 37:0.60 39:0.75 41:0.92 43:0.88 60:0.87 66:0.07 69:0.53 70:0.85 74:0.99 77:0.96 78:0.77 81:0.65 83:0.82 85:1.00 91:0.18 96:0.84 98:0.37 100:0.81 101:0.85 104:0.84 106:0.81 108:0.97 111:0.77 114:0.86 117:0.86 120:0.75 121:0.90 122:0.57 123:0.96 124:0.96 126:0.54 127:0.90 129:0.98 133:0.96 135:0.69 140:0.45 145:0.76 146:0.59 147:0.65 149:0.98 150:0.79 151:0.92 152:0.85 153:0.72 154:0.86 155:0.67 158:0.92 159:0.94 161:0.77 162:0.75 164:0.76 165:0.84 167:0.46 169:0.79 172:0.91 173:0.16 176:0.73 177:0.38 179:0.12 181:0.80 186:0.78 187:0.39 189:0.93 192:0.59 195:0.99 201:0.74 202:0.67 204:0.89 206:0.81 208:0.64 212:0.60 215:0.72 216:0.86 220:0.87 222:0.25 230:0.87 232:0.90 233:0.76 235:0.42 238:0.91 242:0.43 243:0.13 245:0.96 247:0.67 248:0.82 253:0.90 255:0.79 256:0.68 259:0.21 260:0.76 261:0.84 265:0.31 266:0.92 267:0.52 268:0.71 276:0.97 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.97 300:0.94 +1 1:0.74 6:0.94 7:0.81 8:0.65 9:0.80 11:0.57 12:0.51 17:0.26 20:0.77 21:0.30 27:0.11 28:0.91 30:0.74 34:0.53 36:0.86 37:0.79 39:0.75 41:0.88 43:0.87 60:0.97 66:0.43 69:0.56 70:0.42 74:0.95 78:0.78 81:0.65 83:0.86 85:0.99 91:0.20 96:0.87 98:0.52 100:0.82 101:0.86 104:0.89 106:0.81 108:0.79 111:0.78 114:0.86 117:0.86 120:0.78 121:0.90 122:0.57 123:0.77 124:0.82 126:0.54 127:0.55 129:0.93 133:0.84 135:0.60 140:0.45 145:0.52 146:0.13 147:0.18 149:0.97 150:0.79 151:0.93 152:0.75 153:0.77 154:0.85 155:0.67 158:0.92 159:0.76 161:0.77 162:0.86 164:0.70 165:0.84 167:0.51 169:0.80 172:0.79 173:0.37 176:0.73 177:0.38 179:0.27 181:0.80 186:0.79 187:0.39 189:0.94 192:0.59 201:0.74 202:0.55 204:0.89 206:0.81 208:0.64 212:0.55 215:0.72 216:0.85 222:0.25 230:0.87 232:0.93 233:0.76 235:0.42 238:0.88 241:0.18 242:0.61 243:0.09 245:0.87 247:0.39 248:0.85 253:0.91 255:0.79 256:0.58 259:0.21 260:0.79 261:0.77 265:0.53 266:0.71 267:0.52 268:0.64 276:0.83 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43 +1 1:0.74 11:0.57 12:0.51 17:0.26 21:0.40 27:0.45 28:0.91 30:0.31 34:0.68 36:0.17 37:0.50 39:0.75 43:0.82 55:0.71 60:0.87 66:0.10 69:0.69 70:0.81 74:0.93 81:0.61 83:0.84 85:0.93 91:0.04 98:0.32 101:0.76 108:0.82 114:0.82 122:0.67 123:0.63 124:0.93 126:0.54 127:0.71 129:0.96 133:0.93 135:0.78 145:0.49 146:0.13 147:0.18 149:0.83 150:0.76 154:0.77 155:0.67 158:0.87 159:0.88 161:0.77 163:0.81 165:0.83 167:0.52 172:0.54 173:0.40 176:0.73 177:0.38 178:0.55 179:0.38 181:0.80 187:0.68 192:0.59 201:0.74 208:0.64 212:0.18 215:0.67 216:0.77 222:0.25 235:0.52 241:0.27 242:0.61 243:0.11 245:0.76 247:0.60 253:0.76 259:0.21 265:0.34 266:0.94 267:0.65 276:0.77 279:0.86 283:0.71 290:0.82 297:0.36 300:0.70 +1 1:0.57 2:1.00 8:0.91 9:0.72 10:0.97 11:0.54 12:0.20 17:0.34 18:0.78 21:0.54 23:0.97 27:0.35 29:0.77 30:0.55 34:0.61 36:0.64 37:0.59 39:0.98 43:0.27 44:0.88 45:0.89 46:0.88 56:0.97 62:0.98 66:0.83 69:0.55 70:0.75 71:0.73 74:0.51 75:0.95 77:0.70 80:0.98 81:0.33 82:0.99 83:0.60 86:0.75 88:0.98 91:0.58 98:0.83 101:0.69 102:0.96 107:0.92 108:0.42 110:0.89 122:0.99 123:0.40 124:0.39 125:0.88 126:0.31 127:0.66 128:0.97 129:0.05 133:0.45 135:0.97 138:0.96 139:0.77 140:0.80 143:0.99 145:0.92 146:0.93 147:0.95 149:0.67 150:0.37 151:0.85 153:0.48 154:0.26 155:0.57 159:0.69 160:0.88 162:0.86 168:0.97 170:0.82 172:0.93 173:0.43 174:0.98 175:0.75 177:0.88 179:0.23 187:0.87 190:0.98 192:0.56 193:0.97 195:0.83 197:0.99 201:0.54 202:0.50 204:0.83 205:0.97 208:0.64 212:0.78 216:0.45 220:0.74 222:0.25 226:0.96 235:0.74 236:0.91 239:0.96 241:0.12 242:0.27 243:0.84 244:0.83 245:0.90 247:0.92 248:0.76 253:0.41 254:0.84 255:0.70 256:0.58 257:0.65 259:0.21 264:0.93 265:0.83 266:0.75 267:0.97 268:0.59 271:0.90 275:0.99 276:0.88 277:0.69 279:0.77 281:0.91 282:0.73 285:0.49 289:0.88 294:0.99 295:0.98 298:0.99 300:0.84 +1 7:0.66 8:0.90 10:0.99 11:0.54 12:0.20 17:0.57 18:0.94 21:0.40 23:0.98 27:0.28 29:0.77 30:0.56 32:0.67 33:0.97 34:0.29 36:0.98 37:0.86 39:0.98 43:0.60 44:0.88 45:0.98 46:0.88 62:0.99 66:0.63 69:0.81 70:0.78 71:0.73 74:0.68 75:0.95 77:0.70 81:0.32 86:0.91 91:0.27 98:0.76 101:0.87 102:0.96 107:0.95 108:0.65 110:0.90 120:0.59 122:0.91 123:0.63 124:0.65 125:0.88 126:0.31 127:0.74 128:0.98 129:0.59 133:0.76 135:1.00 139:0.89 140:0.45 145:0.74 146:0.97 147:0.61 149:0.80 150:0.33 151:0.56 153:0.48 154:0.20 159:0.84 160:0.88 162:0.61 164:0.71 168:0.98 170:0.82 172:0.96 173:0.63 174:0.99 177:0.88 179:0.15 187:0.68 190:0.99 191:0.70 192:0.49 193:0.96 195:0.95 197:0.99 201:0.55 202:0.72 205:0.98 212:0.81 215:0.67 216:0.61 220:0.91 222:0.25 226:0.98 230:0.68 233:0.76 235:0.52 236:0.92 239:0.98 241:0.35 242:0.15 243:0.21 244:0.61 245:0.97 247:0.99 248:0.57 253:0.50 254:0.84 255:0.69 256:0.71 257:0.65 259:0.21 260:0.59 264:0.93 265:0.76 266:0.80 267:0.96 268:0.73 271:0.90 275:0.98 276:0.95 282:0.75 283:0.82 285:0.49 289:0.89 291:0.97 294:0.96 297:0.36 300:0.94 +1 1:0.57 10:0.96 11:0.54 12:0.20 17:0.61 18:0.91 21:0.71 27:0.11 29:0.77 30:0.55 33:0.97 34:0.96 36:0.99 37:0.84 39:0.98 43:0.44 45:0.99 46:0.88 64:0.77 66:0.94 69:0.90 70:0.69 71:0.73 74:0.61 81:0.50 83:0.60 86:0.89 91:0.20 98:0.88 99:0.95 101:0.69 107:0.94 108:0.81 110:0.90 114:0.64 122:0.91 123:0.79 124:0.36 125:0.88 126:0.43 127:0.83 129:0.05 133:0.38 135:0.82 139:0.84 146:0.99 147:0.41 149:0.73 150:0.38 154:0.33 155:0.46 159:0.86 160:0.88 165:0.70 170:0.82 172:0.98 173:0.79 174:0.98 176:0.62 177:0.38 178:0.55 179:0.14 187:0.87 192:0.45 197:0.97 201:0.56 202:0.36 208:0.54 212:0.73 216:0.65 222:0.25 235:0.95 236:0.92 239:0.94 241:0.39 242:0.14 243:0.07 244:0.61 245:0.92 247:0.98 253:0.53 257:0.93 259:0.21 264:0.93 265:0.88 266:0.75 267:0.91 271:0.90 275:0.99 276:0.95 289:0.89 290:0.64 291:0.97 294:0.98 300:0.84 +1 10:0.98 11:0.48 12:0.20 17:0.64 18:0.89 21:0.78 27:0.10 29:0.77 30:0.19 32:0.64 33:0.97 34:0.56 36:0.95 37:0.81 39:0.98 43:0.26 45:0.93 46:0.88 55:0.59 66:0.74 69:0.19 70:0.91 71:0.73 74:0.59 75:0.96 83:0.60 86:0.82 91:0.46 97:0.94 98:0.82 101:0.52 102:0.94 107:0.93 108:0.15 110:0.90 120:0.57 122:0.98 123:0.15 124:0.47 125:0.88 127:0.90 129:0.05 133:0.72 135:0.99 139:0.82 140:0.87 145:0.63 146:0.95 147:0.96 149:0.64 151:0.50 153:0.48 154:0.14 155:0.52 158:0.75 159:0.77 160:0.88 162:0.54 164:0.55 170:0.82 172:0.93 173:0.14 174:0.99 177:0.96 178:0.48 179:0.23 187:0.99 190:0.99 191:0.77 192:0.51 193:0.97 195:0.89 197:0.99 202:0.56 204:0.81 208:0.54 212:0.78 216:0.80 219:0.90 220:0.91 222:0.25 226:0.98 235:0.52 239:0.97 241:0.21 242:0.45 243:0.77 244:0.83 245:0.88 247:0.91 248:0.50 253:0.45 254:0.97 256:0.45 259:0.21 260:0.57 264:0.93 265:0.82 266:0.54 267:1.00 268:0.46 271:0.90 275:0.95 276:0.89 279:0.77 281:0.91 285:0.45 289:0.89 291:0.97 292:0.95 294:0.94 300:0.84 +1 1:0.59 2:1.00 9:0.71 10:0.97 11:0.48 12:0.20 17:0.32 18:0.91 21:0.13 23:0.98 27:0.30 29:0.77 30:0.57 33:0.97 34:0.31 36:0.99 37:0.53 39:0.98 43:0.21 44:0.86 45:0.96 46:0.88 56:0.97 62:0.98 66:0.26 69:0.62 70:0.83 71:0.73 74:0.61 75:0.95 77:0.70 80:0.98 81:0.37 83:0.54 86:0.87 91:0.31 98:0.57 101:0.52 107:0.94 108:0.83 110:0.90 122:0.97 123:0.82 124:0.84 125:0.88 126:0.31 127:0.61 128:0.96 129:0.81 133:0.83 135:0.95 138:0.95 139:0.84 140:0.45 143:0.99 145:0.58 146:0.51 147:0.57 149:0.47 150:0.41 151:0.71 153:0.77 154:0.46 155:0.55 159:0.80 160:0.88 162:0.86 168:0.96 170:0.82 172:0.84 173:0.41 174:0.98 177:0.96 179:0.18 187:0.98 190:0.98 192:0.51 193:0.95 195:0.92 197:0.98 201:0.57 202:0.57 204:0.78 205:0.98 208:0.54 212:0.74 216:0.88 220:0.74 222:0.25 226:0.96 235:0.22 236:0.91 239:0.96 241:0.18 242:0.15 243:0.27 244:0.83 245:0.95 247:0.99 248:0.73 253:0.46 254:0.80 255:0.70 256:0.64 259:0.21 264:0.93 265:0.58 266:0.75 267:0.98 268:0.66 271:0.90 275:0.98 276:0.91 279:0.79 281:0.91 282:0.73 285:0.49 286:0.99 289:0.89 291:0.97 294:0.97 295:0.98 298:0.99 300:0.84 +1 2:0.99 9:0.71 10:0.92 11:0.48 12:0.20 17:0.30 18:0.70 21:0.78 23:0.97 27:0.11 29:0.77 30:0.30 33:0.97 34:0.97 36:0.72 37:0.84 39:0.98 43:0.22 45:0.85 46:0.88 55:0.71 62:0.97 66:0.53 69:0.91 70:0.81 71:0.73 74:0.36 86:0.69 91:0.67 98:0.62 101:0.52 107:0.91 108:0.81 110:0.89 120:0.64 122:0.98 123:0.80 124:0.64 125:0.88 127:0.79 128:0.97 129:0.05 133:0.60 135:0.97 138:0.98 139:0.66 140:0.98 143:0.99 146:0.88 147:0.41 149:0.26 151:0.81 153:0.46 154:0.16 155:0.53 159:0.79 160:0.88 162:0.46 164:0.54 168:0.97 170:0.82 172:0.76 173:0.85 174:0.97 177:0.05 178:0.54 179:0.42 187:0.68 190:0.95 192:0.56 197:0.96 202:0.82 205:0.97 208:0.49 212:0.54 216:0.65 220:0.93 222:0.25 235:0.22 239:0.91 241:0.66 242:0.29 243:0.11 244:0.83 245:0.84 247:0.96 248:0.73 253:0.32 254:0.88 255:0.72 256:0.58 257:0.97 259:0.21 260:0.64 264:0.93 265:0.63 266:0.80 267:0.69 268:0.58 271:0.90 275:0.96 276:0.74 285:0.55 286:0.99 289:0.88 291:0.97 294:0.96 295:0.98 298:0.99 300:0.70 +1 1:0.65 8:0.85 10:0.96 11:0.48 12:0.20 17:0.57 18:0.91 21:0.22 27:0.45 29:0.77 30:0.71 34:0.68 36:0.38 37:0.50 39:0.98 43:0.29 45:0.97 46:0.88 56:0.97 64:0.77 66:0.81 69:0.69 70:0.64 71:0.73 74:0.57 81:0.59 82:0.98 83:0.54 86:0.87 88:0.98 91:0.17 98:0.81 99:0.83 101:0.69 102:0.94 107:0.94 108:0.52 110:0.90 114:0.80 122:0.91 123:0.50 124:0.39 125:0.88 126:0.53 127:0.52 129:0.05 133:0.36 135:0.81 139:0.83 145:0.49 146:0.95 147:0.96 149:0.74 150:0.49 154:0.48 155:0.50 159:0.72 160:0.88 165:0.63 170:0.82 172:0.93 173:0.55 174:0.98 176:0.62 177:0.96 178:0.55 179:0.21 187:0.68 192:0.49 195:0.87 197:0.98 201:0.64 208:0.64 212:0.78 216:0.77 222:0.25 235:0.52 236:0.92 239:0.95 241:0.18 242:0.21 243:0.83 244:0.83 245:0.91 247:0.98 253:0.59 254:0.74 259:0.21 264:0.93 265:0.81 266:0.71 267:0.44 271:0.90 275:0.97 276:0.87 289:0.89 290:0.80 294:0.97 300:0.84 +1 1:0.68 2:0.99 10:0.99 11:0.54 12:0.20 17:0.77 18:0.85 21:0.13 22:0.93 27:0.28 29:0.77 30:0.38 33:0.97 34:0.88 36:0.90 37:0.86 39:0.98 43:0.24 44:0.88 45:0.93 46:0.88 47:0.93 56:0.97 64:0.77 66:0.62 69:0.45 70:0.87 71:0.73 74:0.61 75:0.96 77:0.70 80:0.99 81:0.65 82:0.98 83:0.60 86:0.83 88:0.98 91:0.25 97:0.89 98:0.67 99:0.83 101:0.69 102:0.95 107:0.94 108:0.35 110:0.90 114:0.76 117:0.86 122:0.98 123:0.34 124:0.65 125:0.88 126:0.53 127:0.82 129:0.05 133:0.73 135:0.98 139:0.84 145:0.63 146:0.87 147:0.89 149:0.34 150:0.57 154:0.55 155:0.57 158:0.75 159:0.74 160:0.88 165:0.63 170:0.82 172:0.94 173:0.31 174:0.99 176:0.60 177:0.96 178:0.55 179:0.19 187:0.39 192:0.57 193:0.96 195:0.86 197:0.99 201:0.67 204:0.84 208:0.64 212:0.89 216:0.61 219:0.90 222:0.25 226:0.95 235:0.60 236:0.92 239:0.98 241:0.02 242:0.13 243:0.68 244:0.61 245:0.94 247:0.98 253:0.59 254:0.80 259:0.21 262:0.93 264:0.93 265:0.68 266:0.71 267:0.89 271:0.90 275:0.99 276:0.92 279:0.79 281:0.86 282:0.73 289:0.89 290:0.76 291:0.97 292:0.87 294:0.99 295:0.98 300:0.94 +1 1:0.59 2:0.99 9:0.71 10:0.97 11:0.48 12:0.20 17:0.32 18:0.91 21:0.13 23:0.95 27:0.30 29:0.77 30:0.57 33:0.97 34:0.28 36:0.99 37:0.53 39:0.98 43:0.21 44:0.86 45:0.96 46:0.88 56:0.97 62:0.97 66:0.26 69:0.63 70:0.83 71:0.73 74:0.60 75:0.95 77:0.70 80:0.98 81:0.37 83:0.54 86:0.87 91:0.22 98:0.57 101:0.52 107:0.94 108:0.83 110:0.90 122:0.97 123:0.82 124:0.84 125:0.88 126:0.31 127:0.61 128:0.96 129:0.81 133:0.83 135:0.95 138:0.95 139:0.84 140:0.45 143:0.99 145:0.58 146:0.51 147:0.57 149:0.47 150:0.41 151:0.65 153:0.48 154:0.46 155:0.54 159:0.80 160:0.88 162:0.86 168:0.96 170:0.82 172:0.84 173:0.42 174:0.98 177:0.96 179:0.18 187:0.95 190:0.98 192:0.50 193:0.95 195:0.92 197:0.98 201:0.57 202:0.36 204:0.78 205:0.95 208:0.54 212:0.74 216:0.88 220:0.74 222:0.25 226:0.95 235:0.22 236:0.91 239:0.96 241:0.07 242:0.15 243:0.27 244:0.83 245:0.95 247:0.99 248:0.63 253:0.46 254:0.80 255:0.69 256:0.45 259:0.21 264:0.93 265:0.58 266:0.75 267:0.98 268:0.46 271:0.90 275:0.98 276:0.91 279:0.75 281:0.91 282:0.73 285:0.49 286:0.99 289:0.89 291:0.97 294:0.97 295:0.98 298:0.99 300:0.84 +1 1:0.69 10:0.99 11:0.64 12:0.20 17:0.32 18:0.91 27:0.51 29:0.77 30:0.84 32:0.80 34:0.66 36:0.70 37:0.70 39:0.98 43:0.79 44:0.93 45:0.66 46:0.97 56:0.97 60:0.87 64:0.77 66:0.46 69:0.72 70:0.45 71:0.73 74:0.89 75:0.99 77:0.70 81:0.81 82:0.99 83:0.72 85:0.97 86:0.97 88:0.99 91:0.06 98:0.71 101:0.96 102:0.98 107:0.95 108:0.75 110:0.90 114:0.98 122:0.67 123:0.74 124:0.76 125:0.88 126:0.54 127:0.62 129:0.81 133:0.74 135:0.98 139:0.97 145:0.97 146:0.62 147:0.05 149:0.99 150:0.62 154:0.72 155:0.53 158:0.92 159:0.79 160:0.88 165:0.93 170:0.82 172:0.91 173:0.54 174:0.97 176:0.73 177:0.05 178:0.55 179:0.18 187:0.68 192:0.57 195:0.92 197:0.97 201:0.68 208:0.64 212:0.61 215:0.96 216:0.55 219:0.95 222:0.25 226:0.98 235:0.42 236:0.97 239:0.99 241:0.03 242:0.19 243:0.06 245:0.96 247:0.96 253:0.76 257:0.97 264:0.93 265:0.72 266:0.75 267:0.65 271:0.90 275:0.99 276:0.92 279:0.80 282:0.88 283:0.82 289:0.89 290:0.98 292:0.95 294:1.00 295:0.99 297:0.36 299:0.88 300:0.84 +1 1:0.58 7:0.65 10:0.97 11:0.54 12:0.20 17:0.73 18:0.74 21:0.30 27:0.28 29:0.77 30:0.30 33:0.97 34:0.85 36:0.98 37:0.86 39:0.98 43:0.26 45:0.87 46:0.88 56:0.97 66:0.77 69:0.45 70:0.96 71:0.73 74:0.41 80:0.98 81:0.40 82:0.98 86:0.74 88:0.98 91:0.22 98:0.85 101:0.69 102:0.94 104:0.94 106:0.81 107:0.93 108:0.35 110:0.90 122:0.85 123:0.33 124:0.43 125:0.88 126:0.43 127:0.85 129:0.05 133:0.59 135:0.99 139:0.70 145:0.70 146:0.97 147:0.98 149:0.41 150:0.40 154:0.55 155:0.47 159:0.81 160:0.88 170:0.82 172:0.94 173:0.25 174:0.98 177:0.96 178:0.55 179:0.21 187:0.39 192:0.46 195:0.91 197:0.98 201:0.60 206:0.81 208:0.49 212:0.75 215:0.67 216:0.61 222:0.25 230:0.67 233:0.66 235:0.52 236:0.92 239:0.95 241:0.01 242:0.13 243:0.79 244:0.61 245:0.92 247:0.98 253:0.36 254:0.80 259:0.21 264:0.93 265:0.85 266:0.84 267:0.59 271:0.90 275:0.99 276:0.91 283:0.67 289:0.88 291:0.97 294:0.99 297:0.36 300:0.94 +1 8:0.74 10:0.97 11:0.54 12:0.20 17:0.85 18:0.82 21:0.54 27:0.52 29:0.77 30:0.53 33:0.97 34:0.93 36:0.64 37:0.64 39:0.98 43:0.46 45:0.91 46:0.88 56:0.97 66:0.88 69:0.56 70:0.79 71:0.73 74:0.51 75:0.96 80:0.98 81:0.26 83:0.60 86:0.81 91:0.22 97:0.89 98:0.71 101:0.87 102:0.94 107:0.92 108:0.43 110:0.90 122:0.97 123:0.42 124:0.37 125:0.88 126:0.23 127:0.54 129:0.05 133:0.36 135:0.98 139:0.78 145:0.50 146:0.82 147:0.85 149:0.50 150:0.28 154:0.44 155:0.54 159:0.59 160:0.88 170:0.82 172:0.84 173:0.50 174:0.96 177:0.96 178:0.55 179:0.39 187:0.68 192:0.55 193:0.97 195:0.77 197:0.97 204:0.83 208:0.64 212:0.85 216:0.38 222:0.25 226:0.96 235:0.60 239:0.96 241:0.07 242:0.17 243:0.89 244:0.83 245:0.64 247:0.92 253:0.41 254:0.86 259:0.21 264:0.93 265:0.71 266:0.38 267:0.99 271:0.90 275:0.96 276:0.68 279:0.75 281:0.91 289:0.88 291:0.97 294:0.98 295:0.98 300:0.84 +1 1:0.66 9:0.71 10:0.97 11:0.48 12:0.20 17:0.55 18:0.80 21:0.13 23:0.98 27:0.10 29:0.77 30:0.21 33:0.97 34:0.75 36:0.90 37:0.81 39:0.98 43:0.28 45:0.89 46:0.88 56:0.97 62:0.97 66:0.89 69:0.17 70:0.94 71:0.73 74:0.45 75:0.95 80:0.98 81:0.46 83:0.54 86:0.78 91:0.49 98:0.88 101:0.52 102:0.94 107:0.92 108:0.14 110:0.89 122:0.98 123:0.13 124:0.36 125:0.88 126:0.31 127:0.79 128:0.96 129:0.05 133:0.36 135:0.99 138:0.97 139:0.73 140:0.90 143:0.98 145:0.45 146:0.90 147:0.92 149:0.44 150:0.50 151:0.57 153:0.48 154:0.46 155:0.53 159:0.59 160:0.88 162:0.58 168:0.96 170:0.82 172:0.85 173:0.20 174:0.97 177:0.96 178:0.48 179:0.42 187:1.00 190:0.99 192:0.47 193:0.95 195:0.76 197:0.98 201:0.62 202:0.67 205:0.98 208:0.49 212:0.75 216:0.80 220:0.82 222:0.25 226:0.96 235:0.42 236:0.91 239:0.95 241:0.44 242:0.29 243:0.89 244:0.83 245:0.65 247:0.93 248:0.57 253:0.38 254:0.80 255:0.69 256:0.64 259:0.21 264:0.93 265:0.88 266:0.47 267:1.00 268:0.65 271:0.90 275:0.95 276:0.75 285:0.49 289:0.88 291:0.97 294:0.96 300:0.84 +1 1:0.64 7:0.70 10:0.95 11:0.51 12:0.20 17:0.61 18:0.82 21:0.40 22:0.93 27:0.30 29:0.77 30:0.66 33:0.97 34:0.34 36:0.36 37:0.62 39:0.98 43:0.63 45:0.94 46:0.93 47:0.93 48:0.91 64:0.77 66:0.27 69:0.81 70:0.87 71:0.73 74:0.60 75:0.97 81:0.66 83:0.72 86:0.81 91:0.18 97:0.97 98:0.39 99:0.99 101:0.96 102:0.94 104:0.80 106:0.81 107:0.93 108:0.65 110:0.90 114:0.78 117:0.86 122:0.91 123:0.62 124:0.89 125:0.88 126:0.53 127:0.36 129:0.81 133:0.89 135:0.96 139:0.83 145:0.82 146:0.79 147:0.25 149:0.66 150:0.48 154:0.35 155:0.53 158:0.77 159:0.81 160:0.88 165:0.86 170:0.82 172:0.67 173:0.58 174:0.95 176:0.63 177:0.88 178:0.55 179:0.27 187:0.68 192:0.58 193:0.98 195:0.96 197:0.95 201:0.63 204:0.81 206:0.81 208:0.64 212:0.40 215:0.67 216:0.44 219:0.91 222:0.25 226:0.98 230:0.73 233:0.76 235:0.68 236:0.94 239:0.96 242:0.19 243:0.19 244:0.61 245:0.81 247:0.96 253:0.59 254:0.84 257:0.65 259:0.21 262:0.93 264:0.93 265:0.41 266:0.84 267:0.23 271:0.90 275:0.97 276:0.79 279:0.79 281:0.91 283:0.82 289:0.89 290:0.78 291:0.97 292:0.95 294:0.96 297:0.36 300:0.94 +1 1:0.56 2:0.99 10:0.96 11:0.57 12:0.20 17:0.38 18:0.74 21:0.64 27:0.45 29:0.77 30:0.41 34:0.91 36:0.18 37:0.50 39:0.98 43:0.43 45:0.89 46:0.93 56:0.97 66:0.19 69:0.70 70:0.92 71:0.73 74:0.40 75:0.95 81:0.30 86:0.77 91:0.23 98:0.58 101:1.00 107:0.91 108:0.53 110:0.89 122:0.99 123:0.82 124:0.83 125:0.88 126:0.31 127:0.59 129:0.05 133:0.81 135:0.93 139:0.72 145:0.50 146:0.73 147:0.78 149:0.54 150:0.33 154:0.33 155:0.46 159:0.76 160:0.88 170:0.82 172:0.75 173:0.53 174:0.98 177:0.96 178:0.55 179:0.29 187:0.68 192:0.44 197:0.98 201:0.54 208:0.49 212:0.67 216:0.77 222:0.25 226:0.95 235:0.80 236:0.91 239:0.94 241:0.53 242:0.21 243:0.49 244:0.83 245:0.87 247:0.94 253:0.35 259:0.21 264:0.93 265:0.46 266:0.87 267:0.65 271:0.90 275:0.98 276:0.83 277:0.95 279:0.75 289:0.88 294:0.98 295:0.98 300:0.84 +1 1:0.69 9:0.73 10:0.95 11:0.54 12:0.20 17:0.95 18:0.69 21:0.13 23:0.95 27:0.36 29:0.77 30:0.26 31:0.89 34:0.98 36:0.60 37:0.92 39:0.98 43:0.22 44:0.88 45:0.89 46:0.88 56:0.97 62:0.97 64:0.77 66:0.71 69:0.88 70:0.78 71:0.73 74:0.49 77:0.70 79:0.89 80:0.98 81:0.74 82:0.99 83:0.60 86:0.71 88:0.98 91:0.29 96:0.72 98:0.71 99:0.99 101:0.69 102:0.96 107:0.91 108:0.77 110:0.89 114:0.85 122:0.98 123:0.75 124:0.46 125:0.88 126:0.53 127:0.81 128:0.96 129:0.05 133:0.59 135:0.60 137:0.89 138:0.95 139:0.75 140:0.45 143:0.99 145:0.77 146:0.81 147:0.30 149:0.32 150:0.59 151:0.66 153:0.48 154:0.40 155:0.64 159:0.61 160:0.88 162:0.86 165:0.86 168:0.96 170:0.82 172:0.80 173:0.91 174:0.97 176:0.63 177:0.05 179:0.44 187:0.39 190:0.77 192:0.87 193:0.97 195:0.76 196:0.90 197:0.98 201:0.67 202:0.36 204:0.83 205:0.95 208:0.64 212:0.68 216:0.25 220:0.74 222:0.25 235:0.68 236:0.94 239:0.95 241:0.44 242:0.54 243:0.12 245:0.77 247:0.82 248:0.63 253:0.64 254:0.97 255:0.73 256:0.45 257:0.97 259:0.21 264:0.93 265:0.72 266:0.75 267:0.44 268:0.46 271:0.90 275:0.93 276:0.73 281:0.91 282:0.75 284:0.88 285:0.61 286:0.99 289:0.89 290:0.85 294:0.96 298:0.99 300:0.70 +1 1:0.63 9:0.71 10:0.97 11:0.48 12:0.20 17:0.53 18:0.91 21:0.40 23:0.95 27:0.10 29:0.77 30:0.66 31:0.88 33:0.97 34:0.58 36:0.88 37:0.81 39:0.98 43:0.56 44:0.88 45:0.95 46:0.88 62:0.97 64:0.77 66:0.49 69:0.87 70:0.60 71:0.73 74:0.65 75:0.96 77:0.70 79:0.87 81:0.58 83:0.60 86:0.86 91:0.14 96:0.70 97:0.97 98:0.68 99:0.95 101:0.52 102:0.95 107:0.94 108:0.75 110:0.90 114:0.76 122:0.91 123:0.73 124:0.64 125:0.88 126:0.43 127:0.86 128:0.95 129:0.05 133:0.60 135:0.97 137:0.88 139:0.86 140:0.45 145:0.74 146:0.82 147:0.69 149:0.73 150:0.47 151:0.53 153:0.48 154:0.17 155:0.55 158:0.75 159:0.66 160:0.88 162:0.86 165:0.70 168:0.95 170:0.82 172:0.82 173:0.87 174:0.97 176:0.62 177:0.70 179:0.32 187:1.00 190:0.95 192:0.57 193:0.97 195:0.81 196:0.90 197:0.98 201:0.62 202:0.36 204:0.81 205:0.95 208:0.54 212:0.78 216:0.80 219:0.90 220:0.91 222:0.25 226:0.98 235:0.68 236:0.92 239:0.97 241:0.37 242:0.24 243:0.44 244:0.83 245:0.91 247:0.91 248:0.53 253:0.61 254:0.80 255:0.70 256:0.45 257:0.84 259:0.21 264:0.93 265:0.68 266:0.71 267:1.00 268:0.46 271:0.90 275:0.91 276:0.82 279:0.79 281:0.91 282:0.74 284:0.88 285:0.55 289:0.89 290:0.76 291:0.97 292:0.95 294:0.92 300:0.55 +1 1:0.66 8:0.85 10:0.94 11:0.42 12:0.20 17:0.69 18:0.67 20:0.95 21:0.40 27:0.07 29:0.77 30:0.08 33:0.97 34:0.80 36:0.68 37:0.94 39:0.98 43:0.08 45:0.66 46:0.88 56:0.97 64:0.77 66:0.69 69:0.23 70:0.88 71:0.73 74:0.41 75:0.95 78:0.98 80:0.98 81:0.60 83:0.60 86:0.69 91:0.44 98:0.69 99:0.83 100:0.73 101:0.46 102:0.94 107:0.89 108:0.18 110:0.89 111:0.95 114:0.78 122:0.98 123:0.18 124:0.42 125:0.88 126:0.53 127:0.64 129:0.05 133:0.36 135:0.84 139:0.70 145:0.61 146:0.46 147:0.52 149:0.09 150:0.51 152:0.88 154:0.54 155:0.54 159:0.26 160:0.88 165:0.63 169:0.72 170:0.82 172:0.54 173:0.49 174:0.93 176:0.63 177:0.96 178:0.55 179:0.75 186:0.98 187:0.21 192:0.55 193:0.97 195:0.56 197:0.97 201:0.65 204:0.81 208:0.64 212:0.89 216:0.50 222:0.25 226:0.96 235:0.74 236:0.94 239:0.94 241:0.52 242:0.58 243:0.73 244:0.83 245:0.59 247:0.67 253:0.57 254:0.74 259:0.21 261:0.91 264:0.93 265:0.69 266:0.23 267:0.59 271:0.90 275:0.91 276:0.42 281:0.91 289:0.89 290:0.78 291:0.97 294:0.95 300:0.55 +2 1:0.74 6:0.98 8:0.97 9:0.80 11:0.57 12:0.20 17:0.91 20:0.79 27:0.01 28:0.99 30:0.76 34:0.92 36:0.80 37:0.97 39:0.86 41:0.82 43:0.69 46:0.98 66:0.54 69:0.88 70:0.62 74:0.80 78:0.84 81:0.83 83:0.81 85:0.97 91:0.61 96:0.83 98:0.33 100:0.95 101:0.85 104:0.58 107:0.98 108:0.88 110:0.92 111:0.90 114:0.98 120:0.72 122:0.57 123:0.88 124:0.76 125:1.00 126:0.54 127:0.30 129:0.89 133:0.83 135:0.34 140:0.45 146:0.13 147:0.18 149:0.89 150:0.89 151:0.90 152:1.00 153:0.69 154:0.59 155:0.67 159:0.55 160:0.96 161:0.55 162:0.86 164:0.62 165:0.92 167:0.89 169:0.99 172:0.73 173:0.86 176:0.73 177:0.38 179:0.31 181:0.48 186:0.84 187:0.21 189:0.99 192:0.59 201:0.74 202:0.46 208:0.64 212:0.67 215:0.96 216:0.85 222:0.25 232:0.76 235:0.05 238:0.98 241:0.77 242:0.63 243:0.12 245:0.74 247:0.39 248:0.80 253:0.86 255:0.79 256:0.45 259:0.21 260:0.73 261:0.99 265:0.35 266:0.71 267:0.99 268:0.55 271:0.90 276:0.70 285:0.62 289:0.90 290:0.98 297:0.36 300:0.84 +1 1:0.74 11:0.57 12:0.20 17:0.86 27:0.01 28:0.99 30:0.76 34:0.94 36:0.80 37:0.97 39:0.86 43:0.68 46:0.99 66:0.54 69:0.89 70:0.58 74:0.82 81:0.83 83:0.80 85:0.97 91:0.42 98:0.36 101:0.85 104:0.58 107:0.98 108:0.87 110:0.92 114:0.98 122:0.57 123:0.86 124:0.69 125:0.88 126:0.54 127:0.27 129:0.89 133:0.78 135:0.32 146:0.13 147:0.18 149:0.91 150:0.89 154:0.57 155:0.67 159:0.49 160:0.88 161:0.55 165:0.92 167:0.80 172:0.75 173:0.89 176:0.73 177:0.38 178:0.55 179:0.27 181:0.48 187:0.21 192:0.59 201:0.74 212:0.69 215:0.96 216:0.85 222:0.25 232:0.76 235:0.05 241:0.70 242:0.63 243:0.12 245:0.78 247:0.39 253:0.78 259:0.21 265:0.38 266:0.47 267:0.65 271:0.90 276:0.72 289:0.90 290:0.98 297:0.36 300:0.70 +4 1:0.74 6:0.98 8:0.98 9:0.80 11:0.57 12:0.20 17:0.30 20:0.97 27:0.01 28:0.99 30:0.76 32:0.80 34:0.96 36:0.65 37:0.97 39:0.86 41:1.00 43:0.65 46:0.98 60:0.99 66:0.68 69:0.90 70:0.62 74:0.84 77:0.89 78:0.94 81:0.83 83:0.91 85:0.96 91:1.00 96:1.00 98:0.35 100:1.00 101:0.84 104:0.58 107:0.98 108:0.87 110:0.92 111:0.99 114:0.98 120:1.00 122:0.57 123:0.87 124:0.47 125:0.97 126:0.54 127:0.24 129:0.59 133:0.64 135:0.32 138:0.99 140:0.45 145:0.86 146:0.45 147:0.51 149:0.86 150:0.89 151:1.00 152:1.00 153:1.00 154:0.55 155:0.67 158:0.92 159:0.46 160:1.00 161:0.55 162:0.59 164:0.99 165:0.92 167:0.98 169:0.99 172:0.73 173:0.91 176:0.73 177:0.38 179:0.31 181:0.48 186:0.94 189:0.99 191:0.99 192:0.59 195:0.80 201:0.74 202:0.99 208:0.64 212:0.77 215:0.96 216:0.85 222:0.25 232:0.76 235:0.05 238:1.00 241:0.99 242:0.63 243:0.23 245:0.71 247:0.39 248:1.00 253:1.00 255:0.79 256:0.99 259:0.21 260:1.00 261:0.99 265:0.37 266:0.63 267:0.87 268:0.99 271:0.90 276:0.62 279:0.86 282:0.88 283:0.82 285:0.62 289:0.90 290:0.98 297:0.36 299:0.88 300:0.84 +1 1:0.74 6:0.83 8:0.70 9:0.80 11:0.57 12:0.20 17:0.45 20:0.80 21:0.22 27:0.54 28:0.99 30:0.73 34:0.37 36:0.27 37:0.49 39:0.86 41:1.00 43:0.86 46:0.96 60:0.97 66:0.05 69:0.58 70:0.66 74:0.93 78:0.74 81:0.66 83:0.90 85:1.00 91:0.63 96:0.99 98:0.15 100:0.77 101:0.80 104:0.93 106:0.81 107:0.98 108:1.00 110:0.92 111:0.74 114:0.90 117:0.86 120:0.97 122:0.43 123:0.43 124:1.00 125:0.97 126:0.54 127:0.99 129:1.00 133:1.00 135:0.28 138:1.00 140:0.45 145:0.39 146:0.75 147:0.79 149:0.97 150:0.80 151:0.99 152:0.77 153:0.95 154:0.83 155:0.67 158:0.92 159:0.99 160:1.00 161:0.55 162:0.60 163:0.81 164:0.97 165:0.86 167:0.72 169:0.75 172:0.57 173:0.08 176:0.73 177:0.38 179:0.12 181:0.48 186:0.75 187:0.87 189:0.85 191:0.80 192:0.59 201:0.74 202:0.96 206:0.81 208:0.64 212:0.21 215:0.79 216:0.50 222:0.25 232:0.94 235:0.31 238:0.84 241:0.56 242:0.59 243:0.12 245:0.90 247:0.60 248:0.99 253:0.99 255:0.79 256:0.97 259:0.21 260:0.97 261:0.75 265:0.14 266:0.99 267:1.00 268:0.97 271:0.90 276:0.97 279:0.86 283:0.82 285:0.62 289:0.90 290:0.90 297:0.36 300:0.98 +1 6:0.98 8:0.90 9:0.80 11:0.57 12:0.20 17:0.88 20:0.74 21:0.78 27:0.01 28:0.99 30:0.76 34:0.97 36:0.50 37:0.97 39:0.86 41:0.82 43:0.69 46:0.98 66:0.74 69:0.88 70:0.62 74:0.76 78:0.79 83:0.76 85:0.96 91:0.49 96:0.80 98:0.38 100:0.84 101:0.84 104:0.58 107:0.98 108:0.84 110:0.92 111:0.80 120:0.69 122:0.57 123:0.84 124:0.42 125:0.99 127:0.24 129:0.59 133:0.64 135:0.81 140:0.80 146:0.13 147:0.18 149:0.86 151:0.87 152:1.00 153:0.48 154:0.59 155:0.67 159:0.41 160:0.96 161:0.55 162:0.53 164:0.57 167:0.89 169:0.99 172:0.73 173:0.89 177:0.38 178:0.42 179:0.33 181:0.48 186:0.80 187:0.21 189:0.99 191:0.90 192:0.59 202:0.58 208:0.64 212:0.81 216:0.85 222:0.25 232:0.76 235:0.05 238:0.92 241:0.77 242:0.63 243:0.15 245:0.64 247:0.39 248:0.78 253:0.82 255:0.79 256:0.45 259:0.21 260:0.70 261:0.99 265:0.40 266:0.54 267:0.65 268:0.46 271:0.90 276:0.58 285:0.62 289:0.90 300:0.84 +1 1:0.74 6:0.95 7:0.81 8:0.95 9:0.80 11:0.57 12:0.20 17:0.94 20:0.82 21:0.05 27:0.08 28:0.99 30:0.87 32:0.80 34:0.53 36:0.35 37:0.93 39:0.86 41:0.93 43:0.93 46:0.95 66:0.24 69:0.29 70:0.32 74:0.72 76:0.85 77:0.89 78:0.74 81:0.77 83:0.82 85:0.90 91:0.51 96:0.92 98:0.18 100:0.79 101:0.77 104:0.58 107:0.96 108:0.92 110:0.90 111:0.75 114:0.95 120:0.87 121:0.97 122:0.43 123:0.92 124:0.73 125:1.00 126:0.54 127:0.89 129:0.81 133:0.67 135:0.68 140:0.45 145:0.87 146:0.13 147:0.18 149:0.68 150:0.86 151:0.96 152:0.92 153:0.86 154:0.93 155:0.67 159:0.81 160:0.98 161:0.55 162:0.70 163:0.81 164:0.79 165:0.90 167:0.86 169:0.80 172:0.21 173:0.15 176:0.73 177:0.38 179:0.87 181:0.48 186:0.75 187:0.39 189:0.98 191:0.75 192:0.59 195:0.92 201:0.74 202:0.72 204:0.89 208:0.64 212:0.30 215:0.91 216:0.62 220:0.62 222:0.25 230:0.87 232:0.76 233:0.76 235:0.12 238:0.97 241:0.75 242:0.63 243:0.28 245:0.54 247:0.30 248:0.92 253:0.92 255:0.79 256:0.71 260:0.87 261:0.81 265:0.19 266:0.54 267:0.98 268:0.75 271:0.90 276:0.19 282:0.88 285:0.62 289:0.90 290:0.95 297:0.36 299:0.88 300:0.33 +2 1:0.74 6:0.86 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.75 20:0.98 21:0.30 27:0.21 28:0.99 30:0.76 34:0.79 36:0.91 37:0.74 39:0.86 41:0.99 43:0.98 46:1.00 60:0.99 66:0.31 69:0.12 70:0.44 74:0.99 78:0.75 81:0.61 83:0.90 85:1.00 91:0.75 96:0.98 98:0.66 100:0.78 101:0.87 104:0.84 106:0.81 107:0.99 108:0.94 110:0.93 111:0.74 114:0.86 117:0.86 120:0.96 121:0.90 122:0.57 123:0.94 124:0.77 125:0.99 126:0.54 127:0.73 129:0.89 133:0.78 135:0.19 140:0.45 146:0.84 147:0.86 149:1.00 150:0.77 151:0.99 152:0.90 153:0.97 154:0.98 155:0.67 158:0.92 159:0.92 160:1.00 161:0.55 162:0.65 164:0.93 165:0.84 167:0.71 169:0.76 172:0.97 173:0.07 176:0.73 177:0.38 179:0.11 181:0.48 186:0.76 187:0.39 189:0.88 191:0.80 192:0.59 201:0.74 202:0.90 204:0.89 206:0.81 208:0.64 212:0.74 215:0.72 216:0.95 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 238:0.91 241:0.51 242:0.63 243:0.20 245:0.99 247:0.55 248:0.99 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.78 265:0.66 266:0.75 267:0.59 268:0.91 271:0.90 276:0.98 279:0.86 283:0.82 285:0.62 289:0.90 290:0.86 297:0.36 300:0.94 +1 1:0.74 6:0.95 7:0.81 8:0.95 9:0.80 11:0.57 12:0.20 17:0.90 20:0.92 21:0.05 27:0.08 28:0.99 30:0.76 32:0.80 34:0.28 36:0.41 37:0.93 39:0.86 41:0.99 43:0.93 46:0.94 60:0.99 66:0.36 69:0.29 70:0.29 74:0.76 76:0.85 77:0.89 78:0.77 81:0.77 83:0.90 85:0.85 91:0.97 96:0.99 98:0.16 100:0.82 101:0.74 104:0.58 107:0.94 108:0.93 110:0.90 111:0.82 114:0.95 120:0.97 121:0.97 122:0.43 123:0.92 124:0.69 125:0.97 126:0.54 127:0.96 129:0.59 133:0.64 135:0.37 140:0.45 145:0.87 146:0.13 147:0.18 149:0.62 150:0.86 151:1.00 152:0.92 153:0.98 154:0.93 155:0.67 158:0.92 159:0.81 160:1.00 161:0.55 162:0.58 164:0.95 165:0.90 167:0.98 169:0.86 172:0.21 173:0.16 176:0.73 177:0.38 179:0.94 181:0.48 186:0.76 189:0.96 191:0.96 192:0.59 195:0.92 201:0.74 202:0.93 204:0.89 208:0.64 212:0.23 215:0.91 216:0.62 222:0.25 230:0.87 232:0.76 233:0.76 235:0.12 238:0.99 241:0.97 242:0.55 243:0.34 245:0.45 247:0.39 248:0.99 253:0.99 255:0.79 256:0.93 260:0.97 261:0.81 265:0.17 266:0.38 267:0.97 268:0.93 271:0.90 276:0.16 279:0.86 282:0.88 283:0.82 285:0.62 289:0.90 290:0.95 297:0.36 299:0.88 300:0.25 +1 1:0.74 6:0.80 8:0.59 9:0.80 11:0.57 12:0.20 17:0.88 20:0.86 27:0.06 28:0.99 30:0.95 34:0.18 36:0.59 37:0.84 39:0.86 41:0.88 43:0.78 46:0.96 66:0.54 69:0.77 74:0.41 78:0.83 81:0.83 83:0.82 85:0.72 91:0.90 96:0.88 98:0.13 100:0.85 101:0.75 104:0.58 107:0.91 108:0.61 110:0.89 111:0.82 114:0.98 120:0.81 123:0.58 125:1.00 126:0.54 127:0.08 129:0.05 135:0.37 140:0.45 146:0.13 147:0.18 149:0.42 150:0.89 151:0.94 152:0.82 153:0.80 154:0.71 155:0.67 159:0.08 160:0.97 161:0.55 162:0.78 164:0.70 165:0.92 167:0.97 169:0.83 172:0.10 173:1.00 176:0.73 177:0.38 179:0.12 181:0.48 186:0.83 189:0.82 191:0.78 192:0.59 201:0.74 202:0.59 208:0.64 215:0.96 216:0.03 222:0.25 232:0.76 235:0.05 238:0.90 241:0.94 243:0.63 248:0.88 253:0.86 255:0.79 256:0.58 259:0.21 260:0.81 261:0.83 265:0.14 267:0.92 268:0.64 271:0.90 285:0.62 289:0.90 290:0.98 297:0.36 300:0.08 +1 1:0.74 11:0.57 12:0.20 17:0.51 21:0.22 27:0.45 28:0.99 30:0.17 34:0.92 36:0.18 37:0.50 39:0.86 43:0.82 46:0.94 55:0.59 66:0.69 69:0.69 70:0.80 74:0.82 81:0.66 83:0.76 85:0.80 91:0.08 98:0.52 101:0.73 107:0.97 108:0.52 110:0.91 114:0.90 122:0.67 123:0.50 124:0.42 125:0.88 126:0.54 127:0.29 129:0.59 133:0.47 135:0.71 145:0.47 146:0.72 147:0.76 149:0.74 150:0.80 154:0.77 155:0.67 159:0.53 160:0.88 161:0.55 165:0.86 167:0.62 172:0.54 173:0.61 176:0.73 177:0.38 178:0.55 179:0.61 181:0.48 187:0.68 192:0.59 201:0.74 212:0.30 215:0.79 216:0.77 222:0.25 235:0.31 241:0.15 242:0.58 243:0.73 245:0.59 247:0.47 253:0.74 259:0.21 265:0.54 266:0.63 267:0.59 271:0.90 276:0.44 289:0.90 290:0.90 297:0.36 300:0.55 +2 1:0.74 6:0.85 7:0.81 8:0.69 9:0.80 11:0.57 12:0.20 17:0.95 20:0.79 27:0.33 28:0.99 30:0.85 32:0.80 34:0.58 36:0.56 39:0.86 41:0.96 43:0.93 46:1.00 60:0.87 66:0.75 69:0.25 70:0.27 74:0.92 77:0.70 78:0.77 81:0.83 83:0.89 85:0.95 91:0.87 96:0.95 98:0.76 100:0.80 101:0.86 104:0.58 107:0.98 108:0.57 110:0.92 111:0.77 114:0.98 120:0.90 121:0.99 122:0.43 123:0.54 124:0.40 125:1.00 126:0.54 127:0.76 129:0.59 133:0.47 135:0.39 140:0.45 145:0.95 146:0.13 147:0.18 149:0.93 150:0.89 151:0.98 152:0.77 153:0.91 154:0.93 155:0.67 158:0.87 159:0.32 160:0.99 161:0.55 162:0.82 164:0.85 165:0.92 167:0.97 169:0.78 172:0.67 173:0.44 176:0.73 177:0.38 179:0.65 181:0.48 186:0.78 189:0.87 191:0.70 192:0.59 195:0.60 201:0.74 202:0.76 204:0.89 208:0.64 212:0.89 215:0.96 216:0.35 222:0.25 230:0.87 232:0.76 233:0.76 235:0.05 238:0.90 241:0.90 242:0.63 243:0.18 245:0.64 247:0.30 248:0.95 253:0.96 255:0.79 256:0.81 259:0.21 260:0.91 261:0.76 265:0.76 266:0.27 267:1.00 268:0.81 271:0.90 276:0.54 279:0.86 282:0.88 283:0.71 285:0.62 289:0.90 290:0.98 297:0.36 299:0.88 300:0.25 +3 1:0.74 6:0.86 7:0.81 9:0.80 11:0.57 12:0.20 17:0.83 20:0.74 21:0.30 27:0.21 28:0.99 30:0.76 34:0.66 36:0.88 37:0.74 39:0.86 41:0.90 43:0.98 46:1.00 66:0.26 69:0.12 70:0.46 74:1.00 78:0.72 81:0.61 83:0.78 85:1.00 91:0.12 96:0.83 98:0.62 100:0.73 101:0.87 104:0.84 107:0.99 108:0.95 110:0.93 111:0.72 114:0.86 120:0.73 121:0.97 122:0.57 123:0.95 124:0.87 125:0.98 126:0.54 127:0.76 129:0.95 133:0.87 135:0.23 140:0.45 146:0.83 147:0.85 149:1.00 150:0.77 151:0.87 152:0.90 153:0.48 154:0.98 155:0.67 159:0.93 160:0.98 161:0.55 162:0.59 164:0.73 165:0.84 167:0.67 169:0.76 172:0.97 173:0.07 176:0.73 177:0.38 179:0.10 181:0.48 186:0.73 187:0.39 192:0.59 201:0.74 202:0.67 204:0.89 208:0.64 212:0.77 215:0.72 216:0.95 220:0.87 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 241:0.37 242:0.63 243:0.19 245:0.99 247:0.39 248:0.81 253:0.90 255:0.79 256:0.64 259:0.21 260:0.74 261:0.78 265:0.63 266:0.75 267:0.96 268:0.66 271:0.90 276:0.98 285:0.62 289:0.90 290:0.86 297:0.36 300:0.84 +2 1:0.74 6:0.89 7:0.81 8:0.81 9:0.80 11:0.57 12:0.20 17:0.97 20:0.82 21:0.05 27:0.07 28:0.99 30:0.45 32:0.80 34:0.19 36:0.55 37:0.78 39:0.86 41:0.88 43:0.74 46:0.93 66:0.27 69:0.85 70:0.25 74:0.63 77:0.70 78:0.84 81:0.77 83:0.80 85:0.72 91:0.88 96:0.84 98:0.09 100:0.89 101:0.71 104:0.58 107:0.92 108:0.71 110:0.89 111:0.85 114:0.95 120:0.90 121:0.90 122:0.43 123:0.69 124:0.72 125:0.96 126:0.54 127:0.33 129:0.05 133:0.62 135:0.82 140:0.90 145:0.69 146:0.13 147:0.18 149:0.41 150:0.86 151:0.85 152:0.81 153:0.45 154:0.64 155:0.67 159:0.49 160:0.97 161:0.55 162:0.63 163:0.89 164:0.91 165:0.90 167:0.96 169:0.86 172:0.10 173:0.85 176:0.73 177:0.38 179:0.96 181:0.48 186:0.84 189:0.92 191:0.94 192:0.59 195:0.76 201:0.74 202:0.87 204:0.89 208:0.64 212:0.61 215:0.91 216:0.25 220:0.62 222:0.25 230:0.87 232:0.76 233:0.76 235:0.12 238:0.93 241:0.87 242:0.63 243:0.46 245:0.38 247:0.30 248:0.82 253:0.84 255:0.79 256:0.88 259:0.21 260:0.91 261:0.83 265:0.09 266:0.17 267:1.00 268:0.88 271:0.90 276:0.18 282:0.88 283:0.71 285:0.62 289:0.90 290:0.95 297:0.36 299:0.88 300:0.18 +3 1:0.74 7:0.81 11:0.57 12:0.20 17:0.87 21:0.30 27:0.21 28:0.99 30:0.76 34:0.68 36:0.77 37:0.74 39:0.86 43:0.98 46:1.00 66:0.27 69:0.12 70:0.47 74:1.00 81:0.61 83:0.75 85:1.00 91:0.08 98:0.62 101:0.87 104:0.84 106:0.81 107:0.99 108:0.96 110:0.93 114:0.86 121:0.90 122:0.57 123:0.96 124:0.89 125:0.88 126:0.54 127:0.75 129:0.96 133:0.90 135:0.23 146:0.27 147:0.33 149:1.00 150:0.77 154:0.98 155:0.67 159:0.93 160:0.88 161:0.55 165:0.84 167:0.55 172:0.97 173:0.06 176:0.73 177:0.38 178:0.55 179:0.10 181:0.48 187:0.39 192:0.59 201:0.74 204:0.89 206:0.81 208:0.64 212:0.78 215:0.72 216:0.95 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 241:0.01 242:0.63 243:0.06 245:0.99 247:0.55 253:0.79 259:0.21 265:0.63 266:0.75 267:0.52 271:0.90 276:0.99 283:0.71 289:0.90 290:0.86 297:0.36 300:0.84 +3 1:0.74 7:0.81 9:0.80 11:0.57 12:0.20 17:0.84 21:0.30 27:0.21 28:0.99 30:0.76 34:0.66 36:0.87 37:0.74 39:0.86 41:0.88 43:0.98 46:1.00 66:0.26 69:0.12 70:0.46 74:1.00 81:0.61 83:0.77 85:1.00 91:0.11 96:0.75 98:0.62 101:0.87 104:0.84 106:0.81 107:0.99 108:0.95 110:0.93 114:0.86 117:0.86 120:0.63 121:0.97 122:0.57 123:0.95 124:0.87 125:0.98 126:0.54 127:0.76 129:0.95 133:0.87 135:0.23 140:0.45 146:0.83 147:0.86 149:1.00 150:0.77 151:0.69 153:0.48 154:0.98 155:0.67 159:0.93 160:0.97 161:0.55 162:0.62 164:0.67 165:0.84 167:0.66 172:0.97 173:0.07 176:0.73 177:0.38 179:0.10 181:0.48 187:0.39 192:0.59 201:0.74 202:0.60 204:0.89 206:0.81 208:0.64 212:0.77 215:0.72 216:0.95 220:0.87 222:0.25 230:0.87 232:0.88 233:0.76 235:0.42 241:0.35 242:0.63 243:0.19 245:0.99 247:0.39 248:0.67 253:0.84 255:0.79 256:0.58 259:0.21 260:0.64 265:0.63 266:0.75 267:0.97 268:0.59 271:0.90 276:0.98 285:0.62 289:0.90 290:0.86 297:0.36 300:0.84 +1 1:0.74 8:0.84 11:0.57 12:0.20 17:0.43 21:0.13 27:0.45 28:0.99 30:0.72 32:0.80 34:0.50 36:0.20 37:0.50 39:0.86 43:0.82 46:0.98 60:0.87 66:0.27 69:0.69 70:0.70 74:0.91 77:0.70 81:0.71 83:0.85 85:0.97 91:0.09 98:0.33 101:0.83 107:0.98 108:0.52 110:0.92 114:0.92 122:0.43 123:0.50 124:0.84 125:0.88 126:0.54 127:0.52 129:0.93 133:0.84 135:0.92 145:0.49 146:0.60 147:0.65 149:0.94 150:0.83 154:0.77 155:0.67 158:0.92 159:0.74 160:0.88 161:0.55 163:0.81 165:0.88 167:0.64 172:0.57 173:0.54 176:0.73 177:0.38 178:0.55 179:0.43 181:0.48 187:0.68 192:0.59 195:0.89 201:0.74 208:0.64 212:0.30 215:0.84 216:0.77 222:0.25 235:0.22 241:0.27 242:0.62 243:0.46 245:0.77 247:0.39 253:0.78 259:0.21 265:0.36 266:0.80 267:0.36 271:0.90 276:0.70 279:0.86 282:0.88 283:0.82 289:0.90 290:0.92 297:0.36 299:0.88 300:0.84 +2 1:0.74 7:0.81 8:0.83 9:0.80 11:0.57 12:0.20 17:0.84 21:0.30 27:0.21 28:0.99 30:0.76 34:0.64 36:0.76 37:0.74 39:0.86 41:0.82 43:0.98 46:1.00 66:0.27 69:0.12 70:0.47 74:1.00 81:0.61 83:0.76 85:1.00 91:0.15 96:0.72 98:0.62 101:0.87 107:0.99 108:0.96 110:0.93 114:0.86 120:0.59 121:0.90 122:0.57 123:0.96 124:0.89 125:0.98 126:0.54 127:0.75 129:0.96 133:0.90 135:0.24 140:0.80 146:0.27 147:0.33 149:1.00 150:0.77 151:0.60 153:0.72 154:0.98 155:0.67 159:0.93 160:0.96 161:0.55 162:0.54 164:0.64 165:0.84 167:0.63 172:0.97 173:0.06 176:0.73 177:0.38 178:0.42 179:0.10 181:0.48 187:0.39 192:0.59 201:0.74 202:0.60 204:0.89 208:0.64 212:0.78 215:0.72 216:0.95 220:0.87 222:0.25 230:0.87 233:0.76 235:0.42 241:0.21 242:0.63 243:0.06 245:0.99 247:0.39 248:0.58 253:0.81 255:0.79 256:0.45 259:0.21 260:0.59 265:0.63 266:0.75 267:0.65 268:0.57 271:0.90 276:0.99 285:0.62 289:0.90 290:0.86 297:0.36 300:0.84 +1 9:0.80 11:0.57 12:0.20 17:0.69 21:0.78 27:0.01 28:0.99 30:0.75 34:0.95 36:0.73 37:0.97 39:0.86 41:0.99 43:0.69 46:0.98 60:0.95 66:0.72 69:0.88 70:0.63 74:0.80 83:0.90 85:0.96 91:0.85 96:0.97 98:0.38 101:0.84 104:0.58 107:0.98 108:0.85 110:0.92 120:0.95 122:0.43 123:0.84 124:0.45 125:0.97 127:0.25 129:0.59 133:0.64 135:0.65 140:0.45 146:0.19 147:0.24 149:0.86 151:0.99 153:0.97 154:0.59 155:0.67 158:0.92 159:0.43 160:1.00 161:0.55 162:0.64 164:0.94 167:0.90 172:0.73 173:0.89 177:0.38 179:0.34 181:0.48 187:0.68 192:0.59 202:0.91 208:0.64 212:0.78 216:0.85 222:0.25 232:0.76 235:0.05 241:0.77 242:0.63 243:0.19 245:0.68 247:0.30 248:0.97 253:0.98 255:0.79 256:0.92 259:0.21 260:0.95 265:0.40 266:0.63 267:0.65 268:0.92 271:0.90 276:0.58 279:0.86 283:0.82 285:0.62 289:0.90 300:0.84 +1 1:0.74 9:0.80 11:0.57 12:0.20 17:0.77 21:0.30 27:0.09 28:0.99 30:0.85 34:0.99 36:0.75 37:0.66 39:0.86 41:0.97 43:0.61 46:0.98 60:0.87 66:0.88 69:0.92 70:0.27 74:0.74 81:0.61 83:0.89 85:0.94 91:0.68 96:0.96 98:0.34 101:0.83 104:0.58 107:0.98 108:0.84 110:0.91 114:0.86 120:0.92 122:0.43 123:0.83 125:0.99 126:0.54 127:0.12 129:0.05 135:0.61 140:0.45 145:0.52 146:0.46 147:0.53 149:0.81 150:0.77 151:0.97 153:0.90 154:0.50 155:0.67 158:0.92 159:0.17 160:0.99 161:0.55 162:0.65 164:0.87 165:0.84 167:0.92 172:0.67 173:0.98 176:0.73 177:0.38 179:0.14 181:0.48 187:0.98 192:0.59 201:0.74 202:0.82 208:0.64 212:0.93 215:0.72 216:0.51 222:0.25 232:0.76 235:0.42 241:0.79 242:0.63 243:0.89 247:0.30 248:0.96 253:0.95 255:0.79 256:0.83 259:0.21 260:0.92 265:0.37 266:0.17 267:0.98 268:0.84 271:0.90 276:0.55 279:0.86 283:0.82 285:0.62 289:0.90 290:0.86 297:0.36 300:0.25 +2 9:0.80 11:0.57 12:0.20 17:0.84 21:0.78 27:0.01 28:0.99 30:0.76 32:0.80 34:0.96 36:0.63 37:0.97 39:0.86 41:0.95 43:0.68 46:0.98 66:0.75 69:0.89 70:0.60 74:0.84 76:0.85 77:0.70 83:0.82 85:0.96 91:0.80 96:0.94 98:0.37 101:0.85 104:0.58 107:0.98 108:0.86 110:0.92 120:0.89 122:0.57 123:0.85 124:0.42 125:0.99 127:0.25 129:0.59 133:0.64 135:0.89 140:0.45 145:0.56 146:0.13 147:0.18 149:0.87 151:0.93 153:0.78 154:0.58 155:0.67 159:0.46 160:0.99 161:0.55 162:0.65 164:0.84 167:0.83 172:0.75 173:0.89 177:0.38 179:0.33 181:0.48 187:0.68 192:0.59 195:0.81 202:0.78 208:0.64 212:0.82 216:0.85 220:0.62 222:0.25 232:0.76 235:0.05 241:0.72 242:0.63 243:0.14 245:0.65 247:0.39 248:0.94 253:0.95 255:0.79 256:0.78 259:0.21 260:0.90 265:0.39 266:0.54 267:0.85 268:0.80 271:0.90 276:0.58 282:0.88 285:0.62 289:0.90 299:0.88 300:0.84 +1 1:0.74 7:0.66 11:0.64 12:0.06 17:0.77 18:0.93 22:0.93 27:0.65 28:0.73 29:0.91 30:0.32 31:0.86 32:0.80 34:0.97 36:0.17 37:0.43 39:0.52 43:0.78 44:0.93 46:0.96 47:0.93 48:0.91 55:0.59 60:0.95 64:0.77 66:0.94 69:0.40 70:0.91 71:0.95 74:0.76 77:0.70 79:0.86 81:0.83 83:0.91 85:0.85 86:0.89 91:0.42 98:0.96 99:0.83 101:0.87 106:0.81 107:0.94 108:0.31 110:1.00 114:0.85 117:0.86 120:0.63 122:0.57 123:0.30 125:0.88 126:0.54 127:0.70 129:0.05 131:0.81 135:0.96 137:0.86 139:0.92 140:0.45 144:0.57 145:0.96 146:0.91 147:0.93 149:0.80 150:0.89 151:0.55 153:0.48 154:0.70 155:0.67 157:0.87 158:0.92 159:0.52 160:0.88 161:0.51 162:0.76 164:0.53 165:0.93 166:0.86 167:0.64 172:0.85 173:0.39 176:0.66 177:0.70 179:0.41 181:0.91 182:0.89 187:0.39 190:0.89 192:0.87 195:0.71 196:0.88 201:0.93 202:0.58 206:0.81 208:0.64 212:0.84 215:0.78 216:0.35 219:0.95 220:0.91 222:0.60 227:0.82 228:0.99 229:0.61 230:0.69 231:0.79 232:0.95 233:0.76 235:0.52 241:0.21 242:0.14 243:0.94 246:0.85 247:0.90 248:0.55 253:0.59 256:0.58 259:0.21 260:0.61 262:0.93 265:0.96 266:0.54 267:0.73 268:0.62 276:0.75 279:0.86 281:0.91 282:0.88 283:0.82 284:0.86 285:0.56 287:0.61 289:0.99 290:0.85 292:0.95 297:0.36 299:0.88 300:0.70 +0 1:0.74 11:0.75 12:0.06 17:0.32 18:0.78 21:0.64 27:0.45 28:0.73 29:0.91 30:0.38 32:0.80 34:0.66 36:0.17 37:0.50 39:0.52 43:0.08 44:0.93 45:0.66 46:0.88 55:0.45 64:0.77 66:0.36 69:0.71 70:0.63 71:0.95 74:0.60 77:0.89 81:0.42 83:0.60 86:0.79 91:0.29 98:0.21 99:0.83 101:0.52 107:0.90 108:0.84 110:0.96 114:0.57 122:0.82 123:0.83 124:0.68 125:0.88 126:0.54 127:0.42 129:0.05 131:0.61 133:0.60 135:0.66 139:0.83 144:0.57 145:0.54 146:0.21 147:0.26 149:0.08 150:0.66 154:0.75 155:0.67 157:0.78 159:0.55 160:0.88 161:0.51 165:0.70 166:0.85 167:0.69 172:0.32 173:0.66 176:0.73 177:0.70 178:0.55 179:0.78 181:0.91 182:0.89 187:0.68 192:0.87 195:0.77 201:0.93 208:0.64 212:0.57 215:0.38 216:0.77 222:0.60 227:0.61 229:0.61 231:0.78 235:0.95 241:0.43 242:0.29 243:0.40 245:0.54 246:0.85 247:0.55 253:0.41 254:0.74 259:0.21 265:0.23 266:0.38 267:0.44 276:0.33 277:0.69 282:0.88 287:0.61 289:0.99 290:0.57 297:0.36 300:0.43 +2 7:0.66 8:0.79 9:0.80 12:0.06 17:0.28 18:0.64 21:0.13 27:0.25 28:0.73 29:0.91 30:0.74 31:0.92 32:0.64 34:0.59 36:0.59 37:0.70 39:0.52 43:0.15 46:0.88 55:0.71 66:0.29 69:0.89 70:0.31 71:0.95 74:0.29 77:0.70 79:0.94 81:0.31 83:0.91 86:0.58 91:0.90 96:0.91 98:0.55 106:0.81 107:0.92 108:0.77 110:0.99 120:0.91 123:0.76 124:0.78 125:0.88 126:0.26 127:0.52 129:0.05 131:0.90 133:0.74 135:0.28 137:0.92 139:0.56 140:0.99 144:0.57 145:0.76 146:0.74 147:0.59 149:0.25 150:0.29 151:0.68 153:0.81 154:0.18 155:0.67 157:0.61 159:0.64 160:0.88 161:0.51 162:0.56 163:0.88 164:0.90 166:0.61 167:0.86 172:0.37 173:0.87 175:0.75 177:0.05 179:0.68 181:0.91 182:0.87 187:0.21 190:0.77 191:0.88 192:0.87 195:0.85 196:0.87 202:0.92 206:0.81 208:0.64 212:0.16 215:0.61 216:0.53 220:0.82 222:0.60 227:0.84 229:0.93 230:0.69 231:0.79 232:0.94 233:0.76 235:0.22 241:0.73 242:0.40 243:0.43 244:0.61 245:0.61 246:0.61 247:0.67 248:0.68 253:0.72 254:0.90 255:0.95 256:0.91 257:0.84 260:0.88 265:0.56 266:0.87 267:0.59 268:0.92 276:0.52 277:0.69 282:0.71 283:0.78 284:0.92 285:0.62 287:0.87 289:0.99 297:0.36 300:0.25 +2 1:0.74 9:0.80 11:0.92 12:0.06 17:0.88 18:0.93 21:0.68 22:0.93 27:0.57 28:0.73 29:0.91 30:0.33 31:0.91 32:0.80 34:0.93 36:0.21 37:0.38 39:0.52 43:0.70 44:0.93 45:0.89 46:0.94 47:0.93 55:0.52 64:0.77 66:0.48 69:0.44 70:0.92 71:0.95 74:0.83 77:0.89 79:0.91 81:0.41 83:0.60 85:0.85 86:0.92 91:0.18 96:0.76 97:0.89 98:0.74 99:0.83 101:0.97 106:0.81 107:0.94 108:0.68 110:1.00 114:0.56 117:0.86 120:0.64 122:0.75 123:0.66 124:0.78 125:0.88 126:0.54 127:0.82 129:0.59 131:0.90 133:0.81 135:0.95 137:0.91 139:0.94 140:0.45 144:0.57 145:0.97 146:0.47 147:0.54 149:0.86 150:0.65 151:0.72 153:0.48 154:0.80 155:0.67 157:0.89 158:0.91 159:0.82 160:0.88 161:0.51 162:0.86 164:0.67 165:0.70 166:0.85 167:0.61 172:0.91 173:0.23 176:0.73 177:0.70 179:0.20 181:0.91 182:0.90 187:0.39 192:0.87 195:0.93 196:0.95 201:0.93 202:0.50 206:0.81 208:0.64 212:0.75 215:0.37 216:0.72 219:0.95 220:0.96 222:0.60 227:0.84 229:0.95 231:0.79 232:0.92 235:0.95 241:0.07 242:0.13 243:0.39 245:0.93 246:0.85 247:0.97 248:0.67 253:0.73 255:0.95 256:0.58 259:0.21 260:0.65 262:0.93 265:0.74 266:0.89 267:0.99 268:0.59 276:0.91 279:0.86 282:0.88 283:0.78 284:0.92 285:0.62 287:0.87 289:0.99 290:0.56 292:0.91 297:0.36 299:0.88 300:0.84 +0 1:0.74 11:0.75 12:0.06 17:0.36 18:0.81 21:0.22 27:0.45 28:0.73 29:0.91 30:0.33 34:0.80 36:0.20 37:0.50 39:0.52 43:0.12 45:0.66 46:0.88 55:0.71 64:0.77 66:0.32 69:0.68 70:0.69 71:0.95 74:0.43 81:0.60 83:0.60 86:0.75 91:0.17 98:0.28 99:0.83 101:0.52 107:0.89 108:0.52 110:0.95 114:0.71 122:0.86 123:0.50 124:0.61 125:0.88 126:0.54 127:0.31 129:0.05 131:0.61 133:0.37 135:0.24 139:0.72 144:0.57 145:0.47 146:0.34 147:0.41 149:0.11 150:0.75 154:0.76 155:0.67 157:0.79 159:0.40 160:0.88 161:0.51 165:0.70 166:0.86 167:0.63 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.82 181:0.91 182:0.89 187:0.68 192:0.87 201:0.93 208:0.64 212:0.19 215:0.51 216:0.77 222:0.60 227:0.61 229:0.61 231:0.78 235:0.52 241:0.15 242:0.75 243:0.49 245:0.54 246:0.85 247:0.35 253:0.52 254:0.74 259:0.21 265:0.30 266:0.47 267:0.36 276:0.24 287:0.61 289:0.99 290:0.71 297:0.36 300:0.43 +1 7:0.72 11:0.85 12:0.06 17:0.78 18:0.98 21:0.13 27:0.64 28:0.73 29:0.91 30:0.38 34:0.26 36:0.37 37:0.27 39:0.52 43:0.63 44:0.85 45:0.77 46:0.94 48:0.98 55:0.45 64:0.77 66:0.45 69:0.07 70:0.87 71:0.95 74:0.70 76:0.85 81:0.34 85:0.80 86:0.91 91:0.33 98:0.71 101:0.97 106:0.81 107:0.94 108:0.86 110:1.00 117:0.86 122:0.86 123:0.86 124:0.78 125:0.88 126:0.26 127:0.93 129:0.81 131:0.61 133:0.81 135:0.63 139:0.88 144:0.57 145:0.52 146:0.37 147:0.43 149:0.66 150:0.31 154:0.85 155:0.58 157:0.90 158:0.72 159:0.79 160:0.88 161:0.51 166:0.73 167:0.55 172:0.88 173:0.09 177:0.70 178:0.55 179:0.23 181:0.91 182:0.79 187:0.68 192:0.59 195:0.90 204:0.85 206:0.81 208:0.54 212:0.82 216:0.42 219:0.87 222:0.60 227:0.61 229:0.61 230:0.74 231:0.76 232:0.99 233:0.73 235:0.12 242:0.70 243:0.15 245:0.92 246:0.61 247:0.79 253:0.41 259:0.21 265:0.72 266:0.80 267:0.36 276:0.89 279:0.82 281:0.91 283:0.78 287:0.61 289:0.99 292:0.91 300:0.84 +2 6:0.99 7:0.66 8:0.95 9:0.80 11:0.85 12:0.06 17:0.13 18:0.70 20:0.98 21:0.71 27:0.17 28:0.73 29:0.91 30:0.21 31:0.99 32:0.68 34:0.28 36:0.64 37:0.55 39:0.52 41:0.92 43:0.59 45:0.73 46:0.93 66:0.47 69:0.76 70:0.92 71:0.95 74:0.62 76:0.85 77:0.70 78:0.91 79:0.99 81:0.23 83:0.91 85:0.72 86:0.81 91:0.99 96:0.99 97:0.89 98:0.26 100:0.99 101:0.97 107:0.92 108:0.85 110:0.99 111:0.99 120:0.99 122:0.57 123:0.85 124:0.67 125:0.96 126:0.26 127:0.81 129:0.59 131:0.90 133:0.61 135:0.65 137:0.99 138:1.00 139:0.84 140:0.95 144:0.57 145:0.65 146:0.19 147:0.30 149:0.38 150:0.23 151:0.97 152:0.97 153:0.96 154:0.71 155:0.67 157:0.87 158:0.91 159:0.63 160:0.98 161:0.51 162:0.69 164:0.98 166:0.61 167:0.99 169:0.98 172:0.41 173:0.74 177:0.05 179:0.80 181:0.91 182:0.90 186:0.91 189:0.99 191:0.95 192:0.87 195:0.80 196:0.98 202:0.98 208:0.64 212:0.58 215:0.37 216:0.71 219:0.95 220:0.74 222:0.60 227:0.84 229:0.94 230:0.69 231:0.79 233:0.73 235:0.88 238:0.99 241:0.95 242:0.38 243:0.23 245:0.58 246:0.61 247:0.47 248:0.95 253:0.96 255:0.95 256:0.98 257:0.84 259:0.21 260:0.98 261:0.98 265:0.28 266:0.54 267:0.85 268:0.99 276:0.31 279:0.86 282:0.71 283:0.78 284:0.99 285:0.62 287:0.87 289:0.99 292:0.91 297:0.36 300:0.70 +1 1:0.74 7:0.72 8:0.65 9:0.80 11:0.64 12:0.06 17:0.66 18:0.96 20:0.81 21:0.54 27:0.40 28:0.73 29:0.91 30:0.15 31:0.87 32:0.80 34:0.99 36:0.32 37:0.31 39:0.52 41:0.82 43:0.96 44:0.93 46:0.96 55:0.59 60:0.87 64:0.77 66:0.94 69:0.27 70:0.79 71:0.95 74:0.78 76:0.94 77:0.96 78:0.85 79:0.87 81:0.48 83:0.91 85:0.88 86:0.90 91:0.40 96:0.69 98:0.99 100:0.73 101:0.87 107:0.93 108:0.21 110:1.00 111:0.82 114:0.59 120:0.55 122:0.86 123:0.20 125:0.98 126:0.54 127:0.89 129:0.05 131:0.90 135:0.39 137:0.87 139:0.93 140:0.45 144:0.57 145:0.84 146:0.92 147:0.94 149:0.87 150:0.71 151:0.52 152:0.77 153:0.48 154:0.96 155:0.67 157:0.90 158:0.92 159:0.54 160:0.96 161:0.51 162:0.86 164:0.57 165:0.93 166:0.85 167:0.75 169:0.72 172:0.85 173:0.29 176:0.73 177:0.70 179:0.44 181:0.91 182:0.90 186:0.85 187:0.21 192:0.87 195:0.71 196:0.90 201:0.93 202:0.36 204:0.85 208:0.64 212:0.71 215:0.39 216:0.24 219:0.95 220:0.87 222:0.60 227:0.84 229:0.95 230:0.74 231:0.79 233:0.76 235:0.88 241:0.62 242:0.16 243:0.94 246:0.85 247:0.91 248:0.51 253:0.49 255:0.95 256:0.45 259:0.21 260:0.56 261:0.80 265:0.99 266:0.63 267:0.65 268:0.46 276:0.75 279:0.86 282:0.88 283:0.82 284:0.89 285:0.62 287:0.87 289:0.99 290:0.59 292:0.95 297:0.36 299:0.99 300:0.55 +1 1:0.74 7:0.81 11:0.75 12:0.06 17:0.78 18:0.98 21:0.59 27:0.61 28:0.73 29:0.91 30:0.21 32:0.64 34:0.99 36:0.17 37:0.42 39:0.52 43:0.24 44:0.85 45:0.93 46:0.88 48:0.91 55:0.71 64:0.77 66:0.71 69:0.35 70:0.76 71:0.95 74:0.83 81:0.42 83:0.60 86:0.96 91:0.40 98:0.76 99:0.83 101:0.52 106:0.81 107:0.94 108:0.75 110:1.00 114:0.58 122:0.86 123:0.73 124:0.44 125:0.88 126:0.54 127:0.43 129:0.59 131:0.61 133:0.46 135:1.00 139:0.96 144:0.57 145:0.92 146:0.76 147:0.80 149:0.37 150:0.66 154:0.70 155:0.67 157:0.95 159:0.73 160:0.88 161:0.51 165:0.70 166:0.86 167:0.63 172:0.93 173:0.20 176:0.73 177:0.70 178:0.55 179:0.17 181:0.91 182:0.89 187:0.39 192:0.87 195:0.91 201:0.93 204:0.89 206:0.81 208:0.64 212:0.80 215:0.39 216:0.24 222:0.60 227:0.61 229:0.61 230:0.86 231:0.78 232:0.99 233:0.73 235:0.84 241:0.15 242:0.23 243:0.28 245:0.96 246:0.85 247:0.93 253:0.46 254:0.74 259:0.21 265:0.76 266:0.63 267:0.65 276:0.90 281:0.91 283:0.78 287:0.61 289:0.99 290:0.58 297:0.36 300:0.70 +1 1:0.74 7:0.81 9:0.80 11:0.85 12:0.06 17:0.73 18:0.98 21:0.54 22:0.93 25:0.88 27:0.63 28:0.73 29:0.91 30:0.17 31:0.95 32:0.80 34:0.97 36:0.24 39:0.52 41:0.82 43:0.74 44:0.93 45:0.95 46:0.95 47:0.93 48:0.91 60:0.87 64:0.77 66:0.68 69:0.23 70:0.92 71:0.95 74:0.95 77:0.89 79:0.94 81:0.47 83:0.91 85:0.94 86:0.99 91:0.25 96:0.84 98:0.86 101:0.97 106:0.81 107:0.94 108:0.62 110:1.00 114:0.59 120:0.74 121:0.97 122:0.57 123:0.59 124:0.56 125:0.98 126:0.54 127:0.90 129:0.89 131:0.90 133:0.78 135:0.99 137:0.95 139:0.99 140:0.45 144:0.57 145:0.99 146:0.29 147:0.35 149:0.90 150:0.71 151:0.86 153:0.48 154:0.86 155:0.67 157:0.96 158:0.92 159:0.87 160:0.96 161:0.51 162:0.86 164:0.67 165:0.93 166:0.86 167:0.67 172:0.97 173:0.11 176:0.73 177:0.70 179:0.15 181:0.91 182:0.90 187:0.98 192:0.87 195:0.95 196:0.98 201:0.93 202:0.50 204:0.89 206:0.81 208:0.64 212:0.69 215:0.39 216:0.15 219:0.95 222:0.60 227:0.84 229:0.95 230:0.87 231:0.79 232:0.83 233:0.76 235:0.80 241:0.32 242:0.31 243:0.09 245:0.96 246:0.85 247:0.90 248:0.82 253:0.89 255:0.95 256:0.58 259:0.21 260:0.75 262:0.93 265:0.86 266:0.75 267:0.87 268:0.59 276:0.95 279:0.86 281:0.91 282:0.88 283:0.82 284:0.92 285:0.62 287:0.87 289:0.99 290:0.59 292:0.95 297:0.36 300:0.84 +1 9:0.80 12:0.06 17:0.15 18:0.79 21:0.54 27:0.72 28:0.73 29:0.91 30:0.76 31:0.97 34:0.40 36:0.71 39:0.52 43:0.16 46:0.88 55:0.84 64:0.77 66:0.36 69:0.85 70:0.15 71:0.95 79:0.99 81:0.24 83:0.60 86:0.60 91:0.98 96:0.88 98:0.52 107:0.89 108:0.70 110:0.93 120:0.82 122:0.86 123:0.68 124:0.67 125:0.88 126:0.26 127:0.58 129:0.05 131:0.90 133:0.62 135:0.75 137:0.97 139:0.59 140:0.98 144:0.57 145:0.39 146:0.49 147:0.05 149:0.14 150:0.24 151:0.72 153:0.48 154:0.11 155:0.67 157:0.61 159:0.39 160:0.88 161:0.51 162:0.59 163:0.97 164:0.84 166:0.73 167:0.99 172:0.16 173:0.95 175:0.75 177:0.05 179:0.96 181:0.91 182:0.61 190:0.89 192:0.87 196:0.87 202:0.94 208:0.64 212:0.42 216:0.52 219:0.87 220:0.62 222:0.60 227:0.84 229:0.61 231:0.79 235:0.60 241:0.96 242:0.75 243:0.26 244:0.83 245:0.40 246:0.61 247:0.30 248:0.84 253:0.81 255:0.95 256:0.94 257:0.84 259:0.21 260:0.81 265:0.53 266:0.23 267:0.73 268:0.94 276:0.26 277:0.69 283:0.78 284:0.98 285:0.62 287:0.61 289:0.99 292:0.91 300:0.13 +2 8:0.92 9:0.80 12:0.06 17:0.15 20:0.82 21:0.78 27:0.51 28:0.73 29:0.91 31:0.95 34:0.61 36:0.32 37:0.97 39:0.52 41:0.82 46:0.88 71:0.95 78:0.87 79:0.94 83:0.91 91:0.93 96:0.92 100:0.96 107:0.88 110:0.88 111:0.92 120:0.91 125:0.96 131:0.90 135:0.82 137:0.95 140:0.98 144:0.57 151:0.86 152:0.78 153:0.82 155:0.67 157:0.61 160:0.96 161:0.51 162:0.72 164:0.89 166:0.61 167:0.99 169:0.94 175:0.97 181:0.91 182:0.89 186:0.87 191:0.80 192:0.87 202:0.88 208:0.64 216:0.33 220:0.74 222:0.60 227:0.84 229:0.94 231:0.79 241:0.95 244:0.93 246:0.61 248:0.80 253:0.80 255:0.95 256:0.89 257:0.93 260:0.89 261:0.86 267:0.65 268:0.90 277:0.95 284:0.93 285:0.62 287:0.87 289:0.99 +2 1:0.74 7:0.81 11:0.85 12:0.06 17:0.66 18:0.98 21:0.47 22:0.93 27:0.34 28:0.73 29:0.91 30:0.15 32:0.80 34:0.98 36:0.23 37:0.66 39:0.52 43:0.74 44:0.93 45:0.95 46:0.95 47:0.93 48:0.91 64:0.77 66:0.69 69:0.23 70:0.93 71:0.95 74:0.93 77:0.89 81:0.51 83:0.91 85:0.94 86:0.99 91:0.23 98:0.87 101:0.97 107:0.94 108:0.62 110:1.00 114:0.60 121:0.97 122:0.57 123:0.60 124:0.49 125:0.88 126:0.54 127:0.93 129:0.81 131:0.61 133:0.67 135:0.99 139:0.99 144:0.57 145:0.99 146:0.29 147:0.35 149:0.89 150:0.73 154:0.86 155:0.67 157:0.96 159:0.87 160:0.88 161:0.51 165:0.93 166:0.86 167:0.68 172:0.97 173:0.11 176:0.73 177:0.70 178:0.55 179:0.15 181:0.91 182:0.89 187:0.98 192:0.87 195:0.95 201:0.93 204:0.89 208:0.64 212:0.70 215:0.41 216:0.46 222:0.60 227:0.61 229:0.61 230:0.87 231:0.79 233:0.76 235:0.80 241:0.37 242:0.29 243:0.09 245:0.97 246:0.85 247:0.90 253:0.50 259:0.21 262:0.93 265:0.87 266:0.75 267:0.69 276:0.95 281:0.91 282:0.88 283:0.78 287:0.61 289:0.99 290:0.60 297:0.36 300:0.84 +1 8:0.90 9:0.80 11:0.85 12:0.06 17:0.13 18:0.65 21:0.40 27:0.17 28:0.73 29:0.91 30:0.11 31:0.96 34:0.56 36:0.55 37:0.55 39:0.52 41:0.82 43:0.51 45:0.73 46:0.93 55:0.84 66:0.44 69:0.94 70:0.97 71:0.95 74:0.47 76:0.99 77:0.98 79:0.96 81:0.34 83:0.91 85:0.72 86:0.75 91:0.90 96:0.95 98:0.64 101:0.97 107:0.93 108:0.67 110:0.99 114:0.58 120:0.94 122:0.77 123:0.65 124:0.82 125:0.96 126:0.26 127:0.77 129:0.59 131:0.90 133:0.82 135:0.94 137:0.96 139:0.72 140:0.97 144:0.57 145:0.88 146:0.19 147:0.37 149:0.43 150:0.30 151:0.93 153:0.89 154:0.46 155:0.67 157:0.86 158:0.72 159:0.72 160:0.96 161:0.51 162:0.75 164:0.94 166:0.78 167:0.88 172:0.65 173:0.97 176:0.55 177:0.05 179:0.49 181:0.91 182:0.90 187:0.21 191:0.88 192:0.87 195:0.87 196:0.92 202:0.94 208:0.64 212:0.37 216:0.71 220:0.74 222:0.60 227:0.84 229:0.94 231:0.79 232:0.80 235:0.52 241:0.75 242:0.60 243:0.13 245:0.75 246:0.61 247:0.76 248:0.85 253:0.85 255:0.95 256:0.95 257:0.84 259:0.21 260:0.91 265:0.65 266:0.89 267:0.96 268:0.96 276:0.70 277:0.69 282:0.71 283:0.78 284:0.95 285:0.62 287:0.87 289:0.99 290:0.58 300:0.84 +0 6:0.86 7:0.81 8:0.91 9:0.80 12:0.06 17:0.59 18:0.66 20:0.98 21:0.13 27:0.13 28:0.48 29:0.97 30:0.91 31:0.95 34:0.22 36:0.47 37:0.91 39:0.67 41:0.82 43:0.16 46:0.88 55:0.59 66:0.69 69:0.33 70:0.13 71:0.85 74:0.57 76:1.00 78:0.88 79:0.95 81:0.38 83:0.91 86:0.70 91:0.97 96:0.94 98:0.79 100:0.95 107:0.89 108:0.26 110:0.89 111:0.91 114:0.72 120:0.86 121:0.90 122:0.77 123:0.25 125:0.96 126:0.26 127:0.28 129:0.05 135:0.18 137:0.95 138:0.98 139:0.78 140:0.98 146:0.28 147:0.35 149:0.10 150:0.33 151:0.87 152:0.90 153:0.78 154:0.54 155:0.67 159:0.12 160:0.96 161:0.84 162:0.74 164:0.84 167:0.92 169:0.93 172:0.21 173:0.85 175:0.75 176:0.61 177:0.38 179:0.94 181:0.74 186:0.88 189:0.88 191:0.94 192:0.87 196:0.94 202:0.91 204:0.89 208:0.64 212:0.95 216:0.48 220:0.74 222:0.48 230:0.87 233:0.76 235:0.12 238:0.95 241:0.94 242:0.63 243:0.73 244:0.61 247:0.30 248:0.77 253:0.90 254:0.93 255:0.95 256:0.93 257:0.65 259:0.21 260:0.83 261:0.94 265:0.79 266:0.12 267:0.96 268:0.94 271:0.26 276:0.23 277:0.69 281:0.91 284:0.93 285:0.62 289:0.95 290:0.72 300:0.13 +3 6:0.88 8:0.76 9:0.76 11:0.64 12:0.06 17:0.18 20:0.86 21:0.40 25:0.88 27:0.41 28:0.48 29:0.97 30:0.56 34:0.56 36:0.59 37:0.63 39:0.67 43:0.65 45:0.73 46:0.88 55:0.52 66:0.84 69:0.15 70:0.46 71:0.85 74:0.50 78:0.84 81:0.32 83:0.60 91:0.85 96:0.76 98:0.93 100:0.91 101:0.52 104:0.58 107:0.93 108:0.12 110:0.94 111:0.86 120:0.72 122:0.41 123:0.12 125:0.88 126:0.26 127:0.57 129:0.05 135:0.47 140:0.87 146:0.58 147:0.64 149:0.51 150:0.29 151:0.65 152:0.82 153:0.46 154:0.54 155:0.58 159:0.21 160:0.88 161:0.84 162:0.83 164:0.70 167:0.91 169:0.89 172:0.54 173:0.48 175:0.75 177:0.38 178:0.42 179:0.79 181:0.74 186:0.84 187:0.21 189:0.89 190:0.89 191:0.84 192:0.59 202:0.64 208:0.54 212:0.81 215:0.42 216:0.24 220:0.91 222:0.48 235:0.42 238:0.95 241:0.89 242:0.42 243:0.85 244:0.61 247:0.55 248:0.66 253:0.57 255:0.74 256:0.68 257:0.65 259:0.21 260:0.68 261:0.88 265:0.93 266:0.27 267:0.28 268:0.70 271:0.26 276:0.43 277:0.69 285:0.56 289:0.93 297:0.36 300:0.33 +1 1:0.74 11:0.83 12:0.06 17:0.16 18:0.77 21:0.40 27:0.45 28:0.48 29:0.97 30:0.45 34:0.79 36:0.18 37:0.50 39:0.67 43:0.82 45:0.77 46:0.93 64:0.77 66:0.18 69:0.69 70:0.87 71:0.85 74:0.66 81:0.56 83:0.91 85:0.80 86:0.87 91:0.22 98:0.27 101:0.97 107:0.94 108:0.52 110:0.94 114:0.62 122:0.57 123:0.50 124:0.85 125:0.88 126:0.54 127:0.57 129:0.59 133:0.82 135:0.92 139:0.83 145:0.50 146:0.52 147:0.58 149:0.71 150:0.75 154:0.77 155:0.67 159:0.78 160:0.88 161:0.84 165:0.93 167:0.63 172:0.37 173:0.51 176:0.73 177:0.70 178:0.55 179:0.58 181:0.74 187:0.68 192:0.87 201:0.93 208:0.64 212:0.48 215:0.42 216:0.77 222:0.48 235:0.60 241:0.43 242:0.17 243:0.39 245:0.68 247:0.79 253:0.48 259:0.21 265:0.29 266:0.80 267:0.65 271:0.26 276:0.58 289:0.94 290:0.62 297:0.36 300:0.70 +1 1:0.74 6:0.82 7:0.66 8:0.60 11:0.64 12:0.06 17:0.32 18:0.86 20:0.86 21:0.40 22:0.93 27:0.62 28:0.48 29:0.97 30:0.36 32:0.68 34:0.39 36:0.85 37:0.35 39:0.67 43:0.66 44:0.93 45:0.83 46:0.88 47:0.93 48:0.91 55:0.52 64:0.77 66:0.54 69:0.23 70:0.69 71:0.85 74:0.89 76:0.85 77:0.89 78:0.78 81:0.54 83:0.60 86:0.93 91:0.11 98:0.33 99:0.83 100:0.80 101:0.52 104:0.98 106:0.81 107:0.94 108:0.18 110:0.94 111:0.77 114:0.62 117:0.86 122:0.77 123:0.18 124:0.63 125:0.88 126:0.54 127:0.21 129:0.05 133:0.60 135:0.79 139:0.93 145:0.84 146:0.49 147:0.55 149:0.51 150:0.71 152:0.84 154:0.89 155:0.67 159:0.41 160:0.88 161:0.84 165:0.70 167:0.61 169:0.79 172:0.65 173:0.19 176:0.73 177:0.70 178:0.55 179:0.30 181:0.74 186:0.78 187:0.39 192:0.87 195:0.81 201:0.93 206:0.81 208:0.64 212:0.88 215:0.42 216:0.40 222:0.48 230:0.69 232:0.70 233:0.76 235:0.60 238:0.87 241:0.35 242:0.50 243:0.63 245:0.74 247:0.74 253:0.51 254:0.86 259:0.21 261:0.82 262:0.93 265:0.35 266:0.38 267:0.36 271:0.26 276:0.60 281:0.91 282:0.77 289:0.94 290:0.62 297:0.36 300:0.55 +0 11:0.64 12:0.06 17:0.51 18:0.63 21:0.78 27:0.45 28:0.48 29:0.97 30:0.76 34:0.63 36:0.19 37:0.50 39:0.67 43:0.78 45:0.66 46:0.88 66:0.64 69:0.77 70:0.15 71:0.85 74:0.42 86:0.73 91:0.06 98:0.11 101:0.52 107:0.89 108:0.61 110:0.90 122:0.57 123:0.58 125:0.88 127:0.07 129:0.05 135:0.61 139:0.70 145:0.42 146:0.18 147:0.23 149:0.53 154:0.71 159:0.09 160:0.88 161:0.84 163:0.97 167:0.55 172:0.16 173:0.78 177:0.70 178:0.55 179:0.09 181:0.74 187:0.68 212:0.95 216:0.77 222:0.48 235:0.97 241:0.12 242:0.82 243:0.69 247:0.12 253:0.32 254:0.74 259:0.21 265:0.12 266:0.12 267:0.23 271:0.26 276:0.18 289:0.88 300:0.13 +1 1:0.74 7:0.66 9:0.80 11:0.64 12:0.06 17:0.22 18:0.83 21:0.30 22:0.93 27:0.62 28:0.48 29:0.97 30:0.27 31:0.87 32:0.74 34:0.28 36:0.81 37:0.35 39:0.67 43:0.42 44:0.93 45:0.81 46:0.88 47:0.93 48:0.91 55:0.52 64:0.77 66:0.53 69:0.21 70:0.83 71:0.85 74:0.91 76:0.85 77:0.70 79:0.87 81:0.59 83:0.60 86:0.92 91:0.08 96:0.69 97:0.89 98:0.41 99:0.83 101:0.52 104:0.81 106:0.81 107:0.95 108:0.17 110:0.95 114:0.65 117:0.86 120:0.56 122:0.77 123:0.16 124:0.64 125:0.88 126:0.54 127:0.23 129:0.05 133:0.60 135:0.73 137:0.87 139:0.94 140:0.45 145:0.79 146:0.59 147:0.65 149:0.26 150:0.74 151:0.52 153:0.69 154:0.88 155:0.67 158:0.86 159:0.44 160:0.88 161:0.84 162:0.86 164:0.62 165:0.70 167:0.61 172:0.63 173:0.18 176:0.73 177:0.70 179:0.33 181:0.74 187:0.39 192:0.87 195:0.81 196:0.91 201:0.93 202:0.46 206:0.81 208:0.64 212:0.86 215:0.44 216:0.40 219:0.95 220:0.87 222:0.48 230:0.69 232:0.70 233:0.76 235:0.52 241:0.35 242:0.45 243:0.62 245:0.73 247:0.79 248:0.52 253:0.55 254:0.86 255:0.95 256:0.45 259:0.21 260:0.56 262:0.93 265:0.43 266:0.47 267:0.52 268:0.55 271:0.26 276:0.61 279:0.86 281:0.91 282:0.83 283:0.67 284:0.90 285:0.62 289:0.94 290:0.65 292:0.87 297:0.36 300:0.70 +1 7:0.66 11:0.64 12:0.06 17:0.73 18:0.64 20:0.76 21:0.40 27:0.42 28:0.48 29:0.97 30:0.21 32:0.78 34:0.83 36:0.27 37:0.62 39:0.67 43:0.63 44:0.93 45:0.66 46:0.88 66:0.72 69:0.73 70:0.37 71:0.85 74:0.51 77:0.70 78:0.72 81:0.32 86:0.68 91:0.44 98:0.58 100:0.73 101:0.52 107:0.91 108:0.56 110:0.91 111:0.72 120:0.55 123:0.53 125:0.88 126:0.26 127:0.17 129:0.05 135:0.88 139:0.77 140:0.45 145:0.69 146:0.58 147:0.64 149:0.33 150:0.29 151:0.48 152:0.75 153:0.48 154:0.52 159:0.21 160:0.88 161:0.84 162:0.86 163:0.81 164:0.53 167:0.61 169:0.72 172:0.27 173:0.79 177:0.70 179:0.75 181:0.74 186:0.73 187:0.39 190:0.89 195:0.66 202:0.36 212:0.42 215:0.42 216:0.49 220:0.87 222:0.48 230:0.69 233:0.76 235:0.42 241:0.35 242:0.45 243:0.75 244:0.61 247:0.35 248:0.48 253:0.36 256:0.45 259:0.21 260:0.55 261:0.73 265:0.59 266:0.23 267:0.65 268:0.46 271:0.26 276:0.23 282:0.86 285:0.47 289:0.92 297:0.36 300:0.25 +1 6:0.90 8:0.88 9:0.76 12:0.06 17:0.82 20:0.93 21:0.59 27:0.56 28:0.48 29:0.97 30:0.95 34:0.33 36:0.70 37:0.24 39:0.67 43:0.18 44:0.90 46:0.88 55:0.92 66:0.20 69:0.21 71:0.85 74:0.53 76:0.94 77:0.70 78:0.80 81:0.24 83:0.60 91:0.91 96:0.82 98:0.12 100:0.87 104:0.58 107:0.88 108:0.17 110:0.88 111:0.81 114:0.57 120:0.79 123:0.16 124:0.61 125:0.88 126:0.26 127:0.49 129:0.59 133:0.47 135:0.42 139:0.75 140:0.92 145:0.63 146:0.05 147:0.05 149:0.09 150:0.24 151:0.65 152:0.86 153:0.72 154:0.12 155:0.67 159:0.19 160:0.88 161:0.84 162:0.85 164:0.71 167:0.92 169:0.84 172:0.05 173:0.56 175:0.91 176:0.61 177:0.05 179:1.00 181:0.74 186:0.80 189:0.92 190:0.89 191:0.75 192:0.87 195:0.55 202:0.70 204:0.85 208:0.64 216:0.11 222:0.48 235:0.68 238:0.95 241:0.97 243:0.40 244:0.83 245:0.36 248:0.63 253:0.68 255:0.74 256:0.77 257:0.84 259:0.21 260:0.73 261:0.86 265:0.12 267:0.23 268:0.77 271:0.26 277:0.87 281:0.91 282:0.77 285:0.56 289:0.93 290:0.57 300:0.08 +4 8:0.83 9:0.76 12:0.06 17:0.24 21:0.78 27:0.08 28:0.48 29:0.97 34:0.61 36:0.28 37:0.82 39:0.67 46:0.88 71:0.85 83:0.60 91:0.95 96:0.94 107:0.88 110:0.88 120:0.82 125:0.88 135:0.83 138:0.99 140:0.96 151:0.70 153:0.48 155:0.58 160:0.88 161:0.84 162:0.74 164:0.81 167:0.92 175:0.97 181:0.74 190:0.89 191:0.90 192:0.59 202:0.90 208:0.54 216:0.43 220:0.62 222:0.48 232:0.78 241:0.94 244:0.93 248:0.79 253:0.87 255:0.74 256:0.92 257:0.93 259:0.21 260:0.77 267:0.52 268:0.93 271:0.26 277:0.95 285:0.56 289:0.93 +2 6:0.89 8:0.72 11:0.64 12:0.06 17:0.26 18:0.84 20:0.88 21:0.78 27:0.21 28:0.48 29:0.97 30:0.14 34:0.75 36:0.83 37:0.74 39:0.67 43:0.57 45:0.85 46:0.88 66:0.92 69:0.64 70:0.80 71:0.85 74:0.88 76:0.94 78:0.80 86:0.93 91:0.51 98:0.77 100:0.85 101:0.52 107:0.96 108:0.49 110:0.96 111:0.80 120:0.74 122:0.57 123:0.46 125:0.88 127:0.26 129:0.05 135:0.17 139:0.92 140:0.45 146:0.75 147:0.79 149:0.28 151:0.63 152:0.83 153:0.72 154:0.77 155:0.58 159:0.31 160:0.88 161:0.84 162:0.52 164:0.53 167:0.62 169:0.83 172:0.79 173:0.69 177:0.70 179:0.31 181:0.74 186:0.80 187:0.39 189:0.90 190:0.89 192:0.51 202:0.62 204:0.85 208:0.54 212:0.90 216:0.95 222:0.48 235:0.52 238:0.92 241:0.41 242:0.23 243:0.93 247:0.86 248:0.61 253:0.46 254:0.74 256:0.45 259:0.21 260:0.67 261:0.84 265:0.77 266:0.27 267:0.76 268:0.57 271:0.26 276:0.69 281:0.91 285:0.47 289:0.94 300:0.55 +2 1:0.69 6:0.94 8:0.97 9:0.80 11:0.64 12:0.06 17:0.01 18:0.70 20:0.91 21:0.13 27:0.06 28:0.48 29:0.97 30:0.45 31:1.00 34:0.40 36:0.67 37:0.96 39:0.67 41:0.82 43:0.57 44:0.90 45:0.66 46:0.88 64:0.77 66:0.27 69:0.51 70:0.25 71:0.85 74:0.59 76:0.85 77:0.96 78:0.92 79:1.00 81:0.54 83:0.91 86:0.70 91:1.00 96:1.00 97:1.00 98:0.36 99:0.83 100:0.98 101:0.52 107:0.89 108:0.39 110:0.89 111:0.96 114:0.75 120:0.78 122:0.80 123:0.37 124:0.61 125:0.96 126:0.44 127:0.63 129:0.59 133:0.47 135:0.28 137:1.00 138:0.99 139:0.82 140:1.00 145:0.63 146:0.31 147:0.37 149:0.28 150:0.58 151:0.87 152:0.85 153:0.99 154:0.46 155:0.67 158:0.79 159:0.31 160:0.96 161:0.84 162:0.75 163:0.96 164:0.65 165:0.70 167:0.93 169:0.97 172:0.10 173:0.67 175:0.75 176:0.66 177:0.38 179:0.98 181:0.74 186:0.91 189:0.95 191:0.96 192:0.87 195:0.59 196:1.00 201:0.68 202:0.97 204:0.85 208:0.64 212:0.61 216:0.68 219:0.92 222:0.48 235:0.22 238:0.97 241:1.00 242:0.63 243:0.46 244:0.61 245:0.40 247:0.30 248:0.77 253:0.99 255:0.95 256:0.98 257:0.65 259:0.21 260:0.76 261:0.96 265:0.38 266:0.17 267:0.69 268:0.98 271:0.26 276:0.16 277:0.69 279:0.82 281:0.91 282:0.77 284:1.00 285:0.62 289:0.94 290:0.75 292:0.95 300:0.18 +2 6:0.87 8:0.93 12:0.79 17:0.03 20:0.76 21:0.13 27:0.25 28:0.46 30:0.21 34:0.75 36:0.99 37:0.86 43:0.39 55:0.59 66:0.20 69:0.55 70:0.37 78:1.00 81:0.94 91:0.95 98:0.22 100:0.94 104:0.74 108:0.42 111:0.99 120:0.87 123:0.57 124:0.73 126:0.54 127:0.56 129:0.81 133:0.67 135:0.63 140:0.45 146:0.22 147:0.27 149:0.34 150:0.89 151:0.62 152:0.75 153:0.98 154:0.30 159:0.43 161:0.80 162:0.51 163:0.81 164:0.94 167:0.86 169:0.91 172:0.10 173:0.59 177:0.05 179:0.96 181:0.81 186:1.00 189:0.89 191:0.87 202:0.95 212:0.42 215:0.84 216:0.50 220:0.62 235:0.12 238:0.80 241:0.98 242:0.82 243:0.40 245:0.40 247:0.12 248:0.82 256:0.91 259:0.21 260:0.88 261:0.81 265:0.17 266:0.23 267:0.69 268:0.92 271:0.40 276:0.21 283:0.60 285:0.62 297:0.36 300:0.25 +2 6:0.91 8:0.95 12:0.79 17:0.11 20:0.97 21:0.78 27:0.08 28:0.46 34:0.56 36:0.52 37:0.93 78:0.93 91:0.97 100:0.95 111:0.93 120:0.87 135:0.47 140:0.87 151:0.71 152:0.89 153:0.72 161:0.80 162:0.47 164:0.93 167:0.86 169:0.92 175:0.75 178:0.48 181:0.81 186:0.93 189:0.92 191:0.93 202:0.98 216:0.70 220:0.62 235:0.31 238:0.90 241:0.95 244:0.61 248:0.82 256:0.91 257:0.65 259:0.21 260:0.88 261:0.94 267:0.65 268:0.92 271:0.40 277:0.69 283:0.60 285:0.62 +2 6:0.87 7:0.81 8:0.86 12:0.79 17:0.12 20:0.97 21:0.54 27:0.14 28:0.46 32:0.80 34:0.28 36:0.47 37:0.80 78:0.94 81:0.86 91:0.98 100:0.96 104:0.73 111:0.94 120:0.92 126:0.54 135:0.37 140:0.45 145:0.54 150:0.70 151:0.75 152:0.90 153:0.84 161:0.80 162:0.51 164:0.97 167:0.86 169:0.94 175:0.75 181:0.81 186:0.94 189:0.89 191:0.90 195:0.53 202:0.97 215:0.58 216:0.59 220:0.62 230:0.87 233:0.76 235:0.60 238:0.92 241:0.95 244:0.61 248:0.89 256:0.95 257:0.65 259:0.21 260:0.93 261:0.95 267:0.73 268:0.96 271:0.40 277:0.69 283:0.60 285:0.62 297:0.36 +2 6:0.89 7:0.81 8:0.91 12:0.79 17:0.11 20:0.97 21:0.30 25:0.88 27:0.11 28:0.46 32:0.80 34:0.39 36:0.57 37:0.90 43:0.28 66:0.36 69:0.77 78:0.93 81:0.91 91:0.95 98:0.11 100:0.93 104:0.76 108:0.60 111:0.92 120:0.89 123:0.58 126:0.54 127:0.07 129:0.05 135:0.39 140:0.80 145:0.72 146:0.05 147:0.05 149:0.12 150:0.83 151:0.62 152:0.89 153:0.98 154:0.21 159:0.05 161:0.80 162:0.49 164:0.96 167:0.85 169:0.91 172:0.05 173:1.00 177:0.05 178:0.42 181:0.81 186:0.93 189:0.90 191:0.90 195:0.57 202:0.98 215:0.72 216:0.66 220:0.62 230:0.87 233:0.76 235:0.31 238:0.89 241:0.89 243:0.51 248:0.84 256:0.95 260:0.89 261:0.93 265:0.12 267:0.59 268:0.96 271:0.40 283:0.60 285:0.62 297:0.36 +2 6:0.92 7:0.81 8:0.83 12:0.79 17:0.02 20:0.95 21:0.78 27:0.34 28:0.46 34:0.26 36:0.42 37:0.46 78:0.89 91:0.87 100:0.94 111:0.90 120:0.59 135:0.51 140:0.92 145:0.85 151:0.51 152:0.88 153:0.88 161:0.80 162:0.47 164:0.79 167:0.83 169:0.92 175:0.75 178:0.51 181:0.81 186:0.89 189:0.93 191:0.70 202:0.91 216:0.40 220:0.74 230:0.65 233:0.63 235:0.12 238:0.93 241:0.84 244:0.61 248:0.52 256:0.71 257:0.65 259:0.21 260:0.59 261:0.92 267:1.00 268:0.74 271:0.40 277:0.69 285:0.62 +1 6:0.88 8:0.86 12:0.79 17:0.03 20:0.92 21:0.78 27:0.17 28:0.46 34:0.20 36:0.35 37:0.65 78:0.91 91:0.99 100:0.90 111:0.89 120:0.88 135:0.51 140:0.80 151:0.80 152:0.86 153:0.93 161:0.80 162:0.48 164:0.93 167:0.85 169:0.88 175:0.75 178:0.42 181:0.81 186:0.90 189:0.89 191:0.91 202:0.96 216:0.56 220:0.62 238:0.88 241:0.90 244:0.61 248:0.84 256:0.90 257:0.65 259:0.21 260:0.89 261:0.91 267:0.76 268:0.91 271:0.40 277:0.69 283:0.60 285:0.62 +2 6:0.87 7:0.81 8:0.90 12:0.79 17:0.45 20:0.78 21:0.74 27:0.34 28:0.46 30:0.33 32:0.80 34:0.34 36:0.65 37:0.83 43:0.33 55:0.52 66:0.35 69:0.68 70:0.92 78:0.97 81:0.73 91:0.84 98:0.59 100:0.94 104:0.58 108:0.71 111:0.95 120:0.75 123:0.69 124:0.70 126:0.54 127:0.44 129:0.81 133:0.67 135:0.54 140:0.45 145:0.79 146:0.29 147:0.36 149:0.65 150:0.54 151:0.59 152:0.76 153:0.91 154:0.25 159:0.66 161:0.80 162:0.66 164:0.87 167:0.85 169:0.92 172:0.51 173:0.57 177:0.05 179:0.54 181:0.81 186:0.97 189:0.88 191:0.83 195:0.87 202:0.81 212:0.39 215:0.46 216:0.27 220:0.62 230:0.87 233:0.76 235:0.88 238:0.89 241:0.92 242:0.82 243:0.43 245:0.74 247:0.12 248:0.68 256:0.81 259:0.21 260:0.76 261:0.84 265:0.60 266:0.75 267:1.00 268:0.83 271:0.40 276:0.59 285:0.62 297:0.36 300:0.84 +2 6:0.94 8:0.83 12:0.79 17:0.28 20:0.94 21:0.78 27:0.21 28:0.46 30:0.09 34:0.37 36:0.64 37:0.74 43:0.36 55:0.59 66:0.05 69:0.61 70:0.99 78:0.80 91:0.90 98:0.18 100:0.85 108:0.89 111:0.81 120:0.94 123:0.96 124:0.99 127:0.76 129:1.00 133:0.99 135:0.30 140:0.80 146:0.05 147:0.05 149:0.67 151:0.83 152:0.91 153:0.96 154:0.27 159:0.96 161:0.80 162:0.47 164:0.88 167:0.64 169:0.84 172:0.51 173:0.15 177:0.05 178:0.42 179:0.21 181:0.81 186:0.81 187:0.39 189:0.96 191:0.79 202:0.94 212:0.23 216:0.95 235:0.31 238:0.92 241:0.62 242:0.82 243:0.06 245:0.81 247:0.12 248:0.91 256:0.84 259:0.21 260:0.94 261:0.85 265:0.16 266:0.98 267:0.69 268:0.85 271:0.40 276:0.91 283:0.82 285:0.62 300:0.94 +1 12:0.79 17:0.55 21:0.59 27:0.45 28:0.46 30:0.45 32:0.80 34:0.92 36:0.25 37:0.50 43:0.32 55:0.59 66:0.49 69:0.70 70:0.25 81:0.84 91:0.20 98:0.30 108:0.53 123:0.51 124:0.48 126:0.54 127:0.33 129:0.59 133:0.47 135:0.78 145:0.45 146:0.38 147:0.45 149:0.28 150:0.65 154:0.24 159:0.44 161:0.80 163:0.87 167:0.48 172:0.16 173:0.70 177:0.05 178:0.55 179:0.96 181:0.81 187:0.68 195:0.72 212:0.61 215:0.55 216:0.77 235:0.68 241:0.07 242:0.82 243:0.60 245:0.39 247:0.12 259:0.21 265:0.32 266:0.17 267:0.36 271:0.40 276:0.16 297:0.36 300:0.18 +2 1:0.69 6:0.91 8:0.81 9:0.79 11:0.78 12:0.06 17:0.40 18:0.84 20:0.91 27:0.06 28:0.57 29:0.62 30:0.33 31:0.96 34:0.58 36:0.69 37:0.68 39:0.37 41:0.94 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 78:0.97 79:0.96 81:0.80 83:0.60 85:0.80 86:0.91 91:0.63 96:0.88 98:0.23 100:0.99 101:0.87 104:0.73 108:0.09 111:0.99 114:0.98 120:0.80 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.87 133:0.59 135:0.88 137:0.96 139:0.88 140:0.45 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 151:0.94 152:0.91 153:0.80 154:0.84 155:0.66 157:0.81 159:0.71 161:0.62 162:0.67 164:0.80 165:0.93 166:0.82 167:0.84 169:1.00 172:0.57 173:0.12 176:0.73 177:0.70 179:0.74 181:0.54 182:0.79 186:0.96 187:0.39 189:0.92 191:0.84 192:0.87 196:0.97 201:0.67 202:0.75 208:0.64 212:0.89 215:0.96 216:0.17 220:0.74 222:0.25 227:0.92 229:0.88 231:0.73 232:0.77 235:0.22 238:0.98 241:0.74 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 248:0.87 253:0.87 255:0.78 256:0.82 259:0.21 260:0.80 261:0.97 265:0.25 266:0.23 267:0.19 268:0.77 271:0.93 276:0.31 284:0.96 285:0.62 287:0.91 290:0.98 297:0.36 300:0.70 +1 1:0.69 6:0.79 7:0.81 8:0.60 9:0.79 11:0.78 12:0.06 17:0.75 18:0.94 20:0.75 21:0.47 27:0.40 28:0.57 29:0.62 30:0.60 31:0.95 32:0.80 34:0.25 36:0.56 37:0.25 39:0.37 41:0.82 43:0.86 44:0.93 45:0.92 60:0.87 64:0.77 66:0.53 69:0.39 70:0.68 71:0.80 74:0.97 77:0.96 78:0.88 79:0.94 81:0.76 83:0.60 85:0.98 86:0.99 91:0.69 96:0.83 98:0.77 99:0.83 100:0.86 101:0.87 104:0.90 108:0.80 111:0.85 114:0.80 120:0.72 121:0.90 122:0.57 123:0.78 124:0.55 126:0.54 127:0.76 129:0.59 131:0.87 133:0.46 135:0.87 137:0.95 139:0.99 140:0.45 144:0.57 145:0.83 146:0.79 147:0.82 149:0.99 150:0.60 151:0.90 152:0.74 153:0.69 154:0.83 155:0.66 157:0.88 158:0.92 159:0.72 161:0.62 162:0.86 164:0.62 165:0.93 166:0.81 167:0.64 169:0.78 172:0.92 173:0.27 176:0.73 177:0.70 179:0.19 181:0.54 182:0.79 186:0.97 187:0.39 189:0.82 192:0.87 195:0.85 196:0.98 201:0.66 202:0.46 204:0.85 208:0.64 212:0.73 215:0.63 216:0.41 219:0.95 222:0.25 227:0.92 229:0.88 230:0.87 231:0.73 232:0.91 233:0.76 235:0.84 238:0.81 241:0.32 242:0.32 243:0.43 245:0.98 246:0.89 247:0.74 248:0.80 253:0.89 255:0.78 256:0.45 259:0.21 260:0.73 261:0.76 265:0.77 266:0.71 267:0.44 268:0.55 271:0.93 276:0.91 279:0.80 281:0.91 282:0.88 283:0.82 284:0.90 285:0.62 287:0.91 290:0.80 292:0.95 297:0.36 299:0.97 300:0.84 +4 1:0.65 6:0.91 7:0.70 8:0.82 9:0.80 11:0.50 12:0.06 17:0.70 20:0.97 21:0.40 27:0.06 28:0.57 29:0.62 30:0.95 31:0.98 32:0.67 34:0.23 36:0.30 37:0.73 39:0.37 41:0.95 43:0.27 45:0.66 66:0.54 69:0.91 71:0.80 74:0.48 77:0.70 78:0.94 79:0.98 81:0.59 83:0.60 91:0.92 96:0.94 98:0.10 99:0.83 100:0.98 101:0.52 104:0.58 108:0.82 111:0.97 114:0.78 120:0.89 123:0.81 126:0.44 127:0.07 129:0.05 131:0.87 135:0.42 137:0.98 140:0.45 144:0.57 145:0.52 146:0.13 147:0.18 149:0.18 150:0.51 151:0.96 152:0.90 153:0.86 154:0.19 155:0.67 157:0.76 159:0.08 161:0.62 162:0.59 164:0.83 165:0.70 166:0.82 167:0.94 169:0.98 172:0.10 173:1.00 175:0.75 176:0.63 177:0.38 179:0.09 181:0.54 182:0.79 186:0.94 189:0.92 191:0.96 192:0.87 195:0.67 201:0.61 202:0.79 204:0.84 208:0.64 215:0.67 216:0.26 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.76 235:0.60 238:0.97 241:0.85 243:0.63 244:0.83 246:0.89 248:0.94 253:0.92 255:0.95 256:0.79 257:0.65 259:0.21 260:0.89 261:0.96 265:0.10 267:0.52 268:0.80 271:0.93 277:0.69 282:0.75 284:0.97 285:0.62 287:0.91 290:0.78 297:0.36 300:0.08 +1 1:0.66 6:0.81 7:0.81 8:0.60 11:0.78 12:0.06 17:0.97 18:0.89 20:0.77 21:0.30 22:0.93 27:0.16 28:0.57 29:0.62 30:0.13 32:0.80 34:0.68 36:0.39 37:0.44 39:0.37 43:0.74 44:0.93 45:0.85 47:0.93 60:0.95 64:0.77 66:0.07 69:0.85 70:0.98 71:0.80 74:0.89 77:0.70 78:0.82 81:0.74 83:0.60 85:0.96 86:0.96 91:0.76 98:0.46 100:0.73 101:0.87 104:0.86 106:0.81 108:0.85 111:0.80 114:0.86 117:0.86 121:0.90 123:0.93 124:0.91 126:0.54 127:0.79 129:0.93 131:0.61 133:0.90 135:0.98 139:0.97 144:0.57 145:0.59 146:0.30 147:0.52 149:0.95 150:0.57 152:0.75 154:0.64 155:0.56 157:0.86 158:0.92 159:0.86 161:0.62 165:0.93 166:0.82 167:0.55 169:0.72 172:0.68 173:0.67 176:0.73 177:0.05 178:0.55 179:0.19 181:0.54 182:0.78 186:0.82 187:0.21 189:0.83 192:0.55 195:0.95 201:0.64 204:0.83 206:0.81 208:0.64 212:0.67 215:0.72 216:0.28 219:0.95 222:0.25 227:0.61 229:0.61 230:0.87 231:0.73 232:0.88 233:0.76 235:0.60 238:0.81 241:0.02 242:0.29 243:0.13 245:0.94 246:0.89 247:0.90 253:0.71 257:0.84 259:0.21 261:0.76 262:0.93 265:0.34 266:0.84 267:0.44 271:0.93 276:0.91 277:0.69 279:0.81 281:0.91 282:0.88 283:0.82 287:0.61 290:0.86 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.63 6:0.91 7:0.70 8:0.80 9:0.79 11:0.50 12:0.06 17:0.95 18:0.73 20:0.78 21:0.59 27:0.16 28:0.57 29:0.62 30:0.21 31:0.94 32:0.63 34:0.45 36:0.49 37:0.72 39:0.37 41:0.82 43:0.56 44:0.92 45:0.81 64:0.77 66:0.20 69:0.44 70:0.91 71:0.80 74:0.55 76:0.94 77:0.70 78:0.88 79:0.93 81:0.61 83:0.60 86:0.77 91:0.82 96:0.80 98:0.48 99:0.83 100:0.86 101:0.52 104:0.58 108:0.82 111:0.86 114:0.73 120:0.69 122:0.51 123:0.81 124:0.82 126:0.54 127:0.46 129:0.81 131:0.87 133:0.78 135:0.32 137:0.94 139:0.77 140:0.45 144:0.57 145:0.77 146:0.19 147:0.25 149:0.42 150:0.49 151:0.86 152:0.87 153:0.48 154:0.65 155:0.66 157:0.81 159:0.63 161:0.62 162:0.86 163:0.79 164:0.57 165:0.70 166:0.81 167:0.72 169:0.82 172:0.32 173:0.33 176:0.70 177:0.70 179:0.65 181:0.54 182:0.78 186:0.88 187:0.39 189:0.92 192:0.87 195:0.83 196:0.93 201:0.61 202:0.36 204:0.83 208:0.64 212:0.16 215:0.55 216:0.30 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.66 235:0.80 238:0.86 241:0.59 242:0.15 243:0.26 244:0.61 245:0.64 246:0.89 247:0.79 248:0.77 253:0.79 254:0.84 255:0.78 256:0.45 259:0.21 260:0.70 261:0.87 265:0.50 266:0.84 267:0.28 268:0.46 271:0.93 276:0.53 282:0.72 284:0.89 285:0.62 287:0.91 290:0.73 297:0.36 300:0.70 +1 1:0.69 7:0.70 11:0.78 12:0.06 17:0.90 18:0.90 21:0.22 22:0.93 27:0.15 28:0.57 29:0.62 30:0.45 32:0.80 34:0.31 36:0.40 37:0.87 39:0.37 43:0.94 44:0.93 45:0.99 47:0.93 48:0.91 60:0.87 64:0.77 66:0.51 69:0.19 70:0.79 71:0.80 74:0.98 77:0.89 81:0.81 83:0.60 85:0.95 86:0.96 91:0.51 98:0.77 99:0.83 101:0.87 104:0.58 108:0.87 114:0.90 122:0.51 123:0.87 124:0.64 126:0.54 127:0.93 129:0.59 131:0.61 133:0.66 135:0.26 139:0.97 144:0.57 145:0.45 146:0.92 147:0.94 149:0.99 150:0.71 154:0.94 155:0.57 157:0.88 158:0.92 159:0.84 161:0.62 165:0.93 166:0.82 167:0.55 172:0.98 173:0.11 176:0.73 177:0.70 178:0.55 179:0.11 181:0.54 182:0.78 187:0.21 191:0.78 192:0.56 195:0.94 201:0.68 204:0.83 208:0.64 212:0.91 215:0.79 216:0.13 219:0.95 222:0.25 227:0.61 229:0.61 230:0.73 231:0.73 232:0.75 233:0.76 235:0.68 241:0.02 242:0.28 243:0.43 245:0.99 246:0.89 247:0.86 253:0.76 259:0.21 262:0.93 265:0.77 266:0.54 267:0.52 271:0.93 276:0.98 279:0.80 281:0.86 282:0.88 283:0.82 287:0.61 290:0.90 292:0.95 297:0.36 299:0.88 300:0.70 +2 1:0.69 12:0.06 17:0.16 18:0.67 20:0.74 25:0.88 27:0.61 28:0.57 29:0.62 34:0.96 36:0.63 37:0.78 39:0.37 43:0.19 64:0.77 66:0.36 69:0.35 71:0.80 78:0.85 81:0.80 83:0.60 86:0.66 87:0.98 91:0.85 98:0.13 100:0.73 104:0.54 108:0.27 111:0.82 114:0.98 123:0.26 126:0.54 127:0.08 129:0.05 131:0.61 135:0.42 139:0.64 144:0.57 146:0.05 147:0.05 149:0.07 150:0.68 152:0.74 154:0.12 155:0.54 157:0.61 159:0.05 161:0.62 165:0.93 166:0.82 167:0.66 169:0.72 172:0.05 173:0.72 175:0.91 176:0.73 177:0.05 178:0.55 181:0.54 182:0.78 186:0.85 187:0.21 192:0.50 201:0.67 208:0.64 215:0.96 216:0.24 222:0.25 227:0.61 229:0.61 231:0.73 232:0.76 235:0.22 241:0.41 243:0.51 244:0.93 246:0.89 253:0.69 257:0.84 259:0.21 261:0.74 265:0.13 267:0.44 271:0.93 277:0.87 287:0.61 290:0.98 297:0.36 +2 1:0.69 6:0.91 8:0.83 11:0.78 12:0.06 17:0.64 18:0.84 20:0.79 27:0.06 28:0.57 29:0.62 30:0.33 34:0.62 36:0.70 37:0.68 39:0.37 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 78:0.97 81:0.80 83:0.60 85:0.80 86:0.91 91:0.53 98:0.23 100:0.99 101:0.87 104:0.73 108:0.09 111:0.99 114:0.98 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.61 133:0.59 135:0.88 139:0.88 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 152:0.92 154:0.84 155:0.54 157:0.81 159:0.71 161:0.62 165:0.93 166:0.82 167:0.72 169:1.00 172:0.57 173:0.12 176:0.73 177:0.70 178:0.55 179:0.74 181:0.54 182:0.78 186:0.98 187:0.39 189:0.93 192:0.50 201:0.67 208:0.64 212:0.89 215:0.96 216:0.17 222:0.25 227:0.61 229:0.61 231:0.73 232:0.77 235:0.22 238:0.97 241:0.57 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 253:0.71 259:0.21 261:0.97 265:0.25 266:0.23 267:0.36 271:0.93 276:0.31 287:0.61 290:0.98 297:0.36 300:0.70 +3 1:0.69 6:0.86 8:0.68 9:0.79 11:0.78 12:0.06 17:0.96 18:0.82 20:0.91 27:0.16 28:0.57 29:0.62 30:0.41 31:0.96 34:0.26 36:0.43 37:0.24 39:0.37 41:0.88 43:0.86 45:0.73 64:0.77 66:0.54 69:0.41 70:0.77 71:0.80 74:0.61 78:0.88 79:0.95 81:0.80 83:0.60 85:0.80 86:0.87 91:0.94 96:0.86 98:0.63 100:0.88 101:0.87 104:0.58 108:0.32 111:0.87 114:0.98 120:0.77 122:0.57 123:0.31 124:0.50 126:0.54 127:0.66 129:0.05 131:0.87 133:0.43 135:0.28 137:0.96 139:0.84 140:0.45 144:0.57 146:0.44 147:0.50 149:0.82 150:0.68 151:0.93 152:0.85 153:0.77 154:0.83 155:0.66 157:0.82 159:0.27 161:0.62 162:0.86 164:0.70 165:0.93 166:0.82 167:0.94 169:0.86 172:0.48 173:0.62 176:0.73 177:0.70 179:0.76 181:0.54 182:0.79 186:0.88 189:0.87 191:0.79 192:0.87 196:0.95 201:0.67 202:0.55 208:0.64 212:0.84 215:0.96 216:0.02 222:0.25 227:0.92 229:0.88 231:0.73 232:0.75 235:0.22 238:0.86 241:0.86 242:0.19 243:0.63 245:0.66 246:0.89 247:0.74 248:0.84 253:0.85 255:0.79 256:0.58 259:0.21 260:0.77 261:0.87 265:0.64 266:0.27 267:0.84 268:0.64 271:0.93 276:0.39 284:0.93 285:0.62 287:0.91 290:0.98 297:0.36 300:0.55 +2 1:0.69 8:0.70 9:0.76 11:0.78 12:0.06 17:0.49 18:0.84 27:0.06 28:0.57 29:0.62 30:0.33 31:0.94 34:0.58 36:0.69 37:0.68 39:0.37 41:0.82 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 79:0.94 81:0.80 83:0.60 85:0.80 86:0.91 91:0.56 96:0.82 98:0.23 101:0.87 104:0.73 108:0.09 114:0.98 120:0.72 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.87 133:0.59 135:0.87 137:0.94 139:0.88 140:0.45 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 151:0.90 153:0.69 154:0.84 155:0.66 157:0.81 159:0.71 161:0.62 162:0.49 164:0.65 165:0.93 166:0.82 167:0.85 172:0.57 173:0.12 176:0.73 177:0.70 179:0.74 181:0.54 182:0.78 187:0.39 191:0.87 192:0.87 196:0.95 201:0.67 202:0.71 208:0.64 212:0.89 215:0.96 216:0.17 222:0.25 227:0.91 229:0.87 231:0.73 232:0.77 235:0.22 241:0.75 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 248:0.80 253:0.83 255:0.78 256:0.75 259:0.21 260:0.73 265:0.25 266:0.23 267:0.23 268:0.58 271:0.93 276:0.31 284:0.90 285:0.62 287:0.91 290:0.98 297:0.36 300:0.70 +2 1:0.69 7:0.81 8:0.85 9:0.75 11:0.50 12:0.06 17:0.90 18:0.73 21:0.30 27:0.16 28:0.57 29:0.62 30:0.30 32:0.80 34:0.67 36:0.55 37:0.72 39:0.37 43:0.60 44:0.92 45:0.77 64:0.77 66:0.84 69:0.43 70:0.48 71:0.80 74:0.69 76:0.94 77:0.98 78:0.85 81:0.81 83:0.60 86:0.75 91:0.84 96:0.86 98:0.75 99:0.95 101:0.52 104:0.58 108:0.33 111:0.83 114:0.82 120:0.78 121:0.90 122:0.51 123:0.32 126:0.54 127:0.25 129:0.05 131:0.87 135:0.37 139:0.85 140:0.45 144:0.57 145:0.79 146:0.44 147:0.50 149:0.46 150:0.73 151:0.81 153:0.77 154:0.66 155:0.65 157:0.81 159:0.16 161:0.62 162:0.61 164:0.73 165:0.93 166:0.81 167:0.86 169:0.84 172:0.54 173:0.72 176:0.70 177:0.70 179:0.61 181:0.54 182:0.79 187:0.39 190:0.89 192:0.86 195:0.54 201:0.68 202:0.67 204:0.85 208:0.64 212:0.92 215:0.67 216:0.30 220:0.62 222:0.25 227:0.92 229:0.88 230:0.87 231:0.73 232:0.75 233:0.76 235:0.84 241:0.76 242:0.24 243:0.85 244:0.61 246:0.89 247:0.74 248:0.85 253:0.86 254:0.80 255:0.73 256:0.68 259:0.21 260:0.78 265:0.76 266:0.17 267:0.52 268:0.68 271:0.93 276:0.44 281:0.91 282:0.88 285:0.56 287:0.91 290:0.82 297:0.36 299:0.88 300:0.33 +2 1:0.69 9:0.79 12:0.06 17:0.64 27:0.16 28:0.57 29:0.62 31:0.94 34:0.67 36:0.67 37:0.31 39:0.37 41:0.82 60:0.87 64:0.77 71:0.80 74:0.46 79:0.93 81:0.80 83:0.60 91:0.62 96:0.80 104:0.73 114:0.98 120:0.69 126:0.54 131:0.87 135:0.32 137:0.94 139:0.74 140:0.45 144:0.57 150:0.68 151:0.86 153:0.48 155:0.66 157:0.61 158:0.86 161:0.62 162:0.50 164:0.57 165:0.93 166:0.82 167:0.75 175:0.97 176:0.73 181:0.54 182:0.78 187:0.68 192:0.87 196:0.92 201:0.67 202:0.62 208:0.64 215:0.96 216:0.30 219:0.95 222:0.25 227:0.92 229:0.88 231:0.73 232:0.77 235:0.22 241:0.65 244:0.97 246:0.89 248:0.77 253:0.81 255:0.78 256:0.45 257:0.93 259:0.21 260:0.70 267:0.23 268:0.46 271:0.93 277:0.95 279:0.80 283:0.67 284:0.89 285:0.62 287:0.91 290:0.98 292:0.88 297:0.36 +1 1:0.69 6:0.91 7:0.70 8:0.77 9:0.76 11:0.50 12:0.06 17:0.88 18:0.73 20:0.82 21:0.54 27:0.16 28:0.57 29:0.62 30:0.30 31:0.90 32:0.71 34:0.55 36:0.49 37:0.72 39:0.37 43:0.60 44:0.92 45:0.77 64:0.77 66:0.84 69:0.45 70:0.48 71:0.80 74:0.65 76:0.94 77:0.96 78:0.87 79:0.89 81:0.76 83:0.60 86:0.75 91:0.92 96:0.80 97:0.89 98:0.84 99:0.99 100:0.84 101:0.52 104:0.58 108:0.35 111:0.85 114:0.76 120:0.68 122:0.51 123:0.34 126:0.54 127:0.34 129:0.05 131:0.87 135:0.26 137:0.90 139:0.78 140:0.80 144:0.57 145:0.87 146:0.44 147:0.50 149:0.46 150:0.70 151:0.59 152:0.87 153:0.72 154:0.66 155:0.66 157:0.81 158:0.76 159:0.16 161:0.62 162:0.70 164:0.76 165:0.70 166:0.81 167:0.94 169:0.82 172:0.54 173:0.79 176:0.70 177:0.70 179:0.70 181:0.54 182:0.79 186:0.87 189:0.88 190:0.89 191:0.93 192:0.87 195:0.55 196:0.91 201:0.68 202:0.68 204:0.82 208:0.64 212:0.92 215:0.58 216:0.30 220:0.62 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.76 235:0.98 238:0.84 241:0.87 242:0.24 243:0.85 244:0.61 246:0.89 247:0.74 248:0.61 253:0.80 254:0.80 255:0.78 256:0.68 259:0.21 260:0.69 261:0.87 265:0.84 266:0.17 267:0.44 268:0.70 271:0.93 276:0.44 279:0.77 282:0.79 283:0.67 284:0.93 285:0.62 287:0.91 290:0.76 297:0.36 300:0.33 +1 1:0.65 6:0.86 7:0.70 8:0.94 9:0.76 12:0.06 17:0.49 20:0.79 21:0.40 27:0.06 28:0.57 29:0.62 34:0.22 36:0.41 37:0.81 39:0.37 71:0.80 74:0.44 78:0.82 81:0.59 83:0.60 91:0.96 96:0.96 97:0.99 99:0.83 100:0.79 104:0.58 111:0.80 114:0.78 120:0.91 126:0.44 131:0.87 135:0.18 140:0.45 144:0.57 150:0.51 151:0.99 152:0.77 153:0.97 155:0.58 157:0.61 158:0.77 161:0.62 162:0.57 164:0.88 165:0.70 166:0.82 167:0.96 169:0.77 175:0.97 176:0.63 181:0.54 182:0.79 186:0.83 189:0.87 190:0.89 191:0.97 192:0.58 201:0.61 202:0.88 204:0.85 208:0.54 215:0.67 216:0.17 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.75 233:0.66 235:0.60 238:0.82 241:0.93 244:0.97 246:0.89 248:0.95 253:0.94 255:0.74 256:0.86 257:0.93 259:0.21 260:0.91 261:0.78 267:0.76 268:0.87 271:0.93 277:0.95 279:0.82 283:0.82 285:0.56 287:0.91 290:0.78 297:0.36 +2 1:0.69 6:0.91 8:0.81 9:0.80 11:0.78 12:0.06 17:0.47 18:0.84 20:0.84 27:0.06 28:0.57 29:0.62 30:0.33 31:0.98 34:0.44 36:0.68 37:0.68 39:0.37 41:0.92 43:0.86 45:0.73 64:0.77 66:0.71 69:0.10 70:0.89 71:0.80 74:0.62 78:0.97 79:0.97 81:0.80 83:0.60 85:0.80 86:0.91 91:0.80 96:0.91 98:0.23 100:0.99 101:0.87 104:0.73 108:0.09 111:0.99 114:0.98 120:0.84 122:0.57 123:0.09 124:0.43 126:0.54 127:0.66 129:0.05 131:0.87 133:0.59 135:0.86 137:0.98 139:0.88 140:0.45 144:0.57 146:0.38 147:0.44 149:0.78 150:0.68 151:0.98 152:0.91 153:0.92 154:0.84 155:0.66 157:0.81 159:0.71 161:0.62 162:0.61 164:0.79 165:0.93 166:0.82 167:0.88 169:1.00 172:0.57 173:0.12 176:0.73 177:0.70 179:0.74 181:0.54 182:0.79 186:0.97 187:0.21 189:0.92 191:0.91 192:0.87 196:0.98 201:0.67 202:0.75 208:0.64 212:0.89 215:0.96 216:0.17 222:0.25 227:0.92 229:0.88 231:0.73 232:0.77 235:0.22 238:0.98 241:0.76 242:0.31 243:0.75 245:0.52 246:0.89 247:0.76 248:0.90 253:0.90 255:0.94 256:0.77 259:0.21 260:0.85 261:0.97 265:0.25 266:0.23 267:0.19 268:0.76 271:0.93 276:0.31 284:0.97 285:0.62 287:0.91 290:0.98 297:0.36 300:0.70 +1 1:0.69 6:0.94 7:0.70 8:0.75 9:0.79 11:0.78 12:0.06 17:0.96 18:0.86 20:0.75 27:0.72 28:0.57 29:0.62 30:0.11 31:0.96 32:0.71 34:0.17 36:0.33 37:0.45 39:0.37 41:0.88 43:0.96 44:0.92 45:0.81 48:0.91 60:0.95 64:0.77 66:0.93 69:0.08 70:0.92 71:0.80 74:0.80 76:0.85 77:0.89 78:0.89 79:0.95 81:0.80 83:0.60 85:0.80 86:0.92 91:0.86 96:0.86 98:1.00 100:0.93 101:0.87 104:0.54 108:0.07 111:0.87 114:0.98 120:0.78 122:0.82 123:0.07 126:0.54 127:0.97 129:0.05 131:0.87 135:0.30 137:0.96 139:0.94 140:0.45 144:0.57 145:0.92 146:0.89 147:0.91 149:0.77 150:0.68 151:0.90 152:0.74 153:0.69 154:0.96 155:0.66 157:0.83 158:0.92 159:0.48 161:0.62 162:0.86 164:0.66 165:0.93 166:0.82 167:0.91 169:0.81 172:0.81 173:0.20 176:0.73 177:0.70 179:0.52 181:0.54 182:0.79 186:0.98 189:0.95 191:0.73 192:0.87 195:0.67 196:0.98 201:0.67 202:0.50 204:0.82 208:0.64 212:0.68 215:0.96 216:0.05 219:0.95 222:0.25 227:0.92 229:0.88 230:0.73 231:0.73 232:0.71 233:0.76 235:0.22 238:0.84 241:0.79 242:0.16 243:0.93 246:0.89 247:0.84 248:0.85 253:0.90 255:0.94 256:0.58 259:0.21 260:0.78 261:0.76 265:1.00 266:0.54 267:0.44 268:0.59 271:0.93 276:0.70 279:0.82 281:0.91 282:0.80 283:0.82 284:0.92 285:0.62 287:0.91 290:0.98 292:0.95 297:0.36 300:0.70 +1 8:0.98 9:0.80 12:0.69 17:0.24 21:0.78 27:0.64 28:0.66 34:0.68 36:0.30 37:0.72 39:0.85 41:0.94 83:0.79 91:0.88 96:0.89 104:0.78 120:0.83 131:0.97 135:0.78 140:0.45 144:0.57 151:0.91 153:0.90 155:0.67 157:0.61 161:0.90 162:0.72 164:0.84 166:0.61 167:0.84 175:0.91 181:0.55 182:0.94 192:0.59 202:0.77 208:0.64 216:0.32 220:0.62 222:0.84 227:0.96 229:0.97 231:0.91 232:0.84 241:0.85 244:0.83 246:0.61 248:0.88 253:0.84 255:0.79 256:0.79 257:0.84 259:0.21 260:0.84 267:0.44 268:0.81 271:0.51 277:0.87 285:0.62 287:0.95 +1 1:0.74 6:0.86 8:0.88 9:0.80 11:0.42 12:0.69 17:0.43 20:0.84 21:0.05 27:0.64 28:0.66 30:0.68 34:0.44 36:0.58 37:0.72 39:0.85 41:0.82 43:0.37 55:0.52 66:0.54 69:0.15 70:0.31 74:0.49 78:0.88 81:0.91 83:0.80 91:0.48 96:0.79 98:0.62 100:0.83 104:0.78 108:0.12 111:0.86 114:0.95 120:0.77 123:0.12 124:0.48 126:0.54 127:0.69 129:0.59 131:0.96 133:0.47 135:0.49 140:0.45 144:0.57 146:0.51 147:0.57 149:0.27 150:0.95 151:0.81 152:0.84 153:0.48 154:0.75 155:0.67 157:0.61 159:0.35 161:0.90 162:0.86 164:0.68 165:0.90 166:0.94 167:0.53 169:0.80 172:0.32 173:0.33 176:0.73 177:0.38 179:0.90 181:0.55 182:0.94 186:0.88 187:0.21 190:0.77 192:0.59 201:0.74 202:0.53 208:0.64 212:0.61 215:0.91 216:0.32 220:0.62 222:0.84 227:0.95 229:0.97 231:0.91 232:0.84 235:0.12 241:0.30 242:0.63 243:0.63 245:0.50 246:0.93 247:0.39 248:0.73 253:0.80 254:0.86 255:0.79 256:0.58 259:0.21 260:0.77 261:0.86 265:0.63 266:0.27 267:0.52 268:0.62 271:0.51 276:0.28 283:0.71 285:0.62 287:0.95 290:0.95 297:0.36 300:0.25 +2 1:0.74 6:0.88 7:0.81 8:0.74 9:0.80 11:0.64 12:0.69 17:0.75 20:0.98 21:0.30 27:0.15 28:0.66 30:0.21 32:0.80 34:0.17 36:0.39 37:0.63 39:0.85 41:0.92 43:0.89 55:0.71 60:0.87 66:0.20 69:0.17 70:0.92 74:0.87 77:0.89 78:0.96 81:0.83 83:0.81 85:0.96 91:0.72 96:0.79 98:0.56 100:0.95 101:0.79 104:0.58 108:0.14 111:0.95 114:0.86 120:0.84 121:0.97 123:0.95 124:0.80 126:0.54 127:1.00 129:0.81 131:0.97 133:0.76 135:0.49 140:0.80 144:0.57 145:0.52 146:0.93 147:0.94 149:0.94 150:0.89 151:0.70 152:0.90 153:0.78 154:0.88 155:0.67 157:0.96 158:0.92 159:0.91 161:0.90 162:0.72 164:0.80 165:0.84 166:0.94 167:0.81 169:0.93 172:0.89 173:0.08 176:0.73 177:0.38 179:0.16 181:0.55 182:0.94 186:0.96 189:0.89 190:0.77 191:0.75 192:0.59 195:0.97 201:0.74 202:0.73 204:0.89 208:0.64 212:0.70 215:0.72 216:0.09 220:0.62 222:0.84 227:0.96 229:0.97 230:0.87 231:0.91 233:0.76 235:0.52 238:0.90 241:0.79 242:0.76 243:0.44 245:0.98 246:0.93 247:0.47 248:0.74 253:0.84 255:0.79 256:0.75 259:0.21 260:0.84 261:0.94 265:0.54 266:0.89 267:0.82 268:0.76 271:0.51 276:0.95 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.86 297:0.36 299:0.88 300:0.94 +1 6:0.96 7:0.81 8:0.75 9:0.80 12:0.69 17:0.02 20:0.89 21:0.30 27:0.16 28:0.66 30:0.21 34:0.76 36:0.93 37:0.84 39:0.85 43:0.26 55:0.84 66:0.36 69:0.12 70:0.67 74:0.67 78:0.90 81:0.41 91:0.51 96:0.77 98:0.63 100:0.87 108:0.74 111:0.88 114:0.55 120:0.71 121:0.90 123:0.73 124:0.68 126:0.32 127:0.85 129:0.05 131:0.96 133:0.62 135:0.83 140:0.80 144:0.57 146:0.25 147:0.31 149:0.28 150:0.35 151:0.77 152:0.95 153:0.48 154:0.75 155:0.67 157:0.61 158:0.82 159:0.51 161:0.90 162:0.62 164:0.76 166:0.74 167:0.60 169:0.97 172:0.27 173:0.21 176:0.53 177:0.38 178:0.42 179:0.89 181:0.55 182:0.61 186:0.90 187:0.68 189:0.91 191:0.82 192:0.59 202:0.70 204:0.89 208:0.64 212:0.16 216:0.54 220:0.62 222:0.84 227:0.95 229:0.61 230:0.87 231:0.91 233:0.76 235:0.31 238:0.86 241:0.53 242:0.72 243:0.39 245:0.50 246:0.61 247:0.39 248:0.71 253:0.72 254:0.92 255:0.79 256:0.71 259:0.21 260:0.70 261:0.97 265:0.63 266:0.54 267:0.19 268:0.71 271:0.51 276:0.36 277:0.69 285:0.62 287:0.61 290:0.55 300:0.43 +2 1:0.74 6:0.87 7:0.76 8:0.83 9:0.80 11:0.64 12:0.69 17:0.20 20:0.97 21:0.13 27:0.38 28:0.66 30:0.21 32:0.80 34:0.21 36:0.63 37:0.88 39:0.85 41:0.98 43:0.97 55:0.71 60:0.97 66:0.61 69:0.10 70:0.64 74:0.77 76:0.85 77:0.70 78:0.92 81:0.86 83:0.85 85:0.80 91:0.86 96:0.94 98:0.56 100:0.94 101:0.74 104:0.76 108:0.09 111:0.92 114:0.92 120:0.95 123:0.09 124:0.47 126:0.54 127:0.94 129:0.59 131:0.97 133:0.47 135:0.20 140:0.45 144:0.57 145:0.44 146:0.68 147:0.73 149:0.70 150:0.92 151:0.84 152:0.90 153:0.77 154:0.97 155:0.67 157:0.86 158:0.87 159:0.63 161:0.90 162:0.60 163:0.75 164:0.95 165:0.88 166:0.94 167:0.86 169:0.92 172:0.48 173:0.15 176:0.73 177:0.38 179:0.81 181:0.55 182:0.94 186:0.92 189:0.89 190:0.77 191:0.83 192:0.59 195:0.78 201:0.74 202:0.93 208:0.64 212:0.36 215:0.84 216:0.14 220:0.74 222:0.84 227:0.96 229:0.97 230:0.79 231:0.91 233:0.76 235:0.22 238:0.90 241:0.91 242:0.61 243:0.68 245:0.62 246:0.93 247:0.39 248:0.94 253:0.95 255:0.79 256:0.94 259:0.21 260:0.95 261:0.92 265:0.57 266:0.47 267:0.76 268:0.94 271:0.51 276:0.33 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.43 +0 11:0.64 12:0.69 17:0.26 21:0.54 27:0.45 28:0.66 30:0.11 34:0.75 36:0.17 37:0.50 39:0.85 43:0.78 55:0.84 66:0.20 69:0.69 70:0.95 74:0.41 81:0.47 85:0.72 91:0.23 98:0.29 101:0.71 108:0.53 123:0.51 124:0.85 126:0.32 127:0.43 129:0.05 131:0.61 133:0.82 135:0.88 144:0.57 145:0.38 146:0.47 147:0.53 149:0.58 150:0.37 154:0.70 157:0.79 159:0.65 161:0.90 163:0.88 166:0.88 167:0.56 172:0.21 173:0.59 177:0.38 178:0.55 179:0.78 181:0.55 182:0.73 187:0.68 212:0.28 215:0.58 216:0.77 222:0.84 227:0.61 229:0.61 231:0.85 235:0.60 241:0.43 242:0.75 243:0.40 245:0.49 246:0.61 247:0.39 253:0.36 259:0.21 265:0.31 266:0.63 267:0.36 271:0.51 276:0.41 283:0.71 287:0.61 297:0.36 300:0.70 +2 1:0.74 7:0.76 9:0.80 11:0.64 12:0.69 17:0.85 21:0.13 27:0.72 28:0.66 30:0.17 32:0.80 34:0.45 36:0.53 39:0.85 41:0.82 43:0.92 55:0.71 60:0.87 66:0.25 69:0.10 70:0.90 74:0.88 77:0.89 81:0.86 83:0.83 85:0.94 91:0.73 96:0.73 98:0.75 101:0.82 104:0.75 108:0.09 114:0.92 120:0.72 123:0.73 124:0.68 126:0.54 127:0.95 129:0.81 131:0.96 133:0.67 135:0.32 140:0.80 144:0.57 145:0.69 146:0.66 147:0.71 149:0.93 150:0.92 151:0.63 153:0.71 154:0.92 155:0.67 157:0.96 158:0.87 159:0.69 161:0.90 162:0.72 164:0.71 165:0.88 166:0.94 167:0.82 172:0.79 173:0.13 176:0.73 177:0.38 179:0.33 181:0.55 182:0.94 190:0.77 192:0.59 195:0.82 201:0.74 202:0.62 208:0.64 212:0.76 215:0.84 216:0.05 220:0.62 222:0.84 227:0.95 229:0.97 230:0.79 231:0.91 233:0.76 235:0.22 241:0.80 242:0.59 243:0.57 245:0.91 246:0.93 247:0.60 248:0.60 253:0.80 255:0.79 256:0.64 259:0.21 260:0.72 265:0.49 266:0.80 267:0.79 268:0.65 271:0.51 276:0.82 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.84 +1 1:0.74 7:0.81 9:0.80 11:0.64 12:0.69 17:0.99 27:0.68 28:0.66 30:0.31 32:0.80 34:0.61 36:0.90 37:0.63 39:0.85 41:0.88 43:0.98 55:0.52 60:0.87 66:0.98 69:0.05 70:0.71 74:0.98 77:0.70 81:0.95 83:0.86 85:0.96 91:0.70 96:0.86 98:0.99 101:0.86 104:0.57 106:0.81 108:0.05 114:0.98 117:0.86 120:0.78 121:0.90 122:0.43 123:0.05 126:0.54 127:0.92 129:0.05 131:0.96 135:0.84 140:0.45 144:0.57 145:0.47 146:0.89 147:0.91 149:0.96 150:0.98 151:0.90 153:0.69 154:0.98 155:0.67 157:0.97 158:0.92 159:0.49 161:0.90 162:0.86 164:0.66 165:0.92 166:0.94 167:0.49 172:0.94 173:0.16 176:0.73 177:0.38 179:0.24 181:0.55 182:0.94 187:0.21 191:0.76 192:0.59 195:0.64 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.92 215:0.96 216:0.10 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 232:0.86 233:0.76 235:0.05 241:0.10 242:0.41 243:0.98 246:0.93 247:0.60 248:0.85 253:0.92 255:0.79 256:0.58 259:0.21 260:0.78 265:0.99 266:0.27 267:0.97 268:0.59 271:0.51 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.98 297:0.36 299:0.88 300:0.55 +1 1:0.74 6:0.95 7:0.76 8:0.84 9:0.80 11:0.64 12:0.69 17:0.94 20:0.88 21:0.13 27:0.63 28:0.66 30:0.15 32:0.80 34:0.50 36:0.66 37:0.80 39:0.85 41:0.82 43:0.93 55:0.45 66:0.95 69:0.10 70:0.88 74:0.86 77:0.89 78:0.88 81:0.86 83:0.78 85:0.91 91:0.65 96:0.74 98:0.99 100:0.84 101:0.82 104:0.58 108:0.09 111:0.86 114:0.92 120:0.62 123:0.09 126:0.54 127:0.92 129:0.05 131:0.96 135:0.30 140:0.45 144:0.57 145:0.69 146:0.92 147:0.94 149:0.93 150:0.92 151:0.69 152:0.83 153:0.48 154:0.92 155:0.67 157:0.95 159:0.54 161:0.90 162:0.86 164:0.57 165:0.88 166:0.94 167:0.81 169:0.82 172:0.87 173:0.19 176:0.73 177:0.38 179:0.40 181:0.55 182:0.94 186:0.88 187:0.21 189:0.95 192:0.59 195:0.71 201:0.74 202:0.36 208:0.64 212:0.73 215:0.84 216:0.02 220:0.74 222:0.84 227:0.95 229:0.97 230:0.79 231:0.91 233:0.76 235:0.22 238:0.83 241:0.78 242:0.61 243:0.95 246:0.93 247:0.60 248:0.65 253:0.81 255:0.79 256:0.45 259:0.21 260:0.63 261:0.86 265:0.99 266:0.63 267:0.17 268:0.46 271:0.51 276:0.78 282:0.88 283:0.71 285:0.62 287:0.95 290:0.92 297:0.36 299:0.88 300:0.70 +1 1:0.74 7:0.81 8:0.60 11:0.64 12:0.69 17:0.84 21:0.22 27:0.72 28:0.66 30:0.14 32:0.80 34:0.45 36:0.96 39:0.85 43:0.94 55:0.52 66:0.53 69:0.25 70:0.86 74:0.85 77:0.70 81:0.82 83:0.76 85:0.85 91:0.66 98:0.71 101:0.75 104:0.79 108:0.20 114:0.90 121:0.90 123:0.19 124:0.72 126:0.54 127:0.98 129:0.59 131:0.61 133:0.76 135:0.72 144:0.57 145:0.69 146:0.89 147:0.91 149:0.86 150:0.88 154:0.94 155:0.67 157:0.89 159:0.76 161:0.90 165:0.86 166:0.94 167:0.76 172:0.83 173:0.17 176:0.73 177:0.38 178:0.55 179:0.34 181:0.55 182:0.94 187:0.21 192:0.59 195:0.88 201:0.74 204:0.89 208:0.64 212:0.60 215:0.79 216:0.08 222:0.84 227:0.61 229:0.61 230:0.87 231:0.91 233:0.76 235:0.31 241:0.75 242:0.74 243:0.62 245:0.87 246:0.93 247:0.60 253:0.75 259:0.21 265:0.71 266:0.89 267:0.84 271:0.51 276:0.83 282:0.88 287:0.61 290:0.90 297:0.36 299:0.88 300:0.70 +1 1:0.74 9:0.80 11:0.64 12:0.69 17:0.45 21:0.13 27:0.38 28:0.66 30:0.74 34:0.39 36:0.83 37:0.88 39:0.85 41:0.88 43:0.97 55:0.84 66:0.35 69:0.10 70:0.55 74:0.60 81:0.86 83:0.78 85:0.80 91:0.54 96:0.78 98:0.59 101:0.74 104:0.76 108:0.83 114:0.92 120:0.66 123:0.82 124:0.67 126:0.54 127:0.81 129:0.59 131:0.96 133:0.64 135:0.58 140:0.45 144:0.57 146:0.50 147:0.57 149:0.69 150:0.92 151:0.69 153:0.48 154:0.97 155:0.67 157:0.86 159:0.66 161:0.90 162:0.86 163:0.94 164:0.67 165:0.88 166:0.94 167:0.62 172:0.45 173:0.14 176:0.73 177:0.38 179:0.70 181:0.55 182:0.94 187:0.39 192:0.59 201:0.74 202:0.50 208:0.64 212:0.30 215:0.84 216:0.14 220:0.74 222:0.84 227:0.95 229:0.97 231:0.91 235:0.22 241:0.58 242:0.59 243:0.48 245:0.67 246:0.93 247:0.39 248:0.71 253:0.79 255:0.79 256:0.58 259:0.21 260:0.67 265:0.60 266:0.71 267:0.76 268:0.59 271:0.51 276:0.52 285:0.62 287:0.95 290:0.92 297:0.36 300:0.55 +0 1:0.74 6:0.82 7:0.76 8:0.61 9:0.80 11:0.64 12:0.69 17:0.32 20:0.90 21:0.05 27:0.50 28:0.66 30:0.54 32:0.80 34:0.52 36:0.95 37:0.42 39:0.85 41:0.88 43:0.97 60:0.95 66:0.83 69:0.60 70:0.51 74:0.93 76:0.94 77:0.98 78:0.92 81:0.85 83:0.88 85:0.93 91:0.65 96:0.83 98:0.89 100:0.83 101:0.85 104:0.82 108:0.46 111:0.89 114:0.95 120:0.79 122:0.57 123:0.44 124:0.37 126:0.54 127:0.58 129:0.05 131:0.97 133:0.39 135:0.49 140:0.45 144:0.57 145:0.56 146:0.74 147:0.05 149:0.91 150:0.91 151:0.83 152:0.93 153:0.48 154:0.97 155:0.67 157:0.96 158:0.87 159:0.33 161:0.90 162:0.77 164:0.75 165:0.88 166:0.94 167:0.54 169:0.81 172:0.71 173:0.74 176:0.73 177:0.05 179:0.56 181:0.55 182:0.94 186:0.92 187:0.21 189:0.82 190:0.77 192:0.59 195:0.71 201:0.74 202:0.65 208:0.64 212:0.80 215:0.91 216:0.92 220:0.74 222:0.84 227:0.95 229:0.97 230:0.79 231:0.91 233:0.76 235:0.52 238:0.82 241:0.37 242:0.37 243:0.10 245:0.57 246:0.93 247:0.55 248:0.81 253:0.89 255:0.79 256:0.64 257:0.65 259:0.21 260:0.80 261:0.90 265:0.89 266:0.27 267:0.52 268:0.69 271:0.51 276:0.60 279:0.86 282:0.88 283:0.71 285:0.62 287:0.95 290:0.95 297:0.99 299:0.97 300:0.43 +1 1:0.74 7:0.81 8:0.80 9:0.80 11:0.64 12:0.69 17:0.76 20:0.86 21:0.22 27:0.72 28:0.66 30:0.14 32:0.80 34:0.33 36:0.96 39:0.85 41:0.88 43:0.94 55:0.52 66:0.53 69:0.25 70:0.86 74:0.85 77:0.70 78:0.87 81:0.82 83:0.78 85:0.85 91:0.69 96:0.77 98:0.71 100:0.86 101:0.75 104:0.79 108:0.20 111:0.85 114:0.90 120:0.65 121:0.90 123:0.19 124:0.72 126:0.54 127:0.98 129:0.59 131:0.96 133:0.76 135:0.74 140:0.45 144:0.57 145:0.69 146:0.89 147:0.91 149:0.86 150:0.88 151:0.69 152:0.81 153:0.48 154:0.94 155:0.67 157:0.89 159:0.76 161:0.90 162:0.78 164:0.70 165:0.86 166:0.94 167:0.83 169:0.84 172:0.83 173:0.17 176:0.73 177:0.38 179:0.34 181:0.55 182:0.94 186:0.87 192:0.59 195:0.88 201:0.74 202:0.59 204:0.89 208:0.64 212:0.60 215:0.79 216:0.08 220:0.82 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 233:0.76 235:0.31 241:0.82 242:0.74 243:0.62 245:0.87 246:0.93 247:0.60 248:0.70 253:0.82 255:0.79 256:0.58 259:0.21 260:0.66 261:0.84 265:0.71 266:0.89 267:0.84 268:0.64 271:0.51 276:0.83 282:0.88 285:0.62 287:0.95 290:0.90 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.82 7:0.81 8:0.65 9:0.80 11:0.64 12:0.69 17:0.69 20:0.82 21:0.30 27:0.41 28:0.66 30:0.09 34:0.59 36:0.57 37:0.63 39:0.85 41:0.82 43:0.89 55:0.59 60:0.87 66:0.13 69:0.17 70:0.94 74:0.82 78:0.94 81:0.83 83:0.84 85:0.96 91:0.20 96:0.80 98:0.39 100:0.85 101:0.78 104:0.54 106:0.81 108:0.97 111:0.91 114:0.86 117:0.86 120:0.69 121:0.90 123:0.94 124:0.95 126:0.54 127:0.98 129:0.93 131:0.96 133:0.96 135:0.32 140:0.80 144:0.57 146:0.63 147:0.68 149:0.92 150:0.89 151:0.87 152:0.79 153:0.48 154:0.88 155:0.67 157:0.96 158:0.92 159:0.93 161:0.90 162:0.62 164:0.57 165:0.84 166:0.94 167:0.46 169:0.82 172:0.86 173:0.07 176:0.73 177:0.38 178:0.42 179:0.16 181:0.55 182:0.94 186:0.94 187:0.39 189:0.84 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.57 215:0.72 216:0.12 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 232:0.94 233:0.76 235:0.52 238:0.84 241:0.01 242:0.75 243:0.15 245:0.93 246:0.93 247:0.47 248:0.78 253:0.85 255:0.79 256:0.45 259:0.21 260:0.70 261:0.85 265:0.34 266:0.92 267:0.79 268:0.46 271:0.51 276:0.95 279:0.86 283:0.82 285:0.62 287:0.95 290:0.86 297:0.36 300:0.94 +2 1:0.74 8:0.91 9:0.80 11:0.64 12:0.69 17:0.59 20:0.79 21:0.22 27:0.49 28:0.66 30:0.30 34:0.58 36:0.93 37:0.42 39:0.85 41:0.88 43:0.96 55:0.84 60:0.87 66:0.24 69:0.15 70:0.80 74:0.70 78:0.93 81:0.82 83:0.83 85:0.80 91:0.65 96:0.79 98:0.56 100:0.89 101:0.77 104:0.89 108:0.73 111:0.91 114:0.90 120:0.67 123:0.43 124:0.71 126:0.54 127:0.81 129:0.81 131:0.96 133:0.67 135:0.74 140:0.45 144:0.57 146:0.13 147:0.18 149:0.72 150:0.88 151:0.73 152:0.81 153:0.77 154:0.96 155:0.67 157:0.87 158:0.87 159:0.43 161:0.90 162:0.86 164:0.70 165:0.86 166:0.94 167:0.53 169:0.80 172:0.32 173:0.27 176:0.73 177:0.38 179:0.79 181:0.55 182:0.94 186:0.93 187:0.21 192:0.59 201:0.74 202:0.55 208:0.64 212:0.14 215:0.79 216:0.94 220:0.74 222:0.84 227:0.95 229:0.97 231:0.91 235:0.31 241:0.30 242:0.78 243:0.23 245:0.60 246:0.93 247:0.35 248:0.73 253:0.81 255:0.79 256:0.58 259:0.21 260:0.68 261:0.84 265:0.51 266:0.71 267:0.36 268:0.64 271:0.51 276:0.41 279:0.86 283:0.71 285:0.62 287:0.95 290:0.90 297:0.36 300:0.55 +2 6:0.96 7:0.81 8:0.87 9:0.80 12:0.69 17:0.09 20:0.98 21:0.22 27:0.16 28:0.66 30:0.76 34:0.20 36:0.87 37:0.84 39:0.85 41:0.97 43:0.31 55:0.84 66:0.36 69:0.80 70:0.15 74:0.62 78:0.97 81:0.73 83:0.84 91:0.98 96:0.97 98:0.27 100:0.99 108:0.64 111:0.98 120:0.98 121:0.90 122:0.43 123:0.61 124:0.52 126:0.32 127:0.19 129:0.05 131:0.97 133:0.39 135:0.74 138:0.99 140:0.92 144:0.57 146:0.30 147:0.05 149:0.23 150:0.54 151:0.97 152:0.95 153:0.96 154:0.23 155:0.67 157:0.61 158:0.86 159:0.23 161:0.90 162:0.69 163:0.96 164:0.98 166:0.88 167:0.87 169:0.97 172:0.10 173:0.88 177:0.05 179:0.94 181:0.55 182:0.94 186:0.97 189:0.97 191:0.93 192:0.59 202:0.96 204:0.89 208:0.64 212:0.95 215:0.79 216:0.54 220:0.82 222:0.84 227:0.96 229:0.97 230:0.87 231:0.91 233:0.76 235:0.22 238:0.97 241:0.97 242:0.82 243:0.34 244:0.61 245:0.37 246:0.61 247:0.12 248:0.97 253:0.96 255:0.79 256:0.97 257:0.65 259:0.21 260:0.98 261:0.97 265:0.29 266:0.12 267:0.85 268:0.97 271:0.51 276:0.16 279:0.86 283:0.71 285:0.62 287:0.95 297:0.36 300:0.13 +1 7:0.81 8:0.75 9:0.80 11:0.64 12:0.69 17:0.59 21:0.05 27:0.34 28:0.66 30:0.14 32:0.72 34:0.50 36:0.35 37:0.36 39:0.85 41:0.82 43:0.56 55:0.45 66:0.16 69:0.94 70:0.93 74:0.61 81:0.87 83:0.76 85:0.91 91:0.35 96:0.80 98:0.52 101:0.73 104:0.80 108:0.97 120:0.83 121:0.90 123:0.91 124:0.92 126:0.32 127:0.70 129:0.89 131:0.97 133:0.92 135:0.87 140:0.87 144:0.57 145:1.00 146:0.60 147:0.87 149:0.88 150:0.72 151:0.87 153:0.48 154:0.45 155:0.67 157:0.90 159:0.93 161:0.90 162:0.73 164:0.77 166:0.88 167:0.54 172:0.92 173:0.75 177:0.05 179:0.12 181:0.55 182:0.94 187:0.21 192:0.59 195:0.99 202:0.69 204:0.89 208:0.64 212:0.78 215:0.91 216:0.84 220:0.62 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 233:0.76 235:0.05 241:0.37 242:0.80 243:0.23 245:0.97 246:0.61 247:0.47 248:0.78 253:0.79 255:0.79 256:0.71 257:0.65 259:0.21 260:0.83 265:0.30 266:0.92 267:0.91 268:0.72 271:0.51 276:0.97 283:0.82 285:0.62 287:0.95 297:0.36 300:0.94 +2 1:0.74 7:0.81 8:0.73 9:0.80 11:0.64 12:0.69 17:0.98 20:0.84 27:0.11 28:0.66 30:0.31 32:0.80 34:0.63 36:0.90 37:0.63 39:0.85 41:0.88 43:0.98 55:0.52 60:0.87 66:0.98 69:0.05 70:0.71 74:0.98 77:0.70 78:0.90 81:0.95 83:0.82 85:0.96 91:0.64 96:0.88 98:0.99 100:0.83 101:0.86 104:0.74 106:0.81 108:0.05 111:0.87 114:0.98 117:0.86 120:0.80 121:0.90 122:0.43 123:0.05 126:0.54 127:0.92 129:0.05 131:0.96 135:0.84 140:0.45 144:0.57 145:0.47 146:0.89 147:0.91 149:0.96 150:0.98 151:0.91 152:0.80 153:0.71 154:0.98 155:0.67 157:0.97 158:0.92 159:0.49 161:0.90 162:0.86 164:0.69 165:0.92 166:0.94 167:0.47 169:0.81 172:0.94 173:0.16 176:0.73 177:0.38 179:0.24 181:0.55 182:0.94 186:0.90 187:0.21 191:0.73 192:0.59 195:0.64 201:0.74 202:0.54 204:0.89 206:0.81 208:0.64 212:0.92 215:0.96 216:0.09 222:0.84 227:0.95 229:0.97 230:0.87 231:0.91 232:0.88 233:0.76 235:0.05 241:0.03 242:0.41 243:0.98 246:0.93 247:0.60 248:0.87 253:0.92 255:0.79 256:0.58 259:0.21 260:0.81 261:0.84 265:0.99 266:0.27 267:0.23 268:0.63 271:0.51 276:0.89 279:0.86 282:0.88 283:0.82 285:0.62 287:0.95 290:0.98 297:0.36 299:0.88 300:0.55 +1 11:0.64 12:0.69 17:0.26 21:0.76 27:0.45 28:0.66 30:0.28 34:0.68 36:0.23 37:0.50 39:0.85 43:0.69 55:0.71 66:0.43 69:0.88 70:0.75 74:0.65 81:0.30 85:0.85 91:0.23 98:0.39 101:0.74 108:0.86 122:0.51 123:0.86 124:0.75 126:0.32 127:0.32 129:0.59 131:0.61 133:0.76 135:0.93 144:0.57 145:0.49 146:0.41 147:0.05 149:0.63 150:0.28 154:0.59 157:0.89 159:0.67 161:0.90 166:0.88 167:0.51 172:0.45 173:0.81 177:0.05 178:0.55 179:0.59 181:0.55 182:0.78 187:0.68 212:0.48 215:0.46 216:0.77 222:0.84 227:0.61 229:0.61 231:0.89 235:0.95 241:0.18 242:0.40 243:0.12 245:0.60 246:0.61 247:0.55 253:0.50 257:0.65 259:0.21 265:0.41 266:0.71 267:0.65 271:0.51 276:0.50 287:0.61 297:0.36 300:0.55 +0 8:0.86 10:0.90 11:0.45 12:0.69 17:0.01 18:0.69 21:0.78 27:0.28 29:0.56 30:0.15 34:0.80 36:0.73 37:0.46 39:0.76 43:0.09 45:0.73 66:0.82 69:0.55 70:0.68 71:1.00 74:0.29 86:0.74 91:0.66 98:0.82 101:0.47 108:0.43 122:0.57 123:0.41 127:0.31 129:0.05 131:0.61 135:0.49 139:0.68 145:0.45 146:0.73 147:0.77 149:0.10 154:0.45 157:0.61 159:0.29 166:0.61 170:0.82 172:0.48 173:0.66 174:0.86 177:0.88 178:0.55 179:0.75 182:0.61 187:0.87 191:0.91 197:0.88 202:0.95 212:0.50 216:0.64 222:0.35 227:0.61 229:0.61 231:0.68 235:0.84 239:0.88 241:0.71 242:0.14 243:0.83 244:0.83 246:0.61 247:0.67 253:0.26 254:0.94 259:0.21 265:0.82 266:0.47 267:0.76 271:0.64 276:0.37 287:0.61 300:0.43 +0 10:0.89 11:0.56 12:0.69 17:0.28 18:0.63 21:0.78 27:0.45 29:0.56 30:0.91 34:0.50 36:0.17 37:0.50 39:0.76 43:0.78 45:0.73 55:0.42 66:0.69 69:0.76 70:0.13 71:1.00 74:0.41 86:0.73 91:0.04 98:0.10 101:0.65 108:0.59 122:0.65 123:0.57 127:0.07 129:0.05 131:0.61 135:0.73 139:0.70 144:0.57 145:0.41 146:0.19 147:0.25 149:0.61 154:0.70 157:0.61 159:0.10 166:0.61 170:0.82 172:0.21 173:0.62 174:0.79 177:0.88 178:0.55 179:0.08 182:0.61 187:0.68 197:0.83 212:0.61 216:0.77 222:0.35 227:0.61 229:0.61 231:0.62 235:0.52 239:0.88 241:0.15 242:0.63 243:0.73 246:0.61 247:0.30 253:0.36 254:0.74 259:0.21 265:0.11 266:0.17 267:0.28 271:0.64 276:0.23 287:0.61 300:0.13 +0 1:0.56 9:0.70 10:0.90 11:0.45 12:0.69 17:0.16 18:0.79 21:0.71 23:0.95 27:0.44 29:0.56 30:0.37 31:0.87 34:0.30 36:1.00 37:0.53 39:0.76 43:0.57 45:0.83 62:0.96 64:0.77 66:0.43 69:0.30 70:0.44 71:1.00 74:0.32 75:0.96 79:0.87 81:0.38 83:0.56 86:0.75 91:0.22 97:0.89 98:0.45 99:0.83 101:0.47 108:0.24 122:0.93 123:0.23 124:0.59 126:0.32 127:0.51 128:0.95 129:0.05 131:0.61 133:0.45 135:1.00 137:0.87 139:0.75 140:0.87 145:0.38 146:0.60 147:0.65 149:0.47 150:0.33 151:0.52 153:0.48 154:0.15 155:0.49 157:0.61 159:0.58 162:0.53 163:0.81 165:0.65 166:0.61 168:0.95 170:0.82 172:0.48 173:0.25 174:0.89 177:0.88 178:0.48 179:0.64 182:0.61 187:0.39 190:0.95 192:0.45 196:0.88 197:0.91 201:0.54 202:0.58 205:0.95 208:0.50 212:0.22 216:0.50 219:0.90 220:0.93 222:0.35 226:0.96 227:0.61 229:0.61 231:0.67 235:0.88 236:0.92 239:0.90 241:0.21 242:0.33 243:0.57 244:0.83 245:0.75 246:0.61 247:0.74 248:0.51 253:0.29 254:0.94 255:0.69 256:0.45 259:0.21 265:0.47 266:0.75 267:0.44 268:0.46 271:0.64 276:0.46 279:0.75 284:0.88 285:0.50 287:0.61 292:0.88 300:0.33 +0 10:0.90 11:0.45 12:0.69 17:0.06 18:0.78 21:0.40 27:0.32 29:0.56 30:0.60 34:0.55 36:0.70 37:0.64 39:0.76 43:0.30 45:0.83 66:0.34 69:0.37 70:0.46 71:1.00 74:0.29 81:0.28 86:0.74 91:0.10 98:0.44 101:0.47 104:0.98 106:0.81 108:0.29 122:0.94 123:0.28 124:0.70 126:0.24 127:0.28 129:0.05 131:0.61 133:0.62 135:0.90 139:0.71 146:0.63 147:0.68 149:0.47 150:0.30 154:0.22 157:0.61 159:0.50 163:0.81 166:0.76 170:0.82 172:0.37 173:0.28 174:0.90 177:0.88 178:0.55 179:0.60 182:0.61 187:0.95 197:0.91 206:0.81 212:0.19 215:0.67 216:0.55 222:0.35 227:0.61 229:0.61 231:0.73 235:0.42 239:0.89 241:0.12 242:0.45 243:0.50 244:0.93 245:0.62 246:0.61 247:0.60 253:0.26 259:0.21 265:0.46 266:0.75 267:0.79 271:0.64 276:0.46 287:0.61 297:0.36 300:0.43 +0 10:0.93 11:0.53 12:0.69 17:0.81 18:0.75 21:0.78 27:0.24 29:0.56 30:0.54 32:0.65 34:0.90 36:0.51 37:0.65 39:0.76 43:0.29 45:0.87 66:0.82 69:0.79 70:0.57 71:1.00 74:0.50 77:0.89 86:0.80 91:0.33 98:0.73 101:0.65 108:0.63 122:0.65 123:0.60 124:0.37 127:0.44 129:0.05 131:0.61 133:0.36 135:0.77 139:0.75 144:0.57 145:0.77 146:0.74 147:0.05 149:0.23 154:0.49 157:0.90 159:0.44 166:0.61 170:0.82 172:0.70 173:0.84 174:0.89 177:0.05 178:0.55 179:0.56 182:0.82 187:0.39 195:0.66 197:0.91 212:0.72 216:0.76 222:0.35 227:0.61 229:0.61 231:0.75 235:0.12 239:0.91 241:0.05 242:0.18 243:0.10 244:0.61 245:0.56 246:0.61 247:0.74 253:0.41 254:0.90 257:0.93 259:0.21 265:0.73 266:0.47 267:0.52 271:0.64 276:0.56 282:0.74 287:0.61 300:0.43 +0 8:0.94 9:0.71 10:0.86 12:0.69 17:0.59 21:0.59 23:0.99 27:0.72 29:0.56 30:0.40 31:0.89 34:0.18 36:0.27 37:0.78 39:0.76 43:0.10 55:0.92 62:0.96 66:0.08 69:0.21 70:0.88 71:1.00 74:0.33 75:0.95 76:0.85 77:0.70 79:0.89 81:0.32 83:0.56 91:0.44 96:0.69 98:0.22 108:0.93 114:0.64 122:0.83 123:0.93 124:0.95 126:0.32 127:0.98 128:0.98 129:0.05 131:0.83 133:0.94 135:0.30 137:0.89 140:0.90 145:0.44 146:0.39 147:0.46 149:0.13 150:0.31 151:0.61 153:0.90 154:0.21 155:0.53 157:0.61 159:0.93 162:0.63 163:0.98 166:0.75 168:0.98 170:0.82 172:0.21 173:0.08 174:0.88 175:0.75 176:0.59 177:0.70 179:0.61 182:0.61 190:0.98 191:0.77 192:0.48 195:0.98 197:0.85 201:0.55 202:0.77 205:0.99 208:0.50 212:0.16 216:0.11 220:0.74 222:0.35 226:0.96 227:0.82 229:0.61 231:0.70 235:0.74 236:0.92 239:0.86 241:0.80 242:0.57 243:0.21 244:0.93 245:0.60 246:0.61 247:0.67 248:0.60 253:0.49 254:0.94 255:0.69 256:0.79 257:0.65 259:0.21 265:0.24 266:0.94 267:0.94 268:0.78 271:0.64 276:0.62 277:0.87 282:0.71 284:0.88 285:0.60 287:0.61 290:0.64 300:0.84 +1 8:0.69 11:0.49 12:0.69 17:0.36 21:0.78 27:0.25 29:0.56 30:0.60 34:0.94 36:0.55 37:0.41 39:0.76 43:0.26 45:0.83 66:0.44 69:0.85 70:0.44 71:1.00 74:0.43 76:0.94 91:0.46 96:0.80 98:0.20 101:0.47 108:0.70 122:0.82 123:0.68 124:0.67 127:0.22 129:0.59 131:0.83 133:0.64 135:0.63 140:0.95 144:0.57 145:0.41 146:0.36 147:0.42 149:0.42 151:0.65 153:0.48 154:0.18 157:0.92 158:0.72 159:0.54 162:0.47 166:0.61 170:0.82 172:0.41 173:0.74 175:0.91 177:0.38 178:0.53 179:0.50 182:0.81 187:0.39 190:0.98 191:0.75 202:0.72 212:0.72 216:0.81 222:0.35 227:0.82 229:0.61 231:0.75 232:0.91 235:0.52 241:0.41 242:0.45 243:0.57 244:0.93 245:0.60 246:0.61 247:0.47 248:0.62 253:0.72 256:0.45 257:0.84 259:0.21 265:0.21 266:0.27 267:0.23 268:0.46 271:0.64 276:0.41 277:0.87 285:0.46 287:0.61 300:0.33 +0 1:0.56 10:0.86 11:0.49 12:0.69 17:0.28 18:0.66 21:0.76 27:0.45 29:0.56 30:0.21 32:0.64 34:0.83 36:0.20 37:0.50 39:0.76 43:0.19 45:0.73 55:0.92 64:0.77 66:0.07 69:0.71 70:0.96 71:1.00 74:0.42 75:0.97 77:0.70 81:0.45 83:0.56 86:0.69 91:0.10 97:0.89 98:0.22 99:0.83 101:0.47 108:0.54 114:0.61 122:0.83 123:0.85 124:0.90 126:0.51 127:0.56 129:0.05 131:0.61 133:0.88 135:0.83 139:0.70 144:0.57 145:0.50 146:0.41 147:0.48 149:0.19 150:0.37 154:0.45 155:0.46 157:0.61 158:0.73 159:0.79 165:0.65 166:0.75 170:0.82 172:0.37 173:0.52 174:0.83 176:0.59 177:0.88 178:0.55 179:0.59 182:0.61 187:0.68 192:0.44 195:0.92 197:0.84 201:0.53 208:0.61 212:0.18 216:0.77 219:0.91 222:0.35 226:0.95 227:0.61 229:0.61 231:0.74 235:0.98 236:0.94 239:0.87 241:0.51 242:0.58 243:0.40 244:0.61 245:0.62 246:0.61 247:0.74 253:0.46 254:0.99 259:0.21 265:0.23 266:0.91 267:0.28 271:0.64 276:0.58 277:0.87 279:0.75 282:0.73 287:0.61 290:0.61 292:0.86 300:0.84 +0 10:0.85 11:0.45 12:0.69 17:0.89 18:0.62 21:0.13 27:0.72 29:0.56 30:0.56 34:0.36 36:0.79 37:0.24 39:0.76 43:0.10 45:0.66 55:0.84 66:0.84 69:0.58 70:0.58 71:1.00 74:0.33 81:0.30 86:0.66 91:0.83 96:0.74 98:0.90 101:0.47 108:0.45 114:0.74 122:0.83 123:0.43 126:0.24 127:0.48 129:0.05 131:0.83 132:0.97 135:0.44 139:0.63 140:0.80 145:0.89 146:0.74 147:0.78 149:0.12 150:0.33 151:0.57 153:0.48 154:0.28 157:0.61 159:0.31 162:0.62 163:0.75 166:0.75 170:0.82 172:0.54 173:0.72 174:0.79 176:0.56 177:0.88 178:0.42 179:0.77 182:0.61 190:0.98 197:0.82 202:0.50 212:0.39 216:0.04 220:0.74 222:0.35 227:0.82 229:0.61 231:0.70 232:0.74 235:0.12 239:0.84 241:0.92 242:0.42 243:0.85 244:0.83 246:0.61 247:0.67 248:0.56 253:0.61 254:0.84 256:0.45 265:0.90 266:0.47 267:0.28 268:0.46 271:0.64 276:0.45 285:0.46 287:0.61 290:0.74 300:0.43 +0 1:0.59 10:0.87 11:0.53 12:0.69 17:0.66 18:0.62 21:0.40 27:0.53 29:0.56 30:0.85 32:0.65 34:0.90 36:0.52 37:0.31 39:0.76 43:0.56 45:0.92 66:0.92 69:0.32 70:0.36 71:1.00 74:0.67 77:0.89 81:0.46 83:0.56 86:0.70 91:0.46 98:0.90 99:0.83 101:0.65 108:0.25 114:0.76 122:0.55 123:0.24 126:0.32 127:0.46 129:0.05 131:0.61 135:0.68 139:0.66 144:0.57 145:0.67 146:0.70 147:0.75 149:0.75 150:0.40 154:0.52 155:0.47 157:0.97 159:0.28 165:0.65 166:0.87 170:0.82 172:0.77 173:0.50 174:0.79 176:0.62 177:0.88 178:0.55 179:0.48 182:0.93 187:0.39 192:0.46 195:0.57 197:0.82 201:0.56 202:0.36 208:0.50 212:0.84 215:0.67 216:0.28 222:0.35 227:0.61 229:0.61 231:0.80 235:0.52 239:0.85 241:0.05 242:0.58 243:0.92 244:0.61 246:0.84 247:0.55 253:0.60 254:0.93 259:0.21 265:0.90 266:0.27 267:0.28 271:0.64 276:0.65 282:0.74 287:0.61 290:0.76 297:0.36 300:0.33 +0 10:0.83 11:0.45 12:0.69 17:0.51 18:0.61 21:0.78 27:0.72 29:0.56 30:0.38 34:0.22 36:0.38 39:0.76 43:0.20 45:0.73 55:0.92 66:0.29 69:0.94 70:0.78 71:1.00 74:0.27 86:0.63 91:0.68 98:0.25 101:0.47 108:0.94 122:0.83 123:0.94 124:0.84 127:0.23 129:0.81 131:0.61 133:0.83 135:0.85 139:0.61 146:0.13 147:0.18 149:0.17 154:0.13 157:0.61 159:0.78 163:0.75 166:0.61 170:0.82 172:0.27 173:0.82 174:0.83 175:0.91 177:0.38 178:0.55 179:0.62 182:0.61 197:0.81 202:0.36 212:0.14 216:0.06 222:0.35 227:0.61 229:0.61 231:0.64 235:0.22 239:0.82 241:0.81 242:0.45 243:0.26 244:0.93 245:0.49 246:0.61 247:0.60 253:0.25 257:0.84 259:0.21 265:0.27 266:0.71 267:0.19 271:0.64 276:0.38 277:0.87 287:0.61 300:0.55 +0 8:0.81 9:0.71 11:0.42 12:0.69 17:0.49 21:0.78 27:0.30 29:0.56 30:0.89 34:0.69 36:0.63 37:0.57 39:0.76 43:0.08 55:0.59 66:0.80 69:0.75 70:0.25 71:1.00 74:0.48 76:0.85 77:0.70 83:0.56 91:0.42 96:0.73 97:0.89 98:0.67 108:0.68 120:0.59 122:0.83 123:0.66 124:0.39 127:0.30 129:0.59 131:0.91 133:0.47 135:0.20 140:0.94 144:0.57 145:0.50 146:0.17 147:0.22 149:0.22 151:0.61 153:0.48 154:0.22 155:0.54 157:0.61 158:0.75 159:0.37 162:0.51 164:0.55 166:0.61 170:0.82 172:0.77 173:0.79 175:0.91 177:0.38 178:0.50 179:0.36 182:0.93 187:0.39 190:0.95 191:0.70 192:0.56 195:0.70 202:0.69 208:0.50 212:0.91 216:0.40 220:0.91 222:0.35 227:0.82 229:0.96 231:0.80 235:0.31 241:0.27 242:0.80 243:0.14 244:0.93 245:0.70 246:0.61 247:0.39 248:0.59 253:0.52 254:0.74 255:0.70 256:0.58 257:0.84 259:0.21 260:0.60 265:0.68 266:0.27 267:0.19 268:0.59 271:0.64 276:0.66 277:0.87 279:0.75 282:0.71 283:0.67 285:0.50 287:0.87 300:0.33 +0 10:0.80 12:0.69 17:0.45 21:0.78 27:0.34 29:0.56 30:0.21 34:0.58 36:0.41 37:0.46 39:0.76 43:0.09 55:0.84 66:0.20 69:0.66 70:0.37 71:1.00 74:0.25 91:0.01 98:0.15 104:0.99 106:0.81 108:0.50 122:0.41 123:0.48 124:0.81 127:0.52 129:0.89 131:0.61 133:0.78 135:0.34 145:0.54 146:0.31 147:0.38 149:0.09 154:0.08 157:0.61 159:0.84 163:0.96 166:0.61 170:0.82 172:0.10 173:0.40 174:0.79 175:0.97 177:0.05 178:0.55 179:0.96 182:0.61 187:0.98 197:0.80 206:0.81 212:0.42 216:0.53 222:0.35 227:0.61 229:0.61 231:0.64 235:0.60 239:0.80 241:0.10 242:0.45 243:0.40 244:0.97 245:0.39 246:0.61 247:0.35 253:0.23 257:0.93 259:0.21 265:0.16 266:0.23 267:0.59 271:0.64 276:0.23 277:0.95 287:0.61 300:0.25 +0 7:0.64 10:0.81 11:0.45 12:0.69 17:0.75 18:0.58 21:0.76 27:0.39 29:0.56 30:0.56 32:0.62 34:0.75 36:0.32 37:0.67 39:0.76 43:0.14 45:0.66 55:0.71 66:0.10 69:0.95 70:0.83 71:1.00 74:0.40 76:0.85 77:0.70 81:0.24 86:0.60 91:0.09 98:0.30 101:0.47 108:0.77 117:0.86 122:0.83 123:0.89 124:0.93 126:0.24 127:0.72 129:0.05 131:0.61 133:0.92 135:0.96 139:0.59 145:0.82 146:0.78 147:0.54 149:0.33 150:0.26 154:0.10 157:0.61 159:0.91 166:0.76 170:0.82 172:0.63 173:0.83 174:0.79 177:0.05 178:0.55 179:0.33 182:0.61 187:0.21 195:0.98 197:0.80 212:0.56 215:0.46 216:0.89 222:0.35 227:0.61 229:0.61 230:0.64 231:0.79 232:1.00 233:0.66 235:0.95 239:0.81 241:0.02 242:0.81 243:0.28 244:0.93 245:0.80 246:0.61 247:0.55 253:0.35 257:0.93 259:0.21 265:0.30 266:0.96 267:0.19 271:0.64 276:0.80 282:0.71 287:0.61 297:0.36 300:0.94 +0 7:0.64 8:0.63 11:0.49 12:0.69 17:0.77 21:0.30 27:0.52 29:0.56 30:0.32 32:0.62 34:0.56 36:0.90 37:0.62 39:0.76 43:0.34 45:0.66 55:0.84 66:0.12 69:0.70 70:0.79 71:1.00 74:0.41 81:0.28 91:0.54 98:0.43 101:0.47 104:0.87 106:0.81 108:0.80 120:0.62 123:0.79 124:0.96 126:0.24 127:0.86 129:0.93 131:0.83 133:0.96 135:0.86 140:0.45 144:0.57 145:0.44 146:0.73 147:0.77 149:0.37 150:0.31 151:0.53 153:0.48 154:0.25 157:0.76 159:0.93 162:0.83 164:0.74 166:0.76 170:0.82 172:0.70 173:0.33 175:0.91 177:0.38 179:0.21 182:0.72 187:0.39 190:0.98 191:0.79 195:0.98 202:0.64 206:0.81 212:0.14 215:0.72 216:0.15 220:0.97 222:0.35 227:0.82 229:0.61 230:0.64 231:0.79 233:0.76 235:0.31 241:0.15 242:0.45 243:0.31 244:0.93 245:0.88 246:0.61 247:0.67 248:0.55 253:0.35 256:0.68 257:0.84 259:0.21 260:0.62 265:0.45 266:0.95 267:0.36 268:0.70 271:0.64 276:0.91 277:0.87 283:0.67 285:0.46 287:0.61 297:0.36 300:0.70 +0 8:0.98 10:0.84 12:0.69 17:0.91 21:0.64 27:0.40 29:0.56 30:0.91 34:0.62 36:0.51 37:0.90 39:0.76 43:0.10 55:0.84 66:0.69 69:0.55 70:0.13 71:1.00 74:0.30 77:0.70 81:0.25 91:0.83 96:0.74 98:0.71 108:0.43 114:0.63 122:0.85 123:0.41 126:0.24 127:0.22 129:0.05 131:0.83 135:0.17 140:0.45 145:0.70 146:0.53 147:0.59 149:0.08 150:0.27 151:0.56 153:0.78 154:0.18 157:0.61 159:0.19 162:0.76 163:0.93 166:0.75 170:0.82 172:0.21 173:0.73 174:0.79 175:0.75 176:0.56 177:0.70 179:0.91 182:0.61 190:0.98 191:0.80 195:0.60 197:0.82 202:0.64 212:0.61 216:0.09 220:0.82 222:0.35 227:0.82 229:0.61 231:0.70 232:0.75 235:0.74 239:0.83 241:0.89 242:0.63 243:0.73 244:0.93 246:0.61 247:0.30 248:0.55 253:0.55 254:0.90 256:0.64 257:0.65 259:0.21 265:0.71 266:0.17 267:0.44 268:0.68 271:0.64 276:0.21 277:0.69 282:0.71 285:0.46 287:0.61 290:0.63 300:0.13 +0 10:0.82 11:0.45 12:0.69 17:0.88 18:0.59 21:0.78 27:0.69 29:0.56 30:0.45 34:0.98 36:0.24 37:0.45 39:0.76 43:0.20 45:0.66 66:0.49 69:0.95 70:0.25 71:1.00 74:0.25 86:0.61 91:0.10 98:0.26 101:0.47 108:0.89 122:0.76 123:0.89 124:0.48 127:0.24 129:0.05 131:0.61 133:0.37 135:0.34 139:0.59 145:0.80 146:0.45 147:0.05 149:0.12 154:0.13 157:0.61 159:0.55 163:0.99 166:0.61 170:0.82 172:0.16 173:0.96 174:0.79 175:0.75 177:0.05 178:0.55 179:0.93 182:0.61 187:0.21 197:0.80 212:0.61 216:0.18 222:0.35 227:0.61 229:0.61 231:0.62 235:0.88 239:0.81 241:0.21 242:0.63 243:0.29 244:0.93 245:0.39 246:0.61 247:0.30 253:0.23 257:0.93 259:0.21 265:0.28 266:0.17 267:0.28 271:0.64 276:0.15 277:0.69 287:0.61 300:0.18 +0 8:0.88 10:0.85 11:0.53 12:0.69 17:0.51 18:0.62 21:0.78 27:0.36 29:0.56 30:0.38 34:0.56 36:0.85 37:0.74 39:0.76 43:0.47 45:0.73 55:0.52 66:0.83 69:0.57 70:0.63 71:1.00 74:0.41 77:0.70 86:0.66 91:0.63 98:0.68 101:0.65 108:0.44 122:0.83 123:0.42 127:0.20 129:0.05 131:0.61 135:0.91 139:0.64 144:0.57 145:0.92 146:0.60 147:0.65 149:0.26 154:0.35 157:0.80 159:0.22 166:0.61 170:0.82 172:0.51 173:0.67 174:0.79 177:0.88 178:0.55 179:0.56 182:0.74 187:0.39 195:0.64 197:0.82 202:0.71 212:0.70 216:0.46 222:0.35 227:0.61 229:0.61 231:0.68 235:0.80 239:0.84 241:0.32 242:0.71 243:0.84 244:0.83 246:0.61 247:0.47 253:0.36 254:0.86 259:0.21 265:0.69 266:0.27 267:0.19 271:0.64 276:0.38 282:0.71 287:0.61 300:0.43 +0 10:0.97 11:0.45 12:0.69 17:0.67 18:0.89 21:0.59 27:0.49 29:0.56 30:0.38 33:0.97 34:0.20 36:0.58 37:0.66 39:0.76 43:0.22 44:0.88 45:0.97 66:0.45 69:0.89 70:0.82 71:1.00 74:0.35 75:0.96 77:0.70 81:0.32 86:0.85 91:0.11 98:0.60 101:0.47 102:0.96 108:0.78 114:0.64 117:0.86 122:0.94 123:0.77 124:0.84 126:0.32 127:0.54 129:0.89 131:0.61 133:0.87 135:0.75 139:0.84 145:0.80 146:0.96 147:0.97 149:0.65 150:0.31 154:0.20 157:0.61 158:0.72 159:0.89 166:0.75 170:0.82 172:0.90 173:0.69 174:0.99 175:0.75 176:0.59 177:0.70 178:0.55 179:0.18 182:0.61 187:0.39 192:0.44 195:0.97 197:0.98 201:0.55 212:0.48 216:0.43 222:0.35 226:0.95 227:0.61 229:0.61 231:0.68 232:0.94 235:0.74 236:0.92 239:0.97 241:0.12 242:0.29 243:0.58 244:0.83 245:0.93 246:0.61 247:0.92 253:0.49 254:0.80 257:0.65 259:0.21 265:0.61 266:0.92 267:0.44 271:0.64 276:0.91 277:0.69 282:0.72 287:0.61 290:0.64 291:0.97 300:0.94 +0 12:0.86 17:0.64 21:0.78 27:0.72 34:0.59 36:0.36 37:0.34 43:0.24 66:0.36 69:0.86 91:0.54 98:0.09 108:0.73 123:0.71 127:0.06 129:0.05 135:0.76 145:0.87 146:0.05 147:0.05 149:0.10 154:0.17 159:0.05 172:0.05 173:0.96 177:0.05 178:0.55 187:0.68 216:0.16 235:0.84 241:0.27 243:0.51 259:0.21 265:0.09 267:0.52 +1 12:0.86 17:0.38 21:0.78 27:0.25 30:0.58 34:0.44 36:0.21 37:0.90 43:0.46 55:0.98 66:0.18 69:0.44 70:0.44 91:0.42 98:0.11 106:0.81 108:0.34 123:0.32 124:0.90 127:0.37 129:0.96 133:0.90 135:0.37 145:0.42 146:0.22 147:0.28 149:0.37 154:0.35 159:0.83 163:0.99 172:0.16 173:0.17 177:0.05 178:0.55 179:0.82 187:0.21 206:0.81 212:0.37 216:0.13 235:0.22 241:0.30 242:0.82 243:0.39 245:0.43 247:0.12 259:0.21 265:0.11 266:0.47 267:0.28 276:0.37 300:0.33 +1 12:0.86 17:0.49 21:0.78 27:0.25 30:0.45 34:0.25 36:0.21 37:0.90 43:0.24 55:0.92 66:0.16 69:0.86 70:0.42 91:0.56 98:0.27 108:0.80 123:0.94 124:0.88 127:0.52 129:0.95 133:0.87 135:0.20 145:0.42 146:0.05 147:0.05 149:0.30 154:0.17 159:0.83 163:0.89 172:0.16 173:0.70 177:0.05 178:0.55 179:0.83 187:0.21 212:0.15 216:0.13 235:0.22 241:0.37 242:0.82 243:0.17 245:0.46 247:0.12 259:0.21 265:0.23 266:0.63 267:0.73 276:0.39 300:0.33 +1 12:0.86 17:0.22 21:0.78 27:0.21 30:0.76 34:0.49 36:0.28 37:0.81 43:0.43 55:0.84 66:0.36 69:0.47 70:0.15 91:0.40 98:0.25 106:0.81 108:0.70 123:0.68 124:0.52 127:0.23 129:0.59 133:0.47 135:0.84 145:0.92 146:0.05 147:0.05 149:0.24 154:0.33 159:0.34 163:0.96 172:0.10 173:0.44 177:0.05 178:0.55 179:0.96 187:0.68 206:0.81 212:0.95 216:0.45 235:0.22 241:0.15 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.27 266:0.12 267:0.59 276:0.15 300:0.13 +0 12:0.86 17:0.22 21:0.78 27:0.02 30:0.76 34:0.73 36:0.27 37:0.92 43:0.36 55:0.96 66:0.13 69:0.62 70:0.15 91:0.51 98:0.07 108:0.47 123:0.46 124:0.75 127:0.40 129:0.81 133:0.67 135:0.68 145:0.87 146:0.05 147:0.05 149:0.23 154:0.27 159:0.54 163:0.97 172:0.05 173:0.56 177:0.05 178:0.55 179:0.99 187:0.39 202:0.49 212:0.95 216:0.99 235:0.42 241:0.62 242:0.82 243:0.34 245:0.37 247:0.12 259:0.21 265:0.07 266:0.12 267:0.65 276:0.17 300:0.13 +0 12:0.86 17:0.69 21:0.78 27:0.71 34:0.62 36:0.33 37:0.32 43:0.24 66:0.36 69:0.86 91:0.66 98:0.09 108:0.72 123:0.71 127:0.06 129:0.05 135:0.74 145:0.65 146:0.05 147:0.05 149:0.10 154:0.17 159:0.05 172:0.05 173:0.96 177:0.05 178:0.55 187:0.68 216:0.10 235:0.95 241:0.32 243:0.51 259:0.21 265:0.09 267:0.44 +0 12:0.86 17:0.32 21:0.78 27:0.21 30:0.95 34:0.49 36:0.37 37:0.81 43:0.24 55:0.84 66:0.54 69:0.85 91:0.38 98:0.27 108:0.71 123:0.69 127:0.11 129:0.05 135:0.86 145:0.92 146:0.36 147:0.42 149:0.16 154:0.17 159:0.14 172:0.10 173:0.87 177:0.05 178:0.55 179:0.76 187:0.39 216:0.45 235:0.22 241:0.35 243:0.63 259:0.21 265:0.29 267:0.69 300:0.08 +0 12:0.86 17:0.34 21:0.78 27:0.21 34:0.23 36:0.24 37:0.81 43:0.22 66:0.36 69:0.90 91:0.27 98:0.07 108:0.79 123:0.78 127:0.06 129:0.05 135:0.83 145:0.92 146:0.05 147:0.05 149:0.09 154:0.15 159:0.05 172:0.05 173:0.93 177:0.05 178:0.55 187:0.39 216:0.45 235:0.22 241:0.39 243:0.51 259:0.21 265:0.07 267:0.28 +0 8:0.91 12:0.86 17:0.49 21:0.78 27:0.72 34:0.24 36:0.36 43:0.22 66:0.36 69:0.89 91:0.72 98:0.06 108:0.79 123:0.77 127:0.05 129:0.05 135:0.88 145:0.98 146:0.05 147:0.05 149:0.09 154:0.15 159:0.05 172:0.05 173:0.85 177:0.05 178:0.55 202:0.50 216:0.04 235:0.31 241:0.94 243:0.51 259:0.21 265:0.07 267:0.28 +0 12:0.86 17:0.61 21:0.78 27:0.25 30:0.85 34:0.37 36:0.19 37:0.90 43:0.26 55:0.92 66:0.09 69:0.82 70:0.28 91:0.38 98:0.23 108:0.66 123:0.82 124:0.95 127:0.42 129:0.99 133:0.95 135:0.24 145:0.42 146:0.52 147:0.58 149:0.41 154:0.18 159:0.85 163:0.96 172:0.16 173:0.58 177:0.05 178:0.55 179:0.65 187:0.21 212:0.37 216:0.13 235:0.22 241:0.35 242:0.82 243:0.27 245:0.50 247:0.12 259:0.21 265:0.24 266:0.38 267:0.52 276:0.53 300:0.25 +1 12:0.86 17:0.59 21:0.78 27:0.21 30:0.95 34:0.76 36:0.29 37:0.81 43:0.29 55:0.59 66:0.54 69:0.76 91:0.49 98:0.23 108:0.59 123:0.57 127:0.11 129:0.05 135:0.79 145:0.92 146:0.23 147:0.29 149:0.19 154:0.21 159:0.11 172:0.10 173:0.92 177:0.05 178:0.55 179:0.64 187:0.39 216:0.45 235:0.97 241:0.27 243:0.63 259:0.21 265:0.25 267:0.44 300:0.08 +2 1:0.74 6:0.88 7:0.81 8:0.76 9:0.80 11:0.92 12:0.37 17:0.43 18:0.94 20:0.98 21:0.30 22:0.93 27:0.21 28:0.76 29:0.86 30:0.70 31:0.95 34:0.49 36:0.74 37:0.74 39:0.56 41:0.82 43:0.97 45:0.83 47:0.93 60:0.87 64:0.77 66:0.95 69:0.15 70:0.50 71:0.63 74:0.92 76:0.85 78:0.88 79:0.94 81:0.61 83:0.91 85:0.91 86:0.98 91:0.33 96:0.84 98:0.82 100:0.92 101:0.87 104:0.84 106:0.81 108:0.12 111:0.89 114:0.65 117:0.86 120:0.74 121:0.90 122:0.80 123:0.12 126:0.54 127:0.31 129:0.05 135:0.51 137:0.95 139:0.98 140:0.45 144:0.85 146:0.82 147:0.85 149:0.95 150:0.77 151:0.92 152:0.90 153:0.72 154:0.97 155:0.67 158:0.92 159:0.38 161:0.76 162:0.67 164:0.64 165:0.93 167:0.67 169:0.90 172:0.86 173:0.23 176:0.73 177:0.70 179:0.27 181:0.74 186:0.88 187:0.39 189:0.90 192:0.87 196:0.98 201:0.93 202:0.53 204:0.89 206:0.81 208:0.64 212:0.89 215:0.44 216:0.95 219:0.95 222:0.60 230:0.87 232:0.89 233:0.76 235:0.52 238:0.91 241:0.24 242:0.32 243:0.95 247:0.82 248:0.81 253:0.88 255:0.95 256:0.45 259:0.21 260:0.75 261:0.91 262:0.93 265:0.82 266:0.27 267:0.52 268:0.57 271:0.60 276:0.77 279:0.86 281:0.91 283:0.82 284:0.91 285:0.62 290:0.65 292:0.95 297:0.36 300:0.55 +3 1:0.74 6:0.98 7:0.81 8:0.96 9:0.80 11:0.92 12:0.37 17:0.05 18:0.91 20:0.98 21:0.22 27:0.18 28:0.76 29:0.86 30:0.70 31:0.99 32:0.80 34:0.88 36:0.92 37:0.85 39:0.56 41:0.97 43:0.95 44:0.93 45:0.77 48:0.97 60:0.99 64:0.77 66:0.92 69:0.19 70:0.48 71:0.63 74:0.88 77:0.70 78:0.95 79:0.99 81:0.67 83:0.91 85:0.88 86:0.96 91:0.84 96:0.95 98:0.94 100:0.99 101:0.87 104:0.58 108:0.15 111:0.99 114:0.71 120:0.90 121:0.90 122:0.57 123:0.15 126:0.54 127:0.62 129:0.05 135:0.72 137:0.99 139:0.97 140:0.45 144:0.85 145:0.42 146:0.82 147:0.85 149:0.91 150:0.80 151:0.99 152:0.96 153:0.94 154:0.95 155:0.67 158:0.92 159:0.38 161:0.76 162:0.81 164:0.90 165:0.93 167:0.91 169:0.98 172:0.77 173:0.33 176:0.73 177:0.70 179:0.53 181:0.74 186:0.94 187:0.21 189:0.98 191:0.78 192:0.87 195:0.62 196:1.00 201:0.93 202:0.84 204:0.89 208:0.64 212:0.61 215:0.51 216:0.72 219:0.95 222:0.60 230:0.87 232:0.77 233:0.76 235:0.42 238:0.98 241:0.76 242:0.27 243:0.92 247:0.79 248:0.94 253:0.96 255:0.95 256:0.89 259:0.21 260:0.90 261:0.98 265:0.94 266:0.54 267:0.23 268:0.89 271:0.60 276:0.65 279:0.86 281:0.91 282:0.88 283:0.82 284:0.99 285:0.62 290:0.71 292:0.95 297:0.36 299:0.88 300:0.43 +2 1:0.74 9:0.80 11:0.92 12:0.37 17:0.15 18:0.85 20:0.83 21:0.54 27:0.25 28:0.76 29:0.86 30:0.66 31:0.94 34:0.61 36:0.88 37:0.77 39:0.56 41:0.82 43:0.89 45:0.85 60:0.87 64:0.77 66:0.26 69:0.52 70:0.52 71:0.63 74:0.82 78:0.78 79:0.93 81:0.49 83:0.91 85:0.85 86:0.93 91:0.05 96:0.80 98:0.56 100:0.73 101:0.87 108:0.75 111:0.77 114:0.59 120:0.69 122:0.57 123:0.73 124:0.74 126:0.54 127:0.41 129:0.81 133:0.67 135:0.83 137:0.94 139:0.93 140:0.45 144:0.85 146:0.31 147:0.37 149:0.89 150:0.72 151:0.87 152:0.80 153:0.48 154:0.87 155:0.67 158:0.91 159:0.50 161:0.76 162:0.86 164:0.57 165:0.93 167:0.71 169:0.72 172:0.54 173:0.49 176:0.73 177:0.70 179:0.41 181:0.74 186:0.79 187:0.68 192:0.87 196:0.96 201:0.93 202:0.36 208:0.64 212:0.69 215:0.39 216:0.72 219:0.95 222:0.60 232:0.91 235:0.74 241:0.44 242:0.22 243:0.36 245:0.83 247:0.82 248:0.77 253:0.84 255:0.95 256:0.45 259:0.21 260:0.70 261:0.79 265:0.58 266:0.47 267:0.44 268:0.46 271:0.60 276:0.69 279:0.86 283:0.78 284:0.89 285:0.62 290:0.59 292:0.91 297:0.36 300:0.43 +1 1:0.74 11:0.92 12:0.37 17:0.32 18:0.73 21:0.59 27:0.45 28:0.76 29:0.86 30:0.87 32:0.69 34:0.79 36:0.21 37:0.50 39:0.56 43:0.81 45:0.85 64:0.77 66:0.49 69:0.70 70:0.48 71:0.63 74:0.71 77:0.70 81:0.47 83:0.91 85:0.80 86:0.82 91:0.09 98:0.45 101:0.87 108:0.53 114:0.58 122:0.51 123:0.51 124:0.56 126:0.54 127:0.34 129:0.59 133:0.46 135:0.66 139:0.78 144:0.85 145:0.50 146:0.58 147:0.64 149:0.81 150:0.71 154:0.77 155:0.67 159:0.49 161:0.76 165:0.93 167:0.64 172:0.54 173:0.67 176:0.73 177:0.70 178:0.55 179:0.54 181:0.74 187:0.68 192:0.87 195:0.75 201:0.93 208:0.64 212:0.59 215:0.39 216:0.77 222:0.60 235:0.80 241:0.12 242:0.41 243:0.60 245:0.76 247:0.60 253:0.44 259:0.21 265:0.47 266:0.63 267:0.28 271:0.60 276:0.44 282:0.77 290:0.58 297:0.36 300:0.70 +1 1:0.69 11:0.92 12:0.37 17:0.47 18:0.73 21:0.74 27:0.45 28:0.76 29:0.86 30:0.75 32:0.69 34:0.70 36:0.45 37:0.50 39:0.56 43:0.79 45:0.92 66:0.69 69:0.70 70:0.58 71:0.63 74:0.79 77:0.70 81:0.32 83:0.60 85:0.80 86:0.84 91:0.05 98:0.72 99:0.83 101:0.87 108:0.54 114:0.54 122:0.75 123:0.51 124:0.44 126:0.44 127:0.44 129:0.05 133:0.43 135:0.83 139:0.81 144:0.85 145:0.54 146:0.88 147:0.90 149:0.89 150:0.51 154:0.72 155:0.58 159:0.63 161:0.76 165:0.70 167:0.66 172:0.79 173:0.61 176:0.66 177:0.70 178:0.55 179:0.38 181:0.74 187:0.68 192:0.59 195:0.83 201:0.68 208:0.54 212:0.57 215:0.36 216:0.77 222:0.60 235:0.95 241:0.21 242:0.44 243:0.73 245:0.84 247:0.76 253:0.44 259:0.21 265:0.72 266:0.71 267:0.19 271:0.60 276:0.69 282:0.77 290:0.54 297:0.36 300:0.70 +1 1:0.74 6:0.87 8:0.72 9:0.80 11:0.93 12:0.37 17:0.18 18:0.82 20:0.91 21:0.30 27:0.06 28:0.76 29:0.86 30:0.42 31:0.95 32:0.78 34:0.68 36:0.99 37:0.82 39:0.56 41:0.93 43:0.74 44:0.93 45:0.85 60:0.87 64:0.77 66:0.49 69:0.83 70:0.77 71:0.63 74:0.77 77:0.70 78:0.84 79:0.94 81:0.61 83:0.91 85:0.80 86:0.91 91:0.25 96:0.88 98:0.61 100:0.88 101:0.97 108:0.84 111:0.84 114:0.65 120:0.79 122:0.75 123:0.83 124:0.57 126:0.54 127:0.30 129:0.59 133:0.46 135:0.89 137:0.95 139:0.94 140:0.45 144:0.85 145:0.88 146:0.41 147:0.48 149:0.65 150:0.77 151:0.80 152:0.91 153:0.48 154:0.65 155:0.67 158:0.91 159:0.67 161:0.76 162:0.69 164:0.80 165:0.93 167:0.72 169:0.85 172:0.70 173:0.70 176:0.73 177:0.70 179:0.32 181:0.74 186:0.85 187:0.39 189:0.88 190:0.77 192:0.87 195:0.90 196:0.97 201:0.93 202:0.74 208:0.64 212:0.60 215:0.44 216:0.92 219:0.95 220:0.62 222:0.60 235:0.52 238:0.89 241:0.49 242:0.15 243:0.45 245:0.88 247:0.88 248:0.80 253:0.87 255:0.95 256:0.75 259:0.21 260:0.78 261:0.87 265:0.62 266:0.71 267:0.59 268:0.76 271:0.60 276:0.69 279:0.86 282:0.86 283:0.78 284:0.96 285:0.62 290:0.65 292:0.91 297:0.36 300:0.70 +2 6:0.98 7:0.81 8:0.98 9:0.80 11:0.92 12:0.37 17:0.28 18:0.90 20:0.83 21:0.78 27:0.18 28:0.76 29:0.86 30:0.66 31:0.95 34:0.86 36:0.74 37:0.85 39:0.56 41:0.90 43:0.86 45:0.88 48:0.97 66:0.94 69:0.27 70:0.56 71:0.63 74:0.78 78:0.92 79:0.95 83:0.91 85:0.85 86:0.94 91:0.46 96:0.84 98:0.97 100:0.97 101:0.87 104:0.58 108:0.21 111:0.95 120:0.77 121:0.90 122:0.75 123:0.20 127:0.77 129:0.05 135:0.68 137:0.95 139:0.93 140:0.87 144:0.85 145:0.39 146:0.93 147:0.94 149:0.88 151:0.87 152:0.96 153:0.48 154:0.83 155:0.67 159:0.56 161:0.76 162:0.62 164:0.72 167:0.77 169:0.98 172:0.84 173:0.27 177:0.70 178:0.48 179:0.45 181:0.74 186:0.92 187:0.21 189:1.00 192:0.87 196:0.97 202:0.68 204:0.89 208:0.64 212:0.42 216:0.72 222:0.60 230:0.87 232:0.77 233:0.76 235:0.12 238:0.96 241:0.63 242:0.28 243:0.94 247:0.79 248:0.82 253:0.85 255:0.95 256:0.68 259:0.21 260:0.76 261:0.98 265:0.97 266:0.63 267:0.76 268:0.69 271:0.60 276:0.74 281:0.91 284:0.94 285:0.62 300:0.55 +1 1:0.69 7:0.72 8:0.91 9:0.76 11:0.93 12:0.37 17:0.20 18:0.64 21:0.30 27:0.21 28:0.76 29:0.86 30:0.21 34:0.21 36:0.56 37:0.74 39:0.56 43:0.72 45:0.99 55:0.71 66:0.54 69:0.97 70:0.81 71:0.63 74:0.88 81:0.46 83:0.60 85:0.80 86:0.70 91:0.75 96:0.98 97:0.99 98:0.79 99:0.83 101:0.97 104:0.84 106:0.81 108:0.96 114:0.63 117:0.86 120:0.96 122:0.77 123:0.96 124:0.86 126:0.44 127:0.83 129:0.59 133:0.91 135:0.17 138:0.99 139:0.68 140:0.45 144:0.85 146:0.90 147:0.88 149:0.94 150:0.56 151:0.96 153:0.95 154:0.14 155:0.58 158:0.79 159:0.96 161:0.76 162:0.67 164:0.92 165:0.70 167:0.72 172:0.99 173:0.80 176:0.66 177:0.38 179:0.10 181:0.74 187:0.39 190:0.77 191:0.80 192:0.59 201:0.68 202:0.91 204:0.85 206:0.81 208:0.54 212:0.58 215:0.44 216:0.95 222:0.60 230:0.75 232:0.89 233:0.76 235:0.42 241:0.47 242:0.54 243:0.16 245:0.99 247:0.90 248:0.98 253:0.99 255:0.74 256:0.93 257:0.65 259:0.21 260:0.95 265:0.79 266:0.94 267:0.23 268:0.93 271:0.60 276:0.99 279:0.82 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.69 6:0.98 7:0.72 8:0.93 9:0.80 11:0.91 12:0.37 17:0.01 20:0.89 21:0.22 27:0.18 28:0.76 29:0.86 30:0.76 31:0.97 32:0.69 34:0.50 36:0.35 37:0.85 39:0.56 41:0.90 43:0.70 45:0.81 66:0.80 69:0.45 70:0.37 71:0.63 74:0.69 77:0.70 78:0.89 79:0.96 81:0.53 83:0.91 91:0.99 96:1.00 97:0.98 98:0.77 99:0.83 100:0.94 101:0.52 104:0.58 108:0.35 111:0.91 114:0.67 120:0.99 122:0.75 123:0.34 126:0.44 127:0.26 129:0.05 135:0.44 137:0.97 138:0.99 140:0.98 144:0.85 145:0.41 146:0.48 147:0.54 149:0.67 150:0.58 151:0.95 152:0.96 153:0.99 154:0.59 155:0.67 158:0.78 159:0.17 161:0.76 162:0.66 164:0.98 165:0.70 167:1.00 169:0.98 172:0.45 173:0.72 175:0.75 176:0.66 177:0.38 179:0.73 181:0.74 186:0.88 189:0.94 191:0.92 192:0.87 195:0.54 201:0.68 202:0.98 204:0.85 208:0.64 212:0.55 215:0.51 216:0.72 222:0.60 230:0.75 232:0.77 233:0.76 235:0.31 238:0.94 241:0.98 242:0.58 243:0.82 244:0.61 247:0.39 248:0.87 253:0.99 255:0.95 256:0.99 257:0.65 259:0.21 260:0.99 261:0.98 265:0.77 266:0.27 267:0.79 268:0.99 271:0.60 276:0.32 277:0.69 279:0.82 282:0.77 283:0.78 284:0.93 285:0.62 290:0.67 297:0.36 300:0.33 +1 1:0.69 7:0.72 8:0.84 9:0.76 11:0.93 12:0.37 17:0.30 18:0.85 21:0.30 27:0.50 28:0.76 29:0.86 30:0.21 32:0.69 34:0.53 36:0.79 37:0.60 39:0.56 43:0.69 45:1.00 66:0.34 69:0.23 70:0.85 71:0.63 74:0.93 77:0.70 81:0.46 83:0.60 85:0.80 86:0.92 91:0.04 96:0.87 97:0.99 98:0.68 99:0.83 101:0.97 104:0.84 106:0.81 108:0.98 114:0.63 117:0.86 120:0.78 122:0.77 123:0.98 124:0.95 126:0.44 127:0.98 129:0.97 133:0.96 135:0.26 139:0.89 140:0.45 144:0.85 145:0.67 146:0.71 147:0.75 149:0.90 150:0.56 151:0.84 153:0.71 154:0.78 155:0.58 158:0.79 159:0.98 161:0.76 162:0.78 164:0.74 165:0.70 167:0.63 172:0.99 173:0.06 176:0.66 177:0.70 179:0.09 181:0.74 187:0.39 190:0.77 191:0.83 192:0.59 195:1.00 201:0.68 202:0.67 204:0.85 206:0.81 208:0.54 212:0.55 215:0.44 216:0.86 220:0.62 222:0.60 230:0.75 232:0.89 233:0.76 235:0.42 241:0.10 242:0.50 243:0.08 245:0.99 247:0.94 248:0.82 253:0.88 255:0.74 256:0.71 259:0.21 260:0.75 265:0.69 266:0.94 267:0.28 268:0.72 271:0.60 276:0.99 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 297:0.36 300:0.84 +2 1:0.74 6:0.95 8:0.88 9:0.80 11:0.92 12:0.37 17:0.73 18:0.94 20:0.92 21:0.30 22:0.93 27:0.44 28:0.76 29:0.86 30:0.43 31:0.95 34:0.29 36:0.80 37:0.45 39:0.56 41:0.88 43:0.96 45:0.93 47:0.93 60:0.95 64:0.77 66:0.36 69:0.19 70:0.85 71:0.63 74:0.86 78:0.87 79:0.94 81:0.61 83:0.91 85:0.93 86:0.97 91:0.22 96:0.84 98:0.63 100:0.90 101:0.87 104:0.88 106:0.81 108:0.92 111:0.87 114:0.65 117:0.86 120:0.74 122:0.57 123:0.92 124:0.83 126:0.54 127:0.85 129:0.93 133:0.84 135:0.82 137:0.95 139:0.96 140:0.45 144:0.85 146:0.32 147:0.39 149:0.93 150:0.77 151:0.92 152:0.86 153:0.72 154:0.96 155:0.67 158:0.92 159:0.86 161:0.76 162:0.55 164:0.70 165:0.93 167:0.66 169:0.88 172:0.89 173:0.10 176:0.73 177:0.70 179:0.19 181:0.74 186:0.87 187:0.21 189:0.96 191:0.70 192:0.87 196:0.97 201:0.93 202:0.67 206:0.81 208:0.64 212:0.57 215:0.44 216:0.39 219:0.95 222:0.60 232:0.91 235:0.52 238:0.91 241:0.21 242:0.16 243:0.13 245:0.95 247:0.96 248:0.81 253:0.87 255:0.95 256:0.58 259:0.21 260:0.75 261:0.89 262:0.93 265:0.64 266:0.84 267:0.82 268:0.64 271:0.60 276:0.92 279:0.86 283:0.82 284:0.93 285:0.62 290:0.65 292:0.95 297:0.36 300:0.94 +3 1:0.64 6:0.96 7:0.81 8:0.92 9:0.80 11:0.75 12:0.37 17:0.03 18:0.75 20:0.98 21:0.47 26:0.99 27:0.17 28:0.91 29:0.91 30:0.54 31:1.00 32:0.80 34:0.86 36:0.76 37:0.62 39:0.29 41:0.99 43:0.80 44:0.93 45:0.81 58:1.00 60:0.99 64:0.77 66:0.88 69:0.73 70:0.56 71:0.57 74:0.83 77:0.70 78:0.98 79:1.00 81:0.76 83:0.91 85:0.80 86:0.82 91:0.89 96:0.99 97:0.98 98:0.79 100:0.99 101:0.87 108:0.56 111:0.99 114:0.80 120:0.98 121:0.90 122:0.75 123:0.53 126:0.54 127:0.27 129:0.05 135:0.28 137:1.00 138:0.99 139:0.92 140:0.45 141:1.00 145:0.44 146:0.73 147:0.77 149:0.78 150:0.60 151:0.99 152:0.94 153:0.96 154:0.74 155:0.67 158:0.92 159:0.29 161:0.87 162:0.78 164:0.97 165:0.93 167:0.84 169:0.99 172:0.67 173:0.81 176:0.73 177:0.70 179:0.49 181:0.55 186:0.98 187:0.21 189:0.97 191:0.80 192:0.87 195:0.62 196:1.00 199:1.00 201:0.62 202:0.94 204:0.84 208:0.64 212:0.75 215:0.63 216:0.92 219:0.95 220:0.74 222:0.60 223:0.99 224:0.98 230:0.87 233:0.76 234:0.98 235:0.80 238:0.96 241:0.78 242:0.33 243:0.89 247:0.74 248:0.99 253:0.99 255:0.95 256:0.96 259:0.21 260:0.98 261:0.97 265:0.79 266:0.54 267:0.19 268:0.96 271:0.38 274:1.00 276:0.54 279:0.86 281:0.91 282:0.88 283:0.82 284:1.00 285:0.62 290:0.80 292:0.95 297:0.36 299:0.88 300:0.43 +0 11:0.57 12:0.37 17:0.47 18:0.65 21:0.64 26:0.95 27:0.45 28:0.91 29:0.91 30:0.70 34:0.50 36:0.39 37:0.50 39:0.29 43:0.66 55:0.71 58:0.93 66:0.19 69:0.70 70:0.62 71:0.57 74:0.82 77:0.70 81:0.30 85:0.80 86:0.74 91:0.03 98:0.46 101:0.87 108:0.88 114:0.66 122:0.77 123:0.51 124:0.87 126:0.26 127:0.48 129:0.59 133:0.85 135:0.86 139:0.72 141:0.91 145:0.49 146:0.88 147:0.90 149:0.66 150:0.33 154:0.56 158:0.73 159:0.86 161:0.87 167:0.56 172:0.75 173:0.41 176:0.60 177:0.70 178:0.55 179:0.20 181:0.55 187:0.68 195:0.97 199:0.94 212:0.71 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.74 241:0.30 242:0.80 243:0.40 245:0.91 247:0.60 253:0.61 259:0.21 265:0.41 266:0.89 267:0.36 271:0.38 274:0.91 276:0.89 282:0.73 290:0.66 300:0.84 +0 11:0.57 12:0.37 17:0.66 18:0.63 21:0.78 26:0.94 27:0.45 28:0.91 29:0.91 30:0.45 34:0.83 36:0.29 37:0.50 39:0.29 43:0.76 58:0.93 66:0.69 69:0.82 70:0.25 71:0.57 74:0.49 85:0.72 86:0.72 91:0.11 98:0.15 101:0.87 108:0.67 122:0.57 123:0.65 127:0.09 129:0.05 135:0.88 139:0.70 141:0.91 145:0.67 146:0.22 147:0.28 149:0.50 154:0.67 159:0.10 161:0.87 167:0.57 172:0.21 173:0.83 177:0.70 178:0.55 179:0.12 181:0.55 187:0.68 199:0.94 212:0.61 216:0.77 222:0.60 223:0.93 224:0.93 234:0.91 235:0.98 241:0.35 242:0.63 243:0.73 247:0.30 253:0.40 259:0.21 265:0.16 266:0.17 267:0.28 271:0.38 274:0.91 276:0.20 300:0.18 +2 1:0.66 6:0.90 7:0.81 8:0.80 9:0.79 11:0.57 12:0.37 17:0.57 18:0.87 20:0.93 21:0.30 26:0.99 27:0.35 28:0.91 29:0.91 30:0.45 31:0.95 32:0.80 34:0.52 36:0.43 37:0.43 39:0.29 41:0.92 43:0.95 44:0.93 55:0.45 58:0.99 60:0.95 64:0.77 66:0.74 69:0.19 70:0.69 71:0.57 74:0.90 77:0.89 78:0.96 79:0.95 81:0.79 83:0.60 85:0.93 86:0.96 91:0.62 96:0.87 98:0.81 100:0.96 101:0.87 104:0.54 108:0.15 111:0.95 114:0.86 120:0.78 121:0.90 122:0.57 123:0.15 124:0.41 126:0.54 127:0.79 129:0.05 133:0.37 135:0.23 137:0.95 139:0.97 140:0.45 141:1.00 145:0.69 146:0.75 147:0.79 149:0.95 150:0.67 151:0.86 152:0.86 153:0.48 154:0.95 155:0.66 158:0.86 159:0.43 161:0.87 162:0.85 164:0.77 165:0.93 167:0.86 169:0.94 172:0.73 173:0.31 176:0.73 177:0.70 179:0.57 181:0.55 186:0.96 189:0.92 192:0.87 195:0.62 196:0.98 199:0.98 201:0.64 202:0.65 204:0.84 208:0.64 212:0.60 215:0.72 216:0.05 219:0.95 220:0.87 222:0.60 223:0.99 224:0.98 230:0.87 232:0.74 233:0.76 234:0.98 235:0.68 238:0.90 241:0.80 242:0.43 243:0.77 245:0.73 247:0.67 248:0.82 253:0.91 255:0.79 256:0.71 259:0.21 260:0.79 261:0.93 265:0.81 266:0.54 267:0.18 268:0.72 271:0.38 274:0.98 276:0.62 279:0.81 281:0.91 282:0.88 283:0.67 284:0.95 285:0.62 290:0.86 292:0.88 297:0.36 299:0.97 300:0.70 +1 1:0.69 7:0.81 9:0.74 11:0.75 12:0.37 17:0.47 18:0.97 21:0.30 22:0.93 26:0.99 27:0.39 28:0.91 29:0.91 30:0.53 31:0.90 32:0.80 34:0.93 36:0.59 37:0.67 39:0.29 41:0.82 43:0.88 44:0.93 45:0.92 47:0.93 58:0.98 60:0.99 64:0.77 66:0.51 69:0.54 70:0.62 71:0.57 74:0.98 77:0.89 79:0.90 81:0.85 83:0.60 85:0.99 86:0.99 91:0.10 96:0.74 98:0.73 101:0.87 104:0.95 106:0.81 108:0.80 114:0.86 117:0.86 120:0.62 121:0.99 122:0.57 123:0.79 124:0.74 126:0.54 127:0.76 129:0.89 133:0.78 135:0.77 137:0.90 139:1.00 140:0.45 141:1.00 145:0.87 146:0.75 147:0.79 149:1.00 150:0.85 151:0.68 153:0.48 154:0.86 155:0.65 158:0.86 159:0.82 161:0.87 162:0.86 164:0.57 165:0.93 167:0.51 172:0.96 173:0.32 176:0.73 177:0.70 179:0.14 181:0.55 187:0.21 192:0.87 195:0.93 196:0.95 199:0.99 201:0.67 202:0.36 204:0.85 206:0.81 208:0.64 212:0.86 215:0.72 216:0.89 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.96 233:0.76 234:0.98 235:0.95 241:0.07 242:0.37 243:0.31 245:0.98 247:0.82 248:0.64 253:0.82 255:0.73 256:0.45 259:0.21 260:0.63 262:0.93 265:0.73 266:0.54 267:0.59 268:0.46 271:0.38 274:0.97 276:0.95 279:0.82 281:0.91 282:0.88 283:0.67 284:0.89 285:0.62 290:0.86 292:0.88 297:0.36 299:0.97 300:0.84 +2 1:0.66 9:0.79 11:0.82 12:0.37 17:0.43 18:0.94 21:0.54 22:0.93 26:0.99 27:0.49 28:0.91 29:0.91 30:0.40 31:0.95 32:0.80 34:0.90 36:0.98 37:0.69 39:0.29 41:0.88 43:0.87 44:0.93 45:0.85 47:0.93 55:0.45 58:0.98 60:0.87 64:0.77 66:0.48 69:0.32 70:0.72 71:0.57 74:0.84 77:0.89 79:0.94 81:0.78 83:0.60 85:0.90 86:0.93 91:0.49 96:0.83 98:0.68 101:0.97 104:0.96 106:0.81 108:0.80 114:0.77 117:0.86 120:0.73 122:0.57 123:0.79 124:0.65 126:0.54 127:0.85 129:0.59 133:0.61 135:0.60 137:0.95 139:0.95 140:0.45 141:1.00 145:0.91 146:0.57 147:0.63 149:0.85 150:0.62 151:0.86 153:0.48 154:0.84 155:0.66 158:0.92 159:0.64 161:0.87 162:0.86 164:0.67 165:0.93 167:0.60 172:0.77 173:0.26 176:0.73 177:0.70 179:0.38 181:0.55 187:0.39 192:0.87 195:0.76 196:0.97 199:0.98 201:0.64 202:0.50 206:0.81 208:0.64 212:0.85 215:0.58 216:0.47 219:0.95 222:0.60 223:0.99 224:0.99 232:0.97 234:0.99 235:0.88 241:0.44 242:0.16 243:0.45 245:0.87 247:0.91 248:0.81 253:0.87 255:0.78 256:0.58 259:0.21 260:0.74 262:0.93 265:0.68 266:0.71 267:0.36 268:0.59 271:0.38 274:0.98 276:0.77 279:0.80 282:0.88 283:0.82 284:0.92 285:0.62 290:0.77 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.64 7:0.81 9:0.79 11:0.82 12:0.37 17:0.26 18:0.96 21:0.47 22:0.93 26:0.99 27:0.21 28:0.91 29:0.91 30:0.21 31:0.94 34:0.73 36:0.83 37:0.74 39:0.29 41:0.82 43:0.95 45:0.92 47:0.93 58:0.98 60:0.87 64:0.77 66:0.85 69:0.23 70:0.88 71:0.57 74:0.95 79:0.93 81:0.76 83:0.60 85:0.98 86:0.98 91:0.22 96:0.87 97:0.94 98:0.89 101:0.97 104:0.84 106:0.81 108:0.18 114:0.80 117:0.86 120:0.78 121:0.90 122:0.57 123:0.18 124:0.39 126:0.54 127:0.74 129:0.59 133:0.61 135:0.44 137:0.94 139:0.98 140:0.80 141:1.00 146:0.99 147:0.99 149:0.97 150:0.60 151:0.86 153:0.48 154:0.95 155:0.66 158:0.92 159:0.87 161:0.87 162:0.86 164:0.66 165:0.93 167:0.57 172:0.97 173:0.10 176:0.73 177:0.70 179:0.15 181:0.55 187:0.39 192:0.87 196:0.97 199:0.99 201:0.62 202:0.50 204:0.84 206:0.81 208:0.64 212:0.67 215:0.63 216:0.95 219:0.95 222:0.60 223:0.99 224:0.99 230:0.87 232:0.89 233:0.76 234:0.98 235:0.80 241:0.35 242:0.24 243:0.86 245:0.92 247:0.93 248:0.77 253:0.92 255:0.79 256:0.58 259:0.21 260:0.79 262:0.93 265:0.89 266:0.75 267:0.84 268:0.59 271:0.38 274:0.97 276:0.95 279:0.82 281:0.91 283:0.82 284:0.89 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 +2 1:0.68 6:0.92 7:0.81 8:0.76 9:0.79 11:0.75 12:0.37 17:0.66 18:0.94 20:0.87 21:0.13 26:0.99 27:0.72 28:0.91 29:0.91 30:0.31 31:0.94 32:0.80 34:0.79 36:0.84 39:0.29 41:0.82 43:0.95 44:0.93 45:0.73 58:0.98 60:0.87 64:0.77 66:0.47 69:0.15 70:0.85 71:0.57 74:0.94 77:0.89 78:0.92 79:0.93 81:0.82 83:0.60 85:0.95 86:0.98 91:0.44 96:0.80 98:0.64 100:0.88 101:0.87 104:0.58 108:0.12 111:0.90 114:0.92 120:0.69 121:0.90 122:0.57 123:0.12 124:0.75 126:0.54 127:0.88 129:0.81 133:0.74 135:0.58 137:0.94 139:0.98 140:0.45 141:1.00 145:0.69 146:0.82 147:0.85 149:0.97 150:0.78 151:0.86 152:0.82 153:0.48 154:0.95 155:0.66 158:0.92 159:0.71 161:0.87 162:0.86 164:0.57 165:0.93 167:0.85 169:0.86 172:0.80 173:0.15 176:0.73 177:0.70 179:0.33 181:0.55 186:0.92 189:0.93 192:0.87 195:0.82 196:0.98 199:0.98 201:0.65 202:0.36 204:0.84 208:0.64 212:0.58 215:0.84 216:0.04 219:0.95 222:0.60 223:0.99 224:0.99 230:0.87 232:0.77 233:0.76 234:0.99 235:0.52 238:0.83 241:0.79 242:0.32 243:0.59 245:0.88 247:0.88 248:0.77 253:0.88 255:0.78 256:0.45 259:0.21 260:0.70 261:0.87 265:0.65 266:0.54 267:0.23 268:0.46 271:0.38 274:0.97 276:0.82 279:0.80 281:0.91 282:0.88 283:0.82 284:0.89 285:0.62 290:0.92 292:0.95 297:0.36 299:0.97 300:0.70 +0 1:0.67 6:0.87 7:0.81 8:0.74 9:0.80 11:0.75 12:0.37 17:0.57 18:0.80 20:0.98 21:0.22 22:0.93 26:0.99 27:0.37 28:0.91 29:0.91 30:0.26 31:0.98 32:0.80 34:0.45 36:0.98 37:0.62 39:0.29 41:0.94 43:0.89 44:0.93 45:0.89 47:0.93 48:0.91 58:0.99 60:0.97 64:0.77 66:0.96 69:0.47 70:0.77 71:0.57 74:0.87 77:0.70 78:0.92 79:0.98 81:0.81 83:0.91 85:0.85 86:0.88 91:0.54 96:0.95 97:0.89 98:0.97 100:0.89 101:0.87 104:0.94 106:0.81 108:0.36 111:0.90 114:0.90 117:0.86 120:0.90 121:0.90 122:0.75 123:0.34 126:0.54 127:0.76 129:0.05 135:0.56 137:0.98 139:0.94 140:0.45 141:1.00 145:0.45 146:0.96 147:0.97 149:0.83 150:0.73 151:0.95 152:0.90 153:0.85 154:0.88 155:0.67 158:0.92 159:0.65 161:0.87 162:0.77 164:0.85 165:0.93 167:0.59 169:0.86 172:0.89 173:0.38 176:0.73 177:0.70 179:0.34 181:0.55 186:0.92 187:0.68 189:0.89 192:0.87 195:0.80 196:0.99 199:0.98 201:0.64 202:0.79 204:0.84 206:0.81 208:0.64 212:0.71 215:0.79 216:0.58 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.96 233:0.76 234:0.98 235:0.60 238:0.84 241:0.39 242:0.24 243:0.96 247:0.90 248:0.91 253:0.96 255:0.95 256:0.82 259:0.21 260:0.90 261:0.90 262:0.93 265:0.97 266:0.63 267:0.23 268:0.83 271:0.38 274:0.99 276:0.81 279:0.82 281:0.91 282:0.88 283:0.82 284:0.98 285:0.62 290:0.90 292:0.95 297:0.36 299:0.88 300:0.55 +2 1:0.64 6:0.91 7:0.81 8:0.74 9:0.80 11:0.82 12:0.37 17:0.18 18:0.95 20:0.98 21:0.47 22:0.93 26:0.99 27:0.21 28:0.91 29:0.91 30:0.21 31:0.98 34:0.75 36:0.87 37:0.74 39:0.29 41:0.94 43:0.95 45:0.92 47:0.93 58:0.99 60:0.98 64:0.77 66:0.85 69:0.23 70:0.89 71:0.57 74:0.94 78:0.92 79:0.98 81:0.76 83:0.60 85:0.97 86:0.98 91:0.46 96:0.93 97:0.89 98:0.89 100:0.92 101:0.97 104:0.84 106:0.81 108:0.18 111:0.91 114:0.80 117:0.86 120:0.89 121:0.97 122:0.57 123:0.18 124:0.39 126:0.54 127:0.73 129:0.59 133:0.61 135:0.47 137:0.98 139:0.98 140:0.45 141:1.00 146:0.99 147:0.99 149:0.96 150:0.60 151:0.96 152:0.90 153:0.88 154:0.95 155:0.67 158:0.92 159:0.87 161:0.87 162:0.70 164:0.85 165:0.93 167:0.57 169:0.90 172:0.97 173:0.10 176:0.73 177:0.70 179:0.15 181:0.55 186:0.92 187:0.39 189:0.92 192:0.87 196:0.99 199:0.99 201:0.62 202:0.78 204:0.85 206:0.81 208:0.64 212:0.67 215:0.63 216:0.95 219:0.95 220:0.74 222:0.60 223:0.99 224:0.99 230:0.87 232:0.89 233:0.76 234:0.98 235:0.80 238:0.87 241:0.32 242:0.24 243:0.86 245:0.92 247:0.93 248:0.92 253:0.96 255:0.94 256:0.79 259:0.21 260:0.89 261:0.91 262:0.93 265:0.89 266:0.75 267:0.88 268:0.81 271:0.38 274:0.99 276:0.94 279:0.82 281:0.91 283:0.82 284:0.97 285:0.62 290:0.80 292:0.95 297:0.36 300:0.84 +2 1:0.69 6:0.90 8:0.68 9:0.71 11:0.75 12:0.37 17:0.22 18:0.97 20:0.86 26:0.99 27:0.70 28:0.91 29:0.91 30:0.19 34:0.47 36:0.97 37:0.32 39:0.29 43:0.85 45:0.95 58:0.93 60:0.87 64:0.77 66:0.54 69:0.61 70:0.93 71:0.57 74:0.90 78:0.87 81:0.85 83:0.60 85:0.95 86:0.97 91:0.31 96:0.72 98:0.76 100:0.86 101:0.87 104:0.90 106:0.81 108:0.79 111:0.86 114:0.98 117:0.86 120:0.59 122:0.57 123:0.78 124:0.83 126:0.54 127:0.72 129:0.81 133:0.89 135:0.73 139:0.97 140:0.45 141:0.91 146:0.77 147:0.80 149:0.96 150:0.86 151:0.57 152:0.81 153:0.48 154:0.81 155:0.58 158:0.92 159:0.91 161:0.87 162:0.76 164:0.68 165:0.93 167:0.57 169:0.84 172:0.96 173:0.25 176:0.73 177:0.70 179:0.14 181:0.55 186:0.87 187:0.39 189:0.91 190:0.95 192:0.57 199:0.99 201:0.67 202:0.58 206:0.81 208:0.64 212:0.51 215:0.96 216:0.35 219:0.95 220:0.91 222:0.60 223:0.99 224:0.93 232:0.93 234:0.91 235:0.31 238:0.82 241:0.35 242:0.19 243:0.31 245:0.95 247:0.91 248:0.58 253:0.79 255:0.70 256:0.58 259:0.21 260:0.59 261:0.84 265:0.76 266:0.94 267:0.28 268:0.62 271:0.38 274:0.91 276:0.95 279:0.80 283:0.82 285:0.56 290:0.98 292:0.95 297:0.36 300:0.84 +1 1:0.64 7:0.81 11:0.82 12:0.37 17:0.51 18:0.96 21:0.47 22:0.93 26:0.99 27:0.50 28:0.91 29:0.91 30:0.21 32:0.80 34:0.80 36:0.88 37:0.60 39:0.29 43:0.95 44:0.93 45:0.93 47:0.93 58:0.93 60:0.95 64:0.77 66:0.72 69:0.27 70:0.89 71:0.57 74:0.96 77:0.70 81:0.76 83:0.60 85:0.98 86:0.98 91:0.07 97:0.89 98:0.88 101:0.97 104:0.84 106:0.81 108:0.21 114:0.80 117:0.86 121:0.90 122:0.57 123:0.20 124:0.46 126:0.54 127:0.88 129:0.59 133:0.61 135:0.61 139:0.99 141:0.91 145:0.70 146:0.99 147:0.99 149:0.98 150:0.60 154:0.95 155:0.57 158:0.92 159:0.89 161:0.87 165:0.93 167:0.50 172:0.98 173:0.10 176:0.73 177:0.70 178:0.55 179:0.14 181:0.55 187:0.39 192:0.56 195:0.97 199:0.99 201:0.62 204:0.84 206:0.81 208:0.64 212:0.60 215:0.63 216:0.86 219:0.95 222:0.60 223:0.99 224:0.93 230:0.87 232:0.89 233:0.76 234:0.91 235:0.80 241:0.03 242:0.24 243:0.75 245:0.98 247:0.93 253:0.72 259:0.21 262:0.93 265:0.88 266:0.80 267:0.52 271:0.38 274:0.91 276:0.96 279:0.82 281:0.91 282:0.88 283:0.82 290:0.80 292:0.95 297:0.36 299:0.88 300:0.84 +2 1:0.66 6:0.92 7:0.69 8:0.83 9:0.80 11:0.82 12:0.37 17:0.55 18:0.95 20:0.96 21:0.30 26:0.99 27:0.47 28:0.91 29:0.91 30:0.21 31:0.97 32:0.80 34:0.59 36:1.00 37:0.49 39:0.29 41:0.95 43:0.76 44:0.93 45:0.81 48:0.91 55:0.45 58:0.99 60:0.99 64:0.77 66:0.82 69:0.81 70:0.85 71:0.57 74:0.89 77:0.70 78:0.88 79:0.97 81:0.79 83:0.60 85:0.95 86:0.97 91:0.44 96:0.90 97:0.94 98:0.69 100:0.84 101:0.97 104:0.95 106:0.81 108:0.65 111:0.85 114:0.86 120:0.83 122:0.57 123:0.63 124:0.39 126:0.54 127:0.24 129:0.05 133:0.37 135:0.90 137:0.97 139:0.98 140:0.45 141:1.00 145:0.59 146:0.95 147:0.28 149:0.93 150:0.67 151:0.94 152:0.89 153:0.82 154:0.68 155:0.66 158:0.92 159:0.68 161:0.87 162:0.79 164:0.84 165:0.93 167:0.57 169:0.82 172:0.92 173:0.63 176:0.73 177:0.05 179:0.15 181:0.55 186:0.88 187:0.87 189:0.93 192:0.87 195:0.94 196:0.99 199:0.99 201:0.64 202:0.75 204:0.82 206:0.81 208:0.64 212:0.70 215:0.72 216:0.66 219:0.95 222:0.60 223:0.99 224:0.98 230:0.71 233:0.66 234:0.98 235:0.68 238:0.83 241:0.35 242:0.24 243:0.15 245:0.89 247:0.94 248:0.89 253:0.93 255:0.94 256:0.79 257:0.84 259:0.21 260:0.84 261:0.87 265:0.70 266:0.75 267:0.73 268:0.80 271:0.38 274:0.99 276:0.86 279:0.82 281:0.86 282:0.88 283:0.82 284:0.97 285:0.62 290:0.86 292:0.95 297:0.36 299:0.88 300:0.84 +1 1:0.62 6:0.86 8:0.71 9:0.75 10:0.97 11:0.86 12:0.92 17:0.69 18:0.92 20:0.87 21:0.47 22:0.93 23:0.99 27:0.30 28:0.96 29:0.86 30:0.52 31:0.95 33:0.97 34:0.37 36:0.18 37:0.71 39:0.55 41:0.82 43:0.76 44:0.88 45:0.98 47:0.93 48:0.91 62:0.99 64:0.77 66:0.45 69:0.95 70:0.81 71:0.81 74:0.86 75:0.98 76:0.85 78:0.99 79:0.94 81:0.61 83:0.91 85:0.80 86:0.92 91:0.11 96:0.84 97:0.98 98:0.56 99:0.95 100:0.98 101:1.00 102:0.96 104:0.58 108:0.93 111:0.98 114:0.78 117:0.86 120:0.79 122:0.82 123:0.93 124:0.87 126:0.51 127:0.96 128:0.98 129:0.59 131:0.84 133:0.89 135:0.71 137:0.95 139:0.92 140:0.97 144:0.76 145:0.67 146:0.88 147:0.80 149:0.89 150:0.47 151:0.77 152:0.86 153:0.89 154:0.20 155:0.66 157:0.86 158:0.81 159:0.89 161:0.60 162:0.55 164:0.75 165:0.81 166:0.77 167:0.45 168:0.98 169:0.95 170:0.82 172:0.95 173:0.88 174:0.97 176:0.70 177:0.70 178:0.42 179:0.15 181:0.66 182:0.80 186:0.99 187:0.87 189:0.88 192:0.98 195:0.96 196:0.97 197:0.96 201:0.60 202:0.81 205:0.98 208:0.64 212:0.77 216:0.29 219:0.94 220:0.62 222:0.35 226:0.98 227:0.91 229:0.89 231:0.67 232:0.95 235:0.68 236:0.95 238:0.92 239:0.97 242:0.15 243:0.22 245:0.96 246:0.93 247:0.99 248:0.70 253:0.88 255:0.78 256:0.80 257:0.65 259:0.21 260:0.79 261:0.94 262:0.93 265:0.57 266:0.89 267:0.23 268:0.79 271:0.16 276:0.96 279:0.81 281:0.91 283:0.82 284:0.97 285:0.62 287:0.95 290:0.78 291:0.97 292:0.95 300:0.84 +2 1:0.67 6:0.85 8:0.64 9:0.76 10:0.93 11:0.84 12:0.92 17:0.30 18:0.83 20:0.88 21:0.22 22:0.93 23:0.97 27:0.35 28:0.96 29:0.86 30:0.33 31:0.94 33:0.97 34:0.58 36:0.43 37:0.48 39:0.55 41:0.88 43:0.85 45:0.89 47:0.93 60:0.95 62:0.98 64:0.77 66:0.45 69:0.62 70:0.96 71:0.81 74:0.78 75:0.99 78:0.90 79:0.94 81:0.79 83:0.91 85:0.72 86:0.84 91:0.17 96:0.82 98:0.35 100:0.88 101:0.96 104:0.94 106:0.81 108:0.47 111:0.88 114:0.90 117:0.86 120:0.68 123:0.45 124:0.93 126:0.54 127:0.85 128:0.97 129:0.05 131:0.84 133:0.95 135:0.93 137:0.94 139:0.85 140:0.87 144:0.76 146:0.84 147:0.86 149:0.79 150:0.62 151:0.81 152:0.95 153:0.77 154:0.81 155:0.66 157:0.76 158:0.86 159:0.91 161:0.60 162:0.52 164:0.69 165:0.93 166:0.77 167:0.46 168:0.97 169:0.85 170:0.82 172:0.75 173:0.27 174:0.86 176:0.73 177:0.88 178:0.48 179:0.39 181:0.66 182:0.80 186:0.90 187:0.87 189:0.85 192:0.99 196:0.95 197:0.89 201:0.66 202:0.72 205:0.97 206:0.81 208:0.64 212:0.39 215:0.79 216:0.64 219:0.95 220:0.62 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.97 235:0.52 236:0.97 238:0.83 239:0.95 241:0.03 242:0.14 243:0.58 245:0.71 246:0.94 247:0.96 248:0.75 253:0.85 255:0.79 256:0.64 259:0.21 260:0.69 261:0.90 262:0.93 265:0.37 266:0.97 267:0.69 268:0.67 271:0.16 276:0.78 279:0.81 283:0.67 284:0.94 285:0.62 287:0.95 290:0.90 291:0.97 292:0.88 297:0.36 300:0.94 +2 1:0.64 6:0.87 8:0.74 9:0.75 10:0.94 11:0.84 12:0.92 17:0.45 18:0.80 20:0.95 21:0.54 22:0.93 23:0.99 27:0.35 28:0.96 29:0.86 30:0.15 31:0.92 33:0.97 34:0.36 36:0.33 37:0.49 39:0.55 41:0.93 43:0.84 45:0.88 47:0.93 60:0.95 62:0.98 64:0.77 66:0.35 69:0.63 70:0.97 71:0.81 74:0.80 75:0.99 78:0.97 79:0.91 81:0.74 83:0.91 85:0.72 86:0.84 91:0.51 96:0.77 98:0.30 100:0.96 101:0.96 104:0.90 106:0.81 108:0.48 111:0.95 114:0.77 117:0.86 120:0.65 122:0.55 123:0.46 124:0.93 126:0.54 127:0.84 128:0.97 129:0.05 131:0.84 133:0.94 135:0.89 137:0.92 139:0.85 140:0.80 144:0.76 146:0.79 147:0.83 149:0.84 150:0.55 151:0.66 152:0.96 153:0.87 154:0.81 155:0.65 157:0.76 158:0.86 159:0.92 161:0.60 162:0.55 164:0.80 165:0.93 166:0.77 167:0.48 168:0.97 169:0.94 170:0.82 172:0.78 173:0.27 174:0.88 176:0.73 177:0.88 178:0.42 179:0.30 181:0.66 182:0.80 186:0.96 187:0.87 189:0.90 191:0.70 192:0.98 196:0.94 197:0.88 201:0.63 202:0.77 205:0.98 206:0.81 208:0.64 212:0.37 215:0.58 216:0.84 219:0.95 220:0.93 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.95 235:0.80 236:0.97 238:0.89 239:0.94 241:0.12 242:0.13 243:0.51 245:0.79 246:0.93 247:0.97 248:0.71 253:0.81 255:0.78 256:0.71 259:0.21 260:0.66 261:0.96 262:0.93 265:0.32 266:0.96 267:0.73 268:0.75 271:0.16 276:0.83 279:0.81 283:0.67 284:0.97 285:0.62 287:0.95 290:0.77 291:0.97 292:0.88 297:0.36 300:0.98 +2 1:0.68 6:0.87 8:0.71 9:0.79 10:0.95 11:0.84 12:0.92 17:0.45 18:0.82 20:0.88 21:0.13 22:0.93 23:0.98 27:0.35 28:0.96 29:0.86 30:0.32 31:0.96 33:0.97 34:0.29 36:0.34 37:0.49 39:0.55 41:0.90 43:0.84 45:0.92 47:0.93 60:0.87 62:0.99 64:0.77 66:0.12 69:0.63 70:0.92 71:0.81 74:0.78 75:0.99 78:0.98 79:0.95 81:0.80 83:0.91 85:0.72 86:0.86 91:0.54 96:0.85 98:0.31 100:0.97 101:0.96 104:0.90 106:0.81 108:0.48 111:0.97 114:0.92 117:0.86 120:0.76 122:0.55 123:0.91 124:0.96 126:0.54 127:0.84 128:0.98 129:0.59 131:0.84 133:0.96 135:0.89 137:0.96 139:0.87 140:0.87 144:0.76 146:0.69 147:0.74 149:0.79 150:0.66 151:0.83 152:0.96 153:0.78 154:0.81 155:0.66 157:0.82 158:0.86 159:0.93 161:0.60 162:0.56 164:0.74 165:0.93 166:0.77 167:0.46 168:0.98 169:0.94 170:0.82 172:0.73 173:0.24 174:0.92 176:0.73 177:0.88 178:0.48 179:0.29 181:0.66 182:0.80 186:0.98 187:0.87 189:0.89 192:0.99 196:0.96 197:0.91 201:0.67 202:0.70 205:0.98 206:0.81 208:0.64 212:0.41 215:0.84 216:0.84 219:0.95 220:0.62 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.95 235:0.42 236:0.97 238:0.91 239:0.95 241:0.03 242:0.16 243:0.44 245:0.79 246:0.94 247:0.97 248:0.83 253:0.88 255:0.95 256:0.64 259:0.21 260:0.77 261:0.96 262:0.93 265:0.25 266:0.96 267:0.59 268:0.68 271:0.16 276:0.84 279:0.80 283:0.67 284:0.95 285:0.62 287:0.95 290:0.92 291:0.97 292:0.88 297:0.36 300:0.94 +1 1:0.65 6:0.87 7:0.70 8:0.73 9:0.73 10:0.95 11:0.84 12:0.92 17:0.49 18:0.78 20:0.85 21:0.40 22:0.93 23:0.96 27:0.51 28:0.96 29:0.86 30:0.72 31:0.90 32:0.67 33:0.97 34:0.80 36:0.76 37:0.37 39:0.55 41:0.82 43:0.90 45:0.91 47:0.93 60:0.87 62:0.97 64:0.77 66:0.33 69:0.45 70:0.62 71:0.81 74:0.80 75:0.99 77:0.70 78:0.90 79:0.89 81:0.76 83:0.91 85:0.72 86:0.86 91:0.14 96:0.73 97:0.89 98:0.54 100:0.83 101:0.96 104:0.80 106:0.81 108:0.35 111:0.87 114:0.82 117:0.86 120:0.61 122:0.55 123:0.34 124:0.77 126:0.54 127:0.46 128:0.96 129:0.59 131:0.84 133:0.73 135:0.95 137:0.90 139:0.87 140:0.80 144:0.76 145:0.83 146:0.74 147:0.78 149:0.86 150:0.58 151:0.63 152:0.81 153:0.69 154:0.89 155:0.65 157:0.82 158:0.86 159:0.64 161:0.60 162:0.59 164:0.62 165:0.93 166:0.77 167:0.45 168:0.96 169:0.81 170:0.82 172:0.63 173:0.33 174:0.89 176:0.73 177:0.88 178:0.42 179:0.39 181:0.66 182:0.80 186:0.90 187:0.21 189:0.89 192:0.98 195:0.84 196:0.92 197:0.91 201:0.65 202:0.55 204:0.80 205:0.95 206:0.81 208:0.64 212:0.54 215:0.67 216:0.49 219:0.95 220:0.82 222:0.35 226:0.96 227:0.91 229:0.88 230:0.73 231:0.67 232:0.89 233:0.76 235:0.68 236:0.97 238:0.82 239:0.96 242:0.14 243:0.50 245:0.80 246:0.93 247:0.86 248:0.61 253:0.74 255:0.73 256:0.45 259:0.21 260:0.61 261:0.84 262:0.93 265:0.55 266:0.63 267:0.44 268:0.55 271:0.16 276:0.72 279:0.81 282:0.75 283:0.67 284:0.90 285:0.62 287:0.95 290:0.82 291:0.97 292:0.88 297:0.36 300:0.84 +1 1:0.64 10:0.91 11:0.75 12:0.92 17:0.30 18:0.70 21:0.54 27:0.45 28:0.96 29:0.86 30:0.61 34:0.83 36:0.23 37:0.50 39:0.55 43:0.58 45:0.87 64:0.77 66:0.64 69:0.70 70:0.53 71:0.81 74:0.67 75:0.99 81:0.69 83:0.69 86:0.78 91:0.14 97:0.94 98:0.65 99:0.95 101:0.65 108:0.53 114:0.77 122:0.55 123:0.51 124:0.46 126:0.54 127:0.40 129:0.05 131:0.61 133:0.39 135:0.86 139:0.81 144:0.76 145:0.44 146:0.78 147:0.81 149:0.59 150:0.54 154:0.73 155:0.49 157:0.79 158:0.84 159:0.54 161:0.60 165:0.81 166:0.77 167:0.48 170:0.82 172:0.61 173:0.66 174:0.83 176:0.73 177:0.88 178:0.55 179:0.58 181:0.66 182:0.79 187:0.68 192:0.48 197:0.86 201:0.63 208:0.64 212:0.30 215:0.58 216:0.77 219:0.95 222:0.35 226:0.95 227:0.61 229:0.61 231:0.67 235:0.80 236:0.97 239:0.93 241:0.15 242:0.14 243:0.69 245:0.71 246:0.93 247:0.79 253:0.61 254:0.80 259:0.21 265:0.66 266:0.71 267:0.28 271:0.16 276:0.55 279:0.79 283:0.66 287:0.61 290:0.77 292:0.87 297:0.36 300:0.43 +2 1:0.65 6:0.85 8:0.72 9:0.74 10:0.94 11:0.84 12:0.92 17:0.43 18:0.75 20:0.95 21:0.40 22:0.93 23:0.98 27:0.35 28:0.96 29:0.86 30:0.43 31:0.91 33:0.97 34:0.44 36:0.68 37:0.48 39:0.55 41:0.92 43:0.85 45:0.90 47:0.93 60:0.95 62:0.98 64:0.77 66:0.46 69:0.62 70:0.87 71:0.81 74:0.81 75:0.99 78:0.90 79:0.91 81:0.76 83:0.91 85:0.72 86:0.84 91:0.37 96:0.76 98:0.41 100:0.87 101:0.96 104:0.94 106:0.81 108:0.47 111:0.88 114:0.82 117:0.86 120:0.64 122:0.83 123:0.46 124:0.86 126:0.54 127:0.77 128:0.97 129:0.05 131:0.84 133:0.88 135:0.92 137:0.91 139:0.85 140:0.87 144:0.76 146:0.82 147:0.85 149:0.85 150:0.58 151:0.64 152:0.95 153:0.78 154:0.81 155:0.65 157:0.77 158:0.86 159:0.87 161:0.60 162:0.50 164:0.75 165:0.93 166:0.77 167:0.45 168:0.97 169:0.85 170:0.82 172:0.77 173:0.34 174:0.83 176:0.73 177:0.88 178:0.48 179:0.36 181:0.66 182:0.80 186:0.90 187:0.87 189:0.88 192:0.98 196:0.93 197:0.87 201:0.65 202:0.77 205:0.98 206:0.81 208:0.64 212:0.55 215:0.67 216:0.64 219:0.95 220:0.91 222:0.35 226:0.96 227:0.91 229:0.89 231:0.67 232:0.97 235:0.68 236:0.97 238:0.83 239:0.94 241:0.01 242:0.19 243:0.59 245:0.79 246:0.93 247:0.93 248:0.68 253:0.80 255:0.77 256:0.68 259:0.21 260:0.65 261:0.90 262:0.93 265:0.43 266:0.91 267:0.65 268:0.70 271:0.16 276:0.77 279:0.81 283:0.67 284:0.95 285:0.62 287:0.95 290:0.82 291:0.97 292:0.88 297:0.36 300:0.94 +1 1:0.67 7:0.70 10:1.00 11:0.86 12:0.92 17:0.40 18:0.95 21:0.22 22:0.93 27:0.41 28:0.96 29:0.86 30:0.85 32:0.80 33:0.97 34:0.83 36:0.18 37:0.44 39:0.55 43:0.82 44:0.93 45:0.96 47:0.93 64:0.77 66:0.88 69:0.50 70:0.48 71:0.81 74:0.97 77:0.70 81:0.75 83:0.69 85:0.95 86:0.99 91:0.12 98:0.83 99:0.95 101:1.00 102:0.98 104:0.96 106:0.81 108:0.38 114:0.90 117:0.86 122:0.93 123:0.37 124:0.37 126:0.54 127:0.74 129:0.05 131:0.61 133:0.45 135:0.87 139:0.99 144:0.76 145:0.42 146:0.95 147:0.96 149:0.98 150:0.61 154:0.85 155:0.54 157:0.88 159:0.74 161:0.60 165:0.81 166:0.77 167:0.48 170:0.82 172:0.97 173:0.34 174:0.96 176:0.73 177:0.88 178:0.55 179:0.17 181:0.66 182:0.79 187:0.87 192:0.56 195:0.86 197:0.98 201:0.66 202:0.36 204:0.81 206:0.81 208:0.64 212:0.85 215:0.79 216:0.75 222:0.35 227:0.61 229:0.61 230:0.73 231:0.67 232:0.98 233:0.66 235:0.52 236:0.97 239:1.00 241:0.15 242:0.17 243:0.89 245:0.93 246:0.94 247:0.92 253:0.75 259:0.21 262:0.93 265:0.83 266:0.54 267:0.44 271:0.16 276:0.93 282:0.88 287:0.61 290:0.90 291:0.97 297:0.36 299:0.88 300:0.70 +2 1:0.65 6:0.89 7:0.69 8:0.75 9:0.73 10:0.92 11:0.75 12:0.92 17:0.32 18:0.75 20:0.96 21:0.40 22:0.93 23:0.98 27:0.26 28:0.96 29:0.86 30:0.17 31:0.90 32:0.74 33:0.97 34:0.64 36:0.33 37:0.70 39:0.55 43:0.23 44:0.93 45:0.93 47:0.93 55:0.71 62:0.97 64:0.77 66:0.33 69:0.93 70:0.85 71:0.81 74:0.72 75:0.99 76:0.85 77:0.70 78:0.98 79:0.89 81:0.72 83:0.69 86:0.77 91:0.33 96:0.74 97:0.98 98:0.82 99:0.95 100:0.96 101:0.65 102:0.98 104:0.79 106:0.81 108:0.93 111:0.96 114:0.82 117:0.86 120:0.61 123:0.92 124:0.83 126:0.54 127:0.98 128:0.96 129:0.59 131:0.84 133:0.81 135:0.85 137:0.90 139:0.88 140:0.45 144:0.76 145:0.54 146:0.94 147:0.73 149:0.71 150:0.57 151:0.62 152:0.96 153:0.48 154:0.27 155:0.65 157:0.82 158:0.85 159:0.94 161:0.60 162:0.66 164:0.73 165:0.81 166:0.77 167:0.48 168:0.96 169:0.92 170:0.82 172:0.96 173:0.71 174:0.99 176:0.73 177:0.05 179:0.12 181:0.66 182:0.80 186:0.98 187:0.21 189:0.90 192:0.98 193:0.98 195:0.99 196:0.93 197:0.96 201:0.65 202:0.65 204:0.80 205:0.98 206:0.81 208:0.64 212:0.61 215:0.67 216:0.52 219:0.95 220:0.91 222:0.35 226:0.95 227:0.91 229:0.89 230:0.71 231:0.67 232:0.88 233:0.76 235:0.68 236:0.97 238:0.87 239:0.96 241:0.15 242:0.54 243:0.07 245:0.99 246:0.93 247:0.99 248:0.62 253:0.73 254:0.74 255:0.73 256:0.64 257:0.93 259:0.21 260:0.62 261:0.95 262:0.93 265:0.82 266:0.80 267:0.98 268:0.67 271:0.16 276:0.97 279:0.81 281:0.91 282:0.83 283:0.66 284:0.94 285:0.62 287:0.95 290:0.82 291:0.97 292:0.87 297:0.36 300:0.84 +1 1:0.61 10:0.89 11:0.75 12:0.92 17:0.45 18:0.67 21:0.68 27:0.45 28:0.96 29:0.86 30:0.17 32:0.74 34:0.96 36:0.18 37:0.50 39:0.55 43:0.12 44:0.93 45:0.73 55:0.52 64:0.77 66:0.49 69:0.70 70:0.80 71:0.81 74:0.55 77:0.70 81:0.65 83:0.69 86:0.75 91:0.09 98:0.61 99:0.95 101:0.65 102:0.98 108:0.74 114:0.70 123:0.72 124:0.55 126:0.54 127:0.33 129:0.05 131:0.61 133:0.45 135:0.74 139:0.79 144:0.76 145:0.52 146:0.41 147:0.47 149:0.11 150:0.49 154:0.68 155:0.48 157:0.76 159:0.46 161:0.60 165:0.81 166:0.77 167:0.48 170:0.82 172:0.45 173:0.69 174:0.79 176:0.73 177:0.88 178:0.55 179:0.65 181:0.66 182:0.79 187:0.68 192:0.46 195:0.73 197:0.83 201:0.57 208:0.64 212:0.30 215:0.49 216:0.77 222:0.35 227:0.61 229:0.61 231:0.67 235:0.95 236:0.97 239:0.92 241:0.15 242:0.33 243:0.40 245:0.68 246:0.93 247:0.76 253:0.55 254:0.90 259:0.21 265:0.62 266:0.63 267:0.23 271:0.16 276:0.46 277:0.69 282:0.82 287:0.61 290:0.70 297:0.36 300:0.55 +2 7:0.69 8:0.70 10:0.87 11:0.75 12:0.92 17:0.55 18:0.61 21:0.40 27:0.26 28:0.96 29:0.86 30:0.19 32:0.71 34:0.64 36:0.37 37:0.70 39:0.55 43:0.23 45:0.91 55:0.45 66:0.33 69:0.93 70:0.85 71:0.81 74:0.64 77:0.70 81:0.33 86:0.67 91:0.44 98:0.66 101:0.65 102:0.94 104:0.91 106:0.81 108:0.85 120:0.58 123:0.84 124:0.90 126:0.24 127:0.97 129:0.59 131:0.61 133:0.91 135:0.71 139:0.65 140:0.96 144:0.76 145:0.58 146:0.98 147:0.73 149:0.71 150:0.36 151:0.51 153:0.45 154:0.24 157:0.76 159:0.92 161:0.60 162:0.47 164:0.64 166:0.61 167:0.50 170:0.82 172:0.95 173:0.76 174:0.96 177:0.38 178:0.54 179:0.13 181:0.66 182:0.72 187:0.21 190:0.95 195:0.98 197:0.91 202:0.77 206:0.81 212:0.77 215:0.67 216:0.52 220:0.93 222:0.35 227:0.61 229:0.61 230:0.71 231:0.63 233:0.76 235:0.42 239:0.87 241:0.24 242:0.72 243:0.10 245:0.97 246:0.61 247:0.98 248:0.51 253:0.48 254:0.80 256:0.58 257:0.84 259:0.21 260:0.58 265:0.67 266:0.80 267:0.98 268:0.57 271:0.16 276:0.97 282:0.79 283:0.67 285:0.46 287:0.61 297:0.36 300:0.84 +1 1:0.68 7:0.70 9:0.73 10:0.99 11:0.86 12:0.92 17:0.51 18:0.92 21:0.13 22:0.93 27:0.24 28:0.96 29:0.86 30:0.45 32:0.80 33:0.97 34:0.83 36:0.40 37:0.47 39:0.55 43:0.85 44:0.93 45:0.96 47:0.93 60:0.87 64:0.77 66:0.34 69:0.15 70:0.74 71:0.81 74:0.91 75:0.99 77:0.70 81:0.80 83:0.91 85:0.85 86:0.97 91:0.06 96:0.80 98:0.72 101:0.96 102:0.98 104:0.91 106:0.81 108:0.12 114:0.92 117:0.86 120:0.69 123:0.75 124:0.67 126:0.54 127:0.84 129:0.59 131:0.61 133:0.64 135:0.96 139:0.97 140:0.45 144:0.76 145:0.42 146:0.77 147:0.81 149:0.95 150:0.66 151:0.85 153:0.48 154:0.81 155:0.63 157:0.86 158:0.86 159:0.73 161:0.60 162:0.86 164:0.56 165:0.93 166:0.77 167:0.46 170:0.82 172:0.90 173:0.14 174:0.93 176:0.73 177:0.88 179:0.17 181:0.66 182:0.79 187:0.68 190:0.89 192:0.86 193:0.98 195:0.84 197:0.95 201:0.67 202:0.36 204:0.83 206:0.81 208:0.64 212:0.87 215:0.84 216:0.14 219:0.95 222:0.35 226:0.96 227:0.61 229:0.61 230:0.73 231:0.67 232:0.99 233:0.76 235:0.42 236:0.97 239:0.99 241:0.05 242:0.52 243:0.50 245:0.97 246:0.94 247:0.94 248:0.77 253:0.88 255:0.72 256:0.45 259:0.21 260:0.70 262:0.93 265:0.56 266:0.84 267:0.18 268:0.46 271:0.16 276:0.93 279:0.80 281:0.91 282:0.88 283:0.67 285:0.50 287:0.61 290:0.92 291:0.97 292:0.88 297:0.36 299:0.88 300:0.70 +1 1:0.67 7:0.69 8:0.76 9:0.72 10:1.00 11:0.86 12:0.92 17:0.36 18:0.95 20:0.75 21:0.22 22:0.93 23:0.96 27:0.41 28:0.96 29:0.86 30:0.86 31:0.89 32:0.77 33:0.97 34:0.74 36:0.39 37:0.44 39:0.55 43:0.81 44:0.93 45:0.97 47:0.93 48:0.91 60:0.87 62:0.98 64:0.77 66:0.92 69:0.52 70:0.49 71:0.81 74:0.98 75:0.99 76:0.85 77:0.89 78:0.95 79:0.89 81:0.75 83:0.91 85:0.95 86:0.99 91:0.29 96:0.72 97:0.98 98:0.83 99:0.95 100:0.73 101:1.00 102:0.98 104:0.96 106:0.81 108:0.40 111:0.92 114:0.90 117:0.86 120:0.59 122:0.89 123:0.38 124:0.37 126:0.54 127:0.59 128:0.96 129:0.05 131:0.84 133:0.45 135:0.70 137:0.89 139:0.99 140:0.45 144:0.76 145:0.69 146:0.94 147:0.95 149:0.98 150:0.61 151:0.62 152:0.74 153:0.48 154:0.84 155:0.65 157:0.88 158:0.86 159:0.69 161:0.60 162:0.86 164:0.57 165:0.81 166:0.77 167:0.46 168:0.96 169:0.72 170:0.82 172:0.97 173:0.39 174:0.98 176:0.73 177:0.88 179:0.16 181:0.66 182:0.80 186:0.95 187:0.68 192:0.98 193:0.95 195:0.85 196:0.94 197:0.99 201:0.66 202:0.36 205:0.95 206:0.81 208:0.64 212:0.87 215:0.79 216:0.75 219:0.95 220:0.82 222:0.35 226:0.96 227:0.91 229:0.88 230:0.71 231:0.67 232:0.98 233:0.76 235:0.52 236:0.97 239:1.00 241:0.02 242:0.19 243:0.92 245:0.90 246:0.94 247:0.96 248:0.60 253:0.80 255:0.72 256:0.45 259:0.21 260:0.60 261:0.76 262:0.93 265:0.83 266:0.54 267:0.91 268:0.46 271:0.16 276:0.93 279:0.82 281:0.91 282:0.85 283:0.67 284:0.89 285:0.62 287:0.95 290:0.90 291:0.97 292:0.88 297:0.36 300:0.84 +2 1:0.60 6:0.89 7:0.69 8:0.79 9:0.74 10:0.85 11:0.46 12:0.92 17:0.59 18:0.61 20:0.77 21:0.30 27:0.26 28:0.96 29:0.86 30:0.08 32:0.67 34:0.42 36:0.32 37:0.70 39:0.55 43:0.23 45:0.97 55:0.71 66:0.23 69:0.93 70:0.95 71:0.81 74:0.76 76:0.85 77:0.70 78:0.93 81:0.53 83:0.56 86:0.60 91:0.38 96:0.88 97:0.94 98:0.49 99:0.83 100:0.91 101:0.47 104:0.79 106:0.81 108:0.85 111:0.91 114:0.82 117:0.86 120:0.78 123:0.84 124:0.96 126:0.32 127:0.99 129:0.59 131:0.61 133:0.96 135:0.54 139:0.58 140:0.45 145:0.44 146:0.98 147:0.73 149:0.81 150:0.48 151:0.93 152:0.96 153:0.78 154:0.15 155:0.56 157:0.61 158:0.76 159:0.96 161:0.60 162:0.70 164:0.78 165:0.65 166:0.61 167:0.50 169:0.92 170:0.82 172:0.96 173:0.64 174:0.99 176:0.63 177:0.05 179:0.11 181:0.66 182:0.61 186:0.93 187:0.21 189:0.91 190:0.89 192:0.58 195:0.99 197:0.92 201:0.57 202:0.73 206:0.81 208:0.50 212:0.60 215:0.72 216:0.52 222:0.35 227:0.61 229:0.61 230:0.71 231:0.64 232:0.88 233:0.76 235:0.42 238:0.86 239:0.84 241:0.30 242:0.45 243:0.07 244:0.61 245:0.98 246:0.61 247:0.99 248:0.85 253:0.90 254:0.88 255:0.72 256:0.75 257:0.93 259:0.21 260:0.78 261:0.95 265:0.50 266:0.94 267:0.69 268:0.76 271:0.16 276:0.98 279:0.78 282:0.75 283:0.67 285:0.50 287:0.61 290:0.82 297:0.36 300:0.94 +2 6:0.80 7:0.81 8:0.59 12:0.37 17:0.64 20:0.94 21:0.68 25:0.88 27:0.25 28:0.76 30:0.62 32:0.80 34:0.34 36:0.45 37:0.23 43:0.33 55:0.59 66:0.47 69:0.69 70:0.34 78:0.77 81:0.88 91:0.92 98:0.31 100:0.78 104:0.58 108:0.70 111:0.76 120:0.85 123:0.68 124:0.52 126:0.54 127:0.38 129:0.59 133:0.47 135:0.54 140:0.45 145:0.76 146:0.19 147:0.24 149:0.35 150:0.75 151:0.73 152:0.95 153:0.78 154:0.24 159:0.28 161:0.50 162:0.66 164:0.87 167:0.99 169:0.76 172:0.21 173:0.84 177:0.05 179:0.91 181:0.63 186:0.77 189:0.82 191:0.82 195:0.62 202:0.81 212:0.30 215:0.49 216:0.07 220:0.82 230:0.87 233:0.76 235:0.80 238:0.84 241:0.93 242:0.82 243:0.37 245:0.46 247:0.12 248:0.79 256:0.81 259:0.21 260:0.86 261:0.80 265:0.33 266:0.27 267:0.52 268:0.83 271:0.71 276:0.17 283:0.60 285:0.62 297:0.36 300:0.25 +1 6:0.85 8:0.78 12:0.37 17:0.62 20:0.76 21:0.05 27:0.55 28:0.76 30:0.60 34:0.52 36:0.83 37:0.59 43:0.42 55:0.84 66:0.34 69:0.48 70:0.41 78:0.76 81:0.99 91:0.63 98:0.76 100:0.79 108:0.84 111:0.76 120:0.93 123:0.83 124:0.68 126:0.54 127:0.81 129:0.81 133:0.67 135:0.61 140:0.45 146:0.87 147:0.89 149:0.75 150:0.98 151:0.79 152:0.75 153:0.90 154:0.32 159:0.78 161:0.50 162:0.86 163:0.75 164:0.86 167:0.66 169:0.77 172:0.68 173:0.30 177:0.05 179:0.40 181:0.63 186:0.77 187:0.68 189:0.87 202:0.75 212:0.22 215:0.91 216:0.34 235:0.05 238:0.90 241:0.27 242:0.82 243:0.49 245:0.87 247:0.12 248:0.90 256:0.79 259:0.21 260:0.93 261:0.75 265:0.76 266:0.63 267:0.91 268:0.82 271:0.71 276:0.78 285:0.62 297:0.36 300:0.55 +1 12:0.37 17:0.78 25:0.88 27:0.72 28:0.76 30:0.95 34:0.18 36:0.36 43:0.29 55:0.98 66:0.20 69:0.75 81:0.99 91:0.81 98:0.09 104:0.54 108:0.58 123:0.56 124:0.61 126:0.54 127:0.33 129:0.59 133:0.47 135:0.56 145:0.69 146:0.05 147:0.05 149:0.17 150:0.99 154:0.21 159:0.30 161:0.50 167:0.95 172:0.05 173:0.86 177:0.05 178:0.55 179:1.00 181:0.63 215:0.96 235:0.05 241:0.80 243:0.40 245:0.36 259:0.21 265:0.09 267:0.44 271:0.71 297:0.36 300:0.08 +0 12:0.37 17:0.47 21:0.59 27:0.45 28:0.76 34:0.85 36:0.22 37:0.50 43:0.32 66:0.36 69:0.70 81:0.91 91:0.20 98:0.06 108:0.53 123:0.51 126:0.54 127:0.05 129:0.05 135:0.58 145:0.50 146:0.05 147:0.05 149:0.13 150:0.83 154:0.23 159:0.05 161:0.50 167:0.72 172:0.05 173:0.51 177:0.05 178:0.55 181:0.63 187:0.68 215:0.55 216:0.77 235:0.68 241:0.51 243:0.51 259:0.21 265:0.06 267:0.73 271:0.71 283:0.60 297:0.36 +2 6:0.98 8:0.94 12:0.37 17:0.20 20:0.75 21:0.13 25:0.88 27:0.03 28:0.76 30:0.95 34:0.31 36:0.30 37:0.95 43:0.27 55:0.99 66:0.20 69:0.79 78:0.94 81:0.98 91:0.86 98:0.07 100:0.96 104:0.58 108:0.62 111:0.95 120:0.89 123:0.60 124:0.61 126:0.54 127:0.41 129:0.59 133:0.47 135:0.42 140:0.87 145:0.76 146:0.05 147:0.05 149:0.15 150:0.97 151:0.75 152:1.00 153:0.84 154:0.20 159:0.53 161:0.50 162:0.69 164:0.85 167:0.86 169:0.96 172:0.05 173:0.77 177:0.05 178:0.48 179:1.00 181:0.63 186:0.93 187:0.21 189:0.98 191:0.98 202:0.79 215:0.84 216:0.65 220:0.74 235:0.12 238:0.97 241:0.74 243:0.40 245:0.36 248:0.84 256:0.80 259:0.21 260:0.89 261:1.00 265:0.07 267:0.69 268:0.82 271:0.71 283:0.60 285:0.62 297:0.36 300:0.08 +0 12:0.37 17:0.22 21:0.68 27:0.45 28:0.76 30:0.21 32:0.80 34:0.76 36:0.19 37:0.50 43:0.32 55:0.92 66:0.10 69:0.70 70:0.67 81:0.88 91:0.10 98:0.21 108:0.53 123:0.90 124:0.86 126:0.54 127:0.37 129:0.93 133:0.84 135:0.65 145:0.49 146:0.36 147:0.43 149:0.38 150:0.75 154:0.24 159:0.73 161:0.50 163:0.86 167:0.70 172:0.16 173:0.52 177:0.05 178:0.55 179:0.82 181:0.63 187:0.68 195:0.91 212:0.16 215:0.49 216:0.77 235:0.84 241:0.44 242:0.82 243:0.39 245:0.46 247:0.12 259:0.21 265:0.21 266:0.54 267:0.28 271:0.71 276:0.36 297:0.36 300:0.43 +3 12:0.37 17:0.47 21:0.13 25:0.88 27:0.03 28:0.76 30:0.95 34:0.29 36:0.26 37:0.95 43:0.25 55:0.99 66:0.20 69:0.83 81:0.98 91:0.46 98:0.07 104:0.58 106:0.81 108:0.68 120:0.57 123:0.66 124:0.61 126:0.54 127:0.33 129:0.59 133:0.47 135:0.76 140:0.80 145:0.76 146:0.05 147:0.05 149:0.14 150:0.97 151:0.49 153:0.48 154:0.18 159:0.53 161:0.50 162:0.62 164:0.67 167:0.88 172:0.05 173:0.81 177:0.05 178:0.42 179:1.00 181:0.63 187:0.39 202:0.60 206:0.81 215:0.84 216:0.65 220:0.74 235:0.12 241:0.75 243:0.40 245:0.36 248:0.50 256:0.58 259:0.21 260:0.58 265:0.07 267:0.89 268:0.59 271:0.71 285:0.62 297:0.36 300:0.08 +2 6:0.98 8:0.66 12:0.37 17:0.32 20:0.74 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.28 37:0.95 43:0.31 55:0.52 66:0.27 69:0.70 70:0.25 78:0.89 81:0.98 91:0.48 98:0.07 100:0.91 104:0.58 108:0.92 111:0.88 120:0.95 123:0.92 124:0.72 126:0.54 127:0.93 129:0.81 133:0.67 135:0.94 140:0.45 145:0.84 146:0.05 147:0.05 149:0.18 150:0.97 151:0.80 152:1.00 153:0.93 154:0.23 159:0.96 161:0.50 162:0.85 163:0.97 164:0.90 167:0.69 169:0.96 172:0.10 173:0.22 177:0.05 179:0.98 181:0.63 186:0.85 187:0.21 189:0.87 191:0.84 202:0.81 212:0.61 215:0.84 216:0.65 235:0.12 238:0.87 241:0.41 242:0.82 243:0.29 245:0.38 247:0.12 248:0.92 256:0.86 259:0.21 260:0.95 261:1.00 265:0.07 266:0.17 267:0.91 268:0.87 271:0.71 276:0.17 285:0.62 297:0.36 300:0.18 +2 7:0.81 8:0.80 12:0.37 17:0.78 20:0.75 21:0.30 27:0.43 28:0.76 30:0.68 32:0.80 34:0.64 36:0.47 37:0.63 43:0.51 55:0.52 66:0.77 69:0.19 70:0.61 78:0.78 81:0.97 91:0.51 98:0.75 100:0.80 106:0.81 108:0.58 111:0.77 120:0.69 123:0.56 124:0.38 126:0.54 127:0.45 129:0.59 133:0.47 135:0.58 140:0.45 145:0.65 146:0.05 147:0.05 149:0.59 150:0.95 151:0.66 152:0.74 153:0.48 154:0.40 159:0.41 161:0.50 162:0.86 164:0.57 167:0.64 169:0.78 172:0.59 173:0.28 177:0.05 179:0.68 181:0.63 186:0.79 187:0.21 195:0.67 202:0.36 206:0.81 212:0.69 215:0.72 216:0.69 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.13 245:0.52 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.74 265:0.75 266:0.47 267:0.23 268:0.46 271:0.71 276:0.48 283:0.60 285:0.62 297:0.36 300:0.55 +2 6:0.81 7:0.81 8:0.61 12:0.37 17:0.45 20:0.90 21:0.30 27:0.21 28:0.76 30:0.58 34:0.47 36:0.67 37:0.74 43:0.52 55:0.84 66:0.36 69:0.10 70:0.33 78:0.76 81:0.97 91:0.63 98:0.57 100:0.78 104:0.84 106:0.81 108:0.09 111:0.75 120:0.82 123:0.09 124:0.59 126:0.54 127:0.32 129:0.59 133:0.47 135:0.19 140:0.45 146:0.61 147:0.66 149:0.47 150:0.95 151:0.78 152:0.87 153:0.89 154:0.41 159:0.39 161:0.50 162:0.83 163:0.86 164:0.82 167:0.75 169:0.76 172:0.27 173:0.20 177:0.05 179:0.80 181:0.63 186:0.77 187:0.39 189:0.83 191:0.76 202:0.71 206:0.81 212:0.37 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.85 241:0.61 242:0.82 243:0.51 245:0.56 247:0.12 248:0.74 256:0.78 259:0.21 260:0.83 261:0.78 265:0.59 266:0.38 267:0.89 268:0.77 271:0.71 276:0.37 283:0.82 285:0.62 297:0.36 300:0.25 +2 6:0.85 8:0.72 12:0.37 17:0.47 20:0.88 21:0.74 28:0.76 30:0.19 32:0.80 34:0.44 36:0.36 37:0.97 43:0.31 55:0.59 66:0.20 69:0.72 70:0.87 78:0.76 81:0.83 91:0.56 98:0.31 100:0.79 104:0.80 108:0.78 111:0.75 120:0.76 123:0.76 124:0.93 126:0.54 127:0.63 129:0.98 133:0.94 135:0.88 140:0.45 145:0.67 146:0.68 147:0.73 149:0.65 150:0.64 151:0.73 152:0.91 153:0.78 154:0.23 159:0.90 161:0.50 162:0.71 163:0.75 164:0.81 167:0.63 169:0.76 172:0.54 173:0.39 177:0.05 179:0.42 181:0.63 186:0.77 187:0.21 189:0.88 191:0.73 195:0.98 202:0.73 212:0.15 215:0.46 216:0.98 220:0.96 235:0.95 238:0.89 241:0.15 242:0.82 243:0.29 245:0.70 247:0.12 248:0.68 256:0.77 259:0.21 260:0.77 261:0.79 265:0.33 266:0.97 267:0.18 268:0.76 271:0.71 276:0.73 283:0.82 285:0.62 297:0.36 300:0.70 +3 6:0.98 8:0.97 12:0.37 17:0.36 20:0.97 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.73 37:0.95 43:0.51 55:0.71 66:0.27 69:0.10 70:0.25 78:0.99 81:0.98 91:0.99 98:0.21 100:0.99 104:0.58 108:0.09 111:1.00 120:0.98 123:0.09 124:0.72 126:0.54 127:0.65 129:0.81 133:0.67 135:0.82 140:0.45 145:0.76 146:0.28 147:0.34 149:0.29 150:0.97 151:0.85 152:1.00 153:0.98 154:0.40 159:0.50 161:0.50 162:0.61 163:0.79 164:0.97 167:0.99 169:0.96 172:0.10 173:0.19 177:0.05 179:0.98 181:0.63 186:0.99 189:0.99 191:1.00 202:0.96 212:0.61 215:0.84 216:0.65 220:0.62 235:0.12 238:1.00 241:0.93 242:0.82 243:0.46 245:0.38 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:1.00 265:0.23 266:0.17 267:0.95 268:0.97 271:0.71 276:0.20 283:0.82 285:0.62 297:0.36 300:0.18 +4 12:0.37 17:0.88 21:0.13 27:0.53 28:0.76 30:0.21 34:0.53 36:0.38 37:0.90 43:0.51 55:0.71 66:0.16 69:0.62 70:0.37 81:0.98 91:0.37 98:0.35 104:0.95 106:0.81 108:0.53 123:0.82 124:0.81 126:0.54 127:0.65 129:0.89 133:0.78 135:0.96 145:0.76 146:0.05 147:0.05 149:0.36 150:0.97 154:0.40 159:0.56 161:0.50 163:0.89 167:0.85 172:0.10 173:0.60 177:0.05 178:0.55 179:0.96 181:0.63 187:0.39 202:0.36 206:0.81 212:0.42 215:0.84 216:0.27 235:0.12 241:0.73 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.21 266:0.23 267:0.82 271:0.71 276:0.24 297:0.36 300:0.25 +2 6:0.81 8:0.60 12:0.37 17:0.93 20:0.84 21:0.59 25:0.88 27:0.72 28:0.76 30:0.76 32:0.80 34:0.39 36:0.36 43:0.49 55:0.52 66:0.64 69:0.37 70:0.15 78:0.74 81:0.98 91:0.65 98:0.65 100:0.77 104:0.54 108:0.29 111:0.74 120:0.68 123:0.28 126:0.54 127:0.19 129:0.05 135:0.69 140:0.45 145:0.54 146:0.29 147:0.35 149:0.29 150:0.97 151:0.64 152:0.80 153:0.46 154:0.37 159:0.12 161:0.50 162:0.80 164:0.79 167:0.97 169:0.75 172:0.16 173:0.79 177:0.05 179:0.94 181:0.63 186:0.75 189:0.83 195:0.53 202:0.68 212:0.95 215:0.55 216:0.04 220:0.62 235:0.80 238:0.83 241:0.86 242:0.82 243:0.69 247:0.12 248:0.63 256:0.73 259:0.21 260:0.70 261:0.75 265:0.66 266:0.12 267:0.28 268:0.74 271:0.71 276:0.16 285:0.62 297:0.36 300:0.13 +0 12:0.69 17:0.53 21:0.78 27:0.45 28:0.87 30:0.75 34:0.84 36:0.22 37:0.50 39:0.49 43:0.26 55:0.52 66:0.67 69:0.79 70:0.22 74:0.57 91:0.10 98:0.28 108:0.62 123:0.60 124:0.46 127:0.32 129:0.59 133:0.47 135:0.79 145:0.47 146:0.40 147:0.47 149:0.70 154:0.19 159:0.51 161:0.60 167:0.65 172:0.73 173:0.75 175:0.75 177:0.05 178:0.55 179:0.38 181:0.81 187:0.68 212:0.89 216:0.77 222:0.21 235:0.84 241:0.18 242:0.78 243:0.71 244:0.61 245:0.81 247:0.39 253:0.46 257:0.65 259:0.21 265:0.30 266:0.27 267:0.28 276:0.39 277:0.69 300:0.25 +2 1:0.74 7:0.78 8:0.75 9:0.80 12:0.69 17:0.36 21:0.30 25:0.88 27:0.17 28:0.87 30:0.15 32:0.77 34:0.56 36:0.21 37:0.88 39:0.49 43:0.38 55:0.71 66:0.82 69:0.48 70:0.68 74:0.83 76:0.94 77:0.89 81:0.68 91:0.51 96:0.71 98:0.91 104:0.54 106:0.81 108:0.37 114:0.86 117:0.86 120:0.58 123:0.35 126:0.54 127:0.51 129:0.05 135:0.94 140:0.80 145:0.77 146:0.76 147:0.80 149:0.49 150:0.73 151:0.58 153:0.48 154:0.28 155:0.67 158:0.84 159:0.32 161:0.60 162:0.62 163:0.81 164:0.57 167:0.69 172:0.48 173:0.61 176:0.73 177:0.38 178:0.42 179:0.83 181:0.81 187:0.68 192:0.59 195:0.63 201:0.74 202:0.50 206:0.81 208:0.64 212:0.30 215:0.72 216:0.65 220:0.87 222:0.21 230:0.81 232:0.93 233:0.76 235:0.42 241:0.37 242:0.33 243:0.83 244:0.61 247:0.47 248:0.57 253:0.75 255:0.79 256:0.45 259:0.21 260:0.59 265:0.91 266:0.47 267:0.44 268:0.46 276:0.40 282:0.85 283:0.71 285:0.62 290:0.86 297:0.36 300:0.43 +0 1:0.74 6:0.79 8:0.59 12:0.69 17:0.40 20:0.85 21:0.71 27:0.45 28:0.87 30:0.42 34:0.98 36:0.17 37:0.50 39:0.49 43:0.33 55:0.59 66:0.26 69:0.63 70:0.81 74:0.89 76:0.85 78:0.81 81:0.44 91:0.17 98:0.50 100:0.82 108:0.81 111:0.80 114:0.68 122:0.67 123:0.46 124:0.58 126:0.54 127:0.38 129:0.05 133:0.39 135:0.88 145:0.52 146:0.67 147:0.72 149:0.49 150:0.61 152:0.82 154:0.72 155:0.67 158:0.85 159:0.55 161:0.60 167:0.71 169:0.79 172:0.48 173:0.55 176:0.73 177:0.38 178:0.55 179:0.61 181:0.81 186:0.81 187:0.68 189:0.81 192:0.59 201:0.74 208:0.64 212:0.52 215:0.48 216:0.77 222:0.21 235:0.88 238:0.86 241:0.44 242:0.73 243:0.46 245:0.74 247:0.55 253:0.69 254:0.84 259:0.21 261:0.81 265:0.43 266:0.71 267:0.65 276:0.49 279:0.86 283:0.66 290:0.68 297:0.36 300:0.70 +2 7:0.78 8:0.70 12:0.69 17:0.73 21:0.59 25:0.88 27:0.17 28:0.87 30:0.95 32:0.77 34:0.55 36:0.37 37:0.88 39:0.49 43:0.27 55:0.92 66:0.54 69:0.78 74:0.63 76:0.85 77:0.70 81:0.29 91:0.69 98:0.56 104:0.54 108:0.61 120:0.72 123:0.59 126:0.32 127:0.17 129:0.05 135:0.65 140:0.87 145:0.82 146:0.46 147:0.52 149:0.17 150:0.27 151:0.66 153:0.48 154:0.19 159:0.17 161:0.60 162:0.59 164:0.77 167:0.81 172:0.10 173:0.91 177:0.38 178:0.48 179:0.97 181:0.81 187:0.21 190:0.77 191:0.88 195:0.63 202:0.72 215:0.55 216:0.65 220:0.91 222:0.21 230:0.81 233:0.76 235:0.68 241:0.69 243:0.63 244:0.61 248:0.65 253:0.49 256:0.71 259:0.21 260:0.72 265:0.57 267:0.44 268:0.72 282:0.85 285:0.50 297:0.36 300:0.08 +1 1:0.74 6:0.81 8:0.60 12:0.69 17:0.64 20:0.99 21:0.47 28:0.87 30:0.43 32:0.77 34:0.29 36:0.24 37:0.97 39:0.49 43:0.36 55:0.71 66:0.18 69:0.54 70:0.82 74:0.86 77:0.70 78:0.82 81:0.66 87:0.98 91:0.51 96:0.90 98:0.60 100:0.84 104:0.80 108:0.88 111:0.81 114:0.80 123:0.88 124:0.85 126:0.54 127:0.85 129:0.89 133:0.83 135:0.34 140:0.45 145:0.58 146:0.69 147:0.73 149:0.69 150:0.72 151:0.64 152:0.99 153:0.46 154:0.71 155:0.67 159:0.79 161:0.60 162:0.86 167:0.64 169:0.80 172:0.59 173:0.35 176:0.73 177:0.38 179:0.37 181:0.81 186:0.82 187:0.21 189:0.82 190:0.77 191:0.91 192:0.59 195:0.90 201:0.74 202:0.59 212:0.50 215:0.63 216:0.98 220:0.62 222:0.21 232:0.72 235:0.68 238:0.88 241:0.12 242:0.55 243:0.37 245:0.85 247:0.60 248:0.75 253:0.92 254:0.80 256:0.79 259:0.21 261:0.86 265:0.61 266:0.80 267:0.79 268:0.68 276:0.80 282:0.85 283:0.71 285:0.50 290:0.80 297:0.36 300:0.70 +0 8:0.82 12:0.69 17:0.38 21:0.59 28:0.87 30:0.87 34:0.49 36:0.25 37:0.97 39:0.49 43:0.36 55:0.52 66:0.08 69:0.55 70:0.46 74:0.90 77:0.70 81:0.46 87:0.98 91:0.87 96:0.97 98:0.22 108:0.84 114:0.64 122:0.67 123:0.40 124:0.84 126:0.32 127:0.92 129:0.93 133:0.84 135:0.80 140:0.45 145:0.63 146:0.56 147:0.62 149:0.56 150:0.37 151:0.79 153:0.91 154:0.27 158:0.84 159:0.91 161:0.60 162:0.66 167:0.74 172:0.51 173:0.22 175:0.75 176:0.56 177:0.05 179:0.61 181:0.81 187:0.21 190:0.77 191:0.92 195:0.97 202:0.89 212:0.54 216:0.98 222:0.21 232:0.72 235:0.74 241:0.57 242:0.77 243:0.25 244:0.61 245:0.69 247:0.55 248:0.92 253:0.98 256:0.91 257:0.65 259:0.21 265:0.23 266:0.87 267:0.73 268:0.91 276:0.49 277:0.69 282:0.84 285:0.50 290:0.64 300:0.70 +0 7:0.78 12:0.69 17:0.97 25:0.88 27:0.72 28:0.87 30:0.55 32:0.75 34:0.17 36:0.27 39:0.49 43:0.44 55:0.71 66:0.86 69:0.07 70:0.52 74:0.55 76:0.85 81:0.67 91:0.76 98:0.96 104:0.54 108:0.06 120:0.65 123:0.06 126:0.32 127:0.72 129:0.05 135:0.24 140:0.45 145:0.40 146:0.75 147:0.79 149:0.62 150:0.48 151:0.61 153:0.48 154:0.33 159:0.31 161:0.60 162:0.86 164:0.56 167:0.97 172:0.61 173:0.27 177:0.38 179:0.74 181:0.81 190:0.77 195:0.57 202:0.36 212:0.81 215:0.96 216:0.02 220:0.62 222:0.21 230:0.81 233:0.76 235:0.02 241:0.82 242:0.78 243:0.87 244:0.61 247:0.39 248:0.59 253:0.45 256:0.45 259:0.21 260:0.66 265:0.96 266:0.27 267:0.52 268:0.46 276:0.50 285:0.50 297:0.36 300:0.43 +3 1:0.74 12:0.69 17:0.36 21:0.47 27:0.48 28:0.87 30:0.38 32:0.77 34:0.20 36:0.25 37:0.55 39:0.49 43:0.45 55:0.71 66:0.25 69:0.19 70:0.83 74:0.88 77:0.70 81:0.66 87:0.98 91:0.31 98:0.65 108:0.89 114:0.80 120:0.69 123:0.88 124:0.74 126:0.54 127:0.88 129:0.81 133:0.67 135:0.47 140:0.45 145:0.58 146:0.74 147:0.78 149:0.69 150:0.72 151:0.66 153:0.48 154:0.70 155:0.67 159:0.81 161:0.60 162:0.86 164:0.56 167:0.73 172:0.67 173:0.12 176:0.73 177:0.38 179:0.35 181:0.81 187:0.39 190:0.77 192:0.59 195:0.90 201:0.74 202:0.36 212:0.42 215:0.63 216:0.92 222:0.21 232:0.76 235:0.68 241:0.53 242:0.55 243:0.39 245:0.91 247:0.67 248:0.63 253:0.73 254:0.80 256:0.45 259:0.21 260:0.70 265:0.66 266:0.80 267:0.65 268:0.46 276:0.81 282:0.85 283:0.82 285:0.50 290:0.80 297:0.36 300:0.70 +2 6:0.98 7:0.78 8:0.92 12:0.69 17:0.16 20:0.85 21:0.64 25:0.88 27:0.17 28:0.87 30:0.45 32:0.77 34:0.39 36:0.33 37:0.88 39:0.49 43:0.33 55:0.45 66:0.69 69:0.63 70:0.25 74:0.66 76:0.85 77:0.70 78:0.95 81:0.28 91:0.97 96:0.97 98:0.64 100:0.99 104:0.54 108:0.48 111:0.99 120:0.98 123:0.46 126:0.32 127:0.19 129:0.05 135:0.54 140:0.80 145:0.83 146:0.36 147:0.42 149:0.30 150:0.27 151:0.83 152:0.93 153:0.96 154:0.24 158:0.84 159:0.14 161:0.60 162:0.52 163:0.81 164:0.98 167:0.99 169:0.96 172:0.21 173:0.90 175:0.75 177:0.05 178:0.42 179:0.87 181:0.81 186:0.95 189:0.99 190:0.77 191:0.98 195:0.55 202:0.98 212:0.61 215:0.53 216:0.65 220:0.74 222:0.21 230:0.81 233:0.76 235:0.74 238:0.99 241:0.89 242:0.82 243:0.73 244:0.61 247:0.12 248:0.98 253:0.97 256:0.99 257:0.65 259:0.21 260:0.98 261:0.96 265:0.65 266:0.17 267:0.82 268:0.98 276:0.20 277:0.69 282:0.85 283:0.82 285:0.62 297:0.36 300:0.18 +0 1:0.74 6:0.81 8:0.61 12:0.69 17:0.57 20:0.80 21:0.68 28:0.87 30:0.17 32:0.77 34:0.61 36:0.21 37:0.97 39:0.49 43:0.36 55:0.52 66:0.92 69:0.56 70:0.85 74:0.86 77:0.70 78:0.77 81:0.52 87:0.98 91:0.42 98:0.95 100:0.81 104:0.80 108:0.43 111:0.77 114:0.70 123:0.41 126:0.54 127:0.66 129:0.05 135:0.74 145:0.59 146:0.92 147:0.93 149:0.57 150:0.63 152:0.99 154:0.69 155:0.67 159:0.54 161:0.60 167:0.62 169:0.80 172:0.78 173:0.54 176:0.73 177:0.38 178:0.55 179:0.52 181:0.81 186:0.78 187:0.21 192:0.59 195:0.71 201:0.74 212:0.49 215:0.49 216:0.98 222:0.21 232:0.72 235:0.88 241:0.07 242:0.45 243:0.92 247:0.67 253:0.68 254:0.84 259:0.21 261:0.86 265:0.95 266:0.63 267:0.36 276:0.67 282:0.85 283:0.71 290:0.70 297:0.36 300:0.55 +1 1:0.74 6:0.84 8:0.65 9:0.80 12:0.69 17:0.09 20:0.96 21:0.40 27:0.13 28:0.87 30:0.30 32:0.75 34:0.26 36:0.59 37:0.31 39:0.49 43:0.34 55:0.71 66:0.44 69:0.59 70:0.92 74:0.84 76:0.94 77:0.70 78:0.85 81:0.63 91:0.42 96:0.72 98:0.38 100:0.87 104:0.87 106:0.81 108:0.45 111:0.85 114:0.82 120:0.74 123:0.43 124:0.74 126:0.54 127:0.86 129:0.05 133:0.74 135:0.97 140:0.45 145:0.82 146:0.78 147:0.81 149:0.49 150:0.69 151:0.56 152:0.89 153:0.72 154:0.73 155:0.67 158:0.84 159:0.87 161:0.60 162:0.64 164:0.73 167:0.64 169:0.85 172:0.57 173:0.31 176:0.73 177:0.38 179:0.61 181:0.81 186:0.85 187:0.87 189:0.86 190:0.77 191:0.73 192:0.59 195:0.95 201:0.74 202:0.66 206:0.81 208:0.64 212:0.60 215:0.67 216:0.81 220:0.91 222:0.21 235:0.52 238:0.89 241:0.12 242:0.45 243:0.58 245:0.69 247:0.60 248:0.57 253:0.75 254:0.86 255:0.79 256:0.64 259:0.21 260:0.75 261:0.88 265:0.40 266:0.75 267:0.73 268:0.68 276:0.52 279:0.86 282:0.84 283:0.82 285:0.62 290:0.82 297:0.36 300:0.84 +2 6:0.98 7:0.78 8:0.92 12:0.69 17:0.45 20:0.90 21:0.59 25:0.88 27:0.17 28:0.87 30:0.95 32:0.77 34:0.37 36:0.23 37:0.88 39:0.49 43:0.38 55:0.84 66:0.64 69:0.50 74:0.65 76:0.85 77:0.70 78:0.85 81:0.29 91:0.90 98:0.74 100:0.93 104:0.54 106:0.81 108:0.38 111:0.88 120:0.93 122:0.43 123:0.37 126:0.32 127:0.23 129:0.05 135:0.32 140:0.80 145:0.82 146:0.43 147:0.49 149:0.26 150:0.27 151:0.73 152:0.93 153:0.81 154:0.28 159:0.16 161:0.60 162:0.60 163:0.94 164:0.89 167:0.78 169:0.96 172:0.16 173:0.77 177:0.38 178:0.42 179:0.97 181:0.81 186:0.85 187:0.21 189:0.96 190:0.77 191:0.92 195:0.57 202:0.85 206:0.81 212:0.95 215:0.55 216:0.65 220:0.97 222:0.21 230:0.81 233:0.76 235:0.68 238:0.96 241:0.66 242:0.82 243:0.69 244:0.61 247:0.12 248:0.90 253:0.50 256:0.87 259:0.21 260:0.93 261:0.96 265:0.74 266:0.12 267:0.23 268:0.86 276:0.19 282:0.85 283:0.82 285:0.50 297:0.36 300:0.08 +0 1:0.74 11:0.57 12:0.69 17:0.73 21:0.59 27:0.38 28:0.87 30:0.71 34:0.86 36:0.50 37:0.95 39:0.49 43:0.80 55:0.71 66:0.32 69:0.73 70:0.58 74:0.74 81:0.58 83:0.73 85:0.85 91:0.12 98:0.62 101:0.74 108:0.56 114:0.74 123:0.83 124:0.67 126:0.54 127:0.45 129:0.59 133:0.64 135:0.74 146:0.86 147:0.89 149:0.80 150:0.72 154:0.75 155:0.67 159:0.72 161:0.60 165:0.70 167:0.64 172:0.73 173:0.58 176:0.73 177:0.38 178:0.55 179:0.31 181:0.81 187:0.68 192:0.59 201:0.74 212:0.30 215:0.55 216:0.71 222:0.21 235:0.84 241:0.15 242:0.62 243:0.51 245:0.88 247:0.47 253:0.65 259:0.21 265:0.62 266:0.63 267:0.87 276:0.79 290:0.74 297:0.36 300:0.55 +1 6:0.82 7:0.81 8:0.62 12:0.06 17:0.38 20:0.95 21:0.78 27:0.08 28:0.68 34:0.61 36:0.55 37:0.40 78:0.91 91:0.86 100:0.87 111:0.89 135:0.68 145:0.99 152:0.88 161:0.71 167:0.52 169:0.85 175:0.75 178:0.55 181:0.54 186:0.91 187:0.39 189:0.84 191:0.78 202:0.79 216:0.24 230:0.87 233:0.76 235:0.52 238:0.82 241:0.41 244:0.61 257:0.65 261:0.88 267:0.59 271:0.51 277:0.69 +2 6:1.00 8:0.96 12:0.06 17:0.94 20:0.74 21:0.78 27:0.04 28:0.68 34:0.40 36:0.56 37:0.96 43:0.42 66:0.36 69:0.52 78:1.00 91:0.67 98:0.08 100:0.99 104:0.82 106:0.81 108:0.40 111:1.00 120:0.72 123:0.38 127:0.06 129:0.05 135:0.89 140:0.45 146:0.05 147:0.05 149:0.15 151:0.70 152:0.75 153:0.69 154:0.32 159:0.05 161:0.71 162:0.86 164:0.62 167:0.54 169:0.98 172:0.05 173:0.51 177:0.05 181:0.54 186:1.00 187:0.39 189:1.00 191:0.85 202:0.46 206:0.81 216:0.31 235:0.60 238:0.80 241:0.49 243:0.51 248:0.66 256:0.45 259:0.21 260:0.73 261:0.80 265:0.08 267:0.28 268:0.55 271:0.51 283:0.82 285:0.62 +2 6:0.93 8:0.85 12:0.06 17:0.83 20:0.76 21:0.59 27:0.54 28:0.68 30:0.45 32:0.80 34:0.49 36:0.39 37:0.58 43:0.51 55:0.45 66:0.77 69:0.27 70:0.33 78:1.00 81:0.93 91:0.93 98:0.94 100:0.88 104:0.57 108:0.21 111:0.99 120:0.74 123:0.20 126:0.54 127:0.61 129:0.05 135:0.30 140:0.87 145:0.54 146:0.50 147:0.57 149:0.46 150:0.89 151:0.70 152:0.74 153:0.71 154:0.40 159:0.18 161:0.71 162:0.69 163:0.81 164:0.82 167:0.80 169:0.86 172:0.37 173:0.65 177:0.05 178:0.48 179:0.92 181:0.54 186:1.00 189:0.94 191:0.82 195:0.53 202:0.75 212:0.52 215:0.55 216:0.07 220:0.96 235:0.74 238:0.81 241:0.84 242:0.82 243:0.79 247:0.12 248:0.67 256:0.77 259:0.21 260:0.75 261:0.78 265:0.94 266:0.27 267:0.18 268:0.78 271:0.51 276:0.29 283:0.82 285:0.62 297:0.36 300:0.25 +1 6:0.82 7:0.81 8:0.63 12:0.06 17:0.30 20:0.95 21:0.78 27:0.06 28:0.68 34:0.92 36:0.59 37:0.34 78:0.98 91:0.80 100:0.92 111:0.95 120:0.53 135:0.37 140:0.87 145:0.52 151:0.46 152:0.88 153:0.46 161:0.71 162:0.51 164:0.70 167:0.67 169:0.90 175:0.75 178:0.48 181:0.54 186:0.98 187:0.21 189:0.84 191:0.73 202:0.71 216:0.10 220:0.96 230:0.87 233:0.76 235:0.84 238:0.83 241:0.69 244:0.61 248:0.46 256:0.64 257:0.65 259:0.21 260:0.53 261:0.92 267:0.44 268:0.63 271:0.51 277:0.69 283:0.60 285:0.62 +2 6:0.88 8:0.80 12:0.06 17:0.40 20:0.81 21:0.78 27:0.21 28:0.68 30:0.21 34:0.67 36:0.97 37:0.74 43:0.48 55:0.52 66:0.54 69:0.34 70:0.37 78:0.88 91:0.71 98:0.19 100:0.88 104:0.84 106:0.81 108:0.27 111:0.87 120:0.93 123:0.26 124:0.45 127:0.38 129:0.59 133:0.47 135:0.20 140:0.45 146:0.42 147:0.49 149:0.35 151:0.79 152:0.78 153:0.90 154:0.37 159:0.82 161:0.71 162:0.65 164:0.89 167:0.49 169:0.88 172:0.21 173:0.13 177:0.05 179:0.94 181:0.54 186:0.88 187:0.39 189:0.91 191:0.76 202:0.84 206:0.81 212:0.42 216:0.95 235:0.31 238:0.87 241:0.27 242:0.82 243:0.63 245:0.40 247:0.12 248:0.90 256:0.85 259:0.21 260:0.93 261:0.81 265:0.21 266:0.23 267:0.65 268:0.86 271:0.51 276:0.14 283:0.82 285:0.62 300:0.25 +2 6:0.90 8:0.84 12:0.06 17:0.36 20:0.96 21:0.78 27:0.07 28:0.68 34:0.25 36:0.23 37:0.78 78:0.96 91:0.93 100:0.93 111:0.94 120:0.61 135:0.92 140:0.87 151:0.56 152:0.94 153:0.72 161:0.71 162:0.46 164:0.64 167:0.83 169:0.89 175:0.75 178:0.48 181:0.54 186:0.96 189:0.92 191:0.94 202:0.80 216:0.37 220:0.62 235:0.42 238:0.84 241:0.99 244:0.61 248:0.55 256:0.45 257:0.65 259:0.21 260:0.62 261:0.92 267:0.85 268:0.57 271:0.51 277:0.69 285:0.62 +1 12:0.06 17:0.55 21:0.59 27:0.45 28:0.68 30:0.91 32:0.80 34:0.94 36:0.18 37:0.50 43:0.32 55:0.92 66:0.69 69:0.70 70:0.13 81:0.85 91:0.27 98:0.53 108:0.53 123:0.51 126:0.54 127:0.16 129:0.05 135:0.66 145:0.49 146:0.59 147:0.64 149:0.29 150:0.67 154:0.24 159:0.21 161:0.71 163:0.86 167:0.46 172:0.21 173:0.72 177:0.05 178:0.55 179:0.79 181:0.54 187:0.68 195:0.70 212:0.61 215:0.55 216:0.77 235:0.74 241:0.07 242:0.82 243:0.73 247:0.12 259:0.21 265:0.54 266:0.17 267:0.73 271:0.51 276:0.22 297:0.36 300:0.13 +1 8:0.85 12:0.06 17:0.28 21:0.76 27:0.45 28:0.68 30:0.21 32:0.80 34:0.63 36:0.18 37:0.50 43:0.32 55:0.59 66:0.36 69:0.71 70:0.37 81:0.71 91:0.17 98:0.19 108:0.94 123:0.94 124:0.67 126:0.54 127:0.32 129:0.81 133:0.67 135:0.83 145:0.54 146:0.05 147:0.05 149:0.21 150:0.53 154:0.24 159:0.84 161:0.71 163:0.99 167:0.48 172:0.16 173:0.39 177:0.05 178:0.55 179:0.93 181:0.54 187:0.68 195:0.97 212:0.42 215:0.46 216:0.77 235:0.97 241:0.18 242:0.82 243:0.26 245:0.40 247:0.12 259:0.21 265:0.21 266:0.23 267:0.36 271:0.51 276:0.21 297:0.36 300:0.25 +2 7:0.81 8:0.84 12:0.06 17:0.86 21:0.78 25:0.88 27:0.72 28:0.68 30:0.27 32:0.80 34:0.22 36:0.32 37:0.71 43:0.52 55:0.59 66:0.35 69:0.07 70:0.71 78:0.95 91:0.62 98:0.71 104:0.58 108:0.61 111:0.92 120:0.63 123:0.59 124:0.71 127:0.73 129:0.81 133:0.67 135:0.28 140:0.45 145:0.49 146:0.35 147:0.42 149:0.71 151:0.56 153:0.77 154:0.41 159:0.58 161:0.71 162:0.77 164:0.69 167:0.56 169:0.82 172:0.59 173:0.13 177:0.05 179:0.52 181:0.54 187:0.21 191:0.82 195:0.76 202:0.58 212:0.55 216:0.07 220:0.62 230:0.87 233:0.76 235:0.05 238:0.81 241:0.55 242:0.82 243:0.27 245:0.80 247:0.12 248:0.58 256:0.58 259:0.21 260:0.64 265:0.72 266:0.54 267:0.69 268:0.63 271:0.51 276:0.68 283:0.60 285:0.62 300:0.55 +2 6:0.87 8:0.77 12:0.06 17:0.28 20:0.77 21:0.78 27:0.21 28:0.68 30:0.21 34:0.67 36:0.96 37:0.74 43:0.48 55:0.52 66:0.54 69:0.34 70:0.37 78:0.91 91:0.65 98:0.19 100:0.93 106:0.81 108:0.27 111:0.90 120:0.85 123:0.26 124:0.45 127:0.38 129:0.59 133:0.47 135:0.24 140:0.80 146:0.42 147:0.49 149:0.35 151:0.73 152:0.76 153:0.81 154:0.37 159:0.82 161:0.71 162:0.52 164:0.79 167:0.47 169:0.90 172:0.21 173:0.13 177:0.05 178:0.42 179:0.94 181:0.54 186:0.94 187:0.39 189:0.89 191:0.70 202:0.79 206:0.81 212:0.42 216:0.95 235:0.31 238:0.87 241:0.15 242:0.82 243:0.63 245:0.40 247:0.12 248:0.79 256:0.75 259:0.21 260:0.86 261:0.81 265:0.21 266:0.23 267:0.69 268:0.74 271:0.51 276:0.14 283:0.82 285:0.62 300:0.25 +2 6:0.89 8:0.81 12:0.06 17:0.28 20:0.75 21:0.78 27:0.07 28:0.68 34:0.36 36:0.28 37:0.78 43:0.23 66:0.36 69:0.87 78:0.99 91:0.96 98:0.07 100:0.91 104:0.76 108:0.74 111:0.97 123:0.73 127:0.05 129:0.05 135:0.47 140:0.96 145:0.76 146:0.05 147:0.05 149:0.10 152:0.94 153:0.81 154:0.16 159:0.05 161:0.71 162:0.46 164:0.65 167:0.76 169:0.89 172:0.05 173:0.85 177:0.05 178:0.53 181:0.54 186:0.99 187:0.39 189:0.85 202:0.85 216:0.37 220:0.97 235:0.52 238:0.90 241:0.77 243:0.51 256:0.45 259:0.21 261:0.92 265:0.07 267:0.28 268:0.58 271:0.51 285:0.62 +2 6:0.89 8:0.77 12:0.06 17:0.72 20:0.95 21:0.78 25:0.88 27:0.04 28:0.68 34:0.42 36:0.21 37:0.66 78:0.99 91:0.90 100:0.97 104:0.76 111:0.98 120:0.63 135:0.75 140:0.45 151:0.56 152:0.88 153:0.77 161:0.71 162:0.86 164:0.69 167:0.81 169:0.95 175:0.75 181:0.54 186:0.99 189:0.90 191:0.75 202:0.54 216:0.04 220:0.62 238:0.88 241:0.87 244:0.61 248:0.58 256:0.58 257:0.65 260:0.64 261:0.94 267:0.52 268:0.63 271:0.51 277:0.69 285:0.62 +2 6:0.89 8:0.86 12:0.06 17:0.82 20:0.74 21:0.47 27:0.53 28:0.68 30:0.21 34:0.83 36:0.37 37:0.87 43:0.44 55:0.45 66:0.72 69:0.47 70:0.37 78:0.98 81:0.89 91:0.90 98:0.76 100:0.90 104:0.58 108:0.36 111:0.95 120:0.63 123:0.34 126:0.54 127:0.25 129:0.05 135:0.30 140:0.45 146:0.58 147:0.63 149:0.37 150:0.77 151:0.56 152:0.74 153:0.72 154:0.34 159:0.21 161:0.71 162:0.86 163:0.75 164:0.69 167:0.82 169:0.87 172:0.27 173:0.64 177:0.05 179:0.90 181:0.54 186:1.00 189:0.91 191:0.82 202:0.53 212:0.42 215:0.63 216:0.02 220:0.62 235:0.52 238:0.81 241:0.94 242:0.82 243:0.75 247:0.12 248:0.57 256:0.58 259:0.21 260:0.64 261:0.76 265:0.76 266:0.23 267:0.44 268:0.62 271:0.51 276:0.23 283:0.60 285:0.62 297:0.36 300:0.25 +2 12:0.06 17:0.61 21:0.30 27:0.12 28:0.68 30:0.38 34:0.33 36:0.21 37:0.66 43:0.36 55:0.92 66:0.20 69:0.61 70:0.50 81:0.92 91:0.91 98:0.37 108:0.72 120:0.94 123:0.70 124:0.85 126:0.54 127:0.63 129:0.93 133:0.84 135:0.21 140:0.80 145:0.86 146:0.05 147:0.05 149:0.44 150:0.85 151:0.74 153:0.83 154:0.27 159:0.59 161:0.71 162:0.67 163:0.94 164:0.94 167:0.69 172:0.21 173:0.57 177:0.05 178:0.42 179:0.83 181:0.54 187:0.87 202:0.91 212:0.28 215:0.72 216:0.39 235:0.31 241:0.71 242:0.82 243:0.16 245:0.49 247:0.12 248:0.91 256:0.92 259:0.21 260:0.94 265:0.40 266:0.63 267:0.44 268:0.92 271:0.51 276:0.42 285:0.62 297:0.36 300:0.33 +2 6:0.94 7:0.81 8:0.85 12:0.06 17:0.84 20:0.78 21:0.22 27:0.11 28:0.68 30:0.45 32:0.80 34:0.47 36:0.69 37:0.84 43:0.52 55:0.71 66:0.26 69:0.17 70:0.64 78:0.99 81:0.99 91:0.89 98:0.57 100:0.93 104:0.78 106:0.81 108:0.71 111:0.97 120:0.82 123:0.69 124:0.83 126:0.54 127:0.75 129:0.93 133:0.84 135:0.81 140:0.45 145:0.84 146:0.05 147:0.05 149:0.68 150:0.99 151:0.75 152:0.76 153:0.84 154:0.41 159:0.81 161:0.71 162:0.69 163:0.75 164:0.75 167:0.53 169:0.93 172:0.61 173:0.11 177:0.05 179:0.41 181:0.54 186:0.99 187:0.21 189:0.95 191:0.89 195:0.92 202:0.67 206:0.81 212:0.23 215:0.79 216:0.45 230:0.87 233:0.76 235:0.60 238:0.84 241:0.47 242:0.82 243:0.08 245:0.81 247:0.12 248:0.74 256:0.68 259:0.21 260:0.82 261:0.83 265:0.58 266:0.87 267:0.95 268:0.70 271:0.51 276:0.76 283:0.82 285:0.62 297:0.36 300:0.55 +2 6:1.00 8:0.94 12:0.06 17:0.91 20:0.76 21:0.78 27:0.04 28:0.68 34:0.50 36:0.54 37:0.96 43:0.42 66:0.36 69:0.52 78:1.00 91:0.79 98:0.08 100:0.98 104:0.80 106:0.81 108:0.40 111:0.99 120:0.77 123:0.38 127:0.06 129:0.05 135:0.89 140:0.45 146:0.05 147:0.05 149:0.15 151:0.74 152:0.74 153:0.83 154:0.32 159:0.05 161:0.71 162:0.53 164:0.65 167:0.54 169:0.98 172:0.05 173:0.51 177:0.05 181:0.54 186:1.00 187:0.39 189:1.00 191:0.96 202:0.64 206:0.81 216:0.31 235:0.60 238:0.96 241:0.50 243:0.51 248:0.69 256:0.45 259:0.21 260:0.77 261:0.79 265:0.08 267:0.69 268:0.58 271:0.51 283:0.82 285:0.62 +0 12:0.99 17:0.83 21:0.78 27:0.64 34:0.70 36:0.41 37:0.69 43:0.30 66:0.36 69:0.75 91:0.35 98:0.10 108:0.58 123:0.56 127:0.07 129:0.05 135:0.18 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 172:0.05 173:0.92 177:0.05 178:0.55 187:0.68 202:0.36 216:0.46 235:0.97 241:0.59 243:0.51 259:0.21 265:0.10 267:0.28 +2 12:0.99 17:0.26 21:0.78 25:0.88 27:0.72 34:0.56 36:0.84 43:0.29 66:0.36 69:0.75 91:0.23 98:0.09 108:0.58 123:0.55 127:0.06 129:0.05 135:0.49 146:0.05 147:0.05 149:0.12 154:0.22 159:0.05 172:0.05 173:0.83 177:0.05 178:0.55 187:0.21 216:0.05 235:0.52 241:0.30 243:0.51 259:0.21 265:0.09 267:0.23 +1 12:0.99 17:0.66 21:0.78 25:0.88 27:0.72 30:0.85 34:0.67 36:0.93 37:0.94 43:0.39 55:0.52 66:0.87 69:0.58 70:0.15 91:0.06 98:0.82 108:0.44 123:0.42 127:0.30 129:0.05 132:0.97 135:0.56 146:0.49 147:0.55 149:0.66 154:0.30 159:0.17 172:0.63 173:0.85 177:0.05 178:0.55 179:0.57 187:0.39 212:0.93 216:0.16 235:0.95 241:0.15 242:0.82 243:0.88 247:0.12 265:0.82 266:0.17 267:0.73 276:0.54 300:0.13 +3 12:0.99 17:0.78 21:0.78 25:0.88 27:0.72 34:0.52 36:0.71 37:0.94 91:0.35 120:0.56 132:0.97 135:0.18 140:0.80 145:0.52 151:0.49 153:0.48 162:0.62 164:0.57 175:0.75 178:0.42 202:0.50 216:0.08 220:0.74 235:0.95 241:0.79 244:0.61 248:0.49 256:0.45 257:0.65 260:0.56 267:0.28 268:0.46 277:0.69 285:0.62 +1 12:0.99 17:0.67 21:0.78 25:0.88 27:0.72 30:0.86 34:0.58 36:0.71 37:0.94 43:0.48 55:0.52 66:0.84 69:0.41 70:0.15 91:0.08 98:0.86 108:0.32 123:0.31 127:0.38 129:0.05 132:0.97 135:0.30 146:0.52 147:0.58 149:0.60 154:0.37 159:0.19 172:0.54 173:0.70 177:0.05 178:0.55 179:0.73 187:0.39 212:0.95 216:0.16 235:0.95 241:0.32 242:0.82 243:0.85 247:0.12 265:0.86 266:0.12 267:0.65 276:0.45 300:0.13 +0 12:0.99 17:0.57 21:0.78 25:0.88 27:0.72 30:0.56 34:0.33 36:0.37 37:0.94 43:0.35 55:0.92 66:0.33 69:0.65 70:0.46 91:0.15 98:0.60 108:0.49 123:0.47 124:0.72 127:0.56 129:0.81 132:0.97 133:0.67 135:0.66 146:0.74 147:0.78 149:0.47 154:0.26 159:0.59 163:0.79 172:0.32 173:0.59 177:0.05 178:0.55 179:0.79 187:0.39 212:0.13 216:0.16 235:0.60 241:0.30 242:0.82 243:0.50 245:0.58 247:0.12 265:0.61 266:0.75 267:0.44 276:0.44 300:0.33 +0 1:0.69 8:0.59 9:0.76 11:0.75 12:0.20 17:0.43 18:0.84 21:0.05 22:0.93 27:0.72 29:0.77 30:0.66 31:0.92 34:0.63 36:0.97 37:0.72 39:0.74 43:0.70 45:0.83 47:0.93 64:0.77 66:0.68 69:0.35 70:0.40 71:0.91 74:0.60 79:0.92 81:0.64 83:0.60 86:0.83 91:0.61 96:0.79 97:0.89 98:0.57 99:0.83 101:0.52 108:0.27 114:0.85 122:0.80 123:0.26 124:0.43 126:0.44 127:0.84 129:0.59 131:0.88 133:0.47 135:0.65 137:0.92 139:0.83 140:0.80 144:0.57 146:0.78 147:0.82 149:0.67 150:0.62 151:0.70 153:0.72 154:0.60 155:0.58 157:0.87 158:0.79 159:0.73 162:0.57 163:0.92 165:0.70 166:0.82 172:0.51 173:0.24 175:0.75 176:0.66 177:0.38 178:0.42 179:0.80 182:0.86 187:0.21 190:0.77 192:0.51 196:0.93 201:0.68 202:0.64 208:0.54 212:0.36 216:0.26 219:0.92 220:0.62 222:0.76 227:0.87 229:0.92 231:0.74 235:0.12 241:0.55 242:0.38 243:0.72 244:0.61 245:0.58 246:0.86 247:0.55 248:0.72 253:0.76 255:0.74 256:0.58 257:0.65 259:0.21 262:0.93 265:0.58 266:0.63 267:0.65 268:0.62 271:0.65 276:0.33 277:0.69 279:0.82 284:0.92 285:0.56 287:0.89 290:0.85 292:0.95 300:0.33 +0 1:0.69 8:0.68 9:0.76 11:0.75 12:0.20 17:0.18 18:0.94 21:0.05 27:0.04 29:0.77 30:0.61 31:0.91 34:0.61 36:0.85 37:0.98 39:0.74 43:0.56 45:0.95 64:0.77 66:0.80 69:0.86 70:0.74 71:0.91 74:0.83 79:0.92 81:0.64 83:0.60 86:0.94 91:0.72 96:0.78 97:0.94 98:0.70 99:0.83 101:0.52 108:0.72 114:0.85 122:0.80 123:0.70 124:0.40 126:0.44 127:0.32 129:0.81 131:0.88 133:0.67 135:0.30 137:0.91 139:0.94 140:0.45 144:0.57 146:0.90 147:0.92 149:0.87 150:0.62 151:0.74 153:0.83 154:0.45 155:0.58 157:0.93 158:0.79 159:0.64 162:0.68 165:0.70 166:0.82 172:0.90 173:0.78 175:0.75 176:0.66 177:0.38 179:0.21 182:0.86 187:0.68 190:0.77 191:0.73 192:0.51 196:0.95 201:0.68 202:0.62 204:0.85 208:0.54 212:0.78 216:0.74 219:0.92 220:0.62 222:0.76 227:0.87 229:0.92 231:0.74 235:0.12 241:0.35 242:0.57 243:0.82 244:0.61 245:0.81 246:0.86 247:0.60 248:0.69 253:0.80 255:0.74 256:0.58 257:0.65 259:0.21 265:0.71 266:0.75 267:0.76 268:0.64 271:0.65 276:0.83 277:0.69 279:0.82 281:0.91 284:0.92 285:0.56 287:0.89 290:0.85 292:0.95 300:0.84 +0 8:0.68 12:0.20 17:0.32 18:0.57 21:0.78 27:0.72 29:0.77 30:0.95 34:0.85 36:0.66 37:0.90 39:0.74 43:0.07 55:1.00 66:0.13 69:0.93 71:0.91 74:0.24 86:0.57 91:0.56 98:0.05 108:0.86 122:0.67 123:0.85 124:0.75 127:0.61 129:0.81 131:0.61 133:0.67 135:0.47 139:0.55 145:0.69 146:0.05 147:0.05 149:0.06 154:0.07 157:0.61 159:0.95 163:1.00 166:0.61 172:0.05 173:0.63 175:0.91 177:0.05 178:0.55 179:0.99 182:0.61 191:0.73 202:0.79 212:0.95 216:0.13 222:0.76 227:0.61 229:0.61 231:0.61 235:0.42 241:0.80 242:0.82 243:0.34 244:0.83 245:0.37 246:0.61 247:0.12 253:0.20 257:0.84 259:0.21 265:0.05 266:0.12 267:0.65 271:0.65 276:0.19 277:0.87 287:0.61 300:0.08 +0 12:0.20 17:0.36 18:0.62 21:0.78 27:0.34 29:0.77 30:0.15 34:0.67 36:0.92 37:0.46 39:0.74 43:0.10 55:0.84 66:0.24 69:0.80 70:0.68 71:0.91 74:0.28 86:0.58 91:0.08 98:0.24 108:0.94 122:0.77 123:0.94 124:0.88 127:0.55 129:0.95 131:0.61 133:0.87 135:0.88 139:0.56 145:0.63 146:0.05 147:0.05 149:0.12 154:0.09 157:0.61 159:0.86 163:0.90 166:0.61 172:0.21 173:0.56 175:0.91 177:0.05 178:0.55 179:0.84 182:0.61 187:0.87 212:0.15 216:0.53 222:0.76 227:0.61 229:0.61 231:0.63 235:0.22 241:0.10 242:0.73 243:0.17 244:0.83 245:0.46 246:0.61 247:0.39 253:0.24 257:0.84 259:0.21 265:0.27 266:0.63 267:0.23 271:0.65 276:0.38 277:0.87 287:0.61 300:0.43 +0 7:0.66 8:0.79 11:0.75 12:0.20 17:0.55 18:0.85 21:0.54 27:0.13 29:0.77 30:0.32 32:0.64 34:0.80 36:0.80 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.35 69:0.81 70:0.84 71:0.91 74:0.62 81:0.25 86:0.83 91:0.54 98:0.44 101:0.52 104:0.86 106:0.81 108:0.65 120:0.65 122:0.80 123:0.63 124:0.82 126:0.26 127:0.39 129:0.05 131:0.61 133:0.81 135:0.61 139:0.80 140:0.45 144:0.57 145:0.50 146:0.76 147:0.54 149:0.69 150:0.25 151:0.55 153:0.48 154:0.58 157:0.86 159:0.74 162:0.84 164:0.70 166:0.61 172:0.61 173:0.66 177:0.38 179:0.40 182:0.77 187:0.39 190:0.89 195:0.92 202:0.65 206:0.81 212:0.52 215:0.39 216:0.65 220:0.62 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.63 242:0.22 243:0.38 244:0.61 245:0.74 246:0.61 247:0.79 248:0.56 253:0.39 256:0.71 257:0.65 259:0.21 260:0.62 265:0.46 266:0.80 267:0.96 268:0.71 271:0.65 276:0.68 283:0.78 285:0.47 287:0.61 297:0.36 300:0.70 +0 1:0.69 11:0.75 12:0.20 17:0.55 18:0.69 21:0.59 27:0.45 29:0.77 30:0.76 34:0.85 36:0.17 37:0.50 39:0.74 43:0.64 45:0.73 64:0.77 66:0.36 69:0.70 70:0.22 71:0.91 74:0.49 81:0.33 83:0.60 86:0.72 91:0.18 97:0.89 98:0.27 99:0.83 101:0.52 108:0.53 114:0.57 122:0.57 123:0.51 124:0.57 126:0.44 127:0.36 129:0.05 131:0.61 133:0.43 135:0.77 139:0.75 144:0.57 145:0.42 146:0.39 147:0.45 149:0.40 150:0.52 154:0.54 155:0.58 157:0.81 158:0.78 159:0.52 163:0.88 165:0.70 166:0.82 172:0.16 173:0.66 176:0.66 177:0.70 178:0.55 179:0.94 182:0.85 187:0.68 192:0.51 201:0.68 208:0.54 212:0.42 216:0.77 219:0.92 222:0.76 227:0.61 229:0.61 231:0.73 235:0.74 241:0.37 242:0.45 243:0.51 244:0.61 245:0.43 246:0.86 247:0.35 253:0.40 259:0.21 265:0.30 266:0.23 267:0.44 271:0.65 276:0.15 279:0.82 287:0.61 290:0.57 292:0.91 300:0.18 +0 12:0.20 17:0.47 18:0.96 21:0.78 27:0.52 29:0.77 30:0.93 34:0.83 36:0.27 37:0.43 39:0.74 43:0.14 55:0.42 66:0.88 69:0.61 70:0.19 71:0.91 74:0.26 86:0.66 91:0.10 98:0.30 108:0.95 122:0.86 123:0.95 124:0.38 127:0.73 129:0.89 131:0.61 133:0.78 135:0.82 139:0.64 145:0.67 146:0.05 147:0.05 149:0.35 154:0.10 157:0.61 159:0.89 166:0.61 172:0.97 173:0.29 175:0.91 177:0.05 178:0.55 179:0.17 182:0.61 187:0.68 202:0.50 212:0.93 216:0.79 222:0.76 227:0.61 229:0.61 231:0.67 235:0.74 241:0.15 242:0.82 243:0.05 244:0.83 245:0.84 246:0.61 247:0.55 253:0.23 257:0.84 259:0.21 265:0.32 266:0.47 267:0.44 271:0.65 276:0.90 277:0.87 287:0.61 300:0.55 +0 1:0.69 8:0.83 11:0.75 12:0.20 17:0.28 18:0.77 21:0.30 27:0.04 29:0.77 30:0.29 34:0.68 36:0.58 37:0.98 39:0.74 43:0.56 45:0.83 55:0.52 64:0.77 66:0.11 69:0.86 70:0.76 71:0.91 74:0.63 81:0.42 83:0.60 86:0.79 91:0.48 97:0.94 98:0.35 99:0.83 101:0.52 108:0.72 114:0.63 122:0.77 123:0.81 124:0.74 126:0.44 127:0.30 129:0.59 131:0.61 133:0.64 135:0.37 139:0.83 144:0.57 146:0.46 147:0.52 149:0.61 150:0.55 154:0.45 155:0.58 157:0.88 158:0.78 159:0.54 165:0.70 166:0.82 172:0.51 173:0.83 175:0.75 176:0.66 177:0.38 178:0.55 179:0.38 182:0.85 187:0.68 192:0.51 201:0.68 204:0.85 208:0.54 212:0.68 216:0.74 219:0.92 222:0.76 227:0.61 229:0.61 231:0.73 235:0.42 241:0.30 242:0.70 243:0.46 244:0.61 245:0.80 246:0.86 247:0.47 253:0.49 257:0.65 259:0.21 265:0.32 266:0.80 267:0.59 271:0.65 276:0.64 277:0.69 279:0.82 281:0.89 287:0.61 290:0.63 292:0.91 300:0.55 +0 7:0.66 8:0.83 11:0.75 12:0.20 17:0.61 18:0.85 21:0.54 27:0.13 29:0.77 30:0.26 32:0.64 34:0.79 36:0.79 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.33 69:0.81 70:0.87 71:0.91 74:0.62 81:0.25 86:0.83 91:0.46 98:0.44 101:0.52 104:0.89 106:0.81 108:0.65 120:0.59 123:0.63 124:0.83 126:0.26 127:0.43 129:0.05 131:0.61 133:0.81 135:0.54 139:0.80 140:0.45 144:0.57 145:0.50 146:0.76 147:0.54 149:0.69 150:0.25 151:0.51 153:0.48 154:0.58 157:0.87 159:0.75 162:0.86 164:0.65 166:0.61 172:0.61 173:0.67 177:0.38 179:0.40 182:0.77 187:0.39 190:0.89 191:0.75 195:0.92 202:0.58 206:0.81 212:0.52 215:0.39 216:0.65 220:0.96 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.32 242:0.19 243:0.37 244:0.61 245:0.76 246:0.61 247:0.79 248:0.51 253:0.39 256:0.64 257:0.65 259:0.21 260:0.58 265:0.46 266:0.87 267:0.97 268:0.66 271:0.65 276:0.71 283:0.78 285:0.47 287:0.61 297:0.36 300:0.70 +0 7:0.66 8:0.85 11:0.75 12:0.20 17:0.73 18:0.85 21:0.54 27:0.13 29:0.77 30:0.19 32:0.64 34:0.81 36:0.84 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.30 69:0.81 70:0.72 71:0.91 74:0.62 81:0.25 86:0.83 91:0.22 98:0.48 101:0.52 104:0.83 106:0.81 108:0.65 123:0.63 124:0.80 126:0.26 127:0.41 129:0.05 131:0.61 133:0.74 135:0.49 139:0.80 144:0.57 145:0.50 146:0.76 147:0.54 149:0.71 150:0.25 154:0.58 157:0.87 159:0.71 166:0.61 172:0.65 173:0.69 177:0.38 178:0.55 179:0.33 182:0.77 187:0.39 191:0.75 195:0.90 206:0.81 212:0.54 215:0.39 216:0.65 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.41 242:0.38 243:0.32 244:0.61 245:0.84 246:0.61 247:0.79 253:0.39 257:0.65 259:0.21 265:0.49 266:0.80 267:0.94 271:0.65 276:0.76 283:0.78 287:0.61 297:0.36 300:0.55 +0 1:0.69 7:0.66 8:0.75 11:0.75 12:0.20 17:0.02 18:0.81 21:0.05 22:0.93 27:0.37 29:0.77 30:0.71 34:0.87 36:0.62 37:0.98 39:0.74 43:0.70 45:0.81 47:0.93 64:0.77 66:0.45 69:0.37 70:0.40 71:0.91 74:0.60 81:0.64 83:0.60 86:0.84 91:0.27 97:0.94 98:0.54 99:0.83 101:0.52 108:0.58 114:0.85 117:0.86 122:0.80 123:0.55 124:0.57 126:0.44 127:0.27 129:0.59 131:0.61 133:0.47 135:0.23 139:0.83 144:0.57 145:0.45 146:0.20 147:0.25 149:0.67 150:0.62 154:0.60 155:0.58 157:0.87 158:0.79 159:0.29 165:0.70 166:0.82 172:0.32 173:0.44 175:0.75 176:0.66 177:0.38 178:0.55 179:0.71 182:0.85 187:0.68 192:0.51 201:0.68 208:0.54 212:0.30 216:0.52 219:0.92 222:0.76 227:0.61 229:0.61 230:0.69 231:0.73 232:0.87 233:0.76 235:0.12 241:0.27 242:0.63 243:0.44 244:0.61 245:0.59 246:0.86 247:0.35 253:0.58 257:0.65 259:0.21 262:0.93 265:0.56 266:0.47 267:0.82 271:0.65 276:0.35 277:0.69 279:0.82 287:0.61 290:0.85 292:0.95 300:0.33 +1 8:0.78 11:0.75 12:0.20 17:0.95 18:0.98 21:0.30 22:0.93 27:0.71 29:0.77 30:0.30 31:0.86 34:0.73 36:0.89 37:0.65 39:0.74 43:0.68 44:0.85 45:0.81 47:0.93 48:0.98 55:0.52 64:0.77 66:0.84 69:0.12 70:0.90 71:0.91 74:0.51 79:0.87 81:0.30 86:0.86 91:0.70 98:0.88 101:0.52 108:0.10 122:0.86 123:0.10 124:0.38 126:0.26 127:0.59 129:0.05 131:0.82 133:0.37 135:0.42 137:0.86 139:0.83 140:0.80 144:0.57 145:0.70 146:0.91 147:0.93 149:0.67 150:0.28 151:0.48 153:0.69 154:0.58 157:0.86 159:0.58 162:0.59 166:0.74 172:0.84 173:0.17 177:0.70 178:0.42 179:0.39 182:0.77 187:0.21 190:0.89 191:0.70 195:0.78 196:0.88 202:0.55 212:0.57 216:0.29 219:0.87 220:0.87 222:0.76 227:0.83 229:0.61 231:0.71 235:0.31 241:0.03 242:0.58 243:0.85 244:0.61 245:0.74 246:0.61 247:0.79 248:0.48 253:0.35 256:0.45 259:0.21 262:0.93 265:0.88 266:0.63 267:0.79 268:0.55 271:0.65 276:0.76 281:0.89 284:0.86 285:0.47 287:0.61 292:0.91 300:0.70 +1 7:0.66 8:0.80 11:0.75 12:0.20 17:0.70 18:0.80 21:0.59 27:0.13 29:0.77 30:0.21 32:0.64 34:0.77 36:0.87 37:0.83 39:0.74 43:0.62 45:0.81 55:0.52 66:0.19 69:0.60 70:0.75 71:0.91 74:0.55 81:0.24 86:0.81 91:0.51 98:0.54 101:0.52 104:0.87 106:0.81 108:0.46 120:0.58 123:0.75 124:0.52 126:0.26 127:0.30 129:0.05 131:0.61 133:0.43 135:0.54 139:0.77 140:0.45 144:0.57 145:0.52 146:0.74 147:0.78 149:0.64 150:0.24 151:0.49 153:0.48 154:0.51 157:0.86 159:0.55 162:0.86 164:0.68 166:0.61 172:0.65 173:0.48 177:0.70 179:0.40 182:0.76 187:0.39 190:0.89 195:0.85 202:0.62 206:0.81 212:0.52 215:0.39 216:0.65 220:0.97 222:0.76 227:0.61 229:0.61 230:0.69 231:0.69 233:0.73 235:0.68 241:0.62 242:0.28 243:0.63 244:0.61 245:0.82 246:0.61 247:0.76 248:0.50 253:0.37 256:0.71 259:0.21 260:0.57 265:0.55 266:0.75 267:0.96 268:0.70 271:0.65 276:0.65 277:0.69 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +1 9:0.76 11:0.75 12:0.20 17:0.73 18:0.91 21:0.64 22:0.93 27:0.27 29:0.77 30:0.27 31:0.89 32:0.64 34:0.88 36:0.99 37:0.87 39:0.74 43:0.70 45:0.85 47:0.93 55:0.71 66:0.92 69:0.41 70:0.66 71:0.91 74:0.60 79:0.88 81:0.24 83:0.60 86:0.87 91:0.54 96:0.72 98:0.96 101:0.52 108:0.32 117:0.86 120:0.53 122:0.80 123:0.31 126:0.26 127:0.70 129:0.05 131:0.87 135:0.79 137:0.89 139:0.83 140:0.80 144:0.57 145:0.93 146:0.93 147:0.95 149:0.75 150:0.24 151:0.59 153:0.48 154:0.60 155:0.58 157:0.89 159:0.57 162:0.86 164:0.54 166:0.61 172:0.79 173:0.37 177:0.70 179:0.52 182:0.86 187:0.39 190:0.77 192:0.51 195:0.76 196:0.91 202:0.54 208:0.54 212:0.29 215:0.38 216:0.43 220:0.99 222:0.76 227:0.86 229:0.92 231:0.73 232:0.92 235:0.74 241:0.65 242:0.24 243:0.92 244:0.61 246:0.61 247:0.76 248:0.58 253:0.50 255:0.74 256:0.58 259:0.21 260:0.53 262:0.93 265:0.96 266:0.54 267:0.98 268:0.63 271:0.65 276:0.68 284:0.87 285:0.62 287:0.89 297:0.36 300:0.43 +0 7:0.66 8:0.86 11:0.75 12:0.20 17:0.61 18:0.85 21:0.54 27:0.13 29:0.77 30:0.21 32:0.64 34:0.84 36:0.62 37:0.83 39:0.74 43:0.69 45:0.83 55:0.52 66:0.12 69:0.81 70:0.77 71:0.91 74:0.62 81:0.25 86:0.83 91:0.40 98:0.44 101:0.52 104:0.86 106:0.81 108:0.65 120:0.54 123:0.85 124:0.80 126:0.26 127:0.32 129:0.05 131:0.61 133:0.74 135:0.49 139:0.80 140:0.45 144:0.57 145:0.50 146:0.75 147:0.54 149:0.70 150:0.25 151:0.46 153:0.48 154:0.58 157:0.86 159:0.72 162:0.86 164:0.53 166:0.61 172:0.63 173:0.66 177:0.38 179:0.29 182:0.77 187:0.39 190:0.89 195:0.93 202:0.36 206:0.81 212:0.51 215:0.39 216:0.65 220:0.93 222:0.76 227:0.61 229:0.61 230:0.69 231:0.70 233:0.73 235:0.60 241:0.57 242:0.39 243:0.33 244:0.61 245:0.84 246:0.61 247:0.79 248:0.46 253:0.39 256:0.45 257:0.65 259:0.21 260:0.54 265:0.44 266:0.75 267:0.98 268:0.46 271:0.65 276:0.76 277:0.87 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +0 8:0.64 12:0.20 17:0.83 18:0.66 21:0.78 27:0.72 29:0.77 30:0.76 34:0.59 36:0.98 39:0.74 43:0.17 55:0.71 66:0.54 69:0.88 70:0.22 71:0.91 74:0.25 86:0.58 91:0.65 98:0.40 108:0.76 122:0.57 123:0.74 124:0.45 127:0.78 129:0.05 131:0.61 133:0.37 135:0.74 139:0.57 145:0.80 146:0.41 147:0.05 149:0.10 154:0.22 157:0.61 159:0.45 163:0.86 166:0.61 172:0.21 173:0.97 177:0.05 178:0.55 179:0.97 182:0.61 202:0.67 212:0.42 216:0.10 222:0.76 227:0.61 229:0.61 231:0.63 235:0.60 241:0.81 242:0.45 243:0.26 244:0.61 245:0.40 246:0.61 247:0.35 253:0.23 254:0.95 257:0.84 259:0.21 265:0.42 266:0.23 267:0.79 271:0.65 276:0.17 287:0.61 300:0.18 +0 1:0.69 8:0.76 11:0.75 12:0.20 17:0.15 18:0.67 21:0.22 27:0.04 29:0.77 30:0.45 34:0.70 36:0.65 37:0.98 39:0.74 43:0.56 45:0.73 64:0.77 66:0.77 69:0.86 70:0.50 71:0.91 74:0.40 76:0.94 81:0.48 83:0.60 86:0.70 91:0.48 98:0.60 99:0.83 101:0.52 108:0.72 114:0.67 122:0.80 123:0.70 126:0.44 127:0.17 129:0.05 131:0.61 135:0.47 139:0.68 144:0.57 146:0.63 147:0.68 149:0.38 150:0.57 154:0.45 155:0.58 157:0.82 158:0.71 159:0.23 165:0.70 166:0.82 172:0.37 173:0.92 175:0.75 176:0.66 177:0.38 178:0.55 179:0.64 182:0.85 187:0.87 192:0.51 201:0.68 208:0.54 212:0.23 216:0.74 222:0.76 227:0.61 229:0.61 231:0.73 232:0.94 235:0.31 241:0.60 242:0.55 243:0.79 244:0.61 246:0.86 247:0.39 253:0.50 257:0.65 259:0.21 265:0.61 266:0.38 267:0.91 271:0.65 276:0.29 277:0.69 287:0.61 290:0.67 300:0.33 +1 1:0.69 11:0.75 12:0.20 17:0.59 18:0.88 21:0.59 22:0.93 27:0.39 29:0.77 30:0.44 34:0.19 36:0.42 37:0.62 39:0.74 43:0.58 45:0.83 47:0.93 66:0.54 69:0.83 70:0.89 71:0.91 74:0.54 81:0.30 86:0.88 91:0.51 98:0.56 101:0.52 108:0.67 114:0.54 122:0.67 123:0.65 124:0.68 126:0.44 127:0.36 129:0.05 131:0.61 133:0.74 135:0.70 139:0.85 144:0.57 145:0.85 146:0.86 147:0.05 149:0.58 150:0.47 154:0.59 155:0.58 157:0.86 159:0.73 166:0.74 172:0.70 173:0.69 176:0.55 177:0.05 178:0.55 179:0.40 182:0.77 187:0.39 192:0.59 201:0.68 208:0.54 212:0.35 215:0.38 216:0.15 219:0.87 222:0.76 227:0.61 229:0.61 231:0.72 235:0.74 241:0.64 242:0.13 243:0.09 245:0.73 246:0.61 247:0.88 253:0.38 254:0.74 257:0.84 259:0.21 262:0.93 265:0.57 266:0.87 267:0.97 271:0.65 276:0.66 287:0.61 290:0.54 292:0.95 297:0.36 300:0.84 +0 1:0.69 11:0.75 12:0.20 17:0.92 18:0.78 22:0.93 27:0.65 29:0.77 30:0.45 34:0.39 36:0.75 37:0.31 39:0.74 43:0.71 45:0.77 47:0.93 48:0.91 64:0.77 66:0.82 69:0.21 70:0.61 71:0.91 74:0.63 76:1.00 77:0.96 81:0.73 83:0.60 86:0.81 91:0.12 97:0.89 98:0.82 99:0.83 101:0.52 108:0.17 114:0.95 117:0.86 122:0.77 123:0.16 126:0.44 127:0.31 129:0.05 131:0.61 132:0.97 135:0.66 139:0.82 144:0.57 145:0.87 146:0.58 147:0.63 149:0.62 150:0.69 154:0.61 155:0.58 157:0.85 158:0.79 159:0.21 165:0.70 166:0.82 172:0.48 173:0.46 176:0.66 177:0.70 178:0.55 179:0.75 182:0.86 187:0.21 192:0.51 195:0.55 201:0.68 204:0.85 208:0.54 212:0.61 216:0.67 219:0.92 222:0.76 227:0.61 229:0.61 231:0.73 232:0.81 235:0.05 241:0.07 242:0.33 243:0.83 244:0.61 246:0.86 247:0.55 253:0.64 259:0.21 262:0.93 265:0.82 266:0.27 267:0.52 271:0.65 276:0.37 279:0.82 281:0.86 282:0.71 287:0.61 290:0.95 292:0.95 300:0.43 +1 7:0.66 8:0.84 11:0.75 12:0.20 17:0.85 18:0.80 21:0.47 27:0.13 29:0.77 30:0.14 32:0.64 34:0.80 36:0.88 37:0.83 39:0.74 43:0.62 45:0.77 55:0.52 66:0.53 69:0.59 70:0.80 71:0.91 74:0.51 81:0.27 86:0.77 91:0.51 98:0.52 101:0.52 104:0.78 106:0.81 108:0.45 120:0.67 123:0.43 124:0.52 126:0.26 127:0.27 129:0.05 131:0.61 133:0.43 135:0.54 139:0.74 140:0.45 144:0.57 145:0.45 146:0.74 147:0.78 149:0.58 150:0.26 151:0.55 153:0.48 154:0.51 157:0.84 159:0.55 162:0.78 164:0.70 166:0.61 172:0.63 173:0.46 177:0.70 179:0.39 182:0.75 187:0.21 190:0.89 191:0.70 195:0.85 202:0.67 206:0.81 212:0.61 215:0.41 216:0.65 220:0.74 222:0.76 227:0.61 229:0.61 230:0.69 231:0.69 233:0.73 235:0.52 241:0.59 242:0.30 243:0.62 244:0.61 245:0.81 246:0.61 247:0.76 248:0.57 253:0.36 256:0.71 259:0.21 260:0.64 265:0.53 266:0.75 267:0.85 268:0.72 271:0.65 276:0.64 283:0.78 285:0.47 287:0.61 297:0.36 300:0.55 +1 7:0.72 8:0.79 11:0.75 12:0.20 17:0.95 18:0.94 21:0.22 22:0.93 27:0.71 29:0.77 30:0.14 31:0.88 32:0.68 34:0.73 36:0.95 37:0.65 39:0.74 43:0.70 44:0.85 45:0.66 47:0.93 48:0.97 55:0.42 64:0.77 66:0.53 69:0.10 70:0.99 71:0.91 74:0.43 79:0.90 81:0.33 86:0.82 91:0.48 98:0.38 101:0.52 108:0.09 120:0.63 122:0.86 123:0.09 124:0.80 126:0.26 127:0.69 129:0.05 131:0.82 133:0.86 135:0.32 137:0.88 139:0.79 140:0.45 144:0.57 145:0.69 146:0.59 147:0.65 149:0.51 150:0.30 151:0.64 153:0.46 154:0.74 157:0.77 159:0.71 162:0.86 164:0.57 166:0.74 172:0.63 173:0.12 177:0.70 179:0.57 182:0.72 187:0.21 190:0.77 191:0.70 192:0.51 195:0.85 196:0.89 202:0.49 212:0.69 216:0.29 219:0.87 220:0.87 222:0.76 227:0.83 229:0.61 230:0.75 231:0.69 233:0.76 235:0.22 241:0.10 242:0.22 243:0.62 245:0.62 246:0.61 247:0.84 248:0.62 253:0.33 254:0.97 255:0.74 256:0.58 259:0.21 260:0.62 262:0.93 265:0.40 266:0.71 267:0.52 268:0.58 271:0.65 276:0.61 281:0.91 284:0.86 285:0.56 287:0.61 292:0.91 300:0.94 +0 1:0.69 7:0.63 12:0.20 17:0.72 18:0.60 21:0.59 27:0.28 29:0.62 30:0.21 32:0.62 34:0.47 36:0.91 37:0.47 39:0.54 43:0.17 55:0.96 66:0.16 69:0.41 70:0.79 71:0.68 74:0.27 77:0.70 81:0.31 86:0.62 91:0.08 98:0.27 108:0.32 114:0.57 123:0.31 124:0.92 126:0.44 127:0.92 129:0.05 133:0.91 135:0.95 139:0.60 144:0.85 145:0.90 146:0.62 147:0.67 149:0.18 150:0.48 154:0.47 155:0.58 159:0.87 163:0.98 172:0.27 173:0.17 175:0.75 176:0.66 177:0.38 178:0.55 179:0.73 187:0.39 192:0.59 195:0.96 201:0.68 208:0.54 212:0.16 215:0.39 216:0.44 222:0.76 230:0.63 233:0.73 235:0.74 241:0.18 242:0.75 243:0.37 244:0.61 245:0.55 247:0.55 253:0.39 254:0.97 257:0.65 259:0.21 265:0.29 266:0.89 267:0.65 271:0.65 276:0.54 277:0.69 282:0.71 290:0.57 297:0.36 300:0.55 +0 8:0.92 11:0.92 12:0.20 17:0.59 18:0.60 21:0.78 27:0.58 29:0.62 30:0.74 34:0.34 36:0.94 37:0.47 39:0.54 43:0.13 45:0.83 66:0.20 69:0.99 70:0.39 71:0.68 74:0.31 85:0.80 86:0.66 91:0.04 98:0.14 101:0.97 108:0.98 122:0.41 123:0.98 124:0.74 127:0.12 129:0.81 133:0.67 135:0.88 139:0.64 144:0.85 146:0.41 147:0.48 149:0.25 154:0.10 159:0.62 172:0.41 173:0.98 177:0.70 178:0.55 179:0.13 187:0.39 202:0.46 212:0.70 216:0.68 222:0.76 235:0.68 241:0.18 242:0.19 243:0.45 245:0.73 247:0.55 253:0.26 259:0.21 265:0.14 266:0.63 267:0.28 271:0.65 276:0.52 300:0.43 +0 11:0.92 12:0.20 17:0.53 18:0.72 21:0.78 27:0.72 29:0.62 30:0.21 34:0.70 36:0.51 39:0.54 43:0.78 45:0.66 66:0.47 69:0.44 70:0.77 71:0.68 74:0.41 85:0.72 86:0.79 91:0.06 98:0.38 101:0.97 104:0.79 108:0.34 123:0.32 124:0.74 127:0.66 129:0.05 133:0.73 135:0.54 139:0.76 144:0.85 146:0.68 147:0.72 149:0.62 154:0.70 159:0.78 172:0.41 173:0.25 177:0.70 178:0.55 179:0.78 187:0.21 212:0.36 216:0.12 222:0.76 235:0.60 241:0.44 242:0.38 243:0.59 245:0.53 247:0.74 253:0.32 259:0.21 265:0.40 266:0.71 267:0.59 271:0.65 276:0.43 300:0.55 +0 1:0.69 7:0.63 12:0.20 17:0.66 18:0.69 21:0.64 27:0.31 29:0.62 30:0.33 32:0.62 34:0.59 36:0.92 37:0.91 39:0.54 43:0.15 55:0.52 66:0.60 69:0.54 70:0.74 71:0.68 74:0.30 81:0.30 86:0.75 91:0.35 98:0.62 104:0.58 106:0.81 108:0.41 114:0.56 123:0.39 124:0.58 126:0.44 127:0.62 129:0.05 133:0.59 135:0.98 139:0.71 144:0.85 145:0.79 146:0.72 147:0.76 149:0.20 150:0.47 154:0.64 155:0.58 159:0.56 172:0.63 173:0.50 176:0.66 177:0.70 178:0.55 179:0.59 187:0.39 192:0.59 195:0.77 201:0.68 206:0.81 208:0.54 212:0.58 215:0.38 216:0.46 222:0.76 230:0.63 233:0.73 235:0.80 241:0.10 242:0.42 243:0.67 245:0.70 247:0.82 253:0.38 254:0.97 265:0.63 266:0.63 267:0.87 271:0.65 276:0.58 290:0.56 297:0.36 300:0.55 +2 1:0.69 11:0.92 12:0.20 17:0.47 18:0.86 21:0.68 27:0.53 29:0.62 30:0.57 34:0.61 36:0.95 37:0.32 39:0.54 43:0.70 45:0.88 64:0.77 66:0.23 69:0.62 70:0.64 71:0.68 74:0.41 81:0.32 83:0.60 85:0.72 86:0.92 91:0.11 98:0.51 99:0.83 101:0.97 108:0.74 114:0.54 122:0.57 123:0.45 124:0.79 126:0.44 127:0.58 129:0.81 132:0.97 133:0.78 135:0.61 139:0.88 144:0.85 145:0.69 146:0.68 147:0.72 149:0.75 150:0.51 154:0.70 155:0.58 159:0.62 165:0.70 172:0.51 173:0.55 176:0.55 177:0.70 178:0.55 179:0.49 187:0.21 192:0.51 201:0.68 208:0.54 212:0.36 216:0.75 222:0.76 235:0.84 241:0.44 242:0.31 243:0.43 245:0.77 247:0.60 253:0.35 265:0.35 266:0.71 267:0.99 271:0.65 276:0.67 290:0.54 300:0.55 +1 11:0.88 12:0.20 17:0.12 18:0.63 21:0.78 27:0.33 29:0.62 30:0.95 34:0.87 36:0.68 37:0.47 39:0.54 43:0.77 66:0.64 69:0.80 71:0.68 74:0.41 85:0.72 86:0.72 91:0.49 98:0.12 101:0.87 108:0.64 122:0.57 123:0.61 127:0.08 129:0.05 135:0.70 139:0.70 144:0.85 145:0.93 146:0.17 147:0.22 149:0.52 154:0.69 159:0.09 163:0.97 172:0.16 173:0.90 177:0.70 178:0.55 179:0.09 187:0.68 202:0.36 212:0.95 216:0.51 222:0.76 235:0.42 241:0.05 242:0.82 243:0.69 247:0.12 253:0.32 259:0.21 265:0.13 266:0.12 267:0.65 271:0.65 276:0.19 300:0.08 +1 11:0.64 12:0.20 17:0.82 18:0.82 21:0.78 22:0.93 27:0.72 29:0.62 30:0.71 34:0.92 36:0.86 39:0.54 43:0.71 45:0.81 47:0.93 66:0.24 69:0.79 70:0.40 71:0.68 74:0.32 83:0.60 86:0.84 91:0.17 97:0.89 98:0.54 101:0.52 104:0.79 108:0.31 117:0.86 122:0.80 123:0.60 124:0.57 127:0.43 129:0.05 133:0.43 135:0.63 139:0.84 145:0.99 146:0.44 147:0.30 149:0.68 154:0.60 155:0.58 158:0.72 159:0.30 172:0.32 173:0.92 177:0.38 178:0.55 179:0.81 187:0.39 192:0.51 208:0.54 212:0.61 216:0.15 219:0.92 222:0.76 232:0.85 235:0.68 241:0.18 242:0.33 243:0.58 244:0.61 245:0.59 247:0.55 253:0.27 257:0.65 259:0.21 262:0.93 265:0.24 266:0.38 267:0.19 271:0.65 276:0.31 279:0.82 292:0.95 300:0.33 +1 1:0.69 8:0.83 9:0.76 11:0.64 12:0.20 17:0.47 18:0.93 21:0.30 22:0.93 27:0.21 29:0.62 30:0.73 31:0.95 34:0.49 36:0.70 37:0.74 39:0.54 43:0.72 45:0.94 47:0.93 64:0.77 66:0.71 69:0.76 70:0.54 71:0.68 74:0.36 79:0.96 81:0.43 83:0.60 86:0.95 91:0.17 96:0.87 97:0.89 98:0.69 99:0.83 101:0.52 108:0.59 114:0.60 117:0.86 122:0.80 123:0.57 124:0.46 126:0.44 127:0.44 129:0.05 133:0.66 135:0.21 137:0.95 139:0.95 140:0.45 146:0.88 147:0.31 149:0.87 150:0.56 151:0.91 153:0.84 154:0.50 155:0.58 158:0.72 159:0.67 162:0.73 165:0.70 172:0.80 173:0.66 176:0.55 177:0.38 179:0.36 187:0.39 190:0.77 192:0.51 196:0.97 201:0.68 202:0.64 204:0.85 208:0.54 212:0.59 216:0.95 219:0.92 222:0.76 232:0.85 235:0.42 241:0.50 242:0.54 243:0.19 245:0.77 247:0.67 248:0.83 253:0.59 254:0.95 255:0.74 256:0.64 257:0.65 259:0.21 262:0.93 265:0.70 266:0.63 267:0.36 268:0.68 271:0.65 276:0.72 279:0.82 281:0.91 284:0.94 285:0.56 290:0.60 292:0.95 300:0.70 +0 12:0.20 17:0.32 18:0.70 21:0.78 27:0.45 29:0.62 30:0.45 34:0.71 36:0.17 37:0.50 39:0.54 43:0.11 55:0.59 66:0.49 69:0.77 70:0.25 71:0.68 74:0.27 86:0.71 91:0.22 98:0.16 108:0.60 122:0.80 123:0.58 124:0.48 127:0.13 129:0.05 133:0.39 135:0.44 139:0.68 145:0.42 146:0.31 147:0.38 149:0.08 154:0.46 159:0.29 163:0.79 172:0.16 173:0.55 175:0.75 177:0.38 178:0.55 179:0.57 187:0.68 212:0.61 216:0.77 222:0.76 235:0.80 241:0.61 242:0.63 243:0.60 244:0.61 245:0.39 247:0.30 253:0.24 254:0.95 257:0.65 259:0.21 265:0.17 266:0.17 267:0.36 271:0.65 276:0.16 277:0.69 300:0.18 +0 8:0.85 12:0.20 17:0.62 18:0.56 21:0.78 27:0.72 29:0.62 30:0.76 34:0.24 36:0.61 39:0.54 43:0.06 55:1.00 66:0.13 69:0.98 70:0.15 71:0.68 74:0.24 86:0.57 91:0.27 98:0.05 108:0.96 122:0.57 123:0.96 124:0.75 127:0.40 129:0.81 133:0.67 135:0.92 139:0.56 145:0.91 146:0.05 147:0.05 149:0.05 154:0.06 159:0.99 163:1.00 172:0.05 173:0.66 175:0.91 177:0.05 178:0.55 179:0.99 202:0.64 212:0.95 216:0.02 222:0.76 235:0.42 241:0.78 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.20 257:0.84 259:0.21 265:0.05 266:0.12 267:0.28 271:0.65 276:0.19 277:0.87 300:0.13 +2 1:0.69 9:0.76 11:0.64 12:0.20 17:0.30 18:0.75 21:0.54 27:0.66 29:0.62 30:0.62 31:0.96 34:0.94 36:0.77 37:0.53 39:0.54 43:0.72 45:0.73 64:0.77 66:0.47 69:0.19 70:0.34 71:0.68 74:0.30 79:0.96 81:0.35 83:0.60 86:0.78 91:0.60 96:0.88 97:0.94 98:0.23 99:0.83 101:0.52 108:0.15 114:0.57 122:0.80 123:0.15 124:0.52 126:0.44 127:0.46 129:0.05 132:0.97 133:0.39 135:0.77 137:0.96 139:0.77 140:0.45 146:0.22 147:0.28 149:0.54 150:0.52 151:0.84 153:0.72 154:0.62 155:0.58 158:0.71 159:0.24 162:0.86 165:0.70 172:0.21 173:0.45 175:0.75 176:0.55 177:0.38 179:0.93 187:0.39 190:0.77 192:0.51 196:0.93 201:0.68 202:0.58 208:0.54 212:0.61 216:0.21 219:0.89 222:0.76 232:0.89 235:0.68 241:0.61 242:0.33 243:0.59 244:0.61 245:0.46 247:0.39 248:0.84 253:0.59 255:0.74 256:0.64 257:0.65 265:0.25 266:0.23 267:0.23 268:0.66 271:0.65 276:0.15 277:0.69 279:0.82 284:0.93 285:0.56 290:0.57 292:0.91 300:0.25 +2 7:0.63 8:0.71 11:0.64 12:0.20 17:0.61 18:0.79 21:0.78 27:0.70 29:0.62 30:0.60 34:0.36 36:1.00 37:0.40 39:0.54 43:0.65 45:0.88 66:0.27 69:0.42 70:0.65 71:0.68 74:0.31 83:0.60 86:0.82 91:0.15 97:0.89 98:0.39 101:0.52 104:0.78 108:0.86 122:0.57 123:0.85 124:0.84 127:0.51 129:0.81 133:0.82 135:0.99 139:0.82 146:0.40 147:0.46 149:0.62 154:0.22 155:0.58 158:0.72 159:0.77 163:0.81 172:0.45 173:0.23 177:0.70 178:0.55 179:0.56 187:0.39 192:0.51 208:0.54 212:0.30 216:0.32 219:0.92 222:0.76 230:0.63 232:0.87 233:0.73 241:0.18 242:0.33 243:0.22 245:0.67 247:0.60 253:0.26 254:0.97 259:0.21 265:0.41 266:0.84 267:0.87 271:0.65 276:0.60 279:0.82 292:0.95 300:0.55 +0 1:0.69 7:0.63 12:0.20 17:0.86 18:0.68 21:0.71 27:0.66 29:0.62 30:0.21 32:0.69 34:0.62 36:0.93 37:0.90 39:0.54 43:0.14 55:0.52 66:0.60 69:0.62 70:0.85 71:0.68 74:0.40 76:0.97 77:0.89 81:0.29 86:0.74 91:0.40 98:0.66 104:0.58 106:0.81 108:0.47 114:0.53 123:0.46 124:0.58 126:0.44 127:0.67 129:0.05 133:0.59 135:0.96 139:0.70 144:0.85 145:0.74 146:0.75 147:0.79 149:0.29 150:0.47 154:0.60 155:0.58 159:0.57 172:0.67 173:0.60 176:0.55 177:0.70 178:0.55 179:0.55 187:0.39 192:0.59 195:0.75 201:0.68 206:0.81 208:0.54 212:0.52 215:0.37 216:0.28 222:0.76 230:0.63 233:0.73 235:0.88 241:0.10 242:0.59 243:0.67 245:0.73 247:0.84 253:0.34 254:0.97 265:0.67 266:0.63 267:0.59 271:0.65 276:0.62 282:0.77 290:0.53 297:0.36 300:0.55 +0 12:0.20 17:0.57 18:0.59 21:0.78 27:0.34 29:0.62 30:0.76 34:0.58 36:0.73 37:0.46 39:0.54 43:0.12 55:0.99 66:0.13 69:0.71 70:0.15 71:0.68 74:0.26 86:0.61 91:0.14 98:0.05 108:0.54 122:0.80 123:0.52 124:0.75 127:0.71 129:0.81 133:0.67 135:0.42 139:0.59 145:0.59 146:0.05 147:0.05 149:0.08 154:0.10 159:0.92 163:0.99 172:0.05 173:0.34 175:0.91 177:0.05 178:0.55 179:0.99 187:0.87 212:0.95 216:0.53 222:0.76 235:0.74 241:0.44 242:0.82 243:0.34 244:0.83 245:0.37 247:0.12 253:0.23 257:0.84 259:0.21 265:0.05 266:0.12 267:0.23 271:0.65 276:0.17 277:0.87 300:0.13 +1 11:0.91 12:0.20 17:0.06 18:0.77 21:0.47 27:0.10 29:0.62 30:0.60 34:0.71 36:0.97 37:0.86 39:0.54 43:0.57 45:0.83 66:0.44 69:0.81 70:0.53 71:0.68 74:0.39 81:0.26 86:0.84 91:0.12 98:0.42 101:0.52 108:0.65 120:0.53 122:0.57 123:0.63 124:0.58 126:0.26 127:0.20 129:0.59 133:0.46 135:0.74 139:0.80 140:0.80 144:0.85 145:0.70 146:0.57 147:0.63 149:0.46 150:0.25 151:0.46 153:0.48 154:0.64 159:0.36 162:0.54 164:0.52 172:0.41 173:0.79 177:0.70 178:0.42 179:0.46 187:0.87 190:0.89 202:0.56 212:0.30 215:0.41 216:0.81 220:0.97 222:0.76 235:0.52 241:0.43 242:0.33 243:0.57 245:0.68 247:0.60 248:0.46 253:0.31 254:0.74 256:0.45 259:0.21 260:0.53 265:0.44 266:0.47 267:0.73 268:0.46 271:0.65 276:0.46 285:0.47 297:0.36 300:0.43 +0 8:0.86 12:0.20 17:0.77 18:0.57 21:0.78 27:0.72 29:0.62 30:0.62 34:0.50 36:0.68 37:0.77 39:0.54 43:0.06 55:0.96 66:0.07 69:0.97 70:0.23 71:0.68 74:0.25 86:0.59 91:0.93 98:0.05 108:0.94 122:0.55 123:0.94 124:0.89 127:0.24 129:0.95 133:0.87 135:0.61 139:0.57 145:0.82 146:0.05 147:0.05 149:0.06 154:0.06 159:0.82 163:0.99 172:0.05 173:0.91 175:0.91 177:0.05 178:0.55 179:0.84 202:0.75 212:0.61 216:0.06 222:0.76 235:0.60 241:0.81 242:0.33 243:0.23 244:0.83 245:0.39 247:0.39 253:0.23 257:0.84 259:0.21 265:0.06 266:0.23 267:0.87 271:0.65 276:0.29 277:0.87 300:0.18 +1 1:0.74 6:0.95 7:0.81 8:0.93 9:0.80 11:0.75 12:0.51 17:0.09 18:0.96 20:0.95 21:0.68 26:0.95 27:0.12 28:0.80 29:0.56 30:0.15 31:0.98 34:0.25 36:0.70 37:0.88 39:0.80 41:0.98 43:0.37 45:0.94 46:0.97 58:1.00 64:0.77 66:0.17 69:0.54 70:0.90 71:0.74 74:0.57 78:0.80 79:0.99 81:0.44 83:0.91 86:0.81 91:0.92 96:0.96 98:0.61 100:0.83 101:0.52 104:0.58 107:1.00 108:0.90 110:0.98 111:0.80 114:0.56 120:0.92 121:0.90 122:0.85 123:0.78 124:0.82 125:0.97 126:0.54 127:0.90 129:0.81 131:0.94 133:0.84 135:0.21 137:0.98 139:0.84 140:0.90 141:0.97 144:0.57 146:0.82 147:0.85 149:0.61 150:0.70 151:0.87 152:0.88 153:0.88 154:0.78 155:0.67 157:0.61 159:0.84 160:1.00 161:0.54 162:0.60 164:0.91 165:0.93 166:0.90 167:0.94 169:0.81 172:0.85 173:0.30 176:0.73 177:0.70 179:0.23 181:0.91 182:0.99 186:0.81 189:0.96 191:0.94 192:0.87 196:0.98 199:1.00 201:0.93 202:0.95 204:0.89 208:0.64 212:0.52 215:0.37 216:0.41 220:0.74 222:0.25 223:0.94 224:0.98 227:0.94 229:0.99 230:0.86 231:0.87 233:0.73 234:0.97 235:0.88 238:0.91 241:0.82 242:0.14 243:0.28 245:0.93 246:0.97 247:0.95 248:0.92 253:0.92 254:0.84 255:0.95 256:0.96 259:0.21 260:0.88 261:0.85 265:0.54 266:0.80 267:0.94 268:0.96 271:0.80 274:1.00 276:0.90 281:0.89 283:0.78 284:0.99 285:0.62 287:0.98 289:0.97 290:0.56 297:0.36 300:0.70 +1 1:0.74 11:0.92 12:0.51 17:0.38 18:0.93 21:0.54 26:0.95 27:0.45 28:0.80 29:0.56 30:0.53 34:0.80 36:0.21 37:0.50 39:0.80 43:0.82 45:0.92 46:0.97 58:0.93 64:0.77 66:0.20 69:0.70 70:0.86 71:0.74 74:0.63 81:0.54 83:0.91 85:0.90 86:0.89 91:0.12 98:0.51 99:0.83 101:0.97 107:1.00 108:0.79 110:0.98 114:0.59 122:0.57 123:0.77 124:0.85 125:0.88 126:0.54 127:0.57 129:0.89 131:0.61 133:0.84 135:0.74 139:0.85 141:0.91 144:0.57 145:0.47 146:0.74 147:0.78 149:0.85 150:0.73 154:0.77 155:0.67 157:0.98 159:0.79 160:0.88 161:0.54 165:1.00 166:0.91 167:0.73 172:0.73 173:0.51 176:0.73 177:0.70 178:0.55 179:0.24 181:0.91 182:0.97 187:0.68 192:0.87 199:0.97 201:0.93 208:0.64 212:0.67 215:0.39 216:0.77 222:0.25 223:0.94 224:0.93 227:0.61 229:0.61 231:0.86 234:0.91 235:0.84 241:0.59 242:0.15 243:0.40 245:0.91 246:0.97 247:0.92 253:0.44 259:0.21 265:0.53 266:0.87 267:0.79 271:0.80 274:0.91 276:0.86 287:0.61 289:0.96 290:0.59 297:0.36 300:0.84 +2 1:0.74 6:0.93 7:0.81 8:0.86 9:0.80 11:0.85 12:0.51 17:0.12 18:0.99 20:0.75 21:0.05 25:0.88 26:0.95 27:0.16 28:0.80 29:0.56 30:0.42 31:1.00 32:0.80 34:0.30 36:0.98 37:0.53 39:0.80 41:0.99 43:0.69 44:0.93 45:0.98 46:0.99 58:1.00 60:0.87 64:0.77 66:0.91 69:0.53 70:0.60 71:0.74 74:0.76 77:0.89 78:0.93 79:1.00 81:0.84 83:0.91 85:0.72 86:0.83 91:0.82 96:0.99 97:1.00 98:0.96 99:0.83 100:0.96 101:0.97 104:0.58 107:1.00 108:0.41 110:0.98 111:0.92 114:0.75 120:0.97 121:0.90 122:0.85 123:0.39 124:0.37 125:0.97 126:0.54 127:0.90 129:0.05 131:0.94 133:0.37 135:0.85 137:1.00 138:0.96 139:0.93 140:0.87 141:0.97 144:0.57 145:0.69 146:0.99 147:0.99 149:0.94 150:0.89 151:0.98 152:0.89 153:0.95 154:0.54 155:0.67 157:0.81 158:0.92 159:0.85 160:1.00 161:0.54 162:0.75 164:0.96 165:1.00 166:0.91 167:0.82 169:0.90 172:0.98 173:0.29 176:0.66 177:0.70 179:0.14 181:0.91 182:0.99 186:0.87 187:0.68 189:0.99 191:0.96 192:0.87 195:0.94 196:1.00 199:1.00 201:0.93 202:0.96 204:0.89 208:0.64 212:0.82 215:0.61 216:0.90 219:0.95 220:0.62 222:0.25 223:0.94 224:0.98 227:0.94 229:0.99 230:0.86 231:0.87 232:0.91 233:0.73 234:0.98 235:0.52 238:0.90 241:0.72 242:0.31 243:0.92 245:0.95 246:0.97 247:0.91 248:0.98 253:0.98 255:0.95 256:0.98 259:0.21 260:0.95 261:0.92 265:0.96 266:0.80 267:0.88 268:0.97 271:0.80 274:1.00 276:0.96 279:0.86 281:0.89 282:0.88 283:0.82 284:1.00 285:0.62 287:0.98 289:0.97 290:0.75 292:0.95 297:0.36 299:0.88 300:0.55 +1 1:0.74 7:0.63 11:0.92 12:0.51 17:0.66 18:0.96 21:0.54 26:0.95 27:0.45 28:0.80 29:0.56 30:0.62 34:0.67 36:0.63 37:0.50 39:0.80 43:0.82 45:0.96 46:0.99 58:0.93 60:0.87 64:0.77 66:0.35 69:0.62 70:0.62 71:0.74 74:0.73 81:0.48 83:0.91 85:0.88 86:0.92 91:0.04 98:0.65 101:0.97 107:1.00 108:0.81 110:0.98 114:0.59 122:0.85 123:0.80 124:0.71 125:0.88 126:0.54 127:0.61 129:0.81 131:0.61 133:0.67 135:0.88 139:0.92 141:0.91 144:0.57 145:0.50 146:0.84 147:0.87 149:0.92 150:0.72 154:0.77 155:0.67 157:0.97 158:0.91 159:0.78 160:0.88 161:0.54 165:0.93 166:0.91 167:0.62 172:0.86 173:0.43 176:0.73 177:0.70 178:0.55 179:0.20 181:0.91 182:0.97 187:0.68 192:0.87 199:0.98 201:0.93 208:0.64 212:0.55 215:0.39 216:0.77 219:0.95 222:0.25 223:0.94 224:0.93 227:0.61 229:0.61 230:0.63 231:0.86 233:0.76 234:0.91 235:0.74 241:0.18 242:0.30 243:0.40 245:0.96 246:0.97 247:0.90 253:0.45 259:0.21 265:0.66 266:0.54 267:0.19 271:0.80 274:0.91 276:0.90 279:0.86 283:0.78 287:0.61 289:0.97 290:0.59 292:0.91 297:0.36 300:0.70 +3 1:0.74 6:0.92 8:0.87 9:0.80 11:0.85 12:0.51 17:0.55 18:0.96 20:0.97 21:0.47 26:0.95 27:0.12 28:0.80 29:0.56 30:0.87 31:0.99 32:0.80 34:0.96 36:0.35 37:0.78 39:0.80 41:0.99 43:0.77 44:0.93 45:0.90 46:0.99 58:1.00 60:0.99 64:0.77 66:0.45 69:0.80 70:0.44 71:0.74 74:0.89 77:0.70 78:0.78 79:0.99 81:0.51 83:0.91 85:0.97 86:0.97 91:0.78 96:0.97 97:0.89 98:0.42 100:0.81 101:0.97 107:1.00 108:0.84 110:0.98 111:0.77 114:0.60 120:0.95 122:0.57 123:0.83 124:0.76 125:0.98 126:0.54 127:0.40 129:0.89 131:0.94 133:0.78 135:0.23 137:0.99 139:0.98 140:0.45 141:0.97 144:0.57 145:0.56 146:0.44 147:0.50 149:0.97 150:0.73 151:0.99 152:0.90 153:0.97 154:0.69 155:0.67 157:0.99 158:0.92 159:0.68 160:1.00 161:0.54 162:0.77 164:0.92 165:0.93 166:0.91 167:0.77 169:0.79 172:0.87 173:0.70 176:0.73 177:0.70 179:0.18 181:0.91 182:0.99 186:0.78 187:0.68 189:0.93 191:0.93 192:0.87 195:0.87 196:1.00 199:1.00 201:0.93 202:0.90 208:0.64 212:0.85 215:0.41 216:0.81 219:0.95 222:0.25 223:0.94 224:0.99 227:0.94 229:1.00 231:0.87 234:0.98 235:0.68 238:0.89 241:0.66 242:0.27 243:0.22 245:0.93 246:0.97 247:0.86 248:0.96 253:0.98 255:0.95 256:0.93 259:0.21 260:0.93 261:0.83 265:0.44 266:0.71 267:0.84 268:0.93 271:0.80 274:1.00 276:0.88 279:0.86 282:0.88 283:0.82 284:0.99 285:0.62 287:0.98 289:0.97 290:0.60 292:0.95 297:0.36 299:0.88 300:0.84 +3 1:0.69 8:0.86 9:0.80 11:0.64 12:0.51 17:0.03 18:0.96 21:0.30 25:0.88 26:0.95 27:0.09 28:0.80 29:0.56 30:0.74 31:0.93 34:0.53 36:0.86 37:0.84 39:0.80 41:0.92 43:0.70 45:0.96 46:0.99 58:0.99 64:0.77 66:0.34 69:0.46 70:0.53 71:0.74 74:0.37 79:0.97 81:0.45 83:0.91 86:0.69 91:0.51 96:0.90 97:0.94 98:0.56 99:0.83 101:0.52 104:0.54 107:1.00 108:0.79 110:0.98 114:0.60 120:0.65 122:0.85 123:0.77 124:0.69 125:0.99 126:0.44 127:0.47 129:0.81 131:0.94 133:0.67 135:0.47 137:0.93 139:0.67 140:0.93 141:0.97 144:0.57 146:0.26 147:0.32 149:0.92 150:0.56 151:0.69 153:0.81 154:0.59 155:0.67 157:0.61 158:0.72 159:0.57 160:0.99 161:0.54 162:0.79 164:0.74 165:0.70 166:0.84 167:0.87 172:0.74 173:0.39 175:0.75 176:0.55 177:0.38 178:0.42 179:0.29 181:0.91 182:0.99 187:0.68 190:0.77 191:0.89 192:0.87 196:0.90 199:0.96 201:0.68 202:0.76 208:0.64 212:0.77 216:0.67 219:0.87 220:0.62 222:0.25 223:0.94 224:0.98 227:0.94 229:0.99 231:0.87 232:0.75 234:0.97 235:0.42 241:0.75 242:0.61 243:0.19 244:0.61 245:0.90 246:0.61 247:0.47 248:0.69 253:0.73 255:0.95 256:0.79 257:0.65 259:0.21 260:0.65 265:0.57 266:0.54 267:0.18 268:0.80 271:0.80 274:0.98 276:0.79 277:0.69 279:0.82 284:0.96 285:0.62 287:0.98 289:0.97 290:0.60 292:0.95 300:0.55 +3 1:0.74 6:0.95 7:0.81 8:0.89 9:0.80 11:0.85 12:0.51 17:0.40 18:0.94 20:0.97 21:0.54 26:0.95 27:0.11 28:0.80 29:0.56 30:0.75 31:0.99 32:0.80 34:0.52 36:0.88 37:0.80 39:0.80 41:0.98 43:0.89 44:0.93 45:0.95 46:0.98 58:1.00 60:0.97 64:0.77 66:0.43 69:0.50 70:0.63 71:0.74 74:0.83 77:0.70 78:0.80 79:1.00 81:0.48 83:0.91 85:0.93 86:0.91 91:0.92 96:0.98 97:0.97 98:0.50 100:0.85 101:0.97 107:1.00 108:0.81 110:0.98 111:0.81 114:0.59 120:0.95 121:0.99 122:0.85 123:0.80 124:0.91 125:0.98 126:0.54 127:0.86 129:0.95 131:0.94 133:0.93 135:0.39 137:0.99 139:0.95 140:0.45 141:0.97 144:0.57 145:0.56 146:0.17 147:0.22 149:0.92 150:0.72 151:0.99 152:0.89 153:0.97 154:0.88 155:0.67 157:0.98 158:0.92 159:0.87 160:1.00 161:0.54 162:0.76 164:0.93 165:0.93 166:0.91 167:0.77 169:0.83 172:0.85 173:0.24 176:0.73 177:0.70 179:0.24 181:0.91 182:0.99 186:0.81 187:0.68 189:0.96 191:0.92 192:0.87 195:0.95 196:1.00 199:1.00 201:0.93 202:0.92 204:0.89 208:0.64 212:0.39 215:0.39 216:0.86 219:0.95 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 230:0.87 231:0.87 233:0.76 234:0.98 235:0.74 238:0.94 241:0.68 242:0.38 243:0.10 245:0.87 246:0.97 247:0.76 248:0.97 253:0.98 255:0.95 256:0.94 259:0.21 260:0.94 261:0.86 265:0.52 266:0.84 267:0.76 268:0.94 271:0.80 274:1.00 276:0.88 279:0.86 281:0.91 282:0.88 283:0.82 284:0.99 285:0.62 287:0.98 289:0.97 290:0.59 292:0.95 297:0.36 299:0.88 300:0.84 +4 1:0.74 6:0.98 8:0.95 9:0.80 11:0.64 12:0.51 17:0.04 18:0.98 20:0.85 21:0.59 25:0.88 26:0.95 27:0.09 28:0.80 29:0.56 30:0.74 31:1.00 34:0.63 36:0.81 37:0.84 39:0.80 41:1.00 43:0.70 45:0.98 46:0.99 58:1.00 60:0.97 64:0.77 66:0.23 69:0.92 70:0.59 71:0.74 74:0.53 78:0.93 79:1.00 81:0.46 83:0.91 86:0.71 91:0.95 96:1.00 97:0.98 98:0.59 100:0.99 101:0.52 104:0.54 107:1.00 108:0.91 110:0.98 111:0.98 114:0.58 120:0.99 122:0.85 123:0.82 124:0.79 125:0.97 126:0.54 127:0.71 129:0.05 131:0.94 133:0.77 135:0.60 137:1.00 138:1.00 139:0.79 140:0.45 141:0.97 144:0.57 146:0.91 147:0.30 149:0.95 150:0.71 151:1.00 152:0.81 153:0.99 154:0.59 155:0.67 157:0.61 158:0.92 159:0.84 160:1.00 161:0.54 162:0.65 164:0.98 165:0.93 166:0.91 167:0.97 169:1.00 172:0.87 173:0.83 176:0.73 177:0.38 179:0.18 181:0.91 182:0.99 186:0.93 187:0.39 189:0.99 191:0.98 192:0.87 196:1.00 199:1.00 201:0.93 202:0.99 208:0.64 212:0.72 215:0.39 216:0.67 219:0.95 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 231:0.87 232:0.75 234:0.97 235:0.80 238:0.99 241:0.92 242:0.60 243:0.11 244:0.61 245:0.96 246:0.97 247:0.67 248:1.00 253:0.99 255:0.95 256:0.99 257:0.65 259:0.21 260:0.99 261:0.95 265:0.56 266:0.71 267:0.65 268:0.99 271:0.80 274:1.00 276:0.92 279:0.86 283:0.82 284:1.00 285:0.62 287:0.98 289:0.97 290:0.58 292:0.95 297:0.36 300:0.84 +3 1:0.74 6:0.97 7:0.81 8:0.88 9:0.80 11:0.92 12:0.51 17:0.49 18:0.99 20:0.97 21:0.30 22:0.93 26:0.95 27:0.21 28:0.80 29:0.56 30:0.68 31:1.00 34:0.34 36:0.74 37:0.74 39:0.80 41:0.99 43:0.97 45:1.00 46:0.99 47:0.93 58:1.00 60:0.95 64:0.77 66:0.09 69:0.15 70:0.66 71:0.74 74:0.88 78:0.77 79:1.00 81:0.59 83:0.91 85:0.99 86:0.96 91:0.80 96:0.99 97:0.97 98:0.39 100:0.86 101:0.97 104:0.84 106:0.81 107:1.00 108:0.98 110:0.98 111:0.80 114:0.65 117:0.86 120:0.96 121:0.90 122:0.80 123:0.98 124:0.99 125:0.98 126:0.54 127:0.97 129:1.00 131:0.94 133:0.99 135:0.18 137:1.00 139:0.97 140:0.45 141:0.97 144:0.57 146:0.54 147:0.60 149:0.98 150:0.77 151:1.00 152:0.90 153:0.98 154:0.97 155:0.67 157:0.99 158:0.92 159:0.98 160:1.00 161:0.54 162:0.70 164:0.93 165:0.93 166:0.91 167:0.77 169:0.84 172:0.97 173:0.05 176:0.73 177:0.70 179:0.09 181:0.91 182:1.00 186:0.78 187:0.39 189:0.97 191:0.76 192:0.87 196:1.00 199:1.00 201:0.93 202:0.92 204:0.89 206:0.81 208:0.64 212:0.57 215:0.44 216:0.95 219:0.95 222:0.25 223:0.94 224:0.99 227:0.94 229:1.00 230:0.87 231:0.87 232:0.90 233:0.76 234:0.98 235:0.52 238:0.97 241:0.66 242:0.33 243:0.07 245:0.99 246:0.97 247:0.96 248:0.98 253:0.99 255:0.95 256:0.94 259:0.21 260:0.96 261:0.85 262:0.93 265:0.41 266:0.95 267:0.59 268:0.94 271:0.80 274:1.00 276:1.00 279:0.86 281:0.91 283:0.82 284:0.99 285:0.62 287:0.98 289:0.97 290:0.65 292:0.95 297:0.36 300:0.94 +4 6:0.98 8:0.97 9:0.80 12:0.51 17:0.01 20:0.90 21:0.78 26:0.95 27:0.04 28:0.80 29:0.56 31:1.00 34:0.77 36:0.26 37:0.96 39:0.80 41:1.00 46:0.88 58:1.00 60:0.87 71:0.74 74:0.47 78:0.93 79:1.00 83:0.91 91:0.99 96:1.00 97:0.89 100:0.99 107:0.88 110:0.88 111:0.99 120:0.98 125:0.98 131:0.94 135:0.86 137:1.00 138:1.00 139:0.74 140:0.45 141:0.97 144:0.57 151:1.00 152:0.84 153:0.99 155:0.67 157:0.61 158:0.92 160:1.00 161:0.54 162:0.65 164:0.97 166:0.61 167:0.97 169:1.00 175:0.97 181:0.91 182:0.99 186:0.93 189:0.99 191:0.97 192:0.87 196:0.99 199:1.00 202:0.98 208:0.64 216:0.60 219:0.95 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 231:0.87 232:0.72 234:0.97 238:0.99 241:0.95 244:0.93 246:0.61 248:0.99 253:0.99 255:0.95 256:0.98 257:0.93 259:0.21 260:0.98 261:0.97 267:0.91 268:0.98 271:0.80 274:1.00 277:0.95 279:0.86 283:0.82 284:1.00 285:0.62 287:0.98 289:0.97 292:0.95 +4 1:0.74 6:0.94 8:0.93 9:0.80 11:0.85 12:0.51 17:0.15 18:0.99 20:0.77 21:0.59 26:0.95 27:0.55 28:0.80 29:0.56 30:0.75 31:0.99 34:0.76 36:0.83 37:0.84 39:0.80 41:0.97 43:0.70 45:0.99 46:1.00 58:1.00 64:0.77 66:0.44 69:0.90 70:0.59 71:0.74 74:0.52 78:0.84 79:0.99 81:0.46 83:0.91 85:0.72 86:0.82 91:0.73 96:0.97 98:0.75 100:0.90 101:0.97 107:1.00 108:0.79 110:0.98 111:0.85 114:0.58 120:0.93 122:0.85 123:0.78 124:0.77 125:0.98 126:0.54 127:0.73 129:0.59 131:0.94 133:0.77 135:0.54 137:0.99 139:0.78 140:0.45 141:0.97 144:0.57 146:0.97 147:0.32 149:0.97 150:0.71 151:0.99 152:0.75 153:0.95 154:0.55 155:0.67 157:0.81 159:0.86 160:1.00 161:0.54 162:0.80 164:0.89 165:0.93 166:0.91 167:0.87 169:0.88 172:0.94 173:0.76 176:0.73 177:0.38 179:0.15 181:0.91 182:0.99 186:0.83 187:0.95 189:0.95 191:0.82 192:0.87 196:0.97 199:0.99 201:0.93 202:0.88 208:0.64 212:0.70 215:0.39 216:0.47 222:0.25 223:0.94 224:0.98 227:0.94 229:1.00 231:0.87 232:0.70 234:0.98 235:0.80 238:0.95 241:0.75 242:0.60 243:0.09 245:0.98 246:0.97 247:0.67 248:0.95 253:0.94 255:0.95 256:0.91 257:0.65 259:0.21 260:0.92 261:0.78 265:0.75 266:0.71 267:0.73 268:0.92 271:0.80 274:1.00 276:0.95 284:0.99 285:0.62 287:0.98 289:0.97 290:0.58 297:0.36 300:0.84 +3 1:0.74 7:0.81 11:0.85 12:0.51 17:0.53 18:0.82 21:0.30 26:0.95 27:0.04 28:0.80 29:0.56 30:0.75 32:0.80 34:0.71 36:0.36 37:0.99 39:0.80 43:0.59 44:0.93 45:0.88 46:0.96 58:0.93 64:0.77 66:0.54 69:0.93 70:0.67 71:0.74 74:0.70 77:0.70 81:0.59 83:0.91 85:0.93 86:0.82 91:0.11 98:0.47 101:0.97 107:0.99 108:0.94 110:0.98 114:0.65 121:0.99 122:0.57 123:0.94 124:0.76 125:0.88 126:0.54 127:0.36 129:0.81 131:0.61 133:0.82 135:0.81 139:0.89 141:0.91 144:0.57 145:0.49 146:0.54 147:0.60 149:0.75 150:0.77 154:0.49 155:0.67 157:0.98 159:0.83 160:0.88 161:0.54 165:0.93 166:0.91 167:0.65 172:0.81 173:0.81 176:0.73 177:0.70 178:0.55 179:0.26 181:0.91 182:0.97 187:0.87 192:0.87 195:0.96 199:0.98 201:0.93 204:0.89 208:0.64 212:0.59 215:0.44 216:0.57 222:0.25 223:0.94 224:0.93 227:0.61 229:0.61 230:0.87 231:0.86 233:0.76 234:0.91 235:0.52 241:0.32 242:0.24 243:0.28 245:0.82 246:0.97 247:0.82 253:0.50 259:0.21 265:0.49 266:0.91 267:0.52 271:0.80 274:0.91 276:0.78 281:0.91 282:0.88 287:0.61 289:0.96 290:0.65 297:0.36 299:0.88 300:0.84 +2 1:0.74 9:0.80 11:0.64 12:0.69 17:0.96 21:0.05 27:0.72 30:0.38 34:0.50 36:0.81 37:0.28 39:0.31 41:0.82 43:0.82 55:0.42 66:0.83 69:0.23 70:0.58 74:0.75 81:0.95 83:0.79 85:0.72 91:0.85 96:0.78 98:0.82 101:0.76 104:0.57 108:0.18 114:0.95 120:0.66 122:0.43 123:0.18 126:0.54 127:0.31 129:0.05 131:1.00 135:0.30 140:0.45 144:0.57 146:0.48 147:0.55 149:0.50 150:0.97 151:0.79 153:0.69 154:0.86 155:0.67 157:0.87 159:0.17 162:0.86 164:0.62 165:0.90 166:0.99 172:0.51 173:0.57 176:0.73 177:0.38 179:0.72 182:0.99 192:0.59 201:0.74 202:0.46 208:0.64 212:0.95 215:0.91 216:0.17 220:0.62 222:0.76 227:0.61 229:0.99 231:0.99 235:0.12 241:0.85 242:0.45 243:0.84 246:0.61 247:0.47 248:0.72 253:0.82 255:0.79 256:0.45 259:0.21 260:0.67 265:0.82 266:0.12 267:0.19 268:0.55 271:0.26 276:0.41 285:0.62 287:0.61 290:0.95 297:0.36 300:0.43 +3 1:0.74 7:0.81 8:0.66 12:0.69 17:0.85 21:0.13 27:0.72 30:0.76 32:0.78 34:0.49 36:0.78 37:0.26 39:0.31 43:0.33 55:0.59 66:0.72 69:0.62 70:0.15 74:0.62 76:0.94 77:0.98 81:0.90 91:0.88 98:0.76 104:0.58 108:0.47 114:0.92 122:0.41 123:0.45 126:0.54 127:0.25 129:0.05 131:0.61 135:0.39 144:0.57 145:0.97 146:0.43 147:0.50 149:0.25 150:0.92 154:0.60 155:0.67 157:0.61 159:0.16 166:0.99 172:0.27 173:0.89 176:0.73 177:0.38 178:0.55 179:0.90 182:0.61 192:0.59 195:0.55 201:0.74 202:0.50 204:0.89 208:0.64 212:0.78 215:0.84 216:0.04 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 232:0.85 233:0.69 235:0.22 241:0.83 242:0.45 243:0.75 246:0.61 247:0.35 253:0.71 254:0.98 259:0.21 265:0.76 266:0.17 267:0.18 271:0.26 276:0.26 282:0.86 287:0.61 290:0.92 297:0.36 300:0.13 +2 11:0.42 12:0.69 17:0.53 21:0.77 27:0.45 30:0.42 34:0.87 36:0.18 37:0.50 39:0.31 43:0.30 55:0.92 66:0.26 69:0.72 70:0.72 74:0.73 81:0.28 91:0.15 98:0.47 108:0.55 114:0.53 122:0.67 123:0.79 124:0.67 126:0.32 127:0.29 129:0.05 131:0.61 133:0.62 135:0.71 144:0.57 145:0.50 146:0.71 147:0.76 149:0.47 150:0.27 154:0.69 157:0.61 159:0.62 163:0.86 166:0.93 172:0.45 173:0.58 176:0.53 177:0.38 178:0.55 179:0.54 182:0.61 187:0.68 212:0.23 216:0.77 222:0.76 227:0.61 229:0.61 231:0.98 235:0.98 241:0.24 242:0.55 243:0.51 245:0.66 246:0.61 247:0.67 253:0.54 254:0.74 259:0.21 265:0.46 266:0.80 267:0.23 271:0.26 276:0.54 277:0.69 287:0.61 290:0.53 300:0.55 +2 1:0.74 11:0.64 12:0.69 17:0.91 21:0.05 27:0.72 30:0.11 34:0.66 36:0.85 37:0.28 39:0.31 43:0.79 66:0.75 69:0.19 70:0.54 74:0.59 81:0.95 83:0.79 85:0.72 91:0.53 98:0.85 101:0.75 104:0.74 108:0.15 114:0.95 120:0.65 122:0.41 123:0.15 126:0.54 127:0.35 129:0.05 131:0.96 135:0.60 140:0.45 144:0.57 146:0.60 147:0.65 149:0.43 150:0.97 151:0.61 153:0.48 154:0.84 155:0.67 157:0.87 159:0.22 162:0.86 163:0.81 164:0.55 165:0.90 166:0.99 172:0.32 173:0.45 176:0.73 177:0.38 179:0.91 182:0.98 187:0.21 190:0.77 192:0.59 201:0.74 202:0.36 212:0.61 215:0.91 216:0.15 220:0.62 222:0.76 227:0.61 229:0.61 231:0.99 235:0.12 241:0.27 242:0.33 243:0.77 246:0.61 247:0.39 248:0.59 253:0.72 256:0.45 259:0.21 260:0.65 265:0.85 266:0.23 267:0.28 268:0.46 271:0.26 276:0.26 285:0.50 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 7:0.76 8:0.61 11:0.42 12:0.69 17:0.66 21:0.13 27:0.72 30:0.95 32:0.78 34:0.81 36:0.72 37:0.26 39:0.31 43:0.86 55:0.52 66:0.54 69:0.10 74:0.64 76:0.94 77:0.98 81:0.90 91:0.81 98:0.46 104:0.58 108:0.09 114:0.92 123:0.09 126:0.54 127:0.14 129:0.05 131:0.61 135:0.21 144:0.57 145:0.89 146:0.19 147:0.25 149:0.49 150:0.92 154:0.83 155:0.67 157:0.61 159:0.10 166:0.99 172:0.10 173:0.79 176:0.73 177:0.38 178:0.55 179:0.93 182:0.61 187:0.21 192:0.59 195:0.53 201:0.74 215:0.84 216:0.04 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.85 233:0.69 235:0.22 241:0.76 243:0.63 246:0.61 253:0.71 254:0.86 259:0.21 265:0.48 267:0.19 271:0.26 282:0.86 287:0.61 290:0.92 297:0.36 300:0.08 +3 1:0.74 7:0.81 8:0.86 9:0.80 11:0.42 12:0.69 17:0.64 21:0.64 27:0.58 30:0.62 32:0.78 34:0.66 36:0.91 37:0.47 39:0.31 43:0.36 55:0.45 66:0.83 69:0.25 70:0.69 74:0.71 76:0.94 77:0.70 81:0.65 91:0.81 96:0.71 98:0.97 104:0.58 108:0.20 114:0.72 120:0.83 123:0.19 126:0.54 127:0.77 129:0.05 131:1.00 135:0.47 140:0.92 144:0.57 145:0.88 146:0.67 147:0.72 149:0.24 150:0.70 151:0.54 153:0.89 154:0.84 155:0.67 157:0.61 159:0.26 162:0.77 164:0.83 166:0.99 172:0.51 173:0.52 176:0.73 177:0.38 179:0.84 182:0.61 190:0.77 191:0.83 192:0.59 195:0.57 201:0.74 202:0.75 204:0.89 208:0.64 212:0.57 215:0.53 216:0.28 220:0.74 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 233:0.69 235:0.84 241:0.89 242:0.60 243:0.84 246:0.61 247:0.39 248:0.55 253:0.65 254:0.80 255:0.79 256:0.78 259:0.21 260:0.83 265:0.97 266:0.27 267:0.52 268:0.80 271:0.26 276:0.38 282:0.86 283:0.71 285:0.62 287:0.61 290:0.72 297:0.36 300:0.55 +1 1:0.74 7:0.76 11:0.42 12:0.69 17:0.51 21:0.47 27:0.58 30:0.76 32:0.78 34:0.73 36:0.69 37:0.47 39:0.31 43:0.34 55:0.45 66:0.72 69:0.49 70:0.22 74:0.64 76:0.85 77:0.70 81:0.78 91:0.49 98:0.63 104:0.58 108:0.38 114:0.80 122:0.41 123:0.36 126:0.54 127:0.18 129:0.05 131:0.61 135:0.54 144:0.57 145:0.88 146:0.31 147:0.37 149:0.13 150:0.80 154:0.83 155:0.67 157:0.61 159:0.13 166:0.99 172:0.27 173:0.84 176:0.73 177:0.38 178:0.55 179:0.79 182:0.61 187:0.21 192:0.59 195:0.53 201:0.74 212:0.78 215:0.63 216:0.28 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.68 241:0.44 242:0.45 243:0.75 246:0.61 247:0.35 253:0.64 254:0.80 259:0.21 265:0.63 266:0.17 267:0.23 271:0.26 276:0.19 282:0.86 287:0.61 290:0.80 297:0.36 300:0.18 +2 1:0.74 7:0.81 9:0.80 11:0.42 12:0.69 17:0.53 21:0.64 27:0.58 30:0.21 32:0.78 34:0.76 36:0.91 37:0.47 39:0.31 43:0.44 55:0.45 66:0.80 69:0.30 70:0.67 74:0.65 76:0.94 77:0.70 81:0.65 91:0.58 96:0.73 98:0.86 104:0.58 108:0.24 114:0.72 120:0.67 123:0.23 126:0.54 127:0.38 129:0.05 131:1.00 135:0.51 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.33 150:0.70 151:0.62 153:0.48 154:0.75 155:0.67 157:0.61 159:0.19 162:0.86 164:0.66 166:0.99 172:0.45 173:0.61 176:0.73 177:0.38 179:0.82 182:0.61 187:0.21 190:0.77 192:0.59 195:0.55 201:0.74 202:0.50 204:0.89 208:0.64 212:0.71 215:0.53 216:0.28 220:0.82 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 233:0.69 235:0.84 241:0.53 242:0.72 243:0.82 246:0.61 247:0.39 248:0.60 253:0.66 254:0.80 255:0.79 256:0.58 259:0.21 260:0.67 265:0.86 266:0.27 267:0.59 268:0.59 271:0.26 276:0.36 282:0.86 285:0.62 287:0.61 290:0.72 297:0.36 300:0.43 +2 7:0.76 8:0.73 9:0.80 11:0.64 12:0.69 17:0.79 21:0.71 27:0.26 30:0.33 32:0.78 34:0.89 36:0.96 37:0.94 39:0.31 41:0.82 43:0.84 55:0.45 66:0.20 69:0.65 70:0.49 74:0.68 77:0.98 81:0.38 83:0.72 85:0.72 91:0.68 96:0.71 98:0.44 101:0.73 104:0.76 108:0.49 120:0.58 123:0.74 124:0.56 126:0.32 127:0.55 129:0.05 131:1.00 133:0.39 135:0.24 140:0.80 144:0.57 145:1.00 146:0.41 147:0.48 149:0.56 150:0.34 151:0.58 153:0.48 154:0.80 155:0.67 157:0.86 159:0.42 162:0.62 164:0.57 166:0.94 172:0.27 173:0.72 177:0.38 178:0.42 179:0.89 182:0.98 187:0.39 191:0.84 192:0.59 195:0.83 202:0.50 208:0.64 212:0.19 215:0.48 216:0.29 220:0.87 222:0.76 227:0.61 229:0.99 230:0.78 231:0.99 233:0.69 235:0.95 241:0.75 242:0.19 243:0.58 245:0.53 246:0.61 247:0.55 248:0.57 253:0.56 255:0.79 256:0.45 259:0.21 260:0.59 265:0.43 266:0.47 267:0.65 268:0.46 271:0.26 276:0.28 277:0.69 282:0.86 285:0.62 287:0.61 297:1.00 300:0.33 +2 7:0.76 8:0.87 11:0.42 12:0.69 17:0.78 21:0.71 27:0.26 30:0.30 32:0.78 34:0.88 36:0.80 37:0.94 39:0.31 43:0.33 55:0.59 66:0.64 69:0.49 70:0.83 74:0.84 77:0.70 81:0.40 91:0.37 98:0.78 104:0.76 108:0.60 122:0.55 123:0.58 124:0.47 126:0.32 127:0.46 129:0.59 131:0.61 133:0.47 135:0.30 144:0.57 145:0.80 146:0.34 147:0.41 149:0.45 150:0.34 154:0.84 157:0.61 159:0.44 166:0.93 172:0.67 173:0.51 177:0.38 178:0.55 179:0.53 182:0.61 187:0.21 195:0.70 202:0.36 212:0.55 215:0.48 216:0.29 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.88 241:0.39 242:0.24 243:0.31 245:0.77 246:0.61 247:0.67 253:0.60 254:0.74 259:0.21 265:0.78 266:0.54 267:0.44 271:0.26 276:0.62 282:0.86 287:0.61 297:0.36 300:0.55 +3 1:0.74 7:0.76 8:0.61 11:0.42 12:0.69 17:0.66 21:0.13 27:0.72 30:0.95 32:0.78 34:0.81 36:0.72 37:0.26 39:0.31 43:0.86 55:0.52 66:0.54 69:0.10 74:0.64 76:0.94 77:0.98 81:0.90 91:0.81 98:0.46 104:0.58 108:0.09 114:0.92 123:0.09 126:0.54 127:0.14 129:0.05 131:0.61 135:0.21 144:0.57 145:0.89 146:0.19 147:0.25 149:0.49 150:0.92 154:0.83 155:0.67 157:0.61 159:0.10 166:0.99 172:0.10 173:0.79 176:0.73 177:0.38 178:0.55 179:0.93 182:0.61 187:0.21 192:0.59 195:0.53 201:0.74 215:0.84 216:0.04 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.85 233:0.69 235:0.22 241:0.76 243:0.63 246:0.61 253:0.71 254:0.86 259:0.21 265:0.48 267:0.19 271:0.26 282:0.86 287:0.61 290:0.92 297:0.36 300:0.08 +1 1:0.74 7:0.81 8:0.58 9:0.80 11:0.42 12:0.69 17:0.75 21:0.74 27:0.58 30:0.62 32:0.78 34:0.62 36:0.90 37:0.47 39:0.31 43:0.21 55:0.45 66:0.75 69:0.91 70:0.34 74:0.68 76:0.98 77:0.89 81:0.55 91:0.57 96:0.69 98:0.26 104:0.58 108:0.81 114:0.67 120:0.66 123:0.80 126:0.54 127:0.11 129:0.05 131:1.00 135:0.65 140:0.45 144:0.57 145:0.84 146:0.32 147:0.39 149:0.08 150:0.64 151:0.50 153:0.48 154:0.52 155:0.67 157:0.61 158:0.82 159:0.13 162:0.86 164:0.75 166:0.99 172:0.32 173:0.98 176:0.73 177:0.38 179:0.23 182:0.61 187:0.21 190:0.77 192:0.59 195:0.72 201:0.74 202:0.61 204:0.89 208:0.64 212:0.84 215:0.46 216:0.28 220:0.97 222:0.76 227:0.61 229:0.61 230:0.84 231:0.99 233:0.69 235:0.97 241:0.27 242:0.33 243:0.77 246:0.61 247:0.39 248:0.51 253:0.59 254:0.80 255:0.79 256:0.68 259:0.21 260:0.66 265:0.28 266:0.17 267:0.82 268:0.70 271:0.26 276:0.24 282:0.86 283:0.71 285:0.62 287:0.61 290:0.67 297:0.36 300:0.25 +2 1:0.74 7:0.76 8:0.60 9:0.80 11:0.42 12:0.69 17:0.82 21:0.05 27:0.72 30:0.58 32:0.78 34:0.47 36:0.77 37:0.25 39:0.31 43:0.70 55:0.42 66:0.80 69:0.87 70:0.33 74:0.70 76:0.97 77:0.96 81:0.94 91:0.56 96:0.84 98:0.39 104:0.58 108:0.74 114:0.95 120:0.74 122:0.43 123:0.72 126:0.54 127:0.13 129:0.05 131:1.00 135:0.44 140:0.45 144:0.57 145:0.98 146:0.41 147:0.48 149:0.62 150:0.96 151:0.82 153:0.77 154:0.60 155:0.67 157:0.61 159:0.15 162:0.86 164:0.70 166:0.99 172:0.45 173:0.95 176:0.73 177:0.38 179:0.29 182:0.61 187:0.21 192:0.59 195:0.65 201:0.74 202:0.55 208:0.64 212:0.90 215:0.91 216:0.09 220:0.62 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.83 233:0.69 235:0.12 241:0.39 242:0.40 243:0.82 246:0.61 247:0.55 248:0.81 253:0.85 254:0.74 255:0.79 256:0.58 259:0.21 260:0.75 265:0.41 266:0.17 267:0.44 268:0.64 271:0.26 276:0.34 282:0.86 285:0.62 287:0.61 290:0.95 297:0.36 300:0.25 +3 1:0.74 9:0.80 11:0.64 12:0.69 17:0.96 21:0.05 27:0.72 30:0.38 34:0.50 36:0.81 37:0.28 39:0.31 41:0.82 43:0.82 55:0.42 66:0.83 69:0.23 70:0.58 74:0.75 81:0.95 83:0.79 85:0.72 91:0.85 96:0.78 98:0.82 101:0.76 104:0.57 108:0.18 114:0.95 120:0.66 122:0.43 123:0.18 126:0.54 127:0.31 129:0.05 131:1.00 135:0.30 140:0.45 144:0.57 146:0.48 147:0.55 149:0.50 150:0.97 151:0.79 153:0.69 154:0.86 155:0.67 157:0.87 159:0.17 162:0.86 164:0.62 165:0.90 166:0.99 172:0.51 173:0.57 176:0.73 177:0.38 179:0.72 182:0.99 192:0.59 201:0.74 202:0.46 208:0.64 212:0.95 215:0.91 216:0.17 220:0.62 222:0.76 227:0.61 229:0.99 231:0.99 235:0.12 241:0.85 242:0.45 243:0.84 246:0.61 247:0.47 248:0.72 253:0.82 255:0.79 256:0.45 259:0.21 260:0.67 265:0.82 266:0.12 267:0.19 268:0.55 271:0.26 276:0.41 285:0.62 287:0.61 290:0.95 297:0.36 300:0.43 +3 1:0.74 7:0.76 8:0.63 9:0.80 11:0.42 12:0.69 17:0.81 21:0.05 27:0.72 30:0.33 32:0.78 34:0.52 36:0.73 37:0.25 39:0.31 43:0.80 55:0.42 66:0.79 69:0.54 70:0.49 74:0.77 76:0.97 77:0.96 81:0.94 91:0.80 96:0.83 98:0.71 104:0.58 108:0.42 114:0.95 120:0.71 122:0.41 123:0.40 126:0.54 127:0.22 129:0.05 131:1.00 135:0.28 140:0.80 144:0.57 145:0.98 146:0.41 147:0.48 149:0.64 150:0.96 151:0.77 153:0.48 154:0.76 155:0.67 157:0.61 159:0.16 162:0.81 164:0.67 166:0.99 172:0.41 173:0.81 176:0.73 177:0.38 179:0.72 182:0.61 191:0.75 192:0.59 195:0.53 201:0.74 202:0.62 208:0.64 212:0.42 215:0.91 216:0.09 220:0.87 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 232:0.83 233:0.69 235:0.12 241:0.88 242:0.19 243:0.80 246:0.61 247:0.55 248:0.79 253:0.85 254:0.74 255:0.79 256:0.64 259:0.21 260:0.72 265:0.72 266:0.27 267:0.28 268:0.67 271:0.26 276:0.29 282:0.86 285:0.62 287:0.61 290:0.95 297:0.36 300:0.33 +2 1:0.74 11:0.64 12:0.69 17:0.93 21:0.05 27:0.72 30:0.38 34:0.50 36:0.78 37:0.28 39:0.31 43:0.82 55:0.42 66:0.83 69:0.23 70:0.58 74:0.75 81:0.95 83:0.79 85:0.72 91:0.80 98:0.82 101:0.76 104:0.74 108:0.18 114:0.95 122:0.43 123:0.18 126:0.54 127:0.31 129:0.05 131:0.61 135:0.26 144:0.57 146:0.48 147:0.55 149:0.50 150:0.97 154:0.86 155:0.67 157:0.87 159:0.17 165:0.90 166:0.99 172:0.51 173:0.57 176:0.73 177:0.38 178:0.55 179:0.72 182:0.98 187:0.21 192:0.59 201:0.74 212:0.95 215:0.91 216:0.15 222:0.76 227:0.61 229:0.61 231:0.99 235:0.12 241:0.75 242:0.45 243:0.84 246:0.61 247:0.47 253:0.75 259:0.21 265:0.82 266:0.12 267:0.19 271:0.26 276:0.41 287:0.61 290:0.95 297:0.36 300:0.43 +2 1:0.74 6:0.94 7:0.76 8:0.85 11:0.64 12:0.37 17:0.89 20:0.76 21:0.13 27:0.58 28:0.93 30:0.68 32:0.80 34:0.76 36:0.37 37:0.55 39:0.29 43:0.90 60:0.87 66:0.19 69:0.45 70:0.48 74:0.96 76:0.85 77:0.70 78:0.99 81:0.89 83:0.84 85:0.97 91:0.51 98:0.50 100:0.90 101:0.84 104:0.83 106:0.81 108:0.35 111:0.97 114:0.92 117:0.86 122:0.43 123:0.72 124:0.77 126:0.54 127:0.34 129:0.81 131:0.61 133:0.76 135:0.49 144:0.57 145:0.56 146:0.73 147:0.77 149:0.97 150:0.94 152:0.77 154:0.89 155:0.67 157:0.95 158:0.92 159:0.68 161:0.78 163:0.81 165:0.88 166:0.95 167:0.52 169:0.84 172:0.70 173:0.27 176:0.73 177:0.38 178:0.55 179:0.28 181:0.76 182:0.89 186:0.99 187:0.21 189:0.95 191:0.87 192:0.59 195:0.91 201:0.74 206:0.81 208:0.64 212:0.40 215:0.84 216:0.17 222:0.76 227:0.61 229:0.61 230:0.79 231:0.92 232:0.88 233:0.76 235:0.22 238:0.81 241:0.24 242:0.54 243:0.51 245:0.84 246:0.99 247:0.60 253:0.79 259:0.21 261:0.82 265:0.45 266:0.38 267:0.52 271:0.36 276:0.77 279:0.86 282:0.88 283:0.82 287:0.61 290:0.92 297:0.36 299:0.88 300:0.43 +1 1:0.74 7:0.81 8:0.59 9:0.80 11:0.64 12:0.37 17:0.69 21:0.13 27:0.41 28:0.93 30:0.84 32:0.80 34:0.95 36:0.42 37:0.35 39:0.29 41:0.93 43:0.89 60:0.99 66:0.35 69:0.47 70:0.49 74:0.94 77:0.70 81:0.89 83:0.87 85:0.97 91:0.23 96:0.92 98:0.42 101:0.85 104:0.98 106:0.81 108:0.36 114:0.92 117:0.86 120:0.85 121:0.90 122:0.43 123:0.35 124:0.83 126:0.54 127:0.82 129:0.93 131:0.97 133:0.84 135:0.81 140:0.45 144:0.57 145:0.79 146:0.64 147:0.69 149:0.96 150:0.94 151:0.93 153:0.78 154:0.88 155:0.67 157:0.95 158:0.92 159:0.73 161:0.78 162:0.86 164:0.78 165:0.88 166:0.95 167:0.51 172:0.63 173:0.34 176:0.73 177:0.38 179:0.49 181:0.76 182:0.90 187:0.99 192:0.59 195:0.87 201:0.74 202:0.66 204:0.89 206:0.81 208:0.64 212:0.49 215:0.84 216:0.75 222:0.76 227:0.99 229:0.95 230:0.84 231:0.92 232:0.99 233:0.69 235:0.22 241:0.21 242:0.63 243:0.51 245:0.76 246:0.99 247:0.30 248:0.91 253:0.95 255:0.79 256:0.73 259:0.21 260:0.86 265:0.44 266:0.71 267:0.52 268:0.75 271:0.36 276:0.68 279:0.86 282:0.88 283:0.82 285:0.62 287:0.99 290:0.92 297:0.36 299:0.88 300:0.70 +1 1:0.74 11:0.64 12:0.37 17:0.78 21:0.22 27:0.45 28:0.93 30:0.75 32:0.80 34:0.74 36:0.19 37:0.50 39:0.29 43:0.82 66:0.84 69:0.68 70:0.47 74:0.99 77:0.70 81:0.85 83:0.76 85:0.99 91:0.07 98:0.75 101:0.87 108:0.52 114:0.90 122:0.43 123:0.50 124:0.38 126:0.54 127:0.44 129:0.59 131:0.61 133:0.47 135:0.63 144:0.57 145:0.49 146:0.89 147:0.91 149:0.99 150:0.91 154:0.77 155:0.67 157:0.96 159:0.62 161:0.78 165:0.86 166:0.95 167:0.49 172:0.95 173:0.60 176:0.73 177:0.38 178:0.55 179:0.17 181:0.76 182:0.89 187:0.68 192:0.59 195:0.82 201:0.74 212:0.91 215:0.79 216:0.77 222:0.76 227:0.61 229:0.61 231:0.92 235:0.31 241:0.10 242:0.60 243:0.85 245:0.93 246:0.99 247:0.55 253:0.79 259:0.21 265:0.76 266:0.54 267:0.19 271:0.36 276:0.90 282:0.88 287:0.61 290:0.90 297:0.36 299:0.88 300:0.84 +1 6:0.87 7:0.81 8:0.79 11:0.64 12:0.37 17:0.97 20:0.78 21:0.78 27:0.72 28:0.93 30:0.95 34:0.39 36:0.68 39:0.29 43:0.86 66:0.54 69:0.60 74:0.57 76:0.94 78:0.92 85:0.72 91:0.75 98:0.17 100:0.89 101:0.77 104:0.54 108:0.45 111:0.90 121:0.90 123:0.44 127:0.10 129:0.05 131:0.61 135:0.30 144:0.57 145:0.70 146:0.13 147:0.18 149:0.49 152:0.76 154:0.83 155:0.67 157:0.80 159:0.08 161:0.78 166:0.61 167:0.77 169:0.86 172:0.10 173:0.98 177:0.38 178:0.55 179:0.23 181:0.76 182:0.74 186:0.91 187:0.21 189:0.89 192:0.59 204:0.89 208:0.64 222:0.76 227:0.61 229:0.61 230:0.87 231:0.69 232:0.74 233:0.76 235:0.42 238:0.84 241:0.76 243:0.63 246:0.61 253:0.46 259:0.21 261:0.80 265:0.19 267:0.28 271:0.36 287:0.61 300:0.08 +2 1:0.74 6:0.89 7:0.81 8:0.75 9:0.80 11:0.64 12:0.37 17:0.38 20:0.98 21:0.30 27:0.21 28:0.93 30:0.70 34:0.55 36:0.88 37:0.74 39:0.29 41:0.96 43:0.98 60:0.97 66:0.61 69:0.12 70:0.57 74:0.99 78:0.89 81:0.81 83:0.84 85:0.99 91:0.60 96:0.95 98:0.81 100:0.93 101:0.86 104:0.84 106:0.81 108:0.70 111:0.90 114:0.86 117:0.86 120:0.90 121:0.90 122:0.57 123:0.68 124:0.66 126:0.54 127:0.64 129:0.89 131:0.97 133:0.78 135:0.18 140:0.45 144:0.57 146:0.48 147:0.54 149:0.99 150:0.87 151:0.98 152:0.98 153:0.91 154:0.98 155:0.67 157:0.96 158:0.92 159:0.84 161:0.78 162:0.65 164:0.85 165:0.84 166:0.95 167:0.50 169:0.89 172:0.94 173:0.09 176:0.73 177:0.38 179:0.17 181:0.76 182:0.90 186:0.89 187:0.39 189:0.92 191:0.70 192:0.59 201:0.74 202:0.81 204:0.89 206:0.81 208:0.64 212:0.58 215:0.72 216:0.95 222:0.76 227:0.99 229:0.95 230:0.87 231:0.92 232:0.89 233:0.76 235:0.42 238:0.91 241:0.18 242:0.58 243:0.21 245:0.95 246:0.99 247:0.60 248:0.94 253:0.97 255:0.79 256:0.83 259:0.21 260:0.90 261:0.92 265:0.81 266:0.54 267:0.85 268:0.83 271:0.36 276:0.93 279:0.86 283:0.82 285:0.62 287:0.99 290:0.86 297:0.36 300:0.70 +2 1:0.74 6:0.89 8:0.69 9:0.80 11:0.64 12:0.37 17:0.78 20:0.79 21:0.05 27:0.19 28:0.93 30:0.62 34:0.19 36:0.31 37:0.64 39:0.29 41:0.82 43:0.79 66:0.61 69:0.75 70:0.23 74:0.62 78:0.91 81:0.93 83:0.79 85:0.80 91:0.85 96:0.74 98:0.66 100:0.73 101:0.78 104:0.58 108:0.59 111:0.88 114:0.95 120:0.62 122:0.57 123:0.56 124:0.43 126:0.54 127:0.37 129:0.05 131:0.97 133:0.39 135:0.73 140:0.45 144:0.57 145:0.44 146:0.55 147:0.05 149:0.63 150:0.97 151:0.69 152:0.98 153:0.48 154:0.72 155:0.67 157:0.84 159:0.29 161:0.78 162:0.86 163:0.93 164:0.57 165:0.90 166:0.95 167:0.60 169:0.95 172:0.27 173:0.88 176:0.73 177:0.05 179:0.91 181:0.76 182:0.89 186:0.91 187:0.21 191:0.82 192:0.59 201:0.74 202:0.36 208:0.64 212:0.61 215:0.91 216:0.17 220:0.74 222:0.76 227:0.99 229:0.94 231:0.92 232:0.77 235:0.12 241:0.54 242:0.63 243:0.23 245:0.42 246:0.99 247:0.35 248:0.65 253:0.78 255:0.79 256:0.45 257:0.65 259:0.21 260:0.63 261:0.97 265:0.67 266:0.23 267:0.23 268:0.46 271:0.36 276:0.27 285:0.62 287:0.99 290:0.95 297:0.36 300:0.18 +2 1:0.74 6:0.82 8:0.61 9:0.80 11:0.64 12:0.37 17:0.49 20:0.98 27:0.53 28:0.93 30:0.40 34:0.24 36:0.90 37:0.37 39:0.29 41:0.88 43:0.98 60:0.87 66:0.60 69:0.05 70:0.93 74:0.92 78:0.93 81:0.97 83:0.87 85:0.96 91:0.68 96:0.86 98:0.65 100:0.85 101:0.82 104:0.81 108:0.72 111:0.90 114:0.98 120:0.77 122:0.57 123:0.70 124:0.74 126:0.54 127:0.91 129:0.89 131:0.97 133:0.83 135:0.89 140:0.45 144:0.57 146:0.19 147:0.25 149:0.94 150:0.99 151:0.94 152:0.90 153:0.80 154:0.98 155:0.67 157:0.94 158:0.87 159:0.82 161:0.78 162:0.64 164:0.70 165:0.92 166:0.95 167:0.54 169:0.83 172:0.81 173:0.08 176:0.73 177:0.38 179:0.38 181:0.76 182:0.90 186:0.92 187:0.21 189:0.84 192:0.59 201:0.74 202:0.63 208:0.64 212:0.51 215:0.96 216:0.21 222:0.76 227:0.99 229:0.94 231:0.92 232:0.86 235:0.05 238:0.82 241:0.35 242:0.40 243:0.17 245:0.80 246:0.99 247:0.67 248:0.84 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 261:0.92 265:0.66 266:0.87 267:0.52 268:0.64 271:0.36 276:0.77 279:0.86 283:0.71 285:0.62 287:0.99 290:0.98 297:0.36 300:0.94 +2 1:0.74 6:0.93 7:0.81 8:0.79 9:0.80 11:0.64 12:0.37 17:0.95 20:0.78 21:0.13 27:0.58 28:0.93 30:0.66 32:0.80 34:0.67 36:0.29 37:0.72 39:0.29 41:0.92 43:0.91 60:0.87 66:0.93 69:0.44 70:0.47 74:0.96 76:0.94 77:1.00 78:0.98 81:0.93 83:0.87 85:0.96 91:0.72 96:0.75 98:0.82 100:0.89 101:0.86 104:0.87 108:0.34 111:0.95 114:0.72 120:0.63 121:0.99 122:0.57 123:0.33 126:0.54 127:0.31 129:0.05 131:0.97 135:0.42 138:0.95 140:0.45 144:0.57 145:0.85 146:0.79 147:0.82 149:0.96 150:0.97 151:0.69 152:0.76 153:0.48 154:0.90 155:0.67 157:0.95 158:0.92 159:0.35 161:0.78 162:0.86 164:0.67 165:0.91 166:0.95 167:0.55 169:0.87 172:0.80 173:0.48 176:0.56 177:0.38 179:0.35 181:0.76 182:0.90 186:0.97 187:0.21 189:0.94 191:0.75 192:0.59 195:0.65 201:0.74 202:0.50 204:0.89 208:0.64 212:0.80 215:0.79 216:0.14 220:0.74 222:0.76 227:0.99 229:0.95 230:0.84 231:0.92 232:0.90 233:0.69 235:0.60 238:0.82 241:0.41 242:0.55 243:0.93 246:0.99 247:0.47 248:0.69 253:0.81 255:0.79 256:0.58 259:0.21 260:0.64 261:0.84 265:0.82 266:0.27 267:0.82 268:0.59 271:0.36 276:0.69 279:0.86 282:0.88 283:0.82 285:0.62 286:0.99 287:0.99 290:0.72 297:0.36 299:0.99 300:0.43 +2 1:0.74 6:0.93 7:0.81 8:0.89 9:0.80 11:0.64 12:0.37 17:0.85 20:0.98 21:0.05 27:0.19 28:0.93 30:0.72 32:0.80 34:0.33 36:0.63 37:0.64 39:0.29 41:0.96 43:0.94 60:0.87 66:0.84 69:0.21 70:0.48 74:0.91 76:0.85 77:0.96 78:0.97 81:0.93 83:0.89 85:0.90 91:0.88 96:0.93 98:0.91 100:0.98 101:0.85 104:0.58 108:0.17 111:0.97 114:0.95 120:0.88 121:0.97 122:0.57 123:0.16 126:0.54 127:0.51 129:0.05 131:0.97 135:0.39 140:0.45 144:0.57 145:0.79 146:0.41 147:0.48 149:0.88 150:0.97 151:0.93 152:0.98 153:0.78 154:0.94 155:0.67 157:0.92 158:0.87 159:0.15 161:0.78 162:0.72 164:0.85 165:0.90 166:0.95 167:0.83 169:0.95 172:0.54 173:0.68 176:0.73 177:0.38 179:0.78 181:0.76 182:0.90 186:0.96 189:0.96 191:0.86 192:0.59 195:0.54 201:0.74 202:0.78 204:0.89 208:0.64 212:0.87 215:0.91 216:0.17 220:0.62 222:0.76 227:0.99 229:0.95 230:0.87 231:0.92 232:0.77 233:0.76 235:0.12 238:0.95 241:0.83 242:0.55 243:0.85 246:0.99 247:0.39 248:0.93 253:0.95 255:0.79 256:0.81 259:0.21 260:0.89 261:0.97 265:0.91 266:0.23 267:0.19 268:0.81 271:0.36 276:0.41 279:0.86 282:0.88 283:0.71 285:0.62 287:0.99 290:0.95 297:0.36 299:0.97 300:0.43 +2 1:0.74 6:0.93 8:0.64 11:0.64 12:0.37 17:0.79 20:0.93 21:0.05 27:0.19 28:0.93 30:0.33 34:0.96 36:0.20 37:0.64 39:0.29 43:0.98 66:0.68 69:0.07 70:0.88 74:0.88 78:0.89 81:0.93 83:0.79 85:0.91 91:0.40 98:0.78 100:0.89 101:0.82 104:0.58 108:0.06 111:0.88 114:0.95 122:0.57 123:0.06 124:0.44 126:0.54 127:0.66 129:0.59 131:0.61 133:0.47 135:0.83 144:0.57 146:0.81 147:0.84 149:0.88 150:0.97 152:0.98 154:0.98 155:0.67 157:0.92 159:0.51 161:0.78 165:0.90 166:0.95 167:0.50 169:0.95 172:0.67 173:0.15 176:0.73 177:0.38 178:0.55 179:0.60 181:0.76 182:0.89 186:0.89 187:0.39 189:0.85 192:0.59 201:0.74 212:0.52 215:0.91 216:0.17 222:0.76 227:0.61 229:0.61 231:0.92 232:0.77 235:0.12 238:0.85 241:0.15 242:0.24 243:0.72 245:0.74 246:0.99 247:0.60 253:0.78 259:0.21 261:0.97 265:0.78 266:0.71 267:0.52 271:0.36 276:0.60 287:0.61 290:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.83 8:0.65 11:0.64 12:0.37 17:0.38 20:0.81 21:0.22 27:0.45 28:0.93 30:0.67 34:0.62 36:0.19 37:0.50 39:0.29 43:0.82 66:0.35 69:0.68 70:0.70 74:0.76 78:0.83 81:0.85 83:0.76 85:0.94 91:0.17 98:0.53 100:0.81 101:0.82 108:0.52 111:0.81 114:0.90 122:0.43 123:0.50 124:0.69 126:0.54 127:0.42 129:0.81 131:0.61 133:0.67 135:0.70 144:0.57 145:0.47 146:0.70 147:0.74 149:0.87 150:0.91 152:0.80 154:0.77 155:0.67 157:0.93 159:0.57 161:0.78 165:0.86 166:0.95 167:0.51 169:0.82 172:0.48 173:0.63 176:0.73 177:0.38 178:0.55 179:0.57 181:0.76 182:0.89 186:0.84 187:0.68 189:0.84 192:0.59 201:0.74 212:0.57 215:0.79 216:0.77 222:0.76 227:0.61 229:0.61 231:0.92 235:0.31 238:0.83 241:0.21 242:0.60 243:0.51 245:0.71 246:0.99 247:0.47 253:0.72 259:0.21 261:0.85 265:0.55 266:0.71 267:0.28 271:0.36 276:0.54 287:0.61 290:0.90 297:0.36 300:0.70 +1 1:0.74 6:0.87 7:0.66 8:0.85 9:0.80 11:0.64 12:0.37 17:0.88 18:0.96 20:0.77 21:0.59 27:0.42 28:0.51 29:0.62 30:0.86 31:0.95 32:0.78 34:0.17 36:0.50 37:0.92 39:0.29 43:0.30 44:0.93 45:0.88 48:0.91 55:0.45 64:0.77 66:0.99 69:0.44 70:0.44 71:0.95 74:0.98 76:0.94 77:0.89 78:0.88 79:0.96 81:0.47 83:0.60 86:0.97 91:0.80 96:0.90 98:1.00 99:0.83 100:0.85 101:0.52 108:0.34 111:0.86 114:0.58 120:0.59 122:0.77 123:0.32 126:0.54 127:0.95 129:0.05 135:0.66 137:0.95 139:0.97 140:0.80 145:0.74 146:0.99 147:0.99 149:0.80 150:0.67 151:0.57 152:0.76 153:0.82 154:0.50 155:0.67 158:0.74 159:0.80 161:0.64 162:0.81 164:0.75 165:0.70 167:0.84 169:0.83 172:0.99 173:0.25 176:0.73 177:0.70 179:0.13 181:0.49 186:0.88 189:0.89 190:0.77 191:0.94 192:0.87 195:0.90 196:0.98 201:0.93 202:0.79 208:0.64 212:0.91 215:0.39 216:0.13 220:0.62 222:0.21 230:0.69 232:0.72 233:0.76 235:0.98 238:0.88 241:0.82 242:0.70 243:1.00 247:0.97 248:0.58 253:0.92 254:0.80 255:0.95 256:0.83 260:0.60 261:0.80 265:1.00 266:0.54 267:0.91 268:0.84 271:0.53 276:0.97 281:0.91 282:0.86 284:0.96 285:0.62 290:0.58 297:0.36 300:0.70 +2 7:0.72 8:0.79 9:0.76 11:0.83 12:0.37 17:0.89 18:0.98 21:0.13 27:0.44 28:0.51 29:0.62 30:0.43 31:0.96 32:0.69 34:0.18 36:0.43 39:0.29 43:0.95 44:0.90 45:0.92 48:0.91 55:0.45 64:0.77 66:0.99 69:0.23 70:0.56 71:0.95 74:0.85 78:0.83 79:0.97 81:0.75 83:0.60 85:0.88 86:0.99 91:0.67 96:0.74 98:1.00 101:0.87 104:0.75 108:0.18 111:0.83 120:0.83 123:0.18 126:0.44 127:0.96 129:0.05 135:0.30 137:0.96 139:0.99 140:0.45 145:0.95 146:0.95 147:0.96 149:0.96 150:0.54 151:0.84 153:0.72 154:0.95 155:0.67 159:0.63 161:0.64 162:0.86 164:0.80 167:0.83 169:0.85 172:0.98 173:0.23 177:0.70 179:0.14 181:0.49 190:0.77 191:0.84 192:0.87 195:0.77 196:0.99 201:0.68 202:0.74 204:0.85 208:0.64 212:0.94 215:0.61 216:0.10 219:0.92 220:0.82 222:0.21 230:0.75 233:0.76 235:0.42 241:0.80 242:0.51 243:0.99 247:0.94 248:0.85 253:0.60 255:0.79 256:0.81 259:0.21 260:0.80 265:1.00 266:0.47 267:0.89 268:0.81 271:0.53 276:0.96 281:0.91 283:0.82 284:0.96 285:0.62 292:0.95 297:0.36 300:0.55 +2 1:0.74 6:0.87 7:0.72 8:0.75 9:0.80 11:0.64 12:0.37 17:0.85 18:1.00 20:0.76 21:0.22 27:0.39 28:0.51 29:0.62 30:0.57 31:0.99 32:0.77 34:0.17 36:0.42 37:0.55 39:0.29 41:0.95 43:0.69 44:0.93 45:0.93 48:0.99 55:0.45 64:0.77 66:0.99 69:0.23 70:0.53 71:0.95 74:0.97 77:0.70 78:0.90 79:1.00 81:0.75 83:0.91 86:1.00 91:0.88 96:0.98 97:1.00 98:1.00 99:0.83 100:0.85 101:0.52 108:0.18 111:0.88 114:0.71 120:0.80 122:0.86 123:0.18 126:0.54 127:0.95 129:0.05 135:0.56 137:0.99 138:0.98 139:1.00 140:0.87 145:0.63 146:0.92 147:0.94 149:0.86 150:0.83 151:0.86 152:0.75 153:0.97 154:0.88 155:0.67 158:0.79 159:0.55 161:0.64 162:0.83 164:0.84 165:0.70 167:0.83 169:0.87 172:0.97 173:0.27 176:0.73 177:0.70 179:0.17 181:0.49 186:0.87 189:0.87 191:0.95 192:0.87 195:0.72 196:1.00 201:0.93 202:0.88 204:0.85 208:0.64 212:0.94 215:0.51 216:0.27 219:0.92 222:0.21 230:0.75 232:0.77 233:0.76 235:0.80 238:0.90 241:0.80 242:0.45 243:0.99 247:0.95 248:0.88 253:0.99 255:0.95 256:0.91 259:0.21 260:0.81 261:0.77 265:1.00 266:0.27 267:0.98 268:0.92 271:0.53 276:0.94 279:0.82 281:0.91 282:0.85 284:0.99 285:0.62 290:0.71 292:0.95 297:0.36 300:0.55 +1 1:0.74 7:0.72 8:0.92 11:0.64 12:0.37 17:0.98 18:0.97 21:0.30 22:0.93 27:0.36 28:0.51 29:0.62 30:0.58 32:0.69 34:0.28 36:0.75 37:0.73 39:0.29 43:0.57 44:0.90 45:0.66 47:0.93 48:0.98 55:0.52 64:0.77 66:0.98 69:0.25 70:0.52 71:0.95 74:0.87 76:0.94 81:0.67 86:0.98 91:0.56 98:0.99 101:0.52 104:0.91 106:0.81 108:0.20 114:0.65 120:0.72 123:0.19 126:0.54 127:0.91 129:0.05 135:0.54 139:0.98 140:0.45 145:0.42 146:0.89 147:0.91 149:0.55 150:0.76 151:0.62 153:0.69 154:0.89 155:0.67 159:0.48 161:0.64 162:0.55 164:0.53 167:0.53 172:0.94 173:0.32 176:0.73 177:0.70 179:0.25 181:0.49 187:0.21 190:0.89 191:0.85 192:0.87 195:0.69 201:0.93 202:0.58 204:0.85 206:0.81 208:0.64 212:0.92 215:0.44 216:0.18 222:0.21 230:0.75 232:0.76 233:0.76 235:0.68 241:0.27 242:0.33 243:0.98 247:0.94 248:0.60 253:0.52 254:0.84 256:0.45 259:0.21 260:0.67 262:0.93 265:0.99 266:0.27 267:0.73 268:0.55 271:0.53 276:0.88 281:0.91 283:0.82 285:0.47 290:0.65 297:0.36 300:0.55 +1 1:0.74 6:0.94 7:0.72 8:0.81 9:0.76 11:0.64 12:0.37 17:0.94 18:0.98 20:0.76 21:0.30 22:0.93 27:0.36 28:0.51 29:0.62 30:0.36 31:0.92 34:0.18 36:0.51 37:0.73 39:0.29 43:0.59 44:0.90 45:0.73 47:0.93 48:0.91 55:0.42 64:0.77 66:0.97 69:0.23 70:0.68 71:0.95 74:0.90 76:0.85 77:0.70 78:0.88 79:0.92 81:0.61 83:0.60 86:0.99 91:0.72 96:0.79 97:0.89 98:0.97 100:0.88 101:0.52 104:0.97 106:0.81 108:0.18 111:0.87 114:0.65 117:0.86 120:0.72 122:0.86 123:0.18 126:0.54 127:0.76 129:0.05 135:0.54 137:0.92 139:0.99 140:0.80 145:0.47 146:0.88 147:0.90 149:0.48 150:0.72 151:0.76 152:0.75 153:0.69 154:0.89 155:0.67 158:0.79 159:0.47 161:0.64 162:0.86 164:0.53 167:0.54 169:0.88 172:0.91 173:0.31 176:0.73 177:0.70 179:0.30 181:0.49 186:0.88 187:0.68 189:0.94 190:0.77 191:0.70 192:0.87 195:0.68 196:0.97 201:0.93 202:0.50 206:0.81 208:0.64 212:0.88 215:0.44 216:0.18 219:0.92 222:0.21 230:0.75 232:0.76 233:0.76 235:0.60 238:0.91 241:0.32 242:0.39 243:0.97 247:0.94 248:0.70 253:0.81 254:0.74 255:0.74 256:0.58 259:0.21 260:0.67 261:0.77 262:0.93 265:0.97 266:0.54 267:0.99 268:0.59 271:0.53 276:0.84 279:0.82 281:0.91 282:0.77 283:0.82 284:0.86 285:0.62 290:0.65 292:0.95 297:0.36 300:0.55 +0 1:0.74 6:0.94 7:0.72 8:0.88 9:0.80 11:0.64 12:0.37 17:0.78 18:0.98 20:0.76 21:0.30 27:0.36 28:0.51 29:0.62 30:0.43 31:0.99 34:0.18 36:0.73 37:0.73 39:0.29 41:0.82 43:0.55 44:0.90 45:0.66 48:0.91 55:0.45 64:0.77 66:0.97 69:0.23 70:0.77 71:0.95 74:0.90 76:0.85 77:0.70 78:0.87 79:0.99 81:0.61 83:0.91 86:0.99 91:0.85 96:0.96 97:0.99 98:1.00 100:0.88 101:0.52 108:0.18 111:0.86 114:0.65 120:0.80 122:0.86 123:0.18 126:0.54 127:0.95 129:0.05 135:0.58 137:0.99 139:0.99 140:0.80 145:0.47 146:0.91 147:0.92 149:0.48 150:0.72 151:0.72 152:0.75 153:0.93 154:0.89 155:0.67 158:0.87 159:0.51 161:0.64 162:0.84 164:0.79 167:0.82 169:0.87 172:0.93 173:0.29 176:0.73 177:0.70 179:0.27 181:0.49 186:0.87 189:0.92 190:0.77 191:0.95 192:0.87 195:0.70 196:1.00 201:0.93 202:0.84 208:0.64 212:0.89 215:0.44 216:0.18 219:0.95 222:0.21 230:0.75 232:0.76 233:0.76 235:0.60 238:0.91 241:0.79 242:0.50 243:0.97 247:0.90 248:0.75 253:0.97 254:0.84 255:0.95 256:0.88 259:0.21 260:0.78 261:0.77 265:1.00 266:0.38 267:0.97 268:0.89 271:0.53 276:0.87 279:0.86 281:0.91 282:0.77 283:0.82 284:0.99 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.90 7:0.72 8:0.80 11:0.64 12:0.37 17:0.96 18:0.98 20:0.80 21:0.64 27:0.21 28:0.51 29:0.62 30:0.21 32:0.78 34:0.56 36:0.48 37:0.88 39:0.29 43:0.67 44:0.93 45:0.83 48:1.00 55:0.42 64:0.77 66:0.96 69:0.38 70:0.64 71:0.95 74:0.87 76:0.85 77:0.89 78:0.91 81:0.45 83:0.60 86:0.99 91:0.40 98:0.93 99:0.83 100:0.89 101:0.52 104:0.94 106:0.81 108:0.30 111:0.89 114:0.57 120:0.69 123:0.28 126:0.54 127:0.58 129:0.05 132:0.97 135:0.42 139:0.99 140:0.45 145:0.69 146:0.77 147:0.80 149:0.70 150:0.67 151:0.60 152:0.85 153:0.48 154:0.88 155:0.67 159:0.33 161:0.64 162:0.86 164:0.53 165:0.70 167:0.56 169:0.86 172:0.88 173:0.52 176:0.73 177:0.70 179:0.32 181:0.49 186:0.91 187:0.68 189:0.91 190:0.89 191:0.87 192:0.87 195:0.60 201:0.93 202:0.36 204:0.85 206:0.81 208:0.64 212:0.93 215:0.38 216:0.16 222:0.21 230:0.75 232:0.77 233:0.76 235:1.00 238:0.90 241:0.41 242:0.16 243:0.96 247:0.92 248:0.59 253:0.47 254:0.84 256:0.45 260:0.66 261:0.89 265:0.93 266:0.27 267:0.82 268:0.46 271:0.53 276:0.80 281:0.91 282:0.86 283:0.82 285:0.47 290:0.57 297:0.36 300:0.55 +1 1:0.74 6:0.90 7:0.72 8:0.80 9:0.80 11:0.92 12:0.37 17:0.86 18:0.99 20:0.96 21:0.30 27:0.10 28:0.51 29:0.62 30:0.53 31:0.99 34:0.24 36:0.87 37:0.32 39:0.29 41:0.88 43:0.74 44:0.90 45:0.95 48:0.98 55:0.45 64:0.77 66:0.99 69:0.36 70:0.69 71:0.95 74:0.98 76:0.94 77:0.70 78:0.94 79:0.99 81:0.73 83:0.91 85:0.72 86:1.00 91:0.82 96:0.96 97:0.89 98:1.00 99:0.83 100:0.94 101:0.97 108:0.28 111:0.93 114:0.65 120:0.88 123:0.27 126:0.54 127:0.95 129:0.05 135:0.54 137:0.99 139:1.00 140:0.80 145:0.74 146:0.94 147:0.95 149:0.88 150:0.81 151:0.80 152:0.93 153:0.89 154:0.89 155:0.67 158:0.87 159:0.59 161:0.64 162:0.83 164:0.84 165:0.70 167:0.83 169:0.92 172:0.96 173:0.34 176:0.73 177:0.70 179:0.19 181:0.49 186:0.94 189:0.92 191:0.92 192:0.87 195:0.75 196:1.00 201:0.93 202:0.86 204:0.85 208:0.64 212:0.88 215:0.44 216:0.19 219:0.95 220:0.62 222:0.21 230:0.75 232:0.72 233:0.76 235:0.95 238:0.94 241:0.80 242:0.14 243:0.99 247:0.97 248:0.83 253:0.97 255:0.95 256:0.90 259:0.21 260:0.86 261:0.94 265:1.00 266:0.27 267:0.97 268:0.90 271:0.53 276:0.92 279:0.86 281:0.91 282:0.77 283:0.82 284:0.99 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 1:0.69 6:0.85 7:0.66 8:0.69 9:0.80 11:0.64 12:0.37 17:0.83 18:0.98 20:0.80 21:0.54 27:0.39 28:0.51 29:0.62 30:0.13 31:0.98 34:0.17 36:0.73 37:0.84 39:0.29 43:0.63 44:0.90 45:0.87 48:0.91 55:0.45 64:0.77 66:0.97 69:0.35 70:0.74 71:0.95 74:0.87 76:0.94 77:0.70 78:0.87 79:0.98 81:0.33 83:0.60 86:0.98 91:0.88 96:0.87 97:0.89 98:0.99 100:0.86 101:0.52 108:0.27 111:0.86 114:0.58 120:0.84 122:0.86 123:0.26 126:0.44 127:0.93 129:0.05 135:0.24 137:0.98 139:0.98 140:0.95 145:0.59 146:0.93 147:0.94 149:0.67 150:0.48 151:0.68 152:0.77 153:0.88 154:0.55 155:0.67 158:0.79 159:0.56 161:0.64 162:0.80 164:0.73 167:0.84 169:0.83 172:0.93 173:0.35 176:0.66 177:0.70 179:0.27 181:0.49 186:0.87 189:0.87 190:0.77 191:0.88 192:0.87 195:0.72 196:0.99 201:0.68 202:0.74 208:0.64 212:0.93 216:0.12 219:0.92 222:0.21 230:0.69 233:0.76 235:0.74 238:0.86 241:0.82 242:0.23 243:0.97 247:0.93 248:0.71 253:0.88 254:0.80 255:0.95 256:0.79 259:0.21 260:0.82 261:0.81 265:0.99 266:0.38 267:0.91 268:0.80 271:0.53 276:0.87 279:0.82 281:0.89 282:0.77 283:0.82 284:0.97 285:0.62 290:0.58 292:0.95 300:0.55 +0 1:0.74 11:0.83 12:0.37 17:0.40 18:0.91 21:0.59 27:0.45 28:0.51 29:0.62 30:0.21 32:0.80 34:0.74 36:0.22 37:0.50 39:0.29 43:0.81 44:0.93 45:0.73 48:0.91 55:0.84 64:0.77 66:0.33 69:0.61 70:0.95 71:0.95 74:0.62 77:0.70 81:0.48 83:0.91 85:0.72 86:0.89 91:0.14 98:0.55 101:0.97 108:0.74 114:0.58 122:0.86 123:0.45 124:0.60 126:0.54 127:0.36 129:0.59 133:0.46 135:0.75 139:0.91 145:0.49 146:0.66 147:0.71 149:0.71 150:0.71 154:0.76 155:0.67 159:0.47 161:0.64 163:0.81 165:0.93 167:0.52 172:0.51 173:0.58 176:0.73 177:0.70 178:0.55 179:0.51 181:0.49 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.27 215:0.39 216:0.77 222:0.21 235:0.84 241:0.24 242:0.52 243:0.50 245:0.79 247:0.79 253:0.42 259:0.21 265:0.56 266:0.75 267:0.89 271:0.53 276:0.59 281:0.89 282:0.88 290:0.58 297:0.36 299:0.88 300:0.84 +0 1:0.74 7:0.72 8:0.88 9:0.80 11:0.83 12:0.37 17:0.91 18:1.00 21:0.22 22:0.93 27:0.10 28:0.51 29:0.62 30:0.36 31:0.94 32:0.80 34:0.33 36:0.55 37:0.63 39:0.29 41:0.82 43:0.71 44:0.93 45:0.85 47:0.93 48:0.97 55:0.45 64:0.77 66:0.99 69:0.38 70:0.59 71:0.95 74:0.96 76:0.85 77:0.70 79:0.94 81:0.87 83:0.91 85:0.72 86:1.00 91:0.61 96:0.84 97:0.89 98:0.96 99:0.83 101:0.97 104:0.75 106:0.81 108:0.30 114:0.71 117:0.86 120:0.63 122:0.77 123:0.28 126:0.54 127:0.72 129:0.05 135:0.73 137:0.94 139:1.00 140:0.80 145:0.47 146:0.90 147:0.91 149:0.83 150:0.92 151:0.72 153:0.48 154:0.85 155:0.67 158:0.79 159:0.49 161:0.64 162:0.86 164:0.57 165:1.00 167:0.55 172:0.97 173:0.40 176:0.73 177:0.70 179:0.17 181:0.49 187:0.68 190:0.77 191:0.83 192:0.87 195:0.69 196:0.98 201:0.93 202:0.50 204:0.85 206:0.81 208:0.64 212:0.93 215:0.51 216:0.19 219:0.92 222:0.21 230:0.74 232:0.81 233:0.73 235:0.97 241:0.39 242:0.22 243:0.99 247:0.93 248:0.67 253:0.88 255:0.95 256:0.58 259:0.21 260:0.64 262:0.93 265:0.96 266:0.27 267:0.69 268:0.59 271:0.53 276:0.93 279:0.82 281:0.91 282:0.88 284:0.92 285:0.62 290:0.71 292:0.95 297:0.36 300:0.55 +1 1:0.74 6:0.95 7:0.72 8:0.92 9:0.80 11:0.92 12:0.37 17:0.91 18:0.97 20:0.96 21:0.30 22:0.93 27:0.04 28:0.51 29:0.62 30:0.16 31:0.96 32:0.69 34:0.36 36:0.53 37:0.73 39:0.29 43:0.77 44:0.90 45:0.92 47:0.93 48:0.99 55:0.45 64:0.77 66:0.98 69:0.33 70:0.77 71:0.95 74:0.93 77:0.70 78:0.98 79:0.97 81:0.61 83:0.60 85:0.88 86:0.99 91:0.56 96:0.70 98:0.96 100:0.98 101:0.97 104:0.92 106:0.81 108:0.26 111:0.98 114:0.65 120:0.82 123:0.25 126:0.54 127:0.72 129:0.05 135:0.79 137:0.96 138:0.96 139:0.99 140:0.80 145:0.70 146:0.93 147:0.95 149:0.89 150:0.72 151:0.53 152:0.97 153:0.80 154:0.80 155:0.67 159:0.58 161:0.64 162:0.86 164:0.75 167:0.53 169:0.96 172:0.96 173:0.30 176:0.73 177:0.70 179:0.18 181:0.49 186:0.97 187:0.68 189:0.97 190:0.77 191:0.95 192:0.87 195:0.74 196:0.99 201:0.93 202:0.63 204:0.85 206:0.81 208:0.64 212:0.92 215:0.44 216:0.24 219:0.92 222:0.21 230:0.75 232:0.78 233:0.76 235:0.60 238:0.97 241:0.30 242:0.39 243:0.99 247:0.97 248:0.54 253:0.56 255:0.95 256:0.73 259:0.21 260:0.79 261:0.97 262:0.93 265:0.96 266:0.27 267:0.97 268:0.72 271:0.53 276:0.92 281:0.91 282:0.77 283:0.82 284:0.95 285:0.62 290:0.65 292:0.95 297:0.36 300:0.55 +2 1:0.74 7:0.72 11:0.64 12:0.37 17:0.95 18:0.86 22:0.93 27:0.34 28:0.51 29:0.62 30:0.67 32:0.69 34:0.40 36:0.21 39:0.29 43:0.61 44:0.93 45:0.73 47:0.93 48:0.91 55:0.52 64:0.77 66:0.96 69:0.10 70:0.39 71:0.95 74:0.83 76:1.00 77:0.89 81:0.87 83:0.60 86:0.93 91:0.35 98:0.94 99:0.83 101:0.52 104:0.94 106:0.81 108:0.09 114:0.85 117:0.86 123:0.09 126:0.54 127:0.63 129:0.05 132:0.97 135:0.93 139:0.94 145:0.82 146:0.82 147:0.85 149:0.55 150:0.91 154:0.79 155:0.67 159:0.38 161:0.64 165:0.70 167:0.57 172:0.88 173:0.26 176:0.66 177:0.70 178:0.55 179:0.33 181:0.49 187:0.39 191:0.76 192:0.87 195:0.64 201:0.93 204:0.89 206:0.81 208:0.64 212:0.88 215:0.78 216:0.05 222:0.21 230:0.75 232:0.70 233:0.76 235:0.31 241:0.44 242:0.52 243:0.96 247:0.91 253:0.59 254:0.95 259:0.21 262:0.93 265:0.94 266:0.27 267:0.36 271:0.53 276:0.79 281:0.91 282:0.83 283:0.82 290:0.85 297:0.36 300:0.43 +2 1:0.74 7:0.72 8:0.92 11:0.64 12:0.37 17:0.99 18:0.98 21:0.30 22:0.93 27:0.36 28:0.51 29:0.62 30:0.36 32:0.69 34:0.22 36:0.75 37:0.73 39:0.29 43:0.57 44:0.90 45:0.77 47:0.93 48:0.98 55:0.45 64:0.77 66:0.98 69:0.25 70:0.61 71:0.95 74:0.88 76:0.94 81:0.67 86:1.00 91:0.48 98:0.98 101:0.52 104:0.91 106:0.81 108:0.20 114:0.65 120:0.72 122:0.86 123:0.19 126:0.54 127:0.85 129:0.05 135:0.49 139:1.00 140:0.45 145:0.42 146:0.91 147:0.92 149:0.67 150:0.76 151:0.62 153:0.69 154:0.89 155:0.67 159:0.51 161:0.64 162:0.55 164:0.53 167:0.52 172:0.96 173:0.30 176:0.73 177:0.70 179:0.20 181:0.49 187:0.21 190:0.89 191:0.85 192:0.87 195:0.71 201:0.93 202:0.58 204:0.85 206:0.81 208:0.64 212:0.93 215:0.44 216:0.18 222:0.21 230:0.75 232:0.76 233:0.76 235:0.68 241:0.24 242:0.30 243:0.98 247:0.94 248:0.60 253:0.52 254:0.74 256:0.45 259:0.21 260:0.67 262:0.93 265:0.98 266:0.27 267:0.65 268:0.55 271:0.53 276:0.92 281:0.91 283:0.82 285:0.47 290:0.65 297:0.36 300:0.55 +1 6:0.81 7:0.81 8:0.62 9:0.80 11:0.83 12:0.37 17:0.84 18:0.99 20:0.86 22:0.93 27:0.34 28:0.51 29:0.62 30:0.12 31:0.98 32:0.64 34:0.55 36:0.67 37:0.36 39:0.29 41:0.82 43:0.68 45:0.77 47:0.93 48:1.00 55:0.52 64:0.77 66:0.30 69:0.30 70:0.87 71:0.95 74:0.70 77:0.70 78:0.88 79:0.98 81:0.80 83:0.91 85:0.90 86:0.99 91:0.54 96:0.83 98:0.68 100:0.90 101:0.97 104:0.80 106:0.81 108:0.88 111:0.88 120:0.87 121:0.90 123:0.87 124:0.77 126:0.44 127:0.71 129:0.81 133:0.74 135:0.94 137:0.98 139:0.99 140:0.92 145:1.00 146:0.92 147:0.93 149:0.91 150:0.58 151:0.91 152:0.83 153:0.84 154:0.57 155:0.67 159:0.90 161:0.64 162:0.86 164:0.81 167:0.50 169:0.86 172:0.98 173:0.10 177:0.70 179:0.10 181:0.49 186:0.88 187:0.21 189:0.83 191:0.84 192:0.87 195:0.97 196:0.99 201:0.68 202:0.73 204:0.89 206:0.81 208:0.64 212:0.89 215:0.96 216:0.84 219:0.92 220:0.87 222:0.21 230:0.87 233:0.76 235:0.05 238:0.90 241:0.15 242:0.45 243:0.28 245:1.00 247:0.95 248:0.81 253:0.82 255:0.95 256:0.79 259:0.21 260:0.85 261:0.86 262:0.93 265:0.69 266:0.84 267:0.98 268:0.81 271:0.53 276:0.99 281:0.91 282:0.73 283:0.82 284:0.96 285:0.62 292:0.95 297:0.36 300:0.94 +1 1:0.74 6:0.90 7:0.72 8:0.80 9:0.80 11:0.85 12:0.37 17:0.95 18:0.98 20:0.79 21:0.05 22:0.93 27:0.10 28:0.51 29:0.62 30:0.45 31:0.98 34:0.34 36:0.36 37:0.32 39:0.29 41:0.88 43:0.69 44:0.90 45:0.93 47:0.93 48:0.98 55:0.45 64:0.77 66:0.98 69:0.25 70:0.59 71:0.95 74:0.96 76:0.94 77:0.70 78:0.86 79:0.97 81:0.86 83:0.91 86:0.99 91:0.56 96:0.87 98:0.97 100:0.86 101:0.87 104:0.94 106:0.81 108:0.20 111:0.85 114:0.89 120:0.86 123:0.19 126:0.54 127:0.73 129:0.05 135:0.39 137:0.98 139:0.99 140:0.80 145:0.74 146:0.88 147:0.90 149:0.87 150:0.89 151:0.86 152:0.93 153:0.80 154:0.89 155:0.67 159:0.46 161:0.64 162:0.86 164:0.76 167:0.60 169:0.92 172:0.96 173:0.32 176:0.73 177:0.70 179:0.20 181:0.49 186:0.86 187:0.68 189:0.88 191:0.78 192:0.87 195:0.68 196:0.99 201:0.93 202:0.65 204:0.85 206:0.81 208:0.64 212:0.93 215:0.78 216:0.19 219:0.92 222:0.21 230:0.75 232:0.72 233:0.76 235:0.42 238:0.88 241:0.52 242:0.23 243:0.98 247:0.97 248:0.86 253:0.92 254:0.74 255:0.95 256:0.83 259:0.21 260:0.85 261:0.94 262:0.93 265:0.97 266:0.27 267:0.94 268:0.74 271:0.53 276:0.91 281:0.91 282:0.77 283:0.82 284:0.95 285:0.62 290:0.89 292:0.95 297:0.36 300:0.55 +1 1:0.69 7:0.66 8:0.82 11:0.64 12:0.37 17:0.96 18:0.95 21:0.40 27:0.47 28:0.51 29:0.62 30:0.56 31:0.93 32:0.78 34:0.44 36:0.81 37:0.84 39:0.29 43:0.57 44:0.93 45:0.73 48:0.91 55:0.45 64:0.77 66:0.97 69:0.32 70:0.66 71:0.95 74:0.85 76:0.94 77:0.70 79:0.93 81:0.54 86:0.97 91:0.64 98:0.97 101:0.52 104:0.86 106:0.81 108:0.25 114:0.62 120:0.74 123:0.24 126:0.54 127:0.73 129:0.05 135:0.54 137:0.93 139:0.98 140:0.80 145:0.61 146:0.88 147:0.90 149:0.60 150:0.60 151:0.81 153:0.48 154:0.68 155:0.58 159:0.46 161:0.64 162:0.86 164:0.63 167:0.55 172:0.92 173:0.37 176:0.73 177:0.70 179:0.27 181:0.49 187:0.21 190:0.77 191:0.70 192:0.59 195:0.68 196:0.97 201:0.74 202:0.50 206:0.81 208:0.64 212:0.92 215:0.42 216:0.13 219:0.92 220:0.62 222:0.21 230:0.69 233:0.76 235:0.74 241:0.39 242:0.28 243:0.97 247:0.92 248:0.73 253:0.50 254:0.90 255:0.74 256:0.58 259:0.21 260:0.69 265:0.97 266:0.27 267:0.36 268:0.59 271:0.53 276:0.86 281:0.89 282:0.86 283:0.82 284:0.87 285:0.56 290:0.62 292:0.95 297:0.36 300:0.55 +0 1:0.69 8:0.58 11:0.64 12:0.37 17:0.40 18:0.94 21:0.13 27:0.45 28:0.51 29:0.62 30:0.17 34:0.69 36:0.22 37:0.50 39:0.29 43:0.64 44:0.90 45:0.77 48:0.91 55:0.84 64:0.77 66:0.47 69:0.59 70:0.89 71:0.95 74:0.59 77:0.70 81:0.62 83:0.60 86:0.91 91:0.18 98:0.70 99:0.83 101:0.52 108:0.45 114:0.75 122:0.86 123:0.43 124:0.58 126:0.44 127:0.43 129:0.05 133:0.43 135:0.92 139:0.91 145:0.50 146:0.83 147:0.86 149:0.58 150:0.61 154:0.61 155:0.58 159:0.58 161:0.64 165:0.70 167:0.51 172:0.63 173:0.51 176:0.66 177:0.70 178:0.55 179:0.46 181:0.49 187:0.68 192:0.51 195:0.79 201:0.68 208:0.54 212:0.30 216:0.77 222:0.21 235:0.31 241:0.18 242:0.60 243:0.59 245:0.84 247:0.84 253:0.54 254:0.98 259:0.21 265:0.70 266:0.75 267:0.44 271:0.53 276:0.67 281:0.89 282:0.77 290:0.75 300:0.70 +1 1:0.74 6:0.87 7:0.66 8:0.73 11:0.64 12:0.37 17:0.95 18:0.91 20:0.77 21:0.59 27:0.42 28:0.51 29:0.62 30:0.88 31:0.93 32:0.78 34:0.20 36:0.46 37:0.92 39:0.29 43:0.30 44:0.93 45:0.85 48:0.91 55:0.45 64:0.77 66:0.99 69:0.47 70:0.39 71:0.95 74:0.96 76:0.94 77:0.89 78:0.88 79:0.93 81:0.49 83:0.60 86:0.96 91:0.33 98:0.97 99:0.83 100:0.86 101:0.52 104:0.91 106:0.81 108:0.36 111:0.86 114:0.58 120:0.69 122:0.77 123:0.35 126:0.54 127:0.74 129:0.05 135:0.44 137:0.93 139:0.96 140:0.45 145:0.74 146:0.95 147:0.96 149:0.77 150:0.68 151:0.81 152:0.76 153:0.48 154:0.50 155:0.67 159:0.62 161:0.64 162:0.86 164:0.54 165:0.70 167:0.52 169:0.83 172:0.98 173:0.40 176:0.73 177:0.70 179:0.15 181:0.49 186:0.88 187:0.68 189:0.88 190:0.77 192:0.87 195:0.78 196:0.96 201:0.93 202:0.36 206:0.81 208:0.64 212:0.91 215:0.39 216:0.13 219:0.92 222:0.21 230:0.69 232:0.72 233:0.76 235:0.99 238:0.87 241:0.21 242:0.73 243:0.99 247:0.93 248:0.73 253:0.49 254:0.80 255:0.74 256:0.45 260:0.68 261:0.80 265:0.97 266:0.38 267:0.73 268:0.46 271:0.53 276:0.95 281:0.91 282:0.86 283:0.82 284:0.87 285:0.56 290:0.58 292:0.95 297:0.36 300:0.70 +1 1:0.74 6:0.82 8:0.64 9:0.80 11:0.92 12:0.37 17:0.95 18:0.99 20:0.80 21:0.30 27:0.53 28:0.51 29:0.62 30:0.53 31:0.96 34:0.18 36:0.36 37:0.67 39:0.29 43:0.70 44:0.90 45:0.98 48:0.98 55:0.52 64:0.77 66:0.99 69:0.43 70:0.57 71:0.95 74:0.99 76:0.85 77:0.70 78:0.92 79:0.96 81:0.76 83:0.91 85:0.80 86:1.00 91:0.73 96:0.89 97:0.94 98:1.00 99:0.99 100:0.90 101:0.97 108:0.33 111:0.90 114:0.65 120:0.59 122:0.80 123:0.32 126:0.54 127:0.95 129:0.05 135:0.60 137:0.96 139:1.00 140:0.45 145:0.49 146:0.97 147:0.98 149:0.97 150:0.85 151:0.56 152:0.77 153:0.86 154:0.67 155:0.67 158:0.79 159:0.73 161:0.64 162:0.84 164:0.78 165:0.93 167:0.81 169:0.87 172:0.98 173:0.31 176:0.73 177:0.70 179:0.14 181:0.49 186:0.92 189:0.84 190:0.77 191:0.87 192:0.87 195:0.84 196:0.98 201:0.93 202:0.75 204:0.85 208:0.64 212:0.86 215:0.44 216:0.08 219:0.92 220:0.91 222:0.21 232:0.77 235:0.98 238:0.88 241:0.78 242:0.23 243:0.99 247:0.94 248:0.58 253:0.91 255:0.95 256:0.78 259:0.21 260:0.59 261:0.83 265:1.00 266:0.38 267:0.97 268:0.81 271:0.53 276:0.96 279:0.82 281:0.89 282:0.77 284:0.97 285:0.62 290:0.65 292:0.95 297:0.36 300:0.70 +1 6:0.90 7:0.66 8:0.90 12:0.37 17:0.08 20:0.88 21:0.30 25:0.88 27:0.05 28:0.51 29:0.62 30:0.91 31:0.95 34:0.31 36:0.82 37:0.92 39:0.29 43:0.18 44:0.87 55:0.59 66:0.69 69:0.33 70:0.13 71:0.95 78:0.88 79:0.96 81:0.65 91:0.97 98:0.86 100:0.92 104:0.58 108:0.26 111:0.89 120:0.99 123:0.25 126:0.26 127:0.38 129:0.05 135:0.21 137:0.95 139:0.64 140:1.00 145:0.89 146:0.45 147:0.51 149:0.15 150:0.47 151:0.81 152:0.83 153:0.99 154:0.12 159:0.16 161:0.64 162:0.67 163:0.81 164:0.97 167:0.85 169:0.90 172:0.21 173:0.69 175:0.91 177:0.05 179:0.97 181:0.49 186:0.88 189:0.91 190:0.77 191:0.98 192:0.51 195:0.64 196:0.89 202:0.97 212:0.61 215:0.44 216:0.51 222:0.21 230:0.69 233:0.76 235:0.42 238:0.92 241:0.88 242:0.82 243:0.73 244:0.83 247:0.12 248:0.76 253:0.20 255:0.74 256:0.98 257:0.84 259:0.21 260:0.98 261:0.88 265:0.86 266:0.17 267:0.93 268:0.98 271:0.53 276:0.20 277:0.87 283:0.82 284:0.96 285:0.56 297:1.00 300:0.13 +3 1:0.74 6:0.90 7:0.72 8:0.84 9:0.80 11:0.83 12:0.37 17:0.90 18:1.00 20:0.77 21:0.22 27:0.10 28:0.51 29:0.62 30:0.40 31:0.99 32:0.69 34:0.17 36:0.47 37:0.63 39:0.29 41:0.95 43:0.71 44:0.90 45:0.81 48:0.97 55:0.45 64:0.77 66:0.99 69:0.35 70:0.62 71:0.95 74:0.91 76:0.85 78:0.90 79:0.99 81:0.84 83:0.91 85:0.72 86:1.00 91:0.89 96:0.94 98:0.99 100:0.86 101:0.97 104:0.75 108:0.27 111:0.90 114:0.71 120:0.94 122:0.86 123:0.26 126:0.54 127:0.89 129:0.05 135:0.71 137:0.99 138:0.96 139:1.00 140:0.80 145:0.52 146:0.89 147:0.91 149:0.78 150:0.90 151:0.86 152:0.75 153:0.95 154:0.85 155:0.67 159:0.47 161:0.64 162:0.82 164:0.88 165:0.93 167:0.83 169:0.90 172:0.96 173:0.40 176:0.73 177:0.70 179:0.19 181:0.49 186:0.86 189:0.92 191:0.95 192:0.87 195:0.66 196:1.00 201:0.93 202:0.83 208:0.64 212:0.93 215:0.51 216:0.19 219:0.92 222:0.21 230:0.74 232:0.81 233:0.73 235:0.84 238:0.92 241:0.81 242:0.21 243:0.99 247:0.96 248:0.92 253:0.96 255:0.95 256:0.87 259:0.21 260:0.93 261:0.78 265:0.99 266:0.27 267:0.93 268:0.88 271:0.53 276:0.92 281:0.89 283:0.82 284:0.99 285:0.62 290:0.71 292:0.95 297:0.36 300:0.55 +1 1:0.74 6:0.95 8:0.86 9:0.80 11:0.92 12:0.37 17:0.89 18:0.97 20:0.93 21:0.30 22:0.93 27:0.04 28:0.51 29:0.62 30:0.28 31:0.97 32:0.80 34:0.59 36:0.82 37:0.73 39:0.29 41:0.88 43:0.80 44:0.93 45:0.89 47:0.93 48:0.98 60:0.87 64:0.77 66:0.32 69:0.37 70:0.74 71:0.95 74:0.94 76:0.85 77:0.89 78:0.93 79:0.96 81:0.66 83:0.91 85:0.91 86:0.99 91:0.38 96:0.88 98:0.68 99:0.83 100:0.94 101:0.97 104:0.95 106:0.81 108:0.71 111:0.92 114:0.61 117:0.86 120:0.80 122:0.80 123:0.28 124:0.60 126:0.54 127:0.61 129:0.59 133:0.46 135:0.87 137:0.97 139:0.99 140:0.45 145:0.91 146:0.67 147:0.72 149:0.94 150:0.79 151:0.93 152:0.97 153:0.77 154:0.82 155:0.67 158:0.92 159:0.44 161:0.64 162:0.86 164:0.69 165:1.00 167:0.52 169:0.96 172:0.82 173:0.42 176:0.66 177:0.70 179:0.27 181:0.49 186:0.93 187:0.68 189:0.94 191:0.91 192:0.87 195:0.66 196:0.99 201:0.93 202:0.54 204:0.85 206:0.81 208:0.64 212:0.89 215:0.42 216:0.24 219:0.95 222:0.21 235:0.84 238:0.94 241:0.24 242:0.19 243:0.49 245:0.95 247:0.96 248:0.87 253:0.92 255:0.95 256:0.58 259:0.21 260:0.81 261:0.97 262:0.93 265:0.60 266:0.27 267:0.84 268:0.63 271:0.53 276:0.84 279:0.86 281:0.91 282:0.88 283:0.82 284:0.93 285:0.62 290:0.61 292:0.95 297:0.36 299:0.88 300:0.70 +1 1:0.74 6:0.79 8:0.58 9:0.80 11:0.57 12:0.37 17:0.32 18:0.82 20:0.77 21:0.05 22:0.93 27:0.30 28:0.51 29:0.62 30:0.38 31:0.95 32:0.80 34:0.36 36:0.88 37:0.31 39:0.29 41:0.82 43:0.77 44:0.93 47:0.93 64:0.77 66:0.64 69:0.80 70:0.63 71:0.95 74:0.62 77:0.89 78:0.83 79:0.94 81:0.86 83:0.91 85:0.80 86:0.87 91:0.56 96:0.83 98:0.26 100:0.80 101:0.87 104:0.90 106:0.81 108:0.64 111:0.81 114:0.89 117:0.86 120:0.72 122:0.57 123:0.62 124:0.44 126:0.54 127:0.17 129:0.05 133:0.37 135:0.58 137:0.95 139:0.89 140:0.45 145:0.91 146:0.33 147:0.24 149:0.71 150:0.91 151:0.90 152:0.75 153:0.69 154:0.69 155:0.67 159:0.23 161:0.64 162:0.86 164:0.62 165:0.93 167:0.50 169:0.79 172:0.45 173:0.83 176:0.73 177:0.05 179:0.42 181:0.49 186:0.83 187:0.39 189:0.81 192:0.87 195:0.71 196:0.96 201:0.93 202:0.46 206:0.81 208:0.64 212:0.78 215:0.78 216:0.89 222:0.21 235:0.31 238:0.83 241:0.15 242:0.29 243:0.26 245:0.55 247:0.55 248:0.80 253:0.82 255:0.95 256:0.45 257:0.84 259:0.21 260:0.73 261:0.76 262:0.93 265:0.28 266:0.27 267:0.95 268:0.55 271:0.53 276:0.30 282:0.88 284:0.90 285:0.62 290:0.89 297:0.36 299:0.88 300:0.43 +2 1:0.74 11:0.64 12:0.37 17:0.45 21:0.54 27:0.28 30:0.62 32:0.80 34:0.56 36:0.23 37:0.58 39:0.39 43:0.79 66:0.36 69:0.77 70:0.28 74:0.78 76:0.85 77:0.89 81:0.70 83:0.74 85:0.88 91:0.29 98:0.50 101:0.79 104:0.93 106:0.81 108:0.60 114:0.77 117:0.86 122:0.43 123:0.58 124:0.60 126:0.54 127:0.27 129:0.59 131:0.61 133:0.47 135:0.94 144:0.57 145:0.65 146:0.61 147:0.66 149:0.75 150:0.80 154:0.72 155:0.67 157:0.96 159:0.42 163:0.81 165:0.79 166:0.97 172:0.32 173:0.77 176:0.73 177:0.38 178:0.55 179:0.68 182:0.96 187:0.39 192:0.59 195:0.74 201:0.74 206:0.81 212:0.57 215:0.58 216:0.90 222:0.76 227:0.61 229:0.61 231:0.96 232:0.95 235:0.74 241:0.18 242:0.45 243:0.51 245:0.62 246:0.88 247:0.47 253:0.68 265:0.52 266:0.23 267:0.44 271:0.38 276:0.41 282:0.88 287:0.61 290:0.77 297:0.36 299:0.88 300:0.25 +2 1:0.74 7:0.78 9:0.80 11:0.42 12:0.37 17:0.69 21:0.22 27:0.59 30:0.20 34:0.66 36:0.28 37:0.86 39:0.39 43:0.94 55:0.52 66:0.63 69:0.25 70:0.94 74:0.80 76:0.98 81:0.80 91:0.46 96:0.75 98:0.57 104:0.84 106:0.81 108:0.70 114:0.69 117:0.86 120:0.63 123:0.68 124:0.51 126:0.54 127:0.36 129:0.59 131:0.99 133:0.64 135:0.18 138:0.95 140:0.45 144:0.57 146:0.29 147:0.35 149:0.77 150:0.82 151:0.77 153:0.48 154:0.94 155:0.67 157:0.61 158:0.84 159:0.46 162:0.86 164:0.66 166:0.97 172:0.65 173:0.26 176:0.56 177:0.38 179:0.50 182:0.61 187:0.21 192:0.59 201:0.74 202:0.50 204:0.89 206:0.81 208:0.64 212:0.81 215:0.72 216:0.49 220:0.82 222:0.76 227:0.92 229:0.61 230:0.81 231:0.96 232:0.82 233:0.69 235:0.31 241:0.12 242:0.51 243:0.27 245:0.67 246:0.61 247:0.60 248:0.73 253:0.77 255:0.79 256:0.58 259:0.21 260:0.64 265:0.58 266:0.54 267:0.94 268:0.59 271:0.38 276:0.58 279:0.86 283:0.82 285:0.62 286:0.99 287:0.61 290:0.69 297:0.36 298:0.99 300:0.84 +0 11:0.64 12:0.37 17:0.32 21:0.78 27:0.45 30:0.41 34:0.81 36:0.18 37:0.50 39:0.39 43:0.75 55:0.45 66:0.49 69:0.78 70:0.84 74:0.78 85:0.72 91:0.06 98:0.27 101:0.70 108:0.61 122:0.67 123:0.59 124:0.65 127:0.47 129:0.05 131:0.61 133:0.62 135:0.66 144:0.57 145:0.52 146:0.48 147:0.54 149:0.52 154:0.66 157:0.81 159:0.71 166:0.61 172:0.45 173:0.66 177:0.38 178:0.55 179:0.72 182:0.74 187:0.68 212:0.54 216:0.77 222:0.76 227:0.61 229:0.61 231:0.85 235:0.52 241:0.21 242:0.77 243:0.60 245:0.59 246:0.61 247:0.39 253:0.57 259:0.21 265:0.30 266:0.63 267:0.79 271:0.38 276:0.40 287:0.61 300:0.70 +1 1:0.74 7:0.76 8:0.91 11:0.64 12:0.37 17:0.62 20:0.93 21:0.40 27:0.15 30:0.11 32:0.77 34:0.73 36:0.35 37:0.83 39:0.39 43:0.80 55:0.45 66:0.61 69:0.40 70:0.72 74:0.80 76:0.85 77:0.70 78:0.72 81:0.71 85:0.72 91:0.53 98:0.50 100:0.73 101:0.72 104:0.96 106:0.81 108:0.31 111:0.72 114:0.82 117:0.86 122:0.43 123:0.30 124:0.64 126:0.54 127:0.69 129:0.05 131:0.61 132:0.97 133:0.74 135:0.99 144:0.57 145:0.50 146:0.59 147:0.65 149:0.69 150:0.76 152:0.93 154:0.85 155:0.67 157:0.82 159:0.55 166:0.97 169:0.72 172:0.54 173:0.37 176:0.73 177:0.38 178:0.55 179:0.72 182:0.75 186:0.73 187:0.87 191:0.73 192:0.59 195:0.72 201:0.74 206:0.81 212:0.61 215:0.67 216:0.62 222:0.76 227:0.61 229:0.61 230:0.79 231:0.96 232:0.91 233:0.76 235:0.52 241:0.01 242:0.51 243:0.68 245:0.55 246:0.61 247:0.47 253:0.71 261:0.73 265:0.52 266:0.63 267:0.28 271:0.38 276:0.51 282:0.85 283:0.82 287:0.61 290:0.82 297:0.36 300:0.43 +2 1:0.74 6:0.97 7:0.76 8:0.93 9:0.80 11:0.42 12:0.37 17:0.38 20:0.95 21:0.05 27:0.02 30:0.91 34:0.44 36:0.21 37:0.92 39:0.39 41:0.92 43:0.82 55:0.71 66:0.69 69:0.67 70:0.22 74:0.82 76:0.85 78:0.92 81:0.90 83:0.80 91:0.70 96:0.97 98:0.58 100:0.98 108:0.87 111:0.97 114:0.74 120:0.94 122:0.67 123:0.86 124:0.46 126:0.54 127:0.52 129:0.59 131:0.99 133:0.64 135:0.81 138:0.96 140:0.80 144:0.57 145:0.89 146:0.13 147:0.18 149:0.70 150:0.92 151:0.94 152:1.00 153:0.86 154:0.78 155:0.67 157:0.61 158:0.84 159:0.74 162:0.77 164:0.90 166:0.98 169:0.96 172:0.70 173:0.52 176:0.56 177:0.38 179:0.53 182:0.96 186:0.92 187:0.39 189:0.98 191:0.80 192:0.59 201:0.74 202:0.85 208:0.64 212:0.69 215:0.84 216:0.99 222:0.76 227:0.92 229:0.98 230:0.79 231:0.96 233:0.76 235:0.12 238:0.98 241:0.74 242:0.63 243:0.15 245:0.67 246:0.61 247:0.67 248:0.98 253:0.97 255:0.79 256:0.88 259:0.21 260:0.94 261:0.97 265:0.59 266:0.75 267:0.65 268:0.89 271:0.38 276:0.59 279:0.86 283:0.71 285:0.62 287:0.90 290:0.74 297:0.36 300:0.25 +2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.37 17:0.57 20:0.92 21:0.30 27:0.34 30:0.09 32:0.72 34:0.62 36:0.35 37:0.36 39:0.39 43:0.68 66:0.36 69:0.55 70:0.95 74:0.98 77:0.70 78:0.72 81:0.77 85:0.85 91:0.42 96:0.71 98:0.64 100:0.73 101:0.73 104:0.80 106:0.81 108:0.43 111:0.72 114:0.86 117:0.86 120:0.58 121:0.90 122:0.57 123:0.41 124:0.68 126:0.54 127:0.39 129:0.81 131:0.99 133:0.67 135:0.97 140:0.45 144:0.57 145:0.96 146:0.94 147:0.95 149:0.86 150:0.80 151:0.58 152:0.86 153:0.48 154:0.76 155:0.67 157:0.91 158:0.85 159:0.80 162:0.86 164:0.57 166:0.97 169:0.72 172:0.87 173:0.30 176:0.73 177:0.38 179:0.16 182:0.81 186:0.73 187:0.21 192:0.59 195:0.95 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.71 215:0.72 216:0.84 220:0.87 222:0.76 227:0.92 229:0.61 230:0.87 231:0.96 232:0.85 233:0.76 235:0.42 241:0.07 242:0.39 243:0.51 245:0.96 246:0.61 247:0.67 248:0.56 253:0.80 255:0.79 256:0.45 259:0.21 260:0.59 261:0.73 265:0.65 266:0.80 267:0.69 268:0.46 271:0.38 276:0.90 279:0.86 282:0.81 283:0.66 285:0.62 287:0.61 290:0.86 297:0.36 300:0.84 +1 11:0.42 12:0.37 17:0.55 21:0.47 27:0.45 30:0.93 34:0.75 36:0.30 37:0.50 39:0.39 43:0.30 55:0.59 66:0.83 69:0.70 70:0.24 74:0.99 77:0.70 81:0.47 91:0.07 98:0.77 108:0.53 114:0.55 122:0.67 123:0.51 124:0.39 126:0.32 127:0.51 129:0.05 131:0.61 133:0.62 135:0.88 144:0.57 145:0.50 146:0.94 147:0.95 149:0.83 150:0.38 154:0.55 157:0.61 158:0.82 159:0.74 166:0.87 172:0.93 173:0.54 176:0.53 177:0.38 178:0.55 179:0.22 182:0.61 187:0.68 195:0.89 212:0.92 216:0.77 222:0.76 227:0.61 229:0.61 231:0.88 235:0.52 241:0.12 242:0.82 243:0.84 245:0.83 246:0.61 247:0.39 253:0.71 254:0.80 259:0.21 265:0.77 266:0.63 267:0.28 271:0.38 276:0.87 282:0.81 287:0.61 290:0.55 300:0.55 +1 1:0.74 6:0.97 7:0.76 8:0.95 9:0.80 11:0.64 12:0.37 17:0.28 20:0.93 21:0.05 27:0.02 30:0.91 34:0.34 36:0.28 37:0.92 39:0.39 41:0.90 43:0.82 55:0.71 66:0.77 69:0.67 70:0.21 74:0.83 76:0.85 78:0.91 81:0.90 83:0.81 85:0.85 91:0.76 96:0.95 98:0.54 100:0.97 101:0.74 108:0.90 111:0.94 114:0.74 120:0.94 122:0.57 123:0.90 124:0.41 126:0.54 127:0.60 129:0.59 131:0.99 133:0.64 135:0.90 138:0.96 140:0.80 144:0.57 145:0.45 146:0.13 147:0.18 149:0.78 150:0.92 151:0.96 152:1.00 153:0.88 154:0.78 155:0.67 157:0.92 158:0.84 159:0.79 162:0.78 164:0.88 166:0.98 169:0.96 172:0.79 173:0.49 176:0.56 177:0.38 179:0.46 182:0.96 186:0.91 187:0.39 192:0.59 201:0.74 202:0.81 208:0.64 212:0.87 215:0.84 216:0.99 222:0.76 227:0.92 229:0.98 230:0.79 231:0.96 233:0.76 235:0.12 241:0.67 242:0.39 243:0.13 245:0.67 246:0.61 247:0.47 248:0.96 253:0.95 255:0.79 256:0.85 259:0.21 260:0.94 261:0.97 265:0.55 266:0.54 267:0.65 268:0.86 271:0.38 276:0.62 279:0.86 283:0.71 285:0.62 287:0.90 290:0.74 297:0.36 300:0.33 +1 1:0.74 7:0.76 8:0.84 11:0.42 12:0.37 17:0.93 20:0.89 21:0.05 27:0.36 30:0.30 32:0.77 34:0.62 36:0.22 37:0.79 39:0.39 43:0.67 55:0.84 66:0.54 69:0.07 70:0.95 74:0.86 76:0.85 77:0.70 78:0.72 81:0.90 91:0.57 98:0.74 100:0.96 104:0.93 106:0.81 108:0.06 111:0.91 114:0.74 117:0.86 120:0.82 122:0.43 123:0.06 124:0.52 126:0.54 127:0.80 129:0.59 131:0.89 133:0.47 135:0.99 140:0.45 144:0.57 145:0.41 146:0.80 147:0.83 149:0.48 150:0.92 151:0.66 152:0.83 153:0.48 154:0.90 155:0.67 157:0.61 159:0.56 162:0.86 164:0.72 166:0.98 169:0.95 172:0.63 173:0.14 176:0.56 177:0.38 179:0.61 182:0.61 186:0.73 187:0.87 190:0.77 192:0.59 195:0.72 201:0.74 202:0.59 206:0.81 212:0.37 215:0.84 216:0.62 220:0.62 222:0.76 227:0.86 229:0.61 230:0.79 231:0.96 232:0.97 233:0.76 235:0.12 241:0.10 242:0.32 243:0.63 245:0.79 246:0.61 247:0.60 248:0.73 253:0.70 256:0.64 259:0.21 260:0.81 261:0.91 265:0.75 266:0.75 267:0.65 268:0.67 271:0.38 276:0.61 282:0.85 283:0.82 285:0.50 287:0.61 290:0.74 297:0.36 300:0.84 +1 1:0.74 7:0.76 8:0.88 9:0.80 11:0.42 12:0.37 17:0.95 21:0.05 27:0.36 30:0.62 32:0.77 34:0.79 36:0.31 37:0.81 39:0.39 43:0.85 55:0.84 66:0.47 69:0.59 70:0.23 74:0.66 76:0.85 77:0.70 81:0.90 91:0.66 96:0.74 98:0.70 104:0.91 106:0.81 108:0.59 114:0.74 117:0.86 120:0.80 123:0.57 124:0.52 126:0.54 127:0.35 129:0.59 131:0.99 133:0.47 135:0.84 138:0.95 140:0.45 144:0.57 145:0.40 146:0.13 147:0.18 149:0.57 150:0.92 151:0.77 153:0.48 154:0.83 155:0.67 157:0.61 159:0.31 162:0.80 163:0.94 164:0.71 166:0.98 172:0.21 173:0.69 176:0.56 177:0.38 179:0.91 182:0.61 187:0.68 190:0.77 192:0.59 195:0.61 201:0.74 202:0.60 206:0.81 208:0.64 212:0.61 215:0.84 216:0.64 220:0.62 222:0.76 227:0.92 229:0.61 230:0.79 231:0.96 232:0.85 233:0.76 235:0.12 241:0.03 242:0.63 243:0.37 245:0.46 246:0.61 247:0.35 248:0.71 253:0.72 255:0.79 256:0.64 259:0.21 260:0.80 265:0.70 266:0.23 267:0.69 268:0.65 271:0.38 276:0.28 282:0.85 283:0.82 285:0.62 286:0.99 287:0.61 290:0.74 297:0.36 298:0.99 300:0.18 +2 1:0.74 8:0.82 9:0.80 11:0.42 12:0.37 17:0.28 21:0.40 27:0.49 30:0.42 32:0.75 34:0.31 36:0.21 37:0.38 39:0.39 41:0.82 43:0.85 60:0.87 66:0.26 69:0.60 70:0.76 74:0.90 77:0.98 81:0.69 83:0.76 91:0.63 96:0.95 98:0.30 104:0.90 106:0.81 108:0.86 114:0.66 117:0.86 120:0.92 122:0.43 123:0.44 124:0.74 126:0.54 127:0.43 129:0.59 131:0.99 133:0.76 135:0.85 138:0.95 140:0.45 144:0.57 145:0.99 146:0.48 147:0.54 149:0.90 150:0.74 151:0.97 153:0.90 154:0.82 155:0.67 157:0.61 158:0.84 159:0.63 162:0.83 164:0.89 166:0.97 172:0.63 173:0.48 176:0.56 177:0.38 179:0.42 182:0.96 187:0.87 192:0.59 195:0.82 201:0.74 202:0.81 206:0.81 208:0.64 212:0.84 215:0.63 216:0.88 222:0.76 227:0.92 229:0.98 231:0.96 232:0.83 235:0.52 241:0.44 242:0.33 243:0.46 245:0.76 246:0.61 247:0.67 248:0.97 253:0.97 255:0.79 256:0.86 260:0.92 265:0.32 266:0.63 267:0.69 268:0.86 271:0.38 276:0.65 279:0.86 282:0.83 283:0.82 285:0.62 286:0.99 287:0.90 290:0.66 297:0.36 300:0.70 +1 8:0.89 12:0.37 17:0.49 21:0.78 27:0.21 30:0.30 34:0.29 36:0.74 37:0.74 39:0.39 43:0.38 55:0.92 66:0.06 69:0.92 70:0.79 74:0.84 91:0.80 96:0.96 98:0.21 108:0.88 120:0.67 122:0.67 123:0.98 124:0.98 127:0.87 129:0.05 131:0.98 133:0.97 135:0.54 140:0.90 146:0.59 147:0.05 149:0.55 151:0.66 153:0.48 154:0.28 157:0.61 159:0.95 162:0.52 163:0.94 164:0.54 166:0.61 172:0.27 173:0.64 177:0.05 178:0.50 179:0.37 182:0.61 187:0.39 190:0.77 191:0.73 202:0.91 212:0.16 216:0.95 220:0.62 222:0.76 227:0.92 229:0.61 231:0.96 235:0.31 241:0.15 242:0.80 243:0.07 244:0.61 245:0.70 246:0.61 247:0.39 248:0.89 253:0.96 256:0.89 257:0.65 259:0.21 260:0.68 265:0.20 266:0.98 267:0.44 268:0.88 271:0.38 276:0.80 277:0.69 283:0.82 285:0.62 287:0.61 300:0.70 +1 1:0.74 11:0.88 12:0.37 17:0.40 21:0.54 27:0.45 28:0.78 30:0.87 32:0.80 34:0.79 36:0.17 37:0.50 39:0.72 43:0.82 66:0.24 69:0.70 70:0.27 74:0.73 77:0.70 81:0.58 83:0.74 85:0.90 91:0.31 98:0.27 101:0.80 108:0.53 114:0.77 122:0.43 123:0.75 124:0.73 126:0.54 127:0.23 129:0.81 133:0.67 135:0.73 144:0.85 145:0.44 146:0.38 147:0.45 149:0.80 150:0.74 154:0.77 155:0.67 159:0.40 161:0.88 163:0.81 165:0.79 167:0.47 172:0.21 173:0.65 176:0.73 177:0.38 178:0.55 179:0.65 181:0.89 187:0.68 192:0.59 195:0.77 201:0.74 212:0.50 215:0.58 216:0.77 222:0.48 235:0.68 241:0.12 242:0.63 243:0.44 245:0.54 247:0.30 253:0.66 259:0.21 265:0.29 266:0.38 267:0.19 271:0.53 276:0.36 282:0.88 290:0.77 297:0.36 299:0.88 300:0.25 +3 1:0.74 6:0.85 8:0.92 9:0.80 11:0.88 12:0.37 17:0.59 20:0.81 21:0.68 28:0.78 30:0.55 32:0.80 34:0.45 36:0.22 37:0.97 39:0.72 41:0.82 43:0.80 60:0.95 66:0.35 69:0.59 70:0.85 74:0.94 77:0.70 78:0.86 81:0.50 83:0.87 85:0.98 91:0.33 96:0.80 98:0.46 100:0.86 101:0.83 104:0.80 108:0.78 111:0.85 114:0.70 120:0.69 122:0.43 123:0.77 124:0.87 126:0.54 127:0.76 129:0.81 133:0.89 135:0.51 140:0.45 144:0.85 145:0.59 146:0.78 147:0.81 149:0.96 150:0.67 151:0.87 152:0.84 153:0.48 154:0.74 155:0.67 158:0.92 159:0.86 161:0.88 162:0.86 164:0.57 165:0.70 167:0.45 169:0.82 172:0.76 173:0.32 176:0.73 177:0.38 179:0.32 181:0.89 186:0.86 187:0.21 192:0.59 195:0.95 201:0.74 202:0.36 208:0.64 212:0.51 215:0.49 216:0.98 222:0.48 232:0.85 235:0.84 242:0.52 243:0.43 245:0.82 247:0.60 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 261:0.84 265:0.48 266:0.89 267:0.73 268:0.46 271:0.53 276:0.81 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.88 300:0.94 +2 1:0.74 11:0.88 12:0.37 17:0.38 21:0.59 27:0.45 28:0.78 30:0.76 34:0.74 36:0.19 37:0.50 39:0.72 43:0.82 66:0.72 69:0.70 70:0.22 74:0.52 81:0.55 83:0.73 85:0.80 91:0.11 98:0.44 101:0.74 108:0.53 114:0.74 122:0.43 123:0.51 126:0.54 127:0.14 129:0.05 135:0.88 144:0.85 145:0.52 146:0.56 147:0.62 149:0.65 150:0.71 154:0.77 155:0.67 159:0.20 161:0.88 163:0.92 165:0.78 167:0.49 172:0.27 173:0.67 176:0.73 177:0.38 178:0.55 179:0.58 181:0.89 187:0.68 192:0.59 201:0.74 212:0.78 215:0.55 216:0.77 222:0.48 235:0.74 241:0.24 242:0.75 243:0.75 247:0.30 253:0.59 259:0.21 265:0.46 266:0.17 267:0.36 271:0.53 276:0.22 290:0.74 297:0.36 300:0.18 +3 1:0.74 9:0.80 11:0.88 12:0.37 17:0.02 21:0.05 27:0.33 28:0.78 30:0.74 34:0.86 36:0.26 37:0.41 39:0.72 41:0.82 43:0.86 60:0.87 66:0.35 69:0.57 70:0.53 74:0.87 77:0.89 81:0.86 83:0.86 85:0.94 91:0.37 96:0.80 98:0.28 101:0.82 108:0.86 114:0.95 120:0.69 122:0.57 123:0.85 124:0.71 126:0.54 127:0.51 129:0.81 133:0.67 135:0.60 140:0.45 144:0.85 145:0.84 146:0.30 147:0.37 149:0.89 150:0.92 151:0.87 153:0.48 154:0.84 155:0.67 158:0.92 159:0.64 161:0.88 162:0.86 164:0.57 165:0.90 167:0.50 172:0.48 173:0.47 176:0.73 177:0.38 179:0.60 181:0.89 187:0.39 192:0.59 195:0.82 201:0.74 202:0.36 208:0.64 212:0.51 215:0.91 216:0.88 222:0.48 235:0.12 241:0.27 242:0.60 243:0.37 245:0.72 247:0.47 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 265:0.31 266:0.75 267:0.36 268:0.46 271:0.53 276:0.46 279:0.86 282:0.81 283:0.82 285:0.62 290:0.95 297:0.36 300:0.55 +0 1:0.74 8:0.60 9:0.80 11:0.88 12:0.37 17:0.12 20:0.92 21:0.22 27:0.64 28:0.78 30:0.32 34:0.34 36:0.55 37:0.45 39:0.72 41:0.99 43:0.93 55:0.92 60:1.00 66:0.06 69:0.32 70:0.86 74:0.66 78:0.83 81:0.76 83:0.90 85:0.80 91:0.76 96:0.98 98:0.16 100:0.78 101:0.68 104:0.79 106:0.81 108:0.25 111:0.82 114:0.90 117:0.86 120:0.96 123:0.97 124:0.96 126:0.54 127:0.95 129:0.05 133:0.96 135:0.30 140:0.45 144:0.85 145:0.41 146:0.22 147:0.27 149:0.73 150:0.85 151:0.99 152:0.92 153:0.95 154:0.93 155:0.67 158:0.92 159:0.94 161:0.88 162:0.65 164:0.95 165:0.86 167:0.65 169:0.79 172:0.21 173:0.08 176:0.73 177:0.38 179:0.69 181:0.89 186:0.79 187:0.21 192:0.59 201:0.74 202:0.92 206:0.81 208:0.64 212:0.14 215:0.79 216:0.87 222:0.48 232:0.85 235:0.31 238:0.83 241:0.66 242:0.78 243:0.29 245:0.52 247:0.39 248:0.98 253:0.98 255:0.79 256:0.94 259:0.21 260:0.96 261:0.81 265:0.09 266:0.89 267:0.97 268:0.93 271:0.53 276:0.56 277:0.69 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.70 +0 1:0.74 6:0.79 7:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.20 20:0.92 21:0.22 27:0.57 28:0.78 30:0.45 32:0.80 34:0.86 36:0.40 37:0.23 39:0.72 41:0.98 43:0.75 60:0.99 66:0.82 69:0.82 70:0.42 74:0.77 77:0.70 78:0.91 81:0.76 83:0.83 85:0.85 91:0.89 96:0.93 98:0.70 100:0.90 101:0.79 104:0.58 108:0.67 111:0.90 114:0.90 120:0.88 121:0.90 122:0.43 123:0.65 126:0.54 127:0.21 129:0.05 135:0.34 140:0.45 144:0.85 145:0.88 146:0.64 147:0.69 149:0.68 150:0.85 151:0.98 152:0.86 153:0.93 154:0.66 155:0.67 158:0.92 159:0.24 161:0.88 162:0.60 164:0.89 165:0.86 167:0.83 169:0.87 172:0.48 173:0.92 176:0.73 177:0.38 179:0.62 181:0.89 186:0.89 189:0.82 191:0.78 192:0.59 195:0.64 201:0.74 202:0.86 204:0.89 208:0.64 212:0.61 215:0.79 216:0.28 222:0.48 230:0.87 232:0.77 233:0.76 235:0.31 238:0.85 241:0.91 242:0.53 243:0.83 247:0.47 248:0.93 253:0.94 255:0.79 256:0.88 259:0.21 260:0.89 261:0.90 265:0.70 266:0.38 267:0.92 268:0.87 271:0.53 276:0.36 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.33 +2 1:0.74 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.06 21:0.40 27:0.64 28:0.78 30:0.95 32:0.80 34:0.47 36:0.87 37:0.70 39:0.72 41:0.98 43:0.96 60:0.95 66:0.54 69:0.21 74:0.74 77:0.70 78:0.96 81:0.66 83:0.90 85:0.72 91:0.88 96:0.97 98:0.23 101:0.77 104:0.58 108:0.17 111:0.94 114:0.82 120:0.94 121:0.90 123:0.16 126:0.54 127:0.11 129:0.05 132:0.97 135:0.42 140:0.45 144:0.85 145:0.61 146:0.13 147:0.18 149:0.53 150:0.79 151:0.93 153:0.78 154:0.96 155:0.67 158:0.87 159:0.08 161:0.88 162:0.76 164:0.92 165:0.83 167:0.83 169:0.91 172:0.10 173:0.84 176:0.73 177:0.38 179:0.42 181:0.89 191:0.75 192:0.59 195:0.53 201:0.74 202:0.86 204:0.89 208:0.64 215:0.67 216:0.19 220:0.87 222:0.48 230:0.87 232:0.77 233:0.76 235:0.52 238:0.86 241:0.90 243:0.63 248:0.97 253:0.97 255:0.79 256:0.89 259:0.21 260:0.94 265:0.25 267:0.69 268:0.89 271:0.53 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.08 +2 1:0.74 6:0.86 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.26 20:0.85 21:0.30 27:0.57 28:0.78 30:0.62 32:0.80 34:0.55 36:0.98 37:0.31 39:0.72 41:0.99 43:0.98 55:0.71 60:0.97 66:0.44 69:0.12 70:0.66 74:0.88 77:0.70 78:0.93 81:0.71 83:0.90 85:0.90 91:0.58 96:0.97 98:0.72 100:0.90 101:0.80 108:0.73 111:0.92 114:0.86 120:0.94 121:0.90 123:0.71 124:0.60 126:0.54 127:0.67 129:0.59 133:0.47 135:0.90 138:0.98 140:0.45 144:0.85 145:0.58 146:0.73 147:0.77 149:0.85 150:0.82 151:0.98 152:0.89 153:0.91 154:0.98 155:0.67 158:0.92 159:0.57 161:0.88 162:0.73 164:0.93 165:0.84 167:0.64 169:0.87 172:0.65 173:0.18 176:0.73 177:0.38 179:0.47 181:0.89 186:0.89 187:0.21 189:0.91 191:0.77 192:0.59 195:0.73 201:0.74 202:0.89 204:0.89 208:0.64 212:0.58 215:0.72 216:0.90 220:0.87 222:0.48 230:0.87 233:0.76 235:0.42 238:0.89 241:0.64 242:0.57 243:0.49 245:0.87 247:0.60 248:0.97 253:0.98 255:0.79 256:0.91 259:0.21 260:0.95 261:0.89 265:0.72 266:0.47 267:0.73 268:0.91 271:0.53 276:0.71 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70 +2 1:0.74 6:0.98 7:0.81 8:0.95 9:0.80 11:0.88 12:0.37 17:0.43 20:0.97 21:0.30 27:0.21 28:0.78 30:0.36 34:0.69 36:0.79 37:0.74 39:0.72 41:0.98 43:0.98 55:0.71 60:0.97 66:0.20 69:0.12 70:0.83 74:0.99 78:0.94 81:0.71 83:0.90 85:0.99 91:0.76 96:0.98 98:0.62 100:0.99 101:0.79 104:0.84 106:0.81 108:0.95 111:0.98 114:0.86 117:0.86 120:0.96 121:0.90 122:0.67 123:0.94 124:0.93 126:0.54 127:0.71 129:0.97 133:0.93 135:0.26 140:0.45 144:0.85 146:0.35 147:0.42 149:0.95 150:0.82 151:0.99 152:0.95 153:0.96 154:0.98 155:0.67 158:0.92 159:0.96 161:0.88 162:0.67 164:0.92 165:0.84 167:0.49 169:0.97 172:0.95 173:0.06 176:0.73 177:0.38 179:0.11 181:0.89 186:0.93 187:0.39 189:0.99 191:0.73 192:0.59 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.48 215:0.72 216:0.95 222:0.48 230:0.87 232:0.89 233:0.76 235:0.42 238:0.98 241:0.24 242:0.70 243:0.11 245:0.98 247:0.60 248:0.98 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.97 265:0.63 266:0.89 267:0.89 268:0.90 271:0.53 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94 +2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.37 17:0.36 20:0.97 21:0.40 27:0.39 28:0.78 30:0.10 34:0.44 36:1.00 37:0.57 39:0.72 41:0.98 43:0.98 55:0.92 60:0.87 66:0.20 69:0.15 70:0.88 74:0.82 78:0.93 81:0.66 83:0.89 85:0.94 91:0.78 96:0.97 98:0.52 100:0.94 101:0.78 108:0.83 111:0.92 114:0.82 120:0.94 123:0.82 124:0.92 126:0.54 127:0.93 129:0.96 133:0.91 135:0.92 140:0.45 144:0.85 146:0.57 147:0.63 149:0.86 150:0.79 151:0.97 152:0.89 153:0.90 154:0.98 155:0.67 158:0.92 159:0.86 161:0.88 162:0.79 164:0.91 165:0.83 167:0.56 169:0.91 172:0.67 173:0.09 176:0.73 177:0.38 179:0.33 181:0.89 186:0.92 187:0.39 189:0.94 192:0.59 201:0.74 202:0.85 208:0.64 212:0.18 215:0.67 216:0.26 220:0.74 222:0.48 232:0.95 235:0.52 238:0.90 241:0.51 242:0.41 243:0.17 245:0.83 247:0.60 248:0.97 253:0.97 255:0.79 256:0.88 259:0.21 260:0.95 261:0.93 265:0.54 266:0.91 267:0.23 268:0.89 271:0.53 276:0.83 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.70 diff --git a/examples/xendcg/rank.train.query b/examples/xendcg/rank.train.query new file mode 100644 index 0000000..769e5aa --- /dev/null +++ b/examples/xendcg/rank.train.query @@ -0,0 +1,201 @@ +1 +13 +5 +8 +19 +12 +18 +5 +14 +13 +8 +9 +16 +11 +21 +14 +21 +9 +14 +11 +20 +18 +13 +20 +22 +22 +13 +17 +10 +13 +12 +13 +13 +23 +18 +13 +20 +12 +22 +14 +13 +23 +13 +14 +14 +5 +13 +15 +14 +14 +16 +16 +15 +21 +22 +10 +22 +18 +25 +16 +12 +12 +15 +15 +25 +13 +9 +12 +8 +16 +25 +19 +24 +12 +16 +10 +16 +9 +17 +15 +7 +9 +15 +14 +16 +17 +8 +17 +12 +18 +23 +10 +12 +12 +4 +14 +12 +15 +27 +16 +20 +13 +19 +13 +17 +17 +16 +12 +15 +14 +14 +19 +12 +23 +18 +16 +9 +23 +11 +15 +8 +10 +10 +16 +11 +15 +22 +16 +17 +23 +16 +22 +17 +14 +12 +14 +20 +15 +17 +15 +15 +22 +9 +21 +9 +17 +16 +15 +13 +13 +15 +14 +18 +21 +14 +17 +15 +14 +16 +12 +17 +19 +16 +11 +18 +11 +13 +14 +9 +16 +15 +16 +25 +9 +13 +22 +16 +18 +20 +14 +11 +9 +16 +19 +19 +11 +11 +13 +14 +14 +13 +16 +6 +21 +16 +12 +16 +11 +24 +12 +10 diff --git a/examples/xendcg/train.conf b/examples/xendcg/train.conf new file mode 100644 index 0000000..1587b9d --- /dev/null +++ b/examples/xendcg/train.conf @@ -0,0 +1,117 @@ +# task type, support train and predict +task = train + +# boosting type, support gbdt for now, alias: boosting, boost +boosting_type = gbdt + +# application type, support following application +# regression , regression task +# binary , binary classification task +# lambdarank , LambdaRank task +# alias: application, app +objective = rank_xendcg + +# eval metrics, support multi metric, delimited by ',' , support following metrics +# l1 +# l2 , default metric for regression +# ndcg , default metric for lambdarank +# auc +# binary_logloss , default metric for binary +# binary_error +metric = ndcg + +# evaluation position for ndcg metric, alias : ndcg_at +ndcg_eval_at = 1,3,5 + +# frequency for metric output +metric_freq = 1 + +# true if need output metric for training data, alias: tranining_metric, train_metric +is_training_metric = true + +# column in data to use as label +label_column = 0 + +# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy. +max_bin = 255 + +# training data +# if existing weight file, should name to "rank.train.weight" +# if existing query file, should name to "rank.train.query" +# alias: train_data, train +data = rank.train + +# validation data, support multi validation data, separated by ',' +# if existing weight file, should name to "rank.test.weight" +# if existing query file, should name to "rank.test.query" +# alias: valid, test, test_data, +valid_data = rank.test + +# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds +num_trees = 100 + +# shrinkage rate , alias: shrinkage_rate +learning_rate = 0.1 + +# number of leaves for one tree, alias: num_leaf +num_leaves = 31 + +# type of tree learner, support following types: +# serial , single machine version +# feature , use feature parallel to train +# data , use data parallel to train +# voting , use voting based parallel to train +# alias: tree +tree_learner = serial + +# Set num_threads and objective_seed for stable unit-tests. Comment out otherwise. +num_threads = 1 +objective_seed = 1025 + +# feature sub-sample, will random select 80% feature to train on each iteration +# alias: sub_feature +feature_fraction = 1.0 + +# Support bagging (data sub-sample), will perform bagging every 5 iterations +bagging_freq = 1 + +# Bagging fraction, will random select 80% data on bagging +# alias: sub_row +bagging_fraction = 0.9 + +# minimal number data for one leaf, use this to deal with over-fit +# alias : min_data_per_leaf, min_data +min_data_in_leaf = 50 + +# minimal sum Hessians for one leaf, use this to deal with over-fit +min_sum_hessian_in_leaf = 5.0 + +# save memory and faster speed for sparse feature, alias: is_sparse +is_enable_sparse = true + +# when data is bigger than memory size, set this to true. otherwise set false will have faster speed +# alias: two_round_loading, two_round +use_two_round_loading = false + +# true if need to save data to binary file and application will auto load data from binary file next time +# alias: is_save_binary, save_binary +is_save_binary_file = false + +# output model file +output_model = LightGBM_model.txt + +# support continuous train from trained gbdt model +# input_model= trained_model.txt + +# output prediction file for predict task +# output_result= prediction.txt + + +# number of machines in distributed training, alias: num_machine +num_machines = 1 + +# local listening port in distributed training, alias: local_port +local_listen_port = 12400 + +# machines list file for distributed training, alias: mlist +machine_list_file = mlist.txt diff --git a/include/LightGBM/application.h b/include/LightGBM/application.h new file mode 100644 index 0000000..518e980 --- /dev/null +++ b/include/LightGBM/application.h @@ -0,0 +1,93 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_ + +#include +#include + +#include +#include + +namespace LightGBM { + +class DatasetLoader; +class Dataset; +class Boosting; +class ObjectiveFunction; +class Metric; + +/*! +* \brief The main entrance of LightGBM. this application has two tasks: +* Train and Predict. +* Train task will train a new model +* Predict task will predict the scores of test data using existing model, +* and save the score to disk. +*/ +class Application { + public: + Application(int argc, char** argv); + + /*! \brief Destructor */ + ~Application(); + + /*! \brief To call this function to run application*/ + inline void Run(); + + private: + /*! \brief Load parameters from command line and config file*/ + void LoadParameters(int argc, char** argv); + + /*! \brief Load data, including training data and validation data*/ + void LoadData(); + + /*! \brief Initialization before training*/ + void InitTrain(); + + /*! \brief Main Training logic */ + void Train(); + + /*! \brief Initializations before prediction */ + void InitPredict(); + + /*! \brief Main predicting logic */ + void Predict(); + + /*! \brief Main Convert model logic */ + void ConvertModel(); + + /*! \brief All configs */ + Config config_; + /*! \brief Training data */ + std::unique_ptr train_data_; + /*! \brief Validation data */ + std::vector> valid_datas_; + /*! \brief Metric for training data */ + std::vector> train_metric_; + /*! \brief Metrics for validation data */ + std::vector>> valid_metrics_; + /*! \brief Boosting object */ + std::unique_ptr boosting_; + /*! \brief Training objective function */ + std::unique_ptr objective_fun_; +}; + + +inline void Application::Run() { + if (config_.task == TaskType::kPredict || config_.task == TaskType::KRefitTree) { + InitPredict(); + Predict(); + } else if (config_.task == TaskType::kConvertModel) { + ConvertModel(); + } else { + InitTrain(); + Train(); + } +} + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_ diff --git a/include/LightGBM/arrow.h b/include/LightGBM/arrow.h new file mode 100644 index 0000000..48bb00e --- /dev/null +++ b/include/LightGBM/arrow.h @@ -0,0 +1,90 @@ +/*! + * Copyright (c) 2026-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Oliver Borchert + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_ + +// The C data interface (ARROW_C_DATA_INTERFACE) is taken from +// https://arrow.apache.org/docs/format/CDataInterface.html#structure-definitions + +// The C stream interface (ARROW_C_STREAM_INTERFACE) is taken from +// https://arrow.apache.org/docs/format/CStreamInterface.html#structure-definition + +#ifdef __cplusplus +#include +#else +#include +#endif + +#ifdef __cplusplus +extern "C" { +#endif + +#ifndef ARROW_C_DATA_INTERFACE +#define ARROW_C_DATA_INTERFACE + +#define ARROW_FLAG_DICTIONARY_ORDERED 1 +#define ARROW_FLAG_NULLABLE 2 +#define ARROW_FLAG_MAP_KEYS_SORTED 4 + +struct ArrowSchema { + // Array type description + const char* format; + const char* name; + const char* metadata; + int64_t flags; + int64_t n_children; + struct ArrowSchema** children; + struct ArrowSchema* dictionary; + + // Release callback + void (*release)(struct ArrowSchema*); + // Opaque producer-specific data + void* private_data; +}; + +struct ArrowArray { + // Array data description + int64_t length; + int64_t null_count; + int64_t offset; + int64_t n_buffers; + int64_t n_children; + const void** buffers; + struct ArrowArray** children; + struct ArrowArray* dictionary; + + // Release callback + void (*release)(struct ArrowArray*); + // Opaque producer-specific data + void* private_data; +}; + +#endif // ARROW_C_DATA_INTERFACE + +#ifndef ARROW_C_STREAM_INTERFACE +#define ARROW_C_STREAM_INTERFACE + +struct ArrowArrayStream { + // Callbacks providing stream functionality + int (*get_schema)(struct ArrowArrayStream*, struct ArrowSchema* out); + int (*get_next)(struct ArrowArrayStream*, struct ArrowArray* out); + const char* (*get_last_error)(struct ArrowArrayStream*); + + // Release callback + void (*release)(struct ArrowArrayStream*); + + // Opaque producer-specific data + void* private_data; +}; + +#endif // ARROW_C_STREAM_INTERFACE + +#ifdef __cplusplus +} +#endif +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_ diff --git a/include/LightGBM/bin.h b/include/LightGBM/bin.h new file mode 100644 index 0000000..b4a4fc1 --- /dev/null +++ b/include/LightGBM/bin.h @@ -0,0 +1,655 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_ + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +enum BinType { + NumericalBin, + CategoricalBin +}; + +enum MissingType { + None, + Zero, + NaN +}; + +typedef double hist_t; +typedef int32_t int_hist_t; +typedef uint64_t hist_cnt_t; +// check at compile time +static_assert(sizeof(hist_t) == sizeof(hist_cnt_t), "Histogram entry size is not correct"); + +const size_t kHistEntrySize = 2 * sizeof(hist_t); +const size_t kInt32HistEntrySize = 2 * sizeof(int_hist_t); +const size_t kInt16HistEntrySize = 2 * sizeof(int16_t); +const int kHistOffset = 2; +const double kSparseThreshold = 0.7; + +#define GET_GRAD(hist, i) hist[(i) << 1] +#define GET_HESS(hist, i) hist[((i) << 1) + 1] + +inline static void HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const hist_t* p1; + hist_t* p2; + while (used_size < len) { + // convert + p1 = reinterpret_cast(src); + p2 = reinterpret_cast(dst); + *p2 += *p1; + src += type_size; + dst += type_size; + used_size += type_size; + } +} + +inline static void Int32HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) { + const int64_t* src_ptr = reinterpret_cast(src); + int64_t* dst_ptr = reinterpret_cast(dst); + const comm_size_t steps = (len + (type_size * 2) - 1) / (type_size * 2); + #pragma omp parallel for schedule(static) num_threads(OMP_NUM_THREADS()) + for (comm_size_t i = 0; i < steps; ++i) { + dst_ptr[i] += src_ptr[i]; + } +} + +inline static void Int16HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) { + const int32_t* src_ptr = reinterpret_cast(src); + int32_t* dst_ptr = reinterpret_cast(dst); + const comm_size_t steps = (len + (type_size * 2) - 1) / (type_size * 2); + #pragma omp parallel for schedule(static) num_threads(OMP_NUM_THREADS()) + for (comm_size_t i = 0; i < steps; ++i) { + dst_ptr[i] += src_ptr[i]; + } +} + +/*! \brief This class used to convert feature values into bin, +* and store some meta information for bin*/ +class BinMapper { + public: + BinMapper(); + BinMapper(const BinMapper& other); + explicit BinMapper(const void* memory); + ~BinMapper(); + + bool CheckAlign(const BinMapper& other) const { + if (num_bin_ != other.num_bin_) { + return false; + } + if (missing_type_ != other.missing_type_) { + return false; + } + if (bin_type_ == BinType::NumericalBin) { + for (int i = 0; i < num_bin_; ++i) { + if (bin_upper_bound_[i] != other.bin_upper_bound_[i]) { + return false; + } + } + } else { + for (int i = 0; i < num_bin_; i++) { + if (bin_2_categorical_[i] != other.bin_2_categorical_[i]) { + return false; + } + } + } + return true; + } + + /*! \brief Get number of bins */ + inline int num_bin() const { return num_bin_; } + + /*! \brief Missing Type */ + inline MissingType missing_type() const { return missing_type_; } + + /*! \brief True if bin is trivial (contains only one bin) */ + inline bool is_trivial() const { return is_trivial_; } + + /*! \brief Sparsity of this bin ( num_zero_bins / num_data ) */ + inline double sparse_rate() const { return sparse_rate_; } + + /*! + * \brief Save binary data to file + * \param file File want to write + */ + void SaveBinaryToFile(BinaryWriter* writer) const; + + /*! + * \brief Mapping bin into feature value + * \param bin + * \return Feature value of this bin + */ + inline double BinToValue(uint32_t bin) const { + if (bin_type_ == BinType::NumericalBin) { + return bin_upper_bound_[bin]; + } else { + return bin_2_categorical_[bin]; + } + } + + /*! + * \brief Maximum categorical value + * \return Maximum categorical value for categorical features, 0 for numerical features + */ + inline int MaxCatValue() const { + if (bin_2_categorical_.size() == 0) { + return 0; + } + int max_cat_value = bin_2_categorical_[0]; + for (size_t i = 1; i < bin_2_categorical_.size(); ++i) { + if (bin_2_categorical_[i] > max_cat_value) { + max_cat_value = bin_2_categorical_[i]; + } + } + return max_cat_value; + } + + /*! + * \brief Get sizes in byte of this object + */ + size_t SizesInByte() const; + + /*! + * \brief Mapping feature value into bin + * \param value + * \return bin for this feature value + */ + inline uint32_t ValueToBin(double value) const; + + /*! + * \brief Get the default bin when value is 0 + * \return default bin + */ + inline uint32_t GetDefaultBin() const { + return default_bin_; + } + + inline uint32_t GetMostFreqBin() const { + return most_freq_bin_; + } + + /*! + * \brief Construct feature value to bin mapper according feature values + * \param values (Sampled) values of this feature, Note: not include zero. + * \param num_values number of values. + * \param total_sample_cnt number of total sample count, equal with values.size() + num_zeros + * \param max_bin The maximal number of bin + * \param min_data_in_bin min number of data in one bin + * \param min_split_data + * \param pre_filter + * \param bin_type Type of this bin + * \param use_missing True to enable missing value handle + * \param zero_as_missing True to use zero as missing value + * \param forced_upper_bounds Vector of split points that must be used (if this has size less than max_bin, remaining splits are found by the algorithm) + */ + void FindBin(double* values, int num_values, size_t total_sample_cnt, int max_bin, int min_data_in_bin, int min_split_data, bool pre_filter, BinType bin_type, + bool use_missing, bool zero_as_missing, const std::vector& forced_upper_bounds); + + /*! + * \brief Serializing this object to buffer + * \param buffer The destination + */ + void CopyTo(char* buffer) const; + + /*! + * \brief Deserializing this object from buffer + * \param buffer The source + */ + void CopyFrom(const char* buffer); + + /*! + * \brief Get bin types + */ + inline BinType bin_type() const { return bin_type_; } + + /*! + * \brief Get bin info + */ + inline std::string bin_info_string() const { + if (bin_type_ == BinType::CategoricalBin) { + return Common::Join(bin_2_categorical_, ":"); + } else { + std::stringstream str_buf; + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + str_buf << '[' << min_val_ << ':' << max_val_ << ']'; + return str_buf.str(); + } + } + + private: + /*! \brief Number of bins */ + int num_bin_; + MissingType missing_type_; + /*! \brief Store upper bound for each bin */ + std::vector bin_upper_bound_; + /*! \brief True if this feature is trivial */ + bool is_trivial_; + /*! \brief Sparse rate of this bins( num_bin0/num_data ) */ + double sparse_rate_; + /*! \brief Type of this bin */ + BinType bin_type_; + /*! \brief Mapper from categorical to bin */ + std::unordered_map categorical_2_bin_; + /*! \brief Mapper from bin to categorical */ + std::vector bin_2_categorical_; + /*! \brief minimal feature value */ + double min_val_; + /*! \brief maximum feature value */ + double max_val_; + /*! \brief bin value of feature value 0 */ + uint32_t default_bin_; + + uint32_t most_freq_bin_; +}; + +/*! \brief Iterator for one bin column */ +class BinIterator { + public: + /*! + * \brief Get bin data on specific row index + * \param idx Index of this data + * \return Bin data + */ + virtual uint32_t Get(data_size_t idx) = 0; + virtual uint32_t RawGet(data_size_t idx) = 0; + virtual void Reset(data_size_t idx) = 0; + virtual ~BinIterator() = default; +}; + +/*! +* \brief Interface for bin data. This class will store bin data for one feature. +* unlike OrderedBin, this class will store data by original order. +* Note that it may cause cache misses when construct histogram, +* but it doesn't need to re-order operation, So it will be faster than OrderedBin for dense feature +*/ +class Bin { + public: + /*! \brief virtual destructor */ + virtual ~Bin() {} + /*! + * \brief Initialize for pushing. By default, no action needed. + * \param num_thread The number of external threads that will be calling the push APIs + * \param omp_max_threads The maximum number of OpenMP threads to allocate for + */ + virtual void InitStreaming(uint32_t /*num_thread*/, int32_t /*omp_max_threads*/) { } + /*! + * \brief Push one record + * \param tid Thread id + * \param idx Index of record + * \param value bin value of record + */ + virtual void Push(int tid, data_size_t idx, uint32_t value) = 0; + + virtual void CopySubrow(const Bin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) = 0; + /*! + * \brief Get bin iterator of this bin for specific feature + * \param min_bin min_bin of current used feature + * \param max_bin max_bin of current used feature + * \param most_freq_bin + * \return Iterator of this bin + */ + virtual BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const = 0; + + /*! + * \brief Save binary data to file + * \param file File want to write + */ + virtual void SaveBinaryToFile(BinaryWriter* writer) const = 0; + + /*! + * \brief Load from memory + * \param memory + * \param local_used_indices + */ + virtual void LoadFromMemory(const void* memory, + const std::vector& local_used_indices) = 0; + + /*! + * \brief Get sizes in byte of this object + */ + virtual size_t SizesInByte() const = 0; + + /*! \brief Number of all data */ + virtual data_size_t num_data() const = 0; + + /*! \brief Get data pointer */ + virtual void* get_data() = 0; + + virtual void ReSize(data_size_t num_data) = 0; + + /*! + * \brief Construct histogram of this feature, + * Note: We use ordered_gradients and ordered_hessians to improve cache hit chance + * The naive solution is using gradients[data_indices[i]] for data_indices[i] to get gradients, + which is not cache friendly, since the access of memory is not continuous. + * ordered_gradients and ordered_hessians are preprocessed, and they are re-ordered by data_indices. + * Ordered_gradients[i] is aligned with data_indices[i]'s gradients (same for ordered_hessians). + * \param data_indices Used data indices in current leaf + * \param start start index in data_indices + * \param end end index in data_indices + * \param ordered_gradients Pointer to gradients, the data_indices[i]-th data's gradient is ordered_gradients[i] + * \param ordered_hessians Pointer to hessians, the data_indices[i]-th data's hessian is ordered_hessians[i] + * \param out Output Result + */ + virtual void ConstructHistogram( + const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt8( + const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt16( + const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt32( + const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* ordered_gradients, const score_t* ordered_hessians, + hist_t* out) const = 0; + + /*! + * \brief Construct histogram of this feature, + * Note: We use ordered_gradients and ordered_hessians to improve cache hit chance + * The naive solution is using gradients[data_indices[i]] for data_indices[i] to get gradients, + which is not cache friendly, since the access of memory is not continuous. + * ordered_gradients and ordered_hessians are preprocessed, and they are re-ordered by data_indices. + * Ordered_gradients[i] is aligned with data_indices[i]'s gradients (same for ordered_hessians). + * \param data_indices Used data indices in current leaf + * \param start start index in data_indices + * \param end end index in data_indices + * \param ordered_gradients Pointer to gradients, the data_indices[i]-th data's gradient is ordered_gradients[i] + * \param out Output Result + */ + virtual void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* ordered_gradients, hist_t* out) const = 0; + + virtual data_size_t Split(uint32_t min_bin, uint32_t max_bin, + uint32_t default_bin, uint32_t most_freq_bin, + MissingType missing_type, bool default_left, + uint32_t threshold, const data_size_t* data_indices, + data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const = 0; + + virtual data_size_t SplitCategorical( + uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin, + const uint32_t* threshold, int num_threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, data_size_t* gt_indices) const = 0; + + virtual data_size_t Split(uint32_t max_bin, uint32_t default_bin, + uint32_t most_freq_bin, MissingType missing_type, + bool default_left, uint32_t threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const = 0; + + virtual data_size_t SplitCategorical( + uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold, + int num_threshold, const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, data_size_t* gt_indices) const = 0; + + /*! + * \brief After pushed all feature data, call this could have better refactor for bin data + */ + virtual void FinishLoad() = 0; + + /*! + * \brief Create object for bin data of one feature, used for dense feature + * \param num_data Total number of data + * \param num_bin Number of bin + * \return The bin data object + */ + static Bin* CreateDenseBin(data_size_t num_data, int num_bin); + + /*! + * \brief Create object for bin data of one feature, used for sparse feature + * \param num_data Total number of data + * \param num_bin Number of bin + * \return The bin data object + */ + static Bin* CreateSparseBin(data_size_t num_data, int num_bin); + + /*! + * \brief Deep copy the bin + */ + virtual Bin* Clone() = 0; + + virtual const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const = 0; + + virtual const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const = 0; +}; + + +class MultiValBin { + public: + virtual ~MultiValBin() {} + + virtual data_size_t num_data() const = 0; + + virtual int32_t num_bin() const = 0; + + virtual double num_element_per_row() const = 0; + + virtual const std::vector& offsets() const = 0; + + virtual void PushOneRow(int tid, data_size_t idx, const std::vector& values) = 0; + + virtual void CopySubrow(const MultiValBin* full_bin, + const data_size_t* used_indices, + data_size_t num_used_indices) = 0; + + virtual MultiValBin* CreateLike(data_size_t num_data, int num_bin, + int num_feature, + double estimate_element_per_row, + const std::vector& offsets) const = 0; + + virtual void CopySubcol(const MultiValBin* full_bin, + const std::vector& used_feature_index, + const std::vector& lower, + const std::vector& upper, + const std::vector& delta) = 0; + + virtual void ReSize(data_size_t num_data, int num_bin, int num_feature, + double estimate_element_per_row, const std::vector& offsets) = 0; + + virtual void CopySubrowAndSubcol( + const MultiValBin* full_bin, const data_size_t* used_indices, + data_size_t num_used_indices, const std::vector& used_feature_index, + const std::vector& lower, const std::vector& upper, + const std::vector& delta) = 0; + + virtual void ConstructHistogram(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramOrdered(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt32(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramOrderedInt32(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt16(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramOrderedInt16(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt8(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const = 0; + + virtual void ConstructHistogramOrderedInt8(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const = 0; + + virtual void FinishLoad() = 0; + + virtual bool IsSparse() = 0; + + static MultiValBin* CreateMultiValBin(data_size_t num_data, int num_bin, + int num_feature, double sparse_rate, const std::vector& offsets); + + static MultiValBin* CreateMultiValDenseBin(data_size_t num_data, int num_bin, + int num_feature, const std::vector& offsets); + + static MultiValBin* CreateMultiValSparseBin(data_size_t num_data, int num_bin, double estimate_element_per_row); + + static constexpr double multi_val_bin_sparse_threshold = 0.25f; + + virtual MultiValBin* Clone() = 0; + + #ifdef USE_CUDA + virtual const void* GetRowWiseData(uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const = 0; + #endif // USE_CUDA +}; + +inline uint32_t BinMapper::ValueToBin(double value) const { + if (std::isnan(value)) { + if (bin_type_ == BinType::CategoricalBin) { + return 0; + } else if (missing_type_ == MissingType::NaN) { + return num_bin_ - 1; + } else { + value = 0.0f; + } + } + if (bin_type_ == BinType::NumericalBin) { + // binary search to find bin + int l = 0; + int r = num_bin_ - 1; + if (missing_type_ == MissingType::NaN) { + r -= 1; + } + while (l < r) { + int m = (r + l - 1) / 2; + if (value <= bin_upper_bound_[m]) { + r = m; + } else { + l = m + 1; + } + } + return l; + } else { + int int_value = static_cast(value); + // convert negative value to NaN bin + if (int_value < 0) { + return 0; + } + if (categorical_2_bin_.count(int_value)) { + return categorical_2_bin_.at(int_value); + } else { + return 0; + } + } +} + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_ diff --git a/include/LightGBM/boosting.h b/include/LightGBM/boosting.h new file mode 100644 index 0000000..8bc1b63 --- /dev/null +++ b/include/LightGBM/boosting.h @@ -0,0 +1,334 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_ + +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { + +/*! \brief forward declaration */ +class Dataset; +class ObjectiveFunction; +class Metric; +struct PredictionEarlyStopInstance; + +/*! +* \brief The interface for Boosting +*/ +class LIGHTGBM_EXPORT Boosting { + public: + /*! \brief virtual destructor */ + virtual ~Boosting() {} + + /*! + * \brief Initialization logic + * \param config Configs for boosting + * \param train_data Training data + * \param objective_function Training objective function + * \param training_metrics Training metric + */ + virtual void Init( + const Config* config, + const Dataset* train_data, + const ObjectiveFunction* objective_function, + const std::vector& training_metrics) = 0; + + /*! + * \brief Merge model from other boosting object + Will insert to the front of current boosting object + * \param other + */ + virtual void MergeFrom(const Boosting* other) = 0; + + /*! + * \brief Shuffle Existing Models + */ + virtual void ShuffleModels(int start_iter, int end_iter) = 0; + + virtual void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, + const std::vector& training_metrics) = 0; + + virtual void ResetConfig(const Config* config) = 0; + + + + /*! + * \brief Add a validation data + * \param valid_data Validation data + * \param valid_metrics Metric for validation data + */ + virtual void AddValidDataset(const Dataset* valid_data, + const std::vector& valid_metrics) = 0; + + virtual void Train(int snapshot_freq, const std::string& model_output_path) = 0; + + /*! + * \brief Update the tree output by new training data + */ + virtual void RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) = 0; + + /*! + * \brief Training logic + * \param gradients nullptr for using default objective, otherwise use self-defined boosting + * \param hessians nullptr for using default objective, otherwise use self-defined boosting + * \return True if cannot train anymore + */ + virtual bool TrainOneIter(const score_t* gradients, const score_t* hessians) = 0; + + /*! + * \brief Rollback one iteration + */ + virtual void RollbackOneIter() = 0; + + /*! + * \brief return current iteration + */ + virtual int GetCurrentIteration() const = 0; + + /*! + * \brief Get evaluation result at data_idx data + * \param data_idx 0: training data, 1: 1st validation data + * \return evaluation result + */ + virtual std::vector GetEvalAt(int data_idx) const = 0; + + /*! + * \brief Get current training score + * \param out_len length of returned score + * \return training score + */ + virtual const double* GetTrainingScore(int64_t* out_len) = 0; + + /*! + * \brief Get prediction result at data_idx data + * \param data_idx 0: training data, 1: 1st validation data + * \return out_len length of returned score + */ + virtual int64_t GetNumPredictAt(int data_idx) const = 0; + + /*! + * \brief Get prediction result at data_idx data + * \param data_idx 0: training data, 1: 1st validation data + * \param result used to store prediction result, should allocate memory before call this function + * \param out_len length of returned score + */ + virtual void GetPredictAt(int data_idx, double* result, int64_t* out_len) = 0; + + virtual int NumPredictOneRow(int start_iteration, int num_iteration, bool is_pred_leaf, bool is_pred_contrib) const = 0; + + /*! + * \brief Prediction for one record, not sigmoid transform + * \param feature_values Feature value on this record + * \param output Prediction result for this record + * \param early_stop Early stopping instance. If nullptr, no early stopping is applied and all models are evaluated. + */ + virtual void PredictRaw(const double* features, double* output, + const PredictionEarlyStopInstance* early_stop) const = 0; + + virtual void PredictRawByMap(const std::unordered_map& features, double* output, + const PredictionEarlyStopInstance* early_stop) const = 0; + + + /*! + * \brief Prediction for one record, sigmoid transformation will be used if needed + * \param feature_values Feature value on this record + * \param output Prediction result for this record + * \param early_stop Early stopping instance. If nullptr, no early stopping is applied and all models are evaluated. + */ + virtual void Predict(const double* features, double* output, + const PredictionEarlyStopInstance* early_stop) const = 0; + + virtual void PredictByMap(const std::unordered_map& features, double* output, + const PredictionEarlyStopInstance* early_stop) const = 0; + + + /*! + * \brief Prediction for one record with leaf index + * \param feature_values Feature value on this record + * \param output Prediction result for this record + */ + virtual void PredictLeafIndex( + const double* features, double* output) const = 0; + + virtual void PredictLeafIndexByMap( + const std::unordered_map& features, double* output) const = 0; + + /*! + * \brief Feature contributions for the model's prediction of one record + * \param feature_values Feature value on this record + * \param output Prediction result for this record + */ + virtual void PredictContrib(const double* features, double* output) const = 0; + + virtual void PredictContribByMap(const std::unordered_map& features, + std::vector>* output) const = 0; + + /*! + * \brief Dump model to json format string + * \param start_iteration The model will be saved start from + * \param num_iteration Number of iterations that want to dump, -1 means dump all + * \param feature_importance_type Type of feature importance, 0: split, 1: gain + * \return Json format string of model + */ + virtual std::string DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const = 0; + + /*! + * \brief Translate model to if-else statement + * \param num_iteration Number of iterations that want to translate, -1 means translate all + * \return if-else format codes of model + */ + virtual std::string ModelToIfElse(int num_iteration) const = 0; + + /*! + * \brief Translate model to if-else statement + * \param num_iteration Number of iterations that want to translate, -1 means translate all + * \param filename Filename that want to save to + * \return is_finish Is training finished or not + */ + virtual bool SaveModelToIfElse(int num_iteration, const char* filename) const = 0; + + /*! + * \brief Save model to file + * \param start_iteration The model will be saved start from + * \param num_iterations Number of model that want to save, -1 means save all + * \param feature_importance_type Type of feature importance, 0: split, 1: gain + * \param filename Filename that want to save to + * \return true if succeeded + */ + virtual bool SaveModelToFile(int start_iteration, int num_iterations, int feature_importance_type, const char* filename) const = 0; + + /*! + * \brief Save model to string + * \param start_iteration The model will be saved start from + * \param num_iterations Number of model that want to save, -1 means save all + * \param feature_importance_type Type of feature importance, 0: split, 1: gain + * \return Non-empty string if succeeded + */ + virtual std::string SaveModelToString(int start_iteration, int num_iterations, int feature_importance_type) const = 0; + + /*! + * \brief Restore from a serialized string + * \param buffer The content of model + * \param len The length of buffer + * \return true if succeeded + */ + virtual bool LoadModelFromString(const char* buffer, size_t len) = 0; + + /*! + * \brief Calculate feature importances + * \param num_iteration Number of model that want to use for feature importance, -1 means use all + * \param importance_type: 0 for split, 1 for gain + * \return vector of feature_importance + */ + virtual std::vector FeatureImportance(int num_iteration, int importance_type) const = 0; + + /*! + * \brief Calculate upper bound value + * \return max possible value + */ + virtual double GetUpperBoundValue() const = 0; + + /*! + * \brief Calculate lower bound value + * \return min possible value + */ + virtual double GetLowerBoundValue() const = 0; + + /*! + * \brief Get max feature index of this model + * \return Max feature index of this model + */ + virtual int MaxFeatureIdx() const = 0; + + /*! + * \brief Get feature names of this model + * \return Feature names of this model + */ + virtual std::vector FeatureNames() const = 0; + + /*! + * \brief Get index of label column + * \return index of label column + */ + virtual int LabelIdx() const = 0; + + /*! + * \brief Get number of weak sub-models + * \return Number of weak sub-models + */ + virtual int NumberOfTotalModel() const = 0; + + /*! + * \brief Get number of models per iteration + * \return Number of models per iteration + */ + virtual int NumModelPerIteration() const = 0; + + /*! + * \brief Get number of classes + * \return Number of classes + */ + virtual int NumberOfClasses() const = 0; + + /*! \brief The prediction should be accurate or not. True will disable early stopping for prediction. */ + virtual bool NeedAccuratePrediction() const = 0; + + /*! + * \brief Initial work for the prediction + * \param start_iteration Start index of the iteration to predict + * \param num_iteration number of used iteration + * \param is_pred_contrib + */ + virtual void InitPredict(int start_iteration, int num_iteration, bool is_pred_contrib) = 0; + + /*! + * \brief Name of submodel + */ + virtual const char* SubModelName() const = 0; + + Boosting() = default; + /*! \brief Disable copy */ + Boosting& operator=(const Boosting&) = delete; + /*! \brief Disable copy */ + Boosting(const Boosting&) = delete; + + static bool LoadFileToBoosting(Boosting* boosting, const char* filename); + + /*! + * \brief Create boosting object + * \param type Type of boosting + * \param format Format of model + * \param config config for boosting + * \param filename name of model file, if existing will continue to train from this model + * \param device_type type of device, can be cpu, gpu or cuda + * \param num_gpu number of GPUs to use + * \return The boosting object + */ + static Boosting* CreateBoosting(const std::string& type, const char* filename, const std::string& device_type, const int num_gpu); + + virtual std::string GetLoadedParam() const = 0; + + virtual bool IsLinear() const { return false; } + + virtual std::string ParserConfigStr() const = 0; +}; + +class GBDTBase : public Boosting { + public: + virtual double GetLeafValue(int tree_idx, int leaf_idx) const = 0; + virtual void SetLeafValue(int tree_idx, int leaf_idx, double val) = 0; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_ diff --git a/include/LightGBM/c_api.h b/include/LightGBM/c_api.h new file mode 100644 index 0000000..ca6d778 --- /dev/null +++ b/include/LightGBM/c_api.h @@ -0,0 +1,1739 @@ +/*! + * \file c_api.h + * \copyright Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * \note + * To avoid type conversion on large data, the most of our exposed interface supports both float32 and float64, + * except the following: + * 1. gradient and Hessian; + * 2. current score for training and validation data. + * . + * The reason is that they are called frequently, and the type conversion on them may be time-cost. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_ + +#include +#include + +#ifdef __cplusplus +#include +#include +#include +#else +#include +#include +#include +#endif + + +typedef void* DatasetHandle; /*!< \brief Handle of dataset. */ +typedef void* BoosterHandle; /*!< \brief Handle of booster. */ +typedef void* FastConfigHandle; /*!< \brief Handle of FastConfig. */ +typedef void* ByteBufferHandle; /*!< \brief Handle of ByteBuffer. */ + +#define C_API_DTYPE_FLOAT32 (0) /*!< \brief float32 (single precision float). */ +#define C_API_DTYPE_FLOAT64 (1) /*!< \brief float64 (double precision float). */ +#define C_API_DTYPE_INT32 (2) /*!< \brief int32. */ +#define C_API_DTYPE_INT64 (3) /*!< \brief int64. */ + +#define C_API_PREDICT_NORMAL (0) /*!< \brief Normal prediction, with transform (if needed). */ +#define C_API_PREDICT_RAW_SCORE (1) /*!< \brief Predict raw score. */ +#define C_API_PREDICT_LEAF_INDEX (2) /*!< \brief Predict leaf index. */ +#define C_API_PREDICT_CONTRIB (3) /*!< \brief Predict feature contributions (SHAP values). */ + +#define C_API_MATRIX_TYPE_CSR (0) /*!< \brief CSR sparse matrix type. */ +#define C_API_MATRIX_TYPE_CSC (1) /*!< \brief CSC sparse matrix type. */ + +#define C_API_FEATURE_IMPORTANCE_SPLIT (0) /*!< \brief Split type of feature importance. */ +#define C_API_FEATURE_IMPORTANCE_GAIN (1) /*!< \brief Gain type of feature importance. */ + +#if defined(_MSC_VER) +# define LIGHTGBM_DEPRECATED(msg) __declspec(deprecated(msg)) /*!< \brief Deprecated function. */ +#elif defined(__GNUC__) || defined(__clang__) +# define LIGHTGBM_DEPRECATED(msg) __attribute__((deprecated(msg))) /*!< \brief Deprecated function. */ +#else +# define LIGHTGBM_DEPRECATED(msg) /*!< \brief Deprecated function. */ +#endif + +/*! + * \brief Get string message of the last error. + * \return Error information + */ +LIGHTGBM_C_EXPORT const char* LGBM_GetLastError(); + +/*! + * \brief Dump all parameter names with their aliases to JSON. + * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer + * \param[out] out_len Actual output length + * \param[out] out_str JSON format string of parameters, should pre-allocate memory + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DumpParamAliases(int64_t buffer_len, + int64_t* out_len, + char* out_str); + +/*! + * \brief Register a callback function for log redirecting. + * \param callback The callback function to register + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_RegisterLogCallback(void (*callback)(const char*)); + +/*! + * \brief Get number of samples based on parameters and total number of rows of data. + * \param num_total_row Number of total rows + * \param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` is used to calculate returned value + * \param[out] out Number of samples. This value is used to pre-allocate memory to hold sample indices when calling ``LGBM_SampleIndices`` + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_GetSampleCount(int32_t num_total_row, + const char* parameters, + int* out); + +/*! + * \brief Create sample indices for total number of rows. + * \note + * You should pre-allocate memory for ``out``, you can get its length by ``LGBM_GetSampleCount``. + * \param num_total_row Number of total rows + * \param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` and ``data_random_seed`` are used to produce the output + * \param[out] out Created indices, type is int32_t + * \param[out] out_len Number of indices + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_SampleIndices(int32_t num_total_row, + const char* parameters, + void* out, + int32_t* out_len); + +/*! + * \brief Get a ByteBuffer value at an index. + * \param handle Handle of byte buffer to be read + * \param index Index of value to return + * \param[out] out_val Byte value at index to return + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_ByteBufferGetAt(ByteBufferHandle handle, int32_t index, uint8_t* out_val); + +/*! + * \brief Free space for byte buffer. + * \param handle Handle of byte buffer to be freed + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_ByteBufferFree(ByteBufferHandle handle); + +/* --- start Dataset interface */ + +/*! + * \brief Load dataset from file (like LightGBM CLI version does). + * \param filename The name of the file + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out A loaded dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromFile(const char* filename, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out); + +/*! + * \brief Allocate the space for dataset and bucket feature bins according to sampled data. + * \param sample_data Sampled data, grouped by the column + * \param sample_indices Indices of sampled data + * \param ncol Number of columns + * \param num_per_col Size of each sampling column + * \param num_sample_row Number of sampled rows + * \param num_local_row Total number of rows local to machine + * \param num_dist_row Number of total distributed rows + * \param parameters Additional parameters + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSampledColumn(double** sample_data, + int** sample_indices, + int32_t ncol, + const int* num_per_col, + int32_t num_sample_row, + int32_t num_local_row, + int64_t num_dist_row, + const char* parameters, + DatasetHandle* out); + +/*! + * \brief Allocate the space for dataset and bucket feature bins according to reference dataset. + * \param reference Used to align bin mapper with other dataset + * \param num_total_row Number of total rows + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateByReference(const DatasetHandle reference, + int64_t num_total_row, + DatasetHandle* out); + +/*! + * \brief Initialize the Dataset for streaming. + * \param dataset Handle of dataset + * \param has_weights Whether the dataset has Metadata weights + * \param has_init_scores Whether the dataset has Metadata initial scores + * \param has_queries Whether the dataset has Metadata queries/groups + * \param nclasses Number of initial score classes + * \param nthreads Number of external threads that will use the PushRows APIs + * \param omp_max_threads Maximum number of OpenMP threads (-1 for default) + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetInitStreaming(DatasetHandle dataset, + int32_t has_weights, + int32_t has_init_scores, + int32_t has_queries, + int32_t nclasses, + int32_t nthreads, + int32_t omp_max_threads); + +/*! + * \brief Allocate the space for dataset and bucket feature bins according to serialized reference dataset. + * \param ref_buffer A binary representation of the dataset schema (feature groups, bins, etc.) + * \param ref_buffer_size The size of the reference array in bytes + * \param num_row Number of total rows the dataset will contain + * \param num_classes Number of classes (will be used only in case of multiclass and specifying initial scores) + * \param parameters Additional parameters + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSerializedReference(const void* ref_buffer, + int32_t ref_buffer_size, + int64_t num_row, + int32_t num_classes, + const char* parameters, + DatasetHandle* out); + +/*! + * \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``. + * \param dataset Handle of dataset + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nrow Number of rows + * \param ncol Number of columns + * \param start_row Row start index + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetPushRows(DatasetHandle dataset, + const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int32_t start_row); + +/*! + * \brief Push data to existing dataset. + * The general flow for a streaming scenario is: + * 1. create Dataset "schema" (e.g. ``LGBM_DatasetCreateFromSampledColumn``) + * 2. init them for thread-safe streaming (``LGBM_DatasetInitStreaming``) + * 3. push data (``LGBM_DatasetPushRowsWithMetadata`` or ``LGBM_DatasetPushRowsByCSRWithMetadata``) + * 4. call ``LGBM_DatasetMarkFinished`` + * \param dataset Handle of dataset + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nrow Number of rows + * \param ncol Number of feature columns + * \param start_row Row start index, i.e., the index at which to start inserting data + * \param label Pointer to array with nrow labels + * \param weight Optional pointer to array with nrow weights + * \param init_score Optional pointer to array with nrow*nclasses initial scores, in column format + * \param query Optional pointer to array with nrow query values + * \param tid The id of the calling thread, from 0...N-1 threads + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsWithMetadata(DatasetHandle dataset, + const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int32_t start_row, + const float* label, + const float* weight, + const double* init_score, + const int32_t* query, + int32_t tid); + +/*! + * \brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``. + * \param dataset Handle of dataset + * \param indptr Pointer to row headers + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to column indices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nindptr Number of rows in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param num_col Number of columns + * \param start_row Row start index + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSR(DatasetHandle dataset, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col, + int64_t start_row); + +/*! + * \brief Push CSR data to existing dataset. (See ``LGBM_DatasetPushRowsWithMetadata`` for more details.) + * \param dataset Handle of dataset + * \param indptr Pointer to row headers + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to column indices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nindptr Number of rows in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param start_row Row start index + * \param label Pointer to array with nindptr-1 labels + * \param weight Optional pointer to array with nindptr-1 weights + * \param init_score Optional pointer to array with (nindptr-1)*nclasses initial scores, in column format + * \param query Optional pointer to array with nindptr-1 query values + * \param tid The id of the calling thread, from 0...N-1 threads + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSRWithMetadata(DatasetHandle dataset, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t start_row, + const float* label, + const float* weight, + const double* init_score, + const int32_t* query, + int32_t tid); + +/*! + * \brief Set whether or not the Dataset waits for a manual MarkFinished call or calls FinishLoad on itself automatically. + * Set to 1 for streaming scenario, and use ``LGBM_DatasetMarkFinished`` to manually finish the Dataset. + * \param dataset Handle of dataset + * \param wait Whether to wait or not (1 or 0) + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetSetWaitForManualFinish(DatasetHandle dataset, int wait); + +/*! + * \brief Mark the Dataset as complete by calling ``dataset->FinishLoad``. + * \param dataset Handle of dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetMarkFinished(DatasetHandle dataset); + +/*! + * \brief Create a dataset from CSR format. + * \param indptr Pointer to row headers + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to column indices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nindptr Number of rows in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param num_col Number of columns + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSR(const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out); + +/*! + * \brief Create a dataset from CSR format through callbacks. + * \param get_row_funptr Pointer to ``std::function>& ret)>`` + * (called for every row and expected to clear and fill ``ret``) + * \param num_rows Number of rows + * \param num_col Number of columns + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr, + int num_rows, + int64_t num_col, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out); + +/*! + * \brief Create a dataset from CSC format. + * \param col_ptr Pointer to column headers + * \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to row indices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param ncol_ptr Number of columns in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param num_row Number of rows + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSC(const void* col_ptr, + int col_ptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t ncol_ptr, + int64_t nelem, + int64_t num_row, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out); + +/*! + * \brief Create dataset from dense matrix. + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nrow Number of rows + * \param ncol Number of columns + * \param is_row_major 1 for row-major, 0 for column-major + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMat(const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int is_row_major, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out); + +/*! + * \brief Create dataset from array of dense matrices. + * \param nmat Number of dense matrices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nrow Number of rows + * \param ncol Number of columns + * \param is_row_major Pointer to the data layouts. 1 for row-major, 0 for column-major + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMats(int32_t nmat, + const void** data, + int data_type, + int32_t* nrow, + int32_t ncol, + int* is_row_major, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out); + +/*! + * \brief Create dataset from Arrow. + * \deprecated This function is deprecated in favor of ``LGBM_DatasetCreateFromArrowStream``. + * \param n_chunks The number of Arrow arrays passed to this function + * \param chunks Pointer to the list of Arrow arrays + * \param schema Pointer to the schema of all Arrow arrays + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT LIGHTGBM_DEPRECATED("Use LGBM_DatasetCreateFromArrowStream instead.") +int LGBM_DatasetCreateFromArrow(int64_t n_chunks, + struct ArrowArray* chunks, + struct ArrowSchema* schema, + const char* parameters, + const DatasetHandle reference, + DatasetHandle *out); + +/*! + * \brief Create dataset from Arrow stream. + * \param stream Arrow stream pointer + * \param parameters Additional parameters + * \param reference Used to align bin mapper with other dataset, nullptr means isn't used + * \param[out] out Created dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromArrowStream(struct ArrowArrayStream* stream, + const char* parameters, + const DatasetHandle reference, + DatasetHandle *out); + +/*! + * \brief Create subset of a data. + * \param handle Handle of full dataset + * \param used_row_indices Indices used in subset + * \param num_used_row_indices Length of ``used_row_indices`` + * \param parameters Additional parameters + * \param[out] out Subset of data + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(const DatasetHandle handle, + const int32_t* used_row_indices, + int32_t num_used_row_indices, + const char* parameters, + DatasetHandle* out); + +/*! + * \brief Save feature names to dataset. + * \param handle Handle of dataset + * \param feature_names Feature names + * \param num_feature_names Number of feature names + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(DatasetHandle handle, + const char** feature_names, + int num_feature_names); + +/*! + * \brief Get feature names of dataset. + * \param handle Handle of dataset + * \param len Number of ``char*`` pointers stored at ``out_strs``. + * If smaller than the max size, only this many strings are copied + * \param[out] num_feature_names Number of feature names + * \param buffer_len Size of pre-allocated strings. + * Content is copied up to ``buffer_len - 1`` and null-terminated + * \param[out] out_buffer_len String sizes required to do the full string copies + * \param[out] feature_names Feature names, should pre-allocate memory + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(DatasetHandle handle, + const int len, + int* num_feature_names, + const size_t buffer_len, + size_t* out_buffer_len, + char** feature_names); + +/*! + * \brief Free space for dataset. + * \param handle Handle of dataset to be freed + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetFree(DatasetHandle handle); + +/*! + * \brief Save dataset to binary file. + * \param handle Handle of dataset + * \param filename The name of the file + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetSaveBinary(DatasetHandle handle, + const char* filename); + +/*! + * \brief Create a dataset schema representation as a binary byte array (excluding data). + * \param handle Handle of dataset + * \param[out] out The output byte array + * \param[out] out_len The length of the output byte array (returned for convenience) + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetSerializeReferenceToBinary(DatasetHandle handle, + ByteBufferHandle* out, + int32_t* out_len); + +/*! + * \brief Save dataset to text file, intended for debugging use only. + * \param handle Handle of dataset + * \param filename The name of the file + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetDumpText(DatasetHandle handle, + const char* filename); + +/*! + * \brief Set vector to a content in info. + * \note + * - \a group only works for ``C_API_DTYPE_INT32``; + * - \a label and \a weight only work for ``C_API_DTYPE_FLOAT32``; + * - \a init_score only works for ``C_API_DTYPE_FLOAT64``. + * \param handle Handle of dataset + * \param field_name Field name, can be \a label, \a weight, \a init_score, \a group + * \param field_data Pointer to data vector + * \param num_element Number of elements in ``field_data`` + * \param type Type of ``field_data`` pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetSetField(DatasetHandle handle, + const char* field_name, + const void* field_data, + int num_element, + int type); + +/*! + * \brief Set vector to a content in info. + * \deprecated This function is deprecated in favor of ``LGBM_DatasetSetFieldFromArrowStream``. + * \note + * - \a group converts input datatype into ``int32``; + * - \a label and \a weight convert input datatype into ``float32``; + * - \a init_score converts input datatype into ``float64``. + * \param handle Handle of dataset + * \param field_name Field name, can be \a label, \a weight, \a init_score, \a group + * \param n_chunks The number of Arrow arrays passed to this function + * \param chunks Pointer to the list of Arrow arrays + * \param schema Pointer to the schema of all Arrow arrays + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT LIGHTGBM_DEPRECATED("Use LGBM_DatasetSetFieldFromArrowStream instead.") +int LGBM_DatasetSetFieldFromArrow(DatasetHandle handle, + const char* field_name, + int64_t n_chunks, + struct ArrowArray* chunks, + struct ArrowSchema* schema); + +/*! + * \brief Set vector to a content in info. + * \note + * - \a group converts input datatype into ``int32``; + * - \a label and \a weight convert input datatype into ``float32``; + * - \a init_score converts input datatype into ``float64``. + * \param handle Handle of dataset + * \param field_name Field name, can be \a label, \a weight, \a init_score, \a group + * \param stream Arrow stream pointer + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetSetFieldFromArrowStream(DatasetHandle handle, + const char* field_name, + struct ArrowArrayStream* stream); + +/*! + * \brief Get info vector from dataset. + * \param handle Handle of dataset + * \param field_name Field name + * \param[out] out_len Used to set result length + * \param[out] out_ptr Pointer to the result + * \param[out] out_type Type of result pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetGetField(DatasetHandle handle, + const char* field_name, + int* out_len, + const void** out_ptr, + int* out_type); + +/*! + * \brief Raise errors for attempts to update dataset parameters. + * \param old_parameters Current dataset parameters + * \param new_parameters New dataset parameters + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParamChecking(const char* old_parameters, + const char* new_parameters); + +/*! + * \brief Get number of data points. + * \param handle Handle of dataset + * \param[out] out The address to hold number of data points + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumData(DatasetHandle handle, + int* out); + +/*! + * \brief Get number of features. + * \param handle Handle of dataset + * \param[out] out The address to hold number of features + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetGetNumFeature(DatasetHandle handle, + int* out); + +/*! + * \brief Get number of bins for feature. + * \param handle Handle of dataset + * \param feature Index of the feature + * \param[out] out The address to hold number of bins + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNumBin(DatasetHandle handle, + int feature, + int* out); + +/*! + * \brief Add features from ``source`` to ``target``. + * \param target The handle of the dataset to add features to + * \param source The handle of the dataset to take features from + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_DatasetAddFeaturesFrom(DatasetHandle target, + DatasetHandle source); + +/* --- start Booster interfaces */ + +/*! +* \brief Get int representing whether booster is fitting linear trees. +* \param handle Handle of booster +* \param[out] out The address to hold linear trees indicator +* \return 0 when succeed, -1 when failure happens +*/ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetLinear(BoosterHandle handle, int* out); + +/*! + * \brief Create a new boosting learner. + * \param train_data Training dataset + * \param parameters Parameters in format 'key1=value1 key2=value2' + * \param[out] out Handle of created booster + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterCreate(const DatasetHandle train_data, + const char* parameters, + BoosterHandle* out); + +/*! + * \brief Load an existing booster from model file. + * \param filename Filename of model + * \param[out] out_num_iterations Number of iterations of this booster + * \param[out] out Handle of created booster + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(const char* filename, + int* out_num_iterations, + BoosterHandle* out); + +/*! + * \brief Load an existing booster from string. + * \param model_str Model string + * \param[out] out_num_iterations Number of iterations of this booster + * \param[out] out Handle of created booster + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(const char* model_str, + int* out_num_iterations, + BoosterHandle* out); + +/*! + * \brief Get parameters as JSON string. + * \param handle Handle of booster + * \param buffer_len Allocated space for string + * \param[out] out_len Actual size of string + * \param[out] out_str JSON string containing parameters + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetLoadedParam(BoosterHandle handle, + int64_t buffer_len, + int64_t* out_len, + char* out_str); + + +/*! + * \brief Free space for booster. + * \param handle Handle of booster to be freed + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterFree(BoosterHandle handle); + +/*! + * \brief Shuffle models. + * \param handle Handle of booster + * \param start_iter The first iteration that will be shuffled + * \param end_iter The last iteration that will be shuffled + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle, + int start_iter, + int end_iter); + +/*! + * \brief Merge model from ``other_handle`` into ``handle``. + * \param handle Handle of booster, will merge another booster into this one + * \param other_handle Other handle of booster + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterMerge(BoosterHandle handle, + BoosterHandle other_handle); + +/*! + * \brief Add new validation data to booster. + * \param handle Handle of booster + * \param valid_data Validation dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterAddValidData(BoosterHandle handle, + const DatasetHandle valid_data); + +/*! + * \brief Reset training data for booster. + * \param handle Handle of booster + * \param train_data Training dataset + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterResetTrainingData(BoosterHandle handle, + const DatasetHandle train_data); + +/*! + * \brief Reset config for booster. + * \param handle Handle of booster + * \param parameters Parameters in format 'key1=value1 key2=value2' + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle, + const char* parameters); + +/*! + * \brief Get number of classes. + * \param handle Handle of booster + * \param[out] out_len Number of classes + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle, + int* out_len); + +/*! + * \brief Update the model for one iteration. + * \param handle Handle of booster + * \param[out] produced_empty_tree 1 means the tree(s) produced by this iteration did not have any splits. + * This usually means that training is "finished" (calling this function again will not change the model's predictions). + * However, that is not always the case. + * For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``), + * it is possible that another call to this function would produce a non-empty tree. + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle, + int* produced_empty_tree); + +/*! + * \brief Refit the tree model using the new data (online learning). + * \param handle Handle of booster + * \param leaf_preds Pointer to predicted leaf indices + * \param nrow Number of rows of ``leaf_preds`` + * \param ncol Number of columns of ``leaf_preds`` + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle, + const int32_t* leaf_preds, + int32_t nrow, + int32_t ncol); + +/*! + * \brief Update the model by specifying gradient and Hessian directly + * (this can be used to support customized loss functions). + * \note + * The length of the arrays referenced by ``grad`` and ``hess`` must be equal to + * ``num_class * num_train_data``, this is not verified by the library, the caller must ensure this. + * \param handle Handle of booster + * \param grad The first order derivative (gradient) statistics + * \param hess The second order derivative (Hessian) statistics + * \param[out] produced_empty_tree 1 means the tree(s) produced by this iteration did not have any splits. + * This usually means that training is "finished" (calling this function again will not change the model's predictions). + * However, that is not always the case. + * For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``), + * it is possible that another call to this function would produce a non-empty tree. + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle, + const float* grad, + const float* hess, + int* produced_empty_tree); + +/*! + * \brief Rollback one iteration. + * \param handle Handle of booster + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterRollbackOneIter(BoosterHandle handle); + +/*! + * \brief Get index of the current boosting iteration. + * \param handle Handle of booster + * \param[out] out_iteration Index of the current boosting iteration + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle, + int* out_iteration); + +/*! + * \brief Get number of trees per iteration. + * \param handle Handle of booster + * \param[out] out_tree_per_iteration Number of trees per iteration + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle, + int* out_tree_per_iteration); + +/*! + * \brief Get number of weak sub-models. + * \param handle Handle of booster + * \param[out] out_models Number of weak sub-models + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, + int* out_models); + +/*! + * \brief Get number of evaluation metrics. + * \param handle Handle of booster + * \param[out] out_len Total number of evaluation metrics + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle, + int* out_len); + +/*! + * \brief Get names of evaluation metrics. + * \param handle Handle of booster + * \param len Number of ``char*`` pointers stored at ``out_strs``. + * If smaller than the max size, only this many strings are copied + * \param[out] out_len Total number of evaluation metrics + * \param buffer_len Size of pre-allocated strings. + * Content is copied up to ``buffer_len - 1`` and null-terminated + * \param[out] out_buffer_len String sizes required to do the full string copies + * \param[out] out_strs Names of evaluation metrics, should pre-allocate memory + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle, + const int len, + int* out_len, + const size_t buffer_len, + size_t* out_buffer_len, + char** out_strs); + +/*! + * \brief Get names of features. + * \param handle Handle of booster + * \param len Number of ``char*`` pointers stored at ``out_strs``. + * If smaller than the max size, only this many strings are copied + * \param[out] out_len Total number of features + * \param buffer_len Size of pre-allocated strings. + * Content is copied up to ``buffer_len - 1`` and null-terminated + * \param[out] out_buffer_len String sizes required to do the full string copies + * \param[out] out_strs Names of features, should pre-allocate memory + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetFeatureNames(BoosterHandle handle, + const int len, + int* out_len, + const size_t buffer_len, + size_t* out_buffer_len, + char** out_strs); + +/*! + * \brief Check that the feature names of the data match the ones used to train the booster. + * \param handle Handle of booster + * \param data_names Array with the feature names in the data + * \param data_num_features Number of features in the data + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterValidateFeatureNames(BoosterHandle handle, + const char** data_names, + int data_num_features); + +/*! + * \brief Get number of features. + * \param handle Handle of booster + * \param[out] out_len Total number of features + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumFeature(BoosterHandle handle, + int* out_len); + +/*! + * \brief Get evaluation for training data and validation data. + * \note + * 1. You should call ``LGBM_BoosterGetEvalNames`` first to get the names of evaluation metrics. + * 2. You should pre-allocate memory for ``out_results``, you can get its length by ``LGBM_BoosterGetEvalCounts``. + * \param handle Handle of booster + * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on + * \param[out] out_len Length of output result + * \param[out] out_results Array with evaluation results + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetEval(BoosterHandle handle, + int data_idx, + int* out_len, + double* out_results); + +/*! + * \brief Get number of predictions for training data and validation data + * (this can be used to support customized evaluation functions). + * \param handle Handle of booster + * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on + * \param[out] out_len Number of predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetNumPredict(BoosterHandle handle, + int data_idx, + int64_t* out_len); + +/*! + * \brief Get prediction for training data and validation data. + * \note + * You should pre-allocate memory for ``out_result``, its length is equal to ``num_class * num_data``. + * \param handle Handle of booster + * \param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetPredict(BoosterHandle handle, + int data_idx, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make prediction for file. + * \param handle Handle of booster + * \param data_filename Filename of file with data + * \param data_has_header Whether file has header or not + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iterations for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param result_filename Filename of result file in which predictions will be written + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForFile(BoosterHandle handle, + const char* data_filename, + int data_has_header, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + const char* result_filename); + +/*! + * \brief Get number of predictions. + * \param handle Handle of booster + * \param num_row Number of rows + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iterations for prediction, <= 0 means no limit + * \param[out] out_len Length of prediction + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterCalcNumPredict(BoosterHandle handle, + int num_row, + int predict_type, + int start_iteration, + int num_iteration, + int64_t* out_len); + +/*! + * \brief Release FastConfig object. + * + * \param fastConfig Handle to the FastConfig object acquired with a ``*FastInit()`` method. + * \return 0 when it succeeds, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_FastConfigFree(FastConfigHandle fastConfig); + +/*! + * \brief Make prediction for a new dataset in CSR format. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param indptr Pointer to row headers + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to column indices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nindptr Number of rows in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param num_col Number of columns + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iterations for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSR(BoosterHandle handle, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make sparse prediction for a new dataset in CSR or CSC format. Currently only used for feature contributions. + * \note + * The outputs are pre-allocated, as they can vary for each invocation, but the shape should be the same: + * - for feature contributions, the shape of sparse matrix will be ``num_class * num_data * (num_feature + 1)``. + * The output indptr_type for the sparse matrix will be the same as the given input indptr_type. + * Call ``LGBM_BoosterFreePredictSparse`` to deallocate resources. + * \param handle Handle of booster + * \param indptr Pointer to row headers for CSR or column headers for CSC + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to column indices for CSR or row indices for CSC + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nindptr Number of entries in ``indptr`` + * \param nelem Number of nonzero elements in the matrix + * \param num_col_or_row Number of columns for CSR or number of rows for CSC + * \param predict_type What should be predicted, only feature contributions supported currently + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iterations for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param matrix_type Type of matrix input and output, can be ``C_API_MATRIX_TYPE_CSR`` or ``C_API_MATRIX_TYPE_CSC`` + * \param[out] out_len Length of output data and output indptr (pointer to an array with two entries where to write them) + * \param[out] out_indptr Pointer to output row headers for CSR or column headers for CSC + * \param[out] out_indices Pointer to sparse column indices for CSR or row indices for CSC + * \param[out] out_data Pointer to sparse data space + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictSparseOutput(BoosterHandle handle, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col_or_row, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int matrix_type, + int64_t* out_len, + void** out_indptr, + int32_t** out_indices, + void** out_data); + +/*! + * \brief Method corresponding to ``LGBM_BoosterPredictSparseOutput`` to free the allocated data. + * \param indptr Pointer to output row headers or column headers to be deallocated + * \param indices Pointer to sparse indices to be deallocated + * \param data Pointer to sparse data space to be deallocated + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterFreePredictSparse(void* indptr, int32_t* indices, void* data, int indptr_type, int data_type); + +/*! + * \brief Make prediction for a new dataset in CSR format. This method re-uses the internal predictor structure + * from previous calls and is optimized for single row invocation. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param indptr Pointer to row headers + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to column indices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nindptr Number of rows in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param num_col Number of columns + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iterations for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForCSRSingleRowFast``. + * + * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed. + * + * \param handle Booster handle + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iterations for prediction, <= 0 means no limit + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param num_col Number of columns + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForCSRSingleRowFast`` + * \return 0 when it succeeds, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFastInit(BoosterHandle handle, + const int predict_type, + const int start_iteration, + const int num_iteration, + const int data_type, + const int64_t num_col, + const char* parameter, + FastConfigHandle *out_fastConfig); + +/*! + * \brief Faster variant of ``LGBM_BoosterPredictForCSRSingleRow``. + * + * Score single rows after setup with ``LGBM_BoosterPredictForCSRSingleRowFastInit``. + * + * By removing the setup steps from this call extra optimizations can be made like + * initializing the config only once, instead of once per call. + * + * \note + * Setting up the number of threads is only done once at ``LGBM_BoosterPredictForCSRSingleRowFastInit`` + * instead of at each prediction. + * If you use a different number of threads in other calls, you need to start the setup process over, + * or that number of threads will be used for these calls as well. + * + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * + * \param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForCSRSingleRowFastInit`` + * \param indptr Pointer to row headers + * \param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to column indices + * \param data Pointer to the data space + * \param nindptr Number of rows in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFast(FastConfigHandle fastConfig_handle, + const void* indptr, + const int indptr_type, + const int32_t* indices, + const void* data, + const int64_t nindptr, + const int64_t nelem, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make prediction for a new dataset in CSC format. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param col_ptr Pointer to column headers + * \param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64`` + * \param indices Pointer to row indices + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param ncol_ptr Number of columns in the matrix + 1 + * \param nelem Number of nonzero elements in the matrix + * \param num_row Number of rows + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iteration for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSC(BoosterHandle handle, + const void* col_ptr, + int col_ptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t ncol_ptr, + int64_t nelem, + int64_t num_row, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make prediction for a new dataset. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nrow Number of rows + * \param ncol Number of columns + * \param is_row_major 1 for row-major, 0 for column-major + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iteration for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMat(BoosterHandle handle, + const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int is_row_major, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make prediction for a new dataset. This method re-uses the internal predictor structure + * from previous calls and is optimized for single row invocation. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param ncol Number columns + * \param is_row_major 1 for row-major, 0 for column-major + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iteration for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle, + const void* data, + int data_type, + int ncol, + int is_row_major, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForMatSingleRowFast``. + * + * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed. + * + * \param handle Booster handle + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iterations for prediction, <= 0 means no limit + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param ncol Number of columns + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForMatSingleRowFast`` + * \return 0 when it succeeds, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFastInit(BoosterHandle handle, + const int predict_type, + const int start_iteration, + const int num_iteration, + const int data_type, + const int32_t ncol, + const char* parameter, + FastConfigHandle *out_fastConfig); + +/*! + * \brief Faster variant of ``LGBM_BoosterPredictForMatSingleRow``. + * + * Score a single row after setup with ``LGBM_BoosterPredictForMatSingleRowFastInit``. + * + * By removing the setup steps from this call extra optimizations can be made like + * initializing the config only once, instead of once per call. + * + * \note + * Setting up the number of threads is only done once at ``LGBM_BoosterPredictForMatSingleRowFastInit`` + * instead of at each prediction. + * If you use a different number of threads in other calls, you need to start the setup process over, + * or that number of threads will be used for these calls as well. + * + * \param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForMatSingleRowFastInit`` + * \param data Single-row array data (no other way than row-major form). + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when it succeeds, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFast(FastConfigHandle fastConfig_handle, + const void* data, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make prediction for a new dataset presented in a form of array of pointers to rows. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param data Pointer to the data space + * \param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64`` + * \param nrow Number of rows + * \param ncol Number columns + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iteration for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMats(BoosterHandle handle, + const void** data, + int data_type, + int32_t nrow, + int32_t ncol, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make prediction for a new dataset. + * \deprecated This function is deprecated in favor of ``LGBM_BoosterPredictForArrowStream``. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param n_chunks The number of Arrow arrays passed to this function + * \param chunks Pointer to the list of Arrow arrays + * \param schema Pointer to the schema of all Arrow arrays + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iteration for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT LIGHTGBM_DEPRECATED("Use LGBM_BoosterPredictForArrowStream instead.") +int LGBM_BoosterPredictForArrow(BoosterHandle handle, + int64_t n_chunks, + struct ArrowArray* chunks, + struct ArrowSchema* schema, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Make prediction for a new dataset. + * \note + * You should pre-allocate memory for ``out_result``: + * - for normal and raw score, its length is equal to ``num_class * num_data``; + * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``; + * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``. + * \param handle Handle of booster + * \param stream Arrow stream pointer + * \param predict_type What should be predicted + * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed); + * - ``C_API_PREDICT_RAW_SCORE``: raw score; + * - ``C_API_PREDICT_LEAF_INDEX``: leaf index; + * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values) + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of iteration for prediction, <= 0 means no limit + * \param parameter Other parameters for prediction, e.g. early stopping for prediction + * \param[out] out_len Length of output result + * \param[out] out_result Pointer to array with predictions + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterPredictForArrowStream(BoosterHandle handle, + struct ArrowArrayStream* stream, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result); + +/*! + * \brief Save model into file. + * \param handle Handle of booster + * \param start_iteration Start index of the iteration that should be saved + * \param num_iteration Index of the iteration that should be saved, <= 0 means save all + * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN`` + * \param filename The name of the file + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModel(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + const char* filename); + +/*! + * \brief Save model to string. + * \param handle Handle of booster + * \param start_iteration Start index of the iteration that should be saved + * \param num_iteration Index of the iteration that should be saved, <= 0 means save all + * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN`` + * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer + * \param[out] out_len Actual output length + * \param[out] out_str String of model, should pre-allocate memory + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterSaveModelToString(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + int64_t buffer_len, + int64_t* out_len, + char* out_str); + +/*! + * \brief Dump model to JSON. + * \param handle Handle of booster + * \param start_iteration Start index of the iteration that should be dumped + * \param num_iteration Index of the iteration that should be dumped, <= 0 means dump all + * \param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN`` + * \param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer + * \param[out] out_len Actual output length + * \param[out] out_str JSON format string of model, should pre-allocate memory + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterDumpModel(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + int64_t buffer_len, + int64_t* out_len, + char* out_str); + +/*! + * \brief Get leaf value. + * \param handle Handle of booster + * \param tree_idx Index of tree + * \param leaf_idx Index of leaf + * \param[out] out_val Output result from the specified leaf + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetLeafValue(BoosterHandle handle, + int tree_idx, + int leaf_idx, + double* out_val); + +/*! + * \brief Set leaf value. + * \param handle Handle of booster + * \param tree_idx Index of tree + * \param leaf_idx Index of leaf + * \param val Leaf value + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterSetLeafValue(BoosterHandle handle, + int tree_idx, + int leaf_idx, + double val); + +/*! + * \brief Get model feature importance. + * \param handle Handle of booster + * \param num_iteration Number of iterations for which feature importance is calculated, <= 0 means use all + * \param importance_type Method of importance calculation: + * - ``C_API_FEATURE_IMPORTANCE_SPLIT``: result contains numbers of times the feature is used in a model; + * - ``C_API_FEATURE_IMPORTANCE_GAIN``: result contains total gains of splits which use the feature + * \param[out] out_results Result array with feature importance + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterFeatureImportance(BoosterHandle handle, + int num_iteration, + int importance_type, + double* out_results); + +/*! + * \brief Get model upper bound value. + * \param handle Handle of booster + * \param[out] out_results Result pointing to max value + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetUpperBoundValue(BoosterHandle handle, + double* out_results); + +/*! + * \brief Get model lower bound value. + * \param handle Handle of booster + * \param[out] out_results Result pointing to min value + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_BoosterGetLowerBoundValue(BoosterHandle handle, + double* out_results); + +/*! + * \brief Initialize the network. + * \param machines List of machines in format 'ip1:port1,ip2:port2' + * \param local_listen_port TCP listen port for local machines + * \param listen_time_out Socket time-out in minutes + * \param num_machines Total number of machines + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_NetworkInit(const char* machines, + int local_listen_port, + int listen_time_out, + int num_machines); + +/*! + * \brief Finalize the network. + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_NetworkFree(); + +/*! + * \brief Initialize the network with external collective functions. + * \param num_machines Total number of machines + * \param rank Rank of local machine + * \param reduce_scatter_ext_fun The external reduce-scatter function + * \param allgather_ext_fun The external allgather function + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_NetworkInitWithFunctions(int num_machines, + int rank, + void* reduce_scatter_ext_fun, + void* allgather_ext_fun); + +/*! + * \brief Set maximum number of threads used by LightGBM routines in this process. + * \param num_threads maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_SetMaxThreads(int num_threads); + +/*! + * \brief Get current maximum number of threads used by LightGBM routines in this process. + * \param[out] out current maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads(). + * \return 0 when succeed, -1 when failure happens + */ +LIGHTGBM_C_EXPORT int LGBM_GetMaxThreads(int* out); + +#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L)) +/*! \brief Inline specifier no-op in C using standards before C99. */ +#define INLINE_FUNCTION +#else +/*! \brief Inline specifier. */ +#define INLINE_FUNCTION inline +#endif + +#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 201112L)) +/*! \brief Thread local specifier no-op in C using standards before C11. */ +#define THREAD_LOCAL +#elif !defined(__cplusplus) +/*! \brief Thread local specifier. */ +#define THREAD_LOCAL _Thread_local +#elif defined(_MSC_VER) +/*! \brief Thread local specifier. */ +#define THREAD_LOCAL __declspec(thread) +#else +/*! \brief Thread local specifier. */ +#define THREAD_LOCAL thread_local +#endif + +/*! + * \brief Handle of error message. + * \return Error message + */ +static char* LastErrorMsg() { static THREAD_LOCAL char err_msg[512] = "Everything is fine"; return err_msg; } + +#ifdef _MSC_VER + #pragma warning(disable : 4996) +#endif +/*! + * \brief Set string message of the last error. + * \note + * This will call unsafe ``sprintf`` when compiled using C standards before C99. + * \param msg Error message + */ +INLINE_FUNCTION void LGBM_SetLastError(const char* msg) { +#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L)) + sprintf(LastErrorMsg(), "%s", msg); /* NOLINT(runtime/printf) */ +#else + const int err_buf_len = 512; + snprintf(LastErrorMsg(), err_buf_len, "%s", msg); +#endif +} + +#endif /* LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_ */ diff --git a/include/LightGBM/config.h b/include/LightGBM/config.h new file mode 100644 index 0000000..7d466cb --- /dev/null +++ b/include/LightGBM/config.h @@ -0,0 +1,1334 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * \note + * - desc and descl2 fields must be written in reStructuredText format; + * - nested sections can be placed only at the bottom of parent's section; + * - [no-automatically-extract] + * - do not automatically extract this parameter into a Config property with the same name in Config::GetMembersFromString(). Use if: + * - specialized extraction logic for this param exists in Config::GetMembersFromString() + * - [no-save] + * - this param should not be saved into a model text representation via Config::SaveMembersToString(). Use if: + * - param is only used by the CLI (especially the "predict" and "convert_model" tasks) + * - param is related to LightGBM writing files (e.g. "output_model", "save_binary") + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_ + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +/*! \brief Types of tasks */ +enum TaskType { + kTrain, kPredict, kConvertModel, KRefitTree, kSaveBinary +}; +const int kDefaultNumLeaves = 31; + +struct Config { + public: + Config() {} + explicit Config(std::unordered_map parameters_map) { + Set(parameters_map); + } + std::string ToString() const; + /*! + * \brief Get string value by specific name of key + * \param params Store the key and value for params + * \param name Name of key + * \param out Value will assign to out if key exists + * \return True if key exists + */ + inline static bool GetString( + const std::unordered_map& params, + const std::string& name, std::string* out); + + /*! + * \brief Get int value by specific name of key + * \param params Store the key and value for params + * \param name Name of key + * \param out Value will assign to out if key exists + * \return True if key exists + */ + inline static bool GetInt( + const std::unordered_map& params, + const std::string& name, int* out); + + /*! + * \brief Get double value by specific name of key + * \param params Store the key and value for params + * \param name Name of key + * \param out Value will assign to out if key exists + * \return True if key exists + */ + inline static bool GetDouble( + const std::unordered_map& params, + const std::string& name, double* out); + + /*! + * \brief Get bool value by specific name of key + * \param params Store the key and value for params + * \param name Name of key + * \param out Value will assign to out if key exists + * \return True if key exists + */ + inline static bool GetBool( + const std::unordered_map& params, + const std::string& name, bool* out); + + /*! + * \brief Sort aliases by length and then alphabetically + * \param x Alias 1 + * \param y Alias 2 + * \return true if x has higher priority than y + */ + inline static bool SortAlias(const std::string& x, const std::string& y); + + static void KeepFirstValues(const std::unordered_map>& params, std::unordered_map* out); + static void KV2Map(std::unordered_map>* params, const char* kv); + static void SetVerbosity(const std::unordered_map>& params); + static std::unordered_map Str2Map(const char* parameters); + + #ifndef __NVCC__ + #pragma region Parameters + + #pragma region Core Parameters + #endif // __NVCC__ + + // [no-automatically-extract] + // [no-save] + // alias = config_file + // desc = path of config file + // desc = **Note**: can be used only in CLI version + std::string config = ""; + + // [no-automatically-extract] + // [no-save] + // type = enum + // default = train + // options = train, predict, convert_model, refit + // alias = task_type + // desc = ``train``, for training, aliases: ``training`` + // desc = ``predict``, for prediction, aliases: ``prediction``, ``test`` + // desc = ``convert_model``, for converting model file into if-else format, see more information in `Convert Parameters <#convert-parameters>`__ + // desc = ``refit``, for refitting existing models with new data, aliases: ``refit_tree`` + // desc = ``save_binary``, load train (and validation) data then save dataset to binary file. Typical usage: ``save_binary`` first, then run multiple ``train`` tasks in parallel using the saved binary file + // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions + TaskType task = TaskType::kTrain; + + // [no-automatically-extract] + // [no-save] + // type = enum + // options = regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie, binary, multiclass, multiclassova, cross_entropy, cross_entropy_lambda, lambdarank, rank_xendcg + // alias = objective_type, app, application, loss + // desc = regression application + // descl2 = ``regression``, L2 loss, aliases: ``regression_l2``, ``l2``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse`` + // descl2 = ``regression_l1``, L1 loss, aliases: ``l1``, ``mean_absolute_error``, ``mae`` + // descl2 = ``huber``, `Huber loss `__ + // descl2 = ``fair``, `Fair loss `__ + // descl2 = ``poisson``, `Poisson regression `__ + // descl2 = ``quantile``, `Quantile regression `__ + // descl2 = ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` + // descl2 = ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed `__ + // descl2 = ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed `__ + // desc = binary classification application + // descl2 = ``binary``, binary `log loss `__ classification (or logistic regression) + // descl2 = requires labels in {0, 1}; see ``cross-entropy`` application for general probability labels in [0, 1] + // desc = multi-class classification application + // descl2 = ``multiclass``, `softmax `__ objective function, aliases: ``softmax`` + // descl2 = ``multiclassova``, `One-vs-All `__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr`` + // descl2 = ``num_class`` should be set as well + // desc = cross-entropy application + // descl2 = ``cross_entropy``, objective function for cross-entropy (with optional linear weights), aliases: ``xentropy`` + // descl2 = ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda`` + // descl2 = label is anything in interval [0, 1] + // desc = ranking application + // descl2 = ``lambdarank``, `lambdarank `__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain`` + // descl2 = ``rank_xendcg``, `XE_NDCG_MART `__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` + // descl2 = ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank`` + // descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect) + // desc = custom objective function (gradients and hessians not computed directly by LightGBM) + // descl2 = ``custom`` + // descl2 = must be passed through parameters explicitly in the C API + // descl2 = **Note**: cannot be used in CLI version + std::string objective = "regression"; + + // [no-automatically-extract] + // [no-save] + // type = enum + // alias = boosting_type, boost + // options = gbdt, rf, dart + // desc = ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt`` + // desc = ``rf``, Random Forest, aliases: ``random_forest`` + // desc = ``dart``, `Dropouts meet Multiple Additive Regression Trees `__ + // descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations + std::string boosting = "gbdt"; + + // [no-automatically-extract] + // type = enum + // options = bagging, goss + // desc = ``bagging``, Randomly Bagging Sampling + // descl2 = **Note**: ``bagging`` is only effective when ``bagging_freq > 0`` and ``bagging_fraction < 1.0`` + // desc = ``goss``, Gradient-based One-Side Sampling + // desc = *New in version 4.0.0* + std::string data_sample_strategy = "bagging"; + + // alias = train, train_data, train_data_file, data_filename + // desc = path of training data, LightGBM will train from this data + // desc = **Note**: can be used only in CLI version + std::string data = ""; + + // alias = test, valid_data, valid_data_file, test_data, test_data_file, valid_filenames + // default = "" + // desc = path(s) of validation/test data, LightGBM will output metrics for these data + // desc = support multiple validation data, separated by ``,`` + // desc = **Note**: can be used only in CLI version + std::vector valid; + + // alias = num_iteration, n_iter, num_tree, num_trees, num_round, num_rounds, nrounds, num_boost_round, n_estimators, max_iter + // check = >=0 + // desc = number of boosting iterations + // desc = **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems + int num_iterations = 100; + + // alias = shrinkage_rate, eta + // check = >0.0 + // desc = shrinkage rate + // desc = in ``dart``, it also affects on normalization weights of dropped trees + double learning_rate = 0.1; + + // default = 31 + // alias = num_leaf, max_leaves, max_leaf, max_leaf_nodes + // check = >1 + // check = <=131072 + // desc = max number of leaves in one tree + int num_leaves = kDefaultNumLeaves; + + // [no-automatically-extract] + // [no-save] + // type = enum + // options = serial, feature, data, voting + // alias = tree, tree_type, tree_learner_type + // desc = ``serial``, single machine tree learner + // desc = ``feature``, feature parallel tree learner, aliases: ``feature_parallel`` + // desc = ``data``, data parallel tree learner, aliases: ``data_parallel`` + // desc = ``voting``, voting parallel tree learner, aliases: ``voting_parallel`` + // desc = refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details + std::string tree_learner = "serial"; + + // alias = num_thread, nthread, nthreads, n_jobs + // desc = used only in ``train``, ``prediction`` and ``refit`` tasks or in correspondent functions of language-specific packages + // desc = number of threads for LightGBM + // desc = ``0`` means default number of threads in OpenMP + // desc = for the best speed, set this to the number of **real CPU cores**, not the number of threads (most CPUs use `hyper-threading `__ to generate 2 threads per CPU core) + // desc = do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows) + // desc = be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal** + // desc = for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication + // desc = **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors + int num_threads = 0; + + // [no-automatically-extract] + // [no-save] + // type = enum + // options = cpu, gpu, cuda + // alias = device + // desc = device for the tree learning + // desc = ``cpu`` supports all LightGBM functionality and is portable across the widest range of operating systems and hardware + // desc = ``cuda`` offers faster training than ``gpu`` or ``cpu``, but only works on GPUs supporting CUDA or ROCm + // desc = ``gpu`` can be faster than ``cpu`` and works on a wider range of GPUs than CUDA + // desc = **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up + // desc = **Note**: for the faster speed, GPU uses 32-bit float point to sum up by default, so this may affect the accuracy for some tasks. You can set ``gpu_use_dp=true`` to enable 64-bit float point, but it will slow down the training + // desc = **Note**: refer to `Installation Guide <./Installation-Guide.rst>`__ to build LightGBM with GPU, CUDA, or ROCm support + std::string device_type = "cpu"; + + // [no-automatically-extract] + // alias = random_seed, random_state + // default = None + // desc = this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc. + // desc = by default, this seed is unused in favor of default values of other seeds + // desc = this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly + int seed = 0; + + // desc = used only with ``cpu`` device type + // desc = setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``) + // desc = when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different + // desc = you can `raise issues `__ in LightGBM GitHub repo when you meet the unstable results + // desc = **Note**: setting this to ``true`` may slow down the training + // desc = **Note**: to avoid potential instability due to numerical issues, please set ``force_col_wise=true`` or ``force_row_wise=true`` when setting ``deterministic=true`` + bool deterministic = false; + + #ifndef __NVCC__ + #pragma endregion + + #pragma region Learning Control Parameters + #endif // __NVCC__ + + // desc = used only with ``cpu`` device type + // desc = set this to ``true`` to force col-wise histogram building + // desc = enabling this is recommended when: + // descl2 = the number of columns is large, or the total number of bins is large + // descl2 = ``num_threads`` is large, e.g. ``> 20`` + // descl2 = you want to reduce memory cost + // desc = **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually + // desc = **Note**: this parameter cannot be used at the same time with ``force_row_wise``, choose only one of them + bool force_col_wise = false; + + // desc = used only with ``cpu`` device type + // desc = set this to ``true`` to force row-wise histogram building + // desc = enabling this is recommended when: + // descl2 = the number of data points is large, and the total number of bins is relatively small + // descl2 = ``num_threads`` is relatively small, e.g. ``<= 16`` + // descl2 = you want to use small ``bagging_fraction`` or ``goss`` sample strategy to speed up + // desc = **Note**: setting this to ``true`` will double the memory cost for Dataset object. If you have not enough memory, you can try setting ``force_col_wise=true`` + // desc = **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually + // desc = **Note**: this parameter cannot be used at the same time with ``force_col_wise``, choose only one of them + bool force_row_wise = false; + + // alias = hist_pool_size + // desc = max cache size in MB for historical histogram + // desc = ``< 0`` means no limit + double histogram_pool_size = -1.0; + + // desc = limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise + // desc = ``<= 0`` means no limit + int max_depth = -1; + + // alias = min_data_per_leaf, min_data, min_child_samples, min_samples_leaf + // check = >=0 + // desc = minimal number of data in one leaf. Can be used to deal with over-fitting + // desc = **Note**: this is an approximation based on the Hessian, so occasionally you may observe splits which produce leaf nodes that have less than this many observations + int min_data_in_leaf = 20; + + // alias = min_sum_hessian_per_leaf, min_sum_hessian, min_hessian, min_child_weight + // check = >=0.0 + // desc = minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting + double min_sum_hessian_in_leaf = 1e-3; + + // alias = sub_row, subsample, bagging + // check = >0.0 + // check = <=1.0 + // desc = like ``feature_fraction``, but this will randomly select part of data without resampling + // desc = can be used to speed up training + // desc = can be used to deal with over-fitting + // desc = **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well + double bagging_fraction = 1.0; + + // alias = pos_sub_row, pos_subsample, pos_bagging + // check = >0.0 + // check = <=1.0 + // desc = used only in ``binary`` application + // desc = used for imbalanced binary classification problem, will randomly sample ``#pos_samples * pos_bagging_fraction`` positive samples in bagging + // desc = should be used together with ``neg_bagging_fraction`` + // desc = set this to ``1.0`` to disable + // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``neg_bagging_fraction`` as well + // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled + // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored + double pos_bagging_fraction = 1.0; + + // alias = neg_sub_row, neg_subsample, neg_bagging + // check = >0.0 + // check = <=1.0 + // desc = used only in ``binary`` application + // desc = used for imbalanced binary classification problem, will randomly sample ``#neg_samples * neg_bagging_fraction`` negative samples in bagging + // desc = should be used together with ``pos_bagging_fraction`` + // desc = set this to ``1.0`` to disable + // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``pos_bagging_fraction`` as well + // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled + // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored + double neg_bagging_fraction = 1.0; + + // alias = subsample_freq + // desc = frequency for bagging + // desc = ``0`` means disable bagging; ``k`` means perform bagging at every ``k`` iteration. Every ``k``-th iteration, LightGBM will randomly select ``bagging_fraction * 100%`` of the data to use for the next ``k`` iterations + // desc = **Note**: bagging is only effective when ``0.0 < bagging_fraction < 1.0`` + int bagging_freq = 0; + + // alias = bagging_fraction_seed + // desc = random seed for bagging + int bagging_seed = 3; + + // desc = whether to do bagging sample by query + // desc = *New in version 4.6.0* + bool bagging_by_query = false; + + // alias = sub_feature, colsample_bytree + // check = >0.0 + // check = <=1.0 + // desc = LightGBM will randomly select a subset of features on each iteration (tree) if ``feature_fraction`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features before training each tree + // desc = can be used to speed up training + // desc = can be used to deal with over-fitting + double feature_fraction = 1.0; + + // alias = sub_feature_bynode, colsample_bynode + // check = >0.0 + // check = <=1.0 + // desc = LightGBM will randomly select a subset of features on each tree node if ``feature_fraction_bynode`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features at each tree node + // desc = can be used to deal with over-fitting + // desc = **Note**: unlike ``feature_fraction``, this cannot speed up training + // desc = **Note**: if both ``feature_fraction`` and ``feature_fraction_bynode`` are smaller than ``1.0``, the final fraction of each node is ``feature_fraction * feature_fraction_bynode`` + double feature_fraction_bynode = 1.0; + + // desc = random seed for ``feature_fraction`` + int feature_fraction_seed = 2; + + // alias = extra_tree + // desc = use extremely randomized trees + // desc = if set to ``true``, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature + // desc = can be used to speed up training + // desc = can be used to deal with over-fitting + bool extra_trees = false; + + // desc = random seed for selecting thresholds when ``extra_trees`` is true + int extra_seed = 6; + + // alias = early_stopping_rounds, early_stopping, n_iter_no_change + // desc = will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds + // desc = ``<= 0`` means disable + // desc = can be used to speed up training + int early_stopping_round = 0; + + // check = >=0.0 + // desc = when early stopping is used (i.e. ``early_stopping_round > 0``), require the early stopping metric to improve by at least this delta to be considered an improvement + // desc = *New in version 4.4.0* + double early_stopping_min_delta = 0.0; + + // desc = LightGBM allows you to provide multiple evaluation metrics. Set this to ``true``, if you want to use only the first metric for early stopping + bool first_metric_only = false; + + // alias = max_tree_output, max_leaf_output + // desc = used to limit the max output of tree leaves + // desc = ``<= 0`` means no constraint + // desc = the final max output of leaves is ``learning_rate * max_delta_step`` + double max_delta_step = 0.0; + + // alias = reg_alpha, l1_regularization + // check = >=0.0 + // desc = L1 regularization + double lambda_l1 = 0.0; + + // alias = reg_lambda, lambda, l2_regularization + // check = >=0.0 + // desc = L2 regularization + double lambda_l2 = 0.0; + + // check = >=0.0 + // desc = linear tree regularization, corresponds to the parameter ``lambda`` in Eq. 3 of `Gradient Boosting with Piece-Wise Linear Regression Trees `__ + double linear_lambda = 0.0; + + // alias = min_split_gain + // check = >=0.0 + // desc = the minimal gain to perform split + // desc = can be used to speed up training + double min_gain_to_split = 0.0; + + // alias = rate_drop + // check = >=0.0 + // check = <=1.0 + // desc = used only in ``dart`` + // desc = dropout rate: a fraction of previous trees to drop during the dropout + double drop_rate = 0.1; + + // desc = used only in ``dart`` + // desc = max number of dropped trees during one boosting iteration + // desc = ``<=0`` means no limit + int max_drop = 50; + + // check = >=0.0 + // check = <=1.0 + // desc = used only in ``dart`` + // desc = probability of skipping the dropout procedure during a boosting iteration + double skip_drop = 0.5; + + // desc = used only in ``dart`` + // desc = set this to ``true``, if you want to use XGBoost DART mode + bool xgboost_dart_mode = false; + + // desc = used only in ``dart`` + // desc = set this to ``true``, if you want to use uniform drop + bool uniform_drop = false; + + // desc = used only in ``dart`` + // desc = random seed to choose dropping models + int drop_seed = 4; + + // check = >=0.0 + // check = <=1.0 + // desc = used only in ``goss`` + // desc = the retain ratio of large gradient data + double top_rate = 0.2; + + // check = >=0.0 + // check = <=1.0 + // desc = used only in ``goss`` + // desc = the retain ratio of small gradient data + double other_rate = 0.1; + + // check = >0 + // desc = used for the categorical features + // desc = minimal number of data per categorical group + int min_data_per_group = 100; + + // check = >0 + // desc = used for the categorical features + // desc = limit number of split points considered for categorical features. See `the documentation on how LightGBM finds optimal splits for categorical features <./Features.rst#optimal-split-for-categorical-features>`_ for more details + // desc = can be used to speed up training + int max_cat_threshold = 32; + + // check = >=0.0 + // desc = used for the categorical features + // desc = L2 regularization in categorical split + double cat_l2 = 10.0; + + // check = >=0.0 + // desc = used for the categorical features + // desc = this can reduce the effect of noises in categorical features, especially for categories with few data + double cat_smooth = 10.0; + + // check = >0 + // desc = used for the categorical features + // desc = when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used + int max_cat_to_onehot = 4; + + // alias = topk + // check = >0 + // desc = used only in ``voting`` tree learner, refer to `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__ + // desc = set this to larger value for more accurate result, but it will slow down the training speed + int top_k = 20; + + // type = multi-int + // alias = mc, monotone_constraint, monotonic_cst + // default = None + // desc = used for constraints of monotonic features + // desc = ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint + // desc = you need to specify all features in order. For example, ``mc=-1,0,1`` means decreasing for the 1st feature, non-constraint for the 2nd feature and increasing for the 3rd feature + std::vector monotone_constraints; + + // type = enum + // alias = monotone_constraining_method, mc_method + // options = basic, intermediate, advanced + // desc = used only if ``monotone_constraints`` is set + // desc = monotone constraints method + // descl2 = ``basic``, the most basic monotone constraints method. It does not slow down the training speed at all, but over-constrains the predictions + // descl2 = ``intermediate``, a `more advanced method `__, which may slow down the training speed very slightly. However, this method is much less constraining than the basic method and should significantly improve the results + // descl2 = ``advanced``, an `even more advanced method `__, which may slow down the training speed. However, this method is even less constraining than the intermediate method and should again significantly improve the results + std::string monotone_constraints_method = "basic"; + + // alias = monotone_splits_penalty, ms_penalty, mc_penalty + // check = >=0.0 + // desc = used only if ``monotone_constraints`` is set + // desc = `monotone penalty `__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter + // desc = if ``0.0`` (the default), no penalization is applied + double monotone_penalty = 0.0; + + // type = multi-double + // alias = feature_contrib, fc, fp, feature_penalty + // default = None + // desc = used to control feature's split gain, will use ``gain[i] = max(0, feature_contri[i]) * gain[i]`` to replace the split gain of i-th feature + // desc = you need to specify all features in order + std::vector feature_contri; + + // alias = fs, forced_splits_filename, forced_splits_file, forced_splits + // desc = path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences + // desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits + // desc = categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values + // desc = **Note**: the forced split logic will be ignored, if the split makes gain worse + // desc = see `this file `__ as an example + std::string forcedsplits_filename = ""; + + // check = >=0.0 + // check = <=1.0 + // desc = decay rate of ``refit`` task, will use ``leaf_output = refit_decay_rate * old_leaf_output + (1.0 - refit_decay_rate) * new_leaf_output`` to refit trees + // desc = used only in ``refit`` task in CLI version or as argument in ``refit`` function in language-specific package + double refit_decay_rate = 0.9; + + // check = >=0.0 + // desc = cost-effective gradient boosting multiplier for all penalties + double cegb_tradeoff = 1.0; + + // check = >=0.0 + // desc = cost-effective gradient-boosting penalty for splitting a node + double cegb_penalty_split = 0.0; + + // type = multi-double + // default = 0,0,...,0 + // desc = cost-effective gradient boosting penalty for using a feature + // desc = applied per data point + std::vector cegb_penalty_feature_lazy; + + // type = multi-double + // default = 0,0,...,0 + // desc = cost-effective gradient boosting penalty for using a feature + // desc = applied once per forest + std::vector cegb_penalty_feature_coupled; + + // check = >= 0.0 + // desc = controls smoothing applied to tree nodes + // desc = helps prevent overfitting on leaves with few samples + // desc = if ``0.0`` (the default), no smoothing is applied + // desc = if ``path_smooth > 0`` then ``min_data_in_leaf`` must be at least ``2`` + // desc = larger values give stronger regularization + // descl2 = the weight of each node is ``w * (n / path_smooth) / (n / path_smooth + 1) + w_p / (n / path_smooth + 1)``, where ``n`` is the number of samples in the node, ``w`` is the optimal node weight to minimise the loss (approximately ``-sum_gradients / sum_hessians``), and ``w_p`` is the weight of the parent node + // descl2 = note that the parent output ``w_p`` itself has smoothing applied, unless it is the root node, so that the smoothing effect accumulates with the tree depth + double path_smooth = 0; + + // desc = controls which features can appear in the same branch + // desc = by default interaction constraints are disabled, to enable them you can specify + // descl2 = for CLI, lists separated by commas, e.g. ``[0,1,2],[2,3]`` + // descl2 = for Python-package, list of lists, e.g. ``[[0, 1, 2], [2, 3]]`` + // descl2 = for R-package, list of character or numeric vectors, e.g. ``list(c("var1", "var2", "var3"), c("var3", "var4"))`` or ``list(c(1L, 2L, 3L), c(3L, 4L))``. Numeric vectors should use 1-based indexing, where ``1L`` is the first feature, ``2L`` is the second feature, etc. + // desc = any two features can only appear in the same branch only if there exists a constraint containing both features + std::string interaction_constraints = ""; + + // alias = verbose + // desc = controls the level of LightGBM's verbosity + // desc = ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug + int verbosity = 1; + + // [no-save] + // alias = model_input, model_in + // desc = filename of input model + // desc = for ``prediction`` task, this model will be applied to prediction data + // desc = for ``train`` task, training will be continued from this model + // desc = **Note**: can be used only in CLI version + std::string input_model = ""; + + // [no-save] + // alias = model_output, model_out + // desc = filename of output model in training + // desc = **Note**: can be used only in CLI version + std::string output_model = "LightGBM_model.txt"; + + // desc = the feature importance type in the saved model file + // desc = ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted) + // desc = **Note**: can be used only in CLI version + int saved_feature_importance_type = 0; + + // [no-save] + // alias = save_period + // desc = frequency of saving model file snapshot + // desc = set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1`` + // desc = **Note**: can be used only in CLI version + int snapshot_freq = -1; + + // desc = whether to use gradient quantization when training + // desc = enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins`` + // desc = with quantized training, most arithmetics in the training process will be integer operations + // desc = gradient quantization can accelerate training, with little accuracy drop in most cases + // desc = **Note**: works only with ``cpu`` and ``cuda`` device type + // desc = *New in version 4.0.0* + bool use_quantized_grad = false; + + // desc = used only if ``use_quantized_grad=true`` + // desc = number of bins to quantization gradients and hessians + // desc = with more bins, the quantized training will be closer to full precision training + // desc = **Note**: works only with ``cpu`` and ``cuda`` device type + // desc = *New in version 4.0.0* + int num_grad_quant_bins = 4; + + // desc = used only if ``use_quantized_grad=true`` + // desc = whether to renew the leaf values with original gradients when quantized training + // desc = renewing is very helpful for good quantized training accuracy for ranking objectives + // desc = **Note**: works only with ``cpu`` and ``cuda`` device type + // desc = *New in version 4.0.0* + bool quant_train_renew_leaf = false; + + // desc = used only if ``use_quantized_grad=true`` + // desc = whether to use stochastic rounding in gradient quantization + // desc = **Note**: works only with ``cpu`` and ``cuda`` device type + // desc = *New in version 4.0.0* + bool stochastic_rounding = true; + + #ifndef __NVCC__ + #pragma endregion + + #pragma region IO Parameters + + #pragma region Dataset Parameters + #endif // __NVCC__ + + // alias = linear_trees + // desc = fit piecewise linear gradient boosting tree + // desc = tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant + // desc = the linear model at each leaf includes all the numerical features in that leaf's branch + // desc = the first tree has constant leaf values + // desc = categorical features are used for splits as normal but are not used in the linear models + // desc = missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R + // desc = it is recommended to rescale data before training so that features have similar mean and standard deviation + // desc = **Note**: works only with ``cpu``, ``gpu`` device type and ``serial`` tree learner + // desc = **Note**: ``regression_l1`` objective is not supported with linear tree boosting + // desc = **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM + // desc = **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves + bool linear_tree = false; + + // alias = max_bins + // check = >1 + // desc = max number of bins that feature values will be bucketed in + // desc = small number of bins may reduce training accuracy but may increase general power (deal with over-fitting) + // desc = LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255`` + int max_bin = 255; + + // type = multi-int + // default = None + // desc = max number of bins for each feature + // desc = if not specified, will use ``max_bin`` for all features + std::vector max_bin_by_feature; + + // check = >0 + // desc = minimal number of data inside one bin + // desc = use this to avoid one-data-one-bin (potential over-fitting) + int min_data_in_bin = 3; + + // alias = subsample_for_bin + // check = >0 + // desc = number of data that sampled to construct feature discrete bins + // desc = setting this to larger value will give better training result, but may increase data loading time + // desc = set this to larger value if data is very sparse + // desc = **Note**: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy + int bin_construct_sample_cnt = 200000; + + // alias = data_seed + // desc = random seed for sampling data to construct histogram bins + int data_random_seed = 1; + + // alias = is_sparse, enable_sparse, sparse + // desc = used to enable/disable sparse optimization + bool is_enable_sparse = true; + + // alias = is_enable_bundle, bundle + // desc = set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree `__ + // desc = **Note**: disabling this may cause the slow training speed for sparse datasets + bool enable_bundle = true; + + // desc = set this to ``false`` to disable the special handle of missing value + bool use_missing = true; + + // desc = set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices) + // desc = set this to ``false`` to use ``na`` for representing missing values + bool zero_as_missing = false; + + // desc = set this to ``true`` (the default) to tell LightGBM to ignore the features that are unsplittable based on ``min_data_in_leaf`` + // desc = as dataset object is initialized only once and cannot be changed after that, you may need to set this to ``false`` when searching parameters with ``min_data_in_leaf``, otherwise features are filtered by ``min_data_in_leaf`` firstly if you don't reconstruct dataset object + // desc = **Note**: setting this to ``false`` may slow down the training + bool feature_pre_filter = true; + + // alias = is_pre_partition + // desc = used for distributed learning (excluding the ``feature_parallel`` mode) + // desc = ``true`` if training data are pre-partitioned, and different machines use different partitions + bool pre_partition = false; + + // alias = two_round_loading, use_two_round_loading + // desc = set this to ``true`` if data file is too big to fit in memory + // desc = by default, LightGBM will map data file to memory and load features from memory. This will provide faster data loading speed, but may cause run out of memory error when the data file is very big + // desc = **Note**: works only in case of loading data directly from text file + bool two_round = false; + + // alias = has_header + // desc = set this to ``true`` if input data has header + // desc = **Note**: works only in case of loading data directly from text file + bool header = false; + + // type = int or string + // alias = label + // desc = used to specify the label column + // desc = use number for index, e.g. ``label=0`` means column\_0 is the label + // desc = add a prefix ``name:`` for column name, e.g. ``label=name:is_click`` + // desc = if omitted, the first column in the training data is used as the label + // desc = **Note**: works only in case of loading data directly from text file + std::string label_column = ""; + + // type = int or string + // alias = weight + // desc = used to specify the weight column + // desc = use number for index, e.g. ``weight=0`` means column\_0 is the weight + // desc = add a prefix ``name:`` for column name, e.g. ``weight=name:weight`` + // desc = **Note**: works only in case of loading data directly from text file + // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0, and weight is column\_1, the correct parameter is ``weight=0`` + // desc = **Note**: weights should be non-negative + std::string weight_column = ""; + + // type = int or string + // alias = group, group_id, query_column, query, query_id + // desc = used to specify the query/group id column + // desc = use number for index, e.g. ``query=0`` means column\_0 is the query id + // desc = add a prefix ``name:`` for column name, e.g. ``query=name:query_id`` + // desc = **Note**: works only in case of loading data directly from text file + // desc = **Note**: data should be grouped by query\_id, for more information, see `Query Data <#query-data>`__ + // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\_0 and query\_id is column\_1, the correct parameter is ``query=0`` + std::string group_column = ""; + + // type = multi-int or string + // alias = ignore_feature, blacklist + // desc = used to specify some ignoring columns in training + // desc = use number for index, e.g. ``ignore_column=0,1,2`` means column\_0, column\_1 and column\_2 will be ignored + // desc = add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored + // desc = **Note**: works only in case of loading data directly from text file + // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` + // desc = **Note**: despite the fact that specified columns will be completely ignored during the training, they still should have a valid format allowing LightGBM to load file successfully + std::string ignore_column = ""; + + // type = multi-int or string + // alias = cat_feature, categorical_column, cat_column, categorical_features + // desc = used to specify categorical features + // desc = use number for index, e.g. ``categorical_feature=0,1,2`` means column\_0, column\_1 and column\_2 are categorical features + // desc = add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features + // desc = **Note**: all values will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package) + // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int`` + // desc = **Note**: all values should be less than ``Int32.MaxValue`` (2147483647) + // desc = **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero + // desc = **Note**: all negative values will be treated as **missing values** + // desc = **Note**: the output cannot be monotonically constrained with respect to a categorical feature + // desc = **Note**: floating point numbers in categorical features will be rounded towards 0 + std::string categorical_feature = ""; + + // desc = path to a ``.json`` file that specifies bin upper bounds for some or all features + // desc = ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning) + // desc = see `this file `__ as an example + std::string forcedbins_filename = ""; + + // [no-save] + // alias = is_save_binary, is_save_binary_file + // desc = if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time + // desc = **Note**: ``init_score`` is not saved in binary file + // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function + bool save_binary = false; + + // desc = use precise floating point number parsing for text parser (e.g. CSV, TSV, LibSVM input) + // desc = **Note**: setting this to ``true`` may lead to much slower text parsing + bool precise_float_parser = false; + + // desc = path to a ``.json`` file that specifies customized parser initialized configuration + // desc = see `lightgbm-transform `__ for usage examples + // desc = **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page `__ + // desc = *New in version 4.0.0* + std::string parser_config_file = ""; + + #ifndef __NVCC__ + #pragma endregion + + #pragma region Predict Parameters + #endif // __NVCC__ + + // [no-save] + // desc = used only in ``prediction`` task + // desc = used to specify from which iteration to start the prediction + // desc = ``<= 0`` means from the first iteration + int start_iteration_predict = 0; + + // [no-save] + // desc = used only in ``prediction`` task + // desc = used to specify how many trained iterations will be used in prediction + // desc = ``<= 0`` means no limit + int num_iteration_predict = -1; + + // [no-save] + // alias = is_predict_raw_score, predict_rawscore, raw_score + // desc = used only in ``prediction`` task + // desc = set this to ``true`` to predict only the raw scores + // desc = set this to ``false`` to predict transformed scores + bool predict_raw_score = false; + + // [no-save] + // alias = is_predict_leaf_index, leaf_index + // desc = used only in ``prediction`` task + // desc = set this to ``true`` to predict with leaf index of all trees + bool predict_leaf_index = false; + + // [no-save] + // alias = is_predict_contrib, contrib + // desc = used only in ``prediction`` task + // desc = set this to ``true`` to estimate `SHAP values `__, which represent how each feature contributes to each prediction + // desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data + // desc = **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package `__ + // desc = **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value + // desc = **Note**: this feature is not implemented for linear trees + bool predict_contrib = false; + + // [no-save] + // desc = used only in ``prediction`` task + // desc = control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data + // desc = if ``false`` (the default), a fatal error will be raised if the number of features in the dataset you predict on differs from the number seen during training + // desc = if ``true``, LightGBM will attempt to predict on whatever data you provide. This is dangerous because you might get incorrect predictions, but you could use it in situations where it is difficult or expensive to generate some features and you are very confident that they were never chosen for splits in the model + // desc = **Note**: be very careful setting this parameter to ``true`` + bool predict_disable_shape_check = false; + + // [no-save] + // desc = used only in ``prediction`` task + // desc = used only in ``classification`` and ``ranking`` applications + // desc = used only for predicting normal or raw scores + // desc = if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy + // desc = **Note**: cannot be used with ``rf`` boosting type or custom objective function + bool pred_early_stop = false; + + // [no-save] + // desc = used only in ``prediction`` task and if ``pred_early_stop=true`` + // desc = the frequency of checking early-stopping prediction + int pred_early_stop_freq = 10; + + // [no-save] + // desc = used only in ``prediction`` task and if ``pred_early_stop=true`` + // desc = the threshold of margin in early-stopping prediction + double pred_early_stop_margin = 10.0; + + // [no-save] + // alias = predict_result, prediction_result, predict_name, prediction_name, pred_name, name_pred + // desc = used only in ``prediction`` task + // desc = filename of prediction result + // desc = **Note**: can be used only in CLI version + std::string output_result = "LightGBM_predict_result.txt"; + + #ifndef __NVCC__ + #pragma endregion + + #pragma region Convert Parameters + #endif // __NVCC__ + + // [no-save] + // desc = used only in ``convert_model`` task + // desc = only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen `__ utility + // desc = if ``convert_model_language`` is set and ``task=train``, the model will be also converted + // desc = **Note**: can be used only in CLI version + std::string convert_model_language = ""; + + // [no-save] + // alias = convert_model_file + // desc = used only in ``convert_model`` task + // desc = output filename of converted model + // desc = **Note**: can be used only in CLI version + std::string convert_model = "gbdt_prediction.cpp"; + + #ifndef __NVCC__ + #pragma endregion + + #pragma endregion + + #pragma region Objective Parameters + #endif // __NVCC__ + + // desc = used only in ``rank_xendcg`` objective + // desc = random seed for objectives, if random process is needed + int objective_seed = 5; + + // check = >0 + // alias = num_classes + // desc = used only in ``multi-class`` classification application + int num_class = 1; + + // alias = unbalance, unbalanced_sets + // desc = used only in ``binary`` and ``multiclassova`` applications + // desc = set this to ``true`` if training data are unbalanced + // desc = **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities + // desc = **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them + bool is_unbalance = false; + + // check = >0.0 + // desc = used only in ``binary`` and ``multiclassova`` applications + // desc = weight of labels with positive class + // desc = **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities + // desc = **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them + double scale_pos_weight = 1.0; + + // check = >0.0 + // desc = used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications + // desc = parameter for the sigmoid function + double sigmoid = 1.0; + + // desc = used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications + // desc = adjusts initial score to the mean of labels for faster convergence + bool boost_from_average = true; + + // desc = used only in ``regression`` application + // desc = used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2`` + // desc = might be useful in case of large-range labels + bool reg_sqrt = false; + + // check = >0.0 + // desc = used only in ``huber`` and ``quantile`` ``regression`` applications + // desc = parameter for `Huber loss `__ and `Quantile regression `__ + double alpha = 0.9; + + // check = >0.0 + // desc = used only in ``fair`` ``regression`` application + // desc = parameter for `Fair loss `__ + double fair_c = 1.0; + + // check = >0.0 + // desc = used only in ``poisson`` ``regression`` application + // desc = parameter for `Poisson regression `__ to safeguard optimization + double poisson_max_delta_step = 0.7; + + // check = >=1.0 + // check = <2.0 + // desc = used only in ``tweedie`` ``regression`` application + // desc = used to control the variance of the tweedie distribution + // desc = set this closer to ``2`` to shift towards a **Gamma** distribution + // desc = set this closer to ``1`` to shift towards a **Poisson** distribution + double tweedie_variance_power = 1.5; + + // check = >0 + // desc = used only in ``lambdarank`` application + // desc = controls the number of top-results to focus on during training, refer to "truncation level" in the Sec. 3 of `LambdaMART paper `__ + // desc = this parameter is closely related to the desirable cutoff ``k`` in the metric **NDCG@k** that we aim at optimizing the ranker for. The optimal setting for this parameter is likely to be slightly higher than ``k`` (e.g., ``k + 3``) to include more pairs of documents to train on, but perhaps not too high to avoid deviating too much from the desired target metric **NDCG@k** + int lambdarank_truncation_level = 30; + + // desc = used only in ``lambdarank`` application + // desc = set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data + // desc = set this to ``false`` to enforce the original lambdarank algorithm + bool lambdarank_norm = true; + + // type = multi-double + // default = 0,1,3,7,15,31,63,...,2^30-1 + // desc = used only in ``lambdarank`` application + // desc = relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains + // desc = separate by ``,`` + std::vector label_gain; + + // check = >=0.0 + // desc = used only in ``lambdarank`` application when positional information is provided and position bias is modeled + // desc = larger values reduce the inferred position bias factors + // desc = *New in version 4.1.0* + double lambdarank_position_bias_regularization = 0.0; + + #ifndef __NVCC__ + #pragma endregion + + #pragma region Metric Parameters + #endif // __NVCC__ + + // [no-automatically-extract] + // [no-save] + // alias = metrics, metric_types + // default = "" + // type = multi-enum + // desc = metric(s) to be evaluated on the evaluation set(s) + // descl2 = ``""`` (empty string or not specified) means that metric corresponding to specified ``objective`` will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added) + // descl2 = ``"None"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom`` + // descl2 = ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1`` + // descl2 = ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression`` + // descl2 = ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root`` + // descl2 = ``quantile``, `Quantile regression `__ + // descl2 = ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error`` + // descl2 = ``huber``, `Huber loss `__ + // descl2 = ``fair``, `Fair loss `__ + // descl2 = ``poisson``, negative log-likelihood for `Poisson regression `__ + // descl2 = ``gamma``, negative log-likelihood for **Gamma** regression + // descl2 = ``gamma_deviance``, residual deviance for **Gamma** regression + // descl2 = ``tweedie``, negative log-likelihood for **Tweedie** regression + // descl2 = ``ndcg``, `NDCG `__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart`` + // descl2 = ``map``, `MAP `__, aliases: ``mean_average_precision`` + // descl2 = ``auc``, `AUC `__ + // descl2 = ``average_precision``, `average precision score `__ + // descl2 = ``r2``, `R-squared `__ + // descl2 = ``binary_logloss``, `log loss `__, aliases: ``binary`` + // descl2 = ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification + // descl2 = ``auc_mu``, `AUC-mu `__ + // descl2 = ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr`` + // descl2 = ``multi_error``, error rate for multi-class classification + // descl2 = ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy`` + // descl2 = ``cross_entropy_lambda``, "intensity-weighted" cross-entropy, aliases: ``xentlambda`` + // descl2 = ``kullback_leibler``, `Kullback-Leibler divergence `__, aliases: ``kldiv`` + // desc = support multiple metrics, separated by ``,`` + std::vector metric; + + // [no-save] + // check = >0 + // alias = output_freq + // desc = frequency for metric output + // desc = **Note**: can be used only in CLI version + int metric_freq = 1; + + // [no-save] + // alias = training_metric, is_training_metric, train_metric + // desc = set this to ``true`` to output metric result over training dataset + // desc = **Note**: can be used only in CLI version + bool is_provide_training_metric = false; + + // type = multi-int + // default = 1,2,3,4,5 + // alias = ndcg_eval_at, ndcg_at, map_eval_at, map_at + // desc = used only with ``ndcg`` and ``map`` metrics + // desc = `NDCG `__ and `MAP `__ evaluation positions, separated by ``,`` + std::vector eval_at; + + // check = >0 + // desc = used only with ``multi_error`` metric + // desc = threshold for top-k multi-error metric + // desc = the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise + // descl2 = more precisely, the error on a sample is ``0`` if there are at least ``num_classes - multi_error_top_k`` predictions strictly less than the prediction on the true class + // desc = when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric + int multi_error_top_k = 1; + + // type = multi-double + // default = None + // desc = used only with ``auc_mu`` metric + // desc = list representing flattened matrix (in row-major order) giving loss weights for classification errors + // desc = list should have ``n * n`` elements, where ``n`` is the number of classes + // desc = the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list + // desc = if not specified, will use equal weights for all classes + std::vector auc_mu_weights; + + #ifndef __NVCC__ + #pragma endregion + + #pragma region Network Parameters + #endif // __NVCC__ + + // check = >0 + // alias = num_machine + // desc = the number of machines for distributed learning application + // desc = this parameter is needed to be set in both **socket** and **MPI** versions + int num_machines = 1; + + // check = >0 + // default = 12400 (random for Dask-package) + // alias = local_port, port + // desc = TCP listen port for local machines + // desc = **Note**: don't forget to allow this port in firewall settings before training + int local_listen_port = 12400; + + // check = >0 + // desc = socket time-out in minutes + int time_out = 120; + + // alias = machine_list_file, machine_list, mlist + // desc = path of file that lists machines for this distributed learning application + // desc = each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator) + // desc = **Note**: can be used only in CLI version + std::string machine_list_filename = ""; + + // alias = workers, nodes + // desc = list of machines in the following format: ``ip1:port1,ip2:port2`` + std::string machines = ""; + + #ifndef __NVCC__ + #pragma endregion + + #pragma region GPU Parameters + #endif // __NVCC__ + + // desc = used only with ``gpu`` device type + // desc = OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform + // desc = ``-1`` means the system-wide default platform + // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details + int gpu_platform_id = -1; + + // desc = OpenCL device ID in the specified platform or CUDA device ID. Each GPU in the selected platform has a unique device ID + // desc = ``-1`` means the default device in the selected platform + // desc = in multi-GPU case (``num_gpu>1``) means ID of the master GPU + // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details + int gpu_device_id = -1; + + // desc = list of CUDA device IDs + // desc = **Note**: can be used only in CUDA implementation (``device_type="cuda"``) and when ``num_gpu>1`` + // desc = if empty, the devices with the smallest IDs will be used + std::string gpu_device_id_list = ""; + + // desc = set this to ``true`` to use double precision math on GPU (by default single precision is used) + // desc = **Note**: can be used only in OpenCL implementation (``device_type="gpu"``), in CUDA implementation only double precision is currently supported + bool gpu_use_dp = false; + + // check = >0 + // desc = number of GPUs used for training in this node + // desc = **Note**: can be used only in CUDA implementation (``device_type="cuda"``) + // desc = if ``0``, only 1 GPU will be used + // desc = used in both single-machine and distributed learning applications + // desc = in distributed learning application, each machine can use different number of GPUs + int num_gpu = 1; + + #ifndef __NVCC__ + #pragma endregion + + #pragma endregion + #endif // __NVCC__ + + size_t file_load_progress_interval_bytes = size_t(10) * 1024 * 1024 * 1024; + + bool is_parallel = false; + bool is_data_based_parallel = false; + LIGHTGBM_EXPORT void Set(const std::unordered_map& params); + static const std::unordered_map& alias_table(); + static const std::unordered_map>& parameter2aliases(); + static const std::unordered_set& parameter_set(); + std::vector> auc_mu_weights_matrix; + std::vector> interaction_constraints_vector; + static const std::unordered_map& ParameterTypes(); + static const std::string DumpAliases(); + + private: + void CheckParamConflict(const std::unordered_map& params); + void GetMembersFromString(const std::unordered_map& params); + std::string SaveMembersToString() const; + void GetAucMuWeights(); + void GetInteractionConstraints(); +}; + +inline bool Config::GetString( + const std::unordered_map& params, + const std::string& name, std::string* out) { + if (params.count(name) > 0 && !params.at(name).empty()) { + *out = params.at(name); + return true; + } + return false; +} + +inline bool Config::GetInt( + const std::unordered_map& params, + const std::string& name, int* out) { + if (params.count(name) > 0 && !params.at(name).empty()) { + if (!Common::AtoiAndCheck(params.at(name).c_str(), out)) { + Log::Fatal("Parameter %s should be of type int, got \"%s\"", + name.c_str(), params.at(name).c_str()); + } + return true; + } + return false; +} + +inline bool Config::GetDouble( + const std::unordered_map& params, + const std::string& name, double* out) { + if (params.count(name) > 0 && !params.at(name).empty()) { + if (!Common::AtofAndCheck(params.at(name).c_str(), out)) { + Log::Fatal("Parameter %s should be of type double, got \"%s\"", + name.c_str(), params.at(name).c_str()); + } + return true; + } + return false; +} + +inline bool Config::GetBool( + const std::unordered_map& params, + const std::string& name, bool* out) { + if (params.count(name) > 0 && !params.at(name).empty()) { + std::string value = params.at(name); + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + if (value == std::string("false") || value == std::string("-")) { + *out = false; + } else if (value == std::string("true") || value == std::string("+")) { + *out = true; + } else { + Log::Fatal("Parameter %s should be \"true\"/\"+\" or \"false\"/\"-\", got \"%s\"", + name.c_str(), params.at(name).c_str()); + } + return true; + } + return false; +} + +inline bool Config::SortAlias(const std::string& x, const std::string& y) { + return x.size() < y.size() || (x.size() == y.size() && x < y); +} + +struct ParameterAlias { + static void KeyAliasTransform(std::unordered_map* params) { + std::unordered_map tmp_map; + for (const auto& pair : *params) { + auto alias = Config::alias_table().find(pair.first); + if (alias != Config::alias_table().end()) { // found alias + auto alias_set = tmp_map.find(alias->second); + if (alias_set != tmp_map.end()) { // alias already set + if (Config::SortAlias(alias_set->second, pair.first)) { + Log::Warning("%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s", + alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(), + pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), params->at(alias_set->second).c_str()); + } else { + Log::Warning("%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%s", + alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(), + pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), pair.second.c_str()); + tmp_map[alias->second] = pair.first; + } + } else { // alias not set + tmp_map.emplace(alias->second, pair.first); + } + } else if (Config::parameter_set().find(pair.first) == Config::parameter_set().end()) { + Log::Warning("Unknown parameter: %s", pair.first.c_str()); + } + } + for (const auto& pair : tmp_map) { + auto alias = params->find(pair.first); + if (alias == params->end()) { // not find + params->emplace(pair.first, params->at(pair.second)); + params->erase(pair.second); + } else { + Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s", + pair.first.c_str(), alias->second.c_str(), pair.second.c_str(), params->at(pair.second).c_str(), + pair.first.c_str(), alias->second.c_str()); + } + } + } +}; + +inline std::string ParseObjectiveAlias(const std::string& type) { + if (type == std::string("regression") || type == std::string("regression_l2") + || type == std::string("mean_squared_error") || type == std::string("mse") || type == std::string("l2") + || type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) { + return "regression"; + } else if (type == std::string("regression_l1") || type == std::string("mean_absolute_error") + || type == std::string("l1") || type == std::string("mae")) { + return "regression_l1"; + } else if (type == std::string("multiclass") || type == std::string("softmax")) { + return "multiclass"; + } else if (type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) { + return "multiclassova"; + } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) { + return "cross_entropy"; + } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) { + return "cross_entropy_lambda"; + } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) { + return "mape"; + } else if (type == std::string("rank_xendcg") || type == std::string("xendcg") || type == std::string("xe_ndcg") + || type == std::string("xe_ndcg_mart") || type == std::string("xendcg_mart")) { + return "rank_xendcg"; + } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) { + return "custom"; + } + return type; +} + +inline std::string ParseMetricAlias(const std::string& type) { + if (type == std::string("regression") || type == std::string("regression_l2") || type == std::string("l2") || type == std::string("mean_squared_error") || type == std::string("mse")) { + return "l2"; + } else if (type == std::string("l2_root") || type == std::string("root_mean_squared_error") || type == std::string("rmse")) { + return "rmse"; + } else if (type == std::string("regression_l1") || type == std::string("l1") || type == std::string("mean_absolute_error") || type == std::string("mae")) { + return "l1"; + } else if (type == std::string("binary_logloss") || type == std::string("binary")) { + return "binary_logloss"; + } else if (type == std::string("ndcg") || type == std::string("lambdarank") || type == std::string("rank_xendcg") + || type == std::string("xendcg") || type == std::string("xe_ndcg") || type == std::string("xe_ndcg_mart") || type == std::string("xendcg_mart")) { + return "ndcg"; + } else if (type == std::string("map") || type == std::string("mean_average_precision")) { + return "map"; + } else if (type == std::string("multi_logloss") || type == std::string("multiclass") || type == std::string("softmax") || type == std::string("multiclassova") || type == std::string("multiclass_ova") || type == std::string("ova") || type == std::string("ovr")) { + return "multi_logloss"; + } else if (type == std::string("xentropy") || type == std::string("cross_entropy")) { + return "cross_entropy"; + } else if (type == std::string("xentlambda") || type == std::string("cross_entropy_lambda")) { + return "cross_entropy_lambda"; + } else if (type == std::string("kldiv") || type == std::string("kullback_leibler")) { + return "kullback_leibler"; + } else if (type == std::string("mean_absolute_percentage_error") || type == std::string("mape")) { + return "mape"; + } else if (type == std::string("none") || type == std::string("null") || type == std::string("custom") || type == std::string("na")) { + return "custom"; + } + return type; +} + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_ diff --git a/include/LightGBM/cuda/cuda_algorithms.hpp b/include/LightGBM/cuda/cuda_algorithms.hpp new file mode 100644 index 0000000..71105b7 --- /dev/null +++ b/include/LightGBM/cuda/cuda_algorithms.hpp @@ -0,0 +1,626 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_ + +#ifdef USE_CUDA + +#ifndef USE_ROCM +#include +#include +#endif +#include + +#include +#include +#include +#include + +#include + +#define GLOBAL_PREFIX_SUM_BLOCK_SIZE (1024) +#define BITONIC_SORT_NUM_ELEMENTS (1024) +#define BITONIC_SORT_DEPTH (11) +#define BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE (10) + +namespace LightGBM { + +template +__device__ __forceinline__ T ShufflePrefixSum(T value, T* shared_mem_buffer) { + const uint32_t mask = 0xffffffff; + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t warpID = threadIdx.x / warpSize; + const uint32_t num_warp = blockDim.x / warpSize; + for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { + const T other_value = __shfl_up_sync(mask, value, offset); + if (warpLane >= offset) { + value += other_value; + } + } + if (warpLane == warpSize - 1) { + shared_mem_buffer[warpID] = value; + } + __syncthreads(); + if (warpID == 0) { + T warp_sum = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); + for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { + const T other_warp_sum = __shfl_up_sync(mask, warp_sum, offset); + if (warpLane >= offset) { + warp_sum += other_warp_sum; + } + } + shared_mem_buffer[warpLane] = warp_sum; + } + __syncthreads(); + const T warp_base = warpID == 0 ? 0 : shared_mem_buffer[warpID - 1]; + return warp_base + value; +} + +template +__device__ __forceinline__ T ShufflePrefixSumExclusive(T value, T* shared_mem_buffer) { + const uint32_t mask = 0xffffffff; + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t warpID = threadIdx.x / warpSize; + const uint32_t num_warp = blockDim.x / warpSize; + for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { + const T other_value = __shfl_up_sync(mask, value, offset); + if (warpLane >= offset) { + value += other_value; + } + } + if (warpLane == warpSize - 1) { + shared_mem_buffer[warpID] = value; + } + __syncthreads(); + if (warpID == 0) { + T warp_sum = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); + for (uint32_t offset = 1; offset < warpSize; offset <<= 1) { + const T other_warp_sum = __shfl_up_sync(mask, warp_sum, offset); + if (warpLane >= offset) { + warp_sum += other_warp_sum; + } + } + shared_mem_buffer[warpLane] = warp_sum; + } + __syncthreads(); + const T warp_base = warpID == 0 ? 0 : shared_mem_buffer[warpID - 1]; + const T inclusive_result = warp_base + value; + if (threadIdx.x % warpSize == warpSize - 1) { + shared_mem_buffer[warpLane] = inclusive_result; + } + __syncthreads(); + T exclusive_result = __shfl_up_sync(mask, inclusive_result, 1); + if (threadIdx.x == 0) { + exclusive_result = 0; + } else if (threadIdx.x % warpSize == 0) { + exclusive_result = shared_mem_buffer[warpLane - 1]; + } + return exclusive_result; +} + +template +void ShufflePrefixSumGlobal(T* values, size_t len, T* block_prefix_sum_buffer); + +template +void GlobalInclusiveArgPrefixSum(const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, size_t n); + +template +__device__ __forceinline__ T ShuffleReduceSumWarp(T value, const data_size_t len) { + if (len > 0) { + const uint32_t mask = 0xffffffff; + for (int offset = warpSize / 2; offset > 0; offset >>= 1) { + value += __shfl_down_sync(mask, value, offset); + } + } + return value; +} + +// reduce values from an 1-dimensional block (block size must be no greater than 1024) +template +__device__ __forceinline__ T ShuffleReduceSum(T value, T* shared_mem_buffer, const size_t len) { + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t warpID = threadIdx.x / warpSize; + const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize)); + value = ShuffleReduceSumWarp(value, warp_len); + if (warpLane == 0) { + shared_mem_buffer[warpID] = value; + } + __syncthreads(); + const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize); + if (warpID == 0) { + value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); + value = ShuffleReduceSumWarp(value, num_warp); + } + return value; +} + +template +__device__ __forceinline__ T ShuffleReduceMaxWarp(T value, const data_size_t len) { + if (len > 0) { + const uint32_t mask = 0xffffffff; + for (int offset = warpSize / 2; offset > 0; offset >>= 1) { + value = max(value, __shfl_down_sync(mask, value, offset)); + } + } + return value; +} + +// reduce values from an 1-dimensional block (block size must be no greater than 1024) +template +__device__ __forceinline__ T ShuffleReduceMax(T value, T* shared_mem_buffer, const size_t len) { + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t warpID = threadIdx.x / warpSize; + const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize)); + value = ShuffleReduceMaxWarp(value, warp_len); + if (warpLane == 0) { + shared_mem_buffer[warpID] = value; + } + __syncthreads(); + const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize); + if (warpID == 0) { + value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0); + value = ShuffleReduceMaxWarp(value, num_warp); + } + return value; +} + +// calculate prefix sum values within an 1-dimensional block in global memory, exclusively +template +__device__ __forceinline__ void GlobalMemoryPrefixSum(T* array, const size_t len) { + const size_t num_values_per_thread = (len + blockDim.x - 1) / blockDim.x; + const size_t start = threadIdx.x * num_values_per_thread; + const size_t end = min(start + num_values_per_thread, len); + T thread_sum = 0; + for (size_t index = start; index < end; ++index) { + thread_sum += array[index]; + } + __shared__ T shared_mem[WARPSIZE]; + const T thread_base = ShufflePrefixSumExclusive(thread_sum, shared_mem); + if (start < end) { + array[start] += thread_base; + } + for (size_t index = start + 1; index < end; ++index) { + array[index] += array[index - 1]; + } +} + +template +__device__ __forceinline__ T ShuffleReduceMinWarp(T value, const data_size_t len) { + if (len > 0) { + const uint32_t mask = (0xffffffff >> (warpSize - len)); + for (int offset = warpSize / 2; offset > 0; offset >>= 1) { + const T other_value = __shfl_down_sync(mask, value, offset); + value = (other_value < value) ? other_value : value; + } + } + return value; +} + +// reduce values from an 1-dimensional block (block size must be no greater than 1024) +template +__device__ __forceinline__ T ShuffleReduceMin(T value, T* shared_mem_buffer, const size_t len) { + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t warpID = threadIdx.x / warpSize; + const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize)); + value = ShuffleReduceMinWarp(value, warp_len); + if (warpLane == 0) { + shared_mem_buffer[warpID] = value; + } + __syncthreads(); + const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize); + if (warpID == 0) { + value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : shared_mem_buffer[0]); + value = ShuffleReduceMinWarp(value, num_warp); + } + return value; +} + +template +void ShuffleReduceMinGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer); + +template +__device__ __forceinline__ void BitonicArgSort_1024(const VAL_T* scores, INDEX_T* indices, const INDEX_T num_items) { + INDEX_T depth = 1; + INDEX_T num_items_aligend = 1; + INDEX_T num_items_ref = num_items - 1; + while (num_items_ref > 0) { + num_items_ref >>= 1; + num_items_aligend <<= 1; + ++depth; + } + for (INDEX_T outer_depth = depth - 1; outer_depth >= 1; --outer_depth) { + const INDEX_T outer_segment_length = 1 << (depth - outer_depth); + const INDEX_T outer_segment_index = threadIdx.x / outer_segment_length; + const bool ascending = ASCENDING ? (outer_segment_index % 2 == 0) : (outer_segment_index % 2 > 0); + for (INDEX_T inner_depth = outer_depth; inner_depth < depth; ++inner_depth) { + const INDEX_T segment_length = 1 << (depth - inner_depth); + const INDEX_T half_segment_length = segment_length >> 1; + const INDEX_T half_segment_index = threadIdx.x / half_segment_length; + if (threadIdx.x < num_items_aligend) { + if (half_segment_index % 2 == 0) { + const INDEX_T index_to_compare = threadIdx.x + half_segment_length; + if ((scores[indices[threadIdx.x]] > scores[indices[index_to_compare]]) == ascending) { + const INDEX_T index = indices[threadIdx.x]; + indices[threadIdx.x] = indices[index_to_compare]; + indices[index_to_compare] = index; + } + } + } + __syncthreads(); + } + } +} + +template +__device__ __forceinline__ void BitonicArgSort_2048(const VAL_T* scores, INDEX_T* indices) { + for (INDEX_T base = 0; base < 2048; base += 1024) { + for (INDEX_T outer_depth = 10; outer_depth >= 1; --outer_depth) { + const INDEX_T outer_segment_length = 1 << (11 - outer_depth); + const INDEX_T outer_segment_index = threadIdx.x / outer_segment_length; + const bool ascending = ((base == 0) ^ ASCENDING) ? (outer_segment_index % 2 > 0) : (outer_segment_index % 2 == 0); + for (INDEX_T inner_depth = outer_depth; inner_depth < 11; ++inner_depth) { + const INDEX_T segment_length = 1 << (11 - inner_depth); + const INDEX_T half_segment_length = segment_length >> 1; + const INDEX_T half_segment_index = threadIdx.x / half_segment_length; + if (half_segment_index % 2 == 0) { + const INDEX_T index_to_compare = threadIdx.x + half_segment_length + base; + if ((scores[indices[threadIdx.x + base]] > scores[indices[index_to_compare]]) == ascending) { + const INDEX_T index = indices[threadIdx.x + base]; + indices[threadIdx.x + base] = indices[index_to_compare]; + indices[index_to_compare] = index; + } + } + __syncthreads(); + } + } + } + const unsigned int index_to_compare = threadIdx.x + 1024; + if (scores[indices[index_to_compare]] > scores[indices[threadIdx.x]]) { + const INDEX_T temp_index = indices[index_to_compare]; + indices[index_to_compare] = indices[threadIdx.x]; + indices[threadIdx.x] = temp_index; + } + __syncthreads(); + for (INDEX_T base = 0; base < 2048; base += 1024) { + for (INDEX_T inner_depth = 1; inner_depth < 11; ++inner_depth) { + const INDEX_T segment_length = 1 << (11 - inner_depth); + const INDEX_T half_segment_length = segment_length >> 1; + const INDEX_T half_segment_index = threadIdx.x / half_segment_length; + if (half_segment_index % 2 == 0) { + const INDEX_T index_to_compare = threadIdx.x + half_segment_length + base; + if (scores[indices[threadIdx.x + base]] < scores[indices[index_to_compare]]) { + const INDEX_T index = indices[threadIdx.x + base]; + indices[threadIdx.x + base] = indices[index_to_compare]; + indices[index_to_compare] = index; + } + } + __syncthreads(); + } + } +} + +template +__device__ void BitonicArgSortDevice(const VAL_T* values, INDEX_T* indices, const int len) { + __shared__ VAL_T shared_values[BLOCK_DIM]; + __shared__ INDEX_T shared_indices[BLOCK_DIM]; + int len_to_shift = len - 1; + int max_depth = 1; + while (len_to_shift > 0) { + len_to_shift >>= 1; + ++max_depth; + } + const int num_blocks = (len + static_cast(BLOCK_DIM) - 1) / static_cast(BLOCK_DIM); + for (int block_index = 0; block_index < num_blocks; ++block_index) { + const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); + if (this_index < len) { + shared_values[threadIdx.x] = values[this_index]; + shared_indices[threadIdx.x] = this_index; + } else { + shared_indices[threadIdx.x] = len; + } + __syncthreads(); + for (int depth = max_depth - 1; depth > max_depth - static_cast(MAX_DEPTH); --depth) { + const int segment_length = (1 << (max_depth - depth)); + const int segment_index = this_index / segment_length; + const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); + { + const int half_segment_length = (segment_length >> 1); + const int half_segment_index = this_index / half_segment_length; + const int num_total_segment = (len + segment_length - 1) / segment_length; + const int offset = (segment_index == num_total_segment - 1 && ascending == ASCENDING) ? + (num_total_segment * segment_length - len) : 0; + if (half_segment_index % 2 == 0) { + const int segment_start = segment_index * segment_length; + if (this_index >= offset + segment_start) { + const int other_index = static_cast(threadIdx.x) + half_segment_length - offset; + const INDEX_T this_data_index = shared_indices[threadIdx.x]; + const INDEX_T other_data_index = shared_indices[other_index]; + const VAL_T this_value = shared_values[threadIdx.x]; + const VAL_T other_value = shared_values[other_index]; + if (other_data_index < len && (this_value > other_value) == ascending) { + shared_indices[threadIdx.x] = other_data_index; + shared_indices[other_index] = this_data_index; + shared_values[threadIdx.x] = other_value; + shared_values[other_index] = this_value; + } + } + } + __syncthreads(); + } + for (int inner_depth = depth + 1; inner_depth < max_depth; ++inner_depth) { + const int half_segment_length = (1 << (max_depth - inner_depth - 1)); + const int half_segment_index = this_index / half_segment_length; + if (half_segment_index % 2 == 0) { + const int other_index = static_cast(threadIdx.x) + half_segment_length; + const INDEX_T this_data_index = shared_indices[threadIdx.x]; + const INDEX_T other_data_index = shared_indices[other_index]; + const VAL_T this_value = shared_values[threadIdx.x]; + const VAL_T other_value = shared_values[other_index]; + if (other_data_index < len && (this_value > other_value) == ascending) { + shared_indices[threadIdx.x] = other_data_index; + shared_indices[other_index] = this_data_index; + shared_values[threadIdx.x] = other_value; + shared_values[other_index] = this_value; + } + } + __syncthreads(); + } + } + if (this_index < len) { + indices[this_index] = shared_indices[threadIdx.x]; + } + __syncthreads(); + } + for (int depth = max_depth - static_cast(MAX_DEPTH); depth >= 1; --depth) { + const int segment_length = (1 << (max_depth - depth)); + { + const int num_total_segment = (len + segment_length - 1) / segment_length; + const int half_segment_length = (segment_length >> 1); + for (int block_index = 0; block_index < num_blocks; ++block_index) { + const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); + const int segment_index = this_index / segment_length; + const int half_segment_index = this_index / half_segment_length; + const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); + const int offset = (segment_index == num_total_segment - 1 && ascending == ASCENDING) ? + (num_total_segment * segment_length - len) : 0; + if (half_segment_index % 2 == 0) { + const int segment_start = segment_index * segment_length; + if (this_index >= offset + segment_start) { + const int other_index = this_index + half_segment_length - offset; + if (other_index < len) { + const INDEX_T this_data_index = indices[this_index]; + const INDEX_T other_data_index = indices[other_index]; + const VAL_T this_value = values[this_data_index]; + const VAL_T other_value = values[other_data_index]; + if ((this_value > other_value) == ascending) { + indices[this_index] = other_data_index; + indices[other_index] = this_data_index; + } + } + } + } + } + __syncthreads(); + } + for (int inner_depth = depth + 1; inner_depth <= max_depth - static_cast(MAX_DEPTH); ++inner_depth) { + const int half_segment_length = (1 << (max_depth - inner_depth - 1)); + for (int block_index = 0; block_index < num_blocks; ++block_index) { + const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); + const int segment_index = this_index / segment_length; + const int half_segment_index = this_index / half_segment_length; + const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); + if (half_segment_index % 2 == 0) { + const int other_index = this_index + half_segment_length; + if (other_index < len) { + const INDEX_T this_data_index = indices[this_index]; + const INDEX_T other_data_index = indices[other_index]; + const VAL_T this_value = values[this_data_index]; + const VAL_T other_value = values[other_data_index]; + if ((this_value > other_value) == ascending) { + indices[this_index] = other_data_index; + indices[other_index] = this_data_index; + } + } + } + __syncthreads(); + } + } + for (int block_index = 0; block_index < num_blocks; ++block_index) { + const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x); + const int segment_index = this_index / segment_length; + const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); + if (this_index < len) { + const INDEX_T index = indices[this_index]; + shared_values[threadIdx.x] = values[index]; + shared_indices[threadIdx.x] = index; + } else { + shared_indices[threadIdx.x] = len; + } + __syncthreads(); + for (int inner_depth = max_depth - static_cast(MAX_DEPTH) + 1; inner_depth < max_depth; ++inner_depth) { + const int half_segment_length = (1 << (max_depth - inner_depth - 1)); + const int half_segment_index = this_index / half_segment_length; + if (half_segment_index % 2 == 0) { + const int other_index = static_cast(threadIdx.x) + half_segment_length; + const INDEX_T this_data_index = shared_indices[threadIdx.x]; + const INDEX_T other_data_index = shared_indices[other_index]; + const VAL_T this_value = shared_values[threadIdx.x]; + const VAL_T other_value = shared_values[other_index]; + if (other_data_index < len && (this_value > other_value) == ascending) { + shared_indices[threadIdx.x] = other_data_index; + shared_indices[other_index] = this_data_index; + shared_values[threadIdx.x] = other_value; + shared_values[other_index] = this_value; + } + } + __syncthreads(); + } + if (this_index < len) { + indices[this_index] = shared_indices[threadIdx.x]; + } + __syncthreads(); + } + } +} + +void BitonicArgSortItemsGlobal( + const double* scores, + const int num_queries, + const data_size_t* cuda_query_boundaries, + data_size_t* out_indices); + +template +void BitonicArgSortGlobal(const VAL_T* values, INDEX_T* indices, const size_t len); + +template +void ShuffleReduceSumGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer); + +template +void ShuffleReduceDotProdGlobal(const VAL_T* values1, const VAL_T* values2, size_t n, REDUCE_T* block_buffer); + +template +__device__ void ShuffleSortedPrefixSumDevice(const VAL_T* in_values, + const INDEX_T* sorted_indices, + REDUCE_VAL_T* out_values, + const INDEX_T num_data) { + __shared__ REDUCE_VAL_T shared_buffer[WARPSIZE]; + const INDEX_T num_data_per_thread = (num_data + static_cast(blockDim.x) - 1) / static_cast(blockDim.x); + const INDEX_T start = num_data_per_thread * static_cast(threadIdx.x); + const INDEX_T end = min(start + num_data_per_thread, num_data); + REDUCE_VAL_T thread_sum = 0; + for (INDEX_T index = start; index < end; ++index) { + thread_sum += static_cast(in_values[sorted_indices[index]]); + } + __syncthreads(); + thread_sum = ShufflePrefixSumExclusive(thread_sum, shared_buffer); + const REDUCE_VAL_T thread_base = shared_buffer[threadIdx.x]; + for (INDEX_T index = start; index < end; ++index) { + out_values[index] = thread_base + static_cast(in_values[sorted_indices[index]]); + } + __syncthreads(); +} + +template +__global__ void PercentileGlobalKernel(const VAL_T* values, + const WEIGHT_T* weights, + const INDEX_T* sorted_indices, + const WEIGHT_REDUCE_T* weights_prefix_sum, + const double alpha, + const INDEX_T len, + VAL_T* out_value) { + if (!USE_WEIGHT) { + const double float_pos = (1.0f - alpha) * len; + const INDEX_T pos = static_cast(float_pos); + if (pos < 1) { + *out_value = values[sorted_indices[0]]; + } else if (pos >= len) { + *out_value = values[sorted_indices[len - 1]]; + } else { + const double bias = float_pos - static_cast(pos); + const VAL_T v1 = values[sorted_indices[pos - 1]]; + const VAL_T v2 = values[sorted_indices[pos]]; + *out_value = static_cast(v1 - (v1 - v2) * bias); + } + } else { + const WEIGHT_REDUCE_T threshold = weights_prefix_sum[len - 1] * (1.0f - alpha); + __shared__ INDEX_T pos; + if (threadIdx.x == 0) { + pos = len; + } + __syncthreads(); + for (INDEX_T index = static_cast(threadIdx.x); index < len; index += static_cast(blockDim.x)) { + if (weights_prefix_sum[index] > threshold && (index == 0 || weights_prefix_sum[index - 1] <= threshold)) { + pos = index; + } + } + __syncthreads(); + pos = min(pos, len - 1); + if (pos == 0 || pos == len - 1) { + *out_value = values[pos]; + } + const VAL_T v1 = values[sorted_indices[pos - 1]]; + const VAL_T v2 = values[sorted_indices[pos]]; + *out_value = static_cast(v1 - (v1 - v2) * (threshold - weights_prefix_sum[pos - 1]) / (weights_prefix_sum[pos] - weights_prefix_sum[pos - 1])); + } +} + +template +void PercentileGlobal(const VAL_T* values, + const WEIGHT_T* weights, + INDEX_T* indices, + WEIGHT_REDUCE_T* weights_prefix_sum, + WEIGHT_REDUCE_T* weights_prefix_sum_buffer, + const double alpha, + const INDEX_T len, + VAL_T* cuda_out_value) { + if (len <= 1) { + CopyFromCUDADeviceToCUDADevice(cuda_out_value, values, 1, __FILE__, __LINE__); + } + BitonicArgSortGlobal(values, indices, len); + SynchronizeCUDADevice(__FILE__, __LINE__); + if (USE_WEIGHT) { + GlobalInclusiveArgPrefixSum(indices, weights, weights_prefix_sum, weights_prefix_sum_buffer, static_cast(len)); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + PercentileGlobalKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(values, weights, indices, weights_prefix_sum, alpha, len, cuda_out_value); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +template +__device__ VAL_T PercentileDevice(const VAL_T* values, + const WEIGHT_T* weights, + INDEX_T* indices, + REDUCE_WEIGHT_T* weights_prefix_sum, + const double alpha, + const INDEX_T len) { + if (len <= 1) { + return values[0]; + } + if (!USE_WEIGHT) { + BitonicArgSortDevice(values, indices, len); + const double float_pos = (1.0f - alpha) * len; + const INDEX_T pos = static_cast(float_pos); + if (pos < 1) { + return values[indices[0]]; + } else if (pos >= len) { + return values[indices[len - 1]]; + } else { + const double bias = float_pos - pos; + const VAL_T v1 = values[indices[pos - 1]]; + const VAL_T v2 = values[indices[pos]]; + return static_cast(v1 - (v1 - v2) * bias); + } + } else { + BitonicArgSortDevice(values, indices, len); + ShuffleSortedPrefixSumDevice(weights, indices, weights_prefix_sum, len); + const REDUCE_WEIGHT_T threshold = weights_prefix_sum[len - 1] * (1.0f - alpha); + __shared__ INDEX_T pos; + if (threadIdx.x == 0) { + pos = len; + } + __syncthreads(); + for (INDEX_T index = static_cast(threadIdx.x); index < len; index += static_cast(blockDim.x)) { + if (weights_prefix_sum[index] > threshold && (index == 0 || weights_prefix_sum[index - 1] <= threshold)) { + pos = index; + } + } + __syncthreads(); + pos = min(pos, len - 1); + if (pos == 0 || pos == len - 1) { + return values[pos]; + } + const VAL_T v1 = values[indices[pos - 1]]; + const VAL_T v2 = values[indices[pos]]; + return static_cast(v1 - (v1 - v2) * (threshold - weights_prefix_sum[pos - 1]) / (weights_prefix_sum[pos] - weights_prefix_sum[pos - 1])); + } +} + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_ diff --git a/include/LightGBM/cuda/cuda_column_data.hpp b/include/LightGBM/cuda/cuda_column_data.hpp new file mode 100644 index 0000000..cdf4877 --- /dev/null +++ b/include/LightGBM/cuda/cuda_column_data.hpp @@ -0,0 +1,152 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + +class CUDAColumnData { + public: + CUDAColumnData(const data_size_t num_data, const int gpu_device_id); + + ~CUDAColumnData(); + + void Init(const int num_columns, + const std::vector& column_data, + const std::vector& column_bin_iterator, + const std::vector& column_bit_type, + const std::vector& feature_max_bin, + const std::vector& feature_min_bin, + const std::vector& feature_offset, + const std::vector& feature_most_freq_bin, + const std::vector& feature_default_bin, + const std::vector& feature_missing_is_zero, + const std::vector& feature_missing_is_na, + const std::vector& feature_mfb_is_zero, + const std::vector& feature_mfb_is_na, + const std::vector& feature_to_column); + + const uint8_t* GetColumnData(const int column_index) const { return data_by_column_[column_index]->RawData(); } + + void CopySubrow(const CUDAColumnData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices); + + uint8_t* const* cuda_data_by_column() const { return cuda_data_by_column_.RawData(); } + + uint32_t feature_min_bin(const int feature_index) const { return feature_min_bin_[feature_index]; } + + uint32_t feature_max_bin(const int feature_index) const { return feature_max_bin_[feature_index]; } + + uint32_t feature_offset(const int feature_index) const { return feature_offset_[feature_index]; } + + uint32_t feature_most_freq_bin(const int feature_index) const { return feature_most_freq_bin_[feature_index]; } + + uint32_t feature_default_bin(const int feature_index) const { return feature_default_bin_[feature_index]; } + + uint8_t feature_missing_is_zero(const int feature_index) const { return feature_missing_is_zero_[feature_index]; } + + uint8_t feature_missing_is_na(const int feature_index) const { return feature_missing_is_na_[feature_index]; } + + uint8_t feature_mfb_is_zero(const int feature_index) const { return feature_mfb_is_zero_[feature_index]; } + + uint8_t feature_mfb_is_na(const int feature_index) const { return feature_mfb_is_na_[feature_index]; } + + const uint32_t* cuda_feature_min_bin() const { return cuda_feature_min_bin_.RawData(); } + + const uint32_t* cuda_feature_max_bin() const { return cuda_feature_max_bin_.RawData(); } + + const uint32_t* cuda_feature_offset() const { return cuda_feature_offset_.RawData(); } + + const uint32_t* cuda_feature_most_freq_bin() const { return cuda_feature_most_freq_bin_.RawData(); } + + const uint32_t* cuda_feature_default_bin() const { return cuda_feature_default_bin_.RawData(); } + + const uint8_t* cuda_feature_missing_is_zero() const { return cuda_feature_missing_is_zero_.RawData(); } + + const uint8_t* cuda_feature_missing_is_na() const { return cuda_feature_missing_is_na_.RawData(); } + + const uint8_t* cuda_feature_mfb_is_zero() const { return cuda_feature_mfb_is_zero_.RawData(); } + + const uint8_t* cuda_feature_mfb_is_na() const { return cuda_feature_mfb_is_na_.RawData(); } + + const int* cuda_feature_to_column() const { return cuda_feature_to_column_.RawData(); } + + const uint8_t* cuda_column_bit_type() const { return cuda_column_bit_type_.RawData(); } + + int feature_to_column(const int feature_index) const { return feature_to_column_[feature_index]; } + + uint8_t column_bit_type(const int column_index) const { return column_bit_type_[column_index]; } + + private: + template + void InitOneColumnData(const void* in_column_data, BinIterator* bin_iterator, CUDAVector* out_column_data_pointer); + + void LaunchCopySubrowKernel(uint8_t* const* in_cuda_data_by_column); + + void InitColumnMetaInfo(); + + void ResizeWhenCopySubrow(const data_size_t num_used_indices); + + std::vector GetDataByColumnPointers(const std::vector>>& data_by_column) const { + std::vector data_by_column_pointers(data_by_column.size(), nullptr); + for (size_t i = 0; i < data_by_column.size(); ++i) { + data_by_column_pointers[i] = reinterpret_cast(data_by_column[i]->RawData()); + } + return data_by_column_pointers; + } + + int gpu_device_id_; + int num_threads_; + data_size_t num_data_; + int num_columns_; + std::vector column_bit_type_; + std::vector feature_min_bin_; + std::vector feature_max_bin_; + std::vector feature_offset_; + std::vector feature_most_freq_bin_; + std::vector feature_default_bin_; + std::vector feature_missing_is_zero_; + std::vector feature_missing_is_na_; + std::vector feature_mfb_is_zero_; + std::vector feature_mfb_is_na_; + CUDAVector cuda_data_by_column_; + std::vector feature_to_column_; + std::vector>> data_by_column_; + + CUDAVector cuda_column_bit_type_; + CUDAVector cuda_feature_min_bin_; + CUDAVector cuda_feature_max_bin_; + CUDAVector cuda_feature_offset_; + CUDAVector cuda_feature_most_freq_bin_; + CUDAVector cuda_feature_default_bin_; + CUDAVector cuda_feature_missing_is_zero_; + CUDAVector cuda_feature_missing_is_na_; + CUDAVector cuda_feature_mfb_is_zero_; + CUDAVector cuda_feature_mfb_is_na_; + CUDAVector cuda_feature_to_column_; + + // used when bagging with subset + CUDAVector cuda_used_indices_; + data_size_t num_used_indices_; + data_size_t cur_subset_buffer_size_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_ diff --git a/include/LightGBM/cuda/cuda_metadata.hpp b/include/LightGBM/cuda/cuda_metadata.hpp new file mode 100644 index 0000000..97f7e06 --- /dev/null +++ b/include/LightGBM/cuda/cuda_metadata.hpp @@ -0,0 +1,59 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_ + +#ifdef USE_CUDA + +#include +#include + +#include + +namespace LightGBM { + +class CUDAMetadata { + public: + explicit CUDAMetadata(const int gpu_device_id); + + ~CUDAMetadata(); + + void Init(const std::vector& label, + const std::vector& weight, + const std::vector& query_boundaries, + const std::vector& query_weights, + const std::vector& init_score); + + void SetLabel(const label_t* label, data_size_t len); + + void SetWeights(const label_t* weights, data_size_t len); + + void SetQuery(const data_size_t* query, const label_t* query_weights, data_size_t num_queries); + + void SetInitScore(const double* init_score, data_size_t len); + + const label_t* cuda_label() const { return cuda_label_.RawData(); } + + const label_t* cuda_weights() const { return cuda_weights_.RawData(); } + + const data_size_t* cuda_query_boundaries() const { return cuda_query_boundaries_.RawData(); } + + const label_t* cuda_query_weights() const { return cuda_query_weights_.RawData(); } + + private: + CUDAVector cuda_label_; + CUDAVector cuda_weights_; + CUDAVector cuda_query_boundaries_; + CUDAVector cuda_query_weights_; + CUDAVector cuda_init_score_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_ diff --git a/include/LightGBM/cuda/cuda_metric.hpp b/include/LightGBM/cuda/cuda_metric.hpp new file mode 100644 index 0000000..9060670 --- /dev/null +++ b/include/LightGBM/cuda/cuda_metric.hpp @@ -0,0 +1,45 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_ + +#ifdef USE_CUDA + +#include +#include + +namespace LightGBM { + +template +class CUDAMetricInterface: public HOST_METRIC { + public: + explicit CUDAMetricInterface(const Config& config): HOST_METRIC(config) { + cuda_labels_ = nullptr; + cuda_weights_ = nullptr; + const int gpu_device_id = config.gpu_device_id >= 0 ? config.gpu_device_id : 0; + SetCUDADevice(gpu_device_id, __FILE__, __LINE__); + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + HOST_METRIC::Init(metadata, num_data); + cuda_labels_ = metadata.cuda_metadata()->cuda_label(); + cuda_weights_ = metadata.cuda_metadata()->cuda_weights(); + } + + bool IsCUDAMetric() const { return true; } + + protected: + const label_t* cuda_labels_; + const label_t* cuda_weights_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_ diff --git a/include/LightGBM/cuda/cuda_nccl_topology.hpp b/include/LightGBM/cuda/cuda_nccl_topology.hpp new file mode 100644 index 0000000..2e78eb4 --- /dev/null +++ b/include/LightGBM/cuda/cuda_nccl_topology.hpp @@ -0,0 +1,234 @@ +/*! + * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_ + +#ifdef USE_CUDA + +#ifdef USE_ROCM +#include +#else +#include +#endif + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +class NCCLTopology { + public: + NCCLTopology(const int master_gpu_device_id, const int num_gpu, const std::string& gpu_device_id_list, const data_size_t global_num_data) { + num_gpu_ = num_gpu; + master_gpu_device_id_ = master_gpu_device_id; + global_num_data_ = global_num_data; + int max_num_gpu = 0; + CUDASUCCESS_OR_FATAL(cudaGetDeviceCount(&max_num_gpu)); + if (gpu_device_id_list != std::string("")) { + std::set gpu_id_set; + std::vector gpu_list_str = Common::Split(gpu_device_id_list.c_str(), ","); + for (const auto& gpu_str : gpu_list_str) { + int gpu_id = 0; + Common::Atoi(gpu_str.c_str(), &gpu_id); + if (gpu_id < 0 || gpu_id >= max_num_gpu) { + Log::Warning("Invalid GPU device ID %d in gpu_device_list is ignored.", gpu_id); + } else { + gpu_id_set.insert(gpu_id); + } + } + for (const int gpu_id : gpu_id_set) { + gpu_list_.push_back(gpu_id); + } + } + if (!gpu_list_.empty() && num_gpu_ != static_cast(gpu_list_.size())) { + Log::Warning("num_gpu = %d is different from the number of valid device IDs in gpu_device_list (%d), using %d GPUs instead.", \ + num_gpu_, static_cast(gpu_list_.size()), static_cast(gpu_list_.size())); + num_gpu_ = static_cast(gpu_list_.size()); + } + + if (!gpu_list_.empty()) { + bool check_master_gpu = false; + for (int i = 0; i < static_cast(gpu_list_.size()); ++i) { + const int gpu_id = gpu_list_[i]; + if (gpu_id == master_gpu_device_id_) { + check_master_gpu = true; + master_gpu_index_ = i; + break; + } + } + if (!check_master_gpu) { + Log::Warning("Master GPU index not in gpu_device_list. Using %d as the master GPU instead.", gpu_list_[0]); + master_gpu_device_id_ = gpu_list_[0]; + master_gpu_index_ = 0; + } + } else { + if (num_gpu_ <= 0) { + num_gpu_ = 1; + } else if (num_gpu_ > max_num_gpu) { + Log::Warning("Only %d GPUs available, using num_gpu = %d.", max_num_gpu, max_num_gpu); + num_gpu_ = max_num_gpu; + } + if (master_gpu_device_id_ < 0 || master_gpu_device_id_ >= num_gpu_) { + Log::Warning("Invalid gpu_device_id = %d for master GPU index, using gpu_device_id = 0 instead.", master_gpu_device_id_); + master_gpu_device_id_ = 0; + master_gpu_index_ = 0; + } + for (int i = 0; i < num_gpu_; ++i) { + gpu_list_.push_back(i); + } + } + + Log::Info("Using GPU devices %s, and local master GPU device %d.", Common::Join(gpu_list_, ",").c_str(), master_gpu_device_id_); + + const int num_threads = OMP_NUM_THREADS(); + if (num_gpu_ > num_threads) { + Log::Fatal("Number of GPUs %d is greater than the number of threads %d. Please use more threads.", num_gpu_, num_threads); + } + + host_threads_.resize(num_gpu_); + } + + ~NCCLTopology() {} + + void InitNCCL() { + nccl_gpu_rank_.resize(num_gpu_, -1); + nccl_communicators_.resize(num_gpu_); + ncclUniqueId nccl_unique_id; + if (Network::num_machines() == 1 || Network::rank() == 0) { + NCCLCHECK(ncclGetUniqueId(&nccl_unique_id)); + } + if (Network::num_machines() > 1) { + std::vector output_buffer(Network::num_machines()); + Network::Allgather( + reinterpret_cast(&nccl_unique_id), + sizeof(ncclUniqueId) / sizeof(char), + reinterpret_cast(output_buffer.data())); + if (Network::rank() > 0) { + nccl_unique_id = output_buffer[0]; + } + } + + if (Network::num_machines() > 1) { + node_rank_offset_.resize(Network::num_machines() + 1, 0); + Network::Allgather( + reinterpret_cast(&num_gpu_), + sizeof(int) / sizeof(char), + reinterpret_cast(node_rank_offset_.data() + 1)); + for (int rank = 1; rank < Network::num_machines() + 1; ++rank) { + node_rank_offset_[rank] += node_rank_offset_[rank - 1]; + } + CHECK_EQ(node_rank_offset_[Network::rank() + 1] - node_rank_offset_[Network::rank()], num_gpu_); + NCCLCHECK(ncclGroupStart()); + for (int gpu_index = 0; gpu_index < num_gpu_; ++gpu_index) { + SetCUDADevice(gpu_list_[gpu_index], __FILE__, __LINE__); + nccl_gpu_rank_[gpu_index] = gpu_index + node_rank_offset_[Network::rank()]; + NCCLCHECK(ncclCommInitRank(&nccl_communicators_[gpu_index], node_rank_offset_.back(), nccl_unique_id, nccl_gpu_rank_[gpu_index])); + } + NCCLCHECK(ncclGroupEnd()); + } else { + NCCLCHECK(ncclGroupStart()); + for (int gpu_index = 0; gpu_index < num_gpu_; ++gpu_index) { + SetCUDADevice(gpu_list_[gpu_index], __FILE__, __LINE__); + nccl_gpu_rank_[gpu_index] = gpu_index; + NCCLCHECK(ncclCommInitRank(&nccl_communicators_[gpu_index], num_gpu_, nccl_unique_id, gpu_index)); + } + NCCLCHECK(ncclGroupEnd()); + } + + // return to master gpu device + CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); + } + + template + void RunPerDevice(const std::vector>& objs, const std::function& func) { + #pragma omp parallel for schedule(static) num_threads(num_gpu_) + for (int i = 0; i < num_gpu_; ++i) { + CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])); + func(objs[i].get()); + } + CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); + } + + template + void InitPerDevice(std::vector>* vec) { + vec->resize(num_gpu_); + #pragma omp parallel for schedule(static) num_threads(num_gpu_) + for (int i = 0; i < num_gpu_; ++i) { + CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])); + RET_T* nccl_info = new RET_T(); + nccl_info->SetNCCLInfo(nccl_communicators_[i], nccl_gpu_rank_[i], i, gpu_list_[i], global_num_data_); + vec->operator[](i).reset(nccl_info); + } + CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); + } + + template + void DispatchPerDevice(std::vector>* objs, const std::function& func) { + for (int i = 0; i < num_gpu_; ++i) { + host_threads_[i] = std::thread([this, i, &func, objs] () { + CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])) + func(objs->operator[](i).get()); + }); + } + for (int i = 0; i < num_gpu_; ++i) { + host_threads_[i].join(); + } + CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); + } + + template + void RunOnMasterDevice(const std::vector>& objs, const std::function& func) { + CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); + func(objs[master_gpu_index_].get()); + } + + template + void RunOnNonMasterDevice(const std::vector>& objs, const std::function& func) { + for (int i = 0; i < num_gpu_; ++i) { + if (i != master_gpu_index_) { + CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i])); + func(objs[i].get()); + } + } + CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_)); + } + + int num_gpu() const { return num_gpu_; } + + int master_gpu_index() const { return master_gpu_index_; } + + int master_gpu_device_id() const { return master_gpu_device_id_; } + + const std::vector& gpu_list() const { return gpu_list_; } + + private: + int num_gpu_; + int master_gpu_index_; + int master_gpu_device_id_; + std::vector gpu_list_; + data_size_t global_num_data_; + + ncclUniqueId nccl_unique_id_; + std::vector node_rank_offset_; + std::vector nccl_gpu_rank_; + std::vector nccl_communicators_; + std::vector host_threads_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_ diff --git a/include/LightGBM/cuda/cuda_objective_function.hpp b/include/LightGBM/cuda/cuda_objective_function.hpp new file mode 100644 index 0000000..050c270 --- /dev/null +++ b/include/LightGBM/cuda/cuda_objective_function.hpp @@ -0,0 +1,98 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2026-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include + +#include +#include + +namespace LightGBM { + +template +class CUDAObjectiveInterface: public HOST_OBJECTIVE, public NCCLInfo { + public: + explicit CUDAObjectiveInterface(const Config& config): HOST_OBJECTIVE(config) { + if (config.num_gpu <= 1) { + const int gpu_device_id = config.gpu_device_id >= 0 ? config.gpu_device_id : 0; + SetCUDADevice(gpu_device_id, __FILE__, __LINE__); + } + } + + explicit CUDAObjectiveInterface(const std::vector& strs): HOST_OBJECTIVE(strs) {} + + void Init(const Metadata& metadata, data_size_t num_data) { + HOST_OBJECTIVE::Init(metadata, num_data); + cuda_labels_ = metadata.cuda_metadata()->cuda_label(); + cuda_weights_ = metadata.cuda_metadata()->cuda_weights(); + } + + void SetNCCLInfo( + ncclComm_t nccl_communicator, + int nccl_gpu_rank, + int local_gpu_rank, + int gpu_device_id, + data_size_t global_num_data) override { + NCCLInfo::SetNCCLInfo(nccl_communicator, nccl_gpu_rank, local_gpu_rank, gpu_device_id, global_num_data); + } + + virtual const double* ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const { + return LaunchConvertOutputCUDAKernel(num_data, input, output); + } + + double BoostFromScore(int class_id) const override { + return LaunchCalcInitScoreKernel(class_id); + } + + bool IsCUDAObjective() const override { return true; } + + void GetGradients(const double* scores, score_t* gradients, score_t* hessians) const override { + LaunchGetGradientsKernel(scores, gradients, hessians); + SynchronizeCUDADevice(__FILE__, __LINE__); + } + + void GetGradientsWithSampledQueries(const double* scores, const data_size_t /*num_sampled_queries*/, const data_size_t* /*sampled_query_indices*/, score_t* gradients, score_t* hessians) const override { + LaunchGetGradientsKernel(scores, gradients, hessians); + SynchronizeCUDADevice(__FILE__, __LINE__); + } + + void RenewTreeOutputCUDA(const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, + const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override { + global_timer.Start("CUDAObjectiveInterface::LaunchRenewTreeOutputCUDAKernel"); + LaunchRenewTreeOutputCUDAKernel(score, data_indices_in_leaf, num_data_in_leaf, data_start_in_leaf, num_leaves, leaf_value); + SynchronizeCUDADevice(__FILE__, __LINE__); + global_timer.Stop("CUDAObjectiveInterface::LaunchRenewTreeOutputCUDAKernel"); + } + + protected: + virtual void LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const = 0; + + virtual double LaunchCalcInitScoreKernel(const int class_id) const { + return HOST_OBJECTIVE::BoostFromScore(class_id); + } + + virtual const double* LaunchConvertOutputCUDAKernel(const data_size_t /*num_data*/, const double* input, double* /*output*/) const { return input; } + + virtual void LaunchRenewTreeOutputCUDAKernel( + const double* /*score*/, const data_size_t* /*data_indices_in_leaf*/, const data_size_t* /*num_data_in_leaf*/, + const data_size_t* /*data_start_in_leaf*/, const int /*num_leaves*/, double* /*leaf_value*/) const {} + + const label_t* cuda_labels_; + const label_t* cuda_weights_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_ diff --git a/include/LightGBM/cuda/cuda_random.hpp b/include/LightGBM/cuda/cuda_random.hpp new file mode 100644 index 0000000..938b184 --- /dev/null +++ b/include/LightGBM/cuda/cuda_random.hpp @@ -0,0 +1,77 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_ + +#ifdef USE_CUDA + +#ifndef USE_ROCM +#include +#include +#endif + +namespace LightGBM { + +/*! +* \brief A wrapper for random generator +*/ +class CUDARandom { + public: + /*! + * \brief Set specific seed + */ + __device__ void SetSeed(int seed) { + x = seed; + } + /*! + * \brief Generate random integer, int16 range. [0, 65536] + * \param lower_bound lower bound + * \param upper_bound upper bound + * \return The random integer between [lower_bound, upper_bound) + */ + __device__ inline int NextShort(int lower_bound, int upper_bound) { + return (RandInt16()) % (upper_bound - lower_bound) + lower_bound; + } + + /*! + * \brief Generate random integer, int32 range + * \param lower_bound lower bound + * \param upper_bound upper bound + * \return The random integer between [lower_bound, upper_bound) + */ + __device__ inline int NextInt(int lower_bound, int upper_bound) { + return (RandInt32()) % (upper_bound - lower_bound) + lower_bound; + } + + /*! + * \brief Generate random float data + * \return The random float between [0.0, 1.0) + */ + __device__ inline float NextFloat() { + // get random float in [0,1) + return static_cast(RandInt16()) / (32768.0f); + } + + private: + __device__ inline int RandInt16() { + x = (214013 * x + 2531011); + return static_cast((x >> 16) & 0x7FFF); + } + + __device__ inline int RandInt32() { + x = (214013 * x + 2531011); + return static_cast(x & 0x7FFFFFFF); + } + + unsigned int x = 123456789; +}; + + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_ diff --git a/include/LightGBM/cuda/cuda_rocm_interop.h b/include/LightGBM/cuda/cuda_rocm_interop.h new file mode 100644 index 0000000..34ab73c --- /dev/null +++ b/include/LightGBM/cuda/cuda_rocm_interop.h @@ -0,0 +1,71 @@ +/*! + * Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_ + +#ifdef USE_CUDA + +#if defined(__HIP_PLATFORM_AMD__) + +// ROCm doesn't have atomicAdd_block, but it should be semantically the same as atomicAdd +#define atomicAdd_block atomicAdd + +// hipify +#include +#define cudaDeviceProp hipDeviceProp_t +#define cudaDeviceSynchronize hipDeviceSynchronize +#define cudaError_t hipError_t +#define cudaFree hipFree +#define cudaFreeHost hipFreeHost +#define cudaGetDevice hipGetDevice +#define cudaGetDeviceCount hipGetDeviceCount +#define cudaGetDeviceProperties hipGetDeviceProperties +#define cudaGetErrorName hipGetErrorName +#define cudaGetErrorString hipGetErrorString +#define cudaGetLastError hipGetLastError +#define cudaHostAlloc hipHostAlloc +#define cudaHostAllocPortable hipHostAllocPortable +#define cudaMalloc hipMalloc +#define cudaMemcpy hipMemcpy +#define cudaMemcpyAsync hipMemcpyAsync +#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice +#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost +#define cudaMemcpyHostToDevice hipMemcpyHostToDevice +#define cudaMemoryTypeHost hipMemoryTypeHost +#define cudaMemset hipMemset +#define cudaPointerAttributes hipPointerAttribute_t +#define cudaPointerGetAttributes hipPointerGetAttributes +#define cudaSetDevice hipSetDevice +#define cudaStreamCreate hipStreamCreate +#define cudaStreamDestroy hipStreamDestroy +#define cudaStreamSynchronize hipStreamSynchronize +#define cudaStream_t hipStream_t +#define cudaSuccess hipSuccess + +// ROCm 7.0 did add __shfl_down_sync et al, but the following hack still works. +// Since mask is full 0xffffffff, we can use __shfl_down instead. +#define __shfl_down_sync(mask, val, offset) __shfl_down(val, offset) +#define __shfl_up_sync(mask, val, offset) __shfl_up(val, offset) + +// warpSize is only allowed for device code. +// HIP header used to define warpSize as a constexpr that was either 32 or 64 +// depending on the target device, and then always set it to 64 for host code. +static inline constexpr int WARP_SIZE_INTERNAL() { +#if defined(__GFX9__) + return 64; +#else // __GFX9__ + return 32; +#endif // __GFX9__ +} +#define WARPSIZE (WARP_SIZE_INTERNAL()) + +#else // __HIP_PLATFORM_AMD__ +// CUDA warpSize is not a constexpr, but always 32 +#define WARPSIZE 32 +#endif // defined(__HIP_PLATFORM_AMD__) || defined(__HIP__) + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_ diff --git a/include/LightGBM/cuda/cuda_row_data.hpp b/include/LightGBM/cuda/cuda_row_data.hpp new file mode 100644 index 0000000..9b91e1b --- /dev/null +++ b/include/LightGBM/cuda/cuda_row_data.hpp @@ -0,0 +1,182 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include +#include +#include +#include + +#include +#include + +#define COPY_SUBROW_BLOCK_SIZE_ROW_DATA (1024) + +#if CUDART_VERSION == 10000 +#define DP_SHARED_HIST_SIZE (5176) +#else +#define DP_SHARED_HIST_SIZE (6144) +#endif +#define SP_SHARED_HIST_SIZE (DP_SHARED_HIST_SIZE * 2) + +namespace LightGBM { + +class CUDARowData { + public: + CUDARowData(const Dataset* train_data, + const TrainingShareStates* train_share_state, + const int gpu_device_id, + const bool gpu_use_dp); + + ~CUDARowData(); + + void Init(const Dataset* train_data, + TrainingShareStates* train_share_state); + + void CopySubrow(const CUDARowData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices); + + void CopySubcol(const CUDARowData* full_set, const std::vector& is_feature_used, const Dataset* train_data); + + void CopySubrowAndSubcol(const CUDARowData* full_set, const data_size_t* used_indices, + const data_size_t num_used_indices, const std::vector& is_feature_used, const Dataset* train_data); + + template + const BIN_TYPE* GetBin() const; + + template + const PTR_TYPE* GetPartitionPtr() const; + + template + const PTR_TYPE* GetRowPtr() const; + + int NumLargeBinPartition() const { return static_cast(large_bin_partitions_.size()); } + + int num_feature_partitions() const { return num_feature_partitions_; } + + int max_num_column_per_partition() const { return max_num_column_per_partition_; } + + bool is_sparse() const { return is_sparse_; } + + uint8_t bit_type() const { return bit_type_; } + + uint8_t row_ptr_bit_type() const { return row_ptr_bit_type_; } + + const int* cuda_feature_partition_column_index_offsets() const { return cuda_feature_partition_column_index_offsets_.RawData(); } + + const uint32_t* cuda_column_hist_offsets() const { return cuda_column_hist_offsets_.RawData(); } + + const uint32_t* cuda_partition_hist_offsets() const { return cuda_partition_hist_offsets_.RawData(); } + + int shared_hist_size() const { return shared_hist_size_; } + + private: + void DivideCUDAFeatureGroups(const Dataset* train_data, TrainingShareStates* share_state); + + template + void GetDenseDataPartitioned(const BIN_TYPE* row_wise_data, std::vector* partitioned_data); + + template + void GetSparseDataPartitioned(const BIN_TYPE* row_wise_data, + const ROW_PTR_TYPE* row_ptr, + std::vector>* partitioned_data, + std::vector>* partitioned_row_ptr, + std::vector* partition_ptr); + + template + void InitSparseData(const BIN_TYPE* host_data, + const ROW_PTR_TYPE* host_row_ptr, + CUDAVector* cuda_data, + CUDAVector* cuda_row_ptr, + CUDAVector* cuda_partition_ptr); + + /*! \brief number of threads to use */ + int num_threads_; + /*! \brief number of training data */ + data_size_t num_data_; + /*! \brief number of bins of all features */ + int num_total_bin_; + /*! \brief number of feature groups in dataset */ + int num_feature_group_; + /*! \brief number of features in dataset */ + int num_feature_; + /*! \brief number of bits used to store each bin value */ + uint8_t bit_type_; + /*! \brief number of bits used to store each row pointer value */ + uint8_t row_ptr_bit_type_; + /*! \brief is sparse row wise data */ + bool is_sparse_; + /*! \brief start column index of each feature partition */ + std::vector feature_partition_column_index_offsets_; + /*! \brief histogram offset of each column */ + std::vector column_hist_offsets_; + /*! \brief histogram offset of each partition */ + std::vector partition_hist_offsets_; + /*! \brief maximum number of columns among all feature partitions */ + int max_num_column_per_partition_; + /*! \brief number of partitions */ + int num_feature_partitions_; + /*! \brief used when bagging with subset, number of used indices */ + data_size_t num_used_indices_; + /*! \brief used when bagging with subset, number of total elements */ + uint64_t num_total_elements_; + /*! \brief used when bagging with column subset, the size of maximum number of feature partitions */ + int cur_num_feature_partition_buffer_size_; + /*! \brief CUDA device ID */ + int gpu_device_id_; + /*! \brief index of partitions with large bins that its histogram cannot fit into shared memory, each large bin partition contains a single column */ + std::vector large_bin_partitions_; + /*! \brief index of partitions with small bins */ + std::vector small_bin_partitions_; + /*! \brief shared memory size used by histogram */ + int shared_hist_size_; + /*! \brief whether to use double precision in histograms per block */ + bool gpu_use_dp_; + + // CUDA memory + + /*! \brief row-wise data stored in CUDA, 8 bits */ + CUDAVector cuda_data_uint8_t_; + /*! \brief row-wise data stored in CUDA, 16 bits */ + CUDAVector cuda_data_uint16_t_; + /*! \brief row-wise data stored in CUDA, 32 bits */ + CUDAVector cuda_data_uint32_t_; + /*! \brief row pointer stored in CUDA, 16 bits */ + CUDAVector cuda_row_ptr_uint16_t_; + /*! \brief row pointer stored in CUDA, 32 bits */ + CUDAVector cuda_row_ptr_uint32_t_; + /*! \brief row pointer stored in CUDA, 64 bits */ + CUDAVector cuda_row_ptr_uint64_t_; + /*! \brief partition bin offsets, 16 bits */ + CUDAVector cuda_partition_ptr_uint16_t_; + /*! \brief partition bin offsets, 32 bits */ + CUDAVector cuda_partition_ptr_uint32_t_; + /*! \brief partition bin offsets, 64 bits */ + CUDAVector cuda_partition_ptr_uint64_t_; + /*! \brief start column index of each feature partition */ + CUDAVector cuda_feature_partition_column_index_offsets_; + /*! \brief histogram offset of each column */ + CUDAVector cuda_column_hist_offsets_; + /*! \brief histogram offset of each partition */ + CUDAVector cuda_partition_hist_offsets_; + /*! \brief block buffer when calculating prefix sum */ + CUDAVector cuda_block_buffer_uint16_t_; + /*! \brief block buffer when calculating prefix sum */ + CUDAVector cuda_block_buffer_uint32_t_; + /*! \brief block buffer when calculating prefix sum */ + CUDAVector cuda_block_buffer_uint64_t_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_ diff --git a/include/LightGBM/cuda/cuda_split_info.hpp b/include/LightGBM/cuda/cuda_split_info.hpp new file mode 100644 index 0000000..0e0e614 --- /dev/null +++ b/include/LightGBM/cuda/cuda_split_info.hpp @@ -0,0 +1,109 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_ + +#ifdef USE_CUDA + +#include + +namespace LightGBM { + +class CUDASplitInfo { + public: + bool is_valid; + int leaf_index; + double gain; + int inner_feature_index; + uint32_t threshold; + bool default_left; + + double left_sum_gradients; + double left_sum_hessians; + int64_t left_sum_of_gradients_hessians; + data_size_t left_count; + double left_gain; + double left_value; + + double right_sum_gradients; + double right_sum_hessians; + int64_t right_sum_of_gradients_hessians; + data_size_t right_count; + double right_gain; + double right_value; + + int num_cat_threshold = 0; + uint32_t* cat_threshold = nullptr; + int* cat_threshold_real = nullptr; + + __host__ __device__ CUDASplitInfo() { + num_cat_threshold = 0; + cat_threshold = nullptr; + cat_threshold_real = nullptr; + } + + __host__ __device__ ~CUDASplitInfo() { + if (num_cat_threshold > 0) { + if (cat_threshold != nullptr) { + CUDASUCCESS_OR_FATAL(cudaFree(cat_threshold)); + } + if (cat_threshold_real != nullptr) { + CUDASUCCESS_OR_FATAL(cudaFree(cat_threshold_real)); + } + } + } + + __host__ __device__ CUDASplitInfo& operator=(const CUDASplitInfo& other) { + is_valid = other.is_valid; + leaf_index = other.leaf_index; + gain = other.gain; + inner_feature_index = other.inner_feature_index; + threshold = other.threshold; + default_left = other.default_left; + + left_sum_gradients = other.left_sum_gradients; + left_sum_hessians = other.left_sum_hessians; + left_count = other.left_count; + left_gain = other.left_gain; + left_value = other.left_value; + + right_sum_gradients = other.right_sum_gradients; + right_sum_hessians = other.right_sum_hessians; + right_count = other.right_count; + right_gain = other.right_gain; + right_value = other.right_value; + + num_cat_threshold = other.num_cat_threshold; + if (num_cat_threshold > 0 && cat_threshold == nullptr) { + cat_threshold = new uint32_t[num_cat_threshold]; + } + if (num_cat_threshold > 0 && cat_threshold_real == nullptr) { + cat_threshold_real = new int[num_cat_threshold]; + } + if (num_cat_threshold > 0) { + if (other.cat_threshold != nullptr) { + for (int i = 0; i < num_cat_threshold; ++i) { + cat_threshold[i] = other.cat_threshold[i]; + } + } + if (other.cat_threshold_real != nullptr) { + for (int i = 0; i < num_cat_threshold; ++i) { + cat_threshold_real[i] = other.cat_threshold_real[i]; + } + } + } + return *this; + } +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_ diff --git a/include/LightGBM/cuda/cuda_tree.hpp b/include/LightGBM/cuda/cuda_tree.hpp new file mode 100644 index 0000000..0de00a9 --- /dev/null +++ b/include/LightGBM/cuda/cuda_tree.hpp @@ -0,0 +1,174 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include +#include + +namespace LightGBM { + +__device__ void SetDecisionTypeCUDA(int8_t* decision_type, bool input, int8_t mask); + +__device__ void SetMissingTypeCUDA(int8_t* decision_type, int8_t input); + +__device__ bool GetDecisionTypeCUDA(int8_t decision_type, int8_t mask); + +__device__ int8_t GetMissingTypeCUDA(int8_t decision_type); + +__device__ bool IsZeroCUDA(double fval); + +class CUDATree : public Tree { + public: + /*! + * \brief Constructor + * \param max_leaves The number of max leaves + * \param track_branch_features Whether to keep track of ancestors of leaf nodes + * \param is_linear Whether the tree has linear models at each leaf + */ + explicit CUDATree(int max_leaves, bool track_branch_features, bool is_linear, + const int gpu_device_id, const bool has_categorical_feature); + + explicit CUDATree(const Tree* host_tree); + + ~CUDATree() noexcept; + + int Split(const int leaf_index, + const int real_feature_index, + const double real_threshold, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info); + + int SplitCategorical( + const int leaf_index, + const int real_feature_index, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info, + uint32_t* cuda_bitset, + size_t cuda_bitset_len, + uint32_t* cuda_bitset_inner, + size_t cuda_bitset_inner_len); + + /*! + * \brief Adding prediction value of this tree model to scores + * \param data The dataset + * \param num_data Number of total data + * \param score Will add prediction to score + */ + void AddPredictionToScore(const Dataset* data, + data_size_t num_data, + double* score) const override; + + /*! + * \brief Adding prediction value of this tree model to scores + * \param data The dataset + * \param used_data_indices Indices of used data + * \param num_data Number of total data + * \param score Will add prediction to score + */ + void AddPredictionToScore(const Dataset* data, + const data_size_t* used_data_indices, + data_size_t num_data, double* score) const override; + + inline void AsConstantTree(double val, int count) override; + + const int* cuda_leaf_parent() const { return cuda_leaf_parent_.RawData(); } + + const int* cuda_left_child() const { return cuda_left_child_.RawData(); } + + const int* cuda_right_child() const { return cuda_right_child_.RawData(); } + + const int* cuda_split_feature_inner() const { return cuda_split_feature_inner_.RawData(); } + + const int* cuda_split_feature() const { return cuda_split_feature_.RawData(); } + + const uint32_t* cuda_threshold_in_bin() const { return cuda_threshold_in_bin_.RawData(); } + + const double* cuda_threshold() const { return cuda_threshold_.RawData(); } + + const int8_t* cuda_decision_type() const { return cuda_decision_type_.RawData(); } + + const double* cuda_leaf_value() const { return cuda_leaf_value_.RawData(); } + + double* cuda_leaf_value_ref() { return cuda_leaf_value_.RawData(); } + + inline void Shrinkage(double rate) override; + + inline void AddBias(double val) override; + + void ToHost(); + + void SyncLeafOutputFromHostToCUDA(); + + void SyncLeafOutputFromCUDAToHost(); + + private: + void InitCUDAMemory(); + + void InitCUDA(); + + void LaunchSplitKernel(const int leaf_index, + const int real_feature_index, + const double real_threshold, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info); + + void LaunchSplitCategoricalKernel( + const int leaf_index, + const int real_feature_index, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info, + size_t cuda_bitset_len, + size_t cuda_bitset_inner_len); + + void LaunchAddPredictionToScoreKernel(const Dataset* data, + const data_size_t* used_data_indices, + data_size_t num_data, double* score) const; + + void LaunchShrinkageKernel(const double rate); + + void LaunchAddBiasKernel(const double val); + + void RecordBranchFeatures(const int left_leaf_index, + const int right_leaf_index, + const int real_feature_index); + + CUDAVector cuda_left_child_; + CUDAVector cuda_right_child_; + CUDAVector cuda_split_feature_inner_; + CUDAVector cuda_split_feature_; + CUDAVector cuda_leaf_depth_; + CUDAVector cuda_leaf_parent_; + CUDAVector cuda_threshold_in_bin_; + CUDAVector cuda_threshold_; + CUDAVector cuda_internal_weight_; + CUDAVector cuda_internal_value_; + CUDAVector cuda_decision_type_; + CUDAVector cuda_leaf_value_; + CUDAVector cuda_leaf_count_; + CUDAVector cuda_leaf_weight_; + CUDAVector cuda_internal_count_; + CUDAVector cuda_split_gain_; + CUDAVector cuda_bitset_; + CUDAVector cuda_bitset_inner_; + CUDAVector cuda_cat_boundaries_; + CUDAVector cuda_cat_boundaries_inner_; + + cudaStream_t cuda_stream_; + + const int num_threads_per_block_add_prediction_to_score_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_ diff --git a/include/LightGBM/cuda/cuda_utils.hu b/include/LightGBM/cuda/cuda_utils.hu new file mode 100644 index 0000000..cb37f3c --- /dev/null +++ b/include/LightGBM/cuda/cuda_utils.hu @@ -0,0 +1,325 @@ +/*! + * Copyright (c) 2020-2021 IBM Corporation, Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_CUDA_CUDA_UTILS_H_ +#define LIGHTGBM_CUDA_CUDA_UTILS_H_ + +#ifdef USE_CUDA + +#if defined(USE_ROCM) +#include +#include +#else +#include +#include +#include +#endif +#include + +#include +#include + +#include +#include +#include + +namespace LightGBM { + +typedef unsigned long long atomic_add_long_t; + +#define CUDASUCCESS_OR_FATAL(ans) { gpuAssert((ans), __FILE__, __LINE__); } +inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) { + if (code != cudaSuccess) { + LightGBM::Log::Fatal("[CUDA] %s %s %d\n", cudaGetErrorString(code), file, line); + if (abort) exit(code); + } +} + +#define CUDASUCCESS_OR_FATAL_OUTER(ans) { gpuAssert((ans), file, line); } + +#define NCCLCHECK(cmd) do { \ + ncclResult_t r = cmd; \ + if (r!= ncclSuccess) { \ + printf("Failed, NCCL error %s:%d '%s'\n", \ + __FILE__,__LINE__,ncclGetErrorString(r)); \ + exit(EXIT_FAILURE); \ + } \ +} while(0) + +void SetCUDADevice(int gpu_device_id, const char* file, int line); + +int GetCUDADevice(const char* file, int line); + +template +void AllocateCUDAMemory(T** out_ptr, size_t size, const char* file, const int line) { + void* tmp_ptr = nullptr; + CUDASUCCESS_OR_FATAL_OUTER(cudaMalloc(&tmp_ptr, size * sizeof(T))); + *out_ptr = reinterpret_cast(tmp_ptr); +} + +template +void CopyFromHostToCUDADevice(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { + void* void_dst_ptr = reinterpret_cast(dst_ptr); + const void* void_src_ptr = reinterpret_cast(src_ptr); + size_t size_in_bytes = size * sizeof(T); + CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyHostToDevice)); +} + +template +void InitCUDAMemoryFromHostMemory(T** dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { + AllocateCUDAMemory(dst_ptr, size, file, line); + CopyFromHostToCUDADevice(*dst_ptr, src_ptr, size, file, line); +} + +template +void CopyFromCUDADeviceToHost(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { + void* void_dst_ptr = reinterpret_cast(dst_ptr); + const void* void_src_ptr = reinterpret_cast(src_ptr); + size_t size_in_bytes = size * sizeof(T); + CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToHost)); +} + +template +void CopyFromCUDADeviceToHostAsync(T* dst_ptr, const T* src_ptr, size_t size, cudaStream_t stream, const char* file, const int line) { + void* void_dst_ptr = reinterpret_cast(dst_ptr); + const void* void_src_ptr = reinterpret_cast(src_ptr); + size_t size_in_bytes = size * sizeof(T); + CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpyAsync(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToHost, stream)); +} + +template +void CopyFromCUDADeviceToCUDADevice(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { + void* void_dst_ptr = reinterpret_cast(dst_ptr); + const void* void_src_ptr = reinterpret_cast(src_ptr); + size_t size_in_bytes = size * sizeof(T); + CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToDevice)); +} + +template +void CopyFromCUDADeviceToCUDADeviceAsync(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) { + void* void_dst_ptr = reinterpret_cast(dst_ptr); + const void* void_src_ptr = reinterpret_cast(src_ptr); + size_t size_in_bytes = size * sizeof(T); + CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpyAsync(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToDevice)); +} + +void SynchronizeCUDADevice(const char* file, const int line); + +void SynchronizeCUDAStream(cudaStream_t cuda_stream, const char* file, const int line); + +template +void SetCUDAMemory(T* dst_ptr, int value, size_t size, const char* file, const int line) { + CUDASUCCESS_OR_FATAL_OUTER(cudaMemset(reinterpret_cast(dst_ptr), value, size * sizeof(T))); + SynchronizeCUDADevice(file, line); +} + +template +void DeallocateCUDAMemory(T** ptr, const char* file, const int line) { + if (*ptr != nullptr) { + CUDASUCCESS_OR_FATAL_OUTER(cudaFree(reinterpret_cast(*ptr))); + *ptr = nullptr; + } +} + +void PrintLastCUDAError(); + +template +class CUDAVector { + public: + CUDAVector() { + size_ = 0; + data_ = nullptr; + } + + explicit CUDAVector(size_t size) { + size_ = size; + AllocateCUDAMemory(&data_, size_, __FILE__, __LINE__); + } + + void Resize(size_t size) { + if (size == size_) { + return; + } + if (size == 0) { + Clear(); + return; + } + T* new_data = nullptr; + AllocateCUDAMemory(&new_data, size, __FILE__, __LINE__); + if (size_ > 0 && data_ != nullptr) { + const size_t size_for_old_content = std::min(size_, size); + CopyFromCUDADeviceToCUDADevice(new_data, data_, size_for_old_content, __FILE__, __LINE__); + } + DeallocateCUDAMemory(&data_, __FILE__, __LINE__); + data_ = new_data; + size_ = size; + } + + void InitFromHostVector(const std::vector& host_vector) { + Resize(host_vector.size()); + CopyFromHostToCUDADevice(data_, host_vector.data(), host_vector.size(), __FILE__, __LINE__); + } + + void InitFromHostMemory(const T* host_memory, size_t len) { + Resize(len); + CopyFromHostToCUDADevice(data_, host_memory, len, __FILE__, __LINE__); + } + + void Clear() { + if (size_ > 0 && data_ != nullptr) { + DeallocateCUDAMemory(&data_, __FILE__, __LINE__); + } + size_ = 0; + } + + void PushBack(const T* values, size_t len) { + T* new_data = nullptr; + AllocateCUDAMemory(&new_data, size_ + len, __FILE__, __LINE__); + if (size_ > 0 && data_ != nullptr) { + CopyFromCUDADeviceToCUDADevice(new_data, data_, size_, __FILE__, __LINE__); + } + CopyFromCUDADeviceToCUDADevice(new_data + size_, values, len, __FILE__, __LINE__); + DeallocateCUDAMemory(&data_, __FILE__, __LINE__); + size_ += len; + data_ = new_data; + } + + size_t Size() const { + return size_; + } + + ~CUDAVector() { + DeallocateCUDAMemory(&data_, __FILE__, __LINE__); + } + + std::vector ToHost() { + std::vector host_vector(size_); + if (size_ > 0 && data_ != nullptr) { + CopyFromCUDADeviceToHost(host_vector.data(), data_, size_, __FILE__, __LINE__); + } + return host_vector; + } + + T* RawData() const { + return data_; + } + + void SetValue(int value) { + SetCUDAMemory(data_, value, size_, __FILE__, __LINE__); + } + + const T* RawDataReadOnly() const { + return data_; + } + + T* MoveTo() { + size_ = 0; + T* old_data = data_; + data_ = nullptr; + return old_data; + } + + template + void MoveFrom(CUDAVector& other, size_t new_size) { + data_ = reinterpret_cast(other.MoveTo()); + size_ = new_size; + } + + private: + T* data_; + size_t size_; +}; + +template +static __device__ T SafeLog(T x) { + if (x > 0) { + return std::log(x); + } else { + return -INFINITY; + } +} + +class NCCLInfo { + public: + NCCLInfo() { + nccl_communicator_ = nullptr; + nccl_gpu_rank_ = -1; + local_gpu_rank_ = -1; + gpu_device_id_ = -1; + num_gpu_in_node_ = 0; + global_num_data_ = 0; + } + + virtual void SetNCCLInfo( + ncclComm_t nccl_communicator, + int nccl_gpu_rank, + int local_gpu_rank, + int gpu_device_id, + data_size_t global_num_data) { + nccl_communicator_ = nccl_communicator; + nccl_gpu_rank_ = nccl_gpu_rank; + local_gpu_rank_ = local_gpu_rank; + gpu_device_id_ = gpu_device_id; + global_num_data_ = global_num_data; + } + + protected: + ncclComm_t nccl_communicator_ = nullptr; + int nccl_gpu_rank_ = -1; + int local_gpu_rank_ = -1; + int gpu_device_id_ = -1; + int num_gpu_in_node_ = 0; + data_size_t global_num_data_ = 0; +}; + +cudaStream_t CUDAStreamCreate(); + +void CUDAStreamDestroy(cudaStream_t cuda_stream); + +void NCCLGroupStart(); + +void NCCLGroupEnd(); + +template +void NCCLAllReduce(const T* send_buffer, T* recv_buffer, size_t count, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream) { + NCCLCHECK(ncclAllReduce(reinterpret_cast(send_buffer), reinterpret_cast(recv_buffer), count, datatype, op, comm, stream)); +} + +template +void NCCLAllReduce(const T* send_buffer, T* recv_buffer, size_t count, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm) { + cudaStream_t nccl_stream; + CUDASUCCESS_OR_FATAL(cudaStreamCreate(&nccl_stream)); + NCCLCHECK(ncclAllReduce(reinterpret_cast(send_buffer), reinterpret_cast(recv_buffer), count, datatype, op, comm, nccl_stream)); + CUDASUCCESS_OR_FATAL(cudaStreamSynchronize(nccl_stream)); + CUDASUCCESS_OR_FATAL(cudaStreamDestroy(nccl_stream)); +} + +template +T NCCLAllReduce(T send_value, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream) { + CUDAVector send_buffer(1); + CopyFromHostToCUDADevice(send_buffer.RawData(), &send_value, 1, __FILE__, __LINE__); + NCCLAllReduce(send_buffer.RawDataReadOnly(), send_buffer.RawData(), 1, datatype, op, comm, stream); + T recv_value = 0; + CopyFromCUDADeviceToHost(&recv_value, send_buffer.RawDataReadOnly(), 1, __FILE__, __LINE__); + return recv_value; +} + +template +T NCCLAllReduce(T send_value, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm) { + CUDAVector send_buffer(1); + CopyFromHostToCUDADevice(send_buffer.RawData(), &send_value, 1, __FILE__, __LINE__); + NCCLAllReduce(send_buffer.RawDataReadOnly(), send_buffer.RawData(), 1, datatype, op, comm); + T recv_value = 0; + CopyFromCUDADeviceToHost(&recv_value, send_buffer.RawDataReadOnly(), 1, __FILE__, __LINE__); + return recv_value; +} + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_CUDA_CUDA_UTILS_H_ diff --git a/include/LightGBM/cuda/vector_cudahost.h b/include/LightGBM/cuda/vector_cudahost.h new file mode 100644 index 0000000..140a200 --- /dev/null +++ b/include/LightGBM/cuda/vector_cudahost.h @@ -0,0 +1,99 @@ +/*! + * Copyright (c) 2020-2021 IBM Corporation, Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_ + +#include + +#ifdef USE_CUDA +#ifndef USE_ROCM +#include +#include +#endif // USE_ROCM +#include +#endif // USE_CUDA +#include + +enum LGBM_Device { + lgbm_device_cpu, + lgbm_device_gpu, + lgbm_device_cuda +}; + +enum Use_Learner { + use_cpu_learner, + use_gpu_learner, + use_cuda_learner +}; + +namespace LightGBM { + +class LGBM_config_ { + public: + static int current_device; // Default: lgbm_device_cpu + static int current_learner; // Default: use_cpu_learner +}; + + +template +struct CHAllocator { + typedef T value_type; + CHAllocator() {} + template CHAllocator(const CHAllocator& other); + T* allocate(std::size_t n) { + T* ptr; + if (n == 0) return NULL; + n = SIZE_ALIGNED(n); + #ifdef USE_CUDA + if (LGBM_config_::current_device == lgbm_device_cuda) { + cudaError_t ret = cudaHostAlloc(reinterpret_cast(&ptr), n*sizeof(T), cudaHostAllocPortable); + if (ret != cudaSuccess) { + Log::Warning("Defaulting to malloc in CHAllocator!!!"); + ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16)); + } + } else { + ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16)); + } + #else + ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16)); + #endif + return ptr; + } + + void deallocate(T* p, std::size_t n) { + (void)n; // UNUSED + if (p == NULL) return; + #ifdef USE_CUDA + if (LGBM_config_::current_device == lgbm_device_cuda) { + cudaPointerAttributes attributes; + CUDASUCCESS_OR_FATAL(cudaPointerGetAttributes(&attributes, p)); + #if CUDA_VERSION >= 10000 || defined(USE_ROCM) + if ((attributes.type == cudaMemoryTypeHost) && (attributes.devicePointer != NULL)) { + CUDASUCCESS_OR_FATAL(cudaFreeHost(p)); + } + #else + if ((attributes.memoryType == cudaMemoryTypeHost) && (attributes.devicePointer != NULL)) { + CUDASUCCESS_OR_FATAL(cudaFreeHost(p)); + } + #endif + } else { + _mm_free(p); + } + #else + _mm_free(p); + #endif + } +}; +template +bool operator==(const CHAllocator&, const CHAllocator&); +template +bool operator!=(const CHAllocator&, const CHAllocator&); + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_ diff --git a/include/LightGBM/dataset.h b/include/LightGBM/dataset.h new file mode 100644 index 0000000..72256da --- /dev/null +++ b/include/LightGBM/dataset.h @@ -0,0 +1,1086 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_ + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace LightGBM { + +/*! \brief forward declaration */ +class DatasetLoader; +/*! +* \brief This class is used to store some meta(non-feature) data for training data, +* e.g. labels, weights, initial scores, query level information. +* +* Some details: +* 1. Label, used for training. +* 2. Weights, weighs of records, optional +* 3. Query Boundaries, necessary for LambdaRank. +* The documents of i-th query is in [ query_boundaries[i], query_boundaries[i+1] ) +* 4. Query Weights, auto calculate by weights and query_boundaries(if both of them are existed) +* the weight for i-th query is sum(query_boundaries[i] , .., query_boundaries[i+1]) / (query_boundaries[i + 1] - query_boundaries[i+1]) +* 5. Initial score. optional. if existing, the model will boost from this score, otherwise will start from 0. +*/ +class Metadata { + public: + /*! + * \brief Null constructor + */ + Metadata(); + /*! + * \brief Initialization will load query level information, since it is need for sampling data + * \param data_filename Filename of data + */ + void Init(const char* data_filename); + /*! + * \brief init as subset + * \param metadata Filename of data + * \param used_indices + * \param num_used_indices + */ + void Init(const Metadata& metadata, const data_size_t* used_indices, data_size_t num_used_indices); + /*! + * \brief Initial with binary memory + * \param memory Pointer to memory + */ + void LoadFromMemory(const void* memory); + /*! \brief Destructor */ + ~Metadata(); + + /*! + * \brief Initial work, will allocate space for label, weight (if exists) and query (if exists) + * \param num_data Number of training data + * \param weight_idx Index of weight column, < 0 means doesn't exists + * \param query_idx Index of query id column, < 0 means doesn't exists + */ + void Init(data_size_t num_data, int weight_idx, int query_idx); + + /*! + * \brief Allocate space for label, weight (if exists), initial score (if exists) and query (if exists) + * \param num_data Number of data + * \param reference Reference metadata + */ + void InitByReference(data_size_t num_data, const Metadata* reference); + + /*! + * \brief Allocate space for label, weight (if exists), initial score (if exists) and query (if exists) + * \param num_data Number of data rows + * \param has_weights Whether the metadata has weights + * \param has_init_scores Whether the metadata has initial scores + * \param has_queries Whether the metadata has queries + * \param nclasses Number of classes for initial scores + */ + void Init(data_size_t num_data, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses); + + /*! + * \brief Partition label by used indices + * \param used_indices Indices of local used + */ + void PartitionLabel(const std::vector& used_indices); + + /*! + * \brief Partition meta data according to local used indices if need + * \param num_all_data Number of total training data, including other machines' data on distributed learning + * \param used_data_indices Indices of local used training data + */ + void CheckOrPartition(data_size_t num_all_data, + const std::vector& used_data_indices); + + void SetLabel(const label_t* label, data_size_t len); + void SetLabel(struct ArrowArrayStream* stream); + void SetLabel(int64_t n_chunks, struct ArrowArray* chunks, struct ArrowSchema* schema); + + void SetWeights(const label_t* weights, data_size_t len); + void SetWeights(struct ArrowArrayStream* stream); + void SetWeights(int64_t n_chunks, struct ArrowArray* chunks, struct ArrowSchema* schema); + + void SetQuery(const data_size_t* query, data_size_t len); + void SetQuery(struct ArrowArrayStream* stream); + void SetQuery(int64_t n_chunks, struct ArrowArray* chunks, struct ArrowSchema* schema); + + void SetPosition(const data_size_t* position, data_size_t len); + + /*! + * \brief Set initial scores + * \param init_score Initial scores, this class will manage memory for init_score. + */ + void SetInitScore(const double* init_score, data_size_t len); + void SetInitScore(struct ArrowArrayStream* stream); + void SetInitScore(int64_t n_chunks, struct ArrowArray* chunks, struct ArrowSchema* schema); + + + /*! + * \brief Save binary data to file + * \param file File want to write + */ + void SaveBinaryToFile(BinaryWriter* writer) const; + + /*! + * \brief Get sizes in byte of this object + */ + size_t SizesInByte() const; + + /*! + * \brief Get pointer of label + * \return Pointer of label + */ + inline const label_t* label() const { return label_.data(); } + + /*! + * \brief Set label for one record + * \param idx Index of this record + * \param value Label value of this record + */ + inline void SetLabelAt(data_size_t idx, label_t value) { + label_[idx] = value; + } + + /*! + * \brief Set Weight for one record + * \param idx Index of this record + * \param value Weight value of this record + */ + inline void SetWeightAt(data_size_t idx, label_t value) { + weights_[idx] = value; + } + + /*! + * \brief Set initial scores for one record. Note that init_score might have multiple columns and is stored in column format. + * \param idx Index of this record + * \param values Initial score values for this record, one per class + */ + inline void SetInitScoreAt(data_size_t idx, const double* values) { + const auto nclasses = num_init_score_classes(); + const double* val_ptr = values; + for (int i = idx; i < nclasses * num_data_; i += num_data_, ++val_ptr) { + init_score_[i] = *val_ptr; + } + } + + /*! + * \brief Set Query Id for one record + * \param idx Index of this record + * \param value Query Id value of this record + */ + inline void SetQueryAt(data_size_t idx, data_size_t value) { + queries_[idx] = static_cast(value); + } + + /*! \brief Load initial scores from file */ + void LoadInitialScore(const std::string& data_filename); + + /*! + * \brief Insert data from a given data to the current data at a specified index + * \param start_index The target index to begin the insertion + * \param count Number of records to insert + * \param labels Pointer to label data + * \param weights Pointer to weight data, or null + * \param init_scores Pointer to init-score data, or null + * \param queries Pointer to query data, or null + */ + void InsertAt(data_size_t start_index, + data_size_t count, + const float* labels, + const float* weights, + const double* init_scores, + const int32_t* queries); + + /*! + * \brief Perform any extra operations after all data has been loaded + */ + void FinishLoad(); + /*! + * \brief Get weights, if not exists, will return nullptr + * \return Pointer of weights + */ + inline const label_t* weights() const { + if (!weights_.empty()) { + return weights_.data(); + } else { + return nullptr; + } + } + + /*! + * \brief Get positions, if does not exist then return nullptr + * \return Pointer of positions + */ + inline const data_size_t* positions() const { + if (!positions_.empty()) { + return positions_.data(); + } else { + return nullptr; + } + } + + /*! + * \brief Get position IDs, if does not exist then return nullptr + * \return Pointer of position IDs + */ + inline const std::string* position_ids() const { + if (!position_ids_.empty()) { + return position_ids_.data(); + } else { + return nullptr; + } + } + + /*! + * \brief Get Number of different position IDs + * \return number of different position IDs + */ + inline size_t num_position_ids() const { + return position_ids_.size(); + } + + /*! + * \brief Get data boundaries on queries, if not exists, will return nullptr + * we assume data will order by query, + * the interval of [query_boundaris[i], query_boundaris[i+1]) + * is the data indices for query i. + * \return Pointer of data boundaries on queries + */ + inline const data_size_t* query_boundaries() const { + if (!query_boundaries_.empty()) { + return query_boundaries_.data(); + } else { + return nullptr; + } + } + + /*! + * \brief Get Number of queries + * \return Number of queries + */ + inline data_size_t num_queries() const { return num_queries_; } + + /*! + * \brief Get weights for queries, if not exists, will return nullptr + * \return Pointer of weights for queries + */ + inline const label_t* query_weights() const { + if (!query_weights_.empty()) { + return query_weights_.data(); + } else { + return nullptr; + } + } + + /*! + * \brief Get initial scores, if not exists, will return nullptr + * \return Pointer of initial scores + */ + inline const double* init_score() const { + if (!init_score_.empty()) { + return init_score_.data(); + } else { + return nullptr; + } + } + + /*! + * \brief Get size of initial scores + */ + inline int64_t num_init_score() const { return num_init_score_; } + + /*! + * \brief Get number of classes + */ + inline int32_t num_init_score_classes() const { + if (num_data_ && num_init_score_) { + return static_cast(num_init_score_ / num_data_); + } + return 1; + } + + /*! \brief Disable copy */ + Metadata& operator=(const Metadata&) = delete; + /*! \brief Disable copy */ + Metadata(const Metadata&) = delete; + + #ifdef USE_CUDA + + CUDAMetadata* cuda_metadata() const { return cuda_metadata_.get(); } + + void CreateCUDAMetadata(const int gpu_device_id); + + #endif // USE_CUDA + + private: + /*! \brief Load wights from file */ + void LoadWeights(); + /*! \brief Load positions from file */ + void LoadPositions(); + /*! \brief Load query boundaries from file */ + void LoadQueryBoundaries(); + /*! \brief Calculate query weights from queries */ + void CalculateQueryWeights(); + /*! \brief Calculate query boundaries from queries */ + void CalculateQueryBoundaries(); + /*! \brief Insert labels at the given index */ + void InsertLabels(const label_t* labels, data_size_t start_index, data_size_t len); + /*! \brief Set labels from pointers to the first element and the end of an iterator. */ + template + void SetLabelsFromIterator(It first, It last); + /*! \brief Insert weights at the given index */ + void InsertWeights(const label_t* weights, data_size_t start_index, data_size_t len); + /*! \brief Set weights from pointers to the first element and the end of an iterator. */ + template + void SetWeightsFromIterator(It first, It last); + /*! \brief Insert initial scores at the given index */ + void InsertInitScores(const double* init_scores, data_size_t start_index, data_size_t len, data_size_t source_size); + /*! \brief Set init scores from pointers to the first element and the end of an iterator. */ + template + void SetInitScoresFromIterator(It first, It last); + /*! \brief Insert queries at the given index */ + void InsertQueries(const data_size_t* queries, data_size_t start_index, data_size_t len); + /*! \brief Set queries from pointers to the first element and the end of an iterator. */ + template + void SetQueriesFromIterator(It first, It last); + /*! \brief Filename of current data */ + std::string data_filename_; + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Number of weights, used to check correct weight file */ + data_size_t num_weights_; + /*! \brief Number of positions, used to check correct position file */ + data_size_t num_positions_; + /*! \brief Label data */ + std::vector label_; + /*! \brief Weights data */ + std::vector weights_; + /*! \brief Positions data */ + std::vector positions_; + /*! \brief Position identifiers */ + std::vector position_ids_; + /*! \brief Query boundaries */ + std::vector query_boundaries_; + /*! \brief Query weights */ + std::vector query_weights_; + /*! \brief Number of queries */ + data_size_t num_queries_; + /*! \brief Number of Initial score, used to check correct weight file */ + int64_t num_init_score_; + /*! \brief Initial score */ + std::vector init_score_; + /*! \brief Queries data */ + std::vector queries_; + /*! \brief mutex for threading safe call */ + std::mutex mutex_; + bool weight_load_from_file_; + bool position_load_from_file_; + bool query_load_from_file_; + bool init_score_load_from_file_; + #ifdef USE_CUDA + std::unique_ptr cuda_metadata_; + #endif // USE_CUDA +}; + + +/*! \brief Interface for Parser */ +class Parser { + public: + typedef const char* (*AtofFunc)(const char* p, double* out); + + /*! \brief Default constructor */ + Parser() {} + + /*! + * \brief Constructor for customized parser. The constructor accepts content not path because need to save/load the config along with model string + */ + explicit Parser(std::string) {} + + /*! \brief virtual destructor */ + virtual ~Parser() {} + + /*! + * \brief Parse one line with label + * \param str One line record, string format, should end with '\0' + * \param out_features Output columns, store in (column_idx, values) + * \param out_label Label will store to this if exists + */ + virtual void ParseOneLine(const char* str, + std::vector>* out_features, double* out_label) const = 0; + + virtual int NumFeatures() const = 0; + + /*! + * \brief Create an object of parser, will auto choose the format depend on file + * \param filename One Filename of data + * \param header whether input file contains header + * \param num_features Pass num_features of this data file if you know, <=0 means don't know + * \param label_idx index of label column + * \param precise_float_parser using precise floating point number parsing if true + * \return Object of parser + */ + static Parser* CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser); + + /*! + * \brief Create an object of parser, could use customized parser, or auto choose the format depend on file + * \param filename One Filename of data + * \param header whether input file contains header + * \param num_features Pass num_features of this data file if you know, <=0 means don't know + * \param label_idx index of label column + * \param precise_float_parser using precise floating point number parsing if true + * \param parser_config_str Customized parser config content + * \return Object of parser + */ + static Parser* CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser, + std::string parser_config_str); + + /*! + * \brief Generate parser config str used for custom parser initialization, may save values of label id and header + * \param filename One Filename of data + * \param parser_config_filename One Filename of parser config + * \param header whether input file contains header + * \param label_idx index of label column + * \return Parser config str + */ + static std::string GenerateParserConfigStr(const char* filename, const char* parser_config_filename, bool header, int label_idx); +}; + +/*! \brief Interface for parser factory, used by customized parser */ +class ParserFactory { + private: + ParserFactory() {} + std::map> object_map_; + + public: + ~ParserFactory() {} + static ParserFactory& getInstance(); + void Register(std::string class_name, std::function objc); + Parser* getObject(std::string class_name, std::string config_str); +}; + +/*! \brief Interface for parser reflector, used by customized parser */ +class ParserReflector { + public: + ParserReflector(std::string class_name, std::function objc) { + ParserFactory::getInstance().Register(class_name, objc); + } + virtual ~ParserReflector() {} +}; + +/*! \brief The main class of data set, +* which are used to training or validation +*/ +class Dataset { + public: + friend DatasetLoader; + + LIGHTGBM_EXPORT Dataset(); + + LIGHTGBM_EXPORT Dataset(data_size_t num_data); + + void Construct( + std::vector>* bin_mappers, + int num_total_features, + const std::vector>& forced_bins, + int** sample_non_zero_indices, + double** sample_values, + const int* num_per_col, + int num_sample_col, + size_t total_sample_cnt, + const Config& io_config); + + /*! \brief Destructor */ + LIGHTGBM_EXPORT ~Dataset(); + + /*! + * \brief Initialize from the given reference + * \param num_data Number of data + * \param reference Reference dataset + */ + LIGHTGBM_EXPORT void InitByReference(data_size_t num_data, const Dataset* reference) { + metadata_.InitByReference(num_data, &reference->metadata()); + } + + LIGHTGBM_EXPORT void InitStreaming(data_size_t num_data, + int32_t has_weights, + int32_t has_init_scores, + int32_t has_queries, + int32_t nclasses, + int32_t nthreads, + int32_t omp_max_threads) { + // Initialize optional max thread count with either parameter or OMP setting + if (omp_max_threads > 0) { + omp_max_threads_ = omp_max_threads; + } else if (omp_max_threads_ <= 0) { + omp_max_threads_ = OMP_NUM_THREADS(); + } + + metadata_.Init(num_data, has_weights, has_init_scores, has_queries, nclasses); + for (int i = 0; i < num_groups_; ++i) { + feature_groups_[i]->InitStreaming(nthreads, omp_max_threads_); + } + } + + LIGHTGBM_EXPORT bool CheckAlign(const Dataset& other) const { + if (num_features_ != other.num_features_) { + return false; + } + if (num_total_features_ != other.num_total_features_) { + return false; + } + if (label_idx_ != other.label_idx_) { + return false; + } + for (int i = 0; i < num_features_; ++i) { + if (!FeatureBinMapper(i)->CheckAlign(*(other.FeatureBinMapper(i)))) { + return false; + } + } + return true; + } + + inline void FinishOneRow(int tid, data_size_t row_idx, const std::vector& is_feature_added) { + if (is_finish_load_) { + return; + } + for (auto fidx : feature_need_push_zeros_) { + if (is_feature_added[fidx]) { + continue; + } + const int group = feature2group_[fidx]; + const int sub_feature = feature2subfeature_[fidx]; + feature_groups_[group]->PushData(tid, sub_feature, row_idx, 0.0f); + } + } + + inline void PushOneValue(int tid, data_size_t row_idx, size_t col_idx, double value) { + if (this->is_finish_load_) + return; + auto feature_idx = this->used_feature_map_[col_idx]; + if (feature_idx >= 0) { + auto group = this->feature2group_[feature_idx]; + auto sub_feature = this->feature2subfeature_[feature_idx]; + this->feature_groups_[group]->PushData(tid, sub_feature, row_idx, value); + if (this->has_raw_) { + auto feat_ind = numeric_feature_map_[feature_idx]; + if (feat_ind >= 0) { + raw_data_[feat_ind][row_idx] = static_cast(value); + } + } + } + } + + inline void PushOneRow(int tid, data_size_t row_idx, const std::vector& feature_values) { + for (size_t i = 0; i < feature_values.size() && i < static_cast(num_total_features_); ++i) { + this->PushOneValue(tid, row_idx, i, feature_values[i]); + } + } + + inline void PushOneRow(int tid, data_size_t row_idx, const std::vector>& feature_values) { + if (is_finish_load_) { + return; + } + std::vector is_feature_added(num_features_, false); + for (auto& inner_data : feature_values) { + if (inner_data.first >= num_total_features_) { + continue; + } + int feature_idx = used_feature_map_[inner_data.first]; + if (feature_idx >= 0) { + is_feature_added[feature_idx] = true; + const int group = feature2group_[feature_idx]; + const int sub_feature = feature2subfeature_[feature_idx]; + feature_groups_[group]->PushData(tid, sub_feature, row_idx, inner_data.second); + if (has_raw_) { + int feat_ind = numeric_feature_map_[feature_idx]; + if (feat_ind >= 0) { + raw_data_[feat_ind][row_idx] = static_cast(inner_data.second); + } + } + } + } + FinishOneRow(tid, row_idx, is_feature_added); + } + + inline void PushOneData(int tid, data_size_t row_idx, int group, int feature_idx, int sub_feature, double value) { + feature_groups_[group]->PushData(tid, sub_feature, row_idx, value); + if (has_raw_) { + int feat_ind = numeric_feature_map_[feature_idx]; + if (feat_ind >= 0) { + raw_data_[feat_ind][row_idx] = static_cast(value); + } + } + } + + inline void InsertMetadataAt(data_size_t start_index, + data_size_t count, + const label_t* labels, + const label_t* weights, + const double* init_scores, + const data_size_t* queries) { + metadata_.InsertAt(start_index, count, labels, weights, init_scores, queries); + } + + inline int RealFeatureIndex(int fidx) const { + return real_feature_idx_[fidx]; + } + + inline int InnerFeatureIndex(int col_idx) const { + return used_feature_map_[col_idx]; + } + inline int Feature2Group(int feature_idx) const { + return feature2group_[feature_idx]; + } + inline int Feature2SubFeature(int feature_idx) const { + return feature2subfeature_[feature_idx]; + } + inline uint64_t GroupBinBoundary(int group_idx) const { + return group_bin_boundaries_[group_idx]; + } + inline uint64_t NumTotalBin() const { + return group_bin_boundaries_.back(); + } + + inline std::vector ValidFeatureIndices() const { + std::vector ret; + for (int i = 0; i < num_total_features_; ++i) { + if (used_feature_map_[i] >= 0) { + ret.push_back(i); + } + } + return ret; + } + void ReSize(data_size_t num_data); + + void CopySubrow(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data); + + void CopySubrowToDevice(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data, int gpu_device_id); + + MultiValBin* GetMultiBinFromSparseFeatures(const std::vector& offsets) const; + + MultiValBin* GetMultiBinFromAllFeatures(const std::vector& offsets) const; + + template + TrainingShareStates* GetShareStates( + score_t* gradients, score_t* hessians, + const std::vector& is_feature_used, bool is_constant_hessian, + bool force_col_wise, bool force_row_wise, const int num_grad_quant_bins) const; + + LIGHTGBM_EXPORT void FinishLoad(); + + bool SetFieldFromArrow(const char* field_name, struct ArrowArrayStream* stream); + + bool SetFieldFromArrow(const char* field_name, int64_t n_chunks, + struct ArrowArray* chunks, struct ArrowSchema* schema); + + LIGHTGBM_EXPORT bool SetFloatField(const char* field_name, const float* field_data, data_size_t num_element); + + LIGHTGBM_EXPORT bool SetDoubleField(const char* field_name, const double* field_data, data_size_t num_element); + + LIGHTGBM_EXPORT bool SetIntField(const char* field_name, const int* field_data, data_size_t num_element); + + LIGHTGBM_EXPORT bool GetFloatField(const char* field_name, data_size_t* out_len, const float** out_ptr); + + LIGHTGBM_EXPORT bool GetDoubleField(const char* field_name, data_size_t* out_len, const double** out_ptr); + + LIGHTGBM_EXPORT bool GetIntField(const char* field_name, data_size_t* out_len, const int** out_ptr); + + /*! + * \brief Save current dataset into binary file, will save to "filename.bin" + */ + LIGHTGBM_EXPORT void SaveBinaryFile(const char* bin_filename); + + /*! + * \brief Serialize the overall Dataset definition/schema to a binary buffer (i.e., without data) + */ + LIGHTGBM_EXPORT void SerializeReference(ByteBuffer* out); + + LIGHTGBM_EXPORT void DumpTextFile(const char* text_filename); + + LIGHTGBM_EXPORT void CopyFeatureMapperFrom(const Dataset* dataset); + + LIGHTGBM_EXPORT void CreateValid(const Dataset* dataset); + + void InitTrain(const std::vector& is_feature_used, + TrainingShareStates* share_state) const; + + template + void ConstructHistogramsInner(const std::vector& is_feature_used, + const data_size_t* data_indices, + data_size_t num_data, const score_t* gradients, + const score_t* hessians, + score_t* ordered_gradients, + score_t* ordered_hessians, + TrainingShareStates* share_state, + hist_t* hist_data) const; + + template + void ConstructHistogramsMultiVal(const data_size_t* data_indices, + data_size_t num_data, + const score_t* gradients, + const score_t* hessians, + TrainingShareStates* share_state, + hist_t* hist_data) const; + + template + inline void ConstructHistograms( + const std::vector& is_feature_used, + const data_size_t* data_indices, data_size_t num_data, + const score_t* gradients, const score_t* hessians, + score_t* ordered_gradients, score_t* ordered_hessians, + TrainingShareStates* share_state, hist_t* hist_data) const { + if (num_data <= 0) { + return; + } + bool use_indices = data_indices != nullptr && (num_data < num_data_); + if (share_state->is_constant_hessian) { + if (use_indices) { + ConstructHistogramsInner( + is_feature_used, data_indices, num_data, gradients, hessians, + ordered_gradients, ordered_hessians, share_state, hist_data); + } else { + ConstructHistogramsInner( + is_feature_used, data_indices, num_data, gradients, hessians, + ordered_gradients, ordered_hessians, share_state, hist_data); + } + } else { + if (use_indices) { + ConstructHistogramsInner( + is_feature_used, data_indices, num_data, gradients, hessians, + ordered_gradients, ordered_hessians, share_state, hist_data); + } else { + ConstructHistogramsInner( + is_feature_used, data_indices, num_data, gradients, hessians, + ordered_gradients, ordered_hessians, share_state, hist_data); + } + } + } + + void FixHistogram(int feature_idx, double sum_gradient, double sum_hessian, hist_t* data) const; + + template + void FixHistogramInt(int feature_idx, int64_t sum_gradient_and_hessian, hist_t* data) const; + + inline data_size_t Split(int feature, const uint32_t* threshold, + int num_threshold, bool default_left, + const data_size_t* data_indices, + data_size_t cnt, data_size_t* lte_indices, + data_size_t* gt_indices) const { + const int group = feature2group_[feature]; + const int sub_feature = feature2subfeature_[feature]; + return feature_groups_[group]->Split( + sub_feature, threshold, num_threshold, default_left, data_indices, + cnt, lte_indices, gt_indices); + } + + inline int SubFeatureBinOffset(int i) const { + const int sub_feature = feature2subfeature_[i]; + if (sub_feature == 0) { + return 1; + } else { + return 0; + } + } + + inline int FeatureNumBin(int i) const { + const int group = feature2group_[i]; + const int sub_feature = feature2subfeature_[i]; + return feature_groups_[group]->bin_mappers_[sub_feature]->num_bin(); + } + + inline int FeatureGroupNumBin(int group) const { + return feature_groups_[group]->num_total_bin_; + } + + inline const BinMapper* FeatureBinMapper(int i) const { + const int group = feature2group_[i]; + const int sub_feature = feature2subfeature_[i]; + return feature_groups_[group]->bin_mappers_[sub_feature].get(); + } + + inline const Bin* FeatureGroupBin(int group) const { + return feature_groups_[group]->bin_data_.get(); + } + + inline BinIterator* FeatureIterator(int i) const { + const int group = feature2group_[i]; + const int sub_feature = feature2subfeature_[i]; + return feature_groups_[group]->SubFeatureIterator(sub_feature); + } + + inline BinIterator* FeatureGroupIterator(int group) const { + return feature_groups_[group]->FeatureGroupIterator(); + } + + inline bool IsMultiGroup(int i) const { + return feature_groups_[i]->is_multi_val_; + } + + inline size_t FeatureGroupSizesInByte(int group) const { + return feature_groups_[group]->FeatureGroupSizesInByte(); + } + + inline void* FeatureGroupData(int group) const { + return feature_groups_[group]->FeatureGroupData(); + } + + const void* GetColWiseData( + const int feature_group_index, + const int sub_feature_index, + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int num_threads) const; + + const void* GetColWiseData( + const int feature_group_index, + const int sub_feature_index, + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const; + + inline double RealThreshold(int i, uint32_t threshold) const { + const int group = feature2group_[i]; + const int sub_feature = feature2subfeature_[i]; + return feature_groups_[group]->bin_mappers_[sub_feature]->BinToValue(threshold); + } + + // given a real threshold, find the closest threshold bin + inline uint32_t BinThreshold(int i, double threshold_double) const { + const int group = feature2group_[i]; + const int sub_feature = feature2subfeature_[i]; + return feature_groups_[group]->bin_mappers_[sub_feature]->ValueToBin(threshold_double); + } + + inline int MaxRealCatValue(int i) const { + const int group = feature2group_[i]; + const int sub_feature = feature2subfeature_[i]; + return feature_groups_[group]->bin_mappers_[sub_feature]->MaxCatValue(); + } + + /*! + * \brief Get meta data pointer + * \return Pointer of meta data + */ + inline const Metadata& metadata() const { return metadata_; } + + /*! \brief Get Number of used features */ + inline int num_features() const { return num_features_; } + + /*! \brief Get number of numeric features */ + inline int num_numeric_features() const { return num_numeric_features_; } + + /*! \brief Get Number of feature groups */ + inline int num_feature_groups() const { return num_groups_;} + + /*! \brief Get Number of total features */ + inline int num_total_features() const { return num_total_features_; } + + /*! \brief Get the index of label column */ + inline int label_idx() const { return label_idx_; } + + /*! \brief Get names of current data set */ + inline const std::vector& feature_names() const { return feature_names_; } + + /*! \brief Get content of parser config file */ + inline const std::string parser_config_str() const { return parser_config_str_; } + + inline void set_feature_names(const std::vector& feature_names) { + if (feature_names.size() != static_cast(num_total_features_)) { + Log::Fatal("Size of feature_names error, should equal with total number of features"); + } + feature_names_ = std::vector(feature_names); + std::unordered_set feature_name_set; + // replace ' ' in feature_names with '_' + bool spaceInFeatureName = false; + for (auto& feature_name : feature_names_) { + // check JSON + if (!Common::CheckAllowedJSON(feature_name)) { + Log::Fatal("Do not support special JSON characters in feature name."); + } + if (feature_name.find(' ') != std::string::npos) { + spaceInFeatureName = true; + std::replace(feature_name.begin(), feature_name.end(), ' ', '_'); + } + if (feature_name_set.count(feature_name) > 0) { + Log::Fatal("Feature (%s) appears more than one time.", feature_name.c_str()); + } + feature_name_set.insert(feature_name); + } + if (spaceInFeatureName) { + Log::Warning("Found whitespace in feature_names, replace with underlines"); + } + } + + inline std::vector feature_infos() const { + std::vector bufs; + for (int i = 0; i < num_total_features_; ++i) { + int fidx = used_feature_map_[i]; + if (fidx < 0) { + bufs.push_back("none"); + } else { + const auto bin_mapper = FeatureBinMapper(fidx); + bufs.push_back(bin_mapper->bin_info_string()); + } + } + return bufs; + } + + /*! \brief Get Number of data */ + inline data_size_t num_data() const { return num_data_; } + + /*! \brief Get whether FinishLoad is automatically called when pushing last row. */ + inline bool wait_for_manual_finish() const { return wait_for_manual_finish_; } + + /*! \brief Get the maximum number of OpenMP threads to allocate for. */ + inline int omp_max_threads() const { return omp_max_threads_; } + + /*! \brief Set whether the Dataset is finished automatically when last row is pushed or with a manual + * MarkFinished API call. Set to true for thread-safe streaming and/or if will be coalesced later. + * FinishLoad should not be called on any Dataset that will be coalesced. + */ + inline void set_wait_for_manual_finish(bool value) { + std::lock_guard lock(mutex_); + wait_for_manual_finish_ = value; + } + + /*! \brief Disable copy */ + Dataset& operator=(const Dataset&) = delete; + /*! \brief Disable copy */ + Dataset(const Dataset&) = delete; + + void AddFeaturesFrom(Dataset* other); + + /*! \brief Get has_raw_ */ + inline bool has_raw() const { return has_raw_; } + + /*! \brief Set has_raw_ */ + inline void SetHasRaw(bool has_raw) { has_raw_ = has_raw; } + + /*! \brief Resize raw_data_ */ + inline void ResizeRaw(int num_rows) { + if (static_cast(raw_data_.size()) > num_numeric_features_) { + raw_data_.resize(num_numeric_features_); + } + for (size_t i = 0; i < raw_data_.size(); ++i) { + raw_data_[i].resize(num_rows); + } + int curr_size = static_cast(raw_data_.size()); + for (int i = curr_size; i < num_numeric_features_; ++i) { + raw_data_.push_back(std::vector(num_rows, 0)); + } + } + + /*! \brief Get pointer to raw_data_ feature */ + inline const float* raw_index(int feat_ind) const { + return raw_data_[numeric_feature_map_[feat_ind]].data(); + } + + inline uint32_t feature_max_bin(const int inner_feature_index) const { + const int feature_group_index = Feature2Group(inner_feature_index); + const int sub_feature_index = feature2subfeature_[inner_feature_index]; + return feature_groups_[feature_group_index]->feature_max_bin(sub_feature_index); + } + + inline uint32_t feature_min_bin(const int inner_feature_index) const { + const int feature_group_index = Feature2Group(inner_feature_index); + const int sub_feature_index = feature2subfeature_[inner_feature_index]; + return feature_groups_[feature_group_index]->feature_min_bin(sub_feature_index); + } + + #ifdef USE_CUDA + + const CUDAColumnData* cuda_column_data() const { + return cuda_column_data_.get(); + } + + #endif // USE_CUDA + + private: + void SerializeHeader(BinaryWriter* serializer); + + size_t GetSerializedHeaderSize(); + + void CreateCUDAColumnData(); + + void CopySubrowHostPart(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data); + + std::string data_filename_; + /*! \brief Store used features */ + std::vector> feature_groups_; + /*! \brief Mapper from real feature index to used index*/ + std::vector used_feature_map_; + /*! \brief Number of used features*/ + int num_features_; + /*! \brief Number of total features*/ + int num_total_features_; + /*! \brief Number of total data*/ + data_size_t num_data_; + /*! \brief Store some label level data*/ + Metadata metadata_; + /*! \brief index of label column */ + int label_idx_ = 0; + /*! \brief store feature names */ + std::vector feature_names_; + /*! \brief serialized versions */ + static const int kSerializedReferenceVersionLength; + static const char* serialized_reference_version; + static const char* binary_file_token; + static const char* binary_serialized_reference_token; + int num_groups_; + std::vector real_feature_idx_; + std::vector feature2group_; + std::vector feature2subfeature_; + std::vector group_bin_boundaries_; + std::vector group_feature_start_; + std::vector group_feature_cnt_; + bool is_finish_load_; + int max_bin_; + std::vector max_bin_by_feature_; + std::vector> forced_bin_bounds_; + int bin_construct_sample_cnt_; + int min_data_in_bin_; + bool use_missing_; + bool zero_as_missing_; + std::vector feature_need_push_zeros_; + std::vector> raw_data_; + bool wait_for_manual_finish_; + int omp_max_threads_ = -1; + bool has_raw_; + /*! map feature (inner index) to its index in the list of numeric (non-categorical) features */ + std::vector numeric_feature_map_; + int num_numeric_features_; + std::string device_type_; + int gpu_device_id_; + /*! \brief mutex for threading safe call */ + std::mutex mutex_; + + #ifdef USE_CUDA + std::unique_ptr cuda_column_data_; + #endif // USE_CUDA + + std::string parser_config_str_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_ diff --git a/include/LightGBM/dataset_loader.h b/include/LightGBM/dataset_loader.h new file mode 100644 index 0000000..6de173f --- /dev/null +++ b/include/LightGBM/dataset_loader.h @@ -0,0 +1,111 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_ + +#include + +#include +#include +#include +#include + +namespace LightGBM { + +class DatasetLoader { + public: + LIGHTGBM_EXPORT DatasetLoader(const Config& io_config, const PredictFunction& predict_fun, int num_class, const char* filename); + + LIGHTGBM_EXPORT ~DatasetLoader(); + + LIGHTGBM_EXPORT Dataset* LoadFromFile(const char* filename, int rank, int num_machines); + + LIGHTGBM_EXPORT Dataset* LoadFromFile(const char* filename) { + return LoadFromFile(filename, 0, 1); + } + + LIGHTGBM_EXPORT Dataset* LoadFromFileAlignWithOtherDataset(const char* filename, const Dataset* train_data); + + LIGHTGBM_EXPORT Dataset* LoadFromSerializedReference(const char* buffer, size_t buffer_size, data_size_t num_data, int32_t num_classes); + + LIGHTGBM_EXPORT Dataset* ConstructFromSampleData(double** sample_values, + int** sample_indices, + int num_col, + const int* num_per_col, + size_t total_sample_size, + data_size_t num_local_data, + int64_t num_dist_data); + + /*! \brief Disable copy */ + DatasetLoader& operator=(const DatasetLoader&) = delete; + /*! \brief Disable copy */ + DatasetLoader(const DatasetLoader&) = delete; + + static std::vector> GetForcedBins(std::string forced_bins_path, int num_total_features, + const std::unordered_set& categorical_features); + + private: + void LoadHeaderFromMemory(Dataset* dataset, const char* buffer); + + Dataset* LoadFromBinFile(const char* data_filename, const char* bin_filename, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices); + + void SetHeader(const char* filename); + + void CheckDataset(const Dataset* dataset, bool is_load_from_binary); + + std::vector LoadTextDataToMemory(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices); + + std::vector SampleTextDataFromMemory(const std::vector& data); + + std::vector SampleTextDataFromFile(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices); + + void ConstructBinMappersFromTextData(int rank, int num_machines, const std::vector& sample_data, const Parser* parser, Dataset* dataset); + + /*! \brief Extract local features from memory */ + void ExtractFeaturesFromMemory(std::vector* text_data, const Parser* parser, Dataset* dataset); + + /*! \brief Extract local features from file */ + void ExtractFeaturesFromFile(const char* filename, const Parser* parser, const std::vector& used_data_indices, Dataset* dataset); + + /*! \brief Check can load from binary file */ + std::string CheckCanLoadFromBin(const char* filename); + + /*! \brief Check the number of bins for categorical features. + * The number of bins for categorical features may exceed the configured maximum value. + * Log warnings when such cases happen. + * + * \param bin_mappers the bin_mappers of all features + * \param max_bin max_bin from Config + * \param max_bin_by_feature max_bin_by_feature from Config + */ + void CheckCategoricalFeatureNumBin(const std::vector>& bin_mappers, const int max_bin, const std::vector& max_bin_by_feature) const; + + const Config& config_; + /*! \brief Random generator*/ + Random random_; + /*! \brief prediction function for initial model */ + const PredictFunction predict_fun_; + /*! \brief number of classes */ + int num_class_; + /*! \brief index of label column */ + int label_idx_; + /*! \brief index of weight column */ + int weight_idx_; + /*! \brief index of group column */ + int group_idx_; + /*! \brief Mapper from real feature index to used index*/ + std::unordered_set ignore_features_; + /*! \brief store feature names */ + std::vector feature_names_; + /*! \brief Mapper from real feature index to used index*/ + std::unordered_set categorical_features_; + /*! \brief Whether to store raw feature values */ + bool store_raw_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_ diff --git a/include/LightGBM/export.h b/include/LightGBM/export.h new file mode 100644 index 0000000..f404fa4 --- /dev/null +++ b/include/LightGBM/export.h @@ -0,0 +1,26 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_ + +/** Macros for exporting symbols in MSVC/GCC/CLANG **/ + +#ifdef __cplusplus +#define LIGHTGBM_EXTERN_C extern "C" +#else +#define LIGHTGBM_EXTERN_C +#endif + + +#ifdef _MSC_VER +#define LIGHTGBM_EXPORT __declspec(dllexport) +#define LIGHTGBM_C_EXPORT LIGHTGBM_EXTERN_C __declspec(dllexport) +#else +#define LIGHTGBM_EXPORT +#define LIGHTGBM_C_EXPORT LIGHTGBM_EXTERN_C +#endif + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_ diff --git a/include/LightGBM/feature_group.h b/include/LightGBM/feature_group.h new file mode 100644 index 0000000..055c620 --- /dev/null +++ b/include/LightGBM/feature_group.h @@ -0,0 +1,633 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_ + +#include +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { + +class Dataset; +class DatasetLoader; +struct TrainingShareStates; +class MultiValBinWrapper; +/*! \brief Using to store data and providing some operations on one feature + * group*/ +class FeatureGroup { + public: + friend Dataset; + friend DatasetLoader; + friend TrainingShareStates; + friend MultiValBinWrapper; + /*! + * \brief Constructor + * \param num_feature number of features of this group + * \param bin_mappers Bin mapper for features + * \param num_data Total number of data + * \param is_enable_sparse True if enable sparse feature + */ + FeatureGroup(int num_feature, int8_t is_multi_val, + std::vector>* bin_mappers, + data_size_t num_data, int group_id) : + num_feature_(num_feature), is_multi_val_(is_multi_val > 0), is_sparse_(false) { + CHECK_EQ(static_cast(bin_mappers->size()), num_feature); + auto& ref_bin_mappers = *bin_mappers; + double sum_sparse_rate = 0.0f; + for (int i = 0; i < num_feature_; ++i) { + bin_mappers_.emplace_back(ref_bin_mappers[i].release()); + sum_sparse_rate += bin_mappers_.back()->sparse_rate(); + } + sum_sparse_rate /= num_feature_; + int offset = 1; + is_dense_multi_val_ = false; + if (sum_sparse_rate < MultiValBin::multi_val_bin_sparse_threshold && is_multi_val_) { + // use dense multi val bin + offset = 0; + is_dense_multi_val_ = true; + } + // use bin at zero to store most_freq_bin only when not using dense multi val bin + num_total_bin_ = offset; + // however, we should force to leave one bin, if dense multi val bin is the first bin + // and its first feature has most freq bin > 0 + if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ && + bin_mappers_[0]->GetMostFreqBin() > 0) { + num_total_bin_ = 1; + } + bin_offsets_.emplace_back(num_total_bin_); + for (int i = 0; i < num_feature_; ++i) { + auto num_bin = bin_mappers_[i]->num_bin(); + if (bin_mappers_[i]->GetMostFreqBin() == 0) { + num_bin -= offset; + } + num_total_bin_ += num_bin; + bin_offsets_.emplace_back(num_total_bin_); + } + CreateBinData(num_data, is_multi_val_, true, false); + } + + FeatureGroup(const FeatureGroup& other, int num_data) { + num_feature_ = other.num_feature_; + is_multi_val_ = other.is_multi_val_; + is_dense_multi_val_ = other.is_dense_multi_val_; + is_sparse_ = other.is_sparse_; + num_total_bin_ = other.num_total_bin_; + bin_offsets_ = other.bin_offsets_; + + bin_mappers_.reserve(other.bin_mappers_.size()); + for (auto& bin_mapper : other.bin_mappers_) { + bin_mappers_.emplace_back(new BinMapper(*bin_mapper)); + } + CreateBinData(num_data, is_multi_val_, !is_sparse_, is_sparse_); + } + + FeatureGroup(std::vector>* bin_mappers, + data_size_t num_data) : num_feature_(1), is_multi_val_(false) { + CHECK_EQ(static_cast(bin_mappers->size()), 1); + // use bin at zero to store default_bin + num_total_bin_ = 1; + is_dense_multi_val_ = false; + bin_offsets_.emplace_back(num_total_bin_); + auto& ref_bin_mappers = *bin_mappers; + for (int i = 0; i < num_feature_; ++i) { + bin_mappers_.emplace_back(ref_bin_mappers[i].release()); + auto num_bin = bin_mappers_[i]->num_bin(); + if (bin_mappers_[i]->GetMostFreqBin() == 0) { + num_bin -= 1; + } + num_total_bin_ += num_bin; + bin_offsets_.emplace_back(num_total_bin_); + } + CreateBinData(num_data, false, false, false); + } + + /*! + * \brief Constructor from memory when data is present + * \param memory Pointer of memory + * \param num_all_data Number of global data + * \param local_used_indices Local used indices, empty means using all data + * \param group_id Id of group + */ + FeatureGroup(const void* memory, + data_size_t num_all_data, + const std::vector& local_used_indices, + int group_id) { + // Load the definition schema first + const char* memory_ptr = LoadDefinitionFromMemory(memory, group_id); + + // Allocate memory for the data + data_size_t num_data = num_all_data; + if (!local_used_indices.empty()) { + num_data = static_cast(local_used_indices.size()); + } + AllocateBins(num_data); + + // Now load the actual data + if (is_multi_val_) { + for (int i = 0; i < num_feature_; ++i) { + multi_bin_data_[i]->LoadFromMemory(memory_ptr, local_used_indices); + memory_ptr += multi_bin_data_[i]->SizesInByte(); + } + } else { + bin_data_->LoadFromMemory(memory_ptr, local_used_indices); + } + } + + /*! + * \brief Constructor from definition in memory (without data) + * \param memory Pointer of memory + * \param local_used_indices Local used indices, empty means using all data + */ + FeatureGroup(const void* memory, data_size_t num_data, int group_id) { + LoadDefinitionFromMemory(memory, group_id); + AllocateBins(num_data); + } + + /*! \brief Destructor */ + ~FeatureGroup() {} + + /*! + * \brief Load the overall definition of the feature group from binary serialized data + * \param memory Pointer of memory + * \param group_id Id of group + */ + const char* LoadDefinitionFromMemory(const void* memory, int group_id) { + const char* memory_ptr = reinterpret_cast(memory); + // get is_sparse + is_multi_val_ = *(reinterpret_cast(memory_ptr)); + memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_multi_val_)); + is_dense_multi_val_ = *(reinterpret_cast(memory_ptr)); + memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_dense_multi_val_)); + is_sparse_ = *(reinterpret_cast(memory_ptr)); + memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_sparse_)); + num_feature_ = *(reinterpret_cast(memory_ptr)); + memory_ptr += VirtualFileWriter::AlignedSize(sizeof(num_feature_)); + + // get bin mapper(s) + bin_mappers_.clear(); + for (int i = 0; i < num_feature_; ++i) { + bin_mappers_.emplace_back(new BinMapper(memory_ptr)); + memory_ptr += bin_mappers_[i]->SizesInByte(); + } + + bin_offsets_.clear(); + int offset = 1; + if (is_dense_multi_val_) { + offset = 0; + } + // use bin at zero to store most_freq_bin only when not using dense multi val bin + num_total_bin_ = offset; + // however, we should force to leave one bin, if dense multi val bin is the first bin + // and its first feature has most freq bin > 0 + if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ && + bin_mappers_[0]->GetMostFreqBin() > 0) { + num_total_bin_ = 1; + } + bin_offsets_.emplace_back(num_total_bin_); + for (int i = 0; i < num_feature_; ++i) { + auto num_bin = bin_mappers_[i]->num_bin(); + if (bin_mappers_[i]->GetMostFreqBin() == 0) { + num_bin -= offset; + } + num_total_bin_ += num_bin; + bin_offsets_.emplace_back(num_total_bin_); + } + + return memory_ptr; + } + + /*! + * \brief Allocate the bins + * \param num_all_data Number of global data + */ + inline void AllocateBins(data_size_t num_data) { + if (is_multi_val_) { + for (int i = 0; i < num_feature_; ++i) { + int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1; + if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) { + multi_bin_data_.emplace_back(Bin::CreateSparseBin(num_data, bin_mappers_[i]->num_bin() + addi)); + } else { + multi_bin_data_.emplace_back(Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi)); + } + } + } else { + if (is_sparse_) { + bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_)); + } else { + bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_)); + } + } + } + + /*! + * \brief Initialize for pushing in a streaming fashion. By default, no action needed. + * \param num_thread The number of external threads that will be calling the push APIs + * \param omp_max_threads The maximum number of OpenMP threads to allocate for + */ + void InitStreaming(int32_t num_thread, int32_t omp_max_threads) { + if (is_multi_val_) { + for (int i = 0; i < num_feature_; ++i) { + multi_bin_data_[i]->InitStreaming(num_thread, omp_max_threads); + } + } else { + bin_data_->InitStreaming(num_thread, omp_max_threads); + } + } + + /*! + * \brief Push one record, will auto convert to bin and push to bin data + * \param tid Thread id + * \param sub_feature_idx Index of the subfeature + * \param line_idx Index of record + * \param value feature value of record + */ + inline void PushData(int tid, int sub_feature_idx, data_size_t line_idx, double value) { + uint32_t bin = bin_mappers_[sub_feature_idx]->ValueToBin(value); + if (bin == bin_mappers_[sub_feature_idx]->GetMostFreqBin()) { + return; + } + if (bin_mappers_[sub_feature_idx]->GetMostFreqBin() == 0) { + bin -= 1; + } + if (is_multi_val_) { + multi_bin_data_[sub_feature_idx]->Push(tid, line_idx, bin + 1); + } else { + bin += bin_offsets_[sub_feature_idx]; + bin_data_->Push(tid, line_idx, bin); + } + } + + void ReSize(int num_data) { + if (!is_multi_val_) { + bin_data_->ReSize(num_data); + } else { + for (int i = 0; i < num_feature_; ++i) { + multi_bin_data_[i]->ReSize(num_data); + } + } + } + + inline void CopySubrow(const FeatureGroup* full_feature, const data_size_t* used_indices, data_size_t num_used_indices) { + if (!is_multi_val_) { + bin_data_->CopySubrow(full_feature->bin_data_.get(), used_indices, num_used_indices); + } else { + for (int i = 0; i < num_feature_; ++i) { + multi_bin_data_[i]->CopySubrow(full_feature->multi_bin_data_[i].get(), used_indices, num_used_indices); + } + } + } + + inline void CopySubrowByCol(const FeatureGroup* full_feature, const data_size_t* used_indices, data_size_t num_used_indices, int fidx) { + if (!is_multi_val_) { + bin_data_->CopySubrow(full_feature->bin_data_.get(), used_indices, num_used_indices); + } else { + multi_bin_data_[fidx]->CopySubrow(full_feature->multi_bin_data_[fidx].get(), used_indices, num_used_indices); + } + } + + void AddFeaturesFrom(const FeatureGroup* other, int group_id) { + CHECK(is_multi_val_); + CHECK(other->is_multi_val_); + // every time when new features are added, we need to reconsider sparse or dense + double sum_sparse_rate = 0.0f; + for (int i = 0; i < num_feature_; ++i) { + sum_sparse_rate += bin_mappers_[i]->sparse_rate(); + } + for (int i = 0; i < other->num_feature_; ++i) { + sum_sparse_rate += other->bin_mappers_[i]->sparse_rate(); + } + sum_sparse_rate /= (num_feature_ + other->num_feature_); + int offset = 1; + is_dense_multi_val_ = false; + if (sum_sparse_rate < MultiValBin::multi_val_bin_sparse_threshold && is_multi_val_) { + // use dense multi val bin + offset = 0; + is_dense_multi_val_ = true; + } + bin_offsets_.clear(); + num_total_bin_ = offset; + // however, we should force to leave one bin, if dense multi val bin is the first bin + // and its first feature has most freq bin > 0 + if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ && + bin_mappers_[0]->GetMostFreqBin() > 0) { + num_total_bin_ = 1; + } + bin_offsets_.emplace_back(num_total_bin_); + for (int i = 0; i < num_feature_; ++i) { + auto num_bin = bin_mappers_[i]->num_bin(); + if (bin_mappers_[i]->GetMostFreqBin() == 0) { + num_bin -= offset; + } + num_total_bin_ += num_bin; + bin_offsets_.emplace_back(num_total_bin_); + } + for (int i = 0; i < other->num_feature_; ++i) { + const auto& other_bin_mapper = other->bin_mappers_[i]; + bin_mappers_.emplace_back(new BinMapper(*other_bin_mapper)); + auto num_bin = other_bin_mapper->num_bin(); + if (other_bin_mapper->GetMostFreqBin() == 0) { + num_bin -= offset; + } + num_total_bin_ += num_bin; + bin_offsets_.emplace_back(num_total_bin_); + multi_bin_data_.emplace_back(other->multi_bin_data_[i]->Clone()); + } + num_feature_ += other->num_feature_; + } + + inline BinIterator* SubFeatureIterator(int sub_feature) { + uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin(); + if (!is_multi_val_) { + uint32_t min_bin = bin_offsets_[sub_feature]; + uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1; + return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin); + } else { + int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1; + uint32_t min_bin = 1; + uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi; + return multi_bin_data_[sub_feature]->GetIterator(min_bin, max_bin, + most_freq_bin); + } + } + + inline void FinishLoad() { + if (is_multi_val_) { + OMP_INIT_EX(); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) + for (int i = 0; i < num_feature_; ++i) { + OMP_LOOP_EX_BEGIN(); + multi_bin_data_[i]->FinishLoad(); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } else { + bin_data_->FinishLoad(); + } + } + + inline BinIterator* FeatureGroupIterator() { + if (is_multi_val_) { + return nullptr; + } + uint32_t min_bin = bin_offsets_[0]; + uint32_t max_bin = bin_offsets_.back() - 1; + uint32_t most_freq_bin = 0; + return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin); + } + + inline size_t FeatureGroupSizesInByte() { + return bin_data_->SizesInByte(); + } + + inline void* FeatureGroupData() { + if (is_multi_val_) { + return nullptr; + } + return bin_data_->get_data(); + } + + inline data_size_t Split(int sub_feature, const uint32_t* threshold, + int num_threshold, bool default_left, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const { + uint32_t default_bin = bin_mappers_[sub_feature]->GetDefaultBin(); + uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin(); + if (!is_multi_val_) { + uint32_t min_bin = bin_offsets_[sub_feature]; + uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1; + if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) { + auto missing_type = bin_mappers_[sub_feature]->missing_type(); + if (num_feature_ == 1) { + return bin_data_->Split(max_bin, default_bin, most_freq_bin, + missing_type, default_left, *threshold, + data_indices, cnt, lte_indices, gt_indices); + } else { + return bin_data_->Split(min_bin, max_bin, default_bin, most_freq_bin, + missing_type, default_left, *threshold, + data_indices, cnt, lte_indices, gt_indices); + } + } else { + if (num_feature_ == 1) { + return bin_data_->SplitCategorical(max_bin, most_freq_bin, threshold, + num_threshold, data_indices, cnt, + lte_indices, gt_indices); + } else { + return bin_data_->SplitCategorical( + min_bin, max_bin, most_freq_bin, threshold, num_threshold, + data_indices, cnt, lte_indices, gt_indices); + } + } + } else { + int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1; + uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi; + if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) { + auto missing_type = bin_mappers_[sub_feature]->missing_type(); + return multi_bin_data_[sub_feature]->Split( + max_bin, default_bin, most_freq_bin, missing_type, default_left, + *threshold, data_indices, cnt, lte_indices, gt_indices); + } else { + return multi_bin_data_[sub_feature]->SplitCategorical( + max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt, + lte_indices, gt_indices); + } + } + } + + /*! + * \brief From bin to feature value + * \param bin + * \return FeatureGroup value of this bin + */ + inline double BinToValue(int sub_feature_idx, uint32_t bin) const { + return bin_mappers_[sub_feature_idx]->BinToValue(bin); + } + + /*! + * \brief Write to binary stream + * \param writer Writer + * \param include_data Whether to write data (true) or just header information (false) + */ + void SerializeToBinary(BinaryWriter* writer, bool include_data = true) const { + writer->AlignedWrite(&is_multi_val_, sizeof(is_multi_val_)); + writer->AlignedWrite(&is_dense_multi_val_, sizeof(is_dense_multi_val_)); + writer->AlignedWrite(&is_sparse_, sizeof(is_sparse_)); + writer->AlignedWrite(&num_feature_, sizeof(num_feature_)); + for (int i = 0; i < num_feature_; ++i) { + bin_mappers_[i]->SaveBinaryToFile(writer); + } + + if (include_data) { + if (is_multi_val_) { + for (int i = 0; i < num_feature_; ++i) { + multi_bin_data_[i]->SaveBinaryToFile(writer); + } + } else { + bin_data_->SaveBinaryToFile(writer); + } + } + } + + /*! + * \brief Get sizes in byte of this object + */ + size_t SizesInByte(bool include_data = true) const { + size_t ret = VirtualFileWriter::AlignedSize(sizeof(is_multi_val_)) + + VirtualFileWriter::AlignedSize(sizeof(is_dense_multi_val_)) + + VirtualFileWriter::AlignedSize(sizeof(is_sparse_)) + + VirtualFileWriter::AlignedSize(sizeof(num_feature_)); + for (int i = 0; i < num_feature_; ++i) { + ret += bin_mappers_[i]->SizesInByte(); + } + if (include_data) { + if (!is_multi_val_) { + ret += bin_data_->SizesInByte(); + } else { + for (int i = 0; i < num_feature_; ++i) { + ret += multi_bin_data_[i]->SizesInByte(); + } + } + } + return ret; + } + + /*! \brief Disable copy */ + FeatureGroup& operator=(const FeatureGroup&) = delete; + + /*! \brief Deep copy */ + FeatureGroup(const FeatureGroup& other, bool should_handle_dense_mv, + int group_id) { + num_feature_ = other.num_feature_; + is_multi_val_ = other.is_multi_val_; + is_dense_multi_val_ = other.is_dense_multi_val_; + is_sparse_ = other.is_sparse_; + num_total_bin_ = other.num_total_bin_; + bin_offsets_ = other.bin_offsets_; + + bin_mappers_.reserve(other.bin_mappers_.size()); + for (auto& bin_mapper : other.bin_mappers_) { + bin_mappers_.emplace_back(new BinMapper(*bin_mapper)); + } + if (!is_multi_val_) { + bin_data_.reset(other.bin_data_->Clone()); + } else { + multi_bin_data_.clear(); + for (int i = 0; i < num_feature_; ++i) { + multi_bin_data_.emplace_back(other.multi_bin_data_[i]->Clone()); + } + } + + if (should_handle_dense_mv && is_dense_multi_val_ && group_id > 0) { + // this feature group was the first feature group, but now no longer is, + // so we need to eliminate its special empty bin for multi val dense bin + if (bin_mappers_[0]->GetMostFreqBin() > 0 && bin_offsets_[0] == 1) { + for (size_t i = 0; i < bin_offsets_.size(); ++i) { + bin_offsets_[i] -= 1; + } + num_total_bin_ -= 1; + } + } + } + + const void* GetColWiseData(const int sub_feature_index, + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int num_threads) const { + if (sub_feature_index >= 0) { + CHECK(is_multi_val_); + return multi_bin_data_[sub_feature_index]->GetColWiseData(bit_type, is_sparse, bin_iterator, num_threads); + } else { + CHECK(!is_multi_val_); + return bin_data_->GetColWiseData(bit_type, is_sparse, bin_iterator, num_threads); + } + } + + const void* GetColWiseData(const int sub_feature_index, + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + if (sub_feature_index >= 0) { + CHECK(is_multi_val_); + return multi_bin_data_[sub_feature_index]->GetColWiseData(bit_type, is_sparse, bin_iterator); + } else { + CHECK(!is_multi_val_); + return bin_data_->GetColWiseData(bit_type, is_sparse, bin_iterator); + } + } + + uint32_t feature_max_bin(const int sub_feature_index) const { + if (!is_multi_val_) { + return bin_offsets_[sub_feature_index + 1] - 1; + } else { + int addi = bin_mappers_[sub_feature_index]->GetMostFreqBin() == 0 ? 0 : 1; + return bin_mappers_[sub_feature_index]->num_bin() - 1 + addi; + } + } + + uint32_t feature_min_bin(const int sub_feature_index) const { + if (!is_multi_val_) { + return bin_offsets_[sub_feature_index]; + } else { + return 1; + } + } + + private: + void CreateBinData(int num_data, bool is_multi_val, bool force_dense, bool force_sparse) { + if (is_multi_val) { + multi_bin_data_.clear(); + for (int i = 0; i < num_feature_; ++i) { + int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1; + if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) { + multi_bin_data_.emplace_back(Bin::CreateSparseBin( + num_data, bin_mappers_[i]->num_bin() + addi)); + } else { + multi_bin_data_.emplace_back( + Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi)); + } + } + is_multi_val_ = true; + } else { + if (force_sparse || + (!force_dense && num_feature_ == 1 && + bin_mappers_[0]->sparse_rate() >= kSparseThreshold)) { + is_sparse_ = true; + bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_)); + } else { + is_sparse_ = false; + bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_)); + } + is_multi_val_ = false; + } + } + + /*! \brief Number of features */ + int num_feature_; + /*! \brief Bin mapper for sub features */ + std::vector> bin_mappers_; + /*! \brief Bin offsets for sub features */ + std::vector bin_offsets_; + /*! \brief Bin data of this feature */ + std::unique_ptr bin_data_; + std::vector> multi_bin_data_; + /*! \brief True if this feature is sparse */ + bool is_multi_val_; + bool is_dense_multi_val_; + bool is_sparse_; + int num_total_bin_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_ diff --git a/include/LightGBM/meta.h b/include/LightGBM/meta.h new file mode 100644 index 0000000..a46645c --- /dev/null +++ b/include/LightGBM/meta.h @@ -0,0 +1,92 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_META_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_META_H_ + +#include +#include +#include +#include +#include +#include +#include + +#if (defined(_MSC_VER) && (defined(_M_IX86) || defined(_M_AMD64))) || defined(__INTEL_COMPILER) || MM_PREFETCH + #include + #define PREFETCH_T0(addr) _mm_prefetch(reinterpret_cast(addr), _MM_HINT_T0) +#elif defined(__GNUC__) + #define PREFETCH_T0(addr) __builtin_prefetch(reinterpret_cast(addr), 0, 3) +#else + #define PREFETCH_T0(addr) do {} while (0) +#endif + +namespace LightGBM { + +/*! \brief Type of data size, it is better to use signed type*/ +typedef int32_t data_size_t; + +// Enable following macro to use double for score_t +// #define SCORE_T_USE_DOUBLE + +// Enable following macro to use double for label_t +// #define LABEL_T_USE_DOUBLE + +/*! \brief Type of score, and gradients */ +#ifdef SCORE_T_USE_DOUBLE +typedef double score_t; +#else +typedef float score_t; +#endif + +/*! \brief Type of metadata, include weight and label */ +#ifdef LABEL_T_USE_DOUBLE +typedef double label_t; +#else +typedef float label_t; +#endif + +const score_t kMinScore = -std::numeric_limits::infinity(); + +const score_t kMaxScore = std::numeric_limits::infinity(); + +const score_t kEpsilon = 1e-15f; + +const double kZeroThreshold = 1e-35f; + + +typedef int32_t comm_size_t; + +using PredictFunction = +std::function>&, double* output)>; + +using PredictSparseFunction = +std::function>&, std::vector>* output)>; + +typedef void(*ReduceFunction)(const char* input, char* output, int type_size, comm_size_t array_size); + + +typedef void(*ReduceScatterFunction)(char* input, comm_size_t input_size, int type_size, + const comm_size_t* block_start, const comm_size_t* block_len, int num_block, char* output, comm_size_t output_size, + const ReduceFunction& reducer); + +typedef void(*AllgatherFunction)(char* input, comm_size_t input_size, const comm_size_t* block_start, + const comm_size_t* block_len, int num_block, char* output, comm_size_t output_size); + + +#define NO_SPECIFIC (-1) + +const int kAlignedSize = 32; + +#define SIZE_ALIGNED(t) ((t) + kAlignedSize - 1) / kAlignedSize * kAlignedSize + +// Refer to https://docs.microsoft.com/en-us/cpp/error-messages/compiler-warnings/compiler-warning-level-4-c4127?view=vs-2019 +#ifdef _MSC_VER +#pragma warning(disable : 4127) +#endif + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_META_H_ diff --git a/include/LightGBM/metric.h b/include/LightGBM/metric.h new file mode 100644 index 0000000..3a2adb1 --- /dev/null +++ b/include/LightGBM/metric.h @@ -0,0 +1,146 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_ + +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace LightGBM { + +/*! +* \brief The interface of metric. +* Metric is used to calculate metric result +*/ +class Metric { + public: + /*! \brief virtual destructor */ + virtual ~Metric() {} + + /*! + * \brief Initialize + * \param test_name Specific name for this metric, will output on log + * \param metadata Label data + * \param num_data Number of data + */ + virtual void Init(const Metadata& metadata, data_size_t num_data) = 0; + + virtual const std::vector& GetName() const = 0; + + virtual double factor_to_bigger_better() const = 0; + /*! + * \brief Calculating and printing metric result + * \param score Current prediction score + */ + virtual std::vector Eval(const double* score, const ObjectiveFunction* objective) const = 0; + + Metric() = default; + /*! \brief Disable copy */ + Metric& operator=(const Metric&) = delete; + /*! \brief Disable copy */ + Metric(const Metric&) = delete; + + /*! + * \brief Create object of metrics + * \param type Specific type of metric + * \param config Config for metric + */ + LIGHTGBM_EXPORT static Metric* CreateMetric(const std::string& type, const Config& config); + + /*! + * \brief Whether boosting is done on CUDA + */ + virtual bool IsCUDAMetric() const { return false; } +}; + +/*! +* \brief Static class, used to calculate DCG score +*/ +class DCGCalculator { + public: + static void DefaultEvalAt(std::vector* eval_at); + static void DefaultLabelGain(std::vector* label_gain); + /*! + * \brief Initial logic + * \param label_gain Gain for labels, default is 2^i - 1 + */ + static void Init(const std::vector& label_gain); + + /*! + * \brief Calculate the DCG score at multi position + * \param ks The positions to evaluate + * \param label Pointer of label + * \param score Pointer of score + * \param num_data Number of data + * \param out Output result + */ + static void CalDCG(const std::vector& ks, + const label_t* label, const double* score, + data_size_t num_data, std::vector* out); + + /*! + * \brief Calculate the Max DCG score at position k + * \param k The position want to eval at + * \param label Pointer of label + * \param num_data Number of data + * \return The max DCG score + */ + static double CalMaxDCGAtK(data_size_t k, + const label_t* label, data_size_t num_data); + + + /*! + * \brief Check the metadata for NDCG and LambdaRank + * \param metadata Metadata + * \param num_queries Number of queries + */ + static void CheckMetadata(const Metadata& metadata, data_size_t num_queries); + + /*! + * \brief Check the label range for NDCG and LambdaRank + * \param label Pointer of label + * \param num_data Number of data + */ + static void CheckLabel(const label_t* label, data_size_t num_data); + + /*! + * \brief Calculate the Max DCG score at multi position + * \param ks The positions want to eval at + * \param label Pointer of label + * \param num_data Number of data + * \param out Output result + */ + static void CalMaxDCG(const std::vector& ks, + const label_t* label, data_size_t num_data, std::vector* out); + + /*! + * \brief Get discount score of position k + * \param k The position + * \return The discount of this position + */ + inline static double GetDiscount(data_size_t k) { return discount_[k]; } + + private: + /*! \brief store gains for different label */ + static std::vector label_gain_; + /*! \brief store discount score for different position */ + static std::vector discount_; + /*! \brief max position for eval */ + static const data_size_t kMaxPosition; +}; + + +} // namespace LightGBM + + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_ diff --git a/include/LightGBM/network.h b/include/LightGBM/network.h new file mode 100644 index 0000000..a2a5eea --- /dev/null +++ b/include/LightGBM/network.h @@ -0,0 +1,318 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_ + +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + +/*! \brief forward declaration */ +class Linkers; + +/*! \brief The network structure for all_gather */ +class BruckMap { + public: + /*! \brief The communication times for one all gather operation */ + int k; + /*! \brief in_ranks[i] means the incoming rank on i-th communication */ + std::vector in_ranks; + /*! \brief out_ranks[i] means the out rank on i-th communication */ + std::vector out_ranks; + BruckMap(); + explicit BruckMap(int n); + /*! + * \brief Create the object of bruck map + * \param rank Rank of this machine + * \param num_machines The total number of machines + * \return The object of bruck map + */ + static BruckMap Construct(int rank, int num_machines); +}; + +/*! +* \brief node type on recursive halving algorithm +* When number of machines is not power of 2, need group machines into power of 2 group. +* And we can let each group has at most 2 machines. +* if the group only has 1 machine. this machine is the normal node +* if the group has 2 machines, this group will have two type of nodes, one is the leader. +* leader will represent this group and communication with others. +*/ +enum RecursiveHalvingNodeType { + Normal, // normal node, 1 group only have 1 machine + GroupLeader, // leader of group when number of machines in this group is 2. + Other // non-leader machines in group +}; + +/*! \brief Network structure for recursive halving algorithm */ +class RecursiveHalvingMap { + public: + /*! \brief Communication times for one recursive halving algorithm */ + int k; + /*! \brief Node type */ + RecursiveHalvingNodeType type; + bool is_power_of_2; + int neighbor; + /*! \brief ranks[i] means the machines that will communicate with on i-th communication*/ + std::vector ranks; + /*! \brief send_block_start[i] means send block start index at i-th communication*/ + std::vector send_block_start; + /*! \brief send_block_start[i] means send block size at i-th communication*/ + std::vector send_block_len; + /*! \brief send_block_start[i] means recv block start index at i-th communication*/ + std::vector recv_block_start; + /*! \brief send_block_start[i] means recv block size at i-th communication*/ + std::vector recv_block_len; + + RecursiveHalvingMap(); + + RecursiveHalvingMap(int k, RecursiveHalvingNodeType _type, bool _is_power_of_2); + + /*! + * \brief Create the object of recursive halving map + * \param rank Rank of this machine + * \param num_machines The total number of machines + * \return The object of recursive halving map + */ + static RecursiveHalvingMap Construct(int rank, int num_machines); +}; + +/*! \brief A static class that contains some collective communication algorithm */ +class Network { + public: + /*! + * \brief Initialize + * \param config Config of network setting + */ + static void Init(Config config); + /*! + * \brief Initialize + */ + static void Init(int num_machines, int rank, ReduceScatterFunction reduce_scatter_ext_fun, AllgatherFunction allgather_ext_fun); + /*! \brief Free this static class */ + static void Dispose(); + /*! \brief Get rank of this machine */ + static int rank(); + /*! \brief Get total number of machines */ + static int num_machines(); + + /*! + * \brief Perform all_reduce. if data size is small, + will perform AllreduceByAllGather, else with call ReduceScatter followed allgather + * \param input Input data + * \param input_size The size of input data + * \param type_size The size of one object in the reduce function + * \param output Output result + * \param reducer Reduce function + */ + static void Allreduce(char* input, comm_size_t input_size, int type_size, + char* output, const ReduceFunction& reducer); + + /*! + * \brief Perform all_reduce by using all_gather. it can be use to reduce communication time when data is small + * \param input Input data + * \param input_size The size of input data + * \param type_size The size of one object in the reduce function + * \param output Output result + * \param reducer Reduce function + */ + static void AllreduceByAllGather(char* input, comm_size_t input_size, int type_size, char* output, + const ReduceFunction& reducer); + + /*! + * \brief Performing all_gather by using Bruck algorithm. + Communication times is O(log(n)), and communication cost is O(send_size * number_machine) + * It can be used when all nodes have same input size. + * \param input Input data + * \param send_size The size of input data + * \param output Output result + */ + static void Allgather(char* input, comm_size_t send_size, char* output); + + /*! + * \brief Performing all_gather by using Bruck algorithm. + Communication times is O(log(n)), and communication cost is O(all_size) + * It can be used when nodes have different input size. + * \param input Input data + * \param block_start The block start for different machines + * \param block_len The block size for different machines + * \param output Output result + * \param all_size The size of output data + */ + static void Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); + + /*! + * \brief Perform reduce scatter by using recursive halving algorithm. + Communication times is O(log(n)), and communication cost is O(input_size) + * \param input Input data + * \param input_size The size of input data + * \param type_size The size of one object in the reduce function + * \param block_start The block start for different machines + * \param block_len The block size for different machines + * \param output Output result + * \param output_size size of output data + * \param reducer Reduce function + */ + static void ReduceScatter(char* input, comm_size_t input_size, int type_size, + const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size, + const ReduceFunction& reducer); + + template + static T GlobalSyncUpByMin(T local) { + T global = local; + Allreduce(reinterpret_cast(&local), + sizeof(local), sizeof(local), + reinterpret_cast(&global), + [] (const char* src, char* dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const T *p1; + T *p2; + while (used_size < len) { + p1 = reinterpret_cast(src); + p2 = reinterpret_cast(dst); + if (*p1 < *p2) { + std::memcpy(dst, src, type_size); + } + src += type_size; + dst += type_size; + used_size += type_size; + } + }); + return global; + } + template + static T GlobalSyncUpByMax(T local) { + T global = local; + Allreduce(reinterpret_cast(&local), + sizeof(local), sizeof(local), + reinterpret_cast(&global), + [] (const char* src, char* dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const T *p1; + T *p2; + while (used_size < len) { + p1 = reinterpret_cast(src); + p2 = reinterpret_cast(dst); + if (*p1 > *p2) { + std::memcpy(dst, src, type_size); + } + src += type_size; + dst += type_size; + used_size += type_size; + } + }); + return global; + } + + template + static T GlobalSyncUpBySum(T local) { + T global = (T)0; + Allreduce(reinterpret_cast(&local), + sizeof(local), sizeof(local), + reinterpret_cast(&global), + [](const char* src, char* dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const T* p1; + T* p2; + while (used_size < len) { + p1 = reinterpret_cast(src); + p2 = reinterpret_cast(dst); + *p2 += *p1; + src += type_size; + dst += type_size; + used_size += type_size; + } + }); + return static_cast(global); + } + + template + static T GlobalSyncUpByMean(T local) { + return static_cast(GlobalSyncUpBySum(local) / num_machines_); + } + + template + static std::vector GlobalSum(std::vector* local) { + std::vector global(local->size(), 0); + Allreduce(reinterpret_cast(local->data()), + static_cast(sizeof(T) * local->size()), sizeof(T), + reinterpret_cast(global.data()), + [](const char* src, char* dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const T *p1; + T *p2; + while (used_size < len) { + p1 = reinterpret_cast(src); + p2 = reinterpret_cast(dst); + *p2 += *p1; + src += type_size; + dst += type_size; + used_size += type_size; + } + }); + return global; + } + + template + static std::vector GlobalArray(T local) { + std::vector global(num_machines_, 0); + int type_size = sizeof(T); + std::vector block_start(num_machines_); + std::vector block_len(num_machines_, type_size); + for (int i = 1; i < num_machines_; ++i) { + block_start[i] = block_start[i - 1] + block_len[i - 1]; + } + Allgather(reinterpret_cast(&local), block_start.data(), block_len.data(), reinterpret_cast(global.data()), type_size*num_machines_); + return global; + } + + private: + static void AllgatherBruck(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); + + static void AllgatherRecursiveDoubling(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); + + static void AllgatherRing(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size); + + static void ReduceScatterRecursiveHalving(char* input, comm_size_t input_size, int type_size, + const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size, + const ReduceFunction& reducer); + + static void ReduceScatterRing(char* input, comm_size_t input_size, int type_size, + const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size, + const ReduceFunction& reducer); + + /*! \brief Number of all machines */ + static THREAD_LOCAL int num_machines_; + /*! \brief Rank of local machine */ + static THREAD_LOCAL int rank_; + /*! \brief The network interface, provide send/recv functions */ + static THREAD_LOCAL std::unique_ptr linkers_; + /*! \brief Bruck map for all gather algorithm*/ + static THREAD_LOCAL BruckMap bruck_map_; + /*! \brief Recursive halving map for reduce scatter */ + static THREAD_LOCAL RecursiveHalvingMap recursive_halving_map_; + /*! \brief Buffer to store block start index */ + static THREAD_LOCAL std::vector block_start_; + /*! \brief Buffer to store block size */ + static THREAD_LOCAL std::vector block_len_; + /*! \brief Buffer */ + static THREAD_LOCAL std::vector buffer_; + /*! \brief Size of buffer_ */ + static THREAD_LOCAL comm_size_t buffer_size_; + /*! \brief Funcs*/ + static THREAD_LOCAL ReduceScatterFunction reduce_scatter_ext_fun_; + static THREAD_LOCAL AllgatherFunction allgather_ext_fun_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_ diff --git a/include/LightGBM/objective_function.h b/include/LightGBM/objective_function.h new file mode 100644 index 0000000..15019be --- /dev/null +++ b/include/LightGBM/objective_function.h @@ -0,0 +1,142 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_ + +#include +#include +#include + +#include +#include + +namespace LightGBM { +/*! +* \brief The interface of Objective Function. +*/ +class ObjectiveFunction { + public: + /*! \brief virtual destructor */ + virtual ~ObjectiveFunction() {} + + /*! + * \brief Initialize + * \param metadata Label data + * \param num_data Number of data + */ + virtual void Init(const Metadata& metadata, data_size_t num_data) = 0; + + /*! + * \brief calculating first order derivative of loss function + * \param score prediction score in this round + * \gradients Output gradients + * \hessians Output hessians + */ + virtual void GetGradients(const double* score, + score_t* gradients, score_t* hessians) const = 0; + + /*! + * \brief calculating first order derivative of loss function, used only for bagging by query in lambdarank + * \param score prediction score in this round + * \param num_sampled_queries number of in-bag queries + * \param sampled_query_indices indices of in-bag queries + * \gradients Output gradients + * \hessians Output hessians + */ + virtual void GetGradientsWithSampledQueries(const double* score, const data_size_t /*num_sampled_queries*/, const data_size_t* /*sampled_query_indices*/, + score_t* gradients, score_t* hessians) const { GetGradients(score, gradients, hessians); } + + virtual const char* GetName() const = 0; + + virtual bool IsConstantHessian() const { return false; } + + virtual bool IsRenewTreeOutput() const { return false; } + + virtual double RenewTreeOutput(double ori_output, std::function, + const data_size_t*, + const data_size_t*, + data_size_t) const { return ori_output; } + + virtual void RenewTreeOutputCUDA(const double* /*score*/, const data_size_t* /*data_indices_in_leaf*/, const data_size_t* /*num_data_in_leaf*/, + const data_size_t* /*data_start_in_leaf*/, const int /*num_leaves*/, double* /*leaf_value*/) const {} + + virtual double BoostFromScore(int /*class_id*/) const { return 0.0; } + + virtual bool ClassNeedTrain(int /*class_id*/) const { return true; } + + virtual bool SkipEmptyClass() const { return false; } + + virtual int NumModelPerIteration() const { return 1; } + + virtual int NumPredictOneRow() const { return 1; } + + /*! \brief The prediction should be accurate or not. True will disable early stopping for prediction. */ + virtual bool NeedAccuratePrediction() const { return true; } + + /*! \brief Return the number of positive samples. Return 0 if no binary classification tasks.*/ + virtual data_size_t NumPositiveData() const { return 0; } + + virtual void ConvertOutput(const double* input, double* output) const { + output[0] = input[0]; + } + + virtual std::string ToString() const = 0; + + ObjectiveFunction() = default; + /*! \brief Disable copy */ + ObjectiveFunction& operator=(const ObjectiveFunction&) = delete; + /*! \brief Disable copy */ + ObjectiveFunction(const ObjectiveFunction&) = delete; + + /*! + * \brief Create object of objective function + * \param type Specific type of objective function + * \param config Config for objective function + */ + LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunction(const std::string& type, + const Config& config); + + /*! + * \brief Load objective function from string object + */ + LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunction(const std::string& str); + + /*! + * \brief Whether boosting is done on CUDA + */ + virtual bool IsCUDAObjective() const { return false; } + + #ifdef USE_CUDA + /*! + * \brief Convert output for CUDA version + */ + virtual const double* ConvertOutputCUDA(data_size_t /*num_data*/, const double* input, double* /*output*/) const { + return input; + } + + virtual bool NeedConvertOutputCUDA () const { return false; } + + virtual void SetNCCLInfo( + ncclComm_t /*nccl_communicator*/, + int /*nccl_gpu_rank*/, + int /*local_gpu_rank*/, + int /*gpu_device_id*/, + data_size_t /*global_num_data*/) {} + + /*! + * \brief Create object of objective function on CUDA + * \param type Specific type of objective function + * \param config Config for objective function + */ + LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunctionCUDA(const std::string& type, + const Config& config); + + #endif // USE_CUDA +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_ diff --git a/include/LightGBM/prediction_early_stop.h b/include/LightGBM/prediction_early_stop.h new file mode 100644 index 0000000..718fefc --- /dev/null +++ b/include/LightGBM/prediction_early_stop.h @@ -0,0 +1,37 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_ + +#include + +#include +#include + +namespace LightGBM { + +struct PredictionEarlyStopInstance { + /// Callback function type for early stopping. + /// Takes current prediction and number of elements in prediction + /// @returns true if prediction should stop according to criterion + using FunctionType = std::function; + + FunctionType callback_function; // callback function itself + int round_period; // call callback_function every `runPeriod` iterations +}; + +struct PredictionEarlyStopConfig { + int round_period; + double margin_threshold; +}; + +/// Create an early stopping algorithm of type `type`, with given round_period and margin threshold +LIGHTGBM_EXPORT PredictionEarlyStopInstance CreatePredictionEarlyStopInstance(const std::string& type, + const PredictionEarlyStopConfig& config); + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_ diff --git a/include/LightGBM/sample_strategy.h b/include/LightGBM/sample_strategy.h new file mode 100644 index 0000000..8f16a17 --- /dev/null +++ b/include/LightGBM/sample_strategy.h @@ -0,0 +1,88 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_ + +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace LightGBM { + +class SampleStrategy { + public: + SampleStrategy() : balanced_bagging_(false), bagging_runner_(0, bagging_rand_block_), need_resize_gradients_(false) {} + + virtual ~SampleStrategy() {} + + static SampleStrategy* CreateSampleStrategy(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration); + + virtual void Bagging(int iter, TreeLearner* tree_learner, score_t* gradients, score_t* hessians) = 0; + + virtual void ResetSampleConfig(const Config* config, bool is_change_dataset) = 0; + + bool is_use_subset() const { return is_use_subset_; } + + data_size_t bag_data_cnt() const { return bag_data_cnt_; } + + std::vector>& bag_data_indices() { return bag_data_indices_; } + + #ifdef USE_CUDA + CUDAVector& cuda_bag_data_indices() { return cuda_bag_data_indices_; } + #endif // USE_CUDA + + void UpdateObjectiveFunction(const ObjectiveFunction* objective_function) { + objective_function_ = objective_function; + } + + void UpdateTrainingData(const Dataset* train_data) { + train_data_ = train_data; + num_data_ = train_data->num_data(); + } + + virtual bool IsHessianChange() const = 0; + + bool NeedResizeGradients() const { return need_resize_gradients_; } + + virtual data_size_t num_sampled_queries() const { return 0; } + + virtual const data_size_t* sampled_query_indices() const { return nullptr; } + + protected: + const Config* config_; + const Dataset* train_data_; + const ObjectiveFunction* objective_function_; + std::vector> bag_data_indices_; + data_size_t bag_data_cnt_; + data_size_t num_data_; + int num_tree_per_iteration_; + std::unique_ptr tmp_subset_; + bool is_use_subset_; + bool balanced_bagging_; + const int bagging_rand_block_ = 1024; + std::vector bagging_rands_; + ParallelPartitionRunner bagging_runner_; + /*! \brief whether need to resize the gradient vectors */ + bool need_resize_gradients_; + + #ifdef USE_CUDA + /*! \brief Buffer for bag_data_indices_ on GPU, used only with cuda */ + CUDAVector cuda_bag_data_indices_; + #endif // USE_CUDA +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_ diff --git a/include/LightGBM/train_share_states.h b/include/LightGBM/train_share_states.h new file mode 100644 index 0000000..56da9a8 --- /dev/null +++ b/include/LightGBM/train_share_states.h @@ -0,0 +1,367 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_ + +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { + +class MultiValBinWrapper { + public: + MultiValBinWrapper(MultiValBin* bin, data_size_t num_data, + const std::vector& feature_groups_contained, const int num_grad_quant_bins); + + bool IsSparse() const { + if (multi_val_bin_ != nullptr) { + return multi_val_bin_->IsSparse(); + } + return false; + } + + void InitTrain(const std::vector& group_feature_start, + const std::vector>& feature_groups, + const std::vector& is_feature_used, + const data_size_t* bagging_use_indices, + data_size_t bagging_indices_cnt); + + template + void HistMove(const std::vector>& hist_buf); + + template + void HistMerge(std::vector>* hist_buf); + + void ResizeHistBuf(std::vector>* hist_buf, + MultiValBin* sub_multi_val_bin, + hist_t* origin_hist_data); + + template + void ConstructHistograms(const data_size_t* data_indices, + data_size_t num_data, + const score_t* gradients, + const score_t* hessians, + std::vector>* hist_buf, + hist_t* origin_hist_data) { + const auto cur_multi_val_bin = (is_use_subcol_ || is_use_subrow_) + ? multi_val_bin_subset_.get() + : multi_val_bin_.get(); + if (cur_multi_val_bin != nullptr) { + global_timer.Start("Dataset::sparse_bin_histogram"); + n_data_block_ = 1; + data_block_size_ = num_data; + Threading::BlockInfo(num_threads_, num_data, min_block_size_, + &n_data_block_, &data_block_size_); + ResizeHistBuf(hist_buf, cur_multi_val_bin, origin_hist_data); + const int inner_hist_bits = (data_block_size_ * num_grad_quant_bins_ < 256 && HIST_BITS == 16) ? 8 : HIST_BITS; + OMP_INIT_EX(); + #pragma omp parallel for schedule(static) num_threads(num_threads_) + for (int block_id = 0; block_id < n_data_block_; ++block_id) { + OMP_LOOP_EX_BEGIN(); + data_size_t start = block_id * data_block_size_; + data_size_t end = std::min(start + data_block_size_, num_data); + if (inner_hist_bits == 8) { + ConstructHistogramsForBlock( + cur_multi_val_bin, start, end, data_indices, gradients, hessians, + block_id, hist_buf); + } else { + ConstructHistogramsForBlock( + cur_multi_val_bin, start, end, data_indices, gradients, hessians, + block_id, hist_buf); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + global_timer.Stop("Dataset::sparse_bin_histogram"); + + global_timer.Start("Dataset::sparse_bin_histogram_merge"); + if (inner_hist_bits == 8) { + HistMerge(hist_buf); + } else { + HistMerge(hist_buf); + } + global_timer.Stop("Dataset::sparse_bin_histogram_merge"); + global_timer.Start("Dataset::sparse_bin_histogram_move"); + if (inner_hist_bits == 8) { + HistMove(*hist_buf); + } else { + HistMove(*hist_buf); + } + global_timer.Stop("Dataset::sparse_bin_histogram_move"); + } + } + + template + void ConstructHistogramsForBlock(const MultiValBin* sub_multi_val_bin, + data_size_t start, data_size_t end, const data_size_t* data_indices, + const score_t* gradients, const score_t* hessians, int block_id, + std::vector>* hist_buf) { + if (USE_QUANT_GRAD) { + if (HIST_BITS == 8) { + int8_t* hist_buf_ptr = reinterpret_cast(hist_buf->data()); + int8_t* data_ptr = hist_buf_ptr + + static_cast(num_bin_aligned_) * block_id * 2; + std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt8HistBufferEntrySize); + if (USE_INDICES) { + if (ORDERED) { + sub_multi_val_bin->ConstructHistogramOrderedInt8(data_indices, start, end, + gradients, hessians, + reinterpret_cast(data_ptr)); + } else { + sub_multi_val_bin->ConstructHistogramInt8(data_indices, start, end, gradients, + hessians, + reinterpret_cast(data_ptr)); + } + } else { + sub_multi_val_bin->ConstructHistogramInt8(start, end, gradients, hessians, + reinterpret_cast(data_ptr)); + } + } else if (HIST_BITS == 16) { + int16_t* data_ptr = reinterpret_cast(origin_hist_data_); + int16_t* hist_buf_ptr = reinterpret_cast(hist_buf->data()); + if (block_id == 0) { + if (is_use_subcol_) { + data_ptr = hist_buf_ptr + hist_buf->size() - 2 * static_cast(num_bin_aligned_); + } + } else { + data_ptr = hist_buf_ptr + + static_cast(num_bin_aligned_) * (block_id - 1) * 2; + } + std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt16HistBufferEntrySize); + if (USE_INDICES) { + if (ORDERED) { + sub_multi_val_bin->ConstructHistogramOrderedInt16(data_indices, start, end, + gradients, hessians, + reinterpret_cast(data_ptr)); + } else { + sub_multi_val_bin->ConstructHistogramInt16(data_indices, start, end, gradients, + hessians, + reinterpret_cast(data_ptr)); + } + } else { + sub_multi_val_bin->ConstructHistogramInt16(start, end, gradients, hessians, + reinterpret_cast(data_ptr)); + } + } else { + int32_t* data_ptr = reinterpret_cast(origin_hist_data_); + int32_t* hist_buf_ptr = reinterpret_cast(hist_buf->data()); + if (block_id == 0) { + if (is_use_subcol_) { + data_ptr = hist_buf_ptr + hist_buf->size() - 2 * static_cast(num_bin_aligned_); + } + } else { + data_ptr = hist_buf_ptr + + static_cast(num_bin_aligned_) * (block_id - 1) * 2; + } + std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt32HistBufferEntrySize); + if (USE_INDICES) { + if (ORDERED) { + sub_multi_val_bin->ConstructHistogramOrderedInt32(data_indices, start, end, + gradients, hessians, + reinterpret_cast(data_ptr)); + } else { + sub_multi_val_bin->ConstructHistogramInt32(data_indices, start, end, gradients, + hessians, + reinterpret_cast(data_ptr)); + } + } else { + sub_multi_val_bin->ConstructHistogramInt32(start, end, gradients, hessians, + reinterpret_cast(data_ptr)); + } + } + } else { + hist_t* data_ptr = origin_hist_data_; + if (block_id == 0) { + if (is_use_subcol_) { + data_ptr = hist_buf->data() + hist_buf->size() - 2 * static_cast(num_bin_aligned_); + } + } else { + data_ptr = hist_buf->data() + + static_cast(num_bin_aligned_) * (block_id - 1) * 2; + } + std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kHistBufferEntrySize); + if (USE_INDICES) { + if (ORDERED) { + sub_multi_val_bin->ConstructHistogramOrdered(data_indices, start, end, + gradients, hessians, data_ptr); + } else { + sub_multi_val_bin->ConstructHistogram(data_indices, start, end, gradients, + hessians, data_ptr); + } + } else { + sub_multi_val_bin->ConstructHistogram(start, end, gradients, hessians, + data_ptr); + } + } + } + + void CopyMultiValBinSubset(const std::vector& group_feature_start, + const std::vector>& feature_groups, + const std::vector& is_feature_used, + const data_size_t* bagging_use_indices, + data_size_t bagging_indices_cnt); + + void SetUseSubrow(bool is_use_subrow) { + is_use_subrow_ = is_use_subrow; + } + + void SetSubrowCopied(bool is_subrow_copied) { + is_subrow_copied_ = is_subrow_copied; + } + + + #ifdef USE_CUDA + const void* GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + if (multi_val_bin_ == nullptr) { + *bit_type = 0; + *total_size = 0; + *is_sparse = false; + return nullptr; + } else { + return multi_val_bin_->GetRowWiseData(bit_type, total_size, is_sparse, out_data_ptr, data_ptr_bit_type); + } + } + #endif // USE_CUDA + + private: + bool is_use_subcol_ = false; + bool is_use_subrow_ = false; + bool is_subrow_copied_ = false; + std::unique_ptr multi_val_bin_; + std::unique_ptr multi_val_bin_subset_; + std::vector hist_move_src_; + std::vector hist_move_dest_; + std::vector hist_move_size_; + const std::vector feature_groups_contained_; + + int num_threads_; + int num_bin_; + int num_bin_aligned_; + int n_data_block_; + int data_block_size_; + int min_block_size_; + int num_data_; + int num_grad_quant_bins_; + + hist_t* origin_hist_data_; + + const size_t kHistBufferEntrySize = 2 * sizeof(hist_t); + const size_t kInt32HistBufferEntrySize = 2 * sizeof(int32_t); + const size_t kInt16HistBufferEntrySize = 2 * sizeof(int16_t); + const size_t kInt8HistBufferEntrySize = 2 * sizeof(int8_t); +}; + +struct TrainingShareStates { + int num_threads = 0; + bool is_col_wise = true; + bool is_constant_hessian = true; + const data_size_t* bagging_use_indices; + data_size_t bagging_indices_cnt; + + TrainingShareStates() { + multi_val_bin_wrapper_.reset(nullptr); + } + + int num_hist_total_bin() const { return num_hist_total_bin_; } + + const std::vector& feature_hist_offsets() const { return feature_hist_offsets_; } + + #ifdef USE_CUDA + const std::vector& column_hist_offsets() const { return column_hist_offsets_; } + #endif // USE_CUDA + + bool IsSparseRowwise() const { + return (multi_val_bin_wrapper_ != nullptr && multi_val_bin_wrapper_->IsSparse()); + } + + void SetMultiValBin(MultiValBin* bin, data_size_t num_data, + const std::vector>& feature_groups, + bool dense_only, bool sparse_only, const int num_grad_quant_bins); + + void CalcBinOffsets(const std::vector>& feature_groups, + std::vector* offsets, bool is_col_wise); + + void InitTrain(const std::vector& group_feature_start, + const std::vector>& feature_groups, + const std::vector& is_feature_used) { + if (multi_val_bin_wrapper_ != nullptr) { + multi_val_bin_wrapper_->InitTrain(group_feature_start, + feature_groups, + is_feature_used, + bagging_use_indices, + bagging_indices_cnt); + } + } + + template + void ConstructHistograms(const data_size_t* data_indices, + data_size_t num_data, + const score_t* gradients, + const score_t* hessians, + hist_t* hist_data) { + if (multi_val_bin_wrapper_ != nullptr) { + multi_val_bin_wrapper_->ConstructHistograms( + data_indices, num_data, gradients, hessians, &hist_buf_, hist_data); + } + } + + void SetUseSubrow(bool is_use_subrow) { + if (multi_val_bin_wrapper_ != nullptr) { + multi_val_bin_wrapper_->SetUseSubrow(is_use_subrow); + } + } + + void SetSubrowCopied(bool is_subrow_copied) { + if (multi_val_bin_wrapper_ != nullptr) { + multi_val_bin_wrapper_->SetSubrowCopied(is_subrow_copied); + } + } + + + #ifdef USE_CUDA + const void* GetRowWiseData(uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) { + if (multi_val_bin_wrapper_ != nullptr) { + return multi_val_bin_wrapper_->GetRowWiseData(bit_type, total_size, is_sparse, out_data_ptr, data_ptr_bit_type); + } else { + *bit_type = 0; + *total_size = 0; + *is_sparse = false; + return nullptr; + } + } + #endif // USE_CUDA + + private: + std::vector feature_hist_offsets_; + #ifdef USE_CUDA + std::vector column_hist_offsets_; + #endif // USE_CUDA + int num_hist_total_bin_ = 0; + std::unique_ptr multi_val_bin_wrapper_; + std::vector> hist_buf_; + int num_total_bin_ = 0; + double num_elements_per_row_ = 0.0f; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_ diff --git a/include/LightGBM/tree.h b/include/LightGBM/tree.h new file mode 100644 index 0000000..74101dd --- /dev/null +++ b/include/LightGBM/tree.h @@ -0,0 +1,732 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_ + +#include +#include + +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +#define kCategoricalMask (1) +#define kDefaultLeftMask (2) + +/*! +* \brief Tree model +*/ +class Tree { + public: + /*! + * \brief Constructor + * \param max_leaves The number of max leaves + * \param track_branch_features Whether to keep track of ancestors of leaf nodes + * \param is_linear Whether the tree has linear models at each leaf + */ + explicit Tree(int max_leaves, bool track_branch_features, bool is_linear); + + /*! + * \brief Constructor, from a string + * \param str Model string + * \param used_len used count of str + */ + Tree(const char* str, size_t* used_len); + + virtual ~Tree() noexcept = default; + + /*! + * \brief Performing a split on tree leaves. + * \param leaf Index of leaf to be split + * \param feature Index of feature; the converted index after removing useless features + * \param real_feature Index of feature, the original index on data + * \param threshold_bin Threshold(bin) of split + * \param threshold_double Threshold on feature value + * \param left_value Model Left child output + * \param right_value Model Right child output + * \param left_cnt Count of left child + * \param right_cnt Count of right child + * \param left_weight Weight of left child + * \param right_weight Weight of right child + * \param gain Split gain + * \param missing_type missing type + * \param default_left default direction for missing value + * \return The index of new leaf. + */ + int Split(int leaf, int feature, int real_feature, uint32_t threshold_bin, + double threshold_double, double left_value, double right_value, + int left_cnt, int right_cnt, double left_weight, double right_weight, + float gain, MissingType missing_type, bool default_left); + + /*! + * \brief Performing a split on tree leaves, with categorical feature + * \param leaf Index of leaf to be split + * \param feature Index of feature; the converted index after removing useless features + * \param real_feature Index of feature, the original index on data + * \param threshold_bin Threshold(bin) of split, use bitset to represent + * \param num_threshold_bin size of threshold_bin + * \param threshold Thresholds of real feature value, use bitset to represent + * \param num_threshold size of threshold + * \param left_value Model Left child output + * \param right_value Model Right child output + * \param left_cnt Count of left child + * \param right_cnt Count of right child + * \param left_weight Weight of left child + * \param right_weight Weight of right child + * \param gain Split gain + * \return The index of new leaf. + */ + int SplitCategorical(int leaf, int feature, int real_feature, const uint32_t* threshold_bin, int num_threshold_bin, + const uint32_t* threshold, int num_threshold, double left_value, double right_value, + int left_cnt, int right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type); + + /*! \brief Get the output of one leaf */ + inline double LeafOutput(int leaf) const { return leaf_value_[leaf]; } + + /*! \brief Set the output of one leaf */ + inline void SetLeafOutput(int leaf, double output) { + leaf_value_[leaf] = MaybeRoundToZero(output); + } + + /*! + * \brief Adding prediction value of this tree model to scores + * \param data The dataset + * \param num_data Number of total data + * \param score Will add prediction to score + */ + virtual void AddPredictionToScore(const Dataset* data, + data_size_t num_data, + double* score) const; + + /*! + * \brief Adding prediction value of this tree model to scores + * \param data The dataset + * \param used_data_indices Indices of used data + * \param num_data Number of total data + * \param score Will add prediction to score + */ + virtual void AddPredictionToScore(const Dataset* data, + const data_size_t* used_data_indices, + data_size_t num_data, double* score) const; + + /*! + * \brief Get upper bound leaf value of this tree model + */ + double GetUpperBoundValue() const; + + /*! + * \brief Get lower bound leaf value of this tree model + */ + double GetLowerBoundValue() const; + + /*! + * \brief Prediction on one record + * \param feature_values Feature value of this record + * \return Prediction result + */ + inline double Predict(const double* feature_values) const; + inline double PredictByMap(const std::unordered_map& feature_values) const; + + inline int PredictLeafIndex(const double* feature_values) const; + inline int PredictLeafIndexByMap(const std::unordered_map& feature_values) const; + + inline void PredictContrib(const double* feature_values, int num_features, double* output); + inline void PredictContribByMap(const std::unordered_map& feature_values, + int num_features, std::unordered_map* output); + + /*! \brief Get Number of leaves*/ + inline int num_leaves() const { return num_leaves_; } + + /*! \brief Get depth of specific leaf*/ + inline int leaf_depth(int leaf_idx) const { return leaf_depth_[leaf_idx]; } + + /*! \brief Get parent of specific leaf*/ + inline int leaf_parent(int leaf_idx) const {return leaf_parent_[leaf_idx]; } + + /*! \brief Get feature of specific split (original feature index)*/ + inline int split_feature(int split_idx) const { return split_feature_[split_idx]; } + + /*! \brief Get feature of specific split*/ + inline int split_feature_inner(int split_idx) const { return split_feature_inner_[split_idx]; } + + /*! \brief Get features on leaf's branch*/ + inline std::vector branch_features(int leaf) const { return branch_features_[leaf]; } + + inline double split_gain(int split_idx) const { return split_gain_[split_idx]; } + + inline double internal_value(int node_idx) const { + return internal_value_[node_idx]; + } + + inline bool IsNumericalSplit(int node_idx) const { + return !GetDecisionType(decision_type_[node_idx], kCategoricalMask); + } + + inline int left_child(int node_idx) const { return left_child_[node_idx]; } + + inline int right_child(int node_idx) const { return right_child_[node_idx]; } + + inline uint32_t threshold_in_bin(int node_idx) const { + return threshold_in_bin_[node_idx]; + } + + /*! \brief Get the number of data points that fall at or below this node*/ + inline int data_count(int node) const { return node >= 0 ? internal_count_[node] : leaf_count_[~node]; } + + /*! + * \brief Shrinkage for the tree's output + * shrinkage rate (a.k.a learning rate) is used to tune the training process + * \param rate The factor of shrinkage + */ + virtual inline void Shrinkage(double rate) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048) + for (int i = 0; i < num_leaves_ - 1; ++i) { + leaf_value_[i] = MaybeRoundToZero(leaf_value_[i] * rate); + internal_value_[i] = MaybeRoundToZero(internal_value_[i] * rate); + if (is_linear_) { + leaf_const_[i] = MaybeRoundToZero(leaf_const_[i] * rate); + for (size_t j = 0; j < leaf_coeff_[i].size(); ++j) { + leaf_coeff_[i][j] = MaybeRoundToZero(leaf_coeff_[i][j] * rate); + } + } + } + leaf_value_[num_leaves_ - 1] = + MaybeRoundToZero(leaf_value_[num_leaves_ - 1] * rate); + if (is_linear_) { + leaf_const_[num_leaves_ - 1] = MaybeRoundToZero(leaf_const_[num_leaves_ - 1] * rate); + for (size_t j = 0; j < leaf_coeff_[num_leaves_ - 1].size(); ++j) { + leaf_coeff_[num_leaves_ - 1][j] = MaybeRoundToZero(leaf_coeff_[num_leaves_ - 1][j] * rate); + } + } + shrinkage_ *= rate; + } + + inline double shrinkage() const { return shrinkage_; } + + virtual inline void AddBias(double val) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048) + for (int i = 0; i < num_leaves_ - 1; ++i) { + leaf_value_[i] = MaybeRoundToZero(leaf_value_[i] + val); + internal_value_[i] = MaybeRoundToZero(internal_value_[i] + val); + } + leaf_value_[num_leaves_ - 1] = + MaybeRoundToZero(leaf_value_[num_leaves_ - 1] + val); + if (is_linear_) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048) + for (int i = 0; i < num_leaves_ - 1; ++i) { + leaf_const_[i] = MaybeRoundToZero(leaf_const_[i] + val); + } + leaf_const_[num_leaves_ - 1] = MaybeRoundToZero(leaf_const_[num_leaves_ - 1] + val); + } + // force to 1.0 + shrinkage_ = 1.0f; + } + + virtual inline void AsConstantTree(double val, int count = 0) { + num_leaves_ = 1; + shrinkage_ = 1.0f; + leaf_value_[0] = val; + if (is_linear_) { + leaf_const_[0] = val; + } + leaf_count_[0] = count; + } + + /*! \brief Serialize this object to string*/ + std::string ToString() const; + + /*! \brief Serialize this object to json*/ + std::string ToJSON() const; + + /*! \brief Serialize linear model of tree node to json*/ + std::string LinearModelToJSON(int index) const; + + /*! \brief Serialize this object to if-else statement*/ + std::string ToIfElse(int index, bool predict_leaf_index) const; + + inline static bool IsZero(double fval) { + return (fval >= -kZeroThreshold && fval <= kZeroThreshold); + } + + inline static double MaybeRoundToZero(double fval) { + return IsZero(fval) ? 0 : fval; + } + + inline static bool GetDecisionType(int8_t decision_type, int8_t mask) { + return (decision_type & mask) > 0; + } + + inline static void SetDecisionType(int8_t* decision_type, bool input, int8_t mask) { + if (input) { + (*decision_type) |= mask; + } else { + (*decision_type) &= (127 - mask); + } + } + + inline static int8_t GetMissingType(int8_t decision_type) { + return (decision_type >> 2) & 3; + } + + inline static void SetMissingType(int8_t* decision_type, int8_t input) { + (*decision_type) &= 3; + (*decision_type) |= (input << 2); + } + + void RecomputeMaxDepth(); + + int NextLeafId() const { return num_leaves_; } + + /*! \brief Get the linear model constant term (bias) of one leaf */ + inline double LeafConst(int leaf) const { return leaf_const_[leaf]; } + + /*! \brief Get the linear model coefficients of one leaf */ + inline std::vector LeafCoeffs(int leaf) const { return leaf_coeff_[leaf]; } + + /*! \brief Get the linear model features of one leaf */ + inline std::vector LeafFeaturesInner(int leaf) const {return leaf_features_inner_[leaf]; } + + /*! \brief Get the linear model features of one leaf */ + inline std::vector LeafFeatures(int leaf) const {return leaf_features_[leaf]; } + + /*! \brief Set the linear model coefficients on one leaf */ + inline void SetLeafCoeffs(int leaf, const std::vector& output) { + leaf_coeff_[leaf].resize(output.size()); + for (size_t i = 0; i < output.size(); ++i) { + leaf_coeff_[leaf][i] = MaybeRoundToZero(output[i]); + } + } + + /*! \brief Set the linear model constant term (bias) on one leaf */ + inline void SetLeafConst(int leaf, double output) { + leaf_const_[leaf] = MaybeRoundToZero(output); + } + + /*! \brief Set the linear model features on one leaf */ + inline void SetLeafFeaturesInner(int leaf, const std::vector& features) { + leaf_features_inner_[leaf] = features; + } + + /*! \brief Set the linear model features on one leaf */ + inline void SetLeafFeatures(int leaf, const std::vector& features) { + leaf_features_[leaf] = features; + } + + inline bool is_linear() const { return is_linear_; } + + #ifdef USE_CUDA + inline bool is_cuda_tree() const { return is_cuda_tree_; } + #endif // USE_CUDA + + inline void SetIsLinear(bool is_linear) { + is_linear_ = is_linear; + } + + protected: + std::string NumericalDecisionIfElse(int node) const; + + std::string CategoricalDecisionIfElse(int node) const; + + inline int NumericalDecision(double fval, int node) const { + uint8_t missing_type = GetMissingType(decision_type_[node]); + if (std::isnan(fval) && missing_type != MissingType::NaN) { + fval = 0.0f; + } + if ((missing_type == MissingType::Zero && IsZero(fval)) + || (missing_type == MissingType::NaN && std::isnan(fval))) { + if (GetDecisionType(decision_type_[node], kDefaultLeftMask)) { + return left_child_[node]; + } else { + return right_child_[node]; + } + } + if (fval <= threshold_[node]) { + return left_child_[node]; + } else { + return right_child_[node]; + } + } + + inline int NumericalDecisionInner(uint32_t fval, int node, uint32_t default_bin, uint32_t max_bin) const { + uint8_t missing_type = GetMissingType(decision_type_[node]); + if ((missing_type == MissingType::Zero && fval == default_bin) + || (missing_type == MissingType::NaN && fval == max_bin)) { + if (GetDecisionType(decision_type_[node], kDefaultLeftMask)) { + return left_child_[node]; + } else { + return right_child_[node]; + } + } + if (fval <= threshold_in_bin_[node]) { + return left_child_[node]; + } else { + return right_child_[node]; + } + } + + inline int CategoricalDecision(double fval, int node) const { + int int_fval; + if (std::isnan(fval)) { + return right_child_[node]; + } else { + int_fval = static_cast(fval); + if (int_fval < 0) { + return right_child_[node]; + } + } + int cat_idx = static_cast(threshold_[node]); + if (Common::FindInBitset(cat_threshold_.data() + cat_boundaries_[cat_idx], + cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx], int_fval)) { + return left_child_[node]; + } + return right_child_[node]; + } + + inline int CategoricalDecisionInner(uint32_t fval, int node) const { + int cat_idx = static_cast(threshold_in_bin_[node]); + if (Common::FindInBitset(cat_threshold_inner_.data() + cat_boundaries_inner_[cat_idx], + cat_boundaries_inner_[cat_idx + 1] - cat_boundaries_inner_[cat_idx], fval)) { + return left_child_[node]; + } + return right_child_[node]; + } + + inline int Decision(double fval, int node) const { + if (GetDecisionType(decision_type_[node], kCategoricalMask)) { + return CategoricalDecision(fval, node); + } else { + return NumericalDecision(fval, node); + } + } + + inline int DecisionInner(uint32_t fval, int node, uint32_t default_bin, uint32_t max_bin) const { + if (GetDecisionType(decision_type_[node], kCategoricalMask)) { + return CategoricalDecisionInner(fval, node); + } else { + return NumericalDecisionInner(fval, node, default_bin, max_bin); + } + } + + inline void Split(int leaf, int feature, int real_feature, double left_value, double right_value, int left_cnt, int right_cnt, + double left_weight, double right_weight, float gain); + /*! + * \brief Find leaf index of which record belongs by features + * \param feature_values Feature value of this record + * \return Leaf index + */ + inline int GetLeaf(const double* feature_values) const; + inline int GetLeafByMap(const std::unordered_map& feature_values) const; + + /*! \brief Serialize one node to json*/ + std::string NodeToJSON(int index) const; + + /*! \brief Serialize one node to if-else statement*/ + std::string NodeToIfElse(int index, bool predict_leaf_index) const; + + std::string NodeToIfElseByMap(int index, bool predict_leaf_index) const; + + double ExpectedValue() const; + + /*! \brief This is used fill in leaf_depth_ after reloading a model*/ + inline void RecomputeLeafDepths(int node = 0, int depth = 0); + + /*! + * \brief Used by TreeSHAP for data we keep about our decision path + */ + struct PathElement { + int feature_index; + double zero_fraction; + double one_fraction; + + // note that pweight is included for convenience and is not tied with the other attributes, + // the pweight of the i'th path element is the permutation weight of paths with i-1 ones in them + double pweight; + + PathElement() {} + PathElement(int i, double z, double o, double w) : feature_index(i), zero_fraction(z), one_fraction(o), pweight(w) {} + }; + + /*! \brief Polynomial time algorithm for SHAP values (arXiv:1706.06060)*/ + void TreeSHAP(const double *feature_values, double *phi, + int node, int unique_depth, + PathElement *parent_unique_path, double parent_zero_fraction, + double parent_one_fraction, int parent_feature_index) const; + + void TreeSHAPByMap(const std::unordered_map& feature_values, + std::unordered_map* phi, + int node, int unique_depth, + PathElement *parent_unique_path, double parent_zero_fraction, + double parent_one_fraction, int parent_feature_index) const; + + /*! \brief Extend our decision path with a fraction of one and zero extensions for TreeSHAP*/ + static void ExtendPath(PathElement *unique_path, int unique_depth, + double zero_fraction, double one_fraction, int feature_index); + + /*! \brief Undo a previous extension of the decision path for TreeSHAP*/ + static void UnwindPath(PathElement *unique_path, int unique_depth, int path_index); + + /*! determine what the total permutation weight would be if we unwound a previous extension in the decision path*/ + static double UnwoundPathSum(const PathElement *unique_path, int unique_depth, int path_index); + + /*! \brief Number of max leaves*/ + int max_leaves_; + /*! \brief Number of current leaves*/ + int num_leaves_; + // following values used for non-leaf node + /*! \brief A non-leaf node's left child */ + std::vector left_child_; + /*! \brief A non-leaf node's right child */ + std::vector right_child_; + /*! \brief A non-leaf node's split feature */ + std::vector split_feature_inner_; + /*! \brief A non-leaf node's split feature, the original index */ + std::vector split_feature_; + /*! \brief A non-leaf node's split threshold in bin */ + std::vector threshold_in_bin_; + /*! \brief A non-leaf node's split threshold in feature value */ + std::vector threshold_; + int num_cat_; + std::vector cat_boundaries_inner_; + std::vector cat_threshold_inner_; + std::vector cat_boundaries_; + std::vector cat_threshold_; + /*! \brief Store the information for categorical feature handle and missing value handle. */ + std::vector decision_type_; + /*! \brief A non-leaf node's split gain */ + std::vector split_gain_; + // used for leaf node + /*! \brief The parent of leaf */ + std::vector leaf_parent_; + /*! \brief Output of leaves */ + std::vector leaf_value_; + /*! \brief weight of leaves */ + std::vector leaf_weight_; + /*! \brief DataCount of leaves */ + std::vector leaf_count_; + /*! \brief Output of non-leaf nodes */ + std::vector internal_value_; + /*! \brief weight of non-leaf nodes */ + std::vector internal_weight_; + /*! \brief DataCount of non-leaf nodes */ + std::vector internal_count_; + /*! \brief Depth for leaves */ + std::vector leaf_depth_; + /*! \brief whether to keep track of ancestor nodes for each leaf (only needed when feature interactions are restricted) */ + bool track_branch_features_; + /*! \brief Features on leaf's branch, original index */ + std::vector> branch_features_; + double shrinkage_; + int max_depth_; + /*! \brief Tree has linear model at each leaf */ + bool is_linear_; + /*! \brief coefficients of linear models on leaves */ + std::vector> leaf_coeff_; + /*! \brief constant term (bias) of linear models on leaves */ + std::vector leaf_const_; + /* \brief features used in leaf linear models; indexing is relative to num_total_features_ */ + std::vector> leaf_features_; + /* \brief features used in leaf linear models; indexing is relative to used_features_ */ + std::vector> leaf_features_inner_; + #ifdef USE_CUDA + /*! \brief Marks whether this tree is a CUDATree */ + bool is_cuda_tree_; + #endif // USE_CUDA +}; + +inline void Tree::Split(int leaf, int feature, int real_feature, + double left_value, double right_value, int left_cnt, int right_cnt, + double left_weight, double right_weight, float gain) { + int new_node_idx = num_leaves_ - 1; + // update parent info + int parent = leaf_parent_[leaf]; + if (parent >= 0) { + // if cur node is left child + if (left_child_[parent] == ~leaf) { + left_child_[parent] = new_node_idx; + } else { + right_child_[parent] = new_node_idx; + } + } + // add new node + split_feature_inner_[new_node_idx] = feature; + split_feature_[new_node_idx] = real_feature; + split_gain_[new_node_idx] = gain; + // add two new leaves + left_child_[new_node_idx] = ~leaf; + right_child_[new_node_idx] = ~num_leaves_; + // update new leaves + leaf_parent_[leaf] = new_node_idx; + leaf_parent_[num_leaves_] = new_node_idx; + // save current leaf value to internal node before change + internal_weight_[new_node_idx] = left_weight + right_weight; + internal_value_[new_node_idx] = leaf_value_[leaf]; + internal_count_[new_node_idx] = left_cnt + right_cnt; + leaf_value_[leaf] = std::isnan(left_value) ? 0.0f : left_value; + leaf_weight_[leaf] = left_weight; + leaf_count_[leaf] = left_cnt; + leaf_value_[num_leaves_] = std::isnan(right_value) ? 0.0f : right_value; + leaf_weight_[num_leaves_] = right_weight; + leaf_count_[num_leaves_] = right_cnt; + // update leaf depth + leaf_depth_[num_leaves_] = leaf_depth_[leaf] + 1; + leaf_depth_[leaf]++; + if (track_branch_features_) { + branch_features_[num_leaves_] = branch_features_[leaf]; + branch_features_[num_leaves_].push_back(split_feature_[new_node_idx]); + branch_features_[leaf].push_back(split_feature_[new_node_idx]); + } +} + +inline double Tree::Predict(const double* feature_values) const { + if (is_linear_) { + int leaf = (num_leaves_ > 1) ? GetLeaf(feature_values) : 0; + double output = leaf_const_[leaf]; + bool nan_found = false; + for (size_t i = 0; i < leaf_features_[leaf].size(); ++i) { + int feat_raw = leaf_features_[leaf][i]; + double feat_val = feature_values[feat_raw]; + if (std::isnan(feat_val)) { + nan_found = true; + break; + } else { + output += leaf_coeff_[leaf][i] * feat_val; + } + } + if (nan_found) { + return LeafOutput(leaf); + } else { + return output; + } + } else { + if (num_leaves_ > 1) { + int leaf = GetLeaf(feature_values); + return LeafOutput(leaf); + } else { + return leaf_value_[0]; + } + } +} + +inline double Tree::PredictByMap(const std::unordered_map& feature_values) const { + if (is_linear_) { + int leaf = (num_leaves_ > 1) ? GetLeafByMap(feature_values) : 0; + double output = leaf_const_[leaf]; + bool nan_found = false; + for (size_t i = 0; i < leaf_features_[leaf].size(); ++i) { + int feat = leaf_features_[leaf][i]; + auto val_it = feature_values.find(feat); + if (val_it != feature_values.end()) { + double feat_val = val_it->second; + if (std::isnan(feat_val)) { + nan_found = true; + break; + } else { + output += leaf_coeff_[leaf][i] * feat_val; + } + } + } + if (nan_found) { + return LeafOutput(leaf); + } else { + return output; + } + } else { + if (num_leaves_ > 1) { + int leaf = GetLeafByMap(feature_values); + return LeafOutput(leaf); + } else { + return leaf_value_[0]; + } + } +} + +inline int Tree::PredictLeafIndex(const double* feature_values) const { + if (num_leaves_ > 1) { + int leaf = GetLeaf(feature_values); + return leaf; + } else { + return 0; + } +} + +inline int Tree::PredictLeafIndexByMap(const std::unordered_map& feature_values) const { + if (num_leaves_ > 1) { + int leaf = GetLeafByMap(feature_values); + return leaf; + } else { + return 0; + } +} + +inline void Tree::PredictContrib(const double* feature_values, int num_features, double* output) { + output[num_features] += ExpectedValue(); + // Run the recursion with preallocated space for the unique path data + if (num_leaves_ > 1) { + CHECK_GE(max_depth_, 0); + const int max_path_len = max_depth_ + 1; + std::vector unique_path_data(max_path_len*(max_path_len + 1) / 2); + TreeSHAP(feature_values, output, 0, 0, unique_path_data.data(), 1, 1, -1); + } +} + +inline void Tree::PredictContribByMap(const std::unordered_map& feature_values, + int num_features, std::unordered_map* output) { + (*output)[num_features] += ExpectedValue(); + // Run the recursion with preallocated space for the unique path data + if (num_leaves_ > 1) { + CHECK_GE(max_depth_, 0); + const int max_path_len = max_depth_ + 1; + std::vector unique_path_data(max_path_len*(max_path_len + 1) / 2); + TreeSHAPByMap(feature_values, output, 0, 0, unique_path_data.data(), 1, 1, -1); + } +} + +inline void Tree::RecomputeLeafDepths(int node, int depth) { + if (node == 0) leaf_depth_.resize(num_leaves()); + if (node < 0) { + leaf_depth_[~node] = depth; + } else { + RecomputeLeafDepths(left_child_[node], depth + 1); + RecomputeLeafDepths(right_child_[node], depth + 1); + } +} + +inline int Tree::GetLeaf(const double* feature_values) const { + int node = 0; + if (num_cat_ > 0) { + while (node >= 0) { + node = Decision(feature_values[split_feature_[node]], node); + } + } else { + while (node >= 0) { + node = NumericalDecision(feature_values[split_feature_[node]], node); + } + } + return ~node; +} + +inline int Tree::GetLeafByMap(const std::unordered_map& feature_values) const { + int node = 0; + if (num_cat_ > 0) { + while (node >= 0) { + node = Decision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node); + } + } else { + while (node >= 0) { + node = NumericalDecision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node); + } + } + return ~node; +} + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_ diff --git a/include/LightGBM/tree_learner.h b/include/LightGBM/tree_learner.h new file mode 100644 index 0000000..8deb67b --- /dev/null +++ b/include/LightGBM/tree_learner.h @@ -0,0 +1,119 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_ + +#include +#include +#include + +#include +#include + +namespace LightGBM { + +using json11_internal_lightgbm::Json; + +/*! \brief forward declaration */ +class Tree; +class Dataset; +class ObjectiveFunction; + +/*! +* \brief Interface for tree learner +*/ +class TreeLearner { + public: + /*! \brief virtual destructor */ + virtual ~TreeLearner() {} + + /*! + * \brief Initialize tree learner with training dataset + * \param train_data The used training data + * \param is_constant_hessian True if all hessians share the same value + */ + virtual void Init(const Dataset* train_data, bool is_constant_hessian) = 0; + + /*! Initialise some temporary storage, only needed for the linear tree; needs to be a method of TreeLearner since we call it in GBDT::RefitTree */ + virtual void InitLinear(const Dataset* /*train_data*/, const int /*max_leaves*/) {} + + virtual void ResetIsConstantHessian(bool is_constant_hessian) = 0; + + virtual void ResetTrainingData(const Dataset* train_data, + bool is_constant_hessian) = 0; + + /*! + * \brief Reset tree configs + * \param config config of tree + */ + virtual void ResetConfig(const Config* config) = 0; + + /*! + * \brief Reset boosting_on_gpu_ + * \param boosting_on_gpu flag for boosting on GPU + */ + virtual void ResetBoostingOnGPU(const bool /*boosting_on_gpu*/) {} + + virtual void SetForcedSplit(const Json* forced_split_json) = 0; + + /*! + * \brief training tree model on dataset + * \param gradients The first order gradients + * \param hessians The second order gradients + * \param is_first_tree If linear tree learning is enabled, first tree needs to be handled differently + * \return A trained tree + */ + virtual Tree* Train(const score_t* gradients, const score_t* hessians, bool is_first_tree) = 0; + + /*! + * \brief use an existing tree to fit the new gradients and hessians. + */ + virtual Tree* FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const = 0; + + virtual Tree* FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t* hessians) const = 0; + + /*! + * \brief Set bagging data + * \param subset subset of bagging + * \param used_indices Used data indices + * \param num_data Number of used data + */ + virtual void SetBaggingData(const Dataset* subset, + const data_size_t* used_indices, + data_size_t num_data) = 0; + + /*! + * \brief Using last trained tree to predict score then adding to out_score; + * \param out_score output score + */ + virtual void AddPredictionToScore(const Tree* tree, double* out_score) const = 0; + + virtual void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function residual_getter, + data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* train_score) const = 0; + + TreeLearner() = default; + /*! \brief Disable copy */ + TreeLearner& operator=(const TreeLearner&) = delete; + /*! \brief Disable copy */ + TreeLearner(const TreeLearner&) = delete; + + /*! + * \brief Create object of tree learner + * \param learner_type Type of tree learner + * \param device_type Type of tree learner + * \param booster_type Type of boosting + * \param config config of tree + */ + static TreeLearner* CreateTreeLearner(const std::string& learner_type, + const std::string& device_type, + const Config* config, + const bool boosting_on_cuda); +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_ diff --git a/include/LightGBM/utils/array_args.h b/include/LightGBM/utils/array_args.h new file mode 100644 index 0000000..406ac46 --- /dev/null +++ b/include/LightGBM/utils/array_args.h @@ -0,0 +1,200 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_ + +#include +#include + +#include +#include +#include + +namespace LightGBM { + +/*! +* \brief Contains some operation for an array, e.g. ArgMax, TopK. +*/ +template +class ArrayArgs { + public: + inline static size_t ArgMaxMT(const std::vector& array) { + int num_threads = OMP_NUM_THREADS(); + std::vector arg_maxs(num_threads, 0); + int n_blocks = Threading::For( + 0, array.size(), 1024, + [&array, &arg_maxs](int i, size_t start, size_t end) { + size_t arg_max = start; + for (size_t j = start + 1; j < end; ++j) { + if (array[j] > array[arg_max]) { + arg_max = j; + } + } + arg_maxs[i] = arg_max; + }); + size_t ret = arg_maxs[0]; + for (int i = 1; i < n_blocks; ++i) { + if (array[arg_maxs[i]] > array[ret]) { + ret = arg_maxs[i]; + } + } + return ret; + } + inline static size_t ArgMax(const std::vector& array) { + if (array.empty()) { + return 0; + } + if (array.size() > 1024) { + return ArgMaxMT(array); + } else { + size_t arg_max = 0; + for (size_t i = 1; i < array.size(); ++i) { + if (array[i] > array[arg_max]) { + arg_max = i; + } + } + return arg_max; + } + } + + inline static size_t ArgMin(const std::vector& array) { + if (array.empty()) { + return 0; + } + size_t arg_min = 0; + for (size_t i = 1; i < array.size(); ++i) { + if (array[i] < array[arg_min]) { + arg_min = i; + } + } + return arg_min; + } + + inline static size_t ArgMax(const VAL_T* array, size_t n) { + if (n <= 0) { + return 0; + } + size_t arg_max = 0; + for (size_t i = 1; i < n; ++i) { + if (array[i] > array[arg_max]) { + arg_max = i; + } + } + return arg_max; + } + + inline static size_t ArgMin(const VAL_T* array, size_t n) { + if (n <= 0) { + return 0; + } + size_t arg_min = 0; + for (size_t i = 1; i < n; ++i) { + if (array[i] < array[arg_min]) { + arg_min = i; + } + } + return arg_min; + } + + inline static void Partition(std::vector* arr, int start, int end, int* l, int* r) { + int i = start - 1; + int j = end - 1; + int p = i; + int q = j; + if (start >= end - 1) { + *l = start - 1; + *r = end; + return; + } + std::vector& ref = *arr; + VAL_T v = ref[end - 1]; + for (;;) { + while (ref[++i] > v) {} + while (v > ref[--j]) { + if (j == start) { + break; + } + } + if (i >= j) { + break; + } + std::swap(ref[i], ref[j]); + if (ref[i] == v) { + p++; + std::swap(ref[p], ref[i]); + } + if (v == ref[j]) { + q--; + std::swap(ref[j], ref[q]); + } + } + std::swap(ref[i], ref[end - 1]); + j = i - 1; + i = i + 1; + for (int k = start; k <= p; k++, j--) { + std::swap(ref[k], ref[j]); + } + for (int k = end - 2; k >= q; k--, i++) { + std::swap(ref[i], ref[k]); + } + *l = j; + *r = i; + } + + // Note: k refer to index here. e.g. k=0 means get the max number. + inline static int ArgMaxAtK(std::vector* arr, int start, int end, int k) { + if (start >= end - 1) { + return start; + } + int l = start; + int r = end - 1; + Partition(arr, start, end, &l, &r); + // if find or all elements are the same. + if ((k > l && k < r) || (l == start - 1 && r == end - 1)) { + return k; + } else if (k <= l) { + return ArgMaxAtK(arr, start, l + 1, k); + } else { + return ArgMaxAtK(arr, r, end, k); + } + } + + // Note: k is 1-based here. e.g. k=3 means get the top-3 numbers. + inline static void MaxK(const std::vector& array, int k, std::vector* out) { + out->clear(); + if (k <= 0) { + return; + } + for (auto val : array) { + out->push_back(val); + } + if (static_cast(k) >= array.size()) { + return; + } + ArgMaxAtK(out, 0, static_cast(out->size()), k - 1); + out->erase(out->begin() + k, out->end()); + } + + inline static void Assign(std::vector* array, VAL_T t, size_t n) { + array->resize(n); + for (size_t i = 0; i < array->size(); ++i) { + (*array)[i] = t; + } + } + + inline static bool CheckAll(const std::vector& array, VAL_T t) { + for (size_t i = 0; i < array.size(); ++i) { + if (array[i] != t) { + return false; + } + } + return true; + } +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_ diff --git a/include/LightGBM/utils/binary_writer.h b/include/LightGBM/utils/binary_writer.h new file mode 100644 index 0000000..eca9920 --- /dev/null +++ b/include/LightGBM/utils/binary_writer.h @@ -0,0 +1,59 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_ + +#include +#include + +namespace LightGBM { + +/*! + * \brief An interface for serializing binary data to a buffer + */ +struct BinaryWriter { + /*! + * \brief Append data to this binary target + * \param data Buffer to write from + * \param bytes Number of bytes to write from buffer + * \return Number of bytes written + */ + virtual size_t Write(const void* data, size_t bytes) = 0; + + /*! + * \brief Append data to this binary target aligned on a given byte size boundary + * \param data Buffer to write from + * \param bytes Number of bytes to write from buffer + * \param alignment The size of bytes to align to in whole increments + * \return Number of bytes written + */ + size_t AlignedWrite(const void* data, size_t bytes, size_t alignment = 8) { + auto ret = Write(data, bytes); + if (bytes % alignment != 0) { + size_t padding = AlignedSize(bytes, alignment) - bytes; + std::vector tmp(padding, 0); + ret += Write(tmp.data(), padding); + } + return ret; + } + + /*! + * \brief The aligned size of a buffer length. + * \param bytes The number of bytes in a buffer + * \param alignment The size of bytes to align to in whole increments + * \return Number of aligned bytes + */ + static size_t AlignedSize(size_t bytes, size_t alignment = 8) { + if (bytes % alignment == 0) { + return bytes; + } else { + return bytes / alignment * alignment + alignment; + } + } +}; +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_ diff --git a/include/LightGBM/utils/byte_buffer.h b/include/LightGBM/utils/byte_buffer.h new file mode 100644 index 0000000..94c0f19 --- /dev/null +++ b/include/LightGBM/utils/byte_buffer.h @@ -0,0 +1,63 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_ + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +/*! + * \brief An implementation for serializing binary data to an auto-expanding memory buffer + */ +struct ByteBuffer final : public BinaryWriter { + ByteBuffer() {} + + explicit ByteBuffer(size_t initial_size) { + buffer_.reserve(initial_size); + } + + size_t Write(const void* data, size_t bytes) { + const char* mem_ptr = static_cast(data); + for (size_t i = 0; i < bytes; ++i) { + buffer_.push_back(mem_ptr[i]); + } + + return bytes; + } + + LIGHTGBM_EXPORT void Reserve(size_t capacity) { + buffer_.reserve(capacity); + } + + LIGHTGBM_EXPORT size_t GetSize() const { + return buffer_.size(); + } + + LIGHTGBM_EXPORT char GetAt(size_t index) const { + return buffer_.at(index); + } + + LIGHTGBM_EXPORT char* Data() { + return buffer_.data(); + } + + private: + std::vector buffer_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_ diff --git a/include/LightGBM/utils/chunked_array.hpp b/include/LightGBM/utils/chunked_array.hpp new file mode 100644 index 0000000..a2b8b46 --- /dev/null +++ b/include/LightGBM/utils/chunked_array.hpp @@ -0,0 +1,261 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Alberto Ferreira + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_CHUNKED_ARRAY_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_CHUNKED_ARRAY_HPP_ + +#include + +#include + +#include +#include +#include + + +namespace LightGBM { + +/** + * Container that manages a dynamic array of fixed-length chunks. + * + * The class also takes care of allocation & release of the underlying + * memory. It can be used with either a high or low-level API. + * + * The high-level API allocates chunks as needed, manages addresses automatically and keeps + * track of number of inserted elements, but is not thread-safe (this is ok as usually input is a streaming iterator). + * For parallel input sources the low-level API must be used. + * + * Note: When using this for `LGBM_DatasetCreateFromMats` use a + * chunk_size multiple of #num_cols for your dataset, so each chunk + * contains "complete" instances. + * + * === High-level insert API intro === + * + * The easiest way to use is: + * 0. ChunkedArray(chunk_size) # Choose appropriate size + * 1. add(value) # as many times as you want (will generate chunks as needed) + * 2. data() or void_data() # retrieves a T** or void** pointer (useful for `LGBM_DatasetCreateFromMats`). + * + * Useful query methods (all O(1)): + * - get_add_count() # total count of added elements. + * - get_chunks_count() # how many chunks are currently allocated. + * - get_current_chunk_added_count() # for the last add() chunk, how many items there are. + * - get_chunk_size() # get constant chunk_size from constructor call. + * + * With those you can generate int32_t sizes[]. Last chunk can be smaller than chunk_size, so, for any i: + * - sizes[i +class ChunkedArray { + public: + explicit ChunkedArray(size_t chunk_size) + : _chunk_size(chunk_size), _last_chunk_idx(0), _last_idx_in_last_chunk(0) { + if (chunk_size == 0) { + Log::Fatal("ChunkedArray chunk size must be larger than 0!"); + } + new_chunk(); + } + + ~ChunkedArray() { + release(); + } + + /** + * Adds a value to the chunks sequentially. + * If the last chunk is full it creates a new one and appends to it. + * + * @param value value to insert. + */ + void add(T value) { + if (!within_bounds(_last_chunk_idx, _last_idx_in_last_chunk)) { + new_chunk(); + ++_last_chunk_idx; + _last_idx_in_last_chunk = 0; + } + + CHECK_EQ(setitem(_last_chunk_idx, _last_idx_in_last_chunk, value), 0); + ++_last_idx_in_last_chunk; + } + + /** + * @return Number of add() calls. + */ + size_t get_add_count() const { + return _last_chunk_idx * _chunk_size + _last_idx_in_last_chunk; + } + + /** + * @return Number of allocated chunks. + */ + size_t get_chunks_count() const { + return _chunks.size(); + } + + /** + * @return Number of elemends add()'ed in the last chunk. + */ + size_t get_last_chunk_add_count() const { + return _last_idx_in_last_chunk; + } + + /** + * Getter for the chunk size set at the constructor. + * + * @return Return the size of chunks. + */ + size_t get_chunk_size() const { + return _chunk_size; + } + + /** + * Returns the pointer to the raw chunks data. + * + * @return T** pointer to raw data. + */ + T **data() noexcept { + return _chunks.data(); + } + + /** + * Returns the pointer to the raw chunks data, but cast to void**. + * This is so ``LGBM_DatasetCreateFromMats`` accepts it. + * + * @return void** pointer to raw data. + */ + void **data_as_void() noexcept { + return reinterpret_cast(_chunks.data()); + } + + /** + * Coalesces (copies chunked data) to a contiguous array of the same type. + * It assumes that ``other`` has enough space to receive that data. + * + * @param other array with elements T of size >= this->get_add_count(). + * @param all_valid_addresses + * If true exports values from all valid addresses independently of add() count. + * Otherwise, exports only up to `get_add_count()` addresses. + */ + void coalesce_to(T *other, bool all_valid_addresses = false) const { + const size_t full_chunks = this->get_chunks_count() - 1; + + // Copy full chunks: + size_t i = 0; + for (size_t chunk = 0; chunk < full_chunks; ++chunk) { + T* chunk_ptr = _chunks[chunk]; + for (size_t in_chunk_idx = 0; in_chunk_idx < _chunk_size; ++in_chunk_idx) { + other[i++] = chunk_ptr[in_chunk_idx]; + } + } + // Copy filled values from last chunk only: + const size_t last_chunk_elems_to_copy = all_valid_addresses ? _chunk_size : this->get_last_chunk_add_count(); + T* chunk_ptr = _chunks[full_chunks]; + for (size_t in_chunk_idx = 0; in_chunk_idx < last_chunk_elems_to_copy; ++in_chunk_idx) { + other[i++] = chunk_ptr[in_chunk_idx]; + } + } + + /** + * Return value from array of chunks. + * + * @param chunk_index index of the chunk + * @param index_within_chunk index within chunk + * @param on_fail_value sentinel value. If out of bounds returns that value. + * + * @return pointer or nullptr if index is out of bounds. + */ + T getitem(size_t chunk_index, size_t index_within_chunk, T on_fail_value) const noexcept { + if (within_bounds(chunk_index, index_within_chunk)) + return _chunks[chunk_index][index_within_chunk]; + else + return on_fail_value; + } + + /** + * Sets the value at a specific address in one of the chunks. + * + * @param chunk_index index of the chunk + * @param index_within_chunk index within chunk + * @param value value to store + * + * @return 0 = success, -1 = out of bounds access. + */ + int setitem(size_t chunk_index, size_t index_within_chunk, T value) noexcept { + if (within_bounds(chunk_index, index_within_chunk)) { + _chunks[chunk_index][index_within_chunk] = value; + return 0; + } else { + return -1; + } + } + + /** + * To reset storage call this. + * Will release existing resources and prepare for reuse. + */ + void clear() noexcept { + release(); + new_chunk(); + } + + /** + * Deletes all the allocated chunks. + * Do not use container after this! See ``clear()`` instead. + */ + void release() noexcept { + std::for_each(_chunks.begin(), _chunks.end(), [](T* c) { delete[] c; }); + _chunks.clear(); + _chunks.shrink_to_fit(); + _last_chunk_idx = 0; + _last_idx_in_last_chunk = 0; + } + + /** + * As the array is dynamic, checks whether a given address is currently within bounds. + * + * @param chunk_index index of the chunk + * @param index_within_chunk index within that chunk + * @return true if that chunk is already allocated and index_within_chunk < chunk size. + */ + inline bool within_bounds(size_t chunk_index, size_t index_within_chunk) const { + return (chunk_index < _chunks.size()) && (index_within_chunk < _chunk_size); + } + + /** + * Adds a new chunk to the array of chunks. Not thread-safe. + */ + void new_chunk() { + _chunks.push_back(new (std::nothrow) T[_chunk_size]); + + // Check memory allocation success: + if (!_chunks[_chunks.size() - 1]) { + release(); + Log::Fatal("Memory exhausted! Cannot allocate new ChunkedArray chunk."); + } + } + + private: + const size_t _chunk_size; + std::vector _chunks; + + // For the add() interface & some of the get_*() queries: + size_t _last_chunk_idx; // +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define FMT_HEADER_ONLY +#include "fast_double_parser.h" +#include "fmt/format.h" + +#ifdef _MSC_VER +#include +#pragma intrinsic(_BitScanReverse) +#endif + +#if defined(_MSC_VER) +#include +#elif MM_MALLOC +#include +// https://gcc.gnu.org/onlinedocs/cpp/Common-Predefined-Macros.html +// https://www.oreilly.com/library/view/mac-os-x/0596003560/ch05s01s02.html +#elif defined(__GNUC__) && defined(HAVE_MALLOC_H) + #include + #define _mm_malloc(a, b) memalign(b, a) + #define _mm_free(a) free(a) +#else +#include +#define _mm_malloc(a, b) malloc(a) +#define _mm_free(a) free(a) +#endif + +namespace LightGBM { + +namespace Common { + +using json11_internal_lightgbm::Json; + +/*! +* Imbues the stream with the C locale. +*/ +static void C_stringstream(std::stringstream &ss) { + ss.imbue(std::locale::classic()); +} + +inline static std::string Trim(std::string str) { + if (str.empty()) { + return str; + } + str.erase(str.find_last_not_of(" \f\n\r\t\v") + 1); + str.erase(0, str.find_first_not_of(" \f\n\r\t\v")); + return str; +} + +inline static std::string RemoveQuotationSymbol(std::string str) { + if (str.empty()) { + return str; + } + str.erase(str.find_last_not_of("'\"") + 1); + str.erase(0, str.find_first_not_of("'\"")); + return str; +} + +inline static bool StartsWith(const std::string& str, const std::string prefix) { + if (str.substr(0, prefix.size()) == prefix) { + return true; + } else { + return false; + } +} + +inline static std::vector Split(const char* c_str, char delimiter) { + std::vector ret; + std::string str(c_str); + size_t i = 0; + size_t pos = 0; + while (pos < str.length()) { + if (str[pos] == delimiter) { + if (i < pos) { + ret.push_back(str.substr(i, pos - i)); + } + ++pos; + i = pos; + } else { + ++pos; + } + } + if (i < pos) { + ret.push_back(str.substr(i)); + } + return ret; +} + +inline static std::vector SplitBrackets(const char* c_str, char left_delimiter, char right_delimiter) { + std::vector ret; + std::string str(c_str); + size_t i = 0; + size_t pos = 0; + bool open = false; + while (pos < str.length()) { + if (str[pos] == left_delimiter) { + open = true; + ++pos; + i = pos; + } else if (str[pos] == right_delimiter && open) { + if (i < pos) { + ret.push_back(str.substr(i, pos - i)); + } + open = false; + ++pos; + } else { + ++pos; + } + } + return ret; +} + +inline static std::vector SplitLines(const char* c_str) { + std::vector ret; + std::string str(c_str); + size_t i = 0; + size_t pos = 0; + while (pos < str.length()) { + if (str[pos] == '\n' || str[pos] == '\r') { + if (i < pos) { + ret.push_back(str.substr(i, pos - i)); + } + // skip the line endings + while (str[pos] == '\n' || str[pos] == '\r') ++pos; + // new begin + i = pos; + } else { + ++pos; + } + } + if (i < pos) { + ret.push_back(str.substr(i)); + } + return ret; +} + +inline static std::vector Split(const char* c_str, const char* delimiters) { + std::vector ret; + std::string str(c_str); + size_t i = 0; + size_t pos = 0; + while (pos < str.length()) { + bool met_delimiters = false; + for (int j = 0; delimiters[j] != '\0'; ++j) { + if (str[pos] == delimiters[j]) { + met_delimiters = true; + break; + } + } + if (met_delimiters) { + if (i < pos) { + ret.push_back(str.substr(i, pos - i)); + } + ++pos; + i = pos; + } else { + ++pos; + } + } + if (i < pos) { + ret.push_back(str.substr(i)); + } + return ret; +} + +inline static std::string GetFromParserConfig(std::string config_str, std::string key) { + // parser config should follow json format. + std::string err; + Json config_json = Json::parse(config_str, &err); + if (!err.empty()) { + Log::Fatal("Invalid parser config: %s. Please check if follow json format.", err.c_str()); + } + return config_json[key].string_value(); +} + +inline static std::string SaveToParserConfig(std::string config_str, std::string key, std::string value) { + std::string err; + Json config_json = Json::parse(config_str, &err); + if (!err.empty()) { + Log::Fatal("Invalid parser config: %s. Please check if follow json format.", err.c_str()); + } + CHECK(config_json.is_object()); + std::map config_map = config_json.object_items(); + config_map.insert(std::pair(key, Json(value))); + return Json(config_map).dump(); +} + +template +inline static const char* Atoi(const char* p, T* out) { + int sign; + T value; + while (*p == ' ') { + ++p; + } + sign = 1; + if (*p == '-') { + sign = -1; + ++p; + } else if (*p == '+') { + ++p; + } + for (value = 0; *p >= '0' && *p <= '9'; ++p) { + value = value * 10 + (*p - '0'); + } + *out = static_cast(sign * value); + while (*p == ' ') { + ++p; + } + return p; +} + +template +inline static double Pow(T base, int power) { + if (power < 0) { + return 1.0 / Pow(base, -power); + } else if (power == 0) { + return 1; + } else if (power % 2 == 0) { + return Pow(base*base, power / 2); + } else if (power % 3 == 0) { + return Pow(base*base*base, power / 3); + } else { + return base * Pow(base, power - 1); + } +} + +inline static const char* Atof(const char* p, double* out) { + int frac; + double sign, value, scale; + *out = NAN; + // Skip leading white space, if any. + while (*p == ' ') { + ++p; + } + // Get sign, if any. + sign = 1.0; + if (*p == '-') { + sign = -1.0; + ++p; + } else if (*p == '+') { + ++p; + } + + // is a number + if ((*p >= '0' && *p <= '9') || *p == '.' || *p == 'e' || *p == 'E') { + // Get digits before decimal point or exponent, if any. + for (value = 0.0; *p >= '0' && *p <= '9'; ++p) { + value = value * 10.0 + (*p - '0'); + } + + // Get digits after decimal point, if any. + if (*p == '.') { + double right = 0.0; + int nn = 0; + ++p; + while (*p >= '0' && *p <= '9') { + right = (*p - '0') + right * 10.0; + ++nn; + ++p; + } + value += right / Pow(10.0, nn); + } + + // Handle exponent, if any. + frac = 0; + scale = 1.0; + if ((*p == 'e') || (*p == 'E')) { + uint32_t expon; + // Get sign of exponent, if any. + ++p; + if (*p == '-') { + frac = 1; + ++p; + } else if (*p == '+') { + ++p; + } + // Get digits of exponent, if any. + for (expon = 0; *p >= '0' && *p <= '9'; ++p) { + expon = expon * 10 + (*p - '0'); + } + if (expon > 308) expon = 308; + // Calculate scaling factor. + while (expon >= 50) { + scale *= 1E50; + expon -= 50; + } + while (expon >= 8) { + scale *= 1E8; + expon -= 8; + } + while (expon > 0) { + scale *= 10.0; + expon -= 1; + } + } + // Return signed and scaled floating point result. + *out = sign * (frac ? (value / scale) : (value * scale)); + } else { + size_t cnt = 0; + while (*(p + cnt) != '\0' && *(p + cnt) != ' ' + && *(p + cnt) != '\t' && *(p + cnt) != ',' + && *(p + cnt) != '\n' && *(p + cnt) != '\r' + && *(p + cnt) != ':') { + ++cnt; + } + if (cnt > 0) { + std::string tmp_str(p, cnt); + std::transform(tmp_str.begin(), tmp_str.end(), tmp_str.begin(), [](unsigned char c){ return std::tolower(c); }); + if (tmp_str == std::string("na") || tmp_str == std::string("nan") || + tmp_str == std::string("null")) { + *out = NAN; + } else if (tmp_str == std::string("inf") || tmp_str == std::string("infinity")) { + *out = sign * 1e308; + } else { + Log::Fatal("Unknown token %s in data file", tmp_str.c_str()); + } + p += cnt; + } + } + + while (*p == ' ') { + ++p; + } + + return p; +} + +// Use fast_double_parse and strtod (if parse failed) to parse double. +inline static const char* AtofPrecise(const char* p, double* out) { + const char* end = fast_double_parser::parse_number(p, out); + + if (end != nullptr) { + return end; + } + + // Rare path: Not in RFC 7159 format. Possible "inf", "nan", etc. Fallback to standard library: + char* end2; + errno = 0; // This is Required before calling strtod. + *out = std::strtod(p, &end2); // strtod is locale aware. + if (end2 == p) { + Log::Fatal("no conversion to double for: %s", p); + } + if (errno == ERANGE) { + Log::Warning("convert to double got underflow or overflow: %s", p); + } + return end2; +} + +inline static bool AtoiAndCheck(const char* p, int* out) { + const char* after = Atoi(p, out); + if (*after != '\0') { + return false; + } + return true; +} + +inline static bool AtofAndCheck(const char* p, double* out) { + const char* after = Atof(p, out); + if (*after != '\0') { + return false; + } + return true; +} + +inline static const char* SkipSpaceAndTab(const char* p) { + while (*p == ' ' || *p == '\t') { + ++p; + } + return p; +} + +inline static const char* SkipReturn(const char* p) { + while (*p == '\n' || *p == '\r' || *p == ' ') { + ++p; + } + return p; +} + +template +inline static std::vector ArrayCast(const std::vector& arr) { + std::vector ret(arr.size()); + for (size_t i = 0; i < arr.size(); ++i) { + ret[i] = static_cast(arr[i]); + } + return ret; +} + +template +struct __StringToTHelper { + T operator()(const std::string& str) const { + T ret = 0; + Atoi(str.c_str(), &ret); + return ret; + } +}; + +template +struct __StringToTHelper { + T operator()(const std::string& str) const { + return static_cast(std::stod(str)); + } +}; + +template +inline static std::vector StringToArray(const std::string& str, char delimiter) { + std::vector strs = Split(str.c_str(), delimiter); + std::vector ret; + ret.reserve(strs.size()); + __StringToTHelper::value> helper; + for (const auto& s : strs) { + ret.push_back(helper(s)); + } + return ret; +} + +template +inline static std::vector> StringToArrayofArrays( + const std::string& str, char left_bracket, char right_bracket, char delimiter) { + std::vector strs = SplitBrackets(str.c_str(), left_bracket, right_bracket); + std::vector> ret; + for (const auto& s : strs) { + ret.push_back(StringToArray(s, delimiter)); + } + return ret; +} + +template +inline static std::vector StringToArray(const std::string& str, int n) { + if (n == 0) { + return std::vector(); + } + std::vector strs = Split(str.c_str(), ' '); + CHECK_EQ(strs.size(), static_cast(n)); + std::vector ret; + ret.reserve(strs.size()); + __StringToTHelper::value> helper; + for (const auto& s : strs) { + ret.push_back(helper(s)); + } + return ret; +} + +template +struct __StringToTHelperFast { + const char* operator()(const char*p, T* out) const { + return Atoi(p, out); + } +}; + +template +struct __StringToTHelperFast { + const char* operator()(const char*p, T* out) const { + double tmp = 0.0f; + auto ret = Atof(p, &tmp); + *out = static_cast(tmp); + return ret; + } +}; + +template +inline static std::vector StringToArrayFast(const std::string& str, int n) { + if (n == 0) { + return std::vector(); + } + auto p_str = str.c_str(); + __StringToTHelperFast::value> helper; + std::vector ret(n); + for (int i = 0; i < n; ++i) { + p_str = helper(p_str, &ret[i]); + } + return ret; +} + +template +inline static std::string Join(const std::vector& strs, const char* delimiter, const bool force_C_locale = false) { + if (strs.empty()) { + return std::string(""); + } + std::stringstream str_buf; + if (force_C_locale) { + C_stringstream(str_buf); + } + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + str_buf << strs[0]; + for (size_t i = 1; i < strs.size(); ++i) { + str_buf << delimiter; + str_buf << strs[i]; + } + return str_buf.str(); +} + +template<> +inline std::string Join(const std::vector& strs, const char* delimiter, const bool force_C_locale) { + if (strs.empty()) { + return std::string(""); + } + std::stringstream str_buf; + if (force_C_locale) { + C_stringstream(str_buf); + } + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + str_buf << static_cast(strs[0]); + for (size_t i = 1; i < strs.size(); ++i) { + str_buf << delimiter; + str_buf << static_cast(strs[i]); + } + return str_buf.str(); +} + +template +inline static std::string Join(const std::vector& strs, size_t start, size_t end, const char* delimiter, const bool force_C_locale = false) { + if (end - start <= 0) { + return std::string(""); + } + start = std::min(start, static_cast(strs.size()) - 1); + end = std::min(end, static_cast(strs.size())); + std::stringstream str_buf; + if (force_C_locale) { + C_stringstream(str_buf); + } + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + str_buf << strs[start]; + for (size_t i = start + 1; i < end; ++i) { + str_buf << delimiter; + str_buf << strs[i]; + } + return str_buf.str(); +} + +inline static int64_t Pow2RoundUp(int64_t x) { + int64_t t = 1; + for (int i = 0; i < 64; ++i) { + if (t >= x) { + return t; + } + t <<= 1; + } + return 0; +} + +/*! + * \brief Do inplace softmax transformation on p_rec + * \param p_rec The input/output vector of the values. + */ +inline static void Softmax(std::vector* p_rec) { + std::vector &rec = *p_rec; + double wmax = rec[0]; + for (size_t i = 1; i < rec.size(); ++i) { + wmax = std::max(rec[i], wmax); + } + double wsum = 0.0f; + for (size_t i = 0; i < rec.size(); ++i) { + rec[i] = std::exp(rec[i] - wmax); + wsum += rec[i]; + } + for (size_t i = 0; i < rec.size(); ++i) { + rec[i] /= static_cast(wsum); + } +} + +inline static void Softmax(const double* input, double* output, int len) { + double wmax = input[0]; + for (int i = 1; i < len; ++i) { + wmax = std::max(input[i], wmax); + } + double wsum = 0.0f; + for (int i = 0; i < len; ++i) { + output[i] = std::exp(input[i] - wmax); + wsum += output[i]; + } + for (int i = 0; i < len; ++i) { + output[i] /= static_cast(wsum); + } +} + +template +std::vector ConstPtrInVectorWrapper(const std::vector>& input) { + std::vector ret; + for (auto t = input.begin(); t !=input.end(); ++t) { + ret.push_back(t->get()); + } + return ret; +} + +template +inline static void SortForPair(std::vector* keys, std::vector* values, size_t start, bool is_reverse = false) { + std::vector> arr; + auto& ref_key = *keys; + auto& ref_value = *values; + for (size_t i = start; i < keys->size(); ++i) { + arr.emplace_back(ref_key[i], ref_value[i]); + } + if (!is_reverse) { + std::stable_sort(arr.begin(), arr.end(), [](const std::pair& a, const std::pair& b) { + return a.first < b.first; + }); + } else { + std::stable_sort(arr.begin(), arr.end(), [](const std::pair& a, const std::pair& b) { + return a.first > b.first; + }); + } + for (size_t i = start; i < arr.size(); ++i) { + ref_key[i] = arr[i].first; + ref_value[i] = arr[i].second; + } +} + +template +inline static std::vector Vector2Ptr(std::vector>* data) { + std::vector ptr(data->size()); + auto& ref_data = *data; + for (size_t i = 0; i < data->size(); ++i) { + ptr[i] = ref_data[i].data(); + } + return ptr; +} + +template +inline static std::vector VectorSize(const std::vector>& data) { + std::vector ret(data.size()); + for (size_t i = 0; i < data.size(); ++i) { + ret[i] = static_cast(data[i].size()); + } + return ret; +} + +inline static double AvoidInf(double x) { + if (std::isnan(x)) { + return 0.0; + } else if (x >= 1e300) { + return 1e300; + } else if (x <= -1e300) { + return -1e300; + } else { + return x; + } +} + +inline static float AvoidInf(float x) { + if (std::isnan(x)) { + return 0.0f; + } else if (x >= 1e38) { + return 1e38f; + } else if (x <= -1e38) { + return -1e38f; + } else { + return x; + } +} + +template inline +static typename std::iterator_traits<_Iter>::value_type* IteratorValType(_Iter) { + return (0); +} + +template inline +static void ParallelSort(_RanIt _First, _RanIt _Last, _Pr _Pred, _VTRanIt*) { + size_t len = _Last - _First; + const size_t kMinInnerLen = 1024; + int num_threads = OMP_NUM_THREADS(); + if (len <= kMinInnerLen || num_threads <= 1) { + std::sort(_First, _Last, _Pred); + return; + } + size_t inner_size = (len + num_threads - 1) / num_threads; + inner_size = std::max(inner_size, kMinInnerLen); + num_threads = static_cast((len + inner_size - 1) / inner_size); +#pragma omp parallel for num_threads(num_threads) schedule(static, 1) + for (int i = 0; i < num_threads; ++i) { + size_t left = inner_size*i; + size_t right = left + inner_size; + right = std::min(right, len); + if (right > left) { + std::sort(_First + left, _First + right, _Pred); + } + } + // Buffer for merge. + std::vector<_VTRanIt> temp_buf(len); + _RanIt buf = temp_buf.begin(); + size_t s = inner_size; + // Recursive merge + while (s < len) { + int loop_size = static_cast((len + s * 2 - 1) / (s * 2)); + #pragma omp parallel for num_threads(num_threads) schedule(static, 1) + for (int i = 0; i < loop_size; ++i) { + size_t left = i * 2 * s; + size_t mid = left + s; + size_t right = mid + s; + right = std::min(len, right); + if (mid >= right) { + continue; + } + std::copy(_First + left, _First + mid, buf + left); + std::merge(buf + left, buf + mid, _First + mid, _First + right, _First + left, _Pred); + } + s *= 2; + } +} + +template inline +static void ParallelSort(_RanIt _First, _RanIt _Last, _Pr _Pred) { + return ParallelSort(_First, _Last, _Pred, IteratorValType(_First)); +} + +// Check that all y[] are in interval [ymin, ymax] (end points included); throws error if not +template +inline static void CheckElementsIntervalClosed(const T *y, T ymin, T ymax, int ny, const char *callername) { + auto fatal_msg = [&y, &ymin, &ymax, &callername](int i) { + std::ostringstream os; + os << "[%s]: does not tolerate element [#%i = " << y[i] << "] outside [" << ymin << ", " << ymax << "]"; + Log::Fatal(os.str().c_str(), callername, i); + }; + for (int i = 1; i < ny; i += 2) { + if (y[i - 1] < y[i]) { + if (y[i - 1] < ymin) { + fatal_msg(i - 1); + } else if (y[i] > ymax) { + fatal_msg(i); + } + } else { + if (y[i - 1] > ymax) { + fatal_msg(i - 1); + } else if (y[i] < ymin) { + fatal_msg(i); + } + } + } + if (ny & 1) { // odd + if (y[ny - 1] < ymin || y[ny - 1] > ymax) { + fatal_msg(ny - 1); + } + } +} + +// One-pass scan over array w with nw elements: find min, max and sum of elements; +// this is useful for checking weight requirements. +template +inline static void ObtainMinMaxSum(const T1 *w, int nw, T1 *mi, T1 *ma, T2 *su) { + T1 minw; + T1 maxw; + T1 sumw; + int i; + if (nw & 1) { // odd + minw = w[0]; + maxw = w[0]; + sumw = w[0]; + i = 2; + } else { // even + if (w[0] < w[1]) { + minw = w[0]; + maxw = w[1]; + } else { + minw = w[1]; + maxw = w[0]; + } + sumw = w[0] + w[1]; + i = 3; + } + for (; i < nw; i += 2) { + if (w[i - 1] < w[i]) { + minw = std::min(minw, w[i - 1]); + maxw = std::max(maxw, w[i]); + } else { + minw = std::min(minw, w[i]); + maxw = std::max(maxw, w[i - 1]); + } + sumw += w[i - 1] + w[i]; + } + if (mi != nullptr) { + *mi = minw; + } + if (ma != nullptr) { + *ma = maxw; + } + if (su != nullptr) { + *su = static_cast(sumw); + } +} + +inline static std::vector EmptyBitset(int n) { + int size = n / 32; + if (n % 32 != 0) ++size; + return std::vector(size); +} + +template +inline static void InsertBitset(std::vector* vec, const T val) { + auto& ref_v = *vec; + int i1 = val / 32; + int i2 = val % 32; + if (static_cast(vec->size()) < i1 + 1) { + vec->resize(i1 + 1, 0); + } + ref_v[i1] |= (1 << i2); +} + +template +inline static std::vector ConstructBitset(const T* vals, int n) { + std::vector ret; + for (int i = 0; i < n; ++i) { + int i1 = vals[i] / 32; + int i2 = vals[i] % 32; + if (static_cast(ret.size()) < i1 + 1) { + ret.resize(i1 + 1, 0); + } + ret[i1] |= (1 << i2); + } + return ret; +} + +template +inline static bool FindInBitset(const uint32_t* bits, int n, T pos) { + int i1 = pos / 32; + if (i1 >= n) { + return false; + } + int i2 = pos % 32; + return (bits[i1] >> i2) & 1; +} + +inline static bool CheckDoubleEqualOrdered(double a, double b) { + double upper = std::nextafter(a, INFINITY); + return b <= upper; +} + +inline static double GetDoubleUpperBound(double a) { + return std::nextafter(a, INFINITY); +} + +inline static size_t GetLine(const char* str) { + auto start = str; + while (*str != '\0' && *str != '\n' && *str != '\r') { + ++str; + } + return str - start; +} + +inline static const char* SkipNewLine(const char* str) { + if (*str == '\r') { + ++str; + } + if (*str == '\n') { + ++str; + } + return str; +} + +template +static int Sign(T x) { + return (x > T(0)) - (x < T(0)); +} + +template +static T SafeLog(T x) { + if (x > 0) { + return std::log(x); + } else { + return -INFINITY; + } +} + +inline bool CheckAllowedJSON(const std::string& s) { + unsigned char char_code; + for (auto c : s) { + char_code = static_cast(c); + if (char_code == 34 // " + || char_code == 44 // , + || char_code == 58 // : + || char_code == 91 // [ + || char_code == 93 // ] + || char_code == 123 // { + || char_code == 125 // } + ) { + return false; + } + } + return true; +} + +inline int RoundInt(double x) { + return static_cast(x + 0.5f); +} + +template +class AlignmentAllocator { + public: + typedef T value_type; + typedef std::size_t size_type; + typedef std::ptrdiff_t difference_type; + + typedef T* pointer; + typedef const T* const_pointer; + + typedef T& reference; + typedef const T& const_reference; + + inline AlignmentAllocator() throw() {} + + template + inline AlignmentAllocator(const AlignmentAllocator&) throw() {} + + inline ~AlignmentAllocator() throw() {} + + inline pointer address(reference r) { + return &r; + } + + inline const_pointer address(const_reference r) const { + return &r; + } + + inline pointer allocate(size_type n) { + return (pointer)_mm_malloc(n * sizeof(value_type), N); + } + + inline void deallocate(pointer p, size_type) { + _mm_free(p); + } + + inline void construct(pointer p, const value_type& wert) { + new (p) value_type(wert); + } + + inline void destroy(pointer p) { + p->~value_type(); + } + + inline size_type max_size() const throw() { + return size_type(-1) / sizeof(value_type); + } + + template + struct rebind { + typedef AlignmentAllocator other; + }; + + bool operator!=(const AlignmentAllocator& other) const { + return !(*this == other); + } + + // Returns true if and only if storage allocated from *this + // can be deallocated from other, and vice versa. + // Always returns true for stateless allocators. + bool operator==(const AlignmentAllocator&) const { + return true; + } +}; + +class Timer { + public: + Timer() { +#ifdef TIMETAG + int num_threads = OMP_NUM_THREADS(); + start_time_.resize(num_threads); + stats_.resize(num_threads); +#endif // TIMETAG + } + + ~Timer() { Print(); } + +#ifdef TIMETAG + void Start(const std::string& name) { + auto tid = omp_get_thread_num(); + start_time_[tid][name] = std::chrono::steady_clock::now(); + } + + void Stop(const std::string& name) { + auto cur_time = std::chrono::steady_clock::now(); + auto tid = omp_get_thread_num(); + if (stats_[tid].find(name) == stats_[tid].end()) { + stats_[tid][name] = std::chrono::duration(0); + } + stats_[tid][name] += cur_time - start_time_[tid][name]; + } + +#else + void Start(const std::string&) {} + + void Stop(const std::string&) {} +#endif // TIMETAG + + void Print() const { +#ifdef TIMETAG + std::unordered_map> + stats(stats_[0].begin(), stats_[0].end()); + for (size_t i = 1; i < stats_.size(); ++i) { + for (auto it = stats_[i].begin(); it != stats_[i].end(); ++it) { + if (stats.find(it->first) == stats.end()) { + stats[it->first] = it->second; + } else { + stats[it->first] += it->second; + } + } + } + std::map> ordered( + stats.begin(), stats.end()); + for (auto it = ordered.begin(); it != ordered.end(); ++it) { + Log::Info("%s costs:\t %f", it->first.c_str(), it->second * 1e-3); + } +#endif // TIMETAG + } +#ifdef TIMETAG + std::vector< + std::unordered_map> + start_time_; + std::vector>> + stats_; +#endif // TIMETAG +}; + +// Note: this class is not thread-safe, don't use it inside omp blocks +class FunctionTimer { + public: +#ifdef TIMETAG + FunctionTimer(const std::string& name, Timer& timer) : timer_(timer) { + timer.Start(name); + name_ = name; + } + + ~FunctionTimer() { timer_.Stop(name_); } + + private: + std::string name_; + Timer& timer_; +#else + FunctionTimer(const std::string&, Timer&) {} +#endif // TIMETAG +}; + +} // namespace Common + +extern Common::Timer global_timer; + + +/*! +* Provides locale-independent alternatives to Common's methods. +* Essential to make models robust to locale settings. +*/ +namespace CommonC { + +template +inline static std::string Join(const std::vector& strs, const char* delimiter) { + return LightGBM::Common::Join(strs, delimiter, true); +} + +template +inline static std::string Join(const std::vector& strs, size_t start, size_t end, const char* delimiter) { + return LightGBM::Common::Join(strs, start, end, delimiter, true); +} + +inline static const char* Atof(const char* p, double* out) { + return LightGBM::Common::Atof(p, out); +} + +template +struct __StringToTHelperFast { + const char* operator()(const char*p, T* out) const { + return LightGBM::Common::Atoi(p, out); + } +}; + +/*! +* \warning Beware that ``Common::Atof`` in ``__StringToTHelperFast``, +* has **less** floating point precision than ``__StringToTHelper``. +* Both versions are kept to maintain bit-for-bit the "legacy" LightGBM behaviour in terms of precision. +* Check ``StringToArrayFast`` and ``StringToArray`` for more details on this. +*/ +template +struct __StringToTHelperFast { + const char* operator()(const char*p, T* out) const { + double tmp = 0.0f; + auto ret = Atof(p, &tmp); + *out = static_cast(tmp); + return ret; + } +}; + +template +struct __StringToTHelper { + T operator()(const std::string& str) const { + T ret = 0; + LightGBM::Common::Atoi(str.c_str(), &ret); + return ret; + } +}; + +/*! +* \warning Beware that ``Common::Atof`` in ``__StringToTHelperFast``, +* has **less** floating point precision than ``__StringToTHelper``. +* Both versions are kept to maintain bit-for-bit the "legacy" LightGBM behaviour in terms of precision. +* Check ``StringToArrayFast`` and ``StringToArray`` for more details on this. +* \note It is possible that ``fast_double_parser::parse_number`` is faster than ``Common::Atof``. +*/ +template +struct __StringToTHelper { + T operator()(const std::string& str) const { + double tmp; + + const char* end = Common::AtofPrecise(str.c_str(), &tmp); + if (end == str.c_str()) { + Log::Fatal("Failed to parse double: %s", str.c_str()); + } + + return static_cast(tmp); + } +}; + + +/*! +* \warning Beware that due to internal use of ``Common::Atof`` in ``__StringToTHelperFast``, +* this method has less precision for floating point numbers than ``StringToArray``, +* which calls ``__StringToTHelper``. +* As such, ``StringToArrayFast`` and ``StringToArray`` are not equivalent! +* Both versions were kept to maintain bit-for-bit the "legacy" LightGBM behaviour in terms of precision. +*/ +template +inline static std::vector StringToArrayFast(const std::string& str, int n) { + if (n == 0) { + return std::vector(); + } + auto p_str = str.c_str(); + __StringToTHelperFast::value> helper; + std::vector ret(n); + for (int i = 0; i < n; ++i) { + p_str = helper(p_str, &ret[i]); + } + return ret; +} + +/*! +* \warning Do not replace calls to this method by ``StringToArrayFast``. +* This method is more precise for floating point numbers. +* Check ``StringToArrayFast`` for more details. +*/ +template +inline static std::vector StringToArray(const std::string& str, int n) { + if (n == 0) { + return std::vector(); + } + std::vector strs = LightGBM::Common::Split(str.c_str(), ' '); + CHECK_EQ(strs.size(), static_cast(n)); + std::vector ret; + ret.reserve(strs.size()); + __StringToTHelper::value> helper; + for (const auto& s : strs) { + ret.push_back(helper(s)); + } + return ret; +} + +/*! +* \warning Do not replace calls to this method by ``StringToArrayFast``. +* This method is more precise for floating point numbers. +* Check ``StringToArrayFast`` for more details. +*/ +template +inline static std::vector StringToArray(const std::string& str, char delimiter) { + std::vector strs = LightGBM::Common::Split(str.c_str(), delimiter); + std::vector ret; + ret.reserve(strs.size()); + __StringToTHelper::value> helper; + for (const auto& s : strs) { + ret.push_back(helper(s)); + } + return ret; +} + +/*! +* Safely formats a value onto a buffer according to a format string and null-terminates it. +* +* \note It checks that the full value was written or forcefully aborts. +* This safety check serves to prevent incorrect internal API usage. +* Correct usage will never incur in this problem: +* - The received buffer size shall be sufficient at all times for the input format string and value. +*/ +template +inline static void format_to_buf(char* buffer, const size_t buf_len, const char* format, const T value) { + auto result = fmt::format_to_n(buffer, buf_len, format, value); + if (result.size >= buf_len) { + Log::Fatal("Numerical conversion failed. Buffer is too small."); + } + buffer[result.size] = '\0'; +} + +template +struct __TToStringHelper { + void operator()(T value, char* buffer, size_t buf_len) const { + format_to_buf(buffer, buf_len, "{}", value); + } +}; + +template +struct __TToStringHelper { + void operator()(T value, char* buffer, size_t buf_len) const { + format_to_buf(buffer, buf_len, "{:g}", value); + } +}; + +template +struct __TToStringHelper { + void operator()(T value, char* buffer, size_t buf_len) const { + format_to_buf(buffer, buf_len, "{:.17g}", value); + } +}; + +/*! +* Converts an array to a string with with values separated by the space character. +* This method replaces Common's ``ArrayToString`` and ``ArrayToStringFast`` functionality +* and is locale-independent. +* +* \note If ``high_precision_output`` is set to true, +* floating point values are output with more digits of precision. +*/ +template +inline static std::string ArrayToString(const std::vector& arr, size_t n) { + if (arr.empty() || n == 0) { + return std::string(""); + } + __TToStringHelper::value, high_precision_output> helper; + const size_t buf_len = high_precision_output ? 32 : 16; + std::vector buffer(buf_len); + std::stringstream str_buf; + Common::C_stringstream(str_buf); + helper(arr[0], buffer.data(), buf_len); + str_buf << buffer.data(); + for (size_t i = 1; i < std::min(n, arr.size()); ++i) { + helper(arr[i], buffer.data(), buf_len); + str_buf << ' ' << buffer.data(); + } + return str_buf.str(); +} + + +} // namespace CommonC + + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_COMMON_H_ diff --git a/include/LightGBM/utils/file_io.h b/include/LightGBM/utils/file_io.h new file mode 100644 index 0000000..36a5966 --- /dev/null +++ b/include/LightGBM/utils/file_io.h @@ -0,0 +1,79 @@ +/*! + * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_ + +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +/*! + * \brief An interface for writing files from buffers + */ +struct VirtualFileWriter : BinaryWriter { + virtual ~VirtualFileWriter() {} + + /*! + * \brief Initialize the writer + * \return True when the file is available for writes + */ + virtual bool Init() = 0; + + /*! + * \brief Create appropriate writer for filename + * \param filename Filename of the data + * \return File writer instance + */ + static std::unique_ptr Make(const std::string& filename); + + /*! + * \brief Check filename existence + * \param filename Filename of the data + * \return True when the file exists + */ + static bool Exists(const std::string& filename); +}; + +/** + * \brief An interface for reading files into buffers + */ +struct VirtualFileReader { + /*! + * \brief Constructor + * \param filename Filename of the data + */ + virtual ~VirtualFileReader() {} + /*! + * \brief Initialize the reader + * \return True when the file is available for read + */ + virtual bool Init() = 0; + /*! + * \brief Read data into buffer + * \param buffer Buffer to read data into + * \param bytes Number of bytes to read + * \return Number of bytes read + */ + virtual size_t Read(void* buffer, size_t bytes) const = 0; + /*! + * \brief Create appropriate reader for filename + * \param filename Filename of the data + * \return File reader instance + */ + static std::unique_ptr Make(const std::string& filename); +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_ diff --git a/include/LightGBM/utils/json11.h b/include/LightGBM/utils/json11.h new file mode 100644 index 0000000..6799605 --- /dev/null +++ b/include/LightGBM/utils/json11.h @@ -0,0 +1,226 @@ +/* Copyright (c) 2013 Dropbox, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + * THE SOFTWARE. + */ + +/* json11 + * + * json11 is a tiny JSON library for C++11, providing JSON parsing and + * serialization. + * + * The core object provided by the library is json11::Json. A Json object + * represents any JSON value: null, bool, number (int or double), string + * (std::string), array (std::vector), or object (std::map). + * + * Json objects act like values: they can be assigned, copied, moved, compared + * for equality or order, etc. There are also helper methods Json::dump, to + * serialize a Json to a string, and Json::parse (static) to parse a std::string + * as a Json object. + * + * Internally, the various types of Json object are represented by the JsonValue + * class hierarchy. + * + * A note on numbers - JSON specifies the syntax of number formatting but not + * its semantics, so some JSON implementations distinguish between integers and + * floating-point numbers, while some don't. In json11, we choose the latter. + * Because some JSON implementations (namely Javascript itself) treat all + * numbers as the same type, distinguishing the two leads to JSON that will be + * *silently* changed by a round-trip through those implementations. Dangerous! + * To avoid that risk, json11 stores all numbers as double internally, but also + * provides integer helpers. + * + * Fortunately, double-precision IEEE754 ('double') can precisely store any + * integer in the range +/-2^53, which includes every 'int' on most systems. + * (Timestamps often use int64 or long long to avoid the Y2038K problem; a + * double storing microseconds since some epoch will be exact for +/- 275 + * years.) + */ +#pragma once + +#include +#include +#include +#include +#include +#include + +namespace json11_internal_lightgbm { + +enum JsonParse { STANDARD, COMMENTS }; + +class JsonValue; + +class Json final { + public: + // Types + enum Type { NUL, NUMBER, BOOL, STRING, ARRAY, OBJECT }; + + // Array and object typedefs + typedef std::vector array; + typedef std::map object; + + // Constructors for the various types of JSON value. + Json() noexcept; // NUL + explicit Json(std::nullptr_t) noexcept; // NUL + explicit Json(double value); // NUMBER + explicit Json(int value); // NUMBER + explicit Json(bool value); // BOOL + explicit Json(const std::string &value); // STRING + explicit Json(std::string &&value); // STRING + explicit Json(const char *value); // STRING + explicit Json(const array &values); // ARRAY + explicit Json(array &&values); // ARRAY + explicit Json(const object &values); // OBJECT + explicit Json(object &&values); // OBJECT + + // Implicit constructor: anything with a to_json() function. + template + explicit Json(const T &t) : Json(t.to_json()) {} + + // Implicit constructor: map-like objects (std::map, std::unordered_map, etc) + template < + class M, + typename std::enable_if< + std::is_constructible< + std::string, decltype(std::declval().begin()->first)>::value && + std::is_constructible< + Json, decltype(std::declval().begin()->second)>::value, + int>::type = 0> + explicit Json(const M &m) : Json(object(m.begin(), m.end())) {} + + // Implicit constructor: vector-like objects (std::list, std::vector, + // std::set, etc) + template ().begin())>::value, + int>::type = 0> + explicit Json(const V &v) : Json(array(v.begin(), v.end())) {} + + // This prevents Json(some_pointer) from accidentally producing a bool. Use + // Json(bool(some_pointer)) if that behavior is desired. + explicit Json(void *) = delete; + + // Accessors + Type type() const; + + bool is_null() const { return type() == NUL; } + bool is_number() const { return type() == NUMBER; } + bool is_bool() const { return type() == BOOL; } + bool is_string() const { return type() == STRING; } + bool is_array() const { return type() == ARRAY; } + bool is_object() const { return type() == OBJECT; } + + // Return the enclosed value if this is a number, 0 otherwise. Note that + // json11 does not distinguish between integer and non-integer numbers - + // number_value() and int_value() can both be applied to a NUMBER-typed + // object. + double number_value() const; + int int_value() const; + + // Return the enclosed value if this is a boolean, false otherwise. + bool bool_value() const; + // Return the enclosed string if this is a string, "" otherwise. + const std::string &string_value() const; + // Return the enclosed std::vector if this is an array, or an empty vector + // otherwise. + const array &array_items() const; + // Return the enclosed std::map if this is an object, or an empty map + // otherwise. + const object &object_items() const; + + // Return a reference to arr[i] if this is an array, Json() otherwise. + const Json &operator[](size_t i) const; + // Return a reference to obj[key] if this is an object, Json() otherwise. + const Json &operator[](const std::string &key) const; + + // Serialize. + void dump(std::string *out) const; + std::string dump() const { + std::string out; + dump(&out); + return out; + } + + // Parse. If parse fails, return Json() and assign an error message to err. + static Json parse(const std::string &in, std::string *err, + JsonParse strategy = JsonParse::STANDARD); + static Json parse(const char *in, std::string *err, + JsonParse strategy = JsonParse::STANDARD) { + if (in) { + return parse(std::string(in), err, strategy); + } else { + *err = "null input"; + return Json(nullptr); + } + } + // Parse multiple objects, concatenated or separated by whitespace + static std::vector parse_multi( + const std::string &in, std::string::size_type *parser_stop_pos, + std::string *err, JsonParse strategy = JsonParse::STANDARD); + + static inline std::vector parse_multi( + const std::string &in, std::string *err, + JsonParse strategy = JsonParse::STANDARD) { + std::string::size_type parser_stop_pos; + return parse_multi(in, &parser_stop_pos, err, strategy); + } + + bool operator==(const Json &rhs) const; + bool operator<(const Json &rhs) const; + bool operator!=(const Json &rhs) const { return !(*this == rhs); } + bool operator<=(const Json &rhs) const { return !(rhs < *this); } + bool operator>(const Json &rhs) const { return (rhs < *this); } + bool operator>=(const Json &rhs) const { return !(*this < rhs); } + + /* has_shape(types, err) + * + * Return true if this is a JSON object and, for each item in types, has a + * field of the given type. If not, return false and set err to a descriptive + * message. + */ + typedef std::initializer_list> shape; + bool has_shape(const shape &types, std::string *err) const; + + private: + std::shared_ptr m_ptr; +}; + +// Internal class hierarchy - JsonValue objects are not exposed to users of this +// API. +class JsonValue { + protected: + friend class Json; + friend class JsonInt; + friend class JsonDouble; + virtual Json::Type type() const = 0; + virtual bool equals(const JsonValue *other) const = 0; + virtual bool less(const JsonValue *other) const = 0; + virtual void dump(std::string *out) const = 0; + virtual double number_value() const; + virtual int int_value() const; + virtual bool bool_value() const; + virtual const std::string &string_value() const; + virtual const Json::array &array_items() const; + virtual const Json &operator[](size_t i) const; + virtual const Json::object &object_items() const; + virtual const Json &operator[](const std::string &key) const; + virtual ~JsonValue() {} +}; + +} // namespace json11_internal_lightgbm diff --git a/include/LightGBM/utils/log.h b/include/LightGBM/utils/log.h new file mode 100644 index 0000000..84d1efd --- /dev/null +++ b/include/LightGBM/utils/log.h @@ -0,0 +1,186 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_ + +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef LGB_R_BUILD + +#ifndef R_NO_REMAP +#define R_NO_REMAP +#endif + +#ifndef R_USE_C99_IN_CXX +#define R_USE_C99_IN_CXX +#endif + +#include +extern "C" void R_FlushConsole(void); +#endif + +namespace LightGBM { + +#if defined(_MSC_VER) +#define THREAD_LOCAL __declspec(thread) +#else +#define THREAD_LOCAL thread_local +#endif + +#ifndef CHECK +#define CHECK(condition) \ + if (!(condition)) \ + Log::Fatal("Check failed: " #condition " at %s, line %d .\n", __FILE__, \ + __LINE__); +#endif + +#ifndef CHECK_EQ +#define CHECK_EQ(a, b) CHECK((a) == (b)) +#endif + +#ifndef CHECK_NE +#define CHECK_NE(a, b) CHECK((a) != (b)) +#endif + +#ifndef CHECK_GE +#define CHECK_GE(a, b) CHECK((a) >= (b)) +#endif + +#ifndef CHECK_LE +#define CHECK_LE(a, b) CHECK((a) <= (b)) +#endif + +#ifndef CHECK_GT +#define CHECK_GT(a, b) CHECK((a) > (b)) +#endif + +#ifndef CHECK_LT +#define CHECK_LT(a, b) CHECK((a) < (b)) +#endif + +#ifndef CHECK_NOTNULL +#define CHECK_NOTNULL(pointer) \ + if ((pointer) == nullptr) \ + LightGBM::Log::Fatal(#pointer " Can't be NULL at %s, line %d .\n", \ + __FILE__, __LINE__); +#endif + +enum class LogLevel : int { + Fatal = -1, + Warning = 0, + Info = 1, + Debug = 2, +}; + +/*! + * \brief A static Log class + */ +class Log { + public: + using Callback = void (*)(const char *); + /*! + * \brief Resets the minimal log level. It is INFO by default. + * \param level The new minimal log level. + */ + static void ResetLogLevel(LogLevel level) { GetLevel() = level; } + + static void ResetCallBack(Callback callback) { GetLogCallBack() = callback; } + + static void Debug(const char *format, ...) { + va_list val; + va_start(val, format); + Write(LogLevel::Debug, "Debug", format, val); + va_end(val); + } + static void Info(const char *format, ...) { + va_list val; + va_start(val, format); + Write(LogLevel::Info, "Info", format, val); + va_end(val); + } + static void Warning(const char *format, ...) { + va_list val; + va_start(val, format); + Write(LogLevel::Warning, "Warning", format, val); + va_end(val); + } + static void Fatal(const char *format, ...) { + va_list val; + const size_t kBufSize = 1024; + char str_buf[kBufSize]; + va_start(val, format); +#ifdef _MSC_VER + vsnprintf_s(str_buf, kBufSize, format, val); +#else + vsnprintf(str_buf, kBufSize, format, val); +#endif + va_end(val); + +// R code should write back to R's error stream, +// otherwise to stderr +#ifndef LGB_R_BUILD + fprintf(stderr, "[LightGBM] [Fatal] %s\n", str_buf); + fflush(stderr); +#else + REprintf("[LightGBM] [Fatal] %s\n", str_buf); + R_FlushConsole(); +#endif + throw std::runtime_error(std::string(str_buf)); + } + + private: + static void Write(LogLevel level, const char *level_str, const char *format, + va_list val) { + if (level <= GetLevel()) { // omit the message with low level +// R code should write back to R's output stream, +// otherwise to stdout +#ifndef LGB_R_BUILD + if (GetLogCallBack() == nullptr) { + printf("[LightGBM] [%s] ", level_str); + vprintf(format, val); + printf("\n"); + fflush(stdout); + } else { + const size_t kBufSize = 512; + char buf[kBufSize]; + snprintf(buf, kBufSize, "[LightGBM] [%s] ", level_str); + GetLogCallBack()(buf); + vsnprintf(buf, kBufSize, format, val); + GetLogCallBack()(buf); + GetLogCallBack()("\n"); + } +#else + Rprintf("[LightGBM] [%s] ", level_str); + Rvprintf(format, val); + Rprintf("\n"); + R_FlushConsole(); +#endif + } + } + + // a trick to use static variable in header file. + // May be not good, but avoid to use an additional cpp file + static LogLevel &GetLevel() { + static THREAD_LOCAL LogLevel level = LogLevel::Info; + return level; + } + + static Callback &GetLogCallBack() { + static THREAD_LOCAL Callback callback = nullptr; + return callback; + } +}; + +} // namespace LightGBM +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_ diff --git a/include/LightGBM/utils/openmp_wrapper.h b/include/LightGBM/utils/openmp_wrapper.h new file mode 100644 index 0000000..6b6286b --- /dev/null +++ b/include/LightGBM/utils/openmp_wrapper.h @@ -0,0 +1,136 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_ + +#include + +// this can only be changed by LGBM_SetMaxThreads() +LIGHTGBM_EXTERN_C int LGBM_MAX_NUM_THREADS; + +// this is modified by OMP_SET_NUM_THREADS(), for example +// by passing num_thread through params +LIGHTGBM_EXTERN_C int LGBM_DEFAULT_NUM_THREADS; + +#ifdef _OPENMP + +#include + +#include + +#include +#include +#include +#include +#include + +/* + Get number of threads to use in OpenMP parallel regions. + + By default, this will return the result of omp_get_max_threads(), + which is OpenMP-implementation dependent but generally can be controlled + by environment variable OMP_NUM_THREADS. + + ref: + - https://www.openmp.org/spec-html/5.0/openmpsu112.html + - https://gcc.gnu.org/onlinedocs/libgomp/omp_005fget_005fmax_005fthreads.html +*/ +LIGHTGBM_EXTERN_C int OMP_NUM_THREADS(); + +/* + Update the default number of threads that'll be used in OpenMP parallel + regions for LightGBM routines where the number of threads aren't directly + supplied. +*/ +LIGHTGBM_EXTERN_C void OMP_SET_NUM_THREADS(int num_threads); + +class ThreadExceptionHelper { + public: + ThreadExceptionHelper() { + ex_ptr_ = nullptr; + } + + ~ThreadExceptionHelper() { + ReThrow(); + } + void ReThrow() { + if (ex_ptr_ != nullptr) { + std::rethrow_exception(ex_ptr_); + } + } + void CaptureException() { + // only catch first exception. + if (ex_ptr_ != nullptr) { + return; + } + std::unique_lock guard(lock_); + if (ex_ptr_ != nullptr) { + return; + } + ex_ptr_ = std::current_exception(); + } + + private: + std::exception_ptr ex_ptr_; + std::mutex lock_; +}; + +#define OMP_INIT_EX() ThreadExceptionHelper omp_except_helper +#define OMP_LOOP_EX_BEGIN() try { +#define OMP_LOOP_EX_END() \ + } \ + catch (std::exception & ex) { \ + Log::Warning(ex.what()); \ + omp_except_helper.CaptureException(); \ + } \ + catch (...) { \ + omp_except_helper.CaptureException(); \ + } +#define OMP_THROW_EX() omp_except_helper.ReThrow() + +#else + +/* + * To be compatible with OpenMP, define a nothrow macro which is used by gcc + * openmp, but not by clang. + * See also https://github.com/dmlc/dmlc-core/blob/3106c1cbdcc9fc9ef3a2c1d2196a7a6f6616c13d/include/dmlc/omp.h#L14 + */ +#if defined(__clang__) +#undef __GOMP_NOTHROW +#define __GOMP_NOTHROW +#elif defined(__cplusplus) +#undef __GOMP_NOTHROW +#define __GOMP_NOTHROW throw() +#else +#undef __GOMP_NOTHROW +#define __GOMP_NOTHROW __attribute__((__nothrow__)) +#endif + +#ifdef _MSC_VER + #pragma warning(disable : 4068) // disable unknown pragma warning +#endif + +#ifdef __cplusplus + extern "C" { +#endif + /** Fall here if no OPENMP support, so just + simulate a single thread running. + All #pragma omp should be ignored by the compiler **/ + inline void OMP_SET_NUM_THREADS(int) __GOMP_NOTHROW {} + inline int omp_get_thread_num() __GOMP_NOTHROW {return 0;} + inline int OMP_NUM_THREADS() __GOMP_NOTHROW { return 1; } +#ifdef __cplusplus +} // extern "C" +#endif + +#define OMP_INIT_EX() +#define OMP_LOOP_EX_BEGIN() +#define OMP_LOOP_EX_END() +#define OMP_THROW_EX() + +#endif + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_ diff --git a/include/LightGBM/utils/pipeline_reader.h b/include/LightGBM/utils/pipeline_reader.h new file mode 100644 index 0000000..133252b --- /dev/null +++ b/include/LightGBM/utils/pipeline_reader.h @@ -0,0 +1,72 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_ + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +/*! +* \brief A pipeline file reader, use 2 threads, one read block from file, the other process the block +*/ +class PipelineReader { + public: + /*! + * \brief Read data from a file, use pipeline methods + * \param filename Filename of data + * \process_fun Process function + */ + static size_t Read(const char* filename, int skip_bytes, const std::function& process_fun) { + auto reader = VirtualFileReader::Make(filename); + if (!reader->Init()) { + return 0; + } + size_t cnt = 0; + const size_t buffer_size = 16 * 1024 * 1024; + // buffer used for the process_fun + auto buffer_process = std::vector(buffer_size); + // buffer used for the file reading + auto buffer_read = std::vector(buffer_size); + size_t read_cnt = 0; + if (skip_bytes > 0) { + // skip first k bytes + read_cnt = reader->Read(buffer_process.data(), skip_bytes); + } + // read first block + read_cnt = reader->Read(buffer_process.data(), buffer_size); + + size_t last_read_cnt = 0; + while (read_cnt > 0) { + // start read thread + std::thread read_worker = std::thread( + [=, &last_read_cnt, &reader, &buffer_read] { + last_read_cnt = reader->Read(buffer_read.data(), buffer_size); + }); + // start process + cnt += process_fun(buffer_process.data(), read_cnt); + // wait for read thread + read_worker.join(); + // exchange the buffer + std::swap(buffer_process, buffer_read); + read_cnt = last_read_cnt; + } + return cnt; + } +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_ diff --git a/include/LightGBM/utils/random.h b/include/LightGBM/utils/random.h new file mode 100644 index 0000000..c065d65 --- /dev/null +++ b/include/LightGBM/utils/random.h @@ -0,0 +1,118 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_ + +#include +#include +#include +#include + +namespace LightGBM { + +/*! +* \brief A wrapper for random generator +*/ +class Random { + public: + /*! + * \brief Constructor, with random seed + */ + Random() { + std::random_device rd; + auto generator = std::mt19937(rd()); + std::uniform_int_distribution distribution(0, x); + x = distribution(generator); + } + /*! + * \brief Constructor, with specific seed + */ + explicit Random(int seed) { + x = seed; + } + /*! + * \brief Generate random integer, int16 range. [0, 65536] + * \param lower_bound lower bound + * \param upper_bound upper bound + * \return The random integer between [lower_bound, upper_bound) + */ + inline int NextShort(int lower_bound, int upper_bound) { + return (RandInt16()) % (upper_bound - lower_bound) + lower_bound; + } + + /*! + * \brief Generate random integer, int32 range + * \param lower_bound lower bound + * \param upper_bound upper bound + * \return The random integer between [lower_bound, upper_bound) + */ + inline int NextInt(int lower_bound, int upper_bound) { + return (RandInt32()) % (upper_bound - lower_bound) + lower_bound; + } + + /*! + * \brief Generate random float data + * \return The random float between [0.0, 1.0) + */ + inline float NextFloat() { + // get random float in [0,1) + return static_cast(RandInt16()) / (32768.0f); + } + /*! + * \brief Sample K data from {0,1,...,N-1} + * \param N + * \param K + * \return K Ordered sampled data from {0,1,...,N-1} + */ + inline std::vector Sample(int N, int K) { + std::vector ret; + ret.reserve(K); + if (K > N || K <= 0) { + return ret; + } else if (K == N) { + for (int i = 0; i < N; ++i) { + ret.push_back(i); + } + } else if (K > 1 && K > (N / std::log2(K))) { + for (int i = 0; i < N; ++i) { + double prob = (K - ret.size()) / static_cast(N - i); + if (NextFloat() < prob) { + ret.push_back(i); + } + } + } else { + std::set sample_set; + for (int r = N - K; r < N; ++r) { + int v = NextInt(0, r + 1); + if (!sample_set.insert(v).second) { + sample_set.insert(r); + } + } + for (auto iter = sample_set.begin(); iter != sample_set.end(); ++iter) { + ret.push_back(*iter); + } + } + return ret; + } + + private: + inline int RandInt16() { + x = (214013 * x + 2531011); + return static_cast((x >> 16) & 0x7FFF); + } + + inline int RandInt32() { + x = (214013 * x + 2531011); + return static_cast(x & 0x7FFFFFFF); + } + + unsigned int x = 123456789; +}; + + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_ diff --git a/include/LightGBM/utils/text_reader.h b/include/LightGBM/utils/text_reader.h new file mode 100644 index 0000000..c4dd3c3 --- /dev/null +++ b/include/LightGBM/utils/text_reader.h @@ -0,0 +1,360 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_ + +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace LightGBM { + +const size_t kGbs = size_t(1024) * 1024 * 1024; + +/*! +* \brief Read text data from file +*/ +template +class TextReader { + public: + /*! + * \brief Constructor + * \param filename Filename of data + * \param is_skip_first_line True if need to skip header + */ + TextReader(const char* filename, bool is_skip_first_line, size_t progress_interval_bytes = SIZE_MAX): + filename_(filename), is_skip_first_line_(is_skip_first_line), read_progress_interval_bytes_(progress_interval_bytes) { + if (is_skip_first_line_) { + auto reader = VirtualFileReader::Make(filename); + if (!reader->Init()) { + Log::Fatal("Could not open %s", filename); + } + std::stringstream str_buf; + char read_c; + size_t nread = reader->Read(&read_c, 1); + while (nread == 1) { + if (read_c == '\n' || read_c == '\r') { + break; + } + str_buf << read_c; + ++skip_bytes_; + nread = reader->Read(&read_c, 1); + } + if (read_c == '\r') { + reader->Read(&read_c, 1); + ++skip_bytes_; + } + if (read_c == '\n') { + reader->Read(&read_c, 1); + ++skip_bytes_; + } + first_line_ = str_buf.str(); + Log::Debug("Skipped header \"%s\" in file %s", first_line_.c_str(), filename_); + } + } + /*! + * \brief Destructor + */ + ~TextReader() { + Clear(); + } + /*! + * \brief Clear cached data + */ + inline void Clear() { + lines_.clear(); + lines_.shrink_to_fit(); + } + /*! + * \brief return first line of data + */ + inline std::string first_line() { + return first_line_; + } + /*! + * \brief Get text data that read from file + * \return Text data, store in std::vector by line + */ + inline std::vector& Lines() { return lines_; } + /*! + * \brief Get joined text data that read from file + * \return Text data, store in std::string, joined all lines by delimiter + */ + inline std::string JoinedLines(std::string delimiter = "\n") { + std::stringstream ss; + for (auto line : lines_) { + ss << line << delimiter; + } + return ss.str(); + } + + INDEX_T ReadAllAndProcess(const std::function& process_fun) { + last_line_ = ""; + INDEX_T total_cnt = 0; + size_t bytes_read = 0; + PipelineReader::Read(filename_, skip_bytes_, + [&process_fun, &bytes_read, &total_cnt, this] + (const char* buffer_process, size_t read_cnt) { + size_t cnt = 0; + size_t i = 0; + size_t last_i = 0; + // skip the break between \r and \n + if (last_line_.size() == 0 && buffer_process[0] == '\n') { + i = 1; + last_i = i; + } + while (i < read_cnt) { + if (buffer_process[i] == '\n' || buffer_process[i] == '\r') { + if (last_line_.size() > 0) { + last_line_.append(buffer_process + last_i, i - last_i); + process_fun(total_cnt, last_line_.c_str(), last_line_.size()); + last_line_ = ""; + } else { + process_fun(total_cnt, buffer_process + last_i, i - last_i); + } + ++cnt; + ++i; + ++total_cnt; + // skip end of line + while ((buffer_process[i] == '\n' || buffer_process[i] == '\r') && i < read_cnt) { + ++i; + } + last_i = i; + } else { + ++i; + } + } + if (last_i != read_cnt) { + last_line_.append(buffer_process + last_i, read_cnt - last_i); + } + + size_t prev_bytes_read = bytes_read; + bytes_read += read_cnt; + if (prev_bytes_read / read_progress_interval_bytes_ < bytes_read / read_progress_interval_bytes_) { + Log::Debug("Read %.1f GBs from %s.", 1.0 * bytes_read / kGbs, filename_); + } + + return cnt; + }); + // if last line of file doesn't contain end of line + if (last_line_.size() > 0) { + Log::Info("Warning: last line of %s has no end of line, still using this line", filename_); + process_fun(total_cnt, last_line_.c_str(), last_line_.size()); + ++total_cnt; + last_line_ = ""; + } + return total_cnt; + } + + /*! + * \brief Read all text data from file in memory + * \return number of lines of text data + */ + INDEX_T ReadAllLines() { + return ReadAllAndProcess( + [=](INDEX_T, const char* buffer, size_t size) { + lines_.emplace_back(buffer, size); + }); + } + + std::vector ReadContent(size_t* out_len) { + std::vector ret; + *out_len = 0; + auto reader = VirtualFileReader::Make(filename_); + if (!reader->Init()) { + return ret; + } + const size_t buffer_size = 16 * 1024 * 1024; + auto buffer_read = std::vector(buffer_size); + size_t read_cnt = 0; + do { + read_cnt = reader->Read(buffer_read.data(), buffer_size); + ret.insert(ret.end(), buffer_read.begin(), buffer_read.begin() + read_cnt); + *out_len += read_cnt; + } while (read_cnt > 0); + return ret; + } + + INDEX_T SampleFromFile(Random* random, INDEX_T sample_cnt, std::vector* out_sampled_data) { + INDEX_T cur_sample_cnt = 0; + return ReadAllAndProcess([=, &random, &cur_sample_cnt, + &out_sampled_data] + (INDEX_T line_idx, const char* buffer, size_t size) { + if (cur_sample_cnt < sample_cnt) { + out_sampled_data->emplace_back(buffer, size); + ++cur_sample_cnt; + } else { + const size_t idx = static_cast(random->NextInt(0, static_cast(line_idx + 1))); + if (idx < static_cast(sample_cnt)) { + out_sampled_data->operator[](idx) = std::string(buffer, size); + } + } + }); + } + /*! + * \brief Read part of text data from file in memory, use filter_fun to filter data + * \param filter_fun Function that perform data filter + * \param out_used_data_indices Store line indices that read text data + * \return The number of total data + */ + INDEX_T ReadAndFilterLines(const std::function& filter_fun, std::vector* out_used_data_indices) { + out_used_data_indices->clear(); + INDEX_T total_cnt = ReadAllAndProcess( + [&filter_fun, &out_used_data_indices, this] + (INDEX_T line_idx , const char* buffer, size_t size) { + bool is_used = filter_fun(line_idx); + if (is_used) { + out_used_data_indices->push_back(line_idx); + lines_.emplace_back(buffer, size); + } + }); + return total_cnt; + } + + INDEX_T SampleAndFilterFromFile(const std::function& filter_fun, std::vector* out_used_data_indices, + Random* random, INDEX_T sample_cnt, std::vector* out_sampled_data) { + INDEX_T cur_sample_cnt = 0; + out_used_data_indices->clear(); + INDEX_T total_cnt = ReadAllAndProcess( + [=, &filter_fun, &out_used_data_indices, &random, &cur_sample_cnt, + &out_sampled_data] + (INDEX_T line_idx, const char* buffer, size_t size) { + bool is_used = filter_fun(line_idx); + if (is_used) { + out_used_data_indices->push_back(line_idx); + if (cur_sample_cnt < sample_cnt) { + out_sampled_data->emplace_back(buffer, size); + ++cur_sample_cnt; + } else { + const size_t idx = static_cast(random->NextInt(0, static_cast(out_used_data_indices->size()))); + if (idx < static_cast(sample_cnt)) { + out_sampled_data->operator[](idx) = std::string(buffer, size); + } + } + } + }); + return total_cnt; + } + + INDEX_T CountLine() { + return ReadAllAndProcess( + [=](INDEX_T, const char*, size_t) { + }); + } + + INDEX_T ReadAllAndProcessParallelWithFilter(const std::function&)>& process_fun, const std::function& filter_fun) { + last_line_ = ""; + INDEX_T total_cnt = 0; + size_t bytes_read = 0; + INDEX_T used_cnt = 0; + PipelineReader::Read(filename_, skip_bytes_, + [&process_fun, &filter_fun, &total_cnt, &bytes_read, &used_cnt, this] + (const char* buffer_process, size_t read_cnt) { + size_t cnt = 0; + size_t i = 0; + size_t last_i = 0; + INDEX_T start_idx = used_cnt; + // skip the break between \r and \n + if (last_line_.size() == 0 && buffer_process[0] == '\n') { + i = 1; + last_i = i; + } + while (i < read_cnt) { + if (buffer_process[i] == '\n' || buffer_process[i] == '\r') { + if (last_line_.size() > 0) { + last_line_.append(buffer_process + last_i, i - last_i); + if (filter_fun(used_cnt, total_cnt)) { + lines_.push_back(last_line_); + ++used_cnt; + } + last_line_ = ""; + } else { + if (filter_fun(used_cnt, total_cnt)) { + lines_.emplace_back(buffer_process + last_i, i - last_i); + ++used_cnt; + } + } + ++cnt; + ++i; + ++total_cnt; + // skip end of line + while ((buffer_process[i] == '\n' || buffer_process[i] == '\r') && i < read_cnt) { + ++i; + } + last_i = i; + } else { + ++i; + } + } + process_fun(start_idx, lines_); + lines_.clear(); + if (last_i != read_cnt) { + last_line_.append(buffer_process + last_i, read_cnt - last_i); + } + + size_t prev_bytes_read = bytes_read; + bytes_read += read_cnt; + if (prev_bytes_read / read_progress_interval_bytes_ < bytes_read / read_progress_interval_bytes_) { + Log::Debug("Read %.1f GBs from %s.", 1.0 * bytes_read / kGbs, filename_); + } + + return cnt; + }); + // if last line of file doesn't contain end of line + if (last_line_.size() > 0) { + Log::Info("Warning: last line of %s has no end of line, still using this line", filename_); + if (filter_fun(used_cnt, total_cnt)) { + lines_.push_back(last_line_); + process_fun(used_cnt, lines_); + } + lines_.clear(); + ++total_cnt; + ++used_cnt; + last_line_ = ""; + } + return total_cnt; + } + + INDEX_T ReadAllAndProcessParallel(const std::function&)>& process_fun) { + return ReadAllAndProcessParallelWithFilter(process_fun, [](INDEX_T, INDEX_T) { return true; }); + } + + INDEX_T ReadPartAndProcessParallel(const std::vector& used_data_indices, const std::function&)>& process_fun) { + return ReadAllAndProcessParallelWithFilter(process_fun, + [&used_data_indices](INDEX_T used_cnt, INDEX_T total_cnt) { + if (static_cast(used_cnt) < used_data_indices.size() && total_cnt == used_data_indices[used_cnt]) { + return true; + } else { + return false; + } + }); + } + + private: + /*! \brief Filename of text data */ + const char* filename_; + /*! \brief Cache the read text data */ + std::vector lines_; + /*! \brief Buffer for last line */ + std::string last_line_; + /*! \brief first line */ + std::string first_line_ = ""; + /*! \brief is skip first line */ + bool is_skip_first_line_ = false; + size_t read_progress_interval_bytes_; + /*! \brief is skip first line */ + int skip_bytes_ = 0; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_ diff --git a/include/LightGBM/utils/threading.h b/include/LightGBM/utils/threading.h new file mode 100644 index 0000000..5d79209 --- /dev/null +++ b/include/LightGBM/utils/threading.h @@ -0,0 +1,201 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_ + +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + +class Threading { + public: + template + static inline void BlockInfo(INDEX_T cnt, INDEX_T min_cnt_per_block, + int* out_nblock, INDEX_T* block_size) { + int num_threads = OMP_NUM_THREADS(); + BlockInfo(num_threads, cnt, min_cnt_per_block, out_nblock, + block_size); + } + + template + static inline void BlockInfo(int num_threads, INDEX_T cnt, + INDEX_T min_cnt_per_block, int* out_nblock, + INDEX_T* block_size) { + *out_nblock = std::min( + num_threads, + static_cast((cnt + min_cnt_per_block - 1) / min_cnt_per_block)); + if (*out_nblock > 1) { + *block_size = SIZE_ALIGNED((cnt + (*out_nblock) - 1) / (*out_nblock)); + } else { + *block_size = cnt; + } + } + + template + static inline void BlockInfoForceSize(int num_threads, INDEX_T cnt, + INDEX_T min_cnt_per_block, + int* out_nblock, INDEX_T* block_size) { + *out_nblock = std::min( + num_threads, + static_cast((cnt + min_cnt_per_block - 1) / min_cnt_per_block)); + if (*out_nblock > 1) { + *block_size = (cnt + (*out_nblock) - 1) / (*out_nblock); + // force the block size to the times of min_cnt_per_block + *block_size = (*block_size + min_cnt_per_block - 1) / min_cnt_per_block * + min_cnt_per_block; + } else { + *block_size = cnt; + } + } + + template + static inline void BlockInfoForceSize(INDEX_T cnt, INDEX_T min_cnt_per_block, + int* out_nblock, INDEX_T* block_size) { + int num_threads = OMP_NUM_THREADS(); + BlockInfoForceSize(num_threads, cnt, min_cnt_per_block, out_nblock, + block_size); + } + + template + static inline int For( + INDEX_T start, INDEX_T end, INDEX_T min_block_size, + const std::function& inner_fun) { + int n_block = 1; + INDEX_T num_inner = end - start; + BlockInfo(num_inner, min_block_size, &n_block, &num_inner); + OMP_INIT_EX(); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) + for (int i = 0; i < n_block; ++i) { + OMP_LOOP_EX_BEGIN(); + INDEX_T inner_start = start + num_inner * i; + INDEX_T inner_end = std::min(end, inner_start + num_inner); + if (inner_start < inner_end) { + inner_fun(i, inner_start, inner_end); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + return n_block; + } +}; + +template +class ParallelPartitionRunner { + public: + ParallelPartitionRunner(INDEX_T num_data, INDEX_T min_block_size) + : min_block_size_(min_block_size) { + num_threads_ = OMP_NUM_THREADS(); + left_.resize(num_data); + if (TWO_BUFFER) { + right_.resize(num_data); + } + offsets_.resize(num_threads_); + left_cnts_.resize(num_threads_); + right_cnts_.resize(num_threads_); + left_write_pos_.resize(num_threads_); + right_write_pos_.resize(num_threads_); + } + + ~ParallelPartitionRunner() {} + + void ReSize(INDEX_T num_data) { + left_.resize(num_data); + if (TWO_BUFFER) { + right_.resize(num_data); + } + } + + template + INDEX_T Run( + INDEX_T cnt, + const std::function& func, + INDEX_T* out) { + int nblock = 1; + INDEX_T inner_size = cnt; + if (FORCE_SIZE) { + Threading::BlockInfoForceSize(num_threads_, cnt, min_block_size_, + &nblock, &inner_size); + } else { + Threading::BlockInfo(num_threads_, cnt, min_block_size_, &nblock, + &inner_size); + } + + OMP_INIT_EX(); +#pragma omp parallel for schedule(static, 1) num_threads(num_threads_) + for (int i = 0; i < nblock; ++i) { + OMP_LOOP_EX_BEGIN(); + INDEX_T cur_start = i * inner_size; + INDEX_T cur_cnt = std::min(inner_size, cnt - cur_start); + offsets_[i] = cur_start; + if (cur_cnt <= 0) { + left_cnts_[i] = 0; + right_cnts_[i] = 0; + continue; + } + auto left_ptr = left_.data() + cur_start; + INDEX_T* right_ptr = nullptr; + if (TWO_BUFFER) { + right_ptr = right_.data() + cur_start; + } + // split data inner, reduce the times of function called + INDEX_T cur_left_count = + func(i, cur_start, cur_cnt, left_ptr, right_ptr); + if (!TWO_BUFFER) { + // reverse for one buffer + std::reverse(left_ptr + cur_left_count, left_ptr + cur_cnt); + } + left_cnts_[i] = cur_left_count; + right_cnts_[i] = cur_cnt - cur_left_count; + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + left_write_pos_[0] = 0; + right_write_pos_[0] = 0; + for (int i = 1; i < nblock; ++i) { + left_write_pos_[i] = left_write_pos_[i - 1] + left_cnts_[i - 1]; + right_write_pos_[i] = right_write_pos_[i - 1] + right_cnts_[i - 1]; + } + data_size_t left_cnt = left_write_pos_[nblock - 1] + left_cnts_[nblock - 1]; + + auto right_start = out + left_cnt; +#pragma omp parallel for schedule(static, 1) num_threads(num_threads_) + for (int i = 0; i < nblock; ++i) { + std::copy_n(left_.data() + offsets_[i], left_cnts_[i], + out + left_write_pos_[i]); + if (TWO_BUFFER) { + std::copy_n(right_.data() + offsets_[i], right_cnts_[i], + right_start + right_write_pos_[i]); + } else { + std::copy_n(left_.data() + offsets_[i] + left_cnts_[i], right_cnts_[i], + right_start + right_write_pos_[i]); + } + } + return left_cnt; + } + + private: + int num_threads_; + INDEX_T min_block_size_; + std::vector left_; + std::vector right_; + std::vector offsets_; + std::vector left_cnts_; + std::vector right_cnts_; + std::vector left_write_pos_; + std::vector right_write_pos_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_ diff --git a/include/LightGBM/utils/yamc/alternate_shared_mutex.hpp b/include/LightGBM/utils/yamc/alternate_shared_mutex.hpp new file mode 100644 index 0000000..c19671a --- /dev/null +++ b/include/LightGBM/utils/yamc/alternate_shared_mutex.hpp @@ -0,0 +1,212 @@ +/* + * alternate_shared_mutex.hpp + * + * MIT License + * + * Copyright (c) 2017 yohhoy + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_ + +#include +#include +#include +#include + +#include "yamc_rwlock_sched.hpp" + +namespace yamc { + +/* + * alternate implementation of shared mutex variants + * + * - yamc::alternate::shared_mutex + * - yamc::alternate::shared_timed_mutex + * - yamc::alternate::basic_shared_mutex + * - yamc::alternate::basic_shared_timed_mutex + */ +namespace alternate { + +namespace detail { + +template +class shared_mutex_base { + protected: + typename RwLockPolicy::state state_; + std::condition_variable cv_; + std::mutex mtx_; + + void lock() { + std::unique_lock lk(mtx_); + RwLockPolicy::before_wait_wlock(state_); + while (RwLockPolicy::wait_wlock(state_)) { + cv_.wait(lk); + } + RwLockPolicy::after_wait_wlock(state_); + RwLockPolicy::acquire_wlock(&state_); + } + + bool try_lock() { + std::lock_guard lk(mtx_); + if (RwLockPolicy::wait_wlock(state_)) return false; + RwLockPolicy::acquire_wlock(state_); + return true; + } + + void unlock() { + std::lock_guard lk(mtx_); + RwLockPolicy::release_wlock(&state_); + cv_.notify_all(); + } + + void lock_shared() { + std::unique_lock lk(mtx_); + while (RwLockPolicy::wait_rlock(state_)) { + cv_.wait(lk); + } + RwLockPolicy::acquire_rlock(&state_); + } + + bool try_lock_shared() { + std::lock_guard lk(mtx_); + if (RwLockPolicy::wait_rlock(state_)) return false; + RwLockPolicy::acquire_rlock(state_); + return true; + } + + void unlock_shared() { + std::lock_guard lk(mtx_); + if (RwLockPolicy::release_rlock(&state_)) { + cv_.notify_all(); + } + } +}; + +} // namespace detail + +template +class basic_shared_mutex : private detail::shared_mutex_base { + using base = detail::shared_mutex_base; + + public: + basic_shared_mutex() = default; + ~basic_shared_mutex() = default; + + basic_shared_mutex(const basic_shared_mutex&) = delete; + basic_shared_mutex& operator=(const basic_shared_mutex&) = delete; + + using base::lock; + using base::try_lock; + using base::unlock; + + using base::lock_shared; + using base::try_lock_shared; + using base::unlock_shared; +}; + +using shared_mutex = basic_shared_mutex; + +template +class basic_shared_timed_mutex + : private detail::shared_mutex_base { + using base = detail::shared_mutex_base; + + using base::cv_; + using base::mtx_; + using base::state_; + + template + bool do_try_lockwait(const std::chrono::time_point& tp) { + std::unique_lock lk(mtx_); + RwLockPolicy::before_wait_wlock(state_); + while (RwLockPolicy::wait_wlock(state_)) { + if (cv_.wait_until(lk, tp) == std::cv_status::timeout) { + if (!RwLockPolicy::wait_wlock(state_)) // re-check predicate + break; + RwLockPolicy::after_wait_wlock(state_); + return false; + } + } + RwLockPolicy::after_wait_wlock(state_); + RwLockPolicy::acquire_wlock(state_); + return true; + } + + template + bool do_try_lock_sharedwait( + const std::chrono::time_point& tp) { + std::unique_lock lk(mtx_); + while (RwLockPolicy::wait_rlock(state_)) { + if (cv_.wait_until(lk, tp) == std::cv_status::timeout) { + if (!RwLockPolicy::wait_rlock(state_)) // re-check predicate + break; + return false; + } + } + RwLockPolicy::acquire_rlock(state_); + return true; + } + + public: + basic_shared_timed_mutex() = default; + ~basic_shared_timed_mutex() = default; + + basic_shared_timed_mutex(const basic_shared_timed_mutex&) = delete; + basic_shared_timed_mutex& operator=(const basic_shared_timed_mutex&) = delete; + + using base::lock; + using base::try_lock; + using base::unlock; + + template + bool try_lock_for(const std::chrono::duration& duration) { + const auto tp = std::chrono::steady_clock::now() + duration; + return do_try_lockwait(tp); + } + + template + bool try_lock_until(const std::chrono::time_point& tp) { + return do_try_lockwait(tp); + } + + using base::lock_shared; + using base::try_lock_shared; + using base::unlock_shared; + + template + bool try_lock_shared_for(const std::chrono::duration& duration) { + const auto tp = std::chrono::steady_clock::now() + duration; + return do_try_lock_sharedwait(tp); + } + + template + bool try_lock_shared_until( + const std::chrono::time_point& tp) { + return do_try_lock_sharedwait(tp); + } +}; + +using shared_timed_mutex = basic_shared_timed_mutex; + +} // namespace alternate +} // namespace yamc + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_ diff --git a/include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp b/include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp new file mode 100644 index 0000000..7618987 --- /dev/null +++ b/include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp @@ -0,0 +1,149 @@ +/* + * yamc_rwlock_sched.hpp + * + * MIT License + * + * Copyright (c) 2017 yohhoy + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_ + +#include +#include + +/// default shared_mutex rwlock policy +#ifndef YAMC_RWLOCK_SCHED_DEFAULT +#define YAMC_RWLOCK_SCHED_DEFAULT yamc::rwlock::ReaderPrefer +#endif + +namespace yamc { + +/* + * readers-writer locking policy for basic_shared_(timed)_mutex + * + * - yamc::rwlock::ReaderPrefer + * - yamc::rwlock::WriterPrefer + */ +namespace rwlock { + +/// Reader prefer scheduling +/// +/// NOTE: +// This policy might introduce "Writer Starvation" if readers continuously +// hold shared lock. PThreads rwlock implementation in Linux use this +// scheduling policy as default. (see also PTHREAD_RWLOCK_PREFER_READER_NP) +// +struct ReaderPrefer { + static const std::size_t writer_mask = ~(~std::size_t(0u) >> 1); // MSB 1bit + static const std::size_t reader_mask = ~std::size_t(0u) >> 1; + + struct state { + std::size_t rwcount = 0; + }; + + static void before_wait_wlock(const state&) {} + static void after_wait_wlock(const state&) {} + + static bool wait_wlock(const state& s) { return (s.rwcount != 0); } + + static void acquire_wlock(state* s) { + assert(!(s->rwcount & writer_mask)); + s->rwcount |= writer_mask; + } + + static void release_wlock(state* s) { + assert(s->rwcount & writer_mask); + s->rwcount &= ~writer_mask; + } + + static bool wait_rlock(const state& s) { return (s.rwcount & writer_mask) != 0; } + + static void acquire_rlock(state* s) { + assert((s->rwcount & reader_mask) < reader_mask); + ++(s->rwcount); + } + + static bool release_rlock(state* s) { + assert(0 < (s->rwcount & reader_mask)); + return (--(s->rwcount) == 0); + } +}; + +/// Writer prefer scheduling +/// +/// NOTE: +/// If there are waiting writer, new readers are blocked until all shared lock +/// are released, +// and the writer thread can get exclusive lock in preference to blocked +// reader threads. This policy might introduce "Reader Starvation" if writers +// continuously request exclusive lock. +/// (see also PTHREAD_RWLOCK_PREFER_WRITER_NONRECURSIVE_NP) +/// +struct WriterPrefer { + static const std::size_t locked = ~(~std::size_t(0u) >> 1); // MSB 1bit + static const std::size_t wait_mask = ~std::size_t(0u) >> 1; + + struct state { + std::size_t nwriter = 0; + std::size_t nreader = 0; + }; + + static void before_wait_wlock(state* s) { + assert((s->nwriter & wait_mask) < wait_mask); + ++(s->nwriter); + } + + static bool wait_wlock(const state& s) { + return ((s.nwriter & locked) || 0 < s.nreader); + } + + static void after_wait_wlock(state* s) { + assert(0 < (s->nwriter & wait_mask)); + --(s->nwriter); + } + + static void acquire_wlock(state* s) { + assert(!(s->nwriter & locked)); + s->nwriter |= locked; + } + + static void release_wlock(state* s) { + assert(s->nwriter & locked); + s->nwriter &= ~locked; + } + + static bool wait_rlock(const state& s) { return (s.nwriter != 0); } + + static void acquire_rlock(state* s) { + assert(!(s->nwriter & locked)); + ++(s->nreader); + } + + static bool release_rlock(state* s) { + assert(0 < s->nreader); + return (--(s->nreader) == 0); + } +}; + +} // namespace rwlock +} // namespace yamc + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_ diff --git a/include/LightGBM/utils/yamc/yamc_shared_lock.hpp b/include/LightGBM/utils/yamc/yamc_shared_lock.hpp new file mode 100644 index 0000000..eb9152e --- /dev/null +++ b/include/LightGBM/utils/yamc/yamc_shared_lock.hpp @@ -0,0 +1,199 @@ +/* + * yamc_shared_lock.hpp + * + * MIT License + * + * Copyright (c) 2017 yohhoy + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_ +#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_ + +#include +#include +#include +#include +#include // std::swap + +/* + * std::shared_lock in C++14 Standard Library + * + * - yamc::shared_lock + */ +namespace yamc { + +template +class shared_lock { + void locking_precondition(const char* emsg) { + if (pm_ == nullptr) { + throw std::system_error( + std::make_error_code(std::errc::operation_not_permitted), emsg); + } + if (owns_) { + throw std::system_error( + std::make_error_code(std::errc::resource_deadlock_would_occur), emsg); + } + } + + public: + using mutex_type = Mutex; + + shared_lock() noexcept = default; + + explicit shared_lock(mutex_type* m) { + m->lock_shared(); + pm_ = m; + owns_ = true; + } + + shared_lock(const mutex_type& m, std::defer_lock_t) noexcept { + pm_ = &m; + owns_ = false; + } + + shared_lock(const mutex_type& m, std::try_to_lock_t) { + pm_ = &m; + owns_ = m.try_lock_shared(); + } + + shared_lock(const mutex_type& m, std::adopt_lock_t) { + pm_ = &m; + owns_ = true; + } + + template + shared_lock(const mutex_type& m, + const std::chrono::time_point& abs_time) { + pm_ = &m; + owns_ = m.try_lock_shared_until(abs_time); + } + + template + shared_lock(const mutex_type& m, + const std::chrono::duration& rel_time) { + pm_ = &m; + owns_ = m.try_lock_shared_for(rel_time); + } + + ~shared_lock() { + if (owns_) { + assert(pm_ != nullptr); + pm_->unlock_shared(); + } + } + + shared_lock(const shared_lock&) = delete; + shared_lock& operator=(const shared_lock&) = delete; + + shared_lock(shared_lock&& rhs) noexcept { + if (pm_ && owns_) { + pm_->unlock_shared(); + } + pm_ = rhs.pm_; + owns_ = rhs.owns_; + rhs.pm_ = nullptr; + rhs.owns_ = false; + } + + shared_lock& operator=(shared_lock&& rhs) noexcept { + if (pm_ && owns_) { + pm_->unlock_shared(); + } + pm_ = rhs.pm_; + owns_ = rhs.owns_; + rhs.pm_ = nullptr; + rhs.owns_ = false; + return *this; + } + + void lock() { + locking_precondition("shared_lock::lock"); + pm_->lock_shared(); + owns_ = true; + } + + bool try_lock() { + locking_precondition("shared_lock::try_lock"); + return (owns_ = pm_->try_lock_shared()); + } + + template + bool try_lock_for(const std::chrono::duration& rel_time) { + locking_precondition("shared_lock::try_lock_for"); + return (owns_ = pm_->try_lock_shared_for(rel_time)); + } + + template + bool try_lock_until( + const std::chrono::time_point& abs_time) { + locking_precondition("shared_lock::try_lock_until"); + return (owns_ = pm_->try_lock_shared_until(abs_time)); + } + + void unlock() { + assert(pm_ != nullptr); + if (!owns_) { + throw std::system_error( + std::make_error_code(std::errc::operation_not_permitted), + "shared_lock::unlock"); + } + pm_->unlock_shared(); + owns_ = false; + } + + void swap(shared_lock& sl) noexcept { + std::swap(pm_, sl.pm_); + std::swap(owns_, sl.owns_); + } + + mutex_type* release() noexcept { + mutex_type* result = pm_; + pm_ = nullptr; + owns_ = false; + return result; + } + + bool owns_lock() const noexcept { return owns_; } + + explicit operator bool() const noexcept { return owns_; } + + mutex_type* mutex() const noexcept { return pm_; } + + private: + mutex_type* pm_ = nullptr; + bool owns_ = false; +}; + +} // namespace yamc + +namespace std { + +/// std::swap() specialization for yamc::shared_lock type +template +void swap(yamc::shared_lock& lhs, + yamc::shared_lock& rhs) noexcept { + lhs.swap(rhs); +} + +} // namespace std + +#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_ diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 0000000..46ecfc5 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,14222 @@ +version: 7 +platforms: +- name: linux-64 +- name: linux-aarch64 +- name: osx-64 +- name: osx-arm64 +- name: win-64 +environments: + default: + channels: + - url: https://conda.anaconda.org/conda-forge/ + packages: + linux-64: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-8_h4a7cf45_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-8_h0358290_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-8_h47877c9_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.33-pthreads_h94d23a6_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.1-h5347b49_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.2-h35e630c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.10.20-h3c07f61_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + linux-aarch64: + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h4777abc_9.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.45.1-default_h1979696_102.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-8_haddc8a3_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.11.0-8_hd72aa62_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.8.1-hfae3067_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.5.2-h376a255_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-15.2.0-h8acb6b2_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-15.2.0-he9431aa_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-15.2.0-he9431aa_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-15.2.0-h1b7bec0_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgomp-15.2.0-h8acb6b2_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.11.0-8_h88aeb00_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblzma-5.8.3-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h86ecc28_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.33-pthreads_h9d3fd7e_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.53.1-h022381a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-15.2.0-hef695bb_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.42.1-h1022ec0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.2-hdc9db2a_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.6-hf8d1292_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.2.6-py310h6e5608f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.6.2-h546c87b_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.10.20-h28be5d3_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.3-hb682ff5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/tk-8.6.13-noxft_h0dc03b3_103.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h85ac4a6_6.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + osx-64: + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/_openmp_mutex-4.5-7_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h500dc9f_9.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/icu-78.3-h25d91c4_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.11.0-8_he492b99_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.11.0-8_h9b27e0a_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-22.1.7-h19cb2f5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.8.1-hcc62823_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.5.2-hd1f9c09_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgcc-15.2.0-h08519bb_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran-15.2.0-h7e5c614_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-15.2.0-hd16e46c_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.11.0-8_h859234e_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/liblzma-5.8.3-hbb4bfdb_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.33-openmp_h9e49c7b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.53.1-h8f8c405_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.2-hbb4bfdb_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-22.1.7-h0d3cbff_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.6-hcc0dc9a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.2.6-py310h07c5b4d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.6.2-hc881268_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.10.20-hea035f4_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/readline-8.3-h68b038d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/scipy-1.15.2-py310hef62574_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-h7142dee_3.conda + osx-arm64: + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.11.0-8_h51639a9_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.11.0-8_hb0561ab_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-22.1.7-h55c6f16_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.8.1-hf6b4638_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.11.0-8_hd9741b5_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblzma-5.8.3-h8088a28_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.33-openmp_he657e61_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.53.1-h1b79a29_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.2-h8088a28_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-22.1.7-hc7d1edf_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.6-h1d4f5a5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.2.6-py310h4d83441_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.2-hd24854e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.10.20-h1b19095_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.15.2-py310h32ab4ed_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda + win-64: + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-h4c7d964_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h0ad9c76_9.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.11.0-8_h8455456_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-8_h2a3cdd5_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.8.1-hac47afa_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.13.0-default_h049141e_1000.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-hc1393d2_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-8_hf9ab0e9_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.3-hfd05255_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.53.1-hf5d6505_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_10.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.3-h692994f_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.3-hbc0d294_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.2-hfd05255_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/llvm-openmp-22.1.7-h4fa8253_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2026.0.0-hac47afa_908.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/onemkl-license-2026.0.0-h57928b3_908.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.2-hf411b9b_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.10.20-hc20f281_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py310h15c175c_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2023.0.0-hd3d4ead_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.26100.0-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.5-h1b7c187_38.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.51.36231-h1b9f54f_38.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.51.36231-h1b9f54f_38.conda + py310: + channels: + - url: https://conda.anaconda.org/conda-forge/ + packages: + linux-64: + - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py310h7c4b9e2_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.20-h5f1c8d9_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.6.12-h2ba76a8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.17-h4ab18f5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.18-h36a0aea_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.4.2-h161de36_10.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.1-h63f54a0_13.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.8-h96d4d28_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.4-hcc7299c_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.5.9-h10bd90f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.16-h36a0aea_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-h36a0aea_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.26.8-h4f3a3cc_11.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.267-h51dfee4_8.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb03c661_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb03c661_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hea6c23e_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.15.1-py310h2fee648_5.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py310hd41b1e2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py310h7c4b9e2_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h24cb091_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.21-py310h25320af_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/epoxy-1.5.10-hb03c661_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.1-h27c8c51_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.63.0-py310h3406613_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.44.6-h2b0a6b4_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.88.1-hee1de02_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/graphviz-14.1.2-h8b86629_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.52-ha5ea40c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/gts-0.7.6-h977cf35_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/h5py-3.16.0-nompi_py310h4aa865e_102.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-14.2.1-h6083320_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.6-nompi_h19486de_109.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/hicolor-icon-theme-0.17-ha770c72_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.5.0-py310haaf941d_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-ha1258a1_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.19.1-h0c24ade_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.1.0-hdb68285_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he02047a_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.5-h088129d_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-16.0.0-hefa796f_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-16.0.0-hac33072_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-16.0.0-hac33072_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-16.0.0-h7e0c224_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb03c661_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb03c661_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb03c661_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h7a8fb5f_6.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.20.0-hcf29cc6_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.127-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-devel-1.7.0-ha4b6fd6_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h73754d4_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-h5fbf134_12.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-devel-1.7.0-ha4b6fd6_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.88.1-h0d30a3d_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-devel-1.7.0-ha4b6fd6_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.23.0-h9be4e54_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.23.0-hc7a4891_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.62.2-h15f2491_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-16.0.0-h6a7eafb_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.19-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.58-h421ea60_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-4.25.3-hd5b35b9_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2023.09.01-h5a48ba9_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.62.3-h4c96295_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.22-h280c20c_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.19.0-hb90f79a_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.1-h9d88235_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.1-h5347b49_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.13.2-hca5e8e5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.3-hca6bf5a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.3-h49c6c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.3.3-py310hb259640_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py310h3406613_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.7.3-py310h62c0568_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.2-py310h03d9f68_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.22.4-py310h4ef5377_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.4-h55fea9a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.2-h35e630c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.0-h17fec99_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-1.3.5-py310hb5077e9_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/pango-1.56.4-hda50119_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-haa7fec5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.2.0-py310h5a73078_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.4-h54a6638_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-1.0.0-py310h3af5803_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.3-py310h5764c6d_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-16.0.0-py310h17c5347_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-16.0.0-py310h6f79a3a_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.10.20-h3c07f61_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py310h3406613_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-27.1.0-py310ha71cbf4_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2023.09.01-h7f4b329_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.30.0-py310hd8f68c5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.4.13-he19d79f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.0.2-py310h1246948_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.7.3-py310hdfbd76f_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/setuptools-59.8.0-py310hff52083_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.6-py310h7c4b9e2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-17.0.1-py310h7c4b9e2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/wayland-1.25.0-hd6090a7_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.47-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.6-he73a12e_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.13-he1eb515_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb03c661_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.7-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcursor-1.2.3-hb9d3cd8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.6-hb9d3cd8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb03c661_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.7-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.2-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.8.3-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxinerama-1.1.6-hecca717_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrandr-1.5.5-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.7-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-xorgproto-2025.1-hb03c661_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h09e67af_11.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py310h139afa4_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.4.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.9-pyh29332c3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.17-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyha191276_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.37.0-pyh8f84b5b_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.8-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.14.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonpointer-3.1.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.26.0-hcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.3.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.19.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.5.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.28.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_widgets-3.0.16-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-1.15.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.17.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.7-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.10.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.0.0-pyhd8ed1ab_5.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.25.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-graphviz-0.20.3-pyh91182bf_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-3.2.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3339-validator-0.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyha191276_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyhc90fa1f_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + linux-aarch64: + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/argon2-cffi-bindings-25.1.0-py310h5b55623_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/at-spi2-atk-2.38.0-h1f2db35_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/at-spi2-core-2.40.3-h1f2db35_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/atk-1.0-2.38.0-hedc4a1f_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-auth-0.7.20-haea164f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-cal-0.6.12-hc544557_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-common-0.9.17-h68df207_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-compression-0.2.18-h7972eaf_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-event-stream-0.4.2-h0672d3d_10.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-http-0.8.1-h54e40d9_13.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-io-0.14.8-h07c5ed3_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-mqtt-0.10.4-h0b2355d_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-s3-0.5.9-h39e75e1_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-sdkutils-0.1.16-h7972eaf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-checksums-0.1.18-h7972eaf_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-crt-cpp-0.26.8-hf6b9791_11.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-sdk-cpp-1.11.267-he30cb05_8.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-he30d5cf_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.1.0-he30d5cf_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-python-1.1.0-py310h57cea71_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h4777abc_9.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/c-ares-1.34.6-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.4-h0b6afd8_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cffi-1.15.1-py310hce94938_5.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py310h586407a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cytoolz-1.1.0-py310ha7967c6_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.16.2-h70963c4_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/debugpy-1.8.21-py310heccc163_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/epoxy-1.5.10-he30d5cf_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.18.1-hba86a56_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.63.0-py310h2d8da20_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.14.3-h8af1aa0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/fribidi-1.0.16-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gdk-pixbuf-2.44.6-h90308e0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gflags-2.2.2-h5ad3122_1005.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/glib-tools-2.88.1-h7439e1d_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/glog-0.7.1-h468a4a4_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/graphite2-1.3.14-hfae3067_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/graphviz-14.1.2-h45e821f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gtk3-3.24.52-h75d4e7a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gts-0.7.6-he293c15_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/h5py-3.16.0-nompi_py310hce7682f_102.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-14.2.0-h1134a53_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/hdf5-1.14.6-nompi_hf95b8e7_109.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/hicolor-icon-theme-0.17-h8af1aa0_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/icu-78.3-hcab7f73_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/keyutils-1.6.3-h86ecc28_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.5.0-py310h65c7496_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/krb5-1.22.2-hfd895c2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.19.1-h9d5b58d_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.45.1-default_h1979696_102.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lerc-4.1.0-h52b7260_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libabseil-20240116.2-cxx17_h0a1ffab_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libaec-1.1.5-hff7e48a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-16.0.0-hda7f696_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-acero-16.0.0-h5ad3122_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-dataset-16.0.0-h5ad3122_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-substrait-16.0.0-h08b7278_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-20_linuxaarch64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.1.0-he30d5cf_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.1.0-he30d5cf_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.1.0-he30d5cf_4.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-20_linuxaarch64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcrc32c-1.1.2-h01db608_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcups-2.3.3-h4f2b762_6.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcurl-8.20.0-hc57f145_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.25-h1af38f5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdrm-2.4.127-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libedit-3.1.20250104-pl5321h976ea20_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libegl-1.7.0-hd24410f_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libegl-devel-1.7.0-hd24410f_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libev-4.33-h31becfc_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libevent-2.1.12-h4ba1bb4_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.8.1-hfae3067_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.5.2-h376a255_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libfreetype-2.14.3-h8af1aa0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libfreetype6-2.14.3-hdae7a39_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-15.2.0-h8acb6b2_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-15.2.0-he9431aa_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgd-2.3.3-h88aa843_12.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-15.2.0-he9431aa_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-15.2.0-he9431aa_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-15.2.0-h1b7bec0_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgl-1.7.0-hd24410f_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgl-devel-1.7.0-hd24410f_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.88.1-h96a7f82_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglvnd-1.7.0-hd24410f_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglx-1.7.0-hd24410f_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglx-devel-1.7.0-hd24410f_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgomp-15.2.0-h8acb6b2_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgoogle-cloud-2.23.0-hd739bbb_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgoogle-cloud-storage-2.23.0-hdb39181_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgrpc-1.62.2-h98a9317_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libiconv-1.18-h90929bb_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.1.4.1-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-20_linuxaarch64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblzma-5.8.3-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libnghttp2-1.68.1-hd3077d7_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h86ecc28_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.25-pthreads_h5a5ec62_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libparquet-16.0.0-h6fe2c6f_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libpciaccess-0.19-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.58-h1abf092_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libprotobuf-4.25.3-hea2c3fa_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libre2-11-2023.09.01-h9d008c2_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/librsvg-2.62.2-hf685517_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsodium-1.0.22-h80f16a2_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.53.1-h022381a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libssh2-1.11.1-h18c354c_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-15.2.0-hef695bb_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-15.2.0-hdbbeba8_19.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libthrift-0.19.0-h043aeee_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.7.1-hdb009f0_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libutf8proc-2.8.0-h812390e_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.42.1-h1022ec0_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.6.0-ha2e29f5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.17.0-h262b8f6_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.13.2-h3c6a4c8_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxml2-16-2.15.3-h79dcc73_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxml2-2.15.3-h869d058_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.2-hdc9db2a_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-4.3.3-py310h6607652_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-c-1.9.4-hd600fc2_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/markupsafe-3.0.3-py310h3b5aacf_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.7.3-py310h0a7f329_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/msgpack-python-1.1.2-py310h0992a49_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.6-hf8d1292_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.22.4-py310h7f880a9_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.4-h5da879a_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.6.2-h546c87b_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/orc-2.0.0-hd7aaf90_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pandas-1.3.5-py310h713c908_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pango-1.56.4-h8547ced_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pcre2-10.47-hf841c20_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-12.2.0-py310hd8fe58f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pixman-0.46.4-h7ac5ae9_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/polars-1.0.0-py310ha82f49b_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/psutil-5.9.3-py310h761cc84_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-h86ecc28_1002.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyarrow-16.0.0-py310h70ff3ff_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyarrow-core-16.0.0-py310hf3ba4ff_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.10.20-h28be5d3_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyyaml-6.0.3-py310h2d8da20_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyzmq-27.1.0-py310hbea53bb_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/re2-2023.09.01-h9caee61_2.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.3-hb682ff5_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/rpds-py-0.30.0-py310haddc216_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/s2n-1.4.13-h52a6840_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/scikit-learn-1.0.2-py310h2a0ddcd_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.7.3-py310hdd6f644_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/setuptools-59.8.0-py310h4c7bcd0_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/snappy-1.2.2-he774c54_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/tk-8.6.13-noxft_h0dc03b3_103.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.5.6-py310ha7967c6_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-17.0.1-py310h5b55623_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/wayland-1.25.0-h4f8a99f_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xkeyboard-config-2.47-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libice-1.1.2-h86ecc28_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libsm-1.2.6-h0808dbd_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libx11-1.8.13-h63a1b12_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.12-he30d5cf_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxcomposite-0.4.7-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxcursor-1.2.3-h86ecc28_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdamage-1.1.6-h86ecc28_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.5-he30d5cf_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxext-1.3.7-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxfixes-6.0.2-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxi-1.8.3-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxinerama-1.1.6-hfae3067_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrandr-1.5.5-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.12-h86ecc28_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h57736b2_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxxf86vm-1.1.7-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-xorgproto-2025.1-he30d5cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/yaml-0.2.5-h80f16a2_3.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zeromq-4.3.5-hec9560f_11.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zlib-ng-2.3.3-ha7cb516_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstandard-0.25.0-py310hef25091_1.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h85ac4a6_6.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.4.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.9-pyh29332c3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.17-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyha191276_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.37.0-pyh8f84b5b_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.8-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.14.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonpointer-3.1.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.26.0-hcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.3.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.19.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.5.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.28.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_widgets-3.0.16-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-1.15.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.17.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.7-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.10.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.0.0-pyhd8ed1ab_5.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.25.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-graphviz-0.20.3-pyh91182bf_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-3.2.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3339-validator-0.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyha191276_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyhc90fa1f_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + osx-64: + - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.4.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.9-pyh29332c3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.17-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyh5552912_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.37.0-pyh8f84b5b_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.8-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.14.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonpointer-3.1.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.26.0-hcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.3.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.19.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.5.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.28.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_widgets-3.0.16-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-1.15.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.17.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.7-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.10.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.0.0-pyhd8ed1ab_5.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.25.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-graphviz-0.20.3-pyh91182bf_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-3.2.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3339-validator-0.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyh5552912_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyhc90fa1f_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/_openmp_mutex-4.5-7_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/argon2-cffi-bindings-25.1.0-py310hd2d5e8d_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/atk-1.0-2.38.0-h4bec284_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-auth-0.7.20-h26f788b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-cal-0.6.12-h7d0aca8_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-common-0.9.17-hec52a4b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-compression-0.2.18-h94d6f14_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-event-stream-0.4.2-h88c3968_10.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-http-0.8.1-h329322f_13.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-io-0.14.8-hb30fd87_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-mqtt-0.10.4-h2c4861c_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-s3-0.5.9-h82d509c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-sdkutils-0.1.16-h94d6f14_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-checksums-0.1.18-h94d6f14_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-crt-cpp-0.26.8-h1c89d3c_11.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/aws-sdk-cpp-1.11.267-h764722f_8.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-1.1.0-h1c43f85_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h1c43f85_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-python-1.1.0-py310h79c4529_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h500dc9f_9.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/c-ares-1.34.6-hb5e19a0_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/cairo-1.18.4-h7656bdc_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/cffi-1.15.1-py310hdca579f_5.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.1-py310hb3b189b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/cytoolz-1.1.0-py310hf70ac88_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/debugpy-1.8.21-py310h3f55cb5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/epoxy-1.5.10-h8616949_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/fontconfig-2.18.1-h7a4440b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.63.0-py310h399bfa0_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/freetype-2.14.3-h694c41f_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/fribidi-1.0.16-h8616949_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/gdk-pixbuf-2.44.6-hae309b2_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/gflags-2.2.2-hac325c4_1005.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/glib-tools-2.88.1-h6437393_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/glog-0.7.1-h2790a97_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/graphite2-1.3.14-h21dd04a_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/graphviz-14.1.2-h44fc223_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/gtk3-3.24.52-hf2d442a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/gts-0.7.6-h53e17e3_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/h5py-3.16.0-nompi_py310hbc26044_102.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/harfbuzz-14.2.0-hf0bc557_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/hdf5-1.14.6-nompi_h13accda_109.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/hicolor-icon-theme-0.17-h694c41f_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/icu-78.3-h25d91c4_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/kiwisolver-1.5.0-py310h323244c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/krb5-1.22.2-h207b36a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/lcms2-2.19.1-h5ea7634_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/lerc-4.1.0-h35c7297_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libabseil-20240116.2-cxx17_hf036a51_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libaec-1.1.5-he7c3a48_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-16.0.0-hb6a69ac_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-16.0.0-hf036a51_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-dataset-16.0.0-hf036a51_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-substrait-16.0.0-h85bc590_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-20_osx64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.1.0-h1c43f85_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlidec-1.1.0-h1c43f85_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlienc-1.1.0-h1c43f85_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-20_osx64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libcrc32c-1.1.2-he49afe7_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-64/libcurl-8.20.0-h8f0b9e4_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-22.1.7-h19cb2f5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.25-h517ebb2_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libedit-3.1.20250104-pl5321ha958ccf_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libev-4.33-h10d778d_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libevent-2.1.12-ha90c15b_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.8.1-hcc62823_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.5.2-hd1f9c09_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libfreetype-2.14.3-h694c41f_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libfreetype6-2.14.3-h58fbd8d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgcc-15.2.0-h08519bb_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgd-2.3.3-hb2c11ec_12.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran-15.2.0-h7e5c614_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-15.2.0-hd16e46c_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libglib-2.88.1-hf28f236_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgoogle-cloud-2.23.0-h651e89d_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgoogle-cloud-storage-2.23.0-ha67e85c_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libgrpc-1.62.2-h384b2fc_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libiconv-1.18-h57a12c2_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libintl-0.25.1-h3184127_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libjpeg-turbo-3.1.4.1-ha1e9b39_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-20_osx64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/liblzma-5.8.3-hbb4bfdb_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libnghttp2-1.68.1-h70048d4_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.25-openmp_hfef2a42_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libparquet-16.0.0-h904a336_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.58-he930e7c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libprotobuf-4.25.3-hd4aba4c_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libre2-11-2023.09.01-h81f5012_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/librsvg-2.62.2-h7321050_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libsodium-1.0.22-ha3d0635_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.53.1-h8f8c405_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libssh2-1.11.1-hed3591d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libthrift-0.19.0-h064b379_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.7.1-ha0a348c_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libutf8proc-2.8.0-he670073_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libwebp-base-1.6.0-hb807250_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.17.0-hf1f96e2_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libxml2-16-2.15.3-h7a90416_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.2-hbb4bfdb_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-22.1.6-h0d3cbff_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/lz4-4.3.3-py310h43af8dd_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/lz4-c-1.9.4-hf0c8a7f_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/markupsafe-3.0.3-py310h399bfa0_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.7.3-py310hf92ae1b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/msgpack-python-1.1.2-py310h8cf47bc_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.6-hcc0dc9a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.22.4-py310hed37afb_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.4-h52bb76a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.6.2-hc881268_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/orc-2.0.0-hf146577_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pandas-1.3.5-py310hdd25497_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-64/pango-1.56.4-hf280016_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pcre2-10.47-h13923f0_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pillow-12.2.0-py310h0c0a54c_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pixman-0.46.4-ha059160_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/polars-1.0.0-py310hbfb72ad_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/psutil-5.9.3-py310h90acd4f_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-64/pthread-stubs-0.4-h00291cd_1002.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pyarrow-16.0.0-py310h1cef2ca_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pyarrow-core-16.0.0-py310h907dfef_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pyobjc-core-12.1-py310hccc919d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pyobjc-framework-cocoa-12.1-py310hb0d6316_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.10.20-hea035f4_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pyyaml-6.0.3-py310hec06124_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/pyzmq-27.1.0-py310h30bd05a_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/re2-2023.09.01-hb168e87_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/readline-8.3-h68b038d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/rpds-py-0.30.0-py310h64024f4_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/scikit-learn-1.0.2-py310h598de7d_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-64/scipy-1.7.3-py310h240c617_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-64/setuptools-59.8.0-py310h2ec42d9_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-64/snappy-1.2.2-h01f5ddf_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-h7142dee_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.6-py310h5cd8a12_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/unicodedata2-17.0.1-py310hf70ac88_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/xorg-libxau-1.0.12-h8616949_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/xorg-libxdmcp-1.1.5-h8616949_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/yaml-0.2.5-h4132b18_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/zeromq-4.3.5-h84953be_11.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/zlib-ng-2.3.3-h8bce59a_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.25.0-py310h3aa7efa_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda + osx-arm64: + - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.4.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2022.12.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.9-pyh29332c3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.17-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyh5552912_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.37.0-pyh8f84b5b_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.8-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.14.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonpointer-3.1.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.26.0-hcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.3.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyhc90fa1f_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.19.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.5.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.28.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_widgets-3.0.16-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-1.15.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.17.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.7-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.10.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.0.0-pyhd8ed1ab_5.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.25.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-graphviz-0.20.3-pyh91182bf_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-3.2.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3339-validator-0.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyh5552912_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyhc90fa1f_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/argon2-cffi-bindings-25.1.0-py310hfe3a0ae_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/atk-1.0-2.38.0-hd03087b_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.7.20-h5cf208e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.6.12-h35c0bb2_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.9.17-h03532ee_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.2.18-h35c0bb2_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.4.2-h7d5773a_10.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.8.1-h00faecf_13.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.14.8-h6dd71cf_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.10.4-h92d7a41_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.5.9-he1e208d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.1.16-h35c0bb2_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.1.18-h35c0bb2_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.26.8-h5cd1ed2_11.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.267-h108e708_8.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-1.1.0-h6caf38d_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.1.0-h6caf38d_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.1.0-py310h1af2607_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.6-hc919400_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.4-he0f2337_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.15.1-py310hdcd7c05_5.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.2.1-py310h21239e6_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cytoolz-1.1.0-py310h72544b6_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.21-py310h19b6747_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/epoxy-1.5.10-hc919400_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fontconfig-2.18.1-h2b252f5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.63.0-py310hb46c203_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.14.3-hce30654_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fribidi-1.0.16-hc919400_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gdk-pixbuf-2.44.6-h4e57454_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gflags-2.2.2-hf9b8971_1005.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/glib-tools-2.88.1-h37541a8_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/glog-0.7.1-heb240a5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/graphite2-1.3.14-hec049ff_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/graphviz-14.1.2-hec8c438_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gtk3-3.24.52-hc0f3e19_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gts-0.7.6-he42f4ea_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/h5py-3.16.0-nompi_py310h0c5f886_102.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-14.2.0-h3103d1b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/hdf5-1.14.6-nompi_had3affe_109.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/hicolor-icon-theme-0.17-hce30654_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.3-hef89b57_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.5.0-py310h34990b0_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/krb5-1.22.2-h385eeb1_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lcms2-2.19.1-hdfa7624_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lerc-4.1.0-h1eee2c3_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20240116.2-cxx17_h00cdb27_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libaec-1.1.5-h8664d51_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-16.0.0-h23f55cf_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-16.0.0-h00cdb27_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-16.0.0-h00cdb27_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-16.0.0-hc68f6b8_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-20_osxarm64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlicommon-1.1.0-h6caf38d_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.1.0-h6caf38d_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlienc-1.1.0-h6caf38d_4.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-20_osxarm64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.20.0-hd5a2499_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-22.1.7-h55c6f16_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.25-hc11a715_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h93a5062_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.8.1-hf6b4638_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype-2.14.3-hce30654_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype6-2.14.3-hdfa99f5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgd-2.3.3-h05bcc79_12.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_19.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libglib-2.88.1-ha08bb59_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.23.0-hbebe991_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.23.0-h8a76758_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.62.2-h9c18a4f_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.18-h23cfdf5_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libintl-0.25.1-h493aca8_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjpeg-turbo-3.1.4.1-h84a0fba_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-20_osxarm64_openblas.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblzma-5.8.3-h8088a28_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libnghttp2-1.68.1-h8f3e76b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.25-openmp_h6c19121_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-16.0.0-hcf52c46_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libpng-1.6.58-h132b30e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-4.25.3-hc39d83c_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libre2-11-2023.09.01-h7b2c953_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/librsvg-2.62.2-he8aa2a2_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsodium-1.0.22-h1a92334_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.53.1-h1b79a29_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libssh2-1.11.1-h1590b86_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libthrift-0.19.0-h026a170_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libtiff-4.7.1-h4030677_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libutf8proc-2.8.0-hc098a78_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.6.0-h07db88b_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxcb-1.17.0-hdb1d25a_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-16-2.15.3-h5ef1a60_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.2-h8088a28_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-22.1.6-hc7d1edf_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-4.3.3-py310hc798581_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.9.4-hb7217d7_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.3-py310hb46c203_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.7.3-py310hd1c642d_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/msgpack-python-1.1.2-py310h0e897d2_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.6-h1d4f5a5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.22.4-py310h0a343b5_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.4-hd9e9057_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.2-hd24854e_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.0-h4aad248_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-1.3.5-py310hdead3df_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pango-1.56.4-hf80efc4_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pcre2-10.47-h30297fc_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-12.2.0-py310hc037f36_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pixman-0.46.4-h81086ad_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-1.0.0-py310h8d151f0_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-5.9.3-py310h8e9501a_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pthread-stubs-0.4-hd74edd7_1002.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-16.0.0-py310h7ed268f_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-16.0.0-py310h75075a0_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyobjc-core-12.1-py310h9d23ab5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyobjc-framework-cocoa-12.1-py310h8579ff5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.10.20-h1b19095_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.3-py310hb46c203_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-27.1.0-py310hf396ece_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2023.09.01-h4cba328_2.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.30.0-py310hf3301a5_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.0.2-py310h5f111c3_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.7.3-py310ha0d8a01_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/setuptools-59.8.0-py310hbe9552e_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.6-py310h72544b6_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/unicodedata2-17.0.1-py310h72544b6_0.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.12-hc919400_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxdmcp-1.1.5-hc919400_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h925e9cb_3.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h10816f8_11.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.25.0-py310hf151d32_1.conda + - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda + win-64: + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-h4c7d964_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.9-pyh29332c3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.17-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyh6dadd2b_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.37.0-pyha7b4d00_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.8-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.14.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonpointer-3.1.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.26.0-hcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.3.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyh6dadd2b_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.19.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.5.7-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.28.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_widgets-3.0.16-pyhcf101f3_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-1.15.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.17.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.7-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.10.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.0.0-pyhd8ed1ab_5.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.25.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyh09c184e_7.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-graphviz-0.20.3-pyh91182bf_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-3.2.1-pyh332efcf_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3339-validator-0.1.4-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyh6dadd2b_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh6dadd2b_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/win_inet_pton-1.1.0-pyh7428d3b_8.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/argon2-cffi-bindings-25.1.0-py310h29418f3_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-auth-0.7.20-h6823eb1_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-cal-0.6.12-hc83774a_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-common-0.9.17-h2466b09_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-compression-0.2.18-hc83774a_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-event-stream-0.4.2-hc6c0aac_10.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-http-0.8.1-hced5053_13.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-io-0.14.8-hebaacdb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-mqtt-0.10.4-hdafd9a4_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-s3-0.5.9-h7a83f0e_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-sdkutils-0.1.16-hc83774a_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-checksums-0.1.18-hc83774a_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-crt-cpp-0.26.8-h672a689_11.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/aws-sdk-cpp-1.11.267-h12f3f85_8.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hfd05255_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-hfd05255_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/brotli-python-1.1.0-py310h73ae2b4_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h0ad9c76_9.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/c-ares-1.34.6-hfd05255_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.4-h477c42c_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/cffi-1.15.1-py310h8d17308_5.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.2.1-py310h232114e_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/debugpy-1.8.21-py310h699e580_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.18.1-hd47e2ca_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/fonttools-4.63.0-py310hdb0e946_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/freetype-2.14.3-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/fribidi-1.0.16-hfd05255_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/getopt-win32-0.1-h6a83c73_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/graphite2-1.3.14-hac47afa_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/graphviz-14.1.2-h4c50273_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/gts-0.7.6-h6b5321d_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/h5py-3.16.0-nompi_py310hb7e4da9_102.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/harfbuzz-14.2.0-h5a1b470_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/hdf5-1.14.6-nompi_hae35d4c_109.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/icu-78.3-h637d24d_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.5.0-py310h1e1005b_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/krb5-1.22.2-h0ea6238_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/lcms2-2.19.1-hf2c6c5f_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/lerc-4.1.0-hd936e49_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libabseil-20240116.2-cxx17_he0c23c2_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libaec-1.1.5-haf901d7_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-16.0.0-h107e38f_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-16.0.0-he0c23c2_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-dataset-16.0.0-he0c23c2_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-substrait-16.0.0-h1f0e801_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-35_h5709861_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-hfd05255_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-hfd05255_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-hfd05255_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-35_h2a3cdd5_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libcrc32c-1.1.2-h0e60522_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.20.0-h8206538_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.25-h51727cc_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libevent-2.1.12-h3671451_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.8.1-hac47afa_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.14.3-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.14.3-hdbac1cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgd-2.3.3-h4974f7c_12.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libglib-2.88.1-h7ce1215_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-2.23.0-h68df31e_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-2.23.0-hb581fae_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.62.2-h5273850_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.11.2-default_hc8275d1_1000.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-hc1393d2_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.4.1-hfd05255_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-35_hf9ab0e9_mkl.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.3-hfd05255_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libparquet-16.0.0-h178134c_1_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.58-h7351971_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libprotobuf-4.25.3-h47a098d_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libre2-11-2023.09.01-hf8d8778_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libsodium-1.0.22-h6a83c73_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.53.1-hf5d6505_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libssh2-1.11.1-h9aa295b_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libthrift-0.19.0-ha2b3283_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.1-h8f73337_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libutf8proc-2.8.0-hb602f4b_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.6.0-h4d5522a_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libxcb-1.16-h013a479_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.9-h741aa76_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.2-hfd05255_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/llvm-openmp-22.1.6-h4fa8253_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.9.4-hcfcfb64_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-5.3.0-7.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/markupsafe-3.0.3-py310hdb0e946_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.7.3-py310hc9baf74_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2024.2.2-h57928b3_16.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.22.4-py310hed7ac4c_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.4-h0e57b4f_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.2-hf411b9b_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.0.0-h7e885a9_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pandas-1.3.5-py310hf5e1058_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/pango-1.56.4-h13911b6_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pcre2-10.47-hd2b5f0e_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pillow-10.4.0-py310h3e38d90_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.4-h5112557_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/polars-1.0.0-py310h4694af4_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-5.9.3-py310h8d17308_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-hcd874cb_1001.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-h2466b09_4.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-16.0.0-py310h3bcd3f7_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-16.0.0-py310hcb4c9cb_0_cpu.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.10.20-hc20f281_0_cpython.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pywin32-311-py310h282bd7d_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pywinpty-2.0.15-py310h9e98ed7_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0.3-py310hdb0e946_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/pyzmq-27.1.0-py310h824d4bc_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/re2-2023.09.01-hd3b24a8_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/rpds-py-0.30.0-py310h034784e_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.0.2-py310h4dafddf_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.7.3-py310h578b7cb_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/setuptools-59.8.0-py310h5588dad_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2021.13.0-h62715c5_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.6-py310h29418f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.26100.0-h57928b3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/unicodedata2-17.0.1-py310h29418f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.5-h1b7c187_38.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.51.36231-h1b9f54f_38.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.51.36231-h1b9f54f_38.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.51.36231-h84cd919_38.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/winpty-0.4.3-4.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-kbproto-1.0.7-hcd874cb_1002.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libice-1.1.1-hcd874cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libsm-1.2.4-hcd874cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libx11-1.8.9-h0076a8d_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxext-1.3.4-hcd874cb_2.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxpm-3.5.17-hcd874cb_0.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxt-1.3.0-hcd874cb_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-xextproto-7.3.0-hcd874cb_1003.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/xorg-xproto-7.0.31-hcd874cb_1007.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h6a83c73_3.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h3a581c9_11.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.25.0-py310h1637853_1.conda + - conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda +packages: +- conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + build_number: 20 + sha256: 1dd3fffd892081df9726d7eb7e0dea6198962ba775bd88842135a4ddb4deb3c9 + md5: a9f577daf3de00bca7c3c76c0ecbd1de + depends: + - __glibc >=2.17,<3.0.a0 + - libgomp >=7.5.0 + constrains: + - openmp_impl <0.0a0 + license: BSD-3-Clause + license_family: BSD + size: 28948 + timestamp: 1770939786096 +- conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py310h7c4b9e2_2.conda + sha256: 5396242c40688b33b57d8564025569598ab4848fd03852bb7415443b9f421fa1 + md5: 7f9a178be0c687e77f7248507737d15e + depends: + - __glibc >=2.17,<3.0.a0 + - cffi >=1.0.1 + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 35370 + timestamp: 1762509501470 +- conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2 + sha256: 26ab9386e80bf196e51ebe005da77d57decf6d989b4f34d96130560bc133479c + md5: 6b889f174df1e0f816276ae69281af4d + depends: + - at-spi2-core >=2.40.0,<2.41.0a0 + - atk-1.0 >=2.36.0 + - dbus >=1.13.6,<2.0a0 + - libgcc-ng >=9.3.0 + - libglib >=2.68.1,<3.0a0 + license: LGPL-2.1-or-later + license_family: LGPL + size: 339899 + timestamp: 1619122953439 +- conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2 + sha256: c4f9b66bd94c40d8f1ce1fad2d8b46534bdefda0c86e3337b28f6c25779f258d + md5: 8cb2fc4cd6cc63f1369cfa318f581cc3 + depends: + - dbus >=1.13.6,<2.0a0 + - libgcc-ng >=9.3.0 + - libglib >=2.68.3,<3.0a0 + - xorg-libx11 + - xorg-libxi + - xorg-libxtst + license: LGPL-2.1-or-later + license_family: LGPL + size: 658390 + timestamp: 1625848454791 +- conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda + sha256: df682395d05050cd1222740a42a551281210726a67447e5258968dd55854302e + md5: f730d54ba9cd543666d7220c9f7ed563 + depends: + - libgcc-ng >=12 + - libglib >=2.80.0,<3.0a0 + - libstdcxx-ng >=12 + constrains: + - atk-1.0 2.38.0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 355900 + timestamp: 1713896169874 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.20-h5f1c8d9_0.conda + sha256: 12f53171a2cb544a83be9866bf41f7a15aa7ff032d9f91ea6fd2ca4c34c84768 + md5: 418775183961dc1ee1c326a473118f98 + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 105856 + timestamp: 1715287622406 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.6.12-h2ba76a8_0.conda + sha256: 1aafd1dcbfefce4e4c78fc5301d24dbdcffc3dcaae41bf43fde326d1525c1869 + md5: da9257187c044a2a8f52507fea68a4c3 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + - openssl >=3.2.1,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 46544 + timestamp: 1714177543437 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.17-h4ab18f5_0.conda + sha256: e3a6fb2fd7079fc92022facbba5eae1b6d7d3ecd28f894bcde4cd3964280c3ee + md5: 97d60c6b52391872febd35fab0a30159 + depends: + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 227784 + timestamp: 1713863751252 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.2.18-h36a0aea_4.conda + sha256: 7f16b562f9644e5dbc66082886d303601e9fb993dc1cf556ad4517bdf87f30aa + md5: ce9d15eeabc21f9936410382e20c2908 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 19189 + timestamp: 1714043806611 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.4.2-h161de36_10.conda + sha256: 31877ce699b8dfc8bad3bb82d908e9b3f3788af6ba6ab3fb141ad673c8d191d2 + md5: a7a334cb2d24e31a9bf0e7e3d01b14cb + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 53763 + timestamp: 1715026272209 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.8.1-h63f54a0_13.conda + sha256: 679f62ea3e7cca58c8068f2770440636e79c645554e4c7ff52036567a755a5d2 + md5: dd5266145d7b778c9e9a0508a503e564 + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-compression >=0.2.18,<0.2.19.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 195229 + timestamp: 1715026240632 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.14.8-h96d4d28_0.conda + sha256: 70fba744853744151087d0bfe5cd65bdc08089cf713b6b83bf81f878c51ab1b6 + md5: 417d99cf69a0e6f40251815ca7622273 + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + - s2n >=1.4.13,<1.4.14.0a0 + license: Apache-2.0 + license_family: Apache + size: 157952 + timestamp: 1714867798089 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.10.4-hcc7299c_2.conda + sha256: fcb732bc33d0aeead35a10eb7ee76494bfc68dcd06c9299e9c381c6e9e93ff7b + md5: 7003778c651fa3ba815cfdf065d769af + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 164142 + timestamp: 1715057917794 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.5.9-h10bd90f_0.conda + sha256: f7e0101a3e629e55d2afc6265c25e8f7dc207119eaed860c682afa2cdd083c1f + md5: 5cd6a7eb8c0e45be0e49a9c6351ff42b + depends: + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - libgcc-ng >=12 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 109841 + timestamp: 1715619697604 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.1.16-h36a0aea_0.conda + sha256: 214fe6443dcd092287f739af2f9bc1d06e20014515363b3569fd4c74144f6a9d + md5: 2555c5ffa3a60fde5a940c5c9f4327cc + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 54920 + timestamp: 1714208472161 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.18-h36a0aea_4.conda + sha256: 224ead1679870e28005bfa7d27e8dd702f09837005610c6b06c52a95641da30b + md5: bd99b76853edcc6fae6a901900bba995 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 50174 + timestamp: 1714050863900 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.26.8-h4f3a3cc_11.conda + sha256: 3fc936c26ca513608977320aba42adbe8d6eaeabdf153076537f60fdcc6dd93d + md5: 09c816a52369923fb5e37823556b1eb7 + depends: + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-mqtt >=0.10.4,<0.10.5.0a0 + - aws-c-s3 >=0.5.9,<0.5.10.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 340309 + timestamp: 1716300886925 +- conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.267-h51dfee4_8.conda + sha256: 8fb8f648d1ae7d4f2005c130686b569eec998f8fda37d0f24e50fc069428484b + md5: 188857656abd6d1a4dcc471c619b0de5 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - libcurl >=8.7.1,<9.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 3620978 + timestamp: 1715175553502 +- conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb03c661_4.conda + sha256: 294526a54fa13635341729f250d0b1cf8f82cad1e6b83130304cbf3b6d8b74cc + md5: eaf3fbd2aa97c212336de38a51fe404e + depends: + - __glibc >=2.17,<3.0.a0 + - brotli-bin 1.1.0 hb03c661_4 + - libbrotlidec 1.1.0 hb03c661_4 + - libbrotlienc 1.1.0 hb03c661_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 19883 + timestamp: 1756599394934 +- conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb03c661_4.conda + sha256: 444903c6e5c553175721a16b7c7de590ef754a15c28c99afbc8a963b35269517 + md5: ca4ed8015764937c81b830f7f5b68543 + depends: + - __glibc >=2.17,<3.0.a0 + - libbrotlidec 1.1.0 hb03c661_4 + - libbrotlienc 1.1.0 hb03c661_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 19615 + timestamp: 1756599385418 +- conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hea6c23e_4.conda + sha256: 29f24d4a937c3a7f4894d6be9d9f9604adbb5506891f0f37bbb7e2dc8fa6bc0a + md5: 6ef43db290647218e1e04c2601675bff + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - libbrotlicommon 1.1.0 hb03c661_4 + license: MIT + license_family: MIT + size: 353838 + timestamp: 1756599456833 +- conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda + sha256: 0b75d45f0bba3e95dc693336fa51f40ea28c980131fec438afb7ce6118ed05f6 + md5: d2ffd7602c02f2b316fd921d39876885 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: bzip2-1.0.6 + license_family: BSD + size: 260182 + timestamp: 1771350215188 +- conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda + sha256: cc9accf72fa028d31c2a038460787751127317dcfa991f8d1f1babf216bb454e + md5: 920bb03579f15389b9e512095ad995b7 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 207882 + timestamp: 1765214722852 +- conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda + sha256: 06525fa0c4e4f56e771a3b986d0fdf0f0fc5a3270830ee47e127a5105bde1b9a + md5: bb6c4808bfa69d6f7f6b07e5846ced37 + depends: + - __glibc >=2.17,<3.0.a0 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libgcc >=14 + - libglib >=2.86.3,<3.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libstdcxx >=14 + - libxcb >=1.17.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - pixman >=0.46.4,<1.0a0 + - xorg-libice >=1.1.2,<2.0a0 + - xorg-libsm >=1.2.6,<2.0a0 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxrender >=0.9.12,<0.10.0a0 + license: LGPL-2.1-only or MPL-1.1 + size: 989514 + timestamp: 1766415934926 +- conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.15.1-py310h2fee648_5.conda + sha256: aff72ce652dc06ec0ebacf73ea1746b5a6a369826460fff850d2a56d6cc6bd7b + md5: fef75d6c60d8a1cc7e6d1707f470a4e2 + depends: + - libffi >=3.4,<4.0a0 + - libgcc-ng >=12 + - pycparser + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 238047 + timestamp: 1695367249369 +- conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.1-py310hd41b1e2_0.conda + sha256: b9283a52ec79bf71325cde80b8845e86bdf9ac80d8b38f95ad47cbaab32447fe + md5: 60ee50b1968f802f2a487ba36d4cce0d + depends: + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - numpy >=1.20 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 241947 + timestamp: 1712430089559 +- conda: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.1.0-py310h7c4b9e2_2.conda + sha256: d523f8b090ef1ca44f72f50a8c76a2df225655bff99b470146029af722eab2fe + md5: 83d52a0c1500d0f8120966b678bcab80 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - toolz >=0.10.0 + license: BSD-3-Clause + license_family: BSD + size: 598894 + timestamp: 1771855874334 +- conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h24cb091_1.conda + sha256: 8bb557af1b2b7983cf56292336a1a1853f26555d9c6cecf1e5b2b96838c9da87 + md5: ce96f2f470d39bd96ce03945af92e280 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + - libzlib >=1.3.1,<2.0a0 + - libglib >=2.86.2,<3.0a0 + - libexpat >=2.7.3,<3.0a0 + license: AFL-2.1 OR GPL-2.0-or-later + size: 447649 + timestamp: 1764536047944 +- conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.21-py310h25320af_0.conda + sha256: 005e50f4e09327ee737408bcda52c22fcff7226d3864a814d6f97ee3d5ae6d67 + md5: cdbb65fde8219076eed589ff728507d6 + depends: + - python + - __glibc >=2.17,<3.0.a0 + - libstdcxx >=14 + - libgcc >=14 + - python_abi 3.10.* *_cp310 + license: MIT + size: 2224979 + timestamp: 1780390153919 +- conda: https://conda.anaconda.org/conda-forge/linux-64/epoxy-1.5.10-hb03c661_2.conda + sha256: a5b51e491fec22bcc1765f5b2c8fff8a97428e9a5a7ee6730095fb9d091b0747 + md5: 057083b06ccf1c2778344b6dabace38b + depends: + - __glibc >=2.17,<3.0.a0 + - libdrm >=2.4.125,<2.5.0a0 + - libegl >=1.7.0,<2.0a0 + - libegl-devel + - libgcc >=14 + - libgl >=1.7.0,<2.0a0 + - libgl-devel + - libglx >=1.7.0,<2.0a0 + - libglx-devel + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxdamage >=1.1.6,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxfixes >=6.0.1,<7.0a0 + - xorg-libxxf86vm >=1.1.6,<2.0a0 + license: MIT + license_family: MIT + size: 411735 + timestamp: 1758743520805 +- conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.1-h27c8c51_0.conda + sha256: 2e50bdcebdf70a865b81f2456bbc586386451ec601c60f2b6cd22b8c40a2d384 + md5: e0e050cfa9fa85fe39632ab11cb7f3e0 + depends: + - __glibc >=2.17,<3.0.a0 + - libexpat >=2.8.1,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libgcc >=14 + - libuuid >=2.42.1,<3.0a0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + size: 281880 + timestamp: 1780450077431 +- conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.63.0-py310h3406613_0.conda + sha256: bca35ffa02f3c56774deb4a8aa39ef71c7cf5fbd01d7b222047b1a8a7194edae + md5: 73c9e3870ca97be05c96962a1606b288 + depends: + - __glibc >=2.17,<3.0.a0 + - brotli + - libgcc >=14 + - munkres + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - unicodedata2 >=15.1.0 + license: MIT + license_family: MIT + size: 2454762 + timestamp: 1778770500028 +- conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda + sha256: c934c385889c7836f034039b43b05ccfa98f53c900db03d8411189892ced090b + md5: 8462b5322567212beeb025f3519fb3e2 + depends: + - libfreetype 2.14.3 ha770c72_0 + - libfreetype6 2.14.3 h73754d4_0 + license: GPL-2.0-only OR FTL + size: 173839 + timestamp: 1774298173462 +- conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda + sha256: 858283ff33d4c033f4971bf440cebff217d5552a5222ba994c49be990dacd40d + md5: f9f81ea472684d75b9dd8d0b328cf655 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: LGPL-2.1-or-later + size: 61244 + timestamp: 1757438574066 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.44.6-h2b0a6b4_0.conda + sha256: c5594497f0646e9079705b3199dbb2d5b13c48173cf110000fa1c8818e2b3e0c + md5: 7892f39a39ed39591a89a28eba03e987 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libglib >=2.86.4,<3.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - liblzma >=5.8.2,<6.0a0 + - libpng >=1.6.56,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: LGPL-2.1-or-later + license_family: LGPL + size: 577414 + timestamp: 1774985848058 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda + sha256: 6c33bf0c4d8f418546ba9c250db4e4221040936aef8956353bc764d4877bc39a + md5: d411fc29e338efb48c5fd4576d71d881 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - libstdcxx >=13 + license: BSD-3-Clause + license_family: BSD + size: 119654 + timestamp: 1726600001928 +- conda: https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.88.1-hee1de02_2.conda + sha256: ae41fd5c867bc4e713a8cc1dc06f5b418026fec116cc222abe33e94235c6b241 + md5: e5a459d2bb98edb88de5a44bfad66b9d + depends: + - libglib ==2.88.1 h0d30a3d_2 + - libffi + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + license: LGPL-2.1-or-later + size: 236955 + timestamp: 1778508800134 +- conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda + sha256: dc824dc1d0aa358e28da2ecbbb9f03d932d976c8dca11214aa1dcdfcbd054ba2 + md5: ff862eebdfeb2fd048ae9dc92510baca + depends: + - gflags >=2.2.2,<2.3.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: BSD-3-Clause + license_family: BSD + size: 143452 + timestamp: 1718284177264 +- conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda + sha256: 25ba37da5c39697a77fce2c9a15e48cf0a84f1464ad2aafbe53d8357a9f6cc8c + md5: 2cd94587f3a401ae05e03a6caf09539d + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + license: LGPL-2.0-or-later + license_family: LGPL + size: 99596 + timestamp: 1755102025473 +- conda: https://conda.anaconda.org/conda-forge/linux-64/graphviz-14.1.2-h8b86629_0.conda + sha256: 48d4aae8d2f7dd038b8c2b6a1b68b7bca13fa6b374b78c09fcc0757fa21234a1 + md5: 341fc61cfe8efa5c72d24db56c776f44 + depends: + - __glibc >=2.17,<3.0.a0 + - adwaita-icon-theme + - cairo >=1.18.4,<2.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.4,<3.0a0 + - gtk3 >=3.24.43,<4.0a0 + - gts >=0.7.6,<0.8.0a0 + - libexpat >=2.7.3,<3.0a0 + - libgcc >=14 + - libgd >=2.3.3,<2.4.0a0 + - libglib >=2.86.3,<3.0a0 + - librsvg >=2.60.0,<3.0a0 + - libstdcxx >=14 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - pango >=1.56.4,<2.0a0 + license: EPL-1.0 + license_family: Other + size: 2426455 + timestamp: 1769427102743 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.52-ha5ea40c_0.conda + sha256: c6bb4f06331bcb0a566d84e0f0fad7af4b9035a03b13e2d5ecfaf13be57e6e10 + md5: bcaea22d85999a4f17918acfab877e61 + depends: + - __glibc >=2.17,<3.0.a0 + - at-spi2-atk >=2.38.0,<3.0a0 + - atk-1.0 >=2.38.0 + - cairo >=1.18.4,<2.0a0 + - epoxy >=1.5.10,<1.6.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.16,<2.0a0 + - gdk-pixbuf >=2.44.5,<3.0a0 + - glib-tools + - harfbuzz >=13.2.1 + - hicolor-icon-theme + - libcups >=2.3.3,<2.4.0a0 + - libcups >=2.3.3,<3.0a0 + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libgcc >=14 + - libglib >=2.86.4,<3.0a0 + - liblzma >=5.8.2,<6.0a0 + - libxkbcommon >=1.13.1,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - pango >=1.56.4,<2.0a0 + - wayland >=1.25.0,<2.0a0 + - xorg-libx11 >=1.8.13,<2.0a0 + - xorg-libxcomposite >=0.4.7,<1.0a0 + - xorg-libxcursor >=1.2.3,<2.0a0 + - xorg-libxdamage >=1.1.6,<2.0a0 + - xorg-libxext >=1.3.7,<2.0a0 + - xorg-libxfixes >=6.0.2,<7.0a0 + - xorg-libxi >=1.8.2,<2.0a0 + - xorg-libxinerama >=1.1.6,<1.2.0a0 + - xorg-libxrandr >=1.5.5,<2.0a0 + - xorg-libxrender >=0.9.12,<0.10.0a0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 5939083 + timestamp: 1774288645605 +- conda: https://conda.anaconda.org/conda-forge/linux-64/gts-0.7.6-h977cf35_4.conda + sha256: b5cd16262fefb836f69dc26d879b6508d29f8a5c5948a966c47fe99e2e19c99b + md5: 4d8df0b0db060d33c9a702ada998a8fe + depends: + - libgcc-ng >=12 + - libglib >=2.76.3,<3.0a0 + - libstdcxx-ng >=12 + license: LGPL-2.0-or-later + license_family: LGPL + size: 318312 + timestamp: 1686545244763 +- conda: https://conda.anaconda.org/conda-forge/linux-64/h5py-3.16.0-nompi_py310h4aa865e_102.conda + sha256: e2f5169e2da674c83e678e7ec0ed6f969e5830a4bd23606ef610b2b18dde1090 + md5: e5f4aa8cdc28aa431bd54f4d8b2ed24f + depends: + - __glibc >=2.17,<3.0.a0 + - cached-property + - hdf5 >=1.14.6,<1.14.7.0a0 + - libgcc >=14 + - numpy >=1.21,<3 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 1252638 + timestamp: 1775581283560 +- conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-14.2.1-h6083320_0.conda + sha256: da9901aa1e20cbc2369fda212039b294dd02bce95f005539bab840b7310bf7d0 + md5: 21ee4640b7c2d94e584349fa12b29b9a + depends: + - __glibc >=2.17,<3.0.a0 + - cairo >=1.18.4,<2.0a0 + - graphite2 >=1.3.14,<2.0a0 + - icu >=78.3,<79.0a0 + - libexpat >=2.8.1,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libgcc >=14 + - libglib >=2.88.1,<3.0a0 + - libstdcxx >=14 + - libzlib >=1.3.2,<2.0a0 + license: MIT + size: 2362258 + timestamp: 1780450503234 +- conda: https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.14.6-nompi_h19486de_109.conda + sha256: 9d2ea00599e2874b295baa7e719c79823ed61fec885e25c55e7dad666fb1cf50 + md5: c0ca97fff3fff46f837c69efedf838b1 + depends: + - __glibc >=2.17,<3.0.a0 + - libaec >=1.1.5,<2.0a0 + - libcurl >=8.20.0,<9.0a0 + - libgcc >=14 + - libgfortran + - libgfortran5 >=14.3.0 + - libstdcxx >=14 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 3719822 + timestamp: 1777518369641 +- conda: https://conda.anaconda.org/conda-forge/linux-64/hicolor-icon-theme-0.17-ha770c72_3.conda + sha256: 6d7e6e1286cb521059fe69696705100a03b006efb914ffe82a2ae97ecbae66b7 + md5: 129e404c5b001f3ef5581316971e3ea0 + license: GPL-2.0-or-later + license_family: GPL + size: 17625 + timestamp: 1771539597968 +- conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda + sha256: fbf86c4a59c2ed05bbffb2ba25c7ed94f6185ec30ecb691615d42342baa1a16a + md5: c80d8a3b84358cb967fa81e7075fbc8a + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + license: MIT + license_family: MIT + size: 12723451 + timestamp: 1773822285671 +- conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda + sha256: 0960d06048a7185d3542d850986d807c6e37ca2e644342dd0c72feefcf26c2a4 + md5: b38117a3c920364aff79f870c984b4a3 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + license: LGPL-2.1-or-later + size: 134088 + timestamp: 1754905959823 +- conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.5.0-py310haaf941d_0.conda + sha256: 44312f8b881a4c77af4be198c8e2e2022e406f58314191c31be8e172382ecdf7 + md5: 8993ab7e5dce89147288dd78686e790c + depends: + - python + - libstdcxx >=14 + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 77809 + timestamp: 1773067043838 +- conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-ha1258a1_0.conda + sha256: 3e307628ca3527448dd1cb14ad7bb9d04d1d28c7d4c5f97ba196ae984571dd25 + md5: fb53fb07ce46a575c5d004bbc96032c2 + depends: + - __glibc >=2.17,<3.0.a0 + - keyutils >=1.6.3,<2.0a0 + - libedit >=3.1.20250104,<3.2.0a0 + - libedit >=3.1.20250104,<4.0a0 + - libgcc >=14 + - libstdcxx >=14 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 1386730 + timestamp: 1769769569681 +- conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.19.1-h0c24ade_1.conda + sha256: 112b5b9462572d970f4abd2912f76a25ee7db158b1e7260163d91dd8a630db84 + md5: 8b3ce45e929cd8e8e5f4d18586b56d8b + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libjpeg-turbo >=3.1.4.1,<4.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: MIT + license_family: MIT + size: 251971 + timestamp: 1780211695895 +- conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45.1-default_hbd61a6d_102.conda + sha256: 3d584956604909ff5df353767f3a2a2f60e07d070b328d109f30ac40cd62df6c + md5: 18335a698559cdbcd86150a48bf54ba6 + depends: + - __glibc >=2.17,<3.0.a0 + - zstd >=1.5.7,<1.6.0a0 + constrains: + - binutils_impl_linux-64 2.45.1 + license: GPL-3.0-only + license_family: GPL + size: 728002 + timestamp: 1774197446916 +- conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.1.0-hdb68285_0.conda + sha256: f84cb54782f7e9cea95e810ea8fef186e0652d0fa73d3009914fa2c1262594e1 + md5: a752488c68f2e7c456bcbd8f16eec275 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + license: Apache-2.0 + license_family: Apache + size: 261513 + timestamp: 1773113328888 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_he02047a_1.conda + sha256: 945396726cadae174a661ce006e3f74d71dbd719219faf7cc74696b267f7b0b5 + md5: c48fc56ec03229f294176923c3265c05 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + constrains: + - abseil-cpp =20240116.2 + - libabseil-static =20240116.2=cxx17* + license: Apache-2.0 + license_family: Apache + size: 1264712 + timestamp: 1720857377573 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libaec-1.1.5-h088129d_0.conda + sha256: 822e4ae421a7e9c04e841323526321185f6659222325e1a9aedec811c686e688 + md5: 86f7414544ae606282352fa1e116b41f + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + license: BSD-2-Clause + license_family: BSD + size: 36544 + timestamp: 1769221884824 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-16.0.0-hefa796f_1_cpu.conda + build_number: 1 + sha256: 3fc94f83be17f7e29f2b32ec37f7d474258405720699141a3b4558972a9e9f60 + md5: 4c7ccde1d72668a6c3bf9e20fb483f8d + depends: + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - aws-sdk-cpp >=1.11.267,<1.11.268.0a0 + - bzip2 >=1.0.8,<2.0a0 + - gflags >=2.2.2,<2.3.0a0 + - glog >=0.7.0,<0.8.0a0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libbrotlidec >=1.1.0,<1.2.0a0 + - libbrotlienc >=1.1.0,<1.2.0a0 + - libgcc-ng >=12 + - libgoogle-cloud >=2.23.0,<2.24.0a0 + - libgoogle-cloud-storage >=2.23.0,<2.24.0a0 + - libre2-11 >=2023.9.1 + - libstdcxx-ng >=12 + - libutf8proc >=2.8.0,<3.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - orc >=2.0.0,<2.0.1.0a0 + - re2 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.6,<1.6.0a0 + - libutf8proc <2.9 + constrains: + - parquet-cpp <0.0a0 + - apache-arrow-proc =*=cpu + - arrow-cpp <0.0a0 + license: Apache-2.0 + license_family: APACHE + size: 8254968 + timestamp: 1715198055113 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-16.0.0-hac33072_1_cpu.conda + build_number: 1 + sha256: 2d6edbcfbd0d9a96b70fe2898127e7c203e61214f7eb8cb8a5b65258da60d0aa + md5: 418842358b0c5d8e94b2bff03696b6e1 + depends: + - libarrow 16.0.0 hefa796f_1_cpu + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: Apache-2.0 + license_family: APACHE + size: 599285 + timestamp: 1715198096740 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-16.0.0-hac33072_1_cpu.conda + build_number: 1 + sha256: f59514cce9f771e6b5885bd8dd2210acff469d2d217cb85a7ffb5eae2b71cabe + md5: 68aac3dcc08dd7630e557d7c21d03d9f + depends: + - libarrow 16.0.0 hefa796f_1_cpu + - libarrow-acero 16.0.0 hac33072_1_cpu + - libgcc-ng >=12 + - libparquet 16.0.0 h6a7eafb_1_cpu + - libstdcxx-ng >=12 + license: Apache-2.0 + license_family: APACHE + size: 580162 + timestamp: 1715198178342 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-16.0.0-h7e0c224_1_cpu.conda + build_number: 1 + sha256: 672d4eb00060e9cdd06360ed9145e69d185cfbf60c70eb10a6375f5f62f27a90 + md5: 0bef58136c2627be09f838add7826e80 + depends: + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libarrow 16.0.0 hefa796f_1_cpu + - libarrow-acero 16.0.0 hac33072_1_cpu + - libarrow-dataset 16.0.0 hac33072_1_cpu + - libgcc-ng >=12 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libstdcxx-ng >=12 + license: Apache-2.0 + license_family: APACHE + size: 549076 + timestamp: 1715198217009 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-8_h4a7cf45_openblas.conda + build_number: 8 + sha256: b2da6bfd72a1c9cb143ccf64bf5b28790cb4eb58bd1cb978f6537b2322f7d48b + md5: 00fc660ab1b2f5ca07e92b4900d10c79 + depends: + - libopenblas >=0.3.33,<0.3.34.0a0 + - libopenblas >=0.3.33,<1.0a0 + constrains: + - blas 2.308 openblas + - mkl <2027 + - libcblas 3.11.0 8*_openblas + - liblapack 3.11.0 8*_openblas + - liblapacke 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 18804 + timestamp: 1779859100675 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda + build_number: 20 + sha256: 8a0ee1de693a9b3da4a11b95ec81b40dd434bd01fa1f5f38f8268cd2146bf8f0 + md5: 2b7bb4f7562c8cf334fc2e20c2d28abc + depends: + - libopenblas >=0.3.25,<0.3.26.0a0 + - libopenblas >=0.3.25,<1.0a0 + constrains: + - liblapacke 3.9.0 20_linux64_openblas + - libcblas 3.9.0 20_linux64_openblas + - blas * openblas + - liblapack 3.9.0 20_linux64_openblas + - mkl <2025 + license: BSD-3-Clause + license_family: BSD + size: 14433 + timestamp: 1700568383457 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb03c661_4.conda + sha256: 2338a92d1de71f10c8cf70f7bb9775b0144a306d75c4812276749f54925612b6 + md5: 1d29d2e33fe59954af82ef54a8af3fe1 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 69333 + timestamp: 1756599354727 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb03c661_4.conda + sha256: fcec0d26f67741b122f0d5eff32f0393d7ebd3ee6bb866ae2f17f3425a850936 + md5: 5cb5a1c9a94a78f5b23684bcb845338d + depends: + - __glibc >=2.17,<3.0.a0 + - libbrotlicommon 1.1.0 hb03c661_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 33406 + timestamp: 1756599364386 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb03c661_4.conda + sha256: d42c7f0afce21d5279a0d54ee9e64a2279d35a07a90e0c9545caae57d6d7dc57 + md5: 2e55011fa483edb8bfe3fd92e860cd79 + depends: + - __glibc >=2.17,<3.0.a0 + - libbrotlicommon 1.1.0 hb03c661_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 289680 + timestamp: 1756599375485 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-8_h0358290_openblas.conda + build_number: 8 + sha256: 1a2bc77bb26520255904a3d9b1f40e6bf0bf9d8d3405c7709dd162282820915a + md5: 33a413f1095f8325e5c30fde3b0d2445 + depends: + - libblas 3.11.0 8_h4a7cf45_openblas + constrains: + - blas 2.308 openblas + - liblapacke 3.11.0 8*_openblas + - liblapack 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 18778 + timestamp: 1779859107964 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda + build_number: 20 + sha256: 0e34fb0f82262f02fcb279ab4a1db8d50875dc98e3019452f8f387e6bf3c0247 + md5: 36d486d72ab64ffea932329a1d3729a3 + depends: + - libblas 3.9.0 20_linux64_openblas + constrains: + - liblapacke 3.9.0 20_linux64_openblas + - blas * openblas + - liblapack 3.9.0 20_linux64_openblas + - mkl <2025 + license: BSD-3-Clause + license_family: BSD + size: 14383 + timestamp: 1700568410580 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2 + sha256: fd1d153962764433fe6233f34a72cdeed5dcf8a883a85769e8295ce940b5b0c5 + md5: c965a5aa0d5c1c37ffc62dff36e28400 + depends: + - libgcc-ng >=9.4.0 + - libstdcxx-ng >=9.4.0 + license: BSD-3-Clause + license_family: BSD + size: 20440 + timestamp: 1633683576494 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-h7a8fb5f_6.conda + sha256: 205c4f19550f3647832ec44e35e6d93c8c206782bdd620c1d7cf66237580ff9c + md5: 49c553b47ff679a6a1e9fc80b9c5a2d4 + depends: + - __glibc >=2.17,<3.0.a0 + - krb5 >=1.22.2,<1.23.0a0 + - libgcc >=14 + - libstdcxx >=14 + - libzlib >=1.3.1,<2.0a0 + license: Apache-2.0 + license_family: Apache + size: 4518030 + timestamp: 1770902209173 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.20.0-hcf29cc6_0.conda + sha256: 75963a5dd913311f59a35dbd307592f4fa754c4808aff9c33edb430c415e38eb + md5: c3cc2864f82a944bc90a7beb4d3b0e88 + depends: + - __glibc >=2.17,<3.0.a0 + - krb5 >=1.22.2,<1.23.0a0 + - libgcc >=14 + - libnghttp2 >=1.68.1,<2.0a0 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: curl + license_family: MIT + size: 468706 + timestamp: 1777461492876 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda + sha256: aa8e8c4be9a2e81610ddf574e05b64ee131fab5e0e3693210c9d6d2fba32c680 + md5: 6c77a605a7a689d17d4819c0f8ac9a00 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 73490 + timestamp: 1761979956660 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.127-hb03c661_0.conda + sha256: 7d3187c11b7ae66c5595a8afd5a7ce352a490527fdf6614cab129bc7f2c16ba3 + md5: d8d16b9b32a3c5df7e5b3350e2cbe058 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libpciaccess >=0.19,<0.20.0a0 + license: MIT + license_family: MIT + size: 311505 + timestamp: 1778975798004 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda + sha256: d789471216e7aba3c184cd054ed61ce3f6dac6f87a50ec69291b9297f8c18724 + md5: c277e0a4d549b03ac1e9d6cbbe3d017b + depends: + - ncurses + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - ncurses >=6.5,<7.0a0 + license: BSD-2-Clause + license_family: BSD + size: 134676 + timestamp: 1738479519902 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_3.conda + sha256: 9a25ea93e8272785405a21d30f84e620befb1d545f6dfaae18f06103b5df0443 + md5: 75e9f795be506c96dd43cb09c7c8d557 + depends: + - __glibc >=2.17,<3.0.a0 + - libglvnd 1.7.0 ha4b6fd6_3 + license: LicenseRef-libglvnd + size: 46500 + timestamp: 1779728188901 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-devel-1.7.0-ha4b6fd6_3.conda + sha256: e4b46919c9bb65930bce238bd2736110ed7b8c30e5cd5394e4e1edb48de54843 + md5: 5bc6d55503483aabe8a90c5e7f49a2a4 + depends: + - __glibc >=2.17,<3.0.a0 + - libegl 1.7.0 ha4b6fd6_3 + - libgl-devel 1.7.0 ha4b6fd6_3 + - xorg-libx11 + license: LicenseRef-libglvnd + size: 31718 + timestamp: 1779728222280 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda + sha256: 1cd6048169fa0395af74ed5d8f1716e22c19a81a8a36f934c110ca3ad4dd27b4 + md5: 172bf1cd1ff8629f2b1179945ed45055 + depends: + - libgcc-ng >=12 + license: BSD-2-Clause + license_family: BSD + size: 112766 + timestamp: 1702146165126 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda + sha256: 2e14399d81fb348e9d231a82ca4d816bf855206923759b69ad006ba482764131 + md5: a1cfcc585f0c42bf8d5546bb1dfb668d + depends: + - libgcc-ng >=12 + - openssl >=3.1.1,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 427426 + timestamp: 1685725977222 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_0.conda + sha256: 363018b25fdb5534c79783d912bd4b685a3547f4fc5996357ad548899b0ee8e7 + md5: 93764a5ca80616e9c10106cdaec92f74 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + constrains: + - expat 2.8.1.* + license: MIT + license_family: MIT + size: 77294 + timestamp: 1779278686680 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda + sha256: 31f19b6a88ce40ebc0d5a992c131f57d919f73c0b92cd1617a5bec83f6e961e6 + md5: a360c33a5abe61c07959e449fa1453eb + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 58592 + timestamp: 1769456073053 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_0.conda + sha256: 38f014a7129e644636e46064ecd6b1945e729c2140e21d75bb476af39e692db2 + md5: e289f3d17880e44b633ba911d57a321b + depends: + - libfreetype6 >=2.14.3 + license: GPL-2.0-only OR FTL + size: 8049 + timestamp: 1774298163029 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h73754d4_0.conda + sha256: 16f020f96da79db1863fcdd8f2b8f4f7d52f177dd4c58601e38e9182e91adf1d + md5: fb16b4b69e3f1dcfe79d80db8fd0c55d + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - freetype >=2.14.3 + license: GPL-2.0-only OR FTL + size: 384575 + timestamp: 1774298162622 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda + sha256: 8e0a3b5e41272e5678499b5dfc4cddb673f9e935de01eb0767ce857001229f46 + md5: 57736f29cc2b0ec0b6c2952d3f101b6a + depends: + - __glibc >=2.17,<3.0.a0 + - _openmp_mutex >=4.5 + constrains: + - libgcc-ng ==15.2.0=*_19 + - libgomp 15.2.0 he0feb66_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 1041084 + timestamp: 1778269013026 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda + sha256: 9dcf54adfaa5e861123c2da4f2f0451a685464ea7e5a41ad91cf67b31d658d98 + md5: 331ee9b72b9dff570d56b1302c5ab37d + depends: + - libgcc 15.2.0 he0feb66_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27694 + timestamp: 1778269016987 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-h5fbf134_12.conda + sha256: 245be793e831170504f36213134f4c24eedaf39e634679809fd5391ad214480b + md5: 88c1c66987cd52a712eea89c27104be6 + depends: + - __glibc >=2.17,<3.0.a0 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libgcc >=14 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + license: GD + license_family: BSD + size: 177306 + timestamp: 1766331805898 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda + sha256: 561a42758ef25b9ce308c4e2cf56daee4f06138385a17e29a492cd928e00be6f + md5: 42bf7eca1a951735fa06c0e3c0d5c8e6 + depends: + - libgfortran5 15.2.0 h68bc16d_19 + constrains: + - libgfortran-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27655 + timestamp: 1778269042954 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-15.2.0-h69a702a_19.conda + sha256: 9ca1d254a3e44e608abec6186b18d372cec21e5253e6da9750f4a8f4780ea0bb + md5: 35d07243abf828674d273aecd1dd537e + depends: + - libgfortran 15.2.0 h69a702a_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27727 + timestamp: 1778269220455 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda + sha256: 057978bb69fea29ed715a9b98adf71015c31baecc4aeb2bfc20d4fd5d83579d4 + md5: 85072b0ad177c966294f129b7c04a2d5 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=15.2.0 + constrains: + - libgfortran 15.2.0 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 2483673 + timestamp: 1778269025089 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_3.conda + sha256: ec353b3076ed8e357ed961d0e9ff6997491cade0e603de5bd18a2e301ac78ebd + md5: f25206d7322c0e9648e8b83694d143ab + depends: + - __glibc >=2.17,<3.0.a0 + - libglvnd 1.7.0 ha4b6fd6_3 + - libglx 1.7.0 ha4b6fd6_3 + license: LicenseRef-libglvnd + size: 133469 + timestamp: 1779728207669 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-devel-1.7.0-ha4b6fd6_3.conda + sha256: 41d7d864ad1f199bdb06ff6cc3931455c8af62f1d2071a08c6fa08affbcb678f + md5: 63e43d278ee5084813fe3c2edf4834ce + depends: + - __glibc >=2.17,<3.0.a0 + - libgl 1.7.0 ha4b6fd6_3 + - libglx-devel 1.7.0 ha4b6fd6_3 + license: LicenseRef-libglvnd + size: 115664 + timestamp: 1779728218325 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.88.1-h0d30a3d_2.conda + sha256: 33eb5d5310a5c2c0a4707a0afa644801c2e08c8f70c45e1f62f354116dfe0970 + md5: 17d484ab9c8179c6a6e5b7dbb5065afc + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libffi >=3.5.2,<3.6.0a0 + - pcre2 >=10.47,<10.48.0a0 + - libzlib >=1.3.2,<2.0a0 + - libiconv >=1.18,<2.0a0 + constrains: + - glib >2.66 + license: LGPL-2.1-or-later + size: 4754097 + timestamp: 1778508800134 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_3.conda + sha256: e019ebe4e3f5cdf23e2f5e58ddf7ade27988c53820115b17b98f218ebcc87748 + md5: eb83f3f8cecc3e9bff9e250817fc69b6 + depends: + - __glibc >=2.17,<3.0.a0 + license: LicenseRef-libglvnd + size: 133586 + timestamp: 1779728183422 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_3.conda + sha256: 2f74713c9ca408ea84e88a30a9028153e7b553e8bb42e06139eac9a753c27da9 + md5: ec3c4350aa0261bf7f87b8ca15c8e80e + depends: + - __glibc >=2.17,<3.0.a0 + - libglvnd 1.7.0 ha4b6fd6_3 + - xorg-libx11 >=1.8.13,<2.0a0 + license: LicenseRef-libglvnd + size: 76586 + timestamp: 1779728199059 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-devel-1.7.0-ha4b6fd6_3.conda + sha256: a17ae2d4cb2de04a20882ae14ec3cc1958e868a4dec81e3d7eca30115ee50e94 + md5: 16b6330783ce0d1ae8d22782173b32c9 + depends: + - __glibc >=2.17,<3.0.a0 + - libglx 1.7.0 ha4b6fd6_3 + - xorg-libx11 >=1.8.13,<2.0a0 + - xorg-xorgproto + license: LicenseRef-libglvnd + size: 27363 + timestamp: 1779728211402 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda + sha256: 5abe4ab9d93f6c9757d654f1969ae2267d4505315c1f2f8fe705fd60af084f1b + md5: faac990cb7aedc7f3a2224f2c9b0c26c + depends: + - __glibc >=2.17,<3.0.a0 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 603817 + timestamp: 1778268942614 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.23.0-h9be4e54_1.conda + sha256: 680f5a9bc45aa905d9da086b16551438553649e05dd6b94b02b379b050602d5e + md5: 1042d8401bb268553f98e60120cdeb40 + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcurl >=8.7.1,<9.0a0 + - libgcc-ng >=12 + - libgrpc >=1.62.2,<1.63.0a0 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libstdcxx-ng >=12 + - openssl >=3.2.1,<4.0a0 + constrains: + - libgoogle-cloud 2.23.0 *_1 + license: Apache-2.0 + license_family: Apache + size: 1214608 + timestamp: 1713798219648 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.23.0-hc7a4891_1.conda + sha256: b85ce8b78e9262670a145a1639e253708e2a9eb9100d60ccec16f8e41d87a4bb + md5: ee99fb9107ffb579b58ee92a5fb14b06 + depends: + - libabseil + - libcrc32c >=1.1.2,<1.2.0a0 + - libcurl + - libgcc-ng >=12 + - libgoogle-cloud 2.23.0 h9be4e54_1 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl + license: Apache-2.0 + license_family: Apache + size: 752661 + timestamp: 1713798390317 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.62.2-h15f2491_0.conda + sha256: 28241ed89335871db33cb6010e9ccb2d9e9b6bb444ddf6884f02f0857363c06a + md5: 8dabe607748cb3d7002ad73cd06f1325 + depends: + - c-ares >=1.28.1,<2.0a0 + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libgcc-ng >=12 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libre2-11 >=2023.9.1 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.2.1,<4.0a0 + - re2 + constrains: + - grpc-cpp =1.62.2 + license: Apache-2.0 + license_family: APACHE + size: 7316832 + timestamp: 1713390645548 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda + sha256: c467851a7312765447155e071752d7bf9bf44d610a5687e32706f480aad2833f + md5: 915f5995e94f60e9a4826e0b0920ee88 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: LGPL-2.1-only + size: 790176 + timestamp: 1754908768807 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.4.1-hb03c661_0.conda + sha256: 10056646c28115b174de81a44e23e3a0a3b95b5347d2e6c45cc6d49d35294256 + md5: 6178c6f2fb254558238ef4e6c56fb782 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + constrains: + - jpeg <0.0.0a + license: IJG AND BSD-3-Clause AND Zlib + size: 633831 + timestamp: 1775962768273 +- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-8_h47877c9_openblas.conda + build_number: 8 + sha256: 168e327d737059553e15cc6ec36d76b9bbb3931c2a7721555fd68b4c9348b247 + md5: 809be8ba8712c77bc7d44c2d99390dc4 + depends: + - libblas 3.11.0 8_h4a7cf45_openblas + constrains: + - blas 2.308 openblas + - libcblas 3.11.0 8*_openblas + - liblapacke 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 18790 + timestamp: 1779859115086 +- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda + build_number: 20 + sha256: ad7745b8d0f2ccb9c3ba7aaa7167d62fc9f02e45eb67172ae5f0dfb5a3b1a2cc + md5: 6fabc51f5e647d09cc010c40061557e0 + depends: + - libblas 3.9.0 20_linux64_openblas + constrains: + - liblapacke 3.9.0 20_linux64_openblas + - libcblas 3.9.0 20_linux64_openblas + - blas * openblas + - mkl <2025 + license: BSD-3-Clause + license_family: BSD + size: 14350 + timestamp: 1700568424034 +- conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda + sha256: ec30e52a3c1bf7d0425380a189d209a52baa03f22fb66dd3eb587acaa765bd6d + md5: b88d90cad08e6bc8ad540cb310a761fb + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + constrains: + - xz 5.8.3.* + license: 0BSD + size: 113478 + timestamp: 1775825492909 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda + sha256: 663444d77a42f2265f54fb8b48c5450bfff4388d9c0f8253dd7855f0d993153f + md5: 2a45e7f8af083626f009645a6481f12d + depends: + - __glibc >=2.17,<3.0.a0 + - c-ares >=1.34.6,<2.0a0 + - libev >=4.33,<4.34.0a0 + - libev >=4.33,<5.0a0 + - libgcc >=14 + - libstdcxx >=14 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 663344 + timestamp: 1773854035739 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda + sha256: 927fe72b054277cde6cb82597d0fcf6baf127dcbce2e0a9d8925a68f1265eef5 + md5: d864d34357c3b65a4b731f78c0801dc4 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + license: LGPL-2.1-only + license_family: GPL + size: 33731 + timestamp: 1750274110928 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.25-pthreads_h413a1c8_0.conda + sha256: 628564517895ee1b09cf72c817548bd80ef1acce6a8214a8520d9f7b44c4cfaf + md5: d172b34a443b95f86089e8229ddc9a17 + depends: + - libgcc-ng >=12 + - libgfortran-ng + - libgfortran5 >=12.3.0 + constrains: + - openblas >=0.3.25,<0.3.26.0a0 + license: BSD-3-Clause + license_family: BSD + size: 5545169 + timestamp: 1700536004164 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.33-pthreads_h94d23a6_0.conda + sha256: 3d9aa85648e5e18a6d66db98b8c4317cc426721ad7a220aa86330d1ccedc8903 + md5: 2d3278b721e40468295ca755c3b84070 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libgfortran + - libgfortran5 >=14.3.0 + constrains: + - openblas >=0.3.33,<0.3.34.0a0 + license: BSD-3-Clause + license_family: BSD + size: 5931919 + timestamp: 1776993658641 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-16.0.0-h6a7eafb_1_cpu.conda + build_number: 1 + sha256: 285347fd823daf0fe869505698b181603f633df8612b8cc43c714074d6ebcf17 + md5: d8146d9d599a8353702c2dd07fe5164d + depends: + - libarrow 16.0.0 hefa796f_1_cpu + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - libthrift >=0.19.0,<0.19.1.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 1182496 + timestamp: 1715198158486 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.19-hb03c661_0.conda + sha256: f41721636a7c2e51bc2c642e1127955ab9c81145470714fdaac44d4d09e4af41 + md5: 33082e13b4769b48cfeb648e15bfe3fc + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 29147 + timestamp: 1773533027610 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.58-h421ea60_0.conda + sha256: 377cfe037f3eeb3b1bf3ad333f724a64d32f315ee1958581fc671891d63d3f89 + md5: eba48a68a1a2b9d3c0d9511548db85db + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libzlib >=1.3.2,<2.0a0 + license: zlib-acknowledgement + size: 317729 + timestamp: 1776315175087 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-4.25.3-hd5b35b9_1.conda + sha256: 8b5e4e31ed93bf36fd14e9cf10cd3af78bb9184d0f1f87878b8d28c0374aa4dc + md5: 06def97690ef90781a91b786cb48a0a9 + depends: + - __glibc >=2.17,<3.0.a0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libgcc >=13 + - libstdcxx >=13 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 2883090 + timestamp: 1727161327039 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2023.09.01-h5a48ba9_2.conda + sha256: 3f3c65fe0e9e328b4c1ebc2b622727cef3e5b81b18228cfa6cf0955bc1ed8eff + md5: 41c69fba59d495e8cf5ffda48a607e35 + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + constrains: + - re2 2023.09.01.* + license: BSD-3-Clause + license_family: BSD + size: 232603 + timestamp: 1708946763521 +- conda: https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.62.3-h4c96295_0.conda + sha256: 5571bd8239d71961d4e3ce972f865b3ea95a91ce0b53d5749fe2dd24254ddbda + md5: 492c8d9b1c564c2e948b6cb4ba0f8261 + depends: + - __glibc >=2.17,<3.0.a0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.18.0,<3.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.6,<3.0a0 + - harfbuzz >=14.2.0 + - libgcc >=14 + - libglib >=2.88.1,<3.0a0 + - libxml2-16 >=2.14.6 + - pango >=1.56.4,<2.0a0 + constrains: + - __glibc >=2.17 + license: LGPL-2.1-or-later + size: 3476570 + timestamp: 1780450632624 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.22-h280c20c_1.conda + sha256: b677bbf1c339d894757c3dcfbb2f88649e499e4991d70ae09a1466da9a6c92d6 + md5: 965e4d531b588b2e42f66fd8e48b056c + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: ISC + size: 269272 + timestamp: 1779163468406 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.1-h0c1763c_0.conda + sha256: 54cdcd3214313b62c2a8ee277e6f42150d9b748264c1b70d958bf735e420ef8d + md5: 7dc38adcbf71e6b38748e919e16e0dce + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libzlib >=1.3.2,<2.0a0 + license: blessing + size: 954962 + timestamp: 1777986471789 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda + sha256: fa39bfd69228a13e553bd24601332b7cfeb30ca11a3ca50bb028108fe90a7661 + md5: eecce068c7e4eddeb169591baac20ac4 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.0,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 304790 + timestamp: 1745608545575 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda + sha256: dff1058c76ec6b8759e41cefa2508162d00e4a5e6721aa68ec3fd10094e702dc + md5: 5794b3bdc38177caf969dabd3af08549 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc 15.2.0 he0feb66_19 + constrains: + - libstdcxx-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 5852044 + timestamp: 1778269036376 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_19.conda + sha256: 0672b6b6e1791c92e8eccad58081a99d614fcf82bca5841f9dfa3c3e658f83b9 + md5: e5ce228e579726c07255dbf90dc62101 + depends: + - libstdcxx 15.2.0 h934c35e_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27776 + timestamp: 1778269074600 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.19.0-hb90f79a_1.conda + sha256: 719add2cf20d144ef9962c57cd0f77178259bdb3aae1cded2e2b2b7c646092f5 + md5: 8cdb7d41faa0260875ba92414c487e2d + depends: + - libevent >=2.1.12,<2.1.13.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.1.3,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 409409 + timestamp: 1695958011498 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.1-h9d88235_1.conda + sha256: e5f8c38625aa6d567809733ae04bb71c161a42e44a9fa8227abe61fa5c60ebe0 + md5: cd5a90476766d53e901500df9215e927 + depends: + - __glibc >=2.17,<3.0.a0 + - lerc >=4.0.0,<5.0a0 + - libdeflate >=1.25,<1.26.0a0 + - libgcc >=14 + - libjpeg-turbo >=3.1.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libstdcxx >=14 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: HPND + size: 435273 + timestamp: 1762022005702 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-hf23e847_1.conda + sha256: 104cf5b427fc914fec63e55f685a39480abeb4beb34bdbc77dea084c8f5a55cb + md5: b1aa0faa95017bca11369bd080487ec4 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + license: MIT + license_family: MIT + size: 80852 + timestamp: 1732829699583 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.1-h5347b49_0.conda + sha256: 3f0edf1280e2f6684a986f821eaa3e123d2694a00b31b96ca0d4a4c12c129231 + md5: 7d0a66598195ef00b6efc55aefc7453b + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: BSD-3-Clause + license_family: BSD + size: 40163 + timestamp: 1779118517630 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda + sha256: 3aed21ab28eddffdaf7f804f49be7a7d701e8f0e46c856d801270b470820a37b + md5: aea31d2e5b1091feca96fcfe945c3cf9 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + constrains: + - libwebp 1.6.0 + license: BSD-3-Clause + license_family: BSD + size: 429011 + timestamp: 1752159441324 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda + sha256: 666c0c431b23c6cec6e492840b176dde533d48b7e6fb8883f5071223433776aa + md5: 92ed62436b625154323d40d5f2f11dd7 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - pthread-stubs + - xorg-libxau >=1.0.11,<2.0a0 + - xorg-libxdmcp + license: MIT + license_family: MIT + size: 395888 + timestamp: 1727278577118 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda + sha256: 6ae68e0b86423ef188196fff6207ed0c8195dd84273cb5623b85aa08033a410c + md5: 5aa797f8787fe7a17d1b0821485b5adc + depends: + - libgcc-ng >=12 + license: LGPL-2.1-or-later + size: 100393 + timestamp: 1702724383534 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.13.2-hca5e8e5_0.conda + sha256: 046f2ff4acebd8729fac03e99c8c307dfb48b6a32894ba8c11576e78f6e76e43 + md5: dc8b067e22b414172bedd8e3f03f3c95 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + - libxcb >=1.17.0,<2.0a0 + - libxml2 + - libxml2-16 >=2.14.6 + - xkeyboard-config + - xorg-libxau >=1.0.12,<2.0a0 + license: MIT/X11 Derivative + license_family: MIT + size: 851166 + timestamp: 1780213397575 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.3-hca6bf5a_0.conda + sha256: 3d44f737c5ae52d5af32682cc1530df433f401f8e58a7533926536244127572a + md5: e79d2c2f24b027aa8d5ab1b1ba3061e7 + depends: + - __glibc >=2.17,<3.0.a0 + - icu >=78.3,<79.0a0 + - libgcc >=14 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - libxml2 2.15.3 + license: MIT + license_family: MIT + size: 559775 + timestamp: 1776376739004 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.3-h49c6c72_0.conda + sha256: 3bc5551720c58591f6ea1146f7d1539c734ed1c40e7b9f5cb8cb7e900c509aba + md5: 995d8c8bad2a3cc8db14675a153dec2b + depends: + - __glibc >=2.17,<3.0.a0 + - icu >=78.3,<79.0a0 + - libgcc >=14 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libxml2-16 2.15.3 hca6bf5a_0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + license_family: MIT + size: 46810 + timestamp: 1776376751152 +- conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda + sha256: 55044c403570f0dc26e6364de4dc5368e5f3fc7ff103e867c487e2b5ab2bcda9 + md5: d87ff7921124eccd67248aa483c23fec + depends: + - __glibc >=2.17,<3.0.a0 + constrains: + - zlib 1.3.2 *_2 + license: Zlib + license_family: Other + size: 63629 + timestamp: 1774072609062 +- conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.3.3-py310hb259640_1.conda + sha256: 432b919e052fd0e0687ae7395f147629ba708457e17e491db1d7a286a83d6f41 + md5: 7a591c0d64176ea154d53eac3ad7f66d + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - lz4-c >=1.9.3,<1.10.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 37077 + timestamp: 1725089581969 +- conda: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda + sha256: 1b4c105a887f9b2041219d57036f72c4739ab9e9fe5a1486f094e58c76b31f5f + md5: 318b08df404f9c9be5712aaa5a6f0bb0 + depends: + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: BSD-2-Clause + license_family: BSD + size: 143402 + timestamp: 1674727076728 +- conda: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py310h3406613_1.conda + sha256: 9f3c34f8a7a8dcfed64221a2e19bbe0094ab2c6df7c029b7df713e52c9c9f229 + md5: 671afe636d2a97759804723f5afc22e0 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - jinja2 >=3.0.0 + license: BSD-3-Clause + license_family: BSD + size: 23899 + timestamp: 1772445369460 +- conda: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.7.3-py310h62c0568_0.conda + sha256: 5e60c8ab40fa69f9bb665d8dee53650fc5554edb1193cb5313589c788b7f1af2 + md5: 15a0409bc579d80e67715f1ce5d864a2 + depends: + - certifi >=2020.06.20 + - contourpy >=1.0.1 + - cycler >=0.10 + - fonttools >=4.22.0 + - freetype >=2.12.1,<3.0a0 + - kiwisolver >=1.0.1 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - numpy >=1.20 + - numpy >=1.22.4,<2.0a0 + - packaging >=20.0 + - pillow >=6.2.0 + - pyparsing >=2.3.1 + - python >=3.10,<3.11.0a0 + - python-dateutil >=2.7 + - python_abi 3.10.* *_cp310 + - tk >=8.6.12,<8.7.0a0 + license: LicenseRef-PSF-2.0 and CC0-1.0 + license_family: PSF + size: 6740990 + timestamp: 1695076957339 +- conda: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.2-py310h03d9f68_1.conda + sha256: 61cf3572d6afa3fa711c5f970a832783d2c281facb7b3b946a6b71a0bac2c592 + md5: 5eea9d8f8fcf49751dab7927cb0dfc3f + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 95105 + timestamp: 1762504073388 +- conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda + sha256: fc89f74bbe362fb29fa3c037697a89bec140b346a2469a90f7936d1d7ea4d8a3 + md5: fc21868a1a5aacc937e7a18747acb8a5 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: X11 AND BSD-3-Clause + size: 918956 + timestamp: 1777422145199 +- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.22.4-py310h4ef5377_0.tar.bz2 + sha256: 89452fcd4270c04dd9e861e0e02cbc97d82f2f7d4b55225f8198ddab4287c480 + md5: a97b04c2d88d24d4a25fd9c069189281 + depends: + - libblas >=3.8.0,<4.0a0 + - libcblas >=3.8.0,<4.0a0 + - libgcc-ng >=12 + - liblapack >=3.8.0,<4.0a0 + - libstdcxx-ng >=12 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 7109018 + timestamp: 1653325635272 +- conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.2.6-py310hefbff90_0.conda + sha256: 0ba94a61f91d67413e60fa8daa85627a8f299b5054b0eff8f93d26da83ec755e + md5: b0cea2c364bf65cd19e023040eeab05d + depends: + - __glibc >=2.17,<3.0.a0 + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libgcc >=13 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx >=13 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 7893263 + timestamp: 1747545075833 +- conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.4-h55fea9a_0.conda + sha256: 3900f9f2dbbf4129cf3ad6acf4e4b6f7101390b53843591c53b00f034343bc4d + md5: 11b3379b191f63139e29c0d19dee24cd + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libpng >=1.6.50,<1.7.0a0 + - libstdcxx >=14 + - libtiff >=4.7.1,<4.8.0a0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-2-Clause + license_family: BSD + size: 355400 + timestamp: 1758489294972 +- conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.2-h35e630c_0.conda + sha256: c0ef482280e38c71a08ad6d71448194b719630345b0c9c60744a2010e8a8e0cb + md5: da1b85b6a87e141f5140bb9924cecab0 + depends: + - __glibc >=2.17,<3.0.a0 + - ca-certificates + - libgcc >=14 + license: Apache-2.0 + license_family: Apache + size: 3167099 + timestamp: 1775587756857 +- conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.0-h17fec99_1.conda + sha256: ccbfb6c2a01259c2c95b5b8139a0c3a8d4ec6240228ad1ac454b41f5fbcfd082 + md5: d2e0ffa6c3452f0a723a0ef1b96fd1cb + depends: + - libgcc-ng >=12 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.5,<1.6.0a0 + license: Apache-2.0 + license_family: Apache + size: 1029252 + timestamp: 1712616110941 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-1.3.5-py310hb5077e9_0.tar.bz2 + sha256: 802df732ec039e34c97230e5c1a172c06837cb909a05f49db4ad0d48b80ffa04 + md5: 188bb9d4ffcde7943e13d959ac2fdb10 + depends: + - libgcc-ng >=9.4.0 + - libstdcxx-ng >=9.4.0 + - numpy >=1.21.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python-dateutil >=2.7.3 + - python_abi 3.10.* *_cp310 + - pytz >=2017.2 + - setuptools <60.0.0 + license: BSD-3-Clause + license_family: BSD + size: 46685333 + timestamp: 1639398825397 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pango-1.56.4-hda50119_1.conda + sha256: 315b52bfa6d1a820f4806f6490d472581438a28e21df175290477caec18972b0 + md5: d53ffc0edc8eabf4253508008493c5bc + depends: + - __glibc >=2.17,<3.0.a0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.16,<2.0a0 + - harfbuzz >=13.2.1 + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libgcc >=14 + - libglib >=2.86.4,<3.0a0 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + license: LGPL-2.1-or-later + size: 458036 + timestamp: 1774281947855 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-haa7fec5_0.conda + sha256: 5e6f7d161356fefd981948bea5139c5aa0436767751a6930cb1ca801ebb113ff + md5: 7a3bff861a6583f1889021facefc08b1 + depends: + - __glibc >=2.17,<3.0.a0 + - bzip2 >=1.0.8,<2.0a0 + - libgcc >=14 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 1222481 + timestamp: 1763655398280 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-12.2.0-py310h5a73078_0.conda + sha256: 24ea3d3ab64ccdb3c2c114d0daa5e8416a50b102848d384d46c3dda59669986f + md5: 440921820f098897562537c5c3cf7ae0 + depends: + - python + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - openjpeg >=2.5.4,<3.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - tk >=8.6.13,<8.7.0a0 + - zlib-ng >=2.3.3,<2.4.0a0 + - lcms2 >=2.18,<3.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - python_abi 3.10.* *_cp310 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libxcb >=1.17.0,<2.0a0 + license: HPND + size: 890549 + timestamp: 1775060059882 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.4-h54a6638_1.conda + sha256: 43d37bc9ca3b257c5dd7bf76a8426addbdec381f6786ff441dc90b1a49143b6a + md5: c01af13bdc553d1a8fbfff6e8db075f0 + depends: + - libgcc >=14 + - libstdcxx >=14 + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + license: MIT + license_family: MIT + size: 450960 + timestamp: 1754665235234 +- conda: https://conda.anaconda.org/conda-forge/linux-64/polars-1.0.0-py310h3af5803_0.conda + sha256: 52c54653bb817ce8a531d5268dd8b449f0af8b4dc12b93c81c7c1be9271be5b8 + md5: 5bb42099da15e3263830a1ebb4c53044 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc-ng >=12 + - numpy >=1.16.0,<2 + - packaging + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - typing_extensions >=4.0.0 + constrains: + - __glibc >=2.17 + license: MIT + license_family: MIT + run_exports: {} + size: 23142897 + timestamp: 1720019257107 +- conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.3-py310h5764c6d_1.tar.bz2 + sha256: d4dc18b0d0e749e02d7a77aa86db3b3b38b2402fbfad4b1c26163e8a4730cdd4 + md5: 9bfec73f587caed65c74e2219895f723 + depends: + - libgcc-ng >=12 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 363097 + timestamp: 1666772009811 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda + sha256: 9c88f8c64590e9567c6c80823f0328e58d3b1efb0e1c539c0315ceca764e0973 + md5: b3c17d95b5a10c6e64a21fa17573e70e + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + license: MIT + license_family: MIT + size: 8252 + timestamp: 1726802366959 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-16.0.0-py310h17c5347_0.conda + sha256: e8a1da80701136b2ba4476488809bda923a5e20849a13219f82de3cc8ae0e979 + md5: 1adbbc9ae829cabb6eaa2444739cc450 + depends: + - libarrow-acero 16.0.0.* + - libarrow-dataset 16.0.0.* + - libarrow-substrait 16.0.0.* + - libparquet 16.0.0.* + - numpy >=1.22.4,<2.0a0 + - pyarrow-core 16.0.0 *_0_* + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: APACHE + size: 25956 + timestamp: 1715177917556 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-16.0.0-py310h6f79a3a_0_cpu.conda + sha256: c829b280ad0f340ee7dd107f9248e1e1686aa9479c97eaa73cf584fddea4f961 + md5: d2cdf578b4626c07539b4793d57c4713 + depends: + - libarrow 16.0.0.* *cpu + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - numpy >=1.22.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - apache-arrow-proc =*=cpu + license: Apache-2.0 + license_family: APACHE + size: 4387257 + timestamp: 1715177759735 +- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.10.20-h3c07f61_0_cpython.conda + sha256: 8ff2ce308faf2588b69c65b120293f59a8f2577b772b34df4e817d220b09e081 + md5: 5d4e2b00d99feacd026859b7fa239dc0 + depends: + - __glibc >=2.17,<3.0.a0 + - bzip2 >=1.0.8,<2.0a0 + - ld_impl_linux-64 >=2.36.1 + - libexpat >=2.7.4,<3.0a0 + - libffi >=3.4,<4.0a0 + - libgcc >=14 + - liblzma >=5.8.2,<6.0a0 + - libnsl >=2.0.1,<2.1.0a0 + - libsqlite >=3.51.2,<4.0a0 + - libuuid >=2.41.3,<3.0a0 + - libxcrypt >=4.4.36 + - libzlib >=1.3.1,<2.0a0 + - ncurses >=6.5,<7.0a0 + - openssl >=3.5.5,<4.0a0 + - readline >=8.3,<9.0a0 + - tk >=8.6.13,<8.7.0a0 + - tzdata + constrains: + - python_abi 3.10.* *_cp310 + license: Python-2.0 + size: 25455342 + timestamp: 1772729810280 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py310h3406613_1.conda + sha256: f23de6cc72541c6081d3d27482dbc9fc5dd03be93126d9155f06d0cf15d6e90e + md5: 2160894f57a40d2d629a34ee8497795f + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 176522 + timestamp: 1770223379599 +- conda: https://conda.anaconda.org/conda-forge/linux-64/pyzmq-27.1.0-py310ha71cbf4_3.conda + sha256: ae68ab6978705044b70138d7930293a7045ce03ae2470a3145d7808bee4c7ef1 + md5: ee924c369aca41fb3e55c4240154d43c + depends: + - python + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - libstdcxx >=14 + - zeromq >=4.3.5,<4.4.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 326664 + timestamp: 1779483880352 +- conda: https://conda.anaconda.org/conda-forge/linux-64/re2-2023.09.01-h7f4b329_2.conda + sha256: f0f520f57e6b58313e8c41abc7dfa48742a05f1681f05654558127b667c769a8 + md5: 8f70e36268dea8eb666ef14c29bd3cda + depends: + - libre2-11 2023.09.01 h5a48ba9_2 + license: BSD-3-Clause + license_family: BSD + size: 26617 + timestamp: 1708946796423 +- conda: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda + sha256: 12ffde5a6f958e285aa22c191ca01bbd3d6e710aa852e00618fa6ddc59149002 + md5: d7d95fc8287ea7bf33e0e7116d2b95ec + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - ncurses >=6.5,<7.0a0 + license: GPL-3.0-only + license_family: GPL + size: 345073 + timestamp: 1765813471974 +- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.30.0-py310hd8f68c5_0.conda + sha256: ac1132a9344c77e19bbbdb966668cf73a861ceec7b075858a52c8e961fb8ea9d + md5: 61ff3f8e00c63bb66903636d0197e962 + depends: + - python + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - python_abi 3.10.* *_cp310 + constrains: + - __glibc >=2.17 + license: MIT + license_family: MIT + size: 382893 + timestamp: 1764543243162 +- conda: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.4.13-he19d79f_0.conda + sha256: a51daaf7d6affe149452b08a5cbc0568a0984306c558728050c56c48f8f11615 + md5: 51db7e9c0cd527aea7691e7405df33bf + depends: + - libgcc-ng >=12 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 346686 + timestamp: 1714594242637 +- conda: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.0.2-py310h1246948_0.tar.bz2 + sha256: ab3ae43b64d96a1db77568685102d5c75d297f4507a02f6c38c7007b05d4b1e5 + md5: c930949cd0b2dc2d022cb1161a2ff701 + depends: + - joblib >=0.11 + - libcblas >=3.8.0,<4.0a0 + - libgcc-ng >=9.4.0 + - libstdcxx-ng >=9.4.0 + - numpy >=1.21.5,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - scipy + - threadpoolctl + license: BSD-3-Clause + license_family: BSD + size: 28299395 + timestamp: 1640464558772 +- conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda + sha256: 4cb98641f870666d365594013701d5691205a0fe81ac3ba7778a23b1cc2caa8e + md5: 8c29cd33b64b2eb78597fa28b5595c8d + depends: + - __glibc >=2.17,<3.0.a0 + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libgcc >=13 + - libgfortran + - libgfortran5 >=13.3.0 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx >=13 + - numpy <2.5 + - numpy >=1.19,<3 + - numpy >=1.23.5 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 16417101 + timestamp: 1739791865060 +- conda: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.7.3-py310hdfbd76f_1.tar.bz2 + sha256: b4212b7d8638e72c923985662de1f8ba8ada660056f89f1798d88d3ed54132e6 + md5: 63b7ee68b9a6cb6945fa726399f0ecc4 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libgcc-ng >=12 + - libgfortran-ng + - libgfortran5 >=10.4.0 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx-ng >=12 + - numpy >=1.21.6,<1.23 + - numpy >=1.21.6,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - libopenblas <0.3.26 + license: BSD-3-Clause + license_family: BSD + size: 24169985 + timestamp: 1667961533601 +- conda: https://conda.anaconda.org/conda-forge/linux-64/setuptools-59.8.0-py310hff52083_1.tar.bz2 + sha256: b56537f3142cfe51c6835379e693a8d3757aac44385f810f0fcd1623738a375d + md5: af2b60144919a0eb9dd1197212e4d3db + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 1050816 + timestamp: 1648692014339 +- conda: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.2-h03e3b7b_1.conda + sha256: 48f3f6a76c34b2cfe80de9ce7f2283ecb55d5ed47367ba91e8bb8104e12b8f11 + md5: 98b6c9dc80eb87b2519b97bcf7e578dd + depends: + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - libstdcxx >=14 + - libgcc >=14 + license: BSD-3-Clause + license_family: BSD + size: 45829 + timestamp: 1762948049098 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda + sha256: cafeec44494f842ffeca27e9c8b0c27ed714f93ac77ddadc6aaf726b5554ebac + md5: cffd3bdd58090148f4cfcd831f4b26ab + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libzlib >=1.3.1,<2.0a0 + constrains: + - xorg-libx11 >=1.8.12,<2.0a0 + license: TCL + license_family: BSD + size: 3301196 + timestamp: 1769460227866 +- conda: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.5.6-py310h7c4b9e2_0.conda + sha256: ca1fa0814d5468b97f6a9916fa3e91e2015ed33ae7bd4a9aca2357c09ea69494 + md5: 2efda50efe9551f1551effbb59a4fb27 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 669010 + timestamp: 1779915941115 +- conda: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-17.0.1-py310h7c4b9e2_0.conda + sha256: 44ecba51c98c3fb2ce3d00295d423d3bb254cde1790eff9818ed328aa608ab28 + md5: 234e9858dd691d3f597147e22cbf16cf + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 410408 + timestamp: 1770909105501 +- conda: https://conda.anaconda.org/conda-forge/linux-64/wayland-1.25.0-hd6090a7_0.conda + sha256: ea374d57a8fcda281a0a89af0ee49a2c2e99cc4ac97cf2e2db7064e74e764bdb + md5: 996583ea9c796e5b915f7d7580b51ea6 + depends: + - __glibc >=2.17,<3.0.a0 + - libexpat >=2.7.4,<3.0a0 + - libffi >=3.5.2,<3.6.0a0 + - libgcc >=14 + - libstdcxx >=14 + license: MIT + license_family: MIT + size: 334139 + timestamp: 1773959575393 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.47-hb03c661_0.conda + sha256: 19c2bb14bec84b0e995b56b752369775c75f1589314b43733948bb5f471a6915 + md5: b56e0c8432b56decafae7e78c5f29ba5 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - xorg-libx11 >=1.8.13,<2.0a0 + license: MIT + license_family: MIT + size: 399291 + timestamp: 1772021302485 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda + sha256: c12396aabb21244c212e488bbdc4abcdef0b7404b15761d9329f5a4a39113c4b + md5: fb901ff28063514abb6046c9ec2c4a45 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + license: MIT + license_family: MIT + size: 58628 + timestamp: 1734227592886 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.6-he73a12e_0.conda + sha256: 277841c43a39f738927145930ff963c5ce4c4dacf66637a3d95d802a64173250 + md5: 1c74ff8c35dcadf952a16f752ca5aa49 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - libuuid >=2.38.1,<3.0a0 + - xorg-libice >=1.1.2,<2.0a0 + license: MIT + license_family: MIT + size: 27590 + timestamp: 1741896361728 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.13-he1eb515_0.conda + sha256: 516d4060139dbb4de49a4dcdc6317a9353fb39ebd47789c14e6fe52de0deee42 + md5: 861fb6ccbc677bb9a9fb2468430b9c6a + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libxcb >=1.17.0,<2.0a0 + license: MIT + license_family: MIT + size: 839652 + timestamp: 1770819209719 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb03c661_1.conda + sha256: 6bc6ab7a90a5d8ac94c7e300cc10beb0500eeba4b99822768ca2f2ef356f731b + md5: b2895afaf55bf96a8c8282a2e47a5de0 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 15321 + timestamp: 1762976464266 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.7-hb03c661_0.conda + sha256: 048c103000af9541c919deef03ae7c5e9c570ffb4024b42ecb58dbde402e373a + md5: f2ba4192d38b6cef2bb2c25029071d90 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxfixes >=6.0.2,<7.0a0 + license: MIT + license_family: MIT + size: 14415 + timestamp: 1770044404696 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcursor-1.2.3-hb9d3cd8_0.conda + sha256: 832f538ade441b1eee863c8c91af9e69b356cd3e9e1350fff4fe36cc573fc91a + md5: 2ccd714aa2242315acaf0a67faea780b + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - xorg-libx11 >=1.8.10,<2.0a0 + - xorg-libxfixes >=6.0.1,<7.0a0 + - xorg-libxrender >=0.9.11,<0.10.0a0 + license: MIT + license_family: MIT + size: 32533 + timestamp: 1730908305254 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.6-hb9d3cd8_0.conda + sha256: 43b9772fd6582bf401846642c4635c47a9b0e36ca08116b3ec3df36ab96e0ec0 + md5: b5fcc7172d22516e1f965490e65e33a4 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - xorg-libx11 >=1.8.10,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxfixes >=6.0.1,<7.0a0 + license: MIT + license_family: MIT + size: 13217 + timestamp: 1727891438799 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb03c661_1.conda + sha256: 25d255fb2eef929d21ff660a0c687d38a6d2ccfbcbf0cc6aa738b12af6e9d142 + md5: 1dafce8548e38671bea82e3f5c6ce22f + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 20591 + timestamp: 1762976546182 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.7-hb03c661_0.conda + sha256: 79c60fc6acfd3d713d6340d3b4e296836a0f8c51602327b32794625826bd052f + md5: 34e54f03dfea3e7a2dcf1453a85f1085 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + license: MIT + license_family: MIT + size: 50326 + timestamp: 1769445253162 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-6.0.2-hb03c661_0.conda + sha256: 83c4c99d60b8784a611351220452a0a85b080668188dce5dfa394b723d7b64f4 + md5: ba231da7fccf9ea1e768caf5c7099b84 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + license: MIT + license_family: MIT + size: 20071 + timestamp: 1759282564045 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.8.3-hb03c661_0.conda + sha256: 495f99c8eacfa4ae2d8fed2a7f2105777af89acdc204df145d2bbbc380ac631b + md5: adba2e334082bb218db806d4c12277c9 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - xorg-libx11 >=1.8.13,<2.0a0 + - xorg-libxext >=1.3.7,<2.0a0 + - xorg-libxfixes >=6.0.2,<7.0a0 + license: MIT + license_family: MIT + size: 47717 + timestamp: 1779111857071 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxinerama-1.1.6-hecca717_0.conda + sha256: 3a9da41aac6dca9d3ff1b53ee18b9d314de88add76bafad9ca2287a494abcd86 + md5: 93f5d4b5c17c8540479ad65f206fea51 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + license: MIT + license_family: MIT + size: 14818 + timestamp: 1769432261050 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrandr-1.5.5-hb03c661_0.conda + sha256: 80ed047a5cb30632c3dc5804c7716131d767089f65877813d4ae855ee5c9d343 + md5: e192019153591938acf7322b6459d36e + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxrender >=0.9.12,<0.10.0a0 + license: MIT + license_family: MIT + size: 30456 + timestamp: 1769445263457 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda + sha256: 044c7b3153c224c6cedd4484dd91b389d2d7fd9c776ad0f4a34f099b3389f4a1 + md5: 96d57aba173e878a2089d5638016dc5e + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - xorg-libx11 >=1.8.10,<2.0a0 + license: MIT + license_family: MIT + size: 33005 + timestamp: 1734229037766 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda + sha256: 752fdaac5d58ed863bbf685bb6f98092fe1a488ea8ebb7ed7b606ccfce08637a + md5: 7bbe9a0cc0df0ac5f5a8ad6d6a11af2f + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=13 + - xorg-libx11 >=1.8.10,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxi >=1.7.10,<2.0a0 + license: MIT + license_family: MIT + size: 32808 + timestamp: 1727964811275 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxxf86vm-1.1.7-hb03c661_0.conda + sha256: 64db17baaf36fa03ed8fae105e2e671a7383e22df4077486646f7dbf12842c9f + md5: 665d152b9c6e78da404086088077c844 + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + license: MIT + license_family: MIT + size: 18701 + timestamp: 1769434732453 +- conda: https://conda.anaconda.org/conda-forge/linux-64/xorg-xorgproto-2025.1-hb03c661_0.conda + sha256: 7a8c64938428c2bfd016359f9cb3c44f94acc256c6167dbdade9f2a1f5ca7a36 + md5: aa8d21be4b461ce612d8f5fb791decae + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + license: MIT + license_family: MIT + size: 570010 + timestamp: 1766154256151 +- conda: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda + sha256: 6d9ea2f731e284e9316d95fa61869fe7bbba33df7929f82693c121022810f4ad + md5: a77f85f77be52ff59391544bfe73390a + depends: + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + license: MIT + license_family: MIT + size: 85189 + timestamp: 1753484064210 +- conda: https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.5-h09e67af_11.conda + sha256: dc9f28dedcb5f35a127fad2d847674d2833369dd616d294e423b8997df31d8a8 + md5: 96b08867e21d4694fa5c2c226e6581b0 + depends: + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - libstdcxx >=14 + - krb5 >=1.22.2,<1.23.0a0 + - libsodium >=1.0.22,<1.0.23.0a0 + license: MPL-2.0 + license_family: MOZILLA + size: 311184 + timestamp: 1779123989774 +- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda + sha256: ea4e50c465d70236408cb0bfe0115609fd14db1adcd8bd30d8918e0291f8a75f + md5: 2aadb0d17215603a82a2a6b0afd9a4cb + depends: + - __glibc >=2.17,<3.0.a0 + - libgcc >=14 + - libstdcxx >=14 + license: Zlib + license_family: Other + size: 122618 + timestamp: 1770167931827 +- conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py310h139afa4_1.conda + sha256: b0103e8bb639dbc6b9de8ef9a18a06b403b687a33dec83c25bd003190942259a + md5: 3741aefc198dfed2e3c9adc79d706bb7 + depends: + - python + - cffi >=1.11 + - zstd >=1.5.7,<1.5.8.0a0 + - libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - zstd >=1.5.7,<1.6.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 455614 + timestamp: 1762512676430 +- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda + sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7 + md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829 + depends: + - __glibc >=2.17,<3.0.a0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 601375 + timestamp: 1764777111296 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-20_gnu.conda + build_number: 20 + sha256: a2527b1d81792a0ccd2c05850960df119c2b6d8f5fdec97f2db7d25dc23b1068 + md5: 468fd3bb9e1f671d36c2cbc677e56f1d + depends: + - libgomp >=7.5.0 + constrains: + - openmp_impl <0.0a0 + license: BSD-3-Clause + license_family: BSD + size: 28926 + timestamp: 1770939656741 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/argon2-cffi-bindings-25.1.0-py310h5b55623_2.conda + sha256: 3470155b43c805aadaab47191cc5bdf0cf71cde43d02801f10238a3331b98626 + md5: 5dc4fbe2b404dd22fe7a025079a12e26 + depends: + - cffi >=1.0.1 + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 37208 + timestamp: 1762509568850 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/at-spi2-atk-2.38.0-h1f2db35_3.tar.bz2 + sha256: c2c2c998d49c061e390537f929e77ce6b023ef22b51a0f55692d6df7327f3358 + md5: 4ea9d4634f3b054549be5e414291801e + depends: + - at-spi2-core >=2.40.0,<2.41.0a0 + - atk-1.0 >=2.36.0 + - dbus >=1.13.6,<2.0a0 + - libgcc-ng >=9.3.0 + - libglib >=2.68.1,<3.0a0 + license: LGPL-2.1-or-later + license_family: LGPL + size: 322172 + timestamp: 1619123713021 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/at-spi2-core-2.40.3-h1f2db35_0.tar.bz2 + sha256: cd48de9674a20133e70a643476accc1a63360c921ab49477638364877937a40d + md5: a12602a94ee402b57063ef74e82016c0 + depends: + - dbus >=1.13.6,<2.0a0 + - libgcc-ng >=9.3.0 + - libglib >=2.68.3,<3.0a0 + - xorg-libx11 + - xorg-libxi + - xorg-libxtst + license: LGPL-2.1-or-later + license_family: LGPL + size: 622407 + timestamp: 1625848355776 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/atk-1.0-2.38.0-hedc4a1f_2.conda + sha256: 69f70048a1a915be7b8ad5d2cbb7bf020baa989b5506e45a676ef4ef5106c4f0 + md5: 9308557e2328f944bd5809c5630761af + depends: + - libgcc-ng >=12 + - libglib >=2.80.0,<3.0a0 + - libstdcxx-ng >=12 + constrains: + - atk-1.0 2.38.0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 358327 + timestamp: 1713898303194 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-auth-0.7.20-haea164f_0.conda + sha256: 3e12105706271384634776a5ebf364f400b887600ab950ad79c2ea892f6e011a + md5: d86c0927fc214cc75f852df9d199629c + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 109770 + timestamp: 1715287592477 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-cal-0.6.12-hc544557_0.conda + sha256: 8889df5424e76c59af6ea665f633b475224026b2bda40ace70073863198b3b5b + md5: 5391a4b44449f773ec4e6b6db0768ecc + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + - openssl >=3.2.1,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 48519 + timestamp: 1714177584882 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-common-0.9.17-h68df207_0.conda + sha256: 7dd384df039924af5d1a15ae6a54df09f092b7df3f7da34cd6696d3b362e93a8 + md5: d6964495a363d9545e79443511796449 + depends: + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 235020 + timestamp: 1713863866993 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-compression-0.2.18-h7972eaf_4.conda + sha256: 2b17a99e836b4277069cebc811af2b69d3cdaa14c665424f07e9e96090abf76b + md5: 5d85b84f1e714fa1ee56ae8533dfcbb1 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 20023 + timestamp: 1714043906148 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-event-stream-0.4.2-h0672d3d_10.conda + sha256: efc9d1cfef0c823b14270a7521df76264a4c202935de3770262d2a71134641e3 + md5: e601f2fc5cc4a9243e83c112694c00d7 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 54954 + timestamp: 1715026269217 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-http-0.8.1-h54e40d9_13.conda + sha256: 55e117b0b5de68eb78c2629238791ad53ab8059870d1829bb483e7b9425ad6ee + md5: b04c2320bc818736fdb3673e101a18ff + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-compression >=0.2.18,<0.2.19.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 188160 + timestamp: 1715026415811 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-io-0.14.8-h07c5ed3_0.conda + sha256: b1121c52c16fd663e23a0d56c9dbaa29a6903ab78cb647ab6879521be398584e + md5: 832ade3e36140d022f8ac8c928f9bbdd + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + - s2n >=1.4.13,<1.4.14.0a0 + license: Apache-2.0 + license_family: Apache + size: 160912 + timestamp: 1714867837300 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-mqtt-0.10.4-h0b2355d_2.conda + sha256: ce4c0b0096122c7db2644e7b972059d354317f992407b9b9c82776eeab94a954 + md5: 7ad03e93f5d584e3a974852b7c0daa4e + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 146030 + timestamp: 1715057469366 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-s3-0.5.9-h39e75e1_0.conda + sha256: e074103668e3d0f1b0ab3dcd908ef76d1230412f63a97b8cf52bd00af0b70a69 + md5: c526363ca483694258fead4c0eeee077 + depends: + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - libgcc-ng >=12 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 114085 + timestamp: 1715619820240 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-c-sdkutils-0.1.16-h7972eaf_0.conda + sha256: 9837d3042566f06b5512a0905e73c7f6045231f658e7bc9be72454602ee3dde3 + md5: 19c149b60cebab6440bfd115587e7109 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 57456 + timestamp: 1714208541846 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-checksums-0.1.18-h7972eaf_4.conda + sha256: 8d0f27cde33d1f86fba4aa438eb9fa32fe350a60ddda222b5581f6f35acc5676 + md5: 5c4cd53b40f29218bfbc4d1519bb29c3 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - libgcc-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 50093 + timestamp: 1714050912129 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-crt-cpp-0.26.8-hf6b9791_11.conda + sha256: c8dce8301a8b17126697a3de79d37cf18ff2c57f50578890a827f0ea876917b2 + md5: 59f26db2dbd0c179255174e67706c4a3 + depends: + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-mqtt >=0.10.4,<0.10.5.0a0 + - aws-c-s3 >=0.5.9,<0.5.10.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: Apache-2.0 + license_family: Apache + size: 268153 + timestamp: 1716300970871 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/aws-sdk-cpp-1.11.267-he30cb05_8.conda + sha256: cf73364b4f5c4c5aa762a12d2abfaaa1ef5fcce960bdadd2093d16d0c18acc70 + md5: 61e5f105b7beb1d05f971e6800b18a35 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - libcurl >=8.7.1,<9.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 3439235 + timestamp: 1715175832166 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-he30d5cf_4.conda + sha256: 41eee0adb3d4e4d57abcf9f079e17d3461399b2b9521662ae9cea1b3ab317df9 + md5: 65e3d3c3bcad1aaaf9df12e7dec3368d + depends: + - brotli-bin 1.1.0 he30d5cf_4 + - libbrotlidec 1.1.0 he30d5cf_4 + - libbrotlienc 1.1.0 he30d5cf_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 20044 + timestamp: 1756599447845 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-bin-1.1.0-he30d5cf_4.conda + sha256: 2f13c3fddd5b7ea10a768561b5a39c811b722a1bb37b3eec3ad8f66636878593 + md5: 42461478386a95cc4535707fc0e2fb57 + depends: + - libbrotlidec 1.1.0 he30d5cf_4 + - libbrotlienc 1.1.0 he30d5cf_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 19507 + timestamp: 1756599439951 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-python-1.1.0-py310h57cea71_4.conda + sha256: b2b7c91df284c343356132c5c6efbb7e4dbee5d0964fee695719df78f97e3bce + md5: c6d7c1743e0cac81664337a27069bdbc + depends: + - libgcc >=14 + - libstdcxx >=14 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - libbrotlicommon 1.1.0 he30d5cf_4 + license: MIT + license_family: MIT + size: 358201 + timestamp: 1756599501502 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h4777abc_9.conda + sha256: b3495077889dde6bb370938e7db82be545c73e8589696ad0843a32221520ad4c + md5: 840d8fc0d7b3209be93080bc20e07f2d + depends: + - libgcc >=14 + license: bzip2-1.0.6 + license_family: BSD + size: 192412 + timestamp: 1771350241232 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/c-ares-1.34.6-he30d5cf_0.conda + sha256: 7ec8a68efe479e2e298558cbc2e79d29430d5c7508254268818c0ae19b206519 + md5: 1dfbec0d08f112103405756181304c16 + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 217215 + timestamp: 1765214743735 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cairo-1.18.4-h0b6afd8_1.conda + sha256: 675db823f3d6fb6bf747fab3b0170ba99b269a07cf6df1e49fff2f9972be9cd1 + md5: 043c13ed3a18396994be9b4fab6572ad + depends: + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libgcc >=14 + - libglib >=2.86.3,<3.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libstdcxx >=14 + - libxcb >=1.17.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - pixman >=0.46.4,<1.0a0 + - xorg-libice >=1.1.2,<2.0a0 + - xorg-libsm >=1.2.6,<2.0a0 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxrender >=0.9.12,<0.10.0a0 + license: LGPL-2.1-only or MPL-1.1 + size: 927045 + timestamp: 1766416003626 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cffi-1.15.1-py310hce94938_5.conda + sha256: 7161b61507ca9f03956ddfec32f0ed7f7e2ada8b667580f12964bd0cbf99c0cb + md5: a437be649ef87035ea4865ccde7665a4 + depends: + - libffi >=3.4,<4.0a0 + - libgcc-ng >=12 + - pycparser + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 256852 + timestamp: 1695368484409 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.1-py310h586407a_0.conda + sha256: 656c5f1415495224d4dcf5f5baad4ca73879c35bb16c433b6bdabd48778ce10f + md5: 2104865045566cb04092d93959960bba + depends: + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - numpy >=1.20 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 247870 + timestamp: 1712430256989 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cytoolz-1.1.0-py310ha7967c6_2.conda + sha256: 732c776c5fad3df81afa3a2c9d97d5fe1ff1d2c9e711965e65b23f7ec63abeb0 + md5: 2e42acb688f76ef8a9dc980d7f97ac94 + depends: + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - toolz >=0.10.0 + license: BSD-3-Clause + license_family: BSD + size: 587934 + timestamp: 1771857311383 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/dbus-1.16.2-h70963c4_1.conda + sha256: 3af801577431af47c0b72a82bb93c654f03072dece0a2a6f92df8a6802f52a22 + md5: a4b6b82427d15f0489cef0df2d82f926 + depends: + - libstdcxx >=14 + - libgcc >=14 + - libglib >=2.86.2,<3.0a0 + - libzlib >=1.3.1,<2.0a0 + - libexpat >=2.7.3,<3.0a0 + license: AFL-2.1 OR GPL-2.0-or-later + size: 480416 + timestamp: 1764536098891 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/debugpy-1.8.21-py310heccc163_0.conda + sha256: 52b282f04e882fe42a077ef680b25f33f6a56dd221d49f35e8f57bdfef9aeab5 + md5: 33ec97d99da73674ab9ae3a5e9851b28 + depends: + - python + - libgcc >=14 + - libstdcxx >=14 + - python 3.10.* *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + size: 2209952 + timestamp: 1780390171485 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/epoxy-1.5.10-he30d5cf_2.conda + sha256: aa562cdd72d2d15b0f2ee4565c8e34f18b52f7135a3f3b1ce727c202425c3bec + md5: 1c50e7c46ccefffe918ac974fa1a6752 + depends: + - libdrm >=2.4.125,<2.5.0a0 + - libegl >=1.7.0,<2.0a0 + - libegl-devel + - libgcc >=14 + - libgl >=1.7.0,<2.0a0 + - libgl-devel + - libglx >=1.7.0,<2.0a0 + - libglx-devel + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxdamage >=1.1.6,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxfixes >=6.0.1,<7.0a0 + - xorg-libxxf86vm >=1.1.6,<2.0a0 + license: MIT + license_family: MIT + size: 422103 + timestamp: 1758743388115 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/fontconfig-2.18.1-hba86a56_0.conda + sha256: 2ccfd118269d363a5506161c4a0d96da46d2f01beecc74e0540a54b4737d0e45 + md5: f4d29a0cd77104a683607319a542ac7e + depends: + - libexpat >=2.8.1,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libgcc >=14 + - libuuid >=2.42.1,<3.0a0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + size: 290522 + timestamp: 1780450108132 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.63.0-py310h2d8da20_0.conda + sha256: 26cc92dbc52fcb6fdc2820492b02eecf94934322a46b96e47d5fe0895a59f7dc + md5: b77749fdc784b67cd564c091a2d38a0d + depends: + - brotli + - libgcc >=14 + - munkres + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - unicodedata2 >=15.1.0 + license: MIT + license_family: MIT + size: 2450690 + timestamp: 1778770412726 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/freetype-2.14.3-h8af1aa0_0.conda + sha256: 5594df70ef3df016b99de44e61b4024b7f3ec3472db83c7ac7723eafa8b26d95 + md5: f11edf8adf0d119148b97f745548390d + depends: + - libfreetype 2.14.3 h8af1aa0_0 + - libfreetype6 2.14.3 hdae7a39_0 + license: GPL-2.0-only OR FTL + size: 173735 + timestamp: 1774301100144 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/fribidi-1.0.16-he30d5cf_0.conda + sha256: 1bfcd715bcb49a0b22d5d1899a22c6ff884b06f8e141eb746f3949752469a422 + md5: f3ac54914f7d3e1d68cb8d891765e5f9 + depends: + - libgcc >=14 + license: LGPL-2.1-or-later + size: 62909 + timestamp: 1757438620177 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gdk-pixbuf-2.44.6-h90308e0_0.conda + sha256: 53ac38045a8c0b6aa9cfaf784443a3744dc86ab4737c1479b44ae85c96926fe1 + md5: bdd860e72c5e10eb4ffa3d61f9b02ee0 + depends: + - libgcc >=14 + - libglib >=2.86.4,<3.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - liblzma >=5.8.2,<6.0a0 + - libpng >=1.6.56,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: LGPL-2.1-or-later + license_family: LGPL + size: 583708 + timestamp: 1774987740322 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gflags-2.2.2-h5ad3122_1005.conda + sha256: 28fe6b40b20454106d5e4ef6947cf298c13cc72a46347bbc49b563cd3a463bfa + md5: 4ff634d515abbf664774b5e1168a9744 + depends: + - libgcc >=13 + - libstdcxx >=13 + license: BSD-3-Clause + license_family: BSD + size: 106638 + timestamp: 1726599967617 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/glib-tools-2.88.1-h7439e1d_2.conda + sha256: a8d09a59c1cd0df08a085f6ed8401139b3301799735511746165e126c29c3fce + md5: 49b98fdeb09f7379d52a167b35b99223 + depends: + - libglib ==2.88.1 h96a7f82_2 + - libffi + - libgcc >=14 + license: LGPL-2.1-or-later + size: 257707 + timestamp: 1778508920982 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/glog-0.7.1-h468a4a4_0.conda + sha256: 920795d4f775a9f47e91c2223e64847f0b212b3fedc56c137c5889e32efe8ba0 + md5: 08940a32c6ced3703d1412dd37df4f62 + depends: + - gflags >=2.2.2,<2.3.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: BSD-3-Clause + license_family: BSD + size: 145811 + timestamp: 1718284208668 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/graphite2-1.3.14-hfae3067_2.conda + sha256: c9b1781fe329e0b77c5addd741e58600f50bef39321cae75eba72f2f381374b7 + md5: 4aa540e9541cc9d6581ab23ff2043f13 + depends: + - libgcc >=14 + - libstdcxx >=14 + license: LGPL-2.0-or-later + license_family: LGPL + size: 102400 + timestamp: 1755102000043 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/graphviz-14.1.2-h45e821f_0.conda + sha256: 0ac1a90696a3250febdb1d63a3161b9aad5dc136e602f7f17df4894bd153162c + md5: 60c3b65c5e60fc70e1b9f0de4d0c2247 + depends: + - adwaita-icon-theme + - cairo >=1.18.4,<2.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.4,<3.0a0 + - gtk3 >=3.24.43,<4.0a0 + - gts >=0.7.6,<0.8.0a0 + - libexpat >=2.7.3,<3.0a0 + - libgcc >=14 + - libgd >=2.3.3,<2.4.0a0 + - libglib >=2.86.3,<3.0a0 + - librsvg >=2.60.0,<3.0a0 + - libstdcxx >=14 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - pango >=1.56.4,<2.0a0 + license: EPL-1.0 + license_family: Other + size: 2578234 + timestamp: 1769427141106 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gtk3-3.24.52-h75d4e7a_0.conda + sha256: 5db1347513253a2d200b111717eb452d4b95e9380db48b6dd2c8bb7bb256d754 + md5: f6a9ed300bcf3217da7f1955f57facec + depends: + - at-spi2-atk >=2.38.0,<3.0a0 + - atk-1.0 >=2.38.0 + - cairo >=1.18.4,<2.0a0 + - epoxy >=1.5.10,<1.6.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.16,<2.0a0 + - gdk-pixbuf >=2.44.5,<3.0a0 + - glib-tools + - harfbuzz >=13.2.1 + - hicolor-icon-theme + - libcups >=2.3.3,<2.4.0a0 + - libcups >=2.3.3,<3.0a0 + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libgcc >=14 + - libglib >=2.86.4,<3.0a0 + - liblzma >=5.8.2,<6.0a0 + - libxkbcommon >=1.13.1,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - pango >=1.56.4,<2.0a0 + - wayland >=1.25.0,<2.0a0 + - xorg-libx11 >=1.8.13,<2.0a0 + - xorg-libxcomposite >=0.4.7,<1.0a0 + - xorg-libxcursor >=1.2.3,<2.0a0 + - xorg-libxdamage >=1.1.6,<2.0a0 + - xorg-libxext >=1.3.7,<2.0a0 + - xorg-libxfixes >=6.0.2,<7.0a0 + - xorg-libxi >=1.8.2,<2.0a0 + - xorg-libxinerama >=1.1.6,<1.2.0a0 + - xorg-libxrandr >=1.5.5,<2.0a0 + - xorg-libxrender >=0.9.12,<0.10.0a0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 6023698 + timestamp: 1774296668544 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/gts-0.7.6-he293c15_4.conda + sha256: 1e9cc30d1c746d5a3399a279f5f642a953f37d9f9c82fd4d55b301e9c2a23f7c + md5: 2aeaeddbd89e84b60165463225814cfc + depends: + - libgcc-ng >=12 + - libglib >=2.76.3,<3.0a0 + - libstdcxx-ng >=12 + license: LGPL-2.0-or-later + license_family: LGPL + size: 332673 + timestamp: 1686545222091 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/h5py-3.16.0-nompi_py310hce7682f_102.conda + sha256: 041f0a90ce9c2060b8f9b000014bbfd3005c45ea54aea178b3a3b12e155fb1e5 + md5: d14cdb5312d50f6bae854e16d535baa4 + depends: + - cached-property + - hdf5 >=1.14.6,<1.14.7.0a0 + - libgcc >=14 + - numpy >=1.21,<3 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 1229859 + timestamp: 1775581333710 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/harfbuzz-14.2.0-h1134a53_0.conda + sha256: cb3e67e8045577b944b64534907d194ebc44e0959e0842847c18a4067eeeb9f6 + md5: 1775defbef30aa990498e753a948cb18 + depends: + - cairo >=1.18.4,<2.0a0 + - graphite2 >=1.3.14,<2.0a0 + - icu >=78.3,<79.0a0 + - libexpat >=2.7.5,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libgcc >=14 + - libglib >=2.86.4,<3.0a0 + - libstdcxx >=14 + - libzlib >=1.3.2,<2.0a0 + license: MIT + license_family: MIT + size: 2800967 + timestamp: 1776783366021 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/hdf5-1.14.6-nompi_hf95b8e7_109.conda + sha256: a8de3cccca5f00d435300392763e16be31b1602bcf5911605e2f645cbbcaf4bb + md5: ad6282512486712666a86d107301c956 + depends: + - libaec >=1.1.5,<2.0a0 + - libcurl >=8.20.0,<9.0a0 + - libgcc >=14 + - libgfortran + - libgfortran5 >=14.3.0 + - libstdcxx >=14 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 3901933 + timestamp: 1777525882185 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/hicolor-icon-theme-0.17-h8af1aa0_3.conda + sha256: 9f7fa161b33de5f001dc43f9d6399ba45b3fb1efb141ee2fec6bf05a48420d63 + md5: af62915a9e4154e16d97f99c64cec14e + license: GPL-2.0-or-later + license_family: GPL + size: 17670 + timestamp: 1771540496764 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/icu-78.3-hcab7f73_0.conda + sha256: 49ba6aed2c6b482bb0ba41078057555d29764299bc947b990708617712ef6406 + md5: 546da38c2fa9efacf203e2ad3f987c59 + depends: + - libgcc >=14 + - libstdcxx >=14 + license: MIT + license_family: MIT + size: 12837286 + timestamp: 1773822650615 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/keyutils-1.6.3-h86ecc28_0.conda + sha256: 5ce830ca274b67de11a7075430a72020c1fb7d486161a82839be15c2b84e9988 + md5: e7df0aab10b9cbb73ab2a467ebfaf8c7 + depends: + - libgcc >=13 + license: LGPL-2.1-or-later + size: 129048 + timestamp: 1754906002667 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/kiwisolver-1.5.0-py310h65c7496_0.conda + sha256: 5e347811fc93d6175c0515cdf2d8d5948c7b10961f4eb1dd0b8be148787ba145 + md5: 5e9dd24bdd7f2dac4fadce87c758b0d6 + depends: + - python + - libstdcxx >=14 + - libgcc >=14 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 84927 + timestamp: 1773067288778 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/krb5-1.22.2-hfd895c2_0.conda + sha256: b53999d888dda53c506b264e8c02b5f5c8e022c781eda0718f007339e6bc90ba + md5: d9ca108bd680ea86a963104b6b3e95ca + depends: + - keyutils >=1.6.3,<2.0a0 + - libedit >=3.1.20250104,<3.2.0a0 + - libedit >=3.1.20250104,<4.0a0 + - libgcc >=14 + - libstdcxx >=14 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 1517436 + timestamp: 1769773395215 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lcms2-2.19.1-h9d5b58d_1.conda + sha256: ed213207bbf11663181941e0931caa9ce748f0544688e8e0fbcf330bca279389 + md5: 9183fda4be2b4ee5760cdb8e540439c8 + depends: + - libgcc >=14 + - libjpeg-turbo >=3.1.4.1,<4.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: MIT + license_family: MIT + size: 296564 + timestamp: 1780211834883 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.45.1-default_h1979696_102.conda + sha256: 7abd913d81a9bf00abb699e8987966baa2065f5132e37e815f92d90fc6bba530 + md5: a21644fc4a83da26452a718dc9468d5f + depends: + - zstd >=1.5.7,<1.6.0a0 + constrains: + - binutils_impl_linux-aarch64 2.45.1 + license: GPL-3.0-only + license_family: GPL + size: 875596 + timestamp: 1774197520746 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lerc-4.1.0-h52b7260_0.conda + sha256: 8957fd460c1c132c8031f65fd5f56ec3807fd71b7cab2c5e2b0937b13404ab36 + md5: d13423b06447113a90b5b1366d4da171 + depends: + - libgcc >=14 + - libstdcxx >=14 + license: Apache-2.0 + license_family: Apache + size: 240444 + timestamp: 1773114901155 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libabseil-20240116.2-cxx17_h0a1ffab_1.conda + sha256: a6e1a6f13fd49c24238373838c266101a2bf3b521b0a36a3a7e586b40f50ec5b + md5: 9cadd103cf89edb2ea68d33728511158 + depends: + - libgcc-ng >=12 + - libstdcxx-ng >=12 + constrains: + - abseil-cpp =20240116.2 + - libabseil-static =20240116.2=cxx17* + license: Apache-2.0 + license_family: Apache + size: 1283386 + timestamp: 1720857389114 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libaec-1.1.5-hff7e48a_0.conda + sha256: 8b74acb52cda5f4ba91f59c85132f582de5db9dceff4e252f49c2525d14c600c + md5: 345c7bf559743adb5375ee3603911065 + depends: + - libgcc >=14 + - libstdcxx >=14 + license: BSD-2-Clause + license_family: BSD + size: 37745 + timestamp: 1769221878827 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-16.0.0-hda7f696_1_cpu.conda + build_number: 1 + sha256: d9da8e52b94ef5a4ccce358bb4c956ccf608bb363097bcb3faf3b8d849d70668 + md5: 102599b2684aca10c24e5ddf9bea44cc + depends: + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - aws-sdk-cpp >=1.11.267,<1.11.268.0a0 + - bzip2 >=1.0.8,<2.0a0 + - gflags >=2.2.2,<2.3.0a0 + - glog >=0.7.0,<0.8.0a0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libbrotlidec >=1.1.0,<1.2.0a0 + - libbrotlienc >=1.1.0,<1.2.0a0 + - libgcc-ng >=13 + - libgoogle-cloud >=2.23.0,<2.24.0a0 + - libgoogle-cloud-storage >=2.23.0,<2.24.0a0 + - libre2-11 >=2023.9.1 + - libstdcxx-ng >=13 + - libutf8proc >=2.8.0,<3.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - orc >=2.0.0,<2.0.1.0a0 + - re2 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.6,<1.6.0a0 + - libutf8proc <2.9 + constrains: + - parquet-cpp <0.0a0 + - arrow-cpp <0.0a0 + - apache-arrow-proc =*=cpu + license: Apache-2.0 + license_family: APACHE + size: 7673923 + timestamp: 1715198315690 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-acero-16.0.0-h5ad3122_1_cpu.conda + build_number: 1 + sha256: ce185433c5272d85dcdc2c44d1fa886a6a06b35a1cb48660166cd868a13d78a5 + md5: 0424b5248295d495e42b949f7be8674c + depends: + - libarrow 16.0.0 hda7f696_1_cpu + - libgcc-ng >=13 + - libstdcxx-ng >=13 + license: Apache-2.0 + license_family: APACHE + size: 575790 + timestamp: 1715198355478 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-dataset-16.0.0-h5ad3122_1_cpu.conda + build_number: 1 + sha256: 79dff6cdcaff6121f611621b57473cf9828533f3349c6380c3f1da13e32c76ed + md5: fe9a1ff426a7caec0cae8df1b6777e9d + depends: + - libarrow 16.0.0 hda7f696_1_cpu + - libarrow-acero 16.0.0 h5ad3122_1_cpu + - libgcc-ng >=13 + - libparquet 16.0.0 h6fe2c6f_1_cpu + - libstdcxx-ng >=13 + license: Apache-2.0 + license_family: APACHE + size: 559308 + timestamp: 1715198446423 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libarrow-substrait-16.0.0-h08b7278_1_cpu.conda + build_number: 1 + sha256: 2dd8a514e73beb76bcdd391fffda6af6d2bd62a2bf8c77356fd9bc5938fcd1bf + md5: 5273ca31950078c221df8b1878d27b3a + depends: + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libarrow 16.0.0 hda7f696_1_cpu + - libarrow-acero 16.0.0 h5ad3122_1_cpu + - libarrow-dataset 16.0.0 h5ad3122_1_cpu + - libgcc-ng >=13 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libstdcxx-ng >=13 + license: Apache-2.0 + license_family: APACHE + size: 542140 + timestamp: 1715198490839 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-8_haddc8a3_openblas.conda + build_number: 8 + sha256: c897399c943168c646f659952f73a9154f9122d7e9b151649dbe075dfdcd484b + md5: 8b44dad125760faa2b3925f5a6e3112d + depends: + - libopenblas >=0.3.33,<0.3.34.0a0 + - libopenblas >=0.3.33,<1.0a0 + constrains: + - libcblas 3.11.0 8*_openblas + - liblapack 3.11.0 8*_openblas + - mkl <2027 + - blas 2.308 openblas + - liblapacke 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 18843 + timestamp: 1779859042591 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.9.0-20_linuxaarch64_openblas.conda + build_number: 20 + sha256: fa8bbe018fd891f62fd4d1a91920b512d89d5a75641899997fd3050b04837732 + md5: 11590ed0fb5cebe7bbfa4bab8d8b07f8 + depends: + - libopenblas >=0.3.25,<0.3.26.0a0 + - libopenblas >=0.3.25,<1.0a0 + constrains: + - blas * openblas + - libcblas 3.9.0 20_linuxaarch64_openblas + - liblapacke 3.9.0 20_linuxaarch64_openblas + - liblapack 3.9.0 20_linuxaarch64_openblas + license: BSD-3-Clause + license_family: BSD + size: 14578 + timestamp: 1700592059130 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlicommon-1.1.0-he30d5cf_4.conda + sha256: fcd4f03086da6d32f23315ae53183e9889d1ce1c551da9dbfacd9cb735867b21 + md5: a94d4448efbf2053f07342bf56ea0607 + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 69327 + timestamp: 1756599414214 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlidec-1.1.0-he30d5cf_4.conda + sha256: 6009cebecb91eda6f8e2cdc0af2ce66598449058d50d1bccacfc7fe0ec7c212b + md5: 2ca8c800d43a86ea1c5108ff9400560e + depends: + - libbrotlicommon 1.1.0 he30d5cf_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 32318 + timestamp: 1756599422767 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libbrotlienc-1.1.0-he30d5cf_4.conda + sha256: d03363005059aa6a0d190c2200b6520631b628058b8643b69107db24977840d7 + md5: 275458cac08857155a1add14524634bb + depends: + - libbrotlicommon 1.1.0 he30d5cf_4 + - libgcc >=14 + license: MIT + license_family: MIT + size: 298363 + timestamp: 1756599431316 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.11.0-8_hd72aa62_openblas.conda + build_number: 8 + sha256: 3ba039f0705022939d90e36c1ed2fcbafd7f5bb77563e3702202ae796b32f4d2 + md5: 76242b7ad6e43809afa8671dd609b4ed + depends: + - libblas 3.11.0 8_haddc8a3_openblas + constrains: + - liblapack 3.11.0 8*_openblas + - liblapacke 3.11.0 8*_openblas + - blas 2.308 openblas + license: BSD-3-Clause + license_family: BSD + size: 18817 + timestamp: 1779859049133 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-20_linuxaarch64_openblas.conda + build_number: 20 + sha256: ab7220e20f7e20336a3c348dcf9d041ad162d55910394f28ddf1f8b49c966dcb + md5: b41e55ae2cb9d3518da2cbe3677b3b3b + depends: + - libblas 3.9.0 20_linuxaarch64_openblas + constrains: + - blas * openblas + - liblapacke 3.9.0 20_linuxaarch64_openblas + - liblapack 3.9.0 20_linuxaarch64_openblas + license: BSD-3-Clause + license_family: BSD + size: 14501 + timestamp: 1700592073277 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcrc32c-1.1.2-h01db608_0.tar.bz2 + sha256: b8b8c57a87da86b3ea24280fd6aa8efaf92f4e684b606bf2db5d3cb06ffbe2ea + md5: 268ee639c17ada0002fb04dd21816cc2 + depends: + - libgcc-ng >=9.4.0 + - libstdcxx-ng >=9.4.0 + license: BSD-3-Clause + license_family: BSD + size: 18669 + timestamp: 1633683724891 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcups-2.3.3-h4f2b762_6.conda + sha256: 41b04f995c9f63af8c4065a35931e46cbc2fdd6b9bf7e4c19f90d53cbb2bc8e5 + md5: 67828c963b17db7dc989fe5d509ef04a + depends: + - krb5 >=1.22.2,<1.23.0a0 + - libgcc >=14 + - libstdcxx >=14 + - libzlib >=1.3.1,<2.0a0 + license: Apache-2.0 + license_family: Apache + size: 4553739 + timestamp: 1770903929794 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libcurl-8.20.0-hc57f145_0.conda + sha256: 1607d3caf58f7d9e9ad9e5f0841737d0cfa47b5501d4e36fbf98e1c645d7393e + md5: 88da514c8db1a7f6a05297941a897af2 + depends: + - krb5 >=1.22.2,<1.23.0a0 + - libgcc >=14 + - libnghttp2 >=1.68.1,<2.0a0 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: curl + license_family: MIT + size: 488942 + timestamp: 1777461485901 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdeflate-1.25-h1af38f5_0.conda + sha256: 48814b73bd462da6eed2e697e30c060ae16af21e9fbed30d64feaf0aad9da392 + md5: a9138815598fe6b91a1d6782ca657b0c + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 71117 + timestamp: 1761979776756 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libdrm-2.4.127-he30d5cf_0.conda + sha256: 2a941ffcd6b09380344c2cb5b198d2743ce4fc30ec9a5c8c83e53368d8015aef + md5: 987d35ad350bb552a30f3d314f6c7655 + depends: + - libgcc >=14 + - libpciaccess >=0.19,<0.20.0a0 + license: MIT + license_family: MIT + size: 345283 + timestamp: 1778975814771 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libedit-3.1.20250104-pl5321h976ea20_0.conda + sha256: c0b27546aa3a23d47919226b3a1635fccdb4f24b94e72e206a751b33f46fd8d6 + md5: fb640d776fc92b682a14e001980825b1 + depends: + - ncurses + - libgcc >=13 + - ncurses >=6.5,<7.0a0 + license: BSD-2-Clause + license_family: BSD + size: 148125 + timestamp: 1738479808948 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libegl-1.7.0-hd24410f_3.conda + sha256: b987d3874edfcd9c7ddca86c003cb04ae51160a72c173a24cd46ab9eeb8886ab + md5: ec017f25e5d01ef9dd81e95ff73ff051 + depends: + - libglvnd 1.7.0 hd24410f_3 + license: LicenseRef-libglvnd + size: 54600 + timestamp: 1779728234591 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libegl-devel-1.7.0-hd24410f_3.conda + sha256: 2b5ae66aa91215e915df3c520fed85a17231a067339b54f0594d93689b684c86 + md5: 8ebac3af4a69a9a41c16442a65bf3ac4 + depends: + - libegl 1.7.0 hd24410f_3 + - libgl-devel 1.7.0 hd24410f_3 + - xorg-libx11 + license: LicenseRef-libglvnd + size: 31552 + timestamp: 1779728260736 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libev-4.33-h31becfc_2.conda + sha256: 973af77e297f1955dd1f69c2cbdc5ab9dfc88388a5576cd152cda178af0fd006 + md5: a9a13cb143bbaa477b1ebaefbe47a302 + depends: + - libgcc-ng >=12 + license: BSD-2-Clause + license_family: BSD + size: 115123 + timestamp: 1702146237623 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libevent-2.1.12-h4ba1bb4_1.conda + sha256: 01333cc7d6e6985dd5700b43660d90e9e58049182017fd24862088ecbe1458e4 + md5: 96ae6083cd1ac9f6bc81631ac835b317 + depends: + - libgcc-ng >=12 + - openssl >=3.1.1,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 438992 + timestamp: 1685726046519 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.8.1-hfae3067_0.conda + sha256: 1fc392b997c6ee2bd3226a7cd870d0edbcbb367e25f9f18dd4a7025fced6efc0 + md5: 513dd884361dfb8a554298ed69b58823 + depends: + - libgcc >=14 + constrains: + - expat 2.8.1.* + license: MIT + license_family: MIT + size: 77140 + timestamp: 1779278671302 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.5.2-h376a255_0.conda + sha256: 3df4c539449aabc3443bbe8c492c01d401eea894603087fca2917aa4e1c2dea9 + md5: 2f364feefb6a7c00423e80dcb12db62a + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 55952 + timestamp: 1769456078358 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libfreetype-2.14.3-h8af1aa0_0.conda + sha256: 752e4f66283d7deb4c6fd47d88df644d8daa2aaa825a54f3bf350a625190192a + md5: a229e22d4d8814a07702b0919d8e6701 + depends: + - libfreetype6 >=2.14.3 + license: GPL-2.0-only OR FTL + size: 8125 + timestamp: 1774301094057 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libfreetype6-2.14.3-hdae7a39_0.conda + sha256: 8e6b27fe4eec4c2fa7b7769a21973734c8dba1de80086fb0213e58375ac09f4c + md5: b99ed99e42dafb27889483b3098cace7 + depends: + - libgcc >=14 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - freetype >=2.14.3 + license: GPL-2.0-only OR FTL + size: 422941 + timestamp: 1774301093473 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-15.2.0-h8acb6b2_19.conda + sha256: 4592b096e553f67799ae70d4b6167eeda3ec74587d68c7aecbf4e7b1df136681 + md5: f35b3f52d0a2ec4ffe3c89ba135cdb9a + depends: + - _openmp_mutex >=4.5 + constrains: + - libgomp 15.2.0 h8acb6b2_19 + - libgcc-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 622462 + timestamp: 1778268755949 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-15.2.0-he9431aa_19.conda + sha256: 1137f93f477f56199ded24117430045a0c02cbe8b10031beac3b9ad2138539d3 + md5: 770cf892e5530f43e63cadc673e85653 + depends: + - libgcc 15.2.0 h8acb6b2_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27738 + timestamp: 1778268759211 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgd-2.3.3-h88aa843_12.conda + sha256: 8b7dfd29a8ce42d28c47a06dcaa7c21f6dad751b72d2352668d680b6ec951630 + md5: b4c9083a19a45047173f380624c7e5f3 + depends: + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libgcc >=14 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + license: GD + license_family: BSD + size: 195554 + timestamp: 1766331786625 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-15.2.0-he9431aa_19.conda + sha256: e5ad94be72634233510b33ba792a3339921bd468f0b8bc6961ea05eded251d9b + md5: c7a5b5decf969ead5ecada83654164cf + depends: + - libgfortran5 15.2.0 h1b7bec0_19 + constrains: + - libgfortran-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27728 + timestamp: 1778268784621 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran-ng-15.2.0-he9431aa_19.conda + sha256: acdf40833bd2bbcea971f46e4bfa86268e6aa5ea2a18ff23393ea737d7acef2e + md5: f034c1f3b3698a0fce2c121358c5d31e + depends: + - libgfortran 15.2.0 he9431aa_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27757 + timestamp: 1778268971051 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgfortran5-15.2.0-h1b7bec0_19.conda + sha256: af8e9bdcaa77f133a8ee4c1ef57ef564d9c45aa262abf9f5ef9b50eb99d96407 + md5: 779dbb494de6d3d6477cab52eb34285a + depends: + - libgcc >=15.2.0 + constrains: + - libgfortran 15.2.0 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 1487244 + timestamp: 1778268767295 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgl-1.7.0-hd24410f_3.conda + sha256: 05c75a2034bdbca29bab467d02ad770ed5e524e4f0670432258f2d8487c95348 + md5: 6e893c36f31502dd195d3d58f455fdbd + depends: + - libglvnd 1.7.0 hd24410f_3 + - libglx 1.7.0 hd24410f_3 + license: LicenseRef-libglvnd + size: 148112 + timestamp: 1779728248678 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgl-devel-1.7.0-hd24410f_3.conda + sha256: b7483884e5e8df362f113d7d7694f0a37ecf6409f1acaaa889f312688917c067 + md5: 3a0adce33b3b8a52c76389db1edfec1b + depends: + - libgl 1.7.0 hd24410f_3 + - libglx-devel 1.7.0 hd24410f_3 + license: LicenseRef-libglvnd + size: 116084 + timestamp: 1779728257534 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglib-2.88.1-h96a7f82_2.conda + sha256: 050285afdb7bd98b1b8fb052af9da31fafde586a49d3b56dd33d5338b2d0e411 + md5: 16d72f76bf6fead4a29efb2fede0a06b + depends: + - libgcc >=14 + - libiconv >=1.18,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - pcre2 >=10.47,<10.48.0a0 + - libffi >=3.5.2,<3.6.0a0 + constrains: + - glib >2.66 + license: LGPL-2.1-or-later + size: 4946648 + timestamp: 1778508920982 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglvnd-1.7.0-hd24410f_3.conda + sha256: ca124e53765a2b123e0ca6ce809c7caf188bb26e5fe125b69099378276d5e66f + md5: a2ad848c0aab2e326c6af08ea20502f4 + license: LicenseRef-libglvnd + size: 146645 + timestamp: 1779728228274 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglx-1.7.0-hd24410f_3.conda + sha256: 2698b415b9f7b692cd64e34db623e1a6e54ed54e78b0b4e5d4ea6762791e9118 + md5: 338faf34b78d053841098c0528699e34 + depends: + - libglvnd 1.7.0 hd24410f_3 + - xorg-libx11 >=1.8.13,<2.0a0 + license: LicenseRef-libglvnd + size: 76704 + timestamp: 1779728242753 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglx-devel-1.7.0-hd24410f_3.conda + sha256: b30433c4f56bec0a7d9d288e0a456ed280183e32f3f4880ada2189fc12804a52 + md5: 3da9719866b95bddcad86c8aec6a8ba2 + depends: + - libglx 1.7.0 hd24410f_3 + - xorg-libx11 >=1.8.13,<2.0a0 + - xorg-xorgproto + license: LicenseRef-libglvnd + size: 27651 + timestamp: 1779728252006 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgomp-15.2.0-h8acb6b2_19.conda + sha256: 2370ef0ffcbae5bede3c4bf136add4abc257245eb91f724c99bb4a43116c5a83 + md5: c5e8a379c4a2ec2aea4ba22758c001d9 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 587387 + timestamp: 1778268674393 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgoogle-cloud-2.23.0-hd739bbb_1.conda + sha256: 4c3b15e81fe86819829357db217e5987658f7128dd67736214d1d310b2332066 + md5: 28b087119988d9cb764fab2384389018 + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcurl >=8.7.1,<9.0a0 + - libgcc-ng >=12 + - libgrpc >=1.62.2,<1.63.0a0 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libstdcxx-ng >=12 + - openssl >=3.2.1,<4.0a0 + constrains: + - libgoogle-cloud 2.23.0 *_1 + license: Apache-2.0 + license_family: Apache + size: 1197540 + timestamp: 1713798589731 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgoogle-cloud-storage-2.23.0-hdb39181_1.conda + sha256: 977dc600e6958b3c88a278567f279c46eb596cbaac284705bdeec8d7f7e24c39 + md5: 0cdda7751432e9ec4255e19becdac10d + depends: + - libabseil + - libcrc32c >=1.1.2,<1.2.0a0 + - libcurl + - libgcc-ng >=12 + - libgoogle-cloud 2.23.0 hd739bbb_1 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl + license: Apache-2.0 + license_family: Apache + size: 694948 + timestamp: 1713798758001 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgrpc-1.62.2-h98a9317_0.conda + sha256: ae5fe7ba0c5c599f0e20fa08be436518b7ef25ab6f705e8c7fcf0d0f34525f72 + md5: 2a669953ec0f08c2cc56bb43fed78de8 + depends: + - c-ares >=1.28.1,<2.0a0 + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libgcc-ng >=12 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libre2-11 >=2023.9.1 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.2.1,<4.0a0 + - re2 + constrains: + - grpc-cpp =1.62.2 + license: Apache-2.0 + license_family: APACHE + size: 7395259 + timestamp: 1713390742813 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libiconv-1.18-h90929bb_2.conda + sha256: 1473451cd282b48d24515795a595801c9b65b567fe399d7e12d50b2d6cdb04d9 + md5: 5a86bf847b9b926f3a4f203339748d78 + depends: + - libgcc >=14 + license: LGPL-2.1-only + size: 791226 + timestamp: 1754910975665 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.1.4.1-he30d5cf_0.conda + sha256: e97ec2af5f09f8f6ea8ecd550055c95ae80fae22015fcfadaa94eafe025c9ccc + md5: a85ba48648f6868016f2741fd9170250 + depends: + - libgcc >=14 + constrains: + - jpeg <0.0.0a + license: IJG AND BSD-3-Clause AND Zlib + size: 693143 + timestamp: 1775962625956 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.11.0-8_h88aeb00_openblas.conda + build_number: 8 + sha256: d269a684afa0b2fdb44d6b60167f854f30410cdb5ee49a7275c026f6b10c8d05 + md5: 3af3f2aa755abc5e91351114ae214f55 + depends: + - libblas 3.11.0 8_haddc8a3_openblas + constrains: + - libcblas 3.11.0 8*_openblas + - liblapacke 3.11.0 8*_openblas + - blas 2.308 openblas + license: BSD-3-Clause + license_family: BSD + size: 18828 + timestamp: 1779859055749 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-20_linuxaarch64_openblas.conda + build_number: 20 + sha256: 69725416cbab36e3df10e3e4ab7925bef80581b4d3c0e57612e4829e8522b323 + md5: e7412a592d9ee7c92026eb1189687271 + depends: + - libblas 3.9.0 20_linuxaarch64_openblas + constrains: + - blas * openblas + - libcblas 3.9.0 20_linuxaarch64_openblas + - liblapacke 3.9.0 20_linuxaarch64_openblas + license: BSD-3-Clause + license_family: BSD + size: 14502 + timestamp: 1700592081278 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblzma-5.8.3-he30d5cf_0.conda + sha256: d61962b9cd54c3554361550203c64d5b65b71e3058a285b66e4b04b9769f0a5c + md5: 76298a9e6d71ee6e832a8d0d7373b261 + depends: + - libgcc >=14 + constrains: + - xz 5.8.3.* + license: 0BSD + size: 126102 + timestamp: 1775828008518 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libnghttp2-1.68.1-hd3077d7_0.conda + sha256: 13782715b9eeebc4ad16d36e84ca569d1495e3516aea3fe546a32caa0a597d82 + md5: be5f0f007a4500a226ef001115535a3d + depends: + - c-ares >=1.34.6,<2.0a0 + - libev >=4.33,<4.34.0a0 + - libev >=4.33,<5.0a0 + - libgcc >=14 + - libstdcxx >=14 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 726928 + timestamp: 1773854039807 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h86ecc28_1.conda + sha256: c0dc4d84198e3eef1f37321299e48e2754ca83fd12e6284754e3cb231357c3a5 + md5: d5d58b2dc3e57073fe22303f5fed4db7 + depends: + - libgcc >=13 + license: LGPL-2.1-only + license_family: GPL + size: 34831 + timestamp: 1750274211000 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.25-pthreads_h5a5ec62_0.conda + sha256: 4b72f9a6e5bd111e5f320293fd1f56b27836d9b87638a78b7ec94c3b3f51a660 + md5: 60e86bc93e3f213278dc5081115fb63b + depends: + - libgcc-ng >=12 + - libgfortran-ng + - libgfortran5 >=12.3.0 + constrains: + - openblas >=0.3.25,<0.3.26.0a0 + license: BSD-3-Clause + license_family: BSD + size: 4543189 + timestamp: 1700535967727 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.33-pthreads_h9d3fd7e_0.conda + sha256: b018ecfb05e75a8eea3f21f6b5c5c2a54b5178bdcf19e2e2df2735740214a8c8 + md5: 58a66cd95e9692f08abe89f55a6f3f12 + depends: + - libgcc >=14 + - libgfortran + - libgfortran5 >=14.3.0 + constrains: + - openblas >=0.3.33,<0.3.34.0a0 + license: BSD-3-Clause + license_family: BSD + size: 5121336 + timestamp: 1776993423004 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libparquet-16.0.0-h6fe2c6f_1_cpu.conda + build_number: 1 + sha256: c4390be64a922328dcd8c62c206e2c8eb2d5927cc3a2e60658fd6281b1354ad7 + md5: b41c31aa61dc6ab727702b819078c4b4 + depends: + - libarrow 16.0.0 hda7f696_1_cpu + - libgcc-ng >=13 + - libstdcxx-ng >=13 + - libthrift >=0.19.0,<0.19.1.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 1119558 + timestamp: 1715198425086 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libpciaccess-0.19-he30d5cf_0.conda + sha256: 5d26d751b7cc4b66e28ed1ae75900956600aaa5c5d874d5a8cf106d3aff834d3 + md5: 462239e256bc180c9c45dd049ba797ee + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 30294 + timestamp: 1773533057559 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libpng-1.6.58-h1abf092_0.conda + sha256: 483eaa53da40a6a3e558709d9f7b1ca388735364ae21a1ba58cf942514649c92 + md5: f51503ac45a4888bce71af9027a2ecc9 + depends: + - libgcc >=14 + - libzlib >=1.3.2,<2.0a0 + license: zlib-acknowledgement + size: 341202 + timestamp: 1776315188425 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libprotobuf-4.25.3-hea2c3fa_1.conda + sha256: dabf4632d39b29444d157c226f4df146fa347c82540c39bf6f9545f2a7d0f40c + md5: c06acb4f972c516696590e6d6ffb69c7 + depends: + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libgcc >=13 + - libstdcxx >=13 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 2613905 + timestamp: 1727160673211 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libre2-11-2023.09.01-h9d008c2_2.conda + sha256: 1da5cfd57091a52c822ec9580694f1e07817e53db43b0407a477daa2d2a16fcd + md5: 387c114aadcaeb02210f646c4b5efca2 + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + constrains: + - re2 2023.09.01.* + license: BSD-3-Clause + license_family: BSD + size: 217529 + timestamp: 1708946830978 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/librsvg-2.62.2-hf685517_0.conda + sha256: 427486a9eae80874223a3f24fdbf16123330f7a7d89cacee88b460123038d0de + md5: aa215afd17a243a4394f2297f83651e2 + depends: + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.6,<3.0a0 + - harfbuzz >=14.2.0 + - libgcc >=14 + - libglib >=2.88.1,<3.0a0 + - libxml2-16 >=2.14.6 + - pango >=1.56.4,<2.0a0 + constrains: + - __glibc >=2.17 + license: LGPL-2.1-or-later + size: 4101153 + timestamp: 1779420312747 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsodium-1.0.22-h80f16a2_1.conda + sha256: 36fb7afb28fecb8d678611e05a96b1e135510369b7fe5e353e407308ef1f6796 + md5: 5cb9cebc948d70e6e7c81670f911dab3 + depends: + - libgcc >=14 + license: ISC + size: 283426 + timestamp: 1779163468728 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.53.1-h022381a_0.conda + sha256: ad03b7d8e4d08001f0df88ee7a56108bb35bae4795a42b9a04cc1abfa822bd07 + md5: 2ec1119217d8f0d086e9a62f3cb0e5ea + depends: + - libgcc >=14 + - libzlib >=1.3.2,<2.0a0 + license: blessing + size: 955361 + timestamp: 1777986487553 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libssh2-1.11.1-h18c354c_0.conda + sha256: 1e289bcce4ee6a5817a19c66e296f3c644dcfa6e562e5c1cba807270798814e7 + md5: eecc495bcfdd9da8058969656f916cc2 + depends: + - libgcc >=13 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.0,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 311396 + timestamp: 1745609845915 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-15.2.0-hef695bb_19.conda + sha256: 1dadc45e599f510dd5f97141dddcdbb9844d9f1430c1f3a38075cf1c58f87b4e + md5: 543fbc8d71f2a0baf04cf88ce96cb8bb + depends: + - libgcc 15.2.0 h8acb6b2_19 + constrains: + - libstdcxx-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 5546559 + timestamp: 1778268777463 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-15.2.0-hdbbeba8_19.conda + sha256: 56b5ec297a988961486694f1c598889c3a697d77a0b42b8cea3faaa12e9bd360 + md5: c82ed61c3ec470c5ec624580e6ba16e4 + depends: + - libstdcxx 15.2.0 hef695bb_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 27803 + timestamp: 1778268813278 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libthrift-0.19.0-h043aeee_1.conda + sha256: 83d38df283ae258eb73807442ccee62364cf50b853d238a5b03092374c7bcf45 + md5: 591ef1567ed4989d824fe35b45e3ae68 + depends: + - libevent >=2.1.12,<2.1.13.0a0 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.1.3,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 400010 + timestamp: 1695958016227 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.7.1-hdb009f0_1.conda + sha256: 7ff79470db39e803e21b8185bc8f19c460666d5557b1378d1b1e857d929c6b39 + md5: 8c6fd84f9c87ac00636007c6131e457d + depends: + - lerc >=4.0.0,<5.0a0 + - libdeflate >=1.25,<1.26.0a0 + - libgcc >=14 + - libjpeg-turbo >=3.1.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libstdcxx >=14 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: HPND + size: 488407 + timestamp: 1762022048105 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libutf8proc-2.8.0-h812390e_1.conda + sha256: 25dc68d188336e1b83f28175b5146c2192e49a3236b340962c97727c6d47ede9 + md5: 83c5f8e4431ad4b6b7e22c4edd898163 + depends: + - libgcc >=13 + license: MIT + license_family: MIT + size: 81515 + timestamp: 1732829682446 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.42.1-h1022ec0_0.conda + sha256: 1628839b062e98b2192857d4da8496ac9ac6b0dbb77aa040c34efc9192c440ee + md5: 0f42f9fedd2a32d798de95a7f65c456f + depends: + - libgcc >=14 + license: BSD-3-Clause + license_family: BSD + size: 43453 + timestamp: 1779118526838 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.6.0-ha2e29f5_0.conda + sha256: b03700a1f741554e8e5712f9b06dd67e76f5301292958cd3cb1ac8c6fdd9ed25 + md5: 24e92d0942c799db387f5c9d7b81f1af + depends: + - libgcc >=14 + constrains: + - libwebp 1.6.0 + license: BSD-3-Clause + license_family: BSD + size: 359496 + timestamp: 1752160685488 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcb-1.17.0-h262b8f6_0.conda + sha256: 461cab3d5650ac6db73a367de5c8eca50363966e862dcf60181d693236b1ae7b + md5: cd14ee5cca2464a425b1dbfc24d90db2 + depends: + - libgcc >=13 + - pthread-stubs + - xorg-libxau >=1.0.11,<2.0a0 + - xorg-libxdmcp + license: MIT + license_family: MIT + size: 397493 + timestamp: 1727280745441 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda + sha256: 6b46c397644091b8a26a3048636d10b989b1bf266d4be5e9474bf763f828f41f + md5: b4df5d7d4b63579d081fd3a4cf99740e + depends: + - libgcc-ng >=12 + license: LGPL-2.1-or-later + size: 114269 + timestamp: 1702724369203 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxkbcommon-1.13.2-h3c6a4c8_0.conda + sha256: 8f44670a714a12589bc82ea179e46ba4a19c4458d5cee765ddd4d5224eccd912 + md5: d6fc9ac66ea61eb662747959d0a68c57 + depends: + - libgcc >=14 + - libstdcxx >=14 + - libxcb >=1.17.0,<2.0a0 + - libxml2 + - libxml2-16 >=2.14.6 + - xkeyboard-config + - xorg-libxau >=1.0.12,<2.0a0 + license: MIT/X11 Derivative + license_family: MIT + size: 875994 + timestamp: 1780213408784 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxml2-16-2.15.3-h79dcc73_0.conda + sha256: ad048a9ca1bf2cdfedb2b0c231050da416c44ee1436a3d1a83b51d2e2deaa842 + md5: 68866231cfe8789e780347f2482df96d + depends: + - icu >=78.3,<79.0a0 + - libgcc >=14 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - libxml2 2.15.3 + license: MIT + license_family: MIT + size: 601948 + timestamp: 1776376758674 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libxml2-2.15.3-h869d058_0.conda + sha256: e3af6af9df73bd3c7a8e4e6c8cc38df3699e7f588b0705c257a8601e40acfbdf + md5: 2cffef27cb2eb9ed1e315a1e269d4335 + depends: + - icu >=78.3,<79.0a0 + - libgcc >=14 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libxml2-16 2.15.3 h79dcc73_0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + license_family: MIT + size: 48101 + timestamp: 1776376766341 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.2-hdc9db2a_2.conda + sha256: eb111e32e5a7313a5bf799c7fb2419051fa2fe7eff74769fac8d5a448b309f7f + md5: 502006882cf5461adced436e410046d1 + constrains: + - zlib 1.3.2 *_2 + license: Zlib + license_family: Other + size: 69833 + timestamp: 1774072605429 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-4.3.3-py310h6607652_1.conda + sha256: 6c25b5c8a64c1d3de058b73d85bd412cd7a1745088a54b72a99c731adc09609d + md5: 47a17755ca99aae307f0e9c041213c99 + depends: + - libgcc >=13 + - lz4-c >=1.9.3,<1.10.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 37864 + timestamp: 1725090560394 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/lz4-c-1.9.4-hd600fc2_0.conda + sha256: 076870eb72411f41c46598c7582a2f3f42ba94c526a2d60a0c8f70a0a7a64429 + md5: 500145a83ed07ce79c8cef24252f366b + depends: + - libgcc-ng >=12 + - libstdcxx-ng >=12 + license: BSD-2-Clause + license_family: BSD + size: 163770 + timestamp: 1674727020254 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/markupsafe-3.0.3-py310h3b5aacf_1.conda + sha256: 3809439f3f7a28893314ac09aeeb04a7663efcfbb48281f9e65a916d5c7b764a + md5: 3a8d4c69d626abcfaede3f89df2599f8 + depends: + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - jinja2 >=3.0.0 + license: BSD-3-Clause + license_family: BSD + size: 24568 + timestamp: 1772446363441 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.7.3-py310h0a7f329_0.conda + sha256: 8e7d5e57c436e1413142cbc37d90d58082469bd7e4ed894779ca0bf31a353a1a + md5: 2a03c16bd326184661e4887633b5b6ba + depends: + - certifi >=2020.06.20 + - contourpy >=1.0.1 + - cycler >=0.10 + - fonttools >=4.22.0 + - freetype >=2.12.1,<3.0a0 + - kiwisolver >=1.0.1 + - libgcc-ng >=12 + - libstdcxx-ng >=12 + - numpy >=1.20 + - numpy >=1.22.4,<2.0a0 + - packaging >=20.0 + - pillow >=6.2.0 + - pyparsing >=2.3.1 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python-dateutil >=2.7 + - python_abi 3.10.* *_cp310 + - tk >=8.6.12,<8.7.0a0 + license: LicenseRef-PSF-2.0 and CC0-1.0 + license_family: PSF + size: 6699316 + timestamp: 1695077105142 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/msgpack-python-1.1.2-py310h0992a49_1.conda + sha256: 663967ac8fbe9a5e32246784d0c6c08be8394dab5c045297121550beb967b127 + md5: 0d9ac8c2994a3681e44edcf9bb1a3e8e + depends: + - libgcc >=14 + - libstdcxx >=14 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 93092 + timestamp: 1762504199089 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.6-hf8d1292_0.conda + sha256: 369db85c5cd8d99dde364ce70725d76511d9c8199e5b820c740414091bf5bcca + md5: b2a43456aa56fe80c2477a5094899eff + depends: + - libgcc >=14 + license: X11 AND BSD-3-Clause + size: 960036 + timestamp: 1777422174534 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.22.4-py310h7f880a9_0.tar.bz2 + sha256: 5735810e977bfcec4e4bff7f439f6a770660dc6d7f0a075c41202d4265f6d01b + md5: 0f1f37329ff81ca4a27f3e5c7a66b963 + depends: + - libblas >=3.8.0,<4.0a0 + - libcblas >=3.8.0,<4.0a0 + - libgcc-ng >=12 + - liblapack >=3.8.0,<4.0a0 + - libstdcxx-ng >=12 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 6742446 + timestamp: 1653325697994 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-2.2.6-py310h6e5608f_0.conda + sha256: d7234b9c45e4863c7d4c5221c1e91d69b0e0009464bf361c3fea47e64dc4adc2 + md5: 9e9f1f279eb02c41bda162a42861adc0 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libgcc >=13 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx >=13 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 6556655 + timestamp: 1747545077963 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.4-h5da879a_0.conda + sha256: bd1bc8bdde5e6c5cbac42d462b939694e40b59be6d0698f668515908640c77b8 + md5: cea962410e327262346d48d01f05936c + depends: + - libgcc >=14 + - libpng >=1.6.50,<1.7.0a0 + - libstdcxx >=14 + - libtiff >=4.7.1,<4.8.0a0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-2-Clause + license_family: BSD + size: 392636 + timestamp: 1758489353577 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.6.2-h546c87b_0.conda + sha256: 348cb74c1530ac241215d047ef65d134cf797af935c97a68655319362b7e6a01 + md5: 3b129669089e4d6a5c6871dbb4669b99 + depends: + - ca-certificates + - libgcc >=14 + license: Apache-2.0 + license_family: Apache + size: 3706406 + timestamp: 1775589602258 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/orc-2.0.0-hd7aaf90_1.conda + sha256: 27725756b444000f81ef2c2d132d03b1a67e89ab00dec944c89ef072061f9705 + md5: 95d5a76acc60426348cdf900f5b8d1f2 + depends: + - libgcc-ng >=12 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libstdcxx-ng >=12 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.5,<1.6.0a0 + license: Apache-2.0 + license_family: Apache + size: 1002468 + timestamp: 1712616308423 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pandas-1.3.5-py310h713c908_0.tar.bz2 + sha256: be06bee1caecca0fc564ec9c3f9cfb41ca158497713f97442b96fb5f5a3be1d7 + md5: ecc962c7cbdcdaf4ff379d3d4fca8e58 + depends: + - libgcc-ng >=9.4.0 + - libstdcxx-ng >=9.4.0 + - numpy >=1.21.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python-dateutil >=2.7.3 + - python_abi 3.10.* *_cp310 + - pytz >=2017.2 + - setuptools <60.0.0 + license: BSD-3-Clause + license_family: BSD + size: 45687125 + timestamp: 1639398975409 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pango-1.56.4-h8547ced_1.conda + sha256: d209c8b0d53c441ee0bc0d8fce0fcae8e7e05755e51b13b6b9da02c7aa032f98 + md5: 3fc7cc25bba3381e77b753578058e3b0 + depends: + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.16,<2.0a0 + - harfbuzz >=13.2.0 + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libgcc >=14 + - libglib >=2.86.4,<3.0a0 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + license: LGPL-2.1-or-later + size: 470441 + timestamp: 1774284032397 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pcre2-10.47-hf841c20_0.conda + sha256: 04df2cee95feba440387f33f878e9f655521e69f4be33a0cd637f07d3d81f0f9 + md5: 1a30c42e32ca0ea216bd0bfe6f842f0b + depends: + - bzip2 >=1.0.8,<2.0a0 + - libgcc >=14 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 1166552 + timestamp: 1763655534263 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-12.2.0-py310hd8fe58f_0.conda + sha256: 21fd9e1014a9f971882de71dd2937f4f3cb6a2698a9c7a8ed3bad04fd76ff425 + md5: 5c028c6afa90ae8ce26bc0dd80a00c31 + depends: + - python + - python 3.10.* *_cpython + - libgcc >=14 + - zlib-ng >=2.3.3,<2.4.0a0 + - tk >=8.6.13,<8.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - lcms2 >=2.18,<3.0a0 + - openjpeg >=2.5.4,<3.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - python_abi 3.10.* *_cp310 + - libxcb >=1.17.0,<2.0a0 + license: HPND + size: 871833 + timestamp: 1775060067775 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pixman-0.46.4-h7ac5ae9_1.conda + sha256: e6b0846a998f2263629cfeac7bca73565c35af13251969f45d385db537a514e4 + md5: 1587081d537bd4ae77d1c0635d465ba5 + depends: + - libgcc >=14 + - libstdcxx >=14 + - libgcc >=14 + license: MIT + license_family: MIT + size: 357913 + timestamp: 1754665583353 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/polars-1.0.0-py310ha82f49b_0.conda + sha256: 528599ec185069c08563d707847b7ab2150df080821581fb9937015e8a8eb577 + md5: 3aba17cda0db617cf0b10c4b575f1344 + depends: + - libgcc-ng >=12 + - numpy >=1.16.0,<2 + - packaging + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - typing_extensions >=4.0.0 + constrains: + - __glibc >=2.17 + license: MIT + license_family: MIT + run_exports: {} + size: 21881463 + timestamp: 1720026875483 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/psutil-5.9.3-py310h761cc84_1.tar.bz2 + sha256: 77a5df2b4b22b6aa8067d69cc22f640e7b930598566e5e5952d0edb3c40dc584 + md5: cfae693f1721172e89aa7cb0a2b139ac + depends: + - libgcc-ng >=12 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 363801 + timestamp: 1666772101566 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pthread-stubs-0.4-h86ecc28_1002.conda + sha256: 977dfb0cb3935d748521dd80262fe7169ab82920afd38ed14b7fee2ea5ec01ba + md5: bb5a90c93e3bac3d5690acf76b4a6386 + depends: + - libgcc >=13 + license: MIT + license_family: MIT + size: 8342 + timestamp: 1726803319942 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyarrow-16.0.0-py310h70ff3ff_0.conda + sha256: e6b73e3cdc40acdf3fd71ec934d024b929e3a26cc8896d16b391d24507af0a49 + md5: a2af4d2f06edce06e62ebaecdfa82f70 + depends: + - libarrow-acero 16.0.0.* + - libarrow-dataset 16.0.0.* + - libarrow-substrait 16.0.0.* + - libparquet 16.0.0.* + - numpy >=1.22.4,<2.0a0 + - pyarrow-core 16.0.0 *_0_* + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: APACHE + size: 26078 + timestamp: 1715178300027 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyarrow-core-16.0.0-py310hf3ba4ff_0_cpu.conda + sha256: c7923903509139043b212c68eaec37b618f79c478ecacda7dea9f75b22c426a5 + md5: b5d146b7e857b8e0323faea889f6f85e + depends: + - libarrow 16.0.0.* *cpu + - libgcc-ng >=13 + - libstdcxx-ng >=13 + - libzlib >=1.2.13,<2.0.0a0 + - numpy >=1.22.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - apache-arrow-proc =*=cpu + license: Apache-2.0 + license_family: APACHE + size: 4265829 + timestamp: 1715206631884 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.10.20-h28be5d3_0_cpython.conda + sha256: 2f16794d69e058404cca633021b284e70253dfb76e4b314d9dbd448aa0354e84 + md5: 5d61c9a2acf7c675f7ae5f6cf51ffb0b + depends: + - bzip2 >=1.0.8,<2.0a0 + - ld_impl_linux-aarch64 >=2.36.1 + - libexpat >=2.7.4,<3.0a0 + - libffi >=3.4,<4.0a0 + - libgcc >=14 + - liblzma >=5.8.2,<6.0a0 + - libnsl >=2.0.1,<2.1.0a0 + - libsqlite >=3.51.2,<4.0a0 + - libuuid >=2.41.3,<3.0a0 + - libxcrypt >=4.4.36 + - libzlib >=1.3.1,<2.0a0 + - ncurses >=6.5,<7.0a0 + - openssl >=3.5.5,<4.0a0 + - readline >=8.3,<9.0a0 + - tk >=8.6.13,<8.7.0a0 + - tzdata + constrains: + - python_abi 3.10.* *_cp310 + license: Python-2.0 + size: 13234644 + timestamp: 1772728813900 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyyaml-6.0.3-py310h2d8da20_1.conda + sha256: 8acefeb5cc4bcf835f33e45b5cc8b350e738b4954f68c3d8051426e851ca2806 + md5: 7e1a74e779e08e3504230f8216be618b + depends: + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 171618 + timestamp: 1770223419125 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/pyzmq-27.1.0-py310hbea53bb_3.conda + sha256: da1e6be2a14bc57552f28ab0b1b5c70f27c1cd61e84d7c9660b0e69cf01fd293 + md5: 4e27a0d210b12b953fac9a2f385e74bb + depends: + - python + - python 3.10.* *_cpython + - libgcc >=14 + - libstdcxx >=14 + - zeromq >=4.3.5,<4.4.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 326862 + timestamp: 1779483883852 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/re2-2023.09.01-h9caee61_2.conda + sha256: 31db9c598bfa7586ac2e3ba06681d676caa5d252b5b68f4b6173edc71f70681e + md5: a9667ab785e1686d53313364c695f58e + depends: + - libre2-11 2023.09.01 h9d008c2_2 + license: BSD-3-Clause + license_family: BSD + size: 26726 + timestamp: 1708946863063 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/readline-8.3-hb682ff5_0.conda + sha256: fe695f9d215e9a2e3dd0ca7f56435ab4df24f5504b83865e3d295df36e88d216 + md5: 3d49cad61f829f4f0e0611547a9cda12 + depends: + - libgcc >=14 + - ncurses >=6.5,<7.0a0 + license: GPL-3.0-only + license_family: GPL + size: 357597 + timestamp: 1765815673644 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/rpds-py-0.30.0-py310haddc216_0.conda + sha256: 502656baaa06ee999d551f4f300f28f556169b6790066c3af3c20340af6970e6 + md5: eb9b36d85a5cf7f986704c153fd621ba + depends: + - python + - libgcc >=14 + - python_abi 3.10.* *_cp310 + constrains: + - __glibc >=2.17 + license: MIT + license_family: MIT + size: 380660 + timestamp: 1764543343007 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/s2n-1.4.13-h52a6840_0.conda + sha256: 056bd14bb9d2ad1504b49e27a3bed9f480c4afd37eb2c898e1b28a3ba0a2b0e3 + md5: b1e9de2093e14202d628481725b5791c + depends: + - libgcc-ng >=12 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 343306 + timestamp: 1714594260650 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/scikit-learn-1.0.2-py310h2a0ddcd_0.tar.bz2 + sha256: 556f6fc384f9a99f15a7d58756b6405be3ea3f777a31f1519d78d6f441ac7796 + md5: 70a995fbd189811cb2ec19309a1a5979 + depends: + - joblib >=0.11 + - libcblas >=3.8.0,<4.0a0 + - libgcc-ng >=9.4.0 + - libstdcxx-ng >=9.4.0 + - numpy >=1.21.5,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - scipy + - threadpoolctl + license: BSD-3-Clause + license_family: BSD + size: 27731570 + timestamp: 1640464751291 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.15.2-py310hf37559f_0.conda + sha256: 4454218b1d3caa962e171ff5833bf0691e3f156098c4eab773a19115cada6426 + md5: 5c9b72f10d2118d943a5eaaf2f396891 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libgcc >=13 + - libgfortran + - libgfortran5 >=13.3.0 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx >=13 + - numpy <2.5 + - numpy >=1.19,<3 + - numpy >=1.23.5 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 16258789 + timestamp: 1739792196278 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.7.3-py310hdd6f644_1.tar.bz2 + sha256: f9ad1ed66ab2f86ebee07ba5c88bd59affca15ec41691e0d16c63a3b2b129fee + md5: 2448d728f46f8e2bac57a730c1300b13 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libgcc-ng >=12 + - libgfortran-ng + - libgfortran5 >=10.4.0 + - liblapack >=3.9.0,<4.0a0 + - libstdcxx-ng >=12 + - numpy >=1.21.6,<1.23 + - numpy >=1.21.6,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - libopenblas <0.3.26 + license: BSD-3-Clause + license_family: BSD + size: 23949863 + timestamp: 1667961969857 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/setuptools-59.8.0-py310h4c7bcd0_1.tar.bz2 + sha256: 0342bbee32b5c1d3f1b2675a307750ba43052b3594de3594cfd07d01d9a7417e + md5: f433fea3506483eb72bf8db578f86810 + depends: + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 1053081 + timestamp: 1648692152585 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/snappy-1.2.2-he774c54_1.conda + sha256: a8a79c53852fb07286407907402caa5a96b6e22b518c4f010be40647f9ee3726 + md5: 3dec912091fb88614afa0af2712c1362 + depends: + - libgcc >=14 + - libstdcxx >=14 + - libgcc >=14 + license: BSD-3-Clause + license_family: BSD + size: 47096 + timestamp: 1762948094646 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/tk-8.6.13-noxft_h0dc03b3_103.conda + sha256: e25c314b52764219f842b41aea2c98a059f06437392268f09b03561e4f6e5309 + md5: 7fc6affb9b01e567d2ef1d05b84aa6ed + depends: + - libgcc >=14 + - libzlib >=1.3.1,<2.0a0 + constrains: + - xorg-libx11 >=1.8.12,<2.0a0 + license: TCL + license_family: BSD + size: 3368666 + timestamp: 1769464148928 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.5.6-py310ha7967c6_0.conda + sha256: adeca9bb09ab8dd6be5338f05cb8eb34e8bda579ed9efa8154d3084b06018fe0 + md5: 72f764623b2034b454e6a88323d6778f + depends: + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 670937 + timestamp: 1779916999997 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-17.0.1-py310h5b55623_0.conda + sha256: f49f8ef6bc2f23a0a0d186fd82ab44005fcf8ecf10556d77836bfbc61fefc431 + md5: 45dc3d0e49a9d54c2955494c6ae1aaf5 + depends: + - libgcc >=14 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 409703 + timestamp: 1770909143982 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/wayland-1.25.0-h4f8a99f_0.conda + sha256: 3cc479df517b0ce110835a1256f91ca568581cb6dfe1c53a0786f0a226039a45 + md5: 0a7a9548726f98d5869fd4c43e110f0f + depends: + - libexpat >=2.7.4,<3.0a0 + - libffi >=3.5.2,<3.6.0a0 + - libgcc >=14 + - libstdcxx >=14 + license: MIT + license_family: MIT + size: 335260 + timestamp: 1773959583826 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xkeyboard-config-2.47-he30d5cf_0.conda + sha256: ec7ff9dffbd41faa31a30fa0724699f05bca000d57c745a195ecdb56888a8605 + md5: 4ac707a4279972357712af099cd1ae50 + depends: + - libgcc >=14 + - xorg-libx11 >=1.8.13,<2.0a0 + license: MIT + license_family: MIT + size: 399629 + timestamp: 1772021320967 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libice-1.1.2-h86ecc28_0.conda + sha256: a2ba1864403c7eb4194dacbfe2777acf3d596feae43aada8d1b478617ce45031 + md5: c8d8ec3e00cd0fd8a231789b91a7c5b7 + depends: + - libgcc >=13 + license: MIT + license_family: MIT + size: 60433 + timestamp: 1734229908988 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libsm-1.2.6-h0808dbd_0.conda + sha256: b86a819cd16f90c01d9d81892155126d01555a20dabd5f3091da59d6309afd0a + md5: 2d1409c50882819cb1af2de82e2b7208 + depends: + - libgcc >=13 + - libuuid >=2.38.1,<3.0a0 + - xorg-libice >=1.1.2,<2.0a0 + license: MIT + license_family: MIT + size: 28701 + timestamp: 1741897678254 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libx11-1.8.13-h63a1b12_0.conda + sha256: cf886160e2ff580d77f7eb8ec1a77c41c2c5b05343e329bc35f0ddf40b8d92ab + md5: 22dd10425ef181e80e130db50675d615 + depends: + - libgcc >=14 + - libxcb >=1.17.0,<2.0a0 + license: MIT + license_family: MIT + size: 869058 + timestamp: 1770819244991 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxau-1.0.12-he30d5cf_1.conda + sha256: e9f6e931feeb2f40e1fdbafe41d3b665f1ab6cb39c5880a1fcf9f79a3f3c84a5 + md5: 1c246e1105000c3660558459e2fd6d43 + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 16317 + timestamp: 1762977521691 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxcomposite-0.4.7-he30d5cf_0.conda + sha256: 554f986ffa781d3393079926931465e61291d1eb8a56a03ad4a8e54b1ae233f4 + md5: 9c639c1abdbfe6759c5beb2c1db4bc13 + depends: + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxfixes >=6.0.2,<7.0a0 + license: MIT + license_family: MIT + size: 14915 + timestamp: 1770044415607 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxcursor-1.2.3-h86ecc28_0.conda + sha256: c5d3692520762322a9598e7448492309f5ee9d8f3aff72d787cf06e77c42507f + md5: f2054759c2203d12d0007005e1f1296d + depends: + - libgcc >=13 + - xorg-libx11 >=1.8.9,<2.0a0 + - xorg-libxfixes >=6.0.1,<7.0a0 + - xorg-libxrender >=0.9.11,<0.10.0a0 + license: MIT + license_family: MIT + size: 34596 + timestamp: 1730908388714 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdamage-1.1.6-h86ecc28_0.conda + sha256: 3afaa2f43eb4cb679fc0c3d9d7c50f0f2c80cc5d3df01d5d5fd60655d0bfa9be + md5: d5773c4e4d64428d7ddaa01f6f845dc7 + depends: + - libgcc >=13 + - xorg-libx11 >=1.8.9,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxfixes >=6.0.1,<7.0a0 + license: MIT + license_family: MIT + size: 13794 + timestamp: 1727891406431 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxdmcp-1.1.5-he30d5cf_1.conda + sha256: 128d72f36bcc8d2b4cdbec07507542e437c7d67f677b7d77b71ed9eeac7d6df1 + md5: bff06dcde4a707339d66d45d96ceb2e2 + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 21039 + timestamp: 1762979038025 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxext-1.3.7-he30d5cf_0.conda + sha256: db2188bc0d844d4e9747bac7f6c1d067e390bd769c5ad897c93f1df759dc5dba + md5: fb42b683034619915863d68dd9df03a3 + depends: + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + license: MIT + license_family: MIT + size: 52409 + timestamp: 1769446753771 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxfixes-6.0.2-he30d5cf_0.conda + sha256: 8cb9c88e25c57e47419e98f04f9ef3154ad96b9f858c88c570c7b91216a64d0e + md5: e8b4056544341daf1d415eaeae7a040c + depends: + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + license: MIT + license_family: MIT + size: 20704 + timestamp: 1759284028146 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxi-1.8.3-he30d5cf_0.conda + sha256: 0c1c7b39763469cfe0e9c6d0f9a39415321f477710719f4c5d63c61ea270271c + md5: f8ad5777ecc217d383a722598dbeb1ac + depends: + - libgcc >=14 + - xorg-libx11 >=1.8.13,<2.0a0 + - xorg-libxext >=1.3.7,<2.0a0 + - xorg-libxfixes >=6.0.2,<7.0a0 + license: MIT + license_family: MIT + size: 49292 + timestamp: 1779113229775 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxinerama-1.1.6-hfae3067_0.conda + sha256: 2039990aa8454f19e8f890ff1e8582f12fa475af7e836b184adc17b88a22c9b8 + md5: a488ab283de297dc0de69796f7467a71 + depends: + - libgcc >=14 + - libstdcxx >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + license: MIT + license_family: MIT + size: 15322 + timestamp: 1769432283298 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrandr-1.5.5-he30d5cf_0.conda + sha256: 9f5196665a8d72f4f119c40dcc4bafeb0b540b102cc7b8b299c2abf599e7919f + md5: 1f64c613f0b8d67e9fb0e165d898fb6b + depends: + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxrender >=0.9.12,<0.10.0a0 + license: MIT + license_family: MIT + size: 31122 + timestamp: 1769445286951 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxrender-0.9.12-h86ecc28_0.conda + sha256: ffd77ee860c9635a28cfda46163dcfe9224dc6248c62404c544ae6b564a0be1f + md5: ae2c2dd0e2d38d249887727db2af960e + depends: + - libgcc >=13 + - xorg-libx11 >=1.8.10,<2.0a0 + license: MIT + license_family: MIT + size: 33649 + timestamp: 1734229123157 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxtst-1.2.5-h57736b2_3.conda + sha256: 6eaffce5a34fc0a16a21ddeaefb597e792a263b1b0c387c1ce46b0a967d558e1 + md5: c05698071b5c8e0da82a282085845860 + depends: + - libgcc >=13 + - xorg-libx11 >=1.8.9,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + - xorg-libxi >=1.7.10,<2.0a0 + license: MIT + license_family: MIT + size: 33786 + timestamp: 1727964907993 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-libxxf86vm-1.1.7-he30d5cf_0.conda + sha256: 6f6f2b32754e09c2334e067ba5d2d38715f2432cd9fb41d6f66de0591f8f4f94 + md5: b15ca02584678f38df6e114c32f93959 + depends: + - libgcc >=14 + - xorg-libx11 >=1.8.12,<2.0a0 + - xorg-libxext >=1.3.6,<2.0a0 + license: MIT + license_family: MIT + size: 19148 + timestamp: 1769434729220 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/xorg-xorgproto-2025.1-he30d5cf_0.conda + sha256: d8a7593362562f66bab992901df6cdc845c6004d15c1ba2d1e3e39e4e4672384 + md5: 999d230bcb0329c11d101118ace392d9 + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 569539 + timestamp: 1766155414260 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/yaml-0.2.5-h80f16a2_3.conda + sha256: 66265e943f32ce02396ad214e27cb35f5b0490b3bd4f064446390f9d67fa5d88 + md5: 032d8030e4a24fe1f72c74423a46fb88 + depends: + - libgcc >=14 + license: MIT + license_family: MIT + size: 88088 + timestamp: 1753484092643 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zeromq-4.3.5-hec9560f_11.conda + sha256: 134bceda31df1ad0dbadb61dd30e7254f5eab398288fcdd8b070946130533b5a + md5: 1ae4f546793d83754d79a43a38154746 + depends: + - libstdcxx >=14 + - libgcc >=14 + - krb5 >=1.22.2,<1.23.0a0 + - libsodium >=1.0.22,<1.0.23.0a0 + license: MPL-2.0 + license_family: MOZILLA + size: 355573 + timestamp: 1779123980042 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zlib-ng-2.3.3-ha7cb516_1.conda + sha256: 638a3a41a4fbfed52d3c60c8ef5a3693b3f12a5b1a3f58fa29f5698d0a0702e2 + md5: f731af71c723065d91b4c01bb822641b + depends: + - libgcc >=14 + - libstdcxx >=14 + license: Zlib + license_family: Other + size: 121046 + timestamp: 1770167944449 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstandard-0.25.0-py310hef25091_1.conda + sha256: 30202c0e21618a421d189c70b71c152f53a6f605f8fb053a7faf844dffd4843b + md5: ef07dc8296f6e4df546b8d41bfb0a1fe + depends: + - python + - cffi >=1.11 + - zstd >=1.5.7,<1.5.8.0a0 + - python 3.10.* *_cpython + - libgcc >=14 + - zstd >=1.5.7,<1.6.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 447338 + timestamp: 1762512738321 +- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/zstd-1.5.7-h85ac4a6_6.conda + sha256: 569990cf12e46f9df540275146da567d9c618c1e9c7a0bc9d9cfefadaed20b75 + md5: c3655f82dcea2aa179b291e7099c1fcc + depends: + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 614429 + timestamp: 1764777145593 +- conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda + sha256: a362b4f5c96a0bf4def96be1a77317e2730af38915eb9bec85e2a92836501ed7 + md5: b3f0179590f3c0637b7eb5309898f79e + depends: + - __unix + - hicolor-icon-theme + - librsvg + license: LGPL-3.0-or-later OR CC-BY-SA-3.0 + license_family: LGPL + size: 631452 + timestamp: 1758743294412 +- conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda + sha256: f09aed24661cd45ba54a43772504f05c0698248734f9ae8cd289d314ac89707e + md5: af2df4b9108808da3dc76710fe50eae2 + depends: + - exceptiongroup >=1.0.2 + - idna >=2.8 + - python >=3.10 + - typing_extensions >=4.5 + - python + constrains: + - trio >=0.32.0 + - uvloop >=0.22.1 + - winloop >=0.2.3 + license: MIT + license_family: MIT + size: 146764 + timestamp: 1774359453364 +- conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda + sha256: 8f032b140ea4159806e4969a68b4a3c0a7cab1ad936eb958a2b5ffe5335e19bf + md5: 54898d0f524c9dee622d44bbb081a8ab + depends: + - python >=3.9 + license: BSD-2-Clause + license_family: BSD + size: 10076 + timestamp: 1733332433806 +- conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + sha256: bea62005badcb98b1ae1796ec5d70ea0fc9539e7d59708ac4e7d41e2f4bb0bad + md5: 8ac12aff0860280ee0cff7fa2cf63f3b + depends: + - argon2-cffi-bindings + - python >=3.9 + - typing-extensions + constrains: + - argon2_cffi ==999 + license: MIT + license_family: MIT + size: 18715 + timestamp: 1749017288144 +- conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda + sha256: 792da8131b1b53ff667bd6fc617ea9087b570305ccb9913deb36b8e12b3b5141 + md5: 85c4f19f377424eafc4ed7911b291642 + depends: + - python >=3.10 + - python-dateutil >=2.7.0 + - python-tzdata + - python + license: Apache-2.0 + license_family: APACHE + size: 113854 + timestamp: 1760831179410 +- conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + sha256: ee4da0f3fe9d59439798ee399ef3e482791e48784873d546e706d0935f9ff010 + md5: 9673a61a297b00016442e022d689faa6 + depends: + - python >=3.10 + constrains: + - astroid >=2,<5 + license: Apache-2.0 + license_family: Apache + size: 28797 + timestamp: 1763410017955 +- conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.3.0-pyhcf101f3_0.conda + sha256: ea8486637cfb89dc26dc9559921640cd1d5fd37e5e02c33d85c94572139f2efe + md5: b85e84cb64c762569cc1a760c2327e0a + depends: + - python >=3.10 + - typing_extensions >=4.0.0 + - python + license: MIT + license_family: MIT + size: 22949 + timestamp: 1773926359134 +- conda: https://conda.anaconda.org/conda-forge/noarch/attrs-26.1.0-pyhcf101f3_0.conda + sha256: 1b6124230bb4e571b1b9401537ecff575b7b109cc3a21ee019f65e083b8399ab + md5: c6b0543676ecb1fb2d7643941fe375f2 + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + size: 64927 + timestamp: 1773935801332 +- conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda + sha256: a14a9ad02101aab25570543a59c5193043b73dc311a25650134ed9e6cb691770 + md5: f1976ce927373500cc19d3c0b2c85177 + depends: + - python >=3.10 + - python + constrains: + - pytz >=2015.7 + license: BSD-3-Clause + license_family: BSD + size: 7684321 + timestamp: 1772555330347 +- conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda + sha256: bf1e71c3c0a5b024e44ff928225a0874fc3c3356ec1a0b6fe719108e6d1288f6 + md5: 5267bef8efea4127aacd1f4e1f149b6e + depends: + - python >=3.10 + - soupsieve >=1.2 + - typing-extensions + license: MIT + license_family: MIT + size: 90399 + timestamp: 1764520638652 +- conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda + sha256: f8ff1f98423674278964a46c93a1766f9e91960d44efd91c6c3ed56a33813f46 + md5: 7c5ebdc286220e8021bf55e6384acd67 + depends: + - python >=3.10 + - webencodings + - python + constrains: + - tinycss2 >=1.1.0,<1.5 + license: Apache-2.0 AND MIT + size: 142008 + timestamp: 1770719370680 +- conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda + sha256: 7c07a865e5e4cca233cc4e0eb3f0f5ff6c90776461687b4fb0b1764133e1fd61 + md5: f11a319b9700b203aa14c295858782b6 + depends: + - bleach ==6.3.0 pyhcf101f3_1 + - tinycss2 + license: Apache-2.0 AND MIT + size: 4409 + timestamp: 1770719370682 +- conda: https://conda.anaconda.org/conda-forge/noarch/bokeh-2.4.3-pyhd8ed1ab_3.tar.bz2 + sha256: f37e33fb11ae76ff07ce726a3dbdf4cd26ffff1b52c126d2d2d136669d6b919f + md5: e4c6e6d99add99cede5328d811cacb21 + depends: + - jinja2 >=2.9 + - numpy >=1.11.3 + - packaging >=16.8 + - pillow >=7.1.0 + - python >=3.7 + - pyyaml >=3.10 + - tornado >=5.1 + - typing_extensions >=3.10.0 + license: BSD-3-Clause + license_family: BSD + size: 13940985 + timestamp: 1660586705876 +- conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-h4c7d964_0.conda + sha256: 86981d764e4ea1883409d30447ff9da46127426d31a63df08315aaded768e652 + md5: c9b86eece2f944541b86441c94117ab3 + depends: + - __win + license: ISC + size: 130182 + timestamp: 1779289939595 +- conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.5.20-hbd8a1cb_0.conda + sha256: 9812a303a1395e1dafbd92e5bc8a1ff6013bcbba0a09c7f03a8d23e43560aa9b + md5: 489b8e97e666c93f68fdb35c3c9b957f + depends: + - __unix + license: ISC + size: 129868 + timestamp: 1779289852439 +- conda: https://conda.anaconda.org/conda-forge/noarch/cached-property-1.5.2-hd8ed1ab_1.tar.bz2 + noarch: python + sha256: 561e6660f26c35d137ee150187d89767c988413c978e1b712d53f27ddf70ea17 + md5: 9b347a7ec10940d3f7941ff6c460b551 + depends: + - cached_property >=1.5.2,<1.5.3.0a0 + license: BSD-3-Clause + license_family: BSD + size: 4134 + timestamp: 1615209571450 +- conda: https://conda.anaconda.org/conda-forge/noarch/cached_property-1.5.2-pyha770c72_1.tar.bz2 + sha256: 6dbf7a5070cc43d90a1e4c2ec0c541c69d8e30a0e25f50ce9f6e4a432e42c5d7 + md5: 576d629e47797577ab0f1b351297ef4a + depends: + - python >=3.6 + license: BSD-3-Clause + license_family: BSD + size: 11065 + timestamp: 1615209567874 +- conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.5.20-pyhd8ed1ab_0.conda + sha256: 645655a3510e38e625da136595f3f16f2130c3263630cc3bc8f60f619ddbe490 + md5: 9fefff2f745ea1cc2ef15211a20c054a + depends: + - python >=3.10 + license: ISC + size: 134201 + timestamp: 1779285131141 +- conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.7-pyhd8ed1ab_0.conda + sha256: 3f9483d62ce24ecd063f8a5a714448445dc8d9e201147c46699fc0033e824457 + md5: a9167b9571f3baa9d448faa2139d1089 + depends: + - python >=3.10 + license: MIT + license_family: MIT + size: 58872 + timestamp: 1775127203018 +- conda: https://conda.anaconda.org/conda-forge/noarch/click-8.4.1-pyhc90fa1f_0.conda + sha256: c253a41cdf898b651a0786cbb76c6d5fc101d0dbbe719f93a124bc4fde5cdd6a + md5: 554304a07e581a85891b15e39ea9f268 + depends: + - __unix + - python + - python >=3.10 + license: BSD-3-Clause + license_family: BSD + size: 104999 + timestamp: 1779900548735 +- conda: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-2.2.1-pyhd8ed1ab_0.conda + sha256: f0c2fd0e842899a05ddd7b147fb26424adf58be0e8e54e5bc68b8f7e67d05dcd + md5: b325bfc4cff7d7f8a868f1f7ecc4ed16 + depends: + - python >=3.6 + license: BSD-3-Clause + license_family: BSD + size: 27929 + timestamp: 1674202405394 +- conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda + sha256: ab29d57dc70786c1269633ba3dff20288b81664d3ff8d21af995742e2bb03287 + md5: 962b9857ee8e7018c22f2776ffa0b2d7 + depends: + - python >=3.9 + license: BSD-3-Clause + license_family: BSD + size: 27011 + timestamp: 1733218222191 +- conda: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.3-pyhe01879c_0.conda + sha256: 576a44729314ad9e4e5ebe055fbf48beb8116b60e58f9070278985b2b634f212 + md5: 2da13f2b299d8e1995bafbbe9689a2f7 + depends: + - python >=3.9 + - python + license: BSD-3-Clause + license_family: BSD + size: 14690 + timestamp: 1753453984907 +- conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhcf101f3_2.conda + sha256: bb47aec5338695ff8efbddbc669064a3b10fe34ad881fb8ad5d64fbfa6910ed1 + md5: 4c2a8fef270f6c69591889b93f9f55c1 + depends: + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 14778 + timestamp: 1764466758386 +- conda: https://conda.anaconda.org/conda-forge/noarch/dask-2022.12.1-pyhd8ed1ab_0.conda + sha256: 02d0bb1e82c0d2c701e9325ea9275db39604fb5c072341ead7621fbe57ac7f95 + md5: 5861b97a50edcd9c1332d21f2995eca1 + depends: + - bokeh >=2.4.2,<3 + - cytoolz >=0.8.2 + - dask-core >=2022.12.1,<2022.12.2.0a0 + - distributed >=2022.12.1,<2022.12.2.0a0 + - jinja2 + - lz4 + - numpy >=1.18 + - pandas >=1.0,<2.0a0 + - python >=3.8 + constrains: + - openssl !=1.1.1e + license: BSD-3-Clause + license_family: BSD + size: 7017 + timestamp: 1671234583829 +- conda: https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.12.1-pyhd8ed1ab_0.conda + sha256: a32e3df02054f9d4588070369f51d2d40d5b203907304da84a027b457dec4a86 + md5: f12878f9839c72f3d51af02fb10da43d + depends: + - click >=7.0 + - cloudpickle >=1.1.1 + - fsspec >=0.6.0 + - packaging >=20.0 + - partd >=0.3.10 + - python >=3.8 + - pyyaml >=5.3.1 + - toolz >=0.8.2 + license: BSD-3-Clause + license_family: BSD + size: 824991 + timestamp: 1671229767314 +- conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.3.1-pyhd8ed1ab_0.conda + sha256: 430bd9d731b265f0bedb3183ac3ecfaa1656390c092b6e864ff8cc1229843c8c + md5: 61dcf784d59ef0bd62c57d982b154ace + depends: + - python >=3.10 + license: BSD-2-Clause + license_family: BSD + size: 16102 + timestamp: 1779115228886 +- conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2 + sha256: 9717a059677553562a8f38ff07f3b9f61727bd614f505658b0a5ecbcf8df89be + md5: 961b3a227b437d82ad7054484cfa71b2 + depends: + - python >=3.6 + license: PSF-2.0 + license_family: PSF + size: 24062 + timestamp: 1615232388757 +- conda: https://conda.anaconda.org/conda-forge/noarch/distributed-2022.12.1-pyhd8ed1ab_0.conda + sha256: 3575ca9a92b4053977c430d400b3fdba6f545a5abd8b4f7003a74bfe980dfa84 + md5: 63cf20ed7b5205cf4c31aadee4c7fd2e + depends: + - click >=7.0 + - cloudpickle >=1.5.0 + - cytoolz >=0.10.0 + - dask-core >=2022.12.1,<2022.12.2.0a0 + - jinja2 + - locket >=1.0.0 + - msgpack-python >=0.6.0 + - packaging >=20.0 + - psutil >=5.0 + - python >=3.8 + - pyyaml + - sortedcontainers !=2.0.0,!=2.0.1 + - tblib >=1.6.0 + - toolz >=0.10.0 + - tornado >=6.0.3 + - urllib3 + - zict >=0.1.3 + constrains: + - openssl !=1.1.1e + license: BSD-3-Clause + license_family: BSD + size: 740184 + timestamp: 1671231961310 +- conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda + sha256: ee6cf346d017d954255bbcbdb424cddea4d14e4ed7e9813e429db1d795d01144 + md5: 8e662bd460bda79b1ea39194e3c4c9ab + depends: + - python >=3.10 + - typing_extensions >=4.6.0 + license: MIT and PSF-2.0 + size: 21333 + timestamp: 1763918099466 +- conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.1-pyhd8ed1ab_0.conda + sha256: 210c8165a58fdbf16e626aac93cc4c14dbd551a01d1516be5ecad795d2422cad + md5: ff9efb7f7469aed3c4a8106ffa29593c + depends: + - python >=3.10 + license: MIT + license_family: MIT + size: 30753 + timestamp: 1756729456476 +- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + sha256: 58d7f40d2940dd0a8aa28651239adbf5613254df0f75789919c4e6762054403b + md5: 0c96522c6bdaed4b1566d11387caaf45 + license: BSD-3-Clause + license_family: BSD + size: 397370 + timestamp: 1566932522327 +- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + sha256: c52a29fdac682c20d252facc50f01e7c2e7ceac52aa9817aaf0bb83f7559ec5c + md5: 34893075a5c9e55cdafac56607368fc6 + license: OFL-1.1 + license_family: Other + size: 96530 + timestamp: 1620479909603 +- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + sha256: 00925c8c055a2275614b4d983e1df637245e19058d79fc7dd1a93b8d9fb4b139 + md5: 4d59c254e01d9cde7957100457e2d5fb + license: OFL-1.1 + license_family: Other + size: 700814 + timestamp: 1620479612257 +- conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + sha256: 2821ec1dc454bd8b9a31d0ed22a7ce22422c0aef163c59f49dfdf915d0f0ca14 + md5: 49023d73832ef61042f6a237cb2687e7 + license: LicenseRef-Ubuntu-Font-Licence-Version-1.0 + license_family: Other + size: 1620504 + timestamp: 1727511233259 +- conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61 + md5: fee5683a3f04bd15cbd8318b096a27ab + depends: + - fonts-conda-forge + license: BSD-3-Clause + license_family: BSD + size: 3667 + timestamp: 1566974674465 +- conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-hc364b38_1.conda + sha256: 54eea8469786bc2291cc40bca5f46438d3e062a399e8f53f013b6a9f50e98333 + md5: a7970cd949a077b7cb9696379d338681 + depends: + - font-ttf-ubuntu + - font-ttf-inconsolata + - font-ttf-dejavu-sans-mono + - font-ttf-source-code-pro + license: BSD-3-Clause + license_family: BSD + size: 4059 + timestamp: 1762351264405 +- conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda + sha256: 2509992ec2fd38ab27c7cdb42cf6cadc566a1cc0d1021a2673475d9fa87c6276 + md5: d3549fd50d450b6d9e7dddff25dd2110 + depends: + - cached-property >=1.3.0 + - python >=3.9,<4 + license: MPL-2.0 + license_family: MOZILLA + size: 16705 + timestamp: 1733327494780 +- conda: https://conda.anaconda.org/conda-forge/noarch/fsspec-2026.4.0-pyhd8ed1ab_0.conda + sha256: 079701b4ff3b317a1a158cabd48cf2b856b8b8d3ef44f152809d9acf20cc8e10 + md5: 2c11aa96ea85ced419de710c1c3a78ff + depends: + - python >=3.10 + license: BSD-3-Clause + license_family: BSD + size: 149694 + timestamp: 1777547807038 +- conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhcf101f3_1.conda + sha256: 96cac6573fd35ae151f4d6979bab6fbc90cb6b1fb99054ba19eb075da9822fcb + md5: b8993c19b0c32a2f7b66cbb58ca27069 + depends: + - python >=3.10 + - typing_extensions + - python + license: MIT + license_family: MIT + size: 39069 + timestamp: 1767729720872 +- conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda + sha256: 84c64443368f84b600bfecc529a1194a3b14c3656ee2e832d15a20e0329b6da3 + md5: 164fc43f0b53b6e3a7bc7dce5e4f1dc9 + depends: + - python >=3.10 + - hyperframe >=6.1,<7 + - hpack >=4.1,<5 + - python + license: MIT + license_family: MIT + size: 95967 + timestamp: 1756364871835 +- conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda + sha256: 6ad78a180576c706aabeb5b4c8ceb97c0cb25f1e112d76495bff23e3779948ba + md5: 0a802cb9888dd14eeefc611f05c40b6e + depends: + - python >=3.9 + license: MIT + license_family: MIT + size: 30731 + timestamp: 1737618390337 +- conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.9-pyh29332c3_0.conda + sha256: 04d49cb3c42714ce533a8553986e1642d0549a05dc5cc48e0d43ff5be6679a5b + md5: 4f14640d58e2cc0aa0819d9d8ba125bb + depends: + - python >=3.9 + - h11 >=0.16 + - h2 >=3,<5 + - sniffio 1.* + - anyio >=4.0,<5.0 + - certifi + - python + license: BSD-3-Clause + license_family: BSD + size: 49483 + timestamp: 1745602916758 +- conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda + sha256: cd0f1de3697b252df95f98383e9edb1d00386bfdd03fdf607fa42fe5fcb09950 + md5: d6989ead454181f4f9bc987d3dc4e285 + depends: + - anyio + - certifi + - httpcore 1.* + - idna + - python >=3.9 + license: BSD-3-Clause + license_family: BSD + size: 63082 + timestamp: 1733663449209 +- conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda + sha256: 77af6f5fe8b62ca07d09ac60127a30d9069fdc3c68d6b256754d0ffb1f7779f8 + md5: 8e6923fc12f1fe8f8c4e5c9f343256ac + depends: + - python >=3.9 + license: MIT + license_family: MIT + size: 17397 + timestamp: 1737618427549 +- conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.17-pyhcf101f3_0.conda + sha256: f9fe1f9e539c544405ccb7ba632d4ba79edf243c05554d76ace073158a80b691 + md5: c75e517ebd7a5c5272fe111e8b162228 + depends: + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 56858 + timestamp: 1779999227630 +- conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda + sha256: 43e2a5497cad1598ff88a3e69f69bc88b7b8f141fa63c60eab5db296317318b8 + md5: ffc17e785d64e12fc311af9184221839 + depends: + - python >=3.10 + - zipp >=3.20 + - python + license: Apache-2.0 + license_family: APACHE + size: 34766 + timestamp: 1779714582554 +- conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda + sha256: e1a9e3b1c8fe62dc3932a616c284b5d8cbe3124bbfbedcf4ce5c828cb166ee19 + md5: 9614359868482abba1bd15ce465e3c42 + depends: + - python >=3.10 + license: MIT + license_family: MIT + size: 13387 + timestamp: 1760831448842 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyh5552912_1.conda + sha256: 5c1f3e874adaf603449f2b135d48f168c5d510088c78c229bda0431268b43b27 + md5: 4b53d436f3fbc02ce3eeaf8ae9bebe01 + depends: + - appnope + - __osx + - comm >=0.1.1 + - debugpy >=1.6.5 + - ipython >=7.23.1 + - jupyter_client >=8.8.0 + - jupyter_core >=5.1,!=6.0.* + - matplotlib-inline >=0.1 + - nest-asyncio >=1.4 + - packaging >=22 + - psutil >=5.7 + - python >=3.10 + - pyzmq >=25 + - tornado >=6.4.1 + - traitlets >=5.4.0 + - python + constrains: + - appnope >=0.1.2 + license: BSD-3-Clause + license_family: BSD + size: 132260 + timestamp: 1770566135697 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyh6dadd2b_1.conda + sha256: 9cdadaeef5abadca4113f92f5589db19f8b7df5e1b81cb0225f7024a3aedefa3 + md5: b3a7d5842f857414d9ae831a799444dd + depends: + - __win + - comm >=0.1.1 + - debugpy >=1.6.5 + - ipython >=7.23.1 + - jupyter_client >=8.8.0 + - jupyter_core >=5.1,!=6.0.* + - matplotlib-inline >=0.1 + - nest-asyncio >=1.4 + - packaging >=22 + - psutil >=5.7 + - python >=3.10 + - pyzmq >=25 + - tornado >=6.4.1 + - traitlets >=5.4.0 + - python + constrains: + - appnope >=0.1.2 + license: BSD-3-Clause + license_family: BSD + size: 132382 + timestamp: 1770566174387 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipykernel-7.2.0-pyha191276_1.conda + sha256: b77ed58eb235e5ad80e742b03caeed4bbc2a2ef064cb9a2deee3b75dfae91b2a + md5: 8b267f517b81c13594ed68d646fd5dcb + depends: + - __linux + - comm >=0.1.1 + - debugpy >=1.6.5 + - ipython >=7.23.1 + - jupyter_client >=8.8.0 + - jupyter_core >=5.1,!=6.0.* + - matplotlib-inline >=0.1 + - nest-asyncio >=1.4 + - packaging >=22 + - psutil >=5.7 + - python >=3.10 + - pyzmq >=25 + - tornado >=6.4.1 + - traitlets >=5.4.0 + - python + constrains: + - appnope >=0.1.2 + license: BSD-3-Clause + license_family: BSD + size: 133644 + timestamp: 1770566133040 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.37.0-pyh8f84b5b_0.conda + sha256: e43fa762183b49c3c3b811d41259e94bb14b7bff4a239b747ef4e1c6bbe2702d + md5: 177cfa19fe3d74c87a8889286dc64090 + depends: + - __unix + - pexpect >4.3 + - decorator + - exceptiongroup + - jedi >=0.16 + - matplotlib-inline + - pickleshare + - prompt-toolkit >=3.0.41,<3.1.0 + - pygments >=2.4.0 + - python >=3.10 + - stack_data + - traitlets >=5.13.0 + - typing_extensions >=4.6 + - python + license: BSD-3-Clause + license_family: BSD + size: 639160 + timestamp: 1748711175284 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipython-8.37.0-pyha7b4d00_0.conda + sha256: 4812e69a1c9d6d43746fa7e8efaf9127d257508249e7192e68cd163511a751ee + md5: 2ffea44095ca39b38b67599e8091bca3 + depends: + - __win + - colorama + - decorator + - exceptiongroup + - jedi >=0.16 + - matplotlib-inline + - pickleshare + - prompt-toolkit >=3.0.41,<3.1.0 + - pygments >=2.4.0 + - python >=3.10 + - stack_data + - traitlets >=5.13.0 + - typing_extensions >=4.6 + - python + license: BSD-3-Clause + license_family: BSD + size: 638940 + timestamp: 1748711254071 +- conda: https://conda.anaconda.org/conda-forge/noarch/ipywidgets-8.1.8-pyhd8ed1ab_0.conda + sha256: 6bb58afb7eabc8b4ac0c7e92707fb498313cc0164cf04e7ba1090dbf49af514b + md5: d68e3f70d1f068f1b66d94822fdc644e + depends: + - comm >=0.1.3 + - ipython >=6.1.0 + - jupyterlab_widgets >=3.0.15,<3.1.0 + - python >=3.10 + - traitlets >=4.3.1 + - widgetsnbextension >=4.0.14,<4.1.0 + license: BSD-3-Clause + license_family: BSD + size: 114376 + timestamp: 1762040524661 +- conda: https://conda.anaconda.org/conda-forge/noarch/isoduration-20.11.0-pyhd8ed1ab_1.conda + sha256: 08e838d29c134a7684bca0468401d26840f41c92267c4126d7b43a6b533b0aed + md5: 0b0154421989637d424ccf0f104be51a + depends: + - arrow >=0.15.0 + - python >=3.9 + license: MIT + license_family: MIT + size: 19832 + timestamp: 1733493720346 +- conda: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda + sha256: 92c4d217e2dc68983f724aa983cca5464dcb929c566627b26a2511159667dba8 + md5: a4f4c5dc9b80bc50e0d3dc4e6e8f1bd9 + depends: + - parso >=0.8.3,<0.9.0 + - python >=3.9 + license: Apache-2.0 AND MIT + size: 843646 + timestamp: 1733300981994 +- conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda + sha256: fc9ca7348a4f25fed2079f2153ecdcf5f9cf2a0bc36c4172420ca09e1849df7b + md5: 04558c96691bed63104678757beb4f8d + depends: + - markupsafe >=2.0 + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 120685 + timestamp: 1764517220861 +- conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda + sha256: 31e05d47970d956206188480b038829d24ac11fe8216409d8584d93d40233878 + md5: 4da50d410f553db77e62ab62ffaa1abc + depends: + - python >=3.7 + - setuptools + license: BSD-3-Clause + license_family: BSD + size: 221200 + timestamp: 1691577306309 +- conda: https://conda.anaconda.org/conda-forge/noarch/json5-0.14.0-pyhd8ed1ab_0.conda + sha256: 9daa95bd164c8fa23b3ab196e906ef806141d749eddce2a08baa064f722d25fa + md5: 1269891272187518a0a75c286f7d0bbf + depends: + - python >=3.10 + license: Apache-2.0 + license_family: APACHE + size: 34731 + timestamp: 1774655440045 +- conda: https://conda.anaconda.org/conda-forge/noarch/jsonpointer-3.1.1-pyhcf101f3_0.conda + sha256: a3d10301b6ff399ba1f3d39e443664804a3d28315a4fb81e745b6817845f70ae + md5: 89bf346df77603055d3c8fe5811691e6 + depends: + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 14190 + timestamp: 1774311356147 +- conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.26.0-pyhcf101f3_0.conda + sha256: db973a37d75db8e19b5f44bbbdaead0c68dde745407f281e2a7fe4db74ec51d7 + md5: ada41c863af263cc4c5fcbaff7c3e4dc + depends: + - attrs >=22.2.0 + - jsonschema-specifications >=2023.3.6 + - python >=3.10 + - referencing >=0.28.4 + - rpds-py >=0.25.0 + - python + license: MIT + license_family: MIT + size: 82356 + timestamp: 1767839954256 +- conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-specifications-2025.9.1-pyhcf101f3_0.conda + sha256: 0a4f3b132f0faca10c89fdf3b60e15abb62ded6fa80aebfc007d05965192aa04 + md5: 439cd0f567d697b20a8f45cb70a1005a + depends: + - python >=3.10 + - referencing >=0.31.0 + - python + license: MIT + license_family: MIT + size: 19236 + timestamp: 1757335715225 +- conda: https://conda.anaconda.org/conda-forge/noarch/jsonschema-with-format-nongpl-4.26.0-hcf101f3_0.conda + sha256: 6886fc61e4e4edd38fd38729976b134e8bd2143f7fce56cc80d7ac7bac99bce1 + md5: 8368d58342d0825f0843dc6acdd0c483 + depends: + - jsonschema >=4.26.0,<4.26.1.0a0 + - fqdn + - idna + - isoduration + - jsonpointer >1.13 + - rfc3339-validator + - rfc3986-validator >0.1.0 + - rfc3987-syntax >=1.1.0 + - uri-template + - webcolors >=24.6.0 + license: MIT + license_family: MIT + size: 4740 + timestamp: 1767839954258 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyter-lsp-2.3.1-pyhcf101f3_0.conda + sha256: 3766e2ae59641c172cec8a821528bfa6bf9543ffaaeb8b358bfd5259dcf18e4e + md5: 0c3b465ceee138b9c39279cc02e5c4a0 + depends: + - importlib-metadata >=4.8.3 + - jupyter_server >=1.1.2 + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 61633 + timestamp: 1775136333147 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.8.0-pyhcf101f3_0.conda + sha256: e402bd119720862a33229624ec23645916a7d47f30e1711a4af9e005162b84f3 + md5: 8a3d6d0523f66cf004e563a50d9392b3 + depends: + - jupyter_core >=5.1 + - python >=3.10 + - python-dateutil >=2.8.2 + - pyzmq >=25.0 + - tornado >=6.4.1 + - traitlets >=5.3 + - python + license: BSD-3-Clause + license_family: BSD + size: 112785 + timestamp: 1767954655912 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyh6dadd2b_0.conda + sha256: ed709a6c25b731e01563521ef338b93986cd14b5bc17f35e9382000864872ccc + md5: a8db462b01221e9f5135be466faeb3e0 + depends: + - __win + - pywin32 + - platformdirs >=2.5 + - python >=3.10 + - traitlets >=5.3 + - python + constrains: + - pywin32 >=300 + license: BSD-3-Clause + license_family: BSD + size: 64679 + timestamp: 1760643889625 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.9.1-pyhc90fa1f_0.conda + sha256: 1d34b80e5bfcd5323f104dbf99a2aafc0e5d823019d626d0dce5d3d356a2a52a + md5: b38fe4e78ee75def7e599843ef4c1ab0 + depends: + - __unix + - python + - platformdirs >=2.5 + - python >=3.10 + - traitlets >=5.3 + - python + constrains: + - pywin32 >=300 + license: BSD-3-Clause + license_family: BSD + size: 65503 + timestamp: 1760643864586 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.1-pyhcf101f3_0.conda + sha256: c7edb5682c6316a95ad781dccb1b6589cd2ec0bf94f23c21152974eb0363b5d7 + md5: bf42ee94c750c0b2e7e998b79ac299ea + depends: + - jsonschema-with-format-nongpl >=4.18.0 + - packaging + - python >=3.10 + - python-json-logger >=2.0.4 + - pyyaml >=5.3 + - referencing + - rfc3339-validator + - rfc3986-validator >=0.1.1 + - traitlets >=5.3 + - python + license: BSD-3-Clause + license_family: BSD + size: 24002 + timestamp: 1776861872237 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.19.0-pyhcf101f3_0.conda + sha256: 896a350a026db8fff26a7884ed841d53cb84f57f914064fbead0628ab23d1da0 + md5: 82525f37e0976e83bbb69bc4d4011665 + depends: + - anyio >=3.1.0 + - argon2-cffi >=21.1 + - jinja2 >=3.0.3 + - jupyter_client >=7.4.4 + - jupyter_core >=4.12,!=5.0.* + - jupyter_events >=0.11.0 + - jupyter_server_terminals >=0.4.4 + - nbconvert-core >=6.4.4 + - nbformat >=5.3.0 + - overrides >=5.0 + - packaging >=22.0 + - prometheus_client >=0.9 + - python >=3.10 + - pyzmq >=24 + - send2trash >=1.8.2 + - terminado >=0.8.3 + - tornado >=6.2.0 + - traitlets >=5.6.0 + - websocket-client >=1.7 + - python + license: BSD-3-Clause + size: 361523 + timestamp: 1780151480958 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server_terminals-0.5.4-pyhcf101f3_0.conda + sha256: 5eda79ed9f53f590031d29346abd183051263227dd9ee667b5ca1133ce297654 + md5: 7b8bace4943e0dc345fc45938826f2b8 + depends: + - python >=3.10 + - terminado >=0.8.3 + - python + license: BSD-3-Clause + license_family: BSD + size: 22052 + timestamp: 1768574057200 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab-4.5.7-pyhd8ed1ab_0.conda + sha256: b85befad5ba1f50c0cc042a2ffb26441d13ffc2f18572dc20d3541476da0c7b9 + md5: 2ffe77234070324e763a6eddabb5f467 + depends: + - async-lru >=1.0.0 + - httpx >=0.25.0,<1 + - ipykernel >=6.5.0,!=6.30.0 + - jinja2 >=3.0.3 + - jupyter-lsp >=2.0.0 + - jupyter_core + - jupyter_server >=2.4.0,<3 + - jupyterlab_server >=2.28.0,<3 + - notebook-shim >=0.2 + - packaging + - python >=3.10 + - setuptools >=41.1.0 + - tomli >=1.2.2 + - tornado >=6.2.0 + - traitlets + license: BSD-3-Clause + license_family: BSD + size: 8861204 + timestamp: 1777483115382 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_pygments-0.3.0-pyhd8ed1ab_2.conda + sha256: dc24b900742fdaf1e077d9a3458fd865711de80bca95fe3c6d46610c532c6ef0 + md5: fd312693df06da3578383232528c468d + depends: + - pygments >=2.4.1,<3 + - python >=3.9 + constrains: + - jupyterlab >=4.0.8,<5.0.0 + license: BSD-3-Clause + license_family: BSD + size: 18711 + timestamp: 1733328194037 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.28.0-pyhcf101f3_0.conda + sha256: 381d2d6a259a3be5f38a69463e0f6c5dcf1844ae113058007b51c3bef13a7cee + md5: a63877cb23de826b1620d3adfccc4014 + depends: + - babel >=2.10 + - jinja2 >=3.0.3 + - json5 >=0.9.0 + - jsonschema >=4.18 + - jupyter_server >=1.21,<3 + - packaging >=21.3 + - python >=3.10 + - requests >=2.31 + - python + license: BSD-3-Clause + license_family: BSD + size: 51621 + timestamp: 1761145478692 +- conda: https://conda.anaconda.org/conda-forge/noarch/jupyterlab_widgets-3.0.16-pyhcf101f3_1.conda + sha256: 5c03de243d7ae6247f39a402f4785d95e61c3be79ef18738e8f17155585d31a8 + md5: dbf8b81974504fa51d34e436ca7ef389 + depends: + - python >=3.10 + - python + constrains: + - jupyterlab >=3,<5 + license: BSD-3-Clause + license_family: BSD + size: 216779 + timestamp: 1762267481404 +- conda: https://conda.anaconda.org/conda-forge/noarch/lark-1.3.1-pyhd8ed1ab_0.conda + sha256: 49570840fb15f5df5d4b4464db8ee43a6d643031a2bc70ef52120a52e3809699 + md5: 9b965c999135d43a3d0f7bd7d024e26a + depends: + - python >=3.10 + license: MIT + license_family: MIT + size: 94312 + timestamp: 1761596921009 +- conda: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 + sha256: 9afe0b5cfa418e8bdb30d8917c5a6cec10372b037924916f1f85b9f4899a67a6 + md5: 91e27ef3d05cc772ce627e51cff111c4 + depends: + - python >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.* + license: BSD-2-Clause + license_family: BSD + size: 8250 + timestamp: 1650660473123 +- conda: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.2.2-pyhd8ed1ab_0.conda + sha256: 35b43d7343f74452307fd018a1cca92b8f68961ff8e2ab6a81ce0a703c9a3764 + md5: 9acc1c385be401d533ff70ef5b50dae6 + depends: + - python >=3.10 + - traitlets + license: BSD-3-Clause + license_family: BSD + size: 15725 + timestamp: 1778264403247 +- conda: https://conda.anaconda.org/conda-forge/noarch/mistune-3.2.1-pyhcf101f3_0.conda + sha256: b52dc6c78fbbe7a3008535cb8bfd87d70d8053e9250bbe16e387470a9df07070 + md5: b97e84d1553b4a1c765b87fff83453ad + depends: + - python >=3.10 + - typing_extensions + - python + license: BSD-3-Clause + license_family: BSD + size: 74567 + timestamp: 1777824616382 +- conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyhd8ed1ab_1.conda + sha256: d09c47c2cf456de5c09fa66d2c3c5035aa1fa228a1983a433c47b876aa16ce90 + md5: 37293a85a0f4f77bbd9cf7aaefc62609 + depends: + - python >=3.9 + license: Apache-2.0 + license_family: Apache + size: 15851 + timestamp: 1749895533014 +- conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-1.15.2-pyhd8ed1ab_0.conda + sha256: d9d298a386a9b13241094eb26ffd0ea4fef2e47a7c983e0221e86a5524b04b5c + md5: 4afe57efb208dacec09458c3cc397456 + depends: + - python >=3.9 + license: MIT + license_family: MIT + run_exports: {} + size: 136297 + timestamp: 1733267301269 +- conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.22.1-pyhcf101f3_0.conda + sha256: dd2744a501f2db0aef084566bf3d0c2b312661dc91beb5a4cc97d27cdda0a959 + md5: 9450fb40fb1e147d0bcbdf07cd02ca96 + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + run_exports: {} + size: 285532 + timestamp: 1780672242196 +- conda: https://conda.anaconda.org/conda-forge/noarch/nbclient-0.10.4-pyhd8ed1ab_0.conda + sha256: 1b66960ee06874ddceeebe375d5f17fb5f393d025a09e15b830ad0c4fffb585b + md5: 00f5b8dafa842e0c27c1cd7296aa4875 + depends: + - jupyter_client >=6.1.12 + - jupyter_core >=4.12,!=5.0.* + - nbformat >=5.1 + - python >=3.8 + - traitlets >=5.4 + license: BSD-3-Clause + license_family: BSD + size: 28473 + timestamp: 1766485646962 +- conda: https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-7.17.1-pyhcf101f3_0.conda + sha256: ab2ac79c5892c5434d50b3542d96645bdaa06d025b6e03734be29200de248ac2 + md5: 2bce0d047658a91b99441390b9b27045 + depends: + - beautifulsoup4 + - bleach-with-css !=5.0.0 + - defusedxml + - importlib-metadata >=3.6 + - jinja2 >=3.0 + - jupyter_core >=4.7 + - jupyterlab_pygments + - markupsafe >=2.0 + - mistune >=2.0.3,<4 + - nbclient >=0.5.0 + - nbformat >=5.7 + - packaging + - pandocfilters >=1.4.1 + - pygments >=2.4.1 + - python >=3.10 + - traitlets >=5.1 + - python + constrains: + - pandoc >=2.9.2,<4.0.0 + - nbconvert ==7.17.1 *_0 + license: BSD-3-Clause + license_family: BSD + size: 202229 + timestamp: 1775615493260 +- conda: https://conda.anaconda.org/conda-forge/noarch/nbformat-5.10.4-pyhd8ed1ab_1.conda + sha256: 7a5bd30a2e7ddd7b85031a5e2e14f290898098dc85bea5b3a5bf147c25122838 + md5: bbe1963f1e47f594070ffe87cdf612ea + depends: + - jsonschema >=2.6 + - jupyter_core >=4.12,!=5.0.* + - python >=3.9 + - python-fastjsonschema >=2.15 + - traitlets >=5.1 + license: BSD-3-Clause + license_family: BSD + size: 100945 + timestamp: 1733402844974 +- conda: https://conda.anaconda.org/conda-forge/noarch/nest-asyncio-1.6.0-pyhd8ed1ab_1.conda + sha256: bb7b21d7fd0445ddc0631f64e66d91a179de4ba920b8381f29b9d006a42788c0 + md5: 598fd7d4d0de2455fb74f56063969a97 + depends: + - python >=3.9 + license: BSD-2-Clause + license_family: BSD + size: 11543 + timestamp: 1733325673691 +- conda: https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.2.4-pyhd8ed1ab_1.conda + sha256: 7b920e46b9f7a2d2aa6434222e5c8d739021dbc5cc75f32d124a8191d86f9056 + md5: e7f89ea5f7ea9401642758ff50a2d9c1 + depends: + - jupyter_server >=1.8,<3 + - python >=3.9 + license: BSD-3-Clause + license_family: BSD + size: 16817 + timestamp: 1733408419340 +- conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda + sha256: 1840bd90d25d4930d60f57b4f38d4e0ae3f5b8db2819638709c36098c6ba770c + md5: e51f1e4089cad105b6cac64bd8166587 + depends: + - python >=3.9 + - typing_utils + license: Apache-2.0 + license_family: APACHE + size: 30139 + timestamp: 1734587755455 +- conda: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda + sha256: 3906abfb6511a3bb309e39b9b1b7bc38f50a723971de2395489fd1f379255890 + md5: 4c06a92e74452cfa53623a81592e8934 + depends: + - python >=3.8 + - python + license: Apache-2.0 + license_family: APACHE + size: 91574 + timestamp: 1777103621679 +- conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2 + sha256: 2bb9ba9857f4774b85900c2562f7e711d08dd48e2add9bee4e1612fbee27e16f + md5: 457c2c8c08e54905d6954e79cb5b5db9 + depends: + - python !=3.0,!=3.1,!=3.2,!=3.3 + license: BSD-3-Clause + license_family: BSD + size: 11627 + timestamp: 1631603397334 +- conda: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.7-pyhcf101f3_0.conda + sha256: 611882f7944b467281c46644ffde6c5145d1a7730388bcde26e7e86819b0998e + md5: 39894c952938276405a1bd30e4ce2caf + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + size: 82472 + timestamp: 1777722955579 +- conda: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda + sha256: 472fc587c63ec4f6eba0cc0b06008a6371e0a08a5986de3cf4e8024a47b4fe6c + md5: 0badf9c54e24cecfb0ad2f99d680c163 + depends: + - locket + - python >=3.9 + - toolz + license: BSD-3-Clause + license_family: BSD + size: 20884 + timestamp: 1715026639309 +- conda: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda + sha256: 202af1de83b585d36445dc1fda94266697341994d1a3328fabde4989e1b3d07a + md5: d0d408b1f18883a944376da5cf8101ea + depends: + - ptyprocess >=0.5 + - python >=3.9 + license: ISC + size: 53561 + timestamp: 1733302019362 +- conda: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda + sha256: e2ac3d66c367dada209fc6da43e645672364b9fd5f9d28b9f016e24b81af475b + md5: 11a9d1d09a3615fc07c3faf79bc0b943 + depends: + - python >=3.9 + license: MIT + license_family: MIT + size: 11748 + timestamp: 1733327448200 +- conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda + sha256: 29b7d75bf81ad11645a8e320b369abdc90a92b93f2a9178e853d9dddf82e5106 + md5: 511fbc2c63d2c73650ad1755e4d357ba + depends: + - python >=3.10,<3.13.0a0 + - setuptools + - wheel + license: MIT + license_family: MIT + size: 1203173 + timestamp: 1780262795392 +- conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.10.0-pyhcf101f3_0.conda + sha256: 9e5e1fd3506ccfc4d444fc4d2d39b0ed097d5d0e3bd3d4bdf6bcc81aaf66860d + md5: 2c5ef45db85d34799771629bd5860fd7 + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + size: 26308 + timestamp: 1779972894916 +- conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.0.0-pyhd8ed1ab_5.tar.bz2 + sha256: c25e1757e4e90638bb1e778aba3ee5f3c01fae9752e3c3929f9be7d367f6c7f3 + md5: 7d301a0d25f424d96175f810935f0da9 + depends: + - python >=3.8 + license: MIT + license_family: MIT + size: 16363 + timestamp: 1667232772321 +- conda: https://conda.anaconda.org/conda-forge/noarch/prometheus_client-0.25.0-pyhd8ed1ab_0.conda + sha256: 4d7ec90d4f9c1f3b4a50623fefe4ebba69f651b102b373f7c0e9dbbfa43d495c + md5: a11ab1f31af799dd93c3a39881528884 + depends: + - python >=3.10 + license: Apache-2.0 + license_family: Apache + size: 57113 + timestamp: 1775771465170 +- conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda + sha256: 4817651a276016f3838957bfdf963386438c70761e9faec7749d411635979bae + md5: edb16f14d920fb3faf17f5ce582942d6 + depends: + - python >=3.10 + - wcwidth + constrains: + - prompt_toolkit 3.0.52 + license: BSD-3-Clause + license_family: BSD + size: 273927 + timestamp: 1756321848365 +- conda: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda + sha256: a7713dfe30faf17508ec359e0bc7e0983f5d94682492469bd462cdaae9c64d83 + md5: 7d9daffbb8d8e0af0f769dbbcd173a54 + depends: + - python >=3.9 + license: ISC + size: 19457 + timestamp: 1733302371990 +- conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda + sha256: 71bd24600d14bb171a6321d523486f6a06f855e75e547fa0cb2a0953b02047f0 + md5: 3bfdfb8dbcdc4af1ae3f9a8eb3948f04 + depends: + - python >=3.9 + license: MIT + license_family: MIT + size: 16668 + timestamp: 1733569518868 +- conda: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda + sha256: e27e0473fc6723311a0bd48b89b616fa1b996a2f7a2b555338cbbcfb9c640568 + md5: 9c5491066224083c41b6d5635ed7107b + depends: + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 55886 + timestamp: 1779293633166 +- conda: https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda + sha256: cf70b2f5ad9ae472b71235e5c8a736c9316df3705746de419b59d442e8348e86 + md5: 16c18772b340887160c79a6acc022db0 + depends: + - python >=3.10 + license: BSD-2-Clause + license_family: BSD + size: 893031 + timestamp: 1774796815820 +- conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.3.2-pyhcf101f3_0.conda + sha256: 417fba4783e528ee732afa82999300859b065dc59927344b4859c64aae7182de + md5: 3687cc0b82a8b4c17e1f0eb7e47163d5 + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + size: 110893 + timestamp: 1769003998136 +- conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyh09c184e_7.conda + sha256: d016e04b0e12063fbee4a2d5fbb9b39a8d191b5a0042f0b8459188aedeabb0ca + md5: e2fd202833c4a981ce8a65974fe4abd1 + depends: + - __win + - python >=3.9 + - win_inet_pton + license: BSD-3-Clause + license_family: BSD + size: 21784 + timestamp: 1733217448189 +- conda: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda + sha256: ba3b032fa52709ce0d9fd388f63d330a026754587a2f461117cac9ab73d8d0d8 + md5: 461219d1a5bd61342293efa2c0c90eac + depends: + - __unix + - python >=3.9 + license: BSD-3-Clause + license_family: BSD + size: 21085 + timestamp: 1733217331982 +- conda: https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda + sha256: 8979721b7f86b183d21103f3ec2734783847d317c1b754f462f407efc7c60886 + md5: a9d145de8c5f064b5fa68fb34725d9f4 + depends: + - colorama + - exceptiongroup >=1.0.0rc8 + - iniconfig + - packaging + - pluggy >=0.12,<2.0 + - python >=3.7 + - tomli >=1.0.0 + constrains: + - pytest-faulthandler >=2 + license: MIT + license_family: MIT + size: 244564 + timestamp: 1704035308916 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda + sha256: d6a17ece93bbd5139e02d2bd7dbfa80bee1a4261dced63f65f679121686bf664 + md5: 5b8d21249ff20967101ffa321cab24e8 + depends: + - python >=3.9 + - six >=1.5 + - python + license: Apache-2.0 + license_family: APACHE + size: 233310 + timestamp: 1751104122689 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda + sha256: df9aa74e9e28e8d1309274648aac08ec447a92512c33f61a8de0afa9ce32ebe8 + md5: 23029aae904a2ba587daba708208012f + depends: + - python >=3.9 + - python + license: BSD-3-Clause + license_family: BSD + size: 244628 + timestamp: 1755304154927 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-graphviz-0.20.3-pyh91182bf_2.conda + sha256: c8f5d3d23b5962524217f33549add8d6c5af22fe839b49603f4588771154a51c + md5: f822f0e13849c2283f72ec4aa120eeaa + depends: + - graphviz >=2.46.1 + - python >=3.9 + license: MIT + license_family: MIT + size: 38220 + timestamp: 1733792086212 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-3.2.1-pyh332efcf_0.conda + sha256: 1c55116c22512cef7b01d55ae49697707f2c1fd829407183c19817e2d300fd8d + md5: 1cd2f3e885162ee1366312bd1b1677fd + depends: + - python >=3.10 + - typing_extensions + license: BSD-2-Clause + license_family: BSD + size: 18969 + timestamp: 1777318679482 +- conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2026.2-pyhd8ed1ab_0.conda + sha256: e943f9c15a6bdba2e1b9f423ab913b3f6b02197b0ef9f8e6b7464d78b59965b9 + md5: f6ad7450fc21e00ecc23812baed6d2e4 + depends: + - python >=3.10 + license: Apache-2.0 + license_family: APACHE + size: 146639 + timestamp: 1777068997932 +- conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.10-8_cp310.conda + build_number: 8 + sha256: 7ad76fa396e4bde336872350124c0819032a9e8a0a40590744ff9527b54351c1 + md5: 05e00f3b21e88bb3d658ac700b2ce58c + constrains: + - python 3.10.* *_cpython + license: BSD-3-Clause + license_family: BSD + size: 6999 + timestamp: 1752805924192 +- conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda + sha256: 5020863d629f584b5c057333a67a7aed43e3ed013ba15dd70f353501ccb5aff6 + md5: 03cb60f505ad3ada0a95277af5faeb1a + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + size: 201747 + timestamp: 1777892201250 +- conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda + sha256: 0577eedfb347ff94d0f2fa6c052c502989b028216996b45c7f21236f25864414 + md5: 870293df500ca7e18bedefa5838a22ab + depends: + - attrs >=22.2.0 + - python >=3.10 + - rpds-py >=0.7.0 + - typing_extensions >=4.4.0 + - python + license: MIT + license_family: MIT + size: 51788 + timestamp: 1760379115194 +- conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda + sha256: 1715246b19c9f85ee022933b4845f2fc14ac9184981b7b7d9b728bec8e9588da + md5: 4a85203c1d80c1059086ae860836ffb9 + depends: + - python >=3.10 + - certifi >=2023.5.7 + - charset-normalizer >=2,<4 + - idna >=2.5,<4 + - urllib3 >=1.26,<3 + - python + constrains: + - chardet >=3.0.2,<8 + license: Apache-2.0 + license_family: APACHE + size: 68709 + timestamp: 1778851103479 +- conda: https://conda.anaconda.org/conda-forge/noarch/rfc3339-validator-0.1.4-pyhd8ed1ab_1.conda + sha256: 2e4372f600490a6e0b3bac60717278448e323cab1c0fecd5f43f7c56535a99c5 + md5: 36de09a8d3e5d5e6f4ee63af49e59706 + depends: + - python >=3.9 + - six + license: MIT + license_family: MIT + size: 10209 + timestamp: 1733600040800 +- conda: https://conda.anaconda.org/conda-forge/noarch/rfc3986-validator-0.1.1-pyh9f0ad1d_0.tar.bz2 + sha256: 2a5b495a1de0f60f24d8a74578ebc23b24aa53279b1ad583755f223097c41c37 + md5: 912a71cc01012ee38e6b90ddd561e36f + depends: + - python + license: MIT + license_family: MIT + size: 7818 + timestamp: 1598024297745 +- conda: https://conda.anaconda.org/conda-forge/noarch/rfc3987-syntax-1.1.0-pyhe01879c_1.conda + sha256: 70001ac24ee62058557783d9c5a7bbcfd97bd4911ef5440e3f7a576f9e43bc92 + md5: 7234f99325263a5af6d4cd195035e8f2 + depends: + - python >=3.9 + - lark >=1.2.2 + - python + license: MIT + license_family: MIT + size: 22913 + timestamp: 1752876729969 +- conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyh5552912_1.conda + sha256: 8fc024bf1a7b99fc833b131ceef4bef8c235ad61ecb95a71a6108be2ccda63e8 + md5: b70e2d44e6aa2beb69ba64206a16e4c6 + depends: + - __osx + - pyobjc-framework-cocoa + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 22519 + timestamp: 1770937603551 +- conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyh6dadd2b_1.conda + sha256: 305446a0b018f285351300463653d3d3457687270e20eda37417b12ee386ef76 + md5: 6ac53f3fff2c416d63511843a04646fa + depends: + - __win + - pywin32 + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 22864 + timestamp: 1770937641143 +- conda: https://conda.anaconda.org/conda-forge/noarch/send2trash-2.1.0-pyha191276_1.conda + sha256: 59656f6b2db07229351dfb3a859c35e57cc8e8bcbc86d4e501bff881a6f771f1 + md5: 28eb91468df04f655a57bcfbb35fc5c5 + depends: + - __linux + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 24108 + timestamp: 1770937597662 +- conda: https://conda.anaconda.org/conda-forge/noarch/setuptools-82.0.1-pyh332efcf_0.conda + sha256: 82088a6e4daa33329a30bc26dc19a98c7c1d3f05c0f73ce9845d4eab4924e9e1 + md5: 8e194e7b992f99a5015edbd4ebd38efd + depends: + - python >=3.10 + license: MIT + license_family: MIT + size: 639697 + timestamp: 1773074868565 +- conda: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda + sha256: 458227f759d5e3fcec5d9b7acce54e10c9e1f4f4b7ec978f3bfd54ce4ee9853d + md5: 3339e3b65d58accf4ca4fb8748ab16b3 + depends: + - python >=3.9 + - python + license: MIT + license_family: MIT + size: 18455 + timestamp: 1753199211006 +- conda: https://conda.anaconda.org/conda-forge/noarch/sniffio-1.3.1-pyhd8ed1ab_2.conda + sha256: dce518f45e24cd03f401cb0616917773159a210c19d601c5f2d4e0e5879d30ad + md5: 03fe290994c5e4ec17293cfb6bdce520 + depends: + - python >=3.10 + license: Apache-2.0 + license_family: Apache + size: 15698 + timestamp: 1762941572482 +- conda: https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_1.conda + sha256: d1e3e06b5cf26093047e63c8cc77b70d970411c5cbc0cb1fad461a8a8df599f7 + md5: 0401a17ae845fa72c7210e206ec5647d + depends: + - python >=3.9 + license: Apache-2.0 + license_family: APACHE + size: 28657 + timestamp: 1738440459037 +- conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.8.4-pyhd8ed1ab_0.conda + sha256: 2afa5fe9331c09b4c4689ddf6ace8fc16c837eae547c57dab325b844072fdd77 + md5: 9e21f087f087f805debe877d88e00a14 + depends: + - python >=3.10 + license: MIT + license_family: MIT + size: 38802 + timestamp: 1779635534390 +- conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda + sha256: 570da295d421661af487f1595045760526964f41471021056e993e73089e9c41 + md5: b1b505328da7a6b246787df4b5a49fbc + depends: + - asttokens + - executing + - pure_eval + - python >=3.9 + license: MIT + license_family: MIT + size: 26988 + timestamp: 1733569565672 +- conda: https://conda.anaconda.org/conda-forge/noarch/tblib-3.2.2-pyhcf101f3_0.conda + sha256: 6b549360f687ee4d11bf85a6d6a276a30f9333df1857adb0fe785f0f8e9bcd60 + md5: f88bb644823094f436792f80fba3207e + depends: + - python >=3.10 + - python + license: BSD-2-Clause + license_family: BSD + size: 19397 + timestamp: 1762956379123 +- conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh6dadd2b_1.conda + sha256: b375e8df0d5710717c31e7c8e93c025c37fa3504aea325c7a55509f64e5d4340 + md5: e43ca10d61e55d0a8ec5d8c62474ec9e + depends: + - __win + - pywinpty >=1.1.0 + - python >=3.10 + - tornado >=6.1.0 + - python + license: BSD-2-Clause + license_family: BSD + size: 23665 + timestamp: 1766513806974 +- conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyhc90fa1f_1.conda + sha256: 6b6727a13d1ca6a23de5e6686500d0669081a117736a87c8abf444d60c1e40eb + md5: 17b43cee5cc84969529d5d0b0309b2cb + depends: + - __unix + - ptyprocess + - python >=3.10 + - tornado >=6.1.0 + - python + license: BSD-2-Clause + license_family: BSD + size: 24749 + timestamp: 1766513766867 +- conda: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda + sha256: 6016672e0e72c4cf23c0cf7b1986283bd86a9c17e8d319212d78d8e9ae42fdfd + md5: 9d64911b31d57ca443e9f1e36b04385f + depends: + - python >=3.9 + license: BSD-3-Clause + license_family: BSD + size: 23869 + timestamp: 1741878358548 +- conda: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.4.0-pyhd8ed1ab_0.conda + sha256: cad582d6f978276522f84bd209a5ddac824742fe2d452af6acf900f8650a73a2 + md5: f1acf5fdefa8300de697982bcb1761c9 + depends: + - python >=3.5 + - webencodings >=0.4 + license: BSD-3-Clause + license_family: BSD + size: 28285 + timestamp: 1729802975370 +- conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda + sha256: 91cafdb64268e43e0e10d30bd1bef5af392e69f00edd34dfaf909f69ab2da6bd + md5: b5325cf06a000c5b14970462ff5e4d58 + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + size: 21561 + timestamp: 1774492402955 +- conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.1.0-pyhd8ed1ab_1.conda + sha256: 4e379e1c18befb134247f56021fdf18e112fb35e64dd1691858b0a0f3bea9a45 + md5: c07a6153f8306e45794774cf9b13bd32 + depends: + - python >=3.10 + license: BSD-3-Clause + license_family: BSD + size: 53978 + timestamp: 1760707830681 +- conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.0-pyhcf101f3_0.conda + sha256: dfb681579be59c2e790c95f7f49b7529a9b0511d6385ad276e3c8988cbd54d2c + md5: 4bada6a6d908a27262af8ebddf4f7492 + depends: + - python >=3.10 + - python + license: BSD-3-Clause + license_family: BSD + size: 115165 + timestamp: 1778074251714 +- conda: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.15.0-h396c80c_0.conda + sha256: 7c2df5721c742c2a47b2c8f960e718c930031663ac1174da67c1ed5999f7938c + md5: edd329d7d3a4ab45dcf905899a7a6115 + depends: + - typing_extensions ==4.15.0 pyhcf101f3_0 + license: PSF-2.0 + license_family: PSF + size: 91383 + timestamp: 1756220668932 +- conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda + sha256: 032271135bca55aeb156cee361c81350c6f3fb203f57d024d7e5a1fc9ef18731 + md5: 0caa1af407ecff61170c9437a808404d + depends: + - python >=3.10 + - python + license: PSF-2.0 + license_family: PSF + size: 51692 + timestamp: 1756220668932 +- conda: https://conda.anaconda.org/conda-forge/noarch/typing_utils-0.1.0-pyhd8ed1ab_1.conda + sha256: 3088d5d873411a56bf988eee774559335749aed6f6c28e07bf933256afb9eb6c + md5: f6d7aa696c67756a650e91e15e88223c + depends: + - python >=3.9 + license: Apache-2.0 + license_family: APACHE + size: 15183 + timestamp: 1733331395943 +- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025c-hc9c84f9_1.conda + sha256: 1d30098909076af33a35017eed6f2953af1c769e273a0626a04722ac4acaba3c + md5: ad659d0a2b3e47e38d829aa8cad2d610 + license: LicenseRef-Public-Domain + size: 119135 + timestamp: 1767016325805 +- conda: https://conda.anaconda.org/conda-forge/noarch/uri-template-1.3.0-pyhd8ed1ab_1.conda + sha256: e0eb6c8daf892b3056f08416a96d68b0a358b7c46b99c8a50481b22631a4dfc0 + md5: e7cb0f5745e4c5035a460248334af7eb + depends: + - python >=3.9 + license: MIT + license_family: MIT + size: 23990 + timestamp: 1733323714454 +- conda: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.5.0-pyhd8ed1ab_0.conda + sha256: 4fb9789154bd666ca74e428d973df81087a697dbb987775bc3198d2215f240f8 + md5: 436c165519e140cb08d246a4472a9d6a + depends: + - brotli-python >=1.0.9 + - h2 >=4,<5 + - pysocks >=1.5.6,<2.0,!=1.5.7 + - python >=3.9 + - zstandard >=0.18.0 + license: MIT + license_family: MIT + size: 101735 + timestamp: 1750271478254 +- conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.7.0-pyhd8ed1ab_0.conda + sha256: 1ee2d8384972ecbf8630ce8a3ea9d16858358ad3e8566675295e66996d5352da + md5: eb9538b8e55069434a18547f43b96059 + depends: + - python >=3.10 + license: MIT + license_family: MIT + size: 82917 + timestamp: 1777744489106 +- conda: https://conda.anaconda.org/conda-forge/noarch/webcolors-25.10.0-pyhd8ed1ab_0.conda + sha256: 21f6c8a20fe050d09bfda3fb0a9c3493936ce7d6e1b3b5f8b01319ee46d6c6f6 + md5: 6639b6b0d8b5a284f027a2003669aa65 + depends: + - python >=3.10 + license: BSD-3-Clause + license_family: BSD + size: 18987 + timestamp: 1761899393153 +- conda: https://conda.anaconda.org/conda-forge/noarch/webencodings-0.5.1-pyhd8ed1ab_3.conda + sha256: 19ff205e138bb056a46f9e3839935a2e60bd1cf01c8241a5e172a422fed4f9c6 + md5: 2841eb5bfc75ce15e9a0054b98dcd64d + depends: + - python >=3.9 + license: BSD-3-Clause + license_family: BSD + size: 15496 + timestamp: 1733236131358 +- conda: https://conda.anaconda.org/conda-forge/noarch/websocket-client-1.9.0-pyhd8ed1ab_0.conda + sha256: 42a2b61e393e61cdf75ced1f5f324a64af25f347d16c60b14117393a98656397 + md5: 2f1ed718fcd829c184a6d4f0f2e07409 + depends: + - python >=3.10 + license: Apache-2.0 + license_family: APACHE + size: 61391 + timestamp: 1759928175142 +- conda: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda + sha256: 9e156ffaefb8463437144326ada4b85d1de17961b9997ac5f1cbbaf747bd8bed + md5: d0e3b2f0030cf4fca58bde71d246e94c + depends: + - packaging >=24.0 + - python >=3.10 + license: MIT + license_family: MIT + size: 33491 + timestamp: 1776878563806 +- conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.15-pyhd8ed1ab_0.conda + sha256: 826af5e2c09e5e45361fa19168f46ff524e7a766022615678c3a670c45895d9a + md5: dc257b7e7cad9b79c1dfba194e92297b + depends: + - python >=3.10 + license: BSD-3-Clause + license_family: BSD + size: 889195 + timestamp: 1762040404362 +- conda: https://conda.anaconda.org/conda-forge/noarch/win_inet_pton-1.1.0-pyh7428d3b_8.conda + sha256: 93807369ab91f230cf9e6e2a237eaa812492fe00face5b38068735858fba954f + md5: 46e441ba871f524e2b067929da3051c2 + depends: + - __win + - python >=3.9 + license: LicenseRef-Public-Domain + size: 9555 + timestamp: 1733130678956 +- conda: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda + sha256: 5488542dceeb9f2874e726646548ecc5608060934d6f9ceaa7c6a48c61f9cc8d + md5: e52c2ef711ccf31bb7f70ca87d144b9e + depends: + - python >=3.9 + license: BSD-3-Clause + license_family: BSD + size: 36341 + timestamp: 1733261642963 +- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda + sha256: 210bd31c22bb88f5e2a167df24c95bb5f152b2ada7502f9b8c49d1f5366db423 + md5: ba3dcdc8584155c97c648ae9c044b7a3 + depends: + - python >=3.10 + - python + license: MIT + license_family: MIT + size: 24190 + timestamp: 1779159948016 +- conda: https://conda.anaconda.org/conda-forge/osx-64/_openmp_mutex-4.5-7_kmp_llvm.conda + build_number: 7 + sha256: 30006902a9274de8abdad5a9f02ef7c8bb3d69a503486af0c1faee30b023e5b7 + md5: eaac87c21aff3ed21ad9656697bb8326 + depends: + - llvm-openmp >=9.0.1 + license: BSD-3-Clause + license_family: BSD + size: 8328 + timestamp: 1764092562779 +- conda: https://conda.anaconda.org/conda-forge/osx-64/argon2-cffi-bindings-25.1.0-py310hd2d5e8d_2.conda + sha256: 18ef1903012161df483df264401aec47e5164c156d1202655209918de87b6149 + md5: 37915464daee9e9cf50b96b79ec722f7 + depends: + - __osx >=10.13 + - cffi >=1.0.1 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 33007 + timestamp: 1762509850346 +- conda: https://conda.anaconda.org/conda-forge/osx-64/atk-1.0-2.38.0-h4bec284_2.conda + sha256: a5972a943764e46478c966b26be61de70dcd7d0cfda4bd0b0c46916ae32e0492 + md5: d9684247c943d492d9aac8687bc5db77 + depends: + - __osx >=10.9 + - libcxx >=16 + - libglib >=2.80.0,<3.0a0 + - libintl >=0.22.5,<1.0a0 + constrains: + - atk-1.0 2.38.0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 349989 + timestamp: 1713896423623 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-auth-0.7.20-h26f788b_0.conda + sha256: 6ab2aaa38c28e55f7fe92805e53bd11b64f4ea5c9ff6c55d4f0d48144eabe2a9 + md5: 3ea43455d3f11bbd25958446c4d52b49 + depends: + - __osx >=10.13 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + license: Apache-2.0 + license_family: Apache + size: 92499 + timestamp: 1715287785926 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-cal-0.6.12-h7d0aca8_0.conda + sha256: 36179a5dd367aa83621be11d9f55dc78aa527139edf71b593316a6fab05dda96 + md5: dcc4242b10a3b12f79f4f9dd6348e2be + depends: + - __osx >=10.9 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 39053 + timestamp: 1714177945236 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-common-0.9.17-hec52a4b_0.conda + sha256: c192e223fa57bb48e0f9a28dd90d53ca3672dc2446a114be668e4e25b693a9c5 + md5: cfa9e62dc4ecd87a8462f4b8e17759dd + depends: + - __osx >=10.9 + license: Apache-2.0 + license_family: Apache + size: 209689 + timestamp: 1713864115198 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-compression-0.2.18-h94d6f14_4.conda + sha256: 846812f681355c7e2aa93c4c7c19437c1e63fdeb951a5a1e37b05b4d17526e4f + md5: a6e4f967ecd995841783aa09c5602d28 + depends: + - __osx >=10.9 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 18100 + timestamp: 1714044111140 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-event-stream-0.4.2-h88c3968_10.conda + sha256: 793d6f796a2aabd3fbf2dabd8b2c2949999693370602f4fa566425ec77c3eadb + md5: 781fa81527a280519822714f85438eb7 + depends: + - __osx >=10.13 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - libcxx >=16 + license: Apache-2.0 + license_family: Apache + size: 46692 + timestamp: 1715026467133 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-http-0.8.1-h329322f_13.conda + sha256: 853fade3e6c891999227de124ef846fc57571d05eeffbccd798f3605db90a693 + md5: 8b4e09f932d11884e71eaa242cf4504b + depends: + - __osx >=10.13 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-compression >=0.2.18,<0.2.19.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + license: Apache-2.0 + license_family: Apache + size: 163036 + timestamp: 1715026431696 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-io-0.14.8-hb30fd87_0.conda + sha256: fd33e3f81cf7211336cac4dfcf9e60f55ec10b8850e1ac68a48db9d2ead7fe0a + md5: cc57fdbc7b8edd8f8b375c496c3fd09a + depends: + - __osx >=10.9 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 138288 + timestamp: 1714868039180 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-mqtt-0.10.4-h2c4861c_2.conda + sha256: 9b0805494815cca4d112392e93d090d791d42dbd6cf2fe15209be3ce6601a03a + md5: 89e3cc40fc4df897212dbfe8c42722ba + depends: + - __osx >=10.13 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + license: Apache-2.0 + license_family: Apache + size: 138585 + timestamp: 1715057936099 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-s3-0.5.9-h82d509c_0.conda + sha256: f1d61415a2b1615d0e0eb01467d84f78f3478066bf187fd2e06bfc2cfec831d8 + md5: f00002a17b7533476c691ae74d10a8dc + depends: + - __osx >=10.13 + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + license: Apache-2.0 + license_family: Apache + size: 94924 + timestamp: 1715619881617 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-c-sdkutils-0.1.16-h94d6f14_0.conda + sha256: 0f62b5bc055c6ba18ec852969d05667826a76f965f10b61b6dce9e2a74af1ed7 + md5: 34851d0e159f757a09c33b7607613eee + depends: + - __osx >=10.9 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 49163 + timestamp: 1714208530814 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-checksums-0.1.18-h94d6f14_4.conda + sha256: 3542b2489caea50698d19c88f6d0fb2d2877b84b070af600edbb7f6d4bcb168d + md5: 253954f35344663379d6433b1a50d663 + depends: + - __osx >=10.9 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 48729 + timestamp: 1714051137012 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-crt-cpp-0.26.8-h1c89d3c_11.conda + sha256: cf44433fb35727807d5a549d8740f307710106d156017931594e523dc01216ab + md5: b23cf40cd51d3e38d61dd0aff904533a + depends: + - __osx >=10.13 + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-mqtt >=0.10.4,<0.10.5.0a0 + - aws-c-s3 >=0.5.9,<0.5.10.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - libcxx >=16 + license: Apache-2.0 + license_family: Apache + size: 287867 + timestamp: 1716301053117 +- conda: https://conda.anaconda.org/conda-forge/osx-64/aws-sdk-cpp-1.11.267-h764722f_8.conda + sha256: fcef996e9884b88dc8b7b8ca6c93817e0e9a00eba891924d1a780ce483eca050 + md5: a3f8bef901b75be9b228702e44bebbad + depends: + - __osx >=10.13 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - libcurl >=8.7.1,<9.0a0 + - libcxx >=16 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 3477658 + timestamp: 1715176680987 +- conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-1.1.0-h1c43f85_4.conda + sha256: 13847b7477bd66d0f718f337e7980c9a32f82ec4e4527c7e0a0983db2d798b8e + md5: 1a0a37da4466d45c00fc818bb6b446b3 + depends: + - __osx >=10.13 + - brotli-bin 1.1.0 h1c43f85_4 + - libbrotlidec 1.1.0 h1c43f85_4 + - libbrotlienc 1.1.0 h1c43f85_4 + license: MIT + license_family: MIT + size: 20022 + timestamp: 1756599872109 +- conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-bin-1.1.0-h1c43f85_4.conda + sha256: 549ea0221019cfb4b370354f2c3ffbd4be1492740e1c73b2cdf9687ed6ad7364 + md5: 718fb8aa4c8cb953982416db9a82b349 + depends: + - __osx >=10.13 + - libbrotlidec 1.1.0 h1c43f85_4 + - libbrotlienc 1.1.0 h1c43f85_4 + license: MIT + license_family: MIT + size: 17311 + timestamp: 1756599830763 +- conda: https://conda.anaconda.org/conda-forge/osx-64/brotli-python-1.1.0-py310h79c4529_4.conda + sha256: b3c6e5fa94ebf109e10bfe1b1612bf440c6d199ff9ca46d3fccff5da545cf7a9 + md5: 7589c76eac45a9353d09753ad909a85c + depends: + - __osx >=10.13 + - libcxx >=19 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - libbrotlicommon 1.1.0 h1c43f85_4 + license: MIT + license_family: MIT + size: 368928 + timestamp: 1756600001648 +- conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-h500dc9f_9.conda + sha256: 9f242f13537ef1ce195f93f0cc162965d6cc79da578568d6d8e50f70dd025c42 + md5: 4173ac3b19ec0a4f400b4f782910368b + depends: + - __osx >=10.13 + license: bzip2-1.0.6 + license_family: BSD + size: 133427 + timestamp: 1771350680709 +- conda: https://conda.anaconda.org/conda-forge/osx-64/c-ares-1.34.6-hb5e19a0_0.conda + sha256: 2f5bc0292d595399df0d168355b4e9820affc8036792d6984bd751fdda2bcaea + md5: fc9a153c57c9f070bebaa7eef30a8f17 + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 186122 + timestamp: 1765215100384 +- conda: https://conda.anaconda.org/conda-forge/osx-64/cairo-1.18.4-h7656bdc_1.conda + sha256: 88e7e1efb6a0f6b1477e617338e0ed3d27d4572a3283f8341ce6143b7118e31a + md5: 9917add2ab43df894b9bb6f5bf485975 + depends: + - __osx >=10.13 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libcxx >=19 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libglib >=2.86.3,<3.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libzlib >=1.3.1,<2.0a0 + - pixman >=0.46.4,<1.0a0 + license: LGPL-2.1-only or MPL-1.1 + size: 896676 + timestamp: 1766416262450 +- conda: https://conda.anaconda.org/conda-forge/osx-64/cffi-1.15.1-py310hdca579f_5.conda + sha256: a48fdef666efec49e419fd6ace47d649670d71b127be4c29b644586c20e0bfe9 + md5: a1362e88124fe095a4690b956567a6d2 + depends: + - libffi >=3.4,<4.0a0 + - pycparser + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 226622 + timestamp: 1695367542919 +- conda: https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.1-py310hb3b189b_0.conda + sha256: 193fbd7c7b95e4692d12140e8c82d1be0c0bfd450edae9a95fd43f607fbb0c80 + md5: 6601d125e2f6c32c8e853da2651e04fd + depends: + - libcxx >=16 + - numpy >=1.20 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 233310 + timestamp: 1712430195722 +- conda: https://conda.anaconda.org/conda-forge/osx-64/cytoolz-1.1.0-py310hf70ac88_2.conda + sha256: 3007aee36c20738049231857ccf4d0b9b46c2238f28fe80fb9b13b492e065249 + md5: 4c35cdab5b30ade33882183489f0f245 + depends: + - __osx >=10.13 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - toolz >=0.10.0 + license: BSD-3-Clause + license_family: BSD + size: 564028 + timestamp: 1771856203125 +- conda: https://conda.anaconda.org/conda-forge/osx-64/debugpy-1.8.21-py310h3f55cb5_0.conda + sha256: 1e7a3a5bdb76945686b55f013961f5d249e6695355cc9883d370f8335a5244d8 + md5: 8bff36befc9c857616bed239629f5dbc + depends: + - python + - __osx >=11.0 + - libcxx >=19 + - python_abi 3.10.* *_cp310 + license: MIT + size: 2233470 + timestamp: 1780390244395 +- conda: https://conda.anaconda.org/conda-forge/osx-64/epoxy-1.5.10-h8616949_2.conda + sha256: d5c466bddf423a788ce5c39af20af41ebaf3de9dc9e807098fc9bf45c3c7db45 + md5: efe7fa6c60b20cb0a3a22e8c3e7b721e + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 283016 + timestamp: 1758743470535 +- conda: https://conda.anaconda.org/conda-forge/osx-64/fontconfig-2.18.1-h7a4440b_0.conda + sha256: 134aed823beae85798607e32b78aa1368afbfbea145a43c974d88269f1013287 + md5: 17925ae2a399d859c0b978934df591e3 + depends: + - __osx >=11.0 + - libexpat >=2.8.1,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libintl >=0.25.1,<1.0a0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + size: 247884 + timestamp: 1780450811484 +- conda: https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.63.0-py310h399bfa0_0.conda + sha256: dee52fe794b40ada2d0f89c04eb8e88d6d77d2ecd59ba8798d6f2a822f788d0e + md5: aa1c9c8f682d8bc872f0bb22bb119859 + depends: + - __osx >=11.0 + - brotli + - munkres + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - unicodedata2 >=15.1.0 + license: MIT + license_family: MIT + size: 2411822 + timestamp: 1778770648181 +- conda: https://conda.anaconda.org/conda-forge/osx-64/freetype-2.14.3-h694c41f_0.conda + sha256: 5ddd46a88a0b6483e3dec52cabb62414504c94ee0e77369a4717f61a656c535a + md5: 6ab1403cc6cb284d56d0464f19251075 + depends: + - libfreetype 2.14.3 h694c41f_0 + - libfreetype6 2.14.3 h58fbd8d_0 + license: GPL-2.0-only OR FTL + size: 174060 + timestamp: 1774298809296 +- conda: https://conda.anaconda.org/conda-forge/osx-64/fribidi-1.0.16-h8616949_0.conda + sha256: 53dd0a6c561cf31038633aaa0d52be05da1f24e86947f06c4e324606c72c7413 + md5: 4422491d30462506b9f2d554ab55e33d + depends: + - __osx >=10.13 + license: LGPL-2.1-or-later + size: 60923 + timestamp: 1757438791418 +- conda: https://conda.anaconda.org/conda-forge/osx-64/gdk-pixbuf-2.44.6-hae309b2_0.conda + sha256: 27a223201fd86f85284c7e218121ac9ecf0be16e0a73eea42776701c8c90c50b + md5: 5f0f81650af65aa247f6fbc25ebcbdd4 + depends: + - __osx >=11.0 + - libglib >=2.86.4,<3.0a0 + - libintl >=0.25.1,<1.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - liblzma >=5.8.2,<6.0a0 + - libpng >=1.6.56,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: LGPL-2.1-or-later + license_family: LGPL + size: 552947 + timestamp: 1774986327487 +- conda: https://conda.anaconda.org/conda-forge/osx-64/gflags-2.2.2-hac325c4_1005.conda + sha256: c0bea66f71a6f4baa8d4f0248e17f65033d558d9e882c0af571b38bcca3e4b46 + md5: a26de8814083a6971f14f9c8c3cb36c2 + depends: + - __osx >=10.13 + - libcxx >=17 + license: BSD-3-Clause + license_family: BSD + size: 84946 + timestamp: 1726600054963 +- conda: https://conda.anaconda.org/conda-forge/osx-64/glib-tools-2.88.1-h6437393_2.conda + sha256: f4e609d1c523de5ce3ae0a5844573b0b0b30d24b380ca044fb689f288f2c9e54 + md5: 71618f9b86b1d1ff2678c3c196045ca1 + depends: + - libglib ==2.88.1 hf28f236_2 + - libffi + - __osx >=11.0 + - libintl >=0.25.1,<1.0a0 + license: LGPL-2.1-or-later + size: 216282 + timestamp: 1778508940832 +- conda: https://conda.anaconda.org/conda-forge/osx-64/glog-0.7.1-h2790a97_0.conda + sha256: dd56547db8625eb5c91bb0a9fbe8bd6f5c7fbf5b6059d46365e94472c46b24f9 + md5: 06cf91665775b0da395229cd4331b27d + depends: + - __osx >=10.13 + - gflags >=2.2.2,<2.3.0a0 + - libcxx >=16 + license: BSD-3-Clause + license_family: BSD + size: 117017 + timestamp: 1718284325443 +- conda: https://conda.anaconda.org/conda-forge/osx-64/graphite2-1.3.14-h21dd04a_2.conda + sha256: c356eb7a42775bd2bae243d9987436cd1a442be214b1580251bb7fdc136d804b + md5: ba63822087afc37e01bf44edcc2479f3 + depends: + - __osx >=10.13 + - libcxx >=19 + license: LGPL-2.0-or-later + license_family: LGPL + size: 85465 + timestamp: 1755102182985 +- conda: https://conda.anaconda.org/conda-forge/osx-64/graphviz-14.1.2-h44fc223_0.conda + sha256: dd6a5e3599a2e07c04f4d33e61ecd5c26738eee9e88b9faa1da0f8b062ac9ca3 + md5: 4c1c78d65d867d032c07303cf38117ba + depends: + - __osx >=10.13 + - adwaita-icon-theme + - cairo >=1.18.4,<2.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.4,<3.0a0 + - gtk3 >=3.24.43,<4.0a0 + - gts >=0.7.6,<0.8.0a0 + - libcxx >=19 + - libexpat >=2.7.3,<3.0a0 + - libgd >=2.3.3,<2.4.0a0 + - libglib >=2.86.3,<3.0a0 + - librsvg >=2.60.0,<3.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - pango >=1.56.4,<2.0a0 + license: EPL-1.0 + license_family: Other + size: 2297868 + timestamp: 1769427939677 +- conda: https://conda.anaconda.org/conda-forge/osx-64/gtk3-3.24.52-hf2d442a_0.conda + sha256: c69a03b1eec71c0a764658d67f81eaf9a316276ae900b107cd8d77766bc13cf8 + md5: 76be17e448c23c6d1c99a56c15b15925 + depends: + - __osx >=11.0 + - atk-1.0 >=2.38.0 + - cairo >=1.18.4,<2.0a0 + - epoxy >=1.5.10,<1.6.0a0 + - fribidi >=1.0.16,<2.0a0 + - gdk-pixbuf >=2.44.5,<3.0a0 + - glib-tools + - harfbuzz >=13.2.1 + - hicolor-icon-theme + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libglib >=2.86.4,<3.0a0 + - libintl >=0.25.1,<1.0a0 + - liblzma >=5.8.2,<6.0a0 + - libzlib >=1.3.2,<2.0a0 + - pango >=1.56.4,<2.0a0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 5269457 + timestamp: 1774289309822 +- conda: https://conda.anaconda.org/conda-forge/osx-64/gts-0.7.6-h53e17e3_4.conda + sha256: d5b82a36f7e9d7636b854e56d1b4fe01c4d895128a7b73e2ec6945b691ff3314 + md5: 848cc963fcfbd063c7a023024aa3bec0 + depends: + - libcxx >=15.0.7 + - libglib >=2.76.3,<3.0a0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 280972 + timestamp: 1686545425074 +- conda: https://conda.anaconda.org/conda-forge/osx-64/h5py-3.16.0-nompi_py310hbc26044_102.conda + sha256: 33123312e04b901050a31fe4654748b703a0ff8295437cb4d52c6e7a05900bbf + md5: 75229b8c21fcb2f628081515cc9aae3f + depends: + - __osx >=11.0 + - cached-property + - hdf5 >=1.14.6,<1.14.7.0a0 + - numpy >=1.21,<3 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 1102072 + timestamp: 1775581714217 +- conda: https://conda.anaconda.org/conda-forge/osx-64/harfbuzz-14.2.0-hf0bc557_0.conda + sha256: ab070b8961569fbdd3e414bee89887f1ca97522c73afb0fa2f055ad775c7dd20 + md5: e7fd9056aa65f6dac6558b39c332c907 + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - graphite2 >=1.3.14,<2.0a0 + - icu >=78.3,<79.0a0 + - libcxx >=19 + - libexpat >=2.7.5,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libglib >=2.86.4,<3.0a0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + license_family: MIT + size: 2148344 + timestamp: 1776778909454 +- conda: https://conda.anaconda.org/conda-forge/osx-64/hdf5-1.14.6-nompi_h13accda_109.conda + sha256: 1e12111fbde6d1754038487d639339d54d32e22b8e6266d217c9b1d6183c532c + md5: 030598e3ee8d7d842918a9ebfa54ff92 + depends: + - __osx >=11.0 + - libaec >=1.1.5,<2.0a0 + - libcurl >=8.20.0,<9.0a0 + - libcxx >=19 + - libgfortran + - libgfortran5 >=14.3.0 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 3523555 + timestamp: 1777519851430 +- conda: https://conda.anaconda.org/conda-forge/osx-64/hicolor-icon-theme-0.17-h694c41f_3.conda + sha256: 3321e8d2c2198ac796b0ae800473173ade528b49f84b6c6e4e112a9704698b41 + md5: 690e5077aaccf8d280a4284d7c9ec6b4 + license: GPL-2.0-or-later + license_family: GPL + size: 17650 + timestamp: 1771539977217 +- conda: https://conda.anaconda.org/conda-forge/osx-64/icu-78.3-h25d91c4_0.conda + sha256: 1294117122d55246bb83ad5b589e2a031aacdf2d0b1f99fd338aa4394f881735 + md5: 627eca44e62e2b665eeec57a984a7f00 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 12273764 + timestamp: 1773822733780 +- conda: https://conda.anaconda.org/conda-forge/osx-64/kiwisolver-1.5.0-py310h323244c_0.conda + sha256: b4e09e978ffd1577a8e3ac780710808e4f033b5165e209beeeba6d6b021166c6 + md5: d0c6ccd12ebc8f0c9a7ed8ee2a3bb022 + depends: + - python + - libcxx >=19 + - __osx >=11.0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 67618 + timestamp: 1773067353228 +- conda: https://conda.anaconda.org/conda-forge/osx-64/krb5-1.22.2-h207b36a_0.conda + sha256: df009385e8262c234c0dae9016540b86dad3d299f0d9366d08e327e8e7731634 + md5: e66e2c52d2fdddcf314ad750fb4ebb4a + depends: + - __osx >=10.13 + - libcxx >=19 + - libedit >=3.1.20250104,<3.2.0a0 + - libedit >=3.1.20250104,<4.0a0 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 1193620 + timestamp: 1769770267475 +- conda: https://conda.anaconda.org/conda-forge/osx-64/lcms2-2.19.1-h5ea7634_1.conda + sha256: 8bae1207dc7cf0e670ae920a549b1d55486514213ca808b8119067cbad0db43a + md5: f8c168eefc1f75ada2e2cd8f2e6212f5 + depends: + - __osx >=11.0 + - libjpeg-turbo >=3.1.4.1,<4.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: MIT + license_family: MIT + size: 229477 + timestamp: 1780211969520 +- conda: https://conda.anaconda.org/conda-forge/osx-64/lerc-4.1.0-h35c7297_0.conda + sha256: f918716c71c8bebbc0c40e1050878aa512fea92c1d17c363ca35650bc60f6c35 + md5: d2fe7e177d1c97c985140bd54e2a5e33 + depends: + - __osx >=11.0 + - libcxx >=19 + license: Apache-2.0 + license_family: Apache + size: 215089 + timestamp: 1773114468701 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libabseil-20240116.2-cxx17_hf036a51_1.conda + sha256: 396d18f39d5207ecae06fddcbc6e5f20865718939bc4e0ea9729e13952833aac + md5: d6c78ca84abed3fea5f308ac83b8f54e + depends: + - __osx >=10.13 + - libcxx >=16 + constrains: + - abseil-cpp =20240116.2 + - libabseil-static =20240116.2=cxx17* + license: Apache-2.0 + license_family: Apache + size: 1124364 + timestamp: 1720857589333 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libaec-1.1.5-he7c3a48_0.conda + sha256: b42ac9c684c730cb97cb3931a0a97aaf791da38bace4f6944eca10de609e5946 + md5: 975f98248cde8d54884c6d1eb5184e13 + depends: + - __osx >=10.13 + - libcxx >=19 + license: BSD-2-Clause + license_family: BSD + size: 30555 + timestamp: 1769222189944 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-16.0.0-hb6a69ac_1_cpu.conda + build_number: 1 + sha256: 0294cddfb13e177c98477333f52804ad0f6437c57446359fb6ef3c97b60612b5 + md5: 736f23ab8a1b814e46597f5c0bb7cfd2 + depends: + - __osx >=10.13 + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - aws-sdk-cpp >=1.11.267,<1.11.268.0a0 + - bzip2 >=1.0.8,<2.0a0 + - glog >=0.7.0,<0.8.0a0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libbrotlidec >=1.1.0,<1.2.0a0 + - libbrotlienc >=1.1.0,<1.2.0a0 + - libcxx >=16 + - libgoogle-cloud >=2.23.0,<2.24.0a0 + - libgoogle-cloud-storage >=2.23.0,<2.24.0a0 + - libre2-11 >=2023.9.1 + - libutf8proc >=2.8.0,<3.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - orc >=2.0.0,<2.0.1.0a0 + - re2 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.6,<1.6.0a0 + - libutf8proc <2.9 + constrains: + - parquet-cpp <0.0a0 + - apache-arrow-proc =*=cpu + - arrow-cpp <0.0a0 + license: Apache-2.0 + license_family: APACHE + size: 5796095 + timestamp: 1715197505186 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-16.0.0-hf036a51_1_cpu.conda + build_number: 1 + sha256: 5f92b35952846bf63bb792b23a3045e65e57fe0d241ffd126cad0ff73c003b95 + md5: b481a2ca0f92f2c51925e81ed97af15f + depends: + - __osx >=10.13 + - libarrow 16.0.0 hb6a69ac_1_cpu + - libcxx >=16 + license: Apache-2.0 + license_family: APACHE + size: 525436 + timestamp: 1715197598479 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-dataset-16.0.0-hf036a51_1_cpu.conda + build_number: 1 + sha256: 614446ada39805f8c4c8a375f89ced46ae2c438f140702b2d6e73c00c919a6af + md5: 1ea34a0b77a52475cb1b5665ca0422f9 + depends: + - __osx >=10.13 + - libarrow 16.0.0 hb6a69ac_1_cpu + - libarrow-acero 16.0.0 hf036a51_1_cpu + - libcxx >=16 + - libparquet 16.0.0 h904a336_1_cpu + license: Apache-2.0 + license_family: APACHE + size: 517647 + timestamp: 1715198132587 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-substrait-16.0.0-h85bc590_1_cpu.conda + build_number: 1 + sha256: a4c7fe04e3e3c6ae7de69f02003bf65df50160bb1b85958554b486ff75c09178 + md5: 10b79b6ff21c63e83d280741a4affdd7 + depends: + - __osx >=10.13 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libarrow 16.0.0 hb6a69ac_1_cpu + - libarrow-acero 16.0.0 hf036a51_1_cpu + - libarrow-dataset 16.0.0 hf036a51_1_cpu + - libcxx >=16 + - libprotobuf >=4.25.3,<4.25.4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 485031 + timestamp: 1715198257699 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.11.0-8_he492b99_openblas.conda + build_number: 8 + sha256: 55cf9f92a2d07c33f8a32c44ff1528ea48fd69677cc003a4532d09b71cb8a316 + md5: 7da1e8ab7c4498db9457c191d82930a3 + depends: + - libopenblas >=0.3.33,<0.3.34.0a0 + - libopenblas >=0.3.33,<1.0a0 + constrains: + - mkl <2027 + - blas 2.308 openblas + - liblapacke 3.11.0 8*_openblas + - libcblas 3.11.0 8*_openblas + - liblapack 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 19048 + timestamp: 1779860008916 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-20_osx64_openblas.conda + build_number: 20 + sha256: 89cac4653b52817d44802d96c13e5f194320e2e4ea805596641d0f3e22e32525 + md5: 1673476d205d14a9042172be795f63cb + depends: + - libopenblas >=0.3.25,<0.3.26.0a0 + - libopenblas >=0.3.25,<1.0a0 + constrains: + - blas * openblas + - liblapack 3.9.0 20_osx64_openblas + - liblapacke 3.9.0 20_osx64_openblas + - libcblas 3.9.0 20_osx64_openblas + license: BSD-3-Clause + license_family: BSD + size: 14739 + timestamp: 1700568675962 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlicommon-1.1.0-h1c43f85_4.conda + sha256: 28c1a5f7dbe68342b7341d9584961216bd16f81aa3c7f1af317680213c00b46a + md5: b8e1ee78815e0ba7835de4183304f96b + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 67948 + timestamp: 1756599727911 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlidec-1.1.0-h1c43f85_4.conda + sha256: a287470602e8380c0bdb5e7a45ba3facac644432d7857f27b39d6ceb0dcbf8e9 + md5: 9cc4be0cc163d793d5d4bcc405c81bf3 + depends: + - __osx >=10.13 + - libbrotlicommon 1.1.0 h1c43f85_4 + license: MIT + license_family: MIT + size: 30743 + timestamp: 1756599755474 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libbrotlienc-1.1.0-h1c43f85_4.conda + sha256: 820caf0a78770758830adbab97fe300104981a5327683830d162b37bc23399e9 + md5: f2c000dc0185561b15de7f969f435e61 + depends: + - __osx >=10.13 + - libbrotlicommon 1.1.0 h1c43f85_4 + license: MIT + license_family: MIT + size: 294904 + timestamp: 1756599789206 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.11.0-8_h9b27e0a_openblas.conda + build_number: 8 + sha256: 50eb650a17a34ea45fe2b31e60a98632d1f8c203308014dcef93043d54612482 + md5: 4f116127b172bbba835c1e0491efd86f + depends: + - libblas 3.11.0 8_he492b99_openblas + constrains: + - liblapacke 3.11.0 8*_openblas + - blas 2.308 openblas + - liblapack 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 19049 + timestamp: 1779860025163 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-20_osx64_openblas.conda + build_number: 20 + sha256: b0a4eab6d22b865d9b0e39f358f17438602621709db66b8da159197bedd2c5eb + md5: b324ad206d39ce529fb9073f9d062062 + depends: + - libblas 3.9.0 20_osx64_openblas + constrains: + - liblapack 3.9.0 20_osx64_openblas + - liblapacke 3.9.0 20_osx64_openblas + - blas * openblas + license: BSD-3-Clause + license_family: BSD + size: 14648 + timestamp: 1700568722960 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libcrc32c-1.1.2-he49afe7_0.tar.bz2 + sha256: 3043869ac1ee84554f177695e92f2f3c2c507b260edad38a0bf3981fce1632ff + md5: 23d6d5a69918a438355d7cbc4c3d54c9 + depends: + - libcxx >=11.1.0 + license: BSD-3-Clause + license_family: BSD + size: 20128 + timestamp: 1633683906221 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libcurl-8.20.0-h8f0b9e4_0.conda + sha256: 5d3d8a82ca43347e96f1d79048921f3a7c25e32514bc7feb53ed2a040dcca54d + md5: 4a0085ccf90dc514f0fc0909a874045e + depends: + - __osx >=11.0 + - krb5 >=1.22.2,<1.23.0a0 + - libnghttp2 >=1.68.1,<2.0a0 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: curl + license_family: MIT + size: 419676 + timestamp: 1777462238769 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-22.1.7-h19cb2f5_0.conda + sha256: c03c298355dea54b729ed6c5f1e6dbd0e2426906039eba8aa2ba1254d005b7d8 + md5: 423373b842c3861da6cfa8c8915798ce + depends: + - __osx >=11.0 + license: Apache-2.0 WITH LLVM-exception + license_family: Apache + size: 564939 + timestamp: 1780442565078 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libdeflate-1.25-h517ebb2_0.conda + sha256: 025f8b1e85dd8254e0ca65f011919fb1753070eb507f03bca317871a884d24de + md5: 31aa65919a729dc48180893f62c25221 + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 70840 + timestamp: 1761980008502 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libedit-3.1.20250104-pl5321ha958ccf_0.conda + sha256: 6cc49785940a99e6a6b8c6edbb15f44c2dd6c789d9c283e5ee7bdfedd50b4cd6 + md5: 1f4ed31220402fcddc083b4bff406868 + depends: + - ncurses + - __osx >=10.13 + - ncurses >=6.5,<7.0a0 + license: BSD-2-Clause + license_family: BSD + size: 115563 + timestamp: 1738479554273 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libev-4.33-h10d778d_2.conda + sha256: 0d238488564a7992942aa165ff994eca540f687753b4f0998b29b4e4d030ff43 + md5: 899db79329439820b7e8f8de41bca902 + license: BSD-2-Clause + license_family: BSD + size: 106663 + timestamp: 1702146352558 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libevent-2.1.12-ha90c15b_1.conda + sha256: e0bd9af2a29f8dd74309c0ae4f17a7c2b8c4b89f875ff1d6540c941eefbd07fb + md5: e38e467e577bd193a7d5de7c2c540b04 + depends: + - openssl >=3.1.1,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 372661 + timestamp: 1685726378869 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.8.1-hcc62823_0.conda + sha256: 460afe7ba0882e6d2fcc0ad1568dce27025110ec09c2b9ce9e3b49d61e52ce6b + md5: f95dc08366f2a452005062b5bcceac51 + depends: + - __osx >=11.0 + constrains: + - expat 2.8.1.* + license: MIT + license_family: MIT + size: 75654 + timestamp: 1779279058576 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.5.2-hd1f9c09_0.conda + sha256: 951958d1792238006fdc6fce7f71f1b559534743b26cc1333497d46e5903a2d6 + md5: 66a0dc7464927d0853b590b6f53ba3ea + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 53583 + timestamp: 1769456300951 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libfreetype-2.14.3-h694c41f_0.conda + sha256: b5daa4cee3beb98a0317e81a20aa507b9f897a9e21b11fe0b2e32852e372f746 + md5: 63b822fcf984c891f0afab2eedfcfaf4 + depends: + - libfreetype6 >=2.14.3 + license: GPL-2.0-only OR FTL + size: 8088 + timestamp: 1774298785964 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libfreetype6-2.14.3-h58fbd8d_0.conda + sha256: 9d34b5b2be6ebdd3bcd9e21d6598d493afce4d3fcd2d419f3356022cb4d746fd + md5: 27515b8ab8bf4abd8d3d90cf11212411 + depends: + - __osx >=11.0 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - freetype >=2.14.3 + license: GPL-2.0-only OR FTL + size: 364828 + timestamp: 1774298783922 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libgcc-15.2.0-h08519bb_19.conda + sha256: 17a5dcd818f89173db51d7d1acd77615cb77db7b4c2b5f571d4dafe559430ab5 + md5: 4bf33d5ca73f4b89d3495285a42414a4 + depends: + - _openmp_mutex + constrains: + - libgomp 15.2.0 19 + - libgcc-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 424164 + timestamp: 1778271183296 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libgd-2.3.3-hb2c11ec_12.conda + sha256: bf7b0c25b6cca5808f4da46c5c363fa1192088b0b46efb730af43f28d52b8f04 + md5: e12673b408d1eb708adb3ecc2f621d78 + depends: + - __osx >=10.13 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libiconv >=1.18,<2.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + license: GD + license_family: BSD + size: 163145 + timestamp: 1766332198196 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran-15.2.0-h7e5c614_19.conda + sha256: 519045363b87b870be779d38f0bfd325d4b787acdaa0a2136a92c1081eff5112 + md5: d362f41203d0a1d2d4940446f95374c9 + depends: + - libgfortran5 15.2.0 hd16e46c_19 + constrains: + - libgfortran-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 139925 + timestamp: 1778271458366 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libgfortran5-15.2.0-hd16e46c_19.conda + sha256: c7f5f6e80357d6d5bc69588c16144205b0c79cf32cd090ccb5afef9d557632af + md5: 1cddb3f7e54f5871297afc0fafa61c2c + depends: + - libgcc >=15.2.0 + constrains: + - libgfortran 15.2.0 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 1063687 + timestamp: 1778271196574 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libglib-2.88.1-hf28f236_2.conda + sha256: 9e10d37f49b4efef3426ac323dd8cec88a48df57d49e335d5aef8eac08ea9226 + md5: 6cf119d472892f945d81187e790cc131 + depends: + - __osx >=11.0 + - pcre2 >=10.47,<10.48.0a0 + - libintl >=0.25.1,<1.0a0 + - libffi >=3.5.2,<3.6.0a0 + - libiconv >=1.18,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - glib >2.66 + license: LGPL-2.1-or-later + size: 4519643 + timestamp: 1778508940832 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libgoogle-cloud-2.23.0-h651e89d_1.conda + sha256: 669cab160b07f1083fa641564549f38d143380ad36b05e16aeb59625e6fbd08a + md5: e39d78408ff66de247fb5fbf60e9255c + depends: + - __osx >=10.13 + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcurl >=8.7.1,<9.0a0 + - libcxx >=16 + - libgrpc >=1.62.2,<1.63.0a0 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - openssl >=3.2.1,<4.0a0 + constrains: + - libgoogle-cloud 2.23.0 *_1 + license: Apache-2.0 + license_family: Apache + size: 852907 + timestamp: 1713800994635 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libgoogle-cloud-storage-2.23.0-ha67e85c_1.conda + sha256: 35a6e7824ab06fd21042260fb4b11788c088b570ba32bd3f873f91ae12810326 + md5: 62798f6e7af787f3cc550ccf70ddb1e3 + depends: + - libabseil + - libcrc32c >=1.1.2,<1.2.0a0 + - libcurl + - libcxx >=16 + - libgoogle-cloud 2.23.0 h651e89d_1 + - libzlib >=1.2.13,<2.0.0a0 + - openssl + license: Apache-2.0 + license_family: Apache + size: 524880 + timestamp: 1713802437812 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libgrpc-1.62.2-h384b2fc_0.conda + sha256: 7c228040e7dac4e5e7e6935a4decf6bc2155cc05fcfb0811d25ccb242d0036ba + md5: 9421f67cf8b4bc976fe5d0c3ab42de18 + depends: + - __osx >=10.13 + - c-ares >=1.28.1,<2.0a0 + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcxx >=16 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libre2-11 >=2023.9.1 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.2.1,<4.0a0 + - re2 + constrains: + - grpc-cpp =1.62.2 + license: Apache-2.0 + license_family: APACHE + size: 5189573 + timestamp: 1713392887258 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libiconv-1.18-h57a12c2_2.conda + sha256: a1c8cecdf9966921e13f0ae921309a1f415dfbd2b791f2117cf7e8f5e61a48b6 + md5: 210a85a1119f97ea7887188d176db135 + depends: + - __osx >=10.13 + license: LGPL-2.1-only + size: 737846 + timestamp: 1754908900138 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libintl-0.25.1-h3184127_1.conda + sha256: 8c352744517bc62d24539d1ecc813b9fdc8a785c780197c5f0b84ec5b0dfe122 + md5: a8e54eefc65645193c46e8b180f62d22 + depends: + - __osx >=10.13 + - libiconv >=1.18,<2.0a0 + license: LGPL-2.1-or-later + size: 96909 + timestamp: 1753343977382 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libjpeg-turbo-3.1.4.1-ha1e9b39_0.conda + sha256: 6b809d8acb6b97bbb1a858eb4ba7b7163c67257b6c3f199dd9d1e0751f4c5b18 + md5: 57cc1464d457d01ac78f5860b9ca1714 + depends: + - __osx >=11.0 + constrains: + - jpeg <0.0.0a + license: IJG AND BSD-3-Clause AND Zlib + size: 587997 + timestamp: 1775963139212 +- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.11.0-8_h859234e_openblas.conda + build_number: 8 + sha256: 56a68fce5a63d4583a42c212324d62ac292376b8bf05986a551bd640e7fa137d + md5: e11ee849bd2a573a0f6e53b1b67ebf37 + depends: + - libblas 3.11.0 8_he492b99_openblas + constrains: + - liblapacke 3.11.0 8*_openblas + - libcblas 3.11.0 8*_openblas + - blas 2.308 openblas + license: BSD-3-Clause + license_family: BSD + size: 19030 + timestamp: 1779860046842 +- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-20_osx64_openblas.conda + build_number: 20 + sha256: d64e11b93dada339cd0dcc057b3f3f6a5114b8c9bdf90cf6c04cbfa75fb02104 + md5: 704bfc2af1288ea973b6755281e6ad32 + depends: + - libblas 3.9.0 20_osx64_openblas + constrains: + - blas * openblas + - liblapacke 3.9.0 20_osx64_openblas + - libcblas 3.9.0 20_osx64_openblas + license: BSD-3-Clause + license_family: BSD + size: 14658 + timestamp: 1700568740660 +- conda: https://conda.anaconda.org/conda-forge/osx-64/liblzma-5.8.3-hbb4bfdb_0.conda + sha256: d9e2006051529aec5578c6efeb13bb6a7200a014b2d5a77a579e83a8049d5f3c + md5: becdfbfe7049fa248e52aa37a9df09e2 + depends: + - __osx >=11.0 + constrains: + - xz 5.8.3.* + license: 0BSD + size: 105724 + timestamp: 1775826029494 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libnghttp2-1.68.1-h70048d4_0.conda + sha256: 899551e16aac9dfb85bfc2fd98b655f4d1b7fea45720ec04ccb93d95b4d24798 + md5: dba4c95e2fe24adcae4b77ebf33559ae + depends: + - __osx >=11.0 + - c-ares >=1.34.6,<2.0a0 + - libcxx >=19 + - libev >=4.33,<4.34.0a0 + - libev >=4.33,<5.0a0 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 606749 + timestamp: 1773854765508 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.25-openmp_hfef2a42_0.conda + sha256: 9895bccdbaa34958ab7dd1f29de66d1dfb94c551c7bb5a663666a500c67ee93c + md5: a01b96f00c3155c830d98a518c7dcbfb + depends: + - libgfortran >=5 + - libgfortran5 >=12.3.0 + - llvm-openmp >=16.0.6 + constrains: + - openblas >=0.3.25,<0.3.26.0a0 + license: BSD-3-Clause + license_family: BSD + size: 6019426 + timestamp: 1700537709900 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.33-openmp_h9e49c7b_0.conda + sha256: 2c2ffe7c3ab7becd47ad308946873d2bdc219625af32a53d10efbaa54b595d31 + md5: 30666a6f0afe1471e999eca7ae5c8179 + depends: + - __osx >=11.0 + - libgfortran + - libgfortran5 >=14.3.0 + - llvm-openmp >=19.1.7 + constrains: + - openblas >=0.3.33,<0.3.34.0a0 + license: BSD-3-Clause + license_family: BSD + size: 6287889 + timestamp: 1776996499823 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libparquet-16.0.0-h904a336_1_cpu.conda + build_number: 1 + sha256: ccdf9684af61d1358951029748c2bf8634c3742ddbf17718fc2d961a5b5dad55 + md5: 106a7c0ae31d4338a5aded187c11b9e6 + depends: + - __osx >=10.13 + - libarrow 16.0.0 hb6a69ac_1_cpu + - libcxx >=16 + - libthrift >=0.19.0,<0.19.1.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 940260 + timestamp: 1715198069217 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libpng-1.6.58-he930e7c_0.conda + sha256: a669b22978e546484d18d99a210801b1823360a266d7035c713d8d1facd035f7 + md5: 9744d43d5200f284260637304a069ddd + depends: + - __osx >=11.0 + - libzlib >=1.3.2,<2.0a0 + license: zlib-acknowledgement + size: 299206 + timestamp: 1776315286816 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libprotobuf-4.25.3-hd4aba4c_1.conda + sha256: f509cb24a164b84553b28837ec1e8311ceb0212a1dbb8c7fd99ca383d461ea6c + md5: 64ad501f0fd74955056169ec9c42c5c0 + depends: + - __osx >=10.13 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libcxx >=17 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 2212274 + timestamp: 1727160957452 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libre2-11-2023.09.01-h81f5012_2.conda + sha256: 384b72a09bd4bb29c1aa085110b2f940dba431587ffb4e2c1a28f605887a1867 + md5: c5c36ec64e3c86504728c38b79011d08 + depends: + - __osx >=10.13 + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcxx >=16 + constrains: + - re2 2023.09.01.* + license: BSD-3-Clause + license_family: BSD + size: 184017 + timestamp: 1708947106275 +- conda: https://conda.anaconda.org/conda-forge/osx-64/librsvg-2.62.2-h7321050_0.conda + sha256: 891a5c97cf7f1795f5e480c9f6c50f4758a77695c5e649809da69d98edc61f87 + md5: 533bb36caff9ef37e59823562e97f925 + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.6,<3.0a0 + - harfbuzz >=14.2.0 + - libglib >=2.88.1,<3.0a0 + - libxml2-16 >=2.14.6 + - pango >=1.56.4,<2.0a0 + constrains: + - __osx >=10.13 + license: LGPL-2.1-or-later + size: 2518248 + timestamp: 1779415219662 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libsodium-1.0.22-ha3d0635_1.conda + sha256: 07f0f37463564d93530fc23e2a77cc1aad58ba8724b1842f8713dbf6cde17cb0 + md5: bf0ce6af8f7628e34cdb399f6aa82e53 + depends: + - __osx >=11.0 + license: ISC + size: 281370 + timestamp: 1779164249823 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libsqlite-3.53.1-h8f8c405_0.conda + sha256: 5e964e07a14180ce20decfd4897e8f81d48ec78c1cbf4af85c5520f535d9510c + md5: 9273c877f78b7486b0dfdd9268327a79 + depends: + - __osx >=11.0 + - icu >=78.3,<79.0a0 + - libzlib >=1.3.2,<2.0a0 + license: blessing + size: 1007171 + timestamp: 1777987093870 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libssh2-1.11.1-hed3591d_0.conda + sha256: 00654ba9e5f73aa1f75c1f69db34a19029e970a4aeb0fa8615934d8e9c369c3c + md5: a6cb15db1c2dc4d3a5f6cf3772e09e81 + depends: + - __osx >=10.13 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.0,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 284216 + timestamp: 1745608575796 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libthrift-0.19.0-h064b379_1.conda + sha256: 4346c25ef6e2ff3d0fc93074238508531188ecd0dbea6414f6cb93a7775072c4 + md5: b152655bfad7c2374ff03be0596052b6 + depends: + - libcxx >=15.0.7 + - libevent >=2.1.12,<2.1.13.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.1.3,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 325415 + timestamp: 1695958330036 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libtiff-4.7.1-ha0a348c_1.conda + sha256: e53424c34147301beae2cd9223ebf593720d94c038b3f03cacd0535e12c9668e + md5: 9d4344f94de4ab1330cdc41c40152ea6 + depends: + - __osx >=10.13 + - lerc >=4.0.0,<5.0a0 + - libcxx >=19 + - libdeflate >=1.25,<1.26.0a0 + - libjpeg-turbo >=3.1.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: HPND + size: 404591 + timestamp: 1762022511178 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libutf8proc-2.8.0-he670073_1.conda + sha256: 2b4c4c2a6051433e5c39943b8886a89fc74543f3b5d8286e5a39c7373f5f6cec + md5: a7ce895b33370269f03650fa30b7c53d + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 79479 + timestamp: 1732829757644 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libwebp-base-1.6.0-hb807250_0.conda + sha256: 00dbfe574b5d9b9b2b519acb07545380a6bc98d1f76a02695be4995d4ec91391 + md5: 7bb6608cf1f83578587297a158a6630b + depends: + - __osx >=10.13 + constrains: + - libwebp 1.6.0 + license: BSD-3-Clause + license_family: BSD + size: 365086 + timestamp: 1752159528504 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libxcb-1.17.0-hf1f96e2_0.conda + sha256: 8896cd5deff6f57d102734f3e672bc17120613647288f9122bec69098e839af7 + md5: bbeca862892e2898bdb45792a61c4afc + depends: + - __osx >=10.13 + - pthread-stubs + - xorg-libxau >=1.0.11,<2.0a0 + - xorg-libxdmcp + license: MIT + license_family: MIT + size: 323770 + timestamp: 1727278927545 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libxml2-16-2.15.3-h7a90416_0.conda + sha256: 437f003e299d77403db42d17e532d686236f357ac5c3d6bf466558c697902597 + md5: c74ae93cd7876e3a9c4b5569d5e29e34 + depends: + - __osx >=11.0 + - icu >=78.3,<79.0a0 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - libxml2 2.15.3 + license: MIT + license_family: MIT + size: 496338 + timestamp: 1776377250079 +- conda: https://conda.anaconda.org/conda-forge/osx-64/libzlib-1.3.2-hbb4bfdb_2.conda + sha256: 4c6da089952b2d70150c74234679d6f7ac04f4a98f9432dec724968f912691e7 + md5: 30439ff30578e504ee5e0b390afc8c65 + depends: + - __osx >=11.0 + constrains: + - zlib 1.3.2 *_2 + license: Zlib + license_family: Other + size: 59000 + timestamp: 1774073052242 +- conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-22.1.6-h0d3cbff_0.conda + sha256: afbea63c0ffed8f150ba41a3e85bd849560f15f879d0f1b5e5fb6b90eca8ea78 + md5: b67316dec3b5c028b6b1bb6fd713c14e + depends: + - __osx >=11.0 + constrains: + - openmp 22.1.6|22.1.6.* + - intel-openmp <0.0a0 + license: Apache-2.0 WITH LLVM-exception + license_family: APACHE + size: 311051 + timestamp: 1779341346370 +- conda: https://conda.anaconda.org/conda-forge/osx-64/llvm-openmp-22.1.7-h0d3cbff_0.conda + sha256: c8eeb6bca45680db8974b78e0524b2ab3c285a9916a0b3356329d1f949b1311b + md5: 301c1db2d75ac8a91f46d21652e08dd6 + depends: + - __osx >=11.0 + constrains: + - openmp 22.1.7|22.1.7.* + - intel-openmp <0.0a0 + license: Apache-2.0 WITH LLVM-exception + size: 310879 + timestamp: 1780456054580 +- conda: https://conda.anaconda.org/conda-forge/osx-64/lz4-4.3.3-py310h43af8dd_1.conda + sha256: 7915f58c89d52c1d7be63d3e76b492678e8917ffa21a1087d4f561976b3abaf6 + md5: 52868c5ead553dc579c72ad55b9e64ee + depends: + - __osx >=10.13 + - lz4-c >=1.9.3,<1.10.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 34230 + timestamp: 1725089619300 +- conda: https://conda.anaconda.org/conda-forge/osx-64/lz4-c-1.9.4-hf0c8a7f_0.conda + sha256: 39aa0c01696e4e202bf5e337413de09dfeec061d89acd5f28e9968b4e93c3f48 + md5: aa04f7143228308662696ac24023f991 + depends: + - libcxx >=14.0.6 + license: BSD-2-Clause + license_family: BSD + size: 156415 + timestamp: 1674727335352 +- conda: https://conda.anaconda.org/conda-forge/osx-64/markupsafe-3.0.3-py310h399bfa0_1.conda + sha256: db3087d9114a3dc529737e90e95f7869cef076a492fd6b92fe9d349bf63f989a + md5: e85337b6741ec3c1144d3175ee127d57 + depends: + - __osx >=11.0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - jinja2 >=3.0.0 + license: BSD-3-Clause + license_family: BSD + size: 23539 + timestamp: 1772445447729 +- conda: https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.7.3-py310hf92ae1b_0.conda + sha256: 22ef3cf3eaf138b0f5773d87a674cde723e131435da8e3d8ae0922280abb9f7c + md5: fbd800a62a65b3c7a7f63c6cc65abc45 + depends: + - __osx >=10.12 + - certifi >=2020.06.20 + - contourpy >=1.0.1 + - cycler >=0.10 + - fonttools >=4.22.0 + - freetype >=2.12.1,<3.0a0 + - kiwisolver >=1.0.1 + - libcxx >=15.0.7 + - numpy >=1.20 + - numpy >=1.22.4,<2.0a0 + - packaging >=20.0 + - pillow >=6.2.0 + - pyparsing >=2.3.1 + - python >=3.10,<3.11.0a0 + - python-dateutil >=2.7 + - python_abi 3.10.* *_cp310 + license: LicenseRef-PSF-2.0 and CC0-1.0 + license_family: PSF + size: 6764048 + timestamp: 1695077273242 +- conda: https://conda.anaconda.org/conda-forge/osx-64/msgpack-python-1.1.2-py310h8cf47bc_1.conda + sha256: a3ee18240486a01f388cec8e843244a8439d8360a03a3127c8a2497238c7bd26 + md5: 7c3b3ec587f1724ab398bc7e0d548b15 + depends: + - __osx >=10.13 + - libcxx >=19 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 83905 + timestamp: 1762504429734 +- conda: https://conda.anaconda.org/conda-forge/osx-64/ncurses-6.6-hcc0dc9a_0.conda + sha256: f5f7e006ff4271305ab4cc08eedd855c67a571793c3d18aff73f645f088a8cae + md5: 31b8740cf1b2588d4e61c81191004061 + depends: + - __osx >=11.0 + license: X11 AND BSD-3-Clause + size: 831711 + timestamp: 1777423052277 +- conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.22.4-py310hed37afb_0.tar.bz2 + sha256: ea4c7eca45a966f849e109733c65bc3d63206ff0079dfeba140d4da4caf908ce + md5: cd4ad16cf8641c1b02c3966945ff00c3 + depends: + - libblas >=3.8.0,<4.0a0 + - libcblas >=3.8.0,<4.0a0 + - libcxx >=13.0.1 + - liblapack >=3.8.0,<4.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 6770310 + timestamp: 1653326099048 +- conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.2.6-py310h07c5b4d_0.conda + sha256: f1851c5726ff1a4de246e385ba442d749a68ef39316c834933ee9b980dbe62df + md5: d79253493dcc76b95221588b98e1eb3c + depends: + - __osx >=10.13 + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=18 + - liblapack >=3.9.0,<4.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 6988856 + timestamp: 1747545137089 +- conda: https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.4-h52bb76a_0.conda + sha256: 9a37ecf9c086f3a50d0132e6087dcbe7ea978d80e2da267fa3199c486529b311 + md5: 46e628da6e796c948fa8ec9d6d10bda3 + depends: + - __osx >=11.0 + - libcxx >=19 + - libpng >=1.6.55,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-2-Clause + license_family: BSD + size: 335227 + timestamp: 1772625294157 +- conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.6.2-hc881268_0.conda + sha256: 334fd49ea31b99114f5afb1ec44555dc8c90640648302a4f8f838ee345d1ec50 + md5: 5cf0ece4375c73d7a5765e83565a69c7 + depends: + - __osx >=11.0 + - ca-certificates + license: Apache-2.0 + license_family: Apache + size: 2776564 + timestamp: 1775589970694 +- conda: https://conda.anaconda.org/conda-forge/osx-64/orc-2.0.0-hf146577_1.conda + sha256: 801367a030bf6eaf10603c575dbaca439283e449e9cd5bb586b600fb591f5221 + md5: 7979dbaf686485e12d48e7ca9fcb5a56 + depends: + - __osx >=10.13 + - libcxx >=16 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.5,<1.6.0a0 + license: Apache-2.0 + license_family: Apache + size: 433233 + timestamp: 1712616573866 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pandas-1.3.5-py310hdd25497_0.tar.bz2 + sha256: f49e1a7bf8f735c8591e5e6a16cd1036089ae3ca53b6fe9863baae41085f1d56 + md5: f38cbc55bd44a66f3cf8879cb12e6ec9 + depends: + - libcxx >=11.1.0 + - numpy >=1.21.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python-dateutil >=2.7.3 + - python_abi 3.10.* *_cp310 + - pytz >=2017.2 + - setuptools <60.0.0 + license: BSD-3-Clause + license_family: BSD + size: 12698094 + timestamp: 1639399022118 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pango-1.56.4-hf280016_1.conda + sha256: c1150e6a405985b25830c18f896d5e89b9777ef7e420bc0b1d88634f9a614769 + md5: 591f9fcbb36fbd50caef590d9b1de614 + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.16,<2.0a0 + - harfbuzz >=13.2.1 + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libglib >=2.86.4,<3.0a0 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + license: LGPL-2.1-or-later + size: 431801 + timestamp: 1774282435173 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pcre2-10.47-h13923f0_0.conda + sha256: 8d64a9d36073346542e5ea042ef8207a45a0069a2e65ce3323ee3146db78134c + md5: 08f970fb2b75f5be27678e077ebedd46 + depends: + - __osx >=10.13 + - bzip2 >=1.0.8,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 1106584 + timestamp: 1763655837207 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pillow-12.2.0-py310h0c0a54c_0.conda + sha256: 63e4c1a37313e04046541582edd7b3533c1bbcf0793b4afd5d836a51f26506b6 + md5: 58b2cc8e01e4c805722159b2ff3ad3da + depends: + - python + - __osx >=11.0 + - zlib-ng >=2.3.3,<2.4.0a0 + - lcms2 >=2.18,<3.0a0 + - openjpeg >=2.5.4,<3.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libwebp-base >=1.6.0,<2.0a0 + - tk >=8.6.13,<8.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libxcb >=1.17.0,<2.0a0 + - python_abi 3.10.* *_cp310 + license: HPND + size: 824060 + timestamp: 1775060319565 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pixman-0.46.4-ha059160_1.conda + sha256: ff8b679079df25aa3ed5daf3f4e3a9c7ee79e7d4b2bd8a21de0f8e7ec7207806 + md5: 742a8552e51029585a32b6024e9f57b4 + depends: + - __osx >=10.13 + - libcxx >=19 + license: MIT + license_family: MIT + size: 390942 + timestamp: 1754665233989 +- conda: https://conda.anaconda.org/conda-forge/osx-64/polars-1.0.0-py310hbfb72ad_0.conda + sha256: 777772f8e18c92b7c594ea6af73d586cef3c1cfa8d8adaf4c911fb0be68ddc54 + md5: 799e7efdcb410413859c4c893654e6d6 + depends: + - __osx >=10.13 + - numpy >=1.16.0,<2 + - packaging + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - typing_extensions >=4.0.0 + constrains: + - __osx >=10.13 + license: MIT + license_family: MIT + run_exports: {} + size: 21836441 + timestamp: 1720022479472 +- conda: https://conda.anaconda.org/conda-forge/osx-64/psutil-5.9.3-py310h90acd4f_1.tar.bz2 + sha256: 57d133d7f7f091493eeb8cb505e50a04bf6e5369aa9961c34ba8206381fafada + md5: 858ba7ca0fc5c2d3df442ad0be5346ce + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 368759 + timestamp: 1666772379243 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pthread-stubs-0.4-h00291cd_1002.conda + sha256: 05944ca3445f31614f8c674c560bca02ff05cb51637a96f665cb2bbe496099e5 + md5: 8bcf980d2c6b17094961198284b8e862 + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 8364 + timestamp: 1726802331537 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pyarrow-16.0.0-py310h1cef2ca_0.conda + sha256: ee958024b93de7dbabe573ce7a9ee3d5c8c16939a37ab71188c952d74e543d9c + md5: f6141b2d0859810d4a2345e211a2312b + depends: + - libarrow-acero 16.0.0.* + - libarrow-dataset 16.0.0.* + - libarrow-substrait 16.0.0.* + - libparquet 16.0.0.* + - numpy >=1.22.4,<2.0a0 + - pyarrow-core 16.0.0 *_0_* + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: APACHE + size: 26055 + timestamp: 1715178264095 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pyarrow-core-16.0.0-py310h907dfef_0_cpu.conda + sha256: b7f1a51a6d64fc388dd9d43869c7582f5211be1705ffe32820dee60feac96972 + md5: ca416679ec79628b1548d6745b22c005 + depends: + - __osx >=10.13 + - libarrow 16.0.0.* *cpu + - libcxx >=16 + - libzlib >=1.2.13,<2.0.0a0 + - numpy >=1.22.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - apache-arrow-proc =*=cpu + license: Apache-2.0 + license_family: APACHE + size: 3855619 + timestamp: 1715178204986 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pyobjc-core-12.1-py310hccc919d_0.conda + sha256: e5ae95d22806df79e76efcadf8ebc9e9b6205f6bb93c3437048ed8a2146c94c0 + md5: a72e3fed287b69db7e729656c5f31ec7 + depends: + - __osx >=10.13 + - libffi >=3.5.2,<3.6.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - setuptools + license: MIT + license_family: MIT + size: 438171 + timestamp: 1763151299649 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pyobjc-framework-cocoa-12.1-py310hb0d6316_0.conda + sha256: 08122ea8910eb4cccc88cb0265c7ce72157847301802b3ecaf747b62b5a1cfa4 + md5: eddbb0b106b76119b30c2bd40f736187 + depends: + - __osx >=10.13 + - libffi >=3.5.2,<3.6.0a0 + - pyobjc-core 12.1.* + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 334562 + timestamp: 1763160790587 +- conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.10.20-hea035f4_0_cpython.conda + sha256: b6b9d6a85003b21ac17cc1485e196906bd704759caaab1315f6f8eeb85f26202 + md5: bc2a1cfdea76213972b98c65be1e2023 + depends: + - __osx >=11.0 + - bzip2 >=1.0.8,<2.0a0 + - libexpat >=2.7.4,<3.0a0 + - libffi >=3.4,<4.0a0 + - liblzma >=5.8.2,<6.0a0 + - libsqlite >=3.51.2,<4.0a0 + - libzlib >=1.3.1,<2.0a0 + - ncurses >=6.5,<7.0a0 + - openssl >=3.5.5,<4.0a0 + - readline >=8.3,<9.0a0 + - tk >=8.6.13,<8.7.0a0 + - tzdata + constrains: + - python_abi 3.10.* *_cp310 + license: Python-2.0 + size: 13083662 + timestamp: 1772730522090 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pyyaml-6.0.3-py310hec06124_1.conda + sha256: 22a9789bdacdf592c052f3f35f6035063fbc2209cc9f00bae1aca0a2628f77f0 + md5: e4a0c0e534140735d29629182216d229 + depends: + - __osx >=10.13 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 166882 + timestamp: 1770223795901 +- conda: https://conda.anaconda.org/conda-forge/osx-64/pyzmq-27.1.0-py310h30bd05a_3.conda + sha256: 9d9c11b9172214f6d2189d7cfb4808f00470e2b87aa053a87ecec1aa3cd67807 + md5: 29fc584cce53b39d884d898867d6ca44 + depends: + - python + - __osx >=11.0 + - libcxx >=19 + - python_abi 3.10.* *_cp310 + - zeromq >=4.3.5,<4.4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 307442 + timestamp: 1779484125528 +- conda: https://conda.anaconda.org/conda-forge/osx-64/re2-2023.09.01-hb168e87_2.conda + sha256: 5739ed2cfa62ed7f828eb4b9e6e69ff1df56cb9a9aacdc296451a3cb647034eb + md5: 266f8ca8528fc7e0fa31066c309ad864 + depends: + - libre2-11 2023.09.01 h81f5012_2 + license: BSD-3-Clause + license_family: BSD + size: 26814 + timestamp: 1708947195067 +- conda: https://conda.anaconda.org/conda-forge/osx-64/readline-8.3-h68b038d_0.conda + sha256: 4614af680aa0920e82b953fece85a03007e0719c3399f13d7de64176874b80d5 + md5: eefd65452dfe7cce476a519bece46704 + depends: + - __osx >=10.13 + - ncurses >=6.5,<7.0a0 + license: GPL-3.0-only + license_family: GPL + size: 317819 + timestamp: 1765813692798 +- conda: https://conda.anaconda.org/conda-forge/osx-64/rpds-py-0.30.0-py310h64024f4_0.conda + sha256: dddf9628fca28d631d91bdfcb6298cf3c80dd6ee929494f1f87221ba88a7f515 + md5: 96e0fa6dc0ba440ecf9868e7d7973514 + depends: + - python + - __osx >=10.13 + - python_abi 3.10.* *_cp310 + constrains: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 367301 + timestamp: 1764543143461 +- conda: https://conda.anaconda.org/conda-forge/osx-64/scikit-learn-1.0.2-py310h598de7d_0.tar.bz2 + sha256: b83e10111ba00ca2e1a2457fba3188aabf8e5ad93d72c66d3c91bfd5445537a1 + md5: 126e9a6003b8caae4d76a2bf7828ec72 + depends: + - joblib >=0.11 + - libcblas >=3.8.0,<4.0a0 + - libcxx >=11.1.0 + - llvm-openmp >=11.1.0 + - numpy >=1.21.5,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - scipy + - threadpoolctl + license: BSD-3-Clause + license_family: BSD + size: 7666779 + timestamp: 1640464634605 +- conda: https://conda.anaconda.org/conda-forge/osx-64/scipy-1.15.2-py310hef62574_0.conda + sha256: da86efbfa72e4eb3e4748e5471d04fdbe3f9887f367b6302c1dcdb155bbf712b + md5: e79860e43d87b020a0254f0b3f5017c5 + depends: + - __osx >=10.13 + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=18 + - libgfortran >=5 + - libgfortran5 >=13.2.0 + - liblapack >=3.9.0,<4.0a0 + - numpy <2.5 + - numpy >=1.19,<3 + - numpy >=1.23.5 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 14682985 + timestamp: 1739792429025 +- conda: https://conda.anaconda.org/conda-forge/osx-64/scipy-1.7.3-py310h240c617_1.tar.bz2 + sha256: 7f950920adac7d1f724779df1463b8995affdeba8850b71d30d2c2580e8c345a + md5: e75258079343e3fb7e6175897ffcb51a + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=14.0.4 + - libgfortran >=5 + - libgfortran5 >=11.3.0 + - liblapack >=3.9.0,<4.0a0 + - numpy >=1.21.6,<1.23 + - numpy >=1.21.6,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + constrains: + - libopenblas <0.3.26 + license: BSD-3-Clause + license_family: BSD + size: 21207900 + timestamp: 1667961942939 +- conda: https://conda.anaconda.org/conda-forge/osx-64/setuptools-59.8.0-py310h2ec42d9_1.tar.bz2 + sha256: 0f1234714683aa2795c4c55fe2b66aaabc533b32b6b1b8777372a0ebb5ece85a + md5: 18cf8b304e46b09327f3d0330837cfdb + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 1053173 + timestamp: 1648692121671 +- conda: https://conda.anaconda.org/conda-forge/osx-64/snappy-1.2.2-h01f5ddf_1.conda + sha256: 1525e6d8e2edf32dabfe2a8e2fc8bf2df81c5ef9f0b5374a3d4ccfa672bfd949 + md5: 2e993292ec18af5cd480932d448598cf + depends: + - libcxx >=19 + - __osx >=10.13 + license: BSD-3-Clause + license_family: BSD + size: 40023 + timestamp: 1762948053450 +- conda: https://conda.anaconda.org/conda-forge/osx-64/tk-8.6.13-h7142dee_3.conda + sha256: 7f0d9c320288532873e2d8486c331ec6d87919c9028208d3f6ac91dc8f99a67b + md5: 6e6efb7463f8cef69dbcb4c2205bf60e + depends: + - __osx >=10.13 + - libzlib >=1.3.1,<2.0a0 + license: TCL + license_family: BSD + size: 3282953 + timestamp: 1769460532442 +- conda: https://conda.anaconda.org/conda-forge/osx-64/tornado-6.5.6-py310h5cd8a12_0.conda + sha256: b0c0b735b66771551e45cb873931071e96a00def757ec7a624edb575d4cf2920 + md5: e97ddaa9f902bb45f9f0533a6efd25a4 + depends: + - __osx >=11.0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 670193 + timestamp: 1779916280925 +- conda: https://conda.anaconda.org/conda-forge/osx-64/unicodedata2-17.0.1-py310hf70ac88_0.conda + sha256: 9abc6246ddf2d55d3ff2cd7920b7de38f8c85ff11961e79df39ed798d9f5faa2 + md5: 453751e05bdf7275e48460f6313636fd + depends: + - __osx >=10.13 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 403729 + timestamp: 1770909458144 +- conda: https://conda.anaconda.org/conda-forge/osx-64/xorg-libxau-1.0.12-h8616949_1.conda + sha256: 928f28bd278c7da674b57d71b2e7f4ac4e7c7ce56b0bf0f60d6a074366a2e76d + md5: 47f1b8b4a76ebd0cd22bd7153e54a4dc + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 13810 + timestamp: 1762977180568 +- conda: https://conda.anaconda.org/conda-forge/osx-64/xorg-libxdmcp-1.1.5-h8616949_1.conda + sha256: b7b291cc5fd4e1223058542fca46f462221027779920dd433d68b98e858a4afc + md5: 435446d9d7db8e094d2c989766cfb146 + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 19067 + timestamp: 1762977101974 +- conda: https://conda.anaconda.org/conda-forge/osx-64/yaml-0.2.5-h4132b18_3.conda + sha256: a335161bfa57b64e6794c3c354e7d49449b28b8d8a7c4ed02bf04c3f009953f9 + md5: a645bb90997d3fc2aea0adf6517059bd + depends: + - __osx >=10.13 + license: MIT + license_family: MIT + size: 79419 + timestamp: 1753484072608 +- conda: https://conda.anaconda.org/conda-forge/osx-64/zeromq-4.3.5-h84953be_11.conda + sha256: 2ba9b6a3131b5a97641068559d38c044f37fd29b54c10dd6d073f1cf81fc4e4c + md5: 8a622d1db89d80a94477b1c03824d611 + depends: + - libcxx >=19 + - __osx >=11.0 + - krb5 >=1.22.2,<1.23.0a0 + - libsodium >=1.0.22,<1.0.23.0a0 + license: MPL-2.0 + license_family: MOZILLA + size: 260817 + timestamp: 1779124180318 +- conda: https://conda.anaconda.org/conda-forge/osx-64/zlib-ng-2.3.3-h8bce59a_1.conda + sha256: 4a1beb656761c7d8c9a53474bfd3932c30d82af5d93a32b8ef626c01c059d981 + md5: b3ecb6480fd46194e3f7dd0ff4445dff + depends: + - __osx >=10.13 + - libcxx >=19 + license: Zlib + license_family: Other + size: 120464 + timestamp: 1770168263684 +- conda: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.25.0-py310h3aa7efa_1.conda + sha256: 3017e30d806b41312fc7c927ce59101bd0bbd046dc921b3a042e5a8109d35b0d + md5: 02457da962c72f8dfcf9783fe5faa7de + depends: + - python + - cffi >=1.11 + - zstd >=1.5.7,<1.5.8.0a0 + - __osx >=10.13 + - zstd >=1.5.7,<1.6.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 452267 + timestamp: 1762512701017 +- conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h3eecb57_6.conda + sha256: 47101a4055a70a4876ffc87b750ab2287b67eca793f21c8224be5e1ee6394d3f + md5: 727109b184d680772e3122f40136d5ca + depends: + - __osx >=10.13 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 528148 + timestamp: 1764777156963 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/_openmp_mutex-4.5-7_kmp_llvm.conda + build_number: 7 + sha256: 7acaa2e0782cad032bdaf756b536874346ac1375745fb250e9bdd6a48a7ab3cd + md5: a44032f282e7d2acdeb1c240308052dd + depends: + - llvm-openmp >=9.0.1 + license: BSD-3-Clause + license_family: BSD + size: 8325 + timestamp: 1764092507920 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/argon2-cffi-bindings-25.1.0-py310hfe3a0ae_2.conda + sha256: 473dc3160d6e14fdc3821a08a3bec7e6c484081544b15c6751155041a54f0352 + md5: f0dab5a02fcca0ef97bae8f0abd436d6 + depends: + - __osx >=11.0 + - cffi >=1.0.1 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 33738 + timestamp: 1762509940984 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/atk-1.0-2.38.0-hd03087b_2.conda + sha256: b0747f9b1bc03d1932b4d8c586f39a35ac97e7e72fe6e63f2b2a2472d466f3c1 + md5: 57301986d02d30d6805fdce6c99074ee + depends: + - __osx >=11.0 + - libcxx >=16 + - libglib >=2.80.0,<3.0a0 + - libintl >=0.22.5,<1.0a0 + constrains: + - atk-1.0 2.38.0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 347530 + timestamp: 1713896411580 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.7.20-h5cf208e_0.conda + sha256: ca095f3d6678d3073b912722a53ac4489f2e78e79e7075731464b7be4210d458 + md5: 8b6134a6e4ab346a4f7c3aaeb90073c8 + depends: + - __osx >=11.0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + license: Apache-2.0 + license_family: Apache + size: 91004 + timestamp: 1715287760264 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.6.12-h35c0bb2_0.conda + sha256: 7ee38b2e1a74d9300a5daa3e5d8e3721da80cfd2b8c4d0fe7d245b6c0b005345 + md5: f3cafdf2cf60715acf0af9fcc6e314af + depends: + - __osx >=11.0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 39878 + timestamp: 1714177800511 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.9.17-h03532ee_0.conda + sha256: aa8320812d64a61d135eae8f14d3a33ee4a8be93d017af8f0f093818574321cc + md5: f6f9dddf7d48c6260eed53331bc368ca + depends: + - __osx >=11.0 + license: Apache-2.0 + license_family: Apache + size: 205818 + timestamp: 1713863980341 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.2.18-h35c0bb2_4.conda + sha256: 9ac120311a289b84f63b5c9b612595d088ee53fcdea6830d8a37b4f9797c0294 + md5: 175f51c07a9a003b2d88234888dc2aa1 + depends: + - __osx >=11.0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 18096 + timestamp: 1714044195309 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.4.2-h7d5773a_10.conda + sha256: c957e08847be2356dabea00f9f29a35bee51c8f3435812b9ffacad5c17b57586 + md5: 117c8e1a963c44316e3eed40592ff580 + depends: + - __osx >=11.0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - libcxx >=16 + license: Apache-2.0 + license_family: Apache + size: 46956 + timestamp: 1715026300336 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.8.1-h00faecf_13.conda + sha256: f47540726b8c7b8d5d7e28d1e64731d1a22b35ec079a8f982740e7ec823ae1b0 + md5: 3ee817ca3693f0a3e05b5605939404a9 + depends: + - __osx >=11.0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-compression >=0.2.18,<0.2.19.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + license: Apache-2.0 + license_family: Apache + size: 151560 + timestamp: 1715026356984 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.14.8-h6dd71cf_0.conda + sha256: 949742437538cc0db5da9e21c92ccb3c32a1426980523412e5cbf6ec1990c073 + md5: 50142d1519ed7fb7bbe588084deab2ae + depends: + - __osx >=11.0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 138046 + timestamp: 1714868179631 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.10.4-h92d7a41_2.conda + sha256: 81437ee8a65a445bec24df4cd584cb9e3d24f48cfa1a03e05f170fe125f96544 + md5: 128936f3f7c991bd9e112af42d1f9c30 + depends: + - __osx >=11.0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + license: Apache-2.0 + license_family: Apache + size: 118084 + timestamp: 1715057770501 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-s3-0.5.9-he1e208d_0.conda + sha256: c5f113e37c670982abc0a6e1b000186e4c94a6838d2a283dce6726ca92a882d0 + md5: 64f5167f45ce390443e4e80ff37671c6 + depends: + - __osx >=11.0 + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + license: Apache-2.0 + license_family: Apache + size: 94038 + timestamp: 1715619886520 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-sdkutils-0.1.16-h35c0bb2_0.conda + sha256: 152df9a5014a27bb33ecba29ce996cfb51e1b6ee825be40d052125f766efbb18 + md5: 75bc7553b500d2edc8f5eed68d04d981 + depends: + - __osx >=11.0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 48666 + timestamp: 1714208582795 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-checksums-0.1.18-h35c0bb2_4.conda + sha256: 3b7320a5b17d3d7dcf816e60f19122635219c4e1be4dd53cf9ae454b9ed2422d + md5: f0177671ec47f34998666eeb7cd227f9 + depends: + - __osx >=11.0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + license: Apache-2.0 + license_family: Apache + size: 49223 + timestamp: 1714051341844 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.26.8-h5cd1ed2_11.conda + sha256: 54bb963190ab67c28fc15fc9bcdd2650487d0184ffed29154c293ba073d1c4b8 + md5: cceb6e6316256f11d2b975bb4e00dd9d + depends: + - __osx >=11.0 + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-mqtt >=0.10.4,<0.10.5.0a0 + - aws-c-s3 >=0.5.9,<0.5.10.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - libcxx >=16 + license: Apache-2.0 + license_family: Apache + size: 224367 + timestamp: 1716301212739 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-sdk-cpp-1.11.267-h108e708_8.conda + sha256: 7e7c6d017b86287e4d645209a094860559188e59aba1cbe31965348aeb5be921 + md5: bd77e7719bc124f92dab0f8c6c682215 + depends: + - __osx >=11.0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - libcurl >=8.7.1,<9.0a0 + - libcxx >=16 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: Apache + size: 3341757 + timestamp: 1715176512891 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-1.1.0-h6caf38d_4.conda + sha256: 8aa8ee52b95fdc3ef09d476cbfa30df722809b16e6dca4a4f80e581012035b7b + md5: ce8659623cea44cc812bc0bfae4041c5 + depends: + - __osx >=11.0 + - brotli-bin 1.1.0 h6caf38d_4 + - libbrotlidec 1.1.0 h6caf38d_4 + - libbrotlienc 1.1.0 h6caf38d_4 + license: MIT + license_family: MIT + size: 20003 + timestamp: 1756599758165 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-bin-1.1.0-h6caf38d_4.conda + sha256: e57d402b02c9287b7c02d9947d7b7b55a4f7d73341c210c233f6b388d4641e08 + md5: ab57f389f304c4d2eb86d8ae46d219c3 + depends: + - __osx >=11.0 + - libbrotlidec 1.1.0 h6caf38d_4 + - libbrotlienc 1.1.0 h6caf38d_4 + license: MIT + license_family: MIT + size: 17373 + timestamp: 1756599741779 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/brotli-python-1.1.0-py310h1af2607_4.conda + sha256: 75cc1a5e99914ca5777713afe8d262e122c203ebbee0366a76338cb750534ac9 + md5: cd63cc758578ca3318f9c479be55dc30 + depends: + - __osx >=11.0 + - libcxx >=19 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - libbrotlicommon 1.1.0 h6caf38d_4 + license: MIT + license_family: MIT + size: 340989 + timestamp: 1756600184408 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-hd037594_9.conda + sha256: 540fe54be35fac0c17feefbdc3e29725cce05d7367ffedfaaa1bdda234b019df + md5: 620b85a3f45526a8bc4d23fd78fc22f0 + depends: + - __osx >=11.0 + license: bzip2-1.0.6 + license_family: BSD + size: 124834 + timestamp: 1771350416561 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-ares-1.34.6-hc919400_0.conda + sha256: 2995f2aed4e53725e5efbc28199b46bf311c3cab2648fc4f10c2227d6d5fa196 + md5: bcb3cba70cf1eec964a03b4ba7775f01 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 180327 + timestamp: 1765215064054 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/cairo-1.18.4-he0f2337_1.conda + sha256: cde9b79ee206fe3ba6ca2dc5906593fb7a1350515f85b2a1135a4ce8ec1539e3 + md5: 36200ecfbbfbcb82063c87725434161f + depends: + - __osx >=11.0 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libcxx >=19 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libglib >=2.86.3,<3.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libzlib >=1.3.1,<2.0a0 + - pixman >=0.46.4,<1.0a0 + license: LGPL-2.1-only or MPL-1.1 + size: 900035 + timestamp: 1766416416791 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/cffi-1.15.1-py310hdcd7c05_5.conda + sha256: 0172cb3085516469fb1411cbb04b5e119cde46fa6931fc1ae67dfeb0e3cfcd29 + md5: d2f47e7bd93517837dea668142f23136 + depends: + - libffi >=3.4,<4.0a0 + - pycparser + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 228904 + timestamp: 1695367572448 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.2.1-py310h21239e6_0.conda + sha256: 3b97cb954719a53ea66e0c024eb9a5ed28da61036a2c74b9104eaac425ee95fd + md5: db10923835b6b8c082b126c7cbbe50ff + depends: + - libcxx >=16 + - numpy >=1.20 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 226024 + timestamp: 1712430306572 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/cytoolz-1.1.0-py310h72544b6_2.conda + sha256: 1c1a03686e9cb478ee3530f6d76954438dac7cea578f021fbeb4be41e0b21a37 + md5: 8e7580a961e68d4a8c4173a88346f93d + depends: + - __osx >=11.0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - toolz >=0.10.0 + license: BSD-3-Clause + license_family: BSD + size: 560250 + timestamp: 1771856766465 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.21-py310h19b6747_0.conda + sha256: bb484d4f00637abafb54aab3869fd82f555cbdab5eb1781f62b557c9dd038b9a + md5: 7bfcf4fc7e30f04fdd188b06331717ff + depends: + - python + - libcxx >=19 + - __osx >=11.0 + - python 3.10.* *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + size: 2225827 + timestamp: 1780390209126 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/epoxy-1.5.10-hc919400_2.conda + sha256: ba685b87529c95a4bf9de140a33d703d57dc46b036e9586ed26890de65c1c0d5 + md5: 3b87dabebe54c6d66a07b97b53ac5874 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 296347 + timestamp: 1758743805063 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/fontconfig-2.18.1-h2b252f5_0.conda + sha256: 8607d8d0b32f9f6fc61ea8c06b537486b78428a04516658222fa4d1d521af765 + md5: 9d928e6a62192141fb6540a3125b1345 + depends: + - __osx >=11.0 + - libexpat >=2.8.1,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libintl >=0.25.1,<1.0a0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + size: 248677 + timestamp: 1780450500773 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.63.0-py310hb46c203_0.conda + sha256: cb78df3179f98d3f9d1e117bcfba653fcaf5520e83722ba2c1d0f8a816ee8b2e + md5: 93853b69991afccdbdbc4151a70bdeae + depends: + - __osx >=11.0 + - brotli + - munkres + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - unicodedata2 >=15.1.0 + license: MIT + license_family: MIT + size: 2396875 + timestamp: 1778770802543 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/freetype-2.14.3-hce30654_0.conda + sha256: 5952bd9db12207a18a112e8924aa2ce8c2f9d57b62584d58a97d2f6afe1ea324 + md5: 6dcc75ba2e04c555e881b72793d3282f + depends: + - libfreetype 2.14.3 hce30654_0 + - libfreetype6 2.14.3 hdfa99f5_0 + license: GPL-2.0-only OR FTL + size: 173313 + timestamp: 1774298702053 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/fribidi-1.0.16-hc919400_0.conda + sha256: d856dc6744ecfba78c5f7df3378f03a75c911aadac803fa2b41a583667b4b600 + md5: 04bdce8d93a4ed181d1d726163c2d447 + depends: + - __osx >=11.0 + license: LGPL-2.1-or-later + size: 59391 + timestamp: 1757438897523 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gdk-pixbuf-2.44.6-h4e57454_0.conda + sha256: 07cbba4e12430de35ea608eb3006cf1f7f63832c4f89a081cd6f3872944c1aa6 + md5: e67ebd2f639f46e52af8531622fa6051 + depends: + - __osx >=11.0 + - libglib >=2.86.4,<3.0a0 + - libintl >=0.25.1,<1.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - liblzma >=5.8.2,<6.0a0 + - libpng >=1.6.56,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: LGPL-2.1-or-later + license_family: LGPL + size: 548309 + timestamp: 1774986047281 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gflags-2.2.2-hf9b8971_1005.conda + sha256: fd56ed8a1dab72ab90d8a8929b6f916a6d9220ca297ff077f8f04c5ed3408e20 + md5: 57a511a5905caa37540eb914dfcbf1fb + depends: + - __osx >=11.0 + - libcxx >=17 + license: BSD-3-Clause + license_family: BSD + size: 82090 + timestamp: 1726600145480 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/glib-tools-2.88.1-h37541a8_2.conda + sha256: 414bdf86a8096d5706293d163359def2e61b8ffd3fe106bbf2028d79e58e6a97 + md5: 8d4580a91948a6c3383a7c2fbfe5311c + depends: + - libglib ==2.88.1 ha08bb59_2 + - libffi + - __osx >=11.0 + - libintl >=0.25.1,<1.0a0 + license: LGPL-2.1-or-later + size: 204902 + timestamp: 1778508895255 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/glog-0.7.1-heb240a5_0.conda + sha256: 9fc77de416953aa959039db72bc41bfa4600ae3ff84acad04a7d0c1ab9552602 + md5: fef68d0a95aa5b84b5c1a4f6f3bf40e1 + depends: + - __osx >=11.0 + - gflags >=2.2.2,<2.3.0a0 + - libcxx >=16 + license: BSD-3-Clause + license_family: BSD + size: 112215 + timestamp: 1718284365403 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/graphite2-1.3.14-hec049ff_2.conda + sha256: c507ae9989dbea7024aa6feaebb16cbf271faac67ac3f0342ef1ab747c20475d + md5: 0fc46fee39e88bbcf5835f71a9d9a209 + depends: + - __osx >=11.0 + - libcxx >=19 + license: LGPL-2.0-or-later + license_family: LGPL + size: 81202 + timestamp: 1755102333712 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/graphviz-14.1.2-hec8c438_0.conda + sha256: 755c72d469330265f80a615912a3b522aef6f26cbc52763862b6a3c492fbf97c + md5: 1f3d859de3ca2bcaa845e92e87d73660 + depends: + - __osx >=11.0 + - adwaita-icon-theme + - cairo >=1.18.4,<2.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.4,<3.0a0 + - gtk3 >=3.24.43,<4.0a0 + - gts >=0.7.6,<0.8.0a0 + - libcxx >=19 + - libexpat >=2.7.3,<3.0a0 + - libgd >=2.3.3,<2.4.0a0 + - libglib >=2.86.3,<3.0a0 + - librsvg >=2.60.0,<3.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - pango >=1.56.4,<2.0a0 + license: EPL-1.0 + license_family: Other + size: 2218284 + timestamp: 1769427599940 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gtk3-3.24.52-hc0f3e19_0.conda + sha256: 26862a9898054b8552e55e609e5ce73c7ef1eb28bbe6fb87f0b9109d73cd09df + md5: 5557a2433b1339b8e536c264afea41ef + depends: + - __osx >=11.0 + - atk-1.0 >=2.38.0 + - cairo >=1.18.4,<2.0a0 + - epoxy >=1.5.10,<1.6.0a0 + - fribidi >=1.0.16,<2.0a0 + - gdk-pixbuf >=2.44.5,<3.0a0 + - glib-tools + - harfbuzz >=13.2.1 + - hicolor-icon-theme + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libglib >=2.86.4,<3.0a0 + - libintl >=0.25.1,<1.0a0 + - liblzma >=5.8.2,<6.0a0 + - libzlib >=1.3.2,<2.0a0 + - pango >=1.56.4,<2.0a0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 9385734 + timestamp: 1774288504338 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gts-0.7.6-he42f4ea_4.conda + sha256: e0f8c7bc1b9ea62ded78ffa848e37771eeaaaf55b3146580513c7266862043ba + md5: 21b4dd3098f63a74cf2aa9159cbef57d + depends: + - libcxx >=15.0.7 + - libglib >=2.76.3,<3.0a0 + license: LGPL-2.0-or-later + license_family: LGPL + size: 304331 + timestamp: 1686545503242 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/h5py-3.16.0-nompi_py310h0c5f886_102.conda + sha256: 515c30d2ac4809703201b746a1656b5bcfbf6ecc45849caec4684d131acbc82e + md5: 54735b6dd3364328d2e1fa9d2601b8be + depends: + - __osx >=11.0 + - cached-property + - hdf5 >=1.14.6,<1.14.7.0a0 + - numpy >=1.21,<3 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 1103506 + timestamp: 1775581597111 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-14.2.0-h3103d1b_0.conda + sha256: 40ccd6a589c60a4cedb2f9921dfa60ea5845b5ce323477b042b6f90218b239f6 + md5: ea75b03886981362d93bb4708ee14811 + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - graphite2 >=1.3.14,<2.0a0 + - icu >=78.3,<79.0a0 + - libcxx >=19 + - libexpat >=2.7.5,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libglib >=2.86.4,<3.0a0 + - libzlib >=1.3.2,<2.0a0 + license: MIT + license_family: MIT + size: 2023669 + timestamp: 1776779039314 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/hdf5-1.14.6-nompi_had3affe_109.conda + sha256: 5c49a8d636c4cd712652012a4a6d96188cff26032eb0c56a80e428c121b1596f + md5: fa70cb619977ab679abfe4b4c4202a35 + depends: + - __osx >=11.0 + - libaec >=1.1.5,<2.0a0 + - libcurl >=8.20.0,<9.0a0 + - libcxx >=19 + - libgfortran + - libgfortran5 >=14.3.0 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 3296683 + timestamp: 1777519194055 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/hicolor-icon-theme-0.17-hce30654_3.conda + sha256: 46a4958f2f916c5938f2a6dc0709f78b175ece42f601d79a04e0276d55d25d07 + md5: cfb39109ac5fa8601eb595d66d5bf156 + license: GPL-2.0-or-later + license_family: GPL + size: 17616 + timestamp: 1771539622983 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/icu-78.3-hef89b57_0.conda + sha256: 3a7907a17e9937d3a46dfd41cffaf815abad59a569440d1e25177c15fd0684e5 + md5: f1182c91c0de31a7abd40cedf6a5ebef + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 12361647 + timestamp: 1773822915649 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/kiwisolver-1.5.0-py310h34990b0_0.conda + sha256: 3d902014b20f2e4a3d5a20fc1a3bd4a66c5ad46e0f3b2031f7c643ae178ecfcf + md5: 5f82c645836131e2d910d5562a598bd3 + depends: + - python + - __osx >=11.0 + - libcxx >=19 + - python 3.10.* *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 66764 + timestamp: 1773067259184 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/krb5-1.22.2-h385eeb1_0.conda + sha256: c0a0bf028fe7f3defcdcaa464e536cf1b202d07451e18ad83fdd169d15bef6ed + md5: e446e1822f4da8e5080a9de93474184d + depends: + - __osx >=11.0 + - libcxx >=19 + - libedit >=3.1.20250104,<3.2.0a0 + - libedit >=3.1.20250104,<4.0a0 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 1160828 + timestamp: 1769770119811 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/lcms2-2.19.1-hdfa7624_1.conda + sha256: ccb5598fad3694e79bf54f0eb812e3b3c3dd63d1497e631f5978800eadb9bcc4 + md5: d2f2c7c10e2957647d45589b7701a453 + depends: + - __osx >=11.0 + - libjpeg-turbo >=3.1.4.1,<4.0a0 + - libtiff >=4.7.1,<4.8.0a0 + license: MIT + license_family: MIT + size: 213747 + timestamp: 1780212240694 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/lerc-4.1.0-h1eee2c3_0.conda + sha256: 66e5ffd301a44da696f3efc2f25d6d94f42a9adc0db06c44ad753ab844148c51 + md5: 095e5749868adab9cae42d4b460e5443 + depends: + - __osx >=11.0 + - libcxx >=19 + license: Apache-2.0 + license_family: Apache + size: 164222 + timestamp: 1773114244984 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20240116.2-cxx17_h00cdb27_1.conda + sha256: a9517c8683924f4b3b9380cdaa50fdd2009cd8d5f3918c92f64394238189d3cb + md5: f16963d88aed907af8b90878b8d8a05c + depends: + - __osx >=11.0 + - libcxx >=16 + constrains: + - abseil-cpp =20240116.2 + - libabseil-static =20240116.2=cxx17* + license: Apache-2.0 + license_family: Apache + size: 1136123 + timestamp: 1720857649214 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libaec-1.1.5-h8664d51_0.conda + sha256: af9cd8db11eb719e38a3340c88bb4882cf19b5b4237d93845224489fc2a13b46 + md5: 13e6d9ae0efbc9d2e9a01a91f4372b41 + depends: + - __osx >=11.0 + - libcxx >=19 + license: BSD-2-Clause + license_family: BSD + size: 30390 + timestamp: 1769222133373 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-16.0.0-h23f55cf_1_cpu.conda + build_number: 1 + sha256: 6c949d96949a850e2c83fc6be019a81c2b43a6270c7ea12b44b23a80051f3c61 + md5: 47b451263143fa1beb369a096e230381 + depends: + - __osx >=11.0 + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - aws-sdk-cpp >=1.11.267,<1.11.268.0a0 + - bzip2 >=1.0.8,<2.0a0 + - glog >=0.7.0,<0.8.0a0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libbrotlidec >=1.1.0,<1.2.0a0 + - libbrotlienc >=1.1.0,<1.2.0a0 + - libcxx >=16 + - libgoogle-cloud >=2.23.0,<2.24.0a0 + - libgoogle-cloud-storage >=2.23.0,<2.24.0a0 + - libre2-11 >=2023.9.1 + - libutf8proc >=2.8.0,<3.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - orc >=2.0.0,<2.0.1.0a0 + - re2 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.6,<1.6.0a0 + - libutf8proc <2.9 + constrains: + - apache-arrow-proc =*=cpu + - arrow-cpp <0.0a0 + - parquet-cpp <0.0a0 + license: Apache-2.0 + license_family: APACHE + size: 5191448 + timestamp: 1715199163002 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-16.0.0-h00cdb27_1_cpu.conda + build_number: 1 + sha256: 39c64ca3481019b18c63dc273ae177ac5dab3fdb150780a9e89eb3476232106c + md5: 6d9d73735ef4641a4a354be645a162b0 + depends: + - __osx >=11.0 + - libarrow 16.0.0 h23f55cf_1_cpu + - libcxx >=16 + license: Apache-2.0 + license_family: APACHE + size: 486768 + timestamp: 1715199293379 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-dataset-16.0.0-h00cdb27_1_cpu.conda + build_number: 1 + sha256: 215d5a7baa684e4bf9268272f6b19544edfcb102bb66bd5f7b87edb4fb7accc0 + md5: acb786ea7ffe5abae111416535f9dc79 + depends: + - __osx >=11.0 + - libarrow 16.0.0 h23f55cf_1_cpu + - libarrow-acero 16.0.0 h00cdb27_1_cpu + - libcxx >=16 + - libparquet 16.0.0 hcf52c46_1_cpu + license: Apache-2.0 + license_family: APACHE + size: 493228 + timestamp: 1715200517566 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-substrait-16.0.0-hc68f6b8_1_cpu.conda + build_number: 1 + sha256: 3129f4d25c58a9f566e29f6a0cfaa37e5409a6d7fef5124ebfc3fc818a583e01 + md5: 760f864ad19350f0c328db944599f42f + depends: + - __osx >=11.0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libarrow 16.0.0 h23f55cf_1_cpu + - libarrow-acero 16.0.0 h00cdb27_1_cpu + - libarrow-dataset 16.0.0 h00cdb27_1_cpu + - libcxx >=16 + - libprotobuf >=4.25.3,<4.25.4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 473109 + timestamp: 1715200758701 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.11.0-8_h51639a9_openblas.conda + build_number: 8 + sha256: 8f5ec18ead0619a9cf0f38b49796c22f6fc0f44850c0df2baea0f5277db16e75 + md5: dbfe729181a32741ae63ecb41eefbac6 + depends: + - libopenblas >=0.3.33,<0.3.34.0a0 + - libopenblas >=0.3.33,<1.0a0 + constrains: + - blas 2.308 openblas + - liblapack 3.11.0 8*_openblas + - liblapacke 3.11.0 8*_openblas + - libcblas 3.11.0 8*_openblas + - mkl <2027 + license: BSD-3-Clause + license_family: BSD + size: 18949 + timestamp: 1779859141315 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-20_osxarm64_openblas.conda + build_number: 20 + sha256: 5b5b8394352c8ca06b15dcc9319d0af3e9f1dc03fc0a6f6deef05d664d6b763a + md5: 49bc8dec26663241ee064b2d7116ec2d + depends: + - libopenblas >=0.3.25,<0.3.26.0a0 + - libopenblas >=0.3.25,<1.0a0 + constrains: + - liblapack 3.9.0 20_osxarm64_openblas + - liblapacke 3.9.0 20_osxarm64_openblas + - libcblas 3.9.0 20_osxarm64_openblas + - blas * openblas + license: BSD-3-Clause + license_family: BSD + size: 14722 + timestamp: 1700568881837 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlicommon-1.1.0-h6caf38d_4.conda + sha256: 023b609ecc35bfee7935d65fcc5aba1a3ba6807cbba144a0730198c0914f7c79 + md5: 231cffe69d41716afe4525c5c1cc5ddd + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 68938 + timestamp: 1756599687687 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlidec-1.1.0-h6caf38d_4.conda + sha256: 7f1cf83a00a494185fc087b00c355674a0f12e924b1b500d2c20519e98fdc064 + md5: cb7e7fe96c9eee23a464afd57648d2cd + depends: + - __osx >=11.0 + - libbrotlicommon 1.1.0 h6caf38d_4 + license: MIT + license_family: MIT + size: 29015 + timestamp: 1756599708339 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libbrotlienc-1.1.0-h6caf38d_4.conda + sha256: a2f2c1c2369360147c46f48124a3a17f5122e78543275ff9788dc91a1d5819dc + md5: 4ce5651ae5cd6eebc5899f9bfe0eac3c + depends: + - __osx >=11.0 + - libbrotlicommon 1.1.0 h6caf38d_4 + license: MIT + license_family: MIT + size: 275791 + timestamp: 1756599724058 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.11.0-8_hb0561ab_openblas.conda + build_number: 8 + sha256: f93efcd44bc24f97c2478c7474d3baa6801a057974f330e1d06bedc33e4c778f + md5: 03a2ef3491da9e5b4d18c03e9f4b3109 + depends: + - libblas 3.11.0 8_h51639a9_openblas + constrains: + - blas 2.308 openblas + - liblapack 3.11.0 8*_openblas + - liblapacke 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 18911 + timestamp: 1779859147634 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-20_osxarm64_openblas.conda + build_number: 20 + sha256: d3a74638f60e034202e373cf2950c69a8d831190d497881d13cbf789434d2489 + md5: 89f4718753c08afe8cda4dd5791ba94c + depends: + - libblas 3.9.0 20_osxarm64_openblas + constrains: + - liblapack 3.9.0 20_osxarm64_openblas + - liblapacke 3.9.0 20_osxarm64_openblas + - blas * openblas + license: BSD-3-Clause + license_family: BSD + size: 14642 + timestamp: 1700568912840 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcrc32c-1.1.2-hbdafb3b_0.tar.bz2 + sha256: 58477b67cc719060b5b069ba57161e20ba69b8695d154a719cb4b60caf577929 + md5: 32bd82a6a625ea6ce090a81c3d34edeb + depends: + - libcxx >=11.1.0 + license: BSD-3-Clause + license_family: BSD + size: 18765 + timestamp: 1633683992603 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.20.0-hd5a2499_0.conda + sha256: 38c0bc634b61e542776e97cfd15d5d41edd304d4e47c333004d2d622439b2381 + md5: 2f57b7d0c6adda88957586b7afd78438 + depends: + - __osx >=11.0 + - krb5 >=1.22.2,<1.23.0a0 + - libnghttp2 >=1.68.1,<2.0a0 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: curl + license_family: MIT + size: 400568 + timestamp: 1777462251987 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-22.1.7-h55c6f16_0.conda + sha256: cceb668dc1b71f054b1036dd83eca2e02c0c3a4b2ba3ad28c74a982d819597a3 + md5: 0325fbe13eb6dd39234eb305ac1b3cb8 + depends: + - __osx >=11.0 + license: Apache-2.0 WITH LLVM-exception + license_family: Apache + size: 568252 + timestamp: 1780441702930 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libdeflate-1.25-hc11a715_0.conda + sha256: 5e0b6961be3304a5f027a8c00bd0967fc46ae162cffb7553ff45c70f51b8314c + md5: a6130c709305cd9828b4e1bd9ba0000c + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 55420 + timestamp: 1761980066242 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libedit-3.1.20250104-pl5321hafb1f1b_0.conda + sha256: 66aa216a403de0bb0c1340a88d1a06adaff66bae2cfd196731aa24db9859d631 + md5: 44083d2d2c2025afca315c7a172eab2b + depends: + - ncurses + - __osx >=11.0 + - ncurses >=6.5,<7.0a0 + license: BSD-2-Clause + license_family: BSD + size: 107691 + timestamp: 1738479560845 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libev-4.33-h93a5062_2.conda + sha256: 95cecb3902fbe0399c3a7e67a5bed1db813e5ab0e22f4023a5e0f722f2cc214f + md5: 36d33e440c31857372a72137f78bacf5 + license: BSD-2-Clause + license_family: BSD + size: 107458 + timestamp: 1702146414478 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libevent-2.1.12-h2757513_1.conda + sha256: 8c136d7586259bb5c0d2b913aaadc5b9737787ae4f40e3ad1beaf96c80b919b7 + md5: 1a109764bff3bdc7bdd84088347d71dc + depends: + - openssl >=3.1.1,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 368167 + timestamp: 1685726248899 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.8.1-hf6b4638_0.conda + sha256: 3133fb6bfa871288b92c8b8752696686a841bf4ffe035aa3038033c9e15b738e + md5: ef22e9ab1dc7c2f334252f565f90b3b8 + depends: + - __osx >=11.0 + constrains: + - expat 2.8.1.* + license: MIT + license_family: MIT + size: 69110 + timestamp: 1779278728511 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.5.2-hcf2aa1b_0.conda + sha256: 6686a26466a527585e6a75cc2a242bf4a3d97d6d6c86424a441677917f28bec7 + md5: 43c04d9cb46ef176bb2a4c77e324d599 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 40979 + timestamp: 1769456747661 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype-2.14.3-hce30654_0.conda + sha256: a047a2f238362a37d484f9620e8cba29f513a933cd9eb68571ad4b270d6f8f3e + md5: f73b109d49568d5d1dda43bb147ae37f + depends: + - libfreetype6 >=2.14.3 + license: GPL-2.0-only OR FTL + size: 8091 + timestamp: 1774298691258 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libfreetype6-2.14.3-hdfa99f5_0.conda + sha256: ff764608e1f2839e95e2cf9b243681475f8778c36af7a42b3f78f476fdbb1dd3 + md5: e98ba7b5f09a5f450eca083d5a1c4649 + depends: + - __osx >=11.0 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - freetype >=2.14.3 + license: GPL-2.0-only OR FTL + size: 338085 + timestamp: 1774298689297 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_19.conda + sha256: 06644fa4d34d57c9e48f4d84b1256f9e5f654fdb37f43acc8a58a396952d42b7 + md5: 644058123986582db33aebd4ae2ca184 + depends: + - _openmp_mutex + constrains: + - libgcc-ng ==15.2.0=*_19 + - libgomp 15.2.0 19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 404080 + timestamp: 1778273064154 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgd-2.3.3-h05bcc79_12.conda + sha256: 269edce527e204a80d3d05673301e0207efcd0dbeebc036a118ceb52690d6341 + md5: fa4a92cfaae9570d89700a292a9ca714 + depends: + - __osx >=11.0 + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libiconv >=1.18,<2.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + license: GD + license_family: BSD + size: 159247 + timestamp: 1766331953491 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_19.conda + sha256: d4837b3b9b30af3132d260225e91ab9dde83be04c59513f500cc81050fb37486 + md5: 1ea03f87cdb1078fbc0e2b2deb63752c + depends: + - libgfortran5 15.2.0 hdae7583_19 + constrains: + - libgfortran-ng ==15.2.0=*_19 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 139675 + timestamp: 1778273280875 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran5-15.2.0-hdae7583_19.conda + sha256: d0a68b7a121d115b80c169e24d1265dcc25a3fe58d107df1bbc430797e226d88 + md5: ba36d8c606a6a53fe0b8c12d47267b3d + depends: + - libgcc >=15.2.0 + constrains: + - libgfortran 15.2.0 + license: GPL-3.0-only WITH GCC-exception-3.1 + license_family: GPL + size: 599691 + timestamp: 1778273075448 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libglib-2.88.1-ha08bb59_2.conda + sha256: 3b32a7a710132d509f2ea38b2f0384414c863533e0fc7ac71b6a0763e4c67424 + md5: 62d6f3b832d7d79ae0c0aa1bb3c325fa + depends: + - __osx >=11.0 + - libintl >=0.25.1,<1.0a0 + - libffi >=3.5.2,<3.6.0a0 + - pcre2 >=10.47,<10.48.0a0 + - libiconv >=1.18,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - glib >2.66 + license: LGPL-2.1-or-later + size: 4439458 + timestamp: 1778508895255 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-2.23.0-hbebe991_1.conda + sha256: db7c0dcebafc001ff9fe0ba618ed611721217b4ceefeef189ab79ef111056c02 + md5: fdbdbd1dc8e8ba458057be0a00db8ab1 + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcurl >=8.7.1,<9.0a0 + - libcxx >=16 + - libgrpc >=1.62.2,<1.63.0a0 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - openssl >=3.2.1,<4.0a0 + constrains: + - libgoogle-cloud 2.23.0 *_1 + license: Apache-2.0 + license_family: Apache + size: 840819 + timestamp: 1713799797441 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgoogle-cloud-storage-2.23.0-h8a76758_1.conda + sha256: 3173b65b7e36e9fa0e6ddec69f39e4dd0e7ada38dbf2c1be006fddc2e7257b0c + md5: 356c74978867e07e12a939a092dcf30d + depends: + - libabseil + - libcrc32c >=1.1.2,<1.2.0a0 + - libcurl + - libcxx >=16 + - libgoogle-cloud 2.23.0 hbebe991_1 + - libzlib >=1.2.13,<2.0.0a0 + - openssl + license: Apache-2.0 + license_family: Apache + size: 509643 + timestamp: 1713801031940 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.62.2-h9c18a4f_0.conda + sha256: d2c5b5a828f6f1242c11e8c91968f48f64446f7dd5cbfa1197545e465eb7d47a + md5: e624fc11026dbb84c549435eccd08623 + depends: + - c-ares >=1.28.1,<2.0a0 + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcxx >=16 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libre2-11 >=2023.9.1 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.2.1,<4.0a0 + - re2 + constrains: + - grpc-cpp =1.62.2 + license: Apache-2.0 + license_family: APACHE + size: 5016525 + timestamp: 1713392846329 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.18-h23cfdf5_2.conda + sha256: de0336e800b2af9a40bdd694b03870ac4a848161b35c8a2325704f123f185f03 + md5: 4d5a7445f0b25b6a3ddbb56e790f5251 + depends: + - __osx >=11.0 + license: LGPL-2.1-only + size: 750379 + timestamp: 1754909073836 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libintl-0.25.1-h493aca8_0.conda + sha256: 99d2cebcd8f84961b86784451b010f5f0a795ed1c08f1e7c76fbb3c22abf021a + md5: 5103f6a6b210a3912faf8d7db516918c + depends: + - __osx >=11.0 + - libiconv >=1.18,<2.0a0 + license: LGPL-2.1-or-later + size: 90957 + timestamp: 1751558394144 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libjpeg-turbo-3.1.4.1-h84a0fba_0.conda + sha256: 17e035ae6a520ff6a6bb5dd93a4a7c3895891f4f9743bcb8c6ef607445a31cd0 + md5: b8a7544c83a67258b0e8592ec6a5d322 + depends: + - __osx >=11.0 + constrains: + - jpeg <0.0.0a + license: IJG AND BSD-3-Clause AND Zlib + size: 555681 + timestamp: 1775962975624 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.11.0-8_hd9741b5_openblas.conda + build_number: 8 + sha256: 8a076fe82142a00fe85f5a5a5351e286e8064f0100fe13608d19182cd0018c25 + md5: 85adeb3d469d082dbd9c8c39e36dec57 + depends: + - libblas 3.11.0 8_h51639a9_openblas + constrains: + - libcblas 3.11.0 8*_openblas + - blas 2.308 openblas + - liblapacke 3.11.0 8*_openblas + license: BSD-3-Clause + license_family: BSD + size: 18925 + timestamp: 1779859153970 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-20_osxarm64_openblas.conda + build_number: 20 + sha256: e13f79828a7752f6e0a74cbe62df80c551285f6c37de86bc3bd9987c97faca57 + md5: 1fefac78f2315455ce2d7f34782eac0a + depends: + - libblas 3.9.0 20_osxarm64_openblas + constrains: + - liblapacke 3.9.0 20_osxarm64_openblas + - libcblas 3.9.0 20_osxarm64_openblas + - blas * openblas + license: BSD-3-Clause + license_family: BSD + size: 14648 + timestamp: 1700568930669 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblzma-5.8.3-h8088a28_0.conda + sha256: 34878d87275c298f1a732c6806349125cebbf340d24c6c23727268184bba051e + md5: b1fd823b5ae54fbec272cea0811bd8a9 + depends: + - __osx >=11.0 + constrains: + - xz 5.8.3.* + license: 0BSD + size: 92472 + timestamp: 1775825802659 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libnghttp2-1.68.1-h8f3e76b_0.conda + sha256: 2bc7bc3978066f2c274ebcbf711850cc9ab92e023e433b9631958a098d11e10a + md5: 6ea18834adbc3b33df9bd9fb45eaf95b + depends: + - __osx >=11.0 + - c-ares >=1.34.6,<2.0a0 + - libcxx >=19 + - libev >=4.33,<4.34.0a0 + - libev >=4.33,<5.0a0 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.5,<4.0a0 + license: MIT + license_family: MIT + size: 576526 + timestamp: 1773854624224 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.25-openmp_h6c19121_0.conda + sha256: b112e0d500bc0314ea8d393efac3ab8c67857e5a2b345348c98e703ee92723e5 + md5: a1843550403212b9dedeeb31466ade03 + depends: + - libgfortran >=5 + - libgfortran5 >=12.3.0 + - llvm-openmp >=16.0.6 + constrains: + - openblas >=0.3.25,<0.3.26.0a0 + license: BSD-3-Clause + license_family: BSD + size: 2896390 + timestamp: 1700535987588 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.33-openmp_he657e61_0.conda + sha256: 9dd455b2d172aeedfa2058d324b5b5822b0bc1b7c1f32cd183d7078540d2f6eb + md5: 909e41855c29f0d52ae630198cd57135 + depends: + - __osx >=11.0 + - libgfortran + - libgfortran5 >=14.3.0 + - llvm-openmp >=19.1.7 + constrains: + - openblas >=0.3.33,<0.3.34.0a0 + license: BSD-3-Clause + license_family: BSD + size: 4304965 + timestamp: 1776995497368 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libparquet-16.0.0-hcf52c46_1_cpu.conda + build_number: 1 + sha256: f064893db5fb8a80d9d97efb76c430c3c0b53675dacb63ff1d54c3ff4556a5ec + md5: 8bc254dbb1411a702e2793fea8ee8dc4 + depends: + - __osx >=11.0 + - libarrow 16.0.0 h23f55cf_1_cpu + - libcxx >=16 + - libthrift >=0.19.0,<0.19.1.0a0 + - openssl >=3.3.0,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 878618 + timestamp: 1715200426767 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libpng-1.6.58-h132b30e_0.conda + sha256: 66eae34546df1f098a67064970c92aa14ae7a7505091889e00468294d2882c36 + md5: 2259ae0949dbe20c0665850365109b27 + depends: + - __osx >=11.0 + - libzlib >=1.3.2,<2.0a0 + license: zlib-acknowledgement + size: 289546 + timestamp: 1776315246750 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libprotobuf-4.25.3-hc39d83c_1.conda + sha256: f51bde2dfe73968ab3090c1098f520b65a8d8f11e945cb13bf74d19e30966b61 + md5: fa77986d9170450c014586ab87e144f8 + depends: + - __osx >=11.0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libcxx >=17 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 2177164 + timestamp: 1727160770879 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libre2-11-2023.09.01-h7b2c953_2.conda + sha256: c8a0a6e7a627dc9c66ffb8858f8f6d499f67fd269b6636b25dc5169760610f05 + md5: 0b7b2ced046d6b5fe6e9d46b1ee0324c + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcxx >=16 + constrains: + - re2 2023.09.01.* + license: BSD-3-Clause + license_family: BSD + size: 171443 + timestamp: 1708947163461 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/librsvg-2.62.2-he8aa2a2_0.conda + sha256: 17c1544598dd50840e74ef17ea5b4fd27408140827f3f2c17c052086d4036a88 + md5: 44d4dc506e384f90cad14e5de90fbe3d + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - gdk-pixbuf >=2.44.6,<3.0a0 + - harfbuzz >=14.2.0 + - libglib >=2.88.1,<3.0a0 + - libxml2-16 >=2.14.6 + - pango >=1.56.4,<2.0a0 + constrains: + - __osx >=11.0 + license: LGPL-2.1-or-later + size: 2397194 + timestamp: 1779415822239 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsodium-1.0.22-h1a92334_1.conda + sha256: 202be45db5726757a8ea1f374f85aacc18c504f5ff15b2558496dff4c8779c48 + md5: 9ed5ab909c449bdcae72322e44875a18 + depends: + - __osx >=11.0 + license: ISC + size: 247352 + timestamp: 1779164136206 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.53.1-h1b79a29_0.conda + sha256: 49daec7c83e70d4efc17b813547824bc2bcf2f7256d84061d24fbfe537da9f74 + md5: 6681822ea9d362953206352371b6a904 + depends: + - __osx >=11.0 + - libzlib >=1.3.2,<2.0a0 + license: blessing + size: 920047 + timestamp: 1777987051643 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libssh2-1.11.1-h1590b86_0.conda + sha256: 8bfe837221390ffc6f111ecca24fa12d4a6325da0c8d131333d63d6c37f27e0a + md5: b68e8f66b94b44aaa8de4583d3d4cc40 + depends: + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.0,<4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 279193 + timestamp: 1745608793272 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libthrift-0.19.0-h026a170_1.conda + sha256: b2c1b30d36f0412c0c0313db76a0236d736f3a9b887b8ed16182f531e4b7cb80 + md5: 4b8b21eb00d9019e9fa351141da2a6ac + depends: + - libcxx >=15.0.7 + - libevent >=2.1.12,<2.1.13.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.1.3,<4.0a0 + license: Apache-2.0 + license_family: APACHE + size: 331154 + timestamp: 1695958512679 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libtiff-4.7.1-h4030677_1.conda + sha256: e9248077b3fa63db94caca42c8dbc6949c6f32f94d1cafad127f9005d9b1507f + md5: e2a72ab2fa54ecb6abab2b26cde93500 + depends: + - __osx >=11.0 + - lerc >=4.0.0,<5.0a0 + - libcxx >=19 + - libdeflate >=1.25,<1.26.0a0 + - libjpeg-turbo >=3.1.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - zstd >=1.5.7,<1.6.0a0 + license: HPND + size: 373892 + timestamp: 1762022345545 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libutf8proc-2.8.0-hc098a78_1.conda + sha256: 7807a98522477a8bf12460402845224f607ab6e1e73ac316b667169f5143cfe5 + md5: ed89b8bf0d74d23ce47bcf566dd36608 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 82462 + timestamp: 1732829832932 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.6.0-h07db88b_0.conda + sha256: a4de3f371bb7ada325e1f27a4ef7bcc81b2b6a330e46fac9c2f78ac0755ea3dd + md5: e5e7d467f80da752be17796b87fe6385 + depends: + - __osx >=11.0 + constrains: + - libwebp 1.6.0 + license: BSD-3-Clause + license_family: BSD + size: 294974 + timestamp: 1752159906788 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxcb-1.17.0-hdb1d25a_0.conda + sha256: bd3816218924b1e43b275863e21a3e13a5db4a6da74cca8e60bc3c213eb62f71 + md5: af523aae2eca6dfa1c8eec693f5b9a79 + depends: + - __osx >=11.0 + - pthread-stubs + - xorg-libxau >=1.0.11,<2.0a0 + - xorg-libxdmcp + license: MIT + license_family: MIT + size: 323658 + timestamp: 1727278733917 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-16-2.15.3-h5ef1a60_0.conda + sha256: ff75b84cdb9e8d123db2fa694a8ac2c2059516b6cbc98ac21fb68e235d0fd354 + md5: 19edaa53885fc8205614b03da2482282 + depends: + - __osx >=11.0 + - icu >=78.3,<79.0a0 + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libzlib >=1.3.2,<2.0a0 + constrains: + - libxml2 2.15.3 + license: MIT + license_family: MIT + size: 466360 + timestamp: 1776377102261 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libzlib-1.3.2-h8088a28_2.conda + sha256: 361415a698514b19a852f5d1123c5da746d4642139904156ddfca7c922d23a05 + md5: bc5a5721b6439f2f62a84f2548136082 + depends: + - __osx >=11.0 + constrains: + - zlib 1.3.2 *_2 + license: Zlib + license_family: Other + size: 47759 + timestamp: 1774072956767 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-22.1.6-hc7d1edf_0.conda + sha256: 12d3652549a9abd30f3cc14797715327b86e91001d11865106eb3e02c40be9da + md5: b8cf70b77b2ed10d5f82e367c327b76b + depends: + - __osx >=11.0 + constrains: + - openmp 22.1.6|22.1.6.* + - intel-openmp <0.0a0 + license: Apache-2.0 WITH LLVM-exception + license_family: APACHE + size: 284850 + timestamp: 1779340584016 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-22.1.7-hc7d1edf_0.conda + sha256: 6bf27376f11198c01a88a1c8234470f45bce0aa7502b7e7988ef03ef5db3a890 + md5: 7c6a5897a8bc5b6d509a4ee9dec7fcc8 + depends: + - __osx >=11.0 + constrains: + - openmp 22.1.7|22.1.7.* + - intel-openmp <0.0a0 + license: Apache-2.0 WITH LLVM-exception + size: 285162 + timestamp: 1780455637760 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-4.3.3-py310hc798581_1.conda + sha256: 6b1faa741c004dec628b5c13dfaf0c02f600f3f5b992e33f0541b4e2ab681e7f + md5: c09f5bd96704b72109c1287e5cac754c + depends: + - __osx >=11.0 + - lz4-c >=1.9.3,<1.10.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 105681 + timestamp: 1725089664030 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/lz4-c-1.9.4-hb7217d7_0.conda + sha256: fc343b8c82efe40819b986e29ba748366514e5ab94a1e1138df195af5f45fa24 + md5: 45505bec548634f7d05e02fb25262cb9 + depends: + - libcxx >=14.0.6 + license: BSD-2-Clause + license_family: BSD + size: 141188 + timestamp: 1674727268278 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/markupsafe-3.0.3-py310hb46c203_1.conda + sha256: c1a7cf542e15d5bcd1efbae5a60a75223f36f4870cc96c19ab05fcde642b0394 + md5: 4d372362aa5dd174b9300828ac29f806 + depends: + - __osx >=11.0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - jinja2 >=3.0.0 + license: BSD-3-Clause + license_family: BSD + size: 23871 + timestamp: 1772445652936 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/matplotlib-base-3.7.3-py310hd1c642d_0.conda + sha256: 9ac5842ab02ca94242c866b98c015acc6345b54739e3c81716051977258bdc18 + md5: f380d68be47c4256da71b0da83936be8 + depends: + - certifi >=2020.06.20 + - contourpy >=1.0.1 + - cycler >=0.10 + - fonttools >=4.22.0 + - freetype >=2.12.1,<3.0a0 + - kiwisolver >=1.0.1 + - libcxx >=15.0.7 + - numpy >=1.20 + - numpy >=1.22.4,<2.0a0 + - packaging >=20.0 + - pillow >=6.2.0 + - pyparsing >=2.3.1 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python-dateutil >=2.7 + - python_abi 3.10.* *_cp310 + license: LicenseRef-PSF-2.0 and CC0-1.0 + license_family: PSF + size: 6768243 + timestamp: 1695077399967 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/msgpack-python-1.1.2-py310h0e897d2_1.conda + sha256: 0fa5e8ebf78e3a27f5e2646b021b2b0988746372dfcd95dd50c2840cef9a0118 + md5: 03c0ac9f01348d35e889dab9b9bb01fb + depends: + - __osx >=11.0 + - libcxx >=19 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 83632 + timestamp: 1762504396042 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ncurses-6.6-h1d4f5a5_0.conda + sha256: 4ea6c620b87bd1d42bb2ccc2c87cd2483fa2d7f9e905b14c223f11ff3f4c455d + md5: 343d10ed5b44030a2f67193905aea159 + depends: + - __osx >=11.0 + license: X11 AND BSD-3-Clause + size: 805509 + timestamp: 1777423252320 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.22.4-py310h0a343b5_0.tar.bz2 + sha256: bec72e79a265dd1ce30ca2bfdf23f90d28b0da4b49136c11f1f864cf9d4b4366 + md5: 7f336a1a159b4ba15e209353029ea8af + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=13.0.1 + - liblapack >=3.9.0,<4.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 6065933 + timestamp: 1653325981698 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.2.6-py310h4d83441_0.conda + sha256: 87704bcd5f4a4f88eaf2a97f07e9825803b58a8003a209b91e89669317523faf + md5: f4bd8ac423d04b3c444b96f2463d3519 + depends: + - __osx >=11.0 + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=18 + - liblapack >=3.9.0,<4.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 5841650 + timestamp: 1747545043441 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.4-hd9e9057_0.conda + sha256: 60aca8b9f94d06b852b296c276b3cf0efba5a6eb9f25feb8708570d3a74f00e4 + md5: 4b5d3a91320976eec71678fad1e3569b + depends: + - __osx >=11.0 + - libcxx >=19 + - libpng >=1.6.55,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-2-Clause + license_family: BSD + size: 319697 + timestamp: 1772625397692 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.6.2-hd24854e_0.conda + sha256: c91bf510c130a1ea1b6ff023e28bac0ccaef869446acd805e2016f69ebdc49ea + md5: 25dcccd4f80f1638428613e0d7c9b4e1 + depends: + - __osx >=11.0 + - ca-certificates + license: Apache-2.0 + license_family: Apache + size: 3106008 + timestamp: 1775587972483 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.0-h4aad248_1.conda + sha256: 1706ed2e71929f5a2bba0e1041c7ecb064031e7b4ab5862777682c8bdc970bd6 + md5: b89ff040a46c45fba6687243e09b8509 + depends: + - __osx >=11.0 + - libcxx >=16 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - snappy >=1.2.0,<1.3.0a0 + - zstd >=1.5.5,<1.6.0a0 + license: Apache-2.0 + license_family: Apache + size: 414513 + timestamp: 1712616646377 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-1.3.5-py310hdead3df_0.tar.bz2 + sha256: d73f022c0b891df6338c1ce481932efbd55d54da878339280b9580a947a7bf7b + md5: 2afa7b4860e65d2bcac3f1ba193565e5 + depends: + - libcxx >=11.1.0 + - numpy >=1.21.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python-dateutil >=2.7.3 + - python_abi 3.10.* *_cp310 + - pytz >=2017.2 + - setuptools <60.0.0 + license: BSD-3-Clause + license_family: BSD + size: 12609991 + timestamp: 1639399090730 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pango-1.56.4-hf80efc4_1.conda + sha256: b57c59cf5abb06d407b3a79017b990ca5bfb10c15a10c62fc29e113f2b12d9a9 + md5: 4b433508ebb295c05dd3d03daf27f7bb + depends: + - __osx >=11.0 + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.16,<2.0a0 + - harfbuzz >=13.2.1 + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libglib >=2.86.4,<3.0a0 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + license: LGPL-2.1-or-later + size: 425743 + timestamp: 1774282709773 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pcre2-10.47-h30297fc_0.conda + sha256: 5e2e443f796f2fd92adf7978286a525fb768c34e12b1ee9ded4000a41b2894ba + md5: 9b4190c4055435ca3502070186eba53a + depends: + - __osx >=11.0 + - bzip2 >=1.0.8,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 850231 + timestamp: 1763655726735 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pillow-12.2.0-py310hc037f36_0.conda + sha256: c109b35803dfa3a066786de3199f3752841ff50242d5dfdb67a08066d4fb3043 + md5: 0e692125473a62d5bee4fc3d90e59f4c + depends: + - python + - __osx >=11.0 + - python 3.10.* *_cpython + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - python_abi 3.10.* *_cp310 + - libxcb >=1.17.0,<2.0a0 + - tk >=8.6.13,<8.7.0a0 + - zlib-ng >=2.3.3,<2.4.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - lcms2 >=2.18,<3.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - openjpeg >=2.5.4,<3.0a0 + - libjpeg-turbo >=3.1.2,<4.0a0 + license: HPND + size: 815393 + timestamp: 1775060469004 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pixman-0.46.4-h81086ad_1.conda + sha256: 29c9b08a9b8b7810f9d4f159aecfd205fce051633169040005c0b7efad4bc718 + md5: 17c3d745db6ea72ae2fce17e7338547f + depends: + - __osx >=11.0 + - libcxx >=19 + license: MIT + license_family: MIT + size: 248045 + timestamp: 1754665282033 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-1.0.0-py310h8d151f0_0.conda + sha256: 934034a8dc88f6ba42a7bcd2f990336000dd1b02d07ee9f2798e47dde4df7d70 + md5: db925aa7aba66ce131502405566428b2 + depends: + - __osx >=11.0 + - numpy >=1.16.0,<2 + - packaging + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - typing_extensions >=4.0.0 + constrains: + - __osx >=11.0 + license: MIT + license_family: MIT + run_exports: {} + size: 19802604 + timestamp: 1720030602826 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-5.9.3-py310h8e9501a_1.tar.bz2 + sha256: b8dfc8b921bc124d24deee430d7ebb8a4585525dbb8867d9833d1b85c79ecde8 + md5: 3ad3a26bf84360feba50dbf5c9e82d74 + depends: + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 369750 + timestamp: 1666772610832 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pthread-stubs-0.4-hd74edd7_1002.conda + sha256: 8ed65e17fbb0ca944bfb8093b60086e3f9dd678c3448b5de212017394c247ee3 + md5: 415816daf82e0b23a736a069a75e9da7 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 8381 + timestamp: 1726802424786 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-16.0.0-py310h7ed268f_0.conda + sha256: 865ed62d01f3acaaea59870086e7754c08f6132a94275166d4a8de3c23a672e3 + md5: 7f1fb12eabab3d85f9cfc0dbdf8730c9 + depends: + - libarrow-acero 16.0.0.* + - libarrow-dataset 16.0.0.* + - libarrow-substrait 16.0.0.* + - libparquet 16.0.0.* + - numpy >=1.22.4,<2.0a0 + - pyarrow-core 16.0.0 *_0_* + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: APACHE + size: 26104 + timestamp: 1715177817837 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyarrow-core-16.0.0-py310h75075a0_0_cpu.conda + sha256: e871ab04ee3e9df650de02d8c01f8b4fc99ad5063c4e6bfae89ddb5807ece1d2 + md5: bacf37a74c92d89c26dd4e7b5ee89fb2 + depends: + - __osx >=11.0 + - libarrow 16.0.0.* *cpu + - libcxx >=16 + - libzlib >=1.2.13,<2.0.0a0 + - numpy >=1.22.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - apache-arrow-proc =*=cpu + license: Apache-2.0 + license_family: APACHE + size: 3793811 + timestamp: 1715177743408 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyobjc-core-12.1-py310h9d23ab5_0.conda + sha256: e51eab618202c48d608ac1f68407bc59a5cc6e7c900ac0e7e4fc393a0e4521ac + md5: e5b81ccd4ad98423938a907181e334c6 + depends: + - __osx >=11.0 + - libffi >=3.5.2,<3.6.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - setuptools + license: MIT + license_family: MIT + size: 430199 + timestamp: 1763151408471 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyobjc-framework-cocoa-12.1-py310h8579ff5_0.conda + sha256: fb48b98a8a42d358534913068f9b6def5ca4a47215fce067aa55714a47708503 + md5: 25151040f66352906df7c7c80a36a93b + depends: + - __osx >=11.0 + - libffi >=3.5.2,<3.6.0a0 + - pyobjc-core 12.1.* + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 336237 + timestamp: 1763160523904 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.10.20-h1b19095_0_cpython.conda + sha256: f0f6fcbb6cfdee5a6b9c03b5b94d2bbe737f3b17a618006c7685cc48992ae667 + md5: 55ec25b0d09379eb11c32dbe09ee28c4 + depends: + - __osx >=11.0 + - bzip2 >=1.0.8,<2.0a0 + - libexpat >=2.7.4,<3.0a0 + - libffi >=3.4,<4.0a0 + - liblzma >=5.8.2,<6.0a0 + - libsqlite >=3.51.2,<4.0a0 + - libzlib >=1.3.1,<2.0a0 + - ncurses >=6.5,<7.0a0 + - openssl >=3.5.5,<4.0a0 + - readline >=8.3,<9.0a0 + - tk >=8.6.13,<8.7.0a0 + - tzdata + constrains: + - python_abi 3.10.* *_cp310 + license: Python-2.0 + size: 12468674 + timestamp: 1772730636766 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyyaml-6.0.3-py310hb46c203_1.conda + sha256: 22f0c040a56bfdb9dfa2072129b67db3f8bf738e52b243573316443d1da853a8 + md5: cdd081d256a691c8adc3cffad215988c + depends: + - __osx >=11.0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 163966 + timestamp: 1770223747482 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pyzmq-27.1.0-py310hf396ece_3.conda + sha256: 36cb5f64bcadff4c597cdba410004d11ca6a181301b3d75314655247594209fa + md5: 17d9e67836fd80fed6612fe91ada82da + depends: + - python + - libcxx >=19 + - python 3.10.* *_cpython + - __osx >=11.0 + - python_abi 3.10.* *_cp310 + - zeromq >=4.3.5,<4.4.0a0 + license: BSD-3-Clause + license_family: BSD + size: 305188 + timestamp: 1779484035951 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/re2-2023.09.01-h4cba328_2.conda + sha256: 0e0d44414381c39a7e6f3da442cb41c637df0dcb383a07425f19c19ccffa0118 + md5: 0342882197116478a42fa4ea35af79c1 + depends: + - libre2-11 2023.09.01 h7b2c953_2 + license: BSD-3-Clause + license_family: BSD + size: 26770 + timestamp: 1708947220914 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/readline-8.3-h46df422_0.conda + sha256: a77010528efb4b548ac2a4484eaf7e1c3907f2aec86123ed9c5212ae44502477 + md5: f8381319127120ce51e081dce4865cf4 + depends: + - __osx >=11.0 + - ncurses >=6.5,<7.0a0 + license: GPL-3.0-only + license_family: GPL + size: 313930 + timestamp: 1765813902568 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.30.0-py310hf3301a5_0.conda + sha256: 842bfe772072b6ca1ebc286e9fcbe95717d1528ec921687076d707d02d64fa61 + md5: 38645c71e72b3cc640eb508d05a28d0c + depends: + - python + - python 3.10.* *_cpython + - __osx >=11.0 + - python_abi 3.10.* *_cp310 + constrains: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 357454 + timestamp: 1764543222201 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.0.2-py310h5f111c3_0.tar.bz2 + sha256: d308b0112fe0436769fe101f6558ffa20bd97f905863b0c143b6ff6bf05ab939 + md5: aa70ea0f2fefd240f3034ad878954376 + depends: + - joblib >=0.11 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=11.1.0 + - llvm-openmp >=11.1.0 + - numpy >=1.21.5,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + - scipy + - threadpoolctl + license: BSD-3-Clause + license_family: BSD + size: 7614791 + timestamp: 1640464695209 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.15.2-py310h32ab4ed_0.conda + sha256: f6ff2c1ba4775300199e8bc0331d2e2ccb5906f58f3835c5426ddc591c9ad7bf + md5: a389f540c808b22b3c696d7aea791a41 + depends: + - __osx >=11.0 + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=18 + - libgfortran >=5 + - libgfortran5 >=13.2.0 + - liblapack >=3.9.0,<4.0a0 + - numpy <2.5 + - numpy >=1.19,<3 + - numpy >=1.23.5 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 13507343 + timestamp: 1739792089317 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.7.3-py310ha0d8a01_1.tar.bz2 + sha256: 6a12ac4bdbc9c259a07ef20f7e995a1092b7bb98ac444a5d398585e0f011d21b + md5: 0caa41ec83eeed757c02f0473d38d5c9 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - libcxx >=14.0.4 + - libgfortran >=5 + - libgfortran5 >=11.3.0 + - liblapack >=3.9.0,<4.0a0 + - numpy >=1.21.6,<1.23 + - numpy >=1.21.6,<2.0a0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + constrains: + - libopenblas <0.3.26 + license: BSD-3-Clause + license_family: BSD + size: 19982129 + timestamp: 1667962773768 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/setuptools-59.8.0-py310hbe9552e_1.tar.bz2 + sha256: ddcb92e7c9d7bfbb0e0e131443999338aa74a03028d4e0c137463ca5f1a331c5 + md5: 41f6df9f13eb8f017eb6ac0ade6ee4f8 + depends: + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 1052030 + timestamp: 1648692161242 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/snappy-1.2.2-hada39a4_1.conda + sha256: cb9305ede19584115f43baecdf09a3866bfcd5bcca0d9e527bd76d9a1dbe2d8d + md5: fca4a2222994acd7f691e57f94b750c5 + depends: + - libcxx >=19 + - __osx >=11.0 + license: BSD-3-Clause + license_family: BSD + size: 38883 + timestamp: 1762948066818 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tk-8.6.13-h010d191_3.conda + sha256: 799cab4b6cde62f91f750149995d149bc9db525ec12595e8a1d91b9317f038b3 + md5: a9d86bc62f39b94c4661716624eb21b0 + depends: + - __osx >=11.0 + - libzlib >=1.3.1,<2.0a0 + license: TCL + license_family: BSD + size: 3127137 + timestamp: 1769460817696 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/tornado-6.5.6-py310h72544b6_0.conda + sha256: 0e5e2a4b41493c6c7f13d5bb94fc50f6c9ed4b11fa551172fe2cb967e005a40d + md5: c48f94f8f0457b65929e874559f0f0d0 + depends: + - __osx >=11.0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 670200 + timestamp: 1779916220748 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/unicodedata2-17.0.1-py310h72544b6_0.conda + sha256: 48e56c2068cce4b2d09cceca65ca1c877ebf550e3ad67af3ed30db162bc89e0b + md5: a35cf23aa03fb8d3a95158253918ed00 + depends: + - __osx >=11.0 + - python >=3.10,<3.11.0a0 + - python >=3.10,<3.11.0a0 *_cpython + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: Apache + size: 416056 + timestamp: 1770910020955 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxau-1.0.12-hc919400_1.conda + sha256: adae11db0f66f86156569415ed79cda75b2dbf4bea48d1577831db701438164f + md5: 78b548eed8227a689f93775d5d23ae09 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 14105 + timestamp: 1762976976084 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/xorg-libxdmcp-1.1.5-hc919400_1.conda + sha256: f7fa0de519d8da589995a1fe78ef74556bb8bc4172079ae3a8d20c3c81354906 + md5: 9d1299ace1924aa8f4e0bc8e71dd0cf7 + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 19156 + timestamp: 1762977035194 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h925e9cb_3.conda + sha256: b03433b13d89f5567e828ea9f1a7d5c5d697bf374c28a4168d71e9464f5dafac + md5: 78a0fe9e9c50d2c381e8ee47e3ea437d + depends: + - __osx >=11.0 + license: MIT + license_family: MIT + size: 83386 + timestamp: 1753484079473 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h10816f8_11.conda + sha256: 01fd50d2801b23b59fafea6bf704a6c5faf0f5969104400eae0e6572cb2e5304 + md5: d31c0e54c4f9c51100ec8c812ee925d1 + depends: + - libcxx >=19 + - __osx >=11.0 + - krb5 >=1.22.2,<1.23.0a0 + - libsodium >=1.0.22,<1.0.23.0a0 + license: MPL-2.0 + license_family: MOZILLA + size: 245404 + timestamp: 1779124076307 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda + sha256: a339606a6b224bb230ff3d711e801934f3b3844271df9720165e0353716580d4 + md5: d99c2a23a31b0172e90f456f580b695e + depends: + - __osx >=11.0 + - libcxx >=19 + license: Zlib + license_family: Other + size: 94375 + timestamp: 1770168363685 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.25.0-py310hf151d32_1.conda + sha256: be3cd0e825959c8401f083319ef97892115e4dfddb661da088648e442d25008a + md5: 82044ec889ec97e36401ef1fc40b05fc + depends: + - python + - cffi >=1.11 + - zstd >=1.5.7,<1.5.8.0a0 + - python 3.10.* *_cpython + - __osx >=11.0 + - python_abi 3.10.* *_cp310 + - zstd >=1.5.7,<1.6.0a0 + license: BSD-3-Clause + license_family: BSD + size: 377436 + timestamp: 1762512758954 +- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda + sha256: 9485ba49e8f47d2b597dd399e88f4802e100851b27c21d7525625b0b4025a5d9 + md5: ab136e4c34e97f34fb621d2592a393d8 + depends: + - __osx >=11.0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 433413 + timestamp: 1764777166076 +- conda: https://conda.anaconda.org/conda-forge/win-64/argon2-cffi-bindings-25.1.0-py310h29418f3_2.conda + sha256: f7302250bc8844057271c3a7ba610f4cd6cf50dba850ed4138a0205edbed8f98 + md5: 16b3afb462e093533f45c21d0ee3a0d7 + depends: + - cffi >=1.0.1 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 38065 + timestamp: 1762509673392 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-auth-0.7.20-h6823eb1_0.conda + sha256: 793d992fd896784263483d5428a3ec797f42ab6ce896fcb549e0a4b6c48540d4 + md5: bdb3ab44dcc47c12d640fc6c14888bc1 + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 100860 + timestamp: 1715288041401 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-cal-0.6.12-hc83774a_0.conda + sha256: 2b65433ffbcaf69649d238c2749794b81dd8e1c7d7baf832efe93f991ca0ed94 + md5: 4dcf49759f88c084396204addd0eb7b1 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 46200 + timestamp: 1714178039916 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-common-0.9.17-h2466b09_0.conda + sha256: c8e58a552ee66729f78bbdf78cfd1a9813ad5f7be2c5f7c460fa2f7c19031d33 + md5: 45f674089045f64c35d1ba0485842b99 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 224504 + timestamp: 1713864277252 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-compression-0.2.18-hc83774a_4.conda + sha256: 7084083b98b4f40542374d6f2b8cb36c40c22cd49a1f4df1da9c9e1278e768de + md5: a9c2159343eb5cd1b62589f209b1e623 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 22670 + timestamp: 1714044311775 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-event-stream-0.4.2-hc6c0aac_10.conda + sha256: de0e11d8690d8430b33213fed285c0872314f4c95b85b59ac7cd4b9b5c12cb78 + md5: 154013fe21be2e4f6206b894537a95a3 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 54934 + timestamp: 1715026670796 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-http-0.8.1-hced5053_13.conda + sha256: ceb7941ae41e11fc287af579261eaca539cc22fcac641aa0ebd254c23aee8c64 + md5: 4bf3b37a30279d31584e3efb0ab2c722 + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-compression >=0.2.18,<0.2.19.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 181269 + timestamp: 1715026840322 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-io-0.14.8-hebaacdb_0.conda + sha256: 89eb826f187901450b4ebb731ad995f71fa317dc417b31ed621b5e16a0f86b94 + md5: e421ac978195e777aae02059bc129479 + depends: + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 159899 + timestamp: 1714868367395 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-mqtt-0.10.4-hdafd9a4_2.conda + sha256: e4d358f85bce410f4b06b4e60d5aa477b141c9d4ee70f8309aa2ed59cde8d299 + md5: 6d3ac34a5145d83e007eeda33b45fdba + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 158222 + timestamp: 1715058316429 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-s3-0.5.9-h7a83f0e_0.conda + sha256: dc43a11f938365792455dfe2b2d26194dfaa4c3367967ab156f2af8666923012 + md5: 1fcee9f1b1ffa9ef65a3c54330b73f0e + depends: + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 105667 + timestamp: 1715620221686 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-c-sdkutils-0.1.16-hc83774a_0.conda + sha256: 79d6542d6896d7d3f94d8f457924a241193bb2a8399ff929e544f774337238eb + md5: 3bdb282923a48cdd48233212a596bdae + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 53570 + timestamp: 1714208897748 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-checksums-0.1.18-hc83774a_4.conda + sha256: 401aa17135aea3af343e7d4730c2ea878bee3da72824850423168549667d3008 + md5: 197196903f52fbd3e55a26b1e30561a6 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 52400 + timestamp: 1714051200378 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-crt-cpp-0.26.8-h672a689_11.conda + sha256: 1f0c33aa118591a4454afb202e6bbfaec956a2e46f54298f242ab0221682fc40 + md5: 13436e11b88d0574524de9e3f3330332 + depends: + - aws-c-auth >=0.7.20,<0.7.21.0a0 + - aws-c-cal >=0.6.12,<0.6.13.0a0 + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-c-http >=0.8.1,<0.8.2.0a0 + - aws-c-io >=0.14.8,<0.14.9.0a0 + - aws-c-mqtt >=0.10.4,<0.10.5.0a0 + - aws-c-s3 >=0.5.9,<0.5.10.0a0 + - aws-c-sdkutils >=0.1.16,<0.1.17.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 248588 + timestamp: 1716301395438 +- conda: https://conda.anaconda.org/conda-forge/win-64/aws-sdk-cpp-1.11.267-h12f3f85_8.conda + sha256: 8717821ee98cc8c4f9f1e5be8a89a40ff68f6139be7b0461640c2db60ebcaf2a + md5: 7f43d81e0a58785839ed2b5bd92984d1 + depends: + - aws-c-common >=0.9.17,<0.9.18.0a0 + - aws-c-event-stream >=0.4.2,<0.4.3.0a0 + - aws-checksums >=0.1.18,<0.1.19.0a0 + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 3426476 + timestamp: 1715176833996 +- conda: https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hfd05255_4.conda + sha256: df2a43cc4a99bd184cb249e62106dfa9f55b3d06df9b5fc67072b0336852ff65 + md5: 441706c019985cf109ced06458e6f742 + depends: + - brotli-bin 1.1.0 hfd05255_4 + - libbrotlidec 1.1.0 hfd05255_4 + - libbrotlienc 1.1.0 hfd05255_4 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 20233 + timestamp: 1756599828380 +- conda: https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-hfd05255_4.conda + sha256: e92c783502d95743b49b650c9276e9c56c7264da55429a5e45655150a6d1b0cf + md5: ef022c8941d7dcc420c8533b0e419733 + depends: + - libbrotlidec 1.1.0 hfd05255_4 + - libbrotlienc 1.1.0 hfd05255_4 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 21425 + timestamp: 1756599802301 +- conda: https://conda.anaconda.org/conda-forge/win-64/brotli-python-1.1.0-py310h73ae2b4_4.conda + sha256: 7d316ca454968256908c9d947726bc8f51f85fc2a2912814e1a3a98600429855 + md5: b53cd64780fbd287d3be3004cb6d7743 + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - libbrotlicommon 1.1.0 hfd05255_4 + license: MIT + license_family: MIT + size: 322865 + timestamp: 1756599996126 +- conda: https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h0ad9c76_9.conda + sha256: 76dfb71df5e8d1c4eded2dbb5ba15bb8fb2e2b0fe42d94145d5eed4c75c35902 + md5: 4cb8e6b48f67de0b018719cdf1136306 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: bzip2-1.0.6 + license_family: BSD + size: 56115 + timestamp: 1771350256444 +- conda: https://conda.anaconda.org/conda-forge/win-64/c-ares-1.34.6-hfd05255_0.conda + sha256: 5e1e2e24ce279f77e421fcc0e5846c944a8a75f7cf6158427c7302b02984291a + md5: 7c6da34e5b6e60b414592c74582e28bf + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 193550 + timestamp: 1765215100218 +- conda: https://conda.anaconda.org/conda-forge/win-64/cairo-1.18.4-h477c42c_1.conda + sha256: 9ee4ad706c5d3e1c6c469785d60e3c2b263eec569be0eac7be33fbaef978bccc + md5: 52ea1beba35b69852d210242dd20f97d + depends: + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libglib >=2.86.3,<3.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libzlib >=1.3.1,<2.0a0 + - pixman >=0.46.4,<1.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: LGPL-2.1-only or MPL-1.1 + size: 1537783 + timestamp: 1766416059188 +- conda: https://conda.anaconda.org/conda-forge/win-64/cffi-1.15.1-py310h8d17308_5.conda + sha256: f4526d682c6c5d8108b0f714067840ac0347209153bea73a76e90aacf7eb8d6e + md5: 2205c496c047bc9cb36d8e06b22b2774 + depends: + - pycparser + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: MIT + license_family: MIT + size: 234388 + timestamp: 1695367664583 +- conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.2.1-py310h232114e_0.conda + sha256: 9a53e5c28fc4348743beee9e2700a64e2378cdc8a383653da0501f05df677600 + md5: 69968a52474279f0c44c08c87752096f + depends: + - numpy >=1.20 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + size: 189962 + timestamp: 1712430301862 +- conda: https://conda.anaconda.org/conda-forge/win-64/debugpy-1.8.21-py310h699e580_0.conda + sha256: a9ecd0b29dd28f818acbd8e9e10f19e62be87fa65d69599f35389cdf6d53e410 + md5: 16b93bdf7695d43652f3b00ffc099195 + depends: + - python + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - python_abi 3.10.* *_cp310 + license: MIT + size: 3489511 + timestamp: 1780390184311 +- conda: https://conda.anaconda.org/conda-forge/win-64/fontconfig-2.18.1-hd47e2ca_0.conda + sha256: 9217184c4a8e82101b0e512b059ae3ff67e3913133b9031edad89ab5341284e4 + md5: abd79bad98c99c1a116154d6de74ea89 + depends: + - libexpat >=2.8.1,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libiconv >=1.18,<2.0a0 + - libintl >=0.22.5,<1.0a0 + - libzlib >=1.3.2,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + size: 202630 + timestamp: 1780450217840 +- conda: https://conda.anaconda.org/conda-forge/win-64/fonttools-4.63.0-py310hdb0e946_0.conda + sha256: c0f6d54b6885abb130493433b1774097b85bef53160db06b67a32f901cc4021e + md5: 9dac7726fecf466ec59e2c52d74dc4d5 + depends: + - brotli + - munkres + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - unicodedata2 >=15.1.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 2039962 + timestamp: 1778770491437 +- conda: https://conda.anaconda.org/conda-forge/win-64/freetype-2.14.3-h57928b3_0.conda + sha256: 70815dbae6ccdfbb0a47269101a260b0a2e11a2ab5c0f7209f325d01bdb18fb7 + md5: 507b36518b5a595edda64066c820a6ef + depends: + - libfreetype 2.14.3 h57928b3_0 + - libfreetype6 2.14.3 hdbac1cb_0 + license: GPL-2.0-only OR FTL + size: 185640 + timestamp: 1774300487600 +- conda: https://conda.anaconda.org/conda-forge/win-64/fribidi-1.0.16-hfd05255_0.conda + sha256: 15011071ee56c216ffe276c8d734427f1f893f275ef733f728d13f610ed89e6e + md5: c27bd87e70f970010c1c6db104b88b18 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: LGPL-2.1-or-later + size: 64394 + timestamp: 1757438741305 +- conda: https://conda.anaconda.org/conda-forge/win-64/getopt-win32-0.1-h6a83c73_3.conda + sha256: d04c4a6c11daa72c4a0242602e1d00c03291ef66ca2d7cd0e171088411d57710 + md5: 49c36fcad2e9af6b91e91f2ce5be8ebd + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + license: LGPL-3.0-only + license_family: LGPL + size: 26238 + timestamp: 1750744808182 +- conda: https://conda.anaconda.org/conda-forge/win-64/graphite2-1.3.14-hac47afa_2.conda + sha256: 5f1714b07252f885a62521b625898326ade6ca25fbc20727cfe9a88f68a54bfd + md5: b785694dd3ec77a011ccf0c24725382b + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: LGPL-2.0-or-later + license_family: LGPL + size: 96336 + timestamp: 1755102441729 +- conda: https://conda.anaconda.org/conda-forge/win-64/graphviz-14.1.2-h4c50273_0.conda + sha256: 58f83755509a19501a9efe40c484727ffa61fcfaf6a237870678a79638fa6982 + md5: afabed4c46b197b89eb974aa038d12db + depends: + - cairo >=1.18.4,<2.0a0 + - getopt-win32 >=0.1,<0.1.1.0a0 + - gts >=0.7.6,<0.8.0a0 + - libexpat >=2.7.3,<3.0a0 + - libgd >=2.3.3,<2.4.0a0 + - libglib >=2.86.3,<3.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - pango >=1.56.4,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: EPL-1.0 + license_family: Other + size: 1223547 + timestamp: 1769427507016 +- conda: https://conda.anaconda.org/conda-forge/win-64/gts-0.7.6-h6b5321d_4.conda + sha256: b79755d2f9fc2113b6949bfc170c067902bc776e2c20da26e746e780f4f5a2d4 + md5: a41f14768d5e377426ad60c613f2923b + depends: + - libglib >=2.76.3,<3.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: LGPL-2.0-or-later + license_family: LGPL + size: 188688 + timestamp: 1686545648050 +- conda: https://conda.anaconda.org/conda-forge/win-64/h5py-3.16.0-nompi_py310hb7e4da9_102.conda + sha256: 0d6f9e2dde8167cd0c579610609e644e2d35f61e9820db6e8728c40dca6b8ab3 + md5: 45271504d1b2880ad7ea541b7d2db85c + depends: + - cached-property + - hdf5 >=1.14.6,<1.14.7.0a0 + - numpy >=1.21,<3 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: BSD-3-Clause + license_family: BSD + size: 1006603 + timestamp: 1775581487436 +- conda: https://conda.anaconda.org/conda-forge/win-64/harfbuzz-14.2.0-h5a1b470_0.conda + sha256: 82abc468768c5130b45e4025fe289e0c84ea015d7fa23059503da212c89097cb + md5: b8862b83b5c899f5b65bcba0298b8478 + depends: + - cairo >=1.18.4,<2.0a0 + - graphite2 >=1.3.14,<2.0a0 + - icu >=78.3,<79.0a0 + - libexpat >=2.7.5,<3.0a0 + - libfreetype >=2.14.3 + - libfreetype6 >=2.14.3 + - libglib >=2.86.4,<3.0a0 + - libzlib >=1.3.2,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: MIT + license_family: MIT + size: 1322557 + timestamp: 1776778816190 +- conda: https://conda.anaconda.org/conda-forge/win-64/hdf5-1.14.6-nompi_hae35d4c_109.conda + sha256: 43e1de9c833f2e0011b1b3da3b57320096a3a5d0d642536b7598c40e7e70ba93 + md5: 72fdc189f6892cbec0b7bdffe44fec4e + depends: + - libaec >=1.1.5,<2.0a0 + - libcurl >=8.20.0,<9.0a0 + - libzlib >=1.3.2,<2.0a0 + - openssl >=3.5.6,<4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: BSD-3-Clause + license_family: BSD + size: 2377680 + timestamp: 1777518205631 +- conda: https://conda.anaconda.org/conda-forge/win-64/icu-78.3-h637d24d_0.conda + sha256: 1bda728d70a619731b278c859eda364146cb5b4b8c739a64da8128353d81d1c4 + md5: 0097b24800cb696915c3dbd1f5335d3f + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 14954024 + timestamp: 1773822508646 +- conda: https://conda.anaconda.org/conda-forge/win-64/kiwisolver-1.5.0-py310h1e1005b_0.conda + sha256: 7d19326d7345c1f35091c7382559bb46f658808cf31c46ed3545886ad0a6c640 + md5: e4359052ebd96c04465c8ea424e9cb4e + depends: + - python + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 73034 + timestamp: 1773067061551 +- conda: https://conda.anaconda.org/conda-forge/win-64/krb5-1.22.2-h0ea6238_0.conda + sha256: eb60f1ad8b597bcf95dee11bc11fe71a8325bc1204cf51d2bb1f2120ffd77761 + md5: 4432f52dc0c8eb6a7a6abc00a037d93c + depends: + - openssl >=3.5.5,<4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 751055 + timestamp: 1769769688841 +- conda: https://conda.anaconda.org/conda-forge/win-64/lcms2-2.19.1-hf2c6c5f_1.conda + sha256: 5ed63a32639a130564a870becb679fd52dfb816666a61ed3c023917389010480 + md5: 1df4012c8a2478699d07bc26af66d41e + depends: + - libjpeg-turbo >=3.1.4.1,<4.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 523194 + timestamp: 1780211799997 +- conda: https://conda.anaconda.org/conda-forge/win-64/lerc-4.1.0-hd936e49_0.conda + sha256: 45df58fca800b552b17c3914cc9ab0d55a82c5172d72b5c44a59c710c06c5473 + md5: 54b231d595bc1ff9bff668dd443ee012 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: Apache-2.0 + license_family: Apache + size: 172395 + timestamp: 1773113455582 +- conda: https://conda.anaconda.org/conda-forge/win-64/libabseil-20240116.2-cxx17_he0c23c2_1.conda + sha256: aafa7993698420ef786c145f660e6822139c02cf9230fbad43efff6d4828defc + md5: 19725e54b7f996e0a5748ec5e9e37ae9 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + constrains: + - libabseil-static =20240116.2=cxx17* + - abseil-cpp =20240116.2 + license: Apache-2.0 + license_family: Apache + size: 1802886 + timestamp: 1720857653184 +- conda: https://conda.anaconda.org/conda-forge/win-64/libaec-1.1.5-haf901d7_0.conda + sha256: e54c08964262c73671d9e80e400333e59c617e0b454476ad68933c0c458156c8 + md5: 43b6385cfad52a7083f2c41984eb4e91 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: BSD-2-Clause + license_family: BSD + size: 34463 + timestamp: 1769221960556 +- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-16.0.0-h107e38f_1_cpu.conda + build_number: 1 + sha256: ceb42e3f720b948cddf533030efb9af9d6e2b70826502d6681881808ff9b26c8 + md5: b3c144707aa41cc222d0e42631a93a0e + depends: + - aws-crt-cpp >=0.26.8,<0.26.9.0a0 + - aws-sdk-cpp >=1.11.267,<1.11.268.0a0 + - bzip2 >=1.0.8,<2.0a0 + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libbrotlidec >=1.1.0,<1.2.0a0 + - libbrotlienc >=1.1.0,<1.2.0a0 + - libcrc32c >=1.1.2,<1.2.0a0 + - libcurl >=8.7.1,<9.0a0 + - libgoogle-cloud >=2.23.0,<2.24.0a0 + - libgoogle-cloud-storage >=2.23.0,<2.24.0a0 + - libre2-11 >=2023.9.1 + - libutf8proc >=2.8.0,<3.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - orc >=2.0.0,<2.0.1.0a0 + - re2 + - snappy >=1.2.0,<1.3.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + - zstd >=1.5.6,<1.6.0a0 + - libutf8proc <2.9 + constrains: + - arrow-cpp <0.0a0 + - parquet-cpp <0.0a0 + - apache-arrow-proc =*=cpu + license: Apache-2.0 + license_family: APACHE + size: 5031876 + timestamp: 1715199497275 +- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-16.0.0-he0c23c2_1_cpu.conda + build_number: 1 + sha256: bf472ee7c1b695faeebc75baf347d840338ae5ec8419d4c1901f292e5855793b + md5: 9cc27cba4053a5b1ef0e4b95839f1b84 + depends: + - libarrow 16.0.0 h107e38f_1_cpu + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: APACHE + size: 445462 + timestamp: 1715199582817 +- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-dataset-16.0.0-he0c23c2_1_cpu.conda + build_number: 1 + sha256: 566e3ee4c69be5d1416fc696c8bc043e055c4f30af377ede50a1768ae99f971f + md5: 5a274380a3da1d6ce66c05ec643e57b5 + depends: + - libarrow 16.0.0 h107e38f_1_cpu + - libarrow-acero 16.0.0 he0c23c2_1_cpu + - libparquet 16.0.0 h178134c_1_cpu + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: APACHE + size: 427473 + timestamp: 1715199880627 +- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-substrait-16.0.0-h1f0e801_1_cpu.conda + build_number: 1 + sha256: af0c822c5e96f78f7eee94e9948a4e308b99d2b316986f3c3732043ed165c15b + md5: 0a0ce4672dd1af883a8fc46976ec01c1 + depends: + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libarrow 16.0.0 h107e38f_1_cpu + - libarrow-acero 16.0.0 he0c23c2_1_cpu + - libarrow-dataset 16.0.0 he0c23c2_1_cpu + - libprotobuf >=4.25.3,<4.25.4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: APACHE + size: 384489 + timestamp: 1715200011755 +- conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.11.0-8_h8455456_mkl.conda + build_number: 8 + sha256: 43a87b59e6d4c68d80b2e4de487b1b54d66fe1f9a06636909b5a5ab9eae27269 + md5: 4a0ce24b1a946ff77ae9eaa7ef015a33 + depends: + - mkl >=2026.0.0,<2027.0a0 + constrains: + - libcblas 3.11.0 8*_mkl + - liblapacke 3.11.0 8*_mkl + - blas 2.308 mkl + - liblapack 3.11.0 8*_mkl + license: BSD-3-Clause + license_family: BSD + size: 68103 + timestamp: 1779859688049 +- conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-35_h5709861_mkl.conda + build_number: 35 + sha256: 4180e7ab27ed03ddf01d7e599002fcba1b32dcb68214ee25da823bac371ed362 + md5: 45d98af023f8b4a7640b1f713ce6b602 + depends: + - mkl >=2024.2.2,<2025.0a0 + constrains: + - blas 2.135 mkl + - liblapack 3.9.0 35*_mkl + - libcblas 3.9.0 35*_mkl + - liblapacke 3.9.0 35*_mkl + license: BSD-3-Clause + license_family: BSD + size: 66044 + timestamp: 1757003486248 +- conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-hfd05255_4.conda + sha256: 65d0aaf1176761291987f37c8481be132060cc3dbe44b1550797bc27d1a0c920 + md5: 58aec7a295039d8614175eae3a4f8778 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 71243 + timestamp: 1756599708777 +- conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlidec-1.1.0-hfd05255_4.conda + sha256: aa03aff197ed503e38145d0d0f17c30382ac1c6d697535db24c98c272ef57194 + md5: bf0ced5177fec8c18a7b51d568590b7c + depends: + - libbrotlicommon 1.1.0 hfd05255_4 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 33430 + timestamp: 1756599740173 +- conda: https://conda.anaconda.org/conda-forge/win-64/libbrotlienc-1.1.0-hfd05255_4.conda + sha256: a593cde3e728a1e0486a19537846380e3ce90ae9d6c22c1412466a49474eeeed + md5: 37f4669f8ac2f04d826440a8f3f42300 + depends: + - libbrotlicommon 1.1.0 hfd05255_4 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 245418 + timestamp: 1756599770744 +- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-8_h2a3cdd5_mkl.conda + build_number: 8 + sha256: 2a5b6555b481df4603e44cba49a6ef727584fd2f3c5235dd4bcb3028fffbdfb5 + md5: 09f1d8e4d2675d34ad2acb115211d10c + depends: + - libblas 3.11.0 8_h8455456_mkl + constrains: + - liblapacke 3.11.0 8*_mkl + - blas 2.308 mkl + - liblapack 3.11.0 8*_mkl + license: BSD-3-Clause + license_family: BSD + size: 68443 + timestamp: 1779859701498 +- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-35_h2a3cdd5_mkl.conda + build_number: 35 + sha256: 88939f6c1b5da75bd26ce663aa437e1224b26ee0dab5e60cecc77600975f397e + md5: 9639091d266e92438582d0cc4cfc8350 + depends: + - libblas 3.9.0 35_h5709861_mkl + constrains: + - blas 2.135 mkl + - liblapack 3.9.0 35*_mkl + - liblapacke 3.9.0 35*_mkl + license: BSD-3-Clause + license_family: BSD + size: 66398 + timestamp: 1757003514529 +- conda: https://conda.anaconda.org/conda-forge/win-64/libcrc32c-1.1.2-h0e60522_0.tar.bz2 + sha256: 75e60fbe436ba8a11c170c89af5213e8bec0418f88b7771ab7e3d9710b70c54e + md5: cd4cc2d0c610c8cb5419ccc979f2d6ce + depends: + - vc >=14.1,<15.0a0 + - vs2015_runtime >=14.16.27012 + license: BSD-3-Clause + license_family: BSD + size: 25694 + timestamp: 1633684287072 +- conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.20.0-h8206538_0.conda + sha256: f4ce5aa835a698532feaa368e804365a7e45a9edebe006a8e1c80505d893c24e + md5: 7bee27a8f0a295117ccb864f30d2d87e + depends: + - krb5 >=1.22.2,<1.23.0a0 + - libssh2 >=1.11.1,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: curl + license_family: MIT + size: 393114 + timestamp: 1777461635732 +- conda: https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.25-h51727cc_0.conda + sha256: 834e4881a18b690d5ec36f44852facd38e13afe599e369be62d29bd675f107ee + md5: e77030e67343e28b084fabd7db0ce43e + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 156818 + timestamp: 1761979842440 +- conda: https://conda.anaconda.org/conda-forge/win-64/libevent-2.1.12-h3671451_1.conda + sha256: af03882afb7a7135288becf340c2f0cf8aa8221138a9a7b108aaeb308a486da1 + md5: 25efbd786caceef438be46da78a7b5ef + depends: + - openssl >=3.1.1,<4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + size: 410555 + timestamp: 1685726568668 +- conda: https://conda.anaconda.org/conda-forge/win-64/libexpat-2.8.1-hac47afa_0.conda + sha256: a65e518c20d1482182bc0f1f6dd5d992f25ca44c3b32307be39ae8310db8f060 + md5: 23eb9474a16d4b9f6f27429989e82002 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - expat 2.8.1.* + license: MIT + license_family: MIT + size: 71280 + timestamp: 1779278786150 +- conda: https://conda.anaconda.org/conda-forge/win-64/libffi-3.5.2-h3d046cb_0.conda + sha256: 59d01f2dfa8b77491b5888a5ab88ff4e1574c9359f7e229da254cdfe27ddc190 + md5: 720b39f5ec0610457b725eb3f396219a + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 45831 + timestamp: 1769456418774 +- conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype-2.14.3-h57928b3_0.conda + sha256: 71fae9ae05563ceec70adceb7bc66faa326a81a6590a8aac8a5074019070a2d8 + md5: d9f70dd06674e26b6d5a657ddd22b568 + depends: + - libfreetype6 >=2.14.3 + license: GPL-2.0-only OR FTL + size: 8379 + timestamp: 1774300468411 +- conda: https://conda.anaconda.org/conda-forge/win-64/libfreetype6-2.14.3-hdbac1cb_0.conda + sha256: 497e9ab7c80f579e1b2850523740d6a543b8020f6b43be6bd6e83b3a6fb7fb32 + md5: f9975a0177ee6cdda10c86d1db1186b0 + depends: + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - freetype >=2.14.3 + license: GPL-2.0-only OR FTL + size: 340180 + timestamp: 1774300467879 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgd-2.3.3-h4974f7c_12.conda + sha256: 9ab562c718bd3fcef5f6189c8e2730c3d9321e05f13749a611630475d41207fc + md5: 3a5b40267fcd31f1ba3a24014fe92044 + depends: + - fontconfig >=2.15.0,<3.0a0 + - fonts-conda-ecosystem + - icu >=78.1,<79.0a0 + - libexpat >=2.7.3,<3.0a0 + - libfreetype >=2.14.1 + - libfreetype6 >=2.14.1 + - libjpeg-turbo >=3.1.2,<4.0a0 + - libpng >=1.6.53,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libwebp-base >=1.6.0,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - xorg-libxpm >=3.5.17,<4.0a0 + license: GD + license_family: BSD + size: 166711 + timestamp: 1766331770351 +- conda: https://conda.anaconda.org/conda-forge/win-64/libglib-2.88.1-h7ce1215_2.conda + sha256: f61277e224e9889c221bb2eac0f57d5aeeb82fc45d3dc326957d251c97444f7c + md5: 5fb838786a8317ebb38056bbe236d3ff + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - libiconv >=1.18,<2.0a0 + - libzlib >=1.3.2,<2.0a0 + - pcre2 >=10.47,<10.48.0a0 + - libintl >=0.22.5,<1.0a0 + - libffi >=3.5.2,<3.6.0a0 + constrains: + - glib >2.66 + license: LGPL-2.1-or-later + size: 4522891 + timestamp: 1778508851933 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-2.23.0-h68df31e_1.conda + sha256: fba9e1d32302eec582bea67958d1c4fac446b231c579ae8fead45ee54f66490d + md5: a0ef5adaf00591f68185bc59c7ebcb48 + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libcurl >=8.7.1,<9.0a0 + - libgrpc >=1.62.2,<1.63.0a0 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + constrains: + - libgoogle-cloud 2.23.0 *_1 + license: Apache-2.0 + license_family: Apache + size: 14424 + timestamp: 1713800484262 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgoogle-cloud-storage-2.23.0-hb581fae_1.conda + sha256: b7be440cb21b2c8c41064f1a334b9117ed5e4f0b98c5315650194161f7702283 + md5: af19093e2d4171ddef39e9d6457c4e2e + depends: + - libabseil + - libcrc32c >=1.1.2,<1.2.0a0 + - libcurl + - libgoogle-cloud 2.23.0 h68df31e_1 + - libzlib >=1.2.13,<2.0.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: Apache + size: 14323 + timestamp: 1713800995993 +- conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.62.2-h5273850_0.conda + sha256: 08794bf5ea0e19ac23ed47d0f8699b5c05c46f14334b41f075e53bac9bbf97d8 + md5: 2939e4b5baecfeac1e8dee5c4f579f1a + depends: + - c-ares >=1.28.1,<2.0a0 + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libre2-11 >=2023.9.1 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.2.1,<4.0a0 + - re2 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + constrains: + - grpc-cpp =1.62.2 + license: Apache-2.0 + license_family: APACHE + size: 16097674 + timestamp: 1713392821679 +- conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.11.2-default_hc8275d1_1000.conda + sha256: 29db3126762be449bf137d0ce6662e0c95ce79e83a0685359012bb86c9ceef0a + md5: 2805c2eb3a74df931b3e2b724fcb965e + depends: + - libxml2 >=2.12.7,<2.14.0a0 + - pthreads-win32 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + size: 2389010 + timestamp: 1727380221363 +- conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.13.0-default_h049141e_1000.conda + sha256: 2ee12e37223dfcd0acd050c80a91150c482b6e2899198521e1800dce66662467 + md5: 6a01c986e30292c715038d2788aa1385 + depends: + - libwinpthread >=12.0.0.r4.gg4f2fc60ca + - libxml2 + - libxml2-16 >=2.14.6 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: BSD-3-Clause + license_family: BSD + size: 2396128 + timestamp: 1770954127918 +- conda: https://conda.anaconda.org/conda-forge/win-64/libiconv-1.18-hc1393d2_2.conda + sha256: 0dcdb1a5f01863ac4e8ba006a8b0dc1a02d2221ec3319b5915a1863254d7efa7 + md5: 64571d1dd6cdcfa25d0664a5950fdaa2 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: LGPL-2.1-only + size: 696926 + timestamp: 1754909290005 +- conda: https://conda.anaconda.org/conda-forge/win-64/libintl-0.22.5-h5728263_3.conda + sha256: c7e4600f28bcada8ea81456a6530c2329312519efcf0c886030ada38976b0511 + md5: 2cf0cf76cc15d360dfa2f17fd6cf9772 + depends: + - libiconv >=1.17,<2.0a0 + license: LGPL-2.1-or-later + size: 95568 + timestamp: 1723629479451 +- conda: https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.1.4.1-hfd05255_0.conda + sha256: 698d57b5b90120270eaa401298319fcb25ea186ae95b340c2f4813ed9171083d + md5: 25a127bad5470852b30b239f030ec95b + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - jpeg <0.0.0a + license: IJG AND BSD-3-Clause AND Zlib + size: 842806 + timestamp: 1775962811457 +- conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-8_hf9ab0e9_mkl.conda + build_number: 8 + sha256: 44999ed04bc0a56de44ee0ac8bd5b3702efd411a8b29491c0e3d3deb8619c94e + md5: d584799b920ecae9b75a2b70743a3de7 + depends: + - libblas 3.11.0 8_h8455456_mkl + constrains: + - libcblas 3.11.0 8*_mkl + - liblapacke 3.11.0 8*_mkl + - blas 2.308 mkl + license: BSD-3-Clause + license_family: BSD + size: 81027 + timestamp: 1779859714698 +- conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-35_hf9ab0e9_mkl.conda + build_number: 35 + sha256: 56e0992fb58eed8f0d5fa165b8621fa150b84aa9af1467ea0a7a9bb7e2fced4f + md5: 0c6ed9d722cecda18f50f17fb3c30002 + depends: + - libblas 3.9.0 35_h5709861_mkl + constrains: + - blas 2.135 mkl + - libcblas 3.9.0 35*_mkl + - liblapacke 3.9.0 35*_mkl + license: BSD-3-Clause + license_family: BSD + size: 78485 + timestamp: 1757003541803 +- conda: https://conda.anaconda.org/conda-forge/win-64/liblzma-5.8.3-hfd05255_0.conda + sha256: d636d1a25234063642f9c531a7bb58d84c1c496411280a36ea000bd122f078f1 + md5: 8f83619ab1588b98dd99c90b0bfc5c6d + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - xz 5.8.3.* + license: 0BSD + size: 106486 + timestamp: 1775825663227 +- conda: https://conda.anaconda.org/conda-forge/win-64/libparquet-16.0.0-h178134c_1_cpu.conda + build_number: 1 + sha256: b3e2492af56931d31e0c00ee1deeba409e14a14faff5d175da5d62f98ec63872 + md5: ac881781f5315841a54bad25d538072f + depends: + - libarrow 16.0.0 h107e38f_1_cpu + - libthrift >=0.19.0,<0.19.1.0a0 + - openssl >=3.3.0,<4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: APACHE + size: 794925 + timestamp: 1715199815061 +- conda: https://conda.anaconda.org/conda-forge/win-64/libpng-1.6.58-h7351971_0.conda + sha256: 218913aeee391460bd0e341b834dbd9c6fa6ae0a4276c0c300266cc99a816a28 + md5: 52f1280563f3b48b5f75414cd2d15dd1 + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - libzlib >=1.3.2,<2.0a0 + license: zlib-acknowledgement + size: 385227 + timestamp: 1776315248638 +- conda: https://conda.anaconda.org/conda-forge/win-64/libprotobuf-4.25.3-h47a098d_1.conda + sha256: 6412e1b25d14187a4a9ccd62c27fb163621aa4c4dd5f8e97e2aaabed5e61598e + md5: 2ab67bf04b060ed5af5bc6999f1d6b31 + depends: + - libabseil * cxx17* + - libabseil >=20240116.2,<20240117.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + size: 5487058 + timestamp: 1727162016965 +- conda: https://conda.anaconda.org/conda-forge/win-64/libre2-11-2023.09.01-hf8d8778_2.conda + sha256: 04331dad30a076ebb24c683197a5feabf4fd9be0fa0e06f416767096f287f900 + md5: cf54cb5077a60797d53a132d37af25fc + depends: + - libabseil * cxx17* + - libabseil >=20240116.1,<20240117.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + constrains: + - re2 2023.09.01.* + license: BSD-3-Clause + license_family: BSD + size: 256561 + timestamp: 1708947458481 +- conda: https://conda.anaconda.org/conda-forge/win-64/libsodium-1.0.22-h6a83c73_1.conda + sha256: de45b71224da77a1c3a7dd48d8885eb957c9f05455d4f0828463293e7144330f + md5: 7d5abf7ca1bd00b43d273f44d93d05dc + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + license: ISC + size: 280234 + timestamp: 1779164124739 +- conda: https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.53.1-hf5d6505_0.conda + sha256: e70562450332ca8954bc16f3455468cca5ef3695c7d7187ecc87f8fc3c70e9eb + md5: 7fea434a17c323256acc510a041b80d7 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: blessing + size: 1304178 + timestamp: 1777986510497 +- conda: https://conda.anaconda.org/conda-forge/win-64/libssh2-1.11.1-h9aa295b_0.conda + sha256: cbdf93898f2e27cefca5f3fe46519335d1fab25c4ea2a11b11502ff63e602c09 + md5: 9dce2f112bfd3400f4f432b3d0ac07b2 + depends: + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.0,<4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + size: 292785 + timestamp: 1745608759342 +- conda: https://conda.anaconda.org/conda-forge/win-64/libthrift-0.19.0-ha2b3283_1.conda + sha256: 89bbc59898c827429a52315c9c0dd888ea73ab1157a8c86098aeae7d13454ac4 + md5: d3432b9d4950e91d2fdf3bed91248ee0 + depends: + - libevent >=2.1.12,<2.1.13.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - openssl >=3.1.3,<4.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: APACHE + size: 612342 + timestamp: 1695958519927 +- conda: https://conda.anaconda.org/conda-forge/win-64/libtiff-4.7.1-h8f73337_1.conda + sha256: f1b8cccaaeea38a28b9cd496694b2e3d372bb5be0e9377c9e3d14b330d1cba8a + md5: 549845d5133100142452812feb9ba2e8 + depends: + - lerc >=4.0.0,<5.0a0 + - libdeflate >=1.25,<1.26.0a0 + - libjpeg-turbo >=3.1.0,<4.0a0 + - liblzma >=5.8.1,<6.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - zstd >=1.5.7,<1.6.0a0 + license: HPND + size: 993166 + timestamp: 1762022118895 +- conda: https://conda.anaconda.org/conda-forge/win-64/libutf8proc-2.8.0-hb602f4b_1.conda + sha256: b9e55f0be8ea5bee960565fd18c232a0ef62af7f007d1d102a3b66c496489d68 + md5: 4dce7215af5e642fe84a07321c0628f6 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: MIT + license_family: MIT + size: 83847 + timestamp: 1732830082137 +- conda: https://conda.anaconda.org/conda-forge/win-64/libwebp-base-1.6.0-h4d5522a_0.conda + sha256: 7b6316abfea1007e100922760e9b8c820d6fc19df3f42fb5aca684cfacb31843 + md5: f9bbae5e2537e3b06e0f7310ba76c893 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - libwebp 1.6.0 + license: BSD-3-Clause + license_family: BSD + size: 279176 + timestamp: 1752159543911 +- conda: https://conda.anaconda.org/conda-forge/win-64/libwinpthread-12.0.0.r4.gg4f2fc60ca-h57928b3_10.conda + sha256: 0fccf2d17026255b6e10ace1f191d0a2a18f2d65088fd02430be17c701f8ffe0 + md5: 8a86073cf3b343b87d03f41790d8b4e5 + depends: + - ucrt + constrains: + - pthreads-win32 <0.0a0 + - msys2-conda-epoch <0.0a0 + license: MIT AND BSD-3-Clause-Clear + size: 36621 + timestamp: 1759768399557 +- conda: https://conda.anaconda.org/conda-forge/win-64/libxcb-1.16-h013a479_1.conda + sha256: abae56e12a4c62730b899fdfb82628a9ac171c4ce144fc9f34ae024957a82a0e + md5: f0b599acdc82d5bc7e3b105833e7c5c8 + depends: + - m2w64-gcc-libs + - m2w64-gcc-libs-core + - pthread-stubs + - xorg-libxau >=1.0.11,<2.0a0 + - xorg-libxdmcp + license: MIT + license_family: MIT + size: 989459 + timestamp: 1724419883091 +- conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-16-2.15.3-h692994f_0.conda + sha256: 8038084c60eda2006d0122d05e3364fe8db0a18935ca6ed0168b5ba5aa33f904 + md5: f7d6fcda29570e20851b78d92ea2154e + depends: + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libzlib >=1.3.2,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - libxml2 2.15.3 + - icu <0.0a0 + license: MIT + license_family: MIT + size: 518869 + timestamp: 1776376971242 +- conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.13.9-h741aa76_0.conda + sha256: 28ac5bbed11644b9e06241ba1dfdac7e3a99e74b69915d45f646717ad9645ca5 + md5: 333d21ab129d5fa5742225bf1d7557a5 + depends: + - libiconv >=1.18,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: MIT + license_family: MIT + size: 1521446 + timestamp: 1761766307746 +- conda: https://conda.anaconda.org/conda-forge/win-64/libxml2-2.15.3-hbc0d294_0.conda + sha256: da68af9d9d28d65a6916db1bef68f8a25c64c4fdcf759f32a2d2f2f143220adf + md5: e3b5acbb857a12f5d59e8d174bc536c0 + depends: + - libiconv >=1.18,<2.0a0 + - liblzma >=5.8.3,<6.0a0 + - libxml2-16 2.15.3 h692994f_0 + - libzlib >=1.3.2,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - icu <0.0a0 + license: MIT + license_family: MIT + size: 43916 + timestamp: 1776376994334 +- conda: https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.2-hfd05255_2.conda + sha256: 88609816e0cc7452bac637aaf65783e5edf4fee8a9f8e22bdc3a75882c536061 + md5: dbabbd6234dea34040e631f87676292f + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - zlib 1.3.2 *_2 + license: Zlib + license_family: Other + size: 58347 + timestamp: 1774072851498 +- conda: https://conda.anaconda.org/conda-forge/win-64/llvm-openmp-22.1.6-h4fa8253_0.conda + sha256: b12aa9c957fadf488888aa4cad6d424d499ffcceefe5d8e9077c4da46308f26b + md5: 1966432ddb4d5e13890dae3758a112d3 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - openmp 22.1.6|22.1.6.* + - intel-openmp <0.0a0 + license: Apache-2.0 WITH LLVM-exception + license_family: APACHE + size: 347116 + timestamp: 1779341186510 +- conda: https://conda.anaconda.org/conda-forge/win-64/llvm-openmp-22.1.7-h4fa8253_0.conda + sha256: 70140a1fa5d7cb801c6be3273b0704b5f0e418e2fff6b12b8ce9db13067a1ed5 + md5: 0ca3373049a5be11689bc2f9b2f3a9d2 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - intel-openmp <0.0a0 + - openmp 22.1.7|22.1.7.* + license: Apache-2.0 WITH LLVM-exception + size: 347536 + timestamp: 1780456277495 +- conda: https://conda.anaconda.org/conda-forge/win-64/lz4-c-1.9.4-hcfcfb64_0.conda + sha256: a0954b4b1590735ea5f3d0f4579c3883f8ac837387afd5b398b241fda85124ab + md5: e34720eb20a33fc3bfb8451dd837ab7a + depends: + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vs2015_runtime >=14.29.30139 + license: BSD-2-Clause + license_family: BSD + size: 134235 + timestamp: 1674728465431 +- conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2 + sha256: 9de95a7996d5366ae0808eef2acbc63f9b11b874aa42375f55379e6715845dc6 + md5: 066552ac6b907ec6d72c0ddab29050dc + depends: + - m2w64-gcc-libs-core + - msys2-conda-epoch ==20160418 + license: GPL, LGPL, FDL, custom + size: 350687 + timestamp: 1608163451316 +- conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-5.3.0-7.tar.bz2 + sha256: 3bd1ab02b7c89a5b153a17be03b36d833f1517ff2a6a77ead7c4a808b88196aa + md5: fe759119b8b3bfa720b8762c6fdc35de + depends: + - m2w64-gcc-libgfortran + - m2w64-gcc-libs-core + - m2w64-gmp + - m2w64-libwinpthread-git + - msys2-conda-epoch ==20160418 + license: GPL3+, partial:GCCRLE, partial:LGPL2+ + size: 532390 + timestamp: 1608163512830 +- conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2 + sha256: 58afdfe859ed2e9a9b1cc06bc408720cb2c3a6a132e59d4805b090d7574f4ee0 + md5: 4289d80fb4d272f1f3b56cfe87ac90bd + depends: + - m2w64-gmp + - m2w64-libwinpthread-git + - msys2-conda-epoch ==20160418 + license: GPL3+, partial:GCCRLE, partial:LGPL2+ + size: 219240 + timestamp: 1608163481341 +- conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2 + sha256: 7e3cd95f554660de45f8323fca359e904e8d203efaf07a4d311e46d611481ed1 + md5: 53a1c73e1e3d185516d7e3af177596d9 + depends: + - msys2-conda-epoch ==20160418 + license: LGPL3 + size: 743501 + timestamp: 1608163782057 +- conda: https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2 + sha256: f63a09b2cae7defae0480f1740015d6235f1861afa6fe2e2d3e10bd0d1314ee0 + md5: 774130a326dee16f1ceb05cc687ee4f0 + depends: + - msys2-conda-epoch ==20160418 + license: MIT, BSD + size: 31928 + timestamp: 1608166099896 +- conda: https://conda.anaconda.org/conda-forge/win-64/markupsafe-3.0.3-py310hdb0e946_1.conda + sha256: 174f03b12af229fe937cceba1fbac3bc02c9845f78cb02d8d5e702562f03ae36 + md5: ad72e0e0432934e97fd356ed334170d9 + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - jinja2 >=3.0.0 + license: BSD-3-Clause + license_family: BSD + size: 26828 + timestamp: 1772445195768 +- conda: https://conda.anaconda.org/conda-forge/win-64/matplotlib-base-3.7.3-py310hc9baf74_0.conda + sha256: b33df121d3c06700ab6046108aedbeda5e6b97f402903979df684f714c866db1 + md5: b8452c3adde5873f2d430381d515529b + depends: + - certifi >=2020.06.20 + - contourpy >=1.0.1 + - cycler >=0.10 + - fonttools >=4.22.0 + - freetype >=2.12.1,<3.0a0 + - kiwisolver >=1.0.1 + - numpy >=1.20 + - numpy >=1.22.4,<2.0a0 + - packaging >=20.0 + - pillow >=6.2.0 + - pyparsing >=2.3.1 + - python >=3.10,<3.11.0a0 + - python-dateutil >=2.7 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: LicenseRef-PSF-2.0 and CC0-1.0 + license_family: PSF + size: 6649341 + timestamp: 1695077594997 +- conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2024.2.2-h57928b3_16.conda + sha256: ce841e7c3898764154a9293c0f92283c1eb28cdacf7a164c94b632a6af675d91 + md5: 5cddc979c74b90cf5e5cda4f97d5d8bb + depends: + - llvm-openmp >=20.1.8 + - tbb 2021.* + license: LicenseRef-IntelSimplifiedSoftwareOct2022 + license_family: Proprietary + size: 103088799 + timestamp: 1753975600547 +- conda: https://conda.anaconda.org/conda-forge/win-64/mkl-2026.0.0-hac47afa_908.conda + sha256: f997bfc9bc4d4e14261cdcd1ad195d64a72ee44dca3145d24c1349f8d1311aa5 + md5: 36ea6e1292e9d5e89374201da79646ef + depends: + - llvm-openmp >=22.1.5 + - onemkl-license 2026.0.0 h57928b3_908 + - tbb >=2023.0.0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: LicenseRef-IntelSimplifiedSoftwareOct2022 + license_family: Proprietary + size: 114354729 + timestamp: 1779293121860 +- conda: https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2 + sha256: 99358d58d778abee4dca82ad29fb58058571f19b0f86138363c260049d4ac7f1 + md5: b0309b72560df66f71a9d5e34a5efdfa + size: 3227 + timestamp: 1608166968312 +- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.22.4-py310hed7ac4c_0.tar.bz2 + sha256: 0e981a01324cc0343c2ca899146cbf3d4e1062f3ac2d5dcaef762e609e16d047 + md5: 268caa71594c0d641b86d89f22e47dc3 + depends: + - libblas >=3.8.0,<4.0a0 + - libcblas >=3.8.0,<4.0a0 + - liblapack >=3.8.0,<4.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - vc >=14.1,<15 + - vs2015_runtime >=14.16.27033 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 6435677 + timestamp: 1653326157566 +- conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.2.6-py310h4987827_0.conda + sha256: 6f628e51763b86a535a723664e3aa1e38cb7147a2697f80b75c1980c1ed52f3e + md5: d2596785ac2cf5bab04e2ee9e5d04041 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + constrains: + - numpy-base <0a0 + license: BSD-3-Clause + license_family: BSD + size: 6596153 + timestamp: 1747545352390 +- conda: https://conda.anaconda.org/conda-forge/win-64/onemkl-license-2026.0.0-h57928b3_908.conda + sha256: 42ad15cbb3bf31830efa04d4b86dd2d5c0dd590c86f98adcd3c8c1f75acf5dd5 + md5: 9c9303e08b50e09f5c23e1dac99d0936 + license: LicenseRef-IntelSimplifiedSoftwareOct2022 + license_family: Proprietary + size: 41580 + timestamp: 1779292867015 +- conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.4-h0e57b4f_0.conda + sha256: 24342dee891a49a9ba92e2018ec0bde56cc07fdaec95275f7a55b96f03ea4252 + md5: e723ab7cc2794c954e1b22fde51c16e4 + depends: + - libpng >=1.6.55,<1.7.0a0 + - libtiff >=4.7.1,<4.8.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: BSD-2-Clause + license_family: BSD + size: 245594 + timestamp: 1772624841727 +- conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.6.2-hf411b9b_0.conda + sha256: feb5815125c60f2be4a411e532db1ed1cd2d7261a6a43c54cb6ae90724e2e154 + md5: 05c7d624cff49dbd8db1ad5ba537a8a3 + depends: + - ca-certificates + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: Apache-2.0 + license_family: Apache + size: 9410183 + timestamp: 1775589779763 +- conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.0.0-h7e885a9_1.conda + sha256: eb8ba5b2c500b990dc75f468dffaf4ba5eca53a8c021b38900247df988d14e4b + md5: f61ae80fe162b09c627473932d5dc8c3 + depends: + - libprotobuf >=4.25.3,<4.25.4.0a0 + - libzlib >=1.2.13,<2.0.0a0 + - lz4-c >=1.9.3,<1.10.0a0 + - snappy >=1.2.0,<1.3.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc >=14.3,<15 + - vc14_runtime >=14.29.30139 + - vc14_runtime >=14.38.33130 + - zstd >=1.5.5,<1.6.0a0 + license: Apache-2.0 + license_family: Apache + size: 925936 + timestamp: 1712616706879 +- conda: https://conda.anaconda.org/conda-forge/win-64/pandas-1.3.5-py310hf5e1058_0.tar.bz2 + sha256: 8b5ae2ad8a09450f5f7443c224ef1272761b185f8010d9cb3342432634b09e34 + md5: d4b5ef00ec931c47dbabe6d329a05531 + depends: + - numpy >=1.21.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python-dateutil >=2.7.3 + - python_abi 3.10.* *_cp310 + - pytz >=2017.2 + - vc >=14.1,<15.0a0 + - vs2015_runtime >=14.16.27012 + - setuptools <60.0.0 + license: BSD-3-Clause + license_family: BSD + size: 11708396 + timestamp: 1639399518453 +- conda: https://conda.anaconda.org/conda-forge/win-64/pango-1.56.4-h13911b6_1.conda + sha256: 3d4e6e541e633f6fd22fc2c1d79ad5ec39503dea3ba04fc3e01d5be904ec7cea + md5: 1f1cf3772ba7d4eef989e4679ddf97f7 + depends: + - cairo >=1.18.4,<2.0a0 + - fontconfig >=2.17.1,<3.0a0 + - fonts-conda-ecosystem + - fribidi >=1.0.16,<2.0a0 + - harfbuzz >=13.2.1 + - libexpat >=2.7.4,<3.0a0 + - libfreetype >=2.14.2 + - libfreetype6 >=2.14.2 + - libglib >=2.86.4,<3.0a0 + - libpng >=1.6.55,<1.7.0a0 + - libzlib >=1.3.2,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: LGPL-2.1-or-later + size: 454919 + timestamp: 1774282149607 +- conda: https://conda.anaconda.org/conda-forge/win-64/pcre2-10.47-hd2b5f0e_0.conda + sha256: 3e9e02174edf02cb4bcdd75668ad7b74b8061791a3bc8bdb8a52ae336761ba3e + md5: 77eaf2336f3ae749e712f63e36b0f0a1 + depends: + - bzip2 >=1.0.8,<2.0a0 + - libzlib >=1.3.1,<2.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: BSD-3-Clause + license_family: BSD + size: 995992 + timestamp: 1763655708300 +- conda: https://conda.anaconda.org/conda-forge/win-64/pillow-10.4.0-py310h3e38d90_1.conda + sha256: fb730c9510ccf16579762db20383eaee447bda3f5f2f0b0691029c87af462c7a + md5: d9a32c4725436b99df60fdc9c14545d1 + depends: + - freetype >=2.12.1,<3.0a0 + - lcms2 >=2.16,<3.0a0 + - libjpeg-turbo >=3.0.0,<4.0a0 + - libtiff >=4.6.0,<4.8.0a0 + - libwebp-base >=1.4.0,<2.0a0 + - libxcb >=1.16,<2.0.0a0 + - libzlib >=1.3.1,<2.0a0 + - openjpeg >=2.5.2,<3.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - tk >=8.6.13,<8.7.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: HPND + size: 42223178 + timestamp: 1726075720583 +- conda: https://conda.anaconda.org/conda-forge/win-64/pixman-0.46.4-h5112557_1.conda + sha256: 246fce4706b3f8b247a7d6142ba8d732c95263d3c96e212b9d63d6a4ab4aff35 + md5: 08c8fa3b419df480d985e304f7884d35 + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + license: MIT + license_family: MIT + size: 542795 + timestamp: 1754665193489 +- conda: https://conda.anaconda.org/conda-forge/win-64/polars-1.0.0-py310h4694af4_0.conda + sha256: 48ecc151e6b6ac76dc0cf184903409d231ff829e751e1dc6dc0b41c42b92d0d3 + md5: 12c2461964b08f39586a1bd726faa42b + depends: + - numpy >=1.16.0,<2 + - packaging + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - typing_extensions >=4.0.0 + - ucrt >=10.0.20348.0 + - vc >=14.3 + - vc14_runtime >=14.40.33810 + license: MIT + license_family: MIT + run_exports: {} + size: 20953419 + timestamp: 1720033234280 +- conda: https://conda.anaconda.org/conda-forge/win-64/psutil-5.9.3-py310h8d17308_1.tar.bz2 + sha256: 19ef1ca333868ffedbec522bc31bcb00fe68de00343d99f071273b518fbf0e9c + md5: 3bc49b6bddc80d51bf6c354a9427edba + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vs2015_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + size: 381266 + timestamp: 1666772448552 +- conda: https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-hcd874cb_1001.tar.bz2 + sha256: bb5a6ddf1a609a63addd6d7b488b0f58d05092ea84e9203283409bff539e202a + md5: a1f820480193ea83582b13249a7e7bd9 + depends: + - m2w64-gcc-libs + license: MIT + license_family: MIT + size: 6417 + timestamp: 1606147814351 +- conda: https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-h2466b09_4.conda + sha256: b989bdcf0a22ba05a238adac1ad3452c11871681f565e509f629e225a26b7d45 + md5: cf98a67a1ec8040b42455002a24f0b0b + depends: + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: LGPL-2.1-or-later + size: 265827 + timestamp: 1728400965968 +- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-16.0.0-py310h3bcd3f7_0.conda + sha256: 61e98a6ddd199ce0c10fe3c1421226105f65c53f826aceae1bb21be4bf1493e8 + md5: 9fa1aafe7d85893231667e55ae2565cb + depends: + - libarrow-acero 16.0.0.* + - libarrow-dataset 16.0.0.* + - libarrow-substrait 16.0.0.* + - libparquet 16.0.0.* + - numpy >=1.22.4,<2.0a0 + - pyarrow-core 16.0.0 *_0_* + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: Apache-2.0 + license_family: APACHE + size: 26385 + timestamp: 1715178622741 +- conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-16.0.0-py310hcb4c9cb_0_cpu.conda + sha256: 09c0d68e864e27fd59acf8ea70f1d53a458a1e0b5c6a385c0853b44ef8664956 + md5: 04698cca703efcaf6d8ebbad8316bbbd + depends: + - libarrow 16.0.0.* *cpu + - libzlib >=1.2.13,<2.0.0a0 + - numpy >=1.22.4,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + constrains: + - apache-arrow-proc =*=cpu + license: Apache-2.0 + license_family: APACHE + size: 3331735 + timestamp: 1715178207152 +- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.10.20-hc20f281_0_cpython.conda + sha256: e71595dd281a9902d7b84f545f16d7d4c0fb62cc6816431301f8f4870c94dc8c + md5: 6c18c24d33a7ac8a4f81c68b8eb8581b + depends: + - bzip2 >=1.0.8,<2.0a0 + - libexpat >=2.7.4,<3.0a0 + - libffi >=3.4,<4.0a0 + - liblzma >=5.8.2,<6.0a0 + - libsqlite >=3.51.2,<4.0a0 + - libzlib >=1.3.1,<2.0a0 + - openssl >=3.5.5,<4.0a0 + - tk >=8.6.13,<8.7.0a0 + - tzdata + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + constrains: + - python_abi 3.10.* *_cp310 + license: Python-2.0 + size: 16028082 + timestamp: 1772728853200 +- conda: https://conda.anaconda.org/conda-forge/win-64/pywin32-311-py310h282bd7d_2.conda + sha256: be0cead92bf70396d7ff6ae08481671b6bd3f46f4c420874d595f34384fa05cb + md5: 03a4ff957aad223e0685fef945fc75ae + depends: + - python + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - python_abi 3.10.* *_cp310 + license: PSF-2.0 + license_family: PSF + size: 6293123 + timestamp: 1779222947139 +- conda: https://conda.anaconda.org/conda-forge/win-64/pywinpty-2.0.15-py310h9e98ed7_1.conda + sha256: b6d9fc08bfb275fcf038e77302d6f3d8429972116acf962401ebf043d6179770 + md5: 2d4cae270689fefe4895ee1690b34bd1 + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + - winpty + license: MIT + license_family: MIT + size: 207271 + timestamp: 1759557302949 +- conda: https://conda.anaconda.org/conda-forge/win-64/pyyaml-6.0.3-py310hdb0e946_1.conda + sha256: 3b643534d7b029073fd0ec1548a032854bb45391bc51dfdf9fec8d327e9f688d + md5: 463566b14434383e34e366143808b4b7 + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - yaml >=0.2.5,<0.3.0a0 + license: MIT + license_family: MIT + size: 157282 + timestamp: 1770223476842 +- conda: https://conda.anaconda.org/conda-forge/win-64/pyzmq-27.1.0-py310h824d4bc_3.conda + sha256: 3830344ca1fe52d0b4db131e7f2d092e7486cf8468320594f26bceb10b33cc94 + md5: 18506e906131297067a6db8311fefa5c + depends: + - python + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - zeromq >=4.3.5,<4.3.6.0a0 + - python_abi 3.10.* *_cp310 + license: BSD-3-Clause + license_family: BSD + size: 305356 + timestamp: 1779483930069 +- conda: https://conda.anaconda.org/conda-forge/win-64/re2-2023.09.01-hd3b24a8_2.conda + sha256: 929744a982215ea19f6f9a9d00c782969cd690bfddeeb650a39df1536af577fe + md5: ffeb985810bc7d103662e1465c758847 + depends: + - libre2-11 2023.09.01 hf8d8778_2 + license: BSD-3-Clause + license_family: BSD + size: 207315 + timestamp: 1708947529390 +- conda: https://conda.anaconda.org/conda-forge/win-64/rpds-py-0.30.0-py310h034784e_0.conda + sha256: a9176da0165e1fdc0582945ec22cbfac03c1bb88120389c7fe0b7406b5fee08f + md5: f2ae7538b9ab9a7cd375fc23e320c2b0 + depends: + - python + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 241000 + timestamp: 1764543082615 +- conda: https://conda.anaconda.org/conda-forge/win-64/scikit-learn-1.0.2-py310h4dafddf_0.tar.bz2 + sha256: dca1257bf95e7a1827d617eb7b170d0391eee3aee72d47199d3f0c0aec5e5d6f + md5: 16493b39df97abf4b9c07c644cbc9aee + depends: + - joblib >=0.11 + - libcblas >=3.8.0,<4.0a0 + - numpy >=1.21.5,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - scipy + - threadpoolctl + - vc >=14.1,<15.0a0 + - vs2015_runtime >=14.16.27012 + license: BSD-3-Clause + license_family: BSD + size: 7245326 + timestamp: 1640465047689 +- conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.15.2-py310h15c175c_0.conda + sha256: f19350c2061b1cdc3151a33c3dd4f71a1a481f9b10ac186674f957814bc839bc + md5: 81798168111d1021e3d815217c444418 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 + - numpy <2.5 + - numpy >=1.19,<3 + - numpy >=1.23.5 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: BSD-3-Clause + license_family: BSD + size: 14352068 + timestamp: 1739793156239 +- conda: https://conda.anaconda.org/conda-forge/win-64/scipy-1.7.3-py310h578b7cb_1.tar.bz2 + sha256: 5fdcd334debd226c48b71e4ed0ec2c3d56b7ebf31476683c7365073048ba363b + md5: fb4296c2e3236ad9f683a91cb41f2357 + depends: + - libblas >=3.9.0,<4.0a0 + - libcblas >=3.9.0,<4.0a0 + - liblapack >=3.9.0,<4.0a0 + - m2w64-gcc-libs + - numpy >=1.21.6,<1.23 + - numpy >=1.21.6,<2.0a0 + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vs2015_runtime >=14.29.30139 + constrains: + - libopenblas <0.3.26 + license: BSD-3-Clause + license_family: BSD + size: 25266979 + timestamp: 1667962529833 +- conda: https://conda.anaconda.org/conda-forge/win-64/setuptools-59.8.0-py310h5588dad_1.tar.bz2 + sha256: a4f03fb6cafdfe92dd80393272c24a5a8d0c0fe658248eba0a36635cc548702e + md5: efc6a279d1806009b6598732e4db32a4 + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + license: MIT + license_family: MIT + size: 1053508 + timestamp: 1648692671479 +- conda: https://conda.anaconda.org/conda-forge/win-64/snappy-1.2.2-h7fa0ca8_1.conda + sha256: d2deda1350abf8c05978b73cf7fe9147dd5c7f2f9b312692d1b98e52efad53c3 + md5: 3075846de68f942150069d4289aaad63 + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + license: BSD-3-Clause + license_family: BSD + size: 67417 + timestamp: 1762948090450 +- conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2021.13.0-h62715c5_1.conda + sha256: 03cc5442046485b03dd1120d0f49d35a7e522930a2ab82f275e938e17b07b302 + md5: 9190dd0a23d925f7602f9628b3aed511 + depends: + - libhwloc >=2.11.2,<2.11.3.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.2,<15 + - vc14_runtime >=14.29.30139 + license: Apache-2.0 + license_family: APACHE + size: 151460 + timestamp: 1732982860332 +- conda: https://conda.anaconda.org/conda-forge/win-64/tbb-2023.0.0-hd3d4ead_2.conda + sha256: 8a4053839b8e997a5965e2dff7d6cf3c77be62d82c0e48c8a04a5ed2d2e73035 + md5: 8ee01a693aecff5432069eaaf1183c45 + depends: + - libhwloc >=2.13.0,<2.13.1.0a0 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: Apache-2.0 + license_family: APACHE + size: 156515 + timestamp: 1778673901757 +- conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h6ed50ae_3.conda + sha256: 0e79810fae28f3b69fe7391b0d43f5474d6bd91d451d5f2bde02f55ae481d5e3 + md5: 0481bfd9814bf525bd4b3ee4b51494c4 + depends: + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: TCL + license_family: BSD + size: 3526350 + timestamp: 1769460339384 +- conda: https://conda.anaconda.org/conda-forge/win-64/tornado-6.5.6-py310h29418f3_0.conda + sha256: 3057704d4ed506f2d93986ea0990eceffdfddb391b126af876469ec653fe12b9 + md5: 11e12d0018e1b23ed7747bc83af8f877 + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: Apache-2.0 + license_family: Apache + size: 674300 + timestamp: 1779916066347 +- conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.26100.0-h57928b3_0.conda + sha256: 3005729dce6f3d3f5ec91dfc49fc75a0095f9cd23bab49efb899657297ac91a5 + md5: 71b24316859acd00bdb8b38f5e2ce328 + constrains: + - vc14_runtime >=14.29.30037 + - vs2015_runtime >=14.29.30037 + license: LicenseRef-MicrosoftWindowsSDK10 + size: 694692 + timestamp: 1756385147981 +- conda: https://conda.anaconda.org/conda-forge/win-64/unicodedata2-17.0.1-py310h29418f3_0.conda + sha256: 0ce3386d49564a30da221cdee59edf113ef27e1ea784dd33f5f39411b7faeccb + md5: 8c34b3ebcfd8d6e4989ae1a2f2a63d03 + depends: + - python >=3.10,<3.11.0a0 + - python_abi 3.10.* *_cp310 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + license: Apache-2.0 + license_family: Apache + size: 406410 + timestamp: 1770909213469 +- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.5-h1b7c187_38.conda + sha256: 61b68e5a4fc71a17f8d64b12e013a2f971ad980bd08e9c389d5e68efe1a67de0 + md5: 774568633f3b26d7a4a6dd4f9ea6d3e1 + depends: + - vc14_runtime >=14.51.36231 + track_features: + - vc14 + license: BSD-3-Clause + license_family: BSD + size: 20187 + timestamp: 1780005880049 +- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.51.36231-h1b9f54f_38.conda + sha256: 957c7c65583c7107a5e76f39756c6361fcb7b0dc101ac7c0aea86e7ca09fe49c + md5: 2cdcd8ea1010920911bb2eacb4c61227 + depends: + - ucrt >=10.0.20348.0 + - vcomp14 14.51.36231 h1b9f54f_38 + constrains: + - vs2015_runtime 14.51.36231.* *_38 + license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime + license_family: Proprietary + size: 740997 + timestamp: 1780005875753 +- conda: https://conda.anaconda.org/conda-forge/win-64/vcomp14-14.51.36231-h1b9f54f_38.conda + sha256: c645fdc1f0f47718431d973386e946754a10200e7ba2c32032560913a970cacd + md5: 63ee70d69d7540e821940dac5d4d9ba2 + depends: + - ucrt >=10.0.20348.0 + constrains: + - vs2015_runtime 14.51.36231.* *_38 + license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime + license_family: Proprietary + size: 123561 + timestamp: 1780005858779 +- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.51.36231-h84cd919_38.conda + sha256: c4f38268563dc6b8322b0191481e5d20002fc6e37b076c15e0b955a553c8b4a0 + md5: 6033851d921b6c33f1c3018205fcba6a + depends: + - vc14_runtime >=14.51.36231 + license: BSD-3-Clause + license_family: BSD + size: 20170 + timestamp: 1780005880423 +- conda: https://conda.anaconda.org/conda-forge/win-64/winpty-0.4.3-4.tar.bz2 + sha256: 9df10c5b607dd30e05ba08cbd940009305c75db242476f4e845ea06008b0a283 + md5: 1cee351bf20b830d991dbe0bc8cd7dfe + license: MIT + license_family: MIT + size: 1176306 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-kbproto-1.0.7-hcd874cb_1002.tar.bz2 + sha256: 5b16e1ca1ecc0d2907f236bc4d8e6ecfd8417db013c862a01afb7f9d78e48c09 + md5: 8d11c1dac4756ca57e78c1bfe173bba4 + depends: + - m2w64-gcc-libs + license: MIT + license_family: MIT + size: 28166 + timestamp: 1610028297505 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libice-1.1.1-hcd874cb_0.conda + sha256: 353e07e311eb10e934f03e0123d0f05d9b3770a70b0c3993e6d11cf74d85689f + md5: 5271e3af4791170e2c55d02818366916 + depends: + - m2w64-gcc-libs + - m2w64-gcc-libs-core + - xorg-libx11 >=1.8.4,<2.0a0 + license: MIT + license_family: MIT + size: 158086 + timestamp: 1685308072189 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libsm-1.2.4-hcd874cb_0.conda + sha256: 3a8cc151142c379d3ec3ec4420395d3a273873d3a45a94cd3038d143f5a519e8 + md5: 25926681339df15918243d9a7cec25a1 + depends: + - m2w64-gcc-libs + - m2w64-gcc-libs-core + - xorg-libice >=1.1.1,<2.0a0 + license: MIT + license_family: MIT + size: 86397 + timestamp: 1685454296879 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libx11-1.8.9-h0076a8d_1.conda + sha256: c378304044321e74c6acd483674f404864a229ab2a8841bf9515bc1a30783e99 + md5: 0296a4de2235cad9ad3112134f8e4519 + depends: + - libxcb >=1.16,<2.0.0a0 + - m2w64-gcc-libs + - m2w64-gcc-libs-core + - xorg-kbproto + - xorg-xextproto >=7.3.0,<8.0a0 + - xorg-xproto + license: MIT + license_family: MIT + size: 814589 + timestamp: 1718847832308 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.conda + sha256: 8c5b976e3b36001bdefdb41fb70415f9c07eff631f1f0155f3225a7649320e77 + md5: c46ba8712093cb0114404ae8a7582e1a + depends: + - m2w64-gcc-libs + - m2w64-gcc-libs-core + license: MIT + license_family: MIT + size: 51297 + timestamp: 1684638355740 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2 + sha256: f51205d33c07d744ec177243e5d9b874002910c731954f2c8da82459be462b93 + md5: 46878ebb6b9cbd8afcf8088d7ef00ece + depends: + - m2w64-gcc-libs + license: MIT + license_family: MIT + size: 67908 + timestamp: 1610072296570 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxext-1.3.4-hcd874cb_2.conda + sha256: 829320f05866ea1cc51924828427f215f4d0db093e748a662e3bb68b764785a4 + md5: 2aa695ac3c56193fd8d526e3b511e021 + depends: + - m2w64-gcc-libs + - xorg-libx11 >=1.7.2,<2.0a0 + - xorg-xextproto + license: MIT + license_family: MIT + size: 221821 + timestamp: 1677038179908 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxpm-3.5.17-hcd874cb_0.conda + sha256: d5cc2f026658e8b85679813bff35c16c857f873ba02489e6eb6e30d5865dacc4 + md5: 029be9b667bf3896fa28bc32adb1bfc3 + depends: + - m2w64-gcc-libs + - m2w64-gcc-libs-core + - xorg-libx11 >=1.8.6,<2.0a0 + - xorg-libxext >=1.3.4,<2.0a0 + - xorg-libxt >=1.3.0,<2.0a0 + - xorg-xextproto >=7.3.0,<8.0a0 + - xorg-xproto + license: MIT + license_family: MIT + size: 195881 + timestamp: 1696449889560 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-libxt-1.3.0-hcd874cb_1.conda + sha256: d513e0c627f098ef6655ce188eca79a672eaf763b0bbf37b228cb46dc82a66ca + md5: 511a29edd2ff3d973f63e54f19dcc06e + depends: + - m2w64-gcc-libs + - m2w64-gcc-libs-core + - xorg-kbproto + - xorg-libice >=1.1.1,<2.0a0 + - xorg-libsm >=1.2.4,<2.0a0 + - xorg-libx11 >=1.8.6,<2.0a0 + - xorg-xproto + license: MIT + license_family: MIT + size: 671704 + timestamp: 1690289114426 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-xextproto-7.3.0-hcd874cb_1003.conda + sha256: 04c0a08fd34fa33406c20f729e8f9cc40e8fd898072b952a5c14280fcf26f2e6 + md5: 6e6c2639620e436bddb7c040cd4f3adb + depends: + - m2w64-gcc-libs + license: MIT + license_family: MIT + size: 31034 + timestamp: 1677037259999 +- conda: https://conda.anaconda.org/conda-forge/win-64/xorg-xproto-7.0.31-hcd874cb_1007.tar.bz2 + sha256: b84cacba8479fa14199c9255fb62e005cacc619e90198c53b1653973709ec331 + md5: 88f3c65d2ad13826a9e0b162063be023 + depends: + - m2w64-gcc-libs + license: MIT + license_family: MIT + size: 75708 + timestamp: 1607292254607 +- conda: https://conda.anaconda.org/conda-forge/win-64/yaml-0.2.5-h6a83c73_3.conda + sha256: 80ee68c1e7683a35295232ea79bcc87279d31ffeda04a1665efdb43cbd50a309 + md5: 433699cba6602098ae8957a323da2664 + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + license: MIT + license_family: MIT + size: 63944 + timestamp: 1753484092156 +- conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h3a581c9_11.conda + sha256: c3e279cb309b153152fcdd6ee6d039ad996d563c849f06be39d85b8e3351df25 + md5: f016c0c5f9c01549b259146614786192 + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - libsodium >=1.0.22,<1.0.23.0a0 + - krb5 >=1.22.2,<1.23.0a0 + license: MPL-2.0 + license_family: MOZILLA + size: 265717 + timestamp: 1779124031378 +- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.25.0-py310h1637853_1.conda + sha256: db2a40dbe124b275fb0b8fdfd6e3b377963849897ab2b4d7696354040c52570b + md5: 1d261480977c268b3b209b7deaca0dd7 + depends: + - python + - cffi >=1.11 + - zstd >=1.5.7,<1.5.8.0a0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - python_abi 3.10.* *_cp310 + - zstd >=1.5.7,<1.6.0a0 + license: BSD-3-Clause + license_family: BSD + size: 364167 + timestamp: 1762512706699 +- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda + sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2 + md5: 053b84beec00b71ea8ff7a4f84b55207 + depends: + - vc >=14.3,<15 + - vc14_runtime >=14.44.35208 + - ucrt >=10.0.20348.0 + - libzlib >=1.3.1,<2.0a0 + license: BSD-3-Clause + license_family: BSD + size: 388453 + timestamp: 1764777142545 diff --git a/pixi.toml b/pixi.toml new file mode 100644 index 0000000..abe8d5d --- /dev/null +++ b/pixi.toml @@ -0,0 +1,44 @@ +[workspace] +channels = ["conda-forge"] +name = "lightgbm" +platforms = ["linux-64", "linux-aarch64", "osx-64", "osx-arm64", "win-64"] + +[dependencies] +narwhals = ">=1.15" +numpy = ">=1.21.3" +pip = "*" +python = ">=3.10" +scipy = "*" + +[feature.py310.dependencies] +# direct dependencies +cffi = "1.15.*" +joblib = "1.3.*" +matplotlib-base = "3.7.*" +narwhals = "1.15.*" +numpy = "1.22.*" +pandas = "1.3.*" +polars = "1.0.*" +pyarrow = "16.0.*" +python = "3.10.*" +python-graphviz = "0.20.*" +scikit-learn = "1.0.*" +scipy = "1.7.*" + +# testing-only packages +cloudpickle = "2.2.*" +h5py = ">=3.10" +ipywidgets = ">=8.1.2" +jupyterlab = "4.5.*" +pluggy = "1.0.*" +psutil = "5.9.3.*" +pytest = "7.4.*" + +# dask dependencies are not needed on Windows, so split out separately here +[feature.py310.target.unix.dependencies] +dask = "2022.12.*" +distributed = "2022.12.*" + +[environments] +default = [] +py310 = ["py310"] diff --git a/python-package/README.rst b/python-package/README.rst new file mode 100644 index 0000000..19cd71d --- /dev/null +++ b/python-package/README.rst @@ -0,0 +1,423 @@ +LightGBM Python-package +======================= + +|License| |Python Versions| |PyPI Version| |PyPI Downloads| |conda Version| |conda Downloads| |API Docs| + +Installation +------------ + +Preparation +''''''''''' + +32-bit Python is not supported. +Please install 64-bit version. +If you have a strong need to install with 32-bit Python, refer to `Build 32-bit Version with 32-bit Python section <#build-32-bit-version-with-32-bit-python>`__. + +| + +Install from `PyPI `_ +'''''''''''''''''''''''''''''''''''''''''''''''''''''''' + +.. code:: sh + + pip install lightgbm + +Compiled library that is included in the wheel file supports both **GPU** (don't confuse with CUDA version) and **CPU** versions out of the box. +This feature is available only for **Windows** and **Linux** currently. +To use **GPU** version you only need to install OpenCL Runtime libraries. +For NVIDIA and AMD GPU they are included in the ordinary drivers for your graphics card, so no action is required. +If you would like your AMD or Intel CPU to act like a GPU (for testing and debugging), +you can install `AMD APP SDK `_ on **Windows** and `PoCL `_ on **Linux**. +Many modern Linux distributions provide packages for PoCL, look for ``pocl-opencl-icd`` on Debian-based distributions and ``pocl`` on RedHat-based distributions. + +For **Windows** users, `VC runtime `_ is needed if **Visual Studio** is not installed. + +For **macOS** users, the **OpenMP** library is needed. +You can install it by the following command: ``brew install libomp``. + +| + +Install Nightly Packages +'''''''''''''''''''''''' + +Python packages are built on each new commit to ``main`` and uploaded to https://anaconda.org/lightgbm-packages. + +Only the latest development version is available there, and can be installed like this: + +.. code:: sh + + pip install --no-deps --index-url https://pypi.anaconda.org/lightgbm-packages/simple lightgbm + +| + +Use LightGBM with PyArrow +************************* + +To install all dependencies needed to use ``PyArrow`` in LightGBM, append ``[arrow]``. + +.. code:: sh + + pip install 'lightgbm[arrow]' + +| + +Use LightGBM with Polars +************************* + +To install all dependencies needed to use ``polars`` in LightGBM, append ``[polars]``. + +.. code:: sh + + pip install 'lightgbm[polars]' + +| + +Use LightGBM with Dask +********************** + +Warning: Dask-package is only tested on macOS and Linux. + +To install all dependencies needed to use ``lightgbm.dask``, append ``[dask]``. + +.. code:: sh + + pip install 'lightgbm[dask]' + +| + +Use LightGBM with pandas +************************ + +To install all dependencies needed to use ``pandas`` in LightGBM, append ``[pandas]``. + +.. code:: sh + + pip install 'lightgbm[pandas]' + +| + +Use LightGBM Plotting Capabilities +********************************** + +To install all dependencies needed to use ``lightgbm.plotting``, append ``[plotting]``. + +.. code:: sh + + pip install 'lightgbm[plotting]' + +| + +Use LightGBM with scikit-learn +****************************** + +To install all dependencies needed to use ``lightgbm.sklearn``, append ``[scikit-learn]``. + +.. code:: sh + + pip install 'lightgbm[scikit-learn]' + +| + +Build from Sources +****************** + +.. code:: sh + + pip install lightgbm --no-binary lightgbm + +For **macOS** users, you can perform installation either with **Apple Clang** or **gcc**. + +- In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in `Installation Guide `__) first. + +- In case you prefer **gcc**, you need to install it (details for installation can be found in `Installation Guide `__) and specify compilers by running ``export CXX=g++-7 CC=gcc-7`` (replace "7" with version of **gcc** installed on your machine) first. + +For **Windows** users, **Visual Studio** (or `VS Build Tools `_) is needed. + +| + +Build Threadless Version +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_OPENMP=OFF + +All requirements, except the **OpenMP** requirement, from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. + +It is **strongly not recommended** to use this version of LightGBM! + +| + +Build MPI Version +~~~~~~~~~~~~~~~~~ + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_MPI=ON + +All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. + +For **Windows** users, compilation with **MinGW-w64** is not supported. + +**MPI** libraries are needed: details for installation can be found in `Installation Guide `__. + +| + +Build GPU Version +~~~~~~~~~~~~~~~~~ + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_GPU=ON + +All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. + +For **macOS** users, the GPU version is not supported. + +**Boost** and **OpenCL** are needed: details for installation can be found in `Installation Guide `__. +Almost always you also need to pass ``OpenCL_INCLUDE_DIR``, ``OpenCL_LIBRARY`` options for **Linux** and ``BOOST_ROOT``, ``BOOST_LIBRARYDIR`` options for **Windows** to **CMake** via ``pip`` options, like + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_GPU=ON --config-settings=cmake.define.OpenCL_INCLUDE_DIR="/usr/local/cuda/include/" --config-settings=cmake.define.OpenCL_LIBRARY="/usr/local/cuda/lib64/libOpenCL.so" + +All available options that can be passed via ``cmake.define.{option}``. + +- BOOST_ROOT + +- Boost_DIR + +- Boost_INCLUDE_DIR + +- BOOST_LIBRARYDIR + +- OpenCL_INCLUDE_DIR + +- OpenCL_LIBRARY + +For more details see `FindBoost `__ and `FindOpenCL `__. + +Don't confuse with `CUDA version <#build-cuda-version>`__. +To use the GPU version within Python, pass ``{"device": "gpu"}`` respectively in parameters. + +| + +Build CUDA Version +~~~~~~~~~~~~~~~~~~ + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_CUDA=ON + + +By default, the library will be built with support for a hard-coded list of GPU architectures +based on the detected CUDA Toolkit version. + +To build the library with support for more architectures, set ``CMAKE_CUDA_ARCHITECTURES``. + +.. code:: sh + + # example: all Blackwell arches, including DGX Spark + pip install \ + --no-binary lightgbm \ + --config-settings=cmake.define.USE_CUDA=ON \ + --config-settings=cmake.define.CMAKE_CUDA_ARCHITECTURES='100;120;121-real;121-virtual' \ + 'lightgbm>=4.7.0' + + # example: just the local GPU + pip install \ + --no-binary lightgbm \ + --config-settings=cmake.define.USE_CUDA=ON \ + --config-settings=cmake.define.CMAKE_CUDA_ARCHITECTURES='native' \ + 'lightgbm>=4.7.0' + +All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. + +For **macOS** and **Windows** users, the CUDA version is not supported. + +**CUDA** library is needed: details for installation can be found in `Installation Guide `__. + +Don't confuse with `GPU version <#build-gpu-version>`__. +To use the CUDA version within Python, pass ``{"device": "cuda"}`` respectively in parameters. + +| + +Build with MinGW-w64 on Windows +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.CMAKE_SH=CMAKE_SH-NOTFOUND --config-settings=cmake.args="-GMinGW Makefiles" + +`MinGW-w64 `_ should be installed first. + +It is recommended to use **Visual Studio** for its better multithreading efficiency in **Windows** for many-core systems +(see `Question 4 `__ +and `Question 8 `__). + +| + +Build 32-bit Version with 32-bit Python +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.args="-AWin32" + +For **Windows** users, compilation with **MinGW-w64** is not supported. + +For **macOS** and **Linux** users, the 32-bit version is not supported. + +It is **strongly not recommended** to use this version of LightGBM! + +| + +Build with Time Costs Output +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: sh + + pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_TIMETAG=ON + +Use this option to make LightGBM output time costs for different internal routines, to investigate and benchmark its performance. + +| + +Install from `conda-forge channel `_ +''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' + +``lightgbm`` conda packages are available from the ``conda-forge`` channel. + +.. code:: sh + + conda install -c conda-forge lightgbm + +These packages support **CPU**, **GPU** and **CUDA** versions out of the box. + +**GPU**-enabled version is available only for **Windows** and **Linux** currently. + +**CUDA**-enabled version (since ``lightgbm>=4.4.0``) is available only for **Linux** currently and will be automatically selected if you are on a system where CUDA is installed. + +| + +Install from GitHub +''''''''''''''''''' + +All requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well. + +.. code:: sh + + git clone --recursive https://github.com/lightgbm-org/LightGBM.git + cd LightGBM + # export CXX=g++-14 CC=gcc-14 # macOS users, if you decided to compile with gcc, don't forget to specify compilers + sh ./build-python.sh install + +Note: ``sudo`` (or administrator rights in **Windows**) may be needed to perform the command. +Run ``sh ./build-python.sh install --user`` to install into user-specific instead of global site-packages directory. + +Run ``sh ./build-python.sh install --no-isolation`` to assume all build and install dependencies are already installed, don't go to the internet to get them. + +| + +Run ``sh ./build-python.sh install --nomp`` to disable **OpenMP** support. +All requirements from `Build Threadless Version section <#build-threadless-version>`__ apply for this installation option as well. + +Run ``sh ./build-python.sh install --mpi`` to enable **MPI** support. +All requirements from `Build MPI Version section <#build-mpi-version>`__ apply for this installation option as well. + +Run ``sh ./build-python.sh install --gpu`` to enable GPU support. +All requirements from `Build GPU Version section <#build-gpu-version>`__ apply for this installation option as well. +To pass additional options to **CMake** use the following syntax: ``sh ./build-python.sh install --gpu --opencl-include-dir="/usr/local/cuda/include/"``, +see `Build GPU Version section <#build-gpu-version>`__ for the complete list of them. + +Run ``sh ./build-python.sh install --cuda`` to enable CUDA support. +All requirements from `Build CUDA Version section <#build-cuda-version>`__ apply for this installation option as well. + +Run ``sh ./build-python.sh install --mingw``, if you want to use **MinGW-w64** on **Windows** instead of **Visual Studio**. +All requirements from `Build with MinGW-w64 on Windows section <#build-with-mingw-w64-on-windows>`__ apply for this installation option as well. + +Run ``sh ./build-python.sh install --bit32``, if you want to use 32-bit version. +All requirements from `Build 32-bit Version with 32-bit Python section <#build-32-bit-version-with-32-bit-python>`__ apply for this installation option as well. + +Run ``sh ./build-python.sh install --time-costs``, if you want to output time costs for different internal routines. +All requirements from `Build with Time Costs Output section <#build-with-time-costs-output>`__ apply for this installation option as well. + +| + +If you get any errors during installation or due to any other reasons, +you may want to build dynamic library from sources by any method you prefer +(see `Installation Guide `__). +For example, you can use ``MSBuild`` tool and `solution file `__ from the repo. + +.. code:: sh + + MSBuild.exe windows/LightGBM.sln /p:Configuration=DLL /p:Platform=x64 /p:PlatformToolset=v143 + +After compiling dynamic library just run ``sh ./build-python.sh install --precompile`` to install the Python-package using that library. + +| + +Build Wheel File +**************** + +You can run ``sh ./build-python.sh bdist_wheel`` to build a wheel file but not install it. + +That script requires some dependencies like ``build``, ``scikit-build-core``, and ``wheel``. +In environments with restricted or no internet access, install those tools and then pass ``--no-isolation``. + +.. code:: sh + + sh ./build-python.sh bdist_wheel --no-isolation + +Troubleshooting +--------------- + +Refer to `FAQ `_. + +Examples +-------- + +Refer to the walk through examples in `Python guide folder `_. + +Supported Python Versions +------------------------- + +This project supports all Python versions until they reach end-of-life. +For details on the support calendar for Python versions, see https://devguide.python.org/versions/. + +Development Guide +----------------- + +To check that a contribution to the package matches its style expectations, run the following from the root of the repo. + +.. code:: sh + + pre-commit run --all-files + +To run the tests locally and compute test coverage, install the Python package using one of the options mentioned above. +Then run the following from the root of the repo. + +.. code:: sh + + pytest \ + --cov=lightgbm \ + --cov-report="term" \ + --cov-report="html:htmlcov" \ + tests/python_package_test/ + +Then open `htmlcov/index.html` to view a clickable coverage report. + +.. |License| image:: https://img.shields.io/github/license/lightgbm-org/lightgbm.svg + :target: https://github.com/lightgbm-org/LightGBM/blob/main/LICENSE +.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/lightgbm.svg?logo=python&logoColor=white + :target: https://pypi.org/project/lightgbm +.. |PyPI Version| image:: https://img.shields.io/pypi/v/lightgbm.svg?logo=pypi&logoColor=white + :target: https://pypi.org/project/lightgbm +.. |PyPI Downloads| image:: https://img.shields.io/pepy/dt/lightgbm?logo=pypi&logoColor=white&label=pypi%20downloads + :target: https://pepy.tech/project/lightgbm +.. |conda Version| image:: https://img.shields.io/conda/vn/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda + :target: https://anaconda.org/conda-forge/lightgbm +.. |conda Downloads| image:: https://img.shields.io/conda/d/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda%20downloads + :target: https://anaconda.org/conda-forge/lightgbm/files +.. |API Docs| image:: https://readthedocs.org/projects/lightgbm/badge/?version=latest + :target: https://lightgbm.readthedocs.io/en/latest/Python-API.html diff --git a/python-package/lightgbm/__init__.py b/python-package/lightgbm/__init__.py new file mode 100644 index 0000000..d827934 --- /dev/null +++ b/python-package/lightgbm/__init__.py @@ -0,0 +1,58 @@ +# coding: utf-8 +"""LightGBM, Light Gradient Boosting Machine. + +Contributors: https://github.com/lightgbm-org/LightGBM/graphs/contributors. +""" + +from pathlib import Path + +# .basic is intentionally loaded as early as possible, to dlopen() lib_lightgbm.{dll,dylib,so} +# and its dependencies as early as possible +from .basic import Booster, Dataset, Sequence, register_logger +from .callback import EarlyStopException, early_stopping, log_evaluation, record_evaluation, reset_parameter +from .engine import CVBooster, cv, train + +try: + from .sklearn import LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor +except ImportError: + pass +try: + from .plotting import create_tree_digraph, plot_importance, plot_metric, plot_split_value_histogram, plot_tree +except ImportError: + pass +try: + from .dask import DaskLGBMClassifier, DaskLGBMRanker, DaskLGBMRegressor +except ImportError: + pass + + +_version_path = Path(__file__).resolve().parent / "VERSION.txt" +if _version_path.is_file(): + __version__ = _version_path.read_text(encoding="utf-8").strip() + +__all__ = [ + "Dataset", + "Booster", + "CVBooster", + "Sequence", + "register_logger", + "train", + "cv", + "LGBMModel", + "LGBMRegressor", + "LGBMClassifier", + "LGBMRanker", + "DaskLGBMRegressor", + "DaskLGBMClassifier", + "DaskLGBMRanker", + "log_evaluation", + "record_evaluation", + "reset_parameter", + "early_stopping", + "EarlyStopException", + "plot_importance", + "plot_split_value_histogram", + "plot_metric", + "plot_tree", + "create_tree_digraph", +] diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py new file mode 100644 index 0000000..258ed01 --- /dev/null +++ b/python-package/lightgbm/basic.py @@ -0,0 +1,5251 @@ +# coding: utf-8 +"""Wrapper for C API of LightGBM.""" + +# This import causes lib_lightgbm.{dll,dylib,so} to be loaded. +# It's intentionally done here, as early as possible, to avoid issues like +# "libgomp.so.1: cannot allocate memory in static TLS block" on aarch64 Linux. +# +# For details, see the "cannot allocate memory in static TLS block" entry in docs/FAQ.rst. +from .libpath import _LIB # isort: skip + +import abc +import ctypes +import inspect +import json +import warnings +from collections import OrderedDict +from copy import deepcopy +from enum import Enum +from functools import wraps +from os import SEEK_END, environ +from os.path import getsize +from pathlib import Path +from tempfile import NamedTemporaryFile +from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, List, Optional, Set, Tuple, Union + +import narwhals as nw +import narwhals.dependencies as nwd +import narwhals.typing as nwt +import numpy as np +import scipy.sparse + +from .compat import PANDAS_INSTALLED, concat, pd_CategoricalDtype, pd_DataFrame, pd_Series + +if TYPE_CHECKING: + from typing import Literal, TypeGuard + + +__all__ = [ + "Booster", + "Dataset", + "LGBMDeprecationWarning", + "LightGBMError", + "register_logger", + "Sequence", +] + +_BoosterHandle = ctypes.c_void_p +_DatasetHandle = ctypes.c_void_p +_ctypes_int_ptr = Union[ + "ctypes._Pointer[ctypes.c_int32]", + "ctypes._Pointer[ctypes.c_int64]", +] +_ctypes_int_array = Union[ + "ctypes.Array[ctypes._Pointer[ctypes.c_int32]]", + "ctypes.Array[ctypes._Pointer[ctypes.c_int64]]", +] +_ctypes_float_ptr = Union[ + "ctypes._Pointer[ctypes.c_float]", + "ctypes._Pointer[ctypes.c_double]", +] +_ctypes_float_array = Union[ + "ctypes.Array[ctypes._Pointer[ctypes.c_float]]", + "ctypes.Array[ctypes._Pointer[ctypes.c_double]]", +] +_LGBM_EvalFunctionResultType = Tuple[str, float, bool] +_LGBM_BoosterBestScoreType = Dict[str, Dict[str, float]] +_LGBM_BoosterEvalMethodResultType = Tuple[str, str, float, bool] +_LGBM_BoosterEvalMethodResultWithStandardDeviationType = Tuple[str, str, float, bool, float] +_LGBM_CategoricalFeatureConfiguration = Union[List[str], List[int], "Literal['auto']"] +_LGBM_FeatureNameConfiguration = Union[List[str], "Literal['auto']"] +_LGBM_GroupType = Union[ + List[float], + List[int], + np.ndarray, + pd_Series, + nwt.IntoSeries, +] +_LGBM_PositionType = Union[ + np.ndarray, + pd_Series, +] +_LGBM_InitScoreType = Union[ + List[float], + List[List[float]], + np.ndarray, + pd_Series, + pd_DataFrame, + nwt.IntoSeries, + nwt.IntoDataFrame, +] +_LGBM_TrainDataType = Union[ + str, + Path, + np.ndarray, + pd_DataFrame, + scipy.sparse.spmatrix, + "Sequence", + List["Sequence"], + List[np.ndarray], + nwt.IntoDataFrame, +] +_LGBM_LabelType = Union[ + List[float], + List[int], + np.ndarray, + pd_Series, + pd_DataFrame, + nwt.IntoSeries, + nwt.IntoDataFrame, +] +_LGBM_PredictDataType = Union[ + str, + Path, + np.ndarray, + pd_DataFrame, + scipy.sparse.spmatrix, + nwt.IntoDataFrame, +] +_LGBM_PredictReturnType = Union[ + np.ndarray, + scipy.sparse.spmatrix, + List[scipy.sparse.spmatrix], +] +_LGBM_PredictSparseReturnType = Union[ + scipy.sparse.spmatrix, + List[scipy.sparse.spmatrix], +] +_LGBM_WeightType = Union[ + List[float], + List[int], + np.ndarray, + pd_Series, + nwt.IntoSeries, +] +_LGBM_SetFieldType = Union[ + List[List[float]], + List[List[int]], + List[float], + List[int], + np.ndarray, + pd_Series, + pd_DataFrame, + nwt.IntoDataFrame, +] + +ZERO_THRESHOLD = 1e-35 + +_MULTICLASS_OBJECTIVES = {"multiclass", "multiclassova", "multiclass_ova", "ova", "ovr", "softmax"} + + +class LightGBMError(Exception): + """Error thrown by LightGBM.""" + + pass + + +def _is_zero(x: float) -> bool: + return -ZERO_THRESHOLD <= x <= ZERO_THRESHOLD + + +def _get_sample_count(total_nrow: int, params: str) -> int: + sample_cnt = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_GetSampleCount( + ctypes.c_int32(total_nrow), + _c_str(params), + ctypes.byref(sample_cnt), + ) + ) + return sample_cnt.value + + +def _np2d_to_np1d(mat: np.ndarray) -> Tuple[np.ndarray, int]: + dtype: "np.typing.DTypeLike" + if mat.dtype in (np.float32, np.float64): + dtype = mat.dtype + else: + dtype = np.float32 + order: "Literal['C', 'F']" + if mat.flags["F_CONTIGUOUS"]: + order = "F" + layout = _C_API_IS_COL_MAJOR + else: + order = "C" + layout = _C_API_IS_ROW_MAJOR + # ensure dtype and order, copies if either do not match + data = np.asarray(mat, dtype=dtype, order=order) + # flatten array without copying + return data.ravel(order=order), layout + + +class _MissingType(Enum): + NONE = "None" + NAN = "NaN" + ZERO = "Zero" + + +class _DummyLogger: + def info(self, msg: str) -> None: + print(msg) # noqa: T201 + + def warning(self, msg: str) -> None: + warnings.warn(msg, stacklevel=3) + + +_LOGGER: Any = _DummyLogger() +_INFO_METHOD_NAME = "info" +_WARNING_METHOD_NAME = "warning" + + +def _has_method(logger: Any, method_name: str) -> bool: + return callable(getattr(logger, method_name, None)) + + +def register_logger( + logger: Any, + info_method_name: str = "info", + warning_method_name: str = "warning", +) -> None: + """Register custom logger. + + Parameters + ---------- + logger : Any + Custom logger. + info_method_name : str, optional (default="info") + Method used to log info messages. + warning_method_name : str, optional (default="warning") + Method used to log warning messages. + """ + if not _has_method(logger, info_method_name) or not _has_method(logger, warning_method_name): + raise TypeError(f"Logger must provide '{info_method_name}' and '{warning_method_name}' method") + + global _LOGGER, _INFO_METHOD_NAME, _WARNING_METHOD_NAME + _LOGGER = logger + _INFO_METHOD_NAME = info_method_name + _WARNING_METHOD_NAME = warning_method_name + + +def _normalize_native_string(func: Callable[[str], None]) -> Callable[[str], None]: + """Join log messages from native library which come by chunks.""" + msg_normalized: List[str] = [] + + @wraps(func) + def wrapper(msg: str) -> None: + nonlocal msg_normalized + if msg.strip() == "": + msg = "".join(msg_normalized) + msg_normalized = [] + return func(msg) + else: + msg_normalized.append(msg) + + return wrapper + + +def _log_info(msg: str) -> None: + getattr(_LOGGER, _INFO_METHOD_NAME)(msg) + + +def _log_warning(msg: str) -> None: + getattr(_LOGGER, _WARNING_METHOD_NAME)(msg) + + +@_normalize_native_string +def _log_native(msg: str) -> None: + getattr(_LOGGER, _INFO_METHOD_NAME)(msg) + + +def _log_callback(msg: bytes) -> None: + """Redirect logs from native library into Python.""" + _log_native(str(msg.decode("utf-8"))) + + +# connect the Python logger to logging in lib_lightgbm +if environ.get("LIGHTGBM_BUILD_DOC", "False") != "True": + _LIB.LGBM_GetLastError.restype = ctypes.c_char_p + callback = ctypes.CFUNCTYPE(None, ctypes.c_char_p) + _LIB.callback = callback(_log_callback) # type: ignore[attr-defined] + if _LIB.LGBM_RegisterLogCallback(_LIB.callback) != 0: + raise LightGBMError(_LIB.LGBM_GetLastError().decode("utf-8")) + + +_NUMERIC_TYPES = (int, float, bool) + + +def _safe_call(ret: int) -> None: + """Check the return value from C API call. + + Parameters + ---------- + ret : int + The return value from C API calls. + """ + if ret != 0: + raise LightGBMError(_LIB.LGBM_GetLastError().decode("utf-8")) + + +def _is_numeric(obj: Any) -> bool: + """Check whether object is a number or not, include numpy number, etc.""" + try: + float(obj) + return True + except (TypeError, ValueError): + # TypeError: obj is not a string or a number + # ValueError: invalid literal + return False + + +def _is_numpy_1d_array(data: Any) -> bool: + """Check whether data is a numpy 1-D array.""" + return isinstance(data, np.ndarray) and len(data.shape) == 1 + + +def _is_numpy_column_array(data: Any) -> bool: + """Check whether data is a column numpy array.""" + if not isinstance(data, np.ndarray): + return False + shape = data.shape + return len(shape) == 2 and shape[1] == 1 + + +def _cast_numpy_array_to_dtype(array: np.ndarray, dtype: "np.typing.DTypeLike") -> np.ndarray: + """Cast numpy array to given dtype.""" + if array.dtype == dtype: + return array + return array.astype(dtype=dtype, copy=False) + + +def _is_1d_list(data: Any) -> bool: + """Check whether data is a 1-D list.""" + return isinstance(data, list) and (not data or _is_numeric(data[0])) + + +def _is_list_of_numpy_arrays(data: Any) -> "TypeGuard[List[np.ndarray]]": + return isinstance(data, list) and all(isinstance(x, np.ndarray) for x in data) + + +def _is_list_of_sequences(data: Any) -> "TypeGuard[List[Sequence]]": + return isinstance(data, list) and all(isinstance(x, Sequence) for x in data) + + +def _is_1d_collection(data: Any) -> bool: + """Check whether data is a 1-D collection.""" + return _is_numpy_1d_array(data) or _is_numpy_column_array(data) or _is_1d_list(data) or isinstance(data, pd_Series) + + +def _list_to_1d_numpy( + *, + data: Any, + dtype: "np.typing.DTypeLike", + name: str, +) -> np.ndarray: + """Convert data to numpy 1-D array.""" + if _is_numpy_1d_array(data): + return _cast_numpy_array_to_dtype(data, dtype) + elif _is_numpy_column_array(data): + _log_warning("Converting column-vector to 1d array") + array = data.ravel() + return _cast_numpy_array_to_dtype(array, dtype) + elif _is_1d_list(data): + return np.asarray(data, dtype=dtype) + elif isinstance(data, pd_Series): + _check_for_bad_pandas_dtypes(data.to_frame().dtypes) + return np.asarray(data, dtype=dtype) # SparseArray should be supported as well + else: + raise TypeError( + f"Wrong type({type(data).__name__}) for {name}.\nIt should be list, numpy 1-D array or pandas Series" + ) + + +def _is_numpy_2d_array(data: Any) -> bool: + """Check whether data is a numpy 2-D array.""" + return isinstance(data, np.ndarray) and len(data.shape) == 2 and data.shape[1] > 1 + + +def _is_2d_list(data: Any) -> bool: + """Check whether data is a 2-D list.""" + return isinstance(data, list) and len(data) > 0 and _is_1d_list(data[0]) + + +def _is_2d_collection(data: Any) -> bool: + """Check whether data is a 2-D collection.""" + return _is_numpy_2d_array(data) or _is_2d_list(data) or isinstance(data, pd_DataFrame) + + +_pycapsule_get_pointer = ctypes.pythonapi.PyCapsule_GetPointer +_pycapsule_get_pointer.restype = ctypes.c_void_p +_pycapsule_get_pointer.argtypes = [ctypes.py_object, ctypes.c_char_p] + + +def _extract_arrow_stream_capsule_pointer(pycapsule: object) -> ctypes.c_void_p: + """Extract the raw pointer from a PyCapsule returned by __arrow_c_stream__.""" + return ctypes.c_void_p(_pycapsule_get_pointer(pycapsule, b"arrow_array_stream")) + + +def _data_to_2d_numpy( + data: Any, + dtype: "np.typing.DTypeLike", + name: str, +) -> np.ndarray: + """Convert data to numpy 2-D array.""" + if _is_numpy_2d_array(data): + return _cast_numpy_array_to_dtype(data, dtype) + if _is_2d_list(data): + return np.array(data, dtype=dtype) + if isinstance(data, pd_DataFrame): + _check_for_bad_pandas_dtypes(data.dtypes) + return _cast_numpy_array_to_dtype(data.values, dtype) + raise TypeError( + f"Wrong type({type(data).__name__}) for {name}.\n" + "It should be list of lists, numpy 2-D array or pandas DataFrame" + ) + + +def _cfloat32_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: + """Convert a ctypes float pointer array to a numpy array.""" + if isinstance(cptr, ctypes.POINTER(ctypes.c_float)): + return np.ctypeslib.as_array(cptr, shape=(length,)).copy() + else: + raise RuntimeError("Expected float pointer") + + +def _cfloat64_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: + """Convert a ctypes double pointer array to a numpy array.""" + if isinstance(cptr, ctypes.POINTER(ctypes.c_double)): + return np.ctypeslib.as_array(cptr, shape=(length,)).copy() + else: + raise RuntimeError("Expected double pointer") + + +def _cint32_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: + """Convert a ctypes int pointer array to a numpy array.""" + if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)): + return np.ctypeslib.as_array(cptr, shape=(length,)).copy() + else: + raise RuntimeError("Expected int32 pointer") + + +def _cint64_array_to_numpy(*, cptr: "ctypes._Pointer", length: int) -> np.ndarray: + """Convert a ctypes int pointer array to a numpy array.""" + if isinstance(cptr, ctypes.POINTER(ctypes.c_int64)): + return np.ctypeslib.as_array(cptr, shape=(length,)).copy() + else: + raise RuntimeError("Expected int64 pointer") + + +def _c_str(string: str) -> ctypes.c_char_p: + """Convert a Python string to C string.""" + return ctypes.c_char_p(string.encode("utf-8")) + + +def _c_array(ctype: type, values: List[Any]) -> ctypes.Array: + """Convert a Python array to C array.""" + return (ctype * len(values))(*values) # type: ignore[operator] + + +def _json_default_with_numpy(obj: Any) -> Any: + """Convert numpy classes to JSON serializable objects.""" + if isinstance(obj, (np.integer, np.floating, np.bool_)): + return obj.item() + elif isinstance(obj, np.ndarray): + return obj.tolist() + else: + return obj + + +def _to_string(x: Union[int, float, str, List]) -> str: + if isinstance(x, list): + val_list = ",".join(str(val) for val in x) + return f"[{val_list}]" + else: + return str(x) + + +def _param_dict_to_str(data: Optional[Dict[str, Any]]) -> str: + """Convert Python dictionary to string, which is passed to C API.""" + if data is None or not data: + return "" + pairs = [] + for key, val in data.items(): + if isinstance(val, (list, tuple, set)) or _is_numpy_1d_array(val): + pairs.append(f"{key}={','.join(map(_to_string, val))}") + elif isinstance(val, (str, Path, _NUMERIC_TYPES)) or _is_numeric(val): + pairs.append(f"{key}={val}") + elif val is not None: + raise TypeError(f"Unknown type of parameter:{key}, got:{type(val).__name__}") + return " ".join(pairs) + + +class _TempFile: + """Proxy class to workaround errors on Windows.""" + + def __enter__(self) -> "_TempFile": + with NamedTemporaryFile(prefix="lightgbm_tmp_", delete=True) as f: + self.name = f.name + self.path = Path(self.name) + return self + + def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: + if self.path.is_file(): + self.path.unlink() + + +# DeprecationWarning is not shown by default, so let's create our own with higher level +# ref: https://peps.python.org/pep-0565/#additional-use-case-for-futurewarning +class LGBMDeprecationWarning(FutureWarning): + """Custom deprecation warning.""" + + pass + + +class _ConfigAliases: + # lazy evaluation to allow import without dynamic library, e.g., for docs generation + aliases = None + + @staticmethod + def _get_all_param_aliases() -> Dict[str, List[str]]: + buffer_len = 1 << 20 + tmp_out_len = ctypes.c_int64(0) + string_buffer = ctypes.create_string_buffer(buffer_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_DumpParamAliases( + ctypes.c_int64(buffer_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + actual_len = tmp_out_len.value + # if buffer length is not long enough, re-allocate a buffer + if actual_len > buffer_len: + string_buffer = ctypes.create_string_buffer(actual_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_DumpParamAliases( + ctypes.c_int64(actual_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + return json.loads( + string_buffer.value.decode("utf-8"), object_hook=lambda obj: {k: [k] + v for k, v in obj.items()} + ) + + @classmethod + def get(cls, *args: str) -> Set[str]: + if cls.aliases is None: + cls.aliases = cls._get_all_param_aliases() + ret = set() + for i in args: + ret.update(cls.get_sorted(i)) + return ret + + @classmethod + def get_sorted(cls, name: str) -> List[str]: + if cls.aliases is None: + cls.aliases = cls._get_all_param_aliases() + return cls.aliases.get(name, [name]) + + @classmethod + def get_by_alias(cls, *args: str) -> Set[str]: + if cls.aliases is None: + cls.aliases = cls._get_all_param_aliases() + ret = set(args) + for arg in args: + for aliases in cls.aliases.values(): + if arg in aliases: + ret.update(aliases) + break + return ret + + +def _choose_param_value(main_param_name: str, params: Dict[str, Any], default_value: Any) -> Dict[str, Any]: + """Get a single parameter value, accounting for aliases. + + Parameters + ---------- + main_param_name : str + Name of the main parameter to get a value for. One of the keys of ``_ConfigAliases``. + params : dict + Dictionary of LightGBM parameters. + default_value : Any + Default value to use for the parameter, if none is found in ``params``. + + Returns + ------- + params : dict + A ``params`` dict with exactly one value for ``main_param_name``, and all aliases ``main_param_name`` removed. + If both ``main_param_name`` and one or more aliases for it are found, the value of ``main_param_name`` will be preferred. + """ + # avoid side effects on passed-in parameters + params = deepcopy(params) + + aliases = _ConfigAliases.get_sorted(main_param_name) + aliases = [a for a in aliases if a != main_param_name] + + # if main_param_name was provided, keep that value and remove all aliases + if main_param_name in params.keys(): + for param in aliases: + params.pop(param, None) + return params + + # if main param name was not found, search for an alias + for param in aliases: + if param in params.keys(): + params[main_param_name] = params[param] + break + + if main_param_name in params.keys(): + for param in aliases: + params.pop(param, None) + return params + + # neither of main_param_name, aliases were found + params[main_param_name] = default_value + + return params + + +_MAX_INT32 = (1 << 31) - 1 + +"""Macro definition of data type in C API of LightGBM""" +_C_API_DTYPE_FLOAT32 = 0 +_C_API_DTYPE_FLOAT64 = 1 +_C_API_DTYPE_INT32 = 2 +_C_API_DTYPE_INT64 = 3 + +"""Macro definition of data order in matrix""" +_C_API_IS_COL_MAJOR = 0 +_C_API_IS_ROW_MAJOR = 1 + +"""Macro definition of prediction type in C API of LightGBM""" +_C_API_PREDICT_NORMAL = 0 +_C_API_PREDICT_RAW_SCORE = 1 +_C_API_PREDICT_LEAF_INDEX = 2 +_C_API_PREDICT_CONTRIB = 3 + +"""Macro definition of sparse matrix type""" +_C_API_MATRIX_TYPE_CSR = 0 +_C_API_MATRIX_TYPE_CSC = 1 + +"""Macro definition of feature importance type""" +_C_API_FEATURE_IMPORTANCE_SPLIT = 0 +_C_API_FEATURE_IMPORTANCE_GAIN = 1 + +"""Data type of data field""" +_FIELD_TYPE_MAPPER = { + "label": _C_API_DTYPE_FLOAT32, + "weight": _C_API_DTYPE_FLOAT32, + "init_score": _C_API_DTYPE_FLOAT64, + "group": _C_API_DTYPE_INT32, + "position": _C_API_DTYPE_INT32, +} + +"""String name to int feature importance type mapper""" +_FEATURE_IMPORTANCE_TYPE_MAPPER = { + "split": _C_API_FEATURE_IMPORTANCE_SPLIT, + "gain": _C_API_FEATURE_IMPORTANCE_GAIN, +} + + +def _convert_from_sliced_object(data: np.ndarray) -> np.ndarray: + """Fix the memory of multi-dimensional sliced object.""" + if isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray): + if not data.flags.c_contiguous: + _log_warning( + "Usage of np.ndarray subset (sliced data) is not recommended " + "due to it will double the peak memory cost in LightGBM." + ) + return np.copy(data) + return data + + +def _c_float_array(data: np.ndarray) -> Tuple[_ctypes_float_ptr, int, np.ndarray]: + """Get pointer of float numpy array / list.""" + if _is_1d_list(data): + data = np.asarray(data) + if _is_numpy_1d_array(data): + data = _convert_from_sliced_object(data) + assert data.flags.c_contiguous + ptr_data: _ctypes_float_ptr + if data.dtype == np.float32: + ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)) + type_data = _C_API_DTYPE_FLOAT32 + elif data.dtype == np.float64: + ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)) + type_data = _C_API_DTYPE_FLOAT64 + else: + raise TypeError(f"Expected np.float32 or np.float64, met type({data.dtype})") + else: + raise TypeError(f"Unknown type({type(data).__name__})") + return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed + + +def _c_int_array(data: np.ndarray) -> Tuple[_ctypes_int_ptr, int, np.ndarray]: + """Get pointer of int numpy array / list.""" + if _is_1d_list(data): + data = np.asarray(data) + if _is_numpy_1d_array(data): + data = _convert_from_sliced_object(data) + assert data.flags.c_contiguous + ptr_data: _ctypes_int_ptr + if data.dtype == np.int32: + ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)) + type_data = _C_API_DTYPE_INT32 + elif data.dtype == np.int64: + ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)) + type_data = _C_API_DTYPE_INT64 + else: + raise TypeError(f"Expected np.int32 or np.int64, met type({data.dtype})") + else: + raise TypeError(f"Unknown type({type(data).__name__})") + return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed + + +def _is_allowed_numpy_dtype(dtype: type) -> bool: + float128 = getattr(np, "float128", type(None)) + return issubclass(dtype, (np.integer, np.floating, np.bool_)) and not issubclass(dtype, (np.timedelta64, float128)) + + +def _check_for_bad_pandas_dtypes(pandas_dtypes_series: pd_Series) -> None: + bad_pandas_dtypes = [ + f"{column_name}: {pandas_dtype}" + for column_name, pandas_dtype in pandas_dtypes_series.items() + if not _is_allowed_numpy_dtype(pandas_dtype.type) + ] + if bad_pandas_dtypes: + raise ValueError( + f"pandas dtypes must be int, float or bool.\nFields with bad pandas dtypes: {', '.join(bad_pandas_dtypes)}" + ) + + +def _pandas_to_numpy( + data: pd_DataFrame, + target_dtype: "np.typing.DTypeLike", +) -> np.ndarray: + _check_for_bad_pandas_dtypes(data.dtypes) + try: + # most common case (no nullable dtypes) + return data.to_numpy(dtype=target_dtype, copy=False) + except TypeError: + # 1.0 <= pd version < 1.1 and nullable dtypes, least common case + # raises error because array is casted to type(pd.NA) and there's no na_value argument + return data.astype(target_dtype, copy=False).values + except ValueError: + # data has nullable dtypes, but we can specify na_value argument and copy will be made + return data.to_numpy(dtype=target_dtype, na_value=np.nan) + + +def _data_from_pandas( + data: pd_DataFrame, + feature_name: _LGBM_FeatureNameConfiguration, + categorical_feature: _LGBM_CategoricalFeatureConfiguration, + pandas_categorical: Optional[List[List]], +) -> Tuple[np.ndarray, List[str], Union[List[str], List[int]], List[List]]: + if len(data.shape) != 2 or data.shape[0] < 1: + raise ValueError("Input data must be 2 dimensional and non empty.") + + # take shallow copy in case we modify categorical columns + # whole column modifications don't change the original df + data = data.copy(deep=False) + + # determine feature names + if feature_name == "auto": + feature_name = [str(col) for col in data.columns] + + # determine categorical features + cat_cols = [ + col for col, dtype in zip(data.columns, data.dtypes, strict=True) if isinstance(dtype, pd_CategoricalDtype) + ] + cat_cols_not_ordered: List[str] = [col for col in cat_cols if not data[col].cat.ordered] + if pandas_categorical is None: # train dataset + pandas_categorical = [list(data[col].cat.categories) for col in cat_cols] + else: + if len(cat_cols) != len(pandas_categorical): + raise ValueError("train and valid dataset categorical_feature do not match.") + for col, category in zip(cat_cols, pandas_categorical, strict=True): + if list(data[col].cat.categories) != list(category): + data[col] = data[col].cat.set_categories(category) + if cat_cols: # cat_cols is list + data[cat_cols] = data[cat_cols].apply(lambda x: x.cat.codes).replace({-1: np.nan}) + + # use cat cols from DataFrame + if categorical_feature == "auto": + categorical_feature = cat_cols_not_ordered + + df_dtypes = [dtype.type for dtype in data.dtypes] + # so that the target dtype considers floats + df_dtypes.append(np.float32) + target_dtype = np.result_type(*df_dtypes) + + return ( + _pandas_to_numpy(data, target_dtype=target_dtype), + feature_name, + categorical_feature, + pandas_categorical, + ) + + +def _dump_pandas_categorical( + pandas_categorical: Optional[List[List]], + file_name: Optional[Union[str, Path]] = None, +) -> str: + categorical_json = json.dumps(pandas_categorical, default=_json_default_with_numpy) + pandas_str = f"\npandas_categorical:{categorical_json}\n" + if file_name is not None: + with open(file_name, "a") as f: + f.write(pandas_str) + return pandas_str + + +def _load_pandas_categorical( + file_name: Optional[Union[str, Path]] = None, + model_str: Optional[str] = None, +) -> Optional[List[List]]: + pandas_key = "pandas_categorical:" + offset = -len(pandas_key) + if file_name is not None: + max_offset = -getsize(file_name) + with open(file_name, "rb") as f: + while True: + offset = max(offset, max_offset) + f.seek(offset, SEEK_END) + lines = f.readlines() + if len(lines) >= 2: + break + offset *= 2 + last_line = lines[-1].decode("utf-8").strip() + if not last_line.startswith(pandas_key): + last_line = lines[-2].decode("utf-8").strip() + elif model_str is not None: + idx = model_str.rfind("\n", 0, offset) + last_line = model_str[idx:].strip() + if last_line.startswith(pandas_key): + return json.loads(last_line[len(pandas_key) :]) + else: + return None + + +class Sequence(abc.ABC): + """ + Generic data access interface. + + Object should support the following operations: + + .. code-block:: + + # Get total row number. + >>> len(seq) + # Random access by row index. Used for data sampling. + >>> seq[10] + # Range data access. Used to read data in batch when constructing Dataset. + >>> seq[0:100] + # Optionally specify batch_size to control range data read size. + >>> seq.batch_size + + - With random access, **data sampling does not need to go through all data**. + - With range data access, there's **no need to read all data into memory thus reduce memory usage**. + + .. versionadded:: 3.3.0 + + Attributes + ---------- + batch_size : int + Default size of a batch. + """ + + batch_size = 4096 # Defaults to read 4K rows in each batch. + + @abc.abstractmethod + def __getitem__(self, idx: Union[int, slice, List[int]]) -> np.ndarray: + """Return data for given row index. + + A basic implementation should look like this: + + .. code-block:: python + + if isinstance(idx, numbers.Integral): + return self._get_one_line(idx) + elif isinstance(idx, slice): + return np.stack([self._get_one_line(i) for i in range(idx.start, idx.stop)]) + elif isinstance(idx, list): + # Only required if using ``Dataset.subset()``. + return np.array([self._get_one_line(i) for i in idx]) + else: + raise TypeError(f"Sequence index must be integer, slice or list, got {type(idx).__name__}") + + Parameters + ---------- + idx : int, slice[int], list[int] + Item index. + + Returns + ------- + result : numpy 1-D array or numpy 2-D array + 1-D array if idx is int, 2-D array if idx is slice or list. + """ + raise NotImplementedError("Sub-classes of lightgbm.Sequence must implement __getitem__()") + + @abc.abstractmethod + def __len__(self) -> int: + """Return row count of this sequence.""" + raise NotImplementedError("Sub-classes of lightgbm.Sequence must implement __len__()") + + +class _InnerPredictor: + """_InnerPredictor of LightGBM. + + Not exposed to user. + Used only for prediction, usually used for continued training. + + .. note:: + + Can be converted from Booster, but cannot be converted to Booster. + """ + + def __init__( + self, + booster_handle: _BoosterHandle, + pandas_categorical: Optional[List[List]], + pred_parameter: Dict[str, Any], + manage_handle: bool, + ): + """Initialize the _InnerPredictor. + + Parameters + ---------- + booster_handle : object + Handle of Booster. + pandas_categorical : list of list, or None + If provided, list of categories for ``pandas`` categorical columns. + Where the ``i``th element of the list contains the categories for the ``i``th categorical feature. + pred_parameter : dict + Other parameters for the prediction. + manage_handle : bool + If ``True``, free the corresponding Booster on the C++ side when this Python object is deleted. + """ + self._handle = booster_handle + self.__is_manage_handle = manage_handle + self.pandas_categorical = pandas_categorical + self.pred_parameter = _param_dict_to_str(pred_parameter) + + out_num_class = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetNumClasses( + self._handle, + ctypes.byref(out_num_class), + ) + ) + self.num_class = out_num_class.value + + @classmethod + def from_booster( + cls, + booster: "Booster", + pred_parameter: Dict[str, Any], + ) -> "_InnerPredictor": + """Initialize an ``_InnerPredictor`` from a ``Booster``. + + Parameters + ---------- + booster : Booster + Booster. + pred_parameter : dict + Other parameters for the prediction. + """ + out_cur_iter = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetCurrentIteration( + booster._handle, + ctypes.byref(out_cur_iter), + ) + ) + return cls( + booster_handle=booster._handle, + pandas_categorical=booster.pandas_categorical, + pred_parameter=pred_parameter, + manage_handle=False, + ) + + @classmethod + def from_model_file( + cls, + model_file: Union[str, Path], + pred_parameter: Dict[str, Any], + ) -> "_InnerPredictor": + """Initialize an ``_InnerPredictor`` from a text file containing a LightGBM model. + + Parameters + ---------- + model_file : str or pathlib.Path + Path to the model file. + pred_parameter : dict + Other parameters for the prediction. + """ + booster_handle = ctypes.c_void_p() + out_num_iterations = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterCreateFromModelfile( + _c_str(str(model_file)), + ctypes.byref(out_num_iterations), + ctypes.byref(booster_handle), + ) + ) + return cls( + booster_handle=booster_handle, + pandas_categorical=_load_pandas_categorical(file_name=model_file), + pred_parameter=pred_parameter, + manage_handle=True, + ) + + def __del__(self) -> None: + try: + if self.__is_manage_handle: + _safe_call(_LIB.LGBM_BoosterFree(self._handle)) + except AttributeError: + pass + + def __getstate__(self) -> Dict[str, Any]: + this = self.__dict__.copy() + this.pop("handle", None) + this.pop("_handle", None) + return this + + def predict( + self, + data: _LGBM_PredictDataType, + start_iteration: int = 0, + num_iteration: int = -1, + raw_score: bool = False, + pred_leaf: bool = False, + pred_contrib: bool = False, + data_has_header: bool = False, + validate_features: bool = False, + ) -> _LGBM_PredictReturnType: + """Predict logic. + + Parameters + ---------- + data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, pyarrow Table or polars DataFrame + Data source for prediction. + If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). + start_iteration : int, optional (default=0) + Start index of the iteration to predict. + num_iteration : int, optional (default=-1) + Iteration used for prediction. + raw_score : bool, optional (default=False) + Whether to predict raw scores. + pred_leaf : bool, optional (default=False) + Whether to predict leaf index. + pred_contrib : bool, optional (default=False) + Whether to predict feature contributions. + data_has_header : bool, optional (default=False) + Whether data has header. + Used only for txt data. + validate_features : bool, optional (default=False) + If True, ensure that the features used to predict match the ones used to train. + Used only if data is pandas DataFrame. + + .. versionadded:: 4.0.0 + + Returns + ------- + result : numpy array, scipy.sparse or list of scipy.sparse + Prediction result. + Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). + """ + if isinstance(data, Dataset): + raise TypeError("Cannot use Dataset instance for prediction, please use raw data instead") + if isinstance(data, pd_DataFrame) and validate_features: + data_names = [str(x) for x in data.columns] + ptr_names = (ctypes.c_char_p * len(data_names))() + ptr_names[:] = [x.encode("utf-8") for x in data_names] + _safe_call( + _LIB.LGBM_BoosterValidateFeatureNames( + self._handle, + ptr_names, + ctypes.c_int(len(data_names)), + ) + ) + + if isinstance(data, pd_DataFrame): + data = _data_from_pandas( + data=data, + feature_name="auto", + categorical_feature="auto", + pandas_categorical=self.pandas_categorical, + )[0] + + predict_type = _C_API_PREDICT_NORMAL + if raw_score: + predict_type = _C_API_PREDICT_RAW_SCORE + if pred_leaf: + predict_type = _C_API_PREDICT_LEAF_INDEX + if pred_contrib: + predict_type = _C_API_PREDICT_CONTRIB + + if isinstance(data, (str, Path)): + with _TempFile() as f: + _safe_call( + _LIB.LGBM_BoosterPredictForFile( + self._handle, + _c_str(str(data)), + ctypes.c_int(data_has_header), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + _c_str(self.pred_parameter), + _c_str(f.name), + ) + ) + preds = np.loadtxt(f.name, dtype=np.float64) + nrow = preds.shape[0] + elif isinstance(data, scipy.sparse.csr_matrix): + # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. + preds, nrow = self.__pred_for_csr( # type: ignore[assignment] + csr=data, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + elif isinstance(data, scipy.sparse.csc_matrix): + # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. + preds, nrow = self.__pred_for_csc( # type: ignore[assignment] + csc=data, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + elif isinstance(data, np.ndarray): + preds, nrow = self.__pred_for_np2d( + mat=data, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + elif nwd.is_into_dataframe(data): + preds, nrow = self.__pred_for_narwhals( + data=nw.from_native(data), + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + elif isinstance(data, list): + try: + data = np.array(data) + except BaseException as err: + raise ValueError("Cannot convert data list to numpy array.") from err + preds, nrow = self.__pred_for_np2d( + mat=data, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + else: + try: + _log_warning("Converting data to scipy sparse matrix.") + csr = scipy.sparse.csr_matrix(data) + except BaseException as err: + raise TypeError(f"Cannot predict data for type {type(data).__name__}") from err + # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. + preds, nrow = self.__pred_for_csr( # type: ignore[assignment] + csr=csr, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + if pred_leaf: + preds = preds.astype(np.int32) + is_sparse = isinstance(preds, (list, scipy.sparse.spmatrix)) + if not is_sparse and (preds.size != nrow or pred_leaf or pred_contrib): + if preds.size % nrow == 0: + preds = preds.reshape(nrow, -1) + else: + raise ValueError(f"Length of predict result ({preds.size}) cannot be divide nrow ({nrow})") + return preds + + def __get_num_preds( + self, + *, + start_iteration: int, + num_iteration: int, + nrow: int, + predict_type: int, + ) -> int: + """Get size of prediction result.""" + if nrow > _MAX_INT32: + raise LightGBMError( + "LightGBM cannot perform prediction for data " + f"with number of rows greater than MAX_INT32 ({_MAX_INT32}).\n" + "You can split your data into chunks " + "and then concatenate predictions for them" + ) + n_preds = ctypes.c_int64(0) + _safe_call( + _LIB.LGBM_BoosterCalcNumPredict( + self._handle, + ctypes.c_int(nrow), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + ctypes.byref(n_preds), + ) + ) + return n_preds.value + + def __inner_predict_np2d( + self, + mat: np.ndarray, + start_iteration: int, + num_iteration: int, + predict_type: int, + preds: Optional[np.ndarray], + ) -> Tuple[np.ndarray, int]: + data, layout = _np2d_to_np1d(mat) + ptr_data, type_ptr_data, _ = _c_float_array(data) + n_preds = self.__get_num_preds( + start_iteration=start_iteration, + num_iteration=num_iteration, + nrow=mat.shape[0], + predict_type=predict_type, + ) + if preds is None: + preds = np.empty(n_preds, dtype=np.float64) + elif len(preds.shape) != 1 or len(preds) != n_preds: + raise ValueError("Wrong length of pre-allocated predict array") + out_num_preds = ctypes.c_int64(0) + _safe_call( + _LIB.LGBM_BoosterPredictForMat( + self._handle, + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int32(mat.shape[0]), + ctypes.c_int32(mat.shape[1]), + ctypes.c_int(layout), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + _c_str(self.pred_parameter), + ctypes.byref(out_num_preds), + preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + ) + if n_preds != out_num_preds.value: + raise ValueError("Wrong length for predict results") + return preds, mat.shape[0] + + def __pred_for_np2d( + self, + mat: np.ndarray, + start_iteration: int, + num_iteration: int, + predict_type: int, + ) -> Tuple[np.ndarray, int]: + """Predict for a 2-D numpy matrix.""" + if len(mat.shape) != 2: + raise ValueError("Input numpy.ndarray or list must be 2 dimensional") + + nrow = mat.shape[0] + if nrow > _MAX_INT32: + sections = np.arange(_MAX_INT32, nrow, _MAX_INT32) + # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal + n_preds = [ + self.__get_num_preds( + start_iteration=start_iteration, + num_iteration=num_iteration, + nrow=int(i), + predict_type=predict_type, + ) + for i in np.diff([0] + list(sections) + [nrow]) + ] + n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum() + preds = np.empty(sum(n_preds), dtype=np.float64) + for chunk, (start_idx_pred, end_idx_pred) in zip( + np.array_split(mat, sections), zip(n_preds_sections, n_preds_sections[1:], strict=True), strict=True + ): + # avoid memory consumption by arrays concatenation operations + self.__inner_predict_np2d( + mat=chunk, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + preds=preds[start_idx_pred:end_idx_pred], + ) + return preds, nrow + else: + return self.__inner_predict_np2d( + mat=mat, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + preds=None, + ) + + def __create_sparse_native( + self, + cs: Union[scipy.sparse.csc_matrix, scipy.sparse.csr_matrix], + out_shape: np.ndarray, + out_ptr_indptr: "ctypes._Pointer", + out_ptr_indices: "ctypes._Pointer", + out_ptr_data: "ctypes._Pointer", + indptr_type: int, + data_type: int, + is_csr: bool, + ) -> _LGBM_PredictSparseReturnType: + # create numpy array from output arrays + data_indices_len = out_shape[0] + indptr_len = out_shape[1] + if indptr_type == _C_API_DTYPE_INT32: + out_indptr = _cint32_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len) + elif indptr_type == _C_API_DTYPE_INT64: + out_indptr = _cint64_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len) + else: + raise TypeError("Expected int32 or int64 type for indptr") + if data_type == _C_API_DTYPE_FLOAT32: + out_data = _cfloat32_array_to_numpy(cptr=out_ptr_data, length=data_indices_len) + elif data_type == _C_API_DTYPE_FLOAT64: + out_data = _cfloat64_array_to_numpy(cptr=out_ptr_data, length=data_indices_len) + else: + raise TypeError("Expected float32 or float64 type for data") + out_indices = _cint32_array_to_numpy(cptr=out_ptr_indices, length=data_indices_len) + # break up indptr based on number of rows (note more than one matrix in multiclass case) + per_class_indptr_shape = cs.indptr.shape[0] + # for CSC there is extra column added + if not is_csr: + per_class_indptr_shape += 1 + out_indptr_arrays = np.split(out_indptr, out_indptr.shape[0] / per_class_indptr_shape) + # reformat output into a csr or csc matrix or list of csr or csc matrices + cs_output_matrices = [] + offset = 0 + for cs_indptr in out_indptr_arrays: + matrix_indptr_len = cs_indptr[cs_indptr.shape[0] - 1] + cs_indices = out_indices[offset + cs_indptr[0] : offset + matrix_indptr_len] + cs_data = out_data[offset + cs_indptr[0] : offset + matrix_indptr_len] + offset += matrix_indptr_len + # same shape as input csr or csc matrix except extra column for expected value + cs_shape = [cs.shape[0], cs.shape[1] + 1] + # note: make sure we copy data as it will be deallocated next + if is_csr: + cs_output_matrices.append(scipy.sparse.csr_matrix((cs_data, cs_indices, cs_indptr), cs_shape)) + else: + cs_output_matrices.append(scipy.sparse.csc_matrix((cs_data, cs_indices, cs_indptr), cs_shape)) + # free the temporary native indptr, indices, and data + _safe_call( + _LIB.LGBM_BoosterFreePredictSparse( + out_ptr_indptr, + out_ptr_indices, + out_ptr_data, + ctypes.c_int(indptr_type), + ctypes.c_int(data_type), + ) + ) + if len(cs_output_matrices) == 1: + return cs_output_matrices[0] + return cs_output_matrices + + def __inner_predict_csr( + self, + csr: scipy.sparse.csr_matrix, + start_iteration: int, + num_iteration: int, + predict_type: int, + preds: Optional[np.ndarray], + ) -> Tuple[np.ndarray, int]: + nrow = len(csr.indptr) - 1 + n_preds = self.__get_num_preds( + start_iteration=start_iteration, + num_iteration=num_iteration, + nrow=nrow, + predict_type=predict_type, + ) + if preds is None: + preds = np.empty(n_preds, dtype=np.float64) + elif len(preds.shape) != 1 or len(preds) != n_preds: + raise ValueError("Wrong length of pre-allocated predict array") + out_num_preds = ctypes.c_int64(0) + + ptr_indptr, type_ptr_indptr, _ = _c_int_array(csr.indptr) + ptr_data, type_ptr_data, _ = _c_float_array(csr.data) + + assert csr.shape[1] <= _MAX_INT32 + csr_indices = csr.indices.astype(np.int32, copy=False) + + _safe_call( + _LIB.LGBM_BoosterPredictForCSR( + self._handle, + ptr_indptr, + ctypes.c_int(type_ptr_indptr), + csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int64(len(csr.indptr)), + ctypes.c_int64(len(csr.data)), + ctypes.c_int64(csr.shape[1]), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + _c_str(self.pred_parameter), + ctypes.byref(out_num_preds), + preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + ) + if n_preds != out_num_preds.value: + raise ValueError("Wrong length for predict results") + return preds, nrow + + def __inner_predict_csr_sparse( + self, + csr: scipy.sparse.csr_matrix, + start_iteration: int, + num_iteration: int, + predict_type: int, + ) -> Tuple[_LGBM_PredictSparseReturnType, int]: + ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr) + ptr_data, type_ptr_data, _ = _c_float_array(csr.data) + csr_indices = csr.indices.astype(np.int32, copy=False) + matrix_type = _C_API_MATRIX_TYPE_CSR + out_ptr_indptr: _ctypes_int_ptr + if type_ptr_indptr == _C_API_DTYPE_INT32: + out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)() + else: + out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)() + out_ptr_indices = ctypes.POINTER(ctypes.c_int32)() + out_ptr_data: _ctypes_float_ptr + if type_ptr_data == _C_API_DTYPE_FLOAT32: + out_ptr_data = ctypes.POINTER(ctypes.c_float)() + else: + out_ptr_data = ctypes.POINTER(ctypes.c_double)() + out_shape = np.empty(2, dtype=np.int64) + _safe_call( + _LIB.LGBM_BoosterPredictSparseOutput( + self._handle, + ptr_indptr, + ctypes.c_int(type_ptr_indptr), + csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int64(len(csr.indptr)), + ctypes.c_int64(len(csr.data)), + ctypes.c_int64(csr.shape[1]), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + _c_str(self.pred_parameter), + ctypes.c_int(matrix_type), + out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), + ctypes.byref(out_ptr_indptr), + ctypes.byref(out_ptr_indices), + ctypes.byref(out_ptr_data), + ) + ) + matrices = self.__create_sparse_native( + cs=csr, + out_shape=out_shape, + out_ptr_indptr=out_ptr_indptr, + out_ptr_indices=out_ptr_indices, + out_ptr_data=out_ptr_data, + indptr_type=type_ptr_indptr, + data_type=type_ptr_data, + is_csr=True, + ) + nrow = len(csr.indptr) - 1 + return matrices, nrow + + def __pred_for_csr( + self, + csr: scipy.sparse.csr_matrix, + start_iteration: int, + num_iteration: int, + predict_type: int, + ) -> Tuple[_LGBM_PredictSparseReturnType, int]: + """Predict for a CSR data.""" + if predict_type == _C_API_PREDICT_CONTRIB: + return self.__inner_predict_csr_sparse( + csr=csr, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + nrow = len(csr.indptr) - 1 + if nrow > _MAX_INT32: + sections = [0] + list(np.arange(_MAX_INT32, nrow, _MAX_INT32)) + [nrow] + # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal + n_preds = [ + self.__get_num_preds( + start_iteration=start_iteration, + num_iteration=num_iteration, + nrow=int(i), + predict_type=predict_type, + ) + for i in np.diff(sections) + ] + n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum() + preds = np.empty(sum(n_preds), dtype=np.float64) + for (start_idx, end_idx), (start_idx_pred, end_idx_pred) in zip( + zip(sections, sections[1:], strict=True), + zip(n_preds_sections, n_preds_sections[1:], strict=True), + strict=True, + ): + # avoid memory consumption by arrays concatenation operations + self.__inner_predict_csr( + csr=csr[start_idx:end_idx], + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + preds=preds[start_idx_pred:end_idx_pred], + ) + return preds, nrow + else: + return self.__inner_predict_csr( + csr=csr, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + preds=None, + ) + + def __inner_predict_sparse_csc( + self, + csc: scipy.sparse.csc_matrix, + start_iteration: int, + num_iteration: int, + predict_type: int, + ) -> Tuple[_LGBM_PredictSparseReturnType, int]: + ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr) + ptr_data, type_ptr_data, _ = _c_float_array(csc.data) + csc_indices = csc.indices.astype(np.int32, copy=False) + matrix_type = _C_API_MATRIX_TYPE_CSC + out_ptr_indptr: _ctypes_int_ptr + if type_ptr_indptr == _C_API_DTYPE_INT32: + out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)() + else: + out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)() + out_ptr_indices = ctypes.POINTER(ctypes.c_int32)() + out_ptr_data: _ctypes_float_ptr + if type_ptr_data == _C_API_DTYPE_FLOAT32: + out_ptr_data = ctypes.POINTER(ctypes.c_float)() + else: + out_ptr_data = ctypes.POINTER(ctypes.c_double)() + out_shape = np.empty(2, dtype=np.int64) + _safe_call( + _LIB.LGBM_BoosterPredictSparseOutput( + self._handle, + ptr_indptr, + ctypes.c_int(type_ptr_indptr), + csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int64(len(csc.indptr)), + ctypes.c_int64(len(csc.data)), + ctypes.c_int64(csc.shape[0]), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + _c_str(self.pred_parameter), + ctypes.c_int(matrix_type), + out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)), + ctypes.byref(out_ptr_indptr), + ctypes.byref(out_ptr_indices), + ctypes.byref(out_ptr_data), + ) + ) + matrices = self.__create_sparse_native( + cs=csc, + out_shape=out_shape, + out_ptr_indptr=out_ptr_indptr, + out_ptr_indices=out_ptr_indices, + out_ptr_data=out_ptr_data, + indptr_type=type_ptr_indptr, + data_type=type_ptr_data, + is_csr=False, + ) + nrow = csc.shape[0] + return matrices, nrow + + def __pred_for_csc( + self, + csc: scipy.sparse.csc_matrix, + start_iteration: int, + num_iteration: int, + predict_type: int, + ) -> Tuple[_LGBM_PredictSparseReturnType, int]: + """Predict for a CSC data.""" + nrow = csc.shape[0] + if nrow > _MAX_INT32: + return self.__pred_for_csr( + csr=csc.tocsr(), + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + if predict_type == _C_API_PREDICT_CONTRIB: + return self.__inner_predict_sparse_csc( + csc=csc, + start_iteration=start_iteration, + num_iteration=num_iteration, + predict_type=predict_type, + ) + n_preds = self.__get_num_preds( + start_iteration=start_iteration, + num_iteration=num_iteration, + nrow=nrow, + predict_type=predict_type, + ) + preds = np.empty(n_preds, dtype=np.float64) + out_num_preds = ctypes.c_int64(0) + + ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr) + ptr_data, type_ptr_data, _ = _c_float_array(csc.data) + + assert csc.shape[0] <= _MAX_INT32 + csc_indices = csc.indices.astype(np.int32, copy=False) + + _safe_call( + _LIB.LGBM_BoosterPredictForCSC( + self._handle, + ptr_indptr, + ctypes.c_int(type_ptr_indptr), + csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int64(len(csc.indptr)), + ctypes.c_int64(len(csc.data)), + ctypes.c_int64(csc.shape[0]), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + _c_str(self.pred_parameter), + ctypes.byref(out_num_preds), + preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + ) + if n_preds != out_num_preds.value: + raise ValueError("Wrong length for predict results") + return preds, nrow + + def __pred_for_narwhals( + self, + data: nw.DataFrame, + start_iteration: int, + num_iteration: int, + predict_type: int, + ) -> Tuple[np.ndarray, int]: + """Predict for a narwhals DataFrame.""" + # Prepare prediction output array + n_preds = self.__get_num_preds( + start_iteration=start_iteration, + num_iteration=num_iteration, + nrow=data.shape[0], + predict_type=predict_type, + ) + preds = np.empty(n_preds, dtype=np.float64) + out_num_preds = ctypes.c_int64(0) + + # Export narwhals DataFrame to Arrow and run prediction + pycapsule = data.__arrow_c_stream__() + _safe_call( + _LIB.LGBM_BoosterPredictForArrowStream( + self._handle, + _extract_arrow_stream_capsule_pointer(pycapsule), + ctypes.c_int(predict_type), + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + _c_str(self.pred_parameter), + ctypes.byref(out_num_preds), + preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + ) + if n_preds != out_num_preds.value: + raise ValueError("Wrong length for predict results") + return preds, data.shape[0] + + def current_iteration(self) -> int: + """Get the index of the current iteration. + + Returns + ------- + cur_iter : int + The index of the current iteration. + """ + out_cur_iter = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetCurrentIteration( + self._handle, + ctypes.byref(out_cur_iter), + ) + ) + return out_cur_iter.value + + +class Dataset: + """ + Dataset in LightGBM. + + LightGBM does not train on raw data. + It discretizes continuous features into histogram bins, tries to combine categorical features, + and automatically handles missing and infinite values. + + This class handles that preprocessing, and holds that alternative representation of the input data. + """ + + def __init__( + self, + data: _LGBM_TrainDataType, + label: Optional[_LGBM_LabelType] = None, + reference: Optional["Dataset"] = None, + weight: Optional[_LGBM_WeightType] = None, + group: Optional[_LGBM_GroupType] = None, + init_score: Optional[_LGBM_InitScoreType] = None, + feature_name: _LGBM_FeatureNameConfiguration = "auto", + categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", + params: Optional[Dict[str, Any]] = None, + free_raw_data: bool = True, + position: Optional[_LGBM_PositionType] = None, + ): + """Initialize Dataset. + + Parameters + ---------- + data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or polars DataFrame + Data source of Dataset. + If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. + label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Label of the data. + reference : Dataset or None, optional (default=None) + If this is Dataset for validation, training data should be used as reference. + weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Weight for each instance. Weights should be non-negative. + group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None, optional (default=None) + Init score for Dataset. + feature_name : list of str, or 'auto', optional (default="auto") + Feature names. + If 'auto' and data is pandas DataFrame, pyarrow Table, or polars DataFrame, data columns names are used. + categorical_feature : list of str or int, or 'auto', optional (default="auto") + Categorical features. + If list of int, interpreted as indices. + If list of str, interpreted as feature names (need to specify ``feature_name`` as well). + If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. + All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). + Large values could be memory consuming. Consider using consecutive integers starting from zero. + All negative values in categorical features will be treated as missing values. + The output cannot be monotonically constrained with respect to a categorical feature. + Floating point numbers in categorical features will be rounded towards 0. + params : dict or None, optional (default=None) + Other parameters for Dataset. + free_raw_data : bool, optional (default=True) + If True, raw data is freed after constructing inner Dataset. + position : numpy 1-D array, pandas Series or None, optional (default=None) + Position of items used in unbiased learning-to-rank task. + """ + self._handle: Optional[_DatasetHandle] = None + self.data = data + self.label = label + self.reference = reference + self.weight = weight + self.group = group + self.position = position + self.init_score = init_score + self.feature_name: _LGBM_FeatureNameConfiguration = feature_name + self.categorical_feature: _LGBM_CategoricalFeatureConfiguration = categorical_feature + self.params = deepcopy(params) + self.free_raw_data = free_raw_data + self.used_indices: Optional[List[int]] = None + self._need_slice = True + self._predictor: Optional[_InnerPredictor] = None + self.pandas_categorical: Optional[List[List]] = None + self._params_back_up: Optional[Dict[str, Any]] = None + self.version = 0 + self._start_row = 0 # Used when pushing rows one by one. + # By default, LightGBM assigns features names like Column_0, Column_1, etc. + # These may be overridden during construction if that raw training data is in a format + # that includes feature names (like a pandas DataFrame) or if the 'feature_name' keyword argument + # is explicitly set. + # + # This is here mostly for scikit-learn's benefit, as it tracks whether input data had feature names. + self._has_non_default_feature_names: bool = False + + def __del__(self) -> None: + try: + self._free_handle() + except AttributeError: + pass + + def _create_sample_indices(self, *, total_nrow: int) -> np.ndarray: + """Get an array of randomly chosen indices from this ``Dataset``. + + Indices are sampled without replacement. + + Parameters + ---------- + total_nrow : int + Total number of rows to sample from. + If this value is greater than the value of parameter ``bin_construct_sample_cnt``, only ``bin_construct_sample_cnt`` indices will be used. + If Dataset has multiple input data, this should be the sum of rows of every file. + + Returns + ------- + indices : numpy array + Indices for sampled data. + """ + param_str = _param_dict_to_str(self.get_params()) + sample_cnt = _get_sample_count(total_nrow, param_str) + indices = np.empty(sample_cnt, dtype=np.int32) + ptr_data, _, _ = _c_int_array(indices) + actual_sample_cnt = ctypes.c_int32(0) + + _safe_call( + _LIB.LGBM_SampleIndices( + ctypes.c_int32(total_nrow), + _c_str(param_str), + ptr_data, + ctypes.byref(actual_sample_cnt), + ) + ) + assert sample_cnt == actual_sample_cnt.value + return indices + + def _init_from_ref_dataset( + self, + total_nrow: int, + ref_dataset: _DatasetHandle, + ) -> "Dataset": + """Create dataset from a reference dataset. + + Parameters + ---------- + total_nrow : int + Number of rows expected to add to dataset. + ref_dataset : object + Handle of reference dataset to extract metadata from. + + Returns + ------- + self : Dataset + Constructed Dataset object. + """ + self._handle = ctypes.c_void_p() + _safe_call( + _LIB.LGBM_DatasetCreateByReference( + ref_dataset, + ctypes.c_int64(total_nrow), + ctypes.byref(self._handle), + ) + ) + return self + + def _init_from_sample( + self, + sample_data: List[np.ndarray], + sample_indices: List[np.ndarray], + sample_cnt: int, + total_nrow: int, + ) -> "Dataset": + """Create Dataset from sampled data structures. + + Parameters + ---------- + sample_data : list of numpy array + Sample data for each column. + sample_indices : list of numpy array + Sample data row index for each column. + sample_cnt : int + Number of samples. + total_nrow : int + Total number of rows for all input files. + + Returns + ------- + self : Dataset + Constructed Dataset object. + """ + ncol = len(sample_indices) + assert len(sample_data) == ncol, "#sample data column != #column indices" + + for i in range(ncol): + if sample_data[i].dtype != np.double: + raise ValueError(f"sample_data[{i}] type {sample_data[i].dtype} is not double") + if sample_indices[i].dtype != np.int32: + raise ValueError(f"sample_indices[{i}] type {sample_indices[i].dtype} is not int32") + + # c type: double** + # each double* element points to start of each column of sample data. + sample_col_ptr: _ctypes_float_array = (ctypes.POINTER(ctypes.c_double) * ncol)() + # c type int** + # each int* points to start of indices for each column + indices_col_ptr: _ctypes_int_array = (ctypes.POINTER(ctypes.c_int32) * ncol)() + for i in range(ncol): + sample_col_ptr[i] = _c_float_array(sample_data[i])[0] + indices_col_ptr[i] = _c_int_array(sample_indices[i])[0] + + num_per_col = np.array([len(d) for d in sample_indices], dtype=np.int32) + num_per_col_ptr, _, _ = _c_int_array(num_per_col) + + self._handle = ctypes.c_void_p() + params_str = _param_dict_to_str(self.get_params()) + _safe_call( + _LIB.LGBM_DatasetCreateFromSampledColumn( + ctypes.cast(sample_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))), + ctypes.cast(indices_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_int32))), + ctypes.c_int32(ncol), + num_per_col_ptr, + ctypes.c_int32(sample_cnt), + ctypes.c_int32(total_nrow), + ctypes.c_int64(total_nrow), + _c_str(params_str), + ctypes.byref(self._handle), + ) + ) + return self + + def _push_rows(self, data: np.ndarray) -> "Dataset": + """Add rows to Dataset. + + Parameters + ---------- + data : numpy 1-D array + New data to add to the Dataset. + + Returns + ------- + self : Dataset + Dataset object. + """ + nrow, ncol = data.shape + data = data.reshape(data.size) + data_ptr, data_type, _ = _c_float_array(data) + + _safe_call( + _LIB.LGBM_DatasetPushRows( + self._handle, + data_ptr, + data_type, + ctypes.c_int32(nrow), + ctypes.c_int32(ncol), + ctypes.c_int32(self._start_row), + ) + ) + self._start_row += nrow + return self + + def get_params(self) -> Dict[str, Any]: + """Get the used parameters in the Dataset. + + Returns + ------- + params : dict + The used parameters in this Dataset object. + """ + if self.params is not None: + # no min_data, nthreads and verbose in this function + dataset_params = _ConfigAliases.get( + "bin_construct_sample_cnt", + "categorical_feature", + "data_random_seed", + "enable_bundle", + "feature_pre_filter", + "forcedbins_filename", + "group_column", + "header", + "ignore_column", + "is_enable_sparse", + "label_column", + "linear_tree", + "max_bin", + "max_bin_by_feature", + "min_data_in_bin", + "pre_partition", + "precise_float_parser", + "two_round", + "use_missing", + "weight_column", + "zero_as_missing", + ) + return {k: v for k, v in self.params.items() if k in dataset_params} + else: + return {} + + def _free_handle(self) -> "Dataset": + if self._handle is not None: + _safe_call(_LIB.LGBM_DatasetFree(self._handle)) + self._handle = None + self._need_slice = True + if self.used_indices is not None: + self.data = None + return self + + def _set_init_score_by_predictor( + self, + predictor: Optional[_InnerPredictor], + data: _LGBM_TrainDataType, + used_indices: Optional[Union[List[int], np.ndarray]], + ) -> "Dataset": + data_has_header = False + if isinstance(data, (str, Path)) and self.params is not None: + # check data has header or not + data_has_header = any(self.params.get(alias, False) for alias in _ConfigAliases.get("header")) + num_data = self.num_data() + if predictor is not None: + init_score: Union[np.ndarray, scipy.sparse.spmatrix] = predictor.predict( + data=data, + raw_score=True, + data_has_header=data_has_header, + ) + init_score = init_score.ravel() + if used_indices is not None: + assert not self._need_slice + if isinstance(data, (str, Path)): + sub_init_score = np.empty(num_data * predictor.num_class, dtype=np.float64) + assert num_data == len(used_indices) + for i in range(len(used_indices)): + for j in range(predictor.num_class): + sub_init_score[i * predictor.num_class + j] = init_score[ + used_indices[i] * predictor.num_class + j + ] + init_score = sub_init_score + if predictor.num_class > 1: + # need to regroup init_score + new_init_score = np.empty(init_score.size, dtype=np.float64) + for i in range(num_data): + for j in range(predictor.num_class): + new_init_score[j * num_data + i] = init_score[i * predictor.num_class + j] + init_score = new_init_score + elif self.init_score is not None: + init_score = np.full_like(self.init_score, fill_value=0.0, dtype=np.float64) + else: + return self + self.set_init_score(init_score) + return self + + def _lazy_init( + self, + data: Optional[_LGBM_TrainDataType], + label: Optional[_LGBM_LabelType], + reference: Optional["Dataset"], + weight: Optional[_LGBM_WeightType], + group: Optional[_LGBM_GroupType], + init_score: Optional[_LGBM_InitScoreType], + predictor: Optional[_InnerPredictor], + feature_name: _LGBM_FeatureNameConfiguration, + categorical_feature: _LGBM_CategoricalFeatureConfiguration, + params: Optional[Dict[str, Any]], + position: Optional[_LGBM_PositionType], + ) -> "Dataset": + if data is None: + self._handle = None + return self + if reference is not None: + self.pandas_categorical = reference.pandas_categorical + categorical_feature = reference.categorical_feature + if isinstance(data, pd_DataFrame): + data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas( + data=data, + feature_name=feature_name, + categorical_feature=categorical_feature, + pandas_categorical=self.pandas_categorical, + ) + if nwd.is_into_dataframe(data) and feature_name == "auto": + feature_name = nw.from_native(data).schema.names() + + # 'feature_name == "auto"' after the block above means no feature names were provided + # by either the data type (DataFrame/pyarrow) or the user's 'feature_name' argument. + # LightGBM will assign auto-generated names like Column_0, Column_1, etc. + self._has_non_default_feature_names = feature_name != "auto" + + # process for args + params = {} if params is None else params + args_names = inspect.signature(self.__class__._lazy_init).parameters.keys() + for key in params.keys(): + if key in args_names: + _log_warning( + f"{key} keyword has been found in `params` and will be ignored.\n" + f"Please use {key} argument of the Dataset constructor to pass this parameter." + ) + # get categorical features + if isinstance(categorical_feature, list): + categorical_indices = set() + feature_dict = {} + if isinstance(feature_name, list): + feature_dict = {name: i for i, name in enumerate(feature_name)} + for name in categorical_feature: + if isinstance(name, str) and name in feature_dict: + categorical_indices.add(feature_dict[name]) + elif isinstance(name, int): + categorical_indices.add(name) + else: + raise TypeError(f"Wrong type({type(name).__name__}) or unknown name({name}) in categorical_feature") + if categorical_indices: + for cat_alias in _ConfigAliases.get("categorical_feature"): + if cat_alias in params: + # If the params[cat_alias] is equal to categorical_indices, do not report the warning. + if not (isinstance(params[cat_alias], list) and set(params[cat_alias]) == categorical_indices): + _log_warning(f"{cat_alias} in param dict is overridden.") + params.pop(cat_alias, None) + params["categorical_column"] = sorted(categorical_indices) + + params_str = _param_dict_to_str(params) + self.params = params + # process for reference dataset + ref_dataset = None + if isinstance(reference, Dataset): + ref_dataset = reference.construct()._handle + elif reference is not None: + raise TypeError("Reference dataset should be None or dataset instance") + # start construct data + if isinstance(data, (str, Path)): + self._handle = ctypes.c_void_p() + _safe_call( + _LIB.LGBM_DatasetCreateFromFile( + _c_str(str(data)), + _c_str(params_str), + ref_dataset, + ctypes.byref(self._handle), + ) + ) + elif isinstance(data, scipy.sparse.csr_matrix): + self.__init_from_csr(csr=data, params_str=params_str, ref_dataset=ref_dataset) + elif isinstance(data, scipy.sparse.csc_matrix): + self.__init_from_csc(csc=data, params_str=params_str, ref_dataset=ref_dataset) + elif isinstance(data, np.ndarray): + self.__init_from_np2d(mat=data, params_str=params_str, ref_dataset=ref_dataset) + elif nwd.is_into_dataframe(data): + self.__init_from_narwhals(data=nw.from_native(data), params_str=params_str, ref_dataset=ref_dataset) + elif isinstance(data, list) and len(data) > 0: + if _is_list_of_numpy_arrays(data): + self.__init_from_list_np2d(mats=data, params_str=params_str, ref_dataset=ref_dataset) + elif _is_list_of_sequences(data): + self.__init_from_seqs(seqs=data, ref_dataset=ref_dataset) + else: + raise TypeError("Data list can only be of ndarray or Sequence") + elif isinstance(data, Sequence): + self.__init_from_seqs(seqs=[data], ref_dataset=ref_dataset) + else: + try: + csr = scipy.sparse.csr_matrix(data) + self.__init_from_csr(csr=csr, params_str=params_str, ref_dataset=ref_dataset) + except BaseException as err: + raise TypeError(f"Cannot initialize Dataset from {type(data).__name__}") from err + if label is not None: + self.set_label(label) + if self.get_label() is None: + raise ValueError("Label should not be None") + if weight is not None: + self.set_weight(weight) + if group is not None: + self.set_group(group) + if position is not None: + self.set_position(position) + if isinstance(predictor, _InnerPredictor): + if self._predictor is None and init_score is not None: + _log_warning("The init_score will be overridden by the prediction of init_model.") + self._set_init_score_by_predictor(predictor=predictor, data=data, used_indices=None) + elif init_score is not None: + self.set_init_score(init_score) + elif predictor is not None: + raise TypeError(f"Wrong predictor type {type(predictor).__name__}") + # set feature names + return self.set_feature_name(feature_name) + + @staticmethod + def _yield_row_from_seqlist(seqs: List[Sequence], indices: Iterable[int]) -> Iterator[np.ndarray]: + offset = 0 + seq_id = 0 + seq = seqs[seq_id] + for row_id in indices: + assert row_id >= offset, "sample indices are expected to be monotonic" + while row_id >= offset + len(seq): + offset += len(seq) + seq_id += 1 + seq = seqs[seq_id] + id_in_seq = row_id - offset + row = seq[id_in_seq] + yield row if row.flags["OWNDATA"] else row.copy() + + def __sample(self, *, seqs: List[Sequence], total_nrow: int) -> Tuple[List[np.ndarray], List[np.ndarray]]: + """Sample data from seqs. + + Mimics behavior in c_api.cpp:LGBM_DatasetCreateFromMats() + + Returns + ------- + sampled_rows, sampled_row_indices + """ + indices = self._create_sample_indices(total_nrow=total_nrow) + + # Select sampled rows, transpose to column order. + sampled = np.array(list(self._yield_row_from_seqlist(seqs, indices))) + sampled = sampled.T + + filtered = [] + filtered_idx = [] + sampled_row_range = np.arange(len(indices), dtype=np.int32) + for col in sampled: + col_predicate = (np.abs(col) > ZERO_THRESHOLD) | np.isnan(col) + filtered_col = col[col_predicate] + filtered_row_idx = sampled_row_range[col_predicate] + + filtered.append(filtered_col) + filtered_idx.append(filtered_row_idx) + + return filtered, filtered_idx + + def __init_from_seqs( + self, + *, + seqs: List[Sequence], + ref_dataset: Optional[_DatasetHandle], + ) -> "Dataset": + """ + Initialize data from list of Sequence objects. + + Sequence: Generic Data Access Object + Supports random access and access by batch if properly defined by user + + Data scheme uniformity are trusted, not checked + """ + total_nrow = sum(len(seq) for seq in seqs) + + # create validation dataset from ref_dataset + if ref_dataset is not None: + self._init_from_ref_dataset(total_nrow, ref_dataset) + else: + param_str = _param_dict_to_str(self.get_params()) + sample_cnt = _get_sample_count(total_nrow, param_str) + + sample_data, col_indices = self.__sample(seqs=seqs, total_nrow=total_nrow) + self._init_from_sample(sample_data, col_indices, sample_cnt, total_nrow) + + for seq in seqs: + nrow = len(seq) + batch_size = getattr(seq, "batch_size", None) or Sequence.batch_size + for start in range(0, nrow, batch_size): + end = min(start + batch_size, nrow) + self._push_rows(seq[start:end]) + return self + + def __init_from_np2d( + self, + *, + mat: np.ndarray, + params_str: str, + ref_dataset: Optional[_DatasetHandle], + ) -> "Dataset": + """Initialize data from a 2-D numpy matrix.""" + if len(mat.shape) != 2: + raise ValueError("Input numpy.ndarray must be 2 dimensional") + + self._handle = ctypes.c_void_p() + data, layout = _np2d_to_np1d(mat) + ptr_data, type_ptr_data, _ = _c_float_array(data) + _safe_call( + _LIB.LGBM_DatasetCreateFromMat( + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int32(mat.shape[0]), + ctypes.c_int32(mat.shape[1]), + ctypes.c_int(layout), + _c_str(params_str), + ref_dataset, + ctypes.byref(self._handle), + ) + ) + return self + + def __init_from_list_np2d( + self, + *, + mats: List[np.ndarray], + params_str: str, + ref_dataset: Optional[_DatasetHandle], + ) -> "Dataset": + """Initialize data from a list of 2-D numpy matrices.""" + ncol = mats[0].shape[1] + nrow = np.empty((len(mats),), np.int32) + ptr_data: _ctypes_float_array + if mats[0].dtype == np.float64: + ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))() + else: + ptr_data = (ctypes.POINTER(ctypes.c_float) * len(mats))() + layouts = (ctypes.c_int * len(mats))() + + holders = [] + type_ptr_data = -1 + + for i, mat in enumerate(mats): + if len(mat.shape) != 2: + raise ValueError("Input numpy.ndarray must be 2 dimensional") + + if mat.shape[1] != ncol: + raise ValueError("Input arrays must have same number of columns") + + nrow[i] = mat.shape[0] + + mat, layout = _np2d_to_np1d(mat) + + chunk_ptr_data, chunk_type_ptr_data, holder = _c_float_array(mat) + if type_ptr_data != -1 and chunk_type_ptr_data != type_ptr_data: + raise ValueError("Input chunks must have same type") + ptr_data[i] = chunk_ptr_data + layouts[i] = layout + type_ptr_data = chunk_type_ptr_data + holders.append(holder) + + self._handle = ctypes.c_void_p() + _safe_call( + _LIB.LGBM_DatasetCreateFromMats( + ctypes.c_int32(len(mats)), + ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))), + ctypes.c_int(type_ptr_data), + nrow.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ctypes.c_int32(ncol), + layouts, + _c_str(params_str), + ref_dataset, + ctypes.byref(self._handle), + ) + ) + return self + + def __init_from_csr( + self, + *, + csr: scipy.sparse.csr_matrix, + params_str: str, + ref_dataset: Optional[_DatasetHandle], + ) -> "Dataset": + """Initialize data from a CSR matrix.""" + if len(csr.indices) != len(csr.data): + raise ValueError(f"Length mismatch: {len(csr.indices)} vs {len(csr.data)}") + self._handle = ctypes.c_void_p() + + ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr) + ptr_data, type_ptr_data, _ = _c_float_array(csr.data) + + assert csr.shape[1] <= _MAX_INT32 + csr_indices = csr.indices.astype(np.int32, copy=False) + + _safe_call( + _LIB.LGBM_DatasetCreateFromCSR( + ptr_indptr, + ctypes.c_int(type_ptr_indptr), + csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int64(len(csr.indptr)), + ctypes.c_int64(len(csr.data)), + ctypes.c_int64(csr.shape[1]), + _c_str(params_str), + ref_dataset, + ctypes.byref(self._handle), + ) + ) + return self + + def __init_from_csc( + self, + *, + csc: scipy.sparse.csc_matrix, + params_str: str, + ref_dataset: Optional[_DatasetHandle], + ) -> "Dataset": + """Initialize data from a CSC matrix.""" + if len(csc.indices) != len(csc.data): + raise ValueError(f"Length mismatch: {len(csc.indices)} vs {len(csc.data)}") + self._handle = ctypes.c_void_p() + + ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr) + ptr_data, type_ptr_data, _ = _c_float_array(csc.data) + + assert csc.shape[0] <= _MAX_INT32 + csc_indices = csc.indices.astype(np.int32, copy=False) + + _safe_call( + _LIB.LGBM_DatasetCreateFromCSC( + ptr_indptr, + ctypes.c_int(type_ptr_indptr), + csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ptr_data, + ctypes.c_int(type_ptr_data), + ctypes.c_int64(len(csc.indptr)), + ctypes.c_int64(len(csc.data)), + ctypes.c_int64(csc.shape[0]), + _c_str(params_str), + ref_dataset, + ctypes.byref(self._handle), + ) + ) + return self + + def __init_from_narwhals( + self, + *, + data: nw.DataFrame, + params_str: str, + ref_dataset: Optional[_DatasetHandle], + ) -> "Dataset": + """Initialize data from a narwhals DataFrame.""" + # Export narwhals DataFrame to Arrow + pycapsule = data.__arrow_c_stream__() + + # Create dataset + self._handle = ctypes.c_void_p() + _safe_call( + _LIB.LGBM_DatasetCreateFromArrowStream( + _extract_arrow_stream_capsule_pointer(pycapsule), + _c_str(params_str), + ref_dataset, + ctypes.byref(self._handle), + ) + ) + return self + + @staticmethod + def _compare_params_for_warning( + *, + params: Dict[str, Any], + other_params: Dict[str, Any], + ignore_keys: Set[str], + ) -> bool: + """Compare two dictionaries with params ignoring some keys. + + It is only for the warning purpose. + + Parameters + ---------- + params : dict + One dictionary with parameters to compare. + other_params : dict + Another dictionary with parameters to compare. + ignore_keys : set + Keys that should be ignored during comparing two dictionaries. + + Returns + ------- + compare_result : bool + Returns whether two dictionaries with params are equal. + """ + for k, v in other_params.items(): + if k not in ignore_keys: + if k not in params or params[k] != v: + return False + for k, v in params.items(): + if k not in ignore_keys: + if k not in other_params or v != other_params[k]: + return False + return True + + def construct(self) -> "Dataset": + """Lazy init. + + Returns + ------- + self : Dataset + Constructed Dataset object. + """ + if self._handle is None: + if self.reference is not None: + reference_params = self.reference.get_params() + params = self.get_params() + if params != reference_params: + if not self._compare_params_for_warning( + params=params, + other_params=reference_params, + ignore_keys=_ConfigAliases.get("categorical_feature"), + ): + _log_warning("Overriding the parameters from Reference Dataset.") + self._update_params(reference_params) + if self.used_indices is None: + # create valid + self._lazy_init( + data=self.data, + label=self.label, + reference=self.reference, + weight=self.weight, + group=self.group, + position=self.position, + init_score=self.init_score, + predictor=self._predictor, + feature_name=self.feature_name, + categorical_feature="auto", + params=self.params, + ) + else: + # construct subset + used_indices = _list_to_1d_numpy( + data=self.used_indices, + dtype=np.int32, + name="used_indices", + ) + assert used_indices.flags.c_contiguous + if self.reference.group is not None: + group_info = np.array(self.reference.group).astype(np.int32, copy=False) + _, self.group = np.unique( + np.repeat(range(len(group_info)), repeats=group_info)[self.used_indices], return_counts=True + ) + self._handle = ctypes.c_void_p() + params_str = _param_dict_to_str(self.params) + _safe_call( + _LIB.LGBM_DatasetGetSubset( + self.reference.construct()._handle, + used_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ctypes.c_int32(used_indices.shape[0]), + _c_str(params_str), + ctypes.byref(self._handle), + ) + ) + if not self.free_raw_data: + self.get_data() + if self.group is not None: + self.set_group(self.group) + if self.position is not None: + self.set_position(self.position) + if self.get_label() is None: + raise ValueError("Label should not be None.") + if ( + isinstance(self._predictor, _InnerPredictor) + and self._predictor is not self.reference._predictor + ): + self.get_data() + self._set_init_score_by_predictor( + predictor=self._predictor, data=self.data, used_indices=used_indices + ) + else: + # create train + self._lazy_init( + data=self.data, + label=self.label, + reference=None, + weight=self.weight, + group=self.group, + init_score=self.init_score, + predictor=self._predictor, + feature_name=self.feature_name, + categorical_feature=self.categorical_feature, + params=self.params, + position=self.position, + ) + if self.free_raw_data: + self.data = None + self.feature_name = self.get_feature_name() + return self + + def create_valid( + self, + data: _LGBM_TrainDataType, + label: Optional[_LGBM_LabelType] = None, + weight: Optional[_LGBM_WeightType] = None, + group: Optional[_LGBM_GroupType] = None, + init_score: Optional[_LGBM_InitScoreType] = None, + params: Optional[Dict[str, Any]] = None, + position: Optional[_LGBM_PositionType] = None, + ) -> "Dataset": + """Create validation data align with current Dataset. + + Parameters + ---------- + data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or polars DataFrame + Data source of Dataset. + If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file. + label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Label of the data. + weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Weight for each instance. Weights should be non-negative. + group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None, optional (default=None) + Init score for Dataset. + params : dict or None, optional (default=None) + Other parameters for validation Dataset. + position : numpy 1-D array, pandas Series or None, optional (default=None) + Position of items used in unbiased learning-to-rank task. + + Returns + ------- + valid : Dataset + Validation Dataset with reference to self. + """ + ret = Dataset( + data, + label=label, + reference=self, + weight=weight, + group=group, + position=position, + init_score=init_score, + params=params, + free_raw_data=self.free_raw_data, + ) + ret._predictor = self._predictor + ret.pandas_categorical = self.pandas_categorical + return ret + + def subset( + self, + used_indices: List[int], + params: Optional[Dict[str, Any]] = None, + ) -> "Dataset": + """Get subset of current Dataset. + + Parameters + ---------- + used_indices : list of int + Indices used to create the subset. + params : dict or None, optional (default=None) + These parameters will be passed to Dataset constructor. + + Returns + ------- + subset : Dataset + Subset of the current Dataset. + """ + if params is None: + params = self.params + ret = Dataset( + None, + reference=self, + feature_name=self.feature_name, + categorical_feature=self.categorical_feature, + params=params, + free_raw_data=self.free_raw_data, + ) + ret._predictor = self._predictor + ret.pandas_categorical = self.pandas_categorical + ret.used_indices = sorted(used_indices) + return ret + + def save_binary(self, filename: Union[str, Path]) -> "Dataset": + """Save Dataset to a binary file. + + .. note:: + + Please note that `init_score` is not saved in binary file. + If you need it, please set it again after loading Dataset. + + Parameters + ---------- + filename : str or pathlib.Path + Name of the output file. + + Returns + ------- + self : Dataset + Returns self. + """ + _safe_call( + _LIB.LGBM_DatasetSaveBinary( + self.construct()._handle, + _c_str(str(filename)), + ) + ) + return self + + def _update_params(self, params: Optional[Dict[str, Any]]) -> "Dataset": + if not params: + return self + params = deepcopy(params) + + def update() -> None: + if not self.params: + self.params = params + else: + self._params_back_up = deepcopy(self.params) + self.params.update(params) + + if self._handle is None: + update() + elif params is not None: + ret = _LIB.LGBM_DatasetUpdateParamChecking( + _c_str(_param_dict_to_str(self.params)), + _c_str(_param_dict_to_str(params)), + ) + if ret != 0: + # could be updated if data is not freed + if self.data is not None: + update() + self._free_handle() + else: + raise LightGBMError(_LIB.LGBM_GetLastError().decode("utf-8")) + return self + + def _reverse_update_params(self) -> "Dataset": + if self._handle is None: + self.params = deepcopy(self._params_back_up) + self._params_back_up = None + return self + + def set_field( + self, + field_name: str, + data: Optional[_LGBM_SetFieldType], + ) -> "Dataset": + """Set property into the Dataset. + + Parameters + ---------- + field_name : str + The field name of the information. + data : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None + The data to be set. + + Returns + ------- + self : Dataset + Dataset with set property. + """ + if self._handle is None: + raise Exception(f"Cannot set {field_name} before construct dataset") + if data is None: + # set to None + _safe_call( + _LIB.LGBM_DatasetSetField( + self._handle, + _c_str(field_name), + None, + ctypes.c_int(0), + ctypes.c_int(_FIELD_TYPE_MAPPER[field_name]), + ) + ) + return self + + # If the data is Arrow, we can just pass it to C + if (nwd.is_into_dataframe(data) or nwd.is_into_series(data)) and not isinstance( + data, (pd_DataFrame, pd_Series) + ): + pycapsule = nw.from_native(data, allow_series=True).__arrow_c_stream__() + _safe_call( + _LIB.LGBM_DatasetSetFieldFromArrowStream( + self._handle, + _c_str(field_name), + _extract_arrow_stream_capsule_pointer(pycapsule), + ) + ) + self.version += 1 + return self + + dtype: "np.typing.DTypeLike" + if field_name == "init_score": + dtype = np.float64 + if _is_1d_collection(data): + data = _list_to_1d_numpy(data=data, dtype=dtype, name=field_name) + elif _is_2d_collection(data): + data = _data_to_2d_numpy(data=data, dtype=dtype, name=field_name) + data = data.ravel(order="F") + else: + raise TypeError( + "init_score must be list, numpy 1-D array or pandas Series.\n" + "In multiclass classification init_score can also be a list of lists, numpy 2-D array or pandas DataFrame." + ) + else: + if field_name in {"group", "position"}: + dtype = np.int32 + else: + dtype = np.float32 + data = _list_to_1d_numpy(data=data, dtype=dtype, name=field_name) + + ptr_data: Union[_ctypes_float_ptr, _ctypes_int_ptr] + if data.dtype == np.float32 or data.dtype == np.float64: + ptr_data, type_data, _ = _c_float_array(data) + elif data.dtype == np.int32: + ptr_data, type_data, _ = _c_int_array(data) + else: + raise TypeError(f"Expected np.float32/64 or np.int32, met type({data.dtype})") + if type_data != _FIELD_TYPE_MAPPER[field_name]: + raise TypeError("Input type error for set_field") + _safe_call( + _LIB.LGBM_DatasetSetField( + self._handle, + _c_str(field_name), + ptr_data, + ctypes.c_int(len(data)), + ctypes.c_int(type_data), + ) + ) + self.version += 1 + return self + + def get_field(self, field_name: str) -> Optional[np.ndarray]: + """Get property from the Dataset. + + Can only be run on a constructed Dataset. + + Unlike ``get_group()``, ``get_init_score()``, ``get_label()``, ``get_position()``, and ``get_weight()``, + this method ignores any raw data passed into ``lgb.Dataset()`` on the Python side, and will only read + data from the constructed C++ ``Dataset`` object. + + Parameters + ---------- + field_name : str + The field name of the information. + + Returns + ------- + info : numpy array or None + A numpy array with information from the Dataset. + """ + if self._handle is None: + raise Exception(f"Cannot get {field_name} before construct Dataset") + tmp_out_len = ctypes.c_int(0) + out_type = ctypes.c_int(0) + ret = ctypes.POINTER(ctypes.c_void_p)() + _safe_call( + _LIB.LGBM_DatasetGetField( + self._handle, + _c_str(field_name), + ctypes.byref(tmp_out_len), + ctypes.byref(ret), + ctypes.byref(out_type), + ) + ) + if out_type.value != _FIELD_TYPE_MAPPER[field_name]: + raise TypeError("Return type error for get_field") + if tmp_out_len.value == 0: + return None + if out_type.value == _C_API_DTYPE_INT32: + arr = _cint32_array_to_numpy( + cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_int32)), + length=tmp_out_len.value, + ) + elif out_type.value == _C_API_DTYPE_FLOAT32: + arr = _cfloat32_array_to_numpy( + cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_float)), + length=tmp_out_len.value, + ) + elif out_type.value == _C_API_DTYPE_FLOAT64: + arr = _cfloat64_array_to_numpy( + cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_double)), + length=tmp_out_len.value, + ) + else: + raise TypeError("Unknown type") + if field_name == "init_score": + num_data = self.num_data() + num_classes = arr.size // num_data + if num_classes > 1: + arr = arr.reshape((num_data, num_classes), order="F") + return arr + + def set_categorical_feature( + self, + categorical_feature: _LGBM_CategoricalFeatureConfiguration, + ) -> "Dataset": + """Set categorical features. + + Parameters + ---------- + categorical_feature : list of str or int, or 'auto' + Names or indices of categorical features. + + Returns + ------- + self : Dataset + Dataset with set categorical features. + """ + if self.categorical_feature == categorical_feature: + return self + if self.data is not None: + if self.categorical_feature is None: + self.categorical_feature = categorical_feature + return self._free_handle() + elif categorical_feature == "auto": + return self + else: + if self.categorical_feature != "auto": + _log_warning( + "categorical_feature in Dataset is overridden.\n" + f"New categorical_feature is {list(categorical_feature)}" + ) + self.categorical_feature = categorical_feature + return self._free_handle() + else: + raise LightGBMError( + "Cannot set categorical feature after freed raw data, " + "set free_raw_data=False when construct Dataset to avoid this." + ) + + def _set_predictor( + self, + predictor: Optional[_InnerPredictor], + ) -> "Dataset": + """Set predictor for continued training. + + It is not recommended for user to call this function. + Please use init_model argument in engine.train() or engine.cv() instead. + """ + if predictor is None and self._predictor is None: + return self + elif isinstance(predictor, _InnerPredictor) and isinstance(self._predictor, _InnerPredictor): + if (predictor == self._predictor) and ( + predictor.current_iteration() == self._predictor.current_iteration() + ): + return self + if self._handle is None: + self._predictor = predictor + elif self.data is not None: + self._predictor = predictor + self._set_init_score_by_predictor( + predictor=self._predictor, + data=self.data, + used_indices=None, + ) + elif self.used_indices is not None and self.reference is not None and self.reference.data is not None: + self._predictor = predictor + self._set_init_score_by_predictor( + predictor=self._predictor, + data=self.reference.data, + used_indices=self.used_indices, + ) + else: + raise LightGBMError( + "Cannot set predictor after freed raw data, " + "set free_raw_data=False when construct Dataset to avoid this." + ) + return self + + def set_reference(self, reference: "Dataset") -> "Dataset": + """Set reference Dataset. + + Parameters + ---------- + reference : Dataset + Reference that is used as a template to construct the current Dataset. + + Returns + ------- + self : Dataset + Dataset with set reference. + """ + self.set_categorical_feature(reference.categorical_feature).set_feature_name( + reference.feature_name + )._set_predictor(reference._predictor) + # we're done if self and reference share a common upstream reference + if self.get_ref_chain().intersection(reference.get_ref_chain()): + return self + if self.data is not None: + self.reference = reference + return self._free_handle() + else: + raise LightGBMError( + "Cannot set reference after freed raw data, " + "set free_raw_data=False when construct Dataset to avoid this." + ) + + def set_feature_name(self, feature_name: _LGBM_FeatureNameConfiguration) -> "Dataset": + """Set feature name. + + Parameters + ---------- + feature_name : list of str + Feature names. + + Returns + ------- + self : Dataset + Dataset with set feature name. + """ + if feature_name != "auto": + self.feature_name = feature_name + self._has_non_default_feature_names = True + if self._handle is not None and feature_name is not None and feature_name != "auto": + if len(feature_name) != self.num_feature(): + raise ValueError( + f"Length of feature_name({len(feature_name)}) and num_feature({self.num_feature()}) don't match" + ) + c_feature_name = [_c_str(name) for name in feature_name] + _safe_call( + _LIB.LGBM_DatasetSetFeatureNames( + self._handle, + _c_array(ctypes.c_char_p, c_feature_name), + ctypes.c_int(len(feature_name)), + ) + ) + return self + + def set_label(self, label: Optional[_LGBM_LabelType]) -> "Dataset": + """Set label of Dataset. + + Parameters + ---------- + label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None + The label information to be set into Dataset. + + Returns + ------- + self : Dataset + Dataset with set label. + """ + self.label = label + if self._handle is not None: + if isinstance(label, pd_DataFrame): + if len(label.columns) > 1: + raise ValueError("DataFrame for label cannot have multiple columns") + label_array = np.ravel(_pandas_to_numpy(label, target_dtype=np.float32)) + elif (nwd.is_into_dataframe(label) or nwd.is_into_series(label)) and not isinstance(label, pd_Series): + label_array = label + else: + label_array = _list_to_1d_numpy(data=label, dtype=np.float32, name="label") + self.set_field("label", label_array) + self.label = self.get_field("label") # original values can be modified at cpp side + return self + + def set_weight( + self, + weight: Optional[_LGBM_WeightType], + ) -> "Dataset": + """Set weight of each instance. + + Parameters + ---------- + weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None + Weight to be set for each data point. Weights should be non-negative. + + Returns + ------- + self : Dataset + Dataset with set weight. + """ + # Check if the weight contains values other than one + if weight is not None: + if nwd.is_into_series(weight): + if (nw.from_native(weight, series_only=True) == 1).all(): + weight = None + elif np.all(weight == 1): + weight = None + self.weight = weight + + # Set field + if self._handle is not None and weight is not None: + if isinstance(weight, pd_Series) or not nwd.is_into_series(weight): + weight = _list_to_1d_numpy(data=weight, dtype=np.float32, name="weight") + self.set_field("weight", weight) + self.weight = self.get_field("weight") # original values can be modified at cpp side + return self + + def set_init_score( + self, + init_score: Optional[_LGBM_InitScoreType], + ) -> "Dataset": + """Set init score of Booster to start from. + + Parameters + ---------- + init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None + Init score for Booster. + + Returns + ------- + self : Dataset + Dataset with set init score. + """ + self.init_score = init_score + if self._handle is not None and init_score is not None: + self.set_field("init_score", init_score) + self.init_score = self.get_field("init_score") # original values can be modified at cpp side + return self + + def set_group( + self, + group: Optional[_LGBM_GroupType], + ) -> "Dataset": + """Set group size of Dataset (used for ranking). + + Parameters + ---------- + group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + + Returns + ------- + self : Dataset + Dataset with set group. + """ + self.group = group + if self._handle is not None and group is not None: + if isinstance(group, pd_Series) or not nwd.is_into_series(group): + group = _list_to_1d_numpy(data=group, dtype=np.int32, name="group") + self.set_field("group", group) + # original values can be modified at cpp side + constructed_group = self.get_field("group") + if constructed_group is not None: + self.group = np.diff(constructed_group) + return self + + def set_position( + self, + position: Optional[_LGBM_PositionType], + ) -> "Dataset": + """Set position of Dataset (used for ranking). + + Parameters + ---------- + position : numpy 1-D array, pandas Series or None, optional (default=None) + Position of items used in unbiased learning-to-rank task. + + Returns + ------- + self : Dataset + Dataset with set position. + """ + self.position = position + if self._handle is not None and position is not None: + position = _list_to_1d_numpy(data=position, dtype=np.int32, name="position") + self.set_field("position", position) + self.position = self.get_field("position") # original values can be modified at cpp side + return self + + def get_feature_name(self) -> List[str]: + """Get the names of columns (features) in the Dataset. + + Returns + ------- + feature_names : list of str + The names of columns (features) in the Dataset. + """ + if self._handle is None: + raise LightGBMError("Cannot get feature_name before construct dataset") + num_feature = self.num_feature() + tmp_out_len = ctypes.c_int(0) + reserved_string_buffer_size = 255 + required_string_buffer_size = ctypes.c_size_t(0) + string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)] + ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] + _safe_call( + _LIB.LGBM_DatasetGetFeatureNames( + self._handle, + ctypes.c_int(num_feature), + ctypes.byref(tmp_out_len), + ctypes.c_size_t(reserved_string_buffer_size), + ctypes.byref(required_string_buffer_size), + ptr_string_buffers, + ) + ) + if num_feature != tmp_out_len.value: + raise ValueError("Length of feature names doesn't equal with num_feature") + actual_string_buffer_size = required_string_buffer_size.value + # if buffer length is not long enough, reallocate buffers + if reserved_string_buffer_size < actual_string_buffer_size: + string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)] + ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] + _safe_call( + _LIB.LGBM_DatasetGetFeatureNames( + self._handle, + ctypes.c_int(num_feature), + ctypes.byref(tmp_out_len), + ctypes.c_size_t(actual_string_buffer_size), + ctypes.byref(required_string_buffer_size), + ptr_string_buffers, + ) + ) + return [string_buffers[i].value.decode("utf-8") for i in range(num_feature)] + + def get_label(self) -> Optional[_LGBM_LabelType]: + """Get the label of the Dataset. + + Returns + ------- + label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None + The label information from the Dataset. + For a constructed ``Dataset``, this will only return a numpy array. + """ + if self.label is None: + self.label = self.get_field("label") + return self.label + + def get_weight(self) -> Optional[_LGBM_WeightType]: + """Get the weight of the Dataset. + + Returns + ------- + weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None + Weight for each data point from the Dataset. Weights should be non-negative. + For a constructed ``Dataset``, this will only return ``None`` or a numpy array. + """ + if self.weight is None: + self.weight = self.get_field("weight") + return self.weight + + def get_init_score(self) -> Optional[_LGBM_InitScoreType]: + """Get the initial score of the Dataset. + + Returns + ------- + init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None + Init score of Booster. + For a constructed ``Dataset``, this will only return ``None`` or a numpy array. + """ + if self.init_score is None: + self.init_score = self.get_field("init_score") + return self.init_score + + def get_data(self) -> Optional[_LGBM_TrainDataType]: + """Get the raw data of the Dataset. + + Returns + ------- + data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table, polars DataFrame, or None + Raw data used in the Dataset construction. + """ + if self._handle is None: + raise Exception("Cannot get data before construct Dataset") + if self._need_slice and self.used_indices is not None and self.reference is not None: + self.data = self.reference.data + if self.data is not None: + if isinstance(self.data, (np.ndarray, scipy.sparse.spmatrix)): + self.data = self.data[self.used_indices, :] + elif isinstance(self.data, pd_DataFrame): + self.data = self.data.iloc[self.used_indices].copy() + elif isinstance(self.data, Sequence): + self.data = self.data[self.used_indices] + elif nwd.is_into_dataframe(self.data): + indices = np.array(self.used_indices) + self.data = nw.from_native(self.data)[indices].to_native() + elif _is_list_of_sequences(self.data) and len(self.data) > 0: + self.data = np.array(list(self._yield_row_from_seqlist(self.data, self.used_indices))) + else: + _log_warning( + f"Cannot subset {type(self.data).__name__} type of raw data.\nReturning original raw data" + ) + self._need_slice = False + if self.data is None: + raise LightGBMError( + "Cannot call `get_data` after freed raw data, " + "set free_raw_data=False when construct Dataset to avoid this." + ) + return self.data + + def get_group(self) -> Optional[_LGBM_GroupType]: + """Get the group of the Dataset. + + Returns + ------- + group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + For a constructed ``Dataset``, this will only return ``None`` or a numpy array. + """ + if self.group is None: + self.group = self.get_field("group") + if self.group is not None: + # group data from LightGBM is boundaries data, need to convert to group size + self.group = np.diff(self.group) + return self.group + + def get_position(self) -> Optional[_LGBM_PositionType]: + """Get the position of the Dataset. + + Returns + ------- + position : numpy 1-D array, pandas Series or None + Position of items used in unbiased learning-to-rank task. + For a constructed ``Dataset``, this will only return ``None`` or a numpy array. + """ + if self.position is None: + self.position = self.get_field("position") + return self.position + + def num_data(self) -> int: + """Get the number of rows in the Dataset. + + Returns + ------- + number_of_rows : int + The number of rows in the Dataset. + """ + if self._handle is not None: + ret = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_DatasetGetNumData( + self._handle, + ctypes.byref(ret), + ) + ) + return ret.value + else: + raise LightGBMError("Cannot get num_data before construct dataset") + + def num_feature(self) -> int: + """Get the number of columns (features) in the Dataset. + + Returns + ------- + number_of_columns : int + The number of columns (features) in the Dataset. + """ + if self._handle is not None: + ret = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_DatasetGetNumFeature( + self._handle, + ctypes.byref(ret), + ) + ) + return ret.value + else: + raise LightGBMError("Cannot get num_feature before construct dataset") + + def feature_num_bin(self, feature: Union[int, str]) -> int: + """Get the number of bins for a feature. + + .. versionadded:: 4.0.0 + + Parameters + ---------- + feature : int or str + Index or name of the feature. + + Returns + ------- + number_of_bins : int + The number of constructed bins for the feature in the Dataset. + """ + if self._handle is not None: + if isinstance(feature, str): + feature_index = self.feature_name.index(feature) + else: + feature_index = feature + ret = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_DatasetGetFeatureNumBin( + self._handle, + ctypes.c_int(feature_index), + ctypes.byref(ret), + ) + ) + return ret.value + else: + raise LightGBMError("Cannot get feature_num_bin before construct dataset") + + def get_ref_chain(self, ref_limit: int = 100) -> Set["Dataset"]: + """Get a chain of Dataset objects. + + Starts with r, then goes to r.reference (if exists), + then to r.reference.reference, etc. + until we hit ``ref_limit`` or a reference loop. + + Parameters + ---------- + ref_limit : int, optional (default=100) + The limit number of references. + + Returns + ------- + ref_chain : set of Dataset + Chain of references of the Datasets. + """ + head = self + ref_chain: Set[Dataset] = set() + while len(ref_chain) < ref_limit: + if isinstance(head, Dataset): + ref_chain.add(head) + if (head.reference is not None) and (head.reference not in ref_chain): + head = head.reference + else: + break + else: + break + return ref_chain + + def add_features_from(self, other: "Dataset") -> "Dataset": + """Add features from other Dataset to the current Dataset. + + Both Datasets must be constructed before calling this method. + + Parameters + ---------- + other : Dataset + The Dataset to take features from. + + Returns + ------- + self : Dataset + Dataset with the new features added. + """ + if self._handle is None or other._handle is None: + raise ValueError("Both source and target Datasets must be constructed before adding features") + _safe_call( + _LIB.LGBM_DatasetAddFeaturesFrom( + self._handle, + other._handle, + ) + ) + was_none = self.data is None + old_self_data_type = type(self.data).__name__ + if other.data is None: + self.data = None + elif self.data is not None: + if isinstance(self.data, np.ndarray): + if isinstance(other.data, np.ndarray): + self.data = np.hstack((self.data, other.data)) + elif isinstance(other.data, scipy.sparse.spmatrix): + self.data = np.hstack((self.data, other.data.toarray())) + elif isinstance(other.data, pd_DataFrame): + self.data = np.hstack((self.data, other.data.values)) + else: + self.data = None + elif isinstance(self.data, scipy.sparse.spmatrix): + sparse_format = self.data.getformat() + if isinstance(other.data, (np.ndarray, scipy.sparse.spmatrix)): + self.data = scipy.sparse.hstack((self.data, other.data), format=sparse_format) + elif isinstance(other.data, pd_DataFrame): + self.data = scipy.sparse.hstack((self.data, other.data.values), format=sparse_format) + else: + self.data = None + elif isinstance(self.data, pd_DataFrame): + if not PANDAS_INSTALLED: + raise LightGBMError( + "Cannot add features to DataFrame type of raw data " + "without pandas installed. " + "Install pandas and restart your session." + ) + if isinstance(other.data, np.ndarray): + self.data = concat((self.data, pd_DataFrame(other.data)), axis=1, ignore_index=True) + elif isinstance(other.data, scipy.sparse.spmatrix): + self.data = concat((self.data, pd_DataFrame(other.data.toarray())), axis=1, ignore_index=True) + elif isinstance(other.data, pd_DataFrame): + self.data = concat((self.data, other.data), axis=1, ignore_index=True) + else: + self.data = None + else: + self.data = None + if self.data is None: + err_msg = ( + f"Cannot add features from {type(other.data).__name__} type of raw data to " + f"{old_self_data_type} type of raw data.\n" + ) + err_msg += ( + "Set free_raw_data=False when construct Dataset to avoid this" if was_none else "Freeing raw data" + ) + _log_warning(err_msg) + self.feature_name = self.get_feature_name() + _log_warning( + "Resetting categorical features.\n" + "You can set new categorical features via ``set_categorical_feature`` method" + ) + self.categorical_feature = "auto" + self.pandas_categorical = None + return self + + def _dump_text(self, filename: Union[str, Path]) -> "Dataset": + """Save Dataset to a text file. + + This format cannot be loaded back in by LightGBM, but is useful for debugging purposes. + + Parameters + ---------- + filename : str or pathlib.Path + Name of the output file. + + Returns + ------- + self : Dataset + Returns self. + """ + _safe_call( + _LIB.LGBM_DatasetDumpText( + self.construct()._handle, + _c_str(str(filename)), + ) + ) + return self + + +_LGBM_CustomObjectiveFunction = Callable[ + [np.ndarray, Dataset], + Tuple[np.ndarray, np.ndarray], +] +_LGBM_CustomEvalFunction = Union[ + Callable[ + [np.ndarray, Dataset], + _LGBM_EvalFunctionResultType, + ], + Callable[ + [np.ndarray, Dataset], + List[_LGBM_EvalFunctionResultType], + ], +] + + +class Booster: + """Booster in LightGBM.""" + + def __init__( + self, + params: Optional[Dict[str, Any]] = None, + train_set: Optional[Dataset] = None, + model_file: Optional[Union[str, Path]] = None, + model_str: Optional[str] = None, + ): + """Initialize the Booster. + + Parameters + ---------- + params : dict or None, optional (default=None) + Parameters for Booster. + train_set : Dataset or None, optional (default=None) + Training dataset. + model_file : str, pathlib.Path or None, optional (default=None) + Path to the model file. + model_str : str or None, optional (default=None) + Model will be loaded from this string. + """ + self._handle = ctypes.c_void_p() + self._network = False + self.__need_reload_eval_info = True + self._train_data_name = "training" + self.__set_objective_to_none = False + self.best_iteration = -1 + self.best_score: _LGBM_BoosterBestScoreType = {} + params = {} if params is None else deepcopy(params) + if train_set is not None: + # Training task + if not isinstance(train_set, Dataset): + raise TypeError(f"Training data should be Dataset instance, met {type(train_set).__name__}") + params = _choose_param_value( + main_param_name="machines", + params=params, + default_value=None, + ) + # if "machines" is given, assume user wants to do distributed learning, and set up network + if params["machines"] is None: + params.pop("machines", None) + else: + machines = params["machines"] + if isinstance(machines, str): + num_machines_from_machine_list = len(machines.split(",")) + elif isinstance(machines, (list, set)): + num_machines_from_machine_list = len(machines) + machines = ",".join(machines) + else: + raise ValueError("Invalid machines in params.") + + params = _choose_param_value( + main_param_name="num_machines", + params=params, + default_value=num_machines_from_machine_list, + ) + params = _choose_param_value( + main_param_name="local_listen_port", + params=params, + default_value=12400, + ) + self.set_network( + machines=machines, + local_listen_port=params["local_listen_port"], + listen_time_out=params.get("time_out", 120), + num_machines=params["num_machines"], + ) + # construct booster object + train_set.construct() + # copy the parameters from train_set + params.update(train_set.get_params()) + params_str = _param_dict_to_str(params) + _safe_call( + _LIB.LGBM_BoosterCreate( + train_set._handle, + _c_str(params_str), + ctypes.byref(self._handle), + ) + ) + # save reference to data + self.train_set = train_set + self.valid_sets: List[Dataset] = [] + self.name_valid_sets: List[str] = [] + self.__num_dataset = 1 + self.__init_predictor = train_set._predictor + if self.__init_predictor is not None: + _safe_call( + _LIB.LGBM_BoosterMerge( + self._handle, + self.__init_predictor._handle, + ) + ) + out_num_class = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetNumClasses( + self._handle, + ctypes.byref(out_num_class), + ) + ) + self.__num_class = out_num_class.value + # buffer for inner predict + self.__inner_predict_buffer: List[Optional[np.ndarray]] = [None] + self.__is_predicted_cur_iter = [False] + self.__get_eval_info() + self.pandas_categorical = train_set.pandas_categorical + self.train_set_version = train_set.version + elif model_file is not None: + # Prediction task + out_num_iterations = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterCreateFromModelfile( + _c_str(str(model_file)), + ctypes.byref(out_num_iterations), + ctypes.byref(self._handle), + ) + ) + out_num_class = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetNumClasses( + self._handle, + ctypes.byref(out_num_class), + ) + ) + self.__num_class = out_num_class.value + self.pandas_categorical = _load_pandas_categorical(file_name=model_file) + if params: + _log_warning("Ignoring params argument, using parameters from model file.") + params = self._get_loaded_param() + elif model_str is not None: + self.model_from_string(model_str) + if params: + _log_warning("Ignoring params argument, using parameters from model string.") + params = self._get_loaded_param() + else: + raise TypeError( + "Need at least one training dataset or model file or model string to create Booster instance" + ) + self.params = params + + def __del__(self) -> None: + try: + if self._network: + self.free_network() + except AttributeError: + pass + try: + if self._handle is not None: + _safe_call(_LIB.LGBM_BoosterFree(self._handle)) + except AttributeError: + pass + + def __copy__(self) -> "Booster": + return self.__deepcopy__(None) + + def __deepcopy__(self, *args: Any, **kwargs: Any) -> "Booster": + model_str = self.model_to_string(num_iteration=-1) + return Booster(model_str=model_str) + + def __getstate__(self) -> Dict[str, Any]: + this = self.__dict__.copy() + handle = this["_handle"] + this.pop("train_set", None) + this.pop("valid_sets", None) + if handle is not None: + this["_handle"] = self.model_to_string(num_iteration=-1) + return this + + def __setstate__(self, state: Dict[str, Any]) -> None: + model_str = state.get("_handle", state.get("handle", None)) + if model_str is not None: + handle = ctypes.c_void_p() + out_num_iterations = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterLoadModelFromString( + _c_str(model_str), + ctypes.byref(out_num_iterations), + ctypes.byref(handle), + ) + ) + state["_handle"] = handle + self.__dict__.update(state) + + def _get_loaded_param(self) -> Dict[str, Any]: + buffer_len = 1 << 20 + tmp_out_len = ctypes.c_int64(0) + string_buffer = ctypes.create_string_buffer(buffer_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_BoosterGetLoadedParam( + self._handle, + ctypes.c_int64(buffer_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + actual_len = tmp_out_len.value + # if buffer length is not long enough, re-allocate a buffer + if actual_len > buffer_len: + string_buffer = ctypes.create_string_buffer(actual_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_BoosterGetLoadedParam( + self._handle, + ctypes.c_int64(actual_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + return json.loads(string_buffer.value.decode("utf-8")) + + def free_dataset(self) -> "Booster": + """Free Booster's Datasets. + + Returns + ------- + self : Booster + Booster without Datasets. + """ + self.__dict__.pop("train_set", None) + self.__dict__.pop("valid_sets", None) + self.__num_dataset = 0 + return self + + def _free_buffer(self) -> "Booster": + self.__inner_predict_buffer = [] + self.__is_predicted_cur_iter = [] + return self + + def set_network( + self, + machines: Union[List[str], Set[str], str], + local_listen_port: int = 12400, + listen_time_out: int = 120, + num_machines: int = 1, + ) -> "Booster": + """Set the network configuration. + + Parameters + ---------- + machines : list, set or str + Names of machines. + local_listen_port : int, optional (default=12400) + TCP listen port for local machines. + listen_time_out : int, optional (default=120) + Socket time-out in minutes. + num_machines : int, optional (default=1) + The number of machines for distributed learning application. + + Returns + ------- + self : Booster + Booster with set network. + """ + if isinstance(machines, (list, set)): + machines = ",".join(machines) + _safe_call( + _LIB.LGBM_NetworkInit( + _c_str(machines), + ctypes.c_int(local_listen_port), + ctypes.c_int(listen_time_out), + ctypes.c_int(num_machines), + ) + ) + self._network = True + return self + + def free_network(self) -> "Booster": + """Free Booster's network. + + Returns + ------- + self : Booster + Booster with freed network. + """ + _safe_call(_LIB.LGBM_NetworkFree()) + self._network = False + return self + + def trees_to_dataframe(self) -> pd_DataFrame: + """Parse the fitted model and return in an easy-to-read pandas DataFrame. + + The returned DataFrame has the following columns. + + - ``tree_index`` : int64, which tree a node belongs to. 0-based, so a value of ``6``, for example, means "this node is in the 7th tree". + - ``node_depth`` : int64, how far a node is from the root of the tree. The root node has a value of ``1``, its direct children are ``2``, etc. + - ``node_index`` : str, unique identifier for a node. + - ``left_child`` : str, ``node_index`` of the child node to the left of a split. ``None`` for leaf nodes. + - ``right_child`` : str, ``node_index`` of the child node to the right of a split. ``None`` for leaf nodes. + - ``parent_index`` : str, ``node_index`` of this node's parent. ``None`` for the root node. + - ``split_feature`` : str, name of the feature used for splitting. ``None`` for leaf nodes. + - ``split_gain`` : float64, gain from adding this split to the tree. ``NaN`` for leaf nodes. + - ``threshold`` : float64, value of the feature used to decide which side of the split a record will go down. ``NaN`` for leaf nodes. + - ``decision_type`` : str, logical operator describing how to compare a value to ``threshold``. + For example, ``split_feature = "Column_10", threshold = 15, decision_type = "<="`` means that + records where ``Column_10 <= 15`` follow the left side of the split, otherwise follows the right side of the split. ``None`` for leaf nodes. + - ``missing_direction`` : str, split direction that missing values should go to. ``None`` for leaf nodes. + - ``missing_type`` : str, describes what types of values are treated as missing. + - ``value`` : float64, predicted value for this leaf node, multiplied by the learning rate. + - ``weight`` : float64 or int64, sum of Hessian (second-order derivative of objective), summed over observations that fall in this node. + - ``count`` : int64, number of records in the training data that fall into this node. + + Returns + ------- + result : pandas DataFrame + Returns a pandas DataFrame of the parsed model. + """ + if not PANDAS_INSTALLED: + raise LightGBMError( + "This method cannot be run without pandas installed. " + "You must install pandas and restart your session to use this method." + ) + + if self.num_trees() == 0: + raise LightGBMError("There are no trees in this Booster and thus nothing to parse") + + def _is_split_node(tree: Dict[str, Any]) -> bool: + return "split_index" in tree.keys() + + def create_node_record( + tree: Dict[str, Any], + node_depth: int = 1, + tree_index: Optional[int] = None, + feature_names: Optional[List[str]] = None, + parent_node: Optional[str] = None, + ) -> Dict[str, Any]: + def _get_node_index( + tree: Dict[str, Any], + tree_index: Optional[int], + ) -> str: + tree_num = f"{tree_index}-" if tree_index is not None else "" + is_split = _is_split_node(tree) + node_type = "S" if is_split else "L" + # if a single node tree it won't have `leaf_index` so return 0 + node_num = tree.get("split_index" if is_split else "leaf_index", 0) + return f"{tree_num}{node_type}{node_num}" + + def _get_split_feature( + *, + tree: Dict[str, Any], + feature_names: Optional[List[str]], + ) -> Optional[str]: + if _is_split_node(tree): + if feature_names is not None: + feature_name = feature_names[tree["split_feature"]] + else: + feature_name = tree["split_feature"] + else: + feature_name = None + return feature_name + + def _is_single_node_tree(tree: Dict[str, Any]) -> bool: + return set(tree.keys()) == {"leaf_value", "leaf_count"} + + # Create the node record, and populate universal data members + node: Dict[str, Union[int, str, None]] = OrderedDict() + node["tree_index"] = tree_index + node["node_depth"] = node_depth + node["node_index"] = _get_node_index(tree, tree_index) + node["left_child"] = None + node["right_child"] = None + node["parent_index"] = parent_node + node["split_feature"] = _get_split_feature(tree=tree, feature_names=feature_names) + node["split_gain"] = None + node["threshold"] = None + node["decision_type"] = None + node["missing_direction"] = None + node["missing_type"] = None + node["value"] = None + node["weight"] = None + node["count"] = None + + # Update values to reflect node type (leaf or split) + if _is_split_node(tree): + node["left_child"] = _get_node_index(tree["left_child"], tree_index) + node["right_child"] = _get_node_index(tree["right_child"], tree_index) + node["split_gain"] = tree["split_gain"] + node["threshold"] = tree["threshold"] + node["decision_type"] = tree["decision_type"] + node["missing_direction"] = "left" if tree["default_left"] else "right" + node["missing_type"] = tree["missing_type"] + node["value"] = tree["internal_value"] + node["weight"] = tree["internal_weight"] + node["count"] = tree["internal_count"] + else: + node["value"] = tree["leaf_value"] + if not _is_single_node_tree(tree): + node["weight"] = tree["leaf_weight"] + node["count"] = tree["leaf_count"] + + return node + + def tree_dict_to_node_list( + tree: Dict[str, Any], + node_depth: int = 1, + tree_index: Optional[int] = None, + feature_names: Optional[List[str]] = None, + parent_node: Optional[str] = None, + ) -> List[Dict[str, Any]]: + node = create_node_record( + tree=tree, + node_depth=node_depth, + tree_index=tree_index, + feature_names=feature_names, + parent_node=parent_node, + ) + + res = [node] + + if _is_split_node(tree): + # traverse the next level of the tree + children = ["left_child", "right_child"] + for child in children: + subtree_list = tree_dict_to_node_list( + tree=tree[child], + node_depth=node_depth + 1, + tree_index=tree_index, + feature_names=feature_names, + parent_node=node["node_index"], + ) + # In tree format, "subtree_list" is a list of node records (dicts), + # and we add node to the list. + res.extend(subtree_list) + return res + + model_dict = self.dump_model() + feature_names = model_dict["feature_names"] + model_list = [] + for tree in model_dict["tree_info"]: + model_list.extend( + tree_dict_to_node_list( + tree=tree["tree_structure"], tree_index=tree["tree_index"], feature_names=feature_names + ) + ) + + return pd_DataFrame(model_list, columns=model_list[0].keys()) + + def set_train_data_name(self, name: str) -> "Booster": + """Set the name to the training Dataset. + + Parameters + ---------- + name : str + Name for the training Dataset. + + Returns + ------- + self : Booster + Booster with set training Dataset name. + """ + self._train_data_name = name + return self + + def add_valid(self, data: Dataset, name: str) -> "Booster": + """Add validation data. + + Parameters + ---------- + data : Dataset + Validation data. + name : str + Name of validation data. + + Returns + ------- + self : Booster + Booster with set validation data. + """ + if not isinstance(data, Dataset): + raise TypeError(f"Validation data should be Dataset instance, met {type(data).__name__}") + if data._predictor is not self.__init_predictor: + raise LightGBMError("Add validation data failed, you should use same predictor for these data") + _safe_call( + _LIB.LGBM_BoosterAddValidData( + self._handle, + data.construct()._handle, + ) + ) + self.valid_sets.append(data) + self.name_valid_sets.append(name) + self.__num_dataset += 1 + self.__inner_predict_buffer.append(None) + self.__is_predicted_cur_iter.append(False) + return self + + def reset_parameter(self, params: Dict[str, Any]) -> "Booster": + """Reset parameters of Booster. + + Parameters + ---------- + params : dict + New parameters for Booster. + + Returns + ------- + self : Booster + Booster with new parameters. + """ + params_str = _param_dict_to_str(params) + if params_str: + _safe_call( + _LIB.LGBM_BoosterResetParameter( + self._handle, + _c_str(params_str), + ) + ) + self.params.update(params) + return self + + def update( + self, + train_set: Optional[Dataset] = None, + fobj: Optional[_LGBM_CustomObjectiveFunction] = None, + ) -> bool: + """Update Booster for one iteration. + + Parameters + ---------- + train_set : Dataset or None, optional (default=None) + Training data. + If None, last training data is used. + fobj : callable or None, optional (default=None) + Customized objective function. + Should accept two parameters: preds, train_data, + and return (grad, hess). + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + Predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task. + train_data : Dataset + The training dataset. + grad : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the first order derivative (gradient) of the loss + with respect to the elements of preds for each sample point. + hess : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the second order derivative (Hessian) of the loss + with respect to the elements of preds for each sample point. + + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], + and grad and hess should be returned in the same format. + + Returns + ------- + produced_empty_tree : bool + ``True`` if the tree(s) produced by this iteration did not have any splits. + This usually means that training is "finished" (calling ``update()`` again + will not change the model's predictions). However, that is not always the + case. For example, if you have added any randomness (like column sampling by + setting ``feature_fraction_bynode < 1.0``), it is possible that another call + to ``update()`` would produce a non-empty tree. + """ + # need reset training data + if train_set is None and self.train_set_version != self.train_set.version: + train_set = self.train_set + is_the_same_train_set = False + else: + is_the_same_train_set = train_set is self.train_set and self.train_set_version == train_set.version + if train_set is not None and not is_the_same_train_set: + if not isinstance(train_set, Dataset): + raise TypeError(f"Training data should be Dataset instance, met {type(train_set).__name__}") + if train_set._predictor is not self.__init_predictor: + raise LightGBMError("Replace training data failed, you should use same predictor for these data") + self.train_set = train_set + _safe_call( + _LIB.LGBM_BoosterResetTrainingData( + self._handle, + self.train_set.construct()._handle, + ) + ) + self.__inner_predict_buffer[0] = None + self.train_set_version = self.train_set.version + produced_empty_tree = ctypes.c_int(0) + if fobj is None: + if self.__set_objective_to_none: + raise LightGBMError("Cannot update due to null objective function.") + _safe_call( + _LIB.LGBM_BoosterUpdateOneIter( + self._handle, + ctypes.byref(produced_empty_tree), + ) + ) + self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)] + return produced_empty_tree.value == 1 + else: + if not self.__set_objective_to_none: + self.reset_parameter({"objective": "none"}).__set_objective_to_none = True + grad, hess = fobj(self.__inner_predict(data_idx=0), self.train_set) + return self.__boost(grad=grad, hess=hess) + + def __boost( + self, + *, + grad: np.ndarray, + hess: np.ndarray, + ) -> bool: + """Boost Booster for one iteration with customized gradient statistics. + + .. note:: + + Score is returned before any transformation, + e.g. it is raw margin instead of probability of positive class for binary task. + For multi-class task, score are numpy 2-D array of shape = [n_samples, n_classes], + and grad and hess should be returned in the same format. + + Parameters + ---------- + grad : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the first order derivative (gradient) of the loss + with respect to the elements of score for each sample point. + hess : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the second order derivative (Hessian) of the loss + with respect to the elements of score for each sample point. + + Returns + ------- + produced_empty_tree : bool + ``True`` if the tree(s) produced by this iteration did not have any splits. + This usually means that training is "finished" (calling ``__boost()`` again + will not change the model's predictions). However, that is not always the + case. For example, if you have added any randomness (like column sampling by + setting ``feature_fraction_bynode < 1.0``), it is possible that another call + to ``__boost()`` would produce a non-empty tree. + """ + if self.__num_class > 1: + grad = grad.ravel(order="F") + hess = hess.ravel(order="F") + grad = _list_to_1d_numpy(data=grad, dtype=np.float32, name="gradient") + hess = _list_to_1d_numpy(data=hess, dtype=np.float32, name="hessian") + assert grad.flags.c_contiguous + assert hess.flags.c_contiguous + if len(grad) != len(hess): + raise ValueError(f"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) don't match") + num_train_data = self.train_set.num_data() + if len(grad) != num_train_data * self.__num_class: + raise ValueError( + f"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) " + f"don't match training data length ({num_train_data}) * " + f"number of models per one iteration ({self.__num_class})" + ) + produced_empty_tree = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterUpdateOneIterCustom( + self._handle, + grad.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + hess.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + ctypes.byref(produced_empty_tree), + ) + ) + self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)] + return produced_empty_tree.value == 1 + + def rollback_one_iter(self) -> "Booster": + """Rollback one iteration. + + Returns + ------- + self : Booster + Booster with rolled back one iteration. + """ + _safe_call(_LIB.LGBM_BoosterRollbackOneIter(self._handle)) + self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)] + return self + + def current_iteration(self) -> int: + """Get the index of the current iteration. + + Returns + ------- + cur_iter : int + The index of the current iteration. + """ + out_cur_iter = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetCurrentIteration( + self._handle, + ctypes.byref(out_cur_iter), + ) + ) + return out_cur_iter.value + + def num_model_per_iteration(self) -> int: + """Get number of models per iteration. + + Returns + ------- + model_per_iter : int + The number of models per iteration. + """ + model_per_iter = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterNumModelPerIteration( + self._handle, + ctypes.byref(model_per_iter), + ) + ) + return model_per_iter.value + + def num_trees(self) -> int: + """Get number of weak sub-models. + + Returns + ------- + num_trees : int + The number of weak sub-models. + """ + num_trees = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterNumberOfTotalModel( + self._handle, + ctypes.byref(num_trees), + ) + ) + return num_trees.value + + def upper_bound(self) -> float: + """Get upper bound value of a model. + + Returns + ------- + upper_bound : float + Upper bound value of the model. + """ + ret = ctypes.c_double(0) + _safe_call( + _LIB.LGBM_BoosterGetUpperBoundValue( + self._handle, + ctypes.byref(ret), + ) + ) + return ret.value + + def lower_bound(self) -> float: + """Get lower bound value of a model. + + Returns + ------- + lower_bound : float + Lower bound value of the model. + """ + ret = ctypes.c_double(0) + _safe_call( + _LIB.LGBM_BoosterGetLowerBoundValue( + self._handle, + ctypes.byref(ret), + ) + ) + return ret.value + + def eval( + self, + data: Dataset, + name: str, + feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None, + ) -> List[_LGBM_BoosterEvalMethodResultType]: + """Evaluate for data. + + Parameters + ---------- + data : Dataset + Data for the evaluating. + name : str + Name of the data. + feval : callable, list of callable, or None, optional (default=None) + Customized evaluation function. + Each evaluation function should accept two parameters: preds, eval_data, + and return (metric_name, metric_value, maximize) or list of such tuples. + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. + If custom objective function is used, predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task in this case. + eval_data : Dataset + A ``Dataset`` to evaluate. + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. + + Returns + ------- + result : list + List with (dataset_name, metric_name, metric_value, maximize) tuples. + """ + if not isinstance(data, Dataset): + raise TypeError("Can only eval for Dataset instance") + data_idx = -1 + if data is self.train_set: + data_idx = 0 + else: + for i in range(len(self.valid_sets)): + if data is self.valid_sets[i]: + data_idx = i + 1 + break + # need to push new valid data + if data_idx == -1: + self.add_valid(data, name) + data_idx = self.__num_dataset - 1 + + return self.__inner_eval(data_name=name, data_idx=data_idx, feval=feval) + + def eval_train( + self, + feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None, + ) -> List[_LGBM_BoosterEvalMethodResultType]: + """Evaluate for training data. + + Parameters + ---------- + feval : callable, list of callable, or None, optional (default=None) + Customized evaluation function. + Each evaluation function should accept two parameters: preds, eval_data, + and return (metric_name, metric_value, maximize) or list of such tuples. + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. + If custom objective function is used, predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task in this case. + eval_data : Dataset + The training dataset. + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. + + Returns + ------- + result : list + List with (train_dataset_name, metric_name, metric_value, maximize) tuples. + """ + return self.__inner_eval(data_name=self._train_data_name, data_idx=0, feval=feval) + + def eval_valid( + self, + feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None, + ) -> List[_LGBM_BoosterEvalMethodResultType]: + """Evaluate for validation data. + + Parameters + ---------- + feval : callable, list of callable, or None, optional (default=None) + Customized evaluation function. + Each evaluation function should accept two parameters: preds, eval_data, + and return (metric_name, metric_value, maximize) or list of such tuples. + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. + If custom objective function is used, predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task in this case. + eval_data : Dataset + The validation dataset. + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. + + Returns + ------- + result : list + List with (validation_dataset_name, metric_name, metric_value, maximize) tuples. + """ + return [ + item + for i in range(1, self.__num_dataset) + for item in self.__inner_eval(data_name=self.name_valid_sets[i - 1], data_idx=i, feval=feval) + ] + + def save_model( + self, + filename: Union[str, Path], + num_iteration: Optional[int] = None, + start_iteration: int = 0, + importance_type: str = "split", + ) -> "Booster": + """Save Booster to file. + + Parameters + ---------- + filename : str or pathlib.Path + Filename to save Booster. + num_iteration : int or None, optional (default=None) + Index of the iteration that should be saved. + If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. + If <= 0, all iterations are saved. + start_iteration : int, optional (default=0) + Start index of the iteration that should be saved. + importance_type : str, optional (default="split") + What type of feature importance should be saved. + If "split", result contains numbers of times the feature is used in a model. + If "gain", result contains total gains of splits which use the feature. + + Returns + ------- + self : Booster + Returns self. + """ + if num_iteration is None: + num_iteration = self.best_iteration + importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] + _safe_call( + _LIB.LGBM_BoosterSaveModel( + self._handle, + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + ctypes.c_int(importance_type_int), + _c_str(str(filename)), + ) + ) + _dump_pandas_categorical(self.pandas_categorical, filename) + return self + + def shuffle_models( + self, + start_iteration: int = 0, + end_iteration: int = -1, + ) -> "Booster": + """Shuffle models. + + Parameters + ---------- + start_iteration : int, optional (default=0) + The first iteration that will be shuffled. + end_iteration : int, optional (default=-1) + The last iteration that will be shuffled. + If <= 0, means the last available iteration. + + Returns + ------- + self : Booster + Booster with shuffled models. + """ + _safe_call( + _LIB.LGBM_BoosterShuffleModels( + self._handle, + ctypes.c_int(start_iteration), + ctypes.c_int(end_iteration), + ) + ) + return self + + def model_from_string(self, model_str: str) -> "Booster": + """Load Booster from a string. + + Parameters + ---------- + model_str : str + Model will be loaded from this string. + + Returns + ------- + self : Booster + Loaded Booster object. + """ + # ensure that existing Booster is freed before replacing it + # with a new one createdfrom file + _safe_call(_LIB.LGBM_BoosterFree(self._handle)) + self._free_buffer() + self._handle = ctypes.c_void_p() + out_num_iterations = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterLoadModelFromString( + _c_str(model_str), + ctypes.byref(out_num_iterations), + ctypes.byref(self._handle), + ) + ) + out_num_class = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetNumClasses( + self._handle, + ctypes.byref(out_num_class), + ) + ) + self.__num_class = out_num_class.value + self.pandas_categorical = _load_pandas_categorical(model_str=model_str) + return self + + def model_to_string( + self, + num_iteration: Optional[int] = None, + start_iteration: int = 0, + importance_type: str = "split", + ) -> str: + """Save Booster to string. + + Parameters + ---------- + num_iteration : int or None, optional (default=None) + Index of the iteration that should be saved. + If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. + If <= 0, all iterations are saved. + start_iteration : int, optional (default=0) + Start index of the iteration that should be saved. + importance_type : str, optional (default="split") + What type of feature importance should be saved. + If "split", result contains numbers of times the feature is used in a model. + If "gain", result contains total gains of splits which use the feature. + + Returns + ------- + str_repr : str + String representation of Booster. + """ + if num_iteration is None: + num_iteration = self.best_iteration + importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] + buffer_len = 1 << 20 + tmp_out_len = ctypes.c_int64(0) + string_buffer = ctypes.create_string_buffer(buffer_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_BoosterSaveModelToString( + self._handle, + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + ctypes.c_int(importance_type_int), + ctypes.c_int64(buffer_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + actual_len = tmp_out_len.value + # if buffer length is not long enough, re-allocate a buffer + if actual_len > buffer_len: + string_buffer = ctypes.create_string_buffer(actual_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_BoosterSaveModelToString( + self._handle, + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + ctypes.c_int(importance_type_int), + ctypes.c_int64(actual_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + ret = string_buffer.value.decode("utf-8") + ret += _dump_pandas_categorical(self.pandas_categorical) + return ret + + def dump_model( + self, + num_iteration: Optional[int] = None, + start_iteration: int = 0, + importance_type: str = "split", + object_hook: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None, + ) -> Dict[str, Any]: + """Dump Booster to JSON format. + + Parameters + ---------- + num_iteration : int or None, optional (default=None) + Index of the iteration that should be dumped. + If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped. + If <= 0, all iterations are dumped. + start_iteration : int, optional (default=0) + Start index of the iteration that should be dumped. + importance_type : str, optional (default="split") + What type of feature importance should be dumped. + If "split", result contains numbers of times the feature is used in a model. + If "gain", result contains total gains of splits which use the feature. + object_hook : callable or None, optional (default=None) + If not None, ``object_hook`` is a function called while parsing the json + string returned by the C API. It may be used to alter the json, to store + specific values while building the json structure. It avoids + walking through the structure again. It saves a significant amount + of time if the number of trees is huge. + Signature is ``def object_hook(node: dict) -> dict``. + None is equivalent to ``lambda node: node``. + See documentation of ``json.loads()`` for further details. + + Returns + ------- + json_repr : dict + JSON format of Booster. + """ + if num_iteration is None: + num_iteration = self.best_iteration + importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] + buffer_len = 1 << 20 + tmp_out_len = ctypes.c_int64(0) + string_buffer = ctypes.create_string_buffer(buffer_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_BoosterDumpModel( + self._handle, + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + ctypes.c_int(importance_type_int), + ctypes.c_int64(buffer_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + actual_len = tmp_out_len.value + # if buffer length is not long enough, reallocate a buffer + if actual_len > buffer_len: + string_buffer = ctypes.create_string_buffer(actual_len) + ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer)) + _safe_call( + _LIB.LGBM_BoosterDumpModel( + self._handle, + ctypes.c_int(start_iteration), + ctypes.c_int(num_iteration), + ctypes.c_int(importance_type_int), + ctypes.c_int64(actual_len), + ctypes.byref(tmp_out_len), + ptr_string_buffer, + ) + ) + ret = json.loads(string_buffer.value.decode("utf-8"), object_hook=object_hook) + ret["pandas_categorical"] = json.loads( + json.dumps( + self.pandas_categorical, + default=_json_default_with_numpy, + ) + ) + return ret + + def predict( + self, + data: _LGBM_PredictDataType, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + raw_score: bool = False, + pred_leaf: bool = False, + pred_contrib: bool = False, + data_has_header: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> _LGBM_PredictReturnType: + """Make a prediction. + + Parameters + ---------- + data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, pyarrow Table or polars DataFrame + Data source for prediction. + If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). + start_iteration : int, optional (default=0) + Start index of the iteration to predict. + If <= 0, starts from the first iteration. + num_iteration : int or None, optional (default=None) + Total number of iterations used in the prediction. + If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; + otherwise, all iterations from ``start_iteration`` are used (no limits). + If <= 0, all iterations from ``start_iteration`` are used (no limits). + raw_score : bool, optional (default=False) + Whether to predict raw scores. + pred_leaf : bool, optional (default=False) + Whether to predict leaf index. + pred_contrib : bool, optional (default=False) + Whether to predict feature contributions. + + .. note:: + + If you want to get more explanations for your model's predictions using SHAP values, + like SHAP interaction values, + you can install the shap package (https://github.com/slundberg/shap). + Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra + column, where the last column is the expected value. + + data_has_header : bool, optional (default=False) + Whether the data has header. + Used only if data is str. + validate_features : bool, optional (default=False) + If True, ensure that the features used to predict match the ones used to train. + Used only if data is pandas DataFrame. + **kwargs + Other parameters for the prediction. + + Returns + ------- + result : numpy array, scipy.sparse or list of scipy.sparse + Prediction result. + Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``). + """ + predictor = _InnerPredictor.from_booster( + booster=self, + pred_parameter=deepcopy(kwargs), + ) + if num_iteration is None: + if start_iteration <= 0: + num_iteration = self.best_iteration + else: + num_iteration = -1 + return predictor.predict( + data=data, + start_iteration=start_iteration, + num_iteration=num_iteration, + raw_score=raw_score, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + data_has_header=data_has_header, + validate_features=validate_features, + ) + + def refit( + self, + data: _LGBM_TrainDataType, + label: _LGBM_LabelType, + decay_rate: float = 0.9, + reference: Optional[Dataset] = None, + weight: Optional[_LGBM_WeightType] = None, + group: Optional[_LGBM_GroupType] = None, + init_score: Optional[_LGBM_InitScoreType] = None, + feature_name: _LGBM_FeatureNameConfiguration = "auto", + categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", + dataset_params: Optional[Dict[str, Any]] = None, + free_raw_data: bool = True, + validate_features: bool = False, + **kwargs: Any, + ) -> "Booster": + """Refit the existing Booster by new data. + + Parameters + ---------- + data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or polars DataFrame + Data source for refit. + If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM). + label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow ChunkedArray, polars Series or None + Label for refit. + decay_rate : float, optional (default=0.9) + Decay rate of refit, + will use ``leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output`` to refit trees. + reference : Dataset or None, optional (default=None) + Reference for ``data``. + + .. versionadded:: 4.0.0 + + weight : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Weight for each ``data`` instance. Weights should be non-negative. + + .. versionadded:: 4.0.0 + + group : list, numpy 1-D array, pandas Series, pyarrow ChunkedArray, polars Series or None, optional (default=None) + Group/query size for ``data``. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + + .. versionadded:: 4.0.0 + + init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow ChunkedArray, pyarrow Table (for multi-class task), polars Series, polars DataFrame (for multi-class task) or None, optional (default=None) + Init score for ``data``. + + .. versionadded:: 4.0.0 + + feature_name : list of str, or 'auto', optional (default="auto") + Feature names for ``data``. + If 'auto' and data is pandas DataFrame, data columns names are used. + + .. versionadded:: 4.0.0 + + categorical_feature : list of str or int, or 'auto', optional (default="auto") + Categorical features for ``data``. + If list of int, interpreted as indices. + If list of str, interpreted as feature names (need to specify ``feature_name`` as well). + If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. + All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). + Large values could be memory consuming. Consider using consecutive integers starting from zero. + All negative values in categorical features will be treated as missing values. + The output cannot be monotonically constrained with respect to a categorical feature. + Floating point numbers in categorical features will be rounded towards 0. + + .. versionadded:: 4.0.0 + + dataset_params : dict or None, optional (default=None) + Other parameters for Dataset ``data``. + + .. versionadded:: 4.0.0 + + free_raw_data : bool, optional (default=True) + If True, raw data is freed after constructing inner Dataset for ``data``. + + .. versionadded:: 4.0.0 + + validate_features : bool, optional (default=False) + If True, ensure that the features used to refit the model match the original ones. + Used only if data is pandas DataFrame. + + .. versionadded:: 4.0.0 + + **kwargs + Other parameters for refit. + These parameters will be passed to ``predict`` method. + + Returns + ------- + result : Booster + Refitted Booster. + """ + if self.__set_objective_to_none: + raise LightGBMError("Cannot refit due to null objective function.") + if dataset_params is None: + dataset_params = {} + predictor = _InnerPredictor.from_booster(booster=self, pred_parameter=deepcopy(kwargs)) + leaf_preds: np.ndarray = predictor.predict( # type: ignore[assignment] + data=data, + start_iteration=-1, + pred_leaf=True, + validate_features=validate_features, + ) + nrow, ncol = leaf_preds.shape + out_is_linear = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetLinear( + self._handle, + ctypes.byref(out_is_linear), + ) + ) + new_params = _choose_param_value( + main_param_name="linear_tree", + params=self.params, + default_value=None, + ) + new_params["linear_tree"] = bool(out_is_linear.value) + new_params.update(dataset_params) + + # 'categorical_feature' can end up in self.params when a Booster + # is created from a model string or file... pre-process to ensure it's passed + # via a keyword argument to the Dataset constructor instead of 'params'. + new_params = _choose_param_value( + main_param_name="categorical_feature", + params=new_params, + default_value=None, + ) + cat_features_from_params = new_params.pop("categorical_feature") + + # reconcile params and keyword argument + if cat_features_from_params: + if categorical_feature == "auto": + categorical_feature = cat_features_from_params + elif cat_features_from_params != categorical_feature: + error_msg = ( + "'categorical_feature' value passed to Booster.refit() is different from " + "'categorical_feature' value found in Booster.params. " + "Preferring the value passed via keyword argument. " + "Using refit() to change which columns are treated as categorical is not supported. " + "If you have a valid use case for this, please open an issue at https://github.com/lightgbm-org/LightGBM/issues." + ) + raise LightGBMError(error_msg) + + train_set = Dataset( + data=data, + label=label, + reference=reference, + weight=weight, + group=group, + init_score=init_score, + feature_name=feature_name, + categorical_feature=categorical_feature, + params=new_params, + free_raw_data=free_raw_data, + ) + new_params["refit_decay_rate"] = decay_rate + new_booster = Booster(new_params, train_set) + # Copy models + _safe_call( + _LIB.LGBM_BoosterMerge( + new_booster._handle, + predictor._handle, + ) + ) + leaf_preds = leaf_preds.reshape(-1) + ptr_data, _, _ = _c_int_array(leaf_preds) + _safe_call( + _LIB.LGBM_BoosterRefit( + new_booster._handle, + ptr_data, + ctypes.c_int32(nrow), + ctypes.c_int32(ncol), + ) + ) + new_booster._network = self._network + return new_booster + + def get_leaf_output(self, tree_id: int, leaf_id: int) -> float: + """Get the output of a leaf. + + Parameters + ---------- + tree_id : int + The index of the tree. + leaf_id : int + The index of the leaf in the tree. + + Returns + ------- + result : float + The output of the leaf. + """ + ret = ctypes.c_double(0) + _safe_call( + _LIB.LGBM_BoosterGetLeafValue( + self._handle, + ctypes.c_int(tree_id), + ctypes.c_int(leaf_id), + ctypes.byref(ret), + ) + ) + return ret.value + + def set_leaf_output( + self, + tree_id: int, + leaf_id: int, + value: float, + ) -> "Booster": + """Set the output of a leaf. + + .. versionadded:: 4.0.0 + + Parameters + ---------- + tree_id : int + The index of the tree. + leaf_id : int + The index of the leaf in the tree. + value : float + Value to set as the output of the leaf. + + Returns + ------- + self : Booster + Booster with the leaf output set. + """ + _safe_call( + _LIB.LGBM_BoosterSetLeafValue( + self._handle, + ctypes.c_int(tree_id), + ctypes.c_int(leaf_id), + ctypes.c_double(value), + ) + ) + return self + + def num_feature(self) -> int: + """Get number of features. + + Returns + ------- + num_feature : int + The number of features. + """ + out_num_feature = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetNumFeature( + self._handle, + ctypes.byref(out_num_feature), + ) + ) + return out_num_feature.value + + def feature_name(self) -> List[str]: + """Get names of features. + + Returns + ------- + result : list of str + List with names of features. + """ + num_feature = self.num_feature() + # Get name of features + tmp_out_len = ctypes.c_int(0) + reserved_string_buffer_size = 255 + required_string_buffer_size = ctypes.c_size_t(0) + string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)] + ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] + _safe_call( + _LIB.LGBM_BoosterGetFeatureNames( + self._handle, + ctypes.c_int(num_feature), + ctypes.byref(tmp_out_len), + ctypes.c_size_t(reserved_string_buffer_size), + ctypes.byref(required_string_buffer_size), + ptr_string_buffers, + ) + ) + if num_feature != tmp_out_len.value: + raise ValueError("Length of feature names doesn't equal with num_feature") + actual_string_buffer_size = required_string_buffer_size.value + # if buffer length is not long enough, reallocate buffers + if reserved_string_buffer_size < actual_string_buffer_size: + string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)] + ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] + _safe_call( + _LIB.LGBM_BoosterGetFeatureNames( + self._handle, + ctypes.c_int(num_feature), + ctypes.byref(tmp_out_len), + ctypes.c_size_t(actual_string_buffer_size), + ctypes.byref(required_string_buffer_size), + ptr_string_buffers, + ) + ) + return [string_buffers[i].value.decode("utf-8") for i in range(num_feature)] + + def feature_importance( + self, + importance_type: str = "split", + iteration: Optional[int] = None, + ) -> np.ndarray: + """Get feature importances. + + Parameters + ---------- + importance_type : str, optional (default="split") + How the importance is calculated. + If "split", result contains numbers of times the feature is used in a model. + If "gain", result contains total gains of splits which use the feature. + iteration : int or None, optional (default=None) + Limit number of iterations in the feature importance calculation. + If None, if the best iteration exists, it is used; otherwise, all trees are used. + If <= 0, all trees are used (no limits). + + Returns + ------- + result : numpy array + Array with feature importances. + """ + if iteration is None: + iteration = self.best_iteration + importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type] + result = np.empty(self.num_feature(), dtype=np.float64) + _safe_call( + _LIB.LGBM_BoosterFeatureImportance( + self._handle, + ctypes.c_int(iteration), + ctypes.c_int(importance_type_int), + result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + ) + if importance_type_int == _C_API_FEATURE_IMPORTANCE_SPLIT: + return result.astype(np.int32) + else: + return result + + def get_split_value_histogram( + self, + feature: Union[int, str], + bins: Optional[Union[int, str]] = None, + xgboost_style: bool = False, + ) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray, pd_DataFrame]: + """Get split value histogram for the specified feature. + + Parameters + ---------- + feature : int or str + The feature name or index the histogram is calculated for. + If int, interpreted as index. + If str, interpreted as name. + + .. warning:: + + Categorical features are not supported. + + bins : int, str or None, optional (default=None) + The maximum number of bins. + If None, or int and > number of unique split values and ``xgboost_style=True``, + the number of bins equals number of unique split values. + If str, it should be one from the list of the supported values by ``numpy.histogram()`` function. + xgboost_style : bool, optional (default=False) + Whether the returned result should be in the same form as it is in XGBoost. + If False, the returned value is tuple of 2 numpy arrays as it is in ``numpy.histogram()`` function. + If True, the returned value is matrix, in which the first column is the right edges of non-empty bins + and the second one is the histogram values. + + Returns + ------- + result_tuple : tuple of 2 numpy arrays + If ``xgboost_style=False``, the values of the histogram of used splitting values for the specified feature + and the bin edges. + result_array_like : numpy array or pandas DataFrame (if pandas is installed) + If ``xgboost_style=True``, the histogram of used splitting values for the specified feature. + """ + + def add(root: Dict[str, Any]) -> None: + """Recursively add thresholds.""" + if "split_index" in root: # non-leaf + if feature_names is not None and isinstance(feature, str): + split_feature = feature_names[root["split_feature"]] + else: + split_feature = root["split_feature"] + if split_feature == feature: + if isinstance(root["threshold"], str): + raise LightGBMError("Cannot compute split value histogram for the categorical feature") + values.append(root["threshold"]) + add(root["left_child"]) + add(root["right_child"]) + + model = self.dump_model() + feature_names = model.get("feature_names") + tree_infos = model["tree_info"] + values: List[float] = [] + for tree_info in tree_infos: + add(tree_info["tree_structure"]) + + if bins is None or isinstance(bins, int) and xgboost_style: + n_unique = len(np.unique(values)) + bins = max(min(n_unique, bins) if bins is not None else n_unique, 1) + hist, bin_edges = np.histogram(values, bins=bins) + if xgboost_style: + ret = np.column_stack((bin_edges[1:], hist)) + ret = ret[ret[:, 1] > 0] + if PANDAS_INSTALLED: + return pd_DataFrame(ret, columns=["SplitValue", "Count"]) + else: + return ret + else: + return hist, bin_edges + + def __inner_eval( + self, + *, + data_name: str, + data_idx: int, + feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]], + ) -> List[_LGBM_BoosterEvalMethodResultType]: + """Evaluate training or validation data.""" + if data_idx >= self.__num_dataset: + raise ValueError("Data_idx should be smaller than number of dataset") + self.__get_eval_info() + ret = [] + if self.__num_inner_eval > 0: + result = np.empty(self.__num_inner_eval, dtype=np.float64) + tmp_out_len = ctypes.c_int(0) + _safe_call( + _LIB.LGBM_BoosterGetEval( + self._handle, + ctypes.c_int(data_idx), + ctypes.byref(tmp_out_len), + result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + ) + if tmp_out_len.value != self.__num_inner_eval: + raise ValueError("Wrong length of eval results") + for i in range(self.__num_inner_eval): + ret.append((data_name, self.__name_inner_eval[i], result[i], self.__higher_better_inner_eval[i])) + if callable(feval): + feval = [feval] + if feval is not None: + if data_idx == 0: + cur_data = self.train_set + else: + cur_data = self.valid_sets[data_idx - 1] + for eval_function in feval: + if eval_function is None: + continue + feval_ret = eval_function(self.__inner_predict(data_idx=data_idx), cur_data) + if isinstance(feval_ret, list): + for metric_name, metric_value, maximize in feval_ret: + ret.append((data_name, metric_name, metric_value, maximize)) + else: + metric_name, metric_value, maximize = feval_ret + ret.append((data_name, metric_name, metric_value, maximize)) + return ret + + def __inner_predict(self, *, data_idx: int) -> np.ndarray: + """Predict for training and validation dataset.""" + if data_idx >= self.__num_dataset: + raise ValueError("Data_idx should be smaller than number of dataset") + if self.__inner_predict_buffer[data_idx] is None: + if data_idx == 0: + n_preds = self.train_set.num_data() * self.__num_class + else: + n_preds = self.valid_sets[data_idx - 1].num_data() * self.__num_class + self.__inner_predict_buffer[data_idx] = np.empty(n_preds, dtype=np.float64) + # avoid to predict many time in one iteration + if not self.__is_predicted_cur_iter[data_idx]: + tmp_out_len = ctypes.c_int64(0) + data_ptr = self.__inner_predict_buffer[data_idx].ctypes.data_as(ctypes.POINTER(ctypes.c_double)) # type: ignore[union-attr] + _safe_call( + _LIB.LGBM_BoosterGetPredict( + self._handle, + ctypes.c_int(data_idx), + ctypes.byref(tmp_out_len), + data_ptr, + ) + ) + if tmp_out_len.value != len(self.__inner_predict_buffer[data_idx]): # type: ignore[arg-type] + raise ValueError(f"Wrong length of predict results for data {data_idx}") + self.__is_predicted_cur_iter[data_idx] = True + result: np.ndarray = self.__inner_predict_buffer[data_idx] # type: ignore[assignment] + if self.__num_class > 1: + num_data = result.size // self.__num_class + result = result.reshape(num_data, self.__num_class, order="F") + return result + + def __get_eval_info(self) -> None: + """Get inner evaluation count and names.""" + if self.__need_reload_eval_info: + self.__need_reload_eval_info = False + out_num_eval = ctypes.c_int(0) + # Get num of inner evals + _safe_call( + _LIB.LGBM_BoosterGetEvalCounts( + self._handle, + ctypes.byref(out_num_eval), + ) + ) + self.__num_inner_eval = out_num_eval.value + if self.__num_inner_eval > 0: + # Get name of eval metrics + tmp_out_len = ctypes.c_int(0) + reserved_string_buffer_size = 255 + required_string_buffer_size = ctypes.c_size_t(0) + string_buffers = [ + ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(self.__num_inner_eval) + ] + ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc] + _safe_call( + _LIB.LGBM_BoosterGetEvalNames( + self._handle, + ctypes.c_int(self.__num_inner_eval), + ctypes.byref(tmp_out_len), + ctypes.c_size_t(reserved_string_buffer_size), + ctypes.byref(required_string_buffer_size), + ptr_string_buffers, + ) + ) + if self.__num_inner_eval != tmp_out_len.value: + raise ValueError("Length of eval names doesn't equal with num_evals") + actual_string_buffer_size = required_string_buffer_size.value + # if buffer length is not long enough, reallocate buffers + if reserved_string_buffer_size < actual_string_buffer_size: + string_buffers = [ + ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(self.__num_inner_eval) + ] + ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)( + *map(ctypes.addressof, string_buffers) + ) # type: ignore[misc] + _safe_call( + _LIB.LGBM_BoosterGetEvalNames( + self._handle, + ctypes.c_int(self.__num_inner_eval), + ctypes.byref(tmp_out_len), + ctypes.c_size_t(actual_string_buffer_size), + ctypes.byref(required_string_buffer_size), + ptr_string_buffers, + ) + ) + self.__name_inner_eval = [string_buffers[i].value.decode("utf-8") for i in range(self.__num_inner_eval)] + self.__higher_better_inner_eval = [ + name.startswith(("auc", "ndcg@", "map@", "average_precision")) for name in self.__name_inner_eval + ] diff --git a/python-package/lightgbm/callback.py b/python-package/lightgbm/callback.py new file mode 100644 index 0000000..11c3bc4 --- /dev/null +++ b/python-package/lightgbm/callback.py @@ -0,0 +1,511 @@ +# coding: utf-8 +"""Callbacks library.""" + +from collections import OrderedDict +from dataclasses import dataclass +from functools import partial +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union + +from .basic import ( + Booster, + _ConfigAliases, + _LGBM_BoosterEvalMethodResultType, + _LGBM_BoosterEvalMethodResultWithStandardDeviationType, + _log_info, + _log_warning, +) + +if TYPE_CHECKING: + from .engine import CVBooster + +__all__ = [ + "EarlyStopException", + "early_stopping", + "log_evaluation", + "record_evaluation", + "reset_parameter", +] + +_EvalResultDict = Dict[str, Dict[str, List[Any]]] +_EvalResultTuple = Union[ + _LGBM_BoosterEvalMethodResultType, + _LGBM_BoosterEvalMethodResultWithStandardDeviationType, +] +_ListOfEvalResultTuples = Union[ + List[_LGBM_BoosterEvalMethodResultType], + List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType], +] + + +class EarlyStopException(Exception): + """Exception of early stopping. + + Raise this from a callback passed in via keyword argument ``callbacks`` + in ``cv()`` or ``train()`` to trigger early stopping. + """ + + def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) -> None: + """Create early stopping exception. + + Parameters + ---------- + best_iteration : int + The best iteration stopped. + 0-based... pass ``best_iteration=2`` to indicate that the third iteration was the best one. + best_score : list of (dataset_name, metric_name, metric_value, maximize) tuple or (dataset_name, metric_name, metric_value, maximize, metric_std_dev) tuple + Scores for each metric, on each validation set, as of the best iteration. + """ + super().__init__() + self.best_iteration = best_iteration + self.best_score = best_score + + +# Callback environment used by callbacks +@dataclass +class CallbackEnv: + model: Union[Booster, "CVBooster"] + params: Dict[str, Any] + iteration: int + begin_iteration: int + end_iteration: int + evaluation_result_list: Optional[_ListOfEvalResultTuples] + + +def _is_using_cv(env: CallbackEnv) -> bool: + """Check if model in callback env is a CVBooster.""" + # this import is here to avoid a circular import + from .engine import CVBooster # noqa: PLC0415 + + return isinstance(env.model, CVBooster) + + +def _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str: + """Format metric string.""" + dataset_name, metric_name, metric_value, *_ = value + out = f"{dataset_name}'s {metric_name}: {metric_value:g}" + # tuples from cv() sometimes have a 5th item, with standard deviation of + # the evaluation metric (taken over all cross-validation folds) + if show_stdv and len(value) == 5: + out += f" + {value[4]:g}" + return out + + +class _LogEvaluationCallback: + """Internal log evaluation callable class.""" + + def __init__(self, period: int = 1, show_stdv: bool = True) -> None: + self.order = 10 + self.before_iteration = False + + self.period = period + self.show_stdv = show_stdv + + def __call__(self, env: CallbackEnv) -> None: + if self.period > 0 and env.evaluation_result_list and (env.iteration + 1) % self.period == 0: + result = "\t".join([_format_eval_result(x, self.show_stdv) for x in env.evaluation_result_list]) + _log_info(f"[{env.iteration + 1}]\t{result}") + + +def log_evaluation(period: int = 1, show_stdv: bool = True) -> _LogEvaluationCallback: + """Create a callback that logs the evaluation results. + + By default, standard output resource is used. + Use ``register_logger()`` function to register a custom logger. + + Note + ---- + Requires at least one validation data. + + Parameters + ---------- + period : int, optional (default=1) + The period to log the evaluation results. + The last boosting stage or the boosting stage found by using ``early_stopping`` callback is also logged. + show_stdv : bool, optional (default=True) + Whether to log stdv (if provided). + + Returns + ------- + callback : _LogEvaluationCallback + The callback that logs the evaluation results every ``period`` boosting iteration(s). + """ + return _LogEvaluationCallback(period=period, show_stdv=show_stdv) + + +class _RecordEvaluationCallback: + """Internal record evaluation callable class.""" + + def __init__(self, eval_result: _EvalResultDict) -> None: + self.order = 20 + self.before_iteration = False + + if not isinstance(eval_result, dict): + raise TypeError("eval_result should be a dictionary") + self.eval_result = eval_result + + def _init(self, env: CallbackEnv) -> None: + if env.evaluation_result_list is None: + raise RuntimeError( + "record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. " + "Please report it at https://github.com/lightgbm-org/LightGBM/issues" + ) + self.eval_result.clear() + for item in env.evaluation_result_list: + dataset_name, metric_name, *_ = item + self.eval_result.setdefault(dataset_name, OrderedDict()) + if len(item) == 4: + self.eval_result[dataset_name].setdefault(metric_name, []) + else: + self.eval_result[dataset_name].setdefault(f"{metric_name}-mean", []) + self.eval_result[dataset_name].setdefault(f"{metric_name}-stdv", []) + + def __call__(self, env: CallbackEnv) -> None: + if env.iteration == env.begin_iteration: + self._init(env) + if env.evaluation_result_list is None: + raise RuntimeError( + "record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. " + "Please report it at https://github.com/lightgbm-org/LightGBM/issues" + ) + for item in env.evaluation_result_list: + # for cv(), 'metric_value' is actually a mean of metric values over all CV folds + dataset_name, metric_name, metric_value, *_ = item + if len(item) == 4: + # train() + self.eval_result[dataset_name][metric_name].append(metric_value) + else: + # cv() + metric_std_dev = item[4] # type: ignore[misc] + self.eval_result[dataset_name][f"{metric_name}-mean"].append(metric_value) + self.eval_result[dataset_name][f"{metric_name}-stdv"].append(metric_std_dev) + + +def record_evaluation(eval_result: Dict[str, Dict[str, List[Any]]]) -> Callable: + """Create a callback that records the evaluation history into ``eval_result``. + + Parameters + ---------- + eval_result : dict + Dictionary used to store all evaluation results of all validation sets. + This should be initialized outside of your call to ``record_evaluation()`` and should be empty. + Any initial contents of the dictionary will be deleted. + + .. rubric:: Example + + With two validation sets named 'eval' and 'train', and one evaluation metric named 'logloss' + this dictionary after finishing a model training process will have the following structure: + + .. code-block:: + + { + 'train': + { + 'logloss': [0.48253, 0.35953, ...] + }, + 'eval': + { + 'logloss': [0.480385, 0.357756, ...] + } + } + + Returns + ------- + callback : _RecordEvaluationCallback + The callback that records the evaluation history into the passed dictionary. + """ + return _RecordEvaluationCallback(eval_result=eval_result) + + +class _ResetParameterCallback: + """Internal reset parameter callable class.""" + + def __init__(self, **kwargs: Union[list, Callable]) -> None: + self.order = 10 + self.before_iteration = True + + self.kwargs = kwargs + + def __call__(self, env: CallbackEnv) -> None: + new_parameters = {} + for key, value in self.kwargs.items(): + if isinstance(value, list): + if len(value) != env.end_iteration - env.begin_iteration: + raise ValueError(f"Length of list {key!r} has to be equal to 'num_boost_round'.") + new_param = value[env.iteration - env.begin_iteration] + elif callable(value): + new_param = value(env.iteration - env.begin_iteration) + else: + raise ValueError( + "Only list and callable values are supported " + "as a mapping from boosting round index to new parameter value." + ) + if new_param != env.params.get(key, None): + new_parameters[key] = new_param + if new_parameters: + if isinstance(env.model, Booster): + env.model.reset_parameter(new_parameters) + else: + # CVBooster holds a list of Booster objects, each needs to be updated + for booster in env.model.boosters: + booster.reset_parameter(new_parameters) + env.params.update(new_parameters) + + +def reset_parameter(**kwargs: Union[list, Callable]) -> Callable: + """Create a callback that resets the parameter after the first iteration. + + .. note:: + + The initial parameter will still take in-effect on first iteration. + + Parameters + ---------- + **kwargs : value should be list or callable + List of parameters for each boosting round + or a callable that calculates the parameter in terms of + current number of round (e.g. yields learning rate decay). + If list lst, parameter = lst[current_round]. + If callable func, parameter = func(current_round). + + Returns + ------- + callback : _ResetParameterCallback + The callback that resets the parameter after the first iteration. + """ + return _ResetParameterCallback(**kwargs) + + +class _EarlyStoppingCallback: + """Internal early stopping callable class.""" + + def __init__( + self, + stopping_rounds: int, + first_metric_only: bool = False, + verbose: bool = True, + min_delta: Union[float, List[float]] = 0.0, + ) -> None: + self.enabled = _should_enable_early_stopping(stopping_rounds) + + self.order = 30 + self.before_iteration = False + + self.stopping_rounds = stopping_rounds + self.first_metric_only = first_metric_only + self.verbose = verbose + self.min_delta = min_delta + + self._reset_storages() + + def _reset_storages(self) -> None: + self.best_score: List[float] = [] + self.best_iter: List[int] = [] + self.best_score_list: List[_ListOfEvalResultTuples] = [] + self.cmp_op: List[Callable[[float, float, float], bool]] = [] + self.first_metric = "" + + def _gt_delta(self, *, curr_score: float, best_score: float, delta: float) -> bool: + return curr_score > best_score + delta + + def _lt_delta(self, *, curr_score: float, best_score: float, delta: float) -> bool: + return curr_score < best_score - delta + + def _is_train_set(self, *, dataset_name: str, env: CallbackEnv) -> bool: + """Check, by name, if a given Dataset is the training data.""" + # for lgb.cv() with eval_train_metric=True, evaluation is also done on the training set + # and those metrics are considered for early stopping + if _is_using_cv(env) and dataset_name == "train": + return True + + # for lgb.train(), it's possible to pass the training data via valid_sets with any name + if isinstance(env.model, Booster) and dataset_name == env.model._train_data_name: + return True + + return False + + def _init(self, env: CallbackEnv) -> None: + if env.evaluation_result_list is None or env.evaluation_result_list == []: + raise ValueError("For early stopping, at least one dataset and eval metric is required for evaluation") + + is_dart = any(env.params.get(alias, "") == "dart" for alias in _ConfigAliases.get("boosting")) + if is_dart: + self.enabled = False + _log_warning("Early stopping is not available in dart mode") + return + + # get details of the first dataset + first_dataset_name, first_metric_name, *_ = env.evaluation_result_list[0] + + # validation sets are guaranteed to not be identical to the training data in cv() + if isinstance(env.model, Booster): + only_train_set = len(env.evaluation_result_list) == 1 and self._is_train_set( + dataset_name=first_dataset_name, + env=env, + ) + if only_train_set: + self.enabled = False + _log_warning("Only training set found, disabling early stopping.") + return + + if self.verbose: + _log_info(f"Training until validation scores don't improve for {self.stopping_rounds} rounds") + + self._reset_storages() + + n_metrics = len({m[1] for m in env.evaluation_result_list}) + n_datasets = len(env.evaluation_result_list) // n_metrics + if isinstance(self.min_delta, list): + if not all(t >= 0 for t in self.min_delta): + raise ValueError("Values for early stopping min_delta must be non-negative.") + if len(self.min_delta) == 0: + if self.verbose: + _log_info("Disabling min_delta for early stopping.") + deltas = [0.0] * n_datasets * n_metrics + elif len(self.min_delta) == 1: + if self.verbose: + _log_info(f"Using {self.min_delta[0]} as min_delta for all metrics.") + deltas = self.min_delta * n_datasets * n_metrics + else: + if len(self.min_delta) != n_metrics: + raise ValueError("Must provide a single value for min_delta or as many as metrics.") + if self.first_metric_only and self.verbose: + _log_info(f"Using only {self.min_delta[0]} as early stopping min_delta.") + deltas = self.min_delta * n_datasets + else: + if self.min_delta < 0: + raise ValueError("Early stopping min_delta must be non-negative.") + if self.min_delta > 0 and n_metrics > 1 and not self.first_metric_only and self.verbose: + _log_info(f"Using {self.min_delta} as min_delta for all metrics.") + deltas = [self.min_delta] * n_datasets * n_metrics + + self.first_metric = first_metric_name + for eval_ret, delta in zip(env.evaluation_result_list, deltas, strict=True): + self.best_iter.append(0) + if eval_ret[3]: # greater is better + self.best_score.append(float("-inf")) + self.cmp_op.append(partial(self._gt_delta, delta=delta)) + else: + self.best_score.append(float("inf")) + self.cmp_op.append(partial(self._lt_delta, delta=delta)) + + def _final_iteration_check(self, *, env: CallbackEnv, metric_name: str, i: int) -> None: + if env.iteration == env.end_iteration - 1: + if self.verbose: + best_score_str = "\t".join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]]) + _log_info( + f"Did not meet early stopping. Best iteration is:\n[{self.best_iter[i] + 1}]\t{best_score_str}" + ) + if self.first_metric_only: + _log_info(f"Evaluated only: {metric_name}") + raise EarlyStopException( + best_iteration=self.best_iter[i], + best_score=self.best_score_list[i], + ) + + def __call__(self, env: CallbackEnv) -> None: + if env.iteration == env.begin_iteration: + self._init(env) + if not self.enabled: + return + if env.evaluation_result_list is None: + raise RuntimeError( + "early_stopping() callback enabled but no evaluation results found. This is a probably bug in LightGBM. " + "Please report it at https://github.com/lightgbm-org/LightGBM/issues" + ) + # self.best_score_list is initialized to an empty list + first_time_updating_best_score_list = self.best_score_list == [] + for i in range(len(env.evaluation_result_list)): + dataset_name, metric_name, metric_value, *_ = env.evaluation_result_list[i] + if first_time_updating_best_score_list or self.cmp_op[i]( # type: ignore[call-arg] + curr_score=metric_value, best_score=self.best_score[i] + ): + self.best_score[i] = metric_value + self.best_iter[i] = env.iteration + if first_time_updating_best_score_list: + self.best_score_list.append(env.evaluation_result_list) + else: + self.best_score_list[i] = env.evaluation_result_list + if self.first_metric_only and self.first_metric != metric_name: + continue # use only the first metric for early stopping + if self._is_train_set( + dataset_name=dataset_name, + env=env, + ): + continue # train data for lgb.cv or sklearn wrapper (underlying lgb.train) + elif env.iteration - self.best_iter[i] >= self.stopping_rounds: + if self.verbose: + eval_result_str = "\t".join( + [_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]] + ) + _log_info(f"Early stopping, best iteration is:\n[{self.best_iter[i] + 1}]\t{eval_result_str}") + if self.first_metric_only: + _log_info(f"Evaluated only: {metric_name}") + raise EarlyStopException( + best_iteration=self.best_iter[i], + best_score=self.best_score_list[i], + ) + self._final_iteration_check(env=env, metric_name=metric_name, i=i) + + +def _should_enable_early_stopping(stopping_rounds: Any) -> bool: + """Check if early stopping should be activated. + + This function will evaluate to True if the early stopping callback should be + activated (i.e. stopping_rounds > 0). It also provides an informative error if the + type is not int. + """ + if not isinstance(stopping_rounds, int): + raise TypeError(f"early_stopping_round should be an integer. Got '{type(stopping_rounds).__name__}'") + return stopping_rounds > 0 + + +def early_stopping( + stopping_rounds: int, + first_metric_only: bool = False, + verbose: bool = True, + min_delta: Union[float, List[float]] = 0.0, +) -> _EarlyStoppingCallback: + """Create a callback that activates early stopping. + + Activates early stopping. + The model will train until the validation score doesn't improve by at least ``min_delta``. + Validation score needs to improve at least every ``stopping_rounds`` round(s) + to continue training. + Requires at least one validation data and one metric. + If there's more than one, will check all of them. But the training data is ignored anyway. + To check only the first metric set ``first_metric_only`` to True. + The index of iteration that has the best performance will be saved in the ``best_iteration`` attribute of a model. + + .. note:: + + If using ``boosting_type="dart"``, this callback has no effect and early stopping + will not be performed. + + Parameters + ---------- + stopping_rounds : int + The possible number of rounds without the trend occurrence. + first_metric_only : bool, optional (default=False) + Whether to use only the first metric for early stopping. + verbose : bool, optional (default=True) + Whether to log message with early stopping information. + By default, standard output resource is used. + Use ``register_logger()`` function to register a custom logger. + min_delta : float or list of float, optional (default=0.0) + Minimum improvement in score to keep training. + If float, this single value is used for all metrics. + If list, its length should match the total number of metrics. + + .. versionadded:: 4.0.0 + + Returns + ------- + callback : _EarlyStoppingCallback + The callback that activates early stopping. + """ + return _EarlyStoppingCallback( + stopping_rounds=stopping_rounds, + first_metric_only=first_metric_only, + verbose=verbose, + min_delta=min_delta, + ) diff --git a/python-package/lightgbm/compat.py b/python-package/lightgbm/compat.py new file mode 100644 index 0000000..57de0c1 --- /dev/null +++ b/python-package/lightgbm/compat.py @@ -0,0 +1,223 @@ +# coding: utf-8 +"""Compatibility library.""" + +import inspect +from typing import TYPE_CHECKING, Any, List + +# scikit-learn is intentionally imported first here, +# see https://github.com/lightgbm-org/LightGBM/issues/6509 +"""sklearn""" +try: + from sklearn import __version__ as _sklearn_version + from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin + from sklearn.preprocessing import LabelEncoder + from sklearn.utils.class_weight import compute_sample_weight + from sklearn.utils.multiclass import check_classification_targets + from sklearn.utils.validation import assert_all_finite, check_array, check_X_y + + try: + from sklearn.exceptions import NotFittedError + from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold + except ImportError: + from sklearn.cross_validation import BaseCrossValidator, GroupKFold, StratifiedKFold + from sklearn.utils.validation import NotFittedError + try: + from sklearn.utils.validation import _check_sample_weight + + # As of https://github.com/scikit-learn/scikit-learn/pull/32212, scikit-learn started raising an error + # when sample weights are all 0. This argument allow_all_zero_weights can be used switch back + # to the old behavior of allowing them. + # + # This can be removed when the minimum scikit-learn version supported here is v1.9. + SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = ( + "allow_all_zero_weights" in inspect.signature(_check_sample_weight).parameters + ) + + except ImportError: + from sklearn.utils.validation import check_consistent_length + + SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = False + + # dummy function to support older version of scikit-learn + def _check_sample_weight(sample_weight: Any, X: Any, dtype: Any = None) -> Any: + check_consistent_length(sample_weight, X) + return sample_weight + + try: + from sklearn.utils.validation import validate_data + except ImportError: + # validate_data() was added in scikit-learn 1.6, this function roughly imitates it for older versions. + # It can be removed when lightgbm's minimum scikit-learn version is at least 1.6. + def validate_data( + _estimator: Any, + X: Any, + y: Any = "no_validation", + accept_sparse: bool = True, + # 'force_all_finite' was renamed to 'ensure_all_finite' in scikit-learn 1.6 + ensure_all_finite: bool = False, + ensure_min_samples: int = 1, + # trap other keyword arguments that only work on scikit-learn >=1.6, like 'reset' + **ignored_kwargs: Any, + ) -> Any: + # it's safe to import _num_features unconditionally because: + # + # * it was first added in scikit-learn 0.24.2 + # * lightgbm cannot be used with scikit-learn versions older than that + # * this validate_data() re-implementation will not be called in scikit-learn>=1.6 + # + from sklearn.utils.validation import _num_features # noqa: PLC0415 + + # _num_features() raises a TypeError on 1-dimensional input. That's a problem + # because scikit-learn's 'check_fit1d' estimator check sets that expectation that + # estimators must raise a ValueError when a 1-dimensional input is passed to fit(). + # + # So here, lightgbm avoids calling _num_features() on 1-dimensional inputs. + if hasattr(X, "shape") and len(X.shape) == 1: + n_features_in_ = 1 + else: + n_features_in_ = _num_features(X) + + no_val_y = isinstance(y, str) and y == "no_validation" + + # NOTE: check_X_y() calls check_array() internally, so only need to call one or the other of them here + if no_val_y: + X = check_array( + X, + accept_sparse=accept_sparse, + force_all_finite=ensure_all_finite, + ensure_min_samples=ensure_min_samples, + ) + else: + X, y = check_X_y( + X, + y, + accept_sparse=accept_sparse, + force_all_finite=ensure_all_finite, + ensure_min_samples=ensure_min_samples, + ) + + # this only needs to be updated at fit() time + _estimator.n_features_in_ = n_features_in_ + + # raise the same error that scikit-learn's `validate_data()` does on scikit-learn>=1.6 + if _estimator.__sklearn_is_fitted__() and _estimator._n_features != n_features_in_: + raise ValueError( + f"X has {n_features_in_} features, but {_estimator.__class__.__name__} " + f"is expecting {_estimator._n_features} features as input." + ) + + if no_val_y: + return X + else: + return X, y + + SKLEARN_INSTALLED = True + _LGBMBaseCrossValidator = BaseCrossValidator + _LGBMModelBase = BaseEstimator + _LGBMRegressorBase = RegressorMixin + _LGBMClassifierBase = ClassifierMixin + _LGBMLabelEncoder = LabelEncoder + LGBMNotFittedError = NotFittedError + _LGBMStratifiedKFold = StratifiedKFold + _LGBMGroupKFold = GroupKFold + _LGBMCheckSampleWeight = _check_sample_weight + _LGBMAssertAllFinite = assert_all_finite + _LGBMCheckClassificationTargets = check_classification_targets + _LGBMComputeSampleWeight = compute_sample_weight + _LGBMValidateData = validate_data +except ImportError: + SKLEARN_INSTALLED = False + SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = False + + class _LGBMModelBase: # type: ignore + """Dummy class for sklearn.base.BaseEstimator.""" + + pass + + class _LGBMClassifierBase: # type: ignore + """Dummy class for sklearn.base.ClassifierMixin.""" + + pass + + class _LGBMRegressorBase: # type: ignore + """Dummy class for sklearn.base.RegressorMixin.""" + + pass + + _LGBMBaseCrossValidator = None + _LGBMLabelEncoder = None + LGBMNotFittedError = ValueError + _LGBMStratifiedKFold = None + _LGBMGroupKFold = None + _LGBMCheckSampleWeight = None + _LGBMAssertAllFinite = None + _LGBMCheckClassificationTargets = None + _LGBMComputeSampleWeight = None + _LGBMValidateData = None + _sklearn_version = None + +# additional scikit-learn imports only for type hints +if TYPE_CHECKING: + # sklearn.utils.Tags can be imported unconditionally once + # lightgbm's minimum scikit-learn version is 1.6 or higher + try: + from sklearn.utils import Tags as _sklearn_Tags + except ImportError: + _sklearn_Tags = None + +"""pandas""" +try: + from pandas import DataFrame as pd_DataFrame + from pandas import Series as pd_Series + from pandas import concat + + try: + from pandas import CategoricalDtype as pd_CategoricalDtype + except ImportError: + from pandas.api.types import CategoricalDtype as pd_CategoricalDtype + PANDAS_INSTALLED = True +except ImportError: + PANDAS_INSTALLED = False + + class pd_Series: # type: ignore + """Dummy class for pandas.Series.""" + + def __init__(self, *args: Any, **kwargs: Any): + pass + + class pd_DataFrame: # type: ignore + """Dummy class for pandas.DataFrame.""" + + def __init__(self, *args: Any, **kwargs: Any): + pass + + class pd_CategoricalDtype: # type: ignore + """Dummy class for pandas.CategoricalDtype.""" + + def __init__(self, *args: Any, **kwargs: Any): + pass + + concat = None + +"""cpu_count()""" + + +def _LGBMCpuCount(only_physical_cores: bool = True) -> int: + ret: int + try: + from joblib import cpu_count # noqa: I001,PLC0415 + + ret = cpu_count(only_physical_cores=only_physical_cores) + except ImportError: + try: + from psutil import cpu_count # noqa: I001,PLC0415 + + ret = cpu_count(logical=not only_physical_cores) or 1 + except ImportError: + from multiprocessing import cpu_count # noqa: I001,PLC0415 + + ret = cpu_count() + return ret + + +__all__: List[str] = [] diff --git a/python-package/lightgbm/dask.py b/python-package/lightgbm/dask.py new file mode 100644 index 0000000..df907fc --- /dev/null +++ b/python-package/lightgbm/dask.py @@ -0,0 +1,1749 @@ +# coding: utf-8 +"""Distributed training with LightGBM and dask.distributed. + +This module enables you to perform distributed training with LightGBM on +dask.Array and dask.DataFrame collections. + +It is based on dask-lightgbm, which was based on dask-xgboost. +""" + +import operator +import socket +from collections import defaultdict +from copy import deepcopy +from enum import Enum, auto +from functools import partial +from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type, Union +from urllib.parse import urlparse + +import numpy as np +import scipy.sparse as ss + +from .basic import LightGBMError, _choose_param_value, _ConfigAliases, _log_info, _log_warning +from .compat import ( + PANDAS_INSTALLED, + SKLEARN_INSTALLED, + LGBMNotFittedError, + concat, + pd_DataFrame, + pd_Series, +) +from .sklearn import ( + LGBMClassifier, + LGBMModel, + LGBMRanker, + LGBMRegressor, + _LGBM_ScikitCustomObjectiveFunction, + _LGBM_ScikitEvalMetricType, + _lgbmmodel_doc_custom_eval_note, + _lgbmmodel_doc_fit, + _lgbmmodel_doc_predict, + _validate_eval_set_Xy, +) + +__all__ = [ + "DaskLGBMClassifier", + "DaskLGBMRanker", + "DaskLGBMRegressor", +] + +if TYPE_CHECKING: + import dask + import distributed + +_DaskCollection = Union["dask.array.Array", "dask.dataframe.DataFrame", "dask.dataframe.Series"] +_DaskMatrixLike = Union["dask.array.Array", "dask.dataframe.DataFrame"] +_DaskVectorLike = Union["dask.array.Array", "dask.dataframe.Series"] +_DaskPart = Union[np.ndarray, pd_DataFrame, pd_Series, ss.spmatrix] + +# catching 'ValueError' here because of this: +# https://github.com/lightgbm-org/LightGBM/issues/6365#issuecomment-2002330003 +# +# That's potentially risky as dask does some significant import-time processing, +# like loading configuration from environment variables and files, and catching +# ValueError here might hide issues with that config-loading. +# +# But in exchange, it's less likely that 'import lightgbm' will fail for +# dask-related reasons, which is beneficial for any workloads that are using +# lightgbm but not its Dask functionality. +# +# 'ValueError' can be removed when LightGBM's Dask floor is '>=2024.4.0'. +_DaskImportErrorTypes = (ImportError, ValueError) + + +class _RemoteSocket: + def acquire(self) -> int: + self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + self.socket.bind(("", 0)) + return self.socket.getsockname()[1] + + def release(self) -> None: + self.socket.close() + + +def _acquire_port() -> Tuple[_RemoteSocket, int]: + s = _RemoteSocket() + port = s.acquire() + return s, port + + +class _DatasetNames(Enum): + """Placeholder names used by lightgbm.dask internals to say 'also evaluate the training data'. + + Avoid duplicating the training data when the validation set refers to elements of training data. + """ + + TRAINSET = auto() + SAMPLE_WEIGHT = auto() + INIT_SCORE = auto() + GROUP = auto() + + +def _get_dask_client(client: Optional["distributed.Client"]) -> "distributed.Client": + """Choose a Dask client to use. + + Parameters + ---------- + client : distributed.Client or None + Dask client. + + Returns + ------- + client : distributed.Client + A Dask client. + """ + from dask.distributed import default_client # noqa: PLC0415 + + if client is None: + return default_client() + else: + return client + + +def _assign_open_ports_to_workers( + *, + client: "distributed.Client", + workers: List[str], +) -> Tuple[Dict[str, "distributed.client.Future"], Dict[str, int]]: + """Assign an open port to each worker. + + Returns + ------- + worker_to_socket_future: dict + mapping from worker address to a future pointing to the remote socket. + worker_to_port: dict + mapping from worker address to an open port in the worker's host. + """ + # Acquire port in worker + worker_to_future = {} + for worker in workers: + worker_to_future[worker] = client.submit( + _acquire_port, + workers=[worker], + allow_other_workers=False, + pure=False, + ) + + # schedule futures to retrieve each element of the tuple + worker_to_socket_future = {} + worker_to_port_future = {} + for worker, socket_future in worker_to_future.items(): + worker_to_socket_future[worker] = client.submit(operator.itemgetter(0), socket_future) + worker_to_port_future[worker] = client.submit(operator.itemgetter(1), socket_future) + + # retrieve ports + worker_to_port = client.gather(worker_to_port_future) + + return worker_to_socket_future, worker_to_port + + +def _concat(seq: List[_DaskPart]) -> _DaskPart: + if isinstance(seq[0], np.ndarray): + return np.concatenate(seq, axis=0) + elif isinstance(seq[0], (pd_DataFrame, pd_Series)): + return concat(seq, axis=0) + elif isinstance(seq[0], ss.spmatrix): + return ss.vstack(seq, format="csr") + else: + raise TypeError( + f"Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got {type(seq[0]).__name__}." + ) + + +def _remove_list_padding(*args: Any) -> List[List[Any]]: + return [[z for z in arg if z is not None] for arg in args] + + +def _pad_eval_names( + *, + lgbm_model: LGBMModel, + required_names: List[str], +) -> LGBMModel: + """Append missing (key, value) pairs to a LightGBM model's evals_result_ and best_score_ OrderedDict attrs based on a set of required eval_set names. + + Allows users to rely on expected eval_set names being present when fitting DaskLGBM estimators with ``eval_set``. + """ + for eval_name in required_names: + if eval_name not in lgbm_model.evals_result_: + lgbm_model.evals_result_[eval_name] = {} + if eval_name not in lgbm_model.best_score_: + lgbm_model.best_score_[eval_name] = {} + + return lgbm_model + + +def _train_part( + *, + params: Dict[str, Any], + model_factory: Type[LGBMModel], + list_of_parts: List[Dict[str, _DaskPart]], + machines: str, + local_listen_port: int, + num_machines: int, + return_model: bool, + time_out: int, + remote_socket: _RemoteSocket, + **kwargs: Any, +) -> Optional[LGBMModel]: + network_params = { + "machines": machines, + "local_listen_port": local_listen_port, + "time_out": time_out, + "num_machines": num_machines, + } + params.update(network_params) + + is_ranker = issubclass(model_factory, LGBMRanker) + + # Concatenate many parts into one + data = _concat([x["data"] for x in list_of_parts]) + label = _concat([x["label"] for x in list_of_parts]) + + if "weight" in list_of_parts[0]: + weight = _concat([x["weight"] for x in list_of_parts]) + else: + weight = None + + if "group" in list_of_parts[0]: + group = _concat([x["group"] for x in list_of_parts]) + else: + group = None + + if "init_score" in list_of_parts[0]: + init_score = _concat([x["init_score"] for x in list_of_parts]) + else: + init_score = None + + # construct local eval_set data. + n_evals = max(len(x.get("eval_set", [])) for x in list_of_parts) + eval_names = kwargs.pop("eval_names", None) + eval_class_weight = kwargs.get("eval_class_weight") + local_eval_set = None + local_eval_names = None + local_eval_sample_weight = None + local_eval_init_score = None + local_eval_group = None + + if n_evals: + has_eval_sample_weight = any(x.get("eval_sample_weight") is not None for x in list_of_parts) + has_eval_init_score = any(x.get("eval_init_score") is not None for x in list_of_parts) + + local_eval_set = [] + evals_result_names = [] + if has_eval_sample_weight: + local_eval_sample_weight = [] + if has_eval_init_score: + local_eval_init_score = [] + if is_ranker: + local_eval_group = [] + + # store indices of eval_set components that were not contained within local parts. + missing_eval_component_idx = [] + + # consolidate parts of each individual eval component. + for i in range(n_evals): + x_e = [] + y_e = [] + w_e = [] + init_score_e = [] + g_e = [] + for part in list_of_parts: + if not part.get("eval_set"): + continue + + # require that eval_name exists in evaluated result data in case dropped due to padding. + # in distributed training the 'training' eval_set is not detected, will have name 'valid_'. + if eval_names: + evals_result_name = eval_names[i] + else: + evals_result_name = f"valid_{i}" + + eval_set = part["eval_set"][i] + if eval_set is _DatasetNames.TRAINSET: + x_e.append(part["data"]) + y_e.append(part["label"]) + else: + x_e.extend(eval_set[0]) + y_e.extend(eval_set[1]) + + if evals_result_name not in evals_result_names: + evals_result_names.append(evals_result_name) + + eval_weight = part.get("eval_sample_weight") + if eval_weight: + if eval_weight[i] is _DatasetNames.SAMPLE_WEIGHT: + w_e.append(part["weight"]) + else: + w_e.extend(eval_weight[i]) + + eval_init_score = part.get("eval_init_score") + if eval_init_score: + if eval_init_score[i] is _DatasetNames.INIT_SCORE: + init_score_e.append(part["init_score"]) + else: + init_score_e.extend(eval_init_score[i]) + + eval_group = part.get("eval_group") + if eval_group: + if eval_group[i] is _DatasetNames.GROUP: + g_e.append(part["group"]) + else: + g_e.extend(eval_group[i]) + + # filter padding from eval parts then _concat each eval_set component. + x_e, y_e, w_e, init_score_e, g_e = _remove_list_padding(x_e, y_e, w_e, init_score_e, g_e) + if x_e: + local_eval_set.append((_concat(x_e), _concat(y_e))) + else: + missing_eval_component_idx.append(i) + continue + + if w_e: + local_eval_sample_weight.append(_concat(w_e)) + if init_score_e: + local_eval_init_score.append(_concat(init_score_e)) + if g_e: + local_eval_group.append(_concat(g_e)) + + # reconstruct eval_set fit args/kwargs depending on which components of eval_set are on worker. + eval_component_idx = [i for i in range(n_evals) if i not in missing_eval_component_idx] + if eval_names: + local_eval_names = [eval_names[i] for i in eval_component_idx] + if eval_class_weight: + kwargs["eval_class_weight"] = [eval_class_weight[i] for i in eval_component_idx] + + if local_eval_set is None: + local_eval_X = None + local_eval_y = None + else: + local_eval_X = tuple(X for X, _ in local_eval_set) + local_eval_y = tuple(y for _, y in local_eval_set) + + model = model_factory(**params) + if remote_socket is not None: + remote_socket.release() + try: + if is_ranker: + model.fit( + data, + label, + sample_weight=weight, + init_score=init_score, + group=group, + eval_X=local_eval_X, + eval_y=local_eval_y, + eval_sample_weight=local_eval_sample_weight, + eval_init_score=local_eval_init_score, + eval_group=local_eval_group, + eval_names=local_eval_names, + **kwargs, + ) + else: + model.fit( + data, + label, + sample_weight=weight, + init_score=init_score, + eval_X=local_eval_X, + eval_y=local_eval_y, + eval_sample_weight=local_eval_sample_weight, + eval_init_score=local_eval_init_score, + eval_names=local_eval_names, + **kwargs, + ) + + finally: + if getattr(model, "fitted_", False): + model.booster_.free_network() + + if n_evals: + # ensure that expected keys for evals_result_ and best_score_ exist regardless of padding. + model = _pad_eval_names(lgbm_model=model, required_names=evals_result_names) + + return model if return_model else None + + +def _split_to_parts(*, data: _DaskCollection, is_matrix: bool) -> List[_DaskPart]: + parts = data.to_delayed() + if isinstance(parts, np.ndarray): + if is_matrix: + assert parts.shape[1] == 1 + else: + assert parts.ndim == 1 or parts.shape[1] == 1 + parts = parts.flatten().tolist() + return parts + + +def _machines_to_worker_map( + *, + machines: str, + worker_addresses: Iterable[str], +) -> Dict[str, int]: + """Create a worker_map from machines list. + + Given ``machines`` and a list of Dask worker addresses, return a mapping where the keys are + ``worker_addresses`` and the values are ports from ``machines``. + + Parameters + ---------- + machines : str + A comma-delimited list of workers, of the form ``ip1:port,ip2:port``. + worker_addresses : list of str + An iterable of Dask worker addresses, of the form ``{protocol}{hostname}:{port}``, where ``port`` is the port Dask's scheduler uses to talk to that worker. + + Returns + ------- + result : Dict[str, int] + Dictionary where keys are work addresses in the form expected by Dask and values are a port for LightGBM to use. + """ + machine_addresses = machines.split(",") + + if len(set(machine_addresses)) != len(machine_addresses): + raise ValueError( + f"Found duplicates in 'machines' ({machines}). Each entry in 'machines' must be a unique IP-port combination." + ) + + machine_to_port = defaultdict(set) + for address in machine_addresses: + host, port = address.split(":") + machine_to_port[host].add(int(port)) + + out = {} + for address in worker_addresses: + worker_host = urlparse(address).hostname + if not worker_host: + raise ValueError(f"Could not parse host name from worker address '{address}'") + out[address] = machine_to_port[worker_host].pop() + + return out + + +def _train( + *, + client: "distributed.Client", + data: _DaskMatrixLike, + label: _DaskCollection, + params: Dict[str, Any], + model_factory: Type[LGBMModel], + sample_weight: Optional[_DaskVectorLike] = None, + init_score: Optional[_DaskCollection] = None, + group: Optional[_DaskVectorLike] = None, + eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, + eval_names: Optional[List[str]] = None, + eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, + eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, + eval_sample_weight: Optional[List[_DaskVectorLike]] = None, + eval_class_weight: Optional[List[Union[dict, str]]] = None, + eval_init_score: Optional[List[_DaskCollection]] = None, + eval_group: Optional[List[_DaskVectorLike]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None, + **kwargs: Any, +) -> LGBMModel: + """Inner train routine. + + Parameters + ---------- + client : distributed.Client + Dask client. + data : Dask Array or Dask DataFrame of shape = [n_samples, n_features] + Input feature matrix. + label : Dask Array, Dask DataFrame or Dask Series of shape = [n_samples] + The target values (class labels in classification, real numbers in regression). + params : dict + Parameters passed to constructor of the local underlying model. + model_factory : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class + Class of the local underlying model. + sample_weight : Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None) + Weights of training data. Weights should be non-negative. + init_score : Dask Array or Dask Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task), or Dask Array or Dask DataFrame of shape = [n_samples, n_classes] (for multi-class task), or None, optional (default=None) + Init score of training data. + group : Dask Array or Dask Series or None, optional (default=None) + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + eval_set : list of (X, y) tuples of Dask data collections, or None, optional (default=None) + List of (X, y) tuple pairs to use as validation sets. + Note, that not all workers may receive chunks of every eval set within ``eval_set``. When the returned + lightgbm estimator is not trained using any chunks of a particular eval set, its corresponding component + of ``evals_result_`` and ``best_score_`` will be empty dictionaries. + eval_names : list of str, or None, optional (default=None) + Unique identifiers for each evaluation dataset. + Should be the same length as ``eval_set`` / ``eval_X``. + eval_X : Dask Array or Dask DataFrame, tuple thereof or None, optional (default=None) + Feature matrix or tuple thereof, e.g. ``(X_val0, X_val1)``, to use as validation sets. + eval_y : Dask Array or Dask DataFrame or Dask Series, tuple thereof or None, optional (default=None) + Target values or tuple thereof, e.g. ``(y_val0, y_val1)``, to use as validation sets. + eval_sample_weight : list of Dask Array or Dask Series, or None, optional (default=None) + Weights for each validation set in eval_set. Weights should be non-negative. + eval_class_weight : list of dict or str, or None, optional (default=None) + Class weights, one dict or str for each validation set in eval_set. + eval_init_score : list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None) + Initial model score for each validation set in eval_set. + eval_group : list of Dask Array or Dask Series, or None, optional (default=None) + Group/query for each validation set in eval_set. + eval_metric : str, callable, list or None, optional (default=None) + If str, it should be a built-in evaluation metric to use. + If callable, it should be a custom evaluation metric, see note below for more details. + If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both. + In either case, the ``metric`` from the Dask model parameters (or inferred from the objective) will be evaluated and used as well. + Default: 'l2' for DaskLGBMRegressor, 'binary(multi)_logloss' for DaskLGBMClassifier, 'ndcg' for DaskLGBMRanker. + eval_at : list or tuple of int, optional (default=None) + The evaluation positions of the specified ranking metric. + **kwargs + Other parameters passed to ``fit`` method of the local underlying model. + + Returns + ------- + model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class + Returns fitted underlying model. + + Note + ---- + + This method handles setting up the following network parameters based on information + about the Dask cluster referenced by ``client``. + + * ``local_listen_port``: port that each LightGBM worker opens a listening socket on, + to accept connections from other workers. This can differ from LightGBM worker + to LightGBM worker, but does not have to. + * ``machines``: a comma-delimited list of all workers in the cluster, in the + form ``ip:port,ip:port``. If running multiple Dask workers on the same host, use different + ports for each worker. For example, for ``LocalCluster(n_workers=3)``, you might + pass ``"127.0.0.1:12400,127.0.0.1:12401,127.0.0.1:12402"``. + * ``num_machines``: number of LightGBM workers. + * ``timeout``: time in minutes to wait before closing unused sockets. + + The default behavior of this function is to generate ``machines`` from the list of + Dask workers which hold some piece of the training data, and to search for an open + port on each worker to be used as ``local_listen_port``. + + If ``machines`` is provided explicitly in ``params``, this function uses the hosts + and ports in that list directly, and does not do any searching. This means that if + any of the Dask workers are missing from the list or any of those ports are not free + when training starts, training will fail. + + If ``local_listen_port`` is provided in ``params`` and ``machines`` is not, this function + constructs ``machines`` from the list of Dask workers which hold some piece of the + training data, assuming that each one will use the same ``local_listen_port``. + """ + try: + from dask import delayed # noqa: PLC0415 + from dask.distributed import wait # noqa: PLC0415 + except _DaskImportErrorTypes as err: + raise LightGBMError("dask is required for lightgbm.dask") from err + + params = deepcopy(params) + + # capture whether local_listen_port or its aliases were provided + listen_port_in_params = any(alias in params for alias in _ConfigAliases.get("local_listen_port")) + + # capture whether machines or its aliases were provided + machines_in_params = any(alias in params for alias in _ConfigAliases.get("machines")) + + params = _choose_param_value( + main_param_name="tree_learner", + params=params, + default_value="data", + ) + allowed_tree_learners = { + "data", + "data_parallel", + "feature", + "feature_parallel", + "voting", + "voting_parallel", + } + if params["tree_learner"] not in allowed_tree_learners: + _log_warning( + f'Parameter tree_learner set to {params["tree_learner"]}, which is not allowed. Using "data" as default' + ) + params["tree_learner"] = "data" + + # Some passed-in parameters can be removed: + # * 'num_machines': set automatically from Dask worker list + # * 'num_threads': overridden to match nthreads on each Dask process + for param_alias in _ConfigAliases.get("num_machines", "num_threads"): + if param_alias in params: + _log_warning(f"Parameter {param_alias} will be ignored.") + params.pop(param_alias) + + # Split arrays/dataframes into parts. Arrange parts into dicts to enforce co-locality + data_parts = _split_to_parts(data=data, is_matrix=True) + label_parts = _split_to_parts(data=label, is_matrix=False) + parts = [{"data": x, "label": y} for (x, y) in zip(data_parts, label_parts, strict=True)] + n_parts = len(parts) + + if sample_weight is not None: + weight_parts = _split_to_parts(data=sample_weight, is_matrix=False) + for i in range(n_parts): + parts[i]["weight"] = weight_parts[i] + + if group is not None: + group_parts = _split_to_parts(data=group, is_matrix=False) + for i in range(n_parts): + parts[i]["group"] = group_parts[i] + + if init_score is not None: + init_score_parts = _split_to_parts(data=init_score, is_matrix=False) + for i in range(n_parts): + parts[i]["init_score"] = init_score_parts[i] + + eval_set = _validate_eval_set_Xy(eval_set=eval_set, eval_X=eval_X, eval_y=eval_y) + # evals_set will to be re-constructed into smaller lists of (X, y) tuples, where + # X and y are each delayed sub-lists of original eval dask Collections. + if eval_set: + # find maximum number of parts in an individual eval set so that we can + # pad eval sets when they come in different sizes. + n_largest_eval_parts = max(x[0].npartitions for x in eval_set) + + eval_sets: Dict[ + int, List[Union[_DatasetNames, Tuple[List[Optional[_DaskMatrixLike]], List[Optional[_DaskVectorLike]]]]] + ] = defaultdict(list) + if eval_sample_weight: + eval_sample_weights: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict( + list + ) + if eval_group: + eval_groups: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict(list) + if eval_init_score: + eval_init_scores: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskMatrixLike]]]]] = defaultdict(list) + + for i, (X_eval, y_eval) in enumerate(eval_set): + n_this_eval_parts = X_eval.npartitions + + # when individual eval set is equivalent to training data, skip recomputing parts. + if X_eval is data and y_eval is label: + for parts_idx in range(n_parts): + eval_sets[parts_idx].append(_DatasetNames.TRAINSET) + else: + eval_x_parts = _split_to_parts(data=X_eval, is_matrix=True) + eval_y_parts = _split_to_parts(data=y_eval, is_matrix=False) + for j in range(n_largest_eval_parts): + parts_idx = j % n_parts + + # add None-padding for individual eval_set member if it is smaller than the largest member. + if j < n_this_eval_parts: + x_e = eval_x_parts[j] + y_e = eval_y_parts[j] + else: + x_e = None + y_e = None + + if j < n_parts: + # first time a chunk of this eval set is added to this part. + eval_sets[parts_idx].append(([x_e], [y_e])) + else: + # append additional chunks of this eval set to this part. + eval_sets[parts_idx][-1][0].append(x_e) # type: ignore[index, union-attr] + eval_sets[parts_idx][-1][1].append(y_e) # type: ignore[index, union-attr] + + if eval_sample_weight: + if eval_sample_weight[i] is sample_weight: + for parts_idx in range(n_parts): + eval_sample_weights[parts_idx].append(_DatasetNames.SAMPLE_WEIGHT) + else: + eval_w_parts = _split_to_parts(data=eval_sample_weight[i], is_matrix=False) + + # ensure that all evaluation parts map uniquely to one part. + for j in range(n_largest_eval_parts): + if j < n_this_eval_parts: + w_e = eval_w_parts[j] + else: + w_e = None + + parts_idx = j % n_parts + if j < n_parts: + eval_sample_weights[parts_idx].append([w_e]) + else: + eval_sample_weights[parts_idx][-1].append(w_e) # type: ignore[union-attr] + + if eval_init_score: + if eval_init_score[i] is init_score: + for parts_idx in range(n_parts): + eval_init_scores[parts_idx].append(_DatasetNames.INIT_SCORE) + else: + eval_init_score_parts = _split_to_parts(data=eval_init_score[i], is_matrix=False) + for j in range(n_largest_eval_parts): + if j < n_this_eval_parts: + init_score_e = eval_init_score_parts[j] + else: + init_score_e = None + + parts_idx = j % n_parts + if j < n_parts: + eval_init_scores[parts_idx].append([init_score_e]) + else: + eval_init_scores[parts_idx][-1].append(init_score_e) # type: ignore[union-attr] + + if eval_group: + if eval_group[i] is group: + for parts_idx in range(n_parts): + eval_groups[parts_idx].append(_DatasetNames.GROUP) + else: + eval_g_parts = _split_to_parts(data=eval_group[i], is_matrix=False) + for j in range(n_largest_eval_parts): + if j < n_this_eval_parts: + g_e = eval_g_parts[j] + else: + g_e = None + + parts_idx = j % n_parts + if j < n_parts: + eval_groups[parts_idx].append([g_e]) + else: + eval_groups[parts_idx][-1].append(g_e) # type: ignore[union-attr] + + # assign sub-eval_set components to worker parts. + for parts_idx, e_set in eval_sets.items(): + parts[parts_idx]["eval_set"] = e_set + if eval_sample_weight: + parts[parts_idx]["eval_sample_weight"] = eval_sample_weights[parts_idx] + if eval_init_score: + parts[parts_idx]["eval_init_score"] = eval_init_scores[parts_idx] + if eval_group: + parts[parts_idx]["eval_group"] = eval_groups[parts_idx] + + # Start computation in the background + parts = list(map(delayed, parts)) + parts = client.compute(parts) + wait(parts) + + for part in parts: + if part.status == "error": # type: ignore + # trigger error locally + return part # type: ignore[return-value] + + # Find locations of all parts and map them to particular Dask workers + key_to_part_dict = {part.key: part for part in parts} # type: ignore + who_has = client.who_has(parts) + worker_map = defaultdict(list) + for key, workers in who_has.items(): + worker_map[next(iter(workers))].append(key_to_part_dict[key]) + + # Check that all workers were provided some of eval_set. Otherwise warn user that validation + # data artifacts may not be populated depending on worker returning final estimator. + if eval_set: + for worker in worker_map: + has_eval_set = False + for part in worker_map[worker]: + if "eval_set" in part.result(): # type: ignore[attr-defined] + has_eval_set = True + break + + if not has_eval_set: + _log_warning( + f"Worker {worker} was not allocated eval_set data. Therefore evals_result_ and best_score_ data may be unreliable. " + "Try rebalancing data across workers." + ) + + # assign general validation set settings to fit kwargs. + if eval_names: + kwargs["eval_names"] = eval_names + if eval_class_weight: + kwargs["eval_class_weight"] = eval_class_weight + if eval_metric: + kwargs["eval_metric"] = eval_metric + if eval_at: + kwargs["eval_at"] = eval_at + + master_worker = next(iter(worker_map)) + worker_ncores = client.ncores() + + # resolve aliases for network parameters and pop the result off params. + # these values are added back in calls to `_train_part()` + params = _choose_param_value( + main_param_name="local_listen_port", + params=params, + default_value=12400, + ) + local_listen_port = params.pop("local_listen_port") + + params = _choose_param_value( + main_param_name="machines", + params=params, + default_value=None, + ) + machines = params.pop("machines") + + # figure out network params + worker_to_socket_future: Dict[str, "distributed.client.Future"] = {} + worker_addresses = worker_map.keys() + if machines is not None: + _log_info("Using passed-in 'machines' parameter") + worker_address_to_port = _machines_to_worker_map( + machines=machines, + worker_addresses=worker_addresses, + ) + else: + if listen_port_in_params: + _log_info("Using passed-in 'local_listen_port' for all workers") + unique_hosts = {urlparse(a).hostname for a in worker_addresses} + if len(unique_hosts) < len(worker_addresses): + msg = ( + "'local_listen_port' was provided in Dask training parameters, but at least one " + "machine in the cluster has multiple Dask worker processes running on it. Please omit " + "'local_listen_port' or pass 'machines'." + ) + raise LightGBMError(msg) + + worker_address_to_port = dict.fromkeys(worker_addresses, local_listen_port) + else: + _log_info("Finding random open ports for workers") + worker_to_socket_future, worker_address_to_port = _assign_open_ports_to_workers( + client=client, + workers=list(worker_map.keys()), + ) + + machines = ",".join( + [f"{urlparse(worker_address).hostname}:{port}" for worker_address, port in worker_address_to_port.items()] + ) + + num_machines = len(worker_address_to_port) + + # Tell each worker to train on the parts that it has locally + # + # This code treats ``_train_part()`` calls as not "pure" because: + # 1. there is randomness in the training process unless parameters ``seed`` + # and ``deterministic`` are set + # 2. even with those parameters set, the output of one ``_train_part()`` call + # relies on global state (it and all the other LightGBM training processes + # coordinate with each other) + futures_classifiers = [ + client.submit( + _train_part, + model_factory=model_factory, + params={**params, "num_threads": worker_ncores[worker]}, + list_of_parts=list_of_parts, + machines=machines, + local_listen_port=worker_address_to_port[worker], + num_machines=num_machines, + time_out=params.get("time_out", 120), + remote_socket=worker_to_socket_future.get(worker, None), + return_model=(worker == master_worker), + workers=[worker], + allow_other_workers=False, + pure=False, + **kwargs, + ) + for worker, list_of_parts in worker_map.items() + ] + + results = client.gather(futures_classifiers) + results = [v for v in results if v] + model = results[0] + + # if network parameters were changed during training, remove them from the + # returned model so that they're generated dynamically on every run based + # on the Dask cluster you're connected to and which workers have pieces of + # the training data + if not listen_port_in_params: + for param in _ConfigAliases.get("local_listen_port"): + model._other_params.pop(param, None) + + if not machines_in_params: + for param in _ConfigAliases.get("machines"): + model._other_params.pop(param, None) + + for param in _ConfigAliases.get("num_machines", "timeout"): + model._other_params.pop(param, None) + + return model + + +def _predict_part( + part: _DaskPart, + *, + model: LGBMModel, + raw_score: bool, + pred_proba: bool, + pred_leaf: bool, + pred_contrib: bool, + **kwargs: Any, +) -> _DaskPart: + result: _DaskPart + if part.shape[0] == 0: + result = np.array([]) + elif pred_proba: + result = model.predict_proba( + part, + raw_score=raw_score, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + **kwargs, + ) + else: + result = model.predict( + part, + raw_score=raw_score, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + **kwargs, + ) + + # dask.DataFrame.map_partitions() expects each call to return a pandas DataFrame or Series + if isinstance(part, pd_DataFrame): + # assert that 'result' is an array, only necessary because predict(..., pred_contrib=True) on + # sparse matrices returns a list. + # + # This can be removed when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved. + error_msg = ( + f"predict(X) for lightgbm.dask estimators should always return an array, not '{type(result)}', when X is a pandas Dataframe. " + "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." + ) + assert hasattr(result, "shape"), error_msg + if len(result.shape) == 2: + result = pd_DataFrame(result, index=part.index) + else: + result = pd_Series(result, index=part.index, name="predictions") + + return result + + +def _predict( + *, + model: LGBMModel, + data: _DaskMatrixLike, + client: "distributed.Client", + raw_score: bool = False, + pred_proba: bool = False, + pred_leaf: bool = False, + pred_contrib: bool = False, + **kwargs: Any, +) -> Union["dask.array.Array", List["dask.array.Array"]]: + """Inner predict routine. + + Parameters + ---------- + model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class + Fitted underlying model. + data : Dask Array or Dask DataFrame of shape = [n_samples, n_features] + Input feature matrix. + raw_score : bool, optional (default=False) + Whether to predict raw scores. + pred_proba : bool, optional (default=False) + Should method return results of ``predict_proba`` (``pred_proba=True``) or ``predict`` (``pred_proba=False``). + pred_leaf : bool, optional (default=False) + Whether to predict leaf index. + pred_contrib : bool, optional (default=False) + Whether to predict feature contributions. + **kwargs + Other parameters passed to ``predict`` or ``predict_proba`` method. + + Returns + ------- + predicted_result : Dask Array of shape = [n_samples] or shape = [n_samples, n_classes] + The predicted values. + X_leaves : Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes] + If ``pred_leaf=True``, the predicted leaf of every tree for each sample. + X_SHAP_values : Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1] + If ``pred_contrib=True``, the feature contributions for each sample. + """ + if not all((PANDAS_INSTALLED, SKLEARN_INSTALLED)): + raise LightGBMError("pandas and scikit-learn are required for lightgbm.dask") + + try: + import dask.array # noqa: PLC0415 + import dask.bag # noqa: PLC0415 + import dask.dataframe # noqa: PLC0415 + from dask import delayed # noqa: PLC0415 + except _DaskImportErrorTypes as err: + raise LightGBMError("dask is required for lightgbm.dask") from err + + if isinstance(data, dask.dataframe.DataFrame): + return data.map_partitions( + _predict_part, + model=model, + raw_score=raw_score, + pred_proba=pred_proba, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + **kwargs, + ).values + elif isinstance(data, dask.array.Array): + # for multi-class classification with sparse matrices, pred_contrib predictions + # are returned as a list of sparse matrices (one per class) + num_classes = model._n_classes + + if num_classes > 2 and pred_contrib and isinstance(data._meta, ss.spmatrix): + predict_function = partial( + _predict_part, + model=model, + raw_score=False, + pred_proba=pred_proba, + pred_leaf=False, + pred_contrib=True, + **kwargs, + ) + + delayed_chunks = data.to_delayed() + bag = dask.bag.from_delayed(delayed_chunks[:, 0]) + + @delayed + def _extract(items: List[Any], i: int) -> Any: + return items[i] + + preds = bag.map_partitions(predict_function) + + # pred_contrib output will have one column per feature, + # plus one more for the base value + num_cols = model.n_features_ + 1 + + nrows_per_chunk = data.chunks[0] + out: List[List[dask.array.Array]] = [[] for _ in range(num_classes)] + + # need to tell Dask the expected type and shape of individual preds + pred_meta = data._meta + + for j, partition in enumerate(preds.to_delayed()): + for i in range(num_classes): + part = dask.array.from_delayed( + value=_extract(partition, i), + shape=(nrows_per_chunk[j], num_cols), + meta=pred_meta, + ) + out[i].append(part) + + # by default, dask.array.concatenate() concatenates sparse arrays into a COO matrix + # the code below is used instead to ensure that the sparse type is preserved during concatenation + if isinstance(pred_meta, ss.csr_matrix): + concat_fn = partial(ss.vstack, format="csr") + elif isinstance(pred_meta, ss.csc_matrix): + concat_fn = partial(ss.vstack, format="csc") + else: + concat_fn = ss.vstack + + # At this point, `out` is a list of lists of delayeds (each of which points to a matrix). + # Concatenate them to return a list of Dask Arrays. + out_arrays: List[dask.array.Array] = [] + for i in range(num_classes): + out_arrays.append( + dask.array.from_delayed( + value=delayed(concat_fn)(out[i]), + shape=(data.shape[0], num_cols), + meta=pred_meta, + ) + ) + + return out_arrays + + data_row = client.compute(data[[0]]).result() + predict_fn = partial( + _predict_part, + model=model, + raw_score=raw_score, + pred_proba=pred_proba, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + **kwargs, + ) + pred_row = predict_fn(data_row) # type: ignore[misc] + chunks: Tuple[int, ...] = (data.chunks[0],) + map_blocks_kwargs = {} + if len(pred_row.shape) > 1: + chunks += (pred_row.shape[1],) + else: + map_blocks_kwargs["drop_axis"] = 1 + return data.map_blocks( + predict_fn, + chunks=chunks, + meta=pred_row, + **map_blocks_kwargs, + ) + else: + raise TypeError(f"Data must be either Dask Array or Dask DataFrame. Got {type(data).__name__}.") + + +class _DaskLGBMModel: + @property + def client_(self) -> "distributed.Client": + """:obj:`distributed.Client`: Dask client. + + This property can be passed in the constructor or updated + with ``model.set_params(client=client)``. + """ + if not getattr(self, "fitted_", False): + raise LGBMNotFittedError("Cannot access property client_ before calling fit().") + + return _get_dask_client(client=self.client) + + def _lgb_dask_getstate(self) -> Dict[Any, Any]: + """Remove un-picklable attributes before serialization.""" + client = self.__dict__.pop("client", None) + self._other_params.pop("client", None) # type: ignore[attr-defined] + out = deepcopy(self.__dict__) + out.update({"client": None}) + self.client = client + return out + + def _lgb_dask_fit( + self, + *, + model_factory: Type[LGBMModel], + X: _DaskMatrixLike, + y: _DaskCollection, + sample_weight: Optional[_DaskVectorLike] = None, + init_score: Optional[_DaskCollection] = None, + group: Optional[_DaskVectorLike] = None, + eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, + eval_names: Optional[List[str]] = None, + eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, + eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, + eval_sample_weight: Optional[List[_DaskVectorLike]] = None, + eval_class_weight: Optional[List[Union[dict, str]]] = None, + eval_init_score: Optional[List[_DaskCollection]] = None, + eval_group: Optional[List[_DaskVectorLike]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None, + **kwargs: Any, + ) -> "_DaskLGBMModel": + if not all((PANDAS_INSTALLED, SKLEARN_INSTALLED)): + raise LightGBMError("pandas and scikit-learn are required for lightgbm.dask") + + params = self.get_params(True) # type: ignore[attr-defined] + params.pop("client", None) + + model = _train( + client=_get_dask_client(self.client), + data=X, + label=y, + params=params, + model_factory=model_factory, + sample_weight=sample_weight, + init_score=init_score, + group=group, + eval_set=eval_set, + eval_names=eval_names, + eval_X=eval_X, + eval_y=eval_y, + eval_sample_weight=eval_sample_weight, + eval_class_weight=eval_class_weight, + eval_init_score=eval_init_score, + eval_group=eval_group, + eval_metric=eval_metric, + eval_at=eval_at, + **kwargs, + ) + + self.set_params(**model.get_params()) # type: ignore[attr-defined] + self._lgb_dask_copy_extra_params(source=model, dest=self) # type: ignore[attr-defined] + + return self + + def _lgb_dask_to_local(self, model_factory: Type[LGBMModel]) -> LGBMModel: + params = self.get_params() # type: ignore[attr-defined] + params.pop("client", None) + model = model_factory(**params) + self._lgb_dask_copy_extra_params(source=self, dest=model) + model._other_params.pop("client", None) + return model + + @staticmethod + def _lgb_dask_copy_extra_params( + *, + source: Union["_DaskLGBMModel", LGBMModel], + dest: Union["_DaskLGBMModel", LGBMModel], + ) -> None: + params = source.get_params() # type: ignore[union-attr] + attributes = source.__dict__ + extra_param_names = set(attributes.keys()).difference(params.keys()) + for name in extra_param_names: + setattr(dest, name, attributes[name]) + + +class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel): + """Distributed version of lightgbm.LGBMClassifier.""" + + def __init__( + self, + *, + boosting_type: str = "gbdt", + num_leaves: int = 31, + max_depth: int = -1, + learning_rate: float = 0.1, + n_estimators: int = 100, + subsample_for_bin: int = 200000, + objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, + class_weight: Optional[Union[dict, str]] = None, + min_split_gain: float = 0.0, + min_child_weight: float = 1e-3, + min_child_samples: int = 20, + subsample: float = 1.0, + subsample_freq: int = 0, + colsample_bytree: float = 1.0, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None, + n_jobs: Optional[int] = None, + importance_type: str = "split", + client: Optional["distributed.Client"] = None, + **kwargs: Any, + ): + """Docstring is inherited from the lightgbm.LGBMClassifier.__init__.""" + self.client = client + super().__init__( + boosting_type=boosting_type, + num_leaves=num_leaves, + max_depth=max_depth, + learning_rate=learning_rate, + n_estimators=n_estimators, + subsample_for_bin=subsample_for_bin, + objective=objective, + class_weight=class_weight, + min_split_gain=min_split_gain, + min_child_weight=min_child_weight, + min_child_samples=min_child_samples, + subsample=subsample, + subsample_freq=subsample_freq, + colsample_bytree=colsample_bytree, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + random_state=random_state, + n_jobs=n_jobs, + importance_type=importance_type, + **kwargs, + ) + + _base_doc = LGBMClassifier.__init__.__doc__ + _before_kwargs, _, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore + __init__.__doc__ = ( + _before_kwargs + + "client : distributed.Client or None, optional (default=None)\n" + + " Dask client. \n" + + " If ``None``, ``distributed.default_client()`` will be used at runtime.\n" + + " The Dask client used by this class will not be saved if the model object is pickled.\n" + + "**kwargs\n" + + _after_kwargs + ) + + def __getstate__(self) -> Dict[Any, Any]: + return self._lgb_dask_getstate() + + def fit( # type: ignore[override] + self, + X: _DaskMatrixLike, + y: _DaskCollection, + sample_weight: Optional[_DaskVectorLike] = None, + init_score: Optional[_DaskCollection] = None, + eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, + eval_names: Optional[List[str]] = None, + eval_sample_weight: Optional[List[_DaskVectorLike]] = None, + eval_class_weight: Optional[List[Union[dict, str]]] = None, + eval_init_score: Optional[List[_DaskCollection]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + *, + eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, + eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, + **kwargs: Any, + ) -> "DaskLGBMClassifier": + """Docstring is inherited from the lightgbm.LGBMClassifier.fit.""" + self._lgb_dask_fit( + model_factory=LGBMClassifier, + X=X, + y=y, + sample_weight=sample_weight, + init_score=init_score, + eval_set=eval_set, + eval_names=eval_names, + eval_X=eval_X, + eval_y=eval_y, + eval_sample_weight=eval_sample_weight, + eval_class_weight=eval_class_weight, + eval_init_score=eval_init_score, + eval_metric=eval_metric, + **kwargs, + ) + return self + + _base_doc = _lgbmmodel_doc_fit.format( + X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", + y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", + sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", + init_score_shape="Dask Array or Dask Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task), or Dask Array or Dask DataFrame of shape = [n_samples, n_classes] (for multi-class task), or None, optional (default=None)", + group_shape="Dask Array or Dask Series or None, optional (default=None)", + eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + eval_init_score_shape="list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None)", + eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + ) + + # DaskLGBMClassifier does not support group, eval_group. + _base_doc = _base_doc[: _base_doc.find("group :")] + _base_doc[_base_doc.find("eval_set :") :] + + _base_doc = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] + + # DaskLGBMClassifier support for callbacks and init_model is not tested + fit.__doc__ = f"""{_base_doc[: _base_doc.find("callbacks :")]}**kwargs + Other parameters passed through to ``LGBMClassifier.fit()``. + + Returns + ------- + self : lightgbm.DaskLGBMClassifier + Returns self. + + {_lgbmmodel_doc_custom_eval_note} + """ + + def predict( + self, + X: _DaskMatrixLike, # type: ignore[override] + raw_score: bool = False, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + pred_leaf: bool = False, + pred_contrib: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> "dask.array.Array": + """Docstring is inherited from the lightgbm.LGBMClassifier.predict.""" + return _predict( + model=self.to_local(), + data=X, + client=_get_dask_client(self.client), + raw_score=raw_score, + start_iteration=start_iteration, + num_iteration=num_iteration, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + validate_features=validate_features, + **kwargs, + ) + + predict.__doc__ = _lgbmmodel_doc_predict.format( + description="Return the predicted value for each sample.", + X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", + output_name="predicted_result", + predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]", + X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", + X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]", + ) + + def predict_proba( + self, + X: _DaskMatrixLike, # type: ignore[override] + raw_score: bool = False, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + pred_leaf: bool = False, + pred_contrib: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> "dask.array.Array": + """Docstring is inherited from the lightgbm.LGBMClassifier.predict_proba.""" + return _predict( + model=self.to_local(), + data=X, + pred_proba=True, + client=_get_dask_client(self.client), + raw_score=raw_score, + start_iteration=start_iteration, + num_iteration=num_iteration, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + validate_features=validate_features, + **kwargs, + ) + + predict_proba.__doc__ = _lgbmmodel_doc_predict.format( + description="Return the predicted probability for each class for each sample.", + X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", + output_name="predicted_probability", + predicted_result_shape="Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]", + X_leaves_shape="Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", + X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]", + ) + + def to_local(self) -> LGBMClassifier: + """Create regular version of lightgbm.LGBMClassifier from the distributed version. + + Returns + ------- + model : lightgbm.LGBMClassifier + Local underlying model. + """ + return self._lgb_dask_to_local(LGBMClassifier) + + +class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel): + """Distributed version of lightgbm.LGBMRegressor.""" + + def __init__( + self, + *, + boosting_type: str = "gbdt", + num_leaves: int = 31, + max_depth: int = -1, + learning_rate: float = 0.1, + n_estimators: int = 100, + subsample_for_bin: int = 200000, + objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, + class_weight: Optional[Union[dict, str]] = None, + min_split_gain: float = 0.0, + min_child_weight: float = 1e-3, + min_child_samples: int = 20, + subsample: float = 1.0, + subsample_freq: int = 0, + colsample_bytree: float = 1.0, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None, + n_jobs: Optional[int] = None, + importance_type: str = "split", + client: Optional["distributed.Client"] = None, + **kwargs: Any, + ): + """Docstring is inherited from the lightgbm.LGBMRegressor.__init__.""" + self.client = client + super().__init__( + boosting_type=boosting_type, + num_leaves=num_leaves, + max_depth=max_depth, + learning_rate=learning_rate, + n_estimators=n_estimators, + subsample_for_bin=subsample_for_bin, + objective=objective, + class_weight=class_weight, + min_split_gain=min_split_gain, + min_child_weight=min_child_weight, + min_child_samples=min_child_samples, + subsample=subsample, + subsample_freq=subsample_freq, + colsample_bytree=colsample_bytree, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + random_state=random_state, + n_jobs=n_jobs, + importance_type=importance_type, + **kwargs, + ) + + _base_doc = LGBMRegressor.__init__.__doc__ + _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore + __init__.__doc__ = ( + _before_kwargs + + "client : distributed.Client or None, optional (default=None)\n" + + " Dask client. \n" + + " If ``None``, ``distributed.default_client()`` will be used at runtime.\n" + + " The Dask client used by this class will not be saved if the model object is pickled.\n" + + "**kwargs\n" + + _after_kwargs + ) + + def __getstate__(self) -> Dict[Any, Any]: + return self._lgb_dask_getstate() + + def fit( # type: ignore[override] + self, + X: _DaskMatrixLike, + y: _DaskCollection, + sample_weight: Optional[_DaskVectorLike] = None, + init_score: Optional[_DaskVectorLike] = None, + eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, + eval_names: Optional[List[str]] = None, + eval_sample_weight: Optional[List[_DaskVectorLike]] = None, + eval_init_score: Optional[List[_DaskVectorLike]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + *, + eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, + eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, + **kwargs: Any, + ) -> "DaskLGBMRegressor": + """Docstring is inherited from the lightgbm.LGBMRegressor.fit.""" + self._lgb_dask_fit( + model_factory=LGBMRegressor, + X=X, + y=y, + sample_weight=sample_weight, + init_score=init_score, + eval_set=eval_set, + eval_names=eval_names, + eval_X=eval_X, + eval_y=eval_y, + eval_sample_weight=eval_sample_weight, + eval_init_score=eval_init_score, + eval_metric=eval_metric, + **kwargs, + ) + return self + + _base_doc = _lgbmmodel_doc_fit.format( + X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", + y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", + sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", + init_score_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", + group_shape="Dask Array or Dask Series or None, optional (default=None)", + eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + eval_init_score_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + ) + + # DaskLGBMRegressor does not support group, eval_class_weight, eval_group. + _base_doc = _base_doc[: _base_doc.find("group :")] + _base_doc[_base_doc.find("eval_set :") :] + + _base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :] + + _base_doc = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] + + # DaskLGBMRegressor support for callbacks and init_model is not tested + fit.__doc__ = f"""{_base_doc[: _base_doc.find("callbacks :")]}**kwargs + Other parameters passed through to ``LGBMRegressor.fit()``. + + Returns + ------- + self : lightgbm.DaskLGBMRegressor + Returns self. + + {_lgbmmodel_doc_custom_eval_note} + """ + + def predict( + self, + X: _DaskMatrixLike, # type: ignore[override] + raw_score: bool = False, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + pred_leaf: bool = False, + pred_contrib: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> "dask.array.Array": + """Docstring is inherited from the lightgbm.LGBMRegressor.predict.""" + return _predict( + model=self.to_local(), + data=X, + client=_get_dask_client(self.client), + raw_score=raw_score, + start_iteration=start_iteration, + num_iteration=num_iteration, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + validate_features=validate_features, + **kwargs, + ) + + predict.__doc__ = _lgbmmodel_doc_predict.format( + description="Return the predicted value for each sample.", + X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", + output_name="predicted_result", + predicted_result_shape="Dask Array of shape = [n_samples]", + X_leaves_shape="Dask Array of shape = [n_samples, n_trees]", + X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1]", + ) + + def to_local(self) -> LGBMRegressor: + """Create regular version of lightgbm.LGBMRegressor from the distributed version. + + Returns + ------- + model : lightgbm.LGBMRegressor + Local underlying model. + """ + return self._lgb_dask_to_local(LGBMRegressor) + + +class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel): + """Distributed version of lightgbm.LGBMRanker.""" + + def __init__( + self, + *, + boosting_type: str = "gbdt", + num_leaves: int = 31, + max_depth: int = -1, + learning_rate: float = 0.1, + n_estimators: int = 100, + subsample_for_bin: int = 200000, + objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, + class_weight: Optional[Union[dict, str]] = None, + min_split_gain: float = 0.0, + min_child_weight: float = 1e-3, + min_child_samples: int = 20, + subsample: float = 1.0, + subsample_freq: int = 0, + colsample_bytree: float = 1.0, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + random_state: Optional[Union[int, np.random.RandomState, "np.random.Generator"]] = None, + n_jobs: Optional[int] = None, + importance_type: str = "split", + client: Optional["distributed.Client"] = None, + **kwargs: Any, + ): + """Docstring is inherited from the lightgbm.LGBMRanker.__init__.""" + self.client = client + super().__init__( + boosting_type=boosting_type, + num_leaves=num_leaves, + max_depth=max_depth, + learning_rate=learning_rate, + n_estimators=n_estimators, + subsample_for_bin=subsample_for_bin, + objective=objective, + class_weight=class_weight, + min_split_gain=min_split_gain, + min_child_weight=min_child_weight, + min_child_samples=min_child_samples, + subsample=subsample, + subsample_freq=subsample_freq, + colsample_bytree=colsample_bytree, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + random_state=random_state, + n_jobs=n_jobs, + importance_type=importance_type, + **kwargs, + ) + + _base_doc = LGBMRanker.__init__.__doc__ + _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition("**kwargs") # type: ignore + __init__.__doc__ = ( + _before_kwargs + + "client : distributed.Client or None, optional (default=None)\n" + + " Dask client. \n" + + " If ``None``, ``distributed.default_client()`` will be used at runtime.\n" + + " The Dask client used by this class will not be saved if the model object is pickled.\n" + + "**kwargs\n" + + _after_kwargs + ) + + def __getstate__(self) -> Dict[Any, Any]: + return self._lgb_dask_getstate() + + def fit( # type: ignore[override] + self, + X: _DaskMatrixLike, + y: _DaskCollection, + sample_weight: Optional[_DaskVectorLike] = None, + init_score: Optional[_DaskVectorLike] = None, + group: Optional[_DaskVectorLike] = None, + eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None, + eval_names: Optional[List[str]] = None, + eval_sample_weight: Optional[List[_DaskVectorLike]] = None, + eval_init_score: Optional[List[_DaskVectorLike]] = None, + eval_group: Optional[List[_DaskVectorLike]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + eval_at: Union[List[int], Tuple[int, ...]] = (1, 2, 3, 4, 5), + *, + eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None, + eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None, + **kwargs: Any, + ) -> "DaskLGBMRanker": + """Docstring is inherited from the lightgbm.LGBMRanker.fit.""" + self._lgb_dask_fit( + model_factory=LGBMRanker, + X=X, + y=y, + sample_weight=sample_weight, + init_score=init_score, + group=group, + eval_set=eval_set, + eval_names=eval_names, + eval_X=eval_X, + eval_y=eval_y, + eval_sample_weight=eval_sample_weight, + eval_init_score=eval_init_score, + eval_group=eval_group, + eval_metric=eval_metric, + eval_at=eval_at, + **kwargs, + ) + return self + + _base_doc = _lgbmmodel_doc_fit.format( + X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", + y_shape="Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]", + sample_weight_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", + init_score_shape="Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)", + group_shape="Dask Array or Dask Series or None, optional (default=None)", + eval_sample_weight_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + eval_init_score_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + eval_group_shape="list of Dask Array or Dask Series, or None, optional (default=None)", + ) + + # DaskLGBMRanker does not support eval_class_weight or early stopping + _base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :] + + _base_doc = ( + _base_doc[: _base_doc.find("feature_name :")] + + "eval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5))\n" + + f"{' ':8}The evaluation positions of the specified metric.\n" + + f"{' ':4}{_base_doc[_base_doc.find('feature_name :') :]}" + ) + + # DaskLGBMRanker support for callbacks and init_model is not tested + fit.__doc__ = f"""{_base_doc[: _base_doc.find("callbacks :")]}**kwargs + Other parameters passed through to ``LGBMRanker.fit()``. + + Returns + ------- + self : lightgbm.DaskLGBMRanker + Returns self. + + {_lgbmmodel_doc_custom_eval_note} + """ + + def predict( + self, + X: _DaskMatrixLike, # type: ignore[override] + raw_score: bool = False, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + pred_leaf: bool = False, + pred_contrib: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> "dask.array.Array": + """Docstring is inherited from the lightgbm.LGBMRanker.predict.""" + return _predict( + model=self.to_local(), + data=X, + client=_get_dask_client(self.client), + raw_score=raw_score, + start_iteration=start_iteration, + num_iteration=num_iteration, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + validate_features=validate_features, + **kwargs, + ) + + predict.__doc__ = _lgbmmodel_doc_predict.format( + description="Return the predicted value for each sample.", + X_shape="Dask Array or Dask DataFrame of shape = [n_samples, n_features]", + output_name="predicted_result", + predicted_result_shape="Dask Array of shape = [n_samples]", + X_leaves_shape="Dask Array of shape = [n_samples, n_trees]", + X_SHAP_values_shape="Dask Array of shape = [n_samples, n_features + 1]", + ) + + def to_local(self) -> LGBMRanker: + """Create regular version of lightgbm.LGBMRanker from the distributed version. + + Returns + ------- + model : lightgbm.LGBMRanker + Local underlying model. + """ + return self._lgb_dask_to_local(LGBMRanker) diff --git a/python-package/lightgbm/engine.py b/python-package/lightgbm/engine.py new file mode 100644 index 0000000..329d66d --- /dev/null +++ b/python-package/lightgbm/engine.py @@ -0,0 +1,872 @@ +# coding: utf-8 +"""Library with training routines of LightGBM.""" + +import copy +import json +from collections import OrderedDict, defaultdict +from operator import attrgetter +from pathlib import Path +from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union + +import numpy as np + +from . import callback +from .basic import ( + Booster, + Dataset, + LightGBMError, + _choose_param_value, + _ConfigAliases, + _InnerPredictor, + _LGBM_BoosterEvalMethodResultType, + _LGBM_BoosterEvalMethodResultWithStandardDeviationType, + _LGBM_CustomObjectiveFunction, + _LGBM_EvalFunctionResultType, + _log_warning, +) +from .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold + +__all__ = [ + "cv", + "CVBooster", + "train", +] + + +_LGBM_CustomMetricFunction = Union[ + Callable[ + [np.ndarray, Dataset], + _LGBM_EvalFunctionResultType, + ], + Callable[ + [np.ndarray, Dataset], + List[_LGBM_EvalFunctionResultType], + ], +] + +_LGBM_PreprocFunction = Callable[ + [Dataset, Dataset, Dict[str, Any]], + Tuple[Dataset, Dataset, Dict[str, Any]], +] + + +def _choose_num_iterations(*, num_boost_round_kwarg: int, params: Dict[str, Any]) -> Dict[str, Any]: + """Choose number of boosting rounds. + + In ``train()`` and ``cv()``, there are multiple ways to provide configuration for + the number of boosting rounds to perform: + + * the ``num_boost_round`` keyword argument + * any of the ``num_iterations`` or its aliases via the ``params`` dictionary + + These should be preferred in the following order (first one found wins): + + 1. ``num_iterations`` provided via ``params`` (because it's the main parameter name) + 2. any other aliases of ``num_iterations`` provided via ``params`` + 3. the ``num_boost_round`` keyword argument + + This function handles that choice, and issuing helpful warnings in the cases where the + result might be surprising. + + Returns + ------- + params : dict + Parameters, with ``"num_iterations"`` set to the preferred value and all other + aliases of ``num_iterations`` removed. + """ + num_iteration_configs_provided = { + alias: params[alias] for alias in _ConfigAliases.get("num_iterations") if alias in params + } + + # now that the relevant information has been pulled out of params, it's safe to overwrite it + # with the content that should be used for training (i.e. with aliases resolved) + params = _choose_param_value( + main_param_name="num_iterations", + params=params, + default_value=num_boost_round_kwarg, + ) + + # if there were not multiple boosting rounds configurations provided in params, + # then by definition they cannot have conflicting values... no need to warn + if len(num_iteration_configs_provided) <= 1: + return params + + # if all the aliases have the same value, no need to warn + if len(set(num_iteration_configs_provided.values())) <= 1: + return params + + # if this line is reached, lightgbm should warn + value_string = ", ".join(f"{alias}={val}" for alias, val in num_iteration_configs_provided.items()) + _log_warning( + f"Found conflicting values for num_iterations provided via 'params': {value_string}. " + f"LightGBM will perform up to {params['num_iterations']} boosting rounds. " + "To be confident in the maximum number of boosting rounds LightGBM will perform and to " + "suppress this warning, modify 'params' so that only one of those is present." + ) + return params + + +def train( + params: Dict[str, Any], + train_set: Dataset, + num_boost_round: int = 100, + valid_sets: Optional[List[Dataset]] = None, + valid_names: Optional[List[str]] = None, + feval: Optional[Union[_LGBM_CustomMetricFunction, List[_LGBM_CustomMetricFunction]]] = None, + init_model: Optional[Union[str, Path, Booster]] = None, + keep_training_booster: bool = False, + callbacks: Optional[List[Callable]] = None, +) -> Booster: + """Perform the training with given parameters. + + Parameters + ---------- + params : dict + Parameters for training. Values passed through ``params`` take precedence over those + supplied via arguments. + train_set : Dataset + Data to be trained on. + num_boost_round : int, optional (default=100) + Number of boosting iterations. + valid_sets : list of Dataset, or None, optional (default=None) + List of data to be evaluated on during training. + valid_names : list of str, or None, optional (default=None) + Names of ``valid_sets``. + feval : callable, list of callable, or None, optional (default=None) + Customized evaluation function. + Each evaluation function should accept two parameters: preds, eval_data, + and return (metric_name, metric_value, maximize) or list of such tuples. + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. + If custom objective function is used, predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task in this case. + eval_data : Dataset + A ``Dataset`` to evaluate. + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. + + To ignore the default metric corresponding to the used objective, + set the ``metric`` parameter to the string ``"None"`` in ``params``. + init_model : str, pathlib.Path, Booster or None, optional (default=None) + Filename of LightGBM model or Booster instance used for continue training. + keep_training_booster : bool, optional (default=False) + Whether the returned Booster will be used to keep training. + If False, the returned value will be converted into _InnerPredictor before returning. + This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster. + When your model is very large and cause the memory error, + you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``. + You can still use _InnerPredictor as ``init_model`` for future continue training. + callbacks : list of callable, or None, optional (default=None) + List of callback functions that are applied at each iteration. + See Callbacks in Python API for more information. + + Note + ---- + A custom objective function can be provided for the ``objective`` parameter. + It should accept two parameters: preds, train_data and return (grad, hess). + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + Predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task. + train_data : Dataset + The training dataset. + grad : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the first order derivative (gradient) of the loss + with respect to the elements of preds for each sample point. + hess : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the second order derivative (Hessian) of the loss + with respect to the elements of preds for each sample point. + + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], + and grad and hess should be returned in the same format. + + Returns + ------- + booster : Booster + The trained Booster model. + """ + if not isinstance(train_set, Dataset): + raise TypeError(f"train() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.") + + if isinstance(valid_sets, list): + for i, valid_item in enumerate(valid_sets): + if not isinstance(valid_item, Dataset): + raise TypeError( + "Every item in valid_sets must be a Dataset object. " + f"Item {i} has type '{type(valid_item).__name__}'." + ) + + # create predictor first + params = copy.deepcopy(params) + params = _choose_param_value( + main_param_name="objective", + params=params, + default_value=None, + ) + fobj: Optional[_LGBM_CustomObjectiveFunction] = None + if callable(params["objective"]): + fobj = params["objective"] + params["objective"] = "none" + + params = _choose_num_iterations(num_boost_round_kwarg=num_boost_round, params=params) + num_boost_round = params["num_iterations"] + if num_boost_round <= 0: + raise ValueError(f"Number of boosting rounds must be greater than 0. Got {num_boost_round}.") + + # setting early stopping via global params should be possible + params = _choose_param_value( + main_param_name="early_stopping_round", + params=params, + default_value=None, + ) + if params["early_stopping_round"] is None: + params.pop("early_stopping_round") + first_metric_only = params.get("first_metric_only", False) + + predictor: Optional[_InnerPredictor] = None + if isinstance(init_model, (str, Path)): + predictor = _InnerPredictor.from_model_file(model_file=init_model, pred_parameter=params) + elif isinstance(init_model, Booster): + predictor = _InnerPredictor.from_booster(booster=init_model, pred_parameter=dict(init_model.params, **params)) + + if predictor is not None: + init_iteration = predictor.current_iteration() + else: + init_iteration = 0 + + train_set._update_params(params)._set_predictor(predictor) + + is_valid_contain_train = False + train_data_name = "training" + reduced_valid_sets = [] + name_valid_sets = [] + if valid_sets is not None: + if isinstance(valid_sets, Dataset): + valid_sets = [valid_sets] + if isinstance(valid_names, str): + valid_names = [valid_names] + for i, valid_data in enumerate(valid_sets): + # reduce cost for prediction training data + if valid_data is train_set: + is_valid_contain_train = True + if valid_names is not None: + train_data_name = valid_names[i] + continue + reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set)) + if valid_names is not None and len(valid_names) > i: + name_valid_sets.append(valid_names[i]) + else: + name_valid_sets.append(f"valid_{i}") + # process callbacks + if callbacks is None: + callbacks_set = set() + else: + for i, cb in enumerate(callbacks): + cb.__dict__.setdefault("order", i - len(callbacks)) + callbacks_set = set(callbacks) + + if callback._should_enable_early_stopping(params.get("early_stopping_round", 0)): + callbacks_set.add( + callback.early_stopping( + stopping_rounds=params["early_stopping_round"], # type: ignore[arg-type] + first_metric_only=first_metric_only, + min_delta=params.get("early_stopping_min_delta", 0.0), + verbose=_choose_param_value( + main_param_name="verbosity", + params=params, + default_value=1, + ).pop("verbosity") + > 0, + ) + ) + + callbacks_before_iter_set = {cb for cb in callbacks_set if getattr(cb, "before_iteration", False)} + callbacks_after_iter_set = callbacks_set - callbacks_before_iter_set + callbacks_before_iter = sorted(callbacks_before_iter_set, key=attrgetter("order")) + callbacks_after_iter = sorted(callbacks_after_iter_set, key=attrgetter("order")) + + # construct booster + try: + booster = Booster(params=params, train_set=train_set) + if is_valid_contain_train: + booster.set_train_data_name(train_data_name) + for valid_set, name_valid_set in zip(reduced_valid_sets, name_valid_sets, strict=True): + booster.add_valid(valid_set, name_valid_set) + finally: + train_set._reverse_update_params() + for valid_set in reduced_valid_sets: + valid_set._reverse_update_params() + booster.best_iteration = 0 + + # start training + for i in range(init_iteration, init_iteration + num_boost_round): + for cb in callbacks_before_iter: + cb( + callback.CallbackEnv( + model=booster, + params=params, + iteration=i, + begin_iteration=init_iteration, + end_iteration=init_iteration + num_boost_round, + evaluation_result_list=None, + ) + ) + + booster.update(fobj=fobj) + + evaluation_result_list: List[_LGBM_BoosterEvalMethodResultType] = [] + # check evaluation result. + if valid_sets is not None: + if is_valid_contain_train: + evaluation_result_list.extend(booster.eval_train(feval)) + evaluation_result_list.extend(booster.eval_valid(feval)) + try: + for cb in callbacks_after_iter: + cb( + callback.CallbackEnv( + model=booster, + params=params, + iteration=i, + begin_iteration=init_iteration, + end_iteration=init_iteration + num_boost_round, + evaluation_result_list=evaluation_result_list, + ) + ) + except callback.EarlyStopException as earlyStopException: + booster.best_iteration = earlyStopException.best_iteration + 1 + # eval results from cv() have a 5th element with the standard deviation of metrics, + # which is not needed for early stopping + evaluation_result_list = [item[:4] for item in earlyStopException.best_score] + break + booster.best_score = defaultdict(OrderedDict) + for dataset_name, metric_name, metric_value, _ in evaluation_result_list: + booster.best_score[dataset_name][metric_name] = metric_value + if not keep_training_booster: + booster.model_from_string(booster.model_to_string()).free_dataset() + return booster + + +class CVBooster: + """CVBooster in LightGBM. + + Auxiliary data structure to hold and redirect all boosters of ``cv()`` function. + This class has the same methods as Booster class. + All method calls, except for the following methods, are actually performed for underlying Boosters and + then all returned results are returned in a list. + + - ``model_from_string()`` + - ``model_to_string()`` + - ``save_model()`` + + Attributes + ---------- + boosters : list of Booster + The list of underlying fitted models. + best_iteration : int + The best iteration of fitted model. + """ + + def __init__( + self, + model_file: Optional[Union[str, Path]] = None, + ): + """Initialize the CVBooster. + + Parameters + ---------- + model_file : str, pathlib.Path or None, optional (default=None) + Path to the CVBooster model file. + """ + self.boosters: List[Booster] = [] + self.best_iteration = -1 + + if model_file is not None: + with open(model_file, "r") as file: + self._from_dict(json.load(file)) + + def _from_dict(self, models: Dict[str, Any]) -> None: + """Load CVBooster from dict.""" + self.best_iteration = models["best_iteration"] + self.boosters = [] + for model_str in models["boosters"]: + self.boosters.append(Booster(model_str=model_str)) + + def _to_dict( + self, + *, + num_iteration: Optional[int], + start_iteration: int, + importance_type: str, + ) -> Dict[str, Any]: + """Serialize CVBooster to dict.""" + models_str = [] + for booster in self.boosters: + models_str.append( + booster.model_to_string( + num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type + ) + ) + return {"boosters": models_str, "best_iteration": self.best_iteration} + + def __getattr__(self, name: str) -> Callable[[Any, Any], List[Any]]: + """Redirect methods call of CVBooster.""" + + def handler_function(*args: Any, **kwargs: Any) -> List[Any]: + """Call methods with each booster, and concatenate their results.""" + ret = [] + for booster in self.boosters: + ret.append(getattr(booster, name)(*args, **kwargs)) + return ret + + return handler_function + + def __getstate__(self) -> Dict[str, Any]: + return vars(self) + + def __setstate__(self, state: Dict[str, Any]) -> None: + vars(self).update(state) + + def model_from_string(self, model_str: str) -> "CVBooster": + """Load CVBooster from a string. + + Parameters + ---------- + model_str : str + Model will be loaded from this string. + + Returns + ------- + self : CVBooster + Loaded CVBooster object. + """ + self._from_dict(json.loads(model_str)) + return self + + def model_to_string( + self, + num_iteration: Optional[int] = None, + start_iteration: int = 0, + importance_type: str = "split", + ) -> str: + """Save CVBooster to JSON string. + + Parameters + ---------- + num_iteration : int or None, optional (default=None) + Index of the iteration that should be saved. + If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. + If <= 0, all iterations are saved. + start_iteration : int, optional (default=0) + Start index of the iteration that should be saved. + importance_type : str, optional (default="split") + What type of feature importance should be saved. + If "split", result contains numbers of times the feature is used in a model. + If "gain", result contains total gains of splits which use the feature. + + Returns + ------- + str_repr : str + JSON string representation of CVBooster. + """ + return json.dumps( + self._to_dict(num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type) + ) + + def save_model( + self, + filename: Union[str, Path], + num_iteration: Optional[int] = None, + start_iteration: int = 0, + importance_type: str = "split", + ) -> "CVBooster": + """Save CVBooster to a file as JSON text. + + Parameters + ---------- + filename : str or pathlib.Path + Filename to save CVBooster. + num_iteration : int or None, optional (default=None) + Index of the iteration that should be saved. + If None, if the best iteration exists, it is saved; otherwise, all iterations are saved. + If <= 0, all iterations are saved. + start_iteration : int, optional (default=0) + Start index of the iteration that should be saved. + importance_type : str, optional (default="split") + What type of feature importance should be saved. + If "split", result contains numbers of times the feature is used in a model. + If "gain", result contains total gains of splits which use the feature. + + Returns + ------- + self : CVBooster + Returns self. + """ + with open(filename, "w") as file: + json.dump( + self._to_dict( + num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type + ), + file, + ) + + return self + + +def _make_n_folds( + *, + full_data: Dataset, + folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]], + nfold: int, + params: Dict[str, Any], + seed: int, + fpreproc: Optional[_LGBM_PreprocFunction], + stratified: bool, + shuffle: bool, + eval_train_metric: bool, +) -> CVBooster: + """Make a n-fold list of Booster from random indices.""" + full_data = full_data.construct() + num_data = full_data.num_data() + if folds is not None: + if not hasattr(folds, "__iter__") and not hasattr(folds, "split"): + raise AttributeError( + "folds should be a generator or iterator of (train_idx, test_idx) tuples " + "or scikit-learn splitter object with split method" + ) + if hasattr(folds, "split"): + group_info = full_data.get_group() + if group_info is not None: + group_info = np.asarray(group_info, dtype=np.int32) + flatted_group = np.repeat(range(len(group_info)), repeats=group_info) + else: + flatted_group = np.zeros(num_data, dtype=np.int32) + folds = folds.split(X=np.empty(num_data), y=full_data.get_label(), groups=flatted_group) + else: + if any( + params.get(obj_alias, "") + in {"lambdarank", "rank_xendcg", "xendcg", "xe_ndcg", "xe_ndcg_mart", "xendcg_mart"} + for obj_alias in _ConfigAliases.get("objective") + ): + if not SKLEARN_INSTALLED: + raise LightGBMError("scikit-learn is required for ranking cv") + # ranking task, split according to groups + group_info = np.asarray(full_data.get_group(), dtype=np.int32) + flatted_group = np.repeat(range(len(group_info)), repeats=group_info) + group_kfold = _LGBMGroupKFold(n_splits=nfold) + folds = group_kfold.split(X=np.empty(num_data), groups=flatted_group) + elif stratified: + if not SKLEARN_INSTALLED: + raise LightGBMError("scikit-learn is required for stratified cv") + skf = _LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed) + folds = skf.split(X=np.empty(num_data), y=full_data.get_label()) + else: + if shuffle: + randidx = np.random.RandomState(seed).permutation(num_data) + else: + randidx = np.arange(num_data) + test_id = np.array_split(randidx, nfold) + train_id = [np.concatenate([test_id[i] for i in range(nfold) if k != i]) for k in range(nfold)] + folds = zip(train_id, test_id, strict=True) + + ret = CVBooster() + for train_idx, test_idx in folds: + train_set = full_data.subset(sorted(train_idx)) + valid_set = full_data.subset(sorted(test_idx)) + # run preprocessing on the data set if needed + if fpreproc is not None: + train_set, valid_set, tparam = fpreproc(train_set, valid_set, params.copy()) + else: + tparam = params + booster_for_fold = Booster(tparam, train_set) + if eval_train_metric: + booster_for_fold.add_valid(train_set, "train") + booster_for_fold.add_valid(valid_set, "valid") + ret.boosters.append(booster_for_fold) + return ret + + +def _agg_cv_result( + raw_results: List[List[_LGBM_BoosterEvalMethodResultType]], +) -> List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]: + """Aggregate cross-validation results.""" + # build up 2 maps, of the form: + # + # OrderedDict{ + # (, ): + # } + # + # OrderedDict{ + # (, ): list[] + # } + # + metric_types: Dict[Tuple[str, str], bool] = OrderedDict() + metric_values: Dict[Tuple[str, str], List[float]] = OrderedDict() + for one_result in raw_results: + for dataset_name, metric_name, metric_value, maximize in one_result: + key = (dataset_name, metric_name) + metric_types[key] = maximize + metric_values.setdefault(key, []) + metric_values[key].append(metric_value) + + # turn that into a list of tuples of the form: + # + # [ + # (, , mean(), , std_dev()) + # ] + return [(k[0], k[1], float(np.mean(v)), metric_types[k], float(np.std(v))) for k, v in metric_values.items()] + + +def cv( + params: Dict[str, Any], + train_set: Dataset, + num_boost_round: int = 100, + folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]] = None, + nfold: int = 5, + stratified: bool = True, + shuffle: bool = True, + metrics: Optional[Union[str, List[str]]] = None, + feval: Optional[Union[_LGBM_CustomMetricFunction, List[_LGBM_CustomMetricFunction]]] = None, + init_model: Optional[Union[str, Path, Booster]] = None, + fpreproc: Optional[_LGBM_PreprocFunction] = None, + seed: int = 0, + callbacks: Optional[List[Callable]] = None, + eval_train_metric: bool = False, + return_cvbooster: bool = False, +) -> Dict[str, Union[List[float], CVBooster]]: + """Perform the cross-validation with given parameters. + + Parameters + ---------- + params : dict + Parameters for training. Values passed through ``params`` take precedence over those + supplied via arguments. + train_set : Dataset + Data to be trained on. + num_boost_round : int, optional (default=100) + Number of boosting iterations. + folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None) + If generator or iterator, it should yield the train and test indices for each fold. + If object, it should be one of the scikit-learn splitter classes + (https://scikit-learn.org/stable/modules/classes.html#splitter-classes) + and have ``split`` method. + This argument has highest priority over other data split arguments. + nfold : int, optional (default=5) + Number of folds in CV. + stratified : bool, optional (default=True) + Whether to perform stratified sampling. + shuffle : bool, optional (default=True) + Whether to shuffle before splitting data. + metrics : str, list of str, or None, optional (default=None) + Evaluation metrics to be monitored while CV. + If not None, the metric in ``params`` will be overridden. + feval : callable, list of callable, or None, optional (default=None) + Customized evaluation function. + Each evaluation function should accept two parameters: preds, eval_data, + and return (metric_name, metric_value, maximize) or list of such tuples. + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes]. + If custom objective function is used, predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task in this case. + eval_data : Dataset + A ``Dataset`` to evaluate. + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. + + To ignore the default metric corresponding to the used objective, + set ``metrics`` to the string ``"None"``. + init_model : str, pathlib.Path, Booster or None, optional (default=None) + Filename of LightGBM model or Booster instance used for continue training. + fpreproc : callable or None, optional (default=None) + Preprocessing function that takes (dtrain, dtest, params) + and returns transformed versions of those. + seed : int, optional (default=0) + Seed used to generate the folds (passed to numpy.random.seed). + callbacks : list of callable, or None, optional (default=None) + List of callback functions that are applied at each iteration. + See Callbacks in Python API for more information. + eval_train_metric : bool, optional (default=False) + Whether to display the train metric in progress. + The score of the metric is calculated again after each training step, so there is some impact on performance. + return_cvbooster : bool, optional (default=False) + Whether to return Booster models trained on each fold through ``CVBooster``. + + Note + ---- + A custom objective function can be provided for the ``objective`` parameter. + It should accept two parameters: preds, train_data and return (grad, hess). + + preds : numpy 1-D array or numpy 2-D array (for multi-class task) + The predicted values. + Predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task. + train_data : Dataset + The training dataset. + grad : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the first order derivative (gradient) of the loss + with respect to the elements of preds for each sample point. + hess : numpy 1-D array or numpy 2-D array (for multi-class task) + The value of the second order derivative (Hessian) of the loss + with respect to the elements of preds for each sample point. + + For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes], + and grad and hess should be returned in the same format. + + Returns + ------- + eval_results : dict + History of evaluation results of each metric. + The dictionary has the following format: + {'valid metric1-mean': [values], 'valid metric1-stdv': [values], + 'valid metric2-mean': [values], 'valid metric2-stdv': [values], + ...}. + If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key. + If ``eval_train_metric=True``, also returns the train metric history. + In this case, the dictionary has the following format: + {'train metric1-mean': [values], 'valid metric1-mean': [values], + 'train metric2-mean': [values], 'valid metric2-mean': [values], + ...}. + """ + if not isinstance(train_set, Dataset): + raise TypeError(f"cv() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.") + + params = copy.deepcopy(params) + params = _choose_param_value( + main_param_name="objective", + params=params, + default_value=None, + ) + fobj: Optional[_LGBM_CustomObjectiveFunction] = None + if callable(params["objective"]): + fobj = params["objective"] + params["objective"] = "none" + + params = _choose_num_iterations(num_boost_round_kwarg=num_boost_round, params=params) + num_boost_round = params["num_iterations"] + if num_boost_round <= 0: + raise ValueError(f"Number of boosting rounds must be greater than 0. Got {num_boost_round}.") + + # setting early stopping via global params should be possible + params = _choose_param_value( + main_param_name="early_stopping_round", + params=params, + default_value=None, + ) + if params["early_stopping_round"] is None: + params.pop("early_stopping_round") + first_metric_only = params.get("first_metric_only", False) + + if isinstance(init_model, (str, Path)): + predictor = _InnerPredictor.from_model_file( + model_file=init_model, + pred_parameter=params, + ) + elif isinstance(init_model, Booster): + predictor = _InnerPredictor.from_booster( + booster=init_model, + pred_parameter=dict(init_model.params, **params), + ) + else: + predictor = None + + if metrics is not None: + for metric_alias in _ConfigAliases.get("metric"): + params.pop(metric_alias, None) + params["metric"] = metrics + + train_set._update_params(params)._set_predictor(predictor) + + results = defaultdict(list) + cvbooster = _make_n_folds( + full_data=train_set, + folds=folds, + nfold=nfold, + params=params, + seed=seed, + fpreproc=fpreproc, + stratified=stratified, + shuffle=shuffle, + eval_train_metric=eval_train_metric, + ) + + # setup callbacks + if callbacks is None: + callbacks_set = set() + else: + for i, cb in enumerate(callbacks): + cb.__dict__.setdefault("order", i - len(callbacks)) + callbacks_set = set(callbacks) + + if callback._should_enable_early_stopping(params.get("early_stopping_round", 0)): + callbacks_set.add( + callback.early_stopping( + stopping_rounds=params["early_stopping_round"], # type: ignore[arg-type] + first_metric_only=first_metric_only, + min_delta=params.get("early_stopping_min_delta", 0.0), + verbose=_choose_param_value( + main_param_name="verbosity", + params=params, + default_value=1, + ).pop("verbosity") + > 0, + ) + ) + + callbacks_before_iter_set = {cb for cb in callbacks_set if getattr(cb, "before_iteration", False)} + callbacks_after_iter_set = callbacks_set - callbacks_before_iter_set + callbacks_before_iter = sorted(callbacks_before_iter_set, key=attrgetter("order")) + callbacks_after_iter = sorted(callbacks_after_iter_set, key=attrgetter("order")) + + for i in range(num_boost_round): + for cb in callbacks_before_iter: + cb( + callback.CallbackEnv( + model=cvbooster, + params=params, + iteration=i, + begin_iteration=0, + end_iteration=num_boost_round, + evaluation_result_list=None, + ) + ) + cvbooster.update(fobj=fobj) # type: ignore[call-arg] + res = _agg_cv_result(cvbooster.eval_valid(feval)) # type: ignore[call-arg] + for dataset_name, metric_name, metric_mean, _, metric_std_dev in res: + results[f"{dataset_name} {metric_name}-mean"].append(metric_mean) + results[f"{dataset_name} {metric_name}-stdv"].append(metric_std_dev) + try: + for cb in callbacks_after_iter: + cb( + callback.CallbackEnv( + model=cvbooster, + params=params, + iteration=i, + begin_iteration=0, + end_iteration=num_boost_round, + evaluation_result_list=res, + ) + ) + except callback.EarlyStopException as earlyStopException: + cvbooster.best_iteration = earlyStopException.best_iteration + 1 + for bst in cvbooster.boosters: + bst.best_iteration = cvbooster.best_iteration + for k in results: + results[k] = results[k][: cvbooster.best_iteration] + break + + if return_cvbooster: + results["cvbooster"] = cvbooster # type: ignore[assignment] + + return dict(results) diff --git a/python-package/lightgbm/libpath.py b/python-package/lightgbm/libpath.py new file mode 100644 index 0000000..e14173f --- /dev/null +++ b/python-package/lightgbm/libpath.py @@ -0,0 +1,49 @@ +# coding: utf-8 +"""Find the path to LightGBM dynamic library files.""" + +import ctypes +from os import environ +from pathlib import Path +from platform import system +from typing import List + +__all__: List[str] = [] + + +def _find_lib_path() -> List[str]: + """Find the path to LightGBM library files. + + Returns + ------- + lib_path: list of str + List of all found library paths to LightGBM. + """ + curr_path = Path(__file__).resolve() + dll_path = [ + curr_path.parents[1], + curr_path.parents[0] / "bin", + curr_path.parents[0] / "lib", + ] + if system() in ("Windows", "Microsoft"): + dll_path.append(curr_path.parents[1] / "Release") + dll_path.append(curr_path.parents[1] / "windows" / "x64" / "DLL") + dll_path = [p / "lib_lightgbm.dll" for p in dll_path] + elif system() == "Darwin": + dll_path = [p / "lib_lightgbm.dylib" for p in dll_path] + else: + dll_path = [p / "lib_lightgbm.so" for p in dll_path] + lib_path = [str(p) for p in dll_path if p.is_file()] + if not lib_path: + dll_path_joined = "\n".join(map(str, dll_path)) + raise Exception(f"Cannot find lightgbm library file in following paths:\n{dll_path_joined}") + return lib_path + + +# we don't need lib_lightgbm while building docs +_LIB: ctypes.CDLL +if environ.get("LIGHTGBM_BUILD_DOC", "False") == "True": + from unittest.mock import Mock # isort: skip + + _LIB = Mock(ctypes.CDLL) # type: ignore +else: + _LIB = ctypes.cdll.LoadLibrary(_find_lib_path()[0]) diff --git a/python-package/lightgbm/plotting.py b/python-package/lightgbm/plotting.py new file mode 100644 index 0000000..c1b6311 --- /dev/null +++ b/python-package/lightgbm/plotting.py @@ -0,0 +1,849 @@ +# coding: utf-8 +"""Plotting library.""" + +import math +from copy import deepcopy +from io import BytesIO +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union + +import numpy as np + +from .basic import Booster, _data_from_pandas, _is_zero, _log_warning, _MissingType +from .compat import pd_DataFrame +from .sklearn import LGBMModel + +__all__ = [ + "create_tree_digraph", + "plot_importance", + "plot_metric", + "plot_split_value_histogram", + "plot_tree", +] + +if TYPE_CHECKING: + import matplotlib + + +def _check_not_tuple_of_2_elements(obj: Any, obj_name: str) -> None: + """Check object is not tuple or does not have 2 elements.""" + if not isinstance(obj, tuple) or len(obj) != 2: + raise TypeError(f"{obj_name} must be a tuple of 2 elements.") + + +def _float2str(value: float, precision: Optional[int]) -> str: + return f"{value:.{precision}f}" if precision is not None and not isinstance(value, str) else str(value) + + +def plot_importance( + booster: Union[Booster, LGBMModel], + ax: "Optional[matplotlib.axes.Axes]" = None, + height: float = 0.2, + xlim: Optional[Tuple[float, float]] = None, + ylim: Optional[Tuple[float, float]] = None, + title: Optional[str] = "Feature importance", + xlabel: Optional[str] = "Feature importance", + ylabel: Optional[str] = "Features", + importance_type: str = "auto", + max_num_features: Optional[int] = None, + ignore_zero: bool = True, + figsize: Optional[Tuple[float, float]] = None, + dpi: Optional[int] = None, + grid: bool = True, + precision: Optional[int] = 3, + **kwargs: Any, +) -> Any: + """Plot model's feature importances. + + Parameters + ---------- + booster : Booster or LGBMModel + Booster or LGBMModel instance which feature importance should be plotted. + ax : matplotlib.axes.Axes or None, optional (default=None) + Target axes instance. + If None, new figure and axes will be created. + height : float, optional (default=0.2) + Bar height, passed to ``ax.barh()``. + xlim : tuple of 2 elements or None, optional (default=None) + Tuple passed to ``ax.xlim()``. + ylim : tuple of 2 elements or None, optional (default=None) + Tuple passed to ``ax.ylim()``. + title : str or None, optional (default="Feature importance") + Axes title. + If None, title is disabled. + xlabel : str or None, optional (default="Feature importance") + X-axis title label. + If None, title is disabled. + @importance_type@ placeholder can be used, and it will be replaced with the value of ``importance_type`` parameter. + ylabel : str or None, optional (default="Features") + Y-axis title label. + If None, title is disabled. + importance_type : str, optional (default="auto") + How the importance is calculated. + If "auto", if ``booster`` parameter is LGBMModel, ``booster.importance_type`` attribute is used; "split" otherwise. + If "split", result contains numbers of times the feature is used in a model. + If "gain", result contains total gains of splits which use the feature. + max_num_features : int or None, optional (default=None) + Max number of top features displayed on plot. + If None or <1, all features will be displayed. + ignore_zero : bool, optional (default=True) + Whether to ignore features with zero importance. + figsize : tuple of 2 elements or None, optional (default=None) + Figure size. + dpi : int or None, optional (default=None) + Resolution of the figure. + grid : bool, optional (default=True) + Whether to add a grid for axes. + precision : int or None, optional (default=3) + Used to restrict the display of floating point values to a certain precision. + **kwargs + Other parameters passed to ``ax.barh()``. + + Returns + ------- + ax : matplotlib.axes.Axes + The plot with model's feature importances. + """ + try: + import matplotlib.pyplot as plt # noqa: PLC0415 + except ImportError as err: + raise ImportError("You must install matplotlib and restart your session to plot importance.") from err + + if isinstance(booster, LGBMModel): + if importance_type == "auto": + importance_type = booster.importance_type + booster = booster.booster_ + elif isinstance(booster, Booster): + if importance_type == "auto": + importance_type = "split" + else: + raise TypeError("booster must be Booster or LGBMModel.") + + importance = booster.feature_importance(importance_type=importance_type) + feature_name = booster.feature_name() + + if not len(importance): + raise ValueError("Booster's feature_importance is empty.") + + tuples = sorted(zip(feature_name, importance, strict=True), key=lambda x: x[1]) + if ignore_zero: + tuples = [x for x in tuples if x[1] > 0] + if max_num_features is not None and max_num_features > 0: + tuples = tuples[-max_num_features:] + if not tuples: + raise ValueError( + "No non-zero feature importances found. The model may have no splits. " + "Use ignore_zero=False to show all features." + ) + labels, values = zip(*tuples, strict=True) + + if ax is None: + if figsize is not None: + _check_not_tuple_of_2_elements(figsize, "figsize") + _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) + + ylocs = np.arange(len(values)) + ax.barh(ylocs, values, align="center", height=height, **kwargs) + + for x, y in zip(values, ylocs, strict=True): + ax.text(x + 1, float(y), _float2str(x, precision) if importance_type == "gain" else x, va="center") + + ax.set_yticks(ylocs) + ax.set_yticklabels(labels) + + if xlim is not None: + _check_not_tuple_of_2_elements(xlim, "xlim") + else: + xlim = (0, max(values) * 1.1) + ax.set_xlim(xlim) + + if ylim is not None: + _check_not_tuple_of_2_elements(ylim, "ylim") + else: + ylim = (-1, len(values)) + ax.set_ylim(ylim) + + if title is not None: + ax.set_title(title) + if xlabel is not None: + xlabel = xlabel.replace("@importance_type@", importance_type) + ax.set_xlabel(xlabel) + if ylabel is not None: + ax.set_ylabel(ylabel) + ax.grid(grid) + return ax + + +def plot_split_value_histogram( + booster: Union[Booster, LGBMModel], + feature: Union[int, str], + bins: Union[int, str, None] = None, + ax: "Optional[matplotlib.axes.Axes]" = None, + width_coef: float = 0.8, + xlim: Optional[Tuple[float, float]] = None, + ylim: Optional[Tuple[float, float]] = None, + title: Optional[str] = "Split value histogram for feature with @index/name@ @feature@", + xlabel: Optional[str] = "Feature split value", + ylabel: Optional[str] = "Count", + figsize: Optional[Tuple[float, float]] = None, + dpi: Optional[int] = None, + grid: bool = True, + **kwargs: Any, +) -> Any: + """Plot split value histogram for the specified feature of the model. + + Parameters + ---------- + booster : Booster or LGBMModel + Booster or LGBMModel instance of which feature split value histogram should be plotted. + feature : int or str + The feature name or index the histogram is plotted for. + If int, interpreted as index. + If str, interpreted as name. + bins : int, str or None, optional (default=None) + The maximum number of bins. + If None, the number of bins equals number of unique split values. + If str, it should be one from the list of the supported values by ``numpy.histogram()`` function. + ax : matplotlib.axes.Axes or None, optional (default=None) + Target axes instance. + If None, new figure and axes will be created. + width_coef : float, optional (default=0.8) + Coefficient for histogram bar width. + xlim : tuple of 2 elements or None, optional (default=None) + Tuple passed to ``ax.xlim()``. + ylim : tuple of 2 elements or None, optional (default=None) + Tuple passed to ``ax.ylim()``. + title : str or None, optional (default="Split value histogram for feature with @index/name@ @feature@") + Axes title. + If None, title is disabled. + @feature@ placeholder can be used, and it will be replaced with the value of ``feature`` parameter. + @index/name@ placeholder can be used, + and it will be replaced with ``index`` word in case of ``int`` type ``feature`` parameter + or ``name`` word in case of ``str`` type ``feature`` parameter. + xlabel : str or None, optional (default="Feature split value") + X-axis title label. + If None, title is disabled. + ylabel : str or None, optional (default="Count") + Y-axis title label. + If None, title is disabled. + figsize : tuple of 2 elements or None, optional (default=None) + Figure size. + dpi : int or None, optional (default=None) + Resolution of the figure. + grid : bool, optional (default=True) + Whether to add a grid for axes. + **kwargs + Other parameters passed to ``ax.bar()``. + + Returns + ------- + ax : matplotlib.axes.Axes + The plot with specified model's feature split value histogram. + """ + try: + import matplotlib.pyplot as plt # noqa: PLC0415 + from matplotlib.ticker import MaxNLocator # noqa: PLC0415 + except ImportError as err: + raise ImportError( + "You must install matplotlib and restart your session to plot split value histogram." + ) from err + + if isinstance(booster, LGBMModel): + booster = booster.booster_ + elif not isinstance(booster, Booster): + raise TypeError("booster must be Booster or LGBMModel.") + + hist, split_bins = booster.get_split_value_histogram(feature=feature, bins=bins, xgboost_style=False) + if np.count_nonzero(hist) == 0: + raise ValueError(f"Cannot plot split value histogram, because feature {feature} was not used in splitting") + width = width_coef * (split_bins[1] - split_bins[0]) + centred = (split_bins[:-1] + split_bins[1:]) / 2 + + if ax is None: + if figsize is not None: + _check_not_tuple_of_2_elements(figsize, "figsize") + _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) + + ax.bar(centred, hist, align="center", width=width, **kwargs) + + if xlim is not None: + _check_not_tuple_of_2_elements(xlim, "xlim") + else: + range_result = split_bins[-1] - split_bins[0] + xlim = (split_bins[0] - range_result * 0.2, split_bins[-1] + range_result * 0.2) + ax.set_xlim(xlim) + + ax.yaxis.set_major_locator(MaxNLocator(integer=True)) + if ylim is not None: + _check_not_tuple_of_2_elements(ylim, "ylim") + else: + ylim = (0, max(hist) * 1.1) + ax.set_ylim(ylim) + + if title is not None: + title = title.replace("@feature@", str(feature)) + title = title.replace("@index/name@", ("name" if isinstance(feature, str) else "index")) + ax.set_title(title) + if xlabel is not None: + ax.set_xlabel(xlabel) + if ylabel is not None: + ax.set_ylabel(ylabel) + ax.grid(grid) + return ax + + +def plot_metric( + booster: Union[Dict, LGBMModel], + metric: Optional[str] = None, + dataset_names: Optional[List[str]] = None, + ax: "Optional[matplotlib.axes.Axes]" = None, + xlim: Optional[Tuple[float, float]] = None, + ylim: Optional[Tuple[float, float]] = None, + title: Optional[str] = "Metric during training", + xlabel: Optional[str] = "Iterations", + ylabel: Optional[str] = "@metric@", + figsize: Optional[Tuple[float, float]] = None, + dpi: Optional[int] = None, + grid: bool = True, +) -> Any: + """Plot one metric during training. + + Parameters + ---------- + booster : dict or LGBMModel + Dictionary returned from ``lightgbm.train()`` or LGBMModel instance. + metric : str or None, optional (default=None) + The metric name to plot. + Only one metric supported because different metrics have various scales. + If None, first metric picked from dictionary (according to hashcode). + dataset_names : list of str, or None, optional (default=None) + List of the dataset names which are used to calculate metric to plot. + If None, all datasets are used. + ax : matplotlib.axes.Axes or None, optional (default=None) + Target axes instance. + If None, new figure and axes will be created. + xlim : tuple of 2 elements or None, optional (default=None) + Tuple passed to ``ax.xlim()``. + ylim : tuple of 2 elements or None, optional (default=None) + Tuple passed to ``ax.ylim()``. + title : str or None, optional (default="Metric during training") + Axes title. + If None, title is disabled. + xlabel : str or None, optional (default="Iterations") + X-axis title label. + If None, title is disabled. + ylabel : str or None, optional (default="@metric@") + Y-axis title label. + If 'auto', metric name is used. + If None, title is disabled. + @metric@ placeholder can be used, and it will be replaced with metric name. + figsize : tuple of 2 elements or None, optional (default=None) + Figure size. + dpi : int or None, optional (default=None) + Resolution of the figure. + grid : bool, optional (default=True) + Whether to add a grid for axes. + + Returns + ------- + ax : matplotlib.axes.Axes + The plot with metric's history over the training. + """ + try: + import matplotlib.pyplot as plt # noqa: PLC0415 + except ImportError as err: + raise ImportError("You must install matplotlib and restart your session to plot metric.") from err + + if isinstance(booster, LGBMModel): + eval_results = deepcopy(booster.evals_result_) + elif isinstance(booster, dict): + eval_results = deepcopy(booster) + elif isinstance(booster, Booster): + raise TypeError( + "booster must be dict or LGBMModel. To use plot_metric with Booster type, first record the metrics using record_evaluation callback then pass that to plot_metric as argument `booster`" + ) + else: + raise TypeError("booster must be dict or LGBMModel.") + + num_data = len(eval_results) + + if not num_data: + raise ValueError("eval results cannot be empty.") + + if ax is None: + if figsize is not None: + _check_not_tuple_of_2_elements(figsize, "figsize") + _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) + + if dataset_names is None: + dataset_names_iter = iter(eval_results.keys()) + elif not isinstance(dataset_names, (list, tuple, set)) or not dataset_names: + raise ValueError("dataset_names should be iterable and cannot be empty") + else: + dataset_names_iter = iter(dataset_names) + + name = next(dataset_names_iter) # take one as sample + metrics_for_one = eval_results[name] + num_metric = len(metrics_for_one) + if metric is None: + if num_metric > 1: + _log_warning("More than one metric available, picking one to plot.") + metric, results = metrics_for_one.popitem() + else: + if metric not in metrics_for_one: + raise KeyError("No given metric in eval results.") + results = metrics_for_one[metric] + num_iteration = len(results) + max_result = max(results) + min_result = min(results) + x_ = range(num_iteration) + ax.plot(x_, results, label=name) + + for name in dataset_names_iter: + metrics_for_one = eval_results[name] + results = metrics_for_one[metric] + max_result = max(*results, max_result) + min_result = min(*results, min_result) + ax.plot(x_, results, label=name) + + ax.legend(loc="best") + + if xlim is not None: + _check_not_tuple_of_2_elements(xlim, "xlim") + else: + xlim = (0, num_iteration) + ax.set_xlim(xlim) + + if ylim is not None: + _check_not_tuple_of_2_elements(ylim, "ylim") + else: + range_result = max_result - min_result + ylim = (min_result - range_result * 0.2, max_result + range_result * 0.2) + ax.set_ylim(ylim) + + if title is not None: + ax.set_title(title) + if xlabel is not None: + ax.set_xlabel(xlabel) + if ylabel is not None: + ylabel = ylabel.replace("@metric@", metric) + ax.set_ylabel(ylabel) + ax.grid(grid) + return ax + + +def _determine_direction_for_numeric_split( + *, + fval: float, + threshold: float, + missing_type_str: str, + default_left: bool, +) -> str: + missing_type = _MissingType(missing_type_str) + if math.isnan(fval) and missing_type != _MissingType.NAN: + fval = 0.0 + if (missing_type == _MissingType.ZERO and _is_zero(fval)) or ( + missing_type == _MissingType.NAN and math.isnan(fval) + ): + direction = "left" if default_left else "right" + else: + direction = "left" if fval <= threshold else "right" + return direction + + +def _determine_direction_for_categorical_split(fval: float, thresholds: str) -> str: + if math.isnan(fval) or int(fval) < 0: + return "right" + int_thresholds = {int(t) for t in thresholds.split("||")} + return "left" if int(fval) in int_thresholds else "right" + + +def _to_graphviz( + *, + tree_info: Dict[str, Any], + show_info: List[str], + feature_names: Union[List[str], None], + precision: Optional[int], + orientation: str, + constraints: Optional[List[int]], + example_case: Optional[Union[np.ndarray, pd_DataFrame]], + max_category_values: int, + **kwargs: Any, +) -> Any: + """Convert specified tree to graphviz instance. + + See: + - https://graphviz.readthedocs.io/en/stable/api.html#digraph + """ + try: + from graphviz import Digraph # noqa: PLC0415 + except ImportError as err: + raise ImportError("You must install graphviz and restart your session to plot tree.") from err + + def add( + root: Dict[str, Any], total_count: int, parent: Optional[str], decision: Optional[str], highlight: bool + ) -> None: + """Recursively add node or edge.""" + fillcolor = "white" + style = "" + tooltip = None + if highlight: + color = "blue" + penwidth = "3" + else: + color = "black" + penwidth = "1" + if "split_index" in root: # non-leaf + shape = "rectangle" + l_dec = "yes" + r_dec = "no" + threshold = root["threshold"] + if root["decision_type"] == "<=": + operator = "≤" + elif root["decision_type"] == "==": + operator = "=" + else: + raise ValueError("Invalid decision type in tree model.") + name = f"split{root['split_index']}" + split_feature = root["split_feature"] + if feature_names is not None: + label = f"{feature_names[split_feature]} {operator}" + else: + label = f"feature {split_feature} {operator} " + direction = None + if example_case is not None: + if root["decision_type"] == "==": + direction = _determine_direction_for_categorical_split( + fval=example_case[split_feature], thresholds=root["threshold"] + ) + else: + direction = _determine_direction_for_numeric_split( + fval=example_case[split_feature], + threshold=root["threshold"], + missing_type_str=root["missing_type"], + default_left=root["default_left"], + ) + if root["decision_type"] == "==": + category_values = root["threshold"].split("||") + if len(category_values) > max_category_values: + tooltip = root["threshold"] + threshold = "||".join(category_values[:2]) + "||...||" + category_values[-1] + + label += f"{_float2str(threshold, precision)}" + for info in ["split_gain", "internal_value", "internal_weight", "internal_count", "data_percentage"]: + if info in show_info: + output = info.split("_")[-1] + if info in {"split_gain", "internal_value", "internal_weight"}: + label += f"
{_float2str(root[info], precision)} {output}" + elif info == "internal_count": + label += f"
{output}: {root[info]}" + elif info == "data_percentage": + label += f"
{_float2str(root['internal_count'] / total_count * 100, 2)}% of data" + + if constraints: + if constraints[root["split_feature"]] == 1: + fillcolor = "#ddffdd" # light green + if constraints[root["split_feature"]] == -1: + fillcolor = "#ffdddd" # light red + style = "filled" + label = f"<{label}>" + add( + root=root["left_child"], + total_count=total_count, + parent=name, + decision=l_dec, + highlight=highlight and direction == "left", + ) + add( + root=root["right_child"], + total_count=total_count, + parent=name, + decision=r_dec, + highlight=highlight and direction == "right", + ) + else: # leaf + shape = "ellipse" + name = f"leaf{root['leaf_index']}" + label = f"leaf {root['leaf_index']}: " + label += f"{_float2str(root['leaf_value'], precision)}" + if "leaf_weight" in show_info: + label += f"
{_float2str(root['leaf_weight'], precision)} weight" + if "leaf_count" in show_info: + label += f"
count: {root['leaf_count']}" + if "data_percentage" in show_info: + label += f"
{_float2str(root['leaf_count'] / total_count * 100, 2)}% of data" + label = f"<{label}>" + graph.node( + name, + label=label, + shape=shape, + style=style, + fillcolor=fillcolor, + color=color, + penwidth=penwidth, + tooltip=tooltip, + ) + if parent is not None: + graph.edge(parent, name, decision, color=color, penwidth=penwidth) + + graph = Digraph(**kwargs) + rankdir = "LR" if orientation == "horizontal" else "TB" + graph.attr("graph", nodesep="0.05", ranksep="0.3", rankdir=rankdir) + if "internal_count" in tree_info["tree_structure"]: + add( + root=tree_info["tree_structure"], + total_count=tree_info["tree_structure"]["internal_count"], + parent=None, + decision=None, + highlight=example_case is not None, + ) + else: + raise Exception("Cannot plot trees with no split") + + if constraints: + # "#ddffdd" is light green, "#ffdddd" is light red + legend = """< + + + + + + + + + + + + +
Monotone constraints
Increasing
Decreasing
+ >""" + graph.node("legend", label=legend, shape="rectangle", color="white") + return graph + + +def create_tree_digraph( + booster: Union[Booster, LGBMModel], + tree_index: int = 0, + show_info: Optional[List[str]] = None, + precision: Optional[int] = 3, + orientation: str = "horizontal", + example_case: Optional[Union[np.ndarray, pd_DataFrame]] = None, + max_category_values: int = 10, + **kwargs: Any, +) -> Any: + """Create a digraph representation of specified tree. + + Each node in the graph represents a node in the tree. + + Non-leaf nodes have labels like ``Column_10 <= 875.9``, which means + "this node splits on the feature named "Column_10", with threshold 875.9". + + Leaf nodes have labels like ``leaf 2: 0.422``, which means "this node is a + leaf node, and the predicted value for records that fall into this node + is 0.422". The number (``2``) is an internal unique identifier and doesn't + have any special meaning. + + .. note:: + + For more information please visit + https://graphviz.readthedocs.io/en/stable/api.html#digraph. + + Parameters + ---------- + booster : Booster or LGBMModel + Booster or LGBMModel instance to be converted. + tree_index : int, optional (default=0) + The index of a target tree to convert. + show_info : list of str, or None, optional (default=None) + What information should be shown in nodes. + + - ``'split_gain'`` : gain from adding this split to the model + - ``'internal_value'`` : raw predicted value that would be produced by this node if it was a leaf node + - ``'internal_count'`` : number of records from the training data that fall into this non-leaf node + - ``'internal_weight'`` : total weight of all nodes that fall into this non-leaf node + - ``'leaf_count'`` : number of records from the training data that fall into this leaf node + - ``'leaf_weight'`` : total weight (sum of Hessian) of all observations that fall into this leaf node + - ``'data_percentage'`` : percentage of training data that fall into this node + precision : int or None, optional (default=3) + Used to restrict the display of floating point values to a certain precision. + orientation : str, optional (default='horizontal') + Orientation of the tree. + Can be 'horizontal' or 'vertical'. + example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None) + Single row with the same structure as the training data. + If not None, the plot will highlight the path that sample takes through the tree. + + .. versionadded:: 4.0.0 + + max_category_values : int, optional (default=10) + The maximum number of category values to display in tree nodes, if the number of thresholds is greater than this value, thresholds will be collapsed and displayed on the label tooltip instead. + + .. warning:: + + Consider wrapping the SVG string of the tree graph with ``IPython.display.HTML`` when running on JupyterLab to get the `tooltip `_ working right. + + Example: + + .. code-block:: python + + from IPython.display import HTML + + graph = lgb.create_tree_digraph(clf, max_category_values=5) + HTML(graph._repr_image_svg_xml()) + + .. versionadded:: 4.0.0 + + **kwargs + Other parameters passed to ``Digraph`` constructor. + Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters. + + Returns + ------- + graph : graphviz.Digraph + The digraph representation of specified tree. + """ + if isinstance(booster, LGBMModel): + booster = booster.booster_ + elif not isinstance(booster, Booster): + raise TypeError("booster must be Booster or LGBMModel.") + + model = booster.dump_model() + tree_infos = model["tree_info"] + feature_names = model.get("feature_names", None) + monotone_constraints = model.get("monotone_constraints", None) + + if tree_index < len(tree_infos): + tree_info = tree_infos[tree_index] + else: + raise IndexError("tree_index is out of range.") + + if show_info is None: + show_info = [] + + if example_case is not None: + if not isinstance(example_case, (np.ndarray, pd_DataFrame)) or example_case.ndim != 2: + raise ValueError("example_case must be a numpy 2-D array or a pandas DataFrame") + if example_case.shape[0] != 1: + raise ValueError("example_case must have a single row.") + if isinstance(example_case, pd_DataFrame): + example_case = _data_from_pandas( + data=example_case, + feature_name="auto", + categorical_feature="auto", + pandas_categorical=booster.pandas_categorical, + )[0] + example_case = example_case[0] + + return _to_graphviz( + tree_info=tree_info, + show_info=show_info, + feature_names=feature_names, + precision=precision, + orientation=orientation, + constraints=monotone_constraints, + example_case=example_case, + max_category_values=max_category_values, + **kwargs, + ) + + +def plot_tree( + booster: Union[Booster, LGBMModel], + ax: "Optional[matplotlib.axes.Axes]" = None, + tree_index: int = 0, + figsize: Optional[Tuple[float, float]] = None, + dpi: Optional[int] = None, + show_info: Optional[List[str]] = None, + precision: Optional[int] = 3, + orientation: str = "horizontal", + example_case: Optional[Union[np.ndarray, pd_DataFrame]] = None, + **kwargs: Any, +) -> Any: + """Plot specified tree. + + Each node in the graph represents a node in the tree. + + Non-leaf nodes have labels like ``Column_10 <= 875.9``, which means + "this node splits on the feature named "Column_10", with threshold 875.9". + + Leaf nodes have labels like ``leaf 2: 0.422``, which means "this node is a + leaf node, and the predicted value for records that fall into this node + is 0.422". The number (``2``) is an internal unique identifier and doesn't + have any special meaning. + + .. note:: + + It is preferable to use ``create_tree_digraph()`` because of its lossless quality + and returned objects can be also rendered and displayed directly inside a Jupyter notebook. + + Parameters + ---------- + booster : Booster or LGBMModel + Booster or LGBMModel instance to be plotted. + ax : matplotlib.axes.Axes or None, optional (default=None) + Target axes instance. + If None, new figure and axes will be created. + tree_index : int, optional (default=0) + The index of a target tree to plot. + figsize : tuple of 2 elements or None, optional (default=None) + Figure size. + dpi : int or None, optional (default=None) + Resolution of the figure. + show_info : list of str, or None, optional (default=None) + What information should be shown in nodes. + + - ``'split_gain'`` : gain from adding this split to the model + - ``'internal_value'`` : raw predicted value that would be produced by this node if it was a leaf node + - ``'internal_count'`` : number of records from the training data that fall into this non-leaf node + - ``'internal_weight'`` : total weight of all nodes that fall into this non-leaf node + - ``'leaf_count'`` : number of records from the training data that fall into this leaf node + - ``'leaf_weight'`` : total weight (sum of Hessian) of all observations that fall into this leaf node + - ``'data_percentage'`` : percentage of training data that fall into this node + precision : int or None, optional (default=3) + Used to restrict the display of floating point values to a certain precision. + orientation : str, optional (default='horizontal') + Orientation of the tree. + Can be 'horizontal' or 'vertical'. + example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None) + Single row with the same structure as the training data. + If not None, the plot will highlight the path that sample takes through the tree. + + .. versionadded:: 4.0.0 + + **kwargs + Other parameters passed to ``Digraph`` constructor. + Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters. + + Returns + ------- + ax : matplotlib.axes.Axes + The plot with single tree. + """ + try: + import matplotlib.image # noqa: PLC0415 + import matplotlib.pyplot as plt # noqa: PLC0415 + except ImportError as err: + raise ImportError("You must install matplotlib and restart your session to plot tree.") from err + + if ax is None: + if figsize is not None: + _check_not_tuple_of_2_elements(figsize, "figsize") + _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi) + + graph = create_tree_digraph( + booster=booster, + tree_index=tree_index, + show_info=show_info, + precision=precision, + orientation=orientation, + example_case=example_case, + **kwargs, + ) + + s = BytesIO() + s.write(graph.pipe(format="png")) + s.seek(0) + img = matplotlib.image.imread(s) + + ax.imshow(img) + ax.axis("off") + return ax diff --git a/python-package/lightgbm/py.typed b/python-package/lightgbm/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/python-package/lightgbm/sklearn.py b/python-package/lightgbm/sklearn.py new file mode 100644 index 0000000..e7266b5 --- /dev/null +++ b/python-package/lightgbm/sklearn.py @@ -0,0 +1,1954 @@ +# coding: utf-8 +"""Scikit-learn wrapper interface for LightGBM.""" + +import copy +import warnings +from inspect import signature +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union + +import narwhals as nw +import narwhals.dependencies as nwd +import narwhals.typing as nwt +import numpy as np +import scipy.sparse + +from .basic import ( + _MULTICLASS_OBJECTIVES, + Booster, + Dataset, + LGBMDeprecationWarning, + LightGBMError, + _choose_param_value, + _ConfigAliases, + _LGBM_BoosterBestScoreType, + _LGBM_CategoricalFeatureConfiguration, + _LGBM_EvalFunctionResultType, + _LGBM_FeatureNameConfiguration, + _LGBM_GroupType, + _LGBM_InitScoreType, + _LGBM_LabelType, + _LGBM_PredictReturnType, + _LGBM_WeightType, + _log_warning, +) +from .callback import _EvalResultDict, record_evaluation +from .compat import ( + SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG, + SKLEARN_INSTALLED, + LGBMNotFittedError, + _LGBMAssertAllFinite, + _LGBMCheckClassificationTargets, + _LGBMCheckSampleWeight, + _LGBMClassifierBase, + _LGBMComputeSampleWeight, + _LGBMCpuCount, + _LGBMLabelEncoder, + _LGBMModelBase, + _LGBMRegressorBase, + _LGBMValidateData, + _sklearn_version, + pd_DataFrame, +) +from .engine import train + +if TYPE_CHECKING: + from .compat import _sklearn_Tags + + +__all__ = [ + "LGBMClassifier", + "LGBMModel", + "LGBMRanker", + "LGBMRegressor", +] + +_LGBM_ScikitMatrixLike = Union[ + List[Union[List[float], List[int]]], + np.ndarray, + pd_DataFrame, + nwt.IntoDataFrame, + scipy.sparse.spmatrix, +] +_LGBM_ScikitCustomObjectiveFunction = Union[ + # f(labels, preds) + Callable[ + [Optional[np.ndarray], np.ndarray], + Tuple[np.ndarray, np.ndarray], + ], + # f(labels, preds, weights) + Callable[ + [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]], + Tuple[np.ndarray, np.ndarray], + ], + # f(labels, preds, weights, group) + Callable[ + [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]], + Tuple[np.ndarray, np.ndarray], + ], +] +_LGBM_ScikitCustomEvalFunction = Union[ + # f(labels, preds) + Callable[ + [Optional[np.ndarray], np.ndarray], + _LGBM_EvalFunctionResultType, + ], + Callable[ + [Optional[np.ndarray], np.ndarray], + List[_LGBM_EvalFunctionResultType], + ], + # f(labels, preds, weights) + Callable[ + [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]], + _LGBM_EvalFunctionResultType, + ], + Callable[ + [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]], + List[_LGBM_EvalFunctionResultType], + ], + # f(labels, preds, weights, group) + Callable[ + [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]], + _LGBM_EvalFunctionResultType, + ], + Callable[ + [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]], + List[_LGBM_EvalFunctionResultType], + ], +] +_LGBM_ScikitEvalMetricType = Union[ + str, + _LGBM_ScikitCustomEvalFunction, + List[Union[str, _LGBM_ScikitCustomEvalFunction]], +] +_LGBM_ScikitValidSet = Tuple[_LGBM_ScikitMatrixLike, _LGBM_LabelType] + + +def _get_group_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]: + group = dataset.get_group() + error_msg = ( + "Estimators in lightgbm.sklearn should only retrieve query groups from a constructed Dataset. " + "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." + ) + assert group is None or isinstance(group, np.ndarray), error_msg + return group + + +def _get_label_from_constructed_dataset(dataset: Dataset) -> np.ndarray: + label = dataset.get_label() + error_msg = ( + "Estimators in lightgbm.sklearn should only retrieve labels from a constructed Dataset. " + "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." + ) + assert isinstance(label, np.ndarray), error_msg + return label + + +def _get_weight_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]: + weight = dataset.get_weight() + error_msg = ( + "Estimators in lightgbm.sklearn should only retrieve weights from a constructed Dataset. " + "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." + ) + assert weight is None or isinstance(weight, np.ndarray), error_msg + return weight + + +class _ObjectiveFunctionWrapper: + """Proxy class for objective function.""" + + def __init__(self, func: _LGBM_ScikitCustomObjectiveFunction): + """Construct a proxy class. + + This class transforms objective function to match objective function with signature ``new_func(preds, dataset)`` + as expected by ``lightgbm.engine.train``. + + Parameters + ---------- + func : callable + Expects a callable with following signatures: + ``func(y_true, y_pred)``, + ``func(y_true, y_pred, weight)`` + or ``func(y_true, y_pred, weight, group)`` + and returns (grad, hess): + + y_true : numpy 1-D array of shape = [n_samples] + The target values. + y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The predicted values. + Predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task. + weight : numpy 1-D array of shape = [n_samples] + The weight of samples. Weights should be non-negative. + group : numpy 1-D array + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task) + The value of the first order derivative (gradient) of the loss + with respect to the elements of y_pred for each sample point. + hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The value of the second order derivative (Hessian) of the loss + with respect to the elements of y_pred for each sample point. + + .. note:: + + For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes], + and grad and hess should be returned in the same format. + """ + self.func = func + + def __call__( + self, + preds: np.ndarray, + dataset: Dataset, + ) -> Tuple[np.ndarray, np.ndarray]: + """Call passed function with appropriate arguments. + + Parameters + ---------- + preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The predicted values. + dataset : Dataset + The training dataset. + + Returns + ------- + grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The value of the first order derivative (gradient) of the loss + with respect to the elements of preds for each sample point. + hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The value of the second order derivative (Hessian) of the loss + with respect to the elements of preds for each sample point. + """ + labels = _get_label_from_constructed_dataset(dataset) + argc = len(signature(self.func).parameters) + if argc == 2: + grad, hess = self.func(labels, preds) # type: ignore[call-arg] + return grad, hess + + weight = _get_weight_from_constructed_dataset(dataset) + if argc == 3: + grad, hess = self.func(labels, preds, weight) # type: ignore[call-arg] + return grad, hess + + if argc == 4: + group = _get_group_from_constructed_dataset(dataset) + return self.func(labels, preds, weight, group) # type: ignore[call-arg] + + raise TypeError(f"Self-defined objective function should have 2, 3 or 4 arguments, got {argc}") + + +class _EvalFunctionWrapper: + """Proxy class for evaluation function.""" + + def __init__(self, func: _LGBM_ScikitCustomEvalFunction): + """Construct a proxy class. + + This class transforms evaluation function to match evaluation function with signature ``new_func(preds, dataset)`` + as expected by ``lightgbm.engine.train``. + + Parameters + ---------- + func : callable + Expects a callable with following signatures: + ``func(y_true, y_pred)``, + ``func(y_true, y_pred, weight)`` + or ``func(y_true, y_pred, weight, group)`` + and returns (metric_name, metric_value, maximize) or + list of (metric_name, metric_value, maximize): + + y_true : numpy 1-D array of shape = [n_samples] + The target values. + y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array shape = [n_samples, n_classes] (for multi-class task) + The predicted values. + In case of custom ``objective``, predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task in this case. + weight : numpy 1-D array of shape = [n_samples] + The weight of samples. Weights should be non-negative. + group : numpy 1-D array + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. + """ + self.func = func + + def __call__( + self, + preds: np.ndarray, + dataset: Dataset, + ) -> Union[_LGBM_EvalFunctionResultType, List[_LGBM_EvalFunctionResultType]]: + """Call passed function with appropriate arguments. + + Parameters + ---------- + preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The predicted values. + dataset : Dataset + The training dataset. + + Returns + ------- + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. + """ + labels = _get_label_from_constructed_dataset(dataset) + argc = len(signature(self.func).parameters) + if argc == 2: + return self.func(labels, preds) # type: ignore[call-arg] + + weight = _get_weight_from_constructed_dataset(dataset) + if argc == 3: + return self.func(labels, preds, weight) # type: ignore[call-arg] + + if argc == 4: + group = _get_group_from_constructed_dataset(dataset) + return self.func(labels, preds, weight, group) # type: ignore[call-arg] + + raise TypeError(f"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}") + + +# documentation templates for LGBMModel methods are shared between the classes in +# this module and those in the ``dask`` module + +_lgbmmodel_doc_fit = """ + Build a gradient boosting model from the training set (X, y). + + Parameters + ---------- + X : {X_shape} + Input feature matrix. + y : {y_shape} + The target values (class labels in classification, real numbers in regression). + sample_weight : {sample_weight_shape} + Weights of training data. Weights should be non-negative. + init_score : {init_score_shape} + Init score of training data. + group : {group_shape} + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + eval_set : list or None, optional (default=None) + .. deprecated:: 4.7.0 + A list of (X, y) tuple pairs to use as validation sets. + Use ``eval_X`` and ``eval_y`` instead. + eval_names : list of str, or None, optional (default=None) + Unique identifiers for each evaluation dataset. + Should be the same length as ``eval_set`` / ``eval_X``. + eval_sample_weight : {eval_sample_weight_shape} + Weights of eval data. Weights should be non-negative. + eval_class_weight : list or None, optional (default=None) + Class weights of eval data. + eval_init_score : {eval_init_score_shape} + Init score of eval data. + eval_group : {eval_group_shape} + Group data of eval data. + eval_metric : str, callable, list or None, optional (default=None) + If str, it should be a built-in evaluation metric to use. + If callable, it should be a custom evaluation metric, see note below for more details. + If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both. + In either case, the ``metric`` from the model parameters will be evaluated and used as well. + Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker. + feature_name : list of str, or 'auto', optional (default='auto') + Feature names. + If 'auto' and data is pandas DataFrame, data columns names are used. + categorical_feature : list of str or int, or 'auto', optional (default='auto') + Categorical features. + If list of int, interpreted as indices. + If list of str, interpreted as feature names (need to specify ``feature_name`` as well). + If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. + All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). + Large values could be memory consuming. Consider using consecutive integers starting from zero. + All negative values in categorical features will be treated as missing values. + The output cannot be monotonically constrained with respect to a categorical feature. + Floating point numbers in categorical features will be rounded towards 0. + callbacks : list of callable, or None, optional (default=None) + List of callback functions that are applied at each iteration. + See Callbacks in Python API for more information. + init_model : str, pathlib.Path, Booster, LGBMModel or None, optional (default=None) + Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training. + eval_X : {X_shape}, or tuple of such inputs, or None, optional (default=None) + Feature matrix or tuple thereof, e.g. ``(X_val0, X_val1)``, to use as validation sets. + eval_y : {y_shape}, or tuple of such inputs, or None, optional (default=None) + Target values or tuple thereof, e.g. ``(y_val0, y_val1)``, to use as validation sets. + + Returns + ------- + self : LGBMModel + Returns self. + """ + +_lgbmmodel_doc_custom_eval_note = """ + Note + ---- + Custom eval function expects a callable with following signatures: + ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or + ``func(y_true, y_pred, weight, group)`` + and returns (metric_name, metric_value, maximize) or + list of (metric_name, metric_value, maximize): + + y_true : numpy 1-D array of shape = [n_samples] + The target values. + y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The predicted values. + In case of custom ``objective``, predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task in this case. + weight : numpy 1-D array of shape = [n_samples] + The weight of samples. Weights should be non-negative. + group : numpy 1-D array + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + metric_name : str + Unique identifier for the metric (e.g. "custom_adjusted_mse"). + metric_value : float + Value of the evaluation metric. + maximize : bool + Are higher values better? e.g. ``True`` for AUC and ``False`` for binary error. +""" + +_lgbmmodel_doc_predict = """ + {description} + + Parameters + ---------- + X : {X_shape} + Input features matrix. + raw_score : bool, optional (default=False) + Whether to predict raw scores. + start_iteration : int, optional (default=0) + Start index of the iteration to predict. + If <= 0, starts from the first iteration. + num_iteration : int or None, optional (default=None) + Total number of iterations used in the prediction. + If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; + otherwise, all iterations from ``start_iteration`` are used (no limits). + If <= 0, all iterations from ``start_iteration`` are used (no limits). + pred_leaf : bool, optional (default=False) + Whether to predict leaf index. + pred_contrib : bool, optional (default=False) + Whether to predict feature contributions. + + .. note:: + + If you want to get more explanations for your model's predictions using SHAP values, + like SHAP interaction values, + you can install the shap package (https://github.com/slundberg/shap). + Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra + column, where the last column is the expected value. + + validate_features : bool, optional (default=False) + If True, ensure that the features used to predict match the ones used to train. + Used only if data is pandas DataFrame. + **kwargs + Other parameters for the prediction. + + Returns + ------- + {output_name} : {predicted_result_shape} + The predicted values. + X_leaves : {X_leaves_shape} + If ``pred_leaf=True``, the predicted leaf of every tree for each sample. + X_SHAP_values : {X_SHAP_values_shape} + If ``pred_contrib=True``, the feature contributions for each sample. + """ + + +def _extract_evaluation_meta_data( + *, + collection: Optional[Union[Dict[Any, Any], List[Any]]], + name: str, + i: int, +) -> Optional[Any]: + """Try to extract the ith element of one of the ``eval_*`` inputs.""" + if collection is None: + return None + elif isinstance(collection, list): + # It's possible, for example, to pass 3 eval sets through `eval_set`, + # but only 1 init_score through `eval_init_score`. + # + # This if-else accounts for that possibility. + if len(collection) > i: + return collection[i] + else: + return None + elif isinstance(collection, dict): + return collection.get(i, None) + else: + raise TypeError(f"{name} should be dict or list") + + +def _validate_eval_set_Xy( + *, + eval_set: Optional[List[_LGBM_ScikitValidSet]], + eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]], + eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]], +) -> Optional[List[_LGBM_ScikitValidSet]]: + """Validate eval args. + + Returns + ------- + eval_set + """ + if eval_set is not None: + msg = "The argument 'eval_set' is deprecated, use 'eval_X' and 'eval_y' instead." + warnings.warn(msg, category=LGBMDeprecationWarning, stacklevel=2) + if eval_X is not None or eval_y is not None: + raise ValueError("Specify either 'eval_set' or 'eval_X' and 'eval_y', but not both.") + if isinstance(eval_set, tuple): + return [eval_set] + else: + return eval_set + if (eval_X is None) != (eval_y is None): + raise ValueError("You must specify eval_X and eval_y, not just one of them.") + if eval_set is None and eval_X is not None: + if isinstance(eval_X, tuple) != isinstance(eval_y, tuple): + raise ValueError("If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.") + if isinstance(eval_X, tuple) and isinstance(eval_y, tuple): + if len(eval_X) != len(eval_y): + raise ValueError("If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.") + if isinstance(eval_X, tuple) and isinstance(eval_y, tuple): + eval_set = list(zip(eval_X, eval_y, strict=True)) + else: + eval_set = [(eval_X, eval_y)] + return eval_set + + +class LGBMModel(_LGBMModelBase): + """Implementation of the scikit-learn API for LightGBM.""" + + def __init__( + self, + *, + boosting_type: str = "gbdt", + num_leaves: int = 31, + max_depth: int = -1, + learning_rate: float = 0.1, + n_estimators: int = 100, + subsample_for_bin: int = 200000, + objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, + class_weight: Optional[Union[Dict, str]] = None, + min_split_gain: float = 0.0, + min_child_weight: float = 1e-3, + min_child_samples: int = 20, + subsample: float = 1.0, + subsample_freq: int = 0, + colsample_bytree: float = 1.0, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, + n_jobs: Optional[int] = None, + importance_type: str = "split", + **kwargs: Any, + ): + r"""Construct a gradient boosting model. + + Parameters + ---------- + boosting_type : str, optional (default='gbdt') + 'gbdt', traditional Gradient Boosting Decision Tree. + 'dart', Dropouts meet Multiple Additive Regression Trees. + 'rf', Random Forest. + num_leaves : int, optional (default=31) + Maximum tree leaves for base learners. + max_depth : int, optional (default=-1) + Maximum tree depth for base learners, <=0 means no limit. + If setting this to a positive value, consider also changing ``num_leaves`` to ``<= 2^max_depth``. + learning_rate : float, optional (default=0.1) + Boosting learning rate. + You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate + in training using ``reset_parameter`` callback. + Note, that this will ignore the ``learning_rate`` argument in training. + n_estimators : int, optional (default=100) + Number of boosted trees to fit. + subsample_for_bin : int, optional (default=200000) + Number of samples for constructing bins. + objective : str, callable or None, optional (default=None) + Specify the learning task and the corresponding learning objective or + a custom objective function to be used (see note below). + Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker. + class_weight : dict, 'balanced' or None, optional (default=None) + Weights associated with classes in the form ``{class_label: weight}``. + Use this parameter only for multi-class classification task; + for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters. + Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities. + You may want to consider performing probability calibration + (https://scikit-learn.org/stable/modules/calibration.html) of your model. + The 'balanced' mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``. + If None, all classes are supposed to have weight one. + Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method) + if ``sample_weight`` is specified. + min_split_gain : float, optional (default=0.) + Minimum loss reduction required to make a further partition on a leaf node of the tree. + min_child_weight : float, optional (default=1e-3) + Minimum sum of instance weight (Hessian) needed in a child (leaf). + min_child_samples : int, optional (default=20) + Minimum number of data needed in a child (leaf). + subsample : float, optional (default=1.) + Subsample ratio of the training instance. + subsample_freq : int, optional (default=0) + Frequency of subsample, <=0 means no enable. + colsample_bytree : float, optional (default=1.) + Subsample ratio of columns when constructing each tree. + reg_alpha : float, optional (default=0.) + L1 regularization term on weights. + reg_lambda : float, optional (default=0.) + L2 regularization term on weights. + random_state : int, RandomState object or None, optional (default=None) + Random number seed. + If int, this number is used to seed the C++ code. + If RandomState or Generator object (numpy), a random integer is picked based on its state to seed the C++ code. + If None, default seeds in C++ code are used. + n_jobs : int or None, optional (default=None) + Number of parallel threads to use for training (can be changed at prediction time by + passing it as an extra keyword argument). + + For better performance, it is recommended to set this to the number of physical cores + in the CPU. + + Negative integers are interpreted as following joblib's formula (n_cpus + 1 + n_jobs), just like + scikit-learn (so e.g. -1 means using all threads). A value of zero corresponds the default number of + threads configured for OpenMP in the system. A value of ``None`` (the default) corresponds + to using the number of physical cores in the system (its correct detection requires + either the ``joblib`` or the ``psutil`` util libraries to be installed). + + .. versionchanged:: 4.0.0 + + importance_type : str, optional (default='split') + The type of feature importance to be filled into ``feature_importances_``. + If 'split', result contains numbers of times the feature is used in a model. + If 'gain', result contains total gains of splits which use the feature. + **kwargs + Other parameters for the model. + Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters. + + .. warning:: + + \*\*kwargs is not supported in sklearn, it may cause unexpected issues. + + Note + ---- + A custom objective function can be provided for the ``objective`` parameter. + In this case, it should have the signature + ``objective(y_true, y_pred) -> grad, hess``, + ``objective(y_true, y_pred, weight) -> grad, hess`` + or ``objective(y_true, y_pred, weight, group) -> grad, hess``: + + y_true : numpy 1-D array of shape = [n_samples] + The target values. + y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The predicted values. + Predicted values are returned before any transformation, + e.g. they are raw margin instead of probability of positive class for binary task. + weight : numpy 1-D array of shape = [n_samples] + The weight of samples. Weights should be non-negative. + group : numpy 1-D array + Group/query data. + Only used in the learning-to-rank task. + sum(group) = n_samples. + For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups, + where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc. + grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The value of the first order derivative (gradient) of the loss + with respect to the elements of y_pred for each sample point. + hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task) + The value of the second order derivative (Hessian) of the loss + with respect to the elements of y_pred for each sample point. + + For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes], + and grad and hess should be returned in the same format. + """ + if not SKLEARN_INSTALLED: + raise LightGBMError( + "scikit-learn is required for lightgbm.sklearn. " + "You must install scikit-learn and restart your session to use this module." + ) + + self.boosting_type = boosting_type + self.objective = objective + self.num_leaves = num_leaves + self.max_depth = max_depth + self.learning_rate = learning_rate + self.n_estimators = n_estimators + self.subsample_for_bin = subsample_for_bin + self.min_split_gain = min_split_gain + self.min_child_weight = min_child_weight + self.min_child_samples = min_child_samples + self.subsample = subsample + self.subsample_freq = subsample_freq + self.colsample_bytree = colsample_bytree + self.reg_alpha = reg_alpha + self.reg_lambda = reg_lambda + self.random_state = random_state + self.n_jobs = n_jobs + self.importance_type = importance_type + self._Booster: Optional[Booster] = None + self._evals_result: _EvalResultDict = {} + self._best_score: _LGBM_BoosterBestScoreType = {} + self._best_iteration: int = -1 + self._other_params: Dict[str, Any] = {} + self._objective = objective + self.class_weight = class_weight + self._class_weight: Optional[Union[Dict, str]] = None + self._class_map: Optional[Dict[int, int]] = None + self._fitted_with_feature_names: bool = False + self._n_features: int = -1 + self._n_features_in: int = -1 + self._classes: Optional[np.ndarray] = None + self._n_classes: int = -1 + self.set_params(**kwargs) + + # scikit-learn 1.6 introduced an __sklearn__tags() method intended to replace _more_tags(). + # _more_tags() can be removed whenever lightgbm's minimum supported scikit-learn version + # is >=1.6. + # ref: https://github.com/lightgbm-org/LightGBM/pull/6651 + def _more_tags(self) -> Dict[str, Any]: + check_sample_weight_str = ( + "In LightGBM, setting a sample's weight to 0 can produce a different result than omitting the sample. " + "Such samples intentionally still affect count-based measures like 'min_data_in_leaf' " + "(https://github.com/lightgbm-org/LightGBM/issues/5626#issuecomment-1712706678) and the estimated distribution " + "of features for Dataset construction (see https://github.com/lightgbm-org/LightGBM/issues/5553)." + ) + # "check_sample_weight_equivalence" can be removed when lightgbm's + # minimum supported scikit-learn version is at least 1.6 + # ref: https://github.com/scikit-learn/scikit-learn/pull/30137 + xfail_checks = { + "check_no_attributes_set_in_init": ( + "scikit-learn incorrectly asserts that private attributes " + "cannot be set in __init__: " + "(see https://github.com/lightgbm-org/LightGBM/issues/2628)" + ), + "check_all_zero_sample_weights_error": ( + "Beginning in scikit-learn 1.9, by default estimators are expected to reject " + "sample weight arrays that are all-0. LightGBM intentionally accepts such arrays. " + "LightGBM supports some operations where training on an all-0-weight input could make sense, " + "like batch updates with training continuation or manual model creation with forced splits." + ), + "check_sample_weight_equivalence": check_sample_weight_str, + "check_sample_weight_equivalence_on_dense_data": check_sample_weight_str, + "check_sample_weight_equivalence_on_sparse_data": check_sample_weight_str, + } + # "check_decision_proba_consistency" can be removed when lightgbm's + # minimum supported scikit-learn version is at least 1.2 + sklearn_major, sklearn_minor, *_ = _sklearn_version.split(".") + if (int(sklearn_major), int(sklearn_minor)) < (1, 2): + xfail_checks["check_decision_proba_consistency"] = ( + "decision_function() returns raw margins while predict_proba() applies sigmoid in C++ " + "independently, causing different tie structures after rounding. " + "scikit-learn >= 1.2 relaxed this check to accept monotonically consistent scores." + ) + return { + "allow_nan": True, + "X_types": ["2darray", "sparse", "1dlabels"], + "_xfail_checks": xfail_checks, + } + + @staticmethod + def _update_sklearn_tags_from_dict( + *, + tags: "_sklearn_Tags", + tags_dict: Dict[str, Any], + ) -> "_sklearn_Tags": + """Update ``sklearn.utils.Tags`` inherited from ``scikit-learn`` base classes. + + ``scikit-learn`` 1.6 introduced a dataclass-based interface for estimator tags. + ref: https://github.com/scikit-learn/scikit-learn/pull/29677 + + This method handles updating that instance based on the value in ``self._more_tags()``. + """ + tags.input_tags.allow_nan = tags_dict["allow_nan"] + tags.input_tags.sparse = "sparse" in tags_dict["X_types"] + tags.target_tags.one_d_labels = "1dlabels" in tags_dict["X_types"] + return tags + + def __sklearn_tags__(self) -> Optional["_sklearn_Tags"]: + # _LGBMModelBase.__sklearn_tags__() cannot be called unconditionally, + # because that method isn't defined for scikit-learn<1.6 + if not hasattr(_LGBMModelBase, "__sklearn_tags__"): + err_msg = ( + "__sklearn_tags__() should not be called when using scikit-learn<1.6. " + f"Detected version: {_sklearn_version}" + ) + raise AttributeError(err_msg) + + # take whatever tags are provided by BaseEstimator, then modify + # them with LightGBM-specific values + return self._update_sklearn_tags_from_dict( + tags=super().__sklearn_tags__(), + tags_dict=self._more_tags(), + ) + + def __sklearn_is_fitted__(self) -> bool: + return getattr(self, "fitted_", False) + + def get_params(self, deep: bool = True) -> Dict[str, Any]: + """Get parameters for this estimator. + + Parameters + ---------- + deep : bool, optional (default=True) + If True, will return the parameters for this estimator and + contained subobjects that are estimators. + + Returns + ------- + params : dict + Parameter names mapped to their values. + """ + # Based on: https://github.com/dmlc/xgboost/blob/bd92b1c9c0db3e75ec3dfa513e1435d518bb535d/python-package/xgboost/sklearn.py#L941 + # which was based on: https://stackoverflow.com/questions/59248211 + # + # `get_params()` flows like this: + # + # 0. Get parameters in subclass (self.__class__) first, by using inspect. + # 1. Get parameters in all parent classes (especially `LGBMModel`). + # 2. Get whatever was passed via `**kwargs`. + # 3. Merge them. + # + # This needs to accommodate being called recursively in the following + # inheritance graphs (and similar for classification and ranking): + # + # DaskLGBMRegressor -> LGBMRegressor -> LGBMModel -> BaseEstimator + # (custom subclass) -> LGBMRegressor -> LGBMModel -> BaseEstimator + # LGBMRegressor -> LGBMModel -> BaseEstimator + # (custom subclass) -> LGBMModel -> BaseEstimator + # LGBMModel -> BaseEstimator + # + params = super().get_params(deep=deep) + cp = copy.copy(self) + # If the immediate parent defines get_params(), use that. + if callable(getattr(cp.__class__.__bases__[0], "get_params", None)): + cp.__class__ = cp.__class__.__bases__[0] + # Otherwise, skip it and assume the next class will have it. + # This is here primarily for cases where the first class in MRO is a scikit-learn mixin. + else: + cp.__class__ = cp.__class__.__bases__[1] + params.update(cp.__class__.get_params(cp, deep)) + params.update(self._other_params) + return params + + def set_params(self, **params: Any) -> "LGBMModel": + """Set the parameters of this estimator. + + Parameters + ---------- + **params + Parameter names with their new values. + + Returns + ------- + self : object + Returns self. + """ + for key, value in params.items(): + setattr(self, key, value) + if hasattr(self, f"_{key}"): + setattr(self, f"_{key}", value) + self._other_params[key] = value + return self + + def _process_params(self, stage: str) -> Dict[str, Any]: + """Process the parameters of this estimator based on its type, parameter aliases, etc. + + Parameters + ---------- + stage : str + Name of the stage (can be ``fit`` or ``predict``) this method is called from. + + Returns + ------- + processed_params : dict + Processed parameter names mapped to their values. + """ + assert stage in {"fit", "predict"} + params = self.get_params() + + params.pop("objective", None) + for alias in _ConfigAliases.get("objective"): + if alias in params: + obj = params.pop(alias) + _log_warning(f"Found '{alias}' in params. Will use it instead of 'objective' argument") + if stage == "fit": + self._objective = obj + if stage == "fit": + if self._objective is None: + if isinstance(self, LGBMRegressor): + self._objective = "regression" + elif isinstance(self, LGBMClassifier): + if self._n_classes > 2: + self._objective = "multiclass" + else: + self._objective = "binary" + elif isinstance(self, LGBMRanker): + self._objective = "lambdarank" + else: + raise ValueError("Unknown LGBMModel type.") + if callable(self._objective): + if stage == "fit": + params["objective"] = _ObjectiveFunctionWrapper(self._objective) + else: + params["objective"] = "None" + else: + params["objective"] = self._objective + + params.pop("importance_type", None) + params.pop("n_estimators", None) + params.pop("class_weight", None) + + if isinstance(params["random_state"], np.random.RandomState): + params["random_state"] = params["random_state"].randint(np.iinfo(np.int32).max) + elif isinstance(params["random_state"], np.random.Generator): + params["random_state"] = int(params["random_state"].integers(np.iinfo(np.int32).max)) + if self._n_classes > 2: + for alias in _ConfigAliases.get("num_class"): + params.pop(alias, None) + params["num_class"] = self._n_classes + if hasattr(self, "_eval_at"): + eval_at = self._eval_at + for alias in _ConfigAliases.get("eval_at"): + if alias in params: + _log_warning(f"Found '{alias}' in params. Will use it instead of 'eval_at' argument") + eval_at = params.pop(alias) + params["eval_at"] = eval_at + + # register default metric for consistency with callable eval_metric case + original_metric = self._objective if isinstance(self._objective, str) else None + if original_metric is None: + # try to deduce from class instance + if isinstance(self, LGBMRegressor): + original_metric = "l2" + elif isinstance(self, LGBMClassifier): + original_metric = "multi_logloss" if self._n_classes > 2 else "binary_logloss" + elif isinstance(self, LGBMRanker): + original_metric = "ndcg" + + # overwrite default metric by explicitly set metric + params = _choose_param_value("metric", params, original_metric) + + # use joblib conventions for negative n_jobs, just like scikit-learn + # at predict time, this is handled later due to the order of parameter updates + if stage == "fit": + params = _choose_param_value("num_threads", params, self.n_jobs) + params["num_threads"] = self._process_n_jobs(params["num_threads"]) + + return params + + def _process_n_jobs(self, n_jobs: Optional[int]) -> int: + """Convert special values of n_jobs to their actual values according to the formulas that apply. + + Parameters + ---------- + n_jobs : int or None + The original value of n_jobs, potentially having special values such as 'None' or + negative integers. + + Returns + ------- + n_jobs : int + The value of n_jobs with special values converted to actual number of threads. + """ + if n_jobs is None: + n_jobs = _LGBMCpuCount(only_physical_cores=True) + elif n_jobs < 0: + n_jobs = max(_LGBMCpuCount(only_physical_cores=False) + 1 + n_jobs, 1) + return n_jobs + + def fit( + self, + X: _LGBM_ScikitMatrixLike, + y: _LGBM_LabelType, + sample_weight: Optional[_LGBM_WeightType] = None, + init_score: Optional[_LGBM_InitScoreType] = None, + group: Optional[_LGBM_GroupType] = None, + eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, + eval_names: Optional[List[str]] = None, + eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, + eval_class_weight: Optional[List[float]] = None, + eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, + eval_group: Optional[List[_LGBM_GroupType]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + feature_name: _LGBM_FeatureNameConfiguration = "auto", + categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", + callbacks: Optional[List[Callable]] = None, + init_model: Optional[Union[str, Path, Booster, "LGBMModel"]] = None, + *, + eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, + eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, + ) -> "LGBMModel": + """Docstring is set after definition, using a template.""" + params = self._process_params(stage="fit") + + # Do not modify original args in fit function + # Refer to https://github.com/lightgbm-org/LightGBM/pull/2619 + eval_metric_list: List[Union[str, _LGBM_ScikitCustomEvalFunction]] + if eval_metric is None: + eval_metric_list = [] + elif isinstance(eval_metric, list): + eval_metric_list = copy.deepcopy(eval_metric) + else: + eval_metric_list = [copy.deepcopy(eval_metric)] + + # Separate built-in from callable evaluation metrics + eval_metrics_callable = [_EvalFunctionWrapper(f) for f in eval_metric_list if callable(f)] + eval_metrics_builtin = [m for m in eval_metric_list if isinstance(m, str)] + + # concatenate metric from params (or default if not provided in params) and eval_metric + params["metric"] = [params["metric"]] if isinstance(params["metric"], (str, type(None))) else params["metric"] + params["metric"] = [e for e in eval_metrics_builtin if e not in params["metric"]] + params["metric"] + params["metric"] = [metric for metric in params["metric"] if metric is not None] + + if isinstance(X, pd_DataFrame): + _X, _y = X, y + self.n_features_in_ = _X.shape[1] + elif nwd.is_into_dataframe(X): + _X, _y = X, y + self.n_features_in_ = nw.from_native(X).shape[1] + else: + # NOTE: _LGBMValidateData() is also responsible for setting n_features_in_ + _X, _y = _LGBMValidateData( + self, + X, + y, + reset=True, + # allow any input type (this validation is done further down, in lgb.Dataset()) + accept_sparse=True, + # do not raise an error if Inf of NaN values are found (LightGBM handles these internally) + ensure_all_finite=False, + # raise an error on 0-row and 1-row inputs + ensure_min_samples=2, + ) + if sample_weight is not None: + if SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG: + sample_weight = _LGBMCheckSampleWeight(sample_weight, _X, allow_all_zero_weights=True) + else: + sample_weight = _LGBMCheckSampleWeight(sample_weight, _X) + + if self._class_weight is None: + self._class_weight = self.class_weight + if self._class_weight is not None: + class_sample_weight = _LGBMComputeSampleWeight(self._class_weight, y) + if sample_weight is None or len(sample_weight) == 0: + sample_weight = class_sample_weight + else: + sample_weight = np.multiply(sample_weight, class_sample_weight) # type: ignore[arg-type] + + train_set = Dataset( + data=_X, + label=_y, + weight=sample_weight, + group=group, + init_score=init_score, + categorical_feature=categorical_feature, + feature_name=feature_name, + params=params, + ) + + valid_sets: List[Dataset] = [] + eval_set = _validate_eval_set_Xy(eval_set=eval_set, eval_X=eval_X, eval_y=eval_y) + if eval_set is not None: + # check eval_group (only relevant for ranking tasks) + if eval_group is not None: + if len(eval_group) != len(eval_set): + raise ValueError( + f"Length of eval_group ({len(eval_group)}) not equal to length of eval_set ({len(eval_set)})" + ) + + for i, valid_data in enumerate(eval_set): + # reduce cost for prediction training data + if valid_data[0] is X and valid_data[1] is y: + valid_set = train_set + else: + valid_weight = _extract_evaluation_meta_data( + collection=eval_sample_weight, + name="eval_sample_weight", + i=i, + ) + valid_class_weight = _extract_evaluation_meta_data( + collection=eval_class_weight, + name="eval_class_weight", + i=i, + ) + if valid_class_weight is not None: + if isinstance(valid_class_weight, dict) and self._class_map is not None: + valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()} + valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1]) + if valid_weight is None or len(valid_weight) == 0: + valid_weight = valid_class_sample_weight + else: + valid_weight = np.multiply(valid_weight, valid_class_sample_weight) + valid_init_score = _extract_evaluation_meta_data( + collection=eval_init_score, + name="eval_init_score", + i=i, + ) + valid_group = _extract_evaluation_meta_data( + collection=eval_group, + name="eval_group", + i=i, + ) + valid_set = Dataset( + data=valid_data[0], + label=valid_data[1], + weight=valid_weight, + group=valid_group, + init_score=valid_init_score, + categorical_feature="auto", + params=params, + ) + + valid_sets.append(valid_set) + + if isinstance(init_model, LGBMModel): + init_model = init_model.booster_ + + if callbacks is None: + callbacks = [] + else: + callbacks = copy.copy(callbacks) # don't use deepcopy here to allow non-serializable objects + + evals_result: _EvalResultDict = {} + callbacks.append(record_evaluation(evals_result)) + + self._Booster = train( + params=params, + train_set=train_set, + num_boost_round=self.n_estimators, + valid_sets=valid_sets, + valid_names=eval_names, + feval=eval_metrics_callable, # type: ignore[arg-type] + init_model=init_model, + callbacks=callbacks, + ) + + # This populates the property self.n_features_, the number of features in the fitted model, + # and so should only be set after fitting. + # + # The related property self._n_features_in, which populates self.n_features_in_, + # is set BEFORE fitting. + self._n_features = self._Booster.num_feature() + + # This attribute informs self.features_in_, but isn't set until here + # because Dataset.construct(), called by train(), is responsible for updating it. + self._fitted_with_feature_names = train_set._has_non_default_feature_names + + self._evals_result = evals_result + self._best_iteration = self._Booster.best_iteration + self._best_score = self._Booster.best_score + + self.fitted_ = True + + # free dataset + self._Booster.free_dataset() + del train_set, valid_sets + return self + + fit.__doc__ = ( + _lgbmmodel_doc_fit.format( + X_shape="numpy array, pandas DataFrame, pyarrow Table, polars DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]", + y_shape="numpy array, pandas DataFrame, pandas Series, list of int or float, pyarrow ChunkedArray or polars Series of shape = [n_samples]", + sample_weight_shape="numpy array, pandas Series, list of int or float, pyarrow ChunkedArray, polars Series of shape = [n_samples] or None, optional (default=None)", + init_score_shape="numpy array, pandas DataFrame, pandas Series, list of int or float, list of lists, pyarrow ChunkedArray, pyarrow Table, polars Series, polars DataFrame of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) or shape = [n_samples, n_classes] (for multi-class task) or None, optional (default=None)", + group_shape="numpy array, pandas Series, pyarrow ChunkedArray, polars Series, list of int or float, or None, optional (default=None)", + eval_sample_weight_shape="list of array (same types as ``sample_weight`` supports), or None, optional (default=None)", + eval_init_score_shape="list of array (same types as ``init_score`` supports), or None, optional (default=None)", + eval_group_shape="list of array (same types as ``group`` supports), or None, optional (default=None)", + ) + + "\n\n" + + _lgbmmodel_doc_custom_eval_note + ) + + def predict( + self, + X: _LGBM_ScikitMatrixLike, + raw_score: bool = False, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + pred_leaf: bool = False, + pred_contrib: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> _LGBM_PredictReturnType: + """Docstring is set after definition, using a template.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("Estimator not fitted, call fit before exploiting the model.") + if not isinstance(X, pd_DataFrame) and not nwd.is_into_dataframe(X): + X = _LGBMValidateData( + self, + X, + # 'y' being omitted = run scikit-learn's check_array() instead of check_X_y() + # + # Prevent scikit-learn from deleting or modifying attributes like 'feature_names_in_' and 'n_features_in_'. + # These shouldn't be changed at predict() time. + reset=False, + # allow any input type (this validation is done further down, in lgb.Dataset()) + accept_sparse=True, + # do not raise an error if Inf of NaN values are found (LightGBM handles these internally) + ensure_all_finite=False, + # raise an error on 0-row inputs + ensure_min_samples=1, + ) + # retrieve original params that possibly can be used in both training and prediction + # and then overwrite them (considering aliases) with params that were passed directly in prediction + predict_params = self._process_params(stage="predict") + for alias in _ConfigAliases.get_by_alias( + "data", + "X", + "raw_score", + "start_iteration", + "num_iteration", + "pred_leaf", + "pred_contrib", + *kwargs.keys(), + ): + predict_params.pop(alias, None) + predict_params.update(kwargs) + + # number of threads can have values with special meaning which is only applied + # in the scikit-learn interface, these should not reach the c++ side as-is + predict_params = _choose_param_value("num_threads", predict_params, self.n_jobs) + predict_params["num_threads"] = self._process_n_jobs(predict_params["num_threads"]) + + return self._Booster.predict( # type: ignore[union-attr] + X, + raw_score=raw_score, + start_iteration=start_iteration, + num_iteration=num_iteration, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + validate_features=validate_features, + **predict_params, + ) + + predict.__doc__ = _lgbmmodel_doc_predict.format( + description="Return the predicted value for each sample.", + X_shape="numpy array, pandas DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]", + output_name="predicted_result", + predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]", + X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", + X_SHAP_values_shape="array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects", + ) + + @property + def n_features_(self) -> int: + """:obj:`int`: The number of features of fitted model.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No n_features found. Need to call fit beforehand.") + return self._n_features + + @property + def n_features_in_(self) -> int: + """:obj:`int`: The number of features of fitted model.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No n_features_in found. Need to call fit beforehand.") + return self._n_features_in + + @n_features_in_.setter + def n_features_in_(self, value: int) -> None: + """Set number of features found in passed-in dataset. + + Starting with ``scikit-learn`` 1.6, ``scikit-learn`` expects to be able to directly + set this property in functions like ``validate_data()``. + + .. note:: + + Do not call ``estimator.n_features_in_ = some_int`` or anything else that invokes + this method. It is only here for compatibility with ``scikit-learn`` validation + functions used internally in ``lightgbm``. + """ + self._n_features_in = value + + @property + def best_score_(self) -> _LGBM_BoosterBestScoreType: + """:obj:`dict`: The best score of fitted model.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No best_score found. Need to call fit beforehand.") + return self._best_score + + @property + def best_iteration_(self) -> int: + """:obj:`int`: The best iteration of fitted model if ``early_stopping()`` callback has been specified.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError( + "No best_iteration found. Need to call fit with early_stopping callback beforehand." + ) + return self._best_iteration + + @property + def objective_(self) -> Union[str, _LGBM_ScikitCustomObjectiveFunction]: + """:obj:`str` or :obj:`callable`: The concrete objective used while fitting this model.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No objective found. Need to call fit beforehand.") + return self._objective # type: ignore[return-value] + + @property + def n_estimators_(self) -> int: + """:obj:`int`: True number of boosting iterations performed. + + This might be less than parameter ``n_estimators`` if early stopping was enabled or + if boosting stopped early due to limits on complexity like ``min_gain_to_split``. + + .. versionadded:: 4.0.0 + """ + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No n_estimators found. Need to call fit beforehand.") + return self._Booster.current_iteration() # type: ignore + + @property + def n_iter_(self) -> int: + """:obj:`int`: True number of boosting iterations performed. + + This might be less than parameter ``n_estimators`` if early stopping was enabled or + if boosting stopped early due to limits on complexity like ``min_gain_to_split``. + + .. versionadded:: 4.0.0 + """ + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No n_iter found. Need to call fit beforehand.") + return self._Booster.current_iteration() # type: ignore + + @property + def booster_(self) -> Booster: + """Booster: The underlying Booster of this model.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No booster found. Need to call fit beforehand.") + return self._Booster # type: ignore[return-value] + + @property + def evals_result_(self) -> _EvalResultDict: + """:obj:`dict`: The evaluation results if validation sets have been specified.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No results found. Need to call fit with eval_set beforehand.") + return self._evals_result + + @property + def feature_importances_(self) -> np.ndarray: + """:obj:`array` of shape = [n_features]: The feature importances (the higher, the more important). + + .. note:: + + ``importance_type`` attribute is passed to the function + to configure the type of importance values to be extracted. + """ + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No feature_importances found. Need to call fit beforehand.") + return self._Booster.feature_importance(importance_type=self.importance_type) # type: ignore[union-attr] + + @property + def feature_name_(self) -> List[str]: + """:obj:`list` of shape = [n_features]: The names of features. + + .. note:: + + If input does not contain feature names, they will be added during fitting in the format ``Column_0``, ``Column_1``, ..., ``Column_N``. + """ + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No feature_name found. Need to call fit beforehand.") + return self._Booster.feature_name() # type: ignore[union-attr] + + @property + def feature_names_in_(self) -> np.ndarray: + """:obj:`array` of shape = [n_features]: scikit-learn compatible version of ``.feature_name_``. + + Only available when training data had feature names (e.g. a pandas DataFrame). + When training was done with data without feature names (e.g. a numpy array), + accessing this attribute raises ``AttributeError``. + + .. versionadded:: 4.5.0 + """ + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No feature_names_in_ found. Need to call fit beforehand.") + if not self._fitted_with_feature_names: + raise AttributeError( + f"'{type(self).__name__}' object has no attribute 'feature_names_in_'. " + "The training data did not have feature names " + "(e.g. was a numpy array rather than a pandas DataFrame)." + ) + return np.array(self.feature_name_) + + @feature_names_in_.deleter + def feature_names_in_(self) -> None: + """Intercept calls to delete ``feature_names_in_``. + + Some code paths in ``scikit-learn`` try to delete the ``feature_names_in_`` attribute + on estimators when a new training dataset that doesn't have features is passed. + LightGBM has custom handling of feature names and has chosen to opt out of this behavior. + + However, that behavior is coupled to ``scikit-learn`` automatically updating + ``n_features_in_`` in those same code paths, which is necessary for compliance + with its API (via argument ``reset`` to functions like ``validate_data()`` and + ``check_array()``). + + .. note:: + + Do not call ``del estimator.feature_names_in_`` or anything else that invokes + this method. It is only here for compatibility with ``scikit-learn`` validation + functions used internally in ``lightgbm``. + """ + pass + + +class LGBMRegressor(_LGBMRegressorBase, LGBMModel): + """LightGBM regressor.""" + + # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for + # docs, help(), and tab completion. + def __init__( + self, + *, + boosting_type: str = "gbdt", + num_leaves: int = 31, + max_depth: int = -1, + learning_rate: float = 0.1, + n_estimators: int = 100, + subsample_for_bin: int = 200000, + objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, + class_weight: Optional[Union[Dict, str]] = None, + min_split_gain: float = 0.0, + min_child_weight: float = 1e-3, + min_child_samples: int = 20, + subsample: float = 1.0, + subsample_freq: int = 0, + colsample_bytree: float = 1.0, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, + n_jobs: Optional[int] = None, + importance_type: str = "split", + **kwargs: Any, + ) -> None: + super().__init__( + boosting_type=boosting_type, + num_leaves=num_leaves, + max_depth=max_depth, + learning_rate=learning_rate, + n_estimators=n_estimators, + subsample_for_bin=subsample_for_bin, + objective=objective, + class_weight=class_weight, + min_split_gain=min_split_gain, + min_child_weight=min_child_weight, + min_child_samples=min_child_samples, + subsample=subsample, + subsample_freq=subsample_freq, + colsample_bytree=colsample_bytree, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + random_state=random_state, + n_jobs=n_jobs, + importance_type=importance_type, + **kwargs, + ) + + __init__.__doc__ = LGBMModel.__init__.__doc__ + + def _more_tags(self) -> Dict[str, Any]: + # handle the case where RegressorMixin possibly provides _more_tags() + if callable(getattr(_LGBMRegressorBase, "_more_tags", None)): + tags = _LGBMRegressorBase._more_tags(self) + else: + tags = {} + # override those with LightGBM-specific preferences + tags.update(LGBMModel._more_tags(self)) + return tags + + def __sklearn_tags__(self) -> "_sklearn_Tags": + return super().__sklearn_tags__() + + def fit( # type: ignore[override] + self, + X: _LGBM_ScikitMatrixLike, + y: _LGBM_LabelType, + sample_weight: Optional[_LGBM_WeightType] = None, + init_score: Optional[_LGBM_InitScoreType] = None, + eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, + eval_names: Optional[List[str]] = None, + eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, + eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + feature_name: _LGBM_FeatureNameConfiguration = "auto", + categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", + callbacks: Optional[List[Callable]] = None, + init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, + *, + eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, + eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, + ) -> "LGBMRegressor": + """Docstring is inherited from the LGBMModel.""" + super().fit( + X, + y, + sample_weight=sample_weight, + init_score=init_score, + eval_set=eval_set, + eval_X=eval_X, + eval_y=eval_y, + eval_names=eval_names, + eval_sample_weight=eval_sample_weight, + eval_init_score=eval_init_score, + eval_metric=eval_metric, + feature_name=feature_name, + categorical_feature=categorical_feature, + callbacks=callbacks, + init_model=init_model, + ) + return self + + _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMRegressor") # type: ignore + _base_doc = ( + _base_doc[: _base_doc.find("group :")] # type: ignore + + _base_doc[_base_doc.find("eval_set :") :] + ) # type: ignore + _base_doc = _base_doc[: _base_doc.find("eval_class_weight :")] + _base_doc[_base_doc.find("eval_init_score :") :] + fit.__doc__ = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] + + +class LGBMClassifier(_LGBMClassifierBase, LGBMModel): + """LightGBM classifier.""" + + # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for + # docs, help(), and tab completion. + def __init__( + self, + *, + boosting_type: str = "gbdt", + num_leaves: int = 31, + max_depth: int = -1, + learning_rate: float = 0.1, + n_estimators: int = 100, + subsample_for_bin: int = 200000, + objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, + class_weight: Optional[Union[Dict, str]] = None, + min_split_gain: float = 0.0, + min_child_weight: float = 1e-3, + min_child_samples: int = 20, + subsample: float = 1.0, + subsample_freq: int = 0, + colsample_bytree: float = 1.0, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, + n_jobs: Optional[int] = None, + importance_type: str = "split", + **kwargs: Any, + ) -> None: + super().__init__( + boosting_type=boosting_type, + num_leaves=num_leaves, + max_depth=max_depth, + learning_rate=learning_rate, + n_estimators=n_estimators, + subsample_for_bin=subsample_for_bin, + objective=objective, + class_weight=class_weight, + min_split_gain=min_split_gain, + min_child_weight=min_child_weight, + min_child_samples=min_child_samples, + subsample=subsample, + subsample_freq=subsample_freq, + colsample_bytree=colsample_bytree, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + random_state=random_state, + n_jobs=n_jobs, + importance_type=importance_type, + **kwargs, + ) + + __init__.__doc__ = LGBMModel.__init__.__doc__ + + def _more_tags(self) -> Dict[str, Any]: + # handle the case where ClassifierMixin possibly provides _more_tags() + if callable(getattr(_LGBMClassifierBase, "_more_tags", None)): + tags = _LGBMClassifierBase._more_tags(self) + else: + tags = {} + # override those with LightGBM-specific preferences + tags.update(LGBMModel._more_tags(self)) + return tags + + def __sklearn_tags__(self) -> "_sklearn_Tags": + tags = super().__sklearn_tags__() + if tags is not None: + tags.classifier_tags.multi_class = True + tags.classifier_tags.multi_label = False + return tags + + def fit( # type: ignore[override] + self, + X: _LGBM_ScikitMatrixLike, + y: _LGBM_LabelType, + sample_weight: Optional[_LGBM_WeightType] = None, + init_score: Optional[_LGBM_InitScoreType] = None, + eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, + eval_names: Optional[List[str]] = None, + eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, + eval_class_weight: Optional[List[float]] = None, + eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + feature_name: _LGBM_FeatureNameConfiguration = "auto", + categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", + callbacks: Optional[List[Callable]] = None, + init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, + *, + eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, + eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, + ) -> "LGBMClassifier": + """Docstring is inherited from the LGBMModel.""" + _LGBMAssertAllFinite(y) + _LGBMCheckClassificationTargets(y) + self._le = _LGBMLabelEncoder().fit(y) + _y = self._le.transform(y) + self._class_map = dict(zip(self._le.classes_, self._le.transform(self._le.classes_), strict=True)) + if isinstance(self.class_weight, dict): + self._class_weight = {self._class_map[k]: v for k, v in self.class_weight.items()} + + self._classes = self._le.classes_ + self._n_classes = len(self._classes) # type: ignore[arg-type] + if self.objective is None: + self._objective = None + + # adjust eval metrics to match whether binary or multiclass + # classification is being performed + if not callable(eval_metric): + if isinstance(eval_metric, list): + eval_metric_list = eval_metric + elif isinstance(eval_metric, str): + eval_metric_list = [eval_metric] + else: + eval_metric_list = [] + if self.__is_multiclass: + for index, metric in enumerate(eval_metric_list): + if metric in {"logloss", "binary_logloss"}: + eval_metric_list[index] = "multi_logloss" + elif metric in {"error", "binary_error"}: + eval_metric_list[index] = "multi_error" + else: + for index, metric in enumerate(eval_metric_list): + if metric in {"logloss", "multi_logloss"}: + eval_metric_list[index] = "binary_logloss" + elif metric in {"error", "multi_error"}: + eval_metric_list[index] = "binary_error" + eval_metric = eval_metric_list + + # do not modify args, as it causes errors in model selection tools + valid_sets: Optional[List[_LGBM_ScikitValidSet]] = None + if eval_set is not None: + if isinstance(eval_set, tuple): + eval_set = [eval_set] + valid_sets = [] + for valid_x, valid_y in eval_set: + if valid_x is X and valid_y is y: + valid_sets.append((valid_x, _y)) + else: + valid_sets.append((valid_x, self._le.transform(valid_y))) + + super().fit( + X, + _y, + sample_weight=sample_weight, + init_score=init_score, + eval_set=valid_sets, + eval_names=eval_names, + eval_X=eval_X, + eval_y=eval_y, + eval_sample_weight=eval_sample_weight, + eval_class_weight=eval_class_weight, + eval_init_score=eval_init_score, + eval_metric=eval_metric, + feature_name=feature_name, + categorical_feature=categorical_feature, + callbacks=callbacks, + init_model=init_model, + ) + return self + + _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMClassifier") # type: ignore + _base_doc = ( + _base_doc[: _base_doc.find("group :")] # type: ignore + + _base_doc[_base_doc.find("eval_set :") :] + ) # type: ignore + fit.__doc__ = _base_doc[: _base_doc.find("eval_group :")] + _base_doc[_base_doc.find("eval_metric :") :] + + def predict( + self, + X: _LGBM_ScikitMatrixLike, + raw_score: bool = False, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + pred_leaf: bool = False, + pred_contrib: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> _LGBM_PredictReturnType: + """Docstring is inherited from the LGBMModel.""" + result = self.predict_proba( + X=X, + raw_score=raw_score, + start_iteration=start_iteration, + num_iteration=num_iteration, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + validate_features=validate_features, + **kwargs, + ) + if callable(self._objective) or raw_score or pred_leaf or pred_contrib: + return result + else: + class_index = np.argmax(result, axis=1) + return self._le.inverse_transform(class_index) + + predict.__doc__ = LGBMModel.predict.__doc__ + + def predict_proba( + self, + X: _LGBM_ScikitMatrixLike, + raw_score: bool = False, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + pred_leaf: bool = False, + pred_contrib: bool = False, + validate_features: bool = False, + **kwargs: Any, + ) -> _LGBM_PredictReturnType: + """Docstring is set after definition, using a template.""" + result = super().predict( + X=X, + raw_score=raw_score, + start_iteration=start_iteration, + num_iteration=num_iteration, + pred_leaf=pred_leaf, + pred_contrib=pred_contrib, + validate_features=validate_features, + **kwargs, + ) + if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib): + _log_warning( + "Cannot compute class probabilities or labels " + "due to the usage of customized objective function.\n" + "Returning raw scores instead." + ) + return result + elif self.__is_multiclass or raw_score or pred_leaf or pred_contrib: # type: ignore [operator] + return result + else: + error_msg = ( + "predict() should return np.ndarray when pred_contrib=False. " + "If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues." + ) + assert isinstance(result, np.ndarray), error_msg + return np.vstack((1.0 - result, result)).transpose() + + predict_proba.__doc__ = _lgbmmodel_doc_predict.format( + description="Return the predicted probability for each class for each sample.", + X_shape="numpy array, pandas DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]", + output_name="predicted_probability", + predicted_result_shape="array-like of shape = [n_samples] or shape = [n_samples, n_classes]", + X_leaves_shape="array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]", + X_SHAP_values_shape="array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects", + ) + + def decision_function( + self, + X: _LGBM_ScikitMatrixLike, + *, + start_iteration: int = 0, + num_iteration: Optional[int] = None, + validate_features: bool = False, + **kwargs: Any, + ) -> _LGBM_PredictReturnType: + """Return the raw margin score for each sample. + + Parameters + ---------- + X : numpy array, pandas DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features] + Input features matrix. + start_iteration : int, optional (default=0) + Start index of the iteration to predict. + If <= 0, starts from the first iteration. + num_iteration : int or None, optional (default=None) + Total number of iterations used in the prediction. + If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; + otherwise, all iterations from ``start_iteration`` are used (no limits). + If <= 0, all iterations from ``start_iteration`` are used (no limits). + validate_features : bool, optional (default=False) + If True, ensure that the features used to predict match the ones used to train. + Used only if data is pandas DataFrame. + **kwargs + Other parameters forwarded to ``predict()``. + + Returns + ------- + raw_score : array-like of shape = [n_samples] or shape = [n_samples, n_classes] + The predicted values. + """ + return super().predict( + X=X, + raw_score=True, + start_iteration=start_iteration, + num_iteration=num_iteration, + validate_features=validate_features, + **kwargs, + ) + + @property + def classes_(self) -> np.ndarray: + """:obj:`array` of shape = [n_classes]: The class label array.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No classes found. Need to call fit beforehand.") + return self._classes # type: ignore[return-value] + + @property + def n_classes_(self) -> int: + """:obj:`int`: The number of classes.""" + if not self.__sklearn_is_fitted__(): + raise LGBMNotFittedError("No classes found. Need to call fit beforehand.") + return self._n_classes + + @property + def __is_multiclass(self) -> bool: + """:obj:`bool`: Indicator of whether the classifier is used for multiclass.""" + return self._n_classes > 2 or (isinstance(self._objective, str) and self._objective in _MULTICLASS_OBJECTIVES) + + +class LGBMRanker(LGBMModel): + """LightGBM ranker. + + .. warning:: + + scikit-learn doesn't support ranking applications yet, + therefore this class is not really compatible with the sklearn ecosystem. + Please use this class mainly for training and applying ranking models in common sklearnish way. + """ + + # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for + # docs, help(), and tab completion. + def __init__( + self, + *, + boosting_type: str = "gbdt", + num_leaves: int = 31, + max_depth: int = -1, + learning_rate: float = 0.1, + n_estimators: int = 100, + subsample_for_bin: int = 200000, + objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None, + class_weight: Optional[Union[Dict, str]] = None, + min_split_gain: float = 0.0, + min_child_weight: float = 1e-3, + min_child_samples: int = 20, + subsample: float = 1.0, + subsample_freq: int = 0, + colsample_bytree: float = 1.0, + reg_alpha: float = 0.0, + reg_lambda: float = 0.0, + random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None, + n_jobs: Optional[int] = None, + importance_type: str = "split", + **kwargs: Any, + ) -> None: + super().__init__( + boosting_type=boosting_type, + num_leaves=num_leaves, + max_depth=max_depth, + learning_rate=learning_rate, + n_estimators=n_estimators, + subsample_for_bin=subsample_for_bin, + objective=objective, + class_weight=class_weight, + min_split_gain=min_split_gain, + min_child_weight=min_child_weight, + min_child_samples=min_child_samples, + subsample=subsample, + subsample_freq=subsample_freq, + colsample_bytree=colsample_bytree, + reg_alpha=reg_alpha, + reg_lambda=reg_lambda, + random_state=random_state, + n_jobs=n_jobs, + importance_type=importance_type, + **kwargs, + ) + + __init__.__doc__ = LGBMModel.__init__.__doc__ + + def fit( # type: ignore[override] + self, + X: _LGBM_ScikitMatrixLike, + y: _LGBM_LabelType, + sample_weight: Optional[_LGBM_WeightType] = None, + init_score: Optional[_LGBM_InitScoreType] = None, + group: Optional[_LGBM_GroupType] = None, + eval_set: Optional[List[_LGBM_ScikitValidSet]] = None, + eval_names: Optional[List[str]] = None, + eval_sample_weight: Optional[List[_LGBM_WeightType]] = None, + eval_init_score: Optional[List[_LGBM_InitScoreType]] = None, + eval_group: Optional[List[_LGBM_GroupType]] = None, + eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None, + eval_at: Union[List[int], Tuple[int, ...]] = (1, 2, 3, 4, 5), + feature_name: _LGBM_FeatureNameConfiguration = "auto", + categorical_feature: _LGBM_CategoricalFeatureConfiguration = "auto", + callbacks: Optional[List[Callable]] = None, + init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None, + *, + eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None, + eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None, + ) -> "LGBMRanker": + """Docstring is inherited from the LGBMModel.""" + # check group data + if group is None: + raise ValueError("Should set group for ranking task") + + if eval_group is None and (eval_set is not None or eval_X is not None or eval_y is not None): + raise ValueError("eval_group cannot be None if any of eval_set, eval_X, or eval_y are provided") + + self._eval_at = eval_at + super().fit( + X, + y, + sample_weight=sample_weight, + init_score=init_score, + group=group, + eval_set=eval_set, + eval_names=eval_names, + eval_X=eval_X, + eval_y=eval_y, + eval_sample_weight=eval_sample_weight, + eval_init_score=eval_init_score, + eval_group=eval_group, + eval_metric=eval_metric, + feature_name=feature_name, + categorical_feature=categorical_feature, + callbacks=callbacks, + init_model=init_model, + ) + return self + + _base_doc = LGBMModel.fit.__doc__.replace("self : LGBMModel", "self : LGBMRanker") # type: ignore + fit.__doc__ = ( + _base_doc[: _base_doc.find("eval_class_weight :")] # type: ignore + + _base_doc[_base_doc.find("eval_init_score :") :] + ) # type: ignore + _base_doc = fit.__doc__ + _before_feature_name, _feature_name, _after_feature_name = _base_doc.partition("feature_name :") + fit.__doc__ = f"""{_before_feature_name}eval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5)) + The evaluation positions of the specified metric. + {_feature_name}{_after_feature_name}""" diff --git a/python-package/pyproject.toml b/python-package/pyproject.toml new file mode 100644 index 0000000..aacf5ca --- /dev/null +++ b/python-package/pyproject.toml @@ -0,0 +1,229 @@ +[project] + +classifiers = [ + "Development Status :: 5 - Production/Stable", + "Intended Audience :: Science/Research", + "Natural Language :: English", + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", + "Operating System :: POSIX", + "Operating System :: Unix", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Topic :: Scientific/Engineering :: Artificial Intelligence" +] +dependencies = [ + "narwhals>=1.15", + "numpy>=1.21.3", + "scipy" +] +description = "LightGBM Python-package" +license = "MIT" +license-files = [ + "LICENSE" +] +maintainers = [ + {name = "Yu Shi", email = "yushi@microsoft.com"} +] +name = "lightgbm" +readme = "README.rst" +requires-python = ">=3.10" +version = "4.6.0.99" + +[project.optional-dependencies] +arrow = [ + "narwhals[pyarrow]", + "pyarrow>=16.0.0" +] +dask = [ + "dask[array,dataframe,distributed]>=2.0.0", + "narwhals[pandas]", + "pandas>=1.3.4" +] +pandas = [ + "narwhals[pandas]", + "pandas>=1.3.4" +] +plotting = [ + "graphviz", + "matplotlib" +] +polars = [ + "narwhals[polars]", + "polars>=1.0.0" +] +scikit-learn = [ + "scikit-learn>=1.0.2" +] + +[project.urls] +homepage = "https://github.com/lightgbm-org/LightGBM" +documentation = "https://lightgbm.readthedocs.io/en/latest/" +repository = "https://github.com/lightgbm-org/LightGBM.git" +changelog = "https://github.com/lightgbm-org/LightGBM/releases" + +# start:build-system +[build-system] + +requires = ["scikit-build-core>=0.11.0"] +build-backend = "scikit_build_core.build" + +# based on https://github.com/scikit-build/scikit-build-core#configuration +[tool.scikit-build] +ninja.version = ">=1.11" +ninja.make-fallback = true +build.verbose = false +build.targets = ["_lightgbm"] +install.strip = true +logging.level = "INFO" +sdist.reproducible = true +wheel.py-api = "py3" +experimental = false +strict-config = false +minimum-version = "build-system.requires" + +[tool.scikit-build.cmake] +version = "CMakeLists.txt" +build-type = "Release" + +[tool.scikit-build.cmake.define] +__BUILD_FOR_PYTHON = "ON" + +# end:build-system + +[tool.mypy] +disallow_untyped_defs = true +exclude = 'build/*|compile/*|docs/*|examples/*|external_libs/*|lightgbm-python/*|.pixi/*|tests/*' +ignore_missing_imports = true + +[tool.rstcheck] +report_level = "WARNING" +ignore_directives = [ + "autoclass", + "autofunction", + "autosummary", + "doxygenfile" +] + +[tool.ruff] +exclude = [ + "build", + "compile", + "external_libs", + "lightgbm-python", +] +line-length = 120 + +# this should be set to the oldest version of python LightGBM supports +target-version = "py310" + +[tool.ruff.format] +docstring-code-format = false +exclude = [ + "build/*.py", + "compile/*.py", + "external_libs/*.py", + "lightgbm-python/*.py", +] +indent-style = "space" +quote-style = "double" +skip-magic-trailing-comma = false + +[tool.ruff.lint] +ignore = [ + # (pydocstyle) Missing docstring in magic method + "D105", + # (pycodestyle) Line too long + "E501", + # (pylint) Too many branches + "PLR0912", + # (pylint) Too many arguments in function definition + "PLR0913", + # (pylint) Too many statements + "PLR0915", + # (pylint) Consider merging multiple comparisons + "PLR1714", + # (pylint) Magic value used in comparison + "PLR2004", + # (pylint) for loop variable overwritten by assignment target + "PLW2901", + # (pylint) use 'elif' instead of 'else' then 'if', to reduce indentation + "PLR5501", + # (flake8-pytest-style) `scope='function'` is implied in `@pytest.fixture()` + "PT003" +] +select = [ + # flake8-bugbear + "B", + # flake8-comprehensions + "C4", + # pydocstyle + "D", + # pycodestyle (errors) + "E", + # pyflakes + "F", + # isort + "I", + # NumPy-specific rules + "NPY", + # pylint + "PL", + # flake8-pytest-style + "PT", + # flake8-return: unnecessary assignment before return + "RET504", + # flake8-return: superfluous-else-raise + "RET506", + # flake8-simplify: use dict.get() instead of an if-else block + "SIM401", + # flake8-print + "T", + # pycodestyle (warnings) + "W", +] + +[tool.ruff.lint.flake8-pytest-style] +raises-extend-require-match-for = ["*Exception", "*Error"] + +[tool.ruff.lint.per-file-ignores] +".ci/create-nuget.py" = [ + # (flake8-print) flake8-print + "T201" +] +"docs/conf.py" = [ + # (flake8-bugbear) raise exceptions with "raise ... from err" + "B904", + # (flake8-print) flake8-print + "T" +] +"examples/*" = [ + # pydocstyle + "D", + # flake8-print + "T" +] +"python-package/lightgbm/basic.py" = [ + # (pylint) Using the global statement is discouraged + "PLW0603" +] +"tests/*" = [ + # (flake8-bugbear) Found useless expression + "B018", + # (pycodestyle) Module level import not at top of file + "E402", + # pydocstyle + "D", + # flake8-print + "T" +] + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ruff.lint.isort] +known-first-party = ["lightgbm"] diff --git a/src/application/application.cpp b/src/application/application.cpp new file mode 100644 index 0000000..8cd95ce --- /dev/null +++ b/src/application/application.cpp @@ -0,0 +1,296 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "predictor.hpp" + +namespace LightGBM { + +Application::Application(int argc, char** argv) { + LoadParameters(argc, argv); + // set number of threads for openmp + OMP_SET_NUM_THREADS(config_.num_threads); + if (config_.data.size() == 0 && config_.task != TaskType::kConvertModel) { + Log::Fatal("No training/prediction data, application quit"); + } + + if (config_.device_type == std::string("cuda")) { + LGBM_config_::current_device = lgbm_device_cuda; + } +} + +Application::~Application() { + if (config_.is_parallel) { + Network::Dispose(); + } +} + +void Application::LoadParameters(int argc, char** argv) { + std::unordered_map> all_params; + std::unordered_map params; + for (int i = 1; i < argc; ++i) { + Config::KV2Map(&all_params, argv[i]); + } + // read parameters from config file + bool config_file_ok = true; + if (all_params.count("config") > 0) { + TextReader config_reader(all_params["config"][0].c_str(), false); + config_reader.ReadAllLines(); + if (!config_reader.Lines().empty()) { + for (auto& line : config_reader.Lines()) { + // remove str after "#" + if (line.size() > 0 && std::string::npos != line.find_first_of("#")) { + line.erase(line.find_first_of("#")); + } + line = Common::Trim(line); + if (line.size() == 0) { + continue; + } + Config::KV2Map(&all_params, line.c_str()); + } + } else { + config_file_ok = false; + } + } + Config::SetVerbosity(all_params); + // de-duplicate params + Config::KeepFirstValues(all_params, ¶ms); + if (!config_file_ok) { + Log::Warning("Config file %s doesn't exist, will ignore", params["config"].c_str()); + } + ParameterAlias::KeyAliasTransform(¶ms); + config_.Set(params); + Log::Info("Finished loading parameters"); +} + +void Application::LoadData() { + auto start_time = std::chrono::high_resolution_clock::now(); + std::unique_ptr predictor; + // prediction is needed if using input initial model(continued train) + PredictFunction predict_fun = nullptr; + // need to continue training + if (boosting_->NumberOfTotalModel() > 0 && config_.task != TaskType::KRefitTree) { + predictor.reset(new Predictor(boosting_.get(), 0, -1, true, false, false, false, -1, -1)); + predict_fun = predictor->GetPredictFunction(); + } + + // sync up random seed for data partition + if (config_.is_data_based_parallel) { + config_.data_random_seed = Network::GlobalSyncUpByMin(config_.data_random_seed); + } + + Log::Debug("Loading train file..."); + DatasetLoader dataset_loader(config_, predict_fun, + config_.num_class, config_.data.c_str()); + // load Training data + if (config_.is_data_based_parallel) { + // load data for distributed training + train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), + Network::rank(), Network::num_machines())); + } else { + // load data for single machine + train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1)); + } + // need save binary file + if (config_.save_binary) { + train_data_->SaveBinaryFile(nullptr); + } + // create training metric + if (config_.is_provide_training_metric) { + for (auto metric_type : config_.metric) { + auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_)); + if (metric == nullptr) { + continue; + } + metric->Init(train_data_->metadata(), train_data_->num_data()); + train_metric_.push_back(std::move(metric)); + } + } + train_metric_.shrink_to_fit(); + + if (!config_.metric.empty()) { + // only when have metrics then need to construct validation data + + // Add validation data, if it exists + for (size_t i = 0; i < config_.valid.size(); ++i) { + Log::Debug("Loading validation file #%zu...", (i + 1)); + // add + auto new_dataset = std::unique_ptr( + dataset_loader.LoadFromFileAlignWithOtherDataset( + config_.valid[i].c_str(), + train_data_.get())); + valid_datas_.push_back(std::move(new_dataset)); + // need save binary file + if (config_.save_binary) { + valid_datas_.back()->SaveBinaryFile(nullptr); + } + + // add metric for validation data + valid_metrics_.emplace_back(); + for (auto metric_type : config_.metric) { + auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_)); + if (metric == nullptr) { + continue; + } + metric->Init(valid_datas_.back()->metadata(), + valid_datas_.back()->num_data()); + valid_metrics_.back().push_back(std::move(metric)); + } + valid_metrics_.back().shrink_to_fit(); + } + valid_datas_.shrink_to_fit(); + valid_metrics_.shrink_to_fit(); + } + auto end_time = std::chrono::high_resolution_clock::now(); + // output used time on each iteration + Log::Info("Finished loading data in %f seconds", + std::chrono::duration(end_time - start_time) * 1e-3); +} + +void Application::InitTrain() { + if (config_.is_parallel) { + // need init network + Network::Init(config_); + Log::Info("Finished initializing network"); + config_.feature_fraction_seed = + Network::GlobalSyncUpByMin(config_.feature_fraction_seed); + config_.feature_fraction = + Network::GlobalSyncUpByMin(config_.feature_fraction); + config_.drop_seed = + Network::GlobalSyncUpByMin(config_.drop_seed); + } + + // create boosting + boosting_.reset( + Boosting::CreateBoosting(config_.boosting, + config_.input_model.c_str(), config_.device_type, config_.num_gpu)); + // create objective function + objective_fun_.reset( + ObjectiveFunction::CreateObjectiveFunction(config_.objective, + config_)); + // load training data + LoadData(); + if (config_.task == TaskType::kSaveBinary) { + Log::Info("Save data as binary finished, exit"); + exit(0); + } + // initialize the objective function + objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); + // initialize the boosting + boosting_->Init(&config_, train_data_.get(), objective_fun_.get(), + Common::ConstPtrInVectorWrapper(train_metric_)); + // add validation data into boosting + for (size_t i = 0; i < valid_datas_.size(); ++i) { + boosting_->AddValidDataset(valid_datas_[i].get(), + Common::ConstPtrInVectorWrapper(valid_metrics_[i])); + Log::Debug("Number of data points in validation set #%zu: %d", i + 1, valid_datas_[i]->num_data()); + } + Log::Info("Finished initializing training"); +} + +void Application::Train() { + Log::Info("Started training..."); + boosting_->Train(config_.snapshot_freq, config_.output_model); + boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type, + config_.output_model.c_str()); + // convert model to if-else statement code + if (config_.convert_model_language == std::string("cpp")) { + boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str()); + } + Log::Info("Finished training"); +} + +void Application::Predict() { + if (config_.task == TaskType::KRefitTree) { + // create predictor + Predictor predictor(boosting_.get(), 0, -1, false, true, false, false, 1, 1); + predictor.Predict(config_.data.c_str(), config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check, + config_.precise_float_parser); + TextReader result_reader(config_.output_result.c_str(), false); + result_reader.ReadAllLines(); + + size_t nrow = result_reader.Lines().size(); + size_t ncol = 0; + if (nrow > 0) { + ncol = Common::StringToArray(result_reader.Lines()[0], '\t').size(); + } + std::vector pred_leaf; + pred_leaf.resize(nrow * ncol); + + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int irow = 0; irow < static_cast(nrow); ++irow) { + auto line_vec = Common::StringToArray(result_reader.Lines()[irow], '\t'); + CHECK_EQ(line_vec.size(), ncol); + for (int i_row_item = 0; i_row_item < static_cast(ncol); ++i_row_item) { + pred_leaf[irow * ncol + i_row_item] = line_vec[i_row_item]; + } + // Free memory + result_reader.Lines()[irow].clear(); + } + DatasetLoader dataset_loader(config_, nullptr, + config_.num_class, config_.data.c_str()); + train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1)); + train_metric_.clear(); + objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective, + config_)); + objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); + boosting_->Init(&config_, train_data_.get(), objective_fun_.get(), + Common::ConstPtrInVectorWrapper(train_metric_)); + + boosting_->RefitTree(pred_leaf.data(), nrow, ncol); + boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type, + config_.output_model.c_str()); + Log::Info("Finished RefitTree"); + } else { + // create predictor + Predictor predictor(boosting_.get(), config_.start_iteration_predict, config_.num_iteration_predict, config_.predict_raw_score, + config_.predict_leaf_index, config_.predict_contrib, + config_.pred_early_stop, config_.pred_early_stop_freq, + config_.pred_early_stop_margin); + predictor.Predict(config_.data.c_str(), + config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check, + config_.precise_float_parser); + Log::Info("Finished prediction"); + } +} + +void Application::InitPredict() { + boosting_.reset( + Boosting::CreateBoosting("gbdt", config_.input_model.c_str(), config_.device_type, config_.num_gpu)); + Log::Info("Finished initializing prediction, total used %d iterations", boosting_->GetCurrentIteration()); +} + +void Application::ConvertModel() { + boosting_.reset( + Boosting::CreateBoosting(config_.boosting, config_.input_model.c_str(), config_.device_type, config_.num_gpu)); + boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str()); +} + + +} // namespace LightGBM diff --git a/src/application/predictor.hpp b/src/application/predictor.hpp new file mode 100644 index 0000000..444f4c6 --- /dev/null +++ b/src/application/predictor.hpp @@ -0,0 +1,303 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_ +#define LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_ + +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +/*! +* \brief Used to predict data with input model +*/ +class Predictor { + public: + /*! + * \brief Constructor + * \param boosting Input boosting model + * \param start_iteration Start index of the iteration to predict + * \param num_iteration Number of boosting round + * \param is_raw_score True if need to predict result with raw score + * \param predict_leaf_index True to output leaf index instead of prediction score + * \param predict_contrib True to output feature contributions instead of prediction score + */ + Predictor(Boosting* boosting, int start_iteration, int num_iteration, bool is_raw_score, + bool predict_leaf_index, bool predict_contrib, bool early_stop, + int early_stop_freq, double early_stop_margin) { + early_stop_ = CreatePredictionEarlyStopInstance( + "none", LightGBM::PredictionEarlyStopConfig()); + if (early_stop && !boosting->NeedAccuratePrediction()) { + PredictionEarlyStopConfig pred_early_stop_config; + CHECK_GT(early_stop_freq, 0); + CHECK_GE(early_stop_margin, 0); + pred_early_stop_config.margin_threshold = early_stop_margin; + pred_early_stop_config.round_period = early_stop_freq; + if (boosting->NumberOfClasses() == 1) { + early_stop_ = + CreatePredictionEarlyStopInstance("binary", pred_early_stop_config); + } else { + early_stop_ = CreatePredictionEarlyStopInstance("multiclass", + pred_early_stop_config); + } + } + + boosting->InitPredict(start_iteration, num_iteration, predict_contrib); + boosting_ = boosting; + num_pred_one_row_ = boosting_->NumPredictOneRow(start_iteration, + num_iteration, predict_leaf_index, predict_contrib); + num_feature_ = boosting_->MaxFeatureIdx() + 1; + predict_buf_.resize( + OMP_NUM_THREADS(), + std::vector>( + num_feature_, 0.0f)); + const int kFeatureThreshold = 100000; + const size_t KSparseThreshold = static_cast(0.01 * num_feature_); + if (predict_leaf_index) { + predict_fun_ = [=](const std::vector>& features, + double* output) { + int tid = omp_get_thread_num(); + if (num_feature_ > kFeatureThreshold && + features.size() < KSparseThreshold) { + auto buf = CopyToPredictMap(features); + boosting_->PredictLeafIndexByMap(buf, output); + } else { + CopyToPredictBuffer(predict_buf_[tid].data(), features); + // get result for leaf index + boosting_->PredictLeafIndex(predict_buf_[tid].data(), output); + ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), + features); + } + }; + } else if (predict_contrib) { + if (boosting_->IsLinear()) { + Log::Fatal("Predicting SHAP feature contributions is not implemented for linear trees."); + } + predict_fun_ = [=](const std::vector>& features, + double* output) { + int tid = omp_get_thread_num(); + CopyToPredictBuffer(predict_buf_[tid].data(), features); + // get feature importances + boosting_->PredictContrib(predict_buf_[tid].data(), output); + ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), + features); + }; + predict_sparse_fun_ = [=](const std::vector>& features, + std::vector>* output) { + auto buf = CopyToPredictMap(features); + // get sparse feature importances + boosting_->PredictContribByMap(buf, output); + }; + + } else { + if (is_raw_score) { + predict_fun_ = [=](const std::vector>& features, + double* output) { + int tid = omp_get_thread_num(); + if (num_feature_ > kFeatureThreshold && + features.size() < KSparseThreshold) { + auto buf = CopyToPredictMap(features); + boosting_->PredictRawByMap(buf, output, &early_stop_); + } else { + CopyToPredictBuffer(predict_buf_[tid].data(), features); + boosting_->PredictRaw(predict_buf_[tid].data(), output, + &early_stop_); + ClearPredictBuffer(predict_buf_[tid].data(), + predict_buf_[tid].size(), features); + } + }; + } else { + predict_fun_ = [=](const std::vector>& features, + double* output) { + int tid = omp_get_thread_num(); + if (num_feature_ > kFeatureThreshold && + features.size() < KSparseThreshold) { + auto buf = CopyToPredictMap(features); + boosting_->PredictByMap(buf, output, &early_stop_); + } else { + CopyToPredictBuffer(predict_buf_[tid].data(), features); + boosting_->Predict(predict_buf_[tid].data(), output, &early_stop_); + ClearPredictBuffer(predict_buf_[tid].data(), + predict_buf_[tid].size(), features); + } + }; + } + } + } + + /*! + * \brief Destructor + */ + ~Predictor() { + } + + inline const PredictFunction& GetPredictFunction() const { + return predict_fun_; + } + + + inline const PredictSparseFunction& GetPredictSparseFunction() const { + return predict_sparse_fun_; + } + + /*! + * \brief predicting on data, then saving result to disk + * \param data_filename Filename of data + * \param result_filename Filename of output result + */ + void Predict(const char* data_filename, const char* result_filename, bool header, bool disable_shape_check, bool precise_float_parser) { + auto writer = VirtualFileWriter::Make(result_filename); + if (!writer->Init()) { + Log::Fatal("Prediction results file %s cannot be created", result_filename); + } + auto label_idx = header ? -1 : boosting_->LabelIdx(); + auto parser = std::unique_ptr(Parser::CreateParser(data_filename, header, boosting_->MaxFeatureIdx() + 1, label_idx, + precise_float_parser, boosting_->ParserConfigStr())); + + if (parser == nullptr) { + Log::Fatal("Could not recognize the data format of data file %s", data_filename); + } + if (!header && !disable_shape_check && parser->NumFeatures() != boosting_->MaxFeatureIdx() + 1) { + Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n" \ + "You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", parser->NumFeatures(), boosting_->MaxFeatureIdx() + 1); + } + TextReader predict_data_reader(data_filename, header); + std::vector feature_remapper(parser->NumFeatures(), -1); + bool need_adjust = false; + // skip raw feature remapping if trained model has parser config str which may contain actual feature names. + if (header && boosting_->ParserConfigStr().empty()) { + std::string first_line = predict_data_reader.first_line(); + std::vector header_words = Common::Split(first_line.c_str(), "\t,"); + std::unordered_map header_mapper; + for (int i = 0; i < static_cast(header_words.size()); ++i) { + if (header_mapper.count(header_words[i]) > 0) { + Log::Fatal("Feature (%s) appears more than one time.", header_words[i].c_str()); + } + header_mapper[header_words[i]] = i; + } + const auto& fnames = boosting_->FeatureNames(); + for (int i = 0; i < static_cast(fnames.size()); ++i) { + if (header_mapper.count(fnames[i]) <= 0) { + Log::Warning("Feature (%s) is missed in data file. If it is weight/query/group/ignore_column, you can ignore this warning.", fnames[i].c_str()); + } else { + feature_remapper[header_mapper.at(fnames[i])] = i; + } + } + for (int i = 0; i < static_cast(feature_remapper.size()); ++i) { + if (feature_remapper[i] >= 0 && i != feature_remapper[i]) { + need_adjust = true; + break; + } + } + } + // function for parse data + std::function>*)> parser_fun; + double tmp_label; + parser_fun = [&parser, &feature_remapper, &tmp_label, need_adjust] + (const char* buffer, std::vector>* feature) { + parser->ParseOneLine(buffer, feature, &tmp_label); + if (need_adjust) { + int i = 0, j = static_cast(feature->size()); + while (i < j) { + if (feature_remapper[(*feature)[i].first] >= 0) { + (*feature)[i].first = feature_remapper[(*feature)[i].first]; + ++i; + } else { + // move the non-used features to the end of the feature vector + std::swap((*feature)[i], (*feature)[--j]); + } + } + feature->resize(i); + } + }; + + std::function&)> + process_fun = [&parser_fun, &writer, this]( + data_size_t, const std::vector& lines) { + std::vector> oneline_features; + std::vector result_to_write(lines.size()); + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(oneline_features) + for (data_size_t i = 0; i < static_cast(lines.size()); ++i) { + OMP_LOOP_EX_BEGIN(); + oneline_features.clear(); + // parser + parser_fun(lines[i].c_str(), &oneline_features); + // predict + std::vector result(num_pred_one_row_); + predict_fun_(oneline_features, result.data()); + auto str_result = Common::Join(result, "\t"); + result_to_write[i] = str_result; + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + for (data_size_t i = 0; i < static_cast(result_to_write.size()); ++i) { + writer->Write(result_to_write[i].c_str(), result_to_write[i].size()); + writer->Write("\n", 1); + } + }; + predict_data_reader.ReadAllAndProcessParallel(process_fun); + } + + private: + void CopyToPredictBuffer(double* pred_buf, const std::vector>& features) { + for (const auto &feature : features) { + if (feature.first < num_feature_) { + pred_buf[feature.first] = feature.second; + } + } + } + + void ClearPredictBuffer(double* pred_buf, size_t buf_size, const std::vector>& features) { + if (features.size() > static_cast(buf_size / 2)) { + std::memset(pred_buf, 0, sizeof(double)*(buf_size)); + } else { + for (const auto &feature : features) { + if (feature.first < num_feature_) { + pred_buf[feature.first] = 0.0f; + } + } + } + } + + std::unordered_map CopyToPredictMap(const std::vector>& features) { + std::unordered_map buf; + for (const auto &feature : features) { + if (feature.first < num_feature_) { + buf[feature.first] = feature.second; + } + } + return buf; + } + + /*! \brief Boosting model */ + const Boosting* boosting_; + /*! \brief function for prediction */ + PredictFunction predict_fun_; + PredictSparseFunction predict_sparse_fun_; + PredictionEarlyStopInstance early_stop_; + int num_feature_; + int num_pred_one_row_; + std::vector>> predict_buf_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_ diff --git a/src/arrow/array.hpp b/src/arrow/array.hpp new file mode 100644 index 0000000..4ad5950 --- /dev/null +++ b/src/arrow/array.hpp @@ -0,0 +1,413 @@ +/*! + * Copyright (c) 2026-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Oliver Borchert + */ + +#ifndef LIGHTGBM_SRC_ARROW_ARRAY_HPP_ +#define LIGHTGBM_SRC_ARROW_ARRAY_HPP_ + +#include +#include + +#include +#include +#include +#include +#include + +#include + +namespace LightGBM { + +/* ------------------------------------------- UTILS ------------------------------------------- */ + +inline struct ArrowSchemaView MakeSchemaView(const struct ArrowSchema* schema) { + struct ArrowSchemaView view; + struct ArrowError error; + auto ret = ArrowSchemaViewInit(&view, schema, &error); + if (ret != NANOARROW_OK) { + throw nanoarrow::Exception("Failed to initialize ArrowSchemaView: " + + std::string(ArrowErrorMessage(&error))); + } + return view; +} + +/* --------------------------------------------------------------------------------------------- */ +/* CHUNKED ARRAY */ +/* --------------------------------------------------------------------------------------------- */ + +class ArrowChunkedArray { + enum ArrowType type_; + nanoarrow::UniqueSchema schema_; + std::vector chunks_; + + public: + class View; + template + class Visitor; + template + class Iterator; + + /** + * @brief Construct a new chunked Arrow array from an array stream. + * The stream is consumed and the chunked Arrow array takes ownership of the schema and all + * chunks. Upon destruction, the release callback is called for the schema and all chunks. + * + * @param stream The Arrow array stream to consume. + */ + explicit ArrowChunkedArray(ArrowArrayStream* stream) { + nanoarrow::UniqueArrayStream stream_(stream); + + // Extract the schema + auto ret = stream_->get_schema(stream_.get(), schema_.get()); + if (ret != NANOARROW_OK) { + throw nanoarrow::Exception("Failed to get schema from Arrow array stream: " + + std::string(stream_->get_last_error(stream_.get()))); + } + + // Turn the schema into a type + type_ = MakeSchemaView(schema_.get()).type; + + // Extract all chunks + while (true) { + nanoarrow::UniqueArray chunk; + auto ret = stream_->get_next(stream_.get(), chunk.get()); + if (ret != NANOARROW_OK) { + throw nanoarrow::Exception("Failed to get next chunk from Arrow array stream: " + + std::string(stream_->get_last_error(stream_.get()))); + } + if (chunk->release == nullptr) break; + chunks_.emplace_back(std::move(chunk)); + } + } + + /** + * @brief Construct a new chunked Arrow array from a list of Arrow arrays and a schema. + * The chunked Arrow array takes ownership of the schema and all chunks. Upon destruction, + * the release callback is called for the schema and all chunks. + * + * @param n_chunks The number of Arrow arrays. + * @param chunks Pointer to the list of Arrow arrays. + * @param schema Pointer to the schema of all Arrow arrays. + */ + explicit ArrowChunkedArray(int64_t n_chunks, struct ArrowArray* chunks, + struct ArrowSchema* schema) { + // Take ownership of schema + schema_ = nanoarrow::UniqueSchema(schema); + type_ = MakeSchemaView(schema_.get()).type; + + // Take ownership of chunks + chunks_.reserve(n_chunks); + for (int64_t i = 0; i < n_chunks; ++i) { + chunks_.emplace_back(&chunks[i]); + } + } + + /** + * @brief Whether the chunked array is a struct and has multiple fields. + * A struct chunked array is typically interpreted as a table. + */ + bool is_struct() const { return type_ == NANOARROW_TYPE_STRUCT; } + + /** + * @brief Get the length of the chunked array as the sum of all chunk lengths. + * + * @return int64_t The total number of elements. + */ + int64_t get_length() const { + int64_t length = 0; + for (const auto& chunk : chunks_) { + length += chunk->length; + } + return length; + } + + /** + * @brief Get the number of fields in the chunked array. + * + * @return int64_t The number of fields. + */ + int64_t get_num_fields() const { + if (!is_struct()) { + Log::Fatal("Expected struct type for array, got %s", ArrowTypeString(type_)); + } + return schema_.get()->n_children; + } + + /** + * @brief Obtain a view on the chunked array to visit its values. + * + * @return View The view on the array. + */ + View view() const { + std::vector chunk_ptrs; + chunk_ptrs.reserve(chunks_.size()); + for (const auto& chunk : chunks_) { + // Skip empty chunks to avoid additional complexity in the iterator + if (chunk->length == 0) continue; + chunk_ptrs.push_back(chunk.get()); + } + return View(type_, schema_.get(), std::move(chunk_ptrs)); + } + + /* ------------------------------------------------------------------------------------------- */ + /* VIEW */ + /* ------------------------------------------------------------------------------------------- */ + + class View { + friend class ArrowChunkedArray; + + enum ArrowType type_; + const struct ArrowSchema* schema_; + std::vector chunks_; + + View(enum ArrowType type, const struct ArrowSchema* schema, + std::vector chunks) + : type_(type), schema_(schema), chunks_(std::move(chunks)) {} + + public: + explicit View(std::vector views) { + type_ = views[0].type_; + schema_ = views[0].schema_; + for (auto it = views.begin(), end = views.end(); it != end; ++it) { + if (it->type_ != type_) { + Log::Fatal("All views must have the same type, but got %s and %s", + ArrowTypeString(it->type_), ArrowTypeString(type_)); + } + chunks_.insert(chunks_.end(), it->chunks_.begin(), it->chunks_.end()); + } + } + + /** + * @brief Obtain a view on the field at the given index. + * This method assumes that the view has a type "struct". + * + * @param field_idx The index of the field to view. + * @return View A view on the field at the given index. + */ + View field(int64_t field_idx) const { + if (type_ != NANOARROW_TYPE_STRUCT) { + Log::Fatal("Expected struct type for array, got %s", ArrowTypeString(type_)); + } + + std::vector chunk_ptrs; + chunk_ptrs.reserve(chunks_.size()); + for (const auto& chunk : chunks_) { + chunk_ptrs.push_back(chunk->children[field_idx]); + } + + auto type = MakeSchemaView(schema_->children[field_idx]).type; + return View(type, schema_->children[field_idx], std::move(chunk_ptrs)); + } + + /** + * @brief Visit the chunked array with a visitor created from the schema type. + * The visitor allows accessing all values from the chunked array, casting to the desired + * output type. + * + * @tparam OutputT The desired output type. + * @tparam F The type of the visitor function, which must be invocable with a + * `Visitor` + * @param f The visitor function to invoke with the created visitor. + * @return decltype(auto) The result of invoking the visitor function with the created visitor. + */ + template + decltype(auto) visit(F&& f) const { + // Switch on the schema type and construct the appropriate visitor + switch (type_) { + case NANOARROW_TYPE_INT8: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_INT16: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_INT32: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_INT64: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_UINT8: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_UINT16: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_UINT32: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_UINT64: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_FLOAT: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_DOUBLE: + return f(Visitor(chunks_)); + case NANOARROW_TYPE_BOOL: + return f(Visitor(chunks_)); + default: + Log::Fatal("Unsupported Arrow type: %s", ArrowTypeString(type_)); + } + } + }; + + /* ------------------------------------------------------------------------------------------- */ + /* VISITOR */ + /* ------------------------------------------------------------------------------------------- */ + + template + class Visitor { + friend class View; + friend class Iterator; + + std::vector chunks_; + std::vector chunk_offsets_; + + explicit Visitor(std::vector chunks) : chunks_(chunks) { + // Derive chunk offsets + chunk_offsets_.reserve(chunks.size() + 1); + chunk_offsets_.push_back(0); + for (const auto& chunk : chunks) { + chunk_offsets_.push_back(chunk_offsets_.back() + chunk->length); + } + } + + ArrowT is_valid(int64_t chunk_idx, int64_t element_idx) const { + auto arr = chunks_[chunk_idx]; + return arr->buffers[0] == nullptr || + ArrowBitGet(static_cast(arr->buffers[0]), arr->offset + element_idx); + } + + ArrowT get(int64_t chunk_idx, int64_t element_idx) const { + auto arr = chunks_[chunk_idx]; + auto idx = arr->offset + element_idx; + if constexpr (std::is_same_v) { + return ArrowBitGet(static_cast(arr->buffers[1]), idx); + } else { + return static_cast(arr->buffers[1])[idx]; + } + } + + public: + Iterator begin() const { return Iterator(*this, 0, 0); } + Iterator end() const { + return Iterator(*this, chunks_.size(), 0); + } + }; + + /* ------------------------------------------------------------------------------------------- */ + /* ITERATOR */ + /* ------------------------------------------------------------------------------------------- */ + + template + class Iterator { + friend class Visitor; + + const Visitor& visitor_; + int64_t chunk_idx_; + int64_t element_idx_; + + Iterator(const Visitor& visitor, int64_t chunk_idx, int64_t element_idx) + : visitor_(visitor), chunk_idx_(chunk_idx), element_idx_(element_idx) {} + + int64_t full_offset() const { return visitor_.chunk_offsets_[chunk_idx_] + element_idx_; } + + public: + using iterator_category = std::random_access_iterator_tag; + using difference_type = int64_t; + using value_type = OutputT; + using pointer = value_type*; + using reference = value_type&; + + /* --------------------------------------- OPERATORS --------------------------------------- */ + + public: + Iterator& operator++() { + if (element_idx_ + 1 >= visitor_.chunks_[chunk_idx_]->length) { + element_idx_ = 0; + chunk_idx_++; + } else { + element_idx_++; + } + return *this; + } + + Iterator& operator+=(int64_t c) { + while (element_idx_ + c >= visitor_.chunks_[chunk_idx_]->length) { + c -= visitor_.chunks_[chunk_idx_]->length - element_idx_; + element_idx_ = 0; + chunk_idx_++; + } + element_idx_ += c; + return *this; + } + + Iterator& operator--() { + if (element_idx_ == 0) { + chunk_idx_--; + element_idx_ = visitor_.chunks_[chunk_idx_]->length - 1; + } else { + element_idx_--; + } + return *this; + } + + Iterator& operator-=(int64_t c) { + while (c > element_idx_) { + c -= element_idx_ + 1; + chunk_idx_--; + element_idx_ = visitor_.chunks_[chunk_idx_]->length - 1; + } + element_idx_ -= c; + return *this; + } + + friend int64_t operator-(const Iterator& a, + const Iterator& b) { + auto full_offset_a = a.full_offset(); + auto full_offset_b = b.full_offset(); + return full_offset_a - full_offset_b; + } + + friend bool operator==(const Iterator& a, + const Iterator& b) { + return a.chunk_idx_ == b.chunk_idx_ && a.element_idx_ == b.element_idx_; + } + + friend bool operator!=(const Iterator& a, + const Iterator& b) { + return !(a == b); + } + + /* ----------------------------------------- VALUE ----------------------------------------- */ + + private: + static constexpr OutputT null_default() { + if constexpr (std::is_floating_point_v) { + return std::numeric_limits::quiet_NaN(); + } else { + return OutputT{0}; + } + } + + OutputT get(int64_t chunk_idx, int64_t element_idx) const { + if (visitor_.is_valid(chunk_idx, element_idx)) { + return static_cast(visitor_.get(chunk_idx, element_idx)); + } else { + return null_default(); + } + } + + public: + OutputT operator*() const { return this->get(chunk_idx_, element_idx_); } + + OutputT operator[](int64_t c) const { + if (visitor_.chunks_.size() == 1) { + return this->get(0, c); + } + auto it = + std::upper_bound(visitor_.chunk_offsets_.begin(), visitor_.chunk_offsets_.end(), c); + auto chunk_idx = std::distance(visitor_.chunk_offsets_.begin(), it) - 1; + auto element_idx = c - visitor_.chunk_offsets_[chunk_idx]; + return this->get(chunk_idx, element_idx); + } + }; +}; + +}; // namespace LightGBM + +#endif // LIGHTGBM_SRC_ARROW_ARRAY_HPP_ diff --git a/src/boosting/bagging.hpp b/src/boosting/bagging.hpp new file mode 100644 index 0000000..37b18d6 --- /dev/null +++ b/src/boosting/bagging.hpp @@ -0,0 +1,297 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_ +#define LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_ + +#include +#include + +namespace LightGBM { + +class BaggingSampleStrategy : public SampleStrategy { + public: + BaggingSampleStrategy(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration) + : need_re_bagging_(false) { + config_ = config; + train_data_ = train_data; + num_data_ = train_data->num_data(); + num_queries_ = train_data->metadata().num_queries(); + query_boundaries_ = train_data->metadata().query_boundaries(); + objective_function_ = objective_function; + num_tree_per_iteration_ = num_tree_per_iteration; + num_threads_ = OMP_NUM_THREADS(); + } + + ~BaggingSampleStrategy() {} + + void Bagging(int iter, TreeLearner* tree_learner, score_t* /*gradients*/, score_t* /*hessians*/) override { + Common::FunctionTimer fun_timer("GBDT::Bagging", global_timer); + // if need bagging + if ((bag_data_cnt_ < num_data_ && iter % config_->bagging_freq == 0) || + need_re_bagging_) { + need_re_bagging_ = false; + if (!config_->bagging_by_query) { + auto left_cnt = bagging_runner_.Run( + num_data_, + [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left, + data_size_t*) { + data_size_t cur_left_count = 0; + if (balanced_bagging_) { + cur_left_count = + BalancedBaggingHelper(cur_start, cur_cnt, left); + } else { + cur_left_count = BaggingHelper(cur_start, cur_cnt, left); + } + return cur_left_count; + }, + bag_data_indices_.data()); + bag_data_cnt_ = left_cnt; + } else { + num_sampled_queries_ = bagging_runner_.Run( + num_queries_, + [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left, + data_size_t*) { + data_size_t cur_left_count = 0; + cur_left_count = BaggingHelper(cur_start, cur_cnt, left); + return cur_left_count; + }, bag_query_indices_.data()); + + sampled_query_boundaries_[0] = 0; + OMP_INIT_EX(); + #pragma omp parallel for schedule(static) num_threads(num_threads_) + for (data_size_t i = 0; i < num_sampled_queries_; ++i) { + OMP_LOOP_EX_BEGIN(); + sampled_query_boundaries_[i + 1] = query_boundaries_[bag_query_indices_[i] + 1] - query_boundaries_[bag_query_indices_[i]]; + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + const int num_blocks = Threading::For(0, num_sampled_queries_ + 1, 128, [this](int thread_index, data_size_t start_index, data_size_t end_index) { + for (data_size_t i = start_index + 1; i < end_index; ++i) { + sampled_query_boundaries_[i] += sampled_query_boundaries_[i - 1]; + } + sampled_query_boundaries_thread_buffer_[thread_index] = sampled_query_boundaries_[end_index - 1]; + }); + + for (int thread_index = 1; thread_index < num_blocks; ++thread_index) { + sampled_query_boundaries_thread_buffer_[thread_index] += sampled_query_boundaries_thread_buffer_[thread_index - 1]; + } + + Threading::For(0, num_sampled_queries_ + 1, 128, [this](int thread_index, data_size_t start_index, data_size_t end_index) { + if (thread_index > 0) { + for (data_size_t i = start_index; i < end_index; ++i) { + sampled_query_boundaries_[i] += sampled_query_boundaries_thread_buffer_[thread_index - 1]; + } + } + }); + + bag_data_cnt_ = sampled_query_boundaries_[num_sampled_queries_]; + + Threading::For(0, num_sampled_queries_, 1, [this](int /*thread_index*/, data_size_t start_index, data_size_t end_index) { + for (data_size_t sampled_query_id = start_index; sampled_query_id < end_index; ++sampled_query_id) { + const data_size_t query_index = bag_query_indices_[sampled_query_id]; + const data_size_t data_index_start = query_boundaries_[query_index]; + const data_size_t data_index_end = query_boundaries_[query_index + 1]; + const data_size_t sampled_query_start = sampled_query_boundaries_[sampled_query_id]; + for (data_size_t i = data_index_start; i < data_index_end; ++i) { + bag_data_indices_[sampled_query_start + i - data_index_start] = i; + } + } + }); + } + Log::Debug("Re-bagging, using %d data to train", bag_data_cnt_); + // set bagging data to tree learner + if (!is_use_subset_) { + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); + tree_learner->SetBaggingData(nullptr, cuda_bag_data_indices_.RawData(), bag_data_cnt_); + } else { + #endif // USE_CUDA + tree_learner->SetBaggingData(nullptr, bag_data_indices_.data(), bag_data_cnt_); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } else { + // get subset + tmp_subset_->ReSize(bag_data_cnt_); + tmp_subset_->CopySubrow(train_data_, bag_data_indices_.data(), + bag_data_cnt_, false); + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); + tree_learner->SetBaggingData(tmp_subset_.get(), cuda_bag_data_indices_.RawData(), + bag_data_cnt_); + } else { + #endif // USE_CUDA + tree_learner->SetBaggingData(tmp_subset_.get(), bag_data_indices_.data(), + bag_data_cnt_); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } + } + } + + void ResetSampleConfig(const Config* config, bool is_change_dataset) override { + need_resize_gradients_ = false; + // if need bagging, create buffer + data_size_t num_pos_data = 0; + if (objective_function_ != nullptr) { + num_pos_data = objective_function_->NumPositiveData(); + } + bool balance_bagging_cond = (config->pos_bagging_fraction < 1.0 || config->neg_bagging_fraction < 1.0) && (num_pos_data > 0); + if ((config->bagging_fraction < 1.0 || balance_bagging_cond) && config->bagging_freq > 0) { + need_re_bagging_ = false; + if (!is_change_dataset && + config_ != nullptr && config_->bagging_fraction == config->bagging_fraction && config_->bagging_freq == config->bagging_freq + && config_->pos_bagging_fraction == config->pos_bagging_fraction && config_->neg_bagging_fraction == config->neg_bagging_fraction) { + config_ = config; + return; + } + config_ = config; + if (balance_bagging_cond) { + balanced_bagging_ = true; + bag_data_cnt_ = static_cast(num_pos_data * config_->pos_bagging_fraction) + + static_cast((num_data_ - num_pos_data) * config_->neg_bagging_fraction); + } else { + bag_data_cnt_ = static_cast(config_->bagging_fraction * num_data_); + } + bag_data_indices_.resize(num_data_); + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + cuda_bag_data_indices_.Resize(num_data_); + } + #endif // USE_CUDA + if (!config_->bagging_by_query) { + bagging_runner_.ReSize(num_data_); + } else { + bagging_runner_.ReSize(num_queries_); + sampled_query_boundaries_.resize(num_queries_ + 1, 0); + sampled_query_boundaries_thread_buffer_.resize(num_threads_, 0); + bag_query_indices_.resize(num_data_); + } + bagging_rands_.clear(); + for (int i = 0; + i < (num_data_ + bagging_rand_block_ - 1) / bagging_rand_block_; ++i) { + bagging_rands_.emplace_back(config_->bagging_seed + i); + } + + double average_bag_rate = + (static_cast(bag_data_cnt_) / num_data_) / config_->bagging_freq; + is_use_subset_ = false; + if (config_->device_type != std::string("cuda")) { + const int group_threshold_usesubset = 100; + const double average_bag_rate_threshold = 0.5; + if (average_bag_rate <= average_bag_rate_threshold + && (train_data_->num_feature_groups() < group_threshold_usesubset)) { + if (tmp_subset_ == nullptr || is_change_dataset) { + tmp_subset_.reset(new Dataset(bag_data_cnt_)); + tmp_subset_->CopyFeatureMapperFrom(train_data_); + } + is_use_subset_ = true; + Log::Debug("Use subset for bagging"); + } + } + + need_re_bagging_ = true; + + if (is_use_subset_ && bag_data_cnt_ < num_data_) { + // resize gradient vectors to copy the customized gradients for using subset data + need_resize_gradients_ = true; + } + } else { + bag_data_cnt_ = num_data_; + bag_data_indices_.clear(); + #ifdef USE_CUDA + cuda_bag_data_indices_.Clear(); + #endif // USE_CUDA + bagging_runner_.ReSize(0); + is_use_subset_ = false; + } + } + + bool IsHessianChange() const override { + return false; + } + + data_size_t num_sampled_queries() const override { + return num_sampled_queries_; + } + + const data_size_t* sampled_query_indices() const override { + return bag_query_indices_.data(); + } + + private: + data_size_t BaggingHelper(data_size_t start, data_size_t cnt, data_size_t* buffer) { + if (cnt <= 0) { + return 0; + } + data_size_t cur_left_cnt = 0; + data_size_t cur_right_pos = cnt; + // random bagging, minimal unit is one record + for (data_size_t i = 0; i < cnt; ++i) { + auto cur_idx = start + i; + if (bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < config_->bagging_fraction) { + buffer[cur_left_cnt++] = cur_idx; + } else { + buffer[--cur_right_pos] = cur_idx; + } + } + return cur_left_cnt; + } + + data_size_t BalancedBaggingHelper(data_size_t start, data_size_t cnt, data_size_t* buffer) { + if (cnt <= 0) { + return 0; + } + auto label_ptr = train_data_->metadata().label(); + data_size_t cur_left_cnt = 0; + data_size_t cur_right_pos = cnt; + // random bagging, minimal unit is one record + for (data_size_t i = 0; i < cnt; ++i) { + auto cur_idx = start + i; + bool is_pos = label_ptr[start + i] > 0; + bool is_in_bag = false; + if (is_pos) { + is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < + config_->pos_bagging_fraction; + } else { + is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < + config_->neg_bagging_fraction; + } + if (is_in_bag) { + buffer[cur_left_cnt++] = cur_idx; + } else { + buffer[--cur_right_pos] = cur_idx; + } + } + return cur_left_cnt; + } + + /*! \brief whether need restart bagging in continued training */ + bool need_re_bagging_; + /*! \brief number of threads */ + int num_threads_; + /*! \brief query boundaries of the in-bag queries */ + std::vector sampled_query_boundaries_; + /*! \brief buffer for calculating sampled_query_boundaries_ */ + std::vector sampled_query_boundaries_thread_buffer_; + /*! \brief in-bag query indices */ + std::vector> bag_query_indices_; + /*! \brief number of queries in the training dataset */ + data_size_t num_queries_; + /*! \brief number of in-bag queries */ + data_size_t num_sampled_queries_; + /*! \brief query boundaries of the whole training dataset */ + const data_size_t* query_boundaries_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_ diff --git a/src/boosting/boosting.cpp b/src/boosting/boosting.cpp new file mode 100644 index 0000000..10b586b --- /dev/null +++ b/src/boosting/boosting.cpp @@ -0,0 +1,102 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include + +#include "dart.hpp" +#include "gbdt.h" +#include "rf.hpp" + +#ifdef USE_CUDA +#include "cuda/nccl_gbdt.hpp" +#endif // USE_CUDA + +namespace LightGBM { + +std::string GetBoostingTypeFromModelFile(const char* filename) { + TextReader model_reader(filename, true); + std::string type = model_reader.first_line(); + return type; +} + +bool Boosting::LoadFileToBoosting(Boosting* boosting, const char* filename) { + auto start_time = std::chrono::steady_clock::now(); + if (boosting != nullptr) { + TextReader model_reader(filename, true); + size_t buffer_len = 0; + auto buffer = model_reader.ReadContent(&buffer_len); + if (!boosting->LoadModelFromString(buffer.data(), buffer_len)) { + return false; + } + } + std::chrono::duration delta = (std::chrono::steady_clock::now() - start_time); + Log::Debug("Time for loading model: %f seconds", 1e-3*delta); + return true; +} + +Boosting* Boosting::CreateBoosting(const std::string& type, const char* filename, + const std::string& + #ifdef USE_CUDA + device_type + #endif // USE_CUDA + , const int + #ifdef USE_CUDA + num_gpu + #endif // USE_CUDA + ) { + if (filename == nullptr || filename[0] == '\0') { + if (type == std::string("gbdt")) { + #ifdef USE_CUDA + if (device_type == std::string("cuda") && num_gpu > 1) { + return new NCCLGBDT(); + } else { + #endif // USE_CUDA + return new GBDT(); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } else if (type == std::string("dart")) { + return new DART(); + } else if (type == std::string("goss")) { + return new GBDT(); + } else if (type == std::string("rf")) { + return new RF(); + } else { + return nullptr; + } + } else { + std::unique_ptr ret; + if (GetBoostingTypeFromModelFile(filename) == std::string("tree")) { + if (type == std::string("gbdt")) { + #ifdef USE_CUDA + if (device_type == std::string("cuda") && num_gpu > 1) { + ret.reset(new NCCLGBDT()); + } else { + #endif // USE_CUDA + ret.reset(new GBDT()); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } else if (type == std::string("dart")) { + ret.reset(new DART()); + } else if (type == std::string("goss")) { + ret.reset(new GBDT()); + } else if (type == std::string("rf")) { + ret.reset(new RF()); + } else { + Log::Fatal("Unknown boosting type %s", type.c_str()); + } + LoadFileToBoosting(ret.get(), filename); + } else { + Log::Fatal("Unknown model format or submodel type in model file %s", filename); + } + return ret.release(); + } +} + +} // namespace LightGBM diff --git a/src/boosting/cuda/cuda_score_updater.cpp b/src/boosting/cuda/cuda_score_updater.cpp new file mode 100644 index 0000000..af1eb47 --- /dev/null +++ b/src/boosting/cuda/cuda_score_updater.cpp @@ -0,0 +1,93 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include "cuda_score_updater.hpp" + +#ifdef USE_CUDA + +namespace LightGBM { + +CUDAScoreUpdater::CUDAScoreUpdater(const Dataset* data, int num_tree_per_iteration, const bool boosting_on_cuda): + ScoreUpdater(data, num_tree_per_iteration), num_threads_per_block_(1024), boosting_on_cuda_(boosting_on_cuda) { + num_data_ = data->num_data(); + int64_t total_size = static_cast(num_data_) * num_tree_per_iteration; + InitCUDA(total_size); + has_init_score_ = false; + const double* init_score = data->metadata().init_score(); + // if exists initial score, will start from it + if (init_score != nullptr) { + if ((data->metadata().num_init_score() % num_data_) != 0 + || (data->metadata().num_init_score() / num_data_) != num_tree_per_iteration) { + Log::Fatal("Number of class for initial score error"); + } + has_init_score_ = true; + CopyFromHostToCUDADevice(cuda_score_.RawData(), init_score, total_size, __FILE__, __LINE__); + } else { + SetCUDAMemory(cuda_score_.RawData(), 0, static_cast(total_size), __FILE__, __LINE__); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + if (boosting_on_cuda_) { + // clear host score buffer + score_.clear(); + score_.shrink_to_fit(); + } +} + +void CUDAScoreUpdater::InitCUDA(const size_t total_size) { + cuda_score_.Resize(total_size); +} + +CUDAScoreUpdater::~CUDAScoreUpdater() {} + +inline void CUDAScoreUpdater::AddScore(double val, int cur_tree_id) { + Common::FunctionTimer fun_timer("CUDAScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + LaunchAddScoreConstantKernel(val, offset); + if (!boosting_on_cuda_) { + CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); + } +} + +inline void CUDAScoreUpdater::AddScore(const Tree* tree, int cur_tree_id) { + Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + tree->AddPredictionToScore(data_, num_data_, cuda_score_.RawData() + offset); + if (!boosting_on_cuda_) { + CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); + } +} + +inline void CUDAScoreUpdater::AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) { + Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + tree_learner->AddPredictionToScore(tree, cuda_score_.RawData() + offset); + if (!boosting_on_cuda_) { + CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); + } +} + +inline void CUDAScoreUpdater::AddScore(const Tree* tree, const data_size_t* data_indices, + data_size_t data_cnt, int cur_tree_id) { + Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + tree->AddPredictionToScore(data_, data_indices, data_cnt, cuda_score_.RawData() + offset); + if (!boosting_on_cuda_) { + CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); + } +} + +inline void CUDAScoreUpdater::MultiplyScore(double val, int cur_tree_id) { + Common::FunctionTimer fun_timer("CUDAScoreUpdater::MultiplyScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + LaunchMultiplyScoreConstantKernel(val, offset); + if (!boosting_on_cuda_) { + CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__); + } +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/boosting/cuda/cuda_score_updater.cu b/src/boosting/cuda/cuda_score_updater.cu new file mode 100644 index 0000000..f89aab0 --- /dev/null +++ b/src/boosting/cuda/cuda_score_updater.cu @@ -0,0 +1,46 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include "cuda_score_updater.hpp" + +#ifdef USE_CUDA + +namespace LightGBM { + +__global__ void AddScoreConstantKernel( + const double val, + const data_size_t num_data, + double* score) { + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (data_index < num_data) { + score[data_index] += val; + } +} + +void CUDAScoreUpdater::LaunchAddScoreConstantKernel(const double val, const size_t offset) { + const int num_blocks = (num_data_ + num_threads_per_block_) / num_threads_per_block_; + Log::Debug("Adding init score = %lf", val); + AddScoreConstantKernel<<>>(val, num_data_, cuda_score_.RawData() + offset); +} + +__global__ void MultiplyScoreConstantKernel( + const double val, + const data_size_t num_data, + double* score) { + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (data_index < num_data) { + score[data_index] *= val; + } +} + +void CUDAScoreUpdater::LaunchMultiplyScoreConstantKernel(const double val, const size_t offset) { + const int num_blocks = (num_data_ + num_threads_per_block_) / num_threads_per_block_; + MultiplyScoreConstantKernel<<>>(val, num_data_, cuda_score_.RawData() + offset); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/boosting/cuda/cuda_score_updater.hpp b/src/boosting/cuda/cuda_score_updater.hpp new file mode 100644 index 0000000..67b0a31 --- /dev/null +++ b/src/boosting/cuda/cuda_score_updater.hpp @@ -0,0 +1,66 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_ +#define LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_ + +#ifdef USE_CUDA + +#include + +#include "../score_updater.hpp" + +namespace LightGBM { + +class CUDAScoreUpdater: public ScoreUpdater { + public: + CUDAScoreUpdater(const Dataset* data, int num_tree_per_iteration, const bool boosting_on_cuda); + + ~CUDAScoreUpdater(); + + void AddScore(double val, int cur_tree_id) override; + + inline void AddScore(const Tree* tree, int cur_tree_id) override; + + void AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) override; + + inline void AddScore(const Tree* tree, const data_size_t* data_indices, + data_size_t data_cnt, int cur_tree_id) override; + + inline void MultiplyScore(double val, int cur_tree_id) override; + + inline const double* score() const override { + if (boosting_on_cuda_) { + return cuda_score_.RawData(); + } else { + return score_.data(); + } + } + + /*! \brief Disable copy */ + CUDAScoreUpdater& operator=(const CUDAScoreUpdater&) = delete; + + CUDAScoreUpdater(const CUDAScoreUpdater&) = delete; + + private: + void InitCUDA(const size_t total_size); + + void LaunchAddScoreConstantKernel(const double val, const size_t offset); + + void LaunchMultiplyScoreConstantKernel(const double val, const size_t offset); + + CUDAVector cuda_score_; + + const int num_threads_per_block_; + + const bool boosting_on_cuda_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_ diff --git a/src/boosting/cuda/nccl_gbdt.cpp b/src/boosting/cuda/nccl_gbdt.cpp new file mode 100644 index 0000000..fc99549 --- /dev/null +++ b/src/boosting/cuda/nccl_gbdt.cpp @@ -0,0 +1,210 @@ +/*! + * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include + +#include +#include + +#include "nccl_gbdt.hpp" +#include "nccl_gbdt_component.hpp" + +#ifdef USE_CUDA + +namespace LightGBM { + +template +NCCLGBDT::NCCLGBDT(): GBDT_T() {} + +template +NCCLGBDT::~NCCLGBDT() {} + +template +void NCCLGBDT::Init( + const Config* gbdt_config, const Dataset* train_data, + const ObjectiveFunction* objective_function, + const std::vector& training_metrics) { + GBDT_T::Init(gbdt_config, train_data, objective_function, training_metrics); + + this->tree_learner_.reset(); + + nccl_topology_.reset(new NCCLTopology(this->config_->gpu_device_id, this->config_->num_gpu, this->config_->gpu_device_id_list, train_data->num_data())); + + nccl_topology_->InitNCCL(); + + nccl_topology_->InitPerDevice(&nccl_gbdt_components_); + nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, gbdt_config, train_data] + (NCCLGBDTComponent* nccl_gbdt_component) { nccl_gbdt_component->Init( + gbdt_config, train_data, this->num_tree_per_iteration_, this->boosting_on_gpu_, this->is_constant_hessian_); + }); +} + +template +void NCCLGBDT::BoostingThread(NCCLGBDTComponent* thread_data) { + const ObjectiveFunction* objective_function = thread_data->objective_function(); + score_t* gradients = thread_data->gradients(); + score_t* hessians = thread_data->hessians(); + const double* score = thread_data->train_score_updater()->score(); + objective_function->GetGradients(score, gradients, hessians); +} + +template +void NCCLGBDT::Boosting() { + Common::FunctionTimer fun_timer("NCCLGBDT::Boosting", global_timer); + if (this->objective_function_ == nullptr) { + Log::Fatal("No object function provided"); + } + nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, BoostingThread); +} + +template +double NCCLGBDT::BoostFromAverage(int class_id, bool update_scorer) { + double init_score = GBDT_T::BoostFromAverage(class_id, update_scorer); + + if (init_score != 0.0) { + nccl_topology_->RunPerDevice(nccl_gbdt_components_, [init_score, class_id] (NCCLGBDTComponent* thread_data) { + thread_data->train_score_updater()->AddScore(init_score, class_id); + }); + } + + return init_score; +} + +template +void NCCLGBDT::TrainTreeLearnerThread(NCCLGBDTComponent* thread_data, const int class_id, const bool is_first_tree) { + const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu(); + const score_t* gradients = thread_data->gradients() + class_id * num_data_in_gpu; + const score_t* hessians = thread_data->hessians() + class_id * num_data_in_gpu; + thread_data->SetTree(thread_data->tree_learner()->Train(gradients, hessians, is_first_tree)); +} + +template +bool NCCLGBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) { + Common::FunctionTimer fun_timer("NCCLGBDT::TrainOneIter", global_timer); + std::vector init_scores(this->num_tree_per_iteration_, 0.0); + // boosting first + if (gradients == nullptr || hessians == nullptr) { + for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) { + init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true); + } + Boosting(); + } else { + nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, gradients, hessians] (NCCLGBDTComponent* thread_data) { + const data_size_t data_start_index = thread_data->data_start_index(); + const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu(); + + for (int class_id = 0; class_id < this->num_class_; ++class_id) { + CopyFromHostToCUDADevice( + thread_data->gradients() + class_id * num_data_in_gpu, + gradients + class_id * this->num_data_ + data_start_index, num_data_in_gpu, __FILE__, __LINE__); + CopyFromHostToCUDADevice( + thread_data->hessians() + class_id * num_data_in_gpu, + hessians + class_id * this->num_data_ + data_start_index, num_data_in_gpu, __FILE__, __LINE__); + } + }); + } + + bool should_continue = false; + for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) { + if (this->class_need_train_[cur_tree_id] && this->train_data_->num_features() > 0) { + if (this->data_sample_strategy_->is_use_subset() && this->data_sample_strategy_->bag_data_cnt() < this->num_data_) { + Log::Fatal("Bagging is not supported for NCCLGBDT"); + } + bool is_first_tree = this->models_.size() < static_cast(this->num_tree_per_iteration_); + nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, + [is_first_tree, cur_tree_id] (NCCLGBDTComponent* thread_data) -> void { + TrainTreeLearnerThread(thread_data, cur_tree_id, is_first_tree); + }); + } + + nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, [cur_tree_id, this, init_scores] (NCCLGBDTComponent* thread_data) -> void { + this->UpdateScoreThread(thread_data, cur_tree_id, this->config_->learning_rate, init_scores[cur_tree_id]); + }); + + nccl_topology_->RunOnMasterDevice(nccl_gbdt_components_, [&should_continue, this, cur_tree_id] (NCCLGBDTComponent* thread_data) -> void { + if (thread_data->new_tree()->num_leaves() > 1) { + should_continue = true; + } + for (auto& score_updater : this->valid_score_updater_) { + score_updater->AddScore(thread_data->new_tree(), cur_tree_id); + } + }); + + if (!should_continue) { + if (this->models_.size() < static_cast(this->num_tree_per_iteration_)) { + Log::Warning("Training stopped with no splits."); + } + } + + // add model + nccl_topology_->RunOnMasterDevice(nccl_gbdt_components_, [this] (NCCLGBDTComponent* thread_data) -> void { + this->models_.emplace_back(thread_data->release_new_tree()); + }); + + nccl_topology_->RunOnNonMasterDevice(nccl_gbdt_components_, [this] (NCCLGBDTComponent* thread_data) -> void { + thread_data->clear_new_tree(); + }); + } + + if (!should_continue) { + Log::Warning("Stopped training because there are no more leaves that meet the split requirements"); + if (this->models_.size() > static_cast(this->num_tree_per_iteration_)) { + for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) { + this->models_.pop_back(); + } + } + return true; + } + + ++this->iter_; + return false; +} + +template +void NCCLGBDT::UpdateScoreThread(NCCLGBDTComponent* thread_data, const int cur_tree_id, const double shrinkage_rate, const double init_score) { + if (thread_data->new_tree()->num_leaves() > 1) { + // TODO(shiyu1994): implement bagging + if (thread_data->objective_function() != nullptr && thread_data->objective_function()->IsRenewTreeOutput()) { + // TODO(shiyu1994): implement renewing + } + thread_data->new_tree()->Shrinkage(shrinkage_rate); + thread_data->train_score_updater()->AddScore( + thread_data->tree_learner(), + thread_data->new_tree(), + cur_tree_id); + if (std::fabs(init_score) > kEpsilon) { + thread_data->new_tree()->AddBias(init_score); + } + } +} + +template +std::vector NCCLGBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const { + if (score == this->train_score_updater_->score()) { + // delegate to per gpu train score updater + std::vector tmp_score(num_data * this->num_class_, 0.0f); + + nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, &tmp_score] (NCCLGBDTComponent* thread_data) { + const data_size_t data_start = thread_data->data_start_index(); + const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu(); + for (int class_id = 0; class_id < this->num_class_; ++class_id) { + CopyFromCUDADeviceToHost(tmp_score.data() + class_id * this->num_data_ + data_start, + thread_data->train_score_updater()->score() + class_id * num_data_in_gpu, + static_cast(num_data_in_gpu), __FILE__, __LINE__); + } + }); + + return metric->Eval(tmp_score.data(), this->objective_function_); + } else { + return GBDT_T::EvalOneMetric(metric, score, num_data); + } +} + +template class NCCLGBDT; + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/boosting/cuda/nccl_gbdt.hpp b/src/boosting/cuda/nccl_gbdt.hpp new file mode 100644 index 0000000..75563b8 --- /dev/null +++ b/src/boosting/cuda/nccl_gbdt.hpp @@ -0,0 +1,146 @@ +/*! + * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_ +#define LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_ + +#ifdef USE_CUDA + +#include + +#include +#include + +#include +#include + +#include + +#include "cuda_score_updater.hpp" +#include "nccl_gbdt_component.hpp" + +#include "../gbdt.h" + +namespace LightGBM { + +template +class NCCLGBDT: public GBDT_T { + public: + NCCLGBDT(); + + ~NCCLGBDT(); + + void Init(const Config* gbdt_config, const Dataset* train_data, + const ObjectiveFunction* objective_function, + const std::vector& training_metrics) override; + + void Boosting() override; + + void RefitTree(const int* /*tree_leaf_prediction*/, const size_t /*nrow*/, const size_t /*ncol*/) override { + Log::Fatal("RefitTree is not supported for NCCLGBDT."); + } + + bool TrainOneIter(const score_t* gradients, const score_t* hessians) override; + + const double* GetTrainingScore(int64_t* /*out_len*/) override { + Log::Fatal("GetTrainingScore is not supported for NCCLGBDT."); + } + + void ResetTrainingData(const Dataset* /*train_data*/, const ObjectiveFunction* /*objective_function*/, + const std::vector& /*training_metrics*/) override { + Log::Fatal("ResetTrainingData is not supported for NCCLGBDT."); + } + + void ResetConfig(const Config* /*gbdt_config*/) override { + Log::Fatal("ResetConfig is not supported for NCCLGBDT."); + } + + private: + struct BoostingThreadData { + int gpu_index; + ObjectiveFunction* gpu_objective_function; + score_t* gradients; + score_t* hessians; + const double* score; + + BoostingThreadData() { + gpu_index = 0; + gpu_objective_function = nullptr; + } + }; + + struct TrainTreeLearnerThreadData { + int gpu_index; + TreeLearner* gpu_tree_learner; + const score_t* gradients; + const score_t* hessians; + bool is_first_time; + int class_id; + data_size_t num_data_in_gpu; + std::unique_ptr tree; + + TrainTreeLearnerThreadData() { + gpu_index = 0; + gpu_tree_learner = nullptr; + gradients = nullptr; + hessians = nullptr; + is_first_time = false; + class_id = 0; + num_data_in_gpu = 0; + tree.reset(nullptr); + } + }; + + struct UpdateScoreThreadData { + int gpu_index; + ScoreUpdater* gpu_score_updater; + TreeLearner* gpu_tree_learner; + Tree* tree; + int cur_tree_id; + + UpdateScoreThreadData() { + gpu_index = 0; + gpu_score_updater = nullptr; + gpu_tree_learner = nullptr; + tree = nullptr; + cur_tree_id = 0; + } + }; + + static void BoostingThread(NCCLGBDTComponent* thread_data); + + static void TrainTreeLearnerThread(NCCLGBDTComponent* thread_data, const int class_id, const bool is_first_tree); + + static void UpdateScoreThread(NCCLGBDTComponent* thread_data, const int cur_tree_id, const double shrinkage_rate, const double init_score); + + double BoostFromAverage(int class_id, bool update_scorer) override; + + void UpdateScore(const std::vector>& tree, const int cur_tree_id); + + void UpdateScore(const Tree* /*tree*/, const int /*cur_tree_id*/) { + Log::Fatal("UpdateScore is not supported for NCCLGBDT."); + } + + void RollbackOneIter() override { + Log::Fatal("RollbackOneIter is not supported for NCCLGBDT."); + } + + std::vector EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const override; + + + int num_threads_; + std::unique_ptr nccl_topology_; + + std::vector nccl_gpu_rank_; + std::vector nccl_communicators_; + + std::vector> nccl_gbdt_components_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_ diff --git a/src/boosting/cuda/nccl_gbdt_component.hpp b/src/boosting/cuda/nccl_gbdt_component.hpp new file mode 100644 index 0000000..70d5b40 --- /dev/null +++ b/src/boosting/cuda/nccl_gbdt_component.hpp @@ -0,0 +1,104 @@ +/*! + * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_ +#define LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_ + +#ifdef USE_CUDA + +#include +#include + +#include +#include +#include + +#include +#include "cuda_score_updater.hpp" +#include "../../treelearner/cuda/cuda_single_gpu_tree_learner.hpp" + +namespace LightGBM { + +class NCCLGBDTComponent: public NCCLInfo { + public: + NCCLGBDTComponent() {} + + ~NCCLGBDTComponent() {} + + void Init(const Config* config, const Dataset* train_data, const int num_tree_per_iteration, const bool boosting_on_gpu, const bool is_constant_hessian) { + CUDASUCCESS_OR_FATAL(cudaGetDeviceCount(&num_gpu_in_node_)); + const data_size_t num_data_per_gpu = (train_data->num_data() + num_gpu_in_node_ - 1) / num_gpu_in_node_; + data_start_index_ = num_data_per_gpu * local_gpu_rank_; + data_end_index_ = std::min(data_start_index_ + num_data_per_gpu, train_data->num_data()); + num_data_in_gpu_ = data_end_index_ - data_start_index_; + + dataset_.reset(new Dataset(num_data_in_gpu_)); + dataset_->ReSize(num_data_in_gpu_); + dataset_->CopyFeatureMapperFrom(train_data); + std::vector used_indices(num_data_in_gpu_); + for (data_size_t data_index = data_start_index_; data_index < data_end_index_; ++data_index) { + used_indices[data_index - data_start_index_] = data_index; + } + dataset_->CopySubrowToDevice(train_data, used_indices.data(), num_data_in_gpu_, true, gpu_device_id_); + + objective_function_.reset(ObjectiveFunction::CreateObjectiveFunctionCUDA(config->objective, *config)); + objective_function_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, train_data->num_data()); + train_score_updater_.reset(new CUDAScoreUpdater(dataset_.get(), num_tree_per_iteration, boosting_on_gpu)); + gradients_.reset(new CUDAVector(num_data_in_gpu_)); + hessians_.reset(new CUDAVector(num_data_in_gpu_)); + tree_learner_.reset(new CUDASingleGPUTreeLearner(config, boosting_on_gpu)); + + tree_learner_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, train_data->num_data()); + + objective_function_->Init(dataset_->metadata(), dataset_->num_data()); + tree_learner_->Init(dataset_.get(), is_constant_hessian); + } + + ObjectiveFunction* objective_function() { return objective_function_.get(); } + + ScoreUpdater* train_score_updater() { return train_score_updater_.get(); } + + score_t* gradients() { return gradients_->RawData(); } + + score_t* hessians() { return hessians_->RawData(); } + + data_size_t num_data_in_gpu() const { return num_data_in_gpu_; } + + CUDASingleGPUTreeLearner* tree_learner() { return tree_learner_.get(); } + + void SetTree(Tree* tree) { + new_tree_.reset(tree); + } + + data_size_t data_start_index() const { return data_start_index_; } + + data_size_t data_end_index() const { return data_end_index_; } + + Tree* new_tree() { return new_tree_.get(); } + + Tree* release_new_tree() { return new_tree_.release(); } + + void clear_new_tree() { new_tree_.reset(nullptr); } + + private: + std::unique_ptr objective_function_; + std::unique_ptr train_score_updater_; + std::unique_ptr> gradients_; + std::unique_ptr> hessians_; + std::unique_ptr dataset_; + std::unique_ptr tree_learner_; + std::unique_ptr new_tree_; + + data_size_t data_start_index_; + data_size_t data_end_index_; + data_size_t num_data_in_gpu_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_ diff --git a/src/boosting/dart.hpp b/src/boosting/dart.hpp new file mode 100644 index 0000000..3d7e91d --- /dev/null +++ b/src/boosting/dart.hpp @@ -0,0 +1,212 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_BOOSTING_DART_HPP_ +#define LIGHTGBM_SRC_BOOSTING_DART_HPP_ + +#include + +#include +#include +#include +#include +#include + +#include "gbdt.h" +#include "score_updater.hpp" + +namespace LightGBM { +/*! +* \brief DART algorithm implementation. including Training, prediction, bagging. +*/ +class DART: public GBDT { + public: + /*! + * \brief Constructor + */ + DART() : GBDT() { } + /*! + * \brief Destructor + */ + ~DART() { } + /*! + * \brief Initialization logic + * \param config Config for boosting + * \param train_data Training data + * \param objective_function Training objective function + * \param training_metrics Training metrics + * \param output_model_filename Filename of output model + */ + void Init(const Config* config, const Dataset* train_data, + const ObjectiveFunction* objective_function, + const std::vector& training_metrics) override { + GBDT::Init(config, train_data, objective_function, training_metrics); + random_for_drop_ = Random(config_->drop_seed); + sum_weight_ = 0.0f; + } + + void ResetConfig(const Config* config) override { + GBDT::ResetConfig(config); + random_for_drop_ = Random(config_->drop_seed); + sum_weight_ = 0.0f; + } + + /*! + * \brief one training iteration + */ + bool TrainOneIter(const score_t* gradient, const score_t* hessian) override { + is_update_score_cur_iter_ = false; + bool ret = GBDT::TrainOneIter(gradient, hessian); + if (ret) { + return ret; + } + // normalize + Normalize(); + if (!config_->uniform_drop) { + tree_weight_.push_back(shrinkage_rate_); + sum_weight_ += shrinkage_rate_; + } + return false; + } + + /*! + * \brief Get current training score + * \param out_len length of returned score + * \return training score + */ + const double* GetTrainingScore(int64_t* out_len) override { + if (!is_update_score_cur_iter_) { + // only drop one time in one iteration + DroppingTrees(); + is_update_score_cur_iter_ = true; + } + *out_len = static_cast(train_score_updater_->num_data()) * num_class_; + return train_score_updater_->score(); + } + + bool EvalAndCheckEarlyStopping() override { + GBDT::OutputMetric(iter_); + return false; + } + + private: + /*! + * \brief drop trees based on drop_rate + */ + void DroppingTrees() { + drop_index_.clear(); + bool is_skip = random_for_drop_.NextFloat() < config_->skip_drop; + // select dropping tree indices based on drop_rate and tree weights + if (!is_skip) { + double drop_rate = config_->drop_rate; + if (!config_->uniform_drop) { + double inv_average_weight = static_cast(tree_weight_.size()) / sum_weight_; + if (config_->max_drop > 0) { + drop_rate = std::min(drop_rate, config_->max_drop * inv_average_weight / sum_weight_); + } + for (int i = 0; i < iter_; ++i) { + if (random_for_drop_.NextFloat() < drop_rate * tree_weight_[i] * inv_average_weight) { + drop_index_.push_back(num_init_iteration_ + i); + if (drop_index_.size() >= static_cast(config_->max_drop)) { + break; + } + } + } + } else { + if (config_->max_drop > 0) { + drop_rate = std::min(drop_rate, config_->max_drop / static_cast(iter_)); + } + for (int i = 0; i < iter_; ++i) { + if (random_for_drop_.NextFloat() < drop_rate) { + drop_index_.push_back(num_init_iteration_ + i); + if (drop_index_.size() >= static_cast(config_->max_drop)) { + break; + } + } + } + } + } + // drop trees + for (auto i : drop_index_) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id; + models_[curr_tree]->Shrinkage(-1.0); + train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); + } + } + if (!config_->xgboost_dart_mode) { + shrinkage_rate_ = config_->learning_rate / (1.0f + static_cast(drop_index_.size())); + } else { + if (drop_index_.empty()) { + shrinkage_rate_ = config_->learning_rate; + } else { + shrinkage_rate_ = config_->learning_rate / (config_->learning_rate + static_cast(drop_index_.size())); + } + } + } + /*! + * \brief normalize dropped trees + * NOTE: num_drop_tree(k), learning_rate(lr), shrinkage_rate_ = lr / (k + 1) + * step 1: shrink tree to -1 -> drop tree + * step 2: shrink tree to k / (k + 1) - 1 from -1, by 1/(k+1) + * -> normalize for valid data + * step 3: shrink tree to k / (k + 1) from k / (k + 1) - 1, by -k + * -> normalize for train data + * end with tree weight = (k / (k + 1)) * old_weight + */ + void Normalize() { + double k = static_cast(drop_index_.size()); + if (!config_->xgboost_dart_mode) { + for (auto i : drop_index_) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id; + // update validation score + models_[curr_tree]->Shrinkage(1.0f / (k + 1.0f)); + for (auto& score_updater : valid_score_updater_) { + score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); + } + // update training score + models_[curr_tree]->Shrinkage(-k); + train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); + } + if (!config_->uniform_drop) { + sum_weight_ -= tree_weight_[i - num_init_iteration_] * (1.0f / (k + 1.0f)); + tree_weight_[i - num_init_iteration_] *= (k / (k + 1.0f)); + } + } + } else { + for (auto i : drop_index_) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id; + // update validation score + models_[curr_tree]->Shrinkage(shrinkage_rate_); + for (auto& score_updater : valid_score_updater_) { + score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); + } + // update training score + models_[curr_tree]->Shrinkage(-k / config_->learning_rate); + train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); + } + if (!config_->uniform_drop) { + sum_weight_ -= tree_weight_[i - num_init_iteration_] * (1.0f / (k + config_->learning_rate));; + tree_weight_[i - num_init_iteration_] *= (k / (k + config_->learning_rate)); + } + } + } + } + /*! \brief The weights of all trees, used to choose drop trees */ + std::vector tree_weight_; + /*! \brief sum weights of all trees */ + double sum_weight_; + /*! \brief The indices of dropping trees */ + std::vector drop_index_; + /*! \brief Random generator, used to select dropping trees */ + Random random_for_drop_; + /*! \brief Flag that the score is update on current iter or not*/ + bool is_update_score_cur_iter_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_BOOSTING_DART_HPP_ diff --git a/src/boosting/gbdt.cpp b/src/boosting/gbdt.cpp new file mode 100644 index 0000000..ba341af --- /dev/null +++ b/src/boosting/gbdt.cpp @@ -0,0 +1,890 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include "gbdt.h" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +Common::Timer global_timer; + +int LGBM_config_::current_device = lgbm_device_cpu; +int LGBM_config_::current_learner = use_cpu_learner; + +GBDT::GBDT() + : iter_(0), + train_data_(nullptr), + config_(nullptr), + objective_function_(nullptr), + early_stopping_round_(0), + early_stopping_min_delta_(0.0), + es_first_metric_only_(false), + max_feature_idx_(0), + num_tree_per_iteration_(1), + num_class_(1), + num_iteration_for_pred_(0), + shrinkage_rate_(0.1f), + num_init_iteration_(0) { + average_output_ = false; + tree_learner_ = nullptr; + linear_tree_ = false; + data_sample_strategy_.reset(nullptr); + gradients_pointer_ = nullptr; + hessians_pointer_ = nullptr; + boosting_on_gpu_ = false; +} + +GBDT::~GBDT() { +} + +void GBDT::Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, + const std::vector& training_metrics) { + CHECK_NOTNULL(train_data); + train_data_ = train_data; + if (!config->monotone_constraints.empty()) { + CHECK_EQ(static_cast(train_data_->num_total_features()), config->monotone_constraints.size()); + } + if (!config->feature_contri.empty()) { + CHECK_EQ(static_cast(train_data_->num_total_features()), config->feature_contri.size()); + } + iter_ = 0; + num_iteration_for_pred_ = 0; + max_feature_idx_ = 0; + num_class_ = config->num_class; + config_ = std::unique_ptr(new Config(*config)); + early_stopping_round_ = config_->early_stopping_round; + early_stopping_min_delta_ = config->early_stopping_min_delta; + es_first_metric_only_ = config_->first_metric_only; + shrinkage_rate_ = config_->learning_rate; + + if (config_->device_type == std::string("cuda")) { + LGBM_config_::current_learner = use_cuda_learner; + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + const int gpu_device_id = config_->gpu_device_id >= 0 ? config_->gpu_device_id : 0; + CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_device_id)); + } + #endif // USE_CUDA + } + + // load forced_splits file + if (!config->forcedsplits_filename.empty()) { + std::ifstream forced_splits_file(config->forcedsplits_filename.c_str()); + std::stringstream buffer; + buffer << forced_splits_file.rdbuf(); + std::string err; + forced_splits_json_ = Json::parse(buffer.str(), &err); + } + + objective_function_ = objective_function; + num_tree_per_iteration_ = num_class_; + if (objective_function_ != nullptr) { + num_tree_per_iteration_ = objective_function_->NumModelPerIteration(); + if (objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) { + Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName()); + } + } + + data_sample_strategy_.reset(SampleStrategy::CreateSampleStrategy(config_.get(), train_data_, objective_function_, num_tree_per_iteration_)); + is_constant_hessian_ = GetIsConstHessian(objective_function); + + boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() && + !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU + + tree_learner_ = std::unique_ptr(TreeLearner::CreateTreeLearner(config_->tree_learner, config_->device_type, + config_.get(), boosting_on_gpu_)); + + // init tree learner + tree_learner_->Init(train_data_, is_constant_hessian_); + tree_learner_->SetForcedSplit(&forced_splits_json_); + + // push training metrics + training_metrics_.clear(); + for (const auto& metric : training_metrics) { + training_metrics_.push_back(metric); + } + training_metrics_.shrink_to_fit(); + + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + train_score_updater_.reset(new CUDAScoreUpdater(train_data_, num_tree_per_iteration_, boosting_on_gpu_)); + } else { + #endif // USE_CUDA + train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_)); + #ifdef USE_CUDA + } + #endif // USE_CUDA + + num_data_ = train_data_->num_data(); + + // get max feature index + max_feature_idx_ = train_data_->num_total_features() - 1; + // get label index + label_idx_ = train_data_->label_idx(); + // get feature names + feature_names_ = train_data_->feature_names(); + feature_infos_ = train_data_->feature_infos(); + monotone_constraints_ = config->monotone_constraints; + // get parser config file content + parser_config_str_ = train_data_->parser_config_str(); + + // check that forced splits does not use feature indices larger than dataset size + CheckForcedSplitFeatures(); + + // if need bagging, create buffer + data_sample_strategy_->ResetSampleConfig(config_.get(), true); + ResetGradientBuffers(); + + class_need_train_ = std::vector(num_tree_per_iteration_, true); + if (objective_function_ != nullptr && objective_function_->SkipEmptyClass()) { + CHECK_EQ(num_tree_per_iteration_, num_class_); + for (int i = 0; i < num_class_; ++i) { + class_need_train_[i] = objective_function_->ClassNeedTrain(i); + } + } + + if (config_->linear_tree) { + linear_tree_ = true; + } +} + +void GBDT::CheckForcedSplitFeatures() { + std::queue forced_split_nodes; + forced_split_nodes.push(forced_splits_json_); + while (!forced_split_nodes.empty()) { + Json node = forced_split_nodes.front(); + forced_split_nodes.pop(); + const int feature_index = node["feature"].int_value(); + if (feature_index > max_feature_idx_) { + Log::Fatal("Forced splits file includes feature index %d, but maximum feature index in dataset is %d", + feature_index, max_feature_idx_); + } + if (node.object_items().count("left") > 0) { + forced_split_nodes.push(node["left"]); + } + if (node.object_items().count("right") > 0) { + forced_split_nodes.push(node["right"]); + } + } +} + +void GBDT::AddValidDataset(const Dataset* valid_data, + const std::vector& valid_metrics) { + if (!train_data_->CheckAlign(*valid_data)) { + Log::Fatal("Cannot add validation data, since it has different bin mappers with training data"); + } + // for a validation dataset, we need its score and metric + auto new_score_updater = + #ifdef USE_CUDA + config_->device_type == std::string("cuda") ? + std::unique_ptr(new CUDAScoreUpdater(valid_data, num_tree_per_iteration_, + objective_function_ != nullptr && objective_function_->IsCUDAObjective())) : + #endif // USE_CUDA + std::unique_ptr(new ScoreUpdater(valid_data, num_tree_per_iteration_)); + // update score + for (int i = 0; i < iter_; ++i) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id; + new_score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); + } + } + valid_score_updater_.push_back(std::move(new_score_updater)); + valid_metrics_.emplace_back(); + for (const auto& metric : valid_metrics) { + valid_metrics_.back().push_back(metric); + } + valid_metrics_.back().shrink_to_fit(); + + if (early_stopping_round_ > 0) { + auto num_metrics = valid_metrics.size(); + if (es_first_metric_only_) { + num_metrics = 1; + } + best_iter_.emplace_back(num_metrics, 0); + best_score_.emplace_back(num_metrics, kMinScore); + best_msg_.emplace_back(num_metrics); + } +} + +void GBDT::Boosting() { + Common::FunctionTimer fun_timer("GBDT::Boosting", global_timer); + if (objective_function_ == nullptr) { + Log::Fatal("No objective function provided"); + } + // objective function will calculate gradients and hessians + int64_t num_score = 0; + if (config_->bagging_by_query) { + data_sample_strategy_->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data()); + objective_function_-> + GetGradientsWithSampledQueries(GetTrainingScore(&num_score), data_sample_strategy_->num_sampled_queries(), data_sample_strategy_->sampled_query_indices(), gradients_pointer_, hessians_pointer_); + } else { + objective_function_-> + GetGradients(GetTrainingScore(&num_score), gradients_pointer_, hessians_pointer_); + } +} + +void GBDT::Train(int snapshot_freq, const std::string& model_output_path) { + Common::FunctionTimer fun_timer("GBDT::Train", global_timer); + bool is_finished = false; + auto start_time = std::chrono::steady_clock::now(); + for (int iter = 0; iter < config_->num_iterations && !is_finished; ++iter) { + is_finished = TrainOneIter(nullptr, nullptr); + if (!is_finished) { + is_finished = EvalAndCheckEarlyStopping(); + } + auto end_time = std::chrono::steady_clock::now(); + // output used time per iteration + Log::Info("%f seconds elapsed, finished iteration %d", std::chrono::duration(end_time - start_time) * 1e-3, iter + 1); + if (snapshot_freq > 0 + && (iter + 1) % snapshot_freq == 0) { + std::string snapshot_out = model_output_path + ".snapshot_iter_" + std::to_string(iter + 1); + SaveModelToFile(0, -1, config_->saved_feature_importance_type, snapshot_out.c_str()); + } + } +} + +void GBDT::RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) { + CHECK_GT(nrow * ncol, 0); + CHECK_EQ(static_cast(num_data_), nrow); + CHECK_EQ(models_.size(), ncol); + + int num_iterations = static_cast(models_.size() / num_tree_per_iteration_); + std::vector leaf_pred(num_data_); + if (linear_tree_) { + std::vector max_leaves_by_thread = std::vector(OMP_NUM_THREADS(), 0); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < static_cast(nrow); ++i) { + int tid = omp_get_thread_num(); + for (size_t j = 0; j < ncol; ++j) { + max_leaves_by_thread[tid] = std::max(max_leaves_by_thread[tid], tree_leaf_prediction[i * ncol + j]); + } + } + int max_leaves = *std::max_element(max_leaves_by_thread.begin(), max_leaves_by_thread.end()); + max_leaves += 1; + tree_learner_->InitLinear(train_data_, max_leaves); + } + + for (int iter = 0; iter < num_iterations; ++iter) { + Boosting(); + for (int tree_id = 0; tree_id < num_tree_per_iteration_; ++tree_id) { + int model_index = iter * num_tree_per_iteration_ + tree_id; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < num_data_; ++i) { + leaf_pred[i] = tree_leaf_prediction[i * ncol + model_index]; + CHECK_LT(leaf_pred[i], models_[model_index]->num_leaves()); + } + size_t offset = static_cast(tree_id) * num_data_; + auto grad = gradients_pointer_ + offset; + auto hess = hessians_pointer_ + offset; + auto new_tree = tree_learner_->FitByExistingTree(models_[model_index].get(), leaf_pred, grad, hess); + train_score_updater_->AddScore(tree_learner_.get(), new_tree, tree_id); + models_[model_index].reset(new_tree); + } + } +} + +/* If the custom "average" is implemented it will be used in place of the label average (if enabled) +* +* An improvement to this is to have options to explicitly choose +* (i) standard average +* (ii) custom average if available +* (iii) any user defined scalar bias (e.g. using a new option "init_score" that overrides (i) and (ii) ) +* +* (i) and (ii) could be selected as say "auto_init_score" = 0 or 1 etc.. +* +*/ +double ObtainAutomaticInitialScore(const ObjectiveFunction* fobj, int class_id) { + double init_score = 0.0; + if (fobj != nullptr) { + init_score = fobj->BoostFromScore(class_id); + } + if (Network::num_machines() > 1) { + init_score = Network::GlobalSyncUpByMean(init_score); + } + return init_score; +} + +double GBDT::BoostFromAverage(int class_id, bool update_scorer) { + Common::FunctionTimer fun_timer("GBDT::BoostFromAverage", global_timer); + // boosting from average label; or customized "average" if implemented for the current objective + if (models_.empty() && !train_score_updater_->has_init_score() && objective_function_ != nullptr) { + if (config_->boost_from_average || (train_data_ != nullptr && train_data_->num_features() == 0)) { + double init_score = ObtainAutomaticInitialScore(objective_function_, class_id); + if (std::fabs(init_score) > kEpsilon) { + if (update_scorer) { + train_score_updater_->AddScore(init_score, class_id); + for (auto& score_updater : valid_score_updater_) { + score_updater->AddScore(init_score, class_id); + } + } + Log::Info("Start training from score %lf", init_score); + return init_score; + } + } else if (std::string(objective_function_->GetName()) == std::string("regression_l1") + || std::string(objective_function_->GetName()) == std::string("quantile") + || std::string(objective_function_->GetName()) == std::string("mape")) { + Log::Warning("Disabling boost_from_average in %s may cause the slow convergence", objective_function_->GetName()); + } + } + return 0.0f; +} + +bool GBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) { + Common::FunctionTimer fun_timer("GBDT::TrainOneIter", global_timer); + std::vector init_scores(num_tree_per_iteration_, 0.0); + // boosting first + if (gradients == nullptr || hessians == nullptr) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true); + } + Boosting(); + gradients = gradients_pointer_; + hessians = hessians_pointer_; + } else { + // use customized objective function + // the check below fails unless objective=custom is provided in the parameters on Booster creation + CHECK(objective_function_ == nullptr); + if (data_sample_strategy_->IsHessianChange()) { + // need to copy customized gradients when using GOSS + int64_t total_size = static_cast(num_data_) * num_tree_per_iteration_; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int64_t i = 0; i < total_size; ++i) { + gradients_[i] = gradients[i]; + hessians_[i] = hessians[i]; + } + CHECK_EQ(gradients_pointer_, gradients_.data()); + CHECK_EQ(hessians_pointer_, hessians_.data()); + gradients = gradients_pointer_; + hessians = hessians_pointer_; + } + } + + // bagging logic + if (!config_->bagging_by_query) { + data_sample_strategy_->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data()); + } + const bool is_use_subset = data_sample_strategy_->is_use_subset(); + const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); + const std::vector>& bag_data_indices = data_sample_strategy_->bag_data_indices(); + + if (objective_function_ == nullptr && is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_ && !data_sample_strategy_->IsHessianChange()) { + ResetGradientBuffers(); + } + + bool should_continue = false; + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + const size_t offset = static_cast(cur_tree_id) * num_data_; + std::unique_ptr new_tree(new Tree(2, false, false)); + if (class_need_train_[cur_tree_id] && train_data_->num_features() > 0) { + auto grad = gradients + offset; + auto hess = hessians + offset; + // need to copy gradients for bagging subset. + if (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_) { + for (int i = 0; i < bag_data_cnt; ++i) { + gradients_pointer_[offset + i] = grad[bag_data_indices[i]]; + hessians_pointer_[offset + i] = hess[bag_data_indices[i]]; + } + grad = gradients_pointer_ + offset; + hess = hessians_pointer_ + offset; + } + bool is_first_tree = models_.size() < static_cast(num_tree_per_iteration_); + new_tree.reset(tree_learner_->Train(grad, hess, is_first_tree)); + } + + if (new_tree->num_leaves() > 1) { + should_continue = true; + auto score_ptr = train_score_updater_->score() + offset; + auto residual_getter = [score_ptr](const label_t* label, int i) {return static_cast(label[i]) - score_ptr[i]; }; + tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter, + num_data_, bag_data_indices.data(), bag_data_cnt, train_score_updater_->score()); + // shrinkage by learning rate + new_tree->Shrinkage(shrinkage_rate_); + // update score + UpdateScore(new_tree.get(), cur_tree_id); + if (std::fabs(init_scores[cur_tree_id]) > kEpsilon) { + new_tree->AddBias(init_scores[cur_tree_id]); + } + } else { + // only add default score one-time + if (models_.size() < static_cast(num_tree_per_iteration_)) { + if (objective_function_ != nullptr && !config_->boost_from_average && !train_score_updater_->has_init_score()) { + init_scores[cur_tree_id] = ObtainAutomaticInitialScore(objective_function_, cur_tree_id); + // updates scores + train_score_updater_->AddScore(init_scores[cur_tree_id], cur_tree_id); + for (auto& score_updater : valid_score_updater_) { + score_updater->AddScore(init_scores[cur_tree_id], cur_tree_id); + } + } + new_tree->AsConstantTree(init_scores[cur_tree_id], num_data_); + } else { + // extend init_scores with zeros + new_tree->AsConstantTree(0, num_data_); + } + } + // add model + models_.push_back(std::move(new_tree)); + } + + if (!should_continue) { + Log::Warning("Stopped training because there are no more leaves that meet the split requirements"); + if (models_.size() > static_cast(num_tree_per_iteration_)) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + models_.pop_back(); + } + } + return true; + } + + ++iter_; + return false; +} + +void GBDT::RollbackOneIter() { + if (iter_ <= 0) { + return; + } + // reset score + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + auto curr_tree = models_.size() - num_tree_per_iteration_ + cur_tree_id; + models_[curr_tree]->Shrinkage(-1.0); + train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); + for (auto& score_updater : valid_score_updater_) { + score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); + } + } + // remove model + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + models_.pop_back(); + } + --iter_; +} + +bool GBDT::EvalAndCheckEarlyStopping() { + bool is_met_early_stopping = false; + // print message for metric + auto best_msg = OutputMetric(iter_); + + + is_met_early_stopping = !best_msg.empty(); + if (is_met_early_stopping) { + Log::Info("Early stopping at iteration %d, the best iteration round is %d", + iter_, iter_ - early_stopping_round_); + Log::Info("Output of best iteration round:\n%s", best_msg.c_str()); + // pop last early_stopping_round_ models + for (int i = 0; i < early_stopping_round_ * num_tree_per_iteration_; ++i) { + models_.pop_back(); + } + } + return is_met_early_stopping; +} + +void GBDT::UpdateScore(const Tree* tree, const int cur_tree_id) { + Common::FunctionTimer fun_timer("GBDT::UpdateScore", global_timer); + // update training score + if (!data_sample_strategy_->is_use_subset()) { + train_score_updater_->AddScore(tree_learner_.get(), tree, cur_tree_id); + + const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); + // we need to predict out-of-bag scores of data for boosting + if (num_data_ - bag_data_cnt > 0) { + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + train_score_updater_->AddScore(tree, data_sample_strategy_->cuda_bag_data_indices().RawData() + bag_data_cnt, num_data_ - bag_data_cnt, cur_tree_id); + } else { + #endif // USE_CUDA + train_score_updater_->AddScore(tree, data_sample_strategy_->bag_data_indices().data() + bag_data_cnt, num_data_ - bag_data_cnt, cur_tree_id); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } + + } else { + train_score_updater_->AddScore(tree, cur_tree_id); + } + + + // update validation score + for (auto& score_updater : valid_score_updater_) { + score_updater->AddScore(tree, cur_tree_id); + } +} + +#ifdef USE_CUDA +std::vector GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const { +#else +std::vector GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t /*num_data*/) const { +#endif // USE_CUDA + #ifdef USE_CUDA + const bool evaluation_on_cuda = metric->IsCUDAMetric(); + if ((boosting_on_gpu_ && evaluation_on_cuda) || (!boosting_on_gpu_ && !evaluation_on_cuda)) { + #endif // USE_CUDA + return metric->Eval(score, objective_function_); + #ifdef USE_CUDA + } else if (boosting_on_gpu_ && !evaluation_on_cuda) { + const size_t total_size = static_cast(num_data) * static_cast(num_tree_per_iteration_); + if (total_size > host_score_.size()) { + host_score_.resize(total_size, 0.0f); + } + CopyFromCUDADeviceToHost(host_score_.data(), score, total_size, __FILE__, __LINE__); + return metric->Eval(host_score_.data(), objective_function_); + } else { + const size_t total_size = static_cast(num_data) * static_cast(num_tree_per_iteration_); + if (total_size > cuda_score_.Size()) { + cuda_score_.Resize(total_size); + } + CopyFromHostToCUDADevice(cuda_score_.RawData(), score, total_size, __FILE__, __LINE__); + return metric->Eval(cuda_score_.RawData(), objective_function_); + } + #endif // USE_CUDA +} + +std::string GBDT::OutputMetric(int iter) { + bool need_output = (iter % config_->metric_freq) == 0; + std::string ret = ""; + std::stringstream msg_buf; + std::vector> meet_early_stopping_pairs; + // print training metric + if (need_output) { + for (auto& sub_metric : training_metrics_) { + auto name = sub_metric->GetName(); + auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data()); + for (size_t k = 0; k < name.size(); ++k) { + std::stringstream tmp_buf; + tmp_buf << "Iteration:" << iter + << ", training " << name[k] + << " : " << scores[k]; + Log::Info(tmp_buf.str().c_str()); + if (early_stopping_round_ > 0) { + msg_buf << tmp_buf.str() << '\n'; + } + } + } + } + // print validation metric + if (need_output || early_stopping_round_ > 0) { + for (size_t i = 0; i < valid_metrics_.size(); ++i) { + for (size_t j = 0; j < valid_metrics_[i].size(); ++j) { + auto test_scores = EvalOneMetric(valid_metrics_[i][j], valid_score_updater_[i]->score(), valid_score_updater_[i]->num_data()); + auto name = valid_metrics_[i][j]->GetName(); + for (size_t k = 0; k < name.size(); ++k) { + std::stringstream tmp_buf; + tmp_buf << "Iteration:" << iter + << ", valid_" << i + 1 << " " << name[k] + << " : " << test_scores[k]; + if (need_output) { + Log::Info(tmp_buf.str().c_str()); + } + if (early_stopping_round_ > 0) { + msg_buf << tmp_buf.str() << '\n'; + } + } + if (es_first_metric_only_ && j > 0) { + continue; + } + if (ret.empty() && early_stopping_round_ > 0) { + auto cur_score = valid_metrics_[i][j]->factor_to_bigger_better() * test_scores.back(); + if (cur_score - best_score_[i][j] > early_stopping_min_delta_) { + best_score_[i][j] = cur_score; + best_iter_[i][j] = iter; + meet_early_stopping_pairs.emplace_back(i, j); + } else { + if (iter - best_iter_[i][j] >= early_stopping_round_) { + ret = best_msg_[i][j]; + } + } + } + } + } + } + for (auto& pair : meet_early_stopping_pairs) { + best_msg_[pair.first][pair.second] = msg_buf.str(); + } + return ret; +} + +/*! \brief Get eval result */ +std::vector GBDT::GetEvalAt(int data_idx) const { + CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size())); + std::vector ret; + if (data_idx == 0) { + for (auto& sub_metric : training_metrics_) { + auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data()); + for (auto score : scores) { + ret.push_back(score); + } + } + } else { + auto used_idx = data_idx - 1; + for (size_t j = 0; j < valid_metrics_[used_idx].size(); ++j) { + auto test_scores = EvalOneMetric(valid_metrics_[used_idx][j], valid_score_updater_[used_idx]->score(), valid_score_updater_[used_idx]->num_data()); + for (auto score : test_scores) { + ret.push_back(score); + } + } + } + return ret; +} + +/*! \brief Get training scores result */ +const double* GBDT::GetTrainingScore(int64_t* out_len) { + *out_len = static_cast(train_score_updater_->num_data()) * num_class_; + return train_score_updater_->score(); +} + +void GBDT::PredictContrib(const double* features, double* output) const { + // set zero + const int num_features = max_feature_idx_ + 1; + std::memset(output, 0, sizeof(double) * num_tree_per_iteration_ * (num_features + 1)); + const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; + for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { + // predict all the trees for one iteration + for (int k = 0; k < num_tree_per_iteration_; ++k) { + models_[i * num_tree_per_iteration_ + k]->PredictContrib(features, num_features, output + k*(num_features + 1)); + } + } +} + +void GBDT::PredictContribByMap(const std::unordered_map& features, + std::vector>* output) const { + const int num_features = max_feature_idx_ + 1; + const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; + for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { + // predict all the trees for one iteration + for (int k = 0; k < num_tree_per_iteration_; ++k) { + models_[i * num_tree_per_iteration_ + k]->PredictContribByMap(features, num_features, &((*output)[k])); + } + } +} + +void GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) { + CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size())); + + const double* raw_scores = nullptr; + data_size_t num_data = 0; + if (data_idx == 0) { + raw_scores = GetTrainingScore(out_len); + num_data = train_score_updater_->num_data(); + } else { + auto used_idx = data_idx - 1; + raw_scores = valid_score_updater_[used_idx]->score(); + num_data = valid_score_updater_[used_idx]->num_data(); + *out_len = static_cast(num_data) * num_class_; + } + #ifdef USE_CUDA + std::vector host_raw_scores; + if (boosting_on_gpu_) { + host_raw_scores.resize(static_cast(*out_len), 0.0); + CopyFromCUDADeviceToHost(host_raw_scores.data(), raw_scores, static_cast(*out_len), __FILE__, __LINE__); + raw_scores = host_raw_scores.data(); + } + #endif // USE_CUDA + if (objective_function_ != nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data; ++i) { + std::vector tree_pred(num_tree_per_iteration_); + for (int j = 0; j < num_tree_per_iteration_; ++j) { + tree_pred[j] = raw_scores[j * num_data + i]; + } + std::vector tmp_result(num_class_); + objective_function_->ConvertOutput(tree_pred.data(), tmp_result.data()); + for (int j = 0; j < num_class_; ++j) { + out_result[j * num_data + i] = static_cast(tmp_result[j]); + } + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data; ++i) { + for (int j = 0; j < num_tree_per_iteration_; ++j) { + out_result[j * num_data + i] = static_cast(raw_scores[j * num_data + i]); + } + } + } +} + +double GBDT::GetUpperBoundValue() const { + double max_value = 0.0; + for (const auto &tree : models_) { + max_value += tree->GetUpperBoundValue(); + } + return max_value; +} + +double GBDT::GetLowerBoundValue() const { + double min_value = 0.0; + for (const auto &tree : models_) { + min_value += tree->GetLowerBoundValue(); + } + return min_value; +} + +void GBDT::ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, + const std::vector& training_metrics) { + if (train_data != train_data_ && !train_data_->CheckAlign(*train_data)) { + Log::Fatal("Cannot reset training data, since new training data has different bin mappers"); + } + + objective_function_ = objective_function; + data_sample_strategy_->UpdateObjectiveFunction(objective_function); + if (objective_function_ != nullptr) { + CHECK_EQ(num_tree_per_iteration_, objective_function_->NumModelPerIteration()); + if (objective_function_->IsRenewTreeOutput() && !config_->monotone_constraints.empty()) { + Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName()); + } + } + is_constant_hessian_ = GetIsConstHessian(objective_function); + + // push training metrics + training_metrics_.clear(); + for (const auto& metric : training_metrics) { + training_metrics_.push_back(metric); + } + training_metrics_.shrink_to_fit(); + + #ifdef USE_CUDA + boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() && + !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU + tree_learner_->ResetBoostingOnGPU(boosting_on_gpu_); + #endif // USE_CUDA + + if (train_data != train_data_) { + train_data_ = train_data; + data_sample_strategy_->UpdateTrainingData(train_data); + // not same training data, need reset score and others + // create score tracker + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + train_score_updater_.reset(new CUDAScoreUpdater(train_data_, num_tree_per_iteration_, boosting_on_gpu_)); + } else { + #endif // USE_CUDA + train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_)); + #ifdef USE_CUDA + } + #endif // USE_CUDA + + // update score + for (int i = 0; i < iter_; ++i) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id; + train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); + } + } + + num_data_ = train_data_->num_data(); + + ResetGradientBuffers(); + + max_feature_idx_ = train_data_->num_total_features() - 1; + label_idx_ = train_data_->label_idx(); + feature_names_ = train_data_->feature_names(); + feature_infos_ = train_data_->feature_infos(); + parser_config_str_ = train_data_->parser_config_str(); + + tree_learner_->ResetTrainingData(train_data, is_constant_hessian_); + data_sample_strategy_->ResetSampleConfig(config_.get(), true); + } else { + tree_learner_->ResetIsConstantHessian(is_constant_hessian_); + } +} + +void GBDT::ResetConfig(const Config* config) { + auto new_config = std::unique_ptr(new Config(*config)); + if (!config->monotone_constraints.empty()) { + CHECK_EQ(static_cast(train_data_->num_total_features()), config->monotone_constraints.size()); + } + if (!config->feature_contri.empty()) { + CHECK_EQ(static_cast(train_data_->num_total_features()), config->feature_contri.size()); + } + if (objective_function_ != nullptr && objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) { + Log::Fatal("Cannot use ``monotone_constraints`` in %s objective, please disable it.", objective_function_->GetName()); + } + early_stopping_round_ = new_config->early_stopping_round; + shrinkage_rate_ = new_config->learning_rate; + if (tree_learner_ != nullptr) { + tree_learner_->ResetConfig(new_config.get()); + } + + boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() && + !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU + tree_learner_->ResetBoostingOnGPU(boosting_on_gpu_); + + if (train_data_ != nullptr) { + data_sample_strategy_->ResetSampleConfig(new_config.get(), false); + if (data_sample_strategy_->NeedResizeGradients()) { + // resize gradient vectors to copy the customized gradients for goss or bagging with subset + ResetGradientBuffers(); + } + } + if (config_.get() != nullptr && config_->forcedsplits_filename != new_config->forcedsplits_filename) { + // load forced_splits file + if (!new_config->forcedsplits_filename.empty()) { + std::ifstream forced_splits_file( + new_config->forcedsplits_filename.c_str()); + std::stringstream buffer; + buffer << forced_splits_file.rdbuf(); + std::string err; + forced_splits_json_ = Json::parse(buffer.str(), &err); + tree_learner_->SetForcedSplit(&forced_splits_json_); + } else { + forced_splits_json_ = Json(); + tree_learner_->SetForcedSplit(nullptr); + } + } + config_.reset(new_config.release()); +} + +void GBDT::ResetGradientBuffers() { + const size_t total_size = static_cast(num_data_) * num_tree_per_iteration_; + const bool is_use_subset = data_sample_strategy_->is_use_subset(); + const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); + if (objective_function_ != nullptr) { + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda") && boosting_on_gpu_) { + if (cuda_gradients_.Size() < total_size) { + cuda_gradients_.Resize(total_size); + cuda_hessians_.Resize(total_size); + } + gradients_pointer_ = cuda_gradients_.RawData(); + hessians_pointer_ = cuda_hessians_.RawData(); + } else { + #endif // USE_CUDA + if (gradients_.size() < total_size) { + gradients_.resize(total_size); + hessians_.resize(total_size); + } + gradients_pointer_ = gradients_.data(); + hessians_pointer_ = hessians_.data(); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } else if (data_sample_strategy_->IsHessianChange() || (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_)) { + if (gradients_.size() < total_size) { + gradients_.resize(total_size); + hessians_.resize(total_size); + } + gradients_pointer_ = gradients_.data(); + hessians_pointer_ = hessians_.data(); + } +} + +} // namespace LightGBM diff --git a/src/boosting/gbdt.h b/src/boosting/gbdt.h new file mode 100644 index 0000000..4d6cf8c --- /dev/null +++ b/src/boosting/gbdt.h @@ -0,0 +1,625 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_BOOSTING_GBDT_H_ +#define LIGHTGBM_SRC_BOOSTING_GBDT_H_ + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "cuda/cuda_score_updater.hpp" +#include "score_updater.hpp" + +namespace LightGBM { + +using json11_internal_lightgbm::Json; + +/*! +* \brief GBDT algorithm implementation. including Training, prediction, bagging. +*/ +class GBDT : public GBDTBase { + public: + /*! + * \brief Constructor + */ + GBDT(); + + /*! + * \brief Destructor + */ + ~GBDT(); + + + /*! + * \brief Initialization logic + * \param gbdt_config Config for boosting + * \param train_data Training data + * \param objective_function Training objective function + * \param training_metrics Training metrics + */ + void Init(const Config* gbdt_config, const Dataset* train_data, + const ObjectiveFunction* objective_function, + const std::vector& training_metrics) override; + + /*! + * \brief Traverse the tree of forced splits and check that all indices are less than the number of features. + */ + void CheckForcedSplitFeatures(); + + /*! + * \brief Merge model from other boosting object. Will insert to the front of current boosting object + * \param other + */ + void MergeFrom(const Boosting* other) override { + auto other_gbdt = reinterpret_cast(other); + // tmp move to other vector + auto original_models = std::move(models_); + models_ = std::vector>(); + // push model from other first + for (const auto& tree : other_gbdt->models_) { + auto new_tree = std::unique_ptr(new Tree(*(tree.get()))); + models_.push_back(std::move(new_tree)); + } + num_init_iteration_ = static_cast(models_.size()) / num_tree_per_iteration_; + // push model in current object + for (const auto& tree : original_models) { + auto new_tree = std::unique_ptr(new Tree(*(tree.get()))); + models_.push_back(std::move(new_tree)); + } + num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_; + } + + void ShuffleModels(int start_iter, int end_iter) override { + int total_iter = static_cast(models_.size()) / num_tree_per_iteration_; + start_iter = std::max(0, start_iter); + if (end_iter <= 0) { + end_iter = total_iter; + } + end_iter = std::min(total_iter, end_iter); + auto original_models = std::move(models_); + std::vector indices(total_iter); + for (int i = 0; i < total_iter; ++i) { + indices[i] = i; + } + Random tmp_rand(17); + for (int i = start_iter; i < end_iter - 1; ++i) { + int j = tmp_rand.NextShort(i + 1, end_iter); + std::swap(indices[i], indices[j]); + } + models_ = std::vector>(); + for (int i = 0; i < total_iter; ++i) { + for (int j = 0; j < num_tree_per_iteration_; ++j) { + int tree_idx = indices[i] * num_tree_per_iteration_ + j; + auto new_tree = std::unique_ptr(new Tree(*(original_models[tree_idx].get()))); + models_.push_back(std::move(new_tree)); + } + } + } + + /*! + * \brief Reset the training data + * \param train_data New Training data + * \param objective_function Training objective function + * \param training_metrics Training metrics + */ + void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, + const std::vector& training_metrics) override; + + /*! + * \brief Reset Boosting Config + * \param gbdt_config Config for boosting + */ + void ResetConfig(const Config* gbdt_config) override; + + /*! + * \brief Adding a validation dataset + * \param valid_data Validation dataset + * \param valid_metrics Metrics for validation dataset + */ + void AddValidDataset(const Dataset* valid_data, + const std::vector& valid_metrics) override; + + /*! + * \brief Perform a full training procedure + * \param snapshot_freq frequency of snapshot + * \param model_output_path path of model file + */ + void Train(int snapshot_freq, const std::string& model_output_path) override; + + void RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) override; + + /*! + * \brief Training logic + * \param gradients nullptr for using default objective, otherwise use self-defined boosting + * \param hessians nullptr for using default objective, otherwise use self-defined boosting + * \return True if cannot train any more + */ + bool TrainOneIter(const score_t* gradients, const score_t* hessians) override; + + /*! + * \brief Rollback one iteration + */ + void RollbackOneIter() override; + + /*! + * \brief Get current iteration + */ + int GetCurrentIteration() const override { return static_cast(models_.size()) / num_tree_per_iteration_; } + + /*! + * \brief Get parameters as a JSON string + */ + std::string GetLoadedParam() const override { + if (loaded_parameter_.empty()) { + return std::string("{}"); + } + const auto param_types = Config::ParameterTypes(); + const auto lines = Common::Split(loaded_parameter_.c_str(), "\n"); + bool first = true; + std::stringstream str_buf; + str_buf << "{"; + for (const auto& line : lines) { + const auto pair = Common::Split(line.c_str(), ":"); + if (pair[1] == " ]") + continue; + const auto param = pair[0].substr(1); + const auto value_str = pair[1].substr(1, pair[1].size() - 2); + auto iter = param_types.find(param); + if (iter == param_types.end()) { + Log::Warning("Ignoring unrecognized parameter '%s' found in model string.", param.c_str()); + continue; + } + std::string param_type = iter->second; + if (first) { + first = false; + str_buf << "\""; + } else { + str_buf << ",\""; + } + str_buf << param << "\": "; + if (param_type == "string") { + str_buf << "\"" << value_str << "\""; + } else if (param_type == "int") { + int value; + Common::Atoi(value_str.c_str(), &value); + str_buf << value; + } else if (param_type == "double") { + double value; + Common::Atof(value_str.c_str(), &value); + str_buf << value; + } else if (param_type == "bool") { + bool value = value_str == "1"; + str_buf << std::boolalpha << value; + } else if (param_type.substr(0, 6) == "vector") { + str_buf << "["; + if (param_type.substr(7, 6) == "string") { + const auto parts = Common::Split(value_str.c_str(), ","); + str_buf << "\"" << Common::Join(parts, "\",\"") << "\""; + } else { + str_buf << value_str; + } + str_buf << "]"; + } + } + str_buf << "}"; + return str_buf.str(); + } + + /*! + * \brief Can use early stopping for prediction or not + * \return True if cannot use early stopping for prediction + */ + bool NeedAccuratePrediction() const override { + if (objective_function_ == nullptr) { + return true; + } else { + return objective_function_->NeedAccuratePrediction(); + } + } + + /*! + * \brief Get evaluation result at data_idx data + * \param data_idx 0: training data, 1: 1st validation data + * \return evaluation result + */ + std::vector GetEvalAt(int data_idx) const override; + + /*! + * \brief Get current training score + * \param out_len length of returned score + * \return training score + */ + const double* GetTrainingScore(int64_t* out_len) override; + + /*! + * \brief Get size of prediction at data_idx data + * \param data_idx 0: training data, 1: 1st validation data + * \return The size of prediction + */ + int64_t GetNumPredictAt(int data_idx) const override { + CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size())); + data_size_t num_data = train_data_->num_data(); + if (data_idx > 0) { + num_data = valid_score_updater_[data_idx - 1]->num_data(); + } + return static_cast(num_data) * num_class_; + } + + /*! + * \brief Get prediction result at data_idx data + * \param data_idx 0: training data, 1: 1st validation data + * \param result used to store prediction result, should allocate memory before call this function + * \param out_len length of returned score + */ + void GetPredictAt(int data_idx, double* out_result, int64_t* out_len) override; + + /*! + * \brief Get number of prediction for one data + * \param start_iteration Start index of the iteration to predict + * \param num_iteration number of used iterations + * \param is_pred_leaf True if predicting leaf index + * \param is_pred_contrib True if predicting feature contribution + * \return number of prediction + */ + inline int NumPredictOneRow(int start_iteration, int num_iteration, bool is_pred_leaf, bool is_pred_contrib) const override { + int num_pred_in_one_row = num_class_; + if (is_pred_leaf) { + int max_iteration = GetCurrentIteration(); + start_iteration = std::max(start_iteration, 0); + start_iteration = std::min(start_iteration, max_iteration); + if (num_iteration > 0) { + num_pred_in_one_row *= static_cast(std::min(max_iteration - start_iteration, num_iteration)); + } else { + num_pred_in_one_row *= (max_iteration - start_iteration); + } + } else if (is_pred_contrib) { + num_pred_in_one_row = num_tree_per_iteration_ * (max_feature_idx_ + 2); // +1 for 0-based indexing, +1 for baseline + } + return num_pred_in_one_row; + } + + void PredictRaw(const double* features, double* output, + const PredictionEarlyStopInstance* earlyStop) const override; + + void PredictRawByMap(const std::unordered_map& features, double* output, + const PredictionEarlyStopInstance* early_stop) const override; + + void Predict(const double* features, double* output, + const PredictionEarlyStopInstance* earlyStop) const override; + + void PredictByMap(const std::unordered_map& features, double* output, + const PredictionEarlyStopInstance* early_stop) const override; + + void PredictLeafIndex(const double* features, double* output) const override; + + void PredictLeafIndexByMap(const std::unordered_map& features, double* output) const override; + + void PredictContrib(const double* features, double* output) const override; + + void PredictContribByMap(const std::unordered_map& features, + std::vector>* output) const override; + + /*! + * \brief Dump model to json format string + * \param start_iteration The model will be saved start from + * \param num_iteration Number of iterations that want to dump, -1 means dump all + * \param feature_importance_type Type of feature importance, 0: split, 1: gain + * \return Json format string of model + */ + std::string DumpModel(int start_iteration, int num_iteration, + int feature_importance_type) const override; + + /*! + * \brief Translate model to if-else statement + * \param num_iteration Number of iterations that want to translate, -1 means translate all + * \return if-else format codes of model + */ + std::string ModelToIfElse(int num_iteration) const override; + + /*! + * \brief Translate model to if-else statement + * \param num_iteration Number of iterations that want to translate, -1 means translate all + * \param filename Filename that want to save to + * \return is_finish Is training finished or not + */ + bool SaveModelToIfElse(int num_iteration, const char* filename) const override; + + /*! + * \brief Save model to file + * \param start_iteration The model will be saved start from + * \param num_iterations Number of model that want to save, -1 means save all + * \param feature_importance_type Type of feature importance, 0: split, 1: gain + * \param filename Filename that want to save to + * \return is_finish Is training finished or not + */ + bool SaveModelToFile(int start_iteration, int num_iterations, + int feature_importance_type, + const char* filename) const override; + + /*! + * \brief Save model to string + * \param start_iteration The model will be saved start from + * \param num_iterations Number of model that want to save, -1 means save all + * \param feature_importance_type Type of feature importance, 0: split, 1: gain + * \return Non-empty string if succeeded + */ + std::string SaveModelToString(int start_iteration, int num_iterations, int feature_importance_type) const override; + + /*! + * \brief Restore from a serialized buffer + */ + bool LoadModelFromString(const char* buffer, size_t len) override; + + /*! + * \brief Calculate feature importances + * \param num_iteration Number of model that want to use for feature importance, -1 means use all + * \param importance_type: 0 for split, 1 for gain + * \return vector of feature_importance + */ + std::vector FeatureImportance(int num_iteration, int importance_type) const override; + + /*! + * \brief Calculate upper bound value + * \return upper bound value + */ + double GetUpperBoundValue() const override; + + /*! + * \brief Calculate lower bound value + * \return lower bound value + */ + double GetLowerBoundValue() const override; + + /*! + * \brief Get max feature index of this model + * \return Max feature index of this model + */ + inline int MaxFeatureIdx() const override { return max_feature_idx_; } + + /*! + * \brief Get feature names of this model + * \return Feature names of this model + */ + inline std::vector FeatureNames() const override { return feature_names_; } + + /*! + * \brief Get index of label column + * \return index of label column + */ + inline int LabelIdx() const override { return label_idx_; } + + /*! + * \brief Get number of weak sub-models + * \return Number of weak sub-models + */ + inline int NumberOfTotalModel() const override { return static_cast(models_.size()); } + + /*! + * \brief Get number of tree per iteration + * \return number of tree per iteration + */ + inline int NumModelPerIteration() const override { return num_tree_per_iteration_; } + + /*! + * \brief Get number of classes + * \return Number of classes + */ + inline int NumberOfClasses() const override { return num_class_; } + + inline void InitPredict(int start_iteration, int num_iteration, bool is_pred_contrib) override { + num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_; + start_iteration = std::max(start_iteration, 0); + start_iteration = std::min(start_iteration, num_iteration_for_pred_); + if (num_iteration > 0) { + num_iteration_for_pred_ = std::min(num_iteration, num_iteration_for_pred_ - start_iteration); + } else { + num_iteration_for_pred_ = num_iteration_for_pred_ - start_iteration; + } + start_iteration_for_pred_ = start_iteration; + + if (is_pred_contrib && !models_initialized_) { + std::lock_guard lock(instance_mutex_); + if (models_initialized_) + return; + + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < static_cast(models_.size()); ++i) { + models_[i]->RecomputeMaxDepth(); + } + + models_initialized_ = true; + } + } + + inline double GetLeafValue(int tree_idx, int leaf_idx) const override { + CHECK(tree_idx >= 0 && static_cast(tree_idx) < models_.size()); + CHECK(leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves()); + return models_[tree_idx]->LeafOutput(leaf_idx); + } + + inline void SetLeafValue(int tree_idx, int leaf_idx, double val) override { + CHECK(tree_idx >= 0 && static_cast(tree_idx) < models_.size()); + CHECK(leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves()); + models_[tree_idx]->SetLeafOutput(leaf_idx, val); + } + + /*! + * \brief Get Type name of this boosting object + */ + const char* SubModelName() const override { return "tree"; } + + bool IsLinear() const override { return linear_tree_; } + + inline std::string ParserConfigStr() const override {return parser_config_str_;} + + protected: + virtual bool GetIsConstHessian(const ObjectiveFunction* objective_function) { + if (objective_function != nullptr && !data_sample_strategy_->IsHessianChange()) { + return objective_function->IsConstantHessian(); + } else { + return false; + } + } + /*! + * \brief Print eval result and check early stopping + */ + virtual bool EvalAndCheckEarlyStopping(); + + /*! + * \brief reset config for bagging + */ + void ResetBaggingConfig(const Config* config, bool is_change_dataset); + + /*! + * \brief calculate the objective function + */ + virtual void Boosting(); + + /*! + * \brief updating score after tree was trained + * \param tree Trained tree of this iteration + * \param cur_tree_id Current tree for multiclass training + */ + virtual void UpdateScore(const Tree* tree, const int cur_tree_id); + + /*! + * \brief eval results for one metric + + */ + virtual std::vector EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const; + + /*! + * \brief Print metric result of current iteration + * \param iter Current iteration + * \return best_msg if met early_stopping + */ + std::string OutputMetric(int iter); + + virtual double BoostFromAverage(int class_id, bool update_scorer); + + /*! + * \brief Reset gradient buffers, must be called after sample strategy is reset + */ + void ResetGradientBuffers(); + + /*! \brief current iteration */ + int iter_; + /*! \brief Pointer to training data */ + const Dataset* train_data_; + /*! \brief Config of gbdt */ + std::unique_ptr config_; + /*! \brief Tree learner, will use this class to learn trees */ + std::unique_ptr tree_learner_; + /*! \brief Objective function */ + const ObjectiveFunction* objective_function_; + /*! \brief Store and update training data's score */ + std::unique_ptr train_score_updater_; + /*! \brief Metrics for training data */ + std::vector training_metrics_; + /*! \brief Store and update validation data's scores */ + std::vector> valid_score_updater_; + /*! \brief Metric for validation data */ + std::vector> valid_metrics_; + /*! \brief Number of rounds for early stopping */ + int early_stopping_round_; + /*! \brief Minimum improvement for early stopping */ + double early_stopping_min_delta_; + /*! \brief Only use first metric for early stopping */ + bool es_first_metric_only_; + /*! \brief Best iteration(s) for early stopping */ + std::vector> best_iter_; + /*! \brief Best score(s) for early stopping */ + std::vector> best_score_; + /*! \brief output message of best iteration */ + std::vector> best_msg_; + /*! \brief Trained models(trees) */ + std::vector> models_; + /*! \brief Max feature index of training data*/ + int max_feature_idx_; + /*! \brief Parser config file content */ + std::string parser_config_str_ = ""; + /*! \brief Are the models initialized (passed RecomputeMaxDepth phase) */ + bool models_initialized_ = false; + /*! \brief Mutex for exclusive models initialization */ + std::mutex instance_mutex_; + +#ifdef USE_CUDA + /*! \brief First order derivative of training data */ + std::vector> gradients_; + /*! \brief Second order derivative of training data */ + std::vector> hessians_; +#else + /*! \brief First order derivative of training data */ + std::vector> gradients_; + /*! \brief Second order derivative of training data */ + std::vector> hessians_; +#endif + /*! \brief Pointer to gradient vector, can be on CPU or GPU */ + score_t* gradients_pointer_; + /*! \brief Pointer to hessian vector, can be on CPU or GPU */ + score_t* hessians_pointer_; + /*! \brief Whether boosting is done on GPU, used for device_type=cuda */ + bool boosting_on_gpu_; + #ifdef USE_CUDA + /*! \brief Gradient vector on GPU */ + CUDAVector cuda_gradients_; + /*! \brief Hessian vector on GPU */ + CUDAVector cuda_hessians_; + /*! \brief Buffer for scores when boosting is on GPU but evaluation is not, used only with device_type=cuda */ + mutable std::vector host_score_; + /*! \brief Buffer for scores when boosting is not on GPU but evaluation is, used only with device_type=cuda */ + mutable CUDAVector cuda_score_; + #endif // USE_CUDA + + /*! \brief Number of training data */ + data_size_t num_data_; + /*! \brief Number of trees per iterations */ + int num_tree_per_iteration_; + /*! \brief Number of class */ + int num_class_; + /*! \brief Index of label column */ + data_size_t label_idx_; + /*! \brief number of used model */ + int num_iteration_for_pred_; + /*! \brief Start iteration of used model */ + int start_iteration_for_pred_; + /*! \brief Shrinkage rate for one iteration */ + double shrinkage_rate_; + /*! \brief Number of loaded initial models */ + int num_init_iteration_; + /*! \brief Feature names */ + std::vector feature_names_; + std::vector feature_infos_; + std::vector class_need_train_; + bool is_constant_hessian_; + std::unique_ptr loaded_objective_; + bool average_output_; + bool need_re_bagging_; + bool balanced_bagging_; + std::string loaded_parameter_; + std::vector monotone_constraints_; + Json forced_splits_json_; + bool linear_tree_; + std::unique_ptr data_sample_strategy_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_BOOSTING_GBDT_H_ diff --git a/src/boosting/gbdt_model_text.cpp b/src/boosting/gbdt_model_text.cpp new file mode 100644 index 0000000..38fafab --- /dev/null +++ b/src/boosting/gbdt_model_text.cpp @@ -0,0 +1,667 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "gbdt.h" + +namespace LightGBM { + +const char* kModelVersion = "v4"; + +std::string GBDT::DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + + str_buf << "{"; + str_buf << "\"name\":\"" << SubModelName() << "\"," << '\n'; + str_buf << "\"version\":\"" << kModelVersion << "\"," << '\n'; + str_buf << "\"num_class\":" << num_class_ << "," << '\n'; + str_buf << "\"num_tree_per_iteration\":" << num_tree_per_iteration_ << "," << '\n'; + str_buf << "\"label_index\":" << label_idx_ << "," << '\n'; + str_buf << "\"max_feature_idx\":" << max_feature_idx_ << "," << '\n'; + if (objective_function_ != nullptr) { + str_buf << "\"objective\":\"" << objective_function_->ToString() << "\",\n"; + } + + str_buf << "\"average_output\":" << (average_output_ ? "true" : "false") << ",\n"; + + str_buf << "\"feature_names\":[\"" << CommonC::Join(feature_names_, "\",\"") + << "\"]," << '\n'; + + str_buf << "\"monotone_constraints\":[" + << CommonC::Join(monotone_constraints_, ",") << "]," << '\n'; + + str_buf << "\"feature_infos\":" << "{"; + bool first_obj = true; + for (size_t i = 0; i < feature_infos_.size(); ++i) { + std::stringstream json_str_buf; + Common::C_stringstream(json_str_buf); + auto strs = Common::Split(feature_infos_[i].c_str(), ":"); + if (strs[0][0] == '[') { + strs[0].erase(0, 1); // remove '[' + strs[1].erase(strs[1].size() - 1); // remove ']' + double max_, min_; + Common::Atof(strs[0].c_str(), &min_); + Common::Atof(strs[1].c_str(), &max_); + json_str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + json_str_buf << "{\"min_value\":" << Common::AvoidInf(min_) << ","; + json_str_buf << "\"max_value\":" << Common::AvoidInf(max_) << ","; + json_str_buf << "\"values\":[]}"; + } else if (strs[0] != "none") { // categorical feature + auto vals = CommonC::StringToArray(feature_infos_[i], ':'); + auto max_idx = ArrayArgs::ArgMax(vals); + auto min_idx = ArrayArgs::ArgMin(vals); + json_str_buf << "{\"min_value\":" << vals[min_idx] << ","; + json_str_buf << "\"max_value\":" << vals[max_idx] << ","; + json_str_buf << "\"values\":[" << CommonC::Join(vals, ",") << "]}"; + } else { // unused feature + continue; + } + if (!first_obj) { + str_buf << ","; + } + str_buf << "\"" << feature_names_[i] << "\":"; + str_buf << json_str_buf.str(); + first_obj = false; + } + str_buf << "}," << '\n'; + + str_buf << "\"tree_info\":["; + int num_used_model = static_cast(models_.size()); + int total_iteration = num_used_model / num_tree_per_iteration_; + start_iteration = std::max(start_iteration, 0); + start_iteration = std::min(start_iteration, total_iteration); + if (num_iteration > 0) { + int end_iteration = start_iteration + num_iteration; + num_used_model = std::min(end_iteration * num_tree_per_iteration_ , num_used_model); + } + int start_model = start_iteration * num_tree_per_iteration_; + for (int i = start_model; i < num_used_model; ++i) { + if (i > start_model) { + str_buf << ","; + } + str_buf << "{"; + str_buf << "\"tree_index\":" << i << ","; + str_buf << models_[i]->ToJSON(); + str_buf << "}"; + } + str_buf << "]," << '\n'; + + std::vector feature_importances = FeatureImportance( + num_iteration, feature_importance_type); + // store the importance first + std::vector> pairs; + for (size_t i = 0; i < feature_importances.size(); ++i) { + size_t feature_importances_int = static_cast(feature_importances[i]); + if (feature_importances_int > 0) { + pairs.emplace_back(feature_importances_int, feature_names_[i]); + } + } + str_buf << '\n' << "\"feature_importances\":" << "{"; + for (size_t i = 0; i < pairs.size(); ++i) { + if (i > 0) { + str_buf << ","; + } + str_buf << "\"" << pairs[i].second << "\":" << std::to_string(pairs[i].first); + } + str_buf << "}" << '\n'; + + str_buf << "}" << '\n'; + + return str_buf.str(); +} + +std::string GBDT::ModelToIfElse(int num_iteration) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + + str_buf << "#include \"gbdt.h\"" << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "#include " << '\n'; + str_buf << "namespace LightGBM {" << '\n'; + + int num_used_model = static_cast(models_.size()); + if (num_iteration > 0) { + num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model); + } + + // PredictRaw + for (int i = 0; i < num_used_model; ++i) { + str_buf << models_[i]->ToIfElse(i, false) << '\n'; + } + + str_buf << "double (*PredictTreePtr[])(const double*) = { "; + for (int i = 0; i < num_used_model; ++i) { + if (i > 0) { + str_buf << " , "; + } + str_buf << "PredictTree" << i; + } + str_buf << " };" << '\n' << '\n'; + + std::stringstream pred_str_buf; + Common::C_stringstream(pred_str_buf); + + pred_str_buf << "\t" << "int early_stop_round_counter = 0;" << '\n'; + pred_str_buf << "\t" << "std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);" << '\n'; + pred_str_buf << "\t" << "for (int i = 0; i < num_iteration_for_pred_; ++i) {" << '\n'; + pred_str_buf << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; + pred_str_buf << "\t\t\t" << "output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);" << '\n'; + pred_str_buf << "\t\t" << "}" << '\n'; + pred_str_buf << "\t\t" << "++early_stop_round_counter;" << '\n'; + pred_str_buf << "\t\t" << "if (early_stop->round_period == early_stop_round_counter) {" << '\n'; + pred_str_buf << "\t\t\t" << "if (early_stop->callback_function(output, num_tree_per_iteration_))" << '\n'; + pred_str_buf << "\t\t\t\t" << "return;" << '\n'; + pred_str_buf << "\t\t\t" << "early_stop_round_counter = 0;" << '\n'; + pred_str_buf << "\t\t" << "}" << '\n'; + pred_str_buf << "\t" << "}" << '\n'; + + str_buf << "void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; + str_buf << pred_str_buf.str(); + str_buf << "}" << '\n'; + str_buf << '\n'; + + // PredictRawByMap + str_buf << "double (*PredictTreeByMapPtr[])(const std::unordered_map&) = { "; + for (int i = 0; i < num_used_model; ++i) { + if (i > 0) { + str_buf << " , "; + } + str_buf << "PredictTree" << i << "ByMap"; + } + str_buf << " };" << '\n' << '\n'; + + std::stringstream pred_str_buf_map; + Common::C_stringstream(pred_str_buf_map); + + pred_str_buf_map << "\t" << "int early_stop_round_counter = 0;" << '\n'; + pred_str_buf_map << "\t" << "std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);" << '\n'; + pred_str_buf_map << "\t" << "for (int i = 0; i < num_iteration_for_pred_; ++i) {" << '\n'; + pred_str_buf_map << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; + pred_str_buf_map << "\t\t\t" << "output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);" << '\n'; + pred_str_buf_map << "\t\t" << "}" << '\n'; + pred_str_buf_map << "\t\t" << "++early_stop_round_counter;" << '\n'; + pred_str_buf_map << "\t\t" << "if (early_stop->round_period == early_stop_round_counter) {" << '\n'; + pred_str_buf_map << "\t\t\t" << "if (early_stop->callback_function(output, num_tree_per_iteration_))" << '\n'; + pred_str_buf_map << "\t\t\t\t" << "return;" << '\n'; + pred_str_buf_map << "\t\t\t" << "early_stop_round_counter = 0;" << '\n'; + pred_str_buf_map << "\t\t" << "}" << '\n'; + pred_str_buf_map << "\t" << "}" << '\n'; + + str_buf << "void GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; + str_buf << pred_str_buf_map.str(); + str_buf << "}" << '\n'; + str_buf << '\n'; + + // Predict + str_buf << "void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; + str_buf << "\t" << "PredictRaw(features, output, early_stop);" << '\n'; + str_buf << "\t" << "if (average_output_) {" << '\n'; + str_buf << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; + str_buf << "\t\t\t" << "output[k] /= num_iteration_for_pred_;" << '\n'; + str_buf << "\t\t" << "}" << '\n'; + str_buf << "\t" << "}" << '\n'; + str_buf << "\t" << "if (objective_function_ != nullptr) {" << '\n'; + str_buf << "\t\t" << "objective_function_->ConvertOutput(output, output);" << '\n'; + str_buf << "\t" << "}" << '\n'; + str_buf << "}" << '\n'; + str_buf << '\n'; + + // PredictByMap + str_buf << "void GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {" << '\n'; + str_buf << "\t" << "PredictRawByMap(features, output, early_stop);" << '\n'; + str_buf << "\t" << "if (average_output_) {" << '\n'; + str_buf << "\t\t" << "for (int k = 0; k < num_tree_per_iteration_; ++k) {" << '\n'; + str_buf << "\t\t\t" << "output[k] /= num_iteration_for_pred_;" << '\n'; + str_buf << "\t\t" << "}" << '\n'; + str_buf << "\t" << "}" << '\n'; + str_buf << "\t" << "if (objective_function_ != nullptr) {" << '\n'; + str_buf << "\t\t" << "objective_function_->ConvertOutput(output, output);" << '\n'; + str_buf << "\t" << "}" << '\n'; + str_buf << "}" << '\n'; + str_buf << '\n'; + + + // PredictLeafIndex + for (int i = 0; i < num_used_model; ++i) { + str_buf << models_[i]->ToIfElse(i, true) << '\n'; + } + + str_buf << "double (*PredictTreeLeafPtr[])(const double*) = { "; + for (int i = 0; i < num_used_model; ++i) { + if (i > 0) { + str_buf << " , "; + } + str_buf << "PredictTree" << i << "Leaf"; + } + str_buf << " };" << '\n' << '\n'; + + str_buf << "void GBDT::PredictLeafIndex(const double* features, double *output) const {" << '\n'; + str_buf << "\t" << "int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;" << '\n'; + str_buf << "\t" << "for (int i = 0; i < total_tree; ++i) {" << '\n'; + str_buf << "\t\t" << "output[i] = (*PredictTreeLeafPtr[i])(features);" << '\n'; + str_buf << "\t" << "}" << '\n'; + str_buf << "}" << '\n'; + + // PredictLeafIndexByMap + str_buf << "double (*PredictTreeLeafByMapPtr[])(const std::unordered_map&) = { "; + for (int i = 0; i < num_used_model; ++i) { + if (i > 0) { + str_buf << " , "; + } + str_buf << "PredictTree" << i << "LeafByMap"; + } + str_buf << " };" << '\n' << '\n'; + + str_buf << "void GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const {" << '\n'; + str_buf << "\t" << "int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;" << '\n'; + str_buf << "\t" << "for (int i = 0; i < total_tree; ++i) {" << '\n'; + str_buf << "\t\t" << "output[i] = (*PredictTreeLeafByMapPtr[i])(features);" << '\n'; + str_buf << "\t" << "}" << '\n'; + str_buf << "}" << '\n'; + + str_buf << "} // namespace LightGBM" << '\n'; + + return str_buf.str(); +} + +bool GBDT::SaveModelToIfElse(int num_iteration, const char* filename) const { + /*! \brief File to write models */ + std::ofstream output_file; + std::ifstream ifs(filename); + if (ifs.good()) { + std::string origin((std::istreambuf_iterator(ifs)), + (std::istreambuf_iterator())); + output_file.open(filename); + output_file << "#define USE_HARD_CODE 0" << '\n'; + output_file << "#ifndef USE_HARD_CODE" << '\n'; + output_file << origin << '\n'; + output_file << "#else" << '\n'; + output_file << ModelToIfElse(num_iteration); + output_file << "#endif" << '\n'; + } else { + output_file.open(filename); + output_file << ModelToIfElse(num_iteration); + } + + ifs.close(); + output_file.close(); + + return static_cast(output_file); +} + +std::string GBDT::SaveModelToString(int start_iteration, int num_iteration, int feature_importance_type) const { + std::stringstream ss; + Common::C_stringstream(ss); + + // output model type + ss << SubModelName() << '\n'; + ss << "version=" << kModelVersion << '\n'; + // output number of class + ss << "num_class=" << num_class_ << '\n'; + ss << "num_tree_per_iteration=" << num_tree_per_iteration_ << '\n'; + // output label index + ss << "label_index=" << label_idx_ << '\n'; + // output max_feature_idx + ss << "max_feature_idx=" << max_feature_idx_ << '\n'; + // output objective + if (objective_function_ != nullptr) { + ss << "objective=" << objective_function_->ToString() << '\n'; + } + + if (average_output_) { + ss << "average_output" << '\n'; + } + + ss << "feature_names=" << CommonC::Join(feature_names_, " ") << '\n'; + + if (monotone_constraints_.size() != 0) { + ss << "monotone_constraints=" << CommonC::Join(monotone_constraints_, " ") + << '\n'; + } + + ss << "feature_infos=" << CommonC::Join(feature_infos_, " ") << '\n'; + + int num_used_model = static_cast(models_.size()); + int total_iteration = num_used_model / num_tree_per_iteration_; + start_iteration = std::max(start_iteration, 0); + start_iteration = std::min(start_iteration, total_iteration); + if (num_iteration > 0) { + int end_iteration = start_iteration + num_iteration; + num_used_model = std::min(end_iteration * num_tree_per_iteration_, num_used_model); + } + + int start_model = start_iteration * num_tree_per_iteration_; + + std::vector tree_strs(num_used_model - start_model); + std::vector tree_sizes(num_used_model - start_model); + // output tree models + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = start_model; i < num_used_model; ++i) { + const int idx = i - start_model; + tree_strs[idx] = "Tree=" + std::to_string(idx) + '\n'; + tree_strs[idx] += models_[i]->ToString() + '\n'; + tree_sizes[idx] = tree_strs[idx].size(); + } + + ss << "tree_sizes=" << CommonC::Join(tree_sizes, " ") << '\n'; + ss << '\n'; + + for (int i = 0; i < num_used_model - start_model; ++i) { + ss << tree_strs[i]; + tree_strs[i].clear(); + } + ss << "end of trees" << "\n"; + std::vector feature_importances = FeatureImportance( + num_iteration, feature_importance_type); + // store the importance first + std::vector> pairs; + for (size_t i = 0; i < feature_importances.size(); ++i) { + size_t feature_importances_int = static_cast(feature_importances[i]); + if (feature_importances_int > 0) { + pairs.emplace_back(feature_importances_int, feature_names_[i]); + } + } + // sort the importance + std::stable_sort(pairs.begin(), pairs.end(), + [](const std::pair& lhs, + const std::pair& rhs) { + return lhs.first > rhs.first; + }); + ss << '\n' << "feature_importances:" << '\n'; + for (size_t i = 0; i < pairs.size(); ++i) { + ss << pairs[i].second << "=" << std::to_string(pairs[i].first) << '\n'; + } + if (config_ != nullptr) { + ss << "\nparameters:" << '\n'; + ss << config_->ToString() << "\n"; + ss << "end of parameters" << '\n'; + } else if (!loaded_parameter_.empty()) { + ss << "\nparameters:" << '\n'; + ss << loaded_parameter_ << "\n"; + ss << "end of parameters" << '\n'; + } + if (!parser_config_str_.empty()) { + ss << "\nparser:" << '\n'; + ss << parser_config_str_ << "\n"; + ss << "end of parser" << '\n'; + } + return ss.str(); +} + +bool GBDT::SaveModelToFile(int start_iteration, int num_iteration, int feature_importance_type, const char* filename) const { + /*! \brief File to write models */ + auto writer = VirtualFileWriter::Make(filename); + if (!writer->Init()) { + Log::Fatal("Model file %s is not available for writes", filename); + } + std::string str_to_write = SaveModelToString(start_iteration, num_iteration, feature_importance_type); + auto size = writer->Write(str_to_write.c_str(), str_to_write.size()); + return size > 0; +} + +bool GBDT::LoadModelFromString(const char* buffer, size_t len) { + // use serialized string to restore this object + models_.clear(); + auto c_str = buffer; + auto p = c_str; + auto end = p + len; + std::unordered_map key_vals; + while (p < end) { + auto line_len = Common::GetLine(p); + if (line_len > 0) { + std::string cur_line(p, line_len); + if (!Common::StartsWith(cur_line, "Tree=")) { + auto strs = Common::Split(cur_line.c_str(), '='); + if (strs.size() == 1) { + key_vals[strs[0]] = ""; + } else if (strs.size() == 2) { + key_vals[strs[0]] = strs[1]; + } else if (strs.size() > 2) { + if (strs[0] == "feature_names") { + key_vals[strs[0]] = cur_line.substr(std::strlen("feature_names=")); + } else if (strs[0] == "monotone_constraints") { + key_vals[strs[0]] = cur_line.substr(std::strlen("monotone_constraints=")); + } else { + // Use first 128 chars to avoid exceed the message buffer. + Log::Fatal("Wrong line at model file: %s", cur_line.substr(0, std::min(128, cur_line.size())).c_str()); + } + } + } else { + break; + } + } + p += line_len; + p = Common::SkipNewLine(p); + } + + // get number of classes + if (key_vals.count("num_class")) { + Common::Atoi(key_vals["num_class"].c_str(), &num_class_); + } else { + Log::Fatal("Model file doesn't specify the number of classes"); + return false; + } + + if (key_vals.count("num_tree_per_iteration")) { + Common::Atoi(key_vals["num_tree_per_iteration"].c_str(), &num_tree_per_iteration_); + } else { + num_tree_per_iteration_ = num_class_; + } + + // get index of label + if (key_vals.count("label_index")) { + Common::Atoi(key_vals["label_index"].c_str(), &label_idx_); + } else { + Log::Fatal("Model file doesn't specify the label index"); + return false; + } + + // get max_feature_idx first + if (key_vals.count("max_feature_idx")) { + Common::Atoi(key_vals["max_feature_idx"].c_str(), &max_feature_idx_); + } else { + Log::Fatal("Model file doesn't specify max_feature_idx"); + return false; + } + + // get average_output + if (key_vals.count("average_output")) { + average_output_ = true; + } + + // get feature names + if (key_vals.count("feature_names")) { + feature_names_ = Common::Split(key_vals["feature_names"].c_str(), ' '); + if (feature_names_.size() != static_cast(max_feature_idx_ + 1)) { + Log::Fatal("Wrong size of feature_names"); + return false; + } + } else { + Log::Fatal("Model file doesn't contain feature_names"); + return false; + } + + // get monotone_constraints + if (key_vals.count("monotone_constraints")) { + monotone_constraints_ = CommonC::StringToArray(key_vals["monotone_constraints"].c_str(), ' '); + if (monotone_constraints_.size() != static_cast(max_feature_idx_ + 1)) { + Log::Fatal("Wrong size of monotone_constraints"); + return false; + } + } + + if (key_vals.count("feature_infos")) { + feature_infos_ = Common::Split(key_vals["feature_infos"].c_str(), ' '); + if (feature_infos_.size() != static_cast(max_feature_idx_ + 1)) { + Log::Fatal("Wrong size of feature_infos"); + return false; + } + } else { + Log::Fatal("Model file doesn't contain feature_infos"); + return false; + } + + if (key_vals.count("objective")) { + auto str = key_vals["objective"]; + loaded_objective_.reset(ObjectiveFunction::CreateObjectiveFunction(ParseObjectiveAlias(str))); + objective_function_ = loaded_objective_.get(); + } + + if (!key_vals.count("tree_sizes")) { + while (p < end) { + auto line_len = Common::GetLine(p); + if (line_len > 0) { + std::string cur_line(p, line_len); + if (Common::StartsWith(cur_line, "Tree=")) { + p += line_len; + p = Common::SkipNewLine(p); + size_t used_len = 0; + models_.emplace_back(new Tree(p, &used_len)); + p += used_len; + } else { + break; + } + } + p = Common::SkipNewLine(p); + } + } else { + std::vector tree_sizes = CommonC::StringToArray(key_vals["tree_sizes"].c_str(), ' '); + std::vector tree_boundaries(tree_sizes.size() + 1, 0); + int num_trees = static_cast(tree_sizes.size()); + for (int i = 0; i < num_trees; ++i) { + tree_boundaries[i + 1] = tree_boundaries[i] + tree_sizes[i]; + models_.emplace_back(nullptr); + } + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < num_trees; ++i) { + OMP_LOOP_EX_BEGIN(); + auto cur_p = p + tree_boundaries[i]; + auto line_len = Common::GetLine(cur_p); + std::string cur_line(cur_p, line_len); + if (Common::StartsWith(cur_line, "Tree=")) { + cur_p += line_len; + cur_p = Common::SkipNewLine(cur_p); + size_t used_len = 0; + models_[i].reset(new Tree(cur_p, &used_len)); + } else { + Log::Fatal("Model format error, expect a tree here. met %s", cur_line.c_str()); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_; + num_init_iteration_ = num_iteration_for_pred_; + iter_ = 0; + bool is_inparameter = false, is_inparser = false; + std::stringstream ss; + Common::C_stringstream(ss); + while (p < end) { + auto line_len = Common::GetLine(p); + if (line_len > 0) { + std::string cur_line(p, line_len); + if (cur_line == std::string("parameters:")) { + is_inparameter = true; + } else if (cur_line == std::string("end of parameters")) { + break; + } else if (is_inparameter) { + ss << cur_line << "\n"; + if (Common::StartsWith(cur_line, "[linear_tree: ")) { + int is_linear = 0; + Common::Atoi(cur_line.substr(14, 1).c_str(), &is_linear); + linear_tree_ = static_cast(is_linear); + } + } + } + p += line_len; + p = Common::SkipNewLine(p); + } + if (!ss.str().empty()) { + loaded_parameter_ = ss.str(); + } + ss.clear(); + ss.str(""); + while (p < end) { + auto line_len = Common::GetLine(p); + if (line_len > 0) { + std::string cur_line(p, line_len); + if (cur_line == std::string("parser:")) { + is_inparser = true; + } else if (cur_line == std::string("end of parser")) { + p += line_len; + p = Common::SkipNewLine(p); + break; + } else if (is_inparser) { + ss << cur_line << "\n"; + } + } + p += line_len; + p = Common::SkipNewLine(p); + } + parser_config_str_ = ss.str(); + ss.clear(); + ss.str(""); + return true; +} + +std::vector GBDT::FeatureImportance(int num_iteration, int importance_type) const { + int num_used_model = static_cast(models_.size()); + if (num_iteration > 0) { + num_iteration += 0; + num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model); + } + + std::vector feature_importances(max_feature_idx_ + 1, 0.0); + if (importance_type == 0) { + for (int iter = 0; iter < num_used_model; ++iter) { + for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) { + if (models_[iter]->split_gain(split_idx) > 0) { +#ifdef DEBUG + CHECK_GE(models_[iter]->split_feature(split_idx), 0); +#endif + feature_importances[models_[iter]->split_feature(split_idx)] += 1.0; + } + } + } + } else if (importance_type == 1) { + for (int iter = 0; iter < num_used_model; ++iter) { + for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) { + if (models_[iter]->split_gain(split_idx) > 0) { +#ifdef DEBUG + CHECK_GE(models_[iter]->split_feature(split_idx), 0); +#endif + feature_importances[models_[iter]->split_feature(split_idx)] += models_[iter]->split_gain(split_idx); + } + } + } + } else { + Log::Fatal("Unknown importance type: only support split=0 and gain=1"); + } + return feature_importances; +} + +} // namespace LightGBM diff --git a/src/boosting/gbdt_prediction.cpp b/src/boosting/gbdt_prediction.cpp new file mode 100644 index 0000000..edd2cae --- /dev/null +++ b/src/boosting/gbdt_prediction.cpp @@ -0,0 +1,100 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include +#include +#include + +#include + +#include "gbdt.h" + +namespace LightGBM { + +void GBDT::PredictRaw(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const { + int early_stop_round_counter = 0; + // set zero + std::memset(output, 0, sizeof(double) * num_tree_per_iteration_); + const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; + for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { + // predict all the trees for one iteration + for (int k = 0; k < num_tree_per_iteration_; ++k) { + output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(features); + } + // check early stopping + ++early_stop_round_counter; + if (early_stop->round_period == early_stop_round_counter) { + if (early_stop->callback_function(output, num_tree_per_iteration_)) { + return; + } + early_stop_round_counter = 0; + } + } +} + +void GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const { + int early_stop_round_counter = 0; + // set zero + std::memset(output, 0, sizeof(double) * num_tree_per_iteration_); + const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_; + for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) { + // predict all the trees for one iteration + for (int k = 0; k < num_tree_per_iteration_; ++k) { + output[k] += models_[i * num_tree_per_iteration_ + k]->PredictByMap(features); + } + // check early stopping + ++early_stop_round_counter; + if (early_stop->round_period == early_stop_round_counter) { + if (early_stop->callback_function(output, num_tree_per_iteration_)) { + return; + } + early_stop_round_counter = 0; + } + } +} + +void GBDT::Predict(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const { + PredictRaw(features, output, early_stop); + if (average_output_) { + for (int k = 0; k < num_tree_per_iteration_; ++k) { + output[k] /= num_iteration_for_pred_; + } + } + if (objective_function_ != nullptr) { + objective_function_->ConvertOutput(output, output); + } +} + +void GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const { + PredictRawByMap(features, output, early_stop); + if (average_output_) { + for (int k = 0; k < num_tree_per_iteration_; ++k) { + output[k] /= num_iteration_for_pred_; + } + } + if (objective_function_ != nullptr) { + objective_function_->ConvertOutput(output, output); + } +} + +void GBDT::PredictLeafIndex(const double* features, double* output) const { + int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_; + int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_; + const auto* models_ptr = models_.data() + start_tree; + for (int i = 0; i < num_trees; ++i) { + output[i] = models_ptr[i]->PredictLeafIndex(features); + } +} + +void GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const { + int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_; + int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_; + const auto* models_ptr = models_.data() + start_tree; + for (int i = 0; i < num_trees; ++i) { + output[i] = models_ptr[i]->PredictLeafIndexByMap(features); + } +} + +} // namespace LightGBM diff --git a/src/boosting/goss.hpp b/src/boosting/goss.hpp new file mode 100644 index 0000000..cb24394 --- /dev/null +++ b/src/boosting/goss.hpp @@ -0,0 +1,173 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifndef LIGHTGBM_SRC_BOOSTING_GOSS_HPP_ +#define LIGHTGBM_SRC_BOOSTING_GOSS_HPP_ + +#include +#include + +#include +#include +#include + +namespace LightGBM { + +class GOSSStrategy : public SampleStrategy { + public: + GOSSStrategy(const Config* config, const Dataset* train_data, int num_tree_per_iteration) { + config_ = config; + train_data_ = train_data; + num_tree_per_iteration_ = num_tree_per_iteration; + num_data_ = train_data->num_data(); + } + + ~GOSSStrategy() { + } + + void Bagging(int iter, TreeLearner* tree_learner, score_t* gradients, score_t* hessians) override { + bag_data_cnt_ = num_data_; + // not subsample for first iterations + if (iter < static_cast(1.0f / config_->learning_rate)) { + return; + } + auto left_cnt = bagging_runner_.Run( + num_data_, + [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left, + data_size_t*) { + data_size_t cur_left_count = 0; + cur_left_count = Helper(cur_start, cur_cnt, left, gradients, hessians); + return cur_left_count; + }, + bag_data_indices_.data()); + bag_data_cnt_ = left_cnt; + // set bagging data to tree learner + if (!is_use_subset_) { + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); + tree_learner->SetBaggingData(nullptr, cuda_bag_data_indices_.RawData(), bag_data_cnt_); + } else { + #endif // USE_CUDA + tree_learner->SetBaggingData(nullptr, bag_data_indices_.data(), bag_data_cnt_); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } else { + // get subset + tmp_subset_->ReSize(bag_data_cnt_); + tmp_subset_->CopySubrow(train_data_, bag_data_indices_.data(), + bag_data_cnt_, false); + #ifdef USE_CUDA + if (config_->device_type == std::string("cuda")) { + CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__); + tree_learner->SetBaggingData(tmp_subset_.get(), cuda_bag_data_indices_.RawData(), + bag_data_cnt_); + } else { + #endif // USE_CUDA + tree_learner->SetBaggingData(tmp_subset_.get(), bag_data_indices_.data(), + bag_data_cnt_); + #ifdef USE_CUDA + } + #endif // USE_CUDA + } + } + + void ResetSampleConfig(const Config* config, bool /*is_change_dataset*/) override { + // Cannot use bagging in GOSS + config_ = config; + need_resize_gradients_ = false; + if (objective_function_ == nullptr) { + // resize gradient vectors to copy the customized gradients for goss + need_resize_gradients_ = true; + } + + CHECK_LE(config_->top_rate + config_->other_rate, 1.0f); + CHECK(config_->top_rate > 0.0f && config_->other_rate > 0.0f); + if (config_->bagging_freq > 0 && config_->bagging_fraction != 1.0f) { + Log::Fatal("Cannot use bagging in GOSS"); + } + Log::Info("Using GOSS"); + balanced_bagging_ = false; + bag_data_indices_.resize(num_data_); + bagging_runner_.ReSize(num_data_); + bagging_rands_.clear(); + for (int i = 0; + i < (num_data_ + bagging_rand_block_ - 1) / bagging_rand_block_; ++i) { + bagging_rands_.emplace_back(config_->bagging_seed + i); + } + is_use_subset_ = false; + if (config_->top_rate + config_->other_rate <= 0.5) { + auto bag_data_cnt = static_cast((config_->top_rate + config_->other_rate) * num_data_); + bag_data_cnt = std::max(1, bag_data_cnt); + tmp_subset_.reset(new Dataset(bag_data_cnt)); + tmp_subset_->CopyFeatureMapperFrom(train_data_); + is_use_subset_ = true; + } + // flag to not bagging first + bag_data_cnt_ = num_data_; + } + + bool IsHessianChange() const override { + return true; + } + + private: + data_size_t Helper(data_size_t start, data_size_t cnt, data_size_t* buffer, score_t* gradients, score_t* hessians) { + if (cnt <= 0) { + return 0; + } + std::vector tmp_gradients(cnt, 0.0f); + for (data_size_t i = 0; i < cnt; ++i) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + size_t idx = static_cast(cur_tree_id) * num_data_ + start + i; + tmp_gradients[i] += std::fabs(gradients[idx] * hessians[idx]); + } + } + data_size_t top_k = static_cast(cnt * config_->top_rate); + data_size_t other_k = static_cast(cnt * config_->other_rate); + top_k = std::max(1, top_k); + ArrayArgs::ArgMaxAtK(&tmp_gradients, 0, static_cast(tmp_gradients.size()), top_k - 1); + score_t threshold = tmp_gradients[top_k - 1]; + + score_t multiply = static_cast(cnt - top_k) / other_k; + data_size_t cur_left_cnt = 0; + data_size_t cur_right_pos = cnt; + data_size_t big_weight_cnt = 0; + for (data_size_t i = 0; i < cnt; ++i) { + auto cur_idx = start + i; + score_t grad = 0.0f; + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + size_t idx = static_cast(cur_tree_id) * num_data_ + cur_idx; + grad += std::fabs(gradients[idx] * hessians[idx]); + } + if (grad >= threshold) { + buffer[cur_left_cnt++] = cur_idx; + ++big_weight_cnt; + } else { + data_size_t sampled = cur_left_cnt - big_weight_cnt; + data_size_t rest_need = other_k - sampled; + data_size_t rest_all = (cnt - i) - (top_k - big_weight_cnt); + double prob = (rest_need) / static_cast(rest_all); + if (bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < prob) { + buffer[cur_left_cnt++] = cur_idx; + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + size_t idx = static_cast(cur_tree_id) * num_data_ + cur_idx; + gradients[idx] *= multiply; + hessians[idx] *= multiply; + } + } else { + buffer[--cur_right_pos] = cur_idx; + } + } + } + return cur_left_cnt; + } +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_BOOSTING_GOSS_HPP_ diff --git a/src/boosting/prediction_early_stop.cpp b/src/boosting/prediction_early_stop.cpp new file mode 100644 index 0000000..d81750f --- /dev/null +++ b/src/boosting/prediction_early_stop.cpp @@ -0,0 +1,94 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include + +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +PredictionEarlyStopInstance CreateNone(const PredictionEarlyStopConfig&) { + return PredictionEarlyStopInstance{ + [](const double*, int) { + return false; + }, + std::numeric_limits::max() // make sure the lambda is almost never called + }; +} + +PredictionEarlyStopInstance CreateMulticlass(const PredictionEarlyStopConfig& config) { + // margin_threshold will be captured by value + const double margin_threshold = config.margin_threshold; + + return PredictionEarlyStopInstance{ + [margin_threshold](const double* pred, int sz) { + if (sz < 2) { + Log::Fatal("Multiclass early stopping needs predictions to be of length two or larger"); + } + + // copy and sort + std::vector votes(static_cast(sz)); + for (int i = 0; i < sz; ++i) { + votes[i] = pred[i]; + } + std::partial_sort(votes.begin(), votes.begin() + 2, votes.end(), std::greater()); + + const auto margin = votes[0] - votes[1]; + + if (margin > margin_threshold) { + return true; + } + + return false; + }, + config.round_period + }; +} + +PredictionEarlyStopInstance CreateBinary(const PredictionEarlyStopConfig& config) { + // margin_threshold will be captured by value + const double margin_threshold = config.margin_threshold; + + return PredictionEarlyStopInstance{ + [margin_threshold](const double* pred, int sz) { + if (sz != 1) { + Log::Fatal("Binary early stopping needs predictions to be of length one"); + } + const auto margin = 2.0 * fabs(pred[0]); + + if (margin > margin_threshold) { + return true; + } + + return false; + }, + config.round_period + }; +} + +PredictionEarlyStopInstance CreatePredictionEarlyStopInstance(const std::string& type, + const PredictionEarlyStopConfig& config) { + if (type == "none") { + return CreateNone(config); + } else if (type == "multiclass") { + return CreateMulticlass(config); + } else if (type == "binary") { + return CreateBinary(config); + } else { + Log::Fatal("Unknown early stopping type: %s", type.c_str()); + } + + // Fix for compiler warnings about reaching end of control + return CreateNone(config); +} + +} // namespace LightGBM diff --git a/src/boosting/rf.hpp b/src/boosting/rf.hpp new file mode 100644 index 0000000..ce2b968 --- /dev/null +++ b/src/boosting/rf.hpp @@ -0,0 +1,237 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_BOOSTING_RF_HPP_ +#define LIGHTGBM_SRC_BOOSTING_RF_HPP_ + +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "gbdt.h" +#include "score_updater.hpp" + +namespace LightGBM { +/*! +* \brief Random Forest implementation +*/ +class RF : public GBDT { + public: + RF() : GBDT() { + average_output_ = true; + } + + ~RF() {} + + void Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, + const std::vector& training_metrics) override { + if (config->data_sample_strategy == std::string("bagging")) { + CHECK((config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f) || + (config->feature_fraction < 1.0f && config->feature_fraction > 0.0f)); + } else { + CHECK_EQ(config->data_sample_strategy, std::string("goss")); + } + GBDT::Init(config, train_data, objective_function, training_metrics); + + if (num_init_iteration_ > 0) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + MultiplyScore(cur_tree_id, 1.0f / num_init_iteration_); + } + } else { + CHECK_EQ(train_data->metadata().init_score(), nullptr); + } + CHECK_EQ(num_tree_per_iteration_, num_class_); + // not shrinkage rate for the RF + shrinkage_rate_ = 1.0f; + // only boosting one time + Boosting(); + if (data_sample_strategy_->is_use_subset() && data_sample_strategy_->bag_data_cnt() < num_data_) { + tmp_grad_.resize(num_data_); + tmp_hess_.resize(num_data_); + } + } + + void ResetConfig(const Config* config) override { + if (config->data_sample_strategy == std::string("bagging")) { + CHECK((config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f) || + (config->feature_fraction < 1.0f && config->feature_fraction > 0.0f)); + } else { + CHECK_EQ(config->data_sample_strategy, std::string("goss")); + } + GBDT::ResetConfig(config); + // not shrinkage rate for the RF + shrinkage_rate_ = 1.0f; + } + + void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function, + const std::vector& training_metrics) override { + GBDT::ResetTrainingData(train_data, objective_function, training_metrics); + if (iter_ + num_init_iteration_ > 0) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + train_score_updater_->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id); + } + } + CHECK_EQ(num_tree_per_iteration_, num_class_); + // only boosting one time + Boosting(); + if (data_sample_strategy_->is_use_subset() && data_sample_strategy_->bag_data_cnt() < num_data_) { + tmp_grad_.resize(num_data_); + tmp_hess_.resize(num_data_); + } + } + + void Boosting() override { + if (objective_function_ == nullptr) { + Log::Fatal("RF mode do not support custom objective function, please use built-in objectives."); + } + init_scores_.resize(num_tree_per_iteration_, 0.0); + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + init_scores_[cur_tree_id] = BoostFromAverage(cur_tree_id, false); + } + size_t total_size = static_cast(num_data_) * num_tree_per_iteration_; + std::vector tmp_scores(total_size, 0.0f); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int j = 0; j < num_tree_per_iteration_; ++j) { + size_t offset = static_cast(j)* num_data_; + for (data_size_t i = 0; i < num_data_; ++i) { + tmp_scores[offset + i] = init_scores_[j]; + } + } + objective_function_-> + GetGradients(tmp_scores.data(), gradients_.data(), hessians_.data()); + } + + bool TrainOneIter(const score_t* gradients, const score_t* hessians) override { + // bagging logic + data_sample_strategy_ ->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data()); + const bool is_use_subset = data_sample_strategy_->is_use_subset(); + const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt(); + const std::vector>& bag_data_indices = data_sample_strategy_->bag_data_indices(); + + // GOSSStrategy->Bagging may modify value of bag_data_cnt_ + if (is_use_subset && bag_data_cnt < num_data_) { + tmp_grad_.resize(num_data_); + tmp_hess_.resize(num_data_); + } + + CHECK_EQ(gradients, nullptr); + CHECK_EQ(hessians, nullptr); + + gradients = gradients_.data(); + hessians = hessians_.data(); + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + std::unique_ptr new_tree(new Tree(2, false, false)); + size_t offset = static_cast(cur_tree_id)* num_data_; + if (class_need_train_[cur_tree_id]) { + auto grad = gradients + offset; + auto hess = hessians + offset; + + if (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_) { + for (int i = 0; i < bag_data_cnt; ++i) { + tmp_grad_[i] = grad[bag_data_indices[i]]; + tmp_hess_[i] = hess[bag_data_indices[i]]; + } + grad = tmp_grad_.data(); + hess = tmp_hess_.data(); + } + + new_tree.reset(tree_learner_->Train(grad, hess, false)); + } + + if (new_tree->num_leaves() > 1) { + double pred = init_scores_[cur_tree_id]; + auto residual_getter = [pred](const label_t* label, int i) {return static_cast(label[i]) - pred; }; + tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter, + num_data_, bag_data_indices.data(), bag_data_cnt, train_score_updater_->score()); + if (std::fabs(init_scores_[cur_tree_id]) > kEpsilon) { + new_tree->AddBias(init_scores_[cur_tree_id]); + } + // update score + MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_)); + UpdateScore(new_tree.get(), cur_tree_id); + MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1)); + } else { + // only add default score one-time + if (models_.size() < static_cast(num_tree_per_iteration_)) { + double output = 0.0; + if (!class_need_train_[cur_tree_id]) { + if (objective_function_ != nullptr) { + output = objective_function_->BoostFromScore(cur_tree_id); + } else { + output = init_scores_[cur_tree_id]; + } + } + new_tree->AsConstantTree(output, num_data_); + MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_)); + UpdateScore(new_tree.get(), cur_tree_id); + MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1)); + } + } + // add model + models_.push_back(std::move(new_tree)); + } + ++iter_; + return false; + } + + void RollbackOneIter() override { + if (iter_ <= 0) { + return; + } + int cur_iter = iter_ + num_init_iteration_ - 1; + // reset score + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + auto curr_tree = cur_iter * num_tree_per_iteration_ + cur_tree_id; + models_[curr_tree]->Shrinkage(-1.0); + MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_)); + train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id); + for (auto& score_updater : valid_score_updater_) { + score_updater->AddScore(models_[curr_tree].get(), cur_tree_id); + } + MultiplyScore(cur_tree_id, 1.0f / (iter_ + num_init_iteration_ - 1)); + } + // remove model + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + models_.pop_back(); + } + --iter_; + } + + void MultiplyScore(const int cur_tree_id, double val) { + train_score_updater_->MultiplyScore(val, cur_tree_id); + for (auto& score_updater : valid_score_updater_) { + score_updater->MultiplyScore(val, cur_tree_id); + } + } + + void AddValidDataset(const Dataset* valid_data, + const std::vector& valid_metrics) override { + GBDT::AddValidDataset(valid_data, valid_metrics); + if (iter_ + num_init_iteration_ > 0) { + for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) { + valid_score_updater_.back()->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id); + } + } + } + + bool NeedAccuratePrediction() const override { + // No early stopping for prediction + return true; + }; + + private: + std::vector tmp_grad_; + std::vector tmp_hess_; + std::vector init_scores_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_BOOSTING_RF_HPP_ diff --git a/src/boosting/sample_strategy.cpp b/src/boosting/sample_strategy.cpp new file mode 100644 index 0000000..f080ff0 --- /dev/null +++ b/src/boosting/sample_strategy.cpp @@ -0,0 +1,28 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include + +#include + +#include "goss.hpp" +#include "bagging.hpp" + +namespace LightGBM { + +SampleStrategy* SampleStrategy::CreateSampleStrategy( + const Config* config, + const Dataset* train_data, + const ObjectiveFunction* objective_function, + int num_tree_per_iteration) { + if (config->data_sample_strategy == std::string("goss")) { + return new GOSSStrategy(config, train_data, num_tree_per_iteration); + } else { + return new BaggingSampleStrategy(config, train_data, objective_function, num_tree_per_iteration); + } +} + +} // namespace LightGBM diff --git a/src/boosting/score_updater.hpp b/src/boosting/score_updater.hpp new file mode 100644 index 0000000..839be3b --- /dev/null +++ b/src/boosting/score_updater.hpp @@ -0,0 +1,129 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_ +#define LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_ + +#include +#include +#include +#include +#include + +#include +#include + +namespace LightGBM { +/*! +* \brief Used to store and update score for data +*/ +class ScoreUpdater { + public: + /*! + * \brief Constructor, will pass a const pointer of dataset + * \param data This class will bind with this data set + */ + ScoreUpdater(const Dataset* data, int num_tree_per_iteration) : data_(data) { + num_data_ = data->num_data(); + int64_t total_size = static_cast(num_data_) * num_tree_per_iteration; + score_.resize(total_size); + // default start score is zero + std::memset(score_.data(), 0, total_size * sizeof(double)); + has_init_score_ = false; + const double* init_score = data->metadata().init_score(); + // if exists initial score, will start from it + if (init_score != nullptr) { + if ((data->metadata().num_init_score() % num_data_) != 0 + || (data->metadata().num_init_score() / num_data_) != num_tree_per_iteration) { + Log::Fatal("Number of class for initial score error"); + } + has_init_score_ = true; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (total_size >= 1024) + for (int64_t i = 0; i < total_size; ++i) { + score_[i] = init_score[i]; + } + } + } + /*! \brief Destructor */ + virtual ~ScoreUpdater() { + } + + inline bool has_init_score() const { return has_init_score_; } + + virtual inline void AddScore(double val, int cur_tree_id) { + Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) + for (int i = 0; i < num_data_; ++i) { + score_[offset + i] += val; + } + } + + virtual inline void MultiplyScore(double val, int cur_tree_id) { + const size_t offset = static_cast(num_data_) * cur_tree_id; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) + for (int i = 0; i < num_data_; ++i) { + score_[offset + i] *= val; + } + } + /*! + * \brief Using tree model to get prediction number, then adding to scores for all data + * Note: this function generally will be used on validation data too. + * \param tree Trained tree model + * \param cur_tree_id Current tree for multiclass training + */ + virtual inline void AddScore(const Tree* tree, int cur_tree_id) { + Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + tree->AddPredictionToScore(data_, num_data_, score_.data() + offset); + } + /*! + * \brief Adding prediction score, only used for training data. + * The training data is partitioned into tree leaves after training + * Based on which We can get prediction quickly. + * \param tree_learner + * \param cur_tree_id Current tree for multiclass training + */ + virtual inline void AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) { + Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + tree_learner->AddPredictionToScore(tree, score_.data() + offset); + } + /*! + * \brief Using tree model to get prediction number, then adding to scores for parts of data + * Used for prediction of training out-of-bag data + * \param tree Trained tree model + * \param data_indices Indices of data that will be processed + * \param data_cnt Number of data that will be processed + * \param cur_tree_id Current tree for multiclass training + */ + virtual inline void AddScore(const Tree* tree, const data_size_t* data_indices, + data_size_t data_cnt, int cur_tree_id) { + Common::FunctionTimer fun_timer("ScoreUpdater::AddScore", global_timer); + const size_t offset = static_cast(num_data_) * cur_tree_id; + tree->AddPredictionToScore(data_, data_indices, data_cnt, score_.data() + offset); + } + /*! \brief Pointer of score */ + virtual inline const double* score() const { return score_.data(); } + + inline data_size_t num_data() const { return num_data_; } + + /*! \brief Disable copy */ + ScoreUpdater& operator=(const ScoreUpdater&) = delete; + /*! \brief Disable copy */ + ScoreUpdater(const ScoreUpdater&) = delete; + + protected: + /*! \brief Number of total data */ + data_size_t num_data_; + /*! \brief Pointer of data set */ + const Dataset* data_; + /*! \brief Scores for data set */ + std::vector> score_; + bool has_init_score_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_ diff --git a/src/c_api.cpp b/src/c_api.cpp new file mode 100644 index 0000000..9bdf085 --- /dev/null +++ b/src/c_api.cpp @@ -0,0 +1,3080 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "application/predictor.hpp" +#ifndef LGB_R_BUILD +#include "arrow/array.hpp" +#endif // LGB_R_BUILD +#include +#include + +namespace LightGBM { + +inline int LGBM_APIHandleException(const std::exception& ex) { + LGBM_SetLastError(ex.what()); + return -1; +} +inline int LGBM_APIHandleException(const std::string& ex) { + LGBM_SetLastError(ex.c_str()); + return -1; +} + +#define API_BEGIN() try { +#define API_END() } \ +catch(std::exception& ex) { return LGBM_APIHandleException(ex); } \ +catch(std::string& ex) { return LGBM_APIHandleException(ex); } \ +catch(...) { return LGBM_APIHandleException("unknown exception"); } \ +return 0; + +#define UNIQUE_LOCK(mtx) \ +std::unique_lock lock(mtx); + +#define SHARED_LOCK(mtx) \ +yamc::shared_lock lock(&mtx); + +const int PREDICTOR_TYPES = 4; + +// Single row predictor to abstract away caching logic +class SingleRowPredictorInner { + public: + PredictFunction predict_function; + int64_t num_pred_in_one_row; + + SingleRowPredictorInner(int predict_type, Boosting* boosting, const Config& config, int start_iter, int num_iter) { + bool is_predict_leaf = false; + bool is_raw_score = false; + bool predict_contrib = false; + if (predict_type == C_API_PREDICT_LEAF_INDEX) { + is_predict_leaf = true; + } else if (predict_type == C_API_PREDICT_RAW_SCORE) { + is_raw_score = true; + } else if (predict_type == C_API_PREDICT_CONTRIB) { + predict_contrib = true; + } + early_stop_ = config.pred_early_stop; + early_stop_freq_ = config.pred_early_stop_freq; + early_stop_margin_ = config.pred_early_stop_margin; + iter_ = num_iter; + predictor_.reset(new Predictor(boosting, start_iter, iter_, is_raw_score, is_predict_leaf, predict_contrib, + early_stop_, early_stop_freq_, early_stop_margin_)); + num_pred_in_one_row = boosting->NumPredictOneRow(start_iter, iter_, is_predict_leaf, predict_contrib); + predict_function = predictor_->GetPredictFunction(); + num_total_model_ = boosting->NumberOfTotalModel(); + } + + ~SingleRowPredictorInner() {} + + bool IsPredictorEqual(const Config& config, int iter, Boosting* boosting) { + return early_stop_ == config.pred_early_stop && + early_stop_freq_ == config.pred_early_stop_freq && + early_stop_margin_ == config.pred_early_stop_margin && + iter_ == iter && + num_total_model_ == boosting->NumberOfTotalModel(); + } + + private: + std::unique_ptr predictor_; + bool early_stop_; + int early_stop_freq_; + double early_stop_margin_; + int iter_; + int num_total_model_; +}; + +/*! + * \brief Object to store resources meant for single-row Fast Predict methods. + * + * For legacy reasons this is called `FastConfig` in the public C API. + * + * Meant to be used by the *Fast* predict methods only. + * It stores the configuration and prediction resources for reuse across predictions. + */ +struct SingleRowPredictor { + public: + SingleRowPredictor(yamc::alternate::shared_mutex *booster_mutex, + const char *parameters, + const int data_type, + const int32_t num_cols, + int predict_type, + Boosting *boosting, + int start_iter, + int num_iter) : config(Config::Str2Map(parameters)), data_type(data_type), num_cols(num_cols), single_row_predictor_inner(predict_type, boosting, config, start_iter, num_iter), booster_mutex(booster_mutex) { + if (!config.predict_disable_shape_check && num_cols != boosting->MaxFeatureIdx() + 1) { + Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n"\ + "You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", num_cols, boosting->MaxFeatureIdx() + 1); + } + } + + void Predict(std::function>(int row_idx)> get_row_fun, + double* out_result, int64_t* out_len) const { + UNIQUE_LOCK(single_row_predictor_mutex) + yamc::shared_lock booster_shared_lock(booster_mutex); + + auto one_row = get_row_fun(0); + single_row_predictor_inner.predict_function(one_row, out_result); + + *out_len = single_row_predictor_inner.num_pred_in_one_row; + } + + public: + Config config; + const int data_type; + const int32_t num_cols; + + private: + SingleRowPredictorInner single_row_predictor_inner; + + // Prevent the booster from being modified while we have a predictor relying on it during prediction + yamc::alternate::shared_mutex *booster_mutex; + + // If several threads try to predict at the same time using the same SingleRowPredictor + // we want them to still provide correct values, so the mutex is necessary due to the shared + // resources in the predictor. + // However the recommended approach is to instantiate one SingleRowPredictor per thread, + // to avoid contention here. + mutable yamc::alternate::shared_mutex single_row_predictor_mutex; +}; + +class Booster { + public: + explicit Booster(const char* filename) { + boosting_.reset(Boosting::CreateBoosting("gbdt", filename, std::string("cpu"), 0)); + } + + Booster(const Dataset* train_data, + const char* parameters) { + auto param = Config::Str2Map(parameters); + config_.Set(param); + OMP_SET_NUM_THREADS(config_.num_threads); + // create boosting + if (config_.input_model.size() > 0) { + Log::Warning("Continued train from model is not supported for c_api,\n" + "please use continued train with input score"); + } + + boosting_.reset(Boosting::CreateBoosting(config_.boosting, nullptr, config_.device_type, config_.num_gpu)); + + train_data_ = train_data; + CreateObjectiveAndMetrics(); + // initialize the boosting + if (config_.tree_learner == std::string("feature")) { + Log::Fatal("Do not support feature parallel in c api"); + } + if (Network::num_machines() == 1 && config_.tree_learner != std::string("serial")) { + Log::Warning("Only find one worker, will switch to serial tree learner"); + config_.tree_learner = "serial"; + } + boosting_->Init(&config_, train_data_, objective_fun_.get(), + Common::ConstPtrInVectorWrapper(train_metric_)); + } + + void MergeFrom(const Booster* other) { + UNIQUE_LOCK(mutex_) + boosting_->MergeFrom(other->boosting_.get()); + } + + ~Booster() { + } + + void CreateObjectiveAndMetrics() { + // create objective function + objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective, + config_)); + if (objective_fun_ == nullptr) { + Log::Info("Using self-defined objective function"); + } + // initialize the objective function + if (objective_fun_ != nullptr) { + objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); + } + + // create training metric + train_metric_.clear(); + for (auto metric_type : config_.metric) { + auto metric = std::unique_ptr( + Metric::CreateMetric(metric_type, config_)); + if (metric == nullptr) { + continue; + } + metric->Init(train_data_->metadata(), train_data_->num_data()); + train_metric_.push_back(std::move(metric)); + } + train_metric_.shrink_to_fit(); + } + + void ResetTrainingData(const Dataset* train_data) { + if (train_data != train_data_) { + UNIQUE_LOCK(mutex_) + train_data_ = train_data; + CreateObjectiveAndMetrics(); + // reset the boosting + boosting_->ResetTrainingData(train_data_, + objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_)); + } + } + + static void CheckDatasetResetConfig( + const Config& old_config, + const std::unordered_map& new_param) { + Config new_config; + new_config.Set(new_param); + if (new_param.count("data_random_seed") && + new_config.data_random_seed != old_config.data_random_seed) { + Log::Fatal("Cannot change data_random_seed after constructed Dataset handle."); + } + if (new_param.count("max_bin") && + new_config.max_bin != old_config.max_bin) { + Log::Fatal("Cannot change max_bin after constructed Dataset handle."); + } + if (new_param.count("max_bin_by_feature") && + new_config.max_bin_by_feature != old_config.max_bin_by_feature) { + Log::Fatal( + "Cannot change max_bin_by_feature after constructed Dataset handle."); + } + if (new_param.count("bin_construct_sample_cnt") && + new_config.bin_construct_sample_cnt != + old_config.bin_construct_sample_cnt) { + Log::Fatal( + "Cannot change bin_construct_sample_cnt after constructed Dataset " + "handle."); + } + if (new_param.count("min_data_in_bin") && + new_config.min_data_in_bin != old_config.min_data_in_bin) { + Log::Fatal( + "Cannot change min_data_in_bin after constructed Dataset handle."); + } + if (new_param.count("use_missing") && + new_config.use_missing != old_config.use_missing) { + Log::Fatal("Cannot change use_missing after constructed Dataset handle."); + } + if (new_param.count("zero_as_missing") && + new_config.zero_as_missing != old_config.zero_as_missing) { + Log::Fatal( + "Cannot change zero_as_missing after constructed Dataset handle."); + } + if (new_param.count("categorical_feature") && + new_config.categorical_feature != old_config.categorical_feature) { + Log::Fatal( + "Cannot change categorical_feature after constructed Dataset " + "handle."); + } + if (new_param.count("feature_pre_filter") && + new_config.feature_pre_filter != old_config.feature_pre_filter) { + Log::Fatal( + "Cannot change feature_pre_filter after constructed Dataset handle."); + } + if (new_param.count("is_enable_sparse") && + new_config.is_enable_sparse != old_config.is_enable_sparse) { + Log::Fatal( + "Cannot change is_enable_sparse after constructed Dataset handle."); + } + if (new_param.count("pre_partition") && + new_config.pre_partition != old_config.pre_partition) { + Log::Fatal( + "Cannot change pre_partition after constructed Dataset handle."); + } + if (new_param.count("enable_bundle") && + new_config.enable_bundle != old_config.enable_bundle) { + Log::Fatal( + "Cannot change enable_bundle after constructed Dataset handle."); + } + if (new_param.count("header") && new_config.header != old_config.header) { + Log::Fatal("Cannot change header after constructed Dataset handle."); + } + if (new_param.count("two_round") && + new_config.two_round != old_config.two_round) { + Log::Fatal("Cannot change two_round after constructed Dataset handle."); + } + if (new_param.count("label_column") && + new_config.label_column != old_config.label_column) { + Log::Fatal( + "Cannot change label_column after constructed Dataset handle."); + } + if (new_param.count("weight_column") && + new_config.weight_column != old_config.weight_column) { + Log::Fatal( + "Cannot change weight_column after constructed Dataset handle."); + } + if (new_param.count("group_column") && + new_config.group_column != old_config.group_column) { + Log::Fatal( + "Cannot change group_column after constructed Dataset handle."); + } + if (new_param.count("ignore_column") && + new_config.ignore_column != old_config.ignore_column) { + Log::Fatal( + "Cannot change ignore_column after constructed Dataset handle."); + } + if (new_param.count("forcedbins_filename")) { + Log::Fatal("Cannot change forced bins after constructed Dataset handle."); + } + if (new_param.count("min_data_in_leaf") && + new_config.min_data_in_leaf < old_config.min_data_in_leaf && + old_config.feature_pre_filter) { + Log::Fatal( + "Reducing `min_data_in_leaf` with `feature_pre_filter=true` may " + "cause unexpected behaviour " + "for features that were pre-filtered by the larger " + "`min_data_in_leaf`.\n" + "You need to set `feature_pre_filter=false` to dynamically change " + "the `min_data_in_leaf`."); + } + if (new_param.count("linear_tree") && new_config.linear_tree != old_config.linear_tree) { + Log::Fatal("Cannot change linear_tree after constructed Dataset handle."); + } + if (new_param.count("precise_float_parser") && + new_config.precise_float_parser != old_config.precise_float_parser) { + Log::Fatal("Cannot change precise_float_parser after constructed Dataset handle."); + } + } + + void ResetConfig(const char* parameters) { + UNIQUE_LOCK(mutex_) + auto param = Config::Str2Map(parameters); + Config new_config; + new_config.Set(param); + if (param.count("num_class") && new_config.num_class != config_.num_class) { + Log::Fatal("Cannot change num_class during training"); + } + if (param.count("boosting") && new_config.boosting != config_.boosting) { + Log::Fatal("Cannot change boosting during training"); + } + if (param.count("metric") && new_config.metric != config_.metric) { + Log::Fatal("Cannot change metric during training"); + } + CheckDatasetResetConfig(config_, param); + + config_.Set(param); + + OMP_SET_NUM_THREADS(config_.num_threads); + + if (param.count("objective")) { + // create objective function + objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective, + config_)); + if (objective_fun_ == nullptr) { + Log::Info("Using self-defined objective function"); + } + // initialize the objective function + if (objective_fun_ != nullptr) { + objective_fun_->Init(train_data_->metadata(), train_data_->num_data()); + } + boosting_->ResetTrainingData(train_data_, + objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_)); + } + + boosting_->ResetConfig(&config_); + } + + void AddValidData(const Dataset* valid_data) { + UNIQUE_LOCK(mutex_) + valid_metrics_.emplace_back(); + for (auto metric_type : config_.metric) { + auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_)); + if (metric == nullptr) { + continue; + } + metric->Init(valid_data->metadata(), valid_data->num_data()); + valid_metrics_.back().push_back(std::move(metric)); + } + valid_metrics_.back().shrink_to_fit(); + boosting_->AddValidDataset(valid_data, + Common::ConstPtrInVectorWrapper(valid_metrics_.back())); + } + + bool TrainOneIter() { + UNIQUE_LOCK(mutex_) + return boosting_->TrainOneIter(nullptr, nullptr); + } + + void Refit(const int32_t* leaf_preds, int32_t nrow, int32_t ncol) { + UNIQUE_LOCK(mutex_) + boosting_->RefitTree(leaf_preds, nrow, ncol); + } + + bool TrainOneIter(const score_t* gradients, const score_t* hessians) { + UNIQUE_LOCK(mutex_) + return boosting_->TrainOneIter(gradients, hessians); + } + + void RollbackOneIter() { + UNIQUE_LOCK(mutex_) + boosting_->RollbackOneIter(); + } + + void SetSingleRowPredictorInner(int start_iteration, int num_iteration, int predict_type, const Config& config) { + UNIQUE_LOCK(mutex_) + if (single_row_predictor_[predict_type].get() == nullptr || + !single_row_predictor_[predict_type]->IsPredictorEqual(config, num_iteration, boosting_.get())) { + single_row_predictor_[predict_type].reset(new SingleRowPredictorInner(predict_type, boosting_.get(), + config, start_iteration, num_iteration)); + } + } + + std::unique_ptr InitSingleRowPredictor(int predict_type, int start_iteration, int num_iteration, int data_type, int32_t num_cols, const char *parameters) { + // Workaround https://github.com/lightgbm-org/LightGBM/issues/6142 by locking here + // This is only a workaround because if predictors are initialized differently it may still behave incorrectly, + // and because multiple racing Predictor initializations through LGBM_BoosterPredictForMat suffers from that same issue of Predictor init writing things in the booster. + // Once #6142 is fixed (predictor doesn't write in the Booster as should have been the case since 1c35c3b9ede9adab8ccc5fd7b4b2b6af188a79f0), this line can be removed. + UNIQUE_LOCK(mutex_) + + return std::unique_ptr(new SingleRowPredictor( + &mutex_, parameters, data_type, num_cols, predict_type, boosting_.get(), start_iteration, num_iteration)); + } + + void PredictSingleRow(int predict_type, int ncol, + std::function>(int row_idx)> get_row_fun, + const Config& config, + double* out_result, int64_t* out_len) const { + if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) { + Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n"\ + "You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", ncol, boosting_->MaxFeatureIdx() + 1); + } + UNIQUE_LOCK(mutex_) + const auto& single_row_predictor = single_row_predictor_[predict_type]; + auto one_row = get_row_fun(0); + auto pred_wrt_ptr = out_result; + single_row_predictor->predict_function(one_row, pred_wrt_ptr); + + *out_len = single_row_predictor->num_pred_in_one_row; + } + + std::shared_ptr CreatePredictor(int start_iteration, int num_iteration, int predict_type, int ncol, const Config& config) const { + if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) { + Log::Fatal("The number of features in data (%d) is not the same as it was in training data (%d).\n" \ + "You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.", ncol, boosting_->MaxFeatureIdx() + 1); + } + bool is_predict_leaf = false; + bool is_raw_score = false; + bool predict_contrib = false; + if (predict_type == C_API_PREDICT_LEAF_INDEX) { + is_predict_leaf = true; + } else if (predict_type == C_API_PREDICT_RAW_SCORE) { + is_raw_score = true; + } else if (predict_type == C_API_PREDICT_CONTRIB) { + predict_contrib = true; + } else { + is_raw_score = false; + } + + return std::make_shared(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib, + config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin); + } + + void Predict(int start_iteration, int num_iteration, int predict_type, int nrow, int ncol, + std::function>(int row_idx)> get_row_fun, + const Config& config, + double* out_result, int64_t* out_len) const { + SHARED_LOCK(mutex_); + auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config); + bool is_predict_leaf = false; + bool predict_contrib = false; + if (predict_type == C_API_PREDICT_LEAF_INDEX) { + is_predict_leaf = true; + } else if (predict_type == C_API_PREDICT_CONTRIB) { + predict_contrib = true; + } + int64_t num_pred_in_one_row = boosting_->NumPredictOneRow(start_iteration, num_iteration, is_predict_leaf, predict_contrib); + auto pred_fun = predictor->GetPredictFunction(); + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < nrow; ++i) { + OMP_LOOP_EX_BEGIN(); + auto one_row = get_row_fun(i); + auto pred_wrt_ptr = out_result + static_cast(num_pred_in_one_row) * i; + pred_fun(one_row, pred_wrt_ptr); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + *out_len = num_pred_in_one_row * nrow; + } + + void PredictSparse(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol, + std::function>(int64_t row_idx)> get_row_fun, + const Config& config, int64_t* out_elements_size, + std::vector>>* agg_ptr, + int32_t** out_indices, void** out_data, int data_type, + bool* is_data_float32_ptr, int num_matrices) const { + auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config); + auto pred_sparse_fun = predictor->GetPredictSparseFunction(); + std::vector>>& agg = *agg_ptr; + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int64_t i = 0; i < nrow; ++i) { + OMP_LOOP_EX_BEGIN(); + auto one_row = get_row_fun(i); + agg[i] = std::vector>(num_matrices); + pred_sparse_fun(one_row, &agg[i]); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + // calculate the nonzero data and indices size + int64_t elements_size = 0; + for (int64_t i = 0; i < static_cast(agg.size()); ++i) { + auto row_vector = agg[i]; + for (int j = 0; j < static_cast(row_vector.size()); ++j) { + elements_size += static_cast(row_vector[j].size()); + } + } + *out_elements_size = elements_size; + *is_data_float32_ptr = false; + // allocate data and indices arrays + if (data_type == C_API_DTYPE_FLOAT32) { + *out_data = new float[elements_size]; + *is_data_float32_ptr = true; + } else if (data_type == C_API_DTYPE_FLOAT64) { + *out_data = new double[elements_size]; + } else { + Log::Fatal("Unknown data type in PredictSparse"); + return; + } + *out_indices = new int32_t[elements_size]; + } + + void PredictSparseCSR(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol, + std::function>(int64_t row_idx)> get_row_fun, + const Config& config, + int64_t* out_len, void** out_indptr, int indptr_type, + int32_t** out_indices, void** out_data, int data_type) const { + SHARED_LOCK(mutex_); + // Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices) + int num_matrices = boosting_->NumModelPerIteration(); + bool is_indptr_int32 = false; + bool is_data_float32 = false; + int64_t indptr_size = (nrow + 1) * num_matrices; + if (indptr_type == C_API_DTYPE_INT32) { + *out_indptr = new int32_t[indptr_size]; + is_indptr_int32 = true; + } else if (indptr_type == C_API_DTYPE_INT64) { + *out_indptr = new int64_t[indptr_size]; + } else { + Log::Fatal("Unknown indptr type in PredictSparseCSR"); + return; + } + // aggregated per row feature contribution results + std::vector>> agg(nrow); + int64_t elements_size = 0; + PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg, + out_indices, out_data, data_type, &is_data_float32, num_matrices); + std::vector row_sizes(num_matrices * nrow); + std::vector row_matrix_offsets(num_matrices * nrow); + std::vector matrix_offsets(num_matrices); + int64_t row_vector_cnt = 0; + for (int m = 0; m < num_matrices; ++m) { + for (int64_t i = 0; i < static_cast(agg.size()); ++i) { + auto row_vector = agg[i]; + auto row_vector_size = row_vector[m].size(); + // keep track of the row_vector sizes for parallelization + row_sizes[row_vector_cnt] = static_cast(row_vector_size); + if (i == 0) { + row_matrix_offsets[row_vector_cnt] = 0; + } else { + row_matrix_offsets[row_vector_cnt] = static_cast(row_sizes[row_vector_cnt - 1] + row_matrix_offsets[row_vector_cnt - 1]); + } + row_vector_cnt++; + } + if (m == 0) { + matrix_offsets[m] = 0; + } + if (m + 1 < num_matrices) { + matrix_offsets[m + 1] = static_cast(matrix_offsets[m] + row_matrix_offsets[row_vector_cnt - 1] + row_sizes[row_vector_cnt - 1]); + } + } + // copy vector results to output for each row + int64_t indptr_index = 0; + for (int m = 0; m < num_matrices; ++m) { + if (is_indptr_int32) { + (reinterpret_cast(*out_indptr))[indptr_index] = 0; + } else { + (reinterpret_cast(*out_indptr))[indptr_index] = 0; + } + indptr_index++; + int64_t matrix_start_index = m * static_cast(agg.size()); + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int64_t i = 0; i < static_cast(agg.size()); ++i) { + OMP_LOOP_EX_BEGIN(); + auto row_vector = agg[i]; + int64_t row_start_index = matrix_start_index + i; + int64_t element_index = row_matrix_offsets[row_start_index] + matrix_offsets[m]; + int64_t indptr_loop_index = indptr_index + i; + for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) { + (*out_indices)[element_index] = it->first; + if (is_data_float32) { + (reinterpret_cast(*out_data))[element_index] = static_cast(it->second); + } else { + (reinterpret_cast(*out_data))[element_index] = it->second; + } + element_index++; + } + int64_t indptr_value = row_matrix_offsets[row_start_index] + row_sizes[row_start_index]; + if (is_indptr_int32) { + (reinterpret_cast(*out_indptr))[indptr_loop_index] = static_cast(indptr_value); + } else { + (reinterpret_cast(*out_indptr))[indptr_loop_index] = indptr_value; + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + indptr_index += static_cast(agg.size()); + } + out_len[0] = elements_size; + out_len[1] = indptr_size; + } + + void PredictSparseCSC(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol, + std::function>(int64_t row_idx)> get_row_fun, + const Config& config, + int64_t* out_len, void** out_col_ptr, int col_ptr_type, + int32_t** out_indices, void** out_data, int data_type) const { + SHARED_LOCK(mutex_); + // Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices) + int num_matrices = boosting_->NumModelPerIteration(); + auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config); + auto pred_sparse_fun = predictor->GetPredictSparseFunction(); + bool is_col_ptr_int32 = false; + bool is_data_float32 = false; + int num_output_cols = ncol + 1; + int col_ptr_size = (num_output_cols + 1) * num_matrices; + if (col_ptr_type == C_API_DTYPE_INT32) { + *out_col_ptr = new int32_t[col_ptr_size]; + is_col_ptr_int32 = true; + } else if (col_ptr_type == C_API_DTYPE_INT64) { + *out_col_ptr = new int64_t[col_ptr_size]; + } else { + Log::Fatal("Unknown col_ptr type in PredictSparseCSC"); + return; + } + // aggregated per row feature contribution results + std::vector>> agg(nrow); + int64_t elements_size = 0; + PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg, + out_indices, out_data, data_type, &is_data_float32, num_matrices); + // calculate number of elements per column to construct + // the CSC matrix with random access + std::vector> column_sizes(num_matrices); + for (int m = 0; m < num_matrices; ++m) { + column_sizes[m] = std::vector(num_output_cols, 0); + for (int64_t i = 0; i < static_cast(agg.size()); ++i) { + auto row_vector = agg[i]; + for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) { + column_sizes[m][it->first] += 1; + } + } + } + // keep track of column counts + std::vector> column_counts(num_matrices); + // keep track of beginning index for each column + std::vector> column_start_indices(num_matrices); + // keep track of beginning index for each matrix + std::vector matrix_start_indices(num_matrices, 0); + int col_ptr_index = 0; + for (int m = 0; m < num_matrices; ++m) { + int64_t col_ptr_value = 0; + column_start_indices[m] = std::vector(num_output_cols, 0); + column_counts[m] = std::vector(num_output_cols, 0); + if (is_col_ptr_int32) { + (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(col_ptr_value); + } else { + (reinterpret_cast(*out_col_ptr))[col_ptr_index] = col_ptr_value; + } + col_ptr_index++; + for (int64_t i = 1; i < static_cast(column_sizes[m].size()); ++i) { + column_start_indices[m][i] = column_sizes[m][i - 1] + column_start_indices[m][i - 1]; + if (is_col_ptr_int32) { + (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(column_start_indices[m][i]); + } else { + (reinterpret_cast(*out_col_ptr))[col_ptr_index] = column_start_indices[m][i]; + } + col_ptr_index++; + } + int64_t last_elem_index = static_cast(column_sizes[m].size()) - 1; + int64_t last_column_start_index = column_start_indices[m][last_elem_index]; + int64_t last_column_size = column_sizes[m][last_elem_index]; + if (is_col_ptr_int32) { + (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(last_column_start_index + last_column_size); + } else { + (reinterpret_cast(*out_col_ptr))[col_ptr_index] = last_column_start_index + last_column_size; + } + if (m + 1 < num_matrices) { + matrix_start_indices[m + 1] = matrix_start_indices[m] + last_column_start_index + last_column_size; + } + col_ptr_index++; + } + // Note: we parallelize across matrices instead of rows because of the column_counts[m][col_idx] increment inside the loop + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int m = 0; m < num_matrices; ++m) { + OMP_LOOP_EX_BEGIN(); + for (int64_t i = 0; i < static_cast(agg.size()); ++i) { + auto row_vector = agg[i]; + for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) { + int64_t col_idx = it->first; + int64_t element_index = column_start_indices[m][col_idx] + + matrix_start_indices[m] + + column_counts[m][col_idx]; + // store the row index + (*out_indices)[element_index] = static_cast(i); + // update column count + column_counts[m][col_idx]++; + if (is_data_float32) { + (reinterpret_cast(*out_data))[element_index] = static_cast(it->second); + } else { + (reinterpret_cast(*out_data))[element_index] = it->second; + } + } + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + out_len[0] = elements_size; + out_len[1] = col_ptr_size; + } + + void Predict(int start_iteration, int num_iteration, int predict_type, const char* data_filename, + int data_has_header, const Config& config, + const char* result_filename) const { + SHARED_LOCK(mutex_) + bool is_predict_leaf = false; + bool is_raw_score = false; + bool predict_contrib = false; + if (predict_type == C_API_PREDICT_LEAF_INDEX) { + is_predict_leaf = true; + } else if (predict_type == C_API_PREDICT_RAW_SCORE) { + is_raw_score = true; + } else if (predict_type == C_API_PREDICT_CONTRIB) { + predict_contrib = true; + } else { + is_raw_score = false; + } + Predictor predictor(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib, + config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin); + bool bool_data_has_header = data_has_header > 0 ? true : false; + predictor.Predict(data_filename, result_filename, bool_data_has_header, config.predict_disable_shape_check, + config.precise_float_parser); + } + + void GetPredictAt(int data_idx, double* out_result, int64_t* out_len) const { + boosting_->GetPredictAt(data_idx, out_result, out_len); + } + + void SaveModelToFile(int start_iteration, int num_iteration, int feature_importance_type, const char* filename) const { + boosting_->SaveModelToFile(start_iteration, num_iteration, feature_importance_type, filename); + } + + void LoadModelFromString(const char* model_str) { + size_t len = std::strlen(model_str); + boosting_->LoadModelFromString(model_str, len); + } + + std::string SaveModelToString(int start_iteration, int num_iteration, + int feature_importance_type) const { + return boosting_->SaveModelToString(start_iteration, + num_iteration, feature_importance_type); + } + + std::string DumpModel(int start_iteration, int num_iteration, + int feature_importance_type) const { + return boosting_->DumpModel(start_iteration, num_iteration, + feature_importance_type); + } + + std::vector FeatureImportance(int num_iteration, int importance_type) const { + return boosting_->FeatureImportance(num_iteration, importance_type); + } + + double UpperBoundValue() const { + SHARED_LOCK(mutex_) + return boosting_->GetUpperBoundValue(); + } + + double LowerBoundValue() const { + SHARED_LOCK(mutex_) + return boosting_->GetLowerBoundValue(); + } + + double GetLeafValue(int tree_idx, int leaf_idx) const { + SHARED_LOCK(mutex_) + return dynamic_cast(boosting_.get())->GetLeafValue(tree_idx, leaf_idx); + } + + void SetLeafValue(int tree_idx, int leaf_idx, double val) { + UNIQUE_LOCK(mutex_) + dynamic_cast(boosting_.get())->SetLeafValue(tree_idx, leaf_idx, val); + } + + void ShuffleModels(int start_iter, int end_iter) { + UNIQUE_LOCK(mutex_) + boosting_->ShuffleModels(start_iter, end_iter); + } + + int GetEvalCounts() const { + SHARED_LOCK(mutex_) + int ret = 0; + for (const auto& metric : train_metric_) { + ret += static_cast(metric->GetName().size()); + } + return ret; + } + + int GetEvalNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const { + SHARED_LOCK(mutex_) + *out_buffer_len = 0; + int idx = 0; + for (const auto& metric : train_metric_) { + for (const auto& name : metric->GetName()) { + if (idx < len) { + std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len)); + out_strs[idx][buffer_len - 1] = '\0'; + } + *out_buffer_len = std::max(name.size() + 1, *out_buffer_len); + ++idx; + } + } + return idx; + } + + int GetFeatureNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const { + SHARED_LOCK(mutex_) + *out_buffer_len = 0; + int idx = 0; + for (const auto& name : boosting_->FeatureNames()) { + if (idx < len) { + std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len)); + out_strs[idx][buffer_len - 1] = '\0'; + } + *out_buffer_len = std::max(name.size() + 1, *out_buffer_len); + ++idx; + } + return idx; + } + + const Boosting* GetBoosting() const { return boosting_.get(); } + + private: + const Dataset* train_data_; + std::unique_ptr boosting_; + std::unique_ptr single_row_predictor_[PREDICTOR_TYPES]; + + /*! \brief All configs */ + Config config_; + /*! \brief Metric for training data */ + std::vector> train_metric_; + /*! \brief Metrics for validation data */ + std::vector>> valid_metrics_; + /*! \brief Training objective function */ + std::unique_ptr objective_fun_; + /*! \brief mutex for threading safe call */ + mutable yamc::alternate::shared_mutex mutex_; +}; + +} // namespace LightGBM + +// explicitly declare symbols from LightGBM namespace +using LightGBM::AllgatherFunction; +#ifndef LGB_R_BUILD +using LightGBM::ArrowChunkedArray; +#endif // LGB_R_BUILD +using LightGBM::Booster; +using LightGBM::Common::CheckElementsIntervalClosed; +using LightGBM::Common::RemoveQuotationSymbol; +using LightGBM::Common::Vector2Ptr; +using LightGBM::Common::VectorSize; +using LightGBM::Config; +using LightGBM::data_size_t; +using LightGBM::Dataset; +using LightGBM::DatasetLoader; +using LightGBM::kZeroThreshold; +using LightGBM::LGBM_APIHandleException; +using LightGBM::Log; +using LightGBM::Network; +using LightGBM::Random; +using LightGBM::ReduceScatterFunction; +using LightGBM::SingleRowPredictor; + +// some help functions used to convert data + +std::function(int row_idx)> +RowFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major); + +std::function>(int row_idx)> +RowPairFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major); + +std::function>(int row_idx)> +RowPairFunctionFromDenseRows(const void** data, int num_col, int data_type); + +template +std::function>(T idx)> +RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices, + const void* data, int data_type, int64_t nindptr, int64_t nelem); + +// Row iterator of on column for CSC matrix +class CSC_RowIterator { + public: + CSC_RowIterator(const void* col_ptr, int col_ptr_type, const int32_t* indices, + const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int col_idx); + ~CSC_RowIterator() {} + // return value at idx, only can access by ascent order + double Get(int idx); + // return next non-zero pair, if index < 0, means no more data + std::pair NextNonZero(); + + private: + int nonzero_idx_ = 0; + int cur_idx_ = -1; + double cur_val_ = 0.0f; + bool is_end_ = false; + std::function(int idx)> iter_fun_; +}; + +// start of c_api functions + +const char* LGBM_GetLastError() { + return LastErrorMsg(); +} + +int LGBM_DumpParamAliases(int64_t buffer_len, + int64_t* out_len, + char* out_str) { + API_BEGIN(); + std::string aliases = Config::DumpAliases(); + *out_len = static_cast(aliases.size()) + 1; + if (*out_len <= buffer_len) { + std::memcpy(out_str, aliases.c_str(), *out_len); + } + API_END(); +} + +int LGBM_RegisterLogCallback(void (*callback)(const char*)) { + API_BEGIN(); + Log::ResetCallBack(callback); + API_END(); +} + +static inline int SampleCount(int32_t total_nrow, const Config& config) { + return static_cast(total_nrow < config.bin_construct_sample_cnt ? total_nrow : config.bin_construct_sample_cnt); +} + +static inline std::vector CreateSampleIndices(int32_t total_nrow, const Config& config) { + Random rand(config.data_random_seed); + int sample_cnt = SampleCount(total_nrow, config); + return rand.Sample(total_nrow, sample_cnt); +} + +int LGBM_GetSampleCount(int32_t num_total_row, + const char* parameters, + int* out) { + API_BEGIN(); + if (out == nullptr) { + Log::Fatal("LGBM_GetSampleCount output is nullptr"); + } + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + + *out = SampleCount(num_total_row, config); + API_END(); +} + +int LGBM_SampleIndices(int32_t num_total_row, + const char* parameters, + void* out, + int32_t* out_len) { + // This API is to keep python binding's behavior the same with C++ implementation. + // Sample count, random seed etc. should be provided in parameters. + API_BEGIN(); + if (out == nullptr) { + Log::Fatal("LGBM_SampleIndices output is nullptr"); + } + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + + auto sample_indices = CreateSampleIndices(num_total_row, config); + memcpy(out, sample_indices.data(), sizeof(int32_t) * sample_indices.size()); + *out_len = static_cast(sample_indices.size()); + API_END(); +} + +int LGBM_ByteBufferGetAt(ByteBufferHandle handle, int32_t index, uint8_t* out_val) { + API_BEGIN(); + LightGBM::ByteBuffer* byteBuffer = reinterpret_cast(handle); + *out_val = byteBuffer->GetAt(index); + API_END(); +} + +int LGBM_ByteBufferFree(ByteBufferHandle handle) { + API_BEGIN(); + delete reinterpret_cast(handle); + API_END(); +} + +int LGBM_DatasetCreateFromFile(const char* filename, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + API_BEGIN(); + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + DatasetLoader loader(config, nullptr, 1, filename); + if (reference == nullptr) { + if (Network::num_machines() == 1) { + *out = loader.LoadFromFile(filename); + } else { + *out = loader.LoadFromFile(filename, Network::rank(), Network::num_machines()); + } + } else { + *out = loader.LoadFromFileAlignWithOtherDataset(filename, + reinterpret_cast(reference)); + } + API_END(); +} + +int LGBM_DatasetCreateFromSampledColumn(double** sample_data, + int** sample_indices, + int32_t ncol, + const int* num_per_col, + int32_t num_sample_row, + int32_t num_local_row, + int64_t num_dist_row, + const char* parameters, + DatasetHandle* out) { + API_BEGIN(); + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + DatasetLoader loader(config, nullptr, 1, nullptr); + *out = loader.ConstructFromSampleData(sample_data, + sample_indices, + ncol, + num_per_col, + num_sample_row, + static_cast(num_local_row), + num_dist_row); + API_END(); +} + +int LGBM_DatasetCreateByReference(const DatasetHandle reference, + int64_t num_total_row, + DatasetHandle* out) { + API_BEGIN(); + std::unique_ptr ret; + data_size_t nrows = static_cast(num_total_row); + ret.reset(new Dataset(nrows)); + const Dataset* reference_dataset = reinterpret_cast(reference); + ret->CreateValid(reference_dataset); + ret->InitByReference(nrows, reference_dataset); + *out = ret.release(); + API_END(); +} + +int LGBM_DatasetCreateFromSerializedReference(const void* ref_buffer, + int32_t ref_buffer_size, + int64_t num_row, + int32_t num_classes, + const char* parameters, + DatasetHandle* out) { + API_BEGIN(); + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + DatasetLoader loader(config, nullptr, 1, nullptr); + *out = loader.LoadFromSerializedReference(static_cast(ref_buffer), + static_cast(ref_buffer_size), + static_cast(num_row), + num_classes); + API_END(); +} + +int LGBM_DatasetInitStreaming(DatasetHandle dataset, + int32_t has_weights, + int32_t has_init_scores, + int32_t has_queries, + int32_t nclasses, + int32_t nthreads, + int32_t omp_max_threads) { + API_BEGIN(); + auto p_dataset = reinterpret_cast(dataset); + auto num_data = p_dataset->num_data(); + p_dataset->InitStreaming(num_data, has_weights, has_init_scores, has_queries, nclasses, nthreads, omp_max_threads); + p_dataset->set_wait_for_manual_finish(true); + API_END(); +} + +int LGBM_DatasetPushRows(DatasetHandle dataset, + const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int32_t start_row) { + API_BEGIN(); + auto p_dataset = reinterpret_cast(dataset); + auto get_row_fun = RowFunctionFromDenseMatrix(data, nrow, ncol, data_type, 1); + if (p_dataset->has_raw()) { + p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); + } + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < nrow; ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + auto one_row = get_row_fun(i); + p_dataset->PushOneRow(tid, start_row + i, one_row); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == p_dataset->num_data())) { + p_dataset->FinishLoad(); + } + API_END(); +} + +int LGBM_DatasetPushRowsWithMetadata(DatasetHandle dataset, + const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int32_t start_row, + const float* labels, + const float* weights, + const double* init_scores, + const int32_t* queries, + int32_t tid) { + API_BEGIN(); +#ifdef LABEL_T_USE_DOUBLE + Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); +#endif + if (!data) { + Log::Fatal("data cannot be null."); + } + auto p_dataset = reinterpret_cast(dataset); + auto get_row_fun = RowFunctionFromDenseMatrix(data, nrow, ncol, data_type, 1); + if (p_dataset->has_raw()) { + p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); + } + + const int max_omp_threads = p_dataset->omp_max_threads() > 0 ? p_dataset->omp_max_threads() : OMP_NUM_THREADS(); + + OMP_INIT_EX(); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < nrow; ++i) { + OMP_LOOP_EX_BEGIN(); + // convert internal thread id to be unique based on external thread id + const int internal_tid = omp_get_thread_num() + (max_omp_threads * tid); + auto one_row = get_row_fun(i); + p_dataset->PushOneRow(internal_tid, start_row + i, one_row); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + p_dataset->InsertMetadataAt(start_row, nrow, labels, weights, init_scores, queries); + + if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == p_dataset->num_data())) { + p_dataset->FinishLoad(); + } + API_END(); +} + +int LGBM_DatasetPushRowsByCSR(DatasetHandle dataset, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t, + int64_t start_row) { + API_BEGIN(); + auto p_dataset = reinterpret_cast(dataset); + auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); + int32_t nrow = static_cast(nindptr - 1); + if (p_dataset->has_raw()) { + p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); + } + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < nrow; ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + auto one_row = get_row_fun(i); + p_dataset->PushOneRow(tid, static_cast(start_row + i), one_row); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == static_cast(p_dataset->num_data()))) { + p_dataset->FinishLoad(); + } + API_END(); +} + +int LGBM_DatasetPushRowsByCSRWithMetadata(DatasetHandle dataset, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t start_row, + const float* labels, + const float* weights, + const double* init_scores, + const int32_t* queries, + int32_t tid) { + API_BEGIN(); +#ifdef LABEL_T_USE_DOUBLE + Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); +#endif + if (!data) { + Log::Fatal("data cannot be null."); + } + auto p_dataset = reinterpret_cast(dataset); + auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); + int32_t nrow = static_cast(nindptr - 1); + if (p_dataset->has_raw()) { + p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow); + } + + const int max_omp_threads = p_dataset->omp_max_threads() > 0 ? p_dataset->omp_max_threads() : OMP_NUM_THREADS(); + + OMP_INIT_EX(); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < nrow; ++i) { + OMP_LOOP_EX_BEGIN(); + // convert internal thread id to be unique based on external thread id + const int internal_tid = omp_get_thread_num() + (max_omp_threads * tid); + auto one_row = get_row_fun(i); + p_dataset->PushOneRow(internal_tid, static_cast(start_row + i), one_row); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + p_dataset->InsertMetadataAt(static_cast(start_row), nrow, labels, weights, init_scores, queries); + + if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == static_cast(p_dataset->num_data()))) { + p_dataset->FinishLoad(); + } + API_END(); +} + +int LGBM_DatasetSetWaitForManualFinish(DatasetHandle dataset, int wait) { + API_BEGIN(); + auto p_dataset = reinterpret_cast(dataset); + p_dataset->set_wait_for_manual_finish(wait); + API_END(); +} + +int LGBM_DatasetMarkFinished(DatasetHandle dataset) { + API_BEGIN(); + auto p_dataset = reinterpret_cast(dataset); + p_dataset->FinishLoad(); + API_END(); +} + +int LGBM_DatasetCreateFromMat(const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int is_row_major, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + return LGBM_DatasetCreateFromMats(1, + &data, + data_type, + &nrow, + ncol, + &is_row_major, + parameters, + reference, + out); +} + +int LGBM_DatasetCreateFromMats(int32_t nmat, + const void** data, + int data_type, + int32_t* nrow, + int32_t ncol, + int* is_row_major, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + API_BEGIN(); + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + std::unique_ptr ret; + int32_t total_nrow = 0; + for (int j = 0; j < nmat; ++j) { + total_nrow += nrow[j]; + } + + std::vector(int row_idx)>> get_row_fun; + for (int j = 0; j < nmat; ++j) { + get_row_fun.push_back(RowFunctionFromDenseMatrix(data[j], nrow[j], ncol, data_type, is_row_major[j])); + } + + if (reference == nullptr) { + // sample data first + auto sample_indices = CreateSampleIndices(total_nrow, config); + int sample_cnt = static_cast(sample_indices.size()); + std::vector> sample_values(ncol); + std::vector> sample_idx(ncol); + + int offset = 0; + int j = 0; + for (size_t i = 0; i < sample_indices.size(); ++i) { + auto idx = sample_indices[i]; + while ((idx - offset) >= nrow[j]) { + offset += nrow[j]; + ++j; + } + + auto row = get_row_fun[j](static_cast(idx - offset)); + for (size_t k = 0; k < row.size(); ++k) { + if (std::fabs(row[k]) > kZeroThreshold || std::isnan(row[k])) { + sample_values[k].emplace_back(row[k]); + sample_idx[k].emplace_back(static_cast(i)); + } + } + } + DatasetLoader loader(config, nullptr, 1, nullptr); + ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), + Vector2Ptr(&sample_idx).data(), + ncol, + VectorSize(sample_values).data(), + sample_cnt, + total_nrow, + total_nrow)); + } else { + ret.reset(new Dataset(total_nrow)); + ret->CreateValid( + reinterpret_cast(reference)); + if (ret->has_raw()) { + ret->ResizeRaw(total_nrow); + } + } + int32_t start_row = 0; + for (int j = 0; j < nmat; ++j) { + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < nrow[j]; ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + auto one_row = get_row_fun[j](i); + ret->PushOneRow(tid, start_row + i, one_row); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + start_row += nrow[j]; + } + ret->FinishLoad(); + *out = ret.release(); + API_END(); +} + +int LGBM_DatasetCreateFromCSR(const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + API_BEGIN(); + if (num_col <= 0) { + Log::Fatal("The number of columns should be greater than zero."); + } else if (num_col >= INT32_MAX) { + Log::Fatal("The number of columns should be smaller than INT32_MAX."); + } + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + std::unique_ptr ret; + auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); + int32_t nrow = static_cast(nindptr - 1); + if (reference == nullptr) { + // sample data first + auto sample_indices = CreateSampleIndices(nrow, config); + int sample_cnt = static_cast(sample_indices.size()); + std::vector> sample_values(num_col); + std::vector> sample_idx(num_col); + for (size_t i = 0; i < sample_indices.size(); ++i) { + auto idx = sample_indices[i]; + auto row = get_row_fun(static_cast(idx)); + for (std::pair& inner_data : row) { + CHECK_LT(inner_data.first, num_col); + if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) { + sample_values[inner_data.first].emplace_back(inner_data.second); + sample_idx[inner_data.first].emplace_back(static_cast(i)); + } + } + } + DatasetLoader loader(config, nullptr, 1, nullptr); + ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), + Vector2Ptr(&sample_idx).data(), + static_cast(num_col), + VectorSize(sample_values).data(), + sample_cnt, + nrow, + nrow)); + } else { + ret.reset(new Dataset(nrow)); + ret->CreateValid( + reinterpret_cast(reference)); + if (ret->has_raw()) { + ret->ResizeRaw(nrow); + } + } + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < static_cast(nindptr - 1); ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + auto one_row = get_row_fun(i); + ret->PushOneRow(tid, i, one_row); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + ret->FinishLoad(); + *out = ret.release(); + API_END(); +} + +int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr, + int num_rows, + int64_t num_col, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + API_BEGIN(); + if (num_col <= 0) { + Log::Fatal("The number of columns should be greater than zero."); + } else if (num_col >= INT32_MAX) { + Log::Fatal("The number of columns should be smaller than INT32_MAX."); + } + auto get_row_fun = *static_cast>&)>*>(get_row_funptr); + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + std::unique_ptr ret; + int32_t nrow = num_rows; + if (reference == nullptr) { + // sample data first + auto sample_indices = CreateSampleIndices(nrow, config); + int sample_cnt = static_cast(sample_indices.size()); + std::vector> sample_values(num_col); + std::vector> sample_idx(num_col); + // local buffer to re-use memory + std::vector> buffer; + for (size_t i = 0; i < sample_indices.size(); ++i) { + auto idx = sample_indices[i]; + get_row_fun(static_cast(idx), buffer); + for (std::pair& inner_data : buffer) { + CHECK_LT(inner_data.first, num_col); + if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) { + sample_values[inner_data.first].emplace_back(inner_data.second); + sample_idx[inner_data.first].emplace_back(static_cast(i)); + } + } + } + DatasetLoader loader(config, nullptr, 1, nullptr); + ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), + Vector2Ptr(&sample_idx).data(), + static_cast(num_col), + VectorSize(sample_values).data(), + sample_cnt, + nrow, + nrow)); + } else { + ret.reset(new Dataset(nrow)); + ret->CreateValid( + reinterpret_cast(reference)); + if (ret->has_raw()) { + ret->ResizeRaw(nrow); + } + } + + OMP_INIT_EX(); + std::vector> thread_buffer; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(thread_buffer) + for (int i = 0; i < num_rows; ++i) { + OMP_LOOP_EX_BEGIN(); + { + const int tid = omp_get_thread_num(); + get_row_fun(i, thread_buffer); + ret->PushOneRow(tid, i, thread_buffer); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + ret->FinishLoad(); + *out = ret.release(); + API_END(); +} + +int LGBM_DatasetCreateFromCSC(const void* col_ptr, + int col_ptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t ncol_ptr, + int64_t nelem, + int64_t num_row, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + API_BEGIN(); + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + std::unique_ptr ret; + int32_t nrow = static_cast(num_row); + if (reference == nullptr) { + // sample data first + auto sample_indices = CreateSampleIndices(nrow, config); + int sample_cnt = static_cast(sample_indices.size()); + std::vector> sample_values(ncol_ptr - 1); + std::vector> sample_idx(ncol_ptr - 1); + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < static_cast(sample_values.size()); ++i) { + OMP_LOOP_EX_BEGIN(); + CSC_RowIterator col_it(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, i); + for (int j = 0; j < sample_cnt; j++) { + auto val = col_it.Get(sample_indices[j]); + if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { + sample_values[i].emplace_back(val); + sample_idx[i].emplace_back(j); + } + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + DatasetLoader loader(config, nullptr, 1, nullptr); + ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), + Vector2Ptr(&sample_idx).data(), + static_cast(sample_values.size()), + VectorSize(sample_values).data(), + sample_cnt, + nrow, + nrow)); + } else { + ret.reset(new Dataset(nrow)); + ret->CreateValid( + reinterpret_cast(reference)); + } + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < static_cast(ncol_ptr - 1); ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + int feature_idx = ret->InnerFeatureIndex(i); + if (feature_idx < 0) { + continue; + } + int group = ret->Feature2Group(feature_idx); + int sub_feature = ret->Feature2SubFeature(feature_idx); + CSC_RowIterator col_it(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, i); + auto bin_mapper = ret->FeatureBinMapper(feature_idx); + if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) { + int row_idx = 0; + while (row_idx < nrow) { + auto pair = col_it.NextNonZero(); + row_idx = pair.first; + // no more data + if (row_idx < 0) { + break; + } + ret->PushOneData(tid, row_idx, group, feature_idx, sub_feature, pair.second); + } + } else { + for (int row_idx = 0; row_idx < nrow; ++row_idx) { + auto val = col_it.Get(row_idx); + ret->PushOneData(tid, row_idx, group, feature_idx, sub_feature, val); + } + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + ret->FinishLoad(); + *out = ret.release(); + API_END(); +} + +#ifndef LGB_R_BUILD +void DatasetCreateFromArrowChunkedArray(ArrowChunkedArray& chunked_array, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + + std::unique_ptr ret; + auto chunked_array_view = chunked_array.view(); + + // Initialize the dataset + if (reference == nullptr) { + // If there is no reference dataset, we first sample indices + auto sample_indices = CreateSampleIndices(static_cast(chunked_array.get_length()), config); + auto sample_count = static_cast(sample_indices.size()); + std::vector> sample_values(chunked_array.get_num_fields()); + std::vector> sample_idx(chunked_array.get_num_fields()); + + // Then, we obtain sample values by parallelizing across columns + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int64_t j = 0; j < chunked_array.get_num_fields(); ++j) { + OMP_LOOP_EX_BEGIN(); + + // Values need to be copied from the record batches. + sample_values[j].reserve(sample_indices.size()); + sample_idx[j].reserve(sample_indices.size()); + + // The chunks are iterated over in the inner loop as columns can be treated independently. + chunked_array_view.field(j).visit([&](auto&& visitor) { + int last_idx = 0; + int i = 0; + auto it = visitor.begin(); + for (auto idx : sample_indices) { + std::advance(it, idx - last_idx); + auto v = *it; + if (std::fabs(v) > kZeroThreshold || std::isnan(v)) { + sample_values[j].emplace_back(v); + sample_idx[j].emplace_back(i); + } + last_idx = idx; + i++; + } + }); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + // Finally, we initialize a loader from the sampled values + DatasetLoader loader(config, nullptr, 1, nullptr); + ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(), + Vector2Ptr(&sample_idx).data(), + static_cast(chunked_array.get_num_fields()), + VectorSize(sample_values).data(), + sample_count, + static_cast(chunked_array.get_length()), + static_cast(chunked_array.get_length()))); + } else { + ret.reset(new Dataset(static_cast(chunked_array.get_length()))); + ret->CreateValid(reinterpret_cast(reference)); + if (ret->has_raw()) { + ret->ResizeRaw(static_cast(chunked_array.get_length())); + } + } + + // After sampling and properly initializing all bins, we can add our data to the dataset. Here, + // we parallelize across rows. + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int64_t j = 0; j < chunked_array.get_num_fields(); ++j) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + data_size_t idx = 0; + chunked_array_view.field(j).visit([&](auto&& visitor) { + for (auto it = visitor.begin(), end = visitor.end(); it != end; ++it) { + ret->PushOneValue(tid, idx++, j, *it); + } + }); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + ret->FinishLoad(); + *out = ret.release(); +} + +[[deprecated("Use LGBM_DatasetCreateFromArrowStream instead.")]] +int LGBM_DatasetCreateFromArrow(int64_t n_chunks, + ArrowArray* chunks, + ArrowSchema* schema, + const char* parameters, + const DatasetHandle reference, + DatasetHandle *out) { + API_BEGIN(); + Log::Warning("LGBM_DatasetCreateFromArrow is deprecated. Please use LGBM_DatasetCreateFromArrowStream instead."); + ArrowChunkedArray chunked_array(n_chunks, chunks, schema); + DatasetCreateFromArrowChunkedArray(chunked_array, parameters, reference, out); + API_END(); +} + +int LGBM_DatasetCreateFromArrowStream(ArrowArrayStream* stream, + const char* parameters, + const DatasetHandle reference, + DatasetHandle *out) { + API_BEGIN(); + ArrowChunkedArray chunked_array(stream); + DatasetCreateFromArrowChunkedArray(chunked_array, parameters, reference, out); + API_END(); +} +#endif // LGB_R_BUILD + +int LGBM_DatasetGetSubset( + const DatasetHandle handle, + const int32_t* used_row_indices, + int32_t num_used_row_indices, + const char* parameters, + DatasetHandle* out) { + API_BEGIN(); + auto param = Config::Str2Map(parameters); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + auto full_dataset = reinterpret_cast(handle); + CHECK_GT(num_used_row_indices, 0); + const int32_t lower = 0; + const int32_t upper = full_dataset->num_data() - 1; + CheckElementsIntervalClosed(used_row_indices, lower, upper, num_used_row_indices, "Used indices of subset"); + if (!std::is_sorted(used_row_indices, used_row_indices + num_used_row_indices)) { + Log::Fatal("used_row_indices should be sorted in Subset"); + } + auto ret = std::unique_ptr(new Dataset(num_used_row_indices)); + ret->CopyFeatureMapperFrom(full_dataset); + ret->CopySubrow(full_dataset, used_row_indices, num_used_row_indices, true); + *out = ret.release(); + API_END(); +} + +int LGBM_DatasetSetFeatureNames( + DatasetHandle handle, + const char** feature_names, + int num_feature_names) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + std::vector feature_names_str; + for (int i = 0; i < num_feature_names; ++i) { + feature_names_str.emplace_back(feature_names[i]); + } + dataset->set_feature_names(feature_names_str); + API_END(); +} + +int LGBM_DatasetGetFeatureNames( + DatasetHandle handle, + const int len, + int* num_feature_names, + const size_t buffer_len, + size_t* out_buffer_len, + char** feature_names) { + API_BEGIN(); + *out_buffer_len = 0; + auto dataset = reinterpret_cast(handle); + auto inside_feature_name = dataset->feature_names(); + *num_feature_names = static_cast(inside_feature_name.size()); + for (int i = 0; i < *num_feature_names; ++i) { + if (i < len) { + std::memcpy(feature_names[i], inside_feature_name[i].c_str(), std::min(inside_feature_name[i].size() + 1, buffer_len)); + feature_names[i][buffer_len - 1] = '\0'; + } + *out_buffer_len = std::max(inside_feature_name[i].size() + 1, *out_buffer_len); + } + API_END(); +} + +#ifdef _MSC_VER + #pragma warning(disable : 4702) +#endif +int LGBM_DatasetFree(DatasetHandle handle) { + API_BEGIN(); + delete reinterpret_cast(handle); + API_END(); +} + +int LGBM_DatasetSaveBinary(DatasetHandle handle, + const char* filename) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + dataset->SaveBinaryFile(filename); + API_END(); +} + +int LGBM_DatasetSerializeReferenceToBinary(DatasetHandle handle, + ByteBufferHandle* out, + int32_t* out_len) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + std::unique_ptr ret; + ret.reset(new LightGBM::ByteBuffer()); + dataset->SerializeReference(ret.get()); + *out_len = static_cast(ret->GetSize()); + *out = ret.release(); + API_END(); +} + +int LGBM_DatasetDumpText(DatasetHandle handle, + const char* filename) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + dataset->DumpTextFile(filename); + API_END(); +} + +int LGBM_DatasetSetField(DatasetHandle handle, + const char* field_name, + const void* field_data, + int num_element, + int type) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + bool is_success = false; + if (type == C_API_DTYPE_FLOAT32) { + is_success = dataset->SetFloatField(field_name, reinterpret_cast(field_data), static_cast(num_element)); + } else if (type == C_API_DTYPE_INT32) { + is_success = dataset->SetIntField(field_name, reinterpret_cast(field_data), static_cast(num_element)); + } else if (type == C_API_DTYPE_FLOAT64) { + is_success = dataset->SetDoubleField(field_name, reinterpret_cast(field_data), static_cast(num_element)); + } + if (!is_success) { + Log::Fatal("Input data type error or field not found"); + } + API_END(); +} + +#ifndef LGB_R_BUILD +[[deprecated("Use LGBM_DatasetSetFieldFromArrowStream instead.")]] +int LGBM_DatasetSetFieldFromArrow(DatasetHandle handle, + const char* field_name, + int64_t n_chunks, + ArrowArray* chunks, + ArrowSchema* schema) { + API_BEGIN(); + Log::Warning("LGBM_DatasetSetFieldFromArrow is deprecated. Please use LGBM_DatasetSetFieldFromArrowStream instead."); + auto dataset = reinterpret_cast(handle); + auto is_success = dataset->SetFieldFromArrow(field_name, n_chunks, chunks, schema); + if (!is_success) { + Log::Fatal("Input field is not supported"); + } + API_END(); +} + +int LGBM_DatasetSetFieldFromArrowStream(DatasetHandle handle, + const char* field_name, + ArrowArrayStream* stream) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + auto is_success = dataset->SetFieldFromArrow(field_name, stream); + if (!is_success) { + Log::Fatal("Input field is not supported"); + } + API_END(); +} +#endif // LGB_R_BUILD + +int LGBM_DatasetGetField(DatasetHandle handle, + const char* field_name, + int* out_len, + const void** out_ptr, + int* out_type) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + bool is_success = false; + if (dataset->GetFloatField(field_name, out_len, reinterpret_cast(out_ptr))) { + *out_type = C_API_DTYPE_FLOAT32; + is_success = true; + } else if (dataset->GetIntField(field_name, out_len, reinterpret_cast(out_ptr))) { + *out_type = C_API_DTYPE_INT32; + is_success = true; + } else if (dataset->GetDoubleField(field_name, out_len, reinterpret_cast(out_ptr))) { + *out_type = C_API_DTYPE_FLOAT64; + is_success = true; + } + if (!is_success) { + Log::Fatal("Field not found"); + } + if (*out_ptr == nullptr) { + *out_len = 0; + } + API_END(); +} + +int LGBM_DatasetUpdateParamChecking(const char* old_parameters, const char* new_parameters) { + API_BEGIN(); + auto old_param = Config::Str2Map(old_parameters); + Config old_config; + old_config.Set(old_param); + auto new_param = Config::Str2Map(new_parameters); + Booster::CheckDatasetResetConfig(old_config, new_param); + API_END(); +} + +int LGBM_DatasetGetNumData(DatasetHandle handle, + int* out) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + *out = dataset->num_data(); + API_END(); +} + +int LGBM_DatasetGetNumFeature(DatasetHandle handle, + int* out) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + *out = dataset->num_total_features(); + API_END(); +} + +int LGBM_DatasetGetFeatureNumBin(DatasetHandle handle, + int feature, + int* out) { + API_BEGIN(); + auto dataset = reinterpret_cast(handle); + int num_features = dataset->num_total_features(); + if (feature < 0 || feature >= num_features) { + Log::Fatal("Tried to retrieve number of bins for feature index %d, " + "but the valid feature indices are [0, %d].", feature, num_features - 1); + } + int inner_idx = dataset->InnerFeatureIndex(feature); + if (inner_idx >= 0) { + *out = dataset->FeatureNumBin(inner_idx); + } else { + *out = 0; + } + API_END(); +} + +int LGBM_DatasetAddFeaturesFrom(DatasetHandle target, + DatasetHandle source) { + API_BEGIN(); + auto target_d = reinterpret_cast(target); + auto source_d = reinterpret_cast(source); + target_d->AddFeaturesFrom(source_d); + API_END(); +} + +// ---- start of booster + +int LGBM_BoosterCreate(const DatasetHandle train_data, + const char* parameters, + BoosterHandle* out) { + API_BEGIN(); + const Dataset* p_train_data = reinterpret_cast(train_data); + auto ret = std::unique_ptr(new Booster(p_train_data, parameters)); + *out = ret.release(); + API_END(); +} + +int LGBM_BoosterCreateFromModelfile( + const char* filename, + int* out_num_iterations, + BoosterHandle* out) { + API_BEGIN(); + auto ret = std::unique_ptr(new Booster(filename)); + *out_num_iterations = ret->GetBoosting()->GetCurrentIteration(); + *out = ret.release(); + API_END(); +} + +int LGBM_BoosterLoadModelFromString( + const char* model_str, + int* out_num_iterations, + BoosterHandle* out) { + API_BEGIN(); + auto ret = std::unique_ptr(new Booster(nullptr)); + ret->LoadModelFromString(model_str); + *out_num_iterations = ret->GetBoosting()->GetCurrentIteration(); + *out = ret.release(); + API_END(); +} + +int LGBM_BoosterGetLoadedParam( + BoosterHandle handle, + int64_t buffer_len, + int64_t* out_len, + char* out_str) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + std::string params = ref_booster->GetBoosting()->GetLoadedParam(); + *out_len = static_cast(params.size()) + 1; + if (*out_len <= buffer_len) { + std::memcpy(out_str, params.c_str(), *out_len); + } + API_END(); +} + +#ifdef _MSC_VER + #pragma warning(disable : 4702) +#endif +int LGBM_BoosterFree(BoosterHandle handle) { + API_BEGIN(); + delete reinterpret_cast(handle); + API_END(); +} + +int LGBM_BoosterShuffleModels(BoosterHandle handle, int start_iter, int end_iter) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->ShuffleModels(start_iter, end_iter); + API_END(); +} + +int LGBM_BoosterMerge(BoosterHandle handle, + BoosterHandle other_handle) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + Booster* ref_other_booster = reinterpret_cast(other_handle); + ref_booster->MergeFrom(ref_other_booster); + API_END(); +} + +int LGBM_BoosterAddValidData(BoosterHandle handle, + const DatasetHandle valid_data) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + const Dataset* p_dataset = reinterpret_cast(valid_data); + ref_booster->AddValidData(p_dataset); + API_END(); +} + +int LGBM_BoosterResetTrainingData(BoosterHandle handle, + const DatasetHandle train_data) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + const Dataset* p_dataset = reinterpret_cast(train_data); + ref_booster->ResetTrainingData(p_dataset); + API_END(); +} + +int LGBM_BoosterResetParameter(BoosterHandle handle, const char* parameters) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->ResetConfig(parameters); + API_END(); +} + +int LGBM_BoosterGetNumClasses(BoosterHandle handle, int* out_len) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_len = ref_booster->GetBoosting()->NumberOfClasses(); + API_END(); +} + +int LGBM_BoosterGetLinear(BoosterHandle handle, int* out) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + if (ref_booster->GetBoosting()->IsLinear()) { + *out = 1; + } else { + *out = 0; + } + API_END(); +} + +int LGBM_BoosterRefit(BoosterHandle handle, const int32_t* leaf_preds, int32_t nrow, int32_t ncol) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->Refit(leaf_preds, nrow, ncol); + API_END(); +} + +int LGBM_BoosterUpdateOneIter(BoosterHandle handle, int* produced_empty_tree) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + if (ref_booster->TrainOneIter()) { + *produced_empty_tree = 1; + } else { + *produced_empty_tree = 0; + } + API_END(); +} + +int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle, + const float* grad, + const float* hess, + int* produced_empty_tree) { + API_BEGIN(); + #ifdef SCORE_T_USE_DOUBLE + (void) handle; // UNUSED VARIABLE + (void) grad; // UNUSED VARIABLE + (void) hess; // UNUSED VARIABLE + (void) produced_empty_tree; // UNUSED VARIABLE + Log::Fatal("Don't support custom loss function when SCORE_T_USE_DOUBLE is enabled"); + #else + Booster* ref_booster = reinterpret_cast(handle); + if (ref_booster->TrainOneIter(grad, hess)) { + *produced_empty_tree = 1; + } else { + *produced_empty_tree = 0; + } + #endif + API_END(); +} + +int LGBM_BoosterRollbackOneIter(BoosterHandle handle) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->RollbackOneIter(); + API_END(); +} + +int LGBM_BoosterGetCurrentIteration(BoosterHandle handle, int* out_iteration) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_iteration = ref_booster->GetBoosting()->GetCurrentIteration(); + API_END(); +} + +int LGBM_BoosterNumModelPerIteration(BoosterHandle handle, int* out_tree_per_iteration) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_tree_per_iteration = ref_booster->GetBoosting()->NumModelPerIteration(); + API_END(); +} + +int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, int* out_models) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_models = ref_booster->GetBoosting()->NumberOfTotalModel(); + API_END(); +} + +int LGBM_BoosterGetEvalCounts(BoosterHandle handle, int* out_len) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_len = ref_booster->GetEvalCounts(); + API_END(); +} + +int LGBM_BoosterGetEvalNames(BoosterHandle handle, + const int len, + int* out_len, + const size_t buffer_len, + size_t* out_buffer_len, + char** out_strs) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_len = ref_booster->GetEvalNames(out_strs, len, buffer_len, out_buffer_len); + API_END(); +} + +int LGBM_BoosterGetFeatureNames(BoosterHandle handle, + const int len, + int* out_len, + const size_t buffer_len, + size_t* out_buffer_len, + char** out_strs) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_len = ref_booster->GetFeatureNames(out_strs, len, buffer_len, out_buffer_len); + API_END(); +} + +int LGBM_BoosterGetNumFeature(BoosterHandle handle, int* out_len) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_len = ref_booster->GetBoosting()->MaxFeatureIdx() + 1; + API_END(); +} + +int LGBM_BoosterGetEval(BoosterHandle handle, + int data_idx, + int* out_len, + double* out_results) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + auto boosting = ref_booster->GetBoosting(); + auto result_buf = boosting->GetEvalAt(data_idx); + *out_len = static_cast(result_buf.size()); + for (size_t i = 0; i < result_buf.size(); ++i) { + (out_results)[i] = static_cast(result_buf[i]); + } + API_END(); +} + +int LGBM_BoosterGetNumPredict(BoosterHandle handle, + int data_idx, + int64_t* out_len) { + API_BEGIN(); + auto boosting = reinterpret_cast(handle)->GetBoosting(); + *out_len = boosting->GetNumPredictAt(data_idx); + API_END(); +} + +int LGBM_BoosterGetPredict(BoosterHandle handle, + int data_idx, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->GetPredictAt(data_idx, out_result, out_len); + API_END(); +} + +int LGBM_BoosterPredictForFile(BoosterHandle handle, + const char* data_filename, + int data_has_header, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + const char* result_filename) { + API_BEGIN(); + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->Predict(start_iteration, num_iteration, predict_type, data_filename, data_has_header, + config, result_filename); + API_END(); +} + +int LGBM_BoosterCalcNumPredict(BoosterHandle handle, + int num_row, + int predict_type, + int start_iteration, + int num_iteration, + int64_t* out_len) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_len = static_cast(num_row) * ref_booster->GetBoosting()->NumPredictOneRow(start_iteration, + num_iteration, predict_type == C_API_PREDICT_LEAF_INDEX, predict_type == C_API_PREDICT_CONTRIB); + API_END(); +} + +// Naming: In future versions of LightGBM, public API named around `FastConfig` should be made named around +// `SingleRowPredictor`, because it is specific to single row prediction, and doesn't actually hold only config. +// For now this is kept as `FastConfig` for backwards compatibility. +// At the same time, one should consider removing the old non-fast single row public API that stores its Predictor +// in the Booster, because that will enable removing these Predictors from the Booster, and associated initialization +// code. +int LGBM_FastConfigFree(FastConfigHandle fastConfig) { + API_BEGIN(); + delete reinterpret_cast(fastConfig); + API_END(); +} + +int LGBM_BoosterPredictForCSR(BoosterHandle handle, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + if (num_col <= 0) { + Log::Fatal("The number of columns should be greater than zero."); + } else if (num_col >= INT32_MAX) { + Log::Fatal("The number of columns should be smaller than INT32_MAX."); + } + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + Booster* ref_booster = reinterpret_cast(handle); + auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); + int nrow = static_cast(nindptr - 1); + ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, static_cast(num_col), get_row_fun, + config, out_result, out_len); + API_END(); +} + +int LGBM_BoosterPredictSparseOutput(BoosterHandle handle, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col_or_row, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int matrix_type, + int64_t* out_len, + void** out_indptr, + int32_t** out_indices, + void** out_data) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + if (matrix_type == C_API_MATRIX_TYPE_CSR) { + if (num_col_or_row <= 0) { + Log::Fatal("The number of columns should be greater than zero."); + } else if (num_col_or_row >= INT32_MAX) { + Log::Fatal("The number of columns should be smaller than INT32_MAX."); + } + auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); + int64_t nrow = nindptr - 1; + ref_booster->PredictSparseCSR(start_iteration, num_iteration, predict_type, nrow, static_cast(num_col_or_row), get_row_fun, + config, out_len, out_indptr, indptr_type, out_indices, out_data, data_type); + } else if (matrix_type == C_API_MATRIX_TYPE_CSC) { + int num_threads = OMP_NUM_THREADS(); + int ncol = static_cast(nindptr - 1); + std::vector> iterators(num_threads, std::vector()); + for (int i = 0; i < num_threads; ++i) { + for (int j = 0; j < ncol; ++j) { + iterators[i].emplace_back(indptr, indptr_type, indices, data, data_type, nindptr, nelem, j); + } + } + std::function>(int64_t row_idx)> get_row_fun = + [&iterators, ncol](int64_t i) { + std::vector> one_row; + one_row.reserve(ncol); + const int tid = omp_get_thread_num(); + for (int j = 0; j < ncol; ++j) { + auto val = iterators[tid][j].Get(static_cast(i)); + if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { + one_row.emplace_back(j, val); + } + } + return one_row; + }; + ref_booster->PredictSparseCSC(start_iteration, num_iteration, predict_type, num_col_or_row, ncol, get_row_fun, config, + out_len, out_indptr, indptr_type, out_indices, out_data, data_type); + } else { + Log::Fatal("Unknown matrix type in LGBM_BoosterPredictSparseOutput"); + } + API_END(); +} + +int LGBM_BoosterFreePredictSparse(void* indptr, int32_t* indices, void* data, int indptr_type, int data_type) { + API_BEGIN(); + if (indptr_type == C_API_DTYPE_INT32) { + delete[] reinterpret_cast(indptr); + } else if (indptr_type == C_API_DTYPE_INT64) { + delete[] reinterpret_cast(indptr); + } else { + Log::Fatal("Unknown indptr type in LGBM_BoosterFreePredictSparse"); + } + delete[] indices; + if (data_type == C_API_DTYPE_FLOAT32) { + delete[] reinterpret_cast(data); + } else if (data_type == C_API_DTYPE_FLOAT64) { + delete[] reinterpret_cast(data); + } else { + Log::Fatal("Unknown data type in LGBM_BoosterFreePredictSparse"); + } + API_END(); +} + +int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle, + const void* indptr, + int indptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t nindptr, + int64_t nelem, + int64_t num_col, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + if (num_col <= 0) { + Log::Fatal("The number of columns should be greater than zero."); + } else if (num_col >= INT32_MAX) { + Log::Fatal("The number of columns should be smaller than INT32_MAX."); + } + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + Booster* ref_booster = reinterpret_cast(handle); + auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem); + ref_booster->SetSingleRowPredictorInner(start_iteration, num_iteration, predict_type, config); + ref_booster->PredictSingleRow(predict_type, static_cast(num_col), get_row_fun, config, out_result, out_len); + API_END(); +} + +int LGBM_BoosterPredictForCSRSingleRowFastInit(BoosterHandle handle, + const int predict_type, + const int start_iteration, + const int num_iteration, + const int data_type, + const int64_t num_col, + const char* parameter, + FastConfigHandle *out_fastConfig) { + API_BEGIN(); + if (num_col <= 0) { + Log::Fatal("The number of columns should be greater than zero."); + } else if (num_col >= INT32_MAX) { + Log::Fatal("The number of columns should be smaller than INT32_MAX."); + } + + Booster* ref_booster = reinterpret_cast(handle); + + std::unique_ptr single_row_predictor = + ref_booster->InitSingleRowPredictor(start_iteration, num_iteration, predict_type, data_type, static_cast(num_col), parameter); + + OMP_SET_NUM_THREADS(single_row_predictor->config.num_threads); + + *out_fastConfig = single_row_predictor.release(); + API_END(); +} + +int LGBM_BoosterPredictForCSRSingleRowFast(FastConfigHandle fastConfig_handle, + const void* indptr, + const int indptr_type, + const int32_t* indices, + const void* data, + const int64_t nindptr, + const int64_t nelem, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + SingleRowPredictor *single_row_predictor = reinterpret_cast(fastConfig_handle); + auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, single_row_predictor->data_type, nindptr, nelem); + single_row_predictor->Predict(get_row_fun, out_result, out_len); + API_END(); +} + + +int LGBM_BoosterPredictForCSC(BoosterHandle handle, + const void* col_ptr, + int col_ptr_type, + const int32_t* indices, + const void* data, + int data_type, + int64_t ncol_ptr, + int64_t nelem, + int64_t num_row, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + int num_threads = OMP_NUM_THREADS(); + int ncol = static_cast(ncol_ptr - 1); + std::vector> iterators(num_threads, std::vector()); + for (int i = 0; i < num_threads; ++i) { + for (int j = 0; j < ncol; ++j) { + iterators[i].emplace_back(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, j); + } + } + std::function>(int row_idx)> get_row_fun = + [&iterators, ncol](int i) { + std::vector> one_row; + one_row.reserve(ncol); + const int tid = omp_get_thread_num(); + for (int j = 0; j < ncol; ++j) { + auto val = iterators[tid][j].Get(i); + if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { + one_row.emplace_back(j, val); + } + } + return one_row; + }; + ref_booster->Predict(start_iteration, num_iteration, predict_type, static_cast(num_row), ncol, get_row_fun, config, + out_result, out_len); + API_END(); +} + +int LGBM_BoosterValidateFeatureNames(BoosterHandle handle, + const char** data_names, + int data_num_features) { + API_BEGIN(); + int booster_num_features; + size_t out_buffer_len; + LGBM_BoosterGetFeatureNames(handle, 0, &booster_num_features, 0, &out_buffer_len, nullptr); + if (booster_num_features != data_num_features) { + Log::Fatal("Model was trained on %d features, but got %d input features to predict.", booster_num_features, data_num_features); + } + std::vector> tmp_names(booster_num_features, std::vector(out_buffer_len)); + std::vector booster_names = Vector2Ptr(&tmp_names); + LGBM_BoosterGetFeatureNames(handle, data_num_features, &booster_num_features, out_buffer_len, &out_buffer_len, booster_names.data()); + for (int i = 0; i < booster_num_features; ++i) { + if (strcmp(data_names[i], booster_names[i]) != 0) { + Log::Fatal("Expected '%s' at position %d but found '%s'", booster_names[i], i, data_names[i]); + } + } + API_END(); +} + +int LGBM_BoosterPredictForMat(BoosterHandle handle, + const void* data, + int data_type, + int32_t nrow, + int32_t ncol, + int is_row_major, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + Booster* ref_booster = reinterpret_cast(handle); + auto get_row_fun = RowPairFunctionFromDenseMatrix(data, nrow, ncol, data_type, is_row_major); + ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, + config, out_result, out_len); + API_END(); +} + +int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle, + const void* data, + int data_type, + int32_t ncol, + int is_row_major, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + Booster* ref_booster = reinterpret_cast(handle); + auto get_row_fun = RowPairFunctionFromDenseMatrix(data, 1, ncol, data_type, is_row_major); + ref_booster->SetSingleRowPredictorInner(start_iteration, num_iteration, predict_type, config); + ref_booster->PredictSingleRow(predict_type, ncol, get_row_fun, config, out_result, out_len); + API_END(); +} + +int LGBM_BoosterPredictForMatSingleRowFastInit(BoosterHandle handle, + const int predict_type, + const int start_iteration, + const int num_iteration, + const int data_type, + const int32_t ncol, + const char* parameter, + FastConfigHandle *out_fastConfig) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + + std::unique_ptr single_row_predictor = + ref_booster->InitSingleRowPredictor(predict_type, start_iteration, num_iteration, data_type, ncol, parameter); + + OMP_SET_NUM_THREADS(single_row_predictor->config.num_threads); + + *out_fastConfig = single_row_predictor.release(); + API_END(); +} + +int LGBM_BoosterPredictForMatSingleRowFast(FastConfigHandle fastConfig_handle, + const void* data, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + SingleRowPredictor *single_row_predictor = reinterpret_cast(fastConfig_handle); + // Single row in row-major format: + auto get_row_fun = RowPairFunctionFromDenseMatrix(data, 1, single_row_predictor->num_cols, single_row_predictor->data_type, 1); + single_row_predictor->Predict(get_row_fun, out_result, out_len); + API_END(); +} + + +int LGBM_BoosterPredictForMats(BoosterHandle handle, + const void** data, + int data_type, + int32_t nrow, + int32_t ncol, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + Booster* ref_booster = reinterpret_cast(handle); + auto get_row_fun = RowPairFunctionFromDenseRows(data, ncol, data_type); + ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, out_result, out_len); + API_END(); +} + +#ifndef LGB_R_BUILD +void LGBM_BoosterPredictForArrowChunkedArray(BoosterHandle handle, + ArrowChunkedArray& chunked_array, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + // Apply the configuration + auto param = Config::Str2Map(parameter); + Config config; + config.Set(param); + OMP_SET_NUM_THREADS(config.num_threads); + + // Set up iterators for all fields + auto chunked_array_view = chunked_array.view(); + + // Collect type-erased accessors for all fields to prevent type lookups on every iteration + std::vector> accessors; + accessors.reserve(chunked_array.get_num_fields()); + for (int64_t j = 0; j < chunked_array.get_num_fields(); ++j) { + chunked_array_view.field(j).visit([&](auto&& visitor) { + accessors.emplace_back([visitor](int64_t i) { return visitor.begin()[i]; }); + }); + } + + // Build row function + auto num_columns = chunked_array.get_num_fields(); + auto row_fn = [num_columns, &accessors] (int row_idx) { + std::vector> result; + result.reserve(num_columns); + for (int64_t j = 0; j < num_columns; ++j) { + result.emplace_back(j, accessors[j](row_idx)); + } + return result; + }; + + // Run prediction + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->Predict(start_iteration, + num_iteration, + predict_type, + static_cast(chunked_array.get_length()), + static_cast(chunked_array.get_num_fields()), + row_fn, + config, + out_result, + out_len); +} + +[[deprecated("Use LGBM_BoosterPredictForArrowStream instead.")]] +int LGBM_BoosterPredictForArrow(BoosterHandle handle, + int64_t n_chunks, + ArrowArray* chunks, + ArrowSchema* schema, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + Log::Warning("LGBM_BoosterPredictForArrow is deprecated. Please use LGBM_BoosterPredictForArrowStream instead."); + ArrowChunkedArray chunked_array(n_chunks, chunks, schema); + LGBM_BoosterPredictForArrowChunkedArray( + handle, chunked_array, predict_type, start_iteration, num_iteration, parameter, out_len, out_result); + API_END(); +} + +int LGBM_BoosterPredictForArrowStream(BoosterHandle handle, + ArrowArrayStream* stream, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + API_BEGIN(); + ArrowChunkedArray chunked_array(stream); + LGBM_BoosterPredictForArrowChunkedArray( + handle, chunked_array, predict_type, start_iteration, num_iteration, parameter, out_len, out_result); + API_END(); +} +#endif // LGB_R_BUILD + +int LGBM_BoosterSaveModel(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + const char* filename) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->SaveModelToFile(start_iteration, num_iteration, + feature_importance_type, filename); + API_END(); +} + +int LGBM_BoosterSaveModelToString(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + int64_t buffer_len, + int64_t* out_len, + char* out_str) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + std::string model = ref_booster->SaveModelToString( + start_iteration, num_iteration, feature_importance_type); + *out_len = static_cast(model.size()) + 1; + if (*out_len <= buffer_len) { + std::memcpy(out_str, model.c_str(), *out_len); + } + API_END(); +} + +int LGBM_BoosterDumpModel(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + int64_t buffer_len, + int64_t* out_len, + char* out_str) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + std::string model = ref_booster->DumpModel(start_iteration, num_iteration, + feature_importance_type); + *out_len = static_cast(model.size()) + 1; + if (*out_len <= buffer_len) { + std::memcpy(out_str, model.c_str(), *out_len); + } + API_END(); +} + +int LGBM_BoosterGetLeafValue(BoosterHandle handle, + int tree_idx, + int leaf_idx, + double* out_val) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + *out_val = static_cast(ref_booster->GetLeafValue(tree_idx, leaf_idx)); + API_END(); +} + +int LGBM_BoosterSetLeafValue(BoosterHandle handle, + int tree_idx, + int leaf_idx, + double val) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + ref_booster->SetLeafValue(tree_idx, leaf_idx, val); + API_END(); +} + +int LGBM_BoosterFeatureImportance(BoosterHandle handle, + int num_iteration, + int importance_type, + double* out_results) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + std::vector feature_importances = ref_booster->FeatureImportance(num_iteration, importance_type); + for (size_t i = 0; i < feature_importances.size(); ++i) { + (out_results)[i] = feature_importances[i]; + } + API_END(); +} + +int LGBM_BoosterGetUpperBoundValue(BoosterHandle handle, + double* out_results) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + double max_value = ref_booster->UpperBoundValue(); + *out_results = max_value; + API_END(); +} + +int LGBM_BoosterGetLowerBoundValue(BoosterHandle handle, + double* out_results) { + API_BEGIN(); + Booster* ref_booster = reinterpret_cast(handle); + double min_value = ref_booster->LowerBoundValue(); + *out_results = min_value; + API_END(); +} + +int LGBM_NetworkInit(const char* machines, + int local_listen_port, + int listen_time_out, + int num_machines) { + API_BEGIN(); + Config config; + config.machines = RemoveQuotationSymbol(std::string(machines)); + config.local_listen_port = local_listen_port; + config.num_machines = num_machines; + config.time_out = listen_time_out; + if (num_machines > 1) { + Network::Init(config); + } + API_END(); +} + +int LGBM_NetworkFree() { + API_BEGIN(); + Network::Dispose(); + API_END(); +} + +int LGBM_NetworkInitWithFunctions(int num_machines, int rank, + void* reduce_scatter_ext_fun, + void* allgather_ext_fun) { + API_BEGIN(); + if (num_machines > 1) { + Network::Init(num_machines, rank, (ReduceScatterFunction)reduce_scatter_ext_fun, (AllgatherFunction)allgather_ext_fun); + } + API_END(); +} + +int LGBM_SetMaxThreads(int num_threads) { + API_BEGIN(); + if (num_threads <= 0) { + LGBM_MAX_NUM_THREADS = -1; + } else { + LGBM_MAX_NUM_THREADS = num_threads; + } + API_END(); +} + +int LGBM_GetMaxThreads(int* out) { + API_BEGIN(); + *out = LGBM_MAX_NUM_THREADS; + API_END(); +} + + +// ---- start of some help functions + + +template +std::function(int row_idx)> +RowFunctionFromDenseMatrix_helper(const void* data, int num_row, int num_col, int is_row_major) { + const T* data_ptr = reinterpret_cast(data); + if (is_row_major) { + return [=] (int row_idx) { + std::vector ret(num_col); + auto tmp_ptr = data_ptr + static_cast(num_col) * row_idx; + for (int i = 0; i < num_col; ++i) { + ret[i] = static_cast(*(tmp_ptr + i)); + } + return ret; + }; + } else { + return [=] (int row_idx) { + std::vector ret(num_col); + for (int i = 0; i < num_col; ++i) { + ret[i] = static_cast(*(data_ptr + static_cast(num_row) * i + row_idx)); + } + return ret; + }; + } +} + +std::function(int row_idx)> +RowFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major) { + if (data_type == C_API_DTYPE_FLOAT32) { + return RowFunctionFromDenseMatrix_helper(data, num_row, num_col, is_row_major); + } else if (data_type == C_API_DTYPE_FLOAT64) { + return RowFunctionFromDenseMatrix_helper(data, num_row, num_col, is_row_major); + } + Log::Fatal("Unknown data type in RowFunctionFromDenseMatrix"); + return nullptr; +} + +std::function>(int row_idx)> +RowPairFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major) { + auto inner_function = RowFunctionFromDenseMatrix(data, num_row, num_col, data_type, is_row_major); + if (inner_function != nullptr) { + return [inner_function] (int row_idx) { + auto raw_values = inner_function(row_idx); + std::vector> ret; + ret.reserve(raw_values.size()); + for (int i = 0; i < static_cast(raw_values.size()); ++i) { + if (std::fabs(raw_values[i]) > kZeroThreshold || std::isnan(raw_values[i])) { + ret.emplace_back(i, raw_values[i]); + } + } + return ret; + }; + } + return nullptr; +} + +// data is array of pointers to individual rows +std::function>(int row_idx)> +RowPairFunctionFromDenseRows(const void** data, int num_col, int data_type) { + return [=](int row_idx) { + auto inner_function = RowFunctionFromDenseMatrix(data[row_idx], 1, num_col, data_type, /* is_row_major */ true); + auto raw_values = inner_function(0); + std::vector> ret; + ret.reserve(raw_values.size()); + for (int i = 0; i < static_cast(raw_values.size()); ++i) { + if (std::fabs(raw_values[i]) > kZeroThreshold || std::isnan(raw_values[i])) { + ret.emplace_back(i, raw_values[i]); + } + } + return ret; + }; +} + +template +std::function>(T idx)> +RowFunctionFromCSR_helper(const void* indptr, const int32_t* indices, const void* data) { + const T1* data_ptr = reinterpret_cast(data); + const T2* ptr_indptr = reinterpret_cast(indptr); + return [=] (T idx) { + std::vector> ret; + int64_t start = ptr_indptr[idx]; + int64_t end = ptr_indptr[idx + 1]; + if (end - start > 0) { + ret.reserve(end - start); + } + for (int64_t i = start; i < end; ++i) { + ret.emplace_back(indices[i], data_ptr[i]); + } + return ret; + }; +} + +template +std::function>(T idx)> +RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t , int64_t ) { + if (data_type == C_API_DTYPE_FLOAT32) { + if (indptr_type == C_API_DTYPE_INT32) { + return RowFunctionFromCSR_helper(indptr, indices, data); + } else if (indptr_type == C_API_DTYPE_INT64) { + return RowFunctionFromCSR_helper(indptr, indices, data); + } + } else if (data_type == C_API_DTYPE_FLOAT64) { + if (indptr_type == C_API_DTYPE_INT32) { + return RowFunctionFromCSR_helper(indptr, indices, data); + } else if (indptr_type == C_API_DTYPE_INT64) { + return RowFunctionFromCSR_helper(indptr, indices, data); + } + } + Log::Fatal("Unknown data type in RowFunctionFromCSR"); + return nullptr; +} + + + +template +std::function(int idx)> IterateFunctionFromCSC_helper(const void* col_ptr, const int32_t* indices, const void* data, int col_idx) { + const T1* data_ptr = reinterpret_cast(data); + const T2* ptr_col_ptr = reinterpret_cast(col_ptr); + int64_t start = ptr_col_ptr[col_idx]; + int64_t end = ptr_col_ptr[col_idx + 1]; + return [=] (int offset) { + int64_t i = static_cast(start + offset); + if (i >= end) { + return std::make_pair(-1, 0.0); + } + int idx = static_cast(indices[i]); + double val = static_cast(data_ptr[i]); + return std::make_pair(idx, val); + }; +} + +std::function(int idx)> +IterateFunctionFromCSC(const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t , int col_idx) { + CHECK(col_idx < ncol_ptr && col_idx >= 0); + if (data_type == C_API_DTYPE_FLOAT32) { + if (col_ptr_type == C_API_DTYPE_INT32) { + return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); + } else if (col_ptr_type == C_API_DTYPE_INT64) { + return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); + } + } else if (data_type == C_API_DTYPE_FLOAT64) { + if (col_ptr_type == C_API_DTYPE_INT32) { + return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); + } else if (col_ptr_type == C_API_DTYPE_INT64) { + return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx); + } + } + Log::Fatal("Unknown data type in CSC matrix"); + return nullptr; +} + +CSC_RowIterator::CSC_RowIterator(const void* col_ptr, int col_ptr_type, const int32_t* indices, + const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int col_idx) { + iter_fun_ = IterateFunctionFromCSC(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, col_idx); +} + +double CSC_RowIterator::Get(int idx) { + while (idx > cur_idx_ && !is_end_) { + auto ret = iter_fun_(nonzero_idx_); + if (ret.first < 0) { + is_end_ = true; + break; + } + cur_idx_ = ret.first; + cur_val_ = ret.second; + ++nonzero_idx_; + } + if (idx == cur_idx_) { + return cur_val_; + } else { + return 0.0f; + } +} + +std::pair CSC_RowIterator::NextNonZero() { + if (!is_end_) { + auto ret = iter_fun_(nonzero_idx_); + ++nonzero_idx_; + if (ret.first < 0) { + is_end_ = true; + } + return ret; + } else { + return std::make_pair(-1, 0.0); + } +} diff --git a/src/cuda/cuda_algorithms.cu b/src/cuda/cuda_algorithms.cu new file mode 100644 index 0000000..e33e1f8 --- /dev/null +++ b/src/cuda/cuda_algorithms.cu @@ -0,0 +1,451 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include +#include + +#include + +namespace LightGBM { + +template +__global__ void ShufflePrefixSumGlobalKernel(T* values, size_t len, T* block_prefix_sum_buffer) { + __shared__ T shared_mem_buffer[WARPSIZE]; + const size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + T value = 0; + if (index < len) { + value = values[index]; + } + const T prefix_sum_value = ShufflePrefixSum(value, shared_mem_buffer); + values[index] = prefix_sum_value; + if (threadIdx.x == blockDim.x - 1) { + block_prefix_sum_buffer[blockIdx.x] = prefix_sum_value; + } +} + +template +__global__ void ShufflePrefixSumGlobalReduceBlockKernel(T* block_prefix_sum_buffer, int num_blocks) { + __shared__ T shared_mem_buffer[WARPSIZE]; + const int num_blocks_per_thread = (num_blocks + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 2) / (GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1); + int thread_block_start = threadIdx.x == 0 ? 0 : (threadIdx.x - 1) * num_blocks_per_thread; + int thread_block_end = threadIdx.x == 0 ? 0 : min(thread_block_start + num_blocks_per_thread, num_blocks); + T base = 0; + for (int block_index = thread_block_start; block_index < thread_block_end; ++block_index) { + base += block_prefix_sum_buffer[block_index]; + } + base = ShufflePrefixSum(base, shared_mem_buffer); + thread_block_start = threadIdx.x == blockDim.x - 1 ? 0 : threadIdx.x * num_blocks_per_thread; + thread_block_end = threadIdx.x == blockDim.x - 1 ? 0 : min(thread_block_start + num_blocks_per_thread, num_blocks); + for (int block_index = thread_block_start + 1; block_index < thread_block_end; ++block_index) { + block_prefix_sum_buffer[block_index] += block_prefix_sum_buffer[block_index - 1]; + } + for (int block_index = thread_block_start; block_index < thread_block_end; ++block_index) { + block_prefix_sum_buffer[block_index] += base; + } +} + +template +__global__ void ShufflePrefixSumGlobalAddBase(size_t len, const T* block_prefix_sum_buffer, T* values) { + const T base = blockIdx.x == 0 ? 0 : block_prefix_sum_buffer[blockIdx.x - 1]; + const size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (index < len) { + values[index] += base; + } +} + +template +void ShufflePrefixSumGlobal(T* values, size_t len, T* block_prefix_sum_buffer) { + const int num_blocks = (static_cast(len) + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; + ShufflePrefixSumGlobalKernel<<>>(values, len, block_prefix_sum_buffer); + ShufflePrefixSumGlobalReduceBlockKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_prefix_sum_buffer, num_blocks); + ShufflePrefixSumGlobalAddBase<<>>(len, block_prefix_sum_buffer, values); +} + +template void ShufflePrefixSumGlobal(uint16_t* values, size_t len, uint16_t* block_prefix_sum_buffer); +template void ShufflePrefixSumGlobal(uint32_t* values, size_t len, uint32_t* block_prefix_sum_buffer); +template void ShufflePrefixSumGlobal(uint64_t* values, size_t len, uint64_t* block_prefix_sum_buffer); + +__global__ void BitonicArgSortItemsGlobalKernel(const double* scores, + const int num_queries, + const data_size_t* cuda_query_boundaries, + data_size_t* out_indices) { + const int query_index_start = static_cast(blockIdx.x) * BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE; + const int query_index_end = min(query_index_start + BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE, num_queries); + for (int query_index = query_index_start; query_index < query_index_end; ++query_index) { + const data_size_t query_item_start = cuda_query_boundaries[query_index]; + const data_size_t query_item_end = cuda_query_boundaries[query_index + 1]; + const data_size_t num_items_in_query = query_item_end - query_item_start; + BitonicArgSortDevice(scores + query_item_start, + out_indices + query_item_start, + num_items_in_query); + __syncthreads(); + } +} + +void BitonicArgSortItemsGlobal( + const double* scores, + const int num_queries, + const data_size_t* cuda_query_boundaries, + data_size_t* out_indices) { + const int num_blocks = (num_queries + BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE - 1) / BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE; + BitonicArgSortItemsGlobalKernel<<>>( + scores, num_queries, cuda_query_boundaries, out_indices); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +template +__global__ void BlockReduceSum(T* block_buffer, const data_size_t num_blocks) { + __shared__ T shared_buffer[WARPSIZE]; + T thread_sum = 0; + for (data_size_t block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) { + thread_sum += block_buffer[block_index]; + } + thread_sum = ShuffleReduceSum(thread_sum, shared_buffer, blockDim.x); + if (threadIdx.x == 0) { + block_buffer[0] = thread_sum; + } +} + +template +__global__ void ShuffleReduceSumGlobalKernel(const VAL_T* values, const data_size_t num_value, REDUCE_T* block_buffer) { + __shared__ REDUCE_T shared_buffer[WARPSIZE]; + const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + const REDUCE_T value = (data_index < num_value ? static_cast(values[data_index]) : 0.0f); + const REDUCE_T reduce_value = ShuffleReduceSum(value, shared_buffer, blockDim.x); + if (threadIdx.x == 0) { + block_buffer[blockIdx.x] = reduce_value; + } +} + +template +void ShuffleReduceSumGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer) { + const data_size_t num_value = static_cast(n); + const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; + ShuffleReduceSumGlobalKernel<<>>(values, num_value, block_buffer); + BlockReduceSum<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks); +} + +template void ShuffleReduceSumGlobal(const label_t* values, size_t n, double* block_buffer); +template void ShuffleReduceSumGlobal(const double* values, size_t n, double* block_buffer); + +template +__global__ void ShuffleReduceMinGlobalKernel(const VAL_T* values, const data_size_t num_value, REDUCE_T* block_buffer) { + __shared__ REDUCE_T shared_buffer[WARPSIZE]; + const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + const REDUCE_T value = (data_index < num_value ? static_cast(values[data_index]) : 0.0f); + const REDUCE_T reduce_value = ShuffleReduceMin(value, shared_buffer, blockDim.x); + if (threadIdx.x == 0) { + block_buffer[blockIdx.x] = reduce_value; + } +} + +template +__global__ void ShuffleBlockReduceMin(T* block_buffer, const data_size_t num_blocks) { + __shared__ T shared_buffer[WARPSIZE]; + T thread_min = 0; + for (data_size_t block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) { + const T value = block_buffer[block_index]; + if (value < thread_min) { + thread_min = value; + } + } + thread_min = ShuffleReduceMin(thread_min, shared_buffer, blockDim.x); + if (threadIdx.x == 0) { + block_buffer[0] = thread_min; + } +} + +template +void ShuffleReduceMinGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer) { + const data_size_t num_value = static_cast(n); + const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; + ShuffleReduceMinGlobalKernel<<>>(values, num_value, block_buffer); + ShuffleBlockReduceMin<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks); +} + +template void ShuffleReduceMinGlobal(const label_t* values, size_t n, double* block_buffer); + +template +__global__ void ShuffleReduceDotProdGlobalKernel(const VAL_T* values1, const VAL_T* values2, const data_size_t num_value, REDUCE_T* block_buffer) { + __shared__ REDUCE_T shared_buffer[WARPSIZE]; + const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + const REDUCE_T value1 = (data_index < num_value ? static_cast(values1[data_index]) : 0.0f); + const REDUCE_T value2 = (data_index < num_value ? static_cast(values2[data_index]) : 0.0f); + const REDUCE_T reduce_value = ShuffleReduceSum(value1 * value2, shared_buffer, blockDim.x); + if (threadIdx.x == 0) { + block_buffer[blockIdx.x] = reduce_value; + } +} + +template +void ShuffleReduceDotProdGlobal(const VAL_T* values1, const VAL_T* values2, size_t n, REDUCE_T* block_buffer) { + const data_size_t num_value = static_cast(n); + const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; + ShuffleReduceDotProdGlobalKernel<<>>(values1, values2, num_value, block_buffer); + BlockReduceSum<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks); +} + +template void ShuffleReduceDotProdGlobal(const label_t* values1, const label_t* values2, size_t n, double* block_buffer); + +template +__global__ void GlobalInclusiveArgPrefixSumKernel( + const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, data_size_t num_data) { + __shared__ REDUCE_T shared_buffer[WARPSIZE]; + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + REDUCE_T value = static_cast(data_index < num_data ? in_values[sorted_indices[data_index]] : 0); + __syncthreads(); + value = ShufflePrefixSum(value, shared_buffer); + if (data_index < num_data) { + out_values[data_index] = value; + } + if (threadIdx.x == blockDim.x - 1) { + block_buffer[blockIdx.x + 1] = value; + } +} + +template +__global__ void GlobalInclusivePrefixSumReduceBlockKernel(T* block_buffer, data_size_t num_blocks) { + __shared__ T shared_buffer[WARPSIZE]; + T thread_sum = 0; + const data_size_t num_blocks_per_thread = (num_blocks + static_cast(blockDim.x)) / static_cast(blockDim.x); + const data_size_t thread_start_block_index = static_cast(threadIdx.x) * num_blocks_per_thread; + const data_size_t thread_end_block_index = min(thread_start_block_index + num_blocks_per_thread, num_blocks + 1); + for (data_size_t block_index = thread_start_block_index; block_index < thread_end_block_index; ++block_index) { + thread_sum += block_buffer[block_index]; + } + ShufflePrefixSumExclusive(thread_sum, shared_buffer); + for (data_size_t block_index = thread_start_block_index; block_index < thread_end_block_index; ++block_index) { + block_buffer[block_index] += thread_sum; + } +} + +template +__global__ void GlobalInclusivePrefixSumAddBlockBaseKernel(const T* block_buffer, T* values, data_size_t num_data) { + const T block_sum_base = block_buffer[blockIdx.x]; + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (data_index < num_data) { + values[data_index] += block_sum_base; + } +} + +template +void GlobalInclusiveArgPrefixSum(const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, size_t n) { + const data_size_t num_data = static_cast(n); + const data_size_t num_blocks = (num_data + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE; + GlobalInclusiveArgPrefixSumKernel<<>>( + sorted_indices, in_values, out_values, block_buffer, num_data); + SynchronizeCUDADevice(__FILE__, __LINE__); + GlobalInclusivePrefixSumReduceBlockKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>( + block_buffer, num_blocks); + SynchronizeCUDADevice(__FILE__, __LINE__); + GlobalInclusivePrefixSumAddBlockBaseKernel<<>>( + block_buffer, out_values, num_data); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +template void GlobalInclusiveArgPrefixSum(const data_size_t* sorted_indices, const label_t* in_values, double* out_values, double* block_buffer, size_t n); + +template +__global__ void BitonicArgSortGlobalKernel(const VAL_T* values, INDEX_T* indices, const int num_total_data) { + const int thread_index = static_cast(threadIdx.x); + const int low = static_cast(blockIdx.x * BITONIC_SORT_NUM_ELEMENTS); + const bool outer_ascending = ASCENDING ? (blockIdx.x % 2 == 0) : (blockIdx.x % 2 == 1); + const VAL_T* values_pointer = values + low; + INDEX_T* indices_pointer = indices + low; + const int num_data = min(BITONIC_SORT_NUM_ELEMENTS, num_total_data - low); + __shared__ VAL_T shared_values[BITONIC_SORT_NUM_ELEMENTS]; + __shared__ INDEX_T shared_indices[BITONIC_SORT_NUM_ELEMENTS]; + if (thread_index < num_data) { + shared_values[thread_index] = values_pointer[thread_index]; + shared_indices[thread_index] = static_cast(thread_index + blockIdx.x * blockDim.x); + } + __syncthreads(); + for (int depth = BITONIC_SORT_DEPTH - 1; depth >= 1; --depth) { + const int segment_length = 1 << (BITONIC_SORT_DEPTH - depth); + const int segment_index = thread_index / segment_length; + const bool ascending = outer_ascending ? (segment_index % 2 == 0) : (segment_index % 2 == 1); + const int num_total_segment = (num_data + segment_length - 1) / segment_length; + { + const int inner_depth = depth; + const int inner_segment_length_half = 1 << (BITONIC_SORT_DEPTH - 1 - inner_depth); + const int inner_segment_index_half = thread_index / inner_segment_length_half; + const int offset = ((inner_segment_index_half >> 1) == num_total_segment - 1 && ascending == outer_ascending) ? + (num_total_segment * segment_length - num_data) : 0; + const int segment_start = segment_index * segment_length; + if (inner_segment_index_half % 2 == 0) { + if (thread_index >= offset + segment_start) { + const int index_to_compare = thread_index + inner_segment_length_half - offset; + const INDEX_T this_index = shared_indices[thread_index]; + const INDEX_T other_index = shared_indices[index_to_compare]; + const VAL_T this_value = shared_values[thread_index]; + const VAL_T other_value = shared_values[index_to_compare]; + if (index_to_compare < num_data && (this_value > other_value) == ascending) { + shared_indices[thread_index] = other_index; + shared_indices[index_to_compare] = this_index; + shared_values[thread_index] = other_value; + shared_values[index_to_compare] = this_value; + } + } + } + __syncthreads(); + } + for (int inner_depth = depth + 1; inner_depth < BITONIC_SORT_DEPTH; ++inner_depth) { + const int inner_segment_length_half = 1 << (BITONIC_SORT_DEPTH - 1 - inner_depth); + const int inner_segment_index_half = thread_index / inner_segment_length_half; + if (inner_segment_index_half % 2 == 0) { + const int index_to_compare = thread_index + inner_segment_length_half; + const INDEX_T this_index = shared_indices[thread_index]; + const INDEX_T other_index = shared_indices[index_to_compare]; + const VAL_T this_value = shared_values[thread_index]; + const VAL_T other_value = shared_values[index_to_compare]; + if (index_to_compare < num_data && (this_value > other_value) == ascending) { + shared_indices[thread_index] = other_index; + shared_indices[index_to_compare] = this_index; + shared_values[thread_index] = other_value; + shared_values[index_to_compare] = this_value; + } + } + __syncthreads(); + } + } + if (thread_index < num_data) { + indices_pointer[thread_index] = shared_indices[thread_index]; + } +} + +template +__global__ void BitonicArgSortMergeKernel(const VAL_T* values, INDEX_T* indices, const int segment_length, const int len) { + const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + const int segment_index = thread_index / segment_length; + const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); + __shared__ VAL_T shared_values[BITONIC_SORT_NUM_ELEMENTS]; + __shared__ INDEX_T shared_indices[BITONIC_SORT_NUM_ELEMENTS]; + const int offset = static_cast(blockIdx.x * blockDim.x); + const int local_len = min(BITONIC_SORT_NUM_ELEMENTS, len - offset); + if (thread_index < len) { + const INDEX_T index = indices[thread_index]; + shared_values[threadIdx.x] = values[index]; + shared_indices[threadIdx.x] = index; + } + __syncthreads(); + int half_segment_length = BITONIC_SORT_NUM_ELEMENTS / 2; + while (half_segment_length >= 1) { + const int half_segment_index = static_cast(threadIdx.x) / half_segment_length; + if (half_segment_index % 2 == 0) { + const int index_to_compare = static_cast(threadIdx.x) + half_segment_length; + const INDEX_T this_index = shared_indices[threadIdx.x]; + const INDEX_T other_index = shared_indices[index_to_compare]; + const VAL_T this_value = shared_values[threadIdx.x]; + const VAL_T other_value = shared_values[index_to_compare]; + if (index_to_compare < local_len && ((this_value > other_value) == ascending)) { + shared_indices[threadIdx.x] = other_index; + shared_indices[index_to_compare] = this_index; + shared_values[threadIdx.x] = other_value; + shared_values[index_to_compare] = this_value; + } + } + __syncthreads(); + half_segment_length >>= 1; + } + if (thread_index < len) { + indices[thread_index] = shared_indices[threadIdx.x]; + } +} + +template +__global__ void BitonicArgCompareKernel(const VAL_T* values, INDEX_T* indices, const int half_segment_length, const int outer_segment_length, const int len) { + const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + const int segment_index = thread_index / outer_segment_length; + const int half_segment_index = thread_index / half_segment_length; + const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1); + if (half_segment_index % 2 == 0) { + const int num_total_segment = (len + outer_segment_length - 1) / outer_segment_length; + if (BEGIN && (half_segment_index >> 1) == num_total_segment - 1 && ascending == ASCENDING) { + const int offset = num_total_segment * outer_segment_length - len; + const int segment_start = segment_index * outer_segment_length; + if (thread_index >= offset + segment_start) { + const int index_to_compare = thread_index + half_segment_length - offset; + if (index_to_compare < len) { + const INDEX_T this_index = indices[thread_index]; + const INDEX_T other_index = indices[index_to_compare]; + if ((values[this_index] > values[other_index]) == ascending) { + indices[thread_index] = other_index; + indices[index_to_compare] = this_index; + } + } + } + } else { + const int index_to_compare = thread_index + half_segment_length; + if (index_to_compare < len) { + const INDEX_T this_index = indices[thread_index]; + const INDEX_T other_index = indices[index_to_compare]; + if ((values[this_index] > values[other_index]) == ascending) { + indices[thread_index] = other_index; + indices[index_to_compare] = this_index; + } + } + } + } +} + +template +void BitonicArgSortGlobalHelper(const VAL_T* values, INDEX_T* indices, const size_t len) { + int max_depth = 1; + int len_to_shift = static_cast(len) - 1; + while (len_to_shift > 0) { + ++max_depth; + len_to_shift >>= 1; + } + const int num_blocks = (static_cast(len) + BITONIC_SORT_NUM_ELEMENTS - 1) / BITONIC_SORT_NUM_ELEMENTS; + BitonicArgSortGlobalKernel<<>>(values, indices, static_cast(len)); + SynchronizeCUDADevice(__FILE__, __LINE__); + for (int depth = max_depth - 11; depth >= 1; --depth) { + const int segment_length = (1 << (max_depth - depth)); + int half_segment_length = (segment_length >> 1); + { + BitonicArgCompareKernel<<>>( + values, indices, half_segment_length, segment_length, static_cast(len)); + SynchronizeCUDADevice(__FILE__, __LINE__); + half_segment_length >>= 1; + } + for (int inner_depth = depth + 1; inner_depth <= max_depth - 11; ++inner_depth) { + BitonicArgCompareKernel<<>>( + values, indices, half_segment_length, segment_length, static_cast(len)); + SynchronizeCUDADevice(__FILE__, __LINE__); + half_segment_length >>= 1; + } + BitonicArgSortMergeKernel<<>>( + values, indices, segment_length, static_cast(len)); + SynchronizeCUDADevice(__FILE__, __LINE__); + } +} + +template <> +void BitonicArgSortGlobal(const double* values, data_size_t* indices, const size_t len) { + BitonicArgSortGlobalHelper(values, indices, len); +} + +template <> +void BitonicArgSortGlobal(const double* values, data_size_t* indices, const size_t len) { + BitonicArgSortGlobalHelper(values, indices, len); +} + +template <> +void BitonicArgSortGlobal(const label_t* values, data_size_t* indices, const size_t len) { + BitonicArgSortGlobalHelper(values, indices, len); +} + +template <> +void BitonicArgSortGlobal(const data_size_t* values, int* indices, const size_t len) { + BitonicArgSortGlobalHelper(values, indices, len); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/cuda/cuda_utils.cpp b/src/cuda/cuda_utils.cpp new file mode 100644 index 0000000..56821a5 --- /dev/null +++ b/src/cuda/cuda_utils.cpp @@ -0,0 +1,61 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifdef USE_CUDA + +#include +#include + +namespace LightGBM { + +void SynchronizeCUDADevice(const char* file, const int line) { + gpuAssert(cudaDeviceSynchronize(), file, line); +} + +void SynchronizeCUDAStream(cudaStream_t cuda_stream, const char* file, const int line) { + gpuAssert(cudaStreamSynchronize(cuda_stream), file, line); +} + +void PrintLastCUDAError() { + const char* error_name = cudaGetErrorName(cudaGetLastError()); + Log::Fatal(error_name); +} + +void SetCUDADevice(int gpu_device_id, const char* file, int line) { + int cur_gpu_device_id = 0; + CUDASUCCESS_OR_FATAL_OUTER(cudaGetDevice(&cur_gpu_device_id)); + if (cur_gpu_device_id != gpu_device_id) { + CUDASUCCESS_OR_FATAL_OUTER(cudaSetDevice(gpu_device_id)); + } +} + +int GetCUDADevice(const char* file, int line) { + int cur_gpu_device_id = 0; + CUDASUCCESS_OR_FATAL_OUTER(cudaGetDevice(&cur_gpu_device_id)); + return cur_gpu_device_id; +} + +cudaStream_t CUDAStreamCreate() { + cudaStream_t cuda_stream; + CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream)); + return cuda_stream; +} + +void CUDAStreamDestroy(cudaStream_t cuda_stream) { + CUDASUCCESS_OR_FATAL(cudaStreamDestroy(cuda_stream)); +} + +void NCCLGroupStart() { + NCCLCHECK(ncclGroupStart()); +} + +void NCCLGroupEnd() { + NCCLCHECK(ncclGroupEnd()); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/io/bin.cpp b/src/io/bin.cpp new file mode 100644 index 0000000..07b1f52 --- /dev/null +++ b/src/io/bin.cpp @@ -0,0 +1,1077 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "dense_bin.hpp" +#include "multi_val_dense_bin.hpp" +#include "multi_val_sparse_bin.hpp" +#include "sparse_bin.hpp" + +namespace LightGBM { + +BinMapper::BinMapper(): num_bin_(1), is_trivial_(true), bin_type_(BinType::NumericalBin) { + bin_upper_bound_.clear(); + bin_upper_bound_.push_back(std::numeric_limits::infinity()); +} + +// deep copy function for BinMapper +BinMapper::BinMapper(const BinMapper& other) { + num_bin_ = other.num_bin_; + missing_type_ = other.missing_type_; + is_trivial_ = other.is_trivial_; + sparse_rate_ = other.sparse_rate_; + bin_type_ = other.bin_type_; + if (bin_type_ == BinType::NumericalBin) { + bin_upper_bound_ = other.bin_upper_bound_; + } else { + bin_2_categorical_ = other.bin_2_categorical_; + categorical_2_bin_ = other.categorical_2_bin_; + } + min_val_ = other.min_val_; + max_val_ = other.max_val_; + default_bin_ = other.default_bin_; + most_freq_bin_ = other.most_freq_bin_; +} + +BinMapper::BinMapper(const void* memory) { + CopyFrom(reinterpret_cast(memory)); +} + +BinMapper::~BinMapper() { +} + +bool NeedFilter(const std::vector& cnt_in_bin, int total_cnt, int filter_cnt, BinType bin_type) { + if (bin_type == BinType::NumericalBin) { + int sum_left = 0; + for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) { + sum_left += cnt_in_bin[i]; + if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) { + return false; + } + } + } else { + if (cnt_in_bin.size() <= 2) { + for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) { + int sum_left = cnt_in_bin[i]; + if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) { + return false; + } + } + } else { + return false; + } + } + return true; +} + +std::vector GreedyFindBin(const double* distinct_values, const int* counts, + int num_distinct_values, int max_bin, + size_t total_cnt, int min_data_in_bin) { + std::vector bin_upper_bound; + CHECK_GT(max_bin, 0); + if (num_distinct_values <= max_bin) { + bin_upper_bound.clear(); + int cur_cnt_inbin = 0; + for (int i = 0; i < num_distinct_values - 1; ++i) { + cur_cnt_inbin += counts[i]; + if (cur_cnt_inbin >= min_data_in_bin) { + auto val = Common::GetDoubleUpperBound((distinct_values[i] + distinct_values[i + 1]) / 2.0); + if (bin_upper_bound.empty() || !Common::CheckDoubleEqualOrdered(bin_upper_bound.back(), val)) { + bin_upper_bound.push_back(val); + cur_cnt_inbin = 0; + } + } + } + cur_cnt_inbin += counts[num_distinct_values - 1]; + bin_upper_bound.push_back(std::numeric_limits::infinity()); + } else { + if (min_data_in_bin > 0) { + max_bin = std::min(max_bin, static_cast(total_cnt / min_data_in_bin)); + max_bin = std::max(max_bin, 1); + } + double mean_bin_size = static_cast(total_cnt) / max_bin; + + // mean size for one bin + int rest_bin_cnt = max_bin; + int rest_sample_cnt = static_cast(total_cnt); + std::vector is_big_count_value(num_distinct_values, false); + for (int i = 0; i < num_distinct_values; ++i) { + if (counts[i] >= mean_bin_size) { + is_big_count_value[i] = true; + --rest_bin_cnt; + rest_sample_cnt -= counts[i]; + } + } + mean_bin_size = static_cast(rest_sample_cnt) / rest_bin_cnt; + std::vector upper_bounds(max_bin, std::numeric_limits::infinity()); + std::vector lower_bounds(max_bin, std::numeric_limits::infinity()); + + int bin_cnt = 0; + lower_bounds[bin_cnt] = distinct_values[0]; + int cur_cnt_inbin = 0; + for (int i = 0; i < num_distinct_values - 1; ++i) { + if (!is_big_count_value[i]) { + rest_sample_cnt -= counts[i]; + } + cur_cnt_inbin += counts[i]; + // need a new bin + if (is_big_count_value[i] || cur_cnt_inbin >= mean_bin_size || + (is_big_count_value[i + 1] && cur_cnt_inbin >= std::max(1.0, mean_bin_size * 0.5f))) { + upper_bounds[bin_cnt] = distinct_values[i]; + ++bin_cnt; + lower_bounds[bin_cnt] = distinct_values[i + 1]; + if (bin_cnt >= max_bin - 1) { + break; + } + cur_cnt_inbin = 0; + if (!is_big_count_value[i]) { + --rest_bin_cnt; + mean_bin_size = rest_sample_cnt / static_cast(rest_bin_cnt); + } + } + } + ++bin_cnt; + // update bin upper bound + bin_upper_bound.clear(); + for (int i = 0; i < bin_cnt - 1; ++i) { + auto val = Common::GetDoubleUpperBound((upper_bounds[i] + lower_bounds[i + 1]) / 2.0); + if (bin_upper_bound.empty() || !Common::CheckDoubleEqualOrdered(bin_upper_bound.back(), val)) { + bin_upper_bound.push_back(val); + } + } + // last bin upper bound + bin_upper_bound.push_back(std::numeric_limits::infinity()); + } + return bin_upper_bound; +} + +std::vector FindBinWithPredefinedBin(const double* distinct_values, const int* counts, + int num_distinct_values, int max_bin, + size_t total_sample_cnt, int min_data_in_bin, + const std::vector& forced_upper_bounds) { + std::vector bin_upper_bound; + + // get number of positive and negative distinct values + int left_cnt = -1; + for (int i = 0; i < num_distinct_values; ++i) { + if (distinct_values[i] > -kZeroThreshold) { + left_cnt = i; + break; + } + } + if (left_cnt < 0) { + left_cnt = num_distinct_values; + } + int right_start = -1; + for (int i = left_cnt; i < num_distinct_values; ++i) { + if (distinct_values[i] > kZeroThreshold) { + right_start = i; + break; + } + } + + // include zero bounds and infinity bound + if (max_bin == 2) { + if (left_cnt == 0) { + bin_upper_bound.push_back(kZeroThreshold); + } else { + bin_upper_bound.push_back(-kZeroThreshold); + } + } else if (max_bin >= 3) { + if (left_cnt > 0) { + bin_upper_bound.push_back(-kZeroThreshold); + } + if (right_start >= 0) { + bin_upper_bound.push_back(kZeroThreshold); + } + } + bin_upper_bound.push_back(std::numeric_limits::infinity()); + + // add forced bounds, excluding zeros since we have already added zero bounds + int max_to_insert = max_bin - static_cast(bin_upper_bound.size()); + int num_inserted = 0; + for (size_t i = 0; i < forced_upper_bounds.size(); ++i) { + if (num_inserted >= max_to_insert) { + break; + } + if (std::fabs(forced_upper_bounds[i]) > kZeroThreshold) { + bin_upper_bound.push_back(forced_upper_bounds[i]); + ++num_inserted; + } + } + std::stable_sort(bin_upper_bound.begin(), bin_upper_bound.end()); + + // find remaining bounds + int free_bins = max_bin - static_cast(bin_upper_bound.size()); + std::vector bounds_to_add; + int value_ind = 0; + for (size_t i = 0; i < bin_upper_bound.size(); ++i) { + int cnt_in_bin = 0; + int distinct_cnt_in_bin = 0; + int bin_start = value_ind; + while ((value_ind < num_distinct_values) && (distinct_values[value_ind] < bin_upper_bound[i])) { + cnt_in_bin += counts[value_ind]; + ++distinct_cnt_in_bin; + ++value_ind; + } + int bins_remaining = max_bin - static_cast(bin_upper_bound.size()) - static_cast(bounds_to_add.size()); + int num_sub_bins = static_cast(std::lround((static_cast(cnt_in_bin) * free_bins / total_sample_cnt))); + num_sub_bins = std::min(num_sub_bins, bins_remaining) + 1; + if (i == bin_upper_bound.size() - 1) { + num_sub_bins = bins_remaining + 1; + } + std::vector new_upper_bounds = GreedyFindBin(distinct_values + bin_start, counts + bin_start, distinct_cnt_in_bin, + num_sub_bins, cnt_in_bin, min_data_in_bin); + bounds_to_add.insert(bounds_to_add.end(), new_upper_bounds.begin(), new_upper_bounds.end() - 1); // last bound is infinity + } + bin_upper_bound.insert(bin_upper_bound.end(), bounds_to_add.begin(), bounds_to_add.end()); + std::stable_sort(bin_upper_bound.begin(), bin_upper_bound.end()); + CHECK_LE(bin_upper_bound.size(), static_cast(max_bin)); + return bin_upper_bound; +} + +std::vector FindBinWithZeroAsOneBin(const double* distinct_values, const int* counts, int num_distinct_values, + int max_bin, size_t total_sample_cnt, int min_data_in_bin) { + std::vector bin_upper_bound; + int left_cnt_data = 0; + int cnt_zero = 0; + int right_cnt_data = 0; + for (int i = 0; i < num_distinct_values; ++i) { + if (distinct_values[i] <= -kZeroThreshold) { + left_cnt_data += counts[i]; + } else if (distinct_values[i] > kZeroThreshold) { + right_cnt_data += counts[i]; + } else { + cnt_zero += counts[i]; + } + } + + int left_cnt = -1; + for (int i = 0; i < num_distinct_values; ++i) { + if (distinct_values[i] > -kZeroThreshold) { + left_cnt = i; + break; + } + } + + if (left_cnt < 0) { + left_cnt = num_distinct_values; + } + + if ((left_cnt > 0) && (max_bin > 1)) { + int left_max_bin = static_cast(static_cast(left_cnt_data) / (total_sample_cnt - cnt_zero) * (max_bin - 1)); + left_max_bin = std::max(1, left_max_bin); + bin_upper_bound = GreedyFindBin(distinct_values, counts, left_cnt, left_max_bin, left_cnt_data, min_data_in_bin); + if (bin_upper_bound.size() > 0) { + bin_upper_bound.back() = -kZeroThreshold; + } + } + + int right_start = -1; + for (int i = left_cnt; i < num_distinct_values; ++i) { + if (distinct_values[i] > kZeroThreshold) { + right_start = i; + break; + } + } + + int right_max_bin = max_bin - 1 - static_cast(bin_upper_bound.size()); + if (right_start >= 0 && right_max_bin > 0) { + auto right_bounds = GreedyFindBin(distinct_values + right_start, counts + right_start, + num_distinct_values - right_start, right_max_bin, right_cnt_data, min_data_in_bin); + bin_upper_bound.push_back(kZeroThreshold); + bin_upper_bound.insert(bin_upper_bound.end(), right_bounds.begin(), right_bounds.end()); + } else { + bin_upper_bound.push_back(std::numeric_limits::infinity()); + } + CHECK_LE(bin_upper_bound.size(), static_cast(max_bin)); + return bin_upper_bound; +} + +std::vector FindBinWithZeroAsOneBin(const double* distinct_values, const int* counts, int num_distinct_values, + int max_bin, size_t total_sample_cnt, int min_data_in_bin, + const std::vector& forced_upper_bounds) { + if (forced_upper_bounds.empty()) { + return FindBinWithZeroAsOneBin(distinct_values, counts, num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin); + } else { + return FindBinWithPredefinedBin(distinct_values, counts, num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin, + forced_upper_bounds); + } +} + +void BinMapper::FindBin(double* values, int num_sample_values, size_t total_sample_cnt, + int max_bin, int min_data_in_bin, int min_split_data, bool pre_filter, BinType bin_type, + bool use_missing, bool zero_as_missing, + const std::vector& forced_upper_bounds) { + int na_cnt = 0; + int non_na_cnt = 0; + for (int i = 0; i < num_sample_values; ++i) { + if (!std::isnan(values[i])) { + values[non_na_cnt++] = values[i]; + } + } + if (!use_missing) { + missing_type_ = MissingType::None; + } else if (zero_as_missing) { + missing_type_ = MissingType::Zero; + } else { + if (non_na_cnt == num_sample_values) { + missing_type_ = MissingType::None; + } else { + missing_type_ = MissingType::NaN; + na_cnt = num_sample_values - non_na_cnt; + } + } + num_sample_values = non_na_cnt; + + bin_type_ = bin_type; + default_bin_ = 0; + int zero_cnt = static_cast(total_sample_cnt - num_sample_values - na_cnt); + // find distinct_values first + std::vector distinct_values; + std::vector counts; // count of data points for each distinct feature value. + + std::stable_sort(values, values + num_sample_values); + + // push zero in the front + if (num_sample_values == 0 || (values[0] > 0.0f && zero_cnt > 0)) { + distinct_values.push_back(0.0f); + counts.push_back(zero_cnt); + } + + if (num_sample_values > 0) { + distinct_values.push_back(values[0]); + counts.push_back(1); + } + + for (int i = 1; i < num_sample_values; ++i) { + if (!Common::CheckDoubleEqualOrdered(values[i - 1], values[i])) { + if (values[i - 1] < 0.0f && values[i] > 0.0f) { + distinct_values.push_back(0.0f); + counts.push_back(zero_cnt); + } + distinct_values.push_back(values[i]); + counts.push_back(1); + } else { + // use the large value + distinct_values.back() = values[i]; + ++counts.back(); + } + } + + // push zero in the back + if (num_sample_values > 0 && values[num_sample_values - 1] < 0.0f && zero_cnt > 0) { + distinct_values.push_back(0.0f); + counts.push_back(zero_cnt); + } + min_val_ = distinct_values.front(); + max_val_ = distinct_values.back(); + std::vector cnt_in_bin; // count of data points in each bin. + int num_distinct_values = static_cast(distinct_values.size()); + if (bin_type_ == BinType::NumericalBin) { + if (missing_type_ == MissingType::Zero) { + bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt, + min_data_in_bin, forced_upper_bounds); + if (bin_upper_bound_.size() == 2) { + missing_type_ = MissingType::None; + } + } else if (missing_type_ == MissingType::None) { + bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt, + min_data_in_bin, forced_upper_bounds); + } else { + bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin - 1, total_sample_cnt - na_cnt, + min_data_in_bin, forced_upper_bounds); + bin_upper_bound_.push_back(NaN); + } + num_bin_ = static_cast(bin_upper_bound_.size()); + { + cnt_in_bin.resize(num_bin_, 0); + int i_bin = 0; + for (int i = 0; i < num_distinct_values; ++i) { + while (distinct_values[i] > bin_upper_bound_[i_bin] && i_bin < num_bin_ - 1) { + ++i_bin; + } + cnt_in_bin[i_bin] += counts[i]; + } + if (missing_type_ == MissingType::NaN) { + cnt_in_bin[num_bin_ - 1] = na_cnt; + } + } + CHECK_LE(num_bin_, max_bin); + } else { + // convert to int type first + std::vector distinct_values_int; + std::vector counts_int; + for (size_t i = 0; i < distinct_values.size(); ++i) { + int val = static_cast(distinct_values[i]); + if (val < 0) { + na_cnt += counts[i]; + Log::Warning("Met negative value in categorical features, will convert it to NaN"); + } else { + if (distinct_values_int.empty() || val != distinct_values_int.back()) { + distinct_values_int.push_back(val); + counts_int.push_back(counts[i]); + } else { + counts_int.back() += counts[i]; + } + } + } + int rest_cnt = static_cast(total_sample_cnt - na_cnt); + if (rest_cnt > 0) { + const int SPARSE_RATIO = 100; + if (distinct_values_int.back() / SPARSE_RATIO > static_cast(distinct_values_int.size())) { + Log::Warning("Met categorical feature which contains sparse values. " + "Consider renumbering to consecutive integers started from zero"); + } + // sort by counts in descending order + Common::SortForPair(&counts_int, &distinct_values_int, 0, true); + // will ignore the categorical of small counts + int cut_cnt = static_cast( + Common::RoundInt((total_sample_cnt - na_cnt) * 0.99f)); + size_t cur_cat_idx = 0; // index of current category. + categorical_2_bin_.clear(); + bin_2_categorical_.clear(); + int used_cnt = 0; + int distinct_cnt = static_cast(distinct_values_int.size()); + if (na_cnt > 0) { + ++distinct_cnt; + } + max_bin = std::min(distinct_cnt, max_bin); + cnt_in_bin.clear(); + + // Push the dummy bin for NaN + bin_2_categorical_.push_back(-1); + categorical_2_bin_[-1] = 0; + cnt_in_bin.push_back(0); + num_bin_ = 1; + while (cur_cat_idx < distinct_values_int.size() + && (used_cnt < cut_cnt || num_bin_ < max_bin)) { + if (counts_int[cur_cat_idx] < min_data_in_bin && cur_cat_idx > 1) { + break; + } + bin_2_categorical_.push_back(distinct_values_int[cur_cat_idx]); + categorical_2_bin_[distinct_values_int[cur_cat_idx]] = static_cast(num_bin_); + used_cnt += counts_int[cur_cat_idx]; + cnt_in_bin.push_back(counts_int[cur_cat_idx]); + ++num_bin_; + ++cur_cat_idx; + } + // Use MissingType::None to represent this bin contains all categoricals + if (cur_cat_idx == distinct_values_int.size() && na_cnt == 0) { + missing_type_ = MissingType::None; + } else { + missing_type_ = MissingType::NaN; + } + // fix count of NaN bin + cnt_in_bin[0] = static_cast(total_sample_cnt - used_cnt); + } + } + + // check trivial(num_bin_ == 1) feature + if (num_bin_ <= 1) { + is_trivial_ = true; + } else { + is_trivial_ = false; + } + // check useless bin + if (!is_trivial_ && pre_filter && NeedFilter(cnt_in_bin, static_cast(total_sample_cnt), min_split_data, bin_type_)) { + is_trivial_ = true; + } + + if (!is_trivial_) { + default_bin_ = ValueToBin(0); + most_freq_bin_ = + static_cast(ArrayArgs::ArgMax(cnt_in_bin)); + const double max_sparse_rate = + static_cast(cnt_in_bin[most_freq_bin_]) / total_sample_cnt; + // When most_freq_bin_ != default_bin_, there are some additional data loading costs. + // so use most_freq_bin_ = default_bin_ when there is not so sparse + if (most_freq_bin_ != default_bin_ && max_sparse_rate < kSparseThreshold) { + most_freq_bin_ = default_bin_; + } + sparse_rate_ = + static_cast(cnt_in_bin[most_freq_bin_]) / total_sample_cnt; + } else { + sparse_rate_ = 1.0f; + } +} + +void BinMapper::CopyTo(char * buffer) const { + std::memcpy(buffer, &num_bin_, sizeof(num_bin_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(num_bin_)); + std::memcpy(buffer, &missing_type_, sizeof(missing_type_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(missing_type_)); + std::memcpy(buffer, &is_trivial_, sizeof(is_trivial_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(is_trivial_)); + std::memcpy(buffer, &sparse_rate_, sizeof(sparse_rate_)); + buffer += sizeof(sparse_rate_); + std::memcpy(buffer, &bin_type_, sizeof(bin_type_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(bin_type_)); + std::memcpy(buffer, &min_val_, sizeof(min_val_)); + buffer += sizeof(min_val_); + std::memcpy(buffer, &max_val_, sizeof(max_val_)); + buffer += sizeof(max_val_); + std::memcpy(buffer, &default_bin_, sizeof(default_bin_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(default_bin_)); + std::memcpy(buffer, &most_freq_bin_, sizeof(most_freq_bin_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_)); + if (bin_type_ == BinType::NumericalBin) { + std::memcpy(buffer, bin_upper_bound_.data(), num_bin_ * sizeof(double)); + } else { + std::memcpy(buffer, bin_2_categorical_.data(), num_bin_ * sizeof(int)); + } +} + +void BinMapper::CopyFrom(const char * buffer) { + std::memcpy(&num_bin_, buffer, sizeof(num_bin_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(num_bin_)); + std::memcpy(&missing_type_, buffer, sizeof(missing_type_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(missing_type_)); + std::memcpy(&is_trivial_, buffer, sizeof(is_trivial_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(is_trivial_)); + std::memcpy(&sparse_rate_, buffer, sizeof(sparse_rate_)); + buffer += sizeof(sparse_rate_); + std::memcpy(&bin_type_, buffer, sizeof(bin_type_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(bin_type_)); + std::memcpy(&min_val_, buffer, sizeof(min_val_)); + buffer += sizeof(min_val_); + std::memcpy(&max_val_, buffer, sizeof(max_val_)); + buffer += sizeof(max_val_); + std::memcpy(&default_bin_, buffer, sizeof(default_bin_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(default_bin_)); + std::memcpy(&most_freq_bin_, buffer, sizeof(most_freq_bin_)); + buffer += VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_)); + if (bin_type_ == BinType::NumericalBin) { + bin_upper_bound_ = std::vector(num_bin_); + std::memcpy(bin_upper_bound_.data(), buffer, num_bin_ * sizeof(double)); + } else { + bin_2_categorical_ = std::vector(num_bin_); + std::memcpy(bin_2_categorical_.data(), buffer, num_bin_ * sizeof(int)); + categorical_2_bin_.clear(); + for (int i = 0; i < num_bin_; ++i) { + categorical_2_bin_[bin_2_categorical_[i]] = static_cast(i); + } + } +} + +void BinMapper::SaveBinaryToFile(BinaryWriter* writer) const { + writer->AlignedWrite(&num_bin_, sizeof(num_bin_)); + writer->AlignedWrite(&missing_type_, sizeof(missing_type_)); + writer->AlignedWrite(&is_trivial_, sizeof(is_trivial_)); + writer->Write(&sparse_rate_, sizeof(sparse_rate_)); + writer->AlignedWrite(&bin_type_, sizeof(bin_type_)); + writer->Write(&min_val_, sizeof(min_val_)); + writer->Write(&max_val_, sizeof(max_val_)); + writer->AlignedWrite(&default_bin_, sizeof(default_bin_)); + writer->AlignedWrite(&most_freq_bin_, sizeof(most_freq_bin_)); + if (bin_type_ == BinType::NumericalBin) { + writer->Write(bin_upper_bound_.data(), sizeof(double) * num_bin_); + } else { + writer->Write(bin_2_categorical_.data(), sizeof(int) * num_bin_); + } +} + +size_t BinMapper::SizesInByte() const { + size_t ret = VirtualFileWriter::AlignedSize(sizeof(num_bin_)) + + VirtualFileWriter::AlignedSize(sizeof(missing_type_)) + + VirtualFileWriter::AlignedSize(sizeof(is_trivial_)) + + sizeof(sparse_rate_) + + VirtualFileWriter::AlignedSize(sizeof(bin_type_)) + + sizeof(min_val_) + sizeof(max_val_) + + VirtualFileWriter::AlignedSize(sizeof(default_bin_)) + + VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_)); + if (bin_type_ == BinType::NumericalBin) { + ret += sizeof(double) * num_bin_; + } else { + ret += sizeof(int) * num_bin_; + } + return ret; +} + +template class DenseBin; +template class DenseBin; +template class DenseBin; +template class DenseBin; + +template class SparseBin; +template class SparseBin; +template class SparseBin; + +template class MultiValDenseBin; +template class MultiValDenseBin; +template class MultiValDenseBin; + +Bin* Bin::CreateDenseBin(data_size_t num_data, int num_bin) { + if (num_bin <= 16) { + return new DenseBin(num_data); + } else if (num_bin <= 256) { + return new DenseBin(num_data); + } else if (num_bin <= 65536) { + return new DenseBin(num_data); + } else { + return new DenseBin(num_data); + } +} + +Bin* Bin::CreateSparseBin(data_size_t num_data, int num_bin) { + if (num_bin <= 256) { + return new SparseBin(num_data); + } else if (num_bin <= 65536) { + return new SparseBin(num_data); + } else { + return new SparseBin(num_data); + } +} + +MultiValBin* MultiValBin::CreateMultiValBin(data_size_t num_data, int num_bin, int num_feature, + double sparse_rate, const std::vector& offsets) { + if (sparse_rate >= multi_val_bin_sparse_threshold) { + const double average_element_per_row = (1.0 - sparse_rate) * num_feature; + return CreateMultiValSparseBin(num_data, num_bin, + average_element_per_row); + } else { + return CreateMultiValDenseBin(num_data, num_bin, num_feature, offsets); + } +} + +MultiValBin* MultiValBin::CreateMultiValDenseBin(data_size_t num_data, + int num_bin, + int num_feature, + const std::vector& offsets) { + // calculate max bin of all features to select the int type in MultiValDenseBin + int max_bin = 0; + for (int i = 0; i < static_cast(offsets.size()) - 1; ++i) { + int feature_bin = offsets[i + 1] - offsets[i]; + if (feature_bin > max_bin) { + max_bin = feature_bin; + } + } + if (max_bin <= 256) { + return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); + } else if (max_bin <= 65536) { + return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); + } else { + return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); + } +} + +MultiValBin* MultiValBin::CreateMultiValSparseBin(data_size_t num_data, + int num_bin, + double estimate_element_per_row) { + size_t estimate_total_entries = + static_cast(estimate_element_per_row * 1.1 * num_data); + if (estimate_total_entries <= std::numeric_limits::max()) { + if (num_bin <= 256) { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } else if (num_bin <= 65536) { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } else { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } + } else if (estimate_total_entries <= std::numeric_limits::max()) { + if (num_bin <= 256) { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } else if (num_bin <= 65536) { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } else { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } + } else { + if (num_bin <= 256) { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } else if (num_bin <= 65536) { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } else { + return new MultiValSparseBin( + num_data, num_bin, estimate_element_per_row); + } + } +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int /*num_threads*/) const { + *is_sparse = false; + *bit_type = 8; + bin_iterator->clear(); + return reinterpret_cast(data_.data()); +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int /*num_threads*/) const { + *is_sparse = false; + *bit_type = 16; + bin_iterator->clear(); + return reinterpret_cast(data_.data()); +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int /*num_threads*/) const { + *is_sparse = false; + *bit_type = 32; + bin_iterator->clear(); + return reinterpret_cast(data_.data()); +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int /*num_threads*/) const { + *is_sparse = false; + *bit_type = 4; + bin_iterator->clear(); + return reinterpret_cast(data_.data()); +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + *is_sparse = false; + *bit_type = 8; + *bin_iterator = nullptr; + return reinterpret_cast(data_.data()); +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + *is_sparse = false; + *bit_type = 16; + *bin_iterator = nullptr; + return reinterpret_cast(data_.data()); +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + *is_sparse = false; + *bit_type = 32; + *bin_iterator = nullptr; + return reinterpret_cast(data_.data()); +} + +template <> +const void* DenseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + *is_sparse = false; + *bit_type = 4; + *bin_iterator = nullptr; + return reinterpret_cast(data_.data()); +} + +template <> +const void* SparseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int num_threads) const { + *is_sparse = true; + *bit_type = 8; + for (int thread_index = 0; thread_index < num_threads; ++thread_index) { + bin_iterator->emplace_back(new SparseBinIterator(this, 0)); + } + return nullptr; +} + +template <> +const void* SparseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int num_threads) const { + *is_sparse = true; + *bit_type = 16; + for (int thread_index = 0; thread_index < num_threads; ++thread_index) { + bin_iterator->emplace_back(new SparseBinIterator(this, 0)); + } + return nullptr; +} + +template <> +const void* SparseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int num_threads) const { + *is_sparse = true; + *bit_type = 32; + for (int thread_index = 0; thread_index < num_threads; ++thread_index) { + bin_iterator->emplace_back(new SparseBinIterator(this, 0)); + } + return nullptr; +} + +template <> +const void* SparseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + *is_sparse = true; + *bit_type = 8; + *bin_iterator = new SparseBinIterator(this, 0); + return nullptr; +} + +template <> +const void* SparseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + *is_sparse = true; + *bit_type = 16; + *bin_iterator = new SparseBinIterator(this, 0); + return nullptr; +} + +template <> +const void* SparseBin::GetColWiseData( + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + *is_sparse = true; + *bit_type = 32; + *bin_iterator = new SparseBinIterator(this, 0); + return nullptr; +} + +#ifdef USE_CUDA +template <> +const void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = data_.data(); + *bit_type = 8; + *total_size = static_cast(num_data_) * static_cast(num_feature_); + CHECK_EQ(*total_size, data_.size()); + *is_sparse = false; + *out_data_ptr = nullptr; + *data_ptr_bit_type = 0; + return to_return; +} + +template <> +const void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint16_t* data_ptr = data_.data(); + const uint8_t* to_return = reinterpret_cast(data_ptr); + *bit_type = 16; + *total_size = static_cast(num_data_) * static_cast(num_feature_); + CHECK_EQ(*total_size, data_.size()); + *is_sparse = false; + *out_data_ptr = nullptr; + *data_ptr_bit_type = 0; + return to_return; +} + +template <> +const void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint32_t* data_ptr = data_.data(); + const uint8_t* to_return = reinterpret_cast(data_ptr); + *bit_type = 32; + *total_size = static_cast(num_data_) * static_cast(num_feature_); + CHECK_EQ(*total_size, data_.size()); + *is_sparse = false; + *out_data_ptr = nullptr; + *data_ptr_bit_type = 0; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = data_.data(); + *bit_type = 8; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 16; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = reinterpret_cast(data_.data()); + *bit_type = 16; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 16; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = reinterpret_cast(data_.data()); + *bit_type = 32; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 16; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = data_.data(); + *bit_type = 8; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 32; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = reinterpret_cast(data_.data()); + *bit_type = 16; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 32; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = reinterpret_cast(data_.data()); + *bit_type = 32; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 32; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = data_.data(); + *bit_type = 8; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 64; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = reinterpret_cast(data_.data()); + *bit_type = 16; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 64; + return to_return; +} + +template <> +const void* MultiValSparseBin::GetRowWiseData( + uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const { + const uint8_t* to_return = reinterpret_cast(data_.data()); + *bit_type = 32; + *total_size = data_.size(); + *is_sparse = true; + *out_data_ptr = reinterpret_cast(row_ptr_.data()); + *data_ptr_bit_type = 64; + return to_return; +} + +#endif // USE_CUDA + +} // namespace LightGBM diff --git a/src/io/config.cpp b/src/io/config.cpp new file mode 100644 index 0000000..8a358c9 --- /dev/null +++ b/src/io/config.cpp @@ -0,0 +1,519 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +void Config::KV2Map(std::unordered_map>* params, const char* kv) { + std::vector tmp_strs = Common::Split(kv, '='); + if (tmp_strs.size() == 2 || tmp_strs.size() == 1) { + std::string key = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[0])); + std::string value = ""; + if (tmp_strs.size() == 2) { + value = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[1])); + } + if (key.size() > 0) { + params->operator[](key).emplace_back(value); + } + } else { + Log::Warning("Unknown parameter %s", kv); + } +} + +void GetFirstValueAsInt(const std::unordered_map>& params, std::string key, int* out) { + const auto pair = params.find(key); + if (pair != params.end()) { + auto candidate = pair->second[0].c_str(); + if (!Common::AtoiAndCheck(candidate, out)) { + Log::Fatal("Parameter %s should be of type int, got \"%s\"", key.c_str(), candidate); + } + } +} + +void Config::SetVerbosity(const std::unordered_map>& params) { + int verbosity = 1; + + // if "verbosity" was found in params, prefer that to any other aliases + const auto verbosity_iter = params.find("verbosity"); + if (verbosity_iter != params.end()) { + GetFirstValueAsInt(params, "verbosity", &verbosity); + } else { + // if "verbose" was found in params and "verbosity" was not, use that value + const auto verbose_iter = params.find("verbose"); + if (verbose_iter != params.end()) { + GetFirstValueAsInt(params, "verbose", &verbosity); + } else { + // if "verbosity" and "verbose" were both missing from params, don't modify LightGBM's log level + return; + } + } + + // otherwise, update LightGBM's log level based on the passed-in value + if (verbosity < 0) { + LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Fatal); + } else if (verbosity == 0) { + LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Warning); + } else if (verbosity == 1) { + LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Info); + } else { + LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Debug); + } +} + +void Config::KeepFirstValues(const std::unordered_map>& params, std::unordered_map* out) { + for (auto pair = params.begin(); pair != params.end(); ++pair) { + auto name = pair->first.c_str(); + auto values = pair->second; + out->emplace(name, values[0]); + for (size_t i = 1; i < pair->second.size(); ++i) { + Log::Warning("%s is set=%s, %s=%s will be ignored. Current value: %s=%s", + name, values[0].c_str(), + name, values[i].c_str(), + name, values[0].c_str()); + } + } +} + +std::unordered_map Config::Str2Map(const char* parameters) { + std::unordered_map> all_params; + std::unordered_map params; + auto args = Common::Split(parameters, " \t\n\r"); + for (auto arg : args) { + KV2Map(&all_params, Common::Trim(arg).c_str()); + } + SetVerbosity(all_params); + KeepFirstValues(all_params, ¶ms); + ParameterAlias::KeyAliasTransform(¶ms); + return params; +} + +void GetBoostingType(const std::unordered_map& params, std::string* boosting) { + std::string value; + if (Config::GetString(params, "boosting", &value)) { + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + if (value == std::string("gbdt") || value == std::string("gbrt")) { + *boosting = "gbdt"; + } else if (value == std::string("dart")) { + *boosting = "dart"; + } else if (value == std::string("goss")) { + *boosting = "goss"; + } else if (value == std::string("rf") || value == std::string("random_forest")) { + *boosting = "rf"; + } else { + Log::Fatal("Unknown boosting type %s", value.c_str()); + } + } +} + +void GetDataSampleStrategy(const std::unordered_map& params, std::string* strategy) { + std::string value; + if (Config::GetString(params, "data_sample_strategy", &value)) { + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + if (value == std::string("goss")) { + *strategy = "goss"; + } else if (value == std::string("bagging")) { + *strategy = "bagging"; + } else { + Log::Fatal("Unknown sample strategy %s", value.c_str()); + } + } +} + +void ParseMetrics(const std::string& value, std::vector* out_metric) { + std::unordered_set metric_sets; + out_metric->clear(); + std::vector metrics = Common::Split(value.c_str(), ','); + for (auto& met : metrics) { + auto type = ParseMetricAlias(met); + if (metric_sets.count(type) <= 0) { + out_metric->push_back(type); + metric_sets.insert(type); + } + } +} + +void GetObjectiveType(const std::unordered_map& params, std::string* objective) { + std::string value; + if (Config::GetString(params, "objective", &value)) { + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + *objective = ParseObjectiveAlias(value); + } +} + +void GetMetricType(const std::unordered_map& params, const std::string& objective, std::vector* metric) { + std::string value; + if (Config::GetString(params, "metric", &value)) { + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + ParseMetrics(value, metric); + } + // add names of objective function if not providing metric + if (metric->empty() && value.size() == 0) { + ParseMetrics(objective, metric); + } +} + +void GetTaskType(const std::unordered_map& params, TaskType* task) { + std::string value; + if (Config::GetString(params, "task", &value)) { + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + if (value == std::string("train") || value == std::string("training")) { + *task = TaskType::kTrain; + } else if (value == std::string("predict") || value == std::string("prediction") + || value == std::string("test")) { + *task = TaskType::kPredict; + } else if (value == std::string("convert_model")) { + *task = TaskType::kConvertModel; + } else if (value == std::string("refit") || value == std::string("refit_tree")) { + *task = TaskType::KRefitTree; + } else if (value == std::string("save_binary")) { + *task = TaskType::kSaveBinary; + } else { + Log::Fatal("Unknown task type %s", value.c_str()); + } + } +} + +void GetDeviceType(const std::unordered_map& params, std::string* device_type) { + std::string value; + if (Config::GetString(params, "device_type", &value)) { + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + if (value == std::string("cpu")) { + *device_type = "cpu"; + } else if (value == std::string("gpu")) { + *device_type = "gpu"; + } else if (value == std::string("cuda")) { + *device_type = "cuda"; + } else { + Log::Fatal("Unknown device type %s", value.c_str()); + } + } +} + +void GetTreeLearnerType(const std::unordered_map& params, std::string* tree_learner) { + std::string value; + if (Config::GetString(params, "tree_learner", &value)) { + std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); }); + if (value == std::string("serial")) { + *tree_learner = "serial"; + } else if (value == std::string("feature") || value == std::string("feature_parallel")) { + *tree_learner = "feature"; + } else if (value == std::string("data") || value == std::string("data_parallel")) { + *tree_learner = "data"; + } else if (value == std::string("voting") || value == std::string("voting_parallel")) { + *tree_learner = "voting"; + } else { + Log::Fatal("Unknown tree learner type %s", value.c_str()); + } + } +} + +void Config::GetAucMuWeights() { + if (auc_mu_weights.empty()) { + // equal weights for all classes + auc_mu_weights_matrix = std::vector> (num_class, std::vector(num_class, 1)); + for (size_t i = 0; i < static_cast(num_class); ++i) { + auc_mu_weights_matrix[i][i] = 0; + } + } else { + auc_mu_weights_matrix = std::vector> (num_class, std::vector(num_class, 0)); + if (auc_mu_weights.size() != static_cast(num_class * num_class)) { + Log::Fatal("auc_mu_weights must have %d elements, but found %zu", num_class * num_class, auc_mu_weights.size()); + } + for (size_t i = 0; i < static_cast(num_class); ++i) { + for (size_t j = 0; j < static_cast(num_class); ++j) { + if (i == j) { + auc_mu_weights_matrix[i][j] = 0; + if (std::fabs(auc_mu_weights[i * num_class + j]) > kZeroThreshold) { + Log::Info("AUC-mu matrix must have zeros on diagonal. Overwriting value in position %zu of auc_mu_weights with 0.", i * num_class + j); + } + } else { + if (std::fabs(auc_mu_weights[i * num_class + j]) < kZeroThreshold) { + Log::Fatal("AUC-mu matrix must have non-zero values for non-diagonal entries. Found zero value in position %zu of auc_mu_weights.", i * num_class + j); + } + auc_mu_weights_matrix[i][j] = auc_mu_weights[i * num_class + j]; + } + } + } + } +} + +void Config::GetInteractionConstraints() { + if (interaction_constraints == "") { + interaction_constraints_vector = std::vector>(); + } else { + interaction_constraints_vector = Common::StringToArrayofArrays(interaction_constraints, '[', ']', ','); + } +} + +void Config::Set(const std::unordered_map& params) { + // generate seeds by seed. + if (GetInt(params, "seed", &seed)) { + Random rand(seed); + int int_max = std::numeric_limits::max(); + data_random_seed = static_cast(rand.NextShort(0, int_max)); + bagging_seed = static_cast(rand.NextShort(0, int_max)); + drop_seed = static_cast(rand.NextShort(0, int_max)); + feature_fraction_seed = static_cast(rand.NextShort(0, int_max)); + objective_seed = static_cast(rand.NextShort(0, int_max)); + extra_seed = static_cast(rand.NextShort(0, int_max)); + } + + GetTaskType(params, &task); + GetBoostingType(params, &boosting); + GetDataSampleStrategy(params, &data_sample_strategy); + GetObjectiveType(params, &objective); + GetMetricType(params, objective, &metric); + GetDeviceType(params, &device_type); + if (device_type == std::string("cuda")) { + LGBM_config_::current_device = lgbm_device_cuda; + } + GetTreeLearnerType(params, &tree_learner); + + GetMembersFromString(params); + + GetAucMuWeights(); + + GetInteractionConstraints(); + + // sort eval_at + std::sort(eval_at.begin(), eval_at.end()); + + std::vector new_valid; + for (size_t i = 0; i < valid.size(); ++i) { + if (valid[i] != data) { + // Only push the non-training data + new_valid.push_back(valid[i]); + } else { + is_provide_training_metric = true; + } + } + valid = new_valid; + + if ((task == TaskType::kSaveBinary) && !save_binary) { + Log::Info("save_binary parameter set to true because task is save_binary"); + save_binary = true; + } + + // check for conflicts + CheckParamConflict(params); +} + +bool CheckMultiClassObjective(const std::string& objective) { + return (objective == std::string("multiclass") || objective == std::string("multiclassova")); +} + +void Config::CheckParamConflict(const std::unordered_map& params) { + // check if objective, metric, and num_class match + int num_class_check = num_class; + bool objective_type_multiclass = CheckMultiClassObjective(objective) || (objective == std::string("custom") && num_class_check > 1); + + if (objective_type_multiclass) { + if (num_class_check <= 1) { + Log::Fatal("Number of classes should be specified and greater than 1 for multiclass training"); + } + } else { + if (task == TaskType::kTrain && num_class_check != 1) { + Log::Fatal("Number of classes must be 1 for non-multiclass training"); + } + } + for (std::string metric_type : metric) { + bool metric_type_multiclass = (CheckMultiClassObjective(metric_type) + || metric_type == std::string("multi_logloss") + || metric_type == std::string("multi_error") + || metric_type == std::string("auc_mu") + || (metric_type == std::string("custom") && num_class_check > 1)); + if ((objective_type_multiclass && !metric_type_multiclass) + || (!objective_type_multiclass && metric_type_multiclass)) { + Log::Fatal("Multiclass objective and metrics don't match"); + } + } + + if (num_machines > 1) { + is_parallel = true; + } else { + is_parallel = false; + tree_learner = "serial"; + } + + bool is_single_tree_learner = tree_learner == std::string("serial"); + + if (is_single_tree_learner) { + is_parallel = false; + num_machines = 1; + } + + if (is_single_tree_learner || tree_learner == std::string("feature")) { + is_data_based_parallel = false; + } else if (tree_learner == std::string("data") + || tree_learner == std::string("voting")) { + is_data_based_parallel = true; + if (histogram_pool_size >= 0 + && tree_learner == std::string("data")) { + Log::Warning("Histogram LRU queue was enabled (histogram_pool_size=%f).\n" + "Will disable this to reduce communication costs", + histogram_pool_size); + // Change pool size to -1 (no limit) when using data parallel to reduce communication costs + histogram_pool_size = -1; + } + } + if (is_data_based_parallel) { + if (!forcedsplits_filename.empty()) { + Log::Fatal("Don't support forcedsplits in %s tree learner", + tree_learner.c_str()); + } + } + + // max_depth defaults to -1, so max_depth>0 implies "you explicitly overrode the default" + // + // Changing max_depth while leaving num_leaves at its default (31) can lead to 2 undesirable situations: + // + // * (0 <= max_depth <= 4) it's not possible to produce a tree with 31 leaves + // - this block reduces num_leaves to 2^max_depth + // * (max_depth > 4) 31 leaves is less than a full depth-wise tree, which might lead to underfitting + // - this block warns about that + // ref: https://github.com/lightgbm-org/LightGBM/issues/2898#issuecomment-1002860601 + if (max_depth > 0 && (params.count("num_leaves") == 0 || params.at("num_leaves").empty())) { + double full_num_leaves = std::pow(2, max_depth); + if (full_num_leaves > num_leaves) { + Log::Warning("Provided parameters constrain tree depth (max_depth=%d) without explicitly setting 'num_leaves'. " + "This can lead to underfitting. To resolve this warning, pass 'num_leaves' (<=%.0f) in params. " + "Alternatively, pass (max_depth=-1) and just use 'num_leaves' to constrain model complexity.", + max_depth, + full_num_leaves); + } + + if (full_num_leaves < num_leaves) { + // Fits in an int, and is more restrictive than the current num_leaves + num_leaves = static_cast(full_num_leaves); + } + } + if (device_type == std::string("gpu")) { + // force col-wise for gpu version + force_col_wise = true; + force_row_wise = false; + if (deterministic) { + Log::Warning("Although \"deterministic\" is set, the results ran by GPU may be non-deterministic."); + } + if (use_quantized_grad) { + Log::Warning("Quantized training is not supported by GPU tree learner. Switch to full precision training."); + use_quantized_grad = false; + } + } else if (device_type == std::string("cuda")) { + // force row-wise for cuda version + force_col_wise = false; + force_row_wise = true; + if (deterministic) { + Log::Warning("Although \"deterministic\" is set, the results ran by GPU may be non-deterministic."); + } + } + // linear tree learner must be serial type and run on CPU device + if (linear_tree) { + if (device_type != std::string("cpu") && device_type != std::string("gpu")) { + device_type = "cpu"; + Log::Warning("Linear tree learner only works with CPU and GPU. Falling back to CPU now."); + } + if (tree_learner != std::string("serial")) { + tree_learner = "serial"; + Log::Warning("Linear tree learner must be serial."); + } + if (zero_as_missing) { + Log::Fatal("zero_as_missing must be false when fitting linear trees."); + } + if (objective == std::string("regression_l1")) { + Log::Fatal("Cannot use regression_l1 objective when fitting linear trees."); + } + } + // min_data_in_leaf must be at least 2 if path smoothing is active. This is because when the split is calculated + // the count is calculated using the proportion of hessian in the leaf which is rounded up to nearest int, so it can + // be 1 when there is actually no data in the leaf. In rare cases this can cause a bug because with path smoothing the + // calculated split gain can be positive even with zero gradient and hessian. + if (path_smooth > kEpsilon && min_data_in_leaf < 2) { + min_data_in_leaf = 2; + Log::Warning("min_data_in_leaf has been increased to 2 because this is required when path smoothing is active."); + } + if (is_parallel && (monotone_constraints_method == std::string("intermediate") || monotone_constraints_method == std::string("advanced"))) { + // In distributed mode, local node doesn't have histograms on all features, cannot perform "intermediate" monotone constraints. + Log::Warning("Cannot use \"intermediate\" or \"advanced\" monotone constraints in distributed learning, auto set to \"basic\" method."); + monotone_constraints_method = "basic"; + } + if (feature_fraction_bynode != 1.0 && (monotone_constraints_method == std::string("intermediate") || monotone_constraints_method == std::string("advanced"))) { + // "intermediate" monotone constraints need to recompute splits. If the features are sampled when computing the + // split initially, then the sampling needs to be recorded or done once again, which is currently not supported + Log::Warning("Cannot use \"intermediate\" or \"advanced\" monotone constraints with feature fraction different from 1, auto set monotone constraints to \"basic\" method."); + monotone_constraints_method = "basic"; + } + if (max_depth > 0 && monotone_penalty >= max_depth) { + Log::Warning("Monotone penalty greater than tree depth. Monotone features won't be used."); + } + if (min_data_in_leaf <= 0 && min_sum_hessian_in_leaf <= kEpsilon) { + Log::Warning( + "Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. " + "Will set min_data_in_leaf to 1."); + min_data_in_leaf = 1; + } + if (boosting == std::string("goss")) { + boosting = std::string("gbdt"); + data_sample_strategy = std::string("goss"); + Log::Warning("Found boosting=goss. For backwards compatibility reasons, LightGBM interprets this as boosting=gbdt, data_sample_strategy=goss." + "To suppress this warning, set data_sample_strategy=goss instead."); + } + + if (bagging_by_query && data_sample_strategy != std::string("bagging")) { + Log::Warning("bagging_by_query=true is only compatible with data_sample_strategy=bagging. Setting bagging_by_query=false."); + bagging_by_query = false; + } +} + +std::string Config::ToString() const { + std::stringstream str_buf; + str_buf << "[boosting: " << boosting << "]\n"; + str_buf << "[objective: " << objective << "]\n"; + str_buf << "[metric: " << Common::Join(metric, ",") << "]\n"; + str_buf << "[tree_learner: " << tree_learner << "]\n"; + str_buf << "[device_type: " << device_type << "]\n"; + str_buf << SaveMembersToString(); + return str_buf.str(); +} + +const std::string Config::DumpAliases() { + auto map = Config::parameter2aliases(); + for (auto& pair : map) { + std::sort(pair.second.begin(), pair.second.end(), SortAlias); + } + std::stringstream str_buf; + str_buf << "{\n"; + bool first = true; + for (const auto& pair : map) { + if (first) { + str_buf << " \""; + first = false; + } else { + str_buf << " , \""; + } + str_buf << pair.first << "\": ["; + if (pair.second.size() > 0) { + str_buf << "\"" << CommonC::Join(pair.second, "\", \"") << "\""; + } + str_buf << "]\n"; + } + str_buf << "}\n"; + return str_buf.str(); +} + +} // namespace LightGBM diff --git a/src/io/config_auto.cpp b/src/io/config_auto.cpp new file mode 100644 index 0000000..7273a94 --- /dev/null +++ b/src/io/config_auto.cpp @@ -0,0 +1,1096 @@ +/*! + * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * \note + * This file is auto generated by LightGBM\.ci\parameter-generator.py from LightGBM\include\LightGBM\config.h file. + */ +#include + +#include +#include +#include +#include + +namespace LightGBM { +const std::unordered_map& Config::alias_table() { + static std::unordered_map aliases({ + {"config_file", "config"}, + {"task_type", "task"}, + {"objective_type", "objective"}, + {"app", "objective"}, + {"application", "objective"}, + {"loss", "objective"}, + {"boosting_type", "boosting"}, + {"boost", "boosting"}, + {"train", "data"}, + {"train_data", "data"}, + {"train_data_file", "data"}, + {"data_filename", "data"}, + {"test", "valid"}, + {"valid_data", "valid"}, + {"valid_data_file", "valid"}, + {"test_data", "valid"}, + {"test_data_file", "valid"}, + {"valid_filenames", "valid"}, + {"num_iteration", "num_iterations"}, + {"n_iter", "num_iterations"}, + {"num_tree", "num_iterations"}, + {"num_trees", "num_iterations"}, + {"num_round", "num_iterations"}, + {"num_rounds", "num_iterations"}, + {"nrounds", "num_iterations"}, + {"num_boost_round", "num_iterations"}, + {"n_estimators", "num_iterations"}, + {"max_iter", "num_iterations"}, + {"shrinkage_rate", "learning_rate"}, + {"eta", "learning_rate"}, + {"num_leaf", "num_leaves"}, + {"max_leaves", "num_leaves"}, + {"max_leaf", "num_leaves"}, + {"max_leaf_nodes", "num_leaves"}, + {"tree", "tree_learner"}, + {"tree_type", "tree_learner"}, + {"tree_learner_type", "tree_learner"}, + {"num_thread", "num_threads"}, + {"nthread", "num_threads"}, + {"nthreads", "num_threads"}, + {"n_jobs", "num_threads"}, + {"device", "device_type"}, + {"random_seed", "seed"}, + {"random_state", "seed"}, + {"hist_pool_size", "histogram_pool_size"}, + {"min_data_per_leaf", "min_data_in_leaf"}, + {"min_data", "min_data_in_leaf"}, + {"min_child_samples", "min_data_in_leaf"}, + {"min_samples_leaf", "min_data_in_leaf"}, + {"min_sum_hessian_per_leaf", "min_sum_hessian_in_leaf"}, + {"min_sum_hessian", "min_sum_hessian_in_leaf"}, + {"min_hessian", "min_sum_hessian_in_leaf"}, + {"min_child_weight", "min_sum_hessian_in_leaf"}, + {"sub_row", "bagging_fraction"}, + {"subsample", "bagging_fraction"}, + {"bagging", "bagging_fraction"}, + {"pos_sub_row", "pos_bagging_fraction"}, + {"pos_subsample", "pos_bagging_fraction"}, + {"pos_bagging", "pos_bagging_fraction"}, + {"neg_sub_row", "neg_bagging_fraction"}, + {"neg_subsample", "neg_bagging_fraction"}, + {"neg_bagging", "neg_bagging_fraction"}, + {"subsample_freq", "bagging_freq"}, + {"bagging_fraction_seed", "bagging_seed"}, + {"sub_feature", "feature_fraction"}, + {"colsample_bytree", "feature_fraction"}, + {"sub_feature_bynode", "feature_fraction_bynode"}, + {"colsample_bynode", "feature_fraction_bynode"}, + {"extra_tree", "extra_trees"}, + {"early_stopping_rounds", "early_stopping_round"}, + {"early_stopping", "early_stopping_round"}, + {"n_iter_no_change", "early_stopping_round"}, + {"max_tree_output", "max_delta_step"}, + {"max_leaf_output", "max_delta_step"}, + {"reg_alpha", "lambda_l1"}, + {"l1_regularization", "lambda_l1"}, + {"reg_lambda", "lambda_l2"}, + {"lambda", "lambda_l2"}, + {"l2_regularization", "lambda_l2"}, + {"min_split_gain", "min_gain_to_split"}, + {"rate_drop", "drop_rate"}, + {"topk", "top_k"}, + {"mc", "monotone_constraints"}, + {"monotone_constraint", "monotone_constraints"}, + {"monotonic_cst", "monotone_constraints"}, + {"monotone_constraining_method", "monotone_constraints_method"}, + {"mc_method", "monotone_constraints_method"}, + {"monotone_splits_penalty", "monotone_penalty"}, + {"ms_penalty", "monotone_penalty"}, + {"mc_penalty", "monotone_penalty"}, + {"feature_contrib", "feature_contri"}, + {"fc", "feature_contri"}, + {"fp", "feature_contri"}, + {"feature_penalty", "feature_contri"}, + {"fs", "forcedsplits_filename"}, + {"forced_splits_filename", "forcedsplits_filename"}, + {"forced_splits_file", "forcedsplits_filename"}, + {"forced_splits", "forcedsplits_filename"}, + {"verbose", "verbosity"}, + {"model_input", "input_model"}, + {"model_in", "input_model"}, + {"model_output", "output_model"}, + {"model_out", "output_model"}, + {"save_period", "snapshot_freq"}, + {"linear_trees", "linear_tree"}, + {"max_bins", "max_bin"}, + {"subsample_for_bin", "bin_construct_sample_cnt"}, + {"data_seed", "data_random_seed"}, + {"is_sparse", "is_enable_sparse"}, + {"enable_sparse", "is_enable_sparse"}, + {"sparse", "is_enable_sparse"}, + {"is_enable_bundle", "enable_bundle"}, + {"bundle", "enable_bundle"}, + {"is_pre_partition", "pre_partition"}, + {"two_round_loading", "two_round"}, + {"use_two_round_loading", "two_round"}, + {"has_header", "header"}, + {"label", "label_column"}, + {"weight", "weight_column"}, + {"group", "group_column"}, + {"group_id", "group_column"}, + {"query_column", "group_column"}, + {"query", "group_column"}, + {"query_id", "group_column"}, + {"ignore_feature", "ignore_column"}, + {"blacklist", "ignore_column"}, + {"cat_feature", "categorical_feature"}, + {"categorical_column", "categorical_feature"}, + {"cat_column", "categorical_feature"}, + {"categorical_features", "categorical_feature"}, + {"is_save_binary", "save_binary"}, + {"is_save_binary_file", "save_binary"}, + {"is_predict_raw_score", "predict_raw_score"}, + {"predict_rawscore", "predict_raw_score"}, + {"raw_score", "predict_raw_score"}, + {"is_predict_leaf_index", "predict_leaf_index"}, + {"leaf_index", "predict_leaf_index"}, + {"is_predict_contrib", "predict_contrib"}, + {"contrib", "predict_contrib"}, + {"predict_result", "output_result"}, + {"prediction_result", "output_result"}, + {"predict_name", "output_result"}, + {"prediction_name", "output_result"}, + {"pred_name", "output_result"}, + {"name_pred", "output_result"}, + {"convert_model_file", "convert_model"}, + {"num_classes", "num_class"}, + {"unbalance", "is_unbalance"}, + {"unbalanced_sets", "is_unbalance"}, + {"metrics", "metric"}, + {"metric_types", "metric"}, + {"output_freq", "metric_freq"}, + {"training_metric", "is_provide_training_metric"}, + {"is_training_metric", "is_provide_training_metric"}, + {"train_metric", "is_provide_training_metric"}, + {"ndcg_eval_at", "eval_at"}, + {"ndcg_at", "eval_at"}, + {"map_eval_at", "eval_at"}, + {"map_at", "eval_at"}, + {"num_machine", "num_machines"}, + {"local_port", "local_listen_port"}, + {"port", "local_listen_port"}, + {"machine_list_file", "machine_list_filename"}, + {"machine_list", "machine_list_filename"}, + {"mlist", "machine_list_filename"}, + {"workers", "machines"}, + {"nodes", "machines"}, + }); + return aliases; +} + +const std::unordered_set& Config::parameter_set() { + static std::unordered_set params({ + "config", + "task", + "objective", + "boosting", + "data_sample_strategy", + "data", + "valid", + "num_iterations", + "learning_rate", + "num_leaves", + "tree_learner", + "num_threads", + "device_type", + "seed", + "deterministic", + "force_col_wise", + "force_row_wise", + "histogram_pool_size", + "max_depth", + "min_data_in_leaf", + "min_sum_hessian_in_leaf", + "bagging_fraction", + "pos_bagging_fraction", + "neg_bagging_fraction", + "bagging_freq", + "bagging_seed", + "bagging_by_query", + "feature_fraction", + "feature_fraction_bynode", + "feature_fraction_seed", + "extra_trees", + "extra_seed", + "early_stopping_round", + "early_stopping_min_delta", + "first_metric_only", + "max_delta_step", + "lambda_l1", + "lambda_l2", + "linear_lambda", + "min_gain_to_split", + "drop_rate", + "max_drop", + "skip_drop", + "xgboost_dart_mode", + "uniform_drop", + "drop_seed", + "top_rate", + "other_rate", + "min_data_per_group", + "max_cat_threshold", + "cat_l2", + "cat_smooth", + "max_cat_to_onehot", + "top_k", + "monotone_constraints", + "monotone_constraints_method", + "monotone_penalty", + "feature_contri", + "forcedsplits_filename", + "refit_decay_rate", + "cegb_tradeoff", + "cegb_penalty_split", + "cegb_penalty_feature_lazy", + "cegb_penalty_feature_coupled", + "path_smooth", + "interaction_constraints", + "verbosity", + "input_model", + "output_model", + "saved_feature_importance_type", + "snapshot_freq", + "use_quantized_grad", + "num_grad_quant_bins", + "quant_train_renew_leaf", + "stochastic_rounding", + "linear_tree", + "max_bin", + "max_bin_by_feature", + "min_data_in_bin", + "bin_construct_sample_cnt", + "data_random_seed", + "is_enable_sparse", + "enable_bundle", + "use_missing", + "zero_as_missing", + "feature_pre_filter", + "pre_partition", + "two_round", + "header", + "label_column", + "weight_column", + "group_column", + "ignore_column", + "categorical_feature", + "forcedbins_filename", + "save_binary", + "precise_float_parser", + "parser_config_file", + "start_iteration_predict", + "num_iteration_predict", + "predict_raw_score", + "predict_leaf_index", + "predict_contrib", + "predict_disable_shape_check", + "pred_early_stop", + "pred_early_stop_freq", + "pred_early_stop_margin", + "output_result", + "convert_model_language", + "convert_model", + "objective_seed", + "num_class", + "is_unbalance", + "scale_pos_weight", + "sigmoid", + "boost_from_average", + "reg_sqrt", + "alpha", + "fair_c", + "poisson_max_delta_step", + "tweedie_variance_power", + "lambdarank_truncation_level", + "lambdarank_norm", + "label_gain", + "lambdarank_position_bias_regularization", + "metric", + "metric_freq", + "is_provide_training_metric", + "eval_at", + "multi_error_top_k", + "auc_mu_weights", + "num_machines", + "local_listen_port", + "time_out", + "machine_list_filename", + "machines", + "gpu_platform_id", + "gpu_device_id", + "gpu_device_id_list", + "gpu_use_dp", + "num_gpu", + }); + return params; +} + +void Config::GetMembersFromString(const std::unordered_map& params) { + std::string tmp_str = ""; + GetString(params, "data", &data); + + if (GetString(params, "valid", &tmp_str)) { + valid = Common::Split(tmp_str.c_str(), ','); + } + + GetInt(params, "num_iterations", &num_iterations); + CHECK_GE(num_iterations, 0); + + GetDouble(params, "learning_rate", &learning_rate); + CHECK_GT(learning_rate, 0.0); + + GetInt(params, "num_leaves", &num_leaves); + CHECK_GT(num_leaves, 1); + CHECK_LE(num_leaves, 131072); + + GetInt(params, "num_threads", &num_threads); + + GetBool(params, "deterministic", &deterministic); + + GetBool(params, "force_col_wise", &force_col_wise); + + GetBool(params, "force_row_wise", &force_row_wise); + + GetDouble(params, "histogram_pool_size", &histogram_pool_size); + + GetInt(params, "max_depth", &max_depth); + + GetInt(params, "min_data_in_leaf", &min_data_in_leaf); + CHECK_GE(min_data_in_leaf, 0); + + GetDouble(params, "min_sum_hessian_in_leaf", &min_sum_hessian_in_leaf); + CHECK_GE(min_sum_hessian_in_leaf, 0.0); + + GetDouble(params, "bagging_fraction", &bagging_fraction); + CHECK_GT(bagging_fraction, 0.0); + CHECK_LE(bagging_fraction, 1.0); + + GetDouble(params, "pos_bagging_fraction", &pos_bagging_fraction); + CHECK_GT(pos_bagging_fraction, 0.0); + CHECK_LE(pos_bagging_fraction, 1.0); + + GetDouble(params, "neg_bagging_fraction", &neg_bagging_fraction); + CHECK_GT(neg_bagging_fraction, 0.0); + CHECK_LE(neg_bagging_fraction, 1.0); + + GetInt(params, "bagging_freq", &bagging_freq); + + GetInt(params, "bagging_seed", &bagging_seed); + + GetBool(params, "bagging_by_query", &bagging_by_query); + + GetDouble(params, "feature_fraction", &feature_fraction); + CHECK_GT(feature_fraction, 0.0); + CHECK_LE(feature_fraction, 1.0); + + GetDouble(params, "feature_fraction_bynode", &feature_fraction_bynode); + CHECK_GT(feature_fraction_bynode, 0.0); + CHECK_LE(feature_fraction_bynode, 1.0); + + GetInt(params, "feature_fraction_seed", &feature_fraction_seed); + + GetBool(params, "extra_trees", &extra_trees); + + GetInt(params, "extra_seed", &extra_seed); + + GetInt(params, "early_stopping_round", &early_stopping_round); + + GetDouble(params, "early_stopping_min_delta", &early_stopping_min_delta); + CHECK_GE(early_stopping_min_delta, 0.0); + + GetBool(params, "first_metric_only", &first_metric_only); + + GetDouble(params, "max_delta_step", &max_delta_step); + + GetDouble(params, "lambda_l1", &lambda_l1); + CHECK_GE(lambda_l1, 0.0); + + GetDouble(params, "lambda_l2", &lambda_l2); + CHECK_GE(lambda_l2, 0.0); + + GetDouble(params, "linear_lambda", &linear_lambda); + CHECK_GE(linear_lambda, 0.0); + + GetDouble(params, "min_gain_to_split", &min_gain_to_split); + CHECK_GE(min_gain_to_split, 0.0); + + GetDouble(params, "drop_rate", &drop_rate); + CHECK_GE(drop_rate, 0.0); + CHECK_LE(drop_rate, 1.0); + + GetInt(params, "max_drop", &max_drop); + + GetDouble(params, "skip_drop", &skip_drop); + CHECK_GE(skip_drop, 0.0); + CHECK_LE(skip_drop, 1.0); + + GetBool(params, "xgboost_dart_mode", &xgboost_dart_mode); + + GetBool(params, "uniform_drop", &uniform_drop); + + GetInt(params, "drop_seed", &drop_seed); + + GetDouble(params, "top_rate", &top_rate); + CHECK_GE(top_rate, 0.0); + CHECK_LE(top_rate, 1.0); + + GetDouble(params, "other_rate", &other_rate); + CHECK_GE(other_rate, 0.0); + CHECK_LE(other_rate, 1.0); + + GetInt(params, "min_data_per_group", &min_data_per_group); + CHECK_GT(min_data_per_group, 0); + + GetInt(params, "max_cat_threshold", &max_cat_threshold); + CHECK_GT(max_cat_threshold, 0); + + GetDouble(params, "cat_l2", &cat_l2); + CHECK_GE(cat_l2, 0.0); + + GetDouble(params, "cat_smooth", &cat_smooth); + CHECK_GE(cat_smooth, 0.0); + + GetInt(params, "max_cat_to_onehot", &max_cat_to_onehot); + CHECK_GT(max_cat_to_onehot, 0); + + GetInt(params, "top_k", &top_k); + CHECK_GT(top_k, 0); + + if (GetString(params, "monotone_constraints", &tmp_str)) { + monotone_constraints = Common::StringToArray(tmp_str, ','); + } + + GetString(params, "monotone_constraints_method", &monotone_constraints_method); + + GetDouble(params, "monotone_penalty", &monotone_penalty); + CHECK_GE(monotone_penalty, 0.0); + + if (GetString(params, "feature_contri", &tmp_str)) { + feature_contri = Common::StringToArray(tmp_str, ','); + } + + GetString(params, "forcedsplits_filename", &forcedsplits_filename); + + GetDouble(params, "refit_decay_rate", &refit_decay_rate); + CHECK_GE(refit_decay_rate, 0.0); + CHECK_LE(refit_decay_rate, 1.0); + + GetDouble(params, "cegb_tradeoff", &cegb_tradeoff); + CHECK_GE(cegb_tradeoff, 0.0); + + GetDouble(params, "cegb_penalty_split", &cegb_penalty_split); + CHECK_GE(cegb_penalty_split, 0.0); + + if (GetString(params, "cegb_penalty_feature_lazy", &tmp_str)) { + cegb_penalty_feature_lazy = Common::StringToArray(tmp_str, ','); + } + + if (GetString(params, "cegb_penalty_feature_coupled", &tmp_str)) { + cegb_penalty_feature_coupled = Common::StringToArray(tmp_str, ','); + } + + GetDouble(params, "path_smooth", &path_smooth); + CHECK_GE(path_smooth, 0.0); + + GetString(params, "interaction_constraints", &interaction_constraints); + + GetInt(params, "verbosity", &verbosity); + + GetString(params, "input_model", &input_model); + + GetString(params, "output_model", &output_model); + + GetInt(params, "saved_feature_importance_type", &saved_feature_importance_type); + + GetInt(params, "snapshot_freq", &snapshot_freq); + + GetBool(params, "use_quantized_grad", &use_quantized_grad); + + GetInt(params, "num_grad_quant_bins", &num_grad_quant_bins); + + GetBool(params, "quant_train_renew_leaf", &quant_train_renew_leaf); + + GetBool(params, "stochastic_rounding", &stochastic_rounding); + + GetBool(params, "linear_tree", &linear_tree); + + GetInt(params, "max_bin", &max_bin); + CHECK_GT(max_bin, 1); + + if (GetString(params, "max_bin_by_feature", &tmp_str)) { + max_bin_by_feature = Common::StringToArray(tmp_str, ','); + } + + GetInt(params, "min_data_in_bin", &min_data_in_bin); + CHECK_GT(min_data_in_bin, 0); + + GetInt(params, "bin_construct_sample_cnt", &bin_construct_sample_cnt); + CHECK_GT(bin_construct_sample_cnt, 0); + + GetInt(params, "data_random_seed", &data_random_seed); + + GetBool(params, "is_enable_sparse", &is_enable_sparse); + + GetBool(params, "enable_bundle", &enable_bundle); + + GetBool(params, "use_missing", &use_missing); + + GetBool(params, "zero_as_missing", &zero_as_missing); + + GetBool(params, "feature_pre_filter", &feature_pre_filter); + + GetBool(params, "pre_partition", &pre_partition); + + GetBool(params, "two_round", &two_round); + + GetBool(params, "header", &header); + + GetString(params, "label_column", &label_column); + + GetString(params, "weight_column", &weight_column); + + GetString(params, "group_column", &group_column); + + GetString(params, "ignore_column", &ignore_column); + + GetString(params, "categorical_feature", &categorical_feature); + + GetString(params, "forcedbins_filename", &forcedbins_filename); + + GetBool(params, "save_binary", &save_binary); + + GetBool(params, "precise_float_parser", &precise_float_parser); + + GetString(params, "parser_config_file", &parser_config_file); + + GetInt(params, "start_iteration_predict", &start_iteration_predict); + + GetInt(params, "num_iteration_predict", &num_iteration_predict); + + GetBool(params, "predict_raw_score", &predict_raw_score); + + GetBool(params, "predict_leaf_index", &predict_leaf_index); + + GetBool(params, "predict_contrib", &predict_contrib); + + GetBool(params, "predict_disable_shape_check", &predict_disable_shape_check); + + GetBool(params, "pred_early_stop", &pred_early_stop); + + GetInt(params, "pred_early_stop_freq", &pred_early_stop_freq); + + GetDouble(params, "pred_early_stop_margin", &pred_early_stop_margin); + + GetString(params, "output_result", &output_result); + + GetString(params, "convert_model_language", &convert_model_language); + + GetString(params, "convert_model", &convert_model); + + GetInt(params, "objective_seed", &objective_seed); + + GetInt(params, "num_class", &num_class); + CHECK_GT(num_class, 0); + + GetBool(params, "is_unbalance", &is_unbalance); + + GetDouble(params, "scale_pos_weight", &scale_pos_weight); + CHECK_GT(scale_pos_weight, 0.0); + + GetDouble(params, "sigmoid", &sigmoid); + CHECK_GT(sigmoid, 0.0); + + GetBool(params, "boost_from_average", &boost_from_average); + + GetBool(params, "reg_sqrt", ®_sqrt); + + GetDouble(params, "alpha", &alpha); + CHECK_GT(alpha, 0.0); + + GetDouble(params, "fair_c", &fair_c); + CHECK_GT(fair_c, 0.0); + + GetDouble(params, "poisson_max_delta_step", &poisson_max_delta_step); + CHECK_GT(poisson_max_delta_step, 0.0); + + GetDouble(params, "tweedie_variance_power", &tweedie_variance_power); + CHECK_GE(tweedie_variance_power, 1.0); + CHECK_LT(tweedie_variance_power, 2.0); + + GetInt(params, "lambdarank_truncation_level", &lambdarank_truncation_level); + CHECK_GT(lambdarank_truncation_level, 0); + + GetBool(params, "lambdarank_norm", &lambdarank_norm); + + if (GetString(params, "label_gain", &tmp_str)) { + label_gain = Common::StringToArray(tmp_str, ','); + } + + GetDouble(params, "lambdarank_position_bias_regularization", &lambdarank_position_bias_regularization); + CHECK_GE(lambdarank_position_bias_regularization, 0.0); + + GetInt(params, "metric_freq", &metric_freq); + CHECK_GT(metric_freq, 0); + + GetBool(params, "is_provide_training_metric", &is_provide_training_metric); + + if (GetString(params, "eval_at", &tmp_str)) { + eval_at = Common::StringToArray(tmp_str, ','); + } + + GetInt(params, "multi_error_top_k", &multi_error_top_k); + CHECK_GT(multi_error_top_k, 0); + + if (GetString(params, "auc_mu_weights", &tmp_str)) { + auc_mu_weights = Common::StringToArray(tmp_str, ','); + } + + GetInt(params, "num_machines", &num_machines); + CHECK_GT(num_machines, 0); + + GetInt(params, "local_listen_port", &local_listen_port); + CHECK_GT(local_listen_port, 0); + + GetInt(params, "time_out", &time_out); + CHECK_GT(time_out, 0); + + GetString(params, "machine_list_filename", &machine_list_filename); + + GetString(params, "machines", &machines); + + GetInt(params, "gpu_platform_id", &gpu_platform_id); + + GetInt(params, "gpu_device_id", &gpu_device_id); + + GetString(params, "gpu_device_id_list", &gpu_device_id_list); + + GetBool(params, "gpu_use_dp", &gpu_use_dp); + + GetInt(params, "num_gpu", &num_gpu); + CHECK_GT(num_gpu, 0); +} + +std::string Config::SaveMembersToString() const { + std::stringstream str_buf; + str_buf << "[data_sample_strategy: " << data_sample_strategy << "]\n"; + str_buf << "[data: " << data << "]\n"; + str_buf << "[valid: " << Common::Join(valid, ",") << "]\n"; + str_buf << "[num_iterations: " << num_iterations << "]\n"; + str_buf << "[learning_rate: " << learning_rate << "]\n"; + str_buf << "[num_leaves: " << num_leaves << "]\n"; + str_buf << "[num_threads: " << num_threads << "]\n"; + str_buf << "[seed: " << seed << "]\n"; + str_buf << "[deterministic: " << deterministic << "]\n"; + str_buf << "[force_col_wise: " << force_col_wise << "]\n"; + str_buf << "[force_row_wise: " << force_row_wise << "]\n"; + str_buf << "[histogram_pool_size: " << histogram_pool_size << "]\n"; + str_buf << "[max_depth: " << max_depth << "]\n"; + str_buf << "[min_data_in_leaf: " << min_data_in_leaf << "]\n"; + str_buf << "[min_sum_hessian_in_leaf: " << min_sum_hessian_in_leaf << "]\n"; + str_buf << "[bagging_fraction: " << bagging_fraction << "]\n"; + str_buf << "[pos_bagging_fraction: " << pos_bagging_fraction << "]\n"; + str_buf << "[neg_bagging_fraction: " << neg_bagging_fraction << "]\n"; + str_buf << "[bagging_freq: " << bagging_freq << "]\n"; + str_buf << "[bagging_seed: " << bagging_seed << "]\n"; + str_buf << "[bagging_by_query: " << bagging_by_query << "]\n"; + str_buf << "[feature_fraction: " << feature_fraction << "]\n"; + str_buf << "[feature_fraction_bynode: " << feature_fraction_bynode << "]\n"; + str_buf << "[feature_fraction_seed: " << feature_fraction_seed << "]\n"; + str_buf << "[extra_trees: " << extra_trees << "]\n"; + str_buf << "[extra_seed: " << extra_seed << "]\n"; + str_buf << "[early_stopping_round: " << early_stopping_round << "]\n"; + str_buf << "[early_stopping_min_delta: " << early_stopping_min_delta << "]\n"; + str_buf << "[first_metric_only: " << first_metric_only << "]\n"; + str_buf << "[max_delta_step: " << max_delta_step << "]\n"; + str_buf << "[lambda_l1: " << lambda_l1 << "]\n"; + str_buf << "[lambda_l2: " << lambda_l2 << "]\n"; + str_buf << "[linear_lambda: " << linear_lambda << "]\n"; + str_buf << "[min_gain_to_split: " << min_gain_to_split << "]\n"; + str_buf << "[drop_rate: " << drop_rate << "]\n"; + str_buf << "[max_drop: " << max_drop << "]\n"; + str_buf << "[skip_drop: " << skip_drop << "]\n"; + str_buf << "[xgboost_dart_mode: " << xgboost_dart_mode << "]\n"; + str_buf << "[uniform_drop: " << uniform_drop << "]\n"; + str_buf << "[drop_seed: " << drop_seed << "]\n"; + str_buf << "[top_rate: " << top_rate << "]\n"; + str_buf << "[other_rate: " << other_rate << "]\n"; + str_buf << "[min_data_per_group: " << min_data_per_group << "]\n"; + str_buf << "[max_cat_threshold: " << max_cat_threshold << "]\n"; + str_buf << "[cat_l2: " << cat_l2 << "]\n"; + str_buf << "[cat_smooth: " << cat_smooth << "]\n"; + str_buf << "[max_cat_to_onehot: " << max_cat_to_onehot << "]\n"; + str_buf << "[top_k: " << top_k << "]\n"; + str_buf << "[monotone_constraints: " << Common::Join(Common::ArrayCast(monotone_constraints), ",") << "]\n"; + str_buf << "[monotone_constraints_method: " << monotone_constraints_method << "]\n"; + str_buf << "[monotone_penalty: " << monotone_penalty << "]\n"; + str_buf << "[feature_contri: " << Common::Join(feature_contri, ",") << "]\n"; + str_buf << "[forcedsplits_filename: " << forcedsplits_filename << "]\n"; + str_buf << "[refit_decay_rate: " << refit_decay_rate << "]\n"; + str_buf << "[cegb_tradeoff: " << cegb_tradeoff << "]\n"; + str_buf << "[cegb_penalty_split: " << cegb_penalty_split << "]\n"; + str_buf << "[cegb_penalty_feature_lazy: " << Common::Join(cegb_penalty_feature_lazy, ",") << "]\n"; + str_buf << "[cegb_penalty_feature_coupled: " << Common::Join(cegb_penalty_feature_coupled, ",") << "]\n"; + str_buf << "[path_smooth: " << path_smooth << "]\n"; + str_buf << "[interaction_constraints: " << interaction_constraints << "]\n"; + str_buf << "[verbosity: " << verbosity << "]\n"; + str_buf << "[saved_feature_importance_type: " << saved_feature_importance_type << "]\n"; + str_buf << "[use_quantized_grad: " << use_quantized_grad << "]\n"; + str_buf << "[num_grad_quant_bins: " << num_grad_quant_bins << "]\n"; + str_buf << "[quant_train_renew_leaf: " << quant_train_renew_leaf << "]\n"; + str_buf << "[stochastic_rounding: " << stochastic_rounding << "]\n"; + str_buf << "[linear_tree: " << linear_tree << "]\n"; + str_buf << "[max_bin: " << max_bin << "]\n"; + str_buf << "[max_bin_by_feature: " << Common::Join(max_bin_by_feature, ",") << "]\n"; + str_buf << "[min_data_in_bin: " << min_data_in_bin << "]\n"; + str_buf << "[bin_construct_sample_cnt: " << bin_construct_sample_cnt << "]\n"; + str_buf << "[data_random_seed: " << data_random_seed << "]\n"; + str_buf << "[is_enable_sparse: " << is_enable_sparse << "]\n"; + str_buf << "[enable_bundle: " << enable_bundle << "]\n"; + str_buf << "[use_missing: " << use_missing << "]\n"; + str_buf << "[zero_as_missing: " << zero_as_missing << "]\n"; + str_buf << "[feature_pre_filter: " << feature_pre_filter << "]\n"; + str_buf << "[pre_partition: " << pre_partition << "]\n"; + str_buf << "[two_round: " << two_round << "]\n"; + str_buf << "[header: " << header << "]\n"; + str_buf << "[label_column: " << label_column << "]\n"; + str_buf << "[weight_column: " << weight_column << "]\n"; + str_buf << "[group_column: " << group_column << "]\n"; + str_buf << "[ignore_column: " << ignore_column << "]\n"; + str_buf << "[categorical_feature: " << categorical_feature << "]\n"; + str_buf << "[forcedbins_filename: " << forcedbins_filename << "]\n"; + str_buf << "[precise_float_parser: " << precise_float_parser << "]\n"; + str_buf << "[parser_config_file: " << parser_config_file << "]\n"; + str_buf << "[objective_seed: " << objective_seed << "]\n"; + str_buf << "[num_class: " << num_class << "]\n"; + str_buf << "[is_unbalance: " << is_unbalance << "]\n"; + str_buf << "[scale_pos_weight: " << scale_pos_weight << "]\n"; + str_buf << "[sigmoid: " << sigmoid << "]\n"; + str_buf << "[boost_from_average: " << boost_from_average << "]\n"; + str_buf << "[reg_sqrt: " << reg_sqrt << "]\n"; + str_buf << "[alpha: " << alpha << "]\n"; + str_buf << "[fair_c: " << fair_c << "]\n"; + str_buf << "[poisson_max_delta_step: " << poisson_max_delta_step << "]\n"; + str_buf << "[tweedie_variance_power: " << tweedie_variance_power << "]\n"; + str_buf << "[lambdarank_truncation_level: " << lambdarank_truncation_level << "]\n"; + str_buf << "[lambdarank_norm: " << lambdarank_norm << "]\n"; + str_buf << "[label_gain: " << Common::Join(label_gain, ",") << "]\n"; + str_buf << "[lambdarank_position_bias_regularization: " << lambdarank_position_bias_regularization << "]\n"; + str_buf << "[eval_at: " << Common::Join(eval_at, ",") << "]\n"; + str_buf << "[multi_error_top_k: " << multi_error_top_k << "]\n"; + str_buf << "[auc_mu_weights: " << Common::Join(auc_mu_weights, ",") << "]\n"; + str_buf << "[num_machines: " << num_machines << "]\n"; + str_buf << "[local_listen_port: " << local_listen_port << "]\n"; + str_buf << "[time_out: " << time_out << "]\n"; + str_buf << "[machine_list_filename: " << machine_list_filename << "]\n"; + str_buf << "[machines: " << machines << "]\n"; + str_buf << "[gpu_platform_id: " << gpu_platform_id << "]\n"; + str_buf << "[gpu_device_id: " << gpu_device_id << "]\n"; + str_buf << "[gpu_device_id_list: " << gpu_device_id_list << "]\n"; + str_buf << "[gpu_use_dp: " << gpu_use_dp << "]\n"; + str_buf << "[num_gpu: " << num_gpu << "]\n"; + return str_buf.str(); +} + +const std::unordered_map>& Config::parameter2aliases() { + static std::unordered_map> map({ + {"config", {"config_file"}}, + {"task", {"task_type"}}, + {"objective", {"objective_type", "app", "application", "loss"}}, + {"boosting", {"boosting_type", "boost"}}, + {"data_sample_strategy", {}}, + {"data", {"train", "train_data", "train_data_file", "data_filename"}}, + {"valid", {"test", "valid_data", "valid_data_file", "test_data", "test_data_file", "valid_filenames"}}, + {"num_iterations", {"num_iteration", "n_iter", "num_tree", "num_trees", "num_round", "num_rounds", "nrounds", "num_boost_round", "n_estimators", "max_iter"}}, + {"learning_rate", {"shrinkage_rate", "eta"}}, + {"num_leaves", {"num_leaf", "max_leaves", "max_leaf", "max_leaf_nodes"}}, + {"tree_learner", {"tree", "tree_type", "tree_learner_type"}}, + {"num_threads", {"num_thread", "nthread", "nthreads", "n_jobs"}}, + {"device_type", {"device"}}, + {"seed", {"random_seed", "random_state"}}, + {"deterministic", {}}, + {"force_col_wise", {}}, + {"force_row_wise", {}}, + {"histogram_pool_size", {"hist_pool_size"}}, + {"max_depth", {}}, + {"min_data_in_leaf", {"min_data_per_leaf", "min_data", "min_child_samples", "min_samples_leaf"}}, + {"min_sum_hessian_in_leaf", {"min_sum_hessian_per_leaf", "min_sum_hessian", "min_hessian", "min_child_weight"}}, + {"bagging_fraction", {"sub_row", "subsample", "bagging"}}, + {"pos_bagging_fraction", {"pos_sub_row", "pos_subsample", "pos_bagging"}}, + {"neg_bagging_fraction", {"neg_sub_row", "neg_subsample", "neg_bagging"}}, + {"bagging_freq", {"subsample_freq"}}, + {"bagging_seed", {"bagging_fraction_seed"}}, + {"bagging_by_query", {}}, + {"feature_fraction", {"sub_feature", "colsample_bytree"}}, + {"feature_fraction_bynode", {"sub_feature_bynode", "colsample_bynode"}}, + {"feature_fraction_seed", {}}, + {"extra_trees", {"extra_tree"}}, + {"extra_seed", {}}, + {"early_stopping_round", {"early_stopping_rounds", "early_stopping", "n_iter_no_change"}}, + {"early_stopping_min_delta", {}}, + {"first_metric_only", {}}, + {"max_delta_step", {"max_tree_output", "max_leaf_output"}}, + {"lambda_l1", {"reg_alpha", "l1_regularization"}}, + {"lambda_l2", {"reg_lambda", "lambda", "l2_regularization"}}, + {"linear_lambda", {}}, + {"min_gain_to_split", {"min_split_gain"}}, + {"drop_rate", {"rate_drop"}}, + {"max_drop", {}}, + {"skip_drop", {}}, + {"xgboost_dart_mode", {}}, + {"uniform_drop", {}}, + {"drop_seed", {}}, + {"top_rate", {}}, + {"other_rate", {}}, + {"min_data_per_group", {}}, + {"max_cat_threshold", {}}, + {"cat_l2", {}}, + {"cat_smooth", {}}, + {"max_cat_to_onehot", {}}, + {"top_k", {"topk"}}, + {"monotone_constraints", {"mc", "monotone_constraint", "monotonic_cst"}}, + {"monotone_constraints_method", {"monotone_constraining_method", "mc_method"}}, + {"monotone_penalty", {"monotone_splits_penalty", "ms_penalty", "mc_penalty"}}, + {"feature_contri", {"feature_contrib", "fc", "fp", "feature_penalty"}}, + {"forcedsplits_filename", {"fs", "forced_splits_filename", "forced_splits_file", "forced_splits"}}, + {"refit_decay_rate", {}}, + {"cegb_tradeoff", {}}, + {"cegb_penalty_split", {}}, + {"cegb_penalty_feature_lazy", {}}, + {"cegb_penalty_feature_coupled", {}}, + {"path_smooth", {}}, + {"interaction_constraints", {}}, + {"verbosity", {"verbose"}}, + {"input_model", {"model_input", "model_in"}}, + {"output_model", {"model_output", "model_out"}}, + {"saved_feature_importance_type", {}}, + {"snapshot_freq", {"save_period"}}, + {"use_quantized_grad", {}}, + {"num_grad_quant_bins", {}}, + {"quant_train_renew_leaf", {}}, + {"stochastic_rounding", {}}, + {"linear_tree", {"linear_trees"}}, + {"max_bin", {"max_bins"}}, + {"max_bin_by_feature", {}}, + {"min_data_in_bin", {}}, + {"bin_construct_sample_cnt", {"subsample_for_bin"}}, + {"data_random_seed", {"data_seed"}}, + {"is_enable_sparse", {"is_sparse", "enable_sparse", "sparse"}}, + {"enable_bundle", {"is_enable_bundle", "bundle"}}, + {"use_missing", {}}, + {"zero_as_missing", {}}, + {"feature_pre_filter", {}}, + {"pre_partition", {"is_pre_partition"}}, + {"two_round", {"two_round_loading", "use_two_round_loading"}}, + {"header", {"has_header"}}, + {"label_column", {"label"}}, + {"weight_column", {"weight"}}, + {"group_column", {"group", "group_id", "query_column", "query", "query_id"}}, + {"ignore_column", {"ignore_feature", "blacklist"}}, + {"categorical_feature", {"cat_feature", "categorical_column", "cat_column", "categorical_features"}}, + {"forcedbins_filename", {}}, + {"save_binary", {"is_save_binary", "is_save_binary_file"}}, + {"precise_float_parser", {}}, + {"parser_config_file", {}}, + {"start_iteration_predict", {}}, + {"num_iteration_predict", {}}, + {"predict_raw_score", {"is_predict_raw_score", "predict_rawscore", "raw_score"}}, + {"predict_leaf_index", {"is_predict_leaf_index", "leaf_index"}}, + {"predict_contrib", {"is_predict_contrib", "contrib"}}, + {"predict_disable_shape_check", {}}, + {"pred_early_stop", {}}, + {"pred_early_stop_freq", {}}, + {"pred_early_stop_margin", {}}, + {"output_result", {"predict_result", "prediction_result", "predict_name", "prediction_name", "pred_name", "name_pred"}}, + {"convert_model_language", {}}, + {"convert_model", {"convert_model_file"}}, + {"objective_seed", {}}, + {"num_class", {"num_classes"}}, + {"is_unbalance", {"unbalance", "unbalanced_sets"}}, + {"scale_pos_weight", {}}, + {"sigmoid", {}}, + {"boost_from_average", {}}, + {"reg_sqrt", {}}, + {"alpha", {}}, + {"fair_c", {}}, + {"poisson_max_delta_step", {}}, + {"tweedie_variance_power", {}}, + {"lambdarank_truncation_level", {}}, + {"lambdarank_norm", {}}, + {"label_gain", {}}, + {"lambdarank_position_bias_regularization", {}}, + {"metric", {"metrics", "metric_types"}}, + {"metric_freq", {"output_freq"}}, + {"is_provide_training_metric", {"training_metric", "is_training_metric", "train_metric"}}, + {"eval_at", {"ndcg_eval_at", "ndcg_at", "map_eval_at", "map_at"}}, + {"multi_error_top_k", {}}, + {"auc_mu_weights", {}}, + {"num_machines", {"num_machine"}}, + {"local_listen_port", {"local_port", "port"}}, + {"time_out", {}}, + {"machine_list_filename", {"machine_list_file", "machine_list", "mlist"}}, + {"machines", {"workers", "nodes"}}, + {"gpu_platform_id", {}}, + {"gpu_device_id", {}}, + {"gpu_device_id_list", {}}, + {"gpu_use_dp", {}}, + {"num_gpu", {}}, + }); + return map; +} + +const std::unordered_map& Config::ParameterTypes() { + static std::unordered_map map({ + {"config", "string"}, + {"objective", "string"}, + {"boosting", "string"}, + {"data_sample_strategy", "string"}, + {"data", "string"}, + {"valid", "vector"}, + {"num_iterations", "int"}, + {"learning_rate", "double"}, + {"num_leaves", "int"}, + {"tree_learner", "string"}, + {"num_threads", "int"}, + {"device_type", "string"}, + {"seed", "int"}, + {"deterministic", "bool"}, + {"force_col_wise", "bool"}, + {"force_row_wise", "bool"}, + {"histogram_pool_size", "double"}, + {"max_depth", "int"}, + {"min_data_in_leaf", "int"}, + {"min_sum_hessian_in_leaf", "double"}, + {"bagging_fraction", "double"}, + {"pos_bagging_fraction", "double"}, + {"neg_bagging_fraction", "double"}, + {"bagging_freq", "int"}, + {"bagging_seed", "int"}, + {"bagging_by_query", "bool"}, + {"feature_fraction", "double"}, + {"feature_fraction_bynode", "double"}, + {"feature_fraction_seed", "int"}, + {"extra_trees", "bool"}, + {"extra_seed", "int"}, + {"early_stopping_round", "int"}, + {"early_stopping_min_delta", "double"}, + {"first_metric_only", "bool"}, + {"max_delta_step", "double"}, + {"lambda_l1", "double"}, + {"lambda_l2", "double"}, + {"linear_lambda", "double"}, + {"min_gain_to_split", "double"}, + {"drop_rate", "double"}, + {"max_drop", "int"}, + {"skip_drop", "double"}, + {"xgboost_dart_mode", "bool"}, + {"uniform_drop", "bool"}, + {"drop_seed", "int"}, + {"top_rate", "double"}, + {"other_rate", "double"}, + {"min_data_per_group", "int"}, + {"max_cat_threshold", "int"}, + {"cat_l2", "double"}, + {"cat_smooth", "double"}, + {"max_cat_to_onehot", "int"}, + {"top_k", "int"}, + {"monotone_constraints", "vector"}, + {"monotone_constraints_method", "string"}, + {"monotone_penalty", "double"}, + {"feature_contri", "vector"}, + {"forcedsplits_filename", "string"}, + {"refit_decay_rate", "double"}, + {"cegb_tradeoff", "double"}, + {"cegb_penalty_split", "double"}, + {"cegb_penalty_feature_lazy", "vector"}, + {"cegb_penalty_feature_coupled", "vector"}, + {"path_smooth", "double"}, + {"interaction_constraints", "vector>"}, + {"verbosity", "int"}, + {"input_model", "string"}, + {"output_model", "string"}, + {"saved_feature_importance_type", "int"}, + {"snapshot_freq", "int"}, + {"use_quantized_grad", "bool"}, + {"num_grad_quant_bins", "int"}, + {"quant_train_renew_leaf", "bool"}, + {"stochastic_rounding", "bool"}, + {"linear_tree", "bool"}, + {"max_bin", "int"}, + {"max_bin_by_feature", "vector"}, + {"min_data_in_bin", "int"}, + {"bin_construct_sample_cnt", "int"}, + {"data_random_seed", "int"}, + {"is_enable_sparse", "bool"}, + {"enable_bundle", "bool"}, + {"use_missing", "bool"}, + {"zero_as_missing", "bool"}, + {"feature_pre_filter", "bool"}, + {"pre_partition", "bool"}, + {"two_round", "bool"}, + {"header", "bool"}, + {"label_column", "string"}, + {"weight_column", "string"}, + {"group_column", "string"}, + {"ignore_column", "vector"}, + {"categorical_feature", "vector"}, + {"forcedbins_filename", "string"}, + {"save_binary", "bool"}, + {"precise_float_parser", "bool"}, + {"parser_config_file", "string"}, + {"start_iteration_predict", "int"}, + {"num_iteration_predict", "int"}, + {"predict_raw_score", "bool"}, + {"predict_leaf_index", "bool"}, + {"predict_contrib", "bool"}, + {"predict_disable_shape_check", "bool"}, + {"pred_early_stop", "bool"}, + {"pred_early_stop_freq", "int"}, + {"pred_early_stop_margin", "double"}, + {"output_result", "string"}, + {"convert_model_language", "string"}, + {"convert_model", "string"}, + {"objective_seed", "int"}, + {"num_class", "int"}, + {"is_unbalance", "bool"}, + {"scale_pos_weight", "double"}, + {"sigmoid", "double"}, + {"boost_from_average", "bool"}, + {"reg_sqrt", "bool"}, + {"alpha", "double"}, + {"fair_c", "double"}, + {"poisson_max_delta_step", "double"}, + {"tweedie_variance_power", "double"}, + {"lambdarank_truncation_level", "int"}, + {"lambdarank_norm", "bool"}, + {"label_gain", "vector"}, + {"lambdarank_position_bias_regularization", "double"}, + {"metric", "vector"}, + {"metric_freq", "int"}, + {"is_provide_training_metric", "bool"}, + {"eval_at", "vector"}, + {"multi_error_top_k", "int"}, + {"auc_mu_weights", "vector"}, + {"num_machines", "int"}, + {"local_listen_port", "int"}, + {"time_out", "int"}, + {"machine_list_filename", "string"}, + {"machines", "string"}, + {"gpu_platform_id", "int"}, + {"gpu_device_id", "int"}, + {"gpu_device_id_list", "string"}, + {"gpu_use_dp", "bool"}, + {"num_gpu", "int"}, + }); + return map; +} + +} // namespace LightGBM diff --git a/src/io/cuda/cuda_column_data.cpp b/src/io/cuda/cuda_column_data.cpp new file mode 100644 index 0000000..701f1b3 --- /dev/null +++ b/src/io/cuda/cuda_column_data.cpp @@ -0,0 +1,227 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifdef USE_CUDA + +#include + +#include +#include + +namespace LightGBM { + +CUDAColumnData::CUDAColumnData(const data_size_t num_data, const int gpu_device_id) { + num_threads_ = OMP_NUM_THREADS(); + num_data_ = num_data; + gpu_device_id_ = gpu_device_id >= 0 ? gpu_device_id : 0; + SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); + data_by_column_.clear(); +} + +CUDAColumnData::~CUDAColumnData() {} + +template +void CUDAColumnData::InitOneColumnData(const void* in_column_data, BinIterator* bin_iterator, CUDAVector* out_column_data_pointer) { + CUDAVector cuda_column_data; + if (!IS_SPARSE) { + if (IS_4BIT) { + std::vector expanded_column_data(num_data_, 0); + const BIN_TYPE* in_column_data_reintrepreted = reinterpret_cast(in_column_data); + for (data_size_t i = 0; i < num_data_; ++i) { + expanded_column_data[i] = static_cast((in_column_data_reintrepreted[i >> 1] >> ((i & 1) << 2)) & 0xf); + } + cuda_column_data.InitFromHostVector(expanded_column_data); + } else { + cuda_column_data.InitFromHostMemory(reinterpret_cast(in_column_data), static_cast(num_data_)); + } + } else { + // need to iterate bin iterator + std::vector expanded_column_data(num_data_, 0); + for (data_size_t i = 0; i < num_data_; ++i) { + expanded_column_data[i] = static_cast(bin_iterator->RawGet(i)); + } + cuda_column_data.InitFromHostVector(expanded_column_data); + } + out_column_data_pointer->MoveFrom(cuda_column_data, sizeof(BIN_TYPE) * cuda_column_data.Size()); +} + +void CUDAColumnData::Init(const int num_columns, + const std::vector& column_data, + const std::vector& column_bin_iterator, + const std::vector& column_bit_type, + const std::vector& feature_max_bin, + const std::vector& feature_min_bin, + const std::vector& feature_offset, + const std::vector& feature_most_freq_bin, + const std::vector& feature_default_bin, + const std::vector& feature_missing_is_zero, + const std::vector& feature_missing_is_na, + const std::vector& feature_mfb_is_zero, + const std::vector& feature_mfb_is_na, + const std::vector& feature_to_column) { + num_columns_ = num_columns; + column_bit_type_ = column_bit_type; + feature_max_bin_ = feature_max_bin; + feature_min_bin_ = feature_min_bin; + feature_offset_ = feature_offset; + feature_most_freq_bin_ = feature_most_freq_bin; + feature_default_bin_ = feature_default_bin; + feature_missing_is_zero_ = feature_missing_is_zero; + feature_missing_is_na_ = feature_missing_is_na; + feature_mfb_is_zero_ = feature_mfb_is_zero; + feature_mfb_is_na_ = feature_mfb_is_na; + for (int column_index = 0; column_index < num_columns_; ++column_index) { + data_by_column_.emplace_back(new CUDAVector()); + } + OMP_INIT_EX(); + #pragma omp parallel num_threads(num_threads_) + { + SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); + #pragma omp for schedule(static) + for (int column_index = 0; column_index < num_columns_; ++column_index) { + OMP_LOOP_EX_BEGIN(); + const int8_t bit_type = column_bit_type[column_index]; + if (column_data[column_index] != nullptr) { + // is dense column + if (bit_type == 4) { + column_bit_type_[column_index] = 8; + InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); + } else if (bit_type == 8) { + InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); + } else if (bit_type == 16) { + InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); + } else if (bit_type == 32) { + InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get()); + } else { + Log::Fatal("Unknown column bit type %d", bit_type); + } + } else { + // is sparse column + if (bit_type == 8) { + InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get()); + } else if (bit_type == 16) { + InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get()); + } else if (bit_type == 32) { + InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get()); + } else { + Log::Fatal("Unknown column bit type %d", bit_type); + } + } + OMP_LOOP_EX_END(); + } + } + OMP_THROW_EX(); + feature_to_column_ = feature_to_column; + cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_)); + InitColumnMetaInfo(); +} + +void CUDAColumnData::CopySubrow( + const CUDAColumnData* full_set, + const data_size_t* used_indices, + const data_size_t num_used_indices) { + num_threads_ = full_set->num_threads_; + num_columns_ = full_set->num_columns_; + column_bit_type_ = full_set->column_bit_type_; + feature_min_bin_ = full_set->feature_min_bin_; + feature_max_bin_ = full_set->feature_max_bin_; + feature_offset_ = full_set->feature_offset_; + feature_most_freq_bin_ = full_set->feature_most_freq_bin_; + feature_default_bin_ = full_set->feature_default_bin_; + feature_missing_is_zero_ = full_set->feature_missing_is_zero_; + feature_missing_is_na_ = full_set->feature_missing_is_na_; + feature_mfb_is_zero_ = full_set->feature_mfb_is_zero_; + feature_mfb_is_na_ = full_set->feature_mfb_is_na_; + feature_to_column_ = full_set->feature_to_column_; + if (cuda_used_indices_.Size() == 0) { + // initialize the subset cuda column data + const size_t num_used_indices_size = static_cast(num_used_indices); + cuda_used_indices_.Resize(num_used_indices_size); + for (int column_index = 0; column_index < num_columns_; ++column_index) { + data_by_column_.emplace_back(new CUDAVector()); + } + OMP_INIT_EX(); + #pragma omp parallel num_threads(num_threads_) + { + SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); + #pragma omp for schedule(static) + for (int column_index = 0; column_index < num_columns_; ++column_index) { + OMP_LOOP_EX_BEGIN(); + const uint8_t bit_type = column_bit_type_[column_index]; + if (bit_type == 8) { + CUDAVector column_data; + column_data.Resize(num_used_indices_size); + data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint8_t) * column_data.Size()); + } else if (bit_type == 16) { + CUDAVector column_data; + column_data.Resize(num_used_indices_size); + data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint16_t) * column_data.Size()); + } else if (bit_type == 32) { + CUDAVector column_data; + column_data.Resize(num_used_indices_size); + data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint32_t) * column_data.Size()); + } + OMP_LOOP_EX_END(); + } + } + OMP_THROW_EX(); + cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_)); + InitColumnMetaInfo(); + cur_subset_buffer_size_ = num_used_indices; + } else { + if (num_used_indices > cur_subset_buffer_size_) { + ResizeWhenCopySubrow(num_used_indices); + cur_subset_buffer_size_ = num_used_indices; + } + } + cuda_used_indices_.InitFromHostMemory(used_indices, static_cast(num_used_indices)); + num_used_indices_ = num_used_indices; + LaunchCopySubrowKernel(full_set->cuda_data_by_column()); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDAColumnData::ResizeWhenCopySubrow(const data_size_t num_used_indices) { + const size_t num_used_indices_size = static_cast(num_used_indices); + cuda_used_indices_.Resize(num_used_indices_size); + OMP_INIT_EX(); + #pragma omp parallel num_threads(num_threads_) + { + SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); + #pragma omp for schedule(static) + for (int column_index = 0; column_index < num_columns_; ++column_index) { + OMP_LOOP_EX_BEGIN(); + const uint8_t bit_type = column_bit_type_[column_index]; + if (bit_type == 8) { + data_by_column_[column_index]->Resize(sizeof(uint8_t) * num_used_indices_size); + } else if (bit_type == 16) { + data_by_column_[column_index]->Resize(sizeof(uint16_t) * num_used_indices_size); + } else if (bit_type == 32) { + data_by_column_[column_index]->Resize(sizeof(uint32_t) * num_used_indices_size); + } + OMP_LOOP_EX_END(); + } + } + OMP_THROW_EX(); + cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_)); +} + +void CUDAColumnData::InitColumnMetaInfo() { + cuda_column_bit_type_.InitFromHostVector(column_bit_type_); + cuda_feature_max_bin_.InitFromHostVector(feature_max_bin_); + cuda_feature_min_bin_.InitFromHostVector(feature_min_bin_); + cuda_feature_offset_.InitFromHostVector(feature_offset_); + cuda_feature_most_freq_bin_.InitFromHostVector(feature_most_freq_bin_); + cuda_feature_default_bin_.InitFromHostVector(feature_default_bin_); + cuda_feature_missing_is_zero_.InitFromHostVector(feature_missing_is_zero_); + cuda_feature_missing_is_na_.InitFromHostVector(feature_missing_is_na_); + cuda_feature_mfb_is_zero_.InitFromHostVector(feature_mfb_is_zero_); + cuda_feature_mfb_is_na_.InitFromHostVector(feature_mfb_is_na_); + cuda_feature_to_column_.InitFromHostVector(feature_to_column_); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/io/cuda/cuda_column_data.cu b/src/io/cuda/cuda_column_data.cu new file mode 100644 index 0000000..83f278b --- /dev/null +++ b/src/io/cuda/cuda_column_data.cu @@ -0,0 +1,62 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + + +#ifdef USE_CUDA + +#include + +#define COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA (1024) + +namespace LightGBM { + +__global__ void CopySubrowKernel_ColumnData( + uint8_t* const* in_cuda_data_by_column, + const uint8_t* cuda_column_bit_type, + const data_size_t* cuda_used_indices, + const data_size_t num_used_indices, + const int num_column, + uint8_t** out_cuda_data_by_column) { + const data_size_t local_data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (local_data_index < num_used_indices) { + for (int column_index = 0; column_index < num_column; ++column_index) { + const uint8_t* in_column_data = in_cuda_data_by_column[column_index]; + uint8_t* out_column_data = out_cuda_data_by_column[column_index]; + const uint8_t bit_type = cuda_column_bit_type[column_index]; + if (bit_type == 8) { + const uint8_t* true_in_column_data = reinterpret_cast(in_column_data); + uint8_t* true_out_column_data = reinterpret_cast(out_column_data); + const data_size_t global_data_index = cuda_used_indices[local_data_index]; + true_out_column_data[local_data_index] = true_in_column_data[global_data_index]; + } else if (bit_type == 16) { + const uint16_t* true_in_column_data = reinterpret_cast(in_column_data); + uint16_t* true_out_column_data = reinterpret_cast(out_column_data); + const data_size_t global_data_index = cuda_used_indices[local_data_index]; + true_out_column_data[local_data_index] = true_in_column_data[global_data_index]; + } else if (bit_type == 32) { + const uint32_t* true_in_column_data = reinterpret_cast(in_column_data); + uint32_t* true_out_column_data = reinterpret_cast(out_column_data); + const data_size_t global_data_index = cuda_used_indices[local_data_index]; + true_out_column_data[local_data_index] = true_in_column_data[global_data_index]; + } + } + } +} + +void CUDAColumnData::LaunchCopySubrowKernel(uint8_t* const* in_cuda_data_by_column) { + const int num_blocks = (num_used_indices_ + COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA - 1) / COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA; + CopySubrowKernel_ColumnData<<>>( + in_cuda_data_by_column, + cuda_column_bit_type_.RawData(), + cuda_used_indices_.RawData(), + num_used_indices_, + num_columns_, + cuda_data_by_column_.RawData()); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/io/cuda/cuda_metadata.cpp b/src/io/cuda/cuda_metadata.cpp new file mode 100644 index 0000000..6f13c6e --- /dev/null +++ b/src/io/cuda/cuda_metadata.cpp @@ -0,0 +1,79 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifdef USE_CUDA + +#include + +#include + +namespace LightGBM { + +CUDAMetadata::CUDAMetadata(const int gpu_device_id) { + if (gpu_device_id >= 0) { + SetCUDADevice(gpu_device_id, __FILE__, __LINE__); + } else { + SetCUDADevice(0, __FILE__, __LINE__); + } +} + +CUDAMetadata::~CUDAMetadata() {} + +void CUDAMetadata::Init(const std::vector& label, + const std::vector& weight, + const std::vector& query_boundaries, + const std::vector& query_weights, + const std::vector& init_score) { + if (label.size() == 0) { + cuda_label_.Clear(); + } else { + cuda_label_.InitFromHostVector(label); + } + if (weight.size() == 0) { + cuda_weights_.Clear(); + } else { + cuda_weights_.InitFromHostVector(weight); + } + if (query_boundaries.size() == 0) { + cuda_query_boundaries_.Clear(); + } else { + cuda_query_boundaries_.InitFromHostVector(query_boundaries); + } + if (query_weights.size() == 0) { + cuda_query_weights_.Clear(); + } else { + cuda_query_weights_.InitFromHostVector(query_weights); + } + if (init_score.size() == 0) { + cuda_init_score_.Clear(); + } else { + cuda_init_score_.InitFromHostVector(init_score); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDAMetadata::SetLabel(const label_t* label, data_size_t len) { + cuda_label_.InitFromHostMemory(label, static_cast(len)); +} + +void CUDAMetadata::SetWeights(const label_t* weights, data_size_t len) { + cuda_weights_.InitFromHostMemory(weights, static_cast(len)); +} + +void CUDAMetadata::SetQuery(const data_size_t* query_boundaries, const label_t* query_weights, data_size_t num_queries) { + cuda_query_boundaries_.InitFromHostMemory(query_boundaries, static_cast(num_queries) + 1); + if (query_weights != nullptr) { + cuda_query_weights_.InitFromHostMemory(query_weights, static_cast(num_queries)); + } +} + +void CUDAMetadata::SetInitScore(const double* init_score, data_size_t len) { + cuda_init_score_.InitFromHostMemory(init_score, len); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/io/cuda/cuda_row_data.cpp b/src/io/cuda/cuda_row_data.cpp new file mode 100644 index 0000000..a65c8d2 --- /dev/null +++ b/src/io/cuda/cuda_row_data.cpp @@ -0,0 +1,438 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifdef USE_CUDA + +#include + +#include + +namespace LightGBM { + +CUDARowData::CUDARowData(const Dataset* train_data, + const TrainingShareStates* train_share_state, + const int gpu_device_id, + const bool gpu_use_dp): +gpu_device_id_(gpu_device_id), +gpu_use_dp_(gpu_use_dp) { + num_threads_ = OMP_NUM_THREADS(); + num_data_ = train_data->num_data(); + const auto& feature_hist_offsets = train_share_state->feature_hist_offsets(); + if (gpu_use_dp_) { + shared_hist_size_ = DP_SHARED_HIST_SIZE; + } else { + shared_hist_size_ = SP_SHARED_HIST_SIZE; + } + if (feature_hist_offsets.empty()) { + num_total_bin_ = 0; + } else { + num_total_bin_ = static_cast(feature_hist_offsets.back()); + } + num_feature_group_ = train_data->num_feature_groups(); + num_feature_ = train_data->num_features(); + if (gpu_device_id >= 0) { + SetCUDADevice(gpu_device_id, __FILE__, __LINE__); + } else { + SetCUDADevice(0, __FILE__, __LINE__); + } +} + +CUDARowData::~CUDARowData() {} + +void CUDARowData::Init(const Dataset* train_data, TrainingShareStates* train_share_state) { + if (num_feature_ == 0) { + return; + } + DivideCUDAFeatureGroups(train_data, train_share_state); + bit_type_ = 0; + size_t total_size = 0; + const void* host_row_ptr = nullptr; + row_ptr_bit_type_ = 0; + const void* host_data = train_share_state->GetRowWiseData(&bit_type_, &total_size, &is_sparse_, &host_row_ptr, &row_ptr_bit_type_); + if (bit_type_ == 8) { + if (!is_sparse_) { + std::vector partitioned_data; + GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data); + cuda_data_uint8_t_.InitFromHostVector(partitioned_data); + } else { + if (row_ptr_bit_type_ == 16) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint8_t_, + &cuda_row_ptr_uint16_t_, + &cuda_partition_ptr_uint16_t_); + } else if (row_ptr_bit_type_ == 32) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint8_t_, + &cuda_row_ptr_uint32_t_, + &cuda_partition_ptr_uint32_t_); + } else if (row_ptr_bit_type_ == 64) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint8_t_, + &cuda_row_ptr_uint64_t_, + &cuda_partition_ptr_uint64_t_); + } else { + Log::Fatal("Unknown data ptr bit type %d", row_ptr_bit_type_); + } + } + } else if (bit_type_ == 16) { + if (!is_sparse_) { + std::vector partitioned_data; + GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data); + cuda_data_uint16_t_.InitFromHostVector(partitioned_data); + } else { + if (row_ptr_bit_type_ == 16) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint16_t_, + &cuda_row_ptr_uint16_t_, + &cuda_partition_ptr_uint16_t_); + } else if (row_ptr_bit_type_ == 32) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint16_t_, + &cuda_row_ptr_uint32_t_, + &cuda_partition_ptr_uint32_t_); + } else if (row_ptr_bit_type_ == 64) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint16_t_, + &cuda_row_ptr_uint64_t_, + &cuda_partition_ptr_uint64_t_); + } else { + Log::Fatal("Unknown data ptr bit type %d", row_ptr_bit_type_); + } + } + } else if (bit_type_ == 32) { + if (!is_sparse_) { + std::vector partitioned_data; + GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data); + cuda_data_uint32_t_.InitFromHostVector(partitioned_data); + } else { + if (row_ptr_bit_type_ == 16) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint32_t_, + &cuda_row_ptr_uint16_t_, + &cuda_partition_ptr_uint16_t_); + } else if (row_ptr_bit_type_ == 32) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint32_t_, + &cuda_row_ptr_uint32_t_, + &cuda_partition_ptr_uint32_t_); + } else if (row_ptr_bit_type_ == 64) { + InitSparseData( + reinterpret_cast(host_data), + reinterpret_cast(host_row_ptr), + &cuda_data_uint32_t_, + &cuda_row_ptr_uint64_t_, + &cuda_partition_ptr_uint64_t_); + } else { + Log::Fatal("Unknown data ptr bit type %d", row_ptr_bit_type_); + } + } + } else { + Log::Fatal("Unknown bit type = %d", bit_type_); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDARowData::DivideCUDAFeatureGroups(const Dataset* train_data, TrainingShareStates* share_state) { + const uint32_t max_num_bin_per_partition = shared_hist_size_ / 2; + const std::vector& column_hist_offsets = share_state->column_hist_offsets(); + std::vector feature_group_num_feature_offsets; + int offsets = 0; + int prev_group_index = -1; + for (int feature_index = 0; feature_index < num_feature_; ++feature_index) { + const int feature_group_index = train_data->Feature2Group(feature_index); + if (prev_group_index == -1 || feature_group_index != prev_group_index) { + feature_group_num_feature_offsets.emplace_back(offsets); + prev_group_index = feature_group_index; + } + ++offsets; + } + CHECK_EQ(offsets, num_feature_); + feature_group_num_feature_offsets.emplace_back(offsets); + + uint32_t start_hist_offset = 0; + feature_partition_column_index_offsets_.clear(); + column_hist_offsets_.clear(); + partition_hist_offsets_.clear(); + feature_partition_column_index_offsets_.emplace_back(0); + partition_hist_offsets_.emplace_back(0); + const int num_feature_groups = train_data->num_feature_groups(); + int column_index = 0; + num_feature_partitions_ = 0; + large_bin_partitions_.clear(); + small_bin_partitions_.clear(); + for (int feature_group_index = 0; feature_group_index < num_feature_groups; ++feature_group_index) { + if (!train_data->IsMultiGroup(feature_group_index)) { + const uint32_t column_feature_hist_start = column_hist_offsets[column_index]; + const uint32_t column_feature_hist_end = column_hist_offsets[column_index + 1]; + const uint32_t num_bin_in_dense_group = column_feature_hist_end - column_feature_hist_start; + + // if one column has too many bins, use a separate partition for that column + if (num_bin_in_dense_group > max_num_bin_per_partition) { + feature_partition_column_index_offsets_.emplace_back(column_index + 1); + start_hist_offset = column_feature_hist_end; + partition_hist_offsets_.emplace_back(start_hist_offset); + large_bin_partitions_.emplace_back(num_feature_partitions_); + ++num_feature_partitions_; + column_hist_offsets_.emplace_back(0); + ++column_index; + continue; + } + + // try if adding this column exceed the maximum number per partition + const uint32_t cur_hist_num_bin = column_feature_hist_end - start_hist_offset; + if (cur_hist_num_bin > max_num_bin_per_partition) { + feature_partition_column_index_offsets_.emplace_back(column_index); + start_hist_offset = column_feature_hist_start; + partition_hist_offsets_.emplace_back(start_hist_offset); + small_bin_partitions_.emplace_back(num_feature_partitions_); + ++num_feature_partitions_; + } + column_hist_offsets_.emplace_back(column_hist_offsets[column_index] - start_hist_offset); + if (feature_group_index == num_feature_groups - 1) { + feature_partition_column_index_offsets_.emplace_back(column_index + 1); + partition_hist_offsets_.emplace_back(column_hist_offsets.back()); + small_bin_partitions_.emplace_back(num_feature_partitions_); + ++num_feature_partitions_; + } + ++column_index; + } else { + const int group_feature_index_start = feature_group_num_feature_offsets[feature_group_index]; + const int num_feature_in_group = feature_group_num_feature_offsets[feature_group_index + 1] - group_feature_index_start; + for (int sub_feature_index = 0; sub_feature_index < num_feature_in_group; ++sub_feature_index) { + const int feature_index = group_feature_index_start + sub_feature_index; + const uint32_t column_feature_hist_start = column_hist_offsets[column_index]; + const uint32_t column_feature_hist_end = column_hist_offsets[column_index + 1]; + const uint32_t num_bin_in_dense_group = column_feature_hist_end - column_feature_hist_start; + + // if one column has too many bins, use a separate partition for that column + if (num_bin_in_dense_group > max_num_bin_per_partition) { + feature_partition_column_index_offsets_.emplace_back(column_index + 1); + start_hist_offset = column_feature_hist_end; + partition_hist_offsets_.emplace_back(start_hist_offset); + large_bin_partitions_.emplace_back(num_feature_partitions_); + ++num_feature_partitions_; + column_hist_offsets_.emplace_back(0); + ++column_index; + continue; + } + + // try if adding this column exceed the maximum number per partition + const uint32_t cur_hist_num_bin = column_feature_hist_end - start_hist_offset; + if (cur_hist_num_bin > max_num_bin_per_partition) { + feature_partition_column_index_offsets_.emplace_back(column_index); + start_hist_offset = column_feature_hist_start; + partition_hist_offsets_.emplace_back(start_hist_offset); + small_bin_partitions_.emplace_back(num_feature_partitions_); + ++num_feature_partitions_; + } + column_hist_offsets_.emplace_back(column_hist_offsets[column_index] - start_hist_offset); + if (feature_group_index == num_feature_groups - 1 && sub_feature_index == num_feature_in_group - 1) { + CHECK_EQ(feature_index, num_feature_ - 1); + feature_partition_column_index_offsets_.emplace_back(column_index + 1); + partition_hist_offsets_.emplace_back(column_hist_offsets.back()); + small_bin_partitions_.emplace_back(num_feature_partitions_); + ++num_feature_partitions_; + } + ++column_index; + } + } + } + column_hist_offsets_.emplace_back(column_hist_offsets.back() - start_hist_offset); + max_num_column_per_partition_ = 0; + for (size_t i = 0; i < feature_partition_column_index_offsets_.size() - 1; ++i) { + const int num_column = feature_partition_column_index_offsets_[i + 1] - feature_partition_column_index_offsets_[i]; + if (num_column > max_num_column_per_partition_) { + max_num_column_per_partition_ = num_column; + } + } + + cuda_feature_partition_column_index_offsets_.InitFromHostVector(feature_partition_column_index_offsets_); + cuda_column_hist_offsets_.InitFromHostVector(column_hist_offsets_); + cuda_partition_hist_offsets_.InitFromHostVector(partition_hist_offsets_); +} + +template +void CUDARowData::GetDenseDataPartitioned(const BIN_TYPE* row_wise_data, std::vector* partitioned_data) { + const int num_total_columns = feature_partition_column_index_offsets_.back(); + partitioned_data->resize(static_cast(num_total_columns) * static_cast(num_data_), 0); + BIN_TYPE* out_data = partitioned_data->data(); + Threading::For(0, num_data_, 512, + [this, num_total_columns, row_wise_data, out_data] (int /*thread_index*/, data_size_t start, data_size_t end) { + for (size_t i = 0; i < feature_partition_column_index_offsets_.size() - 1; ++i) { + const int num_prev_columns = static_cast(feature_partition_column_index_offsets_[i]); + const size_t offset = static_cast(num_data_) * static_cast(num_prev_columns); + const int partition_column_start = feature_partition_column_index_offsets_[i]; + const int partition_column_end = feature_partition_column_index_offsets_[i + 1]; + const int num_columns_in_cur_partition = partition_column_end - partition_column_start; + for (data_size_t data_index = start; data_index < end; ++data_index) { + const size_t data_offset = offset + static_cast(data_index) * num_columns_in_cur_partition; + const size_t read_data_offset = static_cast(data_index) * num_total_columns; + for (int column_index = 0; column_index < num_columns_in_cur_partition; ++column_index) { + const size_t true_column_index = read_data_offset + column_index + partition_column_start; + const BIN_TYPE bin = row_wise_data[true_column_index]; + out_data[data_offset + column_index] = bin; + } + } + } + }); +} + +template +void CUDARowData::GetSparseDataPartitioned( + const BIN_TYPE* row_wise_data, + const DATA_PTR_TYPE* row_ptr, + std::vector>* partitioned_data, + std::vector>* partitioned_row_ptr, + std::vector* partition_ptr) { + const int num_partitions = static_cast(feature_partition_column_index_offsets_.size()) - 1; + partitioned_data->resize(num_partitions); + partitioned_row_ptr->resize(num_partitions); + std::vector thread_max_elements_per_row(num_threads_, 0); + Threading::For(0, num_partitions, 1, + [partitioned_data, partitioned_row_ptr, row_ptr, row_wise_data, &thread_max_elements_per_row, this] (int thread_index, int start, int end) { + for (int partition_index = start; partition_index < end; ++partition_index) { + std::vector& data_for_this_partition = partitioned_data->at(partition_index); + std::vector& row_ptr_for_this_partition = partitioned_row_ptr->at(partition_index); + const int partition_hist_start = partition_hist_offsets_[partition_index]; + const int partition_hist_end = partition_hist_offsets_[partition_index + 1]; + DATA_PTR_TYPE offset = 0; + row_ptr_for_this_partition.clear(); + data_for_this_partition.clear(); + row_ptr_for_this_partition.emplace_back(offset); + for (data_size_t data_index = 0; data_index < num_data_; ++data_index) { + const DATA_PTR_TYPE row_start = row_ptr[data_index]; + const DATA_PTR_TYPE row_end = row_ptr[data_index + 1]; + const BIN_TYPE* row_data_start = row_wise_data + row_start; + const BIN_TYPE* row_data_end = row_wise_data + row_end; + const size_t partition_start_in_row = std::lower_bound(row_data_start, row_data_end, partition_hist_start) - row_data_start; + const size_t partition_end_in_row = std::lower_bound(row_data_start, row_data_end, partition_hist_end) - row_data_start; + for (size_t pos = partition_start_in_row; pos < partition_end_in_row; ++pos) { + const BIN_TYPE bin = row_data_start[pos]; + CHECK_GE(bin, static_cast(partition_hist_start)); + data_for_this_partition.emplace_back(bin - partition_hist_start); + } + CHECK_GE(partition_end_in_row, partition_start_in_row); + const data_size_t num_elements_in_row = partition_end_in_row - partition_start_in_row; + offset += static_cast(num_elements_in_row); + row_ptr_for_this_partition.emplace_back(offset); + if (num_elements_in_row > thread_max_elements_per_row[thread_index]) { + thread_max_elements_per_row[thread_index] = num_elements_in_row; + } + } + } + }); + partition_ptr->clear(); + DATA_PTR_TYPE offset = 0; + partition_ptr->emplace_back(offset); + for (size_t i = 0; i < partitioned_row_ptr->size(); ++i) { + offset += partitioned_row_ptr->at(i).back(); + partition_ptr->emplace_back(offset); + } + max_num_column_per_partition_ = 0; + for (int thread_index = 0; thread_index < num_threads_; ++thread_index) { + if (thread_max_elements_per_row[thread_index] > max_num_column_per_partition_) { + max_num_column_per_partition_ = thread_max_elements_per_row[thread_index]; + } + } +} + +template +void CUDARowData::InitSparseData(const BIN_TYPE* host_data, + const ROW_PTR_TYPE* host_row_ptr, + CUDAVector* cuda_data, + CUDAVector* cuda_row_ptr, + CUDAVector* cuda_partition_ptr) { + std::vector> partitioned_data; + std::vector> partitioned_data_ptr; + std::vector partition_ptr; + GetSparseDataPartitioned(host_data, host_row_ptr, &partitioned_data, &partitioned_data_ptr, &partition_ptr); + cuda_partition_ptr->InitFromHostVector(partition_ptr); + cuda_data->Resize(partition_ptr.back()); + cuda_row_ptr->Resize((num_data_ + 1) * partitioned_data_ptr.size()); + for (size_t i = 0; i < partitioned_data.size(); ++i) { + const std::vector& data_ptr_for_this_partition = partitioned_data_ptr[i]; + const std::vector& data_for_this_partition = partitioned_data[i]; + CopyFromHostToCUDADevice(cuda_data->RawData() + partition_ptr[i], data_for_this_partition.data(), data_for_this_partition.size(), __FILE__, __LINE__); + CopyFromHostToCUDADevice(cuda_row_ptr->RawData() + i * (num_data_ + 1), data_ptr_for_this_partition.data(), data_ptr_for_this_partition.size(), __FILE__, __LINE__); + } +} + +template +const BIN_TYPE* CUDARowData::GetBin() const { + if (bit_type_ == 8) { + return reinterpret_cast(cuda_data_uint8_t_.RawData()); + } else if (bit_type_ == 16) { + return reinterpret_cast(cuda_data_uint16_t_.RawData()); + } else if (bit_type_ == 32) { + return reinterpret_cast(cuda_data_uint32_t_.RawData()); + } else { + Log::Fatal("Unknown bit_type %d for GetBin.", bit_type_); + } +} + +template const uint8_t* CUDARowData::GetBin() const; + +template const uint16_t* CUDARowData::GetBin() const; + +template const uint32_t* CUDARowData::GetBin() const; + +template +const PTR_TYPE* CUDARowData::GetRowPtr() const { + if (row_ptr_bit_type_ == 16) { + return reinterpret_cast(cuda_row_ptr_uint16_t_.RawData()); + } else if (row_ptr_bit_type_ == 32) { + return reinterpret_cast(cuda_row_ptr_uint32_t_.RawData()); + } else if (row_ptr_bit_type_ == 64) { + return reinterpret_cast(cuda_row_ptr_uint64_t_.RawData()); + } else { + Log::Fatal("Unknown row_ptr_bit_type = %d for GetRowPtr.", row_ptr_bit_type_); + } +} + +template const uint16_t* CUDARowData::GetRowPtr() const; + +template const uint32_t* CUDARowData::GetRowPtr() const; + +template const uint64_t* CUDARowData::GetRowPtr() const; + +template +const PTR_TYPE* CUDARowData::GetPartitionPtr() const { + if (row_ptr_bit_type_ == 16) { + return reinterpret_cast(cuda_partition_ptr_uint16_t_.RawData()); + } else if (row_ptr_bit_type_ == 32) { + return reinterpret_cast(cuda_partition_ptr_uint32_t_.RawData()); + } else if (row_ptr_bit_type_ == 64) { + return reinterpret_cast(cuda_partition_ptr_uint64_t_.RawData()); + } else { + Log::Fatal("Unknown row_ptr_bit_type = %d for GetPartitionPtr.", row_ptr_bit_type_); + } +} + +template const uint16_t* CUDARowData::GetPartitionPtr() const; + +template const uint32_t* CUDARowData::GetPartitionPtr() const; + +template const uint64_t* CUDARowData::GetPartitionPtr() const; + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/io/cuda/cuda_tree.cpp b/src/io/cuda/cuda_tree.cpp new file mode 100644 index 0000000..5752f7d --- /dev/null +++ b/src/io/cuda/cuda_tree.cpp @@ -0,0 +1,214 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifdef USE_CUDA + +#include + +namespace LightGBM { + +CUDATree::CUDATree(int max_leaves, bool track_branch_features, bool is_linear, + const int gpu_device_id, const bool has_categorical_feature): +Tree(max_leaves, track_branch_features, is_linear), +num_threads_per_block_add_prediction_to_score_(1024) { + is_cuda_tree_ = true; + if (gpu_device_id >= 0) { + SetCUDADevice(gpu_device_id, __FILE__, __LINE__); + } else { + SetCUDADevice(0, __FILE__, __LINE__); + } + if (has_categorical_feature) { + cuda_cat_boundaries_.Resize(max_leaves); + cuda_cat_boundaries_inner_.Resize(max_leaves); + } + InitCUDAMemory(); +} + +CUDATree::CUDATree(const Tree* host_tree): + Tree(*host_tree), + num_threads_per_block_add_prediction_to_score_(1024) { + is_cuda_tree_ = true; + InitCUDA(); +} + +CUDATree::~CUDATree() { + gpuAssert(cudaStreamDestroy(cuda_stream_), __FILE__, __LINE__); +} + +void CUDATree::InitCUDAMemory() { + cuda_left_child_.Resize(static_cast(max_leaves_)); + cuda_right_child_.Resize(static_cast(max_leaves_)); + cuda_split_feature_inner_.Resize(static_cast(max_leaves_)); + cuda_split_feature_.Resize(static_cast(max_leaves_)); + cuda_leaf_depth_.Resize(static_cast(max_leaves_)); + cuda_leaf_parent_.Resize(static_cast(max_leaves_)); + cuda_threshold_in_bin_.Resize(static_cast(max_leaves_)); + cuda_threshold_.Resize(static_cast(max_leaves_)); + cuda_decision_type_.Resize(static_cast(max_leaves_)); + cuda_leaf_value_.Resize(static_cast(max_leaves_)); + cuda_internal_weight_.Resize(static_cast(max_leaves_)); + cuda_internal_value_.Resize(static_cast(max_leaves_)); + cuda_leaf_weight_.Resize(static_cast(max_leaves_)); + cuda_leaf_count_.Resize(static_cast(max_leaves_)); + cuda_internal_count_.Resize(static_cast(max_leaves_)); + cuda_split_gain_.Resize(static_cast(max_leaves_)); + SetCUDAMemory(cuda_leaf_value_.RawData(), 0.0f, 1, __FILE__, __LINE__); + SetCUDAMemory(cuda_leaf_weight_.RawData(), 0.0f, 1, __FILE__, __LINE__); + SetCUDAMemory(cuda_leaf_parent_.RawData(), -1, 1, __FILE__, __LINE__); + CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream_)); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDATree::InitCUDA() { + cuda_left_child_.InitFromHostVector(left_child_); + cuda_right_child_.InitFromHostVector(right_child_); + cuda_split_feature_inner_.InitFromHostVector(split_feature_inner_); + cuda_split_feature_.InitFromHostVector(split_feature_); + cuda_threshold_in_bin_.InitFromHostVector(threshold_in_bin_); + cuda_threshold_.InitFromHostVector(threshold_); + cuda_leaf_depth_.InitFromHostVector(leaf_depth_); + cuda_decision_type_.InitFromHostVector(decision_type_); + cuda_internal_weight_.InitFromHostVector(internal_weight_); + cuda_internal_value_.InitFromHostVector(internal_value_); + cuda_internal_count_.InitFromHostVector(internal_count_); + cuda_leaf_count_.InitFromHostVector(leaf_count_); + cuda_split_gain_.InitFromHostVector(split_gain_); + cuda_leaf_value_.InitFromHostVector(leaf_value_); + cuda_leaf_weight_.InitFromHostVector(leaf_weight_); + cuda_leaf_parent_.InitFromHostVector(leaf_parent_); + CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream_)); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +int CUDATree::Split(const int leaf_index, + const int real_feature_index, + const double real_threshold, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info) { + LaunchSplitKernel(leaf_index, real_feature_index, real_threshold, missing_type, cuda_split_info); + RecordBranchFeatures(leaf_index, num_leaves_, real_feature_index); + ++num_leaves_; + return num_leaves_ - 1; +} + +int CUDATree::SplitCategorical(const int leaf_index, + const int real_feature_index, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info, + uint32_t* cuda_bitset, + size_t cuda_bitset_len, + uint32_t* cuda_bitset_inner, + size_t cuda_bitset_inner_len) { + LaunchSplitCategoricalKernel(leaf_index, real_feature_index, + missing_type, cuda_split_info, + cuda_bitset_len, cuda_bitset_inner_len); + cuda_bitset_.PushBack(cuda_bitset, cuda_bitset_len); + cuda_bitset_inner_.PushBack(cuda_bitset_inner, cuda_bitset_inner_len); + ++num_leaves_; + ++num_cat_; + RecordBranchFeatures(leaf_index, num_leaves_, real_feature_index); + return num_leaves_ - 1; +} + +void CUDATree::RecordBranchFeatures(const int left_leaf_index, + const int right_leaf_index, + const int real_feature_index) { + if (track_branch_features_) { + branch_features_[right_leaf_index] = branch_features_[left_leaf_index]; + branch_features_[right_leaf_index].push_back(real_feature_index); + branch_features_[left_leaf_index].push_back(real_feature_index); + } +} + +void CUDATree::AddPredictionToScore(const Dataset* data, + data_size_t num_data, + double* score) const { + LaunchAddPredictionToScoreKernel(data, nullptr, num_data, score); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDATree::AddPredictionToScore(const Dataset* data, + const data_size_t* used_data_indices, + data_size_t num_data, double* score) const { + LaunchAddPredictionToScoreKernel(data, used_data_indices, num_data, score); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +inline void CUDATree::Shrinkage(double rate) { + Tree::Shrinkage(rate); + LaunchShrinkageKernel(rate); +} + +inline void CUDATree::AddBias(double val) { + Tree::AddBias(val); + LaunchAddBiasKernel(val); +} + +void CUDATree::ToHost() { + left_child_.resize(max_leaves_ - 1); + right_child_.resize(max_leaves_ - 1); + split_feature_inner_.resize(max_leaves_ - 1); + split_feature_.resize(max_leaves_ - 1); + threshold_in_bin_.resize(max_leaves_ - 1); + threshold_.resize(max_leaves_ - 1); + decision_type_.resize(max_leaves_ - 1, 0); + split_gain_.resize(max_leaves_ - 1); + leaf_parent_.resize(max_leaves_); + leaf_value_.resize(max_leaves_); + leaf_weight_.resize(max_leaves_); + leaf_count_.resize(max_leaves_); + internal_value_.resize(max_leaves_ - 1); + internal_weight_.resize(max_leaves_ - 1); + internal_count_.resize(max_leaves_ - 1); + leaf_depth_.resize(max_leaves_); + + const size_t num_leaves_size = static_cast(num_leaves_); + CopyFromCUDADeviceToHost(left_child_.data(), cuda_left_child_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(right_child_.data(), cuda_right_child_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(split_feature_inner_.data(), cuda_split_feature_inner_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(split_feature_.data(), cuda_split_feature_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(threshold_in_bin_.data(), cuda_threshold_in_bin_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(threshold_.data(), cuda_threshold_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(decision_type_.data(), cuda_decision_type_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(split_gain_.data(), cuda_split_gain_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(leaf_parent_.data(), cuda_leaf_parent_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(leaf_value_.data(), cuda_leaf_value_.RawData(), num_leaves_size, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(leaf_weight_.data(), cuda_leaf_weight_.RawData(), num_leaves_size, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(leaf_count_.data(), cuda_leaf_count_.RawData(), num_leaves_size, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(internal_value_.data(), cuda_internal_value_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(internal_weight_.data(), cuda_internal_weight_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(internal_count_.data(), cuda_internal_count_.RawData(), num_leaves_size - 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(leaf_depth_.data(), cuda_leaf_depth_.RawData(), num_leaves_size, __FILE__, __LINE__); + + if (num_cat_ > 0) { + cuda_cat_boundaries_inner_.Resize(num_cat_ + 1); + cuda_cat_boundaries_.Resize(num_cat_ + 1); + cat_boundaries_ = cuda_cat_boundaries_.ToHost(); + cat_boundaries_inner_ = cuda_cat_boundaries_inner_.ToHost(); + cat_threshold_ = cuda_bitset_.ToHost(); + cat_threshold_inner_ = cuda_bitset_inner_.ToHost(); + } + + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDATree::SyncLeafOutputFromHostToCUDA() { + CopyFromHostToCUDADevice(cuda_leaf_value_.RawData(), leaf_value_.data(), leaf_value_.size(), __FILE__, __LINE__); +} + +void CUDATree::SyncLeafOutputFromCUDAToHost() { + CopyFromCUDADeviceToHost(leaf_value_.data(), cuda_leaf_value_.RawData(), leaf_value_.size(), __FILE__, __LINE__); +} + +void CUDATree::AsConstantTree(double val, int count) { + Tree::AsConstantTree(val, count); + CopyFromHostToCUDADevice(cuda_leaf_value_.RawData(), &val, 1, __FILE__, __LINE__); + CopyFromHostToCUDADevice(cuda_leaf_count_.RawData(), &count, 1, __FILE__, __LINE__); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/io/cuda/cuda_tree.cu b/src/io/cuda/cuda_tree.cu new file mode 100644 index 0000000..8b8c5ee --- /dev/null +++ b/src/io/cuda/cuda_tree.cu @@ -0,0 +1,460 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + + +#ifdef USE_CUDA + +#include + +namespace LightGBM { + +__device__ void SetDecisionTypeCUDA(int8_t* decision_type, bool input, int8_t mask) { + if (input) { + (*decision_type) |= mask; + } else { + (*decision_type) &= (127 - mask); + } +} + +__device__ void SetMissingTypeCUDA(int8_t* decision_type, int8_t input) { + (*decision_type) &= 3; + (*decision_type) |= (input << 2); +} + +__device__ bool GetDecisionTypeCUDA(int8_t decision_type, int8_t mask) { + return (decision_type & mask) > 0; +} + +__device__ int8_t GetMissingTypeCUDA(int8_t decision_type) { + return (decision_type >> 2) & 3; +} + +__device__ bool IsZeroCUDA(double fval) { + return (fval >= -kZeroThreshold && fval <= kZeroThreshold); +} + +template +__device__ bool FindInBitsetCUDA(const uint32_t* bits, int n, T pos) { + int i1 = pos / 32; + if (i1 >= n) { + return false; + } + int i2 = pos % 32; + return (bits[i1] >> i2) & 1; +} + +__global__ void SplitKernel( // split information + const int leaf_index, + const int real_feature_index, + const double real_threshold, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info, + // tree structure + const int num_leaves, + int* leaf_parent, + int* leaf_depth, + int* left_child, + int* right_child, + int* split_feature_inner, + int* split_feature, + float* split_gain, + double* internal_weight, + double* internal_value, + data_size_t* internal_count, + double* leaf_weight, + double* leaf_value, + data_size_t* leaf_count, + int8_t* decision_type, + uint32_t* threshold_in_bin, + double* threshold) { + const int new_node_index = num_leaves - 1; + const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + const int parent_index = leaf_parent[leaf_index]; + if (thread_index == 0) { + if (parent_index >= 0) { + // if cur node is left child + if (left_child[parent_index] == ~leaf_index) { + left_child[parent_index] = new_node_index; + } else { + right_child[parent_index] = new_node_index; + } + } + left_child[new_node_index] = ~leaf_index; + right_child[new_node_index] = ~num_leaves; + leaf_parent[leaf_index] = new_node_index; + leaf_parent[num_leaves] = new_node_index; + } else if (thread_index == 1) { + // add new node + split_feature_inner[new_node_index] = cuda_split_info->inner_feature_index; + } else if (thread_index == 2) { + split_feature[new_node_index] = real_feature_index; + } else if (thread_index == 3) { + split_gain[new_node_index] = static_cast(cuda_split_info->gain); + } else if (thread_index == 4) { + // save current leaf value to internal node before change + internal_weight[new_node_index] = cuda_split_info->left_sum_hessians + cuda_split_info->right_sum_hessians; + leaf_weight[leaf_index] = cuda_split_info->left_sum_hessians; + } else if (thread_index == 5) { + internal_value[new_node_index] = leaf_value[leaf_index]; + leaf_value[leaf_index] = isnan(cuda_split_info->left_value) ? 0.0f : cuda_split_info->left_value; + } else if (thread_index == 6) { + internal_count[new_node_index] = cuda_split_info->left_count + cuda_split_info->right_count; + } else if (thread_index == 7) { + leaf_count[leaf_index] = cuda_split_info->left_count; + } else if (thread_index == 8) { + leaf_value[num_leaves] = isnan(cuda_split_info->right_value) ? 0.0f : cuda_split_info->right_value; + } else if (thread_index == 9) { + leaf_weight[num_leaves] = cuda_split_info->right_sum_hessians; + } else if (thread_index == 10) { + leaf_count[num_leaves] = cuda_split_info->right_count; + } else if (thread_index == 11) { + // update leaf depth + leaf_depth[num_leaves] = leaf_depth[leaf_index] + 1; + leaf_depth[leaf_index]++; + } else if (thread_index == 12) { + decision_type[new_node_index] = 0; + SetDecisionTypeCUDA(&decision_type[new_node_index], false, kCategoricalMask); + SetDecisionTypeCUDA(&decision_type[new_node_index], cuda_split_info->default_left, kDefaultLeftMask); + SetMissingTypeCUDA(&decision_type[new_node_index], static_cast(missing_type)); + } else if (thread_index == 13) { + threshold_in_bin[new_node_index] = cuda_split_info->threshold; + } else if (thread_index == 14) { + threshold[new_node_index] = real_threshold; + } +} + +void CUDATree::LaunchSplitKernel(const int leaf_index, + const int real_feature_index, + const double real_threshold, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info) { + SplitKernel<<<3, 5, 0, cuda_stream_>>>( + // split information + leaf_index, + real_feature_index, + real_threshold, + missing_type, + cuda_split_info, + // tree structure + num_leaves_, + cuda_leaf_parent_.RawData(), + cuda_leaf_depth_.RawData(), + cuda_left_child_.RawData(), + cuda_right_child_.RawData(), + cuda_split_feature_inner_.RawData(), + cuda_split_feature_.RawData(), + cuda_split_gain_.RawData(), + cuda_internal_weight_.RawData(), + cuda_internal_value_.RawData(), + cuda_internal_count_.RawData(), + cuda_leaf_weight_.RawData(), + cuda_leaf_value_.RawData(), + cuda_leaf_count_.RawData(), + cuda_decision_type_.RawData(), + cuda_threshold_in_bin_.RawData(), + cuda_threshold_.RawData()); +} + +__global__ void SplitCategoricalKernel( // split information + const int leaf_index, + const int real_feature_index, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info, + // tree structure + const int num_leaves, + int* leaf_parent, + int* leaf_depth, + int* left_child, + int* right_child, + int* split_feature_inner, + int* split_feature, + float* split_gain, + double* internal_weight, + double* internal_value, + data_size_t* internal_count, + double* leaf_weight, + double* leaf_value, + data_size_t* leaf_count, + int8_t* decision_type, + uint32_t* threshold_in_bin, + double* threshold, + size_t cuda_bitset_len, + size_t cuda_bitset_inner_len, + int num_cat, + int* cuda_cat_boundaries, + int* cuda_cat_boundaries_inner) { + const int new_node_index = num_leaves - 1; + const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + const int parent_index = leaf_parent[leaf_index]; + if (thread_index == 0) { + if (parent_index >= 0) { + // if cur node is left child + if (left_child[parent_index] == ~leaf_index) { + left_child[parent_index] = new_node_index; + } else { + right_child[parent_index] = new_node_index; + } + } + left_child[new_node_index] = ~leaf_index; + right_child[new_node_index] = ~num_leaves; + leaf_parent[leaf_index] = new_node_index; + leaf_parent[num_leaves] = new_node_index; + } else if (thread_index == 1) { + // add new node + split_feature_inner[new_node_index] = cuda_split_info->inner_feature_index; + } else if (thread_index == 2) { + split_feature[new_node_index] = real_feature_index; + } else if (thread_index == 3) { + split_gain[new_node_index] = static_cast(cuda_split_info->gain); + } else if (thread_index == 4) { + // save current leaf value to internal node before change + internal_weight[new_node_index] = cuda_split_info->left_sum_hessians + cuda_split_info->right_sum_hessians; + leaf_weight[leaf_index] = cuda_split_info->left_sum_hessians; + } else if (thread_index == 5) { + internal_value[new_node_index] = leaf_value[leaf_index]; + leaf_value[leaf_index] = isnan(cuda_split_info->left_value) ? 0.0f : cuda_split_info->left_value; + } else if (thread_index == 6) { + internal_count[new_node_index] = cuda_split_info->left_count + cuda_split_info->right_count; + } else if (thread_index == 7) { + leaf_count[leaf_index] = cuda_split_info->left_count; + } else if (thread_index == 8) { + leaf_value[num_leaves] = isnan(cuda_split_info->right_value) ? 0.0f : cuda_split_info->right_value; + } else if (thread_index == 9) { + leaf_weight[num_leaves] = cuda_split_info->right_sum_hessians; + } else if (thread_index == 10) { + leaf_count[num_leaves] = cuda_split_info->right_count; + } else if (thread_index == 11) { + // update leaf depth + leaf_depth[num_leaves] = leaf_depth[leaf_index] + 1; + leaf_depth[leaf_index]++; + } else if (thread_index == 12) { + decision_type[new_node_index] = 0; + SetDecisionTypeCUDA(&decision_type[new_node_index], true, kCategoricalMask); + SetMissingTypeCUDA(&decision_type[new_node_index], static_cast(missing_type)); + } else if (thread_index == 13) { + threshold_in_bin[new_node_index] = num_cat; + } else if (thread_index == 14) { + threshold[new_node_index] = num_cat; + } else if (thread_index == 15) { + if (num_cat == 0) { + cuda_cat_boundaries[num_cat] = 0; + } + cuda_cat_boundaries[num_cat + 1] = cuda_cat_boundaries[num_cat] + cuda_bitset_len; + } else if (thread_index == 16) { + if (num_cat == 0) { + cuda_cat_boundaries_inner[num_cat] = 0; + } + cuda_cat_boundaries_inner[num_cat + 1] = cuda_cat_boundaries_inner[num_cat] + cuda_bitset_inner_len; + } +} + +void CUDATree::LaunchSplitCategoricalKernel(const int leaf_index, + const int real_feature_index, + const MissingType missing_type, + const CUDASplitInfo* cuda_split_info, + size_t cuda_bitset_len, + size_t cuda_bitset_inner_len) { + SplitCategoricalKernel<<<3, 6, 0, cuda_stream_>>>( + // split information + leaf_index, + real_feature_index, + missing_type, + cuda_split_info, + // tree structure + num_leaves_, + cuda_leaf_parent_.RawData(), + cuda_leaf_depth_.RawData(), + cuda_left_child_.RawData(), + cuda_right_child_.RawData(), + cuda_split_feature_inner_.RawData(), + cuda_split_feature_.RawData(), + cuda_split_gain_.RawData(), + cuda_internal_weight_.RawData(), + cuda_internal_value_.RawData(), + cuda_internal_count_.RawData(), + cuda_leaf_weight_.RawData(), + cuda_leaf_value_.RawData(), + cuda_leaf_count_.RawData(), + cuda_decision_type_.RawData(), + cuda_threshold_in_bin_.RawData(), + cuda_threshold_.RawData(), + cuda_bitset_len, + cuda_bitset_inner_len, + num_cat_, + cuda_cat_boundaries_.RawData(), + cuda_cat_boundaries_inner_.RawData()); +} + +__global__ void ShrinkageKernel(const double rate, double* cuda_leaf_value, const int num_leaves) { + const int leaf_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + if (leaf_index < num_leaves) { + cuda_leaf_value[leaf_index] *= rate; + } +} + +void CUDATree::LaunchShrinkageKernel(const double rate) { + const int num_threads_per_block = 1024; + const int num_blocks = (num_leaves_ + num_threads_per_block - 1) / num_threads_per_block; + ShrinkageKernel<<>>(rate, cuda_leaf_value_.RawData(), num_leaves_); +} + +__global__ void AddBiasKernel(const double val, double* cuda_leaf_value, const int num_leaves) { + const int leaf_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + if (leaf_index < num_leaves) { + cuda_leaf_value[leaf_index] += val; + } +} + +void CUDATree::LaunchAddBiasKernel(const double val) { + const int num_threads_per_block = 1024; + const int num_blocks = (num_leaves_ + num_threads_per_block - 1) / num_threads_per_block; + AddBiasKernel<<>>(val, cuda_leaf_value_.RawData(), num_leaves_); +} + +template +__global__ void AddPredictionToScoreKernel( + // dataset information + const data_size_t num_data, + uint8_t* const* cuda_data_by_column, + const uint8_t* cuda_column_bit_type, + const uint32_t* cuda_feature_min_bin, + const uint32_t* cuda_feature_max_bin, + const uint32_t* cuda_feature_offset, + const uint32_t* cuda_feature_default_bin, + const uint32_t* cuda_feature_most_freq_bin, + const int* cuda_feature_to_column, + const data_size_t* cuda_used_indices, + // tree information + const uint32_t* cuda_threshold_in_bin, + const int8_t* cuda_decision_type, + const int* cuda_split_feature_inner, + const int* cuda_left_child, + const int* cuda_right_child, + const double* cuda_leaf_value, + const uint32_t* cuda_bitset_inner, + const int* cuda_cat_boundaries_inner, + // output + double* score) { + const data_size_t inner_data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (inner_data_index < num_data) { + const data_size_t data_index = USE_INDICES ? cuda_used_indices[inner_data_index] : inner_data_index; + int node = 0; + while (node >= 0) { + const int split_feature_inner = cuda_split_feature_inner[node]; + const int column = cuda_feature_to_column[split_feature_inner]; + const uint32_t default_bin = cuda_feature_default_bin[split_feature_inner]; + const uint32_t most_freq_bin = cuda_feature_most_freq_bin[split_feature_inner]; + const uint32_t max_bin = cuda_feature_max_bin[split_feature_inner]; + const uint32_t min_bin = cuda_feature_min_bin[split_feature_inner]; + const uint32_t offset = cuda_feature_offset[split_feature_inner]; + const uint8_t column_bit_type = cuda_column_bit_type[column]; + uint32_t bin = 0; + if (column_bit_type == 8) { + bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]); + } else if (column_bit_type == 16) { + bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]); + } else if (column_bit_type == 32) { + bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]); + } + if (bin >= min_bin && bin <= max_bin) { + bin = bin - min_bin + offset; + } else { + bin = most_freq_bin; + } + const int8_t decision_type = cuda_decision_type[node]; + if (GetDecisionTypeCUDA(decision_type, kCategoricalMask)) { + int cat_idx = static_cast(cuda_threshold_in_bin[node]); + if (FindInBitsetCUDA(cuda_bitset_inner + cuda_cat_boundaries_inner[cat_idx], + cuda_cat_boundaries_inner[cat_idx + 1] - cuda_cat_boundaries_inner[cat_idx], bin)) { + node = cuda_left_child[node]; + } else { + node = cuda_right_child[node]; + } + } else { + const uint32_t threshold_in_bin = cuda_threshold_in_bin[node]; + const int8_t missing_type = GetMissingTypeCUDA(decision_type); + const bool default_left = ((decision_type & kDefaultLeftMask) > 0); + if ((missing_type == 1 && bin == default_bin) || (missing_type == 2 && bin == max_bin)) { + if (default_left) { + node = cuda_left_child[node]; + } else { + node = cuda_right_child[node]; + } + } else { + if (bin <= threshold_in_bin) { + node = cuda_left_child[node]; + } else { + node = cuda_right_child[node]; + } + } + } + } + score[data_index] += cuda_leaf_value[~node]; + } +} + +void CUDATree::LaunchAddPredictionToScoreKernel( + const Dataset* data, + const data_size_t* used_data_indices, + data_size_t num_data, + double* score) const { + const CUDAColumnData* cuda_column_data = data->cuda_column_data(); + const int num_blocks = (num_data + num_threads_per_block_add_prediction_to_score_ - 1) / num_threads_per_block_add_prediction_to_score_; + if (used_data_indices == nullptr) { + AddPredictionToScoreKernel<<>>( + // dataset information + num_data, + cuda_column_data->cuda_data_by_column(), + cuda_column_data->cuda_column_bit_type(), + cuda_column_data->cuda_feature_min_bin(), + cuda_column_data->cuda_feature_max_bin(), + cuda_column_data->cuda_feature_offset(), + cuda_column_data->cuda_feature_default_bin(), + cuda_column_data->cuda_feature_most_freq_bin(), + cuda_column_data->cuda_feature_to_column(), + nullptr, + // tree information + cuda_threshold_in_bin_.RawData(), + cuda_decision_type_.RawData(), + cuda_split_feature_inner_.RawData(), + cuda_left_child_.RawData(), + cuda_right_child_.RawData(), + cuda_leaf_value_.RawData(), + cuda_bitset_inner_.RawDataReadOnly(), + cuda_cat_boundaries_inner_.RawDataReadOnly(), + // output + score); + } else { + AddPredictionToScoreKernel<<>>( + // dataset information + num_data, + cuda_column_data->cuda_data_by_column(), + cuda_column_data->cuda_column_bit_type(), + cuda_column_data->cuda_feature_min_bin(), + cuda_column_data->cuda_feature_max_bin(), + cuda_column_data->cuda_feature_offset(), + cuda_column_data->cuda_feature_default_bin(), + cuda_column_data->cuda_feature_most_freq_bin(), + cuda_column_data->cuda_feature_to_column(), + used_data_indices, + // tree information + cuda_threshold_in_bin_.RawData(), + cuda_decision_type_.RawData(), + cuda_split_feature_inner_.RawData(), + cuda_left_child_.RawData(), + cuda_right_child_.RawData(), + cuda_leaf_value_.RawData(), + cuda_bitset_inner_.RawDataReadOnly(), + cuda_cat_boundaries_inner_.RawDataReadOnly(), + // output + score); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/io/dataset.cpp b/src/io/dataset.cpp new file mode 100644 index 0000000..7a25f2c --- /dev/null +++ b/src/io/dataset.cpp @@ -0,0 +1,1955 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +const int Dataset::kSerializedReferenceVersionLength = 2; +const char* Dataset::serialized_reference_version = "v1"; + +const char* Dataset::binary_file_token = "______LightGBM_Binary_File_Token______\n"; +const char* Dataset::binary_serialized_reference_token = "______LightGBM_Binary_Serialized_Token______\n"; + +Dataset::Dataset() { + data_filename_ = "noname"; + num_data_ = 0; + is_finish_load_ = false; + wait_for_manual_finish_ = false; + has_raw_ = false; +} + +Dataset::Dataset(data_size_t num_data) { + CHECK_GT(num_data, 0); + data_filename_ = "noname"; + num_data_ = num_data; + metadata_.Init(num_data_, NO_SPECIFIC, NO_SPECIFIC); + is_finish_load_ = false; + wait_for_manual_finish_ = false; + group_bin_boundaries_.push_back(0); + has_raw_ = false; +} + +Dataset::~Dataset() {} + +std::vector> OneFeaturePerGroup(const std::vector& used_features) { + std::vector> features_in_group; + features_in_group.resize(used_features.size()); + for (size_t i = 0; i < used_features.size(); ++i) { + features_in_group[i].emplace_back(used_features[i]); + } + return features_in_group; +} + +int GetConflictCount(const std::vector& mark, const int* indices, + int num_indices, data_size_t max_cnt) { + int ret = 0; + for (int i = 0; i < num_indices; ++i) { + if (mark[indices[i]]) { + ++ret; + } + if (ret > max_cnt) { + return -1; + } + } + return ret; +} + +void MarkUsed(std::vector* mark, const int* indices, + data_size_t num_indices) { + auto& ref_mark = *mark; + for (int i = 0; i < num_indices; ++i) { + ref_mark[indices[i]] = true; + } +} + +std::vector FixSampleIndices(const BinMapper* bin_mapper, + int num_total_samples, int num_indices, + const int* sample_indices, + const double* sample_values) { + std::vector ret; + if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) { + return ret; + } + int i = 0, j = 0; + while (i < num_total_samples) { + if (j < num_indices && sample_indices[j] < i) { + ++j; + } else if (j < num_indices && sample_indices[j] == i) { + if (bin_mapper->ValueToBin(sample_values[j]) != + bin_mapper->GetMostFreqBin()) { + ret.push_back(i); + } + ++i; + } else { + ret.push_back(i++); + } + } + return ret; +} + +std::vector> FindGroups( + const std::vector>& bin_mappers, + const std::vector& find_order, int** sample_indices, + const int* num_per_col, int num_sample_col, data_size_t total_sample_cnt, + data_size_t num_data, bool is_use_gpu, bool is_sparse, + std::vector* multi_val_group) { + const int max_search_group = 100; + const int max_bin_per_group = 256; + const data_size_t single_val_max_conflict_cnt = + static_cast(total_sample_cnt / 10000); + multi_val_group->clear(); + + Random rand(num_data); + std::vector> features_in_group; + std::vector> conflict_marks; + std::vector group_used_row_cnt; + std::vector group_total_data_cnt; + std::vector group_num_bin; + + // first round: fill the single val group + for (auto fidx : find_order) { + bool is_filtered_feature = fidx >= num_sample_col; + const data_size_t cur_non_zero_cnt = + is_filtered_feature ? 0 : num_per_col[fidx]; + std::vector available_groups; + for (int gid = 0; gid < static_cast(features_in_group.size()); ++gid) { + auto cur_num_bin = group_num_bin[gid] + bin_mappers[fidx]->num_bin() + + (bin_mappers[fidx]->GetMostFreqBin() == 0 ? -1 : 0); + if (group_total_data_cnt[gid] + cur_non_zero_cnt <= + total_sample_cnt + single_val_max_conflict_cnt) { + if (!is_use_gpu || cur_num_bin <= max_bin_per_group) { + available_groups.push_back(gid); + } + } + } + std::vector search_groups; + if (!available_groups.empty()) { + int last = static_cast(available_groups.size()) - 1; + auto indices = rand.Sample(last, std::min(last, max_search_group - 1)); + // always push the last group + search_groups.push_back(available_groups.back()); + for (auto idx : indices) { + search_groups.push_back(available_groups[idx]); + } + } + int best_gid = -1; + int best_conflict_cnt = -1; + for (auto gid : search_groups) { + const data_size_t rest_max_cnt = single_val_max_conflict_cnt - + group_total_data_cnt[gid] + + group_used_row_cnt[gid]; + const data_size_t cnt = + is_filtered_feature + ? 0 + : GetConflictCount(conflict_marks[gid], sample_indices[fidx], + num_per_col[fidx], rest_max_cnt); + if (cnt >= 0 && cnt <= rest_max_cnt && cnt <= cur_non_zero_cnt / 2) { + best_gid = gid; + best_conflict_cnt = cnt; + break; + } + } + if (best_gid >= 0) { + features_in_group[best_gid].push_back(fidx); + group_total_data_cnt[best_gid] += cur_non_zero_cnt; + group_used_row_cnt[best_gid] += cur_non_zero_cnt - best_conflict_cnt; + if (!is_filtered_feature) { + MarkUsed(&conflict_marks[best_gid], sample_indices[fidx], + num_per_col[fidx]); + } + group_num_bin[best_gid] += + bin_mappers[fidx]->num_bin() + + (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0); + } else { + features_in_group.emplace_back(); + features_in_group.back().push_back(fidx); + conflict_marks.emplace_back(total_sample_cnt, false); + if (!is_filtered_feature) { + MarkUsed(&(conflict_marks.back()), sample_indices[fidx], + num_per_col[fidx]); + } + group_total_data_cnt.emplace_back(cur_non_zero_cnt); + group_used_row_cnt.emplace_back(cur_non_zero_cnt); + group_num_bin.push_back( + 1 + bin_mappers[fidx]->num_bin() + + (bin_mappers[fidx]->GetMostFreqBin() == 0 ? -1 : 0)); + } + } + if (!is_sparse) { + multi_val_group->resize(features_in_group.size(), false); + return features_in_group; + } + std::vector second_round_features; + std::vector> features_in_group2; + std::vector> conflict_marks2; + + const double dense_threshold = 0.4; + for (int gid = 0; gid < static_cast(features_in_group.size()); ++gid) { + const double dense_rate = + static_cast(group_used_row_cnt[gid]) / total_sample_cnt; + if (dense_rate >= dense_threshold) { + features_in_group2.push_back(std::move(features_in_group[gid])); + conflict_marks2.push_back(std::move(conflict_marks[gid])); + } else { + for (auto fidx : features_in_group[gid]) { + second_round_features.push_back(fidx); + } + } + } + + features_in_group = features_in_group2; + conflict_marks = conflict_marks2; + multi_val_group->resize(features_in_group.size(), false); + if (!second_round_features.empty()) { + features_in_group.emplace_back(); + conflict_marks.emplace_back(total_sample_cnt, false); + bool is_multi_val = is_use_gpu ? true : false; + int conflict_cnt = 0; + for (auto fidx : second_round_features) { + features_in_group.back().push_back(fidx); + if (!is_multi_val) { + const int rest_max_cnt = single_val_max_conflict_cnt - conflict_cnt; + const auto cnt = + GetConflictCount(conflict_marks.back(), sample_indices[fidx], + num_per_col[fidx], rest_max_cnt); + conflict_cnt += cnt; + if (cnt < 0 || conflict_cnt > single_val_max_conflict_cnt) { + is_multi_val = true; + continue; + } + MarkUsed(&(conflict_marks.back()), sample_indices[fidx], + num_per_col[fidx]); + } + } + multi_val_group->push_back(is_multi_val); + } + return features_in_group; +} + +std::vector> FastFeatureBundling( + const std::vector>& bin_mappers, + int** sample_indices, double** sample_values, const int* num_per_col, + int num_sample_col, data_size_t total_sample_cnt, + const std::vector& used_features, data_size_t num_data, + bool is_use_gpu, bool is_sparse, std::vector* multi_val_group) { + Common::FunctionTimer fun_timer("Dataset::FastFeatureBundling", global_timer); + std::vector feature_non_zero_cnt; + feature_non_zero_cnt.reserve(used_features.size()); + // put dense feature first + for (auto fidx : used_features) { + if (fidx < num_sample_col) { + feature_non_zero_cnt.emplace_back(num_per_col[fidx]); + } else { + feature_non_zero_cnt.emplace_back(0); + } + } + // sort by non zero cnt + std::vector sorted_idx; + sorted_idx.reserve(used_features.size()); + for (int i = 0; i < static_cast(used_features.size()); ++i) { + sorted_idx.emplace_back(i); + } + // sort by non zero cnt, bigger first + std::stable_sort(sorted_idx.begin(), sorted_idx.end(), + [&feature_non_zero_cnt](int a, int b) { + return feature_non_zero_cnt[a] > feature_non_zero_cnt[b]; + }); + + std::vector feature_order_by_cnt; + feature_order_by_cnt.reserve(sorted_idx.size()); + for (auto sidx : sorted_idx) { + feature_order_by_cnt.push_back(used_features[sidx]); + } + + std::vector> tmp_indices; + std::vector tmp_num_per_col(num_sample_col, 0); + for (auto fidx : used_features) { + if (fidx >= num_sample_col) { + continue; + } + auto ret = FixSampleIndices( + bin_mappers[fidx].get(), static_cast(total_sample_cnt), + num_per_col[fidx], sample_indices[fidx], sample_values[fidx]); + if (!ret.empty()) { + tmp_indices.push_back(ret); + tmp_num_per_col[fidx] = static_cast(ret.size()); + sample_indices[fidx] = tmp_indices.back().data(); + } else { + tmp_num_per_col[fidx] = num_per_col[fidx]; + } + } + std::vector group_is_multi_val, group_is_multi_val2; + auto features_in_group = + FindGroups(bin_mappers, used_features, sample_indices, + tmp_num_per_col.data(), num_sample_col, total_sample_cnt, + num_data, is_use_gpu, is_sparse, &group_is_multi_val); + auto group2 = + FindGroups(bin_mappers, feature_order_by_cnt, sample_indices, + tmp_num_per_col.data(), num_sample_col, total_sample_cnt, + num_data, is_use_gpu, is_sparse, &group_is_multi_val2); + + if (features_in_group.size() > group2.size()) { + features_in_group = group2; + group_is_multi_val = group_is_multi_val2; + } + // shuffle groups + int num_group = static_cast(features_in_group.size()); + Random tmp_rand(num_data); + for (int i = 0; i < num_group - 1; ++i) { + int j = tmp_rand.NextShort(i + 1, num_group); + std::swap(features_in_group[i], features_in_group[j]); + // Using std::swap for vector will cause the wrong result. + std::swap(group_is_multi_val[i], group_is_multi_val[j]); + } + *multi_val_group = group_is_multi_val; + return features_in_group; +} + +void Dataset::Construct(std::vector>* bin_mappers, + int num_total_features, + const std::vector>& forced_bins, + int** sample_non_zero_indices, + double** sample_values, + const int* num_per_col, + int num_sample_col, + size_t total_sample_cnt, + const Config& io_config) { + num_total_features_ = num_total_features; + CHECK_EQ(num_total_features_, static_cast(bin_mappers->size())); + // get num_features + std::vector used_features; + auto& ref_bin_mappers = *bin_mappers; + for (int i = 0; i < static_cast(bin_mappers->size()); ++i) { + if (ref_bin_mappers[i] != nullptr && !ref_bin_mappers[i]->is_trivial()) { + used_features.emplace_back(i); + } + } + if (used_features.empty()) { + Log::Warning( + "There are no meaningful features which satisfy the provided configuration. " + "Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing " + "Dataset might resolve this warning."); + } + auto features_in_group = OneFeaturePerGroup(used_features); + + auto is_sparse = io_config.is_enable_sparse; + if (io_config.device_type == std::string("cuda")) { + LGBM_config_::current_device = lgbm_device_cuda; + if ((io_config.device_type == std::string("cuda")) && is_sparse) { + Log::Warning("Using sparse features with CUDA is currently not supported."); + is_sparse = false; + } + } + + std::vector group_is_multi_val(used_features.size(), 0); + if (io_config.enable_bundle && !used_features.empty()) { + bool lgbm_is_gpu_used = io_config.device_type == std::string("gpu") || io_config.device_type == std::string("cuda"); + features_in_group = FastFeatureBundling( + *bin_mappers, sample_non_zero_indices, sample_values, num_per_col, + num_sample_col, static_cast(total_sample_cnt), + used_features, num_data_, lgbm_is_gpu_used, + is_sparse, &group_is_multi_val); + } + + num_features_ = 0; + for (const auto& fs : features_in_group) { + num_features_ += static_cast(fs.size()); + } + int cur_fidx = 0; + used_feature_map_ = std::vector(num_total_features_, -1); + num_groups_ = static_cast(features_in_group.size()); + real_feature_idx_.resize(num_features_); + feature2group_.resize(num_features_); + feature2subfeature_.resize(num_features_); + feature_need_push_zeros_.clear(); + group_bin_boundaries_.clear(); + uint64_t num_total_bin = 0; + group_bin_boundaries_.push_back(num_total_bin); + group_feature_start_.resize(num_groups_); + group_feature_cnt_.resize(num_groups_); + for (int i = 0; i < num_groups_; ++i) { + auto cur_features = features_in_group[i]; + int cur_cnt_features = static_cast(cur_features.size()); + group_feature_start_[i] = cur_fidx; + group_feature_cnt_[i] = cur_cnt_features; + // get bin_mappers + std::vector> cur_bin_mappers; + for (int j = 0; j < cur_cnt_features; ++j) { + int real_fidx = cur_features[j]; + used_feature_map_[real_fidx] = cur_fidx; + real_feature_idx_[cur_fidx] = real_fidx; + feature2group_[cur_fidx] = i; + feature2subfeature_[cur_fidx] = j; + cur_bin_mappers.emplace_back(ref_bin_mappers[real_fidx].release()); + if (cur_bin_mappers.back()->GetDefaultBin() != + cur_bin_mappers.back()->GetMostFreqBin()) { + feature_need_push_zeros_.push_back(cur_fidx); + } + ++cur_fidx; + } + feature_groups_.emplace_back(std::unique_ptr( + new FeatureGroup(cur_cnt_features, group_is_multi_val[i], &cur_bin_mappers, num_data_, i))); + num_total_bin += feature_groups_[i]->num_total_bin_; + group_bin_boundaries_.push_back(num_total_bin); + } + if (!io_config.max_bin_by_feature.empty()) { + CHECK_EQ(static_cast(num_total_features_), + io_config.max_bin_by_feature.size()); + CHECK_GT(*(std::min_element(io_config.max_bin_by_feature.begin(), + io_config.max_bin_by_feature.end())), 1); + max_bin_by_feature_.resize(num_total_features_); + max_bin_by_feature_.assign(io_config.max_bin_by_feature.begin(), + io_config.max_bin_by_feature.end()); + } + forced_bin_bounds_ = forced_bins; + max_bin_ = io_config.max_bin; + min_data_in_bin_ = io_config.min_data_in_bin; + bin_construct_sample_cnt_ = io_config.bin_construct_sample_cnt; + use_missing_ = io_config.use_missing; + zero_as_missing_ = io_config.zero_as_missing; + has_raw_ = false; + if (io_config.linear_tree) { + has_raw_ = true; + } + numeric_feature_map_ = std::vector(num_features_, -1); + num_numeric_features_ = 0; + for (int i = 0; i < num_features_; ++i) { + if (FeatureBinMapper(i)->bin_type() == BinType::NumericalBin) { + numeric_feature_map_[i] = num_numeric_features_; + ++num_numeric_features_; + } + } + device_type_ = io_config.device_type; + gpu_device_id_ = io_config.gpu_device_id; +} + +void Dataset::FinishLoad() { + if (is_finish_load_) { + return; + } + if (num_groups_ > 0) { + for (int i = 0; i < num_groups_; ++i) { + feature_groups_[i]->FinishLoad(); + } + } + metadata_.FinishLoad(); + + #ifdef USE_CUDA + if (device_type_ == std::string("cuda")) { + CreateCUDAColumnData(); + metadata_.CreateCUDAMetadata(gpu_device_id_); + } else { + cuda_column_data_.reset(nullptr); + } + #endif // USE_CUDA + is_finish_load_ = true; +} + +void PushDataToMultiValBin( + data_size_t num_data, const std::vector most_freq_bins, + const std::vector offsets, + std::vector>>* iters, + MultiValBin* ret) { + Common::FunctionTimer fun_time("Dataset::PushDataToMultiValBin", + global_timer); + if (ret->IsSparse()) { + Threading::For( + 0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) { + std::vector cur_data; + cur_data.reserve(most_freq_bins.size()); + for (size_t j = 0; j < most_freq_bins.size(); ++j) { + (*iters)[tid][j]->Reset(start); + } + for (data_size_t i = start; i < end; ++i) { + cur_data.clear(); + for (size_t j = 0; j < most_freq_bins.size(); ++j) { + // for sparse multi value bin, we store the feature bin values with offset added + auto cur_bin = (*iters)[tid][j]->Get(i); + if (cur_bin == most_freq_bins[j]) { + continue; + } + cur_bin += offsets[j]; + if (most_freq_bins[j] == 0) { + cur_bin -= 1; + } + cur_data.push_back(cur_bin); + } + ret->PushOneRow(tid, i, cur_data); + } + }); + } else { + Threading::For( + 0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) { + std::vector cur_data(most_freq_bins.size(), 0); + for (size_t j = 0; j < most_freq_bins.size(); ++j) { + (*iters)[tid][j]->Reset(start); + } + for (data_size_t i = start; i < end; ++i) { + for (size_t j = 0; j < most_freq_bins.size(); ++j) { + // for dense multi value bin, the feature bin values without offsets are used + auto cur_bin = (*iters)[tid][j]->Get(i); + cur_data[j] = cur_bin; + } + ret->PushOneRow(tid, i, cur_data); + } + }); + } +} + +MultiValBin* Dataset::GetMultiBinFromSparseFeatures(const std::vector& offsets) const { + Common::FunctionTimer fun_time("Dataset::GetMultiBinFromSparseFeatures", + global_timer); + int multi_group_id = -1; + for (int i = 0; i < num_groups_; ++i) { + if (feature_groups_[i]->is_multi_val_) { + if (multi_group_id < 0) { + multi_group_id = i; + } else { + Log::Fatal("Bug. There should be only one multi-val group."); + } + } + } + if (multi_group_id < 0) { + return nullptr; + } + const int num_feature = feature_groups_[multi_group_id]->num_feature_; + int num_threads = OMP_NUM_THREADS(); + + std::vector>> iters(num_threads); + std::vector most_freq_bins; + double sum_sparse_rate = 0; + for (int i = 0; i < num_feature; ++i) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) + for (int tid = 0; tid < num_threads; ++tid) { + iters[tid].emplace_back( + feature_groups_[multi_group_id]->SubFeatureIterator(i)); + } + most_freq_bins.push_back( + feature_groups_[multi_group_id]->bin_mappers_[i]->GetMostFreqBin()); + sum_sparse_rate += + feature_groups_[multi_group_id]->bin_mappers_[i]->sparse_rate(); + } + sum_sparse_rate /= num_feature; + Log::Debug("Dataset::GetMultiBinFromSparseFeatures: sparse rate %f", + sum_sparse_rate); + std::unique_ptr ret; + ret.reset(MultiValBin::CreateMultiValBin(num_data_, offsets.back(), + num_feature, sum_sparse_rate, offsets)); + PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get()); + ret->FinishLoad(); + return ret.release(); +} + +MultiValBin* Dataset::GetMultiBinFromAllFeatures(const std::vector& offsets) const { + Common::FunctionTimer fun_time("Dataset::GetMultiBinFromAllFeatures", + global_timer); + int num_threads = OMP_NUM_THREADS(); + double sum_dense_ratio = 0; + + std::unique_ptr ret; + std::vector>> iters(num_threads); + std::vector most_freq_bins; + int ncol = 0; + for (int gid = 0; gid < num_groups_; ++gid) { + if (feature_groups_[gid]->is_multi_val_) { + ncol += feature_groups_[gid]->num_feature_; + } else { + ++ncol; + } + for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) { + const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid]; + sum_dense_ratio += 1.0f - bin_mapper->sparse_rate(); + } + } + sum_dense_ratio /= ncol; + for (int gid = 0; gid < num_groups_; ++gid) { + if (feature_groups_[gid]->is_multi_val_) { + for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) { + const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid]; + most_freq_bins.push_back(bin_mapper->GetMostFreqBin()); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) + for (int tid = 0; tid < num_threads; ++tid) { + iters[tid].emplace_back( + feature_groups_[gid]->SubFeatureIterator(fid)); + } + } + } else { + most_freq_bins.push_back(0); + for (int tid = 0; tid < num_threads; ++tid) { + iters[tid].emplace_back(feature_groups_[gid]->FeatureGroupIterator()); + } + } + } + CHECK(static_cast(most_freq_bins.size()) == ncol); + Log::Debug("Dataset::GetMultiBinFromAllFeatures: sparse rate %f", + 1.0 - sum_dense_ratio); + ret.reset(MultiValBin::CreateMultiValBin( + num_data_, offsets.back(), static_cast(most_freq_bins.size()), + 1.0 - sum_dense_ratio, offsets)); + PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get()); + ret->FinishLoad(); + return ret.release(); +} + +template +TrainingShareStates* Dataset::GetShareStates( + score_t* gradients, score_t* hessians, + const std::vector& is_feature_used, bool is_constant_hessian, + bool force_col_wise, bool force_row_wise, + const int num_grad_quant_bins) const { + Common::FunctionTimer fun_timer("Dataset::TestMultiThreadingMethod", + global_timer); + if (force_col_wise && force_row_wise) { + Log::Fatal( + "Cannot set both of `force_col_wise` and `force_row_wise` to `true` at " + "the same time"); + } + if (num_groups_ <= 0) { + TrainingShareStates* share_state = new TrainingShareStates(); + share_state->is_col_wise = true; + share_state->is_constant_hessian = is_constant_hessian; + return share_state; + } + if (force_col_wise) { + TrainingShareStates* share_state = new TrainingShareStates(); + std::vector offsets; + share_state->CalcBinOffsets( + feature_groups_, &offsets, true); + share_state->SetMultiValBin(GetMultiBinFromSparseFeatures(offsets), + num_data_, feature_groups_, false, true, num_grad_quant_bins); + share_state->is_col_wise = true; + share_state->is_constant_hessian = is_constant_hessian; + return share_state; + } else if (force_row_wise) { + TrainingShareStates* share_state = new TrainingShareStates(); + std::vector offsets; + share_state->CalcBinOffsets( + feature_groups_, &offsets, false); + share_state->SetMultiValBin(GetMultiBinFromAllFeatures(offsets), num_data_, + feature_groups_, false, false, num_grad_quant_bins); + share_state->is_col_wise = false; + share_state->is_constant_hessian = is_constant_hessian; + return share_state; + } else { + std::unique_ptr sparse_bin; + std::unique_ptr all_bin; + std::unique_ptr col_wise_state; + std::unique_ptr row_wise_state; + col_wise_state.reset(new TrainingShareStates()); + row_wise_state.reset(new TrainingShareStates()); + + std::chrono::duration col_wise_init_time, row_wise_init_time; + auto start_time = std::chrono::steady_clock::now(); + std::vector col_wise_offsets; + col_wise_state->CalcBinOffsets(feature_groups_, &col_wise_offsets, true); + col_wise_state->SetMultiValBin(GetMultiBinFromSparseFeatures(col_wise_offsets), num_data_, + feature_groups_, false, true, num_grad_quant_bins); + col_wise_init_time = std::chrono::steady_clock::now() - start_time; + + start_time = std::chrono::steady_clock::now(); + std::vector row_wise_offsets; + row_wise_state->CalcBinOffsets(feature_groups_, &row_wise_offsets, false); + row_wise_state->SetMultiValBin(GetMultiBinFromAllFeatures(row_wise_offsets), num_data_, + feature_groups_, false, false, num_grad_quant_bins); + row_wise_init_time = std::chrono::steady_clock::now() - start_time; + + uint64_t max_total_bin = std::max(row_wise_state->num_hist_total_bin(), + col_wise_state->num_hist_total_bin()); + std::vector> + hist_data(max_total_bin * 2); + + Log::Debug( + "init for col-wise cost %f seconds, init for row-wise cost %f seconds", + col_wise_init_time * 1e-3, row_wise_init_time * 1e-3); + + col_wise_state->is_col_wise = true; + col_wise_state->is_constant_hessian = is_constant_hessian; + InitTrain(is_feature_used, col_wise_state.get()); + row_wise_state->is_col_wise = false; + row_wise_state->is_constant_hessian = is_constant_hessian; + InitTrain(is_feature_used, row_wise_state.get()); + std::chrono::duration col_wise_time, row_wise_time; + start_time = std::chrono::steady_clock::now(); + ConstructHistograms(is_feature_used, nullptr, num_data_, gradients, + hessians, gradients, hessians, col_wise_state.get(), + hist_data.data()); + col_wise_time = std::chrono::steady_clock::now() - start_time; + start_time = std::chrono::steady_clock::now(); + ConstructHistograms(is_feature_used, nullptr, num_data_, gradients, + hessians, gradients, hessians, row_wise_state.get(), + hist_data.data()); + row_wise_time = std::chrono::steady_clock::now() - start_time; + + if (col_wise_time < row_wise_time) { + auto overhead_cost = row_wise_init_time + row_wise_time + col_wise_time; + Log::Info( + "Auto-choosing col-wise multi-threading, the overhead of testing was " + "%f seconds.\n" + "You can set `force_col_wise=true` to remove the overhead.", + overhead_cost * 1e-3); + return col_wise_state.release(); + } else { + auto overhead_cost = col_wise_init_time + row_wise_time + col_wise_time; + Log::Info( + "Auto-choosing row-wise multi-threading, the overhead of testing was " + "%f seconds.\n" + "You can set `force_row_wise=true` to remove the overhead.\n" + "And if memory is not enough, you can set `force_col_wise=true`.", + overhead_cost * 1e-3); + if (row_wise_state->IsSparseRowwise()) { + Log::Debug("Using Sparse Multi-Val Bin"); + } else { + Log::Debug("Using Dense Multi-Val Bin"); + } + return row_wise_state.release(); + } + } +} + +template TrainingShareStates* Dataset::GetShareStates( + score_t* gradients, score_t* hessians, + const std::vector& is_feature_used, bool is_constant_hessian, + bool force_col_wise, bool force_row_wise, + const int num_grad_quant_bins) const; + +template TrainingShareStates* Dataset::GetShareStates( + score_t* gradients, score_t* hessians, + const std::vector& is_feature_used, bool is_constant_hessian, + bool force_col_wise, bool force_row_wise, + const int num_grad_quant_bins) const; + +template TrainingShareStates* Dataset::GetShareStates( + score_t* gradients, score_t* hessians, + const std::vector& is_feature_used, bool is_constant_hessian, + bool force_col_wise, bool force_row_wise, + const int num_grad_quant_bins) const; + +void Dataset::CopyFeatureMapperFrom(const Dataset* dataset) { + feature_groups_.clear(); + num_features_ = dataset->num_features_; + num_groups_ = dataset->num_groups_; + has_raw_ = dataset->has_raw(); + // copy feature bin mapper data + for (int i = 0; i < num_groups_; ++i) { + feature_groups_.emplace_back( + new FeatureGroup(*dataset->feature_groups_[i], num_data_)); + } + feature_groups_.shrink_to_fit(); + used_feature_map_ = dataset->used_feature_map_; + num_total_features_ = dataset->num_total_features_; + feature_names_ = dataset->feature_names_; + label_idx_ = dataset->label_idx_; + real_feature_idx_ = dataset->real_feature_idx_; + feature2group_ = dataset->feature2group_; + feature2subfeature_ = dataset->feature2subfeature_; + group_bin_boundaries_ = dataset->group_bin_boundaries_; + group_feature_start_ = dataset->group_feature_start_; + group_feature_cnt_ = dataset->group_feature_cnt_; + forced_bin_bounds_ = dataset->forced_bin_bounds_; + feature_need_push_zeros_ = dataset->feature_need_push_zeros_; + max_bin_ = dataset->max_bin_; + min_data_in_bin_ = dataset->min_data_in_bin_; + bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_; + use_missing_ = dataset->use_missing_; + zero_as_missing_ = dataset->zero_as_missing_; +} + +void Dataset::CreateValid(const Dataset* dataset) { + feature_groups_.clear(); + num_features_ = dataset->num_features_; + num_groups_ = num_features_; + max_bin_ = dataset->max_bin_; + min_data_in_bin_ = dataset->min_data_in_bin_; + bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_; + use_missing_ = dataset->use_missing_; + zero_as_missing_ = dataset->zero_as_missing_; + feature2group_.clear(); + feature2subfeature_.clear(); + has_raw_ = dataset->has_raw(); + numeric_feature_map_ = dataset->numeric_feature_map_; + num_numeric_features_ = dataset->num_numeric_features_; + // copy feature bin mapper data + feature_need_push_zeros_.clear(); + group_bin_boundaries_.clear(); + uint64_t num_total_bin = 0; + group_bin_boundaries_.push_back(num_total_bin); + group_feature_start_.resize(num_groups_); + group_feature_cnt_.resize(num_groups_); + for (int i = 0; i < num_features_; ++i) { + std::vector> bin_mappers; + bin_mappers.emplace_back(new BinMapper(*(dataset->FeatureBinMapper(i)))); + if (bin_mappers.back()->GetDefaultBin() != + bin_mappers.back()->GetMostFreqBin()) { + feature_need_push_zeros_.push_back(i); + } + feature_groups_.emplace_back(new FeatureGroup(&bin_mappers, num_data_)); + feature2group_.push_back(i); + feature2subfeature_.push_back(0); + num_total_bin += feature_groups_[i]->num_total_bin_; + group_bin_boundaries_.push_back(num_total_bin); + group_feature_start_[i] = i; + group_feature_cnt_[i] = 1; + } + + feature_groups_.shrink_to_fit(); + used_feature_map_ = dataset->used_feature_map_; + num_total_features_ = dataset->num_total_features_; + feature_names_ = dataset->feature_names_; + label_idx_ = dataset->label_idx_; + real_feature_idx_ = dataset->real_feature_idx_; + forced_bin_bounds_ = dataset->forced_bin_bounds_; + device_type_ = dataset->device_type_; + gpu_device_id_ = dataset->gpu_device_id_; +} + +void Dataset::ReSize(data_size_t num_data) { + if (num_data_ != num_data) { + num_data_ = num_data; + OMP_INIT_EX(); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int group = 0; group < num_groups_; ++group) { + OMP_LOOP_EX_BEGIN(); + feature_groups_[group]->ReSize(num_data_); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } +} + + +void Dataset::CopySubrowHostPart(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data) { + CHECK_EQ(num_used_indices, num_data_); + + std::vector group_ids, subfeature_ids; + group_ids.reserve(num_features_); + subfeature_ids.reserve(num_features_); + for (int group = 0; group < num_groups_; ++group) { + if (fullset->IsMultiGroup(group)) { + for (int sub_feature = 0; sub_feature < + fullset->feature_groups_[group]->num_feature_; ++sub_feature) { + group_ids.emplace_back(group); + subfeature_ids.emplace_back(sub_feature); + } + } else { + group_ids.emplace_back(group); + subfeature_ids.emplace_back(-1); + } + } + int num_copy_tasks = static_cast(group_ids.size()); + + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(dynamic) + for (int task_id = 0; task_id < num_copy_tasks; ++task_id) { + OMP_LOOP_EX_BEGIN(); + int group = group_ids[task_id]; + int subfeature = subfeature_ids[task_id]; + feature_groups_[group]->CopySubrowByCol(fullset->feature_groups_[group].get(), + used_indices, num_used_indices, subfeature); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + if (need_meta_data) { + metadata_.Init(fullset->metadata_, used_indices, num_used_indices); + } + is_finish_load_ = true; + numeric_feature_map_ = fullset->numeric_feature_map_; + num_numeric_features_ = fullset->num_numeric_features_; + if (has_raw_) { + ResizeRaw(num_used_indices); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < num_used_indices; ++i) { + for (int j = 0; j < num_numeric_features_; ++j) { + raw_data_[j][i] = fullset->raw_data_[j][used_indices[i]]; + } + } + } +} + +void Dataset::CopySubrow(const Dataset* fullset, + const data_size_t* used_indices, + data_size_t num_used_indices, bool need_meta_data) { + CopySubrowHostPart(fullset, used_indices, num_used_indices, need_meta_data); + + // update CUDA storage for column data and metadata + device_type_ = fullset->device_type_; + gpu_device_id_ = fullset->gpu_device_id_; + + #ifdef USE_CUDA + if (device_type_ == std::string("cuda")) { + if (cuda_column_data_ == nullptr) { + cuda_column_data_.reset(new CUDAColumnData(num_used_indices, gpu_device_id_)); + metadata_.CreateCUDAMetadata(gpu_device_id_); + } + cuda_column_data_->CopySubrow(fullset->cuda_column_data(), used_indices, num_used_indices); + } + #endif // USE_CUDA +} + +void Dataset::CopySubrowToDevice(const Dataset* fullset, + const data_size_t* used_indices, + data_size_t num_used_indices, bool need_meta_data, + int gpu_device_id) { + CopySubrowHostPart(fullset, used_indices, num_used_indices, need_meta_data); + + // update CUDA storage for column data and metadata + device_type_ = fullset->device_type_; + gpu_device_id_ = gpu_device_id; + + #ifdef USE_CUDA + if (device_type_ == std::string("cuda")) { + if (cuda_column_data_ == nullptr) { + cuda_column_data_.reset(new CUDAColumnData(num_used_indices, gpu_device_id_)); + metadata_.CreateCUDAMetadata(gpu_device_id_); + } + cuda_column_data_->CopySubrow(fullset->cuda_column_data(), used_indices, num_used_indices); + } + #endif // USE_CUDA +} + +#ifndef LGB_R_BUILD +bool Dataset::SetFieldFromArrow(const char* field_name, struct ArrowArrayStream* stream) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("label") || name == std::string("target")) { + metadata_.SetLabel(stream); + } else if (name == std::string("weight") || name == std::string("weights")) { + metadata_.SetWeights(stream); + } else if (name == std::string("init_score")) { + metadata_.SetInitScore(stream); + } else if (name == std::string("query") || name == std::string("group")) { + metadata_.SetQuery(stream); + } else { + return false; + } + return true; +} + +bool Dataset::SetFieldFromArrow(const char* field_name, int64_t n_chunks, + struct ArrowArray* chunks, struct ArrowSchema* schema) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("label") || name == std::string("target")) { + metadata_.SetLabel(n_chunks, chunks, schema); + } else if (name == std::string("weight") || name == std::string("weights")) { + metadata_.SetWeights(n_chunks, chunks, schema); + } else if (name == std::string("init_score")) { + metadata_.SetInitScore(n_chunks, chunks, schema); + } else if (name == std::string("query") || name == std::string("group")) { + metadata_.SetQuery(n_chunks, chunks, schema); + } else { + return false; + } + return true; +} +#endif // LGB_R_BUILD + +bool Dataset::SetFloatField(const char* field_name, const float* field_data, + data_size_t num_element) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("label") || name == std::string("target")) { +#ifdef LABEL_T_USE_DOUBLE + Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); +#else + metadata_.SetLabel(field_data, num_element); +#endif + } else if (name == std::string("weight") || name == std::string("weights")) { +#ifdef LABEL_T_USE_DOUBLE + Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); +#else + metadata_.SetWeights(field_data, num_element); +#endif + } else { + return false; + } + return true; +} + +bool Dataset::SetDoubleField(const char* field_name, const double* field_data, + data_size_t num_element) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("init_score")) { + metadata_.SetInitScore(field_data, num_element); + } else { + return false; + } + return true; +} + +bool Dataset::SetIntField(const char* field_name, const int* field_data, + data_size_t num_element) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("query") || name == std::string("group")) { + metadata_.SetQuery(field_data, num_element); + } else if (name == std::string("position")) { + metadata_.SetPosition(field_data, num_element); + } else { + return false; + } + return true; +} + +bool Dataset::GetFloatField(const char* field_name, data_size_t* out_len, + const float** out_ptr) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("label") || name == std::string("target")) { +#ifdef LABEL_T_USE_DOUBLE + Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); +#else + *out_ptr = metadata_.label(); + *out_len = num_data_; +#endif + } else if (name == std::string("weight") || name == std::string("weights")) { +#ifdef LABEL_T_USE_DOUBLE + Log::Fatal("Don't support LABEL_T_USE_DOUBLE"); +#else + *out_ptr = metadata_.weights(); + *out_len = num_data_; +#endif + } else { + return false; + } + return true; +} + +bool Dataset::GetDoubleField(const char* field_name, data_size_t* out_len, + const double** out_ptr) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("init_score")) { + *out_ptr = metadata_.init_score(); + *out_len = static_cast(metadata_.num_init_score()); + } else { + return false; + } + return true; +} + +bool Dataset::GetIntField(const char* field_name, data_size_t* out_len, + const int** out_ptr) { + std::string name(field_name); + name = Common::Trim(name); + if (name == std::string("query") || name == std::string("group")) { + *out_ptr = metadata_.query_boundaries(); + *out_len = metadata_.num_queries() + 1; + } else if (name == std::string("position")) { + *out_ptr = metadata_.positions(); + *out_len = num_data_; + } else { + return false; + } + return true; +} + +void Dataset::SaveBinaryFile(const char* bin_filename) { + if (bin_filename != nullptr && std::string(bin_filename) == data_filename_) { + Log::Warning("Binary file %s already exists", bin_filename); + return; + } + // if not pass a filename, just append ".bin" of original file + std::string bin_filename_str(data_filename_); + if (bin_filename == nullptr || bin_filename[0] == '\0') { + bin_filename_str.append(".bin"); + bin_filename = bin_filename_str.c_str(); + } + bool is_file_existed = false; + + if (VirtualFileWriter::Exists(bin_filename)) { + is_file_existed = true; + Log::Warning("File %s exists, cannot save binary to it", bin_filename); + } + + if (!is_file_existed) { + auto writer = VirtualFileWriter::Make(bin_filename); + if (!writer->Init()) { + Log::Fatal("Cannot write binary data to %s ", bin_filename); + } + Log::Info("Saving data to binary file %s", bin_filename); + size_t size_of_token = std::strlen(binary_file_token); + writer->AlignedWrite(binary_file_token, size_of_token); + + // Write the basic header information for the dataset + SerializeHeader(writer.get()); + + // get size of meta data + size_t size_of_metadata = metadata_.SizesInByte(); + writer->Write(&size_of_metadata, sizeof(size_of_metadata)); + // write meta data + metadata_.SaveBinaryToFile(writer.get()); + + // write feature data + for (int i = 0; i < num_groups_; ++i) { + // get size of feature + size_t size_of_feature = feature_groups_[i]->SizesInByte(); + writer->Write(&size_of_feature, sizeof(size_of_feature)); + // write feature + feature_groups_[i]->SerializeToBinary(writer.get()); + } + + // write raw data; use row-major order so we can read row-by-row + if (has_raw_) { + for (int i = 0; i < num_data_; ++i) { + for (int j = 0; j < num_features_; ++j) { + int feat_ind = numeric_feature_map_[j]; + if (feat_ind > -1) { + writer->Write(&raw_data_[feat_ind][i], sizeof(float)); + } + } + } + } + } +} + +void Dataset::SerializeReference(ByteBuffer* buffer) { + Log::Info("Saving data reference to binary buffer"); + + // Calculate approximate size of output and reserve space + size_t size_of_token = std::strlen(binary_serialized_reference_token); + size_t initial_capacity = size_of_token + GetSerializedHeaderSize(); + // write feature group definitions + for (int i = 0; i < num_groups_; ++i) { + initial_capacity += feature_groups_[i]->SizesInByte(/* include_data */ false); + } + + // Give a little extra just in case, to avoid unnecessary resizes + buffer->Reserve(static_cast(1.1 * static_cast(initial_capacity))); + + // Write token that marks the data as binary reference, and the version + buffer->AlignedWrite(binary_serialized_reference_token, size_of_token); + buffer->AlignedWrite(serialized_reference_version, kSerializedReferenceVersionLength); + + // Write the basic definition of the overall dataset + SerializeHeader(buffer); + + // write feature group definitions + for (int i = 0; i < num_groups_; ++i) { + // get size of feature + size_t size_of_feature = feature_groups_[i]->SizesInByte(false); + buffer->Write(&size_of_feature, sizeof(size_of_feature)); + // write feature + feature_groups_[i]->SerializeToBinary(buffer, /* include_data */ false); + } +} + +size_t Dataset::GetSerializedHeaderSize() { + size_t size_of_header = + VirtualFileWriter::AlignedSize(sizeof(num_data_)) + + VirtualFileWriter::AlignedSize(sizeof(num_features_)) + + VirtualFileWriter::AlignedSize(sizeof(num_total_features_)) + + VirtualFileWriter::AlignedSize(sizeof(int) * num_total_features_) + + VirtualFileWriter::AlignedSize(sizeof(label_idx_)) + + VirtualFileWriter::AlignedSize(sizeof(num_groups_)) + + 3 * VirtualFileWriter::AlignedSize(sizeof(int) * num_features_) + + sizeof(uint64_t) * (num_groups_ + 1) + + 2 * VirtualFileWriter::AlignedSize(sizeof(int) * num_groups_) + + VirtualFileWriter::AlignedSize(sizeof(int32_t) * num_total_features_) + + VirtualFileWriter::AlignedSize(sizeof(int)) * 3 + + VirtualFileWriter::AlignedSize(sizeof(bool)) * 3; + // size of feature names and forced bins + for (int i = 0; i < num_total_features_; ++i) { + size_of_header += + VirtualFileWriter::AlignedSize(feature_names_[i].size()) + + VirtualFileWriter::AlignedSize(sizeof(int)) + + forced_bin_bounds_[i].size() * sizeof(double) + + VirtualFileWriter::AlignedSize(sizeof(int)); + } + + return size_of_header; +} + +void Dataset::SerializeHeader(BinaryWriter* writer) { + size_t size_of_header = GetSerializedHeaderSize(); + writer->Write(&size_of_header, sizeof(size_of_header)); + + // write header + writer->AlignedWrite(&num_data_, sizeof(num_data_)); + writer->AlignedWrite(&num_features_, sizeof(num_features_)); + writer->AlignedWrite(&num_total_features_, sizeof(num_total_features_)); + writer->AlignedWrite(&label_idx_, sizeof(label_idx_)); + writer->AlignedWrite(&max_bin_, sizeof(max_bin_)); + writer->AlignedWrite(&bin_construct_sample_cnt_, + sizeof(bin_construct_sample_cnt_)); + writer->AlignedWrite(&min_data_in_bin_, sizeof(min_data_in_bin_)); + writer->AlignedWrite(&use_missing_, sizeof(use_missing_)); + writer->AlignedWrite(&zero_as_missing_, sizeof(zero_as_missing_)); + writer->AlignedWrite(&has_raw_, sizeof(has_raw_)); + writer->AlignedWrite(used_feature_map_.data(), + sizeof(int) * num_total_features_); + writer->AlignedWrite(&num_groups_, sizeof(num_groups_)); + writer->AlignedWrite(real_feature_idx_.data(), sizeof(int) * num_features_); + writer->AlignedWrite(feature2group_.data(), sizeof(int) * num_features_); + writer->AlignedWrite(feature2subfeature_.data(), + sizeof(int) * num_features_); + writer->Write(group_bin_boundaries_.data(), + sizeof(uint64_t) * (num_groups_ + 1)); + writer->AlignedWrite(group_feature_start_.data(), + sizeof(int) * num_groups_); + writer->AlignedWrite(group_feature_cnt_.data(), sizeof(int) * num_groups_); + if (max_bin_by_feature_.empty()) { + ArrayArgs::Assign(&max_bin_by_feature_, -1, num_total_features_); + } + writer->AlignedWrite(max_bin_by_feature_.data(), + sizeof(int32_t) * num_total_features_); + if (ArrayArgs::CheckAll(max_bin_by_feature_, -1)) { + max_bin_by_feature_.clear(); + } + // write feature names + for (int i = 0; i < num_total_features_; ++i) { + int str_len = static_cast(feature_names_[i].size()); + writer->AlignedWrite(&str_len, sizeof(int)); + const char* c_str = feature_names_[i].c_str(); + writer->AlignedWrite(c_str, sizeof(char) * str_len); + } + // write forced bins + for (int i = 0; i < num_total_features_; ++i) { + int num_bounds = static_cast(forced_bin_bounds_[i].size()); + writer->AlignedWrite(&num_bounds, sizeof(int)); + + for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) { + writer->Write(&forced_bin_bounds_[i][j], sizeof(double)); + } + } +} + +void Dataset::DumpTextFile(const char* text_filename) { + FILE* file = NULL; +#if _MSC_VER + fopen_s(&file, text_filename, "wt"); +#else + file = fopen(text_filename, "wt"); +#endif + fprintf(file, "num_features: %d\n", num_features_); + fprintf(file, "num_total_features: %d\n", num_total_features_); + fprintf(file, "num_groups: %d\n", num_groups_); + fprintf(file, "num_data: %d\n", num_data_); + fprintf(file, "feature_names: "); + for (auto n : feature_names_) { + fprintf(file, "%s, ", n.c_str()); + } + fprintf(file, "\nmax_bin_by_feature: "); + for (auto i : max_bin_by_feature_) { + fprintf(file, "%d, ", i); + } + fprintf(file, "\n"); + for (auto n : feature_names_) { + fprintf(file, "%s, ", n.c_str()); + } + fprintf(file, "\nforced_bins: "); + for (int i = 0; i < num_total_features_; ++i) { + fprintf(file, "\nfeature %d: ", i); + for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) { + fprintf(file, "%lf, ", forced_bin_bounds_[i][j]); + } + } + std::vector> iterators; + iterators.reserve(num_features_); + for (int j = 0; j < num_features_; ++j) { + auto group_idx = feature2group_[j]; + auto sub_idx = feature2subfeature_[j]; + iterators.emplace_back( + feature_groups_[group_idx]->SubFeatureIterator(sub_idx)); + } + for (data_size_t i = 0; i < num_data_; ++i) { + fprintf(file, "\n"); + for (int j = 0; j < num_total_features_; ++j) { + auto inner_feature_idx = used_feature_map_[j]; + if (inner_feature_idx < 0) { + fprintf(file, "NA, "); + } else { + fprintf(file, "%d, ", iterators[inner_feature_idx]->Get(i)); + } + } + } + fclose(file); +} + +void Dataset::InitTrain(const std::vector& is_feature_used, + TrainingShareStates* share_state) const { + Common::FunctionTimer fun_time("Dataset::InitTrain", global_timer); + share_state->InitTrain(group_feature_start_, + feature_groups_, + is_feature_used); +} + +template +void Dataset::ConstructHistogramsMultiVal(const data_size_t* data_indices, + data_size_t num_data, + const score_t* gradients, + const score_t* hessians, + TrainingShareStates* share_state, + hist_t* hist_data) const { + Common::FunctionTimer fun_time("Dataset::ConstructHistogramsMultiVal", + global_timer); + share_state->ConstructHistograms( + data_indices, num_data, gradients, hessians, hist_data); +} + +template +void Dataset::ConstructHistogramsInner( + const std::vector& is_feature_used, const data_size_t* data_indices, + data_size_t num_data, const score_t* gradients, const score_t* hessians, + score_t* ordered_gradients, score_t* ordered_hessians, + TrainingShareStates* share_state, hist_t* hist_data) const { + if (!share_state->is_col_wise) { + return ConstructHistogramsMultiVal( + data_indices, num_data, gradients, hessians, share_state, hist_data); + } + std::vector used_dense_group; + int multi_val_groud_id = -1; + used_dense_group.reserve(num_groups_); + for (int group = 0; group < num_groups_; ++group) { + const int f_start = group_feature_start_[group]; + const int f_cnt = group_feature_cnt_[group]; + bool is_group_used = false; + for (int j = 0; j < f_cnt; ++j) { + const int fidx = f_start + j; + if (is_feature_used[fidx]) { + is_group_used = true; + break; + } + } + if (is_group_used) { + if (feature_groups_[group]->is_multi_val_) { + multi_val_groud_id = group; + } else { + used_dense_group.push_back(group); + } + } + } + int num_used_dense_group = static_cast(used_dense_group.size()); + global_timer.Start("Dataset::dense_bin_histogram"); + auto ptr_ordered_grad = gradients; + auto ptr_ordered_hess = hessians; + if (num_used_dense_group > 0) { + if (USE_QUANT_GRAD) { + int16_t* ordered_gradients_and_hessians = reinterpret_cast(ordered_gradients); + const int16_t* gradients_and_hessians = reinterpret_cast(gradients); + if (USE_INDICES) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) + for (data_size_t i = 0; i < num_data; ++i) { + ordered_gradients_and_hessians[i] = gradients_and_hessians[data_indices[i]]; + } + ptr_ordered_grad = reinterpret_cast(ordered_gradients); + ptr_ordered_hess = nullptr; + } + } else { + if (USE_INDICES) { + if (USE_HESSIAN) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) + for (data_size_t i = 0; i < num_data; ++i) { + ordered_gradients[i] = gradients[data_indices[i]]; + ordered_hessians[i] = hessians[data_indices[i]]; + } + ptr_ordered_grad = ordered_gradients; + ptr_ordered_hess = ordered_hessians; + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) + for (data_size_t i = 0; i < num_data; ++i) { + ordered_gradients[i] = gradients[data_indices[i]]; + } + ptr_ordered_grad = ordered_gradients; + } + } + } + OMP_INIT_EX(); +#pragma omp parallel for schedule(static) num_threads(share_state->num_threads) + for (int gi = 0; gi < num_used_dense_group; ++gi) { + OMP_LOOP_EX_BEGIN(); + int group = used_dense_group[gi]; + const int num_bin = feature_groups_[group]->num_total_bin_; + if (USE_QUANT_GRAD) { + if (HIST_BITS == 16) { + auto data_ptr = reinterpret_cast(reinterpret_cast(hist_data) + group_bin_boundaries_[group]); + std::memset(reinterpret_cast(data_ptr), 0, + num_bin * kInt16HistEntrySize); + if (USE_HESSIAN) { + if (USE_INDICES) { + feature_groups_[group]->bin_data_->ConstructHistogramInt16( + data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess, + data_ptr); + } else { + feature_groups_[group]->bin_data_->ConstructHistogramInt16( + 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); + } + } else { + if (USE_INDICES) { + feature_groups_[group]->bin_data_->ConstructHistogramInt16( + data_indices, 0, num_data, ptr_ordered_grad, + data_ptr); + } else { + feature_groups_[group]->bin_data_->ConstructHistogramInt16( + 0, num_data, ptr_ordered_grad, data_ptr); + } + } + } else { + auto data_ptr = hist_data + group_bin_boundaries_[group]; + std::memset(reinterpret_cast(data_ptr), 0, + num_bin * kInt32HistEntrySize); + if (USE_HESSIAN) { + if (USE_INDICES) { + feature_groups_[group]->bin_data_->ConstructHistogramInt32( + data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess, + data_ptr); + } else { + feature_groups_[group]->bin_data_->ConstructHistogramInt32( + 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); + } + } else { + if (USE_INDICES) { + feature_groups_[group]->bin_data_->ConstructHistogramInt32( + data_indices, 0, num_data, ptr_ordered_grad, + data_ptr); + } else { + feature_groups_[group]->bin_data_->ConstructHistogramInt32( + 0, num_data, ptr_ordered_grad, data_ptr); + } + } + } + } else { + auto data_ptr = hist_data + group_bin_boundaries_[group] * 2; + std::memset(reinterpret_cast(data_ptr), 0, + num_bin * kHistEntrySize); + if (USE_HESSIAN) { + if (USE_INDICES) { + feature_groups_[group]->bin_data_->ConstructHistogram( + data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess, + data_ptr); + } else { + feature_groups_[group]->bin_data_->ConstructHistogram( + 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr); + } + } else { + if (USE_INDICES) { + feature_groups_[group]->bin_data_->ConstructHistogram( + data_indices, 0, num_data, ptr_ordered_grad, data_ptr); + } else { + feature_groups_[group]->bin_data_->ConstructHistogram( + 0, num_data, ptr_ordered_grad, data_ptr); + } + auto cnt_dst = reinterpret_cast(data_ptr + 1); + for (int i = 0; i < num_bin * 2; i += 2) { + data_ptr[i + 1] = static_cast(cnt_dst[i]) * hessians[0]; + } + } + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + global_timer.Stop("Dataset::dense_bin_histogram"); + if (multi_val_groud_id >= 0) { + if (USE_QUANT_GRAD) { + if (HIST_BITS == 32) { + int32_t* hist_data_ptr = reinterpret_cast(hist_data); + if (num_used_dense_group > 0) { + ConstructHistogramsMultiVal( + data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess, + share_state, + reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); + } else { + ConstructHistogramsMultiVal( + data_indices, num_data, gradients, hessians, share_state, + reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); + } + } else if (HIST_BITS == 16) { + int16_t* hist_data_ptr = reinterpret_cast(hist_data); + if (num_used_dense_group > 0) { + ConstructHistogramsMultiVal( + data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess, + share_state, + reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); + } else { + ConstructHistogramsMultiVal( + data_indices, num_data, gradients, hessians, share_state, + reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2)); + } + } + } else { + if (num_used_dense_group > 0) { + ConstructHistogramsMultiVal( + data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess, + share_state, + hist_data + group_bin_boundaries_[multi_val_groud_id] * 2); + } else { + ConstructHistogramsMultiVal( + data_indices, num_data, gradients, hessians, share_state, + hist_data + group_bin_boundaries_[multi_val_groud_id] * 2); + } + } + } +} + +// explicitly initialize template methods, for cross module call +#define CONSTRUCT_HISTOGRAMS_INNER_PARMA \ + const std::vector& is_feature_used, const data_size_t* data_indices, \ + data_size_t num_data, const score_t* gradients, const score_t* hessians, \ + score_t* ordered_gradients, score_t* ordered_hessians, \ + TrainingShareStates* share_state, hist_t* hist_data + +// explicitly initialize template methods, for cross module call +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +template void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const; + +void Dataset::FixHistogram(int feature_idx, double sum_gradient, + double sum_hessian, hist_t* data) const { + const int group = feature2group_[feature_idx]; + const int sub_feature = feature2subfeature_[feature_idx]; + const BinMapper* bin_mapper = + feature_groups_[group]->bin_mappers_[sub_feature].get(); + const int most_freq_bin = bin_mapper->GetMostFreqBin(); + if (most_freq_bin > 0) { + const int num_bin = bin_mapper->num_bin(); + GET_GRAD(data, most_freq_bin) = sum_gradient; + GET_HESS(data, most_freq_bin) = sum_hessian; + for (int i = 0; i < num_bin; ++i) { + if (i != most_freq_bin) { + GET_GRAD(data, most_freq_bin) -= GET_GRAD(data, i); + GET_HESS(data, most_freq_bin) -= GET_HESS(data, i); + } + } + } +} + +template +void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const { + const int group = feature2group_[feature_idx]; + const int sub_feature = feature2subfeature_[feature_idx]; + const BinMapper* bin_mapper = + feature_groups_[group]->bin_mappers_[sub_feature].get(); + const int most_freq_bin = bin_mapper->GetMostFreqBin(); + PACKED_HIST_BIN_T* data_ptr = reinterpret_cast(data); + PACKED_HIST_ACC_T int_sum_gradient_and_hessian_local = HIST_BITS_ACC == 16 ? + ((static_cast(int_sum_gradient_and_hessian >> 32) << 16) | + static_cast(int_sum_gradient_and_hessian & 0x0000ffff)) : + int_sum_gradient_and_hessian; + if (most_freq_bin > 0) { + const int num_bin = bin_mapper->num_bin(); + if (HIST_BITS_BIN == HIST_BITS_ACC) { + for (int i = 0; i < num_bin; ++i) { + if (i != most_freq_bin) { + int_sum_gradient_and_hessian_local -= data_ptr[i]; + } + } + data_ptr[most_freq_bin] = int_sum_gradient_and_hessian_local; + } else { + CHECK_EQ(HIST_BITS_ACC, 32); + CHECK_EQ(HIST_BITS_BIN, 16); + for (int i = 0; i < num_bin; ++i) { + if (i != most_freq_bin) { + const PACKED_HIST_BIN_T packed_hist = data_ptr[i]; + const PACKED_HIST_ACC_T packed_hist_acc = (static_cast(static_cast(packed_hist >> 16)) << 32) | + static_cast(packed_hist & 0x0000ffff); + int_sum_gradient_and_hessian_local -= packed_hist_acc; + } + } + PACKED_HIST_BIN_T int_sum_gradient_and_hessian_local_bin = + (static_cast(int_sum_gradient_and_hessian_local >> 32) << 16) | static_cast(int_sum_gradient_and_hessian_local & 0x0000ffff); + data_ptr[most_freq_bin] = int_sum_gradient_and_hessian_local_bin; + } + } +} + +template void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const; + +template void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const; + +template +void PushVector(std::vector* dest, const std::vector& src) { + dest->reserve(dest->size() + src.size()); + for (auto i : src) { + dest->push_back(i); + } +} + +template +void PushOffset(std::vector* dest, const std::vector& src, + const T& offset) { + dest->reserve(dest->size() + src.size()); + for (auto i : src) { + dest->push_back(i + offset); + } +} + +template +void PushClearIfEmpty(std::vector* dest, const size_t dest_len, + const std::vector& src, const size_t src_len, + const T& deflt) { + if (!dest->empty() && !src.empty()) { + PushVector(dest, src); + } else if (!dest->empty() && src.empty()) { + for (size_t i = 0; i < src_len; ++i) { + dest->push_back(deflt); + } + } else if (dest->empty() && !src.empty()) { + for (size_t i = 0; i < dest_len; ++i) { + dest->push_back(deflt); + } + PushVector(dest, src); + } +} + +void Dataset::AddFeaturesFrom(Dataset* other) { + if (other->num_data_ != num_data_) { + Log::Fatal( + "Cannot add features from other Dataset with a different number of " + "rows"); + } + if (other->has_raw_ != has_raw_) { + Log::Fatal("Can only add features from other Dataset if both or neither have raw data."); + } + int mv_gid = -1; + int other_mv_gid = -1; + for (int i = 0; i < num_groups_; ++i) { + if (IsMultiGroup(i)) { + mv_gid = i; + } + } + for (int i = 0; i < other->num_groups_; ++i) { + if (other->IsMultiGroup(i)) { + other_mv_gid = i; + } + } + // Only one multi-val group, just simply merge + if (mv_gid < 0 || other_mv_gid < 0) { + PushVector(&feature2subfeature_, other->feature2subfeature_); + PushVector(&group_feature_cnt_, other->group_feature_cnt_); + feature_groups_.reserve(other->feature_groups_.size()); + for (auto& fg : other->feature_groups_) { + const int cur_group_id = static_cast(feature_groups_.size()); + feature_groups_.emplace_back(new FeatureGroup(*fg, true, cur_group_id)); + } + for (auto feature_idx : other->used_feature_map_) { + if (feature_idx >= 0) { + used_feature_map_.push_back(feature_idx + num_features_); + } else { + used_feature_map_.push_back(-1); // Unused feature. + } + } + PushOffset(&real_feature_idx_, other->real_feature_idx_, + num_total_features_); + PushOffset(&feature2group_, other->feature2group_, num_groups_); + auto bin_offset = group_bin_boundaries_.back(); + // Skip the leading 0 when copying group_bin_boundaries. + for (auto i = other->group_bin_boundaries_.begin() + 1; + i < other->group_bin_boundaries_.end(); ++i) { + group_bin_boundaries_.push_back(*i + bin_offset); + } + PushOffset(&group_feature_start_, other->group_feature_start_, + num_features_); + num_groups_ += other->num_groups_; + num_features_ += other->num_features_; + } else { + std::vector> features_in_group; + for (int i = 0; i < num_groups_; ++i) { + int f_start = group_feature_start_[i]; + int f_cnt = group_feature_cnt_[i]; + features_in_group.emplace_back(); + for (int j = 0; j < f_cnt; ++j) { + const int real_fidx = real_feature_idx_[f_start + j]; + features_in_group.back().push_back(real_fidx); + } + } + feature_groups_[mv_gid]->AddFeaturesFrom( + other->feature_groups_[other_mv_gid].get(), mv_gid); + for (int i = 0; i < other->num_groups_; ++i) { + int f_start = other->group_feature_start_[i]; + int f_cnt = other->group_feature_cnt_[i]; + if (i == other_mv_gid) { + for (int j = 0; j < f_cnt; ++j) { + const int real_fidx = other->real_feature_idx_[f_start + j] + num_total_features_; + features_in_group[mv_gid].push_back(real_fidx); + } + } else { + features_in_group.emplace_back(); + for (int j = 0; j < f_cnt; ++j) { + const int real_fidx = other->real_feature_idx_[f_start + j] + num_total_features_; + features_in_group.back().push_back(real_fidx); + } + feature_groups_.emplace_back( + new FeatureGroup(*other->feature_groups_[i], false, -1)); + } + } + // regenerate other fields + num_groups_ += other->num_groups_ - 1; + CHECK(num_groups_ == static_cast(features_in_group.size())); + num_features_ += other->num_features_; + int cur_fidx = 0; + used_feature_map_ = + std::vector(num_total_features_ + other->num_total_features_, -1); + real_feature_idx_.resize(num_features_); + feature2group_.resize(num_features_); + feature2subfeature_.resize(num_features_); + group_feature_start_.resize(num_groups_); + group_feature_cnt_.resize(num_groups_); + + group_bin_boundaries_.clear(); + uint64_t num_total_bin = 0; + group_bin_boundaries_.push_back(num_total_bin); + for (int i = 0; i < num_groups_; ++i) { + auto cur_features = features_in_group[i]; + int cur_cnt_features = static_cast(cur_features.size()); + group_feature_start_[i] = cur_fidx; + group_feature_cnt_[i] = cur_cnt_features; + for (int j = 0; j < cur_cnt_features; ++j) { + int real_fidx = cur_features[j]; + used_feature_map_[real_fidx] = cur_fidx; + real_feature_idx_[cur_fidx] = real_fidx; + feature2group_[cur_fidx] = i; + feature2subfeature_[cur_fidx] = j; + ++cur_fidx; + } + num_total_bin += feature_groups_[i]->num_total_bin_; + group_bin_boundaries_.push_back(num_total_bin); + } + } + std::unordered_set feature_names_set; + for (const auto& val : feature_names_) { + feature_names_set.emplace(val); + } + for (const auto& val : other->feature_names_) { + std::string new_name = val; + int cnt = 2; + while (feature_names_set.count(new_name)) { + new_name = "D" + std::to_string(cnt) + "_" + val; + ++cnt; + } + if (new_name != val) { + Log::Warning( + "Find the same feature name (%s) in Dataset::AddFeaturesFrom, change " + "its name to (%s)", + val.c_str(), new_name.c_str()); + } + feature_names_set.emplace(new_name); + feature_names_.push_back(new_name); + } + PushVector(&forced_bin_bounds_, other->forced_bin_bounds_); + PushClearIfEmpty(&max_bin_by_feature_, num_total_features_, + other->max_bin_by_feature_, other->num_total_features_, -1); + num_total_features_ += other->num_total_features_; + for (size_t i = 0; i < (other->numeric_feature_map_).size(); ++i) { + int feat_ind = other->numeric_feature_map_[i]; + if (feat_ind > -1) { + numeric_feature_map_.push_back(feat_ind + num_numeric_features_); + } else { + numeric_feature_map_.push_back(-1); + } + } + num_numeric_features_ += other->num_numeric_features_; + if (has_raw_) { + for (int i = 0; i < other->num_numeric_features_; ++i) { + raw_data_.push_back(other->raw_data_[i]); + } + } + #ifdef USE_CUDA + if (device_type_ == std::string("cuda")) { + CreateCUDAColumnData(); + } else { + cuda_column_data_ = nullptr; + } + #endif // USE_CUDA +} + +const void* Dataset::GetColWiseData( + const int feature_group_index, + const int sub_feature_index, + uint8_t* bit_type, + bool* is_sparse, + std::vector* bin_iterator, + const int num_threads) const { + return feature_groups_[feature_group_index]->GetColWiseData(sub_feature_index, bit_type, is_sparse, bin_iterator, num_threads); +} + +const void* Dataset::GetColWiseData( + const int feature_group_index, + const int sub_feature_index, + uint8_t* bit_type, + bool* is_sparse, + BinIterator** bin_iterator) const { + return feature_groups_[feature_group_index]->GetColWiseData(sub_feature_index, bit_type, is_sparse, bin_iterator); +} + +#ifdef USE_CUDA +void Dataset::CreateCUDAColumnData() { + cuda_column_data_.reset(new CUDAColumnData(num_data_, gpu_device_id_)); + int num_columns = 0; + std::vector column_data; + std::vector column_bin_iterator; + std::vector column_bit_type; + int feature_index = 0; + std::vector feature_to_column(num_features_, -1); + std::vector feature_max_bins(num_features_, 0); + std::vector feature_min_bins(num_features_, 0); + std::vector feature_offsets(num_features_, 0); + std::vector feature_most_freq_bins(num_features_, 0); + std::vector feature_default_bin(num_features_, 0); + std::vector feature_missing_is_zero(num_features_, 0); + std::vector feature_missing_is_na(num_features_, 0); + std::vector feature_mfb_is_zero(num_features_, 0); + std::vector feature_mfb_is_na(num_features_, 0); + for (int feature_group_index = 0; feature_group_index < num_groups_; ++feature_group_index) { + if (feature_groups_[feature_group_index]->is_multi_val_) { + for (int sub_feature_index = 0; sub_feature_index < feature_groups_[feature_group_index]->num_feature_; ++sub_feature_index) { + uint8_t bit_type = 0; + bool is_sparse = false; + BinIterator* bin_iterator = nullptr; + const void* one_column_data = GetColWiseData(feature_group_index, + sub_feature_index, + &bit_type, + &is_sparse, + &bin_iterator); + column_data.emplace_back(one_column_data); + column_bin_iterator.emplace_back(bin_iterator); + column_bit_type.emplace_back(bit_type); + feature_to_column[feature_index] = num_columns; + ++num_columns; + const BinMapper* feature_bin_mapper = FeatureBinMapper(feature_index); + feature_max_bins[feature_index] = feature_max_bin(feature_index); + feature_min_bins[feature_index] = feature_min_bin(feature_index); + const uint32_t most_freq_bin = feature_bin_mapper->GetMostFreqBin(); + feature_offsets[feature_index] = static_cast(most_freq_bin == 0); + feature_most_freq_bins[feature_index] = most_freq_bin; + feature_default_bin[feature_index] = feature_bin_mapper->GetDefaultBin(); + if (feature_bin_mapper->missing_type() == MissingType::Zero) { + feature_missing_is_zero[feature_index] = 1; + feature_missing_is_na[feature_index] = 0; + if (feature_default_bin[feature_index] == feature_most_freq_bins[feature_index]) { + feature_mfb_is_zero[feature_index] = 1; + } else { + feature_mfb_is_zero[feature_index] = 0; + } + feature_mfb_is_na[feature_index] = 0; + } else if (feature_bin_mapper->missing_type() == MissingType::NaN) { + feature_missing_is_zero[feature_index] = 0; + feature_missing_is_na[feature_index] = 1; + feature_mfb_is_zero[feature_index] = 0; + if (feature_most_freq_bins[feature_index] + feature_min_bins[feature_index] == feature_max_bins[feature_index] && + feature_most_freq_bins[feature_index] > 0) { + feature_mfb_is_na[feature_index] = 1; + } else { + feature_mfb_is_na[feature_index] = 0; + } + } else { + feature_missing_is_zero[feature_index] = 0; + feature_missing_is_na[feature_index] = 0; + feature_mfb_is_zero[feature_index] = 0; + feature_mfb_is_na[feature_index] = 0; + } + ++feature_index; + } + } else { + uint8_t bit_type = 0; + bool is_sparse = false; + BinIterator* bin_iterator = nullptr; + const void* one_column_data = GetColWiseData(feature_group_index, + -1, + &bit_type, + &is_sparse, + &bin_iterator); + column_data.emplace_back(one_column_data); + column_bin_iterator.emplace_back(bin_iterator); + column_bit_type.emplace_back(bit_type); + for (int sub_feature_index = 0; sub_feature_index < feature_groups_[feature_group_index]->num_feature_; ++sub_feature_index) { + feature_to_column[feature_index] = num_columns; + const BinMapper* feature_bin_mapper = FeatureBinMapper(feature_index); + feature_max_bins[feature_index] = feature_max_bin(feature_index); + feature_min_bins[feature_index] = feature_min_bin(feature_index); + const uint32_t most_freq_bin = feature_bin_mapper->GetMostFreqBin(); + feature_offsets[feature_index] = static_cast(most_freq_bin == 0); + feature_most_freq_bins[feature_index] = most_freq_bin; + feature_default_bin[feature_index] = feature_bin_mapper->GetDefaultBin(); + if (feature_bin_mapper->missing_type() == MissingType::Zero) { + feature_missing_is_zero[feature_index] = 1; + feature_missing_is_na[feature_index] = 0; + if (feature_default_bin[feature_index] == feature_most_freq_bins[feature_index]) { + feature_mfb_is_zero[feature_index] = 1; + } else { + feature_mfb_is_zero[feature_index] = 0; + } + feature_mfb_is_na[feature_index] = 0; + } else if (feature_bin_mapper->missing_type() == MissingType::NaN) { + feature_missing_is_zero[feature_index] = 0; + feature_missing_is_na[feature_index] = 1; + feature_mfb_is_zero[feature_index] = 0; + if (feature_most_freq_bins[feature_index] + feature_min_bins[feature_index] == feature_max_bins[feature_index] && + feature_most_freq_bins[feature_index] > 0) { + feature_mfb_is_na[feature_index] = 1; + } else { + feature_mfb_is_na[feature_index] = 0; + } + } else { + feature_missing_is_zero[feature_index] = 0; + feature_missing_is_na[feature_index] = 0; + feature_mfb_is_zero[feature_index] = 0; + feature_mfb_is_na[feature_index] = 0; + } + ++feature_index; + } + ++num_columns; + } + } + cuda_column_data_->Init(num_columns, + column_data, + column_bin_iterator, + column_bit_type, + feature_max_bins, + feature_min_bins, + feature_offsets, + feature_most_freq_bins, + feature_default_bin, + feature_missing_is_zero, + feature_missing_is_na, + feature_mfb_is_zero, + feature_mfb_is_na, + feature_to_column); +} + +#endif // USE_CUDA + +} // namespace LightGBM diff --git a/src/io/dataset_loader.cpp b/src/io/dataset_loader.cpp new file mode 100644 index 0000000..ac69794 --- /dev/null +++ b/src/io/dataset_loader.cpp @@ -0,0 +1,1587 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +using json11_internal_lightgbm::Json; + +DatasetLoader::DatasetLoader(const Config& io_config, const PredictFunction& predict_fun, int num_class, const char* filename) + :config_(io_config), random_(config_.data_random_seed), predict_fun_(predict_fun), num_class_(num_class) { + label_idx_ = 0; + weight_idx_ = NO_SPECIFIC; + group_idx_ = NO_SPECIFIC; + SetHeader(filename); + store_raw_ = false; + if (io_config.linear_tree) { + store_raw_ = true; + } +} + +DatasetLoader::~DatasetLoader() { +} + +void DatasetLoader::SetHeader(const char* filename) { + std::unordered_map name2idx; + std::string name_prefix("name:"); + if (filename != nullptr && CheckCanLoadFromBin(filename) == "") { + TextReader text_reader(filename, config_.header); + + // get column names + if (config_.header) { + std::string first_line = text_reader.first_line(); + feature_names_ = Common::Split(first_line.c_str(), "\t,"); + } else if (!config_.parser_config_file.empty()) { + // support to get header from parser config, so could utilize following label name to id mapping logic. + TextReader parser_config_reader(config_.parser_config_file.c_str(), false); + parser_config_reader.ReadAllLines(); + std::string parser_config_str = parser_config_reader.JoinedLines(); + if (!parser_config_str.empty()) { + std::string header_in_parser_config = Common::GetFromParserConfig(parser_config_str, "header"); + if (!header_in_parser_config.empty()) { + Log::Info("Get raw column names from parser config."); + feature_names_ = Common::Split(header_in_parser_config.c_str(), "\t,"); + } + } + } + + // load label idx first + if (config_.label_column.size() > 0) { + if (Common::StartsWith(config_.label_column, name_prefix)) { + std::string name = config_.label_column.substr(name_prefix.size()); + label_idx_ = -1; + for (int i = 0; i < static_cast(feature_names_.size()); ++i) { + if (name == feature_names_[i]) { + label_idx_ = i; + break; + } + } + if (label_idx_ >= 0) { + Log::Info("Using column %s as label", name.c_str()); + } else { + Log::Fatal("Could not find label column %s in data file \n" + "or data file doesn't contain header", name.c_str()); + } + } else { + if (!Common::AtoiAndCheck(config_.label_column.c_str(), &label_idx_)) { + Log::Fatal("label_column is not a number,\n" + "if you want to use a column name,\n" + "please add the prefix \"name:\" to the column name"); + } + Log::Info("Using column number %d as label", label_idx_); + } + } + + if (!config_.parser_config_file.empty()) { + // if parser config file exists, feature names may be changed after customized parser applied. + // clear here so could use default filled feature names during dataset construction. + // may improve by saving real feature names defined in parser in the future. + if (!feature_names_.empty()) { + feature_names_.clear(); + } + } + + if (!feature_names_.empty()) { + // erase label column name + feature_names_.erase(feature_names_.begin() + label_idx_); + for (size_t i = 0; i < feature_names_.size(); ++i) { + name2idx[feature_names_[i]] = static_cast(i); + } + } + + // load ignore columns + if (config_.ignore_column.size() > 0) { + if (Common::StartsWith(config_.ignore_column, name_prefix)) { + std::string names = config_.ignore_column.substr(name_prefix.size()); + for (auto name : Common::Split(names.c_str(), ',')) { + if (name2idx.count(name) > 0) { + int tmp = name2idx[name]; + ignore_features_.emplace(tmp); + } else { + Log::Fatal("Could not find ignore column %s in data file", name.c_str()); + } + } + } else { + for (auto token : Common::Split(config_.ignore_column.c_str(), ',')) { + int tmp = 0; + if (!Common::AtoiAndCheck(token.c_str(), &tmp)) { + Log::Fatal("ignore_column is not a number,\n" + "if you want to use a column name,\n" + "please add the prefix \"name:\" to the column name"); + } + ignore_features_.emplace(tmp); + } + } + } + // load weight idx + if (config_.weight_column.size() > 0) { + if (Common::StartsWith(config_.weight_column, name_prefix)) { + std::string name = config_.weight_column.substr(name_prefix.size()); + if (name2idx.count(name) > 0) { + weight_idx_ = name2idx[name]; + Log::Info("Using column %s as weight", name.c_str()); + } else { + Log::Fatal("Could not find weight column %s in data file", name.c_str()); + } + } else { + if (!Common::AtoiAndCheck(config_.weight_column.c_str(), &weight_idx_)) { + Log::Fatal("weight_column is not a number,\n" + "if you want to use a column name,\n" + "please add the prefix \"name:\" to the column name"); + } + Log::Info("Using column number %d as weight", weight_idx_); + } + ignore_features_.emplace(weight_idx_); + } + // load group idx + if (config_.group_column.size() > 0) { + if (Common::StartsWith(config_.group_column, name_prefix)) { + std::string name = config_.group_column.substr(name_prefix.size()); + if (name2idx.count(name) > 0) { + group_idx_ = name2idx[name]; + Log::Info("Using column %s as group/query id", name.c_str()); + } else { + Log::Fatal("Could not find group/query column %s in data file", name.c_str()); + } + } else { + if (!Common::AtoiAndCheck(config_.group_column.c_str(), &group_idx_)) { + Log::Fatal("group_column is not a number,\n" + "if you want to use a column name,\n" + "please add the prefix \"name:\" to the column name"); + } + Log::Info("Using column number %d as group/query id", group_idx_); + } + ignore_features_.emplace(group_idx_); + } + } + if (config_.categorical_feature.size() > 0) { + if (Common::StartsWith(config_.categorical_feature, name_prefix)) { + std::string names = config_.categorical_feature.substr(name_prefix.size()); + for (auto name : Common::Split(names.c_str(), ',')) { + if (name2idx.count(name) > 0) { + int tmp = name2idx[name]; + categorical_features_.emplace(tmp); + } else { + Log::Fatal("Could not find categorical_feature %s in data file", name.c_str()); + } + } + } else { + for (auto token : Common::Split(config_.categorical_feature.c_str(), ',')) { + int tmp = 0; + if (!Common::AtoiAndCheck(token.c_str(), &tmp)) { + Log::Fatal("categorical_feature is not a number,\n" + "if you want to use a column name,\n" + "please add the prefix \"name:\" to the column name"); + } + categorical_features_.emplace(tmp); + } + } + } +} + +void CheckSampleSize(size_t sample_cnt, size_t num_data) { + if (static_cast(sample_cnt) / num_data < 0.2f && + sample_cnt < 100000) { + Log::Warning( + "Using too small ``bin_construct_sample_cnt`` may encounter " + "unexpected " + "errors and poor accuracy."); + } +} + +Dataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_machines) { + // don't support query id in data file when using distributed training + if (num_machines > 1 && !config_.pre_partition) { + if (group_idx_ > 0) { + Log::Fatal("Using a query id without pre-partitioning the data file is not supported for distributed training.\n" + "Please use an additional query file or pre-partition the data"); + } + } + auto dataset = std::unique_ptr(new Dataset()); + if (store_raw_) { + dataset->SetHasRaw(true); + } + data_size_t num_global_data = 0; + std::vector used_data_indices; + auto bin_filename = CheckCanLoadFromBin(filename); + bool is_load_from_binary = false; + if (bin_filename.size() == 0) { + dataset->parser_config_str_ = Parser::GenerateParserConfigStr(filename, config_.parser_config_file.c_str(), config_.header, label_idx_); + auto parser = std::unique_ptr(Parser::CreateParser(filename, config_.header, 0, label_idx_, + config_.precise_float_parser, dataset->parser_config_str_)); + if (parser == nullptr) { + Log::Fatal("Could not recognize data format of %s", filename); + } + dataset->data_filename_ = filename; + dataset->label_idx_ = label_idx_; + dataset->metadata_.Init(filename); + if (!config_.two_round) { + // read data to memory + auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, rank, num_machines, &num_global_data, &used_data_indices); + dataset->num_data_ = static_cast(text_data.size()); + // sample data + auto sample_data = SampleTextDataFromMemory(text_data); + CheckSampleSize(sample_data.size(), + static_cast(dataset->num_data_)); + // construct feature bin mappers & clear sample data + ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get()); + std::vector().swap(sample_data); + if (dataset->has_raw()) { + dataset->ResizeRaw(dataset->num_data_); + } + // initialize label + dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); + // extract features + ExtractFeaturesFromMemory(&text_data, parser.get(), dataset.get()); + text_data.clear(); + } else { + // sample data from file + auto sample_data = SampleTextDataFromFile(filename, dataset->metadata_, rank, num_machines, &num_global_data, &used_data_indices); + if (used_data_indices.size() > 0) { + dataset->num_data_ = static_cast(used_data_indices.size()); + } else { + dataset->num_data_ = num_global_data; + } + CheckSampleSize(sample_data.size(), + static_cast(dataset->num_data_)); + // construct feature bin mappers & clear sample data + ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get()); + std::vector().swap(sample_data); + if (dataset->has_raw()) { + dataset->ResizeRaw(dataset->num_data_); + } + // initialize label + dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); + Log::Info("Making second pass..."); + // extract features + ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get()); + } + } else { + // load data from binary file + is_load_from_binary = true; + Log::Info("Load from binary file %s", bin_filename.c_str()); + dataset.reset(LoadFromBinFile(filename, bin_filename.c_str(), rank, num_machines, &num_global_data, &used_data_indices)); + + // checks whether there's a initial score file when loaded from binary data files + // the initial score file should with suffix ".bin.init" + dataset->metadata_.LoadInitialScore(bin_filename); + + dataset->device_type_ = config_.device_type; + dataset->gpu_device_id_ = config_.gpu_device_id; + #ifdef USE_CUDA + if (config_.device_type == std::string("cuda")) { + dataset->CreateCUDAColumnData(); + dataset->metadata_.CreateCUDAMetadata(dataset->gpu_device_id_); + } else { + dataset->cuda_column_data_ = nullptr; + } + #endif // USE_CUDA + } + // check meta data + dataset->metadata_.CheckOrPartition(num_global_data, used_data_indices); + // need to check training data + CheckDataset(dataset.get(), is_load_from_binary); + + return dataset.release(); +} + +Dataset* DatasetLoader::LoadFromFileAlignWithOtherDataset(const char* filename, const Dataset* train_data) { + data_size_t num_global_data = 0; + std::vector used_data_indices; + auto dataset = std::unique_ptr(new Dataset()); + if (store_raw_) { + dataset->SetHasRaw(true); + } + auto bin_filename = CheckCanLoadFromBin(filename); + if (bin_filename.size() == 0) { + auto parser = std::unique_ptr(Parser::CreateParser(filename, config_.header, 0, label_idx_, + config_.precise_float_parser, train_data->parser_config_str_)); + if (parser == nullptr) { + Log::Fatal("Could not recognize data format of %s", filename); + } + dataset->data_filename_ = filename; + dataset->label_idx_ = label_idx_; + dataset->metadata_.Init(filename); + if (!config_.two_round) { + // read data in memory + auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, 0, 1, &num_global_data, &used_data_indices); + dataset->num_data_ = static_cast(text_data.size()); + // initialize label + dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); + dataset->CreateValid(train_data); + if (dataset->has_raw()) { + dataset->ResizeRaw(dataset->num_data_); + } + // extract features + ExtractFeaturesFromMemory(&text_data, parser.get(), dataset.get()); + text_data.clear(); + } else { + TextReader text_reader(filename, config_.header); + // Get number of lines of data file + dataset->num_data_ = static_cast(text_reader.CountLine()); + num_global_data = dataset->num_data_; + // initialize label + dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_); + dataset->CreateValid(train_data); + if (dataset->has_raw()) { + dataset->ResizeRaw(dataset->num_data_); + } + // extract features + ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get()); + } + } else { + // load data from binary file + dataset.reset(LoadFromBinFile(filename, bin_filename.c_str(), 0, 1, &num_global_data, &used_data_indices)); + // checks whether there's a initial score file when loaded from binary data files + // the initial score file should with suffix ".bin.init" + dataset->metadata_.LoadInitialScore(bin_filename); + } + // not need to check validation data + // check meta data + dataset->metadata_.CheckOrPartition(num_global_data, used_data_indices); + return dataset.release(); +} + +Dataset* DatasetLoader::LoadFromSerializedReference(const char* binary_data, size_t buffer_size, data_size_t num_data, int32_t num_classes) { + auto dataset = std::unique_ptr(new Dataset(num_data)); + + auto mem_ptr = binary_data; + + // check token + const size_t size_of_token = std::strlen(Dataset::binary_serialized_reference_token); + size_t size_of_token_in_input = VirtualFileWriter::AlignedSize(sizeof(char) * size_of_token); + if (buffer_size < size_of_token_in_input) { + Log::Fatal("Binary definition file error: token has the wrong size"); + } + if (std::string(mem_ptr, size_of_token) != std::string(Dataset::binary_serialized_reference_token)) { + Log::Fatal("Input file is not LightGBM binary reference file"); + } + mem_ptr += size_of_token_in_input; + + size_t size_of_version = VirtualFileWriter::AlignedSize(Dataset::kSerializedReferenceVersionLength); + std::string version(mem_ptr, Dataset::kSerializedReferenceVersionLength); + if (version != std::string(Dataset::serialized_reference_version)) { + Log::Fatal("Unexpected version of serialized binary data: %s", version.c_str()); + } + mem_ptr += size_of_version; + + size_t size_of_header = *(reinterpret_cast(mem_ptr)); + mem_ptr += sizeof(size_t); + + LoadHeaderFromMemory(dataset.get(), mem_ptr); + dataset->num_data_ = num_data; // update to the given num_data + mem_ptr += size_of_header; + + // read feature group definitions + for (int i = 0; i < dataset->num_groups_; ++i) { + // read feature size + const size_t size_of_feature = *(reinterpret_cast(mem_ptr)); + mem_ptr += sizeof(size_t); + dataset->feature_groups_.emplace_back(std::unique_ptr(new FeatureGroup(mem_ptr, num_data, i))); + mem_ptr += size_of_feature; + } + dataset->feature_groups_.shrink_to_fit(); + + dataset->numeric_feature_map_ = std::vector(dataset->num_features_, false); + dataset->num_numeric_features_ = 0; + for (int i = 0; i < dataset->num_features_; ++i) { + if (dataset->FeatureBinMapper(i)->bin_type() == BinType::CategoricalBin) { + dataset->numeric_feature_map_[i] = -1; + } else { + dataset->numeric_feature_map_[i] = dataset->num_numeric_features_; + ++dataset->num_numeric_features_; + } + } + + int has_weights = config_.weight_column.size() > 0; + int has_init_scores = num_classes > 0; + int has_queries = config_.group_column.size() > 0; + dataset->metadata_.Init(num_data, has_weights, has_init_scores, has_queries, num_classes); + + Log::Info("Loaded reference dataset: %d features, %d num_data", dataset->num_features_, num_data); + + return dataset.release(); +} + +Dataset* DatasetLoader::LoadFromBinFile(const char* data_filename, const char* bin_filename, + int rank, int num_machines, int* num_global_data, + std::vector* used_data_indices) { + auto dataset = std::unique_ptr(new Dataset()); + auto reader = VirtualFileReader::Make(bin_filename); + dataset->data_filename_ = data_filename; + if (!reader->Init()) { + Log::Fatal("Could not read binary data from %s", bin_filename); + } + + // buffer to read binary file + size_t buffer_size = 16 * 1024 * 1024; + auto buffer = std::vector(buffer_size); + + // check token + size_t size_of_token = std::strlen(Dataset::binary_file_token); + size_t read_cnt = reader->Read( + buffer.data(), + VirtualFileWriter::AlignedSize(sizeof(char) * size_of_token)); + if (read_cnt < sizeof(char) * size_of_token) { + Log::Fatal("Binary file error: token has the wrong size"); + } + if (std::string(buffer.data()) != std::string(Dataset::binary_file_token)) { + Log::Fatal("Input file is not LightGBM binary file"); + } + + // read size of header + read_cnt = reader->Read(buffer.data(), sizeof(size_t)); + + if (read_cnt != sizeof(size_t)) { + Log::Fatal("Binary file error: header has the wrong size"); + } + + size_t size_of_head = *(reinterpret_cast(buffer.data())); + + // re-allocate space if not enough + if (size_of_head > buffer_size) { + buffer_size = size_of_head; + buffer.resize(buffer_size); + } + // read header + read_cnt = reader->Read(buffer.data(), size_of_head); + + if (read_cnt != size_of_head) { + Log::Fatal("Binary file error: header is incorrect"); + } + // get header + const char* mem_ptr = buffer.data(); + LoadHeaderFromMemory(dataset.get(), mem_ptr); + + // read size of meta data + read_cnt = reader->Read(buffer.data(), sizeof(size_t)); + + if (read_cnt != sizeof(size_t)) { + Log::Fatal("Binary file error: meta data has the wrong size"); + } + + size_t size_of_metadata = *(reinterpret_cast(buffer.data())); + + // re-allocate space if not enough + if (size_of_metadata > buffer_size) { + buffer_size = size_of_metadata; + buffer.resize(buffer_size); + } + // read meta data + read_cnt = reader->Read(buffer.data(), size_of_metadata); + + if (read_cnt != size_of_metadata) { + Log::Fatal("Binary file error: meta data is incorrect"); + } + // load meta data + dataset->metadata_.LoadFromMemory(buffer.data()); + + *num_global_data = dataset->num_data_; + used_data_indices->clear(); + // sample local used data if need to partition + if (num_machines > 1 && !config_.pre_partition) { + const data_size_t* query_boundaries = dataset->metadata_.query_boundaries(); + if (query_boundaries == nullptr) { + // if not contain query file, minimal sample unit is one record + for (data_size_t i = 0; i < dataset->num_data_; ++i) { + if (random_.NextShort(0, num_machines) == rank) { + used_data_indices->push_back(i); + } + } + } else { + // if contain query file, minimal sample unit is one query + data_size_t num_queries = dataset->metadata_.num_queries(); + data_size_t qid = -1; + bool is_query_used = false; + for (data_size_t i = 0; i < dataset->num_data_; ++i) { + if (qid >= num_queries) { + Log::Fatal("Current query exceeds the range of the query file,\n" + "please ensure the query file is correct"); + } + if (i >= query_boundaries[qid + 1]) { + // if is new query + is_query_used = false; + if (random_.NextShort(0, num_machines) == rank) { + is_query_used = true; + } + ++qid; + } + if (is_query_used) { + used_data_indices->push_back(i); + } + } + } + dataset->num_data_ = static_cast((*used_data_indices).size()); + } + dataset->metadata_.PartitionLabel(*used_data_indices); + // read feature data + for (int i = 0; i < dataset->num_groups_; ++i) { + // read feature size + read_cnt = reader->Read(buffer.data(), sizeof(size_t)); + if (read_cnt != sizeof(size_t)) { + Log::Fatal("Binary file error: feature %d has the wrong size", i); + } + size_t size_of_feature = *(reinterpret_cast(buffer.data())); + // re-allocate space if not enough + if (size_of_feature > buffer_size) { + buffer_size = size_of_feature; + buffer.resize(buffer_size); + } + + read_cnt = reader->Read(buffer.data(), size_of_feature); + + if (read_cnt != size_of_feature) { + Log::Fatal("Binary file error: feature %d is incorrect, read count: %zu", i, read_cnt); + } + dataset->feature_groups_.emplace_back(std::unique_ptr( + new FeatureGroup(buffer.data(), + *num_global_data, + *used_data_indices, i))); + } + dataset->feature_groups_.shrink_to_fit(); + + // raw data + dataset->numeric_feature_map_ = std::vector(dataset->num_features_, false); + dataset->num_numeric_features_ = 0; + for (int i = 0; i < dataset->num_features_; ++i) { + if (dataset->FeatureBinMapper(i)->bin_type() == BinType::CategoricalBin) { + dataset->numeric_feature_map_[i] = -1; + } else { + dataset->numeric_feature_map_[i] = dataset->num_numeric_features_; + ++dataset->num_numeric_features_; + } + } + if (dataset->has_raw()) { + dataset->ResizeRaw(dataset->num_data()); + size_t row_size = dataset->num_numeric_features_ * sizeof(float); + if (row_size > buffer_size) { + buffer_size = row_size; + buffer.resize(buffer_size); + } + for (int i = 0; i < dataset->num_data(); ++i) { + read_cnt = reader->Read(buffer.data(), row_size); + if (read_cnt != row_size) { + Log::Fatal("Binary file error: row %d of raw data is incorrect, read count: %zu", i, read_cnt); + } + mem_ptr = buffer.data(); + const float* tmp_ptr_raw_row = reinterpret_cast(mem_ptr); + for (int j = 0; j < dataset->num_features(); ++j) { + int feat_ind = dataset->numeric_feature_map_[j]; + if (feat_ind >= 0) { + dataset->raw_data_[feat_ind][i] = tmp_ptr_raw_row[feat_ind]; + } + } + mem_ptr += row_size; + } + } + + dataset->is_finish_load_ = true; + return dataset.release(); +} + +Dataset* DatasetLoader::ConstructFromSampleData(double** sample_values, + int** sample_indices, + int num_col, + const int* num_per_col, + size_t total_sample_size, + data_size_t num_local_data, + int64_t num_dist_data) { + CheckSampleSize(total_sample_size, static_cast(num_dist_data)); + int num_total_features = num_col; + if (Network::num_machines() > 1) { + num_total_features = Network::GlobalSyncUpByMax(num_total_features); + } + std::vector> bin_mappers(num_total_features); + // fill feature_names_ if not header + if (feature_names_.empty()) { + for (int i = 0; i < num_col; ++i) { + std::stringstream str_buf; + str_buf << "Column_" << i; + feature_names_.push_back(str_buf.str()); + } + } + if (!config_.max_bin_by_feature.empty()) { + CHECK_EQ(static_cast(num_col), config_.max_bin_by_feature.size()); + CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1); + } + + // get forced split + std::string forced_bins_path = config_.forcedbins_filename; + std::vector> forced_bin_bounds = DatasetLoader::GetForcedBins(forced_bins_path, num_col, categorical_features_); + + const data_size_t filter_cnt = static_cast( + static_cast(config_.min_data_in_leaf * total_sample_size) / num_dist_data); + if (Network::num_machines() == 1) { + // if only one machine, find bin locally + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) + for (int i = 0; i < num_col; ++i) { + OMP_LOOP_EX_BEGIN(); + if (ignore_features_.count(i) > 0) { + bin_mappers[i] = nullptr; + continue; + } + BinType bin_type = BinType::NumericalBin; + if (categorical_features_.count(i)) { + bin_type = BinType::CategoricalBin; + bool feat_is_unconstrained = ((config_.monotone_constraints.size() == 0) || (config_.monotone_constraints[i] == 0)); + if (!feat_is_unconstrained) { + Log::Fatal("The output cannot be monotone with respect to categorical features"); + } + } + bin_mappers[i].reset(new BinMapper()); + if (config_.max_bin_by_feature.empty()) { + bin_mappers[i]->FindBin(sample_values[i], num_per_col[i], total_sample_size, + config_.max_bin, config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, + bin_type, config_.use_missing, config_.zero_as_missing, + forced_bin_bounds[i]); + } else { + bin_mappers[i]->FindBin(sample_values[i], num_per_col[i], total_sample_size, + config_.max_bin_by_feature[i], config_.min_data_in_bin, + filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, + config_.zero_as_missing, forced_bin_bounds[i]); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } else { + // if have multi-machines, need to find bin distributed + // different machines will find bin for different features + int num_machines = Network::num_machines(); + int rank = Network::rank(); + // start and len will store the process feature indices for different machines + // machine i will find bins for features in [ start[i], start[i] + len[i] ) + std::vector start(num_machines); + std::vector len(num_machines); + int step = (num_total_features + num_machines - 1) / num_machines; + if (step < 1) { + step = 1; + } + + start[0] = 0; + for (int i = 0; i < num_machines - 1; ++i) { + len[i] = std::min(step, num_total_features - start[i]); + start[i + 1] = start[i] + len[i]; + } + len[num_machines - 1] = num_total_features - start[num_machines - 1]; + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) + for (int i = 0; i < len[rank]; ++i) { + OMP_LOOP_EX_BEGIN(); + if (ignore_features_.count(start[rank] + i) > 0) { + continue; + } + BinType bin_type = BinType::NumericalBin; + if (categorical_features_.count(start[rank] + i)) { + bin_type = BinType::CategoricalBin; + } + bin_mappers[i].reset(new BinMapper()); + if (num_col <= start[rank] + i) { + continue; + } + if (config_.max_bin_by_feature.empty()) { + bin_mappers[i]->FindBin(sample_values[start[rank] + i], num_per_col[start[rank] + i], + total_sample_size, config_.max_bin, config_.min_data_in_bin, + filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, + forced_bin_bounds[i]); + } else { + bin_mappers[i]->FindBin(sample_values[start[rank] + i], num_per_col[start[rank] + i], + total_sample_size, config_.max_bin_by_feature[start[rank] + i], + config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, + config_.zero_as_missing, forced_bin_bounds[i]); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + comm_size_t self_buf_size = 0; + for (int i = 0; i < len[rank]; ++i) { + if (ignore_features_.count(start[rank] + i) > 0) { + continue; + } + self_buf_size += static_cast(bin_mappers[i]->SizesInByte()); + } + std::vector input_buffer(self_buf_size); + auto cp_ptr = input_buffer.data(); + for (int i = 0; i < len[rank]; ++i) { + if (ignore_features_.count(start[rank] + i) > 0) { + continue; + } + bin_mappers[i]->CopyTo(cp_ptr); + cp_ptr += bin_mappers[i]->SizesInByte(); + // free + bin_mappers[i].reset(nullptr); + } + std::vector size_len = Network::GlobalArray(self_buf_size); + std::vector size_start(num_machines, 0); + for (int i = 1; i < num_machines; ++i) { + size_start[i] = size_start[i - 1] + size_len[i - 1]; + } + comm_size_t total_buffer_size = size_start[num_machines - 1] + size_len[num_machines - 1]; + std::vector output_buffer(total_buffer_size); + // gather global feature bin mappers + Network::Allgather(input_buffer.data(), size_start.data(), size_len.data(), output_buffer.data(), total_buffer_size); + cp_ptr = output_buffer.data(); + // restore features bins from buffer + for (int i = 0; i < num_total_features; ++i) { + if (ignore_features_.count(i) > 0) { + bin_mappers[i] = nullptr; + continue; + } + bin_mappers[i].reset(new BinMapper()); + bin_mappers[i]->CopyFrom(cp_ptr); + cp_ptr += bin_mappers[i]->SizesInByte(); + } + } + CheckCategoricalFeatureNumBin(bin_mappers, config_.max_bin, config_.max_bin_by_feature); + auto dataset = std::unique_ptr(new Dataset(num_local_data)); + dataset->Construct(&bin_mappers, num_total_features, forced_bin_bounds, sample_indices, sample_values, num_per_col, num_col, total_sample_size, config_); + if (dataset->has_raw()) { + dataset->ResizeRaw(num_local_data); + } + dataset->set_feature_names(feature_names_); + return dataset.release(); +} + + +// ---- private functions ---- + +void DatasetLoader::LoadHeaderFromMemory(Dataset* dataset, const char* buffer) { + // get header + const char* mem_ptr = buffer; + dataset->num_data_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_data_)); + dataset->num_features_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_features_)); + dataset->num_total_features_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_total_features_)); + dataset->label_idx_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->label_idx_)); + dataset->max_bin_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->max_bin_)); + dataset->bin_construct_sample_cnt_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->bin_construct_sample_cnt_)); + dataset->min_data_in_bin_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->min_data_in_bin_)); + dataset->use_missing_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->use_missing_)); + dataset->zero_as_missing_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->zero_as_missing_)); + dataset->has_raw_ = *(reinterpret_cast(mem_ptr)); + + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->has_raw_)); + const int* tmp_feature_map = reinterpret_cast(mem_ptr); + dataset->used_feature_map_.clear(); + for (int i = 0; i < dataset->num_total_features_; ++i) { + dataset->used_feature_map_.push_back(tmp_feature_map[i]); + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_total_features_); + // num_groups + dataset->num_groups_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_groups_)); + // real_feature_idx_ + const int* tmp_ptr_real_feature_idx_ = reinterpret_cast(mem_ptr); + dataset->real_feature_idx_.clear(); + for (int i = 0; i < dataset->num_features_; ++i) { + dataset->real_feature_idx_.push_back(tmp_ptr_real_feature_idx_[i]); + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_); + // feature2group + const int* tmp_ptr_feature2group = reinterpret_cast(mem_ptr); + dataset->feature2group_.clear(); + for (int i = 0; i < dataset->num_features_; ++i) { + dataset->feature2group_.push_back(tmp_ptr_feature2group[i]); + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_); + // feature2subfeature + const int* tmp_ptr_feature2subfeature = reinterpret_cast(mem_ptr); + dataset->feature2subfeature_.clear(); + for (int i = 0; i < dataset->num_features_; ++i) { + dataset->feature2subfeature_.push_back(tmp_ptr_feature2subfeature[i]); + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_); + // group_bin_boundaries + const uint64_t* tmp_ptr_group_bin_boundaries = reinterpret_cast(mem_ptr); + dataset->group_bin_boundaries_.clear(); + for (int i = 0; i < dataset->num_groups_ + 1; ++i) { + dataset->group_bin_boundaries_.push_back(tmp_ptr_group_bin_boundaries[i]); + } + mem_ptr += sizeof(uint64_t) * (dataset->num_groups_ + 1); + + // group_feature_start_ + const int* tmp_ptr_group_feature_start = reinterpret_cast(mem_ptr); + dataset->group_feature_start_.clear(); + for (int i = 0; i < dataset->num_groups_; ++i) { + dataset->group_feature_start_.push_back(tmp_ptr_group_feature_start[i]); + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * (dataset->num_groups_)); + + // group_feature_cnt_ + const int* tmp_ptr_group_feature_cnt = reinterpret_cast(mem_ptr); + dataset->group_feature_cnt_.clear(); + for (int i = 0; i < dataset->num_groups_; ++i) { + dataset->group_feature_cnt_.push_back(tmp_ptr_group_feature_cnt[i]); + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * (dataset->num_groups_)); + + if (!config_.max_bin_by_feature.empty()) { + CHECK_EQ(static_cast(dataset->num_total_features_), config_.max_bin_by_feature.size()); + CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1); + dataset->max_bin_by_feature_.resize(dataset->num_total_features_); + dataset->max_bin_by_feature_.assign(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end()); + } else { + const int32_t* tmp_ptr_max_bin_by_feature = reinterpret_cast(mem_ptr); + dataset->max_bin_by_feature_.clear(); + for (int i = 0; i < dataset->num_total_features_; ++i) { + dataset->max_bin_by_feature_.push_back(tmp_ptr_max_bin_by_feature[i]); + } + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int32_t) * (dataset->num_total_features_)); + if (ArrayArgs::CheckAll(dataset->max_bin_by_feature_, -1)) { + dataset->max_bin_by_feature_.clear(); + } + + // get feature names + dataset->feature_names_.clear(); + for (int i = 0; i < dataset->num_total_features_; ++i) { + int str_len = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int)); + std::stringstream str_buf; + auto tmp_arr = reinterpret_cast(mem_ptr); + for (int j = 0; j < str_len; ++j) { + char tmp_char = tmp_arr[j]; + str_buf << tmp_char; + } + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(char) * str_len); + dataset->feature_names_.emplace_back(str_buf.str()); + } + // get forced_bin_bounds_ + dataset->forced_bin_bounds_ = std::vector>(dataset->num_total_features_, std::vector()); + for (int i = 0; i < dataset->num_total_features_; ++i) { + int num_bounds = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int)); + dataset->forced_bin_bounds_[i] = std::vector(); + const double* tmp_ptr_forced_bounds = + reinterpret_cast(mem_ptr); + for (int j = 0; j < num_bounds; ++j) { + double bound = tmp_ptr_forced_bounds[j]; + dataset->forced_bin_bounds_[i].push_back(bound); + } + mem_ptr += num_bounds * sizeof(double); + } +} + +void DatasetLoader::CheckDataset(const Dataset* dataset, bool is_load_from_binary) { + if (dataset->num_data_ <= 0) { + Log::Fatal("Data file %s is empty", dataset->data_filename_.c_str()); + } + if (dataset->feature_names_.size() != static_cast(dataset->num_total_features_)) { + Log::Fatal("Size of feature name error, should be %d, got %d", dataset->num_total_features_, + static_cast(dataset->feature_names_.size())); + } + bool is_feature_order_by_group = true; + int last_group = -1; + int last_sub_feature = -1; + // if features are ordered, not need to use hist_buf + for (int i = 0; i < dataset->num_features_; ++i) { + int group = dataset->feature2group_[i]; + int sub_feature = dataset->feature2subfeature_[i]; + if (group < last_group) { + is_feature_order_by_group = false; + } else if (group == last_group) { + if (sub_feature <= last_sub_feature) { + is_feature_order_by_group = false; + break; + } + } + last_group = group; + last_sub_feature = sub_feature; + } + if (!is_feature_order_by_group) { + Log::Fatal("Features in dataset should be ordered by group"); + } + + if (is_load_from_binary) { + if (dataset->max_bin_ != config_.max_bin) { + Log::Fatal("Dataset was constructed with parameter max_bin=%d. It cannot be changed to %d when loading from binary file.", + dataset->max_bin_, config_.max_bin); + } + if (dataset->min_data_in_bin_ != config_.min_data_in_bin) { + Log::Fatal("Dataset was constructed with parameter min_data_in_bin=%d. It cannot be changed to %d when loading from binary file.", + dataset->min_data_in_bin_, config_.min_data_in_bin); + } + if (dataset->use_missing_ != config_.use_missing) { + Log::Fatal("Dataset was constructed with parameter use_missing=%d. It cannot be changed to %d when loading from binary file.", + dataset->use_missing_, config_.use_missing); + } + if (dataset->zero_as_missing_ != config_.zero_as_missing) { + Log::Fatal("Dataset was constructed with parameter zero_as_missing=%d. It cannot be changed to %d when loading from binary file.", + dataset->zero_as_missing_, config_.zero_as_missing); + } + if (dataset->bin_construct_sample_cnt_ != config_.bin_construct_sample_cnt) { + Log::Fatal("Dataset was constructed with parameter bin_construct_sample_cnt=%d. It cannot be changed to %d when loading from binary file.", + dataset->bin_construct_sample_cnt_, config_.bin_construct_sample_cnt); + } + if ((dataset->max_bin_by_feature_.size() != config_.max_bin_by_feature.size()) || + !std::equal(dataset->max_bin_by_feature_.begin(), dataset->max_bin_by_feature_.end(), + config_.max_bin_by_feature.begin())) { + Log::Fatal("Parameter max_bin_by_feature cannot be changed when loading from binary file."); + } + + if (config_.label_column != "") { + Log::Warning("Parameter label_column works only in case of loading data directly from text file. It will be ignored when loading from binary file."); + } + if (config_.weight_column != "") { + Log::Warning("Parameter weight_column works only in case of loading data directly from text file. It will be ignored when loading from binary file."); + } + if (config_.group_column != "") { + Log::Warning("Parameter group_column works only in case of loading data directly from text file. It will be ignored when loading from binary file."); + } + if (config_.ignore_column != "") { + Log::Warning("Parameter ignore_column works only in case of loading data directly from text file. It will be ignored when loading from binary file."); + } + if (config_.two_round) { + Log::Warning("Parameter two_round works only in case of loading data directly from text file. It will be ignored when loading from binary file."); + } + if (config_.header) { + Log::Warning("Parameter header works only in case of loading data directly from text file. It will be ignored when loading from binary file."); + } + } +} + +std::vector DatasetLoader::LoadTextDataToMemory(const char* filename, const Metadata& metadata, + int rank, int num_machines, int* num_global_data, + std::vector* used_data_indices) { + TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes); + used_data_indices->clear(); + if (num_machines == 1 || config_.pre_partition) { + // read all lines + *num_global_data = text_reader.ReadAllLines(); + } else { // need partition data + // get query data + const data_size_t* query_boundaries = metadata.query_boundaries(); + + if (query_boundaries == nullptr) { + // if not contain query data, minimal sample unit is one record + *num_global_data = text_reader.ReadAndFilterLines([this, rank, num_machines](data_size_t) { + if (random_.NextShort(0, num_machines) == rank) { + return true; + } else { + return false; + } + }, used_data_indices); + } else { + // if contain query data, minimal sample unit is one query + data_size_t num_queries = metadata.num_queries(); + data_size_t qid = -1; + bool is_query_used = false; + *num_global_data = text_reader.ReadAndFilterLines( + [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries] + (data_size_t line_idx) { + if (qid >= num_queries) { + Log::Fatal("Current query exceeds the range of the query file,\n" + "please ensure the query file is correct"); + } + if (line_idx >= query_boundaries[qid + 1]) { + // if is new query + is_query_used = false; + if (random_.NextShort(0, num_machines) == rank) { + is_query_used = true; + } + ++qid; + } + return is_query_used; + }, used_data_indices); + } + } + return std::move(text_reader.Lines()); +} + +std::vector DatasetLoader::SampleTextDataFromMemory(const std::vector& data) { + int sample_cnt = config_.bin_construct_sample_cnt; + if (static_cast(sample_cnt) > data.size()) { + sample_cnt = static_cast(data.size()); + } + auto sample_indices = random_.Sample(static_cast(data.size()), sample_cnt); + std::vector out(sample_indices.size()); + for (size_t i = 0; i < sample_indices.size(); ++i) { + const size_t idx = sample_indices[i]; + out[i] = data[idx]; + } + return out; +} + +std::vector DatasetLoader::SampleTextDataFromFile(const char* filename, const Metadata& metadata, + int rank, int num_machines, int* num_global_data, + std::vector* used_data_indices) { + const data_size_t sample_cnt = static_cast(config_.bin_construct_sample_cnt); + TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes); + std::vector out_data; + if (num_machines == 1 || config_.pre_partition) { + *num_global_data = static_cast(text_reader.SampleFromFile(&random_, sample_cnt, &out_data)); + } else { // need partition data + // get query data + const data_size_t* query_boundaries = metadata.query_boundaries(); + if (query_boundaries == nullptr) { + // if not contain query file, minimal sample unit is one record + *num_global_data = text_reader.SampleAndFilterFromFile([this, rank, num_machines] + (data_size_t) { + if (random_.NextShort(0, num_machines) == rank) { + return true; + } else { + return false; + } + }, used_data_indices, &random_, sample_cnt, &out_data); + } else { + // if contain query file, minimal sample unit is one query + data_size_t num_queries = metadata.num_queries(); + data_size_t qid = -1; + bool is_query_used = false; + *num_global_data = text_reader.SampleAndFilterFromFile( + [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries] + (data_size_t line_idx) { + if (qid >= num_queries) { + Log::Fatal("Query id exceeds the range of the query file, " + "please ensure the query file is correct"); + } + if (line_idx >= query_boundaries[qid + 1]) { + // if is new query + is_query_used = false; + if (random_.NextShort(0, num_machines) == rank) { + is_query_used = true; + } + ++qid; + } + return is_query_used; + }, used_data_indices, &random_, sample_cnt, &out_data); + } + } + return out_data; +} + +void DatasetLoader::ConstructBinMappersFromTextData(int rank, int num_machines, + const std::vector& sample_data, + const Parser* parser, Dataset* dataset) { + auto t1 = std::chrono::high_resolution_clock::now(); + std::vector> sample_values; + std::vector> sample_indices; + std::vector> oneline_features; + double label; + for (int i = 0; i < static_cast(sample_data.size()); ++i) { + oneline_features.clear(); + // parse features + parser->ParseOneLine(sample_data[i].c_str(), &oneline_features, &label); + for (std::pair& inner_data : oneline_features) { + if (static_cast(inner_data.first) >= sample_values.size()) { + sample_values.resize(inner_data.first + 1); + sample_indices.resize(inner_data.first + 1); + } + if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) { + sample_values[inner_data.first].emplace_back(inner_data.second); + sample_indices[inner_data.first].emplace_back(i); + } + } + } + + dataset->feature_groups_.clear(); + dataset->num_total_features_ = std::max(static_cast(sample_values.size()), parser->NumFeatures()); + if (num_machines > 1) { + dataset->num_total_features_ = Network::GlobalSyncUpByMax(dataset->num_total_features_); + } + if (!feature_names_.empty()) { + CHECK_EQ(dataset->num_total_features_, static_cast(feature_names_.size())); + } + + if (!config_.max_bin_by_feature.empty()) { + CHECK_EQ(static_cast(dataset->num_total_features_), config_.max_bin_by_feature.size()); + CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1); + } + + // get forced split + std::string forced_bins_path = config_.forcedbins_filename; + std::vector> forced_bin_bounds = DatasetLoader::GetForcedBins(forced_bins_path, + dataset->num_total_features_, + categorical_features_); + + // check the range of label_idx, weight_idx and group_idx + // skip label check if user input parser config file, + // because label id is got from raw features while dataset features are consistent with customized parser. + if (dataset->parser_config_str_.empty()) { + CHECK(label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_); + } + CHECK(weight_idx_ < 0 || weight_idx_ < dataset->num_total_features_); + CHECK(group_idx_ < 0 || group_idx_ < dataset->num_total_features_); + + // fill feature_names_ if not header + if (feature_names_.empty()) { + for (int i = 0; i < dataset->num_total_features_; ++i) { + std::stringstream str_buf; + str_buf << "Column_" << i; + feature_names_.push_back(str_buf.str()); + } + } + dataset->set_feature_names(feature_names_); + std::vector> bin_mappers(dataset->num_total_features_); + const data_size_t filter_cnt = static_cast( + static_cast(config_.min_data_in_leaf* sample_data.size()) / dataset->num_data_); + // start find bins + if (num_machines == 1) { + // if only one machine, find bin locally + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) + for (int i = 0; i < static_cast(sample_values.size()); ++i) { + OMP_LOOP_EX_BEGIN(); + if (ignore_features_.count(i) > 0) { + bin_mappers[i] = nullptr; + continue; + } + BinType bin_type = BinType::NumericalBin; + if (categorical_features_.count(i)) { + bin_type = BinType::CategoricalBin; + } + bin_mappers[i].reset(new BinMapper()); + if (config_.max_bin_by_feature.empty()) { + bin_mappers[i]->FindBin(sample_values[i].data(), static_cast(sample_values[i].size()), + sample_data.size(), config_.max_bin, config_.min_data_in_bin, + filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, + forced_bin_bounds[i]); + } else { + bin_mappers[i]->FindBin(sample_values[i].data(), static_cast(sample_values[i].size()), + sample_data.size(), config_.max_bin_by_feature[i], + config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, + config_.zero_as_missing, forced_bin_bounds[i]); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } else { + // start and len will store the process feature indices for different machines + // machine i will find bins for features in [ start[i], start[i] + len[i] ) + std::vector start(num_machines); + std::vector len(num_machines); + int step = (dataset->num_total_features_ + num_machines - 1) / num_machines; + if (step < 1) { + step = 1; + } + + start[0] = 0; + for (int i = 0; i < num_machines - 1; ++i) { + len[i] = std::min(step, dataset->num_total_features_ - start[i]); + start[i + 1] = start[i] + len[i]; + } + len[num_machines - 1] = dataset->num_total_features_ - start[num_machines - 1]; + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) + for (int i = 0; i < len[rank]; ++i) { + OMP_LOOP_EX_BEGIN(); + if (ignore_features_.count(start[rank] + i) > 0) { + continue; + } + BinType bin_type = BinType::NumericalBin; + if (categorical_features_.count(start[rank] + i)) { + bin_type = BinType::CategoricalBin; + } + bin_mappers[i].reset(new BinMapper()); + if (static_cast(sample_values.size()) <= start[rank] + i) { + continue; + } + if (config_.max_bin_by_feature.empty()) { + bin_mappers[i]->FindBin(sample_values[start[rank] + i].data(), + static_cast(sample_values[start[rank] + i].size()), + sample_data.size(), config_.max_bin, config_.min_data_in_bin, + filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing, + forced_bin_bounds[i]); + } else { + bin_mappers[i]->FindBin(sample_values[start[rank] + i].data(), + static_cast(sample_values[start[rank] + i].size()), + sample_data.size(), config_.max_bin_by_feature[i], + config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, + config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + comm_size_t self_buf_size = 0; + for (int i = 0; i < len[rank]; ++i) { + if (ignore_features_.count(start[rank] + i) > 0) { + continue; + } + self_buf_size += static_cast(bin_mappers[i]->SizesInByte()); + } + std::vector input_buffer(self_buf_size); + auto cp_ptr = input_buffer.data(); + for (int i = 0; i < len[rank]; ++i) { + if (ignore_features_.count(start[rank] + i) > 0) { + continue; + } + bin_mappers[i]->CopyTo(cp_ptr); + cp_ptr += bin_mappers[i]->SizesInByte(); + // free + bin_mappers[i].reset(nullptr); + } + std::vector size_len = Network::GlobalArray(self_buf_size); + std::vector size_start(num_machines, 0); + for (int i = 1; i < num_machines; ++i) { + size_start[i] = size_start[i - 1] + size_len[i - 1]; + } + comm_size_t total_buffer_size = size_start[num_machines - 1] + size_len[num_machines - 1]; + std::vector output_buffer(total_buffer_size); + // gather global feature bin mappers + Network::Allgather(input_buffer.data(), size_start.data(), size_len.data(), output_buffer.data(), total_buffer_size); + cp_ptr = output_buffer.data(); + // restore features bins from buffer + for (int i = 0; i < dataset->num_total_features_; ++i) { + if (ignore_features_.count(i) > 0) { + bin_mappers[i] = nullptr; + continue; + } + bin_mappers[i].reset(new BinMapper()); + bin_mappers[i]->CopyFrom(cp_ptr); + cp_ptr += bin_mappers[i]->SizesInByte(); + } + } + CheckCategoricalFeatureNumBin(bin_mappers, config_.max_bin, config_.max_bin_by_feature); + dataset->Construct(&bin_mappers, dataset->num_total_features_, forced_bin_bounds, Common::Vector2Ptr(&sample_indices).data(), + Common::Vector2Ptr(&sample_values).data(), + Common::VectorSize(sample_indices).data(), static_cast(sample_indices.size()), sample_data.size(), config_); + if (dataset->has_raw()) { + dataset->ResizeRaw(static_cast(sample_data.size())); + } + + auto t2 = std::chrono::high_resolution_clock::now(); + Log::Info("Construct bin mappers from text data time %.2f seconds", + std::chrono::duration(t2 - t1) * 1e-3); +} + +/*! \brief Extract local features from memory */ +void DatasetLoader::ExtractFeaturesFromMemory(std::vector* text_data, const Parser* parser, Dataset* dataset) { + std::vector> oneline_features; + double tmp_label = 0.0f; + auto& ref_text_data = *text_data; + std::vector feature_row(dataset->num_features_); + if (!predict_fun_) { + OMP_INIT_EX(); + // if doesn't need to prediction with initial model + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row) + for (data_size_t i = 0; i < dataset->num_data_; ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + oneline_features.clear(); + // parser + parser->ParseOneLine(ref_text_data[i].c_str(), &oneline_features, &tmp_label); + // set label + dataset->metadata_.SetLabelAt(i, static_cast(tmp_label)); + // free processed line: + ref_text_data[i].clear(); + // shrink_to_fit will be very slow in linux, and seems not free memory, disable for now + // text_reader_->Lines()[i].shrink_to_fit(); + std::vector is_feature_added(dataset->num_features_, false); + // push data + for (auto& inner_data : oneline_features) { + if (inner_data.first >= dataset->num_total_features_) { + continue; + } + int feature_idx = dataset->used_feature_map_[inner_data.first]; + if (feature_idx >= 0) { + is_feature_added[feature_idx] = true; + // if is used feature + int group = dataset->feature2group_[feature_idx]; + int sub_feature = dataset->feature2subfeature_[feature_idx]; + dataset->feature_groups_[group]->PushData(tid, sub_feature, i, inner_data.second); + if (dataset->has_raw()) { + feature_row[feature_idx] = static_cast(inner_data.second); + } + } else { + if (inner_data.first == weight_idx_) { + dataset->metadata_.SetWeightAt(i, static_cast(inner_data.second)); + } else if (inner_data.first == group_idx_) { + dataset->metadata_.SetQueryAt(i, static_cast(inner_data.second)); + } + } + } + if (dataset->has_raw()) { + for (size_t j = 0; j < feature_row.size(); ++j) { + int feat_ind = dataset->numeric_feature_map_[j]; + if (feat_ind >= 0) { + dataset->raw_data_[feat_ind][i] = feature_row[j]; + } + } + } + dataset->FinishOneRow(tid, i, is_feature_added); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } else { + OMP_INIT_EX(); + // if need to prediction with initial model + std::vector init_score(static_cast(dataset->num_data_) * num_class_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row) + for (data_size_t i = 0; i < dataset->num_data_; ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + oneline_features.clear(); + // parser + parser->ParseOneLine(ref_text_data[i].c_str(), &oneline_features, &tmp_label); + // set initial score + std::vector oneline_init_score(num_class_); + predict_fun_(oneline_features, oneline_init_score.data()); + for (int k = 0; k < num_class_; ++k) { + init_score[k * dataset->num_data_ + i] = static_cast(oneline_init_score[k]); + } + // set label + dataset->metadata_.SetLabelAt(i, static_cast(tmp_label)); + // free processed line: + ref_text_data[i].clear(); + // shrink_to_fit will be very slow in Linux, and seems not free memory, disable for now + // text_reader_->Lines()[i].shrink_to_fit(); + // push data + std::vector is_feature_added(dataset->num_features_, false); + for (auto& inner_data : oneline_features) { + if (inner_data.first >= dataset->num_total_features_) { + continue; + } + int feature_idx = dataset->used_feature_map_[inner_data.first]; + if (feature_idx >= 0) { + is_feature_added[feature_idx] = true; + // if is used feature + int group = dataset->feature2group_[feature_idx]; + int sub_feature = dataset->feature2subfeature_[feature_idx]; + dataset->feature_groups_[group]->PushData(tid, sub_feature, i, inner_data.second); + if (dataset->has_raw()) { + feature_row[feature_idx] = static_cast(inner_data.second); + } + } else { + if (inner_data.first == weight_idx_) { + dataset->metadata_.SetWeightAt(i, static_cast(inner_data.second)); + } else if (inner_data.first == group_idx_) { + dataset->metadata_.SetQueryAt(i, static_cast(inner_data.second)); + } + } + } + dataset->FinishOneRow(tid, i, is_feature_added); + if (dataset->has_raw()) { + for (size_t j = 0; j < feature_row.size(); ++j) { + int feat_ind = dataset->numeric_feature_map_[j]; + if (feat_ind >= 0) { + dataset->raw_data_[feat_ind][i] = feature_row[j]; + } + } + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + // metadata_ will manage space of init_score + dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_); + } + dataset->FinishLoad(); + // text data can be free after loaded feature values + text_data->clear(); +} + +/*! \brief Extract local features from file */ +void DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser* parser, + const std::vector& used_data_indices, Dataset* dataset) { + std::vector init_score; + if (predict_fun_) { + init_score = std::vector(static_cast(dataset->num_data_) * num_class_); + } + std::function&)> process_fun = + [this, &init_score, &parser, &dataset] + (data_size_t start_idx, const std::vector& lines) { + std::vector> oneline_features; + double tmp_label = 0.0f; + std::vector feature_row(dataset->num_features_); + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row) + for (data_size_t i = 0; i < static_cast(lines.size()); ++i) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + oneline_features.clear(); + // parser + parser->ParseOneLine(lines[i].c_str(), &oneline_features, &tmp_label); + // set initial score + if (!init_score.empty()) { + std::vector oneline_init_score(num_class_); + predict_fun_(oneline_features, oneline_init_score.data()); + for (int k = 0; k < num_class_; ++k) { + init_score[k * dataset->num_data_ + start_idx + i] = static_cast(oneline_init_score[k]); + } + } + // set label + dataset->metadata_.SetLabelAt(start_idx + i, static_cast(tmp_label)); + std::vector is_feature_added(dataset->num_features_, false); + // push data + for (auto& inner_data : oneline_features) { + if (inner_data.first >= dataset->num_total_features_) { + continue; + } + int feature_idx = dataset->used_feature_map_[inner_data.first]; + if (feature_idx >= 0) { + is_feature_added[feature_idx] = true; + // if is used feature + int group = dataset->feature2group_[feature_idx]; + int sub_feature = dataset->feature2subfeature_[feature_idx]; + dataset->feature_groups_[group]->PushData(tid, sub_feature, start_idx + i, inner_data.second); + if (dataset->has_raw()) { + feature_row[feature_idx] = static_cast(inner_data.second); + } + } else { + if (inner_data.first == weight_idx_) { + dataset->metadata_.SetWeightAt(start_idx + i, static_cast(inner_data.second)); + } else if (inner_data.first == group_idx_) { + dataset->metadata_.SetQueryAt(start_idx + i, static_cast(inner_data.second)); + } + } + } + if (dataset->has_raw()) { + for (size_t j = 0; j < feature_row.size(); ++j) { + int feat_ind = dataset->numeric_feature_map_[j]; + if (feat_ind >= 0) { + dataset->raw_data_[feat_ind][i] = feature_row[j]; + } + } + } + dataset->FinishOneRow(tid, i, is_feature_added); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + }; + TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes); + if (!used_data_indices.empty()) { + // only need part of data + text_reader.ReadPartAndProcessParallel(used_data_indices, process_fun); + } else { + // need full data + text_reader.ReadAllAndProcessParallel(process_fun); + } + + // metadata_ will manage space of init_score + if (!init_score.empty()) { + dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_); + } + dataset->FinishLoad(); +} + +/*! \brief Check can load from binary file */ +std::string DatasetLoader::CheckCanLoadFromBin(const char* filename) { + std::string bin_filename(filename); + bin_filename.append(".bin"); + + auto reader = VirtualFileReader::Make(bin_filename.c_str()); + + if (!reader->Init()) { + bin_filename = std::string(filename); + reader = VirtualFileReader::Make(bin_filename.c_str()); + if (!reader->Init()) { + Log::Fatal("Cannot open data file %s", bin_filename.c_str()); + } + } + + size_t buffer_size = 256; + auto buffer = std::vector(buffer_size); + // read size of token + size_t size_of_token = std::strlen(Dataset::binary_file_token); + size_t read_cnt = reader->Read(buffer.data(), size_of_token); + if (read_cnt == size_of_token + && std::string(buffer.data()) == std::string(Dataset::binary_file_token)) { + return bin_filename; + } else { + return std::string(); + } +} + +std::vector> DatasetLoader::GetForcedBins(std::string forced_bins_path, int num_total_features, + const std::unordered_set& categorical_features) { + std::vector> forced_bins(num_total_features, std::vector()); + if (forced_bins_path != "") { + std::ifstream forced_bins_stream(forced_bins_path.c_str()); + if (forced_bins_stream.fail()) { + Log::Warning("Could not open %s. Will ignore.", forced_bins_path.c_str()); + } else { + std::stringstream buffer; + buffer << forced_bins_stream.rdbuf(); + std::string err; + Json forced_bins_json = Json::parse(buffer.str(), &err); + CHECK(forced_bins_json.is_array()); + std::vector forced_bins_arr = forced_bins_json.array_items(); + for (size_t i = 0; i < forced_bins_arr.size(); ++i) { + int feature_num = forced_bins_arr[i]["feature"].int_value(); + CHECK_LT(feature_num, num_total_features); + if (categorical_features.count(feature_num)) { + Log::Warning("Feature %d is categorical. Will ignore forced bins for this feature.", feature_num); + } else { + std::vector bounds_arr = forced_bins_arr[i]["bin_upper_bound"].array_items(); + for (size_t j = 0; j < bounds_arr.size(); ++j) { + forced_bins[feature_num].push_back(bounds_arr[j].number_value()); + } + } + } + // remove duplicates + for (int i = 0; i < num_total_features; ++i) { + auto new_end = std::unique(forced_bins[i].begin(), forced_bins[i].end()); + forced_bins[i].erase(new_end, forced_bins[i].end()); + } + } + } + return forced_bins; +} + +void DatasetLoader::CheckCategoricalFeatureNumBin( + const std::vector>& bin_mappers, + const int max_bin, const std::vector& max_bin_by_feature) const { + bool need_warning = false; + if (bin_mappers.size() < 1024) { + for (size_t i = 0; i < bin_mappers.size(); ++i) { + const int max_bin_for_this_feature = max_bin_by_feature.empty() ? max_bin : max_bin_by_feature[i]; + if (bin_mappers[i] != nullptr && bin_mappers[i]->bin_type() == BinType::CategoricalBin && bin_mappers[i]->num_bin() > max_bin_for_this_feature) { + need_warning = true; + break; + } + } + } else { + const int num_threads = OMP_NUM_THREADS(); + std::vector thread_need_warning(num_threads, false); + Threading::For(0, bin_mappers.size(), 1, + [&bin_mappers, &thread_need_warning, &max_bin_by_feature, max_bin] (int thread_index, size_t start, size_t end) { + for (size_t i = start; i < end; ++i) { + thread_need_warning[thread_index] = false; + const int max_bin_for_this_feature = max_bin_by_feature.empty() ? max_bin : max_bin_by_feature[i]; + if (bin_mappers[i] != nullptr && bin_mappers[i]->bin_type() == BinType::CategoricalBin && bin_mappers[i]->num_bin() > max_bin_for_this_feature) { + thread_need_warning[thread_index] = true; + break; + } + } + }); + for (int thread_index = 0; thread_index < num_threads; ++thread_index) { + if (thread_need_warning[thread_index]) { + need_warning = true; + break; + } + } + } + + if (need_warning) { + Log::Warning("Categorical features with more bins than the configured maximum bin number found."); + Log::Warning("For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories."); + } +} + +} // namespace LightGBM diff --git a/src/io/dense_bin.hpp b/src/io/dense_bin.hpp new file mode 100644 index 0000000..5052b0e --- /dev/null +++ b/src/io/dense_bin.hpp @@ -0,0 +1,650 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_IO_DENSE_BIN_HPP_ +#define LIGHTGBM_SRC_IO_DENSE_BIN_HPP_ + +#include +#include + +#include +#include +#include + +namespace LightGBM { + +template +class DenseBin; + +template +class DenseBinIterator : public BinIterator { + public: + explicit DenseBinIterator(const DenseBin* bin_data, + uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin) + : bin_data_(bin_data), + min_bin_(static_cast(min_bin)), + max_bin_(static_cast(max_bin)), + most_freq_bin_(static_cast(most_freq_bin)) { + if (most_freq_bin_ == 0) { + offset_ = 1; + } else { + offset_ = 0; + } + } + inline uint32_t RawGet(data_size_t idx) override; + inline uint32_t Get(data_size_t idx) override; + inline void Reset(data_size_t) override {} + + private: + const DenseBin* bin_data_; + VAL_T min_bin_; + VAL_T max_bin_; + VAL_T most_freq_bin_; + uint8_t offset_; +}; +/*! + * \brief Used to store bins for dense feature + * Use template to reduce memory cost + */ +template +class DenseBin : public Bin { + public: + friend DenseBinIterator; + explicit DenseBin(data_size_t num_data) + : num_data_(num_data) { + if (IS_4BIT) { + CHECK_EQ(sizeof(VAL_T), 1); + data_.resize((num_data_ + 1) / 2, static_cast(0)); + buf_.resize((num_data_ + 1) / 2, static_cast(0)); + } else { + data_.resize(num_data_, static_cast(0)); + } + } + + ~DenseBin() {} + + void Push(int, data_size_t idx, uint32_t value) override { + if (IS_4BIT) { + const int i1 = idx >> 1; + const int i2 = (idx & 1) << 2; + const uint8_t val = static_cast(value) << i2; + if (i2 == 0) { + data_[i1] = val; + } else { + buf_[i1] = val; + } + } else { + data_[idx] = static_cast(value); + } + } + + void ReSize(data_size_t num_data) override { + if (num_data_ != num_data) { + num_data_ = num_data; + if (IS_4BIT) { + data_.resize((num_data_ + 1) / 2, static_cast(0)); + } else { + data_.resize(num_data_); + } + } + } + + BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin) const override; + + template + void ConstructHistogramInner(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const { + data_size_t i = start; + hist_t* grad = out; + hist_t* hess = out + 1; + hist_cnt_t* cnt = reinterpret_cast(hess); + if (USE_PREFETCH) { + const data_size_t pf_offset = 64 / sizeof(VAL_T); + const data_size_t pf_end = end - pf_offset; + for (; i < pf_end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto pf_idx = + USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; + if (IS_4BIT) { + PREFETCH_T0(data_.data() + (pf_idx >> 1)); + } else { + PREFETCH_T0(data_.data() + pf_idx); + } + const auto ti = static_cast(data(idx)) << 1; + if (USE_HESSIAN) { + grad[ti] += ordered_gradients[i]; + hess[ti] += ordered_hessians[i]; + } else { + grad[ti] += ordered_gradients[i]; + ++cnt[ti]; + } + } + } + for (; i < end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto ti = static_cast(data(idx)) << 1; + if (USE_HESSIAN) { + grad[ti] += ordered_gradients[i]; + hess[ti] += ordered_hessians[i]; + } else { + grad[ti] += ordered_gradients[i]; + ++cnt[ti]; + } + } + } + + void ConstructHistogram(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const override { + ConstructHistogramInner( + data_indices, start, end, ordered_gradients, ordered_hessians, out); + } + + void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const override { + ConstructHistogramInner( + nullptr, start, end, ordered_gradients, ordered_hessians, out); + } + + void ConstructHistogram(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramInner(data_indices, start, end, + ordered_gradients, nullptr, out); + } + + void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramInner( + nullptr, start, end, ordered_gradients, nullptr, out); + } + + + template + void ConstructHistogramIntInner(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const { + data_size_t i = start; + PACKED_HIST_T* out_ptr = reinterpret_cast(out); + const int16_t* gradients_ptr = reinterpret_cast(ordered_gradients); + const VAL_T* data_ptr_base = data_.data(); + if (USE_PREFETCH) { + const data_size_t pf_offset = 64 / sizeof(VAL_T); + const data_size_t pf_end = end - pf_offset; + for (; i < pf_end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto pf_idx = + USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; + if (IS_4BIT) { + PREFETCH_T0(data_ptr_base + (pf_idx >> 1)); + } else { + PREFETCH_T0(data_ptr_base + pf_idx); + } + const auto ti = static_cast(data(idx)); + const int16_t gradient_16 = gradients_ptr[i]; + if (USE_HESSIAN) { + const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : + (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); + out_ptr[ti] += gradient_packed; + } else { + const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : + (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (1); + out_ptr[ti] += gradient_packed; + } + } + } + for (; i < end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto ti = static_cast(data(idx)); + const int16_t gradient_16 = gradients_ptr[i]; + if (USE_HESSIAN) { + const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : + (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); + out_ptr[ti] += gradient_packed; + } else { + const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 : + (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (1); + out_ptr[ti] += gradient_packed; + } + } + } + + void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramIntInner( + data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramIntInner( + data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramIntInner( + data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, ordered_gradients, out); + } + + template + data_size_t SplitInner(uint32_t min_bin, uint32_t max_bin, + uint32_t default_bin, uint32_t most_freq_bin, + bool default_left, uint32_t threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const { + auto th = static_cast(threshold + min_bin); + auto t_zero_bin = static_cast(min_bin + default_bin); + if (most_freq_bin == 0) { + --th; + --t_zero_bin; + } + const auto minb = static_cast(min_bin); + const auto maxb = static_cast(max_bin); + data_size_t lte_count = 0; + data_size_t gt_count = 0; + data_size_t* default_indices = gt_indices; + data_size_t* default_count = >_count; + data_size_t* missing_default_indices = gt_indices; + data_size_t* missing_default_count = >_count; + if (most_freq_bin <= threshold) { + default_indices = lte_indices; + default_count = <e_count; + } + if (MISS_IS_ZERO || MISS_IS_NA) { + if (default_left) { + missing_default_indices = lte_indices; + missing_default_count = <e_count; + } + } + if (min_bin < max_bin) { + for (data_size_t i = 0; i < cnt; ++i) { + const data_size_t idx = data_indices[i]; + const auto bin = data(idx); + if ((MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) || + (MISS_IS_NA && !MFB_IS_NA && bin == maxb)) { + missing_default_indices[(*missing_default_count)++] = idx; + } else if ((USE_MIN_BIN && (bin < minb || bin > maxb)) || + (!USE_MIN_BIN && bin == 0)) { + if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { + missing_default_indices[(*missing_default_count)++] = idx; + } else { + default_indices[(*default_count)++] = idx; + } + } else if (bin > th) { + gt_indices[gt_count++] = idx; + } else { + lte_indices[lte_count++] = idx; + } + } + } else { + data_size_t* max_bin_indices = gt_indices; + data_size_t* max_bin_count = >_count; + if (maxb <= th) { + max_bin_indices = lte_indices; + max_bin_count = <e_count; + } + for (data_size_t i = 0; i < cnt; ++i) { + const data_size_t idx = data_indices[i]; + const auto bin = data(idx); + if (MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) { + missing_default_indices[(*missing_default_count)++] = idx; + } else if (bin != maxb) { + if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { + missing_default_indices[(*missing_default_count)++] = idx; + } else { + default_indices[(*default_count)++] = idx; + } + } else { + if (MISS_IS_NA && !MFB_IS_NA) { + missing_default_indices[(*missing_default_count)++] = idx; + } else { + max_bin_indices[(*max_bin_count)++] = idx; + } + } + } + } + return lte_count; + } + + data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin, + uint32_t most_freq_bin, MissingType missing_type, + bool default_left, uint32_t threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { +#define ARGUMENTS \ + min_bin, max_bin, default_bin, most_freq_bin, default_left, threshold, \ + data_indices, cnt, lte_indices, gt_indices + if (missing_type == MissingType::None) { + return SplitInner(ARGUMENTS); + } else if (missing_type == MissingType::Zero) { + if (default_bin == most_freq_bin) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } else { + if (max_bin == most_freq_bin + min_bin && most_freq_bin > 0) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } +#undef ARGUMENTS + } + + data_size_t Split(uint32_t max_bin, uint32_t default_bin, + uint32_t most_freq_bin, MissingType missing_type, + bool default_left, uint32_t threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { +#define ARGUMENTS \ + 1, max_bin, default_bin, most_freq_bin, default_left, threshold, \ + data_indices, cnt, lte_indices, gt_indices + if (missing_type == MissingType::None) { + return SplitInner(ARGUMENTS); + } else if (missing_type == MissingType::Zero) { + if (default_bin == most_freq_bin) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } else { + if (max_bin == most_freq_bin + 1 && most_freq_bin > 0) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } +#undef ARGUMENTS + } + + template + data_size_t SplitCategoricalInner(uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin, + const uint32_t* threshold, + int num_threshold, + const data_size_t* data_indices, + data_size_t cnt, data_size_t* lte_indices, + data_size_t* gt_indices) const { + data_size_t lte_count = 0; + data_size_t gt_count = 0; + data_size_t* default_indices = gt_indices; + data_size_t* default_count = >_count; + int8_t offset = most_freq_bin == 0 ? 1 : 0; + if (most_freq_bin > 0 && + Common::FindInBitset(threshold, num_threshold, most_freq_bin)) { + default_indices = lte_indices; + default_count = <e_count; + } + for (data_size_t i = 0; i < cnt; ++i) { + const data_size_t idx = data_indices[i]; + const uint32_t bin = data(idx); + if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) { + default_indices[(*default_count)++] = idx; + } else if (!USE_MIN_BIN && bin == 0) { + default_indices[(*default_count)++] = idx; + } else if (Common::FindInBitset(threshold, num_threshold, + bin - min_bin + offset)) { + lte_indices[lte_count++] = idx; + } else { + gt_indices[gt_count++] = idx; + } + } + return lte_count; + } + + data_size_t SplitCategorical(uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin, + const uint32_t* threshold, int num_threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { + return SplitCategoricalInner(min_bin, max_bin, most_freq_bin, + threshold, num_threshold, data_indices, + cnt, lte_indices, gt_indices); + } + + data_size_t SplitCategorical(uint32_t max_bin, uint32_t most_freq_bin, + const uint32_t* threshold, int num_threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { + return SplitCategoricalInner(1, max_bin, most_freq_bin, threshold, + num_threshold, data_indices, cnt, + lte_indices, gt_indices); + } + + data_size_t num_data() const override { return num_data_; } + + void* get_data() override { return data_.data(); } + + void FinishLoad() override { + if (IS_4BIT) { + if (buf_.empty()) { + return; + } + int len = (num_data_ + 1) / 2; + for (int i = 0; i < len; ++i) { + data_[i] |= buf_[i]; + } + buf_.clear(); + } + } + + void LoadFromMemory( + const void* memory, + const std::vector& local_used_indices) override { + const VAL_T* mem_data = reinterpret_cast(memory); + if (!local_used_indices.empty()) { + if (IS_4BIT) { + const data_size_t rest = num_data_ & 1; + for (int i = 0; i < num_data_ - rest; i += 2) { + // get old bins + data_size_t idx = local_used_indices[i]; + const auto bin1 = static_cast( + (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf); + idx = local_used_indices[i + 1]; + const auto bin2 = static_cast( + (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf); + // add + const int i1 = i >> 1; + data_[i1] = (bin1 | (bin2 << 4)); + } + if (rest) { + data_size_t idx = local_used_indices[num_data_ - 1]; + data_[num_data_ >> 1] = + (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf; + } + } else { + for (int i = 0; i < num_data_; ++i) { + data_[i] = mem_data[local_used_indices[i]]; + } + } + } else { + for (size_t i = 0; i < data_.size(); ++i) { + data_[i] = mem_data[i]; + } + } + } + + inline VAL_T data(data_size_t idx) const { + if (IS_4BIT) { + return (data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf; + } else { + return data_[idx]; + } + } + + void CopySubrow(const Bin* full_bin, const data_size_t* used_indices, + data_size_t num_used_indices) override { + auto other_bin = dynamic_cast*>(full_bin); + if (IS_4BIT) { + const data_size_t rest = num_used_indices & 1; + for (int i = 0; i < num_used_indices - rest; i += 2) { + data_size_t idx = used_indices[i]; + const auto bin1 = static_cast( + (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf); + idx = used_indices[i + 1]; + const auto bin2 = static_cast( + (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf); + const int i1 = i >> 1; + data_[i1] = (bin1 | (bin2 << 4)); + } + if (rest) { + data_size_t idx = used_indices[num_used_indices - 1]; + data_[num_used_indices >> 1] = + (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf; + } + } else { + for (int i = 0; i < num_used_indices; ++i) { + data_[i] = other_bin->data_[used_indices[i]]; + } + } + } + + void SaveBinaryToFile(BinaryWriter* writer) const override { + writer->AlignedWrite(data_.data(), sizeof(VAL_T) * data_.size()); + } + + size_t SizesInByte() const override { + return VirtualFileWriter::AlignedSize(sizeof(VAL_T) * data_.size()); + } + + DenseBin* Clone() override; + + const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const override; + + const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const override; + + private: + data_size_t num_data_; +#ifdef USE_CUDA + std::vector> data_; +#else + std::vector> data_; +#endif + std::vector buf_; + + DenseBin(const DenseBin& other) + : num_data_(other.num_data_), data_(other.data_) {} +}; + +template +DenseBin* DenseBin::Clone() { + return new DenseBin(*this); +} + +template +uint32_t DenseBinIterator::Get(data_size_t idx) { + auto ret = bin_data_->data(idx); + if (ret >= min_bin_ && ret <= max_bin_) { + return ret - min_bin_ + offset_; + } else { + return most_freq_bin_; + } +} + +template +inline uint32_t DenseBinIterator::RawGet(data_size_t idx) { + return bin_data_->data(idx); +} + +template +BinIterator* DenseBin::GetIterator( + uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const { + return new DenseBinIterator(this, min_bin, max_bin, + most_freq_bin); +} + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_IO_DENSE_BIN_HPP_ diff --git a/src/io/file_io.cpp b/src/io/file_io.cpp new file mode 100644 index 0000000..ab74ed6 --- /dev/null +++ b/src/io/file_io.cpp @@ -0,0 +1,80 @@ +/*! + * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#include + +#include + +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +struct LocalFile : VirtualFileReader, VirtualFileWriter { + LocalFile(const std::string& filename, const std::string& mode) + : filename_(filename), mode_(mode) {} + virtual ~LocalFile() { + if (file_ != NULL) { + fclose(file_); + } + } + + bool Init() { + if (file_ == NULL) { +#if _MSC_VER + fopen_s(&file_, filename_.c_str(), mode_.c_str()); +#else + file_ = fopen(filename_.c_str(), mode_.c_str()); +#endif + } + return file_ != NULL; + } + + bool Exists() const { + LocalFile file(filename_, "rb"); + return file.Init(); + } + + size_t Read(void* buffer, size_t bytes) const { + return fread(buffer, 1, bytes, file_); + } + + size_t Write(const void* buffer, size_t bytes) { + size_t bytes_written = fwrite(buffer, 1, bytes, file_); + if (bytes_written != bytes) { + Log::Fatal( + "Cannot write binary data to %s, wrote %zu of %zu bytes", + filename_.c_str(), bytes_written, bytes); + } + return bytes_written; + } + + private: + FILE* file_ = NULL; + const std::string filename_; + const std::string mode_; +}; + +std::unique_ptr VirtualFileReader::Make( + const std::string& filename) { + return std::unique_ptr(new LocalFile(filename, "rb")); +} + +std::unique_ptr VirtualFileWriter::Make( + const std::string& filename) { + return std::unique_ptr(new LocalFile(filename, "wb")); +} + +bool VirtualFileWriter::Exists(const std::string& filename) { + LocalFile file(filename, "rb"); + return file.Exists(); +} + +} // namespace LightGBM diff --git a/src/io/json11.cpp b/src/io/json11.cpp new file mode 100644 index 0000000..414bff7 --- /dev/null +++ b/src/io/json11.cpp @@ -0,0 +1,785 @@ +/* Copyright (c) 2013 Dropbox, Inc. + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to deal + * in the Software without restriction, including without limitation the rights + * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + * copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in + * all copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + * THE SOFTWARE. + */ +#include + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace json11_internal_lightgbm { + +static const int max_depth = 200; + +using std::initializer_list; +using std::make_shared; +using std::map; +using std::string; +using std::vector; + +using LightGBM::Log; + +/* Helper for representing null - just a do-nothing struct, plus comparison + * operators so the helpers in JsonValue work. We can't use nullptr_t because + * it may not be orderable. + */ +struct NullStruct { + bool operator==(NullStruct) const { return true; } + bool operator<(NullStruct) const { return false; } +}; + +/* * * * * * * * * * * * * * * * * * * * + * Serialization + */ + +static void dump(NullStruct, string *out) { *out += "null"; } + +static void dump(double value, string *out) { + if (std::isfinite(value)) { + char buf[32]; + snprintf(buf, sizeof buf, "%.17g", value); + *out += buf; + } else { + *out += "null"; + } +} + +static void dump(int value, string *out) { + char buf[32]; + snprintf(buf, sizeof buf, "%d", value); + *out += buf; +} + +static void dump(bool value, string *out) { *out += value ? "true" : "false"; } + +static void dump(const string &value, string *out) { + *out += '"'; + for (size_t i = 0; i < value.length(); i++) { + const char ch = value[i]; + if (ch == '\\') { + *out += "\\\\"; + } else if (ch == '"') { + *out += "\\\""; + } else if (ch == '\b') { + *out += "\\b"; + } else if (ch == '\f') { + *out += "\\f"; + } else if (ch == '\n') { + *out += "\\n"; + } else if (ch == '\r') { + *out += "\\r"; + } else if (ch == '\t') { + *out += "\\t"; + } else if (static_cast(ch) <= 0x1f) { + char buf[8]; + snprintf(buf, sizeof buf, "\\u%04x", ch); + *out += buf; + } else if (static_cast(ch) == 0xe2 && + static_cast(value[i + 1]) == 0x80 && + static_cast(value[i + 2]) == 0xa8) { + *out += "\\u2028"; + i += 2; + } else if (static_cast(ch) == 0xe2 && + static_cast(value[i + 1]) == 0x80 && + static_cast(value[i + 2]) == 0xa9) { + *out += "\\u2029"; + i += 2; + } else { + *out += ch; + } + } + *out += '"'; +} + +static void dump(const Json::array &values, string *out) { + bool first = true; + *out += "["; + for (const auto &value : values) { + if (!first) *out += ", "; + value.dump(out); + first = false; + } + *out += "]"; +} + +static void dump(const Json::object &values, string *out) { + bool first = true; + *out += "{"; + for (const auto &kv : values) { + if (!first) *out += ", "; + dump(kv.first, out); + *out += ": "; + kv.second.dump(out); + first = false; + } + *out += "}"; +} + +void Json::dump(string *out) const { m_ptr->dump(out); } + +/* * * * * * * * * * * * * * * * * * * * + * Value wrappers + */ + +template +class Value : public JsonValue { + protected: + // Constructors + explicit Value(const T &value) : m_value(value) {} + explicit Value(T &&value) : m_value(std::move(value)) {} + + // Get type tag + Json::Type type() const override { return tag; } + + // Comparisons + bool equals(const JsonValue *other) const override { + return m_value == static_cast *>(other)->m_value; + } + bool less(const JsonValue *other) const override { + return m_value < (static_cast *>(other)->m_value); + } + + const T m_value; + void dump(string *out) const override { json11_internal_lightgbm::dump(m_value, out); } +}; + +class JsonDouble final : public Value { + double number_value() const override { return m_value; } + int int_value() const override { return static_cast(m_value); } + bool equals(const JsonValue *other) const override { + return m_value == other->number_value(); + } + bool less(const JsonValue *other) const override { + return m_value < other->number_value(); + } + + public: + explicit JsonDouble(double value) : Value(value) {} +}; + +class JsonInt final : public Value { + double number_value() const override { return m_value; } + int int_value() const override { return m_value; } + bool equals(const JsonValue *other) const override { + return m_value == other->number_value(); + } + bool less(const JsonValue *other) const override { + return m_value < other->number_value(); + } + + public: + explicit JsonInt(int value) : Value(value) {} +}; + +class JsonBoolean final : public Value { + bool bool_value() const override { return m_value; } + + public: + explicit JsonBoolean(bool value) : Value(value) {} +}; + +class JsonString final : public Value { + const string &string_value() const override { return m_value; } + + public: + explicit JsonString(const string &value) : Value(value) {} + explicit JsonString(string &&value) : Value(std::move(value)) {} +}; + +class JsonArray final : public Value { + const Json::array &array_items() const override { return m_value; } + const Json &operator[](size_t i) const override; + + public: + explicit JsonArray(const Json::array &value) : Value(value) {} + explicit JsonArray(Json::array &&value) : Value(std::move(value)) {} +}; + +class JsonObject final : public Value { + const Json::object &object_items() const override { return m_value; } + const Json &operator[](const string &key) const override; + + public: + explicit JsonObject(const Json::object &value) : Value(value) {} + explicit JsonObject(Json::object &&value) : Value(std::move(value)) {} +}; + +class JsonNull final : public Value { + public: + JsonNull() : Value({}) {} +}; + +/* * * * * * * * * * * * * * * * * * * * + * Static globals - static-init-safe + */ +struct Statics { + const std::shared_ptr null = make_shared(); + const std::shared_ptr t = make_shared(true); + const std::shared_ptr f = make_shared(false); + const string empty_string; + const vector empty_vector; + const map empty_map; + Statics() {} +}; + +static const Statics &statics() { + static const Statics s{}; + return s; +} + +static const Json &static_null() { + // This has to be separate, not in Statics, because Json() accesses + // statics().null. + static const Json json_null; + return json_null; +} + +/* * * * * * * * * * * * * * * * * * * * + * Constructors + */ + +Json::Json() noexcept : m_ptr(statics().null) {} +Json::Json(std::nullptr_t) noexcept : m_ptr(statics().null) {} +Json::Json(double value) : m_ptr(make_shared(value)) {} +Json::Json(int value) : m_ptr(make_shared(value)) {} +Json::Json(bool value) : m_ptr(value ? statics().t : statics().f) {} +Json::Json(const string &value) : m_ptr(make_shared(value)) {} +Json::Json(string &&value) : m_ptr(make_shared(std::move(value))) {} +Json::Json(const char *value) : m_ptr(make_shared(value)) {} +Json::Json(const Json::array &values) : m_ptr(make_shared(values)) {} +Json::Json(Json::array &&values) + : m_ptr(make_shared(std::move(values))) {} +Json::Json(const Json::object &values) + : m_ptr(make_shared(values)) {} +Json::Json(Json::object &&values) + : m_ptr(make_shared(std::move(values))) {} + +/* * * * * * * * * * * * * * * * * * * * + * Accessors + */ + +Json::Type Json::type() const { return m_ptr->type(); } +double Json::number_value() const { return m_ptr->number_value(); } +int Json::int_value() const { return m_ptr->int_value(); } +bool Json::bool_value() const { return m_ptr->bool_value(); } +const string &Json::string_value() const { return m_ptr->string_value(); } +const vector &Json::array_items() const { return m_ptr->array_items(); } +const map &Json::object_items() const { + return m_ptr->object_items(); +} +const Json &Json::operator[](size_t i) const { return (*m_ptr)[i]; } +const Json &Json::operator[](const string &key) const { return (*m_ptr)[key]; } + +double JsonValue::number_value() const { return 0; } +int JsonValue::int_value() const { return 0; } +bool JsonValue::bool_value() const { return false; } +const string &JsonValue::string_value() const { return statics().empty_string; } +const vector &JsonValue::array_items() const { + return statics().empty_vector; +} +const map &JsonValue::object_items() const { + return statics().empty_map; +} +const Json &JsonValue::operator[](size_t) const { return static_null(); } +const Json &JsonValue::operator[](const string &) const { + return static_null(); +} + +const Json &JsonObject::operator[](const string &key) const { + auto iter = m_value.find(key); + return (iter == m_value.end()) ? static_null() : iter->second; +} +const Json &JsonArray::operator[](size_t i) const { + if (i >= m_value.size()) + return static_null(); + else + return m_value[i]; +} + +/* * * * * * * * * * * * * * * * * * * * + * Comparison + */ + +bool Json::operator==(const Json &other) const { + if (m_ptr == other.m_ptr) return true; + if (m_ptr->type() != other.m_ptr->type()) return false; + + return m_ptr->equals(other.m_ptr.get()); +} + +bool Json::operator<(const Json &other) const { + if (m_ptr == other.m_ptr) return false; + if (m_ptr->type() != other.m_ptr->type()) + return m_ptr->type() < other.m_ptr->type(); + + return m_ptr->less(other.m_ptr.get()); +} + +/* * * * * * * * * * * * * * * * * * * * + * Parsing + */ + +/* esc(c) + * + * Format char c suitable for printing in an error message. + */ +static inline string esc(char c) { + char buf[12]; + if (static_cast(c) >= 0x20 && static_cast(c) <= 0x7f) { + snprintf(buf, sizeof buf, "'%c' (%d)", c, c); + } else { + snprintf(buf, sizeof buf, "(%d)", c); + } + return string(buf); +} + +template +static inline bool in_range(T x, T lower, T upper) { + return (x >= lower && x <= upper); +} + +namespace { +/* JsonParser + * + * Object that tracks all state of an in-progress parse. + */ +struct JsonParser final { + /* State + */ + const char *str; + const size_t str_len; + size_t i; + string *err; + bool failed; + const JsonParse strategy; + + /* fail(msg, err_ret = Json()) + * + * Mark this parse as failed. + */ + Json fail(string &&msg) { return fail(std::move(msg), Json()); } + + template + T fail(string &&msg, const T err_ret) { + if (!failed) *err = std::move(msg); + failed = true; + return err_ret; + } + + /* consume_whitespace() + * + * Advance until the current character is non-whitespace. + */ + void consume_whitespace() { + while (str[i] == ' ' || str[i] == '\r' || str[i] == '\n' || str[i] == '\t') + i++; + } + + /* consume_comment() + * + * Advance comments (c-style inline and multiline). + */ + bool consume_comment() { + bool comment_found = false; + if (str[i] == '/') { + i++; + if (i == str_len) + return fail("Unexpected end of input after start of comment", false); + if (str[i] == '/') { // inline comment + i++; + // advance until next line, or end of input + while (i < str_len && str[i] != '\n') { + i++; + } + comment_found = true; + } else if (str[i] == '*') { // multiline comment + i++; + if (i > str_len - 2) + return fail("Unexpected end of input inside multi-line comment", + false); + // advance until closing tokens + while (!(str[i] == '*' && str[i + 1] == '/')) { + i++; + if (i > str_len - 2) + return fail("Unexpected end of input inside multi-line comment", + false); + } + i += 2; + comment_found = true; + } else { + return fail("Malformed comment", false); + } + } + return comment_found; + } + + /* consume_garbage() + * + * Advance until the current character is non-whitespace and non-comment. + */ + void consume_garbage() { + consume_whitespace(); + if (strategy == JsonParse::COMMENTS) { + bool comment_found = false; + do { + comment_found = consume_comment(); + if (failed) return; + consume_whitespace(); + } while (comment_found); + } + } + + /* get_next_token() + * + * Return the next non-whitespace character. If the end of the input is + * reached, flag an error and return 0. + */ + char get_next_token() { + consume_garbage(); + if (failed) return char{0}; + if (i == str_len) return fail("Unexpected end of input", char{0}); + + return str[i++]; + } + + /* encode_utf8(pt, out) + * + * Encode pt as UTF-8 and add it to out. + */ + void encode_utf8(int64_t pt, string* out) { + if (pt < 0) return; + + if (pt < 0x80) { + *out += static_cast(pt); + } else if (pt < 0x800) { + *out += static_cast((pt >> 6) | 0xC0); + *out += static_cast((pt & 0x3F) | 0x80); + } else if (pt < 0x10000) { + *out += static_cast((pt >> 12) | 0xE0); + *out += static_cast(((pt >> 6) & 0x3F) | 0x80); + *out += static_cast((pt & 0x3F) | 0x80); + } else { + *out += static_cast((pt >> 18) | 0xF0); + *out += static_cast(((pt >> 12) & 0x3F) | 0x80); + *out += static_cast(((pt >> 6) & 0x3F) | 0x80); + *out += static_cast((pt & 0x3F) | 0x80); + } + } + + /* parse_string() + * + * Parse a string, starting at the current position. + */ + string parse_string() { + string out; + int64_t last_escaped_codepoint = -1; + while (true) { + if (i == str_len) return fail("Unexpected end of input in string", ""); + + char ch = str[i++]; + + if (ch == '"') { + encode_utf8(last_escaped_codepoint, &out); + return out; + } + + if (in_range(ch, 0, 0x1f)) + return fail("Unescaped " + esc(ch) + " in string", ""); + + // The usual case: non-escaped characters + if (ch != '\\') { + encode_utf8(last_escaped_codepoint, &out); + last_escaped_codepoint = -1; + out += ch; + continue; + } + + // Handle escapes + if (i == str_len) return fail("Unexpected end of input in string", ""); + + ch = str[i++]; + + if (ch == 'u') { + // Extract 4-byte escape sequence + string esc = string(str + i, 4); + // Explicitly check length of the substring. The following loop + // relies on std::string returning the terminating NUL when + // accessing str[length]. Checking here reduces brittleness. + if (esc.length() < 4) { + return fail("Bad \\u escape: " + esc, ""); + } + for (size_t j = 0; j < 4; j++) { + if (!in_range(esc[j], 'a', 'f') && !in_range(esc[j], 'A', 'F') && + !in_range(esc[j], '0', '9')) + return fail("Bad \\u escape: " + esc, ""); + } + + int64_t codepoint = + static_cast(strtol(esc.data(), nullptr, 16)); + + // JSON specifies that characters outside the BMP shall be encoded as a + // pair of 4-hex-digit \u escapes encoding their surrogate pair + // components. Check whether we're in the middle of such a beast: the + // previous codepoint was an escaped lead (high) surrogate, and this is + // a trail (low) surrogate. + if (in_range(last_escaped_codepoint, 0xD800, 0xDBFF) && + in_range(codepoint, 0xDC00, 0xDFFF)) { + // Reassemble the two surrogate pairs into one astral-plane character, + // per the UTF-16 algorithm. + encode_utf8((((last_escaped_codepoint - 0xD800) << 10) | + (codepoint - 0xDC00)) + + 0x10000, + &out); + last_escaped_codepoint = -1; + } else { + encode_utf8(last_escaped_codepoint, &out); + last_escaped_codepoint = codepoint; + } + + i += 4; + continue; + } + + encode_utf8(last_escaped_codepoint, &out); + last_escaped_codepoint = -1; + + if (ch == 'b') { + out += '\b'; + } else if (ch == 'f') { + out += '\f'; + } else if (ch == 'n') { + out += '\n'; + } else if (ch == 'r') { + out += '\r'; + } else if (ch == 't') { + out += '\t'; + } else if (ch == '"' || ch == '\\' || ch == '/') { + out += ch; + } else { + return fail("Invalid escape character " + esc(ch), ""); + } + } + } + + /* parse_number() + * + * Parse a double. + */ + Json parse_number() { + size_t start_pos = i; + + if (str[i] == '-') i++; + + // Integer part + if (str[i] == '0') { + i++; + if (in_range(str[i], '0', '9')) + return fail("Leading 0s not permitted in numbers"); + } else if (in_range(str[i], '1', '9')) { + i++; + while (in_range(str[i], '0', '9')) i++; + } else { + return fail("Invalid " + esc(str[i]) + " in number"); + } + + if (str[i] != '.' && str[i] != 'e' && str[i] != 'E' && + (i - start_pos) <= + static_cast(std::numeric_limits::digits10)) { + return Json(std::atoi(str + start_pos)); + } + + // Decimal part + if (str[i] == '.') { + i++; + if (!in_range(str[i], '0', '9')) + return fail("At least one digit required in fractional part"); + + while (in_range(str[i], '0', '9')) i++; + } + + // Exponent part + if (str[i] == 'e' || str[i] == 'E') { + i++; + + if (str[i] == '+' || str[i] == '-') i++; + + if (!in_range(str[i], '0', '9')) + return fail("At least one digit required in exponent"); + + while (in_range(str[i], '0', '9')) i++; + } + + return Json(std::strtod(str + start_pos, nullptr)); + } + + /* expect(str, res) + * + * Expect that 'str' starts at the character that was just read. If it does, + * advance the input and return res. If not, flag an error. + */ + Json expect(const string &expected, Json res) { + CHECK_NE(i, 0) + i--; + auto substr = string(str + i, expected.length()); + if (substr == expected) { + i += expected.length(); + return res; + } else { + return fail("Parse error: expected " + expected + ", got " + substr); + } + } + + /* parse_json() + * + * Parse a JSON object. + */ + Json parse_json(int depth) { + if (depth > max_depth) { + return fail("Exceeded maximum nesting depth"); + } + + char ch = get_next_token(); + if (failed) return Json(); + + if (ch == '-' || (ch >= '0' && ch <= '9')) { + i--; + return parse_number(); + } + + if (ch == 't') return expect("true", Json(true)); + + if (ch == 'f') return expect("false", Json(false)); + + if (ch == 'n') return expect("null", Json()); + + if (ch == '"') return Json(parse_string()); + + if (ch == '{') { + map data; + ch = get_next_token(); + if (ch == '}') return Json(data); + + while (1) { + if (ch != '"') return fail("Expected '\"' in object, got " + esc(ch)); + + string key = parse_string(); + if (failed) return Json(); + + ch = get_next_token(); + if (ch != ':') return fail("Expected ':' in object, got " + esc(ch)); + + data[std::move(key)] = parse_json(depth + 1); + if (failed) return Json(); + + ch = get_next_token(); + if (ch == '}') break; + if (ch != ',') return fail("Expected ',' in object, got " + esc(ch)); + + ch = get_next_token(); + } + return Json(data); + } + + if (ch == '[') { + vector data; + ch = get_next_token(); + if (ch == ']') return Json(data); + + while (1) { + i--; + data.push_back(parse_json(depth + 1)); + if (failed) return Json(); + + ch = get_next_token(); + if (ch == ']') break; + if (ch != ',') return fail("Expected ',' in list, got " + esc(ch)); + + ch = get_next_token(); + (void)ch; + } + return Json(data); + } + + return fail("Expected value, got " + esc(ch)); + } +}; +} // namespace + +Json Json::parse(const string &in, string *err, JsonParse strategy) { + JsonParser parser{in.c_str(), in.size(), 0, err, false, strategy}; + Json result = parser.parse_json(0); + + // Check for any trailing garbage + parser.consume_garbage(); + if (parser.failed) return Json(); + if (parser.i != in.size()) + return parser.fail("Unexpected trailing " + esc(in[parser.i])); + + return result; +} + +// Documented in json11.hpp +vector Json::parse_multi(const string &in, + std::string::size_type *parser_stop_pos, + string *err, JsonParse strategy) { + JsonParser parser{in.c_str(), in.size(), 0, err, false, strategy}; + *parser_stop_pos = 0; + vector json_vec; + while (parser.i != in.size() && !parser.failed) { + json_vec.push_back(parser.parse_json(0)); + if (parser.failed) break; + + // Check for another object + parser.consume_garbage(); + if (parser.failed) break; + *parser_stop_pos = parser.i; + } + return json_vec; +} + +/* * * * * * * * * * * * * * * * * * * * + * Shape-checking + */ + +bool Json::has_shape(const shape &types, string *err) const { + if (!is_object()) { + *err = "Expected JSON object, got " + dump(); + return false; + } + + for (auto &item : types) { + if ((*this)[item.first].type() != item.second) { + *err = "Bad type for " + item.first + " in " + dump(); + return false; + } + } + + return true; +} + +} // namespace json11_internal_lightgbm diff --git a/src/io/metadata.cpp b/src/io/metadata.cpp new file mode 100644 index 0000000..897a2c8 --- /dev/null +++ b/src/io/metadata.cpp @@ -0,0 +1,946 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include +#include + +#include +#include +#include +#include + +#ifndef LGB_R_BUILD +#include "../arrow/array.hpp" +#endif // LGB_R_BUILD + +namespace LightGBM { + +Metadata::Metadata() { + num_weights_ = 0; + num_init_score_ = 0; + num_data_ = 0; + num_queries_ = 0; + num_positions_ = 0; + weight_load_from_file_ = false; + position_load_from_file_ = false; + query_load_from_file_ = false; + init_score_load_from_file_ = false; + #ifdef USE_CUDA + cuda_metadata_ = nullptr; + #endif // USE_CUDA +} + +void Metadata::Init(const char* data_filename) { + data_filename_ = data_filename; + // for lambdarank, it needs query data for partition data in distributed learning + LoadQueryBoundaries(); + LoadWeights(); + LoadPositions(); + CalculateQueryWeights(); + LoadInitialScore(data_filename_); +} + +Metadata::~Metadata() { +} + +void Metadata::Init(data_size_t num_data, int weight_idx, int query_idx) { + num_data_ = num_data; + label_ = std::vector(num_data_); + if (weight_idx >= 0) { + if (!weights_.empty()) { + Log::Info("Using weights in data file, ignoring the additional weights file"); + weights_.clear(); + } + weights_ = std::vector(num_data_, 0.0f); + num_weights_ = num_data_; + weight_load_from_file_ = false; + } + if (query_idx >= 0) { + if (!query_boundaries_.empty()) { + Log::Info("Using query id in data file, ignoring the additional query file"); + query_boundaries_.clear(); + } + if (!query_weights_.empty()) { + query_weights_.clear(); + } + queries_ = std::vector(num_data_, 0); + query_load_from_file_ = false; + } +} + +void Metadata::InitByReference(data_size_t num_data, const Metadata* reference) { + int has_weights = reference->num_weights_ > 0; + int has_init_scores = reference->num_init_score_ > 0; + int has_queries = reference->num_queries_ > 0; + int nclasses = reference->num_init_score_classes(); + Init(num_data, has_weights, has_init_scores, has_queries, nclasses); +} + +void Metadata::Init(data_size_t num_data, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses) { + num_data_ = num_data; + label_ = std::vector(num_data_); + if (has_weights) { + if (!weights_.empty()) { + Log::Fatal("Calling Init() on Metadata weights that have already been initialized"); + } + weights_.resize(num_data_, 0.0f); + num_weights_ = num_data_; + weight_load_from_file_ = false; + } + if (has_init_scores) { + if (!init_score_.empty()) { + Log::Fatal("Calling Init() on Metadata initial scores that have already been initialized"); + } + num_init_score_ = static_cast(num_data) * nclasses; + init_score_.resize(num_init_score_, 0); + } + if (has_queries) { + if (!query_weights_.empty()) { + Log::Fatal("Calling Init() on Metadata queries that have already been initialized"); + } + queries_.resize(num_data_, 0); + query_load_from_file_ = false; + } +} + +void Metadata::Init(const Metadata& fullset, const data_size_t* used_indices, data_size_t num_used_indices) { + num_data_ = num_used_indices; + + label_.resize(num_used_indices); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_used_indices >= 1024) + for (data_size_t i = 0; i < num_used_indices; ++i) { + label_[i] = fullset.label_[used_indices[i]]; + } + + if (!fullset.weights_.empty()) { + weights_ = std::vector(num_used_indices); + num_weights_ = num_used_indices; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_used_indices >= 1024) + for (data_size_t i = 0; i < num_used_indices; ++i) { + weights_[i] = fullset.weights_[used_indices[i]]; + } + } else { + num_weights_ = 0; + } + + if (!fullset.init_score_.empty()) { + int num_class = static_cast(fullset.num_init_score_ / fullset.num_data_); + init_score_ = std::vector(static_cast(num_used_indices) * num_class); + num_init_score_ = static_cast(num_used_indices) * num_class; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int k = 0; k < num_class; ++k) { + const size_t offset_dest = static_cast(k) * num_data_; + const size_t offset_src = static_cast(k) * fullset.num_data_; + for (data_size_t i = 0; i < num_used_indices; ++i) { + init_score_[offset_dest + i] = fullset.init_score_[offset_src + used_indices[i]]; + } + } + } else { + num_init_score_ = 0; + } + + if (!fullset.query_boundaries_.empty()) { + std::vector used_query; + data_size_t data_idx = 0; + for (data_size_t qid = 0; qid < num_queries_ && data_idx < num_used_indices; ++qid) { + data_size_t start = fullset.query_boundaries_[qid]; + data_size_t end = fullset.query_boundaries_[qid + 1]; + data_size_t len = end - start; + if (used_indices[data_idx] > start) { + continue; + } else if (used_indices[data_idx] == start) { + if (num_used_indices >= data_idx + len && used_indices[data_idx + len - 1] == end - 1) { + used_query.push_back(qid); + data_idx += len; + } else { + Log::Fatal("Data partition error, data didn't match queries"); + } + } else { + Log::Fatal("Data partition error, data didn't match queries"); + } + } + query_boundaries_ = std::vector(used_query.size() + 1); + num_queries_ = static_cast(used_query.size()); + query_boundaries_[0] = 0; + for (data_size_t i = 0; i < num_queries_; ++i) { + data_size_t qid = used_query[i]; + data_size_t len = fullset.query_boundaries_[qid + 1] - fullset.query_boundaries_[qid]; + query_boundaries_[i + 1] = query_boundaries_[i] + len; + } + } else { + num_queries_ = 0; + } +} + +void Metadata::PartitionLabel(const std::vector& used_indices) { + if (used_indices.empty()) { + return; + } + auto old_label = label_; + num_data_ = static_cast(used_indices.size()); + label_ = std::vector(num_data_); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) + for (data_size_t i = 0; i < num_data_; ++i) { + label_[i] = old_label[used_indices[i]]; + } + old_label.clear(); +} + +void Metadata::CalculateQueryBoundaries() { + if (!queries_.empty()) { + // need convert query_id to boundaries + std::vector tmp_buffer; + data_size_t last_qid = -1; + data_size_t cur_cnt = 0; + for (data_size_t i = 0; i < num_data_; ++i) { + if (last_qid != queries_[i]) { + if (cur_cnt > 0) { + tmp_buffer.push_back(cur_cnt); + } + cur_cnt = 0; + last_qid = queries_[i]; + } + ++cur_cnt; + } + tmp_buffer.push_back(cur_cnt); + query_boundaries_ = std::vector(tmp_buffer.size() + 1); + num_queries_ = static_cast(tmp_buffer.size()); + query_boundaries_[0] = 0; + for (size_t i = 0; i < tmp_buffer.size(); ++i) { + query_boundaries_[i + 1] = query_boundaries_[i] + tmp_buffer[i]; + } + CalculateQueryWeights(); + queries_.clear(); + } +} + +void Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector& used_data_indices) { + if (used_data_indices.empty()) { + CalculateQueryBoundaries(); + // check weights + if (!weights_.empty() && num_weights_ != num_data_) { + weights_.clear(); + num_weights_ = 0; + Log::Fatal("Weights size doesn't match data size"); + } + + // check positions + if (!positions_.empty() && num_positions_ != num_data_) { + Log::Fatal("Positions size (%i) doesn't match data size (%i)", num_positions_, num_data_); + positions_.clear(); + num_positions_ = 0; + } + + // check query boundaries + if (!query_boundaries_.empty() && query_boundaries_[num_queries_] != num_data_) { + query_boundaries_.clear(); + num_queries_ = 0; + Log::Fatal("Query size doesn't match data size"); + } + + // contain initial score file + if (!init_score_.empty() && (num_init_score_ % num_data_) != 0) { + init_score_.clear(); + num_init_score_ = 0; + Log::Fatal("Initial score size doesn't match data size"); + } + } else { + if (!queries_.empty()) { + Log::Fatal("Cannot used query_id for distributed training"); + } + data_size_t num_used_data = static_cast(used_data_indices.size()); + // check weights + if (weight_load_from_file_) { + if (weights_.size() > 0 && num_weights_ != num_all_data) { + weights_.clear(); + num_weights_ = 0; + Log::Fatal("Weights size doesn't match data size"); + } + // get local weights + if (!weights_.empty()) { + auto old_weights = weights_; + num_weights_ = num_data_; + weights_ = std::vector(num_data_); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) + for (int i = 0; i < static_cast(used_data_indices.size()); ++i) { + weights_[i] = old_weights[used_data_indices[i]]; + } + old_weights.clear(); + } + } + // check positions + if (position_load_from_file_) { + if (positions_.size() > 0 && num_positions_ != num_all_data) { + positions_.clear(); + num_positions_ = 0; + Log::Fatal("Positions size (%i) doesn't match data size (%i)", num_positions_, num_data_); + } + // get local positions + if (!positions_.empty()) { + auto old_positions = positions_; + num_positions_ = num_data_; + positions_ = std::vector(num_data_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) + for (int i = 0; i < static_cast(used_data_indices.size()); ++i) { + positions_[i] = old_positions[used_data_indices[i]]; + } + old_positions.clear(); + } + } + if (query_load_from_file_) { + // check query boundaries + if (!query_boundaries_.empty() && query_boundaries_[num_queries_] != num_all_data) { + query_boundaries_.clear(); + num_queries_ = 0; + Log::Fatal("Query size doesn't match data size"); + } + // get local query boundaries + if (!query_boundaries_.empty()) { + std::vector used_query; + data_size_t data_idx = 0; + for (data_size_t qid = 0; qid < num_queries_ && data_idx < num_used_data; ++qid) { + data_size_t start = query_boundaries_[qid]; + data_size_t end = query_boundaries_[qid + 1]; + data_size_t len = end - start; + if (used_data_indices[data_idx] > start) { + continue; + } else if (used_data_indices[data_idx] == start) { + if (num_used_data >= data_idx + len && used_data_indices[data_idx + len - 1] == end - 1) { + used_query.push_back(qid); + data_idx += len; + } else { + Log::Fatal("Data partition error, data didn't match queries"); + } + } else { + Log::Fatal("Data partition error, data didn't match queries"); + } + } + auto old_query_boundaries = query_boundaries_; + query_boundaries_ = std::vector(used_query.size() + 1); + num_queries_ = static_cast(used_query.size()); + query_boundaries_[0] = 0; + for (data_size_t i = 0; i < num_queries_; ++i) { + data_size_t qid = used_query[i]; + data_size_t len = old_query_boundaries[qid + 1] - old_query_boundaries[qid]; + query_boundaries_[i + 1] = query_boundaries_[i] + len; + } + old_query_boundaries.clear(); + } + } + if (init_score_load_from_file_) { + // contain initial score file + if (!init_score_.empty() && (num_init_score_ % num_all_data) != 0) { + init_score_.clear(); + num_init_score_ = 0; + Log::Fatal("Initial score size doesn't match data size"); + } + + // get local initial scores + if (!init_score_.empty()) { + auto old_scores = init_score_; + int num_class = static_cast(num_init_score_ / num_all_data); + num_init_score_ = static_cast(num_data_) * num_class; + init_score_ = std::vector(num_init_score_); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int k = 0; k < num_class; ++k) { + const size_t offset_dest = static_cast(k) * num_data_; + const size_t offset_src = static_cast(k) * num_all_data; + for (size_t i = 0; i < used_data_indices.size(); ++i) { + init_score_[offset_dest + i] = old_scores[offset_src + used_data_indices[i]]; + } + } + old_scores.clear(); + } + } + // re-calculate query weight + CalculateQueryWeights(); + } + if (num_queries_ > 0) { + Log::Debug("Number of queries in %s: %i. Average number of rows per query: %f.", + data_filename_.c_str(), static_cast(num_queries_), static_cast(num_data_) / num_queries_); + } +} + +template +void Metadata::SetInitScoresFromIterator(It first, It last) { + std::lock_guard lock(mutex_); + // Clear init scores on empty input + if (last - first == 0) { + init_score_.clear(); + num_init_score_ = 0; + return; + } + if (((last - first) % num_data_) != 0) { + Log::Fatal("Initial score size doesn't match data size"); + } + if (init_score_.empty()) { + init_score_.resize(last - first); + } + num_init_score_ = last - first; + + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_init_score_ >= 1024) + for (int64_t i = 0; i < num_init_score_; ++i) { + init_score_[i] = Common::AvoidInf(first[i]); + } + init_score_load_from_file_ = false; + + #ifdef USE_CUDA + if (cuda_metadata_ != nullptr) { + cuda_metadata_->SetInitScore(init_score_.data(), init_score_.size()); + } + #endif // USE_CUDA +} + +void Metadata::SetInitScore(const double* init_score, data_size_t len) { + SetInitScoresFromIterator(init_score, init_score + len); +} + +#ifndef LGB_R_BUILD +ArrowChunkedArray::View InitScoreView(const ArrowChunkedArray& chunked_array) { + auto view = chunked_array.view(); + // For multiclass classification, the init scores are provided in multiple columns. In this + // case, we must concatenate all fields of the chunked array. + if (chunked_array.is_struct()) { + std::vector concat_views; + concat_views.reserve(chunked_array.get_num_fields()); + for (int64_t i = 0; i < chunked_array.get_num_fields(); ++i) { + concat_views.push_back(view.field(i)); + } + view = ArrowChunkedArray::View(concat_views); + } + return view; +} + +void Metadata::SetInitScore(struct ArrowArrayStream* stream) { + ArrowChunkedArray chunked_array(stream); + auto view = InitScoreView(chunked_array); + view.visit([&](auto&& visitor) { + SetInitScoresFromIterator(visitor.begin(), visitor.end()); + }); +} + +void Metadata::SetInitScore(int64_t n_chunks, struct ArrowArray* chunks, + struct ArrowSchema* schema) { + ArrowChunkedArray chunked_array(n_chunks, chunks, schema); + auto view = InitScoreView(chunked_array); + view.visit([&](auto&& visitor) { + SetInitScoresFromIterator(visitor.begin(), visitor.end()); + }); +} +#endif // LGB_R_BUILD + +void Metadata::InsertInitScores(const double* init_scores, data_size_t start_index, data_size_t len, data_size_t source_size) { + if (num_init_score_ <= 0) { + Log::Fatal("Inserting initial score data into dataset with no initial scores"); + } + if (start_index + len > num_data_) { + // Note that len here is row count, not num_init_score, so we compare against num_data + Log::Fatal("Inserted initial score data is too large for dataset"); + } + if (init_score_.empty()) { + init_score_.resize(num_init_score_); + } + + int nclasses = num_init_score_classes(); + + for (int32_t col = 0; col < nclasses; ++col) { + int32_t dest_offset = num_data_ * col + start_index; + // We need to use source_size here, because len might not equal size (due to a partially loaded dataset) + int32_t source_offset = source_size * col; + memcpy(init_score_.data() + dest_offset, init_scores + source_offset, sizeof(double) * len); + } + init_score_load_from_file_ = false; + // CUDA is handled after all insertions are complete +} + +template +void Metadata::SetLabelsFromIterator(It first, It last) { + std::lock_guard lock(mutex_); + if (num_data_ != last - first) { + Log::Fatal("Length of labels differs from the length of #data"); + } + if (label_.empty()) { + label_.resize(num_data_); + } + + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) + for (data_size_t i = 0; i < num_data_; ++i) { + label_[i] = Common::AvoidInf(first[i]); + } + + #ifdef USE_CUDA + if (cuda_metadata_ != nullptr) { + cuda_metadata_->SetLabel(label_.data(), label_.size()); + } + #endif // USE_CUDA +} + +void Metadata::SetLabel(const label_t* label, data_size_t len) { + if (label == nullptr) { + Log::Fatal("label cannot be nullptr"); + } + SetLabelsFromIterator(label, label + len); +} + +#ifndef LGB_R_BUILD +void Metadata::SetLabel(struct ArrowArrayStream* stream) { + ArrowChunkedArray chunked_array(stream); + chunked_array.view().visit([&](auto&& visitor) { + SetLabelsFromIterator(visitor.begin(), visitor.end()); + }); +} + +void Metadata::SetLabel(int64_t n_chunks, struct ArrowArray* chunks, + struct ArrowSchema* schema) { + ArrowChunkedArray chunked_array(n_chunks, chunks, schema); + chunked_array.view().visit([&](auto&& visitor) { + SetLabelsFromIterator(visitor.begin(), visitor.end()); + }); +} +#endif // LGB_R_BUILD + +void Metadata::InsertLabels(const label_t* labels, data_size_t start_index, data_size_t len) { + if (labels == nullptr) { + Log::Fatal("label cannot be nullptr"); + } + if (start_index + len > num_data_) { + Log::Fatal("Inserted label data is too large for dataset"); + } + if (label_.empty()) { + label_.resize(num_data_); + } + + memcpy(label_.data() + start_index, labels, sizeof(label_t) * len); + + // CUDA is handled after all insertions are complete +} + +template +void Metadata::SetWeightsFromIterator(It first, It last) { + std::lock_guard lock(mutex_); + // Clear weights on empty input + if (last - first == 0) { + weights_.clear(); + num_weights_ = 0; + return; + } + if (num_data_ != last - first) { + Log::Fatal("Length of weights differs from the length of #data"); + } + if (weights_.empty()) { + weights_.resize(num_data_); + } + num_weights_ = num_data_; + + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_weights_ >= 1024) + for (data_size_t i = 0; i < num_weights_; ++i) { + weights_[i] = Common::AvoidInf(first[i]); + } + CalculateQueryWeights(); + weight_load_from_file_ = false; + + #ifdef USE_CUDA + if (cuda_metadata_ != nullptr) { + cuda_metadata_->SetWeights(weights_.data(), weights_.size()); + } + #endif // USE_CUDA +} + +void Metadata::SetWeights(const label_t* weights, data_size_t len) { + SetWeightsFromIterator(weights, weights + len); +} + +#ifndef LGB_R_BUILD +void Metadata::SetWeights(struct ArrowArrayStream* stream) { + ArrowChunkedArray chunked_array(stream); + chunked_array.view().visit([&](auto&& visitor) { + SetWeightsFromIterator(visitor.begin(), visitor.end()); + }); +} + +void Metadata::SetWeights(int64_t n_chunks, struct ArrowArray* chunks, + struct ArrowSchema* schema) { + ArrowChunkedArray chunked_array(n_chunks, chunks, schema); + chunked_array.view().visit([&](auto&& visitor) { + SetWeightsFromIterator(visitor.begin(), visitor.end()); + }); +} +#endif // LGB_R_BUILD + +void Metadata::InsertWeights(const label_t* weights, data_size_t start_index, data_size_t len) { + if (!weights) { + Log::Fatal("Passed null weights"); + } + if (num_weights_ <= 0) { + Log::Fatal("Inserting weight data into dataset with no weights"); + } + if (start_index + len > num_weights_) { + Log::Fatal("Inserted weight data is too large for dataset"); + } + if (weights_.empty()) { + weights_.resize(num_weights_); + } + + memcpy(weights_.data() + start_index, weights, sizeof(label_t) * len); + + weight_load_from_file_ = false; + // CUDA is handled after all insertions are complete +} + +template +void Metadata::SetQueriesFromIterator(It first, It last) { + std::lock_guard lock(mutex_); + // Clear query boundaries on empty input + if (last - first == 0) { + query_boundaries_.clear(); + num_queries_ = 0; + return; + } + + data_size_t sum = 0; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum) + for (data_size_t i = 0; i < static_cast(last - first); ++i) { + sum += first[i]; + } + if (num_data_ != sum) { + Log::Fatal("Sum of query counts (%i) differs from the length of #data (%i)", num_data_, sum); + } + num_queries_ = last - first; + + query_boundaries_.resize(num_queries_ + 1); + query_boundaries_[0] = 0; + for (data_size_t i = 0; i < num_queries_; ++i) { + query_boundaries_[i + 1] = query_boundaries_[i] + first[i]; + } + CalculateQueryWeights(); + query_load_from_file_ = false; + + #ifdef USE_CUDA + if (cuda_metadata_ != nullptr) { + if (query_weights_.size() > 0) { + CHECK_EQ(query_weights_.size(), static_cast(num_queries_)); + cuda_metadata_->SetQuery(query_boundaries_.data(), query_weights_.data(), num_queries_); + } else { + cuda_metadata_->SetQuery(query_boundaries_.data(), nullptr, num_queries_); + } + } + #endif // USE_CUDA +} + +void Metadata::SetQuery(const data_size_t* query, data_size_t len) { + SetQueriesFromIterator(query, query + len); +} + +#ifndef LGB_R_BUILD +void Metadata::SetQuery(struct ArrowArrayStream* stream) { + ArrowChunkedArray chunked_array(stream); + chunked_array.view().visit([&](auto&& visitor) { + SetQueriesFromIterator(visitor.begin(), visitor.end()); + }); +} + +void Metadata::SetQuery(int64_t n_chunks, struct ArrowArray* chunks, + struct ArrowSchema* schema) { + ArrowChunkedArray chunked_array(n_chunks, chunks, schema); + chunked_array.view().visit([&](auto&& visitor) { + SetQueriesFromIterator(visitor.begin(), visitor.end()); + }); +} +#endif // LGB_R_BUILD + +void Metadata::SetPosition(const data_size_t* positions, data_size_t len) { + std::lock_guard lock(mutex_); + // save to nullptr + if (positions == nullptr || len == 0) { + positions_.clear(); + num_positions_ = 0; + return; + } + #ifdef USE_CUDA + Log::Fatal("Positions in learning to rank is not supported in CUDA version yet."); + #endif // USE_CUDA + if (num_data_ != len) { + Log::Fatal("Positions size (%i) doesn't match data size (%i)", len, num_data_); + } + if (positions_.empty()) { + positions_.resize(num_data_); + } else { + Log::Warning("Overwriting positions in dataset."); + } + num_positions_ = num_data_; + + position_load_from_file_ = false; + + position_ids_.clear(); + std::unordered_map map_id2pos; + for (data_size_t i = 0; i < num_positions_; ++i) { + if (map_id2pos.count(positions[i]) == 0) { + int pos = static_cast(map_id2pos.size()); + map_id2pos[positions[i]] = pos; + position_ids_.push_back(std::to_string(positions[i])); + } + } + + Log::Debug("number of unique positions found = %ld", position_ids_.size()); + + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_positions_ >= 1024) + for (data_size_t i = 0; i < num_positions_; ++i) { + positions_[i] = map_id2pos.at(positions[i]); + } +} + +void Metadata::InsertQueries(const data_size_t* queries, data_size_t start_index, data_size_t len) { + if (!queries) { + Log::Fatal("Passed null queries"); + } + if (queries_.size() <= 0) { + Log::Fatal("Inserting query data into dataset with no queries"); + } + if (static_cast(start_index + len) > queries_.size()) { + Log::Fatal("Inserted query data is too large for dataset"); + } + + memcpy(queries_.data() + start_index, queries, sizeof(data_size_t) * len); + + query_load_from_file_ = false; + // CUDA is handled after all insertions are complete +} + +void Metadata::LoadWeights() { + num_weights_ = 0; + std::string weight_filename(data_filename_); + // default weight file name + weight_filename.append(".weight"); + TextReader reader(weight_filename.c_str(), false); + reader.ReadAllLines(); + if (reader.Lines().empty()) { + return; + } + Log::Info("Loading weights..."); + num_weights_ = static_cast(reader.Lines().size()); + weights_ = std::vector(num_weights_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_weights_; ++i) { + double tmp_weight = 0.0f; + Common::Atof(reader.Lines()[i].c_str(), &tmp_weight); + weights_[i] = Common::AvoidInf(static_cast(tmp_weight)); + } + weight_load_from_file_ = true; +} + +void Metadata::LoadPositions() { + num_positions_ = 0; + std::string position_filename(data_filename_); + // default position file name + position_filename.append(".position"); + TextReader reader(position_filename.c_str(), false); + reader.ReadAllLines(); + if (reader.Lines().empty()) { + return; + } + Log::Info("Loading positions from %s ...", position_filename.c_str()); + num_positions_ = static_cast(reader.Lines().size()); + positions_ = std::vector(num_positions_); + position_ids_ = std::vector(); + std::unordered_map map_id2pos; + for (data_size_t i = 0; i < num_positions_; ++i) { + std::string& line = reader.Lines()[i]; + if (map_id2pos.count(line) == 0) { + map_id2pos[line] = static_cast(position_ids_.size()); + position_ids_.push_back(line); + } + positions_[i] = map_id2pos.at(line); + } + position_load_from_file_ = true; +} + +void Metadata::LoadInitialScore(const std::string& data_filename) { + num_init_score_ = 0; + std::string init_score_filename(data_filename); + init_score_filename = std::string(data_filename); + // default init_score file name + init_score_filename.append(".init"); + TextReader reader(init_score_filename.c_str(), false); + reader.ReadAllLines(); + if (reader.Lines().empty()) { + return; + } + Log::Info("Loading initial scores..."); + + // use first line to count number class + int num_class = static_cast(Common::Split(reader.Lines()[0].c_str(), '\t').size()); + data_size_t num_line = static_cast(reader.Lines().size()); + num_init_score_ = static_cast(num_line) * num_class; + + init_score_ = std::vector(num_init_score_); + if (num_class == 1) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_line; ++i) { + double tmp = 0.0f; + Common::Atof(reader.Lines()[i].c_str(), &tmp); + init_score_[i] = Common::AvoidInf(static_cast(tmp)); + } + } else { + std::vector oneline_init_score; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_line; ++i) { + double tmp = 0.0f; + oneline_init_score = Common::Split(reader.Lines()[i].c_str(), '\t'); + if (static_cast(oneline_init_score.size()) != num_class) { + Log::Fatal("Invalid initial score file. Redundant or insufficient columns"); + } + for (int k = 0; k < num_class; ++k) { + Common::Atof(oneline_init_score[k].c_str(), &tmp); + init_score_[static_cast(k) * num_line + i] = Common::AvoidInf(static_cast(tmp)); + } + } + } + init_score_load_from_file_ = true; +} + +void Metadata::LoadQueryBoundaries() { + num_queries_ = 0; + std::string query_filename(data_filename_); + // default query file name + query_filename.append(".query"); + TextReader reader(query_filename.c_str(), false); + reader.ReadAllLines(); + if (reader.Lines().empty()) { + return; + } + Log::Info("Calculating query boundaries..."); + query_boundaries_ = std::vector(reader.Lines().size() + 1); + num_queries_ = static_cast(reader.Lines().size()); + query_boundaries_[0] = 0; + for (size_t i = 0; i < reader.Lines().size(); ++i) { + int tmp_cnt; + Common::Atoi(reader.Lines()[i].c_str(), &tmp_cnt); + query_boundaries_[i + 1] = query_boundaries_[i] + static_cast(tmp_cnt); + } + query_load_from_file_ = true; +} + +void Metadata::CalculateQueryWeights() { + if (weights_.size() == 0 || query_boundaries_.size() == 0) { + return; + } + query_weights_.clear(); + Log::Info("Calculating query weights..."); + query_weights_ = std::vector(num_queries_); + for (data_size_t i = 0; i < num_queries_; ++i) { + query_weights_[i] = 0.0f; + for (data_size_t j = query_boundaries_[i]; j < query_boundaries_[i + 1]; ++j) { + query_weights_[i] += weights_[j]; + } + query_weights_[i] /= (query_boundaries_[i + 1] - query_boundaries_[i]); + } +} + +void Metadata::InsertAt(data_size_t start_index, + data_size_t count, + const float* labels, + const float* weights, + const double* init_scores, + const int32_t* queries) { + if (num_data_ < count + start_index) { + Log::Fatal("Length of metadata is too long to append #data"); + } + InsertLabels(labels, start_index, count); + if (weights) { + InsertWeights(weights, start_index, count); + } + if (init_scores) { + InsertInitScores(init_scores, start_index, count, count); + } + if (queries) { + InsertQueries(queries, start_index, count); + } +} + +void Metadata::FinishLoad() { + CalculateQueryBoundaries(); +} + +#ifdef USE_CUDA +void Metadata::CreateCUDAMetadata(const int gpu_device_id) { + cuda_metadata_.reset(new CUDAMetadata(gpu_device_id)); + cuda_metadata_->Init(label_, weights_, query_boundaries_, query_weights_, init_score_); +} +#endif // USE_CUDA + +void Metadata::LoadFromMemory(const void* memory) { + const char* mem_ptr = reinterpret_cast(memory); + + num_data_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_data_)); + num_weights_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_weights_)); + num_queries_ = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_queries_)); + + if (!label_.empty()) { + label_.clear(); + } + label_ = std::vector(num_data_); + std::memcpy(label_.data(), mem_ptr, sizeof(label_t) * num_data_); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_data_); + + if (num_weights_ > 0) { + if (!weights_.empty()) { + weights_.clear(); + } + weights_ = std::vector(num_weights_); + std::memcpy(weights_.data(), mem_ptr, sizeof(label_t) * num_weights_); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_weights_); + weight_load_from_file_ = true; + } + if (num_queries_ > 0) { + if (!query_boundaries_.empty()) { + query_boundaries_.clear(); + } + query_boundaries_ = std::vector(num_queries_ + 1); + std::memcpy(query_boundaries_.data(), mem_ptr, sizeof(data_size_t) * (num_queries_ + 1)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(data_size_t) * + (num_queries_ + 1)); + query_load_from_file_ = true; + } + CalculateQueryWeights(); +} + +void Metadata::SaveBinaryToFile(BinaryWriter* writer) const { + writer->AlignedWrite(&num_data_, sizeof(num_data_)); + writer->AlignedWrite(&num_weights_, sizeof(num_weights_)); + writer->AlignedWrite(&num_queries_, sizeof(num_queries_)); + writer->AlignedWrite(label_.data(), sizeof(label_t) * num_data_); + if (!weights_.empty()) { + writer->AlignedWrite(weights_.data(), sizeof(label_t) * num_weights_); + } + if (!query_boundaries_.empty()) { + writer->AlignedWrite(query_boundaries_.data(), + sizeof(data_size_t) * (num_queries_ + 1)); + } + if (num_init_score_ > 0) { + Log::Warning("Please note that `init_score` is not saved in binary file.\n" + "If you need it, please set it again after loading Dataset."); + } +} + +size_t Metadata::SizesInByte() const { + size_t size = VirtualFileWriter::AlignedSize(sizeof(num_data_)) + + VirtualFileWriter::AlignedSize(sizeof(num_weights_)) + + VirtualFileWriter::AlignedSize(sizeof(num_queries_)); + size += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_data_); + if (!weights_.empty()) { + size += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_weights_); + } + if (!query_boundaries_.empty()) { + size += VirtualFileWriter::AlignedSize(sizeof(data_size_t) * + (num_queries_ + 1)); + } + return size; +} + + +} // namespace LightGBM diff --git a/src/io/multi_val_dense_bin.hpp b/src/io/multi_val_dense_bin.hpp new file mode 100644 index 0000000..1315c0f --- /dev/null +++ b/src/io/multi_val_dense_bin.hpp @@ -0,0 +1,360 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_ +#define LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_ + +#include +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { + +template +class MultiValDenseBin : public MultiValBin { + public: + explicit MultiValDenseBin(data_size_t num_data, int num_bin, int num_feature, + const std::vector& offsets) + : num_data_(num_data), num_bin_(num_bin), num_feature_(num_feature), + offsets_(offsets) { + data_.resize(static_cast(num_data_) * num_feature_, static_cast(0)); + } + + ~MultiValDenseBin() { + } + + data_size_t num_data() const override { + return num_data_; + } + + int num_bin() const override { + return num_bin_; + } + + double num_element_per_row() const override { return num_feature_; } + + const std::vector& offsets() const override { return offsets_; } + + void PushOneRow(int , data_size_t idx, const std::vector& values) override { + auto start = RowPtr(idx); + for (auto i = 0; i < num_feature_; ++i) { + data_[start + i] = static_cast(values[i]); + } + } + + void FinishLoad() override { + } + + bool IsSparse() override { + return false; + } + + template + void ConstructHistogramInner(const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* gradients, const score_t* hessians, hist_t* out) const { + data_size_t i = start; + hist_t* grad = out; + hist_t* hess = out + 1; + + if (USE_PREFETCH) { + const data_size_t pf_offset = 32 / sizeof(VAL_T); + const data_size_t pf_end = end - pf_offset; + + for (; i < pf_end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; + if (!ORDERED) { + PREFETCH_T0(gradients + pf_idx); + PREFETCH_T0(hessians + pf_idx); + } + PREFETCH_T0(data_.data() + RowPtr(pf_idx)); + const auto j_start = RowPtr(idx); + const VAL_T* data_ptr = data_.data() + j_start; + const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; + const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; + for (int j = 0; j < num_feature_; ++j) { + const uint32_t bin = static_cast(data_ptr[j]); + const auto ti = (bin + offsets_[j]) << 1; + grad[ti] += gradient; + hess[ti] += hessian; + } + } + } + for (; i < end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto j_start = RowPtr(idx); + const VAL_T* data_ptr = data_.data() + j_start; + const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; + const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; + for (int j = 0; j < num_feature_; ++j) { + const uint32_t bin = static_cast(data_ptr[j]); + const auto ti = (bin + offsets_[j]) << 1; + grad[ti] += gradient; + hess[ti] += hessian; + } + } + } + + void ConstructHistogram(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* hessians, hist_t* out) const override { + ConstructHistogramInner(data_indices, start, end, + gradients, hessians, out); + } + + void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* hessians, + hist_t* out) const override { + ConstructHistogramInner( + nullptr, start, end, gradients, hessians, out); + } + + void ConstructHistogramOrdered(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const override { + ConstructHistogramInner(data_indices, start, end, + gradients, hessians, out); + } + + template + void ConstructHistogramIntInner(const data_size_t* data_indices, data_size_t start, data_size_t end, + const score_t* gradients_and_hessians, hist_t* out) const { + data_size_t i = start; + const VAL_T* data_ptr_base = data_.data(); + const int16_t* gradients_and_hessians_ptr = reinterpret_cast(gradients_and_hessians); + PACKED_HIST_T* out_ptr = reinterpret_cast(out); + + if (USE_PREFETCH) { + const data_size_t pf_offset = 32 / sizeof(VAL_T); + const data_size_t pf_end = end - pf_offset; + + for (; i < pf_end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; + if (!ORDERED) { + PREFETCH_T0(gradients_and_hessians_ptr + pf_idx); + } + PREFETCH_T0(data_ptr_base + RowPtr(pf_idx)); + const auto j_start = RowPtr(idx); + const VAL_T* data_ptr = data_ptr_base + j_start; + const int16_t gradient_16 = gradients_and_hessians_ptr[idx]; + const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : + ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | + static_cast(gradient_16 & 0xff)); + for (int j = 0; j < num_feature_; ++j) { + const uint32_t bin = static_cast(data_ptr[j]); + const auto ti = (bin + offsets_[j]); + out_ptr[ti] += gradient_packed; + } + } + } + for (; i < end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto j_start = RowPtr(idx); + const VAL_T* data_ptr = data_ptr_base + j_start; + const int16_t gradient_16 = gradients_and_hessians_ptr[idx]; + const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : + ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | + static_cast(gradient_16 & 0xff)); + for (int j = 0; j < num_feature_; ++j) { + const uint32_t bin = static_cast(data_ptr[j]); + const auto ti = (bin + offsets_[j]); + out_ptr[ti] += gradient_packed; + } + } + } + + void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* /*hessians*/, hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, gradients, out); + } + + void ConstructHistogramOrderedInt32(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* /*hessians*/, hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, gradients, out); + } + + void ConstructHistogramOrderedInt16(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* /*hessians*/, hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, gradients, out); + } + + void ConstructHistogramOrderedInt8(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + MultiValBin* CreateLike(data_size_t num_data, int num_bin, int num_feature, double, + const std::vector& offsets) const override { + return new MultiValDenseBin(num_data, num_bin, num_feature, offsets); + } + + void ReSize(data_size_t num_data, int num_bin, int num_feature, + double, const std::vector& offsets) override { + num_data_ = num_data; + num_bin_ = num_bin; + num_feature_ = num_feature; + offsets_ = offsets; + size_t new_size = static_cast(num_feature_) * num_data_; + if (data_.size() < new_size) { + data_.resize(new_size, 0); + } + } + + template + void CopyInner(const MultiValBin* full_bin, const data_size_t* used_indices, + data_size_t num_used_indices, + const std::vector& used_feature_index) { + const auto other_bin = + reinterpret_cast*>(full_bin); + if (SUBROW) { + CHECK_EQ(num_data_, num_used_indices); + } + int n_block = 1; + data_size_t block_size = num_data_; + Threading::BlockInfo(num_data_, 1024, &n_block, + &block_size); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) + for (int tid = 0; tid < n_block; ++tid) { + data_size_t start = tid * block_size; + data_size_t end = std::min(num_data_, start + block_size); + for (data_size_t i = start; i < end; ++i) { + const auto j_start = RowPtr(i); + const auto other_j_start = + SUBROW ? other_bin->RowPtr(used_indices[i]) : other_bin->RowPtr(i); + for (int j = 0; j < num_feature_; ++j) { + if (SUBCOL) { + if (other_bin->data_[other_j_start + used_feature_index[j]] > 0) { + data_[j_start + j] = static_cast( + other_bin->data_[other_j_start + used_feature_index[j]]); + } else { + data_[j_start + j] = 0; + } + } else { + data_[j_start + j] = + static_cast(other_bin->data_[other_j_start + j]); + } + } + } + } + } + + + void CopySubrow(const MultiValBin* full_bin, const data_size_t* used_indices, + data_size_t num_used_indices) override { + CopyInner(full_bin, used_indices, num_used_indices, + std::vector()); + } + + void CopySubcol(const MultiValBin* full_bin, + const std::vector& used_feature_index, + const std::vector&, + const std::vector&, + const std::vector&) override { + CopyInner(full_bin, nullptr, num_data_, used_feature_index); + } + + void CopySubrowAndSubcol(const MultiValBin* full_bin, + const data_size_t* used_indices, + data_size_t num_used_indices, + const std::vector& used_feature_index, + const std::vector&, + const std::vector&, + const std::vector&) override { + CopyInner(full_bin, used_indices, num_used_indices, + used_feature_index); + } + + inline size_t RowPtr(data_size_t idx) const { + return static_cast(idx) * num_feature_; + } + + MultiValDenseBin* Clone() override; + + #ifdef USE_CUDA + const void* GetRowWiseData(uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const override; + #endif // USE_CUDA + + private: + data_size_t num_data_; + int num_bin_; + int num_feature_; + std::vector offsets_; + std::vector> data_; + + MultiValDenseBin(const MultiValDenseBin& other) + : num_data_(other.num_data_), num_bin_(other.num_bin_), num_feature_(other.num_feature_), + offsets_(other.offsets_), data_(other.data_) { + } +}; + +template +MultiValDenseBin* MultiValDenseBin::Clone() { + return new MultiValDenseBin(*this); +} + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_ diff --git a/src/io/multi_val_sparse_bin.hpp b/src/io/multi_val_sparse_bin.hpp new file mode 100644 index 0000000..124174b --- /dev/null +++ b/src/io/multi_val_sparse_bin.hpp @@ -0,0 +1,449 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_ +#define LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_ + +#include +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { + +template +class MultiValSparseBin : public MultiValBin { + public: + explicit MultiValSparseBin(data_size_t num_data, int num_bin, + double estimate_element_per_row) + : num_data_(num_data), + num_bin_(num_bin), + estimate_element_per_row_(estimate_element_per_row) { + row_ptr_.resize(num_data_ + 1, 0); + INDEX_T estimate_num_data = static_cast(estimate_element_per_row_ * 1.1 * num_data_); + int num_threads = OMP_NUM_THREADS(); + if (num_threads > 1) { + t_data_.resize(num_threads - 1); + for (size_t i = 0; i < t_data_.size(); ++i) { + t_data_[i].resize(estimate_num_data / num_threads); + } + } + t_size_.resize(num_threads, 0); + data_.resize(estimate_num_data / num_threads); + } + + ~MultiValSparseBin() {} + + data_size_t num_data() const override { return num_data_; } + + int num_bin() const override { return num_bin_; } + + double num_element_per_row() const override { + return estimate_element_per_row_; + } + + const std::vector& offsets() const override { return offsets_; } + + void PushOneRow(int tid, data_size_t idx, + const std::vector& values) override { + const int pre_alloc_size = 50; + row_ptr_[idx + 1] = static_cast(values.size()); + if (tid == 0) { + if (t_size_[tid] + row_ptr_[idx + 1] > + static_cast(data_.size())) { + data_.resize(t_size_[tid] + row_ptr_[idx + 1] * pre_alloc_size); + } + for (auto val : values) { + data_[t_size_[tid]++] = static_cast(val); + } + } else { + if (t_size_[tid] + row_ptr_[idx + 1] > + static_cast(t_data_[tid - 1].size())) { + t_data_[tid - 1].resize(t_size_[tid] + + row_ptr_[idx + 1] * pre_alloc_size); + } + for (auto val : values) { + t_data_[tid - 1][t_size_[tid]++] = static_cast(val); + } + } + } + + void MergeData(const INDEX_T* sizes) { + Common::FunctionTimer fun_time("MultiValSparseBin::MergeData", global_timer); + for (data_size_t i = 0; i < num_data_; ++i) { + row_ptr_[i + 1] += row_ptr_[i]; + } + if (t_data_.size() > 0) { + std::vector offsets(1 + t_data_.size()); + offsets[0] = sizes[0]; + for (size_t tid = 0; tid < t_data_.size() - 1; ++tid) { + offsets[tid + 1] = offsets[tid] + sizes[tid + 1]; + } + data_.resize(row_ptr_[num_data_]); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) + for (int tid = 0; tid < static_cast(t_data_.size()); ++tid) { + std::copy_n(t_data_[tid].data(), sizes[tid + 1], + data_.data() + offsets[tid]); + } + } else { + data_.resize(row_ptr_[num_data_]); + } + } + + void FinishLoad() override { + MergeData(t_size_.data()); + t_size_.clear(); + row_ptr_.shrink_to_fit(); + data_.shrink_to_fit(); + t_data_.clear(); + t_data_.shrink_to_fit(); + // update estimate_element_per_row_ by all data + estimate_element_per_row_ = + static_cast(row_ptr_[num_data_]) / num_data_; + } + + bool IsSparse() override { return true; } + + template + void ConstructHistogramInner(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, hist_t* out) const { + data_size_t i = start; + hist_t* grad = out; + hist_t* hess = out + 1; + const VAL_T* data_ptr = data_.data(); + if (USE_PREFETCH) { + const data_size_t pf_offset = 32 / sizeof(VAL_T); + const data_size_t pf_end = end - pf_offset; + + for (; i < pf_end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto pf_idx = + USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; + if (!ORDERED) { + PREFETCH_T0(gradients + pf_idx); + PREFETCH_T0(hessians + pf_idx); + } + PREFETCH_T0(row_ptr_.data() + pf_idx); + PREFETCH_T0(data_ptr + row_ptr_[pf_idx]); + const auto j_start = RowPtr(idx); + const auto j_end = RowPtr(idx + 1); + const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; + const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; + for (auto j = j_start; j < j_end; ++j) { + const auto ti = static_cast(data_ptr[j]) << 1; + grad[ti] += gradient; + hess[ti] += hessian; + } + } + } + for (; i < end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto j_start = RowPtr(idx); + const auto j_end = RowPtr(idx + 1); + const score_t gradient = ORDERED ? gradients[i] : gradients[idx]; + const score_t hessian = ORDERED ? hessians[i] : hessians[idx]; + for (auto j = j_start; j < j_end; ++j) { + const auto ti = static_cast(data_ptr[j]) << 1; + grad[ti] += gradient; + hess[ti] += hessian; + } + } + } + + void ConstructHistogram(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* hessians, hist_t* out) const override { + ConstructHistogramInner(data_indices, start, end, + gradients, hessians, out); + } + + void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* hessians, + hist_t* out) const override { + ConstructHistogramInner( + nullptr, start, end, gradients, hessians, out); + } + + void ConstructHistogramOrdered(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* hessians, + hist_t* out) const override { + ConstructHistogramInner(data_indices, start, end, + gradients, hessians, out); + } + + template + void ConstructHistogramIntInner(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients_and_hessians, hist_t* out) const { + data_size_t i = start; + PACKED_HIST_T* out_ptr = reinterpret_cast(out); + const int16_t* gradients_and_hessians_ptr = reinterpret_cast(gradients_and_hessians); + const VAL_T* data_ptr = data_.data(); + const INDEX_T* row_ptr_base = row_ptr_.data(); + if (USE_PREFETCH) { + const data_size_t pf_offset = 32 / sizeof(VAL_T); + const data_size_t pf_end = end - pf_offset; + + for (; i < pf_end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto pf_idx = + USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset; + if (!ORDERED) { + PREFETCH_T0(gradients_and_hessians_ptr + pf_idx); + } + PREFETCH_T0(row_ptr_base + pf_idx); + PREFETCH_T0(data_ptr + row_ptr_[pf_idx]); + const auto j_start = RowPtr(idx); + const auto j_end = RowPtr(idx + 1); + const int16_t gradient_16 = ORDERED ? gradients_and_hessians_ptr[i] : gradients_and_hessians_ptr[idx]; + const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : + ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | + static_cast(gradient_16 & 0xff)); + for (auto j = j_start; j < j_end; ++j) { + const auto ti = static_cast(data_ptr[j]); + out_ptr[ti] += gradient_packed; + } + } + } + for (; i < end; ++i) { + const auto idx = USE_INDICES ? data_indices[i] : i; + const auto j_start = RowPtr(idx); + const auto j_end = RowPtr(idx + 1); + const int16_t gradient_16 = ORDERED ? gradients_and_hessians_ptr[i] : gradients_and_hessians_ptr[idx]; + const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : + ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | + static_cast(gradient_16 & 0xff)); + for (auto j = j_start; j < j_end; ++j) { + const auto ti = static_cast(data_ptr[j]); + out_ptr[ti] += gradient_packed; + } + } + } + + void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* /*hessians*/, hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, gradients, out); + } + + void ConstructHistogramOrderedInt32(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* /*hessians*/, hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, gradients, out); + } + + void ConstructHistogramOrderedInt16(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* gradients, + const score_t* /*hessians*/, hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* gradients, const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner( + nullptr, start, end, gradients, out); + } + + void ConstructHistogramOrderedInt8(const data_size_t* data_indices, + data_size_t start, data_size_t end, + const score_t* gradients, + const score_t* /*hessians*/, + hist_t* out) const override { + ConstructHistogramIntInner(data_indices, start, end, + gradients, out); + } + + MultiValBin* CreateLike(data_size_t num_data, int num_bin, int, + double estimate_element_per_row, + const std::vector& /*offsets*/) const override { + return new MultiValSparseBin(num_data, num_bin, + estimate_element_per_row); + } + + void ReSize(data_size_t num_data, int num_bin, int, + double estimate_element_per_row, const std::vector& /*offsets*/) override { + num_data_ = num_data; + num_bin_ = num_bin; + estimate_element_per_row_ = estimate_element_per_row; + INDEX_T estimate_num_data = + static_cast(estimate_element_per_row_ * 1.1 * num_data_); + size_t npart = 1 + t_data_.size(); + INDEX_T avg_num_data = static_cast(estimate_num_data / npart); + if (static_cast(data_.size()) < avg_num_data) { + data_.resize(avg_num_data, 0); + } + for (size_t i = 0; i < t_data_.size(); ++i) { + if (static_cast(t_data_[i].size()) < avg_num_data) { + t_data_[i].resize(avg_num_data, 0); + } + } + if (num_data_ + 1 > static_cast(row_ptr_.size())) { + row_ptr_.resize(num_data_ + 1); + } + } + + template + void CopyInner(const MultiValBin* full_bin, const data_size_t* used_indices, + data_size_t num_used_indices, + const std::vector& lower, + const std::vector& upper, + const std::vector& delta) { + const auto other = + reinterpret_cast*>(full_bin); + if (SUBROW) { + CHECK_EQ(num_data_, num_used_indices); + } + int n_block = 1; + data_size_t block_size = num_data_; + Threading::BlockInfo(static_cast(t_data_.size() + 1), + num_data_, 1024, &n_block, &block_size); + std::vector sizes(t_data_.size() + 1, 0); + const int pre_alloc_size = 50; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) + for (int tid = 0; tid < n_block; ++tid) { + data_size_t start = tid * block_size; + data_size_t end = std::min(num_data_, start + block_size); + auto& buf = (tid == 0) ? data_ : t_data_[tid - 1]; + INDEX_T size = 0; + for (data_size_t i = start; i < end; ++i) { + const auto j_start = + SUBROW ? other->RowPtr(used_indices[i]) : other->RowPtr(i); + const auto j_end = + SUBROW ? other->RowPtr(used_indices[i] + 1) : other->RowPtr(i + 1); + if (size + (j_end - j_start) > static_cast(buf.size())) { + buf.resize(size + (j_end - j_start) * pre_alloc_size); + } + int k = 0; + const auto pre_size = size; + for (auto j = j_start; j < j_end; ++j) { + const auto val = other->data_[j]; + if (SUBCOL) { + while (val >= upper[k]) { + ++k; + } + if (val >= lower[k]) { + buf[size++] = static_cast(val - delta[k]); + } + } else { + buf[size++] = val; + } + } + row_ptr_[i + 1] = size - pre_size; + } + sizes[tid] = size; + } + MergeData(sizes.data()); + } + + void CopySubrow(const MultiValBin* full_bin, const data_size_t* used_indices, + data_size_t num_used_indices) override { + CopyInner(full_bin, used_indices, num_used_indices, + std::vector(), std::vector(), + std::vector()); + } + + void CopySubcol(const MultiValBin* full_bin, const std::vector&, + const std::vector& lower, + const std::vector& upper, + const std::vector& delta) override { + CopyInner(full_bin, nullptr, num_data_, lower, upper, delta); + } + + void CopySubrowAndSubcol(const MultiValBin* full_bin, + const data_size_t* used_indices, + data_size_t num_used_indices, + const std::vector&, + const std::vector& lower, + const std::vector& upper, + const std::vector& delta) override { + CopyInner(full_bin, used_indices, num_used_indices, lower, + upper, delta); + } + + inline INDEX_T RowPtr(data_size_t idx) const { return row_ptr_[idx]; } + + MultiValSparseBin* Clone() override; + + + #ifdef USE_CUDA + const void* GetRowWiseData(uint8_t* bit_type, + size_t* total_size, + bool* is_sparse, + const void** out_data_ptr, + uint8_t* data_ptr_bit_type) const override; + #endif // USE_CUDA + + private: + data_size_t num_data_; + int num_bin_; + double estimate_element_per_row_; + std::vector> data_; + std::vector> + row_ptr_; + std::vector>> + t_data_; + std::vector t_size_; + std::vector offsets_; + + MultiValSparseBin(const MultiValSparseBin& other) + : num_data_(other.num_data_), + num_bin_(other.num_bin_), + estimate_element_per_row_(other.estimate_element_per_row_), + data_(other.data_), + row_ptr_(other.row_ptr_) {} +}; + +template +MultiValSparseBin* MultiValSparseBin::Clone() { + return new MultiValSparseBin(*this); +} + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_ diff --git a/src/io/parser.cpp b/src/io/parser.cpp new file mode 100644 index 0000000..0039d6e --- /dev/null +++ b/src/io/parser.cpp @@ -0,0 +1,319 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include "parser.hpp" + +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +void GetStatistic(const char* str, int* comma_cnt, int* tab_cnt, int* colon_cnt) { + *comma_cnt = 0; + *tab_cnt = 0; + *colon_cnt = 0; + for (int i = 0; str[i] != '\0'; ++i) { + if (str[i] == ',') { + ++(*comma_cnt); + } else if (str[i] == '\t') { + ++(*tab_cnt); + } else if (str[i] == ':') { + ++(*colon_cnt); + } + } +} + +int GetLabelIdxForLibsvm(const std::string& str, int num_features, int label_idx) { + if (num_features <= 0) { + return label_idx; + } + auto str2 = Common::Trim(str); + auto pos_space = str2.find_first_of(" \f\n\r\t\v"); + auto pos_colon = str2.find_first_of(":"); + if (pos_space == std::string::npos || pos_space < pos_colon) { + return label_idx; + } else { + return -1; + } +} + +int GetLabelIdxForTSV(const std::string& str, int num_features, int label_idx) { + if (num_features <= 0) { + return label_idx; + } + auto str2 = Common::Trim(str); + auto tokens = Common::Split(str2.c_str(), '\t'); + if (static_cast(tokens.size()) == num_features) { + return -1; + } else { + return label_idx; + } +} + +int GetLabelIdxForCSV(const std::string& str, int num_features, int label_idx) { + if (num_features <= 0) { + return label_idx; + } + auto str2 = Common::Trim(str); + auto tokens = Common::Split(str2.c_str(), ','); + if (static_cast(tokens.size()) == num_features) { + return -1; + } else { + return label_idx; + } +} + +enum DataType { + INVALID, + CSV, + TSV, + LIBSVM +}; + +void GetLine(std::stringstream* ss, std::string* line, const VirtualFileReader* reader, std::vector* buffer, size_t buffer_size) { + std::getline(*ss, *line); + while (ss->eof()) { + size_t read_len = reader->Read(buffer->data(), buffer_size); + if (read_len <= 0) { + break; + } + ss->clear(); + ss->str(std::string(buffer->data(), read_len)); + std::string tmp; + std::getline(*ss, tmp); + *line += tmp; + } +} + +std::vector ReadKLineFromFile(const char* filename, bool header, int k) { + auto reader = VirtualFileReader::Make(filename); + if (!reader->Init()) { + Log::Fatal("Data file %s doesn't exist.", filename); + } + std::vector ret; + std::string cur_line; + const size_t buffer_size = 1024 * 1024; + auto buffer = std::vector(buffer_size); + size_t read_len = reader->Read(buffer.data(), buffer_size); + if (read_len <= 0) { + Log::Fatal("Data file %s couldn't be read.", filename); + } + std::string read_str = std::string(buffer.data(), read_len); + std::stringstream tmp_file(read_str); + if (header) { + if (!tmp_file.eof()) { + GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); + } + } + for (int i = 0; i < k; ++i) { + if (!tmp_file.eof()) { + GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); + cur_line = Common::Trim(cur_line); + if (!cur_line.empty()) { + ret.push_back(cur_line); + } + } else { + break; + } + } + if (ret.empty()) { + Log::Fatal("Data file %s should have at least one line.", filename); + } else if (ret.size() == 1) { + Log::Warning("Data file %s only has one line.", filename); + } + return ret; +} + +int GetNumColFromLIBSVMFile(const char* filename, bool header) { + auto reader = VirtualFileReader::Make(filename); + if (!reader->Init()) { + Log::Fatal("Data file %s doesn't exist.", filename); + } + std::vector ret; + std::string cur_line; + const size_t buffer_size = 1024 * 1024; + auto buffer = std::vector(buffer_size); + size_t read_len = reader->Read(buffer.data(), buffer_size); + if (read_len <= 0) { + Log::Fatal("Data file %s couldn't be read.", filename); + } + std::string read_str = std::string(buffer.data(), read_len); + std::stringstream tmp_file(read_str); + if (header) { + if (!tmp_file.eof()) { + GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); + } + } + int max_col_idx = 0; + int max_line_idx = 0; + const int stop_round = 1 << 7; + const int max_line = 1 << 13; + for (int i = 0; i < max_line; ++i) { + if (!tmp_file.eof()) { + GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size); + cur_line = Common::Trim(cur_line); + auto colon_pos = cur_line.find_last_of(":"); + auto space_pos = cur_line.find_last_of(" \f\t\v"); + auto sub_str = cur_line.substr(space_pos + 1, space_pos - colon_pos - 1); + int cur_idx = 0; + Common::Atoi(sub_str.c_str(), &cur_idx); + if (cur_idx > max_col_idx) { + max_col_idx = cur_idx; + max_line_idx = i; + } + if (i - max_line_idx >= stop_round) { + break; + } + } else { + break; + } + } + CHECK_GT(max_col_idx, 0); + return max_col_idx; +} + +DataType GetDataType(const char* filename, bool header, + const std::vector& lines, int* num_col) { + DataType type = DataType::INVALID; + if (lines.empty()) { + return type; + } + int comma_cnt = 0; + int tab_cnt = 0; + int colon_cnt = 0; + GetStatistic(lines[0].c_str(), &comma_cnt, &tab_cnt, &colon_cnt); + size_t num_lines = lines.size(); + if (num_lines == 1) { + if (colon_cnt > 0) { + type = DataType::LIBSVM; + } else if (tab_cnt > 0) { + type = DataType::TSV; + } else if (comma_cnt > 0) { + type = DataType::CSV; + } + } else { + int comma_cnt2 = 0; + int tab_cnt2 = 0; + int colon_cnt2 = 0; + GetStatistic(lines[1].c_str(), &comma_cnt2, &tab_cnt2, &colon_cnt2); + if (colon_cnt > 0 || colon_cnt2 > 0) { + type = DataType::LIBSVM; + } else if (tab_cnt == tab_cnt2 && tab_cnt > 0) { + type = DataType::TSV; + } else if (comma_cnt == comma_cnt2 && comma_cnt > 0) { + type = DataType::CSV; + } + if (type == DataType::TSV || type == DataType::CSV) { + // valid the type + for (size_t i = 2; i < num_lines; ++i) { + GetStatistic(lines[i].c_str(), &comma_cnt2, &tab_cnt2, &colon_cnt2); + if (type == DataType::TSV && tab_cnt2 != tab_cnt) { + type = DataType::INVALID; + break; + } else if (type == DataType::CSV && comma_cnt != comma_cnt2) { + type = DataType::INVALID; + break; + } + } + } + } + if (type == DataType::LIBSVM) { + int max_col_idx = GetNumColFromLIBSVMFile(filename, header); + *num_col = max_col_idx + 1; + } else if (type == DataType::CSV) { + *num_col = comma_cnt + 1; + } else if (type == DataType::TSV) { + *num_col = tab_cnt + 1; + } + return type; +} + +// parser factory implementation. +ParserFactory& ParserFactory::getInstance() { + static ParserFactory factory; + return factory; +} + +void ParserFactory::Register(std::string class_name, std::function m_objc) { + if (m_objc) { + object_map_.insert( + std::map>::value_type(class_name, m_objc)); + } +} + +Parser* ParserFactory::getObject(std::string class_name, std::string config_str) { + std::map>::const_iterator iter = + object_map_.find(class_name); + if (iter != object_map_.end()) { + return iter->second(config_str); + } else { + Log::Fatal("Cannot find parser class '%s', please register first or check config format.", class_name.c_str()); + return nullptr; + } +} + +Parser* Parser::CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser) { + const int n_read_line = 32; + auto lines = ReadKLineFromFile(filename, header, n_read_line); + int num_col = 0; + DataType type = GetDataType(filename, header, lines, &num_col); + if (type == DataType::INVALID) { + Log::Fatal("Unknown format of training data. Only CSV, TSV, and LibSVM (zero-based) formatted text files are supported."); + } + std::unique_ptr ret; + int output_label_index = -1; + AtofFunc atof = precise_float_parser ? Common::AtofPrecise : Common::Atof; + if (type == DataType::LIBSVM) { + output_label_index = GetLabelIdxForLibsvm(lines[0], num_features, label_idx); + ret.reset(new LibSVMParser(output_label_index, num_col, atof)); + } else if (type == DataType::TSV) { + output_label_index = GetLabelIdxForTSV(lines[0], num_features, label_idx); + ret.reset(new TSVParser(output_label_index, num_col, atof)); + } else if (type == DataType::CSV) { + output_label_index = GetLabelIdxForCSV(lines[0], num_features, label_idx); + ret.reset(new CSVParser(output_label_index, num_col, atof)); + } + + if (output_label_index < 0 && label_idx >= 0) { + Log::Info("Data file %s doesn't contain a label column.", filename); + } + return ret.release(); +} + +Parser* Parser::CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser, std::string parser_config_str) { + // customized parser add-on. + if (!parser_config_str.empty()) { + std::unique_ptr ret; + std::string class_name = Common::GetFromParserConfig(parser_config_str, "className"); + Log::Info("Custom parser class name: %s", class_name.c_str()); + Parser* p = ParserFactory::getInstance().getObject(class_name, parser_config_str); + ret.reset(p); + return ret.release(); + } + return CreateParser(filename, header, num_features, label_idx, precise_float_parser); +} + +std::string Parser::GenerateParserConfigStr(const char* filename, const char* parser_config_filename, bool header, int label_idx) { + TextReader parser_config_reader(parser_config_filename, false); + parser_config_reader.ReadAllLines(); + std::string parser_config_str = parser_config_reader.JoinedLines(); + if (!parser_config_str.empty()) { + // save header to parser config in case needed. + if (header && Common::GetFromParserConfig(parser_config_str, "header").empty()) { + TextReader text_reader(filename, header); + parser_config_str = Common::SaveToParserConfig(parser_config_str, "header", text_reader.first_line()); + } + // save label id to parser config in case needed. + if (Common::GetFromParserConfig(parser_config_str, "labelId").empty()) { + parser_config_str = Common::SaveToParserConfig(parser_config_str, "labelId", std::to_string(label_idx)); + } + } + return parser_config_str; +} +} // namespace LightGBM diff --git a/src/io/parser.hpp b/src/io/parser.hpp new file mode 100644 index 0000000..0358b67 --- /dev/null +++ b/src/io/parser.hpp @@ -0,0 +1,136 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_IO_PARSER_HPP_ +#define LIGHTGBM_SRC_IO_PARSER_HPP_ + +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + +class CSVParser: public Parser { + public: + explicit CSVParser(int label_idx, int total_columns, AtofFunc atof) + :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) { + } + inline void ParseOneLine(const char* str, + std::vector>* out_features, double* out_label) const override { + int idx = 0; + double val = 0.0f; + int offset = 0; + *out_label = 0.0f; + while (*str != '\0') { + str = atof_(str, &val); + if (idx == label_idx_) { + *out_label = val; + offset = -1; + } else if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { + out_features->emplace_back(idx + offset, val); + } + ++idx; + if (*str == ',') { + ++str; + } else if (*str != '\0') { + Log::Fatal("Input format error when parsing as CSV"); + } + } + } + + inline int NumFeatures() const override { + return total_columns_ - (label_idx_ >= 0); + } + + private: + int label_idx_ = 0; + int total_columns_ = -1; + AtofFunc atof_; +}; + +class TSVParser: public Parser { + public: + explicit TSVParser(int label_idx, int total_columns, AtofFunc atof) + :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) { + } + inline void ParseOneLine(const char* str, + std::vector>* out_features, double* out_label) const override { + int idx = 0; + double val = 0.0f; + int offset = 0; + while (*str != '\0') { + str = atof_(str, &val); + if (idx == label_idx_) { + *out_label = val; + offset = -1; + } else if (std::fabs(val) > kZeroThreshold || std::isnan(val)) { + out_features->emplace_back(idx + offset, val); + } + ++idx; + if (*str == '\t') { + ++str; + } else if (*str != '\0') { + Log::Fatal("Input format error when parsing as TSV"); + } + } + } + + inline int NumFeatures() const override { + return total_columns_ - (label_idx_ >= 0); + } + + private: + int label_idx_ = 0; + int total_columns_ = -1; + AtofFunc atof_; +}; + +class LibSVMParser: public Parser { + public: + explicit LibSVMParser(int label_idx, int total_columns, AtofFunc atof) + :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) { + if (label_idx > 0) { + Log::Fatal("Label should be the first column in a LibSVM file"); + } + } + inline void ParseOneLine(const char* str, + std::vector>* out_features, double* out_label) const override { + int idx = 0; + double val = 0.0f; + if (label_idx_ == 0) { + str = atof_(str, &val); + *out_label = val; + str = Common::SkipSpaceAndTab(str); + } + while (*str != '\0') { + str = Common::Atoi(str, &idx); + str = Common::SkipSpaceAndTab(str); + if (*str == ':') { + ++str; + str = Common::Atof(str, &val); + out_features->emplace_back(idx, val); + } else { + Log::Fatal("Input format error when parsing as LibSVM"); + } + str = Common::SkipSpaceAndTab(str); + } + } + + inline int NumFeatures() const override { + return total_columns_; + } + + private: + int label_idx_ = 0; + int total_columns_ = -1; + AtofFunc atof_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_IO_PARSER_HPP_ diff --git a/src/io/sparse_bin.hpp b/src/io/sparse_bin.hpp new file mode 100644 index 0000000..595ce1e --- /dev/null +++ b/src/io/sparse_bin.hpp @@ -0,0 +1,858 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_ +#define LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_ + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +template +class SparseBin; + +const size_t kNumFastIndex = 64; + +template +class SparseBinIterator : public BinIterator { + public: + SparseBinIterator(const SparseBin* bin_data, uint32_t min_bin, + uint32_t max_bin, uint32_t most_freq_bin) + : bin_data_(bin_data), + min_bin_(static_cast(min_bin)), + max_bin_(static_cast(max_bin)), + most_freq_bin_(static_cast(most_freq_bin)) { + if (most_freq_bin_ == 0) { + offset_ = 1; + } else { + offset_ = 0; + } + Reset(0); + } + SparseBinIterator(const SparseBin* bin_data, data_size_t start_idx) + : bin_data_(bin_data) { + Reset(start_idx); + } + + inline uint32_t RawGet(data_size_t idx) override; + inline VAL_T InnerRawGet(data_size_t idx); + + inline uint32_t Get(data_size_t idx) override { + VAL_T ret = InnerRawGet(idx); + if (ret >= min_bin_ && ret <= max_bin_) { + return ret - min_bin_ + offset_; + } else { + return most_freq_bin_; + } + } + + inline void Reset(data_size_t idx) override; + + private: + const SparseBin* bin_data_; + data_size_t cur_pos_; + data_size_t i_delta_; + VAL_T min_bin_; + VAL_T max_bin_; + VAL_T most_freq_bin_; + uint8_t offset_; +}; + +template +class SparseBin : public Bin { + public: + friend class SparseBinIterator; + + explicit SparseBin(data_size_t num_data) : num_data_(num_data) { + int num_threads = OMP_NUM_THREADS(); + push_buffers_.resize(num_threads); + } + + ~SparseBin() {} + + void InitStreaming(uint32_t num_thread, int32_t omp_max_threads) override { + // Each external thread needs its own set of OpenMP push buffers, + // so allocate num_thread times the maximum number of OMP threads per external thread + push_buffers_.resize(omp_max_threads * num_thread); + }; + + void ReSize(data_size_t num_data) override { num_data_ = num_data; } + + void Push(int tid, data_size_t idx, uint32_t value) override { + auto cur_bin = static_cast(value); + if (cur_bin != 0) { + push_buffers_[tid].emplace_back(idx, cur_bin); + } + } + + BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin) const override; + +#define ACC_GH(hist, i, g, h) \ + const auto ti = static_cast(i) << 1; \ + hist[ti] += g; \ + hist[ti + 1] += h; + + void ConstructHistogram(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const override { + data_size_t i_delta, cur_pos; + InitIndex(data_indices[start], &i_delta, &cur_pos); + data_size_t i = start; + for (;;) { + if (cur_pos < data_indices[i]) { + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } else if (cur_pos > data_indices[i]) { + if (++i >= end) { + break; + } + } else { + const VAL_T bin = vals_[i_delta]; + ACC_GH(out, bin, ordered_gradients[i], ordered_hessians[i]); + if (++i >= end) { + break; + } + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } + } + } + + void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* ordered_hessians, + hist_t* out) const override { + data_size_t i_delta, cur_pos; + InitIndex(start, &i_delta, &cur_pos); + while (cur_pos < start && i_delta < num_vals_) { + cur_pos += deltas_[++i_delta]; + } + while (cur_pos < end && i_delta < num_vals_) { + const VAL_T bin = vals_[i_delta]; + ACC_GH(out, bin, ordered_gradients[cur_pos], ordered_hessians[cur_pos]); + cur_pos += deltas_[++i_delta]; + } + } + + void ConstructHistogram(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + data_size_t i_delta, cur_pos; + InitIndex(data_indices[start], &i_delta, &cur_pos); + data_size_t i = start; + hist_t* grad = out; + hist_cnt_t* cnt = reinterpret_cast(out + 1); + for (;;) { + if (cur_pos < data_indices[i]) { + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } else if (cur_pos > data_indices[i]) { + if (++i >= end) { + break; + } + } else { + const uint32_t ti = static_cast(vals_[i_delta]) << 1; + grad[ti] += ordered_gradients[i]; + ++cnt[ti]; + if (++i >= end) { + break; + } + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } + } + } + + void ConstructHistogram(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + data_size_t i_delta, cur_pos; + InitIndex(start, &i_delta, &cur_pos); + hist_t* grad = out; + hist_cnt_t* cnt = reinterpret_cast(out + 1); + while (cur_pos < start && i_delta < num_vals_) { + cur_pos += deltas_[++i_delta]; + } + while (cur_pos < end && i_delta < num_vals_) { + const uint32_t ti = static_cast(vals_[i_delta]) << 1; + grad[ti] += ordered_gradients[cur_pos]; + ++cnt[ti]; + cur_pos += deltas_[++i_delta]; + } + } +#undef ACC_GH + + template + void ConstructIntHistogramInner(data_size_t start, data_size_t end, + const score_t* ordered_gradients_and_hessians, + hist_t* out) const { + data_size_t i_delta, cur_pos; + InitIndex(start, &i_delta, &cur_pos); + if (USE_HESSIAN) { + PACKED_HIST_T* out_ptr = reinterpret_cast(out); + const int16_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); + while (cur_pos < start && i_delta < num_vals_) { + cur_pos += deltas_[++i_delta]; + } + while (cur_pos < end && i_delta < num_vals_) { + const VAL_T bin = vals_[i_delta]; + const int16_t gradient_16 = gradients_and_hessians_ptr[cur_pos]; + const PACKED_HIST_T gradient_64 = (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); + out_ptr[bin] += gradient_64; + cur_pos += deltas_[++i_delta]; + } + } else { + GRAD_HIST_T* grad = reinterpret_cast(out); + HESS_HIST_T* cnt = reinterpret_cast(out) + 1; + const int8_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); + while (cur_pos < start && i_delta < num_vals_) { + cur_pos += deltas_[++i_delta]; + } + while (cur_pos < end && i_delta < num_vals_) { + const uint32_t ti = static_cast(vals_[i_delta]) << 1; + grad[ti] += gradients_and_hessians_ptr[cur_pos]; + ++cnt[ti]; + cur_pos += deltas_[++i_delta]; + } + } + } + + template + void ConstructIntHistogramInner(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients_and_hessians, + hist_t* out) const { + data_size_t i_delta, cur_pos; + InitIndex(data_indices[start], &i_delta, &cur_pos); + data_size_t i = start; + if (USE_HESSIAN) { + PACKED_HIST_T* out_ptr = reinterpret_cast(out); + const int16_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); + for (;;) { + if (cur_pos < data_indices[i]) { + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } else if (cur_pos > data_indices[i]) { + if (++i >= end) { + break; + } + } else { + const VAL_T bin = vals_[i_delta]; + const int16_t gradient_16 = gradients_and_hessians_ptr[i]; + const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 : + (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff); + out_ptr[bin] += gradient_packed; + if (++i >= end) { + break; + } + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } + } + } else { + GRAD_HIST_T* grad = reinterpret_cast(out); + HESS_HIST_T* cnt = reinterpret_cast(out) + 1; + const int8_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians); + for (;;) { + if (cur_pos < data_indices[i]) { + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } else if (cur_pos > data_indices[i]) { + if (++i >= end) { + break; + } + } else { + const uint32_t ti = static_cast(vals_[i_delta]) << 1; + grad[ti] += gradients_and_hessians_ptr[i << 1]; + ++cnt[ti]; + if (++i >= end) { + break; + } + cur_pos += deltas_[++i_delta]; + if (i_delta >= num_vals_) { + break; + } + } + } + } + } + + void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructIntHistogramInner(start, end, ordered_gradients, out); + } + + void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt32(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + ConstructIntHistogramInner(start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructIntHistogramInner(start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt16(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + ConstructIntHistogramInner(start, end, ordered_gradients, out); + } + + void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + const score_t* /*ordered_hessians*/, + hist_t* out) const override { + ConstructIntHistogramInner(start, end, ordered_gradients, out); + } + + void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, + data_size_t end, const score_t* ordered_gradients, + hist_t* out) const override { + ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out); + } + + void ConstructHistogramInt8(data_size_t start, data_size_t end, + const score_t* ordered_gradients, + hist_t* out) const override { + ConstructIntHistogramInner(start, end, ordered_gradients, out); + } + + inline void NextNonzeroFast(data_size_t* i_delta, + data_size_t* cur_pos) const { + *cur_pos += deltas_[++(*i_delta)]; + if (*i_delta >= num_vals_) { + *cur_pos = num_data_; + } + } + + inline bool NextNonzero(data_size_t* i_delta, data_size_t* cur_pos) const { + *cur_pos += deltas_[++(*i_delta)]; + if (*i_delta < num_vals_) { + return true; + } else { + *cur_pos = num_data_; + return false; + } + } + + template + data_size_t SplitInner(uint32_t min_bin, uint32_t max_bin, + uint32_t default_bin, uint32_t most_freq_bin, + bool default_left, uint32_t threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const { + auto th = static_cast(threshold + min_bin); + auto t_zero_bin = static_cast(min_bin + default_bin); + if (most_freq_bin == 0) { + --th; + --t_zero_bin; + } + const auto minb = static_cast(min_bin); + const auto maxb = static_cast(max_bin); + data_size_t lte_count = 0; + data_size_t gt_count = 0; + data_size_t* default_indices = gt_indices; + data_size_t* default_count = >_count; + data_size_t* missing_default_indices = gt_indices; + data_size_t* missing_default_count = >_count; + if (most_freq_bin <= threshold) { + default_indices = lte_indices; + default_count = <e_count; + } + if (MISS_IS_ZERO || MISS_IS_NA) { + if (default_left) { + missing_default_indices = lte_indices; + missing_default_count = <e_count; + } + } + SparseBinIterator iterator(this, data_indices[0]); + if (min_bin < max_bin) { + for (data_size_t i = 0; i < cnt; ++i) { + const data_size_t idx = data_indices[i]; + const auto bin = iterator.InnerRawGet(idx); + if ((MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) || + (MISS_IS_NA && !MFB_IS_NA && bin == maxb)) { + missing_default_indices[(*missing_default_count)++] = idx; + } else if ((USE_MIN_BIN && (bin < minb || bin > maxb)) || + (!USE_MIN_BIN && bin == 0)) { + if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { + missing_default_indices[(*missing_default_count)++] = idx; + } else { + default_indices[(*default_count)++] = idx; + } + } else if (bin > th) { + gt_indices[gt_count++] = idx; + } else { + lte_indices[lte_count++] = idx; + } + } + } else { + data_size_t* max_bin_indices = gt_indices; + data_size_t* max_bin_count = >_count; + if (maxb <= th) { + max_bin_indices = lte_indices; + max_bin_count = <e_count; + } + for (data_size_t i = 0; i < cnt; ++i) { + const data_size_t idx = data_indices[i]; + const auto bin = iterator.InnerRawGet(idx); + if (MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) { + missing_default_indices[(*missing_default_count)++] = idx; + } else if (bin != maxb) { + if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) { + missing_default_indices[(*missing_default_count)++] = idx; + } else { + default_indices[(*default_count)++] = idx; + } + } else { + if (MISS_IS_NA && !MFB_IS_NA) { + missing_default_indices[(*missing_default_count)++] = idx; + } else { + max_bin_indices[(*max_bin_count)++] = idx; + } + } + } + } + return lte_count; + } + + data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin, + uint32_t most_freq_bin, MissingType missing_type, + bool default_left, uint32_t threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { +#define ARGUMENTS \ + min_bin, max_bin, default_bin, most_freq_bin, default_left, threshold, \ + data_indices, cnt, lte_indices, gt_indices + if (missing_type == MissingType::None) { + return SplitInner(ARGUMENTS); + } else if (missing_type == MissingType::Zero) { + if (default_bin == most_freq_bin) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } else { + if (max_bin == most_freq_bin + min_bin && most_freq_bin > 0) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } +#undef ARGUMENTS + } + + data_size_t Split(uint32_t max_bin, uint32_t default_bin, + uint32_t most_freq_bin, MissingType missing_type, + bool default_left, uint32_t threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { +#define ARGUMENTS \ + 1, max_bin, default_bin, most_freq_bin, default_left, threshold, \ + data_indices, cnt, lte_indices, gt_indices + if (missing_type == MissingType::None) { + return SplitInner(ARGUMENTS); + } else if (missing_type == MissingType::Zero) { + if (default_bin == most_freq_bin) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } else { + if (max_bin == most_freq_bin + 1 && most_freq_bin > 0) { + return SplitInner(ARGUMENTS); + } else { + return SplitInner(ARGUMENTS); + } + } +#undef ARGUMENTS + } + template + data_size_t SplitCategoricalInner(uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin, + const uint32_t* threshold, + int num_threshold, + const data_size_t* data_indices, + data_size_t cnt, data_size_t* lte_indices, + data_size_t* gt_indices) const { + data_size_t lte_count = 0; + data_size_t gt_count = 0; + data_size_t* default_indices = gt_indices; + data_size_t* default_count = >_count; + SparseBinIterator iterator(this, data_indices[0]); + int8_t offset = most_freq_bin == 0 ? 1 : 0; + if (most_freq_bin > 0 && Common::FindInBitset(threshold, num_threshold, most_freq_bin)) { + default_indices = lte_indices; + default_count = <e_count; + } + for (data_size_t i = 0; i < cnt; ++i) { + const data_size_t idx = data_indices[i]; + const uint32_t bin = iterator.RawGet(idx); + if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) { + default_indices[(*default_count)++] = idx; + } else if (!USE_MIN_BIN && bin == 0) { + default_indices[(*default_count)++] = idx; + } else if (Common::FindInBitset(threshold, num_threshold, + bin - min_bin + offset)) { + lte_indices[lte_count++] = idx; + } else { + gt_indices[gt_count++] = idx; + } + } + return lte_count; + } + + data_size_t SplitCategorical(uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin, + const uint32_t* threshold, int num_threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { + return SplitCategoricalInner(min_bin, max_bin, most_freq_bin, + threshold, num_threshold, data_indices, + cnt, lte_indices, gt_indices); + } + + data_size_t SplitCategorical(uint32_t max_bin, uint32_t most_freq_bin, + const uint32_t* threshold, int num_threshold, + const data_size_t* data_indices, data_size_t cnt, + data_size_t* lte_indices, + data_size_t* gt_indices) const override { + return SplitCategoricalInner(1, max_bin, most_freq_bin, threshold, + num_threshold, data_indices, cnt, + lte_indices, gt_indices); + } + + data_size_t num_data() const override { return num_data_; } + + void* get_data() override { return nullptr; } + + void FinishLoad() override { + // get total non zero size + size_t pair_cnt = 0; + for (size_t i = 0; i < push_buffers_.size(); ++i) { + pair_cnt += push_buffers_[i].size(); + } + std::vector>& idx_val_pairs = + push_buffers_[0]; + idx_val_pairs.reserve(pair_cnt); + + for (size_t i = 1; i < push_buffers_.size(); ++i) { + idx_val_pairs.insert(idx_val_pairs.end(), push_buffers_[i].begin(), + push_buffers_[i].end()); + push_buffers_[i].clear(); + push_buffers_[i].shrink_to_fit(); + } + // sort by data index + std::sort(idx_val_pairs.begin(), idx_val_pairs.end(), + [](const std::pair& a, + const std::pair& b) { + return a.first < b.first; + }); + // load delta array + LoadFromPair(idx_val_pairs); + } + + void LoadFromPair( + const std::vector>& idx_val_pairs) { + deltas_.clear(); + vals_.clear(); + deltas_.reserve(idx_val_pairs.size()); + vals_.reserve(idx_val_pairs.size()); + // transform to delta array + data_size_t last_idx = 0; + for (size_t i = 0; i < idx_val_pairs.size(); ++i) { + const data_size_t cur_idx = idx_val_pairs[i].first; + const VAL_T bin = idx_val_pairs[i].second; + data_size_t cur_delta = cur_idx - last_idx; + // disallow the multi-val in one row + if (i > 0 && cur_delta == 0) { + continue; + } + while (cur_delta >= 256) { + deltas_.push_back(255); + vals_.push_back(0); + cur_delta -= 255; + } + deltas_.push_back(static_cast(cur_delta)); + vals_.push_back(bin); + last_idx = cur_idx; + } + // avoid out of range + deltas_.push_back(0); + num_vals_ = static_cast(vals_.size()); + + // reduce memory cost + deltas_.shrink_to_fit(); + vals_.shrink_to_fit(); + + // generate fast index + GetFastIndex(); + } + + void GetFastIndex() { + fast_index_.clear(); + // get shift cnt + data_size_t mod_size = (num_data_ + kNumFastIndex - 1) / kNumFastIndex; + data_size_t pow2_mod_size = 1; + fast_index_shift_ = 0; + while (pow2_mod_size < mod_size) { + pow2_mod_size <<= 1; + ++fast_index_shift_; + } + // build fast index + data_size_t i_delta = -1; + data_size_t cur_pos = 0; + data_size_t next_threshold = 0; + while (NextNonzero(&i_delta, &cur_pos)) { + while (next_threshold <= cur_pos) { + fast_index_.emplace_back(i_delta, cur_pos); + next_threshold += pow2_mod_size; + } + } + // avoid out of range + while (next_threshold < num_data_) { + fast_index_.emplace_back(num_vals_ - 1, cur_pos); + next_threshold += pow2_mod_size; + } + fast_index_.shrink_to_fit(); + } + + void SaveBinaryToFile(BinaryWriter* writer) const override { + writer->AlignedWrite(&num_vals_, sizeof(num_vals_)); + writer->AlignedWrite(deltas_.data(), sizeof(uint8_t) * (num_vals_ + 1)); + writer->AlignedWrite(vals_.data(), sizeof(VAL_T) * num_vals_); + } + + size_t SizesInByte() const override { + return VirtualFileWriter::AlignedSize(sizeof(num_vals_)) + + VirtualFileWriter::AlignedSize(sizeof(uint8_t) * (num_vals_ + 1)) + + VirtualFileWriter::AlignedSize(sizeof(VAL_T) * num_vals_); + } + + void LoadFromMemory( + const void* memory, + const std::vector& local_used_indices) override { + const char* mem_ptr = reinterpret_cast(memory); + data_size_t tmp_num_vals = *(reinterpret_cast(mem_ptr)); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(tmp_num_vals)); + const uint8_t* tmp_delta = reinterpret_cast(mem_ptr); + mem_ptr += VirtualFileWriter::AlignedSize(sizeof(uint8_t) * (tmp_num_vals + 1)); + const VAL_T* tmp_vals = reinterpret_cast(mem_ptr); + + deltas_.clear(); + vals_.clear(); + num_vals_ = tmp_num_vals; + for (data_size_t i = 0; i < num_vals_; ++i) { + deltas_.push_back(tmp_delta[i]); + vals_.push_back(tmp_vals[i]); + } + deltas_.push_back(0); + // reduce memory cost + deltas_.shrink_to_fit(); + vals_.shrink_to_fit(); + + if (local_used_indices.empty()) { + // generate fast index + GetFastIndex(); + } else { + std::vector> tmp_pair; + data_size_t cur_pos = 0; + data_size_t j = -1; + for (data_size_t i = 0; + i < static_cast(local_used_indices.size()); ++i) { + const data_size_t idx = local_used_indices[i]; + while (cur_pos < idx && j < num_vals_) { + NextNonzero(&j, &cur_pos); + } + if (cur_pos == idx && j < num_vals_ && vals_[j] > 0) { + // new row index is i + tmp_pair.emplace_back(i, vals_[j]); + } + } + LoadFromPair(tmp_pair); + } + } + + void CopySubrow(const Bin* full_bin, const data_size_t* used_indices, + data_size_t num_used_indices) override { + auto other_bin = dynamic_cast*>(full_bin); + deltas_.clear(); + vals_.clear(); + data_size_t start = 0; + if (num_used_indices > 0) { + start = used_indices[0]; + } + SparseBinIterator iterator(other_bin, start); + // transform to delta array + data_size_t last_idx = 0; + for (data_size_t i = 0; i < num_used_indices; ++i) { + auto bin = iterator.InnerRawGet(used_indices[i]); + if (bin > 0) { + data_size_t cur_delta = i - last_idx; + while (cur_delta >= 256) { + deltas_.push_back(255); + vals_.push_back(0); + cur_delta -= 255; + } + deltas_.push_back(static_cast(cur_delta)); + vals_.push_back(bin); + last_idx = i; + } + } + // avoid out of range + deltas_.push_back(0); + num_vals_ = static_cast(vals_.size()); + + // reduce memory cost + deltas_.shrink_to_fit(); + vals_.shrink_to_fit(); + + // generate fast index + GetFastIndex(); + } + + SparseBin* Clone() override; + + SparseBin(const SparseBin& other) + : num_data_(other.num_data_), + deltas_(other.deltas_), + vals_(other.vals_), + num_vals_(other.num_vals_), + push_buffers_(other.push_buffers_), + fast_index_(other.fast_index_), + fast_index_shift_(other.fast_index_shift_) {} + + void InitIndex(data_size_t start_idx, data_size_t* i_delta, + data_size_t* cur_pos) const { + auto idx = start_idx >> fast_index_shift_; + if (static_cast(idx) < fast_index_.size()) { + const auto fast_pair = fast_index_[start_idx >> fast_index_shift_]; + *i_delta = fast_pair.first; + *cur_pos = fast_pair.second; + } else { + *i_delta = -1; + *cur_pos = 0; + } + } + + const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const override; + + const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const override; + + private: + data_size_t num_data_; + std::vector> + deltas_; + std::vector> vals_; + data_size_t num_vals_; + std::vector>> push_buffers_; + std::vector> fast_index_; + data_size_t fast_index_shift_; +}; + +template +SparseBin* SparseBin::Clone() { + return new SparseBin(*this); +} + +template +inline uint32_t SparseBinIterator::RawGet(data_size_t idx) { + return InnerRawGet(idx); +} + +template +inline VAL_T SparseBinIterator::InnerRawGet(data_size_t idx) { + while (cur_pos_ < idx) { + bin_data_->NextNonzeroFast(&i_delta_, &cur_pos_); + } + if (cur_pos_ == idx) { + return bin_data_->vals_[i_delta_]; + } else { + return 0; + } +} + +template +inline void SparseBinIterator::Reset(data_size_t start_idx) { + bin_data_->InitIndex(start_idx, &i_delta_, &cur_pos_); +} + +template +BinIterator* SparseBin::GetIterator(uint32_t min_bin, uint32_t max_bin, + uint32_t most_freq_bin) const { + return new SparseBinIterator(this, min_bin, max_bin, most_freq_bin); +} + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_ diff --git a/src/io/train_share_states.cpp b/src/io/train_share_states.cpp new file mode 100644 index 0000000..842f8ac --- /dev/null +++ b/src/io/train_share_states.cpp @@ -0,0 +1,540 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#include + +#include +#include +#include + +namespace LightGBM { + +MultiValBinWrapper::MultiValBinWrapper(MultiValBin* bin, data_size_t num_data, + const std::vector& feature_groups_contained, const int num_grad_quant_bins): + feature_groups_contained_(feature_groups_contained) { + num_threads_ = OMP_NUM_THREADS(); + num_data_ = num_data; + multi_val_bin_.reset(bin); + if (bin == nullptr) { + return; + } + num_bin_ = bin->num_bin(); + num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize; + num_grad_quant_bins_ = num_grad_quant_bins; +} + +void MultiValBinWrapper::InitTrain(const std::vector& group_feature_start, + const std::vector>& feature_groups, + const std::vector& is_feature_used, + const data_size_t* bagging_use_indices, + data_size_t bagging_indices_cnt) { + is_use_subcol_ = false; + if (multi_val_bin_ == nullptr) { + return; + } + CopyMultiValBinSubset(group_feature_start, feature_groups, + is_feature_used, bagging_use_indices, bagging_indices_cnt); + const auto cur_multi_val_bin = (is_use_subcol_ || is_use_subrow_) + ? multi_val_bin_subset_.get() + : multi_val_bin_.get(); + if (cur_multi_val_bin != nullptr) { + num_bin_ = cur_multi_val_bin->num_bin(); + num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize; + auto num_element_per_row = cur_multi_val_bin->num_element_per_row(); + min_block_size_ = std::min(static_cast(0.3f * num_bin_ / + (num_element_per_row + kZeroThreshold)) + 1, 1024); + min_block_size_ = std::max(min_block_size_, 32); + } +} + +template +void MultiValBinWrapper::HistMove(const std::vector>& hist_buf) { + if (!is_use_subcol_ && INNER_HIST_BITS != 8) { + return; + } + if (USE_QUANT_GRAD) { + if (HIST_BITS == 32) { + const int64_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2 - + static_cast(num_bin_aligned_); + #pragma omp parallel for schedule(static) num_threads(num_threads_) + for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) { + std::copy_n(src + hist_move_src_[i] / 2, hist_move_size_[i] / 2, + reinterpret_cast(origin_hist_data_) + hist_move_dest_[i] / 2); + } + } else if (HIST_BITS == 16) { + if (is_use_subcol_) { + const int32_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2 - + static_cast(num_bin_aligned_); + #pragma omp parallel for schedule(static) num_threads(num_threads_) + for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) { + std::copy_n(src + hist_move_src_[i] / 2, hist_move_size_[i] / 2, + reinterpret_cast(origin_hist_data_) + hist_move_dest_[i] / 2); + } + } else { + CHECK_EQ(INNER_HIST_BITS, 8); + const int32_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2; + int32_t* orig_ptr = reinterpret_cast(origin_hist_data_); + #pragma omp parallel for schedule(static) num_threads(num_threads_) + for (int i = 0; i < num_bin_; ++i) { + orig_ptr[i] = src[i]; + } + } + } + } else { + const hist_t* src = hist_buf.data() + hist_buf.size() - + 2 * static_cast(num_bin_aligned_); + #pragma omp parallel for schedule(static) num_threads(num_threads_) + for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) { + std::copy_n(src + hist_move_src_[i], hist_move_size_[i], + origin_hist_data_ + hist_move_dest_[i]); + } + } +} + +template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); + +template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); + +template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); + +template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); + +template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); + +template void MultiValBinWrapper::HistMove(const std::vector>& hist_buf); + +template +void MultiValBinWrapper::HistMerge(std::vector>* hist_buf) { + int n_bin_block = 1; + int bin_block_size = num_bin_; + Threading::BlockInfo(num_threads_, num_bin_, 512, &n_bin_block, + &bin_block_size); + if (USE_QUANT_GRAD) { + if (HIST_BITS == 32) { + int64_t* dst = reinterpret_cast(origin_hist_data_); + if (is_use_subcol_) { + dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2 - static_cast(num_bin_aligned_); + } + #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) + for (int t = 0; t < n_bin_block; ++t) { + const int start = t * bin_block_size; + const int end = std::min(start + bin_block_size, num_bin_); + for (int tid = 1; tid < n_data_block_; ++tid) { + auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * (tid - 1); + for (int i = start; i < end; ++i) { + dst[i] += src_ptr[i]; + } + } + } + } else if (HIST_BITS == 16 && INNER_HIST_BITS == 16) { + int32_t* dst = reinterpret_cast(origin_hist_data_); + if (is_use_subcol_) { + dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2 - static_cast(num_bin_aligned_); + } + #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) + for (int t = 0; t < n_bin_block; ++t) { + const int start = t * bin_block_size; + const int end = std::min(start + bin_block_size, num_bin_); + for (int tid = 1; tid < n_data_block_; ++tid) { + auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * (tid - 1); + for (int i = start; i < end; ++i) { + dst[i] += src_ptr[i]; + } + } + } + } else if (HIST_BITS == 16 && INNER_HIST_BITS == 8) { + int32_t* dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2; + std::memset(reinterpret_cast(dst), 0, num_bin_ * kInt16HistBufferEntrySize); + #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) + for (int t = 0; t < n_bin_block; ++t) { + const int start = t * bin_block_size; + const int end = std::min(start + bin_block_size, num_bin_); + for (int tid = 0; tid < n_data_block_; ++tid) { + auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * tid; + for (int i = start; i < end; ++i) { + const int16_t packed_hist = src_ptr[i]; + const int32_t packed_hist_int32 = (static_cast(static_cast(packed_hist >> 8)) << 16) | static_cast(packed_hist & 0x00ff); + dst[i] += packed_hist_int32; + } + } + } + } + } else { + hist_t* dst = origin_hist_data_; + if (is_use_subcol_) { + dst = hist_buf->data() + hist_buf->size() - 2 * static_cast(num_bin_aligned_); + } + #pragma omp parallel for schedule(static, 1) num_threads(num_threads_) + for (int t = 0; t < n_bin_block; ++t) { + const int start = t * bin_block_size; + const int end = std::min(start + bin_block_size, num_bin_); + for (int tid = 1; tid < n_data_block_; ++tid) { + auto src_ptr = hist_buf->data() + static_cast(num_bin_aligned_) * 2 * (tid - 1); + for (int i = start * 2; i < end * 2; ++i) { + dst[i] += src_ptr[i]; + } + } + } + } +} + +template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); + +template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); + +template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); + +template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); + +template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); + +template void MultiValBinWrapper::HistMerge(std::vector>* hist_buf); + +void MultiValBinWrapper::ResizeHistBuf(std::vector>* hist_buf, + MultiValBin* sub_multi_val_bin, + hist_t* origin_hist_data) { + num_bin_ = sub_multi_val_bin->num_bin(); + num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize; + origin_hist_data_ = origin_hist_data; + size_t new_buf_size = static_cast(n_data_block_) * static_cast(num_bin_aligned_) * 2; + if (hist_buf->size() < new_buf_size) { + hist_buf->resize(new_buf_size); + } +} + +void MultiValBinWrapper::CopyMultiValBinSubset( + const std::vector& group_feature_start, + const std::vector>& feature_groups, + const std::vector& is_feature_used, + const data_size_t* bagging_use_indices, + data_size_t bagging_indices_cnt) { + double sum_used_dense_ratio = 0.0; + double sum_dense_ratio = 0.0; + int num_used = 0; + int total = 0; + std::vector used_feature_index; + for (int i : feature_groups_contained_) { + int f_start = group_feature_start[i]; + if (feature_groups[i]->is_multi_val_) { + for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { + const auto dense_rate = + 1.0 - feature_groups[i]->bin_mappers_[j]->sparse_rate(); + if (is_feature_used[f_start + j]) { + ++num_used; + used_feature_index.push_back(total); + sum_used_dense_ratio += dense_rate; + } + sum_dense_ratio += dense_rate; + ++total; + } + } else { + bool is_group_used = false; + double dense_rate = 0; + for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { + if (is_feature_used[f_start + j]) { + is_group_used = true; + } + dense_rate += 1.0 - feature_groups[i]->bin_mappers_[j]->sparse_rate(); + } + if (is_group_used) { + ++num_used; + used_feature_index.push_back(total); + sum_used_dense_ratio += dense_rate; + } + sum_dense_ratio += dense_rate; + ++total; + } + } + const double k_subfeature_threshold = 0.6; + if (sum_used_dense_ratio >= sum_dense_ratio * k_subfeature_threshold) { + // only need to copy subset + if (is_use_subrow_ && !is_subrow_copied_) { + if (multi_val_bin_subset_ == nullptr) { + multi_val_bin_subset_.reset(multi_val_bin_->CreateLike( + bagging_indices_cnt, multi_val_bin_->num_bin(), total, + multi_val_bin_->num_element_per_row(), multi_val_bin_->offsets())); + } else { + multi_val_bin_subset_->ReSize( + bagging_indices_cnt, multi_val_bin_->num_bin(), total, + multi_val_bin_->num_element_per_row(), multi_val_bin_->offsets()); + } + multi_val_bin_subset_->CopySubrow( + multi_val_bin_.get(), bagging_use_indices, + bagging_indices_cnt); + // avoid to copy subset many times + is_subrow_copied_ = true; + } + } else { + is_use_subcol_ = true; + std::vector upper_bound; + std::vector lower_bound; + std::vector delta; + std::vector offsets; + hist_move_src_.clear(); + hist_move_dest_.clear(); + hist_move_size_.clear(); + + const int offset = multi_val_bin_->IsSparse() ? 1 : 0; + int num_total_bin = offset; + int new_num_total_bin = offset; + offsets.push_back(static_cast(new_num_total_bin)); + for (int i : feature_groups_contained_) { + int f_start = group_feature_start[i]; + if (feature_groups[i]->is_multi_val_) { + for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { + const auto& bin_mapper = feature_groups[i]->bin_mappers_[j]; + if (i == 0 && j == 0 && bin_mapper->GetMostFreqBin() > 0) { + num_total_bin = 1; + } + int cur_num_bin = bin_mapper->num_bin(); + if (bin_mapper->GetMostFreqBin() == 0) { + cur_num_bin -= offset; + } + num_total_bin += cur_num_bin; + if (is_feature_used[f_start + j]) { + new_num_total_bin += cur_num_bin; + offsets.push_back(static_cast(new_num_total_bin)); + lower_bound.push_back(num_total_bin - cur_num_bin); + upper_bound.push_back(num_total_bin); + + hist_move_src_.push_back( + (new_num_total_bin - cur_num_bin) * 2); + hist_move_dest_.push_back((num_total_bin - cur_num_bin) * + 2); + hist_move_size_.push_back(cur_num_bin * 2); + delta.push_back(num_total_bin - new_num_total_bin); + } + } + } else { + bool is_group_used = false; + for (int j = 0; j < feature_groups[i]->num_feature_; ++j) { + if (is_feature_used[f_start + j]) { + is_group_used = true; + break; + } + } + int cur_num_bin = feature_groups[i]->bin_offsets_.back() - offset; + num_total_bin += cur_num_bin; + if (is_group_used) { + new_num_total_bin += cur_num_bin; + offsets.push_back(static_cast(new_num_total_bin)); + lower_bound.push_back(num_total_bin - cur_num_bin); + upper_bound.push_back(num_total_bin); + + hist_move_src_.push_back( + (new_num_total_bin - cur_num_bin) * 2); + hist_move_dest_.push_back((num_total_bin - cur_num_bin) * + 2); + hist_move_size_.push_back(cur_num_bin * 2); + delta.push_back(num_total_bin - new_num_total_bin); + } + } + } + // avoid out of range + lower_bound.push_back(num_total_bin); + upper_bound.push_back(num_total_bin); + data_size_t num_data = is_use_subrow_ ? bagging_indices_cnt : num_data_; + if (multi_val_bin_subset_ == nullptr) { + multi_val_bin_subset_.reset(multi_val_bin_->CreateLike( + num_data, new_num_total_bin, num_used, sum_used_dense_ratio, offsets)); + } else { + multi_val_bin_subset_->ReSize(num_data, new_num_total_bin, + num_used, sum_used_dense_ratio, offsets); + } + if (is_use_subrow_) { + multi_val_bin_subset_->CopySubrowAndSubcol( + multi_val_bin_.get(), bagging_use_indices, + bagging_indices_cnt, used_feature_index, lower_bound, + upper_bound, delta); + // may need to recopy subset + is_subrow_copied_ = false; + } else { + multi_val_bin_subset_->CopySubcol( + multi_val_bin_.get(), used_feature_index, lower_bound, upper_bound, delta); + } + } +} + +void TrainingShareStates::CalcBinOffsets(const std::vector>& feature_groups, + std::vector* offsets, bool in_is_col_wise) { + offsets->clear(); + feature_hist_offsets_.clear(); + if (in_is_col_wise) { + uint32_t cur_num_bin = 0; + uint32_t hist_cur_num_bin = 0; + for (int group = 0; group < static_cast(feature_groups.size()); ++group) { + const std::unique_ptr& feature_group = feature_groups[group]; + if (feature_group->is_multi_val_) { + if (feature_group->is_dense_multi_val_) { + for (int i = 0; i < feature_group->num_feature_; ++i) { + const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; + if (group == 0 && i == 0 && bin_mapper->GetMostFreqBin() > 0) { + cur_num_bin += 1; + hist_cur_num_bin += 1; + } + offsets->push_back(cur_num_bin); + feature_hist_offsets_.push_back(hist_cur_num_bin); + int num_bin = bin_mapper->num_bin(); + hist_cur_num_bin += num_bin; + if (bin_mapper->GetMostFreqBin() == 0) { + feature_hist_offsets_.back() += 1; + } + cur_num_bin += num_bin; + } + offsets->push_back(cur_num_bin); + CHECK(cur_num_bin == feature_group->bin_offsets_.back()); + } else { + cur_num_bin += 1; + hist_cur_num_bin += 1; + for (int i = 0; i < feature_group->num_feature_; ++i) { + offsets->push_back(cur_num_bin); + feature_hist_offsets_.push_back(hist_cur_num_bin); + const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; + int num_bin = bin_mapper->num_bin(); + if (bin_mapper->GetMostFreqBin() == 0) { + num_bin -= 1; + } + hist_cur_num_bin += num_bin; + cur_num_bin += num_bin; + } + offsets->push_back(cur_num_bin); + CHECK(cur_num_bin == feature_group->bin_offsets_.back()); + } + } else { + for (int i = 0; i < feature_group->num_feature_; ++i) { + feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i]); + } + hist_cur_num_bin += feature_group->bin_offsets_.back(); + } + } + feature_hist_offsets_.push_back(hist_cur_num_bin); + num_hist_total_bin_ = static_cast(feature_hist_offsets_.back()); + } else { + double sum_dense_ratio = 0.0f; + int ncol = 0; + for (int gid = 0; gid < static_cast(feature_groups.size()); ++gid) { + if (feature_groups[gid]->is_multi_val_) { + ncol += feature_groups[gid]->num_feature_; + } else { + ++ncol; + } + for (int fid = 0; fid < feature_groups[gid]->num_feature_; ++fid) { + const auto& bin_mapper = feature_groups[gid]->bin_mappers_[fid]; + sum_dense_ratio += 1.0f - bin_mapper->sparse_rate(); + } + } + sum_dense_ratio /= ncol; + const bool is_sparse_row_wise = (1.0f - sum_dense_ratio) >= + MultiValBin::multi_val_bin_sparse_threshold ? 1 : 0; + if (is_sparse_row_wise) { + int cur_num_bin = 1; + uint32_t hist_cur_num_bin = 1; + for (int group = 0; group < static_cast(feature_groups.size()); ++group) { + const std::unique_ptr& feature_group = feature_groups[group]; + if (feature_group->is_multi_val_) { + for (int i = 0; i < feature_group->num_feature_; ++i) { + offsets->push_back(cur_num_bin); + feature_hist_offsets_.push_back(hist_cur_num_bin); + const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; + int num_bin = bin_mapper->num_bin(); + if (bin_mapper->GetMostFreqBin() == 0) { + num_bin -= 1; + } + cur_num_bin += num_bin; + hist_cur_num_bin += num_bin; + } + } else { + offsets->push_back(cur_num_bin); + cur_num_bin += feature_group->bin_offsets_.back() - 1; + for (int i = 0; i < feature_group->num_feature_; ++i) { + feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i] - 1); + } + hist_cur_num_bin += feature_group->bin_offsets_.back() - 1; + } + } + offsets->push_back(cur_num_bin); + feature_hist_offsets_.push_back(hist_cur_num_bin); + } else { + int cur_num_bin = 0; + uint32_t hist_cur_num_bin = 0; + for (int group = 0; group < static_cast(feature_groups.size()); ++group) { + const std::unique_ptr& feature_group = feature_groups[group]; + if (feature_group->is_multi_val_) { + for (int i = 0; i < feature_group->num_feature_; ++i) { + const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i]; + if (group == 0 && i == 0 && bin_mapper->GetMostFreqBin() > 0) { + cur_num_bin += 1; + hist_cur_num_bin += 1; + } + offsets->push_back(cur_num_bin); + feature_hist_offsets_.push_back(hist_cur_num_bin); + int num_bin = bin_mapper->num_bin(); + cur_num_bin += num_bin; + hist_cur_num_bin += num_bin; + if (bin_mapper->GetMostFreqBin() == 0) { + feature_hist_offsets_.back() += 1; + } + } + } else { + offsets->push_back(cur_num_bin); + cur_num_bin += feature_group->bin_offsets_.back(); + for (int i = 0; i < feature_group->num_feature_; ++i) { + feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i]); + } + hist_cur_num_bin += feature_group->bin_offsets_.back(); + } + } + offsets->push_back(cur_num_bin); + feature_hist_offsets_.push_back(hist_cur_num_bin); + } + num_hist_total_bin_ = static_cast(feature_hist_offsets_.back()); + } + #ifdef USE_CUDA + column_hist_offsets_ = *offsets; + #endif // USE_CUDA +} + +void TrainingShareStates::SetMultiValBin(MultiValBin* bin, data_size_t num_data, + const std::vector>& feature_groups, + bool dense_only, bool sparse_only, const int num_grad_quant_bins) { + num_threads = OMP_NUM_THREADS(); + if (bin == nullptr) { + return; + } + std::vector feature_groups_contained; + for (int group = 0; group < static_cast(feature_groups.size()); ++group) { + const auto& feature_group = feature_groups[group]; + if (feature_group->is_multi_val_) { + if (!dense_only) { + feature_groups_contained.push_back(group); + } + } else if (!sparse_only) { + feature_groups_contained.push_back(group); + } + } + num_total_bin_ += bin->num_bin(); + num_elements_per_row_ += bin->num_element_per_row(); + multi_val_bin_wrapper_.reset(new MultiValBinWrapper( + bin, num_data, feature_groups_contained, num_grad_quant_bins)); +} + +} // namespace LightGBM diff --git a/src/io/tree.cpp b/src/io/tree.cpp new file mode 100644 index 0000000..86d02a1 --- /dev/null +++ b/src/io/tree.cpp @@ -0,0 +1,1060 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +Tree::Tree(int max_leaves, bool track_branch_features, bool is_linear) + :max_leaves_(max_leaves), track_branch_features_(track_branch_features) { + left_child_.resize(max_leaves_ - 1); + right_child_.resize(max_leaves_ - 1); + split_feature_inner_.resize(max_leaves_ - 1); + split_feature_.resize(max_leaves_ - 1); + threshold_in_bin_.resize(max_leaves_ - 1); + threshold_.resize(max_leaves_ - 1); + decision_type_.resize(max_leaves_ - 1, 0); + split_gain_.resize(max_leaves_ - 1); + leaf_parent_.resize(max_leaves_); + leaf_value_.resize(max_leaves_); + leaf_weight_.resize(max_leaves_); + leaf_count_.resize(max_leaves_); + internal_value_.resize(max_leaves_ - 1); + internal_weight_.resize(max_leaves_ - 1); + internal_count_.resize(max_leaves_ - 1); + leaf_depth_.resize(max_leaves_); + if (track_branch_features_) { + branch_features_ = std::vector>(max_leaves_); + } + // root is in the depth 0 + leaf_depth_[0] = 0; + num_leaves_ = 1; + leaf_value_[0] = 0.0f; + leaf_weight_[0] = 0.0f; + leaf_parent_[0] = -1; + shrinkage_ = 1.0f; + num_cat_ = 0; + cat_boundaries_.push_back(0); + cat_boundaries_inner_.push_back(0); + max_depth_ = -1; + is_linear_ = is_linear; + if (is_linear_) { + leaf_coeff_.resize(max_leaves_); + leaf_const_ = std::vector(max_leaves_, 0); + leaf_features_.resize(max_leaves_); + leaf_features_inner_.resize(max_leaves_); + } + #ifdef USE_CUDA + is_cuda_tree_ = false; + #endif // USE_CUDA +} + +int Tree::Split(int leaf, int feature, int real_feature, uint32_t threshold_bin, + double threshold_double, double left_value, double right_value, + int left_cnt, int right_cnt, double left_weight, double right_weight, float gain, + MissingType missing_type, bool default_left) { + Split(leaf, feature, real_feature, left_value, right_value, left_cnt, right_cnt, left_weight, right_weight, gain); + int new_node_idx = num_leaves_ - 1; + decision_type_[new_node_idx] = 0; + SetDecisionType(&decision_type_[new_node_idx], false, kCategoricalMask); + SetDecisionType(&decision_type_[new_node_idx], default_left, kDefaultLeftMask); + SetMissingType(&decision_type_[new_node_idx], static_cast(missing_type)); + threshold_in_bin_[new_node_idx] = threshold_bin; + threshold_[new_node_idx] = threshold_double; + ++num_leaves_; + return num_leaves_ - 1; +} + +int Tree::SplitCategorical(int leaf, int feature, int real_feature, const uint32_t* threshold_bin, int num_threshold_bin, + const uint32_t* threshold, int num_threshold, double left_value, double right_value, + data_size_t left_cnt, data_size_t right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type) { + Split(leaf, feature, real_feature, left_value, right_value, left_cnt, right_cnt, left_weight, right_weight, gain); + int new_node_idx = num_leaves_ - 1; + decision_type_[new_node_idx] = 0; + SetDecisionType(&decision_type_[new_node_idx], true, kCategoricalMask); + SetMissingType(&decision_type_[new_node_idx], static_cast(missing_type)); + threshold_in_bin_[new_node_idx] = num_cat_; + threshold_[new_node_idx] = num_cat_; + ++num_cat_; + cat_boundaries_.push_back(cat_boundaries_.back() + num_threshold); + for (int i = 0; i < num_threshold; ++i) { + cat_threshold_.push_back(threshold[i]); + } + cat_boundaries_inner_.push_back(cat_boundaries_inner_.back() + num_threshold_bin); + for (int i = 0; i < num_threshold_bin; ++i) { + cat_threshold_inner_.push_back(threshold_bin[i]); + } + ++num_leaves_; + return num_leaves_ - 1; +} + +#define PredictionFun(niter, fidx_in_iter, start_pos, decision_fun, iter_idx, \ + data_idx) \ + std::vector> iter((niter)); \ + for (int i = 0; i < (niter); ++i) { \ + iter[i].reset(data->FeatureIterator((fidx_in_iter))); \ + iter[i]->Reset((start_pos)); \ + } \ + for (data_size_t i = start; i < end; ++i) { \ + int node = 0; \ + while (node >= 0) { \ + node = decision_fun(iter[(iter_idx)]->Get((data_idx)), node, \ + default_bins[node], max_bins[node]); \ + } \ + score[(data_idx)] += static_cast(leaf_value_[~node]); \ + }\ + + +#define PredictionFunLinear(niter, fidx_in_iter, start_pos, decision_fun, \ + iter_idx, data_idx) \ + std::vector> iter((niter)); \ + for (int i = 0; i < (niter); ++i) { \ + iter[i].reset(data->FeatureIterator((fidx_in_iter))); \ + iter[i]->Reset((start_pos)); \ + } \ + for (data_size_t i = start; i < end; ++i) { \ + int node = 0; \ + if (num_leaves_ > 1) { \ + while (node >= 0) { \ + node = decision_fun(iter[(iter_idx)]->Get((data_idx)), node, \ + default_bins[node], max_bins[node]); \ + } \ + node = ~node; \ + } \ + double add_score = leaf_const_[node]; \ + bool nan_found = false; \ + const double* coeff_ptr = leaf_coeff_[node].data(); \ + const float** data_ptr = feat_ptr[node].data(); \ + for (size_t j = 0; j < leaf_features_inner_[node].size(); ++j) { \ + float feat_val = data_ptr[j][(data_idx)]; \ + if (std::isnan(feat_val)) { \ + nan_found = true; \ + break; \ + } \ + add_score += coeff_ptr[j] * feat_val; \ + } \ + if (nan_found) { \ + score[(data_idx)] += leaf_value_[node]; \ + } else { \ + score[(data_idx)] += add_score; \ + } \ +}\ + + +void Tree::AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const { + if (!is_linear_ && num_leaves_ <= 1) { + if (leaf_value_[0] != 0.0f) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) + for (data_size_t i = 0; i < num_data; ++i) { + score[i] += leaf_value_[0]; + } + } + return; + } + std::vector default_bins(num_leaves_ - 1); + std::vector max_bins(num_leaves_ - 1); + for (int i = 0; i < num_leaves_ - 1; ++i) { + const int fidx = split_feature_inner_[i]; + auto bin_mapper = data->FeatureBinMapper(fidx); + default_bins[i] = bin_mapper->GetDefaultBin(); + max_bins[i] = bin_mapper->num_bin() - 1; + } + if (is_linear_) { + std::vector> feat_ptr(num_leaves_); + for (int leaf_num = 0; leaf_num < num_leaves_; ++leaf_num) { + for (int feat : leaf_features_inner_[leaf_num]) { + feat_ptr[leaf_num].push_back(data->raw_index(feat)); + } + } + if (num_cat_ > 0) { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], start, DecisionInner, node, i); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(data->num_features(), i, start, DecisionInner, split_feature_inner_[node], i); + }); + } + } else { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], start, NumericalDecisionInner, node, i); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(data->num_features(), i, start, NumericalDecisionInner, split_feature_inner_[node], i); + }); + } + } + } else { + if (num_cat_ > 0) { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(num_leaves_ - 1, split_feature_inner_[i], start, DecisionInner, node, i); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(data->num_features(), i, start, DecisionInner, split_feature_inner_[node], i); + }); + } + } else { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(num_leaves_ - 1, split_feature_inner_[i], start, NumericalDecisionInner, node, i); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(data->num_features(), i, start, NumericalDecisionInner, split_feature_inner_[node], i); + }); + } + } + } +} + +void Tree::AddPredictionToScore(const Dataset* data, + const data_size_t* used_data_indices, + data_size_t num_data, double* score) const { + if (!is_linear_ && num_leaves_ <= 1) { + if (leaf_value_[0] != 0.0f) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024) + for (data_size_t i = 0; i < num_data; ++i) { + score[used_data_indices[i]] += leaf_value_[0]; + } + } + return; + } + std::vector default_bins(num_leaves_ - 1); + std::vector max_bins(num_leaves_ - 1); + for (int i = 0; i < num_leaves_ - 1; ++i) { + const int fidx = split_feature_inner_[i]; + auto bin_mapper = data->FeatureBinMapper(fidx); + default_bins[i] = bin_mapper->GetDefaultBin(); + max_bins[i] = bin_mapper->num_bin() - 1; + } + if (is_linear_) { + std::vector> feat_ptr(num_leaves_); + for (int leaf_num = 0; leaf_num < num_leaves_; ++leaf_num) { + for (int feat : leaf_features_inner_[leaf_num]) { + feat_ptr[leaf_num].push_back(data->raw_index(feat)); + } + } + if (num_cat_ > 0) { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], DecisionInner, + node, used_data_indices[i]); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(data->num_features(), i, used_data_indices[start], DecisionInner, split_feature_inner_[node], used_data_indices[i]); + }); + } + } else { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], NumericalDecisionInner, + node, used_data_indices[i]); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr] + (int, data_size_t start, data_size_t end) { + PredictionFunLinear(data->num_features(), i, used_data_indices[start], NumericalDecisionInner, + split_feature_inner_[node], used_data_indices[i]); + }); + } + } + } else { + if (num_cat_ > 0) { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], DecisionInner, node, used_data_indices[i]); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(data->num_features(), i, used_data_indices[start], DecisionInner, split_feature_inner_[node], used_data_indices[i]); + }); + } + } else { + if (data->num_features() > num_leaves_ - 1) { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], NumericalDecisionInner, node, used_data_indices[i]); + }); + } else { + Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins] + (int, data_size_t start, data_size_t end) { + PredictionFun(data->num_features(), i, used_data_indices[start], NumericalDecisionInner, split_feature_inner_[node], used_data_indices[i]); + }); + } + } + } +} + +#undef PredictionFun +#undef PredictionFunLinear + +double Tree::GetUpperBoundValue() const { + double upper_bound = leaf_value_[0]; + for (int i = 1; i < num_leaves_; ++i) { + if (leaf_value_[i] > upper_bound) { + upper_bound = leaf_value_[i]; + } + } + return upper_bound; +} + +double Tree::GetLowerBoundValue() const { + double lower_bound = leaf_value_[0]; + for (int i = 1; i < num_leaves_; ++i) { + if (leaf_value_[i] < lower_bound) { + lower_bound = leaf_value_[i]; + } + } + return lower_bound; +} + +std::string Tree::ToString() const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + + using CommonC::ArrayToString; + + str_buf << "num_leaves=" << num_leaves_ << '\n'; + str_buf << "num_cat=" << num_cat_ << '\n'; + str_buf << "split_feature=" + << ArrayToString(split_feature_, num_leaves_ - 1) << '\n'; + str_buf << "split_gain=" + << ArrayToString(split_gain_, num_leaves_ - 1) << '\n'; + str_buf << "threshold=" + << ArrayToString(threshold_, num_leaves_ - 1) << '\n'; + str_buf << "decision_type=" + << ArrayToString(Common::ArrayCast(decision_type_), num_leaves_ - 1) << '\n'; + str_buf << "left_child=" + << ArrayToString(left_child_, num_leaves_ - 1) << '\n'; + str_buf << "right_child=" + << ArrayToString(right_child_, num_leaves_ - 1) << '\n'; + str_buf << "leaf_value=" + << ArrayToString(leaf_value_, num_leaves_) << '\n'; + str_buf << "leaf_weight=" + << ArrayToString(leaf_weight_, num_leaves_) << '\n'; + str_buf << "leaf_count=" + << ArrayToString(leaf_count_, num_leaves_) << '\n'; + str_buf << "internal_value=" + << ArrayToString(internal_value_, num_leaves_ - 1) << '\n'; + str_buf << "internal_weight=" + << ArrayToString(internal_weight_, num_leaves_ - 1) << '\n'; + str_buf << "internal_count=" + << ArrayToString(internal_count_, num_leaves_ - 1) << '\n'; + if (num_cat_ > 0) { + str_buf << "cat_boundaries=" + << ArrayToString(cat_boundaries_, num_cat_ + 1) << '\n'; + str_buf << "cat_threshold=" + << ArrayToString(cat_threshold_, cat_threshold_.size()) << '\n'; + } + str_buf << "is_linear=" << is_linear_ << '\n'; + + if (is_linear_) { + str_buf << "leaf_const=" + << ArrayToString(leaf_const_, num_leaves_) << '\n'; + std::vector num_feat(num_leaves_); + for (int i = 0; i < num_leaves_; ++i) { + num_feat[i] = static_cast(leaf_coeff_[i].size()); + } + str_buf << "num_features=" + << ArrayToString(num_feat, num_leaves_) << '\n'; + str_buf << "leaf_features="; + for (int i = 0; i < num_leaves_; ++i) { + if (num_feat[i] > 0) { + str_buf << ArrayToString(leaf_features_[i], leaf_features_[i].size()) << ' '; + } + str_buf << ' '; + } + str_buf << '\n'; + str_buf << "leaf_coeff="; + for (int i = 0; i < num_leaves_; ++i) { + if (num_feat[i] > 0) { + str_buf << ArrayToString(leaf_coeff_[i], leaf_coeff_[i].size()) << ' '; + } + str_buf << ' '; + } + str_buf << '\n'; + } + str_buf << "shrinkage=" << shrinkage_ << '\n'; + str_buf << '\n'; + + return str_buf.str(); +} + +std::string Tree::ToJSON() const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + str_buf << "\"num_leaves\":" << num_leaves_ << "," << '\n'; + str_buf << "\"num_cat\":" << num_cat_ << "," << '\n'; + str_buf << "\"shrinkage\":" << shrinkage_ << "," << '\n'; + if (num_leaves_ == 1) { + str_buf << "\"tree_structure\":{"; + str_buf << "\"leaf_value\":" << leaf_value_[0] << ", " << '\n'; + if (is_linear_) { + str_buf << "\"leaf_count\":" << leaf_count_[0] << ", " << '\n'; + str_buf << LinearModelToJSON(0); + } else { + str_buf << "\"leaf_count\":" << leaf_count_[0]; + } + str_buf << "}" << '\n'; + } else { + str_buf << "\"tree_structure\":" << NodeToJSON(0) << '\n'; + } + return str_buf.str(); +} + +std::string Tree::LinearModelToJSON(int index) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + str_buf << "\"leaf_const\":" << leaf_const_[index] << "," << "\n"; + int num_features = static_cast(leaf_features_[index].size()); + if (num_features > 0) { + str_buf << "\"leaf_features\":["; + for (int i = 0; i < num_features - 1; ++i) { + str_buf << leaf_features_[index][i] << ", "; + } + str_buf << leaf_features_[index][num_features - 1] << "]" << ", " << "\n"; + str_buf << "\"leaf_coeff\":["; + for (int i = 0; i < num_features - 1; ++i) { + str_buf << leaf_coeff_[index][i] << ", "; + } + str_buf << leaf_coeff_[index][num_features - 1] << "]" << "\n"; + } else { + str_buf << "\"leaf_features\":[],\n"; + str_buf << "\"leaf_coeff\":[]\n"; + } + return str_buf.str(); +} + +std::string Tree::NodeToJSON(int index) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + if (index >= 0) { + // non-leaf + str_buf << "{" << '\n'; + str_buf << "\"split_index\":" << index << "," << '\n'; + str_buf << "\"split_feature\":" << split_feature_[index] << "," << '\n'; + str_buf << "\"split_gain\":" << Common::AvoidInf(split_gain_[index]) << "," << '\n'; + if (GetDecisionType(decision_type_[index], kCategoricalMask)) { + int cat_idx = static_cast(threshold_[index]); + std::vector cats; + for (int i = cat_boundaries_[cat_idx]; i < cat_boundaries_[cat_idx + 1]; ++i) { + for (int j = 0; j < 32; ++j) { + int cat = (i - cat_boundaries_[cat_idx]) * 32 + j; + if (Common::FindInBitset(cat_threshold_.data() + cat_boundaries_[cat_idx], + cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx], cat)) { + cats.push_back(cat); + } + } + } + str_buf << "\"threshold\":\"" << CommonC::Join(cats, "||") << "\"," << '\n'; + str_buf << "\"decision_type\":\"==\"," << '\n'; + } else { + str_buf << "\"threshold\":" << Common::AvoidInf(threshold_[index]) << "," << '\n'; + str_buf << "\"decision_type\":\"<=\"," << '\n'; + } + if (GetDecisionType(decision_type_[index], kDefaultLeftMask)) { + str_buf << "\"default_left\":true," << '\n'; + } else { + str_buf << "\"default_left\":false," << '\n'; + } + uint8_t missing_type = GetMissingType(decision_type_[index]); + if (missing_type == MissingType::None) { + str_buf << "\"missing_type\":\"None\"," << '\n'; + } else if (missing_type == MissingType::Zero) { + str_buf << "\"missing_type\":\"Zero\"," << '\n'; + } else { + str_buf << "\"missing_type\":\"NaN\"," << '\n'; + } + str_buf << "\"internal_value\":" << internal_value_[index] << "," << '\n'; + str_buf << "\"internal_weight\":" << internal_weight_[index] << "," << '\n'; + str_buf << "\"internal_count\":" << internal_count_[index] << "," << '\n'; + str_buf << "\"left_child\":" << NodeToJSON(left_child_[index]) << "," << '\n'; + str_buf << "\"right_child\":" << NodeToJSON(right_child_[index]) << '\n'; + str_buf << "}"; + } else { + // leaf + index = ~index; + str_buf << "{" << '\n'; + str_buf << "\"leaf_index\":" << index << "," << '\n'; + str_buf << "\"leaf_value\":" << leaf_value_[index] << "," << '\n'; + str_buf << "\"leaf_weight\":" << leaf_weight_[index] << "," << '\n'; + if (is_linear_) { + str_buf << "\"leaf_count\":" << leaf_count_[index] << "," << '\n'; + str_buf << LinearModelToJSON(index); + } else { + str_buf << "\"leaf_count\":" << leaf_count_[index] << '\n'; + } + str_buf << "}"; + } + return str_buf.str(); +} + +std::string Tree::NumericalDecisionIfElse(int node) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + uint8_t missing_type = GetMissingType(decision_type_[node]); + bool default_left = GetDecisionType(decision_type_[node], kDefaultLeftMask); + if (missing_type != MissingType::NaN) { + str_buf << "if (std::isnan(fval)) fval = 0.0;"; + } + if (missing_type == MissingType::Zero) { + if (default_left) { + str_buf << "if (Tree::IsZero(fval)) {"; + } else { + str_buf << "if (!Tree::IsZero(fval)) {"; + } + } else if (missing_type == MissingType::NaN) { + if (default_left) { + str_buf << "if (std::isnan(fval)) {"; + } else { + str_buf << "if (!std::isnan(fval)) {"; + } + } else { + str_buf << "if (fval <= " << threshold_[node] << ") {"; + } + return str_buf.str(); +} + +std::string Tree::CategoricalDecisionIfElse(int node) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + int cat_idx = static_cast(threshold_[node]); + str_buf << "if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast(fval); }"; + str_buf << "if (int_fval >= 0 && int_fval < 32 * ("; + str_buf << cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx]; + str_buf << ") && (((cat_threshold[" << cat_boundaries_[cat_idx]; + str_buf << " + int_fval / 32] >> (int_fval & 31)) & 1))) {"; + return str_buf.str(); +} + +std::string Tree::ToIfElse(int index, bool predict_leaf_index) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + str_buf << "double PredictTree" << index; + if (predict_leaf_index) { + str_buf << "Leaf"; + } + str_buf << "(const double* arr) { "; + if (num_leaves_ <= 1) { + str_buf << "return " << leaf_value_[0] << ";"; + } else { + str_buf << "const std::vector cat_threshold = {"; + for (size_t i = 0; i < cat_threshold_.size(); ++i) { + if (i != 0) { + str_buf << ","; + } + str_buf << cat_threshold_[i]; + } + str_buf << "};"; + // use this for the missing value conversion + str_buf << "double fval = 0.0f; "; + if (num_cat_ > 0) { + str_buf << "int int_fval = 0; "; + } + str_buf << NodeToIfElse(0, predict_leaf_index); + } + str_buf << " }" << '\n'; + + // Predict func by Map to ifelse + str_buf << "double PredictTree" << index; + if (predict_leaf_index) { + str_buf << "LeafByMap"; + } else { + str_buf << "ByMap"; + } + str_buf << "(const std::unordered_map& arr) { "; + if (num_leaves_ <= 1) { + str_buf << "return " << leaf_value_[0] << ";"; + } else { + str_buf << "const std::vector cat_threshold = {"; + for (size_t i = 0; i < cat_threshold_.size(); ++i) { + if (i != 0) { + str_buf << ","; + } + str_buf << cat_threshold_[i]; + } + str_buf << "};"; + // use this for the missing value conversion + str_buf << "double fval = 0.0f; "; + if (num_cat_ > 0) { + str_buf << "int int_fval = 0; "; + } + str_buf << NodeToIfElseByMap(0, predict_leaf_index); + } + str_buf << " }" << '\n'; + + return str_buf.str(); +} + +std::string Tree::NodeToIfElse(int index, bool predict_leaf_index) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + if (index >= 0) { + // non-leaf + str_buf << "fval = arr[" << split_feature_[index] << "];"; + if (GetDecisionType(decision_type_[index], kCategoricalMask) == 0) { + str_buf << NumericalDecisionIfElse(index); + } else { + str_buf << CategoricalDecisionIfElse(index); + } + // left subtree + str_buf << NodeToIfElse(left_child_[index], predict_leaf_index); + str_buf << " } else { "; + // right subtree + str_buf << NodeToIfElse(right_child_[index], predict_leaf_index); + str_buf << " }"; + } else { + // leaf + str_buf << "return "; + if (predict_leaf_index) { + str_buf << ~index; + } else { + str_buf << leaf_value_[~index]; + } + str_buf << ";"; + } + + return str_buf.str(); +} + +std::string Tree::NodeToIfElseByMap(int index, bool predict_leaf_index) const { + std::stringstream str_buf; + Common::C_stringstream(str_buf); + str_buf << std::setprecision(std::numeric_limits::digits10 + 2); + if (index >= 0) { + // non-leaf + str_buf << "fval = arr.count(" << split_feature_[index] << ") > 0 ? arr.at(" << split_feature_[index] << ") : 0.0f;"; + if (GetDecisionType(decision_type_[index], kCategoricalMask) == 0) { + str_buf << NumericalDecisionIfElse(index); + } else { + str_buf << CategoricalDecisionIfElse(index); + } + // left subtree + str_buf << NodeToIfElseByMap(left_child_[index], predict_leaf_index); + str_buf << " } else { "; + // right subtree + str_buf << NodeToIfElseByMap(right_child_[index], predict_leaf_index); + str_buf << " }"; + } else { + // leaf + str_buf << "return "; + if (predict_leaf_index) { + str_buf << ~index; + } else { + str_buf << leaf_value_[~index]; + } + str_buf << ";"; + } + + return str_buf.str(); +} + +Tree::Tree(const char* str, size_t* used_len) { + auto p = str; + std::unordered_map key_vals; + const int max_num_line = 22; + int read_line = 0; + while (read_line < max_num_line) { + if (*p == '\r' || *p == '\n') break; + auto start = p; + while (*p != '=') ++p; + std::string key(start, p - start); + ++p; + start = p; + while (*p != '\r' && *p != '\n') ++p; + key_vals[key] = std::string(start, p - start); + ++read_line; + if (*p == '\r') ++p; + if (*p == '\n') ++p; + } + *used_len = p - str; + + if (key_vals.count("num_leaves") <= 0) { + Log::Fatal("Tree model should contain num_leaves field"); + } + + Common::Atoi(key_vals["num_leaves"].c_str(), &num_leaves_); + + if (key_vals.count("num_cat") <= 0) { + Log::Fatal("Tree model should contain num_cat field"); + } + + Common::Atoi(key_vals["num_cat"].c_str(), &num_cat_); + + if (key_vals.count("leaf_value")) { + leaf_value_ = CommonC::StringToArray(key_vals["leaf_value"], num_leaves_); + } else { + Log::Fatal("Tree model string format error, should contain leaf_value field"); + } + + if (key_vals.count("shrinkage")) { + CommonC::Atof(key_vals["shrinkage"].c_str(), &shrinkage_); + } else { + shrinkage_ = 1.0f; + } + + if (key_vals.count("is_linear")) { + int is_linear_int; + Common::Atoi(key_vals["is_linear"].c_str(), &is_linear_int); + is_linear_ = static_cast(is_linear_int); + } else { + is_linear_ = false; + } + + if (key_vals.count("leaf_count")) { + leaf_count_ = CommonC::StringToArrayFast(key_vals["leaf_count"], num_leaves_); + } else { + leaf_count_.resize(num_leaves_); + } + + #ifdef USE_CUDA + is_cuda_tree_ = false; + #endif // USE_CUDA + + if ((num_leaves_ <= 1) && !is_linear_) { + return; + } + + if (key_vals.count("left_child")) { + left_child_ = CommonC::StringToArrayFast(key_vals["left_child"], num_leaves_ - 1); + } else { + Log::Fatal("Tree model string format error, should contain left_child field"); + } + + if (key_vals.count("right_child")) { + right_child_ = CommonC::StringToArrayFast(key_vals["right_child"], num_leaves_ - 1); + } else { + Log::Fatal("Tree model string format error, should contain right_child field"); + } + + if (key_vals.count("split_feature")) { + split_feature_ = CommonC::StringToArrayFast(key_vals["split_feature"], num_leaves_ - 1); + } else { + Log::Fatal("Tree model string format error, should contain split_feature field"); + } + + if (key_vals.count("threshold")) { + threshold_ = CommonC::StringToArray(key_vals["threshold"], num_leaves_ - 1); + } else { + Log::Fatal("Tree model string format error, should contain threshold field"); + } + + if (key_vals.count("split_gain")) { + split_gain_ = CommonC::StringToArrayFast(key_vals["split_gain"], num_leaves_ - 1); + } else { + split_gain_.resize(num_leaves_ - 1); + } + + if (key_vals.count("internal_count")) { + internal_count_ = CommonC::StringToArrayFast(key_vals["internal_count"], num_leaves_ - 1); + } else { + internal_count_.resize(num_leaves_ - 1); + } + + if (key_vals.count("internal_value")) { + internal_value_ = CommonC::StringToArrayFast(key_vals["internal_value"], num_leaves_ - 1); + } else { + internal_value_.resize(num_leaves_ - 1); + } + + if (key_vals.count("internal_weight")) { + internal_weight_ = CommonC::StringToArrayFast(key_vals["internal_weight"], num_leaves_ - 1); + } else { + internal_weight_.resize(num_leaves_ - 1); + } + + if (key_vals.count("leaf_weight")) { + leaf_weight_ = CommonC::StringToArray(key_vals["leaf_weight"], num_leaves_); + } else { + leaf_weight_.resize(num_leaves_); + } + + if (key_vals.count("decision_type")) { + decision_type_ = CommonC::StringToArrayFast(key_vals["decision_type"], num_leaves_ - 1); + } else { + decision_type_ = std::vector(num_leaves_ - 1, 0); + } + + if (is_linear_) { + if (key_vals.count("leaf_const")) { + leaf_const_ = Common::StringToArray(key_vals["leaf_const"], num_leaves_); + } else { + leaf_const_.resize(num_leaves_); + } + std::vector num_feat; + if (key_vals.count("num_features")) { + num_feat = Common::StringToArrayFast(key_vals["num_features"], num_leaves_); + } + leaf_coeff_.resize(num_leaves_); + leaf_features_.resize(num_leaves_); + leaf_features_inner_.resize(num_leaves_); + if (num_feat.size() > 0) { + int total_num_feat = 0; + for (size_t i = 0; i < num_feat.size(); ++i) { + total_num_feat += num_feat[i]; + } + std::vector all_leaf_features; + if (key_vals.count("leaf_features")) { + all_leaf_features = Common::StringToArrayFast(key_vals["leaf_features"], total_num_feat); + } + std::vector all_leaf_coeff; + if (key_vals.count("leaf_coeff")) { + all_leaf_coeff = Common::StringToArray(key_vals["leaf_coeff"], total_num_feat); + } + int sum_num_feat = 0; + for (int i = 0; i < num_leaves_; ++i) { + if (num_feat[i] > 0) { + if (key_vals.count("leaf_features")) { + leaf_features_[i].assign(all_leaf_features.begin() + sum_num_feat, all_leaf_features.begin() + sum_num_feat + num_feat[i]); + } + if (key_vals.count("leaf_coeff")) { + leaf_coeff_[i].assign(all_leaf_coeff.begin() + sum_num_feat, all_leaf_coeff.begin() + sum_num_feat + num_feat[i]); + } + } + sum_num_feat += num_feat[i]; + } + } + } + + if (num_cat_ > 0) { + if (key_vals.count("cat_boundaries")) { + cat_boundaries_ = CommonC::StringToArrayFast(key_vals["cat_boundaries"], num_cat_ + 1); + } else { + Log::Fatal("Tree model should contain cat_boundaries field."); + } + + if (key_vals.count("cat_threshold")) { + cat_threshold_ = CommonC::StringToArrayFast(key_vals["cat_threshold"], cat_boundaries_.back()); + } else { + Log::Fatal("Tree model should contain cat_threshold field"); + } + } + max_depth_ = -1; +} + +void Tree::ExtendPath(PathElement *unique_path, int unique_depth, + double zero_fraction, double one_fraction, int feature_index) { + unique_path[unique_depth].feature_index = feature_index; + unique_path[unique_depth].zero_fraction = zero_fraction; + unique_path[unique_depth].one_fraction = one_fraction; + unique_path[unique_depth].pweight = (unique_depth == 0 ? 1 : 0); + for (int i = unique_depth - 1; i >= 0; i--) { + unique_path[i + 1].pweight += one_fraction*unique_path[i].pweight*(i + 1) + / static_cast(unique_depth + 1); + unique_path[i].pweight = zero_fraction*unique_path[i].pweight*(unique_depth - i) + / static_cast(unique_depth + 1); + } +} + +void Tree::UnwindPath(PathElement *unique_path, int unique_depth, int path_index) { + const double one_fraction = unique_path[path_index].one_fraction; + const double zero_fraction = unique_path[path_index].zero_fraction; + double next_one_portion = unique_path[unique_depth].pweight; + + for (int i = unique_depth - 1; i >= 0; --i) { + if (one_fraction != 0) { + const double tmp = unique_path[i].pweight; + unique_path[i].pweight = next_one_portion*(unique_depth + 1) + / static_cast((i + 1)*one_fraction); + next_one_portion = tmp - unique_path[i].pweight*zero_fraction*(unique_depth - i) + / static_cast(unique_depth + 1); + } else { + unique_path[i].pweight = (unique_path[i].pweight*(unique_depth + 1)) + / static_cast(zero_fraction*(unique_depth - i)); + } + } + + for (int i = path_index; i < unique_depth; ++i) { + unique_path[i].feature_index = unique_path[i + 1].feature_index; + unique_path[i].zero_fraction = unique_path[i + 1].zero_fraction; + unique_path[i].one_fraction = unique_path[i + 1].one_fraction; + } +} + +double Tree::UnwoundPathSum(const PathElement *unique_path, int unique_depth, int path_index) { + const double one_fraction = unique_path[path_index].one_fraction; + const double zero_fraction = unique_path[path_index].zero_fraction; + double next_one_portion = unique_path[unique_depth].pweight; + double total = 0; + for (int i = unique_depth - 1; i >= 0; --i) { + if (one_fraction != 0) { + const double tmp = next_one_portion*(unique_depth + 1) + / static_cast((i + 1)*one_fraction); + total += tmp; + next_one_portion = unique_path[i].pweight - tmp*zero_fraction*((unique_depth - i) + / static_cast(unique_depth + 1)); + } else { + total += (unique_path[i].pweight / zero_fraction) / ((unique_depth - i) + / static_cast(unique_depth + 1)); + } + } + return total; +} + +// recursive computation of SHAP values for a decision tree +void Tree::TreeSHAP(const double *feature_values, double *phi, + int node, int unique_depth, + PathElement *parent_unique_path, double parent_zero_fraction, + double parent_one_fraction, int parent_feature_index) const { + // extend the unique path + PathElement* unique_path = parent_unique_path + unique_depth; + if (unique_depth > 0) { + std::copy(parent_unique_path, parent_unique_path + unique_depth, unique_path); + } + ExtendPath(unique_path, unique_depth, parent_zero_fraction, + parent_one_fraction, parent_feature_index); + + // leaf node + if (node < 0) { + for (int i = 1; i <= unique_depth; ++i) { + const double w = UnwoundPathSum(unique_path, unique_depth, i); + const PathElement &el = unique_path[i]; + phi[el.feature_index] += w*(el.one_fraction - el.zero_fraction)*leaf_value_[~node]; + } + + // internal node + } else { + const int hot_index = Decision(feature_values[split_feature_[node]], node); + const int cold_index = (hot_index == left_child_[node] ? right_child_[node] : left_child_[node]); + const double w = data_count(node); + const double hot_zero_fraction = data_count(hot_index) / w; + const double cold_zero_fraction = data_count(cold_index) / w; + double incoming_zero_fraction = 1; + double incoming_one_fraction = 1; + + // see if we have already split on this feature, + // if so we undo that split so we can redo it for this node + int path_index = 0; + for (; path_index <= unique_depth; ++path_index) { + if (unique_path[path_index].feature_index == split_feature_[node]) break; + } + if (path_index != unique_depth + 1) { + incoming_zero_fraction = unique_path[path_index].zero_fraction; + incoming_one_fraction = unique_path[path_index].one_fraction; + UnwindPath(unique_path, unique_depth, path_index); + unique_depth -= 1; + } + + TreeSHAP(feature_values, phi, hot_index, unique_depth + 1, unique_path, + hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_feature_[node]); + + TreeSHAP(feature_values, phi, cold_index, unique_depth + 1, unique_path, + cold_zero_fraction*incoming_zero_fraction, 0, split_feature_[node]); + } +} + +// recursive sparse computation of SHAP values for a decision tree +void Tree::TreeSHAPByMap(const std::unordered_map& feature_values, std::unordered_map* phi, + int node, int unique_depth, + PathElement *parent_unique_path, double parent_zero_fraction, + double parent_one_fraction, int parent_feature_index) const { + // extend the unique path + PathElement* unique_path = parent_unique_path + unique_depth; + if (unique_depth > 0) { + std::copy(parent_unique_path, parent_unique_path + unique_depth, unique_path); + } + ExtendPath(unique_path, unique_depth, parent_zero_fraction, + parent_one_fraction, parent_feature_index); + + // leaf node + if (node < 0) { + for (int i = 1; i <= unique_depth; ++i) { + const double w = UnwoundPathSum(unique_path, unique_depth, i); + const PathElement &el = unique_path[i]; + (*phi)[el.feature_index] += w*(el.one_fraction - el.zero_fraction)*leaf_value_[~node]; + } + + // internal node + } else { + const int hot_index = Decision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node); + const int cold_index = (hot_index == left_child_[node] ? right_child_[node] : left_child_[node]); + const double w = data_count(node); + const double hot_zero_fraction = data_count(hot_index) / w; + const double cold_zero_fraction = data_count(cold_index) / w; + double incoming_zero_fraction = 1; + double incoming_one_fraction = 1; + + // see if we have already split on this feature, + // if so we undo that split so we can redo it for this node + int path_index = 0; + for (; path_index <= unique_depth; ++path_index) { + if (unique_path[path_index].feature_index == split_feature_[node]) break; + } + if (path_index != unique_depth + 1) { + incoming_zero_fraction = unique_path[path_index].zero_fraction; + incoming_one_fraction = unique_path[path_index].one_fraction; + UnwindPath(unique_path, unique_depth, path_index); + unique_depth -= 1; + } + + TreeSHAPByMap(feature_values, phi, hot_index, unique_depth + 1, unique_path, + hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_feature_[node]); + + TreeSHAPByMap(feature_values, phi, cold_index, unique_depth + 1, unique_path, + cold_zero_fraction*incoming_zero_fraction, 0, split_feature_[node]); + } +} + +double Tree::ExpectedValue() const { + if (num_leaves_ == 1) return LeafOutput(0); + const double total_count = internal_count_[0]; + double exp_value = 0.0; + for (int i = 0; i < num_leaves(); ++i) { + exp_value += (leaf_count_[i] / total_count)*LeafOutput(i); + } + return exp_value; +} + +void Tree::RecomputeMaxDepth() { + if (num_leaves_ == 1) { + max_depth_ = 0; + } else { + if (leaf_depth_.size() == 0) { + RecomputeLeafDepths(0, 0); + } + max_depth_ = leaf_depth_[0]; + for (int i = 1; i < num_leaves(); ++i) { + if (max_depth_ < leaf_depth_[i]) max_depth_ = leaf_depth_[i]; + } + } +} + +} // namespace LightGBM diff --git a/src/main.cpp b/src/main.cpp new file mode 100644 index 0000000..b1abf7e --- /dev/null +++ b/src/main.cpp @@ -0,0 +1,46 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include + +#ifdef USE_MPI + #include "network/linkers.h" +#endif + +int main(int argc, char** argv) { + bool success = false; + try { + LightGBM::Application app(argc, argv); + app.Run(); + +#ifdef USE_MPI + LightGBM::Linkers::MpiFinalizeIfIsParallel(); +#endif + + success = true; + } + catch (const std::exception& ex) { + std::cerr << "Met Exceptions:" << std::endl; + std::cerr << ex.what() << std::endl; + } + catch (const std::string& ex) { + std::cerr << "Met Exceptions:" << std::endl; + std::cerr << ex << std::endl; + } + catch (...) { + std::cerr << "Unknown Exceptions" << std::endl; + } + + if (!success) { +#ifdef USE_MPI + LightGBM::Linkers::MpiAbortIfIsParallel(); +#endif + + exit(-1); + } +} diff --git a/src/metric/binary_metric.hpp b/src/metric/binary_metric.hpp new file mode 100644 index 0000000..d7d0fdb --- /dev/null +++ b/src/metric/binary_metric.hpp @@ -0,0 +1,389 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_ + +#include +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { + +/*! +* \brief Metric for binary classification task. +* Use static class "PointWiseLossCalculator" to calculate loss point-wise +*/ +template +class BinaryMetric: public Metric { + public: + explicit BinaryMetric(const Config&) { + } + + virtual ~BinaryMetric() { + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back(PointWiseLossCalculator::Name()); + + num_data_ = num_data; + // get label + label_ = metadata.label(); + + // get weights + weights_ = metadata.weights(); + + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + sum_weights_ = 0.0f; + for (data_size_t i = 0; i < num_data; ++i) { + sum_weights_ += weights_[i]; + } + } + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return -1.0f; + } + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { + double sum_loss = 0.0f; + if (objective == nullptr) { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i]); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i]) * weights_[i]; + } + } + } else { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double prob = 0; + objective->ConvertOutput(&score[i], &prob); + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double prob = 0; + objective->ConvertOutput(&score[i], &prob); + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob) * weights_[i]; + } + } + } + double loss = sum_loss / sum_weights_; + return std::vector(1, loss); + } + + protected: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Pointer of weighs */ + const label_t* weights_; + /*! \brief Sum weights */ + double sum_weights_; + /*! \brief Name of test set */ + std::vector name_; +}; + +/*! +* \brief Log loss metric for binary classification task. +*/ +class BinaryLoglossMetric: public BinaryMetric { + public: + explicit BinaryLoglossMetric(const Config& config) :BinaryMetric(config) {} + + inline static double LossOnPoint(label_t label, double prob) { + if (label <= 0) { + if (1.0f - prob > kEpsilon) { + return -std::log(1.0f - prob); + } + } else { + if (prob > kEpsilon) { + return -std::log(prob); + } + } + return -std::log(kEpsilon); + } + + inline static const char* Name() { + return "binary_logloss"; + } +}; +/*! +* \brief Error rate metric for binary classification task. +*/ +class BinaryErrorMetric: public BinaryMetric { + public: + explicit BinaryErrorMetric(const Config& config) :BinaryMetric(config) {} + + inline static double LossOnPoint(label_t label, double prob) { + if (prob <= 0.5f) { + return label > 0; + } else { + return label <= 0; + } + } + + inline static const char* Name() { + return "binary_error"; + } +}; + +/*! +* \brief Auc Metric for binary classification task. +*/ +class AUCMetric: public Metric { + public: + explicit AUCMetric(const Config&) { + } + + virtual ~AUCMetric() { + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return 1.0f; + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back("auc"); + + num_data_ = num_data; + // get label + label_ = metadata.label(); + // get weights + weights_ = metadata.weights(); + + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + sum_weights_ = 0.0f; + for (data_size_t i = 0; i < num_data; ++i) { + sum_weights_ += weights_[i]; + } + } + } + + std::vector Eval(const double* score, const ObjectiveFunction*) const override { + // get indices sorted by score, descent order + std::vector sorted_idx; + for (data_size_t i = 0; i < num_data_; ++i) { + sorted_idx.emplace_back(i); + } + Common::ParallelSort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); + // temp sum of positive label + double cur_pos = 0.0f; + // total sum of positive label + double sum_pos = 0.0f; + // accumulate of AUC + double accum = 0.0f; + // temp sum of negative label + double cur_neg = 0.0f; + double threshold = score[sorted_idx[0]]; + if (weights_ == nullptr) { // no weights + for (data_size_t i = 0; i < num_data_; ++i) { + const label_t cur_label = label_[sorted_idx[i]]; + const double cur_score = score[sorted_idx[i]]; + // new threshold + if (cur_score != threshold) { + threshold = cur_score; + // accumulate + accum += cur_neg*(cur_pos * 0.5f + sum_pos); + sum_pos += cur_pos; + // reset + cur_neg = cur_pos = 0.0f; + } + cur_neg += (cur_label <= 0); + cur_pos += (cur_label > 0); + } + } else { // has weights + for (data_size_t i = 0; i < num_data_; ++i) { + const label_t cur_label = label_[sorted_idx[i]]; + const double cur_score = score[sorted_idx[i]]; + const label_t cur_weight = weights_[sorted_idx[i]]; + // new threshold + if (cur_score != threshold) { + threshold = cur_score; + // accumulate + accum += cur_neg*(cur_pos * 0.5f + sum_pos); + sum_pos += cur_pos; + // reset + cur_neg = cur_pos = 0.0f; + } + cur_neg += (cur_label <= 0)*cur_weight; + cur_pos += (cur_label > 0)*cur_weight; + } + } + accum += cur_neg*(cur_pos * 0.5f + sum_pos); + sum_pos += cur_pos; + double auc = 1.0f; + if (sum_pos > 0.0f && sum_pos != sum_weights_) { + auc = accum / (sum_pos *(sum_weights_ - sum_pos)); + } + return std::vector(1, auc); + } + + private: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Pointer of weighs */ + const label_t* weights_; + /*! \brief Sum weights */ + double sum_weights_; + /*! \brief Name of test set */ + std::vector name_; +}; + + +/*! +* \brief Average Precision Metric for binary classification task. +*/ +class AveragePrecisionMetric: public Metric { + public: + explicit AveragePrecisionMetric(const Config&) { + } + + virtual ~AveragePrecisionMetric() { + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return 1.0f; + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back("average_precision"); + + num_data_ = num_data; + // get label + label_ = metadata.label(); + // get weights + weights_ = metadata.weights(); + + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + sum_weights_ = 0.0f; + for (data_size_t i = 0; i < num_data; ++i) { + sum_weights_ += weights_[i]; + } + } + } + + std::vector Eval(const double* score, const ObjectiveFunction*) const override { + // get indices sorted by score, descending order + std::vector sorted_idx; + for (data_size_t i = 0; i < num_data_; ++i) { + sorted_idx.emplace_back(i); + } + Common::ParallelSort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); + // temp sum of positive label + double cur_actual_pos = 0.0f; + // total sum of positive label + double sum_actual_pos = 0.0f; + // total sum of predicted positive + double sum_pred_pos = 0.0f; + // accumulated precision + double accum_prec = 1.0f; + // accumulated pr-auc + double accum = 0.0f; + // temp sum of negative label + double cur_neg = 0.0f; + double threshold = score[sorted_idx[0]]; + if (weights_ == nullptr) { // no weights + for (data_size_t i = 0; i < num_data_; ++i) { + const label_t cur_label = label_[sorted_idx[i]]; + const double cur_score = score[sorted_idx[i]]; + // new threshold + if (cur_score != threshold) { + threshold = cur_score; + // accumulate + sum_actual_pos += cur_actual_pos; + sum_pred_pos += cur_actual_pos + cur_neg; + accum_prec = sum_actual_pos / sum_pred_pos; + accum += cur_actual_pos * accum_prec; + // reset + cur_neg = cur_actual_pos = 0.0f; + } + cur_neg += (cur_label <= 0); + cur_actual_pos += (cur_label > 0); + } + } else { // has weights + for (data_size_t i = 0; i < num_data_; ++i) { + const label_t cur_label = label_[sorted_idx[i]]; + const double cur_score = score[sorted_idx[i]]; + const label_t cur_weight = weights_[sorted_idx[i]]; + // new threshold + if (cur_score != threshold) { + threshold = cur_score; + // accumulate + sum_actual_pos += cur_actual_pos; + sum_pred_pos += cur_actual_pos + cur_neg; + accum_prec = sum_actual_pos / sum_pred_pos; + accum += cur_actual_pos * accum_prec; + // reset + cur_neg = cur_actual_pos = 0.0f; + } + cur_neg += (cur_label <= 0) * cur_weight; + cur_actual_pos += (cur_label > 0) * cur_weight; + } + } + sum_actual_pos += cur_actual_pos; + sum_pred_pos += cur_actual_pos + cur_neg; + accum_prec = sum_actual_pos / sum_pred_pos; + accum += cur_actual_pos * accum_prec; + double ap = 1.0f; + if (sum_actual_pos > 0.0f && sum_actual_pos != sum_weights_) { + ap = accum / sum_actual_pos; + } + return std::vector(1, ap); + } + + private: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Pointer of weighs */ + const label_t* weights_; + /*! \brief Sum weights */ + double sum_weights_; + /*! \brief Name of test set */ + std::vector name_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_ diff --git a/src/metric/cuda/cuda_binary_metric.cpp b/src/metric/cuda/cuda_binary_metric.cpp new file mode 100644 index 0000000..c9435ee --- /dev/null +++ b/src/metric/cuda/cuda_binary_metric.cpp @@ -0,0 +1,35 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_binary_metric.hpp" + +#include + +namespace LightGBM { + +template +std::vector CUDABinaryMetricInterface::Eval(const double* score, const ObjectiveFunction* objective) const { + const double* score_convert = score; + if (objective != nullptr && objective->NeedConvertOutputCUDA()) { + this->score_convert_buffer_.Resize(static_cast(this->num_data_) * static_cast(this->num_class_)); + score_convert = objective->ConvertOutputCUDA(this->num_data_, score, this->score_convert_buffer_.RawData()); + } + double sum_loss = 0.0, sum_weight = 0.0; + this->LaunchEvalKernel(score_convert, &sum_loss, &sum_weight); + const double eval_score = sum_loss / sum_weight; + return std::vector{eval_score}; +} + +CUDABinaryLoglossMetric::CUDABinaryLoglossMetric(const Config& config):CUDABinaryMetricInterface(config) {} + +CUDABinaryErrorMetric::CUDABinaryErrorMetric(const Config& config):CUDABinaryMetricInterface(config) {} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/metric/cuda/cuda_binary_metric.hpp b/src/metric/cuda/cuda_binary_metric.hpp new file mode 100644 index 0000000..10bc24f --- /dev/null +++ b/src/metric/cuda/cuda_binary_metric.hpp @@ -0,0 +1,73 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_ + +#ifdef USE_CUDA + +#include +#include + +#include + +#include "cuda_regression_metric.hpp" +#include "../binary_metric.hpp" + +namespace LightGBM { + +template +class CUDABinaryMetricInterface: public CUDAPointwiseMetricInterface { + public: + explicit CUDABinaryMetricInterface(const Config& config): CUDAPointwiseMetricInterface(config) {} + + virtual ~CUDABinaryMetricInterface() {} + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override; +}; + +class CUDABinaryLoglossMetric: public CUDABinaryMetricInterface { + public: + explicit CUDABinaryLoglossMetric(const Config& config); + + virtual ~CUDABinaryLoglossMetric() {} + + __device__ static double MetricOnPointCUDA(label_t label, double score, const double /*param*/) { + // score should have been converted to probability + if (label <= 0) { + if (1.0f - score > kEpsilon) { + return -log(1.0f - score); + } + } else { + if (score > kEpsilon) { + return -log(score); + } + } + return -log(kEpsilon); + } +}; + +class CUDABinaryErrorMetric: public CUDABinaryMetricInterface { + public: + explicit CUDABinaryErrorMetric(const Config& config); + + virtual ~CUDABinaryErrorMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, const double /*param*/) { + if (score <= 0.5f) { + return label > 0; + } else { + return label <= 0; + } + } +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_ diff --git a/src/metric/cuda/cuda_pointwise_metric.cpp b/src/metric/cuda/cuda_pointwise_metric.cpp new file mode 100644 index 0000000..c29586b --- /dev/null +++ b/src/metric/cuda/cuda_pointwise_metric.cpp @@ -0,0 +1,52 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_binary_metric.hpp" +#include "cuda_pointwise_metric.hpp" +#include "cuda_regression_metric.hpp" + +namespace LightGBM { + +template +void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data) { + CUDAMetricInterface::Init(metadata, num_data); + const int max_num_reduce_blocks = (this->num_data_ + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD; + if (this->cuda_weights_ == nullptr) { + reduce_block_buffer_.Resize(max_num_reduce_blocks); + } else { + reduce_block_buffer_.Resize(max_num_reduce_blocks * 2); + } + const int max_num_reduce_blocks_inner = (max_num_reduce_blocks + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD; + if (this->cuda_weights_ == nullptr) { + reduce_block_buffer_inner_.Resize(max_num_reduce_blocks_inner); + } else { + reduce_block_buffer_inner_.Resize(max_num_reduce_blocks_inner * 2); + } +} + +// Regression metrics +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); + +// Binary metrics +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data); + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/metric/cuda/cuda_pointwise_metric.cu b/src/metric/cuda/cuda_pointwise_metric.cu new file mode 100644 index 0000000..3c5c6e3 --- /dev/null +++ b/src/metric/cuda/cuda_pointwise_metric.cu @@ -0,0 +1,85 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include +#include + +#include "cuda_binary_metric.hpp" +#include "cuda_pointwise_metric.hpp" +#include "cuda_regression_metric.hpp" + +namespace LightGBM { + +template +__global__ void EvalKernel(const data_size_t num_data, const label_t* labels, const label_t* weights, + const double* scores, double* reduce_block_buffer, const double param) { + __shared__ double shared_mem_buffer[WARPSIZE]; + const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + double point_metric = 0.0; + if (index < num_data) { + point_metric = USE_WEIGHTS ? + CUDA_METRIC::MetricOnPointCUDA(labels[index], scores[index], param) * weights[index] : + CUDA_METRIC::MetricOnPointCUDA(labels[index], scores[index], param); + } + const double block_sum_point_metric = ShuffleReduceSum(point_metric, shared_mem_buffer, NUM_DATA_PER_EVAL_THREAD); + if (threadIdx.x == 0) { + reduce_block_buffer[blockIdx.x] = block_sum_point_metric; + } + if (USE_WEIGHTS) { + double weight = 0.0; + if (index < num_data) { + weight = static_cast(weights[index]); + const double block_sum_weight = ShuffleReduceSum(weight, shared_mem_buffer, NUM_DATA_PER_EVAL_THREAD); + if (threadIdx.x == 0) { + reduce_block_buffer[blockIdx.x + gridDim.x] = block_sum_weight; + } + } + } +} + +template +void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const { + const int num_blocks = (this->num_data_ + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD; + if (this->cuda_weights_ != nullptr) { + EvalKernel<<>>( + this->num_data_, this->cuda_labels_, this->cuda_weights_, score, reduce_block_buffer_.RawData(), GetParamFromConfig()); + } else { + EvalKernel<<>>( + this->num_data_, this->cuda_labels_, this->cuda_weights_, score, reduce_block_buffer_.RawData(), GetParamFromConfig()); + } + ShuffleReduceSumGlobal(reduce_block_buffer_.RawData(), num_blocks, reduce_block_buffer_inner_.RawData()); + CopyFromCUDADeviceToHost(sum_loss, reduce_block_buffer_inner_.RawData(), 1, __FILE__, __LINE__); + *sum_weight = static_cast(this->num_data_); + if (this->cuda_weights_ != nullptr) { + ShuffleReduceSumGlobal(reduce_block_buffer_.RawData() + num_blocks, num_blocks, reduce_block_buffer_inner_.RawData()); + CopyFromCUDADeviceToHost(sum_weight, reduce_block_buffer_inner_.RawData(), 1, __FILE__, __LINE__); + } +} + +// Regression metrics +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; + +// Binary metrics +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; +template void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const; + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/metric/cuda/cuda_pointwise_metric.hpp b/src/metric/cuda/cuda_pointwise_metric.hpp new file mode 100644 index 0000000..6963cbf --- /dev/null +++ b/src/metric/cuda/cuda_pointwise_metric.hpp @@ -0,0 +1,46 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_ + +#ifdef USE_CUDA + +#include +#include + +#include + +#define NUM_DATA_PER_EVAL_THREAD (1024) + +namespace LightGBM { + +template +class CUDAPointwiseMetricInterface: public CUDAMetricInterface { + public: + explicit CUDAPointwiseMetricInterface(const Config& config): CUDAMetricInterface(config), num_class_(config.num_class) {} + + virtual ~CUDAPointwiseMetricInterface() {} + + void Init(const Metadata& metadata, data_size_t num_data) override; + + protected: + void LaunchEvalKernel(const double* score_convert, double* sum_loss, double* sum_weight) const; + + virtual double GetParamFromConfig() const { return 0.0; } + + mutable CUDAVector score_convert_buffer_; + CUDAVector reduce_block_buffer_; + CUDAVector reduce_block_buffer_inner_; + const int num_class_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_ diff --git a/src/metric/cuda/cuda_regression_metric.cpp b/src/metric/cuda/cuda_regression_metric.cpp new file mode 100644 index 0000000..e9115f1 --- /dev/null +++ b/src/metric/cuda/cuda_regression_metric.cpp @@ -0,0 +1,53 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include + +#include "cuda_regression_metric.hpp" + +namespace LightGBM { + +template +std::vector CUDARegressionMetricInterface::Eval(const double* score, const ObjectiveFunction* objective) const { + const double* score_convert = score; + if (objective != nullptr && objective->NeedConvertOutputCUDA()) { + this->score_convert_buffer_.Resize(static_cast(this->num_data_) * static_cast(this->num_class_)); + score_convert = objective->ConvertOutputCUDA(this->num_data_, score, this->score_convert_buffer_.RawData()); + } + double sum_loss = 0.0, sum_weight = 0.0; + this->LaunchEvalKernel(score_convert, &sum_loss, &sum_weight); + const double eval_score = this->AverageLoss(sum_loss, sum_weight); + return std::vector{eval_score}; +} + +CUDARMSEMetric::CUDARMSEMetric(const Config& config): CUDARegressionMetricInterface(config) {} + +CUDAL2Metric::CUDAL2Metric(const Config& config): CUDARegressionMetricInterface(config) {} + +CUDAQuantileMetric::CUDAQuantileMetric(const Config& config): CUDARegressionMetricInterface(config), alpha_(config.alpha) {} + +CUDAL1Metric::CUDAL1Metric(const Config& config): CUDARegressionMetricInterface(config) {} + +CUDAHuberLossMetric::CUDAHuberLossMetric(const Config& config): CUDARegressionMetricInterface(config), alpha_(config.alpha) {} + +CUDAFairLossMetric::CUDAFairLossMetric(const Config& config): CUDARegressionMetricInterface(config) , fair_c_(config.fair_c) {} + +CUDAPoissonMetric::CUDAPoissonMetric(const Config& config): CUDARegressionMetricInterface(config) {} + +CUDAMAPEMetric::CUDAMAPEMetric(const Config& config): CUDARegressionMetricInterface(config) {} + +CUDAGammaMetric::CUDAGammaMetric(const Config& config): CUDARegressionMetricInterface(config) {} + +CUDAGammaDevianceMetric::CUDAGammaDevianceMetric(const Config& config): CUDARegressionMetricInterface(config) {} + +CUDATweedieMetric::CUDATweedieMetric(const Config& config): CUDARegressionMetricInterface(config) , tweedie_variance_power_(config.tweedie_variance_power) {} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/metric/cuda/cuda_regression_metric.hpp b/src/metric/cuda/cuda_regression_metric.hpp new file mode 100644 index 0000000..1433c0f --- /dev/null +++ b/src/metric/cuda/cuda_regression_metric.hpp @@ -0,0 +1,216 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_ + +#ifdef USE_CUDA + +#include +#include + +#include + +#include "cuda_pointwise_metric.hpp" +#include "../regression_metric.hpp" + +namespace LightGBM { + +template +class CUDARegressionMetricInterface: public CUDAPointwiseMetricInterface { + public: + explicit CUDARegressionMetricInterface(const Config& config): + CUDAPointwiseMetricInterface(config) {} + + virtual ~CUDARegressionMetricInterface() {} + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override; +}; + +class CUDARMSEMetric: public CUDARegressionMetricInterface { + public: + explicit CUDARMSEMetric(const Config& config); + + virtual ~CUDARMSEMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { + return (score - label) * (score - label); + } +}; + +class CUDAL2Metric : public CUDARegressionMetricInterface { + public: + explicit CUDAL2Metric(const Config& config); + + virtual ~CUDAL2Metric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { + return (score - label) * (score - label); + } +}; + +class CUDAQuantileMetric : public CUDARegressionMetricInterface { + public: + explicit CUDAQuantileMetric(const Config& config); + + virtual ~CUDAQuantileMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double alpha) { + double delta = label - score; + if (delta < 0) { + return (alpha - 1.0f) * delta; + } else { + return alpha * delta; + } + } + + double GetParamFromConfig() const override { + return alpha_; + } + + private: + const double alpha_; +}; + +class CUDAL1Metric : public CUDARegressionMetricInterface { + public: + explicit CUDAL1Metric(const Config& config); + + virtual ~CUDAL1Metric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { + return std::fabs(score - label); + } +}; + +class CUDAHuberLossMetric : public CUDARegressionMetricInterface { + public: + explicit CUDAHuberLossMetric(const Config& config); + + virtual ~CUDAHuberLossMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double alpha) { + const double diff = score - label; + if (std::abs(diff) <= alpha) { + return 0.5f * diff * diff; + } else { + return alpha * (std::abs(diff) - 0.5f * alpha); + } + } + + double GetParamFromConfig() const override { + return alpha_; + } + private: + const double alpha_; +}; + +class CUDAFairLossMetric : public CUDARegressionMetricInterface { + public: + explicit CUDAFairLossMetric(const Config& config); + + virtual ~CUDAFairLossMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double fair_c) { + const double x = std::fabs(score - label); + const double c = fair_c; + return c * x - c * c * std::log1p(x / c); + } + + double GetParamFromConfig() const override { + return fair_c_; + } + + private: + const double fair_c_; +}; + +class CUDAPoissonMetric : public CUDARegressionMetricInterface { + public: + explicit CUDAPoissonMetric(const Config& config); + + virtual ~CUDAPoissonMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { + const double eps = 1e-10f; + if (score < eps) { + score = eps; + } + return score - label * std::log(score); + } +}; + +class CUDAMAPEMetric : public CUDARegressionMetricInterface { + public: + explicit CUDAMAPEMetric(const Config& config); + + virtual ~CUDAMAPEMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { + return std::fabs((label - score)) / fmax(1.0f, std::fabs(label)); + } +}; + +class CUDAGammaMetric : public CUDARegressionMetricInterface { + public: + explicit CUDAGammaMetric(const Config& config); + + virtual ~CUDAGammaMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { + const double psi = 1.0; + const double theta = -1.0 / score; + const double a = psi; + const double b = -SafeLog(-theta); + const double c = 1. / psi * SafeLog(label / psi) - SafeLog(label) - 0; // 0 = std::lgamma(1.0 / psi) = std::lgamma(1.0); + return -((label * theta - b) / a + c); + } +}; + +class CUDAGammaDevianceMetric : public CUDARegressionMetricInterface { + public: + explicit CUDAGammaDevianceMetric(const Config& config); + + virtual ~CUDAGammaDevianceMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) { + const double epsilon = 1.0e-9; + const double tmp = label / (score + epsilon); + return tmp - SafeLog(tmp) - 1; + } +}; + +class CUDATweedieMetric : public CUDARegressionMetricInterface { + public: + explicit CUDATweedieMetric(const Config& config); + + virtual ~CUDATweedieMetric() {} + + __device__ inline static double MetricOnPointCUDA(label_t label, double score, double tweedie_variance_power) { + const double rho = tweedie_variance_power; + const double eps = 1e-10f; + if (score < eps) { + score = eps; + } + const double a = label * std::exp((1 - rho) * std::log(score)) / (1 - rho); + const double b = std::exp((2 - rho) * std::log(score)) / (2 - rho); + return -a + b; + } + + double GetParamFromConfig() const override { + return tweedie_variance_power_; + } + + private: + const double tweedie_variance_power_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_ diff --git a/src/metric/dcg_calculator.cpp b/src/metric/dcg_calculator.cpp new file mode 100644 index 0000000..593a045 --- /dev/null +++ b/src/metric/dcg_calculator.cpp @@ -0,0 +1,174 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include +#include + +#include +#include +#include + +namespace LightGBM { + +/*! \brief Declaration for some static members */ +std::vector DCGCalculator::label_gain_; +std::vector DCGCalculator::discount_; +const data_size_t DCGCalculator::kMaxPosition = 10000; + + +void DCGCalculator::DefaultEvalAt(std::vector* eval_at) { + auto& ref_eval_at = *eval_at; + if (ref_eval_at.empty()) { + for (int i = 1; i <= 5; ++i) { + ref_eval_at.push_back(i); + } + } else { + for (size_t i = 0; i < eval_at->size(); ++i) { + CHECK_GT(ref_eval_at[i], 0); + } + } +} + +void DCGCalculator::DefaultLabelGain(std::vector* label_gain) { + if (!label_gain->empty()) { + return; + } + // label_gain = 2^i - 1, may overflow, so we use 31 here + const int max_label = 31; + label_gain->push_back(0.0f); + for (int i = 1; i < max_label; ++i) { + label_gain->push_back(static_cast((1 << i) - 1)); + } +} + +void DCGCalculator::Init(const std::vector& input_label_gain) { + label_gain_.resize(input_label_gain.size()); + for (size_t i = 0; i < input_label_gain.size(); ++i) { + label_gain_[i] = static_cast(input_label_gain[i]); + } + discount_.resize(kMaxPosition); + for (data_size_t i = 0; i < kMaxPosition; ++i) { + discount_[i] = 1.0 / std::log2(2.0 + i); + } +} + +double DCGCalculator::CalMaxDCGAtK(data_size_t k, const label_t* label, data_size_t num_data) { + double ret = 0.0f; + // counts for all labels + std::vector label_cnt(label_gain_.size(), 0); + for (data_size_t i = 0; i < num_data; ++i) { + ++label_cnt[static_cast(label[i])]; + } + int top_label = static_cast(label_gain_.size()) - 1; + + if (k > num_data) { + k = num_data; + } + // start from top label, and accumulate DCG + for (data_size_t j = 0; j < k; ++j) { + while (top_label > 0 && label_cnt[top_label] <= 0) { + top_label -= 1; + } + if (top_label < 0) { + break; + } + ret += discount_[j] * label_gain_[top_label]; + label_cnt[top_label] -= 1; + } + return ret; +} + +void DCGCalculator::CalMaxDCG(const std::vector& ks, + const label_t* label, + data_size_t num_data, + std::vector* out) { + std::vector label_cnt(label_gain_.size(), 0); + // counts for all labels + for (data_size_t i = 0; i < num_data; ++i) { + ++label_cnt[static_cast(label[i])]; + } + double cur_result = 0.0f; + data_size_t cur_left = 0; + int top_label = static_cast(label_gain_.size()) - 1; + // calculate k Max DCG by one pass + for (size_t i = 0; i < ks.size(); ++i) { + data_size_t cur_k = ks[i]; + if (cur_k > num_data) { + cur_k = num_data; + } + for (data_size_t j = cur_left; j < cur_k; ++j) { + while (top_label > 0 && label_cnt[top_label] <= 0) { + top_label -= 1; + } + if (top_label < 0) { + break; + } + cur_result += discount_[j] * label_gain_[top_label]; + label_cnt[top_label] -= 1; + } + (*out)[i] = cur_result; + cur_left = cur_k; + } +} + +void DCGCalculator::CalDCG(const std::vector& ks, const label_t* label, + const double * score, data_size_t num_data, std::vector* out) { + // get sorted indices by score + std::vector sorted_idx(num_data); + for (data_size_t i = 0; i < num_data; ++i) { + sorted_idx[i] = i; + } + std::stable_sort(sorted_idx.begin(), sorted_idx.end(), + [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); + + double cur_result = 0.0f; + data_size_t cur_left = 0; + // calculate multi dcg by one pass + for (size_t i = 0; i < ks.size(); ++i) { + data_size_t cur_k = ks[i]; + if (cur_k > num_data) { + cur_k = num_data; + } + for (data_size_t j = cur_left; j < cur_k; ++j) { + data_size_t idx = sorted_idx[j]; + cur_result += label_gain_[static_cast(label[idx])] * discount_[j]; + } + (*out)[i] = cur_result; + cur_left = cur_k; + } +} + +void DCGCalculator::CheckMetadata(const Metadata& metadata, data_size_t num_queries) { + const data_size_t* query_boundaries = metadata.query_boundaries(); + if (num_queries > 0 && query_boundaries != nullptr) { + for (data_size_t i = 0; i < num_queries; i++) { + data_size_t num_rows = query_boundaries[i + 1] - query_boundaries[i]; + if (num_rows > kMaxPosition) { + Log::Fatal("Number of rows %i exceeds upper limit of %i for a query", static_cast(num_rows), static_cast(kMaxPosition)); + } + } + } +} + + +void DCGCalculator::CheckLabel(const label_t* label, data_size_t num_data) { + for (data_size_t i = 0; i < num_data; ++i) { + label_t delta = std::fabs(label[i] - static_cast(label[i])); + if (delta > kEpsilon) { + Log::Fatal("label should be int type (met %f) for ranking task,\n" + "for the gain of label, please set the label_gain parameter", label[i]); + } + + if (label[i] < 0) { + Log::Fatal("Label should be non-negative (met %f) for ranking task", label[i]); + } + + if (static_cast(label[i]) >= label_gain_.size()) { + Log::Fatal("Label %zu is not less than the number of label mappings (%zu)", static_cast(label[i]), label_gain_.size()); + } + } +} + +} // namespace LightGBM diff --git a/src/metric/map_metric.hpp b/src/metric/map_metric.hpp new file mode 100644 index 0000000..dfa2c08 --- /dev/null +++ b/src/metric/map_metric.hpp @@ -0,0 +1,169 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_ + +#include +#include +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { + +class MapMetric:public Metric { + public: + explicit MapMetric(const Config& config) { + // get eval position + eval_at_ = config.eval_at; + DCGCalculator::DefaultEvalAt(&eval_at_); + } + + ~MapMetric() { + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + for (auto k : eval_at_) { + name_.emplace_back(std::string("map@") + std::to_string(k)); + } + num_data_ = num_data; + // get label + label_ = metadata.label(); + // get query boundaries + query_boundaries_ = metadata.query_boundaries(); + if (query_boundaries_ == nullptr) { + Log::Fatal("For MAP metric, there should be query information"); + } + num_queries_ = metadata.num_queries(); + Log::Info("Total groups: %d, total data: %d", num_queries_, num_data_); + // get query weights + query_weights_ = metadata.query_weights(); + if (query_weights_ == nullptr) { + sum_query_weights_ = static_cast(num_queries_); + } else { + sum_query_weights_ = 0.0f; + for (data_size_t i = 0; i < num_queries_; ++i) { + sum_query_weights_ += query_weights_[i]; + } + } + + npos_per_query_.resize(num_queries_, 0); + for (data_size_t i = 0; i < num_queries_; ++i) { + for (data_size_t j = query_boundaries_[i]; j < query_boundaries_[i + 1]; ++j) { + if (label_[j] > 0.5f) { + ++npos_per_query_[i]; + } + } + } + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return 1.0f; + } + + void CalMapAtK(std::vector ks, data_size_t npos, const label_t* label, + const double* score, data_size_t num_data, std::vector* out) const { + // get sorted indices by score + std::vector sorted_idx; + for (data_size_t i = 0; i < num_data; ++i) { + sorted_idx.emplace_back(i); + } + std::stable_sort(sorted_idx.begin(), sorted_idx.end(), + [score](data_size_t a, data_size_t b) {return score[a] > score[b]; }); + + int num_hit = 0; + double sum_ap = 0.0f; + data_size_t cur_left = 0; + for (size_t i = 0; i < ks.size(); ++i) { + data_size_t cur_k = static_cast(ks[i]); + if (cur_k > num_data) { + cur_k = num_data; + } + for (data_size_t j = cur_left; j < cur_k; ++j) { + data_size_t idx = sorted_idx[j]; + if (label[idx] > 0.5f) { + ++num_hit; + sum_ap += static_cast(num_hit) / (j + 1.0f); + } + } + if (npos > 0) { + (*out)[i] = sum_ap / std::min(npos, cur_k); + } else { + (*out)[i] = 1.0f; + } + cur_left = cur_k; + } + } + std::vector Eval(const double* score, const ObjectiveFunction*) const override { + // some buffers for multi-threading sum up + int num_threads = OMP_NUM_THREADS(); + std::vector> result_buffer_; + for (int i = 0; i < num_threads; ++i) { + result_buffer_.emplace_back(eval_at_.size(), 0.0f); + } + std::vector tmp_map(eval_at_.size(), 0.0f); + if (query_weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) firstprivate(tmp_map) + for (data_size_t i = 0; i < num_queries_; ++i) { + const int tid = omp_get_thread_num(); + CalMapAtK(eval_at_, npos_per_query_[i], label_ + query_boundaries_[i], + score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_map); + for (size_t j = 0; j < eval_at_.size(); ++j) { + result_buffer_[tid][j] += tmp_map[j]; + } + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) firstprivate(tmp_map) + for (data_size_t i = 0; i < num_queries_; ++i) { + const int tid = omp_get_thread_num(); + CalMapAtK(eval_at_, npos_per_query_[i], label_ + query_boundaries_[i], + score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_map); + for (size_t j = 0; j < eval_at_.size(); ++j) { + result_buffer_[tid][j] += tmp_map[j] * query_weights_[i]; + } + } + } + // Get final average MAP + std::vector result(eval_at_.size(), 0.0f); + for (size_t j = 0; j < result.size(); ++j) { + for (int i = 0; i < num_threads; ++i) { + result[j] += result_buffer_[i][j]; + } + result[j] /= sum_query_weights_; + } + return result; + } + + private: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Query boundaries information */ + const data_size_t* query_boundaries_; + /*! \brief Number of queries */ + data_size_t num_queries_; + /*! \brief Weights of queries */ + const label_t* query_weights_; + /*! \brief Sum weights of queries */ + double sum_query_weights_; + /*! \brief Evaluate position of Nmap */ + std::vector eval_at_; + std::vector name_; + std::vector npos_per_query_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_ diff --git a/src/metric/metric.cpp b/src/metric/metric.cpp new file mode 100644 index 0000000..1b18576 --- /dev/null +++ b/src/metric/metric.cpp @@ -0,0 +1,142 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include + +#include "binary_metric.hpp" +#include "map_metric.hpp" +#include "multiclass_metric.hpp" +#include "rank_metric.hpp" +#include "regression_metric.hpp" +#include "xentropy_metric.hpp" + +#include "cuda/cuda_binary_metric.hpp" +#include "cuda/cuda_regression_metric.hpp" + +namespace LightGBM { + +Metric* Metric::CreateMetric(const std::string& type, const Config& config) { + #ifdef USE_CUDA + if (config.device_type == std::string("cuda") && config.boosting == std::string("gbdt")) { + if (type == std::string("l2")) { + return new CUDAL2Metric(config); + } else if (type == std::string("rmse")) { + return new CUDARMSEMetric(config); + } else if (type == std::string("l1")) { + return new CUDAL1Metric(config); + } else if (type == std::string("quantile")) { + return new CUDAQuantileMetric(config); + } else if (type == std::string("huber")) { + return new CUDAHuberLossMetric(config); + } else if (type == std::string("fair")) { + return new CUDAFairLossMetric(config); + } else if (type == std::string("poisson")) { + return new CUDAPoissonMetric(config); + } else if (type == std::string("binary_logloss")) { + return new CUDABinaryLoglossMetric(config); + } else if (type == std::string("binary_error")) { + return new CUDABinaryErrorMetric(config); + } else if (type == std::string("auc")) { + Log::Warning("Metric auc is not implemented in cuda version. Fall back to evaluation on CPU."); + return new AUCMetric(config); + } else if (type == std::string("average_precision")) { + Log::Warning("Metric average_precision is not implemented in cuda version. Fall back to evaluation on CPU."); + return new AveragePrecisionMetric(config); + } else if (type == std::string("auc_mu")) { + Log::Warning("Metric auc_mu is not implemented in cuda version. Fall back to evaluation on CPU."); + return new AucMuMetric(config); + } else if (type == std::string("ndcg")) { + Log::Warning("Metric ndcg is not implemented in cuda version. Fall back to evaluation on CPU."); + return new NDCGMetric(config); + } else if (type == std::string("map")) { + Log::Warning("Metric map is not implemented in cuda version. Fall back to evaluation on CPU."); + return new MapMetric(config); + } else if (type == std::string("multi_logloss")) { + Log::Warning("Metric multi_logloss is not implemented in cuda version. Fall back to evaluation on CPU."); + return new MultiSoftmaxLoglossMetric(config); + } else if (type == std::string("multi_error")) { + Log::Warning("Metric multi_error is not implemented in cuda version. Fall back to evaluation on CPU."); + return new MultiErrorMetric(config); + } else if (type == std::string("cross_entropy")) { + Log::Warning("Metric cross_entropy is not implemented in cuda version. Fall back to evaluation on CPU."); + return new CrossEntropyMetric(config); + } else if (type == std::string("cross_entropy_lambda")) { + Log::Warning("Metric cross_entropy_lambda is not implemented in cuda version. Fall back to evaluation on CPU."); + return new CrossEntropyLambdaMetric(config); + } else if (type == std::string("kullback_leibler")) { + Log::Warning("Metric kullback_leibler is not implemented in cuda version. Fall back to evaluation on CPU."); + return new KullbackLeiblerDivergence(config); + } else if (type == std::string("mape")) { + return new CUDAMAPEMetric(config); + } else if (type == std::string("gamma")) { + return new CUDAGammaMetric(config); + } else if (type == std::string("gamma_deviance")) { + return new CUDAGammaDevianceMetric(config); + } else if (type == std::string("tweedie")) { + return new CUDATweedieMetric(config); + } else if (type == std::string("r2")) { + Log::Warning("Metric r2 is not implemented in cuda version. Fall back to evaluation on CPU."); + return new R2Metric(config); + } + } else { + #endif // USE_CUDA + if (type == std::string("l2")) { + return new L2Metric(config); + } else if (type == std::string("rmse")) { + return new RMSEMetric(config); + } else if (type == std::string("l1")) { + return new L1Metric(config); + } else if (type == std::string("quantile")) { + return new QuantileMetric(config); + } else if (type == std::string("huber")) { + return new HuberLossMetric(config); + } else if (type == std::string("fair")) { + return new FairLossMetric(config); + } else if (type == std::string("poisson")) { + return new PoissonMetric(config); + } else if (type == std::string("binary_logloss")) { + return new BinaryLoglossMetric(config); + } else if (type == std::string("binary_error")) { + return new BinaryErrorMetric(config); + } else if (type == std::string("auc")) { + return new AUCMetric(config); + } else if (type == std::string("average_precision")) { + return new AveragePrecisionMetric(config); + } else if (type == std::string("auc_mu")) { + return new AucMuMetric(config); + } else if (type == std::string("ndcg")) { + return new NDCGMetric(config); + } else if (type == std::string("map")) { + return new MapMetric(config); + } else if (type == std::string("multi_logloss")) { + return new MultiSoftmaxLoglossMetric(config); + } else if (type == std::string("multi_error")) { + return new MultiErrorMetric(config); + } else if (type == std::string("cross_entropy")) { + return new CrossEntropyMetric(config); + } else if (type == std::string("cross_entropy_lambda")) { + return new CrossEntropyLambdaMetric(config); + } else if (type == std::string("kullback_leibler")) { + return new KullbackLeiblerDivergence(config); + } else if (type == std::string("mape")) { + return new MAPEMetric(config); + } else if (type == std::string("gamma")) { + return new GammaMetric(config); + } else if (type == std::string("gamma_deviance")) { + return new GammaDevianceMetric(config); + } else if (type == std::string("tweedie")) { + return new TweedieMetric(config); + } else if (type == std::string("r2")) { + return new R2Metric(config); + } + #ifdef USE_CUDA + } + #endif // USE_CUDA + return nullptr; +} + +} // namespace LightGBM diff --git a/src/metric/multiclass_metric.hpp b/src/metric/multiclass_metric.hpp new file mode 100644 index 0000000..ff27650 --- /dev/null +++ b/src/metric/multiclass_metric.hpp @@ -0,0 +1,369 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_ + +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { +/*! +* \brief Metric for multiclass task. +* Use static class "PointWiseLossCalculator" to calculate loss point-wise +*/ +template +class MulticlassMetric: public Metric { + public: + explicit MulticlassMetric(const Config& config) :config_(config) { + num_class_ = config.num_class; + } + + virtual ~MulticlassMetric() { + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back(PointWiseLossCalculator::Name(config_)); + num_data_ = num_data; + // get label + label_ = metadata.label(); + // get weights + weights_ = metadata.weights(); + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + sum_weights_ = 0.0f; + for (data_size_t i = 0; i < num_data_; ++i) { + sum_weights_ += weights_[i]; + } + } + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return -1.0f; + } + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { + double sum_loss = 0.0; + int num_tree_per_iteration = num_class_; + int num_pred_per_row = num_class_; + if (objective != nullptr) { + num_tree_per_iteration = objective->NumModelPerIteration(); + num_pred_per_row = objective->NumPredictOneRow(); + } + if (objective != nullptr) { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector raw_score(num_tree_per_iteration); + for (int k = 0; k < num_tree_per_iteration; ++k) { + size_t idx = static_cast(num_data_) * k + i; + raw_score[k] = static_cast(score[idx]); + } + std::vector rec(num_pred_per_row); + objective->ConvertOutput(raw_score.data(), rec.data()); + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector raw_score(num_tree_per_iteration); + for (int k = 0; k < num_tree_per_iteration; ++k) { + size_t idx = static_cast(num_data_) * k + i; + raw_score[k] = static_cast(score[idx]); + } + std::vector rec(num_pred_per_row); + objective->ConvertOutput(raw_score.data(), rec.data()); + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_) * weights_[i]; + } + } + } else { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector rec(num_tree_per_iteration); + for (int k = 0; k < num_tree_per_iteration; ++k) { + size_t idx = static_cast(num_data_) * k + i; + rec[k] = static_cast(score[idx]); + } + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + std::vector rec(num_tree_per_iteration); + for (int k = 0; k < num_tree_per_iteration; ++k) { + size_t idx = static_cast(num_data_) * k + i; + rec[k] = static_cast(score[idx]); + } + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_) * weights_[i]; + } + } + } + double loss = sum_loss / sum_weights_; + return std::vector(1, loss); + } + + private: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Pointer of weighs */ + const label_t* weights_; + /*! \brief Sum weights */ + double sum_weights_; + /*! \brief Name of this test set */ + std::vector name_; + int num_class_; + /*! \brief config parameters*/ + Config config_; +}; + +/*! \brief top-k error for multiclass task; if k=1 (default) this is the usual multi-error */ +class MultiErrorMetric: public MulticlassMetric { + public: + explicit MultiErrorMetric(const Config& config) :MulticlassMetric(config) {} + + inline static double LossOnPoint(label_t label, std::vector* score, const Config& config) { + size_t k = static_cast(label); + auto& ref_score = *score; + int num_larger = 0; + for (size_t i = 0; i < score->size(); ++i) { + if (ref_score[i] >= ref_score[k]) ++num_larger; + if (num_larger > config.multi_error_top_k) return 1.0f; + } + return 0.0f; + } + + inline static const std::string Name(const Config& config) { + if (config.multi_error_top_k == 1) { + return "multi_error"; + } else { + return "multi_error@" + std::to_string(config.multi_error_top_k); + } + } +}; + +/*! \brief Logloss for multiclass task */ +class MultiSoftmaxLoglossMetric: public MulticlassMetric { + public: + explicit MultiSoftmaxLoglossMetric(const Config& config) :MulticlassMetric(config) {} + + inline static double LossOnPoint(label_t label, std::vector* score, const Config&) { + size_t k = static_cast(label); + auto& ref_score = *score; + if (ref_score[k] > kEpsilon) { + return static_cast(-std::log(ref_score[k])); + } else { + return -std::log(kEpsilon); + } + } + + inline static const std::string Name(const Config&) { + return "multi_logloss"; + } +}; + +/*! \brief AUC mu for multiclass task*/ +class AucMuMetric : public Metric { + public: + explicit AucMuMetric(const Config& config) : config_(config) { + num_class_ = config.num_class; + class_weights_ = config.auc_mu_weights_matrix; + } + + virtual ~AucMuMetric() {} + + const std::vector& GetName() const override { return name_; } + + double factor_to_bigger_better() const override { return 1.0f; } + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back("auc_mu"); + + num_data_ = num_data; + label_ = metadata.label(); + + // get weights + weights_ = metadata.weights(); + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + sum_weights_ = 0.0f; + for (data_size_t i = 0; i < num_data_; ++i) { + sum_weights_ += weights_[i]; + } + } + + // sort the data indices by true class + sorted_data_idx_ = std::vector(num_data_, 0); + for (data_size_t i = 0; i < num_data_; ++i) { + sorted_data_idx_[i] = i; + } + Common::ParallelSort(sorted_data_idx_.begin(), sorted_data_idx_.end(), + [this](data_size_t a, data_size_t b) { return label_[a] < label_[b]; }); + + // get size of each class + class_sizes_ = std::vector(num_class_, 0); + for (data_size_t i = 0; i < num_data_; ++i) { + data_size_t curr_label = static_cast(label_[i]); + ++class_sizes_[curr_label]; + } + + // get total weight of data in each class + class_data_weights_ = std::vector(num_class_, 0); + if (weights_ != nullptr) { + for (data_size_t i = 0; i < num_data_; ++i) { + data_size_t curr_label = static_cast(label_[i]); + class_data_weights_[curr_label] += weights_[i]; + } + } + } + + std::vector Eval(const double* score, const ObjectiveFunction*) const override { + // the notation follows that used in the paper introducing the auc-mu metric: + // https://proceedings.mlr.press/v97/kleiman19a.html + + auto S = std::vector>(num_class_, std::vector(num_class_, 0)); + int i_start = 0; + for (int i = 0; i < num_class_; ++i) { + int j_start = i_start + class_sizes_[i]; + for (int j = i + 1; j < num_class_; ++j) { + std::vector curr_v; + for (int k = 0; k < num_class_; ++k) { + curr_v.emplace_back(class_weights_[i][k] - class_weights_[j][k]); + } + double t1 = curr_v[i] - curr_v[j]; + // extract the data indices belonging to class i or j + std::vector class_i_j_indices; + class_i_j_indices.assign(sorted_data_idx_.begin() + i_start, sorted_data_idx_.begin() + i_start + class_sizes_[i]); + class_i_j_indices.insert(class_i_j_indices.end(), + sorted_data_idx_.begin() + j_start, sorted_data_idx_.begin() + j_start + class_sizes_[j]); + // sort according to distance from separating hyperplane + std::vector> dist; + for (data_size_t k = 0; static_cast(k) < class_i_j_indices.size(); ++k) { + data_size_t a = class_i_j_indices[k]; + double v_a = 0; + for (int m = 0; m < num_class_; ++m) { + v_a += curr_v[m] * score[num_data_ * m + a]; + } + dist.push_back(std::pair(a, t1 * v_a)); + } + Common::ParallelSort(dist.begin(), dist.end(), + [this](std::pair a, std::pair b) { + // if scores are equal, put j class first + if (std::fabs(a.second - b.second) < kEpsilon) { + return label_[a.first] > label_[b.first]; + } else if (a.second < b.second) { + return true; + } else { + return false; + } + }); + // calculate AUC + double num_j = 0; + double last_j_dist = 0; + double num_current_j = 0; + if (weights_ == nullptr) { + for (size_t k = 0; k < dist.size(); ++k) { + data_size_t a = dist[k].first; + double curr_dist = dist[k].second; + if (label_[a] == i) { + if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { + S[i][j] += num_j - 0.5 * num_current_j; // members of class j with same distance as a contribute 0.5 + } else { + S[i][j] += num_j; + } + } else { + ++num_j; + if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { + ++num_current_j; + } else { + last_j_dist = dist[k].second; + num_current_j = 1; + } + } + } + } else { + for (size_t k = 0; k < dist.size(); ++k) { + data_size_t a = dist[k].first; + double curr_dist = dist[k].second; + double curr_weight = weights_[a]; + if (label_[a] == i) { + if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { + S[i][j] += curr_weight * (num_j - 0.5 * num_current_j); // members of class j with same distance as a contribute 0.5 + } else { + S[i][j] += curr_weight * num_j; + } + } else { + num_j += curr_weight; + if (std::fabs(curr_dist - last_j_dist) < kEpsilon) { + num_current_j += curr_weight; + } else { + last_j_dist = dist[k].second; + num_current_j = curr_weight; + } + } + } + } + j_start += class_sizes_[j]; + } + i_start += class_sizes_[i]; + } + double ans = 0; + for (int i = 0; i < num_class_; ++i) { + for (int j = i + 1; j < num_class_; ++j) { + if (weights_ == nullptr) { + ans += (S[i][j] / class_sizes_[i]) / class_sizes_[j]; + } else { + ans += (S[i][j] / class_data_weights_[i]) / class_data_weights_[j]; + } + } + } + ans = (2.0 * ans / num_class_) / (num_class_ - 1); + return std::vector(1, ans); + } + + private: + /*! \brief Number of data*/ + data_size_t num_data_; + /*! \brief Pointer to label*/ + const label_t* label_; + /*! \brief Name of this metric*/ + std::vector name_; + /*! \brief Number of classes*/ + int num_class_; + /*! \brief Class auc-mu weights*/ + std::vector> class_weights_; + /*! \brief Data weights */ + const label_t* weights_; + /*! \brief Sum of data weights */ + double sum_weights_; + /*! \brief Sum of data weights in each class*/ + std::vector class_data_weights_; + /*! \brief Number of data in each class*/ + std::vector class_sizes_; + /*! \brief config parameters*/ + Config config_; + /*! \brief index to data, sorted by true class*/ + std::vector sorted_data_idx_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_ diff --git a/src/metric/rank_metric.hpp b/src/metric/rank_metric.hpp new file mode 100644 index 0000000..8e41696 --- /dev/null +++ b/src/metric/rank_metric.hpp @@ -0,0 +1,170 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_ + +#include +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + +class NDCGMetric:public Metric { + public: + explicit NDCGMetric(const Config& config) { + // get eval position + eval_at_ = config.eval_at; + auto label_gain = config.label_gain; + DCGCalculator::DefaultEvalAt(&eval_at_); + DCGCalculator::DefaultLabelGain(&label_gain); + // initialize DCG calculator + DCGCalculator::Init(label_gain); + } + + ~NDCGMetric() { + } + void Init(const Metadata& metadata, data_size_t num_data) override { + for (auto k : eval_at_) { + name_.emplace_back(std::string("ndcg@") + std::to_string(k)); + } + num_data_ = num_data; + // get label + label_ = metadata.label(); + num_queries_ = metadata.num_queries(); + DCGCalculator::CheckMetadata(metadata, num_queries_); + DCGCalculator::CheckLabel(label_, num_data_); + // get query boundaries + query_boundaries_ = metadata.query_boundaries(); + if (query_boundaries_ == nullptr) { + Log::Fatal("The NDCG metric requires query information"); + } + // get query weights + query_weights_ = metadata.query_weights(); + if (query_weights_ == nullptr) { + sum_query_weights_ = static_cast(num_queries_); + } else { + sum_query_weights_ = 0.0f; + for (data_size_t i = 0; i < num_queries_; ++i) { + sum_query_weights_ += query_weights_[i]; + } + } + inverse_max_dcgs_.resize(num_queries_); + // cache the inverse max DCG for all queries, used to calculate NDCG + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_queries_; ++i) { + inverse_max_dcgs_[i].resize(eval_at_.size(), 0.0f); + DCGCalculator::CalMaxDCG(eval_at_, label_ + query_boundaries_[i], + query_boundaries_[i + 1] - query_boundaries_[i], + &inverse_max_dcgs_[i]); + for (size_t j = 0; j < inverse_max_dcgs_[i].size(); ++j) { + if (inverse_max_dcgs_[i][j] > 0.0f) { + inverse_max_dcgs_[i][j] = 1.0f / inverse_max_dcgs_[i][j]; + } else { + // marking negative for all negative queries. + // if one meet this query, it's ndcg will be set as -1. + inverse_max_dcgs_[i][j] = -1.0f; + } + } + } + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return 1.0f; + } + + std::vector Eval(const double* score, const ObjectiveFunction*) const override { + int num_threads = OMP_NUM_THREADS(); + // some buffers for multi-threading sum up + std::vector> result_buffer_; + for (int i = 0; i < num_threads; ++i) { + result_buffer_.emplace_back(eval_at_.size(), 0.0f); + } + std::vector tmp_dcg(eval_at_.size(), 0.0f); + if (query_weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(tmp_dcg) + for (data_size_t i = 0; i < num_queries_; ++i) { + const int tid = omp_get_thread_num(); + // if all doc in this query are all negative, let its NDCG=1 + if (inverse_max_dcgs_[i][0] <= 0.0f) { + for (size_t j = 0; j < eval_at_.size(); ++j) { + result_buffer_[tid][j] += 1.0f; + } + } else { + // calculate DCG + DCGCalculator::CalDCG(eval_at_, label_ + query_boundaries_[i], + score + query_boundaries_[i], + query_boundaries_[i + 1] - query_boundaries_[i], &tmp_dcg); + // calculate NDCG + for (size_t j = 0; j < eval_at_.size(); ++j) { + result_buffer_[tid][j] += tmp_dcg[j] * inverse_max_dcgs_[i][j]; + } + } + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(tmp_dcg) + for (data_size_t i = 0; i < num_queries_; ++i) { + const int tid = omp_get_thread_num(); + // if all doc in this query are all negative, let its NDCG=1 + if (inverse_max_dcgs_[i][0] <= 0.0f) { + for (size_t j = 0; j < eval_at_.size(); ++j) { + result_buffer_[tid][j] += 1.0f; + } + } else { + // calculate DCG + DCGCalculator::CalDCG(eval_at_, label_ + query_boundaries_[i], + score + query_boundaries_[i], + query_boundaries_[i + 1] - query_boundaries_[i], &tmp_dcg); + // calculate NDCG + for (size_t j = 0; j < eval_at_.size(); ++j) { + result_buffer_[tid][j] += tmp_dcg[j] * inverse_max_dcgs_[i][j] * query_weights_[i]; + } + } + } + } + // Get final average NDCG + std::vector result(eval_at_.size(), 0.0f); + for (size_t j = 0; j < result.size(); ++j) { + for (int i = 0; i < num_threads; ++i) { + result[j] += result_buffer_[i][j]; + } + result[j] /= sum_query_weights_; + } + return result; + } + + private: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Name of test set */ + std::vector name_; + /*! \brief Query boundaries information */ + const data_size_t* query_boundaries_; + /*! \brief Number of queries */ + data_size_t num_queries_; + /*! \brief Weights of queries */ + const label_t* query_weights_; + /*! \brief Sum weights of queries */ + double sum_query_weights_; + /*! \brief Evaluate position of NDCG */ + std::vector eval_at_; + /*! \brief Cache the inverse max dcg for all queries */ + std::vector> inverse_max_dcgs_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_ diff --git a/src/metric/regression_metric.hpp b/src/metric/regression_metric.hpp new file mode 100644 index 0000000..4556e9c --- /dev/null +++ b/src/metric/regression_metric.hpp @@ -0,0 +1,433 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_ + +#include +#include + +#include +#include +#include +#include + +namespace LightGBM { +/*! +* \brief Metric for regression task. +* Use static class "PointWiseLossCalculator" to calculate loss point-wise +*/ +template +class RegressionMetric: public Metric { + public: + explicit RegressionMetric(const Config& config) :config_(config) { + } + + virtual ~RegressionMetric() { + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return -1.0f; + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back(PointWiseLossCalculator::Name()); + num_data_ = num_data; + // get label + label_ = metadata.label(); + // get weights + weights_ = metadata.weights(); + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + sum_weights_ = 0.0f; + for (data_size_t i = 0; i < num_data_; ++i) { + sum_weights_ += weights_[i]; + } + } + for (data_size_t i = 0; i < num_data_; ++i) { + PointWiseLossCalculator::CheckLabel(label_[i]); + } + } + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { + double sum_loss = 0.0f; + if (objective == nullptr) { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i], config_); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + // add loss + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i], config_) * weights_[i]; + } + } + } else { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + // add loss + double t = 0; + objective->ConvertOutput(&score[i], &t); + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], t, config_); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + // add loss + double t = 0; + objective->ConvertOutput(&score[i], &t); + sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], t, config_) * weights_[i]; + } + } + } + double loss = PointWiseLossCalculator::AverageLoss(sum_loss, sum_weights_); + return std::vector(1, loss); + } + + inline static double AverageLoss(double sum_loss, double sum_weights) { + return sum_loss / sum_weights; + } + + inline static void CheckLabel(label_t) { + } + + protected: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Pointer of weighs */ + const label_t* weights_; + /*! \brief Sum weights */ + double sum_weights_; + /*! \brief Name of this test set */ + Config config_; + std::vector name_; +}; + +/*! \brief RMSE loss for regression task */ +class RMSEMetric: public RegressionMetric { + public: + explicit RMSEMetric(const Config& config) :RegressionMetric(config) {} + + inline static double LossOnPoint(label_t label, double score, const Config&) { + return (score - label)*(score - label); + } + + inline static double AverageLoss(double sum_loss, double sum_weights) { + // need sqrt the result for RMSE loss + return std::sqrt(sum_loss / sum_weights); + } + + inline static const char* Name() { + return "rmse"; + } +}; + +/*! \brief L2 loss for regression task */ +class L2Metric: public RegressionMetric { + public: + explicit L2Metric(const Config& config) :RegressionMetric(config) {} + + inline static double LossOnPoint(label_t label, double score, const Config&) { + return (score - label)*(score - label); + } + + inline static const char* Name() { + return "l2"; + } +}; + +/*! \brief Quantile loss for regression task */ +class QuantileMetric : public RegressionMetric { + public: + explicit QuantileMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config& config) { + double delta = label - score; + if (delta < 0) { + return (config.alpha - 1.0f) * delta; + } else { + return config.alpha * delta; + } + } + + inline static const char* Name() { + return "quantile"; + } +}; + + +/*! \brief L1 loss for regression task */ +class L1Metric: public RegressionMetric { + public: + explicit L1Metric(const Config& config) :RegressionMetric(config) {} + + inline static double LossOnPoint(label_t label, double score, const Config&) { + return std::fabs(score - label); + } + inline static const char* Name() { + return "l1"; + } +}; + +/*! \brief Huber loss for regression task */ +class HuberLossMetric: public RegressionMetric { + public: + explicit HuberLossMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config& config) { + const double diff = score - label; + if (std::abs(diff) <= config.alpha) { + return 0.5f * diff * diff; + } else { + return config.alpha * (std::abs(diff) - 0.5f * config.alpha); + } + } + + inline static const char* Name() { + return "huber"; + } +}; + +/*! \brief Fair loss for regression task */ +// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html +class FairLossMetric: public RegressionMetric { + public: + explicit FairLossMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config& config) { + const double x = std::fabs(score - label); + const double c = config.fair_c; + return c * x - c * c * std::log1p(x / c); + } + + inline static const char* Name() { + return "fair"; + } +}; + +/*! \brief Poisson regression loss for regression task */ +class PoissonMetric: public RegressionMetric { + public: + explicit PoissonMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config&) { + const double eps = 1e-10f; + if (score < eps) { + score = eps; + } + return score - label * std::log(score); + } + inline static const char* Name() { + return "poisson"; + } +}; + + +/*! \brief MAPE regression loss for regression task */ +class MAPEMetric : public RegressionMetric { + public: + explicit MAPEMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config&) { + return std::fabs((label - score)) / std::max(1.0f, std::fabs(label)); + } + inline static const char* Name() { + return "mape"; + } +}; + +class GammaMetric : public RegressionMetric { + public: + explicit GammaMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config&) { + const double psi = 1.0; + const double theta = -1.0 / score; + const double a = psi; + const double b = -Common::SafeLog(-theta); + const double c = 1. / psi * Common::SafeLog(label / psi) - Common::SafeLog(label) - 0; // 0 = std::lgamma(1.0 / psi) = std::lgamma(1.0); + return -((label * theta - b) / a + c); + } + inline static const char* Name() { + return "gamma"; + } + + inline static void CheckLabel(label_t label) { + CHECK_GT(label, 0); + } +}; + + +class GammaDevianceMetric : public RegressionMetric { + public: + explicit GammaDevianceMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config&) { + const double epsilon = 1.0e-9; + const double tmp = label / (score + epsilon); + return tmp - Common::SafeLog(tmp) - 1; + } + inline static const char* Name() { + return "gamma_deviance"; + } + inline static double AverageLoss(double sum_loss, double) { + return sum_loss * 2; + } + inline static void CheckLabel(label_t label) { + CHECK_GT(label, 0); + } +}; + +class TweedieMetric : public RegressionMetric { + public: + explicit TweedieMetric(const Config& config) :RegressionMetric(config) { + } + + inline static double LossOnPoint(label_t label, double score, const Config& config) { + const double rho = config.tweedie_variance_power; + const double eps = 1e-10f; + if (score < eps) { + score = eps; + } + const double a = label * std::exp((1 - rho) * std::log(score)) / (1 - rho); + const double b = std::exp((2 - rho) * std::log(score)) / (2 - rho); + return -a + b; + } + inline static const char* Name() { + return "tweedie"; + } +}; + + +class R2Metric: public Metric { + public: + explicit R2Metric(const Config& config) :config_(config) {} + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return 1.0f; + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back("r2"); + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + + double sum_label = 0.0f; + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_label) + for (data_size_t i = 0; i < num_data_; ++i) { + sum_label += label_[i]; + } + } else { + double local_sum_weights = 0.0f; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_sum_weights, sum_label) + for (data_size_t i = 0; i < num_data_; ++i) { + local_sum_weights += weights_[i]; + sum_label += label_[i] * weights_[i]; + } + sum_weights_ = local_sum_weights; + } + label_mean_ = sum_label / sum_weights_; + + total_sum_squares_ = 0.0f; + double local_total_sum_squares = 0.0f; + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_total_sum_squares) + for (data_size_t i = 0; i < num_data_; ++i) { + double diff = label_[i] - label_mean_; + local_total_sum_squares += diff * diff; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_total_sum_squares) + for (data_size_t i = 0; i < num_data_; ++i) { + double diff = label_[i] - label_mean_; + local_total_sum_squares += diff * diff * weights_[i]; + } + } + total_sum_squares_ = local_total_sum_squares; + } + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { + double residual_sum_squares = 0.0f; + if (objective == nullptr) { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) + for (data_size_t i = 0; i < num_data_; ++i) { + double diff = label_[i] - score[i]; + residual_sum_squares += diff * diff; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) + for (data_size_t i = 0; i < num_data_; ++i) { + double diff = label_[i] - score[i]; + residual_sum_squares += diff * diff * weights_[i]; + } + } + } else { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) + for (data_size_t i = 0; i < num_data_; ++i) { + double t = 0; + objective->ConvertOutput(&score[i], &t); + double diff = label_[i] - t; + residual_sum_squares += diff * diff; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares) + for (data_size_t i = 0; i < num_data_; ++i) { + double t = 0; + objective->ConvertOutput(&score[i], &t); + double diff = label_[i] - t; + residual_sum_squares += diff * diff * weights_[i]; + } + } + } + + double r2 = 1.0 - (residual_sum_squares / total_sum_squares_); + if (std::fabs(total_sum_squares_) < kZeroThreshold) { + return std::vector(1, std::fabs(residual_sum_squares) < kZeroThreshold ? 1.0 : 0.0); + } + return std::vector(1, r2); + } + + protected: + data_size_t num_data_; + const label_t* label_; + const label_t* weights_; + double sum_weights_; + Config config_; + std::vector name_; + + // Custom members for R2 calculation + double label_mean_; + double total_sum_squares_; +}; + + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_ diff --git a/src/metric/xentropy_metric.hpp b/src/metric/xentropy_metric.hpp new file mode 100644 index 0000000..bff0828 --- /dev/null +++ b/src/metric/xentropy_metric.hpp @@ -0,0 +1,359 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_ +#define LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_ + +#include +#include +#include +#include + +#include +#include +#include +#include + +/* + * Implements three related metrics: + * + * (1) standard cross-entropy that can be used for continuous labels in [0, 1] + * (2) "intensity-weighted" cross-entropy, also for continuous labels in [0, 1] + * (3) Kullback-Leibler divergence, also for continuous labels in [0, 1] + * + * (3) adds an offset term to (1); the entropy of the label + * + * See xentropy_objective.hpp for further details. + * + */ + +namespace LightGBM { + +// label should be in interval [0, 1]; +// prob should be in interval (0, 1); prob is clipped if needed +inline static double XentLoss(label_t label, double prob) { + const double log_arg_epsilon = 1.0e-12; + double a = label; + if (prob > log_arg_epsilon) { + a *= std::log(prob); + } else { + a *= std::log(log_arg_epsilon); + } + double b = 1.0f - label; + if (1.0f - prob > log_arg_epsilon) { + b *= std::log(1.0f - prob); + } else { + b *= std::log(log_arg_epsilon); + } + return - (a + b); +} + +// hhat >(=) 0 assumed; and weight > 0 required; but not checked here +inline static double XentLambdaLoss(label_t label, label_t weight, double hhat) { + return XentLoss(label, 1.0f - std::exp(-weight * hhat)); +} + +// Computes the (negative) entropy for label p; p should be in interval [0, 1]; +// This is used to presum the KL-divergence offset term (to be _added_ to the cross-entropy loss). +// NOTE: x*log(x) = 0 for x=0,1; so only add when in (0, 1); avoid log(0)*0 +inline static double YentLoss(double p) { + double hp = 0.0; + if (p > 0) hp += p * std::log(p); + double q = 1.0f - p; + if (q > 0) hp += q * std::log(q); + return hp; +} + +// +// CrossEntropyMetric : "xentropy" : (optional) weights are used linearly +// +class CrossEntropyMetric : public Metric { + public: + explicit CrossEntropyMetric(const Config&) {} + virtual ~CrossEntropyMetric() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back("cross_entropy"); + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + + CHECK_NOTNULL(label_); + + // ensure that labels are in interval [0, 1], interval ends included + Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str()); + Log::Info("[%s:%s]: (metric) labels passed interval [0, 1] check", GetName()[0].c_str(), __func__); + + // check that weights are non-negative and sum is positive + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + label_t minw; + Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sum_weights_); + if (minw < 0.0f) { + Log::Fatal("[%s:%s]: (metric) weights not allowed to be negative", GetName()[0].c_str(), __func__); + } + } + + // check weight sum (may fail to be zero) + if (sum_weights_ <= 0.0f) { + Log::Fatal("[%s:%s]: sum-of-weights = %f is non-positive", __func__, GetName()[0].c_str(), sum_weights_); + } + Log::Info("[%s:%s]: sum-of-weights = %f", GetName()[0].c_str(), __func__, sum_weights_); + } + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { + double sum_loss = 0.0f; + if (objective == nullptr) { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + sum_loss += XentLoss(label_[i], score[i]); // NOTE: does not work unless score is a probability + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + sum_loss += XentLoss(label_[i], score[i]) * weights_[i]; // NOTE: does not work unless score is a probability + } + } + } else { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double p = 0; + objective->ConvertOutput(&score[i], &p); + sum_loss += XentLoss(label_[i], p); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double p = 0; + objective->ConvertOutput(&score[i], &p); + sum_loss += XentLoss(label_[i], p) * weights_[i]; + } + } + } + double loss = sum_loss / sum_weights_; + return std::vector(1, loss); + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return -1.0f; // negative means smaller loss is better, positive means larger loss is better + } + + private: + /*! \brief Number of data points */ + data_size_t num_data_; + /*! \brief Pointer to label */ + const label_t* label_; + /*! \brief Pointer to weights */ + const label_t* weights_; + /*! \brief Sum of weights */ + double sum_weights_; + /*! \brief Name of this metric */ + std::vector name_; +}; + +// +// CrossEntropyLambdaMetric : "xentlambda" : (optional) weights have a different meaning than for "xentropy" +// ATTENTION: Supposed to be used when the objective also is "xentlambda" +// +class CrossEntropyLambdaMetric : public Metric { + public: + explicit CrossEntropyLambdaMetric(const Config&) {} + virtual ~CrossEntropyLambdaMetric() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back("cross_entropy_lambda"); + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + + CHECK_NOTNULL(label_); + Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str()); + Log::Info("[%s:%s]: (metric) labels passed interval [0, 1] check", GetName()[0].c_str(), __func__); + + // check all weights are strictly positive; throw error if not + if (weights_ != nullptr) { + label_t minw; + Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), static_cast(nullptr)); + if (minw <= 0.0f) { + Log::Fatal("[%s:%s]: (metric) all weights must be positive", GetName()[0].c_str(), __func__); + } + } + } + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { + double sum_loss = 0.0f; + if (objective == nullptr) { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double hhat = std::log1p(std::exp(score[i])); // auto-convert + sum_loss += XentLambdaLoss(label_[i], 1.0f, hhat); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double hhat = std::log1p(std::exp(score[i])); // auto-convert + sum_loss += XentLambdaLoss(label_[i], weights_[i], hhat); + } + } + } else { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double hhat = 0; + objective->ConvertOutput(&score[i], &hhat); // NOTE: this only works if objective = "xentlambda" + sum_loss += XentLambdaLoss(label_[i], 1.0f, hhat); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double hhat = 0; + objective->ConvertOutput(&score[i], &hhat); // NOTE: this only works if objective = "xentlambda" + sum_loss += XentLambdaLoss(label_[i], weights_[i], hhat); + } + } + } + return std::vector(1, sum_loss / static_cast(num_data_)); + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return -1.0f; + } + + private: + /*! \brief Number of data points */ + data_size_t num_data_; + /*! \brief Pointer to label */ + const label_t* label_; + /*! \brief Pointer to weights */ + const label_t* weights_; + /*! \brief Name of this metric */ + std::vector name_; +}; + +// +// KullbackLeiblerDivergence : "kldiv" : (optional) weights are used linearly +// +class KullbackLeiblerDivergence : public Metric { + public: + explicit KullbackLeiblerDivergence(const Config&) {} + virtual ~KullbackLeiblerDivergence() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + name_.emplace_back("kullback_leibler"); + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + + CHECK_NOTNULL(label_); + Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str()); + Log::Info("[%s:%s]: (metric) labels passed interval [0, 1] check", GetName()[0].c_str(), __func__); + + if (weights_ == nullptr) { + sum_weights_ = static_cast(num_data_); + } else { + label_t minw; + Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sum_weights_); + if (minw < 0.0f) { + Log::Fatal("[%s:%s]: (metric) at least one weight is negative", GetName()[0].c_str(), __func__); + } + } + + // check weight sum + if (sum_weights_ <= 0.0f) { + Log::Fatal("[%s:%s]: sum-of-weights = %f is non-positive", GetName()[0].c_str(), __func__, sum_weights_); + } + + Log::Info("[%s:%s]: sum-of-weights = %f", GetName()[0].c_str(), __func__, sum_weights_); + + // evaluate offset term + presum_label_entropy_ = 0.0f; + if (weights_ == nullptr) { + for (data_size_t i = 0; i < num_data; ++i) { + presum_label_entropy_ += YentLoss(label_[i]); + } + } else { + for (data_size_t i = 0; i < num_data; ++i) { + presum_label_entropy_ += YentLoss(label_[i]) * weights_[i]; + } + } + presum_label_entropy_ /= sum_weights_; + + // communicate the value of the offset term to be added + Log::Info("%s offset term = %f", GetName()[0].c_str(), presum_label_entropy_); + } + + std::vector Eval(const double* score, const ObjectiveFunction* objective) const override { + double sum_loss = 0.0f; + if (objective == nullptr) { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + sum_loss += XentLoss(label_[i], score[i]); // NOTE: does not work unless score is a probability + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + sum_loss += XentLoss(label_[i], score[i]) * weights_[i]; // NOTE: does not work unless score is a probability + } + } + } else { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double p = 0; + objective->ConvertOutput(&score[i], &p); + sum_loss += XentLoss(label_[i], p); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss) + for (data_size_t i = 0; i < num_data_; ++i) { + double p = 0; + objective->ConvertOutput(&score[i], &p); + sum_loss += XentLoss(label_[i], p) * weights_[i]; + } + } + } + double loss = presum_label_entropy_ + sum_loss / sum_weights_; + return std::vector(1, loss); + } + + const std::vector& GetName() const override { + return name_; + } + + double factor_to_bigger_better() const override { + return -1.0f; + } + + private: + /*! \brief Number of data points */ + data_size_t num_data_; + /*! \brief Pointer to label */ + const label_t* label_; + /*! \brief Pointer to weights */ + const label_t* weights_; + /*! \brief Sum of weights */ + double sum_weights_; + /*! \brief Offset term to cross-entropy; precomputed during init */ + double presum_label_entropy_; + /*! \brief Name of this metric */ + std::vector name_; +}; + +} // end namespace LightGBM + +#endif // LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_ diff --git a/src/network/linker_topo.cpp b/src/network/linker_topo.cpp new file mode 100644 index 0000000..c25f543 --- /dev/null +++ b/src/network/linker_topo.cpp @@ -0,0 +1,182 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + + +BruckMap::BruckMap() { + k = 0; +} + +BruckMap::BruckMap(int n) { + k = n; + // default set to -1 + for (int i = 0; i < n; ++i) { + in_ranks.push_back(-1); + out_ranks.push_back(-1); + } +} + +BruckMap BruckMap::Construct(int rank, int num_machines) { + // distance at k-th communication, distance[k] = 2^k + std::vector distance; + int k = 0; + for (k = 0; (1 << k) < num_machines; ++k) { + distance.push_back(1 << k); + } + BruckMap bruckMap(k); + for (int j = 0; j < k; ++j) { + // set incoming rank at k-th communication + const int in_rank = (rank + distance[j]) % num_machines; + bruckMap.in_ranks[j] = in_rank; + // set outgoing rank at k-th communication + const int out_rank = (rank - distance[j] + num_machines) % num_machines; + bruckMap.out_ranks[j] = out_rank; + } + return bruckMap; +} + +RecursiveHalvingMap::RecursiveHalvingMap() { + k = 0; +} + +RecursiveHalvingMap::RecursiveHalvingMap(int in_k, RecursiveHalvingNodeType _type, bool _is_power_of_2) { + type = _type; + k = in_k; + is_power_of_2 = _is_power_of_2; + if (type != RecursiveHalvingNodeType::Other) { + for (int i = 0; i < k; ++i) { + // default set as -1 + ranks.push_back(-1); + send_block_start.push_back(-1); + send_block_len.push_back(-1); + recv_block_start.push_back(-1); + recv_block_len.push_back(-1); + } + } +} + +RecursiveHalvingMap RecursiveHalvingMap::Construct(int rank, int num_machines) { + // construct all recursive halving map for all machines + int k = 0; + while ((1 << k) <= num_machines) { + ++k; + } + // let 1 << k <= num_machines + --k; + // distance of each communication + std::vector distance; + for (int i = 0; i < k; ++i) { + distance.push_back(1 << (k - 1 - i)); + } + + if ((1 << k) == num_machines) { + RecursiveHalvingMap rec_map(k, RecursiveHalvingNodeType::Normal, true); + // if num_machines = 2^k, don't need to group machines + for (int i = 0; i < k; ++i) { + // communication direction, %2 == 0 is positive + const int dir = ((rank / distance[i]) % 2 == 0) ? 1 : -1; + // neighbor at k-th communication + const int next_node_idx = rank + dir * distance[i]; + rec_map.ranks[i] = next_node_idx; + // receive data block at k-th communication + const int recv_block_start = rank / distance[i]; + rec_map.recv_block_start[i] = recv_block_start * distance[i]; + rec_map.recv_block_len[i] = distance[i]; + // send data block at k-th communication + const int send_block_start = next_node_idx / distance[i]; + rec_map.send_block_start[i] = send_block_start * distance[i]; + rec_map.send_block_len[i] = distance[i]; + } + return rec_map; + } else { + // if num_machines != 2^k, need to group machines + + int lower_power_of_2 = 1 << k; + + int rest = num_machines - lower_power_of_2; + + std::vector node_type(num_machines); + for (int i = 0; i < num_machines; ++i) { + node_type[i] = RecursiveHalvingNodeType::Normal; + } + // group, two machine in one group, total "rest" groups will have 2 machines. + for (int i = 0; i < rest; ++i) { + int right = num_machines - i * 2 - 1; + int left = num_machines - i * 2 - 2; + // let left machine as group leader + node_type[left] = RecursiveHalvingNodeType::GroupLeader; + node_type[right] = RecursiveHalvingNodeType::Other; + } + int group_cnt = 0; + // cache block information for groups, group with 2 machines will have double block size + std::vector group_block_start(lower_power_of_2); + std::vector group_block_len(lower_power_of_2, 0); + // convert from group to node leader + std::vector group_to_node(lower_power_of_2); + // convert from node to group + std::vector node_to_group(num_machines); + + for (int i = 0; i < num_machines; ++i) { + // meet new group + if (node_type[i] == RecursiveHalvingNodeType::Normal || node_type[i] == RecursiveHalvingNodeType::GroupLeader) { + group_to_node[group_cnt++] = i; + } + node_to_group[i] = group_cnt - 1; + // add block len for this group + group_block_len[group_cnt - 1]++; + } + // calculate the group block start + group_block_start[0] = 0; + for (int i = 1; i < lower_power_of_2; ++i) { + group_block_start[i] = group_block_start[i - 1] + group_block_len[i - 1]; + } + + RecursiveHalvingMap rec_map(k, node_type[rank], false); + if (node_type[rank] == RecursiveHalvingNodeType::Other) { + rec_map.neighbor = rank - 1; + // not need to construct + return rec_map; + } + if (node_type[rank] == RecursiveHalvingNodeType::GroupLeader) { + rec_map.neighbor = rank + 1; + } + const int cur_group_idx = node_to_group[rank]; + for (int i = 0; i < k; ++i) { + const int dir = ((cur_group_idx / distance[i]) % 2 == 0) ? 1 : -1; + const int next_node_idx = group_to_node[(cur_group_idx + dir * distance[i])]; + rec_map.ranks[i] = next_node_idx; + // get receive block information + const int recv_block_start = cur_group_idx / distance[i]; + rec_map.recv_block_start[i] = group_block_start[static_cast(recv_block_start) * distance[i]]; + int recv_block_len = 0; + // accumulate block len + for (int j = 0; j < distance[i]; ++j) { + recv_block_len += group_block_len[recv_block_start * distance[i] + j]; + } + rec_map.recv_block_len[i] = recv_block_len; + // get send block information + const int send_block_start = (cur_group_idx + dir * distance[i]) / distance[i]; + rec_map.send_block_start[i] = group_block_start[static_cast(send_block_start) * distance[i]]; + int send_block_len = 0; + // accumulate block len + for (int j = 0; j < distance[i]; ++j) { + send_block_len += group_block_len[send_block_start * distance[i] + j]; + } + rec_map.send_block_len[i] = send_block_len; + } + return rec_map; + } +} + +} // namespace LightGBM diff --git a/src/network/linkers.h b/src/network/linkers.h new file mode 100644 index 0000000..0515bed --- /dev/null +++ b/src/network/linkers.h @@ -0,0 +1,329 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_NETWORK_LINKERS_H_ +#define LIGHTGBM_SRC_NETWORK_LINKERS_H_ + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#ifdef USE_SOCKET +#include "socket_wrapper.hpp" +#endif + +#ifdef USE_MPI +#include +#define MPI_SAFE_CALL(mpi_return) CHECK((mpi_return) == MPI_SUCCESS) +#endif + +namespace LightGBM { + +/*! +* \brief A network basic communication wrapper. +* Will wrap low level communication methods, e.g. mpi, socket and so on. +* This class will wrap all linkers to other machines if needs +*/ +class Linkers { + public: + Linkers() { + is_init_ = false; + } + /*! + * \brief Constructor + * \param config Config of network settings + */ + explicit Linkers(Config config); + /*! + * \brief Destructor + */ + ~Linkers(); + /*! + * \brief Recv data, blocking + * \param rank Which rank will send data to local machine + * \param data Pointer of receive data + * \param len Recv size, will block until receive len size of data + */ + inline void Recv(int rank, char* data, int len) const; + + inline void Recv(int rank, char* data, int64_t len) const; + + /*! + * \brief Send data, blocking + * \param rank Which rank local machine will send to + * \param data Pointer of send data + * \param len Send size + */ + inline void Send(int rank, char* data, int len) const; + + inline void Send(int rank, char* data, int64_t len) const; + /*! + * \brief Send and Recv at same time, blocking + * \param send_rank + * \param send_data + * \param send_len + * \param recv_rank + * \param recv_data + * \param recv_len + */ + inline void SendRecv(int send_rank, char* send_data, int send_len, + int recv_rank, char* recv_data, int recv_len); + + inline void SendRecv(int send_rank, char* send_data, int64_t send_len, + int recv_rank, char* recv_data, int64_t recv_len); + /*! + * \brief Get rank of local machine + */ + inline int rank(); + /*! + * \brief Get total number of machines + */ + inline int num_machines(); + /*! + * \brief Get Bruck map of this network + */ + inline const BruckMap& bruck_map(); + /*! + * \brief Get Recursive Halving map of this network + */ + inline const RecursiveHalvingMap& recursive_halving_map(); + + #ifdef USE_SOCKET + /*! + * \brief Bind local listen to port + * \param port Local listen port + */ + void TryBind(int port); + /*! + * \brief Set socket to rank + * \param rank + * \param socket + */ + void SetLinker(int rank, const TcpSocket& socket); + /*! + * \brief Thread for listening + * \param incoming_cnt Number of incoming machines + */ + void ListenThread(int incoming_cnt); + /*! + * \brief Construct network topo + */ + void Construct(); + /*! + * \brief Parser machines information from file + * \param machines + * \param filename + */ + void ParseMachineList(const std::string& machines, const std::string& filename); + /*! + * \brief Check one linker is connected or not + * \param rank + * \return True if linker is connected + */ + bool CheckLinker(int rank); + /*! + * \brief Print connected linkers + */ + void PrintLinkers(); + + #endif // USE_SOCKET + + #ifdef USE_MPI + + /*! + * \brief Check if MPI has been initialized + */ + static bool IsMpiInitialized(); + + /*! + * \brief Finalize the MPI session if it was initialized + */ + static void MpiFinalizeIfIsParallel(); + + /*! + * \brief Abort the MPI session if it was initialized (called in case there was a error that needs abrupt ending) + */ + static void MpiAbortIfIsParallel(); + + #endif + + private: + /*! \brief Rank of local machine */ + int rank_; + /*! \brief Total number machines */ + int num_machines_; + /*! \brief Bruck map */ + BruckMap bruck_map_; + /*! \brief Recursive Halving map */ + RecursiveHalvingMap recursive_halving_map_; + + std::chrono::duration network_time_; + + bool is_init_; + + #ifdef USE_SOCKET + /*! \brief use to store client ips */ + std::vector client_ips_; + /*! \brief use to store client ports */ + std::vector client_ports_; + /*! \brief time out for sockets, in minutes */ + int socket_timeout_; + /*! \brief Local listen ports */ + int local_listen_port_; + /*! \brief Linkers */ + std::vector> linkers_; + /*! \brief Local socket listener */ + std::unique_ptr listener_; + #endif // USE_SOCKET +}; + + +inline int Linkers::rank() { + return rank_; +} + +inline int Linkers::num_machines() { + return num_machines_; +} + +inline const BruckMap& Linkers::bruck_map() { + return bruck_map_; +} + +inline const RecursiveHalvingMap& Linkers::recursive_halving_map() { + return recursive_halving_map_; +} + +inline void Linkers::Recv(int rank, char* data, int64_t len) const { + int64_t used = 0; + do { + int cur_size = static_cast(std::min(len - used, INT32_MAX)); + Recv(rank, data + used, cur_size); + used += cur_size; + } while (used < len); +} + +inline void Linkers::Send(int rank, char* data, int64_t len) const { + int64_t used = 0; + do { + int cur_size = static_cast(std::min(len - used, INT32_MAX)); + Send(rank, data + used, cur_size); + used += cur_size; + } while (used < len); +} + +inline void Linkers::SendRecv(int send_rank, char* send_data, int64_t send_len, + int recv_rank, char* recv_data, int64_t recv_len) { + auto start_time = std::chrono::high_resolution_clock::now(); + std::thread send_worker( + [this, send_rank, send_data, send_len]() { + Send(send_rank, send_data, send_len); + }); + Recv(recv_rank, recv_data, recv_len); + send_worker.join(); + // wait for send complete + auto end_time = std::chrono::high_resolution_clock::now(); + // output used time on each iteration + network_time_ += std::chrono::duration(end_time - start_time); +} + +#ifdef USE_SOCKET + +inline void Linkers::Recv(int rank, char* data, int len) const { + int recv_cnt = 0; + while (recv_cnt < len) { + recv_cnt += linkers_[rank]->Recv(data + recv_cnt, + // len - recv_cnt + std::min(len - recv_cnt, SocketConfig::kMaxReceiveSize)); + } +} + +inline void Linkers::Send(int rank, char* data, int len) const { + if (len <= 0) { + return; + } + int send_cnt = 0; + while (send_cnt < len) { + send_cnt += linkers_[rank]->Send(data + send_cnt, len - send_cnt); + } +} + +inline void Linkers::SendRecv(int send_rank, char* send_data, int send_len, + int recv_rank, char* recv_data, int recv_len) { + auto start_time = std::chrono::high_resolution_clock::now(); + if (send_len < SocketConfig::kSocketBufferSize) { + // if buffer is enough, send will non-blocking + Send(send_rank, send_data, send_len); + Recv(recv_rank, recv_data, recv_len); + } else { + // if buffer is not enough, use another thread to send, since send will be blocking + std::thread send_worker( + [this, send_rank, send_data, send_len]() { + Send(send_rank, send_data, send_len); + }); + Recv(recv_rank, recv_data, recv_len); + send_worker.join(); + } + // wait for send complete + auto end_time = std::chrono::high_resolution_clock::now(); + // output used time on each iteration + network_time_ += std::chrono::duration(end_time - start_time); +} + +#endif // USE_SOCKET + +#ifdef USE_MPI + +inline void Linkers::Recv(int rank, char* data, int len) const { + MPI_Status status; + int read_cnt = 0; + while (read_cnt < len) { + MPI_SAFE_CALL(MPI_Recv(data + read_cnt, len - read_cnt, MPI_BYTE, rank, MPI_ANY_TAG, MPI_COMM_WORLD, &status)); + int cur_cnt; + MPI_SAFE_CALL(MPI_Get_count(&status, MPI_BYTE, &cur_cnt)); + read_cnt += cur_cnt; + } +} + +inline void Linkers::Send(int rank, char* data, int len) const { + if (len <= 0) { + return; + } + MPI_Status status; + MPI_Request send_request; + MPI_SAFE_CALL(MPI_Isend(data, len, MPI_BYTE, rank, 0, MPI_COMM_WORLD, &send_request)); + MPI_SAFE_CALL(MPI_Wait(&send_request, &status)); +} + +inline void Linkers::SendRecv(int send_rank, char* send_data, int send_len, + int recv_rank, char* recv_data, int recv_len) { + MPI_Request send_request; + // send first, non-blocking + MPI_SAFE_CALL(MPI_Isend(send_data, send_len, MPI_BYTE, send_rank, 0, MPI_COMM_WORLD, &send_request)); + // then receive, blocking + MPI_Status status; + int read_cnt = 0; + while (read_cnt < recv_len) { + MPI_SAFE_CALL(MPI_Recv(recv_data + read_cnt, recv_len - read_cnt, MPI_BYTE, recv_rank, 0, MPI_COMM_WORLD, &status)); + int cur_cnt; + MPI_SAFE_CALL(MPI_Get_count(&status, MPI_BYTE, &cur_cnt)); + read_cnt += cur_cnt; + } + // wait for send complete + MPI_SAFE_CALL(MPI_Wait(&send_request, &status)); +} + +#endif // USE_MPI +} // namespace LightGBM +#endif // LIGHTGBM_SRC_NETWORK_LINKERS_H_ diff --git a/src/network/linkers_mpi.cpp b/src/network/linkers_mpi.cpp new file mode 100644 index 0000000..f119703 --- /dev/null +++ b/src/network/linkers_mpi.cpp @@ -0,0 +1,64 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifdef USE_MPI + +#include "linkers.h" + +#include + +namespace LightGBM { + +Linkers::Linkers(Config) { + is_init_ = false; + int argc = 0; + char**argv = nullptr; + int flag = 0; + MPI_SAFE_CALL(MPI_Initialized(&flag)); // test if MPI has been initialized + if (!flag) { // if MPI not started, start it + MPI_SAFE_CALL(MPI_Init_thread(&argc, &argv, MPI_THREAD_SERIALIZED, &flag)); + } + MPI_SAFE_CALL(MPI_Comm_size(MPI_COMM_WORLD, &num_machines_)); + MPI_SAFE_CALL(MPI_Comm_rank(MPI_COMM_WORLD, &rank_)); + // wait for all client start up + MPI_SAFE_CALL(MPI_Barrier(MPI_COMM_WORLD)); + bruck_map_ = BruckMap::Construct(rank_, num_machines_); + recursive_halving_map_ = RecursiveHalvingMap::Construct(rank_, num_machines_); + is_init_ = true; +} + +Linkers::~Linkers() { + // Don't call MPI_Finalize() here: If the destructor was called because only this node had an exception, calling MPI_Finalize() will cause all nodes to hang. + // Instead we will handle finalize/abort for MPI in main(). +} + +bool Linkers::IsMpiInitialized() { + int is_mpi_init; + MPI_SAFE_CALL(MPI_Initialized(&is_mpi_init)); + return is_mpi_init; +} + +void Linkers::MpiFinalizeIfIsParallel() { + if (IsMpiInitialized()) { + Log::Debug("Finalizing MPI session."); + MPI_SAFE_CALL(MPI_Finalize()); + } +} + +void Linkers::MpiAbortIfIsParallel() { + try { + if (IsMpiInitialized()) { + std::cerr << "Aborting MPI communication." << std::endl << std::flush; + MPI_SAFE_CALL(MPI_Abort(MPI_COMM_WORLD, -1));; + } + } + catch (...) { + std::cerr << "Exception was raised before aborting MPI. Aborting process..." << std::endl << std::flush; + abort(); + } +} + +} // namespace LightGBM +#endif // USE_MPI diff --git a/src/network/linkers_socket.cpp b/src/network/linkers_socket.cpp new file mode 100644 index 0000000..3f73257 --- /dev/null +++ b/src/network/linkers_socket.cpp @@ -0,0 +1,241 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifdef USE_SOCKET + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "linkers.h" + +namespace LightGBM { + +Linkers::Linkers(Config config) { + is_init_ = false; + // start up socket + TcpSocket::Startup(); + network_time_ = std::chrono::duration(0); + num_machines_ = config.num_machines; + local_listen_port_ = config.local_listen_port; + socket_timeout_ = config.time_out; + rank_ = -1; + // parse clients from file + ParseMachineList(config.machines, config.machine_list_filename); + + if (rank_ == -1) { + // get ip list of local machine + std::unordered_set local_ip_list = TcpSocket::GetLocalIpList(); + // get local rank + for (size_t i = 0; i < client_ips_.size(); ++i) { + if (local_ip_list.count(client_ips_[i]) > 0 && client_ports_[i] == local_listen_port_) { + rank_ = static_cast(i); + break; + } + } + } + if (rank_ == -1) { + Log::Fatal("Machine list file doesn't contain the local machine"); + } + // construct listener + listener_ = std::unique_ptr(new TcpSocket()); + TryBind(local_listen_port_); + + for (int i = 0; i < num_machines_; ++i) { + linkers_.push_back(nullptr); + } + + // construct communication topo + bruck_map_ = BruckMap::Construct(rank_, num_machines_); + recursive_halving_map_ = RecursiveHalvingMap::Construct(rank_, num_machines_); + + // construct linkers + Construct(); + // free listener + listener_->Close(); + is_init_ = true; +} + +Linkers::~Linkers() { + if (is_init_) { + for (size_t i = 0; i < linkers_.size(); ++i) { + if (linkers_[i] != nullptr) { + linkers_[i]->Close(); + } + } + TcpSocket::Finalize(); + Log::Info("Finished linking network in %f seconds", network_time_ * 1e-3); + } +} + +void Linkers::ParseMachineList(const std::string& machines, const std::string& filename) { + std::vector lines; + if (machines.empty()) { + TextReader machine_list_reader(filename.c_str(), false); + machine_list_reader.ReadAllLines(); + if (machine_list_reader.Lines().empty()) { + Log::Fatal("Machine list file %s doesn't exist", filename.c_str()); + } + lines = machine_list_reader.Lines(); + } else { + lines = Common::Split(machines.c_str(), ','); + } + for (auto& line : lines) { + line = Common::Trim(line); + if (line.find("rank=") != std::string::npos) { + std::vector str_after_split = Common::Split(line.c_str(), '='); + Common::Atoi(str_after_split[1].c_str(), &rank_); + continue; + } + std::vector str_after_split = Common::Split(line.c_str(), ' '); + if (str_after_split.size() != 2) { + str_after_split = Common::Split(line.c_str(), ':'); + if (str_after_split.size() != 2) { + continue; + } + } + if (client_ips_.size() >= static_cast(num_machines_)) { + Log::Warning("machine_list size is larger than the parameter num_machines, ignoring redundant entries"); + break; + } + str_after_split[0] = Common::Trim(str_after_split[0]); + str_after_split[1] = Common::Trim(str_after_split[1]); + client_ips_.push_back(str_after_split[0]); + client_ports_.push_back(atoi(str_after_split[1].c_str())); + } + if (client_ips_.empty()) { + Log::Fatal("Cannot find any ip and port.\n" + "Please check machine_list_filename or machines parameter"); + } + if (client_ips_.size() != static_cast(num_machines_)) { + Log::Warning("World size is larger than the machine_list size, change world size to %zu", client_ips_.size()); + num_machines_ = static_cast(client_ips_.size()); + } +} + +void Linkers::TryBind(int port) { + Log::Info("Trying to bind port %d...", port); + if (listener_->Bind(port)) { + Log::Info("Binding port %d succeeded", port); + } else { + Log::Fatal("Binding port %d failed", port); + } +} + +void Linkers::SetLinker(int rank, const TcpSocket& socket) { + linkers_[rank].reset(new TcpSocket(socket)); + // set timeout + linkers_[rank]->SetTimeout(socket_timeout_ * 1000 * 60); +} + +void Linkers::ListenThread(int incoming_cnt) { + Log::Info("Listening..."); + char buffer[100]; + int connected_cnt = 0; + while (connected_cnt < incoming_cnt) { + // accept incoming socket + TcpSocket handler = listener_->Accept(); + if (handler.IsClosed()) { + continue; + } + // receive rank + int read_cnt = 0; + int size_of_int = static_cast(sizeof(int)); + while (read_cnt < size_of_int) { + int cur_read_cnt = handler.Recv(buffer + read_cnt, size_of_int - read_cnt); + read_cnt += cur_read_cnt; + } + int* ptr_in_rank = reinterpret_cast(buffer); + int in_rank = *ptr_in_rank; + if (in_rank < 0 || in_rank >= num_machines_) { + Log::Fatal("Invalid rank %d found during initialization of linkers. The world size is %d.", in_rank, num_machines_); + } + // add new socket + SetLinker(in_rank, handler); + ++connected_cnt; + } +} + +void Linkers::Construct() { + // save ranks that need to connect with + std::unordered_map need_connect; + for (int i = 0; i < num_machines_; ++i) { + if (i != rank_) { + need_connect[i] = 1; + } + } + int incoming_cnt = 0; + for (auto it = need_connect.begin(); it != need_connect.end(); ++it) { + int machine_rank = it->first; + if (machine_rank < rank_) { + ++incoming_cnt; + } + } + + // start listener + listener_->SetTimeout(socket_timeout_ * 1000 * 60); + listener_->Listen(incoming_cnt); + std::thread listen_thread(&Linkers::ListenThread, this, incoming_cnt); + const int connect_fail_constant_factor = 20; + const int connect_fail_retries_scale_factor = static_cast(num_machines_ / connect_fail_constant_factor); + const int connect_fail_retry_cnt = std::max(connect_fail_constant_factor, connect_fail_retries_scale_factor); + const int connect_fail_retry_first_delay_interval = 200; // 0.2 s + const float connect_fail_retry_delay_factor = 1.3f; + // start connect + for (auto it = need_connect.begin(); it != need_connect.end(); ++it) { + int out_rank = it->first; + // let smaller rank connect to larger rank + if (out_rank > rank_) { + int connect_fail_delay_time = connect_fail_retry_first_delay_interval; + for (int i = 0; i < connect_fail_retry_cnt; ++i) { + TcpSocket cur_socket; + if (cur_socket.Connect(client_ips_[out_rank].c_str(), client_ports_[out_rank])) { + // send local rank + cur_socket.Send(reinterpret_cast(&rank_), sizeof(rank_)); + SetLinker(out_rank, cur_socket); + break; + } else { + Log::Warning("Connecting to rank %d failed, waiting for %d milliseconds", out_rank, connect_fail_delay_time); + cur_socket.Close(); + std::this_thread::sleep_for(std::chrono::milliseconds(connect_fail_delay_time)); + connect_fail_delay_time = static_cast(connect_fail_delay_time * connect_fail_retry_delay_factor); + } + } + } + } + // wait for listener + listen_thread.join(); + // print connected linkers + PrintLinkers(); +} + +bool Linkers::CheckLinker(int rank) { + if (linkers_[rank] == nullptr || linkers_[rank]->IsClosed()) { + return false; + } + return true; +} + +void Linkers::PrintLinkers() { + for (int i = 0; i < num_machines_; ++i) { + if (CheckLinker(i)) { + Log::Info("Connected to rank %d", i); + } + } +} + +} // namespace LightGBM + +#endif // USE_SOCKET diff --git a/src/network/network.cpp b/src/network/network.cpp new file mode 100644 index 0000000..d556dfe --- /dev/null +++ b/src/network/network.cpp @@ -0,0 +1,332 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include + +#include +#include +#include +#include +#include + +#include "linkers.h" + +namespace LightGBM { + +// static member definition +THREAD_LOCAL int Network::num_machines_ = 1; +THREAD_LOCAL int Network::rank_ = 0; +THREAD_LOCAL std::unique_ptr Network::linkers_; +THREAD_LOCAL BruckMap Network::bruck_map_; +THREAD_LOCAL RecursiveHalvingMap Network::recursive_halving_map_; +THREAD_LOCAL std::vector Network::block_start_; +THREAD_LOCAL std::vector Network::block_len_; +THREAD_LOCAL comm_size_t Network::buffer_size_ = 0; +THREAD_LOCAL std::vector Network::buffer_; +THREAD_LOCAL ReduceScatterFunction Network::reduce_scatter_ext_fun_ = nullptr; +THREAD_LOCAL AllgatherFunction Network::allgather_ext_fun_ = nullptr; + + +void Network::Init(Config config) { + if (config.num_machines > 1) { + linkers_.reset(new Linkers(config)); + rank_ = linkers_->rank(); + num_machines_ = linkers_->num_machines(); + bruck_map_ = linkers_->bruck_map(); + recursive_halving_map_ = linkers_->recursive_halving_map(); + block_start_ = std::vector(num_machines_); + block_len_ = std::vector(num_machines_); + buffer_size_ = 1024 * 1024; + buffer_.resize(buffer_size_); + Log::Info("Local rank: %d, total number of machines: %d", rank_, num_machines_); + } +} + +void Network::Init(int num_machines, int rank, + ReduceScatterFunction reduce_scatter_ext_fun, AllgatherFunction allgather_ext_fun) { + if (num_machines > 1) { + rank_ = rank; + num_machines_ = num_machines; + block_start_ = std::vector(num_machines_); + block_len_ = std::vector(num_machines_); + buffer_size_ = 1024 * 1024; + buffer_.resize(buffer_size_); + reduce_scatter_ext_fun_ = reduce_scatter_ext_fun; + allgather_ext_fun_ = allgather_ext_fun; + Log::Info("Local rank: %d, total number of machines: %d", rank_, num_machines_); + } +} + +void Network::Dispose() { + num_machines_ = 1; + rank_ = 0; + linkers_.reset(new Linkers()); + reduce_scatter_ext_fun_ = nullptr; + allgather_ext_fun_ = nullptr; +} + +void Network::Allreduce(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer) { + if (num_machines_ <= 1) { + Log::Fatal("Please initialize the network interface first"); + } + comm_size_t count = input_size / type_size; + // if small package or small count , do it by all gather.(reduce the communication times.) + if (count < num_machines_ || input_size < 4096) { + AllreduceByAllGather(input, input_size, type_size, output, reducer); + return; + } + // assign the blocks to every rank. + comm_size_t step = (count + num_machines_ - 1) / num_machines_; + if (step < 1) { + step = 1; + } + block_start_[0] = 0; + for (int i = 0; i < num_machines_ - 1; ++i) { + block_len_[i] = std::min(step * type_size, input_size - block_start_[i]); + block_start_[i + 1] = block_start_[i] + block_len_[i]; + } + block_len_[num_machines_ - 1] = input_size - block_start_[num_machines_ - 1]; + // do reduce scatter + ReduceScatter(input, input_size, type_size, block_start_.data(), block_len_.data(), output, input_size, reducer); + // do all gather + Allgather(output, block_start_.data(), block_len_.data(), output, input_size); +} + +void Network::AllreduceByAllGather(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer) { + if (num_machines_ <= 1) { + Log::Fatal("Please initialize the network interface first"); + } + // assign blocks + comm_size_t all_size = input_size * num_machines_; + block_start_[0] = 0; + block_len_[0] = input_size; + for (int i = 1; i < num_machines_; ++i) { + block_start_[i] = block_start_[i - 1] + block_len_[i - 1]; + block_len_[i] = input_size; + } + // need use buffer here, since size of "output" is smaller than size after all gather + if (input_size*num_machines_ > buffer_size_) { + buffer_size_ = input_size*num_machines_; + buffer_.resize(buffer_size_); + } + + Allgather(input, block_start_.data(), block_len_.data(), buffer_.data(), all_size); + for (int i = 1; i < num_machines_; ++i) { + reducer(buffer_.data() + block_start_[i], buffer_.data() + block_start_[0], type_size, input_size); + } + // copy back + std::memcpy(output, buffer_.data(), input_size); +} + +void Network::Allgather(char* input, comm_size_t send_size, char* output) { + if (num_machines_ <= 1) { + Log::Fatal("Please initialize the network interface first"); + return; + } + // assign blocks + block_start_[0] = 0; + block_len_[0] = send_size; + for (int i = 1; i < num_machines_; ++i) { + block_start_[i] = block_start_[i - 1] + block_len_[i - 1]; + block_len_[i] = send_size; + } + // start all gather + Allgather(input, block_start_.data(), block_len_.data(), output, send_size * num_machines_); +} + +void Network::Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size) { + if (num_machines_ <= 1) { + Log::Fatal("Please initialize the network interface first"); + } + if (allgather_ext_fun_ != nullptr) { + return allgather_ext_fun_(input, block_len[rank_], block_start, block_len, num_machines_, output, all_size); + } + const comm_size_t kRingThreshold = 10 * 1024 * 1024; // 10MB + const int kRingNodeThreshold = 64; + if (all_size > kRingThreshold && num_machines_ < kRingNodeThreshold) { + // when num_machines is small and data is large + AllgatherRing(input, block_start, block_len, output, all_size); + } else if (recursive_halving_map_.is_power_of_2) { + AllgatherRecursiveDoubling(input, block_start, block_len, output, all_size); + } else { + AllgatherBruck(input, block_start, block_len, output, all_size); + } +} + +void Network::AllgatherBruck(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size) { + comm_size_t write_pos = 0; + // use output as receive buffer + std::memcpy(output, input, block_len[rank_]); + write_pos += block_len[rank_]; + int accumulated_block = 1; + for (int i = 0; i < bruck_map_.k; ++i) { + // get current local block size + int cur_block_size = std::min(1 << i, num_machines_ - accumulated_block); + // get out rank + int out_rank = bruck_map_.out_ranks[i]; + // get in rank + int in_rank = bruck_map_.in_ranks[i]; + // get send information + comm_size_t need_send_len = 0; + // get recv information + comm_size_t need_recv_len = 0; + for (int j = 0; j < cur_block_size; ++j) { + need_send_len += block_len[(rank_ + j) % num_machines_]; + need_recv_len += block_len[(rank_ + accumulated_block + j) % num_machines_]; + } + // send and recv at same time + linkers_->SendRecv(out_rank, output, need_send_len, in_rank, output + write_pos, need_recv_len); + write_pos += need_recv_len; + accumulated_block += cur_block_size; + } + // rotate in-place + std::reverse(output, output + all_size); + std::reverse(output, output + block_start[rank_]); + std::reverse(output + block_start[rank_], output + all_size); +} + +void Network::AllgatherRecursiveDoubling(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t) { + // use output as receive buffer + std::memcpy(output + block_start[rank_], input, block_len[rank_]); + for (int i = 0; i < bruck_map_.k; ++i) { + // get current local block size + int cur_step = 1 << i; + const int vgroup = rank_ / cur_step; + const int vrank = vgroup * cur_step; + int target = rank_ + cur_step; + int target_vrank = (vgroup + 1) * cur_step; + if (vgroup & 1) { + target = rank_ - cur_step; + target_vrank = (vgroup - 1) * cur_step; + } + // get send information + comm_size_t need_send_len = 0; + // get recv information + comm_size_t need_recv_len = 0; + for (int j = 0; j < cur_step; ++j) { + need_send_len += block_len[(vrank + j)]; + need_recv_len += block_len[(target_vrank + j)]; + } + // send and recv at same time + linkers_->SendRecv(target, output + block_start[vrank], need_send_len, + target, output + block_start[target_vrank], need_recv_len); + } +} + +void Network::AllgatherRing(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t) { + // use output as receive buffer + std::memcpy(output + block_start[rank_], input, block_len[rank_]); + int out_rank = (rank_ + 1) % num_machines_; + int in_rank = (rank_ - 1 + num_machines_) % num_machines_; + int out_block = rank_; + int in_block = in_rank; + for (int i = 1; i < num_machines_; ++i) { + // send and recv at same time + linkers_->SendRecv(out_rank, output + block_start[out_block], block_len[out_block], + in_rank, output + block_start[in_block], block_len[in_block]); + out_block = (out_block - 1 + num_machines_) % num_machines_; + in_block = (in_block - 1 + num_machines_) % num_machines_; + } +} + +void Network::ReduceScatter(char* input, comm_size_t input_size, int type_size, + const comm_size_t* block_start, const comm_size_t* block_len, char* output, + comm_size_t output_size, const ReduceFunction& reducer) { + if (num_machines_ <= 1) { + Log::Fatal("Please initialize the network interface first"); + } + if (reduce_scatter_ext_fun_ != nullptr) { + return reduce_scatter_ext_fun_(input, input_size, type_size, block_start, block_len, num_machines_, output, output_size, reducer); + } + const comm_size_t kRingThreshold = 10 * 1024 * 1024; // 10MB + if (recursive_halving_map_.is_power_of_2 || input_size < kRingThreshold) { + ReduceScatterRecursiveHalving(input, input_size, type_size, block_start, block_len, output, output_size, reducer); + } else { + ReduceScatterRing(input, input_size, type_size, block_start, block_len, output, output_size, reducer); + } +} + +void Network::ReduceScatterRecursiveHalving(char* input, comm_size_t input_size, int type_size, + const comm_size_t* block_start, const comm_size_t* block_len, char* output, + comm_size_t, const ReduceFunction& reducer) { + if (!recursive_halving_map_.is_power_of_2) { + if (recursive_halving_map_.type == RecursiveHalvingNodeType::Other) { + // send local data to neighbor first + linkers_->Send(recursive_halving_map_.neighbor, input, input_size); + } else if (recursive_halving_map_.type == RecursiveHalvingNodeType::GroupLeader) { + // receive neighbor data first + int need_recv_cnt = input_size; + linkers_->Recv(recursive_halving_map_.neighbor, output, need_recv_cnt); + // reduce + reducer(output, input, type_size, input_size); + } + } + if (recursive_halving_map_.type != RecursiveHalvingNodeType::Other) { + for (int i = 0; i < recursive_halving_map_.k; ++i) { + // get target + int target = recursive_halving_map_.ranks[i]; + comm_size_t send_block_start = recursive_halving_map_.send_block_start[i]; + comm_size_t recv_block_start = recursive_halving_map_.recv_block_start[i]; + // get send information + comm_size_t send_size = 0; + for (int j = 0; j < recursive_halving_map_.send_block_len[i]; ++j) { + send_size += block_len[send_block_start + j]; + } + // get recv information + comm_size_t need_recv_cnt = 0; + for (int j = 0; j < recursive_halving_map_.recv_block_len[i]; ++j) { + need_recv_cnt += block_len[recv_block_start + j]; + } + // send and recv at same time + linkers_->SendRecv(target, input + block_start[send_block_start], send_size, target, output, need_recv_cnt); + // reduce + reducer(output, input + block_start[recv_block_start], type_size, need_recv_cnt); + } + } + if (!recursive_halving_map_.is_power_of_2) { + if (recursive_halving_map_.type == RecursiveHalvingNodeType::GroupLeader) { + // send result to neighbor + linkers_->Send(recursive_halving_map_.neighbor, + input + block_start[recursive_halving_map_.neighbor], + block_len[recursive_halving_map_.neighbor]); + } else if (recursive_halving_map_.type == RecursiveHalvingNodeType::Other) { + // receive result from neighbor + int need_recv_cnt = block_len[rank_]; + linkers_->Recv(recursive_halving_map_.neighbor, output, need_recv_cnt); + return; + } + } + // copy result + std::memcpy(output, input + block_start[rank_], block_len[rank_]); +} + +void Network::ReduceScatterRing(char* input, comm_size_t, int type_size, + const comm_size_t* block_start, const comm_size_t* block_len, char* output, + comm_size_t, const ReduceFunction& reducer) { + const int out_rank = (rank_ + 1) % num_machines_; + const int in_rank = (rank_ - 1 + num_machines_) % num_machines_; + int out_block = in_rank; + int in_block = (in_rank - 1 + num_machines_) % num_machines_; + for (int i = 1; i < num_machines_; ++i) { + linkers_->SendRecv(out_rank, input + block_start[out_block], block_len[out_block], + in_rank, output, block_len[in_block]); + reducer(output, input + block_start[in_block], type_size, block_len[in_block]); + out_block = (out_block - 1 + num_machines_) % num_machines_; + in_block = (in_block - 1 + num_machines_) % num_machines_; + } + std::memcpy(output, input + block_start[rank_], block_len[rank_]); +} + +int Network::rank() { + return rank_; +} + +int Network::num_machines() { + return num_machines_; +} + +} // namespace LightGBM diff --git a/src/network/socket_wrapper.hpp b/src/network/socket_wrapper.hpp new file mode 100644 index 0000000..9b65eee --- /dev/null +++ b/src/network/socket_wrapper.hpp @@ -0,0 +1,333 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_ +#define LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_ +#ifdef USE_SOCKET + +#include + +#include +#include +#include +#include + +#if defined(_WIN32) + +#ifdef _MSC_VER +#define NOMINMAX +#endif + +#include +#include +#include + +#else + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#endif // defined(_WIN32) + +#ifdef _MSC_VER +#pragma comment(lib, "Ws2_32.lib") +#pragma comment(lib, "IPHLPAPI.lib") +#endif + +namespace LightGBM { + +#ifndef _WIN32 + +typedef int SOCKET; +const int INVALID_SOCKET = -1; +#define SOCKET_ERROR -1 + +#endif + +#ifdef _WIN32 + +#define MALLOC(x) HeapAlloc(GetProcessHeap(), 0, (x)) +#define FREE(x) HeapFree(GetProcessHeap(), 0, (x)) + +// existence of inet_pton is checked in CMakeLists.txt and configure.win, then stored in WIN_HAS_INET_PTON +#ifndef WIN_HAS_INET_PTON +inline int inet_pton(int af, const char *src, void *dst) { + struct sockaddr_storage ss; + int size = sizeof(ss); + char src_copy[INET6_ADDRSTRLEN + 1]; + + ZeroMemory(&ss, sizeof(ss)); + /* stupid non-const API */ + strncpy(src_copy, src, INET6_ADDRSTRLEN + 1); + src_copy[INET6_ADDRSTRLEN] = 0; + + if (WSAStringToAddress(src_copy, af, NULL, (struct sockaddr *)&ss, &size) == 0) { + switch (af) { + case AF_INET: + *(struct in_addr *)dst = ((struct sockaddr_in *)&ss)->sin_addr; + return 1; + case AF_INET6: + *(struct in6_addr *)dst = ((struct sockaddr_in6 *)&ss)->sin6_addr; + return 1; + } + } + return 0; +} +#endif +#endif + +namespace SocketConfig { +const int kSocketBufferSize = 100 * 1000; +const int kMaxReceiveSize = 100 * 1000; +const int kNoDelay = 1; +} + +class TcpSocket { + public: + TcpSocket() { + sockfd_ = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP); + if (sockfd_ == INVALID_SOCKET) { + Log::Fatal("Socket construction error"); + return; + } + ConfigSocket(); + } + + explicit TcpSocket(SOCKET socket) { + sockfd_ = socket; + if (sockfd_ == INVALID_SOCKET) { + Log::Fatal("Passed socket error"); + return; + } + ConfigSocket(); + } + + TcpSocket(const TcpSocket &object) { + sockfd_ = object.sockfd_; + ConfigSocket(); + } + ~TcpSocket() { + } + inline void SetTimeout(int timeout_ms) { +#if defined(_WIN32) + DWORD timeout = static_cast(timeout_ms); + setsockopt(sockfd_, SOL_SOCKET, SO_RCVTIMEO, reinterpret_cast(&timeout), sizeof(timeout)); +#else + struct timeval tv; + tv.tv_sec = timeout_ms / 1000; + tv.tv_usec = (timeout_ms % 1000) * 1000; + setsockopt(sockfd_, SOL_SOCKET, SO_RCVTIMEO, &tv, sizeof(tv)); +#endif + } + inline void ConfigSocket() { + if (sockfd_ == INVALID_SOCKET) { + return; + } + + if (setsockopt(sockfd_, SOL_SOCKET, SO_RCVBUF, reinterpret_cast(&SocketConfig::kSocketBufferSize), sizeof(SocketConfig::kSocketBufferSize)) != 0) { + Log::Warning("Set SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at least"); + } + + if (setsockopt(sockfd_, SOL_SOCKET, SO_SNDBUF, reinterpret_cast(&SocketConfig::kSocketBufferSize), sizeof(SocketConfig::kSocketBufferSize)) != 0) { + Log::Warning("Set SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at least"); + } + if (setsockopt(sockfd_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&SocketConfig::kNoDelay), sizeof(SocketConfig::kNoDelay)) != 0) { + Log::Warning("Set TCP_NODELAY failed"); + } + } + + inline static void Startup() { +#if defined(_WIN32) + WSADATA wsa_data; + if (WSAStartup(MAKEWORD(2, 2), &wsa_data) == -1) { + Log::Fatal("Socket error: WSAStartup error"); + } + if (LOBYTE(wsa_data.wVersion) != 2 || HIBYTE(wsa_data.wVersion) != 2) { + WSACleanup(); + Log::Fatal("Socket error: Winsock.dll version error"); + } +#else +#endif + } + inline static void Finalize() { +#if defined(_WIN32) + WSACleanup(); +#endif + } + + inline static int GetLastError() { +#if defined(_WIN32) + return WSAGetLastError(); +#else + return errno; +#endif + } + + + +#if defined(_WIN32) + inline static std::unordered_set GetLocalIpList() { + std::unordered_set ip_list; + char buffer[512]; + // get hostName + if (gethostname(buffer, sizeof(buffer)) == SOCKET_ERROR) { + Log::Fatal("Error code %d, when getting local host name", WSAGetLastError()); + } + // push local ip + PIP_ADAPTER_INFO pAdapterInfo; + PIP_ADAPTER_INFO pAdapter = NULL; + DWORD dwRetVal = 0; + ULONG ulOutBufLen = sizeof(IP_ADAPTER_INFO); + pAdapterInfo = reinterpret_cast(MALLOC(sizeof(IP_ADAPTER_INFO))); + if (pAdapterInfo == NULL) { + Log::Fatal("GetAdaptersinfo error: allocating memory"); + } + // Make an initial call to GetAdaptersInfo to get + // the necessary size into the ulOutBufLen variable + if (GetAdaptersInfo(pAdapterInfo, &ulOutBufLen) == ERROR_BUFFER_OVERFLOW) { + FREE(pAdapterInfo); + pAdapterInfo = reinterpret_cast(MALLOC(ulOutBufLen)); + if (pAdapterInfo == NULL) { + Log::Fatal("GetAdaptersinfo error: allocating memory"); + } + } + if ((dwRetVal = GetAdaptersInfo(pAdapterInfo, &ulOutBufLen)) == NO_ERROR) { + pAdapter = pAdapterInfo; + while (pAdapter) { + ip_list.insert(pAdapter->IpAddressList.IpAddress.String); + pAdapter = pAdapter->Next; + } + } else { + Log::Fatal("GetAdaptersinfo error: code %d", dwRetVal); + } + if (pAdapterInfo) + FREE(pAdapterInfo); + return ip_list; + } +#else + inline static std::unordered_set GetLocalIpList() { + std::unordered_set ip_list; + struct ifaddrs * ifAddrStruct = NULL; + struct ifaddrs * ifa = NULL; + void * tmpAddrPtr = NULL; + + getifaddrs(&ifAddrStruct); + + for (ifa = ifAddrStruct; ifa != NULL; ifa = ifa->ifa_next) { + if (!ifa->ifa_addr) { + continue; + } + if (ifa->ifa_addr->sa_family == AF_INET) { + // NOLINTNEXTLINE + tmpAddrPtr = &((struct sockaddr_in *)ifa->ifa_addr)->sin_addr; + char addressBuffer[INET_ADDRSTRLEN]; + inet_ntop(AF_INET, tmpAddrPtr, addressBuffer, INET_ADDRSTRLEN); + ip_list.insert(std::string(addressBuffer)); + } + } + if (ifAddrStruct != NULL) freeifaddrs(ifAddrStruct); + return ip_list; + } +#endif + inline static sockaddr_in GetAddress(const char* url, int port) { + sockaddr_in addr = sockaddr_in(); + std::memset(&addr, 0, sizeof(sockaddr_in)); + inet_pton(AF_INET, url, &addr.sin_addr); + addr.sin_family = AF_INET; + addr.sin_port = htons(static_cast(port)); + return addr; + } + + inline bool Bind(int port) { + sockaddr_in local_addr = GetAddress("0.0.0.0", port); + if (bind(sockfd_, reinterpret_cast(&local_addr), sizeof(sockaddr_in)) == 0) { + return true; + } + return false; + } + + inline bool Connect(const char *url, int port) { + sockaddr_in server_addr = GetAddress(url, port); + if (connect(sockfd_, reinterpret_cast(&server_addr), sizeof(sockaddr_in)) == 0) { + return true; + } + return false; + } + + inline void Listen(int backlog = 128) { + listen(sockfd_, backlog); + } + + inline TcpSocket Accept() { + SOCKET newfd = accept(sockfd_, NULL, NULL); + if (newfd == INVALID_SOCKET) { + int err_code = GetLastError(); +#if defined(_WIN32) + Log::Fatal("Socket accept error (code: %d)", err_code); +#else + Log::Fatal("Socket accept error, %s (code: %d)", std::strerror(err_code), err_code); +#endif + } + return TcpSocket(newfd); + } + + inline int Send(const char *buf_, int len, int flag = 0) { + int cur_cnt = send(sockfd_, buf_, len, flag); + if (cur_cnt == SOCKET_ERROR) { + int err_code = GetLastError(); +#if defined(_WIN32) + Log::Fatal("Socket send error (code: %d)", err_code); +#else + Log::Fatal("Socket send error, %s (code: %d)", std::strerror(err_code), err_code); +#endif + } + return cur_cnt; + } + + inline int Recv(char *buf_, int len, int flags = 0) { + int cur_cnt = recv(sockfd_, buf_ , len , flags); + if (cur_cnt == SOCKET_ERROR) { + int err_code = GetLastError(); +#if defined(_WIN32) + Log::Fatal("Socket recv error (code: %d)", err_code); +#else + Log::Fatal("Socket recv error, %s (code: %d)", std::strerror(err_code), err_code); +#endif + } + return cur_cnt; + } + + inline bool IsClosed() { + return sockfd_ == INVALID_SOCKET; + } + + inline void Close() { + if (!IsClosed()) { +#if defined(_WIN32) + closesocket(sockfd_); +#else + close(sockfd_); +#endif + sockfd_ = INVALID_SOCKET; + } + } + + private: + SOCKET sockfd_; +}; + +} // namespace LightGBM +#endif // USE_SOCKET +#endif // LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_ diff --git a/src/objective/binary_objective.hpp b/src/objective/binary_objective.hpp new file mode 100644 index 0000000..970a7e8 --- /dev/null +++ b/src/objective/binary_objective.hpp @@ -0,0 +1,217 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_ + +#include +#include + +#include +#include +#include +#include +#include + +namespace LightGBM { +/*! +* \brief Objective function for binary classification +*/ +class BinaryLogloss: public ObjectiveFunction { + public: + explicit BinaryLogloss(const Config& config, + std::function is_pos = nullptr) + : deterministic_(config.deterministic) { + sigmoid_ = static_cast(config.sigmoid); + if (sigmoid_ <= 0.0) { + Log::Fatal("Sigmoid parameter %f should be greater than zero", sigmoid_); + } + is_unbalance_ = config.is_unbalance; + scale_pos_weight_ = static_cast(config.scale_pos_weight); + if (is_unbalance_ && std::fabs(scale_pos_weight_ - 1.0f) > 1e-6) { + Log::Fatal("Cannot set is_unbalance and scale_pos_weight at the same time"); + } + is_pos_ = is_pos; + if (is_pos_ == nullptr) { + is_pos_ = [](label_t label) { return label > 0; }; + } + } + + explicit BinaryLogloss(const std::vector& strs) + : deterministic_(false) { + sigmoid_ = -1; + for (auto str : strs) { + auto tokens = Common::Split(str.c_str(), ':'); + if (tokens.size() == 2) { + if (tokens[0] == std::string("sigmoid")) { + Common::Atof(tokens[1].c_str(), &sigmoid_); + } + } + } + if (sigmoid_ <= 0.0) { + Log::Fatal("Sigmoid parameter %f should be greater than zero", sigmoid_); + } + } + + ~BinaryLogloss() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + data_size_t cnt_positive = 0; + data_size_t cnt_negative = 0; + // count for positive and negative samples + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:cnt_positive, cnt_negative) + for (data_size_t i = 0; i < num_data_; ++i) { + if (is_pos_(label_[i])) { + ++cnt_positive; + } else { + ++cnt_negative; + } + } + num_pos_data_ = cnt_positive; + if (Network::num_machines() > 1) { + cnt_positive = Network::GlobalSyncUpBySum(cnt_positive); + cnt_negative = Network::GlobalSyncUpBySum(cnt_negative); + } + need_train_ = true; + if (cnt_negative == 0 || cnt_positive == 0) { + Log::Warning("Contains only one class"); + // not need to boost. + need_train_ = false; + } + Log::Info("Number of positive: %d, number of negative: %d", cnt_positive, cnt_negative); + // use -1 for negative class, and 1 for positive class + label_val_[0] = -1; + label_val_[1] = 1; + // weight for label + label_weights_[0] = 1.0f; + label_weights_[1] = 1.0f; + // if using unbalance, change the labels weight + if (is_unbalance_ && cnt_positive > 0 && cnt_negative > 0) { + if (cnt_positive > cnt_negative) { + label_weights_[1] = 1.0f; + label_weights_[0] = static_cast(cnt_positive) / cnt_negative; + } else { + label_weights_[1] = static_cast(cnt_negative) / cnt_positive; + label_weights_[0] = 1.0f; + } + } + label_weights_[1] *= scale_pos_weight_; + } + + void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { + if (!need_train_) { + return; + } + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + // get label and label weights + const int is_pos = is_pos_(label_[i]); + const int label = label_val_[is_pos]; + const double label_weight = label_weights_[is_pos]; + // calculate gradients and hessians + const double response = -label * sigmoid_ / (1.0f + std::exp(label * sigmoid_ * score[i])); + const double abs_response = fabs(response); + gradients[i] = static_cast(response * label_weight); + hessians[i] = static_cast(abs_response * (sigmoid_ - abs_response) * label_weight); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + // get label and label weights + const int is_pos = is_pos_(label_[i]); + const int label = label_val_[is_pos]; + const double label_weight = label_weights_[is_pos]; + // calculate gradients and hessians + const double response = -label * sigmoid_ / (1.0f + std::exp(label * sigmoid_ * score[i])); + const double abs_response = fabs(response); + gradients[i] = static_cast(response * label_weight * weights_[i]); + hessians[i] = static_cast(abs_response * (sigmoid_ - abs_response) * label_weight * weights_[i]); + } + } + } + + // implement custom average to boost from (if enabled among options) + double BoostFromScore(int) const override { + double suml = 0.0f; + double sumw = 0.0f; + if (weights_ != nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_) + for (data_size_t i = 0; i < num_data_; ++i) { + suml += is_pos_(label_[i]) * weights_[i]; + sumw += weights_[i]; + } + } else { + sumw = static_cast(num_data_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_) + for (data_size_t i = 0; i < num_data_; ++i) { + suml += is_pos_(label_[i]); + } + } + if (Network::num_machines() > 1) { + suml = Network::GlobalSyncUpBySum(suml); + sumw = Network::GlobalSyncUpBySum(sumw); + } + double pavg = suml / sumw; + pavg = std::min(pavg, 1.0 - kEpsilon); + pavg = std::max(pavg, kEpsilon); + double initscore = std::log(pavg / (1.0f - pavg)) / sigmoid_; + Log::Info("[%s:%s]: pavg=%f -> initscore=%f", GetName(), __func__, pavg, initscore); + return initscore; + } + + bool ClassNeedTrain(int /*class_id*/) const override { + return need_train_; + } + + const char* GetName() const override { + return "binary"; + } + + void ConvertOutput(const double* input, double* output) const override { + output[0] = 1.0f / (1.0f + std::exp(-sigmoid_ * input[0])); + } + + std::string ToString() const override { + std::stringstream str_buf; + str_buf << GetName() << " "; + str_buf << "sigmoid:" << sigmoid_; + return str_buf.str(); + } + + bool SkipEmptyClass() const override { return true; } + + bool NeedAccuratePrediction() const override { return false; } + + data_size_t NumPositiveData() const override { return num_pos_data_; } + + protected: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Number of positive samples */ + data_size_t num_pos_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief True if using unbalance training */ + bool is_unbalance_; + /*! \brief Sigmoid parameter */ + double sigmoid_; + /*! \brief Values for positive and negative labels */ + int label_val_[2]; + /*! \brief Weights for positive and negative labels */ + double label_weights_[2]; + /*! \brief Weights for data */ + const label_t* weights_; + double scale_pos_weight_; + std::function is_pos_; + bool need_train_; + const bool deterministic_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_ diff --git a/src/objective/cuda/cuda_binary_objective.cpp b/src/objective/cuda/cuda_binary_objective.cpp new file mode 100644 index 0000000..e03b2b5 --- /dev/null +++ b/src/objective/cuda/cuda_binary_objective.cpp @@ -0,0 +1,58 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_binary_objective.hpp" + +#include +#include + +namespace LightGBM { + +CUDABinaryLogloss::CUDABinaryLogloss(const Config& config): +CUDAObjectiveInterface(config), ova_class_id_(-1) { + cuda_label_ = nullptr; + cuda_weights_ = nullptr; +} + +CUDABinaryLogloss::CUDABinaryLogloss(const Config& config, const int ova_class_id): +CUDAObjectiveInterface(config), ova_class_id_(ova_class_id) { + is_pos_ = [ova_class_id](label_t label) { return static_cast(label) == ova_class_id; }; +} + +CUDABinaryLogloss::CUDABinaryLogloss(const std::vector& strs): CUDAObjectiveInterface(strs) {} + +CUDABinaryLogloss::~CUDABinaryLogloss() {} + +void CUDABinaryLogloss::Init(const Metadata& metadata, data_size_t num_data) { + CUDAObjectiveInterface::Init(metadata, num_data); + if (ova_class_id_ == -1) { + cuda_label_ = metadata.cuda_metadata()->cuda_label(); + cuda_ova_label_.Clear(); + } else { + cuda_ova_label_.Resize(static_cast(num_data)); + CopyFromHostToCUDADevice(cuda_ova_label_.RawData(), metadata.cuda_metadata()->cuda_label(), static_cast(num_data), __FILE__, __LINE__); + LaunchResetOVACUDALabelKernel(); + cuda_label_ = cuda_ova_label_.RawData(); + } + cuda_weights_ = metadata.cuda_metadata()->cuda_weights(); + cuda_boost_from_score_.Resize(1); + SetCUDAMemory(cuda_boost_from_score_.RawData(), 0, 1, __FILE__, __LINE__); + cuda_sum_weights_.Resize(1); + SetCUDAMemory(cuda_sum_weights_.RawData(), 0, 1, __FILE__, __LINE__); + if (label_weights_[0] != 1.0f || label_weights_[1] != 1.0f) { + cuda_label_weights_.Resize(2); + CopyFromHostToCUDADevice(cuda_label_weights_.RawData(), label_weights_, 2, __FILE__, __LINE__); + } else { + cuda_label_weights_.Clear(); + } +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_binary_objective.cu b/src/objective/cuda/cuda_binary_objective.cu new file mode 100644 index 0000000..bc6e9f4 --- /dev/null +++ b/src/objective/cuda/cuda_binary_objective.cu @@ -0,0 +1,225 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include "cuda_binary_objective.hpp" + +#include + +#include + +namespace LightGBM { + +template +__global__ void BoostFromScoreKernel_1_BinaryLogloss(const label_t* cuda_labels, const data_size_t num_data, double* out_cuda_sum_labels, + double* out_cuda_sum_weights, const label_t* cuda_weights) { + __shared__ double shared_buffer[WARPSIZE]; + const uint32_t mask = 0xffffffff; + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t warpID = threadIdx.x / warpSize; + const uint32_t num_warp = blockDim.x / warpSize; + const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + double label_value = 0.0; + double weight_value = 0.0; + if (index < num_data) { + if (USE_WEIGHT) { + const label_t cuda_label = cuda_labels[index]; + const double sample_weight = cuda_weights[index]; + const label_t label = cuda_label > 0 ? 1 : 0; + label_value = label * sample_weight; + weight_value = sample_weight; + } else { + const label_t cuda_label = cuda_labels[index]; + label_value = cuda_label > 0 ? 1 : 0; + } + } + for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { + label_value += __shfl_down_sync(mask, label_value, offset); + } + if (warpLane == 0) { + shared_buffer[warpID] = label_value; + } + __syncthreads(); + if (warpID == 0) { + label_value = (warpLane < num_warp ? shared_buffer[warpLane] : 0); + for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { + label_value += __shfl_down_sync(mask, label_value, offset); + } + } + __syncthreads(); + if (USE_WEIGHT) { + for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { + weight_value += __shfl_down_sync(mask, weight_value, offset); + } + if (warpLane == 0) { + shared_buffer[warpID] = weight_value; + } + __syncthreads(); + if (warpID == 0) { + weight_value = (warpLane < num_warp ? shared_buffer[warpLane] : 0); + for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) { + weight_value += __shfl_down_sync(mask, weight_value, offset); + } + } + __syncthreads(); + } + if (threadIdx.x == 0) { + atomicAdd_system(out_cuda_sum_labels, label_value); + if (USE_WEIGHT) { + atomicAdd_system(out_cuda_sum_weights, weight_value); + } + } +} + +template +__global__ void BoostFromScoreKernel_2_BinaryLogloss(double* out_cuda_sum_labels, double* out_cuda_sum_weights, + const data_size_t num_data, const double sigmoid) { + const double suml = *out_cuda_sum_labels; + const double sumw = USE_WEIGHT ? *out_cuda_sum_weights : static_cast(num_data); + double pavg = suml / sumw; + pavg = min(pavg, 1.0 - kEpsilon); + pavg = max(pavg, kEpsilon); + const double init_score = log(pavg / (1.0f - pavg)) / sigmoid; + *out_cuda_sum_weights = pavg; + *out_cuda_sum_labels = init_score; +} + +double CUDABinaryLogloss::LaunchCalcInitScoreKernel(const int /*class_id*/) const { + const int num_blocks = (num_data_ + CALC_INIT_SCORE_BLOCK_SIZE_BINARY - 1) / CALC_INIT_SCORE_BLOCK_SIZE_BINARY; + SetCUDAMemory(cuda_boost_from_score_.RawData(), 0, 1, __FILE__, __LINE__); + if (cuda_weights_ == nullptr) { + BoostFromScoreKernel_1_BinaryLogloss<<>> + (cuda_label_, num_data_, cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), cuda_weights_); + } else { + BoostFromScoreKernel_1_BinaryLogloss<<>> + (cuda_label_, num_data_, cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), cuda_weights_); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + if (cuda_weights_ == nullptr) { + if (nccl_communicator_ == nullptr) { + BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_); + } else { + NCCLAllReduce(cuda_boost_from_score_.RawData(), cuda_boost_from_score_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_); + const data_size_t global_num_data = NCCLAllReduce(num_data_, ncclInt32, ncclSum, nccl_communicator_); + BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), global_num_data, sigmoid_); + } + } else { + if (nccl_communicator_ == nullptr) { + BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_); + } else { + NCCLAllReduce(cuda_boost_from_score_.RawData(), cuda_boost_from_score_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_); + NCCLAllReduce(cuda_sum_weights_.RawData(), cuda_sum_weights_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_); + BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_); + } + } + SynchronizeCUDADevice(__FILE__, __LINE__); + double boost_from_score = 0.0f; + CopyFromCUDADeviceToHost(&boost_from_score, cuda_boost_from_score_.RawData(), 1, __FILE__, __LINE__); + double pavg = 0.0f; + CopyFromCUDADeviceToHost(&pavg, cuda_sum_weights_.RawData(), 1, __FILE__, __LINE__); + // for some test cases in test_utilities.py which check the log output + Log::Info("[%s:%s]: pavg=%f -> initscore=%f", GetName(), "BoostFromScore", pavg, boost_from_score); + return boost_from_score; +} + +template +__global__ void GetGradientsKernel_BinaryLogloss(const double* cuda_scores, const label_t* cuda_labels, + const double* cuda_label_weights, const label_t* cuda_weights, + const double sigmoid, const data_size_t num_data, + score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); + if (data_index < num_data) { + const label_t cuda_label = static_cast(cuda_labels[data_index]); + const int label = cuda_label > 0 ? 1 : -1; + const double response = -label * sigmoid / (1.0f + exp(label * sigmoid * cuda_scores[data_index])); + const double abs_response = fabs(response); + if (!USE_WEIGHT) { + if (USE_LABEL_WEIGHT) { + const double label_weight = cuda_label_weights[label]; + cuda_out_gradients[data_index] = static_cast(response * label_weight); + cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * label_weight); + } else { + cuda_out_gradients[data_index] = static_cast(response); + cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response)); + } + } else { + const double sample_weight = cuda_weights[data_index]; + if (USE_LABEL_WEIGHT) { + const double label_weight = cuda_label_weights[label]; + cuda_out_gradients[data_index] = static_cast(response * label_weight * sample_weight); + cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * label_weight * sample_weight); + } else { + cuda_out_gradients[data_index] = static_cast(response * sample_weight); + cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * sample_weight); + } + } + } +} + +#define GetGradientsKernel_BinaryLogloss_ARGS \ + scores, \ + cuda_label_, \ + cuda_label_weights_.RawData(), \ + cuda_weights_, \ + sigmoid_, \ + num_data_, \ + gradients, \ + hessians + +void CUDABinaryLogloss::LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY; + if (cuda_label_weights_.Size() == 0) { + if (cuda_weights_ == nullptr) { + GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); + } else { + GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); + } + } else { + if (cuda_weights_ == nullptr) { + GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); + } else { + GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS); + } + } +} + +#undef GetGradientsKernel_BinaryLogloss_ARGS + +__global__ void ConvertOutputCUDAKernel_BinaryLogloss(const double sigmoid, const data_size_t num_data, const double* input, double* output) { + const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + if (data_index < num_data) { + output[data_index] = 1.0f / (1.0f + exp(-sigmoid * input[data_index])); + } +} + +const double* CUDABinaryLogloss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const { + const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY; + ConvertOutputCUDAKernel_BinaryLogloss<<>>(sigmoid_, num_data, input, output); + return output; +} + +__global__ void ResetOVACUDALabelKernel( + const int ova_class_id, + const data_size_t num_data, + label_t* cuda_label) { + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (data_index < num_data) { + const int int_label = static_cast(cuda_label[data_index]); + cuda_label[data_index] = (int_label == ova_class_id ? 1.0f : 0.0f); + } +} + +void CUDABinaryLogloss::LaunchResetOVACUDALabelKernel() const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY; + ResetOVACUDALabelKernel<<>>(ova_class_id_, num_data_, cuda_ova_label_.RawData()); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_binary_objective.hpp b/src/objective/cuda/cuda_binary_objective.hpp new file mode 100644 index 0000000..5eef8d3 --- /dev/null +++ b/src/objective/cuda/cuda_binary_objective.hpp @@ -0,0 +1,64 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_ + +#ifdef USE_CUDA + +#define GET_GRADIENTS_BLOCK_SIZE_BINARY (1024) +#define CALC_INIT_SCORE_BLOCK_SIZE_BINARY (1024) + +#include + +#include +#include + +#include "../binary_objective.hpp" + +namespace LightGBM { + +class CUDABinaryLogloss : public CUDAObjectiveInterface { + public: + explicit CUDABinaryLogloss(const Config& config); + + explicit CUDABinaryLogloss(const Config& config, const int ova_class_id); + + explicit CUDABinaryLogloss(const std::vector& strs); + + ~CUDABinaryLogloss(); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + bool NeedConvertOutputCUDA() const override { return true; } + + private: + void LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const override; + + double LaunchCalcInitScoreKernel(const int class_id) const override; + + const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override; + + void LaunchResetOVACUDALabelKernel() const; + + // CUDA memory, held by other objects + const label_t* cuda_label_; + CUDAVector cuda_ova_label_; + const label_t* cuda_weights_; + + // CUDA memory, held by this object + CUDAVector cuda_boost_from_score_; + CUDAVector cuda_sum_weights_; + CUDAVector cuda_label_weights_; + const int ova_class_id_ = -1; +}; + +} // namespace LightGBM + +#endif // USE_CUDA + +#endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_ diff --git a/src/objective/cuda/cuda_multiclass_objective.cpp b/src/objective/cuda/cuda_multiclass_objective.cpp new file mode 100644 index 0000000..7449c7e --- /dev/null +++ b/src/objective/cuda/cuda_multiclass_objective.cpp @@ -0,0 +1,63 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifdef USE_CUDA + +#include "cuda_multiclass_objective.hpp" + +#include +#include + +namespace LightGBM { + +CUDAMulticlassSoftmax::CUDAMulticlassSoftmax(const Config& config): CUDAObjectiveInterface(config) {} + +CUDAMulticlassSoftmax::CUDAMulticlassSoftmax(const std::vector& strs): CUDAObjectiveInterface(strs) {} + +CUDAMulticlassSoftmax::~CUDAMulticlassSoftmax() {} + +void CUDAMulticlassSoftmax::Init(const Metadata& metadata, data_size_t num_data) { + CUDAObjectiveInterface::Init(metadata, num_data); + cuda_softmax_buffer_.Resize(static_cast(num_data) * static_cast(num_class_)); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + + +CUDAMulticlassOVA::CUDAMulticlassOVA(const Config& config): CUDAObjectiveInterface(config) { + for (int i = 0; i < num_class_; ++i) { + cuda_binary_loss_.emplace_back(new CUDABinaryLogloss(config, i)); + } +} + +CUDAMulticlassOVA::CUDAMulticlassOVA(const std::vector& strs): CUDAObjectiveInterface(strs) {} + +CUDAMulticlassOVA::~CUDAMulticlassOVA() {} + +void CUDAMulticlassOVA::Init(const Metadata& metadata, data_size_t num_data) { + MulticlassOVA::Init(metadata, num_data); + for (int i = 0; i < num_class_; ++i) { + cuda_binary_loss_[i]->Init(metadata, num_data); + } +} + +void CUDAMulticlassOVA::GetGradients(const double* score, score_t* gradients, score_t* hessians) const { + for (int i = 0; i < num_class_; ++i) { + int64_t offset = static_cast(num_data_) * i; + cuda_binary_loss_[i]->GetGradients(score + offset, gradients + offset, hessians + offset); + } +} + +const double* CUDAMulticlassOVA::ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const { + for (int i = 0; i < num_class_; ++i) { + cuda_binary_loss_[i]->ConvertOutputCUDA(num_data, input + i * num_data, output + i * num_data); + } + return output; +} + + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_multiclass_objective.cu b/src/objective/cuda/cuda_multiclass_objective.cu new file mode 100644 index 0000000..b610f14 --- /dev/null +++ b/src/objective/cuda/cuda_multiclass_objective.cu @@ -0,0 +1,109 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#ifdef USE_CUDA + +#include + +#include "cuda_multiclass_objective.hpp" + +namespace LightGBM { + +__device__ void SoftmaxCUDA(double* softmax_buffer, int len) { + double wmax = softmax_buffer[0]; + for (int i = 1; i < len; ++i) { + wmax = max(softmax_buffer[i], wmax); + } + double wsum = 0.0f; + for (int i = 0; i < len; ++i) { + softmax_buffer[i] = exp(softmax_buffer[i] - wmax); + wsum += softmax_buffer[i]; + } + for (int i = 0; i < len; ++i) { + softmax_buffer[i] /= static_cast(wsum); + } +} + +template +__global__ void GetGradientsKernel_MulticlassSoftmax( + const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, + const double factor, const int num_class, const data_size_t num_data, + double* cuda_softmax_buffer, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (data_index < num_data) { + const data_size_t offset = data_index * num_class; + double* softmax_result = cuda_softmax_buffer + offset; + for (int k = 0; k < num_class; ++k) { + softmax_result[k] = cuda_scores[k * num_data + data_index]; + } + SoftmaxCUDA(softmax_result, num_class); + if (!USE_WEIGHT) { + for (int k = 0; k < num_class; ++k) { + const double p = softmax_result[k]; + size_t idx = static_cast(num_data) * k + data_index; + if (static_cast(cuda_labels[data_index]) == k) { + cuda_out_gradients[idx] = static_cast(p - 1.0f); + } else { + cuda_out_gradients[idx] = static_cast(p); + } + cuda_out_hessians[idx] = static_cast(factor * p * (1.0f - p)); + } + } else { + for (int k = 0; k < num_class; ++k) { + const double p = softmax_result[k]; + const double weight = cuda_weights[data_index]; + size_t idx = static_cast(num_data) * k + data_index; + if (static_cast(cuda_labels[data_index]) == k) { + cuda_out_gradients[idx] = static_cast((p - 1.0f) * weight); + } else { + cuda_out_gradients[idx] = static_cast(p * weight); + } + cuda_out_hessians[idx] = static_cast((factor * p * (1.0f - p)) * weight); + } + } + } +} + +void CUDAMulticlassSoftmax::LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_MULTICLASS - 1) / GET_GRADIENTS_BLOCK_SIZE_MULTICLASS; + if (cuda_weights_ == nullptr) { + GetGradientsKernel_MulticlassSoftmax<<>>( + scores, cuda_labels_, cuda_weights_, factor_, num_class_, num_data_, + cuda_softmax_buffer_.RawData(), gradients, hessians); + } else { + GetGradientsKernel_MulticlassSoftmax<<>>( + scores, cuda_labels_, cuda_weights_, factor_, num_class_, num_data_, + cuda_softmax_buffer_.RawData(), gradients, hessians); + } +} + +__global__ void ConvertOutputCUDAKernel_MulticlassSoftmax( + const int num_class, const data_size_t num_data, const double* input, double* cuda_softmax_buffer, double* output) { + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (data_index < num_data) { + const data_size_t offset = data_index * num_class; + double* cuda_softmax_buffer_ptr = cuda_softmax_buffer + offset; + for (int class_index = 0; class_index < num_class; ++class_index) { + cuda_softmax_buffer_ptr[class_index] = input[class_index * num_data + data_index]; + } + SoftmaxCUDA(cuda_softmax_buffer_ptr, num_class); + for (int class_index = 0; class_index < num_class; ++class_index) { + output[class_index * num_data + data_index] = cuda_softmax_buffer_ptr[class_index]; + } + } +} + +const double* CUDAMulticlassSoftmax::LaunchConvertOutputCUDAKernel( + const data_size_t num_data, const double* input, double* output) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_MULTICLASS - 1) / GET_GRADIENTS_BLOCK_SIZE_MULTICLASS; + ConvertOutputCUDAKernel_MulticlassSoftmax<<>>( + num_class_, num_data, input, cuda_softmax_buffer_.RawData(), output); + return output; +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_multiclass_objective.hpp b/src/objective/cuda/cuda_multiclass_objective.hpp new file mode 100644 index 0000000..0f075f0 --- /dev/null +++ b/src/objective/cuda/cuda_multiclass_objective.hpp @@ -0,0 +1,79 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_MULTICLASS_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_MULTICLASS_OBJECTIVE_HPP_ + +#ifdef USE_CUDA + +#include + +#include +#include +#include + +#include "cuda_binary_objective.hpp" + +#include "../multiclass_objective.hpp" + +#define GET_GRADIENTS_BLOCK_SIZE_MULTICLASS (1024) + +namespace LightGBM { + +class CUDAMulticlassSoftmax: public CUDAObjectiveInterface { + public: + explicit CUDAMulticlassSoftmax(const Config& config); + + explicit CUDAMulticlassSoftmax(const std::vector& strs); + + ~CUDAMulticlassSoftmax(); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + private: + void LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const; + + const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const; + + // CUDA memory, held by this object + CUDAVector cuda_softmax_buffer_; +}; + + +class CUDAMulticlassOVA: public CUDAObjectiveInterface { + public: + explicit CUDAMulticlassOVA(const Config& config); + + explicit CUDAMulticlassOVA(const std::vector& strs); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override; + + const double* ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const override; + + double BoostFromScore(int class_id) const override { + return cuda_binary_loss_[class_id]->BoostFromScore(0); + } + + bool ClassNeedTrain(int class_id) const override { + return cuda_binary_loss_[class_id]->ClassNeedTrain(0); + } + + ~CUDAMulticlassOVA(); + + bool IsCUDAObjective() const override { return true; } + + private: + void LaunchGetGradientsKernel(const double* /*scores*/, score_t* /*gradients*/, score_t* /*hessians*/) const {} + + std::vector> cuda_binary_loss_; +}; + + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_MULTICLASS_OBJECTIVE_HPP_ diff --git a/src/objective/cuda/cuda_rank_objective.cpp b/src/objective/cuda/cuda_rank_objective.cpp new file mode 100644 index 0000000..e2b81c2 --- /dev/null +++ b/src/objective/cuda/cuda_rank_objective.cpp @@ -0,0 +1,68 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include +#include + +#include "cuda_rank_objective.hpp" + +namespace LightGBM { + +CUDALambdarankNDCG::CUDALambdarankNDCG(const Config& config): CUDALambdaRankObjectiveInterface(config) {} + +CUDALambdarankNDCG::CUDALambdarankNDCG(const std::vector& strs): CUDALambdaRankObjectiveInterface(strs) {} + +CUDALambdarankNDCG::~CUDALambdarankNDCG() {} + +void CUDALambdarankNDCG::Init(const Metadata& metadata, data_size_t num_data) { + CUDALambdaRankObjectiveInterface::Init(metadata, num_data); + cuda_inverse_max_dcgs_.Resize(this->inverse_max_dcgs_.size()); + CopyFromHostToCUDADevice(cuda_inverse_max_dcgs_.RawData(), this->inverse_max_dcgs_.data(), this->inverse_max_dcgs_.size(), __FILE__, __LINE__); + cuda_label_gain_.Resize(this->label_gain_.size()); + CopyFromHostToCUDADevice(cuda_label_gain_.RawData(), this->label_gain_.data(), this->label_gain_.size(), __FILE__, __LINE__); +} + + +CUDARankXENDCG::CUDARankXENDCG(const Config& config): CUDALambdaRankObjectiveInterface(config) {} + +CUDARankXENDCG::CUDARankXENDCG(const std::vector& strs): CUDALambdaRankObjectiveInterface(strs) {} + +CUDARankXENDCG::~CUDARankXENDCG() {} + +void CUDARankXENDCG::Init(const Metadata& metadata, data_size_t num_data) { + CUDALambdaRankObjectiveInterface::Init(metadata, num_data); + for (data_size_t i = 0; i < num_queries_; ++i) { + rands_.emplace_back(seed_ + i); + } + item_rands_.resize(num_data, 0.0f); + cuda_item_rands_.Resize(static_cast(num_data)); + if (max_items_in_query_aligned_ >= 2048) { + cuda_params_buffer_.Resize(static_cast(num_data_)); + } +} + +void CUDARankXENDCG::GenerateItemRands() const { + const int num_threads = OMP_NUM_THREADS(); + OMP_INIT_EX(); + #pragma omp parallel for schedule(static) num_threads(num_threads) + for (data_size_t i = 0; i < num_queries_; ++i) { + OMP_LOOP_EX_BEGIN(); + const data_size_t start = query_boundaries_[i]; + const data_size_t end = query_boundaries_[i + 1]; + for (data_size_t j = start; j < end; ++j) { + item_rands_[j] = rands_[i].NextFloat(); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_rank_objective.cu b/src/objective/cuda/cuda_rank_objective.cu new file mode 100644 index 0000000..112bde9 --- /dev/null +++ b/src/objective/cuda/cuda_rank_objective.cu @@ -0,0 +1,666 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include "cuda_rank_objective.hpp" + +#include +#include +#include + +namespace LightGBM { + +template +__global__ void GetGradientsKernel_LambdarankNDCG(const double* cuda_scores, const label_t* cuda_labels, const data_size_t num_data, + const data_size_t num_queries, const data_size_t* cuda_query_boundaries, const double* cuda_inverse_max_dcgs, + const bool norm, const double sigmoid, const int truncation_level, const double* cuda_label_gain, const data_size_t num_rank_label, + score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + __shared__ score_t shared_scores[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; + __shared__ uint16_t shared_indices[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; + __shared__ score_t shared_lambdas[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; + __shared__ score_t shared_hessians[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024]; + __shared__ double shared_label_gain[NUM_RANK_LABEL > 1024 ? 1 : NUM_RANK_LABEL]; + const double* label_gain_ptr = nullptr; + if (NUM_RANK_LABEL <= 1024) { + for (uint32_t i = threadIdx.x; i < num_rank_label; i += blockDim.x) { + shared_label_gain[i] = cuda_label_gain[i]; + } + __syncthreads(); + label_gain_ptr = shared_label_gain; + } else { + label_gain_ptr = cuda_label_gain; + } + const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; + const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); + for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { + const double inverse_max_dcg = cuda_inverse_max_dcgs[query_index]; + const data_size_t query_start = cuda_query_boundaries[query_index]; + const data_size_t query_end = cuda_query_boundaries[query_index + 1]; + const data_size_t query_item_count = query_end - query_start; + const double* cuda_scores_pointer = cuda_scores + query_start; + score_t* cuda_out_gradients_pointer = cuda_out_gradients + query_start; + score_t* cuda_out_hessians_pointer = cuda_out_hessians + query_start; + const label_t* cuda_label_pointer = cuda_labels + query_start; + if (threadIdx.x < query_item_count) { + shared_scores[threadIdx.x] = cuda_scores_pointer[threadIdx.x]; + shared_indices[threadIdx.x] = static_cast(threadIdx.x); + shared_lambdas[threadIdx.x] = 0.0f; + shared_hessians[threadIdx.x] = 0.0f; + } else { + shared_scores[threadIdx.x] = kMinScore; + shared_indices[threadIdx.x] = static_cast(threadIdx.x); + } + if (MAX_ITEM_GREATER_THAN_1024) { + if (query_item_count > 1024) { + const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024; + if (threadIdx_x_plus_1024 < query_item_count) { + shared_scores[threadIdx_x_plus_1024] = cuda_scores_pointer[threadIdx_x_plus_1024]; + shared_indices[threadIdx_x_plus_1024] = static_cast(threadIdx_x_plus_1024); + shared_lambdas[threadIdx_x_plus_1024] = 0.0f; + shared_hessians[threadIdx_x_plus_1024] = 0.0f; + } else { + shared_scores[threadIdx_x_plus_1024] = kMinScore; + shared_indices[threadIdx_x_plus_1024] = static_cast(threadIdx_x_plus_1024); + } + } + } + __syncthreads(); + if (MAX_ITEM_GREATER_THAN_1024) { + if (query_item_count > 1024) { + BitonicArgSort_2048(shared_scores, shared_indices); + } else { + BitonicArgSort_1024(shared_scores, shared_indices, static_cast(query_item_count)); + } + } else { + BitonicArgSort_1024(shared_scores, shared_indices, static_cast(query_item_count)); + } + __syncthreads(); + // get best and worst score + const double best_score = shared_scores[shared_indices[0]]; + data_size_t worst_idx = query_item_count - 1; + if (worst_idx > 0 && shared_scores[shared_indices[worst_idx]] == kMinScore) { + worst_idx -= 1; + } + const double worst_score = shared_scores[shared_indices[worst_idx]]; + __shared__ double sum_lambdas; + if (threadIdx.x == 0) { + sum_lambdas = 0.0f; + } + __syncthreads(); + // start accumulate lambdas by pairs that contain at least one document above truncation level + const data_size_t num_items_i = min(query_item_count - 1, truncation_level); + const data_size_t num_j_per_i = query_item_count - 1; + const data_size_t s = num_j_per_i - num_items_i + 1; + const data_size_t num_pairs = (num_j_per_i + s) * num_items_i / 2; + double thread_sum_lambdas = 0.0f; + for (data_size_t pair_index = static_cast(threadIdx.x); pair_index < num_pairs; pair_index += static_cast(blockDim.x)) { + const double square = 2 * static_cast(pair_index) + s * s - s; + const double sqrt_result = floor(sqrt(square)); + const data_size_t row_index = static_cast(floor(sqrt(square - sqrt_result)) + 1 - s); + const data_size_t i = num_items_i - 1 - row_index; + const data_size_t j = num_j_per_i - (pair_index - (2 * s + row_index - 1) * row_index / 2); + if (cuda_label_pointer[shared_indices[i]] != cuda_label_pointer[shared_indices[j]] && shared_scores[shared_indices[j]] != kMinScore) { + data_size_t high_rank, low_rank; + if (cuda_label_pointer[shared_indices[i]] > cuda_label_pointer[shared_indices[j]]) { + high_rank = i; + low_rank = j; + } else { + high_rank = j; + low_rank = i; + } + const data_size_t high = shared_indices[high_rank]; + const int high_label = static_cast(cuda_label_pointer[high]); + const double high_score = shared_scores[high]; + const double high_label_gain = label_gain_ptr[high_label]; + const double high_discount = log2(2.0f + high_rank); + const data_size_t low = shared_indices[low_rank]; + const int low_label = static_cast(cuda_label_pointer[low]); + const double low_score = shared_scores[low]; + const double low_label_gain = label_gain_ptr[low_label]; + const double low_discount = log2(2.0f + low_rank); + + const double delta_score = high_score - low_score; + + // get dcg gap + const double dcg_gap = high_label_gain - low_label_gain; + // get discount of this pair + const double paired_discount = fabs(high_discount - low_discount); + // get delta NDCG + double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg; + // regular the delta_pair_NDCG by score distance + if (norm && best_score != worst_score) { + delta_pair_NDCG /= (0.01f + fabs(delta_score)); + } + // calculate lambda for this pair + double p_lambda = 1.0f / (1.0f + exp(sigmoid * delta_score)); + double p_hessian = p_lambda * (1.0f - p_lambda); + // update + p_lambda *= -sigmoid * delta_pair_NDCG; + p_hessian *= sigmoid * sigmoid * delta_pair_NDCG; + atomicAdd_block(shared_lambdas + low, -static_cast(p_lambda)); + atomicAdd_block(shared_hessians + low, static_cast(p_hessian)); + atomicAdd_block(shared_lambdas + high, static_cast(p_lambda)); + atomicAdd_block(shared_hessians + high, static_cast(p_hessian)); + // lambda is negative, so use minus to accumulate + thread_sum_lambdas -= 2 * p_lambda; + } + } + atomicAdd_block(&sum_lambdas, thread_sum_lambdas); + __syncthreads(); + if (norm && sum_lambdas > 0) { + const double norm_factor = log2(1 + sum_lambdas) / sum_lambdas; + if (threadIdx.x < static_cast(query_item_count)) { + cuda_out_gradients_pointer[threadIdx.x] = static_cast(shared_lambdas[threadIdx.x] * norm_factor); + cuda_out_hessians_pointer[threadIdx.x] = static_cast(shared_hessians[threadIdx.x] * norm_factor); + } + if (MAX_ITEM_GREATER_THAN_1024) { + if (query_item_count > 1024) { + const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024; + if (threadIdx_x_plus_1024 < static_cast(query_item_count)) { + cuda_out_gradients_pointer[threadIdx_x_plus_1024] = static_cast(shared_lambdas[threadIdx_x_plus_1024] * norm_factor); + cuda_out_hessians_pointer[threadIdx_x_plus_1024] = static_cast(shared_hessians[threadIdx_x_plus_1024] * norm_factor); + } + } + } + } else { + if (threadIdx.x < static_cast(query_item_count)) { + cuda_out_gradients_pointer[threadIdx.x] = static_cast(shared_lambdas[threadIdx.x]); + cuda_out_hessians_pointer[threadIdx.x] = static_cast(shared_hessians[threadIdx.x]); + } + if (MAX_ITEM_GREATER_THAN_1024) { + if (query_item_count > 1024) { + const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024; + if (threadIdx_x_plus_1024 < static_cast(query_item_count)) { + cuda_out_gradients_pointer[threadIdx_x_plus_1024] = static_cast(shared_lambdas[threadIdx_x_plus_1024]); + cuda_out_hessians_pointer[threadIdx_x_plus_1024] = static_cast(shared_hessians[threadIdx_x_plus_1024]); + } + } + } + } + __syncthreads(); + } +} + +template +__global__ void GetGradientsKernel_LambdarankNDCG_Sorted( + const double* cuda_scores, const int* cuda_item_indices_buffer, const label_t* cuda_labels, const data_size_t num_data, + const data_size_t num_queries, const data_size_t* cuda_query_boundaries, const double* cuda_inverse_max_dcgs, + const bool norm, const double sigmoid, const int truncation_level, const double* cuda_label_gain, const data_size_t num_rank_label, + score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + __shared__ double shared_label_gain[NUM_RANK_LABEL > 1024 ? 1 : NUM_RANK_LABEL]; + const double* label_gain_ptr = nullptr; + if (NUM_RANK_LABEL <= 1024) { + for (uint32_t i = threadIdx.x; i < static_cast(num_rank_label); i += blockDim.x) { + shared_label_gain[i] = cuda_label_gain[i]; + } + __syncthreads(); + label_gain_ptr = shared_label_gain; + } else { + label_gain_ptr = cuda_label_gain; + } + const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; + const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); + for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { + const double inverse_max_dcg = cuda_inverse_max_dcgs[query_index]; + const data_size_t query_start = cuda_query_boundaries[query_index]; + const data_size_t query_end = cuda_query_boundaries[query_index + 1]; + const data_size_t query_item_count = query_end - query_start; + const double* cuda_scores_pointer = cuda_scores + query_start; + const int* cuda_item_indices_buffer_pointer = cuda_item_indices_buffer + query_start; + score_t* cuda_out_gradients_pointer = cuda_out_gradients + query_start; + score_t* cuda_out_hessians_pointer = cuda_out_hessians + query_start; + const label_t* cuda_label_pointer = cuda_labels + query_start; + // get best and worst score + const double best_score = cuda_scores_pointer[cuda_item_indices_buffer_pointer[0]]; + data_size_t worst_idx = query_item_count - 1; + if (worst_idx > 0 && cuda_scores_pointer[cuda_item_indices_buffer_pointer[worst_idx]] == kMinScore) { + worst_idx -= 1; + } + const double worst_score = cuda_scores_pointer[cuda_item_indices_buffer_pointer[worst_idx]]; + __shared__ double sum_lambdas; + if (threadIdx.x == 0) { + sum_lambdas = 0.0f; + } + for (int item_index = static_cast(threadIdx.x); item_index < query_item_count; item_index += static_cast(blockDim.x)) { + cuda_out_gradients_pointer[item_index] = 0.0f; + cuda_out_hessians_pointer[item_index] = 0.0f; + } + __syncthreads(); + // start accumulate lambdas by pairs that contain at least one document above truncation level + const data_size_t num_items_i = min(query_item_count - 1, truncation_level); + const data_size_t num_j_per_i = query_item_count - 1; + const data_size_t s = num_j_per_i - num_items_i + 1; + const data_size_t num_pairs = (num_j_per_i + s) * num_items_i / 2; + double thread_sum_lambdas = 0.0f; + for (data_size_t pair_index = static_cast(threadIdx.x); pair_index < num_pairs; pair_index += static_cast(blockDim.x)) { + const double square = 2 * static_cast(pair_index) + s * s - s; + const double sqrt_result = floor(sqrt(square)); + const data_size_t row_index = static_cast(floor(sqrt(square - sqrt_result)) + 1 - s); + const data_size_t i = num_items_i - 1 - row_index; + const data_size_t j = num_j_per_i - (pair_index - (2 * s + row_index - 1) * row_index / 2); + if (j > i) { + // skip pairs with the same labels + if (cuda_label_pointer[cuda_item_indices_buffer_pointer[i]] != cuda_label_pointer[cuda_item_indices_buffer_pointer[j]] && cuda_scores_pointer[cuda_item_indices_buffer_pointer[j]] != kMinScore) { + data_size_t high_rank, low_rank; + if (cuda_label_pointer[cuda_item_indices_buffer_pointer[i]] > cuda_label_pointer[cuda_item_indices_buffer_pointer[j]]) { + high_rank = i; + low_rank = j; + } else { + high_rank = j; + low_rank = i; + } + const data_size_t high = cuda_item_indices_buffer_pointer[high_rank]; + const int high_label = static_cast(cuda_label_pointer[high]); + const double high_score = cuda_scores_pointer[high]; + const double high_label_gain = label_gain_ptr[high_label]; + const double high_discount = log2(2.0f + high_rank); + const data_size_t low = cuda_item_indices_buffer_pointer[low_rank]; + const int low_label = static_cast(cuda_label_pointer[low]); + const double low_score = cuda_scores_pointer[low]; + const double low_label_gain = label_gain_ptr[low_label]; + const double low_discount = log2(2.0f + low_rank); + + const double delta_score = high_score - low_score; + + // get dcg gap + const double dcg_gap = high_label_gain - low_label_gain; + // get discount of this pair + const double paired_discount = fabs(high_discount - low_discount); + // get delta NDCG + double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg; + // regular the delta_pair_NDCG by score distance + if (norm && best_score != worst_score) { + delta_pair_NDCG /= (0.01f + fabs(delta_score)); + } + // calculate lambda for this pair + double p_lambda = 1.0f / (1.0f + exp(sigmoid * delta_score)); + double p_hessian = p_lambda * (1.0f - p_lambda); + // update + p_lambda *= -sigmoid * delta_pair_NDCG; + p_hessian *= sigmoid * sigmoid * delta_pair_NDCG; + atomicAdd_block(cuda_out_gradients_pointer + low, -static_cast(p_lambda)); + atomicAdd_block(cuda_out_hessians_pointer + low, static_cast(p_hessian)); + atomicAdd_block(cuda_out_gradients_pointer + high, static_cast(p_lambda)); + atomicAdd_block(cuda_out_hessians_pointer + high, static_cast(p_hessian)); + // lambda is negative, so use minus to accumulate + thread_sum_lambdas -= 2 * p_lambda; + } + } + } + atomicAdd_block(&sum_lambdas, thread_sum_lambdas); + __syncthreads(); + if (norm && sum_lambdas > 0) { + const double norm_factor = log2(1 + sum_lambdas) / sum_lambdas; + for (int item_index = static_cast(threadIdx.x); item_index < query_item_count; item_index += static_cast(blockDim.x)) { + cuda_out_gradients_pointer[item_index] *= norm_factor; + cuda_out_hessians_pointer[item_index] *= norm_factor; + } + } + __syncthreads(); + } +} + +void CUDALambdarankNDCG::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_queries_ + NUM_QUERY_PER_BLOCK - 1) / NUM_QUERY_PER_BLOCK; + const data_size_t num_rank_label = static_cast(label_gain_.size()); + const int device_index = GetCUDADevice(__FILE__, __LINE__); + cudaDeviceProp device_prop; + CUDASUCCESS_OR_FATAL(cudaGetDeviceProperties(&device_prop, device_index)); + + #define GetGradientsKernel_LambdarankNDCG_ARGS \ + score, cuda_labels_, num_data_, \ + num_queries_, cuda_query_boundaries_, cuda_inverse_max_dcgs_.RawData(), \ + norm_, sigmoid_, truncation_level_, cuda_label_gain_.RawData(), num_rank_label, \ + gradients, hessians + + #define GetGradientsKernel_LambdarankNDCG_Sorted_ARGS \ + score, cuda_item_indices_buffer_.RawData(), cuda_labels_, num_data_, \ + num_queries_, cuda_query_boundaries_, cuda_inverse_max_dcgs_.RawData(), \ + norm_, sigmoid_, truncation_level_, cuda_label_gain_.RawData(), num_rank_label, \ + gradients, hessians + + if (max_items_in_query_aligned_ <= 1024) { + if (num_rank_label <= 32 && device_prop.warpSize == 32) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 64) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 128) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 256) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 512) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 1024) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } + } else if (max_items_in_query_aligned_ <= 2048) { + if (num_rank_label <= 32 && device_prop.warpSize == 32) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 64) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 128) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 256) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 512) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else if (num_rank_label <= 1024) { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } else { + GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS); + } + } else { + BitonicArgSortItemsGlobal(score, num_queries_, cuda_query_boundaries_, cuda_item_indices_buffer_.RawData()); + if (num_rank_label <= 32 && device_prop.warpSize == 32) { + GetGradientsKernel_LambdarankNDCG_Sorted<32><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); + } else if (num_rank_label <= 64) { + GetGradientsKernel_LambdarankNDCG_Sorted<64><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); + } else if (num_rank_label <= 128) { + GetGradientsKernel_LambdarankNDCG_Sorted<128><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); + } else if (num_rank_label <= 256) { + GetGradientsKernel_LambdarankNDCG_Sorted<256><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); + } else if (num_rank_label <= 512) { + GetGradientsKernel_LambdarankNDCG_Sorted<512><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); + } else if (num_rank_label <= 1024) { + GetGradientsKernel_LambdarankNDCG_Sorted<1024><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); + } else { + GetGradientsKernel_LambdarankNDCG_Sorted<2048><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS); + } + } + SynchronizeCUDADevice(__FILE__, __LINE__); + + #undef GetGradientsKernel_LambdarankNDCG_ARGS + #undef GetGradientsKernel_LambdarankNDCG_Sorted_ARGS +} + + +__device__ __forceinline__ double CUDAPhi(const label_t l, double g) { + return pow(2.0f, static_cast(l)) - g; +} + +template +__global__ void GetGradientsKernel_RankXENDCG_SharedMemory( + const double* cuda_scores, + const label_t* cuda_labels, + const double* cuda_item_rands, + const data_size_t num_data, + const data_size_t num_queries, + const data_size_t* cuda_query_boundaries, + score_t* cuda_out_gradients, + score_t* cuda_out_hessians) { + const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; + const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); + for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { + const data_size_t item_index_start = cuda_query_boundaries[query_index]; + const data_size_t item_index_end = cuda_query_boundaries[query_index + 1]; + const data_size_t query_item_count = item_index_end - item_index_start; + score_t* cuda_out_gradients_pointer = cuda_out_gradients + item_index_start; + score_t* cuda_out_hessians_pointer = cuda_out_hessians + item_index_start; + const label_t* cuda_labels_pointer = cuda_labels + item_index_start; + const double* cuda_scores_pointer = cuda_scores + item_index_start; + const double* cuda_item_rands_pointer = cuda_item_rands + item_index_start; + const data_size_t block_reduce_size = query_item_count >= 1024 ? 1024 : query_item_count; + __shared__ double shared_rho[SHARED_MEMORY_SIZE]; + // assert that warpSize == 32 + __shared__ double shared_buffer[1024 / WARPSIZE]; + __shared__ double shared_params[SHARED_MEMORY_SIZE]; + __shared__ score_t shared_lambdas[SHARED_MEMORY_SIZE]; + __shared__ double reduce_result; + if (query_item_count <= 1) { + for (data_size_t i = 0; i <= query_item_count; ++i) { + cuda_out_gradients_pointer[i] = 0.0f; + cuda_out_hessians_pointer[i] = 0.0f; + } + __syncthreads(); + } else { + // compute softmax + double thread_reduce_result = kMinScore; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double rho = cuda_scores_pointer[i]; + shared_rho[i] = rho; + if (rho > thread_reduce_result) { + thread_reduce_result = rho; + } + } + __syncthreads(); + thread_reduce_result = ShuffleReduceMax(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double exp_value = exp(shared_rho[i] - reduce_result); + shared_rho[i] = exp_value; + thread_reduce_result += exp_value; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + shared_rho[i] /= reduce_result; + } + __syncthreads(); + + // compute params + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double param_value = CUDAPhi(cuda_labels_pointer[i], cuda_item_rands_pointer[i]); + shared_params[i] = param_value; + thread_reduce_result += param_value; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + reduce_result = 1.0f / max(kEpsilon, reduce_result); + } + __syncthreads(); + const double inv_denominator = reduce_result; + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double term = -shared_params[i] * inv_denominator + shared_rho[i]; + shared_lambdas[i] = static_cast(term); + shared_params[i] = term / (1.0f - shared_rho[i]); + thread_reduce_result += shared_params[i]; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + const double sum_l1 = reduce_result; + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double term = shared_rho[i] * (sum_l1 - shared_params[i]); + shared_lambdas[i] += static_cast(term); + shared_params[i] = term / (1.0f - shared_rho[i]); + thread_reduce_result += shared_params[i]; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + const double sum_l2 = reduce_result; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + shared_lambdas[i] += static_cast(shared_rho[i] * (sum_l2 - shared_params[i])); + cuda_out_hessians_pointer[i] = static_cast(shared_rho[i] * (1.0f - shared_rho[i])); + } + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + cuda_out_gradients_pointer[i] = shared_lambdas[i]; + } + __syncthreads(); + } + } +} + +__global__ void GetGradientsKernel_RankXENDCG_GlobalMemory( + const double* cuda_scores, + const label_t* cuda_labels, + const double* cuda_item_rands, + const data_size_t num_data, + const data_size_t num_queries, + const data_size_t* cuda_query_boundaries, + double* cuda_params_buffer, + score_t* cuda_out_gradients, + score_t* cuda_out_hessians) { + const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK; + const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries); + for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) { + const data_size_t item_index_start = cuda_query_boundaries[query_index]; + const data_size_t item_index_end = cuda_query_boundaries[query_index + 1]; + const data_size_t query_item_count = item_index_end - item_index_start; + score_t* cuda_out_gradients_pointer = cuda_out_gradients + item_index_start; + score_t* cuda_out_hessians_pointer = cuda_out_hessians + item_index_start; + const label_t* cuda_labels_pointer = cuda_labels + item_index_start; + const double* cuda_scores_pointer = cuda_scores + item_index_start; + const double* cuda_item_rands_pointer = cuda_item_rands + item_index_start; + double* cuda_params_buffer_pointer = cuda_params_buffer + item_index_start; + const data_size_t block_reduce_size = query_item_count > 1024 ? 1024 : query_item_count; + // assert that warpSize == 32, so we use buffer size 1024 / 32 = 32 + __shared__ double shared_buffer[1024 / WARPSIZE]; + __shared__ double reduce_result; + if (query_item_count <= 1) { + for (data_size_t i = 0; i <= query_item_count; ++i) { + cuda_out_gradients_pointer[i] = 0.0f; + cuda_out_hessians_pointer[i] = 0.0f; + } + __syncthreads(); + } else { + // compute softmax + double thread_reduce_result = kMinScore; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double rho = cuda_scores_pointer[i]; + if (rho > thread_reduce_result) { + thread_reduce_result = rho; + } + } + __syncthreads(); + thread_reduce_result = ShuffleReduceMax(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double exp_value = exp(cuda_scores_pointer[i] - reduce_result); + cuda_out_hessians_pointer[i] = exp_value; + thread_reduce_result += exp_value; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + // store probability into hessians + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + cuda_out_hessians_pointer[i] /= reduce_result; + } + __syncthreads(); + + // compute params + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double param_value = CUDAPhi(cuda_labels_pointer[i], cuda_item_rands_pointer[i]); + cuda_params_buffer_pointer[i] = param_value; + thread_reduce_result += param_value; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + reduce_result = 1.0f / max(kEpsilon, reduce_result); + } + __syncthreads(); + const double inv_denominator = reduce_result; + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double term = -cuda_params_buffer_pointer[i] * inv_denominator + cuda_out_hessians_pointer[i]; + cuda_out_gradients_pointer[i] = static_cast(term); + const double param = term / (1.0f - cuda_out_hessians_pointer[i]); + cuda_params_buffer_pointer[i] = param; + thread_reduce_result += param; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + const double sum_l1 = reduce_result; + thread_reduce_result = 0.0f; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double term = cuda_out_hessians_pointer[i] * (sum_l1 - cuda_params_buffer_pointer[i]); + cuda_out_gradients_pointer[i] += static_cast(term); + const double param = term / (1.0f - cuda_out_hessians_pointer[i]); + cuda_params_buffer_pointer[i] = param; + thread_reduce_result += param; + } + thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size); + if (threadIdx.x == 0) { + reduce_result = thread_reduce_result; + } + __syncthreads(); + const double sum_l2 = reduce_result; + for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) { + const double prob = cuda_out_hessians_pointer[i]; + cuda_out_gradients_pointer[i] += static_cast(prob * (sum_l2 - cuda_params_buffer_pointer[i])); + cuda_out_hessians_pointer[i] = static_cast(prob * (1.0f - prob)); + } + __syncthreads(); + } + } +} + +void CUDARankXENDCG::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + GenerateItemRands(); + CopyFromHostToCUDADevice(cuda_item_rands_.RawData(), item_rands_.data(), item_rands_.size(), __FILE__, __LINE__); + + const int num_blocks = (num_queries_ + NUM_QUERY_PER_BLOCK - 1) / NUM_QUERY_PER_BLOCK; + if (max_items_in_query_aligned_ <= 1024) { + GetGradientsKernel_RankXENDCG_SharedMemory<1024><<>>( + score, + cuda_labels_, + cuda_item_rands_.RawData(), + num_data_, + num_queries_, + cuda_query_boundaries_, + gradients, + hessians); + } else if (max_items_in_query_aligned_ <= 2 * 1024) { + GetGradientsKernel_RankXENDCG_SharedMemory<2 * 1024><<>>( + score, + cuda_labels_, + cuda_item_rands_.RawData(), + num_data_, + num_queries_, + cuda_query_boundaries_, + gradients, + hessians); + } else { + GetGradientsKernel_RankXENDCG_GlobalMemory<<>>( + score, + cuda_labels_, + cuda_item_rands_.RawData(), + num_data_, + num_queries_, + cuda_query_boundaries_, + cuda_params_buffer_.RawData(), + gradients, + hessians); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_rank_objective.hpp b/src/objective/cuda/cuda_rank_objective.hpp new file mode 100644 index 0000000..2523325 --- /dev/null +++ b/src/objective/cuda/cuda_rank_objective.hpp @@ -0,0 +1,123 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_ + +#ifdef USE_CUDA + +#define NUM_QUERY_PER_BLOCK (10) + +#include +#include + +#include +#include +#include + +#include "../rank_objective.hpp" + +namespace LightGBM { + +template +class CUDALambdaRankObjectiveInterface : public CUDAObjectiveInterface { + public: + explicit CUDALambdaRankObjectiveInterface(const Config& config): CUDAObjectiveInterface(config) {} + + explicit CUDALambdaRankObjectiveInterface(const std::vector& strs): CUDAObjectiveInterface(strs) {} + + ~CUDALambdaRankObjectiveInterface() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + CUDAObjectiveInterface::Init(metadata, num_data); + + const int num_threads = OMP_NUM_THREADS(); + std::vector thread_max_num_items_in_query(num_threads); + Threading::For(0, this->num_queries_, 1, + [this, &thread_max_num_items_in_query] (int thread_index, data_size_t start, data_size_t end) { + for (data_size_t query_index = start; query_index < end; ++query_index) { + const data_size_t query_item_count = this->query_boundaries_[query_index + 1] - this->query_boundaries_[query_index]; + if (query_item_count > thread_max_num_items_in_query[thread_index]) { + thread_max_num_items_in_query[thread_index] = query_item_count; + } + } + }); + data_size_t max_items_in_query = 0; + for (int thread_index = 0; thread_index < num_threads; ++thread_index) { + if (thread_max_num_items_in_query[thread_index] > max_items_in_query) { + max_items_in_query = thread_max_num_items_in_query[thread_index]; + } + } + max_items_in_query_aligned_ = 1; + --max_items_in_query; + while (max_items_in_query > 0) { + max_items_in_query >>= 1; + max_items_in_query_aligned_ <<= 1; + } + if (max_items_in_query_aligned_ > 2048) { + cuda_item_indices_buffer_.Resize(static_cast(metadata.query_boundaries()[metadata.num_queries()])); + } + this->cuda_labels_ = metadata.cuda_metadata()->cuda_label(); + cuda_query_boundaries_ = metadata.cuda_metadata()->cuda_query_boundaries(); + } + + protected: + // CUDA memory, held by this object + CUDAVector cuda_item_indices_buffer_; + + // CUDA memory, held by other objects + const data_size_t* cuda_query_boundaries_; + + // Host memory + int max_items_in_query_aligned_; +}; + + +class CUDALambdarankNDCG: public CUDALambdaRankObjectiveInterface { + public: + explicit CUDALambdarankNDCG(const Config& config); + + explicit CUDALambdarankNDCG(const std::vector& strs); + + void Init(const Metadata& mdtadata, data_size_t num_data) override; + + ~CUDALambdarankNDCG(); + + private: + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; + + // CUDA memory, held by this object + CUDAVector cuda_inverse_max_dcgs_; + CUDAVector cuda_label_gain_; +}; + + +class CUDARankXENDCG : public CUDALambdaRankObjectiveInterface { + public: + explicit CUDARankXENDCG(const Config& config); + + explicit CUDARankXENDCG(const std::vector& strs); + + ~CUDARankXENDCG(); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + protected: + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const; + + void GenerateItemRands() const; + + mutable std::vector item_rands_; + CUDAVector cuda_item_rands_; + CUDAVector cuda_params_buffer_; +}; + + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_ diff --git a/src/objective/cuda/cuda_regression_objective.cpp b/src/objective/cuda/cuda_regression_objective.cpp new file mode 100644 index 0000000..e678349 --- /dev/null +++ b/src/objective/cuda/cuda_regression_objective.cpp @@ -0,0 +1,110 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_regression_objective.hpp" + +#include +#include + +namespace LightGBM { + +CUDARegressionL2loss::CUDARegressionL2loss(const Config& config): +CUDARegressionObjectiveInterface(config) {} + +CUDARegressionL2loss::CUDARegressionL2loss(const std::vector& strs): +CUDARegressionObjectiveInterface(strs) {} + +CUDARegressionL2loss::~CUDARegressionL2loss() {} + +void CUDARegressionL2loss::Init(const Metadata& metadata, data_size_t num_data) { + CUDARegressionObjectiveInterface::Init(metadata, num_data); +} + +CUDARegressionL1loss::CUDARegressionL1loss(const Config& config): +CUDARegressionObjectiveInterface(config) {} + +CUDARegressionL1loss::CUDARegressionL1loss(const std::vector& strs): +CUDARegressionObjectiveInterface(strs) {} + +CUDARegressionL1loss::~CUDARegressionL1loss() {} + +void CUDARegressionL1loss::Init(const Metadata& metadata, data_size_t num_data) { + CUDARegressionObjectiveInterface::Init(metadata, num_data); + cuda_data_indices_buffer_.Resize(static_cast(num_data)); + cuda_percentile_result_.Resize(1); + if (cuda_weights_ != nullptr) { + const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION + 1; + cuda_weights_prefix_sum_.Resize(static_cast(num_data)); + cuda_weights_prefix_sum_buffer_.Resize(static_cast(num_blocks)); + cuda_weight_by_leaf_buffer_.Resize(static_cast(num_data)); + } + cuda_residual_buffer_.Resize(static_cast(num_data)); +} + + +CUDARegressionHuberLoss::CUDARegressionHuberLoss(const Config& config): +CUDARegressionObjectiveInterface(config) {} + +CUDARegressionHuberLoss::CUDARegressionHuberLoss(const std::vector& strs): +CUDARegressionObjectiveInterface(strs) {} + +CUDARegressionHuberLoss::~CUDARegressionHuberLoss() {} + + +CUDARegressionFairLoss::CUDARegressionFairLoss(const Config& config): +CUDARegressionObjectiveInterface(config) {} + +CUDARegressionFairLoss::CUDARegressionFairLoss(const std::vector& strs): +CUDARegressionObjectiveInterface(strs) {} + +CUDARegressionFairLoss::~CUDARegressionFairLoss() {} + + +CUDARegressionPoissonLoss::CUDARegressionPoissonLoss(const Config& config): +CUDARegressionObjectiveInterface(config) {} + +CUDARegressionPoissonLoss::CUDARegressionPoissonLoss(const std::vector& strs): +CUDARegressionObjectiveInterface(strs) {} + +CUDARegressionPoissonLoss::~CUDARegressionPoissonLoss() {} + +void CUDARegressionPoissonLoss::Init(const Metadata& metadata, data_size_t num_data) { + CUDARegressionObjectiveInterface::Init(metadata, num_data); + LaunchCheckLabelKernel(); +} + +double CUDARegressionPoissonLoss::LaunchCalcInitScoreKernel(const int class_id) const { + return Common::SafeLog(CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(class_id)); +} + + +CUDARegressionQuantileloss::CUDARegressionQuantileloss(const Config& config): +CUDARegressionObjectiveInterface(config) {} + +CUDARegressionQuantileloss::CUDARegressionQuantileloss(const std::vector& strs): +CUDARegressionObjectiveInterface(strs) {} + +CUDARegressionQuantileloss::~CUDARegressionQuantileloss() {} + +void CUDARegressionQuantileloss::Init(const Metadata& metadata, data_size_t num_data) { + CUDARegressionObjectiveInterface::Init(metadata, num_data); + cuda_data_indices_buffer_.Resize(static_cast(num_data)); + cuda_percentile_result_.Resize(1); + if (cuda_weights_ != nullptr) { + const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION + 1; + cuda_weights_prefix_sum_.Resize(static_cast(num_data)); + cuda_weights_prefix_sum_buffer_.Resize(static_cast(num_blocks)); + cuda_weight_by_leaf_buffer_.Resize(static_cast(num_data)); + } + cuda_residual_buffer_.Resize(static_cast(num_data)); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_regression_objective.cu b/src/objective/cuda/cuda_regression_objective.cu new file mode 100644 index 0000000..2d2a428 --- /dev/null +++ b/src/objective/cuda/cuda_regression_objective.cu @@ -0,0 +1,482 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_regression_objective.hpp" +#include + +namespace LightGBM { + +template +void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data) { + CUDAObjectiveInterface::Init(metadata, num_data); + const data_size_t num_get_gradients_blocks = (this->num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + cuda_block_buffer_.Resize(static_cast(num_get_gradients_blocks)); + if (this->sqrt_) { + cuda_trans_label_.Resize(this->trans_label_.size()); + CopyFromHostToCUDADevice(cuda_trans_label_.RawData(), this->trans_label_.data(), this->trans_label_.size(), __FILE__, __LINE__); + this->cuda_labels_ = cuda_trans_label_.RawData(); + } +} + +template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); +template void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data); + +template +double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int /*class_id*/) const { + double label_sum = 0.0f, weight_sum = 0.0f; + if (this->cuda_weights_ == nullptr) { + ShuffleReduceSumGlobal(this->cuda_labels_, + static_cast(this->num_data_), cuda_block_buffer_.RawData()); + CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); + weight_sum = static_cast(this->num_data_); + } else { + ShuffleReduceDotProdGlobal(this->cuda_labels_, + this->cuda_weights_, static_cast(this->num_data_), cuda_block_buffer_.RawData()); + CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); + ShuffleReduceSumGlobal(this->cuda_weights_, + static_cast(this->num_data_), cuda_block_buffer_.RawData()); + CopyFromCUDADeviceToHost(&weight_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); + } + return label_sum / weight_sum; +} + +template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; +template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; +template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; +template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; +template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; +template double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const; + +__global__ void ConvertOutputCUDAKernel_Regression(const bool sqrt, const data_size_t num_data, const double* input, double* output) { + const int data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + if (data_index < num_data) { + if (sqrt) { + const double sign = input[data_index] >= 0.0f ? 1 : -1; + output[data_index] = sign * input[data_index] * input[data_index]; + } else { + output[data_index] = input[data_index]; + } + } +} + +const double* CUDARegressionL2loss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const { + const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + if (sqrt_) { + ConvertOutputCUDAKernel_Regression<<>>(sqrt_, num_data, input, output); + return output; + } else { + return input; + } +} + +template +__global__ void GetGradientsKernel_RegressionL2(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, + score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); + if (data_index < num_data) { + if (!USE_WEIGHT) { + cuda_out_gradients[data_index] = static_cast(cuda_scores[data_index] - cuda_labels[data_index]); + cuda_out_hessians[data_index] = 1.0f; + } else { + const score_t weight = static_cast(cuda_weights[data_index]); + cuda_out_gradients[data_index] = static_cast(cuda_scores[data_index] - cuda_labels[data_index]) * weight; + cuda_out_hessians[data_index] = weight; + } + } +} + +void CUDARegressionL2loss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + if (cuda_weights_ == nullptr) { + GetGradientsKernel_RegressionL2<<>>(score, cuda_labels_, nullptr, num_data_, gradients, hessians); + } else { + GetGradientsKernel_RegressionL2<<>>(score, cuda_labels_, cuda_weights_, num_data_, gradients, hessians); + } +} + + +double CUDARegressionL1loss::LaunchCalcInitScoreKernel(const int /*class_id*/) const { + const double alpha = 0.5f; + if (cuda_weights_ == nullptr) { + PercentileGlobal( + cuda_labels_, nullptr, cuda_data_indices_buffer_.RawData(), nullptr, nullptr, alpha, num_data_, cuda_percentile_result_.RawData()); + } else { + PercentileGlobal( + cuda_labels_, cuda_weights_, cuda_data_indices_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), + cuda_weights_prefix_sum_buffer_.RawData(), alpha, num_data_, cuda_percentile_result_.RawData()); + } + label_t percentile_result = 0.0f; + CopyFromCUDADeviceToHost(&percentile_result, cuda_percentile_result_.RawData(), 1, __FILE__, __LINE__); + SynchronizeCUDADevice(__FILE__, __LINE__); + return static_cast(percentile_result); +} + +template +__global__ void GetGradientsKernel_RegressionL1(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, + score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); + if (data_index < num_data) { + if (!USE_WEIGHT) { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + cuda_out_gradients[data_index] = static_cast((diff > 0.0f) - (diff < 0.0f)); + cuda_out_hessians[data_index] = 1.0f; + } else { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + const score_t weight = static_cast(cuda_weights[data_index]); + cuda_out_gradients[data_index] = static_cast((diff > 0.0f) - (diff < 0.0f)) * weight; + cuda_out_hessians[data_index] = weight; + } + } +} + +void CUDARegressionL1loss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + if (cuda_weights_ == nullptr) { + GetGradientsKernel_RegressionL1<<>>(score, cuda_labels_, nullptr, num_data_, gradients, hessians); + } else { + GetGradientsKernel_RegressionL1<<>>(score, cuda_labels_, cuda_weights_, num_data_, gradients, hessians); + } +} + +template +__global__ void RenewTreeOutputCUDAKernel_RegressionL1( + const double* score, + const label_t* label, + const label_t* weight, + double* residual_buffer, + label_t* weight_by_leaf, + double* weight_prefix_sum_buffer, + const data_size_t* data_indices_in_leaf, + const data_size_t* num_data_in_leaf, + const data_size_t* data_start_in_leaf, + data_size_t* data_indices_buffer, + double* leaf_value) { + const int leaf_index = static_cast(blockIdx.x); + const data_size_t data_start = data_start_in_leaf[leaf_index]; + const data_size_t num_data = num_data_in_leaf[leaf_index]; + data_size_t* data_indices_buffer_pointer = data_indices_buffer + data_start; + const label_t* weight_by_leaf_pointer = weight_by_leaf + data_start; + double* weight_prefix_sum_buffer_pointer = weight_prefix_sum_buffer + data_start; + const double* residual_buffer_pointer = residual_buffer + data_start; + const double alpha = 0.5f; + for (data_size_t inner_data_index = data_start + static_cast(threadIdx.x); + inner_data_index < data_start + num_data; inner_data_index += static_cast(blockDim.x)) { + const data_size_t data_index = data_indices_in_leaf[inner_data_index]; + const label_t data_label = label[data_index]; + const double data_score = score[data_index]; + residual_buffer[inner_data_index] = static_cast(data_label) - data_score; + if (USE_WEIGHT) { + weight_by_leaf[inner_data_index] = weight[data_index]; + } + } + __syncthreads(); + const double renew_leaf_value = PercentileDevice( + residual_buffer_pointer, weight_by_leaf_pointer, data_indices_buffer_pointer, + weight_prefix_sum_buffer_pointer, alpha, num_data); + if (threadIdx.x == 0) { + leaf_value[leaf_index] = renew_leaf_value; + } +} + +void CUDARegressionL1loss::LaunchRenewTreeOutputCUDAKernel( + const double* score, + const data_size_t* data_indices_in_leaf, + const data_size_t* num_data_in_leaf, + const data_size_t* data_start_in_leaf, + const int num_leaves, + double* leaf_value) const { + if (cuda_weights_ == nullptr) { + RenewTreeOutputCUDAKernel_RegressionL1<<>>( + score, + cuda_labels_, + cuda_weights_, + cuda_residual_buffer_.RawData(), + cuda_weight_by_leaf_buffer_.RawData(), + cuda_weights_prefix_sum_.RawData(), + data_indices_in_leaf, + num_data_in_leaf, + data_start_in_leaf, + cuda_data_indices_buffer_.RawData(), + leaf_value); + } else { + RenewTreeOutputCUDAKernel_RegressionL1<<>>( + score, + cuda_labels_, + cuda_weights_, + cuda_residual_buffer_.RawData(), + cuda_weight_by_leaf_buffer_.RawData(), + cuda_weights_prefix_sum_.RawData(), + data_indices_in_leaf, + num_data_in_leaf, + data_start_in_leaf, + cuda_data_indices_buffer_.RawData(), + leaf_value); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + + +template +__global__ void GetGradientsKernel_Huber(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, + const double alpha, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); + if (data_index < num_data) { + if (!USE_WEIGHT) { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + if (fabs(diff) <= alpha) { + cuda_out_gradients[data_index] = static_cast(diff); + } else { + const score_t sign = static_cast((diff > 0.0f) - (diff < 0.0f)); + cuda_out_gradients[data_index] = static_cast(sign * alpha); + } + cuda_out_hessians[data_index] = 1.0f; + } else { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + const score_t weight = static_cast(cuda_weights[data_index]); + if (fabs(diff) <= alpha) { + cuda_out_gradients[data_index] = static_cast(diff) * weight; + } else { + const score_t sign = static_cast((diff > 0.0f) - (diff < 0.0f)); + cuda_out_gradients[data_index] = static_cast(sign * alpha) * weight; + } + cuda_out_hessians[data_index] = weight; + } + } +} + +void CUDARegressionHuberLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + if (cuda_weights_ == nullptr) { + GetGradientsKernel_Huber<<>>(score, cuda_labels_, nullptr, num_data_, alpha_, gradients, hessians); + } else { + GetGradientsKernel_Huber<<>>(score, cuda_labels_, cuda_weights_, num_data_, alpha_, gradients, hessians); + } +} + + +template +__global__ void GetGradientsKernel_Fair(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, + const double c, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); + if (data_index < num_data) { + if (!USE_WEIGHT) { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + cuda_out_gradients[data_index] = static_cast(c * diff / (fabs(diff) + c)); + cuda_out_hessians[data_index] = static_cast(c * c / ((fabs(diff) + c) * (fabs(diff) + c))); + } else { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + const score_t weight = static_cast(cuda_weights[data_index]); + cuda_out_gradients[data_index] = static_cast(c * diff / (fabs(diff) + c) * weight); + cuda_out_hessians[data_index] = static_cast(c * c / ((fabs(diff) + c) * (fabs(diff) + c)) * weight); + } + } +} + +void CUDARegressionFairLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + if (cuda_weights_ == nullptr) { + GetGradientsKernel_Fair<<>>(score, cuda_labels_, nullptr, num_data_, c_, gradients, hessians); + } else { + GetGradientsKernel_Fair<<>>(score, cuda_labels_, cuda_weights_, num_data_, c_, gradients, hessians); + } +} + +void CUDARegressionPoissonLoss::LaunchCheckLabelKernel() const { + ShuffleReduceSumGlobal(cuda_labels_, static_cast(num_data_), cuda_block_buffer_.RawData()); + double label_sum = 0.0f; + CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); + + ShuffleReduceMinGlobal(cuda_labels_, static_cast(num_data_), cuda_block_buffer_.RawData()); + double label_min = 0.0f; + CopyFromCUDADeviceToHost(&label_min, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__); + + if (label_min < 0.0f) { + Log::Fatal("[%s]: at least one target label is negative", GetName()); + } + if (label_sum == 0.0f) { + Log::Fatal("[%s]: sum of labels is zero", GetName()); + } +} + +template +__global__ void GetGradientsKernel_Poisson(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data, + const double max_delta_step, score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); + const double exp_max_delta_step = std::exp(max_delta_step); + if (data_index < num_data) { + if (!USE_WEIGHT) { + const double exp_score = exp(cuda_scores[data_index]); + cuda_out_gradients[data_index] = static_cast(exp_score - cuda_labels[data_index]); + cuda_out_hessians[data_index] = static_cast(exp_score * exp_max_delta_step); + } else { + const double exp_score = exp(cuda_scores[data_index]); + const score_t weight = static_cast(cuda_weights[data_index]); + cuda_out_gradients[data_index] = static_cast((exp_score - cuda_labels[data_index]) * weight); + cuda_out_hessians[data_index] = static_cast(exp_score * exp_max_delta_step * weight); + } + } +} + +void CUDARegressionPoissonLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + if (cuda_weights_ == nullptr) { + GetGradientsKernel_Poisson<<>>( + score, cuda_labels_, nullptr, num_data_, max_delta_step_, gradients, hessians); + } else { + GetGradientsKernel_Poisson<<>>( + score, cuda_labels_, cuda_weights_, num_data_, max_delta_step_, gradients, hessians); + } +} + +__global__ void ConvertOutputCUDAKernel_Regression_Poisson(const data_size_t num_data, const double* input, double* output) { + const int data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x); + if (data_index < num_data) { + output[data_index] = exp(input[data_index]); + } +} + +const double* CUDARegressionPoissonLoss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const { + const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + ConvertOutputCUDAKernel_Regression_Poisson<<>>(num_data, input, output); + return output; +} + + +double CUDARegressionQuantileloss::LaunchCalcInitScoreKernel(const int /*class_id*/) const { + if (cuda_weights_ == nullptr) { + PercentileGlobal( + cuda_labels_, nullptr, cuda_data_indices_buffer_.RawData(), nullptr, nullptr, alpha_, num_data_, cuda_percentile_result_.RawData()); + } else { + PercentileGlobal( + cuda_labels_, cuda_weights_, cuda_data_indices_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(), + cuda_weights_prefix_sum_buffer_.RawData(), alpha_, num_data_, cuda_percentile_result_.RawData()); + } + label_t percentile_result = 0.0f; + CopyFromCUDADeviceToHost(&percentile_result, cuda_percentile_result_.RawData(), 1, __FILE__, __LINE__); + SynchronizeCUDADevice(__FILE__, __LINE__); + return static_cast(percentile_result); +} + +template +__global__ void RenewTreeOutputCUDAKernel_RegressionQuantile( + const double* score, + const label_t* label, + const label_t* weight, + double* residual_buffer, + label_t* weight_by_leaf, + double* weight_prefix_sum_buffer, + const data_size_t* data_indices_in_leaf, + const data_size_t* num_data_in_leaf, + const data_size_t* data_start_in_leaf, + data_size_t* data_indices_buffer, + double* leaf_value, + const double alpha) { + const int leaf_index = static_cast(blockIdx.x); + const data_size_t data_start = data_start_in_leaf[leaf_index]; + const data_size_t num_data = num_data_in_leaf[leaf_index]; + data_size_t* data_indices_buffer_pointer = data_indices_buffer + data_start; + const label_t* weight_by_leaf_pointer = weight_by_leaf + data_start; + double* weight_prefix_sum_buffer_pointer = weight_prefix_sum_buffer + data_start; + const double* residual_buffer_pointer = residual_buffer + data_start; + for (data_size_t inner_data_index = data_start + static_cast(threadIdx.x); inner_data_index < data_start + num_data; inner_data_index += static_cast(blockDim.x)) { + const data_size_t data_index = data_indices_in_leaf[inner_data_index]; + const label_t data_label = label[data_index]; + const double data_score = score[data_index]; + residual_buffer[inner_data_index] = static_cast(data_label) - data_score; + if (USE_WEIGHT) { + weight_by_leaf[inner_data_index] = weight[data_index]; + } + } + __syncthreads(); + const double renew_leaf_value = PercentileDevice( + residual_buffer_pointer, weight_by_leaf_pointer, data_indices_buffer_pointer, + weight_prefix_sum_buffer_pointer, alpha, num_data); + if (threadIdx.x == 0) { + leaf_value[leaf_index] = renew_leaf_value; + } +} + +void CUDARegressionQuantileloss::LaunchRenewTreeOutputCUDAKernel( + const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, + const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const { + if (cuda_weights_ == nullptr) { + RenewTreeOutputCUDAKernel_RegressionQuantile<<>>( + score, + cuda_labels_, + cuda_weights_, + cuda_residual_buffer_.RawData(), + cuda_weight_by_leaf_buffer_.RawData(), + cuda_weights_prefix_sum_.RawData(), + data_indices_in_leaf, + num_data_in_leaf, + data_start_in_leaf, + cuda_data_indices_buffer_.RawData(), + leaf_value, + alpha_); + } else { + RenewTreeOutputCUDAKernel_RegressionQuantile<<>>( + score, + cuda_labels_, + cuda_weights_, + cuda_residual_buffer_.RawData(), + cuda_weight_by_leaf_buffer_.RawData(), + cuda_weights_prefix_sum_.RawData(), + data_indices_in_leaf, + num_data_in_leaf, + data_start_in_leaf, + cuda_data_indices_buffer_.RawData(), + leaf_value, + alpha_); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +template +__global__ void GetGradientsKernel_RegressionQuantile(const double* cuda_scores, const label_t* cuda_labels, + const label_t* cuda_weights, const data_size_t num_data, const double alpha, + score_t* cuda_out_gradients, score_t* cuda_out_hessians) { + const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x); + if (data_index < num_data) { + if (!USE_WEIGHT) { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + if (diff >= 0.0f) { + cuda_out_gradients[data_index] = (1.0f - alpha); + } else { + cuda_out_gradients[data_index] = -alpha; + } + cuda_out_hessians[data_index] = 1.0f; + } else { + const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]); + const score_t weight = static_cast(cuda_weights[data_index]); + if (diff >= 0.0f) { + cuda_out_gradients[data_index] = (1.0f - alpha) * weight; + } else { + cuda_out_gradients[data_index] = -alpha * weight; + } + cuda_out_hessians[data_index] = weight; + } + } +} + +void CUDARegressionQuantileloss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const { + const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION; + if (cuda_weights_ == nullptr) { + GetGradientsKernel_RegressionQuantile<<>>(score, cuda_labels_, nullptr, num_data_, alpha_, gradients, hessians); + } else { + GetGradientsKernel_RegressionQuantile<<>>(score, cuda_labels_, cuda_weights_, num_data_, alpha_, gradients, hessians); + } +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/objective/cuda/cuda_regression_objective.hpp b/src/objective/cuda/cuda_regression_objective.hpp new file mode 100644 index 0000000..2e8584b --- /dev/null +++ b/src/objective/cuda/cuda_regression_objective.hpp @@ -0,0 +1,168 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_ + +#ifdef USE_CUDA + +#define GET_GRADIENTS_BLOCK_SIZE_REGRESSION (1024) + +#include + +#include +#include + +#include "../regression_objective.hpp" + +namespace LightGBM { + +template +class CUDARegressionObjectiveInterface: public CUDAObjectiveInterface { + public: + explicit CUDARegressionObjectiveInterface(const Config& config): CUDAObjectiveInterface(config) {} + + explicit CUDARegressionObjectiveInterface(const std::vector& strs): CUDAObjectiveInterface(strs) {} + + void Init(const Metadata& metadata, data_size_t num_data) override; + + protected: + double LaunchCalcInitScoreKernel(const int class_id) const override; + + CUDAVector cuda_block_buffer_; + CUDAVector cuda_trans_label_; +}; + +class CUDARegressionL2loss : public CUDARegressionObjectiveInterface { + public: + explicit CUDARegressionL2loss(const Config& config); + + explicit CUDARegressionL2loss(const std::vector& strs); + + ~CUDARegressionL2loss(); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + protected: + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; + + const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override; + + bool NeedConvertOutputCUDA() const override { return sqrt_; } +}; + + +class CUDARegressionL1loss : public CUDARegressionObjectiveInterface { + public: + explicit CUDARegressionL1loss(const Config& config); + + explicit CUDARegressionL1loss(const std::vector& strs); + + ~CUDARegressionL1loss(); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + protected: + CUDAVector cuda_data_indices_buffer_; + CUDAVector cuda_weights_prefix_sum_; + CUDAVector cuda_weights_prefix_sum_buffer_; + CUDAVector cuda_residual_buffer_; + CUDAVector cuda_weight_by_leaf_buffer_; + CUDAVector cuda_percentile_result_; + + double LaunchCalcInitScoreKernel(const int class_id) const override; + + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; + + void LaunchRenewTreeOutputCUDAKernel( + const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, + const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override; +}; + + +class CUDARegressionHuberLoss : public CUDARegressionObjectiveInterface { + public: + explicit CUDARegressionHuberLoss(const Config& config); + + explicit CUDARegressionHuberLoss(const std::vector& strs); + + ~CUDARegressionHuberLoss(); + + private: + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; +}; + + +// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html +class CUDARegressionFairLoss : public CUDARegressionObjectiveInterface { + public: + explicit CUDARegressionFairLoss(const Config& config); + + explicit CUDARegressionFairLoss(const std::vector& strs); + + ~CUDARegressionFairLoss(); + + private: + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; +}; + + +class CUDARegressionPoissonLoss : public CUDARegressionObjectiveInterface { + public: + explicit CUDARegressionPoissonLoss(const Config& config); + + explicit CUDARegressionPoissonLoss(const std::vector& strs); + + ~CUDARegressionPoissonLoss(); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + private: + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; + + const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override; + + bool NeedConvertOutputCUDA() const override { return true; } + + double LaunchCalcInitScoreKernel(const int class_id) const override; + + void LaunchCheckLabelKernel() const; +}; + + +class CUDARegressionQuantileloss : public CUDARegressionObjectiveInterface { + public: + explicit CUDARegressionQuantileloss(const Config& config); + + explicit CUDARegressionQuantileloss(const std::vector& strs); + + ~CUDARegressionQuantileloss(); + + void Init(const Metadata& metadata, data_size_t num_data) override; + + protected: + void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override; + + double LaunchCalcInitScoreKernel(const int class_id) const override; + + void LaunchRenewTreeOutputCUDAKernel( + const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf, + const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override; + + CUDAVector cuda_data_indices_buffer_; + CUDAVector cuda_weights_prefix_sum_; + CUDAVector cuda_weights_prefix_sum_buffer_; + CUDAVector cuda_residual_buffer_; + CUDAVector cuda_weight_by_leaf_buffer_; + CUDAVector cuda_percentile_result_; +}; + + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_ diff --git a/src/objective/multiclass_objective.hpp b/src/objective/multiclass_objective.hpp new file mode 100644 index 0000000..3541847 --- /dev/null +++ b/src/objective/multiclass_objective.hpp @@ -0,0 +1,280 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ + +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "binary_objective.hpp" + +namespace LightGBM { +/*! +* \brief Objective function for multiclass classification, use softmax as objective functions +*/ +class MulticlassSoftmax: public ObjectiveFunction { + public: + explicit MulticlassSoftmax(const Config& config) { + num_class_ = config.num_class; + // This factor is to rescale the redundant form of K-classification, to the non-redundant form. + // In the traditional settings of K-classification, there is one redundant class, whose output is set to 0 (like the class 0 in binary classification). + // This is from the Friedman GBDT paper. + factor_ = static_cast(num_class_) / (num_class_ - 1.0f); + } + + explicit MulticlassSoftmax(const std::vector& strs) { + num_class_ = -1; + for (auto str : strs) { + auto tokens = Common::Split(str.c_str(), ':'); + if (tokens.size() == 2) { + if (tokens[0] == std::string("num_class")) { + Common::Atoi(tokens[1].c_str(), &num_class_); + } + } + } + if (num_class_ < 0) { + Log::Fatal("Objective should contain num_class field"); + } + factor_ = static_cast(num_class_) / (num_class_ - 1.0f); + } + + ~MulticlassSoftmax() { + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + label_int_.resize(num_data_); + class_init_probs_.resize(num_class_, 0.0); + double sum_weight = 0.0; + for (int i = 0; i < num_data_; ++i) { + label_int_[i] = static_cast(label_[i]); + if (label_int_[i] < 0 || label_int_[i] >= num_class_) { + Log::Fatal("Label must be in [0, %d), but found %d in label", num_class_, label_int_[i]); + } + if (weights_ == nullptr) { + class_init_probs_[label_int_[i]] += 1.0; + } else { + class_init_probs_[label_int_[i]] += weights_[i]; + sum_weight += weights_[i]; + } + } + if (weights_ == nullptr) { + sum_weight = num_data_; + } + if (Network::num_machines() > 1) { + sum_weight = Network::GlobalSyncUpBySum(sum_weight); + for (int i = 0; i < num_class_; ++i) { + class_init_probs_[i] = Network::GlobalSyncUpBySum(class_init_probs_[i]); + } + } + for (int i = 0; i < num_class_; ++i) { + class_init_probs_[i] /= sum_weight; + } + } + + void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { + if (weights_ == nullptr) { + std::vector rec; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(rec) + for (data_size_t i = 0; i < num_data_; ++i) { + rec.resize(num_class_); + for (int k = 0; k < num_class_; ++k) { + size_t idx = static_cast(num_data_) * k + i; + rec[k] = static_cast(score[idx]); + } + Common::Softmax(&rec); + for (int k = 0; k < num_class_; ++k) { + auto p = rec[k]; + size_t idx = static_cast(num_data_) * k + i; + if (label_int_[i] == k) { + gradients[idx] = static_cast(p - 1.0f); + } else { + gradients[idx] = static_cast(p); + } + hessians[idx] = static_cast(factor_ * p * (1.0f - p)); + } + } + } else { + std::vector rec; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(rec) + for (data_size_t i = 0; i < num_data_; ++i) { + rec.resize(num_class_); + for (int k = 0; k < num_class_; ++k) { + size_t idx = static_cast(num_data_) * k + i; + rec[k] = static_cast(score[idx]); + } + Common::Softmax(&rec); + for (int k = 0; k < num_class_; ++k) { + auto p = rec[k]; + size_t idx = static_cast(num_data_) * k + i; + if (label_int_[i] == k) { + gradients[idx] = static_cast((p - 1.0f) * weights_[i]); + } else { + gradients[idx] = static_cast(p * weights_[i]); + } + hessians[idx] = static_cast((factor_ * p * (1.0f - p))* weights_[i]); + } + } + } + } + + void ConvertOutput(const double* input, double* output) const override { + Common::Softmax(input, output, num_class_); + } + + const char* GetName() const override { + return "multiclass"; + } + + std::string ToString() const override { + std::stringstream str_buf; + str_buf << GetName() << " "; + str_buf << "num_class:" << num_class_; + return str_buf.str(); + } + + bool SkipEmptyClass() const override { return true; } + + int NumModelPerIteration() const override { return num_class_; } + + int NumPredictOneRow() const override { return num_class_; } + + bool NeedAccuratePrediction() const override { return false; } + + double BoostFromScore(int class_id) const override { + return std::log(std::max(kEpsilon, class_init_probs_[class_id])); + } + + bool ClassNeedTrain(int class_id) const override { + if (std::fabs(class_init_probs_[class_id]) <= kEpsilon + || std::fabs(class_init_probs_[class_id]) >= 1.0 - kEpsilon) { + return false; + } else { + return true; + } + } + + protected: + double factor_; + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Number of classes */ + int num_class_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Corresponding integers of label_ */ + std::vector label_int_; + /*! \brief Weights for data */ + const label_t* weights_; + std::vector class_init_probs_; +}; + +/*! +* \brief Objective function for multiclass classification, use one-vs-all binary objective function +*/ +class MulticlassOVA: public ObjectiveFunction { + public: + explicit MulticlassOVA(const Config& config) { + num_class_ = config.num_class; + for (int i = 0; i < num_class_; ++i) { + binary_loss_.emplace_back( + new BinaryLogloss(config, [i](label_t label) { return static_cast(label) == i; })); + } + sigmoid_ = config.sigmoid; + } + + explicit MulticlassOVA(const std::vector& strs) { + num_class_ = -1; + sigmoid_ = -1; + for (auto str : strs) { + auto tokens = Common::Split(str.c_str(), ':'); + if (tokens.size() == 2) { + if (tokens[0] == std::string("num_class")) { + Common::Atoi(tokens[1].c_str(), &num_class_); + } else if (tokens[0] == std::string("sigmoid")) { + Common::Atof(tokens[1].c_str(), &sigmoid_); + } + } + } + if (num_class_ < 0) { + Log::Fatal("Objective should contain num_class field"); + } + if (sigmoid_ <= 0.0) { + Log::Fatal("Sigmoid parameter %f should be greater than zero", sigmoid_); + } + } + + ~MulticlassOVA() { + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + for (int i = 0; i < num_class_; ++i) { + binary_loss_[i]->Init(metadata, num_data); + } + } + + void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { + for (int i = 0; i < num_class_; ++i) { + int64_t offset = static_cast(num_data_) * i; + binary_loss_[i]->GetGradients(score + offset, gradients + offset, hessians + offset); + } + } + + const char* GetName() const override { + return "multiclassova"; + } + + void ConvertOutput(const double* input, double* output) const override { + for (int i = 0; i < num_class_; ++i) { + output[i] = 1.0f / (1.0f + std::exp(-sigmoid_ * input[i])); + } + } + + std::string ToString() const override { + std::stringstream str_buf; + str_buf << GetName() << " "; + str_buf << "num_class:" << num_class_ << " "; + str_buf << "sigmoid:" << sigmoid_; + return str_buf.str(); + } + + bool SkipEmptyClass() const override { return true; } + + int NumModelPerIteration() const override { return num_class_; } + + int NumPredictOneRow() const override { return num_class_; } + + bool NeedAccuratePrediction() const override { return false; } + + double BoostFromScore(int class_id) const override { + return binary_loss_[class_id]->BoostFromScore(0); + } + + bool ClassNeedTrain(int class_id) const override { + return binary_loss_[class_id]->ClassNeedTrain(0); + } + + protected: + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Number of classes */ + int num_class_; + std::vector> binary_loss_; + double sigmoid_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_ diff --git a/src/objective/objective_function.cpp b/src/objective/objective_function.cpp new file mode 100644 index 0000000..c5a5cea --- /dev/null +++ b/src/objective/objective_function.cpp @@ -0,0 +1,164 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include + +#include + +#include "binary_objective.hpp" +#include "multiclass_objective.hpp" +#include "rank_objective.hpp" +#include "regression_objective.hpp" +#include "xentropy_objective.hpp" + +#include + +#include "cuda/cuda_binary_objective.hpp" +#include "cuda/cuda_multiclass_objective.hpp" +#include "cuda/cuda_rank_objective.hpp" +#include "cuda/cuda_regression_objective.hpp" + +namespace LightGBM { + +#ifdef USE_CUDA +ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunctionCUDA(const std::string& type, const Config& config) { + if (type == std::string("regression")) { + return new CUDARegressionL2loss(config); + } else if (type == std::string("regression_l1")) { + return new CUDARegressionL1loss(config); + } else if (type == std::string("quantile")) { + return new CUDARegressionQuantileloss(config); + } else if (type == std::string("huber")) { + return new CUDARegressionHuberLoss(config); + } else if (type == std::string("fair")) { + return new CUDARegressionFairLoss(config); + } else if (type == std::string("poisson")) { + return new CUDARegressionPoissonLoss(config); + } else if (type == std::string("binary")) { + return new CUDABinaryLogloss(config); + } else if (type == std::string("lambdarank")) { + return new CUDALambdarankNDCG(config); + } else if (type == std::string("rank_xendcg")) { + return new CUDARankXENDCG(config); + } else if (type == std::string("multiclass")) { + return new CUDAMulticlassSoftmax(config); + } else if (type == std::string("multiclassova")) { + return new CUDAMulticlassOVA(config); + } else if (type == std::string("cross_entropy")) { + Log::Warning("Objective cross_entropy is not implemented in cuda version. Fall back to boosting on CPU."); + return new CrossEntropy(config); + } else if (type == std::string("cross_entropy_lambda")) { + Log::Warning("Objective cross_entropy_lambda is not implemented in cuda version. Fall back to boosting on CPU."); + return new CrossEntropyLambda(config); + } else if (type == std::string("mape")) { + Log::Warning("Objective mape is not implemented in cuda version. Fall back to boosting on CPU."); + return new RegressionMAPELOSS(config); + } else if (type == std::string("gamma")) { + Log::Warning("Objective gamma is not implemented in cuda version. Fall back to boosting on CPU."); + return new RegressionGammaLoss(config); + } else if (type == std::string("tweedie")) { + Log::Warning("Objective tweedie is not implemented in cuda version. Fall back to boosting on CPU."); + return new RegressionTweedieLoss(config); + } else if (type == std::string("custom")) { + Log::Warning("Using customized objective with cuda. This requires copying gradients from CPU to GPU, which can be slow."); + return nullptr; + } +} +#endif // USE_CUDA + +ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& type, const Config& config) { + #ifdef USE_CUDA + if (config.device_type == std::string("cuda") && + config.data_sample_strategy != std::string("goss") && + config.boosting != std::string("rf")) { + return CreateObjectiveFunctionCUDA(type, config); + } else { + #endif // USE_CUDA + if (type == std::string("regression")) { + return new RegressionL2loss(config); + } else if (type == std::string("regression_l1")) { + return new RegressionL1loss(config); + } else if (type == std::string("quantile")) { + return new RegressionQuantileloss(config); + } else if (type == std::string("huber")) { + return new RegressionHuberLoss(config); + } else if (type == std::string("fair")) { + return new RegressionFairLoss(config); + } else if (type == std::string("poisson")) { + return new RegressionPoissonLoss(config); + } else if (type == std::string("binary")) { + return new BinaryLogloss(config); + } else if (type == std::string("lambdarank")) { + return new LambdarankNDCG(config); + } else if (type == std::string("rank_xendcg")) { + return new RankXENDCG(config); + } else if (type == std::string("multiclass")) { + return new MulticlassSoftmax(config); + } else if (type == std::string("multiclassova")) { + return new MulticlassOVA(config); + } else if (type == std::string("cross_entropy")) { + return new CrossEntropy(config); + } else if (type == std::string("cross_entropy_lambda")) { + return new CrossEntropyLambda(config); + } else if (type == std::string("mape")) { + return new RegressionMAPELOSS(config); + } else if (type == std::string("gamma")) { + return new RegressionGammaLoss(config); + } else if (type == std::string("tweedie")) { + return new RegressionTweedieLoss(config); + } else if (type == std::string("custom")) { + return nullptr; + } + #ifdef USE_CUDA + } + #endif // USE_CUDA + Log::Fatal("Unknown objective type name: %s", type.c_str()); + return nullptr; +} + +ObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& str) { + auto strs = Common::Split(str.c_str(), ' '); + auto type = strs[0]; + if (type == std::string("regression")) { + return new RegressionL2loss(strs); + } else if (type == std::string("regression_l1")) { + return new RegressionL1loss(strs); + } else if (type == std::string("quantile")) { + return new RegressionQuantileloss(strs); + } else if (type == std::string("huber")) { + return new RegressionHuberLoss(strs); + } else if (type == std::string("fair")) { + return new RegressionFairLoss(strs); + } else if (type == std::string("poisson")) { + return new RegressionPoissonLoss(strs); + } else if (type == std::string("binary")) { + return new BinaryLogloss(strs); + } else if (type == std::string("lambdarank")) { + return new LambdarankNDCG(strs); + } else if (type == std::string("rank_xendcg")) { + return new RankXENDCG(strs); + } else if (type == std::string("multiclass")) { + return new MulticlassSoftmax(strs); + } else if (type == std::string("multiclassova")) { + return new MulticlassOVA(strs); + } else if (type == std::string("cross_entropy")) { + return new CrossEntropy(strs); + } else if (type == std::string("cross_entropy_lambda")) { + return new CrossEntropyLambda(strs); + } else if (type == std::string("mape")) { + return new RegressionMAPELOSS(strs); + } else if (type == std::string("gamma")) { + return new RegressionGammaLoss(strs); + } else if (type == std::string("tweedie")) { + return new RegressionTweedieLoss(strs); + } else if (type == std::string("custom")) { + return nullptr; + } + Log::Fatal("Unknown objective type name: %s", type.c_str()); + return nullptr; +} + +} // namespace LightGBM diff --git a/src/objective/rank_objective.hpp b/src/objective/rank_objective.hpp new file mode 100644 index 0000000..512f731 --- /dev/null +++ b/src/objective/rank_objective.hpp @@ -0,0 +1,466 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_ + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +/*! + * \brief Objective function for Ranking + */ +class RankingObjective : public ObjectiveFunction { + public: + explicit RankingObjective(const Config& config) + : seed_(config.objective_seed) { + learning_rate_ = config.learning_rate; + position_bias_regularization_ = config.lambdarank_position_bias_regularization; + } + + explicit RankingObjective(const std::vector&) : seed_(0) {} + + ~RankingObjective() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + // get label + label_ = metadata.label(); + // get weights + weights_ = metadata.weights(); + // get positions + positions_ = metadata.positions(); + // get position ids + position_ids_ = metadata.position_ids(); + // get number of different position ids + num_position_ids_ = static_cast(metadata.num_position_ids()); + // get boundaries + query_boundaries_ = metadata.query_boundaries(); + if (query_boundaries_ == nullptr) { + Log::Fatal("Ranking tasks require query information"); + } + num_queries_ = metadata.num_queries(); + // initialize position bias vectors + pos_biases_.resize(num_position_ids_, 0.0); + } + + void GetGradientsWithSampledQueries(const double* score, const data_size_t num_sampled_queries, const data_size_t* sampled_query_indices, + score_t* gradients, score_t* hessians) const override { + const data_size_t num_queries = (sampled_query_indices == nullptr ? num_queries_ : num_sampled_queries); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) + for (data_size_t i = 0; i < num_queries; ++i) { + const data_size_t query_index = (sampled_query_indices == nullptr ? i : sampled_query_indices[i]); + const data_size_t start = query_boundaries_[query_index]; + const data_size_t cnt = query_boundaries_[query_index + 1] - query_boundaries_[query_index]; + std::vector score_adjusted; + if (num_position_ids_ > 0) { + for (data_size_t j = 0; j < cnt; ++j) { + score_adjusted.push_back(score[start + j] + pos_biases_[positions_[start + j]]); + } + } + GetGradientsForOneQuery(query_index, cnt, label_ + start, num_position_ids_ > 0 ? score_adjusted.data() : score + start, + gradients + start, hessians + start); + if (weights_ != nullptr) { + for (data_size_t j = 0; j < cnt; ++j) { + gradients[start + j] = + static_cast(gradients[start + j] * weights_[start + j]); + hessians[start + j] = + static_cast(hessians[start + j] * weights_[start + j]); + } + } + } + if (num_position_ids_ > 0) { + UpdatePositionBiasFactors(gradients, hessians); + } + } + + void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { + GetGradientsWithSampledQueries(score, num_queries_, nullptr, gradients, hessians); + } + + virtual void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt, + const label_t* label, + const double* score, score_t* lambdas, + score_t* hessians) const = 0; + + virtual void UpdatePositionBiasFactors(const score_t* /*lambdas*/, const score_t* /*hessians*/) const {} + + const char* GetName() const override = 0; + + std::string ToString() const override { + std::stringstream str_buf; + str_buf << GetName(); + return str_buf.str(); + } + + bool NeedAccuratePrediction() const override { return false; } + + protected: + int seed_; + data_size_t num_queries_; + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Pointer of weights */ + const label_t* weights_; + /*! \brief Pointer of positions */ + const data_size_t* positions_; + /*! \brief Pointer of position IDs */ + const std::string* position_ids_; + /*! \brief Pointer of label */ + data_size_t num_position_ids_; + /*! \brief Query boundaries */ + const data_size_t* query_boundaries_; + /*! \brief Position bias factors */ + mutable std::vector pos_biases_; + /*! \brief Learning rate to update position bias factors */ + double learning_rate_; + /*! \brief Position bias regularization */ + double position_bias_regularization_; +}; + +/*! + * \brief Objective function for LambdaRank with NDCG + */ +class LambdarankNDCG : public RankingObjective { + public: + explicit LambdarankNDCG(const Config& config) + : RankingObjective(config), + sigmoid_(config.sigmoid), + norm_(config.lambdarank_norm), + truncation_level_(config.lambdarank_truncation_level) { + label_gain_ = config.label_gain; + // initialize DCG calculator + DCGCalculator::DefaultLabelGain(&label_gain_); + DCGCalculator::Init(label_gain_); + sigmoid_table_.clear(); + inverse_max_dcgs_.clear(); + if (sigmoid_ <= 0.0) { + Log::Fatal("Sigmoid param %f should be greater than zero", sigmoid_); + } + } + + explicit LambdarankNDCG(const std::vector& strs) + : RankingObjective(strs) {} + + ~LambdarankNDCG() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + RankingObjective::Init(metadata, num_data); + DCGCalculator::CheckMetadata(metadata, num_queries_); + DCGCalculator::CheckLabel(label_, num_data_); + inverse_max_dcgs_.resize(num_queries_); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_queries_; ++i) { + inverse_max_dcgs_[i] = DCGCalculator::CalMaxDCGAtK( + truncation_level_, label_ + query_boundaries_[i], + query_boundaries_[i + 1] - query_boundaries_[i]); + + if (inverse_max_dcgs_[i] > 0.0) { + inverse_max_dcgs_[i] = 1.0f / inverse_max_dcgs_[i]; + } + } + // construct Sigmoid table to speed up Sigmoid transform + ConstructSigmoidTable(); + } + + inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt, + const label_t* label, const double* score, + score_t* lambdas, + score_t* hessians) const override { + // get max DCG on current query + const double inverse_max_dcg = inverse_max_dcgs_[query_id]; + // initialize with zero + for (data_size_t i = 0; i < cnt; ++i) { + lambdas[i] = 0.0f; + hessians[i] = 0.0f; + } + // get sorted indices for scores + std::vector sorted_idx(cnt); + for (data_size_t i = 0; i < cnt; ++i) { + sorted_idx[i] = i; + } + std::stable_sort( + sorted_idx.begin(), sorted_idx.end(), + [score](data_size_t a, data_size_t b) { return score[a] > score[b]; }); + // get best and worst score + const double best_score = score[sorted_idx[0]]; + data_size_t worst_idx = cnt - 1; + if (worst_idx > 0 && score[sorted_idx[worst_idx]] == kMinScore) { + worst_idx -= 1; + } + const double worst_score = score[sorted_idx[worst_idx]]; + double sum_lambdas = 0.0; + // start accumulate lambdas by pairs that contain at least one document above truncation level + for (data_size_t i = 0; i < cnt - 1 && i < truncation_level_; ++i) { + if (score[sorted_idx[i]] == kMinScore) { + continue; + } + for (data_size_t j = i + 1; j < cnt; ++j) { + if (score[sorted_idx[j]] == kMinScore) { + continue; + } + // skip pairs with the same labels + if (label[sorted_idx[i]] == label[sorted_idx[j]]) { + continue; + } + data_size_t high_rank, low_rank; + if (label[sorted_idx[i]] > label[sorted_idx[j]]) { + high_rank = i; + low_rank = j; + } else { + high_rank = j; + low_rank = i; + } + const data_size_t high = sorted_idx[high_rank]; + const int high_label = static_cast(label[high]); + const double high_score = score[high]; + const double high_label_gain = label_gain_[high_label]; + const double high_discount = DCGCalculator::GetDiscount(high_rank); + const data_size_t low = sorted_idx[low_rank]; + const int low_label = static_cast(label[low]); + const double low_score = score[low]; + const double low_label_gain = label_gain_[low_label]; + const double low_discount = DCGCalculator::GetDiscount(low_rank); + + const double delta_score = high_score - low_score; + + // get dcg gap + const double dcg_gap = high_label_gain - low_label_gain; + // get discount of this pair + const double paired_discount = fabs(high_discount - low_discount); + // get delta NDCG + double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg; + // regular the delta_pair_NDCG by score distance + if (norm_ && best_score != worst_score) { + delta_pair_NDCG /= (0.01f + fabs(delta_score)); + } + // calculate lambda for this pair + double p_lambda = GetSigmoid(delta_score); + double p_hessian = p_lambda * (1.0f - p_lambda); + // update + p_lambda *= -sigmoid_ * delta_pair_NDCG; + p_hessian *= sigmoid_ * sigmoid_ * delta_pair_NDCG; + lambdas[low] -= static_cast(p_lambda); + hessians[low] += static_cast(p_hessian); + lambdas[high] += static_cast(p_lambda); + hessians[high] += static_cast(p_hessian); + // lambda is negative, so use minus to accumulate + sum_lambdas -= 2 * p_lambda; + } + } + if (norm_ && sum_lambdas > 0) { + double norm_factor = std::log2(1 + sum_lambdas) / sum_lambdas; + for (data_size_t i = 0; i < cnt; ++i) { + lambdas[i] = static_cast(lambdas[i] * norm_factor); + hessians[i] = static_cast(hessians[i] * norm_factor); + } + } + } + + inline double GetSigmoid(double score) const { + if (score <= min_sigmoid_input_) { + // too small, use lower bound + return sigmoid_table_[0]; + } else if (score >= max_sigmoid_input_) { + // too large, use upper bound + return sigmoid_table_[_sigmoid_bins - 1]; + } else { + return sigmoid_table_[static_cast((score - min_sigmoid_input_) * + sigmoid_table_idx_factor_)]; + } + } + + void ConstructSigmoidTable() { + // get boundary + min_sigmoid_input_ = min_sigmoid_input_ / sigmoid_ / 2; + max_sigmoid_input_ = -min_sigmoid_input_; + sigmoid_table_.resize(_sigmoid_bins); + // get score to bin factor + sigmoid_table_idx_factor_ = + _sigmoid_bins / (max_sigmoid_input_ - min_sigmoid_input_); + // cache + for (size_t i = 0; i < _sigmoid_bins; ++i) { + const double score = i / sigmoid_table_idx_factor_ + min_sigmoid_input_; + sigmoid_table_[i] = 1.0f / (1.0f + std::exp(score * sigmoid_)); + } + } + + void UpdatePositionBiasFactors(const score_t* lambdas, const score_t* hessians) const override { + /// get number of threads + int num_threads = OMP_NUM_THREADS(); + // create per-thread buffers for first and second derivatives of utility w.r.t. position bias factors + std::vector bias_first_derivatives(num_position_ids_ * num_threads, 0.0); + std::vector bias_second_derivatives(num_position_ids_ * num_threads, 0.0); + std::vector instance_counts(num_position_ids_ * num_threads, 0); + #pragma omp parallel for schedule(guided) num_threads(num_threads) + for (data_size_t i = 0; i < num_data_; i++) { + // get thread ID + const int tid = omp_get_thread_num(); + size_t offset = static_cast(positions_[i] + tid * num_position_ids_); + // accumulate first derivatives of utility w.r.t. position bias factors, for each position + bias_first_derivatives[offset] -= lambdas[i]; + // accumulate second derivatives of utility w.r.t. position bias factors, for each position + bias_second_derivatives[offset] -= hessians[i]; + instance_counts[offset]++; + } + #pragma omp parallel for schedule(guided) num_threads(num_threads) + for (data_size_t i = 0; i < num_position_ids_; i++) { + double bias_first_derivative = 0.0; + double bias_second_derivative = 0.0; + int instance_count = 0; + // aggregate derivatives from per-thread buffers + for (int tid = 0; tid < num_threads; tid++) { + size_t offset = static_cast(i + tid * num_position_ids_); + bias_first_derivative += bias_first_derivatives[offset]; + bias_second_derivative += bias_second_derivatives[offset]; + instance_count += instance_counts[offset]; + } + // L2 regularization on position bias factors + bias_first_derivative -= pos_biases_[i] * position_bias_regularization_ * instance_count; + bias_second_derivative -= position_bias_regularization_ * instance_count; + // do Newton-Raphson step to update position bias factors + pos_biases_[i] += learning_rate_ * bias_first_derivative / (std::abs(bias_second_derivative) + 0.001); + } + LogDebugPositionBiasFactors(); + } + + const char* GetName() const override { return "lambdarank"; } + + protected: + void LogDebugPositionBiasFactors() const { + std::stringstream message_stream; + message_stream << std::setw(15) << "position" + << std::setw(15) << "bias_factor" + << std::endl; + Log::Debug(message_stream.str().c_str()); + message_stream.str(""); + for (int i = 0; i < num_position_ids_; ++i) { + message_stream << std::setw(15) << position_ids_[i] + << std::setw(15) << pos_biases_[i]; + Log::Debug(message_stream.str().c_str()); + message_stream.str(""); + } + } + /*! \brief Sigmoid param */ + double sigmoid_; + /*! \brief Normalize the lambdas or not */ + bool norm_; + /*! \brief Truncation position for max DCG */ + int truncation_level_; + /*! \brief Cache inverse max DCG, speed up calculation */ + std::vector inverse_max_dcgs_; + /*! \brief Cache result for sigmoid transform to speed up */ + std::vector sigmoid_table_; + /*! \brief Gains for labels */ + std::vector label_gain_; + /*! \brief Number of bins in simoid table */ + size_t _sigmoid_bins = 1024 * 1024; + /*! \brief Minimal input of sigmoid table */ + double min_sigmoid_input_ = -50; + /*! \brief Maximal input of Sigmoid table */ + double max_sigmoid_input_ = 50; + /*! \brief Factor that covert score to bin in Sigmoid table */ + double sigmoid_table_idx_factor_; +}; + +/*! + * \brief Implementation of the learning-to-rank objective function, XE_NDCG + * [arxiv.org/abs/1911.09798]. + */ +class RankXENDCG : public RankingObjective { + public: + explicit RankXENDCG(const Config& config) : RankingObjective(config) {} + + explicit RankXENDCG(const std::vector& strs) + : RankingObjective(strs) {} + + ~RankXENDCG() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + RankingObjective::Init(metadata, num_data); + for (data_size_t i = 0; i < num_queries_; ++i) { + rands_.emplace_back(seed_ + i); + } + } + + inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt, + const label_t* label, const double* score, + score_t* lambdas, + score_t* hessians) const override { + // Skip groups with too few items. + if (cnt <= 1) { + for (data_size_t i = 0; i < cnt; ++i) { + lambdas[i] = 0.0f; + hessians[i] = 0.0f; + } + return; + } + + // Turn scores into a probability distribution using Softmax. + std::vector rho(cnt, 0.0); + Common::Softmax(score, rho.data(), cnt); + + // An auxiliary buffer of parameters used to form the ground-truth + // distribution and compute the loss. + std::vector params(cnt); + + double inv_denominator = 0; + for (data_size_t i = 0; i < cnt; ++i) { + params[i] = Phi(label[i], rands_[query_id].NextFloat()); + inv_denominator += params[i]; + } + // sum_labels will always be positive number + inv_denominator = 1. / std::max(kEpsilon, inv_denominator); + + // Approximate gradients and inverse Hessian. + // First order terms. + double sum_l1 = 0.0; + for (data_size_t i = 0; i < cnt; ++i) { + double term = -params[i] * inv_denominator + rho[i]; + lambdas[i] = static_cast(term); + // Params will now store terms needed to compute second-order terms. + params[i] = term / (1. - rho[i]); + sum_l1 += params[i]; + } + // Second order terms. + double sum_l2 = 0.0; + for (data_size_t i = 0; i < cnt; ++i) { + double term = rho[i] * (sum_l1 - params[i]); + lambdas[i] += static_cast(term); + // Params will now store terms needed to compute third-order terms. + params[i] = term / (1. - rho[i]); + sum_l2 += params[i]; + } + for (data_size_t i = 0; i < cnt; ++i) { + lambdas[i] += static_cast(rho[i] * (sum_l2 - params[i])); + hessians[i] = static_cast(rho[i] * (1.0 - rho[i])); + } + } + + double Phi(const label_t l, double g) const { + return Common::Pow(2, static_cast(l)) - g; + } + + const char* GetName() const override { return "rank_xendcg"; } + + protected: + mutable std::vector rands_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_ diff --git a/src/objective/regression_objective.hpp b/src/objective/regression_objective.hpp new file mode 100644 index 0000000..fbaa66a --- /dev/null +++ b/src/objective/regression_objective.hpp @@ -0,0 +1,764 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_OBJECTIVE_REGRESSION_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_REGRESSION_OBJECTIVE_HPP_ + +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + +#define PercentileFun(T, data_reader, cnt_data, alpha) \ + { \ + if (cnt_data <= 1) { \ + return data_reader(0); \ + } \ + std::vector ref_data(cnt_data); \ + for (data_size_t i = 0; i < cnt_data; ++i) { \ + ref_data[i] = data_reader(i); \ + } \ + const double float_pos = static_cast(cnt_data - 1) * (1.0 - alpha); \ + const data_size_t pos = static_cast(float_pos) + 1; \ + if (pos < 1) { \ + return ref_data[ArrayArgs::ArgMax(ref_data)]; \ + } else if (pos >= cnt_data) { \ + return ref_data[ArrayArgs::ArgMin(ref_data)]; \ + } else { \ + const double bias = float_pos - (pos - 1); \ + if (pos > cnt_data / 2) { \ + ArrayArgs::ArgMaxAtK(&ref_data, 0, cnt_data, pos - 1); \ + T v1 = ref_data[pos - 1]; \ + T v2 = ref_data[pos + ArrayArgs::ArgMax(ref_data.data() + pos, \ + cnt_data - pos)]; \ + return static_cast(v1 - (v1 - v2) * bias); \ + } else { \ + ArrayArgs::ArgMaxAtK(&ref_data, 0, cnt_data, pos); \ + T v2 = ref_data[pos]; \ + T v1 = ref_data[ArrayArgs::ArgMin(ref_data.data(), pos)]; \ + return static_cast(v1 - (v1 - v2) * bias); \ + } \ + } \ + }\ + +#define WeightedPercentileFun(T, data_reader, weight_reader, cnt_data, alpha) \ + { \ + if (cnt_data <= 1) { \ + return data_reader(0); \ + } \ + std::vector sorted_idx(cnt_data); \ + for (data_size_t i = 0; i < cnt_data; ++i) { \ + sorted_idx[i] = i; \ + } \ + std::stable_sort(sorted_idx.begin(), sorted_idx.end(), \ + [&](data_size_t a, data_size_t b) { \ + return data_reader(a) < data_reader(b); \ + }); \ + std::vector weighted_cdf(cnt_data); \ + weighted_cdf[0] = weight_reader(sorted_idx[0]); \ + for (data_size_t i = 1; i < cnt_data; ++i) { \ + weighted_cdf[i] = weighted_cdf[i - 1] + weight_reader(sorted_idx[i]); \ + } \ + double threshold = weighted_cdf[cnt_data - 1] * alpha; \ + size_t pos = std::upper_bound(weighted_cdf.begin(), weighted_cdf.end(), \ + threshold) - \ + weighted_cdf.begin(); \ + pos = std::min(pos, static_cast(cnt_data - 1)); \ + if (pos == 0 || pos == static_cast(cnt_data - 1)) { \ + return data_reader(sorted_idx[pos]); \ + } \ + CHECK_GE(threshold, weighted_cdf[pos - 1]); \ + CHECK_LT(threshold, weighted_cdf[pos]); \ + T v1 = data_reader(sorted_idx[pos - 1]); \ + T v2 = data_reader(sorted_idx[pos]); \ + if (weighted_cdf[pos] - weighted_cdf[pos - 1] >= 1.0) { \ + return static_cast((threshold - weighted_cdf[pos - 1]) / \ + (weighted_cdf[pos] - weighted_cdf[pos - 1]) * \ + (v2 - v1) + \ + v1); \ + } else { \ + return static_cast(v1); \ + } \ + }\ + +/*! +* \brief Objective function for regression +*/ +class RegressionL2loss: public ObjectiveFunction { + public: + explicit RegressionL2loss(const Config& config) + : deterministic_(config.deterministic) { + sqrt_ = config.reg_sqrt; + } + + explicit RegressionL2loss(const std::vector& strs) + : deterministic_(false) { + sqrt_ = false; + for (auto str : strs) { + if (str == std::string("sqrt")) { + sqrt_ = true; + } + } + } + + ~RegressionL2loss() { + } + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + label_ = metadata.label(); + if (sqrt_) { + trans_label_.resize(num_data_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data; ++i) { + trans_label_[i] = Common::Sign(label_[i]) * std::sqrt(std::fabs(label_[i])); + } + label_ = trans_label_.data(); + } + weights_ = metadata.weights(); + } + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + gradients[i] = static_cast(score[i] - label_[i]); + hessians[i] = 1.0f; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + gradients[i] = static_cast(static_cast((score[i] - label_[i])) * weights_[i]); + hessians[i] = static_cast(weights_[i]); + } + } + } + + const char* GetName() const override { + return "regression"; + } + + void ConvertOutput(const double* input, double* output) const override { + if (sqrt_) { + output[0] = Common::Sign(input[0]) * input[0] * input[0]; + } else { + output[0] = input[0]; + } + } + + std::string ToString() const override { + std::stringstream str_buf; + str_buf << GetName(); + if (sqrt_) { + str_buf << " sqrt"; + } + return str_buf.str(); + } + + bool IsConstantHessian() const override { + if (weights_ == nullptr) { + return true; + } else { + return false; + } + } + + double BoostFromScore(int) const override { + double suml = 0.0f; + double sumw = 0.0f; + if (weights_ != nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_) + for (data_size_t i = 0; i < num_data_; ++i) { + suml += static_cast(label_[i]) * weights_[i]; + sumw += weights_[i]; + } + } else { + sumw = static_cast(num_data_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_) + for (data_size_t i = 0; i < num_data_; ++i) { + suml += label_[i]; + } + } + return suml / sumw; + } + + protected: + bool sqrt_; + /*! \brief Number of data */ + data_size_t num_data_; + /*! \brief Pointer of label */ + const label_t* label_; + /*! \brief Pointer of weights */ + const label_t* weights_; + std::vector trans_label_; + const bool deterministic_; +}; + +/*! +* \brief L1 regression loss +*/ +class RegressionL1loss: public RegressionL2loss { + public: + explicit RegressionL1loss(const Config& config): RegressionL2loss(config) { + } + + explicit RegressionL1loss(const std::vector& strs): RegressionL2loss(strs) { + } + + ~RegressionL1loss() {} + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double diff = score[i] - label_[i]; + gradients[i] = static_cast(Common::Sign(diff)); + hessians[i] = 1.0f; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double diff = score[i] - label_[i]; + gradients[i] = static_cast(Common::Sign(diff) * weights_[i]); + hessians[i] = weights_[i]; + } + } + } + + double BoostFromScore(int) const override { + const double alpha = 0.5; + if (weights_ != nullptr) { + #define data_reader(i) (label_[i]) + #define weight_reader(i) (weights_[i]) + WeightedPercentileFun(label_t, data_reader, weight_reader, num_data_, alpha); + #undef data_reader + #undef weight_reader + } else { + #define data_reader(i) (label_[i]) + PercentileFun(label_t, data_reader, num_data_, alpha); + #undef data_reader + } + } + + bool IsRenewTreeOutput() const override { return true; } + + double RenewTreeOutput(double, std::function residual_getter, + const data_size_t* index_mapper, + const data_size_t* bagging_mapper, + data_size_t num_data_in_leaf) const override { + const double alpha = 0.5; + if (weights_ == nullptr) { + if (bagging_mapper == nullptr) { + #define data_reader(i) (residual_getter(label_, index_mapper[i])) + PercentileFun(double, data_reader, num_data_in_leaf, alpha); + #undef data_reader + } else { + #define data_reader(i) (residual_getter(label_, bagging_mapper[index_mapper[i]])) + PercentileFun(double, data_reader, num_data_in_leaf, alpha); + #undef data_reader + } + } else { + if (bagging_mapper == nullptr) { + #define data_reader(i) (residual_getter(label_, index_mapper[i])) + #define weight_reader(i) (weights_[index_mapper[i]]) + WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha); + #undef data_reader + #undef weight_reader + } else { + #define data_reader(i) (residual_getter(label_, bagging_mapper[index_mapper[i]])) + #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]]) + WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha); + #undef data_reader + #undef weight_reader + } + } + } + + const char* GetName() const override { + return "regression_l1"; + } +}; + +/*! +* \brief Huber regression loss +*/ +class RegressionHuberLoss: public RegressionL2loss { + public: + explicit RegressionHuberLoss(const Config& config): RegressionL2loss(config) { + alpha_ = static_cast(config.alpha); + if (sqrt_) { + Log::Warning("Cannot use sqrt transform in %s Regression, will auto disable it", GetName()); + sqrt_ = false; + } + } + + explicit RegressionHuberLoss(const std::vector& strs): RegressionL2loss(strs) { + if (sqrt_) { + Log::Warning("Cannot use sqrt transform in %s Regression, will auto disable it", GetName()); + sqrt_ = false; + } + } + + ~RegressionHuberLoss() { + } + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double diff = score[i] - label_[i]; + if (std::abs(diff) <= alpha_) { + gradients[i] = static_cast(diff); + } else { + gradients[i] = static_cast(Common::Sign(diff) * alpha_); + } + hessians[i] = 1.0f; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double diff = score[i] - label_[i]; + if (std::abs(diff) <= alpha_) { + gradients[i] = static_cast(diff * weights_[i]); + } else { + gradients[i] = static_cast(Common::Sign(diff) * static_cast(weights_[i]) * alpha_); + } + hessians[i] = static_cast(weights_[i]); + } + } + } + + const char* GetName() const override { + return "huber"; + } + + protected: + /*! \brief delta for Huber loss */ + double alpha_; +}; + + +// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html +class RegressionFairLoss: public RegressionL2loss { + public: + explicit RegressionFairLoss(const Config& config): RegressionL2loss(config) { + c_ = static_cast(config.fair_c); + } + + explicit RegressionFairLoss(const std::vector& strs): RegressionL2loss(strs) { + } + + ~RegressionFairLoss() {} + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double x = score[i] - label_[i]; + gradients[i] = static_cast(c_ * x / (std::fabs(x) + c_)); + hessians[i] = static_cast(c_ * c_ / ((std::fabs(x) + c_) * (std::fabs(x) + c_))); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double x = score[i] - label_[i]; + gradients[i] = static_cast(c_ * x / (std::fabs(x) + c_) * weights_[i]); + hessians[i] = static_cast(c_ * c_ / ((std::fabs(x) + c_) * (std::fabs(x) + c_)) * weights_[i]); + } + } + } + + const char* GetName() const override { + return "fair"; + } + + bool IsConstantHessian() const override { + return false; + } + + protected: + /*! \brief c for Fair loss */ + double c_; +}; + + +/*! +* \brief Objective function for Poisson regression +*/ +class RegressionPoissonLoss: public RegressionL2loss { + public: + explicit RegressionPoissonLoss(const Config& config): RegressionL2loss(config) { + max_delta_step_ = static_cast(config.poisson_max_delta_step); + if (sqrt_) { + Log::Warning("Cannot use sqrt transform in %s Regression, will auto disable it", GetName()); + sqrt_ = false; + } + } + + explicit RegressionPoissonLoss(const std::vector& strs): RegressionL2loss(strs) { + } + + ~RegressionPoissonLoss() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + if (sqrt_) { + Log::Warning("Cannot use sqrt transform in %s Regression, will auto disable it", GetName()); + sqrt_ = false; + } + RegressionL2loss::Init(metadata, num_data); + // Safety check of labels + label_t miny; + double sumy; + Common::ObtainMinMaxSum(label_, num_data_, &miny, static_cast(nullptr), &sumy); + if (miny < 0.0f) { + Log::Fatal("[%s]: at least one target label is negative", GetName()); + } + if (sumy == 0.0f) { + Log::Fatal("[%s]: sum of labels is zero", GetName()); + } + } + + /* Parametrize with unbounded internal score "f"; then + * loss = exp(f) - label * f + * grad = exp(f) - label + * hess = exp(f) + * + * And the output is exp(f); so the associated metric get s=exp(f) + * so that its loss = s - label * log(s); a little awkward maybe. + * + */ + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + double exp_max_delta_step_ = std::exp(max_delta_step_); + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + double exp_score = std::exp(score[i]); + gradients[i] = static_cast(exp_score - label_[i]); + hessians[i] = static_cast(exp_score * exp_max_delta_step_); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + double exp_score = std::exp(score[i]); + gradients[i] = static_cast((exp_score - label_[i]) * weights_[i]); + hessians[i] = static_cast(exp_score * exp_max_delta_step_ * weights_[i]); + } + } + } + + void ConvertOutput(const double* input, double* output) const override { + output[0] = std::exp(input[0]); + } + + const char* GetName() const override { + return "poisson"; + } + + double BoostFromScore(int) const override { + return Common::SafeLog(RegressionL2loss::BoostFromScore(0)); + } + + bool IsConstantHessian() const override { + return false; + } + + protected: + /*! \brief used to safeguard optimization */ + double max_delta_step_; +}; + +class RegressionQuantileloss : public RegressionL2loss { + public: + explicit RegressionQuantileloss(const Config& config): RegressionL2loss(config) { + alpha_ = static_cast(config.alpha); + CHECK(alpha_ > 0 && alpha_ < 1); + } + + explicit RegressionQuantileloss(const std::vector& strs): RegressionL2loss(strs) { + } + + ~RegressionQuantileloss() {} + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + score_t delta = static_cast(score[i] - label_[i]); + if (delta >= 0) { + gradients[i] = (1.0f - alpha_); + } else { + gradients[i] = -alpha_; + } + hessians[i] = 1.0f; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + score_t delta = static_cast(score[i] - label_[i]); + if (delta >= 0) { + gradients[i] = static_cast((1.0f - alpha_) * weights_[i]); + } else { + gradients[i] = static_cast(-alpha_ * weights_[i]); + } + hessians[i] = static_cast(weights_[i]); + } + } + } + + const char* GetName() const override { + return "quantile"; + } + + double BoostFromScore(int) const override { + if (weights_ != nullptr) { + #define data_reader(i) (label_[i]) + #define weight_reader(i) (weights_[i]) + WeightedPercentileFun(label_t, data_reader, weight_reader, num_data_, alpha_); + #undef data_reader + #undef weight_reader + } else { + #define data_reader(i) (label_[i]) + PercentileFun(label_t, data_reader, num_data_, alpha_); + #undef data_reader + } + } + + bool IsRenewTreeOutput() const override { return true; } + + double RenewTreeOutput(double, std::function residual_getter, + const data_size_t* index_mapper, + const data_size_t* bagging_mapper, + data_size_t num_data_in_leaf) const override { + if (weights_ == nullptr) { + if (bagging_mapper == nullptr) { + #define data_reader(i) (residual_getter(label_, index_mapper[i])) + PercentileFun(double, data_reader, num_data_in_leaf, alpha_); + #undef data_reader + } else { + #define data_reader(i) (residual_getter(label_, bagging_mapper[index_mapper[i]])) + PercentileFun(double, data_reader, num_data_in_leaf, alpha_); + #undef data_reader + } + } else { + if (bagging_mapper == nullptr) { + #define data_reader(i) (residual_getter(label_, index_mapper[i])) + #define weight_reader(i) (weights_[index_mapper[i]]) + WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_); + #undef data_reader + #undef weight_reader + } else { + #define data_reader(i) (residual_getter(label_, bagging_mapper[index_mapper[i]])) + #define weight_reader(i) (weights_[bagging_mapper[index_mapper[i]]]) + WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha_); + #undef data_reader + #undef weight_reader + } + } + } + + protected: + score_t alpha_; +}; + + +/*! +* \brief MAPE Regression Loss +*/ +class RegressionMAPELOSS : public RegressionL1loss { + public: + explicit RegressionMAPELOSS(const Config& config) : RegressionL1loss(config) { + } + + explicit RegressionMAPELOSS(const std::vector& strs) : RegressionL1loss(strs) { + } + + ~RegressionMAPELOSS() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + RegressionL2loss::Init(metadata, num_data); + for (data_size_t i = 0; i < num_data_; ++i) { + if (std::fabs(label_[i]) < 1) { + Log::Warning( + "Some label values are < 1 in absolute value. MAPE is unstable with such values, " + "so LightGBM rounds them to 1.0 when calculating MAPE."); + break; + } + } + label_weight_.resize(num_data); + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + label_weight_[i] = 1.0f / std::max(1.0f, std::fabs(label_[i])); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + label_weight_[i] = 1.0f / std::max(1.0f, std::fabs(label_[i])) * weights_[i]; + } + } + } + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double diff = score[i] - label_[i]; + gradients[i] = static_cast(Common::Sign(diff) * label_weight_[i]); + hessians[i] = 1.0f; + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double diff = score[i] - label_[i]; + gradients[i] = static_cast(Common::Sign(diff) * label_weight_[i]); + hessians[i] = weights_[i]; + } + } + } + + double BoostFromScore(int) const override { + const double alpha = 0.5; + #define data_reader(i) (label_[i]) + #define weight_reader(i) (label_weight_[i]) + WeightedPercentileFun(label_t, data_reader, weight_reader, num_data_, alpha); + #undef data_reader + #undef weight_reader + } + + bool IsRenewTreeOutput() const override { return true; } + + double RenewTreeOutput(double, std::function residual_getter, + const data_size_t* index_mapper, + const data_size_t* bagging_mapper, + data_size_t num_data_in_leaf) const override { + const double alpha = 0.5; + if (bagging_mapper == nullptr) { + #define data_reader(i) (residual_getter(label_, index_mapper[i])) + #define weight_reader(i) (label_weight_[index_mapper[i]]) + WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha); + #undef data_reader + #undef weight_reader + } else { + #define data_reader(i) (residual_getter(label_, bagging_mapper[index_mapper[i]])) + #define weight_reader(i) (label_weight_[bagging_mapper[index_mapper[i]]]) + WeightedPercentileFun(double, data_reader, weight_reader, num_data_in_leaf, alpha); + #undef data_reader + #undef weight_reader + } + } + + const char* GetName() const override { + return "mape"; + } + + bool IsConstantHessian() const override { + return true; + } + + private: + std::vector label_weight_; +}; + + + +/*! +* \brief Objective function for Gamma regression +*/ +class RegressionGammaLoss : public RegressionPoissonLoss { + public: + explicit RegressionGammaLoss(const Config& config) : RegressionPoissonLoss(config) { + } + + explicit RegressionGammaLoss(const std::vector& strs) : RegressionPoissonLoss(strs) { + } + + ~RegressionGammaLoss() {} + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + double exp_score = std::exp(-score[i]); + gradients[i] = static_cast(1.0 - label_[i] * exp_score); + hessians[i] = static_cast(label_[i] * exp_score); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + double exp_score = std::exp(-score[i]); + gradients[i] = static_cast((1.0 - label_[i] * exp_score) * weights_[i]); + hessians[i] = static_cast(label_[i] * exp_score * weights_[i]); + } + } + } + + const char* GetName() const override { + return "gamma"; + } +}; + +/*! +* \brief Objective function for Tweedie regression +*/ +class RegressionTweedieLoss: public RegressionPoissonLoss { + public: + explicit RegressionTweedieLoss(const Config& config) : RegressionPoissonLoss(config) { + rho_ = config.tweedie_variance_power; + } + + explicit RegressionTweedieLoss(const std::vector& strs) : RegressionPoissonLoss(strs) { + } + + ~RegressionTweedieLoss() {} + + void GetGradients(const double* score, score_t* gradients, + score_t* hessians) const override { + if (weights_ == nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + double exp_1_score = std::exp((1 - rho_) * score[i]); + double exp_2_score = std::exp((2 - rho_) * score[i]); + gradients[i] = static_cast(-label_[i] * exp_1_score + exp_2_score); + hessians[i] = static_cast(-label_[i] * (1 - rho_) * exp_1_score + + (2 - rho_) * exp_2_score); + } + } else { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + double exp_1_score = std::exp((1 - rho_) * score[i]); + double exp_2_score = std::exp((2 - rho_) * score[i]); + gradients[i] = static_cast((-label_[i] * exp_1_score + exp_2_score) * weights_[i]); + hessians[i] = static_cast((-label_[i] * (1 - rho_) * exp_1_score + + (2 - rho_) * exp_2_score) * weights_[i]); + } + } + } + + const char* GetName() const override { + return "tweedie"; + } + + private: + double rho_; +}; + +#undef PercentileFun +#undef WeightedPercentileFun + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_OBJECTIVE_REGRESSION_OBJECTIVE_HPP_ diff --git a/src/objective/xentropy_objective.hpp b/src/objective/xentropy_objective.hpp new file mode 100644 index 0000000..cf3e512 --- /dev/null +++ b/src/objective/xentropy_objective.hpp @@ -0,0 +1,317 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_ +#define LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_ + +#include +#include +#include + +#include +#include +#include +#include +#include + +/* + * Implements gradients and Hessians for the following point losses. + * Target y is anything in interval [0, 1]. + * + * (1) CrossEntropy; "xentropy"; + * + * loss(y, p, w) = { -(1-y)*log(1-p)-y*log(p) }*w, + * with probability p = 1/(1+exp(-f)), where f is being boosted + * + * ConvertToOutput: f -> p + * + * (2) CrossEntropyLambda; "xentlambda" + * + * loss(y, p, w) = -(1-y)*log(1-p)-y*log(p), + * with p = 1-exp(-lambda*w), lambda = log(1+exp(f)), f being boosted, and w > 0 + * + * ConvertToOutput: f -> lambda + * + * (1) and (2) are the same if w=1; but outputs still differ. + * + */ + +namespace LightGBM { +/*! +* \brief Objective function for cross-entropy (with optional linear weights) +*/ +class CrossEntropy: public ObjectiveFunction { + public: + explicit CrossEntropy(const Config& config) + : deterministic_(config.deterministic) {} + + explicit CrossEntropy(const std::vector&) + : deterministic_(false) { + } + + ~CrossEntropy() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + + CHECK_NOTNULL(label_); + Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()); + Log::Info("[%s:%s]: (objective) labels passed interval [0, 1] check", GetName(), __func__); + + if (weights_ != nullptr) { + label_t minw; + double sumw; + Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sumw); + if (minw < 0.0f) { + Log::Fatal("[%s]: at least one weight is negative", GetName()); + } + if (sumw == 0.0f) { + Log::Fatal("[%s]: sum of weights is zero", GetName()); + } + } + } + + void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { + // z = expit(score) = 1 / (1 + exp(-score)) + // gradient = z - label = expit(score) - label + // Numerically more stable, see http://fa.bianp.net/blog/2019/evaluate_logistic/ + // if score < 0: + // exp_tmp = exp(score) + // return ((1 - label) * exp_tmp - label) / (1 + exp_tmp) + // else: + // exp_tmp = exp(-score) + // return ((1 - label) - label * exp_tmp) / (1 + exp_tmp) + // Note that optimal speed would be achieved, at the cost of precision, by + // return expit(score) - y_true + // i.e. no "if else" and an own inline implementation of expit. + // The case distinction score < 0 in the stable implementation does not + // provide significant better precision apart from protecting overflow of exp(..). + // The branch (if else), however, can incur runtime costs of up to 30%. + // Instead, we help branch prediction by almost always ending in the first if clause + // and making the second branch (else) a bit simpler. This has the exact same + // precision but is faster than the stable implementation. + // As branching criteria, we use the same cutoff as in log1pexp, see link above. + // Note that the maximal value to get gradient = -1 with label = 1 is -37.439198610162731 + // (based on mpmath), and scipy.special.logit(np.finfo(float).eps) ~ -36.04365. + if (weights_ == nullptr) { + // compute pointwise gradients and Hessians with implied unit weights + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + if (score[i] > -37.0) { + const double exp_tmp = std::exp(-score[i]); + gradients[i] = static_cast(((1.0f - label_[i]) - label_[i] * exp_tmp) / (1.0f + exp_tmp)); + hessians[i] = static_cast(exp_tmp / ((1 + exp_tmp) * (1 + exp_tmp))); + } else { + const double exp_tmp = std::exp(score[i]); + gradients[i] = static_cast(exp_tmp - label_[i]); + hessians[i] = static_cast(exp_tmp); + } + } + } else { + // compute pointwise gradients and Hessians with given weights + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + if (score[i] > -37.0) { + const double exp_tmp = std::exp(-score[i]); + gradients[i] = static_cast(((1.0f - label_[i]) - label_[i] * exp_tmp) / (1.0f + exp_tmp) * weights_[i]); + hessians[i] = static_cast(exp_tmp / ((1 + exp_tmp) * (1 + exp_tmp)) * weights_[i]); + } else { + const double exp_tmp = std::exp(score[i]); + gradients[i] = static_cast((exp_tmp - label_[i]) * weights_[i]); + hessians[i] = static_cast(exp_tmp * weights_[i]); + } + } + } + } + + const char* GetName() const override { + return "cross_entropy"; + } + + // convert score to a probability + void ConvertOutput(const double* input, double* output) const override { + output[0] = 1.0f / (1.0f + std::exp(-input[0])); + } + + std::string ToString() const override { + std::stringstream str_buf; + str_buf << GetName(); + return str_buf.str(); + } + + // implement custom average to boost from (if enabled among options) + double BoostFromScore(int) const override { + double suml = 0.0f; + double sumw = 0.0f; + if (weights_ != nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_) + + for (data_size_t i = 0; i < num_data_; ++i) { + suml += static_cast(label_[i]) * weights_[i]; + sumw += weights_[i]; + } + } else { + sumw = static_cast(num_data_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_) + + for (data_size_t i = 0; i < num_data_; ++i) { + suml += label_[i]; + } + } + double pavg = suml / sumw; + pavg = std::min(pavg, 1.0 - kEpsilon); + pavg = std::max(pavg, kEpsilon); + double initscore = std::log(pavg / (1.0f - pavg)); + Log::Info("[%s:%s]: pavg = %f -> initscore = %f", GetName(), __func__, pavg, initscore); + return initscore; + } + + private: + /*! \brief Number of data points */ + data_size_t num_data_; + /*! \brief Pointer for label */ + const label_t* label_; + /*! \brief Weights for data */ + const label_t* weights_; + const bool deterministic_; +}; + +/*! +* \brief Objective function for alternative parameterization of cross-entropy (see top of file for explanation) +*/ +class CrossEntropyLambda: public ObjectiveFunction { + public: + explicit CrossEntropyLambda(const Config& config) + : deterministic_(config.deterministic) { + min_weight_ = max_weight_ = 0.0f; + } + + explicit CrossEntropyLambda(const std::vector&) + : deterministic_(false) {} + + ~CrossEntropyLambda() {} + + void Init(const Metadata& metadata, data_size_t num_data) override { + num_data_ = num_data; + label_ = metadata.label(); + weights_ = metadata.weights(); + + CHECK_NOTNULL(label_); + Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()); + Log::Info("[%s:%s]: (objective) labels passed interval [0, 1] check", GetName(), __func__); + + if (weights_ != nullptr) { + Common::ObtainMinMaxSum(weights_, num_data_, &min_weight_, &max_weight_, static_cast(nullptr)); + if (min_weight_ <= 0.0f) { + Log::Fatal("[%s]: at least one weight is non-positive", GetName()); + } + + // Issue an info statement about this ratio + double weight_ratio = max_weight_ / min_weight_; + Log::Info("[%s:%s]: min, max weights = %f, %f; ratio = %f", + GetName(), __func__, + min_weight_, max_weight_, + weight_ratio); + } else { + // all weights are implied to be unity; no need to do anything + } + } + + void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override { + if (weights_ == nullptr) { + // compute pointwise gradients and Hessians with implied unit weights; exactly equivalent to CrossEntropy with unit weights + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double z = 1.0f / (1.0f + std::exp(-score[i])); + gradients[i] = static_cast(z - label_[i]); + hessians[i] = static_cast(z * (1.0f - z)); + } + } else { + // compute pointwise gradients and Hessians with given weights + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data_; ++i) { + const double w = weights_[i]; + const double y = label_[i]; + const double epf = std::exp(score[i]); + const double hhat = std::log1p(epf); + const double z = 1.0f - std::exp(-w*hhat); + const double enf = 1.0f / epf; // = std::exp(-score[i]); + gradients[i] = static_cast((1.0f - y / z) * w / (1.0f + enf)); + const double c = 1.0f / (1.0f - z); + double d = 1.0f + epf; + const double a = w * epf / (d * d); + d = c - 1.0f; + const double b = (c / (d * d) ) * (1.0f + w * epf - c); + hessians[i] = static_cast(a * (1.0f + y * b)); + } + } + } + + const char* GetName() const override { + return "cross_entropy_lambda"; + } + + // + // ATTENTION: the function output is the "normalized exponential parameter" lambda > 0, not the probability + // + // If this code would read: output[0] = 1.0f / (1.0f + std::exp(-input[0])); + // The output would still not be the probability unless the weights are unity. + // + // Let z = 1 / (1 + exp(-f)), then prob(z) = 1-(1-z)^w, where w is the weight for the specific point. + // + + void ConvertOutput(const double* input, double* output) const override { + output[0] = std::log1p(std::exp(input[0])); + } + + std::string ToString() const override { + std::stringstream str_buf; + str_buf << GetName(); + return str_buf.str(); + } + + double BoostFromScore(int) const override { + double suml = 0.0f; + double sumw = 0.0f; + if (weights_ != nullptr) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_) + + for (data_size_t i = 0; i < num_data_; ++i) { + suml += static_cast(label_[i]) * weights_[i]; + sumw += weights_[i]; + } + } else { + sumw = static_cast(num_data_); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_) + + for (data_size_t i = 0; i < num_data_; ++i) { + suml += label_[i]; + } + } + double havg = suml / sumw; + double initscore = std::log(std::expm1(havg)); + Log::Info("[%s:%s]: havg = %f -> initscore = %f", GetName(), __func__, havg, initscore); + return initscore; + } + + private: + /*! \brief Number of data points */ + data_size_t num_data_; + /*! \brief Pointer for label */ + const label_t* label_; + /*! \brief Weights for data */ + const label_t* weights_; + /*! \brief Minimum weight found during init */ + label_t min_weight_; + /*! \brief Maximum weight found during init */ + label_t max_weight_; + const bool deterministic_; +}; + +} // end namespace LightGBM + +#endif // LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_ diff --git a/src/treelearner/col_sampler.hpp b/src/treelearner/col_sampler.hpp new file mode 100644 index 0000000..2f3b159 --- /dev/null +++ b/src/treelearner/col_sampler.hpp @@ -0,0 +1,208 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_COL_SAMPLER_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_COL_SAMPLER_HPP_ + +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { +class ColSampler { + public: + explicit ColSampler(const Config* config) + : fraction_bytree_(config->feature_fraction), + fraction_bynode_(config->feature_fraction_bynode), + seed_(config->feature_fraction_seed), + random_(config->feature_fraction_seed) { + for (auto constraint : config->interaction_constraints_vector) { + std::unordered_set constraint_set(constraint.begin(), constraint.end()); + interaction_constraints_.push_back(constraint_set); + } + } + + static int GetCnt(size_t total_cnt, double fraction) { + const int min = std::min(1, static_cast(total_cnt)); + int used_feature_cnt = static_cast(Common::RoundInt(total_cnt * fraction)); + return std::max(used_feature_cnt, min); + } + + void SetTrainingData(const Dataset* train_data) { + train_data_ = train_data; + is_feature_used_.resize(train_data_->num_features(), 1); + valid_feature_indices_ = train_data->ValidFeatureIndices(); + if (fraction_bytree_ >= 1.0f) { + need_reset_bytree_ = false; + used_cnt_bytree_ = static_cast(valid_feature_indices_.size()); + } else { + need_reset_bytree_ = true; + used_cnt_bytree_ = + GetCnt(valid_feature_indices_.size(), fraction_bytree_); + } + ResetByTree(); + } + + void SetConfig(const Config* config) { + fraction_bytree_ = config->feature_fraction; + fraction_bynode_ = config->feature_fraction_bynode; + is_feature_used_.resize(train_data_->num_features(), 1); + // seed is changed + if (seed_ != config->feature_fraction_seed) { + seed_ = config->feature_fraction_seed; + random_ = Random(seed_); + } + if (fraction_bytree_ >= 1.0f) { + need_reset_bytree_ = false; + used_cnt_bytree_ = static_cast(valid_feature_indices_.size()); + } else { + need_reset_bytree_ = true; + used_cnt_bytree_ = + GetCnt(valid_feature_indices_.size(), fraction_bytree_); + } + ResetByTree(); + } + + void ResetByTree() { + if (need_reset_bytree_) { + std::memset(is_feature_used_.data(), 0, + sizeof(int8_t) * is_feature_used_.size()); + used_feature_indices_ = random_.Sample( + static_cast(valid_feature_indices_.size()), used_cnt_bytree_); + int omp_loop_size = static_cast(used_feature_indices_.size()); + +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (omp_loop_size >= 1024) + for (int i = 0; i < omp_loop_size; ++i) { + int used_feature = valid_feature_indices_[used_feature_indices_[i]]; + int inner_feature_index = train_data_->InnerFeatureIndex(used_feature); + is_feature_used_[inner_feature_index] = 1; + } + } + } + + std::vector GetByNode(const Tree* tree, int leaf) { + // get interaction constraints for current branch + std::unordered_set allowed_features; + if (!interaction_constraints_.empty()) { + std::vector branch_features = tree->branch_features(leaf); + allowed_features.insert(branch_features.begin(), branch_features.end()); + for (auto constraint : interaction_constraints_) { + int num_feat_found = 0; + if (branch_features.size() == 0) { + allowed_features.insert(constraint.begin(), constraint.end()); + } + for (int feat : branch_features) { + if (constraint.count(feat) == 0) { + break; + } + ++num_feat_found; + if (num_feat_found == static_cast(branch_features.size())) { + allowed_features.insert(constraint.begin(), constraint.end()); + break; + } + } + } + } + + std::vector ret(train_data_->num_features(), 0); + if (fraction_bynode_ >= 1.0f) { + if (interaction_constraints_.empty()) { + return std::vector(train_data_->num_features(), 1); + } else { + for (int feat : allowed_features) { + int inner_feat = train_data_->InnerFeatureIndex(feat); + if (inner_feat >= 0) { + ret[inner_feat] = 1; + } + } + return ret; + } + } + if (need_reset_bytree_) { + auto used_feature_cnt = GetCnt(used_feature_indices_.size(), fraction_bynode_); + std::vector* allowed_used_feature_indices; + std::vector filtered_feature_indices; + if (interaction_constraints_.empty()) { + allowed_used_feature_indices = &used_feature_indices_; + } else { + for (int feat_ind : used_feature_indices_) { + if (allowed_features.count(valid_feature_indices_[feat_ind]) == 1) { + filtered_feature_indices.push_back(feat_ind); + } + } + used_feature_cnt = std::min(used_feature_cnt, static_cast(filtered_feature_indices.size())); + allowed_used_feature_indices = &filtered_feature_indices; + } + auto sampled_indices = random_.Sample( + static_cast((*allowed_used_feature_indices).size()), used_feature_cnt); + int omp_loop_size = static_cast(sampled_indices.size()); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (omp_loop_size >= 1024) + for (int i = 0; i < omp_loop_size; ++i) { + int used_feature = + valid_feature_indices_[(*allowed_used_feature_indices)[sampled_indices[i]]]; + int inner_feature_index = train_data_->InnerFeatureIndex(used_feature); + ret[inner_feature_index] = 1; + } + } else { + auto used_feature_cnt = + GetCnt(valid_feature_indices_.size(), fraction_bynode_); + std::vector* allowed_valid_feature_indices; + std::vector filtered_feature_indices; + if (interaction_constraints_.empty()) { + allowed_valid_feature_indices = &valid_feature_indices_; + } else { + for (int feat : valid_feature_indices_) { + if (allowed_features.count(feat) == 1) { + filtered_feature_indices.push_back(feat); + } + } + allowed_valid_feature_indices = &filtered_feature_indices; + used_feature_cnt = std::min(used_feature_cnt, static_cast(filtered_feature_indices.size())); + } + auto sampled_indices = random_.Sample( + static_cast((*allowed_valid_feature_indices).size()), used_feature_cnt); + int omp_loop_size = static_cast(sampled_indices.size()); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (omp_loop_size >= 1024) + for (int i = 0; i < omp_loop_size; ++i) { + int used_feature = (*allowed_valid_feature_indices)[sampled_indices[i]]; + int inner_feature_index = train_data_->InnerFeatureIndex(used_feature); + ret[inner_feature_index] = 1; + } + } + return ret; + } + + const std::vector& is_feature_used_bytree() const { + return is_feature_used_; + } + + void SetIsFeatureUsedByTree(int fid, bool val) { + is_feature_used_[fid] = val; + } + + private: + const Dataset* train_data_; + double fraction_bytree_; + double fraction_bynode_; + bool need_reset_bytree_; + int used_cnt_bytree_; + int seed_; + Random random_; + std::vector is_feature_used_; + std::vector used_feature_indices_; + std::vector valid_feature_indices_; + /*! \brief interaction constraints index in original (raw data) features */ + std::vector> interaction_constraints_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_COL_SAMPLER_HPP_ diff --git a/src/treelearner/cost_effective_gradient_boosting.hpp b/src/treelearner/cost_effective_gradient_boosting.hpp new file mode 100644 index 0000000..85851ac --- /dev/null +++ b/src/treelearner/cost_effective_gradient_boosting.hpp @@ -0,0 +1,175 @@ +/*! + * Copyright (c) 2019-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2019-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_ + +#include +#include +#include +#include +#include + +#include + +#include "data_partition.hpp" +#include "serial_tree_learner.h" +#include "split_info.hpp" + +namespace LightGBM { + +class CostEfficientGradientBoosting { + public: + explicit CostEfficientGradientBoosting(const SerialTreeLearner* tree_learner) + : init_(false), tree_learner_(tree_learner) {} + static bool IsEnable(const Config* config) { + if (config->cegb_tradeoff >= 1.0f && config->cegb_penalty_split <= 0.0f && + config->cegb_penalty_feature_coupled.empty() && + config->cegb_penalty_feature_lazy.empty()) { + return false; + } else { + return true; + } + } + + void Init() { + auto train_data = tree_learner_->train_data_; + if (!init_) { + splits_per_leaf_.resize( + static_cast(tree_learner_->config_->num_leaves) * + train_data->num_features()); + is_feature_used_in_split_.clear(); + is_feature_used_in_split_.resize(train_data->num_features()); + } + + if (!tree_learner_->config_->cegb_penalty_feature_coupled.empty() && + tree_learner_->config_->cegb_penalty_feature_coupled.size() != + static_cast(train_data->num_total_features())) { + Log::Fatal( + "cegb_penalty_feature_coupled should be the same size as feature " + "number."); + } + if (!tree_learner_->config_->cegb_penalty_feature_lazy.empty()) { + if (tree_learner_->config_->cegb_penalty_feature_lazy.size() != + static_cast(train_data->num_total_features())) { + Log::Fatal( + "cegb_penalty_feature_lazy should be the same size as feature " + "number."); + } + if (!init_) { + feature_used_in_data_ = Common::EmptyBitset(train_data->num_features() * + tree_learner_->num_data_); + } + } + init_ = true; + } + + void BeforeTrain() { + // clear the splits in splits_per_leaf_ + Threading::For(0, splits_per_leaf_.size(), 1024, + [this] (int /*thread_index*/, size_t start, size_t end) { + for (size_t i = start; i < end; ++i) { + splits_per_leaf_[i].Reset(); + } + }); + } + + double DeltaGain(int feature_index, int real_fidx, int leaf_index, + int num_data_in_leaf, SplitInfo split_info) { + auto config = tree_learner_->config_; + double delta = + config->cegb_tradeoff * config->cegb_penalty_split * num_data_in_leaf; + if (!config->cegb_penalty_feature_coupled.empty() && + !is_feature_used_in_split_[feature_index]) { + delta += config->cegb_tradeoff * + config->cegb_penalty_feature_coupled[real_fidx]; + } + if (!config->cegb_penalty_feature_lazy.empty()) { + delta += config->cegb_tradeoff * + CalculateOndemandCosts(feature_index, real_fidx, leaf_index); + } + splits_per_leaf_[static_cast(leaf_index) * + tree_learner_->train_data_->num_features() + + feature_index] = split_info; + return delta; + } + + void UpdateLeafBestSplits(Tree* tree, int best_leaf, + const SplitInfo* best_split_info, + std::vector* best_split_per_leaf) { + auto config = tree_learner_->config_; + auto train_data = tree_learner_->train_data_; + const int inner_feature_index = + train_data->InnerFeatureIndex(best_split_info->feature); + auto& ref_best_split_per_leaf = *best_split_per_leaf; + if (!config->cegb_penalty_feature_coupled.empty() && + !is_feature_used_in_split_[inner_feature_index]) { + is_feature_used_in_split_[inner_feature_index] = true; + for (int i = 0; i < tree->num_leaves(); ++i) { + if (i == best_leaf) continue; + auto split = &splits_per_leaf_[static_cast(i) * + train_data->num_features() + + inner_feature_index]; + split->gain += + config->cegb_tradeoff * + config->cegb_penalty_feature_coupled[best_split_info->feature]; + // Avoid to update the leaf that cannot split + if (ref_best_split_per_leaf[i].gain > kMinScore && + *split > ref_best_split_per_leaf[i]) { + ref_best_split_per_leaf[i] = *split; + } + } + } + if (!config->cegb_penalty_feature_lazy.empty()) { + data_size_t cnt_leaf_data = 0; + auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf( + best_leaf, &cnt_leaf_data); + for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) { + int real_idx = tmp_idx[i_input]; + Common::InsertBitset( + &feature_used_in_data_, + train_data->num_data() * inner_feature_index + real_idx); + } + } + } + + private: + double CalculateOndemandCosts(int feature_index, int real_fidx, + int leaf_index) const { + if (tree_learner_->config_->cegb_penalty_feature_lazy.empty()) { + return 0.0f; + } + auto train_data = tree_learner_->train_data_; + double penalty = + tree_learner_->config_->cegb_penalty_feature_lazy[real_fidx]; + + double total = 0.0f; + data_size_t cnt_leaf_data = 0; + auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf( + leaf_index, &cnt_leaf_data); + + for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) { + int real_idx = tmp_idx[i_input]; + if (Common::FindInBitset( + feature_used_in_data_.data(), + train_data->num_data() * train_data->num_features(), + train_data->num_data() * feature_index + real_idx)) { + continue; + } + total += penalty; + } + return total; + } + bool init_; + const SerialTreeLearner* tree_learner_; + std::vector splits_per_leaf_; + std::vector is_feature_used_in_split_; + std::vector feature_used_in_data_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_ diff --git a/src/treelearner/cuda/cuda_best_split_finder.cpp b/src/treelearner/cuda/cuda_best_split_finder.cpp new file mode 100644 index 0000000..f61e469 --- /dev/null +++ b/src/treelearner/cuda/cuda_best_split_finder.cpp @@ -0,0 +1,380 @@ +/*! + * Copyright (c) 2021 Microsoft Corporation. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include +#include + +#include "cuda_best_split_finder.hpp" +#include "cuda_leaf_splits.hpp" + +namespace LightGBM { + +CUDABestSplitFinder::CUDABestSplitFinder( + const hist_t* cuda_hist, + const Dataset* train_data, + const std::vector& feature_hist_offsets, + const bool select_features_by_node, + const Config* config): + num_features_(train_data->num_features()), + num_leaves_(config->num_leaves), + feature_hist_offsets_(feature_hist_offsets), + lambda_l1_(config->lambda_l1), + lambda_l2_(config->lambda_l2), + min_data_in_leaf_(config->min_data_in_leaf), + min_sum_hessian_in_leaf_(config->min_sum_hessian_in_leaf), + min_gain_to_split_(config->min_gain_to_split), + cat_smooth_(config->cat_smooth), + cat_l2_(config->cat_l2), + max_cat_threshold_(config->max_cat_threshold), + min_data_per_group_(config->min_data_per_group), + max_cat_to_onehot_(config->max_cat_to_onehot), + extra_trees_(config->extra_trees), + extra_seed_(config->extra_seed), + use_smoothing_(config->path_smooth > 0), + path_smooth_(config->path_smooth), + num_total_bin_(feature_hist_offsets.empty() ? 0 : static_cast(feature_hist_offsets.back())), + select_features_by_node_(select_features_by_node), + cuda_hist_(cuda_hist) { + InitFeatureMetaInfo(train_data); + if (has_categorical_feature_ && config->use_quantized_grad) { + Log::Fatal("Quantized training on GPU with categorical features is not supported yet."); + } +} + +CUDABestSplitFinder::~CUDABestSplitFinder() { + gpuAssert(cudaStreamDestroy(cuda_streams_[0]), __FILE__, __LINE__); + gpuAssert(cudaStreamDestroy(cuda_streams_[1]), __FILE__, __LINE__); + cuda_streams_.clear(); + cuda_streams_.shrink_to_fit(); +} + +void CUDABestSplitFinder::InitFeatureMetaInfo(const Dataset* train_data) { + feature_missing_type_.resize(num_features_); + feature_mfb_offsets_.resize(num_features_); + feature_default_bins_.resize(num_features_); + feature_num_bins_.resize(num_features_); + max_num_bin_in_feature_ = 0; + has_categorical_feature_ = false; + max_num_categorical_bin_ = 0; + is_categorical_.resize(train_data->num_features(), 0); + for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) { + const BinMapper* bin_mapper = train_data->FeatureBinMapper(inner_feature_index); + if (bin_mapper->bin_type() == BinType::CategoricalBin) { + has_categorical_feature_ = true; + is_categorical_[inner_feature_index] = 1; + if (bin_mapper->num_bin() > max_num_categorical_bin_) { + max_num_categorical_bin_ = bin_mapper->num_bin(); + } + } + const MissingType missing_type = bin_mapper->missing_type(); + feature_missing_type_[inner_feature_index] = missing_type; + feature_mfb_offsets_[inner_feature_index] = static_cast(bin_mapper->GetMostFreqBin() == 0); + feature_default_bins_[inner_feature_index] = bin_mapper->GetDefaultBin(); + feature_num_bins_[inner_feature_index] = static_cast(bin_mapper->num_bin()); + const int num_bin_hist = bin_mapper->num_bin() - feature_mfb_offsets_[inner_feature_index]; + if (num_bin_hist > max_num_bin_in_feature_) { + max_num_bin_in_feature_ = num_bin_hist; + } + } + if (max_num_bin_in_feature_ > NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER) { + use_global_memory_ = true; + } else { + use_global_memory_ = false; + } +} + +void CUDABestSplitFinder::Init() { + InitCUDAFeatureMetaInfo(); + cuda_streams_.resize(2); + CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_streams_[0])); + CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_streams_[1])); + cuda_best_split_info_buffer_.Resize(8); + if (use_global_memory_) { + cuda_feature_hist_grad_buffer_.Resize(static_cast(num_total_bin_)); + cuda_feature_hist_hess_buffer_.Resize(static_cast(num_total_bin_)); + if (has_categorical_feature_) { + cuda_feature_hist_stat_buffer_.Resize(static_cast(num_total_bin_)); + cuda_feature_hist_index_buffer_.Resize(static_cast(num_total_bin_)); + } + } + + if (select_features_by_node_) { + is_feature_used_by_smaller_node_.Resize(num_features_); + is_feature_used_by_larger_node_.Resize(num_features_); + } +} + +void CUDABestSplitFinder::InitCUDAFeatureMetaInfo() { + cuda_is_feature_used_bytree_.Resize(static_cast(num_features_)); + + // initialize split find task information (a split find task is one pass through the histogram of a feature) + num_tasks_ = 0; + for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) { + const uint32_t num_bin = feature_num_bins_[inner_feature_index]; + const MissingType missing_type = feature_missing_type_[inner_feature_index]; + if (num_bin > 2 && missing_type != MissingType::None && !is_categorical_[inner_feature_index]) { + num_tasks_ += 2; + } else { + ++num_tasks_; + } + } + split_find_tasks_.resize(num_tasks_); + split_find_tasks_.shrink_to_fit(); + int cur_task_index = 0; + for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) { + const uint32_t num_bin = feature_num_bins_[inner_feature_index]; + const MissingType missing_type = feature_missing_type_[inner_feature_index]; + if (num_bin > 2 && missing_type != MissingType::None && !is_categorical_[inner_feature_index]) { + if (missing_type == MissingType::Zero) { + SplitFindTask* new_task = &split_find_tasks_[cur_task_index]; + new_task->reverse = false; + new_task->skip_default_bin = true; + new_task->na_as_missing = false; + new_task->inner_feature_index = inner_feature_index; + new_task->assume_out_default_left = false; + new_task->is_categorical = false; + uint32_t num_bin = feature_num_bins_[inner_feature_index]; + new_task->is_one_hot = false; + new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; + new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; + new_task->default_bin = feature_default_bins_[inner_feature_index]; + new_task->num_bin = num_bin; + ++cur_task_index; + + new_task = &split_find_tasks_[cur_task_index]; + new_task->reverse = true; + new_task->skip_default_bin = true; + new_task->na_as_missing = false; + new_task->inner_feature_index = inner_feature_index; + new_task->assume_out_default_left = true; + new_task->is_categorical = false; + num_bin = feature_num_bins_[inner_feature_index]; + new_task->is_one_hot = false; + new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; + new_task->default_bin = feature_default_bins_[inner_feature_index]; + new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; + new_task->num_bin = num_bin; + ++cur_task_index; + } else { + SplitFindTask* new_task = &split_find_tasks_[cur_task_index]; + new_task->reverse = false; + new_task->skip_default_bin = false; + new_task->na_as_missing = true; + new_task->inner_feature_index = inner_feature_index; + new_task->assume_out_default_left = false; + new_task->is_categorical = false; + uint32_t num_bin = feature_num_bins_[inner_feature_index]; + new_task->is_one_hot = false; + new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; + new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; + new_task->default_bin = feature_default_bins_[inner_feature_index]; + new_task->num_bin = num_bin; + ++cur_task_index; + + new_task = &split_find_tasks_[cur_task_index]; + new_task->reverse = true; + new_task->skip_default_bin = false; + new_task->na_as_missing = true; + new_task->inner_feature_index = inner_feature_index; + new_task->assume_out_default_left = true; + new_task->is_categorical = false; + num_bin = feature_num_bins_[inner_feature_index]; + new_task->is_one_hot = false; + new_task->hist_offset = feature_hist_offsets_[inner_feature_index]; + new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index]; + new_task->default_bin = feature_default_bins_[inner_feature_index]; + new_task->num_bin = num_bin; + ++cur_task_index; + } + } else { + SplitFindTask& new_task = split_find_tasks_[cur_task_index]; + const uint32_t num_bin = feature_num_bins_[inner_feature_index]; + if (is_categorical_[inner_feature_index]) { + new_task.reverse = false; + new_task.is_categorical = true; + new_task.is_one_hot = (static_cast(num_bin) <= max_cat_to_onehot_); + } else { + new_task.reverse = true; + new_task.is_categorical = false; + new_task.is_one_hot = false; + } + new_task.skip_default_bin = false; + new_task.na_as_missing = false; + new_task.inner_feature_index = inner_feature_index; + if (missing_type != MissingType::NaN && !is_categorical_[inner_feature_index]) { + new_task.assume_out_default_left = true; + } else { + new_task.assume_out_default_left = false; + } + new_task.hist_offset = feature_hist_offsets_[inner_feature_index]; + new_task.mfb_offset = feature_mfb_offsets_[inner_feature_index]; + new_task.default_bin = feature_default_bins_[inner_feature_index]; + new_task.num_bin = num_bin; + ++cur_task_index; + } + } + CHECK_EQ(cur_task_index, static_cast(split_find_tasks_.size())); + + if (extra_trees_) { + cuda_randoms_.Resize(num_tasks_ * 2); + LaunchInitCUDARandomKernel(); + } + + const int num_task_blocks = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK; + const size_t cuda_best_leaf_split_info_buffer_size = static_cast(num_task_blocks) * static_cast(num_leaves_); + + cuda_leaf_best_split_info_.Resize(cuda_best_leaf_split_info_buffer_size); + + cuda_split_find_tasks_.Resize(num_tasks_); + CopyFromHostToCUDADevice(cuda_split_find_tasks_.RawData(), + split_find_tasks_.data(), + split_find_tasks_.size(), + __FILE__, + __LINE__); + + const size_t output_buffer_size = 2 * static_cast(num_tasks_); + cuda_best_split_info_.Resize(output_buffer_size); + + max_num_categories_in_split_ = std::min(max_cat_threshold_, max_num_categorical_bin_ / 2); + cuda_cat_threshold_feature_.Resize(max_num_categories_in_split_ * output_buffer_size); + cuda_cat_threshold_real_feature_.Resize(max_num_categories_in_split_ * output_buffer_size); + cuda_cat_threshold_leaf_.Resize(max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size); + cuda_cat_threshold_real_leaf_.Resize(max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size); + AllocateCatVectors(cuda_leaf_best_split_info_.RawData(), cuda_cat_threshold_leaf_.RawData(), cuda_cat_threshold_real_leaf_.RawData(), cuda_best_leaf_split_info_buffer_size); + AllocateCatVectors(cuda_best_split_info_.RawData(), cuda_cat_threshold_feature_.RawData(), cuda_cat_threshold_real_feature_.RawData(), output_buffer_size); +} + +void CUDABestSplitFinder::ResetTrainingData( + const hist_t* cuda_hist, + const Dataset* train_data, + const std::vector& feature_hist_offsets) { + cuda_hist_ = cuda_hist; + num_features_ = train_data->num_features(); + feature_hist_offsets_ = feature_hist_offsets; + InitFeatureMetaInfo(train_data); + cuda_is_feature_used_bytree_.Clear(); + cuda_best_split_info_.Clear(); + InitCUDAFeatureMetaInfo(); +} + +void CUDABestSplitFinder::ResetConfig(const Config* config, const hist_t* cuda_hist) { + num_leaves_ = config->num_leaves; + lambda_l1_ = config->lambda_l1; + lambda_l2_ = config->lambda_l2; + min_data_in_leaf_ = config->min_data_in_leaf; + min_sum_hessian_in_leaf_ = config->min_sum_hessian_in_leaf; + min_gain_to_split_ = config->min_gain_to_split; + cat_smooth_ = config->cat_smooth; + cat_l2_ = config->cat_l2; + max_cat_threshold_ = config->max_cat_threshold; + min_data_per_group_ = config->min_data_per_group; + max_cat_to_onehot_ = config->max_cat_to_onehot; + extra_trees_ = config->extra_trees; + extra_seed_ = config->extra_seed; + use_smoothing_ = (config->path_smooth > 0.0f); + path_smooth_ = config->path_smooth; + cuda_hist_ = cuda_hist; + + const int num_task_blocks = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK; + size_t cuda_best_leaf_split_info_buffer_size = static_cast(num_task_blocks) * static_cast(num_leaves_); + cuda_leaf_best_split_info_.Resize(cuda_best_leaf_split_info_buffer_size); + max_num_categories_in_split_ = std::min(max_cat_threshold_, max_num_categorical_bin_ / 2); + size_t total_cat_threshold_size = max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size; + cuda_cat_threshold_leaf_.Resize(total_cat_threshold_size); + cuda_cat_threshold_real_leaf_.Resize(total_cat_threshold_size); + AllocateCatVectors(cuda_leaf_best_split_info_.RawData(), cuda_cat_threshold_leaf_.RawData(), cuda_cat_threshold_real_leaf_.RawData(), cuda_best_leaf_split_info_buffer_size); + + cuda_best_leaf_split_info_buffer_size = 2 * static_cast(num_tasks_); + total_cat_threshold_size = max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size; + cuda_cat_threshold_feature_.Resize(total_cat_threshold_size); + cuda_cat_threshold_real_feature_.Resize(total_cat_threshold_size); + AllocateCatVectors(cuda_best_split_info_.RawData(), cuda_cat_threshold_feature_.RawData(), cuda_cat_threshold_real_feature_.RawData(), cuda_best_leaf_split_info_buffer_size); +} + +void CUDABestSplitFinder::BeforeTrain(const std::vector& is_feature_used_bytree) { + CopyFromHostToCUDADevice(cuda_is_feature_used_bytree_.RawData(), + is_feature_used_bytree.data(), + is_feature_used_bytree.size(), __FILE__, __LINE__); +} + +void CUDABestSplitFinder::FindBestSplitsForLeaf( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const CUDALeafSplitsStruct* larger_leaf_splits, + const int smaller_leaf_index, + const int larger_leaf_index, + const data_size_t num_data_in_smaller_leaf, + const data_size_t num_data_in_larger_leaf, + const double sum_hessians_in_smaller_leaf, + const double sum_hessians_in_larger_leaf, + const score_t* grad_scale, + const score_t* hess_scale, + const uint8_t smaller_num_bits_in_histogram_bins, + const uint8_t larger_num_bits_in_histogram_bins) { + const bool is_smaller_leaf_valid = (num_data_in_smaller_leaf > min_data_in_leaf_ && + sum_hessians_in_smaller_leaf > min_sum_hessian_in_leaf_); + const bool is_larger_leaf_valid = (num_data_in_larger_leaf > min_data_in_leaf_ && + sum_hessians_in_larger_leaf > min_sum_hessian_in_leaf_ && larger_leaf_index >= 0); + if (grad_scale != nullptr && hess_scale != nullptr) { + LaunchFindBestSplitsDiscretizedForLeafKernel(smaller_leaf_splits, larger_leaf_splits, + smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid, + grad_scale, hess_scale, smaller_num_bits_in_histogram_bins, larger_num_bits_in_histogram_bins, num_data_in_smaller_leaf, num_data_in_larger_leaf); + } else { + LaunchFindBestSplitsForLeafKernel(smaller_leaf_splits, larger_leaf_splits, + smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid, num_data_in_smaller_leaf, num_data_in_larger_leaf); + } + global_timer.Start("CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel"); + LaunchSyncBestSplitForLeafKernel(smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid); + SynchronizeCUDADevice(__FILE__, __LINE__); + global_timer.Stop("CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel"); +} + +const CUDASplitInfo* CUDABestSplitFinder::FindBestFromAllSplits( + const int cur_num_leaves, + const int smaller_leaf_index, + const int larger_leaf_index, + int* smaller_leaf_best_split_feature, + uint32_t* smaller_leaf_best_split_threshold, + uint8_t* smaller_leaf_best_split_default_left, + int* larger_leaf_best_split_feature, + uint32_t* larger_leaf_best_split_threshold, + uint8_t* larger_leaf_best_split_default_left, + int* best_leaf_index, + int* num_cat_threshold) { + LaunchFindBestFromAllSplitsKernel( + cur_num_leaves, + smaller_leaf_index, + larger_leaf_index, + smaller_leaf_best_split_feature, + smaller_leaf_best_split_threshold, + smaller_leaf_best_split_default_left, + larger_leaf_best_split_feature, + larger_leaf_best_split_threshold, + larger_leaf_best_split_default_left, + best_leaf_index, + num_cat_threshold); + SynchronizeCUDADevice(__FILE__, __LINE__); + return cuda_leaf_best_split_info_.RawData() + (*best_leaf_index); +} + +void CUDABestSplitFinder::AllocateCatVectors(CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len) { + LaunchAllocateCatVectorsKernel(cuda_split_infos, cat_threshold_vec, cat_threshold_real_vec, len); +} + +void CUDABestSplitFinder::SetUsedFeatureByNode(const std::vector& is_feature_used_by_smaller_node, + const std::vector& is_feature_used_by_larger_node) { + if (select_features_by_node_) { + CopyFromHostToCUDADevice(is_feature_used_by_smaller_node_.RawData(), + is_feature_used_by_smaller_node.data(), is_feature_used_by_smaller_node.size(), __FILE__, __LINE__); + CopyFromHostToCUDADevice(is_feature_used_by_larger_node_.RawData(), + is_feature_used_by_larger_node.data(), is_feature_used_by_larger_node.size(), __FILE__, __LINE__); + } +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_best_split_finder.cu b/src/treelearner/cuda/cuda_best_split_finder.cu new file mode 100644 index 0000000..fb359fb --- /dev/null +++ b/src/treelearner/cuda/cuda_best_split_finder.cu @@ -0,0 +1,2268 @@ +/*! + * Copyright (c) 2021 Microsoft Corporation. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include "cuda_best_split_finder.hpp" + +#include +#include + +#include +#include + +namespace LightGBM { + +__device__ void ReduceBestGainWarp(double gain, bool found, uint32_t thread_index, double* out_gain, bool* out_found, uint32_t* out_thread_index) { + const uint32_t mask = 0xffffffff; + const uint32_t warpLane = threadIdx.x % warpSize; + for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { + const bool other_found = __shfl_down_sync(mask, found, offset); + const double other_gain = __shfl_down_sync(mask, gain, offset); + const uint32_t other_thread_index = __shfl_down_sync(mask, thread_index, offset); + if ((other_found && found && other_gain > gain) || (!found && other_found)) { + found = other_found; + gain = other_gain; + thread_index = other_thread_index; + } + } + if (warpLane == 0) { + *out_gain = gain; + *out_found = found; + *out_thread_index = thread_index; + } +} + +__device__ uint32_t ReduceBestGainBlock(double gain, bool found, uint32_t thread_index) { + const uint32_t mask = 0xffffffff; + for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { + const bool other_found = __shfl_down_sync(mask, found, offset); + const double other_gain = __shfl_down_sync(mask, gain, offset); + const uint32_t other_thread_index = __shfl_down_sync(mask, thread_index, offset); + if ((other_found && found && other_gain > gain) || (!found && other_found)) { + found = other_found; + gain = other_gain; + thread_index = other_thread_index; + } + } + return thread_index; +} + +__device__ uint32_t ReduceBestGain(double gain, bool found, uint32_t thread_index, + double* shared_gain_buffer, bool* shared_found_buffer, uint32_t* shared_thread_index_buffer) { + const uint32_t warpID = threadIdx.x / warpSize; + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t num_warp = blockDim.x / warpSize; + ReduceBestGainWarp(gain, found, thread_index, shared_gain_buffer + warpID, shared_found_buffer + warpID, shared_thread_index_buffer + warpID); + __syncthreads(); + if (warpID == 0) { + gain = warpLane < num_warp ? shared_gain_buffer[warpLane] : kMinScore; + found = warpLane < num_warp ? shared_found_buffer[warpLane] : false; + thread_index = warpLane < num_warp ? shared_thread_index_buffer[warpLane] : 0; + thread_index = ReduceBestGainBlock(gain, found, thread_index); + } + return thread_index; +} + +__device__ void ReduceBestGainForLeaves(double* gain, int* leaves, int cuda_cur_num_leaves) { + const unsigned int tid = threadIdx.x; + for (unsigned int s = 1; s < cuda_cur_num_leaves; s *= 2) { + if (tid % (2 * s) == 0 && (tid + s) < cuda_cur_num_leaves) { + const uint32_t tid_s = tid + s; + if ((leaves[tid] == -1 && leaves[tid_s] != -1) || (leaves[tid] != -1 && leaves[tid_s] != -1 && gain[tid_s] > gain[tid])) { + gain[tid] = gain[tid_s]; + leaves[tid] = leaves[tid_s]; + } + } + __syncthreads(); + } +} + +__device__ void ReduceBestGainForLeavesWarp(double gain, int leaf_index, double* out_gain, int* out_leaf_index) { + const uint32_t mask = 0xffffffff; + const uint32_t warpLane = threadIdx.x % warpSize; + for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { + const int other_leaf_index = __shfl_down_sync(mask, leaf_index, offset); + const double other_gain = __shfl_down_sync(mask, gain, offset); + if ((leaf_index != -1 && other_leaf_index != -1 && other_gain > gain) || (leaf_index == -1 && other_leaf_index != -1)) { + gain = other_gain; + leaf_index = other_leaf_index; + } + } + if (warpLane == 0) { + *out_gain = gain; + *out_leaf_index = leaf_index; + } +} + +__device__ int ReduceBestGainForLeavesBlock(double gain, int leaf_index) { + const uint32_t mask = 0xffffffff; + for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) { + const int other_leaf_index = __shfl_down_sync(mask, leaf_index, offset); + const double other_gain = __shfl_down_sync(mask, gain, offset); + if ((leaf_index != -1 && other_leaf_index != -1 && other_gain > gain) || (leaf_index == -1 && other_leaf_index != -1)) { + gain = other_gain; + leaf_index = other_leaf_index; + } + } + return leaf_index; +} + +__device__ int ReduceBestGainForLeaves(double gain, int leaf_index, double* shared_gain_buffer, int* shared_leaf_index_buffer) { + const uint32_t warpID = threadIdx.x / warpSize; + const uint32_t warpLane = threadIdx.x % warpSize; + const uint32_t num_warp = blockDim.x / warpSize; + ReduceBestGainForLeavesWarp(gain, leaf_index, shared_gain_buffer + warpID, shared_leaf_index_buffer + warpID); + __syncthreads(); + if (warpID == 0) { + gain = warpLane < num_warp ? shared_gain_buffer[warpLane] : kMinScore; + leaf_index = warpLane < num_warp ? shared_leaf_index_buffer[warpLane] : -1; + leaf_index = ReduceBestGainForLeavesBlock(gain, leaf_index); + } + return leaf_index; +} + +template +__device__ void FindBestSplitsForLeafKernelInner( + // input feature information + const hist_t* feature_hist_ptr, + // input task information + const SplitFindTask* task, + CUDARandom* cuda_random, + // input config parameter values + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + // input parent node information + const double parent_gain, + const double sum_gradients, + const double sum_hessians, + const data_size_t num_data, + const double parent_output, + // output parameters + CUDASplitInfo* cuda_best_split_info) { + const double cnt_factor = num_data / sum_hessians; + const double min_gain_shift = parent_gain + min_gain_to_split; + + cuda_best_split_info->is_valid = false; + + hist_t local_grad_hist = 0.0f; + hist_t local_hess_hist = 0.0f; + double local_gain = 0.0f; + bool threshold_found = false; + uint32_t threshold_value = 0; + __shared__ int rand_threshold; + if (USE_RAND && threadIdx.x == 0) { + if (task->num_bin - 2 > 0) { + rand_threshold = cuda_random->NextInt(0, task->num_bin - 2); + } + } + __shared__ uint32_t best_thread_index; + __shared__ double shared_double_buffer[WARPSIZE]; + __shared__ bool shared_bool_buffer[WARPSIZE]; + __shared__ uint32_t shared_int_buffer[WARPSIZE]; + const unsigned int threadIdx_x = threadIdx.x; + const bool skip_sum = REVERSE ? + (task->skip_default_bin && (task->num_bin - 1 - threadIdx_x) == static_cast(task->default_bin)) : + (task->skip_default_bin && (threadIdx_x + task->mfb_offset) == static_cast(task->default_bin)); + const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset; + if (!REVERSE) { + if (task->na_as_missing && task->mfb_offset == 1) { + if (threadIdx_x < static_cast(task->num_bin) && threadIdx_x > 0) { + const unsigned int bin_offset = (threadIdx_x - 1) << 1; + local_grad_hist = feature_hist_ptr[bin_offset]; + local_hess_hist = feature_hist_ptr[bin_offset + 1]; + } + } else { + if (threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { + const unsigned int bin_offset = threadIdx_x << 1; + local_grad_hist = feature_hist_ptr[bin_offset]; + local_hess_hist = feature_hist_ptr[bin_offset + 1]; + } + } + } else { + if (threadIdx_x >= static_cast(task->na_as_missing) && + threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { + const unsigned int read_index = feature_num_bin_minus_offset - 1 - threadIdx_x; + const unsigned int bin_offset = read_index << 1; + local_grad_hist = feature_hist_ptr[bin_offset]; + local_hess_hist = feature_hist_ptr[bin_offset + 1]; + } + } + __syncthreads(); + if (!REVERSE && task->na_as_missing && task->mfb_offset == 1) { + const hist_t sum_gradients_non_default = ShuffleReduceSum(local_grad_hist, shared_double_buffer, blockDim.x); + __syncthreads(); + const hist_t sum_hessians_non_default = ShuffleReduceSum(local_hess_hist, shared_double_buffer, blockDim.x); + if (threadIdx_x == 0) { + local_grad_hist += (sum_gradients - sum_gradients_non_default); + local_hess_hist += (sum_hessians - sum_hessians_non_default); + } + } + if (threadIdx_x == 0) { + local_hess_hist += kEpsilon; + } + local_gain = kMinScore; + local_grad_hist = ShufflePrefixSum(local_grad_hist, shared_double_buffer); + __syncthreads(); + local_hess_hist = ShufflePrefixSum(local_hess_hist, shared_double_buffer); + if (REVERSE) { + if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x <= task->num_bin - 2 && !skip_sum) { + const double sum_right_gradient = local_grad_hist; + const double sum_right_hessian = local_hess_hist; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double sum_left_gradient = sum_gradients - sum_right_gradient; + const double sum_left_hessian = sum_hessians - sum_right_hessian; + const data_size_t left_count = num_data - right_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || static_cast(task->num_bin - 2 - threadIdx_x) == rand_threshold)) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + lambda_l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_value = static_cast(task->num_bin - 2 - threadIdx_x); + threshold_found = true; + } + } + } + } else { + const uint32_t end = (task->na_as_missing && task->mfb_offset == 1) ? static_cast(task->num_bin - 2) : feature_num_bin_minus_offset - 2; + if (threadIdx_x <= end && !skip_sum) { + const double sum_left_gradient = local_grad_hist; + const double sum_left_hessian = local_hess_hist; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = num_data - left_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || static_cast(threadIdx_x + task->mfb_offset) == rand_threshold)) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + lambda_l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_value = (task->na_as_missing && task->mfb_offset == 1) ? + static_cast(threadIdx_x) : + static_cast(threadIdx_x + task->mfb_offset); + threshold_found = true; + } + } + } + } + __syncthreads(); + const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_bool_buffer, shared_int_buffer); + if (threadIdx_x == 0) { + best_thread_index = result; + } + __syncthreads(); + if (threshold_found && threadIdx_x == best_thread_index) { + cuda_best_split_info->is_valid = true; + cuda_best_split_info->threshold = threshold_value; + cuda_best_split_info->gain = local_gain; + cuda_best_split_info->default_left = task->assume_out_default_left; + if (REVERSE) { + const double sum_right_gradient = local_grad_hist; + const double sum_right_hessian = local_hess_hist - kEpsilon; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double sum_left_gradient = sum_gradients - sum_right_gradient; + const double sum_left_hessian = sum_hessians - sum_right_hessian - kEpsilon; + const data_size_t left_count = num_data - right_count; + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, right_output); + } else { + const double sum_left_gradient = local_grad_hist; + const double sum_left_hessian = local_hess_hist - kEpsilon; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian - kEpsilon; + const data_size_t right_count = num_data - left_count; + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, right_output); + } + } +} + +template +__device__ void FindBestSplitsDiscretizedForLeafKernelInner( + // input feature information + const BIN_HIST_TYPE* feature_hist_ptr, + // input task information + const SplitFindTask* task, + CUDARandom* cuda_random, + // input config parameter values + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + // input parent node information + const double parent_gain, + const int64_t sum_gradients_hessians, + const data_size_t num_data, + const double parent_output, + // gradient scale + const double grad_scale, + const double hess_scale, + // output parameters + CUDASplitInfo* cuda_best_split_info) { + const double sum_hessians = static_cast(sum_gradients_hessians & 0x00000000ffffffff) * hess_scale; + const double cnt_factor = num_data / sum_hessians; + const double min_gain_shift = parent_gain + min_gain_to_split; + + cuda_best_split_info->is_valid = false; + + ACC_HIST_TYPE local_grad_hess_hist = 0; + double local_gain = 0.0f; + bool threshold_found = false; + uint32_t threshold_value = 0; + __shared__ int rand_threshold; + if (USE_RAND && threadIdx.x == 0) { + if (task->num_bin - 2 > 0) { + rand_threshold = cuda_random->NextInt(0, task->num_bin - 2); + } + } + __shared__ uint32_t best_thread_index; + __shared__ double shared_double_buffer[WARPSIZE]; + __shared__ bool shared_bool_buffer[WARPSIZE]; + __shared__ uint32_t shared_int_buffer[2 * WARPSIZE]; // need 2 * WARPSIZE since the actual ACC_HIST_TYPE could be long int + const unsigned int threadIdx_x = threadIdx.x; + const bool skip_sum = REVERSE ? + (task->skip_default_bin && (task->num_bin - 1 - threadIdx_x) == static_cast(task->default_bin)) : + (task->skip_default_bin && (threadIdx_x + task->mfb_offset) == static_cast(task->default_bin)); + const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset; + if (!REVERSE) { + if (threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { + const unsigned int bin_offset = threadIdx_x; + if (USE_16BIT_BIN_HIST && !USE_16BIT_ACC_HIST) { + const int32_t local_grad_hess_hist_int32 = feature_hist_ptr[bin_offset]; + local_grad_hess_hist = (static_cast(static_cast(local_grad_hess_hist_int32 >> 16)) << 32) | (static_cast(local_grad_hess_hist_int32 & 0x0000ffff)); + } else { + local_grad_hess_hist = feature_hist_ptr[bin_offset]; + } + } + } else { + if (threadIdx_x >= static_cast(task->na_as_missing) && + threadIdx_x < feature_num_bin_minus_offset && !skip_sum) { + const unsigned int read_index = feature_num_bin_minus_offset - 1 - threadIdx_x; + if (USE_16BIT_BIN_HIST && !USE_16BIT_ACC_HIST) { + const int32_t local_grad_hess_hist_int32 = feature_hist_ptr[read_index]; + local_grad_hess_hist = (static_cast(static_cast(local_grad_hess_hist_int32 >> 16)) << 32) | (static_cast(local_grad_hess_hist_int32 & 0x0000ffff)); + } else { + local_grad_hess_hist = feature_hist_ptr[read_index]; + } + } + } + __syncthreads(); + local_gain = kMinScore; + local_grad_hess_hist = ShufflePrefixSum(local_grad_hess_hist, reinterpret_cast(shared_int_buffer)); + double sum_left_gradient = 0.0f; + double sum_left_hessian = 0.0f; + double sum_right_gradient = 0.0f; + double sum_right_hessian = 0.0f; + data_size_t left_count = 0; + data_size_t right_count = 0; + int64_t sum_left_gradient_hessian = 0; + int64_t sum_right_gradient_hessian = 0; + if (REVERSE) { + if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x <= task->num_bin - 2 && !skip_sum) { + sum_right_gradient_hessian = USE_16BIT_ACC_HIST ? + (static_cast(static_cast(local_grad_hess_hist >> 16)) << 32) | static_cast(local_grad_hess_hist & 0x0000ffff) : + local_grad_hess_hist; + sum_right_gradient = static_cast(static_cast((sum_right_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale; + sum_right_hessian = static_cast(static_cast(sum_right_gradient_hessian & 0x00000000ffffffff)) * hess_scale; + right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + sum_left_gradient_hessian = sum_gradients_hessians - sum_right_gradient_hessian; + sum_left_gradient = static_cast(static_cast((sum_left_gradient_hessian & 0xffffffff00000000)>> 32)) * grad_scale; + sum_left_hessian = static_cast(static_cast(sum_left_gradient_hessian & 0x00000000ffffffff)) * hess_scale; + left_count = num_data - right_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || static_cast(task->num_bin - 2 - threadIdx_x) == rand_threshold)) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient, + sum_right_hessian + kEpsilon, lambda_l1, + lambda_l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_value = static_cast(task->num_bin - 2 - threadIdx_x); + threshold_found = true; + } + } + } + } else { + if (threadIdx_x <= feature_num_bin_minus_offset - 2 && !skip_sum) { + sum_left_gradient_hessian = USE_16BIT_ACC_HIST ? + (static_cast(static_cast(local_grad_hess_hist >> 16)) << 32) | static_cast(local_grad_hess_hist & 0x0000ffff) : + local_grad_hess_hist; + sum_left_gradient = static_cast(static_cast((sum_left_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale; + sum_left_hessian = static_cast(static_cast(sum_left_gradient_hessian & 0x00000000ffffffff)) * hess_scale; + left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + sum_right_gradient_hessian = sum_gradients_hessians - sum_left_gradient_hessian; + sum_right_gradient = static_cast(static_cast((sum_right_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale; + sum_right_hessian = static_cast(static_cast(sum_right_gradient_hessian & 0x00000000ffffffff)) * hess_scale; + right_count = num_data - left_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || static_cast(threadIdx_x + task->mfb_offset) == rand_threshold)) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient, + sum_right_hessian + kEpsilon, lambda_l1, + lambda_l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_value = static_cast(threadIdx_x + task->mfb_offset); + threshold_found = true; + } + } + } + } + __syncthreads(); + const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_bool_buffer, shared_int_buffer); + if (threadIdx_x == 0) { + best_thread_index = result; + } + __syncthreads(); + if (threshold_found && threadIdx_x == best_thread_index) { + cuda_best_split_info->is_valid = true; + cuda_best_split_info->threshold = threshold_value; + cuda_best_split_info->gain = local_gain; + cuda_best_split_info->default_left = task->assume_out_default_left; + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_sum_of_gradients_hessians = sum_left_gradient_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_sum_of_gradients_hessians = sum_right_gradient_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, right_output); + } +} + +template +__device__ void FindBestSplitsForLeafKernelCategoricalInner( + // input feature information + const hist_t* feature_hist_ptr, + // input task information + const SplitFindTask* task, + CUDARandom* cuda_random, + // input config parameter values + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + const double cat_smooth, + const double cat_l2, + const int max_cat_threshold, + const int min_data_per_group, + // input parent node information + const double parent_gain, + const double sum_gradients, + const double sum_hessians, + const data_size_t num_data, + const double parent_output, + // output parameters + CUDASplitInfo* cuda_best_split_info) { + __shared__ double shared_gain_buffer[WARPSIZE]; + __shared__ bool shared_found_buffer[WARPSIZE]; + __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; + __shared__ uint32_t best_thread_index; + const double cnt_factor = num_data / sum_hessians; + const double min_gain_shift = parent_gain + min_gain_to_split; + double l2 = lambda_l2; + + double local_gain = min_gain_shift; + bool threshold_found = false; + + cuda_best_split_info->is_valid = false; + + const int bin_start = 1 - task->mfb_offset; + const int bin_end = task->num_bin - task->mfb_offset; + const int threadIdx_x = static_cast(threadIdx.x); + + __shared__ int rand_threshold; + + if (task->is_one_hot) { + if (USE_RAND && threadIdx.x == 0) { + rand_threshold = 0; + if (bin_end > bin_start) { + rand_threshold = cuda_random->NextInt(bin_start, bin_end); + } + } + __syncthreads(); + if (threadIdx_x >= bin_start && threadIdx_x < bin_end) { + const int bin_offset = (threadIdx_x << 1); + const hist_t grad = feature_hist_ptr[bin_offset]; + const hist_t hess = feature_hist_ptr[bin_offset + 1]; + data_size_t cnt = + static_cast(__double2int_rn(hess * cnt_factor)); + if (cnt >= min_data_in_leaf && hess >= min_sum_hessian_in_leaf) { + const data_size_t other_count = num_data - cnt; + if (other_count >= min_data_in_leaf) { + const double sum_other_hessian = sum_hessians - hess - kEpsilon; + if (sum_other_hessian >= min_sum_hessian_in_leaf && (!USE_RAND || static_cast(threadIdx_x) == rand_threshold)) { + const double sum_other_gradient = sum_gradients - grad; + double current_gain = CUDALeafSplits::GetSplitGains( + sum_other_gradient, sum_other_hessian, grad, + hess + kEpsilon, lambda_l1, + l2, path_smooth, other_count, cnt, parent_output); + if (current_gain > min_gain_shift) { + local_gain = current_gain; + threshold_found = true; + } + } + } + } + } + __syncthreads(); + const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); + if (threadIdx_x == 0) { + best_thread_index = result; + } + __syncthreads(); + if (threshold_found && threadIdx_x == best_thread_index) { + cuda_best_split_info->is_valid = true; + cuda_best_split_info->num_cat_threshold = 1; + cuda_best_split_info->gain = local_gain - min_gain_shift; + *(cuda_best_split_info->cat_threshold) = static_cast(threadIdx_x + task->mfb_offset); + cuda_best_split_info->default_left = false; + const int bin_offset = (threadIdx_x << 1); + const hist_t sum_left_gradient = feature_hist_ptr[bin_offset]; + const hist_t sum_left_hessian = feature_hist_ptr[bin_offset + 1]; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, right_output); + } + } else { + __shared__ double shared_value_buffer[NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER]; + __shared__ int16_t shared_index_buffer[NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER]; + __shared__ uint16_t shared_mem_buffer_uint16[WARPSIZE]; + __shared__ double shared_mem_buffer_double[WARPSIZE]; + __shared__ int used_bin; + l2 += cat_l2; + uint16_t is_valid_bin = 0; + int best_dir = 0; + double best_sum_left_gradient = 0.0f; + double best_sum_left_hessian = 0.0f; + if (threadIdx_x >= bin_start && threadIdx_x < bin_end) { + const int bin_offset = (threadIdx_x << 1); + const double hess = feature_hist_ptr[bin_offset + 1]; + if (__double2int_rn(hess * cnt_factor) >= cat_smooth) { + const double grad = feature_hist_ptr[bin_offset]; + shared_value_buffer[threadIdx_x] = grad / (hess + cat_smooth); + is_valid_bin = 1; + } else { + shared_value_buffer[threadIdx_x] = kMaxScore; + } + } else { + shared_value_buffer[threadIdx_x] = kMaxScore; + } + shared_index_buffer[threadIdx_x] = threadIdx_x; + __syncthreads(); + const int local_used_bin = ShuffleReduceSum(is_valid_bin, shared_mem_buffer_uint16, blockDim.x); + if (threadIdx_x == 0) { + used_bin = local_used_bin; + } + __syncthreads(); + BitonicArgSort_1024(shared_value_buffer, shared_index_buffer, bin_end); + __syncthreads(); + const int max_num_cat = min(max_cat_threshold, (used_bin + 1) / 2); + + if (USE_RAND) { + rand_threshold = 0; + const int max_threshold = max(min(max_num_cat, used_bin) - 1, 0); + if (max_threshold > 0) { + rand_threshold = cuda_random->NextInt(0, max_threshold); + } + } + + // left to right + double grad = 0.0f; + double hess = 0.0f; + if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { + const int bin_offset = (shared_index_buffer[threadIdx_x] << 1); + grad = feature_hist_ptr[bin_offset]; + hess = feature_hist_ptr[bin_offset + 1]; + } + if (threadIdx_x == 0) { + hess += kEpsilon; + } + __syncthreads(); + double sum_left_gradient = ShufflePrefixSum(grad, shared_mem_buffer_double); + __syncthreads(); + double sum_left_hessian = ShufflePrefixSum(hess, shared_mem_buffer_double); + if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = num_data - left_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || threadIdx_x == static_cast(rand_threshold))) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > local_gain) { + local_gain = current_gain; + threshold_found = true; + best_dir = 1; + best_sum_left_gradient = sum_left_gradient; + best_sum_left_hessian = sum_left_hessian; + } + } + } + __syncthreads(); + + // right to left + grad = 0.0f; + hess = 0.0f; + if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { + const int bin_offset = (shared_index_buffer[used_bin - 1 - threadIdx_x] << 1); + grad = feature_hist_ptr[bin_offset]; + hess = feature_hist_ptr[bin_offset + 1]; + } + if (threadIdx_x == 0) { + hess += kEpsilon; + } + __syncthreads(); + sum_left_gradient = ShufflePrefixSum(grad, shared_mem_buffer_double); + __syncthreads(); + sum_left_hessian = ShufflePrefixSum(hess, shared_mem_buffer_double); + if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) { + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = num_data - left_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || threadIdx_x == static_cast(rand_threshold))) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > local_gain) { + local_gain = current_gain; + threshold_found = true; + best_dir = -1; + best_sum_left_gradient = sum_left_gradient; + best_sum_left_hessian = sum_left_hessian; + } + } + } + __syncthreads(); + + const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); + if (threadIdx_x == 0) { + best_thread_index = result; + } + __syncthreads(); + if (threshold_found && threadIdx_x == best_thread_index) { + cuda_best_split_info->is_valid = true; + cuda_best_split_info->num_cat_threshold = threadIdx_x + 1; + cuda_best_split_info->gain = local_gain - min_gain_shift; + if (best_dir == 1) { + for (int i = 0; i < threadIdx_x + 1; ++i) { + (cuda_best_split_info->cat_threshold)[i] = shared_index_buffer[i] + task->mfb_offset; + } + } else { + for (int i = 0; i < threadIdx_x + 1; ++i) { + (cuda_best_split_info->cat_threshold)[i] = shared_index_buffer[used_bin - 1 - i] + task->mfb_offset; + } + } + cuda_best_split_info->default_left = false; + const hist_t sum_left_gradient = best_sum_left_gradient; + const hist_t sum_left_hessian = best_sum_left_hessian; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, right_output); + } + } +} + +template +__global__ void FindBestSplitsForLeafKernel( + // input feature information + const int8_t* is_feature_used_bytree, + // input task information + const int num_tasks, + const SplitFindTask* tasks, + CUDARandom* cuda_randoms, + // input leaf information + const CUDALeafSplitsStruct* smaller_leaf_splits, + const CUDALeafSplitsStruct* larger_leaf_splits, + // input config parameter values + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const double cat_smooth, + const double cat_l2, + const int max_cat_threshold, + const int min_data_per_group, + // output + CUDASplitInfo* cuda_best_split_info, + // global num data in leaf + const data_size_t global_num_data_in_smaller_leaf, + const data_size_t global_num_data_in_larger_leaf) { + const unsigned int task_index = blockIdx.x; + const SplitFindTask* task = tasks + task_index; + const int inner_feature_index = task->inner_feature_index; + const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain; + const double sum_gradients = IS_LARGER ? larger_leaf_splits->sum_of_gradients : smaller_leaf_splits->sum_of_gradients; + const double sum_hessians = (IS_LARGER ? larger_leaf_splits->sum_of_hessians : smaller_leaf_splits->sum_of_hessians) + 2 * kEpsilon; + const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf; + const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value; + const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index; + CUDASplitInfo* out = cuda_best_split_info + output_offset; + CUDARandom* cuda_random = USE_RAND ? + (IS_LARGER ? cuda_randoms + task_index * 2 + 1 : cuda_randoms + task_index * 2) : nullptr; + if (is_feature_used_bytree[inner_feature_index]) { + const hist_t* hist_ptr = (IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset * 2; + if (task->is_categorical) { + FindBestSplitsForLeafKernelCategoricalInner( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + cat_smooth, + cat_l2, + max_cat_threshold, + min_data_per_group, + // input parent node information + parent_gain, + sum_gradients, + sum_hessians, + num_data, + parent_output, + // output parameters + out); + } else { + if (!task->reverse) { + FindBestSplitsForLeafKernelInner( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients, + sum_hessians, + num_data, + parent_output, + // output parameters + out); + } else { + FindBestSplitsForLeafKernelInner( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients, + sum_hessians, + num_data, + parent_output, + // output parameters + out); + } + } + } else { + out->is_valid = false; + } +} + + +template +__global__ void FindBestSplitsDiscretizedForLeafKernel( + // input feature information + const int8_t* is_feature_used_bytree, + // input task information + const int num_tasks, + const SplitFindTask* tasks, + CUDARandom* cuda_randoms, + // input leaf information + const CUDALeafSplitsStruct* smaller_leaf_splits, + const CUDALeafSplitsStruct* larger_leaf_splits, + const uint8_t smaller_leaf_num_bits_in_histogram_bin, + const uint8_t larger_leaf_num_bits_in_histogram_bin, + // input config parameter values + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const double cat_smooth, + const double cat_l2, + const int max_cat_threshold, + const int min_data_per_group, + const int max_cat_to_onehot, + // gradient scale + const score_t* grad_scale, + const score_t* hess_scale, + // output + CUDASplitInfo* cuda_best_split_info, + // global num data in leaf + const data_size_t global_num_data_in_smaller_leaf, + const data_size_t global_num_data_in_larger_leaf) { + const unsigned int task_index = blockIdx.x; + const SplitFindTask* task = tasks + task_index; + const int inner_feature_index = task->inner_feature_index; + const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain; + const int64_t sum_gradients_hessians = IS_LARGER ? larger_leaf_splits->sum_of_gradients_hessians : smaller_leaf_splits->sum_of_gradients_hessians; + const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf; + const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value; + const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index; + CUDASplitInfo* out = cuda_best_split_info + output_offset; + CUDARandom* cuda_random = USE_RAND ? + (IS_LARGER ? cuda_randoms + task_index * 2 + 1 : cuda_randoms + task_index * 2) : nullptr; + const bool use_16bit_bin = IS_LARGER ? (larger_leaf_num_bits_in_histogram_bin <= 16) : (smaller_leaf_num_bits_in_histogram_bin <= 16); + if (is_feature_used_bytree[inner_feature_index]) { + if (task->is_categorical) { + __threadfence(); // ensure store issued before trap +#if defined(USE_ROCM) + __builtin_trap(); +#else + asm("trap;"); +#endif + } else { + if (!task->reverse) { + if (use_16bit_bin) { + const int32_t* hist_ptr = + reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; + FindBestSplitsDiscretizedForLeafKernelInner( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients_hessians, + num_data, + parent_output, + // gradient scale + *grad_scale, + *hess_scale, + // output parameters + out); + } else { + const int32_t* hist_ptr = + reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; + FindBestSplitsDiscretizedForLeafKernelInner( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients_hessians, + num_data, + parent_output, + // gradient scale + *grad_scale, + *hess_scale, + // output parameters + out); + } + } else { + if (use_16bit_bin) { + const int32_t* hist_ptr = + reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; + FindBestSplitsDiscretizedForLeafKernelInner( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients_hessians, + num_data, + parent_output, + // gradient scale + *grad_scale, + *hess_scale, + // output parameters + out); + } else { + const int32_t* hist_ptr = + reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset; + FindBestSplitsDiscretizedForLeafKernelInner( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients_hessians, + num_data, + parent_output, + // gradient scale + *grad_scale, + *hess_scale, + // output parameters + out); + } + } + } + } else { + out->is_valid = false; + } +} + +template +__device__ void FindBestSplitsForLeafKernelInner_GlobalMemory( + // input feature information + const hist_t* feature_hist_ptr, + // input task information + const SplitFindTask* task, + CUDARandom* cuda_random, + // input config parameter values + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + // input parent node information + const double parent_gain, + const double sum_gradients, + const double sum_hessians, + const data_size_t num_data, + const double parent_output, + // output parameters + CUDASplitInfo* cuda_best_split_info, + // buffer + hist_t* hist_grad_buffer_ptr, + hist_t* hist_hess_buffer_ptr) { + const double cnt_factor = num_data / sum_hessians; + const double min_gain_shift = parent_gain + min_gain_to_split; + + cuda_best_split_info->is_valid = false; + double local_gain = 0.0f; + bool threshold_found = false; + uint32_t threshold_value = 0; + __shared__ int rand_threshold; + if (USE_RAND && threadIdx.x == 0) { + if (task->num_bin - 2 > 0) { + rand_threshold = cuda_random->NextInt(0, task->num_bin - 2); + } + } + __shared__ uint32_t best_thread_index; + __shared__ double shared_double_buffer[WARPSIZE]; + __shared__ bool shared_found_buffer[WARPSIZE]; + __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; + const unsigned int threadIdx_x = threadIdx.x; + const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset; + if (!REVERSE) { + if (task->na_as_missing && task->mfb_offset == 1) { + uint32_t bin_start = threadIdx_x > 0 ? threadIdx_x : blockDim.x; + hist_t thread_sum_gradients = 0.0f; + hist_t thread_sum_hessians = 0.0f; + for (unsigned int bin = bin_start; bin < static_cast(task->num_bin); bin += blockDim.x) { + const unsigned int bin_offset = (bin - 1) << 1; + const hist_t grad = feature_hist_ptr[bin_offset]; + const hist_t hess = feature_hist_ptr[bin_offset + 1]; + hist_grad_buffer_ptr[bin] = grad; + hist_hess_buffer_ptr[bin] = hess; + thread_sum_gradients += grad; + thread_sum_hessians += hess; + } + const hist_t sum_gradients_non_default = ShuffleReduceSum(thread_sum_gradients, shared_double_buffer, blockDim.x); + __syncthreads(); + const hist_t sum_hessians_non_default = ShuffleReduceSum(thread_sum_hessians, shared_double_buffer, blockDim.x); + if (threadIdx_x == 0) { + hist_grad_buffer_ptr[0] = sum_gradients - sum_gradients_non_default; + hist_hess_buffer_ptr[0] = sum_hessians - sum_hessians_non_default; + } + } else { + for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) { + const bool skip_sum = + (task->skip_default_bin && (bin + task->mfb_offset) == static_cast(task->default_bin)); + if (!skip_sum) { + const unsigned int bin_offset = bin << 1; + hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; + hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; + } else { + hist_grad_buffer_ptr[bin] = 0.0f; + hist_hess_buffer_ptr[bin] = 0.0f; + } + } + } + } else { + for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) { + const bool skip_sum = bin >= static_cast(task->na_as_missing) && + (task->skip_default_bin && (task->num_bin - 1 - bin) == static_cast(task->default_bin)); + if (!skip_sum) { + const unsigned int read_index = feature_num_bin_minus_offset - 1 - bin; + const unsigned int bin_offset = read_index << 1; + hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; + hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; + } else { + hist_grad_buffer_ptr[bin] = 0.0f; + hist_hess_buffer_ptr[bin] = 0.0f; + } + } + } + __syncthreads(); + if (threadIdx_x == 0) { + hist_hess_buffer_ptr[0] += kEpsilon; + } + local_gain = kMinScore; + GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(feature_num_bin_minus_offset)); + __syncthreads(); + GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(feature_num_bin_minus_offset)); + if (REVERSE) { + for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) { + const bool skip_sum = (bin >= static_cast(task->na_as_missing) && + (task->skip_default_bin && (task->num_bin - 1 - bin) == static_cast(task->default_bin))); + if (!skip_sum) { + const double sum_right_gradient = hist_grad_buffer_ptr[bin]; + const double sum_right_hessian = hist_hess_buffer_ptr[bin]; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double sum_left_gradient = sum_gradients - sum_right_gradient; + const double sum_left_hessian = sum_hessians - sum_right_hessian; + const data_size_t left_count = num_data - right_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || static_cast(task->num_bin - 2 - bin) == rand_threshold)) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + lambda_l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_value = static_cast(task->num_bin - 2 - bin); + threshold_found = true; + } + } + } + } + } else { + const uint32_t end = (task->na_as_missing && task->mfb_offset == 1) ? static_cast(task->num_bin - 2) : feature_num_bin_minus_offset - 2; + for (unsigned int bin = threadIdx_x; bin <= end; bin += blockDim.x) { + const bool skip_sum = + (task->skip_default_bin && (bin + task->mfb_offset) == static_cast(task->default_bin)); + if (!skip_sum) { + const double sum_left_gradient = hist_grad_buffer_ptr[bin]; + const double sum_left_hessian = hist_hess_buffer_ptr[bin]; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = num_data - left_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf && + (!USE_RAND || static_cast(bin + task->mfb_offset) == rand_threshold)) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + lambda_l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_value = (task->na_as_missing && task->mfb_offset == 1) ? + bin : static_cast(bin + task->mfb_offset); + threshold_found = true; + } + } + } + } + } + __syncthreads(); + const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_found_buffer, shared_thread_index_buffer); + if (threadIdx_x == 0) { + best_thread_index = result; + } + __syncthreads(); + if (threshold_found && threadIdx_x == best_thread_index) { + cuda_best_split_info->is_valid = true; + cuda_best_split_info->threshold = threshold_value; + cuda_best_split_info->gain = local_gain; + cuda_best_split_info->default_left = task->assume_out_default_left; + if (REVERSE) { + const unsigned int best_bin = static_cast(task->num_bin - 2 - threshold_value); + const double sum_right_gradient = hist_grad_buffer_ptr[best_bin]; + const double sum_right_hessian = hist_hess_buffer_ptr[best_bin] - kEpsilon; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double sum_left_gradient = sum_gradients - sum_right_gradient; + const double sum_left_hessian = sum_hessians - sum_right_hessian - kEpsilon; + const data_size_t left_count = num_data - right_count; + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, right_output); + } else { + const unsigned int best_bin = (task->na_as_missing && task->mfb_offset == 1) ? + threshold_value : static_cast(threshold_value - task->mfb_offset); + const double sum_left_gradient = hist_grad_buffer_ptr[best_bin]; + const double sum_left_hessian = hist_hess_buffer_ptr[best_bin] - kEpsilon; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian - kEpsilon; + const data_size_t right_count = num_data - left_count; + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, lambda_l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, lambda_l2, right_output); + } + } +} + +template +__device__ void FindBestSplitsForLeafKernelCategoricalInner_GlobalMemory( + // input feature information + const hist_t* feature_hist_ptr, + // input task information + const SplitFindTask* task, + CUDARandom* cuda_random, + // input config parameter values + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + const double cat_smooth, + const double cat_l2, + const int max_cat_threshold, + const int min_data_per_group, + // input parent node information + const double parent_gain, + const double sum_gradients, + const double sum_hessians, + const data_size_t num_data, + const double parent_output, + // buffer + hist_t* hist_grad_buffer_ptr, + hist_t* hist_hess_buffer_ptr, + hist_t* hist_stat_buffer_ptr, + data_size_t* hist_index_buffer_ptr, + // output parameters + CUDASplitInfo* cuda_best_split_info) { + __shared__ double shared_gain_buffer[WARPSIZE]; + __shared__ bool shared_found_buffer[WARPSIZE]; + __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; + __shared__ uint32_t best_thread_index; + const double cnt_factor = num_data / sum_hessians; + const double min_gain_shift = parent_gain + min_gain_to_split; + double l2 = lambda_l2; + + double local_gain = kMinScore; + bool threshold_found = false; + + cuda_best_split_info->is_valid = false; + + __shared__ int rand_threshold; + + const int bin_start = 1 - task->mfb_offset; + const int bin_end = task->num_bin - task->mfb_offset; + int best_threshold = -1; + const int threadIdx_x = static_cast(threadIdx.x); + if (task->is_one_hot) { + if (USE_RAND && threadIdx.x == 0) { + rand_threshold = 0; + if (bin_end > bin_start) { + rand_threshold = cuda_random->NextInt(bin_start, bin_end); + } + } + __syncthreads(); + for (int bin = bin_start + threadIdx_x; bin < bin_end; bin += static_cast(blockDim.x)) { + const int bin_offset = (bin << 1); + const hist_t grad = feature_hist_ptr[bin_offset]; + const hist_t hess = feature_hist_ptr[bin_offset + 1]; + data_size_t cnt = + static_cast(__double2int_rn(hess * cnt_factor)); + if (cnt >= min_data_in_leaf && hess >= min_sum_hessian_in_leaf) { + const data_size_t other_count = num_data - cnt; + if (other_count >= min_data_in_leaf) { + const double sum_other_hessian = sum_hessians - hess - kEpsilon; + if (sum_other_hessian >= min_sum_hessian_in_leaf && (!USE_RAND || bin == rand_threshold)) { + const double sum_other_gradient = sum_gradients - grad; + double current_gain = CUDALeafSplits::GetSplitGains( + sum_other_gradient, sum_other_hessian, grad, + hess + kEpsilon, lambda_l1, + l2, path_smooth, other_count, cnt, parent_output); + if (current_gain > min_gain_shift) { + best_threshold = bin; + local_gain = current_gain - min_gain_shift; + threshold_found = true; + } + } + } + } + } + __syncthreads(); + const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); + if (threadIdx_x == 0) { + best_thread_index = result; + } + __syncthreads(); + if (threshold_found && threadIdx_x == best_thread_index) { + cuda_best_split_info->is_valid = true; + cuda_best_split_info->num_cat_threshold = 1; + cuda_best_split_info->cat_threshold = new uint32_t[1]; + *(cuda_best_split_info->cat_threshold) = static_cast(best_threshold); + cuda_best_split_info->default_left = false; + const int bin_offset = (best_threshold << 1); + const hist_t sum_left_gradient = feature_hist_ptr[bin_offset]; + const hist_t sum_left_hessian = feature_hist_ptr[bin_offset + 1]; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, right_output); + } + } else { + __shared__ uint16_t shared_mem_buffer_uint16[WARPSIZE]; + __shared__ int used_bin; + l2 += cat_l2; + uint16_t is_valid_bin = 0; + int best_dir = 0; + double best_sum_left_gradient = 0.0f; + double best_sum_left_hessian = 0.0f; + for (int bin = 0; bin < bin_end; bin += static_cast(blockDim.x)) { + if (bin >= bin_start) { + const int bin_offset = (bin << 1); + const double hess = feature_hist_ptr[bin_offset + 1]; + if (__double2int_rn(hess * cnt_factor) >= cat_smooth) { + const double grad = feature_hist_ptr[bin_offset]; + hist_stat_buffer_ptr[bin] = grad / (hess + cat_smooth); + hist_index_buffer_ptr[bin] = threadIdx_x; + is_valid_bin = 1; + } else { + hist_stat_buffer_ptr[bin] = kMaxScore; + hist_index_buffer_ptr[bin] = -1; + } + } + } + __syncthreads(); + const int local_used_bin = ShuffleReduceSum(is_valid_bin, shared_mem_buffer_uint16, blockDim.x); + if (threadIdx_x == 0) { + used_bin = local_used_bin; + } + __syncthreads(); + BitonicArgSortDevice( + hist_stat_buffer_ptr, hist_index_buffer_ptr, task->num_bin - task->mfb_offset); + const int max_num_cat = min(max_cat_threshold, (used_bin + 1) / 2); + if (USE_RAND) { + rand_threshold = 0; + const int max_threshold = max(min(max_num_cat, used_bin) - 1, 0); + if (max_threshold > 0) { + rand_threshold = cuda_random->NextInt(0, max_threshold); + } + } + __syncthreads(); + + // left to right + for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { + const int bin_offset = (hist_index_buffer_ptr[bin] << 1); + hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; + hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; + } + if (threadIdx_x == 0) { + hist_hess_buffer_ptr[0] += kEpsilon; + } + __syncthreads(); + GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(bin_end)); + __syncthreads(); + GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(bin_end)); + for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { + const double sum_left_gradient = hist_grad_buffer_ptr[bin]; + const double sum_left_hessian = hist_hess_buffer_ptr[bin]; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = num_data - left_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_found = true; + best_dir = 1; + best_sum_left_gradient = sum_left_gradient; + best_sum_left_hessian = sum_left_hessian; + best_threshold = bin; + } + } + } + __syncthreads(); + + // right to left + for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { + const int bin_offset = (hist_index_buffer_ptr[used_bin - 1 - bin] << 1); + hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset]; + hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1]; + } + if (threadIdx_x == 0) { + hist_hess_buffer_ptr[0] += kEpsilon; + } + __syncthreads(); + GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(bin_end)); + __syncthreads(); + GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(bin_end)); + for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) { + const double sum_left_gradient = hist_grad_buffer_ptr[bin]; + const double sum_left_hessian = hist_hess_buffer_ptr[bin]; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = num_data - left_count; + if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf && + sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf) { + double current_gain = CUDALeafSplits::GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, lambda_l1, + l2, path_smooth, left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain > min_gain_shift) { + local_gain = current_gain - min_gain_shift; + threshold_found = true; + best_dir = -1; + best_sum_left_gradient = sum_left_gradient; + best_sum_left_hessian = sum_left_hessian; + best_threshold = bin; + } + } + } + __syncthreads(); + + const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); + if (threadIdx_x == 0) { + best_thread_index = result; + } + __syncthreads(); + if (threshold_found && threadIdx_x == best_thread_index) { + cuda_best_split_info->is_valid = true; + cuda_best_split_info->num_cat_threshold = best_threshold + 1; + cuda_best_split_info->cat_threshold = new uint32_t[best_threshold + 1]; + cuda_best_split_info->gain = local_gain; + if (best_dir == 1) { + for (int i = 0; i < best_threshold + 1; ++i) { + (cuda_best_split_info->cat_threshold)[i] = hist_index_buffer_ptr[i] + task->mfb_offset; + } + } else { + for (int i = 0; i < best_threshold + 1; ++i) { + (cuda_best_split_info->cat_threshold)[i] = hist_index_buffer_ptr[used_bin - 1 - i] + task->mfb_offset; + } + } + cuda_best_split_info->default_left = false; + const hist_t sum_left_gradient = best_sum_left_gradient; + const hist_t sum_left_hessian = best_sum_left_hessian; + const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor)); + const double sum_right_gradient = sum_gradients - sum_left_gradient; + const double sum_right_hessian = sum_hessians - sum_left_hessian; + const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor)); + const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output); + const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output); + cuda_best_split_info->left_sum_gradients = sum_left_gradient; + cuda_best_split_info->left_sum_hessians = sum_left_hessian; + cuda_best_split_info->left_count = left_count; + cuda_best_split_info->right_sum_gradients = sum_right_gradient; + cuda_best_split_info->right_sum_hessians = sum_right_hessian; + cuda_best_split_info->right_count = right_count; + cuda_best_split_info->left_value = left_output; + cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient, + sum_left_hessian, lambda_l1, l2, left_output); + cuda_best_split_info->right_value = right_output; + cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient, + sum_right_hessian, lambda_l1, l2, right_output); + } + } +} + +template +__global__ void FindBestSplitsForLeafKernel_GlobalMemory( + // input feature information + const int8_t* is_feature_used_bytree, + // input task information + const int num_tasks, + const SplitFindTask* tasks, + CUDARandom* cuda_randoms, + // input leaf information + const CUDALeafSplitsStruct* smaller_leaf_splits, + const CUDALeafSplitsStruct* larger_leaf_splits, + // input config parameter values + const data_size_t min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const double min_gain_to_split, + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const double cat_smooth, + const double cat_l2, + const int max_cat_threshold, + const int min_data_per_group, + // output + CUDASplitInfo* cuda_best_split_info, + // global num data in leaf + const data_size_t global_num_data_in_smaller_leaf, + const data_size_t global_num_data_in_larger_leaf, + // buffer + hist_t* feature_hist_grad_buffer, + hist_t* feature_hist_hess_buffer, + hist_t* feature_hist_stat_buffer, + data_size_t* feature_hist_index_buffer) { + const unsigned int task_index = blockIdx.x; + const SplitFindTask* task = tasks + task_index; + const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain; + const double sum_gradients = IS_LARGER ? larger_leaf_splits->sum_of_gradients : smaller_leaf_splits->sum_of_gradients; + const double sum_hessians = (IS_LARGER ? larger_leaf_splits->sum_of_hessians : smaller_leaf_splits->sum_of_hessians) + 2 * kEpsilon; + const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf; + const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value; + const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index; + CUDASplitInfo* out = cuda_best_split_info + output_offset; + CUDARandom* cuda_random = USE_RAND ? + (IS_LARGER ? cuda_randoms + task_index * 2 + 1: cuda_randoms + task_index * 2) : nullptr; + if (is_feature_used_bytree[task->inner_feature_index]) { + const uint32_t hist_offset = task->hist_offset; + const hist_t* hist_ptr = (IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + hist_offset * 2; + hist_t* hist_grad_buffer_ptr = feature_hist_grad_buffer + hist_offset * 2; + hist_t* hist_hess_buffer_ptr = feature_hist_hess_buffer + hist_offset * 2; + hist_t* hist_stat_buffer_ptr = feature_hist_stat_buffer + hist_offset * 2; + data_size_t* hist_index_buffer_ptr = feature_hist_index_buffer + hist_offset * 2; + if (task->is_categorical) { + FindBestSplitsForLeafKernelCategoricalInner_GlobalMemory( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + cat_smooth, + cat_l2, + max_cat_threshold, + min_data_per_group, + // input parent node information + parent_gain, + sum_gradients, + sum_hessians, + num_data, + parent_output, + // buffer + hist_grad_buffer_ptr, + hist_hess_buffer_ptr, + hist_stat_buffer_ptr, + hist_index_buffer_ptr, + // output parameters + out); + } else { + if (!task->reverse) { + FindBestSplitsForLeafKernelInner_GlobalMemory( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients, + sum_hessians, + num_data, + parent_output, + // output parameters + out, + // buffer + hist_grad_buffer_ptr, + hist_hess_buffer_ptr); + } else { + FindBestSplitsForLeafKernelInner_GlobalMemory( + // input feature information + hist_ptr, + // input task information + task, + cuda_random, + // input config parameter values + lambda_l1, + lambda_l2, + path_smooth, + min_data_in_leaf, + min_sum_hessian_in_leaf, + min_gain_to_split, + // input parent node information + parent_gain, + sum_gradients, + sum_hessians, + num_data, + parent_output, + // output parameters + out, + // buffer + hist_grad_buffer_ptr, + hist_hess_buffer_ptr); + } + } + } else { + out->is_valid = false; + } +} + +#define LaunchFindBestSplitsForLeafKernel_PARAMS \ + const CUDALeafSplitsStruct* smaller_leaf_splits, \ + const CUDALeafSplitsStruct* larger_leaf_splits, \ + const int smaller_leaf_index, \ + const int larger_leaf_index, \ + const bool is_smaller_leaf_valid, \ + const bool is_larger_leaf_valid, \ + const data_size_t global_num_data_in_smaller_leaf, \ + const data_size_t global_num_data_in_larger_leaf + +#define LaunchFindBestSplitsForLeafKernel_ARGS \ + smaller_leaf_splits, \ + larger_leaf_splits, \ + smaller_leaf_index, \ + larger_leaf_index, \ + is_smaller_leaf_valid, \ + is_larger_leaf_valid, \ + global_num_data_in_smaller_leaf, \ + global_num_data_in_larger_leaf + +#define FindBestSplitsForLeafKernel_ARGS \ + num_tasks_, \ + cuda_split_find_tasks_.RawData(), \ + cuda_randoms_.RawData(), \ + smaller_leaf_splits, \ + larger_leaf_splits, \ + min_data_in_leaf_, \ + min_sum_hessian_in_leaf_, \ + min_gain_to_split_, \ + lambda_l1_, \ + lambda_l2_, \ + path_smooth_, \ + cat_smooth_, \ + cat_l2_, \ + max_cat_threshold_, \ + min_data_per_group_, \ + cuda_best_split_info_.RawData(), \ + global_num_data_in_smaller_leaf, \ + global_num_data_in_larger_leaf + +#define GlobalMemory_Buffer_ARGS \ + cuda_feature_hist_grad_buffer_.RawData(), \ + cuda_feature_hist_hess_buffer_.RawData(), \ + cuda_feature_hist_stat_buffer_.RawData(), \ + cuda_feature_hist_index_buffer_.RawData() + +void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernel(LaunchFindBestSplitsForLeafKernel_PARAMS) { + if (!is_smaller_leaf_valid && !is_larger_leaf_valid) { + return; + } + if (!extra_trees_) { + LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_ARGS); + } else { + LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_ARGS); + } +} + +template +void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_PARAMS) { + if (lambda_l1_ <= 0.0f) { + LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_ARGS); + } else { + LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_ARGS); + } +} + +template +void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_PARAMS) { + if (!use_smoothing_) { + LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_ARGS); + } else { + LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_ARGS); + } +} + +template +void CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_PARAMS) { + const int8_t* is_feature_used_by_smaller_node = cuda_is_feature_used_bytree_.RawData(); + const int8_t* is_feature_used_by_larger_node = cuda_is_feature_used_bytree_.RawData(); + if (select_features_by_node_) { + is_feature_used_by_smaller_node = is_feature_used_by_smaller_node_.RawData(); + is_feature_used_by_larger_node = is_feature_used_by_larger_node_.RawData(); + } + if (!use_global_memory_) { + if (is_smaller_leaf_valid) { + FindBestSplitsForLeafKernel + <<>> + (is_feature_used_by_smaller_node, FindBestSplitsForLeafKernel_ARGS); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + if (is_larger_leaf_valid) { + FindBestSplitsForLeafKernel + <<>> + (is_feature_used_by_larger_node, FindBestSplitsForLeafKernel_ARGS); + } + } else { + if (is_smaller_leaf_valid) { + FindBestSplitsForLeafKernel_GlobalMemory + <<>> + (is_feature_used_by_smaller_node, FindBestSplitsForLeafKernel_ARGS, GlobalMemory_Buffer_ARGS); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + if (is_larger_leaf_valid) { + FindBestSplitsForLeafKernel_GlobalMemory + <<>> + (is_feature_used_by_larger_node, FindBestSplitsForLeafKernel_ARGS, GlobalMemory_Buffer_ARGS); + } + } +} + +#undef LaunchFindBestSplitsForLeafKernel_PARAMS +#undef FindBestSplitsForLeafKernel_ARGS +#undef GlobalMemory_Buffer_ARGS + + +#define LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS \ + const CUDALeafSplitsStruct* smaller_leaf_splits, \ + const CUDALeafSplitsStruct* larger_leaf_splits, \ + const int smaller_leaf_index, \ + const int larger_leaf_index, \ + const bool is_smaller_leaf_valid, \ + const bool is_larger_leaf_valid, \ + const score_t* grad_scale, \ + const score_t* hess_scale, \ + const uint8_t smaller_num_bits_in_histogram_bins, \ + const uint8_t larger_num_bits_in_histogram_bins, \ + const data_size_t global_num_data_in_smaller_leaf, \ + const data_size_t global_num_data_in_larger_leaf + +#define LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS \ + smaller_leaf_splits, \ + larger_leaf_splits, \ + smaller_leaf_index, \ + larger_leaf_index, \ + is_smaller_leaf_valid, \ + is_larger_leaf_valid, \ + grad_scale, \ + hess_scale, \ + smaller_num_bits_in_histogram_bins, \ + larger_num_bits_in_histogram_bins, \ + global_num_data_in_smaller_leaf, \ + global_num_data_in_larger_leaf + +#define FindBestSplitsDiscretizedForLeafKernel_ARGS \ + cuda_is_feature_used_bytree_.RawData(), \ + num_tasks_, \ + cuda_split_find_tasks_.RawData(), \ + cuda_randoms_.RawData(), \ + smaller_leaf_splits, \ + larger_leaf_splits, \ + smaller_num_bits_in_histogram_bins, \ + larger_num_bits_in_histogram_bins, \ + min_data_in_leaf_, \ + min_sum_hessian_in_leaf_, \ + min_gain_to_split_, \ + lambda_l1_, \ + lambda_l2_, \ + path_smooth_, \ + cat_smooth_, \ + cat_l2_, \ + max_cat_threshold_, \ + min_data_per_group_, \ + max_cat_to_onehot_, \ + grad_scale, \ + hess_scale, \ + cuda_best_split_info_.RawData(), \ + global_num_data_in_smaller_leaf, \ + global_num_data_in_larger_leaf + +void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernel(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { + if (!is_smaller_leaf_valid && !is_larger_leaf_valid) { + return; + } + if (!extra_trees_) { + LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); + } else { + LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); + } +} + +template +void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { + if (lambda_l1_ <= 0.0f) { + LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); + } else { + LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); + } +} + +template +void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { + if (!use_smoothing_) { + LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); + } else { + LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS); + } +} + +template +void CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) { + if (!use_global_memory_) { + if (is_smaller_leaf_valid) { + FindBestSplitsDiscretizedForLeafKernel + <<>> + (FindBestSplitsDiscretizedForLeafKernel_ARGS); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + if (is_larger_leaf_valid) { + FindBestSplitsDiscretizedForLeafKernel + <<>> + (FindBestSplitsDiscretizedForLeafKernel_ARGS); + } + } else { + // TODO(shiyu1994) + } +} + +#undef LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS +#undef LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS +#undef FindBestSplitsDiscretizedForLeafKernel_ARGS + + +__device__ void ReduceBestSplit(bool* found, double* gain, uint32_t* shared_read_index, + uint32_t num_features_aligned) { + const uint32_t threadIdx_x = threadIdx.x; + for (unsigned int s = 1; s < num_features_aligned; s <<= 1) { + if (threadIdx_x % (2 * s) == 0 && (threadIdx_x + s) < num_features_aligned) { + const uint32_t pos_to_compare = threadIdx_x + s; + if ((!found[threadIdx_x] && found[pos_to_compare]) || + (found[threadIdx_x] && found[pos_to_compare] && gain[threadIdx_x] < gain[pos_to_compare])) { + found[threadIdx_x] = found[pos_to_compare]; + gain[threadIdx_x] = gain[pos_to_compare]; + shared_read_index[threadIdx_x] = shared_read_index[pos_to_compare]; + } + } + __syncthreads(); + } +} + +__global__ void SyncBestSplitForLeafKernel(const int smaller_leaf_index, const int larger_leaf_index, + CUDASplitInfo* cuda_leaf_best_split_info, + // input parameters + const SplitFindTask* tasks, + const CUDASplitInfo* cuda_best_split_info, + const int num_tasks, + const int num_tasks_aligned, + const int num_blocks_per_leaf, + const bool larger_only, + const int num_leaves) { + __shared__ double shared_gain_buffer[WARPSIZE]; + __shared__ bool shared_found_buffer[WARPSIZE]; + __shared__ uint32_t shared_thread_index_buffer[WARPSIZE]; + const uint32_t threadIdx_x = threadIdx.x; + const uint32_t blockIdx_x = blockIdx.x; + + bool best_found = false; + double best_gain = kMinScore; + uint32_t shared_read_index = 0; + + const bool is_smaller = (blockIdx_x < static_cast(num_blocks_per_leaf) && !larger_only); + const uint32_t leaf_block_index = (is_smaller || larger_only) ? blockIdx_x : (blockIdx_x - static_cast(num_blocks_per_leaf)); + const int task_index = static_cast(leaf_block_index * blockDim.x + threadIdx_x); + const uint32_t read_index = is_smaller ? static_cast(task_index) : static_cast(task_index + num_tasks); + if (task_index < num_tasks) { + best_found = cuda_best_split_info[read_index].is_valid; + best_gain = cuda_best_split_info[read_index].gain; + shared_read_index = read_index; + } else { + best_found = false; + } + + __syncthreads(); + const uint32_t best_read_index = ReduceBestGain(best_gain, best_found, shared_read_index, + shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer); + if (threadIdx.x == 0) { + const int leaf_index_ref = is_smaller ? smaller_leaf_index : larger_leaf_index; + const unsigned buffer_write_pos = static_cast(leaf_index_ref) + leaf_block_index * num_leaves; + CUDASplitInfo* cuda_split_info = cuda_leaf_best_split_info + buffer_write_pos; + const CUDASplitInfo* best_split_info = cuda_best_split_info + best_read_index; + if (best_split_info->is_valid) { + *cuda_split_info = *best_split_info; + cuda_split_info->inner_feature_index = is_smaller ? tasks[best_read_index].inner_feature_index : + tasks[static_cast(best_read_index) - num_tasks].inner_feature_index; + cuda_split_info->is_valid = true; + } else { + cuda_split_info->gain = kMinScore; + cuda_split_info->is_valid = false; + } + } +} + +__global__ void SyncBestSplitForLeafKernelAllBlocks( + const int smaller_leaf_index, + const int larger_leaf_index, + const unsigned int num_blocks_per_leaf, + const int num_leaves, + CUDASplitInfo* cuda_leaf_best_split_info, + const bool larger_only) { + if (!larger_only) { + if (blockIdx.x == 0) { + CUDASplitInfo* smaller_leaf_split_info = cuda_leaf_best_split_info + smaller_leaf_index; + for (unsigned int block_index = 1; block_index < num_blocks_per_leaf; ++block_index) { + const unsigned int leaf_read_pos = static_cast(smaller_leaf_index) + block_index * static_cast(num_leaves); + const CUDASplitInfo* other_split_info = cuda_leaf_best_split_info + leaf_read_pos; + if ((other_split_info->is_valid && smaller_leaf_split_info->is_valid && + other_split_info->gain > smaller_leaf_split_info->gain) || + (!smaller_leaf_split_info->is_valid && other_split_info->is_valid)) { + *smaller_leaf_split_info = *other_split_info; + } + } + } + } + if (larger_leaf_index >= 0) { + if (blockIdx.x == 1 || larger_only) { + CUDASplitInfo* larger_leaf_split_info = cuda_leaf_best_split_info + larger_leaf_index; + for (unsigned int block_index = 1; block_index < num_blocks_per_leaf; ++block_index) { + const unsigned int leaf_read_pos = static_cast(larger_leaf_index) + block_index * static_cast(num_leaves); + const CUDASplitInfo* other_split_info = cuda_leaf_best_split_info + leaf_read_pos; + if ((other_split_info->is_valid && larger_leaf_split_info->is_valid && + other_split_info->gain > larger_leaf_split_info->gain) || + (!larger_leaf_split_info->is_valid && other_split_info->is_valid)) { + *larger_leaf_split_info = *other_split_info; + } + } + } + } +} + +__global__ void SetInvalidLeafSplitInfoKernel( + CUDASplitInfo* cuda_leaf_best_split_info, + const bool is_smaller_leaf_valid, + const bool is_larger_leaf_valid, + const int smaller_leaf_index, + const int larger_leaf_index) { + if (!is_smaller_leaf_valid) { + cuda_leaf_best_split_info[smaller_leaf_index].is_valid = false; + } + if (!is_larger_leaf_valid && larger_leaf_index >= 0) { + cuda_leaf_best_split_info[larger_leaf_index].is_valid = false; + } +} + +void CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel( + const int host_smaller_leaf_index, + const int host_larger_leaf_index, + const bool is_smaller_leaf_valid, + const bool is_larger_leaf_valid) { + if (!is_smaller_leaf_valid || !is_larger_leaf_valid) { + SetInvalidLeafSplitInfoKernel<<<1, 1>>>( + cuda_leaf_best_split_info_.RawData(), + is_smaller_leaf_valid, is_larger_leaf_valid, + host_smaller_leaf_index, host_larger_leaf_index); + } + if (!is_smaller_leaf_valid && !is_larger_leaf_valid) { + return; + } + int num_tasks = num_tasks_; + int num_tasks_aligned = 1; + num_tasks -= 1; + while (num_tasks > 0) { + num_tasks_aligned <<= 1; + num_tasks >>= 1; + } + const int num_blocks_per_leaf = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK; + if (host_larger_leaf_index >= 0 && is_smaller_leaf_valid && is_larger_leaf_valid) { + SyncBestSplitForLeafKernel<<>>( + host_smaller_leaf_index, + host_larger_leaf_index, + cuda_leaf_best_split_info_.RawData(), + cuda_split_find_tasks_.RawData(), + cuda_best_split_info_.RawData(), + num_tasks_, + num_tasks_aligned, + num_blocks_per_leaf, + false, + num_leaves_); + if (num_blocks_per_leaf > 1) { + SyncBestSplitForLeafKernelAllBlocks<<<1, 1, 0, cuda_streams_[0]>>>( + host_smaller_leaf_index, + host_larger_leaf_index, + num_blocks_per_leaf, + num_leaves_, + cuda_leaf_best_split_info_.RawData(), + false); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + SyncBestSplitForLeafKernel<<>>( + host_smaller_leaf_index, + host_larger_leaf_index, + cuda_leaf_best_split_info_.RawData(), + cuda_split_find_tasks_.RawData(), + cuda_best_split_info_.RawData(), + num_tasks_, + num_tasks_aligned, + num_blocks_per_leaf, + true, + num_leaves_); + if (num_blocks_per_leaf > 1) { + SyncBestSplitForLeafKernelAllBlocks<<<1, 1, 0, cuda_streams_[1]>>>( + host_smaller_leaf_index, + host_larger_leaf_index, + num_blocks_per_leaf, + num_leaves_, + cuda_leaf_best_split_info_.RawData(), + true); + } + } else { + const bool larger_only = (!is_smaller_leaf_valid && is_larger_leaf_valid); + SyncBestSplitForLeafKernel<<>>( + host_smaller_leaf_index, + host_larger_leaf_index, + cuda_leaf_best_split_info_.RawData(), + cuda_split_find_tasks_.RawData(), + cuda_best_split_info_.RawData(), + num_tasks_, + num_tasks_aligned, + num_blocks_per_leaf, + larger_only, + num_leaves_); + if (num_blocks_per_leaf > 1) { + SynchronizeCUDADevice(__FILE__, __LINE__); + SyncBestSplitForLeafKernelAllBlocks<<<1, 1>>>( + host_smaller_leaf_index, + host_larger_leaf_index, + num_blocks_per_leaf, + num_leaves_, + cuda_leaf_best_split_info_.RawData(), + larger_only); + } + } +} + +__global__ void FindBestFromAllSplitsKernel(const int cur_num_leaves, + CUDASplitInfo* cuda_leaf_best_split_info, + int* cuda_best_split_info_buffer) { + __shared__ double gain_shared_buffer[WARPSIZE]; + __shared__ int leaf_index_shared_buffer[WARPSIZE]; + double thread_best_gain = kMinScore; + int thread_best_leaf_index = -1; + const int threadIdx_x = static_cast(threadIdx.x); + for (int leaf_index = threadIdx_x; leaf_index < cur_num_leaves; leaf_index += static_cast(blockDim.x)) { + const double leaf_best_gain = cuda_leaf_best_split_info[leaf_index].gain; + if (cuda_leaf_best_split_info[leaf_index].is_valid && leaf_best_gain > thread_best_gain) { + thread_best_gain = leaf_best_gain; + thread_best_leaf_index = leaf_index; + } + } + const int best_leaf_index = ReduceBestGainForLeaves(thread_best_gain, thread_best_leaf_index, gain_shared_buffer, leaf_index_shared_buffer); + if (threadIdx_x == 0) { + cuda_best_split_info_buffer[6] = best_leaf_index; + if (best_leaf_index != -1) { + cuda_leaf_best_split_info[best_leaf_index].is_valid = false; + cuda_leaf_best_split_info[cur_num_leaves].is_valid = false; + cuda_best_split_info_buffer[7] = cuda_leaf_best_split_info[best_leaf_index].num_cat_threshold; + } + } +} + +__global__ void PrepareLeafBestSplitInfo(const int smaller_leaf_index, const int larger_leaf_index, + int* cuda_best_split_info_buffer, + const CUDASplitInfo* cuda_leaf_best_split_info) { + const unsigned int threadIdx_x = blockIdx.x; + if (threadIdx_x == 0) { + cuda_best_split_info_buffer[0] = cuda_leaf_best_split_info[smaller_leaf_index].inner_feature_index; + } else if (threadIdx_x == 1) { + cuda_best_split_info_buffer[1] = cuda_leaf_best_split_info[smaller_leaf_index].threshold; + } else if (threadIdx_x == 2) { + cuda_best_split_info_buffer[2] = cuda_leaf_best_split_info[smaller_leaf_index].default_left; + } + if (larger_leaf_index >= 0) { + if (threadIdx_x == 3) { + cuda_best_split_info_buffer[3] = cuda_leaf_best_split_info[larger_leaf_index].inner_feature_index; + } else if (threadIdx_x == 4) { + cuda_best_split_info_buffer[4] = cuda_leaf_best_split_info[larger_leaf_index].threshold; + } else if (threadIdx_x == 5) { + cuda_best_split_info_buffer[5] = cuda_leaf_best_split_info[larger_leaf_index].default_left; + } + } +} + +void CUDABestSplitFinder::LaunchFindBestFromAllSplitsKernel( + const int cur_num_leaves, + const int smaller_leaf_index, const int larger_leaf_index, + int* smaller_leaf_best_split_feature, + uint32_t* smaller_leaf_best_split_threshold, + uint8_t* smaller_leaf_best_split_default_left, + int* larger_leaf_best_split_feature, + uint32_t* larger_leaf_best_split_threshold, + uint8_t* larger_leaf_best_split_default_left, + int* best_leaf_index, + int* num_cat_threshold) { + FindBestFromAllSplitsKernel<<<1, NUM_THREADS_FIND_BEST_LEAF, 0, cuda_streams_[1]>>>(cur_num_leaves, + cuda_leaf_best_split_info_.RawData(), + cuda_best_split_info_buffer_.RawData()); + PrepareLeafBestSplitInfo<<<6, 1, 0, cuda_streams_[0]>>>(smaller_leaf_index, larger_leaf_index, + cuda_best_split_info_buffer_.RawData(), + cuda_leaf_best_split_info_.RawData()); + std::vector host_leaf_best_split_info_buffer(8, 0); + SynchronizeCUDADevice(__FILE__, __LINE__); + CopyFromCUDADeviceToHost(host_leaf_best_split_info_buffer.data(), cuda_best_split_info_buffer_.RawData(), 8, __FILE__, __LINE__); + *smaller_leaf_best_split_feature = host_leaf_best_split_info_buffer[0]; + *smaller_leaf_best_split_threshold = static_cast(host_leaf_best_split_info_buffer[1]); + *smaller_leaf_best_split_default_left = static_cast(host_leaf_best_split_info_buffer[2]); + if (larger_leaf_index >= 0) { + *larger_leaf_best_split_feature = host_leaf_best_split_info_buffer[3]; + *larger_leaf_best_split_threshold = static_cast(host_leaf_best_split_info_buffer[4]); + *larger_leaf_best_split_default_left = static_cast(host_leaf_best_split_info_buffer[5]); + } + *best_leaf_index = host_leaf_best_split_info_buffer[6]; + *num_cat_threshold = host_leaf_best_split_info_buffer[7]; +} + +__global__ void AllocateCatVectorsKernel( + CUDASplitInfo* cuda_split_infos, size_t len, + const int max_num_categories_in_split, + const bool has_categorical_feature, + uint32_t* cat_threshold_vec, + int* cat_threshold_real_vec) { + const size_t i = threadIdx.x + blockIdx.x * blockDim.x; + if (i < len) { + if (has_categorical_feature) { + cuda_split_infos[i].cat_threshold = cat_threshold_vec + i * max_num_categories_in_split; + cuda_split_infos[i].cat_threshold_real = cat_threshold_real_vec + i * max_num_categories_in_split; + cuda_split_infos[i].num_cat_threshold = 0; + } else { + cuda_split_infos[i].cat_threshold = nullptr; + cuda_split_infos[i].cat_threshold_real = nullptr; + cuda_split_infos[i].num_cat_threshold = 0; + } + } +} + +void CUDABestSplitFinder::LaunchAllocateCatVectorsKernel( + CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len) { + const int num_blocks = (static_cast(len) + NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER - 1) / NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER; + AllocateCatVectorsKernel<<>>( + cuda_split_infos, len, max_num_categories_in_split_, has_categorical_feature_, cat_threshold_vec, cat_threshold_real_vec); +} + +__global__ void InitCUDARandomKernel( + const int seed, + const int num_tasks, + CUDARandom* cuda_randoms) { + const int task_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (task_index < num_tasks) { + cuda_randoms[task_index].SetSeed(seed + task_index); + } +} + +void CUDABestSplitFinder::LaunchInitCUDARandomKernel() { + const int num_blocks = (static_cast(cuda_randoms_.Size()) + + NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER - 1) / NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER; + InitCUDARandomKernel<<>>(extra_seed_, + static_cast(cuda_randoms_.Size()), cuda_randoms_.RawData()); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_best_split_finder.hpp b/src/treelearner/cuda/cuda_best_split_finder.hpp new file mode 100644 index 0000000..4508f04 --- /dev/null +++ b/src/treelearner/cuda/cuda_best_split_finder.hpp @@ -0,0 +1,249 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_BEST_SPLIT_FINDER_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_BEST_SPLIT_FINDER_HPP_ + +#ifdef USE_CUDA + +#include +#include + +#include + +#include +#include + +#include "cuda_leaf_splits.hpp" + +#define NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER (256) +#define NUM_THREADS_FIND_BEST_LEAF (256) +#define NUM_TASKS_PER_SYNC_BLOCK (1024) + +namespace LightGBM { + +struct SplitFindTask { + int inner_feature_index; + bool reverse; + bool skip_default_bin; + bool na_as_missing; + bool assume_out_default_left; + bool is_categorical; + bool is_one_hot; + uint32_t hist_offset; + uint8_t mfb_offset; + uint32_t num_bin; + uint32_t default_bin; + int rand_threshold; +}; + +class CUDABestSplitFinder { + public: + CUDABestSplitFinder( + const hist_t* cuda_hist, + const Dataset* train_data, + const std::vector& feature_hist_offsets, + const bool select_features_by_node, + const Config* config); + + ~CUDABestSplitFinder(); + + void InitFeatureMetaInfo(const Dataset* train_data); + + void Init(); + + void InitCUDAFeatureMetaInfo(); + + void BeforeTrain(const std::vector& is_feature_used_bytree); + + void FindBestSplitsForLeaf( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const CUDALeafSplitsStruct* larger_leaf_splits, + const int smaller_leaf_index, + const int larger_leaf_index, + const data_size_t num_data_in_smaller_leaf, + const data_size_t num_data_in_larger_leaf, + const double sum_hessians_in_smaller_leaf, + const double sum_hessians_in_larger_leaf, + const score_t* grad_scale, + const score_t* hess_scale, + const uint8_t smaller_num_bits_in_histogram_bins, + const uint8_t larger_num_bits_in_histogram_bins); + + const CUDASplitInfo* FindBestFromAllSplits( + const int cur_num_leaves, + const int smaller_leaf_index, + const int larger_leaf_index, + int* smaller_leaf_best_split_feature, + uint32_t* smaller_leaf_best_split_threshold, + uint8_t* smaller_leaf_best_split_default_left, + int* larger_leaf_best_split_feature, + uint32_t* larger_leaf_best_split_threshold, + uint8_t* larger_leaf_best_split_default_left, + int* best_leaf_index, + int* num_cat_threshold); + + void ResetTrainingData( + const hist_t* cuda_hist, + const Dataset* train_data, + const std::vector& feature_hist_offsets); + + void ResetConfig(const Config* config, const hist_t* cuda_hist); + + void SetUsedFeatureByNode(const std::vector& is_feature_used_by_smaller_node, + const std::vector& is_feature_used_by_larger_node); + + private: + #define LaunchFindBestSplitsForLeafKernel_PARAMS \ + const CUDALeafSplitsStruct* smaller_leaf_splits, \ + const CUDALeafSplitsStruct* larger_leaf_splits, \ + const int smaller_leaf_index, \ + const int larger_leaf_index, \ + const bool is_smaller_leaf_valid, \ + const bool is_larger_leaf_valid, \ + const data_size_t global_num_data_in_smaller_leaf, \ + const data_size_t global_num_data_in_larger_leaf + + void LaunchFindBestSplitsForLeafKernel(LaunchFindBestSplitsForLeafKernel_PARAMS); + + template + void LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_PARAMS); + + template + void LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_PARAMS); + + template + void LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_PARAMS); + + #undef LaunchFindBestSplitsForLeafKernel_PARAMS + + #define LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS \ + const CUDALeafSplitsStruct* smaller_leaf_splits, \ + const CUDALeafSplitsStruct* larger_leaf_splits, \ + const int smaller_leaf_index, \ + const int larger_leaf_index, \ + const bool is_smaller_leaf_valid, \ + const bool is_larger_leaf_valid, \ + const score_t* grad_scale, \ + const score_t* hess_scale, \ + const uint8_t smaller_num_bits_in_histogram_bins, \ + const uint8_t larger_num_bits_in_histogram_bins, \ + const data_size_t global_num_data_in_smaller_leaf, \ + const data_size_t global_num_data_in_larger_leaf + + void LaunchFindBestSplitsDiscretizedForLeafKernel(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS); + + template + void LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS); + + template + void LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS); + + template + void LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS); + + #undef LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS + + void LaunchSyncBestSplitForLeafKernel( + const int host_smaller_leaf_index, + const int host_larger_leaf_index, + const bool is_smaller_leaf_valid, + const bool is_larger_leaf_valid); + + void LaunchFindBestFromAllSplitsKernel( + const int cur_num_leaves, + const int smaller_leaf_index, + const int larger_leaf_index, + int* smaller_leaf_best_split_feature, + uint32_t* smaller_leaf_best_split_threshold, + uint8_t* smaller_leaf_best_split_default_left, + int* larger_leaf_best_split_feature, + uint32_t* larger_leaf_best_split_threshold, + uint8_t* larger_leaf_best_split_default_left, + int* best_leaf_index, + data_size_t* num_cat_threshold); + + void AllocateCatVectors(CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len); + + void LaunchAllocateCatVectorsKernel(CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len); + + void LaunchInitCUDARandomKernel(); + + // Host memory + int num_features_; + int num_leaves_; + int max_num_bin_in_feature_; + std::vector feature_hist_offsets_; + std::vector feature_mfb_offsets_; + std::vector feature_default_bins_; + std::vector feature_num_bins_; + std::vector feature_missing_type_; + double lambda_l1_; + double lambda_l2_; + data_size_t min_data_in_leaf_; + double min_sum_hessian_in_leaf_; + double min_gain_to_split_; + double cat_smooth_; + double cat_l2_; + int max_cat_threshold_; + int min_data_per_group_; + int max_cat_to_onehot_; + bool extra_trees_; + int extra_seed_; + bool use_smoothing_; + double path_smooth_; + std::vector cuda_streams_; + // for best split find tasks + std::vector split_find_tasks_; + int num_tasks_; + // use global memory + bool use_global_memory_; + // number of total bins in the dataset + const int num_total_bin_; + // has categorical feature + bool has_categorical_feature_; + // maximum number of bins of categorical features + int max_num_categorical_bin_; + // marks whether a feature is categorical + std::vector is_categorical_; + // whether need to select features by node + bool select_features_by_node_; + + // CUDA memory, held by this object + // for per leaf best split information + CUDAVector cuda_leaf_best_split_info_; + // for best split information when finding best split + CUDAVector cuda_best_split_info_; + // best split information buffer, to be copied to host + CUDAVector cuda_best_split_info_buffer_; + // find best split task information + CUDAVector cuda_split_find_tasks_; + CUDAVector cuda_is_feature_used_bytree_; + // used when finding best split with global memory + CUDAVector cuda_feature_hist_grad_buffer_; + CUDAVector cuda_feature_hist_hess_buffer_; + CUDAVector cuda_feature_hist_stat_buffer_; + CUDAVector cuda_feature_hist_index_buffer_; + CUDAVector cuda_cat_threshold_leaf_; + CUDAVector cuda_cat_threshold_real_leaf_; + CUDAVector cuda_cat_threshold_feature_; + CUDAVector cuda_cat_threshold_real_feature_; + int max_num_categories_in_split_; + // used for extremely randomized trees + CUDAVector cuda_randoms_; + // features used by node + CUDAVector is_feature_used_by_smaller_node_; + CUDAVector is_feature_used_by_larger_node_; + + // CUDA memory, held by other object + const hist_t* cuda_hist_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_BEST_SPLIT_FINDER_HPP_ diff --git a/src/treelearner/cuda/cuda_data_partition.cpp b/src/treelearner/cuda/cuda_data_partition.cpp new file mode 100644 index 0000000..11410a3 --- /dev/null +++ b/src/treelearner/cuda/cuda_data_partition.cpp @@ -0,0 +1,352 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include +#include +#include + +#include "cuda_data_partition.hpp" + +namespace LightGBM { + +CUDADataPartition::CUDADataPartition( + const Dataset* train_data, + const int num_total_bin, + const int num_leaves, + const int num_threads, + const bool use_quantized_grad, + hist_t* cuda_hist): + + num_data_(train_data->num_data()), + num_features_(train_data->num_features()), + num_total_bin_(num_total_bin), + num_leaves_(num_leaves), + num_threads_(num_threads), + use_quantized_grad_(use_quantized_grad), + cuda_hist_(cuda_hist) { + CalcBlockDim(num_data_); + max_num_split_indices_blocks_ = grid_dim_; + cur_num_leaves_ = 1; + cuda_column_data_ = train_data->cuda_column_data(); + + is_categorical_feature_.resize(train_data->num_features(), false); + is_single_feature_in_column_.resize(train_data->num_features(), false); + for (int feature_index = 0; feature_index < train_data->num_features(); ++feature_index) { + if (train_data->FeatureBinMapper(feature_index)->bin_type() == BinType::CategoricalBin) { + is_categorical_feature_[feature_index] = true; + } + const int feature_group_index = train_data->Feature2Group(feature_index); + if (!train_data->IsMultiGroup(feature_group_index)) { + if ((feature_index == 0 || train_data->Feature2Group(feature_index - 1) != feature_group_index) && + (feature_index == train_data->num_features() - 1 || train_data->Feature2Group(feature_index + 1) != feature_group_index)) { + is_single_feature_in_column_[feature_index] = true; + } + } else { + is_single_feature_in_column_[feature_index] = true; + } + } +} + +CUDADataPartition::~CUDADataPartition() { + CUDASUCCESS_OR_FATAL(cudaStreamDestroy(cuda_streams_[0])); + CUDASUCCESS_OR_FATAL(cudaStreamDestroy(cuda_streams_[1])); + CUDASUCCESS_OR_FATAL(cudaStreamDestroy(cuda_streams_[2])); + CUDASUCCESS_OR_FATAL(cudaStreamDestroy(cuda_streams_[3])); + cuda_streams_.clear(); + cuda_streams_.shrink_to_fit(); +} + +void CUDADataPartition::Init() { + // allocate CUDA memory + cuda_data_indices_.Resize(static_cast(num_data_)); + cuda_leaf_data_start_.Resize(static_cast(num_leaves_)); + cuda_leaf_data_end_.Resize(static_cast(num_leaves_)); + cuda_leaf_num_data_.Resize(static_cast(num_leaves_)); + // leave some space for alignment + cuda_block_to_left_offset_.Resize(static_cast(num_data_)); + cuda_data_index_to_leaf_index_.Resize(static_cast(num_data_)); + cuda_block_data_to_left_offset_.Resize(static_cast(max_num_split_indices_blocks_) + 1); + cuda_block_data_to_right_offset_.Resize(static_cast(max_num_split_indices_blocks_) + 1); + SetCUDAMemory(cuda_block_data_to_left_offset_.RawData(), 0, static_cast(max_num_split_indices_blocks_) + 1, __FILE__, __LINE__); + SetCUDAMemory(cuda_block_data_to_right_offset_.RawData(), 0, static_cast(max_num_split_indices_blocks_) + 1, __FILE__, __LINE__); + cuda_out_data_indices_in_leaf_.Resize(static_cast(num_data_)); + cuda_hist_pool_.Resize(static_cast(num_leaves_)); + CopyFromHostToCUDADevice(cuda_hist_pool_.RawData(), &cuda_hist_, 1, __FILE__, __LINE__); + + cuda_split_info_buffer_.Resize(18); + + cuda_leaf_output_.Resize(static_cast(num_leaves_)); + + cuda_streams_.resize(4); + gpuAssert(cudaStreamCreate(&cuda_streams_[0]), __FILE__, __LINE__); + gpuAssert(cudaStreamCreate(&cuda_streams_[1]), __FILE__, __LINE__); + gpuAssert(cudaStreamCreate(&cuda_streams_[2]), __FILE__, __LINE__); + gpuAssert(cudaStreamCreate(&cuda_streams_[3]), __FILE__, __LINE__); + + cuda_num_data_.InitFromHostVector(std::vector{num_data_}); + use_bagging_ = false; + used_indices_ = nullptr; +} + +void CUDADataPartition::BeforeTrain() { + if (!use_bagging_) { + LaunchFillDataIndicesBeforeTrain(); + } + SetCUDAMemory(cuda_leaf_num_data_.RawData(), 0, static_cast(num_leaves_), __FILE__, __LINE__); + SetCUDAMemory(cuda_leaf_data_start_.RawData(), 0, static_cast(num_leaves_), __FILE__, __LINE__); + SetCUDAMemory(cuda_leaf_data_end_.RawData(), 0, static_cast(num_leaves_), __FILE__, __LINE__); + SynchronizeCUDADevice(__FILE__, __LINE__); + if (!use_bagging_) { + CopyFromCUDADeviceToCUDADevice(cuda_leaf_num_data_.RawData(), cuda_num_data_.RawData(), 1, __FILE__, __LINE__); + CopyFromCUDADeviceToCUDADevice(cuda_leaf_data_end_.RawData(), cuda_num_data_.RawData(), 1, __FILE__, __LINE__); + } else { + CopyFromHostToCUDADevice(cuda_leaf_num_data_.RawData(), &num_used_indices_, 1, __FILE__, __LINE__); + CopyFromHostToCUDADevice(cuda_leaf_data_end_.RawData(), &num_used_indices_, 1, __FILE__, __LINE__); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + CopyFromHostToCUDADevice(cuda_hist_pool_.RawData(), &cuda_hist_, 1, __FILE__, __LINE__); +} + +void CUDADataPartition::Split( + // input best split info + const CUDASplitInfo* best_split_info, + const int left_leaf_index, + const int right_leaf_index, + const int leaf_best_split_feature, + const uint32_t leaf_best_split_threshold, + const uint32_t* categorical_bitset, + const int categorical_bitset_len, + const uint8_t leaf_best_split_default_left, + const data_size_t num_data_in_leaf, + const data_size_t leaf_data_start, + // for leaf information update + CUDALeafSplitsStruct* smaller_leaf_splits, + CUDALeafSplitsStruct* larger_leaf_splits, + // gather information for CPU, used for launching kernels + data_size_t* left_leaf_num_data, + data_size_t* right_leaf_num_data, + data_size_t* left_leaf_start, + data_size_t* right_leaf_start, + double* left_leaf_sum_of_hessians, + double* right_leaf_sum_of_hessians, + double* left_leaf_sum_of_gradients, + double* right_leaf_sum_of_gradients, + data_size_t* global_left_leaf_num_data, + data_size_t* global_right_leaf_num_data) { + CalcBlockDim(num_data_in_leaf); + global_timer.Start("GenDataToLeftBitVector"); + GenDataToLeftBitVector(num_data_in_leaf, + leaf_best_split_feature, + leaf_best_split_threshold, + categorical_bitset, + categorical_bitset_len, + leaf_best_split_default_left, + leaf_data_start, + left_leaf_index, + right_leaf_index); + global_timer.Stop("GenDataToLeftBitVector"); + global_timer.Start("SplitInner"); + + SplitInner(num_data_in_leaf, + best_split_info, + left_leaf_index, + right_leaf_index, + smaller_leaf_splits, + larger_leaf_splits, + left_leaf_num_data, + right_leaf_num_data, + left_leaf_start, + right_leaf_start, + left_leaf_sum_of_hessians, + right_leaf_sum_of_hessians, + left_leaf_sum_of_gradients, + right_leaf_sum_of_gradients, + global_left_leaf_num_data, + global_right_leaf_num_data); + global_timer.Stop("SplitInner"); +} + +void CUDADataPartition::GenDataToLeftBitVector( + const data_size_t num_data_in_leaf, + const int split_feature_index, + const uint32_t split_threshold, + const uint32_t* categorical_bitset, + const int categorical_bitset_len, + const uint8_t split_default_left, + const data_size_t leaf_data_start, + const int left_leaf_index, + const int right_leaf_index) { + if (is_categorical_feature_[split_feature_index]) { + LaunchGenDataToLeftBitVectorCategoricalKernel( + num_data_in_leaf, + split_feature_index, + categorical_bitset, + categorical_bitset_len, + split_default_left, + leaf_data_start, + left_leaf_index, + right_leaf_index); + } else { + LaunchGenDataToLeftBitVectorKernel( + num_data_in_leaf, + split_feature_index, + split_threshold, + split_default_left, + leaf_data_start, + left_leaf_index, + right_leaf_index); + } +} + +void CUDADataPartition::SplitInner( + const data_size_t num_data_in_leaf, + const CUDASplitInfo* best_split_info, + const int left_leaf_index, + const int right_leaf_index, + // for leaf splits information update + CUDALeafSplitsStruct* smaller_leaf_splits, + CUDALeafSplitsStruct* larger_leaf_splits, + data_size_t* left_leaf_num_data, + data_size_t* right_leaf_num_data, + data_size_t* left_leaf_start, + data_size_t* right_leaf_start, + double* left_leaf_sum_of_hessians, + double* right_leaf_sum_of_hessians, + double* left_leaf_sum_of_gradients, + double* right_leaf_sum_of_gradients, + data_size_t* global_left_leaf_num_data, + data_size_t* global_right_leaf_num_data) { + LaunchSplitInnerKernel( + num_data_in_leaf, + best_split_info, + left_leaf_index, + right_leaf_index, + smaller_leaf_splits, + larger_leaf_splits, + left_leaf_num_data, + right_leaf_num_data, + left_leaf_start, + right_leaf_start, + left_leaf_sum_of_hessians, + right_leaf_sum_of_hessians, + left_leaf_sum_of_gradients, + right_leaf_sum_of_gradients, + global_left_leaf_num_data, + global_right_leaf_num_data); + ++cur_num_leaves_; +} + +void CUDADataPartition::UpdateTrainScore(const Tree* tree, double* scores) { + const CUDATree* cuda_tree = nullptr; + std::unique_ptr cuda_tree_ptr; + if (tree->is_cuda_tree()) { + cuda_tree = reinterpret_cast(tree); + } else { + cuda_tree_ptr.reset(new CUDATree(tree)); + cuda_tree = cuda_tree_ptr.get(); + } + if (use_bagging_) { + // we need restore the order of indices in cuda_data_indices_ + CopyFromCUDADeviceToCUDADevice(cuda_data_indices_.RawData(), used_indices_, static_cast(num_used_indices_), __FILE__, __LINE__); + } + LaunchAddPredictionToScoreKernel(cuda_tree->cuda_leaf_value(), scores); +} + +void CUDADataPartition::CalcBlockDim(const data_size_t num_data_in_leaf) { + const int min_num_blocks = num_data_in_leaf <= 100 ? 1 : 80; + const int num_blocks = std::max(min_num_blocks, (num_data_in_leaf + SPLIT_INDICES_BLOCK_SIZE_DATA_PARTITION - 1) / SPLIT_INDICES_BLOCK_SIZE_DATA_PARTITION); + int split_indices_block_size_data_partition = (num_data_in_leaf + num_blocks - 1) / num_blocks - 1; + CHECK_GT(split_indices_block_size_data_partition, 0); + int split_indices_block_size_data_partition_aligned = 1; + while (split_indices_block_size_data_partition > 0) { + split_indices_block_size_data_partition_aligned <<= 1; + split_indices_block_size_data_partition >>= 1; + } + const int num_blocks_final = (num_data_in_leaf + split_indices_block_size_data_partition_aligned - 1) / split_indices_block_size_data_partition_aligned; + grid_dim_ = num_blocks_final; + block_dim_ = split_indices_block_size_data_partition_aligned; +} + +void CUDADataPartition::SetUsedDataIndices(const data_size_t* used_indices, const data_size_t num_used_indices) { + use_bagging_ = true; + num_used_indices_ = num_used_indices; + used_indices_ = used_indices; + CopyFromCUDADeviceToCUDADevice(cuda_data_indices_.RawData(), used_indices, static_cast(num_used_indices), __FILE__, __LINE__); + LaunchFillDataIndexToLeafIndex(); +} + +void CUDADataPartition::ResetTrainingData(const Dataset* train_data, const int num_total_bin, hist_t* cuda_hist) { + const data_size_t old_num_data = num_data_; + num_data_ = train_data->num_data(); + num_features_ = train_data->num_features(); + num_total_bin_ = num_total_bin; + cuda_column_data_ = train_data->cuda_column_data(); + cuda_hist_ = cuda_hist; + CopyFromHostToCUDADevice(cuda_hist_pool_.RawData(), &cuda_hist_, 1, __FILE__, __LINE__); + CopyFromHostToCUDADevice(cuda_num_data_.RawData(), &num_data_, 1, __FILE__, __LINE__); + if (num_data_ > old_num_data) { + CalcBlockDim(num_data_); + const int old_max_num_split_indices_blocks = max_num_split_indices_blocks_; + max_num_split_indices_blocks_ = grid_dim_; + if (max_num_split_indices_blocks_ > old_max_num_split_indices_blocks) { + cuda_block_data_to_left_offset_.Resize(static_cast(max_num_split_indices_blocks_) + 1); + cuda_block_data_to_right_offset_.Resize(static_cast(max_num_split_indices_blocks_) + 1); + SetCUDAMemory(cuda_block_data_to_left_offset_.RawData(), 0, static_cast(max_num_split_indices_blocks_) + 1, __FILE__, __LINE__); + SetCUDAMemory(cuda_block_data_to_right_offset_.RawData(), 0, static_cast(max_num_split_indices_blocks_) + 1, __FILE__, __LINE__); + } + cuda_data_indices_.Resize(static_cast(num_data_)); + cuda_block_to_left_offset_.Resize(static_cast(num_data_)); + cuda_data_index_to_leaf_index_.Resize(static_cast(num_data_)); + cuda_out_data_indices_in_leaf_.Resize(static_cast(num_data_)); + } + used_indices_ = nullptr; + use_bagging_ = false; + num_used_indices_ = 0; + cur_num_leaves_ = 1; +} + +void CUDADataPartition::ResetConfig(const Config* config, hist_t* cuda_hist) { + num_threads_ = OMP_NUM_THREADS(); + num_leaves_ = config->num_leaves; + cuda_hist_ = cuda_hist; + cuda_leaf_data_start_.Resize(static_cast(num_leaves_)); + cuda_leaf_data_end_.Resize(static_cast(num_leaves_)); + cuda_leaf_num_data_.Resize(static_cast(num_leaves_)); + cuda_hist_pool_.Resize(static_cast(num_leaves_)); + cuda_leaf_output_.Resize(static_cast(num_leaves_)); +} + +void CUDADataPartition::SetBaggingSubset(const Dataset* subset) { + num_used_indices_ = subset->num_data(); + used_indices_ = nullptr; + use_bagging_ = true; + cuda_column_data_ = subset->cuda_column_data(); +} + +void CUDADataPartition::ResetByLeafPred(const std::vector& leaf_pred, int num_leaves) { + if (leaf_pred.size() != static_cast(num_data_)) { + cuda_data_index_to_leaf_index_.Clear(); + cuda_data_index_to_leaf_index_.InitFromHostVector(leaf_pred); + num_data_ = static_cast(leaf_pred.size()); + } else { + CopyFromHostToCUDADevice(cuda_data_index_to_leaf_index_.RawData(), leaf_pred.data(), leaf_pred.size(), __FILE__, __LINE__); + } + num_leaves_ = num_leaves; + cur_num_leaves_ = num_leaves; +} + +void CUDADataPartition::ReduceLeafGradStat( + const score_t* gradients, const score_t* hessians, + CUDATree* tree, double* leaf_grad_stat_buffer, double* leaf_hess_state_buffer) const { + LaunchReduceLeafGradStat(gradients, hessians, tree, leaf_grad_stat_buffer, leaf_hess_state_buffer); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_data_partition.cu b/src/treelearner/cuda/cuda_data_partition.cu new file mode 100644 index 0000000..dd40bf4 --- /dev/null +++ b/src/treelearner/cuda/cuda_data_partition.cu @@ -0,0 +1,1169 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include "cuda_data_partition.hpp" + +#include +#include +#include + +#include +#include + +namespace LightGBM { + +__global__ void FillDataIndicesBeforeTrainKernel(const data_size_t num_data, + data_size_t* data_indices, int* cuda_data_index_to_leaf_index) { + const unsigned int data_index = threadIdx.x + blockIdx.x * blockDim.x; + if (data_index < num_data) { + data_indices[data_index] = data_index; + cuda_data_index_to_leaf_index[data_index] = 0; + } +} + +__global__ void FillDataIndexToLeafIndexKernel( + const data_size_t num_data, + const data_size_t* data_indices, + int* data_index_to_leaf_index) { + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (data_index < num_data) { + data_index_to_leaf_index[data_indices[data_index]] = 0; + } +} + +void CUDADataPartition::LaunchFillDataIndicesBeforeTrain() { + const data_size_t num_data_in_root = root_num_data(); + const int num_blocks = (num_data_in_root + FILL_INDICES_BLOCK_SIZE_DATA_PARTITION - 1) / FILL_INDICES_BLOCK_SIZE_DATA_PARTITION; + FillDataIndicesBeforeTrainKernel<<>>(num_data_in_root, cuda_data_indices_.RawData(), cuda_data_index_to_leaf_index_.RawData()); +} + +void CUDADataPartition::LaunchFillDataIndexToLeafIndex() { + const data_size_t num_data_in_root = root_num_data(); + const int num_blocks = (num_data_in_root + FILL_INDICES_BLOCK_SIZE_DATA_PARTITION - 1) / FILL_INDICES_BLOCK_SIZE_DATA_PARTITION; + FillDataIndexToLeafIndexKernel<<>>(num_data_in_root, cuda_data_indices_.RawData(), cuda_data_index_to_leaf_index_.RawData()); +} + +__device__ __forceinline__ void PrepareOffset(const data_size_t num_data_in_leaf, uint16_t* block_to_left_offset, + data_size_t* block_to_left_offset_buffer, data_size_t* block_to_right_offset_buffer, + const uint16_t thread_to_left_offset_cnt, uint16_t* shared_mem_buffer) { + const unsigned int threadIdx_x = threadIdx.x; + const unsigned int blockDim_x = blockDim.x; + const uint16_t thread_to_left_offset = ShufflePrefixSum(thread_to_left_offset_cnt, shared_mem_buffer); + const data_size_t num_data_in_block = (blockIdx.x + 1) * blockDim_x <= num_data_in_leaf ? static_cast(blockDim_x) : + num_data_in_leaf - static_cast(blockIdx.x * blockDim_x); + if (static_cast(threadIdx_x) < num_data_in_block) { + block_to_left_offset[threadIdx_x] = thread_to_left_offset; + } + if (threadIdx_x == blockDim_x - 1) { + if (num_data_in_block > 0) { + const data_size_t data_to_left = static_cast(thread_to_left_offset); + block_to_left_offset_buffer[blockIdx.x + 1] = data_to_left; + block_to_right_offset_buffer[blockIdx.x + 1] = num_data_in_block - data_to_left; + } else { + block_to_left_offset_buffer[blockIdx.x + 1] = 0; + block_to_right_offset_buffer[blockIdx.x + 1] = 0; + } + } +} + +template +__device__ bool CUDAFindInBitset(const uint32_t* bits, int n, T pos) { + int i1 = pos / 32; + if (i1 >= n) { + return false; + } + int i2 = pos % 32; + return (bits[i1] >> i2) & 1; +} + + + +#define UpdateDataIndexToLeafIndexKernel_PARAMS \ + const BIN_TYPE* column_data, \ + const data_size_t num_data_in_leaf, \ + const data_size_t* data_indices_in_leaf, \ + const uint32_t th, \ + const uint32_t t_zero_bin, \ + const uint32_t max_bin, \ + const uint32_t min_bin, \ + const int left_leaf_index, \ + const int right_leaf_index, \ + const int default_leaf_index, \ + const int missing_default_leaf_index + +#define UpdateDataIndexToLeafIndex_ARGS \ + column_data, \ + num_data_in_leaf, \ + data_indices_in_leaf, th, \ + t_zero_bin, \ + max_bin, \ + min_bin, \ + left_leaf_index, \ + right_leaf_index, \ + default_leaf_index, \ + missing_default_leaf_index + +template +__global__ void UpdateDataIndexToLeafIndexKernel( + UpdateDataIndexToLeafIndexKernel_PARAMS, + int* cuda_data_index_to_leaf_index) { + const unsigned int local_data_index = blockIdx.x * blockDim.x + threadIdx.x; + if (local_data_index < num_data_in_leaf) { + const unsigned int global_data_index = data_indices_in_leaf[local_data_index]; + const uint32_t bin = static_cast(column_data[global_data_index]); + if (!MIN_IS_MAX) { + if ((MISSING_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) || + (MISSING_IS_NA && !MFB_IS_NA && bin == max_bin)) { + cuda_data_index_to_leaf_index[global_data_index] = missing_default_leaf_index; + } else if ((USE_MIN_BIN && (bin < min_bin || bin > max_bin)) || + (!USE_MIN_BIN && bin == 0)) { + if ((MISSING_IS_NA && MFB_IS_NA) || (MISSING_IS_ZERO && MFB_IS_ZERO)) { + cuda_data_index_to_leaf_index[global_data_index] = missing_default_leaf_index; + } else { + cuda_data_index_to_leaf_index[global_data_index] = default_leaf_index; + } + } else if (bin > th) { + cuda_data_index_to_leaf_index[global_data_index] = right_leaf_index; + } else { + cuda_data_index_to_leaf_index[global_data_index] = left_leaf_index; + } + } else { + if (MISSING_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) { + cuda_data_index_to_leaf_index[global_data_index] = missing_default_leaf_index; + } else if (bin != max_bin) { + if ((MISSING_IS_NA && MFB_IS_NA) || (MISSING_IS_ZERO && MFB_IS_ZERO)) { + cuda_data_index_to_leaf_index[global_data_index] = missing_default_leaf_index; + } else { + cuda_data_index_to_leaf_index[global_data_index] = default_leaf_index; + } + } else { + if (MISSING_IS_NA && !MFB_IS_NA) { + cuda_data_index_to_leaf_index[global_data_index] = missing_default_leaf_index; + } else { + if (!MAX_TO_LEFT) { + cuda_data_index_to_leaf_index[global_data_index] = right_leaf_index; + } else { + cuda_data_index_to_leaf_index[global_data_index] = left_leaf_index; + } + } + } + } + } +} + +template +void CUDADataPartition::LaunchUpdateDataIndexToLeafIndexKernel( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool missing_is_zero, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column) { + if (min_bin < max_bin) { + if (!missing_is_zero) { + LaunchUpdateDataIndexToLeafIndexKernel_Inner0 + (UpdateDataIndexToLeafIndex_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_to_left, is_single_feature_in_column); + } else { + LaunchUpdateDataIndexToLeafIndexKernel_Inner0 + (UpdateDataIndexToLeafIndex_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_to_left, is_single_feature_in_column); + } + } else { + if (!missing_is_zero) { + LaunchUpdateDataIndexToLeafIndexKernel_Inner0 + (UpdateDataIndexToLeafIndex_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_to_left, is_single_feature_in_column); + } else { + LaunchUpdateDataIndexToLeafIndexKernel_Inner0 + (UpdateDataIndexToLeafIndex_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_to_left, is_single_feature_in_column); + } + } +} + +template +void CUDADataPartition::LaunchUpdateDataIndexToLeafIndexKernel_Inner0( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column) { + if (!missing_is_na) { + LaunchUpdateDataIndexToLeafIndexKernel_Inner1 + (UpdateDataIndexToLeafIndex_ARGS, mfb_is_zero, mfb_is_na, max_to_left, is_single_feature_in_column); + } else { + LaunchUpdateDataIndexToLeafIndexKernel_Inner1 + (UpdateDataIndexToLeafIndex_ARGS, mfb_is_zero, mfb_is_na, max_to_left, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchUpdateDataIndexToLeafIndexKernel_Inner1( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column) { + if (!mfb_is_zero) { + LaunchUpdateDataIndexToLeafIndexKernel_Inner2 + (UpdateDataIndexToLeafIndex_ARGS, mfb_is_na, max_to_left, is_single_feature_in_column); + } else { + LaunchUpdateDataIndexToLeafIndexKernel_Inner2 + (UpdateDataIndexToLeafIndex_ARGS, mfb_is_na, max_to_left, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchUpdateDataIndexToLeafIndexKernel_Inner2( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column) { + if (!mfb_is_na) { + LaunchUpdateDataIndexToLeafIndexKernel_Inner3 + (UpdateDataIndexToLeafIndex_ARGS, max_to_left, is_single_feature_in_column); + } else { + LaunchUpdateDataIndexToLeafIndexKernel_Inner3 + (UpdateDataIndexToLeafIndex_ARGS, max_to_left, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchUpdateDataIndexToLeafIndexKernel_Inner3( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool max_to_left, + const bool is_single_feature_in_column) { + if (!max_to_left) { + LaunchUpdateDataIndexToLeafIndexKernel_Inner4 + (UpdateDataIndexToLeafIndex_ARGS, is_single_feature_in_column); + } else { + LaunchUpdateDataIndexToLeafIndexKernel_Inner4 + (UpdateDataIndexToLeafIndex_ARGS, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchUpdateDataIndexToLeafIndexKernel_Inner4( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool is_single_feature_in_column) { + if (!is_single_feature_in_column) { + UpdateDataIndexToLeafIndexKernel + <<>>( + UpdateDataIndexToLeafIndex_ARGS, + cuda_data_index_to_leaf_index_.RawData()); + } else { + UpdateDataIndexToLeafIndexKernel + <<>>( + UpdateDataIndexToLeafIndex_ARGS, + cuda_data_index_to_leaf_index_.RawData()); + } +} + +#define GenDataToLeftBitVectorKernel_PARAMS \ + const BIN_TYPE* column_data, \ + const data_size_t num_data_in_leaf, \ + const data_size_t* data_indices_in_leaf, \ + const uint32_t th, \ + const uint32_t t_zero_bin, \ + const uint32_t max_bin, \ + const uint32_t min_bin, \ + const uint8_t split_default_to_left, \ + const uint8_t split_missing_default_to_left + +#define GenBitVector_ARGS \ + column_data, \ + num_data_in_leaf, \ + data_indices_in_leaf, \ + th, \ + t_zero_bin, \ + max_bin, \ + min_bin, \ + split_default_to_left, \ + split_missing_default_to_left + +template +__global__ void GenDataToLeftBitVectorKernel( + GenDataToLeftBitVectorKernel_PARAMS, + uint16_t* block_to_left_offset, + data_size_t* block_to_left_offset_buffer, + data_size_t* block_to_right_offset_buffer) { + __shared__ uint16_t shared_mem_buffer[WARPSIZE]; + uint16_t thread_to_left_offset_cnt = 0; + const unsigned int local_data_index = blockIdx.x * blockDim.x + threadIdx.x; + if (local_data_index < num_data_in_leaf) { + const unsigned int global_data_index = data_indices_in_leaf[local_data_index]; + const uint32_t bin = static_cast(column_data[global_data_index]); + if (!MIN_IS_MAX) { + if ((MISSING_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) || + (MISSING_IS_NA && !MFB_IS_NA && bin == max_bin)) { + thread_to_left_offset_cnt = split_missing_default_to_left; + } else if ((USE_MIN_BIN && (bin < min_bin || bin > max_bin)) || + (!USE_MIN_BIN && bin == 0)) { + if ((MISSING_IS_NA && MFB_IS_NA) || (MISSING_IS_ZERO || MFB_IS_ZERO)) { + thread_to_left_offset_cnt = split_missing_default_to_left; + } else { + thread_to_left_offset_cnt = split_default_to_left; + } + } else if (bin <= th) { + thread_to_left_offset_cnt = 1; + } + } else { + if (MISSING_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) { + thread_to_left_offset_cnt = split_missing_default_to_left; + } else if (bin != max_bin) { + if ((MISSING_IS_NA && MFB_IS_NA) || (MISSING_IS_ZERO && MFB_IS_ZERO)) { + thread_to_left_offset_cnt = split_missing_default_to_left; + } else { + thread_to_left_offset_cnt = split_default_to_left; + } + } else { + if (MISSING_IS_NA && !MFB_IS_NA) { + thread_to_left_offset_cnt = split_missing_default_to_left; + } else if (MAX_TO_LEFT) { + thread_to_left_offset_cnt = 1; + } + } + } + } + __syncthreads(); + PrepareOffset(num_data_in_leaf, block_to_left_offset + blockIdx.x * blockDim.x, block_to_left_offset_buffer, block_to_right_offset_buffer, + thread_to_left_offset_cnt, shared_mem_buffer); +} + +template +void CUDADataPartition::LaunchGenDataToLeftBitVectorKernelInner( + GenDataToLeftBitVectorKernel_PARAMS, + const bool missing_is_zero, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column) { + if (min_bin < max_bin) { + if (!missing_is_zero) { + LaunchGenDataToLeftBitVectorKernelInner0 + (GenBitVector_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } else { + LaunchGenDataToLeftBitVectorKernelInner0 + (GenBitVector_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } + } else { + if (!missing_is_zero) { + LaunchGenDataToLeftBitVectorKernelInner0 + (GenBitVector_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } else { + LaunchGenDataToLeftBitVectorKernelInner0 + (GenBitVector_ARGS, missing_is_na, mfb_is_zero, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } + } +} + +template +void CUDADataPartition::LaunchGenDataToLeftBitVectorKernelInner0( + GenDataToLeftBitVectorKernel_PARAMS, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column) { + if (!missing_is_na) { + LaunchGenDataToLeftBitVectorKernelInner1 + (GenBitVector_ARGS, mfb_is_zero, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } else { + LaunchGenDataToLeftBitVectorKernelInner1 + (GenBitVector_ARGS, mfb_is_zero, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchGenDataToLeftBitVectorKernelInner1( + GenDataToLeftBitVectorKernel_PARAMS, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column) { + if (!mfb_is_zero) { + LaunchGenDataToLeftBitVectorKernelInner2 + (GenBitVector_ARGS, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } else { + LaunchGenDataToLeftBitVectorKernelInner2 + (GenBitVector_ARGS, mfb_is_na, max_bin_to_left, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchGenDataToLeftBitVectorKernelInner2( + GenDataToLeftBitVectorKernel_PARAMS, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column) { + if (!mfb_is_na) { + LaunchGenDataToLeftBitVectorKernelInner3 + + (GenBitVector_ARGS, max_bin_to_left, is_single_feature_in_column); + } else { + LaunchGenDataToLeftBitVectorKernelInner3 + + (GenBitVector_ARGS, max_bin_to_left, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchGenDataToLeftBitVectorKernelInner3( + GenDataToLeftBitVectorKernel_PARAMS, + const bool max_bin_to_left, + const bool is_single_feature_in_column) { + if (!max_bin_to_left) { + LaunchGenDataToLeftBitVectorKernelInner4 + + (GenBitVector_ARGS, is_single_feature_in_column); + } else { + LaunchGenDataToLeftBitVectorKernelInner4 + + (GenBitVector_ARGS, is_single_feature_in_column); + } +} + +template +void CUDADataPartition::LaunchGenDataToLeftBitVectorKernelInner4( + GenDataToLeftBitVectorKernel_PARAMS, + const bool is_single_feature_in_column) { + if (!is_single_feature_in_column) { + GenDataToLeftBitVectorKernel + + <<>>(GenBitVector_ARGS, + cuda_block_to_left_offset_.RawData(), cuda_block_data_to_left_offset_.RawData(), cuda_block_data_to_right_offset_.RawData()); + } else { + GenDataToLeftBitVectorKernel + + <<>>(GenBitVector_ARGS, + cuda_block_to_left_offset_.RawData(), cuda_block_data_to_left_offset_.RawData(), cuda_block_data_to_right_offset_.RawData()); + } +} + +void CUDADataPartition::LaunchGenDataToLeftBitVectorKernel( + const data_size_t num_data_in_leaf, + const int split_feature_index, + const uint32_t split_threshold, + const uint8_t split_default_left, + const data_size_t leaf_data_start, + const int left_leaf_index, + const int right_leaf_index) { + const bool missing_is_zero = static_cast(cuda_column_data_->feature_missing_is_zero(split_feature_index)); + const bool missing_is_na = static_cast(cuda_column_data_->feature_missing_is_na(split_feature_index)); + const bool mfb_is_zero = static_cast(cuda_column_data_->feature_mfb_is_zero(split_feature_index)); + const bool mfb_is_na = static_cast(cuda_column_data_->feature_mfb_is_na(split_feature_index)); + const bool is_single_feature_in_column = is_single_feature_in_column_[split_feature_index]; + const uint32_t default_bin = cuda_column_data_->feature_default_bin(split_feature_index); + const uint32_t most_freq_bin = cuda_column_data_->feature_most_freq_bin(split_feature_index); + const uint32_t min_bin = is_single_feature_in_column ? 1 : cuda_column_data_->feature_min_bin(split_feature_index); + const uint32_t max_bin = cuda_column_data_->feature_max_bin(split_feature_index); + uint32_t th = split_threshold + min_bin; + uint32_t t_zero_bin = min_bin + default_bin; + if (most_freq_bin == 0) { + --th; + --t_zero_bin; + } + uint8_t split_default_to_left = 0; + uint8_t split_missing_default_to_left = 0; + int default_leaf_index = right_leaf_index; + int missing_default_leaf_index = right_leaf_index; + if (most_freq_bin <= split_threshold) { + split_default_to_left = 1; + default_leaf_index = left_leaf_index; + } + if (missing_is_zero || missing_is_na) { + if (split_default_left) { + split_missing_default_to_left = 1; + missing_default_leaf_index = left_leaf_index; + } + } + const int column_index = cuda_column_data_->feature_to_column(split_feature_index); + const uint8_t bit_type = cuda_column_data_->column_bit_type(column_index); + + const bool max_bin_to_left = (max_bin <= th); + + const data_size_t* data_indices_in_leaf = cuda_data_indices_.RawData() + leaf_data_start; + const void* column_data_pointer = cuda_column_data_->GetColumnData(column_index); + + if (bit_type == 8) { + const uint8_t* column_data = reinterpret_cast(column_data_pointer); + LaunchGenDataToLeftBitVectorKernelInner( + GenBitVector_ARGS, + missing_is_zero, + missing_is_na, + mfb_is_zero, + mfb_is_na, + max_bin_to_left, + is_single_feature_in_column); + LaunchUpdateDataIndexToLeafIndexKernel( + UpdateDataIndexToLeafIndex_ARGS, + missing_is_zero, + missing_is_na, + mfb_is_zero, + mfb_is_na, + max_bin_to_left, + is_single_feature_in_column); + } else if (bit_type == 16) { + const uint16_t* column_data = reinterpret_cast(column_data_pointer); + LaunchGenDataToLeftBitVectorKernelInner( + GenBitVector_ARGS, + missing_is_zero, + missing_is_na, + mfb_is_zero, + mfb_is_na, + max_bin_to_left, + is_single_feature_in_column); + LaunchUpdateDataIndexToLeafIndexKernel( + UpdateDataIndexToLeafIndex_ARGS, + missing_is_zero, + missing_is_na, + mfb_is_zero, + mfb_is_na, + max_bin_to_left, + is_single_feature_in_column); + } else if (bit_type == 32) { + const uint32_t* column_data = reinterpret_cast(column_data_pointer); + LaunchGenDataToLeftBitVectorKernelInner( + GenBitVector_ARGS, + missing_is_zero, + missing_is_na, + mfb_is_zero, + mfb_is_na, + max_bin_to_left, + is_single_feature_in_column); + LaunchUpdateDataIndexToLeafIndexKernel( + UpdateDataIndexToLeafIndex_ARGS, + missing_is_zero, + missing_is_na, + mfb_is_zero, + mfb_is_na, + max_bin_to_left, + is_single_feature_in_column); + } +} + +#undef UpdateDataIndexToLeafIndexKernel_PARAMS +#undef UpdateDataIndexToLeafIndex_ARGS +#undef GenDataToLeftBitVectorKernel_PARAMS +#undef GenBitVector_ARGS + +template +__global__ void UpdateDataIndexToLeafIndexKernel_Categorical( + const data_size_t num_data_in_leaf, const data_size_t* data_indices_in_leaf, + const uint32_t* bitset, const int bitset_len, const BIN_TYPE* column_data, + // values from feature + const uint32_t max_bin, const uint32_t min_bin, const int8_t mfb_offset, + int* cuda_data_index_to_leaf_index, const int left_leaf_index, const int right_leaf_index, + const int default_leaf_index) { + const unsigned int local_data_index = blockIdx.x * blockDim.x + threadIdx.x; + if (local_data_index < num_data_in_leaf) { + const unsigned int global_data_index = data_indices_in_leaf[local_data_index]; + const uint32_t bin = static_cast(column_data[global_data_index]); + if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) { + cuda_data_index_to_leaf_index[global_data_index] = default_leaf_index; + } else if (!USE_MIN_BIN && bin == 0) { + cuda_data_index_to_leaf_index[global_data_index] = default_leaf_index; + } else if (CUDAFindInBitset(bitset, bitset_len, bin - min_bin + mfb_offset)) { + cuda_data_index_to_leaf_index[global_data_index] = left_leaf_index; + } else { + cuda_data_index_to_leaf_index[global_data_index] = right_leaf_index; + } + } +} + +// for categorical features +template +__global__ void GenDataToLeftBitVectorKernel_Categorical( + const data_size_t num_data_in_leaf, const data_size_t* data_indices_in_leaf, + const uint32_t* bitset, int bitset_len, const BIN_TYPE* column_data, + // values from feature + const uint32_t max_bin, const uint32_t min_bin, const int8_t mfb_offset, + const uint8_t split_default_to_left, + uint16_t* block_to_left_offset, + data_size_t* block_to_left_offset_buffer, data_size_t* block_to_right_offset_buffer) { + __shared__ uint16_t shared_mem_buffer[WARPSIZE]; + uint16_t thread_to_left_offset_cnt = 0; + const unsigned int local_data_index = blockIdx.x * blockDim.x + threadIdx.x; + if (local_data_index < num_data_in_leaf) { + const unsigned int global_data_index = data_indices_in_leaf[local_data_index]; + const uint32_t bin = static_cast(column_data[global_data_index]); + if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) { + thread_to_left_offset_cnt = split_default_to_left; + } else if (!USE_MIN_BIN && bin == 0) { + thread_to_left_offset_cnt = split_default_to_left; + } else if (CUDAFindInBitset(bitset, bitset_len, bin - min_bin + mfb_offset)) { + thread_to_left_offset_cnt = 1; + } + } + __syncthreads(); + PrepareOffset(num_data_in_leaf, block_to_left_offset + blockIdx.x * blockDim.x, block_to_left_offset_buffer, block_to_right_offset_buffer, + thread_to_left_offset_cnt, shared_mem_buffer); +} + +#define GenBitVector_Categorical_ARGS \ + num_data_in_leaf, data_indices_in_leaf, \ + bitset, bitset_len, \ + column_data, max_bin, min_bin, mfb_offset, split_default_to_left, \ + cuda_block_to_left_offset_.RawData(), cuda_block_data_to_left_offset_.RawData(), cuda_block_data_to_right_offset_.RawData() + +#define UpdateDataIndexToLeafIndex_Categorical_ARGS \ + num_data_in_leaf, data_indices_in_leaf, \ + bitset, bitset_len, \ + column_data, max_bin, min_bin, mfb_offset, \ + cuda_data_index_to_leaf_index_.RawData(), left_leaf_index, right_leaf_index, default_leaf_index + +void CUDADataPartition::LaunchGenDataToLeftBitVectorCategoricalKernel( + const data_size_t num_data_in_leaf, + const int split_feature_index, + const uint32_t* bitset, + const int bitset_len, + const uint8_t split_default_left, + const data_size_t leaf_data_start, + const int left_leaf_index, + const int right_leaf_index) { + const data_size_t* data_indices_in_leaf = cuda_data_indices_.RawData() + leaf_data_start; + const int column_index = cuda_column_data_->feature_to_column(split_feature_index); + const uint8_t bit_type = cuda_column_data_->column_bit_type(column_index); + const bool is_single_feature_in_column = is_single_feature_in_column_[split_feature_index]; + const uint32_t min_bin = is_single_feature_in_column ? 1 : cuda_column_data_->feature_min_bin(split_feature_index); + const uint32_t max_bin = cuda_column_data_->feature_max_bin(split_feature_index); + const uint32_t most_freq_bin = cuda_column_data_->feature_most_freq_bin(split_feature_index); + const uint32_t default_bin = cuda_column_data_->feature_default_bin(split_feature_index); + const void* column_data_pointer = cuda_column_data_->GetColumnData(column_index); + const int8_t mfb_offset = static_cast(most_freq_bin == 0); + std::vector host_bitset(bitset_len, 0); + CopyFromCUDADeviceToHost(host_bitset.data(), bitset, bitset_len, __FILE__, __LINE__); + uint8_t split_default_to_left = 0; + int default_leaf_index = right_leaf_index; + if (most_freq_bin > 0 && Common::FindInBitset(host_bitset.data(), bitset_len, most_freq_bin)) { + split_default_to_left = 1; + default_leaf_index = left_leaf_index; + } + if (bit_type == 8) { + const uint8_t* column_data = reinterpret_cast(column_data_pointer); + if (is_single_feature_in_column) { + GenDataToLeftBitVectorKernel_Categorical<<>>(GenBitVector_Categorical_ARGS); + UpdateDataIndexToLeafIndexKernel_Categorical<<>>(UpdateDataIndexToLeafIndex_Categorical_ARGS); + } else { + GenDataToLeftBitVectorKernel_Categorical<<>>(GenBitVector_Categorical_ARGS); + UpdateDataIndexToLeafIndexKernel_Categorical<<>>(UpdateDataIndexToLeafIndex_Categorical_ARGS); + } + } else if (bit_type == 16) { + const uint16_t* column_data = reinterpret_cast(column_data_pointer); + if (is_single_feature_in_column) { + GenDataToLeftBitVectorKernel_Categorical<<>>(GenBitVector_Categorical_ARGS); + UpdateDataIndexToLeafIndexKernel_Categorical<<>>(UpdateDataIndexToLeafIndex_Categorical_ARGS); + } else { + GenDataToLeftBitVectorKernel_Categorical<<>>(GenBitVector_Categorical_ARGS); + UpdateDataIndexToLeafIndexKernel_Categorical<<>>(UpdateDataIndexToLeafIndex_Categorical_ARGS); + } + } else if (bit_type == 32) { + const uint32_t* column_data = reinterpret_cast(column_data_pointer); + if (is_single_feature_in_column) { + GenDataToLeftBitVectorKernel_Categorical<<>>(GenBitVector_Categorical_ARGS); + UpdateDataIndexToLeafIndexKernel_Categorical<<>>(UpdateDataIndexToLeafIndex_Categorical_ARGS); + } else { + GenDataToLeftBitVectorKernel_Categorical<<>>(GenBitVector_Categorical_ARGS); + UpdateDataIndexToLeafIndexKernel_Categorical<<>>(UpdateDataIndexToLeafIndex_Categorical_ARGS); + } + } +} + +#undef GenBitVector_Categorical_ARGS +#undef UpdateDataIndexToLeafIndex_Categorical_ARGS + +__global__ void AggregateBlockOffsetKernel0( + const int left_leaf_index, + const int right_leaf_index, + data_size_t* block_to_left_offset_buffer, + data_size_t* block_to_right_offset_buffer, data_size_t* cuda_leaf_data_start, + data_size_t* cuda_leaf_data_end, data_size_t* cuda_leaf_num_data, const data_size_t* cuda_data_indices, + const data_size_t num_blocks) { + __shared__ uint32_t shared_mem_buffer[WARPSIZE]; + __shared__ uint32_t to_left_total_count; + const data_size_t num_data_in_leaf = cuda_leaf_num_data[left_leaf_index]; + const unsigned int blockDim_x = blockDim.x; + const unsigned int threadIdx_x = threadIdx.x; + const data_size_t num_blocks_plus_1 = num_blocks + 1; + const uint32_t num_blocks_per_thread = (num_blocks_plus_1 + blockDim_x - 1) / blockDim_x; + const uint32_t remain = num_blocks_plus_1 - ((num_blocks_per_thread - 1) * blockDim_x); + const uint32_t remain_offset = remain * num_blocks_per_thread; + uint32_t thread_start_block_index = 0; + uint32_t thread_end_block_index = 0; + if (threadIdx_x < remain) { + thread_start_block_index = threadIdx_x * num_blocks_per_thread; + thread_end_block_index = min(thread_start_block_index + num_blocks_per_thread, num_blocks_plus_1); + } else { + thread_start_block_index = remain_offset + (num_blocks_per_thread - 1) * (threadIdx_x - remain); + thread_end_block_index = min(thread_start_block_index + num_blocks_per_thread - 1, num_blocks_plus_1); + } + if (threadIdx.x == 0) { + block_to_right_offset_buffer[0] = 0; + } + __syncthreads(); + for (uint32_t block_index = thread_start_block_index + 1; block_index < thread_end_block_index; ++block_index) { + block_to_left_offset_buffer[block_index] += block_to_left_offset_buffer[block_index - 1]; + block_to_right_offset_buffer[block_index] += block_to_right_offset_buffer[block_index - 1]; + } + __syncthreads(); + uint32_t block_to_left_offset = 0; + uint32_t block_to_right_offset = 0; + if (thread_start_block_index < thread_end_block_index && thread_start_block_index > 1) { + block_to_left_offset = block_to_left_offset_buffer[thread_start_block_index - 1]; + block_to_right_offset = block_to_right_offset_buffer[thread_start_block_index - 1]; + } + block_to_left_offset = ShufflePrefixSum(block_to_left_offset, shared_mem_buffer); + __syncthreads(); + block_to_right_offset = ShufflePrefixSum(block_to_right_offset, shared_mem_buffer); + if (threadIdx_x == blockDim_x - 1) { + to_left_total_count = block_to_left_offset + block_to_left_offset_buffer[num_blocks]; + } + __syncthreads(); + const uint32_t to_left_thread_block_offset = block_to_left_offset; + const uint32_t to_right_thread_block_offset = block_to_right_offset + to_left_total_count; + for (uint32_t block_index = thread_start_block_index; block_index < thread_end_block_index; ++block_index) { + block_to_left_offset_buffer[block_index] += to_left_thread_block_offset; + block_to_right_offset_buffer[block_index] += to_right_thread_block_offset; + } + __syncthreads(); + if (blockIdx.x == 0 && threadIdx.x == 0) { + const data_size_t old_leaf_data_end = cuda_leaf_data_end[left_leaf_index]; + cuda_leaf_data_end[left_leaf_index] = cuda_leaf_data_start[left_leaf_index] + static_cast(to_left_total_count); + cuda_leaf_num_data[left_leaf_index] = static_cast(to_left_total_count); + cuda_leaf_data_start[right_leaf_index] = cuda_leaf_data_end[left_leaf_index]; + cuda_leaf_data_end[right_leaf_index] = old_leaf_data_end; + cuda_leaf_num_data[right_leaf_index] = num_data_in_leaf - static_cast(to_left_total_count); + } +} + +__global__ void AggregateBlockOffsetKernel1( + const int left_leaf_index, + const int right_leaf_index, + data_size_t* block_to_left_offset_buffer, + data_size_t* block_to_right_offset_buffer, data_size_t* cuda_leaf_data_start, + data_size_t* cuda_leaf_data_end, data_size_t* cuda_leaf_num_data, const data_size_t* cuda_data_indices, + const data_size_t num_blocks) { + __shared__ uint32_t shared_mem_buffer[WARPSIZE]; + __shared__ uint32_t to_left_total_count; + const data_size_t num_data_in_leaf = cuda_leaf_num_data[left_leaf_index]; + const unsigned int threadIdx_x = threadIdx.x; + uint32_t block_to_left_offset = 0; + uint32_t block_to_right_offset = 0; + if (threadIdx_x < static_cast(num_blocks)) { + block_to_left_offset = block_to_left_offset_buffer[threadIdx_x + 1]; + block_to_right_offset = block_to_right_offset_buffer[threadIdx_x + 1]; + } + block_to_left_offset = ShufflePrefixSum(block_to_left_offset, shared_mem_buffer); + __syncthreads(); + block_to_right_offset = ShufflePrefixSum(block_to_right_offset, shared_mem_buffer); + if (threadIdx.x == blockDim.x - 1) { + to_left_total_count = block_to_left_offset; + } + __syncthreads(); + if (threadIdx_x < static_cast(num_blocks)) { + block_to_left_offset_buffer[threadIdx_x + 1] = block_to_left_offset; + block_to_right_offset_buffer[threadIdx_x + 1] = block_to_right_offset + to_left_total_count; + } + if (threadIdx_x == 0) { + block_to_right_offset_buffer[0] = to_left_total_count; + } + __syncthreads(); + if (blockIdx.x == 0 && threadIdx.x == 0) { + const data_size_t old_leaf_data_end = cuda_leaf_data_end[left_leaf_index]; + cuda_leaf_data_end[left_leaf_index] = cuda_leaf_data_start[left_leaf_index] + static_cast(to_left_total_count); + cuda_leaf_num_data[left_leaf_index] = static_cast(to_left_total_count); + cuda_leaf_data_start[right_leaf_index] = cuda_leaf_data_end[left_leaf_index]; + cuda_leaf_data_end[right_leaf_index] = old_leaf_data_end; + cuda_leaf_num_data[right_leaf_index] = num_data_in_leaf - static_cast(to_left_total_count); + } +} + +template +__global__ void SplitTreeStructureKernel(const int left_leaf_index, + const int right_leaf_index, + data_size_t* block_to_left_offset_buffer, + data_size_t* block_to_right_offset_buffer, data_size_t* cuda_leaf_data_start, + data_size_t* cuda_leaf_data_end, data_size_t* cuda_leaf_num_data, const data_size_t* cuda_data_indices, + const CUDASplitInfo* best_split_info, + // for leaf splits information update + CUDALeafSplitsStruct* smaller_leaf_splits, + CUDALeafSplitsStruct* larger_leaf_splits, + const int num_total_bin, + hist_t* cuda_hist, hist_t** cuda_hist_pool, + double* cuda_leaf_output, + int* cuda_split_info_buffer) { + const unsigned int to_left_total_cnt = cuda_leaf_num_data[left_leaf_index]; + double* cuda_split_info_buffer_for_hessians = reinterpret_cast(cuda_split_info_buffer + 8); + const unsigned int global_thread_index = blockIdx.x * blockDim.x + threadIdx.x; + if (global_thread_index == 0) { + cuda_leaf_output[left_leaf_index] = best_split_info->left_value; + } else if (global_thread_index == 1) { + cuda_leaf_output[right_leaf_index] = best_split_info->right_value; + } else if (global_thread_index == 2) { + cuda_split_info_buffer[0] = left_leaf_index; + } else if (global_thread_index == 3) { + cuda_split_info_buffer[1] = cuda_leaf_num_data[left_leaf_index]; + } else if (global_thread_index == 4) { + cuda_split_info_buffer[2] = cuda_leaf_data_start[left_leaf_index]; + } else if (global_thread_index == 5) { + cuda_split_info_buffer[3] = right_leaf_index; + } else if (global_thread_index == 6) { + cuda_split_info_buffer[4] = cuda_leaf_num_data[right_leaf_index]; + } else if (global_thread_index == 7) { + cuda_split_info_buffer[5] = cuda_leaf_data_start[right_leaf_index]; + } else if (global_thread_index == 8) { + cuda_split_info_buffer_for_hessians[0] = best_split_info->left_sum_hessians; + cuda_split_info_buffer_for_hessians[2] = best_split_info->left_sum_gradients; + } else if (global_thread_index == 9) { + cuda_split_info_buffer_for_hessians[1] = best_split_info->right_sum_hessians; + cuda_split_info_buffer_for_hessians[3] = best_split_info->right_sum_gradients; + } + + bool left_is_smaller = USE_NCCL ? + cuda_split_info_buffer[16] < cuda_split_info_buffer[17] : + cuda_leaf_num_data[left_leaf_index] < cuda_leaf_num_data[right_leaf_index]; + + if (left_is_smaller) { + if (global_thread_index == 0) { + hist_t* parent_hist_ptr = cuda_hist_pool[left_leaf_index]; + cuda_hist_pool[right_leaf_index] = parent_hist_ptr; + cuda_hist_pool[left_leaf_index] = USE_GRAD_DISCRETIZED ? + cuda_hist + right_leaf_index * num_total_bin : + cuda_hist + 2 * right_leaf_index * num_total_bin; + smaller_leaf_splits->hist_in_leaf = cuda_hist_pool[left_leaf_index]; + larger_leaf_splits->hist_in_leaf = cuda_hist_pool[right_leaf_index]; + } else if (global_thread_index == 1) { + smaller_leaf_splits->sum_of_gradients = best_split_info->left_sum_gradients; + } else if (global_thread_index == 2) { + smaller_leaf_splits->sum_of_hessians = best_split_info->left_sum_hessians; + } else if (global_thread_index == 3) { + smaller_leaf_splits->num_data_in_leaf = to_left_total_cnt; + } else if (global_thread_index == 4) { + smaller_leaf_splits->gain = best_split_info->left_gain; + } else if (global_thread_index == 5) { + smaller_leaf_splits->leaf_value = best_split_info->left_value; + } else if (global_thread_index == 6) { + smaller_leaf_splits->data_indices_in_leaf = cuda_data_indices; + } else if (global_thread_index == 7) { + larger_leaf_splits->leaf_index = right_leaf_index; + } else if (global_thread_index == 8) { + larger_leaf_splits->sum_of_gradients = best_split_info->right_sum_gradients; + } else if (global_thread_index == 9) { + larger_leaf_splits->sum_of_hessians = best_split_info->right_sum_hessians; + } else if (global_thread_index == 10) { + larger_leaf_splits->num_data_in_leaf = cuda_leaf_num_data[right_leaf_index]; + } else if (global_thread_index == 11) { + larger_leaf_splits->gain = best_split_info->right_gain; + } else if (global_thread_index == 12) { + larger_leaf_splits->leaf_value = best_split_info->right_value; + } else if (global_thread_index == 13) { + larger_leaf_splits->data_indices_in_leaf = cuda_data_indices + cuda_leaf_num_data[left_leaf_index]; + } else if (global_thread_index == 14) { + cuda_split_info_buffer[6] = left_leaf_index; + } else if (global_thread_index == 15) { + cuda_split_info_buffer[7] = right_leaf_index; + } else if (global_thread_index == 16) { + smaller_leaf_splits->leaf_index = left_leaf_index; + } + } else { + if (global_thread_index == 0) { + larger_leaf_splits->leaf_index = left_leaf_index; + } else if (global_thread_index == 1) { + larger_leaf_splits->sum_of_gradients = best_split_info->left_sum_gradients; + } else if (global_thread_index == 2) { + larger_leaf_splits->sum_of_hessians = best_split_info->left_sum_hessians; + } else if (global_thread_index == 3) { + larger_leaf_splits->num_data_in_leaf = to_left_total_cnt; + } else if (global_thread_index == 4) { + larger_leaf_splits->gain = best_split_info->left_gain; + } else if (global_thread_index == 5) { + larger_leaf_splits->leaf_value = best_split_info->left_value; + } else if (global_thread_index == 6) { + larger_leaf_splits->data_indices_in_leaf = cuda_data_indices; + } else if (global_thread_index == 7) { + smaller_leaf_splits->leaf_index = right_leaf_index; + } else if (global_thread_index == 8) { + smaller_leaf_splits->sum_of_gradients = best_split_info->right_sum_gradients; + } else if (global_thread_index == 9) { + smaller_leaf_splits->sum_of_hessians = best_split_info->right_sum_hessians; + } else if (global_thread_index == 10) { + smaller_leaf_splits->num_data_in_leaf = cuda_leaf_num_data[right_leaf_index]; + } else if (global_thread_index == 11) { + smaller_leaf_splits->gain = best_split_info->right_gain; + } else if (global_thread_index == 12) { + smaller_leaf_splits->leaf_value = best_split_info->right_value; + } else if (global_thread_index == 13) { + smaller_leaf_splits->data_indices_in_leaf = cuda_data_indices + cuda_leaf_num_data[left_leaf_index]; + } else if (global_thread_index == 14) { + cuda_hist_pool[right_leaf_index] = cuda_hist + 2 * right_leaf_index * num_total_bin; + smaller_leaf_splits->hist_in_leaf = cuda_hist_pool[right_leaf_index]; + } else if (global_thread_index == 15) { + larger_leaf_splits->hist_in_leaf = cuda_hist_pool[left_leaf_index]; + } else if (global_thread_index == 16) { + cuda_split_info_buffer[6] = right_leaf_index; + } else if (global_thread_index == 17) { + cuda_split_info_buffer[7] = left_leaf_index; + } + } +} + +__global__ void SplitInnerKernel(const int left_leaf_index, const int right_leaf_index, + const data_size_t* cuda_leaf_data_start, const data_size_t* cuda_leaf_num_data, + const data_size_t* cuda_data_indices, + const data_size_t* block_to_left_offset_buffer, const data_size_t* block_to_right_offset_buffer, + const uint16_t* block_to_left_offset, data_size_t* out_data_indices_in_leaf) { + const data_size_t leaf_num_data_offset = cuda_leaf_data_start[left_leaf_index]; + const data_size_t num_data_in_leaf = cuda_leaf_num_data[left_leaf_index] + cuda_leaf_num_data[right_leaf_index]; + const unsigned int threadIdx_x = threadIdx.x; + const unsigned int blockDim_x = blockDim.x; + const unsigned int global_thread_index = blockIdx.x * blockDim_x + threadIdx_x; + const data_size_t* cuda_data_indices_in_leaf = cuda_data_indices + leaf_num_data_offset; + const uint16_t* block_to_left_offset_ptr = block_to_left_offset + blockIdx.x * blockDim_x; + const uint32_t to_right_block_offset = block_to_right_offset_buffer[blockIdx.x]; + const uint32_t to_left_block_offset = block_to_left_offset_buffer[blockIdx.x]; + data_size_t* left_out_data_indices_in_leaf = out_data_indices_in_leaf + to_left_block_offset; + data_size_t* right_out_data_indices_in_leaf = out_data_indices_in_leaf + to_right_block_offset; + if (static_cast(global_thread_index) < num_data_in_leaf) { + const uint32_t thread_to_left_offset = (threadIdx_x == 0 ? 0 : block_to_left_offset_ptr[threadIdx_x - 1]); + const bool to_left = block_to_left_offset_ptr[threadIdx_x] > thread_to_left_offset; + if (to_left) { + left_out_data_indices_in_leaf[thread_to_left_offset] = cuda_data_indices_in_leaf[global_thread_index]; + } else { + const uint32_t thread_to_right_offset = threadIdx.x - thread_to_left_offset; + right_out_data_indices_in_leaf[thread_to_right_offset] = cuda_data_indices_in_leaf[global_thread_index]; + } + } +} + +__global__ void CopyDataIndicesKernel( + const data_size_t num_data_in_leaf, + const data_size_t* out_data_indices_in_leaf, + data_size_t* cuda_data_indices) { + const unsigned int threadIdx_x = threadIdx.x; + const unsigned int global_thread_index = blockIdx.x * blockDim.x + threadIdx_x; + if (global_thread_index < num_data_in_leaf) { + cuda_data_indices[global_thread_index] = out_data_indices_in_leaf[global_thread_index]; + } +} + +void CUDADataPartition::LaunchSplitInnerKernel( + const data_size_t num_data_in_leaf, + const CUDASplitInfo* best_split_info, + const int left_leaf_index, + const int right_leaf_index, + // for leaf splits information update + CUDALeafSplitsStruct* smaller_leaf_splits, + CUDALeafSplitsStruct* larger_leaf_splits, + data_size_t* left_leaf_num_data_ref, + data_size_t* right_leaf_num_data_ref, + data_size_t* left_leaf_start_ref, + data_size_t* right_leaf_start_ref, + double* left_leaf_sum_of_hessians_ref, + double* right_leaf_sum_of_hessians_ref, + double* left_leaf_sum_of_gradients_ref, + double* right_leaf_sum_of_gradients_ref, + data_size_t* global_left_leaf_num_data, + data_size_t* global_right_leaf_num_data) { + int num_blocks_final_ref = grid_dim_ - 1; + int num_blocks_final_aligned = 1; + while (num_blocks_final_ref > 0) { + num_blocks_final_aligned <<= 1; + num_blocks_final_ref >>= 1; + } + global_timer.Start("CUDADataPartition::AggregateBlockOffsetKernel"); + + if (grid_dim_ > AGGREGATE_BLOCK_SIZE_DATA_PARTITION) { + AggregateBlockOffsetKernel0<<<1, AGGREGATE_BLOCK_SIZE_DATA_PARTITION, 0, cuda_streams_[0]>>>( + left_leaf_index, + right_leaf_index, + cuda_block_data_to_left_offset_.RawData(), + cuda_block_data_to_right_offset_.RawData(), cuda_leaf_data_start_.RawData(), cuda_leaf_data_end_.RawData(), + cuda_leaf_num_data_.RawData(), cuda_data_indices_.RawData(), + grid_dim_); + } else { + AggregateBlockOffsetKernel1<<<1, num_blocks_final_aligned, 0, cuda_streams_[0]>>>( + left_leaf_index, + right_leaf_index, + cuda_block_data_to_left_offset_.RawData(), + cuda_block_data_to_right_offset_.RawData(), cuda_leaf_data_start_.RawData(), cuda_leaf_data_end_.RawData(), + cuda_leaf_num_data_.RawData(), cuda_data_indices_.RawData(), + grid_dim_); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + global_timer.Stop("CUDADataPartition::AggregateBlockOffsetKernel"); + + if (nccl_communicator_ != nullptr) { + NCCLGroupStart(); + NCCLAllReduce( + cuda_leaf_num_data_.RawData() + left_leaf_index, + cuda_split_info_buffer_.RawData() + 16, + 1, ncclInt32, ncclSum, nccl_communicator_, cuda_streams_[0]); + NCCLAllReduce( + cuda_leaf_num_data_.RawData() + right_leaf_index, + cuda_split_info_buffer_.RawData() + 17, + 1, ncclInt32, ncclSum, nccl_communicator_, cuda_streams_[0]); + NCCLGroupEnd(); + } + + global_timer.Start("CUDADataPartition::SplitInnerKernel"); + SplitInnerKernel<<>>( + left_leaf_index, right_leaf_index, cuda_leaf_data_start_.RawData(), cuda_leaf_num_data_.RawData(), cuda_data_indices_.RawData(), + cuda_block_data_to_left_offset_.RawData(), cuda_block_data_to_right_offset_.RawData(), cuda_block_to_left_offset_.RawData(), + cuda_out_data_indices_in_leaf_.RawData()); + global_timer.Stop("CUDADataPartition::SplitInnerKernel"); + SynchronizeCUDADevice(__FILE__, __LINE__); + + global_timer.Start("CUDADataPartition::SplitTreeStructureKernel"); + +#define SPLIT_TREE_ARGS \ + left_leaf_index, right_leaf_index, \ + cuda_block_data_to_left_offset_.RawData(), \ + cuda_block_data_to_right_offset_.RawData(), cuda_leaf_data_start_.RawData(), cuda_leaf_data_end_.RawData(), \ + cuda_leaf_num_data_.RawData(), cuda_out_data_indices_in_leaf_.RawData(), \ + best_split_info, \ + smaller_leaf_splits, \ + larger_leaf_splits, \ + num_total_bin_, \ + cuda_hist_, \ + cuda_hist_pool_.RawData(), \ + cuda_leaf_output_.RawData(), cuda_split_info_buffer_.RawData() + + if (nccl_communicator_ != nullptr) { + if (use_quantized_grad_) { + SplitTreeStructureKernel<<<4, 5, 0, cuda_streams_[0]>>>(SPLIT_TREE_ARGS); + } else { + SplitTreeStructureKernel<<<4, 5, 0, cuda_streams_[0]>>>(SPLIT_TREE_ARGS); + } + } else { + if (use_quantized_grad_) { + SplitTreeStructureKernel<<<4, 5, 0, cuda_streams_[0]>>>(SPLIT_TREE_ARGS); + } else { + SplitTreeStructureKernel<<<4, 5, 0, cuda_streams_[0]>>>(SPLIT_TREE_ARGS); + } + } + +#undef SPLIT_TREE_ARGS + global_timer.Stop("CUDADataPartition::SplitTreeStructureKernel"); + std::vector cpu_split_info_buffer(18); + const double* cpu_sum_hessians_info = reinterpret_cast(cpu_split_info_buffer.data() + 8); + global_timer.Start("CUDADataPartition::CopyFromCUDADeviceToHostAsync"); + CopyFromCUDADeviceToHostAsync(cpu_split_info_buffer.data(), cuda_split_info_buffer_.RawData(), 18, cuda_streams_[0], __FILE__, __LINE__); + SynchronizeCUDADevice(__FILE__, __LINE__); + global_timer.Stop("CUDADataPartition::CopyFromCUDADeviceToHostAsync"); + const data_size_t left_leaf_num_data = cpu_split_info_buffer[1]; + const data_size_t left_leaf_data_start = cpu_split_info_buffer[2]; + const data_size_t right_leaf_num_data = cpu_split_info_buffer[4]; + global_timer.Start("CUDADataPartition::CopyDataIndicesKernel"); + CopyDataIndicesKernel<<>>( + left_leaf_num_data + right_leaf_num_data, cuda_out_data_indices_in_leaf_.RawData(), cuda_data_indices_.RawData() + left_leaf_data_start); + global_timer.Stop("CUDADataPartition::CopyDataIndicesKernel"); + const data_size_t right_leaf_data_start = cpu_split_info_buffer[5]; + *left_leaf_num_data_ref = left_leaf_num_data; + *left_leaf_start_ref = left_leaf_data_start; + *right_leaf_num_data_ref = right_leaf_num_data; + *right_leaf_start_ref = right_leaf_data_start; + *left_leaf_sum_of_hessians_ref = cpu_sum_hessians_info[0]; + *right_leaf_sum_of_hessians_ref = cpu_sum_hessians_info[1]; + *left_leaf_sum_of_gradients_ref = cpu_sum_hessians_info[2]; + *right_leaf_sum_of_gradients_ref = cpu_sum_hessians_info[3]; + if (nccl_communicator_ != nullptr) { + *global_left_leaf_num_data = cpu_split_info_buffer[16]; + *global_right_leaf_num_data = cpu_split_info_buffer[17]; + } +} + +template +__global__ void AddPredictionToScoreKernel( + const data_size_t* data_indices_in_leaf, + const double* leaf_value, double* cuda_scores, + const int* cuda_data_index_to_leaf_index, const data_size_t num_data) { + const unsigned int threadIdx_x = threadIdx.x; + const unsigned int blockIdx_x = blockIdx.x; + const unsigned int blockDim_x = blockDim.x; + const data_size_t local_data_index = static_cast(blockIdx_x * blockDim_x + threadIdx_x); + if (local_data_index < num_data) { + if (USE_BAGGING) { + const data_size_t global_data_index = data_indices_in_leaf[local_data_index]; + const int leaf_index = cuda_data_index_to_leaf_index[global_data_index]; + const double leaf_prediction_value = leaf_value[leaf_index]; + cuda_scores[global_data_index] += leaf_prediction_value; + } else { + const int leaf_index = cuda_data_index_to_leaf_index[local_data_index]; + const double leaf_prediction_value = leaf_value[leaf_index]; + cuda_scores[local_data_index] += leaf_prediction_value; + } + } +} + +void CUDADataPartition::LaunchAddPredictionToScoreKernel(const double* leaf_value, double* cuda_scores) { + global_timer.Start("CUDADataPartition::AddPredictionToScoreKernel"); + const data_size_t num_data_in_root = root_num_data(); + const int num_blocks = (num_data_in_root + FILL_INDICES_BLOCK_SIZE_DATA_PARTITION - 1) / FILL_INDICES_BLOCK_SIZE_DATA_PARTITION; + if (use_bagging_) { + AddPredictionToScoreKernel<<>>( + cuda_data_indices_.RawData(), leaf_value, cuda_scores, cuda_data_index_to_leaf_index_.RawData(), num_data_in_root); + } else { + AddPredictionToScoreKernel<<>>( + cuda_data_indices_.RawData(), leaf_value, cuda_scores, cuda_data_index_to_leaf_index_.RawData(), num_data_in_root); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + global_timer.Stop("CUDADataPartition::AddPredictionToScoreKernel"); +} + +__global__ void RenewDiscretizedTreeLeavesKernel( + const score_t* gradients, + const score_t* hessians, + const data_size_t* data_indices, + const data_size_t* leaf_data_start, + const data_size_t* leaf_num_data, + double* leaf_grad_stat_buffer, + double* leaf_hess_stat_buffer, + double* leaf_values) { + __shared__ double shared_mem_buffer[WARPSIZE]; + const int leaf_index = static_cast(blockIdx.x); + const data_size_t* data_indices_in_leaf = data_indices + leaf_data_start[leaf_index]; + const data_size_t num_data_in_leaf = leaf_num_data[leaf_index]; + double sum_gradients = 0.0f; + double sum_hessians = 0.0f; + for (data_size_t inner_data_index = static_cast(threadIdx.x); + inner_data_index < num_data_in_leaf; inner_data_index += static_cast(blockDim.x)) { + const data_size_t data_index = data_indices_in_leaf[inner_data_index]; + const score_t gradient = gradients[data_index]; + const score_t hessian = hessians[data_index]; + sum_gradients += static_cast(gradient); + sum_hessians += static_cast(hessian); + } + sum_gradients = ShuffleReduceSum(sum_gradients, shared_mem_buffer, blockDim.x); + __syncthreads(); + sum_hessians = ShuffleReduceSum(sum_hessians, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + leaf_grad_stat_buffer[leaf_index] = sum_gradients; + leaf_hess_stat_buffer[leaf_index] = sum_hessians; + } +} + +void CUDADataPartition::LaunchReduceLeafGradStat( + const score_t* gradients, const score_t* hessians, + CUDATree* tree, double* leaf_grad_stat_buffer, double* leaf_hess_state_buffer) const { + const int num_blocks = tree->num_leaves(); + RenewDiscretizedTreeLeavesKernel<<>>( + gradients, + hessians, + cuda_data_indices_.RawData(), + cuda_leaf_data_start_.RawData(), + cuda_leaf_num_data_.RawData(), + leaf_grad_stat_buffer, + leaf_hess_state_buffer, + tree->cuda_leaf_value_ref()); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_data_partition.hpp b/src/treelearner/cuda/cuda_data_partition.hpp new file mode 100644 index 0000000..f6dc386 --- /dev/null +++ b/src/treelearner/cuda/cuda_data_partition.hpp @@ -0,0 +1,406 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_DATA_PARTITION_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_DATA_PARTITION_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include + +#include + +#include +#include +#include + +#include "cuda_leaf_splits.hpp" + +#define FILL_INDICES_BLOCK_SIZE_DATA_PARTITION (1024) +#define SPLIT_INDICES_BLOCK_SIZE_DATA_PARTITION (1024) +#define AGGREGATE_BLOCK_SIZE_DATA_PARTITION (1024) + +namespace LightGBM { + +class CUDADataPartition: public NCCLInfo { + public: + CUDADataPartition( + const Dataset* train_data, + const int num_total_bin, + const int num_leaves, + const int num_threads, + const bool use_quantized_grad, + hist_t* cuda_hist); + + ~CUDADataPartition(); + + void Init(); + + void BeforeTrain(); + + void Split( + // input best split info + const CUDASplitInfo* best_split_info, + const int left_leaf_index, + const int right_leaf_index, + const int leaf_best_split_feature, + const uint32_t leaf_best_split_threshold, + const uint32_t* categorical_bitset, + const int categorical_bitset_len, + const uint8_t leaf_best_split_default_left, + const data_size_t num_data_in_leaf, + const data_size_t leaf_data_start, + // for leaf information update + CUDALeafSplitsStruct* smaller_leaf_splits, + CUDALeafSplitsStruct* larger_leaf_splits, + // gather information for CPU, used for launching kernels + data_size_t* left_leaf_num_data, + data_size_t* right_leaf_num_data, + data_size_t* left_leaf_start, + data_size_t* right_leaf_start, + double* left_leaf_sum_of_hessians, + double* right_leaf_sum_of_hessians, + double* left_leaf_sum_of_gradients, + double* right_leaf_sum_of_gradients, + data_size_t* global_left_leaf_num_data, + data_size_t* global_right_leaf_num_data); + + void UpdateTrainScore(const Tree* tree, double* cuda_scores); + + void SetUsedDataIndices(const data_size_t* used_indices, const data_size_t num_used_indices); + + void SetBaggingSubset(const Dataset* subset); + + void ResetTrainingData(const Dataset* train_data, const int num_total_bin, hist_t* cuda_hist); + + void ResetConfig(const Config* config, hist_t* cuda_hist); + + void ResetByLeafPred(const std::vector& leaf_pred, int num_leaves); + + void ReduceLeafGradStat( + const score_t* gradients, const score_t* hessians, + CUDATree* tree, double* leaf_grad_stat_buffer, double* leaf_hess_state_buffer) const; + + data_size_t root_num_data() const { + if (use_bagging_) { + return num_used_indices_; + } else { + return num_data_; + } + } + + const data_size_t* cuda_data_indices() const { return cuda_data_indices_.RawData(); } + + const data_size_t* cuda_leaf_num_data() const { return cuda_leaf_num_data_.RawData(); } + + const data_size_t* cuda_leaf_data_start() const { return cuda_leaf_data_start_.RawData(); } + + const int* cuda_data_index_to_leaf_index() const { return cuda_data_index_to_leaf_index_.RawData(); } + + bool use_bagging() const { return use_bagging_; } + + private: + void CalcBlockDim(const data_size_t num_data_in_leaf); + + void GenDataToLeftBitVector( + const data_size_t num_data_in_leaf, + const int split_feature_index, + const uint32_t split_threshold, + const uint32_t* categorical_bitset, + const int categorical_bitset_len, + const uint8_t split_default_left, + const data_size_t leaf_data_start, + const int left_leaf_index, + const int right_leaf_index); + + void SplitInner( + // input best split info + const data_size_t num_data_in_leaf, + const CUDASplitInfo* best_split_info, + const int left_leaf_index, + const int right_leaf_index, + // for leaf splits information update + CUDALeafSplitsStruct* smaller_leaf_splits, + CUDALeafSplitsStruct* larger_leaf_splits, + // gather information for CPU, used for launching kernels + data_size_t* left_leaf_num_data, + data_size_t* right_leaf_num_data, + data_size_t* left_leaf_start, + data_size_t* right_leaf_start, + double* left_leaf_sum_of_hessians, + double* right_leaf_sum_of_hessians, + double* left_leaf_sum_of_gradients, + double* right_leaf_sum_of_gradients, + data_size_t* global_left_leaf_num_data, + data_size_t* global_right_leaf_num_data); + + // kernel launch functions + void LaunchFillDataIndicesBeforeTrain(); + + void LaunchSplitInnerKernel( + // input best split info + const data_size_t num_data_in_leaf, + const CUDASplitInfo* best_split_info, + const int left_leaf_index, + const int right_leaf_index, + // for leaf splits information update + CUDALeafSplitsStruct* smaller_leaf_splits, + CUDALeafSplitsStruct* larger_leaf_splits, + // gather information for CPU, used for launching kernels + data_size_t* left_leaf_num_data, + data_size_t* right_leaf_num_data, + data_size_t* left_leaf_start, + data_size_t* right_leaf_start, + double* left_leaf_sum_of_hessians, + double* right_leaf_sum_of_hessians, + double* left_leaf_sum_of_gradients, + double* right_leaf_sum_of_gradients, + data_size_t* global_left_leaf_num_data, + data_size_t* global_right_leaf_num_data); + + void LaunchGenDataToLeftBitVectorKernel( + const data_size_t num_data_in_leaf, + const int split_feature_index, + const uint32_t split_threshold, + const uint8_t split_default_left, + const data_size_t leaf_data_start, + const int left_leaf_index, + const int right_leaf_index); + + void LaunchGenDataToLeftBitVectorCategoricalKernel( + const data_size_t num_data_in_leaf, + const int split_feature_index, + const uint32_t* bitset, + const int bitset_len, + const uint8_t split_default_left, + const data_size_t leaf_data_start, + const int left_leaf_index, + const int right_leaf_index); + +#define GenDataToLeftBitVectorKernel_PARAMS \ + const BIN_TYPE* column_data, \ + const data_size_t num_data_in_leaf, \ + const data_size_t* data_indices_in_leaf, \ + const uint32_t th, \ + const uint32_t t_zero_bin, \ + const uint32_t max_bin, \ + const uint32_t min_bin, \ + const uint8_t split_default_to_left, \ + const uint8_t split_missing_default_to_left + + template + void LaunchGenDataToLeftBitVectorKernelInner( + GenDataToLeftBitVectorKernel_PARAMS, + const bool missing_is_zero, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column); + + template + void LaunchGenDataToLeftBitVectorKernelInner0( + GenDataToLeftBitVectorKernel_PARAMS, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column); + + template + void LaunchGenDataToLeftBitVectorKernelInner1( + GenDataToLeftBitVectorKernel_PARAMS, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column); + + template + void LaunchGenDataToLeftBitVectorKernelInner2( + GenDataToLeftBitVectorKernel_PARAMS, + const bool mfb_is_na, + const bool max_bin_to_left, + const bool is_single_feature_in_column); + + template + void LaunchGenDataToLeftBitVectorKernelInner3( + GenDataToLeftBitVectorKernel_PARAMS, + const bool max_bin_to_left, + const bool is_single_feature_in_column); + + template + void LaunchGenDataToLeftBitVectorKernelInner4( + GenDataToLeftBitVectorKernel_PARAMS, + const bool is_single_feature_in_column); + +#undef GenDataToLeftBitVectorKernel_PARAMS + +#define UpdateDataIndexToLeafIndexKernel_PARAMS \ + const BIN_TYPE* column_data, \ + const data_size_t num_data_in_leaf, \ + const data_size_t* data_indices_in_leaf, \ + const uint32_t th, \ + const uint32_t t_zero_bin, \ + const uint32_t max_bin_ref, \ + const uint32_t min_bin_ref, \ + const int left_leaf_index, \ + const int right_leaf_index, \ + const int default_leaf_index, \ + const int missing_default_leaf_index + + template + void LaunchUpdateDataIndexToLeafIndexKernel( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool missing_is_zero, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column); + + template + void LaunchUpdateDataIndexToLeafIndexKernel_Inner0( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool missing_is_na, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column); + + template + void LaunchUpdateDataIndexToLeafIndexKernel_Inner1( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool mfb_is_zero, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column); + + template + void LaunchUpdateDataIndexToLeafIndexKernel_Inner2( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool mfb_is_na, + const bool max_to_left, + const bool is_single_feature_in_column); + + template + void LaunchUpdateDataIndexToLeafIndexKernel_Inner3( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool max_to_left, + const bool is_single_feature_in_column); + + template + void LaunchUpdateDataIndexToLeafIndexKernel_Inner4( + UpdateDataIndexToLeafIndexKernel_PARAMS, + const bool is_single_feature_in_column); + +#undef UpdateDataIndexToLeafIndexKernel_PARAMS + + void LaunchAddPredictionToScoreKernel(const double* leaf_value, double* cuda_scores); + + void LaunchFillDataIndexToLeafIndex(); + + void LaunchReduceLeafGradStat( + const score_t* gradients, const score_t* hessians, + CUDATree* tree, double* leaf_grad_stat_buffer, double* leaf_hess_state_buffer) const; + + // Host memory + + // dataset information + /*! \brief number of training data */ + data_size_t num_data_; + /*! \brief number of features in training data */ + int num_features_; + /*! \brief number of total bins in training data */ + int num_total_bin_; + /*! \brief bin data stored by column */ + const CUDAColumnData* cuda_column_data_; + /*! \brief grid dimension when splitting one leaf */ + int grid_dim_; + /*! \brief block dimension when splitting one leaf */ + int block_dim_; + /*! \brief data indices used in this iteration */ + const data_size_t* used_indices_; + /*! \brief marks whether a feature is a categorical feature */ + std::vector is_categorical_feature_; + /*! \brief marks whether a feature is the only feature in its group */ + std::vector is_single_feature_in_column_; + + // config information + /*! \brief maximum number of leaves in a tree */ + int num_leaves_; + /*! \brief number of threads */ + int num_threads_; + /*! \brief whether to use quantized gradients */ + bool use_quantized_grad_; + + // per iteration information + /*! \brief whether bagging is used in this iteration */ + bool use_bagging_; + /*! \brief number of used data indices in this iteration */ + data_size_t num_used_indices_; + + // tree structure information + /*! \brief current number of leaves in tree */ + int cur_num_leaves_; + + // split algorithm related + /*! \brief maximum number of blocks to aggregate after finding bit vector by blocks */ + int max_num_split_indices_blocks_; + + // CUDA streams + /*! \brief cuda streams used for asynchronizing kernel computing and memory copy */ + std::vector cuda_streams_; + + + // CUDA memory, held by this object + + // tree structure information + /*! \brief data indices by leaf */ + CUDAVector cuda_data_indices_; + /*! \brief start position of each leaf in cuda_data_indices_ */ + CUDAVector cuda_leaf_data_start_; + /*! \brief end position of each leaf in cuda_data_indices_ */ + CUDAVector cuda_leaf_data_end_; + /*! \brief number of data in each leaf */ + CUDAVector cuda_leaf_num_data_; + /*! \brief records the histogram of each leaf */ + CUDAVector cuda_hist_pool_; + /*! \brief records the value of each leaf */ + CUDAVector cuda_leaf_output_; + + // split data algorithm related + CUDAVector cuda_block_to_left_offset_; + /*! \brief maps data index to leaf index, for adding scores to training data set */ + CUDAVector cuda_data_index_to_leaf_index_; + /*! \brief prefix sum of number of data going to left in all blocks */ + CUDAVector cuda_block_data_to_left_offset_; + /*! \brief prefix sum of number of data going to right in all blocks */ + CUDAVector cuda_block_data_to_right_offset_; + /*! \brief buffer for splitting data indices, will be copied back to cuda_data_indices_ after split */ + CUDAVector cuda_out_data_indices_in_leaf_; + + // split tree structure algorithm related + /*! \brief buffer to store split information, prepared to be copied to cpu */ + CUDAVector cuda_split_info_buffer_; + + // dataset information + /*! \brief number of data in training set, for initialization of cuda_leaf_num_data_ and cuda_leaf_data_end_ */ + CUDAVector cuda_num_data_; + + + // CUDA memory, held by other object + + // dataset information + /*! \brief beginning of histograms, for initialization of cuda_hist_pool_ */ + hist_t* cuda_hist_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_DATA_PARTITION_HPP_ diff --git a/src/treelearner/cuda/cuda_gradient_discretizer.cu b/src/treelearner/cuda/cuda_gradient_discretizer.cu new file mode 100644 index 0000000..cbae24d --- /dev/null +++ b/src/treelearner/cuda/cuda_gradient_discretizer.cu @@ -0,0 +1,185 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include + +#include + +#include "cuda_gradient_discretizer.hpp" + +namespace LightGBM { + +__global__ void ReduceMinMaxKernel( + const data_size_t num_data, + const score_t* input_gradients, + const score_t* input_hessians, + score_t* grad_min_block_buffer, + score_t* grad_max_block_buffer, + score_t* hess_min_block_buffer, + score_t* hess_max_block_buffer) { + __shared__ score_t shared_mem_buffer[WARPSIZE]; + const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + score_t grad_max_val = kMinScore; + score_t grad_min_val = kMaxScore; + score_t hess_max_val = kMinScore; + score_t hess_min_val = kMaxScore; + if (index < num_data) { + grad_max_val = input_gradients[index]; + grad_min_val = input_gradients[index]; + hess_max_val = input_hessians[index]; + hess_min_val = input_hessians[index]; + } + grad_min_val = ShuffleReduceMin(grad_min_val, shared_mem_buffer, blockDim.x); + __syncthreads(); + grad_max_val = ShuffleReduceMax(grad_max_val, shared_mem_buffer, blockDim.x); + __syncthreads(); + hess_min_val = ShuffleReduceMin(hess_min_val, shared_mem_buffer, blockDim.x); + __syncthreads(); + hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + grad_min_block_buffer[blockIdx.x] = grad_min_val; + grad_max_block_buffer[blockIdx.x] = grad_max_val; + hess_min_block_buffer[blockIdx.x] = hess_min_val; + hess_max_block_buffer[blockIdx.x] = hess_max_val; + } +} + +__global__ void ReduceBlockMinMaxKernel( + const int num_blocks, + const int grad_discretize_bins, + score_t* grad_min_block_buffer, + score_t* grad_max_block_buffer, + score_t* hess_min_block_buffer, + score_t* hess_max_block_buffer) { + __shared__ score_t shared_mem_buffer[WARPSIZE]; + score_t grad_max_val = kMinScore; + score_t grad_min_val = kMaxScore; + score_t hess_max_val = kMinScore; + score_t hess_min_val = kMaxScore; + for (int block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) { + grad_min_val = min(grad_min_val, grad_min_block_buffer[block_index]); + grad_max_val = max(grad_max_val, grad_max_block_buffer[block_index]); + hess_min_val = min(hess_min_val, hess_min_block_buffer[block_index]); + hess_max_val = max(hess_max_val, hess_max_block_buffer[block_index]); + } + grad_min_val = ShuffleReduceMin(grad_min_val, shared_mem_buffer, blockDim.x); + __syncthreads(); + grad_max_val = ShuffleReduceMax(grad_max_val, shared_mem_buffer, blockDim.x); + __syncthreads(); + hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x); + __syncthreads(); + hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + const score_t grad_abs_max = max(fabs(grad_min_val), fabs(grad_max_val)); + const score_t hess_abs_max = max(fabs(hess_min_val), fabs(hess_max_val)); + grad_min_block_buffer[0] = 1.0f / (grad_abs_max / (grad_discretize_bins / 2)); + grad_max_block_buffer[0] = (grad_abs_max / (grad_discretize_bins / 2)); + hess_min_block_buffer[0] = 1.0f / (hess_abs_max / (grad_discretize_bins)); + hess_max_block_buffer[0] = (hess_abs_max / (grad_discretize_bins)); + } +} + +template +__global__ void DiscretizeGradientsKernel( + const data_size_t num_data, + const score_t* input_gradients, + const score_t* input_hessians, + const score_t* grad_scale_ptr, + const score_t* hess_scale_ptr, + const int iter, + const int* random_values_use_start, + const score_t* gradient_random_values, + const score_t* hessian_random_values, + const int grad_discretize_bins, + int8_t* output_gradients_and_hessians) { + const int start = random_values_use_start[iter]; + const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + const score_t grad_scale = *grad_scale_ptr; + const score_t hess_scale = *hess_scale_ptr; + int16_t* output_gradients_and_hessians_ptr = reinterpret_cast(output_gradients_and_hessians); + if (index < num_data) { + if (STOCHASTIC_ROUNDING) { + const data_size_t index_offset = (index + start) % num_data; + const score_t gradient = input_gradients[index]; + const score_t hessian = input_hessians[index]; + const score_t gradient_random_value = gradient_random_values[index_offset]; + const score_t hessian_random_value = hessian_random_values[index_offset]; + output_gradients_and_hessians_ptr[2 * index + 1] = gradient > 0.0f ? + static_cast(gradient * grad_scale + gradient_random_value) : + static_cast(gradient * grad_scale - gradient_random_value); + output_gradients_and_hessians_ptr[2 * index] = static_cast(hessian * hess_scale + hessian_random_value); + } else { + const score_t gradient = input_gradients[index]; + const score_t hessian = input_hessians[index]; + output_gradients_and_hessians_ptr[2 * index + 1] = gradient > 0.0f ? + static_cast(gradient * grad_scale + 0.5) : + static_cast(gradient * grad_scale - 0.5); + output_gradients_and_hessians_ptr[2 * index] = static_cast(hessian * hess_scale + 0.5); + } + } +} + +void CUDAGradientDiscretizer::DiscretizeGradients( + const data_size_t num_data, + const score_t* input_gradients, + const score_t* input_hessians) { + ReduceMinMaxKernel<<>>( + num_data, input_gradients, input_hessians, + grad_min_block_buffer_.RawData(), + grad_max_block_buffer_.RawData(), + hess_min_block_buffer_.RawData(), + hess_max_block_buffer_.RawData()); + SynchronizeCUDADevice(__FILE__, __LINE__); + ReduceBlockMinMaxKernel<<<1, CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE>>>( + num_reduce_blocks_, + num_grad_quant_bins_, + grad_min_block_buffer_.RawData(), + grad_max_block_buffer_.RawData(), + hess_min_block_buffer_.RawData(), + hess_max_block_buffer_.RawData()); + SynchronizeCUDADevice(__FILE__, __LINE__); + + if (nccl_communicator_ != nullptr) { + SynchronizeCUDADevice(__FILE__, __LINE__); + cudaStream_t cuda_stream = CUDAStreamCreate(); + NCCLGroupStart(); + NCCLAllReduce(grad_min_block_buffer_.RawDataReadOnly(), grad_min_block_buffer_.RawData(), 1, ncclFloat32, ncclMin, nccl_communicator_, cuda_stream); + NCCLAllReduce(hess_min_block_buffer_.RawDataReadOnly(), hess_min_block_buffer_.RawData(), 1, ncclFloat32, ncclMin, nccl_communicator_, cuda_stream); + NCCLAllReduce(grad_max_block_buffer_.RawDataReadOnly(), grad_max_block_buffer_.RawData(), 1, ncclFloat32, ncclMax, nccl_communicator_, cuda_stream); + NCCLAllReduce(hess_max_block_buffer_.RawDataReadOnly(), hess_max_block_buffer_.RawData(), 1, ncclFloat32, ncclMax, nccl_communicator_, cuda_stream); + NCCLGroupEnd(); + SynchronizeCUDAStream(cuda_stream, __FILE__, __LINE__); + CUDAStreamDestroy(cuda_stream); + } + + #define DiscretizeGradientsKernel_ARGS \ + num_data, \ + input_gradients, \ + input_hessians, \ + grad_min_block_buffer_.RawData(), \ + hess_min_block_buffer_.RawData(), \ + iter_, \ + random_values_use_start_.RawData(), \ + gradient_random_values_.RawData(), \ + hessian_random_values_.RawData(), \ + num_grad_quant_bins_, \ + discretized_gradients_and_hessians_.RawData() + + if (stochastic_rounding_) { + DiscretizeGradientsKernel<<>>(DiscretizeGradientsKernel_ARGS); + } else { + DiscretizeGradientsKernel<<>>(DiscretizeGradientsKernel_ARGS); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + ++iter_; +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_gradient_discretizer.hpp b/src/treelearner/cuda/cuda_gradient_discretizer.hpp new file mode 100644 index 0000000..0ba930b --- /dev/null +++ b/src/treelearner/cuda/cuda_gradient_discretizer.hpp @@ -0,0 +1,121 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include +#include + +#include +#include +#include + +#include "cuda_leaf_splits.hpp" +#include "../gradient_discretizer.hpp" + +namespace LightGBM { + +#define CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE (1024) + +class CUDAGradientDiscretizer: public GradientDiscretizer, public NCCLInfo { + public: + CUDAGradientDiscretizer(int num_grad_quant_bins, int num_trees, int random_seed, bool is_constant_hessian, bool stochastic_roudning): + GradientDiscretizer(num_grad_quant_bins, num_trees, random_seed, is_constant_hessian, stochastic_roudning) { + } + + ~CUDAGradientDiscretizer() {} + + void DiscretizeGradients( + const data_size_t num_data, + const score_t* input_gradients, + const score_t* input_hessians) override; + + const int8_t* discretized_gradients_and_hessians() const override { return discretized_gradients_and_hessians_.RawData(); } + + double grad_scale() const override { + Log::Fatal("grad_scale() of CUDAGradientDiscretizer should not be called."); + return 0.0; + } + + double hess_scale() const override { + Log::Fatal("hess_scale() of CUDAGradientDiscretizer should not be called."); + return 0.0; + } + + const score_t* grad_scale_ptr() const { return grad_max_block_buffer_.RawData(); } + + const score_t* hess_scale_ptr() const { return hess_max_block_buffer_.RawData(); } + + void Init(const data_size_t num_data, const int num_leaves, + const int num_features, const Dataset* train_data) override { + GradientDiscretizer::Init(num_data, num_leaves, num_features, train_data); + discretized_gradients_and_hessians_.Resize(num_data * 2); + num_reduce_blocks_ = (num_data + CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE - 1) / CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE; + grad_min_block_buffer_.Resize(num_reduce_blocks_); + grad_max_block_buffer_.Resize(num_reduce_blocks_); + hess_min_block_buffer_.Resize(num_reduce_blocks_); + hess_max_block_buffer_.Resize(num_reduce_blocks_); + random_values_use_start_.Resize(num_trees_); + gradient_random_values_.Resize(num_data); + hessian_random_values_.Resize(num_data); + + std::vector gradient_random_values(num_data, 0.0f); + std::vector hessian_random_values(num_data, 0.0f); + std::vector random_values_use_start(num_trees_, 0); + + const int num_threads = OMP_NUM_THREADS(); + + std::mt19937 random_values_use_start_eng = std::mt19937(random_seed_); + std::uniform_int_distribution random_values_use_start_dist = std::uniform_int_distribution(0, num_data); + for (int tree_index = 0; tree_index < num_trees_; ++tree_index) { + random_values_use_start[tree_index] = random_values_use_start_dist(random_values_use_start_eng); + } + + int num_blocks = 0; + data_size_t block_size = 0; + Threading::BlockInfo(num_data, 512, &num_blocks, &block_size); + #pragma omp parallel for schedule(static, 1) num_threads(num_threads) + for (int thread_id = 0; thread_id < num_blocks; ++thread_id) { + const data_size_t start = thread_id * block_size; + const data_size_t end = std::min(start + block_size, num_data); + std::mt19937 gradient_random_values_eng(random_seed_ + thread_id); + std::uniform_real_distribution gradient_random_values_dist(0.0f, 1.0f); + std::mt19937 hessian_random_values_eng(random_seed_ + thread_id + num_threads); + std::uniform_real_distribution hessian_random_values_dist(0.0f, 1.0f); + for (data_size_t i = start; i < end; ++i) { + gradient_random_values[i] = gradient_random_values_dist(gradient_random_values_eng); + hessian_random_values[i] = hessian_random_values_dist(hessian_random_values_eng); + } + } + + CopyFromHostToCUDADevice(gradient_random_values_.RawData(), gradient_random_values.data(), gradient_random_values.size(), __FILE__, __LINE__); + CopyFromHostToCUDADevice(hessian_random_values_.RawData(), hessian_random_values.data(), hessian_random_values.size(), __FILE__, __LINE__); + CopyFromHostToCUDADevice(random_values_use_start_.RawData(), random_values_use_start.data(), random_values_use_start.size(), __FILE__, __LINE__); + iter_ = 0; + } + + protected: + mutable CUDAVector discretized_gradients_and_hessians_; + mutable CUDAVector grad_min_block_buffer_; + mutable CUDAVector grad_max_block_buffer_; + mutable CUDAVector hess_min_block_buffer_; + mutable CUDAVector hess_max_block_buffer_; + CUDAVector random_values_use_start_; + CUDAVector gradient_random_values_; + CUDAVector hessian_random_values_; + int num_reduce_blocks_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_ diff --git a/src/treelearner/cuda/cuda_histogram_constructor.cpp b/src/treelearner/cuda/cuda_histogram_constructor.cpp new file mode 100644 index 0000000..141ff26 --- /dev/null +++ b/src/treelearner/cuda/cuda_histogram_constructor.cpp @@ -0,0 +1,192 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_histogram_constructor.hpp" + +#include +#include + +namespace LightGBM { + +CUDAHistogramConstructor::CUDAHistogramConstructor( + const Dataset* train_data, + const int num_leaves, + const int num_threads, + const std::vector& feature_hist_offsets, + const int min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const int gpu_device_id, + const bool gpu_use_dp, + const bool use_quantized_grad, + const int num_grad_quant_bins): + num_data_(train_data->num_data()), + num_features_(train_data->num_features()), + num_leaves_(num_leaves), + num_threads_(num_threads), + min_data_in_leaf_(min_data_in_leaf), + min_sum_hessian_in_leaf_(min_sum_hessian_in_leaf), + gpu_device_id_(gpu_device_id), + gpu_use_dp_(gpu_use_dp), + use_quantized_grad_(use_quantized_grad), + num_grad_quant_bins_(num_grad_quant_bins) { + InitFeatureMetaInfo(train_data, feature_hist_offsets); + cuda_row_data_.reset(nullptr); +} + +CUDAHistogramConstructor::~CUDAHistogramConstructor() { + gpuAssert(cudaStreamDestroy(cuda_stream_), __FILE__, __LINE__); +} + +void CUDAHistogramConstructor::InitFeatureMetaInfo(const Dataset* train_data, const std::vector& feature_hist_offsets) { + need_fix_histogram_features_.clear(); + need_fix_histogram_features_num_bin_aligend_.clear(); + feature_num_bins_.clear(); + feature_most_freq_bins_.clear(); + for (int feature_index = 0; feature_index < train_data->num_features(); ++feature_index) { + const BinMapper* bin_mapper = train_data->FeatureBinMapper(feature_index); + const uint32_t most_freq_bin = bin_mapper->GetMostFreqBin(); + if (most_freq_bin != 0) { + need_fix_histogram_features_.emplace_back(feature_index); + uint32_t num_bin_ref = static_cast(bin_mapper->num_bin()) - 1; + uint32_t num_bin_aligned = 1; + while (num_bin_ref > 0) { + num_bin_aligned <<= 1; + num_bin_ref >>= 1; + } + need_fix_histogram_features_num_bin_aligend_.emplace_back(num_bin_aligned); + } + feature_num_bins_.emplace_back(static_cast(bin_mapper->num_bin())); + feature_most_freq_bins_.emplace_back(most_freq_bin); + } + feature_hist_offsets_.clear(); + for (size_t i = 0; i < feature_hist_offsets.size(); ++i) { + feature_hist_offsets_.emplace_back(feature_hist_offsets[i]); + } + if (feature_hist_offsets.empty()) { + num_total_bin_ = 0; + } else { + num_total_bin_ = static_cast(feature_hist_offsets.back()); + } +} + +void CUDAHistogramConstructor::BeforeTrain(const score_t* gradients, const score_t* hessians) { + cuda_gradients_ = gradients; + cuda_hessians_ = hessians; + cuda_hist_.SetValue(0); +} + +void CUDAHistogramConstructor::Init(const Dataset* train_data, TrainingShareStates* share_state) { + cuda_hist_.Resize(static_cast(num_total_bin_ * 2 * num_leaves_)); + cuda_hist_.SetValue(0); + + cuda_feature_num_bins_.InitFromHostVector(feature_num_bins_); + cuda_feature_hist_offsets_.InitFromHostVector(feature_hist_offsets_); + cuda_feature_most_freq_bins_.InitFromHostVector(feature_most_freq_bins_); + + cuda_row_data_.reset(new CUDARowData(train_data, share_state, gpu_device_id_, gpu_use_dp_)); + cuda_row_data_->Init(train_data, share_state); + + CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream_)); + + cuda_need_fix_histogram_features_.InitFromHostVector(need_fix_histogram_features_); + cuda_need_fix_histogram_features_num_bin_aligned_.InitFromHostVector(need_fix_histogram_features_num_bin_aligend_); + + if (cuda_row_data_->NumLargeBinPartition() > 0) { + int grid_dim_x = 0, grid_dim_y = 0, block_dim_x = 0, block_dim_y = 0; + CalcConstructHistogramKernelDim(&grid_dim_x, &grid_dim_y, &block_dim_x, &block_dim_y, num_data_); + const size_t buffer_size = static_cast(grid_dim_y) * static_cast(num_total_bin_); + if (!use_quantized_grad_) { + if (gpu_use_dp_) { + // need to double the size of histogram buffer in global memory when using double precision in histogram construction + cuda_hist_buffer_.Resize(buffer_size * 4); + } else { + cuda_hist_buffer_.Resize(buffer_size * 2); + } + } else { + // use only half the size of histogram buffer in global memory when quantized training since each gradient and hessian takes only 2 bytes + cuda_hist_buffer_.Resize(buffer_size); + } + } + hist_buffer_for_num_bit_change_.Resize(num_total_bin_ * 2); +} + +void CUDAHistogramConstructor::ConstructHistogramForLeaf( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* /*cuda_larger_leaf_splits*/, + const data_size_t global_num_data_in_smaller_leaf, + const data_size_t global_num_data_in_larger_leaf, + const data_size_t num_data_in_smaller_leaf, + const data_size_t /*num_data_in_larger_leaf*/, + const double sum_hessians_in_smaller_leaf, + const double sum_hessians_in_larger_leaf, + const uint8_t num_bits_in_histogram_bins) { +if ((global_num_data_in_smaller_leaf <= min_data_in_leaf_ || sum_hessians_in_smaller_leaf <= min_sum_hessian_in_leaf_) && + (global_num_data_in_larger_leaf <= min_data_in_leaf_ || sum_hessians_in_larger_leaf <= min_sum_hessian_in_leaf_)) { + return; + } + LaunchConstructHistogramKernel(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDAHistogramConstructor::SubtractHistogramForLeaf( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits, + const bool use_quantized_grad, + const uint8_t parent_num_bits_in_histogram_bins, + const uint8_t smaller_num_bits_in_histogram_bins, + const uint8_t larger_num_bits_in_histogram_bins) { + global_timer.Start("CUDAHistogramConstructor::ConstructHistogramForLeaf::LaunchSubtractHistogramKernel"); + LaunchSubtractHistogramKernel(cuda_smaller_leaf_splits, cuda_larger_leaf_splits, use_quantized_grad, + parent_num_bits_in_histogram_bins, smaller_num_bits_in_histogram_bins, larger_num_bits_in_histogram_bins); + global_timer.Stop("CUDAHistogramConstructor::ConstructHistogramForLeaf::LaunchSubtractHistogramKernel"); +} + +void CUDAHistogramConstructor::CalcConstructHistogramKernelDim( + int* grid_dim_x, + int* grid_dim_y, + int* block_dim_x, + int* block_dim_y, + const data_size_t num_data_in_smaller_leaf) { + *block_dim_x = cuda_row_data_->max_num_column_per_partition(); + *block_dim_y = NUM_THREADS_PER_BLOCK / cuda_row_data_->max_num_column_per_partition(); + *grid_dim_x = cuda_row_data_->num_feature_partitions(); + *grid_dim_y = std::max(min_grid_dim_y_, + ((num_data_in_smaller_leaf + NUM_DATA_PER_THREAD - 1) / NUM_DATA_PER_THREAD + (*block_dim_y) - 1) / (*block_dim_y)); +} + +void CUDAHistogramConstructor::ResetTrainingData(const Dataset* train_data, TrainingShareStates* share_states) { + num_data_ = train_data->num_data(); + num_features_ = train_data->num_features(); + InitFeatureMetaInfo(train_data, share_states->feature_hist_offsets()); + + cuda_hist_.Resize(static_cast(num_total_bin_ * 2 * num_leaves_)); + cuda_hist_.SetValue(0); + cuda_feature_num_bins_.InitFromHostVector(feature_num_bins_); + cuda_feature_hist_offsets_.InitFromHostVector(feature_hist_offsets_); + cuda_feature_most_freq_bins_.InitFromHostVector(feature_most_freq_bins_); + + cuda_row_data_.reset(new CUDARowData(train_data, share_states, gpu_device_id_, gpu_use_dp_)); + cuda_row_data_->Init(train_data, share_states); + + cuda_need_fix_histogram_features_.InitFromHostVector(need_fix_histogram_features_); + cuda_need_fix_histogram_features_num_bin_aligned_.InitFromHostVector(need_fix_histogram_features_num_bin_aligend_); +} + +void CUDAHistogramConstructor::ResetConfig(const Config* config) { + num_threads_ = OMP_NUM_THREADS(); + num_leaves_ = config->num_leaves; + min_data_in_leaf_ = config->min_data_in_leaf; + min_sum_hessian_in_leaf_ = config->min_sum_hessian_in_leaf; + cuda_hist_.Resize(static_cast(num_total_bin_ * 2 * num_leaves_)); + cuda_hist_.SetValue(0); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_histogram_constructor.cu b/src/treelearner/cuda/cuda_histogram_constructor.cu new file mode 100644 index 0000000..34c7654 --- /dev/null +++ b/src/treelearner/cuda/cuda_histogram_constructor.cu @@ -0,0 +1,963 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include "cuda_histogram_constructor.hpp" + +#include +#include + +#include + +namespace LightGBM { + +template +__global__ void CUDAConstructHistogramDenseKernel( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const score_t* cuda_gradients, + const score_t* cuda_hessians, + const BIN_TYPE* data, + const uint32_t* column_hist_offsets, + const uint32_t* column_hist_offsets_full, + const int* feature_partition_column_index_offsets, + const data_size_t num_data) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + __shared__ HIST_TYPE shared_hist[SHARED_HIST_SIZE]; + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; + const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; + const BIN_TYPE* data_ptr = data + static_cast(partition_column_start) * num_data; + const int num_columns_in_partition = partition_column_end - partition_column_start; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist[i] = 0.0f; + } + __syncthreads(); + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (static_cast(blockIdx_y) * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const int column_index = static_cast(threadIdx.x) + partition_column_start; + if (threadIdx.x < static_cast(num_columns_in_partition)) { + HIST_TYPE* shared_hist_ptr = shared_hist + (column_hist_offsets[column_index] << 1); + for (data_size_t inner_data_index = static_cast(threadIdx.y); inner_data_index < block_num_data; inner_data_index += blockDim.y) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const score_t grad = cuda_gradients[data_index]; + const score_t hess = cuda_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[static_cast(data_index) * num_columns_in_partition + threadIdx.x]); + const uint32_t pos = bin << 1; + HIST_TYPE* pos_ptr = shared_hist_ptr + pos; + atomicAdd_block(pos_ptr, grad); + atomicAdd_block(pos_ptr + 1, hess); + } + } + __syncthreads(); + hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); + } +} + +template +__global__ void CUDAConstructHistogramSparseKernel( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const score_t* cuda_gradients, + const score_t* cuda_hessians, + const BIN_TYPE* data, + const DATA_PTR_TYPE* row_ptr, + const DATA_PTR_TYPE* partition_ptr, + const uint32_t* column_hist_offsets_full, + const data_size_t num_data) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + __shared__ HIST_TYPE shared_hist[SHARED_HIST_SIZE]; + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const DATA_PTR_TYPE* block_row_ptr = row_ptr + static_cast(blockIdx.x) * (num_data + 1); + const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist[i] = 0.0f; + } + __syncthreads(); + const unsigned int threadIdx_y = threadIdx.y; + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; + const data_size_t remainder = block_num_data % blockDim.y; + const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); + data_size_t inner_data_index = static_cast(threadIdx_y); + for (data_size_t i = 0; i < num_iteration_this; ++i) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; + const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; + const DATA_PTR_TYPE row_size = row_end - row_start; + if (threadIdx.x < row_size) { + const score_t grad = cuda_gradients[data_index]; + const score_t hess = cuda_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); + const uint32_t pos = bin << 1; + HIST_TYPE* pos_ptr = shared_hist + pos; + atomicAdd_block(pos_ptr, grad); + atomicAdd_block(pos_ptr + 1, hess); + } + inner_data_index += blockDim.y; + } + __syncthreads(); + hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); + } +} + +template +__global__ void CUDAConstructHistogramDenseKernel_GlobalMemory( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const score_t* cuda_gradients, + const score_t* cuda_hessians, + const BIN_TYPE* data, + const uint32_t* column_hist_offsets, + const uint32_t* column_hist_offsets_full, + const int* feature_partition_column_index_offsets, + const data_size_t num_data, + HIST_TYPE* global_hist_buffer) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; + const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; + const BIN_TYPE* data_ptr = data + static_cast(partition_column_start) * num_data; + const int num_columns_in_partition = partition_column_end - partition_column_start; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + const int num_total_bin = column_hist_offsets_full[gridDim.x]; + HIST_TYPE* shared_hist = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start) * 2; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist[i] = 0.0f; + } + __syncthreads(); + const unsigned int threadIdx_y = threadIdx.y; + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (static_cast(blockIdx_y) * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; + const data_size_t remainder = block_num_data % blockDim.y; + const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); + data_size_t inner_data_index = static_cast(threadIdx_y); + const int column_index = static_cast(threadIdx.x) + partition_column_start; + if (threadIdx.x < static_cast(num_columns_in_partition)) { + HIST_TYPE* shared_hist_ptr = shared_hist + (column_hist_offsets[column_index] << 1); + for (data_size_t i = 0; i < num_iteration_this; ++i) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const score_t grad = cuda_gradients[data_index]; + const score_t hess = cuda_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[static_cast(data_index) * num_columns_in_partition + threadIdx.x]); + const uint32_t pos = bin << 1; + HIST_TYPE* pos_ptr = shared_hist_ptr + pos; + atomicAdd_block(pos_ptr, grad); + atomicAdd_block(pos_ptr + 1, hess); + inner_data_index += blockDim.y; + } + } + __syncthreads(); + hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); + } +} + +template +__global__ void CUDAConstructHistogramSparseKernel_GlobalMemory( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const score_t* cuda_gradients, + const score_t* cuda_hessians, + const BIN_TYPE* data, + const DATA_PTR_TYPE* row_ptr, + const DATA_PTR_TYPE* partition_ptr, + const uint32_t* column_hist_offsets_full, + const data_size_t num_data, + HIST_TYPE* global_hist_buffer) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const DATA_PTR_TYPE* block_row_ptr = row_ptr + static_cast(blockIdx.x) * (num_data + 1); + const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1; + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + const int num_total_bin = column_hist_offsets_full[gridDim.x]; + HIST_TYPE* shared_hist = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start) * 2; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist[i] = 0.0f; + } + __syncthreads(); + const unsigned int threadIdx_y = threadIdx.y; + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; + const data_size_t remainder = block_num_data % blockDim.y; + const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); + data_size_t inner_data_index = static_cast(threadIdx_y); + for (data_size_t i = 0; i < num_iteration_this; ++i) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; + const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; + const DATA_PTR_TYPE row_size = row_end - row_start; + if (threadIdx.x < row_size) { + const score_t grad = cuda_gradients[data_index]; + const score_t hess = cuda_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); + const uint32_t pos = bin << 1; + HIST_TYPE* pos_ptr = shared_hist + pos; + atomicAdd_block(pos_ptr, grad); + atomicAdd_block(pos_ptr + 1, hess); + } + inner_data_index += blockDim.y; + } + __syncthreads(); + hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1); + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]); + } +} + +template +__global__ void CUDAConstructDiscretizedHistogramDenseKernel( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const int32_t* cuda_gradients_and_hessians, + const BIN_TYPE* data, + const uint32_t* column_hist_offsets, + const uint32_t* column_hist_offsets_full, + const int* feature_partition_column_index_offsets, + const data_size_t num_data) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + __shared__ int16_t shared_hist[SHARED_HIST_SIZE]; + int32_t* shared_hist_packed = reinterpret_cast(shared_hist); + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; + const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; + const BIN_TYPE* data_ptr = data + partition_column_start * num_data; + const int num_columns_in_partition = partition_column_end - partition_column_start; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist_packed[i] = 0; + } + __syncthreads(); + const unsigned int threadIdx_y = threadIdx.y; + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; + const data_size_t remainder = block_num_data % blockDim.y; + const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); + data_size_t inner_data_index = static_cast(threadIdx_y); + const int column_index = static_cast(threadIdx.x) + partition_column_start; + if (threadIdx.x < static_cast(num_columns_in_partition)) { + int32_t* shared_hist_ptr = shared_hist_packed + (column_hist_offsets[column_index]); + for (data_size_t i = 0; i < num_iteration_this; ++i) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[data_index * num_columns_in_partition + threadIdx.x]); + int32_t* pos_ptr = shared_hist_ptr + bin; + atomicAdd_block(pos_ptr, grad_and_hess); + inner_data_index += blockDim.y; + } + } + __syncthreads(); + if (USE_16BIT_HIST) { + int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); + } + } else { + atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); + atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); + } + } +} + +template +__global__ void CUDAConstructDiscretizedHistogramSparseKernel( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const int32_t* cuda_gradients_and_hessians, + const BIN_TYPE* data, + const DATA_PTR_TYPE* row_ptr, + const DATA_PTR_TYPE* partition_ptr, + const uint32_t* column_hist_offsets_full, + const data_size_t num_data) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + __shared__ int16_t shared_hist[SHARED_HIST_SIZE]; + int32_t* shared_hist_packed = reinterpret_cast(shared_hist); + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const DATA_PTR_TYPE* block_row_ptr = row_ptr + blockIdx.x * (num_data + 1); + const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist_packed[i] = 0.0f; + } + __syncthreads(); + const unsigned int threadIdx_y = threadIdx.y; + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; + const data_size_t remainder = block_num_data % blockDim.y; + const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); + data_size_t inner_data_index = static_cast(threadIdx_y); + for (data_size_t i = 0; i < num_iteration_this; ++i) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; + const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; + const DATA_PTR_TYPE row_size = row_end - row_start; + if (threadIdx.x < row_size) { + const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); + int32_t* pos_ptr = shared_hist_packed + bin; + atomicAdd_block(pos_ptr, grad_and_hess); + } + inner_data_index += blockDim.y; + } + __syncthreads(); + if (USE_16BIT_HIST) { + int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); + } + } else { + atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); + atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); + } + } +} + +template +__global__ void CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const int32_t* cuda_gradients_and_hessians, + const BIN_TYPE* data, + const uint32_t* column_hist_offsets, + const uint32_t* column_hist_offsets_full, + const int* feature_partition_column_index_offsets, + const data_size_t num_data, + int32_t* global_hist_buffer) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x]; + const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1]; + const BIN_TYPE* data_ptr = data + partition_column_start * num_data; + const int num_columns_in_partition = partition_column_end - partition_column_start; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); + const int num_total_bin = column_hist_offsets_full[gridDim.x]; + int32_t* shared_hist_packed = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start); + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist_packed[i] = 0; + } + __syncthreads(); + const unsigned int threadIdx_y = threadIdx.y; + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; + const data_size_t remainder = block_num_data % blockDim.y; + const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); + data_size_t inner_data_index = static_cast(threadIdx_y); + const int column_index = static_cast(threadIdx.x) + partition_column_start; + if (threadIdx.x < static_cast(num_columns_in_partition)) { + int32_t* shared_hist_ptr = shared_hist_packed + (column_hist_offsets[column_index]); + for (data_size_t i = 0; i < num_iteration_this; ++i) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[data_index * num_columns_in_partition + threadIdx.x]); + int32_t* pos_ptr = shared_hist_ptr + bin; + atomicAdd_block(pos_ptr, grad_and_hess); + inner_data_index += blockDim.y; + } + } + __syncthreads(); + if (USE_16BIT_HIST) { + int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); + } + } else { + atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); + atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); + } + } +} + +template +__global__ void CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory( + const CUDALeafSplitsStruct* smaller_leaf_splits, + const int32_t* cuda_gradients_and_hessians, + const BIN_TYPE* data, + const DATA_PTR_TYPE* row_ptr, + const DATA_PTR_TYPE* partition_ptr, + const uint32_t* column_hist_offsets_full, + const data_size_t num_data, + int32_t* global_hist_buffer) { + const int dim_y = static_cast(gridDim.y * blockDim.y); + const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf; + const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y; + const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf; + const int num_total_bin = column_hist_offsets_full[gridDim.x]; + const unsigned int num_threads_per_block = blockDim.x * blockDim.y; + const DATA_PTR_TYPE* block_row_ptr = row_ptr + blockIdx.x * (num_data + 1); + const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x]; + const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x]; + const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1]; + const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start); + const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x; + int32_t* shared_hist_packed = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start); + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + shared_hist_packed[i] = 0.0f; + } + __syncthreads(); + const unsigned int threadIdx_y = threadIdx.y; + const unsigned int blockIdx_y = blockIdx.y; + const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread; + const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start; + data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y))); + const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y; + const data_size_t remainder = block_num_data % blockDim.y; + const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder); + data_size_t inner_data_index = static_cast(threadIdx_y); + for (data_size_t i = 0; i < num_iteration_this; ++i) { + const data_size_t data_index = data_indices_ref_this_block[inner_data_index]; + const DATA_PTR_TYPE row_start = block_row_ptr[data_index]; + const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1]; + const DATA_PTR_TYPE row_size = row_end - row_start; + if (threadIdx.x < row_size) { + const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index]; + const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]); + int32_t* pos_ptr = shared_hist_packed + bin; + atomicAdd_block(pos_ptr, grad_and_hess); + } + inner_data_index += blockDim.y; + } + __syncthreads(); + if (USE_16BIT_HIST) { + int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess); + } + } else { + atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start; + for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) { + const int32_t packed_grad_hess = shared_hist_packed[i]; + const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff)); + atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64)); + } + } +} + +void CUDAHistogramConstructor::LaunchConstructHistogramKernel( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins) { + if (cuda_row_data_->shared_hist_size() == DP_SHARED_HIST_SIZE && gpu_use_dp_) { + LaunchConstructHistogramKernelInner(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else if (cuda_row_data_->shared_hist_size() == SP_SHARED_HIST_SIZE && !gpu_use_dp_) { + LaunchConstructHistogramKernelInner(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else { + Log::Fatal("Unknown shared histogram size %d", cuda_row_data_->shared_hist_size()); + } +} + +template +void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins) { + if (cuda_row_data_->bit_type() == 8) { + LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else if (cuda_row_data_->bit_type() == 16) { + LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else if (cuda_row_data_->bit_type() == 32) { + LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else { + Log::Fatal("Unknown bit_type = %d", cuda_row_data_->bit_type()); + } +} + +template +void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner0( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins) { + if (cuda_row_data_->row_ptr_bit_type() == 16) { + LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else if (cuda_row_data_->row_ptr_bit_type() == 32) { + LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else if (cuda_row_data_->row_ptr_bit_type() == 64) { + LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else { + if (!cuda_row_data_->is_sparse()) { + LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else { + Log::Fatal("Unknown row_ptr_bit_type = %d", cuda_row_data_->row_ptr_bit_type()); + } + } +} + +template +void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner1( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins) { + if (cuda_row_data_->NumLargeBinPartition() == 0) { + LaunchConstructHistogramKernelInner2(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } else { + LaunchConstructHistogramKernelInner2(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins); + } +} + +template +void CUDAHistogramConstructor::LaunchConstructHistogramKernelInner2( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins) { + int grid_dim_x = 0; + int grid_dim_y = 0; + int block_dim_x = 0; + int block_dim_y = 0; + CalcConstructHistogramKernelDim(&grid_dim_x, &grid_dim_y, &block_dim_x, &block_dim_y, num_data_in_smaller_leaf); + dim3 grid_dim(grid_dim_x, grid_dim_y); + dim3 block_dim(block_dim_x, block_dim_y); + if (use_quantized_grad_) { + if (USE_GLOBAL_MEM_BUFFER) { + if (cuda_row_data_->is_sparse()) { + if (num_bits_in_histogram_bins <= 16) { + CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->GetRowPtr(), + cuda_row_data_->GetPartitionPtr(), + cuda_row_data_->cuda_partition_hist_offsets(), + num_data_, + reinterpret_cast(cuda_hist_buffer_.RawData())); + } else { + CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->GetRowPtr(), + cuda_row_data_->GetPartitionPtr(), + cuda_row_data_->cuda_partition_hist_offsets(), + num_data_, + reinterpret_cast(cuda_hist_buffer_.RawData())); + } + } else { + if (num_bits_in_histogram_bins <= 16) { + CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->cuda_column_hist_offsets(), + cuda_row_data_->cuda_partition_hist_offsets(), + cuda_row_data_->cuda_feature_partition_column_index_offsets(), + num_data_, + reinterpret_cast(cuda_hist_buffer_.RawData())); + } else { + CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->cuda_column_hist_offsets(), + cuda_row_data_->cuda_partition_hist_offsets(), + cuda_row_data_->cuda_feature_partition_column_index_offsets(), + num_data_, + reinterpret_cast(cuda_hist_buffer_.RawData())); + } + } + } else { + if (cuda_row_data_->is_sparse()) { + if (num_bits_in_histogram_bins <= 16) { + CUDAConstructDiscretizedHistogramSparseKernel<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->GetRowPtr(), + cuda_row_data_->GetPartitionPtr(), + cuda_row_data_->cuda_partition_hist_offsets(), + num_data_); + } else { + CUDAConstructDiscretizedHistogramSparseKernel<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->GetRowPtr(), + cuda_row_data_->GetPartitionPtr(), + cuda_row_data_->cuda_partition_hist_offsets(), + num_data_); + } + } else { + if (num_bits_in_histogram_bins <= 16) { + CUDAConstructDiscretizedHistogramDenseKernel<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->cuda_column_hist_offsets(), + cuda_row_data_->cuda_partition_hist_offsets(), + cuda_row_data_->cuda_feature_partition_column_index_offsets(), + num_data_); + } else { + CUDAConstructDiscretizedHistogramDenseKernel<<>>( + cuda_smaller_leaf_splits, + reinterpret_cast(cuda_gradients_), + cuda_row_data_->GetBin(), + cuda_row_data_->cuda_column_hist_offsets(), + cuda_row_data_->cuda_partition_hist_offsets(), + cuda_row_data_->cuda_feature_partition_column_index_offsets(), + num_data_); + } + } + } + } else { + if (!USE_GLOBAL_MEM_BUFFER) { + if (cuda_row_data_->is_sparse()) { + CUDAConstructHistogramSparseKernel<<>>( + cuda_smaller_leaf_splits, + cuda_gradients_, cuda_hessians_, + cuda_row_data_->GetBin(), + cuda_row_data_->GetRowPtr(), + cuda_row_data_->GetPartitionPtr(), + cuda_row_data_->cuda_partition_hist_offsets(), + num_data_); + } else { + CUDAConstructHistogramDenseKernel<<>>( + cuda_smaller_leaf_splits, + cuda_gradients_, cuda_hessians_, + cuda_row_data_->GetBin(), + cuda_row_data_->cuda_column_hist_offsets(), + cuda_row_data_->cuda_partition_hist_offsets(), + cuda_row_data_->cuda_feature_partition_column_index_offsets(), + num_data_); + } + } else { + if (cuda_row_data_->is_sparse()) { + CUDAConstructHistogramSparseKernel_GlobalMemory<<>>( + cuda_smaller_leaf_splits, + cuda_gradients_, cuda_hessians_, + cuda_row_data_->GetBin(), + cuda_row_data_->GetRowPtr(), + cuda_row_data_->GetPartitionPtr(), + cuda_row_data_->cuda_partition_hist_offsets(), + num_data_, + reinterpret_cast(cuda_hist_buffer_.RawData())); + } else { + CUDAConstructHistogramDenseKernel_GlobalMemory<<>>( + cuda_smaller_leaf_splits, + cuda_gradients_, cuda_hessians_, + cuda_row_data_->GetBin(), + cuda_row_data_->cuda_column_hist_offsets(), + cuda_row_data_->cuda_partition_hist_offsets(), + cuda_row_data_->cuda_feature_partition_column_index_offsets(), + num_data_, + reinterpret_cast(cuda_hist_buffer_.RawData())); + } + } + } +} + +__global__ void SubtractHistogramKernel( + const int num_total_bin, + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits) { + const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x; + const int cuda_larger_leaf_index = cuda_larger_leaf_splits->leaf_index; + if (cuda_larger_leaf_index >= 0) { + const hist_t* smaller_leaf_hist = cuda_smaller_leaf_splits->hist_in_leaf; + hist_t* larger_leaf_hist = cuda_larger_leaf_splits->hist_in_leaf; + if (global_thread_index < 2 * num_total_bin) { + larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index]; + } + } +} + +__global__ void FixHistogramKernel( + const uint32_t* cuda_feature_num_bins, + const uint32_t* cuda_feature_hist_offsets, + const uint32_t* cuda_feature_most_freq_bins, + const int* cuda_need_fix_histogram_features, + const uint32_t* cuda_need_fix_histogram_features_num_bin_aligned, + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits) { + __shared__ hist_t shared_mem_buffer[WARPSIZE]; + const unsigned int blockIdx_x = blockIdx.x; + const int feature_index = cuda_need_fix_histogram_features[blockIdx_x]; + const uint32_t num_bin_aligned = cuda_need_fix_histogram_features_num_bin_aligned[blockIdx_x]; + const uint32_t feature_hist_offset = cuda_feature_hist_offsets[feature_index]; + const uint32_t most_freq_bin = cuda_feature_most_freq_bins[feature_index]; + const double leaf_sum_gradients = cuda_smaller_leaf_splits->sum_of_gradients; + const double leaf_sum_hessians = cuda_smaller_leaf_splits->sum_of_hessians; + hist_t* feature_hist = cuda_smaller_leaf_splits->hist_in_leaf + feature_hist_offset * 2; + const unsigned int threadIdx_x = threadIdx.x; + const uint32_t num_bin = cuda_feature_num_bins[feature_index]; + const uint32_t hist_pos = threadIdx_x << 1; + const hist_t bin_gradient = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[hist_pos] : 0.0f; + const hist_t bin_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[hist_pos + 1] : 0.0f; + const hist_t sum_gradient = ShuffleReduceSum(bin_gradient, shared_mem_buffer, num_bin_aligned); + const hist_t sum_hessian = ShuffleReduceSum(bin_hessian, shared_mem_buffer, num_bin_aligned); + if (threadIdx_x == 0) { + feature_hist[most_freq_bin << 1] = leaf_sum_gradients - sum_gradient; + feature_hist[(most_freq_bin << 1) + 1] = leaf_sum_hessians - sum_hessian; + } +} + +template +__global__ void SubtractHistogramDiscretizedKernel( + const int num_total_bin, + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits, + hist_t* num_bit_change_buffer) { + const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x; + const int cuda_larger_leaf_index_ref = cuda_larger_leaf_splits->leaf_index; + if (cuda_larger_leaf_index_ref >= 0) { + if (PARENT_USE_16BIT_HIST) { + const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); + int32_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); + if (global_thread_index < num_total_bin) { + larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index]; + } + } else if (LARGER_USE_16BIT_HIST) { + int32_t* buffer = reinterpret_cast(num_bit_change_buffer); + const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); + int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); + if (global_thread_index < num_total_bin) { + const int64_t parent_hist_item = larger_leaf_hist[global_thread_index]; + const int32_t smaller_hist_item = smaller_leaf_hist[global_thread_index]; + const int64_t smaller_hist_item_int64 = (static_cast(static_cast(smaller_hist_item >> 16)) << 32) | + static_cast(smaller_hist_item & 0x0000ffff); + const int64_t larger_hist_item = parent_hist_item - smaller_hist_item_int64; + buffer[global_thread_index] = static_cast(static_cast(larger_hist_item >> 32) << 16) | + static_cast(larger_hist_item & 0x000000000000ffff); + } + } else if (SMALLER_USE_16BIT_HIST) { + const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); + int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); + if (global_thread_index < num_total_bin) { + const int64_t parent_hist_item = larger_leaf_hist[global_thread_index]; + const int32_t smaller_hist_item = smaller_leaf_hist[global_thread_index]; + const int64_t smaller_hist_item_int64 = (static_cast(static_cast(smaller_hist_item >> 16)) << 32) | + static_cast(smaller_hist_item & 0x0000ffff); + const int64_t larger_hist_item = parent_hist_item - smaller_hist_item_int64; + larger_leaf_hist[global_thread_index] = larger_hist_item; + } + } else { + const int64_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf); + int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); + if (global_thread_index < num_total_bin) { + larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index]; + } + } + } +} + +__global__ void CopyChangedNumBitHistogram( + const int num_total_bin, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits, + hist_t* num_bit_change_buffer) { + int32_t* hist_dst = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf); + const int32_t* hist_src = reinterpret_cast(num_bit_change_buffer); + const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x; + if (global_thread_index < static_cast(num_total_bin)) { + hist_dst[global_thread_index] = hist_src[global_thread_index]; + } +} + +template +__global__ void FixHistogramDiscretizedKernel( + const uint32_t* cuda_feature_num_bins, + const uint32_t* cuda_feature_hist_offsets, + const uint32_t* cuda_feature_most_freq_bins, + const int* cuda_need_fix_histogram_features, + const uint32_t* cuda_need_fix_histogram_features_num_bin_aligned, + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits) { + __shared__ int64_t shared_mem_buffer[WARPSIZE]; + const unsigned int blockIdx_x = blockIdx.x; + const int feature_index = cuda_need_fix_histogram_features[blockIdx_x]; + const uint32_t num_bin_aligned = cuda_need_fix_histogram_features_num_bin_aligned[blockIdx_x]; + const uint32_t feature_hist_offset = cuda_feature_hist_offsets[feature_index]; + const uint32_t most_freq_bin = cuda_feature_most_freq_bins[feature_index]; + if (USE_16BIT_HIST) { + const int64_t leaf_sum_gradients_hessians_int64 = cuda_smaller_leaf_splits->sum_of_gradients_hessians; + const int32_t leaf_sum_gradients_hessians = + (static_cast(leaf_sum_gradients_hessians_int64 >> 32) << 16) | static_cast(leaf_sum_gradients_hessians_int64 & 0x000000000000ffff); + int32_t* feature_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf) + feature_hist_offset; + const unsigned int threadIdx_x = threadIdx.x; + const uint32_t num_bin = cuda_feature_num_bins[feature_index]; + const int32_t bin_gradient_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[threadIdx_x] : 0; + const int32_t sum_gradient_hessian = ShuffleReduceSum( + bin_gradient_hessian, + reinterpret_cast(shared_mem_buffer), + num_bin_aligned); + if (threadIdx_x == 0) { + feature_hist[most_freq_bin] = leaf_sum_gradients_hessians - sum_gradient_hessian; + } + } else { + const int64_t leaf_sum_gradients_hessians = cuda_smaller_leaf_splits->sum_of_gradients_hessians; + int64_t* feature_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf) + feature_hist_offset; + const unsigned int threadIdx_x = threadIdx.x; + const uint32_t num_bin = cuda_feature_num_bins[feature_index]; + const int64_t bin_gradient_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[threadIdx_x] : 0; + const int64_t sum_gradient_hessian = ShuffleReduceSum(bin_gradient_hessian, shared_mem_buffer, num_bin_aligned); + if (threadIdx_x == 0) { + feature_hist[most_freq_bin] = leaf_sum_gradients_hessians - sum_gradient_hessian; + } + } +} + +void CUDAHistogramConstructor::LaunchSubtractHistogramKernel( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits, + const bool use_discretized_grad, + const uint8_t parent_num_bits_in_histogram_bins, + const uint8_t smaller_num_bits_in_histogram_bins, + const uint8_t larger_num_bits_in_histogram_bins) { + if (!use_discretized_grad) { + const int num_subtract_threads = 2 * num_total_bin_; + const int num_subtract_blocks = (num_subtract_threads + SUBTRACT_BLOCK_SIZE - 1) / SUBTRACT_BLOCK_SIZE; + global_timer.Start("CUDAHistogramConstructor::FixHistogramKernel"); + if (need_fix_histogram_features_.size() > 0) { + FixHistogramKernel<<>>( + cuda_feature_num_bins_.RawData(), + cuda_feature_hist_offsets_.RawData(), + cuda_feature_most_freq_bins_.RawData(), + cuda_need_fix_histogram_features_.RawData(), + cuda_need_fix_histogram_features_num_bin_aligned_.RawData(), + cuda_smaller_leaf_splits); + } + global_timer.Stop("CUDAHistogramConstructor::FixHistogramKernel"); + global_timer.Start("CUDAHistogramConstructor::SubtractHistogramKernel"); + SubtractHistogramKernel<<>>( + num_total_bin_, + cuda_smaller_leaf_splits, + cuda_larger_leaf_splits); + global_timer.Stop("CUDAHistogramConstructor::SubtractHistogramKernel"); + } else { + const int num_subtract_threads = num_total_bin_; + const int num_subtract_blocks = (num_subtract_threads + SUBTRACT_BLOCK_SIZE - 1) / SUBTRACT_BLOCK_SIZE; + global_timer.Start("CUDAHistogramConstructor::FixHistogramDiscretizedKernel"); + if (need_fix_histogram_features_.size() > 0) { + if (smaller_num_bits_in_histogram_bins <= 16) { + FixHistogramDiscretizedKernel<<>>( + cuda_feature_num_bins_.RawData(), + cuda_feature_hist_offsets_.RawData(), + cuda_feature_most_freq_bins_.RawData(), + cuda_need_fix_histogram_features_.RawData(), + cuda_need_fix_histogram_features_num_bin_aligned_.RawData(), + cuda_smaller_leaf_splits); + } else { + FixHistogramDiscretizedKernel<<>>( + cuda_feature_num_bins_.RawData(), + cuda_feature_hist_offsets_.RawData(), + cuda_feature_most_freq_bins_.RawData(), + cuda_need_fix_histogram_features_.RawData(), + cuda_need_fix_histogram_features_num_bin_aligned_.RawData(), + cuda_smaller_leaf_splits); + } + } + global_timer.Stop("CUDAHistogramConstructor::FixHistogramDiscretizedKernel"); + global_timer.Start("CUDAHistogramConstructor::SubtractHistogramDiscretizedKernel"); + if (parent_num_bits_in_histogram_bins <= 16) { + CHECK_LE(smaller_num_bits_in_histogram_bins, 16); + CHECK_LE(larger_num_bits_in_histogram_bins, 16); + SubtractHistogramDiscretizedKernel<<>>( + num_total_bin_, + cuda_smaller_leaf_splits, + cuda_larger_leaf_splits, + hist_buffer_for_num_bit_change_.RawData()); + } else if (larger_num_bits_in_histogram_bins <= 16) { + CHECK_LE(smaller_num_bits_in_histogram_bins, 16); + SubtractHistogramDiscretizedKernel<<>>( + num_total_bin_, + cuda_smaller_leaf_splits, + cuda_larger_leaf_splits, + hist_buffer_for_num_bit_change_.RawData()); + CopyChangedNumBitHistogram<<>>( + num_total_bin_, + cuda_larger_leaf_splits, + hist_buffer_for_num_bit_change_.RawData()); + } else if (smaller_num_bits_in_histogram_bins <= 16) { + SubtractHistogramDiscretizedKernel<<>>( + num_total_bin_, + cuda_smaller_leaf_splits, + cuda_larger_leaf_splits, + hist_buffer_for_num_bit_change_.RawData()); + } else { + SubtractHistogramDiscretizedKernel<<>>( + num_total_bin_, + cuda_smaller_leaf_splits, + cuda_larger_leaf_splits, + hist_buffer_for_num_bit_change_.RawData()); + } + global_timer.Stop("CUDAHistogramConstructor::SubtractHistogramDiscretizedKernel"); + } +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_histogram_constructor.hpp b/src/treelearner/cuda/cuda_histogram_constructor.hpp new file mode 100644 index 0000000..18d0029 --- /dev/null +++ b/src/treelearner/cuda/cuda_histogram_constructor.hpp @@ -0,0 +1,199 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_HISTOGRAM_CONSTRUCTOR_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_HISTOGRAM_CONSTRUCTOR_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include +#include + +#include +#include + +#include "cuda_leaf_splits.hpp" + +#define NUM_DATA_PER_THREAD (400) +#define NUM_THREADS_PER_BLOCK (504) +#define NUM_FEATURE_PER_THREAD_GROUP (28) +#define SUBTRACT_BLOCK_SIZE (1024) +#define FIX_HISTOGRAM_SHARED_MEM_SIZE (1024) +#define FIX_HISTOGRAM_BLOCK_SIZE (512) +#define USED_HISTOGRAM_BUFFER_NUM (8) + +namespace LightGBM { + +class CUDAHistogramConstructor { + public: + CUDAHistogramConstructor( + const Dataset* train_data, + const int num_leaves, + const int num_threads, + const std::vector& feature_hist_offsets, + const int min_data_in_leaf, + const double min_sum_hessian_in_leaf, + const int gpu_device_id, + const bool gpu_use_dp, + const bool use_discretized_grad, + const int grad_discretized_bins); + + ~CUDAHistogramConstructor(); + + void Init(const Dataset* train_data, TrainingShareStates* share_state); + + void ConstructHistogramForLeaf( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits, + const data_size_t global_num_data_in_smaller_leaf, + const data_size_t global_num_data_in_larger_leaf, + const data_size_t num_data_in_smaller_leaf, + const data_size_t num_data_in_larger_leaf, + const double sum_hessians_in_smaller_leaf, + const double sum_hessians_in_larger_leaf, + const uint8_t num_bits_in_histogram_bins); + + void SubtractHistogramForLeaf( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits, + const bool use_discretized_grad, + const uint8_t parent_num_bits_in_histogram_bins, + const uint8_t smaller_num_bits_in_histogram_bins, + const uint8_t larger_num_bits_in_histogram_bins); + + void ResetTrainingData(const Dataset* train_data, TrainingShareStates* share_states); + + void ResetConfig(const Config* config); + + void BeforeTrain(const score_t* gradients, const score_t* hessians); + + const hist_t* cuda_hist() const { return cuda_hist_.RawData(); } + + hist_t* cuda_hist_pointer() { return cuda_hist_.RawData(); } + + private: + void InitFeatureMetaInfo(const Dataset* train_data, const std::vector& feature_hist_offsets); + + void CalcConstructHistogramKernelDim( + int* grid_dim_x, + int* grid_dim_y, + int* block_dim_x, + int* block_dim_y, + const data_size_t num_data_in_smaller_leaf); + + template + void LaunchConstructHistogramKernelInner( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins); + + template + void LaunchConstructHistogramKernelInner0( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins); + + template + void LaunchConstructHistogramKernelInner1( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins); + + template + void LaunchConstructHistogramKernelInner2( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins); + + void LaunchConstructHistogramKernel( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const data_size_t num_data_in_smaller_leaf, + const uint8_t num_bits_in_histogram_bins); + + void LaunchSubtractHistogramKernel( + const CUDALeafSplitsStruct* cuda_smaller_leaf_splits, + const CUDALeafSplitsStruct* cuda_larger_leaf_splits, + const bool use_discretized_grad, + const uint8_t parent_num_bits_in_histogram_bins, + const uint8_t smaller_num_bits_in_histogram_bins, + const uint8_t larger_num_bits_in_histogram_bins); + + // Host memory + + /*! \brief size of training data */ + data_size_t num_data_; + /*! \brief number of features in training data */ + int num_features_; + /*! \brief maximum number of leaves */ + int num_leaves_; + /*! \brief number of threads */ + int num_threads_; + /*! \brief total number of bins in histogram */ + int num_total_bin_; + /*! \brief number of bins per feature */ + std::vector feature_num_bins_; + /*! \brief offsets in histogram of all features */ + std::vector feature_hist_offsets_; + /*! \brief most frequent bins in each feature */ + std::vector feature_most_freq_bins_; + /*! \brief minimum number of data allowed per leaf */ + int min_data_in_leaf_; + /*! \brief minimum sum value of hessians allowed per leaf */ + double min_sum_hessian_in_leaf_; + /*! \brief cuda stream for histogram construction */ + cudaStream_t cuda_stream_; + /*! \brief indices of feature whose histograms need to be fixed */ + std::vector need_fix_histogram_features_; + /*! \brief aligned number of bins of the features whose histograms need to be fixed */ + std::vector need_fix_histogram_features_num_bin_aligend_; + /*! \brief minimum number of blocks allowed in the y dimension */ + const int min_grid_dim_y_ = 160; + + + // CUDA memory, held by this object + + /*! \brief CUDA row wise data */ + std::unique_ptr cuda_row_data_; + /*! \brief number of bins per feature */ + CUDAVector cuda_feature_num_bins_; + /*! \brief offsets in histogram of all features */ + CUDAVector cuda_feature_hist_offsets_; + /*! \brief most frequent bins in each feature */ + CUDAVector cuda_feature_most_freq_bins_; + /*! \brief CUDA histograms */ + CUDAVector cuda_hist_; + /*! \brief CUDA histograms buffer for each block */ + CUDAVector cuda_hist_buffer_; + /*! \brief indices of feature whose histograms need to be fixed */ + CUDAVector cuda_need_fix_histogram_features_; + /*! \brief aligned number of bins of the features whose histograms need to be fixed */ + CUDAVector cuda_need_fix_histogram_features_num_bin_aligned_; + /*! \brief histogram buffer used in histogram subtraction with different number of bits for histogram bins */ + CUDAVector hist_buffer_for_num_bit_change_; + + // CUDA memory, held by other object + + /*! \brief gradients on CUDA */ + const score_t* cuda_gradients_; + /*! \brief hessians on CUDA */ + const score_t* cuda_hessians_; + + /*! \brief GPU device index */ + const int gpu_device_id_; + /*! \brief use double precision histogram per block */ + const bool gpu_use_dp_; + /*! \brief whether to use quantized gradients */ + const bool use_quantized_grad_; + /*! \brief the number of bins to quantized gradients */ + const int num_grad_quant_bins_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_HISTOGRAM_CONSTRUCTOR_HPP_ diff --git a/src/treelearner/cuda/cuda_leaf_splits.cpp b/src/treelearner/cuda/cuda_leaf_splits.cpp new file mode 100644 index 0000000..5b5dec5 --- /dev/null +++ b/src/treelearner/cuda/cuda_leaf_splits.cpp @@ -0,0 +1,79 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_leaf_splits.hpp" + +namespace LightGBM { + +CUDALeafSplits::CUDALeafSplits(const data_size_t num_data): +num_data_(num_data) {} + +CUDALeafSplits::~CUDALeafSplits() {} + +void CUDALeafSplits::Init(const bool use_quantized_grad) { + num_blocks_init_from_gradients_ = (num_data_ + NUM_THREADS_PER_BLOCK_LEAF_SPLITS - 1) / NUM_THREADS_PER_BLOCK_LEAF_SPLITS; + + // allocate more memory for sum reduction in CUDA + // only the first element records the final sum + cuda_sum_of_gradients_buffer_.Resize(static_cast(num_blocks_init_from_gradients_)); + cuda_sum_of_hessians_buffer_.Resize(static_cast(num_blocks_init_from_gradients_)); + if (use_quantized_grad) { + cuda_sum_of_gradients_hessians_buffer_.Resize(static_cast(num_blocks_init_from_gradients_)); + } + + cuda_struct_.Resize(1); +} + +void CUDALeafSplits::InitValues() { + LaunchInitValuesEmptyKernel(); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDALeafSplits::InitValues( + const double lambda_l1, const double lambda_l2, + const score_t* cuda_gradients, const score_t* cuda_hessians, + const data_size_t* cuda_bagging_data_indices, const data_size_t* cuda_data_indices_in_leaf, + const data_size_t num_used_indices, hist_t* cuda_hist_in_leaf, + double* root_sum_gradients, double* root_sum_hessians) { + cuda_gradients_ = cuda_gradients; + cuda_hessians_ = cuda_hessians; + cuda_sum_of_gradients_buffer_.SetValue(0); + cuda_sum_of_hessians_buffer_.SetValue(0); + LaunchInitValuesKernel(lambda_l1, lambda_l2, cuda_bagging_data_indices, cuda_data_indices_in_leaf, num_used_indices, cuda_hist_in_leaf); + CopyFromCUDADeviceToHost(root_sum_gradients, cuda_sum_of_gradients_buffer_.RawData(), 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(root_sum_hessians, cuda_sum_of_hessians_buffer_.RawData(), 1, __FILE__, __LINE__); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDALeafSplits::InitValues( + const double lambda_l1, const double lambda_l2, + const int16_t* cuda_gradients_and_hessians, + const data_size_t* cuda_bagging_data_indices, + const data_size_t* cuda_data_indices_in_leaf, const data_size_t num_used_indices, + hist_t* cuda_hist_in_leaf, double* root_sum_gradients, double* root_sum_hessians, + const score_t* grad_scale, const score_t* hess_scale) { + cuda_gradients_ = reinterpret_cast(cuda_gradients_and_hessians); + cuda_hessians_ = nullptr; + LaunchInitValuesKernel(lambda_l1, lambda_l2, cuda_bagging_data_indices, cuda_data_indices_in_leaf, num_used_indices, cuda_hist_in_leaf, grad_scale, hess_scale); + CopyFromCUDADeviceToHost(root_sum_gradients, cuda_sum_of_gradients_buffer_.RawData(), 1, __FILE__, __LINE__); + CopyFromCUDADeviceToHost(root_sum_hessians, cuda_sum_of_hessians_buffer_.RawData(), 1, __FILE__, __LINE__); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDALeafSplits::Resize(const data_size_t num_data) { + num_data_ = num_data; + num_blocks_init_from_gradients_ = (num_data + NUM_THREADS_PER_BLOCK_LEAF_SPLITS - 1) / NUM_THREADS_PER_BLOCK_LEAF_SPLITS; + cuda_sum_of_gradients_buffer_.Resize(static_cast(num_blocks_init_from_gradients_)); + cuda_sum_of_hessians_buffer_.Resize(static_cast(num_blocks_init_from_gradients_)); + cuda_sum_of_gradients_hessians_buffer_.Resize(static_cast(num_blocks_init_from_gradients_)); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_leaf_splits.cu b/src/treelearner/cuda/cuda_leaf_splits.cu new file mode 100644 index 0000000..d263028 --- /dev/null +++ b/src/treelearner/cuda/cuda_leaf_splits.cu @@ -0,0 +1,415 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + + +#ifdef USE_CUDA + +#include "cuda_leaf_splits.hpp" +#include +#include + +namespace LightGBM { + +template +__global__ void CUDAInitValuesKernel1(const score_t* cuda_gradients, const score_t* cuda_hessians, + const data_size_t num_data, const data_size_t* cuda_bagging_data_indices, + double* cuda_sum_of_gradients, double* cuda_sum_of_hessians) { + __shared__ double shared_mem_buffer[WARPSIZE]; + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + double gradient = 0.0f; + double hessian = 0.0f; + if (data_index < num_data) { + gradient = USE_INDICES ? cuda_gradients[cuda_bagging_data_indices[data_index]] : cuda_gradients[data_index]; + hessian = USE_INDICES ? cuda_hessians[cuda_bagging_data_indices[data_index]] : cuda_hessians[data_index]; + } + const double block_sum_gradient = ShuffleReduceSum(gradient, shared_mem_buffer, blockDim.x); + __syncthreads(); + const double block_sum_hessian = ShuffleReduceSum(hessian, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + cuda_sum_of_gradients[blockIdx.x] += block_sum_gradient; + cuda_sum_of_hessians[blockIdx.x] += block_sum_hessian; + } +} + +__global__ void CUDAInitValuesKernel2( + const double lambda_l1, + const double lambda_l2, + const int num_blocks_to_reduce, + double* cuda_sum_of_gradients, + double* cuda_sum_of_hessians, + const data_size_t num_data, + const data_size_t* cuda_data_indices_in_leaf, + hist_t* cuda_hist_in_leaf, + CUDALeafSplitsStruct* cuda_struct) { + __shared__ double shared_mem_buffer[WARPSIZE]; + double thread_sum_of_gradients = 0.0f; + double thread_sum_of_hessians = 0.0f; + for (int block_index = static_cast(threadIdx.x); block_index < num_blocks_to_reduce; block_index += static_cast(blockDim.x)) { + thread_sum_of_gradients += cuda_sum_of_gradients[block_index]; + thread_sum_of_hessians += cuda_sum_of_hessians[block_index]; + } + const double sum_of_gradients = ShuffleReduceSum(thread_sum_of_gradients, shared_mem_buffer, blockDim.x); + __syncthreads(); + const double sum_of_hessians = ShuffleReduceSum(thread_sum_of_hessians, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + cuda_sum_of_hessians[0] = sum_of_hessians; + cuda_struct->leaf_index = 0; + cuda_struct->sum_of_gradients = sum_of_gradients; + cuda_struct->sum_of_hessians = sum_of_hessians; + cuda_struct->num_data_in_leaf = num_data; + const bool use_l1 = lambda_l1 > 0.0f; + if (!use_l1) { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + if (!use_l1) { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + cuda_struct->data_indices_in_leaf = cuda_data_indices_in_leaf; + cuda_struct->hist_in_leaf = cuda_hist_in_leaf; + } +} + +template +__global__ void CUDAInitValuesKernel3(const int16_t* cuda_gradients_and_hessians, + const data_size_t num_data, const data_size_t* cuda_bagging_data_indices, + double* cuda_sum_of_gradients, double* cuda_sum_of_hessians, int64_t* cuda_sum_of_hessians_hessians, + const score_t* grad_scale_pointer, const score_t* hess_scale_pointer) { + const score_t grad_scale = *grad_scale_pointer; + const score_t hess_scale = *hess_scale_pointer; + __shared__ int64_t shared_mem_buffer[WARPSIZE]; + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + int64_t int_gradient = 0; + int64_t int_hessian = 0; + if (data_index < num_data) { + int_gradient = USE_INDICES ? cuda_gradients_and_hessians[2 * cuda_bagging_data_indices[data_index] + 1] : + cuda_gradients_and_hessians[2 * data_index + 1]; + int_hessian = USE_INDICES ? cuda_gradients_and_hessians[2 * cuda_bagging_data_indices[data_index]] : + cuda_gradients_and_hessians[2 * data_index]; + } + const int64_t block_sum_gradient = ShuffleReduceSum(int_gradient, shared_mem_buffer, blockDim.x); + __syncthreads(); + const int64_t block_sum_hessian = ShuffleReduceSum(int_hessian, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + cuda_sum_of_gradients[blockIdx.x] = block_sum_gradient * grad_scale; + cuda_sum_of_hessians[blockIdx.x] = block_sum_hessian * hess_scale; + cuda_sum_of_hessians_hessians[blockIdx.x] = ((block_sum_gradient << 32) | block_sum_hessian); + } +} + +__global__ void CUDAInitValuesKernel4( + const double lambda_l1, + const double lambda_l2, + const int num_blocks_to_reduce, + double* cuda_sum_of_gradients, + double* cuda_sum_of_hessians, + int64_t* cuda_sum_of_gradients_hessians, + const data_size_t num_data, + const data_size_t* cuda_data_indices_in_leaf, + hist_t* cuda_hist_in_leaf, + CUDALeafSplitsStruct* cuda_struct) { + __shared__ double shared_mem_buffer[WARPSIZE]; + double thread_sum_of_gradients = 0.0f; + double thread_sum_of_hessians = 0.0f; + int64_t thread_sum_of_gradients_hessians = 0; + for (int block_index = static_cast(threadIdx.x); block_index < num_blocks_to_reduce; block_index += static_cast(blockDim.x)) { + thread_sum_of_gradients += cuda_sum_of_gradients[block_index]; + thread_sum_of_hessians += cuda_sum_of_hessians[block_index]; + thread_sum_of_gradients_hessians += cuda_sum_of_gradients_hessians[block_index]; + } + const double sum_of_gradients = ShuffleReduceSum(thread_sum_of_gradients, shared_mem_buffer, blockDim.x); + __syncthreads(); + const double sum_of_hessians = ShuffleReduceSum(thread_sum_of_hessians, shared_mem_buffer, blockDim.x); + __syncthreads(); + const double sum_of_gradients_hessians = ShuffleReduceSum( + thread_sum_of_gradients_hessians, + reinterpret_cast(shared_mem_buffer), + blockDim.x); + if (threadIdx.x == 0) { + cuda_sum_of_hessians[0] = sum_of_hessians; + cuda_struct->leaf_index = 0; + cuda_struct->sum_of_gradients = sum_of_gradients; + cuda_struct->sum_of_hessians = sum_of_hessians; + cuda_struct->sum_of_gradients_hessians = sum_of_gradients_hessians; + cuda_struct->num_data_in_leaf = num_data; + const bool use_l1 = lambda_l1 > 0.0f; + if (!use_l1) { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + if (!use_l1) { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + cuda_struct->data_indices_in_leaf = cuda_data_indices_in_leaf; + cuda_struct->hist_in_leaf = cuda_hist_in_leaf; + } +} + +__global__ void InitValuesEmptyKernel(CUDALeafSplitsStruct* cuda_struct) { + cuda_struct->leaf_index = -1; + cuda_struct->sum_of_gradients = 0.0f; + cuda_struct->sum_of_hessians = 0.0f; + cuda_struct->num_data_in_leaf = 0; + cuda_struct->gain = 0.0f; + cuda_struct->leaf_value = 0.0f; + cuda_struct->data_indices_in_leaf = nullptr; + cuda_struct->hist_in_leaf = nullptr; +} + +__global__ void ReduceGradKernel( + const int num_blocks_to_reduce, + double* cuda_sum_of_gradients, + double* cuda_sum_of_hessians, + const data_size_t num_data) { + __shared__ double shared_mem_buffer[WARPSIZE]; + double thread_sum_of_gradients = 0.0f; + double thread_sum_of_hessians = 0.0f; + for (int block_index = static_cast(threadIdx.x); block_index < num_blocks_to_reduce; block_index += static_cast(blockDim.x)) { + thread_sum_of_gradients += cuda_sum_of_gradients[block_index]; + thread_sum_of_hessians += cuda_sum_of_hessians[block_index]; + } + const double sum_of_gradients = ShuffleReduceSum(thread_sum_of_gradients, shared_mem_buffer, blockDim.x); + __syncthreads(); + const double sum_of_hessians = ShuffleReduceSum(thread_sum_of_hessians, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + cuda_sum_of_gradients[0] = sum_of_gradients; + cuda_sum_of_hessians[0] = sum_of_hessians; + } +} + +__global__ void ReduceGradKernel( + const int num_blocks_to_reduce, + double* cuda_sum_of_gradients, + double* cuda_sum_of_hessians, + int64_t* cuda_sum_of_gradients_hessians, + const data_size_t num_data) { + __shared__ double shared_mem_buffer[WARPSIZE]; + double thread_sum_of_gradients = 0.0f; + double thread_sum_of_hessians = 0.0f; + int64_t thread_sum_of_gradients_hessians = 0; + for (int block_index = static_cast(threadIdx.x); block_index < num_blocks_to_reduce; block_index += static_cast(blockDim.x)) { + thread_sum_of_gradients += cuda_sum_of_gradients[block_index]; + thread_sum_of_hessians += cuda_sum_of_hessians[block_index]; + thread_sum_of_gradients_hessians += cuda_sum_of_gradients_hessians[block_index]; + } + const double sum_of_gradients = ShuffleReduceSum(thread_sum_of_gradients, shared_mem_buffer, blockDim.x); + __syncthreads(); + const double sum_of_hessians = ShuffleReduceSum(thread_sum_of_hessians, shared_mem_buffer, blockDim.x); + __syncthreads(); + const int64_t sum_of_gradients_hessians = ShuffleReduceSum( + thread_sum_of_gradients_hessians, + reinterpret_cast(shared_mem_buffer), + blockDim.x); + if (threadIdx.x == 0) { + cuda_sum_of_gradients[0] = sum_of_gradients; + cuda_sum_of_hessians[0] = sum_of_hessians; + cuda_sum_of_gradients_hessians[0] = sum_of_gradients_hessians; + } +} + +__global__ void CUDAInitSetValuesKernel( + const double lambda_l1, + const double lambda_l2, + double* cuda_sum_of_gradients, + double* cuda_sum_of_hessians, + const data_size_t num_data, + const data_size_t* cuda_data_indices_in_leaf, + hist_t* cuda_hist_in_leaf, + CUDALeafSplitsStruct* cuda_struct) { + if (threadIdx.x == 0) { + const double sum_of_gradients = cuda_sum_of_gradients[0]; + const double sum_of_hessians = cuda_sum_of_hessians[0]; + cuda_struct->leaf_index = 0; + cuda_struct->sum_of_gradients = sum_of_gradients; + cuda_struct->sum_of_hessians = sum_of_hessians; + cuda_struct->num_data_in_leaf = num_data; + const bool use_l1 = lambda_l1 > 0.0f; + if (!use_l1) { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + if (!use_l1) { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + cuda_struct->data_indices_in_leaf = cuda_data_indices_in_leaf; + cuda_struct->hist_in_leaf = cuda_hist_in_leaf; + } +} + +__global__ void CUDAInitSetValuesKernel( + const double lambda_l1, + const double lambda_l2, + double* cuda_sum_of_gradients, + double* cuda_sum_of_hessians, + int64_t* cuda_sum_of_gradients_hessians, + const data_size_t num_data, + const data_size_t* cuda_data_indices_in_leaf, + hist_t* cuda_hist_in_leaf, + CUDALeafSplitsStruct* cuda_struct) { + if (threadIdx.x == 0) { + const double sum_of_gradients = cuda_sum_of_gradients[0]; + const double sum_of_hessians = cuda_sum_of_hessians[0]; + const int64_t sum_of_gradients_hessians = cuda_sum_of_gradients_hessians[0]; + cuda_struct->leaf_index = 0; + cuda_struct->sum_of_gradients = sum_of_gradients; + cuda_struct->sum_of_hessians = sum_of_hessians; + cuda_struct->sum_of_gradients_hessians = sum_of_gradients_hessians; + cuda_struct->num_data_in_leaf = num_data; + const bool use_l1 = lambda_l1 > 0.0f; + if (!use_l1) { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->gain = CUDALeafSplits::GetLeafGain(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + if (!use_l1) { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + // no smoothing on root node + cuda_struct->leaf_value = + CUDALeafSplits::CalculateSplittedLeafOutput(sum_of_gradients, sum_of_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + cuda_struct->data_indices_in_leaf = cuda_data_indices_in_leaf; + cuda_struct->hist_in_leaf = cuda_hist_in_leaf; + } +} + +void CUDALeafSplits::LaunchInitValuesEmptyKernel() { + InitValuesEmptyKernel<<<1, 1>>>(cuda_struct_.RawData()); +} + +void CUDALeafSplits::LaunchInitValuesKernel( + const double lambda_l1, const double lambda_l2, + const data_size_t* cuda_bagging_data_indices, + const data_size_t* cuda_data_indices_in_leaf, + const data_size_t num_used_indices, + hist_t* cuda_hist_in_leaf) { + if (cuda_bagging_data_indices == nullptr) { + CUDAInitValuesKernel1<<>>( + cuda_gradients_, cuda_hessians_, num_used_indices, nullptr, cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData()); + } else { + CUDAInitValuesKernel1<<>>( + cuda_gradients_, cuda_hessians_, num_used_indices, cuda_bagging_data_indices, cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData()); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + + if (nccl_communicator_ != nullptr) { + ReduceGradKernel<<<1, NUM_THREADS_PER_BLOCK_LEAF_SPLITS>>>(num_blocks_init_from_gradients_, cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData(), num_used_indices); + SynchronizeCUDADevice(__FILE__, __LINE__); + cudaStream_t cuda_stream = CUDAStreamCreate(); + NCCLGroupStart(); + NCCLAllReduce(cuda_sum_of_gradients_buffer_.RawData(), cuda_sum_of_gradients_buffer_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_, cuda_stream); + NCCLAllReduce(cuda_sum_of_hessians_buffer_.RawData(), cuda_sum_of_hessians_buffer_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_, cuda_stream); + NCCLGroupEnd(); + SynchronizeCUDAStream(cuda_stream, __FILE__, __LINE__); + CUDAStreamDestroy(cuda_stream); + CUDAInitSetValuesKernel<<<1, 1>>>(lambda_l1, lambda_l2, cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData(), num_used_indices, + cuda_data_indices_in_leaf, cuda_hist_in_leaf, cuda_struct_.RawData()); + } else { + CUDAInitValuesKernel2<<<1, NUM_THREADS_PER_BLOCK_LEAF_SPLITS>>>( + lambda_l1, lambda_l2, + num_blocks_init_from_gradients_, + cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData(), + num_used_indices, + cuda_data_indices_in_leaf, + cuda_hist_in_leaf, + cuda_struct_.RawData()); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDALeafSplits::LaunchInitValuesKernel( + const double lambda_l1, const double lambda_l2, + const data_size_t* cuda_bagging_data_indices, + const data_size_t* cuda_data_indices_in_leaf, + const data_size_t num_used_indices, + hist_t* cuda_hist_in_leaf, + const score_t* grad_scale, + const score_t* hess_scale) { + if (cuda_bagging_data_indices == nullptr) { + CUDAInitValuesKernel3<<>>( + reinterpret_cast(cuda_gradients_), num_used_indices, nullptr, cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData(), cuda_sum_of_gradients_hessians_buffer_.RawData(), grad_scale, hess_scale); + } else { + CUDAInitValuesKernel3<<>>( + reinterpret_cast(cuda_gradients_), num_used_indices, cuda_bagging_data_indices, cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData(), cuda_sum_of_gradients_hessians_buffer_.RawData(), grad_scale, hess_scale); + } + + SynchronizeCUDADevice(__FILE__, __LINE__); + + if (nccl_communicator_ != nullptr) { + ReduceGradKernel<<<1, NUM_THREADS_PER_BLOCK_LEAF_SPLITS>>>(num_blocks_init_from_gradients_, + cuda_sum_of_gradients_buffer_.RawData(), cuda_sum_of_hessians_buffer_.RawData(), cuda_sum_of_gradients_hessians_buffer_.RawData(), + num_used_indices); + SynchronizeCUDADevice(__FILE__, __LINE__); + + cudaStream_t cuda_stream = CUDAStreamCreate(); + NCCLGroupStart(); + NCCLAllReduce(cuda_sum_of_gradients_buffer_.RawData(), cuda_sum_of_gradients_buffer_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_, cuda_stream); + NCCLAllReduce(cuda_sum_of_hessians_buffer_.RawData(), cuda_sum_of_hessians_buffer_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_, cuda_stream); + NCCLAllReduce(cuda_sum_of_gradients_hessians_buffer_.RawData(), cuda_sum_of_gradients_hessians_buffer_.RawData(), 1, ncclInt64, ncclSum, nccl_communicator_, cuda_stream); + NCCLGroupEnd(); + SynchronizeCUDAStream(cuda_stream, __FILE__, __LINE__); + CUDAStreamDestroy(cuda_stream); + CUDAInitSetValuesKernel<<<1, 1>>>(lambda_l1, lambda_l2, cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData(), cuda_sum_of_gradients_hessians_buffer_.RawData(), num_used_indices, + cuda_data_indices_in_leaf, cuda_hist_in_leaf, cuda_struct_.RawData()); + } else { + CUDAInitValuesKernel4<<<1, NUM_THREADS_PER_BLOCK_LEAF_SPLITS>>>( + lambda_l1, lambda_l2, + num_blocks_init_from_gradients_, + cuda_sum_of_gradients_buffer_.RawData(), + cuda_sum_of_hessians_buffer_.RawData(), + cuda_sum_of_gradients_hessians_buffer_.RawData(), + num_used_indices, + cuda_data_indices_in_leaf, + cuda_hist_in_leaf, + cuda_struct_.RawData()); + } + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_leaf_splits.hpp b/src/treelearner/cuda/cuda_leaf_splits.hpp new file mode 100644 index 0000000..cb3e7dc --- /dev/null +++ b/src/treelearner/cuda/cuda_leaf_splits.hpp @@ -0,0 +1,180 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_LEAF_SPLITS_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_LEAF_SPLITS_HPP_ + +#ifdef USE_CUDA + +#include +#include +#include +#include + +#define NUM_THREADS_PER_BLOCK_LEAF_SPLITS (1024) +#define NUM_DATA_THREAD_ADD_LEAF_SPLITS (6) + +namespace LightGBM { + +struct CUDALeafSplitsStruct { + public: + int leaf_index; + double sum_of_gradients; + double sum_of_hessians; + int64_t sum_of_gradients_hessians; + data_size_t num_data_in_leaf; + double gain; + double leaf_value; + const data_size_t* data_indices_in_leaf; + hist_t* hist_in_leaf; +}; + +class CUDALeafSplits: public NCCLInfo { + public: + explicit CUDALeafSplits(const data_size_t num_data); + + ~CUDALeafSplits(); + + void Init(const bool use_quantized_grad); + + void InitValues( + const double lambda_l1, const double lambda_l2, + const score_t* cuda_gradients, const score_t* cuda_hessians, + const data_size_t* cuda_bagging_data_indices, + const data_size_t* cuda_data_indices_in_leaf, const data_size_t num_used_indices, + hist_t* cuda_hist_in_leaf, double* root_sum_gradients, double* root_sum_hessians); + + void InitValues( + const double lambda_l1, const double lambda_l2, + const int16_t* cuda_gradients_and_hessians, + const data_size_t* cuda_bagging_data_indices, + const data_size_t* cuda_data_indices_in_leaf, const data_size_t num_used_indices, + hist_t* cuda_hist_in_leaf, double* root_sum_gradients, double* root_sum_hessians, + const score_t* grad_scale, const score_t* hess_scale); + + void InitValues(); + + const CUDALeafSplitsStruct* GetCUDAStruct() const { return cuda_struct_.RawDataReadOnly(); } + + CUDALeafSplitsStruct* GetCUDAStructRef() { return cuda_struct_.RawData(); } + + void Resize(const data_size_t num_data); + + __device__ static double ThresholdL1(double s, double l1) { + const double reg_s = fmax(0.0, fabs(s) - l1); + if (s >= 0.0f) { + return reg_s; + } else { + return -reg_s; + } + } + + template + __device__ static double CalculateSplittedLeafOutput(double sum_gradients, + double sum_hessians, double l1, double l2, + double path_smooth, data_size_t num_data, + double parent_output) { + double ret; + if (USE_L1) { + ret = -ThresholdL1(sum_gradients, l1) / (sum_hessians + l2); + } else { + ret = -sum_gradients / (sum_hessians + l2); + } + if (USE_SMOOTHING) { + ret = ret * (num_data / path_smooth) / (num_data / path_smooth + 1) \ + + parent_output / (num_data / path_smooth + 1); + } + return ret; + } + + template + __device__ static double GetLeafGainGivenOutput(double sum_gradients, + double sum_hessians, double l1, + double l2, double output) { + if (USE_L1) { + const double sg_l1 = ThresholdL1(sum_gradients, l1); + return -(2.0 * sg_l1 * output + (sum_hessians + l2) * output * output); + } else { + return -(2.0 * sum_gradients * output + + (sum_hessians + l2) * output * output); + } + } + + template + __device__ static double GetLeafGain(double sum_gradients, double sum_hessians, + double l1, double l2, + double path_smooth, data_size_t num_data, + double parent_output) { + if (!USE_SMOOTHING) { + if (USE_L1) { + const double sg_l1 = ThresholdL1(sum_gradients, l1); + return (sg_l1 * sg_l1) / (sum_hessians + l2); + } else { + return (sum_gradients * sum_gradients) / (sum_hessians + l2); + } + } else { + const double output = CalculateSplittedLeafOutput( + sum_gradients, sum_hessians, l1, l2, path_smooth, num_data, parent_output); + return GetLeafGainGivenOutput(sum_gradients, sum_hessians, l1, l2, output); + } + } + + template + __device__ static double GetSplitGains(double sum_left_gradients, + double sum_left_hessians, + double sum_right_gradients, + double sum_right_hessians, + double l1, double l2, + double path_smooth, + data_size_t left_count, + data_size_t right_count, + double parent_output) { + return GetLeafGain(sum_left_gradients, + sum_left_hessians, + l1, l2, path_smooth, left_count, parent_output) + + GetLeafGain(sum_right_gradients, + sum_right_hessians, + l1, l2, path_smooth, right_count, parent_output); + } + + private: + void LaunchInitValuesEmptyKernel(); + + void LaunchInitValuesKernel( + const double lambda_l1, const double lambda_l2, + const data_size_t* cuda_bagging_data_indices, + const data_size_t* cuda_data_indices_in_leaf, + const data_size_t num_used_indices, + hist_t* cuda_hist_in_leaf); + + void LaunchInitValuesKernel( + const double lambda_l1, const double lambda_l2, + const data_size_t* cuda_bagging_data_indices, + const data_size_t* cuda_data_indices_in_leaf, + const data_size_t num_used_indices, + hist_t* cuda_hist_in_leaf, + const score_t* grad_scale, + const score_t* hess_scale); + + // Host memory + data_size_t num_data_; + int num_blocks_init_from_gradients_; + + // CUDA memory, held by this object + CUDAVector cuda_struct_; + CUDAVector cuda_sum_of_gradients_buffer_; + CUDAVector cuda_sum_of_hessians_buffer_; + CUDAVector cuda_sum_of_gradients_hessians_buffer_; + + // CUDA memory, held by other object + const score_t* cuda_gradients_; + const score_t* cuda_hessians_; +}; + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_LEAF_SPLITS_HPP_ diff --git a/src/treelearner/cuda/cuda_single_gpu_tree_learner.cpp b/src/treelearner/cuda/cuda_single_gpu_tree_learner.cpp new file mode 100644 index 0000000..54beeb9 --- /dev/null +++ b/src/treelearner/cuda/cuda_single_gpu_tree_learner.cpp @@ -0,0 +1,726 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#ifdef USE_CUDA + +#include "cuda_single_gpu_tree_learner.hpp" + +#include +#include +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { + +CUDASingleGPUTreeLearner::CUDASingleGPUTreeLearner(const Config* config, const bool boosting_on_cuda): SerialTreeLearner(config), boosting_on_cuda_(boosting_on_cuda) {} + +CUDASingleGPUTreeLearner::~CUDASingleGPUTreeLearner() { + if (nccl_communicator_ != nullptr) { + CUDAStreamDestroy(nccl_stream_); + } +} + +void CUDASingleGPUTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian) { + SerialTreeLearner::Init(train_data, is_constant_hessian); + num_threads_ = OMP_NUM_THREADS(); + // use the first gpu by default + if (nccl_communicator_ == nullptr) { + gpu_device_id_ = config_->gpu_device_id >= 0 ? config_->gpu_device_id : 0; + SetCUDADevice(gpu_device_id_, __FILE__, __LINE__); + } + cuda_smaller_leaf_splits_.reset(new CUDALeafSplits(num_data_)); + cuda_smaller_leaf_splits_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, global_num_data_); + cuda_smaller_leaf_splits_->Init(config_->use_quantized_grad); + cuda_larger_leaf_splits_.reset(new CUDALeafSplits(num_data_)); + cuda_larger_leaf_splits_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, global_num_data_); + cuda_larger_leaf_splits_->Init(config_->use_quantized_grad); + + cuda_histogram_constructor_.reset(new CUDAHistogramConstructor(train_data_, config_->num_leaves, num_threads_, + share_state_->feature_hist_offsets(), + config_->min_data_in_leaf, config_->min_sum_hessian_in_leaf, gpu_device_id_, config_->gpu_use_dp, + config_->use_quantized_grad, config_->num_grad_quant_bins)); + cuda_histogram_constructor_->Init(train_data_, share_state_.get()); + + const auto& feature_hist_offsets = share_state_->feature_hist_offsets(); + num_total_bin_ = feature_hist_offsets.empty() ? 0 : static_cast(feature_hist_offsets.back()); + cuda_data_partition_.reset(new CUDADataPartition( + train_data_, num_total_bin_, config_->num_leaves, num_threads_, config_->use_quantized_grad, + cuda_histogram_constructor_->cuda_hist_pointer())); + cuda_data_partition_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, global_num_data_); + cuda_data_partition_->Init(); + + select_features_by_node_ = !config_->interaction_constraints_vector.empty() || config_->feature_fraction_bynode < 1.0; + cuda_best_split_finder_.reset(new CUDABestSplitFinder(cuda_histogram_constructor_->cuda_hist(), + train_data_, this->share_state_->feature_hist_offsets(), select_features_by_node_, config_)); + cuda_best_split_finder_->Init(); + + leaf_best_split_feature_.resize(config_->num_leaves, -1); + leaf_best_split_threshold_.resize(config_->num_leaves, 0); + leaf_best_split_default_left_.resize(config_->num_leaves, 0); + leaf_num_data_.resize(config_->num_leaves, 0); + leaf_data_start_.resize(config_->num_leaves, 0); + leaf_sum_gradients_.resize(config_->num_leaves, 0.0f); + leaf_sum_hessians_.resize(config_->num_leaves, 0.0f); + + if (!boosting_on_cuda_) { + cuda_gradients_.Resize(static_cast(num_data_)); + cuda_hessians_.Resize(static_cast(num_data_)); + } + AllocateBitset(); + + leaf_stat_buffer_size_ = 0; + num_cat_threshold_ = 0; + + if (config_->use_quantized_grad) { + cuda_leaf_gradient_stat_buffer_.Resize(config_->num_leaves); + cuda_leaf_hessian_stat_buffer_.Resize(config_->num_leaves); + cuda_gradient_discretizer_.reset(new CUDAGradientDiscretizer( + config_->num_grad_quant_bins, config_->num_iterations, config_->seed, is_constant_hessian, config_->stochastic_rounding)); + cuda_gradient_discretizer_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, global_num_data_); + cuda_gradient_discretizer_->Init(num_data_, config_->num_leaves, train_data_->num_features(), train_data_); + } else { + cuda_gradient_discretizer_.reset(nullptr); + } + + #ifdef DEBUG + host_gradients_.resize(num_data_, 0.0f); + host_hessians_.resize(num_data_, 0.0f); + #endif // DEBUG +} + +void CUDASingleGPUTreeLearner::BeforeTrain() { + const data_size_t root_num_data = cuda_data_partition_->root_num_data(); + if (!boosting_on_cuda_) { + CopyFromHostToCUDADevice(cuda_gradients_.RawData(), gradients_, static_cast(num_data_), __FILE__, __LINE__); + CopyFromHostToCUDADevice(cuda_hessians_.RawData(), hessians_, static_cast(num_data_), __FILE__, __LINE__); + gradients_ = cuda_gradients_.RawData(); + hessians_ = cuda_hessians_.RawData(); + } + + #ifdef DEBUG + CopyFromCUDADeviceToHost(host_gradients_.data(), gradients_, static_cast(num_data_), __FILE__, __LINE__); + CopyFromCUDADeviceToHost(host_hessians_.data(), hessians_, static_cast(num_data_), __FILE__, __LINE__); + #endif // DEBUG + + const data_size_t* leaf_splits_init_indices = + cuda_data_partition_->use_bagging() ? cuda_data_partition_->cuda_data_indices() : nullptr; + cuda_data_partition_->BeforeTrain(); + if (config_->use_quantized_grad) { + cuda_gradient_discretizer_->DiscretizeGradients(num_data_, gradients_, hessians_); + cuda_histogram_constructor_->BeforeTrain( + reinterpret_cast(cuda_gradient_discretizer_->discretized_gradients_and_hessians()), nullptr); + cuda_smaller_leaf_splits_->InitValues( + config_->lambda_l1, + config_->lambda_l2, + reinterpret_cast(cuda_gradient_discretizer_->discretized_gradients_and_hessians()), + leaf_splits_init_indices, + cuda_data_partition_->cuda_data_indices(), + root_num_data, + cuda_histogram_constructor_->cuda_hist_pointer(), + &leaf_sum_gradients_[0], + &leaf_sum_hessians_[0], + cuda_gradient_discretizer_->grad_scale_ptr(), + cuda_gradient_discretizer_->hess_scale_ptr()); + cuda_gradient_discretizer_->SetNumBitsInHistogramBin(0, -1, root_num_data, 0); + if (nccl_communicator_ != nullptr) { + cuda_gradient_discretizer_->SetNumBitsInHistogramBin(0, -1, global_num_data_, 0); + } + } else { + cuda_histogram_constructor_->BeforeTrain(gradients_, hessians_); + cuda_smaller_leaf_splits_->InitValues( + config_->lambda_l1, + config_->lambda_l2, + gradients_, + hessians_, + leaf_splits_init_indices, + cuda_data_partition_->cuda_data_indices(), + root_num_data, + cuda_histogram_constructor_->cuda_hist_pointer(), + &leaf_sum_gradients_[0], + &leaf_sum_hessians_[0]); + } + leaf_num_data_[0] = root_num_data; + cuda_larger_leaf_splits_->InitValues(); + col_sampler_.ResetByTree(); + cuda_best_split_finder_->BeforeTrain(col_sampler_.is_feature_used_bytree()); + leaf_data_start_[0] = 0; + smaller_leaf_index_ = 0; + larger_leaf_index_ = -1; + + if (nccl_communicator_ != nullptr) { + leaf_to_hist_index_map_.resize(config_->num_leaves, -1); + leaf_to_hist_index_map_[0] = 0; + global_num_data_in_leaf_[0] = global_num_data_; + } +} + +void CUDASingleGPUTreeLearner::AddPredictionToScore(const Tree* tree, double* out_score) const { + cuda_data_partition_->UpdateTrainScore(tree, out_score); +} + +Tree* CUDASingleGPUTreeLearner::Train(const score_t* gradients, + const score_t* hessians, bool /*is_first_tree*/) { + gradients_ = gradients; + hessians_ = hessians; + global_timer.Start("CUDASingleGPUTreeLearner::BeforeTrain"); + BeforeTrain(); + global_timer.Stop("CUDASingleGPUTreeLearner::BeforeTrain"); + const bool track_branch_features = !(config_->interaction_constraints_vector.empty()); + std::unique_ptr tree(new CUDATree(config_->num_leaves, track_branch_features, + config_->linear_tree, gpu_device_id_, has_categorical_feature_)); + // set the root value by hand, as it is not handled by splits + tree->SetLeafOutput(0, CUDALeafSplits::CalculateSplittedLeafOutput( + leaf_sum_gradients_[smaller_leaf_index_], leaf_sum_hessians_[smaller_leaf_index_], + config_->lambda_l1, config_->lambda_l2, config_->path_smooth, + static_cast(num_data_), 0)); + tree->SyncLeafOutputFromHostToCUDA(); + for (int i = 0; i < config_->num_leaves - 1; ++i) { + global_timer.Start("CUDASingleGPUTreeLearner::ConstructHistogramForLeaf"); + const data_size_t global_num_data_in_smaller_leaf = nccl_communicator_ != nullptr ? + global_num_data_in_leaf_[smaller_leaf_index_] : + leaf_num_data_[smaller_leaf_index_]; + const data_size_t global_num_data_in_larger_leaf = nccl_communicator_ != nullptr ? + (larger_leaf_index_ < 0 ? 0 : global_num_data_in_leaf_[larger_leaf_index_]) : + (larger_leaf_index_ < 0 ? 0 : leaf_num_data_[larger_leaf_index_]); + const data_size_t num_data_in_smaller_leaf = leaf_num_data_[smaller_leaf_index_]; + const data_size_t num_data_in_larger_leaf = larger_leaf_index_ < 0 ? 0 : leaf_num_data_[larger_leaf_index_]; + const double sum_hessians_in_smaller_leaf = leaf_sum_hessians_[smaller_leaf_index_]; + const double sum_hessians_in_larger_leaf = larger_leaf_index_ < 0 ? 0 : leaf_sum_hessians_[larger_leaf_index_]; + const uint8_t num_bits_in_histogram_bins = config_->use_quantized_grad ? (nccl_communicator_ != nullptr ? + cuda_gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_index_) : + cuda_gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_index_)) : 0; + cuda_histogram_constructor_->ConstructHistogramForLeaf( + cuda_smaller_leaf_splits_->GetCUDAStruct(), + cuda_larger_leaf_splits_->GetCUDAStruct(), + global_num_data_in_smaller_leaf, + global_num_data_in_larger_leaf, + num_data_in_smaller_leaf, + num_data_in_larger_leaf, + sum_hessians_in_smaller_leaf, + sum_hessians_in_larger_leaf, + num_bits_in_histogram_bins); + global_timer.Stop("CUDASingleGPUTreeLearner::ConstructHistogramForLeaf"); + + global_timer.Start("CUDASingleGPUTreeLearner::NCCLReduceHistogram"); + if (nccl_communicator_ != nullptr) { + NCCLReduceHistogram(); + } + global_timer.Stop("CUDASingleGPUTreeLearner::NCCLReduceHistogram"); + + global_timer.Start("CUDASingleGPUTreeLearner::FindBestSplitsForLeaf"); + uint8_t parent_num_bits_bin = 0; + uint8_t smaller_num_bits_bin = 0; + uint8_t larger_num_bits_bin = 0; + if (config_->use_quantized_grad) { + if (larger_leaf_index_ != -1) { + const int parent_leaf_index = std::min(smaller_leaf_index_, larger_leaf_index_); + if (nccl_communicator_ != nullptr) { + parent_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInNode(parent_leaf_index); + smaller_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_index_); + larger_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_index_); + } else { + parent_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInNode(parent_leaf_index); + smaller_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_index_); + larger_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_index_); + } + } else { + if (nccl_communicator_ != nullptr) { + parent_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(0); + smaller_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(0); + larger_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(0); + } else { + parent_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(0); + smaller_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(0); + larger_num_bits_bin = cuda_gradient_discretizer_->GetHistBitsInLeaf(0); + } + } + } else { + parent_num_bits_bin = 0; + smaller_num_bits_bin = 0; + larger_num_bits_bin = 0; + } + cuda_histogram_constructor_->SubtractHistogramForLeaf( + cuda_smaller_leaf_splits_->GetCUDAStruct(), + cuda_larger_leaf_splits_->GetCUDAStruct(), + config_->use_quantized_grad, + parent_num_bits_bin, + smaller_num_bits_bin, + larger_num_bits_bin); + + SelectFeatureByNode(tree.get()); + + if (config_->use_quantized_grad) { + const uint8_t smaller_leaf_num_bits_bin = nccl_communicator_ == nullptr ? + cuda_gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_index_) : + cuda_gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_index_); + const uint8_t larger_leaf_num_bits_bin = larger_leaf_index_ < 0 ? 32 : (nccl_communicator_ == nullptr ? + cuda_gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_index_) : + cuda_gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_index_)); + cuda_best_split_finder_->FindBestSplitsForLeaf( + cuda_smaller_leaf_splits_->GetCUDAStruct(), + cuda_larger_leaf_splits_->GetCUDAStruct(), + smaller_leaf_index_, larger_leaf_index_, + global_num_data_in_smaller_leaf, global_num_data_in_larger_leaf, + sum_hessians_in_smaller_leaf, sum_hessians_in_larger_leaf, + cuda_gradient_discretizer_->grad_scale_ptr(), + cuda_gradient_discretizer_->hess_scale_ptr(), + smaller_leaf_num_bits_bin, + larger_leaf_num_bits_bin); + } else { + cuda_best_split_finder_->FindBestSplitsForLeaf( + cuda_smaller_leaf_splits_->GetCUDAStruct(), + cuda_larger_leaf_splits_->GetCUDAStruct(), + smaller_leaf_index_, larger_leaf_index_, + global_num_data_in_smaller_leaf, global_num_data_in_larger_leaf, + sum_hessians_in_smaller_leaf, sum_hessians_in_larger_leaf, + nullptr, nullptr, 0, 0); + } + + global_timer.Stop("CUDASingleGPUTreeLearner::FindBestSplitsForLeaf"); + global_timer.Start("CUDASingleGPUTreeLearner::FindBestFromAllSplits"); + const CUDASplitInfo* best_split_info = nullptr; + if (larger_leaf_index_ >= 0) { + best_split_info = cuda_best_split_finder_->FindBestFromAllSplits( + tree->num_leaves(), + smaller_leaf_index_, + larger_leaf_index_, + &leaf_best_split_feature_[smaller_leaf_index_], + &leaf_best_split_threshold_[smaller_leaf_index_], + &leaf_best_split_default_left_[smaller_leaf_index_], + &leaf_best_split_feature_[larger_leaf_index_], + &leaf_best_split_threshold_[larger_leaf_index_], + &leaf_best_split_default_left_[larger_leaf_index_], + &best_leaf_index_, + &num_cat_threshold_); + } else { + best_split_info = cuda_best_split_finder_->FindBestFromAllSplits( + tree->num_leaves(), + smaller_leaf_index_, + larger_leaf_index_, + &leaf_best_split_feature_[smaller_leaf_index_], + &leaf_best_split_threshold_[smaller_leaf_index_], + &leaf_best_split_default_left_[smaller_leaf_index_], + nullptr, + nullptr, + nullptr, + &best_leaf_index_, + &num_cat_threshold_); + } + global_timer.Stop("CUDASingleGPUTreeLearner::FindBestFromAllSplits"); + + if (best_leaf_index_ == -1) { + Log::Warning("No further splits with positive gain, training stopped with %d leaves.", (i + 1)); + break; + } + + global_timer.Start("CUDASingleGPUTreeLearner::Split"); + if (num_cat_threshold_ > 0) { + ConstructBitsetForCategoricalSplit(best_split_info); + } + + int right_leaf_index = 0; + if (train_data_->FeatureBinMapper(leaf_best_split_feature_[best_leaf_index_])->bin_type() == BinType::CategoricalBin) { + right_leaf_index = tree->SplitCategorical(best_leaf_index_, + train_data_->RealFeatureIndex(leaf_best_split_feature_[best_leaf_index_]), + train_data_->FeatureBinMapper(leaf_best_split_feature_[best_leaf_index_])->missing_type(), + best_split_info, + cuda_bitset_, + cuda_bitset_len_, + cuda_bitset_inner_, + cuda_bitset_inner_len_); + } else { + right_leaf_index = tree->Split(best_leaf_index_, + train_data_->RealFeatureIndex(leaf_best_split_feature_[best_leaf_index_]), + train_data_->RealThreshold(leaf_best_split_feature_[best_leaf_index_], + leaf_best_split_threshold_[best_leaf_index_]), + train_data_->FeatureBinMapper(leaf_best_split_feature_[best_leaf_index_])->missing_type(), + best_split_info); + } + + cuda_data_partition_->Split(best_split_info, + best_leaf_index_, + right_leaf_index, + leaf_best_split_feature_[best_leaf_index_], + leaf_best_split_threshold_[best_leaf_index_], + cuda_bitset_inner_, + static_cast(cuda_bitset_inner_len_), + leaf_best_split_default_left_[best_leaf_index_], + leaf_num_data_[best_leaf_index_], + leaf_data_start_[best_leaf_index_], + cuda_smaller_leaf_splits_->GetCUDAStructRef(), + cuda_larger_leaf_splits_->GetCUDAStructRef(), + &leaf_num_data_[best_leaf_index_], + &leaf_num_data_[right_leaf_index], + &leaf_data_start_[best_leaf_index_], + &leaf_data_start_[right_leaf_index], + &leaf_sum_hessians_[best_leaf_index_], + &leaf_sum_hessians_[right_leaf_index], + &leaf_sum_gradients_[best_leaf_index_], + &leaf_sum_gradients_[right_leaf_index], + global_num_data_in_leaf_.data() + best_leaf_index_, + global_num_data_in_leaf_.data() + right_leaf_index); + #ifdef DEBUG + CheckSplitValid(best_leaf_index_, right_leaf_index); + #endif // DEBUG + + if (nccl_communicator_ != nullptr) { + smaller_leaf_index_ = (global_num_data_in_leaf_[best_leaf_index_] < global_num_data_in_leaf_[right_leaf_index] ? best_leaf_index_ : right_leaf_index); + larger_leaf_index_ = (smaller_leaf_index_ == best_leaf_index_ ? right_leaf_index : best_leaf_index_); + const int best_leaf_hist_index = leaf_to_hist_index_map_[best_leaf_index_]; + leaf_to_hist_index_map_[smaller_leaf_index_] = right_leaf_index; + leaf_to_hist_index_map_[larger_leaf_index_] = best_leaf_hist_index; + } else { + smaller_leaf_index_ = (leaf_num_data_[best_leaf_index_] < leaf_num_data_[right_leaf_index] ? best_leaf_index_ : right_leaf_index); + larger_leaf_index_ = (smaller_leaf_index_ == best_leaf_index_ ? right_leaf_index : best_leaf_index_); + } + + if (config_->use_quantized_grad) { + cuda_gradient_discretizer_->SetNumBitsInHistogramBin( + best_leaf_index_, right_leaf_index, leaf_num_data_[best_leaf_index_], leaf_num_data_[right_leaf_index]); + if (nccl_communicator_ != nullptr) { + cuda_gradient_discretizer_->SetNumBitsInHistogramBin( + best_leaf_index_, right_leaf_index, global_num_data_in_leaf_[best_leaf_index_], global_num_data_in_leaf_[right_leaf_index]); + } + } + global_timer.Stop("CUDASingleGPUTreeLearner::Split"); + } + SynchronizeCUDADevice(__FILE__, __LINE__); + if (config_->use_quantized_grad && config_->quant_train_renew_leaf) { + global_timer.Start("CUDASingleGPUTreeLearner::RenewDiscretizedTreeLeaves"); + RenewDiscretizedTreeLeaves(tree.get()); + global_timer.Stop("CUDASingleGPUTreeLearner::RenewDiscretizedTreeLeaves"); + } + tree->ToHost(); + return tree.release(); +} + +void CUDASingleGPUTreeLearner::ResetTrainingData( + const Dataset* train_data, + bool is_constant_hessian) { + SerialTreeLearner::ResetTrainingData(train_data, is_constant_hessian); + CHECK_EQ(num_features_, train_data_->num_features()); + cuda_histogram_constructor_->ResetTrainingData(train_data, share_state_.get()); + cuda_data_partition_->ResetTrainingData(train_data, + static_cast(share_state_->feature_hist_offsets().back()), + cuda_histogram_constructor_->cuda_hist_pointer()); + cuda_best_split_finder_->ResetTrainingData( + cuda_histogram_constructor_->cuda_hist(), + train_data, + share_state_->feature_hist_offsets()); + cuda_smaller_leaf_splits_->Resize(num_data_); + cuda_larger_leaf_splits_->Resize(num_data_); + CHECK_EQ(is_constant_hessian, share_state_->is_constant_hessian); + if (!boosting_on_cuda_) { + cuda_gradients_.Resize(static_cast(num_data_)); + cuda_hessians_.Resize(static_cast(num_data_)); + } +} + +void CUDASingleGPUTreeLearner::ResetConfig(const Config* config) { + const int old_num_leaves = config_->num_leaves; + SerialTreeLearner::ResetConfig(config); + if (config_->gpu_device_id >= 0 && config_->gpu_device_id != gpu_device_id_) { + Log::Fatal("Changing gpu device ID by resetting configuration parameter is not allowed for CUDA tree learner."); + } + num_threads_ = OMP_NUM_THREADS(); + if (config_->num_leaves != old_num_leaves) { + leaf_best_split_feature_.resize(config_->num_leaves, -1); + leaf_best_split_threshold_.resize(config_->num_leaves, 0); + leaf_best_split_default_left_.resize(config_->num_leaves, 0); + leaf_num_data_.resize(config_->num_leaves, 0); + leaf_data_start_.resize(config_->num_leaves, 0); + leaf_sum_gradients_.resize(config_->num_leaves, 0.0f); + leaf_sum_hessians_.resize(config_->num_leaves, 0.0f); + } + cuda_histogram_constructor_->ResetConfig(config); + cuda_best_split_finder_->ResetConfig(config, cuda_histogram_constructor_->cuda_hist()); + cuda_data_partition_->ResetConfig(config, cuda_histogram_constructor_->cuda_hist_pointer()); +} + +void CUDASingleGPUTreeLearner::SetBaggingData(const Dataset* /*subset*/, + const data_size_t* used_indices, data_size_t num_data) { + cuda_data_partition_->SetUsedDataIndices(used_indices, num_data); +} + +void CUDASingleGPUTreeLearner::RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function residual_getter, + data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* train_score) const { + CHECK(tree->is_cuda_tree()); + CUDATree* cuda_tree = reinterpret_cast(tree); + if (obj != nullptr && obj->IsRenewTreeOutput()) { + CHECK_LE(cuda_tree->num_leaves(), data_partition_->num_leaves()); + if (boosting_on_cuda_) { + obj->RenewTreeOutputCUDA(train_score, cuda_data_partition_->cuda_data_indices(), + cuda_data_partition_->cuda_leaf_num_data(), cuda_data_partition_->cuda_leaf_data_start(), + cuda_tree->num_leaves(), cuda_tree->cuda_leaf_value_ref()); + cuda_tree->SyncLeafOutputFromCUDAToHost(); + } else { + const data_size_t* bag_mapper = nullptr; + if (total_num_data != num_data_) { + CHECK_EQ(bag_cnt, num_data_); + bag_mapper = bag_indices; + } + std::vector n_nozeroworker_perleaf(cuda_tree->num_leaves(), 1); + int num_machines = Network::num_machines(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < cuda_tree->num_leaves(); ++i) { + const double output = static_cast(cuda_tree->LeafOutput(i)); + data_size_t cnt_leaf_data = leaf_num_data_[i]; + std::vector index_mapper(cnt_leaf_data, -1); + CopyFromCUDADeviceToHost(index_mapper.data(), + cuda_data_partition_->cuda_data_indices() + leaf_data_start_[i], + static_cast(cnt_leaf_data), __FILE__, __LINE__); + if (cnt_leaf_data > 0) { + const double new_output = obj->RenewTreeOutput(output, residual_getter, index_mapper.data(), bag_mapper, cnt_leaf_data); + cuda_tree->SetLeafOutput(i, new_output); + } else { + CHECK_GT(num_machines, 1); + cuda_tree->SetLeafOutput(i, 0.0); + n_nozeroworker_perleaf[i] = 0; + } + } + if (num_machines > 1) { + std::vector outputs(cuda_tree->num_leaves()); + for (int i = 0; i < cuda_tree->num_leaves(); ++i) { + outputs[i] = static_cast(cuda_tree->LeafOutput(i)); + } + outputs = Network::GlobalSum(&outputs); + n_nozeroworker_perleaf = Network::GlobalSum(&n_nozeroworker_perleaf); + for (int i = 0; i < cuda_tree->num_leaves(); ++i) { + cuda_tree->SetLeafOutput(i, outputs[i] / n_nozeroworker_perleaf[i]); + } + } + } + cuda_tree->SyncLeafOutputFromHostToCUDA(); + } +} + +Tree* CUDASingleGPUTreeLearner::FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const { + std::unique_ptr cuda_tree(new CUDATree(old_tree)); + cuda_leaf_gradient_stat_buffer_.SetValue(0); + cuda_leaf_hessian_stat_buffer_.SetValue(0); + ReduceLeafStat(cuda_tree.get(), gradients, hessians, cuda_data_partition_->cuda_data_indices()); + cuda_tree->SyncLeafOutputFromCUDAToHost(); + return cuda_tree.release(); +} + +Tree* CUDASingleGPUTreeLearner::FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t* hessians) const { + cuda_data_partition_->ResetByLeafPred(leaf_pred, old_tree->num_leaves()); + refit_num_data_ = static_cast(leaf_pred.size()); + data_size_t buffer_size = static_cast(old_tree->num_leaves()); + if (old_tree->num_leaves() > 2048) { + const int num_block = (refit_num_data_ + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + buffer_size *= static_cast(num_block + 1); + } + if (static_cast(buffer_size) > cuda_leaf_gradient_stat_buffer_.Size()) { + cuda_leaf_gradient_stat_buffer_.Resize(buffer_size); + cuda_leaf_hessian_stat_buffer_.Resize(buffer_size); + } + return FitByExistingTree(old_tree, gradients, hessians); +} + +void CUDASingleGPUTreeLearner::ReduceLeafStat( + CUDATree* old_tree, const score_t* gradients, const score_t* hessians, const data_size_t* num_data_in_leaf) const { + LaunchReduceLeafStatKernel(gradients, hessians, num_data_in_leaf, old_tree->cuda_leaf_parent(), + old_tree->cuda_left_child(), old_tree->cuda_right_child(), + old_tree->num_leaves(), refit_num_data_, old_tree->cuda_leaf_value_ref(), old_tree->shrinkage()); +} + +void CUDASingleGPUTreeLearner::ConstructBitsetForCategoricalSplit( + const CUDASplitInfo* best_split_info) { + LaunchConstructBitsetForCategoricalSplitKernel(best_split_info); +} + +void CUDASingleGPUTreeLearner::AllocateBitset() { + has_categorical_feature_ = false; + categorical_bin_offsets_.clear(); + categorical_bin_offsets_.push_back(0); + categorical_bin_to_value_.clear(); + for (int i = 0; i < train_data_->num_features(); ++i) { + const BinMapper* bin_mapper = train_data_->FeatureBinMapper(i); + if (bin_mapper->bin_type() == BinType::CategoricalBin) { + has_categorical_feature_ = true; + break; + } + } + if (has_categorical_feature_) { + int max_cat_value = 0; + int max_cat_num_bin = 0; + for (int i = 0; i < train_data_->num_features(); ++i) { + const BinMapper* bin_mapper = train_data_->FeatureBinMapper(i); + if (bin_mapper->bin_type() == BinType::CategoricalBin) { + max_cat_value = std::max(bin_mapper->MaxCatValue(), max_cat_value); + max_cat_num_bin = std::max(bin_mapper->num_bin(), max_cat_num_bin); + } + } + // std::max(..., 1UL) to avoid error in the case when there are NaN's in the categorical values + const size_t cuda_bitset_max_size = std::max(static_cast((max_cat_value + 31) / 32), 1UL); + const size_t cuda_bitset_inner_max_size = std::max(static_cast((max_cat_num_bin + 31) / 32), 1UL); + AllocateCUDAMemory(&cuda_bitset_, cuda_bitset_max_size, __FILE__, __LINE__); + AllocateCUDAMemory(&cuda_bitset_inner_, cuda_bitset_inner_max_size, __FILE__, __LINE__); + const int max_cat_in_split = std::min(config_->max_cat_threshold, max_cat_num_bin / 2); + const int num_blocks = (max_cat_in_split + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + AllocateCUDAMemory(&cuda_block_bitset_len_buffer_, num_blocks, __FILE__, __LINE__); + + for (int i = 0; i < train_data_->num_features(); ++i) { + const BinMapper* bin_mapper = train_data_->FeatureBinMapper(i); + if (bin_mapper->bin_type() == BinType::CategoricalBin) { + categorical_bin_offsets_.push_back(bin_mapper->num_bin()); + } else { + categorical_bin_offsets_.push_back(0); + } + } + for (size_t i = 1; i < categorical_bin_offsets_.size(); ++i) { + categorical_bin_offsets_[i] += categorical_bin_offsets_[i - 1]; + } + categorical_bin_to_value_.resize(categorical_bin_offsets_.back(), 0); + for (int i = 0; i < train_data_->num_features(); ++i) { + const BinMapper* bin_mapper = train_data_->FeatureBinMapper(i); + if (bin_mapper->bin_type() == BinType::CategoricalBin) { + const int offset = categorical_bin_offsets_[i]; + for (int bin = 0; bin < bin_mapper->num_bin(); ++bin) { + categorical_bin_to_value_[offset + bin] = static_cast(bin_mapper->BinToValue(bin)); + } + } + } + InitCUDAMemoryFromHostMemory(&cuda_categorical_bin_offsets_, categorical_bin_offsets_.data(), categorical_bin_offsets_.size(), __FILE__, __LINE__); + InitCUDAMemoryFromHostMemory(&cuda_categorical_bin_to_value_, categorical_bin_to_value_.data(), categorical_bin_to_value_.size(), __FILE__, __LINE__); + } else { + cuda_bitset_ = nullptr; + cuda_bitset_inner_ = nullptr; + } + cuda_bitset_len_ = 0; + cuda_bitset_inner_len_ = 0; +} + +void CUDASingleGPUTreeLearner::ResetBoostingOnGPU(const bool boosting_on_cuda) { + boosting_on_cuda_ = boosting_on_cuda; + cuda_gradients_.Clear(); + cuda_hessians_.Clear(); + if (!boosting_on_cuda_) { + cuda_gradients_.Resize(static_cast(num_data_)); + cuda_hessians_.Resize(static_cast(num_data_)); + } +} + +void CUDASingleGPUTreeLearner::SelectFeatureByNode(const Tree* tree) { + if (select_features_by_node_) { + // use feature interaction constraint or sample features by node + const std::vector& is_feature_used_by_smaller_node = col_sampler_.GetByNode(tree, smaller_leaf_index_); + std::vector is_feature_used_by_larger_node; + if (larger_leaf_index_ >= 0) { + is_feature_used_by_larger_node = col_sampler_.GetByNode(tree, larger_leaf_index_); + } + cuda_best_split_finder_->SetUsedFeatureByNode(is_feature_used_by_smaller_node, is_feature_used_by_larger_node); + } +} + +#ifdef DEBUG +void CUDASingleGPUTreeLearner::CheckSplitValid( + const int left_leaf, + const int right_leaf) { + std::vector left_data_indices(leaf_num_data_[left_leaf]); + std::vector right_data_indices(leaf_num_data_[right_leaf]); + CopyFromCUDADeviceToHost(left_data_indices.data(), + cuda_data_partition_->cuda_data_indices() + leaf_data_start_[left_leaf], + leaf_num_data_[left_leaf], __FILE__, __LINE__); + CopyFromCUDADeviceToHost(right_data_indices.data(), + cuda_data_partition_->cuda_data_indices() + leaf_data_start_[right_leaf], + leaf_num_data_[right_leaf], __FILE__, __LINE__); + double sum_left_gradients = 0.0f, sum_left_hessians = 0.0f; + double sum_right_gradients = 0.0f, sum_right_hessians = 0.0f; + for (size_t i = 0; i < left_data_indices.size(); ++i) { + const data_size_t index = left_data_indices[i]; + sum_left_gradients += host_gradients_[index]; + sum_left_hessians += host_hessians_[index]; + } + for (size_t i = 0; i < right_data_indices.size(); ++i) { + const data_size_t index = right_data_indices[i]; + sum_right_gradients += host_gradients_[index]; + sum_right_hessians += host_hessians_[index]; + } + if (nccl_communicator_ != nullptr) { + sum_left_gradients = NCCLAllReduce(sum_left_gradients, ncclFloat64, ncclSum, nccl_communicator_); + sum_left_hessians = NCCLAllReduce(sum_left_hessians, ncclFloat64, ncclSum, nccl_communicator_); + sum_right_gradients = NCCLAllReduce(sum_right_gradients, ncclFloat64, ncclSum, nccl_communicator_); + sum_right_hessians = NCCLAllReduce(sum_right_hessians, ncclFloat64, ncclSum, nccl_communicator_); + } + CHECK_LE(std::fabs(sum_left_gradients - leaf_sum_gradients_[left_leaf]), 1e-6f); + CHECK_LE(std::fabs(sum_left_hessians - leaf_sum_hessians_[left_leaf]), 1e-6f); + CHECK_LE(std::fabs(sum_right_gradients - leaf_sum_gradients_[right_leaf]), 1e-6f); + CHECK_LE(std::fabs(sum_right_hessians - leaf_sum_hessians_[right_leaf]), 1e-6f); +} +#endif // DEBUG + +void CUDASingleGPUTreeLearner::RenewDiscretizedTreeLeaves(CUDATree* cuda_tree) { + cuda_data_partition_->ReduceLeafGradStat( + gradients_, hessians_, cuda_tree, + cuda_leaf_gradient_stat_buffer_.RawData(), + cuda_leaf_hessian_stat_buffer_.RawData()); + LaunchCalcLeafValuesGivenGradStat(cuda_tree, cuda_data_partition_->cuda_data_indices()); + SynchronizeCUDADevice(__FILE__, __LINE__); +} + +void CUDASingleGPUTreeLearner::SetNCCLInfo( + ncclComm_t nccl_communicator, + int nccl_gpu_rank, + int local_gpu_rank, + int gpu_device_id, + data_size_t global_num_data) { + NCCLInfo::SetNCCLInfo(nccl_communicator, nccl_gpu_rank, local_gpu_rank, gpu_device_id, global_num_data); + leaf_to_hist_index_map_.resize(config_->num_leaves - 1); + global_num_data_in_leaf_.resize(config_->num_leaves, 0); + nccl_stream_ = CUDAStreamCreate(); +} + +void CUDASingleGPUTreeLearner::NCCLReduceHistogram() { + if (config_->use_quantized_grad) { + hist_t* smaller_leaf_hist_pointer = cuda_histogram_constructor_->cuda_hist_pointer() + + leaf_to_hist_index_map_[smaller_leaf_index_] * num_total_bin_; + const int8_t bit_size = cuda_gradient_discretizer_->GetHistBitsInNode(smaller_leaf_index_); + if (bit_size == 32) { + NCCLAllReduce( + reinterpret_cast(smaller_leaf_hist_pointer), + reinterpret_cast(smaller_leaf_hist_pointer), + static_cast(num_total_bin_), + ncclInt64, + ncclSum, + nccl_communicator_, + nccl_stream_); + } else if (bit_size <= 16) { + NCCLAllReduce( + reinterpret_cast(smaller_leaf_hist_pointer), + reinterpret_cast(smaller_leaf_hist_pointer), + static_cast(num_total_bin_), + ncclInt32, + ncclSum, + nccl_communicator_, + nccl_stream_); + } + } else { + hist_t* smaller_leaf_hist_pointer = cuda_histogram_constructor_->cuda_hist_pointer() + + leaf_to_hist_index_map_[smaller_leaf_index_] * num_total_bin_ * 2; + NCCLAllReduce( + smaller_leaf_hist_pointer, + smaller_leaf_hist_pointer, + static_cast(num_total_bin_) * 2, + ncclFloat64, + ncclSum, + nccl_communicator_, + nccl_stream_); + } + SynchronizeCUDAStream(nccl_stream_, __FILE__, __LINE__); +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_single_gpu_tree_learner.cu b/src/treelearner/cuda/cuda_single_gpu_tree_learner.cu new file mode 100644 index 0000000..3bf7412 --- /dev/null +++ b/src/treelearner/cuda/cuda_single_gpu_tree_learner.cu @@ -0,0 +1,296 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved. + */ + +#ifdef USE_CUDA + +#include "cuda_single_gpu_tree_learner.hpp" + +#include +#include + +#include + +namespace LightGBM { + +__global__ void ReduceLeafStatKernel_SharedMemory( + const score_t* gradients, + const score_t* hessians, + const int num_leaves, + const data_size_t num_data, + const int* data_index_to_leaf_index, + double* leaf_grad_stat_buffer, + double* leaf_hess_stat_buffer) { + extern __shared__ double shared_mem[]; + double* shared_grad_sum = shared_mem; + double* shared_hess_sum = shared_mem + num_leaves; + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + for (int leaf_index = static_cast(threadIdx.x); leaf_index < num_leaves; leaf_index += static_cast(blockDim.x)) { + shared_grad_sum[leaf_index] = 0.0f; + shared_hess_sum[leaf_index] = 0.0f; + } + __syncthreads(); + if (data_index < num_data) { + const int leaf_index = data_index_to_leaf_index[data_index]; + atomicAdd_block(shared_grad_sum + leaf_index, gradients[data_index]); + atomicAdd_block(shared_hess_sum + leaf_index, hessians[data_index]); + } + __syncthreads(); + for (int leaf_index = static_cast(threadIdx.x); leaf_index < num_leaves; leaf_index += static_cast(blockDim.x)) { + atomicAdd_system(leaf_grad_stat_buffer + leaf_index, shared_grad_sum[leaf_index]); + atomicAdd_system(leaf_hess_stat_buffer + leaf_index, shared_hess_sum[leaf_index]); + } +} + +__global__ void ReduceLeafStatKernel_GlobalMemory( + const score_t* gradients, + const score_t* hessians, + const int num_leaves, + const data_size_t num_data, + const int* data_index_to_leaf_index, + double* leaf_grad_stat_buffer, + double* leaf_hess_stat_buffer) { + const size_t offset = static_cast(num_leaves) * (blockIdx.x + 1); + double* grad_sum = leaf_grad_stat_buffer + offset; + double* hess_sum = leaf_hess_stat_buffer + offset; + const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + for (int leaf_index = static_cast(threadIdx.x); leaf_index < num_leaves; leaf_index += static_cast(blockDim.x)) { + grad_sum[leaf_index] = 0.0f; + hess_sum[leaf_index] = 0.0f; + } + __syncthreads(); + if (data_index < num_data) { + const int leaf_index = data_index_to_leaf_index[data_index]; + atomicAdd_block(grad_sum + leaf_index, gradients[data_index]); + atomicAdd_block(hess_sum + leaf_index, hessians[data_index]); + } + __syncthreads(); + for (int leaf_index = static_cast(threadIdx.x); leaf_index < num_leaves; leaf_index += static_cast(blockDim.x)) { + atomicAdd_system(leaf_grad_stat_buffer + leaf_index, grad_sum[leaf_index]); + atomicAdd_system(leaf_hess_stat_buffer + leaf_index, hess_sum[leaf_index]); + } +} + +template +__global__ void CalcRefitLeafOutputKernel( + const int num_leaves, + const double* leaf_grad_stat_buffer, + const double* leaf_hess_stat_buffer, + const data_size_t* num_data_in_leaf, + const int* leaf_parent, + const int* left_child, + const int* right_child, + const double lambda_l1, + const double lambda_l2, + const double path_smooth, + const double shrinkage_rate, + const double refit_decay_rate, + double* leaf_value) { + const int leaf_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (leaf_index < num_leaves) { + const double sum_gradients = leaf_grad_stat_buffer[leaf_index]; + const double sum_hessians = leaf_hess_stat_buffer[leaf_index]; + const data_size_t num_data = num_data_in_leaf[leaf_index]; + const double old_leaf_value = leaf_value[leaf_index]; + double new_leaf_value = 0.0f; + if (!USE_SMOOTHING) { + new_leaf_value = CUDALeafSplits::CalculateSplittedLeafOutput(sum_gradients, sum_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } else { + const int parent = leaf_parent[leaf_index]; + if (parent >= 0) { + const int sibliing = left_child[parent] == leaf_index ? right_child[parent] : left_child[parent]; + const double sum_gradients_of_parent = sum_gradients + leaf_grad_stat_buffer[sibliing]; + const double sum_hessians_of_parent = sum_hessians + leaf_hess_stat_buffer[sibliing]; + const data_size_t num_data_in_parent = num_data + num_data_in_leaf[sibliing]; + const double parent_output = + CUDALeafSplits::CalculateSplittedLeafOutput( + sum_gradients_of_parent, sum_hessians_of_parent, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + new_leaf_value = CUDALeafSplits::CalculateSplittedLeafOutput( + sum_gradients, sum_hessians, lambda_l1, lambda_l2, path_smooth, num_data_in_parent, parent_output); + } else { + new_leaf_value = CUDALeafSplits::CalculateSplittedLeafOutput(sum_gradients, sum_hessians, lambda_l1, lambda_l2, 0.0f, 0, 0.0f); + } + } + if (isnan(new_leaf_value)) { + new_leaf_value = 0.0f; + } else { + new_leaf_value *= shrinkage_rate; + } + leaf_value[leaf_index] = refit_decay_rate * old_leaf_value + (1.0f - refit_decay_rate) * new_leaf_value; + } +} + +void CUDASingleGPUTreeLearner::LaunchReduceLeafStatKernel( + const score_t* gradients, const score_t* hessians, const data_size_t* num_data_in_leaf, + const int* leaf_parent, const int* left_child, const int* right_child, const int num_leaves, + const data_size_t num_data, double* cuda_leaf_value, const double shrinkage_rate) const { + int num_block = (num_data + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + if (num_leaves <= 2048) { + ReduceLeafStatKernel_SharedMemory<<>>( + gradients, hessians, num_leaves, num_data, cuda_data_partition_->cuda_data_index_to_leaf_index(), + cuda_leaf_gradient_stat_buffer_.RawData(), cuda_leaf_hessian_stat_buffer_.RawData()); + } else { + ReduceLeafStatKernel_GlobalMemory<<>>( + gradients, hessians, num_leaves, num_data, cuda_data_partition_->cuda_data_index_to_leaf_index(), + cuda_leaf_gradient_stat_buffer_.RawData(), cuda_leaf_hessian_stat_buffer_.RawData()); + } + const bool use_l1 = config_->lambda_l1 > 0.0f; + const bool use_smoothing = config_->path_smooth > 0.0f; + num_block = (num_leaves + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + + #define CalcRefitLeafOutputKernel_ARGS \ + num_leaves, cuda_leaf_gradient_stat_buffer_.RawData(), cuda_leaf_hessian_stat_buffer_.RawData(), num_data_in_leaf, \ + leaf_parent, left_child, right_child, \ + config_->lambda_l1, config_->lambda_l2, config_->path_smooth, \ + shrinkage_rate, config_->refit_decay_rate, cuda_leaf_value + + if (!use_l1) { + if (!use_smoothing) { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } else { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } + } else { + if (!use_smoothing) { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } else { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } + } + #undef CalcRefitLeafOutputKernel_ARGS +} + +template +__global__ void CalcBitsetLenKernel(const CUDASplitInfo* best_split_info, size_t* out_len_buffer) { + __shared__ size_t shared_mem_buffer[WARPSIZE]; + const T* vals = nullptr; + if (IS_INNER) { + vals = reinterpret_cast(best_split_info->cat_threshold); + } else { + vals = reinterpret_cast(best_split_info->cat_threshold_real); + } + const int i = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + size_t len = 0; + if (i < best_split_info->num_cat_threshold) { + const T val = vals[i]; + len = (val / WARPSIZE) + 1; + } + const size_t block_max_len = ShuffleReduceMax(len, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + out_len_buffer[blockIdx.x] = block_max_len; + } +} + +__global__ void ReduceBlockMaxLen(size_t* out_len_buffer, const int num_blocks) { + __shared__ size_t shared_mem_buffer[WARPSIZE]; + size_t max_len = 0; + for (int i = static_cast(threadIdx.x); i < num_blocks; i += static_cast(blockDim.x)) { + max_len = max(out_len_buffer[i], max_len); + } + const size_t all_max_len = ShuffleReduceMax(max_len, shared_mem_buffer, blockDim.x); + if (threadIdx.x == 0) { + out_len_buffer[0] = max_len; + } +} + +template +__global__ void CUDAConstructBitsetKernel(const CUDASplitInfo* best_split_info, uint32_t* out, size_t cuda_bitset_len) { + const T* vals = nullptr; + if (IS_INNER) { + vals = reinterpret_cast(best_split_info->cat_threshold); + } else { + vals = reinterpret_cast(best_split_info->cat_threshold_real); + } + const int i = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (i < best_split_info->num_cat_threshold) { + const T val = vals[i]; + // can use add instead of or here, because each bit will only be added once + atomicAdd_system(out + (val / WARPSIZE), (0x1 << (val % WARPSIZE))); + } +} + +__global__ void SetRealThresholdKernel( + const CUDASplitInfo* best_split_info, + const int* categorical_bin_to_value, + const int* categorical_bin_offsets) { + const int num_cat_threshold = best_split_info->num_cat_threshold; + const int* categorical_bin_to_value_ptr = categorical_bin_to_value + categorical_bin_offsets[best_split_info->inner_feature_index]; + int* cat_threshold_real = best_split_info->cat_threshold_real; + const uint32_t* cat_threshold = best_split_info->cat_threshold; + const int index = static_cast(threadIdx.x + blockIdx.x * blockDim.x); + if (index < num_cat_threshold) { + cat_threshold_real[index] = categorical_bin_to_value_ptr[cat_threshold[index]]; + } +} + +template +void CUDAConstructBitset(const CUDASplitInfo* best_split_info, const int num_cat_threshold, uint32_t* out, size_t bitset_len) { + const int num_blocks = (num_cat_threshold + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + // clear the bitset vector first + SetCUDAMemory(out, 0, bitset_len, __FILE__, __LINE__); + CUDAConstructBitsetKernel<<>>(best_split_info, out, bitset_len); +} + +template +size_t CUDABitsetLen(const CUDASplitInfo* best_split_info, const int num_cat_threshold, size_t* out_len_buffer) { + const int num_blocks = (num_cat_threshold + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + CalcBitsetLenKernel<<>>(best_split_info, out_len_buffer); + ReduceBlockMaxLen<<<1, CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE>>>(out_len_buffer, num_blocks); + size_t host_max_len = 0; + CopyFromCUDADeviceToHost(&host_max_len, out_len_buffer, 1, __FILE__, __LINE__); + return host_max_len; +} + +void CUDASingleGPUTreeLearner::LaunchConstructBitsetForCategoricalSplitKernel( + const CUDASplitInfo* best_split_info) { + const int num_blocks = (num_cat_threshold_ + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + SetRealThresholdKernel<<>> + (best_split_info, cuda_categorical_bin_to_value_, cuda_categorical_bin_offsets_); + cuda_bitset_inner_len_ = CUDABitsetLen(best_split_info, num_cat_threshold_, cuda_block_bitset_len_buffer_); + CUDAConstructBitset(best_split_info, num_cat_threshold_, cuda_bitset_inner_, cuda_bitset_inner_len_); + cuda_bitset_len_ = CUDABitsetLen(best_split_info, num_cat_threshold_, cuda_block_bitset_len_buffer_); + CUDAConstructBitset(best_split_info, num_cat_threshold_, cuda_bitset_, cuda_bitset_len_); +} + +void CUDASingleGPUTreeLearner::LaunchCalcLeafValuesGivenGradStat( + CUDATree* cuda_tree, const data_size_t* num_data_in_leaf) { + #define CalcRefitLeafOutputKernel_ARGS \ + cuda_tree->num_leaves(), cuda_leaf_gradient_stat_buffer_.RawData(), cuda_leaf_hessian_stat_buffer_.RawData(), num_data_in_leaf, \ + cuda_tree->cuda_leaf_parent(), cuda_tree->cuda_left_child(), cuda_tree->cuda_right_child(), \ + config_->lambda_l1, config_->lambda_l2, config_->path_smooth, \ + 1.0f, config_->refit_decay_rate, cuda_tree->cuda_leaf_value_ref() + const bool use_l1 = config_->lambda_l1 > 0.0f; + const bool use_smoothing = config_->path_smooth > 0.0f; + const int num_block = (cuda_tree->num_leaves() + CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE - 1) / CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE; + if (!use_l1) { + if (!use_smoothing) { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } else { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } + } else { + if (!use_smoothing) { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } else { + CalcRefitLeafOutputKernel + <<>>(CalcRefitLeafOutputKernel_ARGS); + } + } + + #undef CalcRefitLeafOutputKernel_ARGS +} + +} // namespace LightGBM + +#endif // USE_CUDA diff --git a/src/treelearner/cuda/cuda_single_gpu_tree_learner.hpp b/src/treelearner/cuda/cuda_single_gpu_tree_learner.hpp new file mode 100644 index 0000000..0601356 --- /dev/null +++ b/src/treelearner/cuda/cuda_single_gpu_tree_learner.hpp @@ -0,0 +1,183 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_SINGLE_GPU_TREE_LEARNER_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_SINGLE_GPU_TREE_LEARNER_HPP_ + +#include +#include + +#ifdef USE_CUDA + +#include "cuda_leaf_splits.hpp" +#include "cuda_histogram_constructor.hpp" +#include "cuda_data_partition.hpp" +#include "cuda_best_split_finder.hpp" + +#include "cuda_gradient_discretizer.hpp" +#include "../serial_tree_learner.h" + +namespace LightGBM { + +#define CUDA_SINGLE_GPU_TREE_LEARNER_BLOCK_SIZE (1024) + +class CUDASingleGPUTreeLearner: public SerialTreeLearner, public NCCLInfo { + public: + explicit CUDASingleGPUTreeLearner(const Config* config, const bool boosting_on_cuda); + + ~CUDASingleGPUTreeLearner(); + + void Init(const Dataset* train_data, bool is_constant_hessian) override; + + void ResetTrainingData(const Dataset* train_data, + bool is_constant_hessian) override; + + Tree* Train(const score_t* gradients, const score_t *hessians, bool is_first_tree) override; + + void SetBaggingData(const Dataset* subset, const data_size_t* used_indices, data_size_t num_data) override; + + void AddPredictionToScore(const Tree* tree, double* out_score) const override; + + void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function residual_getter, + data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* train_score) const override; + + void ResetConfig(const Config* config) override; + + Tree* FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const override; + + Tree* FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t* hessians) const override; + + void ResetBoostingOnGPU(const bool boosting_on_gpu) override; + + void SetNCCLInfo( + ncclComm_t nccl_communicator, + int nccl_gpu_rank, + int local_gpu_rank, + int gpu_device_id, + data_size_t global_num_data) override; + + protected: + void BeforeTrain() override; + + void ReduceLeafStat(CUDATree* old_tree, const score_t* gradients, const score_t* hessians, const data_size_t* num_data_in_leaf) const; + + void LaunchReduceLeafStatKernel(const score_t* gradients, const score_t* hessians, const data_size_t* num_data_in_leaf, + const int* leaf_parent, const int* left_child, const int* right_child, + const int num_leaves, const data_size_t num_data, double* cuda_leaf_value, const double shrinkage_rate) const; + + void ConstructBitsetForCategoricalSplit(const CUDASplitInfo* best_split_info); + + void LaunchConstructBitsetForCategoricalSplitKernel(const CUDASplitInfo* best_split_info); + + void AllocateBitset(); + + void SelectFeatureByNode(const Tree* tree); + + #ifdef DEBUG + void CheckSplitValid( + const int left_leaf, const int right_leaf); + #endif // DEBUG + + void RenewDiscretizedTreeLeaves(CUDATree* cuda_tree); + + void LaunchCalcLeafValuesGivenGradStat(CUDATree* cuda_tree, const data_size_t* num_data_in_leaf); + + void NCCLReduceHistogram(); + + // number of threads on CPU + int num_threads_; + + // CUDA components for tree training + + // leaf splits information for smaller and larger leaves + std::unique_ptr cuda_smaller_leaf_splits_; + std::unique_ptr cuda_larger_leaf_splits_; + // data partition that partitions data indices into different leaves + std::unique_ptr cuda_data_partition_; + // for histogram construction + std::unique_ptr cuda_histogram_constructor_; + // for best split information finding, given the histograms + std::unique_ptr cuda_best_split_finder_; + // gradient discretizer for quantized training + std::unique_ptr cuda_gradient_discretizer_; + + std::vector leaf_best_split_feature_; + std::vector leaf_best_split_threshold_; + std::vector leaf_best_split_default_left_; + std::vector leaf_num_data_; + std::vector leaf_data_start_; + std::vector leaf_sum_gradients_; + std::vector leaf_sum_hessians_; + int smaller_leaf_index_; + int larger_leaf_index_; + int best_leaf_index_; + int num_cat_threshold_; + bool has_categorical_feature_; + // whether need to select features by node + bool select_features_by_node_; + + std::vector categorical_bin_to_value_; + std::vector categorical_bin_offsets_; + + mutable CUDAVector cuda_leaf_gradient_stat_buffer_; + mutable CUDAVector cuda_leaf_hessian_stat_buffer_; + mutable data_size_t leaf_stat_buffer_size_; + mutable data_size_t refit_num_data_; + uint32_t* cuda_bitset_; + size_t cuda_bitset_len_; + uint32_t* cuda_bitset_inner_; + size_t cuda_bitset_inner_len_; + size_t* cuda_block_bitset_len_buffer_; + int* cuda_categorical_bin_to_value_; + int* cuda_categorical_bin_offsets_; + + /*! \brief gradients on CUDA */ + CUDAVector cuda_gradients_; + /*! \brief hessians on CUDA */ + CUDAVector cuda_hessians_; + /*! \brief whether boosting is done on CUDA */ + bool boosting_on_cuda_; + + // members used in multi-GPU training + /*! \brief cuda stream for nccl operations */ + cudaStream_t nccl_stream_; + /*! \brief index map from leaf index to histogram index */ + std::vector leaf_to_hist_index_map_; + /*! \brief number of total histogram bins */ + int num_total_bin_; + /*! \brief global number of data in the leaves across */ + std::vector global_num_data_in_leaf_; + + #ifdef DEBUG + /*! \brief gradients on CPU */ + std::vector host_gradients_; + /*! \brief hessians on CPU */ + std::vector host_hessians_; + #endif // DEBUG +}; + +} // namespace LightGBM + +#else // USE_CUDA + +// When GPU support is not compiled in, quit with an error message + +namespace LightGBM { + +class CUDASingleGPUTreeLearner: public SerialTreeLearner { + public: + #pragma warning(disable : 4702) + explicit CUDASingleGPUTreeLearner(const Config* tree_config, const bool /*boosting_on_cuda*/) : SerialTreeLearner(tree_config) { + Log::Fatal("CUDA Tree Learner was not enabled in this build.\n" + "Please recompile with CMake option -DUSE_CUDA=1 (NVIDIA GPUs) or -DUSE_ROCM=1 (AMD GPUs)"); + } +}; + +} // namespace LightGBM + +#endif // USE_CUDA +#endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_SINGLE_GPU_TREE_LEARNER_HPP_ diff --git a/src/treelearner/data_parallel_tree_learner.cpp b/src/treelearner/data_parallel_tree_learner.cpp new file mode 100644 index 0000000..cb33355 --- /dev/null +++ b/src/treelearner/data_parallel_tree_learner.cpp @@ -0,0 +1,470 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include +#include +#include +#include + +#include "parallel_tree_learner.h" + +namespace LightGBM { + +template +DataParallelTreeLearner::DataParallelTreeLearner(const Config* config):TREELEARNER_T(config) { +} + +template +DataParallelTreeLearner::~DataParallelTreeLearner() { +} + +template +void DataParallelTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian) { + // initialize SerialTreeLearner + TREELEARNER_T::Init(train_data, is_constant_hessian); + // Get local rank and global machine size + rank_ = Network::rank(); + num_machines_ = Network::num_machines(); + + auto max_cat_threshold = this->config_->max_cat_threshold; + // need to be able to hold smaller and larger best splits in SyncUpGlobalBestSplit + size_t split_info_size = static_cast(SplitInfo::Size(max_cat_threshold) * 2); + size_t histogram_size = this->config_->use_quantized_grad ? + static_cast(this->share_state_->num_hist_total_bin() * kInt32HistEntrySize) : + static_cast(this->share_state_->num_hist_total_bin() * kHistEntrySize); + + // allocate buffer for communication + size_t buffer_size = std::max(histogram_size, split_info_size); + + input_buffer_.resize(buffer_size); + output_buffer_.resize(buffer_size); + + is_feature_aggregated_.resize(this->num_features_); + + block_start_.resize(num_machines_); + block_len_.resize(num_machines_); + + if (this->config_->use_quantized_grad) { + block_start_int16_.resize(num_machines_); + block_len_int16_.resize(num_machines_); + } + + buffer_write_start_pos_.resize(this->num_features_); + buffer_read_start_pos_.resize(this->num_features_); + + if (this->config_->use_quantized_grad) { + buffer_write_start_pos_int16_.resize(this->num_features_); + buffer_read_start_pos_int16_.resize(this->num_features_); + } + + global_data_count_in_leaf_.resize(this->config_->num_leaves); +} + +template +void DataParallelTreeLearner::ResetConfig(const Config* config) { + TREELEARNER_T::ResetConfig(config); + global_data_count_in_leaf_.resize(this->config_->num_leaves); +} + +template +void DataParallelTreeLearner::PrepareBufferPos( + const std::vector>& feature_distribution, + std::vector* block_start, + std::vector* block_len, + std::vector* buffer_write_start_pos, + std::vector* buffer_read_start_pos, + comm_size_t* reduce_scatter_size, + size_t hist_entry_size) { + // get block start and block len for reduce scatter + *reduce_scatter_size = 0; + for (int i = 0; i < num_machines_; ++i) { + (*block_len)[i] = 0; + for (auto fid : feature_distribution[i]) { + auto num_bin = this->train_data_->FeatureNumBin(fid); + if (this->train_data_->FeatureBinMapper(fid)->GetMostFreqBin() == 0) { + num_bin -= 1; + } + (*block_len)[i] += num_bin * hist_entry_size; + } + *reduce_scatter_size += (*block_len)[i]; + } + + (*block_start)[0] = 0; + for (int i = 1; i < num_machines_; ++i) { + (*block_start)[i] = (*block_start)[i - 1] + (*block_len)[i - 1]; + } + + // get buffer_write_start_pos + int bin_size = 0; + for (int i = 0; i < num_machines_; ++i) { + for (auto fid : feature_distribution[i]) { + (*buffer_write_start_pos)[fid] = bin_size; + auto num_bin = this->train_data_->FeatureNumBin(fid); + if (this->train_data_->FeatureBinMapper(fid)->GetMostFreqBin() == 0) { + num_bin -= 1; + } + bin_size += num_bin * hist_entry_size; + } + } + + // get buffer_read_start_pos + bin_size = 0; + for (auto fid : feature_distribution[rank_]) { + (*buffer_read_start_pos)[fid] = bin_size; + auto num_bin = this->train_data_->FeatureNumBin(fid); + if (this->train_data_->FeatureBinMapper(fid)->GetMostFreqBin() == 0) { + num_bin -= 1; + } + bin_size += num_bin * hist_entry_size; + } +} + +template +void DataParallelTreeLearner::BeforeTrain() { + TREELEARNER_T::BeforeTrain(); + // generate feature partition for current tree + std::vector> feature_distribution(num_machines_, std::vector()); + std::vector num_bins_distributed(num_machines_, 0); + for (int i = 0; i < this->train_data_->num_total_features(); ++i) { + int inner_feature_index = this->train_data_->InnerFeatureIndex(i); + if (inner_feature_index == -1) { + continue; + } + if (this->col_sampler_.is_feature_used_bytree()[inner_feature_index]) { + int cur_min_machine = static_cast(ArrayArgs::ArgMin(num_bins_distributed)); + feature_distribution[cur_min_machine].push_back(inner_feature_index); + auto num_bin = this->train_data_->FeatureNumBin(inner_feature_index); + if (this->train_data_->FeatureBinMapper(inner_feature_index)->GetMostFreqBin() == 0) { + num_bin -= 1; + } + num_bins_distributed[cur_min_machine] += num_bin; + } + is_feature_aggregated_[inner_feature_index] = false; + } + // get local used feature + for (auto fid : feature_distribution[rank_]) { + is_feature_aggregated_[fid] = true; + } + + // get block start and block len for reduce scatter + if (this->config_->use_quantized_grad) { + PrepareBufferPos(feature_distribution, &block_start_, &block_len_, &buffer_write_start_pos_, + &buffer_read_start_pos_, &reduce_scatter_size_, kInt32HistEntrySize); + PrepareBufferPos(feature_distribution, &block_start_int16_, &block_len_int16_, &buffer_write_start_pos_int16_, + &buffer_read_start_pos_int16_, &reduce_scatter_size_int16_, kInt16HistEntrySize); + } else { + PrepareBufferPos(feature_distribution, &block_start_, &block_len_, &buffer_write_start_pos_, + &buffer_read_start_pos_, &reduce_scatter_size_, kHistEntrySize); + } + + if (this->config_->use_quantized_grad) { + // sync global data sumup info + std::tuple data(this->smaller_leaf_splits_->num_data_in_leaf(), + this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians(), + this->smaller_leaf_splits_->int_sum_gradients_and_hessians()); + int size = sizeof(data); + std::memcpy(input_buffer_.data(), &data, size); + // global sumup reduce + Network::Allreduce(input_buffer_.data(), size, sizeof(std::tuple), output_buffer_.data(), [](const char *src, char *dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const std::tuple *p1; + std::tuple *p2; + while (used_size < len) { + p1 = reinterpret_cast *>(src); + p2 = reinterpret_cast *>(dst); + std::get<0>(*p2) = std::get<0>(*p2) + std::get<0>(*p1); + std::get<1>(*p2) = std::get<1>(*p2) + std::get<1>(*p1); + std::get<2>(*p2) = std::get<2>(*p2) + std::get<2>(*p1); + std::get<3>(*p2) = std::get<3>(*p2) + std::get<3>(*p1); + src += type_size; + dst += type_size; + used_size += type_size; + } + }); + // copy back + std::memcpy(reinterpret_cast(&data), output_buffer_.data(), size); + // set global sumup info + this->smaller_leaf_splits_->Init(std::get<1>(data), std::get<2>(data), std::get<3>(data)); + // init global data count in leaf + global_data_count_in_leaf_[0] = std::get<0>(data); + // reset hist num bits according to global num data + this->gradient_discretizer_->template SetNumBitsInHistogramBin(0, -1, GetGlobalDataCountInLeaf(0), 0); + } else { + // sync global data sumup info + std::tuple data(this->smaller_leaf_splits_->num_data_in_leaf(), + this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians()); + int size = sizeof(data); + std::memcpy(input_buffer_.data(), &data, size); + // global sumup reduce + Network::Allreduce(input_buffer_.data(), size, sizeof(std::tuple), output_buffer_.data(), [](const char *src, char *dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const std::tuple *p1; + std::tuple *p2; + while (used_size < len) { + p1 = reinterpret_cast *>(src); + p2 = reinterpret_cast *>(dst); + std::get<0>(*p2) = std::get<0>(*p2) + std::get<0>(*p1); + std::get<1>(*p2) = std::get<1>(*p2) + std::get<1>(*p1); + std::get<2>(*p2) = std::get<2>(*p2) + std::get<2>(*p1); + src += type_size; + dst += type_size; + used_size += type_size; + } + }); + // copy back + std::memcpy(reinterpret_cast(&data), output_buffer_.data(), size); + // set global sumup info + this->smaller_leaf_splits_->Init(std::get<1>(data), std::get<2>(data)); + // init global data count in leaf + global_data_count_in_leaf_[0] = std::get<0>(data); + } +} + +template +void DataParallelTreeLearner::FindBestSplits(const Tree* tree) { + TREELEARNER_T::ConstructHistograms( + this->col_sampler_.is_feature_used_bytree(), true); + const int smaller_leaf_index = this->smaller_leaf_splits_->leaf_index(); + const data_size_t local_data_on_smaller_leaf = this->data_partition_->leaf_count(smaller_leaf_index); + if (local_data_on_smaller_leaf <= 0) { + // clear histogram buffer before synchronizing + // otherwise histogram contents from the previous iteration will be sent + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + if (this->col_sampler_.is_feature_used_bytree()[feature_index] == false) + continue; + const BinMapper* feature_bin_mapper = this->train_data_->FeatureBinMapper(feature_index); + const int offset = static_cast(feature_bin_mapper->GetMostFreqBin() == 0); + const int num_bin = feature_bin_mapper->num_bin(); + if (this->config_->use_quantized_grad) { + int32_t* hist_ptr = this->smaller_leaf_histogram_array_[feature_index].RawDataInt32(); + std::memset(reinterpret_cast(hist_ptr), 0, (num_bin - offset) * kInt32HistEntrySize); + int16_t* hist_ptr_int16 = this->smaller_leaf_histogram_array_[feature_index].RawDataInt16(); + std::memset(reinterpret_cast(hist_ptr_int16), 0, (num_bin - offset) * kInt16HistEntrySize); + } else { + hist_t* hist_ptr = this->smaller_leaf_histogram_array_[feature_index].RawData(); + std::memset(reinterpret_cast(hist_ptr), 0, (num_bin - offset) * kHistEntrySize); + } + } + } + // construct local histograms + global_timer.Start("DataParallelTreeLearner::ReduceHistogram"); + global_timer.Start("DataParallelTreeLearner::ReduceHistogram::Copy"); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + if (this->col_sampler_.is_feature_used_bytree()[feature_index] == false) + continue; + // copy to buffer + if (this->config_->use_quantized_grad) { + const uint8_t local_smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->smaller_leaf_splits_->leaf_index()); + const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->smaller_leaf_splits_->leaf_index()); + if (smaller_leaf_num_bits <= 16) { + std::memcpy(input_buffer_.data() + buffer_write_start_pos_int16_[feature_index], + this->smaller_leaf_histogram_array_[feature_index].RawDataInt16(), + this->smaller_leaf_histogram_array_[feature_index].SizeOfInt16Histogram()); + } else { + if (local_smaller_leaf_num_bits == 32) { + std::memcpy(input_buffer_.data() + buffer_write_start_pos_[feature_index], + this->smaller_leaf_histogram_array_[feature_index].RawDataInt32(), + this->smaller_leaf_histogram_array_[feature_index].SizeOfInt32Histogram()); + } else { + this->smaller_leaf_histogram_array_[feature_index].CopyFromInt16ToInt32( + input_buffer_.data() + buffer_write_start_pos_[feature_index]); + } + } + } else { + std::memcpy(input_buffer_.data() + buffer_write_start_pos_[feature_index], + this->smaller_leaf_histogram_array_[feature_index].RawData(), + this->smaller_leaf_histogram_array_[feature_index].SizeOfHistogram()); + } + } + global_timer.Stop("DataParallelTreeLearner::ReduceHistogram::Copy"); + // Reduce scatter for histogram + global_timer.Start("DataParallelTreeLearner::ReduceHistogram::ReduceScatter"); + if (!this->config_->use_quantized_grad) { + Network::ReduceScatter(input_buffer_.data(), reduce_scatter_size_, sizeof(hist_t), block_start_.data(), + block_len_.data(), output_buffer_.data(), static_cast(output_buffer_.size()), &HistogramSumReducer); + } else { + const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->smaller_leaf_splits_->leaf_index()); + if (smaller_leaf_num_bits <= 16) { + Network::ReduceScatter(input_buffer_.data(), reduce_scatter_size_int16_, sizeof(int16_t), block_start_int16_.data(), + block_len_int16_.data(), output_buffer_.data(), static_cast(output_buffer_.size()), &Int16HistogramSumReducer); + } else { + Network::ReduceScatter(input_buffer_.data(), reduce_scatter_size_, sizeof(int_hist_t), block_start_.data(), + block_len_.data(), output_buffer_.data(), static_cast(output_buffer_.size()), &Int32HistogramSumReducer); + } + } + global_timer.Stop("DataParallelTreeLearner::ReduceHistogram::ReduceScatter"); + global_timer.Stop("DataParallelTreeLearner::ReduceHistogram"); + this->FindBestSplitsFromHistograms( + this->col_sampler_.is_feature_used_bytree(), true, tree); +} + +template +void DataParallelTreeLearner::FindBestSplitsFromHistograms(const std::vector&, bool, const Tree* tree) { + std::vector smaller_bests_per_thread(this->share_state_->num_threads); + std::vector larger_bests_per_thread(this->share_state_->num_threads); + std::vector smaller_node_used_features = + this->col_sampler_.GetByNode(tree, this->smaller_leaf_splits_->leaf_index()); + std::vector larger_node_used_features = + this->col_sampler_.GetByNode(tree, this->larger_leaf_splits_->leaf_index()); + double smaller_leaf_parent_output = this->GetParentOutput(tree, this->smaller_leaf_splits_.get()); + double larger_leaf_parent_output = this->GetParentOutput(tree, this->larger_leaf_splits_.get()); + + if (this->config_->use_quantized_grad && this->larger_leaf_splits_ != nullptr && this->larger_leaf_splits_->leaf_index() >= 0) { + const int parent_index = std::min(this->smaller_leaf_splits_->leaf_index(), this->larger_leaf_splits_->leaf_index()); + const uint8_t parent_num_bits = this->gradient_discretizer_->template GetHistBitsInNode(parent_index); + const uint8_t larger_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->larger_leaf_splits_->leaf_index()); + const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->smaller_leaf_splits_->leaf_index()); + if (parent_num_bits > 16 && larger_leaf_num_bits <= 16) { + CHECK_LE(smaller_leaf_num_bits, 16); + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!is_feature_aggregated_[feature_index]) continue; + this->larger_leaf_histogram_array_[feature_index].CopyToBuffer(this->gradient_discretizer_->GetChangeHistBitsBuffer(feature_index)); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + } + + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!is_feature_aggregated_[feature_index]) continue; + const int tid = omp_get_thread_num(); + const int real_feature_index = this->train_data_->RealFeatureIndex(feature_index); + // restore global histograms from buffer + if (this->config_->use_quantized_grad) { + const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->smaller_leaf_splits_->leaf_index()); + if (smaller_leaf_num_bits <= 16) { + this->smaller_leaf_histogram_array_[feature_index].FromMemoryInt16( + output_buffer_.data() + buffer_read_start_pos_int16_[feature_index]); + } else { + this->smaller_leaf_histogram_array_[feature_index].FromMemoryInt32( + output_buffer_.data() + buffer_read_start_pos_[feature_index]); + } + } else { + this->smaller_leaf_histogram_array_[feature_index].FromMemory( + output_buffer_.data() + buffer_read_start_pos_[feature_index]); + } + + if (this->config_->use_quantized_grad) { + const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->smaller_leaf_splits_->leaf_index()); + const int64_t int_sum_gradient_and_hessian = this->smaller_leaf_splits_->int_sum_gradients_and_hessians(); + if (smaller_leaf_num_bits <= 16) { + this->train_data_->template FixHistogramInt( + feature_index, + int_sum_gradient_and_hessian, + reinterpret_cast(this->smaller_leaf_histogram_array_[feature_index].RawDataInt16())); + } else { + this->train_data_->template FixHistogramInt( + feature_index, + int_sum_gradient_and_hessian, + reinterpret_cast(this->smaller_leaf_histogram_array_[feature_index].RawDataInt32())); + } + } else { + this->train_data_->FixHistogram(feature_index, + this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians(), + this->smaller_leaf_histogram_array_[feature_index].RawData()); + } + + this->ComputeBestSplitForFeature( + this->smaller_leaf_histogram_array_, feature_index, real_feature_index, + smaller_node_used_features[feature_index], + GetGlobalDataCountInLeaf(this->smaller_leaf_splits_->leaf_index()), + this->smaller_leaf_splits_.get(), + &smaller_bests_per_thread[tid], + smaller_leaf_parent_output); + + // only root leaf + if (this->larger_leaf_splits_ == nullptr || this->larger_leaf_splits_->leaf_index() < 0) continue; + + // construct histgroms for large leaf, we init larger leaf as the parent, so we can just subtract the smaller leaf's histograms + if (this->config_->use_quantized_grad) { + const int parent_index = std::min(this->smaller_leaf_splits_->leaf_index(), this->larger_leaf_splits_->leaf_index()); + const uint8_t parent_num_bits = this->gradient_discretizer_->template GetHistBitsInNode(parent_index); + const uint8_t larger_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->larger_leaf_splits_->leaf_index()); + const uint8_t smaller_leaf_num_bits = this->gradient_discretizer_->template GetHistBitsInLeaf(this->smaller_leaf_splits_->leaf_index()); + if (parent_num_bits <= 16) { + CHECK_LE(smaller_leaf_num_bits, 16); + CHECK_LE(larger_leaf_num_bits, 16); + this->larger_leaf_histogram_array_[feature_index].template Subtract( + this->smaller_leaf_histogram_array_[feature_index]); + } else if (larger_leaf_num_bits <= 16) { + CHECK_LE(smaller_leaf_num_bits, 16); + this->larger_leaf_histogram_array_[feature_index].template Subtract( + this->smaller_leaf_histogram_array_[feature_index], this->gradient_discretizer_->GetChangeHistBitsBuffer(feature_index)); + } else if (smaller_leaf_num_bits <= 16) { + this->larger_leaf_histogram_array_[feature_index].template Subtract( + this->smaller_leaf_histogram_array_[feature_index]); + } else { + this->larger_leaf_histogram_array_[feature_index].template Subtract( + this->smaller_leaf_histogram_array_[feature_index]); + } + } else { + this->larger_leaf_histogram_array_[feature_index].Subtract( + this->smaller_leaf_histogram_array_[feature_index]); + } + + this->ComputeBestSplitForFeature( + this->larger_leaf_histogram_array_, feature_index, real_feature_index, + larger_node_used_features[feature_index], + GetGlobalDataCountInLeaf(this->larger_leaf_splits_->leaf_index()), + this->larger_leaf_splits_.get(), + &larger_bests_per_thread[tid], + larger_leaf_parent_output); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + auto smaller_best_idx = ArrayArgs::ArgMax(smaller_bests_per_thread); + int leaf = this->smaller_leaf_splits_->leaf_index(); + this->best_split_per_leaf_[leaf] = smaller_bests_per_thread[smaller_best_idx]; + + if (this->larger_leaf_splits_ != nullptr && this->larger_leaf_splits_->leaf_index() >= 0) { + leaf = this->larger_leaf_splits_->leaf_index(); + auto larger_best_idx = ArrayArgs::ArgMax(larger_bests_per_thread); + this->best_split_per_leaf_[leaf] = larger_bests_per_thread[larger_best_idx]; + } + + SplitInfo smaller_best_split, larger_best_split; + smaller_best_split = this->best_split_per_leaf_[this->smaller_leaf_splits_->leaf_index()]; + // find local best split for larger leaf + if (this->larger_leaf_splits_->leaf_index() >= 0) { + larger_best_split = this->best_split_per_leaf_[this->larger_leaf_splits_->leaf_index()]; + } + + // sync global best info + SyncUpGlobalBestSplit(input_buffer_.data(), input_buffer_.data(), &smaller_best_split, &larger_best_split, this->config_->max_cat_threshold); + + // set best split + this->best_split_per_leaf_[this->smaller_leaf_splits_->leaf_index()] = smaller_best_split; + if (this->larger_leaf_splits_->leaf_index() >= 0) { + this->best_split_per_leaf_[this->larger_leaf_splits_->leaf_index()] = larger_best_split; + } +} + +template +void DataParallelTreeLearner::Split(Tree* tree, int best_Leaf, int* left_leaf, int* right_leaf) { + TREELEARNER_T::SplitInner(tree, best_Leaf, left_leaf, right_leaf, false); + const SplitInfo& best_split_info = this->best_split_per_leaf_[best_Leaf]; + // need update global number of data in leaf + global_data_count_in_leaf_[*left_leaf] = best_split_info.left_count; + global_data_count_in_leaf_[*right_leaf] = best_split_info.right_count; + // reset hist num bits according to global num data + if (this->config_->use_quantized_grad) { + this->gradient_discretizer_->template SetNumBitsInHistogramBin(*left_leaf, *right_leaf, GetGlobalDataCountInLeaf(*left_leaf), GetGlobalDataCountInLeaf(*right_leaf)); + } +} + +// instantiate template classes, otherwise linker cannot find the code +template class DataParallelTreeLearner; +template class DataParallelTreeLearner; + +} // namespace LightGBM diff --git a/src/treelearner/data_partition.hpp b/src/treelearner/data_partition.hpp new file mode 100644 index 0000000..0aafed9 --- /dev/null +++ b/src/treelearner/data_partition.hpp @@ -0,0 +1,171 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_DATA_PARTITION_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_DATA_PARTITION_HPP_ + +#include +#include +#include +#include + +#include +#include +#include + +namespace LightGBM { +/*! +* \brief DataPartition is used to store the the partition of data on tree. +*/ +class DataPartition { + public: + DataPartition(data_size_t num_data, int num_leaves) + : num_data_(num_data), num_leaves_(num_leaves), runner_(num_data, 512) { + leaf_begin_.resize(num_leaves_); + leaf_count_.resize(num_leaves_); + indices_.resize(num_data_); + used_data_indices_ = nullptr; + } + + void ResetLeaves(int num_leaves) { + num_leaves_ = num_leaves; + leaf_begin_.resize(num_leaves_); + leaf_count_.resize(num_leaves_); + } + + void ResetNumData(int num_data) { + num_data_ = num_data; + indices_.resize(num_data_); + runner_.ReSize(num_data_); + } + + ~DataPartition() { + } + + /*! + * \brief Init, will put all data on the root(leaf_idx = 0) + */ + void Init() { + std::fill(leaf_begin_.begin(), leaf_begin_.end(), 0); + std::fill(leaf_count_.begin(), leaf_count_.end(), 0); + if (used_data_indices_ == nullptr) { + // if using all data + leaf_count_[0] = num_data_; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024) + for (data_size_t i = 0; i < num_data_; ++i) { + indices_[i] = i; + } + } else { + // if bagging + leaf_count_[0] = used_data_count_; + std::memcpy(indices_.data(), used_data_indices_, used_data_count_ * sizeof(data_size_t)); + } + } + + void ResetByLeafPred(const std::vector& leaf_pred, int num_leaves) { + ResetLeaves(num_leaves); + std::vector> indices_per_leaf(num_leaves_); + for (data_size_t i = 0; i < static_cast(leaf_pred.size()); ++i) { + indices_per_leaf[leaf_pred[i]].push_back(i); + } + data_size_t offset = 0; + for (int i = 0; i < num_leaves_; ++i) { + leaf_begin_[i] = offset; + leaf_count_[i] = static_cast(indices_per_leaf[i].size()); + std::copy(indices_per_leaf[i].begin(), indices_per_leaf[i].end(), indices_.begin() + leaf_begin_[i]); + offset += leaf_count_[i]; + } + } + + /*! + * \brief Get the data indices of one leaf + * \param leaf index of leaf + * \param indices output data indices + * \return number of data on this leaf + */ + const data_size_t* GetIndexOnLeaf(int leaf, data_size_t* out_len) const { + // copy reference, maybe unsafe, but faster + data_size_t begin = leaf_begin_[leaf]; + *out_len = leaf_count_[leaf]; + return indices_.data() + begin; + } + + /*! + * \brief Split the data + * \param leaf index of leaf + * \param feature_bins feature bin data + * \param threshold threshold that want to split + * \param right_leaf index of right leaf + */ + void Split(int leaf, const Dataset* dataset, int feature, + const uint32_t* threshold, int num_threshold, bool default_left, + int right_leaf) { + Common::FunctionTimer fun_timer("DataPartition::Split", global_timer); + // get leaf boundary + const data_size_t begin = leaf_begin_[leaf]; + const data_size_t cnt = leaf_count_[leaf]; + auto left_start = indices_.data() + begin; + const auto left_cnt = runner_.Run( + cnt, + [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left, + data_size_t* right) { + return dataset->Split(feature, threshold, num_threshold, default_left, + left_start + cur_start, cur_cnt, left, right); + }, + left_start); + leaf_count_[leaf] = left_cnt; + leaf_begin_[right_leaf] = left_cnt + begin; + leaf_count_[right_leaf] = cnt - left_cnt; + } + + /*! + * \brief SetLabelAt used data indices before training, used for bagging + * \param used_data_indices indices of used data + * \param num_used_data number of used data + */ + void SetUsedDataIndices(const data_size_t* used_data_indices, data_size_t num_used_data) { + used_data_indices_ = used_data_indices; + used_data_count_ = num_used_data; + } + + /*! + * \brief Get number of data on one leaf + * \param leaf index of leaf + * \return number of data of this leaf + */ + data_size_t leaf_count(int leaf) const { return leaf_count_[leaf]; } + + /*! + * \brief Get leaf begin + * \param leaf index of leaf + * \return begin index of this leaf + */ + data_size_t leaf_begin(int leaf) const { return leaf_begin_[leaf]; } + + const data_size_t* indices() const { return indices_.data(); } + + /*! \brief Get number of leaves */ + int num_leaves() const { return num_leaves_; } + + private: + /*! \brief Number of all data */ + data_size_t num_data_; + /*! \brief Number of all leaves */ + int num_leaves_; + /*! \brief start index of data on one leaf */ + std::vector leaf_begin_; + /*! \brief number of data on one leaf */ + std::vector leaf_count_; + /*! \brief Store all data's indices, order by leaf[data_in_leaf0,..,data_leaf1,..] */ + std::vector> indices_; + /*! \brief used data indices, used for bagging */ + const data_size_t* used_data_indices_; + /*! \brief used data count, used for bagging */ + data_size_t used_data_count_; + ParallelPartitionRunner runner_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_DATA_PARTITION_HPP_ diff --git a/src/treelearner/feature_histogram.cpp b/src/treelearner/feature_histogram.cpp new file mode 100644 index 0000000..3c66ee7 --- /dev/null +++ b/src/treelearner/feature_histogram.cpp @@ -0,0 +1,746 @@ +/*! + * Copyright (c) 2024-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2024-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#include "feature_histogram.hpp" + +#include +#include + +namespace LightGBM { + +void FeatureHistogram::FuncForCategorical() { + if (meta_->config->extra_trees) { + if (meta_->config->monotone_constraints.empty()) { + FuncForCategoricalL1(); + } else { + FuncForCategoricalL1(); + } + } else { + if (meta_->config->monotone_constraints.empty()) { + FuncForCategoricalL1(); + } else { + FuncForCategoricalL1(); + } + } +} + +template +void FeatureHistogram::FuncForCategoricalL1() { + if (meta_->config->path_smooth > kEpsilon) { + FuncForCategoricalL2(); + } else { + FuncForCategoricalL2(); + } +} + +template +void FeatureHistogram::FuncForCategoricalL2() { + if (meta_->config->use_quantized_grad) { +#define LAMBDA_PARAMS_INT \ + int64_t int_sum_gradient_and_hessian, \ + const double grad_scale, const double hess_scale, \ + const uint8_t hist_bits_bin, const uint8_t hist_bits_acc, \ + data_size_t num_data, \ + const FeatureConstraint* constraints, \ + double parent_output, \ + SplitInfo* output + +#define ARGUMENTS_INT \ + int_sum_gradient_and_hessian, grad_scale, hess_scale, num_data, constraints, parent_output, output + + if (meta_->config->lambda_l1 > 0) { + if (meta_->config->max_delta_step > 0) { + int_find_best_threshold_fun_ = [=] (LAMBDA_PARAMS_INT) { + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + if (hist_bits_bin <= 16) { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } + } + }; + } else { + int_find_best_threshold_fun_ = [=] (LAMBDA_PARAMS_INT) { + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + if (hist_bits_bin <= 16) { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } + } + }; + } + } else { + if (meta_->config->max_delta_step > 0) { + int_find_best_threshold_fun_ = [=] (LAMBDA_PARAMS_INT) { + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + if (hist_bits_bin <= 16) { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } + } + }; + } else { + int_find_best_threshold_fun_ = [=] (LAMBDA_PARAMS_INT) { + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + if (hist_bits_bin <= 16) { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } else { + FindBestThresholdCategoricalIntInner(ARGUMENTS_INT); + } + } + }; + } + } +#undef LAMBDA_ARGUMENTS_INT +#undef ARGUMENTS_INT + } else { +#define ARGUMENTS \ + std::placeholders::_1, std::placeholders::_2, std::placeholders::_3, \ + std::placeholders::_4, std::placeholders::_5, std::placeholders::_6 + if (meta_->config->lambda_l1 > 0) { + if (meta_->config->max_delta_step > 0) { + find_best_threshold_fun_ = + std::bind(&FeatureHistogram::FindBestThresholdCategoricalInner< + USE_RAND, USE_MC, true, true, USE_SMOOTHING>, + this, ARGUMENTS); + } else { + find_best_threshold_fun_ = + std::bind(&FeatureHistogram::FindBestThresholdCategoricalInner< + USE_RAND, USE_MC, true, false, USE_SMOOTHING>, + this, ARGUMENTS); + } + } else { + if (meta_->config->max_delta_step > 0) { + find_best_threshold_fun_ = + std::bind(&FeatureHistogram::FindBestThresholdCategoricalInner< + USE_RAND, USE_MC, false, true, USE_SMOOTHING>, + this, ARGUMENTS); + } else { + find_best_threshold_fun_ = + std::bind(&FeatureHistogram::FindBestThresholdCategoricalInner< + USE_RAND, USE_MC, false, false, USE_SMOOTHING>, + this, ARGUMENTS); + } + } +#undef ARGUMENTS + } +} + +template +void FeatureHistogram::FindBestThresholdCategoricalInner(double sum_gradient, + double sum_hessian, + data_size_t num_data, + const FeatureConstraint* constraints, + double parent_output, + SplitInfo* output) { + is_splittable_ = false; + output->default_left = false; + double best_gain = kMinScore; + data_size_t best_left_count = 0; + double best_sum_left_gradient = 0; + double best_sum_left_hessian = 0; + double gain_shift; + if (USE_MC) { + constraints->InitCumulativeConstraints(true); + } + if (USE_SMOOTHING) { + gain_shift = GetLeafGainGivenOutput( + sum_gradient, sum_hessian, meta_->config->lambda_l1, meta_->config->lambda_l2, parent_output); + } else { + // Need special case for no smoothing to preserve existing behaviour. If no smoothing, the parent output is calculated + // with the larger categorical l2, whereas min_split_gain uses the original l2. + gain_shift = GetLeafGain(sum_gradient, sum_hessian, + meta_->config->lambda_l1, meta_->config->lambda_l2, meta_->config->max_delta_step, 0, + num_data, 0); + } + + double min_gain_shift = gain_shift + meta_->config->min_gain_to_split; + const int8_t offset = meta_->offset; + const int bin_start = 1 - offset; + const int bin_end = meta_->num_bin - offset; + int used_bin = -1; + + std::vector sorted_idx; + double l2 = meta_->config->lambda_l2; + bool use_onehot = meta_->num_bin <= meta_->config->max_cat_to_onehot; + int best_threshold = -1; + int best_dir = 1; + const double cnt_factor = num_data / sum_hessian; + int rand_threshold = 0; + if (use_onehot) { + if (USE_RAND) { + if (bin_end - bin_start > 0) { + rand_threshold = meta_->rand.NextInt(bin_start, bin_end); + } + } + for (int t = bin_start; t < bin_end; ++t) { + const auto grad = GET_GRAD(data_, t); + const auto hess = GET_HESS(data_, t); + data_size_t cnt = + static_cast(Common::RoundInt(hess * cnt_factor)); + // if data not enough, or sum hessian too small + if (cnt < meta_->config->min_data_in_leaf || + hess < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t other_count = num_data - cnt; + // if data not enough + if (other_count < meta_->config->min_data_in_leaf) { + continue; + } + + double sum_other_hessian = sum_hessian - hess - kEpsilon; + // if sum hessian too small + if (sum_other_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + + double sum_other_gradient = sum_gradient - grad; + if (USE_RAND) { + if (t != rand_threshold) { + continue; + } + } + // current split gain + double current_gain = GetSplitGains( + sum_other_gradient, sum_other_hessian, grad, hess + kEpsilon, + meta_->config->lambda_l1, l2, meta_->config->max_delta_step, + constraints, 0, meta_->config->path_smooth, other_count, cnt, parent_output); + // gain with split is worse than without split + if (current_gain <= min_gain_shift) { + continue; + } + + // mark as able to be split + is_splittable_ = true; + // better split point + if (current_gain > best_gain) { + best_threshold = t; + best_sum_left_gradient = grad; + best_sum_left_hessian = hess + kEpsilon; + best_left_count = cnt; + best_gain = current_gain; + } + } + } else { + for (int i = bin_start; i < bin_end; ++i) { + if (Common::RoundInt(GET_HESS(data_, i) * cnt_factor) >= + meta_->config->cat_smooth) { + sorted_idx.push_back(i); + } + } + used_bin = static_cast(sorted_idx.size()); + + l2 += meta_->config->cat_l2; + + auto ctr_fun = [this](double sum_grad, double sum_hess) { + return (sum_grad) / (sum_hess + meta_->config->cat_smooth); + }; + std::stable_sort( + sorted_idx.begin(), sorted_idx.end(), [this, &ctr_fun](int i, int j) { + return ctr_fun(GET_GRAD(data_, i), GET_HESS(data_, i)) < + ctr_fun(GET_GRAD(data_, j), GET_HESS(data_, j)); + }); + + std::vector find_direction(1, 1); + std::vector start_position(1, 0); + find_direction.push_back(-1); + start_position.push_back(used_bin - 1); + const int max_num_cat = + std::min(meta_->config->max_cat_threshold, (used_bin + 1) / 2); + int max_threshold = std::max(std::min(max_num_cat, used_bin) - 1, 0); + if (USE_RAND) { + if (max_threshold > 0) { + rand_threshold = meta_->rand.NextInt(0, max_threshold); + } + } + + is_splittable_ = false; + for (size_t out_i = 0; out_i < find_direction.size(); ++out_i) { + auto dir = find_direction[out_i]; + auto start_pos = start_position[out_i]; + data_size_t min_data_per_group = meta_->config->min_data_per_group; + data_size_t cnt_cur_group = 0; + double sum_left_gradient = 0.0f; + double sum_left_hessian = kEpsilon; + data_size_t left_count = 0; + for (int i = 0; i < used_bin && i < max_num_cat; ++i) { + auto t = sorted_idx[start_pos]; + start_pos += dir; + const auto grad = GET_GRAD(data_, t); + const auto hess = GET_HESS(data_, t); + data_size_t cnt = + static_cast(Common::RoundInt(hess * cnt_factor)); + + sum_left_gradient += grad; + sum_left_hessian += hess; + left_count += cnt; + cnt_cur_group += cnt; + + if (left_count < meta_->config->min_data_in_leaf || + sum_left_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t right_count = num_data - left_count; + if (right_count < meta_->config->min_data_in_leaf || + right_count < min_data_per_group) { + break; + } + + double sum_right_hessian = sum_hessian - sum_left_hessian; + if (sum_right_hessian < meta_->config->min_sum_hessian_in_leaf) { + break; + } + + if (cnt_cur_group < min_data_per_group) { + continue; + } + + cnt_cur_group = 0; + + double sum_right_gradient = sum_gradient - sum_left_gradient; + if (USE_RAND) { + if (i != rand_threshold) { + continue; + } + } + double current_gain = GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, constraints, 0, meta_->config->path_smooth, + left_count, right_count, parent_output); + if (current_gain <= min_gain_shift) { + continue; + } + is_splittable_ = true; + if (current_gain > best_gain) { + best_left_count = left_count; + best_sum_left_gradient = sum_left_gradient; + best_sum_left_hessian = sum_left_hessian; + best_threshold = i; + best_gain = current_gain; + best_dir = dir; + } + } + } + } + + if (is_splittable_) { + output->left_output = CalculateSplittedLeafOutput( + best_sum_left_gradient, best_sum_left_hessian, + meta_->config->lambda_l1, l2, meta_->config->max_delta_step, + constraints->LeftToBasicConstraint(), meta_->config->path_smooth, best_left_count, parent_output); + output->left_count = best_left_count; + output->left_sum_gradient = best_sum_left_gradient; + output->left_sum_hessian = best_sum_left_hessian - kEpsilon; + output->right_output = CalculateSplittedLeafOutput( + sum_gradient - best_sum_left_gradient, + sum_hessian - best_sum_left_hessian, meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, constraints->RightToBasicConstraint(), meta_->config->path_smooth, + num_data - best_left_count, parent_output); + output->right_count = num_data - best_left_count; + output->right_sum_gradient = sum_gradient - best_sum_left_gradient; + output->right_sum_hessian = + sum_hessian - best_sum_left_hessian - kEpsilon; + output->gain = best_gain - min_gain_shift; + if (use_onehot) { + output->num_cat_threshold = 1; + output->cat_threshold = + std::vector(1, static_cast(best_threshold + offset)); + } else { + output->num_cat_threshold = best_threshold + 1; + output->cat_threshold = + std::vector(output->num_cat_threshold); + if (best_dir == 1) { + for (int i = 0; i < output->num_cat_threshold; ++i) { + auto t = sorted_idx[i] + offset; + output->cat_threshold[i] = t; + } + } else { + for (int i = 0; i < output->num_cat_threshold; ++i) { + auto t = sorted_idx[used_bin - 1 - i] + offset; + output->cat_threshold[i] = t; + } + } + } + output->monotone_type = 0; + } +} + +template < +bool USE_RAND, bool USE_MC, bool USE_L1, bool USE_MAX_OUTPUT, bool USE_SMOOTHING, +typename PACKED_HIST_BIN_T, typename PACKED_HIST_ACC_T, typename HIST_BIN_T, typename HIST_ACC_T, +int HIST_BITS_BIN, int HIST_BITS_ACC +> +void FeatureHistogram::FindBestThresholdCategoricalIntInner(int64_t int_sum_gradient_and_hessian, + const double grad_scale, const double hess_scale, + data_size_t num_data, + const FeatureConstraint* constraints, + double parent_output, + SplitInfo* output) { + is_splittable_ = false; + output->default_left = false; + double best_gain = kMinScore; + PACKED_HIST_ACC_T best_sum_left_gradient_and_hessian = 0; + double gain_shift; + if (USE_MC) { + constraints->InitCumulativeConstraints(true); + } + + PACKED_HIST_ACC_T local_int_sum_gradient_and_hessian = + HIST_BITS_ACC == 16 ? + ((static_cast(int_sum_gradient_and_hessian >> 32) << 16) | static_cast(int_sum_gradient_and_hessian & 0x0000ffff)) : + static_cast(int_sum_gradient_and_hessian); + + // recover sum of gradient and hessian from the sum of quantized gradient and hessian + double sum_gradient = static_cast(static_cast(int_sum_gradient_and_hessian >> 32)) * grad_scale; + double sum_hessian = static_cast(static_cast(int_sum_gradient_and_hessian & 0x00000000ffffffff)) * hess_scale; + if (USE_SMOOTHING) { + gain_shift = GetLeafGainGivenOutput( + sum_gradient, sum_hessian, meta_->config->lambda_l1, meta_->config->lambda_l2, parent_output); + } else { + // Need special case for no smoothing to preserve existing behaviour. If no smoothing, the parent output is calculated + // with the larger categorical l2, whereas min_split_gain uses the original l2. + gain_shift = GetLeafGain(sum_gradient, sum_hessian, + meta_->config->lambda_l1, meta_->config->lambda_l2, meta_->config->max_delta_step, 0, + num_data, 0); + } + + double min_gain_shift = gain_shift + meta_->config->min_gain_to_split; + const int8_t offset = meta_->offset; + const int bin_start = 1 - offset; + const int bin_end = meta_->num_bin - offset; + int used_bin = -1; + + std::vector sorted_idx; + double l2 = meta_->config->lambda_l2; + bool use_onehot = meta_->num_bin <= meta_->config->max_cat_to_onehot; + int best_threshold = -1; + int best_dir = 1; + const double cnt_factor = static_cast(num_data) / + static_cast(static_cast(int_sum_gradient_and_hessian & 0x00000000ffffffff)); + int rand_threshold = 0; + + const PACKED_HIST_BIN_T* data_ptr = nullptr; + if (HIST_BITS_BIN == 16) { + data_ptr = reinterpret_cast(data_int16_); + } else { + data_ptr = reinterpret_cast(data_); + } + + if (use_onehot) { + if (USE_RAND) { + if (bin_end - bin_start > 0) { + rand_threshold = meta_->rand.NextInt(bin_start, bin_end); + } + } + for (int t = bin_start; t < bin_end; ++t) { + const PACKED_HIST_BIN_T grad_and_hess = data_ptr[t]; + const uint32_t int_hess = HIST_BITS_BIN == 16 ? + static_cast(grad_and_hess & 0x0000ffff) : + static_cast(grad_and_hess & 0x00000000ffffffff); + data_size_t cnt = + static_cast(Common::RoundInt(int_hess * cnt_factor)); + const double hess = int_hess * hess_scale; + // if data not enough, or sum hessian too small + if (cnt < meta_->config->min_data_in_leaf || + hess < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t other_count = num_data - cnt; + // if data not enough + if (other_count < meta_->config->min_data_in_leaf) { + continue; + } + + const PACKED_HIST_ACC_T grad_and_hess_acc = HIST_BITS_ACC != HIST_BITS_BIN ? + ((static_cast(static_cast(grad_and_hess >> HIST_BITS_BIN)) << HIST_BITS_ACC) | + (static_cast(grad_and_hess & 0x0000ffff))) : + grad_and_hess; + const PACKED_HIST_ACC_T sum_other_grad_and_hess = local_int_sum_gradient_and_hessian - grad_and_hess_acc; + const uint32_t sum_other_hess_int = HIST_BITS_ACC == 16 ? + static_cast(sum_other_grad_and_hess & 0x0000ffff) : + static_cast(sum_other_grad_and_hess & 0x00000000ffffffff); + double sum_other_hessian = sum_other_hess_int * hess_scale; + // if sum hessian too small + if (sum_other_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + + const int32_t int_grad = HIST_BITS_ACC == 16 ? + static_cast(static_cast(grad_and_hess_acc >> 16)) : + static_cast(static_cast(grad_and_hess_acc) >> 32); + const double grad = int_grad * grad_scale; + + const int32_t sum_other_grad_int = HIST_BITS_ACC == 16 ? + static_cast(static_cast(sum_other_grad_and_hess >> 16)) : + static_cast(static_cast(sum_other_grad_and_hess) >> 32); + const double sum_other_gradient = sum_other_grad_int * grad_scale; + + if (USE_RAND) { + if (t != rand_threshold) { + continue; + } + } + // current split gain + double current_gain = GetSplitGains( + sum_other_gradient, sum_other_hessian, grad, hess, + meta_->config->lambda_l1, l2, meta_->config->max_delta_step, + constraints, 0, meta_->config->path_smooth, other_count, cnt, parent_output); + // gain with split is worse than without split + if (current_gain <= min_gain_shift) { + continue; + } + + // mark as able to be split + is_splittable_ = true; + // better split point + if (current_gain > best_gain) { + best_threshold = t; + best_sum_left_gradient_and_hessian = grad_and_hess_acc; + best_gain = current_gain; + } + } + } else { + for (int i = bin_start; i < bin_end; ++i) { + const PACKED_HIST_BIN_T int_grad_and_hess = data_ptr[i]; + const uint32_t int_hess = HIST_BITS_BIN == 16 ? + static_cast(int_grad_and_hess & 0x0000ffff) : + static_cast(int_grad_and_hess & 0x00000000ffffffff); + const int cnt = Common::RoundInt(int_hess * cnt_factor); + if (cnt >= meta_->config->cat_smooth) { + sorted_idx.push_back(i); + } + } + used_bin = static_cast(sorted_idx.size()); + + l2 += meta_->config->cat_l2; + + auto ctr_fun = [this](double sum_grad, double sum_hess) { + return (sum_grad) / (sum_hess + meta_->config->cat_smooth); + }; + std::stable_sort( + sorted_idx.begin(), sorted_idx.end(), [data_ptr, &ctr_fun, grad_scale, hess_scale](int i, int j) { + const PACKED_HIST_BIN_T int_grad_and_hess_i = data_ptr[i]; + const PACKED_HIST_BIN_T int_grad_and_hess_j = data_ptr[j]; + const int32_t int_grad_i = HIST_BITS_BIN == 16 ? + static_cast(static_cast(int_grad_and_hess_i >> 16)) : + static_cast(static_cast(int_grad_and_hess_i) >> 32); + const uint32_t int_hess_i = HIST_BITS_BIN == 16 ? + static_cast(int_grad_and_hess_i & 0x0000ffff) : + static_cast(int_grad_and_hess_i & 0x00000000ffffffff); + const int32_t int_grad_j = HIST_BITS_BIN == 16 ? + static_cast(static_cast(int_grad_and_hess_j >> 16)) : + static_cast(static_cast(int_grad_and_hess_j) >> 32); + const uint32_t int_hess_j = HIST_BITS_BIN == 16 ? + static_cast(int_grad_and_hess_j & 0x0000ffff) : + static_cast(int_grad_and_hess_j & 0x00000000ffffffff); + + const double grad_i = int_grad_i * grad_scale; + const double hess_i = int_hess_i * hess_scale; + const double grad_j = int_grad_j * grad_scale; + const double hess_j = int_hess_j * hess_scale; + + return ctr_fun(grad_i, hess_i) < ctr_fun(grad_j, hess_j); + }); + + std::vector find_direction(1, 1); + std::vector start_position(1, 0); + find_direction.push_back(-1); + start_position.push_back(used_bin - 1); + const int max_num_cat = + std::min(meta_->config->max_cat_threshold, (used_bin + 1) / 2); + int max_threshold = std::max(std::min(max_num_cat, used_bin) - 1, 0); + if (USE_RAND) { + if (max_threshold > 0) { + rand_threshold = meta_->rand.NextInt(0, max_threshold); + } + } + + is_splittable_ = false; + for (size_t out_i = 0; out_i < find_direction.size(); ++out_i) { + auto dir = find_direction[out_i]; + auto start_pos = start_position[out_i]; + data_size_t min_data_per_group = meta_->config->min_data_per_group; + data_size_t cnt_cur_group = 0; + PACKED_HIST_ACC_T int_sum_left_gradient_and_hessian = 0; + data_size_t left_count = 0; + for (int i = 0; i < used_bin && i < max_num_cat; ++i) { + auto t = sorted_idx[start_pos]; + start_pos += dir; + PACKED_HIST_BIN_T int_grad_and_hess = data_ptr[t]; + + uint32_t int_hess = HIST_BITS_BIN == 16 ? + static_cast(int_grad_and_hess & 0x0000ffff) : + static_cast(int_grad_and_hess & 0x00000000ffffffff); + data_size_t cnt = + static_cast(Common::RoundInt(int_hess * cnt_factor)); + + if (HIST_BITS_ACC != HIST_BITS_BIN) { + PACKED_HIST_ACC_T int_grad_and_hess_acc = + (static_cast(static_cast(int_grad_and_hess & 0xffff0000)) << 32) | + (static_cast(int_grad_and_hess & 0x0000ffff)); + int_sum_left_gradient_and_hessian += int_grad_and_hess_acc; + } else { + int_sum_left_gradient_and_hessian += int_grad_and_hess; + } + + left_count += cnt; + cnt_cur_group += cnt; + + const uint32_t int_left_sum_hessian = HIST_BITS_ACC == 16 ? + static_cast(int_sum_left_gradient_and_hessian & 0x0000ffff) : + static_cast(int_sum_left_gradient_and_hessian & 0x00000000ffffffff); + const double sum_left_hessian = int_left_sum_hessian * hess_scale; + + if (left_count < meta_->config->min_data_in_leaf || + sum_left_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t right_count = num_data - left_count; + if (right_count < meta_->config->min_data_in_leaf || + right_count < min_data_per_group) { + break; + } + + const PACKED_HIST_ACC_T int_sum_right_gradient_and_hessian = local_int_sum_gradient_and_hessian - int_sum_left_gradient_and_hessian; + const uint32_t int_right_sum_hessian = HIST_BITS_ACC == 16 ? + static_cast(int_sum_right_gradient_and_hessian & 0x0000ffff) : + static_cast(int_sum_right_gradient_and_hessian & 0x00000000ffffffff); + const double sum_right_hessian = int_right_sum_hessian * hess_scale; + + if (sum_right_hessian < meta_->config->min_sum_hessian_in_leaf) { + break; + } + + if (cnt_cur_group < min_data_per_group) { + continue; + } + + cnt_cur_group = 0; + + const int32_t int_sum_left_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(int_sum_left_gradient_and_hessian >> 16)) : + static_cast(static_cast(int_sum_left_gradient_and_hessian) >> 32); + const double sum_left_gradient = int_sum_left_gradient * grad_scale; + + const int32_t int_sum_right_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(int_sum_right_gradient_and_hessian >> 16)) : + static_cast(static_cast(int_sum_right_gradient_and_hessian) >> 32); + const double sum_right_gradient = int_sum_right_gradient * grad_scale; + + if (USE_RAND) { + if (i != rand_threshold) { + continue; + } + } + double current_gain = GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, constraints, 0, meta_->config->path_smooth, + left_count, right_count, parent_output); + if (current_gain <= min_gain_shift) { + continue; + } + is_splittable_ = true; + if (current_gain > best_gain) { + best_sum_left_gradient_and_hessian = int_sum_left_gradient_and_hessian; + best_threshold = i; + best_gain = current_gain; + best_dir = dir; + } + } + } + } + + if (is_splittable_) { + const int32_t int_best_sum_left_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(best_sum_left_gradient_and_hessian >> 16)) : + static_cast(static_cast(best_sum_left_gradient_and_hessian) >> 32); + const uint32_t int_best_sum_left_hessian = HIST_BITS_ACC == 16 ? + static_cast(best_sum_left_gradient_and_hessian & 0x0000ffff) : + static_cast(best_sum_left_gradient_and_hessian & 0x00000000ffffffff); + const double best_sum_left_gradient = int_best_sum_left_gradient * grad_scale; + const double best_sum_left_hessian = int_best_sum_left_hessian * hess_scale; + + const PACKED_HIST_ACC_T best_sum_right_gradient_and_hessian = local_int_sum_gradient_and_hessian - best_sum_left_gradient_and_hessian; + const int32_t int_best_sum_right_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(best_sum_right_gradient_and_hessian >> 16)) : + static_cast(static_cast(best_sum_right_gradient_and_hessian) >> 32); + const uint32_t int_best_sum_right_hessian = HIST_BITS_ACC == 16 ? + static_cast(best_sum_right_gradient_and_hessian & 0x0000ffff) : + static_cast(best_sum_right_gradient_and_hessian & 0x00000000ffffffff); + const double best_sum_right_gradient = int_best_sum_right_gradient * grad_scale; + const double best_sum_right_hessian = int_best_sum_right_hessian * hess_scale; + + const data_size_t best_left_count = Common::RoundInt(static_cast(int_best_sum_left_hessian) * cnt_factor); + const data_size_t best_right_count = Common::RoundInt(static_cast(int_best_sum_right_hessian) * cnt_factor); + + const int64_t best_sum_left_gradient_and_hessian_int64 = HIST_BITS_ACC == 16 ? + ((static_cast(static_cast(best_sum_left_gradient_and_hessian >> 16)) << 32) | + static_cast(best_sum_left_gradient_and_hessian & 0x0000ffff)) : + best_sum_left_gradient_and_hessian; + const int64_t best_sum_right_gradient_and_hessian_int64 = int_sum_gradient_and_hessian - best_sum_left_gradient_and_hessian_int64; + + output->left_output = CalculateSplittedLeafOutput( + best_sum_left_gradient, best_sum_left_hessian, + meta_->config->lambda_l1, l2, meta_->config->max_delta_step, + constraints->LeftToBasicConstraint(), meta_->config->path_smooth, best_left_count, parent_output); + output->left_count = best_left_count; + output->left_sum_gradient = best_sum_left_gradient; + output->left_sum_hessian = best_sum_left_hessian; + output->right_output = CalculateSplittedLeafOutput( + best_sum_right_gradient, + best_sum_right_hessian, meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, constraints->RightToBasicConstraint(), meta_->config->path_smooth, + best_right_count, parent_output); + output->right_count = best_right_count; + output->right_sum_gradient = best_sum_right_gradient; + output->right_sum_hessian = best_sum_right_hessian; + output->gain = best_gain - min_gain_shift; + + output->left_sum_gradient_and_hessian = best_sum_left_gradient_and_hessian_int64; + output->right_sum_gradient_and_hessian = best_sum_right_gradient_and_hessian_int64; + if (use_onehot) { + output->num_cat_threshold = 1; + output->cat_threshold = + std::vector(1, static_cast(best_threshold + offset)); + } else { + output->num_cat_threshold = best_threshold + 1; + output->cat_threshold = + std::vector(output->num_cat_threshold); + if (best_dir == 1) { + for (int i = 0; i < output->num_cat_threshold; ++i) { + auto t = sorted_idx[i] + offset; + output->cat_threshold[i] = t; + } + } else { + for (int i = 0; i < output->num_cat_threshold; ++i) { + auto t = sorted_idx[used_bin - 1 - i] + offset; + output->cat_threshold[i] = t; + } + } + } + output->monotone_type = 0; + } +} + +} // namespace LightGBM diff --git a/src/treelearner/feature_histogram.hpp b/src/treelearner/feature_histogram.hpp new file mode 100644 index 0000000..461e43c --- /dev/null +++ b/src/treelearner/feature_histogram.hpp @@ -0,0 +1,1598 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_FEATURE_HISTOGRAM_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_FEATURE_HISTOGRAM_HPP_ + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "monotone_constraints.hpp" +#include "split_info.hpp" + +namespace LightGBM { + +class FeatureMetainfo { + public: + int num_bin; + MissingType missing_type; + int8_t offset = 0; + uint32_t default_bin; + int8_t monotone_type = 0; + double penalty = 1.0; + /*! \brief pointer of tree config */ + const Config* config; + BinType bin_type; + /*! \brief random number generator for extremely randomized trees */ + mutable Random rand; +}; +/*! + * \brief FeatureHistogram is used to construct and store a histogram for a + * feature. + */ +class FeatureHistogram { + public: + FeatureHistogram() { data_ = nullptr; } + + ~FeatureHistogram() {} + + /*! \brief Disable copy */ + FeatureHistogram& operator=(const FeatureHistogram&) = delete; + /*! \brief Disable copy */ + FeatureHistogram(const FeatureHistogram&) = delete; + + /*! + * \brief Init the feature histogram + * \param feature the feature data for this histogram + * \param min_num_data_one_leaf minimal number of data in one leaf + */ + void Init(hist_t* data, int16_t* data_int16, const FeatureMetainfo* meta) { + meta_ = meta; + data_ = data; + data_int16_ = data_int16; + ResetFunc(); + } + + /*! + * \brief Init the feature histogram + * \param feature the feature data for this histogram + * \param min_num_data_one_leaf minimal number of data in one leaf + */ + void Init(hist_t* data, const FeatureMetainfo* meta) { + meta_ = meta; + data_ = data; + data_int16_ = nullptr; + ResetFunc(); + } + + void ResetFunc() { + if (meta_->bin_type == BinType::NumericalBin) { + FuncForNumrical(); + } else { + FuncForCategorical(); + } + } + + hist_t* RawData() { return data_; } + + int32_t* RawDataInt32() { return reinterpret_cast(data_); } + + int16_t* RawDataInt16() { return data_int16_; } + + /*! + * \brief Subtract current histograms with other + * \param other The histogram that want to subtract + */ + template + void Subtract(const FeatureHistogram& other, const int32_t* buffer = nullptr) { + if (USE_DIST_GRAD) { + const THIS_HIST_T* this_int_data = THIS_HIST_BITS == 16 ? + reinterpret_cast(data_int16_) : + (RESULT_HIST_BITS == 16 ? + reinterpret_cast(buffer) : + reinterpret_cast(data_)); + const OTHER_HIST_T* other_int_data = OTHER_HIST_BITS == 16 ? + reinterpret_cast(other.data_int16_) : + reinterpret_cast(other.data_); + RESULT_HIST_T* result_int_data = RESULT_HIST_BITS == 16 ? + reinterpret_cast(data_int16_) : + reinterpret_cast(data_); + if (THIS_HIST_BITS == 32 && OTHER_HIST_BITS == 16 && RESULT_HIST_BITS == 32) { + for (int i = 0; i < meta_->num_bin - meta_->offset; ++i) { + const int32_t other_grad_hess = static_cast(other_int_data[i]); + const int64_t this_grad_hess = this_int_data[i]; + const int64_t other_grad_hess_int64 = + (static_cast(static_cast(other_grad_hess >> 16)) << 32) | + (static_cast(other_grad_hess & 0x0000ffff)); + const int64_t result_grad_hess = this_grad_hess - other_grad_hess_int64; + result_int_data[i] = static_cast(result_grad_hess); + } + } else if (THIS_HIST_BITS == 32 && OTHER_HIST_BITS == 16 && RESULT_HIST_BITS == 16) { + for (int i = 0; i < meta_->num_bin - meta_->offset; ++i) { + const int32_t other_grad_hess = static_cast(other_int_data[i]); + const int64_t this_grad_hess = this_int_data[i]; + const int64_t other_grad_hess_int64 = + (static_cast(static_cast(other_grad_hess >> 16)) << 32) | + (static_cast(other_grad_hess & 0x0000ffff)); + const int64_t result_grad_hess = this_grad_hess - other_grad_hess_int64; + const int32_t result_grad_hess_int32 = + (static_cast(result_grad_hess >> 32) << 16) | + static_cast(result_grad_hess & 0x00000000ffffffff); + result_int_data[i] = result_grad_hess_int32; + } + } else { + for (int i = 0; i < meta_->num_bin - meta_->offset; ++i) { + result_int_data[i] = this_int_data[i] - other_int_data[i]; + } + } + } else { + for (int i = 0; i < (meta_->num_bin - meta_->offset) * 2; ++i) { + data_[i] -= other.data_[i]; + } + } + } + + void CopyToBuffer(int32_t* buffer) { + const int64_t* data_ptr = reinterpret_cast(data_); + int64_t* buffer_ptr = reinterpret_cast(buffer); + for (int i = 0; i < meta_->num_bin - meta_->offset; ++i) { + buffer_ptr[i] = data_ptr[i]; + } + } + + void CopyFromInt16ToInt32(char* buffer) { + const int32_t* int16_data = reinterpret_cast(RawDataInt16()); + int64_t* int32_data = reinterpret_cast(buffer); + for (int i = 0; i < meta_->num_bin - meta_->offset; ++i) { + const int32_t int16_val = int16_data[i]; + int32_data[i] = (static_cast(static_cast(int16_val >> 16)) << 32) | + static_cast(int16_val & 0x0000ffff); + } + } + + void FindBestThreshold(double sum_gradient, double sum_hessian, + data_size_t num_data, + const FeatureConstraint* constraints, + double parent_output, + SplitInfo* output) { + output->default_left = true; + output->gain = kMinScore; + find_best_threshold_fun_(sum_gradient, sum_hessian + 2 * kEpsilon, num_data, + constraints, parent_output, output); + output->gain *= meta_->penalty; + } + + void FindBestThresholdInt(int64_t sum_gradient_and_hessian, + double grad_scale, double hess_scale, + const uint8_t num_bits_bin, + const uint8_t num_bits_acc, + data_size_t num_data, + const FeatureConstraint* constraints, + double parent_output, + SplitInfo* output) { + output->default_left = true; + output->gain = kMinScore; + int_find_best_threshold_fun_(sum_gradient_and_hessian, grad_scale, hess_scale, num_bits_bin, num_bits_acc, num_data, + constraints, parent_output, output); + output->gain *= meta_->penalty; + } + + template + double BeforeNumerical(double sum_gradient, double sum_hessian, double parent_output, data_size_t num_data, + SplitInfo* output, int* rand_threshold) { + is_splittable_ = false; + output->monotone_type = meta_->monotone_type; + + double gain_shift = GetLeafGain( + sum_gradient, sum_hessian, meta_->config->lambda_l1, meta_->config->lambda_l2, + meta_->config->max_delta_step, meta_->config->path_smooth, num_data, parent_output); + *rand_threshold = 0; + if (USE_RAND) { + if (meta_->num_bin - 2 > 0) { + *rand_threshold = meta_->rand.NextInt(0, meta_->num_bin - 2); + } + } + return gain_shift + meta_->config->min_gain_to_split; + } + + template + double BeforeNumericalInt(int64_t sum_gradient_and_hessian, double grad_scale, double hess_scale, double parent_output, data_size_t num_data, + SplitInfo* output, int* rand_threshold) { + is_splittable_ = false; + output->monotone_type = meta_->monotone_type; + const int32_t int_sum_gradient = static_cast(sum_gradient_and_hessian >> 32); + const uint32_t int_sum_hessian = static_cast(sum_gradient_and_hessian & 0x00000000ffffffff); + const double sum_gradient = static_cast(int_sum_gradient) * grad_scale; + const double sum_hessian = static_cast(int_sum_hessian) * hess_scale; + double gain_shift = GetLeafGain( + sum_gradient, sum_hessian, meta_->config->lambda_l1, meta_->config->lambda_l2, + meta_->config->max_delta_step, meta_->config->path_smooth, num_data, parent_output); + *rand_threshold = 0; + if (USE_RAND) { + if (meta_->num_bin - 2 > 0) { + *rand_threshold = meta_->rand.NextInt(0, meta_->num_bin - 2); + } + } + return gain_shift + meta_->config->min_gain_to_split; + } + + void FuncForNumrical() { + if (meta_->config->extra_trees) { + if (meta_->config->monotone_constraints.empty()) { + FuncForNumricalL1(); + } else { + FuncForNumricalL1(); + } + } else { + if (meta_->config->monotone_constraints.empty()) { + FuncForNumricalL1(); + } else { + FuncForNumricalL1(); + } + } + } + template + void FuncForNumricalL1() { + if (meta_->config->lambda_l1 > 0) { + if (meta_->config->max_delta_step > 0) { + FuncForNumricalL2(); + } else { + FuncForNumricalL2(); + } + } else { + if (meta_->config->max_delta_step > 0) { + FuncForNumricalL2(); + } else { + FuncForNumricalL2(); + } + } + } + + template + void FuncForNumricalL2() { + if (meta_->config->path_smooth > kEpsilon) { + FuncForNumricalL3(); + } else { + FuncForNumricalL3(); + } + } + + template + void FuncForNumricalL3() { + if (meta_->config->use_quantized_grad) { +#define TEMPLATE_PREFIX_INT USE_RAND, USE_MC, USE_L1, USE_MAX_OUTPUT, USE_SMOOTHING +#define LAMBDA_ARGUMENTS_INT \ + int64_t sum_gradient_and_hessian, double grad_scale, double hess_scale, const uint8_t hist_bits_bin, const uint8_t hist_bits_acc, data_size_t num_data, \ + const FeatureConstraint* constraints, double parent_output, SplitInfo *output +#define BEFORE_ARGUMENTS_INT sum_gradient_and_hessian, grad_scale, hess_scale, parent_output, num_data, output, &rand_threshold +#define FUNC_ARGUMENTS_INT \ + sum_gradient_and_hessian, grad_scale, hess_scale, num_data, constraints, min_gain_shift, \ + output, rand_threshold, parent_output + + if (meta_->num_bin > 2 && meta_->missing_type != MissingType::None) { + if (meta_->missing_type == MissingType::Zero) { + int_find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS_INT) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumericalInt( + BEFORE_ARGUMENTS_INT); + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + if (hist_bits_bin == 32) { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } + } + }; + } else { + int_find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS_INT) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumericalInt( + BEFORE_ARGUMENTS_INT); + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + if (hist_bits_bin == 32) { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } + } + }; + } + } else { + if (meta_->missing_type != MissingType::NaN) { + int_find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS_INT) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumericalInt( + BEFORE_ARGUMENTS_INT); + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + if (hist_bits_bin == 32) { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } + } + }; + } else { + int_find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS_INT) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumericalInt( + BEFORE_ARGUMENTS_INT); + if (hist_bits_acc <= 16) { + CHECK_LE(hist_bits_bin, 16); + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + if (hist_bits_bin == 32) { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } else { + FindBestThresholdSequentiallyInt( + FUNC_ARGUMENTS_INT); + } + } + output->default_left = false; + }; + } + } +#undef TEMPLATE_PREFIX_INT +#undef LAMBDA_ARGUMENTS_INT +#undef BEFORE_ARGUMENTS_INT +#undef FUNC_ARGURMENTS_INT + } else { +#define TEMPLATE_PREFIX USE_RAND, USE_MC, USE_L1, USE_MAX_OUTPUT, USE_SMOOTHING +#define LAMBDA_ARGUMENTS \ + double sum_gradient, double sum_hessian, data_size_t num_data, \ + const FeatureConstraint* constraints, double parent_output, SplitInfo *output +#define BEFORE_ARGUMENTS sum_gradient, sum_hessian, parent_output, num_data, output, &rand_threshold +#define FUNC_ARGUMENTS \ + sum_gradient, sum_hessian, num_data, constraints, min_gain_shift, \ + output, rand_threshold, parent_output + + if (meta_->num_bin > 2 && meta_->missing_type != MissingType::None) { + if (meta_->missing_type == MissingType::Zero) { + find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumerical( + BEFORE_ARGUMENTS); + FindBestThresholdSequentially( + FUNC_ARGUMENTS); + FindBestThresholdSequentially( + FUNC_ARGUMENTS); + }; + } else { + find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumerical( + BEFORE_ARGUMENTS); + FindBestThresholdSequentially( + FUNC_ARGUMENTS); + FindBestThresholdSequentially( + FUNC_ARGUMENTS); + }; + } + } else { + if (meta_->missing_type != MissingType::NaN) { + find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumerical( + BEFORE_ARGUMENTS); + FindBestThresholdSequentially( + FUNC_ARGUMENTS); + }; + } else { + find_best_threshold_fun_ = [=](LAMBDA_ARGUMENTS) { + int rand_threshold = 0; + double min_gain_shift = + BeforeNumerical( + BEFORE_ARGUMENTS); + FindBestThresholdSequentially( + FUNC_ARGUMENTS); + output->default_left = false; + }; + } + } +#undef TEMPLATE_PREFIX +#undef LAMBDA_ARGUMENTS +#undef BEFORE_ARGUMENTS +#undef FUNC_ARGURMENTS + } + } + + void FuncForCategorical(); + + template + void FuncForCategoricalL1(); + + template + void FuncForCategoricalL2(); + + template + void FindBestThresholdCategoricalInner(double sum_gradient, + double sum_hessian, + data_size_t num_data, + const FeatureConstraint* constraints, + double parent_output, + SplitInfo* output); + + template + void FindBestThresholdCategoricalIntInner(int64_t int_sum_gradient_and_hessian, + const double grad_scale, const double hess_scale, + data_size_t num_data, + const FeatureConstraint* constraints, + double parent_output, + SplitInfo* output); + + void GatherInfoForThreshold(double sum_gradient, double sum_hessian, + uint32_t threshold, data_size_t num_data, + double parent_output, SplitInfo* output) { + if (meta_->bin_type == BinType::NumericalBin) { + GatherInfoForThresholdNumerical(sum_gradient, sum_hessian, threshold, + num_data, parent_output, output); + } else { + GatherInfoForThresholdCategorical(sum_gradient, sum_hessian, threshold, + num_data, parent_output, output); + } + } + + void GatherInfoForThresholdNumerical(double sum_gradient, double sum_hessian, + uint32_t threshold, data_size_t num_data, + double parent_output, SplitInfo* output) { + bool use_smoothing = meta_->config->path_smooth > kEpsilon; + if (use_smoothing) { + GatherInfoForThresholdNumericalInner(sum_gradient, sum_hessian, + threshold, num_data, + parent_output, output); + } else { + GatherInfoForThresholdNumericalInner(sum_gradient, sum_hessian, + threshold, num_data, + parent_output, output); + } + } + + template + void GatherInfoForThresholdNumericalInner(double sum_gradient, double sum_hessian, + uint32_t threshold, data_size_t num_data, + double parent_output, SplitInfo* output) { + double gain_shift = GetLeafGainGivenOutput( + sum_gradient, sum_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, parent_output); + double min_gain_shift = gain_shift + meta_->config->min_gain_to_split; + + // do stuff here + const int8_t offset = meta_->offset; + + double sum_right_gradient = 0.0f; + double sum_right_hessian = kEpsilon; + data_size_t right_count = 0; + + // set values + bool use_na_as_missing = false; + bool skip_default_bin = false; + if (meta_->missing_type == MissingType::Zero) { + skip_default_bin = true; + } else if (meta_->missing_type == MissingType::NaN) { + use_na_as_missing = true; + } + + int t = meta_->num_bin - 1 - offset - use_na_as_missing; + const int t_end = 1 - offset; + const double cnt_factor = num_data / sum_hessian; + // from right to left, and we don't need data in bin0 + for (; t >= t_end; --t) { + if (static_cast(t + offset) <= threshold) { + break; + } + + // need to skip default bin + if (skip_default_bin && + (t + offset) == static_cast(meta_->default_bin)) { + continue; + } + const auto grad = GET_GRAD(data_, t); + const auto hess = GET_HESS(data_, t); + data_size_t cnt = + static_cast(Common::RoundInt(hess * cnt_factor)); + sum_right_gradient += grad; + sum_right_hessian += hess; + right_count += cnt; + } + double sum_left_gradient = sum_gradient - sum_right_gradient; + double sum_left_hessian = sum_hessian - sum_right_hessian; + data_size_t left_count = num_data - right_count; + double current_gain = + GetLeafGain( + sum_left_gradient, sum_left_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + meta_->config->path_smooth, left_count, parent_output) + + GetLeafGain( + sum_right_gradient, sum_right_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + meta_->config->path_smooth, right_count, parent_output); + + // gain with split is worse than without split + if (std::isnan(current_gain) || current_gain <= min_gain_shift) { + output->gain = kMinScore; + Log::Warning( + "'Forced Split' will be ignored since the gain getting worse."); + return; + } + + // update split information + output->threshold = threshold; + output->left_output = CalculateSplittedLeafOutput( + sum_left_gradient, sum_left_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + meta_->config->path_smooth, left_count, parent_output); + output->left_count = left_count; + output->left_sum_gradient = sum_left_gradient; + output->left_sum_hessian = sum_left_hessian - kEpsilon; + output->right_output = CalculateSplittedLeafOutput( + sum_gradient - sum_left_gradient, sum_hessian - sum_left_hessian, + meta_->config->lambda_l1, meta_->config->lambda_l2, + meta_->config->max_delta_step, meta_->config->path_smooth, + right_count, parent_output); + output->right_count = num_data - left_count; + output->right_sum_gradient = sum_gradient - sum_left_gradient; + output->right_sum_hessian = sum_hessian - sum_left_hessian - kEpsilon; + output->gain = current_gain - min_gain_shift; + output->default_left = true; + } + + void GatherInfoForThresholdCategorical(double sum_gradient, double sum_hessian, + uint32_t threshold, data_size_t num_data, + double parent_output, SplitInfo* output) { + bool use_smoothing = meta_->config->path_smooth > kEpsilon; + if (use_smoothing) { + GatherInfoForThresholdCategoricalInner(sum_gradient, sum_hessian, threshold, + num_data, parent_output, output); + } else { + GatherInfoForThresholdCategoricalInner(sum_gradient, sum_hessian, threshold, + num_data, parent_output, output); + } + } + + template + void GatherInfoForThresholdCategoricalInner(double sum_gradient, + double sum_hessian, uint32_t threshold, + data_size_t num_data, double parent_output, + SplitInfo* output) { + // get SplitInfo for a given one-hot categorical split. + output->default_left = false; + double gain_shift = GetLeafGainGivenOutput( + sum_gradient, sum_hessian, meta_->config->lambda_l1, meta_->config->lambda_l2, parent_output); + double min_gain_shift = gain_shift + meta_->config->min_gain_to_split; + if (threshold >= static_cast(meta_->num_bin) || threshold == 0) { + output->gain = kMinScore; + Log::Warning("Invalid categorical threshold split"); + return; + } + const double cnt_factor = num_data / sum_hessian; + const auto grad = GET_GRAD(data_, threshold - meta_->offset); + const auto hess = GET_HESS(data_, threshold - meta_->offset); + data_size_t cnt = + static_cast(Common::RoundInt(hess * cnt_factor)); + + double l2 = meta_->config->lambda_l2; + data_size_t left_count = cnt; + data_size_t right_count = num_data - left_count; + double sum_left_hessian = hess + kEpsilon; + double sum_right_hessian = sum_hessian - sum_left_hessian; + double sum_left_gradient = grad; + double sum_right_gradient = sum_gradient - sum_left_gradient; + // current split gain + double current_gain = + GetLeafGain(sum_right_gradient, sum_right_hessian, + meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, + meta_->config->path_smooth, right_count, + parent_output) + + GetLeafGain(sum_left_gradient, sum_left_hessian, + meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, + meta_->config->path_smooth, left_count, + parent_output); + if (std::isnan(current_gain) || current_gain <= min_gain_shift) { + output->gain = kMinScore; + Log::Warning( + "'Forced Split' will be ignored since the gain getting worse."); + return; + } + output->left_output = CalculateSplittedLeafOutput( + sum_left_gradient, sum_left_hessian, meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, meta_->config->path_smooth, left_count, + parent_output); + output->left_count = left_count; + output->left_sum_gradient = sum_left_gradient; + output->left_sum_hessian = sum_left_hessian - kEpsilon; + output->right_output = CalculateSplittedLeafOutput( + sum_right_gradient, sum_right_hessian, meta_->config->lambda_l1, l2, + meta_->config->max_delta_step, meta_->config->path_smooth, right_count, + parent_output); + output->right_count = right_count; + output->right_sum_gradient = sum_gradient - sum_left_gradient; + output->right_sum_hessian = sum_right_hessian - kEpsilon; + output->gain = current_gain - min_gain_shift; + output->num_cat_threshold = 1; + output->cat_threshold = std::vector(1, threshold); + } + + /*! + * \brief Binary size of this histogram + */ + int SizeOfHistogram() const { + return (meta_->num_bin - meta_->offset) * kHistEntrySize; + } + + int SizeOfInt32Histogram() const { + return (meta_->num_bin - meta_->offset) * kInt32HistEntrySize; + } + + int SizeOfInt16Histogram() const { + return (meta_->num_bin - meta_->offset) * kInt16HistEntrySize; + } + + /*! + * \brief Restore histogram from memory + */ + void FromMemory(char* memory_data) { + std::memcpy(data_, memory_data, + (meta_->num_bin - meta_->offset) * kHistEntrySize); + } + + void FromMemoryInt32(char* memory_data) { + std::memcpy(data_, memory_data, + (meta_->num_bin - meta_->offset) * kInt32HistEntrySize); + } + + void FromMemoryInt16(char* memory_data) { + std::memcpy(data_int16_, memory_data, + (meta_->num_bin - meta_->offset) * kInt16HistEntrySize); + } + + /*! + * \brief True if this histogram can be splitted + */ + bool is_splittable() const { return is_splittable_; } + + /*! + * \brief Set splittable to this histogram + */ + void set_is_splittable(bool val) { is_splittable_ = val; } + + static double ThresholdL1(double s, double l1) { + const double reg_s = std::max(0.0, std::fabs(s) - l1); + return Common::Sign(s) * reg_s; + } + + template + static double CalculateSplittedLeafOutput(double sum_gradients, + double sum_hessians, double l1, + double l2, double max_delta_step, + double smoothing, data_size_t num_data, + double parent_output) { + double ret; + if (USE_L1) { + ret = -ThresholdL1(sum_gradients, l1) / (sum_hessians + l2); + } else { + ret = -sum_gradients / (sum_hessians + l2); + } + if (USE_MAX_OUTPUT) { + if (max_delta_step > 0 && std::fabs(ret) > max_delta_step) { + ret = Common::Sign(ret) * max_delta_step; + } + } + if (USE_SMOOTHING) { + ret = ret * (num_data / smoothing) / (num_data / smoothing + 1) \ + + parent_output / (num_data / smoothing + 1); + } + return ret; + } + + template + static double CalculateSplittedLeafOutput( + double sum_gradients, double sum_hessians, double l1, double l2, + double max_delta_step, const BasicConstraint& constraints, + double smoothing, data_size_t num_data, double parent_output) { + double ret = CalculateSplittedLeafOutput( + sum_gradients, sum_hessians, l1, l2, max_delta_step, smoothing, num_data, parent_output); + if (USE_MC) { + if (ret < constraints.min) { + ret = constraints.min; + } else if (ret > constraints.max) { + ret = constraints.max; + } + } + return ret; + } + + private: + template + static double GetSplitGains(double sum_left_gradients, + double sum_left_hessians, + double sum_right_gradients, + double sum_right_hessians, double l1, double l2, + double max_delta_step, + const FeatureConstraint* constraints, + int8_t monotone_constraint, + double smoothing, + data_size_t left_count, + data_size_t right_count, + double parent_output) { + if (!USE_MC) { + return GetLeafGain(sum_left_gradients, + sum_left_hessians, l1, l2, + max_delta_step, smoothing, + left_count, parent_output) + + GetLeafGain(sum_right_gradients, + sum_right_hessians, l1, l2, + max_delta_step, smoothing, + right_count, parent_output); + } else { + double left_output = + CalculateSplittedLeafOutput( + sum_left_gradients, sum_left_hessians, l1, l2, max_delta_step, + constraints->LeftToBasicConstraint(), smoothing, left_count, parent_output); + double right_output = + CalculateSplittedLeafOutput( + sum_right_gradients, sum_right_hessians, l1, l2, max_delta_step, + constraints->RightToBasicConstraint(), smoothing, right_count, parent_output); + if (((monotone_constraint > 0) && (left_output > right_output)) || + ((monotone_constraint < 0) && (left_output < right_output))) { + return 0; + } + return GetLeafGainGivenOutput( + sum_left_gradients, sum_left_hessians, l1, l2, left_output) + + GetLeafGainGivenOutput( + sum_right_gradients, sum_right_hessians, l1, l2, right_output); + } + } + + template + static double GetLeafGain(double sum_gradients, double sum_hessians, + double l1, double l2, double max_delta_step, + double smoothing, data_size_t num_data, double parent_output) { + if (!USE_MAX_OUTPUT && !USE_SMOOTHING) { + if (USE_L1) { + const double sg_l1 = ThresholdL1(sum_gradients, l1); + return (sg_l1 * sg_l1) / (sum_hessians + l2); + } else { + return (sum_gradients * sum_gradients) / (sum_hessians + l2); + } + } else { + double output = CalculateSplittedLeafOutput( + sum_gradients, sum_hessians, l1, l2, max_delta_step, smoothing, num_data, parent_output); + return GetLeafGainGivenOutput(sum_gradients, sum_hessians, l1, l2, output); + } + } + + template + static double GetLeafGainGivenOutput(double sum_gradients, + double sum_hessians, double l1, + double l2, double output) { + if (USE_L1) { + const double sg_l1 = ThresholdL1(sum_gradients, l1); + return -(2.0 * sg_l1 * output + (sum_hessians + l2) * output * output); + } else { + return -(2.0 * sum_gradients * output + + (sum_hessians + l2) * output * output); + } + } + + template + void FindBestThresholdSequentially(double sum_gradient, double sum_hessian, + data_size_t num_data, + const FeatureConstraint* constraints, + double min_gain_shift, SplitInfo* output, + int rand_threshold, double parent_output) { + const int8_t offset = meta_->offset; + double best_sum_left_gradient = NAN; + double best_sum_left_hessian = NAN; + double best_gain = kMinScore; + data_size_t best_left_count = 0; + uint32_t best_threshold = static_cast(meta_->num_bin); + const double cnt_factor = num_data / sum_hessian; + + BasicConstraint best_right_constraints; + BasicConstraint best_left_constraints; + bool constraint_update_necessary = + USE_MC && constraints->ConstraintDifferentDependingOnThreshold(); + + if (USE_MC) { + constraints->InitCumulativeConstraints(REVERSE); + } + + if (REVERSE) { + double sum_right_gradient = 0.0f; + double sum_right_hessian = kEpsilon; + data_size_t right_count = 0; + + int t = meta_->num_bin - 1 - offset - NA_AS_MISSING; + const int t_end = 1 - offset; + + // from right to left, and we don't need data in bin0 + for (; t >= t_end; --t) { + // need to skip default bin + if (SKIP_DEFAULT_BIN) { + if ((t + offset) == static_cast(meta_->default_bin)) { + continue; + } + } + const auto grad = GET_GRAD(data_, t); + const auto hess = GET_HESS(data_, t); + data_size_t cnt = + static_cast(Common::RoundInt(hess * cnt_factor)); + sum_right_gradient += grad; + sum_right_hessian += hess; + right_count += cnt; + // if data not enough, or sum hessian too small + if (right_count < meta_->config->min_data_in_leaf || + sum_right_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t left_count = num_data - right_count; + // if data not enough + if (left_count < meta_->config->min_data_in_leaf) { + break; + } + + double sum_left_hessian = sum_hessian - sum_right_hessian; + // if sum hessian too small + if (sum_left_hessian < meta_->config->min_sum_hessian_in_leaf) { + break; + } + + double sum_left_gradient = sum_gradient - sum_right_gradient; + if (USE_RAND) { + if (t - 1 + offset != rand_threshold) { + continue; + } + } + + if (USE_MC && constraint_update_necessary) { + constraints->Update(t + offset); + } + + // current split gain + double current_gain = GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + constraints, meta_->monotone_type, meta_->config->path_smooth, + left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain <= min_gain_shift) { + continue; + } + + // mark as able to be split + is_splittable_ = true; + // better split point + if (current_gain > best_gain) { + if (USE_MC) { + best_right_constraints = constraints->RightToBasicConstraint(); + best_left_constraints = constraints->LeftToBasicConstraint(); + if (best_right_constraints.min > best_right_constraints.max || + best_left_constraints.min > best_left_constraints.max) { + continue; + } + } + best_left_count = left_count; + best_sum_left_gradient = sum_left_gradient; + best_sum_left_hessian = sum_left_hessian; + // left is <= threshold, right is > threshold. so this is t-1 + best_threshold = static_cast(t - 1 + offset); + best_gain = current_gain; + } + } + } else { + double sum_left_gradient = 0.0f; + double sum_left_hessian = kEpsilon; + data_size_t left_count = 0; + + int t = 0; + const int t_end = meta_->num_bin - 2 - offset; + + if (NA_AS_MISSING) { + if (offset == 1) { + sum_left_gradient = sum_gradient; + sum_left_hessian = sum_hessian - kEpsilon; + left_count = num_data; + for (int i = 0; i < meta_->num_bin - offset; ++i) { + const auto grad = GET_GRAD(data_, i); + const auto hess = GET_HESS(data_, i); + data_size_t cnt = + static_cast(Common::RoundInt(hess * cnt_factor)); + sum_left_gradient -= grad; + sum_left_hessian -= hess; + left_count -= cnt; + } + t = -1; + } + } + + for (; t <= t_end; ++t) { + if (SKIP_DEFAULT_BIN) { + if ((t + offset) == static_cast(meta_->default_bin)) { + continue; + } + } + if (t >= 0) { + sum_left_gradient += GET_GRAD(data_, t); + sum_left_hessian += GET_HESS(data_, t); + left_count += static_cast( + Common::RoundInt(GET_HESS(data_, t) * cnt_factor)); + } + // if data not enough, or sum hessian too small + if (left_count < meta_->config->min_data_in_leaf || + sum_left_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t right_count = num_data - left_count; + // if data not enough + if (right_count < meta_->config->min_data_in_leaf) { + break; + } + + double sum_right_hessian = sum_hessian - sum_left_hessian; + // if sum Hessian too small + if (sum_right_hessian < meta_->config->min_sum_hessian_in_leaf) { + break; + } + + double sum_right_gradient = sum_gradient - sum_left_gradient; + if (USE_RAND) { + if (t + offset != rand_threshold) { + continue; + } + } + // current split gain + double current_gain = GetSplitGains( + sum_left_gradient, sum_left_hessian, sum_right_gradient, + sum_right_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + constraints, meta_->monotone_type, meta_->config->path_smooth, left_count, + right_count, parent_output); + // gain with split is worse than without split + if (current_gain <= min_gain_shift) { + continue; + } + + // mark as able to be split + is_splittable_ = true; + // better split point + if (current_gain > best_gain) { + if (USE_MC) { + best_right_constraints = constraints->RightToBasicConstraint(); + best_left_constraints = constraints->LeftToBasicConstraint(); + if (best_right_constraints.min > best_right_constraints.max || + best_left_constraints.min > best_left_constraints.max) { + continue; + } + } + best_left_count = left_count; + best_sum_left_gradient = sum_left_gradient; + best_sum_left_hessian = sum_left_hessian; + best_threshold = static_cast(t + offset); + best_gain = current_gain; + } + } + } + + if (is_splittable_ && best_gain > output->gain + min_gain_shift) { + // update split information + output->threshold = best_threshold; + output->left_output = + CalculateSplittedLeafOutput( + best_sum_left_gradient, best_sum_left_hessian, + meta_->config->lambda_l1, meta_->config->lambda_l2, + meta_->config->max_delta_step, best_left_constraints, meta_->config->path_smooth, + best_left_count, parent_output); + output->left_count = best_left_count; + output->left_sum_gradient = best_sum_left_gradient; + output->left_sum_hessian = best_sum_left_hessian - kEpsilon; + output->right_output = + CalculateSplittedLeafOutput( + sum_gradient - best_sum_left_gradient, + sum_hessian - best_sum_left_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + best_right_constraints, meta_->config->path_smooth, num_data - best_left_count, + parent_output); + output->right_count = num_data - best_left_count; + output->right_sum_gradient = sum_gradient - best_sum_left_gradient; + output->right_sum_hessian = + sum_hessian - best_sum_left_hessian - kEpsilon; + output->gain = best_gain - min_gain_shift; + output->default_left = REVERSE; + } + } + + template + void FindBestThresholdSequentiallyInt(int64_t int_sum_gradient_and_hessian, + const double grad_scale, const double hess_scale, + data_size_t num_data, + const FeatureConstraint* constraints, + double min_gain_shift, SplitInfo* output, + int rand_threshold, double parent_output) { + const int8_t offset = meta_->offset; + PACKED_HIST_ACC_T best_sum_left_gradient_and_hessian = 0; + PACKED_HIST_ACC_T local_int_sum_gradient_and_hessian = + HIST_BITS_ACC == 16 ? + ((static_cast(int_sum_gradient_and_hessian >> 32) << 16) | static_cast(int_sum_gradient_and_hessian & 0x0000ffff)) : + static_cast(int_sum_gradient_and_hessian); + double best_gain = kMinScore; + uint32_t best_threshold = static_cast(meta_->num_bin); + const double cnt_factor = static_cast(num_data) / + static_cast(static_cast(int_sum_gradient_and_hessian & 0x00000000ffffffff)); + + BasicConstraint best_right_constraints; + BasicConstraint best_left_constraints; + bool constraint_update_necessary = + USE_MC && constraints->ConstraintDifferentDependingOnThreshold(); + + if (USE_MC) { + constraints->InitCumulativeConstraints(REVERSE); + } + + const PACKED_HIST_BIN_T* data_ptr = nullptr; + if (HIST_BITS_BIN == 16) { + data_ptr = reinterpret_cast(data_int16_); + } else { + data_ptr = reinterpret_cast(data_); + } + if (REVERSE) { + PACKED_HIST_ACC_T sum_right_gradient_and_hessian = 0; + + int t = meta_->num_bin - 1 - offset - NA_AS_MISSING; + const int t_end = 1 - offset; + + // from right to left, and we don't need data in bin0 + for (; t >= t_end; --t) { + // need to skip default bin + if (SKIP_DEFAULT_BIN) { + if ((t + offset) == static_cast(meta_->default_bin)) { + continue; + } + } + const PACKED_HIST_BIN_T grad_and_hess = data_ptr[t]; + if (HIST_BITS_ACC != HIST_BITS_BIN) { + const PACKED_HIST_ACC_T grad_and_hess_acc = HIST_BITS_BIN == 16 ? + ((static_cast(static_cast(grad_and_hess >> HIST_BITS_BIN)) << HIST_BITS_ACC) | + (static_cast(grad_and_hess & 0x0000ffff))) : + ((static_cast(static_cast(grad_and_hess >> HIST_BITS_BIN)) << HIST_BITS_ACC) | + (static_cast(grad_and_hess & 0x00000000ffffffff))); + sum_right_gradient_and_hessian += grad_and_hess_acc; + } else { + sum_right_gradient_and_hessian += grad_and_hess; + } + const uint32_t int_sum_right_hessian = HIST_BITS_ACC == 16 ? + static_cast(sum_right_gradient_and_hessian & 0x0000ffff) : + static_cast(sum_right_gradient_and_hessian & 0x00000000ffffffff); + data_size_t right_count = Common::RoundInt(int_sum_right_hessian * cnt_factor); + double sum_right_hessian = int_sum_right_hessian * hess_scale; + // if data not enough, or sum hessian too small + if (right_count < meta_->config->min_data_in_leaf || + sum_right_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t left_count = num_data - right_count; + // if data not enough + if (left_count < meta_->config->min_data_in_leaf) { + break; + } + + const PACKED_HIST_ACC_T sum_left_gradient_and_hessian = local_int_sum_gradient_and_hessian - sum_right_gradient_and_hessian; + const uint32_t int_sum_left_hessian = HIST_BITS_ACC == 16 ? + static_cast(sum_left_gradient_and_hessian & 0x0000ffff) : + static_cast(sum_left_gradient_and_hessian & 0x00000000ffffffff); + double sum_left_hessian = int_sum_left_hessian * hess_scale; + // if sum hessian too small + if (sum_left_hessian < meta_->config->min_sum_hessian_in_leaf) { + break; + } + + double sum_right_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(sum_right_gradient_and_hessian >> 16)) * grad_scale : + static_cast(static_cast(static_cast(sum_right_gradient_and_hessian) >> 32)) * grad_scale; + double sum_left_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(sum_left_gradient_and_hessian >> 16)) * grad_scale : + static_cast(static_cast(static_cast(sum_left_gradient_and_hessian) >> 32)) * grad_scale; + if (USE_RAND) { + if (t - 1 + offset != rand_threshold) { + continue; + } + } + + if (USE_MC && constraint_update_necessary) { + constraints->Update(t + offset); + } + + // current split gain + double current_gain = GetSplitGains( + sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient, + sum_right_hessian + kEpsilon, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + constraints, meta_->monotone_type, meta_->config->path_smooth, + left_count, right_count, parent_output); + // gain with split is worse than without split + if (current_gain <= min_gain_shift) { + continue; + } + + // mark as able to be split + is_splittable_ = true; + // better split point + if (current_gain > best_gain) { + if (USE_MC) { + best_right_constraints = constraints->RightToBasicConstraint(); + best_left_constraints = constraints->LeftToBasicConstraint(); + if (best_right_constraints.min > best_right_constraints.max || + best_left_constraints.min > best_left_constraints.max) { + continue; + } + } + best_sum_left_gradient_and_hessian = sum_left_gradient_and_hessian; + // left is <= threshold, right is > threshold. so this is t-1 + best_threshold = static_cast(t - 1 + offset); + best_gain = current_gain; + } + } + } else { + PACKED_HIST_ACC_T sum_left_gradient_and_hessian = 0; + + int t = 0; + const int t_end = meta_->num_bin - 2 - offset; + + if (NA_AS_MISSING) { + if (offset == 1) { + sum_left_gradient_and_hessian = local_int_sum_gradient_and_hessian; + for (int i = 0; i < meta_->num_bin - offset; ++i) { + const PACKED_HIST_BIN_T grad_and_hess = data_ptr[i]; + if (HIST_BITS_ACC != HIST_BITS_BIN) { + const PACKED_HIST_ACC_T grad_and_hess_acc = HIST_BITS_BIN == 16 ? + ((static_cast(static_cast(grad_and_hess >> HIST_BITS_BIN)) << HIST_BITS_ACC) | + (static_cast(grad_and_hess & 0x0000ffff))) : + ((static_cast(static_cast(grad_and_hess >> HIST_BITS_BIN)) << HIST_BITS_ACC) | + (static_cast(grad_and_hess & 0x00000000ffffffff))); + sum_left_gradient_and_hessian -= grad_and_hess_acc; + } else { + sum_left_gradient_and_hessian -= grad_and_hess; + } + } + t = -1; + } + } + + for (; t <= t_end; ++t) { + if (SKIP_DEFAULT_BIN) { + if ((t + offset) == static_cast(meta_->default_bin)) { + continue; + } + } + if (t >= 0) { + const PACKED_HIST_BIN_T grad_and_hess = data_ptr[t]; + if (HIST_BITS_ACC != HIST_BITS_BIN) { + const PACKED_HIST_ACC_T grad_and_hess_acc = HIST_BITS_BIN == 16 ? + ((static_cast(static_cast(grad_and_hess >> HIST_BITS_BIN)) << HIST_BITS_ACC) | + (static_cast(grad_and_hess & 0x0000ffff))) : + ((static_cast(static_cast(grad_and_hess >> HIST_BITS_BIN)) << HIST_BITS_ACC) | + (static_cast(grad_and_hess & 0x00000000ffffffff))); + sum_left_gradient_and_hessian += grad_and_hess_acc; + } else { + sum_left_gradient_and_hessian += grad_and_hess; + } + } + // if data not enough, or sum hessian too small + const uint32_t int_sum_left_hessian = HIST_BITS_ACC == 16 ? + static_cast(sum_left_gradient_and_hessian & 0x0000ffff) : + static_cast(sum_left_gradient_and_hessian & 0x00000000ffffffff); + const data_size_t left_count = Common::RoundInt(static_cast(int_sum_left_hessian) * cnt_factor); + const double sum_left_hessian = static_cast(int_sum_left_hessian) * hess_scale; + if (left_count < meta_->config->min_data_in_leaf || + sum_left_hessian < meta_->config->min_sum_hessian_in_leaf) { + continue; + } + data_size_t right_count = num_data - left_count; + // if data not enough + if (right_count < meta_->config->min_data_in_leaf) { + break; + } + + const PACKED_HIST_ACC_T sum_right_gradient_and_hessian = local_int_sum_gradient_and_hessian - sum_left_gradient_and_hessian; + const uint32_t int_sum_right_hessian = HIST_BITS_ACC == 16 ? + static_cast(sum_right_gradient_and_hessian & 0x0000ffff) : + static_cast(sum_right_gradient_and_hessian & 0x00000000ffffffff); + const double sum_right_hessian = static_cast(int_sum_right_hessian) * hess_scale; + // if sum Hessian too small + if (sum_right_hessian < meta_->config->min_sum_hessian_in_leaf) { + break; + } + + double sum_right_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(sum_right_gradient_and_hessian >> 16)) * grad_scale : + static_cast(static_cast(static_cast(sum_right_gradient_and_hessian) >> 32)) * grad_scale; + double sum_left_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(sum_left_gradient_and_hessian >> 16)) * grad_scale : + static_cast(static_cast(static_cast(sum_left_gradient_and_hessian) >> 32)) * grad_scale; + if (USE_RAND) { + if (t + offset != rand_threshold) { + continue; + } + } + // current split gain + double current_gain = GetSplitGains( + sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient, + sum_right_hessian + kEpsilon, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + constraints, meta_->monotone_type, meta_->config->path_smooth, left_count, + right_count, parent_output); + // gain with split is worse than without split + if (current_gain <= min_gain_shift) { + continue; + } + + // mark as able to be split + is_splittable_ = true; + // better split point + if (current_gain > best_gain) { + if (USE_MC) { + best_right_constraints = constraints->RightToBasicConstraint(); + best_left_constraints = constraints->LeftToBasicConstraint(); + if (best_right_constraints.min > best_right_constraints.max || + best_left_constraints.min > best_left_constraints.max) { + continue; + } + } + best_sum_left_gradient_and_hessian = sum_left_gradient_and_hessian; + best_threshold = static_cast(t + offset); + best_gain = current_gain; + } + } + } + + if (is_splittable_ && best_gain > output->gain + min_gain_shift) { + const int32_t int_best_sum_left_gradient = HIST_BITS_ACC == 16 ? + static_cast(static_cast(best_sum_left_gradient_and_hessian >> 16)) : + static_cast(static_cast(best_sum_left_gradient_and_hessian) >> 32); + const uint32_t int_best_sum_left_hessian = HIST_BITS_ACC == 16 ? + static_cast(best_sum_left_gradient_and_hessian & 0x0000ffff) : + static_cast(best_sum_left_gradient_and_hessian & 0x00000000ffffffff); + const double best_sum_left_gradient = static_cast(int_best_sum_left_gradient) * grad_scale; + const double best_sum_left_hessian = static_cast(int_best_sum_left_hessian) * hess_scale; + const int64_t best_sum_left_gradient_and_hessian_int64 = HIST_BITS_ACC == 16 ? + ((static_cast(static_cast(best_sum_left_gradient_and_hessian >> 16)) << 32) | + static_cast(best_sum_left_gradient_and_hessian & 0x0000ffff)) : + best_sum_left_gradient_and_hessian; + const int64_t best_sum_right_gradient_and_hessian = int_sum_gradient_and_hessian - best_sum_left_gradient_and_hessian_int64; + const int32_t int_best_sum_right_gradient = static_cast(best_sum_right_gradient_and_hessian >> 32); + const uint32_t int_best_sum_right_hessian = static_cast(best_sum_right_gradient_and_hessian & 0x00000000ffffffff); + const double best_sum_right_gradient = static_cast(int_best_sum_right_gradient) * grad_scale; + const double best_sum_right_hessian = static_cast(int_best_sum_right_hessian) * hess_scale; + const data_size_t best_left_count = Common::RoundInt(static_cast(int_best_sum_left_hessian) * cnt_factor); + const data_size_t best_right_count = Common::RoundInt(static_cast(int_best_sum_right_hessian) * cnt_factor); + // update split information + output->threshold = best_threshold; + output->left_output = + CalculateSplittedLeafOutput( + best_sum_left_gradient, best_sum_left_hessian, + meta_->config->lambda_l1, meta_->config->lambda_l2, + meta_->config->max_delta_step, best_left_constraints, meta_->config->path_smooth, + best_left_count, parent_output); + output->left_count = best_left_count; + output->left_sum_gradient = best_sum_left_gradient; + output->left_sum_hessian = best_sum_left_hessian; + output->left_sum_gradient_and_hessian = best_sum_left_gradient_and_hessian_int64; + output->right_output = + CalculateSplittedLeafOutput( + best_sum_right_gradient, + best_sum_right_hessian, meta_->config->lambda_l1, + meta_->config->lambda_l2, meta_->config->max_delta_step, + best_right_constraints, meta_->config->path_smooth, best_right_count, + parent_output); + output->right_count = best_right_count; + output->right_sum_gradient = best_sum_right_gradient; + output->right_sum_hessian = best_sum_right_hessian; + output->right_sum_gradient_and_hessian = best_sum_right_gradient_and_hessian; + output->gain = best_gain - min_gain_shift; + output->default_left = REVERSE; + } + } + + const FeatureMetainfo* meta_; + /*! \brief sum of gradient of each bin */ + hist_t* data_; + int16_t* data_int16_; + bool is_splittable_ = true; + + std::function + find_best_threshold_fun_; + + std::function + int_find_best_threshold_fun_; +}; + +class HistogramPool { + public: + /*! + * \brief Constructor + */ + HistogramPool() { + cache_size_ = 0; + total_size_ = 0; + } + + /*! + * \brief Destructor + */ + ~HistogramPool() {} + + /*! + * \brief Reset pool size + * \param cache_size Max cache size + * \param total_size Total size will be used + */ + void Reset(int cache_size, int total_size) { + cache_size_ = cache_size; + // at least need 2 bucket to store smaller leaf and larger leaf + CHECK_GE(cache_size_, 2); + total_size_ = total_size; + if (cache_size_ > total_size_) { + cache_size_ = total_size_; + } + is_enough_ = (cache_size_ == total_size_); + if (!is_enough_) { + mapper_.resize(total_size_); + inverse_mapper_.resize(cache_size_); + last_used_time_.resize(cache_size_); + ResetMap(); + } + } + + /*! + * \brief Reset mapper + */ + void ResetMap() { + if (!is_enough_) { + cur_time_ = 0; + std::fill(mapper_.begin(), mapper_.end(), -1); + std::fill(inverse_mapper_.begin(), inverse_mapper_.end(), -1); + std::fill(last_used_time_.begin(), last_used_time_.end(), 0); + } + } + template + static void SetFeatureInfo(const Dataset* train_data, const Config* config, + std::vector* feature_meta) { + auto& ref_feature_meta = *feature_meta; + const int num_feature = train_data->num_features(); + ref_feature_meta.resize(num_feature); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_feature >= 1024) + for (int i = 0; i < num_feature; ++i) { + if (USE_DATA) { + ref_feature_meta[i].num_bin = train_data->FeatureNumBin(i); + ref_feature_meta[i].default_bin = + train_data->FeatureBinMapper(i)->GetDefaultBin(); + ref_feature_meta[i].missing_type = + train_data->FeatureBinMapper(i)->missing_type(); + if (train_data->FeatureBinMapper(i)->GetMostFreqBin() == 0) { + ref_feature_meta[i].offset = 1; + } else { + ref_feature_meta[i].offset = 0; + } + ref_feature_meta[i].bin_type = + train_data->FeatureBinMapper(i)->bin_type(); + } + if (USE_CONFIG) { + const int real_fidx = train_data->RealFeatureIndex(i); + if (!config->monotone_constraints.empty()) { + ref_feature_meta[i].monotone_type = + config->monotone_constraints[real_fidx]; + } else { + ref_feature_meta[i].monotone_type = 0; + } + if (!config->feature_contri.empty()) { + ref_feature_meta[i].penalty = config->feature_contri[real_fidx]; + } else { + ref_feature_meta[i].penalty = 1.0; + } + ref_feature_meta[i].rand = Random(config->extra_seed + i); + } + ref_feature_meta[i].config = config; + } + } + + void DynamicChangeSize(const Dataset* train_data, int num_total_bin, + const std::vector& offsets, const Config* config, + int cache_size, int total_size) { + if (feature_metas_.empty()) { + SetFeatureInfo(train_data, config, &feature_metas_); + uint64_t bin_cnt_over_features = 0; + for (int i = 0; i < train_data->num_features(); ++i) { + bin_cnt_over_features += + static_cast(feature_metas_[i].num_bin); + } + Log::Info("Total Bins %d", bin_cnt_over_features); + } + int old_cache_size = static_cast(pool_.size()); + Reset(cache_size, total_size); + + if (cache_size > old_cache_size) { + pool_.resize(cache_size); + data_.resize(cache_size); + } + + if (config->use_quantized_grad) { + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = old_cache_size; i < cache_size; ++i) { + OMP_LOOP_EX_BEGIN(); + pool_[i].reset(new FeatureHistogram[train_data->num_features()]); + data_[i].resize(num_total_bin); + for (int j = 0; j < train_data->num_features(); ++j) { + int16_t* data_ptr = reinterpret_cast(data_[i].data()); + pool_[i][j].Init(data_[i].data() + offsets[j], data_ptr + 2 * offsets[j], &feature_metas_[j]); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } else { + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = old_cache_size; i < cache_size; ++i) { + OMP_LOOP_EX_BEGIN(); + pool_[i].reset(new FeatureHistogram[train_data->num_features()]); + data_[i].resize(num_total_bin * 2); + for (int j = 0; j < train_data->num_features(); ++j) { + pool_[i][j].Init(data_[i].data() + offsets[j] * 2, &feature_metas_[j]); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + } + + void ResetConfig(const Dataset* train_data, const Config* config) { + CHECK_GT(train_data->num_features(), 0); + const Config* old_config = feature_metas_[0].config; + SetFeatureInfo(train_data, config, &feature_metas_); + // if need to reset the function pointers + if (old_config->lambda_l1 != config->lambda_l1 || + old_config->monotone_constraints != config->monotone_constraints || + old_config->extra_trees != config->extra_trees || + old_config->max_delta_step != config->max_delta_step || + old_config->path_smooth != config->path_smooth) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < cache_size_; ++i) { + for (int j = 0; j < train_data->num_features(); ++j) { + pool_[i][j].ResetFunc(); + } + } + } + } + + /*! + * \brief Get data for the specific index + * \param idx which index want to get + * \param out output data will store into this + * \return True if this index is in the pool, False if this index is not in + * the pool + */ + bool Get(int idx, FeatureHistogram** out) { + if (is_enough_) { + *out = pool_[idx].get(); + return true; + } else if (mapper_[idx] >= 0) { + int slot = mapper_[idx]; + *out = pool_[slot].get(); + last_used_time_[slot] = ++cur_time_; + return true; + } else { + // choose the least used slot + int slot = static_cast(ArrayArgs::ArgMin(last_used_time_)); + *out = pool_[slot].get(); + last_used_time_[slot] = ++cur_time_; + + // reset previous mapper + if (inverse_mapper_[slot] >= 0) mapper_[inverse_mapper_[slot]] = -1; + + // update current mapper + mapper_[idx] = slot; + inverse_mapper_[slot] = idx; + return false; + } + } + + /*! + * \brief Move data from one index to another index + * \param src_idx + * \param dst_idx + */ + void Move(int src_idx, int dst_idx) { + if (is_enough_) { + std::swap(pool_[src_idx], pool_[dst_idx]); + return; + } + if (mapper_[src_idx] < 0) { + return; + } + // get slot of src idx + int slot = mapper_[src_idx]; + // reset src_idx + mapper_[src_idx] = -1; + + // move to dst idx + mapper_[dst_idx] = slot; + last_used_time_[slot] = ++cur_time_; + inverse_mapper_[slot] = dst_idx; + } + + private: + std::vector> pool_; + std::vector< + std::vector>> + data_; + std::vector feature_metas_; + int cache_size_; + int total_size_; + bool is_enough_ = false; + std::vector mapper_; + std::vector inverse_mapper_; + std::vector last_used_time_; + int cur_time_ = 0; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_FEATURE_HISTOGRAM_HPP_ diff --git a/src/treelearner/feature_parallel_tree_learner.cpp b/src/treelearner/feature_parallel_tree_learner.cpp new file mode 100644 index 0000000..1e286ad --- /dev/null +++ b/src/treelearner/feature_parallel_tree_learner.cpp @@ -0,0 +1,84 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include +#include + +#include "parallel_tree_learner.h" + +namespace LightGBM { + + +template +FeatureParallelTreeLearner::FeatureParallelTreeLearner(const Config* config):TREELEARNER_T(config) { +} + +template +FeatureParallelTreeLearner::~FeatureParallelTreeLearner() { +} + +template +void FeatureParallelTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian) { + TREELEARNER_T::Init(train_data, is_constant_hessian); + rank_ = Network::rank(); + num_machines_ = Network::num_machines(); + + auto max_cat_threshold = this->config_->max_cat_threshold; + // need to be able to hold smaller and larger best splits in SyncUpGlobalBestSplit + int split_info_size = SplitInfo::Size(max_cat_threshold) * 2; + + input_buffer_.resize(split_info_size); + output_buffer_.resize(split_info_size); +} + + +template +void FeatureParallelTreeLearner::BeforeTrain() { + TREELEARNER_T::BeforeTrain(); + // get feature partition + std::vector> feature_distribution(num_machines_, std::vector()); + std::vector num_bins_distributed(num_machines_, 0); + for (int i = 0; i < this->train_data_->num_total_features(); ++i) { + int inner_feature_index = this->train_data_->InnerFeatureIndex(i); + if (inner_feature_index == -1) { + continue; + } + if (this->col_sampler_.is_feature_used_bytree()[inner_feature_index]) { + int cur_min_machine = static_cast(ArrayArgs::ArgMin(num_bins_distributed)); + feature_distribution[cur_min_machine].push_back(inner_feature_index); + num_bins_distributed[cur_min_machine] += this->train_data_->FeatureNumBin(inner_feature_index); + this->col_sampler_.SetIsFeatureUsedByTree(inner_feature_index, false); + } + } + // get local used features + for (auto fid : feature_distribution[rank_]) { + this->col_sampler_.SetIsFeatureUsedByTree(fid, true); + } +} + +template +void FeatureParallelTreeLearner::FindBestSplitsFromHistograms( + const std::vector& is_feature_used, bool use_subtract, const Tree* tree) { + TREELEARNER_T::FindBestSplitsFromHistograms(is_feature_used, use_subtract, tree); + SplitInfo smaller_best_split, larger_best_split; + // get best split at smaller leaf + smaller_best_split = this->best_split_per_leaf_[this->smaller_leaf_splits_->leaf_index()]; + // find local best split for larger leaf + if (this->larger_leaf_splits_->leaf_index() >= 0) { + larger_best_split = this->best_split_per_leaf_[this->larger_leaf_splits_->leaf_index()]; + } + // sync global best info + SyncUpGlobalBestSplit(input_buffer_.data(), input_buffer_.data(), &smaller_best_split, &larger_best_split, this->config_->max_cat_threshold); + // update best split + this->best_split_per_leaf_[this->smaller_leaf_splits_->leaf_index()] = smaller_best_split; + if (this->larger_leaf_splits_->leaf_index() >= 0) { + this->best_split_per_leaf_[this->larger_leaf_splits_->leaf_index()] = larger_best_split; + } +} + +// instantiate template classes, otherwise linker cannot find the code +template class FeatureParallelTreeLearner; +template class FeatureParallelTreeLearner; +} // namespace LightGBM diff --git a/src/treelearner/gpu_tree_learner.cpp b/src/treelearner/gpu_tree_learner.cpp new file mode 100644 index 0000000..60e73c2 --- /dev/null +++ b/src/treelearner/gpu_tree_learner.cpp @@ -0,0 +1,1132 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifdef USE_GPU + +#include "gpu_tree_learner.h" + +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "../io/dense_bin.hpp" + +#define GPU_DEBUG 0 + +namespace LightGBM { + +GPUTreeLearner::GPUTreeLearner(const Config* config) + :SerialTreeLearner(config) { + use_bagging_ = false; + Log::Info("This is the GPU trainer!!"); +} + +GPUTreeLearner::~GPUTreeLearner() { + if (ptr_pinned_gradients_) { + queue_.enqueue_unmap_buffer(pinned_gradients_, ptr_pinned_gradients_); + } + if (ptr_pinned_hessians_) { + queue_.enqueue_unmap_buffer(pinned_hessians_, ptr_pinned_hessians_); + } + if (ptr_pinned_feature_masks_) { + queue_.enqueue_unmap_buffer(pinned_feature_masks_, ptr_pinned_feature_masks_); + } +} + +void GPUTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian) { + // initialize SerialTreeLearner + SerialTreeLearner::Init(train_data, is_constant_hessian); + // some additional variables needed for GPU trainer + num_feature_groups_ = train_data_->num_feature_groups(); + // Initialize GPU buffers and kernels + InitGPU(config_->gpu_platform_id, config_->gpu_device_id); +} + +// some functions used for debugging the GPU histogram construction +#if GPU_DEBUG > 0 + +void PrintHistograms(hist_t* h, size_t size) { + double total_hess = 0; + for (size_t i = 0; i < size; ++i) { + printf("%03lu=%9.3g,%9.3g\t", i, GET_GRAD(h, i), GET_HESS(h, i)); + if ((i & 3) == 3) + printf("\n"); + total_hess += GET_HESS(h, i); + } + printf("\nSum hessians: %9.3g\n", total_hess); +} + +union Float_t { + int64_t i; + double f; + static int64_t ulp_diff(Float_t a, Float_t b) { + return abs(a.i - b.i); + } +}; + + +void CompareHistograms(hist_t* h1, hist_t* h2, size_t size, int feature_id) { + size_t i; + Float_t a, b; + for (i = 0; i < size; ++i) { + a.f = GET_GRAD(h1, i); + b.f = GET_GRAD(h2, i); + int32_t ulps = Float_t::ulp_diff(a, b); + if (ulps > 0) { + // printf("grad %g != %g (%d ULPs)\n", GET_GRAD(h1, i), GET_GRAD(h2, i), ulps); + // goto err; + } + a.f = GET_HESS(h1, i); + b.f = GET_HESS(h2, i); + ulps = Float_t::ulp_diff(a, b); + if (std::fabs(a.f - b.f) >= 1e-20) { + printf("hessian %g != %g (%d ULPs)\n", GET_HESS(h1, i), GET_HESS(h2, i), ulps); + goto err; + } + } + return; +err: + Log::Warning("Mismatched histograms found for feature %d at location %lu.", feature_id, i); + std::cin.get(); + PrintHistograms(h1, size); + printf("\n"); + PrintHistograms(h2, size); + std::cin.get(); +} +#endif + +int GPUTreeLearner::GetNumWorkgroupsPerFeature(data_size_t leaf_num_data) { + // we roughly want 256 workgroups per device, and we have num_dense_feature4_ feature tuples. + // also guarantee that there are at least 2K examples per workgroup + double x = 256.0 / num_dense_feature4_; + int exp_workgroups_per_feature = static_cast(ceil(log2(x))); + double t = leaf_num_data / 1024.0; + #if GPU_DEBUG >= 4 + printf("Computing histogram for %d examples and (%d * %d) feature groups\n", leaf_num_data, dword_features_, num_dense_feature4_); + printf("We can have at most %d workgroups per feature4 for efficiency reasons.\n" + "Best workgroup size per feature for full utilization is %d\n", static_cast(ceil(t)), (1 << exp_workgroups_per_feature)); + #endif + exp_workgroups_per_feature = std::min(exp_workgroups_per_feature, static_cast(ceil(log(static_cast(t))/log(2.0)))); + if (exp_workgroups_per_feature < 0) + exp_workgroups_per_feature = 0; + if (exp_workgroups_per_feature > kMaxLogWorkgroupsPerFeature) + exp_workgroups_per_feature = kMaxLogWorkgroupsPerFeature; + // return 0; + return exp_workgroups_per_feature; +} + +void GPUTreeLearner::GPUHistogram(data_size_t leaf_num_data, bool use_all_features) { + // we have already copied ordered gradients, ordered Hessians and indices to GPU + // decide the best number of workgroups working on one feature4 tuple + // set work group size based on feature size + // each 2^exp_workgroups_per_feature workgroups work on a feature4 tuple + int exp_workgroups_per_feature = GetNumWorkgroupsPerFeature(leaf_num_data); + int num_workgroups = (1 << exp_workgroups_per_feature) * num_dense_feature4_; + if (num_workgroups > preallocd_max_num_wg_) { + preallocd_max_num_wg_ = num_workgroups; + Log::Info("Increasing preallocd_max_num_wg_ to %d for launching more workgroups", preallocd_max_num_wg_); + device_subhistograms_.reset(new boost::compute::vector( + preallocd_max_num_wg_ * dword_features_ * device_bin_size_ * hist_bin_entry_sz_, ctx_)); + // we need to refresh the kernel arguments after reallocating + for (int i = 0; i <= kMaxLogWorkgroupsPerFeature; ++i) { + // The only argument that needs to be changed later is num_data_ + histogram_kernels_[i].set_arg(7, *device_subhistograms_); + histogram_allfeats_kernels_[i].set_arg(7, *device_subhistograms_); + histogram_fulldata_kernels_[i].set_arg(7, *device_subhistograms_); + } + } + #if GPU_DEBUG >= 4 + printf("Setting exp_workgroups_per_feature to %d, using %u work groups\n", exp_workgroups_per_feature, num_workgroups); + printf("Constructing histogram with %d examples\n", leaf_num_data); + #endif + + // the GPU kernel will process all features in one call, and each + // 2^exp_workgroups_per_feature (compile time constant) workgroup will + // process one feature4 tuple + + if (use_all_features) { + histogram_allfeats_kernels_[exp_workgroups_per_feature].set_arg(4, leaf_num_data); + } else { + histogram_kernels_[exp_workgroups_per_feature].set_arg(4, leaf_num_data); + } + // for the root node, indices are not copied + if (leaf_num_data != num_data_) { + indices_future_.wait(); + } + // for constant hessian, hessians are not copied except for the root node + if (!share_state_->is_constant_hessian) { + hessians_future_.wait(); + } + gradients_future_.wait(); + // there will be 2^exp_workgroups_per_feature = num_workgroups / num_dense_feature4 sub-histogram per feature4 + // and we will launch num_feature workgroups for this kernel + // will launch threads for all features + // the queue should be asynchronous, and we will can WaitAndGetHistograms() before we start processing dense feature groups + if (leaf_num_data == num_data_) { + kernel_wait_obj_ = boost::compute::wait_list( + queue_.enqueue_1d_range_kernel(histogram_fulldata_kernels_[exp_workgroups_per_feature], 0, num_workgroups * 256, 256)); + } else { + if (use_all_features) { + kernel_wait_obj_ = boost::compute::wait_list( + queue_.enqueue_1d_range_kernel(histogram_allfeats_kernels_[exp_workgroups_per_feature], 0, num_workgroups * 256, 256)); + } else { + kernel_wait_obj_ = boost::compute::wait_list( + queue_.enqueue_1d_range_kernel(histogram_kernels_[exp_workgroups_per_feature], 0, num_workgroups * 256, 256)); + } + } + // copy the results asynchronously. Size depends on if double precision is used + size_t output_size = num_dense_feature4_ * dword_features_ * device_bin_size_ * hist_bin_entry_sz_; + boost::compute::event histogram_wait_event; + host_histogram_outputs_ = reinterpret_cast(queue_.enqueue_map_buffer_async( + device_histogram_outputs_, boost::compute::command_queue::map_read, 0, output_size, histogram_wait_event, kernel_wait_obj_)); + // we will wait for this object in WaitAndGetHistograms + histograms_wait_obj_ = boost::compute::wait_list(histogram_wait_event); +} + +template +void GPUTreeLearner::WaitAndGetHistograms(hist_t* histograms) { + HistType* hist_outputs = reinterpret_cast(host_histogram_outputs_); + // when the output is ready, the computation is done + histograms_wait_obj_.wait(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < num_dense_feature_groups_; ++i) { + if (!feature_masks_[i]) { + continue; + } + int dense_group_index = dense_feature_group_map_[i]; + auto old_histogram_array = histograms + train_data_->GroupBinBoundary(dense_group_index) * 2; + int bin_size = train_data_->FeatureGroupNumBin(dense_group_index); + if (device_bin_mults_[i] == 1) { + for (int j = 0; j < bin_size; ++j) { + GET_GRAD(old_histogram_array, j) = GET_GRAD(hist_outputs, i * device_bin_size_+ j); + GET_HESS(old_histogram_array, j) = GET_HESS(hist_outputs, i * device_bin_size_+ j); + } + } else { + // values of this feature has been redistributed to multiple bins; need a reduction here + int ind = 0; + for (int j = 0; j < bin_size; ++j) { + double sum_g = 0.0, sum_h = 0.0; + for (int k = 0; k < device_bin_mults_[i]; ++k) { + sum_g += GET_GRAD(hist_outputs, i * device_bin_size_+ ind); + sum_h += GET_HESS(hist_outputs, i * device_bin_size_+ ind); + ind++; + } + GET_GRAD(old_histogram_array, j) = sum_g; + GET_HESS(old_histogram_array, j) = sum_h; + } + } + } + queue_.enqueue_unmap_buffer(device_histogram_outputs_, host_histogram_outputs_); +} + +void GPUTreeLearner::AllocateGPUMemory() { + num_dense_feature_groups_ = 0; + for (int i = 0; i < num_feature_groups_; ++i) { + if (!train_data_->IsMultiGroup(i)) { + num_dense_feature_groups_++; + } + } + // how many feature-group tuples we have + num_dense_feature4_ = (num_dense_feature_groups_ + (dword_features_ - 1)) / dword_features_; + // leave some safe margin for prefetching + // 256 work-items per workgroup. Each work-item prefetches one tuple for that feature + int allocated_num_data_ = num_data_ + 256 * (1 << kMaxLogWorkgroupsPerFeature); + // clear sparse/dense maps + dense_feature_group_map_.clear(); + device_bin_mults_.clear(); + sparse_feature_group_map_.clear(); + // do nothing if no features can be processed on GPU + if (!num_dense_feature_groups_) { + Log::Warning("GPU acceleration is disabled because no non-trivial dense features can be found"); + return; + } + // allocate memory for all features (FIXME: 4 GB barrier on some devices, need to split to multiple buffers) + device_features_.reset(); + device_features_ = std::unique_ptr>(new boost::compute::vector(static_cast(num_dense_feature4_) * num_data_, ctx_)); + // unpin old buffer if necessary before destructing them + if (ptr_pinned_gradients_) { + queue_.enqueue_unmap_buffer(pinned_gradients_, ptr_pinned_gradients_); + } + if (ptr_pinned_hessians_) { + queue_.enqueue_unmap_buffer(pinned_hessians_, ptr_pinned_hessians_); + } + if (ptr_pinned_feature_masks_) { + queue_.enqueue_unmap_buffer(pinned_feature_masks_, ptr_pinned_feature_masks_); + } + // make ordered_gradients and Hessians larger (including extra room for prefetching), and pin them + ordered_gradients_.reserve(allocated_num_data_); + ordered_hessians_.reserve(allocated_num_data_); + pinned_gradients_ = boost::compute::buffer(); // deallocate + pinned_gradients_ = boost::compute::buffer(ctx_, allocated_num_data_ * sizeof(score_t), + boost::compute::memory_object::read_write | boost::compute::memory_object::use_host_ptr, + ordered_gradients_.data()); + ptr_pinned_gradients_ = queue_.enqueue_map_buffer(pinned_gradients_, boost::compute::command_queue::map_write_invalidate_region, + 0, allocated_num_data_ * sizeof(score_t)); + pinned_hessians_ = boost::compute::buffer(); // deallocate + pinned_hessians_ = boost::compute::buffer(ctx_, allocated_num_data_ * sizeof(score_t), + boost::compute::memory_object::read_write | boost::compute::memory_object::use_host_ptr, + ordered_hessians_.data()); + ptr_pinned_hessians_ = queue_.enqueue_map_buffer(pinned_hessians_, boost::compute::command_queue::map_write_invalidate_region, + 0, allocated_num_data_ * sizeof(score_t)); + // allocate space for gradients and Hessians on device + // we will copy gradients and Hessians in after ordered_gradients_ and ordered_hessians_ are constructed + device_gradients_ = boost::compute::buffer(); // deallocate + device_gradients_ = boost::compute::buffer(ctx_, allocated_num_data_ * sizeof(score_t), + boost::compute::memory_object::read_only, nullptr); + device_hessians_ = boost::compute::buffer(); // deallocate + device_hessians_ = boost::compute::buffer(ctx_, allocated_num_data_ * sizeof(score_t), + boost::compute::memory_object::read_only, nullptr); + // allocate feature mask, for disabling some feature-groups' histogram calculation + feature_masks_.resize(num_dense_feature4_ * dword_features_); + device_feature_masks_ = boost::compute::buffer(); // deallocate + device_feature_masks_ = boost::compute::buffer(ctx_, num_dense_feature4_ * dword_features_, + boost::compute::memory_object::read_only, nullptr); + pinned_feature_masks_ = boost::compute::buffer(ctx_, num_dense_feature4_ * dword_features_, + boost::compute::memory_object::read_write | boost::compute::memory_object::use_host_ptr, + feature_masks_.data()); + ptr_pinned_feature_masks_ = queue_.enqueue_map_buffer(pinned_feature_masks_, boost::compute::command_queue::map_write_invalidate_region, + 0, num_dense_feature4_ * dword_features_); + memset(ptr_pinned_feature_masks_, 0, num_dense_feature4_ * dword_features_); + // copy indices to the device + device_data_indices_.reset(); + device_data_indices_ = std::unique_ptr>(new boost::compute::vector(allocated_num_data_, ctx_)); + boost::compute::fill(device_data_indices_->begin(), device_data_indices_->end(), 0, queue_); + // histogram bin entry size depends on the precision (single/double) + hist_bin_entry_sz_ = config_->gpu_use_dp ? sizeof(hist_t) * 2 : sizeof(gpu_hist_t) * 2; + Log::Info("Size of histogram bin entry: %d", hist_bin_entry_sz_); + // create output buffer, each feature has a histogram with device_bin_size_ bins, + // each work group generates a sub-histogram of dword_features_ features. + if (!device_subhistograms_) { + // only initialize once here, as this will not need to change when ResetTrainingData() is called + device_subhistograms_ = std::unique_ptr>(new boost::compute::vector( + preallocd_max_num_wg_ * dword_features_ * device_bin_size_ * hist_bin_entry_sz_, ctx_)); + } + // create atomic counters for inter-group coordination + sync_counters_.reset(); + sync_counters_ = std::unique_ptr>(new boost::compute::vector( + num_dense_feature4_, ctx_)); + boost::compute::fill(sync_counters_->begin(), sync_counters_->end(), 0, queue_); + // The output buffer is allocated to host directly, to overlap compute and data transfer + device_histogram_outputs_ = boost::compute::buffer(); // deallocate + device_histogram_outputs_ = boost::compute::buffer(ctx_, num_dense_feature4_ * dword_features_ * device_bin_size_ * hist_bin_entry_sz_, + boost::compute::memory_object::write_only | boost::compute::memory_object::alloc_host_ptr, nullptr); + // find the dense feature-groups and group then into Feature4 data structure (several feature-groups packed into 4 bytes) + int k = 0, copied_feature4 = 0; + std::vector dense_dword_ind(dword_features_); + for (int i = 0; i < num_feature_groups_; ++i) { + // looking for dword_features_ non-sparse feature-groups + if (!train_data_->IsMultiGroup(i)) { + dense_dword_ind[k] = i; + // decide if we need to redistribute the bin + double t = device_bin_size_ / static_cast(train_data_->FeatureGroupNumBin(i)); + // multiplier must be a power of 2 + device_bin_mults_.push_back(static_cast(round(pow(2, floor(log2(t)))))); + // device_bin_mults_.push_back(1); + #if GPU_DEBUG >= 1 + printf("feature-group %d using multiplier %d\n", i, device_bin_mults_.back()); + #endif + k++; + } else { + sparse_feature_group_map_.push_back(i); + } + // found + if (k == dword_features_) { + k = 0; + for (int j = 0; j < dword_features_; ++j) { + dense_feature_group_map_.push_back(dense_dword_ind[j]); + } + copied_feature4++; + } + } + // for data transfer time + auto start_time = std::chrono::steady_clock::now(); + // Now generate new data structure feature4, and copy data to the device + int nthreads = std::min(OMP_NUM_THREADS(), static_cast(dense_feature_group_map_.size()) / dword_features_); + nthreads = std::max(nthreads, 1); + std::vector host4_vecs(nthreads); + std::vector host4_bufs(nthreads); + std::vector host4_ptrs(nthreads); + // preallocate arrays for all threads, and pin them + for (int i = 0; i < nthreads; ++i) { + host4_vecs[i] = reinterpret_cast(boost::alignment::aligned_alloc(4096, num_data_ * sizeof(Feature4))); + host4_bufs[i] = boost::compute::buffer(ctx_, num_data_ * sizeof(Feature4), + boost::compute::memory_object::read_write | boost::compute::memory_object::use_host_ptr, + host4_vecs[i]); + host4_ptrs[i] = reinterpret_cast(queue_.enqueue_map_buffer(host4_bufs[i], boost::compute::command_queue::map_write_invalidate_region, + 0, num_data_ * sizeof(Feature4))); + } + // building Feature4 bundles; each thread handles dword_features_ features + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < static_cast(dense_feature_group_map_.size() / dword_features_); ++i) { + int tid = omp_get_thread_num(); + Feature4* host4 = host4_ptrs[tid]; + auto dense_ind = dense_feature_group_map_.begin() + i * dword_features_; + auto dev_bin_mult = device_bin_mults_.begin() + i * dword_features_; + #if GPU_DEBUG >= 1 + printf("Copying feature group "); + for (int l = 0; l < dword_features_; ++l) { + printf("%d ", dense_ind[l]); + } + printf("to devices\n"); + #endif + if (dword_features_ == 8) { + // one feature datapoint is 4 bits + BinIterator* bin_iters[8]; + for (int s_idx = 0; s_idx < 8; ++s_idx) { + bin_iters[s_idx] = train_data_->FeatureGroupIterator(dense_ind[s_idx]); + if (dynamic_cast*>(bin_iters[s_idx]) == 0) { + Log::Fatal("GPU tree learner assumes that all bins are Dense4bitsBin when num_bin <= 16, but feature %d is not", dense_ind[s_idx]); + } + } + // this guarantees that the RawGet() function is inlined, rather than using virtual function dispatching + DenseBinIterator iters[8] = { + *static_cast*>(bin_iters[0]), + *static_cast*>(bin_iters[1]), + *static_cast*>(bin_iters[2]), + *static_cast*>(bin_iters[3]), + *static_cast*>(bin_iters[4]), + *static_cast*>(bin_iters[5]), + *static_cast*>(bin_iters[6]), + *static_cast*>(bin_iters[7])}; + for (int j = 0; j < num_data_; ++j) { + host4[j].s[0] = (uint8_t)((iters[0].RawGet(j) * dev_bin_mult[0] + ((j+0) & (dev_bin_mult[0] - 1))) + |((iters[1].RawGet(j) * dev_bin_mult[1] + ((j+1) & (dev_bin_mult[1] - 1))) << 4)); + host4[j].s[1] = (uint8_t)((iters[2].RawGet(j) * dev_bin_mult[2] + ((j+2) & (dev_bin_mult[2] - 1))) + |((iters[3].RawGet(j) * dev_bin_mult[3] + ((j+3) & (dev_bin_mult[3] - 1))) << 4)); + host4[j].s[2] = (uint8_t)((iters[4].RawGet(j) * dev_bin_mult[4] + ((j+4) & (dev_bin_mult[4] - 1))) + |((iters[5].RawGet(j) * dev_bin_mult[5] + ((j+5) & (dev_bin_mult[5] - 1))) << 4)); + host4[j].s[3] = (uint8_t)((iters[6].RawGet(j) * dev_bin_mult[6] + ((j+6) & (dev_bin_mult[6] - 1))) + |((iters[7].RawGet(j) * dev_bin_mult[7] + ((j+7) & (dev_bin_mult[7] - 1))) << 4)); + } + } else if (dword_features_ == 4) { + // one feature datapoint is one byte + for (int s_idx = 0; s_idx < 4; ++s_idx) { + BinIterator* bin_iter = train_data_->FeatureGroupIterator(dense_ind[s_idx]); + // this guarantees that the RawGet() function is inlined, rather than using virtual function dispatching + if (dynamic_cast*>(bin_iter) != 0) { + // Dense bin + DenseBinIterator iter = *static_cast*>(bin_iter); + for (int j = 0; j < num_data_; ++j) { + host4[j].s[s_idx] = (uint8_t)(iter.RawGet(j) * dev_bin_mult[s_idx] + ((j+s_idx) & (dev_bin_mult[s_idx] - 1))); + } + } else if (dynamic_cast*>(bin_iter) != 0) { + // Dense 4-bit bin + DenseBinIterator iter = *static_cast*>(bin_iter); + for (int j = 0; j < num_data_; ++j) { + host4[j].s[s_idx] = (uint8_t)(iter.RawGet(j) * dev_bin_mult[s_idx] + ((j+s_idx) & (dev_bin_mult[s_idx] - 1))); + } + } else { + Log::Fatal("Bug in GPU tree builder: only DenseBin and Dense4bitsBin are supported"); + } + } + } else { + Log::Fatal("Bug in GPU tree builder: dword_features_ can only be 4 or 8"); + } + #pragma omp critical + queue_.enqueue_write_buffer(device_features_->get_buffer(), + static_cast(i) * num_data_ * sizeof(Feature4), num_data_ * sizeof(Feature4), host4); + #if GPU_DEBUG >= 1 + printf("first example of feature-group tuple is: %d %d %d %d\n", host4[0].s[0], host4[0].s[1], host4[0].s[2], host4[0].s[3]); + printf("Feature-groups copied to device with multipliers "); + for (int l = 0; l < dword_features_; ++l) { + printf("%d ", dev_bin_mult[l]); + } + printf("\n"); + #endif + } + // working on the remaining (less than dword_features_) feature groups + if (k != 0) { + Feature4* host4 = host4_ptrs[0]; + if (dword_features_ == 8) { + memset(host4, 0, num_data_ * sizeof(Feature4)); + } + #if GPU_DEBUG >= 1 + printf("%d features left\n", k); + #endif + for (int i = 0; i < k; ++i) { + if (dword_features_ == 8) { + BinIterator* bin_iter = train_data_->FeatureGroupIterator(dense_dword_ind[i]); + if (dynamic_cast*>(bin_iter) != 0) { + DenseBinIterator iter = *static_cast*>(bin_iter); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int j = 0; j < num_data_; ++j) { + host4[j].s[i >> 1] |= (uint8_t)((iter.RawGet(j) * device_bin_mults_[copied_feature4 * dword_features_ + i] + + ((j+i) & (device_bin_mults_[copied_feature4 * dword_features_ + i] - 1))) + << ((i & 1) << 2)); + } + } else { + Log::Fatal("GPU tree learner assumes that all bins are Dense4bitsBin when num_bin <= 16, but feature %d is not", dense_dword_ind[i]); + } + } else if (dword_features_ == 4) { + BinIterator* bin_iter = train_data_->FeatureGroupIterator(dense_dword_ind[i]); + if (dynamic_cast*>(bin_iter) != 0) { + DenseBinIterator iter = *static_cast*>(bin_iter); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int j = 0; j < num_data_; ++j) { + host4[j].s[i] = (uint8_t)(iter.RawGet(j) * device_bin_mults_[copied_feature4 * dword_features_ + i] + + ((j+i) & (device_bin_mults_[copied_feature4 * dword_features_ + i] - 1))); + } + } else if (dynamic_cast*>(bin_iter) != 0) { + DenseBinIterator iter = *static_cast*>(bin_iter); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int j = 0; j < num_data_; ++j) { + host4[j].s[i] = (uint8_t)(iter.RawGet(j) * device_bin_mults_[copied_feature4 * dword_features_ + i] + + ((j+i) & (device_bin_mults_[copied_feature4 * dword_features_ + i] - 1))); + } + } else { + Log::Fatal("BUG in GPU tree builder: only DenseBin and Dense4bitsBin are supported"); + } + } else { + Log::Fatal("Bug in GPU tree builder: dword_features_ can only be 4 or 8"); + } + } + // fill the leftover features + if (dword_features_ == 8) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int j = 0; j < num_data_; ++j) { + for (int i = k; i < dword_features_; ++i) { + // fill this empty feature with some "random" value + host4[j].s[i >> 1] |= (uint8_t)((j & 0xf) << ((i & 1) << 2)); + } + } + } else if (dword_features_ == 4) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int j = 0; j < num_data_; ++j) { + for (int i = k; i < dword_features_; ++i) { + // fill this empty feature with some "random" value + host4[j].s[i] = (uint8_t)j; + } + } + } + // copying the last 1 to (dword_features - 1) feature-groups in the last tuple + queue_.enqueue_write_buffer(device_features_->get_buffer(), + (num_dense_feature4_ - 1) * static_cast(num_data_) * sizeof(Feature4), num_data_ * sizeof(Feature4), host4); + #if GPU_DEBUG >= 1 + printf("Last features copied to device\n"); + #endif + for (int i = 0; i < k; ++i) { + dense_feature_group_map_.push_back(dense_dword_ind[i]); + } + } + // deallocate pinned space for feature copying + for (int i = 0; i < nthreads; ++i) { + queue_.enqueue_unmap_buffer(host4_bufs[i], host4_ptrs[i]); + host4_bufs[i] = boost::compute::buffer(); + boost::alignment::aligned_free(host4_vecs[i]); + } + // data transfer time + std::chrono::duration end_time = std::chrono::steady_clock::now() - start_time; + Log::Info("%d dense feature groups (%.2f MB) transferred to GPU in %f secs. %d sparse feature groups", + dense_feature_group_map_.size(), ((dense_feature_group_map_.size() + (dword_features_ - 1)) / dword_features_) * num_data_ * sizeof(Feature4) / (1024.0 * 1024.0), + end_time * 1e-3, sparse_feature_group_map_.size()); + #if GPU_DEBUG >= 1 + printf("Dense feature group list (size %lu): ", dense_feature_group_map_.size()); + for (int i = 0; i < num_dense_feature_groups_; ++i) { + printf("%d ", dense_feature_group_map_[i]); + } + printf("\n"); + printf("Sparse feature group list (size %lu): ", sparse_feature_group_map_.size()); + for (int i = 0; i < num_feature_groups_ - num_dense_feature_groups_; ++i) { + printf("%d ", sparse_feature_group_map_[i]); + } + printf("\n"); + #endif +} + +std::string GPUTreeLearner::GetBuildLog(const std::string &opts) { + boost::compute::program program = boost::compute::program::create_with_source(kernel_source_, ctx_); + try { + program.build(opts); + } + catch (boost::compute::opencl_error &e) { + auto error_code = e.error_code(); + std::string log("No log available.\n"); + // for other types of failure, build log might not be available; program.build_log() can crash + if (error_code == CL_INVALID_PROGRAM || error_code == CL_BUILD_PROGRAM_FAILURE) { + try { + log = program.build_log(); + } + catch(...) { + // Something bad happened. Just return "No log available." + } + } + return log; + } + // build is okay, log may contain warnings + return program.build_log(); +} + +void GPUTreeLearner::BuildGPUKernels() { + Log::Info("Compiling OpenCL Kernel with %d bins...", device_bin_size_); + // destroy any old kernels + histogram_kernels_.clear(); + histogram_allfeats_kernels_.clear(); + histogram_fulldata_kernels_.clear(); + // create OpenCL kernels for different number of workgroups per feature + histogram_kernels_.resize(kMaxLogWorkgroupsPerFeature+1); + histogram_allfeats_kernels_.resize(kMaxLogWorkgroupsPerFeature+1); + histogram_fulldata_kernels_.resize(kMaxLogWorkgroupsPerFeature+1); + // currently we don't use constant memory + int use_constants = 0; + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) + for (int i = 0; i <= kMaxLogWorkgroupsPerFeature; ++i) { + OMP_LOOP_EX_BEGIN(); + boost::compute::program program; + std::ostringstream opts; + // compile the GPU kernel depending if double precision is used, constant hessian is used, etc. + opts << " -D POWER_FEATURE_WORKGROUPS=" << i + << " -D USE_CONSTANT_BUF=" << use_constants << " -D USE_DP_FLOAT=" << int(config_->gpu_use_dp) + << " -D CONST_HESSIAN=" << int(share_state_->is_constant_hessian) + << " -cl-mad-enable -cl-no-signed-zeros -cl-fast-relaxed-math"; + #if GPU_DEBUG >= 1 + std::cout << "Building GPU kernels with options: " << opts.str() << std::endl; + #endif + // kernel with indices in an array + try { + program = boost::compute::program::build_with_source(kernel_source_, ctx_, opts.str()); + } + catch (boost::compute::opencl_error &e) { + #pragma omp critical + { + std::cerr << "Build Options:" << opts.str() << std::endl; + std::cerr << "Build Log:" << std::endl << GetBuildLog(opts.str()) << std::endl; + Log::Fatal("Cannot build GPU program: %s", e.what()); + } + } + histogram_kernels_[i] = program.create_kernel(kernel_name_); + + // kernel with all features enabled, with eliminated branches + opts << " -D ENABLE_ALL_FEATURES=1"; + try { + program = boost::compute::program::build_with_source(kernel_source_, ctx_, opts.str()); + } + catch (boost::compute::opencl_error &e) { + #pragma omp critical + { + std::cerr << "Build Options:" << opts.str() << std::endl; + std::cerr << "Build Log:" << std::endl << GetBuildLog(opts.str()) << std::endl; + Log::Fatal("Cannot build GPU program: %s", e.what()); + } + } + histogram_allfeats_kernels_[i] = program.create_kernel(kernel_name_); + + // kernel with all data indices (for root node, and assumes that root node always uses all features) + opts << " -D IGNORE_INDICES=1"; + try { + program = boost::compute::program::build_with_source(kernel_source_, ctx_, opts.str()); + } + catch (boost::compute::opencl_error &e) { + #pragma omp critical + { + std::cerr << "Build Options:" << opts.str() << std::endl; + std::cerr << "Build Log:" << std::endl << GetBuildLog(opts.str()) << std::endl; + Log::Fatal("Cannot build GPU program: %s", e.what()); + } + } + histogram_fulldata_kernels_[i] = program.create_kernel(kernel_name_); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + Log::Info("GPU programs have been built"); +} + +void GPUTreeLearner::SetupKernelArguments() { + // do nothing if no features can be processed on GPU + if (!num_dense_feature_groups_) { + return; + } + for (int i = 0; i <= kMaxLogWorkgroupsPerFeature; ++i) { + // The only argument that needs to be changed later is num_data_ + if (share_state_->is_constant_hessian) { + // hessian is passed as a parameter, but it is not available now. + // hessian will be set in BeforeTrain() + histogram_kernels_[i].set_args(*device_features_, device_feature_masks_, num_data_, + *device_data_indices_, num_data_, device_gradients_, 0.0f, + *device_subhistograms_, *sync_counters_, device_histogram_outputs_); + histogram_allfeats_kernels_[i].set_args(*device_features_, device_feature_masks_, num_data_, + *device_data_indices_, num_data_, device_gradients_, 0.0f, + *device_subhistograms_, *sync_counters_, device_histogram_outputs_); + histogram_fulldata_kernels_[i].set_args(*device_features_, device_feature_masks_, num_data_, + *device_data_indices_, num_data_, device_gradients_, 0.0f, + *device_subhistograms_, *sync_counters_, device_histogram_outputs_); + } else { + histogram_kernels_[i].set_args(*device_features_, device_feature_masks_, num_data_, + *device_data_indices_, num_data_, device_gradients_, device_hessians_, + *device_subhistograms_, *sync_counters_, device_histogram_outputs_); + histogram_allfeats_kernels_[i].set_args(*device_features_, device_feature_masks_, num_data_, + *device_data_indices_, num_data_, device_gradients_, device_hessians_, + *device_subhistograms_, *sync_counters_, device_histogram_outputs_); + histogram_fulldata_kernels_[i].set_args(*device_features_, device_feature_masks_, num_data_, + *device_data_indices_, num_data_, device_gradients_, device_hessians_, + *device_subhistograms_, *sync_counters_, device_histogram_outputs_); + } + } +} + +void GPUTreeLearner::InitGPU(int platform_id, int device_id) { + // Get the max bin size, used for selecting best GPU kernel + max_num_bin_ = 0; + #if GPU_DEBUG >= 1 + printf("bin size: "); + #endif + for (int i = 0; i < num_feature_groups_; ++i) { + if (train_data_->IsMultiGroup(i)) { + continue; + } + #if GPU_DEBUG >= 1 + printf("%d, ", train_data_->FeatureGroupNumBin(i)); + #endif + max_num_bin_ = std::max(max_num_bin_, train_data_->FeatureGroupNumBin(i)); + } + #if GPU_DEBUG >= 1 + printf("\n"); + #endif + // initialize GPU + dev_ = boost::compute::system::default_device(); + if (platform_id >= 0 && device_id >= 0) { + const std::vector platforms = boost::compute::system::platforms(); + if (static_cast(platforms.size()) > platform_id) { + const std::vector platform_devices = platforms[platform_id].devices(); + if (static_cast(platform_devices.size()) > device_id) { + Log::Info("Using requested OpenCL platform %d device %d", platform_id, device_id); + dev_ = platform_devices[device_id]; + } + } + } + // determine which kernel to use based on the max number of bins + if (max_num_bin_ <= 16) { + // the +9 skips extra characters ")", newline, "#endif" and newline at the beginning + kernel_source_ = kernel16_src_ + 9; + kernel_name_ = "histogram16"; + device_bin_size_ = 16; + dword_features_ = 8; + } else if (max_num_bin_ <= 64) { + // the +9 skips extra characters ")", newline, "#endif" and newline at the beginning + kernel_source_ = kernel64_src_ + 9; + kernel_name_ = "histogram64"; + device_bin_size_ = 64; + dword_features_ = 4; + } else if (max_num_bin_ <= 256) { + // the +9 skips extra characters ")", newline, "#endif" and newline at the beginning + kernel_source_ = kernel256_src_ + 9; + kernel_name_ = "histogram256"; + device_bin_size_ = 256; + dword_features_ = 4; + } else { + Log::Fatal("bin size %d cannot run on GPU", max_num_bin_); + } + + // ignore the feature groups that contain categorical features when producing warnings about max_bin. + // these groups may contain larger number of bins due to categorical features, but not due to the setting of max_bin. + int max_num_bin_no_categorical = 0; + int cur_feature_group = 0; + bool categorical_feature_found = false; + for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) { + const int feature_group = train_data_->Feature2Group(inner_feature_index); + const BinMapper* feature_bin_mapper = train_data_->FeatureBinMapper(inner_feature_index); + if (feature_bin_mapper->bin_type() == BinType::CategoricalBin) { + categorical_feature_found = true; + } + if (feature_group != cur_feature_group || inner_feature_index == num_features_ - 1) { + if (!categorical_feature_found) { + max_num_bin_no_categorical = std::max(max_num_bin_no_categorical, train_data_->FeatureGroupNumBin(cur_feature_group)); + } + categorical_feature_found = false; + cur_feature_group = feature_group; + } + } + if (max_num_bin_no_categorical == 65) { + Log::Warning("Setting max_bin to 63 is suggested for best performance"); + } + if (max_num_bin_no_categorical == 17) { + Log::Warning("Setting max_bin to 15 is suggested for best performance"); + } + ctx_ = boost::compute::context(dev_); + queue_ = boost::compute::command_queue(ctx_, dev_); + Log::Info("Using GPU Device: %s, Vendor: %s", dev_.name().c_str(), dev_.vendor().c_str()); + BuildGPUKernels(); + AllocateGPUMemory(); + // setup GPU kernel arguments after we allocating all the buffers + SetupKernelArguments(); +} + +Tree* GPUTreeLearner::Train(const score_t* gradients, const score_t *hessians, bool is_first_tree) { + return SerialTreeLearner::Train(gradients, hessians, is_first_tree); +} + +void GPUTreeLearner::ResetTrainingDataInner(const Dataset* train_data, bool is_constant_hessian, bool reset_multi_val_bin) { + SerialTreeLearner::ResetTrainingDataInner(train_data, is_constant_hessian, reset_multi_val_bin); + num_feature_groups_ = train_data_->num_feature_groups(); + // GPU memory has to been reallocated because data may have been changed + AllocateGPUMemory(); + // setup GPU kernel arguments after we allocating all the buffers + SetupKernelArguments(); +} + +void GPUTreeLearner::ResetIsConstantHessian(bool is_constant_hessian) { + if (is_constant_hessian != share_state_->is_constant_hessian) { + SerialTreeLearner::ResetIsConstantHessian(is_constant_hessian); + BuildGPUKernels(); + SetupKernelArguments(); + } +} + +void GPUTreeLearner::BeforeTrain() { + #if GPU_DEBUG >= 2 + printf("Copying initial full gradients and hessians to device\n"); + #endif + // Copy initial full hessians and gradients to GPU. + // We start copying as early as possible, instead of at ConstructHistogram(). + if (!use_bagging_ && num_dense_feature_groups_) { + if (!share_state_->is_constant_hessian) { + hessians_future_ = queue_.enqueue_write_buffer_async(device_hessians_, 0, num_data_ * sizeof(score_t), hessians_); + } else { + // setup hessian parameters only + score_t const_hessian = hessians_[0]; + for (int i = 0; i <= kMaxLogWorkgroupsPerFeature; ++i) { + // hessian is passed as a parameter + histogram_kernels_[i].set_arg(6, const_hessian); + histogram_allfeats_kernels_[i].set_arg(6, const_hessian); + histogram_fulldata_kernels_[i].set_arg(6, const_hessian); + } + } + gradients_future_ = queue_.enqueue_write_buffer_async(device_gradients_, 0, num_data_ * sizeof(score_t), gradients_); + } + + SerialTreeLearner::BeforeTrain(); + + // use bagging + if (data_partition_->leaf_count(0) != num_data_ && num_dense_feature_groups_) { + // On GPU, we start copying indices, gradients and Hessians now, instead at ConstructHistogram() + // copy used gradients and Hessians to ordered buffer + const data_size_t* indices = data_partition_->indices(); + data_size_t cnt = data_partition_->leaf_count(0); + #if GPU_DEBUG > 0 + printf("Using bagging, examples count = %d\n", cnt); + #endif + // transfer the indices to GPU + indices_future_ = boost::compute::copy_async(indices, indices + cnt, device_data_indices_->begin(), queue_); + if (!share_state_->is_constant_hessian) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < cnt; ++i) { + ordered_hessians_[i] = hessians_[indices[i]]; + } + // transfer hessian to GPU + hessians_future_ = queue_.enqueue_write_buffer_async(device_hessians_, 0, cnt * sizeof(score_t), ordered_hessians_.data()); + } else { + // setup hessian parameters only + score_t const_hessian = hessians_[indices[0]]; + for (int i = 0; i <= kMaxLogWorkgroupsPerFeature; ++i) { + // hessian is passed as a parameter + histogram_kernels_[i].set_arg(6, const_hessian); + histogram_allfeats_kernels_[i].set_arg(6, const_hessian); + histogram_fulldata_kernels_[i].set_arg(6, const_hessian); + } + } + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < cnt; ++i) { + ordered_gradients_[i] = gradients_[indices[i]]; + } + // transfer gradients to GPU + gradients_future_ = queue_.enqueue_write_buffer_async(device_gradients_, 0, cnt * sizeof(score_t), ordered_gradients_.data()); + } +} + +bool GPUTreeLearner::BeforeFindBestSplit(const Tree* tree, int left_leaf, int right_leaf) { + int smaller_leaf; + data_size_t num_data_in_left_child = GetGlobalDataCountInLeaf(left_leaf); + data_size_t num_data_in_right_child = GetGlobalDataCountInLeaf(right_leaf); + // only have root + if (right_leaf < 0) { + smaller_leaf = -1; + } else if (num_data_in_left_child < num_data_in_right_child) { + smaller_leaf = left_leaf; + } else { + smaller_leaf = right_leaf; + } + + // Copy indices, gradients and Hessians as early as possible + if (smaller_leaf >= 0 && num_dense_feature_groups_) { + // only need to initialize for smaller leaf + // Get leaf boundary + const data_size_t* indices = data_partition_->indices(); + data_size_t begin = data_partition_->leaf_begin(smaller_leaf); + data_size_t end = begin + data_partition_->leaf_count(smaller_leaf); + + // copy indices to the GPU: + #if GPU_DEBUG >= 2 + Log::Info("Copying indices, gradients and Hessians to GPU..."); + printf("Indices size %d being copied (left = %d, right = %d)\n", end - begin, num_data_in_left_child, num_data_in_right_child); + #endif + indices_future_ = boost::compute::copy_async(indices + begin, indices + end, device_data_indices_->begin(), queue_); + + if (!share_state_->is_constant_hessian) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = begin; i < end; ++i) { + ordered_hessians_[i - begin] = hessians_[indices[i]]; + } + // copy ordered Hessians to the GPU: + hessians_future_ = queue_.enqueue_write_buffer_async(device_hessians_, 0, (end - begin) * sizeof(score_t), ptr_pinned_hessians_); + } + + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = begin; i < end; ++i) { + ordered_gradients_[i - begin] = gradients_[indices[i]]; + } + // copy ordered gradients to the GPU: + gradients_future_ = queue_.enqueue_write_buffer_async(device_gradients_, 0, (end - begin) * sizeof(score_t), ptr_pinned_gradients_); + + #if GPU_DEBUG >= 2 + Log::Info("Gradients/Hessians/indices copied to device with size %d", end - begin); + #endif + } + return SerialTreeLearner::BeforeFindBestSplit(tree, left_leaf, right_leaf); +} + +bool GPUTreeLearner::ConstructGPUHistogramsAsync( + const std::vector& is_feature_used, + const data_size_t* data_indices, data_size_t num_data, + const score_t* gradients, const score_t* hessians, + score_t* ordered_gradients, score_t* ordered_hessians) { + if (num_data <= 0) { + return false; + } + // do nothing if no features can be processed on GPU + if (!num_dense_feature_groups_) { + return false; + } + + // copy data indices if it is not null + if (data_indices != nullptr && num_data != num_data_) { + indices_future_ = boost::compute::copy_async(data_indices, data_indices + num_data, device_data_indices_->begin(), queue_); + } + // generate and copy ordered_gradients if gradients is not null + if (gradients != nullptr) { + if (num_data != num_data_) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data; ++i) { + ordered_gradients[i] = gradients[data_indices[i]]; + } + gradients_future_ = queue_.enqueue_write_buffer_async(device_gradients_, 0, num_data * sizeof(score_t), ptr_pinned_gradients_); + } else { + gradients_future_ = queue_.enqueue_write_buffer_async(device_gradients_, 0, num_data * sizeof(score_t), gradients); + } + } + // generate and copy ordered_hessians if Hessians is not null + if (hessians != nullptr && !share_state_->is_constant_hessian) { + if (num_data != num_data_) { + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (data_size_t i = 0; i < num_data; ++i) { + ordered_hessians[i] = hessians[data_indices[i]]; + } + hessians_future_ = queue_.enqueue_write_buffer_async(device_hessians_, 0, num_data * sizeof(score_t), ptr_pinned_hessians_); + } else { + hessians_future_ = queue_.enqueue_write_buffer_async(device_hessians_, 0, num_data * sizeof(score_t), hessians); + } + } + // converted indices in is_feature_used to feature-group indices + std::vector is_feature_group_used(num_feature_groups_, 0); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_features_ >= 2048) + for (int i = 0; i < num_features_; ++i) { + if (is_feature_used[i]) { + is_feature_group_used[train_data_->Feature2Group(i)] = 1; + } + } + // construct the feature masks for dense feature-groups + int used_dense_feature_groups = 0; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) reduction(+:used_dense_feature_groups) if (num_dense_feature_groups_ >= 2048) + for (int i = 0; i < num_dense_feature_groups_; ++i) { + if (is_feature_group_used[dense_feature_group_map_[i]]) { + feature_masks_[i] = 1; + ++used_dense_feature_groups; + } else { + feature_masks_[i] = 0; + } + } + bool use_all_features = used_dense_feature_groups == num_dense_feature_groups_; + // if no feature group is used, just return and do not use GPU + if (used_dense_feature_groups == 0) { + return false; + } +#if GPU_DEBUG >= 1 + printf("Feature masks:\n"); + for (unsigned int i = 0; i < feature_masks_.size(); ++i) { + printf("%d ", feature_masks_[i]); + } + printf("\n"); + printf("%d feature groups, %d used, %d\n", num_dense_feature_groups_, used_dense_feature_groups, use_all_features); +#endif + // if not all feature groups are used, we need to transfer the feature mask to GPU + // otherwise, we will use a specialized GPU kernel with all feature groups enabled + if (!use_all_features) { + queue_.enqueue_write_buffer(device_feature_masks_, 0, num_dense_feature4_ * dword_features_, ptr_pinned_feature_masks_); + } + // All data have been prepared, now run the GPU kernel + GPUHistogram(num_data, use_all_features); + return true; +} + +void GPUTreeLearner::ConstructHistograms(const std::vector& is_feature_used, bool use_subtract) { + std::vector is_sparse_feature_used(num_features_, 0); + std::vector is_dense_feature_used(num_features_, 0); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < num_features_; ++feature_index) { + if (!col_sampler_.is_feature_used_bytree()[feature_index]) continue; + if (!is_feature_used[feature_index]) continue; + if (train_data_->IsMultiGroup(train_data_->Feature2Group(feature_index))) { + is_sparse_feature_used[feature_index] = 1; + } else { + is_dense_feature_used[feature_index] = 1; + } + } + // construct smaller leaf + hist_t* ptr_smaller_leaf_hist_data = smaller_leaf_histogram_array_[0].RawData() - kHistOffset; + // ConstructGPUHistogramsAsync will return true if there are available feature groups dispatched to GPU + bool is_gpu_used = ConstructGPUHistogramsAsync(is_feature_used, + nullptr, smaller_leaf_splits_->num_data_in_leaf(), + nullptr, nullptr, + nullptr, nullptr); + // then construct sparse features on CPU + train_data_->ConstructHistograms(is_sparse_feature_used, + smaller_leaf_splits_->data_indices(), smaller_leaf_splits_->num_data_in_leaf(), + gradients_, hessians_, + ordered_gradients_.data(), ordered_hessians_.data(), + share_state_.get(), + ptr_smaller_leaf_hist_data); + // wait for GPU to finish, only if GPU is actually used + if (is_gpu_used) { + if (config_->gpu_use_dp) { + // use double precision + WaitAndGetHistograms(ptr_smaller_leaf_hist_data); + } else { + // use single precision + WaitAndGetHistograms(ptr_smaller_leaf_hist_data); + } + } + + // Compare GPU histogram with CPU histogram, useful for debugging GPU code problem + // #define GPU_DEBUG_COMPARE + #ifdef GPU_DEBUG_COMPARE + for (int i = 0; i < num_dense_feature_groups_; ++i) { + if (!feature_masks_[i]) + continue; + int dense_feature_group_index = dense_feature_group_map_[i]; + size_t size = train_data_->FeatureGroupNumBin(dense_feature_group_index); + hist_t* ptr_smaller_leaf_hist_data = smaller_leaf_histogram_array_[0].RawData() - kHistOffset; + hist_t* current_histogram = ptr_smaller_leaf_hist_data + train_data_->GroupBinBoundary(dense_feature_group_index) * 2; + hist_t* gpu_histogram = new hist_t[size * 2]; + data_size_t num_data = smaller_leaf_splits_->num_data_in_leaf(); + printf("Comparing histogram for feature %d size %d, %lu bins\n", dense_feature_group_index, num_data, size); + std::copy(current_histogram, current_histogram + size * 2, gpu_histogram); + std::memset(current_histogram, 0, size * sizeof(hist_t) * 2); + if (train_data_->FeatureGroupBin(dense_feature_group_index) == nullptr) { + continue; + } + if (num_data != num_data_) { + train_data_->FeatureGroupBin(dense_feature_group_index)->ConstructHistogram( + smaller_leaf_splits_->data_indices(), + 0, + num_data, + ordered_gradients_.data(), + ordered_hessians_.data(), + current_histogram); + } else { + train_data_->FeatureGroupBin(dense_feature_group_index)->ConstructHistogram( + 0, + num_data, + gradients_, + hessians_, + current_histogram); + } + CompareHistograms(gpu_histogram, current_histogram, size, dense_feature_group_index); + std::copy(gpu_histogram, gpu_histogram + size * 2, current_histogram); + delete [] gpu_histogram; + } + #endif + + if (larger_leaf_histogram_array_ != nullptr && !use_subtract) { + // construct larger leaf + hist_t* ptr_larger_leaf_hist_data = larger_leaf_histogram_array_[0].RawData() - kHistOffset; + is_gpu_used = ConstructGPUHistogramsAsync(is_feature_used, + larger_leaf_splits_->data_indices(), larger_leaf_splits_->num_data_in_leaf(), + gradients_, hessians_, + ordered_gradients_.data(), ordered_hessians_.data()); + // then construct sparse features on CPU + train_data_->ConstructHistograms(is_sparse_feature_used, + larger_leaf_splits_->data_indices(), larger_leaf_splits_->num_data_in_leaf(), + gradients_, hessians_, + ordered_gradients_.data(), ordered_hessians_.data(), + share_state_.get(), + ptr_larger_leaf_hist_data); + // wait for GPU to finish, only if GPU is actually used + if (is_gpu_used) { + if (config_->gpu_use_dp) { + // use double precision + WaitAndGetHistograms(ptr_larger_leaf_hist_data); + } else { + // use single precision + WaitAndGetHistograms(ptr_larger_leaf_hist_data); + } + } + } +} + +void GPUTreeLearner::FindBestSplits(const Tree* tree) { + SerialTreeLearner::FindBestSplits(tree); + +#if GPU_DEBUG >= 3 + for (int feature_index = 0; feature_index < num_features_; ++feature_index) { + if (!col_sampler_.is_feature_used_bytree()[feature_index]) continue; + if (parent_leaf_histogram_array_ != nullptr + && !parent_leaf_histogram_array_[feature_index].is_splittable()) { + smaller_leaf_histogram_array_[feature_index].set_is_splittable(false); + continue; + } + size_t bin_size = train_data_->FeatureNumBin(feature_index) + 1; + printf("Feature %d smaller leaf:\n", feature_index); + PrintHistograms(smaller_leaf_histogram_array_[feature_index].RawData() - kHistOffset, bin_size); + if (larger_leaf_splits_ == nullptr || larger_leaf_splits_->leaf_index() < 0) { + continue; + } + printf("Feature %d larger leaf:\n", feature_index); + PrintHistograms(larger_leaf_histogram_array_[feature_index].RawData() - kHistOffset, bin_size); + } +#endif +} + +void GPUTreeLearner::Split(Tree* tree, int best_Leaf, int* left_leaf, int* right_leaf) { + const SplitInfo& best_split_info = best_split_per_leaf_[best_Leaf]; +#if GPU_DEBUG >= 2 + printf("Splitting leaf %d with feature %d thresh %d gain %f stat %f %f %f %f\n", best_Leaf, best_split_info.feature, best_split_info.threshold, best_split_info.gain, best_split_info.left_sum_gradient, best_split_info.right_sum_gradient, best_split_info.left_sum_hessian, best_split_info.right_sum_hessian); +#endif + SerialTreeLearner::Split(tree, best_Leaf, left_leaf, right_leaf); + if (Network::num_machines() == 1) { + // do some sanity check for the GPU algorithm + if (best_split_info.left_count < best_split_info.right_count) { + if ((best_split_info.left_count != smaller_leaf_splits_->num_data_in_leaf()) || + (best_split_info.right_count!= larger_leaf_splits_->num_data_in_leaf())) { + Log::Fatal("Bug in GPU histogram! split %d: %d, smaller_leaf: %d, larger_leaf: %d\n", best_split_info.left_count, best_split_info.right_count, smaller_leaf_splits_->num_data_in_leaf(), larger_leaf_splits_->num_data_in_leaf()); + } + } else { + smaller_leaf_splits_->Init(*right_leaf, data_partition_.get(), best_split_info.right_sum_gradient, best_split_info.right_sum_hessian, best_split_info.right_output); + larger_leaf_splits_->Init(*left_leaf, data_partition_.get(), best_split_info.left_sum_gradient, best_split_info.left_sum_hessian, best_split_info.left_output); + if ((best_split_info.left_count != larger_leaf_splits_->num_data_in_leaf()) || + (best_split_info.right_count!= smaller_leaf_splits_->num_data_in_leaf())) { + Log::Fatal("Bug in GPU histogram! split %d: %d, smaller_leaf: %d, larger_leaf: %d\n", best_split_info.left_count, best_split_info.right_count, smaller_leaf_splits_->num_data_in_leaf(), larger_leaf_splits_->num_data_in_leaf()); + } + } + } +} + +} // namespace LightGBM +#endif // USE_GPU diff --git a/src/treelearner/gpu_tree_learner.h b/src/treelearner/gpu_tree_learner.h new file mode 100644 index 0000000..3c9f217 --- /dev/null +++ b/src/treelearner/gpu_tree_learner.h @@ -0,0 +1,288 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_GPU_TREE_LEARNER_H_ +#define LIGHTGBM_SRC_TREELEARNER_GPU_TREE_LEARNER_H_ + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include "data_partition.hpp" +#include "feature_histogram.hpp" +#include "leaf_splits.hpp" +#include "serial_tree_learner.h" +#include "split_info.hpp" + +#ifdef USE_GPU + +#define BOOST_COMPUTE_THREAD_SAFE +#define BOOST_COMPUTE_HAVE_THREAD_LOCAL +// Use Boost.Compute on-disk kernel cache +#define BOOST_COMPUTE_USE_OFFLINE_CACHE +#include +#include +#include + +namespace LightGBM { + +using json11_internal_lightgbm::Json; + +/*! +* \brief GPU-based parallel learning algorithm. +*/ +class GPUTreeLearner: public SerialTreeLearner { + public: + explicit GPUTreeLearner(const Config* tree_config); + ~GPUTreeLearner(); + void Init(const Dataset* train_data, bool is_constant_hessian) override; + void ResetTrainingDataInner(const Dataset* train_data, bool is_constant_hessian, bool reset_multi_val_bin) override; + void ResetIsConstantHessian(bool is_constant_hessian) override; + Tree* Train(const score_t* gradients, const score_t *hessians, bool is_first_tree) override; + + void SetBaggingData(const Dataset* subset, const data_size_t* used_indices, data_size_t num_data) override { + SerialTreeLearner::SetBaggingData(subset, used_indices, num_data); + if (subset == nullptr && used_indices != nullptr) { + // determine if we are using bagging before we construct the data partition + // thus we can start data movement to GPU earlier + if (num_data != num_data_) { + use_bagging_ = true; + return; + } + } + use_bagging_ = false; + } + + protected: + void BeforeTrain() override; + bool BeforeFindBestSplit(const Tree* tree, int left_leaf, int right_leaf) override; + void FindBestSplits(const Tree* tree) override; + void Split(Tree* tree, int best_Leaf, int* left_leaf, int* right_leaf) override; + void ConstructHistograms(const std::vector& is_feature_used, bool use_subtract) override; + + private: + /*! \brief 4-byte feature tuple used by GPU kernels */ + struct Feature4 { + uint8_t s[4]; + }; + + typedef float gpu_hist_t; + + /*! + * \brief Find the best number of workgroups processing one feature for maximizing efficiency + * \param leaf_num_data The number of data examples on the current leaf being processed + * \return Log2 of the best number for workgroups per feature, in range 0...kMaxLogWorkgroupsPerFeature + */ + int GetNumWorkgroupsPerFeature(data_size_t leaf_num_data); + + /*! + * \brief Initialize GPU device, context and command queues + * Also compiles the OpenCL kernel + * \param platform_id OpenCL platform ID + * \param device_id OpenCL device ID + */ + void InitGPU(int platform_id, int device_id); + + /*! + * \brief Allocate memory for GPU computation + */ + void AllocateGPUMemory(); + + /*! + * \brief Compile OpenCL GPU source code to kernel binaries + */ + void BuildGPUKernels(); + + /*! + * \brief Returns OpenCL kernel build log when compiled with option opts + * \param opts OpenCL build options + * \return OpenCL build log + */ + std::string GetBuildLog(const std::string &opts); + + /*! + * \brief Setup GPU kernel arguments, preparing for launching + */ + void SetupKernelArguments(); + + /*! + * \brief Compute GPU feature histogram for the current leaf. + * Indices, gradients and Hessians have been copied to the device. + * \param leaf_num_data Number of data on current leaf + * \param use_all_features Set to true to not use feature masks, with a faster kernel + */ + void GPUHistogram(data_size_t leaf_num_data, bool use_all_features); + + /*! + * \brief Wait for GPU kernel execution and read histogram + * \param histograms Destination of histogram results from GPU. + */ + template + void WaitAndGetHistograms(hist_t* histograms); + + /*! + * \brief Construct GPU histogram asynchronously. + * Interface is similar to Dataset::ConstructHistograms(). + * \param is_feature_used A predicate vector for enabling each feature + * \param data_indices Array of data example IDs to be included in histogram, will be copied to GPU. + * Set to nullptr to skip copy to GPU. + * \param num_data Number of data examples to be included in histogram + * \param gradients Array of gradients for all examples. + * \param hessians Array of Hessians for all examples. + * \param ordered_gradients Ordered gradients will be generated and copied to GPU when gradients is not nullptr, + * Set gradients to nullptr to skip copy to GPU. + * \param ordered_hessians Ordered Hessians will be generated and copied to GPU when hessians is not nullptr, + * Set hessians to nullptr to skip copy to GPU. + * \return true if GPU kernel is launched, false if GPU is not used + */ + bool ConstructGPUHistogramsAsync( + const std::vector& is_feature_used, + const data_size_t* data_indices, data_size_t num_data, + const score_t* gradients, const score_t* hessians, + score_t* ordered_gradients, score_t* ordered_hessians); + + + /*! brief Log2 of max number of workgroups per feature*/ + const int kMaxLogWorkgroupsPerFeature = 10; // 2^10 + /*! brief Max total number of workgroups with preallocated workspace. + * If we use more than this number of workgroups, we have to reallocate subhistograms */ + int preallocd_max_num_wg_ = 1024; + + /*! \brief True if bagging is used */ + bool use_bagging_; + + /*! \brief GPU device object */ + boost::compute::device dev_; + /*! \brief GPU context object */ + boost::compute::context ctx_; + /*! \brief GPU command queue object */ + boost::compute::command_queue queue_; + /*! \brief GPU kernel for 256 bins */ + const char *kernel256_src_ = { + #include "ocl/histogram256.cl" + }; + /*! \brief GPU kernel for 64 bins */ + const char *kernel64_src_ = { + #include "ocl/histogram64.cl" + }; + /*! \brief GPU kernel for 16 bins */ + const char *kernel16_src_ = { + #include "ocl/histogram16.cl" + }; + /*! \brief Currently used kernel source */ + std::string kernel_source_; + /*! \brief Currently used kernel name */ + std::string kernel_name_; + + /*! \brief an array of histogram kernels with different number + of workgroups per feature */ + std::vector histogram_kernels_; + /*! \brief an array of histogram kernels with different number + of workgroups per feature, with all features enabled to avoid branches */ + std::vector histogram_allfeats_kernels_; + /*! \brief an array of histogram kernels with different number + of workgroups per feature, and processing the whole dataset */ + std::vector histogram_fulldata_kernels_; + /*! \brief total number of feature-groups */ + int num_feature_groups_; + /*! \brief total number of dense feature-groups, which will be processed on GPU */ + int num_dense_feature_groups_; + /*! \brief On GPU we read one DWORD (4-byte) of features of one example once. + * With bin size > 16, there are 4 features per DWORD. + * With bin size <=16, there are 8 features per DWORD. + * */ + int dword_features_; + /*! \brief total number of dense feature-group tuples on GPU. + * Each feature tuple is 4-byte (4 features if each feature takes a byte) */ + int num_dense_feature4_; + /*! \brief Max number of bins of training data, used to determine + * which GPU kernel to use */ + int max_num_bin_; + /*! \brief Used GPU kernel bin size (64, 256) */ + int device_bin_size_; + /*! \brief Size of histogram bin entry, depending if single or double precision is used */ + size_t hist_bin_entry_sz_; + /*! \brief Indices of all dense feature-groups */ + std::vector dense_feature_group_map_; + /*! \brief Indices of all sparse feature-groups */ + std::vector sparse_feature_group_map_; + /*! \brief Multipliers of all dense feature-groups, used for redistributing bins */ + std::vector device_bin_mults_; + /*! \brief GPU memory object holding the training data */ + std::unique_ptr> device_features_; + /*! \brief GPU memory object holding the ordered gradient */ + boost::compute::buffer device_gradients_; + /*! \brief Pinned memory object for ordered gradient */ + boost::compute::buffer pinned_gradients_; + /*! \brief Pointer to pinned memory of ordered gradient */ + void * ptr_pinned_gradients_ = nullptr; + /*! \brief GPU memory object holding the ordered hessian */ + boost::compute::buffer device_hessians_; + /*! \brief Pinned memory object for ordered hessian */ + boost::compute::buffer pinned_hessians_; + /*! \brief Pointer to pinned memory of ordered hessian */ + void * ptr_pinned_hessians_ = nullptr; + /*! \brief A vector of feature mask. 1 = feature used, 0 = feature not used */ + std::vector> feature_masks_; + /*! \brief GPU memory object holding the feature masks */ + boost::compute::buffer device_feature_masks_; + /*! \brief Pinned memory object for feature masks */ + boost::compute::buffer pinned_feature_masks_; + /*! \brief Pointer to pinned memory of feature masks */ + void * ptr_pinned_feature_masks_ = nullptr; + /*! \brief GPU memory object holding indices of the leaf being processed */ + std::unique_ptr> device_data_indices_; + /*! \brief GPU memory object holding counters for workgroup coordination */ + std::unique_ptr> sync_counters_; + /*! \brief GPU memory object holding temporary sub-histograms per workgroup */ + std::unique_ptr> device_subhistograms_; + /*! \brief Host memory object for histogram output (GPU will write to Host memory directly) */ + boost::compute::buffer device_histogram_outputs_; + /*! \brief Host memory pointer for histogram outputs */ + void * host_histogram_outputs_; + /*! \brief OpenCL waitlist object for waiting for data transfer before kernel execution */ + boost::compute::wait_list kernel_wait_obj_; + /*! \brief OpenCL waitlist object for reading output histograms after kernel execution */ + boost::compute::wait_list histograms_wait_obj_; + /*! \brief Asynchronous waiting object for copying indices */ + boost::compute::future indices_future_; + /*! \brief Asynchronous waiting object for copying gradients */ + boost::compute::event gradients_future_; + /*! \brief Asynchronous waiting object for copying Hessians */ + boost::compute::event hessians_future_; +}; + +} // namespace LightGBM +#else + +// When GPU support is not compiled in, quit with an error message + +namespace LightGBM { + +class GPUTreeLearner: public SerialTreeLearner { + public: + #ifdef _MSC_VER + #pragma warning(disable : 4702) + #endif + explicit GPUTreeLearner(const Config* tree_config) : SerialTreeLearner(tree_config) { + Log::Fatal("GPU Tree Learner was not enabled in this build.\n" + "Please recompile with CMake option -DUSE_GPU=1"); + } +}; + +} // namespace LightGBM + +#endif // USE_GPU + +#endif // LIGHTGBM_SRC_TREELEARNER_GPU_TREE_LEARNER_H_ diff --git a/src/treelearner/gradient_discretizer.cpp b/src/treelearner/gradient_discretizer.cpp new file mode 100644 index 0000000..5da72f3 --- /dev/null +++ b/src/treelearner/gradient_discretizer.cpp @@ -0,0 +1,263 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ + +#include "gradient_discretizer.hpp" +#include + +#include +#include +#include + +namespace LightGBM { + +void GradientDiscretizer::Init( + const data_size_t num_data, const int num_leaves, + const int num_features, const Dataset* train_data) { + discretized_gradients_and_hessians_vector_.resize(num_data * 2); + gradient_random_values_.resize(num_data); + hessian_random_values_.resize(num_data); + random_values_use_start_eng_ = std::mt19937(random_seed_); + random_values_use_start_dist_ = std::uniform_int_distribution(0, num_data); + + const int num_threads = OMP_NUM_THREADS(); + int num_blocks = 0; + data_size_t block_size = 0; + Threading::BlockInfo(num_data, 512, &num_blocks, &block_size); + #pragma omp parallel for schedule(static, 1) num_threads(num_threads) + for (int thread_id = 0; thread_id < num_blocks; ++thread_id) { + const data_size_t start = thread_id * block_size; + const data_size_t end = std::min(start + block_size, num_data); + std::mt19937 gradient_random_values_eng(random_seed_ + thread_id); + std::uniform_real_distribution gradient_random_values_dist(0.0f, 1.0f); + std::mt19937 hessian_random_values_eng(random_seed_ + thread_id + num_threads); + std::uniform_real_distribution hessian_random_values_dist(0.0f, 1.0f); + for (data_size_t i = start; i < end; ++i) { + gradient_random_values_[i] = gradient_random_values_dist(gradient_random_values_eng); + hessian_random_values_[i] = hessian_random_values_dist(hessian_random_values_eng); + } + } + + max_gradient_abs_ = 0.0f; + max_hessian_abs_ = 0.0f; + + gradient_scale_ = 0.0f; + hessian_scale_ = 0.0f; + inverse_gradient_scale_ = 0.0f; + inverse_hessian_scale_ = 0.0f; + + num_leaves_ = num_leaves; + leaf_num_bits_in_histogram_bin_.resize(num_leaves_, 0); + node_num_bits_in_histogram_bin_.resize(num_leaves_, 0); + global_leaf_num_bits_in_histogram_bin_.resize(num_leaves_, 0); + global_node_num_bits_in_histogram_bin_.resize(num_leaves_, 0); + + leaf_grad_hess_stats_.resize(num_leaves_ * 2, 0.0); + change_hist_bits_buffer_.resize(num_features); + #pragma omp parallel for schedule(static) num_threads(num_threads) + for (int feature_index = 0; feature_index < num_features; ++feature_index) { + const BinMapper* bin_mapper = train_data->FeatureBinMapper(feature_index); + change_hist_bits_buffer_[feature_index].resize((bin_mapper->num_bin() - static_cast(bin_mapper->GetMostFreqBin() == 0)) * 2); + } + + ordered_int_gradients_and_hessians_.resize(2 * num_data); +} + +void GradientDiscretizer::DiscretizeGradients( + const data_size_t num_data, + const score_t* input_gradients, + const score_t* input_hessians) { + double max_gradient = std::fabs(input_gradients[0]); + double max_hessian = std::fabs(input_hessians[0]); + const int num_threads = OMP_NUM_THREADS(); + std::vector thread_max_gradient(num_threads, max_gradient); + std::vector thread_max_hessian(num_threads, max_hessian); + Threading::For(0, num_data, 1024, + [input_gradients, input_hessians, &thread_max_gradient, &thread_max_hessian] + (int, data_size_t start, data_size_t end) { + int thread_id = omp_get_thread_num(); + for (data_size_t i = start; i < end; ++i) { + double fabs_grad = std::fabs(input_gradients[i]); + double fabs_hess = std::fabs(input_hessians[i]); + if (fabs_grad > thread_max_gradient[thread_id]) { + thread_max_gradient[thread_id] = fabs_grad; + } + if (fabs_hess > thread_max_hessian[thread_id]) { + thread_max_hessian[thread_id] = fabs_hess; + } + }}); + max_gradient = thread_max_gradient[0]; + max_hessian = thread_max_hessian[0]; + for (int thread_id = 1; thread_id < num_threads; ++thread_id) { + if (max_gradient < thread_max_gradient[thread_id]) { + max_gradient = thread_max_gradient[thread_id]; + } + if (max_hessian < thread_max_hessian[thread_id]) { + max_hessian = thread_max_hessian[thread_id]; + } + } + if (Network::num_machines() > 1) { + max_gradient = Network::GlobalSyncUpByMax(max_gradient); + max_hessian = Network::GlobalSyncUpByMax(max_hessian); + } + max_gradient_abs_ = max_gradient; + max_hessian_abs_ = max_hessian; + gradient_scale_ = max_gradient_abs_ / static_cast(num_grad_quant_bins_ / 2); + if (is_constant_hessian_) { + hessian_scale_ = max_hessian_abs_; + } else { + hessian_scale_ = max_hessian_abs_ / static_cast(num_grad_quant_bins_); + } + inverse_gradient_scale_ = 1.0f / gradient_scale_; + inverse_hessian_scale_ = 1.0f / hessian_scale_; + + const int random_values_use_start = random_values_use_start_dist_(random_values_use_start_eng_); + int8_t* discretized_int8 = discretized_gradients_and_hessians_vector_.data(); + if (stochastic_rounding_) { + if (is_constant_hessian_) { + #pragma omp parallel for schedule(static) num_threads(num_threads) + for (data_size_t i = 0; i < num_data; ++i) { + const double gradient = input_gradients[i]; + const data_size_t random_value_pos = (i + random_values_use_start) % num_data; + discretized_int8[2 * i + 1] = gradient >= 0.0f ? + static_cast(gradient * inverse_gradient_scale_ + gradient_random_values_[random_value_pos]) : + static_cast(gradient * inverse_gradient_scale_ - gradient_random_values_[random_value_pos]); + discretized_int8[2 * i] = static_cast(1); + } + } else { + #pragma omp parallel for schedule(static) num_threads(num_threads) + for (data_size_t i = 0; i < num_data; ++i) { + const double gradient = input_gradients[i]; + const data_size_t random_value_pos = (i + random_values_use_start) % num_data; + discretized_int8[2 * i + 1] = gradient >= 0.0f ? + static_cast(gradient * inverse_gradient_scale_ + gradient_random_values_[random_value_pos]) : + static_cast(gradient * inverse_gradient_scale_ - gradient_random_values_[random_value_pos]); + discretized_int8[2 * i] = static_cast(input_hessians[i] * inverse_hessian_scale_ + hessian_random_values_[random_value_pos]); + } + } + } else { + if (is_constant_hessian_) { + #pragma omp parallel for schedule(static) num_threads(num_threads) + for (data_size_t i = 0; i < num_data; ++i) { + const double gradient = input_gradients[i]; + discretized_int8[2 * i + 1] = gradient >= 0.0f ? + static_cast(gradient * inverse_gradient_scale_ + 0.5) : + static_cast(gradient * inverse_gradient_scale_ - 0.5); + discretized_int8[2 * i] = static_cast(1); + } + } else { + #pragma omp parallel for schedule(static) num_threads(num_threads) + for (data_size_t i = 0; i < num_data; ++i) { + const double gradient = input_gradients[i]; + discretized_int8[2 * i + 1] = gradient >= 0.0f ? + static_cast(gradient * inverse_gradient_scale_ + 0.5) : + static_cast(gradient * inverse_gradient_scale_ - 0.5); + discretized_int8[2 * i] = static_cast(input_hessians[i] * inverse_hessian_scale_ + 0.5); + } + } + } +} + +template +void GradientDiscretizer::SetNumBitsInHistogramBin( + const int left_leaf_index, const int right_leaf_index, + const data_size_t num_data_in_left_leaf, const data_size_t num_data_in_right_leaf) { + std::vector& leaf_num_bits_in_histogram_bin = IS_GLOBAL ? + global_leaf_num_bits_in_histogram_bin_ : leaf_num_bits_in_histogram_bin_; + std::vector& node_num_bits_in_histogram_bin = IS_GLOBAL ? + global_node_num_bits_in_histogram_bin_ : node_num_bits_in_histogram_bin_; + if (right_leaf_index == -1) { + const uint64_t max_stat_per_bin = static_cast(num_data_in_left_leaf) * static_cast(num_grad_quant_bins_); + if (max_stat_per_bin < 256) { + leaf_num_bits_in_histogram_bin[left_leaf_index] = 8; + } else if (max_stat_per_bin < 65536) { + leaf_num_bits_in_histogram_bin[left_leaf_index] = 16; + } else { + leaf_num_bits_in_histogram_bin[left_leaf_index] = 32; + } + } else { + const uint64_t max_stat_left_per_bin = static_cast(num_data_in_left_leaf) * static_cast(num_grad_quant_bins_); + const uint64_t max_stat_right_per_bin = static_cast(num_data_in_right_leaf) * static_cast(num_grad_quant_bins_); + node_num_bits_in_histogram_bin[left_leaf_index] = leaf_num_bits_in_histogram_bin[left_leaf_index]; + if (max_stat_left_per_bin < 256) { + leaf_num_bits_in_histogram_bin[left_leaf_index] = 8; + } else if (max_stat_left_per_bin < 65536) { + leaf_num_bits_in_histogram_bin[left_leaf_index] = 16; + } else { + leaf_num_bits_in_histogram_bin[left_leaf_index] = 32; + } + if (max_stat_right_per_bin < 256) { + leaf_num_bits_in_histogram_bin[right_leaf_index] = 8; + } else if (max_stat_right_per_bin < 65536) { + leaf_num_bits_in_histogram_bin[right_leaf_index] = 16; + } else { + leaf_num_bits_in_histogram_bin[right_leaf_index] = 32; + } + } +} + +template void GradientDiscretizer::SetNumBitsInHistogramBin( + const int left_leaf_index, const int right_leaf_index, + const data_size_t num_data_in_left_leaf, const data_size_t num_data_in_right_leaf); + +template void GradientDiscretizer::SetNumBitsInHistogramBin( + const int left_leaf_index, const int right_leaf_index, + const data_size_t num_data_in_left_leaf, const data_size_t num_data_in_right_leaf); + +void GradientDiscretizer::RenewIntGradTreeOutput( + Tree* tree, const Config* config, const DataPartition* data_partition, + const score_t* gradients, const score_t* hessians, + const std::function& leaf_index_to_global_num_data) { + global_timer.Start("GradientDiscretizer::RenewIntGradTreeOutput"); + if (config->tree_learner == std::string("data")) { + for (int leaf_id = 0; leaf_id < tree->num_leaves(); ++leaf_id) { + data_size_t leaf_cnt = 0; + const data_size_t* data_indices = data_partition->GetIndexOnLeaf(leaf_id, &leaf_cnt); + double sum_gradient = 0.0f, sum_hessian = 0.0f; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_gradient, sum_hessian) + for (data_size_t i = 0; i < leaf_cnt; ++i) { + const data_size_t index = data_indices[i]; + const score_t grad = gradients[index]; + const score_t hess = hessians[index]; + sum_gradient += grad; + sum_hessian += hess; + } + leaf_grad_hess_stats_[2 * leaf_id] = sum_gradient; + leaf_grad_hess_stats_[2 * leaf_id + 1] = sum_hessian; + } + std::vector global_leaf_grad_hess_stats = Network::GlobalSum(&leaf_grad_hess_stats_); + for (int leaf_id = 0; leaf_id < tree->num_leaves(); ++leaf_id) { + const double sum_gradient = global_leaf_grad_hess_stats[2 * leaf_id]; + const double sum_hessian = global_leaf_grad_hess_stats[2 * leaf_id + 1]; + const double leaf_output = FeatureHistogram::CalculateSplittedLeafOutput( + sum_gradient, sum_hessian, + config->lambda_l1, config->lambda_l2, config->max_delta_step, config->path_smooth, + leaf_index_to_global_num_data(leaf_id), 0.0f); + tree->SetLeafOutput(leaf_id, leaf_output); + } + } else { + for (int leaf_id = 0; leaf_id < tree->num_leaves(); ++leaf_id) { + data_size_t leaf_cnt = 0; + const data_size_t* data_indices = data_partition->GetIndexOnLeaf(leaf_id, &leaf_cnt); + double sum_gradient = 0.0f, sum_hessian = 0.0f; + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_gradient, sum_hessian) + for (data_size_t i = 0; i < leaf_cnt; ++i) { + const data_size_t index = data_indices[i]; + const score_t grad = gradients[index]; + const score_t hess = hessians[index]; + sum_gradient += grad; + sum_hessian += hess; + } + const double leaf_output = FeatureHistogram::CalculateSplittedLeafOutput(sum_gradient, sum_hessian, + config->lambda_l1, config->lambda_l2, config->max_delta_step, config->path_smooth, + leaf_cnt, 0.0f); + tree->SetLeafOutput(leaf_id, leaf_output); + } + } + global_timer.Stop("GradientDiscretizer::RenewIntGradTreeOutput"); +} + +} // namespace LightGBM diff --git a/src/treelearner/gradient_discretizer.hpp b/src/treelearner/gradient_discretizer.hpp new file mode 100644 index 0000000..7ca02e7 --- /dev/null +++ b/src/treelearner/gradient_discretizer.hpp @@ -0,0 +1,129 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_GRADIENT_DISCRETIZER_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_GRADIENT_DISCRETIZER_HPP_ + +#include +#include +#include +#include + +#include +#include + +#include "data_partition.hpp" +#include "feature_histogram.hpp" + +namespace LightGBM { + +class GradientDiscretizer { + public: + GradientDiscretizer(int num_grad_quant_bins, int num_trees, int random_seed, bool is_constant_hessian, const bool stochastic_rounding) { + num_grad_quant_bins_ = num_grad_quant_bins; + iter_ = 0; + num_trees_ = num_trees; + random_seed_ = random_seed; + is_constant_hessian_ = is_constant_hessian; + stochastic_rounding_ = stochastic_rounding; + } + + virtual ~GradientDiscretizer() {} + + virtual void DiscretizeGradients( + const data_size_t num_data, + const score_t* input_gradients, + const score_t* input_hessians); + + virtual const int8_t* discretized_gradients_and_hessians() const { + return discretized_gradients_and_hessians_vector_.data(); + } + + virtual double grad_scale() const { + return gradient_scale_; + } + + virtual double hess_scale() const { + return hessian_scale_; + } + + virtual void Init( + const data_size_t num_data, const int num_leaves, + const int num_features, const Dataset* train_data); + + template + void SetNumBitsInHistogramBin( + const int left_leaf_index, const int right_leaf_index, + const data_size_t num_data_in_left_leaf, const data_size_t num_data_in_right_leaf); + + template + int8_t GetHistBitsInLeaf(const int leaf_index) { + if (IS_GLOBAL) { + return global_leaf_num_bits_in_histogram_bin_[leaf_index]; + } else { + return leaf_num_bits_in_histogram_bin_[leaf_index]; + } + } + + template + int8_t GetHistBitsInNode(const int node_index) { + if (IS_GLOBAL) { + return global_node_num_bits_in_histogram_bin_[node_index]; + } else { + return node_num_bits_in_histogram_bin_[node_index]; + } + } + + int8_t* ordered_int_gradients_and_hessians() { + return ordered_int_gradients_and_hessians_.data(); + } + + void RenewIntGradTreeOutput( + Tree* tree, const Config* config, const DataPartition* data_partition, + const score_t* gradients, const score_t* hessians, + const std::function& leaf_index_to_global_num_data); + + int32_t* GetChangeHistBitsBuffer(const int feature_index) { + return change_hist_bits_buffer_[feature_index].data(); + } + + protected: + int num_grad_quant_bins_; + int iter_; + int num_trees_; + int random_seed_; + bool stochastic_rounding_; + + std::vector gradient_random_values_; + std::vector hessian_random_values_; + std::mt19937 random_values_use_start_eng_; + std::uniform_int_distribution random_values_use_start_dist_; + std::vector discretized_gradients_and_hessians_vector_; + std::vector ordered_int_gradients_and_hessians_; + + double max_gradient_abs_; + double max_hessian_abs_; + + double gradient_scale_; + double hessian_scale_; + double inverse_gradient_scale_; + double inverse_hessian_scale_; + + bool is_constant_hessian_; + int num_leaves_; + + std::vector leaf_num_bits_in_histogram_bin_; + std::vector node_num_bits_in_histogram_bin_; + std::vector global_leaf_num_bits_in_histogram_bin_; + std::vector global_node_num_bits_in_histogram_bin_; + + std::vector leaf_grad_hess_stats_; + std::vector> change_hist_bits_buffer_; +}; + +} // namespace LightGBM + +#endif // LIGHTGBM_SRC_TREELEARNER_GRADIENT_DISCRETIZER_HPP_ diff --git a/src/treelearner/leaf_splits.hpp b/src/treelearner/leaf_splits.hpp new file mode 100644 index 0000000..6a9e367 --- /dev/null +++ b/src/treelearner/leaf_splits.hpp @@ -0,0 +1,277 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_LEAF_SPLITS_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_LEAF_SPLITS_HPP_ + +#include +#include +#include + +#include +#include + +#include "data_partition.hpp" + +namespace LightGBM { + +/*! +* \brief used to find split candidates for a leaf +*/ +class LeafSplits { + public: + LeafSplits(data_size_t num_data, const Config* config) + : deterministic_(false), + num_data_in_leaf_(num_data), + num_data_(num_data), + data_indices_(nullptr), weight_(0) { + if (config != nullptr) { + deterministic_ = config->deterministic; + } + } + void ResetNumData(data_size_t num_data) { + num_data_ = num_data; + num_data_in_leaf_ = num_data; + } + ~LeafSplits() { + } + + /*! + * \brief Init split on current leaf on partial data. + * \param leaf Index of current leaf + * \param data_partition current data partition + * \param sum_gradients + * \param sum_hessians + * \param weight + */ + void Init(int leaf, const DataPartition* data_partition, double sum_gradients, + double sum_hessians, double weight) { + leaf_index_ = leaf; + data_indices_ = data_partition->GetIndexOnLeaf(leaf, &num_data_in_leaf_); + sum_gradients_ = sum_gradients; + sum_hessians_ = sum_hessians; + weight_ = weight; + } + + /*! + * \brief Init split on current leaf on partial data. + * \param leaf Index of current leaf + * \param data_partition current data partition + * \param sum_gradients + * \param sum_hessians + * \param sum_gradients_and_hessians + * \param weight + */ + void Init(int leaf, const DataPartition* data_partition, double sum_gradients, + double sum_hessians, int64_t sum_gradients_and_hessians, double weight) { + leaf_index_ = leaf; + data_indices_ = data_partition->GetIndexOnLeaf(leaf, &num_data_in_leaf_); + sum_gradients_ = sum_gradients; + sum_hessians_ = sum_hessians; + int_sum_gradients_and_hessians_ = sum_gradients_and_hessians; + weight_ = weight; + } + + /*! + * \brief Init split on current leaf on partial data. + * \param leaf Index of current leaf + * \param sum_gradients + * \param sum_hessians + */ + void Init(int leaf, double sum_gradients, double sum_hessians) { + leaf_index_ = leaf; + sum_gradients_ = sum_gradients; + sum_hessians_ = sum_hessians; + } + + /*! + * \brief Init splits on the current leaf, it will traverse all data to sum up the results + * \param gradients + * \param hessians + */ + void Init(const score_t* gradients, const score_t* hessians) { + num_data_in_leaf_ = num_data_; + leaf_index_ = 0; + data_indices_ = nullptr; + double tmp_sum_gradients = 0.0f; + double tmp_sum_hessians = 0.0f; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians) if (num_data_in_leaf_ >= 1024 && !deterministic_) + for (data_size_t i = 0; i < num_data_in_leaf_; ++i) { + tmp_sum_gradients += gradients[i]; + tmp_sum_hessians += hessians[i]; + } + sum_gradients_ = tmp_sum_gradients; + sum_hessians_ = tmp_sum_hessians; + } + + + /*! + * \brief Init splits on the current leaf, it will traverse all data to sum up the results + * \param int_gradients_and_hessians Discretized gradients and hessians + * \param grad_scale Scaling factor to recover original gradients from discretized gradients + * \param hess_scale Scaling factor to recover original hessians from discretized hessians + */ + void Init(const int8_t* int_gradients_and_hessians, + const double grad_scale, const double hess_scale) { + num_data_in_leaf_ = num_data_; + leaf_index_ = 0; + data_indices_ = nullptr; + double tmp_sum_gradients = 0.0f; + double tmp_sum_hessians = 0.0f; + const int16_t* packed_int_gradients_and_hessians = reinterpret_cast(int_gradients_and_hessians); + int64_t tmp_sum_gradients_and_hessians = 0; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians, tmp_sum_gradients_and_hessians) if (num_data_in_leaf_ >= 1024 && !deterministic_) + for (data_size_t i = 0; i < num_data_in_leaf_; ++i) { + tmp_sum_gradients += int_gradients_and_hessians[2 * i + 1] * grad_scale; + tmp_sum_hessians += int_gradients_and_hessians[2 * i] * hess_scale; + const int16_t packed_int_grad_and_hess = packed_int_gradients_and_hessians[i]; + const int64_t packed_long_int_grad_and_hess = + (static_cast(static_cast(packed_int_grad_and_hess >> 8)) << 32) | + (static_cast(packed_int_grad_and_hess & 0x00ff)); + tmp_sum_gradients_and_hessians += packed_long_int_grad_and_hess; + } + sum_gradients_ = tmp_sum_gradients; + sum_hessians_ = tmp_sum_hessians; + int_sum_gradients_and_hessians_ = tmp_sum_gradients_and_hessians; + } + + + /*! + * \brief Init splits on current leaf of partial data. + * \param leaf Index of current leaf + * \param data_partition current data partition + * \param gradients + * \param hessians + */ + void Init(int leaf, const DataPartition* data_partition, + const score_t* gradients, const score_t* hessians) { + leaf_index_ = leaf; + data_indices_ = data_partition->GetIndexOnLeaf(leaf, &num_data_in_leaf_); + double tmp_sum_gradients = 0.0f; + double tmp_sum_hessians = 0.0f; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians) if (num_data_in_leaf_ >= 1024 && !deterministic_) + for (data_size_t i = 0; i < num_data_in_leaf_; ++i) { + const data_size_t idx = data_indices_[i]; + tmp_sum_gradients += gradients[idx]; + tmp_sum_hessians += hessians[idx]; + } + sum_gradients_ = tmp_sum_gradients; + sum_hessians_ = tmp_sum_hessians; + } + + + /*! + * \brief Init splits on current leaf of partial data. + * \param leaf Index of current leaf + * \param data_partition current data partition + * \param int_gradients_and_hessians Discretized gradients and hessians + * \param grad_scale Scaling factor to recover original gradients from discretized gradients + * \param hess_scale Scaling factor to recover original hessians from discretized hessians + */ + void Init(int leaf, const DataPartition* data_partition, + const int8_t* int_gradients_and_hessians, + const score_t grad_scale, const score_t hess_scale) { + leaf_index_ = leaf; + data_indices_ = data_partition->GetIndexOnLeaf(leaf, &num_data_in_leaf_); + double tmp_sum_gradients = 0.0f; + double tmp_sum_hessians = 0.0f; + const int16_t* packed_int_gradients_and_hessians = reinterpret_cast(int_gradients_and_hessians); + int64_t tmp_sum_gradients_and_hessians = 0; +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) reduction(+:tmp_sum_gradients, tmp_sum_hessians, tmp_sum_gradients_and_hessians) if (num_data_in_leaf_ >= 1024 && !deterministic_) + for (data_size_t i = 0; i < num_data_in_leaf_; ++i) { + const data_size_t idx = data_indices_[i]; + tmp_sum_gradients += int_gradients_and_hessians[2 * idx + 1] * grad_scale; + tmp_sum_hessians += int_gradients_and_hessians[2 * idx] * hess_scale; + const int16_t packed_int_grad_and_hess = packed_int_gradients_and_hessians[i]; + const int64_t packed_long_int_grad_and_hess = + (static_cast(static_cast(packed_int_grad_and_hess >> 8)) << 32) | + (static_cast(packed_int_grad_and_hess & 0x00ff)); + tmp_sum_gradients_and_hessians += packed_long_int_grad_and_hess; + } + sum_gradients_ = tmp_sum_gradients; + sum_hessians_ = tmp_sum_hessians; + int_sum_gradients_and_hessians_ = tmp_sum_gradients_and_hessians; + } + + + /*! + * \brief Init splits on current leaf, only update sum_gradients and sum_hessians + * \param sum_gradients + * \param sum_hessians + */ + void Init(double sum_gradients, double sum_hessians) { + leaf_index_ = 0; + sum_gradients_ = sum_gradients; + sum_hessians_ = sum_hessians; + } + + /*! + * \brief Init splits on current leaf, only update sum_gradients and sum_hessians + * \param sum_gradients + * \param sum_hessians + * \param int_sum_gradients_and_hessians + */ + void Init(double sum_gradients, double sum_hessians, int64_t int_sum_gradients_and_hessians) { + leaf_index_ = 0; + sum_gradients_ = sum_gradients; + sum_hessians_ = sum_hessians; + int_sum_gradients_and_hessians_ = int_sum_gradients_and_hessians; + } + + /*! + * \brief Init splits on current leaf + */ + void Init() { + leaf_index_ = -1; + data_indices_ = nullptr; + num_data_in_leaf_ = 0; + } + + + /*! \brief Get current leaf index */ + int leaf_index() const { return leaf_index_; } + + /*! \brief Get number of data in current leaf */ + data_size_t num_data_in_leaf() const { return num_data_in_leaf_; } + + /*! \brief Get sum of gradients of current leaf */ + double sum_gradients() const { return sum_gradients_; } + + /*! \brief Get sum of Hessians of current leaf */ + double sum_hessians() const { return sum_hessians_; } + + /*! \brief Get sum of discretized gradients and Hessians of current leaf */ + int64_t int_sum_gradients_and_hessians() const { return int_sum_gradients_and_hessians_; } + + /*! \brief Get indices of data of current leaf */ + const data_size_t* data_indices() const { return data_indices_; } + + /*! \brief Get weight of current leaf */ + double weight() const { return weight_; } + + + + private: + bool deterministic_; + /*! \brief current leaf index */ + int leaf_index_; + /*! \brief number of data on current leaf */ + data_size_t num_data_in_leaf_; + /*! \brief number of all training data */ + data_size_t num_data_; + /*! \brief sum of gradients of current leaf */ + double sum_gradients_; + /*! \brief sum of Hessians of current leaf */ + double sum_hessians_; + /*! \brief sum of discretized gradients and Hessians of current leaf */ + int64_t int_sum_gradients_and_hessians_; + /*! \brief indices of data of current leaf */ + const data_size_t* data_indices_; + /*! \brief weight of current leaf */ + double weight_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_LEAF_SPLITS_HPP_ diff --git a/src/treelearner/linear_tree_learner.cpp b/src/treelearner/linear_tree_learner.cpp new file mode 100644 index 0000000..a57cdb7 --- /dev/null +++ b/src/treelearner/linear_tree_learner.cpp @@ -0,0 +1,406 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include "linear_tree_learner.h" + +#include + +#include +#include +#include + +namespace LightGBM { + +template +void LinearTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian) { + TREE_LEARNER_TYPE::Init(train_data, is_constant_hessian); + LinearTreeLearner::InitLinear(train_data, this->config_->num_leaves); +} + +template +void LinearTreeLearner::InitLinear(const Dataset* train_data, const int max_leaves) { + leaf_map_ = std::vector(train_data->num_data(), -1); + contains_nan_ = std::vector(train_data->num_features(), 0); + // identify features containing nans +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feat = 0; feat < train_data->num_features(); ++feat) { + auto bin_mapper = this->train_data_->FeatureBinMapper(feat); + if (bin_mapper->bin_type() == BinType::NumericalBin) { + const float* feat_ptr = this->train_data_->raw_index(feat); + for (int i = 0; i < train_data->num_data(); ++i) { + if (std::isnan(feat_ptr[i])) { + contains_nan_[feat] = 1; + break; + } + } + } + } + any_nan_ = false; + for (int feat = 0; feat < train_data->num_features(); ++feat) { + if (contains_nan_[feat]) { + any_nan_ = true; + break; + } + } + // preallocate the matrix used to calculate linear model coefficients + int max_num_feat = std::min(max_leaves, this->train_data_->num_numeric_features()); + XTHX_.clear(); + XTg_.clear(); + for (int i = 0; i < max_leaves; ++i) { + // store only upper triangular half of matrix as an array, in row-major order + // this requires (max_num_feat + 1) * (max_num_feat + 2) / 2 entries (including the constant terms of the regression) + // we add another 8 to ensure cache lines are not shared among processors + XTHX_.push_back(std::vector((max_num_feat + 1) * (max_num_feat + 2) / 2 + 8, 0)); + XTg_.push_back(std::vector(max_num_feat + 9, 0.0)); + } + XTHX_by_thread_.clear(); + XTg_by_thread_.clear(); + int max_threads = OMP_NUM_THREADS(); + for (int i = 0; i < max_threads; ++i) { + XTHX_by_thread_.push_back(XTHX_); + XTg_by_thread_.push_back(XTg_); + } +} + +template +Tree* LinearTreeLearner::Train(const score_t* gradients, const score_t *hessians, bool is_first_tree) { + Common::FunctionTimer fun_timer("SerialTreeLearner::Train", global_timer); + this->gradients_ = gradients; + this->hessians_ = hessians; + int num_threads = OMP_NUM_THREADS(); + if (this->share_state_->num_threads != num_threads && this->share_state_->num_threads > 0) { + Log::Warning( + "Detected that num_threads changed during training (from %d to %d), " + "it may cause unexpected errors.", + this->share_state_->num_threads, num_threads); + } + this->share_state_->num_threads = num_threads; + + // some initial works before training + this->BeforeTrain(); + + auto tree = std::unique_ptr(new Tree(this->config_->num_leaves, true, true)); + auto tree_ptr = tree.get(); + this->constraints_->ShareTreePointer(tree_ptr); + + // root leaf + int left_leaf = 0; + int cur_depth = 1; + // only root leaf can be splitted on first time + int right_leaf = -1; + + int init_splits = this->ForceSplits(tree_ptr, &left_leaf, &right_leaf, &cur_depth); + + for (int split = init_splits; split < this->config_->num_leaves - 1; ++split) { + // some initial works before finding best split + if (this->BeforeFindBestSplit(tree_ptr, left_leaf, right_leaf)) { + // find best threshold for every feature + this->FindBestSplits(tree_ptr); + } + // Get a leaf with max split gain + int best_leaf = static_cast(ArrayArgs::ArgMax(this->best_split_per_leaf_)); + // Get split information for best leaf + const SplitInfo& best_leaf_SplitInfo = this->best_split_per_leaf_[best_leaf]; + // cannot split, quit + if (best_leaf_SplitInfo.gain <= 0.0) { + Log::Warning("No further splits with positive gain, best gain: %f", best_leaf_SplitInfo.gain); + break; + } + // split tree with best leaf + this->Split(tree_ptr, best_leaf, &left_leaf, &right_leaf); + cur_depth = std::max(cur_depth, tree->leaf_depth(left_leaf)); + } + + bool has_nan = false; + if (any_nan_) { + for (int i = 0; i < tree->num_leaves() - 1 ; ++i) { + if (contains_nan_[tree_ptr->split_feature_inner(i)]) { + has_nan = true; + break; + } + } + } + + GetLeafMap(tree_ptr); + + if (has_nan) { + CalculateLinear(tree_ptr, false, this->gradients_, this->hessians_, is_first_tree); + } else { + CalculateLinear(tree_ptr, false, this->gradients_, this->hessians_, is_first_tree); + } + + Log::Debug("Trained a tree with leaves = %d and depth = %d", tree->num_leaves(), cur_depth); + return tree.release(); +} + +template +Tree* LinearTreeLearner::FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t *hessians) const { + auto tree = TREE_LEARNER_TYPE::FitByExistingTree(old_tree, gradients, hessians); + bool has_nan = false; + if (any_nan_) { + for (int i = 0; i < tree->num_leaves() - 1 ; ++i) { + if (contains_nan_[this->train_data_->InnerFeatureIndex(tree->split_feature(i))]) { + has_nan = true; + break; + } + } + } + GetLeafMap(tree); + if (has_nan) { + CalculateLinear(tree, true, gradients, hessians, false); + } else { + CalculateLinear(tree, true, gradients, hessians, false); + } + return tree; +} + +template +Tree* LinearTreeLearner::FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t *hessians) const { + this->data_partition_->ResetByLeafPred(leaf_pred, old_tree->num_leaves()); + return LinearTreeLearner::FitByExistingTree(old_tree, gradients, hessians); +} + +template +void LinearTreeLearner::GetLeafMap(Tree* tree) const { + std::fill(leaf_map_.begin(), leaf_map_.end(), -1); + // map data to leaf number + const data_size_t* ind = this->data_partition_->indices(); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(dynamic) + for (int i = 0; i < tree->num_leaves(); ++i) { + data_size_t idx = this->data_partition_->leaf_begin(i); + for (int j = 0; j < this->data_partition_->leaf_count(i); ++j) { + leaf_map_[ind[idx + j]] = i; + } + } +} + + +template +template +void LinearTreeLearner::CalculateLinear(Tree* tree, bool is_refit, const score_t* gradients, const score_t* hessians, bool is_first_tree) const { + tree->SetIsLinear(true); + int num_leaves = tree->num_leaves(); + int num_threads = OMP_NUM_THREADS(); + if (is_first_tree) { + for (int leaf_num = 0; leaf_num < num_leaves; ++leaf_num) { + tree->SetLeafConst(leaf_num, tree->LeafOutput(leaf_num)); + } + return; + } + + // calculate coefficients using the method described in Eq 3 of https://arxiv.org/abs/1802.05640 + // the coefficients vector is given by + // - (X_T * H * X + lambda) ^ (-1) * (X_T * g) + // where: + // X is the matrix where the first column is the feature values and the second is all ones, + // H is the diagonal matrix of the hessian, + // lambda is the diagonal matrix with diagonal entries equal to the regularisation term linear_lambda + // g is the vector of gradients + // the subscript _T denotes the transpose + + // create array of pointers to raw data, and coefficient matrices, for each leaf + std::vector> leaf_features; + std::vector leaf_num_features; + std::vector> raw_data_ptr; + size_t max_num_features = 0; + for (int i = 0; i < num_leaves; ++i) { + std::vector raw_features; + if (is_refit) { + raw_features = tree->LeafFeatures(i); + } else { + raw_features = tree->branch_features(i); + } + std::sort(raw_features.begin(), raw_features.end()); + auto new_end = std::unique(raw_features.begin(), raw_features.end()); + raw_features.erase(new_end, raw_features.end()); + std::vector numerical_features; + std::vector data_ptr; + for (size_t j = 0; j < raw_features.size(); ++j) { + int feat = this->train_data_->InnerFeatureIndex(raw_features[j]); + auto bin_mapper = this->train_data_->FeatureBinMapper(feat); + if (bin_mapper->bin_type() == BinType::NumericalBin) { + numerical_features.push_back(feat); + data_ptr.push_back(this->train_data_->raw_index(feat)); + } + } + leaf_features.push_back(numerical_features); + raw_data_ptr.push_back(data_ptr); + leaf_num_features.push_back(static_cast(numerical_features.size())); + if (numerical_features.size() > max_num_features) { + max_num_features = numerical_features.size(); + } + } + // clear the coefficient matrices +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < num_threads; ++i) { + for (int leaf_num = 0; leaf_num < num_leaves; ++leaf_num) { + size_t num_feat = leaf_features[leaf_num].size(); + std::fill(XTHX_by_thread_[i][leaf_num].begin(), XTHX_by_thread_[i][leaf_num].begin() + (num_feat + 1) * (num_feat + 2) / 2, 0.0f); + std::fill(XTg_by_thread_[i][leaf_num].begin(), XTg_by_thread_[i][leaf_num].begin() + num_feat + 1, 0.0f); + } + } +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int leaf_num = 0; leaf_num < num_leaves; ++leaf_num) { + size_t num_feat = leaf_features[leaf_num].size(); + std::fill(XTHX_[leaf_num].begin(), XTHX_[leaf_num].begin() + (num_feat + 1) * (num_feat + 2) / 2, 0.0f); + std::fill(XTg_[leaf_num].begin(), XTg_[leaf_num].begin() + num_feat + 1, 0.0f); + } + std::vector> num_nonzero; + for (int i = 0; i < num_threads; ++i) { + if (HAS_NAN) { + num_nonzero.push_back(std::vector(num_leaves, 0)); + } + } + OMP_INIT_EX(); +#pragma omp parallel num_threads(OMP_NUM_THREADS()) if (this->num_data_ > 1024) + { + std::vector curr_row(max_num_features + 1); + int tid = omp_get_thread_num(); +#pragma omp for schedule(static) + for (int i = 0; i < this->num_data_; ++i) { + OMP_LOOP_EX_BEGIN(); + int leaf_num = leaf_map_[i]; + if (leaf_num < 0) { + continue; + } + bool nan_found = false; + int num_feat = leaf_num_features[leaf_num]; + for (int feat = 0; feat < num_feat; ++feat) { + if (HAS_NAN) { + float val = raw_data_ptr[leaf_num][feat][i]; + if (std::isnan(val)) { + nan_found = true; + break; + } + num_nonzero[tid][leaf_num] += 1; + curr_row[feat] = val; + } else { + curr_row[feat] = raw_data_ptr[leaf_num][feat][i]; + } + } + if (HAS_NAN) { + if (nan_found) { + continue; + } + } + curr_row[num_feat] = 1.0; + float h = static_cast(hessians[i]); + float g = static_cast(gradients[i]); + int j = 0; + for (int feat1 = 0; feat1 < num_feat + 1; ++feat1) { + double f1_val = static_cast(curr_row[feat1]); + XTg_by_thread_[tid][leaf_num][feat1] += f1_val * g; + f1_val *= h; + for (int feat2 = feat1; feat2 < num_feat + 1; ++feat2) { + XTHX_by_thread_[tid][leaf_num][j] += f1_val * curr_row[feat2]; + ++j; + } + } + OMP_LOOP_EX_END(); + } + } + OMP_THROW_EX(); + auto total_nonzero = std::vector(tree->num_leaves()); + // aggregate results from different threads + for (int tid = 0; tid < num_threads; ++tid) { +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int leaf_num = 0; leaf_num < num_leaves; ++leaf_num) { + size_t num_feat = leaf_features[leaf_num].size(); + for (size_t j = 0; j < (num_feat + 1) * (num_feat + 2) / 2; ++j) { + XTHX_[leaf_num][j] += XTHX_by_thread_[tid][leaf_num][j]; + } + for (size_t feat1 = 0; feat1 < num_feat + 1; ++feat1) { + XTg_[leaf_num][feat1] += XTg_by_thread_[tid][leaf_num][feat1]; + } + if (HAS_NAN) { + total_nonzero[leaf_num] += num_nonzero[tid][leaf_num]; + } + } + } + if (!HAS_NAN) { + for (int leaf_num = 0; leaf_num < num_leaves; ++leaf_num) { + total_nonzero[leaf_num] = this->data_partition_->leaf_count(leaf_num); + } + } + double shrinkage = tree->shrinkage(); + double decay_rate = this->config_->refit_decay_rate; + // copy into eigen matrices and solve +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int leaf_num = 0; leaf_num < num_leaves; ++leaf_num) { + if (total_nonzero[leaf_num] < static_cast(leaf_features[leaf_num].size()) + 1) { + if (is_refit) { + double old_const = tree->LeafConst(leaf_num); + tree->SetLeafConst(leaf_num, decay_rate * old_const + (1.0 - decay_rate) * tree->LeafOutput(leaf_num) * shrinkage); + tree->SetLeafCoeffs(leaf_num, std::vector(leaf_features[leaf_num].size(), 0)); + tree->SetLeafFeaturesInner(leaf_num, leaf_features[leaf_num]); + } else { + tree->SetLeafConst(leaf_num, tree->LeafOutput(leaf_num)); + } + continue; + } + size_t num_feat = leaf_features[leaf_num].size(); + Eigen::MatrixXd XTHX_mat(num_feat + 1, num_feat + 1); + Eigen::MatrixXd XTg_mat(num_feat + 1, 1); + size_t j = 0; + for (size_t feat1 = 0; feat1 < num_feat + 1; ++feat1) { + for (size_t feat2 = feat1; feat2 < num_feat + 1; ++feat2) { + XTHX_mat(feat1, feat2) = XTHX_[leaf_num][j]; + XTHX_mat(feat2, feat1) = XTHX_mat(feat1, feat2); + if ((feat1 == feat2) && (feat1 < num_feat)) { + XTHX_mat(feat1, feat2) += this->config_->linear_lambda; + } + ++j; + } + XTg_mat(feat1) = XTg_[leaf_num][feat1]; + } + Eigen::MatrixXd coeffs = - XTHX_mat.fullPivLu().inverse() * XTg_mat; + std::vector coeffs_vec; + std::vector features_new; + std::vector old_coeffs = tree->LeafCoeffs(leaf_num); + for (size_t i = 0; i < leaf_features[leaf_num].size(); ++i) { + if (is_refit) { + features_new.push_back(leaf_features[leaf_num][i]); + coeffs_vec.push_back(decay_rate * old_coeffs[i] + (1.0 - decay_rate) * coeffs(i) * shrinkage); + } else { + if (coeffs(i) < -kZeroThreshold || coeffs(i) > kZeroThreshold) { + coeffs_vec.push_back(coeffs(i)); + int feat = leaf_features[leaf_num][i]; + features_new.push_back(feat); + } + } + } + // update the tree properties + tree->SetLeafFeaturesInner(leaf_num, features_new); + std::vector features_raw(features_new.size()); + for (size_t i = 0; i < features_new.size(); ++i) { + features_raw[i] = this->train_data_->RealFeatureIndex(features_new[i]); + } + tree->SetLeafFeatures(leaf_num, features_raw); + tree->SetLeafCoeffs(leaf_num, coeffs_vec); + if (is_refit) { + double old_const = tree->LeafConst(leaf_num); + tree->SetLeafConst(leaf_num, decay_rate * old_const + (1.0 - decay_rate) * coeffs(num_feat) * shrinkage); + } else { + tree->SetLeafConst(leaf_num, coeffs(num_feat)); + } + } +} + +template void LinearTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian); +template void LinearTreeLearner::InitLinear(const Dataset* train_data, const int max_leaves); +template Tree* LinearTreeLearner::Train(const score_t* gradients, const score_t *hessians, bool is_first_tree); +template Tree* LinearTreeLearner::FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t *hessians) const; +template Tree* LinearTreeLearner::FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t *hessians) const; + +template void LinearTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian); +template void LinearTreeLearner::InitLinear(const Dataset* train_data, const int max_leaves); +template Tree* LinearTreeLearner::Train(const score_t* gradients, const score_t *hessians, bool is_first_tree); +template Tree* LinearTreeLearner::FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t *hessians) const; +template Tree* LinearTreeLearner::FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t *hessians) const; + +} // namespace LightGBM diff --git a/src/treelearner/linear_tree_learner.h b/src/treelearner/linear_tree_learner.h new file mode 100644 index 0000000..a16aff7 --- /dev/null +++ b/src/treelearner/linear_tree_learner.h @@ -0,0 +1,130 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_LINEAR_TREE_LEARNER_H_ +#define LIGHTGBM_SRC_TREELEARNER_LINEAR_TREE_LEARNER_H_ + +#include +#include +#include +#include +#include + +#include "gpu_tree_learner.h" +#include "serial_tree_learner.h" + +namespace LightGBM { + +template +class LinearTreeLearner: public TREE_LEARNER_TYPE { + public: + explicit LinearTreeLearner(const Config* config) : TREE_LEARNER_TYPE(config) {} + + void Init(const Dataset* train_data, bool is_constant_hessian) override; + + void InitLinear(const Dataset* train_data, const int max_leaves) override; + + Tree* Train(const score_t* gradients, const score_t *hessians, bool is_first_tree) override; + + /*! \brief Create array mapping dataset to leaf index, used for linear trees */ + void GetLeafMap(Tree* tree) const; + + template + void CalculateLinear(Tree* tree, bool is_refit, const score_t* gradients, const score_t* hessians, bool is_first_tree) const; + + Tree* FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const override; + + Tree* FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t* hessians) const override; + + void AddPredictionToScore(const Tree* tree, + double* out_score) const override { + CHECK_LE(tree->num_leaves(), this->data_partition_->num_leaves()); + bool has_nan = false; + if (any_nan_) { + for (int i = 0; i < tree->num_leaves() - 1 ; ++i) { + // use split_feature because split_feature_inner doesn't work when refitting existing tree + if (contains_nan_[this->train_data_->InnerFeatureIndex(tree->split_feature(i))]) { + has_nan = true; + break; + } + } + } + if (has_nan) { + AddPredictionToScoreInner(tree, out_score); + } else { + AddPredictionToScoreInner(tree, out_score); + } + } + + template + void AddPredictionToScoreInner(const Tree* tree, double* out_score) const { + int num_leaves = tree->num_leaves(); + std::vector leaf_const(num_leaves); + std::vector> leaf_coeff(num_leaves); + std::vector> feat_ptr(num_leaves); + std::vector leaf_output(num_leaves); + std::vector leaf_num_features(num_leaves); + for (int leaf_num = 0; leaf_num < num_leaves; ++leaf_num) { + leaf_const[leaf_num] = tree->LeafConst(leaf_num); + leaf_coeff[leaf_num] = tree->LeafCoeffs(leaf_num); + leaf_output[leaf_num] = tree->LeafOutput(leaf_num); + for (int feat : tree->LeafFeaturesInner(leaf_num)) { + feat_ptr[leaf_num].push_back(this->train_data_->raw_index(feat)); + } + leaf_num_features[leaf_num] = static_cast(feat_ptr[leaf_num].size()); + } + OMP_INIT_EX(); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) if (this->num_data_ > 1024) + for (int i = 0; i < this->num_data_; ++i) { + OMP_LOOP_EX_BEGIN(); + int leaf_num = leaf_map_[i]; + if (leaf_num < 0) { + continue; + } + double output = leaf_const[leaf_num]; + int num_feat = leaf_num_features[leaf_num]; + if (HAS_NAN) { + bool nan_found = false; + for (int feat_ind = 0; feat_ind < num_feat; ++feat_ind) { + float val = feat_ptr[leaf_num][feat_ind][i]; + if (std::isnan(val)) { + nan_found = true; + break; + } + output += val * leaf_coeff[leaf_num][feat_ind]; + } + if (nan_found) { + out_score[i] += leaf_output[leaf_num]; + } else { + out_score[i] += output; + } + } else { + for (int feat_ind = 0; feat_ind < num_feat; ++feat_ind) { + output += feat_ptr[leaf_num][feat_ind][i] * leaf_coeff[leaf_num][feat_ind]; + } + out_score[i] += output; + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + + protected: + /*! \brief whether numerical features contain any nan values */ + std::vector contains_nan_; + /*! whether any numerical feature contains a nan value */ + bool any_nan_; + /*! \brief map dataset to leaves */ + mutable std::vector leaf_map_; + /*! \brief temporary storage for calculating linear model coefficients */ + mutable std::vector> XTHX_; + mutable std::vector> XTg_; + mutable std::vector>> XTHX_by_thread_; + mutable std::vector>> XTg_by_thread_; +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_LINEAR_TREE_LEARNER_H_ diff --git a/src/treelearner/monotone_constraints.hpp b/src/treelearner/monotone_constraints.hpp new file mode 100644 index 0000000..8f48e1a --- /dev/null +++ b/src/treelearner/monotone_constraints.hpp @@ -0,0 +1,1187 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for + * license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_MONOTONE_CONSTRAINTS_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_MONOTONE_CONSTRAINTS_HPP_ + +#include + +#include +#include +#include +#include +#include +#include + +#include "split_info.hpp" + +namespace LightGBM { + +class LeafConstraintsBase; + +struct BasicConstraint { + double min = -std::numeric_limits::max(); + double max = std::numeric_limits::max(); + + BasicConstraint(double min, double max) : min(min), max(max) {} + + BasicConstraint() = default; +}; + +struct FeatureConstraint { + virtual void InitCumulativeConstraints(bool) const {} + virtual void Update(int) const {} + virtual BasicConstraint LeftToBasicConstraint() const = 0; + virtual BasicConstraint RightToBasicConstraint() const = 0; + virtual bool ConstraintDifferentDependingOnThreshold() const = 0; + virtual ~FeatureConstraint() {} +}; + +struct ConstraintEntry { + virtual ~ConstraintEntry() {} + virtual void Reset() = 0; + virtual void UpdateMin(double new_min) = 0; + virtual void UpdateMax(double new_max) = 0; + virtual bool UpdateMinAndReturnBoolIfChanged(double new_min) = 0; + virtual bool UpdateMaxAndReturnBoolIfChanged(double new_max) = 0; + virtual ConstraintEntry *clone() const = 0; + + virtual void RecomputeConstraintsIfNeeded(LeafConstraintsBase *, int, int, + uint32_t) {} + + virtual FeatureConstraint *GetFeatureConstraint(int feature_index) = 0; +}; + +// used by both BasicLeafConstraints and IntermediateLeafConstraints +struct BasicConstraintEntry : ConstraintEntry, + FeatureConstraint, + BasicConstraint { + bool ConstraintDifferentDependingOnThreshold() const final { return false; } + + BasicConstraintEntry *clone() const final { + return new BasicConstraintEntry(*this); + }; + + void Reset() final { + min = -std::numeric_limits::max(); + max = std::numeric_limits::max(); + } + + void UpdateMin(double new_min) final { min = std::max(new_min, min); } + + void UpdateMax(double new_max) final { max = std::min(new_max, max); } + + bool UpdateMinAndReturnBoolIfChanged(double new_min) final { + if (new_min > min) { + min = new_min; + return true; + } + return false; + } + + bool UpdateMaxAndReturnBoolIfChanged(double new_max) final { + if (new_max < max) { + max = new_max; + return true; + } + return false; + } + + BasicConstraint LeftToBasicConstraint() const final { return *this; } + + BasicConstraint RightToBasicConstraint() const final { return *this; } + + FeatureConstraint *GetFeatureConstraint(int) final { return this; } +}; + +struct FeatureMinOrMaxConstraints { + std::vector constraints; + // the constraint number i is valid on the slice + // [thresholds[i]:threshold[i+1]) + // if threshold[i+1] does not exist, then it is valid for thresholds following + // threshold[i] + std::vector thresholds; + + FeatureMinOrMaxConstraints() { + constraints.reserve(32); + thresholds.reserve(32); + } + + size_t Size() const { return thresholds.size(); } + + explicit FeatureMinOrMaxConstraints(double extremum) { + constraints.reserve(32); + thresholds.reserve(32); + + constraints.push_back(extremum); + thresholds.push_back(0); + } + + void Reset(double extremum) { + constraints.resize(1); + constraints[0] = extremum; + thresholds.resize(1); + thresholds[0] = 0; + } + + void UpdateMin(double min) { + for (size_t j = 0; j < constraints.size(); ++j) { + if (min > constraints[j]) { + constraints[j] = min; + } + } + } + + void UpdateMax(double max) { + for (size_t j = 0; j < constraints.size(); ++j) { + if (max < constraints[j]) { + constraints[j] = max; + } + } + } +}; + +struct CumulativeFeatureConstraint { + std::vector thresholds_min_constraints; + std::vector thresholds_max_constraints; + std::vector cumulative_min_constraints_left_to_right; + std::vector cumulative_min_constraints_right_to_left; + std::vector cumulative_max_constraints_left_to_right; + std::vector cumulative_max_constraints_right_to_left; + size_t index_min_constraints_left_to_right; + size_t index_min_constraints_right_to_left; + size_t index_max_constraints_left_to_right; + size_t index_max_constraints_right_to_left; + + static void CumulativeExtremum( + const double &(*extremum_function)(const double &, const double &), + bool is_direction_from_left_to_right, + std::vector* cumulative_extremum) { + if (cumulative_extremum->size() == 1) { + return; + } + +#ifdef DEBUG + CHECK_NE(cumulative_extremum->size(), 0); +#endif + + size_t n_exts = cumulative_extremum->size(); + int step = is_direction_from_left_to_right ? 1 : -1; + size_t start = is_direction_from_left_to_right ? 0 : n_exts - 1; + size_t end = is_direction_from_left_to_right ? n_exts - 1 : 0; + + for (auto i = start; i != end; i = i + step) { + (*cumulative_extremum)[i + step] = extremum_function( + (*cumulative_extremum)[i + step], (*cumulative_extremum)[i]); + } + } + + CumulativeFeatureConstraint() = default; + + CumulativeFeatureConstraint(FeatureMinOrMaxConstraints min_constraints, + FeatureMinOrMaxConstraints max_constraints, + bool REVERSE) { + thresholds_min_constraints = min_constraints.thresholds; + thresholds_max_constraints = max_constraints.thresholds; + cumulative_min_constraints_left_to_right = min_constraints.constraints; + cumulative_min_constraints_right_to_left = min_constraints.constraints; + cumulative_max_constraints_left_to_right = max_constraints.constraints; + cumulative_max_constraints_right_to_left = max_constraints.constraints; + + const double &(*min)(const double &, const double &) = std::min; + const double &(*max)(const double &, const double &) = std::max; + CumulativeExtremum(max, true, &cumulative_min_constraints_left_to_right); + CumulativeExtremum(max, false, &cumulative_min_constraints_right_to_left); + CumulativeExtremum(min, true, &cumulative_max_constraints_left_to_right); + CumulativeExtremum(min, false, &cumulative_max_constraints_right_to_left); + + if (REVERSE) { + index_min_constraints_left_to_right = + thresholds_min_constraints.size() - 1; + index_min_constraints_right_to_left = + thresholds_min_constraints.size() - 1; + index_max_constraints_left_to_right = + thresholds_max_constraints.size() - 1; + index_max_constraints_right_to_left = + thresholds_max_constraints.size() - 1; + } else { + index_min_constraints_left_to_right = 0; + index_min_constraints_right_to_left = 0; + index_max_constraints_left_to_right = 0; + index_max_constraints_right_to_left = 0; + } + } + + void Update(int threshold) { + while ( + static_cast( + thresholds_min_constraints[index_min_constraints_left_to_right]) > + threshold - 1) { + index_min_constraints_left_to_right -= 1; + } + while ( + static_cast( + thresholds_min_constraints[index_min_constraints_right_to_left]) > + threshold) { + index_min_constraints_right_to_left -= 1; + } + while ( + static_cast( + thresholds_max_constraints[index_max_constraints_left_to_right]) > + threshold - 1) { + index_max_constraints_left_to_right -= 1; + } + while ( + static_cast( + thresholds_max_constraints[index_max_constraints_right_to_left]) > + threshold) { + index_max_constraints_right_to_left -= 1; + } + } + + double GetRightMin() const { + return cumulative_min_constraints_right_to_left + [index_min_constraints_right_to_left]; + } + double GetRightMax() const { + return cumulative_max_constraints_right_to_left + [index_max_constraints_right_to_left]; + } + double GetLeftMin() const { + return cumulative_min_constraints_left_to_right + [index_min_constraints_left_to_right]; + } + double GetLeftMax() const { + return cumulative_max_constraints_left_to_right + [index_max_constraints_left_to_right]; + } +}; + +struct AdvancedFeatureConstraints : FeatureConstraint { + FeatureMinOrMaxConstraints min_constraints; + FeatureMinOrMaxConstraints max_constraints; + mutable CumulativeFeatureConstraint cumulative_feature_constraint; + bool min_constraints_to_be_recomputed = false; + bool max_constraints_to_be_recomputed = false; + + void InitCumulativeConstraints(bool REVERSE) const final { + cumulative_feature_constraint = + CumulativeFeatureConstraint(min_constraints, max_constraints, REVERSE); + } + + void Update(int threshold) const final { + cumulative_feature_constraint.Update(threshold); + } + + FeatureMinOrMaxConstraints &GetMinConstraints() { return min_constraints; } + + FeatureMinOrMaxConstraints &GetMaxConstraints() { return max_constraints; } + + bool ConstraintDifferentDependingOnThreshold() const final { + return min_constraints.Size() > 1 || max_constraints.Size() > 1; + } + + BasicConstraint RightToBasicConstraint() const final { + return BasicConstraint(cumulative_feature_constraint.GetRightMin(), + cumulative_feature_constraint.GetRightMax()); + } + + BasicConstraint LeftToBasicConstraint() const final { + return BasicConstraint(cumulative_feature_constraint.GetLeftMin(), + cumulative_feature_constraint.GetLeftMax()); + } + + void Reset() { + min_constraints.Reset(-std::numeric_limits::max()); + max_constraints.Reset(std::numeric_limits::max()); + } + + void UpdateMax(double new_max, bool trigger_a_recompute) { + if (trigger_a_recompute) { + max_constraints_to_be_recomputed = true; + } + max_constraints.UpdateMax(new_max); + } + + bool FeatureMaxConstraintsToBeUpdated() const { + return max_constraints_to_be_recomputed; + } + + bool FeatureMinConstraintsToBeUpdated() const { + return min_constraints_to_be_recomputed; + } + + void ResetUpdates() { + min_constraints_to_be_recomputed = false; + max_constraints_to_be_recomputed = false; + } + + void UpdateMin(double new_min, bool trigger_a_recompute) { + if (trigger_a_recompute) { + min_constraints_to_be_recomputed = true; + } + min_constraints.UpdateMin(new_min); + } +}; + +class LeafConstraintsBase { + public: + virtual ~LeafConstraintsBase() {} + virtual const ConstraintEntry* Get(int leaf_idx) = 0; + virtual FeatureConstraint* GetFeatureConstraint(int leaf_idx, int feature_index) = 0; + virtual void Reset() = 0; + virtual void BeforeSplit(int leaf, int new_leaf, + int8_t monotone_type) = 0; + virtual std::vector Update( + bool is_numerical_split, + int leaf, int new_leaf, int8_t monotone_type, double right_output, + double left_output, int split_feature, const SplitInfo& split_info, + const std::vector& best_split_per_leaf) = 0; + + virtual void GoUpToFindConstrainingLeaves( + int, int, + std::vector*, + std::vector*, + std::vector*, + FeatureMinOrMaxConstraints*, bool , + uint32_t, uint32_t, uint32_t) {} + + virtual void RecomputeConstraintsIfNeeded( + LeafConstraintsBase *constraints_, + int feature_for_constraint, int leaf_idx, uint32_t it_end) = 0; + + inline static LeafConstraintsBase* Create(const Config* config, int num_leaves, int num_features); + + double ComputeMonotoneSplitGainPenalty(int leaf_index, double penalization) { + int depth = tree_->leaf_depth(leaf_index); + if (penalization >= depth + 1.) { + return kEpsilon; + } + if (penalization <= 1.) { + return 1. - penalization / pow(2., depth) + kEpsilon; + } + return 1. - pow(2, penalization - 1. - depth) + kEpsilon; + } + + void ShareTreePointer(const Tree* tree) { + tree_ = tree; + } + + protected: + const Tree* tree_; +}; + +// used by AdvancedLeafConstraints +struct AdvancedConstraintEntry : ConstraintEntry { + std::vector constraints; + + AdvancedConstraintEntry *clone() const final { + return new AdvancedConstraintEntry(*this); + }; + + void RecomputeConstraintsIfNeeded(LeafConstraintsBase *constraints_, + int feature_for_constraint, int leaf_idx, + uint32_t it_end) final { + if (constraints[feature_for_constraint] + .FeatureMinConstraintsToBeUpdated() || + constraints[feature_for_constraint] + .FeatureMaxConstraintsToBeUpdated()) { + FeatureMinOrMaxConstraints &constraints_to_be_updated = + constraints[feature_for_constraint].FeatureMinConstraintsToBeUpdated() + ? constraints[feature_for_constraint].GetMinConstraints() + : constraints[feature_for_constraint].GetMaxConstraints(); + + constraints_to_be_updated.Reset( + constraints[feature_for_constraint].FeatureMinConstraintsToBeUpdated() + ? -std::numeric_limits::max() + : std::numeric_limits::max()); + + std::vector features_of_splits_going_up_from_original_leaf = + std::vector(); + std::vector thresholds_of_splits_going_up_from_original_leaf = + std::vector(); + std::vector was_original_leaf_right_child_of_split = + std::vector(); + constraints_->GoUpToFindConstrainingLeaves( + feature_for_constraint, leaf_idx, + &features_of_splits_going_up_from_original_leaf, + &thresholds_of_splits_going_up_from_original_leaf, + &was_original_leaf_right_child_of_split, &constraints_to_be_updated, + constraints[feature_for_constraint] + .FeatureMinConstraintsToBeUpdated(), + 0, it_end, it_end); + constraints[feature_for_constraint].ResetUpdates(); + } + } + + // for each feature, an array of constraints needs to be stored + explicit AdvancedConstraintEntry(int num_features) { + constraints.resize(num_features); + } + + void Reset() final { + for (size_t i = 0; i < constraints.size(); ++i) { + constraints[i].Reset(); + } + } + + void UpdateMin(double new_min) final { + for (size_t i = 0; i < constraints.size(); ++i) { + constraints[i].UpdateMin(new_min, false); + } + } + + void UpdateMax(double new_max) final { + for (size_t i = 0; i < constraints.size(); ++i) { + constraints[i].UpdateMax(new_max, false); + } + } + + bool UpdateMinAndReturnBoolIfChanged(double new_min) final { + for (size_t i = 0; i < constraints.size(); ++i) { + constraints[i].UpdateMin(new_min, true); + } + // even if nothing changed, this could have been unconstrained so it needs + // to be recomputed from the beginning + return true; + } + + bool UpdateMaxAndReturnBoolIfChanged(double new_max) final { + for (size_t i = 0; i < constraints.size(); ++i) { + constraints[i].UpdateMax(new_max, true); + } + // even if nothing changed, this could have been unconstrained so it needs + // to be recomputed from the beginning + return true; + } + + FeatureConstraint *GetFeatureConstraint(int feature_index) final { + return &constraints[feature_index]; + } +}; + +class BasicLeafConstraints : public LeafConstraintsBase { + public: + explicit BasicLeafConstraints(int num_leaves) : num_leaves_(num_leaves) { + for (int i = 0; i < num_leaves; ++i) { + entries_.emplace_back(new BasicConstraintEntry()); + } + } + + void Reset() override { + for (auto& entry : entries_) { + entry->Reset(); + } + } + + void RecomputeConstraintsIfNeeded( + LeafConstraintsBase* constraints_, + int feature_for_constraint, int leaf_idx, uint32_t it_end) override { + entries_[~leaf_idx]->RecomputeConstraintsIfNeeded(constraints_, feature_for_constraint, leaf_idx, it_end); + } + + void BeforeSplit(int, int, int8_t) override {} + + std::vector Update(bool is_numerical_split, int leaf, int new_leaf, + int8_t monotone_type, double right_output, + double left_output, int, const SplitInfo& , + const std::vector&) override { + entries_[new_leaf].reset(entries_[leaf]->clone()); + if (is_numerical_split) { + double mid = (left_output + right_output) / 2.0f; + if (monotone_type < 0) { + entries_[leaf]->UpdateMin(mid); + entries_[new_leaf]->UpdateMax(mid); + } else if (monotone_type > 0) { + entries_[leaf]->UpdateMax(mid); + entries_[new_leaf]->UpdateMin(mid); + } + } + return std::vector(); + } + + const ConstraintEntry* Get(int leaf_idx) override { return entries_[leaf_idx].get(); } + + FeatureConstraint* GetFeatureConstraint(int leaf_idx, int feature_index) final { + return entries_[leaf_idx]->GetFeatureConstraint(feature_index); + } + + protected: + int num_leaves_; + std::vector> entries_; +}; + +class IntermediateLeafConstraints : public BasicLeafConstraints { + public: + explicit IntermediateLeafConstraints(const Config* config, int num_leaves) + : BasicLeafConstraints(num_leaves), config_(config) { + leaf_is_in_monotone_subtree_.resize(num_leaves_, false); + node_parent_.resize(num_leaves_ - 1, -1); + leaves_to_update_.reserve(num_leaves_); + } + + void Reset() override { + BasicLeafConstraints::Reset(); + std::fill_n(leaf_is_in_monotone_subtree_.begin(), num_leaves_, false); + std::fill_n(node_parent_.begin(), num_leaves_ - 1, -1); + leaves_to_update_.clear(); + } + + void BeforeSplit(int leaf, int new_leaf, + int8_t monotone_type) override { + if (monotone_type != 0 || leaf_is_in_monotone_subtree_[leaf]) { + leaf_is_in_monotone_subtree_[leaf] = true; + leaf_is_in_monotone_subtree_[new_leaf] = true; + } +#ifdef DEBUG + CHECK_GE(new_leaf - 1, 0); + CHECK_LT(static_cast(new_leaf - 1), node_parent_.size()); +#endif + node_parent_[new_leaf - 1] = tree_->leaf_parent(leaf); + } + + void UpdateConstraintsWithOutputs(bool is_numerical_split, int leaf, + int new_leaf, int8_t monotone_type, + double right_output, double left_output) { + entries_[new_leaf].reset(entries_[leaf]->clone()); + if (is_numerical_split) { + if (monotone_type < 0) { + entries_[leaf]->UpdateMin(right_output); + entries_[new_leaf]->UpdateMax(left_output); + } else if (monotone_type > 0) { + entries_[leaf]->UpdateMax(right_output); + entries_[new_leaf]->UpdateMin(left_output); + } + } + } + + std::vector Update(bool is_numerical_split, int leaf, + int new_leaf, int8_t monotone_type, + double right_output, double left_output, + int split_feature, const SplitInfo& split_info, + const std::vector& best_split_per_leaf) final { + leaves_to_update_.clear(); + if (leaf_is_in_monotone_subtree_[leaf]) { + UpdateConstraintsWithOutputs(is_numerical_split, leaf, new_leaf, + monotone_type, right_output, left_output); + + // Initialize variables to store information while going up the tree + int depth = tree_->leaf_depth(new_leaf) - 1; + + std::vector features_of_splits_going_up_from_original_leaf; + std::vector thresholds_of_splits_going_up_from_original_leaf; + std::vector was_original_leaf_right_child_of_split; + + features_of_splits_going_up_from_original_leaf.reserve(depth); + thresholds_of_splits_going_up_from_original_leaf.reserve(depth); + was_original_leaf_right_child_of_split.reserve(depth); + + GoUpToFindLeavesToUpdate(tree_->leaf_parent(new_leaf), + &features_of_splits_going_up_from_original_leaf, + &thresholds_of_splits_going_up_from_original_leaf, + &was_original_leaf_right_child_of_split, + split_feature, split_info, split_info.threshold, + best_split_per_leaf); + } + return leaves_to_update_; + } + + bool OppositeChildShouldBeUpdated( + bool is_split_numerical, + const std::vector& features_of_splits_going_up_from_original_leaf, + int inner_feature, + const std::vector& was_original_leaf_right_child_of_split, + bool is_in_right_child) { + // if the split is categorical, it is not handled by this optimisation, + // so the code will have to go down in the other child subtree to see if + // there are leaves to update + // even though it may sometimes be unnecessary + if (is_split_numerical) { + // only branches containing leaves that are contiguous to the original + // leaf need to be updated + // therefore, for the same feature, there is no use going down from the + // second time going up on the right (or on the left) + for (size_t split_idx = 0; + split_idx < features_of_splits_going_up_from_original_leaf.size(); + ++split_idx) { + if (features_of_splits_going_up_from_original_leaf[split_idx] == + inner_feature && + (was_original_leaf_right_child_of_split[split_idx] == + is_in_right_child)) { + return false; + } + } + return true; + } else { + return false; + } + } + + // Recursive function that goes up the tree, and then down to find leaves that + // have constraints to be updated + void GoUpToFindLeavesToUpdate( + int node_idx, + std::vector* features_of_splits_going_up_from_original_leaf, + std::vector* thresholds_of_splits_going_up_from_original_leaf, + std::vector* was_original_leaf_right_child_of_split, + int split_feature, const SplitInfo& split_info, uint32_t split_threshold, + const std::vector& best_split_per_leaf) { +#ifdef DEBUG + CHECK_GE(node_idx, 0); + CHECK_LT(static_cast(node_idx), node_parent_.size()); +#endif + int parent_idx = node_parent_[node_idx]; + // if not at the root + if (parent_idx != -1) { + int inner_feature = tree_->split_feature_inner(parent_idx); + int feature = tree_->split_feature(parent_idx); + int8_t monotone_type = config_->monotone_constraints[feature]; + bool is_in_right_child = tree_->right_child(parent_idx) == node_idx; + bool is_split_numerical = tree_->IsNumericalSplit(parent_idx); + + // this is just an optimisation not to waste time going down in subtrees + // where there won't be any leaf to update + bool opposite_child_should_be_updated = OppositeChildShouldBeUpdated( + is_split_numerical, *features_of_splits_going_up_from_original_leaf, + inner_feature, *was_original_leaf_right_child_of_split, + is_in_right_child); + + if (opposite_child_should_be_updated) { + // if there is no monotone constraint on a split, + // then there is no relationship between its left and right leaves' values + if (monotone_type != 0) { + // these variables correspond to the current split we encounter going + // up the tree + int left_child_idx = tree_->left_child(parent_idx); + int right_child_idx = tree_->right_child(parent_idx); + bool left_child_is_curr_idx = (left_child_idx == node_idx); + int opposite_child_idx = + (left_child_is_curr_idx) ? right_child_idx : left_child_idx; + bool update_max_constraints_in_opposite_child_leaves = + (monotone_type < 0) ? left_child_is_curr_idx + : !left_child_is_curr_idx; + + // the opposite child needs to be updated + // so the code needs to go down in the the opposite child + // to see which leaves' constraints need to be updated + GoDownToFindLeavesToUpdate( + opposite_child_idx, + *features_of_splits_going_up_from_original_leaf, + *thresholds_of_splits_going_up_from_original_leaf, + *was_original_leaf_right_child_of_split, + update_max_constraints_in_opposite_child_leaves, split_feature, + split_info, true, true, split_threshold, best_split_per_leaf); + } + + // if opposite_child_should_be_updated, then it means the path to come up there was relevant, + // i.e. that it will be helpful going down to determine which leaf + // is actually contiguous to the original 2 leaves and should be updated + // so the variables associated with the split need to be recorded + was_original_leaf_right_child_of_split->push_back( + tree_->right_child(parent_idx) == node_idx); + thresholds_of_splits_going_up_from_original_leaf->push_back( + tree_->threshold_in_bin(parent_idx)); + features_of_splits_going_up_from_original_leaf->push_back( + tree_->split_feature_inner(parent_idx)); + } + + // since current node is not the root, keep going up + GoUpToFindLeavesToUpdate( + parent_idx, features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split, split_feature, split_info, + split_threshold, best_split_per_leaf); + } + } + + void GoDownToFindLeavesToUpdate( + int node_idx, + const std::vector& features_of_splits_going_up_from_original_leaf, + const std::vector& + thresholds_of_splits_going_up_from_original_leaf, + const std::vector& was_original_leaf_right_child_of_split, + bool update_max_constraints, int split_feature, + const SplitInfo& split_info, bool use_left_leaf, bool use_right_leaf, + uint32_t split_threshold, + const std::vector& best_split_per_leaf) { + // if leaf + if (node_idx < 0) { + int leaf_idx = ~node_idx; + + // splits that are not to be used shall not be updated, + // included leaf at max depth + if (best_split_per_leaf[leaf_idx].gain == kMinScore) { + return; + } + + std::pair min_max_constraints; + bool something_changed = false; + // if the current leaf is contiguous with both the new right leaf and the new left leaf + // then it may need to be greater than the max of the 2 or smaller than the min of the 2 + // otherwise, if the current leaf is contiguous with only one of the 2 new leaves, + // then it may need to be greater or smaller than it + if (use_right_leaf && use_left_leaf) { + min_max_constraints = + std::minmax(split_info.right_output, split_info.left_output); + } else if (use_right_leaf && !use_left_leaf) { + min_max_constraints = std::pair( + split_info.right_output, split_info.right_output); + } else { + min_max_constraints = std::pair(split_info.left_output, + split_info.left_output); + } + +#ifdef DEBUG + if (update_max_constraints) { + CHECK_GE(min_max_constraints.first, tree_->LeafOutput(leaf_idx)); + } else { + CHECK_LE(min_max_constraints.second, tree_->LeafOutput(leaf_idx)); + } +#endif + // depending on which split made the current leaf and the original leaves contiguous, + // either the min constraint or the max constraint of the current leaf need to be updated + if (!update_max_constraints) { + something_changed = entries_[leaf_idx]->UpdateMinAndReturnBoolIfChanged( + min_max_constraints.second); + } else { + something_changed = entries_[leaf_idx]->UpdateMaxAndReturnBoolIfChanged( + min_max_constraints.first); + } + // If constraints were not updated, then there is no need to update the leaf + if (!something_changed) { + return; + } + leaves_to_update_.push_back(leaf_idx); + + } else { // if node + // check if the children are contiguous with the original leaf + std::pair keep_going_left_right = ShouldKeepGoingLeftRight( + node_idx, features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split); + int inner_feature = tree_->split_feature_inner(node_idx); + uint32_t threshold = tree_->threshold_in_bin(node_idx); + bool is_split_numerical = tree_->IsNumericalSplit(node_idx); + bool use_left_leaf_for_update_right = true; + bool use_right_leaf_for_update_left = true; + // if the split is on the same feature (categorical variables not supported) + // then depending on the threshold, + // the current left child may not be contiguous with the original right leaf, + // or the current right child may not be contiguous with the original left leaf + if (is_split_numerical && inner_feature == split_feature) { + if (threshold >= split_threshold) { + use_left_leaf_for_update_right = false; + } + if (threshold <= split_threshold) { + use_right_leaf_for_update_left = false; + } + } + + // go down left + if (keep_going_left_right.first) { + GoDownToFindLeavesToUpdate( + tree_->left_child(node_idx), + features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split, update_max_constraints, + split_feature, split_info, use_left_leaf, + use_right_leaf_for_update_left && use_right_leaf, split_threshold, + best_split_per_leaf); + } + // go down right + if (keep_going_left_right.second) { + GoDownToFindLeavesToUpdate( + tree_->right_child(node_idx), + features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split, update_max_constraints, + split_feature, split_info, + use_left_leaf_for_update_right && use_left_leaf, use_right_leaf, + split_threshold, best_split_per_leaf); + } + } + } + + std::pair ShouldKeepGoingLeftRight( + int node_idx, + const std::vector& features_of_splits_going_up_from_original_leaf, + const std::vector& + thresholds_of_splits_going_up_from_original_leaf, + const std::vector& was_original_leaf_right_child_of_split) { + int inner_feature = tree_->split_feature_inner(node_idx); + uint32_t threshold = tree_->threshold_in_bin(node_idx); + bool is_split_numerical = tree_->IsNumericalSplit(node_idx); + + bool keep_going_right = true; + bool keep_going_left = true; + // left and right nodes are checked to find out if they are contiguous with + // the original leaves if so the algorithm should keep going down these nodes + // to update constraints + if (is_split_numerical) { + for (size_t i = 0; + i < features_of_splits_going_up_from_original_leaf.size(); ++i) { + if (features_of_splits_going_up_from_original_leaf[i] == + inner_feature) { + if (threshold >= + thresholds_of_splits_going_up_from_original_leaf[i] && + !was_original_leaf_right_child_of_split[i]) { + keep_going_right = false; + if (!keep_going_left) { + break; + } + } + if (threshold <= + thresholds_of_splits_going_up_from_original_leaf[i] && + was_original_leaf_right_child_of_split[i]) { + keep_going_left = false; + if (!keep_going_right) { + break; + } + } + } + } + } + return std::pair(keep_going_left, keep_going_right); + } + + protected: + const Config* config_; + std::vector leaves_to_update_; + // add parent node information + std::vector node_parent_; + // Keeps track of the monotone splits above the leaf + std::vector leaf_is_in_monotone_subtree_; +}; + +class AdvancedLeafConstraints : public IntermediateLeafConstraints { + public: + AdvancedLeafConstraints(const Config *config, int num_leaves, + int num_features) + : IntermediateLeafConstraints(config, num_leaves) { + for (int i = 0; i < num_leaves; ++i) { + entries_[i].reset(new AdvancedConstraintEntry(num_features)); + } + } + + // at any point in time, for an index i, the constraint constraint[i] has to + // be valid on [threshold[i]: threshold[i + 1]) (or [threshold[i]: +inf) if i + // is the last index of the array) + void UpdateConstraints(FeatureMinOrMaxConstraints* feature_constraint, + double extremum, uint32_t it_start, uint32_t it_end, + bool use_max_operator, uint32_t last_threshold) { + bool start_done = false; + bool end_done = false; + // previous constraint have to be tracked + // for example when adding a constraints cstr2 on thresholds [1:2), + // on an existing constraints cstr1 on thresholds [0, +inf), + // the thresholds and constraints must become + // [0, 1, 2] and [cstr1, cstr2, cstr1] + // so since we loop through thresholds only once, + // the previous constraint that still applies needs to be recorded + double previous_constraint = use_max_operator + ? -std::numeric_limits::max() + : std::numeric_limits::max(); + double current_constraint; + for (size_t i = 0; i < feature_constraint->thresholds.size(); ++i) { + current_constraint = feature_constraint->constraints[i]; + // easy case when the thresholds match + if (feature_constraint->thresholds[i] == it_start) { + feature_constraint->constraints[i] = + (use_max_operator) + ? std::max(extremum, feature_constraint->constraints[i]) + : std::min(extremum, feature_constraint->constraints[i]); + start_done = true; + } + if (feature_constraint->thresholds[i] > it_start) { + // existing constraint is updated if there is a need for it + if (feature_constraint->thresholds[i] < it_end) { + feature_constraint->constraints[i] = + (use_max_operator) + ? std::max(extremum, feature_constraint->constraints[i]) + : std::min(extremum, feature_constraint->constraints[i]); + } + // when thresholds don't match, a new threshold + // and a new constraint may need to be inserted + if (!start_done) { + start_done = true; + if ((use_max_operator && extremum > previous_constraint) || + (!use_max_operator && extremum < previous_constraint)) { + feature_constraint->constraints.insert( + feature_constraint->constraints.begin() + i, extremum); + feature_constraint->thresholds.insert( + feature_constraint->thresholds.begin() + i, it_start); + ++i; + } + } + } + // easy case when the end thresholds match + if (feature_constraint->thresholds[i] == it_end) { + end_done = true; + break; + } + // if they don't then, the previous constraint needs to be added back + // where the current one ends + if (feature_constraint->thresholds[i] > it_end) { + if (i != 0 && + previous_constraint != feature_constraint->constraints[i - 1]) { + feature_constraint->constraints.insert( + feature_constraint->constraints.begin() + i, previous_constraint); + feature_constraint->thresholds.insert( + feature_constraint->thresholds.begin() + i, it_end); + } + end_done = true; + break; + } + // If 2 successive constraints are the same then the second one may as + // well be deleted + if (i != 0 && feature_constraint->constraints[i] == + feature_constraint->constraints[i - 1]) { + feature_constraint->constraints.erase( + feature_constraint->constraints.begin() + i); + feature_constraint->thresholds.erase( + feature_constraint->thresholds.begin() + i); + previous_constraint = current_constraint; + --i; + } + previous_constraint = current_constraint; + } + // if the loop didn't get to an index greater than it_start, it needs to be + // added at the end + if (!start_done) { + if ((use_max_operator && + extremum > feature_constraint->constraints.back()) || + (!use_max_operator && + extremum < feature_constraint->constraints.back())) { + feature_constraint->constraints.push_back(extremum); + feature_constraint->thresholds.push_back(it_start); + } else { + end_done = true; + } + } + // if we didn't get to an index after it_end, then the previous constraint + // needs to be set back, unless it_end goes up to the last bin of the feature + if (!end_done && it_end != last_threshold && + previous_constraint != feature_constraint->constraints.back()) { + feature_constraint->constraints.push_back(previous_constraint); + feature_constraint->thresholds.push_back(it_end); + } + } + + // this function is called only when computing constraints when the monotone + // precise mode is set to true + // it makes sure that it is worth it to visit a branch, as it could + // not contain any relevant constraint (for example if the a branch + // with bigger values is also constraining the original leaf, then + // it is useless to visit the branch with smaller values) + std::pair + LeftRightContainsRelevantInformation(bool min_constraints_to_be_updated, + int feature, + bool split_feature_is_inner_feature) { + if (split_feature_is_inner_feature) { + return std::pair(true, true); + } + int8_t monotone_type = config_->monotone_constraints[feature]; + if (monotone_type == 0) { + return std::pair(true, true); + } + if ((monotone_type == -1 && min_constraints_to_be_updated) || + (monotone_type == 1 && !min_constraints_to_be_updated)) { + return std::pair(true, false); + } else { + // Same as + // if ((monotone_type == 1 && min_constraints_to_be_updated) || + // (monotone_type == -1 && !min_constraints_to_be_updated)) + return std::pair(false, true); + } + } + + // this function goes down in a subtree to find the + // constraints that would apply on the original leaf + void GoDownToFindConstrainingLeaves( + int feature_for_constraint, int root_monotone_feature, int node_idx, + bool min_constraints_to_be_updated, uint32_t it_start, uint32_t it_end, + const std::vector &features_of_splits_going_up_from_original_leaf, + const std::vector & + thresholds_of_splits_going_up_from_original_leaf, + const std::vector &was_original_leaf_right_child_of_split, + FeatureMinOrMaxConstraints* feature_constraint, uint32_t last_threshold) { + double extremum; + // if leaf, then constraints need to be updated according to its value + if (node_idx < 0) { + extremum = tree_->LeafOutput(~node_idx); +#ifdef DEBUG + CHECK(it_start < it_end); +#endif + UpdateConstraints(feature_constraint, extremum, it_start, it_end, + min_constraints_to_be_updated, last_threshold); + } else { // if node, keep going down the tree + // check if the children are contiguous to the original leaf and therefore + // potentially constraining + std::pair keep_going_left_right = ShouldKeepGoingLeftRight( + node_idx, features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split); + int inner_feature = tree_->split_feature_inner(node_idx); + int feature = tree_->split_feature(node_idx); + uint32_t threshold = tree_->threshold_in_bin(node_idx); + + bool split_feature_is_inner_feature = + (inner_feature == feature_for_constraint); + bool split_feature_is_monotone_feature = + (root_monotone_feature == feature_for_constraint); + // make sure that both children contain values that could + // potentially help determine the true constraints for the original leaf + std::pair left_right_contain_relevant_information = + LeftRightContainsRelevantInformation( + min_constraints_to_be_updated, feature, + split_feature_is_inner_feature && + !split_feature_is_monotone_feature); + // if both children are contiguous to the original leaf + // but one contains values greater than the other + // then no need to go down in both + if (keep_going_left_right.first && + (left_right_contain_relevant_information.first || + !keep_going_left_right.second)) { + // update thresholds based on going left + uint32_t new_it_end = split_feature_is_inner_feature + ? std::min(threshold + 1, it_end) + : it_end; + GoDownToFindConstrainingLeaves( + feature_for_constraint, root_monotone_feature, + tree_->left_child(node_idx), min_constraints_to_be_updated, + it_start, new_it_end, + features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split, feature_constraint, + last_threshold); + } + if (keep_going_left_right.second && + (left_right_contain_relevant_information.second || + !keep_going_left_right.first)) { + // update thresholds based on going right + uint32_t new_it_start = split_feature_is_inner_feature + ? std::max(threshold + 1, it_start) + : it_start; + GoDownToFindConstrainingLeaves( + feature_for_constraint, root_monotone_feature, + tree_->right_child(node_idx), min_constraints_to_be_updated, + new_it_start, it_end, + features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split, feature_constraint, + last_threshold); + } + } + } + + // this function is only used if the monotone precise mode is enabled + // it recursively goes up the tree then down to find leaf that + // are constraining the current leaf + void GoUpToFindConstrainingLeaves( + int feature_for_constraint, int node_idx, + std::vector* features_of_splits_going_up_from_original_leaf, + std::vector* thresholds_of_splits_going_up_from_original_leaf, + std::vector* was_original_leaf_right_child_of_split, + FeatureMinOrMaxConstraints* feature_constraint, + bool min_constraints_to_be_updated, uint32_t it_start, uint32_t it_end, + uint32_t last_threshold) final { + int parent_idx = + (node_idx < 0) ? tree_->leaf_parent(~node_idx) : node_parent_[node_idx]; + // if not at the root + if (parent_idx != -1) { + int inner_feature = tree_->split_feature_inner(parent_idx); + int feature = tree_->split_feature(parent_idx); + int8_t monotone_type = config_->monotone_constraints[feature]; + bool is_in_right_child = tree_->right_child(parent_idx) == node_idx; + bool is_split_numerical = tree_->IsNumericalSplit(parent_idx); + uint32_t threshold = tree_->threshold_in_bin(parent_idx); + + // by going up, more information about the position of the + // original leaf are gathered so the starting and ending + // thresholds can be updated, which will save some time later + if ((feature_for_constraint == inner_feature) && is_split_numerical) { + if (is_in_right_child) { + it_start = std::max(threshold, it_start); + } else { + it_end = std::min(threshold + 1, it_end); + } +#ifdef DEBUG + CHECK(it_start < it_end); +#endif + } + + // this is just an optimisation not to waste time going down in subtrees + // where there won't be any new constraining leaf + bool opposite_child_necessary_to_update_constraints = + OppositeChildShouldBeUpdated( + is_split_numerical, + *features_of_splits_going_up_from_original_leaf, inner_feature, + *was_original_leaf_right_child_of_split, is_in_right_child); + + if (opposite_child_necessary_to_update_constraints) { + // if there is no monotone constraint on a split, + // then there is no relationship between its left and right leaves' + // values + if (monotone_type != 0) { + int left_child_idx = tree_->left_child(parent_idx); + int right_child_idx = tree_->right_child(parent_idx); + bool left_child_is_curr_idx = (left_child_idx == node_idx); + + bool update_min_constraints_in_curr_child_leaf = + (monotone_type < 0) ? left_child_is_curr_idx + : !left_child_is_curr_idx; + if (update_min_constraints_in_curr_child_leaf == + min_constraints_to_be_updated) { + int opposite_child_idx = + (left_child_is_curr_idx) ? right_child_idx : left_child_idx; + + // go down in the opposite branch to find potential + // constraining leaves + GoDownToFindConstrainingLeaves( + feature_for_constraint, inner_feature, opposite_child_idx, + min_constraints_to_be_updated, it_start, it_end, + *features_of_splits_going_up_from_original_leaf, + *thresholds_of_splits_going_up_from_original_leaf, + *was_original_leaf_right_child_of_split, feature_constraint, + last_threshold); + } + } + // if opposite_child_should_be_updated, then it means the path to come + // up there was relevant, + // i.e. that it will be helpful going down to determine which leaf + // is actually contiguous to the original leaf and constraining + // so the variables associated with the split need to be recorded + was_original_leaf_right_child_of_split->push_back(is_in_right_child); + thresholds_of_splits_going_up_from_original_leaf->push_back(threshold); + features_of_splits_going_up_from_original_leaf->push_back(inner_feature); + } + + // since current node is not the root, keep going up + if (parent_idx != 0) { + GoUpToFindConstrainingLeaves( + feature_for_constraint, parent_idx, + features_of_splits_going_up_from_original_leaf, + thresholds_of_splits_going_up_from_original_leaf, + was_original_leaf_right_child_of_split, feature_constraint, + min_constraints_to_be_updated, it_start, it_end, last_threshold); + } + } + } +}; + +LeafConstraintsBase* LeafConstraintsBase::Create(const Config* config, + int num_leaves, int num_features) { + if (config->monotone_constraints_method == "intermediate") { + return new IntermediateLeafConstraints(config, num_leaves); + } + if (config->monotone_constraints_method == "advanced") { + return new AdvancedLeafConstraints(config, num_leaves, num_features); + } + return new BasicLeafConstraints(num_leaves); +} + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_MONOTONE_CONSTRAINTS_HPP_ diff --git a/src/treelearner/ocl/histogram16.cl b/src/treelearner/ocl/histogram16.cl new file mode 100644 index 0000000..c0993ee --- /dev/null +++ b/src/treelearner/ocl/histogram16.cl @@ -0,0 +1,767 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * \brief This file can either be read and passed to an OpenCL compiler directly, + * or included in a C++11 source file as a string literal. + */ +#ifndef __OPENCL_VERSION__ +// If we are including this file in C++, +// the entire source file following (except the last #endif) will become +// a raw string literal. The extra ")" is just for matching parentheses +// to make the editor happy. The extra ")" and extra endif will be skipped. +// DO NOT add anything between here and the next #ifdef, otherwise you need +// to modify the skip count at the end of this file. +R""() +#endif + +#ifndef _HISTOGRAM_16_KERNEL_ +#define _HISTOGRAM_16_KERNEL_ + +#pragma OPENCL EXTENSION cl_khr_local_int32_base_atomics : enable +#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics : enable + +// Configurable options: +// NUM_BANKS should be a power of 2 +#ifndef NUM_BANKS +#define NUM_BANKS 8 +#endif +// how many bits in thread ID represent the bank = log2(NUM_BANKS) +#ifndef BANK_BITS +#define BANK_BITS 3 +#endif +// use double precision or not +#ifndef USE_DP_FLOAT +#define USE_DP_FLOAT 0 +#endif +// ignore hessian, and use the local memory for hessian as an additional bank for gradient +#ifndef CONST_HESSIAN +#define CONST_HESSIAN 0 +#endif + + +#define LOCAL_SIZE_0 256 +#define NUM_BINS 16 +// if USE_DP_FLOAT is set to 1, we will use double precision for the accumulator +#if USE_DP_FLOAT == 1 +#pragma OPENCL EXTENSION cl_khr_fp64 : enable +#pragma OPENCL EXTENSION cl_khr_int64_base_atomics : enable +typedef double acc_type; +typedef ulong acc_int_type; +#define as_acc_type as_double +#define as_acc_int_type as_ulong +#else +typedef float acc_type; +typedef uint acc_int_type; +#define as_acc_type as_float +#define as_acc_int_type as_uint +#endif +// number of features to process in a 4-byte feature tuple +#define DWORD_FEATURES 8 +// number of bits per feature +#define FEATURE_BITS (sizeof(uchar4) * 8 / DWORD_FEATURES) +// bit mask for number of features to process in a 4-byte feature tuple +#define DWORD_FEATURES_MASK (DWORD_FEATURES - 1) +// log2 of number of features to process in a 4-byte feature tuple +#define LOG2_DWORD_FEATURES 3 +// mask for getting the bank ID +#define BANK_MASK (NUM_BANKS - 1) +// 8 features, each has a gradient and a hessian +#define HG_BIN_MULT (NUM_BANKS * DWORD_FEATURES * 2) +// 8 features, each has a counter +#define CNT_BIN_MULT (NUM_BANKS * DWORD_FEATURES) +// local memory size in bytes +#define LOCAL_MEM_SIZE (DWORD_FEATURES * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS * NUM_BANKS) + +// unroll the atomic operation for a few times. Takes more code space, +// but compiler can generate better code for faster atomics. +#define UNROLL_ATOMIC 1 + +// Options passed by compiler at run time: +// IGNORE_INDICES will be set when the kernel does not +// #define IGNORE_INDICES +// #define POWER_FEATURE_WORKGROUPS 10 + +// use all features and do not use feature mask +#ifndef ENABLE_ALL_FEATURES +#define ENABLE_ALL_FEATURES 1 +#endif + +// detect Nvidia platforms +#ifdef cl_nv_pragma_unroll +#define NVIDIA 1 +#endif + +// use binary patching for AMD GCN 1.2 or newer +#ifndef AMD_USE_DS_ADD_F32 +#define AMD_USE_DS_ADD_F32 0 +#endif + +typedef uint data_size_t; +typedef float score_t; + +#define ATOMIC_FADD_SUB1 { \ + expected.f_val = current.f_val; \ + next.f_val = expected.f_val + val; \ + current.u_val = atom_cmpxchg((volatile __local acc_int_type *)addr, expected.u_val, next.u_val); \ + if (current.u_val == expected.u_val) \ + goto end; \ + } +#define ATOMIC_FADD_SUB2 ATOMIC_FADD_SUB1 \ + ATOMIC_FADD_SUB1 +#define ATOMIC_FADD_SUB4 ATOMIC_FADD_SUB2 \ + ATOMIC_FADD_SUB2 +#define ATOMIC_FADD_SUB8 ATOMIC_FADD_SUB4 \ + ATOMIC_FADD_SUB4 +#define ATOMIC_FADD_SUB16 ATOMIC_FADD_SUB8 \ + ATOMIC_FADD_SUB8 +#define ATOMIC_FADD_SUB32 ATOMIC_FADD_SUB16\ + ATOMIC_FADD_SUB16 +#define ATOMIC_FADD_SUB64 ATOMIC_FADD_SUB32\ + ATOMIC_FADD_SUB32 + + +// atomic add for float number in local memory +inline void atomic_local_add_f(__local acc_type *addr, const float val) +{ + union{ + acc_int_type u_val; + acc_type f_val; + } next, expected, current; +#if (NVIDIA == 1 && USE_DP_FLOAT == 0) + float res = 0; + asm volatile ("atom.shared.add.f32 %0, [%1], %2;" : "=f"(res) : "l"(addr), "f"(val)); +#elif (AMD_USE_DS_ADD_F32 == 1 && USE_DP_FLAT == 0) + // this instruction (DS_AND_U32) will be patched into a DS_ADD_F32 + // we need to hack here because DS_ADD_F32 is not exposed via OpenCL + atom_and((__local acc_int_type *)addr, as_acc_int_type(val)); +#else + current.f_val = *addr; + #if UNROLL_ATOMIC == 1 + // provide a fast path + // then do the complete loop + // this should work on all devices + ATOMIC_FADD_SUB8 + ATOMIC_FADD_SUB4 + #endif + do { + expected.f_val = current.f_val; + next.f_val = expected.f_val + val; + current.u_val = atom_cmpxchg((volatile __local acc_int_type *)addr, expected.u_val, next.u_val); + } while (current.u_val != expected.u_val); + end: + ; +#endif +} + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ +// this function will be called by histogram16 +// we have one sub-histogram of one feature in registers, and need to read others +void within_kernel_reduction16x8(uchar8 feature_mask, + __global const acc_type* restrict feature4_sub_hist, + const uint skip_id, + acc_type stat_val, + const ushort num_sub_hist, + __global acc_type* restrict output_buf, + __local acc_type * restrict local_hist) { + const ushort ltid = get_local_id(0); // range 0 - 255 + const ushort lsize = LOCAL_SIZE_0; + ushort feature_id = ltid & DWORD_FEATURES_MASK; // range 0 - 7 + uchar is_hessian_first = (ltid >> LOG2_DWORD_FEATURES) & 1; // hessian or gradient + ushort bin_id = ltid >> (LOG2_DWORD_FEATURES + 1); // range 0 - 16 + ushort i; + #if POWER_FEATURE_WORKGROUPS != 0 + // if there is only 1 work group, no need to do the reduction + // add all sub-histograms for 4 features + __global const acc_type* restrict p = feature4_sub_hist + ltid; + for (i = 0; i < skip_id; ++i) { + // 256 threads working on 8 features' 16 bins, gradient and Hessian + stat_val += *p; + p += NUM_BINS * DWORD_FEATURES * 2; + } + // skip the counters we already have + p += 2 * DWORD_FEATURES * NUM_BINS; + for (i = i + 1; i < num_sub_hist; ++i) { + stat_val += *p; + p += NUM_BINS * DWORD_FEATURES * 2; + } + #endif + // printf("thread %d:feature=%d, bin_id=%d, hessian=%d, stat_val=%f, cnt=%d", ltid, feature_id, bin_id, is_hessian_first, stat_val, cnt_val); + // now overwrite the local_hist for final reduction and output + // reverse the f7...f0 order to match the real order + feature_id = DWORD_FEATURES_MASK - feature_id; + local_hist[feature_id * 2 * NUM_BINS + bin_id * 2 + is_hessian_first] = stat_val; + barrier(CLK_LOCAL_MEM_FENCE); + for (i = ltid; i < DWORD_FEATURES * 2 * NUM_BINS; i += lsize) { + output_buf[i] = local_hist[i]; + } +} + + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ + +__attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1))) +#if USE_CONSTANT_BUF == 1 +__kernel void histogram16(__global const uchar4* restrict feature_data_base, + __constant const uchar8* restrict feature_masks __attribute__((max_constant_size(65536))), + const data_size_t feature_size, + __constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))), + const data_size_t num_data, + __constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))), +#if CONST_HESSIAN == 0 + __constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))), +#else + const score_t const_hessian, +#endif + __global char* restrict output_buf, + __global volatile int * sync_counters, + __global acc_type* restrict hist_buf_base) { +#else +__kernel void histogram16(__global const uchar4* feature_data_base, + __constant const uchar8* restrict feature_masks __attribute__((max_constant_size(65536))), + const data_size_t feature_size, + __global const data_size_t* data_indices, + const data_size_t num_data, + __global const score_t* ordered_gradients, +#if CONST_HESSIAN == 0 + __global const score_t* ordered_hessians, +#else + const score_t const_hessian, +#endif + __global char* restrict output_buf, + __global volatile int * sync_counters, + __global acc_type* restrict hist_buf_base) { +#endif + // allocate the local memory array aligned with float2, to guarantee correct alignment on NVIDIA platforms + // otherwise a "Misaligned Address" exception may occur + __local float2 shared_array[LOCAL_MEM_SIZE/sizeof(float2)]; + const uint gtid = get_global_id(0); + const uint gsize = get_global_size(0); + const ushort ltid = get_local_id(0); + const ushort lsize = LOCAL_SIZE_0; // get_local_size(0); + const ushort group_id = get_group_id(0); + + // local memory per workgroup is 12 KB + // clear local memory + __local uint * ptr = (__local uint *) shared_array; + for (int i = ltid; i < LOCAL_MEM_SIZE/sizeof(uint); i += lsize) { + ptr[i] = 0; + } + barrier(CLK_LOCAL_MEM_FENCE); + // gradient/hessian histograms + // assume this starts at 32 * 4 = 128-byte boundary + // each bank: 2 * 8 * 16 * size_of(float) = 1 KB + // there are 8 banks (sub-histograms) used by 256 threads total 8 KB + /* memory layout of gh_hist: + ----------------------------------------------------------------------------------------------- + bk0_g_f0_bin0 bk0_g_f1_bin0 bk0_g_f2_bin0 bk0_g_f3_bin0 bk0_g_f4_bin0 bk0_g_f5_bin0 bk0_g_f6_bin0 bk0_g_f7_bin0 + bk0_h_f0_bin0 bk0_h_f1_bin0 bk0_h_f2_bin0 bk0_h_f3_bin0 bk0_h_f4_bin0 bk0_h_f5_bin0 bk0_h_f6_bin0 bk0_h_f7_bin0 + bk1_g_f0_bin0 bk1_g_f1_bin0 bk1_g_f2_bin0 bk1_g_f3_bin0 bk1_g_f4_bin0 bk1_g_f5_bin0 bk1_g_f6_bin0 bk1_g_f7_bin0 + bk1_h_f0_bin0 bk1_h_f1_bin0 bk1_h_f2_bin0 bk1_h_f3_bin0 bk1_h_f4_bin0 bk1_h_f5_bin0 bk1_h_f6_bin0 bk1_h_f7_bin0 + bk2_g_f0_bin0 bk2_g_f1_bin0 bk2_g_f2_bin0 bk2_g_f3_bin0 bk2_g_f4_bin0 bk2_g_f5_bin0 bk2_g_f6_bin0 bk2_g_f7_bin0 + bk2_h_f0_bin0 bk2_h_f1_bin0 bk2_h_f2_bin0 bk2_h_f3_bin0 bk2_h_f4_bin0 bk2_h_f5_bin0 bk2_h_f6_bin0 bk2_h_f7_bin0 + bk3_g_f0_bin0 bk3_g_f1_bin0 bk3_g_f2_bin0 bk3_g_f3_bin0 bk3_g_f4_bin0 bk3_g_f5_bin0 bk3_g_f6_bin0 bk3_g_f7_bin0 + bk3_h_f0_bin0 bk3_h_f1_bin0 bk3_h_f2_bin0 bk3_h_f3_bin0 bk3_h_f4_bin0 bk3_h_f5_bin0 bk3_h_f6_bin0 bk3_h_f7_bin0 + bk4_g_f0_bin0 bk4_g_f1_bin0 bk4_g_f2_bin0 bk4_g_f3_bin0 bk4_g_f4_bin0 bk4_g_f5_bin0 bk4_g_f6_bin0 bk4_g_f7_bin0 + bk4_h_f0_bin0 bk4_h_f1_bin0 bk4_h_f2_bin0 bk4_h_f3_bin0 bk4_h_f4_bin0 bk4_h_f5_bin0 bk4_h_f6_bin0 bk4_h_f7_bin0 + bk5_g_f0_bin0 bk5_g_f1_bin0 bk5_g_f2_bin0 bk5_g_f3_bin0 bk5_g_f4_bin0 bk5_g_f5_bin0 bk5_g_f6_bin0 bk5_g_f7_bin0 + bk5_h_f0_bin0 bk5_h_f1_bin0 bk5_h_f2_bin0 bk5_h_f3_bin0 bk5_h_f4_bin0 bk5_h_f5_bin0 bk5_h_f6_bin0 bk5_h_f7_bin0 + bk6_g_f0_bin0 bk6_g_f1_bin0 bk6_g_f2_bin0 bk6_g_f3_bin0 bk6_g_f4_bin0 bk6_g_f5_bin0 bk6_g_f6_bin0 bk6_g_f7_bin0 + bk6_h_f0_bin0 bk6_h_f1_bin0 bk6_h_f2_bin0 bk6_h_f3_bin0 bk6_h_f4_bin0 bk6_h_f5_bin0 bk6_h_f6_bin0 bk6_h_f7_bin0 + bk7_g_f0_bin0 bk7_g_f1_bin0 bk7_g_f2_bin0 bk7_g_f3_bin0 bk7_g_f4_bin0 bk7_g_f5_bin0 bk7_g_f6_bin0 bk7_g_f7_bin0 + bk7_h_f0_bin0 bk7_h_f1_bin0 bk7_h_f2_bin0 bk7_h_f3_bin0 bk7_h_f4_bin0 bk7_h_f5_bin0 bk7_h_f6_bin0 bk7_h_f7_bin0 + ... + bk0_g_f0_bin16 bk0_g_f1_bin16 bk0_g_f2_bin16 bk0_g_f3_bin16 bk0_g_f4_bin16 bk0_g_f5_bin16 bk0_g_f6_bin16 bk0_g_f7_bin16 + bk0_h_f0_bin16 bk0_h_f1_bin16 bk0_h_f2_bin16 bk0_h_f3_bin16 bk0_h_f4_bin16 bk0_h_f5_bin16 bk0_h_f6_bin16 bk0_h_f7_bin16 + bk1_g_f0_bin16 bk1_g_f1_bin16 bk1_g_f2_bin16 bk1_g_f3_bin16 bk1_g_f4_bin16 bk1_g_f5_bin16 bk1_g_f6_bin16 bk1_g_f7_bin16 + bk1_h_f0_bin16 bk1_h_f1_bin16 bk1_h_f2_bin16 bk1_h_f3_bin16 bk1_h_f4_bin16 bk1_h_f5_bin16 bk1_h_f6_bin16 bk1_h_f7_bin16 + bk2_g_f0_bin16 bk2_g_f1_bin16 bk2_g_f2_bin16 bk2_g_f3_bin16 bk2_g_f4_bin16 bk2_g_f5_bin16 bk2_g_f6_bin16 bk2_g_f7_bin16 + bk2_h_f0_bin16 bk2_h_f1_bin16 bk2_h_f2_bin16 bk2_h_f3_bin16 bk2_h_f4_bin16 bk2_h_f5_bin16 bk2_h_f6_bin16 bk2_h_f7_bin16 + bk3_g_f0_bin16 bk3_g_f1_bin16 bk3_g_f2_bin16 bk3_g_f3_bin16 bk3_g_f4_bin16 bk3_g_f5_bin16 bk3_g_f6_bin16 bk3_g_f7_bin16 + bk3_h_f0_bin16 bk3_h_f1_bin16 bk3_h_f2_bin16 bk3_h_f3_bin16 bk3_h_f4_bin16 bk3_h_f5_bin16 bk3_h_f6_bin16 bk3_h_f7_bin16 + bk4_g_f0_bin16 bk4_g_f1_bin16 bk4_g_f2_bin16 bk4_g_f3_bin16 bk4_g_f4_bin16 bk4_g_f5_bin16 bk4_g_f6_bin16 bk4_g_f7_bin16 + bk4_h_f0_bin16 bk4_h_f1_bin16 bk4_h_f2_bin16 bk4_h_f3_bin16 bk4_h_f4_bin16 bk4_h_f5_bin16 bk4_h_f6_bin16 bk4_h_f7_bin16 + bk5_g_f0_bin16 bk5_g_f1_bin16 bk5_g_f2_bin16 bk5_g_f3_bin16 bk5_g_f4_bin16 bk5_g_f5_bin16 bk5_g_f6_bin16 bk5_g_f7_bin16 + bk5_h_f0_bin16 bk5_h_f1_bin16 bk5_h_f2_bin16 bk5_h_f3_bin16 bk5_h_f4_bin16 bk5_h_f5_bin16 bk5_h_f6_bin16 bk5_h_f7_bin16 + bk6_g_f0_bin16 bk6_g_f1_bin16 bk6_g_f2_bin16 bk6_g_f3_bin16 bk6_g_f4_bin16 bk6_g_f5_bin16 bk6_g_f6_bin16 bk6_g_f7_bin16 + bk6_h_f0_bin16 bk6_h_f1_bin16 bk6_h_f2_bin16 bk6_h_f3_bin16 bk6_h_f4_bin16 bk6_h_f5_bin16 bk6_h_f6_bin16 bk6_h_f7_bin16 + bk7_g_f0_bin16 bk7_g_f1_bin16 bk7_g_f2_bin16 bk7_g_f3_bin16 bk7_g_f4_bin16 bk7_g_f5_bin16 bk7_g_f6_bin16 bk7_g_f7_bin16 + bk7_h_f0_bin16 bk7_h_f1_bin16 bk7_h_f2_bin16 bk7_h_f3_bin16 bk7_h_f4_bin16 bk7_h_f5_bin16 bk7_h_f6_bin16 bk7_h_f7_bin16 + ----------------------------------------------------------------------------------------------- + */ + // with this organization, the LDS/shared memory bank is independent of the bin value + // all threads within a quarter-wavefront (half-warp) will not have any bank conflict + + __local acc_type * gh_hist = (__local acc_type *)shared_array; + // counter histogram + // each bank: 8 * 16 * size_of(uint) = 0.5 KB + // there are 8 banks used by 256 threads total 4 KB + /* memory layout in cnt_hist: + ----------------------------------------------- + bk0_c_f0_bin0 bk0_c_f1_bin0 bk0_c_f2_bin0 bk0_c_f3_bin0 bk0_c_f4_bin0 bk0_c_f5_bin0 bk0_c_f6_bin0 bk0_c_f7_bin0 + bk1_c_f0_bin0 bk1_c_f1_bin0 bk1_c_f2_bin0 bk1_c_f3_bin0 bk1_c_f4_bin0 bk1_c_f5_bin0 bk1_c_f6_bin0 bk1_c_f7_bin0 + bk2_c_f0_bin0 bk2_c_f1_bin0 bk2_c_f2_bin0 bk2_c_f3_bin0 bk2_c_f4_bin0 bk2_c_f5_bin0 bk2_c_f6_bin0 bk2_c_f7_bin0 + bk3_c_f0_bin0 bk3_c_f1_bin0 bk3_c_f2_bin0 bk3_c_f3_bin0 bk3_c_f4_bin0 bk3_c_f5_bin0 bk3_c_f6_bin0 bk3_c_f7_bin0 + bk4_c_f0_bin0 bk4_c_f1_bin0 bk4_c_f2_bin0 bk4_c_f3_bin0 bk4_c_f4_bin0 bk4_c_f5_bin0 bk4_c_f6_bin0 bk4_c_f7_bin0 + bk5_c_f0_bin0 bk5_c_f1_bin0 bk5_c_f2_bin0 bk5_c_f3_bin0 bk5_c_f4_bin0 bk5_c_f5_bin0 bk5_c_f6_bin0 bk5_c_f7_bin0 + bk6_c_f0_bin0 bk6_c_f1_bin0 bk6_c_f2_bin0 bk6_c_f3_bin0 bk6_c_f4_bin0 bk6_c_f5_bin0 bk6_c_f6_bin0 bk6_c_f7_bin0 + bk7_c_f0_bin0 bk7_c_f1_bin0 bk7_c_f2_bin0 bk7_c_f3_bin0 bk7_c_f4_bin0 bk7_c_f5_bin0 bk7_c_f6_bin0 bk7_c_f7_bin0 + ... + bk0_c_f0_bin16 bk0_c_f1_bin16 bk0_c_f2_bin16 bk0_c_f3_bin16 bk0_c_f4_bin16 bk0_c_f5_bin16 bk0_c_f6_bin16 bk0_c_f7_bin0 + bk1_c_f0_bin16 bk1_c_f1_bin16 bk1_c_f2_bin16 bk1_c_f3_bin16 bk1_c_f4_bin16 bk1_c_f5_bin16 bk1_c_f6_bin16 bk1_c_f7_bin0 + bk2_c_f0_bin16 bk2_c_f1_bin16 bk2_c_f2_bin16 bk2_c_f3_bin16 bk2_c_f4_bin16 bk2_c_f5_bin16 bk2_c_f6_bin16 bk2_c_f7_bin0 + bk3_c_f0_bin16 bk3_c_f1_bin16 bk3_c_f2_bin16 bk3_c_f3_bin16 bk3_c_f4_bin16 bk3_c_f5_bin16 bk3_c_f6_bin16 bk3_c_f7_bin0 + bk4_c_f0_bin16 bk4_c_f1_bin16 bk4_c_f2_bin16 bk4_c_f3_bin16 bk4_c_f4_bin16 bk4_c_f5_bin16 bk4_c_f6_bin16 bk4_c_f7_bin0 + bk5_c_f0_bin16 bk5_c_f1_bin16 bk5_c_f2_bin16 bk5_c_f3_bin16 bk5_c_f4_bin16 bk5_c_f5_bin16 bk5_c_f6_bin16 bk5_c_f7_bin0 + bk6_c_f0_bin16 bk6_c_f1_bin16 bk6_c_f2_bin16 bk6_c_f3_bin16 bk6_c_f4_bin16 bk6_c_f5_bin16 bk6_c_f6_bin16 bk6_c_f7_bin0 + bk7_c_f0_bin16 bk7_c_f1_bin16 bk7_c_f2_bin16 bk7_c_f3_bin16 bk7_c_f4_bin16 bk7_c_f5_bin16 bk7_c_f6_bin16 bk7_c_f7_bin0 + ----------------------------------------------- + */ + #if CONST_HESSIAN == 1 + __local uint * cnt_hist = (__local uint *)(gh_hist + 2 * DWORD_FEATURES * NUM_BINS * NUM_BANKS); + #endif + + // thread 0, 1, 2, 3, 4, 5, 6, 7 compute histograms for gradients first + // thread 8, 9, 10, 11, 12, 13, 14, 15 compute histograms for Hessians first + // etc. + uchar is_hessian_first = (ltid >> LOG2_DWORD_FEATURES) & 1; + // thread 0-15 write result to bank0, 16-31 to bank1, 32-47 to bank2, 48-63 to bank3, etc + ushort bank = (ltid >> (LOG2_DWORD_FEATURES + 1)) & BANK_MASK; + + ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS; + // each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant) + // feature_size is the number of examples per feature + __global const uchar4* feature_data = feature_data_base + group_feature * feature_size; + // size of threads that process this feature4 + const uint subglobal_size = lsize * (1 << POWER_FEATURE_WORKGROUPS); + // equivalent thread ID in this subgroup for this feature4 + const uint subglobal_tid = gtid - group_feature * subglobal_size; + // extract feature mask, when a byte is set to 0, that feature is disabled + #if ENABLE_ALL_FEATURES == 1 + // hopefully the compiler will propagate the constants and eliminate all branches + uchar8 feature_mask = (uchar8)(0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff); + #else + uchar8 feature_mask = feature_masks[group_feature]; + #endif + // exit if all features are masked + if (!as_ulong(feature_mask)) { + return; + } + + // STAGE 1: read feature data, and gradient and hessian + // first half of the threads read feature data from global memory + // 4 features stored in a tuple MSB...(0, 1, 2, 3)...LSB + // We will prefetch data into the "next" variable at the beginning of each iteration + uchar4 feature4; + uchar4 feature4_next; + // offset used to rotate feature4 vector, & 0x7 + ushort offset = (ltid & DWORD_FEATURES_MASK); + #if ENABLE_ALL_FEATURES == 0 + // rotate feature_mask to match the feature order of each thread + feature_mask = as_uchar8(rotate(as_ulong(feature_mask), (ulong)offset*8)); + #endif + // store gradient and hessian + float stat1, stat2; + float stat1_next, stat2_next; + ushort bin, addr, addr2; + data_size_t ind; + data_size_t ind_next; + stat1 = ordered_gradients[subglobal_tid]; + #if CONST_HESSIAN == 0 + stat2 = ordered_hessians[subglobal_tid]; + #endif + #ifdef IGNORE_INDICES + ind = subglobal_tid; + #else + ind = data_indices[subglobal_tid]; + #endif + feature4 = feature_data[ind]; + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ + // there are 2^POWER_FEATURE_WORKGROUPS workgroups processing each feature4 + for (uint i = subglobal_tid; i < num_data; i += subglobal_size) { + // prefetch the next iteration variables + // we don't need boundary check because we have made the buffer larger + stat1_next = ordered_gradients[i + subglobal_size]; + #if CONST_HESSIAN == 0 + stat2_next = ordered_hessians[i + subglobal_size]; + #endif + #ifdef IGNORE_INDICES + // we need to check to bounds here + ind_next = i + subglobal_size < num_data ? i + subglobal_size : i; + // start load next feature as early as possible + feature4_next = feature_data[ind_next]; + #else + ind_next = data_indices[i + subglobal_size]; + #endif + #if CONST_HESSIAN == 0 + // swap gradient and hessian for threads 8, 9, 10, 11, 12, 13, 14, 15 + float tmp = stat1; + stat1 = is_hessian_first ? stat2 : stat1; + stat2 = is_hessian_first ? tmp : stat2; + // stat1 = select(stat1, stat2, is_hessian_first); + // stat2 = select(stat2, tmp, is_hessian_first); + #endif + + // STAGE 2: accumulate gradient and hessian + offset = (ltid & DWORD_FEATURES_MASK); + // printf("thread %x, %08x -> %08x", ltid, as_uint(feature4), rotate(as_uint(feature4), (uint)(offset * FEATURE_BITS))); + feature4 = as_uchar4(rotate(as_uint(feature4), (uint)(offset * FEATURE_BITS))); + if (feature_mask.s7) { + bin = feature4.s3 >> 4; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 0, 1, 2, 3, 4, 5, 6 ,7's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 0, 1, 2, 3, 4, 5, 6, 7's Hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 0, 1, 2, 3, 4, 5, 6, 7's Hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 0, 1, 2, 3, 4, 5, 6, 7's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s6) { + bin = feature4.s3 & 0xf; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 1, 2, 3, 4, 5, 6 ,7, 0's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 1, 2, 3, 4, 5, 6, 7, 0's Hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 1, 2, 3, 4, 5, 6, 7, 0's Hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 1, 2, 3, 4, 5, 6, 7, 0's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s5) { + bin = feature4.s2 >> 4; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 2, 3, 4, 5, 6, 7, 0, 1's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 2, 3, 4, 5, 6, 7, 0, 1's Hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 2, 3, 4, 5, 6, 7, 0, 1's Hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 2, 3, 4, 5, 6, 7, 0, 1's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s4) { + bin = feature4.s2 & 0xf; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 3, 4, 5, 6, 7, 0, 1, 2's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 3, 4, 5, 6, 7, 0, 1, 2's Hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 3, 4, 5, 6, 7, 0, 1, 2's Hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 3, 4, 5, 6, 7, 0, 1, 2's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + + + // prefetch the next iteration variables + // we don't need boundary check because if it is out of boundary, ind_next = 0 + #ifndef IGNORE_INDICES + feature4_next = feature_data[ind_next]; + #endif + + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s3) { + bin = feature4.s1 >> 4; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 4, 5, 6, 7, 0, 1, 2, 3's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 4, 5, 6, 7, 0, 1, 2, 3's hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 4, 5, 6, 7, 0, 1, 2, 3's hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 4, 5, 6, 7, 0, 1, 2, 3's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s2) { + bin = feature4.s1 & 0xf; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 5, 6, 7, 0, 1, 2, 3, 4's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 5, 6, 7, 0, 1, 2, 3, 4's Hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 5, 6, 7, 0, 1, 2, 3, 4's Hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 5, 6, 7, 0, 1, 2, 3, 4's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s1) { + bin = feature4.s0 >> 4; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 6, 7, 0, 1, 2, 3, 4, 5's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 6, 7, 0, 1, 2, 3, 4, 5's Hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 6, 7, 0, 1, 2, 3, 4, 5's Hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 6, 7, 0, 1, 2, 3, 4, 5's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s0) { + bin = feature4.s0 & 0xf; + addr = bin * HG_BIN_MULT + bank * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + offset; + addr2 = addr + DWORD_FEATURES - 2 * DWORD_FEATURES * is_hessian_first; + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 7, 0, 1, 2, 3, 4, 5, 6's gradients for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 7, 0, 1, 2, 3, 4, 5, 6's Hessians for example 8, 9, 10, 11, 12, 13, 14, 15 + atomic_local_add_f(gh_hist + addr, stat1); + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 7, 0, 1, 2, 3, 4, 5, 6's Hessians for example 0, 1, 2, 3, 4, 5, 6, 7 + // thread 8, 9, 10, 11, 12, 13, 14, 15 now process feature 7, 0, 1, 2, 3, 4, 5, 6's gradients for example 8, 9, 10, 11, 12, 13, 14, 15 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, stat2); + #endif + } + #if CONST_HESSIAN == 1 + // STAGE 3: accumulate counter + // there are 8 counters for 8 features + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 0, 1, 2, 3, 4, 5, 6, 7's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (ltid & DWORD_FEATURES_MASK); + if (feature_mask.s7) { + bin = feature4.s3 >> 4; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (0)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 1, 2, 3, 4, 5, 6, 7, 0's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s6) { + bin = feature4.s3 & 0xf; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (1)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 2, 3, 4, 5, 6, 7, 0, 1's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s5) { + bin = feature4.s2 >> 4; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (2)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 3, 4, 5, 6, 7, 0, 1, 2's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s4) { + bin = feature4.s2 & 0xf; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (3)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 4, 5, 6, 7, 0, 1, 2, 3's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s3) { + bin = feature4.s1 >> 4; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (4)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 5, 6, 7, 0, 1, 2, 3, 4's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s2) { + bin = feature4.s1 & 0xf; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (5)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 6, 7, 0, 1, 2, 3, 4, 5's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s1) { + bin = feature4.s0 >> 4; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (6)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3, 4, 5, 6, 7 now process feature 7, 0, 1, 2, 3, 4, 5, 6's counts for example 0, 1, 2, 3, 4, 5, 6, 7 + offset = (offset + 1) & DWORD_FEATURES_MASK; + if (feature_mask.s0) { + bin = feature4.s0 & 0xf; + addr = bin * CNT_BIN_MULT + bank * DWORD_FEATURES + offset; + // printf("thread %x add counter %d feature %d (7)\n", ltid, bin, offset); + atom_inc(cnt_hist + addr); + } + #endif + stat1 = stat1_next; + stat2 = stat2_next; + feature4 = feature4_next; + } + barrier(CLK_LOCAL_MEM_FENCE); + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ + + #if ENABLE_ALL_FEATURES == 0 + // restore feature_mask + feature_mask = feature_masks[group_feature]; + #endif + + // now reduce the 4 banks of subhistograms into 1 + acc_type stat_val = 0.0f; + uint cnt_val = 0; + // 256 threads, working on 8 features and 16 bins, 2 stats + // so each thread has an independent feature/bin/stat to work on. + const ushort feature_id = ltid & DWORD_FEATURES_MASK; // bits 0 - 2 of ltid, range 0 - 7 + ushort bin_id = ltid >> (LOG2_DWORD_FEATURES + 1); // bits 3 is is_hessian_first; bits 4 - 7 range 0 - 16 is bin ID + offset = (ltid >> (LOG2_DWORD_FEATURES + 1)) & BANK_MASK; // helps avoid LDS bank conflicts + for (int i = 0; i < NUM_BANKS; ++i) { + ushort bank_id = (i + offset) & BANK_MASK; + stat_val += gh_hist[bin_id * HG_BIN_MULT + bank_id * 2 * DWORD_FEATURES + is_hessian_first * DWORD_FEATURES + feature_id]; + } + #if CONST_HESSIAN == 1 + if (ltid < LOCAL_SIZE_0 / 2) { + // first 128 threads accumulate the 8 * 16 = 128 counter values + bin_id = ltid >> LOG2_DWORD_FEATURES; // bits 3 - 6 range 0 - 16 is bin ID + offset = (ltid >> LOG2_DWORD_FEATURES) & BANK_MASK; // helps avoid LDS bank conflicts + for (int i = 0; i < NUM_BANKS; ++i) { + ushort bank_id = (i + offset) & BANK_MASK; + cnt_val += cnt_hist[bin_id * CNT_BIN_MULT + bank_id * DWORD_FEATURES + feature_id]; + } + } + #endif + + // now thread 0 - 7 holds feature 0 - 7's gradient for bin 0 and counter bin 0 + // now thread 8 - 15 holds feature 0 - 7's hessian for bin 0 and counter bin 1 + // now thread 16- 23 holds feature 0 - 7's gradient for bin 1 and counter bin 2 + // now thread 24- 31 holds feature 0 - 7's hessian for bin 1 and counter bin 3 + // etc, + +#if CONST_HESSIAN == 1 + // Combine the two banks into one, and fill the Hessians with counter value * hessian constant + barrier(CLK_LOCAL_MEM_FENCE); + gh_hist[ltid] = stat_val; + if (ltid < LOCAL_SIZE_0 / 2) { + cnt_hist[ltid] = cnt_val; + } + barrier(CLK_LOCAL_MEM_FENCE); + if (is_hessian_first) { + // these are the Hessians + // thread 8 - 15 read counters stored by thread 0 - 7 + // thread 24- 31 read counters stored by thread 8 - 15 + // thread 40- 47 read counters stored by thread 16- 23, etc + stat_val = const_hessian * + cnt_hist[((ltid - DWORD_FEATURES) >> (LOG2_DWORD_FEATURES + 1)) * DWORD_FEATURES + (ltid & DWORD_FEATURES_MASK)]; + } + else { + // these are the gradients + // thread 0 - 7 read gradients stored by thread 8 - 15 + // thread 16- 23 read gradients stored by thread 24- 31 + // thread 32- 39 read gradients stored by thread 40- 47, etc + stat_val += gh_hist[ltid + DWORD_FEATURES]; + } + barrier(CLK_LOCAL_MEM_FENCE); +#endif + + // write to output + // write gradients and Hessians histogram for all 4 features + // output data in linear order for further reduction + // output size = 4 (features) * 2 (counters) * 64 (bins) * sizeof(float) + /* memory layout of output: + g_f0_bin0 g_f1_bin0 g_f2_bin0 g_f3_bin0 g_f4_bin0 g_f5_bin0 g_f6_bin0 g_f7_bin0 + h_f0_bin0 h_f1_bin0 h_f2_bin0 h_f3_bin0 h_f4_bin0 h_f5_bin0 h_f6_bin0 h_f7_bin0 + g_f0_bin1 g_f1_bin1 g_f2_bin1 g_f3_bin1 g_f4_bin1 g_f5_bin1 g_f6_bin1 g_f7_bin1 + h_f0_bin1 h_f1_bin1 h_f2_bin1 h_f3_bin1 h_f4_bin1 h_f5_bin1 h_f6_bin1 h_f7_bin1 + ... + ... + g_f0_bin16 g_f1_bin16 g_f2_bin16 g_f3_bin16 g_f4_bin16 g_f5_bin16 g_f6_bin16 g_f7_bin16 + h_f0_bin16 h_f1_bin16 h_f2_bin16 h_f3_bin16 h_f4_bin16 h_f5_bin16 h_f6_bin16 h_f7_bin16 + c_f0_bin0 c_f1_bin0 c_f2_bin0 c_f3_bin0 c_f4_bin0 c_f5_bin0 c_f6_bin0 c_f7_bin0 + c_f0_bin1 c_f1_bin1 c_f2_bin1 c_f3_bin1 c_f4_bin1 c_f5_bin1 c_f6_bin1 c_f7_bin1 + ... + c_f0_bin16 c_f1_bin16 c_f2_bin16 c_f3_bin16 c_f4_bin16 c_f5_bin16 c_f6_bin16 c_f7_bin16 + */ + // if there is only one workgroup processing this feature4, don't even need to write + uint feature4_id = (group_id >> POWER_FEATURE_WORKGROUPS); + #if POWER_FEATURE_WORKGROUPS != 0 + __global acc_type * restrict output = (__global acc_type * restrict)output_buf + group_id * DWORD_FEATURES * 2 * NUM_BINS; + // if g_val and h_val are double, they are converted to float here + // write gradients and Hessians for 8 features + output[0 * DWORD_FEATURES * NUM_BINS + ltid] = stat_val; + barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE); + mem_fence(CLK_GLOBAL_MEM_FENCE); + // To avoid the cost of an extra reducing kernel, we have to deal with some + // gray area in OpenCL. We want the last work group that process this feature to + // make the final reduction, and other threads will just quit. + // This requires that the results written by other workgroups available to the + // last workgroup (memory consistency) + #if NVIDIA == 1 + // this is equivalent to CUDA __threadfence(); + // ensure the writes above goes to main memory and other workgroups can see it + asm volatile("{\n\tmembar.gl;\n\t}\n\t" :::"memory"); + #else + // FIXME: how to do the above on AMD GPUs?? + // GCN ISA says that the all writes will bypass L1 cache (write through), + // however when the last thread is reading sub-histogram data we have to + // make sure that no part of data is modified in local L1 cache of other workgroups. + // Otherwise reading can be a problem (atomic operations to get consistency). + // But in our case, the sub-histogram of this workgroup cannot be in the cache + // of another workgroup, so the following trick will work just fine. + #endif + // Now, we want one workgroup to do the final reduction. + // Other workgroups processing the same feature quit. + // The is done by using an global atomic counter. + // On AMD GPUs ideally this should be done in GDS, + // but currently there is no easy way to access it via OpenCL. + __local uint * counter_val = (__local uint *)(gh_hist + 2 * DWORD_FEATURES * NUM_BINS * NUM_BANKS); + if (ltid == 0) { + // all workgroups processing the same feature add this counter + *counter_val = atom_inc(sync_counters + feature4_id); + } + // make sure everyone in this workgroup is here + barrier(CLK_LOCAL_MEM_FENCE); + // everyone in this workgroup: if we are the last workgroup, then do reduction! + if (*counter_val == (1 << POWER_FEATURE_WORKGROUPS) - 1) { + if (ltid == 0) { + // printf("workgroup %d start reduction!\n", group_id); + // printf("feature_data[0] = %d %d %d %d", feature_data[0].s0, feature_data[0].s1, feature_data[0].s2, feature_data[0].s3); + // clear the sync counter for using it next time + sync_counters[feature4_id] = 0; + } + #else + // only 1 work group, no need to increase counter + // the reduction will become a simple copy + if (1) { + barrier(CLK_LOCAL_MEM_FENCE); + #endif + // locate our feature4's block in output memory + uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS); + __global acc_type const * restrict feature4_subhists = + (__global acc_type *)output_buf + output_offset * DWORD_FEATURES * 2 * NUM_BINS; + // skip reading the data already in local memory + uint skip_id = group_id ^ output_offset; + // locate output histogram location for this feature4 + __global acc_type* restrict hist_buf = hist_buf_base + feature4_id * DWORD_FEATURES * 2 * NUM_BINS; + within_kernel_reduction16x8(feature_mask, feature4_subhists, skip_id, stat_val, + 1 << POWER_FEATURE_WORKGROUPS, hist_buf, (__local acc_type *)shared_array); + } +} + +// The following line ends the string literal, adds an extra #endif at the end +// )"" "\n#endif" +#endif diff --git a/src/treelearner/ocl/histogram256.cl b/src/treelearner/ocl/histogram256.cl new file mode 100644 index 0000000..70d11f7 --- /dev/null +++ b/src/treelearner/ocl/histogram256.cl @@ -0,0 +1,792 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * \brief This file can either be read and passed to an OpenCL compiler directly, + * or included in a C++11 source file as a string literal. + */ +#ifndef __OPENCL_VERSION__ +// If we are including this file in C++, +// the entire source file following (except the last #endif) will become +// a raw string literal. The extra ")" is just for matching parentheses +// to make the editor happy. The extra ")" and extra endif will be skipped. +// DO NOT add anything between here and the next #ifdef, otherwise you need +// to modify the skip count at the end of this file. +R""() +#endif + +#ifndef _HISTOGRAM_256_KERNEL_ +#define _HISTOGRAM_256_KERNEL_ + +#pragma OPENCL EXTENSION cl_khr_local_int32_base_atomics : enable +#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics : enable + +// use double precision or not +#ifndef USE_DP_FLOAT +#define USE_DP_FLOAT 0 +#endif +// ignore hessian, and use the local memory for hessian as an additional bank for gradient +#ifndef CONST_HESSIAN +#define CONST_HESSIAN 0 +#endif + +#define LOCAL_SIZE_0 256 +#define NUM_BINS 256 +#if USE_DP_FLOAT == 1 +#pragma OPENCL EXTENSION cl_khr_fp64 : enable +#pragma OPENCL EXTENSION cl_khr_int64_base_atomics : enable +typedef double acc_type; +typedef ulong acc_int_type; +#define as_acc_type as_double +#define as_acc_int_type as_ulong +#else +typedef float acc_type; +typedef uint acc_int_type; +#define as_acc_type as_float +#define as_acc_int_type as_uint +#endif +#define LOCAL_MEM_SIZE (4 * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS) + +// unroll the atomic operation for a few times. Takes more code space, +// but compiler can generate better code for faster atomics. +#define UNROLL_ATOMIC 1 + +// Options passed by compiler at run time: +// IGNORE_INDICES will be set when the kernel does not +// #define IGNORE_INDICES +// #define POWER_FEATURE_WORKGROUPS 10 + +// detect Nvidia platforms +#ifdef cl_nv_pragma_unroll +#define NVIDIA 1 +#endif + +// use all features and do not use feature mask +#ifndef ENABLE_ALL_FEATURES +#define ENABLE_ALL_FEATURES 1 +#endif + +// use binary patching for AMD GCN 1.2 or newer +#ifndef AMD_USE_DS_ADD_F32 +#define AMD_USE_DS_ADD_F32 0 +#endif + +typedef uint data_size_t; +typedef float score_t; + + +#define ATOMIC_FADD_SUB1 { \ + expected.f_val = current.f_val; \ + next.f_val = expected.f_val + val; \ + current.u_val = atom_cmpxchg((volatile __local acc_int_type *)addr, expected.u_val, next.u_val); \ + if (current.u_val == expected.u_val) \ + goto end; \ + } +#define ATOMIC_FADD_SUB2 ATOMIC_FADD_SUB1 \ + ATOMIC_FADD_SUB1 +#define ATOMIC_FADD_SUB4 ATOMIC_FADD_SUB2 \ + ATOMIC_FADD_SUB2 +#define ATOMIC_FADD_SUB8 ATOMIC_FADD_SUB4 \ + ATOMIC_FADD_SUB4 +#define ATOMIC_FADD_SUB16 ATOMIC_FADD_SUB8 \ + ATOMIC_FADD_SUB8 +#define ATOMIC_FADD_SUB32 ATOMIC_FADD_SUB16\ + ATOMIC_FADD_SUB16 +#define ATOMIC_FADD_SUB64 ATOMIC_FADD_SUB32\ + ATOMIC_FADD_SUB32 + + +// atomic add for float number in local memory +inline void atomic_local_add_f(__local acc_type *addr, const float val) +{ + union{ + acc_int_type u_val; + acc_type f_val; + } next, expected, current; +#if (NVIDIA == 1 && USE_DP_FLOAT == 0) + float res = 0; + asm volatile ("atom.shared.add.f32 %0, [%1], %2;" : "=f"(res) : "l"(addr), "f"(val)); +#elif (AMD_USE_DS_ADD_F32 == 1 && USE_DP_FLAT == 0) + // this instruction (DS_AND_U32) will be patched into a DS_ADD_F32 + // we need to hack here because DS_ADD_F32 is not exposed via OpenCL + atom_and((__local acc_int_type *)addr, as_acc_int_type(val)); +#else + current.f_val = *addr; + #if UNROLL_ATOMIC == 1 + // provide a fast path + // then do the complete loop + // this should work on all devices + ATOMIC_FADD_SUB8 + ATOMIC_FADD_SUB4 + ATOMIC_FADD_SUB2 + #endif + do { + expected.f_val = current.f_val; + next.f_val = expected.f_val + val; + current.u_val = atom_cmpxchg((volatile __local acc_int_type *)addr, expected.u_val, next.u_val); + } while (current.u_val != expected.u_val); + end: + ; +#endif +} + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ +// this function will be called by histogram256 +// we have one sub-histogram of one feature in local memory, and need to read others +void within_kernel_reduction256x4(uchar4 feature_mask, + __global const acc_type* restrict feature4_sub_hist, + const uint skip_id, + const uint old_val_f0_cont_bin0, + const ushort num_sub_hist, + __global acc_type* restrict output_buf, + __local acc_type* restrict local_hist) { + const ushort ltid = get_local_id(0); + const ushort lsize = LOCAL_SIZE_0; + // initialize register counters from our local memory + // TODO: try to avoid bank conflict here + acc_type f0_grad_bin = local_hist[ltid * 8]; + acc_type f1_grad_bin = local_hist[ltid * 8 + 1]; + acc_type f2_grad_bin = local_hist[ltid * 8 + 2]; + acc_type f3_grad_bin = local_hist[ltid * 8 + 3]; + acc_type f0_hess_bin = local_hist[ltid * 8 + 4]; + acc_type f1_hess_bin = local_hist[ltid * 8 + 5]; + acc_type f2_hess_bin = local_hist[ltid * 8 + 6]; + acc_type f3_hess_bin = local_hist[ltid * 8 + 7]; + ushort i; + // printf("%d-pre(skip %d): %f %f %f %f %f %f %f %f %d %d %d %d", ltid, skip_id, f0_grad_bin, f1_grad_bin, f2_grad_bin, f3_grad_bin, f0_hess_bin, f1_hess_bin, f2_hess_bin, f3_hess_bin, f0_cont_bin, f1_cont_bin, f2_cont_bin, f3_cont_bin); +#if POWER_FEATURE_WORKGROUPS != 0 + // add all sub-histograms for 4 features + __global const acc_type* restrict p = feature4_sub_hist + ltid; + for (i = 0; i < skip_id; ++i) { + if (feature_mask.s3) { + f0_grad_bin += *p; p += NUM_BINS; + f0_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + if (feature_mask.s2) { + f1_grad_bin += *p; p += NUM_BINS; + f1_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + if (feature_mask.s1) { + f2_grad_bin += *p; p += NUM_BINS; + f2_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + if (feature_mask.s0) { + f3_grad_bin += *p; p += NUM_BINS; + f3_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + } + // skip the counters we already have + p += 2 * 4 * NUM_BINS; + for (i = i + 1; i < num_sub_hist; ++i) { + if (feature_mask.s3) { + f0_grad_bin += *p; p += NUM_BINS; + f0_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + if (feature_mask.s2) { + f1_grad_bin += *p; p += NUM_BINS; + f1_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + if (feature_mask.s1) { + f2_grad_bin += *p; p += NUM_BINS; + f2_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + if (feature_mask.s0) { + f3_grad_bin += *p; p += NUM_BINS; + f3_hess_bin += *p; p += NUM_BINS; + } + else { + p += 2 * NUM_BINS; + } + } + // printf("%d-aft: %f %f %f %f %f %f %f %f %d %d %d %d", ltid, f0_grad_bin, f1_grad_bin, f2_grad_bin, f3_grad_bin, f0_hess_bin, f1_hess_bin, f2_hess_bin, f3_hess_bin, f0_cont_bin, f1_cont_bin, f2_cont_bin, f3_cont_bin); + #endif + // now overwrite the local_hist for final reduction and output + barrier(CLK_LOCAL_MEM_FENCE); + #if USE_DP_FLOAT == 0 + // reverse the f3...f0 order to match the real order + local_hist[0 * 2 * NUM_BINS + ltid * 2 + 0] = f3_grad_bin; + local_hist[0 * 2 * NUM_BINS + ltid * 2 + 1] = f3_hess_bin; + local_hist[1 * 2 * NUM_BINS + ltid * 2 + 0] = f2_grad_bin; + local_hist[1 * 2 * NUM_BINS + ltid * 2 + 1] = f2_hess_bin; + local_hist[2 * 2 * NUM_BINS + ltid * 2 + 0] = f1_grad_bin; + local_hist[2 * 2 * NUM_BINS + ltid * 2 + 1] = f1_hess_bin; + local_hist[3 * 2 * NUM_BINS + ltid * 2 + 0] = f0_grad_bin; + local_hist[3 * 2 * NUM_BINS + ltid * 2 + 1] = f0_hess_bin; + barrier(CLK_LOCAL_MEM_FENCE); + /* + for (ushort i = ltid; i < 4 * 3 * NUM_BINS; i += lsize) { + output_buf[i] = local_hist[i]; + } + */ + i = ltid; + if (feature_mask.s0) { + output_buf[i] = local_hist[i]; + output_buf[i + NUM_BINS] = local_hist[i + NUM_BINS]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s1) { + output_buf[i] = local_hist[i]; + output_buf[i + NUM_BINS] = local_hist[i + NUM_BINS]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s2) { + output_buf[i] = local_hist[i]; + output_buf[i + NUM_BINS] = local_hist[i + NUM_BINS]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s3 && i < 4 * 2 * NUM_BINS) { + output_buf[i] = local_hist[i]; + output_buf[i + NUM_BINS] = local_hist[i + NUM_BINS]; + } + #else + // when double precision is used, we need to write twice, because local memory size is not enough + local_hist[0 * 2 * NUM_BINS + ltid * 2 + 0] = f3_grad_bin; + local_hist[0 * 2 * NUM_BINS + ltid * 2 + 1] = f3_hess_bin; + local_hist[1 * 2 * NUM_BINS + ltid * 2 + 0] = f2_grad_bin; + local_hist[1 * 2 * NUM_BINS + ltid * 2 + 1] = f2_hess_bin; + barrier(CLK_LOCAL_MEM_FENCE); + /* + for (ushort i = ltid; i < 2 * 3 * NUM_BINS; i += lsize) { + output_buf[i] = local_hist[i]; + } + */ + i = ltid; + if (feature_mask.s0) { + output_buf[i] = local_hist[i]; + output_buf[i + NUM_BINS] = local_hist[i + NUM_BINS]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s1) { + output_buf[i] = local_hist[i]; + output_buf[i + NUM_BINS] = local_hist[i + NUM_BINS]; + } + barrier(CLK_LOCAL_MEM_FENCE); + local_hist[0 * 2 * NUM_BINS + ltid * 2 + 0] = f1_grad_bin; + local_hist[0 * 2 * NUM_BINS + ltid * 2 + 1] = f1_hess_bin; + local_hist[1 * 2 * NUM_BINS + ltid * 2 + 0] = f0_grad_bin; + local_hist[1 * 2 * NUM_BINS + ltid * 2 + 1] = f0_hess_bin; + barrier(CLK_LOCAL_MEM_FENCE); + /* + for (ushort i = ltid; i < 2 * 3 * NUM_BINS; i += lsize) { + output_buf[i + 2 * 3 * NUM_BINS] = local_hist[i]; + } + */ + i = ltid; + if (feature_mask.s2) { + output_buf[i + 2 * 2 * NUM_BINS] = local_hist[i]; + output_buf[i + 2 * 2 * NUM_BINS + NUM_BINS] = local_hist[i + NUM_BINS]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s3) { + output_buf[i + 2 * 2 * NUM_BINS] = local_hist[i]; + output_buf[i + 2 * 2 * NUM_BINS + NUM_BINS] = local_hist[i + NUM_BINS]; + } + #endif +} + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ +__attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1))) +#if USE_CONSTANT_BUF == 1 +__kernel void histogram256(__global const uchar4* restrict feature_data_base, + __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), + const data_size_t feature_size, + __constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))), + const data_size_t num_data, + __constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))), +#if CONST_HESSIAN == 0 + __constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))), +#else + const score_t const_hessian, +#endif + __global char* restrict output_buf, + __global volatile int * sync_counters, + __global acc_type* restrict hist_buf_base) { +#else +__kernel void histogram256(__global const uchar4* feature_data_base, + __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), + const data_size_t feature_size, + __global const data_size_t* data_indices, + const data_size_t num_data, + __global const score_t* ordered_gradients, +#if CONST_HESSIAN == 0 + __global const score_t* ordered_hessians, +#else + const score_t const_hessian, +#endif + __global char* restrict output_buf, + __global volatile int * sync_counters, + __global acc_type* restrict hist_buf_base) { +#endif + // allocate the local memory array aligned with float2, to guarantee correct alignment on NVIDIA platforms + // otherwise a "Misaligned Address" exception may occur + __local float2 shared_array[LOCAL_MEM_SIZE/sizeof(float2)]; + const uint gtid = get_global_id(0); + const uint gsize = get_global_size(0); + const ushort ltid = get_local_id(0); + const ushort lsize = LOCAL_SIZE_0; // get_local_size(0); + const ushort group_id = get_group_id(0); + + // local memory per workgroup is 12 KB + // clear local memory + __local uint * ptr = (__local uint *) shared_array; + for (int i = ltid; i < LOCAL_MEM_SIZE/sizeof(uint); i += lsize) { + ptr[i] = 0; + } + barrier(CLK_LOCAL_MEM_FENCE); + // gradient/hessian histograms + // assume this starts at 32 * 4 = 128-byte boundary + // total size: 2 * 4 * 256 * size_of(float) = 8 KB + // organization: each feature/grad/hessian is at a different bank, + // as independent of the feature value as possible + __local acc_type * gh_hist = (__local acc_type *)shared_array; + // counter histogram + // total size: 4 * 256 * size_of(uint) = 4 KB + #if CONST_HESSIAN == 1 + __local uint * cnt_hist = (__local uint *)(gh_hist + 2 * 4 * NUM_BINS); + #endif + + // thread 0, 1, 2, 3 compute histograms for gradients first + // thread 4, 5, 6, 7 compute histograms for Hessians first + // etc. + uchar is_hessian_first = (ltid >> 2) & 1; + + ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS; + // each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant) + // feature_size is the number of examples per feature + __global const uchar4* feature_data = feature_data_base + group_feature * feature_size; + // size of threads that process this feature4 + const uint subglobal_size = lsize * (1 << POWER_FEATURE_WORKGROUPS); + // equivalent thread ID in this subgroup for this feature4 + const uint subglobal_tid = gtid - group_feature * subglobal_size; + // extract feature mask, when a byte is set to 0, that feature is disabled + #if ENABLE_ALL_FEATURES == 1 + // hopefully the compiler will propagate the constants and eliminate all branches + uchar4 feature_mask = (uchar4)(0xff, 0xff, 0xff, 0xff); + #else + uchar4 feature_mask = feature_masks[group_feature]; + #endif + // exit if all features are masked + if (!as_uint(feature_mask)) { + return; + } + + // STAGE 1: read feature data, and gradient and hessian + // first half of the threads read feature data from global memory + // 4 features stored in a tuple MSB...(0, 1, 2, 3)...LSB + // We will prefetch data into the "next" variable at the beginning of each iteration + uchar4 feature4; + uchar4 feature4_next; + uchar4 feature4_prev; + // offset used to rotate feature4 vector + ushort offset = (ltid & 0x3); + // store gradient and hessian + float stat1, stat2; + float stat1_next, stat2_next; + ushort bin, addr, addr2; + data_size_t ind; + data_size_t ind_next; + stat1 = ordered_gradients[subglobal_tid]; + #if CONST_HESSIAN == 0 + stat2 = ordered_hessians[subglobal_tid]; + #endif + #ifdef IGNORE_INDICES + ind = subglobal_tid; + #else + ind = data_indices[subglobal_tid]; + #endif + feature4 = feature_data[ind]; + feature4_prev = feature4; + feature4_prev = as_uchar4(rotate(as_uint(feature4_prev), (uint)offset*8)); + #if ENABLE_ALL_FEATURES == 0 + // rotate feature_mask to match the feature order of each thread + feature_mask = as_uchar4(rotate(as_uint(feature_mask), (uint)offset*8)); + #endif + acc_type s3_stat1 = 0.0f, s3_stat2 = 0.0f; + acc_type s2_stat1 = 0.0f, s2_stat2 = 0.0f; + acc_type s1_stat1 = 0.0f, s1_stat2 = 0.0f; + acc_type s0_stat1 = 0.0f, s0_stat2 = 0.0f; + + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ + // there are 2^POWER_FEATURE_WORKGROUPS workgroups processing each feature4 + for (uint i = subglobal_tid; i < num_data; i += subglobal_size) { + // prefetch the next iteration variables + // we don't need boundary check because we have made the buffer larger + stat1_next = ordered_gradients[i + subglobal_size]; + #if CONST_HESSIAN == 0 + stat2_next = ordered_hessians[i + subglobal_size]; + #endif + #ifdef IGNORE_INDICES + // we need to check to bounds here + ind_next = i + subglobal_size < num_data ? i + subglobal_size : i; + // start load next feature as early as possible + feature4_next = feature_data[ind_next]; + #else + ind_next = data_indices[i + subglobal_size]; + #endif + #if CONST_HESSIAN == 0 + // swap gradient and hessian for threads 4, 5, 6, 7 + float tmp = stat1; + stat1 = is_hessian_first ? stat2 : stat1; + stat2 = is_hessian_first ? tmp : stat2; + // stat1 = select(stat1, stat2, is_hessian_first); + // stat2 = select(stat2, tmp, is_hessian_first); + #endif + + // STAGE 2: accumulate gradient and hessian + offset = (ltid & 0x3); + feature4 = as_uchar4(rotate(as_uint(feature4), (uint)offset*8)); + bin = feature4.s3; + if ((bin != feature4_prev.s3) && feature_mask.s3) { + // printf("%3d (%4d): writing s3 %d %d offset %d", ltid, i, bin, feature4_prev.s3, offset); + bin = feature4_prev.s3; + feature4_prev.s3 = feature4.s3; + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s3_stat1); + // thread 0, 1, 2, 3 now process feature 0, 1, 2, 3's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 0, 1, 2, 3's Hessians for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s3_stat2); + #endif + // thread 0, 1, 2, 3 now process feature 0, 1, 2, 3's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 0, 1, 2, 3's gradients for example 4, 5, 6, 7 + s3_stat1 = stat1; + s3_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s3 %d", ltid, i, bin); + s3_stat1 += stat1; + s3_stat2 += stat2; + } + + bin = feature4.s2; + offset = (offset + 1) & 0x3; + if ((bin != feature4_prev.s2) && feature_mask.s2) { + // printf("%3d (%4d): writing s2 %d %d feature %d", ltid, i, bin, feature4_prev.s2, offset); + bin = feature4_prev.s2; + feature4_prev.s2 = feature4.s2; + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s2_stat1); + // thread 0, 1, 2, 3 now process feature 1, 2, 3, 0's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 1, 2, 3, 0's Hessians for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s2_stat2); + #endif + // thread 0, 1, 2, 3 now process feature 1, 2, 3, 0's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 1, 2, 3, 0's gradients for example 4, 5, 6, 7 + s2_stat1 = stat1; + s2_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s2 %d", ltid, i, bin); + s2_stat1 += stat1; + s2_stat2 += stat2; + } + + + // prefetch the next iteration variables + // we don't need boundary check because if it is out of boundary, ind_next = 0 + #ifndef IGNORE_INDICES + feature4_next = feature_data[ind_next]; + #endif + + bin = feature4.s1; + offset = (offset + 1) & 0x3; + if ((bin != feature4_prev.s1) && feature_mask.s1) { + // printf("%3d (%4d): writing s1 %d %d feature %d", ltid, i, bin, feature4_prev.s1, offset); + bin = feature4_prev.s1; + feature4_prev.s1 = feature4.s1; + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s1_stat1); + // thread 0, 1, 2, 3 now process feature 2, 3, 0, 1's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 2, 3, 0, 1's Hessians for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s1_stat2); + #endif + // thread 0, 1, 2, 3 now process feature 2, 3, 0, 1's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 2, 3, 0, 1's gradients for example 4, 5, 6, 7 + s1_stat1 = stat1; + s1_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s1 %d", ltid, i, bin); + s1_stat1 += stat1; + s1_stat2 += stat2; + } + + bin = feature4.s0; + offset = (offset + 1) & 0x3; + if ((bin != feature4_prev.s0) && feature_mask.s0) { + // printf("%3d (%4d): writing s0 %d %d feature %d", ltid, i, bin, feature4_prev.s0, offset); + bin = feature4_prev.s0; + feature4_prev.s0 = feature4.s0; + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s0_stat1); + // thread 0, 1, 2, 3 now process feature 3, 0, 1, 2's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 3, 0, 1, 2's Hessians for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s0_stat2); + #endif + // thread 0, 1, 2, 3 now process feature 3, 0, 1, 2's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 3, 0, 1, 2's gradients for example 4, 5, 6, 7 + s0_stat1 = stat1; + s0_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s0 %d", ltid, i, bin); + s0_stat1 += stat1; + s0_stat2 += stat2; + } + #if CONST_HESSIAN == 1 + // STAGE 3: accumulate counter + // there are 4 counters for 4 features + // thread 0, 1, 2, 3 now process feature 0, 1, 2, 3's counts for example 0, 1, 2, 3 + offset = (ltid & 0x3); + if (feature_mask.s3) { + bin = feature4.s3; + addr = bin * 4 + offset; + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3 now process feature 1, 2, 3, 0's counts for example 0, 1, 2, 3 + offset = (offset + 1) & 0x3; + if (feature_mask.s2) { + bin = feature4.s2; + addr = bin * 4 + offset; + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3 now process feature 2, 3, 0, 1's counts for example 0, 1, 2, 3 + offset = (offset + 1) & 0x3; + if (feature_mask.s1) { + bin = feature4.s1; + addr = bin * 4 + offset; + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3 now process feature 3, 0, 1, 2's counts for example 0, 1, 2, 3 + offset = (offset + 1) & 0x3; + if (feature_mask.s0) { + bin = feature4.s0; + addr = bin * 4 + offset; + atom_inc(cnt_hist + addr); + } + #endif + stat1 = stat1_next; + stat2 = stat2_next; + feature4 = feature4_next; + } + + bin = feature4_prev.s3; + offset = (ltid & 0x3); + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s3_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s3_stat2); + #endif + + bin = feature4_prev.s2; + offset = (offset + 1) & 0x3; + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s2_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s2_stat2); + #endif + + bin = feature4_prev.s1; + offset = (offset + 1) & 0x3; + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s1_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s1_stat2); + #endif + + bin = feature4_prev.s0; + offset = (offset + 1) & 0x3; + addr = bin * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s0_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s0_stat2); + #endif + barrier(CLK_LOCAL_MEM_FENCE); + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ + #if ENABLE_ALL_FEATURES == 0 + // restore feature_mask + feature_mask = feature_masks[group_feature]; + #endif + + #if CONST_HESSIAN == 1 + barrier(CLK_LOCAL_MEM_FENCE); + // make a final reduction + offset = ltid & 0x3; // helps avoid LDS bank conflicts + gh_hist[ltid * 8 + offset] += gh_hist[ltid * 8 + offset + 4]; + gh_hist[ltid * 8 + offset + 4] = const_hessian * cnt_hist[ltid * 4 + offset]; + offset = (offset + 1) & 0x3; + gh_hist[ltid * 8 + offset] += gh_hist[ltid * 8 + offset + 4]; + gh_hist[ltid * 8 + offset + 4] = const_hessian * cnt_hist[ltid * 4 + offset]; + offset = (offset + 1) & 0x3; + gh_hist[ltid * 8 + offset] += gh_hist[ltid * 8 + offset + 4]; + gh_hist[ltid * 8 + offset + 4] = const_hessian * cnt_hist[ltid * 4 + offset]; + offset = (offset + 1) & 0x3; + gh_hist[ltid * 8 + offset] += gh_hist[ltid * 8 + offset + 4]; + gh_hist[ltid * 8 + offset + 4] = const_hessian * cnt_hist[ltid * 4 + offset]; + barrier(CLK_LOCAL_MEM_FENCE); + #endif + + // write to output + // write gradients and hessians histogram for all 4 features + /* memory layout in gh_hist (total 2 * 4 * 256 * sizeof(float) = 8 KB): + ----------------------------------------------------------------------------------------------- + g_f0_bin0 g_f1_bin0 g_f2_bin0 g_f3_bin0 h_f0_bin0 h_f1_bin0 h_f2_bin0 h_f3_bin0 + g_f0_bin1 g_f1_bin1 g_f2_bin1 g_f3_bin1 h_f0_bin1 h_f1_bin1 h_f2_bin1 h_f3_bin1 + ... + g_f0_bin255 g_f1_bin255 g_f2_bin255 g_f3_bin255 h_f0_bin255 h_f1_bin255 h_f2_bin255 h_f3_bin255 + ----------------------------------------------------------------------------------------------- + */ + /* memory layout in cnt_hist (total 4 * 256 * sizeof(uint) = 4 KB): + ----------------------------------------------- + c_f0_bin0 c_f1_bin0 c_f2_bin0 c_f3_bin0 + c_f0_bin1 c_f1_bin1 c_f2_bin1 c_f3_bin1 + ... + c_f0_bin255 c_f1_bin255 c_f2_bin255 c_f3_bin255 + ----------------------------------------------- + */ + // output data in linear order for further reduction + // output size = 4 (features) * 3 (counters) * 256 (bins) * sizeof(float) + /* memory layout of output: + -------------------------------------------- + g_f0_bin0 g_f0_bin1 ... g_f0_bin255 \ + h_f0_bin0 h_f0_bin1 ... h_f0_bin255 | + c_f0_bin0 c_f0_bin1 ... c_f0_bin255 | + g_f1_bin0 g_f1_bin1 ... g_f1_bin255 | + h_f1_bin0 h_f1_bin1 ... h_f1_bin255 | + c_f1_bin0 c_f1_bin1 ... c_f1_bin255 |--- 1 sub-histogram block + g_f2_bin0 g_f2_bin1 ... g_f2_bin255 | + h_f2_bin0 h_f2_bin1 ... h_f2_bin255 | + c_f2_bin0 c_f2_bin1 ... c_f2_bin255 | + g_f3_bin0 g_f3_bin1 ... g_f3_bin255 | + h_f3_bin0 h_f3_bin1 ... h_f3_bin255 | + c_f3_bin0 c_f3_bin1 ... c_f3_bin255 / + -------------------------------------------- + */ + uint feature4_id = (group_id >> POWER_FEATURE_WORKGROUPS); + // if there is only one workgroup processing this feature4, don't even need to write + #if POWER_FEATURE_WORKGROUPS != 0 + __global acc_type * restrict output = (__global acc_type * restrict)output_buf + group_id * 4 * 2 * NUM_BINS; + // write gradients and hessians + __global acc_type * restrict ptr_f = output; + for (ushort j = 0; j < 4; ++j) { + for (ushort i = ltid; i < 2 * NUM_BINS; i += lsize) { + // even threads read gradients, odd threads read hessians + // FIXME: 2-way bank conflict + acc_type value = gh_hist[i * 4 + j]; + ptr_f[(i & 1) * NUM_BINS + (i >> 1)] = value; + } + ptr_f += 2 * NUM_BINS; + } + barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE); + mem_fence(CLK_GLOBAL_MEM_FENCE); + // To avoid the cost of an extra reducing kernel, we have to deal with some + // gray area in OpenCL. We want the last work group that process this feature to + // make the final reduction, and other threads will just quit. + // This requires that the results written by other workgroups available to the + // last workgroup (memory consistency) + #if NVIDIA == 1 + // this is equivalent to CUDA __threadfence(); + // ensure the writes above goes to main memory and other workgroups can see it + asm volatile("{\n\tmembar.gl;\n\t}\n\t" :::"memory"); + #else + // FIXME: how to do the above on AMD GPUs?? + // GCN ISA says that the all writes will bypass L1 cache (write through), + // however when the last thread is reading sub-histogram data we have to + // make sure that no part of data is modified in local L1 cache of other workgroups. + // Otherwise reading can be a problem (atomic operations to get consistency). + // But in our case, the sub-histogram of this workgroup cannot be in the cache + // of another workgroup, so the following trick will work just fine. + #endif + // Now, we want one workgroup to do the final reduction. + // Other workgroups processing the same feature quit. + // The is done by using an global atomic counter. + // On AMD GPUs ideally this should be done in GDS, + // but currently there is no easy way to access it via OpenCL. + __local uint * counter_val = (__local uint *)(gh_hist + 2 * 4 * NUM_BINS);; + // backup the old value + uint old_val = *counter_val; + if (ltid == 0) { + // all workgroups processing the same feature add this counter + *counter_val = atom_inc(sync_counters + feature4_id); + } + // make sure everyone in this workgroup is here + barrier(CLK_LOCAL_MEM_FENCE); + // everyone in this workgroup: if we are the last workgroup, then do reduction! + if (*counter_val == (1 << POWER_FEATURE_WORKGROUPS) - 1) { + if (ltid == 0) { + // printf("workgroup %d: %g %g %g %g %g %g %g %g\n", group_id, gh_hist[0], gh_hist[1], gh_hist[2], gh_hist[3], gh_hist[4], gh_hist[5], gh_hist[6], gh_hist[7]); + // printf("feature_data[0] = %d %d %d %d", feature_data[0].s0, feature_data[0].s1, feature_data[0].s2, feature_data[0].s3); + // clear the sync counter for using it next time + sync_counters[feature4_id] = 0; + } + #else + // only 1 work group, no need to increase counter + // the reduction will become a simple copy + if (1) { + uint old_val; // dummy + #endif + // locate our feature4's block in output memory + uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS); + __global acc_type const * restrict feature4_subhists = + (__global acc_type *)output_buf + output_offset * 4 * 2 * NUM_BINS; + // skip reading the data already in local memory + uint skip_id = group_id ^ output_offset; + // locate output histogram location for this feature4 + __global acc_type* restrict hist_buf = hist_buf_base + feature4_id * 4 * 2 * NUM_BINS; + within_kernel_reduction256x4(feature_mask, feature4_subhists, skip_id, old_val, 1 << POWER_FEATURE_WORKGROUPS, + hist_buf, (__local acc_type *)shared_array); + // if (ltid == 0) + // printf("workgroup %d reduction done, %g %g %g %g %g %g %g %g\n", group_id, hist_buf[0], hist_buf[3*NUM_BINS], hist_buf[2*3*NUM_BINS], hist_buf[3*3*NUM_BINS], hist_buf[1], hist_buf[3*NUM_BINS+1], hist_buf[2*3*NUM_BINS+1], hist_buf[3*3*NUM_BINS+1]); + } +} + +// The following line ends the string literal, adds an extra #endif at the end +// )"" "\n#endif" +#endif diff --git a/src/treelearner/ocl/histogram64.cl b/src/treelearner/ocl/histogram64.cl new file mode 100644 index 0000000..f3e2e57 --- /dev/null +++ b/src/treelearner/ocl/histogram64.cl @@ -0,0 +1,743 @@ +/*! + * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * \brief This file can either be read and passed to an OpenCL compiler directly, + * or included in a C++11 source file as a string literal. + */ +#ifndef __OPENCL_VERSION__ +// If we are including this file in C++, +// the entire source file following (except the last #endif) will become +// a raw string literal. The extra ")" is just for matching parentheses +// to make the editor happy. The extra ")" and extra endif will be skipped. +// DO NOT add anything between here and the next #ifdef, otherwise you need +// to modify the skip count at the end of this file. +R""() +#endif + +#ifndef _HISTOGRAM_64_KERNEL_ +#define _HISTOGRAM_64_KERNEL_ + +#pragma OPENCL EXTENSION cl_khr_local_int32_base_atomics : enable +#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics : enable + +// Configurable options: +// NUM_BANKS should be a power of 2 +#ifndef NUM_BANKS +#define NUM_BANKS 4 +#endif +// how many bits in thread ID represent the bank = log2(NUM_BANKS) +#ifndef BANK_BITS +#define BANK_BITS 2 +#endif +// use double precision or not +#ifndef USE_DP_FLOAT +#define USE_DP_FLOAT 0 +#endif +// ignore hessian, and use the local memory for hessian as an additional bank for gradient +#ifndef CONST_HESSIAN +#define CONST_HESSIAN 0 +#endif + + +#define LOCAL_SIZE_0 256 +#define NUM_BINS 64 +// if USE_DP_FLOAT is set to 1, we will use double precision for the accumulator +#if USE_DP_FLOAT == 1 +#pragma OPENCL EXTENSION cl_khr_fp64 : enable +#pragma OPENCL EXTENSION cl_khr_int64_base_atomics : enable +typedef double acc_type; +typedef ulong acc_int_type; +#define as_acc_type as_double +#define as_acc_int_type as_ulong +#else +typedef float acc_type; +typedef uint acc_int_type; +#define as_acc_type as_float +#define as_acc_int_type as_uint +#endif +// mask for getting the bank ID +#define BANK_MASK (NUM_BANKS - 1) +// 4 features, each has a gradient and a hessian +#define HG_BIN_MULT (NUM_BANKS * 4 * 2) +// 4 features, each has a counter +#define CNT_BIN_MULT (NUM_BANKS * 4) +// local memory size in bytes +#define LOCAL_MEM_SIZE (4 * (sizeof(uint) + 2 * sizeof(acc_type)) * NUM_BINS * NUM_BANKS) + +// unroll the atomic operation for a few times. Takes more code space, +// but compiler can generate better code for faster atomics. +#define UNROLL_ATOMIC 1 + +// Options passed by compiler at run time: +// IGNORE_INDICES will be set when the kernel does not +// #define IGNORE_INDICES +// #define POWER_FEATURE_WORKGROUPS 10 + +// use all features and do not use feature mask +#ifndef ENABLE_ALL_FEATURES +#define ENABLE_ALL_FEATURES 1 +#endif + +// detect Nvidia platforms +#ifdef cl_nv_pragma_unroll +#define NVIDIA 1 +#endif + +// use binary patching for AMD GCN 1.2 or newer +#ifndef AMD_USE_DS_ADD_F32 +#define AMD_USE_DS_ADD_F32 0 +#endif + +typedef uint data_size_t; +typedef float score_t; + +#define ATOMIC_FADD_SUB1 { \ + expected.f_val = current.f_val; \ + next.f_val = expected.f_val + val; \ + current.u_val = atom_cmpxchg((volatile __local acc_int_type *)addr, expected.u_val, next.u_val); \ + if (current.u_val == expected.u_val) \ + goto end; \ + } +#define ATOMIC_FADD_SUB2 ATOMIC_FADD_SUB1 \ + ATOMIC_FADD_SUB1 +#define ATOMIC_FADD_SUB4 ATOMIC_FADD_SUB2 \ + ATOMIC_FADD_SUB2 +#define ATOMIC_FADD_SUB8 ATOMIC_FADD_SUB4 \ + ATOMIC_FADD_SUB4 +#define ATOMIC_FADD_SUB16 ATOMIC_FADD_SUB8 \ + ATOMIC_FADD_SUB8 +#define ATOMIC_FADD_SUB32 ATOMIC_FADD_SUB16\ + ATOMIC_FADD_SUB16 +#define ATOMIC_FADD_SUB64 ATOMIC_FADD_SUB32\ + ATOMIC_FADD_SUB32 + + +// atomic add for float number in local memory +inline void atomic_local_add_f(__local acc_type *addr, const float val) +{ + union{ + acc_int_type u_val; + acc_type f_val; + } next, expected, current; +#if (NVIDIA == 1 && USE_DP_FLOAT == 0) + float res = 0; + asm volatile ("atom.shared.add.f32 %0, [%1], %2;" : "=f"(res) : "l"(addr), "f"(val)); +#elif (AMD_USE_DS_ADD_F32 == 1 && USE_DP_FLAT == 0) + // this instruction (DS_AND_U32) will be patched into a DS_ADD_F32 + // we need to hack here because DS_ADD_F32 is not exposed via OpenCL + atom_and((__local acc_int_type *)addr, as_acc_int_type(val)); +#else + current.f_val = *addr; + #if UNROLL_ATOMIC == 1 + // provide a fast path + // then do the complete loop + // this should work on all devices + ATOMIC_FADD_SUB8 + ATOMIC_FADD_SUB4 + ATOMIC_FADD_SUB2 + #endif + do { + expected.f_val = current.f_val; + next.f_val = expected.f_val + val; + current.u_val = atom_cmpxchg((volatile __local acc_int_type *)addr, expected.u_val, next.u_val); + } while (current.u_val != expected.u_val); + end: + ; +#endif +} + + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ +// this function will be called by histogram64 +// we have one sub-histogram of one feature in registers, and need to read others +void within_kernel_reduction64x4(uchar4 feature_mask, + __global const acc_type* restrict feature4_sub_hist, + const uint skip_id, + acc_type g_val, acc_type h_val, + const ushort num_sub_hist, + __global acc_type* restrict output_buf, + __local acc_type * restrict local_hist) { + const ushort ltid = get_local_id(0); // range 0 - 255 + const ushort lsize = LOCAL_SIZE_0; + ushort feature_id = ltid & 3; // range 0 - 4 + const ushort bin_id = ltid >> 2; // range 0 - 63W + ushort i; + #if POWER_FEATURE_WORKGROUPS != 0 + // if there is only 1 work group, no need to do the reduction + // add all sub-histograms for 4 features + __global const acc_type* restrict p = feature4_sub_hist + ltid; + for (i = 0; i < skip_id; ++i) { + g_val += *p; p += NUM_BINS * 4; // 256 threads working on 4 features' 64 bins + h_val += *p; p += NUM_BINS * 4; + } + // skip the counters we already have + p += 2 * 4 * NUM_BINS; + for (i = i + 1; i < num_sub_hist; ++i) { + g_val += *p; p += NUM_BINS * 4; + h_val += *p; p += NUM_BINS * 4; + } + #endif + // printf("thread %d: g_val=%f, h_val=%f cnt=%d", ltid, g_val, h_val, cnt_val); + // now overwrite the local_hist for final reduction and output + // reverse the f3...f0 order to match the real order + feature_id = 3 - feature_id; + local_hist[feature_id * 2 * NUM_BINS + bin_id * 2 + 0] = g_val; + local_hist[feature_id * 2 * NUM_BINS + bin_id * 2 + 1] = h_val; + barrier(CLK_LOCAL_MEM_FENCE); + i = ltid; + if (feature_mask.s0 && i < 1 * 2 * NUM_BINS) { + output_buf[i] = local_hist[i]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s1 && i < 2 * 2 * NUM_BINS) { + output_buf[i] = local_hist[i]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s2 && i < 3 * 2 * NUM_BINS) { + output_buf[i] = local_hist[i]; + } + i += 1 * 2 * NUM_BINS; + if (feature_mask.s3 && i < 4 * 2 * NUM_BINS) { + output_buf[i] = local_hist[i]; + } +} + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ +__attribute__((reqd_work_group_size(LOCAL_SIZE_0, 1, 1))) +#if USE_CONSTANT_BUF == 1 +__kernel void histogram64(__global const uchar4* restrict feature_data_base, + __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), + const data_size_t feature_size, + __constant const data_size_t* restrict data_indices __attribute__((max_constant_size(65536))), + const data_size_t num_data, + __constant const score_t* restrict ordered_gradients __attribute__((max_constant_size(65536))), +#if CONST_HESSIAN == 0 + __constant const score_t* restrict ordered_hessians __attribute__((max_constant_size(65536))), +#else + const score_t const_hessian, +#endif + __global char* restrict output_buf, + __global volatile int * sync_counters, + __global acc_type* restrict hist_buf_base) { +#else +__kernel void histogram64(__global const uchar4* feature_data_base, + __constant const uchar4* restrict feature_masks __attribute__((max_constant_size(65536))), + const data_size_t feature_size, + __global const data_size_t* data_indices, + const data_size_t num_data, + __global const score_t* ordered_gradients, +#if CONST_HESSIAN == 0 + __global const score_t* ordered_hessians, +#else + const score_t const_hessian, +#endif + __global char* restrict output_buf, + __global volatile int * sync_counters, + __global acc_type* restrict hist_buf_base) { +#endif + // allocate the local memory array aligned with float2, to guarantee correct alignment on NVIDIA platforms + // otherwise a "Misaligned Address" exception may occur + __local float2 shared_array[LOCAL_MEM_SIZE/sizeof(float2)]; + const uint gtid = get_global_id(0); + const uint gsize = get_global_size(0); + const ushort ltid = get_local_id(0); + const ushort lsize = LOCAL_SIZE_0; // get_local_size(0); + const ushort group_id = get_group_id(0); + + // local memory per workgroup is 12 KB + // clear local memory + __local uint * ptr = (__local uint *) shared_array; + for (int i = ltid; i < LOCAL_MEM_SIZE/sizeof(uint); i += lsize) { + ptr[i] = 0; + } + barrier(CLK_LOCAL_MEM_FENCE); + // gradient/hessian histograms + // assume this starts at 32 * 4 = 128-byte boundary + // each bank: 2 * 4 * 64 * size_of(float) = 2 KB + // there are 4 banks (sub-histograms) used by 256 threads total 8 KB + /* memory layout of gh_hist: + ----------------------------------------------------------------------------------------------- + bk0_g_f0_bin0 bk0_g_f1_bin0 bk0_g_f2_bin0 bk0_g_f3_bin0 bk0_h_f0_bin0 bk0_h_f1_bin0 bk0_h_f2_bin0 bk0_h_f3_bin0 + bk1_g_f0_bin0 bk1_g_f1_bin0 bk1_g_f2_bin0 bk1_g_f3_bin0 bk1_h_f0_bin0 bk1_h_f1_bin0 bk1_h_f2_bin0 bk1_h_f3_bin0 + bk2_g_f0_bin0 bk2_g_f1_bin0 bk2_g_f2_bin0 bk2_g_f3_bin0 bk2_h_f0_bin0 bk2_h_f1_bin0 bk2_h_f2_bin0 bk2_h_f3_bin0 + bk3_g_f0_bin0 bk3_g_f1_bin0 bk3_g_f2_bin0 bk3_g_f3_bin0 bk3_h_f0_bin0 bk3_h_f1_bin0 bk3_h_f2_bin0 bk3_h_f3_bin0 + bk0_g_f0_bin1 bk0_g_f1_bin1 bk0_g_f2_bin1 bk0_g_f3_bin1 bk0_h_f0_bin1 bk0_h_f1_bin1 bk0_h_f2_bin1 bk0_h_f3_bin1 + bk1_g_f0_bin1 bk1_g_f1_bin1 bk1_g_f2_bin1 bk1_g_f3_bin1 bk1_h_f0_bin1 bk1_h_f1_bin1 bk1_h_f2_bin1 bk1_h_f3_bin1 + bk2_g_f0_bin1 bk2_g_f1_bin1 bk2_g_f2_bin1 bk2_g_f3_bin1 bk2_h_f0_bin1 bk2_h_f1_bin1 bk2_h_f2_bin1 bk2_h_f3_bin1 + bk3_g_f0_bin1 bk3_g_f1_bin1 bk3_g_f2_bin1 bk3_g_f3_bin1 bk3_h_f0_bin1 bk3_h_f1_bin1 bk3_h_f2_bin1 bk3_h_f3_bin1 + ... + bk0_g_f0_bin64 bk0_g_f1_bin64 bk0_g_f2_bin64 bk0_g_f3_bin64 bk0_h_f0_bin64 bk0_h_f1_bin64 bk0_h_f2_bin64 bk0_h_f3_bin64 + bk1_g_f0_bin64 bk1_g_f1_bin64 bk1_g_f2_bin64 bk1_g_f3_bin64 bk1_h_f0_bin64 bk1_h_f1_bin64 bk1_h_f2_bin64 bk1_h_f3_bin64 + bk2_g_f0_bin64 bk2_g_f1_bin64 bk2_g_f2_bin64 bk2_g_f3_bin64 bk2_h_f0_bin64 bk2_h_f1_bin64 bk2_h_f2_bin64 bk2_h_f3_bin64 + bk3_g_f0_bin64 bk3_g_f1_bin64 bk3_g_f2_bin64 bk3_g_f3_bin64 bk3_h_f0_bin64 bk3_h_f1_bin64 bk3_h_f2_bin64 bk3_h_f3_bin64 + ----------------------------------------------------------------------------------------------- + */ + // with this organization, the LDS/shared memory bank is independent of the bin value + // all threads within a quarter-wavefront (half-warp) will not have any bank conflict + + __local acc_type * gh_hist = (__local acc_type *)shared_array; + // counter histogram + // each bank: 4 * 64 * size_of(uint) = 1 KB + // there are 4 banks used by 256 threads total 4 KB + /* memory layout in cnt_hist: + ----------------------------------------------- + bk0_c_f0_bin0 bk0_c_f1_bin0 bk0_c_f2_bin0 bk0_c_f3_bin0 + bk1_c_f0_bin0 bk1_c_f1_bin0 bk1_c_f2_bin0 bk1_c_f3_bin0 + bk2_c_f0_bin0 bk2_c_f1_bin0 bk2_c_f2_bin0 bk2_c_f3_bin0 + bk3_c_f0_bin0 bk3_c_f1_bin0 bk3_c_f2_bin0 bk3_c_f3_bin0 + bk0_c_f0_bin1 bk0_c_f1_bin1 bk0_c_f2_bin1 bk0_c_f3_bin1 + bk1_c_f0_bin1 bk1_c_f1_bin1 bk1_c_f2_bin1 bk1_c_f3_bin1 + bk2_c_f0_bin1 bk2_c_f1_bin1 bk2_c_f2_bin1 bk2_c_f3_bin1 + bk3_c_f0_bin1 bk3_c_f1_bin1 bk3_c_f2_bin1 bk3_c_f3_bin1 + ... + bk0_c_f0_bin64 bk0_c_f1_bin64 bk0_c_f2_bin64 bk0_c_f3_bin64 + bk1_c_f0_bin64 bk1_c_f1_bin64 bk1_c_f2_bin64 bk1_c_f3_bin64 + bk2_c_f0_bin64 bk2_c_f1_bin64 bk2_c_f2_bin64 bk2_c_f3_bin64 + bk3_c_f0_bin64 bk3_c_f1_bin64 bk3_c_f2_bin64 bk3_c_f3_bin64 + ----------------------------------------------- + */ + #if CONST_HESSIAN == 1 + __local uint * cnt_hist = (__local uint *)(gh_hist + 2 * 4 * NUM_BINS * NUM_BANKS); + #endif + + // thread 0, 1, 2, 3 compute histograms for gradients first + // thread 4, 5, 6, 7 compute histograms for Hessians first + // etc. + uchar is_hessian_first = (ltid >> 2) & 1; + // thread 0-7 write result to bank0, 8-15 to bank1, 16-23 to bank2, 24-31 to bank3 + ushort bank = (ltid >> 3) & BANK_MASK; + + ushort group_feature = group_id >> POWER_FEATURE_WORKGROUPS; + // each 2^POWER_FEATURE_WORKGROUPS workgroups process on one feature (compile-time constant) + // feature_size is the number of examples per feature + __global const uchar4* feature_data = feature_data_base + group_feature * feature_size; + // size of threads that process this feature4 + const uint subglobal_size = lsize * (1 << POWER_FEATURE_WORKGROUPS); + // equivalent thread ID in this subgroup for this feature4 + const uint subglobal_tid = gtid - group_feature * subglobal_size; + // extract feature mask, when a byte is set to 0, that feature is disabled + #if ENABLE_ALL_FEATURES == 1 + // hopefully the compiler will propagate the constants and eliminate all branches + uchar4 feature_mask = (uchar4)(0xff, 0xff, 0xff, 0xff); + #else + uchar4 feature_mask = feature_masks[group_feature]; + #endif + // exit if all features are masked + if (!as_uint(feature_mask)) { + return; + } + + // STAGE 1: read feature data, and gradient and hessian + // first half of the threads read feature data from global memory + // 4 features stored in a tuple MSB...(0, 1, 2, 3)...LSB + // We will prefetch data into the "next" variable at the beginning of each iteration + uchar4 feature4; + uchar4 feature4_next; + uchar4 feature4_prev; + // offset used to rotate feature4 vector + ushort offset = (ltid & 0x3); + // store gradient and hessian + float stat1, stat2; + float stat1_next, stat2_next; + ushort bin, addr, addr2; + data_size_t ind; + data_size_t ind_next; + stat1 = ordered_gradients[subglobal_tid]; + #if CONST_HESSIAN == 0 + stat2 = ordered_hessians[subglobal_tid]; + #endif + #ifdef IGNORE_INDICES + ind = subglobal_tid; + #else + ind = data_indices[subglobal_tid]; + #endif + feature4 = feature_data[ind]; + feature4 = as_uchar4(as_uint(feature4) & 0x3f3f3f3f); + feature4_prev = feature4; + feature4_prev = as_uchar4(rotate(as_uint(feature4_prev), (uint)offset*8)); + #if ENABLE_ALL_FEATURES == 0 + // rotate feature_mask to match the feature order of each thread + feature_mask = as_uchar4(rotate(as_uint(feature_mask), (uint)offset*8)); + #endif + acc_type s3_stat1 = 0.0f, s3_stat2 = 0.0f; + acc_type s2_stat1 = 0.0f, s2_stat2 = 0.0f; + acc_type s1_stat1 = 0.0f, s1_stat2 = 0.0f; + acc_type s0_stat1 = 0.0f, s0_stat2 = 0.0f; + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ + // there are 2^POWER_FEATURE_WORKGROUPS workgroups processing each feature4 + for (uint i = subglobal_tid; i < num_data; i += subglobal_size) { + // prefetch the next iteration variables + // we don't need boundary check because we have made the buffer larger + stat1_next = ordered_gradients[i + subglobal_size]; + #if CONST_HESSIAN == 0 + stat2_next = ordered_hessians[i + subglobal_size]; + #endif + #ifdef IGNORE_INDICES + // we need to check to bounds here + ind_next = i + subglobal_size < num_data ? i + subglobal_size : i; + // start load next feature as early as possible + feature4_next = feature_data[ind_next]; + #else + ind_next = data_indices[i + subglobal_size]; + #endif + #if CONST_HESSIAN == 0 + // swap gradient and hessian for threads 4, 5, 6, 7 + float tmp = stat1; + stat1 = is_hessian_first ? stat2 : stat1; + stat2 = is_hessian_first ? tmp : stat2; + // stat1 = select(stat1, stat2, is_hessian_first); + // stat2 = select(stat2, tmp, is_hessian_first); + #endif + + // STAGE 2: accumulate gradient and hessian + offset = (ltid & 0x3); + feature4 = as_uchar4(rotate(as_uint(feature4), (uint)offset*8)); + bin = feature4.s3; + if ((bin != feature4_prev.s3) && feature_mask.s3) { + // printf("%3d (%4d): writing s3 %d %d offset %d", ltid, i, bin, feature4_prev.s3, offset); + bin = feature4_prev.s3; + feature4_prev.s3 = feature4.s3; + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + // thread 0, 1, 2, 3 now process feature 0, 1, 2, 3's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 0, 1, 2, 3's Hessians for example 4, 5, 6, 7 + atomic_local_add_f(gh_hist + addr, s3_stat1); + // thread 0, 1, 2, 3 now process feature 0, 1, 2, 3's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 0, 1, 2, 3's gradients for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s3_stat2); + #endif + s3_stat1 = stat1; + s3_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s3 %d", ltid, i, bin); + s3_stat1 += stat1; + s3_stat2 += stat2; + } + + bin = feature4.s2; + offset = (offset + 1) & 0x3; + if ((bin != feature4_prev.s2) && feature_mask.s2) { + // printf("%3d (%4d): writing s2 %d %d feature %d", ltid, i, bin, feature4_prev.s2, offset); + bin = feature4_prev.s2; + feature4_prev.s2 = feature4.s2; + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + // thread 0, 1, 2, 3 now process feature 1, 2, 3, 0's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 1, 2, 3, 0's Hessians for example 4, 5, 6, 7 + atomic_local_add_f(gh_hist + addr, s2_stat1); + // thread 0, 1, 2, 3 now process feature 1, 2, 3, 0's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 1, 2, 3, 0's gradients for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s2_stat2); + #endif + s2_stat1 = stat1; + s2_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s2 %d", ltid, i, bin); + s2_stat1 += stat1; + s2_stat2 += stat2; + } + + + // prefetch the next iteration variables + // we don't need boundary check because if it is out of boundary, ind_next = 0 + #ifndef IGNORE_INDICES + feature4_next = feature_data[ind_next]; + #endif + + bin = feature4.s1 & 0x3f; + offset = (offset + 1) & 0x3; + if ((bin != feature4_prev.s1) && feature_mask.s1) { + // printf("%3d (%4d): writing s1 %d %d feature %d", ltid, i, bin, feature4_prev.s1, offset); + bin = feature4_prev.s1; + feature4_prev.s1 = feature4.s1; + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + // thread 0, 1, 2, 3 now process feature 2, 3, 0, 1's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 2, 3, 0, 1's Hessians for example 4, 5, 6, 7 + atomic_local_add_f(gh_hist + addr, s1_stat1); + // thread 0, 1, 2, 3 now process feature 2, 3, 0, 1's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 2, 3, 0, 1's gradients for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s1_stat2); + #endif + s1_stat1 = stat1; + s1_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s1 %d", ltid, i, bin); + s1_stat1 += stat1; + s1_stat2 += stat2; + } + + bin = feature4.s0; + offset = (offset + 1) & 0x3; + if ((bin != feature4_prev.s0) && feature_mask.s0) { + // printf("%3d (%4d): writing s0 %d %d feature %d", ltid, i, bin, feature4_prev.s0, offset); + bin = feature4_prev.s0; + feature4_prev.s0 = feature4.s0; + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + // thread 0, 1, 2, 3 now process feature 3, 0, 1, 2's gradients for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 3, 0, 1, 2's Hessians for example 4, 5, 6, 7 + atomic_local_add_f(gh_hist + addr, s0_stat1); + // thread 0, 1, 2, 3 now process feature 3, 0, 1, 2's Hessians for example 0, 1, 2, 3 + // thread 4, 5, 6, 7 now process feature 3, 0, 1, 2's gradients for example 4, 5, 6, 7 + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s0_stat2); + #endif + s0_stat1 = stat1; + s0_stat2 = stat2; + } + else { + // printf("%3d (%4d): acc s0 %d", ltid, i, bin); + s0_stat1 += stat1; + s0_stat2 += stat2; + } + #if CONST_HESSIAN == 1 + // STAGE 3: accumulate counter + // there are 4 counters for 4 features + // thread 0, 1, 2, 3 now process feature 0, 1, 2, 3's counts for example 0, 1, 2, 3 + offset = (ltid & 0x3); + if (feature_mask.s3) { + bin = feature4.s3; + addr = bin * CNT_BIN_MULT + bank * 4 + offset; + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3 now process feature 1, 2, 3, 0's counts for example 0, 1, 2, 3 + offset = (offset + 1) & 0x3; + if (feature_mask.s2) { + bin = feature4.s2; + addr = bin * CNT_BIN_MULT + bank * 4 + offset; + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3 now process feature 2, 3, 0, 1's counts for example 0, 1, 2, 3 + offset = (offset + 1) & 0x3; + if (feature_mask.s1) { + bin = feature4.s1; + addr = bin * CNT_BIN_MULT + bank * 4 + offset; + atom_inc(cnt_hist + addr); + } + // thread 0, 1, 2, 3 now process feature 3, 0, 1, 2's counts for example 0, 1, 2, 3 + offset = (offset + 1) & 0x3; + if (feature_mask.s0) { + bin = feature4.s0; + addr = bin * CNT_BIN_MULT + bank * 4 + offset; + atom_inc(cnt_hist + addr); + } + #endif + stat1 = stat1_next; + stat2 = stat2_next; + feature4 = feature4_next; + feature4 = as_uchar4(as_uint(feature4) & 0x3f3f3f3f); + } + + bin = feature4_prev.s3; + offset = (ltid & 0x3); + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s3_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s3_stat2); + #endif + + bin = feature4_prev.s2; + offset = (offset + 1) & 0x3; + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s2_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s2_stat2); + #endif + + bin = feature4_prev.s1; + offset = (offset + 1) & 0x3; + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s1_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s1_stat2); + #endif + + bin = feature4_prev.s0; + offset = (offset + 1) & 0x3; + addr = bin * HG_BIN_MULT + bank * 8 + is_hessian_first * 4 + offset; + addr2 = addr + 4 - 8 * is_hessian_first; + atomic_local_add_f(gh_hist + addr, s0_stat1); + #if CONST_HESSIAN == 0 + atomic_local_add_f(gh_hist + addr2, s0_stat2); + #endif + barrier(CLK_LOCAL_MEM_FENCE); + +/* Makes MSVC happy with long string literal +)"" +R""() +*/ + #if ENABLE_ALL_FEATURES == 0 + // restore feature_mask + feature_mask = feature_masks[group_feature]; + #endif + + // now reduce the 4 banks of subhistograms into 1 + /* memory layout of gh_hist: + ----------------------------------------------------------------------------------------------- + bk0_g_f0_bin0 bk0_g_f1_bin0 bk0_g_f2_bin0 bk0_g_f3_bin0 bk0_h_f0_bin0 bk0_h_f1_bin0 bk0_h_f2_bin0 bk0_h_f3_bin0 + bk1_g_f0_bin0 bk1_g_f1_bin0 bk1_g_f2_bin0 bk1_g_f3_bin0 bk1_h_f0_bin0 bk1_h_f1_bin0 bk1_h_f2_bin0 bk1_h_f3_bin0 + bk2_g_f0_bin0 bk2_g_f1_bin0 bk2_g_f2_bin0 bk2_g_f3_bin0 bk2_h_f0_bin0 bk2_h_f1_bin0 bk2_h_f2_bin0 bk2_h_f3_bin0 + bk3_g_f0_bin0 bk3_g_f1_bin0 bk3_g_f2_bin0 bk3_g_f3_bin0 bk3_h_f0_bin0 bk3_h_f1_bin0 bk3_h_f2_bin0 bk3_h_f3_bin0 + bk0_g_f0_bin1 bk0_g_f1_bin1 bk0_g_f2_bin1 bk0_g_f3_bin1 bk0_h_f0_bin1 bk0_h_f1_bin1 bk0_h_f2_bin1 bk0_h_f3_bin1 + bk1_g_f0_bin1 bk1_g_f1_bin1 bk1_g_f2_bin1 bk1_g_f3_bin1 bk1_h_f0_bin1 bk1_h_f1_bin1 bk1_h_f2_bin1 bk1_h_f3_bin1 + bk2_g_f0_bin1 bk2_g_f1_bin1 bk2_g_f2_bin1 bk2_g_f3_bin1 bk2_h_f0_bin1 bk2_h_f1_bin1 bk2_h_f2_bin1 bk2_h_f3_bin1 + bk3_g_f0_bin1 bk3_g_f1_bin1 bk3_g_f2_bin1 bk3_g_f3_bin1 bk3_h_f0_bin1 bk3_h_f1_bin1 bk3_h_f2_bin1 bk3_h_f3_bin1 + ... + bk0_g_f0_bin64 bk0_g_f1_bin64 bk0_g_f2_bin64 bk0_g_f3_bin64 bk0_h_f0_bin64 bk0_h_f1_bin64 bk0_h_f2_bin64 bk0_h_f3_bin64 + bk1_g_f0_bin64 bk1_g_f1_bin64 bk1_g_f2_bin64 bk1_g_f3_bin64 bk1_h_f0_bin64 bk1_h_f1_bin64 bk1_h_f2_bin64 bk1_h_f3_bin64 + bk2_g_f0_bin64 bk2_g_f1_bin64 bk2_g_f2_bin64 bk2_g_f3_bin64 bk2_h_f0_bin64 bk2_h_f1_bin64 bk2_h_f2_bin64 bk2_h_f3_bin64 + bk3_g_f0_bin64 bk3_g_f1_bin64 bk3_g_f2_bin64 bk3_g_f3_bin64 bk3_h_f0_bin64 bk3_h_f1_bin64 bk3_h_f2_bin64 bk3_h_f3_bin64 + ----------------------------------------------------------------------------------------------- + */ + /* memory layout in cnt_hist: + ----------------------------------------------- + bk0_c_f0_bin0 bk0_c_f1_bin0 bk0_c_f2_bin0 bk0_c_f3_bin0 + bk1_c_f0_bin0 bk1_c_f1_bin0 bk1_c_f2_bin0 bk1_c_f3_bin0 + bk2_c_f0_bin0 bk2_c_f1_bin0 bk2_c_f2_bin0 bk2_c_f3_bin0 + bk3_c_f0_bin0 bk3_c_f1_bin0 bk3_c_f2_bin0 bk3_c_f3_bin0 + bk0_c_f0_bin1 bk0_c_f1_bin1 bk0_c_f2_bin1 bk0_c_f3_bin1 + bk1_c_f0_bin1 bk1_c_f1_bin1 bk1_c_f2_bin1 bk1_c_f3_bin1 + bk2_c_f0_bin1 bk2_c_f1_bin1 bk2_c_f2_bin1 bk2_c_f3_bin1 + bk3_c_f0_bin1 bk3_c_f1_bin1 bk3_c_f2_bin1 bk3_c_f3_bin1 + ... + bk0_c_f0_bin64 bk0_c_f1_bin64 bk0_c_f2_bin64 bk0_c_f3_bin64 + bk1_c_f0_bin64 bk1_c_f1_bin64 bk1_c_f2_bin64 bk1_c_f3_bin64 + bk2_c_f0_bin64 bk2_c_f1_bin64 bk2_c_f2_bin64 bk2_c_f3_bin64 + bk3_c_f0_bin64 bk3_c_f1_bin64 bk3_c_f2_bin64 bk3_c_f3_bin64 + ----------------------------------------------- + */ + acc_type g_val = 0.0f; + acc_type h_val = 0.0f; + uint cnt_val = 0; + // 256 threads, working on 4 features and 64 bins, + // so each thread has an independent feature/bin to work on. + const ushort feature_id = ltid & 3; // range 0 - 4 + const ushort bin_id = ltid >> 2; // range 0 - 63 + offset = (ltid >> 2) & BANK_MASK; // helps avoid LDS bank conflicts + for (int i = 0; i < NUM_BANKS; ++i) { + ushort bank_id = (i + offset) & BANK_MASK; + g_val += gh_hist[bin_id * HG_BIN_MULT + bank_id * 8 + feature_id]; + h_val += gh_hist[bin_id * HG_BIN_MULT + bank_id * 8 + feature_id + 4]; + #if CONST_HESSIAN == 1 + cnt_val += cnt_hist[bin_id * CNT_BIN_MULT + bank_id * 4 + feature_id]; + #endif + } + // now thread 0 - 3 holds feature 0, 1, 2, 3's gradient, hessian and count bin 0 + // now thread 4 - 7 holds feature 0, 1, 2, 3's gradient, hessian and count bin 1 + // etc, + + #if CONST_HESSIAN == 1 + g_val += h_val; + h_val = cnt_val * const_hessian; + #endif + // write to output + // write gradients and Hessians histogram for all 4 features + // output data in linear order for further reduction + // output size = 4 (features) * 3 (counters) * 64 (bins) * sizeof(float) + /* memory layout of output: + g_f0_bin0 g_f1_bin0 g_f2_bin0 g_f3_bin0 + g_f0_bin1 g_f1_bin1 g_f2_bin1 g_f3_bin1 + ... + g_f0_bin63 g_f1_bin63 g_f2_bin63 g_f3_bin63 + h_f0_bin0 h_f1_bin0 h_f2_bin0 h_f3_bin0 + h_f0_bin1 h_f1_bin1 h_f2_bin1 h_f3_bin1 + ... + h_f0_bin63 h_f1_bin63 h_f2_bin63 h_f3_bin63 + c_f0_bin0 c_f1_bin0 c_f2_bin0 c_f3_bin0 + c_f0_bin1 c_f1_bin1 c_f2_bin1 c_f3_bin1 + ... + c_f0_bin63 c_f1_bin63 c_f2_bin63 c_f3_bin63 + */ + // if there is only one workgroup processing this feature4, don't even need to write + uint feature4_id = (group_id >> POWER_FEATURE_WORKGROUPS); + #if POWER_FEATURE_WORKGROUPS != 0 + __global acc_type * restrict output = (__global acc_type * restrict)output_buf + group_id * 4 * 2 * NUM_BINS; + // if g_val and h_val are double, they are converted to float here + // write gradients for 4 features + output[0 * 4 * NUM_BINS + ltid] = g_val; + // write Hessians for 4 features + output[1 * 4 * NUM_BINS + ltid] = h_val; + barrier(CLK_LOCAL_MEM_FENCE | CLK_GLOBAL_MEM_FENCE); + mem_fence(CLK_GLOBAL_MEM_FENCE); + // To avoid the cost of an extra reducing kernel, we have to deal with some + // gray area in OpenCL. We want the last work group that process this feature to + // make the final reduction, and other threads will just quit. + // This requires that the results written by other workgroups available to the + // last workgroup (memory consistency) + #if NVIDIA == 1 + // this is equivalent to CUDA __threadfence(); + // ensure the writes above goes to main memory and other workgroups can see it + asm volatile("{\n\tmembar.gl;\n\t}\n\t" :::"memory"); + #else + // FIXME: how to do the above on AMD GPUs?? + // GCN ISA says that the all writes will bypass L1 cache (write through), + // however when the last thread is reading sub-histogram data we have to + // make sure that no part of data is modified in local L1 cache of other workgroups. + // Otherwise reading can be a problem (atomic operations to get consistency). + // But in our case, the sub-histogram of this workgroup cannot be in the cache + // of another workgroup, so the following trick will work just fine. + #endif + // Now, we want one workgroup to do the final reduction. + // Other workgroups processing the same feature quit. + // The is done by using an global atomic counter. + // On AMD GPUs ideally this should be done in GDS, + // but currently there is no easy way to access it via OpenCL. + __local uint * counter_val = (__local uint *)(gh_hist + 2 * 4 * NUM_BINS * NUM_BANKS);; + if (ltid == 0) { + // all workgroups processing the same feature add this counter + *counter_val = atom_inc(sync_counters + feature4_id); + } + // make sure everyone in this workgroup is here + barrier(CLK_LOCAL_MEM_FENCE); + // everyone in this workgroup: if we are the last workgroup, then do reduction! + if (*counter_val == (1 << POWER_FEATURE_WORKGROUPS) - 1) { + if (ltid == 0) { + // printf("workgroup %d start reduction!\n", group_id); + // printf("feature_data[0] = %d %d %d %d", feature_data[0].s0, feature_data[0].s1, feature_data[0].s2, feature_data[0].s3); + // clear the sync counter for using it next time + sync_counters[feature4_id] = 0; + } + #else + // only 1 work group, no need to increase counter + // the reduction will become a simple copy + if (1) { + barrier(CLK_LOCAL_MEM_FENCE); + #endif + // locate our feature4's block in output memory + uint output_offset = (feature4_id << POWER_FEATURE_WORKGROUPS); + __global acc_type const * restrict feature4_subhists = + (__global acc_type *)output_buf + output_offset * 4 * 2 * NUM_BINS; + // skip reading the data already in local memory + uint skip_id = group_id ^ output_offset; + // locate output histogram location for this feature4 + __global acc_type* restrict hist_buf = hist_buf_base + feature4_id * 4 * 2 * NUM_BINS; + within_kernel_reduction64x4(feature_mask, feature4_subhists, skip_id, g_val, h_val, + 1 << POWER_FEATURE_WORKGROUPS, hist_buf, (__local acc_type *)shared_array); + } +} + +// The following line ends the string literal, adds an extra #endif at the end +// )"" "\n#endif" +#endif diff --git a/src/treelearner/parallel_tree_learner.h b/src/treelearner/parallel_tree_learner.h new file mode 100644 index 0000000..25a978c --- /dev/null +++ b/src/treelearner/parallel_tree_learner.h @@ -0,0 +1,236 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_PARALLEL_TREE_LEARNER_H_ +#define LIGHTGBM_SRC_TREELEARNER_PARALLEL_TREE_LEARNER_H_ + +#include +#include + +#include +#include +#include + +#include "gpu_tree_learner.h" +#include "serial_tree_learner.h" + +namespace LightGBM { + +/*! +* \brief Feature parallel learning algorithm. +* Different machine will find best split on different features, then sync global best split +* It is recommended used when #data is small or #feature is large +*/ +template +class FeatureParallelTreeLearner: public TREELEARNER_T { + public: + explicit FeatureParallelTreeLearner(const Config* config); + ~FeatureParallelTreeLearner(); + void Init(const Dataset* train_data, bool is_constant_hessian) override; + + protected: + void BeforeTrain() override; + void FindBestSplitsFromHistograms(const std::vector& is_feature_used, bool use_subtract, const Tree* tree) override; + + private: + /*! \brief rank of local machine */ + int rank_; + /*! \brief Number of machines of this parallel task */ + int num_machines_; + /*! \brief Buffer for network send */ + std::vector input_buffer_; + /*! \brief Buffer for network receive */ + std::vector output_buffer_; +}; + +/*! +* \brief Data parallel learning algorithm. +* Workers use local data to construct histograms locally, then sync up global histograms. +* It is recommended used when #data is large or #feature is small +*/ +template +class DataParallelTreeLearner: public TREELEARNER_T { + public: + explicit DataParallelTreeLearner(const Config* config); + ~DataParallelTreeLearner(); + void Init(const Dataset* train_data, bool is_constant_hessian) override; + void ResetConfig(const Config* config) override; + + protected: + void BeforeTrain() override; + void FindBestSplits(const Tree* tree) override; + void FindBestSplitsFromHistograms(const std::vector& is_feature_used, bool use_subtract, const Tree* tree) override; + void Split(Tree* tree, int best_Leaf, int* left_leaf, int* right_leaf) override; + + inline data_size_t GetGlobalDataCountInLeaf(int leaf_idx) const override { + if (leaf_idx >= 0) { + return global_data_count_in_leaf_[leaf_idx]; + } else { + return 0; + } + } + + void PrepareBufferPos( + const std::vector>& feature_distribution, + std::vector* block_start, + std::vector* block_len, + std::vector* buffer_write_start_pos, + std::vector* buffer_read_start_pos, + comm_size_t* reduce_scatter_size, + size_t hist_entry_size); + + private: + /*! \brief Rank of local machine */ + int rank_; + /*! \brief Number of machines of this parallel task */ + int num_machines_; + /*! \brief Buffer for network send */ + std::vector> input_buffer_; + /*! \brief Buffer for network receive */ + std::vector> output_buffer_; + /*! \brief different machines will aggregate histograms for different features, + use this to mark local aggregate features*/ + std::vector is_feature_aggregated_; + /*! \brief Block start index for reduce scatter */ + std::vector block_start_; + /*! \brief Block size for reduce scatter */ + std::vector block_len_; + /*! \brief Block start index for reduce scatter with int16 histograms */ + std::vector block_start_int16_; + /*! \brief Block size for reduce scatter with int16 histograms */ + std::vector block_len_int16_; + /*! \brief Write positions for feature histograms */ + std::vector buffer_write_start_pos_; + /*! \brief Read positions for local feature histograms */ + std::vector buffer_read_start_pos_; + /*! \brief Write positions for feature histograms with int16 histograms*/ + std::vector buffer_write_start_pos_int16_; + /*! \brief Read positions for local feature histograms with int16 histograms */ + std::vector buffer_read_start_pos_int16_; + /*! \brief Size for reduce scatter */ + comm_size_t reduce_scatter_size_; + /*! \brief Size for reduce scatter with int16 histogram*/ + comm_size_t reduce_scatter_size_int16_; + /*! \brief Store global number of data in leaves */ + std::vector global_data_count_in_leaf_; +}; + +/*! +* \brief Voting based data parallel learning algorithm. +* Like data parallel, but not aggregate histograms for all features. +* Here using voting to reduce features, and only aggregate histograms for selected features. +* When #data is large and #feature is large, you can use this to have better speed-up +*/ +template +class VotingParallelTreeLearner: public TREELEARNER_T { + public: + explicit VotingParallelTreeLearner(const Config* config); + ~VotingParallelTreeLearner() { } + void Init(const Dataset* train_data, bool is_constant_hessian) override; + void ResetConfig(const Config* config) override; + + protected: + void BeforeTrain() override; + bool BeforeFindBestSplit(const Tree* tree, int left_leaf, int right_leaf) override; + void FindBestSplits(const Tree* tree) override; + void FindBestSplitsFromHistograms(const std::vector& is_feature_used, bool use_subtract, const Tree* tree) override; + void Split(Tree* tree, int best_Leaf, int* left_leaf, int* right_leaf) override; + + inline data_size_t GetGlobalDataCountInLeaf(int leaf_idx) const override { + if (leaf_idx >= 0) { + return global_data_count_in_leaf_[leaf_idx]; + } else { + return 0; + } + } + /*! + * \brief Perform global voting + * \param leaf_idx index of leaf + * \param splits All splits from local voting + * \param out Result of global voting, only store feature indices + */ + void GlobalVoting(int leaf_idx, const std::vector& splits, + std::vector* out); + /*! + * \brief Copy local histogram to buffer + * \param smaller_top_features Selected features for smaller leaf + * \param larger_top_features Selected features for larger leaf + */ + void CopyLocalHistogram(const std::vector& smaller_top_features, + const std::vector& larger_top_features); + + private: + /*! \brief Tree config used in local mode */ + Config local_config_; + /*! \brief Voting size */ + int top_k_; + /*! \brief Rank of local machine*/ + int rank_; + /*! \brief Number of machines */ + int num_machines_; + /*! \brief Buffer for network send */ + std::vector input_buffer_; + /*! \brief Buffer for network receive */ + std::vector output_buffer_; + /*! \brief different machines will aggregate histograms for different features, + use this to mark local aggregate features*/ + std::vector smaller_is_feature_aggregated_; + /*! \brief different machines will aggregate histograms for different features, + use this to mark local aggregate features*/ + std::vector larger_is_feature_aggregated_; + /*! \brief Block start index for reduce scatter */ + std::vector block_start_; + /*! \brief Block size for reduce scatter */ + std::vector block_len_; + /*! \brief Read positions for feature histograms at smaller leaf */ + std::vector smaller_buffer_read_start_pos_; + /*! \brief Read positions for feature histograms at larger leaf */ + std::vector larger_buffer_read_start_pos_; + /*! \brief Size for reduce scatter */ + comm_size_t reduce_scatter_size_; + /*! \brief Store global number of data in leaves */ + std::vector global_data_count_in_leaf_; + /*! \brief Store global split information for smaller leaf */ + std::unique_ptr smaller_leaf_splits_global_; + /*! \brief Store global split information for larger leaf */ + std::unique_ptr larger_leaf_splits_global_; + /*! \brief Store global histogram for smaller leaf */ + std::unique_ptr smaller_leaf_histogram_array_global_; + /*! \brief Store global histogram for larger leaf */ + std::unique_ptr larger_leaf_histogram_array_global_; + + std::vector smaller_leaf_histogram_data_; + std::vector larger_leaf_histogram_data_; + std::vector feature_metas_; +}; + +// To-do: reduce the communication cost by using bitset to communicate. +inline void SyncUpGlobalBestSplit(char* input_buffer_, char* output_buffer_, SplitInfo* smaller_best_split, SplitInfo* larger_best_split, int max_cat_threshold) { + // sync global best info + int size = SplitInfo::Size(max_cat_threshold); + smaller_best_split->CopyTo(input_buffer_); + larger_best_split->CopyTo(input_buffer_ + size); + Network::Allreduce(input_buffer_, size * 2, size, output_buffer_, + [] (const char* src, char* dst, int size, comm_size_t len) { + comm_size_t used_size = 0; + LightSplitInfo p1, p2; + while (used_size < len) { + p1.CopyFrom(src); + p2.CopyFrom(dst); + if (p1 > p2) { + std::memcpy(dst, src, size); + } + src += size; + dst += size; + used_size += size; + } + }); + // copy back + smaller_best_split->CopyFrom(output_buffer_); + larger_best_split->CopyFrom(output_buffer_ + size); +} + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_PARALLEL_TREE_LEARNER_H_ diff --git a/src/treelearner/serial_tree_learner.cpp b/src/treelearner/serial_tree_learner.cpp new file mode 100644 index 0000000..23d7525 --- /dev/null +++ b/src/treelearner/serial_tree_learner.cpp @@ -0,0 +1,1118 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include "serial_tree_learner.h" + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include "cost_effective_gradient_boosting.hpp" + +namespace LightGBM { + +SerialTreeLearner::SerialTreeLearner(const Config* config) + : config_(config), col_sampler_(config) { + gradient_discretizer_ = nullptr; +} + +SerialTreeLearner::~SerialTreeLearner() { +} + +void SerialTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian) { + train_data_ = train_data; + num_data_ = train_data_->num_data(); + num_features_ = train_data_->num_features(); + int max_cache_size = 0; + // Get the max size of pool + if (config_->histogram_pool_size <= 0) { + max_cache_size = config_->num_leaves; + } else { + size_t total_histogram_size = 0; + for (int i = 0; i < train_data_->num_features(); ++i) { + total_histogram_size += kHistEntrySize * train_data_->FeatureNumBin(i); + } + max_cache_size = static_cast(config_->histogram_pool_size * 1024 * 1024 / total_histogram_size); + } + // at least need 2 leaves + max_cache_size = std::max(2, max_cache_size); + max_cache_size = std::min(max_cache_size, config_->num_leaves); + + // push split information for all leaves + best_split_per_leaf_.resize(config_->num_leaves); + constraints_.reset(LeafConstraintsBase::Create(config_, config_->num_leaves, train_data_->num_features())); + + // initialize splits for leaf + smaller_leaf_splits_.reset(new LeafSplits(train_data_->num_data(), config_)); + larger_leaf_splits_.reset(new LeafSplits(train_data_->num_data(), config_)); + + // initialize data partition + data_partition_.reset(new DataPartition(num_data_, config_->num_leaves)); + col_sampler_.SetTrainingData(train_data_); + // initialize ordered gradients and hessians + ordered_gradients_.resize(num_data_); + ordered_hessians_.resize(num_data_); + + if (config_->use_quantized_grad) { + gradient_discretizer_.reset(new GradientDiscretizer(config_->num_grad_quant_bins, config_->num_iterations, config_->seed, is_constant_hessian, config_->stochastic_rounding)); + gradient_discretizer_->Init(num_data_, config_->num_leaves, num_features_, train_data_); + } + + GetShareStates(train_data_, is_constant_hessian, true); + histogram_pool_.DynamicChangeSize(train_data_, + share_state_->num_hist_total_bin(), + share_state_->feature_hist_offsets(), + config_, max_cache_size, config_->num_leaves); + Log::Info("Number of data points in the train set: %d, number of used features: %d", num_data_, num_features_); + if (CostEfficientGradientBoosting::IsEnable(config_)) { + cegb_.reset(new CostEfficientGradientBoosting(this)); + cegb_->Init(); + } +} + +void SerialTreeLearner::GetShareStates(const Dataset* dataset, + bool is_constant_hessian, + bool is_first_time) { + if (is_first_time) { + if (config_->use_quantized_grad) { + share_state_.reset(dataset->GetShareStates( + reinterpret_cast(gradient_discretizer_->ordered_int_gradients_and_hessians()), nullptr, + col_sampler_.is_feature_used_bytree(), is_constant_hessian, + config_->force_col_wise, config_->force_row_wise, config_->num_grad_quant_bins)); + } else { + share_state_.reset(dataset->GetShareStates( + ordered_gradients_.data(), ordered_hessians_.data(), + col_sampler_.is_feature_used_bytree(), is_constant_hessian, + config_->force_col_wise, config_->force_row_wise, config_->num_grad_quant_bins)); + } + } else { + CHECK_NOTNULL(share_state_); + // cannot change is_hist_col_wise during training + if (config_->use_quantized_grad) { + share_state_.reset(dataset->GetShareStates( + reinterpret_cast(gradient_discretizer_->ordered_int_gradients_and_hessians()), nullptr, + col_sampler_.is_feature_used_bytree(), is_constant_hessian, + share_state_->is_col_wise, !share_state_->is_col_wise, config_->num_grad_quant_bins)); + } else { + share_state_.reset(dataset->GetShareStates( + ordered_gradients_.data(), ordered_hessians_.data(), col_sampler_.is_feature_used_bytree(), + is_constant_hessian, share_state_->is_col_wise, + !share_state_->is_col_wise, config_->num_grad_quant_bins)); + } + } + CHECK_NOTNULL(share_state_); +} + +void SerialTreeLearner::ResetTrainingDataInner(const Dataset* train_data, + bool is_constant_hessian, + bool reset_multi_val_bin) { + train_data_ = train_data; + num_data_ = train_data_->num_data(); + CHECK_EQ(num_features_, train_data_->num_features()); + + // initialize splits for leaf + smaller_leaf_splits_->ResetNumData(num_data_); + larger_leaf_splits_->ResetNumData(num_data_); + + // initialize data partition + data_partition_->ResetNumData(num_data_); + if (reset_multi_val_bin) { + col_sampler_.SetTrainingData(train_data_); + GetShareStates(train_data_, is_constant_hessian, false); + } + + // initialize ordered gradients and hessians + ordered_gradients_.resize(num_data_); + ordered_hessians_.resize(num_data_); + if (cegb_ != nullptr) { + cegb_->Init(); + } +} + +void SerialTreeLearner::ResetConfig(const Config* config) { + if (config_->num_leaves != config->num_leaves) { + config_ = config; + int max_cache_size = 0; + // Get the max size of pool + if (config->histogram_pool_size <= 0) { + max_cache_size = config_->num_leaves; + } else { + size_t total_histogram_size = 0; + for (int i = 0; i < train_data_->num_features(); ++i) { + total_histogram_size += kHistEntrySize * train_data_->FeatureNumBin(i); + } + max_cache_size = static_cast(config_->histogram_pool_size * 1024 * 1024 / total_histogram_size); + } + // at least need 2 leaves + max_cache_size = std::max(2, max_cache_size); + max_cache_size = std::min(max_cache_size, config_->num_leaves); + histogram_pool_.DynamicChangeSize(train_data_, + share_state_->num_hist_total_bin(), + share_state_->feature_hist_offsets(), + config_, max_cache_size, config_->num_leaves); + + // push split information for all leaves + best_split_per_leaf_.resize(config_->num_leaves); + data_partition_->ResetLeaves(config_->num_leaves); + } else { + config_ = config; + } + col_sampler_.SetConfig(config_); + histogram_pool_.ResetConfig(train_data_, config_); + if (CostEfficientGradientBoosting::IsEnable(config_)) { + if (cegb_ == nullptr) { + cegb_.reset(new CostEfficientGradientBoosting(this)); + } + cegb_->Init(); + } + constraints_.reset(LeafConstraintsBase::Create(config_, config_->num_leaves, train_data_->num_features())); +} + +Tree* SerialTreeLearner::Train(const score_t* gradients, const score_t *hessians, bool /*is_first_tree*/) { + Common::FunctionTimer fun_timer("SerialTreeLearner::Train", global_timer); + gradients_ = gradients; + hessians_ = hessians; + int num_threads = OMP_NUM_THREADS(); + if (share_state_->num_threads != num_threads && share_state_->num_threads > 0) { + Log::Warning( + "Detected that num_threads changed during training (from %d to %d), " + "it may cause unexpected errors.", + share_state_->num_threads, num_threads); + } + share_state_->num_threads = num_threads; + + if (config_->use_quantized_grad) { + gradient_discretizer_->DiscretizeGradients(num_data_, gradients_, hessians_); + } + + // some initial works before training + BeforeTrain(); + + bool track_branch_features = !(config_->interaction_constraints_vector.empty()); + auto tree = std::unique_ptr(new Tree(config_->num_leaves, track_branch_features, false)); + auto tree_ptr = tree.get(); + constraints_->ShareTreePointer(tree_ptr); + + // set the root value by hand, as it is not handled by splits + tree->SetLeafOutput(0, FeatureHistogram::CalculateSplittedLeafOutput( + smaller_leaf_splits_->sum_gradients(), smaller_leaf_splits_->sum_hessians(), + config_->lambda_l1, config_->lambda_l2, config_->max_delta_step, + BasicConstraint(), config_->path_smooth, static_cast(num_data_), 0)); + + // root leaf + int left_leaf = 0; + int cur_depth = 1; + // only root leaf can be splitted on first time + int right_leaf = -1; + + int init_splits = ForceSplits(tree_ptr, &left_leaf, &right_leaf, &cur_depth); + + for (int split = init_splits; split < config_->num_leaves - 1; ++split) { + // some initial works before finding best split + if (BeforeFindBestSplit(tree_ptr, left_leaf, right_leaf)) { + // find best threshold for every feature + FindBestSplits(tree_ptr); + } + // Get a leaf with max split gain + int best_leaf = static_cast(ArrayArgs::ArgMax(best_split_per_leaf_)); + // Get split information for best leaf + const SplitInfo& best_leaf_SplitInfo = best_split_per_leaf_[best_leaf]; + // cannot split, quit + if (best_leaf_SplitInfo.gain <= 0.0) { + Log::Warning("No further splits with positive gain, best gain: %f", best_leaf_SplitInfo.gain); + break; + } + // split tree with best leaf + Split(tree_ptr, best_leaf, &left_leaf, &right_leaf); + cur_depth = std::max(cur_depth, tree->leaf_depth(left_leaf)); + } + + if (config_->use_quantized_grad && config_->quant_train_renew_leaf) { + gradient_discretizer_->RenewIntGradTreeOutput(tree.get(), config_, data_partition_.get(), gradients_, hessians_, + [this] (int leaf_index) { return GetGlobalDataCountInLeaf(leaf_index); }); + } + + Log::Debug("Trained a tree with leaves = %d and depth = %d", tree->num_leaves(), cur_depth); + return tree.release(); +} + +Tree* SerialTreeLearner::FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t *hessians) const { + auto tree = std::unique_ptr(new Tree(*old_tree)); + CHECK_GE(data_partition_->num_leaves(), tree->num_leaves()); + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < tree->num_leaves(); ++i) { + OMP_LOOP_EX_BEGIN(); + data_size_t cnt_leaf_data = 0; + auto tmp_idx = data_partition_->GetIndexOnLeaf(i, &cnt_leaf_data); + double sum_grad = 0.0f; + double sum_hess = kEpsilon; + for (data_size_t j = 0; j < cnt_leaf_data; ++j) { + auto idx = tmp_idx[j]; + sum_grad += gradients[idx]; + sum_hess += hessians[idx]; + } + double output; + if ((config_->path_smooth > kEpsilon) & (i > 0)) { + output = FeatureHistogram::CalculateSplittedLeafOutput( + sum_grad, sum_hess, config_->lambda_l1, config_->lambda_l2, + config_->max_delta_step, config_->path_smooth, cnt_leaf_data, tree->leaf_parent(i)); + } else { + output = FeatureHistogram::CalculateSplittedLeafOutput( + sum_grad, sum_hess, config_->lambda_l1, config_->lambda_l2, + config_->max_delta_step, config_->path_smooth, cnt_leaf_data, 0); + } + auto old_leaf_output = tree->LeafOutput(i); + auto new_leaf_output = output * tree->shrinkage(); + tree->SetLeafOutput(i, config_->refit_decay_rate * old_leaf_output + (1.0 - config_->refit_decay_rate) * new_leaf_output); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + return tree.release(); +} + +Tree* SerialTreeLearner::FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t *hessians) const { + data_partition_->ResetByLeafPred(leaf_pred, old_tree->num_leaves()); + return FitByExistingTree(old_tree, gradients, hessians); +} + +void SerialTreeLearner::BeforeTrain() { + Common::FunctionTimer fun_timer("SerialTreeLearner::BeforeTrain", global_timer); + // reset histogram pool + histogram_pool_.ResetMap(); + + col_sampler_.ResetByTree(); + train_data_->InitTrain(col_sampler_.is_feature_used_bytree(), share_state_.get()); + // initialize data partition + data_partition_->Init(); + + constraints_->Reset(); + + // reset the splits for leaves + for (int i = 0; i < config_->num_leaves; ++i) { + best_split_per_leaf_[i].Reset(); + } + + // Sumup for root + if (data_partition_->leaf_count(0) == num_data_) { + // use all data + if (!config_->use_quantized_grad) { + smaller_leaf_splits_->Init(gradients_, hessians_); + } else { + smaller_leaf_splits_->Init( + gradient_discretizer_->discretized_gradients_and_hessians(), + gradient_discretizer_->grad_scale(), + gradient_discretizer_->hess_scale()); + } + } else { + // use bagging, only use part of data + if (!config_->use_quantized_grad) { + smaller_leaf_splits_->Init(0, data_partition_.get(), gradients_, hessians_); + } else { + smaller_leaf_splits_->Init( + 0, data_partition_.get(), + gradient_discretizer_->discretized_gradients_and_hessians(), + static_cast(gradient_discretizer_->grad_scale()), + static_cast(gradient_discretizer_->hess_scale())); + } + } + + larger_leaf_splits_->Init(); + + if (cegb_ != nullptr) { + cegb_->BeforeTrain(); + } + + if (config_->use_quantized_grad && config_->tree_learner != std::string("data")) { + gradient_discretizer_->SetNumBitsInHistogramBin(0, -1, data_partition_->leaf_count(0), 0); + } +} + +bool SerialTreeLearner::BeforeFindBestSplit(const Tree* tree, int left_leaf, int right_leaf) { + Common::FunctionTimer fun_timer("SerialTreeLearner::BeforeFindBestSplit", global_timer); + // check depth of current leaf + if (config_->max_depth > 0) { + // only need to check left leaf, since right leaf is in same level of left leaf + if (tree->leaf_depth(left_leaf) >= config_->max_depth) { + best_split_per_leaf_[left_leaf].gain = kMinScore; + if (right_leaf >= 0) { + best_split_per_leaf_[right_leaf].gain = kMinScore; + } + return false; + } + } + data_size_t num_data_in_left_child = GetGlobalDataCountInLeaf(left_leaf); + data_size_t num_data_in_right_child = GetGlobalDataCountInLeaf(right_leaf); + // no enough data to continue + if (num_data_in_right_child < static_cast(config_->min_data_in_leaf * 2) + && num_data_in_left_child < static_cast(config_->min_data_in_leaf * 2)) { + best_split_per_leaf_[left_leaf].gain = kMinScore; + if (right_leaf >= 0) { + best_split_per_leaf_[right_leaf].gain = kMinScore; + } + return false; + } + parent_leaf_histogram_array_ = nullptr; + // only have root + if (right_leaf < 0) { + histogram_pool_.Get(left_leaf, &smaller_leaf_histogram_array_); + larger_leaf_histogram_array_ = nullptr; + } else if (num_data_in_left_child < num_data_in_right_child) { + // put parent(left) leaf's histograms into larger leaf's histograms + if (histogram_pool_.Get(left_leaf, &larger_leaf_histogram_array_)) { + parent_leaf_histogram_array_ = larger_leaf_histogram_array_; + } + histogram_pool_.Move(left_leaf, right_leaf); + histogram_pool_.Get(left_leaf, &smaller_leaf_histogram_array_); + } else { + // put parent(left) leaf's histograms to larger leaf's histograms + if (histogram_pool_.Get(left_leaf, &larger_leaf_histogram_array_)) { + parent_leaf_histogram_array_ = larger_leaf_histogram_array_; + } + histogram_pool_.Get(right_leaf, &smaller_leaf_histogram_array_); + } + return true; +} + +void SerialTreeLearner::FindBestSplits(const Tree* tree) { + FindBestSplits(tree, nullptr); +} + +void SerialTreeLearner::FindBestSplits(const Tree* tree, const std::set* force_features) { + std::vector is_feature_used(num_features_, 0); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 256) if (num_features_ >= 512) + for (int feature_index = 0; feature_index < num_features_; ++feature_index) { + if (!col_sampler_.is_feature_used_bytree()[feature_index] && (force_features == nullptr || force_features->find(feature_index) == force_features->end())) continue; + if (parent_leaf_histogram_array_ != nullptr + && !parent_leaf_histogram_array_[feature_index].is_splittable()) { + smaller_leaf_histogram_array_[feature_index].set_is_splittable(false); + continue; + } + is_feature_used[feature_index] = 1; + } + bool use_subtract = parent_leaf_histogram_array_ != nullptr; + + ConstructHistograms(is_feature_used, use_subtract); + FindBestSplitsFromHistograms(is_feature_used, use_subtract, tree); +} + +void SerialTreeLearner::ConstructHistograms( + const std::vector& is_feature_used, bool use_subtract) { + Common::FunctionTimer fun_timer("SerialTreeLearner::ConstructHistograms", + global_timer); + // construct smaller leaf + if (config_->use_quantized_grad) { + const uint8_t smaller_leaf_num_bits = gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_splits_->leaf_index()); + hist_t* ptr_smaller_leaf_hist_data = + smaller_leaf_num_bits <= 16 ? + reinterpret_cast(smaller_leaf_histogram_array_[0].RawDataInt16() - kHistOffset) : + reinterpret_cast(smaller_leaf_histogram_array_[0].RawDataInt32() - kHistOffset); + #define SMALLER_LEAF_ARGS \ + is_feature_used, smaller_leaf_splits_->data_indices(), \ + smaller_leaf_splits_->num_data_in_leaf(), \ + reinterpret_cast(gradient_discretizer_->discretized_gradients_and_hessians()), \ + nullptr, \ + reinterpret_cast(gradient_discretizer_->ordered_int_gradients_and_hessians()), \ + nullptr, \ + share_state_.get(), \ + reinterpret_cast(ptr_smaller_leaf_hist_data) + if (smaller_leaf_num_bits <= 16) { + train_data_->ConstructHistograms(SMALLER_LEAF_ARGS); + } else { + train_data_->ConstructHistograms(SMALLER_LEAF_ARGS); + } + #undef SMALLER_LEAF_ARGS + if (larger_leaf_histogram_array_ && !use_subtract) { + const uint8_t larger_leaf_num_bits = gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_splits_->leaf_index()); + hist_t* ptr_larger_leaf_hist_data = + larger_leaf_num_bits <= 16 ? + reinterpret_cast(larger_leaf_histogram_array_[0].RawDataInt16() - kHistOffset) : + reinterpret_cast(larger_leaf_histogram_array_[0].RawDataInt32() - kHistOffset); + #define LARGER_LEAF_ARGS \ + is_feature_used, larger_leaf_splits_->data_indices(), \ + larger_leaf_splits_->num_data_in_leaf(), \ + reinterpret_cast(gradient_discretizer_->discretized_gradients_and_hessians()), \ + nullptr, \ + reinterpret_cast(gradient_discretizer_->ordered_int_gradients_and_hessians()), \ + nullptr, \ + share_state_.get(), \ + reinterpret_cast(ptr_larger_leaf_hist_data) + if (larger_leaf_num_bits <= 16) { + train_data_->ConstructHistograms(LARGER_LEAF_ARGS); + } else { + train_data_->ConstructHistograms(LARGER_LEAF_ARGS); + } + #undef LARGER_LEAF_ARGS + } + } else { + hist_t* ptr_smaller_leaf_hist_data = + smaller_leaf_histogram_array_[0].RawData() - kHistOffset; + train_data_->ConstructHistograms( + is_feature_used, smaller_leaf_splits_->data_indices(), + smaller_leaf_splits_->num_data_in_leaf(), gradients_, hessians_, + ordered_gradients_.data(), ordered_hessians_.data(), share_state_.get(), + ptr_smaller_leaf_hist_data); + if (larger_leaf_histogram_array_ != nullptr && !use_subtract) { + // construct larger leaf + hist_t* ptr_larger_leaf_hist_data = + larger_leaf_histogram_array_[0].RawData() - kHistOffset; + train_data_->ConstructHistograms( + is_feature_used, larger_leaf_splits_->data_indices(), + larger_leaf_splits_->num_data_in_leaf(), gradients_, hessians_, + ordered_gradients_.data(), ordered_hessians_.data(), share_state_.get(), + ptr_larger_leaf_hist_data); + } + } +} + +void SerialTreeLearner::FindBestSplitsFromHistograms( + const std::vector& is_feature_used, bool use_subtract, const Tree* tree) { + Common::FunctionTimer fun_timer( + "SerialTreeLearner::FindBestSplitsFromHistograms", global_timer); + std::vector smaller_best(share_state_->num_threads); + std::vector larger_best(share_state_->num_threads); + std::vector smaller_node_used_features = col_sampler_.GetByNode(tree, smaller_leaf_splits_->leaf_index()); + std::vector larger_node_used_features; + double smaller_leaf_parent_output = GetParentOutput(tree, smaller_leaf_splits_.get()); + double larger_leaf_parent_output = 0; + if (larger_leaf_splits_ != nullptr && larger_leaf_splits_->leaf_index() >= 0) { + larger_leaf_parent_output = GetParentOutput(tree, larger_leaf_splits_.get()); + } + if (larger_leaf_splits_->leaf_index() >= 0) { + larger_node_used_features = col_sampler_.GetByNode(tree, larger_leaf_splits_->leaf_index()); + } + + if (use_subtract && config_->use_quantized_grad) { + const int parent_index = std::min(smaller_leaf_splits_->leaf_index(), larger_leaf_splits_->leaf_index()); + const uint8_t parent_hist_bits = gradient_discretizer_->GetHistBitsInNode(parent_index); + const uint8_t larger_hist_bits = gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_splits_->leaf_index()); + if (parent_hist_bits > 16 && larger_hist_bits <= 16) { + OMP_INIT_EX(); + #pragma omp parallel for schedule(static) num_threads(share_state_->num_threads) + for (int feature_index = 0; feature_index < num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!is_feature_used[feature_index]) { + continue; + } + larger_leaf_histogram_array_[feature_index].CopyToBuffer(gradient_discretizer_->GetChangeHistBitsBuffer(feature_index)); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + } + + OMP_INIT_EX(); +// find splits +#pragma omp parallel for schedule(static) num_threads(share_state_->num_threads) + for (int feature_index = 0; feature_index < num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!is_feature_used[feature_index]) { + continue; + } + const int tid = omp_get_thread_num(); + if (config_->use_quantized_grad) { + const uint8_t hist_bits_bin = gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_splits_->leaf_index()); + const int64_t int_sum_gradient_and_hessian = smaller_leaf_splits_->int_sum_gradients_and_hessians(); + if (hist_bits_bin <= 16) { + train_data_->FixHistogramInt( + feature_index, int_sum_gradient_and_hessian, + reinterpret_cast(smaller_leaf_histogram_array_[feature_index].RawDataInt16())); + } else { + train_data_->FixHistogramInt( + feature_index, int_sum_gradient_and_hessian, + reinterpret_cast(smaller_leaf_histogram_array_[feature_index].RawDataInt32())); + } + } else { + train_data_->FixHistogram( + feature_index, smaller_leaf_splits_->sum_gradients(), + smaller_leaf_splits_->sum_hessians(), + smaller_leaf_histogram_array_[feature_index].RawData()); + } + int real_fidx = train_data_->RealFeatureIndex(feature_index); + + ComputeBestSplitForFeature(smaller_leaf_histogram_array_, feature_index, + real_fidx, + smaller_node_used_features[feature_index], + smaller_leaf_splits_->num_data_in_leaf(), + smaller_leaf_splits_.get(), &smaller_best[tid], + smaller_leaf_parent_output); + + // only has root leaf + if (larger_leaf_splits_ == nullptr || + larger_leaf_splits_->leaf_index() < 0) { + continue; + } + + if (use_subtract) { + if (config_->use_quantized_grad) { + const int parent_index = std::min(smaller_leaf_splits_->leaf_index(), larger_leaf_splits_->leaf_index()); + const uint8_t parent_hist_bits = gradient_discretizer_->GetHistBitsInNode(parent_index); + const uint8_t smaller_hist_bits = gradient_discretizer_->GetHistBitsInLeaf(smaller_leaf_splits_->leaf_index()); + const uint8_t larger_hist_bits = gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_splits_->leaf_index()); + if (parent_hist_bits <= 16) { + CHECK_LE(smaller_hist_bits, 16); + CHECK_LE(larger_hist_bits, 16); + larger_leaf_histogram_array_[feature_index].Subtract( + smaller_leaf_histogram_array_[feature_index]); + } else if (larger_hist_bits <= 16) { + CHECK_LE(smaller_hist_bits, 16); + larger_leaf_histogram_array_[feature_index].Subtract( + smaller_leaf_histogram_array_[feature_index], gradient_discretizer_->GetChangeHistBitsBuffer(feature_index)); + } else if (smaller_hist_bits <= 16) { + larger_leaf_histogram_array_[feature_index].Subtract( + smaller_leaf_histogram_array_[feature_index]); + } else { + larger_leaf_histogram_array_[feature_index].Subtract( + smaller_leaf_histogram_array_[feature_index]); + } + } else { + larger_leaf_histogram_array_[feature_index].Subtract( + smaller_leaf_histogram_array_[feature_index]); + } + } else { + if (config_->use_quantized_grad) { + const int64_t int_sum_gradient_and_hessian = larger_leaf_splits_->int_sum_gradients_and_hessians(); + const uint8_t hist_bits_bin = gradient_discretizer_->GetHistBitsInLeaf(larger_leaf_splits_->leaf_index()); + if (hist_bits_bin <= 16) { + train_data_->FixHistogramInt( + feature_index, int_sum_gradient_and_hessian, + reinterpret_cast(larger_leaf_histogram_array_[feature_index].RawDataInt16())); + } else { + train_data_->FixHistogramInt( + feature_index, int_sum_gradient_and_hessian, + reinterpret_cast(larger_leaf_histogram_array_[feature_index].RawDataInt32())); + } + } else { + train_data_->FixHistogram( + feature_index, larger_leaf_splits_->sum_gradients(), + larger_leaf_splits_->sum_hessians(), + larger_leaf_histogram_array_[feature_index].RawData()); + } + } + + ComputeBestSplitForFeature(larger_leaf_histogram_array_, feature_index, + real_fidx, + larger_node_used_features[feature_index], + larger_leaf_splits_->num_data_in_leaf(), + larger_leaf_splits_.get(), &larger_best[tid], + larger_leaf_parent_output); + + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + auto smaller_best_idx = ArrayArgs::ArgMax(smaller_best); + int leaf = smaller_leaf_splits_->leaf_index(); + best_split_per_leaf_[leaf] = smaller_best[smaller_best_idx]; + + if (larger_leaf_splits_ != nullptr && + larger_leaf_splits_->leaf_index() >= 0) { + leaf = larger_leaf_splits_->leaf_index(); + auto larger_best_idx = ArrayArgs::ArgMax(larger_best); + best_split_per_leaf_[leaf] = larger_best[larger_best_idx]; + } +} + +int32_t SerialTreeLearner::ForceSplits(Tree* tree, int* left_leaf, + int* right_leaf, int *cur_depth) { + bool abort_last_forced_split = false; + if (forced_split_json_ == nullptr) { + return 0; + } + int32_t result_count = 0; + // start at root leaf + *left_leaf = 0; + std::queue> q; + Json left = *forced_split_json_; + Json right; + bool left_smaller = true; + std::unordered_map forceSplitMap; + q.push(std::make_pair(left, *left_leaf)); + + // Histogram construction require parent features. + std::set force_split_features = FindAllForceFeatures(*forced_split_json_); + while (!q.empty()) { + if (BeforeFindBestSplit(tree, *left_leaf, *right_leaf)) { + FindBestSplits(tree, &force_split_features); + } + + // then, compute own splits + SplitInfo left_split; + SplitInfo right_split; + + if (!left.is_null()) { + const int left_feature = left["feature"].int_value(); + const double left_threshold_double = left["threshold"].number_value(); + const int left_inner_feature_index = train_data_->InnerFeatureIndex(left_feature); + const uint32_t left_threshold = train_data_->BinThreshold( + left_inner_feature_index, left_threshold_double); + auto leaf_histogram_array = (left_smaller) ? smaller_leaf_histogram_array_ : larger_leaf_histogram_array_; + auto left_leaf_splits = (left_smaller) ? smaller_leaf_splits_.get() : larger_leaf_splits_.get(); + leaf_histogram_array[left_inner_feature_index].GatherInfoForThreshold( + left_leaf_splits->sum_gradients(), + left_leaf_splits->sum_hessians(), + left_threshold, + left_leaf_splits->num_data_in_leaf(), + left_leaf_splits->weight(), + &left_split); + left_split.feature = left_feature; + forceSplitMap[*left_leaf] = left_split; + if (left_split.gain < 0) { + forceSplitMap.erase(*left_leaf); + } + } + + if (!right.is_null()) { + const int right_feature = right["feature"].int_value(); + const double right_threshold_double = right["threshold"].number_value(); + const int right_inner_feature_index = train_data_->InnerFeatureIndex(right_feature); + const uint32_t right_threshold = train_data_->BinThreshold( + right_inner_feature_index, right_threshold_double); + auto leaf_histogram_array = (left_smaller) ? larger_leaf_histogram_array_ : smaller_leaf_histogram_array_; + auto right_leaf_splits = (left_smaller) ? larger_leaf_splits_.get() : smaller_leaf_splits_.get(); + leaf_histogram_array[right_inner_feature_index].GatherInfoForThreshold( + right_leaf_splits->sum_gradients(), + right_leaf_splits->sum_hessians(), + right_threshold, + right_leaf_splits->num_data_in_leaf(), + right_leaf_splits->weight(), + &right_split); + right_split.feature = right_feature; + forceSplitMap[*right_leaf] = right_split; + if (right_split.gain < 0) { + forceSplitMap.erase(*right_leaf); + } + } + + std::pair pair = q.front(); + q.pop(); + int current_leaf = pair.second; + // split info should exist because searching in bfs fashion - should have added from parent + if (forceSplitMap.find(current_leaf) == forceSplitMap.end()) { + abort_last_forced_split = true; + break; + } + best_split_per_leaf_[current_leaf] = forceSplitMap[current_leaf]; + Split(tree, current_leaf, left_leaf, right_leaf); + left_smaller = best_split_per_leaf_[current_leaf].left_count < + best_split_per_leaf_[current_leaf].right_count; + left = Json(); + right = Json(); + if ((pair.first).object_items().count("left") > 0) { + left = (pair.first)["left"]; + if (left.object_items().count("feature") > 0 && left.object_items().count("threshold") > 0) { + q.push(std::make_pair(left, *left_leaf)); + } + } + if ((pair.first).object_items().count("right") > 0) { + right = (pair.first)["right"]; + if (right.object_items().count("feature") > 0 && right.object_items().count("threshold") > 0) { + q.push(std::make_pair(right, *right_leaf)); + } + } + result_count++; + *(cur_depth) = std::max(*(cur_depth), tree->leaf_depth(*left_leaf)); + } + if (abort_last_forced_split) { + int best_leaf = + static_cast(ArrayArgs::ArgMax(best_split_per_leaf_)); + const SplitInfo& best_leaf_SplitInfo = best_split_per_leaf_[best_leaf]; + if (best_leaf_SplitInfo.gain <= 0.0) { + Log::Warning("No further splits with positive gain, best gain: %f", + best_leaf_SplitInfo.gain); + return config_->num_leaves; + } + Split(tree, best_leaf, left_leaf, right_leaf); + *(cur_depth) = std::max(*(cur_depth), tree->leaf_depth(*left_leaf)); + ++result_count; + } + return result_count; +} + +std::set SerialTreeLearner::FindAllForceFeatures(Json force_split_leaf_setting) { + std::set force_features; + std::queue force_split_leaves; + + force_split_leaves.push(force_split_leaf_setting); + + while (!force_split_leaves.empty()) { + Json split_leaf = force_split_leaves.front(); + force_split_leaves.pop(); + + const int feature_index = split_leaf["feature"].int_value(); + const int feature_inner_index = train_data_->InnerFeatureIndex(feature_index); + force_features.insert(feature_inner_index); + + if (split_leaf.object_items().count("left") > 0) { + force_split_leaves.push(split_leaf["left"]); + } + + if (split_leaf.object_items().count("right") > 0) { + force_split_leaves.push(split_leaf["right"]); + } + } + + return force_features; +} + +void SerialTreeLearner::SplitInner(Tree* tree, int best_leaf, int* left_leaf, + int* right_leaf, bool update_cnt) { + Common::FunctionTimer fun_timer("SerialTreeLearner::SplitInner", global_timer); + SplitInfo& best_split_info = best_split_per_leaf_[best_leaf]; + const int inner_feature_index = + train_data_->InnerFeatureIndex(best_split_info.feature); + if (cegb_ != nullptr) { + cegb_->UpdateLeafBestSplits(tree, best_leaf, &best_split_info, + &best_split_per_leaf_); + } + *left_leaf = best_leaf; + auto next_leaf_id = tree->NextLeafId(); + + // update before tree split + constraints_->BeforeSplit(best_leaf, next_leaf_id, + best_split_info.monotone_type); + + bool is_numerical_split = + train_data_->FeatureBinMapper(inner_feature_index)->bin_type() == + BinType::NumericalBin; + if (is_numerical_split) { + auto threshold_double = train_data_->RealThreshold( + inner_feature_index, best_split_info.threshold); + data_partition_->Split(best_leaf, train_data_, inner_feature_index, + &best_split_info.threshold, 1, + best_split_info.default_left, next_leaf_id); + if (update_cnt) { + // don't need to update this in data-based parallel model + best_split_info.left_count = data_partition_->leaf_count(*left_leaf); + best_split_info.right_count = data_partition_->leaf_count(next_leaf_id); + } + // split tree, will return right leaf + *right_leaf = tree->Split( + best_leaf, inner_feature_index, best_split_info.feature, + best_split_info.threshold, threshold_double, + static_cast(best_split_info.left_output), + static_cast(best_split_info.right_output), + static_cast(best_split_info.left_count), + static_cast(best_split_info.right_count), + static_cast(best_split_info.left_sum_hessian), + static_cast(best_split_info.right_sum_hessian), + // store the true split gain in tree model + static_cast(best_split_info.gain + config_->min_gain_to_split), + train_data_->FeatureBinMapper(inner_feature_index)->missing_type(), + best_split_info.default_left); + } else { + std::vector cat_bitset_inner = + Common::ConstructBitset(best_split_info.cat_threshold.data(), + best_split_info.num_cat_threshold); + std::vector threshold_int(best_split_info.num_cat_threshold); + for (int i = 0; i < best_split_info.num_cat_threshold; ++i) { + threshold_int[i] = static_cast(train_data_->RealThreshold( + inner_feature_index, best_split_info.cat_threshold[i])); + } + std::vector cat_bitset = Common::ConstructBitset( + threshold_int.data(), best_split_info.num_cat_threshold); + + data_partition_->Split(best_leaf, train_data_, inner_feature_index, + cat_bitset_inner.data(), + static_cast(cat_bitset_inner.size()), + best_split_info.default_left, next_leaf_id); + + if (update_cnt) { + // don't need to update this in data-based parallel model + best_split_info.left_count = data_partition_->leaf_count(*left_leaf); + best_split_info.right_count = data_partition_->leaf_count(next_leaf_id); + } + + *right_leaf = tree->SplitCategorical( + best_leaf, inner_feature_index, best_split_info.feature, + cat_bitset_inner.data(), static_cast(cat_bitset_inner.size()), + cat_bitset.data(), static_cast(cat_bitset.size()), + static_cast(best_split_info.left_output), + static_cast(best_split_info.right_output), + static_cast(best_split_info.left_count), + static_cast(best_split_info.right_count), + static_cast(best_split_info.left_sum_hessian), + static_cast(best_split_info.right_sum_hessian), + // store the true split gain in tree model + static_cast(best_split_info.gain + config_->min_gain_to_split), + train_data_->FeatureBinMapper(inner_feature_index)->missing_type()); + } + +#ifdef DEBUG + CHECK(*right_leaf == next_leaf_id); +#endif + + // init the leaves that used on next iteration + if (!config_->use_quantized_grad) { + if (best_split_info.left_count < best_split_info.right_count) { + CHECK_GT(best_split_info.left_count, 0); + smaller_leaf_splits_->Init(*left_leaf, data_partition_.get(), + best_split_info.left_sum_gradient, + best_split_info.left_sum_hessian, + best_split_info.left_output); + larger_leaf_splits_->Init(*right_leaf, data_partition_.get(), + best_split_info.right_sum_gradient, + best_split_info.right_sum_hessian, + best_split_info.right_output); + } else { + CHECK_GT(best_split_info.right_count, 0); + smaller_leaf_splits_->Init(*right_leaf, data_partition_.get(), + best_split_info.right_sum_gradient, + best_split_info.right_sum_hessian, + best_split_info.right_output); + larger_leaf_splits_->Init(*left_leaf, data_partition_.get(), + best_split_info.left_sum_gradient, + best_split_info.left_sum_hessian, + best_split_info.left_output); + } + } else { + if (best_split_info.left_count < best_split_info.right_count) { + CHECK_GT(best_split_info.left_count, 0); + smaller_leaf_splits_->Init(*left_leaf, data_partition_.get(), + best_split_info.left_sum_gradient, + best_split_info.left_sum_hessian, + best_split_info.left_sum_gradient_and_hessian, + best_split_info.left_output); + larger_leaf_splits_->Init(*right_leaf, data_partition_.get(), + best_split_info.right_sum_gradient, + best_split_info.right_sum_hessian, + best_split_info.right_sum_gradient_and_hessian, + best_split_info.right_output); + } else { + CHECK_GT(best_split_info.right_count, 0); + smaller_leaf_splits_->Init(*right_leaf, data_partition_.get(), + best_split_info.right_sum_gradient, + best_split_info.right_sum_hessian, + best_split_info.right_sum_gradient_and_hessian, + best_split_info.right_output); + larger_leaf_splits_->Init(*left_leaf, data_partition_.get(), + best_split_info.left_sum_gradient, + best_split_info.left_sum_hessian, + best_split_info.left_sum_gradient_and_hessian, + best_split_info.left_output); + } + } + if (config_->use_quantized_grad && config_->tree_learner != std::string("data")) { + gradient_discretizer_->SetNumBitsInHistogramBin(*left_leaf, *right_leaf, + data_partition_->leaf_count(*left_leaf), + data_partition_->leaf_count(*right_leaf)); + } + + #ifdef DEBUG + CheckSplit(best_split_info, *left_leaf, *right_leaf); + #endif + + auto leaves_need_update = constraints_->Update( + is_numerical_split, *left_leaf, *right_leaf, + best_split_info.monotone_type, best_split_info.right_output, + best_split_info.left_output, inner_feature_index, best_split_info, + best_split_per_leaf_); + // update leave outputs if needed + for (auto leaf : leaves_need_update) { + RecomputeBestSplitForLeaf(tree, leaf, &best_split_per_leaf_[leaf]); + } +} + +void SerialTreeLearner::RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function residual_getter, + data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* /*train_score*/) const { + if (obj != nullptr && obj->IsRenewTreeOutput()) { + CHECK_LE(tree->num_leaves(), data_partition_->num_leaves()); + const data_size_t* bag_mapper = nullptr; + if (total_num_data != num_data_) { + CHECK_EQ(bag_cnt, num_data_); + bag_mapper = bag_indices; + } + std::vector n_nozeroworker_perleaf(tree->num_leaves(), 1); + int num_machines = Network::num_machines(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int i = 0; i < tree->num_leaves(); ++i) { + const double output = static_cast(tree->LeafOutput(i)); + data_size_t cnt_leaf_data = 0; + auto index_mapper = data_partition_->GetIndexOnLeaf(i, &cnt_leaf_data); + if (cnt_leaf_data > 0) { + // bag_mapper[index_mapper[i]] + const double new_output = obj->RenewTreeOutput(output, residual_getter, index_mapper, bag_mapper, cnt_leaf_data); + tree->SetLeafOutput(i, new_output); + } else { + CHECK_GT(num_machines, 1); + tree->SetLeafOutput(i, 0.0); + n_nozeroworker_perleaf[i] = 0; + } + } + if (num_machines > 1) { + std::vector outputs(tree->num_leaves()); + for (int i = 0; i < tree->num_leaves(); ++i) { + outputs[i] = static_cast(tree->LeafOutput(i)); + } + outputs = Network::GlobalSum(&outputs); + n_nozeroworker_perleaf = Network::GlobalSum(&n_nozeroworker_perleaf); + for (int i = 0; i < tree->num_leaves(); ++i) { + tree->SetLeafOutput(i, outputs[i] / n_nozeroworker_perleaf[i]); + } + } + } +} + +void SerialTreeLearner::ComputeBestSplitForFeature( + FeatureHistogram* histogram_array_, int feature_index, int real_fidx, + int8_t is_feature_used, int num_data, const LeafSplits* leaf_splits, + SplitInfo* best_split, double parent_output) { + bool is_feature_numerical = train_data_->FeatureBinMapper(feature_index) + ->bin_type() == BinType::NumericalBin; + if (is_feature_numerical & !config_->monotone_constraints.empty()) { + constraints_->RecomputeConstraintsIfNeeded( + constraints_.get(), feature_index, ~(leaf_splits->leaf_index()), + train_data_->FeatureNumBin(feature_index)); + } + SplitInfo new_split; + if (config_->use_quantized_grad) { + const uint8_t hist_bits_bin = gradient_discretizer_->GetHistBitsInLeaf(leaf_splits->leaf_index()); + histogram_array_[feature_index].FindBestThresholdInt( + leaf_splits->int_sum_gradients_and_hessians(), + gradient_discretizer_->grad_scale(), + gradient_discretizer_->hess_scale(), + hist_bits_bin, + hist_bits_bin, + num_data, + constraints_->GetFeatureConstraint(leaf_splits->leaf_index(), feature_index), parent_output, &new_split); + } else { + histogram_array_[feature_index].FindBestThreshold( + leaf_splits->sum_gradients(), leaf_splits->sum_hessians(), num_data, + constraints_->GetFeatureConstraint(leaf_splits->leaf_index(), feature_index), parent_output, &new_split); + } + new_split.feature = real_fidx; + if (cegb_ != nullptr) { + new_split.gain -= + cegb_->DeltaGain(feature_index, real_fidx, leaf_splits->leaf_index(), + num_data, new_split); + } + if (new_split.monotone_type != 0) { + double penalty = constraints_->ComputeMonotoneSplitGainPenalty( + leaf_splits->leaf_index(), config_->monotone_penalty); + new_split.gain *= penalty; + } + // it is needed to filter the features after the above code. + // Otherwise, the `is_splittable` in `FeatureHistogram` will be wrong, and cause some features being accidentally filtered in the later nodes. + if (new_split > *best_split && is_feature_used) { + *best_split = new_split; + } +} + +double SerialTreeLearner::GetParentOutput(const Tree* tree, const LeafSplits* leaf_splits) const { + double parent_output; + if (tree->num_leaves() == 1) { + // for root leaf the "parent" output is its own output because we don't apply any smoothing to the root + parent_output = FeatureHistogram::CalculateSplittedLeafOutput( + leaf_splits->sum_gradients(), leaf_splits->sum_hessians(), config_->lambda_l1, + config_->lambda_l2, config_->max_delta_step, BasicConstraint(), + config_->path_smooth, static_cast(leaf_splits->num_data_in_leaf()), 0); + } else { + parent_output = leaf_splits->weight(); + } + return parent_output; +} + +void SerialTreeLearner::RecomputeBestSplitForLeaf(Tree* tree, int leaf, SplitInfo* split) { + FeatureHistogram* histogram_array_; + if (!histogram_pool_.Get(leaf, &histogram_array_)) { + Log::Warning( + "Get historical Histogram for leaf %d failed, will skip the " + "``RecomputeBestSplitForLeaf``", + leaf); + return; + } + double sum_gradients = split->left_sum_gradient + split->right_sum_gradient; + double sum_hessians = split->left_sum_hessian + split->right_sum_hessian; + int num_data = split->left_count + split->right_count; + + std::vector bests(share_state_->num_threads); + LeafSplits leaf_splits(num_data, config_); + leaf_splits.Init(leaf, sum_gradients, sum_hessians); + + // can't use GetParentOutput because leaf_splits doesn't have weight property set + double parent_output = 0; + if (config_->path_smooth > kEpsilon) { + parent_output = FeatureHistogram::CalculateSplittedLeafOutput( + sum_gradients, sum_hessians, config_->lambda_l1, config_->lambda_l2, config_->max_delta_step, + BasicConstraint(), config_->path_smooth, static_cast(num_data), 0); + } + + OMP_INIT_EX(); +// find splits +std::vector node_used_features = col_sampler_.GetByNode(tree, leaf); +#pragma omp parallel for schedule(static) num_threads(share_state_->num_threads) + for (int feature_index = 0; feature_index < num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!col_sampler_.is_feature_used_bytree()[feature_index] || + !histogram_array_[feature_index].is_splittable()) { + continue; + } + const int tid = omp_get_thread_num(); + int real_fidx = train_data_->RealFeatureIndex(feature_index); + ComputeBestSplitForFeature(histogram_array_, feature_index, real_fidx, node_used_features[feature_index], + num_data, &leaf_splits, &bests[tid], parent_output); + + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + auto best_idx = ArrayArgs::ArgMax(bests); + *split = bests[best_idx]; +} + +#ifdef DEBUG +void SerialTreeLearner::CheckSplit(const SplitInfo& best_split_info, const int left_leaf_index, const int right_leaf_index) { + data_size_t num_data_in_left = 0; + data_size_t num_data_in_right = 0; + const data_size_t* data_indices_in_left = data_partition_->GetIndexOnLeaf(left_leaf_index, &num_data_in_left); + const data_size_t* data_indices_in_right = data_partition_->GetIndexOnLeaf(right_leaf_index, &num_data_in_right); + if (config_->use_quantized_grad) { + int32_t sum_left_gradient = 0; + int32_t sum_left_hessian = 0; + int32_t sum_right_gradient = 0; + int32_t sum_right_hessian = 0; + const int8_t* discretized_grad_and_hess = gradient_discretizer_->discretized_gradients_and_hessians(); + for (data_size_t i = 0; i < num_data_in_left; ++i) { + const data_size_t index = data_indices_in_left[i]; + sum_left_gradient += discretized_grad_and_hess[2 * index + 1]; + sum_left_hessian += discretized_grad_and_hess[2 * index]; + } + for (data_size_t i = 0; i < num_data_in_right; ++i) { + const data_size_t index = data_indices_in_right[i]; + sum_right_gradient += discretized_grad_and_hess[2 * index + 1]; + sum_right_hessian += discretized_grad_and_hess[2 * index]; + } + Log::Warning("============================ start leaf split info ============================"); + Log::Warning("left_leaf_index = %d, right_leaf_index = %d", left_leaf_index, right_leaf_index); + Log::Warning("num_data_in_left = %d, num_data_in_right = %d", num_data_in_left, num_data_in_right); + Log::Warning("sum_left_gradient = %d, best_split_info->left_sum_gradient_and_hessian.gradient = %d", sum_left_gradient, + static_cast(best_split_info.left_sum_gradient_and_hessian >> 32)); + Log::Warning("sum_left_hessian = %d, best_split_info->left_sum_gradient_and_hessian.hessian = %d", sum_left_hessian, + static_cast(best_split_info.left_sum_gradient_and_hessian & 0x00000000ffffffff)); + Log::Warning("sum_right_gradient = %d, best_split_info->right_sum_gradient_and_hessian.gradient = %d", sum_right_gradient, + static_cast(best_split_info.right_sum_gradient_and_hessian >> 32)); + Log::Warning("sum_right_hessian = %d, best_split_info->right_sum_gradient_and_hessian.hessian = %d", sum_right_hessian, + static_cast(best_split_info.right_sum_gradient_and_hessian & 0x00000000ffffffff)); + CHECK_EQ(num_data_in_left, best_split_info.left_count); + CHECK_EQ(num_data_in_right, best_split_info.right_count); + CHECK_EQ(sum_left_gradient, static_cast(best_split_info.left_sum_gradient_and_hessian >> 32)) + CHECK_EQ(sum_left_hessian, static_cast(best_split_info.left_sum_gradient_and_hessian & 0x00000000ffffffff)); + CHECK_EQ(sum_right_gradient, static_cast(best_split_info.right_sum_gradient_and_hessian >> 32)); + CHECK_EQ(sum_right_hessian, static_cast(best_split_info.right_sum_gradient_and_hessian & 0x00000000ffffffff)); + Log::Warning("============================ end leaf split info ============================"); + } +} +#endif + +} // namespace LightGBM diff --git a/src/treelearner/serial_tree_learner.h b/src/treelearner/serial_tree_learner.h new file mode 100644 index 0000000..b753827 --- /dev/null +++ b/src/treelearner/serial_tree_learner.h @@ -0,0 +1,251 @@ +/*! + * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_SERIAL_TREE_LEARNER_H_ +#define LIGHTGBM_SRC_TREELEARNER_SERIAL_TREE_LEARNER_H_ + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include "col_sampler.hpp" +#include "data_partition.hpp" +#include "feature_histogram.hpp" +#include "gradient_discretizer.hpp" +#include "leaf_splits.hpp" +#include "monotone_constraints.hpp" +#include "split_info.hpp" + +#ifdef USE_GPU +// Use 4KBytes aligned allocator for ordered gradients and ordered Hessians when GPU is enabled. +// This is necessary to pin the two arrays in memory and make transferring faster. +#include +#endif + +namespace LightGBM { + +using json11_internal_lightgbm::Json; + +/*! \brief forward declaration */ +class CostEfficientGradientBoosting; + +/*! +* \brief Used for learning a tree by single machine +*/ +class SerialTreeLearner: public TreeLearner { + public: + friend CostEfficientGradientBoosting; + explicit SerialTreeLearner(const Config* config); + + ~SerialTreeLearner(); + + void Init(const Dataset* train_data, bool is_constant_hessian) override; + + void ResetTrainingData(const Dataset* train_data, + bool is_constant_hessian) override { + ResetTrainingDataInner(train_data, is_constant_hessian, true); + } + + void ResetIsConstantHessian(bool is_constant_hessian) override { + share_state_->is_constant_hessian = is_constant_hessian; + } + + virtual void ResetTrainingDataInner(const Dataset* train_data, + bool is_constant_hessian, + bool reset_multi_val_bin); + + void ResetConfig(const Config* config) override; + + inline void SetForcedSplit(const Json* forced_split_json) override { + if (forced_split_json != nullptr && !forced_split_json->is_null()) { + forced_split_json_ = forced_split_json; + } else { + forced_split_json_ = nullptr; + } + } + + Tree* Train(const score_t* gradients, const score_t *hessians, bool is_first_tree) override; + + Tree* FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const override; + + Tree* FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred, + const score_t* gradients, const score_t* hessians) const override; + + void SetBaggingData(const Dataset* subset, const data_size_t* used_indices, data_size_t num_data) override { + if (subset == nullptr) { + data_partition_->SetUsedDataIndices(used_indices, num_data); + share_state_->SetUseSubrow(false); + } else { + ResetTrainingDataInner(subset, share_state_->is_constant_hessian, false); + share_state_->SetUseSubrow(true); + share_state_->SetSubrowCopied(false); + share_state_->bagging_use_indices = used_indices; + share_state_->bagging_indices_cnt = num_data; + } + } + + void AddPredictionToScore(const Tree* tree, + double* out_score) const override { + CHECK_LE(tree->num_leaves(), data_partition_->num_leaves()); + if (tree->num_leaves() <= 1) { + return; + } +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1) + for (int i = 0; i < tree->num_leaves(); ++i) { + double output = static_cast(tree->LeafOutput(i)); + data_size_t cnt_leaf_data = 0; + auto tmp_idx = data_partition_->GetIndexOnLeaf(i, &cnt_leaf_data); + for (data_size_t j = 0; j < cnt_leaf_data; ++j) { + out_score[tmp_idx[j]] += output; + } + } + } + + void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function residual_getter, + data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* train_score) const override; + + /*! \brief Get output of parent node, used for path smoothing */ + double GetParentOutput(const Tree* tree, const LeafSplits* leaf_splits) const; + + protected: + void ComputeBestSplitForFeature(FeatureHistogram* histogram_array_, + int feature_index, int real_fidx, + int8_t is_feature_used, int num_data, + const LeafSplits* leaf_splits, + SplitInfo* best_split, double parent_output); + + + void GetShareStates(const Dataset* dataset, bool is_constant_hessian, bool is_first_time); + + void RecomputeBestSplitForLeaf(Tree* tree, int leaf, SplitInfo* split); + + /*! + * \brief Some initial works before training + */ + virtual void BeforeTrain(); + + /*! + * \brief Some initial works before FindBestSplit + */ + virtual bool BeforeFindBestSplit(const Tree* tree, int left_leaf, int right_leaf); + + virtual void FindBestSplits(const Tree* tree); + + virtual void FindBestSplits(const Tree* tree, const std::set* force_features); + + virtual void ConstructHistograms(const std::vector& is_feature_used, bool use_subtract); + + virtual void FindBestSplitsFromHistograms(const std::vector& is_feature_used, bool use_subtract, const Tree*); + + /*! + * \brief Partition tree and data according best split. + * \param tree Current tree, will be splitted on this function. + * \param best_leaf The index of leaf that will be splitted. + * \param left_leaf The index of left leaf after splitted. + * \param right_leaf The index of right leaf after splitted. + */ + inline virtual void Split(Tree* tree, int best_leaf, int* left_leaf, + int* right_leaf) { + SplitInner(tree, best_leaf, left_leaf, right_leaf, true); + } + + void SplitInner(Tree* tree, int best_leaf, int* left_leaf, int* right_leaf, + bool update_cnt); + + /* Force splits with forced_split_json dict and then return num splits forced.*/ + int32_t ForceSplits(Tree* tree, int* left_leaf, int* right_leaf, + int* cur_depth); + + std::set FindAllForceFeatures(Json force_split_leaf_setting); + + #ifdef DEBUG + void CheckSplit(const SplitInfo& best_split_info, const int left_leaf_index, const int right_leaf_index); + #endif + + /*! + * \brief Get the number of data in a leaf + * \param leaf_idx The index of leaf + * \return The number of data in the leaf_idx leaf + */ + inline virtual data_size_t GetGlobalDataCountInLeaf(int leaf_idx) const; + + /*! \brief number of data */ + data_size_t num_data_; + /*! \brief number of features */ + int num_features_; + /*! \brief training data */ + const Dataset* train_data_; + /*! \brief gradients of current iteration */ + const score_t* gradients_; + /*! \brief hessians of current iteration */ + const score_t* hessians_; + /*! \brief training data partition on leaves */ + std::unique_ptr data_partition_; + /*! \brief pointer to histograms array of parent of current leaves */ + FeatureHistogram* parent_leaf_histogram_array_; + /*! \brief pointer to histograms array of smaller leaf */ + FeatureHistogram* smaller_leaf_histogram_array_; + /*! \brief pointer to histograms array of larger leaf */ + FeatureHistogram* larger_leaf_histogram_array_; + /*! \brief store best split points for all leaves */ + std::vector best_split_per_leaf_; + /*! \brief store best split per feature for all leaves */ + std::vector splits_per_leaf_; + /*! \brief stores minimum and maximum constraints for each leaf */ + std::unique_ptr constraints_; + + /*! \brief stores best thresholds for all feature for smaller leaf */ + std::unique_ptr smaller_leaf_splits_; + /*! \brief stores best thresholds for all feature for larger leaf */ + std::unique_ptr larger_leaf_splits_; +#if defined(USE_GPU) + /*! \brief gradients of current iteration, ordered for cache optimized, aligned to 4K page */ + std::vector> ordered_gradients_; + /*! \brief hessians of current iteration, ordered for cache optimized, aligned to 4K page */ + std::vector> ordered_hessians_; +#elif defined(USE_CUDA) + /*! \brief gradients of current iteration, ordered for cache optimized */ + std::vector> ordered_gradients_; + /*! \brief hessians of current iteration, ordered for cache optimized */ + std::vector> ordered_hessians_; +#else + /*! \brief gradients of current iteration, ordered for cache optimized */ + std::vector> ordered_gradients_; + /*! \brief hessians of current iteration, ordered for cache optimized */ + std::vector> ordered_hessians_; +#endif + /*! \brief used to cache historical histogram to speed up*/ + HistogramPool histogram_pool_; + /*! \brief config of tree learner*/ + const Config* config_; + ColSampler col_sampler_; + const Json* forced_split_json_; + std::unique_ptr share_state_; + std::unique_ptr cegb_; + std::unique_ptr gradient_discretizer_; +}; + +inline data_size_t SerialTreeLearner::GetGlobalDataCountInLeaf(int leaf_idx) const { + if (leaf_idx >= 0) { + return data_partition_->leaf_count(leaf_idx); + } else { + return 0; + } +} + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_SERIAL_TREE_LEARNER_H_ diff --git a/src/treelearner/split_info.hpp b/src/treelearner/split_info.hpp new file mode 100644 index 0000000..48fc67a --- /dev/null +++ b/src/treelearner/split_info.hpp @@ -0,0 +1,294 @@ +/*! + * Copyright (c) 2016 Microsoft Corporation. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_SRC_TREELEARNER_SPLIT_INFO_HPP_ +#define LIGHTGBM_SRC_TREELEARNER_SPLIT_INFO_HPP_ + +#include + +#include +#include +#include +#include +#include +#include + +namespace LightGBM { + +/*! +* \brief Used to store some information for gain split point +*/ +struct SplitInfo { + public: + /*! \brief Feature index */ + int feature = -1; + /*! \brief Split threshold */ + uint32_t threshold = 0; + /*! \brief Left number of data after split */ + data_size_t left_count = 0; + /*! \brief Right number of data after split */ + data_size_t right_count = 0; + int num_cat_threshold = 0; + /*! \brief Left output after split */ + double left_output = 0.0; + /*! \brief Right output after split */ + double right_output = 0.0; + /*! \brief Split gain */ + double gain = kMinScore; + /*! \brief Left sum gradient after split */ + double left_sum_gradient = 0; + /*! \brief Left sum hessian after split */ + double left_sum_hessian = 0; + /*! \brief Left sum discretized gradient and hessian after split */ + int64_t left_sum_gradient_and_hessian = 0; + /*! \brief Right sum gradient after split */ + double right_sum_gradient = 0; + /*! \brief Right sum hessian after split */ + double right_sum_hessian = 0; + /*! \brief Right sum discretized gradient and hessian after split */ + int64_t right_sum_gradient_and_hessian = 0; + std::vector cat_threshold; + /*! \brief True if default split is left */ + bool default_left = true; + int8_t monotone_type = 0; + inline static int Size(int max_cat_threshold) { + return 2 * sizeof(int) + sizeof(uint32_t) + sizeof(bool) + sizeof(double) * 7 + sizeof(data_size_t) * 2 + max_cat_threshold * sizeof(uint32_t) + sizeof(int8_t) + sizeof(int64_t)*2; + } + + inline void CopyTo(char* buffer) const { + std::memcpy(buffer, &feature, sizeof(feature)); + buffer += sizeof(feature); + std::memcpy(buffer, &left_count, sizeof(left_count)); + buffer += sizeof(left_count); + std::memcpy(buffer, &right_count, sizeof(right_count)); + buffer += sizeof(right_count); + std::memcpy(buffer, &gain, sizeof(gain)); + buffer += sizeof(gain); + std::memcpy(buffer, &threshold, sizeof(threshold)); + buffer += sizeof(threshold); + std::memcpy(buffer, &left_output, sizeof(left_output)); + buffer += sizeof(left_output); + std::memcpy(buffer, &right_output, sizeof(right_output)); + buffer += sizeof(right_output); + std::memcpy(buffer, &left_sum_gradient, sizeof(left_sum_gradient)); + buffer += sizeof(left_sum_gradient); + std::memcpy(buffer, &left_sum_hessian, sizeof(left_sum_hessian)); + buffer += sizeof(left_sum_hessian); + std::memcpy(buffer, &left_sum_gradient_and_hessian, sizeof(left_sum_gradient_and_hessian)); + buffer += sizeof(left_sum_gradient_and_hessian); + std::memcpy(buffer, &right_sum_gradient, sizeof(right_sum_gradient)); + buffer += sizeof(right_sum_gradient); + std::memcpy(buffer, &right_sum_hessian, sizeof(right_sum_hessian)); + buffer += sizeof(right_sum_hessian); + std::memcpy(buffer, &right_sum_gradient_and_hessian, sizeof(right_sum_gradient_and_hessian)); + buffer += sizeof(right_sum_gradient_and_hessian); + std::memcpy(buffer, &default_left, sizeof(default_left)); + buffer += sizeof(default_left); + std::memcpy(buffer, &monotone_type, sizeof(monotone_type)); + buffer += sizeof(monotone_type); + std::memcpy(buffer, &num_cat_threshold, sizeof(num_cat_threshold)); + buffer += sizeof(num_cat_threshold); + std::memcpy(buffer, cat_threshold.data(), sizeof(uint32_t) * num_cat_threshold); + } + + void CopyFrom(const char* buffer) { + std::memcpy(&feature, buffer, sizeof(feature)); + buffer += sizeof(feature); + std::memcpy(&left_count, buffer, sizeof(left_count)); + buffer += sizeof(left_count); + std::memcpy(&right_count, buffer, sizeof(right_count)); + buffer += sizeof(right_count); + std::memcpy(&gain, buffer, sizeof(gain)); + buffer += sizeof(gain); + std::memcpy(&threshold, buffer, sizeof(threshold)); + buffer += sizeof(threshold); + std::memcpy(&left_output, buffer, sizeof(left_output)); + buffer += sizeof(left_output); + std::memcpy(&right_output, buffer, sizeof(right_output)); + buffer += sizeof(right_output); + std::memcpy(&left_sum_gradient, buffer, sizeof(left_sum_gradient)); + buffer += sizeof(left_sum_gradient); + std::memcpy(&left_sum_hessian, buffer, sizeof(left_sum_hessian)); + buffer += sizeof(left_sum_hessian); + std::memcpy(&left_sum_gradient_and_hessian, buffer, sizeof(left_sum_gradient_and_hessian)); + buffer += sizeof(left_sum_gradient_and_hessian); + std::memcpy(&right_sum_gradient, buffer, sizeof(right_sum_gradient)); + buffer += sizeof(right_sum_gradient); + std::memcpy(&right_sum_hessian, buffer, sizeof(right_sum_hessian)); + buffer += sizeof(right_sum_hessian); + std::memcpy(&right_sum_gradient_and_hessian, buffer, sizeof(right_sum_gradient_and_hessian)); + buffer += sizeof(right_sum_gradient_and_hessian); + std::memcpy(&default_left, buffer, sizeof(default_left)); + buffer += sizeof(default_left); + std::memcpy(&monotone_type, buffer, sizeof(monotone_type)); + buffer += sizeof(monotone_type); + std::memcpy(&num_cat_threshold, buffer, sizeof(num_cat_threshold)); + buffer += sizeof(num_cat_threshold); + cat_threshold.resize(num_cat_threshold); + std::memcpy(cat_threshold.data(), buffer, sizeof(uint32_t) * num_cat_threshold); + } + + inline void Reset() { + // initialize with -1 and -inf gain + feature = -1; + gain = kMinScore; + } + + inline bool operator > (const SplitInfo& si) const { + double local_gain = this->gain; + double other_gain = si.gain; + // replace nan with -inf + if (local_gain == NAN) { + local_gain = kMinScore; + } + // replace nan with -inf + if (other_gain == NAN) { + other_gain = kMinScore; + } + if (local_gain != other_gain) { + return local_gain > other_gain; + } + + // if gains are identical, choose the feature with the smaller index + int local_feature = this->feature; + int other_feature = si.feature; + // replace -1 with max int + if (local_feature == -1) { + local_feature = INT32_MAX; + } + // replace -1 with max int + if (other_feature == -1) { + other_feature = INT32_MAX; + } + return local_feature < other_feature; + } + + /*! \brief test if a candidate SplitInfo is equivalent to this one */ + inline bool operator == (const SplitInfo& si) const { + double local_gain = this->gain; + double other_gain = si.gain; + // replace nan with -inf + if (local_gain == NAN) { + local_gain = kMinScore; + } + // replace nan with -inf + if (other_gain == NAN) { + other_gain = kMinScore; + } + if (local_gain != other_gain) { + return false; + } + + // if same gain, splits are only equal if they also use the same feature + int local_feature = this->feature; + int other_feature = si.feature; + // replace -1 with max int + if (local_feature == -1) { + local_feature = INT32_MAX; + } + // replace -1 with max int + if (other_feature == -1) { + other_feature = INT32_MAX; + } + return local_feature == other_feature; + } +}; + +struct LightSplitInfo { + public: + /*! \brief Feature index */ + int feature = -1; + /*! \brief Split gain */ + double gain = kMinScore; + /*! \brief Left number of data after split */ + data_size_t left_count = 0; + /*! \brief Right number of data after split */ + data_size_t right_count = 0; + + inline void Reset() { + // initialize with -1 and -inf gain + feature = -1; + gain = kMinScore; + } + + void CopyFrom(const SplitInfo& other) { + feature = other.feature; + gain = other.gain; + left_count = other.left_count; + right_count = other.right_count; + } + + void CopyFrom(const char* buffer) { + std::memcpy(&feature, buffer, sizeof(feature)); + buffer += sizeof(feature); + std::memcpy(&left_count, buffer, sizeof(left_count)); + buffer += sizeof(left_count); + std::memcpy(&right_count, buffer, sizeof(right_count)); + buffer += sizeof(right_count); + std::memcpy(&gain, buffer, sizeof(gain)); + buffer += sizeof(gain); + } + + inline bool operator > (const LightSplitInfo& si) const { + double local_gain = this->gain; + double other_gain = si.gain; + // replace nan with -inf + if (local_gain == NAN) { + local_gain = kMinScore; + } + // replace nan with -inf + if (other_gain == NAN) { + other_gain = kMinScore; + } + if (local_gain != other_gain) { + return local_gain > other_gain; + } + + // if gains are identical, choose the feature with the smaller index + int local_feature = this->feature; + int other_feature = si.feature; + // replace -1 with max int + if (local_feature == -1) { + local_feature = INT32_MAX; + } + // replace -1 with max int + if (other_feature == -1) { + other_feature = INT32_MAX; + } + return local_feature < other_feature; + } + + /*! \brief test if a candidate LightSplitInfo is equivalent to this one */ + inline bool operator == (const LightSplitInfo& si) const { + double local_gain = this->gain; + double other_gain = si.gain; + // replace nan with -inf + if (local_gain == NAN) { + local_gain = kMinScore; + } + // replace nan with -inf + if (other_gain == NAN) { + other_gain = kMinScore; + } + if (local_gain != other_gain) { + return false; + } + + // if same gain, splits are only equal if they also use the same feature + int local_feature = this->feature; + int other_feature = si.feature; + // replace -1 with max int + if (local_feature == -1) { + local_feature = INT32_MAX; + } + // replace -1 with max int + if (other_feature == -1) { + other_feature = INT32_MAX; + } + return local_feature == other_feature; + } +}; + +} // namespace LightGBM +#endif // LIGHTGBM_SRC_TREELEARNER_SPLIT_INFO_HPP_ diff --git a/src/treelearner/tree_learner.cpp b/src/treelearner/tree_learner.cpp new file mode 100644 index 0000000..20104ad --- /dev/null +++ b/src/treelearner/tree_learner.cpp @@ -0,0 +1,57 @@ +/*! + * Copyright (c) 2016 Microsoft Corporation. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include + +#include "gpu_tree_learner.h" +#include "linear_tree_learner.h" +#include "parallel_tree_learner.h" +#include "serial_tree_learner.h" +#include "cuda/cuda_single_gpu_tree_learner.hpp" + +namespace LightGBM { + +TreeLearner* TreeLearner::CreateTreeLearner(const std::string& learner_type, const std::string& device_type, + const Config* config, const bool boosting_on_cuda) { + if (device_type == std::string("cpu")) { + if (learner_type == std::string("serial")) { + if (config->linear_tree) { + return new LinearTreeLearner(config); + } else { + return new SerialTreeLearner(config); + } + } else if (learner_type == std::string("feature")) { + return new FeatureParallelTreeLearner(config); + } else if (learner_type == std::string("data")) { + return new DataParallelTreeLearner(config); + } else if (learner_type == std::string("voting")) { + return new VotingParallelTreeLearner(config); + } + } else if (device_type == std::string("gpu")) { + if (learner_type == std::string("serial")) { + if (config->linear_tree) { + return new LinearTreeLearner(config); + } else { + return new GPUTreeLearner(config); + } + } else if (learner_type == std::string("feature")) { + return new FeatureParallelTreeLearner(config); + } else if (learner_type == std::string("data")) { + return new DataParallelTreeLearner(config); + } else if (learner_type == std::string("voting")) { + return new VotingParallelTreeLearner(config); + } + } else if (device_type == std::string("cuda")) { + if (learner_type == std::string("serial")) { + return new CUDASingleGPUTreeLearner(config, boosting_on_cuda); + } else { + Log::Fatal("Currently cuda version only supports training on a single machine."); + } + } + return nullptr; +} + +} // namespace LightGBM diff --git a/src/treelearner/voting_parallel_tree_learner.cpp b/src/treelearner/voting_parallel_tree_learner.cpp new file mode 100644 index 0000000..a1050d8 --- /dev/null +++ b/src/treelearner/voting_parallel_tree_learner.cpp @@ -0,0 +1,515 @@ +/*! + * Copyright (c) 2016 Microsoft Corporation. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include +#include +#include +#include +#include + +#include "parallel_tree_learner.h" + +namespace LightGBM { + +template +VotingParallelTreeLearner::VotingParallelTreeLearner(const Config* config):TREELEARNER_T(config) { + top_k_ = this->config_->top_k; +} + +template +void VotingParallelTreeLearner::Init(const Dataset* train_data, bool is_constant_hessian) { + TREELEARNER_T::Init(train_data, is_constant_hessian); + rank_ = Network::rank(); + num_machines_ = Network::num_machines(); + + // limit top k + if (top_k_ > this->num_features_) { + top_k_ = this->num_features_; + } + // get max bin + int max_bin = 0; + for (int i = 0; i < this->num_features_; ++i) { + if (max_bin < this->train_data_->FeatureNumBin(i)) { + max_bin = this->train_data_->FeatureNumBin(i); + } + } + // calculate buffer size + size_t buffer_size = 2 * top_k_ * std::max(max_bin * kHistEntrySize, sizeof(LightSplitInfo) * num_machines_); + auto max_cat_threshold = this->config_->max_cat_threshold; + // need to be able to hold smaller and larger best splits in SyncUpGlobalBestSplit + size_t split_info_size = static_cast(SplitInfo::Size(max_cat_threshold) * 2); + buffer_size = std::max(buffer_size, split_info_size); + // left and right on same time, so need double size + input_buffer_.resize(buffer_size); + output_buffer_.resize(buffer_size); + + smaller_is_feature_aggregated_.resize(this->num_features_); + larger_is_feature_aggregated_.resize(this->num_features_); + + block_start_.resize(num_machines_); + block_len_.resize(num_machines_); + + smaller_buffer_read_start_pos_.resize(this->num_features_); + larger_buffer_read_start_pos_.resize(this->num_features_); + global_data_count_in_leaf_.resize(this->config_->num_leaves); + + smaller_leaf_splits_global_.reset(new LeafSplits(train_data->num_data(), this->config_)); + larger_leaf_splits_global_.reset(new LeafSplits(train_data->num_data(), this->config_)); + + local_config_ = *this->config_; + local_config_.min_data_in_leaf /= num_machines_; + local_config_.min_sum_hessian_in_leaf /= num_machines_; + + this->histogram_pool_.ResetConfig(train_data, &local_config_); + + // initialize histograms for global + smaller_leaf_histogram_array_global_.reset(new FeatureHistogram[this->num_features_]); + larger_leaf_histogram_array_global_.reset(new FeatureHistogram[this->num_features_]); + std::vector offsets = this->share_state_->feature_hist_offsets(); + int num_total_bin = this->share_state_->num_hist_total_bin(); + smaller_leaf_histogram_data_.resize(num_total_bin * 2); + larger_leaf_histogram_data_.resize(num_total_bin * 2); + HistogramPool::SetFeatureInfo(train_data, this->config_, &feature_metas_); + for (int j = 0; j < train_data->num_features(); ++j) { + smaller_leaf_histogram_array_global_[j].Init(smaller_leaf_histogram_data_.data() + offsets[j] * 2, &feature_metas_[j]); + larger_leaf_histogram_array_global_[j].Init(larger_leaf_histogram_data_.data() + offsets[j] * 2, &feature_metas_[j]); + } +} + +template +void VotingParallelTreeLearner::ResetConfig(const Config* config) { + TREELEARNER_T::ResetConfig(config); + + local_config_ = *this->config_; + local_config_.min_data_in_leaf /= num_machines_; + local_config_.min_sum_hessian_in_leaf /= num_machines_; + + this->histogram_pool_.ResetConfig(this->train_data_, &local_config_); + global_data_count_in_leaf_.resize(this->config_->num_leaves); + + HistogramPool::SetFeatureInfo(this->train_data_, config, &feature_metas_); +} + +template +void VotingParallelTreeLearner::BeforeTrain() { + TREELEARNER_T::BeforeTrain(); + // sync global data sumup info + std::tuple data(this->smaller_leaf_splits_->num_data_in_leaf(), this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians()); + int size = sizeof(std::tuple); + std::memcpy(input_buffer_.data(), &data, size); + + Network::Allreduce(input_buffer_.data(), size, sizeof(std::tuple), output_buffer_.data(), [](const char *src, char *dst, int type_size, comm_size_t len) { + comm_size_t used_size = 0; + const std::tuple *p1; + std::tuple *p2; + while (used_size < len) { + p1 = reinterpret_cast *>(src); + p2 = reinterpret_cast *>(dst); + std::get<0>(*p2) = std::get<0>(*p2) + std::get<0>(*p1); + std::get<1>(*p2) = std::get<1>(*p2) + std::get<1>(*p1); + std::get<2>(*p2) = std::get<2>(*p2) + std::get<2>(*p1); + src += type_size; + dst += type_size; + used_size += type_size; + } + }); + + std::memcpy(reinterpret_cast(&data), output_buffer_.data(), size); + + // set global sumup info + smaller_leaf_splits_global_->Init(std::get<1>(data), std::get<2>(data)); + larger_leaf_splits_global_->Init(); + // init global data count in leaf + global_data_count_in_leaf_[0] = std::get<0>(data); +} + +template +bool VotingParallelTreeLearner::BeforeFindBestSplit(const Tree* tree, int left_leaf, int right_leaf) { + if (TREELEARNER_T::BeforeFindBestSplit(tree, left_leaf, right_leaf)) { + data_size_t num_data_in_left_child = GetGlobalDataCountInLeaf(left_leaf); + data_size_t num_data_in_right_child = GetGlobalDataCountInLeaf(right_leaf); + if (right_leaf < 0) { + return true; + } else if (num_data_in_left_child < num_data_in_right_child) { + // get local sumup + this->smaller_leaf_splits_->Init(left_leaf, this->data_partition_.get(), this->gradients_, this->hessians_); + this->larger_leaf_splits_->Init(right_leaf, this->data_partition_.get(), this->gradients_, this->hessians_); + } else { + // get local sumup + this->smaller_leaf_splits_->Init(right_leaf, this->data_partition_.get(), this->gradients_, this->hessians_); + this->larger_leaf_splits_->Init(left_leaf, this->data_partition_.get(), this->gradients_, this->hessians_); + } + return true; + } else { + return false; + } +} + +template +void VotingParallelTreeLearner::GlobalVoting(int leaf_idx, const std::vector& splits, std::vector* out) { + out->clear(); + if (leaf_idx < 0) { + return; + } + // get mean number on machines + score_t mean_num_data = GetGlobalDataCountInLeaf(leaf_idx) / static_cast(num_machines_); + std::vector feature_best_split(this->train_data_->num_total_features() , LightSplitInfo()); + for (auto & split : splits) { + int fid = split.feature; + if (fid < 0) { + continue; + } + // weighted gain + double gain = split.gain * (split.left_count + split.right_count) / mean_num_data; + if (gain > feature_best_split[fid].gain) { + feature_best_split[fid] = split; + feature_best_split[fid].gain = gain; + } + } + // get top k + std::vector top_k_splits; + ArrayArgs::MaxK(feature_best_split, top_k_, &top_k_splits); + std::stable_sort(top_k_splits.begin(), top_k_splits.end(), std::greater()); + for (auto& split : top_k_splits) { + if (split.gain == kMinScore || split.feature == -1) { + continue; + } + out->push_back(split.feature); + } +} + +template +void VotingParallelTreeLearner::CopyLocalHistogram(const std::vector& smaller_top_features, const std::vector& larger_top_features) { + for (int i = 0; i < this->num_features_; ++i) { + smaller_is_feature_aggregated_[i] = false; + larger_is_feature_aggregated_[i] = false; + } + size_t total_num_features = smaller_top_features.size() + larger_top_features.size(); + size_t average_feature = (total_num_features + num_machines_ - 1) / num_machines_; + size_t used_num_features = 0, smaller_idx = 0, larger_idx = 0; + block_start_[0] = 0; + reduce_scatter_size_ = 0; + // Copy histogram to buffer, and Get local aggregate features + for (int i = 0; i < num_machines_; ++i) { + size_t cur_size = 0, cur_used_features = 0; + size_t cur_total_feature = std::min(average_feature, total_num_features - used_num_features); + // copy histograms. + while (cur_used_features < cur_total_feature) { + // copy smaller leaf histograms first + if (smaller_idx < smaller_top_features.size()) { + int inner_feature_index = this->train_data_->InnerFeatureIndex(smaller_top_features[smaller_idx]); + ++cur_used_features; + // mark local aggregated feature + if (i == rank_) { + smaller_is_feature_aggregated_[inner_feature_index] = true; + smaller_buffer_read_start_pos_[inner_feature_index] = static_cast(cur_size); + } + // copy + std::memcpy(input_buffer_.data() + reduce_scatter_size_, this->smaller_leaf_histogram_array_[inner_feature_index].RawData(), this->smaller_leaf_histogram_array_[inner_feature_index].SizeOfHistogram()); + cur_size += this->smaller_leaf_histogram_array_[inner_feature_index].SizeOfHistogram(); + reduce_scatter_size_ += this->smaller_leaf_histogram_array_[inner_feature_index].SizeOfHistogram(); + ++smaller_idx; + } + if (cur_used_features >= cur_total_feature) { + break; + } + // then copy larger leaf histograms + if (larger_idx < larger_top_features.size()) { + int inner_feature_index = this->train_data_->InnerFeatureIndex(larger_top_features[larger_idx]); + ++cur_used_features; + // mark local aggregated feature + if (i == rank_) { + larger_is_feature_aggregated_[inner_feature_index] = true; + larger_buffer_read_start_pos_[inner_feature_index] = static_cast(cur_size); + } + // copy + std::memcpy(input_buffer_.data() + reduce_scatter_size_, this->larger_leaf_histogram_array_[inner_feature_index].RawData(), this->larger_leaf_histogram_array_[inner_feature_index].SizeOfHistogram()); + cur_size += this->larger_leaf_histogram_array_[inner_feature_index].SizeOfHistogram(); + reduce_scatter_size_ += this->larger_leaf_histogram_array_[inner_feature_index].SizeOfHistogram(); + ++larger_idx; + } + } + used_num_features += cur_used_features; + block_len_[i] = static_cast(cur_size); + if (i < num_machines_ - 1) { + block_start_[i + 1] = block_start_[i] + block_len_[i]; + } + } +} + +template +void VotingParallelTreeLearner::FindBestSplits(const Tree* tree) { + // use local data to find local best splits + std::vector is_feature_used(this->num_features_, 0); +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + if (!this->col_sampler_.is_feature_used_bytree()[feature_index]) continue; + if (this->parent_leaf_histogram_array_ != nullptr + && !this->parent_leaf_histogram_array_[feature_index].is_splittable()) { + this->smaller_leaf_histogram_array_[feature_index].set_is_splittable(false); + continue; + } + is_feature_used[feature_index] = 1; + } + bool use_subtract = true; + if (this->parent_leaf_histogram_array_ == nullptr) { + use_subtract = false; + } + TREELEARNER_T::ConstructHistograms(is_feature_used, use_subtract); + + const int smaller_leaf_index = this->smaller_leaf_splits_->leaf_index(); + const data_size_t local_data_on_smaller_leaf = this->data_partition_->leaf_count(smaller_leaf_index); + if (local_data_on_smaller_leaf <= 0) { + // clear histogram buffer before synchronizing + // otherwise histogram contents from the previous iteration will be sent + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!is_feature_used[feature_index]) { + continue; + } + const BinMapper* feature_bin_mapper = this->train_data_->FeatureBinMapper(feature_index); + const int num_bin = feature_bin_mapper->num_bin(); + const int offset = static_cast(feature_bin_mapper->GetMostFreqBin() == 0); + hist_t* hist_ptr = this->smaller_leaf_histogram_array_[feature_index].RawData(); + std::memset(reinterpret_cast(hist_ptr), 0, (num_bin - offset) * kHistEntrySize); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + + if (this->larger_leaf_splits_ != nullptr) { + const int larger_leaf_index = this->larger_leaf_splits_->leaf_index(); + if (larger_leaf_index >= 0) { + const data_size_t local_data_on_larger_leaf = this->data_partition_->leaf_count(larger_leaf_index); + if (local_data_on_larger_leaf <= 0) { + OMP_INIT_EX(); + #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!is_feature_used[feature_index]) { + continue; + } + const BinMapper* feature_bin_mapper = this->train_data_->FeatureBinMapper(feature_index); + const int num_bin = feature_bin_mapper->num_bin(); + const int offset = static_cast(feature_bin_mapper->GetMostFreqBin() == 0); + hist_t* hist_ptr = this->larger_leaf_histogram_array_[feature_index].RawData(); + std::memset(reinterpret_cast(hist_ptr), 0, (num_bin - offset) * kHistEntrySize); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + } + } + } + + std::vector smaller_bestsplit_per_features(this->num_features_); + std::vector larger_bestsplit_per_features(this->num_features_); + double smaller_leaf_parent_output = this->GetParentOutput(tree, this->smaller_leaf_splits_.get()); + double larger_leaf_parent_output = this->GetParentOutput(tree, this->larger_leaf_splits_.get()); + OMP_INIT_EX(); + // find splits +#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + if (!is_feature_used[feature_index]) { + continue; + } + const int real_feature_index = this->train_data_->RealFeatureIndex(feature_index); + this->train_data_->FixHistogram(feature_index, + this->smaller_leaf_splits_->sum_gradients(), this->smaller_leaf_splits_->sum_hessians(), + this->smaller_leaf_histogram_array_[feature_index].RawData()); + + this->ComputeBestSplitForFeature( + this->smaller_leaf_histogram_array_, feature_index, real_feature_index, + true, this->smaller_leaf_splits_->num_data_in_leaf(), + this->smaller_leaf_splits_.get(), + &smaller_bestsplit_per_features[feature_index], + smaller_leaf_parent_output); + // only has root leaf + if (this->larger_leaf_splits_ == nullptr || this->larger_leaf_splits_->leaf_index() < 0) { + continue; + } + + if (use_subtract) { + this->larger_leaf_histogram_array_[feature_index].Subtract(this->smaller_leaf_histogram_array_[feature_index]); + } else { + this->train_data_->FixHistogram(feature_index, this->larger_leaf_splits_->sum_gradients(), this->larger_leaf_splits_->sum_hessians(), + this->larger_leaf_histogram_array_[feature_index].RawData()); + } + this->ComputeBestSplitForFeature( + this->larger_leaf_histogram_array_, feature_index, real_feature_index, + true, this->larger_leaf_splits_->num_data_in_leaf(), + this->larger_leaf_splits_.get(), + &larger_bestsplit_per_features[feature_index], + larger_leaf_parent_output); + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + std::vector smaller_top_k_splits, larger_top_k_splits; + // local voting + ArrayArgs::MaxK(smaller_bestsplit_per_features, top_k_, &smaller_top_k_splits); + ArrayArgs::MaxK(larger_bestsplit_per_features, top_k_, &larger_top_k_splits); + + std::vector smaller_top_k_light_splits(top_k_); + std::vector larger_top_k_light_splits(top_k_); + for (int i = 0; i < top_k_; ++i) { + smaller_top_k_light_splits[i].CopyFrom(smaller_top_k_splits[i]); + larger_top_k_light_splits[i].CopyFrom(larger_top_k_splits[i]); + } + + // gather + int offset = 0; + for (int i = 0; i < top_k_; ++i) { + std::memcpy(input_buffer_.data() + offset, &smaller_top_k_light_splits[i], sizeof(LightSplitInfo)); + offset += sizeof(LightSplitInfo); + std::memcpy(input_buffer_.data() + offset, &larger_top_k_light_splits[i], sizeof(LightSplitInfo)); + offset += sizeof(LightSplitInfo); + } + Network::Allgather(input_buffer_.data(), offset, output_buffer_.data()); + // get all top-k from all machines + std::vector smaller_top_k_splits_global; + std::vector larger_top_k_splits_global; + offset = 0; + for (int i = 0; i < num_machines_; ++i) { + for (int j = 0; j < top_k_; ++j) { + smaller_top_k_splits_global.push_back(LightSplitInfo()); + std::memcpy(&smaller_top_k_splits_global.back(), output_buffer_.data() + offset, sizeof(LightSplitInfo)); + offset += sizeof(LightSplitInfo); + larger_top_k_splits_global.push_back(LightSplitInfo()); + std::memcpy(&larger_top_k_splits_global.back(), output_buffer_.data() + offset, sizeof(LightSplitInfo)); + offset += sizeof(LightSplitInfo); + } + } + // global voting + std::vector smaller_top_features, larger_top_features; + GlobalVoting(this->smaller_leaf_splits_->leaf_index(), smaller_top_k_splits_global, &smaller_top_features); + GlobalVoting(this->larger_leaf_splits_->leaf_index(), larger_top_k_splits_global, &larger_top_features); + // copy local histograms to buffer + CopyLocalHistogram(smaller_top_features, larger_top_features); + + // Reduce scatter for histogram + Network::ReduceScatter(input_buffer_.data(), reduce_scatter_size_, sizeof(hist_t), block_start_.data(), block_len_.data(), + output_buffer_.data(), static_cast(output_buffer_.size()), &HistogramSumReducer); + + this->FindBestSplitsFromHistograms(is_feature_used, false, tree); +} + +template +void VotingParallelTreeLearner::FindBestSplitsFromHistograms(const std::vector&, bool, const Tree* tree) { + std::vector smaller_bests_per_thread(this->share_state_->num_threads); + std::vector larger_bests_per_thread(this->share_state_->num_threads); + std::vector smaller_node_used_features = + this->col_sampler_.GetByNode(tree, this->smaller_leaf_splits_->leaf_index()); + std::vector larger_node_used_features = + this->col_sampler_.GetByNode(tree, this->larger_leaf_splits_->leaf_index()); + double smaller_leaf_parent_output = this->GetParentOutput(tree, this->smaller_leaf_splits_global_.get()); + double larger_leaf_parent_output = this->GetParentOutput(tree, this->larger_leaf_splits_global_.get()); + // find best split from local aggregated histograms + OMP_INIT_EX(); +#pragma omp parallel for schedule(static) num_threads(this->share_state_->num_threads) + for (int feature_index = 0; feature_index < this->num_features_; ++feature_index) { + OMP_LOOP_EX_BEGIN(); + const int tid = omp_get_thread_num(); + const int real_feature_index = this->train_data_->RealFeatureIndex(feature_index); + if (smaller_is_feature_aggregated_[feature_index]) { + // restore from buffer + smaller_leaf_histogram_array_global_[feature_index].FromMemory( + output_buffer_.data() + smaller_buffer_read_start_pos_[feature_index]); + + this->train_data_->FixHistogram(feature_index, + smaller_leaf_splits_global_->sum_gradients(), smaller_leaf_splits_global_->sum_hessians(), + smaller_leaf_histogram_array_global_[feature_index].RawData()); + + this->ComputeBestSplitForFeature( + smaller_leaf_histogram_array_global_.get(), feature_index, + real_feature_index, smaller_node_used_features[feature_index], + GetGlobalDataCountInLeaf(smaller_leaf_splits_global_->leaf_index()), + smaller_leaf_splits_global_.get(), &smaller_bests_per_thread[tid], + smaller_leaf_parent_output); + } + + if (larger_is_feature_aggregated_[feature_index]) { + // restore from buffer + larger_leaf_histogram_array_global_[feature_index].FromMemory(output_buffer_.data() + larger_buffer_read_start_pos_[feature_index]); + + this->train_data_->FixHistogram(feature_index, + larger_leaf_splits_global_->sum_gradients(), larger_leaf_splits_global_->sum_hessians(), + larger_leaf_histogram_array_global_[feature_index].RawData()); + + this->ComputeBestSplitForFeature( + larger_leaf_histogram_array_global_.get(), feature_index, + real_feature_index, + larger_node_used_features[feature_index], + GetGlobalDataCountInLeaf(larger_leaf_splits_global_->leaf_index()), + larger_leaf_splits_global_.get(), &larger_bests_per_thread[tid], + larger_leaf_parent_output); + } + OMP_LOOP_EX_END(); + } + OMP_THROW_EX(); + + auto smaller_best_idx = ArrayArgs::ArgMax(smaller_bests_per_thread); + int leaf = this->smaller_leaf_splits_->leaf_index(); + this->best_split_per_leaf_[leaf] = smaller_bests_per_thread[smaller_best_idx]; + + if (this->larger_leaf_splits_ != nullptr && this->larger_leaf_splits_->leaf_index() >= 0) { + leaf = this->larger_leaf_splits_->leaf_index(); + auto larger_best_idx = ArrayArgs::ArgMax(larger_bests_per_thread); + this->best_split_per_leaf_[leaf] = larger_bests_per_thread[larger_best_idx]; + } + + // find local best + SplitInfo smaller_best_split, larger_best_split; + smaller_best_split = this->best_split_per_leaf_[this->smaller_leaf_splits_->leaf_index()]; + // find local best split for larger leaf + if (this->larger_leaf_splits_->leaf_index() >= 0) { + larger_best_split = this->best_split_per_leaf_[this->larger_leaf_splits_->leaf_index()]; + } + // sync global best info + SyncUpGlobalBestSplit(input_buffer_.data(), input_buffer_.data(), &smaller_best_split, &larger_best_split, this->config_->max_cat_threshold); + + // copy back + this->best_split_per_leaf_[smaller_leaf_splits_global_->leaf_index()] = smaller_best_split; + if (larger_best_split.feature >= 0 && larger_leaf_splits_global_->leaf_index() >= 0) { + this->best_split_per_leaf_[larger_leaf_splits_global_->leaf_index()] = larger_best_split; + } +} + +template +void VotingParallelTreeLearner::Split(Tree* tree, int best_Leaf, int* left_leaf, int* right_leaf) { + TREELEARNER_T::SplitInner(tree, best_Leaf, left_leaf, right_leaf, false); + const SplitInfo& best_split_info = this->best_split_per_leaf_[best_Leaf]; + // set the global number of data for leaves + global_data_count_in_leaf_[*left_leaf] = best_split_info.left_count; + global_data_count_in_leaf_[*right_leaf] = best_split_info.right_count; + // init the global sumup info + if (best_split_info.left_count < best_split_info.right_count) { + smaller_leaf_splits_global_->Init(*left_leaf, this->data_partition_.get(), + best_split_info.left_sum_gradient, + best_split_info.left_sum_hessian, + best_split_info.left_output); + larger_leaf_splits_global_->Init(*right_leaf, this->data_partition_.get(), + best_split_info.right_sum_gradient, + best_split_info.right_sum_hessian, + best_split_info.right_output); + } else { + smaller_leaf_splits_global_->Init(*right_leaf, this->data_partition_.get(), + best_split_info.right_sum_gradient, + best_split_info.right_sum_hessian, + best_split_info.right_output); + larger_leaf_splits_global_->Init(*left_leaf, this->data_partition_.get(), + best_split_info.left_sum_gradient, + best_split_info.left_sum_hessian, + best_split_info.left_output); + } +} + +// instantiate template classes, otherwise linker cannot find the code +template class VotingParallelTreeLearner; +template class VotingParallelTreeLearner; +} // namespace LightGBM diff --git a/src/utils/openmp_wrapper.cpp b/src/utils/openmp_wrapper.cpp new file mode 100644 index 0000000..957c8f9 --- /dev/null +++ b/src/utils/openmp_wrapper.cpp @@ -0,0 +1,44 @@ +/*! + * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +int LGBM_MAX_NUM_THREADS = -1; + +int LGBM_DEFAULT_NUM_THREADS = -1; + +#ifdef _OPENMP + +#include + +int OMP_NUM_THREADS() { + int default_num_threads = 1; + + if (LGBM_DEFAULT_NUM_THREADS > 0) { + // if LightGBM-specific default has been set, ignore OpenMP-global config + default_num_threads = LGBM_DEFAULT_NUM_THREADS; + } else { + // otherwise, default to OpenMP-global config + default_num_threads = omp_get_max_threads(); + } + + // ensure that if LGBM_SetMaxThreads() was ever called, LightGBM doesn't + // use more than that many threads + if (LGBM_MAX_NUM_THREADS > 0 && default_num_threads > LGBM_MAX_NUM_THREADS) { + return LGBM_MAX_NUM_THREADS; + } + + return default_num_threads; +} + +void OMP_SET_NUM_THREADS(int num_threads) { + if (num_threads <= 0) { + LGBM_DEFAULT_NUM_THREADS = -1; + } else { + LGBM_DEFAULT_NUM_THREADS = num_threads; + } +} + +#endif // _OPENMP diff --git a/swig/ChunkedArray_API_extensions.i b/swig/ChunkedArray_API_extensions.i new file mode 100644 index 0000000..46df854 --- /dev/null +++ b/swig/ChunkedArray_API_extensions.i @@ -0,0 +1,28 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +/** + * Wrap chunked_array.hpp class for SWIG usage. + * + * Author: Alberto Ferreira + */ + +%{ +#include "../include/LightGBM/utils/chunked_array.hpp" +%} + +%include "../include/LightGBM/utils/chunked_array.hpp" + +using LightGBM::ChunkedArray; + +%template(int32ChunkedArray) ChunkedArray; +/* Unfortunately, for the time being, + * SWIG has issues generating the overloads to coalesce_to() + * for larger integral types + * so we won't support that for now: + */ +//%template(int64ChunkedArray) ChunkedArray; +%template(floatChunkedArray) ChunkedArray; +%template(doubleChunkedArray) ChunkedArray; diff --git a/swig/StringArray.hpp b/swig/StringArray.hpp new file mode 100644 index 0000000..72d9c3b --- /dev/null +++ b/swig/StringArray.hpp @@ -0,0 +1,141 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Alberto Ferreira + */ +#ifndef LIGHTGBM_SWIG_STRINGARRAY_HPP_ +#define LIGHTGBM_SWIG_STRINGARRAY_HPP_ + +#include +#include +#include +#include + +/** + * Container that manages an array of fixed-length strings. + * + * To be compatible with SWIG's `various.i` extension module, + * the array of pointers to char* must be NULL-terminated: + * [char*, char*, char*, ..., NULL] + * This implies that the length of this array is bigger + * by 1 element than the number of char* it stores. + * I.e., _num_elements == _array.size()-1 + * + * The class also takes care of allocation of the underlying + * char* memory. + */ +class StringArray { + public: + StringArray(size_t num_elements, size_t string_size) + : _string_size(string_size), + _array(num_elements + 1, nullptr) { + _allocate_strings(num_elements, string_size); + } + + ~StringArray() { + _release_strings(); + } + + /** + * Returns the pointer to the raw array. + * Notice its size is greater than the number of stored strings by 1. + * + * @return char** pointer to raw data (null-terminated). + */ + char **data() noexcept { + return _array.data(); + } + + /** + * Return char* from the array of size _string_size+1. + * Notice the last element in _array is already + * considered out of bounds. + * + * @param index Index of the element to retrieve. + * @return pointer or nullptr if index is out of bounds. + */ + char *getitem(size_t index) noexcept { + if (_in_bounds(index)) + return _array[index]; + else + return nullptr; + } + + /** + * Safely copies the full content data + * into one of the strings in the array. + * If that is not possible, returns error (-1). + * + * @param index index of the string in the array. + * @param content content to store + * + * @return In case index results in out of bounds access, + * or content + 1 (null-terminator byte) doesn't fit + * into the target string (_string_size), it errors out + * and returns -1. + */ + int setitem(size_t index, const std::string &content) noexcept { + if (_in_bounds(index) && content.size() < _string_size) { + std::strcpy(_array[index], content.c_str()); // NOLINT + return 0; + } else { + return -1; + } + } + + /** + * @return number of stored strings. + */ + size_t get_num_elements() noexcept { + return _array.size() - 1; + } + + private: + /** + * Returns true if and only if within bounds. + * Notice that it excludes the last element of _array (NULL). + * + * @param index index of the element + * @return bool true if within bounds + */ + bool _in_bounds(size_t index) noexcept { + return index < get_num_elements(); + } + + /** + * Allocate an array of fixed-length strings. + * + * Since a NULL-terminated array is required by SWIG's `various.i`, + * the size of the array is actually `num_elements + 1` but only + * num_elements are filled. + * + * @param num_elements Number of strings to store in the array. + * @param string_size The size of each string in the array. + */ + void _allocate_strings(size_t num_elements, size_t string_size) { + for (size_t i = 0; i < num_elements; ++i) { + // Leave space for \0 terminator: + _array[i] = new (std::nothrow) char[string_size + 1]; + + // Check memory allocation: + if (!_array[i]) { + _release_strings(); + throw std::bad_alloc(); + } + } + } + + /** + * Deletes the allocated strings. + */ + void _release_strings() noexcept { + std::for_each(_array.begin(), _array.end(), [](char* c) { delete[] c; }); + } + + const size_t _string_size; + std::vector _array; +}; + +#endif // LIGHTGBM_SWIG_STRINGARRAY_HPP_ diff --git a/swig/StringArray.i b/swig/StringArray.i new file mode 100644 index 0000000..8585276 --- /dev/null +++ b/swig/StringArray.i @@ -0,0 +1,98 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Alberto Ferreira + */ +/** + * This wraps the StringArray.hpp class for SWIG usage, + * adding the basic C-style wrappers needed to make it + * usable for the users of the low-level lightgbmJNI API. + */ + +%{ +#include "../swig/StringArray.hpp" +%} + +// Use SWIG's `various.i` to get a String[] directly in one call: +%apply char **STRING_ARRAY {char **StringArrayHandle_get_strings}; + +%inline %{ + + typedef void* StringArrayHandle; + + /** + * @brief Creates a new StringArray and returns its handle. + * + * @param num_strings number of strings to store. + * @param string_size the maximum number of characters that can be stored in each string. + * @return StringArrayHandle or nullptr in case of allocation failure. + */ + StringArrayHandle StringArrayHandle_create(size_t num_strings, size_t string_size) { + try { + return new StringArray(num_strings, string_size); + } catch (std::bad_alloc &/*e*/) { + return nullptr; + } + } + + /** + * @brief Free the StringArray object. + * + * @param handle StringArray handle. + */ + void StringArrayHandle_free(StringArrayHandle handle) + { + delete reinterpret_cast(handle); + } + + /** + * @brief Return the raw pointer to the array of strings. + * Wrapped in Java into String[] automatically. + * + * @param handle StringArray handle. + * @return Raw pointer to the string array which `various.i` maps to String[]. + */ + char **StringArrayHandle_get_strings(StringArrayHandle handle) + { + return reinterpret_cast(handle)->data(); + } + + /** + * For the end user to extract a specific string from the StringArray object. + * + * @param handle StringArray handle. + * @param index index of the string to retrieve from the array. + * @return raw pointer to string at index, or nullptr if out of bounds. + */ + char *StringArrayHandle_get_string(StringArrayHandle handle, int index) + { + return reinterpret_cast(handle)->getitem(index); + } + + /** + * @brief Replaces one string of the array at index with the new content. + * + * @param handle StringArray handle. + * @param index Index of the string to replace + * @param new_content The content to replace + * @return 0 (success) or -1 (error) in case of out of bounds index or too large content. + */ + int StringArrayHandle_set_string(StringArrayHandle handle, size_t index, const char* new_content) + { + return reinterpret_cast(handle)->setitem(index, std::string(new_content)); + } + + /** + * @brief Retrieve the number of strings in the StringArray. + * + * @param handle StringArray handle. + * @return number of strings that the array stores. + */ + size_t StringArrayHandle_get_num_elements(StringArrayHandle handle) + { + return reinterpret_cast(handle)->get_num_elements(); + } + +%} diff --git a/swig/StringArray_API_extensions.i b/swig/StringArray_API_extensions.i new file mode 100644 index 0000000..f3ca2c8 --- /dev/null +++ b/swig/StringArray_API_extensions.i @@ -0,0 +1,148 @@ +/*! + * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +/** + * This SWIG interface extension provides support to + * allocate, return and manage arrays of strings through + * the class StringArray. + * + * This is then used to generate wrappers that return newly-allocated + * arrays of strings, so the user can them access them easily as a String[] + * on the Java side by a single call to StringArray::data(), and even manipulate + * them. + * + * It also implements working wrappers to: + * - LGBM_BoosterGetEvalNames (re-implemented with new API) + * - LGBM_BoosterGetFeatureNames (original non-wrapped version didn't work). + * where the wrappers names end with "SWIG". + */ + + +#include + +%include "./StringArray.i" + +%inline %{ + + #define API_OK_OR_VALUE(api_return, return_value) if (api_return == -1) return return_value + #define API_OK_OR_NULL(api_return) API_OK_OR_VALUE(api_return, nullptr) + + /** + * @brief Wraps LGBM_BoosterGetEvalNames. + * + * In case of success a new StringArray is created and returned, + * which you're responsible for freeing, + * @see StringArrayHandle_free(). + * In case of failure such resource is freed and nullptr is returned. + * Check for that case with null (lightgbmlib) or 0 (lightgbmlibJNI). + * + * @param handle Booster handle + * @return StringArrayHandle with the eval names (or nullptr in case of error) + */ + StringArrayHandle LGBM_BoosterGetEvalNamesSWIG(BoosterHandle handle) + { + int eval_counts; + size_t string_size; + std::unique_ptr strings(nullptr); + + // Retrieve required allocation space: + API_OK_OR_NULL(LGBM_BoosterGetEvalNames(handle, + 0, &eval_counts, + 0, &string_size, + nullptr)); + + try { + strings.reset(new StringArray(eval_counts, string_size)); + } catch (std::bad_alloc &/*e*/) { + LGBM_SetLastError("Failure to allocate memory."); + return nullptr; + } + + API_OK_OR_NULL(LGBM_BoosterGetEvalNames(handle, + eval_counts, &eval_counts, + string_size, &string_size, + strings->data())); + + return strings.release(); + } + + /** + * @brief Wraps LGBM_BoosterGetFeatureNames. + * + * Allocates a new StringArray. You must free it yourself if it succeeds. + * @see StringArrayHandle_free(). + * In case of failure such resource is freed and nullptr is returned. + * Check for that case with null (lightgbmlib) or 0 (lightgbmlibJNI). + * + * @param handle Booster handle + * @return StringArrayHandle with the feature names (or nullptr in case of error) + */ + StringArrayHandle LGBM_BoosterGetFeatureNamesSWIG(BoosterHandle handle) + { + int num_features; + size_t max_feature_name_size; + std::unique_ptr strings(nullptr); + + // Retrieve required allocation space: + API_OK_OR_NULL(LGBM_BoosterGetFeatureNames(handle, + 0, &num_features, + 0, &max_feature_name_size, + nullptr)); + + try { + strings.reset(new StringArray(num_features, max_feature_name_size)); + } catch (std::bad_alloc &/*e*/) { + LGBM_SetLastError("Failure to allocate memory."); + return nullptr; + } + + API_OK_OR_NULL(LGBM_BoosterGetFeatureNames(handle, + num_features, &num_features, + max_feature_name_size, &max_feature_name_size, + strings->data())); + + return strings.release(); + } + + + /** + * @brief Wraps LGBM_DatasetGetFeatureNames. Has the same limitations as a + * LGBM_BoosterGetFeatureNames: + * + * Allocates a new StringArray. You must free it yourself if it succeeds. + * @see StringArrayHandle_free(). + * In case of failure such resource is freed and nullptr is returned. + * Check for that case with null (lightgbmlib) or 0 (lightgbmlibJNI). + * + * @param handle Booster handle + * @return StringArrayHandle with the feature names (or nullptr in case of error) + */ + StringArrayHandle LGBM_DatasetGetFeatureNamesSWIG(BoosterHandle handle) + { + int num_features; + size_t max_feature_name_size; + std::unique_ptr strings(nullptr); + + // Retrieve required allocation space: + API_OK_OR_NULL(LGBM_DatasetGetFeatureNames(handle, + 0, &num_features, + 0, &max_feature_name_size, + nullptr)); + try { + strings.reset(new StringArray(num_features, max_feature_name_size)); + } catch (std::bad_alloc &/*e*/) { + LGBM_SetLastError("Failure to allocate memory."); + return nullptr; + } + + API_OK_OR_NULL(LGBM_DatasetGetFeatureNames(handle, + num_features, &num_features, + max_feature_name_size, &max_feature_name_size, + strings->data())); + + return strings.release(); + } + +%} diff --git a/swig/lightgbmlib.i b/swig/lightgbmlib.i new file mode 100644 index 0000000..c3e220c --- /dev/null +++ b/swig/lightgbmlib.i @@ -0,0 +1,289 @@ +/*! + * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +/* lightgbmlib.i */ +%module lightgbmlib +%ignore LGBM_BoosterSaveModelToString; +%ignore LGBM_BoosterGetEvalNames; +%ignore LGBM_BoosterGetFeatureNames; +%{ +/* Includes the header in the wrapper code */ +#include "../include/LightGBM/export.h" +#include "../include/LightGBM/utils/log.h" +#include "../include/LightGBM/utils/common.h" +#include "../include/LightGBM/c_api.h" +%} + +%include "various.i" +%include "carrays.i" +%include "cpointer.i" +%include "stdint.i" + +/* Note: instead of using array_functions for string array we apply a typemap instead. + Future char** parameter names should be added to the typemap. +*/ +%apply char **STRING_ARRAY { char **feature_names, char **out_strs } + +/* header files */ +%include "../include/LightGBM/export.h" +%include "../include/LightGBM/c_api.h" + +%typemap(in, numinputs = 0) JNIEnv *jenv %{ + $1 = jenv; +%} + +%newobject LGBM_BoosterSaveModelToStringSWIG; +%newobject LGBM_BoosterDumpModelSWIG; + +%inline %{ + char * LGBM_BoosterSaveModelToStringSWIG(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + int64_t buffer_len, + int64_t* out_len) { + char* dst = new char[buffer_len]; + int result = LGBM_BoosterSaveModelToString(handle, start_iteration, num_iteration, feature_importance_type, buffer_len, out_len, dst); + // Reallocate to use larger length + if (*out_len > buffer_len) { + delete [] dst; + int64_t realloc_len = *out_len; + dst = new char[realloc_len]; + result = LGBM_BoosterSaveModelToString(handle, start_iteration, num_iteration, feature_importance_type, realloc_len, out_len, dst); + } + if (result != 0) { + return nullptr; + } + return dst; + } + + char * LGBM_BoosterDumpModelSWIG(BoosterHandle handle, + int start_iteration, + int num_iteration, + int feature_importance_type, + int64_t buffer_len, + int64_t* out_len) { + char* dst = new char[buffer_len]; + int result = LGBM_BoosterDumpModel(handle, start_iteration, num_iteration, feature_importance_type, buffer_len, out_len, dst); + // Reallocate to use larger length + if (*out_len > buffer_len) { + delete [] dst; + int64_t realloc_len = *out_len; + dst = new char[realloc_len]; + result = LGBM_BoosterDumpModel(handle, start_iteration, num_iteration, feature_importance_type, realloc_len, out_len, dst); + } + if (result != 0) { + return nullptr; + } + return dst; + } + + int LGBM_BoosterPredictForMatSingle(JNIEnv *jenv, + jdoubleArray data, + BoosterHandle handle, + int data_type, + int ncol, + int is_row_major, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + double* data0 = (double*)jenv->GetPrimitiveArrayCritical(data, 0); + + int ret = LGBM_BoosterPredictForMatSingleRow(handle, data0, data_type, ncol, is_row_major, predict_type, start_iteration, + num_iteration, parameter, out_len, out_result); + + jenv->ReleasePrimitiveArrayCritical(data, data0, JNI_ABORT); + + return ret; + } + + /*! \brief Even faster variant of `LGBM_BoosterPredictForMatSingle`. + * + * Uses `LGBM_BoosterPredictForMatSingleRowFast` which is faster + * than `LGBM_BoosterPredictForMatSingleRow` and the trick of + * `LGBM_BoosterPredictForMatSingle` to capture the Java data array + * using `GetPrimitiveArrayCritical`, which can yield faster access + * to the array if the JVM passes the actual address to the C++ side + * instead of performing a copy. + */ + int LGBM_BoosterPredictForMatSingleRowFastCriticalSWIG(JNIEnv *jenv, + jdoubleArray data, + FastConfigHandle handle, + int64_t* out_len, + double* out_result) { + double* data0 = (double*)jenv->GetPrimitiveArrayCritical(data, 0); + + int ret = LGBM_BoosterPredictForMatSingleRowFast(handle, data0, out_len, out_result); + + jenv->ReleasePrimitiveArrayCritical(data, data0, JNI_ABORT); + + return ret; + } + + int LGBM_BoosterPredictForCSRSingle(JNIEnv *jenv, + jintArray indices, + jdoubleArray values, + int numNonZeros, + BoosterHandle handle, + int indptr_type, + int data_type, + int64_t nelem, + int64_t num_col, + int predict_type, + int start_iteration, + int num_iteration, + const char* parameter, + int64_t* out_len, + double* out_result) { + // Alternatives + // - GetIntArrayElements: performs copy + // - GetDirectBufferAddress: fails on wrapped array + // Some words of warning for GetPrimitiveArrayCritical + // https://stackoverflow.com/questions/23258357/whats-the-trade-off-between-using-getprimitivearraycritical-and-getprimitivety + + jboolean isCopy; + int* indices0 = (int*)jenv->GetPrimitiveArrayCritical(indices, &isCopy); + double* values0 = (double*)jenv->GetPrimitiveArrayCritical(values, &isCopy); + + int32_t ind[2] = { 0, numNonZeros }; + + int ret = LGBM_BoosterPredictForCSRSingleRow(handle, ind, indptr_type, indices0, values0, data_type, 2, + nelem, num_col, predict_type, start_iteration, num_iteration, parameter, out_len, out_result); + + jenv->ReleasePrimitiveArrayCritical(values, values0, JNI_ABORT); + jenv->ReleasePrimitiveArrayCritical(indices, indices0, JNI_ABORT); + + return ret; + } + + /*! \brief Even faster variant of `LGBM_BoosterPredictForCSRSingle`. + * + * Uses `LGBM_BoosterPredictForCSRSingleRowFast` which is faster + * than `LGBM_BoosterPredictForMatSingleRow` and the trick of + * `LGBM_BoosterPredictForCSRSingle` to capture the Java data array + * using `GetPrimitiveArrayCritical`, which can yield faster access + * to the array if the JVM passes the actual address to the C++ side + * instead of performing a copy. + */ + int LGBM_BoosterPredictForCSRSingleRowFastCriticalSWIG(JNIEnv *jenv, + jintArray indices, + jdoubleArray values, + int numNonZeros, + FastConfigHandle handle, + int indptr_type, + int64_t nelem, + int64_t* out_len, + double* out_result) { + // Alternatives + // - GetIntArrayElements: performs copy + // - GetDirectBufferAddress: fails on wrapped array + // Some words of warning for GetPrimitiveArrayCritical + // https://stackoverflow.com/questions/23258357/whats-the-trade-off-between-using-getprimitivearraycritical-and-getprimitivety + + jboolean isCopy; + int* indices0 = (int*)jenv->GetPrimitiveArrayCritical(indices, &isCopy); + double* values0 = (double*)jenv->GetPrimitiveArrayCritical(values, &isCopy); + + int32_t ind[2] = { 0, numNonZeros }; + + int ret = LGBM_BoosterPredictForCSRSingleRowFast(handle, ind, indptr_type, indices0, values0, 2, + nelem, out_len, out_result); + + jenv->ReleasePrimitiveArrayCritical(values, values0, JNI_ABORT); + jenv->ReleasePrimitiveArrayCritical(indices, indices0, JNI_ABORT); + + return ret; + } + + #include + #include + + struct CSRDirect { + jintArray indices; + jdoubleArray values; + int* indices0; + double* values0; + int size; + }; + + int LGBM_DatasetCreateFromCSRSpark(JNIEnv *jenv, + jobjectArray arrayOfSparseVector, + int num_rows, + int64_t num_col, + const char* parameters, + const DatasetHandle reference, + DatasetHandle* out) { + jclass sparseVectorClass = jenv->FindClass("org/apache/spark/ml/linalg/SparseVector"); + jmethodID sparseVectorIndices = jenv->GetMethodID(sparseVectorClass, "indices", "()[I"); + jmethodID sparseVectorValues = jenv->GetMethodID(sparseVectorClass, "values", "()[D"); + + std::vector jniCache; + jniCache.reserve(num_rows); + + // this needs to be done ahead of time as row_func is invoked from multiple threads + // these threads would have to be registered with the JVM and also unregistered. + // It is not clear if that can be achieved with OpenMP + for (int i = 0; i < num_rows; i++) { + // get the row + jobject objSparseVec = jenv->GetObjectArrayElement(arrayOfSparseVector, i); + + // get the size, indices and values + auto indices = (jintArray)jenv->CallObjectMethod(objSparseVec, sparseVectorIndices); + if (jenv->ExceptionCheck()) { + return -1; + } + auto values = (jdoubleArray)jenv->CallObjectMethod(objSparseVec, sparseVectorValues); + if (jenv->ExceptionCheck()) { + return -1; + } + int size = jenv->GetArrayLength(indices); + + // Note: when testing on larger data (e.g. 288k rows per partition and 36mio rows total) + // using GetPrimitiveArrayCritical resulted in a dead-lock + // lock arrays + // int* indices0 = (int*)jenv->GetPrimitiveArrayCritical(indices, 0); + // double* values0 = (double*)jenv->GetPrimitiveArrayCritical(values, 0); + // in test-usecase an alternative to GetPrimitiveArrayCritical as it performs copies + int* indices0 = (int *)jenv->GetIntArrayElements(indices, 0); + double* values0 = jenv->GetDoubleArrayElements(values, 0); + + jniCache.push_back({indices, values, indices0, values0, size}); + } + + // type is important here as we want a std::function, rather than a lambda + std::function>& ret)> row_func = [&](int row_num, std::vector>& ret) { + auto& jc = jniCache[row_num]; + ret.clear(); // reset size, but not free() + ret.reserve(jc.size); // make sure we have enough allocated + + // copy data + int* indices0p = jc.indices0; + double* values0p = jc.values0; + int* indices0e = indices0p + jc.size; + + for (; indices0p != indices0e; ++indices0p, ++values0p) + ret.emplace_back(*indices0p, *values0p); + }; + + int ret = LGBM_DatasetCreateFromCSRFunc(&row_func, num_rows, num_col, parameters, reference, out); + + for (auto& jc : jniCache) { + // jenv->ReleasePrimitiveArrayCritical(jc.values, jc.values0, JNI_ABORT); + // jenv->ReleasePrimitiveArrayCritical(jc.indices, jc.indices0, JNI_ABORT); + jenv->ReleaseDoubleArrayElements(jc.values, jc.values0, JNI_ABORT); + jenv->ReleaseIntArrayElements(jc.indices, (jint *)jc.indices0, JNI_ABORT); + } + + return ret; + } +%} + + +%include "pointer_manipulation.i" +%include "StringArray_API_extensions.i" +%include "ChunkedArray_API_extensions.i" diff --git a/swig/pointer_manipulation.i b/swig/pointer_manipulation.i new file mode 100644 index 0000000..2465d65 --- /dev/null +++ b/swig/pointer_manipulation.i @@ -0,0 +1,151 @@ +/*! + * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +/*! + * This SWIG interface extension provides support to + * the pointer manipulation methods present in the standard + * SWIG wrappers, but with support for larger arrays. + * + * SWIG provides this in https://github.com/swig/swig/blob/master/Lib/carrays.i + * but the standard methods only provide arrays with up to + * max(int32_t) elements. + * + * The `long_array_functions` wrappers extend this + * to arrays of size max(int64_t) instead of max(int32_t). + */ + +%pointer_functions(uint8_t, bytep) +%pointer_functions(int, intp) +%pointer_functions(long, longp) +%pointer_functions(double, doublep) +%pointer_functions(float, floatp) +%pointer_functions(int64_t, int64_tp) +%pointer_functions(int32_t, int32_tp) +%pointer_functions(size_t, size_tp) + +%pointer_cast(int64_t *, long *, int64_t_to_long_ptr) +%pointer_cast(int64_t *, double *, int64_t_to_double_ptr) +%pointer_cast(int32_t *, int *, int32_t_to_int_ptr) +%pointer_cast(long *, int64_t *, long_to_int64_t_ptr) +%pointer_cast(double *, int64_t *, double_to_int64_t_ptr) +%pointer_cast(int *, int32_t *, int_to_int32_t_ptr) + +%pointer_cast(double *, void *, double_to_voidp_ptr) +%pointer_cast(float *, void *, float_to_voidp_ptr) +%pointer_cast(int *, void *, int_to_voidp_ptr) +%pointer_cast(uint8_t *, void *, byte_to_voidp_ptr) +%pointer_cast(int32_t *, void *, int32_t_to_voidp_ptr) +%pointer_cast(int64_t *, void *, int64_t_to_voidp_ptr) + +%pointer_cast(void *, double **, void_to_doublep_ptr) + +/* Custom pointer manipulation template */ +%define %pointer_manipulation(TYPE, NAME) +%{ + static TYPE *new_##NAME() { %} + %{ TYPE* NAME = new TYPE; return NAME; %} + %{} + + static void delete_##NAME(TYPE *self) { %} + %{ if (self) delete self; %} + %{} + %} + +TYPE *new_##NAME(); +void delete_##NAME(TYPE *self); + +%enddef + +%define %pointer_dereference(TYPE, NAME) +%{ + static TYPE NAME ##_value(TYPE *self) { + TYPE NAME = *self; + return NAME; + } +%} + +TYPE NAME##_value(TYPE *self); + +%enddef + +%define %pointer_handle(TYPE, NAME) +%{ + static TYPE* NAME ##_handle() { %} + %{ TYPE* NAME = new TYPE; *NAME = (TYPE)operator new(sizeof(int*)); return NAME; %} + %{} +%} + +TYPE *NAME##_handle(); + +%enddef + +%define %long_array_functions(TYPE,NAME) +%{ + static TYPE *new_##NAME(int64_t nelements) { %} + %{ return new TYPE[nelements](); %} + %{} + + static void delete_##NAME(TYPE *ary) { %} + %{ delete [] ary; %} + %{} + + static TYPE NAME##_getitem(TYPE *ary, int64_t index) { + return ary[index]; + } + static void NAME##_setitem(TYPE *ary, int64_t index, TYPE value) { + ary[index] = value; + } + %} + +TYPE *new_##NAME(int64_t nelements); +void delete_##NAME(TYPE *ary); +TYPE NAME##_getitem(TYPE *ary, int64_t index); +void NAME##_setitem(TYPE *ary, int64_t index, TYPE value); + +%enddef + +/* Custom template for arrays of pointers */ +%define %ptr_array_functions(TYPE,NAME) +%{ + static TYPE **new_##NAME(int64_t nelements) { %} + %{ return new TYPE*[nelements](); %} + %{} + + static void delete_##NAME(TYPE **ary) { %} + %{ delete [] ary; %} + %{} + + static TYPE *NAME##_getitem(TYPE **ary, int64_t index) { + return ary[index]; + } + static void NAME##_setitem(TYPE **ary, int64_t index, TYPE *value) { + ary[index] = value; + } + %} + +TYPE **new_##NAME(int64_t nelements); +void delete_##NAME(TYPE **ary); +TYPE *NAME##_getitem(TYPE **ary, int64_t index); +void NAME##_setitem(TYPE **ary, int64_t index, TYPE *value); + +%enddef + +%long_array_functions(uint8_t, byteArray) +%long_array_functions(double, doubleArray) +%long_array_functions(float, floatArray) +%long_array_functions(int32_t, intArray) +%long_array_functions(int64_t, longArray) + +%ptr_array_functions(void, voidPtrArray) +%ptr_array_functions(double, doublePtrArray) +%ptr_array_functions(int, intPtrArray) + +%pointer_manipulation(void*, voidpp) + +/* Allow dereferencing of void** to void* */ +%pointer_dereference(void*, voidpp) + +/* Allow retrieving handle to void** */ +%pointer_handle(void*, voidpp) diff --git a/tests/c_api_test/test_.py b/tests/c_api_test/test_.py new file mode 100644 index 0000000..bad38b8 --- /dev/null +++ b/tests/c_api_test/test_.py @@ -0,0 +1,266 @@ +# coding: utf-8 +import ctypes +from pathlib import Path +from platform import system + +import numpy as np +from scipy import sparse + +try: + from lightgbm.basic import _LIB as LIB +except ModuleNotFoundError: + print("Could not import lightgbm Python-package, looking for lib_lightgbm at the repo root") + if system() in ("Windows", "Microsoft"): + lib_file = Path(__file__).absolute().parents[2] / "Release" / "lib_lightgbm.dll" + else: + lib_file = Path(__file__).absolute().parents[2] / "lib_lightgbm.so" + LIB = ctypes.cdll.LoadLibrary(lib_file) + +LIB.LGBM_GetLastError.restype = ctypes.c_char_p + +dtype_float32 = 0 +dtype_float64 = 1 +dtype_int32 = 2 +dtype_int64 = 3 + + +def c_str(string): + return ctypes.c_char_p(str(string).encode("utf-8")) + + +def load_from_file(filename, reference): + ref = None + if reference is not None: + ref = reference + handle = ctypes.c_void_p() + LIB.LGBM_DatasetCreateFromFile(c_str(str(filename)), c_str("max_bin=15"), ref, ctypes.byref(handle)) + print(LIB.LGBM_GetLastError()) + num_data = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data)) + num_feature = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature)) + print(f"#data: {num_data.value} #feature: {num_feature.value}") + return handle + + +def save_to_binary(handle, filename): + LIB.LGBM_DatasetSaveBinary(handle, c_str(filename)) + + +def load_from_csr(filename, reference): + data = np.loadtxt(str(filename), dtype=np.float64) + csr = sparse.csr_matrix(data[:, 1:]) + label = data[:, 0].astype(np.float32) + handle = ctypes.c_void_p() + ref = None + if reference is not None: + ref = reference + + LIB.LGBM_DatasetCreateFromCSR( + csr.indptr.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ctypes.c_int(dtype_int32), + csr.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + csr.data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ctypes.c_int(dtype_float64), + ctypes.c_int64(len(csr.indptr)), + ctypes.c_int64(len(csr.data)), + ctypes.c_int64(csr.shape[1]), + c_str("max_bin=15"), + ref, + ctypes.byref(handle), + ) + num_data = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data)) + num_feature = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature)) + LIB.LGBM_DatasetSetField( + handle, + c_str("label"), + label.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + ctypes.c_int(len(label)), + ctypes.c_int(dtype_float32), + ) + print(f"#data: {num_data.value} #feature: {num_feature.value}") + return handle + + +def load_from_csc(filename, reference): + data = np.loadtxt(str(filename), dtype=np.float64) + csc = sparse.csc_matrix(data[:, 1:]) + label = data[:, 0].astype(np.float32) + handle = ctypes.c_void_p() + ref = None + if reference is not None: + ref = reference + + LIB.LGBM_DatasetCreateFromCSC( + csc.indptr.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + ctypes.c_int(dtype_int32), + csc.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)), + csc.data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ctypes.c_int(dtype_float64), + ctypes.c_int64(len(csc.indptr)), + ctypes.c_int64(len(csc.data)), + ctypes.c_int64(csc.shape[0]), + c_str("max_bin=15"), + ref, + ctypes.byref(handle), + ) + num_data = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data)) + num_feature = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature)) + LIB.LGBM_DatasetSetField( + handle, + c_str("label"), + label.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + ctypes.c_int(len(label)), + ctypes.c_int(dtype_float32), + ) + print(f"#data: {num_data.value} #feature: {num_feature.value}") + return handle + + +def load_from_mat(filename, reference): + mat = np.loadtxt(str(filename), dtype=np.float64) + label = mat[:, 0].astype(np.float32) + mat = mat[:, 1:] + data = np.asarray(mat.reshape(mat.size), dtype=np.float64) + handle = ctypes.c_void_p() + ref = None + if reference is not None: + ref = reference + + LIB.LGBM_DatasetCreateFromMat( + data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ctypes.c_int(dtype_float64), + ctypes.c_int32(mat.shape[0]), + ctypes.c_int32(mat.shape[1]), + ctypes.c_int(1), + c_str("max_bin=15"), + ref, + ctypes.byref(handle), + ) + num_data = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data)) + num_feature = ctypes.c_int(0) + LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature)) + LIB.LGBM_DatasetSetField( + handle, + c_str("label"), + label.ctypes.data_as(ctypes.POINTER(ctypes.c_float)), + ctypes.c_int(len(label)), + ctypes.c_int(dtype_float32), + ) + print(f"#data: {num_data.value} #feature: {num_feature.value}") + return handle + + +def free_dataset(handle): + LIB.LGBM_DatasetFree(handle) + + +def test_dataset(tmp_path): + binary_example_dir = Path(__file__).absolute().parents[2] / "examples" / "binary_classification" + train = load_from_file(binary_example_dir / "binary.train", None) + test = load_from_mat(binary_example_dir / "binary.test", train) + free_dataset(test) + test = load_from_csr(binary_example_dir / "binary.test", train) + free_dataset(test) + test = load_from_csc(binary_example_dir / "binary.test", train) + free_dataset(test) + train_binary = str(tmp_path / "train.binary.bin") + save_to_binary(train, train_binary) + free_dataset(train) + train = load_from_file(train_binary, None) + free_dataset(train) + + +def test_booster(tmp_path): + binary_example_dir = Path(__file__).absolute().parents[2] / "examples" / "binary_classification" + train = load_from_mat(binary_example_dir / "binary.train", None) + test = load_from_mat(binary_example_dir / "binary.test", train) + booster = ctypes.c_void_p() + model_path = tmp_path / "model.txt" + LIB.LGBM_BoosterCreate(train, c_str("app=binary metric=auc num_leaves=31 verbose=0"), ctypes.byref(booster)) + LIB.LGBM_BoosterAddValidData(booster, test) + produced_empty_tree = ctypes.c_int(0) + for i in range(1, 51): + LIB.LGBM_BoosterUpdateOneIter(booster, ctypes.byref(produced_empty_tree)) + result = np.array([0.0], dtype=np.float64) + out_len = ctypes.c_int(0) + LIB.LGBM_BoosterGetEval( + booster, ctypes.c_int(0), ctypes.byref(out_len), result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)) + ) + if i % 10 == 0: + print(f"{i} iteration test AUC {result[0]:.6f}") + LIB.LGBM_BoosterSaveModel(booster, ctypes.c_int(0), ctypes.c_int(-1), ctypes.c_int(0), c_str(str(model_path))) + LIB.LGBM_BoosterFree(booster) + free_dataset(train) + free_dataset(test) + booster2 = ctypes.c_void_p() + num_total_model = ctypes.c_int(0) + LIB.LGBM_BoosterCreateFromModelfile(c_str(str(model_path)), ctypes.byref(num_total_model), ctypes.byref(booster2)) + data = np.loadtxt(str(binary_example_dir / "binary.test"), dtype=np.float64) + mat = data[:, 1:] + preds = np.empty(mat.shape[0], dtype=np.float64) + num_preds = ctypes.c_int64(0) + data = np.asarray(mat.reshape(mat.size), dtype=np.float64) + LIB.LGBM_BoosterPredictForMat( + booster2, + data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ctypes.c_int(dtype_float64), + ctypes.c_int32(mat.shape[0]), + ctypes.c_int32(mat.shape[1]), + ctypes.c_int(1), + ctypes.c_int(1), + ctypes.c_int(0), + ctypes.c_int(25), + c_str(""), + ctypes.byref(num_preds), + preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), + ) + LIB.LGBM_BoosterPredictForFile( + booster2, + c_str(str(binary_example_dir / "binary.test")), + ctypes.c_int(0), + ctypes.c_int(0), + ctypes.c_int(0), + ctypes.c_int(25), + c_str(""), + c_str(tmp_path / "preds.txt"), + ) + LIB.LGBM_BoosterPredictForFile( + booster2, + c_str(str(binary_example_dir / "binary.test")), + ctypes.c_int(0), + ctypes.c_int(0), + ctypes.c_int(10), + ctypes.c_int(25), + c_str(""), + c_str(tmp_path / "preds.txt"), + ) + LIB.LGBM_BoosterFree(booster2) + + +def test_max_thread_control(): + # at initialization, should be -1 + num_threads = ctypes.c_int(0) + ret = LIB.LGBM_GetMaxThreads(ctypes.byref(num_threads)) + assert ret == 0 + assert num_threads.value == -1 + + # updating that value through the C API should work + ret = LIB.LGBM_SetMaxThreads(ctypes.c_int(6)) + assert ret == 0 + + ret = LIB.LGBM_GetMaxThreads(ctypes.byref(num_threads)) + assert ret == 0 + assert num_threads.value == 6 + + # resetting to any negative number should set it to -1 + ret = LIB.LGBM_SetMaxThreads(ctypes.c_int(-123)) + assert ret == 0 + ret = LIB.LGBM_GetMaxThreads(ctypes.byref(num_threads)) + assert ret == 0 + assert num_threads.value == -1 diff --git a/tests/cpp_tests/predict.conf b/tests/cpp_tests/predict.conf new file mode 100644 index 0000000..795af14 --- /dev/null +++ b/tests/cpp_tests/predict.conf @@ -0,0 +1,5 @@ +data=../data/categorical.data + +input_model=LightGBM_model.txt + +task=predict diff --git a/tests/cpp_tests/test.py b/tests/cpp_tests/test.py new file mode 100644 index 0000000..b9a49e0 --- /dev/null +++ b/tests/cpp_tests/test.py @@ -0,0 +1,7 @@ +# coding: utf-8 +from pathlib import Path + +import numpy as np + +preds = [np.loadtxt(str(name)) for name in Path(__file__).absolute().parent.glob("*.pred")] +np.testing.assert_allclose(preds[0], preds[1]) diff --git a/tests/cpp_tests/test_array_args.cpp b/tests/cpp_tests/test_array_args.cpp new file mode 100644 index 0000000..b34e1bd --- /dev/null +++ b/tests/cpp_tests/test_array_args.cpp @@ -0,0 +1,54 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include +#include +#include + +#include +#include + +using LightGBM::data_size_t; +using LightGBM::score_t; +using LightGBM::ArrayArgs; + + +TEST(Partition, JustWorks) { + std::vector gradients({0.5f, 5.0f, 1.0f, 2.0f, 2.0f}); + data_size_t middle_begin, middle_end; + + ArrayArgs::Partition(&gradients, 0, static_cast(gradients.size()), &middle_begin, &middle_end); + + EXPECT_EQ(gradients[middle_begin + 1], gradients[middle_end - 1]); + EXPECT_GT(gradients[0], gradients[middle_begin + 1]); + EXPECT_GT(gradients[middle_begin + 1], gradients.back()); +} + +TEST(Partition, PartitionOneElement) { + std::vector gradients({0.5f}); + data_size_t middle_begin, middle_end; + ArrayArgs::Partition(&gradients, 0, static_cast(gradients.size()), &middle_begin, &middle_end); + EXPECT_EQ(gradients[middle_begin + 1], gradients[middle_end - 1]); +} + +TEST(Partition, Empty) { + std::vector gradients; + data_size_t middle_begin, middle_end; + ArrayArgs::Partition(&gradients, 0, static_cast(gradients.size()), &middle_begin, &middle_end); + + EXPECT_EQ(middle_begin, -1); + EXPECT_EQ(middle_end, 0); +} + +TEST(Partition, AllEqual) { + std::vector gradients({0.5f, 0.5f, 0.5f}); + data_size_t middle_begin, middle_end; + ArrayArgs::Partition(&gradients, 0, static_cast(gradients.size()), &middle_begin, &middle_end); + + EXPECT_EQ(gradients[middle_begin + 1], gradients[middle_end - 1]); + EXPECT_EQ(middle_begin, -1); + EXPECT_EQ(middle_end, 3); +} diff --git a/tests/cpp_tests/test_arrow.cpp b/tests/cpp_tests/test_arrow.cpp new file mode 100644 index 0000000..cc159cd --- /dev/null +++ b/tests/cpp_tests/test_arrow.cpp @@ -0,0 +1,216 @@ +/*! + * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Oliver Borchert + */ + +#include + +#include +#include +#include + +#include + +#include "../../src/arrow/array.hpp" + +using LightGBM::ArrowChunkedArray; + +namespace { + +// Build an ArrowArrayStream from a schema and a list of chunk arrays. Takes ownership of the +// passed schema and chunks. +nanoarrow::UniqueArrayStream MakeStream(nanoarrow::UniqueSchema schema, + std::vector chunks) { + nanoarrow::UniqueArrayStream stream; + nanoarrow::VectorArrayStream(schema.get(), std::move(chunks)).ToArrayStream(stream.get()); + return stream; +} + +nanoarrow::UniqueSchema MakePrimitiveSchema(ArrowType type) { + nanoarrow::UniqueSchema schema; + EXPECT_EQ(ArrowSchemaInitFromType(schema.get(), type), NANOARROW_OK); + return schema; +} + +nanoarrow::UniqueSchema MakeStructSchema(const std::vector& field_types) { + nanoarrow::UniqueSchema schema; + ArrowSchemaInit(schema.get()); + EXPECT_EQ(ArrowSchemaSetTypeStruct(schema.get(), field_types.size()), NANOARROW_OK); + for (size_t i = 0; i < field_types.size(); ++i) { + EXPECT_EQ(ArrowSchemaSetType(schema->children[i], field_types[i]), NANOARROW_OK); + } + return schema; +} + +template +nanoarrow::UniqueArray MakePrimitiveArray(ArrowType type, const std::vector& values, + const std::vector& null_indices = {}, + int64_t offset = 0) { + nanoarrow::UniqueArray array; + EXPECT_EQ(ArrowArrayInitFromType(array.get(), type), NANOARROW_OK); + EXPECT_EQ(ArrowArrayStartAppending(array.get()), NANOARROW_OK); + size_t null_idx_pos = 0; + for (size_t i = 0; i < values.size(); ++i) { + if (null_idx_pos < null_indices.size() && + null_indices[null_idx_pos] == static_cast(i)) { + EXPECT_EQ(ArrowArrayAppendNull(array.get(), 1), NANOARROW_OK); + ++null_idx_pos; + } else { + if (type == NANOARROW_TYPE_BOOL) { + EXPECT_EQ(ArrowArrayAppendInt(array.get(), values[i] ? 1 : 0), NANOARROW_OK); + } else { + EXPECT_EQ(ArrowArrayAppendDouble(array.get(), static_cast(values[i])), + NANOARROW_OK); + } + } + } + EXPECT_EQ(ArrowArrayFinishBuildingDefault(array.get(), nullptr), NANOARROW_OK); + + // Apply slicing offset (tests the consumer's handling of `array->offset`). + if (offset > 0) { + array->offset += offset; + array->length -= offset; + } + return array; +} + +} // namespace + +TEST(ArrowChunkedArrayTest, GetLength) { + // Single chunk + { + auto schema = MakePrimitiveSchema(NANOARROW_TYPE_FLOAT); + std::vector chunks; + chunks.emplace_back(MakePrimitiveArray(NANOARROW_TYPE_FLOAT, {1, 2})); + ArrowChunkedArray chunked_array(MakeStream(std::move(schema), std::move(chunks)).get()); + ASSERT_EQ(chunked_array.get_length(), 2); + } + + // Multiple chunks + { + auto schema = MakePrimitiveSchema(NANOARROW_TYPE_FLOAT); + std::vector chunks; + chunks.emplace_back(MakePrimitiveArray(NANOARROW_TYPE_FLOAT, {1, 2})); + chunks.emplace_back(MakePrimitiveArray(NANOARROW_TYPE_FLOAT, {3, 4, 5, 6})); + ArrowChunkedArray chunked_array(MakeStream(std::move(schema), std::move(chunks)).get()); + ASSERT_EQ(chunked_array.get_length(), 6); + } + + // Sliced chunk via offset + { + auto schema = MakePrimitiveSchema(NANOARROW_TYPE_BOOL); + std::vector chunks; + chunks.emplace_back( + MakePrimitiveArray(NANOARROW_TYPE_BOOL, {true, false, true, true}, {}, 1)); + ArrowChunkedArray chunked_array(MakeStream(std::move(schema), std::move(chunks)).get()); + ASSERT_EQ(chunked_array.get_length(), 3); + } +} + +TEST(ArrowChunkedArrayTest, GetFields) { + auto schema = MakeStructSchema({NANOARROW_TYPE_FLOAT, NANOARROW_TYPE_FLOAT}); + + nanoarrow::UniqueArray array; + ASSERT_EQ(ArrowArrayInitFromSchema(array.get(), schema.get(), nullptr), NANOARROW_OK); + ASSERT_EQ(ArrowArrayStartAppending(array.get()), NANOARROW_OK); + std::vector dat1 = {1, 2, 3}; + std::vector dat2 = {4, 5, 6}; + for (size_t i = 0; i < dat1.size(); ++i) { + ASSERT_EQ(ArrowArrayAppendDouble(array->children[0], dat1[i]), NANOARROW_OK); + ASSERT_EQ(ArrowArrayAppendDouble(array->children[1], dat2[i]), NANOARROW_OK); + ASSERT_EQ(ArrowArrayFinishElement(array.get()), NANOARROW_OK); + } + ASSERT_EQ(ArrowArrayFinishBuildingDefault(array.get(), nullptr), NANOARROW_OK); + + std::vector chunks; + chunks.emplace_back(std::move(array)); + ArrowChunkedArray chunked_array(MakeStream(std::move(schema), std::move(chunks)).get()); + + ASSERT_EQ(chunked_array.get_length(), 3); + ASSERT_EQ(chunked_array.get_num_fields(), 2); + + int32_t first0 = 0, first1 = 0; + chunked_array.view().field(0).visit([&](auto v) { first0 = *v.begin(); }); + chunked_array.view().field(1).visit([&](auto v) { first1 = *v.begin(); }); + ASSERT_EQ(first0, 1); + ASSERT_EQ(first1, 4); +} + +TEST(ArrowChunkedArrayTest, IteratorArithmetic) { + auto schema = MakePrimitiveSchema(NANOARROW_TYPE_FLOAT); + std::vector chunks; + chunks.emplace_back(MakePrimitiveArray(NANOARROW_TYPE_FLOAT, {1, 2})); + chunks.emplace_back(MakePrimitiveArray(NANOARROW_TYPE_FLOAT, {3, 4, 5, 6})); + chunks.emplace_back(MakePrimitiveArray(NANOARROW_TYPE_FLOAT, {7})); + ArrowChunkedArray chunked_array(MakeStream(std::move(schema), std::move(chunks)).get()); + + chunked_array.view().visit([](auto v) { + auto it = v.begin(); + EXPECT_EQ(*it, 1); + ++it; + EXPECT_EQ(*it, 2); + ++it; + EXPECT_EQ(*it, 3); + it += 2; + EXPECT_EQ(*it, 5); + it += 2; + EXPECT_EQ(*it, 7); + + auto begin = v.begin(); + EXPECT_EQ(begin[0], 1); + EXPECT_EQ(begin[1], 2); + EXPECT_EQ(begin[2], 3); + EXPECT_EQ(begin[6], 7); + + auto end = v.end(); + EXPECT_EQ(end - it, 1); + EXPECT_EQ(end - v.begin(), 7); + }); +} + +TEST(ArrowChunkedArrayTest, BooleanIterator) { + auto schema = MakePrimitiveSchema(NANOARROW_TYPE_BOOL); + std::vector chunks; + chunks.emplace_back(MakePrimitiveArray(NANOARROW_TYPE_BOOL, {false, true, false}, {2})); + chunks.emplace_back(MakePrimitiveArray( + NANOARROW_TYPE_BOOL, {false, false, false, false, true, true, true, true, false, true}, {}, + 1)); + ArrowChunkedArray chunked_array(MakeStream(std::move(schema), std::move(chunks)).get()); + + chunked_array.view().visit([](auto v) { + auto it = v.begin(); + // First chunk + EXPECT_EQ(*it, 0); + EXPECT_EQ(*(++it), 1); + EXPECT_TRUE(std::isnan(*(++it))); + + // Second chunk + EXPECT_EQ(*(++it), 0); + it += 3; + EXPECT_EQ(*it, 1); + it += 4; + EXPECT_EQ(*it, 0); + EXPECT_EQ(*(++it), 1); + + EXPECT_EQ(++it, v.end()); + }); +} + +TEST(ArrowChunkedArrayTest, OffsetAndValidity) { + auto schema = MakePrimitiveSchema(NANOARROW_TYPE_FLOAT); + std::vector chunks; + chunks.emplace_back( + MakePrimitiveArray(NANOARROW_TYPE_FLOAT, {0, 1, 2, 3, 4, 5, 6}, {2, 3}, 2)); + ArrowChunkedArray chunked_array(MakeStream(std::move(schema), std::move(chunks)).get()); + + chunked_array.view().visit([](auto v) { + auto it = v.begin(); + EXPECT_TRUE(std::isnan(*it)); + EXPECT_TRUE(std::isnan(*(++it))); + EXPECT_EQ(it[2], 4); + EXPECT_EQ(it[4], 6); + }); +} diff --git a/tests/cpp_tests/test_arrow_deprecated.cpp b/tests/cpp_tests/test_arrow_deprecated.cpp new file mode 100644 index 0000000..67f4d7b --- /dev/null +++ b/tests/cpp_tests/test_arrow_deprecated.cpp @@ -0,0 +1,220 @@ +/*! + * Copyright (c) 2026-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2026-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Oliver Borchert + */ + +#if defined(_MSC_VER) +#pragma warning(push) +#pragma warning(disable : 4996) +#elif defined(__GNUC__) || defined(__clang__) +#pragma GCC diagnostic push +#pragma GCC diagnostic ignored "-Wdeprecated-declarations" +#endif + +#include + +#include + +#include + +#include + +namespace { + +nanoarrow::UniqueSchema MakePrimitiveSchema(ArrowType type) { + nanoarrow::UniqueSchema schema; + EXPECT_EQ(ArrowSchemaInitFromType(schema.get(), type), NANOARROW_OK); + return schema; +} + +nanoarrow::UniqueSchema MakeFloatStructSchema(int n_fields) { + nanoarrow::UniqueSchema schema; + ArrowSchemaInit(schema.get()); + EXPECT_EQ(ArrowSchemaSetTypeStruct(schema.get(), n_fields), NANOARROW_OK); + for (int i = 0; i < n_fields; ++i) { + EXPECT_EQ(ArrowSchemaSetType(schema->children[i], NANOARROW_TYPE_FLOAT), NANOARROW_OK); + } + return schema; +} + +nanoarrow::UniqueArray MakeFloatArray(const std::vector& values) { + nanoarrow::UniqueArray array; + EXPECT_EQ(ArrowArrayInitFromType(array.get(), NANOARROW_TYPE_FLOAT), NANOARROW_OK); + EXPECT_EQ(ArrowArrayStartAppending(array.get()), NANOARROW_OK); + for (auto v : values) { + EXPECT_EQ(ArrowArrayAppendDouble(array.get(), v), NANOARROW_OK); + } + EXPECT_EQ(ArrowArrayFinishBuildingDefault(array.get(), nullptr), NANOARROW_OK); + return array; +} + +nanoarrow::UniqueArray MakeFloatStructArray(const struct ArrowSchema* schema, + const std::vector>& columns) { + nanoarrow::UniqueArray array; + EXPECT_EQ(ArrowArrayInitFromSchema(array.get(), schema, nullptr), NANOARROW_OK); + EXPECT_EQ(ArrowArrayStartAppending(array.get()), NANOARROW_OK); + const size_t n = columns[0].size(); + for (size_t i = 0; i < n; ++i) { + for (size_t c = 0; c < columns.size(); ++c) { + EXPECT_EQ(ArrowArrayAppendDouble(array->children[c], columns[c][i]), NANOARROW_OK); + } + EXPECT_EQ(ArrowArrayFinishElement(array.get()), NANOARROW_OK); + } + EXPECT_EQ(ArrowArrayFinishBuildingDefault(array.get(), nullptr), NANOARROW_OK); + return array; +} + +} // namespace + +TEST(ArrowDeprecatedTest, DatasetCreateFromArrow) { + auto schema = MakeFloatStructSchema(2); + std::vector> columns = { + {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f}, + {6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f}}; + auto array = MakeFloatStructArray(schema.get(), columns); + + // Move ownership of schema and array out of the unique wrappers; the + // deprecated API takes ownership of both. + ArrowSchema raw_schema; + schema.move(&raw_schema); + std::vector raw_chunks(1); + array.move(&raw_chunks[0]); + + DatasetHandle handle = nullptr; + int result = LGBM_DatasetCreateFromArrow( + static_cast(raw_chunks.size()), raw_chunks.data(), &raw_schema, + "max_bin=15", nullptr, &handle); + ASSERT_EQ(result, 0); + ASSERT_NE(handle, nullptr); + + int num_data = 0; + int num_feature = 0; + ASSERT_EQ(LGBM_DatasetGetNumData(handle, &num_data), 0); + ASSERT_EQ(LGBM_DatasetGetNumFeature(handle, &num_feature), 0); + EXPECT_EQ(num_data, 6); + EXPECT_EQ(num_feature, 2); + + ASSERT_EQ(LGBM_DatasetFree(handle), 0); +} + +TEST(ArrowDeprecatedTest, DatasetSetFieldFromArrow) { + // Create a small dataset from a dense matrix. + std::vector data = {1.0, 2.0, + 3.0, 4.0, + 5.0, 6.0, + 7.0, 8.0}; + DatasetHandle handle = nullptr; + ASSERT_EQ(LGBM_DatasetCreateFromMat(data.data(), C_API_DTYPE_FLOAT64, 4, 2, 1, + "max_bin=15", nullptr, &handle), + 0); + + // Set the label using the deprecated Arrow API. + std::vector label_values = {0.0f, 1.0f, 0.0f, 1.0f}; + auto label_schema = MakePrimitiveSchema(NANOARROW_TYPE_FLOAT); + auto label_array = MakeFloatArray(label_values); + + ArrowSchema raw_schema; + label_schema.move(&raw_schema); + std::vector raw_chunks(1); + label_array.move(&raw_chunks[0]); + + ASSERT_EQ(LGBM_DatasetSetFieldFromArrow( + handle, "label", static_cast(raw_chunks.size()), + raw_chunks.data(), &raw_schema), + 0); + + int out_len = 0; + const void* out_ptr = nullptr; + int out_type = 0; + ASSERT_EQ(LGBM_DatasetGetField(handle, "label", &out_len, &out_ptr, &out_type), 0); + EXPECT_EQ(out_type, C_API_DTYPE_FLOAT32); + ASSERT_EQ(out_len, static_cast(label_values.size())); + const float* read = static_cast(out_ptr); + for (size_t i = 0; i < label_values.size(); ++i) { + EXPECT_FLOAT_EQ(read[i], label_values[i]); + } + + ASSERT_EQ(LGBM_DatasetFree(handle), 0); +} + +TEST(ArrowDeprecatedTest, BoosterPredictForArrow) { + // Train a tiny booster. + const int nrow = 8; + const int ncol = 2; + std::vector data = {1.0, 1.0, + 2.0, 2.0, + 3.0, 3.0, + 4.0, 4.0, + 5.0, 5.0, + 6.0, 6.0, + 7.0, 7.0, + 8.0, 8.0}; + std::vector labels = {0, 0, 0, 0, 1, 1, 1, 1}; + + DatasetHandle dataset = nullptr; + ASSERT_EQ(LGBM_DatasetCreateFromMat(data.data(), C_API_DTYPE_FLOAT64, nrow, ncol, 1, + "max_bin=15", nullptr, &dataset), + 0); + ASSERT_EQ(LGBM_DatasetSetField(dataset, "label", labels.data(), + static_cast(labels.size()), C_API_DTYPE_FLOAT32), + 0); + + BoosterHandle booster = nullptr; + ASSERT_EQ(LGBM_BoosterCreate(dataset, + "objective=binary metric=auc num_leaves=3 verbose=-1", + &booster), + 0); + for (int i = 0; i < 3; ++i) { + int finished = 0; + ASSERT_EQ(LGBM_BoosterUpdateOneIter(booster, &finished), 0); + } + + // Predict using the deprecated Arrow API. + auto schema = MakeFloatStructSchema(ncol); + std::vector> columns = { + {1.0f, 4.0f, 8.0f}, + {1.0f, 4.0f, 8.0f}}; + auto array = MakeFloatStructArray(schema.get(), columns); + + ArrowSchema raw_schema; + schema.move(&raw_schema); + std::vector raw_chunks(1); + array.move(&raw_chunks[0]); + + const int n_predict_rows = static_cast(columns[0].size()); + std::vector arrow_out(n_predict_rows, 0.0); + int64_t arrow_written = 0; + ASSERT_EQ(LGBM_BoosterPredictForArrow( + booster, static_cast(raw_chunks.size()), raw_chunks.data(), + &raw_schema, C_API_PREDICT_NORMAL, 0, -1, "", &arrow_written, + arrow_out.data()), + 0); + ASSERT_EQ(arrow_written, n_predict_rows); + + // Compare against LGBM_BoosterPredictForMat with equivalent data. + std::vector mat_data = {1.0, 1.0, + 4.0, 4.0, + 8.0, 8.0}; + std::vector mat_out(n_predict_rows, 0.0); + int64_t mat_written = 0; + ASSERT_EQ(LGBM_BoosterPredictForMat(booster, mat_data.data(), C_API_DTYPE_FLOAT64, + n_predict_rows, ncol, 1, C_API_PREDICT_NORMAL, 0, + -1, "", &mat_written, mat_out.data()), + 0); + ASSERT_EQ(mat_written, n_predict_rows); + for (int i = 0; i < n_predict_rows; ++i) { + EXPECT_DOUBLE_EQ(arrow_out[i], mat_out[i]); + } + + ASSERT_EQ(LGBM_BoosterFree(booster), 0); + ASSERT_EQ(LGBM_DatasetFree(dataset), 0); +} + +#if defined(_MSC_VER) +#pragma warning(pop) +#elif defined(__GNUC__) || defined(__clang__) +#pragma GCC diagnostic pop +#endif diff --git a/tests/cpp_tests/test_byte_buffer.cpp b/tests/cpp_tests/test_byte_buffer.cpp new file mode 100644 index 0000000..6f4cde8 --- /dev/null +++ b/tests/cpp_tests/test_byte_buffer.cpp @@ -0,0 +1,73 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include +#include + +#include +#include + +using LightGBM::ByteBuffer; + + +TEST(ByteBuffer, JustWorks) { + std::unique_ptr buffer; + buffer.reset(new ByteBuffer()); + + int cumulativeSize = 0; + EXPECT_EQ(cumulativeSize, buffer->GetSize()); + + int8_t int8Val = 34; + cumulativeSize += sizeof(int8_t); + buffer->Write(&int8Val, sizeof(int8_t)); + EXPECT_EQ(cumulativeSize, buffer->GetSize()); + EXPECT_EQ(int8Val, buffer->GetAt(cumulativeSize - 1)); + + int16_t int16Val = 33; + cumulativeSize += sizeof(int16_t); + buffer->Write(&int16Val, sizeof(int16_t)); + EXPECT_EQ(cumulativeSize, buffer->GetSize()); + int16_t serializedInt16 = 0; + char* int16Ptr = reinterpret_cast(&serializedInt16); + for (unsigned int i = 0; i < sizeof(int16_t); i++) { + int16Ptr[i] = buffer->GetAt(cumulativeSize - (sizeof(int16_t) - i)); + } + EXPECT_EQ(int16Val, serializedInt16); + + int64_t int64Val = 35; + cumulativeSize += sizeof(int64_t); + buffer->Write(&int64Val, sizeof(int64_t)); + EXPECT_EQ(cumulativeSize, buffer->GetSize()); + int64_t serializedInt64 = 0; + char* int64Ptr = reinterpret_cast(&serializedInt64); + for (unsigned int i = 0; i < sizeof(int64_t); i++) { + int64Ptr[i] = buffer->GetAt(cumulativeSize - (sizeof(int64_t) - i)); + } + EXPECT_EQ(int64Val, serializedInt64); + + double doubleVal = 36.6; + cumulativeSize += sizeof(double); + buffer->Write(&doubleVal, sizeof(doubleVal)); + EXPECT_EQ(cumulativeSize, buffer->GetSize()); + double serializedDouble = 0; + char* doublePtr = reinterpret_cast(&serializedDouble); + for (unsigned int i = 0; i < sizeof(double); i++) { + doublePtr[i] = buffer->GetAt(cumulativeSize - (sizeof(double) - i)); + } + EXPECT_EQ(doubleVal, serializedDouble); + + const int charSize = 3; + char charArrayVal[charSize] = { 'a', 'b', 'c' }; + cumulativeSize += charSize; + buffer->Write(charArrayVal, charSize); + EXPECT_EQ(cumulativeSize, buffer->GetSize()); + for (int i = 0; i < charSize; i++) { + EXPECT_EQ(charArrayVal[i], buffer->GetAt(cumulativeSize - (charSize - i))); + } + + // Test that Data() points to first value written + EXPECT_EQ(int8Val, *buffer->Data()); +} diff --git a/tests/cpp_tests/test_chunked_array.cpp b/tests/cpp_tests/test_chunked_array.cpp new file mode 100644 index 0000000..7aa2493 --- /dev/null +++ b/tests/cpp_tests/test_chunked_array.cpp @@ -0,0 +1,265 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + * + * Author: Alberto Ferreira + */ +#include + +#include + +#include "../include/LightGBM/utils/chunked_array.hpp" + +using LightGBM::ChunkedArray; + +/*! + Helper util to compare two vectors. + + Don't compare floating point vectors this way! +*/ +template +testing::AssertionResult are_vectors_equal(const std::vector &a, const std::vector &b) { + if (a.size() != b.size()) { + return testing::AssertionFailure() + << "Vectors differ in size: " + << a.size() << " != " << b.size(); + } + + for (size_t i = 0; i < a.size(); ++i) { + if (a[i] != b[i]) { + return testing::AssertionFailure() + << "Vectors differ at least at position " << i << ": " + << a[i] << " != " << b[i]; + } + } + + return testing::AssertionSuccess(); +} + + +class ChunkedArrayTest : public testing::Test { + protected: + void SetUp() override { + } + + void add_items_to_array(const std::vector &vec, ChunkedArray *ca) { + for (auto v : vec) { + ca->add(v); + } + } + + /*! + Ensures that if coalesce_to() is called upon the ChunkedArray, + it would yield the same contents as vec + */ + testing::AssertionResult coalesced_output_equals_vec(const ChunkedArray &ca, const std::vector &vec, + const bool all_addresses = false) { + std::vector out(vec.size()); + ca.coalesce_to(out.data(), all_addresses); + return are_vectors_equal(out, vec); + } + + // Constants + const std::vector REF_VEC = {1, 5, 2, 4, 9, 8, 7}; + const size_t CHUNK_SIZE = 3; + const size_t OUT_OF_BOUNDS_OFFSET = 4; + + ChunkedArray ca_ = ChunkedArray(CHUNK_SIZE); // chunked_array(0), std::runtime_error); +} + +/*! get_chunk_size() should return the size used in the constructor */ +TEST_F(ChunkedArrayTest, constructorWithChunkSize) { + for (size_t chunk_size = 1; chunk_size < 10; ++chunk_size) { + ChunkedArray chunked_array(chunk_size); + ASSERT_EQ(chunked_array.get_chunk_size(), chunk_size); + } +} + +/*! + get_chunk_size() should return the size used in the constructor + independently of array manipulations. +*/ +TEST_F(ChunkedArrayTest, getChunkSizeIsConstant) { + for (size_t i = 0; i < 3 * CHUNK_SIZE; ++i) { + ASSERT_EQ(ca_.get_chunk_size(), CHUNK_SIZE); + ca_.add(0); + } +} + + +/*! + get_add_count() should return the number of add calls, + independently of the number of chunks used. +*/ +TEST_F(ChunkedArrayTest, getChunksCount) { + ASSERT_EQ(ca_.get_chunks_count(), 1); // ChunkedArray always starts with 1 chunk. + + for (size_t i = 0; i < 3 * CHUNK_SIZE; ++i) { + ca_.add(0); + int expected_chunks = static_cast(i / CHUNK_SIZE) + 1; + ASSERT_EQ(ca_.get_chunks_count(), expected_chunks) << "with " << i << " add() call(s) " + << "and CHUNK_SIZE==" << CHUNK_SIZE << "."; + } +} + +/*! + get_add_count() should return the number of add calls, + independently of the number of chunks used. +*/ +TEST_F(ChunkedArrayTest, getAddCount) { + for (size_t i = 0; i < 3 * CHUNK_SIZE; ++i) { + ASSERT_EQ(ca_.get_add_count(), i); + ca_.add(0); + } +} + +/*! + Ensure coalesce_to() works and dumps all the inserted data correctly. + + If the ChunkedArray is created from a sequence of add() calls, coalescing to + an output array after multiple add operations should yield the same + exact data at both input and output. +*/ +TEST_F(ChunkedArrayTest, coalesceTo) { + std::vector out(REF_VEC.size()); + add_items_to_array(REF_VEC, &ca_); + + ca_.coalesce_to(out.data()); + + ASSERT_TRUE(are_vectors_equal(REF_VEC, out)); +} + +/*! + After clear the ChunkedArray() should still be usable. +*/ +TEST_F(ChunkedArrayTest, clear) { + const std::vector ref_vec2 = {1, 2, 5, -1}; + add_items_to_array(REF_VEC, &ca_); + // Start with some content: + ASSERT_TRUE(coalesced_output_equals_vec(ca_, REF_VEC)); + + // Clear & re-use: + ca_.clear(); + add_items_to_array(ref_vec2, &ca_); + + // Output should match new content: + ASSERT_TRUE(coalesced_output_equals_vec(ca_, ref_vec2)); +} + +/*! + Ensure ChunkedArray is safe against double-frees. +*/ +TEST_F(ChunkedArrayTest, doubleFreeSafe) { + ca_.release(); // Cannot be used any longer from now on. + ca_.release(); // Ensure we don't segfault. + + SUCCEED(); +} + +/*! + Ensure size computations in the getters are correct. +*/ +TEST_F(ChunkedArrayTest, totalArraySizeMatchesLastChunkAddCount) { + add_items_to_array(REF_VEC, &ca_); + + const size_t first_chunks_add_count = (ca_.get_chunks_count() - 1) * ca_.get_chunk_size(); + const size_t last_chunk_add_count = ca_.get_last_chunk_add_count(); + + EXPECT_EQ(first_chunks_add_count, static_cast(REF_VEC.size() / CHUNK_SIZE) * CHUNK_SIZE); + EXPECT_EQ(last_chunk_add_count, REF_VEC.size() % CHUNK_SIZE); + EXPECT_EQ(first_chunks_add_count + last_chunk_add_count, ca_.get_add_count()); +} + +/*! + Assert all values are correct and at the expected addresses throughout the + several chunks. + + This uses getitem() to reach each individual address of any of the chunks. + + A sentinel value of -1 is used to check for invalid addresses. + This would occur if there was an improper data layout with the chunks. +*/ +TEST_F(ChunkedArrayTest, dataLayoutTestThroughGetitem) { + add_items_to_array(REF_VEC, &ca_); + + for (size_t i = 0, chunk = 0, in_chunk_idx = 0; i < REF_VEC.size(); ++i) { + int value = ca_.getitem(chunk, in_chunk_idx, -1); // -1 works as sentinel value (bad layout found) + + EXPECT_EQ(value, REF_VEC[i]) << " for address (chunk,in_chunk_idx) = (" << chunk << "," << in_chunk_idx << ")"; + + if (++in_chunk_idx == ca_.get_chunk_size()) { + in_chunk_idx = 0; + ++chunk; + } + } +} + +/*! + Perform an array of setitem & getitem at valid and invalid addresses. + We use several random addresses and trials to avoid writing much code. + + By testing a random number of addresses many more times than the size of the test space + we are almost guaranteed to cover all possible search addresses. + + We also gradually add more chunks to the ChunkedArray and re-run more trials + to ensure the valid/invalid addresses are updated. + + With each valid update we add to a "memory" vector the latest inserted values. + This is used at the end to ensure all values were stored properly, including after + value overrides. +*/ +TEST_F(ChunkedArrayTest, testDataLayoutWithAdvancedInsertionAPI) { + const size_t MAX_CHUNKS_SEARCH = 5; + const size_t MAX_IN_CHUNK_SEARCH_IDX = 2 * CHUNK_SIZE; + // Number of trials for each new ChunkedArray configuration. Pass 100 times over the search space: + const size_t N_TRIALS = MAX_CHUNKS_SEARCH * MAX_IN_CHUNK_SEARCH_IDX * 100; + const int INVALID = -1; // A negative value signaling the requested value lives in an invalid address. + const int UNINITIALIZED = -99; // A negative value to signal this was never updated. + std::vector ref_values(MAX_CHUNKS_SEARCH * CHUNK_SIZE, UNINITIALIZED); // Memorize latest inserted values. + + // Each outer loop iteration changes the test by adding +1 chunk. We start with 1 chunk only: + for (size_t chunks = 1; chunks < MAX_CHUNKS_SEARCH; ++chunks) { + EXPECT_EQ(ca_.get_chunks_count(), chunks); + + // Sweep valid and invalid addresses with a ChunkedArray with `chunks` chunks: + for (size_t trial = 0; trial < N_TRIALS; ++trial) { + // Compute a new trial address & value & if it is a valid address: + const size_t trial_chunk = std::rand() % MAX_CHUNKS_SEARCH; + const size_t trial_in_chunk_idx = std::rand() % MAX_IN_CHUNK_SEARCH_IDX; + const int trial_value = std::rand() % 99999; + const bool valid_address = (trial_chunk < chunks) & (trial_in_chunk_idx < CHUNK_SIZE); + + // Insert item. If at a valid address, 0 is returned, otherwise, -1 is returned: + EXPECT_EQ(ca_.setitem(trial_chunk, trial_in_chunk_idx, trial_value), + valid_address ? 0 : -1); + // If at valid address, check that the stored value is correct & remember it for the future: + if (valid_address) { + // Check the just-stored value with getitem(): + EXPECT_EQ(ca_.getitem(trial_chunk, trial_in_chunk_idx, INVALID), trial_value); + + // Also store the just-stored value for future tracking: + ref_values[trial_chunk * CHUNK_SIZE + trial_in_chunk_idx] = trial_value; + } + } + + ca_.new_chunk(); // Just finished a round of trials. Now add a new chunk. Valid addresses will be expanded. + } + + // Final check: ensure even with overrides, all valid insertions store the latest value at that address: + std::vector coalesced_out(MAX_CHUNKS_SEARCH * CHUNK_SIZE, UNINITIALIZED); + ca_.coalesce_to(coalesced_out.data(), true); // Export all valid addresses. + for (size_t i = 0; i < ref_values.size(); ++i) { + if (ref_values[i] != UNINITIALIZED) { + // Test in 2 ways that the values are correctly laid out in memory: + EXPECT_EQ(ca_.getitem(i / CHUNK_SIZE, i % CHUNK_SIZE, INVALID), ref_values[i]); + EXPECT_EQ(coalesced_out[i], ref_values[i]); + } + } +} diff --git a/tests/cpp_tests/test_common.cpp b/tests/cpp_tests/test_common.cpp new file mode 100755 index 0000000..98dbd47 --- /dev/null +++ b/tests/cpp_tests/test_common.cpp @@ -0,0 +1,154 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +#include + +#include "../include/LightGBM/utils/common.h" + + +// This is a basic test for floating number parsing. +// Most of the test cases come from: +// https://github.com/dmlc/xgboost/blob/master/tests/cpp/common/test_charconv.cc +// https://github.com/Alexhuszagh/rust-lexical/blob/master/data/test-parse-unittests/strtod_tests.toml +class AtofPreciseTest : public testing::Test { + public: + struct AtofTestCase { + const char* data; + double expected; + }; + + static double TestAtofPrecise( + const char* data, double expected, bool test_eq = true) { + double got = 0; + const char* end = LightGBM::Common::AtofPrecise(data, &got); + EXPECT_TRUE(end != data) << "fail to parse: " << data; + EXPECT_EQ(*end, '\0') << "not parsing to end: " << data; + if (test_eq) { + EXPECT_EQ(expected, got) << "parse string: " << data; + } + return got; + } + + static double Int64Bits2Double(uint64_t v) { + union { + uint64_t i; + double d; + } conv; + conv.i = v; + return conv.d; + } +}; + +TEST_F(AtofPreciseTest, Basic) { + AtofTestCase test_cases[] = { + { "0", 0.0 }, + { "0E0", 0.0 }, + { "-0E0", 0.0 }, + { "-0", -0.0 }, + { "1", 1.0 }, + { "1E0", 1.0 }, + { "-1", -1.0 }, + { "-1E0", -1.0 }, + { "123456.0", 123456.0 }, + { "432E1", 432E1 }, + { "1.2345678", 1.2345678 }, + { "2.4414062E-4", 2.4414062E-4 }, + { "3.0540412E5", 3.0540412E5 }, + { "3.355445E7", 3.355445E7 }, + { "1.1754944E-38", 1.1754944E-38 }, + }; + + for (auto const& test : test_cases) { + TestAtofPrecise(test.data, test.expected); + } +} + +TEST_F(AtofPreciseTest, CornerCases) { + AtofTestCase test_cases[] = { + { "1e-400", 0.0 }, + { "2.4703282292062326e-324", 0.0 }, + { "4.9406564584124654e-324", Int64Bits2Double(0x0000000000000001LU) }, + { "8.44291197326099e-309", Int64Bits2Double(0x0006123400000001LU) }, + // FLT_MAX + { "3.40282346638528859811704183484516925440e38", + static_cast(std::numeric_limits::max()) }, + // FLT_MIN + { "1.1754943508222875079687365372222456778186655567720875215087517062784172594547271728515625e-38", + static_cast(std::numeric_limits::min()) }, + // DBL_MAX (1 + (1 - 2^-52)) * 2^1023 = (2^53 - 1) * 2^971 + { "17976931348623157081452742373170435679807056752584499659891747680315" + "72607800285387605895586327668781715404589535143824642343213268894641" + "82768467546703537516986049910576551282076245490090389328944075868508" + "45513394230458323690322294816580855933212334827479782620414472316873" + "8177180919299881250404026184124858368", std::numeric_limits::max() }, + { "1.7976931348623158e+308", std::numeric_limits::max() }, + // 2^971 * (2^53 - 1 + 1/2) : the smallest number resolving to inf + {"179769313486231580793728971405303415079934132710037826936173778980444" + "968292764750946649017977587207096330286416692887910946555547851940402" + "630657488671505820681908902000708383676273854845817711531764475730270" + "069855571366959622842914819860834936475292719074168444365510704342711" + "559699508093042880177904174497792", std::numeric_limits::infinity() }, + // Near DBL_MIN + { "2.2250738585072009e-308", Int64Bits2Double(0x000fffffffffffffLU) }, + // DBL_MIN 2^-1022 + { "2.2250738585072012e-308", std::numeric_limits::min() }, + { "2.2250738585072014e-308", std::numeric_limits::min() }, + }; + + for (auto const& test : test_cases) { + TestAtofPrecise(test.data, test.expected); + } +} + +TEST_F(AtofPreciseTest, ErrorInput) { + double got = 0; + ASSERT_THROW(LightGBM::Common::AtofPrecise("x1", &got), std::runtime_error); +} + +TEST_F(AtofPreciseTest, NaN) { + AtofTestCase test_cases[] = { + { "nan", std::numeric_limits::quiet_NaN() }, + { "NaN", std::numeric_limits::quiet_NaN() }, + { "NAN", std::numeric_limits::quiet_NaN() }, + // The behavior for parsing -nan depends on implementation. + // Thus we skip binary check for negative nan. + { "-nan", -std::numeric_limits::quiet_NaN() }, + { "-NaN", -std::numeric_limits::quiet_NaN() }, + { "-NAN", -std::numeric_limits::quiet_NaN() }, + }; + + for (auto const& test : test_cases) { + double got = TestAtofPrecise(test.data, test.expected, false); + + EXPECT_TRUE(std::isnan(got)) << "not parsed as NaN: " << test.data; + if (got > 0) { + // See comment in test_cases. + EXPECT_EQ(memcmp(&got, &test.expected, sizeof(test.expected)), 0) + << "parsed NaN is not the same for every bit: " << test.data; + } + } +} + +TEST_F(AtofPreciseTest, Inf) { + AtofTestCase test_cases[] = { + { "inf", std::numeric_limits::infinity() }, + { "Inf", std::numeric_limits::infinity() }, + { "INF", std::numeric_limits::infinity() }, + { "-inf", -std::numeric_limits::infinity() }, + { "-Inf", -std::numeric_limits::infinity() }, + { "-INF", -std::numeric_limits::infinity() }, + }; + + for (auto const& test : test_cases) { + double got = TestAtofPrecise(test.data, test.expected, false); + + EXPECT_EQ(LightGBM::Common::Sign(test.expected), LightGBM::Common::Sign(got)) << "sign differs parsing: " << test.data; + EXPECT_TRUE(std::isinf(got)) << "not parsed as infinite: " << test.data; + EXPECT_EQ(memcmp(&got, &test.expected, sizeof(test.expected)), 0) + << "parsed infinite is not the same for every bit: " << test.data; + } +} diff --git a/tests/cpp_tests/test_main.cpp b/tests/cpp_tests/test_main.cpp new file mode 100644 index 0000000..5df6d0f --- /dev/null +++ b/tests/cpp_tests/test_main.cpp @@ -0,0 +1,12 @@ +/*! + * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#include + +int main(int argc, char** argv) { + testing::InitGoogleTest(&argc, argv); + testing::FLAGS_gtest_death_test_style = "threadsafe"; + return RUN_ALL_TESTS(); +} diff --git a/tests/cpp_tests/test_serialize.cpp b/tests/cpp_tests/test_serialize.cpp new file mode 100644 index 0000000..eb81024 --- /dev/null +++ b/tests/cpp_tests/test_serialize.cpp @@ -0,0 +1,86 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include +#include +#include +#include +#include +#include + +#include +#include + +using LightGBM::ByteBuffer; +using LightGBM::Dataset; +using LightGBM::Log; +using LightGBM::TestUtils; + +TEST(Serialization, JustWorks) { + // Load some test data + DatasetHandle dataset_handle; + const char* params = "max_bin=15"; + int result = TestUtils::LoadDatasetFromExamples("binary_classification/binary.test", params, &dataset_handle); + EXPECT_EQ(0, result) << "LoadDatasetFromExamples result code: " << result; + + Dataset* dataset; + bool succeeded = true; + std::string exceptionText(""); + try { + dataset = static_cast(dataset_handle); + + // Serialize the reference + ByteBufferHandle buffer_handle; + int32_t buffer_len; + result = LGBM_DatasetSerializeReferenceToBinary(dataset_handle, &buffer_handle, &buffer_len); + EXPECT_EQ(0, result) << "LGBM_DatasetSerializeReferenceToBinary result code: " << result; + + ByteBuffer* buffer = nullptr; + Dataset* deserialized_dataset = nullptr; + try { + buffer = static_cast(buffer_handle); + + // Deserialize the reference + DatasetHandle deserialized_dataset_handle; + result = LGBM_DatasetCreateFromSerializedReference(buffer->Data(), + static_cast(buffer->GetSize()), + dataset->num_data(), + 0, // num_classes + params, + &deserialized_dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetCreateFromSerializedReference result code: " << result; + + // Confirm 1 successful API call + deserialized_dataset = static_cast(deserialized_dataset_handle); + EXPECT_EQ(dataset->num_data(), deserialized_dataset->num_data()); + } catch (std::exception& ex) { + succeeded = false; + exceptionText = std::string(ex.what()); + } + + // Free memory + if (buffer) { + result = LGBM_ByteBufferFree(buffer); + EXPECT_EQ(0, result) << "LGBM_ByteBufferFree result code: " << result; + } + if (deserialized_dataset) { + result = LGBM_DatasetFree(deserialized_dataset); + EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result; + } + } catch (std::exception& ex) { + succeeded = false; + exceptionText = std::string(ex.what()); + } + + if (dataset) { + result = LGBM_DatasetFree(dataset); + EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result; + } + + if (!succeeded) { + FAIL() << "Test Serialization failed with exception: " << exceptionText; + } +} diff --git a/tests/cpp_tests/test_single_row.cpp b/tests/cpp_tests/test_single_row.cpp new file mode 100644 index 0000000..2281708 --- /dev/null +++ b/tests/cpp_tests/test_single_row.cpp @@ -0,0 +1,190 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include +#include +#include + +#include +#include +#include +#include + +using LightGBM::TestUtils; + +void test_predict_type(int predict_type, int num_predicts) { + // Load some test data + int result; + + DatasetHandle train_dataset; + result = TestUtils::LoadDatasetFromExamples("binary_classification/binary.train", "max_bin=15", &train_dataset); + EXPECT_EQ(0, result) << "LoadDatasetFromExamples train result code: " << result; + + BoosterHandle booster_handle; + result = LGBM_BoosterCreate(train_dataset, "app=binary metric=auc num_leaves=31 verbose=0", &booster_handle); + EXPECT_EQ(0, result) << "LGBM_BoosterCreate result code: " << result; + + for (int i = 0; i < 51; i++) { + int produced_empty_tree; + result = LGBM_BoosterUpdateOneIter( + booster_handle, + &produced_empty_tree); + EXPECT_EQ(0, result) << "LGBM_BoosterUpdateOneIter result code: " << result; + } + + int n_features; + result = LGBM_BoosterGetNumFeature( + booster_handle, + &n_features); + EXPECT_EQ(0, result) << "LGBM_BoosterGetNumFeature result code: " << result; + EXPECT_EQ(28, n_features) << "LGBM_BoosterGetNumFeature number of features: " << n_features; + + // Run a single row prediction and compare with regular Mat prediction: + int64_t output_size; + result = LGBM_BoosterCalcNumPredict( + booster_handle, + 1, + predict_type, // predict_type + 0, // start_iteration + -1, // num_iteration + &output_size); + EXPECT_EQ(0, result) << "LGBM_BoosterCalcNumPredict result code: " << result; + EXPECT_EQ(num_predicts, output_size) << "LGBM_BoosterCalcNumPredict output size: " << output_size; + + std::ifstream test_file("examples/binary_classification/binary.test"); + std::vector test; + double x; + int test_set_size = 0; + while (test_file >> x) { + if (test_set_size % (n_features + 1) == 0) { + // Drop the result from the dataset, we only care about checking that prediction results are equal + // in both cases + test_file >> x; + test_set_size++; + } + test.push_back(x); + test_set_size++; + } + EXPECT_EQ(test_set_size % (n_features + 1), 0) << "Test size mismatch with dataset size (%)"; + test_set_size /= (n_features + 1); + EXPECT_EQ(test_set_size, 500) << "Improperly parsed test file (test_set_size)"; + EXPECT_EQ(test.size(), test_set_size * n_features) << "Improperly parsed test file (test len)"; + + std::vector mat_output(output_size * test_set_size, -1); + int64_t written; + result = LGBM_BoosterPredictForMat( + booster_handle, + &test[0], + C_API_DTYPE_FLOAT64, + test_set_size, // nrow + n_features, // ncol + 1, // is_row_major + predict_type, // predict_type + 0, // start_iteration + -1, // num_iteration + "", + &written, + &mat_output[0]); + EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMat result code: " << result; + + // Test LGBM_BoosterPredictForMat in multi-threaded mode + const int kNThreads = 10; + const int numIterations = 5; + std::vector predict_for_mat_threads(kNThreads); + for (int i = 0; i < kNThreads; i++) { + predict_for_mat_threads[i] = std::thread( + [ + i, test_set_size, output_size, n_features, + test = &test[0], booster_handle, predict_type, numIterations + ]() { + for (int j = 0; j < numIterations; j++) { + int result; + std::vector mat_output(output_size * test_set_size, -1); + int64_t written; + result = LGBM_BoosterPredictForMat( + booster_handle, + &test[0], + C_API_DTYPE_FLOAT64, + test_set_size, // nrow + n_features, // ncol + 1, // is_row_major + predict_type, // predict_type + 0, // start_iteration + -1, // num_iteration + "", + &written, + &mat_output[0]); + EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMat result code: " << result; + } + }); + } + for (std::thread& t : predict_for_mat_threads) { + t.join(); + } + + // Now let's run with the single row fast prediction API: + FastConfigHandle fast_configs[kNThreads]; + for (int i = 0; i < kNThreads; i++) { + result = LGBM_BoosterPredictForMatSingleRowFastInit( + booster_handle, + predict_type, // predict_type + 0, // start_iteration + -1, // num_iteration + C_API_DTYPE_FLOAT64, + n_features, + "", + &fast_configs[i]); + EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMatSingleRowFastInit result code: " << result; + } + + std::vector single_row_output(output_size * test_set_size, -1); + std::vector single_row_threads(kNThreads); + int batch_size = (test_set_size + kNThreads - 1) / kNThreads; // round up + for (int i = 0; i < kNThreads; i++) { + single_row_threads[i] = std::thread( + [ + i, batch_size, test_set_size, output_size, n_features, + test = &test[0], fast_configs = &fast_configs[0], single_row_output = &single_row_output[0] + ]() { + int result; + int64_t written; + for (int j = i * batch_size; j < std::min((i + 1) * batch_size, test_set_size); j++) { + result = LGBM_BoosterPredictForMatSingleRowFast( + fast_configs[i], + &test[j * n_features], + &written, + &single_row_output[j * output_size]); + EXPECT_EQ(0, result) << "LGBM_BoosterPredictForMatSingleRowFast result code: " << result; + EXPECT_EQ(written, output_size) << "LGBM_BoosterPredictForMatSingleRowFast unexpected written output size"; + } + }); + } + for (std::thread& t : single_row_threads) { + t.join(); + } + + EXPECT_EQ(single_row_output, mat_output) << "LGBM_BoosterPredictForMatSingleRowFast output mismatch with LGBM_BoosterPredictForMat"; + + // Free all: + for (int i = 0; i < kNThreads; i++) { + result = LGBM_FastConfigFree(fast_configs[i]); + EXPECT_EQ(0, result) << "LGBM_FastConfigFree result code: " << result; + } + + result = LGBM_BoosterFree(booster_handle); + EXPECT_EQ(0, result) << "LGBM_BoosterFree result code: " << result; + + result = LGBM_DatasetFree(train_dataset); + EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result; +} + +TEST(SingleRow, Normal) { + test_predict_type(C_API_PREDICT_NORMAL, 1); +} + +TEST(SingleRow, Contrib) { + test_predict_type(C_API_PREDICT_CONTRIB, 29); +} diff --git a/tests/cpp_tests/test_stream.cpp b/tests/cpp_tests/test_stream.cpp new file mode 100644 index 0000000..fb75883 --- /dev/null +++ b/tests/cpp_tests/test_stream.cpp @@ -0,0 +1,356 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include +#include +#include +#include +#include + +#include +#include +#include + +using LightGBM::Dataset; +using LightGBM::Log; +using LightGBM::TestUtils; + +void test_stream_dense( + int8_t creation_type, + DatasetHandle ref_dataset_handle, + int32_t nrows, + int32_t ncols, + int32_t nclasses, + int batch_count, + const std::vector* features, + const std::vector* labels, + const std::vector* weights, + const std::vector* init_scores, + const std::vector* groups) { + Log::Info("Streaming %d rows dense data with a batch size of %d", nrows, batch_count); + DatasetHandle dataset_handle = nullptr; + Dataset* dataset = nullptr; + + int has_weights = weights != nullptr; + int has_init_scores = init_scores != nullptr; + int has_queries = groups != nullptr; + + bool succeeded = true; + std::string exceptionText(""); + + try { + int result = 0; + switch (creation_type) { + case 0: { + Log::Info("Creating Dataset using LGBM_DatasetCreateFromSampledColumn, %d rows dense data with a batch size of %d", nrows, batch_count); + + // construct sample data first (use all data for convenience and since size is small) + std::vector> sample_values(ncols); + std::vector> sample_idx(ncols); + const double* current_val = features->data(); + for (int32_t idx = 0; idx < nrows; ++idx) { + for (int32_t k = 0; k < ncols; ++k) { + if (std::fabs(*current_val) > 1e-35f || std::isnan(*current_val)) { + sample_values[k].emplace_back(*current_val); + sample_idx[k].emplace_back(static_cast(idx)); + } + current_val++; + } + } + + std::vector sample_sizes; + std::vector sample_values_ptrs; + std::vector sample_idx_ptrs; + for (int32_t i = 0; i < ncols; ++i) { + sample_values_ptrs.push_back(sample_values[i].data()); + sample_idx_ptrs.push_back(sample_idx[i].data()); + sample_sizes.push_back(static_cast(sample_values[i].size())); + } + + result = LGBM_DatasetCreateFromSampledColumn( + sample_values_ptrs.data(), + sample_idx_ptrs.data(), + ncols, + sample_sizes.data(), + nrows, + nrows, + nrows, + "max_bin=15", + &dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetCreateFromSampledColumn result code: " << result; + + result = LGBM_DatasetInitStreaming(dataset_handle, has_weights, has_init_scores, has_queries, nclasses, 1, -1); + EXPECT_EQ(0, result) << "LGBM_DatasetInitStreaming result code: " << result; + break; + } + + case 1: + Log::Info("Creating Dataset using LGBM_DatasetCreateByReference, %d rows dense data with a batch size of %d", nrows, batch_count); + result = LGBM_DatasetCreateByReference(ref_dataset_handle, nrows, &dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetCreateByReference result code: " << result; + break; + } + + dataset = static_cast(dataset_handle); + + Log::Info("Streaming dense dataset, %d rows dense data with a batch size of %d", nrows, batch_count); + TestUtils::StreamDenseDataset( + dataset_handle, + nrows, + ncols, + nclasses, + batch_count, + features, + labels, + weights, + init_scores, + groups); + + dataset->FinishLoad(); + + TestUtils::AssertMetadata(&dataset->metadata(), + labels, + weights, + init_scores, + groups); + } + catch (std::exception& ex) { + succeeded = false; + exceptionText = std::string(ex.what()); + } + + if (dataset_handle) { + int result = LGBM_DatasetFree(dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result; + } + + if (!succeeded) { + FAIL() << "Test Dense Stream failed with exception: " << exceptionText; + } +} + +void test_stream_sparse( + int8_t creation_type, + DatasetHandle ref_dataset_handle, + int32_t nrows, + int32_t ncols, + int32_t nclasses, + int batch_count, + const std::vector* indptr, + const std::vector* indices, + const std::vector* vals, + const std::vector* labels, + const std::vector* weights, + const std::vector* init_scores, + const std::vector* groups) { + Log::Info("Streaming %d rows sparse data with a batch size of %d", nrows, batch_count); + DatasetHandle dataset_handle = nullptr; + Dataset* dataset = nullptr; + + int has_weights = weights != nullptr; + int has_init_scores = init_scores != nullptr; + int has_queries = groups != nullptr; + + bool succeeded = true; + std::string exceptionText(""); + + try { + int result = 0; + switch (creation_type) { + case 0: { + Log::Info("Creating Dataset using LGBM_DatasetCreateFromSampledColumn, %d rows sparse data with a batch size of %d", nrows, batch_count); + + std::vector> sample_values(ncols); + std::vector> sample_idx(ncols); + for (size_t i = 0; i < indptr->size() - 1; ++i) { + int start_index = indptr->at(i); + int stop_index = indptr->at(i + 1); + for (int32_t j = start_index; j < stop_index; ++j) { + auto val = vals->at(j); + auto idx = indices->at(j); + if (std::fabs(val) > 1e-35f || std::isnan(val)) { + sample_values[idx].emplace_back(val); + sample_idx[idx].emplace_back(static_cast(i)); + } + } + } + + std::vector sample_sizes; + std::vector sample_values_ptrs; + std::vector sample_idx_ptrs; + for (int32_t i = 0; i < ncols; ++i) { + sample_values_ptrs.push_back(sample_values[i].data()); + sample_idx_ptrs.push_back(sample_idx[i].data()); + sample_sizes.push_back(static_cast(sample_values[i].size())); + } + + result = LGBM_DatasetCreateFromSampledColumn( + sample_values_ptrs.data(), + sample_idx_ptrs.data(), + ncols, + sample_sizes.data(), + nrows, + nrows, + nrows, + "max_bin=15", + &dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetCreateFromSampledColumn result code: " << result; + + dataset = static_cast(dataset_handle); + dataset->InitStreaming(nrows, has_weights, has_init_scores, has_queries, nclasses, 2, -1); + break; + } + + case 1: + Log::Info("Creating Dataset using LGBM_DatasetCreateByReference, %d rows sparse data with a batch size of %d", nrows, batch_count); + result = LGBM_DatasetCreateByReference(ref_dataset_handle, nrows, &dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetCreateByReference result code: " << result; + break; + } + + dataset = static_cast(dataset_handle); + + Log::Info("Streaming sparse dataset, %d rows sparse data with a batch size of %d", nrows, batch_count); + TestUtils::StreamSparseDataset( + dataset_handle, + nrows, + nclasses, + batch_count, + indptr, + indices, + vals, + labels, + weights, + init_scores, + groups); + + dataset->FinishLoad(); + + TestUtils::AssertMetadata(&dataset->metadata(), + labels, + weights, + init_scores, + groups); + } + catch (std::exception& ex) { + succeeded = false; + exceptionText = std::string(ex.what()); + } + + if (dataset_handle) { + int result = LGBM_DatasetFree(dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result; + } + + if (!succeeded) { + FAIL() << "Test Sparse Stream failed with exception: " << exceptionText; + } +} + +TEST(Stream, PushDenseRowsWithMetadata) { + // Load some test data + DatasetHandle ref_dataset_handle; + const char* params = "max_bin=15"; + // Use the smaller ".test" data because we don't care about the actual data and it's smaller + int result = TestUtils::LoadDatasetFromExamples("binary_classification/binary.test", params, &ref_dataset_handle); + EXPECT_EQ(0, result) << "LoadDatasetFromExamples result code: " << result; + + Dataset* ref_dataset = static_cast(ref_dataset_handle); + auto noriginalrows = ref_dataset->num_data(); + Log::Info("Row count: %d", noriginalrows); + Log::Info("Feature group count: %d", ref_dataset->num_features()); + + // Add some fake initial_scores and groups so we can test streaming them + int nclasses = 2; // choose > 1 just to test multi-class handling + std::vector unused_init_scores; + unused_init_scores.resize(noriginalrows * nclasses); + std::vector unused_groups; + unused_groups.assign(noriginalrows, 1); + result = LGBM_DatasetSetField(ref_dataset_handle, "init_score", unused_init_scores.data(), noriginalrows * nclasses, 1); + EXPECT_EQ(0, result) << "LGBM_DatasetSetField init_score result code: " << result; + result = LGBM_DatasetSetField(ref_dataset_handle, "group", unused_groups.data(), noriginalrows, 2); + EXPECT_EQ(0, result) << "LGBM_DatasetSetField group result code: " << result; + + // Now use the reference dataset schema to make some testable Datasets with N rows each + int32_t nrows = 1000; + int32_t ncols = ref_dataset->num_features(); + std::vector features; + std::vector labels; + std::vector weights; + std::vector init_scores; + std::vector groups; + + Log::Info("Creating random data"); + TestUtils::CreateRandomDenseData(nrows, ncols, nclasses, &features, &labels, &weights, &init_scores, &groups); + + const std::vector batch_counts = { 1, nrows / 100, nrows / 10, nrows }; + const std::vector creation_types = { 0, 1 }; + + for (size_t i = 0; i < creation_types.size(); ++i) { // from sampled data or reference + for (size_t j = 0; j < batch_counts.size(); ++j) { + auto type = creation_types[i]; + auto batch_count = batch_counts[j]; + test_stream_dense(type, ref_dataset_handle, nrows, ncols, nclasses, batch_count, &features, &labels, &weights, &init_scores, &groups); + } + } + + result = LGBM_DatasetFree(ref_dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result; +} + +TEST(Stream, PushSparseRowsWithMetadata) { + // Load some test data + DatasetHandle ref_dataset_handle; + const char* params = "max_bin=15"; + // Use the smaller ".test" data because we don't care about the actual data and it's smaller + int result = TestUtils::LoadDatasetFromExamples("binary_classification/binary.test", params, &ref_dataset_handle); + EXPECT_EQ(0, result) << "LoadDatasetFromExamples result code: " << result; + + Dataset* ref_dataset = static_cast(ref_dataset_handle); + auto noriginalrows = ref_dataset->num_data(); + Log::Info("Row count: %d", noriginalrows); + Log::Info("Feature group count: %d", ref_dataset->num_features()); + + // Add some fake initial_scores and groups so we can test streaming them + int32_t nclasses = 2; + std::vector unused_init_scores; + unused_init_scores.resize(noriginalrows * nclasses); + std::vector unused_groups; + unused_groups.assign(noriginalrows, 1); + result = LGBM_DatasetSetField(ref_dataset_handle, "init_score", unused_init_scores.data(), noriginalrows * nclasses, 1); + EXPECT_EQ(0, result) << "LGBM_DatasetSetField init_score result code: " << result; + result = LGBM_DatasetSetField(ref_dataset_handle, "group", unused_groups.data(), noriginalrows, 2); + EXPECT_EQ(0, result) << "LGBM_DatasetSetField group result code: " << result; + + // Now use the reference dataset schema to make some testable Datasets with N rows each + int32_t nrows = 1000; + int32_t ncols = ref_dataset->num_features(); + std::vector indptr; + std::vector indices; + std::vector vals; + std::vector labels; + std::vector weights; + std::vector init_scores; + std::vector groups; + + Log::Info("Creating random data"); + float sparse_percent = .1f; + TestUtils::CreateRandomSparseData(nrows, ncols, nclasses, sparse_percent, &indptr, &indices, &vals, &labels, &weights, &init_scores, &groups); + + const std::vector batch_counts = { 1, nrows / 100, nrows / 10, nrows }; + const std::vector creation_types = { 0, 1 }; + + for (size_t i = 0; i < creation_types.size(); ++i) { // from sampled data or reference + for (size_t j = 0; j < batch_counts.size(); ++j) { + auto type = creation_types[i]; + auto batch_count = batch_counts[j]; + test_stream_sparse(type, ref_dataset_handle, nrows, ncols, nclasses, batch_count, &indptr, &indices, &vals, &labels, &weights, &init_scores, &groups); + } + } + + result = LGBM_DatasetFree(ref_dataset_handle); + EXPECT_EQ(0, result) << "LGBM_DatasetFree result code: " << result; +} diff --git a/tests/cpp_tests/testutils.cpp b/tests/cpp_tests/testutils.cpp new file mode 100644 index 0000000..47e6743 --- /dev/null +++ b/tests/cpp_tests/testutils.cpp @@ -0,0 +1,441 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ + +#include +#include +#include +#include + +#include +#include +#include +#include + +using LightGBM::Log; +using LightGBM::Random; + +namespace LightGBM { + +/*! +* Creates a Dataset from the internal repository examples. +*/ +int TestUtils::LoadDatasetFromExamples(const char* filename, const char* config, DatasetHandle* out) { + std::string fullPath("examples/"); + fullPath += filename; + Log::Info("Debug sample data path: %s", fullPath.c_str()); + return LGBM_DatasetCreateFromFile( + fullPath.c_str(), + config, + nullptr, + out); +} + +/*! +* Creates fake data in the passed vectors. +*/ +void TestUtils::CreateRandomDenseData( + int32_t nrows, + int32_t ncols, + int32_t nclasses, + std::vector* features, + std::vector* labels, + std::vector* weights, + std::vector* init_scores, + std::vector* groups) { + Random rand(42); + features->reserve(nrows * ncols); + + for (int32_t row = 0; row < nrows; row++) { + for (int32_t col = 0; col < ncols; col++) { + features->push_back(rand.NextFloat()); + } + } + + CreateRandomMetadata(nrows, nclasses, labels, weights, init_scores, groups); +} + +/*! +* Creates fake data in the passed vectors. +*/ +void TestUtils::CreateRandomSparseData( + int32_t nrows, + int32_t ncols, + int32_t nclasses, + float sparse_percent, + std::vector* indptr, + std::vector* indices, + std::vector* values, + std::vector* labels, + std::vector* weights, + std::vector* init_scores, + std::vector* groups) { + Random rand(42); + indptr->reserve(static_cast(nrows + 1)); + indices->reserve(static_cast(sparse_percent * nrows * ncols)); + values->reserve(static_cast(sparse_percent * nrows * ncols)); + + indptr->push_back(0); + for (int32_t row = 0; row < nrows; row++) { + for (int32_t col = 0; col < ncols; col++) { + float rnd = rand.NextFloat(); + if (rnd < sparse_percent) { + indices->push_back(col); + values->push_back(rand.NextFloat()); + } + } + indptr->push_back(static_cast(indices->size() - 1)); + } + + CreateRandomMetadata(nrows, nclasses, labels, weights, init_scores, groups); +} + +/*! +* Creates fake data in the passed vectors. +*/ +void TestUtils::CreateRandomMetadata(int32_t nrows, + int32_t nclasses, + std::vector* labels, + std::vector* weights, + std::vector* init_scores, + std::vector* groups) { + Random rand(42); + labels->reserve(nrows); + if (weights) { + weights->reserve(nrows); + } + if (init_scores) { + init_scores->reserve(nrows * nclasses); + } + if (groups) { + groups->reserve(nrows); + } + + int32_t group = 0; + + for (int32_t row = 0; row < nrows; row++) { + labels->push_back(rand.NextFloat()); + if (weights) { + weights->push_back(rand.NextFloat()); + } + if (init_scores) { + for (int32_t i = 0; i < nclasses; i++) { + init_scores->push_back(rand.NextFloat()); + } + } + if (groups) { + if (rand.NextFloat() > 0.95) { + group++; + } + groups->push_back(group); + } + } +} + +void TestUtils::StreamDenseDataset(DatasetHandle dataset_handle, + int32_t nrows, + int32_t ncols, + int32_t nclasses, + int32_t batch_count, + const std::vector* features, + const std::vector* labels, + const std::vector* weights, + const std::vector* init_scores, + const std::vector* groups) { + int result = LGBM_DatasetSetWaitForManualFinish(dataset_handle, 1); + EXPECT_EQ(0, result) << "LGBM_DatasetSetWaitForManualFinish result code: " << result; + + Log::Info(" Begin StreamDenseDataset"); + if ((nrows % batch_count) != 0) { + Log::Fatal("This utility method only handles nrows that are a multiple of batch_count"); + } + + const double* features_ptr = features->data(); + const float* labels_ptr = labels->data(); + const float* weights_ptr = nullptr; + if (weights) { + weights_ptr = weights->data(); + } + + // Since init_scores are in a column format, but need to be pushed as rows, we have to extract each batch + std::vector init_score_batch; + const double* init_scores_ptr = nullptr; + if (init_scores) { + init_score_batch.reserve(nclasses * batch_count); + init_scores_ptr = init_score_batch.data(); + } + + const int32_t* groups_ptr = nullptr; + if (groups) { + groups_ptr = groups->data(); + } + + auto start_time = std::chrono::steady_clock::now(); + + for (int32_t i = 0; i < nrows; i += batch_count) { + if (init_scores) { + init_scores_ptr = CreateInitScoreBatch(&init_score_batch, i, nrows, nclasses, batch_count, init_scores); + } + + result = LGBM_DatasetPushRowsWithMetadata(dataset_handle, + features_ptr, + 1, + batch_count, + ncols, + i, + labels_ptr, + weights_ptr, + init_scores_ptr, + groups_ptr, + 0); + EXPECT_EQ(0, result) << "LGBM_DatasetPushRowsWithMetadata result code: " << result; + if (result != 0) { + FAIL() << "LGBM_DatasetPushRowsWithMetadata failed"; // This forces an immediate failure, which EXPECT_EQ does not + } + + features_ptr += batch_count * ncols; + labels_ptr += batch_count; + if (weights_ptr) { + weights_ptr += batch_count; + } + if (groups_ptr) { + groups_ptr += batch_count; + } + } + + auto cur_time = std::chrono::steady_clock::now(); + Log::Info(" Time: %d", cur_time - start_time); +} + +void TestUtils::StreamSparseDataset(DatasetHandle dataset_handle, + int32_t nrows, + int32_t nclasses, + int32_t batch_count, + const std::vector* indptr, + const std::vector* indices, + const std::vector* values, + const std::vector* labels, + const std::vector* weights, + const std::vector* init_scores, + const std::vector* groups) { + int result = LGBM_DatasetSetWaitForManualFinish(dataset_handle, 1); + EXPECT_EQ(0, result) << "LGBM_DatasetSetWaitForManualFinish result code: " << result; + + Log::Info(" Begin StreamSparseDataset"); + if ((nrows % batch_count) != 0) { + Log::Fatal("This utility method only handles nrows that are a multiple of batch_count"); + } + + const int32_t* indptr_ptr = indptr->data(); + const int32_t* indices_ptr = indices->data(); + const double* values_ptr = values->data(); + const float* labels_ptr = labels->data(); + const float* weights_ptr = nullptr; + if (weights) { + weights_ptr = weights->data(); + } + + const int32_t* groups_ptr = nullptr; + if (groups) { + groups_ptr = groups->data(); + } + + auto start_time = std::chrono::steady_clock::now(); + + // Use multiple threads to test concurrency + int thread_count = 2; + if (nrows == batch_count) { + thread_count = 1; // If pushing all rows in 1 batch, we cannot have multiple threads + } + std::vector threads; + threads.reserve(thread_count); + for (int32_t t = 0; t < thread_count; ++t) { + std::thread th(TestUtils::PushSparseBatch, + dataset_handle, + nrows, + nclasses, + batch_count, + indptr, + indptr_ptr, + indices_ptr, + values_ptr, + labels_ptr, + weights_ptr, + init_scores, + groups_ptr, + thread_count, + t); + threads.push_back(std::move(th)); + } + + for (auto& t : threads) t.join(); + + auto cur_time = std::chrono::steady_clock::now(); + Log::Info(" Time: %d", cur_time - start_time); +} + +/*! + * Pushes data from 1 thread into a Dataset based on thread_id and nrows. + * e.g. with 100 rows, thread 0 will push rows 0-49, and thread 2 will push rows 50-99. + * Note that rows are still pushed in microbatches within their range. + */ +void TestUtils::PushSparseBatch(DatasetHandle dataset_handle, + int32_t nrows, + int32_t nclasses, + int32_t batch_count, + const std::vector* indptr, + const int32_t* indptr_ptr, + const int32_t* indices_ptr, + const double* values_ptr, + const float* labels_ptr, + const float* weights_ptr, + const std::vector* init_scores, + const int32_t* groups_ptr, + int32_t thread_count, + int32_t thread_id) { + int32_t threadChunkSize = nrows / thread_count; + int32_t startIndex = threadChunkSize * thread_id; + int32_t stopIndex = startIndex + threadChunkSize; + + indptr_ptr += threadChunkSize * thread_id; + labels_ptr += threadChunkSize * thread_id; + if (weights_ptr) { + weights_ptr += threadChunkSize * thread_id; + } + if (groups_ptr) { + groups_ptr += threadChunkSize * thread_id; + } + + for (int32_t i = startIndex; i < stopIndex; i += batch_count) { + // Since init_scores are in a column format, but need to be pushed as rows, we have to extract each batch + std::vector init_score_batch; + const double* init_scores_ptr = nullptr; + if (init_scores) { + init_score_batch.reserve(nclasses * batch_count); + init_scores_ptr = CreateInitScoreBatch(&init_score_batch, i, nrows, nclasses, batch_count, init_scores); + } + + int32_t nelem = indptr->at(i + batch_count - 1) - indptr->at(i); + + int result = LGBM_DatasetPushRowsByCSRWithMetadata(dataset_handle, + indptr_ptr, + 2, + indices_ptr, + values_ptr, + 1, + batch_count + 1, + nelem, + i, + labels_ptr, + weights_ptr, + init_scores_ptr, + groups_ptr, + thread_id); + EXPECT_EQ(0, result) << "LGBM_DatasetPushRowsByCSRWithMetadata result code: " << result; + if (result != 0) { + FAIL() << "LGBM_DatasetPushRowsByCSRWithMetadata failed"; // This forces an immediate failure, which EXPECT_EQ does not + } + + indptr_ptr += batch_count; + labels_ptr += batch_count; + if (weights_ptr) { + weights_ptr += batch_count; + } + if (groups_ptr) { + groups_ptr += batch_count; + } + } +} + + +void TestUtils::AssertMetadata(const Metadata* metadata, + const std::vector* ref_labels, + const std::vector* ref_weights, + const std::vector* ref_init_scores, + const std::vector* ref_groups) { + const float* labels = metadata->label(); + auto nTotal = static_cast(ref_labels->size()); + for (auto i = 0; i < nTotal; i++) { + EXPECT_EQ(ref_labels->at(i), labels[i]) << "Inserted data: " << ref_labels->at(i) << " at " << i; + if (ref_labels->at(i) != labels[i]) { + FAIL() << "Mismatched labels"; // This forces an immediate failure, which EXPECT_EQ does not + } + } + + const float* weights = metadata->weights(); + if (weights) { + if (!ref_weights) { + FAIL() << "Expected null weights"; + } + for (auto i = 0; i < nTotal; i++) { + EXPECT_EQ(ref_weights->at(i), weights[i]) << "Inserted data: " << ref_weights->at(i); + if (ref_weights->at(i) != weights[i]) { + FAIL() << "Mismatched weights"; // This forces an immediate failure, which EXPECT_EQ does not + } + } + } else if (ref_weights) { + FAIL() << "Expected non-null weights"; + } + + const double* init_scores = metadata->init_score(); + if (init_scores) { + if (!ref_init_scores) { + FAIL() << "Expected null init_scores"; + } + for (size_t i = 0; i < ref_init_scores->size(); i++) { + EXPECT_EQ(ref_init_scores->at(i), init_scores[i]) << "Inserted data: " << ref_init_scores->at(i) << " Index: " << i; + if (ref_init_scores->at(i) != init_scores[i]) { + FAIL() << "Mismatched init_scores"; // This forces an immediate failure, which EXPECT_EQ does not + } + } + } else if (ref_init_scores) { + FAIL() << "Expected non-null init_scores"; + } + + const int32_t* query_boundaries = metadata->query_boundaries(); + if (query_boundaries) { + if (!ref_groups) { + FAIL() << "Expected null query_boundaries"; + } + // Calculate expected boundaries + std::vector ref_query_boundaries; + ref_query_boundaries.push_back(0); + int group_val = ref_groups->at(0); + for (auto i = 1; i < nTotal; i++) { + if (ref_groups->at(i) != group_val) { + ref_query_boundaries.push_back(i); + group_val = ref_groups->at(i); + } + } + ref_query_boundaries.push_back(nTotal); + + for (size_t i = 0; i < ref_query_boundaries.size(); i++) { + EXPECT_EQ(ref_query_boundaries[i], query_boundaries[i]) << "Inserted data: " << ref_query_boundaries[i]; + if (ref_query_boundaries[i] != query_boundaries[i]) { + FAIL() << "Mismatched query_boundaries"; // This forces an immediate failure, which EXPECT_EQ does not + } + } + } else if (ref_groups) { + FAIL() << "Expected non-null query_boundaries"; + } +} + +const double* TestUtils::CreateInitScoreBatch(std::vector* init_score_batch, + int32_t index, + int32_t nrows, + int32_t nclasses, + int32_t batch_count, + const std::vector* original_init_scores) { + // Extract a set of rows from the column-based format (still maintaining column based format) + init_score_batch->clear(); + for (int32_t c = 0; c < nclasses; c++) { + for (int32_t row = index; row < index + batch_count; row++) { + init_score_batch->push_back(original_init_scores->at(row + nrows * c)); + } + } + return init_score_batch->data(); +} + +} // namespace LightGBM diff --git a/tests/cpp_tests/testutils.h b/tests/cpp_tests/testutils.h new file mode 100644 index 0000000..58f2d6d --- /dev/null +++ b/tests/cpp_tests/testutils.h @@ -0,0 +1,125 @@ +/*! + * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved. + * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved. + * Licensed under the MIT License. See LICENSE file in the project root for license information. + */ +#ifndef LIGHTGBM_TESTS_CPP_TESTS_TESTUTILS_H_ +#define LIGHTGBM_TESTS_CPP_TESTS_TESTUTILS_H_ + +#include +#include + +#include + +using LightGBM::Metadata; + +namespace LightGBM { + +class TestUtils { + public: + /*! + * Creates a Dataset from the internal repository examples. + */ + static int LoadDatasetFromExamples(const char* filename, const char* config, DatasetHandle* out); + + + /*! + * Creates a dense Dataset of random values. + */ + static void CreateRandomDenseData(int32_t nrows, + int32_t ncols, + int32_t nclasses, + std::vector* features, + std::vector* labels, + std::vector* weights, + std::vector* init_scores, + std::vector* groups); + + /*! + * Creates a CSR sparse Dataset of random values. + */ + static void CreateRandomSparseData(int32_t nrows, + int32_t ncols, + int32_t nclasses, + float sparse_percent, + std::vector* indptr, + std::vector* indices, + std::vector* values, + std::vector* labels, + std::vector* weights, + std::vector* init_scores, + std::vector* groups); + + /*! + * Creates a batch of Metadata of random values. + */ + static void CreateRandomMetadata(int32_t nrows, + int32_t nclasses, + std::vector* labels, + std::vector* weights, + std::vector* init_scores, + std::vector* groups); + + /*! + * Pushes nrows of data to a Dataset in batches of batch_count. + */ + static void StreamDenseDataset(DatasetHandle dataset_handle, + int32_t nrows, + int32_t ncols, + int32_t nclasses, + int32_t batch_count, + const std::vector* features, + const std::vector* labels, + const std::vector* weights, + const std::vector* init_scores, + const std::vector* groups); + + /*! + * Pushes nrows of data to a Dataset in batches of batch_count. + */ + static void StreamSparseDataset(DatasetHandle dataset_handle, + int32_t nrows, + int32_t nclasses, + int32_t batch_count, + const std::vector* indptr, + const std::vector* indices, + const std::vector* values, + const std::vector* labels, + const std::vector* weights, + const std::vector* init_scores, + const std::vector* groups); + + /*! + * Validates metadata against reference vectors. + */ + static void AssertMetadata(const Metadata* metadata, + const std::vector* labels, + const std::vector* weights, + const std::vector* init_scores, + const std::vector* groups); + + static const double* CreateInitScoreBatch(std::vector* init_score_batch, + int32_t index, + int32_t nrows, + int32_t nclasses, + int32_t batch_count, + const std::vector* original_init_scores); + + private: + static void PushSparseBatch(DatasetHandle dataset_handle, + int32_t nrows, + int32_t nclasses, + int32_t batch_count, + const std::vector* indptr, + const int32_t* indptr_ptr, + const int32_t* indices_ptr, + const double* values_ptr, + const float* labels_ptr, + const float* weights_ptr, + const std::vector* init_scores, + const int32_t* groups_ptr, + int32_t thread_count, + int32_t thread_id); +}; +} // namespace LightGBM +#endif // LIGHTGBM_TESTS_CPP_TESTS_TESTUTILS_H_ diff --git a/tests/cpp_tests/train.conf b/tests/cpp_tests/train.conf new file mode 100644 index 0000000..002bbf7 --- /dev/null +++ b/tests/cpp_tests/train.conf @@ -0,0 +1,7 @@ +data=../data/categorical.data + +app=binary + +num_trees=10 + +categorical_column=0,1,4,5,6 diff --git a/tests/data/categorical.data b/tests/data/categorical.data new file mode 100644 index 0000000..e39c1e6 --- /dev/null +++ b/tests/data/categorical.data @@ -0,0 +1,7000 @@ +1 0:7 1:2 2:3 3:20 4:15 5:38 6:29 7:201 +0 0:5 1:15 2:2 3:1859 4:1 5:156 6:164 7:2475 +0 0:2 1:12 2:6 3:648 4:13 5:29 6:38 7:201 +1 0:10 1:26 2:5 3:1235 4:14 5:82 6:205 7:931 +0 0:6 1:18 2:1 3:737 4:12 5:224 6:162 7:2176 +0 0:4 1:12 3:1845 4:18 5:83 6:49 7:1491 +0 0:3 2:3 3:1652 4:20 5:2 6:180 7:332 +0 0:3 1:21 2:3 3:2010 4:16 5:216 6:69 7:911 +0 0:3 1:3 3:1555 4:1 5:84 6:81 7:1192 +0 0:8 1:2 2:6 3:1008 4:16 5:216 6:228 7:130 +0 0:6 1:29 2:4 3:2128 4:19 5:224 6:38 7:280 +0 0:1 1:30 3:2028 4:1 5:84 6:211 7:1456 +1 0:6 1:1 3:1746 4:16 5:197 6:267 7:78 +1 0:11 1:11 2:4 3:2019 4:13 5:100 6:247 7:416 +0 0:2 1:30 2:3 3:1228 4:21 5:19 6:62 7:554 +0 0:2 1:23 2:2 3:820 4:5 5:82 6:141 7:1208 +0 0:5 1:29 2:1 3:1201 4:7 5:19 6:30 7:134 +0 0:1 1:11 2:6 3:1217 4:18 5:83 6:294 7:1506 +0 0:6 1:26 2:1 3:1244 4:3 5:274 6:223 7:859 +0 0:3 1:5 2:3 3:1120 4:3 5:102 6:16 7:261 +0 0:4 1:13 2:1 3:909 4:18 5:216 6:231 7:412 +1 0:4 1:1 2:3 3:1923 4:10 5:19 6:231 7:526 +1 0:10 1:1 2:3 3:1711 4:1 5:84 6:162 7:1055 +0 0:6 1:13 2:4 3:730 4:20 5:150 6:107 7:1093 +0 0:1 1:21 2:6 3:950 4:1 5:163 6:172 7:2615 +0 0:3 1:18 2:1 3:815 4:18 5:140 6:257 7:2253 +0 0:7 1:7 3:1712 4:13 5:38 6:65 7:640 +0 0:7 1:13 2:5 3:810 4:20 5:79 6:21 7:189 +0 0:6 1:5 2:3 3:1544 4:3 5:261 6:164 7:954 +0 0:6 1:3 2:1 3:1752 4:19 5:168 6:231 7:335 +0 1:17 2:2 3:1301 4:21 5:141 6:188 7:469 +0 0:1 1:20 2:5 3:1456 4:7 5:25 6:107 7:1173 +1 0:9 1:24 3:1409 4:16 5:114 6:266 7:224 +0 0:6 1:27 2:2 3:1956 4:1 5:279 6:162 7:1372 +0 0:2 1:16 2:4 3:924 4:13 5:137 6:218 7:510 +0 0:11 1:20 2:3 3:1757 4:1 5:289 6:284 7:869 +1 0:5 1:18 2:5 3:2006 4:10 5:49 6:83 7:1217 +0 0:7 1:25 2:2 3:922 4:7 5:75 6:227 7:1569 +0 0:11 1:2 3:818 4:10 5:146 6:294 7:838 +0 0:8 1:4 2:1 3:651 4:4 5:21 6:156 7:1522 +0 0:11 1:26 3:1432 4:19 5:217 6:226 7:211 +0 0:6 1:2 2:1 3:1344 4:10 5:19 6:81 7:547 +0 0:11 1:8 2:6 3:1246 4:13 5:81 6:218 7:147 +0 0:8 1:4 3:1608 4:14 5:203 6:110 7:491 +0 0:4 1:19 3:1800 4:14 5:184 6:89 7:229 +0 0:2 1:5 2:1 3:744 4:10 5:49 6:38 7:370 +1 0:10 1:7 2:1 3:1042 4:14 5:203 6:256 7:1416 +0 0:7 1:24 2:1 3:551 4:16 5:179 6:218 7:222 +0 0:6 1:28 2:3 3:2031 4:13 5:295 6:201 7:142 +0 0:10 1:11 2:2 3:1714 4:14 5:90 6:170 7:501 +0 0:7 1:4 2:5 3:1402 4:20 5:279 6:184 7:880 +1 0:10 1:16 2:3 3:1317 4:22 5:257 6:227 7:509 +0 0:10 1:3 2:5 3:1235 4:22 5:169 6:227 7:355 +0 0:7 1:12 2:5 3:1637 4:15 5:66 6:81 7:322 +0 0:2 1:12 2:6 3:1249 4:7 5:270 6:74 7:1449 +1 0:6 1:25 2:1 3:1435 4:20 5:225 6:36 7:1448 +0 0:2 1:27 3:1547 4:21 5:141 6:191 7:554 +0 0:9 1:6 2:5 3:929 4:20 5:155 6:219 7:543 +0 0:2 1:11 2:3 3:1952 4:20 5:253 6:227 7:843 +1 0:8 1:19 2:1 3:1653 4:18 5:140 6:267 7:2419 +0 0:5 1:25 2:4 3:1917 4:14 5:47 6:89 7:240 +0 0:2 1:2 2:4 3:1838 4:13 5:84 6:168 7:785 +1 1:25 2:2 3:1914 4:13 5:84 6:203 7:821 +1 0:11 1:11 2:5 3:241 4:4 5:156 6:294 7:1005 +0 0:7 1:7 3:1230 4:21 5:63 6:79 7:162 +0 0:7 1:30 3:640 4:1 5:168 6:182 7:1107 +0 0:8 1:13 2:2 3:1844 4:14 5:203 6:81 7:931 +1 0:8 1:16 2:5 3:1945 4:18 5:83 6:266 7:1024 +1 1:8 3:1424 4:7 5:75 6:294 7:773 +0 0:7 3:1435 4:20 5:253 6:49 7:1407 +0 0:11 1:22 2:5 3:900 4:1 5:191 6:226 7:1013 +0 0:2 2:2 3:1748 4:20 5:134 6:258 7:192 +0 0:6 1:11 2:6 3:1004 4:18 5:161 6:267 7:414 +0 0:3 1:12 2:1 3:1650 4:15 5:75 6:52 7:214 +1 0:2 1:20 2:1 3:1719 4:16 5:106 6:275 7:532 +0 0:3 1:25 2:6 3:1055 4:15 5:120 6:74 7:416 +0 0:11 1:3 2:1 3:1723 4:22 5:151 6:133 7:216 +0 0:3 1:6 2:4 3:1347 4:14 5:180 6:205 7:393 +0 0:9 1:6 2:5 3:1327 4:19 5:225 6:82 7:602 +0 0:8 1:4 2:1 3:1609 4:19 5:161 6:99 7:2227 +0 0:3 1:20 2:3 3:1725 4:8 5:212 6:19 7:761 +1 0:3 1:30 2:4 3:1004 4:19 5:82 6:46 7:437 +1 0:11 1:11 2:4 3:1921 4:21 5:46 6:99 7:266 +0 0:11 1:7 2:4 3:924 4:19 5:65 6:13 7:646 +0 0:4 1:15 2:2 3:1154 4:15 5:75 6:253 7:461 +0 0:8 1:25 2:6 3:1012 4:1 5:161 6:272 7:386 +0 0:2 1:15 2:3 3:1742 4:20 5:209 6:48 7:325 +0 0:2 1:11 2:3 3:2214 4:1 5:84 6:82 7:641 +0 0:1 1:30 2:6 3:1828 4:1 5:191 6:83 7:1121 +1 1:8 2:1 3:2238 4:22 5:225 6:87 7:351 +0 0:10 1:25 2:3 3:1116 4:3 5:261 6:279 7:978 +0 0:4 2:2 3:722 4:7 5:252 6:275 7:626 +0 1:25 2:2 3:955 4:22 5:216 6:35 7:116 +0 0:5 1:5 3:624 4:16 5:44 6:275 7:357 +1 0:3 1:4 2:2 3:1513 4:16 5:262 6:246 7:462 +0 0:7 1:17 2:3 3:650 4:20 5:184 6:215 7:423 +0 1:5 2:5 3:1105 4:22 5:225 6:314 7:160 +0 0:9 1:17 2:1 3:2010 4:19 5:224 6:155 7:742 +1 0:4 1:25 2:5 3:1511 4:19 5:82 6:222 7:857 +0 0:7 1:12 2:5 3:914 4:1 5:108 6:164 7:2342 +0 0:5 1:25 2:5 3:731 4:1 5:270 6:83 7:988 +0 0:2 1:9 2:4 3:830 4:20 5:225 6:162 7:256 +0 0:5 1:13 3:1527 4:18 5:161 6:82 7:629 +0 0:9 1:18 2:1 3:849 4:1 5:84 6:266 7:1660 +0 0:5 1:8 2:3 3:800 4:10 5:289 6:49 7:842 +0 0:11 1:28 2:3 3:824 4:16 5:93 6:275 7:200 +0 0:9 2:5 3:705 4:14 5:192 6:89 7:238 +0 0:8 1:8 2:4 3:1610 4:1 5:84 6:227 7:868 +0 0:5 1:26 2:5 3:631 4:16 5:240 6:267 7:199 +0 0:6 1:5 2:4 3:1912 4:7 5:100 6:19 7:745 +0 0:3 1:6 2:3 3:843 4:5 5:100 6:184 7:938 +1 2:6 3:1217 4:1 5:38 6:218 7:867 +1 1:13 2:4 3:2101 4:16 5:219 6:164 7:49 +0 0:1 1:26 2:3 3:1456 4:20 5:253 6:184 7:1040 +0 0:6 1:9 2:1 3:2010 4:7 5:38 6:294 7:1185 +0 0:11 1:8 2:5 3:1525 4:10 5:52 6:38 7:549 +0 0:7 1:24 2:1 3:635 4:9 5:19 6:82 7:1199 +0 0:1 1:29 2:6 3:1325 4:7 5:19 6:49 7:576 +0 0:8 1:10 3:1040 4:20 5:2 6:227 7:328 +0 0:11 1:18 2:1 3:1611 4:18 5:90 6:218 7:235 +0 1:4 2:4 3:1419 4:13 5:134 6:83 7:247 +0 0:4 1:13 2:1 3:1347 4:16 5:262 6:48 7:326 +0 0:6 1:1 3:1552 4:19 5:65 6:231 7:366 +1 0:5 1:30 2:3 3:1645 4:14 5:38 6:89 7:632 +0 0:1 1:11 2:6 3:636 4:3 5:16 6:101 7:261 +0 0:9 1:17 2:1 3:1830 4:20 5:66 6:226 7:405 +0 0:3 1:20 2:3 3:1205 4:16 5:83 6:103 7:844 +0 0:3 1:22 2:6 3:1406 4:14 5:2 6:205 7:981 +0 0:7 1:9 2:2 3:950 4:20 5:246 6:275 7:422 +0 0:5 1:7 2:2 3:716 4:16 5:83 6:275 7:391 +0 0:5 1:5 3:1428 4:16 5:142 6:218 7:588 +1 0:8 1:22 2:4 3:1732 4:20 5:49 6:184 7:787 +0 0:10 1:9 2:4 3:1024 4:20 5:48 6:277 7:358 +0 0:3 1:2 3:650 4:5 5:100 6:222 7:1024 +0 0:9 1:23 2:5 3:1815 4:20 5:146 6:164 7:1814 +0 0:6 1:18 2:1 3:2039 4:13 5:84 6:45 7:383 +0 0:10 1:15 2:1 3:1948 4:16 5:273 6:164 7:135 +0 0:5 1:13 3:715 4:20 5:47 6:184 7:1011 +0 0:5 1:18 2:6 3:1657 4:19 5:155 6:64 7:329 +0 0:11 1:21 2:3 3:1827 4:14 5:103 6:205 7:223 +1 0:10 1:1 2:3 3:1825 4:20 5:184 6:134 7:937 +1 0:7 1:24 2:2 3:1749 4:13 5:84 6:188 7:432 +0 0:6 1:3 2:2 3:2025 4:4 5:156 6:277 7:2521 +0 0:2 1:26 2:6 3:653 4:20 5:134 6:284 7:687 +0 0:5 1:11 2:2 3:1944 4:12 5:221 6:162 7:762 +0 0:1 1:24 3:700 4:15 5:90 6:74 7:229 +0 0:7 1:13 2:6 3:1325 4:20 5:79 6:165 7:293 +0 0:8 1:19 3:1115 4:13 5:265 6:83 7:190 +0 0:2 1:27 3:1908 4:16 5:252 6:164 7:109 +0 0:10 1:25 2:4 3:1105 4:7 5:287 6:19 7:223 +0 0:7 1:10 2:4 3:1451 4:16 5:270 6:238 7:541 +0 0:10 1:22 2:2 3:1310 4:20 5:163 6:272 7:308 +0 0:2 1:20 3:707 4:14 5:203 6:99 7:1008 +0 0:1 1:11 2:6 3:1128 4:20 5:161 6:251 7:345 +0 0:9 1:16 3:1434 4:10 5:84 6:19 7:732 +1 0:11 1:10 2:1 3:1842 4:14 5:82 6:205 7:931 +1 0:10 1:22 2:3 3:411 4:3 5:16 6:266 7:1449 +0 0:1 1:28 2:4 3:728 4:1 5:262 6:83 7:1464 +0 0:2 1:7 2:3 3:1657 4:19 5:203 6:64 7:930 +1 0:8 1:10 3:2222 4:10 5:19 6:294 7:406 +1 0:11 1:2 2:6 3:2101 4:12 5:261 6:162 7:866 +0 0:1 1:9 2:2 3:1437 4:20 5:190 6:294 7:1204 +1 0:6 1:10 2:1 3:2040 4:6 5:140 6:156 7:228 +0 0:2 1:30 2:2 3:641 4:18 5:89 6:82 7:589 +0 1:28 2:4 3:636 4:4 5:108 6:171 7:2327 +1 0:3 1:10 3:1733 4:5 5:19 6:99 7:745 +0 0:11 1:17 2:6 3:1916 4:16 5:216 6:181 7:222 +0 0:11 1:14 2:4 3:846 4:18 5:140 6:206 7:954 +0 0:1 1:5 2:5 3:1012 4:16 5:216 6:31 7:1060 +0 0:11 1:4 2:2 3:1735 4:15 5:238 6:170 7:270 +0 0:8 1:27 3:635 4:15 5:141 6:74 7:871 +0 0:7 1:15 3:1700 4:20 5:161 6:48 7:223 +0 1:12 2:3 3:1955 4:7 5:19 6:231 7:526 +0 0:11 1:2 2:6 3:1237 4:1 5:84 6:49 7:1217 +0 1:13 2:4 3:2018 4:19 5:65 6:192 7:737 +0 0:3 2:4 3:1124 4:20 5:134 6:162 7:1235 +1 0:10 1:18 2:4 3:1755 4:20 5:270 6:162 7:368 +0 0:10 1:3 2:5 3:2048 4:20 5:161 6:227 7:256 +0 0:11 1:2 3:1854 4:15 5:63 6:74 7:221 +0 1:3 2:2 3:1216 4:19 5:65 6:227 7:1774 +0 0:6 1:20 2:3 3:1847 4:14 5:203 6:49 7:936 +0 0:9 1:3 2:2 3:1115 4:21 5:100 6:247 7:416 +0 2:6 3:859 4:5 5:63 6:267 7:2161 +0 0:1 1:17 2:2 3:1000 4:13 5:242 6:81 7:227 +0 0:7 1:6 2:6 3:1940 4:19 5:108 6:81 7:899 +0 0:7 1:4 2:4 3:1439 4:21 5:141 6:173 7:374 +0 0:7 1:1 2:1 3:1950 4:20 5:184 6:226 7:668 +0 0:6 2:4 3:925 4:11 5:133 6:213 7:100 +1 0:7 1:15 2:1 3:1440 4:20 5:267 6:164 7:308 +0 1:27 2:4 3:1108 4:13 5:38 6:29 7:201 +0 0:2 1:13 2:1 3:747 4:3 5:261 6:227 7:1107 +0 1:29 2:5 3:1759 4:13 5:84 6:268 7:364 +0 0:10 1:19 2:6 3:1015 4:19 5:82 6:184 7:759 +0 0:1 1:10 2:3 3:2054 4:5 5:141 6:272 7:1608 +0 0:3 1:9 3:1255 4:4 5:38 6:184 7:1121 +0 0:9 1:28 2:4 3:727 4:20 5:289 6:222 7:174 +0 0:6 1:14 2:5 3:1152 4:10 5:203 6:184 7:1310 +0 0:2 1:13 2:1 3:908 4:20 5:163 6:227 7:370 +1 0:9 1:13 2:3 3:1550 4:20 5:161 6:219 7:2155 +0 1:13 2:5 3:1137 4:13 5:216 6:64 7:599 +0 1:12 2:3 3:1007 4:7 5:19 6:258 7:874 +0 0:9 1:8 3:642 4:21 5:189 6:141 7:554 +0 0:6 1:8 3:1357 4:4 5:182 6:38 7:1121 +0 1:12 2:4 3:1505 4:4 5:156 6:274 7:1597 +0 0:3 1:23 2:4 3:658 4:3 5:261 6:48 7:937 +0 0:11 1:18 2:1 3:810 4:19 5:90 6:227 7:1671 +0 0:6 1:11 2:5 3:959 4:16 5:216 6:311 7:522 +0 0:1 1:6 2:5 3:1829 4:1 5:84 6:277 7:1431 +0 0:1 1:19 2:3 3:1210 4:20 5:261 6:227 7:1107 +0 0:4 1:5 3:1734 4:1 5:83 6:218 7:888 +0 1:13 2:4 3:2025 4:16 5:246 6:164 7:390 +1 0:3 1:24 2:5 3:1343 4:13 5:260 6:193 7:911 +0 0:10 3:1420 4:16 5:36 6:82 7:1013 +0 0:2 1:28 2:1 3:547 4:3 5:102 6:16 7:261 +0 0:7 1:8 2:2 3:2125 4:20 5:37 6:114 7:287 +0 1:17 2:2 3:1304 4:18 5:262 6:223 7:550 +1 0:3 1:13 2:3 3:2200 4:1 5:216 6:193 7:1197 +0 1:26 2:3 3:718 4:19 5:207 6:64 7:156 +0 0:9 1:19 2:3 3:649 4:18 5:262 6:223 7:550 +0 0:1 1:2 2:1 3:2135 4:13 5:163 6:260 7:89 +1 1:25 2:2 3:640 4:21 5:46 6:62 7:489 +0 0:9 1:14 2:5 3:1031 4:18 5:83 6:267 7:967 +1 0:1 1:7 3:704 4:5 5:21 6:99 7:1504 +0 0:5 1:9 2:5 3:1229 4:16 5:152 6:164 7:123 +0 0:2 1:21 2:1 3:655 4:20 5:272 6:164 7:373 +0 0:1 1:3 2:3 3:2318 4:1 5:161 6:193 7:2175 +0 0:4 1:22 2:4 3:1627 4:20 5:49 6:62 7:314 +0 0:7 1:1 2:1 3:2203 4:15 5:19 6:52 7:528 +0 0:9 1:4 2:3 3:1245 4:20 5:225 6:146 7:1489 +0 0:3 1:22 2:5 3:533 4:10 5:194 6:19 7:633 +0 0:8 1:1 2:5 3:1050 4:16 5:83 6:275 7:391 +0 0:6 2:4 3:1740 4:15 5:168 6:58 7:641 +0 0:10 1:8 2:2 3:935 4:1 5:84 6:153 7:1046 +0 0:3 1:18 2:1 3:1547 4:16 5:126 6:82 7:624 +0 0:8 1:12 2:1 3:1259 4:8 5:67 6:19 7:490 +1 0:8 1:28 2:2 3:2110 4:20 5:161 6:284 7:1372 +0 0:5 1:25 2:5 3:552 4:7 5:146 6:19 7:432 +0 0:9 1:3 2:1 3:1749 4:18 5:156 6:164 7:2475 +0 0:4 1:10 2:6 3:1509 4:22 5:65 6:141 7:913 +0 0:10 1:7 2:2 3:1043 4:16 5:161 6:238 7:173 +0 0:6 1:4 2:3 3:1122 4:7 5:225 6:74 7:1569 +1 0:1 1:19 2:4 3:1952 4:13 5:168 6:64 7:544 +1 1:15 2:6 3:1717 4:20 5:66 6:227 7:1671 +0 1:2 2:2 3:1643 4:1 5:21 6:83 7:190 +0 1:10 2:3 3:748 4:7 5:19 6:219 7:516 +0 0:2 1:15 2:2 3:752 4:18 5:31 6:82 7:455 +1 0:11 1:28 2:3 3:1658 4:1 5:225 6:83 7:868 +0 1:17 2:1 3:935 4:15 5:20 6:74 7:411 +0 0:10 1:2 2:3 3:1034 4:5 5:141 6:193 7:964 +0 1:23 2:6 3:1004 4:1 5:236 6:83 7:1126 +0 0:11 1:21 2:4 3:1000 4:20 5:79 6:134 7:239 +0 0:1 1:26 2:3 3:657 4:18 5:168 6:218 7:733 +0 0:5 1:15 2:2 3:2028 4:8 5:19 6:224 7:247 +0 0:6 1:22 3:705 4:20 5:48 6:277 7:358 +1 0:10 1:30 2:1 3:2323 4:7 5:19 6:256 7:515 +0 1:12 2:4 3:649 4:21 5:140 6:62 7:288 +0 0:2 1:20 2:1 3:1911 4:21 5:141 6:194 7:984 +0 0:10 1:24 2:2 3:630 4:8 5:149 6:19 7:406 +0 0:9 1:13 2:3 3:1919 4:13 5:255 6:164 7:89 +0 0:5 1:18 2:5 3:658 4:19 5:49 6:64 7:361 +0 0:8 1:22 2:4 3:1610 4:18 5:262 6:156 7:2586 +0 0:1 1:15 3:2130 4:20 5:161 6:257 7:258 +1 0:3 1:24 2:6 3:1024 4:19 5:262 6:162 7:414 +0 0:7 1:15 2:1 3:600 4:15 5:82 6:170 7:214 +0 0:8 1:17 2:5 3:1101 4:21 5:100 6:22 7:583 +0 0:11 1:15 2:4 3:1720 4:13 5:242 6:170 7:431 +0 0:10 1:15 2:2 3:1607 4:14 5:203 6:99 7:1008 +0 0:2 1:20 3:1814 4:1 5:84 6:2 7:569 +0 0:6 1:18 2:2 3:1743 4:3 5:261 6:211 7:671 +0 0:4 1:30 2:3 3:800 4:1 5:100 6:164 7:2454 +0 0:2 1:4 3:1350 4:1 5:279 6:193 7:1068 +0 1:13 2:4 3:1110 4:8 5:141 6:83 7:224 +0 0:11 1:4 2:2 3:1148 4:5 5:253 6:99 7:1569 +0 0:7 3:2235 4:3 5:261 6:140 7:2306 +0 0:2 1:30 2:3 3:558 4:19 5:168 6:81 7:214 +0 0:11 1:25 3:1304 4:1 5:251 6:218 7:1120 +0 0:3 2:3 3:1449 4:3 5:261 6:279 7:978 +0 0:2 1:6 2:2 3:1246 4:19 5:65 6:99 7:529 +0 0:7 1:6 3:1353 4:14 5:180 6:205 7:393 +0 1:22 3:1841 4:18 5:83 6:215 7:472 +0 0:8 2:3 3:657 4:14 5:203 6:170 7:1020 +1 0:9 1:4 2:2 3:1228 4:19 5:84 6:226 7:1302 +0 1:9 2:1 3:1543 4:14 5:204 6:188 7:349 +0 0:1 1:6 2:6 3:1849 4:19 5:274 6:227 7:338 +0 0:9 1:25 3:1145 4:7 5:270 6:211 7:588 +0 0:2 1:5 2:1 3:953 4:16 5:252 6:164 7:109 +0 0:5 1:7 2:3 3:2115 4:10 5:19 6:146 7:432 +0 1:25 2:1 3:804 4:7 5:19 6:99 7:745 +1 0:9 1:26 2:2 3:1935 4:1 5:216 6:141 7:925 +0 0:3 1:1 2:5 3:1044 4:8 5:19 6:191 7:147 +0 0:5 1:11 2:2 3:1604 4:13 5:279 6:38 7:1046 +0 0:5 1:8 2:4 3:1155 4:18 5:216 6:81 7:612 +0 0:10 1:19 2:5 3:540 4:5 5:203 6:141 7:1034 +0 0:8 1:1 2:5 3:1332 4:14 5:90 6:218 7:235 +1 0:2 1:9 2:4 3:2039 4:18 5:83 6:226 7:1557 +0 0:9 1:14 2:5 3:759 4:19 5:82 6:170 7:214 +0 0:2 1:25 2:5 3:853 4:20 5:21 6:78 7:189 +1 0:4 1:18 2:5 3:1850 4:5 5:141 6:81 7:1208 +0 0:2 1:7 2:3 3:1158 4:19 5:161 6:213 7:2695 +0 0:2 1:11 2:2 3:1919 4:18 5:83 6:284 7:770 +0 0:11 1:1 2:5 3:1757 4:21 5:63 6:49 7:314 +0 0:11 1:6 2:4 3:1533 4:16 5:48 6:267 7:326 +0 0:6 1:11 2:6 3:1846 4:14 5:133 6:213 7:100 +0 0:11 1:21 2:4 3:724 4:13 5:84 6:121 7:134 +0 0:8 1:21 2:3 3:858 4:1 5:161 6:83 7:1055 +0 1:22 3:558 4:1 5:204 6:193 7:674 +1 0:1 1:18 2:3 3:2046 4:13 5:216 6:64 7:599 +1 0:8 1:29 2:3 3:1439 4:18 5:16 6:267 7:2018 +0 0:2 1:12 2:6 3:2056 4:4 5:156 6:294 7:1005 +0 0:5 1:16 2:4 3:1253 4:7 5:2 6:74 7:1240 +0 0:11 1:14 2:4 3:625 4:13 5:216 6:135 7:738 +0 0:4 1:10 2:5 3:1932 4:13 5:216 6:219 7:717 +1 0:3 1:11 2:5 3:1348 4:4 5:156 6:247 7:426 +0 0:4 1:20 2:1 3:919 4:13 5:242 6:170 7:431 +0 0:7 1:9 2:3 3:1917 4:14 5:203 6:284 7:449 +0 0:7 1:4 2:4 3:1044 4:18 5:82 6:218 7:612 +0 0:4 1:13 3:1556 4:19 5:65 6:259 7:213 +0 0:5 1:30 2:3 3:1020 4:8 5:19 6:124 7:153 +0 0:11 1:15 2:5 3:659 4:22 5:309 6:227 7:160 +0 0:8 1:14 2:2 3:900 4:1 5:38 6:83 7:1562 +0 0:2 2:1 3:818 4:1 5:191 6:156 7:1090 +0 0:2 1:22 2:4 3:944 4:16 5:216 6:214 7:693 +0 0:10 1:30 2:1 3:1709 4:7 5:224 6:275 7:1926 +0 0:9 1:22 2:6 3:952 4:21 5:29 6:99 7:393 +0 0:9 1:4 2:3 3:816 4:18 5:262 6:266 7:679 +0 0:3 1:2 2:6 3:1537 4:1 5:84 6:193 7:1121 +0 0:2 1:9 2:4 3:927 4:16 5:215 6:275 7:558 +0 1:5 2:5 3:717 4:19 5:19 6:81 7:547 +0 1:12 2:3 3:1027 4:14 5:90 6:47 7:240 +0 0:8 1:9 2:5 3:821 4:21 5:100 6:253 7:246 +0 0:6 1:12 2:2 3:824 4:17 5:184 6:274 7:2057 +0 0:2 1:30 2:3 3:902 4:2 5:209 6:133 7:2409 +0 0:4 1:13 2:1 3:1750 4:21 5:73 6:141 7:975 +0 0:4 2:1 3:1704 4:7 5:285 6:19 7:793 +0 0:10 1:29 3:2351 4:12 5:161 6:223 7:762 +0 0:1 1:26 2:2 3:915 4:20 5:184 6:134 7:937 +0 1:23 2:6 3:1345 4:20 5:150 6:222 7:1052 +1 0:3 2:3 3:1929 4:20 5:184 6:219 7:704 +0 0:8 1:5 2:1 3:1720 4:20 5:79 6:214 7:181 +0 0:5 1:21 2:2 3:1558 4:16 5:233 6:275 7:521 +1 0:6 1:18 2:2 3:1800 4:13 5:134 6:83 7:247 +0 0:4 1:17 2:5 3:738 4:13 5:216 6:137 7:510 +1 0:1 1:1 3:1511 4:19 5:65 6:184 7:468 +0 0:9 1:3 2:2 3:2116 4:19 5:289 6:162 7:1984 +0 0:8 1:20 2:2 3:1649 4:20 5:134 6:49 7:1246 +0 0:7 1:20 2:6 3:1730 4:20 5:161 6:164 7:236 +0 0:3 1:7 2:4 3:859 4:5 5:2 6:141 7:744 +0 0:2 1:6 2:2 3:1333 4:11 5:133 6:158 7:163 +0 0:3 1:28 2:2 3:845 4:20 5:242 6:49 7:255 +0 0:6 1:2 2:1 3:2020 4:15 5:75 6:192 7:741 +0 0:7 1:4 2:4 3:912 4:7 5:75 6:193 7:948 +0 0:1 1:2 2:1 3:1145 4:15 5:232 6:74 7:608 +0 0:4 1:4 3:840 4:14 5:252 6:205 7:1532 +1 0:7 1:10 2:3 3:1639 4:12 5:225 6:279 7:338 +0 0:4 1:29 2:2 3:1730 4:15 5:156 6:193 7:1090 +0 0:6 1:20 2:3 3:915 4:15 5:155 6:74 7:614 +0 0:8 1:10 2:6 3:1824 4:8 5:195 6:19 7:448 +0 0:3 1:16 2:5 3:2002 4:8 5:19 6:51 7:191 +1 0:1 1:29 2:5 3:2114 4:19 5:224 6:218 7:678 +0 0:1 1:30 2:6 3:1602 4:10 5:245 6:226 7:198 +0 0:3 1:26 3:1020 4:10 5:140 6:19 7:533 +0 0:6 1:22 2:6 3:1103 4:6 5:140 6:253 7:289 +0 0:1 1:17 2:1 3:1659 4:18 5:216 6:279 7:1726 +0 0:11 1:23 2:4 3:1337 4:12 5:252 6:162 7:258 +0 0:4 1:12 2:6 3:1520 4:18 5:84 6:82 7:641 +0 0:9 2:5 3:1533 4:7 5:180 6:19 7:692 +0 1:9 2:2 3:1756 4:16 5:255 6:267 7:262 +0 0:1 1:23 3:1825 4:7 5:36 6:19 7:214 +1 0:1 1:28 2:4 3:1911 4:14 5:186 6:122 7:591 +0 0:11 1:4 2:1 3:855 4:20 5:237 6:182 7:1234 +0 0:1 1:11 3:1044 4:16 5:37 6:164 7:674 +0 0:2 1:1 2:3 3:820 4:20 5:246 6:266 7:564 +0 0:3 1:19 2:1 3:601 4:1 5:224 6:193 7:1013 +0 0:2 1:7 2:2 3:548 4:16 5:70 6:275 7:410 +0 0:4 1:19 2:6 3:1333 4:5 5:187 6:141 7:316 +1 0:3 1:14 2:3 3:1315 4:20 5:272 6:266 7:605 +0 0:2 1:27 3:801 4:1 5:216 6:82 7:888 +0 0:7 1:21 2:6 3:1616 4:6 5:38 6:140 7:413 +0 0:10 1:3 2:5 3:929 4:1 5:182 6:218 7:1005 +0 0:3 1:29 2:3 3:653 4:13 5:84 6:74 7:812 +0 0:7 1:20 2:6 3:1349 4:16 5:83 6:188 7:872 +0 0:7 1:19 2:5 3:1010 4:20 5:134 6:211 7:1642 +0 0:5 1:2 2:4 3:1457 4:19 5:237 6:64 7:683 +0 0:8 1:25 2:6 3:1805 4:22 5:225 6:275 7:507 +0 0:9 1:27 2:3 3:1838 4:13 5:292 6:83 7:237 +0 0:1 1:27 2:4 3:603 4:4 5:289 6:156 7:1005 +1 0:9 1:29 2:4 3:812 4:7 5:37 6:19 7:1838 +1 0:10 1:24 2:3 3:1452 4:16 5:228 6:275 7:150 +0 1:5 2:5 3:843 4:13 5:84 6:268 7:364 +0 1:25 2:2 3:1850 4:22 5:56 6:64 7:242 +0 0:7 3:932 4:7 5:19 6:146 7:432 +1 0:9 1:27 2:2 3:1804 4:7 5:100 6:74 7:569 +0 0:8 1:14 2:3 3:1156 4:9 5:83 6:227 7:602 +0 0:3 1:28 2:3 3:1635 4:15 5:75 6:205 7:596 +0 0:1 1:28 2:4 3:1039 4:10 5:38 6:226 7:280 +0 0:9 1:13 2:3 3:720 4:14 5:90 6:19 7:594 +0 0:5 1:19 2:6 3:2019 4:16 5:163 6:269 7:348 +0 0:2 1:30 2:2 3:1627 4:19 5:224 6:227 7:2075 +0 0:11 1:29 2:3 3:615 4:20 5:108 6:184 7:178 +0 0:5 1:7 2:2 3:1022 4:19 5:285 6:226 7:228 +0 1:15 3:1735 4:20 5:63 6:186 7:307 +0 0:10 1:3 2:4 3:1714 4:16 5:163 6:103 7:209 +0 0:7 1:17 2:2 3:1849 4:21 5:63 6:140 7:288 +1 0:2 1:7 2:3 3:2041 4:13 5:168 6:65 7:478 +0 0:10 1:27 2:5 3:639 4:16 5:261 6:223 7:129 +1 0:10 1:20 3:907 4:19 5:65 6:83 7:936 +0 0:5 1:5 3:2015 4:20 5:272 6:257 7:480 +1 0:3 1:7 2:5 3:1741 4:13 5:216 6:196 7:139 +1 0:1 1:2 2:2 3:2118 4:15 5:75 6:258 7:1024 +0 0:8 1:4 3:752 4:13 5:216 6:188 7:491 +1 0:10 1:2 2:3 3:2200 4:20 5:36 6:192 7:938 +0 0:10 1:11 2:1 3:2110 4:21 5:141 6:182 7:643 +0 1:24 2:1 3:820 4:13 5:267 6:279 7:342 +0 1:28 2:4 3:933 4:1 5:216 6:89 7:235 +0 0:10 1:4 2:5 3:1536 4:21 5:100 6:13 7:143 +0 0:5 1:3 2:5 3:1702 4:12 5:261 6:227 7:1107 +0 0:8 1:19 2:1 3:836 4:1 5:236 6:83 7:1126 +0 0:6 2:5 3:1504 4:7 5:100 6:74 7:569 +0 0:8 1:14 2:2 3:1552 4:19 5:84 6:231 7:1068 +0 0:10 1:28 2:6 3:1729 4:14 5:203 6:267 7:1589 +0 0:1 1:18 2:2 3:619 4:21 5:123 6:141 7:986 +0 0:1 1:7 3:759 4:7 5:252 6:156 7:2446 +0 0:1 1:16 3:1626 4:16 5:262 6:275 7:599 +0 0:8 1:26 3:1238 4:20 5:289 6:150 7:1034 +1 0:6 1:23 2:6 3:1555 4:20 5:209 6:217 7:361 +0 0:1 1:15 3:728 4:7 5:225 6:74 7:1569 +1 0:8 1:1 2:4 3:2017 4:1 5:84 6:64 7:936 +0 0:6 1:10 2:1 3:828 4:16 5:270 6:299 7:601 +0 0:9 1:6 2:5 3:952 4:21 5:25 6:99 7:116 +0 0:5 1:22 2:4 3:758 4:8 5:183 6:19 7:619 +0 0:3 1:18 3:1725 4:14 5:90 6:188 7:610 +0 0:2 1:5 2:1 3:947 4:1 5:84 6:38 7:1562 +0 0:4 1:12 2:6 3:1516 4:5 5:280 6:99 7:1634 +0 0:4 1:22 2:3 3:904 4:1 5:216 6:279 7:1726 +0 0:6 1:10 2:1 3:1442 4:19 5:38 6:81 7:399 +0 0:2 1:28 2:1 3:1802 4:20 5:48 6:272 7:296 +1 0:2 1:18 2:6 3:920 4:20 5:79 6:182 7:461 +0 0:10 1:26 2:4 3:1044 4:1 5:84 6:258 7:247 +0 0:11 1:13 2:2 3:1830 4:14 5:83 6:205 7:680 +0 0:5 1:30 2:3 3:1308 4:1 5:84 6:206 7:448 +0 0:11 1:25 2:6 3:1401 4:16 5:270 6:301 7:175 +1 0:3 1:1 2:5 3:2019 4:21 5:63 6:218 7:316 +0 0:7 1:23 3:1309 4:8 5:150 6:74 7:626 +0 0:3 1:17 3:1130 4:10 5:140 6:19 7:533 +0 0:5 2:2 3:1502 4:16 5:172 6:82 7:423 +0 0:10 1:19 2:5 3:1538 4:21 5:141 6:189 7:316 +0 0:5 1:4 2:6 3:2020 4:13 5:216 6:135 7:738 +1 0:7 1:24 2:1 3:833 4:6 5:140 6:231 7:183 +1 1:10 2:2 3:749 4:16 5:163 6:314 7:237 +0 0:11 2:3 3:1054 4:5 5:83 6:99 7:1605 +0 0:9 1:9 3:2028 4:16 5:262 6:37 7:522 +1 0:8 1:6 2:2 3:2031 4:19 5:224 6:184 7:861 +1 0:9 1:19 2:2 3:1235 4:15 5:192 6:74 7:318 +1 0:6 1:16 3:827 4:5 5:82 6:99 7:199 +0 0:7 1:3 2:4 3:849 4:16 5:216 6:268 7:438 +0 0:11 1:2 3:1727 4:14 5:90 6:110 7:56 +0 0:8 1:1 2:5 3:1521 4:18 5:225 6:267 7:651 +1 1:20 2:4 3:1542 4:21 5:141 6:218 7:925 +0 0:10 1:29 3:1455 4:15 5:75 6:283 7:812 +0 0:4 1:26 2:5 3:1057 4:10 5:223 6:184 7:662 +0 0:6 1:20 2:4 3:1441 4:22 5:225 6:69 7:551 +0 0:5 1:6 2:2 3:1618 4:21 5:171 6:141 7:374 +0 0:7 1:16 2:2 3:1549 4:1 5:216 6:193 7:1197 +0 0:11 1:3 3:1742 4:18 5:140 6:294 7:810 +0 0:6 1:9 2:1 3:1225 4:20 5:134 6:49 7:1246 +0 0:2 1:7 2:3 3:1411 4:21 5:36 6:141 7:657 +1 0:1 1:23 2:6 3:2130 4:15 5:182 6:258 7:1040 +0 0:1 1:12 2:3 3:1917 4:19 5:82 6:83 7:1192 +0 0:10 1:9 2:4 3:724 4:19 5:163 6:64 7:2125 +1 0:5 1:25 2:4 3:1930 4:5 5:100 6:274 7:1608 +0 0:3 1:25 2:6 3:645 4:18 5:84 6:82 7:641 +0 0:7 1:1 2:1 3:838 4:14 5:186 6:82 7:872 +0 0:9 1:16 3:636 4:22 5:21 6:218 7:978 +1 0:6 1:19 2:3 3:1848 4:9 5:83 6:186 7:895 +0 0:5 1:30 2:2 3:1309 4:8 5:51 6:19 7:191 +0 0:1 1:2 2:2 3:1030 4:9 5:83 6:184 7:1545 +0 0:2 1:15 2:3 3:1046 4:16 5:272 6:267 7:86 +0 0:4 1:25 2:4 3:1528 4:19 5:224 6:267 7:2521 +0 0:10 1:16 2:2 3:1744 4:11 5:211 6:133 7:100 +1 0:5 1:5 2:1 3:2222 4:14 5:203 6:255 7:76 +0 0:10 1:29 3:1455 4:20 5:182 6:65 7:802 +0 0:10 1:10 2:4 3:620 4:8 5:238 6:19 7:1027 +0 1:12 2:3 3:727 4:12 5:262 6:162 7:414 +0 0:11 1:4 2:2 3:600 4:9 5:36 6:82 7:1013 +0 0:4 1:27 3:932 4:13 5:100 6:38 7:200 +0 0:7 1:6 3:708 4:9 5:270 6:82 7:391 +0 0:10 1:26 2:4 3:1511 4:1 5:84 6:218 7:802 +0 0:2 1:11 2:2 3:1300 4:13 5:84 6:165 7:282 +0 0:10 1:8 2:2 3:2055 4:15 5:52 6:74 7:214 +0 0:4 1:2 2:5 3:551 4:3 5:163 6:266 7:954 +0 0:7 1:10 2:4 3:740 4:20 5:209 6:257 7:446 +1 0:1 1:18 2:3 3:2001 4:20 5:72 6:134 7:187 +1 0:9 1:9 3:1553 4:21 5:141 6:169 7:201 +1 0:6 1:5 2:4 3:1952 4:18 5:225 6:218 7:1440 +0 0:1 1:22 2:1 3:1815 4:8 5:19 6:188 7:332 +1 0:5 1:4 3:1057 4:13 5:38 6:49 7:370 +1 0:1 1:9 2:1 3:1017 4:20 5:48 6:162 7:223 +0 0:11 1:27 2:2 3:939 4:16 5:216 6:268 7:438 +0 0:1 1:20 2:5 3:1832 4:20 5:224 6:192 7:290 +0 0:6 1:6 2:5 3:1232 4:13 5:295 6:218 7:224 +0 0:8 2:3 3:705 4:20 5:63 6:186 7:307 +0 0:10 1:10 2:4 3:1136 4:14 5:154 6:188 7:189 +0 0:6 1:15 2:6 3:748 4:19 5:248 6:64 7:573 +1 0:8 1:27 3:1850 4:20 5:184 6:265 7:271 +0 0:1 1:16 2:1 3:2236 4:19 5:262 6:64 7:2296 +1 0:4 1:6 2:1 3:1536 4:1 5:216 6:227 7:1440 +1 1:22 3:1755 4:1 5:83 6:83 7:641 +0 0:3 1:6 2:3 3:526 4:11 5:133 6:172 7:102 +0 0:7 1:10 2:3 3:1245 4:20 5:134 6:21 7:148 +0 1:23 2:6 3:2038 4:13 5:216 6:268 7:438 +0 0:11 1:18 3:957 4:7 5:49 6:19 7:576 +0 0:7 1:26 2:3 3:1918 4:16 5:83 6:69 7:72 +1 0:3 1:10 3:1153 4:10 5:19 6:38 7:946 +0 0:1 1:23 2:6 3:1127 4:18 5:156 6:267 7:2586 +0 0:5 1:28 2:1 3:1957 4:10 5:49 6:184 7:787 +0 0:1 1:15 3:1203 4:1 5:191 6:285 7:1107 +0 0:7 1:24 2:1 3:623 4:11 5:133 6:213 7:100 +0 0:1 1:28 2:5 3:1501 4:20 5:209 6:227 7:646 +0 0:5 1:1 2:4 3:1616 4:10 5:184 6:283 7:1035 +0 0:1 1:24 2:1 3:925 4:22 5:294 6:227 7:110 +1 0:8 1:29 2:3 3:1030 4:20 5:163 6:36 7:1797 +0 0:6 1:13 2:4 3:1112 4:21 5:100 6:247 7:416 +0 0:1 1:12 2:3 3:1534 4:10 5:289 6:49 7:842 +0 0:11 1:17 3:2311 4:1 5:163 6:38 7:2611 +0 0:10 1:9 2:3 3:1508 4:20 5:21 6:162 7:1090 +0 0:1 1:25 2:2 3:625 4:15 5:141 6:74 7:871 +0 0:1 1:5 2:4 3:853 4:5 5:108 6:141 7:965 +1 0:4 1:15 2:2 3:1827 4:16 5:246 6:275 7:422 +0 0:10 1:28 3:1919 4:18 5:262 6:266 7:679 +0 0:2 1:10 2:6 3:1200 4:13 5:84 6:121 7:134 +0 0:7 1:16 2:1 3:557 4:8 5:98 6:19 7:350 +1 0:11 1:11 2:4 3:2024 4:7 5:19 6:164 7:1946 +0 0:1 1:11 2:6 3:1001 4:14 5:122 6:205 7:408 +1 0:2 1:5 2:1 3:1241 4:13 5:216 6:140 7:589 +0 0:8 1:4 2:1 3:1459 4:19 5:225 6:83 7:868 +0 0:11 1:2 2:6 3:1146 4:19 5:84 6:226 7:1302 +1 0:1 1:19 2:4 3:1737 4:10 5:19 6:170 7:761 +0 0:9 1:6 2:5 3:1530 4:22 5:123 6:64 7:83 +0 0:9 1:16 3:829 4:18 5:83 6:272 7:948 +0 0:6 1:13 2:3 3:831 4:1 5:19 6:193 7:595 +0 0:3 1:11 2:4 3:846 4:8 5:184 6:19 7:590 +0 0:6 1:10 2:1 3:1950 4:13 5:19 6:218 7:606 +1 0:11 1:16 2:6 3:117 4:7 5:163 6:74 7:1900 +1 0:9 1:5 2:3 3:2232 4:5 5:141 6:162 7:1222 +0 0:10 1:23 2:1 3:1806 4:16 5:294 6:275 7:601 +0 0:3 1:7 2:4 3:1650 4:20 5:36 6:217 7:1751 +0 0:4 1:30 2:2 3:1916 4:21 5:141 6:78 7:217 +0 0:2 1:3 2:5 3:802 4:13 5:216 6:113 7:157 +0 0:5 1:18 2:5 3:1116 4:21 5:141 6:265 7:788 +0 0:8 1:16 2:4 3:2110 4:5 5:141 6:21 7:140 +0 0:3 1:12 2:2 3:625 4:1 5:163 6:83 7:1235 +0 0:5 1:3 2:6 3:1202 4:7 5:19 6:74 7:373 +0 0:3 1:29 2:4 3:2102 4:3 5:261 6:257 7:1050 +0 0:8 1:2 2:6 3:1641 4:16 5:97 6:267 7:451 +0 0:10 1:8 2:3 3:1808 4:20 5:215 6:277 7:389 +1 0:9 1:26 2:2 3:911 4:16 5:2 6:267 7:896 +0 0:10 1:17 2:4 3:1217 4:21 5:232 6:141 7:489 +0 0:8 1:26 2:6 3:1443 4:21 5:140 6:62 7:288 +0 0:2 1:7 2:2 3:632 4:1 5:191 6:274 7:1045 +1 0:5 1:30 2:3 3:937 4:1 5:156 6:274 7:1597 +0 0:7 1:22 2:1 3:554 4:15 5:299 6:170 7:647 +0 0:8 1:11 2:4 3:828 4:16 5:62 6:164 7:86 +0 0:11 1:12 2:2 3:2138 4:19 5:90 6:162 7:1750 +0 0:7 1:19 2:5 3:642 4:13 5:84 6:88 7:624 +0 0:9 1:8 3:2052 4:22 5:140 6:38 7:413 +0 0:3 1:5 2:2 3:901 4:18 5:262 6:162 7:414 +0 1:29 2:5 3:612 4:14 5:203 6:162 7:1300 +1 0:3 1:17 3:1903 4:10 5:19 6:184 7:403 +0 0:1 1:20 2:4 3:1449 4:15 5:75 6:72 7:173 +0 0:10 1:23 2:1 3:1649 4:14 5:250 6:205 7:76 +0 0:11 1:15 2:4 3:912 4:13 5:168 6:38 7:185 +0 0:5 1:29 2:1 3:2210 4:7 5:19 6:247 7:356 +0 0:7 1:3 2:3 3:714 4:21 5:216 6:141 7:925 +1 0:6 1:12 2:2 3:2015 4:18 5:216 6:182 7:403 +0 0:8 1:29 2:3 3:730 4:20 5:36 6:134 7:670 +0 0:3 1:9 3:1227 4:1 5:84 6:299 7:813 +0 0:7 1:23 2:1 3:2055 4:21 5:100 6:290 7:194 +0 0:1 1:1 2:1 3:1430 4:20 5:15 6:78 7:324 +0 0:2 1:30 2:2 3:620 4:5 5:262 6:141 7:1635 +0 0:10 1:3 2:4 3:1741 4:13 5:178 6:83 7:309 +0 0:2 1:9 2:5 3:821 4:19 5:182 6:64 7:468 +0 0:10 1:25 2:3 3:635 4:14 5:272 6:205 7:1518 +0 0:10 1:20 2:6 3:1355 4:19 5:168 6:81 7:214 +0 0:6 1:14 2:4 3:1848 4:12 5:163 6:227 7:370 +0 0:1 1:21 2:6 3:1003 4:10 5:289 6:19 7:406 +1 0:3 1:16 2:5 3:1109 4:16 5:262 6:48 7:326 +0 0:3 1:1 2:6 3:1359 4:1 5:236 6:218 7:1652 +0 0:7 1:7 3:1007 4:18 5:90 6:218 7:235 +0 0:2 1:27 2:6 3:1535 4:21 5:141 6:169 7:201 +0 0:7 1:8 2:2 3:1228 4:10 5:19 6:79 7:432 +1 0:2 1:27 3:823 4:13 5:232 6:83 7:604 +0 0:4 1:19 2:6 3:753 4:15 5:75 6:265 7:83 +0 0:3 1:1 2:5 3:1942 4:1 5:84 6:247 7:1062 +0 1:2 2:1 3:1850 4:21 5:45 6:141 7:253 +0 0:7 1:4 2:4 3:1705 4:20 5:204 6:30 7:321 +1 0:8 1:4 2:1 3:1417 4:8 5:122 6:19 7:640 +0 0:11 1:23 2:4 3:1225 4:8 5:299 6:74 7:226 +1 0:10 1:11 2:1 3:2246 4:7 5:19 6:49 7:576 +0 0:8 1:26 3:640 4:21 5:141 6:83 7:224 +1 0:2 1:26 2:5 3:1249 4:19 5:65 6:259 7:213 +0 0:10 1:17 2:3 3:1835 4:20 5:229 6:186 7:402 +0 0:9 1:10 2:1 3:1424 4:18 5:262 6:257 7:447 +0 1:10 2:3 3:1719 4:18 5:168 6:218 7:733 +0 0:7 1:1 2:1 3:1633 4:14 5:191 6:205 7:1501 +0 0:2 1:25 2:5 3:1315 4:8 5:19 6:252 7:357 +0 0:7 1:15 3:1801 4:14 5:216 6:89 7:235 +1 0:9 1:21 2:5 3:1428 4:20 5:134 6:173 7:393 +0 0:5 1:18 2:5 3:1617 4:5 5:141 6:184 7:853 +0 0:10 1:4 2:5 3:805 4:20 5:213 6:227 7:1037 +0 0:1 1:8 2:1 3:1329 4:14 5:90 6:47 7:240 +0 0:10 1:8 2:3 3:1134 4:13 5:1 6:83 7:158 +0 0:1 1:18 2:2 3:1700 4:20 5:95 6:78 7:562 +1 0:5 1:16 2:4 3:1109 4:18 5:216 6:140 7:589 +0 0:8 1:3 3:1629 4:4 5:168 6:107 7:1076 +0 0:6 1:29 2:4 3:703 4:10 5:224 6:184 7:861 +0 0:5 1:14 2:1 3:1729 4:5 5:204 6:141 7:305 +0 0:6 1:18 2:2 3:1535 4:14 5:90 6:186 7:229 +0 0:2 1:16 2:3 3:1521 4:19 5:229 6:184 7:834 +0 0:4 1:13 3:1656 4:4 5:169 6:211 7:353 +0 0:8 1:3 2:6 3:615 4:15 5:90 6:156 7:508 +0 0:4 1:5 2:1 3:1425 4:17 5:182 6:186 7:989 +0 0:4 1:19 2:6 3:850 4:5 5:66 6:141 7:986 +0 0:8 1:17 2:5 3:654 4:18 5:216 6:83 7:802 +0 0:9 1:18 2:1 3:1659 4:18 5:262 6:133 7:2398 +0 0:8 1:12 3:804 4:7 5:156 6:267 7:2586 +0 0:5 1:28 2:1 3:811 4:15 5:75 6:253 7:461 +1 0:10 1:16 2:2 3:1444 4:20 5:289 6:258 7:972 +0 0:10 1:29 2:1 3:1011 4:14 5:203 6:89 7:528 +0 0:8 1:26 3:2138 4:19 5:65 6:240 7:683 +1 0:7 1:16 2:2 3:1001 4:1 5:25 6:218 7:783 +0 0:6 1:4 2:2 3:2210 4:20 5:272 6:217 7:389 +0 0:9 1:21 2:5 3:1052 4:20 5:180 6:78 7:461 +0 1:1 3:731 4:21 5:141 6:74 7:871 +1 0:11 1:4 2:1 3:1342 4:1 5:261 6:284 7:1710 +0 0:10 1:16 2:2 3:1416 4:6 5:156 6:140 7:228 +0 0:8 1:21 2:2 3:945 4:5 5:19 6:99 7:745 +0 0:8 1:16 2:5 3:1509 4:1 5:2 6:83 7:569 +0 1:7 3:1056 4:18 5:168 6:82 7:1619 +0 0:5 1:16 2:4 3:1033 4:16 5:71 6:275 7:320 +0 0:6 1:12 2:3 3:2255 4:19 5:225 6:64 7:1774 +0 0:6 1:5 2:3 3:745 4:20 5:163 6:284 7:1593 +0 0:3 1:1 2:5 3:1040 4:15 5:190 6:74 7:741 +1 0:7 1:16 2:1 3:1742 4:13 5:242 6:284 7:667 +0 0:6 1:18 2:2 3:108 4:5 5:163 6:141 7:1379 +0 0:11 1:26 2:1 3:1818 4:19 5:161 6:205 7:1300 +0 0:6 1:22 3:1417 4:13 5:226 6:83 7:673 +0 0:2 1:23 2:3 3:740 4:16 5:163 6:272 7:308 +0 0:5 1:19 3:630 4:14 5:32 6:205 7:386 +0 0:9 1:26 2:2 3:1954 4:22 5:216 6:20 7:160 +0 0:7 1:5 2:6 3:903 4:21 5:100 6:297 7:1215 +0 0:9 1:22 2:6 3:1108 4:21 5:63 6:226 7:363 +0 0:9 1:1 2:6 3:1340 4:15 5:288 6:74 7:181 +1 0:11 1:4 2:2 3:2304 4:22 5:83 6:91 7:121 +0 0:6 1:7 2:6 3:1741 4:21 5:100 6:89 7:487 +0 0:6 1:30 2:5 3:1025 4:13 5:242 6:38 7:612 +0 0:5 1:16 2:4 3:1810 4:20 5:184 6:275 7:1258 +1 0:1 1:17 2:1 3:829 4:4 5:169 6:211 7:353 +0 0:7 1:3 2:3 3:836 4:7 5:19 6:218 7:606 +0 0:5 1:28 2:1 3:1335 4:20 5:49 6:240 7:328 +0 0:10 1:4 2:6 3:1140 4:4 5:38 6:156 7:187 +0 0:6 1:2 3:1700 4:20 5:2 6:184 7:1552 +0 1:9 2:1 3:1915 4:21 5:63 6:253 7:245 +1 0:10 1:22 2:3 3:1617 4:17 5:168 6:134 7:1428 +0 1:26 2:3 3:741 4:16 5:215 6:164 7:47 +1 0:2 1:14 2:2 3:1718 4:19 5:25 6:64 7:644 +0 0:7 1:3 2:4 3:937 4:1 5:84 6:164 7:1235 +0 0:3 1:27 2:1 3:1922 4:19 5:65 6:294 7:508 +0 0:2 1:7 2:3 3:1303 4:7 5:19 6:247 7:356 +0 0:6 2:4 3:618 4:6 5:182 6:304 7:532 +0 0:11 1:19 2:2 3:1823 4:19 5:38 6:226 7:280 +1 1:23 3:2003 4:4 5:156 6:256 7:1074 +0 0:11 1:29 2:4 3:1728 4:14 5:84 6:205 7:852 +0 0:6 1:5 2:3 3:1103 4:7 5:36 6:19 7:214 +1 1:1 2:1 3:1813 4:13 5:216 6:74 7:264 +0 1:19 2:4 3:748 4:7 5:168 6:19 7:761 +0 0:1 1:2 2:1 3:1225 4:20 5:180 6:49 7:967 +0 0:7 1:15 2:1 3:844 4:1 5:38 6:267 7:2704 +1 0:7 1:30 2:1 3:1053 4:14 5:203 6:19 7:906 +0 0:9 1:23 2:6 3:931 4:14 5:186 6:308 7:369 +0 0:6 1:1 2:6 3:1329 4:18 5:163 6:223 7:834 +1 0:6 1:9 2:1 3:2008 4:13 5:79 6:21 7:189 +0 0:1 1:22 2:1 3:1103 4:1 5:19 6:218 7:606 +0 0:3 1:28 2:3 3:1013 4:7 5:84 6:19 7:732 +1 0:1 1:22 2:1 3:1739 4:14 5:203 6:164 7:1536 +0 0:10 1:13 2:6 3:1404 4:19 5:254 6:64 7:213 +1 0:2 1:6 2:2 3:1740 4:8 5:145 6:19 7:377 +0 0:11 1:26 3:1614 4:7 5:78 6:19 7:366 +0 0:5 1:7 2:3 3:2226 4:14 5:90 6:120 7:288 +1 0:9 1:4 2:3 3:1601 4:5 5:38 6:99 7:200 +0 0:1 1:24 2:1 3:1550 4:14 5:182 6:188 7:683 +0 0:10 1:24 2:3 3:1304 4:20 5:95 6:2 7:223 +0 0:11 1:25 3:751 4:22 5:216 6:247 7:647 +0 0:2 1:22 2:4 3:1522 4:20 5:2 6:257 7:628 +0 0:11 1:11 2:4 3:738 4:19 5:82 6:38 7:399 +0 0:4 1:1 2:4 3:617 4:5 5:83 6:141 7:861 +0 0:4 1:25 2:4 3:918 4:14 5:90 6:194 7:238 +0 1:18 2:2 3:855 4:20 5:213 6:186 7:423 +0 0:9 1:29 2:5 3:1305 4:16 5:20 6:218 7:160 +0 0:5 1:8 2:4 3:803 4:18 5:163 6:140 7:2288 +0 1:1 3:1616 4:19 5:65 6:123 7:83 +0 0:1 1:23 3:1659 4:22 5:124 6:218 7:577 +0 0:4 1:28 3:1006 4:18 5:216 6:279 7:1726 +0 0:8 1:22 2:4 3:1905 4:7 5:220 6:170 7:1035 +0 0:2 1:13 3:506 4:13 5:29 6:38 7:201 +1 0:7 1:24 2:2 3:1818 4:20 5:79 6:94 7:562 +0 0:11 1:16 2:5 3:742 4:12 5:63 6:227 7:1737 +1 0:8 1:13 2:1 3:1738 4:7 5:19 6:234 7:272 +0 0:8 1:12 3:1535 4:15 5:75 6:66 7:205 +1 0:10 1:27 2:6 3:1615 4:16 5:83 6:284 7:770 +0 0:8 1:14 2:2 3:1235 4:7 5:224 6:19 7:665 +1 0:11 1:2 2:6 3:1848 4:20 5:49 6:47 7:281 +0 1:10 2:3 3:550 4:10 5:100 6:19 7:745 +0 0:3 1:25 3:838 4:19 5:229 6:256 7:966 +1 0:3 2:4 3:1237 4:1 5:294 6:83 7:813 +0 0:3 1:30 2:5 3:802 4:1 5:221 6:218 7:1739 +0 0:9 1:12 2:2 3:1000 4:20 5:242 6:36 7:443 +1 0:8 1:28 2:1 3:857 4:17 5:82 6:186 7:601 +0 0:3 1:9 2:6 3:1833 4:7 5:83 6:19 7:1199 +0 0:6 1:9 3:1806 4:1 5:83 6:83 7:641 +0 0:2 1:22 2:3 3:1958 4:20 5:134 6:227 7:1020 +1 0:10 1:4 2:5 3:1020 4:14 5:203 6:227 7:1276 +0 0:10 1:28 3:1150 4:16 5:83 6:204 7:679 +0 0:7 1:28 2:5 3:1301 4:14 5:203 6:81 7:931 +0 0:11 1:30 2:4 3:1128 4:12 5:161 6:38 7:2381 +0 0:5 1:1 2:3 3:1942 4:16 5:93 6:275 7:200 +0 0:1 1:18 2:3 3:803 4:19 5:262 6:226 7:2521 +0 0:6 1:4 2:3 3:1900 4:15 5:75 6:64 7:335 +0 0:3 1:29 2:3 3:1340 4:20 5:279 6:186 7:251 +0 0:5 1:18 2:6 3:751 4:16 5:44 6:275 7:357 +0 0:5 1:16 2:3 3:1346 4:14 5:203 6:140 7:908 +0 0:2 1:23 2:2 3:1837 4:7 5:19 6:222 7:545 +0 0:6 1:6 2:5 3:820 4:20 5:171 6:78 7:296 +0 0:2 1:20 3:945 4:20 5:225 6:215 7:1037 +0 0:2 1:9 2:5 3:1048 4:8 5:156 6:19 7:760 +0 0:1 1:22 3:1159 4:1 5:279 6:83 7:551 +0 0:11 1:27 2:2 3:1352 4:19 5:168 6:38 7:185 +1 1:17 2:1 3:2012 4:18 5:216 6:83 7:802 +0 1:17 2:1 3:1359 4:1 5:141 6:193 7:964 +0 0:9 1:28 2:4 3:844 4:18 5:262 6:164 7:337 +0 0:1 1:24 3:1201 4:16 5:213 6:275 7:839 +0 0:5 1:27 2:6 3:2019 4:16 5:262 6:103 7:158 +0 0:7 1:13 2:5 3:959 4:7 5:278 6:19 7:445 +1 1:25 2:1 3:2005 4:16 5:163 6:275 7:590 +0 0:2 1:6 2:2 3:610 4:15 5:285 6:74 7:527 +0 0:9 1:14 2:4 3:921 4:16 5:216 6:65 7:296 +1 0:9 1:11 2:5 3:2032 4:1 5:84 6:82 7:641 +1 1:11 2:6 3:1518 4:14 5:89 6:205 7:232 +1 1:1 3:625 4:13 5:66 6:170 7:478 +0 0:7 1:21 2:6 3:810 4:16 5:206 6:82 7:197 +0 0:1 1:1 3:1535 4:13 5:75 6:218 7:264 +1 0:5 1:7 2:2 3:1554 4:7 5:161 6:19 7:1747 +0 0:1 1:22 3:825 4:1 5:216 6:83 7:802 +0 1:20 2:5 3:2033 4:17 5:251 6:186 7:1105 +0 0:4 1:4 2:6 3:1133 4:1 5:261 6:156 7:2421 +0 0:6 1:4 2:3 3:738 4:19 5:224 6:182 7:1038 +0 0:10 3:706 4:14 5:203 6:194 7:297 +0 0:11 1:27 2:2 3:706 4:7 5:100 6:19 7:745 +1 0:1 1:16 3:1934 4:3 5:215 6:266 7:956 +0 0:5 1:15 2:3 3:1357 4:19 5:224 6:164 7:2401 +0 0:8 1:22 2:5 3:835 4:20 5:25 6:49 7:283 +0 0:1 1:17 2:1 3:657 4:1 5:216 6:89 7:235 +0 0:6 1:18 2:1 3:1857 4:13 5:216 6:142 7:588 +1 0:1 1:9 2:2 3:1710 4:21 5:100 6:250 7:278 +0 0:5 1:13 3:1317 4:18 5:191 6:140 7:921 +0 0:5 1:1 2:4 3:2044 4:21 5:63 6:265 7:304 +1 1:22 3:1606 4:14 5:90 6:218 7:235 +1 0:11 1:9 2:6 3:1028 4:7 5:19 6:266 7:2182 +0 0:8 1:20 2:1 3:1444 4:15 5:75 6:20 7:411 +0 0:2 1:29 2:1 3:1055 4:8 5:75 6:304 7:226 +0 0:11 1:21 2:3 3:1647 4:7 5:252 6:275 7:626 +0 0:4 1:27 2:6 3:1034 4:18 5:161 6:218 7:1515 +0 0:8 1:17 2:5 3:838 4:21 5:168 6:62 7:418 +1 0:9 1:23 2:5 3:1811 4:21 5:303 6:141 7:510 +1 0:10 1:28 3:1611 4:21 5:119 6:141 7:376 +1 0:10 1:26 2:4 3:1519 4:1 5:182 6:83 7:984 +1 0:11 1:20 2:3 3:1334 4:20 5:209 6:164 7:337 +1 0:3 1:4 2:1 3:1607 4:10 5:168 6:19 7:761 +0 0:5 1:14 2:2 3:1902 4:8 5:223 6:19 7:508 +0 0:1 1:14 2:6 3:1229 4:1 5:108 6:83 7:1119 +0 1:16 2:1 3:1042 4:1 5:84 6:311 7:281 +0 0:10 1:25 2:3 3:1742 4:14 5:21 6:205 7:1042 +0 0:7 1:24 2:1 3:1437 4:8 5:3 6:19 7:146 +0 0:8 1:9 2:6 3:1210 4:13 5:216 6:122 7:137 +0 0:5 1:28 2:1 3:1001 4:19 5:82 6:170 7:214 +0 1:18 2:3 3:1420 4:15 5:75 6:283 7:812 +0 0:4 1:26 2:5 3:624 4:13 5:98 6:218 7:273 +0 0:10 1:19 2:5 3:938 4:1 5:84 6:250 7:1158 +0 0:5 1:26 2:6 3:1250 4:15 5:75 6:268 7:491 +0 1:1 3:830 4:14 5:262 6:205 7:1589 +0 0:9 1:30 2:5 3:2127 4:7 5:19 6:81 7:547 +0 0:8 1:18 3:1357 4:14 5:186 6:19 7:332 +0 0:10 1:16 2:2 3:1955 4:15 5:278 6:74 7:812 +0 0:9 1:10 2:1 3:1220 4:20 5:260 6:186 7:271 +0 0:9 1:21 2:5 3:1232 4:19 5:252 6:227 7:304 +0 0:2 1:8 2:4 3:1219 4:19 5:224 6:162 7:2176 +0 0:9 1:10 2:2 3:1454 4:20 5:224 6:186 7:668 +0 0:8 1:6 2:3 3:1003 4:18 5:216 6:279 7:1726 +0 0:1 1:17 2:2 3:1514 4:19 5:82 6:146 7:499 +0 0:6 1:21 2:4 3:1842 4:8 5:19 6:173 7:453 +0 0:5 1:14 2:1 3:1315 4:8 5:289 6:58 7:371 +0 0:4 1:28 3:902 4:16 5:83 6:299 7:639 +0 0:6 1:23 2:6 3:1512 4:8 5:78 6:19 7:366 +0 0:8 1:23 2:3 3:955 4:20 5:178 6:78 7:319 +0 1:16 2:1 3:2045 4:20 5:79 6:173 7:296 +0 0:8 1:26 2:6 3:1038 4:1 5:84 6:164 7:1235 +0 1:23 2:6 3:1400 4:20 5:171 6:78 7:296 +1 1:23 2:6 3:2009 4:5 5:100 6:222 7:1024 +0 0:4 1:8 2:3 3:811 4:14 5:103 6:205 7:223 +0 0:10 1:26 2:5 3:1314 4:22 5:294 6:227 7:110 +1 0:6 1:22 3:1653 4:3 5:161 6:266 7:866 +0 0:10 1:17 2:4 3:858 4:1 5:21 6:164 7:1242 +1 0:8 1:28 2:1 3:1221 4:13 5:84 6:194 7:853 +0 0:10 1:26 2:4 3:1344 4:21 5:45 6:141 7:253 +0 0:8 1:9 2:5 3:1227 4:21 5:63 6:247 7:416 +0 0:7 1:24 2:2 3:1058 4:7 5:19 6:198 7:302 +0 1:8 2:1 3:1004 4:13 5:84 6:15 7:313 +1 0:9 1:5 2:4 3:1553 4:19 5:225 6:107 7:1972 +0 0:11 1:28 2:3 3:945 4:1 5:84 6:218 7:802 +0 0:2 1:10 2:5 3:1013 4:10 5:251 6:110 7:1138 +0 0:3 1:21 2:4 3:1024 4:20 5:49 6:184 7:787 +0 0:6 1:14 2:5 3:903 4:3 5:82 6:164 7:2311 +0 0:1 1:16 3:1653 4:7 5:270 6:218 7:1249 +0 0:5 1:7 2:2 3:1516 4:1 5:204 6:83 7:448 +0 0:7 1:16 2:1 3:1322 4:21 5:141 6:284 7:668 +0 0:2 1:2 2:4 3:1457 4:15 5:75 6:146 7:98 +0 0:6 1:19 2:3 3:1233 4:20 5:134 6:49 7:1246 +0 0:4 1:22 2:3 3:1720 4:7 5:19 6:83 7:732 +0 0:3 1:2 2:6 3:813 4:10 5:229 6:184 7:834 +0 0:8 1:28 2:2 3:1450 4:22 5:21 6:227 7:872 +0 0:6 1:1 2:6 3:2223 4:16 5:270 6:50 7:347 +0 0:5 1:11 2:3 3:724 4:21 5:187 6:141 7:316 +0 1:25 2:2 3:1918 4:20 5:184 6:219 7:704 +0 0:2 1:28 3:1730 4:20 5:134 6:78 7:239 +0 0:4 1:3 2:5 3:1345 4:20 5:95 6:134 7:677 +0 0:11 1:14 2:4 3:1840 4:15 5:122 6:74 7:268 +0 0:10 1:2 2:4 3:1021 4:2 5:133 6:213 7:100 +0 0:3 1:11 2:4 3:1842 4:20 5:253 6:136 7:233 +0 0:5 1:10 2:6 3:1155 4:22 5:117 6:227 7:438 +0 0:5 1:24 2:3 3:1848 4:14 5:203 6:188 7:700 +0 0:10 1:10 2:5 3:1531 4:4 5:269 6:156 7:1597 +1 0:2 1:1 2:4 3:1640 4:1 5:84 6:170 7:1389 +0 0:6 1:25 3:1155 4:20 5:164 6:78 7:293 +0 0:5 1:25 2:4 3:1351 4:18 5:221 6:218 7:1739 +0 0:2 1:5 3:1722 4:7 5:279 6:19 7:483 +0 0:4 1:1 2:3 3:1323 4:15 5:182 6:247 7:534 +0 0:2 1:29 2:2 3:554 4:13 5:263 6:83 7:364 +0 0:11 1:13 2:3 3:1358 4:18 5:221 6:164 7:834 +1 0:10 1:4 2:5 3:2042 4:19 5:224 6:253 7:257 +1 0:9 1:13 2:4 3:1854 4:18 5:184 6:82 7:895 +0 0:9 1:23 2:5 3:1401 4:8 5:19 6:169 7:503 +0 0:8 1:8 2:4 3:1439 4:16 5:197 6:267 7:78 +0 0:9 1:21 2:5 3:948 4:10 5:49 6:283 7:880 +0 0:8 1:22 2:4 3:1343 4:16 5:114 6:266 7:224 +0 0:3 1:27 2:1 3:1128 4:7 5:168 6:81 7:214 +0 0:3 1:30 2:4 3:710 4:8 5:226 6:19 7:564 +0 0:11 1:17 3:1825 4:11 5:158 6:133 7:163 +0 0:8 1:19 2:1 3:1117 4:18 5:272 6:82 7:910 +0 0:10 1:22 2:2 3:833 4:6 5:140 6:25 7:326 +0 0:9 1:9 2:1 3:1610 4:1 5:191 6:156 7:1090 +0 0:6 1:1 3:936 4:13 5:168 6:247 7:431 +0 0:1 1:24 3:1728 4:15 5:156 6:146 7:664 +0 0:9 1:11 2:5 3:1509 4:1 5:84 6:21 7:190 +0 0:2 1:26 2:5 3:608 4:8 5:226 6:19 7:564 +0 0:7 1:10 2:3 3:1820 4:16 5:202 6:275 7:436 +0 0:4 1:19 2:6 3:815 4:1 5:216 6:205 7:334 +0 0:7 1:3 2:3 3:1711 4:5 5:100 6:83 7:1372 +0 0:5 1:28 3:930 4:15 5:75 6:24 7:225 +1 0:7 1:9 2:3 3:1735 4:20 5:2 6:180 7:332 +0 1:19 2:4 3:820 4:15 5:260 6:74 7:83 +0 0:6 1:7 2:6 3:1227 4:5 5:100 6:19 7:745 +0 0:11 1:21 2:4 3:1231 4:10 5:19 6:110 7:644 +1 0:7 1:15 2:1 3:1233 4:13 5:38 6:247 7:612 +0 0:7 1:20 2:6 3:1900 4:18 5:140 6:272 7:2400 +0 0:11 1:6 2:3 3:1125 4:15 5:65 6:74 7:335 +1 0:5 1:13 2:1 3:1553 4:14 5:146 6:294 7:838 +0 0:6 1:15 2:5 3:1151 4:5 5:90 6:141 7:1076 +0 0:6 1:18 2:1 3:1256 4:5 5:141 6:226 7:1324 +0 0:5 1:14 2:2 3:1659 4:14 5:141 6:205 7:1034 +0 0:3 1:10 3:2038 4:12 5:225 6:267 7:651 +1 0:1 1:19 2:4 3:1242 4:8 5:270 6:238 7:541 +0 0:11 1:5 2:3 3:1720 4:14 5:203 6:227 7:1276 +0 0:6 1:18 2:1 3:729 4:7 5:182 6:38 7:1121 +0 0:1 1:10 2:3 3:1849 4:20 5:184 6:49 7:611 +0 0:7 1:23 3:1723 4:14 5:90 6:188 7:610 +0 0:8 1:3 3:1009 4:1 5:84 6:272 7:1438 +0 0:8 1:9 2:6 3:1221 4:11 5:133 6:213 7:100 +0 0:6 1:10 2:1 3:720 4:13 5:38 6:81 7:399 +0 0:10 1:20 2:6 3:1242 4:21 5:63 6:284 7:487 +0 0:10 1:17 2:4 3:1005 4:18 5:83 6:205 7:680 +0 0:2 1:2 2:4 3:1650 4:1 5:213 6:218 7:416 +0 0:7 1:20 2:5 3:1749 4:16 5:117 6:275 7:217 +1 0:10 1:25 2:3 3:2046 4:13 5:156 6:247 7:426 +0 0:9 1:18 2:2 3:551 4:21 5:167 6:141 7:201 +0 0:3 1:21 2:4 3:1151 4:1 5:84 6:128 7:678 +1 0:1 1:22 3:2020 4:15 5:289 6:74 7:773 +1 0:2 1:8 2:4 3:1028 4:19 5:216 6:226 7:678 +0 0:7 1:13 2:5 3:1347 4:20 5:209 6:275 7:588 +0 0:8 1:29 2:2 3:753 4:21 5:141 6:45 7:253 +0 1:4 2:4 3:950 4:13 5:84 6:180 7:309 +1 1:6 2:5 3:1341 4:19 5:289 6:64 7:508 +0 0:2 1:30 2:2 3:1852 4:14 5:213 6:205 7:282 +1 0:10 1:7 2:1 3:1459 4:8 5:3 6:19 7:146 +0 0:8 1:21 2:3 3:1850 4:21 5:63 6:253 7:245 +0 0:10 1:30 2:1 3:1810 4:18 5:216 6:240 7:849 +0 0:9 1:4 2:3 3:642 4:20 5:225 6:49 7:1999 +0 0:8 1:19 3:2130 4:8 5:260 6:74 7:83 +0 0:2 1:5 2:1 3:940 4:21 5:141 6:69 7:809 +1 0:1 1:6 2:6 3:1012 4:22 5:133 6:151 7:216 +0 0:3 1:1 2:6 3:1654 4:4 5:38 6:156 7:187 +0 0:6 1:14 2:4 3:1515 4:20 5:274 6:277 7:404 +0 0:8 1:5 2:1 3:1225 4:15 5:295 6:74 7:396 +0 0:4 1:5 2:1 3:1245 4:18 5:83 6:140 7:1452 +0 0:3 1:3 2:1 3:926 4:14 5:203 6:82 7:680 +0 0:6 1:6 2:4 3:558 4:1 5:209 6:83 7:1456 +0 0:4 1:1 2:3 3:1215 4:20 5:48 6:211 7:325 +0 0:1 1:4 2:4 3:807 4:1 5:84 6:205 7:852 +0 0:11 1:23 2:5 3:958 4:20 5:184 6:89 7:229 +0 0:1 1:11 3:651 4:4 5:38 6:156 7:187 +0 0:9 1:11 2:6 3:1058 4:19 5:82 6:170 7:214 +0 0:8 1:25 2:6 3:1125 4:8 5:50 6:275 7:347 +0 0:4 1:12 2:6 3:544 4:13 5:104 6:164 7:209 +0 0:9 2:4 3:803 4:7 5:19 6:267 7:2139 +1 0:6 1:12 2:3 3:1730 4:20 5:21 6:94 7:528 +0 0:6 1:11 2:5 3:850 4:15 5:75 6:72 7:173 +0 0:8 1:8 2:5 3:2100 4:19 5:225 6:2 7:328 +1 1:11 3:1150 4:16 5:262 6:258 7:1482 +0 0:11 1:15 2:4 3:904 4:5 5:63 6:294 7:927 +0 0:1 1:4 2:3 3:1726 4:4 5:209 6:171 7:353 +0 1:26 2:2 3:1855 4:13 5:38 6:49 7:370 +0 1:17 2:1 3:849 4:18 5:229 6:82 7:1290 +0 0:5 1:25 2:5 3:1009 4:22 5:179 6:218 7:222 +1 0:10 1:16 2:2 3:959 4:7 5:19 6:186 7:590 +0 0:10 1:2 2:4 3:958 4:16 5:163 6:238 7:110 +0 0:10 1:3 2:4 3:857 4:7 5:66 6:19 7:446 +1 0:5 1:8 2:3 3:1344 4:7 5:134 6:19 7:696 +0 0:10 1:27 2:6 3:759 4:19 5:252 6:227 7:304 +0 0:10 1:27 2:5 3:2038 4:16 5:270 6:132 7:402 +0 0:7 1:21 3:2338 4:18 5:225 6:218 7:1440 +1 0:6 1:29 2:4 3:1041 4:6 5:36 6:140 7:542 +0 0:7 1:17 2:2 3:1407 4:1 5:292 6:83 7:237 +0 0:11 1:15 2:4 3:1245 4:20 5:225 6:258 7:843 +0 0:6 1:30 2:5 3:1517 4:15 5:65 6:170 7:544 +1 0:3 1:13 2:2 3:1305 4:20 5:180 6:227 7:1044 +1 0:10 1:13 3:1742 4:13 5:122 6:218 7:137 +0 0:7 1:24 2:1 3:615 4:1 5:242 6:193 7:700 +1 0:3 1:9 2:6 3:1216 4:4 5:289 6:156 7:1005 +1 0:11 1:12 2:2 3:1351 4:13 5:267 6:257 7:417 +0 1:14 2:6 3:1035 4:1 5:155 6:83 7:919 +1 0:8 1:1 2:4 3:2010 4:13 5:38 6:99 7:200 +0 0:3 1:22 2:6 3:2041 4:1 5:191 6:140 7:921 +0 0:4 1:12 3:900 4:22 5:216 6:252 7:531 +0 0:10 1:10 2:5 3:1416 4:15 5:38 6:247 7:612 +0 0:10 1:14 3:1302 4:14 5:203 6:19 7:906 +0 0:11 1:2 3:1326 4:1 5:245 6:83 7:1158 +0 0:6 1:26 2:1 3:711 4:16 5:163 6:27 7:109 +0 0:10 2:1 3:1420 4:21 5:100 6:194 7:725 +0 0:11 1:23 2:4 3:1849 4:18 5:83 6:140 7:1452 +0 0:4 1:16 2:3 3:907 4:4 5:38 6:184 7:1121 +0 1:30 3:1841 4:18 5:66 6:218 7:296 +0 1:11 2:6 3:610 4:8 5:226 6:19 7:564 +0 1:18 2:2 3:2045 4:8 5:122 6:74 7:268 +0 0:10 1:9 2:4 3:2107 4:5 5:141 6:2 7:744 +0 0:2 1:1 2:3 3:1120 4:18 5:262 6:38 7:2704 +0 0:7 1:3 2:4 3:1740 4:7 5:19 6:36 7:214 +0 0:11 1:27 2:2 3:1536 4:13 5:279 6:38 7:1046 +1 0:1 1:28 2:5 3:1925 4:8 5:19 6:191 7:147 +0 0:10 1:7 2:2 3:1555 4:16 5:262 6:190 7:329 +0 0:2 1:22 2:4 3:1131 4:18 5:161 6:218 7:1515 +0 0:4 1:26 2:6 3:928 4:19 5:224 6:285 7:1605 +0 0:9 1:27 2:3 3:555 4:14 5:21 6:188 7:559 +0 0:6 1:18 2:2 3:648 4:1 5:82 6:83 7:1192 +0 1:30 3:834 4:14 5:146 6:256 7:946 +1 0:7 1:10 2:4 3:1115 4:22 5:38 6:140 7:413 +1 0:3 1:19 2:2 3:1511 4:5 5:100 6:89 7:487 +0 0:2 1:12 2:6 3:1335 4:13 5:168 6:64 7:544 +0 0:10 2:1 3:921 4:8 5:19 6:192 7:952 +0 0:7 1:6 3:1220 4:13 5:84 6:234 7:604 +0 0:8 1:25 2:6 3:1445 4:22 5:142 6:227 7:870 +0 0:7 1:29 3:1003 4:1 5:163 6:284 7:1593 +0 0:6 1:15 2:6 3:1225 4:16 5:270 6:48 7:574 +0 0:11 1:23 2:4 3:636 4:9 5:274 6:82 7:846 +0 0:6 1:8 3:1852 4:18 5:216 6:107 7:1182 +0 0:9 1:7 2:6 3:1756 4:1 5:38 6:83 7:1562 +0 0:11 1:28 2:2 3:823 4:20 5:215 6:36 7:1751 +1 0:3 1:20 2:3 3:1342 4:14 5:203 6:99 7:1008 +0 1:23 3:1247 4:20 5:15 6:78 7:324 +0 0:10 1:5 2:6 3:1717 4:7 5:58 6:19 7:259 +0 0:3 1:7 2:5 3:1530 4:20 5:182 6:2 7:1552 +1 0:8 1:2 2:5 3:1500 4:20 5:163 6:211 7:337 +0 0:10 1:13 2:6 3:955 4:20 5:274 6:227 7:338 +0 0:5 1:23 2:3 3:2245 4:19 5:161 6:184 7:2039 +0 0:5 1:2 2:4 3:930 4:20 5:246 6:272 7:188 +0 0:10 1:15 2:2 3:711 4:20 5:49 6:62 7:314 +1 0:10 1:3 2:5 3:2130 4:13 5:84 6:88 7:624 +0 0:9 1:12 2:3 3:1538 4:5 5:204 6:141 7:305 +0 0:1 1:16 2:1 3:1626 4:21 5:141 6:198 7:427 +0 0:10 1:13 2:6 3:559 4:19 5:146 6:64 7:428 +0 0:8 1:3 3:1850 4:13 5:163 6:202 7:267 +0 0:5 1:21 2:1 3:556 4:16 5:200 6:267 7:77 +0 0:1 1:27 2:3 3:1305 4:20 5:161 6:257 7:258 +0 0:3 1:16 2:5 3:1901 4:19 5:65 6:192 7:737 +0 0:2 1:22 2:3 3:1149 4:18 5:83 6:299 7:639 +0 0:6 1:27 2:2 3:2112 4:19 5:65 6:240 7:683 +0 0:10 1:16 2:2 3:1038 4:1 5:84 6:247 7:1062 +0 0:3 1:26 2:1 3:2026 4:4 5:269 6:156 7:1597 +0 0:6 1:4 2:2 3:1007 4:19 5:229 6:227 7:1813 +1 0:3 1:12 2:1 3:1610 4:20 5:261 6:211 7:671 +1 0:8 1:13 2:2 3:1655 4:15 5:190 6:19 7:952 +1 0:1 1:15 3:2000 4:19 5:65 6:38 7:728 +0 0:7 1:1 2:2 3:2111 4:21 5:141 6:121 7:166 +0 0:9 1:3 2:2 3:854 4:21 5:203 6:99 7:1008 +0 0:7 1:27 2:4 3:1220 4:7 5:83 6:275 7:391 +0 0:2 1:26 2:5 3:1829 4:16 5:264 6:275 7:269 +0 0:5 1:14 2:1 3:639 4:18 5:274 6:82 7:846 +0 0:4 1:29 2:1 3:1459 4:18 5:216 6:170 7:733 +0 0:5 1:17 2:4 3:1255 4:13 5:84 6:273 7:228 +1 0:1 1:14 2:5 3:1651 4:18 5:38 6:267 7:2704 +0 1:20 2:4 3:655 4:21 5:100 6:265 7:642 +0 0:8 1:19 2:1 3:1522 4:13 5:216 6:176 7:215 +1 0:1 1:16 3:1836 4:8 5:19 6:64 7:227 +0 0:7 1:12 2:5 3:1134 4:21 5:141 6:2 7:744 +0 0:7 1:11 3:1619 4:1 5:38 6:83 7:1562 +0 0:1 1:16 3:1341 4:1 5:279 6:81 7:719 +0 0:7 1:29 3:2038 4:20 5:48 6:211 7:325 +0 0:2 1:13 3:1410 4:20 5:36 6:155 7:484 +0 0:3 1:11 2:4 3:2209 4:16 5:262 6:262 7:191 +0 0:10 1:9 2:3 3:1216 4:1 5:246 6:83 7:1345 +0 0:9 1:3 2:2 3:1123 4:3 5:258 6:101 7:373 +0 0:10 1:27 2:6 3:1210 4:8 5:19 6:30 7:134 +0 0:2 1:17 2:4 3:933 4:1 5:84 6:267 7:1464 +0 1:13 2:4 3:1130 4:7 5:168 6:81 7:214 +0 0:2 1:26 2:6 3:817 4:7 5:163 6:19 7:1946 +0 0:7 1:13 2:5 3:1615 4:21 5:82 6:99 7:199 +1 0:2 1:6 2:1 3:933 4:13 5:216 6:214 7:693 +0 1:28 2:5 3:651 4:9 5:83 6:83 7:641 +1 0:10 1:4 2:5 3:825 4:3 5:157 6:266 7:909 +0 0:6 1:4 2:2 3:552 4:16 5:73 6:218 7:416 +0 0:5 1:16 2:4 3:1432 4:1 5:272 6:83 7:1431 +0 1:9 2:2 3:1657 4:1 5:84 6:36 7:631 +0 0:1 1:24 2:1 3:708 4:7 5:156 6:275 7:1989 +1 0:6 1:16 2:6 3:1205 4:13 5:84 6:122 7:931 +0 0:2 1:17 2:5 3:1140 4:20 5:225 6:214 7:833 +1 0:3 1:14 2:4 3:1436 4:21 5:100 6:247 7:416 +0 0:4 1:27 3:852 4:21 5:168 6:62 7:418 +1 0:9 1:2 2:1 3:1049 4:21 5:279 6:141 7:668 +1 1:9 2:1 3:1236 4:18 5:48 6:267 7:326 +0 0:2 1:11 2:3 3:1552 4:21 5:100 6:64 7:529 +1 0:5 1:19 3:2225 4:20 5:163 6:211 7:337 +0 0:4 1:27 3:1910 4:21 5:141 6:79 7:929 +0 0:9 1:5 2:4 3:1026 4:1 5:155 6:83 7:919 +1 0:10 1:24 2:3 3:2038 4:20 5:49 6:134 7:1246 +0 0:9 1:4 2:3 3:1110 4:15 5:20 6:74 7:411 +0 0:1 2:6 3:1205 4:16 5:197 6:267 7:78 +0 0:5 1:27 2:6 3:620 4:15 5:124 6:74 7:317 +0 0:5 1:21 2:2 3:2126 4:7 5:19 6:81 7:547 +0 0:3 1:11 2:4 3:1654 4:13 5:166 6:83 7:785 +0 0:7 1:8 2:1 3:625 4:14 5:84 6:188 7:432 +0 0:11 1:3 2:1 3:1506 4:16 5:202 6:275 7:436 +0 0:11 1:25 3:1018 4:14 5:90 6:156 7:508 +0 0:9 1:21 2:4 3:955 4:7 5:163 6:294 7:2158 +0 0:3 1:14 2:4 3:640 4:20 5:136 6:134 7:276 +0 0:1 1:21 2:6 3:1836 4:20 5:190 6:226 7:290 +0 1:11 2:6 3:936 4:15 5:140 6:74 7:388 +0 0:1 1:27 2:3 3:1737 4:21 5:36 6:62 7:448 +0 0:3 1:9 2:6 3:1455 4:18 5:140 6:267 7:2419 +0 0:4 1:30 2:2 3:726 4:12 5:289 6:227 7:1788 +0 0:3 1:28 2:3 3:1604 4:5 5:141 6:99 7:1400 +0 0:10 1:11 2:1 3:1226 4:13 5:252 6:164 7:109 +0 0:4 1:6 2:2 3:613 4:9 5:141 6:82 7:861 +0 0:4 1:25 2:4 3:1216 4:18 5:83 6:38 7:1754 +1 0:7 1:22 2:2 3:2245 4:13 5:216 6:173 7:552 +0 0:4 1:2 2:5 3:819 4:16 5:292 6:275 7:926 +0 0:7 1:21 2:6 3:636 4:18 5:262 6:227 7:651 +0 0:3 1:27 2:2 3:700 4:13 5:213 6:218 7:416 +0 0:6 1:21 2:5 3:1400 4:16 5:83 6:188 7:872 +0 0:8 1:6 2:2 3:1931 4:14 5:19 6:89 7:594 +1 0:4 1:16 2:3 3:1223 4:5 5:204 6:141 7:305 +0 0:9 1:8 2:6 3:1401 4:14 5:90 6:290 7:374 +0 0:8 1:9 2:5 3:1515 4:15 5:207 6:74 7:489 +0 0:8 1:11 2:4 3:753 4:13 5:216 6:142 7:588 +1 0:6 1:16 2:6 3:1334 4:19 5:224 6:155 7:742 +1 0:9 1:29 2:4 3:2005 4:19 5:204 6:64 7:651 +0 0:2 1:9 2:5 3:744 4:20 5:47 6:186 7:468 +0 0:8 1:20 2:2 3:1424 4:13 5:164 6:83 7:282 +1 0:9 1:26 2:2 3:1244 4:22 5:140 6:259 7:515 +1 0:8 1:18 3:1932 4:10 5:223 6:170 7:288 +0 1:10 2:2 3:1854 4:4 5:156 6:256 7:1074 +0 1:28 2:5 3:1256 4:20 5:224 6:186 7:668 +0 0:2 1:30 2:3 3:1110 4:13 5:84 6:311 7:281 +0 0:6 1:15 2:6 3:2115 4:10 5:19 6:142 7:781 +1 0:7 1:20 2:5 3:1644 4:18 5:83 6:218 7:888 +0 0:4 1:20 2:1 3:1415 4:3 5:157 6:266 7:909 +0 0:11 1:13 2:3 3:700 4:22 5:90 6:64 7:500 +0 0:8 1:18 2:6 3:1052 4:21 5:121 6:141 7:166 +0 0:6 1:17 3:823 4:21 5:78 6:99 7:884 +0 0:4 1:4 3:1550 4:16 5:62 6:164 7:86 +0 0:11 1:22 2:5 3:726 4:13 5:84 6:154 7:408 +1 0:6 1:2 3:1109 4:1 5:84 6:162 7:1055 +1 0:10 1:1 2:2 3:932 4:21 5:100 6:64 7:529 +0 0:7 1:23 2:1 3:1214 4:16 5:255 6:267 7:262 +0 0:10 1:20 3:1721 4:15 5:20 6:74 7:411 +1 0:6 1:1 3:1109 4:5 5:141 6:274 7:2007 +1 0:9 1:17 3:2100 4:20 5:242 6:294 7:587 +0 0:6 1:3 2:1 3:855 4:21 5:100 6:182 7:1092 +0 1:19 2:4 3:1020 4:21 5:279 6:99 7:872 +0 0:6 1:18 2:2 3:1748 4:18 5:38 6:267 7:2704 +0 0:8 1:11 2:3 3:850 4:20 5:95 6:78 7:562 +0 0:4 1:2 2:4 3:720 4:20 5:274 6:211 7:371 +0 0:5 2:2 3:630 4:20 5:209 6:186 7:1844 +1 0:7 1:29 2:6 3:2008 4:21 5:45 6:141 7:253 +0 1:18 2:3 3:1940 4:20 5:79 6:2 7:580 +0 0:5 1:12 2:6 3:912 4:16 5:253 6:82 7:794 +0 0:3 1:10 3:817 4:21 5:100 6:46 7:266 +0 0:8 1:28 2:1 3:649 4:1 5:279 6:193 7:1068 +0 0:3 1:8 2:6 3:824 4:20 5:163 6:227 7:370 +0 0:9 1:28 2:3 3:655 4:20 5:95 6:2 7:223 +0 0:1 1:17 2:1 3:941 4:20 5:272 6:162 7:397 +1 1:27 2:3 3:2038 4:5 5:100 6:258 7:1569 +0 0:5 1:9 2:5 3:1609 4:5 5:141 6:82 7:861 +0 0:1 1:3 2:2 3:1221 4:9 5:83 6:267 7:967 +0 0:11 1:23 2:4 3:2025 4:16 5:202 6:275 7:436 +0 0:6 1:19 2:3 3:821 4:19 5:25 6:64 7:644 +0 0:9 1:8 2:6 3:1257 4:13 5:142 6:218 7:588 +0 0:5 1:2 2:4 3:626 4:19 5:108 6:64 7:631 +0 0:7 1:11 2:1 3:1725 4:14 5:90 6:25 7:548 +0 0:2 1:19 3:936 4:1 5:84 6:251 7:1345 +0 0:2 1:12 2:6 3:1854 4:14 5:203 6:111 7:197 +0 0:10 1:1 2:3 3:1824 4:21 5:141 6:299 7:936 +0 0:1 1:6 2:6 3:843 4:10 5:184 6:83 7:797 +0 0:4 1:22 2:3 3:1438 4:10 5:289 6:19 7:406 +0 0:10 1:14 2:1 3:1535 4:15 5:217 6:38 7:468 +0 0:2 1:21 2:2 3:1117 4:14 5:198 6:205 7:449 +0 0:3 1:18 3:1326 4:1 5:221 6:218 7:1739 +0 0:9 1:15 2:5 3:1853 4:18 5:140 6:184 7:758 +0 0:2 1:18 2:5 3:1644 4:16 5:37 6:82 7:649 +0 0:7 1:28 2:6 3:2038 4:16 5:83 6:238 7:776 +0 1:23 2:6 3:1448 4:14 5:186 6:170 7:963 +0 0:2 1:23 2:2 3:1052 4:3 5:225 6:223 7:1009 +0 0:4 1:12 2:6 3:708 4:19 5:82 6:64 7:331 +0 0:6 1:28 2:4 3:1445 4:20 5:49 6:284 7:737 +0 0:4 1:22 2:3 3:1651 4:14 5:216 6:188 7:491 +1 0:10 1:9 2:4 3:2128 4:13 5:216 6:62 7:316 +0 0:10 1:11 2:1 3:2158 4:1 5:84 6:82 7:641 +1 0:10 1:8 2:2 3:1824 4:1 5:253 6:83 7:247 +1 0:3 1:14 2:4 3:1945 4:14 5:186 6:258 7:625 +0 0:6 1:22 2:6 3:707 4:3 5:157 6:159 7:234 +1 0:8 1:27 3:1853 4:18 5:38 6:164 7:2611 +1 0:9 1:24 3:945 4:15 5:156 6:253 7:264 +0 0:10 1:15 2:1 3:816 4:7 5:19 6:292 7:223 +1 0:6 1:12 2:3 3:1915 4:15 5:217 6:74 7:485 +0 0:7 1:15 3:558 4:16 5:148 6:257 7:94 +0 0:7 3:1200 4:13 5:38 6:49 7:370 +0 0:9 1:5 2:4 3:720 4:7 5:209 6:19 7:2130 +0 0:9 1:10 2:1 3:941 4:1 5:216 6:38 7:867 +0 0:6 1:3 2:2 3:830 4:14 5:19 6:205 7:906 +0 0:3 1:5 2:2 3:1810 4:20 5:225 6:215 7:1037 +0 0:8 1:5 2:2 3:1341 4:21 5:279 6:141 7:668 +1 0:5 1:24 2:3 3:1736 4:1 5:84 6:164 7:1235 +1 0:9 2:5 3:1309 4:19 5:204 6:64 7:651 +0 0:6 1:9 3:614 4:19 5:168 6:231 7:335 +0 0:3 1:16 2:5 3:1755 4:20 5:274 6:211 7:371 +1 0:9 1:27 2:3 3:2132 4:10 5:19 6:194 7:669 +0 0:3 1:1 2:5 3:1320 4:20 5:49 6:227 7:1999 +0 0:8 1:8 2:5 3:1534 4:13 5:253 6:78 7:248 +1 0:4 1:16 2:4 3:2053 4:17 5:184 6:170 7:725 +0 0:1 1:10 2:3 3:725 4:20 5:150 6:49 7:220 +1 1:7 3:2315 4:8 5:19 6:134 7:696 +0 0:7 2:6 3:2025 4:20 5:48 6:162 7:223 +0 0:2 1:10 2:6 3:638 4:14 5:100 6:188 7:946 +0 1:14 2:6 3:748 4:16 5:216 6:168 7:323 +0 0:4 1:10 2:6 3:836 4:22 5:225 6:299 7:110 +0 0:9 1:11 2:5 3:1309 4:12 5:225 6:82 7:602 +0 0:1 1:14 2:5 3:649 4:21 5:183 6:62 7:280 +0 0:8 1:18 2:6 3:1808 4:19 5:100 6:64 7:529 +0 0:5 1:26 2:6 3:738 4:20 5:134 6:21 7:148 +0 0:9 1:23 2:6 3:1222 4:13 5:84 6:134 7:247 +0 0:1 2:5 3:1227 4:7 5:108 6:74 7:932 +0 0:2 1:29 2:1 3:1324 4:7 5:294 6:19 7:1541 +0 0:9 1:4 2:2 3:853 4:12 5:225 6:247 7:1891 +0 1:29 2:5 3:2025 4:1 5:84 6:272 7:1438 +0 0:5 1:23 2:2 3:1211 4:1 5:2 6:218 7:1118 +1 0:3 1:14 2:4 3:1240 4:20 5:49 6:134 7:1246 +0 1:6 2:6 3:1821 4:19 5:225 6:162 7:256 +0 0:5 1:15 2:3 3:659 4:22 5:168 6:140 7:229 +0 0:3 1:9 2:6 3:858 4:13 5:154 6:83 7:408 +1 0:9 1:16 3:1807 4:13 5:24 6:218 7:122 +1 0:2 1:26 2:6 3:646 4:19 5:21 6:227 7:872 +0 0:6 1:25 3:1045 4:20 5:209 6:227 7:646 +0 0:11 1:23 2:4 3:948 4:20 5:209 6:266 7:671 +1 1:22 2:1 3:858 4:18 5:216 6:279 7:1726 +0 0:3 1:2 2:6 3:1231 4:19 5:65 6:247 7:130 +0 0:3 1:11 2:4 3:1422 4:1 5:108 6:83 7:1119 +1 0:4 1:11 2:3 3:1210 4:20 5:134 6:180 7:441 +0 0:4 1:2 2:5 3:810 4:19 5:155 6:226 7:742 +0 0:11 2:3 3:1110 4:20 5:134 6:186 7:937 +0 0:7 1:26 2:3 3:845 4:8 5:212 6:275 7:866 +0 0:11 1:27 2:1 3:1214 4:1 5:236 6:83 7:1126 +0 0:9 1:5 2:3 3:2248 4:16 5:163 6:217 7:47 +0 0:7 1:17 2:3 3:1100 4:20 5:163 6:227 7:370 +0 0:10 1:4 2:5 3:1045 4:21 5:190 6:99 7:209 +0 0:4 1:25 2:5 3:1900 4:11 5:158 6:133 7:163 +0 0:7 1:20 2:5 3:2258 4:3 5:261 6:38 7:2496 +0 0:1 1:28 2:5 3:1434 4:20 5:114 6:275 7:546 +0 0:7 1:10 2:4 3:1359 4:13 5:171 6:218 7:552 +0 0:10 1:30 2:2 3:615 4:8 5:226 6:19 7:564 +1 0:8 1:30 2:3 3:2054 4:18 5:140 6:164 7:2288 +0 0:2 1:14 2:2 3:836 4:8 5:19 6:118 7:300 +1 0:10 1:14 3:2332 4:10 5:19 6:64 7:227 +0 0:5 1:14 2:1 3:1432 4:15 5:171 6:74 7:513 +0 0:5 1:22 2:4 3:922 4:14 5:90 6:194 7:238 +0 0:7 1:6 3:2207 4:7 5:19 6:21 7:813 +0 0:9 1:25 2:1 3:1133 4:16 5:257 6:267 7:191 +0 0:4 1:7 2:2 3:1856 4:1 5:279 6:83 7:551 +1 0:6 2:5 3:1715 4:16 5:216 6:228 7:130 +0 0:7 1:19 2:5 3:1325 4:13 5:216 6:185 7:594 +0 0:6 1:13 2:4 3:1340 4:19 5:216 6:227 7:1440 +0 0:1 1:3 2:3 3:1856 4:20 5:267 6:162 7:386 +0 0:1 1:26 2:3 3:1045 4:18 5:163 6:184 7:2217 +0 0:4 1:16 2:4 3:848 4:11 5:221 6:133 7:2603 +0 0:10 1:6 3:745 4:15 5:75 6:253 7:461 +0 0:9 1:17 2:1 3:849 4:18 5:229 6:82 7:1290 +0 1:19 2:3 3:558 4:19 5:285 6:226 7:228 +0 0:7 1:17 2:2 3:719 4:7 5:168 6:222 7:1035 +0 1:10 2:2 3:1625 4:21 5:100 6:46 7:266 +0 0:4 1:12 2:6 3:1336 4:6 5:140 6:137 7:595 +0 0:9 1:11 2:5 3:1748 4:1 5:269 6:38 7:1674 +0 0:6 1:5 2:4 3:636 4:13 5:167 6:83 7:351 +0 0:9 1:28 2:3 3:1321 4:19 5:224 6:192 7:290 +1 0:9 1:30 2:6 3:640 4:3 5:16 6:220 7:549 +0 0:6 1:15 2:5 3:1200 4:20 5:261 6:227 7:1107 +0 1:8 3:1202 4:5 5:100 6:227 7:2133 +0 0:11 1:14 2:3 3:1115 4:21 5:213 6:141 7:781 +0 1:10 2:2 3:609 4:21 5:90 6:62 7:95 +0 0:6 1:26 2:1 3:1708 4:16 5:299 6:218 7:475 +0 0:10 1:15 2:2 3:1246 4:13 5:38 6:247 7:612 +0 1:10 2:2 3:817 4:7 5:225 6:74 7:1569 +0 0:2 1:12 2:6 3:703 4:14 5:163 6:188 7:1619 +0 0:8 1:22 2:4 3:1529 4:5 5:261 6:141 7:1874 +0 0:6 1:18 2:1 3:1020 4:20 5:134 6:258 7:192 +0 0:3 1:29 2:4 3:2055 4:14 5:90 6:81 7:405 +0 0:7 1:7 3:556 4:13 5:63 6:170 7:418 +0 0:3 1:22 2:6 3:1217 4:20 5:184 6:146 7:162 +0 1:7 2:6 3:1741 4:5 5:141 6:82 7:861 +0 0:7 1:19 2:5 3:1403 4:13 5:194 6:218 7:139 +1 0:9 2:5 3:1433 4:10 5:19 6:186 7:590 +0 0:7 1:15 3:2113 4:1 5:216 6:182 7:403 +0 0:5 1:26 2:5 3:903 4:13 5:84 6:141 7:224 +0 0:10 1:29 2:1 3:1354 4:19 5:82 6:170 7:214 +0 0:7 1:19 2:4 3:1850 4:14 5:110 6:89 7:56 +0 0:6 1:14 2:5 3:955 4:8 5:270 6:173 7:1156 +0 0:1 1:8 2:1 3:1319 4:13 5:38 6:284 7:1046 +0 1:13 2:4 3:1507 4:1 5:84 6:218 7:802 +0 0:3 1:26 2:1 3:1003 4:16 5:270 6:37 7:291 +0 0:10 1:21 3:2005 4:20 5:253 6:162 7:1069 +0 0:6 1:20 2:4 3:607 4:1 5:204 6:193 7:674 +0 0:5 1:22 2:3 3:558 4:7 5:245 6:19 7:481 +0 1:3 2:3 3:1001 4:5 5:100 6:62 7:404 +1 0:4 1:25 2:4 3:1427 4:19 5:65 6:294 7:508 +0 0:2 1:2 2:4 3:1202 4:7 5:19 6:222 7:545 +0 0:3 1:2 3:1813 4:20 5:161 6:48 7:223 +0 0:8 1:18 3:1331 4:21 5:21 6:62 7:1174 +0 0:2 1:6 2:1 3:959 4:14 5:90 6:184 7:957 +0 0:9 1:8 2:6 3:2133 4:16 5:163 6:262 7:155 +0 0:8 1:6 2:2 3:1126 4:14 5:203 6:218 7:334 +0 0:4 1:2 2:5 3:1527 4:5 5:182 6:141 7:853 +0 0:1 1:5 2:5 3:708 4:16 5:216 6:231 7:412 +0 0:6 1:17 3:650 4:15 5:138 6:74 7:123 +0 0:7 1:4 2:5 3:658 4:5 5:253 6:141 7:191 +1 0:9 1:19 2:2 3:2000 4:20 5:292 6:227 7:935 +0 0:1 1:8 2:1 3:657 4:3 5:84 6:266 7:1660 +0 0:10 1:11 2:1 3:1732 4:8 5:19 6:122 7:640 +0 0:7 1:23 3:1020 4:21 5:13 6:62 7:424 +0 0:3 2:4 3:2123 4:21 5:141 6:169 7:201 +0 0:5 1:9 2:4 3:2004 4:8 5:19 6:183 7:79 +0 0:1 1:10 2:3 3:916 4:1 5:95 6:83 7:551 +0 0:4 1:30 2:3 3:1430 4:8 5:19 6:47 7:712 +0 0:3 1:28 2:2 3:814 4:20 5:279 6:227 7:1262 +1 0:5 1:28 2:1 3:2040 4:15 5:279 6:74 7:307 +0 0:4 1:13 2:1 3:1119 4:7 5:278 6:19 7:445 +0 0:6 1:1 3:910 4:20 5:225 6:275 7:507 +1 0:2 1:29 2:2 3:728 4:13 5:84 6:45 7:383 +0 0:8 1:28 2:2 3:2105 4:18 5:216 6:49 7:622 +0 0:5 1:19 3:1850 4:11 5:133 6:172 7:102 +0 0:1 1:7 2:6 3:1855 4:20 5:261 6:227 7:1107 +0 0:4 1:2 2:5 3:1740 4:15 5:156 6:64 7:541 +0 0:7 1:11 3:951 4:19 5:224 6:182 7:1038 +1 0:2 1:12 2:6 3:1200 4:8 5:19 6:51 7:191 +0 0:4 1:17 2:5 3:708 4:7 5:180 6:19 7:692 +0 0:11 1:8 2:5 3:1258 4:16 5:262 6:277 7:86 +0 0:4 1:8 2:3 3:1341 4:20 5:261 6:37 7:399 +0 0:6 1:28 2:4 3:1135 4:10 5:90 6:19 7:594 +0 0:1 1:30 2:6 3:1120 4:20 5:161 6:227 7:256 +0 0:6 1:30 2:5 3:1842 4:1 5:84 6:206 7:448 +0 0:7 1:26 2:4 3:810 4:7 5:270 6:227 7:507 +0 0:6 1:3 2:1 3:1859 4:15 5:36 6:74 7:230 +0 0:10 1:11 2:2 3:1333 4:16 5:228 6:275 7:150 +0 1:17 2:1 3:647 4:18 5:216 6:231 7:412 +0 0:5 1:21 2:2 3:1635 4:8 5:166 6:19 7:303 +0 0:5 1:16 2:3 3:2204 4:7 5:19 6:21 7:813 +0 0:10 1:7 2:1 3:1610 4:20 5:182 6:186 7:989 +0 0:8 1:6 2:3 3:1415 4:1 5:21 6:83 7:190 +1 0:1 1:3 2:3 3:1812 4:21 5:63 6:218 7:316 +0 0:8 1:9 2:5 3:1752 4:1 5:156 6:164 7:2475 +0 0:3 1:16 2:5 3:1306 4:7 5:119 6:19 7:352 +0 1:6 2:6 3:1240 4:22 5:225 6:69 7:551 +0 0:2 1:29 2:2 3:2257 4:18 5:262 6:38 7:2704 +1 0:5 1:30 2:2 3:2139 4:1 5:216 6:205 7:334 +1 0:7 1:11 2:1 3:1158 4:14 5:82 6:188 7:762 +0 0:9 1:28 2:4 3:1738 4:11 5:170 6:133 7:102 +1 0:3 1:28 2:3 3:1954 4:4 5:242 6:156 7:426 +1 0:9 1:28 2:4 3:1658 4:20 5:180 6:164 7:1363 +0 0:8 1:26 2:6 3:800 4:20 5:217 6:184 7:655 +0 0:5 1:6 2:2 3:527 4:10 5:223 6:19 7:508 +1 0:10 1:28 3:845 4:19 5:82 6:222 7:857 +0 0:6 1:5 2:3 3:1052 4:6 5:140 6:256 7:890 +0 0:8 1:2 2:5 3:1209 4:5 5:267 6:141 7:1608 +0 0:2 1:17 2:5 3:1546 4:1 5:133 6:164 7:2556 +0 0:11 1:10 2:1 3:1235 4:13 5:168 6:231 7:335 +0 1:18 2:2 3:1733 4:15 5:278 6:74 7:812 +0 0:1 2:5 3:1544 4:16 5:270 6:227 7:507 +1 0:6 1:21 2:5 3:736 4:18 5:272 6:164 7:373 +0 0:2 1:7 2:2 3:1254 4:16 5:202 6:275 7:436 +1 0:9 1:25 3:2155 4:20 5:184 6:164 7:1750 +0 0:3 1:20 2:3 3:1551 4:1 5:84 6:189 7:468 +1 0:11 1:21 2:3 3:1630 4:20 5:225 6:134 7:1020 +0 0:10 1:3 2:5 3:33 4:5 5:163 6:62 7:2053 +0 0:6 1:6 2:5 3:1738 4:21 5:180 6:141 7:643 +0 0:5 1:24 2:3 3:1010 4:16 5:113 6:218 7:157 +0 0:2 1:14 2:2 3:707 4:5 5:100 6:83 7:1372 +0 0:8 1:28 2:1 3:1410 4:12 5:261 6:227 7:1107 +0 0:2 1:7 2:3 3:1544 4:22 5:169 6:227 7:355 +0 0:5 1:15 2:2 3:1217 4:1 5:84 6:164 7:1235 +0 0:4 1:5 3:815 4:15 5:182 6:256 7:133 +0 0:5 1:20 3:1720 4:16 5:59 6:267 7:153 +0 0:1 1:3 2:2 3:1014 4:1 5:216 6:205 7:334 +0 0:7 2:6 3:2050 4:7 5:75 6:206 7:700 +0 0:7 1:1 2:2 3:1415 4:20 5:272 6:48 7:358 +0 0:1 1:6 2:5 3:958 4:1 5:216 6:240 7:849 +1 0:2 1:27 3:1131 4:3 5:157 6:271 7:95 +1 0:2 1:19 2:6 3:2004 4:1 5:191 6:19 7:595 +0 0:6 1:2 3:900 4:13 5:168 6:65 7:478 +0 0:1 1:13 2:4 3:1223 4:7 5:19 6:123 7:306 +1 0:6 1:5 2:4 3:1334 4:5 5:108 6:99 7:1065 +0 0:10 1:19 2:6 3:1251 4:21 5:141 6:308 7:510 +0 0:9 1:13 2:4 3:1325 4:22 5:216 6:252 7:531 +0 0:3 1:12 2:2 3:1829 4:7 5:168 6:38 7:185 +0 1:29 2:5 3:1045 4:10 5:155 6:19 7:270 +1 0:10 1:12 2:6 3:1800 4:15 5:156 6:247 7:426 +0 0:1 1:19 2:3 3:1727 4:13 5:135 6:218 7:738 +0 0:4 1:28 2:1 3:1343 4:18 5:262 6:279 7:372 +0 0:8 1:1 2:5 3:1614 4:19 5:225 6:251 7:601 +0 0:2 1:11 2:3 3:1045 4:15 5:75 6:156 7:589 +0 0:4 1:26 2:6 3:1359 4:17 5:161 6:133 7:2762 +0 0:7 1:19 2:4 3:1558 4:18 5:216 6:205 7:334 +0 0:9 1:26 2:1 3:1009 4:7 5:270 6:74 7:1449 +0 0:9 1:21 2:4 3:1037 4:1 5:216 6:284 7:258 +0 1:3 2:2 3:943 4:21 5:237 6:99 7:160 +0 0:10 1:19 2:6 3:614 4:1 5:36 6:83 7:631 +0 0:2 1:6 2:1 3:618 4:7 5:140 6:19 7:533 +0 0:5 1:18 2:5 3:2349 4:12 5:161 6:82 7:629 +0 0:10 1:13 2:6 3:1614 4:18 5:216 6:25 7:783 +0 0:3 1:3 3:2016 4:1 5:216 6:272 7:1829 +0 0:9 2:4 3:1106 4:11 5:133 6:151 7:216 +0 0:6 1:2 2:1 3:923 4:8 5:19 6:242 7:1027 +1 0:7 1:19 2:5 3:1805 4:1 5:163 6:83 7:1235 +0 0:6 1:2 2:1 3:1445 4:13 5:242 6:25 7:532 +0 0:2 1:5 3:1206 4:20 5:272 6:227 7:647 +0 0:5 1:17 2:5 3:830 4:20 5:66 6:49 7:336 +0 0:10 1:12 2:6 3:749 4:16 5:216 6:46 7:763 +1 0:9 1:4 2:2 3:1654 4:1 5:279 6:193 7:1068 +0 0:7 1:26 2:3 3:1523 4:14 5:160 6:89 7:74 +0 0:2 1:21 2:1 3:1615 4:20 5:225 6:217 7:325 +1 0:2 1:22 2:3 3:1154 4:18 5:221 6:218 7:1739 +0 0:3 1:26 3:1317 4:1 5:38 6:83 7:1562 +1 0:3 1:24 2:5 3:1653 4:5 5:100 6:266 7:2401 +0 0:3 1:3 2:1 3:908 4:20 5:161 6:173 7:1295 +1 0:2 1:21 2:1 3:1459 4:18 5:140 6:267 7:2419 +0 0:2 1:17 2:5 3:1650 4:14 5:203 6:122 7:408 +0 1:5 2:5 3:1950 4:20 5:49 6:36 7:588 +0 0:6 1:6 2:4 3:1911 4:21 5:141 6:194 7:984 +0 0:4 1:3 2:5 3:1727 4:8 5:292 6:19 7:674 +1 0:7 1:21 2:6 3:901 4:9 5:83 6:227 7:602 +0 0:7 1:1 2:2 3:538 4:5 5:204 6:141 7:305 +0 0:9 1:12 2:2 3:1310 4:20 5:180 6:164 7:1363 +1 0:10 1:25 2:4 3:2025 4:21 5:100 6:124 7:594 +1 0:9 1:28 2:3 3:2118 4:1 5:100 6:218 7:719 +1 0:3 1:27 2:1 3:1826 4:20 5:134 6:155 7:816 +0 0:4 1:12 2:6 3:1007 4:16 5:270 6:299 7:601 +0 0:8 1:15 2:3 3:1113 4:4 5:108 6:170 7:1076 +1 0:1 1:13 2:5 3:1458 4:8 5:105 6:19 7:331 +0 0:2 1:16 2:3 3:726 4:1 5:90 6:193 7:1145 +0 0:1 1:13 2:4 3:721 4:1 5:83 6:193 7:1709 +0 0:5 1:13 2:1 3:838 4:7 5:75 6:162 7:1678 +0 0:3 1:27 2:2 3:1840 4:10 5:182 6:49 7:787 +0 0:9 1:26 2:1 3:1232 4:12 5:252 6:227 7:304 +0 1:17 2:1 3:805 4:10 5:19 6:193 7:595 +0 1:10 2:2 3:1301 4:19 5:47 6:64 7:546 +0 0:11 1:15 2:4 3:1005 4:1 5:182 6:193 7:193 +0 0:3 1:2 3:632 4:13 5:21 6:247 7:1162 +0 0:9 1:24 3:1349 4:16 5:104 6:82 7:844 +0 0:2 1:20 2:1 3:802 4:1 5:163 6:83 7:1235 +1 0:5 1:17 2:5 3:1627 4:1 5:279 6:193 7:1068 +0 1:18 2:2 3:1720 4:13 5:265 6:83 7:190 +1 0:8 1:29 2:3 3:1625 4:20 5:184 6:247 7:632 +0 0:8 1:9 2:6 3:1748 4:13 5:200 6:164 7:267 +0 0:7 1:9 2:3 3:1738 4:16 5:188 6:223 7:222 +0 0:5 1:5 3:939 4:1 5:156 6:274 7:1597 +0 1:18 2:2 3:1456 4:19 5:220 6:64 7:590 +0 0:6 1:15 2:5 3:2025 4:16 5:270 6:126 7:463 +0 0:10 1:4 2:6 3:1707 4:5 5:63 6:164 7:2053 +0 0:9 1:27 2:3 3:1355 4:16 5:201 6:218 7:109 +0 0:7 1:17 2:2 3:2015 4:13 5:242 6:99 7:416 +0 0:8 1:29 2:3 3:846 4:18 5:262 6:158 7:2367 +1 0:5 1:16 2:3 3:1541 4:7 5:270 6:164 7:590 +0 0:7 1:8 2:2 3:2226 4:18 5:163 6:184 7:2217 +1 0:7 1:5 2:6 3:1507 4:8 5:19 6:135 7:780 +0 1:2 2:2 3:1202 4:1 5:84 6:284 7:551 +0 0:5 1:24 2:3 3:538 4:7 5:224 6:19 7:665 +1 0:8 1:11 2:3 3:938 4:15 5:75 6:214 7:756 +0 1:16 2:1 3:1103 4:10 5:108 6:19 7:581 +0 0:10 1:19 2:6 3:1024 4:20 5:246 6:162 7:345 +1 0:10 1:25 2:3 3:1449 4:16 5:146 6:218 7:177 +1 0:7 1:2 2:3 3:1447 4:18 5:184 6:82 7:895 +0 0:10 1:13 3:930 4:22 5:65 6:234 7:488 +0 0:5 1:28 3:2240 4:16 5:262 6:245 7:199 +1 0:3 1:2 2:6 3:1633 4:5 5:38 6:99 7:200 +0 0:8 1:27 2:1 3:734 4:13 5:63 6:218 7:316 +0 0:9 1:25 3:847 4:12 5:2 6:227 7:328 +0 1:26 2:3 3:2126 4:19 5:224 6:38 7:280 +0 1:3 2:3 3:1057 4:20 5:15 6:78 7:324 +0 0:3 1:6 2:4 3:822 4:1 5:141 6:83 7:224 +0 0:5 1:5 3:1027 4:16 5:89 6:218 7:299 +0 0:5 1:19 3:851 4:21 5:140 6:99 7:213 +0 0:8 1:13 2:2 3:755 4:16 5:83 6:275 7:391 +1 0:10 1:28 2:6 3:1057 4:13 5:299 6:218 7:475 +0 0:8 1:12 3:2027 4:1 5:133 6:83 7:3784 +0 0:4 1:22 2:3 3:1253 4:7 5:262 6:275 7:599 +0 0:4 2:1 3:1327 4:8 5:222 6:19 7:247 +0 0:2 1:26 2:6 3:716 4:21 5:165 6:141 7:127 +0 0:11 1:20 2:3 3:1531 4:9 5:83 6:284 7:770 +0 0:8 1:2 2:5 3:1152 4:8 5:56 6:19 7:106 +1 0:4 1:19 2:6 3:1912 4:7 5:83 6:19 7:1199 +1 0:3 1:22 2:6 3:2039 4:18 5:216 6:185 7:594 +1 0:11 1:20 2:3 3:2157 4:1 5:156 6:164 7:2475 +1 0:7 1:18 2:3 3:1241 4:21 5:299 6:141 7:772 +0 0:4 1:16 2:3 3:938 4:3 5:215 6:266 7:956 +0 0:11 1:15 2:5 3:1005 4:22 5:225 6:262 7:509 +0 0:2 1:4 3:1000 4:20 5:21 6:78 7:189 +0 0:7 1:25 2:3 3:1034 4:5 5:100 6:107 7:1065 +1 0:8 1:11 2:3 3:1705 4:20 5:163 6:211 7:337 +0 0:6 1:19 2:3 3:1130 4:20 5:272 6:162 7:397 +0 0:1 1:19 2:3 3:715 4:20 5:66 6:162 7:1772 +0 0:10 1:10 2:4 3:1514 4:20 5:190 6:36 7:938 +0 0:7 1:30 3:1218 4:1 5:267 6:83 7:1438 +1 0:8 1:15 2:4 3:2135 4:20 5:182 6:25 7:1050 +0 0:11 1:2 2:6 3:2014 4:20 5:2 6:227 7:328 +0 1:14 2:6 3:1237 4:21 5:100 6:64 7:529 +0 0:5 1:14 2:2 3:1539 4:5 5:100 6:247 7:416 +1 1:21 2:6 3:1450 4:20 5:161 6:164 7:236 +0 0:7 1:21 3:1659 4:21 5:63 6:122 7:214 +1 0:8 1:19 3:1925 4:20 5:260 6:186 7:271 +0 0:9 1:30 2:5 3:1926 4:14 5:203 6:258 7:1097 +0 0:4 1:1 2:4 3:1208 4:14 5:90 6:188 7:610 +0 0:11 1:9 3:1017 4:13 5:38 6:81 7:399 +0 1:16 2:1 3:1133 4:16 5:216 6:231 7:412 +1 0:4 1:6 2:2 3:2050 4:20 5:163 6:272 7:308 +0 0:10 1:5 3:1222 4:1 5:100 6:193 7:1086 +0 0:6 1:22 2:6 3:620 4:14 5:204 6:188 7:349 +0 0:9 1:18 2:1 3:1205 4:15 5:38 6:247 7:612 +0 0:3 1:11 2:5 3:1020 4:19 5:229 6:64 7:366 +0 0:11 1:1 2:5 3:630 4:20 5:225 6:162 7:256 +0 0:5 1:15 2:2 3:1417 4:7 5:19 6:49 7:576 +0 0:5 1:7 2:2 3:2158 4:7 5:163 6:294 7:2158 +0 0:4 1:20 2:1 3:1050 4:8 5:19 6:224 7:247 +1 0:3 1:18 3:1440 4:20 5:161 6:217 7:197 +0 0:9 1:7 2:6 3:1214 4:13 5:29 6:38 7:201 +1 0:11 1:10 2:1 3:1602 4:19 5:216 6:162 7:1515 +0 0:1 1:22 3:2049 4:21 5:63 6:182 7:694 +0 0:3 1:10 3:631 4:18 5:216 6:107 7:1182 +0 0:2 1:11 2:3 3:716 4:15 5:292 6:74 7:646 +1 0:7 1:12 2:4 3:1603 4:5 5:224 6:141 7:1324 +0 1:23 2:6 3:1111 4:7 5:2 6:19 7:1269 +0 0:8 1:27 3:1536 4:13 5:173 6:83 7:394 +1 0:8 1:21 2:2 3:2008 4:7 5:124 6:19 7:153 +0 0:6 1:10 2:1 3:2333 4:15 5:19 6:74 7:373 +0 0:4 1:19 2:6 3:1529 4:5 5:251 6:62 7:1025 +0 0:10 1:2 2:3 3:703 4:9 5:83 6:267 7:967 +0 0:7 1:24 2:2 3:2259 4:10 5:19 6:52 7:528 +0 0:11 1:28 2:3 3:1315 4:18 5:267 6:218 7:1829 +0 1:29 2:6 3:1310 4:21 5:192 6:99 7:725 +0 0:10 1:29 3:600 4:13 5:38 6:81 7:399 +0 0:11 1:11 2:5 3:629 4:18 5:267 6:218 7:1829 +0 0:8 1:28 2:1 3:525 4:8 5:247 6:19 7:357 +0 0:11 1:23 2:4 3:1237 4:10 5:182 6:19 7:403 +0 0:5 1:17 2:5 3:1240 4:20 5:13 6:49 7:288 +1 0:5 1:9 2:5 3:1524 4:1 5:84 6:89 7:987 +0 0:5 1:12 3:832 4:14 5:289 6:89 7:983 +0 0:1 1:14 2:6 3:1325 4:18 5:83 6:218 7:888 +0 0:6 1:27 2:2 3:1002 4:18 5:216 6:275 7:1249 +0 0:7 1:7 2:1 3:1250 4:21 5:245 6:99 7:278 +0 0:6 1:9 3:1145 4:5 5:141 6:272 7:1608 +0 0:1 1:24 2:1 3:1523 4:10 5:19 6:89 7:594 +1 0:3 1:23 2:5 3:1134 4:13 5:5 6:83 7:89 +1 0:4 1:22 2:3 3:1820 4:20 5:289 6:30 7:460 +0 0:6 1:16 3:2111 4:10 5:19 6:256 7:515 +0 0:5 1:26 2:5 3:1050 4:20 5:267 6:48 7:296 +1 0:2 1:23 2:3 3:1539 4:20 5:63 6:162 7:1825 +0 0:9 1:28 2:3 3:625 4:1 5:137 6:83 7:603 +0 1:4 2:4 3:653 4:8 5:134 6:19 7:696 +0 0:7 1:5 2:5 3:1717 4:7 5:66 6:74 7:116 +0 0:8 1:11 2:4 3:1214 4:10 5:146 6:256 7:946 +0 0:10 1:2 2:4 3:2302 4:14 5:161 6:110 7:1733 +0 0:2 1:16 2:3 3:850 4:16 5:276 6:218 7:174 +0 0:9 1:23 2:6 3:818 4:16 5:216 6:188 7:491 +0 0:8 1:16 2:4 3:625 4:5 5:38 6:99 7:200 +0 0:11 1:17 3:719 4:1 5:137 6:83 7:603 +1 0:7 1:13 2:5 3:1632 4:3 5:16 6:9 7:252 +0 0:9 1:8 2:6 3:1159 4:19 5:82 6:64 7:331 +0 0:5 1:14 2:2 3:1850 4:20 5:237 6:226 7:238 +0 0:8 1:22 2:5 3:716 4:7 5:163 6:19 7:1946 +0 0:9 1:7 2:6 3:1345 4:13 5:84 6:186 7:797 +0 0:8 1:4 3:2254 4:3 5:261 6:101 7:1533 +0 0:5 1:29 2:2 3:1514 4:10 5:224 6:19 7:665 +0 0:4 1:14 2:1 3:1558 4:11 5:133 6:213 7:100 +1 0:8 1:19 2:1 3:820 4:10 5:19 6:231 7:526 +1 1:14 2:6 3:1654 4:7 5:19 6:219 7:516 +1 0:2 1:28 2:1 3:1838 4:13 5:56 6:218 7:501 +0 0:5 1:26 2:5 3:1852 4:14 5:203 6:120 7:252 +0 0:5 1:29 2:1 3:1255 4:20 5:261 6:275 7:689 +0 0:8 1:5 2:2 3:701 4:16 5:270 6:82 7:391 +0 0:1 1:20 2:4 3:628 4:11 5:133 6:213 7:100 +0 0:5 1:11 2:3 3:603 4:16 5:132 6:275 7:402 +0 0:9 1:10 2:2 3:1631 4:20 5:79 6:134 7:239 +0 0:7 1:15 3:557 4:13 5:242 6:81 7:227 +0 1:6 2:5 3:746 4:1 5:262 6:156 7:2586 +0 0:2 1:12 2:6 3:930 4:20 5:134 6:78 7:239 +0 0:10 1:4 2:6 3:1329 4:18 5:83 6:140 7:1452 +0 0:7 1:9 2:3 3:1128 4:1 5:141 6:218 7:925 +0 1:5 2:4 3:1330 4:21 5:10 6:141 7:190 +0 0:11 1:10 3:1957 4:15 5:36 6:74 7:230 +0 0:5 1:7 2:3 3:1234 4:7 5:186 6:19 7:332 +0 0:8 2:2 3:1802 4:13 5:124 6:218 7:577 +0 0:7 1:27 2:5 3:1446 4:1 5:261 6:218 7:1721 +0 0:5 1:28 3:635 4:16 5:252 6:164 7:109 +1 0:4 1:15 2:2 3:1345 4:7 5:47 6:19 7:712 +0 0:4 1:14 2:2 3:1607 4:18 5:216 6:164 7:1745 +0 0:7 1:23 3:614 4:3 5:261 6:164 7:954 +1 0:3 1:5 2:3 3:1959 4:21 5:51 6:99 7:602 +1 0:7 1:2 2:3 3:1640 4:20 5:161 6:82 7:629 +0 0:1 1:13 2:4 3:1215 4:20 5:272 6:162 7:397 +0 0:1 1:21 2:5 3:1110 4:8 5:299 6:294 7:547 +0 0:10 1:6 2:1 3:1043 4:14 5:203 6:284 7:449 +0 0:8 1:3 3:1424 4:7 5:19 6:294 7:406 +1 0:3 1:23 2:5 3:1952 4:13 5:303 6:83 7:626 +0 0:10 1:3 2:4 3:1631 4:14 5:225 6:205 7:1276 +0 0:11 1:10 2:1 3:1620 4:1 5:168 6:83 7:1389 +1 0:11 1:25 3:1653 4:20 5:225 6:164 7:370 +1 0:6 1:30 2:6 3:2310 4:22 5:161 6:214 7:987 +0 0:4 1:20 2:1 3:1427 4:14 5:203 6:267 7:1589 +0 0:5 1:13 3:1426 4:1 5:251 6:218 7:1120 +0 0:9 1:19 2:2 3:1232 4:16 5:188 6:223 7:222 +0 0:3 1:30 2:5 3:1709 4:20 5:180 6:82 7:533 +0 0:3 1:25 3:1013 4:22 5:83 6:87 7:251 +0 0:9 1:2 2:1 3:1357 4:20 5:161 6:48 7:223 +0 0:4 1:29 2:2 3:810 4:10 5:229 6:184 7:834 +1 0:9 1:5 2:3 3:1855 4:20 5:163 6:272 7:308 +0 0:11 1:22 2:5 3:1329 4:20 5:246 6:223 7:444 +0 0:9 1:9 2:1 3:16 4:1 5:163 6:83 7:1235 +0 0:1 1:20 2:4 3:1003 4:8 5:122 6:19 7:640 +1 0:9 1:6 2:4 3:2200 4:8 5:122 6:19 7:640 +1 0:9 1:1 2:6 3:2120 4:1 5:84 6:21 7:190 +1 0:3 1:17 2:6 3:2225 4:20 5:289 6:222 7:174 +0 0:3 1:10 2:1 3:1319 4:8 5:19 6:234 7:272 +0 0:2 1:23 2:2 3:1830 4:20 5:108 6:49 7:925 +0 0:5 1:30 2:2 3:1443 4:19 5:65 6:182 7:809 +0 0:11 1:28 2:2 3:1531 4:11 5:170 6:133 7:102 +0 0:11 1:4 2:2 3:2256 4:19 5:163 6:64 7:2125 +1 0:10 1:26 2:4 3:1150 4:7 5:154 6:19 7:341 +0 0:3 1:7 2:5 3:613 4:3 5:261 6:227 7:1107 +0 0:6 1:5 2:3 3:740 4:7 5:203 6:19 7:906 +0 0:11 1:23 2:4 3:1657 4:6 5:13 6:140 7:325 +1 0:8 1:14 2:3 3:1947 4:7 5:171 6:19 7:453 +0 0:6 1:18 2:2 3:1607 4:16 5:221 6:275 7:630 +0 0:11 1:18 3:1438 4:3 5:262 6:238 7:421 +0 0:9 1:26 2:1 3:1425 4:16 5:83 6:260 7:916 +0 0:11 1:15 2:4 3:1534 4:19 5:65 6:253 7:573 +0 0:3 1:10 3:1021 4:14 5:216 6:89 7:235 +0 0:9 1:2 2:1 3:717 4:20 5:49 6:25 7:283 +0 0:10 1:21 3:1130 4:20 5:36 6:65 7:338 +0 0:5 1:7 2:2 3:733 4:1 5:108 6:218 7:1182 +0 0:4 1:15 2:3 3:1653 4:22 5:140 6:182 7:926 +0 0:8 1:15 2:4 3:604 4:1 5:252 6:218 7:1723 +0 0:2 1:3 2:5 3:1240 4:15 5:75 6:36 7:230 +0 0:11 1:18 3:2107 4:5 5:274 6:99 7:2433 +0 0:1 1:20 2:5 3:820 4:20 5:79 6:258 7:248 +1 0:4 1:6 2:1 3:2322 4:18 5:262 6:99 7:2565 +0 0:2 1:2 2:4 3:725 4:7 5:100 6:275 7:1968 +1 1:14 2:5 3:1834 4:1 5:140 6:164 7:2288 +0 1:22 2:1 3:1346 4:7 5:186 6:19 7:332 +0 0:6 1:3 2:1 3:540 4:7 5:225 6:19 7:1587 +0 1:8 3:2033 4:7 5:270 6:267 7:599 +0 0:8 1:15 2:4 3:1028 4:20 5:2 6:299 7:321 +1 0:5 1:1 2:3 3:2109 4:1 5:216 6:82 7:888 +0 0:2 1:11 2:3 3:1425 4:13 5:162 6:83 7:140 +0 0:6 1:3 2:2 3:1637 4:1 5:84 6:272 7:1438 +0 0:5 1:21 2:1 3:1300 4:20 5:2 6:162 7:487 +0 0:3 1:6 2:4 3:1213 4:20 5:134 6:78 7:239 +0 0:6 1:11 2:6 3:2134 4:1 5:84 6:284 7:551 +0 0:3 1:21 2:4 3:1752 4:7 5:224 6:19 7:665 +1 0:8 1:12 3:1911 4:12 5:272 6:227 7:647 +1 0:7 1:5 2:5 3:1338 4:13 5:84 6:123 7:999 +0 0:8 1:1 2:5 3:1923 4:7 5:191 6:19 7:595 +1 0:8 1:13 2:1 3:1725 4:1 5:84 6:162 7:1055 +0 0:8 1:2 2:5 3:940 4:7 5:108 6:19 7:581 +0 0:2 1:25 2:5 3:1545 4:14 5:90 6:36 7:457 +0 0:3 1:12 2:2 3:934 4:20 5:237 6:49 7:328 +0 0:1 1:17 2:1 3:918 4:18 5:289 6:140 7:810 +0 0:10 1:13 2:6 3:809 4:3 5:102 6:43 7:503 +0 0:11 1:9 2:6 3:1802 4:16 5:2 6:275 7:493 +0 0:9 1:5 2:3 3:1126 4:7 5:270 6:235 7:521 +0 0:2 1:1 2:3 3:628 4:7 5:237 6:19 7:903 +0 0:11 1:3 3:1429 4:21 5:140 6:62 7:288 +0 0:4 1:13 3:1148 4:13 5:216 6:24 7:122 +1 0:10 1:13 2:6 3:1339 4:19 5:65 6:184 7:468 +0 0:10 1:24 2:3 3:832 4:16 5:163 6:257 7:109 +0 0:8 1:29 2:2 3:1628 4:16 5:70 6:164 7:833 +0 0:6 1:20 2:3 3:1240 4:7 5:30 6:19 7:134 +0 0:6 1:11 2:5 3:1448 4:18 5:216 6:284 7:258 +0 0:10 1:13 3:600 4:1 5:221 6:83 7:1616 +1 0:10 2:1 3:1845 4:1 5:108 6:83 7:1119 +0 0:11 1:13 2:3 3:1701 4:20 5:134 6:154 7:359 +0 0:9 1:27 2:3 3:1837 4:21 5:65 6:141 7:913 +1 0:7 1:16 2:1 3:1812 4:3 5:114 6:266 7:224 +0 0:7 1:9 2:3 3:1836 4:21 5:260 6:141 7:788 +0 0:9 1:3 2:1 3:1702 4:15 5:122 6:74 7:268 +0 0:7 1:23 2:1 3:1353 4:13 5:252 6:272 7:417 +0 1:18 2:3 3:1847 4:5 5:100 6:218 7:719 +0 0:1 1:16 2:1 3:1812 4:19 5:225 6:284 7:1262 +0 0:11 1:30 2:4 3:1337 4:15 5:217 6:74 7:485 +1 0:11 1:21 2:4 3:1058 4:7 5:19 6:193 7:595 +0 0:5 1:20 3:1906 4:6 5:140 6:231 7:183 +0 0:2 1:17 2:5 3:2033 4:5 5:100 6:266 7:2401 +0 0:8 1:25 2:5 3:942 4:13 5:212 6:83 7:175 +0 0:7 1:20 2:6 3:1620 4:20 5:150 6:49 7:220 +0 0:7 1:11 2:1 3:1155 4:21 5:278 6:62 7:967 +0 0:5 1:24 2:3 3:1439 4:7 5:75 6:192 7:741 +1 0:2 1:30 2:3 3:2018 4:7 5:82 6:19 7:547 +0 1:1 3:1914 4:21 5:141 6:140 7:1190 +1 0:10 1:23 2:2 3:2118 4:13 5:38 6:284 7:1046 +0 0:8 1:28 2:2 3:1441 4:9 5:261 6:82 7:1024 +0 0:9 1:15 2:6 3:1201 4:20 5:267 6:257 7:417 +0 0:6 1:4 2:2 3:1122 4:4 5:46 6:156 7:267 +1 0:9 1:12 2:2 3:1200 4:20 5:225 6:211 7:646 +0 0:6 1:20 2:3 3:1800 4:1 5:220 6:83 7:1103 +0 0:10 1:17 2:4 3:759 4:13 5:163 6:272 7:308 +0 1:25 2:1 3:751 4:7 5:182 6:170 7:950 +0 0:6 1:22 2:6 3:1440 4:20 5:161 6:223 7:762 +0 0:8 1:26 2:6 3:1245 4:7 5:75 6:294 7:773 +0 0:1 1:25 2:2 3:1552 4:19 5:65 6:107 7:631 +1 0:2 1:13 2:1 3:1736 4:19 5:269 6:64 7:1474 +0 1:17 2:2 3:709 4:10 5:83 6:19 7:1199 +0 0:10 1:30 2:2 3:1947 4:19 5:65 6:253 7:573 +0 0:8 1:8 2:4 3:954 4:18 5:216 6:215 7:416 +0 0:7 1:7 2:1 3:555 4:9 5:36 6:82 7:1013 +0 0:8 1:21 2:3 3:1636 4:5 5:100 6:38 7:200 +0 0:10 1:30 2:2 3:610 4:16 5:80 6:218 7:240 +0 0:9 1:28 2:4 3:1621 4:5 5:38 6:99 7:200 +0 0:6 1:10 2:1 3:2129 4:7 5:262 6:156 7:2586 +0 0:10 1:10 2:4 3:625 4:3 5:102 6:266 7:1533 +0 0:11 1:28 2:3 3:823 4:14 5:262 6:205 7:1589 +0 0:9 1:18 2:2 3:835 4:20 5:108 6:294 7:197 +0 0:2 1:26 2:6 3:2008 4:20 5:225 6:21 7:872 +1 0:3 1:23 2:4 3:1205 4:7 5:168 6:222 7:1035 +1 0:4 1:3 2:6 3:1507 4:22 5:140 6:209 7:372 +0 0:2 1:3 2:6 3:916 4:7 5:75 6:256 7:879 +0 1:4 2:3 3:825 4:18 5:140 6:164 7:2288 +0 0:3 1:23 2:5 3:654 4:7 5:182 6:156 7:944 +0 0:1 1:27 2:3 3:1729 4:12 5:180 6:227 7:1044 +0 0:5 1:20 2:1 3:635 4:20 5:209 6:275 7:588 +0 0:3 1:27 2:1 3:2055 4:5 5:141 6:215 7:781 +0 0:9 1:28 2:3 3:1538 4:12 5:252 6:227 7:304 +0 0:4 1:27 2:6 3:1949 4:15 5:75 6:290 7:527 +0 0:11 1:21 2:4 3:1124 4:10 5:19 6:182 7:692 +0 0:1 1:12 2:3 3:1819 4:16 5:262 6:69 7:963 +0 0:7 1:8 2:1 3:2249 4:12 5:161 6:205 7:1300 +0 0:8 1:30 2:3 3:1732 4:13 5:35 6:218 7:116 +0 0:8 1:22 2:4 3:1251 4:21 5:63 6:284 7:487 +1 0:5 1:5 3:1727 4:8 5:19 6:11 7:143 +0 0:7 1:25 2:3 3:1912 4:7 5:270 6:37 7:291 +0 1:2 2:2 3:1245 4:20 5:209 6:257 7:446 +0 0:4 1:10 2:5 3:935 4:15 5:238 6:38 7:95 +1 1:23 2:6 3:1455 4:1 5:84 6:218 7:802 +0 0:2 1:13 2:1 3:955 4:21 5:285 6:99 7:194 +0 0:4 1:22 2:3 3:812 4:19 5:224 6:294 7:920 +0 0:4 2:2 3:1627 4:21 5:178 6:141 7:429 +1 0:3 1:26 3:1550 4:20 5:209 6:162 7:407 +0 0:7 1:9 2:3 3:1946 4:18 5:216 6:223 7:1739 +0 1:9 2:2 3:1706 4:7 5:19 6:231 7:526 +0 0:3 1:20 2:2 3:1225 4:13 5:72 6:83 7:354 +0 0:6 1:19 2:2 3:912 4:14 5:90 6:181 7:98 +0 1:16 3:1421 4:6 5:140 6:47 7:284 +1 0:9 1:2 3:1740 4:10 5:19 6:226 7:665 +1 0:11 1:11 2:4 3:2218 4:15 5:38 6:242 7:95 +0 0:10 1:25 2:4 3:2012 4:10 5:229 6:19 7:526 +1 1:10 2:2 3:1024 4:16 5:262 6:245 7:199 +0 0:2 1:10 2:6 3:1434 4:13 5:232 6:83 7:604 +0 0:6 1:25 2:1 3:604 4:22 5:156 6:140 7:228 +0 1:11 3:730 4:20 5:225 6:211 7:646 +0 0:1 1:19 2:3 3:1426 4:18 5:163 6:82 7:862 +0 0:1 1:25 2:2 3:1513 4:15 5:75 6:23 7:499 +0 0:3 1:22 2:6 3:1108 4:13 5:163 6:162 7:236 +1 0:9 1:10 2:1 3:1751 4:18 5:168 6:82 7:1619 +0 0:8 1:10 2:6 3:1700 4:20 5:209 6:164 7:337 +0 0:9 1:28 2:4 3:954 4:16 5:163 6:262 7:155 +0 1:5 2:4 3:957 4:1 5:141 6:83 7:224 +1 0:3 1:5 2:3 3:842 4:16 5:200 6:82 7:960 +0 0:5 1:7 2:3 3:606 4:10 5:224 6:19 7:665 +0 0:10 1:20 2:6 3:2240 4:7 5:19 6:146 7:432 +0 0:6 1:3 2:2 3:815 4:8 5:75 6:124 7:317 +0 0:2 1:28 2:1 3:1922 4:4 5:38 6:231 7:496 +0 0:9 1:27 2:3 3:1648 4:1 5:25 6:218 7:783 +0 0:11 1:21 2:4 3:754 4:13 5:217 6:193 7:802 +0 0:9 1:24 3:645 4:14 5:90 6:64 7:500 +0 1:5 2:5 3:729 4:5 5:182 6:99 7:938 +0 0:7 1:16 2:1 3:1157 4:19 5:224 6:240 7:238 +0 0:9 1:3 2:1 3:955 4:13 5:186 6:218 7:491 +0 0:2 1:5 3:1922 4:19 5:65 6:170 7:544 +1 0:9 1:27 2:2 3:2000 4:13 5:168 6:38 7:185 +0 0:9 1:9 2:1 3:941 4:7 5:19 6:274 7:1547 +0 0:8 1:5 2:1 3:655 4:20 5:163 6:134 7:1390 +1 0:8 1:28 2:1 3:911 4:19 5:224 6:231 7:267 +0 0:1 1:8 3:1747 4:10 5:19 6:222 7:545 +0 0:5 1:7 2:3 3:1620 4:20 5:272 6:164 7:373 +0 0:10 1:5 3:1343 4:4 5:269 6:156 7:1597 +1 1:25 2:1 3:853 4:20 5:253 6:134 7:192 +1 0:2 1:20 2:1 3:1602 4:3 5:272 6:266 7:605 +0 0:9 1:4 2:3 3:1020 4:18 5:83 6:284 7:770 +0 0:7 1:20 2:6 3:1206 4:14 5:221 6:205 7:1426 +0 0:8 1:9 2:5 3:2040 4:7 5:270 6:164 7:590 +0 0:5 1:30 2:2 3:1657 4:21 5:100 6:140 7:213 +1 0:8 1:27 3:1518 4:20 5:182 6:284 7:880 +0 0:7 3:1950 4:20 5:272 6:227 7:647 +0 0:5 1:30 2:3 3:1527 4:3 5:272 6:266 7:605 +0 0:11 1:2 3:718 4:1 5:100 6:193 7:1086 +1 0:8 1:18 3:1632 4:20 5:79 6:94 7:562 +0 0:1 1:26 2:2 3:700 4:20 5:150 6:294 7:1034 +0 0:9 1:2 3:1555 4:19 5:229 6:226 7:267 +0 0:4 1:9 2:4 3:1845 4:18 5:213 6:82 7:472 +1 0:5 1:12 3:1439 4:1 5:84 6:189 7:468 +0 0:10 1:2 2:3 3:600 4:15 5:253 6:184 7:1040 +0 0:11 1:12 2:2 3:719 4:7 5:229 6:74 7:256 +0 0:11 1:29 2:3 3:1058 4:13 5:84 6:113 7:859 +0 0:5 1:9 2:5 3:836 4:9 5:224 6:82 7:1557 +0 0:5 1:29 2:1 3:827 4:21 5:100 6:253 7:246 +1 0:8 1:12 2:1 3:1718 4:22 5:35 6:218 7:116 +0 0:1 1:8 3:934 4:16 5:215 6:275 7:558 +0 0:4 1:1 2:3 3:700 4:10 5:38 6:49 7:370 +0 0:11 1:16 2:6 3:2006 4:20 5:180 6:164 7:1363 +0 0:7 1:6 2:6 3:1212 4:19 5:225 6:231 7:1813 +0 0:9 1:24 3:1645 4:8 5:35 6:19 7:533 +0 0:2 1:3 2:5 3:714 4:8 5:19 6:49 7:576 +0 0:8 1:3 2:6 3:1246 4:19 5:65 6:155 7:329 +0 0:7 1:26 2:4 3:1448 4:13 5:84 6:163 7:140 +0 0:8 1:13 2:2 3:1733 4:21 5:141 6:219 7:1201 +0 0:4 1:8 2:4 3:1924 4:13 5:84 6:308 7:626 +1 0:3 1:20 2:2 3:2114 4:10 5:140 6:19 7:533 +0 0:5 2:1 3:1005 4:20 5:270 6:37 7:291 +0 0:11 1:28 2:2 3:1734 4:14 5:90 6:290 7:374 +0 0:6 1:6 2:5 3:558 4:7 5:182 6:19 7:403 +1 0:1 1:29 2:5 3:1242 4:7 5:19 6:140 7:533 +0 0:2 1:26 2:6 3:1802 4:20 5:161 6:37 7:520 +0 0:7 1:30 3:1125 4:5 5:225 6:99 7:2133 +0 0:6 1:17 2:1 3:815 4:13 5:257 6:164 7:155 +1 0:3 1:19 2:1 3:1122 4:20 5:209 6:186 7:1844 +0 0:11 1:8 2:6 3:605 4:22 5:47 6:218 7:473 +0 0:9 1:23 2:5 3:1501 4:16 5:262 6:6 7:250 +0 0:1 1:1 3:1031 4:1 5:84 6:238 7:1126 +1 1:11 3:2228 4:14 5:203 6:2 7:981 +0 0:11 1:16 2:6 3:1629 4:13 5:216 6:64 7:599 +0 0:9 1:29 2:4 3:706 4:21 5:47 6:62 7:192 +1 0:1 1:14 2:6 3:1955 4:8 5:229 6:19 7:526 +0 0:6 1:23 2:6 3:1522 4:10 5:19 6:140 7:533 +0 0:3 1:24 2:5 3:818 4:1 5:216 6:266 7:1721 +0 0:7 1:24 2:2 3:834 4:16 5:201 6:218 7:109 +0 0:9 1:6 2:5 3:1655 4:3 5:252 6:266 7:1050 +0 0:3 1:27 2:2 3:744 4:20 5:164 6:78 7:293 +1 0:6 1:25 2:1 3:714 4:19 5:19 6:226 7:665 +1 0:3 1:6 2:3 3:1505 4:18 5:270 6:218 7:1249 +1 0:3 1:12 2:1 3:1745 4:16 5:216 6:69 7:911 +0 0:2 1:2 2:5 3:1050 4:21 5:237 6:99 7:160 +0 0:9 1:15 2:6 3:956 4:4 5:108 6:99 7:1065 +1 1:5 2:5 3:1627 4:20 5:184 6:162 7:1521 +0 0:3 1:23 2:4 3:904 4:16 5:272 6:164 7:373 +0 0:5 1:19 3:630 4:20 5:212 6:134 7:419 +0 0:10 1:29 2:1 3:944 4:17 5:209 6:213 7:2349 +0 0:10 1:14 3:744 4:8 5:19 6:119 7:352 +1 1:28 2:4 3:2210 4:15 5:75 6:64 7:335 +0 0:9 1:3 2:2 3:723 4:13 5:47 6:83 7:1212 +0 0:1 1:29 2:5 3:1749 4:14 5:192 6:205 7:297 +0 0:7 1:30 2:1 3:1359 4:21 5:155 6:99 7:821 +1 0:9 1:2 3:1641 4:5 5:141 6:82 7:861 +0 0:11 1:17 2:6 3:945 4:20 5:184 6:240 7:842 +0 0:9 2:5 3:1003 4:14 5:47 6:89 7:240 +0 0:8 1:7 2:4 3:910 4:20 5:279 6:227 7:1262 +0 0:7 1:23 2:1 3:923 4:8 5:19 6:196 7:633 +0 0:6 1:23 2:6 3:1240 4:1 5:163 6:99 7:2454 +0 0:2 1:13 2:1 3:1055 4:15 5:89 6:74 7:505 +0 0:4 1:13 3:1139 4:5 5:141 6:227 7:1009 +0 1:28 2:5 3:1101 4:5 5:141 6:38 7:1597 +0 0:6 1:26 2:2 3:1239 4:16 5:71 6:82 7:230 +0 0:3 1:4 2:2 3:1411 4:13 5:89 6:218 7:299 +1 0:10 1:1 2:3 3:1152 4:1 5:84 6:238 7:1126 +0 0:10 1:13 3:944 4:13 5:5 6:83 7:89 +0 0:5 1:12 2:6 3:2044 4:16 5:215 6:164 7:47 +0 0:9 1:10 2:2 3:1139 4:1 5:180 6:218 7:403 +0 0:6 1:29 2:4 3:727 4:18 5:204 6:218 7:837 +1 0:4 1:15 2:2 3:1358 4:8 5:212 6:19 7:761 +0 0:4 1:20 2:1 3:1640 4:16 5:216 6:20 7:160 +0 1:2 2:1 3:1655 4:20 5:184 6:2 7:1121 +0 0:11 1:28 2:2 3:2000 4:15 5:137 6:19 7:151 +0 0:5 1:13 3:1855 4:6 5:229 6:140 7:183 +1 0:6 1:9 2:1 3:1202 4:1 5:163 6:140 7:2288 +1 1:12 2:3 3:1605 4:18 5:270 6:218 7:1249 +0 0:8 1:6 2:3 3:730 4:20 5:49 6:222 7:883 +0 0:6 1:2 3:652 4:16 5:257 6:267 7:191 +1 0:1 1:9 2:2 3:2024 4:8 5:19 6:215 7:821 +0 0:6 1:9 3:1045 4:20 5:36 6:186 7:395 +1 0:6 1:14 2:5 3:1915 4:20 5:224 6:164 7:2401 +0 0:5 1:1 2:4 3:2121 4:3 5:215 6:266 7:956 +0 0:7 1:24 2:2 3:1710 4:20 5:274 6:277 7:404 +1 0:5 1:13 3:2151 4:13 5:84 6:234 7:604 +0 0:1 1:18 2:2 3:1334 4:13 5:36 6:218 7:409 +0 1:22 3:859 4:5 5:182 6:99 7:938 +1 0:5 1:26 2:6 3:1900 4:13 5:216 6:36 7:409 +0 0:8 1:13 2:1 3:1116 4:19 5:65 6:294 7:508 +0 0:10 1:26 2:4 3:857 4:13 5:168 6:38 7:185 +1 0:11 1:26 2:1 3:1738 4:16 5:217 6:218 7:717 +1 0:11 1:30 2:5 3:828 4:13 5:67 6:83 7:693 +0 0:11 1:13 2:2 3:1356 4:19 5:225 6:226 7:2075 +0 0:5 1:22 2:3 3:2047 4:1 5:84 6:267 7:1464 +0 0:5 1:2 2:4 3:800 4:8 5:154 6:81 7:860 +0 1:13 2:4 3:1211 4:13 5:121 6:83 7:134 +0 0:6 1:26 2:1 3:631 4:7 5:108 6:156 7:1069 +0 0:3 1:20 2:2 3:704 4:7 5:95 6:19 7:1282 +1 0:3 1:11 2:5 3:1117 4:13 5:38 6:81 7:399 +0 0:10 1:14 2:1 3:650 4:19 5:155 6:64 7:329 +1 0:1 1:3 2:3 3:1600 4:20 5:184 6:140 7:577 +1 0:5 1:10 2:6 3:1650 4:7 5:168 6:19 7:761 +0 0:1 1:15 2:6 3:1913 4:18 5:83 6:258 7:794 +0 0:11 1:13 2:3 3:1234 4:13 5:84 6:97 7:642 +0 0:1 1:2 2:2 3:1534 4:3 5:261 6:159 7:680 +0 0:5 1:7 2:2 3:700 4:20 5:252 6:211 7:446 +0 0:11 1:29 2:3 3:910 4:7 5:75 6:222 7:892 +0 0:10 1:19 2:5 3:1920 4:20 5:180 6:240 7:1234 +0 0:5 1:23 2:2 3:1444 4:21 5:229 6:99 7:319 +1 0:6 1:21 2:5 3:1532 4:3 5:261 6:159 7:680 +1 0:10 1:8 2:3 3:1125 4:8 5:98 6:19 7:350 +0 0:10 1:26 2:4 3:551 4:18 5:163 6:211 7:337 +1 0:6 1:28 2:4 3:2250 4:5 5:108 6:99 7:1065 +1 0:2 1:28 3:1924 4:15 5:75 6:51 7:404 +0 0:10 2:1 3:1626 4:20 5:267 6:279 7:342 +0 0:4 1:5 3:706 4:16 5:172 6:82 7:423 +0 0:7 1:5 2:6 3:630 4:19 5:224 6:231 7:267 +1 0:11 1:17 2:6 3:1807 4:19 5:65 6:222 7:590 +0 1:5 2:5 3:1301 4:13 5:194 6:83 7:691 +1 0:6 1:25 3:800 4:14 5:203 6:83 7:852 +0 0:10 1:1 2:2 3:709 4:6 5:146 6:140 7:476 +1 0:2 1:21 2:1 3:1025 4:11 5:261 6:213 7:2640 +0 1:10 2:3 3:955 4:18 5:216 6:267 7:1846 +0 0:1 1:23 3:857 4:2 5:274 6:213 7:2513 +0 0:6 1:8 3:1252 4:16 5:309 6:164 7:237 +1 0:1 1:27 2:4 3:1742 4:14 5:90 6:192 7:609 +0 0:7 1:19 2:4 3:1927 4:6 5:140 6:231 7:183 +1 0:7 1:14 2:6 3:1217 4:3 5:261 6:157 7:909 +1 0:6 1:18 2:2 3:634 4:14 5:84 6:205 7:852 +1 0:8 1:4 2:1 3:1800 4:22 5:204 6:64 7:651 +0 0:11 1:3 3:958 4:14 5:203 6:120 7:252 +0 0:1 1:28 2:5 3:1404 4:7 5:220 6:19 7:545 +0 0:8 1:26 3:1005 4:22 5:83 6:69 7:72 +0 0:5 1:29 2:1 3:802 4:12 5:225 6:251 7:601 +1 0:4 1:16 2:3 3:1434 4:19 5:38 6:170 7:185 +0 0:11 1:14 2:4 3:2249 4:19 5:161 6:140 7:2066 +0 0:9 1:15 2:5 3:2127 4:14 5:203 6:31 7:748 +0 0:3 1:26 2:1 3:1715 4:20 5:79 6:206 7:437 +0 0:5 1:5 3:2224 4:16 5:163 6:103 7:209 +0 0:5 1:25 2:4 3:1425 4:16 5:274 6:164 7:36 +0 0:6 1:1 3:1752 4:7 5:191 6:19 7:595 +0 0:1 1:24 2:1 3:1500 4:7 5:19 6:256 7:515 +0 0:3 1:19 2:2 3:1717 4:7 5:245 6:19 7:481 +1 0:2 1:3 2:6 3:2037 4:20 5:36 6:182 7:491 +1 0:10 1:20 3:2217 4:13 5:216 6:255 7:268 +0 0:9 1:24 2:6 3:1010 4:13 5:216 6:214 7:693 +0 0:11 1:5 2:2 3:1737 4:16 5:106 6:275 7:532 +0 1:10 2:3 3:805 4:20 5:294 6:164 7:451 +0 0:7 1:5 2:5 3:1607 4:7 5:182 6:156 7:944 +0 0:6 1:22 2:6 3:705 4:20 5:21 6:227 7:872 +0 0:3 1:23 2:4 3:1307 4:21 5:141 6:188 7:469 +0 0:5 1:4 2:6 3:655 4:20 5:180 6:184 7:1072 +0 0:7 1:11 3:1655 4:1 5:168 6:218 7:733 +0 0:11 1:14 2:4 3:720 4:20 5:270 6:162 7:368 +0 0:11 1:25 3:725 4:21 5:37 6:141 7:1482 +0 0:3 1:2 3:1716 4:1 5:133 6:218 7:4243 +0 0:4 1:7 2:2 3:1412 4:4 5:156 6:222 7:1028 +0 0:11 1:5 2:2 3:703 4:12 5:163 6:227 7:370 +0 0:1 1:4 2:3 3:950 4:7 5:220 6:19 7:545 +0 0:11 1:26 3:1533 4:16 5:163 6:257 7:109 +0 0:6 1:4 2:2 3:809 4:16 5:206 6:82 7:197 +0 0:7 1:22 2:2 3:1546 4:13 5:201 6:218 7:109 +0 0:5 1:24 2:3 3:1241 4:13 5:274 6:272 7:342 +0 0:7 1:25 2:2 3:1913 4:14 5:211 6:158 7:84 +0 0:4 1:22 2:3 3:1034 4:6 5:140 6:124 7:383 +0 0:2 1:12 3:935 4:15 5:242 6:38 7:612 +0 0:2 1:5 3:1859 4:13 5:142 6:218 7:588 +1 1:8 3:2121 4:7 5:100 6:19 7:745 +0 1:13 2:5 3:1030 4:15 5:123 6:74 7:330 +0 0:2 1:26 2:6 3:737 4:8 5:19 6:188 7:332 +0 0:6 1:13 2:3 3:826 4:1 5:83 6:83 7:641 +1 0:1 1:18 2:3 3:2222 4:20 5:161 6:227 7:256 +0 0:11 1:30 2:4 3:1006 4:1 5:262 6:164 7:337 +0 0:6 1:18 2:2 3:1830 4:16 5:252 6:275 7:626 +0 0:9 1:8 2:6 3:858 4:1 5:216 6:193 7:1197 +0 0:3 1:28 2:2 3:2139 4:4 5:169 6:156 7:2465 +0 0:11 1:30 2:4 3:908 4:16 5:216 6:259 7:773 +0 0:5 1:15 2:3 3:830 4:7 5:75 6:223 7:1975 +0 0:1 2:5 3:1403 4:10 5:19 6:225 7:508 +0 0:4 1:3 2:5 3:1450 4:20 5:209 6:2 7:889 +0 0:4 1:6 2:2 3:743 4:1 5:242 6:218 7:647 +0 0:1 1:15 2:6 3:1330 4:13 5:216 6:188 7:491 +0 0:6 1:8 3:2146 4:1 5:216 6:89 7:235 +0 0:7 1:28 2:5 3:1755 4:10 5:19 6:49 7:576 +0 0:7 1:2 2:2 3:2248 4:10 5:19 6:83 7:732 +0 0:8 1:28 2:2 3:2015 4:20 5:79 6:21 7:189 +1 0:5 1:10 2:5 3:1828 4:20 5:213 6:186 7:423 +0 0:10 1:9 2:4 3:1310 4:18 5:163 6:218 7:1745 +0 0:10 1:8 2:3 3:1725 4:14 5:250 6:205 7:76 +0 0:10 1:29 3:929 4:19 5:224 6:184 7:861 +0 0:10 1:18 2:4 3:1225 4:21 5:299 6:141 7:772 +0 1:18 2:2 3:1614 4:19 5:65 6:99 7:529 +1 0:8 1:9 2:5 3:1619 4:19 5:224 6:247 7:336 +0 0:10 1:7 2:2 3:2158 4:16 5:261 6:114 7:224 +0 1:20 2:5 3:1651 4:16 5:270 6:82 7:391 +0 0:9 1:13 2:4 3:725 4:20 5:161 6:215 7:1099 +0 0:9 1:8 3:1020 4:15 5:253 6:19 7:874 +0 0:1 1:25 2:2 3:1518 4:15 5:182 6:123 7:534 +1 0:9 1:13 2:3 3:1735 4:20 5:150 6:107 7:1093 +0 0:5 1:28 3:747 4:17 5:168 6:186 7:725 +0 0:2 1:4 3:1308 4:19 5:225 6:294 7:1788 +1 2:6 3:1454 4:8 5:146 6:19 7:432 +0 0:10 1:15 2:2 3:1657 4:19 5:38 6:170 7:185 +0 0:6 1:4 2:2 3:1226 4:18 5:140 6:218 7:589 +0 0:5 1:10 2:6 3:1853 4:19 5:82 6:170 7:214 +0 0:2 1:8 2:4 3:810 4:18 5:90 6:82 7:1123 +0 0:4 1:25 2:4 3:1452 4:1 5:216 6:205 7:334 +0 0:7 1:21 3:1135 4:1 5:187 6:83 7:468 +0 1:18 2:3 3:2150 4:22 5:140 6:156 7:228 +1 0:9 1:2 3:1239 4:7 5:19 6:38 7:946 +0 0:9 1:10 2:2 3:1035 4:21 5:30 6:141 7:562 +0 0:8 1:5 2:2 3:637 4:3 5:221 6:267 7:550 +0 1:4 2:4 3:1802 4:20 5:2 6:134 7:759 +0 0:4 1:3 2:6 3:1657 4:1 5:83 6:83 7:641 +0 0:4 1:25 2:4 3:2000 4:16 5:132 6:275 7:402 +0 0:8 1:13 2:2 3:1254 4:14 5:203 6:194 7:297 +0 0:5 1:26 2:6 3:1252 4:7 5:58 6:19 7:259 +0 0:4 1:15 2:3 3:759 4:18 5:224 6:82 7:1557 +1 0:1 1:8 2:1 3:2120 4:15 5:75 6:194 7:318 +0 0:3 1:8 2:5 3:2045 4:20 5:79 6:182 7:461 +0 0:4 1:27 3:825 4:1 5:38 6:218 7:867 +0 0:11 1:13 2:2 3:903 4:14 5:25 6:89 7:548 +0 0:7 1:29 3:1441 4:9 5:267 6:82 7:948 +1 1:28 2:4 3:1145 4:20 5:161 6:279 7:226 +0 2:5 3:1240 4:20 5:13 6:49 7:288 +0 1:9 2:2 3:2149 4:18 5:140 6:294 7:810 +0 1:29 2:6 3:1215 4:20 5:49 6:240 7:328 +1 0:9 1:5 2:3 3:2000 4:14 5:90 6:258 7:1215 +0 0:7 1:25 2:3 3:1645 4:21 5:192 6:99 7:725 +0 0:5 1:3 2:5 3:901 4:10 5:49 6:38 7:370 +0 0:8 1:3 2:6 3:807 4:3 5:218 6:216 7:183 +0 0:5 1:24 2:3 3:623 4:1 5:216 6:25 7:783 +1 0:8 1:11 2:4 3:2022 4:1 5:163 6:21 7:1242 +0 0:3 1:27 2:2 3:1103 4:19 5:90 6:227 7:1671 +0 0:7 1:30 2:1 3:1205 4:15 5:122 6:74 7:268 +0 0:5 1:22 2:3 3:1214 4:8 5:113 6:19 7:508 +0 0:8 1:25 2:6 3:1437 4:5 5:141 6:99 7:1400 +0 0:8 1:29 2:3 3:923 4:14 5:224 6:89 7:453 +0 0:9 1:4 2:2 3:630 4:20 5:134 6:78 7:239 +0 0:10 1:6 2:1 3:1044 4:14 5:203 6:170 7:1020 +0 0:8 1:5 2:2 3:704 4:5 5:253 6:141 7:191 +0 0:9 1:8 3:1237 4:1 5:84 6:227 7:868 +1 0:2 1:26 2:6 3:1609 4:1 5:19 6:83 7:732 +1 0:8 1:23 2:4 3:1450 4:10 5:52 6:19 7:528 +0 0:4 1:29 2:1 3:828 4:11 5:170 6:133 7:102 +0 0:1 1:16 2:1 3:2116 4:20 5:161 6:164 7:236 +0 0:2 1:20 3:929 4:19 5:146 6:81 7:499 +0 0:4 1:28 2:1 3:651 4:18 5:182 6:218 7:1005 +0 0:7 1:14 3:2000 4:20 5:180 6:284 7:237 +0 0:7 1:20 2:6 3:1255 4:7 5:19 6:38 7:946 +0 0:5 1:17 2:5 3:1638 4:19 5:82 6:38 7:399 +0 0:8 1:9 2:6 3:1911 4:18 5:262 6:48 7:326 +0 0:4 1:26 2:5 3:1430 4:13 5:268 6:83 7:228 +0 0:2 1:3 2:6 3:1904 4:14 5:260 6:89 7:306 +0 0:7 1:13 2:6 3:1003 4:7 5:19 6:162 7:1747 +1 1:5 2:5 3:1952 4:21 5:248 6:99 7:246 +0 0:11 1:16 2:6 3:911 4:15 5:75 6:297 7:646 +0 1:29 2:6 3:1453 4:10 5:19 6:107 7:581 +0 0:3 1:23 2:5 3:1554 4:8 5:19 6:56 7:106 +0 0:2 1:17 2:5 3:1204 4:1 5:21 6:218 7:978 +0 0:5 1:5 2:1 3:1129 4:11 5:170 6:133 7:102 +1 0:5 1:23 2:3 3:1858 4:20 5:79 6:94 7:562 +1 0:3 1:27 2:2 3:2005 4:20 5:79 6:21 7:189 +0 0:6 1:4 2:2 3:1423 4:18 5:229 6:218 7:412 +1 0:4 1:9 2:5 3:1202 4:16 5:262 6:262 7:191 +0 0:6 1:11 2:6 3:806 4:1 5:83 6:83 7:641 +0 0:4 1:3 2:6 3:1424 4:22 5:65 6:284 7:575 +0 0:6 1:13 2:4 3:925 4:20 5:161 6:217 7:197 +1 0:1 2:6 3:1255 4:7 5:191 6:19 7:595 +0 0:2 1:13 3:1343 4:14 5:90 6:240 7:614 +0 0:1 1:23 3:648 4:16 5:103 6:82 7:627 +1 0:1 1:20 2:5 3:1757 4:20 5:49 6:240 7:328 +0 0:5 1:24 2:3 3:2045 4:12 5:225 6:267 7:651 +0 0:1 1:25 2:1 3:2037 4:21 5:63 6:79 7:162 +0 0:9 1:30 2:6 3:639 4:20 5:252 6:162 7:258 +0 1:9 2:2 3:958 4:19 5:82 6:240 7:357 +0 0:6 1:29 2:4 3:553 4:18 5:289 6:140 7:810 +0 0:8 1:6 2:3 3:650 4:2 5:272 6:279 7:404 +1 0:8 1:19 2:1 3:1155 4:16 5:216 6:58 7:760 +0 0:6 2:5 3:637 4:16 5:155 6:218 7:865 +0 0:2 1:14 2:1 3:2001 4:18 5:161 6:164 7:236 +0 0:3 1:23 2:4 3:742 4:3 5:209 6:223 7:543 +0 0:9 2:4 3:1446 4:10 5:19 6:142 7:781 +0 0:6 1:20 2:3 3:552 4:18 5:25 6:218 7:783 +1 0:2 1:12 2:6 3:1737 4:7 5:217 6:19 7:516 +0 0:4 1:4 3:559 4:16 5:276 6:218 7:174 +0 0:7 1:25 2:2 3:1007 4:7 5:212 6:19 7:761 +0 0:2 1:20 2:1 3:937 4:20 5:184 6:226 7:668 +0 0:2 1:2 2:4 3:1000 4:20 5:161 6:227 7:256 +0 0:1 1:19 2:3 3:1413 4:19 5:216 6:226 7:678 +0 0:11 1:3 3:1600 4:20 5:49 6:222 7:883 +0 0:7 1:30 2:1 3:2300 4:8 5:19 6:191 7:147 +0 0:2 1:2 2:5 3:1929 4:20 5:184 6:36 7:395 +0 1:18 2:3 3:1631 4:14 5:171 6:188 7:129 +0 0:1 1:20 2:5 3:1210 4:19 5:204 6:64 7:651 +0 0:9 1:6 2:4 3:901 4:16 5:239 6:82 7:301 +0 0:6 1:30 2:5 3:931 4:18 5:216 6:294 7:1012 +0 0:4 1:6 2:1 3:1605 4:14 5:237 6:89 7:614 +0 0:1 1:3 2:2 3:841 4:21 5:63 7:339 +0 0:9 1:20 2:3 3:828 4:13 5:216 6:290 7:607 +0 0:4 1:30 2:2 3:918 4:10 5:19 6:134 7:696 +0 0:7 1:11 3:1217 4:13 5:66 6:38 7:640 +0 0:5 2:1 3:1540 4:20 5:49 6:222 7:883 +1 1:25 2:2 3:2039 4:5 5:191 6:99 7:1086 +0 0:11 1:23 2:5 3:753 4:16 5:239 6:82 7:301 +0 0:10 1:23 2:2 3:1742 4:1 5:84 6:21 7:190 +1 0:10 1:13 2:6 3:2020 4:20 5:267 6:251 7:188 +0 0:5 1:13 2:1 3:723 4:11 5:133 6:172 7:102 +0 0:6 1:12 2:2 3:1315 4:10 5:163 6:83 7:1235 +0 0:4 1:17 2:4 3:652 4:3 5:221 6:211 7:543 +0 0:5 1:18 2:5 3:1345 4:20 5:253 6:78 7:248 +0 0:5 1:5 2:1 3:1012 4:19 5:289 6:64 7:508 +0 0:2 1:17 2:4 3:949 4:16 5:270 6:117 7:217 +0 0:10 1:2 2:3 3:1631 4:11 5:133 6:158 7:163 +0 0:1 1:25 2:1 3:1131 4:1 5:38 6:218 7:867 +0 0:10 1:12 2:5 3:510 4:16 5:114 6:275 7:546 +0 0:8 1:6 2:3 3:1944 4:7 5:108 6:25 7:1173 +0 0:9 1:24 3:1037 4:16 5:216 6:2 7:1118 +0 0:1 1:30 3:1528 4:20 5:163 6:211 7:337 +0 0:9 1:23 2:5 3:1919 4:6 5:140 6:289 7:251 +0 0:1 1:26 2:2 3:1025 4:15 5:89 6:74 7:505 +1 0:1 1:18 2:3 3:1704 4:4 5:285 6:184 7:1053 +0 0:3 1:3 3:1815 4:3 5:209 6:266 7:671 +0 0:8 1:1 2:5 3:1812 4:13 5:84 6:302 7:181 +1 0:8 1:14 2:3 3:1608 4:20 5:180 6:214 7:313 +1 0:11 1:29 2:3 3:1058 4:13 5:199 6:120 7:134 +0 0:4 1:18 2:6 3:922 4:21 5:141 6:45 7:253 +1 0:11 1:19 2:2 3:1029 4:1 5:267 6:83 7:1438 +0 0:5 1:27 3:957 4:20 5:225 6:82 7:602 +0 0:7 1:6 3:758 4:14 5:37 6:205 7:1142 +1 0:10 1:11 2:1 3:1855 4:21 5:154 6:141 7:351 +1 0:6 1:14 2:4 3:1645 4:20 5:161 6:251 7:345 +0 0:11 1:25 3:1652 4:10 5:19 6:35 7:533 +0 0:2 1:8 2:3 3:1248 4:3 5:221 6:162 7:762 +0 1:28 2:4 3:632 4:17 5:184 6:99 7:711 +1 0:9 1:14 2:5 3:1851 4:1 5:191 6:83 7:1121 +0 1:12 2:3 3:934 4:18 5:141 6:218 7:925 +1 0:6 1:5 2:3 3:1325 4:1 5:216 6:267 7:1846 +0 0:3 1:12 2:2 3:1321 4:4 5:140 6:156 7:228 +1 0:3 1:4 2:1 3:1659 4:19 5:224 6:64 7:448 +1 0:11 1:4 2:2 3:1634 4:22 5:123 6:218 7:590 +0 1:21 2:5 3:1635 4:20 5:66 6:227 7:1671 +0 0:9 1:11 2:5 3:1223 4:13 5:216 6:293 7:214 +0 0:7 1:3 2:4 3:1545 4:13 5:277 6:83 7:113 +0 0:4 1:16 2:4 3:1751 4:8 5:207 6:19 7:317 +0 1:14 2:6 3:2125 4:5 5:261 6:16 7:1449 +0 0:3 1:5 2:2 3:1752 4:19 5:224 6:227 7:2075 +1 0:2 1:6 2:2 3:1531 4:1 5:216 6:226 7:678 +1 0:1 1:26 2:3 3:1303 4:8 5:111 6:19 7:954 +0 0:8 1:23 2:3 3:723 4:7 5:261 6:275 7:689 +1 0:3 1:6 2:4 3:1651 4:10 5:168 6:19 7:761 +0 0:7 1:9 2:2 3:1447 4:14 5:261 6:213 7:2640 +1 0:3 1:14 2:3 3:1808 4:3 5:16 6:266 7:1449 +1 0:3 1:12 2:1 3:2115 4:7 5:19 6:258 7:874 +0 1:14 2:6 3:734 4:1 5:279 6:83 7:551 +0 1:27 2:4 3:841 4:3 5:261 6:38 7:2496 +0 0:2 1:17 2:4 3:734 4:1 5:163 6:99 7:2454 +0 0:7 1:25 2:3 3:1310 4:20 5:246 6:272 7:188 +0 0:2 1:25 2:5 3:716 4:1 5:137 6:83 7:603 +0 0:5 1:6 2:2 3:1657 4:10 5:19 6:206 7:425 +0 0:6 1:22 3:1420 4:22 5:65 6:295 7:120 +0 0:7 1:8 2:1 3:1613 4:16 5:1 6:141 7:307 +0 0:1 1:10 2:3 3:857 4:18 5:216 6:81 7:612 +0 0:9 1:26 2:2 3:614 4:20 5:66 6:49 7:336 +0 0:10 1:5 2:6 3:1844 4:18 5:140 6:164 7:2288 +0 0:4 1:29 2:2 3:1327 4:21 5:45 6:141 7:253 +0 0:1 1:27 2:3 3:805 4:10 5:229 6:184 7:834 +0 1:7 2:6 3:1225 4:6 5:36 6:140 7:542 +0 0:10 1:19 2:6 3:2023 4:9 5:270 6:82 7:391 +0 0:9 1:16 2:6 3:920 4:7 5:75 6:223 7:1975 +0 1:22 2:1 3:645 4:20 5:63 6:49 7:314 +0 0:5 1:14 2:2 3:603 4:16 5:262 6:277 7:86 +0 0:9 1:7 2:5 3:829 4:8 5:19 6:308 7:264 +0 0:3 1:5 2:3 3:554 4:19 5:38 6:170 7:185 +1 0:8 1:16 2:4 3:1117 4:20 5:225 6:49 7:1999 +1 0:2 2:2 3:1039 4:1 5:216 6:251 7:1671 +0 0:9 1:1 3:636 4:20 5:178 6:21 7:294 +0 0:1 1:25 2:1 3:933 4:15 5:137 6:74 7:325 +1 0:8 1:7 2:3 3:2317 4:18 5:262 6:226 7:2521 +0 0:10 1:12 2:6 3:1410 4:20 5:49 6:65 7:336 +0 0:8 1:5 2:2 3:556 4:18 5:225 6:267 7:651 +0 0:7 1:29 2:6 3:2136 4:13 5:163 6:260 7:89 +0 0:5 1:5 3:1633 4:19 5:278 6:64 7:547 +0 0:6 1:29 2:4 3:1637 4:7 5:75 6:266 7:1964 +0 0:8 1:7 2:3 3:1820 4:20 5:66 6:284 7:410 +1 0:11 1:3 3:1330 4:13 5:84 6:311 7:281 +1 0:9 1:2 2:1 3:1533 4:3 5:261 6:279 7:978 +0 0:6 1:3 2:1 3:1539 4:7 5:75 6:49 7:430 +1 0:10 1:30 2:2 3:1741 4:20 5:224 6:134 7:1335 +0 0:2 1:13 3:930 4:16 5:104 6:164 7:209 +0 0:6 1:6 2:5 3:1504 4:19 5:82 6:294 7:814 +0 0:7 1:4 2:4 3:930 4:16 5:274 6:164 7:36 +0 0:8 1:6 2:2 3:2144 4:1 5:163 6:38 7:2611 +0 0:10 1:29 2:1 3:729 4:19 5:108 6:227 7:1972 +0 0:1 1:29 2:5 3:1745 4:1 5:84 6:297 7:237 +0 1:25 2:2 3:1252 4:21 5:180 6:141 7:643 +0 0:6 1:25 3:955 4:20 5:217 6:49 7:159 +0 0:8 1:9 2:6 3:1018 4:19 5:225 6:164 7:370 +0 0:7 1:11 2:1 3:1702 4:19 5:140 6:227 7:1956 +0 0:7 1:14 3:48 4:3 5:16 6:266 7:1449 +0 0:9 1:25 2:1 3:1652 4:14 5:90 6:146 7:231 +0 0:4 1:11 2:2 3:2014 4:19 5:65 6:123 7:83 +0 0:8 1:2 2:5 3:620 4:16 5:253 6:82 7:794 +0 1:12 2:4 3:950 4:21 5:66 6:62 7:112 +0 0:4 1:5 2:1 3:658 4:19 5:182 6:81 7:759 +0 0:9 1:22 2:6 3:1526 4:21 5:141 6:167 7:127 +0 0:10 1:26 2:4 3:1757 4:7 5:19 6:16 7:3417 +1 0:3 1:18 3:1752 4:14 5:100 6:89 7:487 +0 0:5 1:10 2:6 3:1310 4:20 5:261 6:272 7:697 +1 0:7 1:9 2:3 3:1950 4:20 5:267 6:162 7:386 +0 0:6 1:10 2:2 3:800 4:22 5:65 6:284 7:575 +0 0:3 1:10 2:1 3:1450 4:9 5:221 6:82 7:992 +0 0:11 1:15 2:4 3:830 4:15 5:242 6:170 7:431 +0 1:29 2:5 3:935 4:20 5:225 6:47 7:1912 +0 0:9 1:4 2:2 3:1306 4:1 5:84 6:219 7:1213 +0 0:9 1:21 2:4 3:1300 4:5 5:141 6:146 7:845 +0 0:5 1:13 2:1 3:1125 4:7 5:168 6:222 7:1035 +0 0:2 1:30 2:3 3:1751 4:15 5:80 6:74 7:64 +0 0:1 1:28 2:5 3:1307 4:1 5:216 6:205 7:334 +0 0:9 1:26 2:2 3:1745 4:16 5:252 6:164 7:109 +0 0:8 1:25 2:5 3:1550 4:20 5:182 6:206 7:550 +0 0:1 2:5 3:1225 4:18 5:216 6:162 7:1515 +0 0:1 1:22 3:1825 4:20 5:279 6:265 7:254 +0 0:6 1:22 2:6 3:937 4:19 5:163 6:226 7:2401 +0 0:1 1:15 3:1321 4:5 5:100 6:257 7:2425 +0 0:5 1:16 2:3 3:801 4:12 5:146 6:227 7:1489 +0 0:6 1:1 2:6 3:1224 4:8 5:232 6:294 7:330 +0 0:8 1:5 2:1 3:2237 4:16 5:262 6:245 7:199 +0 0:7 1:3 2:3 3:1902 4:14 5:47 6:89 7:240 +0 0:10 1:11 2:1 3:1344 4:19 5:65 6:259 7:213 +0 0:11 1:3 3:845 4:20 5:184 6:227 7:1444 +0 0:8 1:25 2:6 3:1016 4:19 5:108 6:226 7:992 +0 0:6 1:3 2:1 3:919 4:5 5:100 6:222 7:1024 +0 0:9 1:17 3:910 4:8 5:75 6:252 7:282 +1 0:10 1:9 2:3 3:2132 4:9 5:83 6:162 7:629 +1 0:7 1:9 2:2 3:1521 4:15 5:75 6:79 7:64 +0 0:5 1:3 2:6 3:1745 4:13 5:19 6:218 7:606 +0 0:4 2:1 3:808 4:6 5:285 6:140 7:296 +0 0:6 1:25 2:1 3:903 4:13 5:25 6:247 7:532 +0 0:6 1:23 2:5 3:1611 4:12 5:225 6:162 7:256 +0 0:1 1:23 3:625 4:19 5:289 6:226 7:920 +0 0:10 1:28 3:1316 4:1 5:21 6:83 7:190 +0 0:3 1:28 2:2 3:1945 4:18 5:221 6:267 7:550 +0 0:8 1:7 2:3 3:1256 4:14 5:216 6:205 7:334 +1 0:9 1:10 2:1 3:1906 4:10 5:168 6:52 7:396 +0 0:10 1:4 2:6 3:2118 4:1 5:84 6:89 7:987 +0 0:6 1:6 2:4 3:2005 4:15 5:73 6:74 7:173 +0 0:6 1:7 2:6 3:850 4:1 5:84 6:206 7:448 +0 0:1 1:19 2:3 3:657 4:13 5:216 6:311 7:522 +0 0:10 1:1 2:3 3:557 4:16 5:263 6:218 7:438 +0 0:7 1:26 2:3 3:625 4:20 5:225 6:279 7:338 +1 0:9 1:21 2:5 3:1352 4:1 5:216 6:193 7:1197 +0 0:8 1:6 2:3 3:1930 4:20 5:161 6:48 7:223 +1 0:7 1:15 3:1240 4:10 5:19 6:294 7:406 +0 0:2 1:9 2:5 3:1301 4:16 5:83 6:111 7:483 +0 0:7 1:11 2:1 3:1724 4:18 5:100 6:218 7:719 +1 0:6 1:12 2:2 3:1535 4:20 5:49 6:186 7:611 +1 0:4 1:1 2:4 3:2003 4:20 5:184 6:266 7:1733 +0 0:5 1:27 2:6 3:1550 4:20 5:161 6:164 7:236 +0 0:3 1:30 2:5 3:1522 4:1 5:180 6:83 7:460 +0 0:10 1:19 2:6 3:857 4:19 5:229 6:82 7:1290 +0 0:10 1:6 2:1 3:622 4:22 5:65 6:140 7:321 +0 0:2 1:7 2:2 3:1110 4:20 5:184 6:182 7:405 +0 0:2 1:19 3:1744 4:18 5:216 6:226 7:678 +0 0:2 1:14 2:2 3:1101 4:19 5:225 6:162 7:256 +1 0:7 1:17 2:2 3:2011 4:3 5:261 6:16 7:1449 +0 0:11 1:8 2:5 3:617 4:14 5:279 6:205 7:449 +1 0:8 1:12 2:1 3:1435 4:22 5:129 6:82 7:142 +1 1:20 2:4 3:1720 4:20 5:79 6:134 7:239 +0 0:4 1:1 2:3 3:1910 4:15 5:75 6:138 7:123 +0 0:7 1:8 2:1 3:2116 4:10 5:19 6:164 7:1946 +0 0:5 1:13 2:1 3:1334 4:13 5:155 6:218 7:865 +0 1:3 2:2 3:1343 4:12 5:267 6:162 7:386 +0 0:11 1:22 2:6 3:1529 4:21 5:141 6:10 7:190 +0 0:10 1:13 2:6 3:835 4:9 5:83 6:279 7:846 +0 0:1 1:9 2:2 3:858 4:7 5:19 6:279 7:1919 +0 0:2 1:11 2:2 3:1608 4:20 5:225 6:215 7:1037 +0 0:2 1:3 2:6 3:1855 4:20 5:79 6:134 7:239 +0 1:19 2:3 3:1745 4:20 5:49 6:219 7:159 +0 0:11 1:13 2:2 3:645 4:1 5:216 6:38 7:867 +0 0:6 1:30 2:5 3:839 4:16 5:163 6:217 7:47 +0 0:7 1:25 2:2 3:1303 4:15 5:216 6:74 7:264 +0 0:6 1:22 3:2125 4:4 5:169 6:38 7:2602 +0 0:11 1:21 2:4 3:1510 4:22 5:95 6:227 7:347 +0 0:11 1:20 2:2 3:938 4:15 5:75 6:297 7:646 +0 0:5 1:5 2:1 3:836 4:21 5:100 6:247 7:416 +0 0:8 1:30 2:3 3:1406 4:19 5:65 6:170 7:544 +0 0:5 1:10 2:5 3:530 4:10 5:110 6:19 7:644 +0 0:2 1:18 2:5 3:934 4:13 5:262 6:279 7:372 +0 0:8 1:30 2:4 3:1316 4:7 5:137 6:19 7:151 +0 1:16 3:1407 4:16 5:64 6:141 7:74 +0 0:5 1:20 2:1 3:756 4:1 5:191 6:294 7:204 +0 0:7 1:19 2:5 3:1500 4:19 5:225 6:218 7:1440 +0 1:29 2:5 3:600 4:8 5:167 6:19 7:503 +1 0:10 1:30 2:1 3:1649 4:14 5:237 6:89 7:614 +0 0:4 1:18 2:6 3:1430 4:10 5:19 6:89 7:594 +1 0:9 1:10 2:2 3:2205 4:20 5:163 6:272 7:308 +0 0:4 1:7 2:2 3:2307 4:15 5:75 6:99 7:569 +0 2:5 3:805 4:20 5:213 6:227 7:1037 +0 0:7 1:19 2:4 3:1002 4:18 5:216 6:211 7:1835 +1 0:11 1:5 2:3 3:1321 4:4 5:108 6:99 7:1065 +0 0:3 1:28 2:3 3:1011 4:14 5:90 6:99 7:487 +0 0:2 1:9 2:5 3:645 4:1 5:187 6:83 7:468 +0 0:5 1:17 2:4 3:659 4:8 5:288 6:19 7:549 +0 0:2 1:28 2:1 3:2122 4:19 5:83 6:162 7:629 +0 0:5 1:5 2:1 3:853 4:22 5:212 6:227 7:833 +1 0:2 1:14 2:1 3:2052 4:16 5:246 6:267 7:192 +0 0:11 1:2 2:6 3:2111 4:14 5:90 6:38 7:632 +0 0:4 1:14 2:1 3:600 4:8 5:73 6:19 7:363 +0 0:8 1:8 2:5 3:1743 4:19 5:224 6:81 7:119 +1 2:5 3:1449 4:5 5:141 6:227 7:1009 +0 0:7 1:22 2:1 3:1922 4:16 5:188 6:267 7:329 +0 0:7 1:14 2:6 3:1649 4:13 5:171 6:83 7:304 +1 1:9 2:2 3:1930 4:16 5:221 6:266 7:129 +0 0:8 1:21 2:2 3:900 4:8 5:279 6:19 7:483 +1 0:6 1:4 2:2 3:1254 4:8 5:232 6:294 7:330 +0 0:2 1:21 2:2 3:1423 4:20 5:49 6:65 7:336 +0 0:8 1:11 2:4 3:1727 4:1 5:269 6:184 7:1189 +0 0:8 1:8 2:4 3:840 4:1 5:82 6:218 7:612 +0 0:6 1:23 2:5 3:1420 4:20 5:180 6:186 7:405 +0 0:2 1:13 3:809 4:1 5:216 6:141 7:925 +0 0:1 1:17 2:2 3:1101 4:21 5:42 6:141 7:308 +0 0:5 1:8 2:3 3:558 4:19 5:237 6:226 7:238 +0 0:11 1:14 2:3 3:1343 4:19 5:38 6:81 7:399 +0 1:13 2:5 3:703 4:5 5:100 6:274 7:1608 +0 0:3 1:30 2:4 3:714 4:1 5:19 6:170 7:761 +0 0:10 1:18 2:5 3:1305 4:1 5:163 6:83 7:1235 +0 0:1 1:19 2:3 3:1058 4:19 5:65 6:256 7:600 +0 0:10 1:1 2:2 3:1121 4:7 5:161 6:74 7:1678 +0 1:11 2:6 3:838 4:16 5:270 6:114 7:546 +0 0:1 1:25 2:1 3:1051 4:7 5:262 6:74 7:2036 +0 0:3 1:6 2:3 3:518 4:21 5:164 6:141 7:458 +0 0:11 2:3 3:1817 4:18 5:272 6:164 7:373 +1 0:7 1:21 2:6 3:1140 4:9 5:267 6:82 7:948 +0 0:4 1:3 2:6 3:1850 4:20 5:215 6:211 7:361 +0 0:6 1:20 2:3 3:1115 4:20 5:237 6:294 7:1137 +0 0:7 1:19 2:4 3:657 4:7 5:220 6:19 7:545 +0 0:4 1:28 3:1441 4:1 5:274 6:83 7:1205 +0 0:8 1:18 3:1234 4:15 5:75 6:293 7:181 +0 0:4 1:10 2:6 3:1834 4:15 5:260 6:74 7:83 +0 0:1 1:23 3:2057 4:16 5:262 6:260 7:262 +0 0:1 1:11 3:2230 4:7 5:19 6:62 7:554 +1 0:11 1:10 2:1 3:1110 4:15 5:49 6:38 7:370 +0 0:11 1:15 2:4 3:1237 4:4 5:182 6:38 7:1121 +0 0:4 1:7 2:2 3:815 4:10 5:184 6:184 7:989 +0 0:2 1:26 2:5 3:1326 4:21 5:141 6:123 7:986 +0 0:2 1:2 2:4 3:1532 4:20 5:184 6:184 7:989 +0 0:10 1:6 3:811 4:12 5:274 6:227 7:338 +1 0:11 1:30 2:5 3:1525 4:22 5:225 6:299 7:110 +0 0:6 1:12 2:2 3:623 4:10 5:140 6:19 7:533 +0 0:4 1:9 2:4 3:1138 4:8 5:256 6:19 7:566 +0 0:5 1:18 2:6 3:1652 4:7 5:270 6:140 7:1827 +0 0:2 2:1 3:1221 4:19 5:262 6:64 7:2296 +0 0:9 1:14 2:5 3:2049 4:18 5:216 6:294 7:1012 +0 0:6 1:14 2:4 3:1520 4:12 5:225 6:162 7:256 +1 0:4 1:22 2:4 3:42 4:22 5:212 6:162 7:987 +1 0:9 1:10 2:2 3:1625 4:9 5:19 6:82 7:1199 +0 1:8 3:1018 4:14 5:90 6:13 7:488 +0 0:3 1:3 3:1238 4:8 5:19 6:104 7:331 +0 0:10 1:2 2:3 3:713 4:20 5:49 6:107 7:925 +0 0:10 1:9 2:4 3:1625 4:3 5:102 6:266 7:1533 +0 0:4 1:1 2:3 3:1445 4:7 5:289 6:19 7:406 +0 0:6 1:1 2:6 3:1730 4:20 5:95 6:162 7:584 +0 0:6 1:5 2:4 3:1447 4:1 5:280 6:274 7:68 +0 0:6 1:23 2:6 3:1535 4:5 5:100 6:83 7:1372 +0 0:2 1:29 2:1 3:1348 4:16 5:163 6:27 7:109 +0 0:3 1:24 2:6 3:859 4:5 5:108 6:141 7:965 +0 0:5 1:23 2:3 3:1045 4:21 5:45 6:141 7:253 +0 0:10 2:1 3:614 4:19 5:237 6:81 7:357 +0 0:1 1:18 2:3 3:719 4:20 5:150 6:294 7:1034 +0 0:1 1:25 2:2 3:750 4:16 5:88 6:275 7:338 +0 0:7 1:19 2:4 3:1046 4:5 5:141 6:81 7:1208 +0 0:1 1:21 2:5 3:1102 4:18 5:216 6:81 7:612 +0 0:10 2:1 3:1248 4:1 5:253 6:83 7:247 +0 0:7 1:10 2:4 3:758 4:7 5:299 6:19 7:152 +1 1:23 2:6 3:1930 4:20 5:274 6:277 7:404 +0 0:6 1:5 2:4 3:1252 4:8 5:19 6:209 7:317 +0 0:1 1:26 2:2 3:622 4:5 5:66 6:141 7:986 +0 0:7 1:9 2:3 3:1458 4:18 5:38 6:140 7:413 +0 0:8 2:3 3:840 4:20 5:272 6:217 7:389 +0 0:8 1:7 2:4 3:1747 4:10 5:182 6:19 7:403 +0 0:6 1:9 3:1752 4:18 5:216 6:13 7:723 +0 0:2 1:30 2:3 3:1855 4:13 5:163 6:257 7:109 +0 0:6 1:7 2:5 3:1112 4:13 5:65 6:83 7:936 +0 1:30 3:1139 4:10 5:49 6:107 7:925 +0 0:1 1:18 2:2 3:9 4:5 5:221 6:141 7:1825 +0 0:3 1:20 2:2 3:827 4:7 5:47 6:19 7:712 +1 0:9 1:2 2:1 3:1436 4:18 5:272 6:82 7:910 +0 0:7 1:2 2:2 3:1900 4:15 5:75 6:36 7:230 +1 0:4 1:25 2:5 3:1306 4:3 5:86 6:16 7:329 +0 0:7 1:9 2:2 3:1530 4:20 5:49 6:25 7:283 +0 0:4 1:14 2:2 3:1902 4:13 5:90 6:218 7:235 +0 0:2 1:19 3:554 4:21 5:216 6:62 7:316 +0 0:8 1:5 2:1 3:1812 4:9 5:83 6:257 7:853 +0 0:1 1:24 2:1 3:1246 4:5 5:21 6:141 7:140 +0 0:6 1:21 2:4 3:1628 4:19 5:65 6:205 7:930 +0 0:2 1:20 2:1 3:830 4:5 5:90 6:141 7:1076 +0 0:1 1:5 2:4 3:749 4:5 5:141 6:81 7:1208 +1 1:19 2:4 3:1118 4:20 5:225 6:257 7:304 +0 0:2 1:3 2:6 3:2207 4:20 5:161 6:257 7:258 +0 0:1 1:17 2:1 3:645 4:8 5:274 6:275 7:588 +0 0:11 1:1 2:5 3:1015 4:20 5:180 6:275 7:919 +0 1:2 2:2 3:524 4:11 5:133 6:172 7:102 +0 0:9 1:20 2:4 3:746 4:14 5:66 6:188 7:518 +0 0:10 1:29 2:1 3:704 4:5 5:100 6:206 7:1167 +1 0:3 1:13 2:3 3:1930 4:21 5:140 6:99 7:213 +0 0:9 1:26 2:2 3:1354 4:20 5:36 6:134 7:670 +0 0:3 1:25 2:6 3:737 4:19 5:224 6:162 7:2176 +0 0:11 1:17 3:1420 4:5 5:161 6:99 7:2227 +0 0:10 1:26 2:4 3:644 4:1 5:84 6:182 7:460 +0 0:11 1:1 2:6 3:1900 4:15 5:168 6:250 7:292 +0 0:5 1:6 2:2 3:1732 4:22 5:145 6:64 7:185 +0 0:3 1:17 3:1105 4:11 5:133 6:158 7:163 +0 0:1 1:13 2:5 3:1319 4:18 5:270 6:82 7:391 +0 0:7 1:10 2:4 3:1905 4:20 5:164 6:2 7:289 +1 0:9 1:1 3:1432 4:20 5:215 6:162 7:197 +1 0:9 1:4 2:2 3:1040 4:8 5:270 6:21 7:1085 +0 0:2 1:5 2:1 3:727 4:19 5:83 6:227 7:602 +0 0:11 1:28 2:3 3:1239 4:10 5:19 6:155 7:270 +0 0:4 1:9 2:4 3:1707 4:3 5:102 6:16 7:261 +0 0:2 1:17 2:4 3:1551 4:19 5:229 6:226 7:267 +0 0:6 1:1 2:6 3:1041 4:14 5:182 6:205 7:1310 +0 0:5 1:3 2:5 3:1228 4:21 5:10 6:141 7:190 +0 0:3 1:4 2:2 3:808 4:16 5:261 6:114 7:224 +0 0:8 1:16 2:4 3:1159 4:1 5:168 6:284 7:887 +0 0:11 2:4 3:838 4:20 5:274 6:272 7:342 +0 0:9 1:2 3:1714 4:13 5:267 6:257 7:417 +0 0:9 1:25 3:800 4:16 5:253 6:267 7:1482 +0 0:8 1:6 2:3 3:1938 4:16 5:188 6:267 7:329 +0 0:7 1:4 2:5 3:1930 4:8 5:19 6:149 7:406 +0 0:7 1:27 2:4 3:610 4:14 5:253 6:188 7:625 +0 0:6 1:3 2:2 3:1402 4:1 5:269 6:99 7:1608 +1 0:6 1:22 2:6 3:1640 4:1 5:280 6:156 7:1623 +1 0:10 1:20 2:6 3:2130 4:8 5:19 6:215 7:821 +0 0:5 1:1 2:4 3:1738 4:19 5:224 6:256 7:992 +0 0:5 1:25 2:4 3:848 4:15 5:75 6:295 7:216 +0 0:3 1:8 2:6 3:2030 4:17 5:133 6:227 7:2917 +0 0:9 1:5 2:3 3:740 4:8 5:189 6:19 7:147 +0 0:11 1:21 2:3 3:1245 4:16 5:46 6:218 7:763 +0 0:6 1:20 2:4 3:1920 4:22 5:140 6:170 7:229 +0 0:3 1:27 2:2 3:2242 4:10 5:19 6:225 7:508 +0 0:1 1:15 3:1658 4:19 5:168 6:81 7:214 +0 0:8 1:13 2:1 3:1209 4:21 5:216 6:62 7:316 +0 0:1 1:22 3:726 4:1 5:294 6:218 7:1437 +0 0:7 1:29 2:6 3:1510 4:9 5:161 6:82 7:629 +0 0:1 1:15 2:6 3:1724 4:1 5:82 6:284 7:719 +0 0:10 1:27 2:6 3:1332 4:21 5:84 6:141 7:224 +0 0:8 1:15 2:3 3:935 4:20 5:184 6:266 7:1733 +0 0:6 1:16 2:6 3:602 4:6 5:182 6:140 7:758 +0 0:3 1:22 2:6 3:1147 4:19 5:161 6:257 7:258 +0 0:7 1:8 2:2 3:1339 4:16 5:216 6:219 7:717 +0 0:8 1:26 3:1650 4:22 5:247 6:218 7:531 +1 0:4 1:19 2:6 3:1835 4:12 5:83 6:227 7:602 +1 0:10 1:10 2:4 3:930 4:15 5:100 6:74 7:569 +1 0:6 1:28 2:4 3:943 4:14 5:221 6:205 7:1426 +0 0:7 1:10 2:4 3:1116 4:13 5:168 6:247 7:431 +0 0:11 1:11 2:4 3:1313 4:14 5:203 6:156 7:1028 +0 0:6 1:3 2:1 3:1250 4:1 5:133 6:267 7:2398 +0 0:8 1:8 2:4 3:755 4:20 5:252 6:36 7:1751 +0 0:11 1:4 2:1 3:1220 4:20 5:79 6:258 7:248 +0 0:7 1:22 2:2 3:1152 4:18 5:163 6:218 7:1745 +0 0:11 1:3 3:1713 4:6 5:47 6:140 7:284 +0 0:8 1:29 2:3 3:2017 4:4 5:169 6:211 7:353 +0 0:11 1:23 2:5 3:2247 4:18 5:211 6:267 7:2338 +0 1:7 2:6 3:1605 4:19 5:65 6:182 7:809 +0 0:8 1:27 2:1 3:943 4:16 5:163 6:260 7:89 +1 1:4 2:4 3:1448 4:21 5:100 6:203 7:799 +0 1:16 3:1314 4:14 5:203 6:192 7:1092 +1 0:8 1:10 3:1905 4:1 5:267 6:83 7:1438 +0 0:8 1:5 2:2 3:710 4:16 5:172 6:82 7:423 +0 0:2 1:30 2:2 3:1302 4:14 5:225 6:205 7:1276 +0 0:6 1:5 2:4 3:710 4:20 5:294 6:164 7:451 +0 0:11 1:19 2:2 3:620 4:10 5:47 6:19 7:712 +0 0:10 1:26 2:4 3:1818 4:18 5:84 6:82 7:641 +0 0:7 1:14 3:1012 4:16 5:83 6:194 7:895 +0 0:7 1:30 3:1023 4:14 5:163 6:89 7:1979 +0 0:6 1:11 2:6 3:852 4:21 5:225 6:141 7:1009 +0 0:5 1:17 2:5 3:739 4:7 5:25 6:222 7:1133 +0 0:5 1:5 2:1 3:2013 4:10 5:110 6:19 7:644 +0 0:5 1:8 2:3 3:1733 4:18 5:83 6:16 7:2406 +0 0:10 1:1 2:2 3:735 4:20 5:272 6:48 7:358 +1 0:3 2:3 3:1809 4:16 5:70 6:218 7:911 +0 0:3 1:14 2:3 3:2120 4:20 5:36 6:186 7:395 +0 0:3 2:3 3:915 4:20 5:49 6:294 7:842 +0 0:2 1:23 2:2 3:1017 4:16 5:83 6:30 7:1082 +1 0:7 1:18 2:4 3:1809 4:5 5:100 6:38 7:200 +1 0:11 1:25 2:6 3:1713 4:13 5:191 6:36 7:806 +0 0:7 1:4 2:4 3:1800 4:20 5:36 6:206 7:471 +1 0:9 1:2 2:1 3:1820 4:15 5:155 6:170 7:834 +0 0:1 1:28 2:5 3:1345 4:20 5:79 6:21 7:189 +0 0:4 1:22 2:4 3:2037 4:5 5:100 6:184 7:938 +0 0:10 1:28 3:1719 4:18 5:25 6:218 7:783 +0 0:1 1:6 2:6 3:606 4:18 5:261 6:82 7:1024 +0 0:7 1:12 2:4 3:803 4:12 5:163 6:162 7:236 +0 0:10 1:26 2:4 3:1552 4:8 5:171 6:275 7:1156 +1 0:6 1:18 2:1 3:1502 4:15 5:166 6:74 7:70 +0 0:1 1:27 2:4 3:1735 4:5 5:83 6:99 7:1605 +0 0:5 1:28 2:1 3:1417 4:5 5:161 6:99 7:2227 +0 0:3 1:17 3:1325 4:9 5:270 6:82 7:391 +0 0:9 1:18 2:2 3:646 4:21 5:49 6:99 7:169 +0 0:4 1:25 2:4 3:914 4:15 5:75 6:265 7:83 +1 0:1 1:12 2:3 3:1246 4:19 5:220 6:231 7:952 +1 0:9 1:6 2:5 3:1615 4:15 5:66 6:294 7:829 +0 0:2 1:13 3:1525 4:13 5:252 6:164 7:109 +0 0:8 1:1 2:4 3:1616 4:7 5:63 6:19 7:554 +0 0:5 1:17 2:5 3:812 4:7 5:168 6:184 7:950 +0 0:9 1:17 2:1 3:750 4:19 5:220 6:226 7:951 +0 1:10 2:3 3:648 4:1 5:25 6:274 7:1666 +0 1:26 2:2 3:943 4:1 5:84 6:272 7:1438 +0 0:2 1:22 2:4 3:1839 4:20 5:66 6:226 7:405 +0 0:11 1:26 3:1729 4:13 5:194 6:218 7:139 +0 1:3 2:2 3:2323 4:18 5:133 6:82 7:3365 +0 0:7 1:28 2:6 3:1233 4:19 5:145 6:64 7:185 +0 0:7 1:12 2:5 3:605 4:8 5:238 6:19 7:1027 +0 0:10 1:15 2:2 3:1402 4:18 5:261 6:218 7:1721 +0 0:6 1:10 2:1 3:1558 4:3 5:294 6:266 7:1216 +0 0:11 1:5 2:2 3:1129 4:7 5:19 6:81 7:547 +0 0:8 1:7 2:3 3:1220 4:20 5:134 6:136 7:276 +0 0:1 1:11 3:745 4:19 5:279 6:227 7:1262 +0 0:7 1:5 2:5 3:1555 4:16 5:262 6:6 7:250 +0 0:5 1:27 3:1852 4:16 5:296 6:275 7:175 +1 0:10 1:28 2:6 3:2112 4:3 5:16 6:157 7:571 +1 0:8 1:20 2:2 3:2225 4:20 5:224 6:192 7:290 +0 0:9 1:10 2:1 3:1223 4:18 5:83 6:206 7:1062 +0 0:8 1:21 2:2 3:1246 4:19 5:65 6:38 7:728 +1 0:10 1:11 2:2 3:901 4:10 5:163 6:146 7:1814 +0 0:11 1:22 2:5 3:2005 4:20 5:49 6:222 7:883 +0 0:10 1:20 3:1907 4:3 5:261 6:272 7:697 +1 0:5 2:1 3:1530 4:15 5:80 6:19 7:432 +1 0:4 1:9 2:5 3:1841 4:8 5:196 6:19 7:302 +0 0:3 1:23 2:5 3:1753 4:3 5:216 6:266 7:1721 +0 0:5 1:15 2:2 3:1419 4:20 5:267 6:162 7:386 +1 0:2 1:2 2:5 3:1654 4:1 5:225 6:218 7:1440 +1 0:5 1:14 2:1 3:1858 4:7 5:285 6:19 7:793 +1 0:11 1:5 2:3 3:1454 4:15 5:146 6:156 7:664 +0 0:9 1:18 2:2 3:509 4:16 5:152 6:164 7:123 +0 0:11 1:13 2:3 3:1535 4:21 5:63 6:192 7:544 +1 0:10 1:25 2:3 3:2045 4:20 5:49 6:240 7:328 +0 0:8 1:28 2:1 3:859 4:13 5:168 6:36 7:764 +0 0:8 1:19 2:1 3:1237 4:16 5:71 6:82 7:230 +0 0:1 1:23 2:6 3:739 4:1 5:274 6:83 7:1205 +0 0:10 1:13 2:6 3:912 4:12 5:161 6:164 7:236 +0 0:4 1:29 2:2 3:642 4:1 5:84 6:89 7:987 +1 1:6 2:5 3:2304 4:12 5:225 6:267 7:651 +1 0:8 1:1 2:4 3:2000 4:8 5:19 6:113 7:508 +0 1:30 3:1328 4:8 5:193 6:19 7:443 +0 1:25 2:1 3:1050 4:21 5:100 6:146 7:644 +0 0:11 1:6 2:3 3:1100 4:18 5:216 6:184 7:1005 +0 0:8 2:2 3:1558 4:13 5:84 6:180 7:309 +0 0:6 1:15 2:6 3:2000 4:20 5:225 6:251 7:601 +0 0:7 1:16 2:2 3:1216 4:13 5:200 6:164 7:267 +0 0:1 2:5 3:922 4:5 5:141 6:257 7:1303 +0 0:6 1:11 2:6 3:1950 4:20 5:36 6:65 7:338 +0 0:3 2:3 3:1406 4:19 5:220 6:64 7:590 +0 1:2 2:2 3:1303 4:19 5:65 6:256 7:600 +0 0:10 1:25 2:3 3:810 4:21 5:141 6:140 7:1190 +0 0:10 1:7 2:2 3:1830 4:8 5:222 6:19 7:247 +0 0:9 1:8 2:6 3:1135 4:14 5:203 6:243 7:490 +0 0:7 1:28 2:6 3:1900 4:10 5:224 6:294 7:920 +0 0:3 1:15 2:5 3:1535 4:7 5:19 6:62 7:554 +0 0:3 1:22 2:5 3:2100 4:16 5:294 6:164 7:451 +0 0:3 1:9 3:1956 4:20 5:134 6:297 7:453 +0 0:4 1:27 3:607 4:13 5:66 6:83 7:927 +0 0:11 1:27 2:2 3:626 4:21 5:141 6:78 7:217 +0 0:11 1:13 2:3 3:1146 4:20 5:163 6:277 7:373 +0 0:7 1:24 2:2 3:1550 4:7 5:63 6:74 7:221 +0 0:6 1:1 3:2049 4:13 5:168 6:65 7:478 +1 0:9 1:20 2:3 3:1520 4:21 5:100 6:77 7:884 +1 0:2 1:7 2:3 3:1327 4:18 5:83 6:182 7:533 +0 1:12 2:4 3:1140 4:7 5:182 6:38 7:1121 +0 0:1 1:14 2:5 3:1635 4:20 5:2 6:180 7:332 +0 0:3 1:6 2:4 3:1450 4:1 5:274 6:218 7:1726 +0 0:9 1:30 2:5 3:2331 4:18 5:161 6:140 7:2066 +0 0:8 2:2 3:920 4:21 5:278 6:141 7:796 +0 0:5 1:20 3:1125 4:19 5:229 6:64 7:366 +0 0:4 1:17 2:4 3:1550 4:20 5:49 6:25 7:283 +0 0:2 1:12 3:1304 4:5 5:141 6:226 7:1324 +0 0:5 1:14 2:2 3:638 4:10 5:49 6:38 7:370 +1 0:2 1:20 3:2143 4:18 5:216 6:140 7:589 +1 0:8 1:17 2:6 3:1936 4:22 5:246 6:227 7:601 +0 0:1 1:9 2:2 3:1307 4:5 5:141 6:49 7:1235 +0 0:6 1:30 2:5 3:753 4:1 5:38 6:222 7:1197 +0 0:10 1:14 3:630 4:1 5:252 6:284 7:1558 +0 0:3 1:11 2:5 3:1645 4:13 5:252 6:164 7:109 +0 0:6 1:8 3:1053 4:18 5:216 6:193 7:1197 +1 0:11 1:22 2:6 3:1940 4:21 5:46 6:99 7:266 +0 0:3 1:15 2:4 3:510 4:14 5:103 6:205 7:223 +0 0:7 1:27 2:4 3:1614 4:12 5:225 6:266 7:1107 +0 0:2 1:30 2:3 3:1607 4:5 5:262 6:99 7:2565 +1 0:9 1:9 3:1526 4:21 5:66 6:99 7:462 +0 0:10 1:11 2:1 3:1127 4:7 5:108 6:38 7:1237 +0 0:10 1:17 2:3 3:1737 4:20 5:63 6:186 7:307 +1 0:5 1:3 2:6 3:1220 4:20 5:184 6:227 7:1444 +0 0:6 1:3 2:2 3:620 4:14 5:90 6:188 7:610 +0 0:3 1:29 2:4 3:1218 4:1 5:84 6:140 7:1172 +0 0:6 1:26 2:1 3:927 4:5 5:141 6:227 7:1009 +1 0:5 1:14 2:1 3:2059 4:8 5:288 6:19 7:549 +0 0:2 1:10 2:5 3:1440 4:8 5:19 6:224 7:247 +0 0:6 1:28 2:3 3:1603 4:13 5:84 6:121 7:134 +0 0:6 1:6 2:5 3:1329 4:14 5:184 6:89 7:229 +0 0:2 1:28 2:1 3:1653 4:15 5:47 6:156 7:301 +0 0:8 1:14 2:2 3:1242 4:13 5:84 6:123 7:999 +0 0:10 1:20 3:550 4:20 5:272 6:227 7:647 +0 1:9 2:2 3:1051 4:18 5:216 6:140 7:589 +0 2:6 3:935 4:15 5:216 6:74 7:264 +1 1:20 2:5 3:1135 4:19 5:225 6:277 7:647 +0 0:10 1:30 2:1 3:742 4:5 5:141 6:193 7:964 +0 0:11 1:22 2:6 3:1650 4:7 5:80 6:19 7:432 +0 0:8 1:4 2:1 3:1040 4:18 5:216 6:182 7:403 +0 0:10 1:29 3:1415 4:18 5:156 6:267 7:2586 +0 0:7 1:4 2:5 3:2300 4:10 5:19 6:294 7:406 +0 0:8 1:13 2:1 3:616 4:16 5:233 6:82 7:853 +0 0:4 1:12 2:6 3:721 4:10 5:142 6:19 7:781 +0 0:2 2:2 3:1645 4:22 5:153 6:82 7:406 +0 1:30 3:1454 4:18 5:216 6:279 7:1726 +0 0:7 1:4 2:4 3:1153 4:14 5:190 6:89 7:609 +0 1:15 3:1035 4:20 5:184 6:36 7:395 +0 1:26 2:2 3:811 4:14 5:270 6:205 7:991 +1 0:3 1:8 2:5 3:1400 4:14 5:267 6:205 7:1576 +0 0:5 1:26 2:5 3:1014 4:7 5:19 6:222 7:545 +0 0:1 1:9 2:2 3:1828 4:19 5:225 6:284 7:1262 +0 0:4 1:14 2:1 3:1407 4:4 5:220 6:156 7:1028 +1 0:6 1:3 2:2 3:1915 4:8 5:73 6:19 7:363 +0 0:4 1:26 2:5 3:625 4:14 5:49 6:89 7:408 +0 0:8 1:22 2:5 3:818 4:1 5:84 6:38 7:1562 +0 0:5 1:15 2:3 3:930 4:10 5:49 6:184 7:787 +0 0:8 1:20 2:1 3:1420 4:20 5:184 6:89 7:229 +1 0:2 1:25 2:5 3:1734 4:16 5:202 6:275 7:436 +0 0:1 1:23 3:2148 4:19 5:65 6:226 7:448 +0 0:11 1:16 2:5 3:1010 4:1 5:84 6:251 7:1345 +0 0:2 1:20 3:750 4:20 5:225 6:275 7:507 +0 0:9 1:19 2:2 3:100 4:14 5:16 6:205 7:2518 +1 0:5 1:3 2:6 3:1238 4:7 5:215 6:19 7:1900 +0 0:10 3:1704 4:14 5:204 6:89 7:926 +0 0:6 1:20 2:3 3:611 4:14 5:224 6:205 7:980 +0 0:7 1:20 2:6 3:1101 4:22 5:83 6:69 7:72 +0 0:6 1:4 2:2 3:605 4:16 5:71 6:275 7:320 +0 0:5 1:15 2:2 3:1927 4:1 5:242 6:193 7:700 +0 0:10 1:16 2:3 3:2038 4:16 5:163 6:299 7:451 +0 0:4 1:30 2:2 3:815 4:13 5:84 6:122 7:931 +0 0:5 1:9 2:4 3:627 4:18 5:156 6:267 7:2586 +0 0:8 1:8 2:5 3:1016 4:7 5:163 6:156 7:2475 +0 0:8 1:21 2:2 3:1141 4:8 5:186 6:74 7:403 +0 1:22 2:1 3:905 4:20 5:83 6:162 7:629 +0 0:5 1:20 3:751 4:18 5:38 6:267 7:2704 +0 0:6 1:7 2:6 3:1210 4:20 5:37 6:266 7:399 +0 0:11 1:5 2:2 3:1533 4:18 5:82 6:218 7:612 +0 0:4 1:18 2:5 3:1402 4:1 5:36 6:83 7:631 +0 0:1 1:9 2:1 3:1421 4:5 5:141 6:297 7:429 +0 1:28 2:5 3:1020 4:16 5:270 6:182 7:919 +0 1:25 2:1 3:2007 4:6 5:38 6:140 7:413 +1 0:6 1:8 3:1818 4:8 5:196 6:19 7:302 +0 0:3 1:30 2:5 3:1154 4:14 5:221 6:205 7:1426 +0 0:11 1:8 2:6 3:1300 4:7 5:146 6:19 7:432 +0 0:11 1:18 3:930 4:12 5:161 6:227 7:256 +1 0:10 1:28 3:1238 4:14 5:262 6:205 7:1589 +1 0:7 1:5 2:6 3:2133 4:20 5:204 6:134 7:303 +0 0:8 1:29 2:2 3:1450 4:13 5:21 6:247 7:1162 +0 0:9 1:27 2:3 3:1052 4:20 5:182 6:36 7:616 +1 1:5 2:4 3:824 4:8 5:11 6:19 7:143 +0 0:8 1:16 2:5 3:849 4:20 5:178 6:162 7:796 +0 0:10 1:2 2:4 3:1448 4:13 5:306 6:83 7:281 +0 0:9 1:7 2:5 3:1350 4:20 5:134 6:21 7:148 +1 0:3 2:3 3:825 4:7 5:38 6:256 7:1249 +0 1:20 2:5 3:1743 4:20 5:225 6:277 7:647 +0 0:3 1:22 2:5 3:1732 4:21 5:63 6:194 7:328 +0 0:11 1:6 2:4 3:1644 4:15 5:75 6:89 7:229 +0 0:3 1:8 2:5 3:743 4:5 5:251 6:99 7:1068 +0 0:4 1:20 2:1 3:2200 4:20 5:134 6:78 7:239 +0 0:4 2:2 3:927 4:21 5:100 6:284 7:872 +1 0:2 1:25 2:4 3:1410 4:20 5:161 6:217 7:197 +0 0:10 1:5 2:6 3:741 4:18 5:82 6:218 7:612 +0 0:4 1:6 2:1 3:947 4:12 5:225 6:140 7:1956 +0 0:2 1:2 2:4 3:805 4:12 5:225 6:251 7:601 +0 0:4 1:9 2:5 3:940 4:20 5:48 6:211 7:325 +0 0:2 1:1 2:3 3:1532 4:7 5:19 6:170 7:761 +0 0:3 1:21 2:4 3:1803 4:18 5:38 6:164 7:2611 +0 0:5 1:20 2:1 3:1030 4:20 5:161 6:272 7:386 +0 0:4 1:26 2:6 3:1206 4:5 5:141 6:99 7:1400 +0 0:8 1:30 2:3 3:1421 4:7 5:66 6:74 7:116 +0 0:8 1:20 2:2 3:1844 4:21 5:141 6:250 7:1157 +0 0:5 1:13 3:715 4:18 5:225 6:218 7:1440 +0 0:2 1:11 2:3 3:638 4:19 5:19 6:226 7:665 +0 0:4 1:25 2:4 3:2337 4:7 5:163 6:19 7:1946 +1 0:3 1:2 2:6 3:1631 4:16 5:80 6:218 7:240 +0 0:7 1:20 2:5 3:823 4:21 5:167 6:141 7:201 +0 0:9 1:6 2:5 3:816 4:16 5:261 6:114 7:224 +0 0:1 1:23 2:6 3:1828 4:16 5:216 6:290 7:607 +0 0:11 1:27 2:2 3:1048 4:3 5:169 6:266 7:965 +0 0:3 1:30 2:5 3:2343 4:4 5:209 6:107 7:2575 +0 0:2 1:11 2:2 3:950 4:8 5:19 6:88 7:743 +0 0:10 1:17 2:4 3:818 4:7 5:163 6:19 7:1946 +0 0:9 1:14 2:4 3:850 4:20 5:242 6:49 7:255 +0 0:3 1:27 2:2 3:552 4:1 5:168 6:218 7:733 +0 0:3 1:29 2:4 3:1100 4:20 5:253 6:94 7:496 +0 1:24 3:1717 4:14 5:108 6:89 7:1127 +0 0:7 1:8 2:2 3:2128 4:1 5:252 6:156 7:2446 +0 0:9 1:30 2:5 3:1056 4:1 5:168 6:218 7:733 +0 0:8 1:9 2:5 3:2135 4:7 5:19 6:82 7:1199 +0 0:11 1:26 2:1 3:800 4:8 5:45 6:19 7:449 +0 0:4 1:4 3:859 4:14 5:141 6:89 7:1076 +0 0:3 1:15 2:4 3:1117 4:19 5:65 6:155 7:329 +0 0:5 1:19 2:6 3:800 4:13 5:216 6:265 7:286 +0 0:9 2:5 3:958 4:20 5:213 6:186 7:423 +0 0:6 1:5 2:4 3:1215 4:1 5:267 6:218 7:1829 +0 0:11 1:16 2:6 3:1810 4:9 5:251 6:82 7:1606 +0 0:10 1:8 2:2 3:1524 4:13 5:216 6:140 7:589 +0 0:4 1:14 2:1 3:1925 4:3 5:261 6:159 7:680 +1 0:2 2:2 3:1700 4:1 5:84 6:193 7:1121 +0 0:2 1:17 2:5 3:1510 4:21 5:260 6:141 7:788 +0 0:8 2:2 3:1542 4:1 5:191 6:164 7:2342 +0 0:5 1:25 2:4 3:1224 4:7 5:95 6:19 7:1282 +0 0:6 1:14 2:4 3:805 4:13 5:216 6:47 7:473 +1 0:1 1:25 2:2 3:1443 4:5 5:141 6:218 7:925 +0 0:3 1:24 2:6 3:642 4:13 5:72 6:83 7:354 +0 0:10 1:9 2:3 3:1425 4:15 5:238 6:38 7:95 +0 0:2 1:27 2:6 3:855 4:14 5:261 6:89 7:1927 +0 0:2 1:4 2:6 3:1705 4:16 5:163 6:277 7:373 +0 0:7 3:745 4:20 5:224 6:134 7:1335 +0 0:4 1:13 2:1 3:722 4:16 5:2 6:275 7:493 +0 0:9 1:8 2:6 3:1228 4:7 5:75 6:267 7:2036 +1 0:10 1:25 2:4 3:1845 4:3 5:16 6:101 7:261 +0 0:4 1:4 3:1436 4:1 5:216 6:83 7:802 +0 0:5 1:6 2:2 3:1026 4:1 5:216 6:82 7:888 +0 0:11 1:11 2:4 3:1825 4:20 5:267 6:217 7:333 +0 0:6 1:3 2:1 3:1348 4:6 5:140 6:289 7:251 +0 0:1 1:18 2:3 3:652 4:18 5:213 6:82 7:472 +0 0:2 1:17 2:5 3:1057 4:1 5:216 6:284 7:258 +0 0:3 1:16 2:5 3:1920 4:20 5:267 6:266 7:697 +0 0:9 2:4 3:1316 4:12 5:225 6:267 7:651 +0 0:9 1:20 2:3 3:910 4:16 5:270 6:126 7:463 +0 0:9 1:22 3:1224 4:7 5:19 6:275 7:1589 +0 0:9 1:11 2:6 3:659 4:13 5:298 6:83 7:103 +0 0:10 1:30 2:2 3:1734 4:20 5:251 6:49 7:919 +0 0:11 1:27 2:1 3:1236 4:3 5:163 6:81 7:2311 +0 0:1 2:5 3:2047 4:1 5:84 6:142 7:328 +0 0:2 1:25 2:5 3:2049 4:18 5:216 6:294 7:1012 +0 0:11 1:25 2:6 3:617 4:18 5:47 6:218 7:473 +0 1:2 2:2 3:759 4:19 5:65 6:81 7:331 +0 0:9 1:21 2:5 3:545 4:8 5:192 6:19 7:669 +0 0:6 1:12 2:3 3:1038 4:8 5:74 6:19 7:83 +0 0:8 1:1 2:4 3:1519 4:7 5:161 6:74 7:1678 +0 0:10 1:27 2:5 3:554 4:5 5:204 6:141 7:305 +0 0:8 1:9 2:5 3:620 4:5 5:191 6:141 7:964 +1 0:4 1:12 2:6 3:1717 4:19 5:82 6:64 7:331 +0 0:11 1:9 2:6 3:1020 4:20 5:184 6:211 7:1844 +0 0:5 1:29 2:1 3:2045 4:13 5:84 6:5 7:89 +0 2:5 3:1700 4:14 5:90 6:47 7:240 +0 0:9 1:10 2:2 3:1816 4:20 5:49 6:47 7:281 +1 0:8 1:19 3:1824 4:4 5:156 6:107 7:1069 +1 0:4 1:6 2:2 3:1310 4:8 5:19 6:242 7:1027 +0 0:2 1:16 2:4 3:613 4:16 5:236 6:82 7:776 +0 0:5 1:29 2:2 3:827 4:7 5:90 6:19 7:594 +0 0:8 1:8 2:4 3:1840 4:19 5:224 6:256 7:992 +0 0:3 1:3 2:1 3:2126 4:4 5:156 6:250 7:288 +1 1:22 2:1 3:2212 4:5 5:182 6:99 7:938 +0 0:7 1:23 2:1 3:1120 4:21 5:186 6:99 7:946 +0 0:5 1:20 2:1 3:2113 4:16 5:255 6:164 7:89 +1 0:4 1:13 2:1 3:1620 4:20 5:163 6:211 7:337 +1 0:7 1:24 2:2 3:1537 4:3 5:267 6:223 7:569 +0 0:11 1:23 2:5 3:1335 4:15 5:75 6:168 7:70 +0 0:11 1:14 2:3 3:1650 4:7 5:75 6:257 7:1865 +0 0:8 2:3 3:1047 4:4 5:169 6:140 7:2277 +0 0:8 1:20 2:2 3:1659 4:16 5:270 6:70 7:320 +0 1:10 2:2 3:845 4:6 5:140 6:51 7:401 +0 0:6 1:28 2:4 3:1212 4:1 5:84 6:49 7:1217 +1 0:8 1:30 2:4 3:1907 4:20 5:209 6:257 7:446 +0 0:6 1:28 2:4 3:851 4:18 5:274 6:82 7:846 +0 0:5 1:21 2:2 3:1552 4:19 5:65 6:164 7:2125 +0 0:3 1:1 2:5 3:1741 4:10 5:19 6:193 7:595 +0 0:11 1:8 2:6 3:940 4:1 5:38 6:274 7:1674 +1 0:3 1:21 2:4 3:1332 4:4 5:156 6:107 7:1069 +0 0:5 1:14 2:2 3:1715 4:8 5:19 6:104 7:331 +0 0:7 1:24 2:2 3:830 4:11 5:158 6:133 7:163 +0 0:1 1:27 2:4 3:1605 4:20 5:161 6:272 7:386 +1 0:9 1:6 2:4 3:1057 4:5 5:141 6:2 7:744 +0 0:2 1:19 3:750 4:19 5:65 6:89 7:500 +0 0:10 1:30 2:2 3:1242 4:22 5:129 6:82 7:142 +0 1:4 2:3 3:850 4:14 5:90 6:182 7:629 +0 0:2 1:1 2:3 3:551 4:21 5:166 6:62 7:275 +0 0:7 1:24 2:2 3:643 4:7 5:252 6:74 7:1865 +1 0:8 1:1 2:4 3:1333 4:16 5:83 6:174 7:423 +0 0:6 1:30 2:6 3:1342 4:5 5:253 6:141 7:191 +0 0:1 1:29 2:5 3:530 4:15 5:238 6:74 7:810 +0 0:4 1:11 2:2 3:1255 4:20 5:261 6:275 7:689 +0 0:11 2:4 3:1508 4:20 5:225 6:162 7:256 +0 0:9 1:30 2:5 3:1223 4:18 5:83 6:206 7:1062 +0 0:6 1:13 2:3 3:856 4:5 5:274 6:141 7:1347 +0 0:3 1:4 2:1 3:1547 4:18 5:84 6:218 7:802 +0 0:6 1:2 3:700 4:7 5:182 6:156 7:944 +0 1:16 3:700 4:15 5:82 6:137 7:613 +0 0:1 1:27 2:3 3:1645 4:20 5:134 6:162 7:1235 +0 0:2 1:5 2:1 3:937 4:5 5:47 6:99 7:282 +0 1:11 3:855 4:8 5:19 6:198 7:302 +0 0:9 1:22 3:1850 4:14 5:66 6:205 7:627 +0 0:4 1:7 2:2 3:1018 4:1 5:279 6:164 7:1593 +0 0:4 1:22 2:4 3:1214 4:14 5:25 6:89 7:548 +0 0:1 1:5 2:4 3:1139 4:15 5:75 6:234 7:608 +0 0:3 1:30 2:5 3:710 4:20 5:163 6:162 7:236 +0 0:8 1:28 2:1 3:905 4:21 5:100 6:205 7:1008 +0 0:9 1:9 3:752 4:5 5:100 6:205 7:1008 +0 0:9 1:23 2:5 3:2112 4:13 5:216 6:214 7:693 +0 0:6 1:20 2:4 3:1055 4:15 5:83 6:74 7:1069 +0 0:11 1:17 2:6 3:811 4:5 5:146 6:141 7:845 +0 0:9 1:9 2:1 3:839 4:14 5:261 6:133 7:2677 +0 0:10 1:12 2:5 3:1124 4:1 5:216 6:99 7:719 +0 0:7 1:23 3:1838 4:6 5:46 6:140 7:442 +1 0:6 1:7 2:6 3:1630 4:13 5:216 6:47 7:473 +0 0:10 1:19 2:6 3:1448 4:20 5:163 6:299 7:451 +1 0:2 1:1 2:4 3:1005 4:7 5:156 6:19 7:760 +1 0:5 1:16 2:3 3:1513 4:20 5:182 6:30 7:478 +0 0:4 1:1 2:3 3:1300 4:20 5:184 6:240 7:842 +0 0:6 1:19 2:2 3:1921 4:19 5:204 6:64 7:651 +0 0:10 1:25 2:4 3:1128 4:4 5:38 6:171 7:2602 +0 0:5 1:11 2:2 3:1628 4:15 5:75 6:52 7:214 +0 0:11 1:6 2:4 3:1216 4:3 5:261 6:16 7:1449 +0 0:10 1:21 2:1 3:2201 4:3 5:83 6:16 7:2406 +0 0:4 1:7 2:3 3:1545 4:21 5:265 6:141 7:192 +0 0:3 1:20 2:3 3:630 4:15 5:124 6:170 7:610 +0 0:1 1:12 2:3 3:1718 4:18 5:216 6:81 7:612 +0 0:4 1:30 2:2 3:2010 4:20 5:161 6:164 7:236 +0 0:2 1:3 2:6 3:937 4:15 5:38 6:36 7:943 +0 0:8 1:14 2:3 3:1048 4:20 5:270 6:2 7:493 +0 0:5 1:19 2:6 3:1612 4:1 5:216 6:247 7:647 +1 0:9 1:16 2:6 3:822 4:13 5:216 6:265 7:286 +0 0:4 1:14 2:2 3:1720 4:22 5:89 6:227 7:1149 +0 1:28 2:5 3:1348 4:13 5:82 6:38 7:399 +0 0:2 1:26 2:5 3:820 4:20 5:237 6:182 7:1234 +1 0:1 1:28 2:5 3:1945 4:14 5:203 6:164 7:1536 +0 0:9 1:2 3:925 4:4 5:182 6:156 7:944 +0 0:8 1:10 3:640 4:22 5:216 6:160 7:179 +0 0:6 1:18 2:2 3:931 4:1 5:270 6:83 7:988 +1 0:3 1:8 2:5 3:1731 4:16 5:83 6:275 7:391 +0 0:8 1:19 3:1716 4:21 5:265 6:141 7:192 +0 0:7 1:6 2:6 3:1251 4:11 5:158 6:213 7:84 +0 0:1 1:11 2:6 3:1935 4:9 5:225 6:82 7:602 +0 0:4 1:12 3:600 4:19 5:141 6:227 7:1009 +0 0:2 1:16 2:3 3:1005 4:20 5:225 6:240 7:2277 +0 0:6 1:21 2:5 3:615 4:9 5:267 6:82 7:948 +0 0:9 1:19 2:2 3:1150 4:20 5:21 6:78 7:189 +0 0:2 1:30 2:3 3:1310 4:18 5:216 6:123 7:590 +0 0:8 1:27 3:743 4:13 5:216 6:228 7:130 +0 0:6 1:8 3:1350 4:1 5:203 6:83 7:852 +0 0:3 2:4 3:851 4:19 5:163 6:64 7:2125 +0 0:1 1:19 2:3 3:1123 4:16 5:262 6:37 7:522 +0 0:11 1:15 2:4 3:1157 4:7 5:156 6:164 7:2475 +0 0:3 1:22 2:5 3:1538 4:7 5:289 6:156 7:1005 +0 0:4 1:12 3:1319 4:13 5:84 6:121 7:134 +0 0:2 1:13 2:1 3:2247 4:19 5:261 6:64 7:2279 +0 0:6 1:30 2:5 3:2253 4:19 5:163 6:64 7:2125 +0 0:5 1:24 2:4 3:1427 4:21 5:141 6:165 7:458 +1 0:4 1:4 3:1440 4:20 5:161 6:266 7:866 +0 0:1 1:5 2:4 3:705 4:20 5:184 6:162 7:1521 +1 0:7 1:11 3:2036 4:4 5:108 6:170 7:1076 +0 0:7 1:20 2:6 3:1901 4:5 5:141 6:294 7:787 +1 1:15 2:6 3:1847 4:1 5:216 6:184 7:1005 +0 0:6 1:15 2:6 3:1825 4:22 5:169 6:227 7:355 +0 0:7 1:2 2:3 3:551 4:13 5:66 6:83 7:927 +0 0:8 1:8 2:4 3:800 4:13 5:84 6:71 7:354 +0 0:3 1:21 2:3 3:1853 4:1 5:216 6:275 7:1249 +0 0:8 1:15 2:4 3:1125 4:19 5:161 6:205 7:1300 +0 0:10 1:26 2:4 3:910 4:7 5:75 6:107 7:932 +0 0:3 1:3 2:1 3:1720 4:13 5:56 6:83 7:695 +0 0:9 1:8 2:6 3:638 4:4 5:108 6:171 7:2327 +0 0:11 1:8 2:5 3:1501 4:14 5:203 6:47 7:734 +0 0:9 1:24 2:6 3:1314 4:13 5:164 6:83 7:282 +1 0:8 1:30 2:4 3:919 4:14 5:90 6:218 7:235 +0 0:2 1:12 2:6 3:700 4:20 5:90 6:36 7:457 +0 0:4 1:16 2:3 3:1003 4:13 5:84 6:1 7:158 +0 0:9 1:4 2:3 3:2002 4:20 5:224 6:294 7:920 +0 0:3 1:20 2:2 3:1813 4:5 5:261 6:16 7:1449 +0 0:3 1:11 2:4 3:956 4:7 5:270 6:226 7:1926 +0 0:9 1:1 2:6 3:1029 4:7 5:19 6:299 7:1541 +1 0:2 1:26 2:5 3:19 4:18 5:163 6:162 7:236 +1 0:10 1:25 2:3 3:1930 4:20 5:114 6:223 7:279 +0 0:1 1:5 2:4 3:1512 4:19 5:82 6:64 7:331 +1 0:3 1:14 2:3 3:1516 4:13 5:146 6:193 7:1021 +0 0:4 1:15 2:3 3:1352 4:7 5:75 6:107 7:932 +0 0:10 1:8 2:2 3:1210 4:1 5:108 6:83 7:1119 +0 0:2 1:23 2:3 3:629 4:3 5:16 6:101 7:261 +1 0:10 1:24 2:3 3:1813 4:14 5:100 6:188 7:946 +1 1:27 2:3 3:1951 4:21 5:66 6:99 7:462 +0 0:5 1:27 3:1737 4:1 5:84 6:258 7:247 +0 1:5 2:4 3:1414 4:12 5:225 6:211 7:646 +0 0:10 1:18 2:4 3:928 4:4 5:156 6:47 7:301 +0 0:5 1:22 2:4 3:825 4:18 5:182 6:82 7:1545 +0 0:5 1:7 2:2 3:635 4:15 5:245 6:170 7:292 +0 0:6 1:7 2:6 3:1630 4:19 5:38 6:64 7:728 +0 0:3 1:23 2:5 3:1226 4:10 5:224 6:107 7:992 +0 0:11 1:10 3:1231 4:19 5:242 6:64 7:130 +0 0:5 1:28 2:1 3:1605 4:21 5:95 6:141 7:667 +0 0:8 1:4 2:1 3:1357 4:1 5:84 6:226 7:1302 +0 0:5 1:10 2:5 3:917 4:10 5:19 6:164 7:1946 +0 0:6 1:18 2:2 3:1318 4:13 5:36 6:218 7:409 +0 0:11 1:12 2:2 3:1512 4:13 5:38 6:170 7:185 +0 1:20 2:4 3:1225 4:15 5:213 6:275 7:839 +0 0:2 1:4 2:6 3:546 4:13 5:1 6:83 7:158 +0 0:6 1:28 2:3 3:1601 4:14 5:190 6:89 7:609 +0 0:1 1:27 2:3 3:643 4:16 5:213 6:218 7:416 +0 1:8 3:1330 4:15 5:242 6:74 7:390 +0 0:4 1:17 2:4 3:1222 4:16 5:70 6:218 7:911 +0 0:7 1:2 2:2 3:1230 4:15 5:75 6:36 7:230 +0 0:7 1:8 2:2 3:1146 4:13 5:173 6:83 7:394 +1 0:11 1:16 2:6 3:2136 4:10 5:184 6:205 7:349 +1 0:11 1:6 2:3 3:1920 4:20 5:161 6:165 7:775 +0 0:11 1:5 2:2 3:1845 4:16 5:83 6:30 7:1082 +0 0:3 1:22 2:6 3:654 4:19 5:38 6:274 7:1674 +0 0:9 1:1 3:859 4:14 5:47 6:205 7:734 +0 0:11 1:4 2:2 3:707 4:21 5:30 6:99 7:850 +1 0:9 1:22 2:6 3:953 4:8 5:245 6:19 7:481 +0 0:5 1:21 2:2 3:1412 4:1 5:168 6:218 7:733 +0 0:9 1:23 2:6 3:1045 4:13 5:216 6:142 7:588 +0 0:8 1:1 2:5 3:2103 4:5 5:141 6:279 7:1347 +0 0:9 1:29 2:4 3:2000 4:8 5:98 6:74 7:170 +0 0:6 1:7 2:6 3:1850 4:15 5:182 6:81 7:759 +0 0:1 1:22 2:1 3:950 4:20 5:95 6:134 7:677 +0 0:7 1:22 2:1 3:1918 4:7 5:19 6:182 7:692 +0 0:11 1:29 2:3 3:1110 4:7 5:289 6:19 7:406 +1 0:7 1:3 2:3 3:1600 4:20 5:155 6:294 7:180 +0 0:9 1:15 2:5 3:1307 4:19 5:225 6:226 7:2075 +0 0:5 1:1 2:4 3:811 4:5 5:289 6:99 7:998 +0 0:8 1:25 2:5 3:1450 4:1 5:84 6:182 7:460 +0 0:9 1:17 2:1 3:1223 4:14 5:90 6:265 7:306 +1 0:8 1:12 3:1901 4:1 5:19 6:193 7:595 +0 0:8 1:9 2:5 3:745 4:10 5:49 6:283 7:880 +1 0:6 1:6 2:5 3:1154 4:16 5:188 6:223 7:222 +1 0:8 1:22 2:4 3:10 4:12 5:161 6:82 7:629 +0 0:2 1:2 2:4 3:1135 4:21 5:141 6:15 7:519 +1 0:10 1:29 3:1705 4:7 5:156 6:222 7:1028 +0 0:7 1:8 2:2 3:750 4:13 5:171 6:83 7:304 +0 0:6 1:16 3:1228 4:3 5:26 6:16 7:399 +0 0:6 1:21 2:5 3:1259 4:1 5:269 6:285 7:68 +0 0:7 1:29 3:1454 4:16 5:216 6:268 7:438 +0 0:10 1:20 2:6 3:1729 4:21 5:78 6:99 7:884 +0 1:18 2:2 3:1854 4:16 5:270 6:143 7:188 +0 0:11 1:1 2:6 3:1112 4:7 5:232 6:19 7:272 +1 0:5 1:25 2:4 3:1000 4:20 5:204 6:227 7:1301 +0 0:8 1:26 3:2027 4:7 5:168 6:38 7:185 +0 0:10 1:23 2:1 3:956 4:13 5:84 6:60 7:685 +0 0:11 1:28 2:3 3:1854 4:5 5:141 6:170 7:1416 +0 0:3 1:1 2:5 3:753 4:10 5:19 6:184 7:403 +0 0:4 1:29 2:2 3:1013 4:22 5:225 6:258 7:843 +0 0:5 1:16 2:3 3:547 4:3 5:266 6:157 7:95 +0 0:5 1:1 2:4 3:820 4:13 5:163 6:260 7:89 +0 0:2 1:16 2:4 3:1459 4:13 5:66 6:247 7:361 +0 0:8 1:26 3:1540 4:20 5:267 6:257 7:417 +0 0:9 1:1 3:1112 4:20 5:150 6:49 7:220 +0 0:6 1:19 2:3 3:719 4:10 5:163 6:19 7:1946 +0 0:5 1:12 3:630 4:14 5:32 6:205 7:386 +1 0:5 1:19 2:6 3:1930 4:14 5:90 6:19 7:594 +1 0:11 1:2 2:6 3:1639 4:10 5:82 6:19 7:547 +0 0:5 1:11 2:2 3:1040 4:4 5:156 6:184 7:944 +0 0:8 1:30 2:4 3:929 4:7 5:25 6:19 7:859 +0 0:10 1:29 2:1 3:1640 4:18 5:163 6:140 7:2288 +0 0:1 1:1 3:655 4:19 5:38 6:170 7:185 +0 0:1 1:11 3:1751 4:5 5:141 6:217 7:1334 +0 0:9 2:4 3:1012 4:1 5:163 6:83 7:1235 +0 0:2 1:30 2:3 3:1026 4:22 5:140 6:156 7:228 +0 0:4 1:15 2:2 3:1101 4:16 5:270 6:301 7:175 +0 0:9 1:5 2:4 3:959 4:19 5:100 6:227 7:2133 +0 0:1 1:7 3:839 4:4 5:209 6:140 7:2408 +0 0:3 1:7 2:5 3:1053 4:20 5:83 6:134 7:883 +0 0:10 1:24 2:2 3:1258 4:7 5:182 6:19 7:403 +1 0:5 1:23 2:3 3:1328 4:18 5:163 6:140 7:2288 +0 0:5 1:1 2:4 3:1312 4:16 5:216 6:120 7:174 +0 0:4 1:13 3:1654 4:13 5:168 6:81 7:214 +0 0:10 1:9 2:3 3:2032 4:19 5:229 6:107 7:994 +0 0:1 1:23 2:6 3:1825 4:13 5:5 6:83 7:89 +0 1:28 2:5 3:1710 4:22 5:225 6:314 7:160 +0 0:7 1:7 2:1 3:626 4:13 5:191 6:64 7:650 +0 0:11 1:5 2:2 3:1114 4:13 5:216 6:304 7:475 +0 0:4 1:16 2:4 3:910 4:14 5:242 6:89 7:501 +0 0:11 1:22 2:5 3:949 4:9 5:161 6:82 7:629 +0 0:5 1:19 2:6 3:1509 4:6 5:140 6:124 7:383 +0 0:8 1:23 2:3 3:727 4:7 5:251 6:74 7:879 +1 0:5 1:10 2:6 3:1454 4:20 5:163 6:211 7:337 +0 0:8 1:13 2:1 3:1552 4:14 5:141 6:188 7:469 +0 0:4 1:11 2:2 3:1555 4:15 5:279 6:74 7:307 +0 0:6 1:20 2:4 3:1750 4:20 5:184 6:89 7:229 +0 0:4 1:1 2:4 3:1421 4:1 5:216 6:146 7:177 +0 0:5 1:2 2:5 3:701 4:13 5:110 6:218 7:223 +0 0:3 1:4 2:2 3:628 4:8 5:181 6:19 7:79 +0 0:9 1:16 2:6 3:700 4:20 5:209 6:257 7:446 +0 0:9 1:30 2:5 3:1507 4:19 5:82 6:242 7:482 +0 0:7 1:23 3:1224 4:13 5:242 6:170 7:431 +0 1:8 2:1 3:1251 4:1 5:82 6:193 7:920 +0 0:9 2:5 3:653 4:20 5:49 6:184 7:787 +0 0:10 1:11 2:2 3:1217 4:1 5:83 6:83 7:641 +1 0:8 1:19 2:1 3:1229 4:5 5:221 6:141 7:1825 +0 1:2 2:2 3:2122 4:15 5:19 6:79 7:432 +1 0:9 1:24 3:1124 4:5 5:191 6:99 7:1086 +0 0:8 1:9 2:5 3:758 4:16 5:216 6:311 7:522 +0 0:11 1:20 2:2 3:1810 4:16 5:262 6:262 7:191 +0 0:3 1:1 2:6 3:1559 4:9 5:19 6:82 7:1199 +0 0:9 1:26 2:2 3:2040 4:4 5:169 6:38 7:2602 +0 0:9 1:29 2:5 3:1709 4:1 5:182 6:83 7:984 +0 0:11 1:26 3:1158 4:1 5:38 6:164 7:2611 +0 0:10 1:16 2:3 3:1013 4:14 5:90 6:294 7:983 +0 0:9 1:11 2:6 3:924 4:13 5:262 6:279 7:372 +0 0:8 1:25 2:6 3:1129 4:10 5:224 6:294 7:920 +0 0:4 1:27 2:6 3:1701 4:17 5:184 6:83 7:797 +0 0:3 1:14 2:3 3:1616 4:5 5:253 6:141 7:191 +0 0:1 1:25 2:2 3:1016 4:20 5:163 6:36 7:1797 +0 0:3 1:4 2:1 3:2225 4:19 5:224 6:81 7:119 +1 0:8 1:12 2:1 3:625 4:20 5:261 6:37 7:399 +0 0:7 1:19 2:4 3:1023 4:16 5:83 6:174 7:423 +0 0:5 1:3 2:6 3:741 4:21 5:141 6:284 7:668 +0 0:11 1:1 2:5 3:735 4:8 5:51 6:74 7:404 +0 0:4 1:4 3:943 4:1 5:253 6:83 7:247 +0 0:8 1:4 3:842 4:18 5:262 6:213 7:2338 +0 0:1 1:27 2:4 3:1018 4:16 5:71 6:82 7:230 +0 0:3 1:12 2:1 3:1058 4:13 5:38 6:247 7:612 +0 0:1 1:17 2:1 3:1726 4:6 5:36 6:140 7:542 +0 0:4 1:28 3:1233 4:7 5:224 6:19 7:665 +0 0:7 1:13 2:5 3:1328 4:14 5:25 6:205 7:1050 +0 0:6 1:5 2:3 3:1126 4:21 5:63 7:339 +1 0:1 1:29 2:5 3:1424 4:1 5:269 6:99 7:1608 +0 0:9 1:22 3:2141 4:22 5:140 6:47 7:284 +0 0:5 1:20 2:1 3:935 4:4 5:209 6:140 7:2408 +0 0:7 1:11 2:1 3:1521 4:13 5:84 6:116 7:140 +0 0:6 1:22 3:1445 4:22 5:225 6:117 7:438 +0 0:8 1:11 2:4 3:1335 4:20 5:225 6:231 7:1813 +0 1:16 3:1250 4:15 5:279 6:156 7:892 +0 0:6 1:27 2:3 3:1705 4:8 5:194 6:19 7:633 +1 0:1 1:5 2:5 3:1919 4:5 5:141 6:155 7:817 +0 0:11 1:21 2:3 3:2020 4:20 5:49 6:186 7:611 +0 0:9 1:5 2:4 3:1915 4:9 5:83 6:162 7:629 +0 0:6 1:5 2:4 3:1019 4:15 5:75 6:137 7:325 +0 0:11 1:12 2:1 3:1133 4:8 5:19 6:177 7:369 +1 1:16 3:1047 4:10 5:110 6:19 7:644 +0 0:6 1:3 2:1 3:1415 4:20 5:163 6:186 7:1750 +0 0:5 1:20 2:1 3:700 4:20 5:49 6:150 7:220 +1 1:13 2:4 3:1644 4:20 5:95 6:227 7:347 +1 0:6 1:13 2:4 3:2215 4:19 5:19 6:162 7:1747 +0 0:2 1:23 2:3 3:801 4:7 5:270 6:257 7:626 +0 0:1 1:9 2:1 3:1205 4:16 5:13 6:218 7:723 +1 0:7 1:6 3:1631 4:14 5:203 6:19 7:906 +0 0:3 1:10 2:1 3:1301 4:14 5:203 6:81 7:931 +0 1:18 2:2 3:2124 4:1 5:252 6:156 7:2446 +0 0:9 1:2 2:1 3:1058 4:15 5:75 6:168 7:70 +1 0:8 1:30 2:3 3:1725 4:21 5:213 6:99 7:1133 +0 0:9 1:7 2:6 3:1906 4:16 5:270 6:279 7:588 +1 0:6 1:28 2:4 3:1925 4:19 5:245 6:226 7:198 +0 0:6 1:1 3:1219 4:1 5:84 6:48 7:1231 +0 0:10 1:17 2:4 3:1903 4:7 5:19 6:283 7:445 +0 0:1 1:21 2:5 3:722 4:13 5:89 6:218 7:299 +0 0:6 1:8 3:1427 4:1 5:270 6:83 7:988 +0 1:18 2:2 3:552 4:5 5:204 6:141 7:305 +0 0:9 1:18 2:1 3:829 4:7 5:83 6:275 7:391 +0 0:1 1:14 2:6 3:810 4:14 5:156 6:205 7:1028 +0 0:5 1:28 2:1 3:855 4:18 5:140 6:38 7:413 +0 0:7 1:16 2:2 3:710 4:22 5:254 6:64 7:213 +0 0:5 2:1 3:1946 4:10 5:38 6:49 7:370 +0 0:10 1:6 3:1505 4:8 5:19 7:692 +0 0:9 1:10 2:2 3:700 4:8 5:19 6:186 7:590 +0 0:11 1:28 2:3 3:836 4:5 5:221 6:141 7:1825 +0 0:1 1:30 3:1635 4:20 5:36 6:184 7:616 +0 0:7 1:2 2:2 3:1321 4:14 5:203 6:141 7:1034 +0 0:10 1:29 2:1 3:1615 4:4 5:285 6:184 7:1053 +0 0:6 1:3 2:1 3:1656 4:18 5:262 6:164 7:337 +0 0:10 1:26 2:5 3:844 4:18 5:262 6:218 7:1846 +0 0:5 1:8 2:4 3:1958 4:10 5:108 6:19 7:581 +1 0:9 1:22 3:930 4:15 5:156 6:36 7:766 +1 0:5 1:23 2:3 3:1406 4:16 5:262 6:260 7:262 +0 0:8 1:26 3:1250 4:20 5:246 6:37 7:335 +0 0:9 1:14 2:4 3:715 4:20 5:260 6:186 7:271 +0 0:6 1:9 3:1549 4:12 5:161 6:38 7:2381 +0 0:10 1:10 2:5 3:1952 4:16 5:216 6:258 7:1041 +0 0:4 1:26 2:6 3:2025 4:19 5:225 6:211 7:646 +0 0:9 1:7 2:6 3:911 4:18 5:182 6:140 7:758 +0 0:11 1:17 3:1308 4:14 5:203 6:99 7:1008 +1 0:11 2:4 3:1923 4:13 5:168 6:81 7:214 +0 0:10 1:13 3:1323 4:20 5:21 6:78 7:189 +0 0:8 1:12 3:2053 4:15 5:75 6:218 7:264 +0 0:11 1:5 2:3 3:707 4:10 5:203 6:186 7:349 +1 0:5 1:1 2:4 3:928 4:13 5:38 6:49 7:370 +0 0:1 1:8 3:900 4:15 5:63 6:74 7:221 +0 1:25 2:1 3:1735 4:10 5:19 6:110 7:644 +1 1:5 2:5 3:1827 4:19 5:224 6:227 7:2075 +1 0:2 1:11 2:3 3:1802 4:19 5:224 6:81 7:119 +0 0:8 1:6 2:2 3:838 4:21 5:100 6:247 7:416 +0 0:2 1:2 2:5 3:1452 4:20 5:253 6:78 7:248 +0 0:10 1:6 2:1 3:1357 4:18 5:216 6:253 7:528 +0 0:11 1:20 2:3 3:1621 4:20 5:289 6:107 7:197 +0 0:9 1:18 2:1 3:602 4:6 5:63 6:140 7:288 +0 0:9 1:29 2:5 3:1807 4:20 5:224 6:231 7:267 +0 0:10 1:18 2:4 3:1320 4:9 5:83 6:83 7:641 +0 0:8 1:10 2:6 3:1204 4:1 5:262 6:83 7:1464 +0 0:10 1:11 2:1 3:2058 4:1 5:84 6:99 7:1372 +0 0:2 1:18 2:6 3:910 4:1 5:216 6:256 7:1120 +0 0:11 1:29 2:3 3:956 4:1 5:84 6:133 7:3784 +0 0:5 1:13 3:647 4:19 5:25 6:226 7:196 +0 0:3 1:2 3:1645 4:13 5:252 6:164 7:109 +0 0:6 1:27 2:2 3:957 4:18 5:216 6:81 7:612 +0 0:1 1:10 2:3 3:932 4:20 5:161 6:48 7:223 +0 0:8 1:15 2:4 3:1020 4:13 5:216 6:60 7:196 +1 0:8 1:23 2:4 3:1855 4:5 5:100 6:193 7:1086 +0 0:7 1:2 2:2 3:2100 4:15 5:75 6:215 7:614 +0 0:8 1:28 2:2 3:615 4:20 5:161 6:227 7:256 +0 1:17 2:1 3:1711 4:11 5:163 6:133 7:2556 +1 0:8 1:9 2:5 3:2010 4:6 5:140 6:267 7:2419 +0 0:5 1:11 2:2 3:1547 4:16 5:172 6:82 7:423 +0 0:2 1:4 2:6 3:1309 4:12 5:225 6:49 7:1999 +1 0:11 1:6 2:4 3:1811 4:5 5:25 6:62 7:475 +0 0:1 1:7 3:1229 4:21 5:192 6:62 7:328 +0 0:2 1:15 2:3 3:1054 4:18 5:38 6:82 7:1754 +0 0:6 2:5 3:1425 4:20 5:270 6:162 7:368 +1 1:5 2:5 3:1056 4:8 5:189 6:19 7:147 +0 1:10 2:2 3:524 4:10 5:84 6:19 7:732 +0 0:3 2:4 3:722 4:1 5:137 6:83 7:603 +1 0:7 1:24 2:2 3:1848 4:3 5:261 6:16 7:1449 +0 0:1 1:25 2:2 3:1246 4:14 5:163 6:188 7:1619 +0 0:8 1:6 2:2 3:1758 4:19 5:65 6:209 7:156 +0 0:6 1:17 3:1215 4:14 5:192 6:205 7:297 +0 1:9 2:2 3:1931 4:14 5:203 6:156 7:1028 +0 0:8 1:2 2:6 3:801 4:7 5:21 6:19 7:813 +0 0:7 1:23 3:1030 4:6 5:140 6:192 7:418 +0 0:5 1:13 2:1 3:1015 4:20 5:279 6:78 7:546 +0 0:3 2:3 3:650 4:14 5:111 6:205 7:197 +0 0:10 1:28 3:1345 4:14 5:90 6:47 7:240 +0 0:9 1:21 2:4 3:1230 4:6 5:140 6:265 7:451 +0 0:2 1:14 2:1 3:1450 4:15 5:254 6:74 7:515 +0 0:9 1:27 2:2 3:1114 4:16 5:261 6:223 7:129 +0 0:7 1:22 2:1 3:1629 4:21 5:141 6:78 7:217 +0 0:8 1:28 2:1 3:1510 4:7 5:204 6:74 7:700 +1 0:8 2:3 3:2110 4:5 5:49 6:62 7:314 +0 0:6 1:14 2:5 3:858 4:10 5:52 6:256 7:991 +0 0:2 1:1 2:4 3:1457 4:19 5:82 6:170 7:214 +0 0:11 1:26 3:1154 4:1 5:156 6:164 7:2475 +0 0:7 1:2 2:2 3:1417 4:21 5:224 6:62 7:363 +0 1:30 2:6 3:1029 4:21 5:100 6:22 7:583 +0 0:11 1:1 2:6 3:726 4:2 5:133 6:213 7:100 +1 0:1 1:11 2:6 3:1132 4:16 5:206 6:82 7:197 +0 0:4 1:26 2:5 3:1330 4:15 5:75 6:89 7:229 +0 0:8 1:7 2:3 3:2253 4:8 5:19 6:307 7:208 +0 0:5 1:19 3:2120 4:20 5:252 6:211 7:446 +0 0:10 1:19 2:6 3:1345 4:1 5:38 6:83 7:1562 +1 1:22 2:1 3:1005 4:15 5:156 6:74 7:589 +0 0:4 1:6 2:1 3:534 4:10 5:52 6:19 7:528 +0 0:10 2:1 3:646 4:14 5:229 6:89 7:201 +0 0:4 1:17 2:5 3:958 4:19 5:82 6:253 7:296 +1 0:9 1:12 2:3 3:1348 4:14 5:180 6:89 7:629 +0 0:7 1:28 2:5 3:1008 4:13 5:216 6:36 7:409 +0 0:5 1:20 2:1 3:940 4:20 5:83 6:227 7:602 +0 0:10 1:19 2:5 3:1048 4:21 5:164 6:141 7:458 +1 0:5 1:24 2:4 3:1623 4:18 5:216 6:162 7:1515 +0 0:2 1:12 2:6 3:838 4:14 5:163 6:205 7:1536 +1 0:11 1:1 2:6 3:1000 4:22 5:216 6:58 7:760 +0 0:4 1:19 3:1602 4:7 5:63 6:74 7:221 +0 0:6 1:23 2:5 3:2247 4:19 5:224 6:240 7:238 +1 0:3 1:25 2:6 3:2023 4:4 5:100 6:222 7:1024 +0 0:11 1:1 2:5 3:1048 4:21 5:212 6:141 7:395 +0 0:1 1:7 2:6 3:1310 4:3 5:221 6:164 7:834 +0 0:1 1:25 2:1 3:2111 4:1 5:84 6:19 7:732 +0 1:27 2:3 3:953 4:1 5:84 6:227 7:868 +1 0:1 1:21 2:6 3:1403 4:8 5:190 6:19 7:952 +0 0:5 1:9 2:5 3:1953 4:13 5:216 6:304 7:475 +0 0:11 1:13 2:2 3:1333 4:14 5:186 6:258 7:625 +0 0:7 1:14 2:6 3:1615 4:20 5:209 6:279 7:371 +0 0:7 1:6 3:1538 4:15 5:253 6:74 7:1024 +0 0:1 1:3 2:2 3:2053 4:1 5:84 6:137 7:603 +0 1:9 2:2 3:1905 4:5 5:141 6:19 7:689 +0 0:5 1:7 2:2 3:1940 4:20 5:161 6:258 7:1069 +0 0:10 1:25 2:3 3:2040 4:15 5:242 6:74 7:390 +0 1:16 3:2115 4:16 5:216 6:196 7:139 +0 0:11 1:13 2:3 3:1945 4:14 5:184 6:205 7:349 +1 0:5 1:8 2:3 3:1958 4:8 5:19 6:119 7:352 +0 0:10 1:11 2:1 3:622 4:1 5:84 6:206 7:448 +0 0:11 1:9 2:6 3:2129 4:13 5:252 6:164 7:109 +0 0:1 1:18 2:3 3:1427 4:18 5:262 6:164 7:337 +0 0:2 1:22 2:3 3:1628 4:3 5:163 6:266 7:954 +1 0:2 1:15 2:2 3:1421 4:7 5:182 6:156 7:944 +0 0:5 1:23 2:2 3:1508 4:7 5:220 6:25 7:1133 +1 0:9 1:23 2:6 3:946 4:14 5:203 6:37 7:1142 +1 1:27 2:4 3:1205 4:20 5:289 6:36 7:612 +0 0:10 1:6 3:934 4:12 5:246 6:227 7:601 +0 0:4 1:15 2:2 3:1400 4:14 5:90 6:184 7:957 +1 0:3 1:17 2:6 3:2005 4:1 5:84 6:49 7:1217 +0 0:9 1:27 2:3 3:1254 4:16 5:32 6:82 7:517 +0 0:6 1:30 2:6 3:910 4:15 5:180 6:74 7:539 +0 0:2 1:21 2:2 3:1429 4:20 5:134 6:78 7:239 +0 0:5 1:9 2:5 3:1757 4:1 5:156 6:267 7:2586 +0 0:1 1:7 3:1043 4:20 5:49 6:62 7:314 +0 0:11 1:25 3:1418 4:16 5:48 6:82 7:850 +0 0:5 1:16 2:3 3:940 4:13 5:84 6:5 7:89 +0 0:4 1:30 2:2 3:1828 4:21 5:186 6:141 7:469 +0 0:9 1:18 2:1 3:2203 4:15 5:19 6:52 7:528 +0 0:6 1:17 2:1 3:1615 4:20 5:225 6:164 7:370 +1 0:4 1:22 2:3 3:1714 4:7 5:252 6:275 7:626 +0 0:11 1:22 2:6 3:1359 4:4 5:251 6:156 7:1074 +1 0:2 1:7 2:3 3:1649 4:1 5:38 6:257 7:2588 +0 0:8 1:16 2:4 3:1852 4:7 5:108 6:164 7:2342 +0 0:10 1:10 2:4 3:2202 4:15 5:75 6:304 7:226 +1 0:3 1:16 2:6 3:1246 4:1 5:21 6:218 7:978 +1 0:7 1:15 2:1 3:2118 4:1 5:84 6:218 7:802 +0 0:1 1:19 2:4 3:632 4:20 5:180 6:162 7:1140 +0 0:11 1:21 2:4 3:628 4:21 5:100 6:124 7:594 +0 0:9 1:19 2:3 3:754 4:4 5:100 6:184 7:938 +0 0:7 1:19 2:5 3:1243 4:14 5:13 6:89 7:488 +0 0:7 1:24 2:2 3:656 4:16 5:270 6:267 7:599 +0 1:1 2:1 3:815 4:20 5:36 6:164 7:1797 +0 0:1 1:15 2:6 3:541 4:7 5:224 6:19 7:665 +0 0:9 1:21 2:4 3:630 4:20 5:161 6:186 7:1521 +1 0:4 1:19 3:1418 4:14 5:182 6:205 7:1310 +1 0:10 1:24 2:3 3:1455 4:1 5:19 6:218 7:606 +0 0:11 1:30 2:4 3:600 4:20 5:209 6:257 7:446 +0 0:11 1:4 2:2 3:1058 4:15 5:75 6:49 7:430 +0 0:1 1:17 2:1 3:2024 4:16 5:132 6:275 7:402 +0 0:5 1:24 2:3 3:1105 4:15 5:75 6:122 7:268 +0 0:1 1:26 2:2 3:1056 4:13 5:84 6:74 7:812 +0 1:21 2:6 3:636 4:9 5:246 6:82 7:804 +1 0:6 1:3 2:2 3:1741 4:8 5:89 6:19 7:743 +0 0:3 1:23 2:5 3:2123 4:21 5:141 6:63 7:74 +0 0:6 1:2 2:1 3:1150 4:20 5:90 6:36 7:457 +0 0:9 1:22 3:626 4:14 5:82 6:89 7:405 +0 0:9 1:25 3:1010 4:4 5:108 6:156 7:1069 +0 0:8 1:16 2:4 3:2112 4:6 5:140 6:47 7:284 +0 0:2 1:3 2:6 3:1545 4:1 5:133 6:164 7:2556 +0 0:10 1:26 2:5 3:2259 4:18 5:163 6:162 7:236 +0 0:2 1:27 3:1922 4:14 5:90 6:258 7:1215 +0 0:10 1:19 2:5 3:727 4:21 5:141 6:234 7:489 +0 0:11 1:18 3:1423 4:14 5:146 6:205 7:503 +1 0:9 1:16 3:1847 4:14 5:203 6:146 7:503 +0 0:1 1:4 2:3 3:632 4:1 5:279 6:193 7:1068 +0 0:11 1:25 3:1815 4:16 5:294 6:275 7:601 +0 0:6 1:20 2:3 3:1358 4:18 5:216 6:81 7:612 +0 1:18 2:3 3:1629 4:1 5:191 6:156 7:1090 +0 0:5 1:30 2:3 3:838 4:21 5:242 6:141 7:1043 +0 0:4 1:12 2:6 3:1837 4:14 5:203 6:64 7:930 +0 0:1 1:21 2:6 3:902 4:1 5:168 6:83 7:1389 +0 0:2 1:14 2:2 3:731 4:21 5:141 6:214 7:395 +0 0:3 1:13 2:2 3:1129 4:5 5:78 6:99 7:884 +0 0:8 1:14 2:3 3:1825 4:14 5:82 6:89 7:405 +0 0:3 1:4 2:2 3:2109 4:16 5:270 6:27 7:534 +0 0:6 1:9 3:945 4:20 5:161 6:266 7:866 +1 0:11 1:15 2:5 3:2023 4:1 5:19 6:83 7:732 +0 0:1 1:15 2:6 3:655 4:5 5:108 6:99 7:1065 +0 0:3 1:25 2:6 3:829 4:17 5:184 6:274 7:2057 +0 0:11 1:13 2:3 3:1237 4:21 5:141 6:169 7:201 +1 1:22 3:2046 4:19 5:229 6:81 7:205 +0 0:2 1:22 2:3 3:1335 4:18 5:252 6:267 7:447 +0 0:10 1:30 2:2 3:655 4:4 5:156 6:47 7:301 +0 0:8 1:27 2:1 3:1611 4:10 5:180 6:19 7:692 +1 1:16 2:1 3:1621 4:21 5:192 6:62 7:328 +0 0:1 1:18 2:3 3:1431 4:13 5:113 6:83 7:859 +0 0:5 1:23 2:3 3:610 4:20 5:161 6:251 7:345 +0 0:4 1:19 2:6 3:708 4:14 5:203 6:274 7:2404 +0 0:5 1:8 2:4 3:2110 4:20 5:272 6:257 7:480 +0 0:5 1:26 2:5 3:1813 4:18 5:224 6:164 7:2401 +0 0:8 1:16 2:5 3:1531 4:3 5:16 6:266 7:1449 +0 0:6 1:8 3:1026 4:18 5:49 6:82 7:1491 +0 0:7 1:17 2:2 3:1421 4:21 5:46 6:62 7:489 +0 0:7 1:15 3:1223 4:1 5:246 6:83 7:1345 +0 0:11 1:27 2:2 3:1742 4:13 5:168 6:247 7:431 +0 0:11 1:20 2:2 3:2035 4:20 5:279 6:215 7:342 +0 0:8 1:7 2:3 3:1719 4:7 5:156 6:81 7:213 +1 0:11 1:10 3:2133 4:20 5:63 6:49 7:314 +0 0:6 1:18 2:2 3:708 4:3 5:274 6:211 7:371 +0 0:3 1:26 2:1 3:728 4:1 5:84 6:219 7:1213 +0 0:11 1:20 2:3 3:1121 4:18 5:216 6:231 7:412 +1 0:3 2:4 3:2042 4:13 5:84 6:304 7:772 +0 0:10 1:3 2:5 3:2032 4:18 5:211 6:267 7:2338 +0 0:4 1:20 3:1214 4:13 5:242 6:284 7:667 +1 0:7 1:22 2:1 3:1720 4:20 5:161 6:62 7:1825 +0 0:6 1:25 2:1 3:804 4:15 5:168 6:247 7:431 +0 0:9 1:29 2:4 3:845 4:13 5:84 6:173 7:304 +0 0:5 1:14 2:2 3:1443 4:20 5:289 6:30 7:460 +0 0:2 1:12 3:700 4:2 5:211 6:133 7:100 +1 0:8 1:7 2:4 3:2118 4:18 5:216 6:211 7:1835 +0 0:1 1:21 2:5 3:1115 4:20 5:274 6:227 7:338 +0 0:4 1:11 2:3 3:1339 4:21 5:49 6:99 7:169 +0 0:1 1:9 2:2 3:1610 4:18 5:38 6:82 7:1754 +1 0:8 1:13 2:2 3:802 4:21 5:141 6:297 7:429 +0 0:9 1:25 3:805 4:1 5:84 6:164 7:1235 +0 0:11 1:5 2:2 3:1134 4:13 5:191 6:265 7:911 +1 0:5 1:7 2:3 3:1037 4:14 5:203 6:122 7:408 +0 0:10 1:11 2:2 3:1716 4:20 5:225 6:65 7:1671 +0 1:17 2:2 3:2241 4:19 5:161 6:294 7:1984 +0 0:4 1:17 2:4 3:2208 4:7 5:19 6:297 7:674 +0 0:7 1:9 2:2 3:1251 4:11 5:211 6:133 7:100 +0 0:11 1:3 3:1317 4:12 5:224 6:227 7:2075 +0 0:1 1:10 2:2 3:630 4:5 5:292 6:141 7:429 +0 0:1 1:7 2:6 3:1921 4:21 5:141 6:42 7:308 +1 0:3 1:4 2:1 3:1320 4:20 5:134 6:78 7:239 +0 0:6 1:12 2:2 3:1814 4:21 5:154 6:141 7:351 +0 0:7 1:13 2:5 3:1118 4:16 5:83 6:103 7:844 +0 0:5 1:17 2:4 3:1347 4:1 5:84 6:2 7:569 +0 0:9 1:27 2:3 3:2100 4:15 5:75 6:58 7:497 +1 0:3 1:17 2:6 3:2118 4:5 5:100 6:141 7:1400 +1 0:6 1:30 2:6 3:1911 4:13 5:65 6:218 7:599 +0 0:3 1:8 2:5 3:1511 4:7 5:19 6:162 7:1747 +0 0:1 1:17 2:2 3:1537 4:21 5:270 6:141 7:1195 +0 0:2 1:12 3:1854 4:19 5:274 6:162 7:226 +0 0:8 2:3 3:2022 4:18 5:83 6:162 7:629 +1 0:6 1:22 2:6 3:1309 4:1 5:280 6:38 7:1693 +0 0:3 1:11 2:4 3:1720 4:18 5:184 6:82 7:895 +0 1:1 3:652 4:18 5:83 6:162 7:629 +0 0:1 1:27 2:3 3:1228 4:16 5:106 6:275 7:532 +0 1:6 2:5 3:842 4:18 5:83 6:83 7:641 +0 0:1 1:11 2:6 3:856 4:14 5:216 6:89 7:235 +0 0:2 1:10 2:6 3:1155 4:13 5:163 6:272 7:308 +0 0:1 1:5 2:4 3:740 4:1 5:217 6:83 7:1213 +0 0:1 1:13 2:5 3:1029 4:20 5:209 6:279 7:371 +1 0:10 1:27 2:6 3:1909 4:21 5:63 6:25 7:475 +0 0:3 1:11 2:5 3:1405 4:5 5:100 6:257 7:2425 +0 0:11 1:28 2:2 3:1224 4:13 5:122 6:170 7:618 +0 0:6 1:3 2:1 3:1635 4:20 5:190 6:226 7:290 +0 0:10 1:23 2:1 3:905 4:12 5:225 6:146 7:1489 +1 0:3 1:1 2:5 3:1805 4:1 5:84 6:99 7:1372 +0 0:8 1:13 2:1 3:1820 4:20 5:13 6:49 7:288 +0 0:1 1:4 2:3 3:1356 4:18 5:215 6:82 7:819 +0 0:10 1:4 2:5 3:835 4:13 5:84 6:308 7:626 +0 0:8 1:23 2:4 3:1853 4:15 5:75 6:81 7:411 +1 0:2 1:8 2:4 3:1414 4:20 5:2 6:186 7:1121 +0 0:2 1:14 2:1 3:1255 4:20 5:220 6:150 7:1052 +0 0:7 1:1 2:2 3:1549 4:21 5:72 6:141 7:201 +0 0:1 1:11 2:6 3:2257 4:19 5:163 6:64 7:2125 +0 0:5 1:3 2:5 3:1440 4:20 5:242 6:49 7:255 +1 0:9 1:11 2:6 3:1744 4:20 5:221 6:211 7:543 +0 0:8 1:5 2:2 3:1823 4:13 5:65 6:218 7:599 +1 0:3 1:13 2:3 3:1608 4:21 5:36 6:141 7:657 +0 0:2 1:26 2:6 3:1845 4:19 5:108 6:64 7:631 +0 0:5 1:26 2:6 3:1557 4:18 5:280 6:274 7:68 +0 0:3 1:14 2:4 3:705 4:20 5:289 6:182 7:1048 +1 0:5 1:3 2:5 3:906 4:1 5:168 6:83 7:1389 +0 0:3 2:3 3:1720 4:20 5:267 6:48 7:296 +0 0:11 1:1 2:5 3:1359 4:19 5:38 6:64 7:728 +0 0:10 1:16 2:2 3:940 4:15 5:75 6:297 7:646 +0 0:10 1:24 2:3 3:924 4:13 5:122 6:218 7:137 +0 0:7 1:1 2:2 3:1310 4:16 5:270 6:269 7:269 +0 0:5 1:29 2:1 3:2137 4:12 5:225 6:2 7:328 +0 0:7 1:26 2:3 3:1651 4:1 5:133 6:83 7:3784 +0 0:10 1:30 2:2 3:1441 4:14 5:213 6:205 7:282 +1 0:1 1:14 2:5 3:1845 4:8 5:203 6:275 7:991 +1 0:4 1:10 2:5 3:652 4:14 5:262 6:205 7:1589 +0 0:4 1:25 2:5 3:758 4:17 5:168 6:186 7:725 +0 0:3 1:22 2:6 3:1159 4:18 5:216 6:257 7:1723 +0 0:9 1:20 2:3 3:1430 4:18 5:224 6:218 7:678 +0 0:8 1:6 2:2 3:749 4:14 5:179 6:205 7:463 +0 0:9 1:11 2:6 3:918 4:13 5:216 6:250 7:642 +0 0:6 1:15 2:6 3:1410 4:20 5:163 6:211 7:337 +0 0:5 1:29 2:1 3:905 4:6 5:140 6:89 7:383 +1 0:10 1:1 2:3 3:1642 4:5 5:21 6:99 7:1504 +0 0:11 1:30 2:4 3:1226 4:19 5:224 6:38 7:280 +1 0:2 1:29 2:1 3:954 4:12 5:262 6:162 7:414 +0 0:6 1:7 2:6 3:710 4:22 5:272 6:227 7:647 +0 0:10 1:6 3:649 4:19 5:82 6:231 7:205 +0 0:11 1:23 2:5 3:1448 4:20 5:180 6:186 7:405 +0 0:5 1:10 2:5 3:619 4:21 5:58 6:141 7:925 +0 0:4 2:1 3:1230 4:20 5:279 6:173 7:296 +0 0:5 2:1 3:652 4:14 5:203 6:188 7:700 +1 0:6 1:15 2:6 3:2046 4:8 5:19 6:183 7:79 +0 0:11 1:2 2:6 3:820 4:20 5:25 6:49 7:283 +1 0:5 1:11 2:2 3:1455 4:8 5:171 6:19 7:453 +0 0:7 1:14 3:720 4:1 5:262 6:193 7:2585 +0 0:4 1:30 2:2 3:1658 4:5 5:21 6:141 7:140 +0 0:4 1:7 2:2 3:1310 4:20 5:184 6:284 7:251 +1 0:2 1:12 3:1202 4:8 5:124 6:19 7:153 +0 0:5 1:15 2:2 3:1140 4:20 5:209 6:164 7:337 +0 0:1 1:6 2:5 3:731 4:8 5:19 6:304 7:152 +0 0:7 1:29 3:1226 4:13 5:171 6:218 7:552 +0 0:2 1:29 2:1 3:1250 4:19 5:65 6:192 7:737 +0 0:6 1:11 2:6 3:734 4:1 5:274 6:83 7:1205 +1 0:10 1:29 3:1752 4:18 5:190 6:218 7:843 +0 0:8 1:19 2:1 3:948 4:18 5:292 6:82 7:541 +0 0:7 1:7 3:1505 4:7 5:225 6:74 7:1569 +0 0:1 1:9 2:2 3:610 4:18 5:163 6:82 7:862 +0 0:2 1:18 2:5 3:1508 4:6 5:140 6:25 7:326 +0 0:8 1:25 2:6 3:1649 4:13 5:173 6:83 7:394 +0 0:10 1:14 3:1926 4:1 5:279 6:297 7:351 +0 0:10 1:7 2:2 3:919 4:19 5:65 6:47 7:546 +0 0:11 1:5 2:2 3:849 4:7 5:19 6:250 7:481 +0 0:6 2:4 3:742 4:7 5:252 6:19 7:1891 +0 0:6 1:25 2:1 3:1232 4:18 5:83 6:99 7:1605 +1 1:29 2:5 3:1925 4:15 5:82 6:137 7:613 +0 0:3 1:16 2:5 3:1546 4:12 5:215 6:227 7:325 +1 0:1 1:13 2:4 3:1501 4:7 5:279 6:19 7:483 +0 0:2 1:19 3:1721 4:16 5:296 6:275 7:175 +1 0:3 1:23 2:5 3:1856 4:7 5:25 6:19 7:859 +0 0:1 1:1 2:1 3:705 4:16 5:194 6:218 7:139 +1 0:8 1:10 2:6 3:1256 4:20 5:215 6:162 7:197 +0 0:9 1:2 3:1459 4:1 5:84 6:257 7:1171 +1 0:3 1:6 2:3 3:1426 4:7 5:262 6:19 7:2139 +0 0:1 1:20 2:5 3:653 4:18 5:37 6:218 7:1437 +0 0:10 1:14 2:1 3:1733 4:21 5:141 6:45 7:253 +0 0:1 1:7 2:6 3:759 4:18 5:262 6:82 7:967 +0 0:5 1:15 2:3 3:1603 4:13 5:135 6:218 7:738 +0 1:7 2:6 3:1540 4:7 5:203 6:275 7:991 +0 0:11 1:15 2:5 3:731 4:20 5:270 6:211 7:588 +0 0:9 1:12 2:3 3:1704 4:20 5:225 6:257 7:304 +0 0:6 1:13 2:4 3:1930 4:9 5:84 6:82 7:641 +0 1:25 2:1 3:815 4:3 5:252 6:266 7:1050 +0 0:10 1:6 2:1 3:1758 4:16 5:163 6:103 7:209 +0 0:2 1:8 2:4 3:1455 4:16 5:126 6:275 7:463 +0 0:9 1:14 2:5 3:1037 4:10 5:192 6:19 7:669 +0 0:9 1:8 2:6 3:1923 4:7 5:25 6:19 7:859 +0 0:6 1:14 2:4 3:1451 4:10 5:108 6:83 7:1119 +1 0:11 1:29 2:4 3:1921 4:14 5:289 6:146 7:838 +1 1:14 2:6 3:1616 4:1 5:262 6:164 7:337 +0 0:10 1:7 2:2 3:1215 4:21 5:232 6:141 7:489 +0 0:11 1:10 3:2156 4:18 5:252 6:140 7:2253 +0 0:8 1:28 2:1 3:2145 4:20 5:161 6:257 7:258 +1 0:4 1:17 2:4 3:900 4:20 5:209 6:48 7:325 +1 0:1 1:23 3:1220 4:15 5:260 6:74 7:83 +0 0:9 1:9 3:759 4:18 5:216 6:81 7:612 +1 0:5 1:23 2:2 3:2320 4:7 5:216 6:19 7:606 +0 0:6 1:20 2:4 3:658 4:7 5:168 6:19 7:761 +1 0:5 1:3 2:6 3:1036 4:14 5:36 6:89 7:457 +0 0:7 1:23 3:758 4:10 5:19 6:184 7:403 +0 1:19 2:3 3:845 4:20 5:63 6:186 7:307 +0 0:4 1:27 2:6 3:846 4:13 5:216 6:146 7:177 +0 0:10 1:2 2:3 3:1345 4:21 5:217 6:62 7:435 +0 0:1 1:7 2:6 3:1251 4:16 5:200 6:267 7:77 +0 0:2 1:13 2:1 3:858 4:16 5:292 6:82 7:541 +0 1:5 2:4 3:845 4:6 5:140 6:304 7:419 +0 0:6 1:30 2:5 3:1212 4:1 5:216 6:299 7:1437 +0 0:9 1:24 3:2004 4:13 5:110 6:218 7:223 +0 0:2 1:4 2:6 3:1811 4:1 5:84 6:275 7:988 +0 0:9 1:25 3:1345 4:20 5:108 6:294 7:197 +1 0:8 1:11 2:4 3:2140 4:13 5:191 6:250 7:826 +0 0:4 1:29 2:1 3:1051 4:12 5:163 6:162 7:236 +0 0:7 1:18 2:3 3:1940 4:20 5:136 6:258 7:233 +0 1:27 2:3 3:945 4:20 5:25 6:49 7:283 +0 0:1 1:24 2:1 3:1251 4:5 5:274 6:141 7:1347 +0 0:8 1:14 2:2 3:1340 4:3 5:261 6:164 7:954 +0 0:4 1:8 2:4 3:920 4:8 5:19 6:134 7:696 +0 0:9 1:2 3:1007 4:20 5:204 6:184 7:550 +1 0:1 1:14 2:6 3:2000 4:7 5:19 6:214 7:761 +1 1:22 3:1638 4:13 5:260 6:218 7:286 +0 0:6 1:26 2:1 3:1230 4:20 5:13 6:49 7:288 +1 0:6 1:14 2:4 3:1751 4:8 5:19 6:17 7:540 +0 0:7 1:23 3:1511 4:12 5:216 6:162 7:1515 +0 1:8 3:2214 4:7 5:19 6:137 7:151 +1 0:10 1:13 3:1030 4:13 5:216 6:89 7:235 +0 0:11 1:13 2:3 3:1323 4:20 5:36 6:211 7:1959 +0 0:11 1:19 2:1 3:1929 4:6 5:140 6:58 7:441 +0 0:2 1:29 2:1 3:846 4:7 5:2 6:74 7:1240 +0 0:7 1:11 3:2053 4:21 5:63 6:284 7:487 +0 0:7 1:5 2:5 3:1810 4:19 5:190 6:64 7:737 +0 0:4 2:2 3:1225 4:20 5:267 6:48 7:296 +0 0:10 1:2 2:3 3:2305 4:15 5:75 6:155 7:614 +0 0:11 1:5 2:2 3:1858 4:13 5:75 6:218 7:264 +0 0:10 1:27 2:6 3:914 4:20 5:48 6:272 7:296 +0 0:8 1:27 3:708 4:14 5:90 6:146 7:231 +0 0:4 1:7 2:2 3:1357 4:19 5:65 6:209 7:156 +0 0:4 1:19 2:6 3:1055 4:20 5:209 6:162 7:407 +0 0:4 1:22 2:3 3:1744 4:7 5:19 6:146 7:432 +1 1:18 2:2 3:1308 4:4 5:182 6:156 7:944 +0 0:8 1:7 2:3 3:1306 4:12 5:225 6:275 7:507 +1 0:7 1:15 2:1 3:1047 4:7 5:182 6:19 7:403 +0 1:13 2:4 3:1045 4:1 5:252 6:83 7:1171 +0 0:1 1:1 3:749 4:1 5:49 6:83 7:1217 +0 0:9 1:2 3:1921 4:13 5:216 6:196 7:139 +0 0:10 3:850 4:7 5:261 6:19 7:2182 +0 0:2 1:26 2:6 3:1141 4:21 5:213 6:99 7:1133 +0 0:11 1:17 3:1005 4:1 5:168 6:83 7:1389 +0 0:5 1:19 3:1347 4:10 5:49 6:38 7:370 +0 0:10 1:24 2:2 3:1305 4:20 5:161 6:299 7:365 +0 0:6 1:11 2:6 3:1502 4:1 5:289 6:83 7:929 +1 0:2 1:16 2:3 3:1435 4:20 5:274 6:227 7:338 +0 0:10 1:3 2:5 3:730 4:7 5:163 6:156 7:2475 +0 0:8 1:5 2:2 3:1917 4:7 5:19 6:82 7:1199 +0 0:7 1:23 2:1 3:908 4:7 5:242 6:19 7:356 +0 0:11 1:3 3:1139 4:15 5:75 6:64 7:335 +0 0:8 1:14 2:3 3:1927 4:7 5:270 6:277 7:532 +0 0:5 1:15 2:3 3:1221 4:19 5:252 6:227 7:304 +0 0:9 1:17 3:745 4:15 5:182 6:51 7:380 +0 0:9 1:3 2:2 3:1732 4:20 5:252 6:277 7:480 +0 0:5 1:11 2:2 3:1150 4:20 5:95 6:258 7:496 +0 0:5 1:16 2:3 3:701 4:14 5:203 6:162 7:1300 +0 0:1 1:11 2:6 3:1111 4:16 5:83 6:50 7:525 +1 0:11 1:27 2:1 3:1356 4:7 5:261 6:275 7:689 +1 0:3 1:12 2:2 3:1436 4:1 5:163 6:258 7:1210 +0 0:2 2:2 3:2000 4:15 5:75 6:29 7:906 +0 0:6 1:12 2:2 3:1810 4:20 5:79 6:15 7:324 +0 0:2 1:17 2:5 3:2058 4:1 5:191 6:83 7:1121 +0 0:1 1:1 2:1 3:1330 4:7 5:19 6:155 7:270 +0 0:8 1:4 3:20 4:6 5:161 6:140 7:2066 +0 0:7 1:25 2:2 3:1541 4:16 5:262 6:190 7:329 +0 0:8 1:26 2:6 3:1802 4:3 5:261 6:257 7:1050 +0 0:11 1:30 2:5 3:733 4:16 5:255 6:82 7:916 +0 0:10 1:6 2:1 3:1732 4:14 5:261 6:89 7:1927 +1 0:8 1:27 2:1 3:2207 4:14 5:90 6:49 7:408 +0 0:10 1:13 3:1740 4:1 5:100 6:164 7:2454 +0 0:1 1:29 2:5 3:1115 4:18 5:216 6:122 7:137 +0 0:3 1:23 2:4 3:825 4:20 5:253 6:136 7:233 +0 0:7 1:13 2:5 3:1121 4:16 5:37 6:275 7:291 +0 0:4 1:16 2:3 3:1304 4:1 5:83 6:218 7:888 +0 1:15 3:651 4:18 5:83 6:162 7:629 +1 0:9 1:12 2:3 3:1757 4:14 5:90 6:265 7:306 +0 0:6 1:6 2:5 3:1749 4:18 5:216 6:140 7:589 +0 0:1 1:20 2:5 3:1757 4:21 5:171 6:141 7:374 +0 0:7 1:12 2:5 3:915 4:20 5:225 6:134 7:1020 +0 0:8 1:18 2:6 3:1607 4:21 5:100 6:253 7:246 +0 0:8 1:1 2:5 3:651 4:7 5:83 6:74 7:1069 +0 0:8 1:26 3:920 4:14 5:108 6:89 7:1127 +0 0:8 1:27 3:1650 4:21 5:79 6:141 7:217 +0 0:6 1:30 2:6 3:2157 4:14 5:262 6:89 7:2079 +0 1:14 2:6 3:1652 4:21 5:254 6:99 7:708 +0 0:6 1:15 2:5 3:1750 4:1 5:279 6:164 7:1593 +1 0:1 1:1 3:1842 4:1 5:156 6:193 7:1090 +0 0:1 1:13 2:5 3:1710 4:20 5:184 6:192 7:838 +0 0:8 1:29 2:3 3:1840 4:20 5:161 6:277 7:397 +1 0:8 1:28 2:1 3:1634 4:14 5:262 6:133 7:2398 +0 1:28 2:4 3:2128 4:6 5:140 6:156 7:228 +0 0:10 1:19 2:6 3:831 4:13 5:201 6:218 7:109 +0 0:5 1:21 2:2 3:815 4:20 5:49 6:227 7:1999 +0 0:4 2:2 3:1001 4:21 5:46 6:99 7:266 +0 0:9 1:26 2:1 3:1841 4:21 5:141 6:74 7:871 +0 0:5 1:15 2:2 3:936 4:3 5:221 6:162 7:762 +1 0:2 1:4 2:6 3:2015 4:15 5:168 6:74 7:585 +0 1:1 3:604 4:14 5:100 6:89 7:487 +1 1:3 2:2 3:2210 4:20 5:134 6:21 7:148 +0 0:10 1:12 2:5 3:1621 4:1 5:2 6:83 7:569 +0 0:7 1:25 2:3 3:1242 4:19 5:65 6:81 7:331 +0 0:9 1:1 3:811 4:7 5:38 6:256 7:1249 +0 0:8 1:15 2:4 3:1730 4:1 5:84 6:193 7:1121 +0 1:18 2:3 3:1947 4:1 5:279 6:164 7:1593 +0 0:9 1:19 2:2 3:529 4:16 5:93 6:275 7:200 +0 0:4 1:27 2:6 3:1302 4:8 5:19 6:11 7:143 +0 0:3 1:12 2:1 3:1529 4:14 5:203 6:226 7:980 +0 0:11 1:13 2:2 3:1057 4:3 5:304 6:237 7:31 +0 0:8 1:8 2:4 3:1807 4:7 5:82 6:19 7:547 +0 0:3 1:18 2:1 3:1926 4:8 5:270 6:235 7:521 +0 0:4 1:26 2:5 3:1631 4:16 5:44 6:275 7:357 +0 0:5 1:25 2:5 3:830 4:20 5:209 6:164 7:337 +0 0:5 1:26 2:5 3:1008 4:12 5:225 6:62 7:1737 +0 1:11 3:712 4:14 5:122 6:205 7:408 +0 0:11 1:16 2:6 3:751 4:1 5:84 6:162 7:1055 +0 0:4 1:15 2:3 3:1459 4:7 5:78 6:19 7:366 +0 0:11 1:16 2:5 3:626 4:18 5:209 6:164 7:337 +0 0:1 1:5 2:5 3:1857 4:9 5:83 6:83 7:641 +0 0:5 1:26 2:6 3:749 4:13 5:123 6:193 7:710 +0 0:8 1:5 2:2 3:1747 4:16 5:83 6:275 7:391 +1 0:3 1:16 2:5 3:1059 4:14 5:203 6:256 7:1416 +0 0:1 2:6 3:902 4:18 5:38 6:218 7:867 +1 0:5 1:13 3:1545 4:20 5:2 6:227 7:328 +0 0:1 1:5 2:5 3:745 4:1 5:156 6:164 7:2475 +0 0:9 1:24 2:6 3:1644 4:18 5:140 6:206 7:954 +0 1:13 2:4 3:846 4:13 5:66 6:218 7:296 +0 0:8 1:9 2:6 3:954 4:7 5:19 6:218 7:606 +0 0:7 1:26 2:3 3:1720 4:13 5:242 6:156 7:426 +0 0:11 1:11 2:4 3:1213 4:9 5:83 6:164 7:862 +0 0:10 1:15 2:1 3:727 4:1 5:274 6:83 7:1205 +0 0:7 1:8 2:2 3:1205 4:14 5:267 6:205 7:1576 +0 0:10 1:29 2:1 3:1036 4:18 5:84 6:82 7:641 +0 1:13 2:5 3:641 4:7 5:252 6:74 7:1865 +1 0:10 1:24 2:3 3:1020 4:22 5:78 6:140 7:703 +0 0:2 1:4 2:6 3:919 4:16 5:246 6:267 7:192 +0 0:7 1:19 2:5 3:1240 4:20 5:36 6:21 7:756 +0 1:27 2:3 3:635 4:13 5:82 6:247 7:227 +0 1:28 2:5 3:639 4:1 5:163 6:284 7:1593 +0 0:11 1:3 2:1 3:455 4:16 5:114 6:275 7:546 +0 0:8 1:3 2:6 3:840 4:20 5:182 6:186 7:989 +0 0:3 2:4 3:1134 4:5 5:141 6:267 7:1635 +0 0:10 1:25 2:3 3:1120 4:21 5:63 6:25 7:475 +0 0:3 1:25 2:6 3:706 4:5 5:161 6:141 7:1222 +0 0:11 1:4 2:2 3:730 4:20 5:246 6:162 7:345 +0 0:9 1:19 2:2 3:1150 4:20 5:161 6:164 7:236 +0 0:7 1:7 2:1 3:1047 4:14 5:180 6:205 7:393 +0 0:10 1:8 2:2 3:1255 4:20 5:178 6:78 7:319 +0 0:3 1:24 2:5 3:1950 4:6 5:140 6:162 7:2066 +0 1:11 2:6 3:624 4:14 5:262 6:205 7:1589 +1 0:6 1:14 2:4 3:1807 4:21 5:63 6:218 7:316 +0 0:10 1:28 2:6 3:1336 4:12 5:272 6:162 7:397 +0 0:4 1:8 2:3 3:1715 4:15 5:156 6:38 7:187 +1 0:8 1:29 2:3 3:1604 4:19 5:65 6:38 7:728 +1 0:7 1:26 2:4 3:2005 4:20 5:209 6:48 7:325 +0 0:8 1:13 2:2 3:628 4:7 5:163 6:275 7:590 +0 0:10 1:8 2:2 3:657 4:7 5:287 6:19 7:223 +1 0:7 1:17 2:3 3:1945 4:8 5:303 6:19 7:264 +0 0:1 1:3 2:3 3:1717 4:21 5:140 6:62 7:288 +0 0:11 1:25 3:1824 4:10 5:224 6:107 7:992 +0 0:9 1:7 2:5 3:952 4:16 5:252 6:275 7:626 +0 0:5 1:14 2:2 3:1804 4:9 5:289 6:82 7:1506 +0 0:1 1:8 3:1127 4:14 5:203 6:31 7:748 +0 1:29 2:6 3:2052 4:1 5:84 6:223 7:1616 +1 0:8 1:9 2:6 3:2030 4:16 5:216 6:75 7:213 +1 0:3 1:18 2:1 3:1503 4:16 5:48 6:275 7:574 +1 0:7 1:13 2:5 3:1247 4:13 5:229 6:193 7:1013 +0 0:8 1:19 3:659 4:7 5:285 6:19 7:793 +1 0:2 1:21 2:2 3:104 4:18 5:262 6:266 7:679 +0 0:1 1:26 2:2 3:822 4:7 5:220 6:19 7:545 +1 0:10 1:30 2:1 3:1940 4:13 5:84 6:88 7:624 +0 0:1 1:30 3:851 4:3 5:272 6:266 7:605 +1 0:2 1:21 2:1 3:2016 4:10 5:182 6:110 7:1011 +0 0:5 1:16 2:3 3:1650 4:20 5:182 6:49 7:787 +0 0:1 1:9 2:1 3:1800 4:18 5:216 6:38 7:867 +0 0:2 1:19 2:6 3:840 4:20 5:161 6:146 7:1591 +0 1:15 3:1754 4:21 5:196 6:141 7:427 +0 0:7 1:1 2:1 3:1234 4:7 5:224 6:19 7:665 +0 0:3 1:15 2:5 3:508 4:14 5:111 6:205 7:197 +0 0:9 1:22 3:1703 4:18 5:84 6:218 7:802 +0 1:14 2:6 3:1152 4:19 5:82 6:184 7:759 +0 0:3 1:8 2:6 3:1130 4:19 5:161 6:64 7:1916 +0 0:6 1:23 2:5 3:1515 4:20 5:261 6:186 7:1733 +0 0:8 1:21 2:3 3:1610 4:19 5:65 6:49 7:361 +0 0:10 1:25 2:4 3:1135 4:13 5:84 6:270 7:190 +0 0:2 1:7 2:2 3:631 4:16 5:240 6:267 7:199 +0 0:5 1:21 2:2 3:1010 4:20 5:260 6:49 7:495 +0 0:8 1:19 2:1 3:855 4:7 5:220 6:19 7:545 +0 0:4 1:6 2:1 3:1148 4:18 5:163 6:172 7:2615 +0 0:3 1:13 2:2 3:37 4:1 5:163 6:83 7:1235 +0 0:11 1:29 2:3 3:1339 4:13 5:267 6:257 7:417 +0 0:1 1:19 2:4 3:729 4:20 5:260 6:227 7:1506 +1 0:10 1:29 3:1702 4:1 5:216 6:311 7:522 +0 0:1 1:5 2:4 3:1438 4:3 5:16 6:266 7:1449 +0 0:9 1:25 2:1 3:941 4:16 5:114 6:275 7:546 +0 0:8 1:10 2:6 3:1625 4:10 5:19 6:146 7:432 +0 1:19 2:3 3:600 4:16 5:60 6:218 7:196 +0 0:1 1:30 3:1325 4:5 5:261 6:141 7:1874 +0 0:4 1:1 2:3 3:1250 4:20 5:161 6:164 7:236 +0 0:2 1:19 2:6 3:828 4:1 5:216 6:266 7:1721 +0 0:4 1:12 3:608 4:18 5:262 6:223 7:550 +0 0:6 1:25 2:1 3:1855 4:14 5:146 6:89 7:231 +0 0:6 1:14 2:4 3:705 4:20 5:225 6:21 7:872 +0 0:1 1:8 2:1 3:1513 4:16 5:163 6:260 7:89 +0 0:1 1:24 2:1 3:1620 4:21 5:42 6:141 7:308 +0 0:7 1:14 2:6 3:1714 4:7 5:182 6:19 7:403 +0 0:3 1:9 3:810 4:20 5:36 6:186 7:395 +0 0:9 1:25 3:1206 4:21 5:212 6:141 7:395 +0 0:1 1:26 2:2 3:1113 4:16 5:221 6:246 7:116 +0 0:5 1:1 2:4 3:1001 4:7 5:19 6:275 7:1589 +0 0:4 1:5 2:1 3:1526 4:16 5:252 6:164 7:109 +0 0:8 1:17 2:6 3:934 4:5 5:141 6:217 7:1334 +0 0:8 1:22 2:4 3:1142 4:7 5:140 6:19 7:533 +0 0:2 1:13 3:755 4:18 5:183 6:218 7:594 +0 1:14 2:6 3:956 4:19 5:224 6:227 7:2075 +0 0:2 1:7 2:2 3:826 4:8 5:19 6:145 7:377 +0 1:13 2:4 3:917 4:14 5:203 6:279 7:1522 +0 0:10 1:22 2:3 3:1821 4:20 5:161 6:251 7:345 +1 0:4 1:1 2:4 3:2133 4:20 5:79 6:15 7:324 +1 0:8 1:16 2:5 3:1450 4:22 5:156 6:140 7:228 +0 0:7 1:13 2:6 3:1910 4:14 5:184 6:205 7:349 +1 0:10 1:8 2:2 3:1716 4:12 5:16 6:162 7:2304 +0 0:8 1:8 2:5 3:1635 4:20 5:225 6:2 7:328 +1 0:3 1:26 3:704 4:7 5:215 6:19 7:1900 +0 0:1 1:11 3:1719 4:14 5:90 6:226 7:453 +0 0:5 1:12 3:1559 4:19 5:163 6:227 7:370 +0 0:11 1:30 2:5 3:1210 4:22 5:140 6:170 7:229 +0 0:10 1:5 3:1936 4:11 5:133 6:158 7:163 +0 0:10 1:27 2:5 3:1102 4:8 5:20 6:19 7:765 +1 0:3 1:17 2:6 3:1601 4:19 5:65 6:58 7:168 +0 0:2 1:7 2:2 3:2331 4:7 5:221 6:19 7:2172 +1 0:5 1:7 2:3 3:1743 4:5 5:100 6:272 7:2548 +0 0:2 1:28 3:1536 4:13 5:173 6:83 7:394 +1 0:3 1:8 2:6 3:1200 4:14 5:203 6:60 7:221 +0 0:5 1:6 2:2 3:1120 4:20 5:289 6:186 7:997 +0 0:10 1:7 2:1 3:948 4:16 5:163 6:272 7:308 +1 0:5 1:20 2:1 3:2313 4:21 5:141 6:71 7:201 +0 0:4 1:28 2:1 3:2146 4:3 5:221 6:16 7:1542 +1 0:9 1:16 3:1914 4:18 5:83 6:294 7:1506 +1 0:6 1:20 2:4 3:1616 4:18 5:270 6:218 7:1249 +0 0:9 1:21 2:4 3:1210 4:20 5:95 6:21 7:528 +0 0:11 1:25 2:6 3:907 4:19 5:269 6:226 7:1576 +0 0:6 1:1 3:1800 4:15 5:183 6:74 7:429 +0 0:1 1:24 2:1 3:940 4:19 5:161 6:267 7:414 +1 0:10 1:28 2:6 3:1655 4:5 5:161 6:99 7:2227 +0 0:10 1:25 2:3 3:1224 4:1 5:216 6:141 7:925 +0 0:5 1:13 3:912 4:10 5:38 6:49 7:370 +0 0:7 1:17 2:2 3:2058 4:1 5:163 6:156 7:2475 +0 0:1 1:19 2:4 3:2129 4:1 5:158 6:164 7:2504 +0 0:9 1:18 2:2 3:1628 4:7 5:168 6:81 7:214 +0 0:1 1:22 2:1 3:1418 4:16 5:209 6:164 7:337 +1 0:6 1:14 2:5 3:1505 4:15 5:168 6:247 7:431 +0 0:4 1:14 2:2 3:1151 4:20 5:163 6:299 7:451 +0 0:4 1:14 2:1 3:1142 4:7 5:155 6:74 7:614 +0 0:10 1:17 2:3 3:923 4:1 5:180 6:218 7:403 +0 0:7 1:30 2:1 3:1013 4:14 5:179 6:89 7:98 +0 0:10 1:9 2:4 3:1200 4:15 5:156 6:231 7:340 +0 0:5 1:22 2:3 3:1610 4:20 5:49 6:65 7:336 +0 0:9 1:25 3:2010 4:19 5:168 6:47 7:292 +0 0:6 2:5 3:1718 4:18 5:140 6:25 7:326 +0 0:11 1:1 2:6 3:1000 4:13 5:82 6:156 7:213 +0 0:6 1:18 2:2 3:2024 4:16 5:161 6:103 7:258 +0 0:6 1:14 2:5 3:1450 4:13 5:163 6:162 7:236 +1 0:3 1:10 2:1 3:1730 4:5 5:2 6:141 7:744 +0 0:5 1:7 2:2 3:1129 4:16 5:37 6:275 7:291 +0 0:4 1:13 2:1 3:1723 4:16 5:228 6:275 7:150 +1 0:8 1:14 2:3 3:30 4:13 5:216 6:205 7:334 +0 0:2 1:14 2:2 3:1732 4:14 5:90 6:247 7:501 +0 1:11 2:6 3:1030 4:3 5:274 6:211 7:371 +0 0:1 1:11 2:6 3:1314 4:7 5:203 6:19 7:906 +0 1:11 3:901 4:7 5:19 6:49 7:576 +0 0:8 1:28 2:2 3:1327 4:8 5:19 6:13 7:852 +0 0:3 1:10 3:1726 4:4 5:156 6:107 7:1069 +0 0:7 1:15 3:1029 4:16 5:270 6:311 7:1004 +0 1:23 2:6 3:1230 4:19 5:65 6:185 7:413 +0 0:3 1:8 2:5 3:854 4:12 5:203 6:162 7:1300 +0 0:1 1:28 2:5 3:1033 4:20 5:83 6:134 7:883 +1 0:1 1:4 2:3 3:1415 4:15 5:193 6:156 7:948 +0 0:1 1:4 2:3 3:1041 4:21 5:122 6:99 7:605 +1 1:4 2:4 3:1314 4:16 5:276 6:218 7:174 +1 0:5 1:21 2:1 3:919 4:21 5:140 6:99 7:213 +0 0:10 1:13 3:1145 4:19 5:155 6:64 7:329 +0 1:10 2:2 3:744 4:18 5:220 6:218 7:1144 +0 0:8 1:9 2:6 3:1148 4:7 5:182 6:38 7:1121 +0 1:13 2:4 3:1800 4:19 5:108 6:81 7:899 +0 0:4 1:30 2:2 3:818 4:1 5:279 6:294 7:869 +0 0:1 1:19 2:3 3:1855 4:8 5:85 6:19 7:171 +0 0:3 1:27 2:2 3:1502 4:13 5:36 6:218 7:409 +0 0:1 1:17 2:1 3:640 4:20 5:261 6:272 7:697 +0 0:6 1:20 2:3 3:1855 4:7 5:75 6:294 7:773 +0 0:9 1:28 2:4 3:1324 4:20 5:184 6:89 7:229 +0 0:7 1:24 2:1 3:1731 4:7 5:38 6:170 7:185 +0 0:6 1:2 3:1517 4:7 5:75 6:170 7:585 +0 0:1 1:24 2:1 3:1924 4:19 5:65 6:49 7:361 +0 0:3 1:26 2:1 3:1910 4:7 5:108 6:162 7:2174 +0 0:6 1:23 2:6 3:917 4:22 5:65 6:259 7:213 +1 1:4 2:3 3:1310 4:16 5:228 6:275 7:150 +0 0:11 1:7 2:5 3:1701 4:14 5:90 6:47 7:240 +1 0:5 1:28 2:1 3:1914 4:21 5:100 6:36 7:748 +0 0:10 1:26 2:4 3:1126 4:16 5:163 6:152 7:123 +0 1:7 3:830 4:15 5:124 6:184 7:449 +0 1:19 2:4 3:1201 4:14 5:122 6:89 7:120 +0 0:6 1:10 2:2 3:1934 4:19 5:65 6:38 7:728 +0 0:7 2:6 3:1130 4:16 5:5 6:141 7:159 +0 0:2 1:18 2:5 3:1850 4:20 5:163 6:134 7:1390 +0 1:18 2:3 3:1935 4:1 5:82 6:218 7:612 +1 0:6 1:27 2:3 3:2016 4:22 5:140 6:170 7:229 +0 0:6 1:22 2:6 3:1712 4:18 5:163 6:82 7:862 +1 0:7 1:12 2:5 3:1542 4:13 5:226 6:218 7:130 +0 0:5 1:9 2:5 3:1206 4:13 5:19 6:170 7:761 +0 0:11 1:22 2:6 3:1210 4:18 5:161 6:82 7:629 +0 0:11 1:3 2:1 3:652 4:15 5:123 6:170 7:461 +0 1:13 2:4 3:1112 4:7 5:30 6:74 7:396 +0 1:18 2:3 3:709 4:19 5:224 6:141 7:1324 +1 0:3 1:12 2:2 3:1142 4:19 5:65 6:256 7:600 +0 0:8 1:29 2:3 3:804 4:21 5:65 6:141 7:913 +0 0:2 1:19 3:805 4:18 5:221 6:267 7:550 +0 0:4 1:26 2:5 3:1414 4:3 5:157 6:266 7:909 +0 0:10 1:28 2:6 3:939 4:7 5:19 6:64 7:227 +1 0:3 1:8 2:6 3:1734 4:20 5:180 6:164 7:1363 +0 0:2 1:11 2:3 3:1235 4:20 5:272 6:164 7:373 +1 0:8 1:5 2:2 3:2220 4:22 5:140 6:231 7:183 +0 0:1 1:2 2:2 3:1452 4:20 5:182 6:247 7:534 +0 0:4 1:20 2:1 3:1920 4:13 5:84 6:60 7:685 +0 0:10 1:21 2:1 3:1439 4:20 5:229 6:226 7:267 +0 0:6 1:28 2:4 3:1544 4:19 5:65 6:184 7:468 +0 0:8 1:7 2:3 3:1337 4:13 5:267 6:257 7:417 +0 0:2 1:27 2:6 3:915 4:20 5:225 6:62 7:1737 +1 0:7 1:26 2:3 3:2108 4:10 5:224 6:107 7:992 +0 0:10 1:18 2:5 3:1142 4:20 5:163 6:182 7:1363 +0 0:4 1:30 2:3 3:1030 4:7 5:75 6:133 7:4433 +0 0:3 1:26 3:1351 4:7 5:156 6:294 7:1005 +0 0:7 1:23 2:1 3:1342 4:1 5:245 6:83 7:1158 +1 0:9 1:30 2:5 3:2149 4:7 5:75 6:83 7:812 +0 0:1 1:10 2:3 3:1229 4:13 5:123 6:218 7:590 +0 0:11 1:5 2:3 3:2129 4:20 5:184 6:49 7:611 +0 0:7 1:11 3:1925 4:20 5:182 6:49 7:787 +0 0:4 1:19 3:1418 4:7 5:19 6:107 7:581 +0 0:6 1:23 2:5 3:1158 4:19 5:224 6:155 7:742 +0 0:9 1:8 3:909 4:22 5:225 6:214 7:833 +0 0:5 1:29 2:1 3:1034 4:1 5:163 6:83 7:1235 +0 0:4 1:26 2:5 3:1109 4:16 5:163 6:152 7:123 +0 0:1 1:27 2:3 3:1350 4:20 5:267 6:48 7:296 +0 0:9 1:22 2:6 3:1741 4:15 5:259 6:74 7:383 +0 0:8 1:19 2:1 3:1949 4:1 5:84 6:186 7:797 +0 0:11 1:18 2:1 3:1919 4:22 5:225 6:103 7:493 +0 0:3 1:27 2:2 3:1846 4:20 5:215 6:227 7:325 +0 0:5 1:8 2:4 3:1729 4:5 5:141 6:21 7:140 +0 0:10 1:26 2:5 3:548 4:13 5:226 6:218 7:130 +1 0:10 1:20 2:6 3:1054 4:1 5:90 6:218 7:235 +0 0:9 1:15 2:5 3:1102 4:16 5:240 6:267 7:199 +0 2:6 3:1900 4:14 5:289 6:89 7:983 +0 0:1 1:27 2:3 3:1144 4:13 5:84 6:270 7:190 +0 1:3 2:2 3:754 4:14 5:216 6:205 7:334 +0 0:1 1:15 2:6 3:1745 4:20 5:237 6:49 7:328 +1 0:10 1:29 2:1 3:1055 4:15 5:156 6:62 7:425 +0 0:8 1:26 3:1205 4:8 5:80 6:19 7:432 +1 0:10 1:3 2:5 3:1705 4:8 5:19 6:141 7:689 +0 0:5 1:1 2:4 3:1617 4:11 5:170 6:133 7:102 +0 0:9 1:9 2:1 3:1243 4:13 5:104 6:164 7:209 +0 0:1 1:18 2:3 3:1253 4:1 5:279 6:170 7:887 +0 1:10 2:3 3:1143 4:13 5:267 6:257 7:417 +0 0:6 1:4 2:2 3:714 4:12 5:192 6:227 7:1460 +1 0:3 1:3 2:1 3:1721 4:21 5:22 6:99 7:583 +0 0:7 1:22 2:2 3:626 4:1 5:84 6:226 7:1302 +0 0:11 2:3 3:527 4:12 5:261 6:227 7:1107 +0 0:9 1:1 2:6 3:1400 4:20 5:272 6:227 7:647 +0 0:11 1:27 2:2 3:834 4:13 5:84 6:308 7:626 +1 0:4 1:4 3:1143 4:13 5:84 6:113 7:859 +0 0:1 1:1 2:1 3:848 4:5 5:83 6:141 7:861 +0 0:3 1:24 2:5 3:1237 4:1 5:294 6:83 7:813 +1 0:9 1:5 2:4 3:12 4:18 5:216 6:227 7:1440 +0 0:4 1:19 2:6 3:1456 4:19 5:82 6:256 7:892 +0 0:6 1:7 2:6 3:654 4:14 5:203 6:162 7:1300 +0 0:10 3:1205 4:1 5:216 6:81 7:612 +0 0:9 1:24 3:1125 4:5 5:289 6:99 7:998 +1 0:6 2:4 3:1615 4:8 5:189 6:74 7:475 +0 0:7 1:5 2:5 3:1715 4:20 5:237 6:49 7:328 +1 0:5 1:24 2:3 3:1745 4:20 5:184 6:240 7:842 +0 1:5 2:4 3:916 4:21 5:63 6:284 7:487 +0 0:1 1:24 2:1 3:1945 4:16 5:257 6:267 7:191 +0 0:8 1:23 2:3 3:1309 4:20 5:209 6:164 7:337 +0 0:4 1:4 3:1537 4:14 5:90 6:141 7:1076 +0 0:3 1:17 3:1413 4:16 5:240 6:267 7:199 +0 0:11 1:20 2:2 3:1627 4:1 5:216 6:83 7:802 +0 0:3 1:6 2:4 3:1022 4:4 5:242 6:156 7:426 +1 0:11 1:10 3:2053 4:15 5:242 6:74 7:390 +0 0:11 1:1 2:5 3:1240 4:7 5:108 6:38 7:1237 +0 0:8 1:28 2:1 3:1602 4:18 5:213 6:82 7:472 +0 0:9 2:4 3:845 4:18 5:83 6:279 7:846 +1 0:2 1:4 3:2326 4:22 5:216 6:36 7:409 +1 0:2 1:21 2:1 3:2059 4:13 5:168 6:62 7:418 +0 0:5 1:22 2:4 3:905 4:15 5:238 6:74 7:810 +0 1:9 2:2 3:1726 4:7 5:279 6:19 7:483 +0 0:1 1:17 2:1 3:1535 4:20 5:270 6:182 7:919 +0 0:2 1:22 2:3 3:1432 4:16 5:163 6:221 7:49 +0 0:3 1:17 3:1853 4:2 5:133 6:172 7:102 +0 0:10 1:10 2:5 3:1123 4:21 5:38 6:99 7:200 +0 0:6 1:16 2:6 3:2226 4:3 5:261 6:184 7:2553 +0 0:1 1:14 2:5 3:923 4:14 5:108 6:89 7:1127 +0 0:1 1:6 2:5 3:1900 4:19 5:65 6:205 7:930 +0 0:2 1:2 2:4 3:626 4:21 5:75 6:141 7:871 +1 0:9 1:11 2:6 3:2233 4:4 5:156 6:227 7:2153 +0 1:25 2:2 3:1600 4:22 5:192 6:218 7:67 +1 1:11 2:6 3:823 4:3 5:261 6:159 7:680 +0 0:5 1:19 2:6 3:1103 4:4 5:38 6:256 7:1249 +0 0:11 1:12 2:1 3:1548 4:21 5:141 6:284 7:668 +0 0:3 1:11 2:4 3:1948 4:18 5:90 6:218 7:235 +0 0:11 1:4 2:2 3:848 4:5 5:100 6:107 7:1065 +1 0:6 1:15 2:6 3:1922 4:19 5:224 6:231 7:267 +1 0:1 1:8 3:1803 4:9 5:83 6:294 7:1506 +0 0:4 1:22 2:4 3:719 4:14 5:82 6:188 7:762 +1 0:10 1:6 3:1748 4:5 5:289 6:99 7:998 +0 0:1 1:29 2:6 3:2008 4:5 5:191 6:99 7:1086 +0 0:9 1:3 2:2 3:2032 4:7 5:19 6:30 7:134 +0 0:9 1:13 2:4 3:834 4:16 5:213 6:275 7:839 +0 1:25 2:1 3:659 4:14 5:90 6:231 7:201 +0 0:6 1:20 2:3 3:1349 4:5 5:100 6:164 7:2454 +1 0:10 1:14 2:1 3:1312 4:7 5:108 6:74 7:932 +0 0:4 1:5 2:1 3:1100 4:13 5:84 6:74 7:812 +0 2:6 3:1828 4:16 5:63 6:218 7:316 +0 0:2 1:2 2:5 3:1114 4:16 5:215 6:164 7:47 +0 0:6 1:4 2:2 3:1856 4:13 5:248 6:218 7:528 +0 0:6 1:26 2:2 3:750 4:16 5:216 6:259 7:773 +0 0:3 1:13 2:2 3:723 4:7 5:182 6:38 7:1121 +0 1:2 2:2 3:1503 4:1 5:216 6:284 7:258 +0 0:4 1:26 2:5 3:853 4:5 5:63 6:49 7:314 +0 0:3 1:20 2:3 3:859 4:21 5:63 6:83 7:1021 +0 1:16 2:1 3:1005 4:20 5:182 6:36 7:616 +1 1:16 3:1755 4:12 5:49 6:227 7:1999 +0 1:29 2:5 3:1530 4:11 5:158 6:133 7:163 +1 0:10 1:29 3:1938 4:14 5:186 6:170 7:963 +0 0:6 1:3 2:2 3:1818 4:19 5:237 6:64 7:683 +0 0:1 1:10 2:3 3:1513 4:21 5:292 6:141 7:429 +0 0:7 1:3 2:4 3:812 4:8 5:19 6:293 7:549 +0 0:3 1:9 2:6 3:557 4:16 5:188 6:267 7:329 +0 0:4 1:4 3:1632 4:15 5:75 6:252 7:282 +0 0:6 1:23 2:6 3:912 4:16 5:254 6:218 7:773 +0 0:4 1:14 2:2 3:1057 4:1 5:82 6:83 7:1192 +1 0:6 1:21 2:5 3:1936 4:13 5:84 6:203 7:821 +0 0:1 1:11 2:6 3:2050 4:21 5:100 6:290 7:194 +0 0:10 1:23 2:1 3:931 4:14 5:203 6:223 7:1426 +0 0:9 1:11 2:6 3:1904 4:1 5:84 6:137 7:603 +0 0:8 1:9 2:5 3:26 4:4 5:272 6:156 7:2521 +1 0:9 1:28 2:3 3:1912 4:20 5:49 6:134 7:1246 +0 0:8 1:19 2:1 3:843 4:7 5:19 6:256 7:515 +0 0:3 1:26 2:1 3:1533 4:20 5:261 6:211 7:671 +0 0:8 1:15 2:4 3:1717 4:20 5:274 6:277 7:404 +1 0:4 1:29 2:1 3:2026 4:18 5:83 6:31 7:455 +0 0:5 1:29 2:1 3:2100 4:8 5:75 6:145 7:500 +0 0:2 1:5 2:1 3:739 4:18 5:82 6:218 7:612 +1 0:8 1:3 3:1550 4:5 5:253 6:99 7:1569 +0 0:2 1:4 2:6 3:1055 4:15 5:82 6:38 7:399 +0 0:8 1:30 2:4 3:817 4:7 5:75 6:184 7:756 +0 0:1 1:2 2:2 3:1359 4:13 5:226 6:83 7:673 +0 0:3 1:23 2:5 3:1155 4:20 5:184 6:89 7:229 +0 0:1 1:20 2:4 3:1602 4:1 5:163 6:156 7:2475 +1 0:8 1:6 2:2 3:2222 4:1 5:84 6:19 7:732 +0 0:10 1:21 3:2045 4:20 5:225 6:217 7:325 +0 0:6 1:27 2:2 3:1240 4:20 5:163 6:206 7:1671 +0 0:9 1:23 2:6 3:1055 4:5 5:168 6:141 7:1416 +0 0:4 1:26 2:6 3:1550 4:15 5:299 6:74 7:226 +0 0:11 1:27 2:2 3:835 4:20 5:134 6:30 7:570 +0 0:5 1:26 2:6 3:1754 4:1 5:191 6:99 7:1086 +0 0:9 1:3 2:1 3:822 4:7 5:163 6:19 7:1946 +0 0:9 1:26 2:2 3:850 4:20 5:79 6:21 7:189 +0 0:2 1:3 2:5 3:1155 4:5 5:100 6:38 7:200 +0 0:9 1:11 2:5 3:740 4:20 5:21 6:165 7:341 +0 0:6 1:1 2:6 3:1808 4:12 5:225 6:99 7:2133 +0 0:7 1:13 2:5 3:819 4:16 5:163 6:257 7:109 +0 0:9 1:29 2:4 3:1721 4:1 5:80 6:83 7:861 +0 0:3 1:12 2:2 3:1240 4:15 5:216 6:156 7:740 +0 0:6 1:11 2:6 3:1225 4:20 5:182 6:150 7:972 +0 0:5 1:25 2:5 3:1657 4:13 5:252 6:272 7:417 +0 0:9 1:6 2:4 3:822 4:20 5:272 6:164 7:373 +0 0:10 1:24 2:2 3:1109 4:7 5:108 6:38 7:1237 +0 0:7 1:18 2:4 3:940 4:16 5:270 6:204 7:436 +0 0:11 1:6 2:3 3:958 4:13 5:121 6:83 7:134 +0 0:11 1:12 2:1 3:2051 4:12 5:225 6:162 7:256 +0 0:2 1:30 2:3 3:1500 4:15 5:75 6:58 7:497 +0 0:1 1:11 3:2140 4:16 5:270 6:114 7:546 +1 0:9 1:5 2:3 3:2230 4:20 5:63 6:49 7:314 +1 0:8 1:1 2:4 3:1753 4:21 5:141 6:71 7:201 +0 0:4 1:3 2:6 3:1741 4:19 5:82 6:226 7:119 +1 0:5 1:20 3:1800 4:20 5:184 6:184 7:989 +0 0:1 1:23 2:6 3:1330 4:1 5:84 6:218 7:802 +0 0:8 1:11 2:3 3:1823 4:16 5:6 6:277 7:207 +0 0:5 1:10 2:6 3:855 4:15 5:168 6:30 7:866 +0 0:5 1:29 2:2 3:1602 4:18 5:262 6:218 7:1846 +0 0:10 1:21 2:1 3:1149 4:3 5:261 6:271 7:862 +0 0:7 1:26 2:4 3:1904 4:1 5:216 6:267 7:1846 +0 0:2 1:5 2:1 3:727 4:19 5:252 6:227 7:304 +0 0:1 1:16 3:500 4:15 5:156 6:19 7:760 +0 0:9 1:24 2:6 3:532 4:15 5:30 6:74 7:396 +0 0:2 1:8 2:4 3:1214 4:16 5:216 6:60 7:196 +0 0:1 1:23 2:6 3:2125 4:3 5:261 6:211 7:671 +0 0:3 1:18 2:1 3:1235 4:5 5:204 6:141 7:305 +0 0:6 1:30 2:5 3:1658 4:14 5:166 6:89 7:296 +1 0:4 1:7 2:3 3:1414 4:14 5:50 6:205 7:874 +1 0:4 1:10 2:5 3:1020 4:15 5:75 6:173 7:513 +0 0:7 1:4 2:4 3:755 4:20 5:184 6:247 7:632 +0 0:1 1:14 2:6 3:1257 4:1 5:279 6:218 7:258 +1 0:5 1:22 2:4 3:1110 4:20 5:163 6:162 7:236 +0 0:3 1:16 2:5 3:650 4:20 5:90 6:227 7:1671 +0 0:9 1:21 2:4 3:1233 4:1 5:279 6:218 7:258 +0 0:5 1:3 2:5 3:945 4:8 5:19 6:79 7:432 +0 0:4 1:9 2:4 3:1512 4:18 5:216 6:164 7:1745 +1 1:9 2:1 3:828 4:16 5:202 6:82 7:679 +0 0:11 1:16 2:6 3:2030 4:19 5:224 6:89 7:453 +0 0:5 1:29 2:1 3:1229 4:13 5:168 6:247 7:431 +0 0:6 1:29 2:5 3:1631 4:8 5:19 6:73 7:83 +0 0:7 1:16 2:1 3:1830 4:12 5:215 6:227 7:325 +0 0:3 2:4 3:1530 4:20 5:252 6:162 7:258 +0 0:10 1:2 2:4 3:1723 4:7 5:25 6:74 7:661 +0 0:6 1:1 2:6 3:2045 4:20 5:260 6:49 7:495 +0 0:4 1:18 2:6 3:1742 4:19 5:269 6:226 7:1576 +0 0:6 1:18 2:1 3:748 4:19 5:224 6:184 7:861 +1 0:10 1:29 3:1855 4:14 5:203 6:47 7:734 +0 0:2 1:27 2:6 3:1200 4:20 5:95 6:180 7:246 +0 0:8 1:26 3:605 4:9 5:192 6:82 7:895 +0 0:11 1:3 2:1 3:1755 4:20 5:237 6:184 7:1073 +0 0:2 1:20 2:1 3:1730 4:1 5:84 6:214 7:175 +0 0:2 1:3 2:5 3:2056 4:14 5:203 6:89 7:528 +0 0:2 1:19 3:1055 4:22 5:232 6:64 7:488 +0 0:5 1:9 2:5 3:1527 4:18 5:191 6:218 7:1197 +0 0:5 1:29 2:2 3:806 4:22 5:186 6:82 7:872 +0 0:11 2:3 3:1624 4:12 5:270 6:227 7:507 +0 0:11 1:21 2:4 3:558 4:1 5:168 6:218 7:733 +0 0:9 1:18 2:2 3:800 4:15 5:75 6:89 7:229 +1 0:5 1:26 2:6 3:2133 4:18 5:216 6:205 7:334 +0 1:16 2:1 3:1107 4:7 5:220 6:38 7:1197 +0 0:9 1:1 2:6 3:1006 4:21 5:248 6:99 7:246 +0 0:1 1:6 2:6 3:958 4:16 5:270 6:153 7:205 +0 0:2 1:6 2:1 3:1459 4:19 5:289 6:64 7:508 +0 2:5 3:932 4:5 5:262 6:62 7:2161 +0 0:3 1:22 2:5 3:1834 4:5 5:141 6:297 7:429 +0 0:7 1:7 2:1 3:755 4:3 5:261 6:211 7:671 +1 0:3 1:26 3:1205 4:19 5:224 6:162 7:2176 +1 0:10 1:29 3:1149 4:6 5:140 6:247 7:224 +0 0:7 1:30 3:1736 4:13 5:171 6:83 7:304 +0 0:10 1:5 3:1754 4:21 5:141 6:165 7:458 +0 0:11 1:1 2:5 3:1110 4:14 5:83 6:89 7:1123 +0 1:20 2:4 3:2125 4:7 5:84 6:164 7:1235 +0 0:5 1:15 2:2 3:934 4:5 5:141 6:133 7:3904 +1 0:1 1:18 2:2 3:1835 4:21 5:47 6:99 7:282 +0 0:11 1:1 2:5 3:1251 4:10 5:19 6:188 7:332 +0 2:6 3:2055 4:20 5:279 6:173 7:296 +0 1:15 3:1138 4:20 5:267 6:251 7:188 +0 0:6 1:28 2:4 3:812 4:4 5:156 6:250 7:288 +1 0:9 1:15 2:6 3:959 4:1 5:168 6:83 7:1389 +1 0:10 1:29 3:2100 4:1 5:191 6:184 7:193 +0 0:3 1:29 2:3 3:1703 4:5 5:82 6:99 7:199 +0 0:10 1:14 2:1 3:1129 4:18 5:48 6:267 7:326 +0 0:5 1:25 2:4 3:1700 4:15 5:192 6:74 7:318 +1 0:2 1:13 3:31 4:7 5:163 6:107 7:2342 +0 0:1 1:19 2:4 3:1910 4:4 5:272 6:171 7:387 +0 0:7 1:10 2:3 3:1510 4:7 5:38 6:19 7:946 +0 0:3 1:19 2:2 3:947 4:17 5:163 6:213 7:2486 +0 0:2 1:27 3:1600 4:20 5:289 6:265 7:727 +1 0:5 1:1 2:4 3:1600 4:20 5:242 6:36 7:443 +0 0:3 1:1 2:5 3:635 4:20 5:155 6:36 7:484 +0 0:3 1:19 2:1 3:1610 4:20 5:182 6:47 7:1011 +0 0:1 1:29 2:5 3:854 4:15 5:75 6:123 7:330 +1 0:10 1:16 2:2 3:630 4:20 5:225 6:2 7:328 +1 0:8 1:22 2:5 3:1835 4:5 5:141 6:182 7:643 +0 0:5 1:9 2:4 3:1706 4:18 5:279 6:82 7:770 +0 0:1 1:23 2:6 3:738 4:18 5:270 6:218 7:1249 +0 0:4 1:11 2:2 3:2108 4:19 5:82 6:13 7:318 +1 0:8 1:2 2:6 3:1910 4:20 5:163 6:182 7:1363 +0 1:18 2:3 3:659 4:10 5:203 6:19 7:906 +0 0:7 1:19 2:5 3:1930 4:20 5:279 6:214 7:462 +1 0:3 1:14 2:4 3:807 4:16 5:294 6:275 7:601 +0 0:10 1:5 3:1659 4:14 5:19 6:188 7:332 +1 0:8 1:5 2:2 3:1718 4:13 5:156 6:247 7:426 +0 0:4 1:19 3:704 4:19 5:251 6:81 7:892 +1 0:6 1:20 2:4 3:2023 4:1 5:84 6:82 7:641 +0 1:23 3:1254 4:22 5:18 6:82 7:125 +0 0:2 2:2 3:1313 4:4 5:38 6:140 7:413 +0 0:2 1:5 2:1 3:1942 4:22 5:216 6:20 7:160 +0 0:2 1:16 2:4 3:2124 4:16 5:163 6:152 7:123 +0 0:2 1:7 2:3 3:730 4:22 5:88 6:82 7:251 +1 0:6 1:26 2:2 3:2105 4:20 5:36 6:155 7:484 +1 0:5 1:9 2:5 3:1205 4:9 5:83 6:49 7:1491 +0 1:16 3:638 4:16 5:261 6:223 7:129 +0 0:1 1:28 2:4 3:1558 4:10 5:19 6:250 7:481 +0 0:8 1:25 2:6 3:2018 4:11 5:211 6:133 7:100 +0 0:3 2:3 3:2039 4:18 5:182 6:218 7:1005 +0 0:6 1:22 3:742 4:16 5:294 6:275 7:601 +0 0:3 1:12 2:1 3:2113 4:14 5:203 6:115 7:284 +0 0:6 1:21 2:5 3:1450 4:19 5:182 6:231 7:834 +0 0:4 1:26 2:5 3:845 4:20 5:279 6:182 7:237 +0 0:10 1:15 2:2 3:655 4:19 5:182 6:81 7:759 +0 0:6 1:26 2:2 3:1140 4:21 5:141 6:194 7:984 +0 0:10 1:2 2:4 3:1437 4:3 5:261 6:16 7:1449 +1 1:6 2:6 3:1056 4:22 5:51 6:218 7:666 +0 0:1 1:21 2:6 3:1041 4:13 5:84 6:196 7:691 +0 0:4 1:7 2:2 3:820 4:15 5:19 6:72 7:363 +0 0:8 1:3 2:6 3:842 4:7 5:84 6:275 7:988 +1 0:4 1:11 2:3 3:1927 4:16 5:83 6:299 7:639 +0 0:8 1:13 2:1 3:1840 4:1 5:292 6:83 7:237 +0 0:3 1:13 2:2 3:726 4:13 5:186 6:193 7:860 +1 0:5 1:12 3:2101 4:7 5:75 6:164 7:1900 +0 0:11 1:17 2:6 3:856 4:16 5:270 6:31 7:387 +0 0:6 1:20 2:3 3:856 4:14 5:245 6:89 7:456 +1 0:2 1:5 3:1950 4:20 5:246 6:164 7:390 +0 0:6 1:2 2:1 3:1358 4:16 5:70 6:82 7:72 +0 0:6 1:27 2:3 3:907 4:20 5:279 6:186 7:251 +0 0:5 1:21 2:2 3:558 4:16 5:294 6:164 7:451 +0 0:3 1:29 2:4 3:1714 4:4 5:155 6:156 7:829 +0 0:7 1:21 2:6 3:1045 4:20 5:36 6:186 7:395 +0 0:5 1:25 2:4 3:1715 4:14 5:49 6:188 7:787 +0 0:1 1:10 2:2 3:1850 4:20 5:48 6:272 7:296 +0 0:10 1:29 2:1 3:635 4:5 5:242 6:99 7:416 +0 0:5 1:4 2:6 3:1745 4:15 5:75 6:155 7:614 +0 0:2 1:30 2:3 3:738 4:19 5:65 6:226 7:448 +1 0:3 1:7 2:5 3:1345 4:22 5:216 6:219 7:717 +0 0:6 1:25 3:1922 4:15 5:124 6:184 7:449 +0 0:10 1:26 2:5 3:604 4:14 5:253 6:188 7:625 +0 0:1 1:26 2:2 3:1945 4:18 5:163 6:267 7:337 +0 0:9 1:2 3:1543 4:19 5:65 6:240 7:683 +1 0:6 1:19 2:3 3:1812 4:19 5:161 6:82 7:629 +0 1:18 2:3 3:1227 4:16 5:83 6:243 7:301 +0 0:10 1:4 2:6 3:605 4:1 5:224 6:83 7:1302 +0 0:11 1:2 3:532 4:10 5:108 6:19 7:581 +0 0:3 1:24 2:5 3:636 4:19 5:25 6:64 7:644 +0 0:1 1:23 3:1659 4:1 5:84 6:299 7:813 +0 1:20 2:4 3:1137 4:5 5:108 6:99 7:1065 +0 0:7 1:6 2:6 3:951 4:13 5:163 6:260 7:89 +0 0:8 1:25 2:5 3:855 4:21 5:141 6:173 7:374 +0 0:1 1:14 2:5 3:1905 4:20 5:36 6:155 7:484 +0 0:8 1:19 2:1 3:1256 4:20 5:229 6:184 7:834 +0 1:18 2:3 3:1908 4:22 5:225 6:258 7:843 +1 0:7 1:8 2:2 3:1201 4:13 5:216 6:80 7:147 +0 0:2 1:16 2:3 3:1923 4:13 5:242 6:38 7:612 +0 0:2 1:22 2:4 3:1704 4:21 5:100 6:219 7:284 +0 0:2 1:6 2:1 3:1155 4:20 5:252 6:162 7:258 +0 0:9 1:5 2:3 3:959 4:1 5:272 6:83 7:1431 +0 0:11 1:9 2:6 3:1053 4:1 5:84 6:21 7:190 +0 0:8 1:14 2:2 3:1027 4:4 5:156 6:253 7:264 +0 0:8 1:1 2:4 3:915 4:14 5:203 6:272 7:1576 +1 0:10 1:10 2:4 3:1215 4:13 5:295 6:218 7:224 +0 0:10 1:9 2:4 3:2024 4:7 5:19 6:81 7:547 +0 0:8 1:7 2:3 3:1956 4:19 5:191 6:64 7:650 +0 0:7 1:25 2:2 3:750 4:13 5:36 6:81 7:562 +1 0:1 1:17 2:2 3:1816 4:13 5:268 6:83 7:228 +1 0:10 1:25 2:3 3:2028 4:1 5:168 6:182 7:1107 +0 0:9 1:17 3:956 4:4 5:169 6:211 7:353 +0 0:11 1:20 2:2 3:1846 4:18 5:216 6:82 7:888 +0 0:11 1:25 2:6 3:1016 4:3 5:215 6:266 7:956 +0 0:10 1:9 2:3 3:1156 4:16 5:213 6:275 7:839 +0 0:7 1:12 2:5 3:1623 4:13 5:216 6:196 7:139 +0 0:7 1:26 2:4 3:749 4:13 5:84 6:188 7:432 +0 0:9 1:27 2:2 3:1048 4:20 5:182 6:49 7:787 +0 0:11 1:23 2:4 3:857 4:14 5:126 6:205 7:887 +0 0:7 1:23 3:926 4:21 5:141 6:78 7:217 +0 0:5 1:16 2:4 3:1217 4:14 5:192 6:205 7:297 +0 1:25 2:1 3:2054 4:7 5:82 6:19 7:547 +0 0:8 1:25 2:6 3:1728 4:19 5:224 6:193 7:1013 +0 0:1 1:4 2:4 3:913 4:20 5:279 6:215 7:342 +0 0:10 1:10 2:5 3:2158 4:7 5:211 6:164 7:2486 +0 0:10 1:6 2:1 3:623 4:13 5:120 6:218 7:174 +0 0:11 1:1 2:5 3:1426 4:7 5:223 6:19 7:508 +1 0:7 1:2 2:3 3:1815 4:21 5:141 6:215 7:781 +0 0:2 1:7 2:2 3:1859 4:7 5:84 6:74 7:812 +1 0:1 1:12 2:4 3:1201 4:1 5:216 6:205 7:334 +0 0:6 1:18 2:2 3:808 4:7 5:229 6:19 7:526 +0 0:5 1:30 2:2 3:1023 4:14 5:120 6:89 7:288 +0 0:3 1:28 2:3 3:943 4:13 5:242 6:38 7:612 +0 0:4 1:4 3:2208 4:1 5:84 6:2 7:569 +1 0:9 1:27 2:3 3:1548 4:13 5:229 6:193 7:1013 +0 0:6 1:7 2:5 3:1041 4:10 5:84 6:19 7:732 +0 0:9 1:24 3:602 4:13 5:229 6:218 7:412 +0 0:2 1:9 2:4 3:910 4:20 5:180 6:284 7:237 +1 0:11 1:16 2:6 3:1311 4:21 5:36 6:99 7:748 +0 0:11 1:23 2:5 3:900 4:13 5:216 6:65 7:296 +0 0:2 1:28 3:945 4:20 5:225 6:211 7:646 +0 0:10 2:1 3:636 4:13 5:191 6:64 7:650 +0 0:5 1:23 2:2 3:1455 4:13 5:306 6:83 7:281 +0 0:5 1:7 2:3 3:849 4:18 5:37 6:82 7:649 +0 0:2 1:18 2:6 3:707 4:14 5:114 6:205 7:1175 +0 0:6 1:6 2:4 3:2110 4:14 5:90 6:256 7:1085 +0 0:1 1:16 2:1 3:1154 4:7 5:19 6:234 7:272 +0 0:11 1:28 2:2 3:1540 4:20 5:246 6:275 7:422 +1 0:8 1:11 2:3 3:2008 4:8 5:171 6:74 7:513 +0 0:6 1:4 2:2 3:702 4:14 5:146 6:162 7:1591 +0 0:9 1:18 2:2 3:717 4:1 5:82 6:284 7:719 +0 0:9 1:4 2:2 3:1023 4:21 5:141 6:45 7:253 +0 0:11 1:18 2:1 3:2132 4:16 5:270 6:114 7:546 +1 0:6 1:23 2:6 3:1827 4:1 5:204 6:218 7:837 +1 0:11 1:15 2:4 3:1811 4:13 5:84 6:1 7:158 +0 0:6 1:5 2:3 3:1358 4:15 5:248 6:19 7:749 +0 0:8 1:26 2:6 3:1533 4:15 5:75 6:24 7:225 +0 0:6 1:20 2:4 3:1056 4:1 5:19 6:193 7:595 +0 1:22 3:1101 4:16 5:262 6:190 7:329 +0 0:11 1:16 2:5 3:1320 4:15 5:52 6:19 7:528 +0 0:7 1:17 2:3 3:632 4:20 5:253 6:94 7:496 +0 1:19 2:3 3:1351 4:18 5:216 6:62 7:316 +1 1:15 3:858 4:19 5:82 6:83 7:1192 +0 0:4 1:5 2:1 3:1900 4:7 5:75 6:279 7:1877 +1 0:5 1:3 2:6 3:2040 4:20 5:274 6:211 7:371 +0 0:4 1:1 2:4 3:1625 4:16 5:163 6:257 7:109 +0 0:11 1:2 2:6 3:705 4:7 5:156 6:107 7:1069 +0 0:9 1:19 2:3 3:813 4:21 5:100 6:253 7:246 +0 0:10 1:4 2:5 3:2035 4:15 5:75 6:265 7:83 +0 0:7 1:24 2:2 3:702 4:13 5:63 6:193 7:1080 +0 0:2 1:26 2:6 3:1018 4:20 5:161 6:186 7:1521 +0 0:11 1:4 2:2 3:555 4:7 5:38 6:74 7:752 +0 0:1 1:13 2:5 3:1005 4:22 5:225 6:202 7:598 +0 0:2 1:6 2:2 3:1600 4:16 5:163 6:202 7:267 +1 0:11 1:27 2:1 3:1316 4:21 5:165 6:141 7:127 +0 0:7 1:29 3:1214 4:14 5:90 6:194 7:238 +0 0:7 1:9 2:3 3:1955 4:20 5:272 6:162 7:397 +0 0:4 1:1 2:4 3:1233 4:21 5:27 6:141 7:1428 +1 0:1 1:25 2:1 3:1300 4:20 5:261 6:251 7:564 +1 0:7 1:10 2:3 3:1849 4:19 5:19 6:81 7:547 +0 0:3 1:2 2:6 3:1811 4:19 5:36 6:64 7:329 +1 0:8 1:27 3:1949 4:20 5:225 6:277 7:647 +0 0:6 1:19 2:2 3:1925 4:20 5:79 6:94 7:562 +0 0:2 1:1 2:4 3:1110 4:19 5:252 6:162 7:258 +0 0:7 1:17 2:2 3:1124 4:7 5:220 6:19 7:545 +1 0:3 1:6 2:3 3:1030 4:20 5:161 6:279 7:226 +1 0:11 1:9 2:6 3:1535 4:20 5:161 6:25 7:2298 +0 0:10 1:8 2:3 3:625 4:20 5:178 6:134 7:441 +0 0:8 1:15 2:3 3:1249 4:1 5:236 6:83 7:1126 +1 0:11 1:16 2:5 3:843 4:3 5:218 6:216 7:183 +0 0:2 1:28 3:852 4:15 5:168 6:247 7:431 +1 0:9 1:25 2:1 3:1748 4:1 5:225 6:83 7:868 +0 0:10 1:4 2:6 3:724 4:20 5:108 6:184 7:178 +0 0:5 1:23 2:2 3:1205 4:20 5:252 6:49 7:2295 +0 0:9 1:14 2:4 3:1450 4:8 5:75 6:304 7:226 +1 0:10 1:30 2:1 3:1945 4:7 5:182 6:19 7:403 +1 0:5 1:15 2:3 3:1010 4:14 5:90 6:122 7:120 +0 0:7 1:5 2:6 3:2352 4:1 5:161 6:193 7:2175 +0 0:3 1:7 2:4 3:1242 4:17 5:184 6:83 7:797 +0 1:30 3:744 4:14 5:82 6:89 7:405 +1 0:9 1:30 2:5 3:1437 4:21 5:141 6:71 7:201 +0 0:11 1:1 2:5 3:1500 4:16 5:270 6:143 7:188 +1 0:5 1:2 2:4 3:1958 4:5 5:203 6:141 7:1034 +0 0:10 1:20 2:6 3:1647 4:7 5:25 6:294 7:1111 +0 0:10 1:27 2:5 3:554 4:4 5:156 6:41 7:1576 +0 0:8 1:20 2:1 3:1418 4:5 5:252 6:141 7:1303 +0 0:7 1:10 2:4 3:1512 4:20 5:90 6:227 7:1671 +0 0:9 1:11 2:6 3:719 4:19 5:225 6:275 7:507 +0 0:8 1:9 2:6 3:1909 4:14 5:186 6:36 7:200 +0 0:2 1:2 2:4 3:1553 4:18 5:83 6:38 7:1754 +0 0:8 1:10 2:6 3:1856 4:5 5:168 6:141 7:1416 +0 0:10 1:14 2:1 3:1319 4:5 5:141 6:218 7:925 +0 0:3 1:7 2:5 3:1312 4:20 5:229 6:184 7:834 +0 0:9 1:30 2:6 3:1233 4:14 5:225 6:205 7:1276 +0 1:15 3:1657 4:13 5:192 6:83 7:853 +0 1:22 2:1 3:721 4:5 5:100 6:205 7:1008 +0 0:4 1:14 2:2 3:546 4:13 5:1 6:83 7:158 +0 0:4 1:25 2:4 3:1308 4:21 5:37 6:141 7:1482 +1 0:3 1:6 2:3 3:636 4:13 5:257 6:164 7:155 +0 0:4 1:17 2:4 3:1321 4:21 5:190 6:99 7:209 +0 1:28 2:4 3:1842 4:6 5:140 6:46 7:442 +0 0:7 1:11 3:1948 4:7 5:19 6:219 7:516 +0 0:10 1:21 2:1 3:1536 4:19 5:272 6:227 7:647 +0 0:3 1:12 2:1 3:2032 4:8 5:19 6:24 7:596 +0 1:26 2:3 3:1701 4:21 5:63 6:170 7:418 +1 0:3 1:2 2:6 3:2257 4:19 5:161 6:226 7:2176 +1 0:6 1:13 2:3 3:716 4:19 5:269 6:226 7:1576 +0 0:3 1:9 3:1812 4:16 5:83 6:143 7:458 +1 1:7 3:2318 4:10 5:19 6:134 7:696 +0 0:1 1:10 2:2 3:2006 4:18 5:216 6:182 7:403 +1 1:22 2:1 3:2039 4:4 5:182 6:156 7:944 +0 0:9 1:10 2:2 3:1329 4:22 5:225 6:103 7:493 +0 0:6 1:9 3:1856 4:5 5:141 6:247 7:1043 +0 0:2 1:10 2:6 3:2000 4:19 5:224 6:253 7:257 +0 0:1 1:24 2:1 3:1053 4:7 5:19 6:198 7:302 +0 0:10 1:28 2:6 3:600 4:16 5:141 6:218 7:925 +0 0:8 1:17 2:5 3:1857 4:5 5:289 6:141 7:787 +1 0:9 1:5 2:3 3:1449 4:8 5:171 6:19 7:453 +1 0:11 1:14 2:3 3:1608 4:21 5:141 6:247 7:1043 +0 0:1 1:1 2:1 3:1745 4:18 5:83 6:257 7:853 +0 0:1 1:30 3:1304 4:14 5:19 6:188 7:332 +1 0:11 1:5 2:2 3:844 4:13 5:216 6:173 7:552 +0 0:9 1:25 3:1110 4:20 5:49 6:173 7:912 +0 0:6 1:6 2:4 3:1025 4:12 5:225 6:164 7:370 +0 0:4 1:6 2:1 3:1515 4:1 5:84 6:193 7:1121 +0 0:11 1:28 2:2 3:1835 4:14 5:186 6:99 7:946 +0 0:8 1:4 3:2052 4:13 5:216 6:35 7:116 +0 0:7 1:1 2:1 3:653 4:7 5:156 6:266 7:2421 +0 0:6 1:10 2:1 3:1440 4:18 5:83 6:279 7:846 +1 0:1 1:9 2:2 3:1235 4:15 5:30 6:74 7:396 +0 1:11 2:6 3:623 4:7 5:233 6:275 7:521 +0 0:9 1:17 2:1 3:727 4:21 5:141 6:231 7:1117 +0 0:1 1:3 2:3 3:2105 4:20 5:79 6:284 7:546 +0 0:8 1:10 3:1205 4:8 5:23 6:19 7:714 +0 0:7 1:4 2:4 3:1123 4:14 5:203 6:111 7:197 +0 0:5 1:27 2:6 3:1652 4:14 5:75 6:89 7:229 +1 0:6 1:2 2:1 3:1316 4:14 5:216 6:205 7:334 +0 0:8 1:27 3:856 4:9 5:182 6:82 7:1545 +1 0:9 1:30 2:5 3:537 4:16 5:114 6:275 7:546 +0 0:1 1:15 3:1323 4:20 5:272 6:48 7:358 +0 1:8 2:1 3:731 4:16 5:163 6:61 7:86 +0 0:8 1:7 2:3 3:1945 4:14 5:186 6:162 7:1416 +0 0:11 1:2 2:6 3:1736 4:19 5:65 6:266 7:2279 +0 0:4 1:20 2:1 3:840 4:20 5:225 6:266 7:1107 +1 0:8 1:20 2:1 3:1930 4:14 5:186 6:82 7:872 +0 0:10 1:20 3:544 4:21 5:270 6:141 7:1195 +0 0:9 1:26 2:1 3:1105 4:1 5:216 6:266 7:1721 +0 0:2 1:22 2:3 3:822 4:16 5:270 6:2 7:493 +0 0:2 2:1 3:1154 4:7 5:168 6:19 7:761 +0 0:2 1:7 2:2 3:1434 4:5 5:141 6:83 7:224 +0 0:5 1:27 2:6 3:833 4:18 5:191 6:140 7:921 +0 0:5 1:20 3:1717 4:3 5:102 6:16 7:261 +0 0:7 1:7 2:1 3:1500 4:8 5:19 6:295 7:227 +0 0:4 1:2 2:4 3:1227 4:13 5:216 6:196 7:139 +0 0:11 1:9 3:1015 4:16 5:83 6:194 7:895 +0 0:1 1:3 2:3 3:1233 4:19 5:207 6:64 7:156 +0 0:6 1:2 3:1136 4:14 5:203 6:50 7:874 +0 0:1 1:27 2:3 3:1137 4:1 5:38 6:218 7:867 +0 0:7 3:1840 4:20 5:49 6:164 7:2329 +0 0:6 1:23 2:5 3:531 4:13 5:1 6:83 7:158 +0 0:10 1:6 2:1 3:648 4:19 5:224 6:64 7:448 +0 0:8 1:28 2:1 3:1214 4:5 5:182 6:62 7:895 +0 0:6 1:28 2:3 3:722 4:16 5:126 6:275 7:463 +0 0:3 1:8 2:5 3:1319 4:5 5:100 6:257 7:2425 +0 0:4 1:20 3:736 4:21 5:63 6:146 7:261 +0 0:11 2:3 3:1742 4:1 5:279 6:83 7:551 +0 0:3 1:3 3:1054 4:7 5:191 6:19 7:595 +0 0:2 1:14 2:2 3:1040 4:7 5:270 6:82 7:391 +0 0:6 1:29 2:4 3:942 4:1 5:84 6:82 7:641 +0 0:7 2:6 3:1129 4:20 5:252 6:227 7:304 +0 0:8 1:27 2:1 3:1208 4:14 5:90 6:110 7:56 +0 1:14 2:6 3:1333 4:19 5:229 6:218 7:412 +0 0:7 1:23 2:1 3:830 4:20 5:79 6:134 7:239 +1 0:7 1:6 2:6 3:49 4:1 5:163 6:83 7:1235 +0 0:8 1:21 2:3 3:1213 4:18 5:83 6:215 7:472 +0 0:1 1:5 2:4 3:829 4:7 5:82 6:170 7:214 +0 0:4 1:15 2:2 3:601 4:7 5:124 6:19 7:153 +0 0:7 1:29 3:1526 4:21 5:36 6:99 7:748 +0 0:5 1:10 2:6 3:600 4:8 5:109 6:19 7:273 +1 0:3 1:26 3:2100 4:20 5:260 6:284 7:254 +0 0:10 1:4 2:6 3:1153 4:14 5:161 6:205 7:1300 +0 0:4 1:15 2:2 3:929 4:21 5:229 6:99 7:319 +0 0:7 1:12 2:4 3:702 4:1 5:186 6:83 7:432 +0 0:10 1:13 2:6 3:1635 4:15 5:25 6:256 7:1180 +0 0:8 1:22 2:5 3:906 4:5 5:182 6:99 7:938 +0 0:6 1:16 2:6 3:1645 4:20 5:253 6:227 7:843 +0 0:7 1:8 2:1 3:909 4:21 5:63 6:79 7:162 +0 0:11 1:7 2:5 3:944 4:7 5:19 6:285 7:1599 +0 0:1 1:25 2:2 3:727 4:1 5:21 6:272 7:1476 +0 0:10 1:29 2:1 3:1229 4:13 5:84 6:62 7:1021 +0 0:11 1:12 2:1 3:1307 4:7 5:196 6:19 7:302 +0 0:8 1:1 2:4 3:1410 4:20 5:134 6:2 7:759 +0 0:3 1:14 2:4 3:1656 4:21 5:2 6:141 7:744 +0 0:11 1:21 2:4 3:1244 4:19 5:161 6:49 7:2106 +0 0:1 1:18 2:3 3:843 4:20 5:190 6:226 7:290 +0 0:9 1:12 2:2 3:2037 4:1 5:134 6:21 7:148 +1 0:5 1:2 2:5 3:1115 4:20 5:163 6:162 7:236 +1 0:10 1:24 2:3 3:1107 4:22 5:216 6:52 7:344 +0 0:7 1:7 3:1325 4:12 5:225 6:266 7:1107 +1 0:10 1:10 2:4 3:1719 4:1 5:279 6:218 7:258 +0 0:7 3:929 4:16 5:221 6:275 7:630 +0 0:3 2:4 3:1024 4:20 5:246 6:162 7:345 +0 0:1 1:24 3:1326 4:11 5:158 6:213 7:84 +0 1:19 2:3 3:1702 4:7 5:19 6:13 7:852 +0 0:3 1:7 2:5 3:2229 4:4 5:209 6:38 7:2693 +0 0:4 1:6 2:2 3:1513 4:1 5:84 6:99 7:1372 +0 0:11 1:7 2:4 3:2308 4:12 5:161 6:38 7:2381 +0 0:3 1:5 2:2 3:1238 4:13 5:216 6:293 7:214 +0 0:11 1:7 2:5 3:1638 4:22 5:84 6:64 7:936 +1 0:10 1:4 2:5 3:1809 4:7 5:191 6:19 7:595 +1 0:4 1:8 2:4 3:725 4:20 5:184 6:257 7:1728 +0 0:5 1:4 3:754 4:21 5:141 6:182 7:643 +0 0:7 1:22 2:1 3:1858 4:15 5:75 6:51 7:404 +0 0:9 1:2 3:1225 4:14 5:90 6:186 7:229 +0 1:7 2:6 3:1057 4:10 5:134 6:19 7:696 +1 0:1 1:27 2:4 3:1712 4:20 5:134 6:49 7:1246 +0 0:5 1:30 2:2 3:939 4:13 5:216 6:194 7:67 +0 0:2 1:1 2:4 3:1944 4:20 5:49 6:30 7:682 +0 0:6 1:17 3:1504 4:6 5:123 6:184 7:534 +1 0:10 1:17 2:4 3:2029 4:20 5:90 6:284 7:440 +0 0:5 1:16 2:4 3:2239 4:7 5:19 6:193 7:595 +0 0:1 1:15 2:6 3:735 4:20 5:182 6:182 7:1072 +0 0:2 1:28 3:1958 4:18 5:83 6:114 7:836 +0 0:11 1:25 3:643 4:14 5:213 6:188 7:538 +1 0:4 1:22 2:4 3:1929 4:9 5:83 6:89 7:1123 +0 0:7 1:18 2:4 3:1544 4:21 5:141 6:182 7:643 +0 0:2 1:27 3:1445 4:10 5:207 6:19 7:317 +0 0:8 1:9 2:5 3:1127 4:15 5:245 6:74 7:413 +0 1:4 2:4 3:1820 4:22 5:104 6:227 7:493 +0 0:7 1:11 3:1850 4:20 5:252 6:211 7:446 +0 0:5 1:25 2:5 3:1420 4:22 5:168 6:140 7:229 +0 0:9 1:20 2:3 3:1356 4:13 5:212 6:218 7:693 +0 0:7 1:23 2:1 3:807 4:16 5:192 6:82 7:895 +1 0:8 1:18 2:6 3:1926 4:19 5:65 6:209 7:156 +1 0:4 1:10 2:6 3:2029 4:4 5:38 6:156 7:187 +0 0:4 1:15 2:3 3:1816 4:18 5:262 6:82 7:967 +0 0:9 1:24 3:846 4:1 5:2 6:83 7:569 +0 0:11 2:4 3:539 4:13 5:142 6:83 7:328 +0 0:8 1:10 2:6 3:853 4:15 5:75 6:123 7:330 +0 0:7 1:14 2:6 3:1415 4:20 5:79 6:297 7:237 +0 0:11 1:19 2:2 3:1931 4:16 5:262 6:2 7:896 +1 0:7 1:1 2:2 3:2020 4:20 5:134 6:78 7:239 +0 0:5 1:11 2:2 3:1345 4:15 5:75 6:215 7:614 +0 0:8 1:28 2:2 3:1319 4:8 5:51 6:19 7:191 +0 1:2 2:2 3:721 4:21 5:100 6:206 7:1167 +1 0:8 1:3 3:1733 4:20 5:237 6:49 7:328 +0 0:11 2:4 3:1207 4:16 5:241 6:275 7:526 +0 0:8 1:9 2:6 3:1833 4:19 5:49 6:226 7:90 +0 0:3 1:5 2:2 3:557 4:7 5:163 6:19 7:1946 +0 0:1 1:11 2:6 3:930 4:8 5:226 6:19 7:564 +0 0:7 1:23 3:1906 4:1 5:84 6:21 7:190 +0 0:6 1:21 2:4 3:720 4:20 5:274 6:211 7:371 +0 0:1 1:8 2:1 3:702 4:10 5:38 6:226 7:280 +0 0:11 1:11 2:5 3:1048 4:20 5:182 6:258 7:1040 +0 0:5 1:24 2:3 3:953 4:1 5:108 6:83 7:1119 +0 0:10 1:30 2:2 3:1707 4:3 5:209 6:266 7:671 +0 0:6 1:12 2:3 3:1853 4:14 5:203 6:89 7:528 +1 0:1 1:7 3:1422 4:21 5:279 6:99 7:872 +1 0:8 1:27 3:2254 4:7 5:19 6:77 7:366 +0 0:3 1:11 2:4 3:1251 4:15 5:75 6:83 7:812 +0 0:6 1:4 2:3 3:627 4:9 5:19 6:82 7:1199 +0 0:8 1:27 3:851 4:4 5:47 6:156 7:301 +0 0:5 1:29 2:2 3:725 4:13 5:306 6:218 7:522 +0 0:7 2:6 3:1937 4:19 5:289 6:226 7:920 +0 0:7 1:24 2:1 3:658 4:4 5:156 6:48 7:2465 +0 0:1 1:26 2:2 3:805 4:8 5:270 6:258 7:1086 +0 0:9 1:7 2:6 3:640 4:19 5:47 6:81 7:296 +0 0:3 1:22 2:5 3:1109 4:19 5:224 6:192 7:290 +1 1:28 2:4 3:2319 4:18 5:262 6:140 7:2419 +0 0:11 1:29 2:3 3:1221 4:14 5:146 6:89 7:231 +0 0:6 1:2 3:1215 4:20 5:289 6:240 7:1137 +1 0:8 1:1 2:4 3:1339 4:18 5:216 6:123 7:590 +1 0:9 1:13 2:3 3:1359 4:3 5:157 6:266 7:909 +0 0:5 1:25 2:4 3:920 4:13 5:75 6:218 7:264 +0 0:5 1:11 2:2 3:1735 4:20 5:215 6:272 7:333 +1 0:1 1:1 3:1935 4:20 5:49 6:62 7:314 +0 0:9 1:26 2:1 3:734 4:21 5:141 6:119 7:376 +1 0:5 1:7 2:3 3:915 4:8 5:19 6:47 7:712 +0 0:4 1:16 2:4 3:729 4:4 5:108 6:140 7:901 +0 0:9 1:2 2:1 3:805 4:19 5:224 6:64 7:448 +0 0:10 1:11 2:2 3:1111 4:1 5:246 6:218 7:1671 +0 0:4 1:29 2:2 3:1553 4:16 5:216 6:120 7:174 +0 0:11 1:21 2:4 3:1402 4:19 5:251 6:226 7:992 +0 1:17 2:2 3:1720 4:22 5:225 6:103 7:493 +0 0:4 1:13 2:1 3:1243 4:22 5:140 6:123 7:239 +1 0:8 1:16 2:5 3:2100 4:10 5:223 6:19 7:508 +1 1:21 2:5 3:1925 4:17 5:269 6:186 7:2057 +0 0:9 1:15 2:5 3:1722 4:7 5:163 6:133 7:2556 +0 0:7 1:14 3:1815 4:20 5:229 6:226 7:267 +0 1:5 2:5 3:1454 4:7 5:270 6:162 7:368 +0 0:8 1:18 3:1848 4:20 5:164 6:2 7:289 +0 0:3 1:12 2:1 3:1720 4:8 5:270 6:194 7:1246 +0 0:3 1:19 2:1 3:600 4:20 5:209 6:164 7:337 +0 0:10 1:17 2:4 3:758 4:18 5:216 6:81 7:612 +0 0:1 1:17 2:1 3:1500 4:20 5:163 6:211 7:337 +1 0:8 1:15 2:4 3:1820 4:7 5:75 6:275 7:1449 +0 0:5 1:13 2:1 3:906 4:14 5:90 6:146 7:231 +0 0:7 1:13 2:5 3:745 4:8 5:45 6:19 7:449 +1 0:10 1:6 2:1 3:2206 4:18 5:216 6:122 7:137 +0 0:7 1:12 2:5 3:840 4:14 5:90 6:170 7:501 +0 0:4 1:8 2:3 3:1936 4:14 5:186 6:45 7:319 +0 0:8 1:1 2:4 3:2046 4:16 5:270 6:243 7:508 +0 0:5 1:21 2:2 3:820 4:1 5:216 6:267 7:1846 +0 0:6 1:19 2:2 3:1755 4:15 5:204 6:74 7:700 +0 0:9 1:20 2:4 3:659 4:7 5:289 6:74 7:773 +0 0:5 1:11 2:2 3:1453 4:1 5:84 6:69 7:592 +1 1:11 2:6 3:1601 4:19 5:237 6:226 7:238 +0 0:11 1:23 2:5 3:1557 4:20 5:48 6:272 7:296 +0 1:24 2:1 3:549 4:7 5:163 6:19 7:1946 +0 0:6 1:11 2:6 3:1759 4:5 5:141 6:162 7:1222 +0 0:3 1:1 2:6 3:1840 4:22 5:65 6:141 7:913 +0 0:11 1:18 3:2046 4:18 5:158 6:164 7:2504 +0 0:6 1:9 2:1 3:1755 4:10 5:19 6:186 7:590 +0 0:10 1:8 2:3 3:623 4:15 5:30 6:170 7:866 +0 0:5 1:2 2:5 3:1756 4:1 5:156 6:83 7:1391 +0 1:6 2:5 3:758 4:7 5:153 6:275 7:205 +0 0:4 1:30 2:2 3:1548 4:5 5:141 6:89 7:1076 +0 0:6 1:20 2:3 3:857 4:21 5:203 6:99 7:1008 +1 0:5 1:3 2:6 3:1017 4:18 5:216 6:227 7:1440 +0 0:8 1:16 2:4 3:1141 4:5 5:141 6:162 7:1222 +0 0:10 1:21 2:1 3:1313 4:7 5:163 6:275 7:590 +0 0:5 1:14 2:1 3:1128 4:1 5:163 6:218 7:1745 +1 0:3 1:23 2:4 3:1515 4:18 5:216 6:164 7:1745 +1 0:5 1:12 2:6 3:1904 4:5 5:141 6:83 7:224 +0 0:4 2:2 3:1649 4:14 5:203 6:215 7:282 +0 0:7 1:5 2:5 3:1934 4:10 5:65 6:19 7:227 +0 0:11 1:25 3:2247 4:5 5:141 6:21 7:140 +0 0:2 1:20 2:1 3:1125 4:13 5:38 6:247 7:612 +0 0:7 1:9 2:2 3:1123 4:18 5:216 6:206 7:837 +0 0:8 1:11 2:3 3:1803 4:13 5:260 6:218 7:286 +1 0:3 1:13 2:3 3:2024 4:3 5:252 6:223 7:933 +0 0:11 1:27 2:2 3:1051 4:20 5:49 6:65 7:336 +1 0:8 1:30 2:4 3:1910 4:20 5:251 6:150 7:1101 +1 1:22 3:1551 4:14 5:90 6:164 7:1979 +0 0:2 1:18 2:6 3:2119 4:16 5:270 6:202 7:607 +1 1:19 2:3 3:2225 4:7 5:19 6:304 7:152 +0 0:8 1:12 2:1 3:1722 4:7 5:83 6:19 7:1199 +0 0:9 1:30 2:5 3:1720 4:8 5:171 6:74 7:513 +0 0:11 1:6 2:4 3:1746 4:19 5:108 6:81 7:899 +0 0:11 1:30 2:5 3:1249 4:5 5:182 6:99 7:938 +0 0:7 1:29 2:6 3:1535 4:1 5:163 6:284 7:1593 +0 0:9 1:21 2:5 3:817 4:18 5:216 6:277 7:1781 +0 0:6 1:9 3:1509 4:8 5:146 6:74 7:98 +0 0:8 1:16 2:4 3:1135 4:21 5:75 6:141 7:871 +0 0:11 1:28 2:3 3:1155 4:20 5:190 6:226 7:290 +0 0:7 1:16 2:2 3:1758 4:3 5:261 6:114 7:224 +0 0:8 1:1 2:4 3:624 4:16 5:262 6:277 7:86 +0 0:3 1:15 2:4 3:1637 4:12 5:192 6:227 7:1460 +0 0:2 1:19 2:6 3:2213 4:16 5:262 6:96 7:451 +0 0:5 1:30 2:2 3:2116 4:10 5:19 6:188 7:332 +0 0:2 1:9 2:4 3:1755 4:7 5:19 6:250 7:481 +0 0:3 1:7 2:4 3:1519 4:21 5:47 6:62 7:192 +0 0:9 1:13 2:3 3:1353 4:1 5:84 6:227 7:868 +0 0:4 1:3 2:5 3:1619 4:13 5:213 6:218 7:416 +0 0:2 1:18 2:5 3:1404 4:21 5:285 6:99 7:194 +0 0:10 1:10 2:5 3:759 4:20 5:279 6:162 7:1372 +0 0:3 1:18 3:1926 4:21 5:141 6:58 7:925 +0 0:10 3:1705 4:20 5:2 6:227 7:328 +0 1:18 2:3 3:1654 4:9 5:270 6:82 7:391 +0 0:8 1:26 3:1025 4:20 5:289 6:240 7:1137 +0 0:8 1:19 3:1910 4:8 5:75 6:284 7:307 +0 0:10 1:26 2:4 3:604 4:21 5:217 6:141 7:1201 +0 0:10 1:30 2:1 3:620 4:21 5:51 6:141 7:878 +1 0:2 1:20 2:1 3:2120 4:20 5:83 6:186 7:895 +1 0:11 1:10 3:959 4:14 5:90 6:218 7:235 +0 0:4 1:18 2:5 3:649 4:5 5:203 6:141 7:1034 +0 0:5 1:3 2:5 3:657 4:5 5:100 6:193 7:1086 +0 0:10 1:11 2:1 3:758 4:5 5:25 6:141 7:1507 +0 0:7 1:24 2:1 3:1656 4:19 5:224 6:184 7:861 +0 0:6 1:17 2:1 3:854 4:19 5:82 6:170 7:214 +0 0:5 1:5 2:1 3:1350 4:20 5:21 6:136 7:273 +1 0:9 1:6 2:4 3:1155 4:20 5:242 6:226 7:336 +0 0:5 1:21 2:2 3:1730 4:20 5:146 6:49 7:515 +0 0:6 1:25 2:1 3:1902 4:19 5:108 6:64 7:631 +0 0:1 1:23 3:1713 4:19 5:225 6:266 7:1107 +0 0:11 1:28 2:3 3:813 4:13 5:216 6:60 7:196 +0 0:6 1:13 2:4 3:652 4:8 5:226 6:19 7:564 +0 0:10 1:21 2:1 3:2054 4:20 5:184 6:231 7:402 +1 1:4 2:4 3:1525 4:20 5:182 6:265 7:718 +0 0:6 1:26 2:1 3:600 4:16 5:299 6:218 7:475 +0 0:10 2:1 3:1106 4:7 5:251 6:19 7:515 +1 0:10 1:26 2:4 3:1744 4:7 5:270 6:82 7:391 +0 0:8 1:29 2:3 3:755 4:16 5:132 6:275 7:402 +0 0:10 1:27 2:5 3:1058 4:21 5:121 6:141 7:166 +0 0:5 1:24 2:3 3:808 4:1 5:279 6:294 7:869 +1 0:5 1:1 2:4 3:2056 4:19 5:224 6:107 7:992 +0 1:12 2:4 3:1836 4:16 5:262 6:27 7:238 +0 0:1 1:13 2:5 3:1908 4:20 5:237 6:294 7:1137 +0 1:16 2:1 3:619 4:1 5:30 6:83 7:597 +0 0:10 1:11 2:1 3:641 4:5 5:100 6:218 7:719 +0 0:11 1:15 2:4 3:810 4:18 5:225 6:267 7:651 +1 0:9 1:2 2:1 3:2005 4:20 5:90 6:284 7:440 +0 0:9 1:15 2:6 3:1544 4:19 5:82 6:38 7:399 +0 0:11 1:3 3:1355 4:16 5:264 6:275 7:269 +1 0:2 1:1 2:3 3:1900 4:20 5:209 6:257 7:446 +0 0:4 1:19 3:1041 4:7 5:19 6:170 7:761 +0 0:2 1:27 2:6 3:1854 4:15 5:256 6:74 7:203 +1 0:2 1:6 2:2 3:1603 4:21 5:141 6:94 7:667 +0 0:1 1:17 2:1 3:1655 4:20 5:134 6:284 7:687 +0 0:8 1:2 2:5 3:935 4:20 5:294 6:162 7:365 +0 0:4 1:3 2:5 3:757 4:14 5:84 6:205 7:852 +1 0:2 1:28 2:1 3:2050 4:15 5:168 6:30 7:866 +0 0:5 1:25 2:4 3:1918 4:7 5:100 6:19 7:745 +0 0:5 1:19 2:6 3:556 4:14 5:270 6:205 7:991 +0 0:6 1:29 2:5 3:735 4:22 5:58 6:64 7:168 +0 0:4 1:28 2:1 3:2207 4:14 5:203 6:194 7:297 +0 0:1 1:3 2:3 3:1816 4:1 5:161 6:83 7:1055 +0 0:9 1:6 2:5 3:1820 4:19 5:225 6:2 7:328 +0 0:4 1:11 2:3 3:1836 4:13 5:183 6:218 7:594 +0 0:3 1:4 2:2 3:1558 4:19 5:65 6:58 7:168 +1 0:5 1:14 2:1 3:1824 4:19 5:224 6:253 7:257 +0 0:5 2:1 3:1915 4:1 5:84 6:188 7:432 +0 0:2 2:1 3:1349 4:18 5:279 6:82 7:770 +0 0:7 2:6 3:1830 4:20 5:225 6:275 7:507 +0 0:5 1:27 2:6 3:618 4:6 5:140 6:38 7:413 +0 0:1 1:19 2:4 3:856 4:7 5:19 6:226 7:665 +0 0:9 1:26 2:1 3:1457 4:13 5:156 6:38 7:187 +0 1:8 2:1 3:1804 4:18 5:156 6:267 7:2586 +0 0:8 1:27 2:1 3:748 4:18 5:261 6:267 7:679 +0 0:9 1:9 2:1 3:1304 4:18 5:216 6:164 7:1745 +0 0:2 1:5 2:1 3:1705 4:22 5:274 6:227 7:338 +0 0:3 1:26 3:819 4:21 5:178 6:141 7:429 +0 0:2 1:8 2:3 3:1350 4:20 5:237 6:226 7:238 +0 1:15 3:1621 4:20 5:267 6:164 7:308 +0 0:7 1:14 2:6 3:1429 4:13 5:216 6:215 7:416 +0 1:21 2:6 3:1004 4:15 5:75 6:120 7:416 +0 0:5 1:7 2:2 3:1431 4:21 5:141 6:15 7:519 +0 0:10 1:21 2:1 3:900 4:2 5:133 6:213 7:100 +0 0:9 1:1 2:6 3:1928 4:7 5:168 6:81 7:214 +0 0:5 1:26 2:5 3:810 4:8 5:183 6:19 7:619 +0 0:7 1:4 2:4 3:1050 4:1 5:134 6:21 7:148 +1 0:3 1:24 2:5 3:1707 4:19 5:224 6:164 7:2401 +0 0:9 1:21 2:4 3:1820 4:1 5:216 6:275 7:1249 +0 0:8 1:29 2:3 3:1054 4:5 5:168 6:141 7:1416 +1 0:1 1:19 2:4 3:2022 4:7 5:19 6:211 7:2130 +1 0:5 1:16 2:4 3:2024 4:7 5:19 6:51 7:191 +0 1:16 3:1020 4:21 5:39 6:141 7:79 +1 0:7 1:24 2:2 3:854 4:21 5:165 6:141 7:127 +0 0:5 1:20 2:1 3:1844 4:14 5:203 6:25 7:1050 +0 0:5 1:25 2:5 3:1240 4:8 5:19 6:3 7:146 +0 0:6 1:19 2:2 3:1710 4:20 5:242 6:227 7:1891 +0 0:8 1:22 2:5 3:2152 4:7 5:163 6:38 7:2611 +0 0:8 1:29 2:3 3:1150 4:14 5:155 6:89 7:814 +0 0:1 1:4 2:3 3:1514 4:15 5:98 6:74 7:170 +0 0:3 1:24 2:6 3:1854 4:16 5:83 6:203 7:826 +0 0:9 1:13 2:4 3:1603 4:7 5:19 6:123 7:306 +0 0:10 1:29 2:1 3:1514 4:5 5:225 6:141 7:1009 +0 0:4 1:28 3:1010 4:14 5:201 6:89 7:312 +1 0:9 1:6 2:4 3:2300 4:8 5:19 6:122 7:640 +1 0:11 1:3 2:1 3:1936 4:9 5:184 6:82 7:895 +0 0:3 1:6 2:4 3:1325 4:15 5:13 6:74 7:623 +0 0:8 1:6 2:3 3:1153 4:16 5:83 6:103 7:844 +1 0:6 1:3 2:2 3:1655 4:18 5:216 6:258 7:1041 +0 0:8 1:17 2:6 3:1648 4:21 5:63 6:79 7:162 +0 0:10 1:2 2:3 3:1000 4:7 5:156 6:107 7:1069 +0 2:5 3:959 4:6 5:229 6:140 7:183 +1 0:7 1:14 3:1635 4:8 5:19 6:270 7:552 +0 0:8 1:30 2:4 3:2134 4:8 5:19 6:13 7:852 +1 0:3 1:10 3:1048 4:4 5:108 6:99 7:1065 +0 0:10 1:21 3:742 4:3 5:274 6:266 7:978 +0 0:8 1:15 2:4 3:1843 4:22 5:170 6:133 7:102 +1 0:3 1:17 2:6 3:2054 4:20 5:49 6:162 7:2106 +1 0:3 1:23 2:4 3:2239 4:1 5:216 6:164 7:1745 +0 0:11 1:20 2:2 3:1312 4:5 5:141 6:193 7:964 +0 0:2 1:11 2:3 3:1523 4:1 5:84 6:294 7:929 +0 0:5 1:13 2:1 3:1130 4:15 5:242 6:38 7:612 +0 0:3 1:26 2:1 3:1658 4:16 5:264 6:275 7:269 +0 0:8 1:21 2:3 3:1024 4:20 5:184 6:294 7:997 +0 0:9 1:26 2:2 3:1112 4:10 5:19 6:247 7:356 +0 0:6 1:28 2:4 3:1011 4:10 5:203 6:19 7:906 +0 0:6 1:8 3:1952 4:21 5:141 6:227 7:1009 +0 0:7 1:13 2:6 3:1000 4:20 5:209 6:275 7:588 +0 0:11 1:7 2:5 3:1318 4:14 5:203 6:170 7:1020 +0 0:5 1:21 2:1 3:1915 4:7 5:75 6:250 7:413 +0 0:10 1:30 2:1 3:905 4:20 5:36 6:257 7:1751 +0 0:4 1:5 3:910 4:20 5:225 6:62 7:1737 +0 0:7 1:23 2:1 3:1831 4:18 5:83 6:215 7:472 +0 0:2 1:30 2:3 3:1842 4:19 5:191 6:64 7:650 +0 0:11 1:26 3:1349 4:18 5:203 6:218 7:334 +0 0:7 1:23 2:1 3:1740 4:19 5:65 6:227 7:1774 +0 0:4 1:30 2:2 3:1855 4:1 5:253 6:83 7:247 +0 0:11 1:1 2:6 3:553 4:13 5:194 6:218 7:139 +0 1:24 3:2010 4:20 5:209 6:279 7:371 +1 1:29 2:6 3:1420 4:19 5:262 6:162 7:414 +1 1:2 2:2 3:1725 4:13 5:216 6:89 7:235 +0 0:3 1:23 2:4 3:1120 4:20 5:163 6:251 7:390 +0 0:10 1:23 2:1 3:536 4:15 5:113 6:74 7:136 +0 0:7 1:25 2:2 3:2040 4:15 5:90 6:74 7:229 +1 0:9 1:4 2:3 3:1000 4:21 5:100 6:250 7:278 +1 0:9 1:5 2:4 3:1307 4:20 5:225 6:251 7:601 +0 0:8 1:28 2:1 3:1014 4:13 5:216 6:176 7:215 +0 0:9 1:14 2:4 3:844 4:13 5:216 6:146 7:177 +0 0:7 1:12 2:5 3:1150 4:15 5:242 6:74 7:390 +0 0:7 1:2 2:2 3:1140 4:20 5:180 6:2 7:718 +0 0:6 1:2 2:1 3:1721 4:10 5:35 6:19 7:533 +0 1:21 2:5 3:1414 4:16 5:255 6:82 7:916 +0 0:6 1:5 2:4 3:1036 4:15 5:75 6:66 7:205 +0 0:3 1:29 2:4 3:2112 4:5 5:141 6:21 7:140 +0 0:10 1:29 2:1 3:2005 4:3 5:221 6:257 7:933 +0 0:4 1:15 2:3 3:1600 4:15 5:299 6:74 7:226 +0 1:6 2:5 3:515 4:8 5:288 6:74 7:181 +1 0:1 1:29 2:5 3:1657 4:1 5:191 6:156 7:1090 +0 0:1 1:28 2:5 3:1007 4:7 5:19 6:52 7:528 +0 0:6 1:18 2:1 3:1526 4:13 5:84 6:116 7:140 +0 0:9 1:12 2:3 3:833 4:7 5:25 6:294 7:1111 +0 0:5 1:14 2:2 3:1047 4:10 5:223 6:184 7:662 +1 0:2 1:19 2:6 3:1743 4:1 5:66 6:218 7:296 +0 0:8 1:4 2:1 3:1223 4:18 5:83 6:140 7:1452 +1 0:10 1:26 2:5 3:1346 4:13 5:263 6:83 7:364 +0 1:11 2:6 3:1143 4:7 5:84 6:164 7:1235 +0 1:1 3:1740 4:13 5:60 6:83 7:685 +0 0:3 1:17 2:6 3:1618 4:14 5:269 6:89 7:1929 +0 0:7 1:8 2:1 3:1155 4:21 5:229 6:99 7:319 +0 0:1 1:3 2:3 3:658 4:21 5:100 6:140 7:213 +0 0:7 1:27 2:4 3:635 4:20 5:272 6:227 7:647 +0 0:3 1:20 2:2 3:1010 4:9 5:184 6:82 7:895 +0 0:6 1:26 2:1 3:632 4:19 5:252 6:226 7:2369 +1 0:4 1:26 2:6 3:1601 4:13 5:216 6:219 7:717 +0 0:3 1:13 2:3 3:1413 4:19 5:225 6:211 7:646 +0 1:29 2:5 3:1330 4:1 5:216 6:146 7:177 +0 0:1 1:27 2:4 3:1225 4:13 5:216 6:268 7:438 +0 0:10 1:11 2:1 3:817 4:5 5:100 6:274 7:1608 +0 0:3 1:19 2:2 3:1424 4:13 5:216 6:142 7:588 +0 0:11 2:4 3:1457 4:18 5:38 6:140 7:413 +1 0:8 1:1 2:4 3:2157 4:18 5:216 6:49 7:622 +0 0:10 1:15 2:1 3:1450 4:16 5:262 6:275 7:599 +0 0:1 1:29 2:5 3:1518 4:1 5:84 6:38 7:1562 +0 0:9 1:27 2:3 3:1621 4:20 5:134 6:136 7:276 +0 1:24 2:1 3:740 4:20 5:37 6:266 7:399 +0 0:11 1:9 2:6 3:2110 4:20 5:289 6:222 7:174 +1 0:3 1:6 2:3 3:1845 4:21 5:51 6:99 7:602 +0 0:4 1:5 2:1 3:1644 4:1 5:216 6:272 7:1829 +0 0:6 1:15 2:5 3:1320 4:20 5:246 6:37 7:335 +0 0:9 1:13 2:4 3:741 4:13 5:82 6:247 7:227 +0 0:7 1:10 2:4 3:650 4:21 5:56 6:141 7:686 +0 1:4 2:3 3:1907 4:16 5:83 6:30 7:1082 +0 0:3 1:1 2:5 3:2049 4:5 5:141 6:257 7:1303 +0 0:10 1:1 2:2 3:2005 4:20 5:48 6:162 7:223 +0 0:4 1:4 2:6 3:1427 4:7 5:108 6:19 7:581 +0 0:9 1:6 2:5 3:646 4:21 5:49 6:99 7:169 +0 0:10 1:23 2:1 3:1358 4:8 5:270 6:227 7:507 +0 0:8 1:11 2:4 3:1615 4:1 5:279 6:83 7:551 +0 0:3 1:22 2:6 3:745 4:20 5:180 6:82 7:533 +1 0:4 1:18 2:6 3:1235 4:20 5:225 6:214 7:833 +0 0:7 1:30 2:1 3:1747 4:21 5:141 6:299 7:936 +0 0:4 1:15 2:2 3:2050 4:20 5:279 6:49 7:737 +0 0:1 1:22 3:824 4:7 5:270 6:19 7:1589 +0 0:3 1:9 3:1804 4:19 5:224 6:82 7:1557 +0 0:5 2:2 3:1129 4:10 5:38 6:19 7:946 +0 0:5 1:29 2:2 3:1105 4:15 5:156 6:155 7:829 +0 0:8 1:25 2:6 3:615 4:18 5:267 6:82 7:948 +0 0:1 1:23 2:6 3:824 4:14 5:90 6:162 7:1750 +1 0:3 1:5 2:2 3:1816 4:21 5:201 6:99 7:799 +1 0:8 1:15 2:4 3:1423 4:21 5:36 6:141 7:657 +0 1:5 2:4 3:1600 4:20 5:79 6:134 7:239 +1 1:3 2:2 3:1035 4:20 5:49 6:222 7:883 +0 0:6 1:19 2:2 3:2223 4:16 5:163 6:227 7:370 +0 0:5 1:7 2:2 3:1600 4:13 5:168 6:81 7:214 +0 0:10 1:24 2:2 3:600 4:1 5:209 6:83 7:1456 +0 0:11 1:21 2:3 3:630 4:20 5:134 6:78 7:239 +1 0:4 1:25 2:4 3:1255 4:18 5:216 6:184 7:1005 +0 0:2 1:30 2:2 3:830 4:21 5:100 6:253 7:246 +0 0:6 1:5 2:3 3:1235 4:20 5:180 6:186 7:405 +0 0:7 1:15 2:1 3:2105 4:16 5:270 6:202 7:607 +1 0:10 1:11 2:2 3:1101 4:21 5:242 6:99 7:416 +0 0:11 1:28 2:2 3:1815 4:14 5:224 6:89 7:453 +1 0:9 1:29 2:5 3:1017 4:1 5:253 6:83 7:247 +0 0:3 1:5 2:3 3:1358 4:15 6:74 7:503 +0 0:4 1:22 2:4 3:637 4:16 5:70 6:82 7:72 +0 0:5 1:21 2:2 3:1849 4:15 5:75 6:81 7:411 +0 0:5 1:18 2:6 3:700 4:20 5:220 6:49 7:883 +0 0:2 1:19 3:755 4:4 5:100 6:256 7:1068 +0 0:8 1:20 2:2 3:1714 4:9 5:83 6:162 7:629 +0 0:1 1:17 2:2 3:1825 4:13 5:274 6:272 7:342 +0 0:1 1:19 2:4 3:1137 4:14 5:203 6:2 7:981 +0 0:11 1:2 2:6 3:612 4:9 5:192 6:82 7:895 +0 0:4 1:4 2:6 3:958 4:19 5:182 6:226 7:861 +0 0:3 1:17 2:6 3:924 4:7 5:225 6:19 7:1587 +0 0:11 1:18 2:1 3:1736 4:16 5:83 6:2 7:349 +0 0:4 1:29 2:2 3:934 4:1 5:168 6:83 7:1389 +0 0:5 1:6 2:2 3:1300 4:14 5:25 6:205 7:1050 +0 0:1 1:12 2:4 3:1810 4:18 5:203 6:82 7:680 +0 0:5 1:17 2:4 3:626 4:14 5:83 6:205 7:680 +0 0:8 1:19 2:1 3:1639 4:20 5:267 6:164 7:308 +0 0:2 1:2 2:5 3:2029 4:16 5:282 6:275 7:223 +0 0:8 1:4 3:930 4:20 5:163 6:211 7:337 +0 0:8 1:25 2:5 3:1155 4:1 5:84 6:213 7:3711 +0 0:1 1:27 2:3 3:1121 4:18 5:141 6:82 7:861 +0 0:2 1:18 2:6 3:828 4:7 5:270 6:140 7:1827 +0 0:9 1:23 2:6 3:924 4:13 5:84 6:51 7:922 +0 0:5 1:7 2:2 3:1953 4:1 5:84 6:297 7:237 +0 0:9 1:9 2:1 3:937 4:13 5:81 6:218 7:147 +0 0:11 2:4 3:927 4:20 5:225 6:217 7:325 +0 0:2 1:28 2:1 3:1313 4:1 5:95 6:218 7:1236 +0 0:5 1:11 2:2 3:642 4:5 5:212 6:141 7:395 +0 0:10 1:9 2:3 3:1223 4:11 5:151 6:133 7:216 +1 0:8 1:26 3:817 4:20 5:209 6:164 7:337 +0 0:1 1:28 2:4 3:635 4:20 5:108 6:155 7:318 +0 1:2 2:2 3:2344 4:19 5:161 6:277 7:397 +0 1:24 3:1032 4:18 5:140 6:184 7:758 +0 0:10 1:24 2:2 3:1715 4:7 5:270 6:205 7:991 +0 0:5 1:6 2:2 3:1221 4:1 5:163 6:218 7:1745 +1 0:5 1:18 2:5 3:1155 4:20 5:224 6:206 7:1088 +0 0:5 1:15 2:2 3:1517 4:5 5:19 6:141 7:689 +1 0:10 1:30 2:2 3:2040 4:15 5:156 6:250 7:288 +0 0:3 1:21 2:3 3:559 4:1 5:84 6:193 7:1121 +1 0:9 1:3 2:1 3:2354 4:10 5:19 6:231 7:526 +0 0:11 1:30 2:4 3:1730 4:7 5:38 6:170 7:185 +0 1:18 2:3 3:1452 4:16 5:53 6:275 7:221 +0 0:2 1:3 2:6 3:551 4:18 5:100 6:218 7:719 +0 1:7 3:1124 4:7 5:83 6:19 7:1199 +1 0:1 1:19 2:4 3:1142 4:1 5:212 6:83 7:175 +0 0:7 1:17 2:2 3:1745 4:14 5:279 6:205 7:449 +0 0:10 1:18 2:4 3:859 4:21 5:141 6:165 7:458 +0 0:1 1:22 2:1 3:1235 4:20 5:48 6:277 7:358 +0 1:16 3:2330 4:7 5:19 6:49 7:576 +0 0:6 1:29 2:5 3:657 4:14 5:90 6:205 7:528 +0 0:3 1:18 2:1 3:1025 4:22 5:151 6:133 7:216 +0 0:9 1:3 2:1 3:2033 4:1 5:191 6:294 7:204 +0 0:6 1:12 2:3 3:749 4:18 5:38 6:82 7:1754 +0 0:7 1:11 2:1 3:1304 4:18 5:216 6:122 7:137 +0 0:7 1:27 2:4 3:1303 4:14 5:203 6:49 7:936 +0 0:10 1:30 2:1 3:1230 4:14 5:221 6:89 7:1953 +0 0:1 1:25 2:2 3:557 4:1 5:49 6:193 7:946 +1 0:3 1:14 2:4 3:959 4:19 5:65 6:256 7:600 +1 0:1 1:16 2:1 3:1101 4:4 5:48 6:156 7:2465 +0 0:4 1:2 2:5 3:850 4:14 5:19 6:89 7:594 +1 0:11 1:13 2:2 3:2105 4:13 5:84 6:45 7:383 +0 0:9 1:28 2:4 3:1859 4:5 5:141 6:89 7:1076 +0 2:6 3:2103 4:22 5:83 6:243 7:301 +0 0:5 1:1 2:4 3:1150 4:15 5:90 6:74 7:229 +0 0:5 1:20 3:930 4:20 5:21 6:164 7:1242 +0 0:1 1:18 2:3 3:1401 4:4 5:289 6:156 7:1005 +1 0:1 1:4 2:4 3:1936 4:10 5:134 6:19 7:696 +0 0:6 1:6 2:4 3:640 4:20 5:252 6:227 7:304 +1 0:4 1:25 2:4 3:1815 4:20 5:36 6:182 7:491 +0 0:11 1:15 2:4 3:1749 4:3 5:221 6:267 7:550 +0 0:3 1:2 3:847 4:18 5:261 6:267 7:679 +0 0:10 1:30 2:2 3:1738 4:13 5:84 6:234 7:604 +0 0:10 1:9 2:4 3:1228 4:21 5:63 6:194 7:328 +0 0:10 1:13 3:1253 4:16 5:48 6:267 7:326 +0 0:6 1:18 2:1 3:555 4:18 5:221 6:82 7:992 +0 0:10 1:17 2:3 3:640 4:4 5:269 6:156 7:1597 +0 0:4 1:15 2:2 3:1845 4:7 5:19 6:2 7:1269 +0 0:5 1:3 2:5 3:1100 4:15 5:19 6:52 7:528 +0 0:5 1:10 2:5 3:1826 4:8 5:134 6:19 7:696 +1 0:10 1:4 2:5 3:1208 4:16 5:262 6:2 7:896 +0 1:3 2:3 3:1820 4:11 5:133 6:158 7:163 +0 0:11 1:25 3:2028 4:22 5:158 6:133 7:163 +1 0:5 1:11 2:3 3:1405 4:8 5:265 6:19 7:552 +1 1:5 2:5 3:1814 4:10 5:65 6:49 7:361 +0 0:5 1:8 2:3 3:712 4:14 5:203 6:99 7:1008 +0 0:3 1:4 2:1 3:1526 4:21 5:141 6:78 7:217 +0 0:3 1:9 2:6 3:1910 4:14 5:203 6:203 7:228 +0 0:9 1:29 2:4 3:1323 4:13 5:216 6:188 7:491 +1 1:24 3:1958 4:7 5:19 6:170 7:761 +0 0:10 1:22 2:3 3:1307 4:1 5:274 6:83 7:1205 +0 0:8 1:2 2:5 3:1424 4:13 5:267 6:279 7:342 +0 0:5 1:28 3:841 4:13 5:242 6:99 7:416 +1 0:10 1:2 2:3 3:1825 4:20 5:267 6:162 7:386 +0 1:17 2:1 3:1824 4:21 5:100 6:205 7:1008 +0 0:10 1:24 2:2 3:614 4:8 5:113 6:19 7:508 +1 0:5 1:29 2:1 3:1530 4:21 5:63 6:140 7:288 +0 0:9 1:28 2:4 3:1016 4:18 5:221 6:267 7:550 +0 0:3 1:24 2:6 3:1248 4:14 5:182 6:188 7:683 +0 0:1 1:16 2:1 3:2152 4:18 5:140 6:218 7:589 +0 0:6 1:17 2:1 3:722 4:16 5:221 6:275 7:630 +1 0:6 1:5 2:4 3:1600 4:20 5:161 6:134 7:1235 +0 0:9 1:22 3:915 4:10 5:223 6:19 7:508 +0 0:2 1:30 2:2 3:1320 4:19 5:224 6:38 7:280 +0 0:11 1:3 3:1142 4:9 5:83 6:49 7:1491 +1 0:8 1:18 3:1736 4:20 5:184 6:206 7:825 +0 0:1 1:27 2:4 3:930 4:8 5:181 6:19 7:79 +0 0:8 1:6 2:3 3:625 4:20 5:215 6:277 7:389 +0 0:6 1:26 2:1 3:1847 4:17 5:168 6:186 7:725 +0 0:2 1:1 2:3 3:650 4:5 5:108 6:99 7:1065 +0 0:11 1:28 2:2 3:1145 4:20 5:171 6:78 7:296 +0 0:10 1:9 2:3 3:626 4:14 5:289 6:188 7:656 +0 0:5 1:29 2:1 3:1103 4:1 5:84 6:89 7:987 +1 0:1 1:27 2:4 3:1654 4:8 5:19 6:169 7:503 +0 0:1 1:13 2:4 3:635 4:16 5:240 6:267 7:199 +0 0:6 1:9 3:1630 4:20 5:37 6:266 7:399 +1 0:11 1:6 2:4 3:1353 4:4 5:156 6:222 7:1028 +0 1:9 2:2 3:655 4:7 5:203 6:19 7:906 +0 0:4 1:30 2:2 3:1912 4:7 5:75 6:170 7:585 +0 0:1 1:23 2:6 3:1931 4:13 5:306 6:218 7:522 +0 0:9 1:22 2:6 3:740 4:15 5:186 6:74 7:403 +0 0:10 1:19 2:6 3:947 4:16 5:163 6:260 7:89 +1 0:5 1:10 2:6 3:1852 4:13 5:75 6:83 7:812 +0 0:1 1:26 2:3 3:554 4:3 5:266 6:157 7:95 +0 0:9 1:15 2:5 3:822 4:1 5:215 6:83 7:1189 +1 0:9 1:17 2:1 3:1710 4:20 5:224 6:134 7:1335 +0 1:4 2:3 3:1122 4:4 5:108 6:140 7:901 +0 0:3 1:2 3:2050 4:16 5:152 6:164 7:123 +0 1:13 2:5 3:1123 4:1 5:83 6:83 7:641 +0 0:9 1:17 2:1 3:1554 4:2 5:133 6:213 7:100 +0 0:10 1:17 2:4 3:1200 4:22 5:216 6:111 7:462 +0 0:7 1:8 2:1 3:2040 4:20 5:163 6:299 7:451 +0 0:11 1:2 3:1157 4:5 5:100 6:279 7:2433 +1 0:11 1:18 3:2313 4:10 5:19 6:226 7:665 +0 0:2 1:8 2:3 3:1741 4:21 5:201 6:99 7:799 +0 0:4 1:28 2:1 3:1355 4:11 5:133 6:164 7:2556 +1 0:2 1:13 2:1 3:1820 4:15 5:156 6:38 7:187 +0 0:8 1:28 2:2 3:817 4:8 5:184 6:19 7:590 +0 1:29 2:6 3:1841 4:13 5:64 6:83 7:164 +0 1:15 2:6 3:1456 4:7 5:19 6:258 7:874 +0 0:8 1:2 2:6 3:1835 4:20 5:90 6:186 7:229 +0 0:3 1:24 2:6 3:1538 4:20 5:134 6:49 7:1246 +0 0:5 1:3 2:6 3:932 4:4 5:156 6:222 7:1028 +0 0:9 1:3 2:2 3:1849 4:16 5:215 6:164 7:47 +0 0:5 1:17 2:5 3:1153 4:7 5:215 6:19 7:1900 +1 0:7 1:10 2:3 3:1849 4:18 5:84 6:82 7:641 +0 0:11 1:20 2:2 3:2202 4:1 5:211 6:164 7:2486 +0 0:1 2:6 3:623 4:16 5:241 6:223 7:116 +0 0:2 1:7 2:3 3:906 4:20 5:15 6:78 7:324 +0 0:11 1:14 2:4 3:959 4:19 5:225 6:267 7:651 +0 0:3 1:30 2:5 3:1355 4:16 5:163 6:238 7:110 +1 0:2 1:6 2:2 3:1731 4:21 5:140 6:62 7:288 +0 0:4 1:9 2:4 3:546 4:13 5:29 6:38 7:201 +1 0:1 2:5 3:1608 4:8 5:19 6:253 7:749 +0 0:5 1:23 2:3 3:913 4:1 5:225 6:218 7:1440 +0 0:3 1:18 2:1 3:1237 4:18 5:163 6:140 7:2288 +1 0:11 1:2 3:1915 4:8 5:19 6:209 7:317 +0 2:5 3:2248 4:16 5:262 6:103 7:158 +0 0:3 1:23 2:4 3:1900 4:8 5:290 6:19 7:227 +0 0:6 1:7 2:5 3:529 4:7 5:155 6:19 7:270 +0 0:11 1:18 2:1 3:1050 4:20 5:209 6:36 7:1959 +0 0:4 1:27 2:6 3:2036 4:1 5:191 6:218 7:1197 +0 0:9 1:15 2:6 3:2043 4:15 5:75 6:259 7:515 +0 0:11 1:3 3:1700 4:1 5:156 6:164 7:2475 +0 0:1 1:5 2:5 3:1310 4:20 5:209 6:266 7:671 +1 0:3 1:14 2:4 3:2059 4:16 5:270 6:301 7:175 +0 0:1 1:22 3:946 4:19 5:65 6:164 7:2125 +0 1:12 2:4 3:1416 4:15 5:75 6:188 7:403 +0 0:4 1:13 2:1 3:1030 4:20 5:209 6:279 7:371 +0 0:9 1:12 2:2 3:840 4:16 5:80 6:218 7:240 +0 0:7 2:6 3:1226 4:19 5:13 6:64 7:646 +1 0:9 1:17 3:1920 4:20 5:161 6:297 7:1076 +0 0:10 1:22 2:2 3:1203 4:18 5:84 6:218 7:802 +0 0:4 1:17 2:4 3:1817 4:11 5:211 6:133 7:100 +0 0:7 1:18 2:4 3:2132 4:14 5:90 6:231 7:201 +0 0:3 1:9 3:1850 4:20 5:161 6:82 7:629 +0 0:2 1:1 2:4 3:2139 4:14 5:90 6:140 7:383 +1 0:4 1:4 2:6 3:1300 4:8 5:246 6:275 7:422 +0 0:10 1:27 2:5 3:1105 4:20 5:161 6:186 7:1521 +0 0:1 1:1 2:1 3:845 4:22 5:225 6:251 7:601 +0 0:1 1:11 2:6 3:1256 4:5 5:216 6:99 7:719 +0 0:6 1:11 2:6 3:1055 4:19 5:168 6:81 7:214 +0 0:3 1:12 2:2 3:2100 4:20 5:108 6:49 7:925 +0 0:7 1:9 2:3 3:652 4:13 5:255 6:83 7:1315 +0 0:2 1:22 2:4 3:1805 4:1 5:38 6:218 7:867 +0 0:6 1:25 3:2103 4:21 5:141 6:69 7:809 +0 0:6 1:12 2:3 3:1143 4:7 5:95 6:19 7:1282 +0 0:7 1:9 2:2 3:1755 4:20 5:146 6:164 7:1814 +0 1:1 3:1845 4:7 5:220 6:170 7:1035 +0 1:13 2:4 3:1442 4:6 5:140 6:89 7:383 +0 0:5 1:21 2:2 3:1404 4:20 5:215 6:162 7:197 +0 0:10 1:12 2:6 3:610 4:8 5:265 6:19 7:552 +1 0:7 1:21 3:1047 4:8 5:47 6:19 7:712 +0 1:1 3:622 4:16 5:279 6:82 7:770 +1 0:9 1:6 2:4 3:2309 4:20 5:209 6:164 7:337 +0 0:4 1:16 2:4 3:758 4:8 5:183 6:19 7:619 +0 2:6 3:956 4:20 5:49 6:36 7:588 +0 0:6 1:26 2:1 3:925 4:21 5:63 6:265 7:304 +1 0:10 1:2 2:4 3:1719 4:14 5:203 6:99 7:1008 +1 0:1 1:6 2:6 3:1423 4:3 5:261 6:171 7:965 +0 0:7 1:23 3:1703 4:10 5:19 6:188 7:332 +0 0:11 1:23 2:4 3:630 4:20 5:2 6:164 7:677 +0 0:5 2:1 3:757 4:18 5:216 6:82 7:888 +0 0:5 1:1 2:3 3:1341 4:14 5:186 6:205 7:700 +0 0:7 1:14 2:6 3:711 4:21 5:136 6:141 7:295 +0 0:7 1:2 2:2 3:1601 4:14 5:25 6:89 7:548 +1 0:8 2:3 3:1901 4:8 5:213 6:19 7:821 +0 0:3 1:16 2:6 3:1848 4:16 5:252 6:275 7:626 +0 0:9 1:22 2:6 3:1027 4:19 5:289 6:81 7:814 +0 0:3 1:22 2:6 3:1515 4:20 5:48 6:211 7:325 +0 0:8 1:4 3:1127 4:7 5:38 6:170 7:185 +0 0:7 1:21 3:1410 4:20 5:224 6:155 7:742 +1 0:4 1:12 3:1925 4:20 5:267 6:279 7:342 +0 0:9 1:1 2:6 3:620 4:1 5:294 6:83 7:813 +0 0:4 1:11 2:3 3:504 4:14 5:111 6:205 7:197 +0 0:6 1:28 2:4 3:2050 4:8 5:19 6:119 7:352 +0 1:29 2:5 3:904 4:14 6:89 7:424 +0 0:2 1:12 3:1305 4:14 5:203 6:25 7:1050 +1 0:3 1:28 2:2 3:1720 4:21 5:25 6:99 7:116 +0 0:7 1:6 2:6 3:947 4:7 5:30 6:19 7:134 +0 0:9 1:5 2:4 3:2109 4:17 5:133 6:164 7:2556 +0 0:1 1:17 2:1 3:2133 4:14 5:203 6:284 7:449 +0 0:1 1:17 2:1 3:850 4:7 5:19 6:250 7:481 +0 0:2 1:7 2:3 3:738 4:7 5:156 6:19 7:760 +0 0:5 1:5 2:1 3:653 4:3 5:261 6:217 7:956 +0 0:9 1:18 2:2 3:1419 4:15 5:75 6:283 7:812 +0 0:2 1:12 3:1306 4:1 5:216 6:205 7:334 +1 0:10 1:25 2:3 3:820 4:1 5:163 6:267 7:337 +1 0:1 1:24 3:1955 4:13 5:84 6:154 7:408 +0 0:7 1:30 2:1 3:1210 4:5 5:100 6:274 7:1608 +0 0:2 2:1 3:1741 4:16 5:83 6:202 7:960 +0 0:9 1:17 2:1 3:1430 4:8 5:203 6:275 7:991 +1 0:8 1:1 2:5 3:1942 4:16 5:97 6:267 7:451 +0 0:11 1:15 2:5 3:1144 4:7 5:19 6:2 7:1269 +0 0:11 1:2 2:6 3:1109 4:19 5:65 6:81 7:331 +0 0:1 1:16 2:1 3:2104 4:1 5:84 6:164 7:1235 +0 0:6 1:18 2:2 3:1420 4:1 5:279 6:266 7:1710 +0 0:11 1:21 2:4 3:1125 4:8 5:135 6:19 7:780 +0 0:1 1:7 3:1312 4:13 5:216 6:64 7:599 +0 0:5 1:19 3:704 4:1 5:237 6:83 7:1529 +1 0:3 1:14 2:4 3:1545 4:20 5:134 6:82 7:883 +1 0:7 1:18 2:4 3:2026 4:13 5:45 6:83 7:383 +0 1:21 2:6 3:1110 4:7 5:36 6:19 7:214 +0 0:2 1:29 2:2 3:1441 4:20 5:252 6:272 7:417 +0 0:5 2:1 3:959 4:16 5:216 6:111 7:462 +0 0:1 1:8 2:1 3:701 4:13 5:297 6:83 7:181 +0 0:5 1:6 2:2 3:1810 4:16 5:163 6:299 7:451 +0 0:2 1:29 2:2 3:833 4:19 5:262 6:64 7:2296 +0 0:7 1:3 2:4 3:1527 4:4 5:209 6:156 7:2576 +0 0:3 1:4 2:2 3:2236 4:14 5:90 6:122 7:120 +0 0:2 1:5 2:1 3:2133 4:20 5:163 6:272 7:308 +0 0:11 1:10 2:1 3:1340 4:2 5:151 6:133 7:216 +0 0:9 1:26 2:2 3:1621 4:10 5:19 6:186 7:590 +1 0:8 1:13 2:2 3:846 4:1 5:242 6:193 7:700 +1 0:4 1:14 2:2 3:1258 4:22 5:309 6:227 7:160 +0 0:10 1:25 2:3 3:1617 4:19 5:65 6:205 7:930 +0 0:6 1:15 2:6 3:724 4:7 5:260 6:19 7:321 +0 0:10 1:6 3:1253 4:14 5:203 6:88 7:232 +0 0:3 1:11 2:4 3:1916 4:14 5:203 6:226 7:980 +0 0:5 1:20 3:845 4:5 5:100 6:89 7:487 +0 0:10 1:27 2:5 3:1226 4:14 5:163 6:89 7:1979 +0 0:7 1:3 2:4 3:1740 4:20 5:49 6:222 7:883 +1 1:19 2:3 3:1213 4:1 5:141 6:218 7:925 +0 0:3 1:3 3:755 4:20 5:289 6:107 7:197 +0 0:1 1:19 2:4 3:1140 4:14 5:171 6:188 7:129 +1 1:14 2:6 3:1459 4:19 5:224 6:83 7:1302 +0 0:1 1:17 2:2 3:1316 4:16 5:270 6:92 7:200 +0 0:3 1:11 2:4 3:1350 4:1 5:84 6:279 7:1205 +1 0:5 1:1 2:3 3:2147 4:7 5:19 6:182 7:692 +0 0:10 1:3 2:4 3:742 4:13 5:164 6:83 7:282 +0 1:30 3:957 4:22 5:82 6:253 7:296 +0 0:10 1:14 3:1715 4:20 5:272 6:266 7:605 +0 0:4 1:2 2:5 3:1320 4:20 5:36 6:25 7:852 +0 0:11 1:7 2:4 3:2023 4:7 5:270 6:37 7:291 +0 1:5 2:4 3:2042 4:19 5:224 6:65 7:405 +0 0:8 1:7 2:4 3:644 4:14 5:201 6:205 7:228 +1 0:7 1:2 2:2 3:1000 4:8 5:19 6:224 7:247 +0 1:29 2:5 3:1505 4:20 5:161 6:48 7:223 +0 0:1 1:24 3:2223 4:16 5:270 6:105 7:532 +1 0:3 1:5 2:3 3:1624 4:10 5:49 6:79 7:406 +0 0:1 1:10 2:2 3:556 4:13 5:64 6:83 7:164 +0 0:8 1:3 3:1648 4:19 5:82 6:64 7:331 +0 0:5 1:15 2:3 3:1512 4:1 5:163 6:267 7:337 +0 0:1 1:11 2:6 3:1136 4:14 5:203 6:227 7:1276 +0 0:6 1:3 2:2 3:942 4:22 5:65 6:123 7:83 +0 0:8 1:16 2:4 3:539 4:13 5:15 6:83 7:313 +1 0:2 1:5 2:1 3:2150 4:20 5:270 6:211 7:588 +0 0:6 1:7 2:5 3:1926 4:19 5:65 6:222 7:590 +0 0:3 1:10 2:1 3:2135 4:13 5:163 6:257 7:109 +1 0:6 1:25 3:757 4:16 5:252 6:164 7:109 +0 0:11 1:28 2:2 3:1755 4:7 5:251 6:19 7:515 +0 0:6 1:8 2:6 3:1307 4:14 5:213 6:205 7:282 +0 0:3 1:28 2:2 3:1215 4:15 5:113 6:74 7:136 +0 0:10 1:2 2:3 3:1100 4:20 5:79 6:134 7:239 +0 0:10 1:3 2:5 3:1935 4:18 5:83 6:83 7:641 +0 0:1 1:7 2:6 3:1035 4:20 5:182 6:231 7:834 +1 0:2 1:17 2:5 3:829 4:21 5:192 6:141 7:984 +1 0:9 1:13 2:4 3:2059 4:20 5:289 6:247 7:587 +1 0:6 2:5 3:2110 4:1 5:84 6:182 7:460 +0 0:6 1:7 2:5 3:1042 4:14 5:141 6:205 7:1034 +0 1:8 2:1 3:1910 4:20 5:252 6:227 7:304 +0 0:11 1:30 2:5 3:1200 4:20 5:178 6:78 7:319 +0 0:1 1:10 2:2 3:1054 4:16 5:202 6:275 7:436 +0 0:5 1:22 2:4 3:1724 4:19 5:108 6:231 7:994 +0 0:5 1:7 2:2 3:2100 4:20 5:163 6:211 7:337 +0 0:8 1:4 2:1 3:837 4:18 5:140 6:274 7:1571 +0 0:7 1:27 2:4 3:2052 4:16 5:296 6:275 7:175 +0 0:10 1:12 2:5 3:559 4:7 5:123 6:19 7:306 +0 0:1 1:29 2:5 3:537 4:21 5:136 6:141 7:295 +0 0:2 1:20 3:1758 4:8 5:270 6:227 7:507 +0 0:6 1:8 2:6 3:1010 4:20 5:180 6:36 7:491 +1 1:11 3:146 4:7 5:19 6:184 7:403 +0 0:1 1:10 2:3 3:2045 4:10 5:49 6:253 7:277 +0 0:1 1:16 2:1 3:1547 4:13 5:216 6:250 7:642 +0 0:3 1:17 2:6 3:1121 4:19 5:182 6:64 7:468 +0 0:9 1:21 2:4 3:959 4:13 5:84 6:134 7:247 +0 0:11 1:13 2:2 3:1352 4:14 5:186 6:83 7:432 +1 0:6 1:19 2:3 3:1056 4:18 5:83 6:184 7:1545 +1 0:4 1:9 2:5 3:1530 4:15 5:46 6:74 7:702 +0 0:11 1:11 2:5 3:1245 4:8 5:75 6:81 7:411 +0 0:1 1:22 2:1 3:1848 4:1 5:84 6:218 7:802 +1 0:3 1:29 2:4 3:1100 4:13 5:65 6:218 7:599 +0 0:5 1:8 2:4 3:1800 4:10 5:49 6:19 7:576 +0 0:2 1:13 3:1337 4:1 5:84 6:30 7:597 +0 0:9 1:18 2:1 3:1137 4:14 5:203 6:31 7:748 +1 1:1 3:1350 4:13 5:168 6:311 7:1147 +0 0:5 1:8 2:3 3:2100 4:20 5:49 6:65 7:336 +1 0:2 1:19 3:1218 4:20 5:161 6:258 7:1069 +1 0:5 1:1 2:4 3:1752 4:20 5:49 6:219 7:159 +0 0:3 1:24 2:5 3:611 4:18 5:38 6:140 7:413 +0 0:7 1:7 3:654 4:4 5:156 6:162 7:2248 +0 0:3 1:20 2:2 3:700 4:20 5:134 6:78 7:239 +0 0:3 1:3 2:1 3:1357 4:14 5:90 6:250 7:456 +0 0:2 1:29 2:2 3:2304 4:7 5:221 6:19 7:2172 +0 0:8 1:18 3:1224 4:10 5:108 6:226 7:992 +0 0:3 1:2 3:2105 4:20 5:253 6:162 7:1069 +0 0:9 2:5 3:1838 4:16 5:83 6:69 7:72 +0 0:4 1:30 2:3 3:1020 4:7 5:108 6:19 7:581 +1 0:1 1:29 2:6 3:1220 4:8 5:40 6:19 7:238 +0 0:11 1:2 3:1531 4:20 5:267 6:162 7:386 +0 0:11 1:29 2:4 3:1638 4:5 5:187 6:141 7:316 +0 0:5 2:2 3:1002 4:14 5:122 6:205 7:408 +0 0:8 1:1 2:5 3:1705 4:20 5:242 6:49 7:255 +0 0:10 1:30 2:2 3:1129 4:20 5:253 6:162 7:1069 +0 0:3 1:23 2:5 3:1533 4:14 5:90 6:122 7:120 +0 0:10 3:500 4:8 5:229 6:19 7:526 +0 0:2 1:9 2:5 3:914 4:20 5:161 6:173 7:1295 +0 0:5 1:27 2:6 3:930 4:7 5:38 6:170 7:185 +0 0:1 1:25 2:1 3:1541 4:21 5:141 6:205 7:1034 +0 0:3 1:30 2:5 3:1243 4:1 5:84 6:99 7:1372 +0 0:11 1:11 2:4 3:748 4:1 5:279 6:81 7:719 +0 0:7 1:3 2:4 3:700 4:3 5:84 6:266 7:1660 +1 0:1 1:11 2:6 3:1816 4:14 5:90 6:83 7:987 +0 0:11 1:6 2:3 3:818 4:16 5:270 6:230 7:150 +0 0:9 1:14 2:4 3:1156 4:19 5:285 6:226 7:228 +0 0:2 1:28 3:1641 4:16 5:163 6:278 7:135 +0 0:7 1:3 2:4 3:2135 4:22 5:140 6:259 7:515 +0 0:3 1:21 2:4 3:1202 4:21 5:279 6:62 7:487 +0 0:6 1:3 2:2 3:1700 4:10 5:223 6:170 7:288 +0 0:11 1:12 2:1 3:1612 4:14 5:65 6:89 7:500 +0 0:8 2:3 3:557 4:18 5:262 6:82 7:967 +0 0:5 1:30 2:3 3:829 4:9 5:83 6:279 7:846 +0 0:9 1:26 2:2 3:626 4:16 5:216 6:231 7:412 +0 0:4 1:18 2:6 3:1254 4:19 5:161 6:227 7:256 +0 0:11 1:22 2:6 3:2200 4:18 5:216 6:162 7:1515 +0 0:10 1:7 2:2 3:1612 4:1 5:161 6:83 7:1055 +0 0:9 1:4 2:3 3:1121 4:20 5:108 6:49 7:925 +0 0:4 1:19 3:1744 4:1 5:38 6:218 7:867 +1 0:11 1:1 2:6 3:1022 4:18 5:225 6:267 7:651 +1 0:4 1:20 3:1522 4:13 5:216 6:265 7:286 +0 0:1 1:13 2:5 3:831 4:19 5:82 6:256 7:892 +0 0:1 1:12 2:3 3:855 4:20 5:161 6:211 7:407 +0 1:16 3:1359 4:18 5:251 6:218 7:1120 +0 0:5 1:23 2:2 3:1016 4:15 5:80 6:74 7:64 +0 0:9 1:22 2:6 3:1029 4:1 5:180 6:218 7:403 +0 0:3 1:3 3:1739 4:21 5:141 6:180 7:429 +0 0:2 1:13 2:1 3:606 4:18 5:209 6:140 7:2408 +0 0:6 1:4 2:3 3:1333 4:18 5:267 6:218 7:1829 +0 1:24 2:1 3:1548 4:5 5:141 6:164 7:1379 +1 0:4 1:29 2:1 3:1650 4:8 5:119 6:19 7:352 +0 0:8 1:10 3:1058 4:14 5:192 6:188 7:556 +0 0:5 1:11 2:3 3:1729 4:1 5:84 6:38 7:1562 +0 0:9 1:25 3:1930 4:14 5:224 6:89 7:453 +0 0:11 1:19 2:2 3:1730 4:21 5:141 6:78 7:217 +0 0:7 1:7 2:1 3:900 4:8 5:119 6:19 7:352 +0 0:4 1:10 2:6 3:1752 4:5 5:141 6:257 7:1303 +0 0:5 1:22 2:3 3:1400 4:1 5:216 6:170 7:733 +0 0:6 1:6 2:5 3:1915 4:7 5:19 6:294 7:406 +1 1:8 2:1 3:1710 4:13 5:82 6:218 7:612 +0 0:8 1:23 2:4 3:1319 4:20 5:246 6:272 7:188 +1 0:3 1:16 2:5 3:1837 4:13 5:156 6:284 7:892 +0 0:3 1:29 2:3 3:1340 4:20 5:47 6:162 7:1987 +0 0:4 1:16 2:3 3:1026 4:16 5:188 6:267 7:329 +1 0:5 1:1 2:4 3:1555 4:1 5:216 6:267 7:1846 +0 0:9 1:9 3:1202 4:18 5:262 6:133 7:2398 +0 0:5 1:13 2:1 3:1044 4:16 5:163 6:279 7:36 +1 1:22 2:1 3:1642 4:21 5:146 6:99 7:644 +1 0:3 1:16 2:6 3:1008 4:16 5:114 6:275 7:546 +0 0:11 1:17 3:1842 4:20 5:270 6:227 7:507 +0 0:8 1:23 2:4 3:1538 4:22 5:201 6:218 7:109 +0 0:1 1:8 3:1532 4:3 5:267 6:223 7:569 +1 0:6 1:1 3:725 4:4 5:169 6:211 7:353 +0 1:29 2:6 3:1020 4:20 5:212 6:182 7:313 +0 0:3 1:6 2:3 3:1017 4:1 5:216 6:299 7:1437 +0 1:26 2:3 3:2006 4:18 5:83 6:48 7:850 +1 0:5 1:28 3:645 4:8 5:45 6:19 7:449 +0 0:1 1:8 3:1027 4:13 5:66 6:170 7:478 +0 0:11 1:14 2:4 3:1118 4:21 5:63 6:19 7:554 +0 0:10 1:28 3:2034 4:4 5:289 6:156 7:1005 +0 0:11 1:13 2:2 3:721 4:19 5:191 6:226 7:1013 +0 0:2 1:2 2:4 3:859 4:15 5:75 6:226 7:507 +0 0:8 1:6 2:2 3:630 4:20 5:36 6:49 7:588 +0 0:5 1:10 2:6 3:1934 4:18 5:262 6:164 7:337 +0 0:9 1:12 2:2 3:1445 4:8 5:75 6:72 7:173 +0 0:10 1:11 2:1 3:930 4:20 5:180 6:297 7:223 +0 0:3 1:6 2:4 3:1526 4:15 5:75 6:264 7:383 +1 0:10 1:29 2:1 3:13 4:19 5:161 6:218 7:1515 +0 0:8 1:9 2:5 3:806 4:18 5:156 6:267 7:2586 +0 0:8 1:30 2:4 3:741 4:19 5:38 6:162 7:2381 +0 0:9 1:12 2:2 3:1618 4:14 5:303 6:188 7:369 +0 0:9 1:4 2:3 3:2056 4:5 5:141 6:211 7:1628 +0 0:7 1:7 2:1 3:953 4:11 5:261 6:213 7:2640 +0 0:1 1:17 2:2 3:2033 4:4 5:220 6:156 7:1028 +0 0:8 1:8 2:5 3:2352 4:1 5:163 6:274 7:3386 +0 0:2 1:26 2:5 3:815 4:14 5:270 6:205 7:991 +0 0:5 2:2 3:1006 4:7 5:146 6:19 7:432 +0 0:6 1:26 2:1 3:1314 4:12 5:224 6:227 7:2075 +0 0:7 1:3 2:4 3:1010 4:11 5:170 6:133 7:102 +0 0:2 1:1 2:3 3:906 4:13 5:216 6:234 7:794 +0 0:6 1:23 2:6 3:1008 4:10 5:52 6:19 7:528 +0 0:1 1:26 2:2 3:1554 4:13 5:38 6:247 7:612 +1 0:5 1:18 2:5 3:838 4:20 5:161 6:49 7:2106 +0 0:8 1:4 2:1 3:1147 4:21 5:141 6:42 7:308 +0 0:5 1:21 2:1 3:551 4:21 5:260 6:62 7:304 +0 0:1 1:7 2:6 3:1754 4:21 5:141 6:69 7:809 +1 1:15 3:1407 4:13 5:84 6:198 7:540 +0 0:7 1:10 2:4 3:640 4:20 5:36 6:294 7:612 +1 0:5 1:25 2:4 3:1645 4:20 5:274 6:162 7:226 +0 0:3 1:14 2:4 3:932 4:21 5:141 6:270 7:192 +0 0:6 1:15 2:6 3:1548 4:13 5:154 6:83 7:408 +0 0:3 1:23 2:4 3:1230 4:7 5:225 6:275 7:507 +0 0:1 1:28 2:5 3:1446 4:18 5:216 6:82 7:888 +0 0:9 1:23 2:6 3:732 4:20 5:182 6:36 7:616 +0 0:11 1:22 2:6 3:1428 4:18 5:184 6:82 7:895 +0 0:11 1:2 2:6 3:1220 4:11 5:133 6:213 7:100 +0 0:6 1:22 3:2251 4:19 5:161 6:226 7:2176 +0 0:11 1:10 2:1 3:1141 4:21 5:165 6:141 7:127 +0 0:1 1:4 2:4 3:1743 4:16 5:270 6:215 7:839 +1 0:11 1:10 2:1 3:918 4:18 5:156 6:267 7:2586 +1 0:3 1:17 3:1123 4:1 5:251 6:83 7:1017 +0 0:2 1:22 2:3 3:938 4:7 5:163 6:19 7:1946 +0 0:6 1:28 2:4 3:1558 4:19 5:207 6:64 7:156 +0 0:3 1:6 2:3 3:1405 4:15 5:75 6:215 7:614 +0 1:13 2:5 3:1613 4:5 5:253 6:141 7:191 +0 0:10 1:12 2:5 3:1925 4:20 5:224 6:186 7:668 +0 0:1 1:6 2:6 3:1843 4:18 5:83 6:214 7:495 +1 0:8 1:20 2:2 3:1240 4:22 5:60 6:218 7:196 +0 0:4 1:26 2:5 3:1754 4:3 5:16 6:26 7:399 +1 0:4 1:8 2:4 3:2029 4:5 5:100 6:107 7:1065 +1 0:9 1:23 2:6 3:1038 4:3 5:261 6:277 7:605 +1 0:1 1:12 2:4 3:1502 4:19 5:65 6:170 7:544 +1 0:10 1:14 3:1219 4:14 5:90 6:275 7:1481 +0 0:10 1:27 2:6 3:1121 4:10 5:224 6:294 7:920 +0 0:4 1:9 2:5 3:1325 4:13 5:84 6:142 7:328 +0 0:11 1:2 3:1243 4:13 5:242 6:38 7:612 +0 0:6 1:25 3:2108 4:7 5:19 6:123 7:306 +0 0:1 1:7 3:934 4:10 5:19 6:83 7:732 +0 0:7 1:6 3:738 4:13 5:216 6:36 7:409 +0 0:2 1:29 2:2 3:1456 4:19 5:289 6:231 7:873 +0 0:8 1:20 2:1 3:856 4:19 5:269 6:226 7:1576 +1 0:3 1:14 2:3 3:2124 4:20 5:279 6:173 7:296 +1 0:1 1:29 2:5 3:1651 4:1 5:191 6:99 7:1086 +0 0:8 1:18 2:6 3:1020 4:15 5:260 6:74 7:83 +0 0:6 1:25 3:707 4:12 5:225 6:251 7:601 +0 0:1 1:27 2:3 3:1639 4:6 5:140 6:156 7:228 +0 0:10 1:10 2:5 3:1457 4:21 5:75 6:141 7:871 +0 1:8 2:1 3:1140 4:20 5:224 6:247 7:336 +0 0:8 1:27 3:730 4:20 5:209 6:266 7:671 +0 0:11 1:9 2:6 3:2035 4:20 5:184 6:162 7:1521 +0 0:11 1:7 2:4 3:1739 4:1 5:269 6:140 7:1571 +1 0:5 1:29 2:1 3:1939 4:19 5:224 6:162 7:2176 +0 0:2 1:14 2:2 3:1549 4:13 5:216 6:123 7:590 +0 0:11 1:11 2:5 3:840 4:1 5:163 6:83 7:1235 +0 0:4 1:2 2:4 3:1251 4:15 5:75 6:293 7:181 +1 0:8 1:2 2:6 3:1107 4:8 5:270 6:141 7:1195 +0 0:1 1:11 2:6 3:640 4:20 5:209 6:275 7:588 +0 0:3 1:28 2:2 3:922 4:10 5:19 6:267 7:2139 +0 0:5 1:11 2:2 3:1140 4:15 5:75 6:24 7:225 +1 0:4 1:18 2:5 3:2146 4:18 5:216 6:164 7:1745 +0 0:5 1:5 3:1513 4:3 5:261 6:159 7:680 +1 0:5 1:28 3:1754 4:14 5:90 6:140 7:383 +0 0:6 1:23 2:5 3:2325 4:1 5:211 6:267 7:2338 +1 0:8 1:7 2:3 3:1945 4:8 5:19 6:168 7:303 +0 0:3 1:12 2:2 3:654 4:5 5:163 6:99 7:2454 +0 1:21 2:6 3:1740 4:20 5:66 6:284 7:410 +0 0:3 1:11 2:4 3:600 4:8 5:73 6:19 7:363 +0 0:4 1:20 2:1 3:804 4:1 5:49 6:218 7:622 +0 0:6 1:15 2:5 3:1115 4:6 5:66 6:140 7:299 +0 0:1 1:24 2:1 3:700 4:7 5:221 6:19 7:2172 +0 0:4 1:30 2:3 3:2030 4:20 5:134 6:78 7:239 +0 0:5 1:15 2:2 3:842 4:5 5:100 6:267 7:2565 +0 0:3 1:6 2:4 3:1636 4:1 5:161 6:284 7:1372 +1 0:7 1:5 2:6 3:1559 4:16 5:83 6:102 7:627 +0 1:12 2:3 3:1428 4:21 5:100 6:240 7:160 +1 0:1 1:17 2:1 3:2003 4:14 5:203 6:194 7:297 +0 0:11 1:22 2:5 3:1404 4:16 5:93 6:275 7:200 +0 0:6 1:1 3:1143 4:21 5:123 6:141 7:986 +0 0:5 1:1 2:4 3:1427 4:18 5:19 6:218 7:606 +0 0:8 1:18 3:1225 4:21 5:63 6:170 7:418 +0 0:9 2:4 3:623 4:19 5:180 6:226 7:1038 +0 0:10 1:28 3:1105 4:18 5:261 6:82 7:1024 +0 0:7 1:1 2:1 3:1140 4:20 5:79 6:180 7:319 +0 0:3 1:9 3:1815 4:20 5:36 6:186 7:395 +0 0:4 1:4 2:6 3:1838 4:7 5:229 6:19 7:526 +0 0:6 1:1 3:1133 4:1 5:100 6:83 7:1372 +0 0:7 1:14 2:6 3:1015 4:16 5:117 6:82 7:212 +0 0:10 1:25 2:3 3:1210 4:5 5:289 6:141 7:787 +0 0:6 1:29 2:4 3:600 4:20 5:21 6:134 7:148 +0 0:3 1:8 2:6 3:1318 4:21 5:141 6:122 7:1042 +0 0:7 1:25 2:2 3:1715 4:18 5:140 6:25 7:326 +0 0:6 1:4 2:3 3:1341 4:5 5:100 6:218 7:719 +0 0:6 1:20 2:3 3:1109 4:4 5:161 6:156 7:2248 +0 0:4 1:3 2:5 3:803 4:13 5:64 6:83 7:164 +0 0:1 1:28 2:4 3:944 4:15 5:75 6:120 7:416 +1 1:4 2:4 3:1400 4:20 5:161 6:299 7:365 +0 0:8 1:15 2:4 3:1632 4:14 5:32 6:205 7:386 +0 0:2 1:4 2:6 3:1045 4:19 5:168 6:64 7:544 +0 0:1 1:11 3:810 4:10 5:19 6:250 7:481 +0 0:6 1:1 2:6 3:1352 4:17 5:82 6:186 7:601 +0 0:11 1:15 2:4 3:1313 4:14 5:161 6:194 7:1524 +0 0:4 1:30 2:2 3:1427 4:21 5:141 6:218 7:925 +0 0:6 1:12 2:3 3:1153 4:19 5:168 6:81 7:214 +0 0:3 1:24 2:5 3:1842 4:7 5:19 6:38 7:946 +0 0:6 1:12 2:3 3:1555 4:7 5:108 6:38 7:1237 +0 0:8 1:28 2:1 3:1303 4:21 5:279 6:99 7:872 +0 0:5 2:1 3:1227 4:14 5:192 6:205 7:297 +0 0:3 1:1 2:6 3:2004 4:4 5:156 6:222 7:1028 +0 0:7 1:20 2:6 3:655 4:8 5:105 6:19 7:331 +0 0:10 1:3 2:4 3:1140 4:15 5:75 6:140 7:388 +0 0:7 1:28 2:5 3:1755 4:21 5:63 6:226 7:363 +0 0:11 1:6 2:3 3:1534 4:18 5:66 6:218 7:296 +1 1:2 2:2 3:1057 4:22 5:216 6:194 7:67 +0 0:7 3:1702 4:14 5:122 6:89 7:120 +0 0:6 1:17 2:1 3:1629 4:13 5:163 6:257 7:109 +0 0:3 1:29 2:3 3:1929 4:6 5:140 6:51 7:401 +0 0:1 1:14 2:6 3:2112 4:5 5:274 6:99 7:2433 +0 0:6 1:14 2:5 3:946 4:13 5:84 6:259 7:925 +1 0:8 1:5 2:1 3:2117 4:13 5:216 6:79 7:240 +0 0:9 1:23 2:5 3:735 4:21 5:141 6:119 7:376 +0 0:7 1:14 3:1814 4:7 5:19 6:234 7:272 +0 0:7 1:17 2:2 3:2012 4:18 5:83 6:227 7:602 +0 1:5 2:5 3:647 4:18 5:163 6:223 7:834 +0 0:10 1:6 2:1 3:1541 4:19 5:252 6:227 7:304 +0 0:9 1:21 2:5 3:755 4:22 5:117 6:227 7:438 +0 0:8 1:17 2:5 3:855 4:13 5:84 6:112 7:228 +1 0:4 1:16 2:3 3:1239 4:1 5:84 6:213 7:3711 +0 0:10 1:28 2:6 3:1005 4:20 5:253 6:49 7:1407 +1 0:10 1:17 2:3 3:2025 4:10 5:182 6:110 7:1011 +0 0:10 1:12 2:5 3:921 4:1 5:279 6:266 7:1710 +0 0:6 2:4 3:1917 4:16 5:48 6:267 7:326 +0 0:6 1:22 2:6 3:1203 4:18 5:224 6:218 7:678 +0 0:4 1:28 3:1949 4:15 5:229 6:74 7:256 +0 0:8 1:27 3:920 4:20 5:36 6:258 7:822 +0 0:2 1:19 2:6 3:1502 4:14 5:122 6:89 7:120 +0 0:1 1:12 2:4 3:612 4:16 5:255 6:267 7:262 +0 0:5 1:18 2:6 3:1105 4:8 5:19 6:100 7:646 +0 0:11 1:22 2:5 3:1031 4:1 5:100 6:218 7:719 +0 0:9 1:26 2:2 3:1453 4:7 5:75 6:170 7:585 +0 0:11 1:29 2:3 3:35 4:7 5:83 6:19 7:1199 +0 0:5 1:15 2:3 3:1356 4:21 5:100 6:64 7:529 +0 0:9 1:3 2:2 3:1411 4:14 5:83 6:205 7:680 +0 0:9 1:20 2:3 3:1351 4:1 5:21 6:83 7:190 +1 0:6 1:4 2:3 3:2020 4:5 5:203 6:99 7:1008 +0 0:5 1:24 2:3 3:1700 4:20 5:161 6:164 7:236 +0 0:5 1:13 3:1805 4:5 5:83 6:141 7:861 +0 0:4 2:1 3:1640 4:16 5:36 6:82 7:1013 +0 0:5 1:26 2:5 3:1248 4:20 5:182 6:182 7:1072 +0 0:2 1:13 3:1042 4:21 5:270 6:141 7:1195 +0 0:10 1:20 3:801 4:7 5:19 6:184 7:403 +0 1:13 2:5 3:1433 4:20 5:161 6:164 7:236 +0 0:3 1:11 2:5 3:1843 4:20 5:161 6:272 7:386 +0 1:1 3:854 4:7 5:80 6:19 7:432 +0 0:11 1:30 2:5 3:717 4:16 5:201 6:218 7:109 +0 0:5 1:16 2:4 3:1615 4:1 5:191 6:83 7:1121 +0 0:8 1:3 3:1639 4:13 5:51 6:83 7:922 +0 0:10 1:22 2:2 3:1134 4:4 5:47 6:156 7:301 +0 0:9 1:17 3:1003 4:12 5:225 6:223 7:1009 +1 0:7 1:25 2:3 3:1842 4:22 5:161 6:251 7:345 +0 0:3 1:17 2:6 3:2101 4:5 5:141 6:275 7:1195 +0 0:2 1:1 2:3 3:1341 4:1 5:216 6:89 7:235 +0 0:1 1:4 2:4 3:1157 4:13 5:163 6:257 7:109 +0 0:5 1:2 2:5 3:2328 4:5 5:211 6:164 7:2486 +0 0:3 1:19 2:2 3:1507 4:21 5:196 6:141 7:427 +0 1:20 2:4 3:1451 4:1 5:84 6:64 7:936 +0 0:3 1:10 2:1 3:1249 4:16 5:172 6:82 7:423 +0 0:4 1:5 3:1544 4:7 5:19 6:308 7:264 +0 0:3 1:13 2:2 3:2109 4:12 5:215 6:162 7:197 +0 0:7 1:6 2:6 3:1101 4:16 5:270 6:70 7:320 +0 0:11 1:13 2:2 3:1905 4:16 5:221 6:267 7:550 +1 0:6 1:21 2:4 3:832 4:7 5:19 6:38 7:946 +0 0:9 1:17 2:1 3:700 4:13 5:110 6:218 7:223 +0 0:10 1:6 3:1036 4:1 5:294 6:218 7:1437 +0 0:11 1:12 2:1 3:1215 4:20 5:49 6:240 7:328 +0 0:9 1:3 2:1 3:1540 4:16 5:70 6:82 7:72 +0 0:2 1:28 3:2035 4:15 5:75 6:293 7:181 +0 0:11 1:3 3:1853 4:21 5:63 6:253 7:245 +0 0:8 1:1 2:4 3:730 4:20 5:225 6:2 7:328 +0 0:8 1:28 2:2 3:1831 4:13 5:137 6:83 7:603 +0 0:5 1:7 2:3 3:817 4:22 5:140 6:89 7:383 +1 0:6 1:8 3:1954 4:19 5:65 6:222 7:590 +0 0:3 1:20 2:3 3:1351 4:19 5:82 6:294 7:814 +0 0:10 1:30 2:2 3:1522 4:20 5:279 6:186 7:251 +0 0:7 1:12 2:5 3:2242 4:4 5:156 6:290 7:209 +1 0:3 1:8 2:6 3:1630 4:18 5:216 6:64 7:599 +0 0:6 1:9 3:555 4:3 5:261 6:164 7:954 +0 0:4 1:18 2:5 3:1010 4:20 5:225 6:217 7:325 +0 0:10 2:1 3:1450 4:13 5:163 6:162 7:236 +0 0:11 1:29 2:3 3:1227 4:14 5:19 6:89 7:594 +0 0:8 1:11 2:3 3:622 4:13 5:19 6:218 7:606 +0 0:11 1:17 3:1258 4:11 5:133 6:213 7:100 +0 0:1 1:22 2:1 3:931 4:19 5:161 6:267 7:414 +0 0:9 1:2 3:1320 4:21 5:136 6:141 7:295 +0 0:11 1:21 2:3 3:1723 4:14 5:140 6:205 7:908 +0 0:3 1:9 3:808 4:1 5:216 6:193 7:1197 +0 0:10 1:23 2:1 3:742 4:21 5:141 6:222 7:956 +0 0:5 1:23 2:2 3:1040 4:6 5:140 6:290 7:296 +0 0:8 1:16 2:4 3:2030 4:7 5:270 6:153 7:205 +0 0:9 1:8 3:702 4:19 5:217 6:64 7:290 +0 0:1 1:22 2:1 3:541 4:5 5:187 6:141 7:316 +0 0:4 1:2 2:4 3:1259 4:7 5:114 6:275 7:546 +0 0:6 1:9 3:1519 4:19 5:65 6:65 7:346 +1 0:8 2:3 3:1502 4:15 5:75 6:311 7:561 +1 0:10 1:14 3:1529 4:1 5:191 6:141 7:964 +0 0:9 1:12 2:2 3:619 4:5 5:83 6:141 7:861 +0 0:2 1:4 2:6 3:2041 4:19 5:224 6:290 7:228 +0 0:5 1:1 2:3 3:1657 4:13 5:163 6:257 7:109 +0 1:18 2:2 3:1423 4:5 5:141 6:62 7:1091 +0 0:7 1:18 2:4 3:1425 4:16 5:83 6:204 7:679 +0 0:4 1:5 2:1 3:1440 4:20 5:15 6:78 7:324 +0 0:2 1:6 2:1 3:1613 4:1 5:84 6:82 7:641 +0 0:7 1:3 2:4 3:741 4:13 5:216 6:24 7:122 +0 0:2 1:29 2:2 3:903 4:20 5:209 6:266 7:671 +0 0:1 1:25 2:1 3:721 4:1 5:163 6:83 7:1235 +0 1:10 2:3 3:1806 4:18 5:168 6:218 7:733 +0 0:1 1:1 3:706 4:10 5:65 6:49 7:361 +0 1:16 3:55 4:1 5:215 6:83 7:1189 +0 0:6 1:29 2:4 3:944 4:14 5:90 6:240 7:614 +1 0:2 1:4 3:1716 4:13 5:84 6:234 7:604 +0 1:2 2:1 3:1936 4:7 5:19 6:234 7:272 +0 0:6 1:9 2:1 3:753 4:8 5:19 6:225 7:508 +0 0:6 1:25 2:1 3:1435 4:22 5:294 6:227 7:110 +0 0:1 1:16 2:1 3:1944 4:20 5:289 6:25 7:1111 +0 0:9 1:10 2:1 3:1136 4:11 5:211 6:133 7:100 +0 0:9 1:19 2:3 3:620 4:21 5:285 6:62 7:317 +1 0:6 1:2 3:2006 4:1 5:82 6:218 7:612 +0 0:4 1:13 2:1 3:1643 4:1 5:161 6:164 7:236 +0 0:6 1:17 3:1329 4:7 5:168 6:81 7:214 +1 0:3 1:9 2:6 3:1914 4:13 5:84 6:194 7:853 +0 0:6 1:3 2:2 3:1248 4:7 5:19 6:49 7:576 +0 0:3 1:8 2:5 3:1015 4:10 5:119 6:19 7:352 +0 0:9 1:17 3:1943 4:13 5:84 6:15 7:313 +1 0:2 1:20 3:1550 4:20 5:279 6:89 7:440 +0 0:1 1:21 2:6 3:1410 4:5 5:19 6:99 7:745 +1 0:5 1:3 2:5 3:1415 4:20 5:49 6:2 7:1670 +0 0:9 1:29 2:4 3:1640 4:13 5:84 6:5 7:89 +1 0:9 1:1 2:6 3:2059 4:10 5:204 6:19 7:425 +1 0:8 1:28 2:2 3:1954 4:14 5:90 6:226 7:453 +0 0:2 1:12 2:6 3:1116 4:3 5:102 6:16 7:261 +0 0:1 1:5 2:4 3:2216 4:19 5:262 6:231 7:2254 +0 0:1 2:6 3:1123 4:16 5:270 6:287 7:223 +0 0:7 1:15 2:1 3:1038 4:15 5:75 6:7 7:542 +1 0:6 1:2 3:1416 4:14 5:253 6:188 7:625 +0 0:3 1:2 3:1514 4:21 5:100 6:214 7:1325 +0 0:1 1:13 2:5 3:810 4:8 5:270 6:37 7:291 +0 0:9 1:15 2:5 3:1939 4:1 5:19 6:83 7:732 +0 0:1 1:5 2:4 3:1010 4:8 5:270 6:173 7:1156 +0 0:3 1:23 2:4 3:1321 4:1 5:84 6:79 7:861 +0 0:2 1:4 3:1259 4:21 5:141 6:94 7:667 +0 0:11 1:1 2:6 3:26 4:14 5:163 6:205 7:1536 +0 0:8 1:16 2:4 3:1921 4:18 5:83 6:214 7:495 +0 0:9 1:6 2:4 3:1055 4:20 5:161 6:275 7:368 +0 0:11 1:6 2:4 3:918 4:8 5:19 6:242 7:1027 +0 1:15 3:1702 4:19 5:229 6:64 7:366 +0 0:4 1:4 2:6 3:1416 4:21 5:217 6:62 7:435 +1 0:8 1:18 2:6 3:1719 4:19 5:25 6:226 7:196 +0 1:28 2:5 3:1147 4:5 5:163 6:62 7:2053 +1 0:6 1:15 2:6 3:2040 4:15 5:38 6:155 7:1011 +1 0:11 1:7 2:4 3:1845 4:15 5:75 6:196 7:350 +0 0:5 1:21 2:2 3:1800 4:13 5:84 6:142 7:328 +1 1:14 2:6 3:1215 4:7 5:19 6:141 7:689 +0 0:4 1:4 2:6 3:2239 4:4 5:209 6:38 7:2693 +0 0:8 1:18 3:1813 4:16 5:163 6:277 7:373 +0 0:7 1:20 2:5 3:1721 4:8 5:306 6:19 7:589 +0 0:5 1:10 2:5 3:1355 4:13 5:274 6:267 7:372 +0 0:3 1:17 3:1153 4:1 5:163 6:162 7:236 +0 0:3 1:9 3:927 4:20 5:182 6:140 7:758 +0 0:11 1:21 2:4 3:720 4:13 5:257 6:164 7:155 +0 0:6 1:14 2:5 3:1105 4:3 5:83 6:223 7:992 +1 0:5 1:14 2:2 3:2019 4:5 5:216 6:141 7:925 +0 0:11 1:8 2:6 3:827 4:16 5:270 6:162 7:368 +0 0:9 1:16 3:1856 4:19 5:38 6:170 7:185 +0 0:11 1:30 2:4 3:1422 4:7 5:19 6:267 7:2139 +0 0:9 1:4 2:2 3:933 4:15 5:156 6:247 7:426 +0 1:2 2:1 3:1347 4:14 5:90 6:231 7:201 +0 0:7 1:11 2:1 3:933 4:21 5:141 6:27 7:1428 +0 0:10 1:24 2:3 3:547 4:1 5:38 6:193 7:1258 +0 0:7 1:21 2:6 3:1445 4:7 5:168 6:222 7:1035 +0 0:7 1:27 2:5 3:642 4:3 5:261 6:257 7:1050 +0 0:9 1:3 2:1 3:1351 4:21 5:135 6:62 7:425 +0 0:11 1:23 2:5 3:1440 4:1 5:216 6:299 7:1437 +0 0:7 1:23 3:1321 4:15 5:242 6:74 7:390 +0 0:7 1:3 2:3 3:1129 4:7 5:38 6:170 7:185 +0 1:10 2:2 3:556 4:18 5:140 6:218 7:589 +0 0:4 1:7 2:2 3:1920 4:20 5:272 6:279 7:404 +0 0:6 1:13 2:3 3:902 4:5 5:141 6:49 7:1235 +0 2:5 3:530 4:15 5:284 6:74 7:583 +0 0:3 1:3 2:1 3:2145 4:20 5:270 6:162 7:368 +0 0:8 1:15 2:4 3:922 4:11 5:133 6:213 7:100 +0 0:2 1:16 2:3 3:825 4:18 5:140 6:82 7:1452 +0 0:7 1:9 2:2 3:2224 4:14 5:90 6:122 7:120 +0 0:6 1:25 3:932 4:4 5:156 6:46 7:267 +0 0:5 1:25 2:5 3:2014 4:16 5:83 6:50 7:525 +0 0:5 1:24 2:4 3:1045 4:21 5:141 6:122 7:1042 +0 0:4 1:22 2:4 3:945 4:20 5:83 6:227 7:602 +0 1:16 2:1 3:1144 4:20 5:272 6:257 7:480 +1 0:3 1:14 2:3 3:1520 4:20 5:225 6:297 7:935 +0 1:3 2:3 3:736 4:5 5:141 6:107 7:965 +1 0:5 1:29 2:2 3:1921 4:9 5:192 6:82 7:895 +0 0:8 1:6 2:2 3:1105 4:13 5:38 6:29 7:201 +1 0:2 1:5 3:739 4:14 5:90 6:184 7:957 +0 0:7 1:19 2:5 3:1855 4:22 5:216 6:51 7:666 +0 0:10 1:3 2:4 3:1613 4:19 5:84 6:81 7:1192 +1 1:29 2:6 3:1652 4:20 5:253 6:134 7:192 +0 0:9 1:11 2:6 3:2102 4:19 5:274 6:162 7:226 +0 1:4 2:4 3:805 4:13 5:113 6:218 7:157 +0 0:6 1:27 2:3 3:742 4:16 5:294 6:275 7:601 +0 0:7 1:8 2:2 3:1225 4:18 5:203 6:218 7:334 +0 0:10 1:23 2:2 3:1349 4:19 5:224 6:218 7:678 +0 0:6 1:29 2:4 3:1239 4:1 5:253 6:284 7:786 +0 0:6 1:17 2:1 3:1718 4:20 5:180 6:294 7:1048 +0 0:1 1:18 2:3 3:1055 4:1 5:216 6:164 7:1745 +0 0:4 1:26 2:6 3:1702 4:19 5:224 6:155 7:742 +0 0:3 1:25 3:755 4:1 5:245 6:83 7:1158 +0 1:14 2:6 3:1959 4:18 5:216 6:114 7:1498 +0 0:9 1:18 2:2 3:1930 4:15 5:156 6:231 7:340 +0 0:11 1:14 2:3 3:720 4:20 5:155 6:49 7:663 +0 0:11 1:8 2:5 3:1812 4:18 5:224 6:267 7:2521 +0 0:4 1:11 2:3 3:1031 4:21 5:47 6:62 7:192 +0 0:3 1:19 2:2 3:1620 4:1 5:84 6:99 7:1372 +0 0:8 1:16 2:4 3:1025 4:13 5:216 6:188 7:491 +0 0:2 1:18 2:6 3:900 4:9 5:289 6:82 7:1506 +0 0:1 1:10 2:2 3:1444 4:7 5:19 6:240 7:903 +0 0:5 1:23 2:3 3:2207 4:4 5:252 6:156 7:2446 +1 0:8 1:8 2:5 3:1815 4:5 5:182 6:141 7:853 +0 0:11 1:7 2:4 3:1850 4:15 5:75 6:242 7:810 +0 0:9 1:3 2:2 3:1843 4:21 5:141 6:265 7:788 +0 0:5 1:9 2:4 3:620 4:13 5:60 6:218 7:196 +0 0:11 1:3 3:1454 4:1 5:252 6:83 7:1171 +0 0:11 1:18 2:1 3:619 4:16 5:255 6:275 7:615 +0 0:3 1:9 3:2046 4:20 5:48 6:227 7:369 +0 0:7 1:4 2:4 3:840 4:18 5:163 6:156 7:2475 +1 0:3 1:5 2:3 3:715 4:15 5:156 6:47 7:301 +0 0:1 1:1 3:1620 4:14 5:216 6:205 7:334 +0 0:8 1:5 2:2 3:835 4:5 5:100 6:83 7:1372 +1 0:8 1:10 3:2219 4:18 5:262 6:266 7:679 +0 0:10 1:8 2:3 3:1634 4:13 5:216 6:203 7:109 +0 0:6 1:19 2:3 3:755 4:10 5:161 6:83 7:1055 +1 0:5 1:3 2:6 3:1610 4:22 5:216 6:51 7:666 +1 0:1 1:12 2:4 3:1855 4:14 5:168 6:205 7:1020 +0 0:6 1:30 2:5 3:1700 4:20 5:2 6:227 7:328 +0 0:3 1:7 2:5 3:1219 4:1 5:163 6:193 7:2342 +0 0:1 1:9 2:1 3:1026 4:1 5:146 6:83 7:762 +1 0:7 1:12 2:4 3:2025 4:7 5:19 6:217 7:1900 +0 0:7 1:5 2:6 3:1655 4:20 5:184 6:211 7:1844 +0 0:9 1:8 3:1945 4:4 5:289 6:38 7:1185 +0 0:11 1:1 2:5 3:611 4:13 5:226 6:83 7:673 +0 0:11 1:3 2:1 3:1321 4:21 5:178 6:141 7:429 +0 0:3 1:11 2:4 3:1412 4:21 5:141 6:205 7:1034 +0 0:10 1:28 3:858 4:20 5:155 6:294 7:180 +0 1:26 2:3 3:1148 4:7 5:49 6:19 7:576 +0 0:5 1:13 2:1 3:757 4:7 5:38 6:184 7:1121 +0 0:8 1:2 2:6 3:52 4:14 5:261 6:205 7:1399 +0 0:10 1:12 2:6 3:1724 4:14 5:203 6:275 7:991 +1 0:10 1:29 2:1 3:1206 4:22 5:242 6:140 7:224 +1 0:2 1:8 2:3 3:1805 4:15 5:75 6:294 7:773 +0 0:7 1:14 2:6 3:2102 4:19 5:224 6:240 7:238 +0 0:3 1:19 2:2 3:723 4:14 5:203 6:266 7:1399 +0 0:2 1:9 2:5 3:959 4:20 5:209 6:227 7:646 +1 0:1 1:27 2:4 3:1803 4:16 5:276 6:218 7:174 +0 0:8 1:12 2:1 3:1033 4:14 5:90 6:49 7:408 +0 0:10 1:16 2:3 3:1435 4:15 5:75 6:58 7:497 +0 0:2 1:9 2:5 3:733 4:21 5:141 6:284 7:668 +1 0:3 1:30 2:4 3:1430 4:20 5:163 6:227 7:370 +0 0:6 1:27 2:3 3:1241 4:3 5:102 6:16 7:261 +0 0:4 1:3 2:5 3:1730 4:7 5:19 6:107 7:581 +0 0:4 1:7 2:2 3:851 4:1 5:216 6:193 7:1197 +0 0:3 1:13 2:2 3:1633 4:1 5:84 6:238 7:1126 +0 0:11 1:20 2:3 3:1029 4:20 5:178 6:94 7:246 +0 0:10 1:21 3:1245 4:20 5:292 6:227 7:935 +0 0:11 1:19 2:2 3:706 4:5 5:161 6:141 7:1222 +0 0:3 1:12 2:1 3:1826 4:21 5:196 6:141 7:427 +1 0:10 3:1123 4:13 5:84 6:120 7:932 +0 0:2 1:4 2:6 3:1542 4:1 5:84 6:81 7:1192 +0 0:9 1:26 2:1 3:1648 4:14 5:203 6:82 7:680 +0 0:4 1:28 3:1448 4:1 5:294 6:83 7:813 +0 1:10 2:3 3:651 4:1 5:262 6:193 7:2585 +0 0:8 1:28 2:1 3:710 4:20 5:150 6:184 7:972 +1 0:10 1:22 2:2 3:2037 4:20 5:225 6:275 7:507 +0 0:5 1:4 2:6 3:1904 4:13 5:140 6:218 7:589 +0 0:3 1:3 3:1509 4:16 5:202 6:275 7:436 +0 1:27 2:4 3:1515 4:21 5:306 6:141 7:438 +0 0:4 1:3 2:5 3:1131 4:7 5:274 6:275 7:588 +1 0:8 1:15 2:4 3:2018 4:5 5:269 6:99 7:1608 +0 0:3 1:25 2:6 3:825 4:20 5:225 6:257 7:304 +0 0:8 1:28 2:1 3:1607 4:16 5:262 6:246 7:462 +0 0:9 1:4 2:3 3:1606 4:1 5:191 6:83 7:1121 +0 0:4 1:28 3:2049 4:5 5:141 6:227 7:1009 +0 0:7 1:11 3:1910 4:20 5:272 6:279 7:404 +0 0:2 2:2 3:907 4:21 5:141 6:180 7:429 +0 0:7 1:23 2:1 3:919 4:14 5:49 6:89 7:408 +0 0:6 1:28 2:4 3:1026 4:21 5:141 6:64 7:913 +1 0:7 1:27 2:5 3:1753 4:16 5:270 6:143 7:188 +0 0:11 1:4 2:2 3:904 4:2 5:133 6:158 7:163 +0 0:8 1:8 2:5 3:1704 4:7 5:168 6:19 7:761 +1 0:2 1:18 2:6 3:1936 4:19 5:225 6:19 7:1587 +0 0:2 1:5 3:1244 4:1 5:84 6:141 7:224 +0 0:9 1:1 2:6 3:1513 4:19 5:146 6:226 7:587 +0 0:6 1:22 2:6 3:730 4:20 5:134 6:78 7:239 +0 0:5 1:24 2:3 3:812 4:19 5:65 6:162 7:1916 +0 0:9 1:17 2:1 3:1829 4:17 5:168 6:186 7:725 +1 0:4 1:3 2:6 3:2050 4:20 5:260 6:49 7:495 +0 0:11 1:5 2:2 3:1441 4:7 5:19 6:79 7:432 +1 1:9 2:1 3:105 4:17 5:163 6:146 7:1814 +1 0:4 1:5 3:739 4:20 5:204 6:36 7:471 +0 0:9 1:30 2:5 3:1832 4:14 5:203 6:215 7:282 +0 0:9 1:23 2:5 3:1414 4:1 5:168 6:193 7:1097 +0 0:7 1:19 2:4 3:1419 4:1 5:84 6:81 7:1192 +0 0:8 1:27 3:1611 4:16 5:163 6:27 7:109 +0 0:9 1:28 2:3 3:1847 4:14 5:203 6:120 7:252 +0 0:4 1:20 3:1230 4:14 5:262 6:89 7:2079 +0 0:8 1:21 2:3 3:541 4:13 5:265 6:83 7:190 +1 0:8 1:17 2:6 3:1803 4:20 5:270 6:164 7:590 +0 1:22 3:2110 4:15 5:75 6:139 7:642 +0 0:10 1:21 3:1025 4:9 5:83 6:36 7:1013 +0 1:30 2:6 3:618 4:1 5:180 6:218 7:403 +1 0:7 1:3 2:3 3:1658 4:8 5:19 6:71 7:876 +0 0:3 1:8 2:5 3:1812 4:19 5:224 6:250 7:198 +0 0:3 1:2 3:1015 4:22 5:170 6:133 7:102 +1 0:9 1:19 2:3 3:1259 4:1 5:84 6:170 7:1389 +0 0:10 1:7 2:1 3:2035 4:15 5:56 6:74 7:278 +0 0:11 1:19 2:2 3:1646 4:5 5:63 6:99 7:404 +0 0:8 1:21 2:2 3:655 4:18 5:216 6:25 7:783 +0 0:10 1:11 2:2 3:2107 4:14 5:203 6:275 7:991 +0 0:4 1:8 2:4 3:635 4:18 5:262 6:162 7:414 +0 0:5 1:1 2:3 3:655 4:12 5:100 6:227 7:2133 +0 0:6 1:25 3:929 4:6 5:216 6:140 7:589 +0 0:11 1:10 3:812 4:16 5:270 6:48 7:574 +0 0:2 1:15 2:2 3:1742 4:21 5:292 6:99 7:1215 +0 0:8 1:8 2:5 3:1442 4:7 5:19 6:170 7:761 +0 0:8 1:19 3:1400 4:15 5:63 6:74 7:221 +0 0:9 1:29 2:4 3:2135 4:20 5:180 6:36 7:491 +0 0:2 1:27 2:6 3:1420 4:21 5:82 6:62 7:310 +0 0:11 1:15 2:5 3:1934 4:7 5:19 6:294 7:406 +0 0:1 1:17 2:1 3:1250 4:11 5:211 6:133 7:100 +0 0:7 1:11 2:1 3:1913 4:19 5:65 6:81 7:331 +0 0:7 2:6 3:2310 4:15 5:75 6:99 7:569 +0 0:7 1:25 2:3 3:935 4:15 5:19 6:156 7:760 +0 0:6 1:10 2:2 3:1629 4:21 5:164 6:141 7:458 +0 0:5 1:24 2:3 3:1027 4:4 5:182 6:156 7:944 +0 0:4 1:9 2:5 3:1928 4:19 5:65 6:155 7:329 +0 0:5 1:14 2:2 3:1303 4:16 5:95 6:82 7:563 +0 0:3 1:9 3:624 4:18 5:216 6:294 7:1012 +0 0:3 1:28 2:2 3:1250 4:16 5:163 6:69 7:833 +0 0:6 1:23 2:6 3:2012 4:10 5:182 6:79 7:808 +1 0:2 1:20 3:1102 4:1 5:84 6:21 7:190 +0 1:5 2:5 3:1557 4:19 5:65 6:193 7:650 +1 0:11 1:5 2:3 3:2120 4:22 5:225 6:141 7:1009 +1 0:10 1:25 2:3 3:1715 4:19 5:182 6:64 7:468 +1 0:7 1:2 2:3 3:1355 4:8 5:51 6:19 7:191 +0 0:6 2:5 3:1503 4:7 5:270 6:164 7:590 +0 0:2 1:6 2:1 3:2206 4:3 5:261 6:114 7:224 +0 0:10 1:3 2:4 3:1651 4:16 5:21 6:218 7:978 +0 0:10 1:30 2:2 3:1457 4:10 5:19 6:182 7:692 +0 0:2 1:26 2:6 3:1245 4:5 5:184 6:99 7:711 +0 0:8 1:10 3:1008 4:8 5:124 6:74 7:317 +0 0:5 1:23 2:3 3:1707 4:18 5:245 6:218 7:642 +0 0:3 1:2 2:6 3:701 4:13 5:65 6:170 7:544 +0 0:7 1:10 2:4 3:1410 4:16 5:163 6:27 7:109 +1 0:2 1:9 2:4 3:1509 4:20 5:134 6:30 7:570 +0 0:4 1:22 2:3 3:1331 4:6 5:140 6:13 7:325 +0 0:3 1:3 2:1 3:1131 4:19 5:65 6:170 7:544 +0 0:7 1:17 2:3 3:2048 4:3 5:163 6:114 7:945 +0 0:8 2:3 3:915 4:20 5:161 6:272 7:386 +0 0:2 1:25 2:5 3:750 4:13 5:38 6:170 7:185 +0 0:3 1:30 2:5 3:1546 4:1 5:269 6:38 7:1674 +1 0:11 1:6 2:3 3:900 4:20 5:108 6:49 7:925 +1 0:7 1:26 2:4 3:1406 4:1 5:269 6:83 7:2165 +0 0:2 1:20 2:1 3:1432 4:21 5:203 6:62 7:622 +0 0:2 1:4 3:1948 4:19 5:242 6:162 7:2027 +1 0:8 1:23 2:4 3:1613 4:7 5:168 6:222 7:1035 +0 0:8 1:5 2:1 3:1712 4:11 5:133 6:151 7:216 +0 1:12 2:3 3:746 4:12 5:19 6:227 7:1587 +0 1:15 2:6 3:1640 4:20 5:209 6:164 7:337 +0 0:3 1:20 2:2 3:744 4:7 5:19 6:146 7:432 +0 0:9 1:30 2:5 3:1422 4:13 5:216 6:56 7:501 +0 0:2 1:6 2:1 3:1131 4:14 5:203 6:50 7:874 +0 0:3 1:5 2:2 3:1435 4:10 5:52 6:19 7:528 +0 1:14 2:5 3:1628 4:5 5:83 6:141 7:861 +0 0:7 1:18 2:3 3:700 4:16 5:262 6:260 7:262 +0 0:9 1:1 3:1024 4:18 5:224 6:82 7:1557 +0 1:2 2:1 3:559 4:18 5:216 6:170 7:733 +0 1:10 2:2 3:1203 4:7 5:80 6:19 7:432 +0 0:1 1:17 2:1 3:1106 4:18 5:100 6:82 7:1605 +0 0:4 1:25 2:5 3:646 4:13 5:38 6:156 7:187 +0 0:7 1:1 2:1 3:1014 4:7 5:19 6:36 7:214 +0 0:4 2:2 3:822 4:4 5:38 6:107 7:1237 +0 0:4 1:17 2:4 3:1828 4:7 5:254 6:19 7:215 +0 1:7 2:6 3:2204 4:7 5:19 6:83 7:732 +0 0:7 1:17 2:2 3:1108 4:15 5:75 6:160 7:258 +0 0:5 2:2 3:657 4:19 5:224 6:227 7:2075 +0 0:11 1:10 3:655 4:18 5:108 6:218 7:1182 +0 0:4 1:1 2:3 3:950 4:15 5:56 6:19 7:106 +1 0:11 1:9 2:6 3:1421 4:6 5:38 6:140 7:413 +0 0:10 1:20 2:6 3:1344 4:16 5:163 6:260 7:89 +0 0:5 1:15 2:3 3:1203 4:15 5:122 6:74 7:268 +1 0:9 1:9 2:1 3:2203 4:18 5:158 6:218 7:4213 +0 0:4 1:7 2:3 3:927 4:7 5:49 6:19 7:576 +0 0:9 1:3 2:2 3:1332 4:13 5:38 6:284 7:1046 +0 0:6 1:1 2:6 3:1245 4:13 5:65 6:83 7:936 +0 0:8 2:3 3:651 4:18 5:262 6:38 7:2704 +0 0:10 1:19 2:6 3:1800 4:15 5:168 6:64 7:544 +0 0:10 1:8 2:2 3:2011 4:16 5:83 6:68 7:392 +0 0:4 1:30 2:3 3:900 4:13 5:154 6:83 7:408 +1 0:8 1:22 2:5 3:2012 4:19 5:225 6:164 7:370 +0 0:8 1:3 2:6 3:957 4:21 5:140 6:99 7:213 +0 1:23 2:6 3:1749 4:18 5:216 6:140 7:589 +0 0:4 1:6 2:1 3:1905 4:6 5:217 6:140 7:157 +1 0:2 1:9 2:5 3:1156 4:20 5:36 6:62 7:448 +0 0:2 1:22 2:4 3:700 4:14 5:90 6:227 7:1671 +1 0:8 1:14 2:3 3:1614 4:9 5:83 6:284 7:770 +0 0:6 1:10 2:1 3:700 4:17 5:184 6:267 7:1855 +0 0:10 1:19 2:5 3:2235 4:18 5:163 6:184 7:2217 +0 0:10 1:24 2:2 3:929 4:7 5:25 6:294 7:1111 +0 0:10 1:3 2:5 3:1514 4:7 5:19 6:206 7:425 +0 0:3 1:26 2:1 3:1916 4:22 5:140 6:170 7:229 +0 0:1 2:5 3:1340 4:7 5:108 6:156 7:1069 +0 0:11 1:3 2:1 3:1129 4:14 5:203 6:2 7:981 +1 0:2 1:2 2:4 3:2040 4:20 5:225 6:186 7:1444 +1 0:10 1:6 3:2217 4:7 5:19 6:69 7:1185 +0 0:11 1:9 3:605 4:19 5:237 6:81 7:357 +0 0:4 1:2 2:5 3:1034 4:15 5:75 6:7 7:542 +0 0:11 2:4 3:1128 4:4 5:251 6:99 7:1068 +0 0:11 1:13 2:2 3:1826 4:7 5:168 6:38 7:185 +1 0:4 1:16 2:3 3:1138 4:20 5:242 6:226 7:336 +0 0:5 1:5 2:1 3:1731 4:1 5:280 6:156 7:1623 +1 0:7 2:6 3:1708 4:8 5:75 6:308 7:608 +1 0:10 1:8 2:2 3:838 4:1 5:84 6:279 7:1205 +1 1:19 2:3 3:1440 4:3 5:261 6:227 7:1107 +1 0:9 1:6 2:4 3:1150 4:15 5:123 6:74 7:330 +1 0:11 1:3 2:1 3:1108 4:18 5:83 6:294 7:1506 +1 0:2 1:21 2:1 3:1419 4:16 5:80 6:218 7:240 +0 0:8 1:25 2:5 3:1004 4:20 5:182 6:36 7:616 +0 0:7 1:8 2:1 3:1045 4:7 5:75 6:65 7:116 +0 0:3 1:7 2:4 3:2009 4:14 5:186 6:141 7:469 +0 0:9 1:6 2:5 3:1107 4:21 5:279 6:99 7:872 +0 0:11 1:30 2:5 3:1534 4:7 5:19 6:49 7:576 +0 0:9 1:24 3:2247 4:19 5:158 6:227 7:2860 +0 0:3 1:2 2:6 3:1459 4:16 5:272 6:164 7:373 +0 0:5 1:12 2:6 3:1946 4:16 5:104 6:164 7:209 +0 1:14 2:5 3:2020 4:16 5:70 6:275 7:410 +0 0:4 1:8 2:3 3:1225 4:8 5:19 6:168 7:303 +0 0:10 1:1 2:2 3:1155 4:16 5:270 6:132 7:402 +1 0:1 2:6 3:1656 4:14 5:90 6:205 7:528 +0 0:9 2:4 3:1011 4:18 5:216 6:258 7:1041 +0 0:11 2:4 3:1136 4:20 5:215 6:211 7:361 +0 0:10 1:9 2:4 3:1623 4:18 5:83 6:49 7:1491 +0 0:10 3:1637 4:10 5:38 6:49 7:370 +0 0:10 1:30 2:2 3:1420 4:21 5:224 6:62 7:363 +1 0:5 1:28 3:733 4:1 5:267 6:21 7:1476 +0 0:5 1:29 2:1 3:1915 4:1 5:84 6:188 7:432 +0 0:8 1:7 2:3 3:1135 4:20 5:108 6:294 7:197 +1 0:2 1:3 2:6 3:1748 4:18 5:46 6:218 7:763 +0 0:1 1:19 2:4 3:656 4:5 5:163 6:99 7:2454 +0 0:3 1:19 2:1 3:1524 4:10 5:182 6:49 7:787 +1 0:7 1:9 2:2 3:1212 4:18 5:289 6:218 7:1012 +1 1:25 2:1 3:703 4:4 5:108 6:171 7:2327 +0 0:2 1:11 2:2 3:753 4:19 5:168 6:38 7:185 +0 0:5 1:2 2:4 3:1804 4:18 5:262 6:218 7:1846 +0 0:10 1:20 2:6 3:815 4:7 5:19 6:275 7:1589 +0 0:5 1:11 2:3 3:1451 4:19 5:82 6:47 7:296 +0 0:4 1:8 2:4 3:1058 4:1 5:146 6:83 7:762 +0 0:8 1:19 3:738 4:1 5:84 6:82 7:641 +0 0:4 1:3 2:6 3:810 4:20 5:161 6:217 7:197 +0 0:11 1:20 2:2 3:1925 4:21 5:100 6:64 7:529 +1 0:9 1:4 2:3 3:2045 4:20 5:184 6:247 7:632 +1 0:1 1:7 2:6 3:1611 4:10 5:19 6:64 7:227 +0 0:4 1:27 3:625 4:9 5:52 6:82 7:1224 +0 0:2 1:1 2:3 3:1414 4:18 5:252 6:82 7:853 +0 1:16 2:1 3:1907 4:5 5:182 6:99 7:938 +0 0:5 1:16 2:4 3:1733 4:1 5:84 6:19 7:732 +0 0:2 1:13 3:1008 4:7 5:49 6:19 7:576 +0 0:2 1:13 2:1 3:1012 4:13 5:63 6:170 7:418 +1 0:1 1:3 2:2 3:1720 4:8 5:19 6:97 7:350 +0 0:4 1:27 2:6 3:1118 4:10 5:19 6:35 7:533 +0 0:2 1:27 2:6 3:1123 4:16 5:83 6:208 7:197 +0 0:1 1:15 2:6 3:1313 4:4 5:156 6:184 7:944 +0 0:3 1:24 2:6 3:827 4:1 5:216 6:91 7:1007 +0 1:22 2:1 3:1250 4:20 5:83 6:227 7:602 +0 1:21 2:6 3:1553 4:7 5:19 6:83 7:732 +0 0:7 1:10 2:4 3:1932 4:20 5:289 6:107 7:197 +1 0:5 1:18 2:5 3:1136 4:6 5:124 6:184 7:449 +0 0:7 1:1 2:2 3:1440 4:20 5:253 6:36 7:822 +0 0:6 1:20 2:3 3:620 4:11 5:133 6:213 7:100 +0 0:4 1:29 2:2 3:826 4:1 5:90 6:218 7:235 +1 0:9 1:7 2:6 3:1347 4:1 5:274 6:83 7:1205 +1 0:9 1:26 2:2 3:1435 4:8 5:184 6:19 7:590 +0 0:2 1:18 2:5 3:1228 4:1 5:84 6:257 7:1171 +0 0:2 1:22 2:4 3:1140 4:1 5:191 6:274 7:1045 +0 0:11 1:27 2:1 3:856 4:12 5:225 6:141 7:1009 +1 0:2 1:8 2:4 3:1550 4:7 5:278 6:19 7:445 +0 0:11 1:26 3:1811 4:12 5:156 6:227 7:2153 +0 0:1 1:13 2:4 3:1642 4:16 5:163 6:61 7:86 +0 0:1 1:2 2:1 3:1204 4:13 5:84 6:60 7:685 +0 0:5 1:1 2:3 3:1425 4:16 5:141 6:273 7:321 +1 0:2 1:15 2:2 3:834 4:16 5:201 6:218 7:109 +0 0:8 1:1 2:5 3:1445 4:15 5:21 6:74 7:958 +1 0:6 1:30 2:5 3:1450 4:20 5:221 6:37 7:344 +0 0:9 1:29 2:4 3:839 4:18 5:90 6:82 7:1123 +0 0:5 1:1 2:3 3:1516 4:3 5:274 6:266 7:978 +0 0:8 1:20 2:1 3:701 4:7 5:163 6:156 7:2475 +0 0:11 2:4 3:1137 4:8 5:213 6:19 7:821 +1 0:3 1:28 2:2 3:1514 4:1 5:84 6:247 7:1062 +0 0:10 1:15 2:2 3:1934 4:16 5:270 6:126 7:463 +0 0:10 1:13 2:6 3:547 4:13 5:306 6:83 7:281 +1 0:4 1:20 3:2131 4:14 5:203 6:37 7:1142 +1 0:7 1:22 2:2 3:1513 4:1 5:84 6:170 7:1389 +0 0:8 1:9 2:6 3:614 4:13 5:81 6:218 7:147 +0 1:21 2:6 3:1914 4:14 5:90 6:83 7:987 +0 0:10 1:25 2:4 3:1028 4:20 5:217 6:49 7:159 +0 0:8 1:10 3:1819 4:10 5:49 6:107 7:925 +0 0:11 1:1 2:5 3:2104 4:18 5:16 6:218 7:2846 +0 0:1 1:2 2:2 3:1355 4:20 5:49 6:256 7:919 +0 0:11 1:2 3:644 4:19 5:289 6:64 7:508 +1 0:8 1:14 2:2 3:2316 4:15 5:156 6:58 7:636 +0 0:1 1:9 2:2 3:1332 4:1 5:279 6:107 7:1056 +0 0:5 1:28 3:848 4:7 5:75 6:275 7:1449 +0 0:1 1:11 2:6 3:1738 4:13 5:84 6:47 7:1212 +0 0:7 1:3 2:3 3:2226 4:8 5:19 6:11 7:143 +0 0:11 1:28 2:3 3:1851 4:19 5:251 6:64 7:600 +0 0:7 1:14 2:6 3:1958 4:18 5:161 6:267 7:414 +1 0:10 1:22 2:3 3:1946 4:20 5:292 6:284 7:351 +0 0:8 1:12 3:1615 4:20 5:212 6:78 7:181 +1 0:4 1:8 2:4 3:2212 4:18 5:83 6:19 7:1199 +0 0:2 1:22 2:4 3:1043 4:20 5:272 6:257 7:480 +0 0:5 1:7 2:2 3:630 4:16 5:59 6:267 7:153 +0 0:11 1:18 2:1 3:1304 4:16 5:270 6:48 7:574 +0 0:9 1:6 2:5 3:1746 4:5 5:141 6:206 7:305 +0 0:11 2:3 3:1000 4:20 5:272 6:257 7:480 +0 1:9 2:1 3:1705 4:20 5:279 6:275 7:1156 +0 0:1 1:21 2:5 3:829 4:14 5:186 6:164 7:1619 +0 0:9 1:16 3:1321 4:16 5:111 6:218 7:462 +0 0:5 1:19 2:6 3:1237 4:5 5:215 6:141 7:1334 +0 0:4 1:18 2:6 3:1644 4:14 5:216 6:205 7:334 +0 0:6 1:27 2:2 3:1335 4:15 5:75 6:64 7:335 +0 0:1 1:13 2:5 3:1543 4:10 5:83 6:19 7:1199 +1 0:5 1:12 3:2010 4:20 5:221 6:277 7:479 +1 0:8 1:19 3:1306 4:21 5:90 6:99 7:487 +0 0:5 1:4 2:6 3:1514 4:21 5:63 6:146 7:261 +0 0:5 1:8 2:3 3:1738 4:21 5:237 6:62 7:540 +0 0:5 1:12 2:6 3:647 4:13 5:84 6:79 7:861 +0 0:4 1:26 2:5 3:1807 4:5 5:63 6:184 7:895 +0 0:4 1:25 2:5 3:740 4:20 5:182 6:247 7:534 +0 0:9 1:11 2:5 3:1900 4:19 5:224 6:294 7:920 +1 1:19 2:3 3:1146 4:12 5:225 6:217 7:325 +1 0:1 1:13 2:5 3:1347 4:20 5:49 6:47 7:281 +0 0:1 1:9 2:2 3:1231 4:13 5:216 6:194 7:67 +1 0:6 1:19 2:3 3:2116 4:13 5:84 6:45 7:383 +0 1:10 2:3 3:1005 4:13 5:186 6:218 7:491 +0 0:4 1:8 2:4 3:2128 4:7 5:19 6:250 7:481 +0 0:4 1:15 2:3 3:1121 4:21 5:75 6:99 7:569 +1 0:11 1:26 3:1838 4:14 5:203 6:114 7:1175 +0 0:3 1:29 2:4 3:553 4:5 5:38 6:62 7:563 +0 0:3 1:4 2:1 3:1612 4:12 5:225 6:205 7:1276 +0 0:9 1:24 2:6 3:647 4:16 5:163 6:272 7:308 +0 0:2 1:29 2:2 3:555 4:18 5:262 6:140 7:2419 +0 0:10 1:9 2:4 3:730 4:8 5:189 6:19 7:147 +0 1:12 2:3 3:1635 4:5 5:16 6:266 7:1449 +0 0:2 1:10 2:6 3:1500 4:21 5:63 6:218 7:316 +0 0:9 1:29 2:5 3:618 4:5 5:30 6:141 7:562 +0 0:3 1:29 2:3 3:705 4:20 5:182 6:192 7:1142 +0 0:1 1:20 2:5 3:1259 4:20 5:83 6:227 7:602 +0 0:10 1:4 2:5 3:1904 4:13 5:84 6:260 7:1315 +0 0:2 1:12 2:6 3:830 4:20 5:229 6:226 7:267 +0 0:3 1:10 2:1 3:1325 4:8 5:19 6:108 7:273 +0 0:4 1:8 2:3 3:2044 4:8 5:122 6:74 7:268 +0 0:1 1:13 2:4 3:737 4:16 5:216 6:196 7:139 +0 0:1 1:9 2:2 3:1741 4:21 5:25 6:62 7:475 +0 0:10 1:23 2:1 3:1734 4:1 5:269 6:49 7:1565 +0 0:3 1:12 2:2 3:929 4:19 5:65 6:256 7:600 +0 0:2 1:12 3:1656 4:5 5:163 6:141 7:1379 +0 1:14 2:5 3:712 4:18 5:83 6:227 7:602 +1 1:14 2:6 3:1119 4:21 5:123 6:99 7:446 +0 1:24 2:1 3:644 4:19 5:83 6:64 7:1337 +1 0:6 1:13 2:4 3:1920 4:20 5:49 6:284 7:737 +0 0:3 1:20 2:2 3:1845 4:13 5:64 6:83 7:164 +0 0:8 1:5 2:2 3:1926 4:18 5:83 6:267 7:967 +0 0:11 1:14 2:3 3:739 4:19 5:65 6:184 7:468 +0 0:11 1:26 3:2155 4:15 5:156 6:58 7:636 +1 0:3 1:27 2:1 3:1046 4:18 5:216 6:205 7:334 +0 0:6 1:23 2:5 3:1641 4:15 5:38 6:81 7:399 +0 0:6 1:26 2:1 3:720 4:7 5:192 6:19 7:669 +0 0:2 1:25 2:4 3:1610 4:20 5:161 6:251 7:345 +0 0:3 1:9 2:6 3:2123 4:5 5:141 6:21 7:140 +0 0:6 1:10 2:2 3:2225 4:16 5:163 6:257 7:109 +0 0:1 1:26 2:2 3:1230 4:16 5:209 6:164 7:337 +0 0:8 1:9 2:6 3:2228 4:7 5:261 6:19 7:2182 +0 0:3 1:25 2:6 3:1659 4:20 5:164 6:78 7:293 +1 0:6 1:26 2:2 3:1742 4:18 5:168 6:218 7:733 +0 0:6 1:25 3:856 4:14 5:63 6:205 7:622 +0 0:7 1:14 3:1532 4:14 5:203 6:231 7:726 +0 0:7 1:28 2:5 3:2014 4:1 5:191 6:164 7:2342 +1 0:11 1:16 2:6 3:1508 4:18 5:216 6:82 7:888 +0 0:3 1:19 2:1 3:1408 4:21 5:183 6:62 7:280 +0 0:10 1:10 2:4 3:1945 4:20 5:279 6:134 7:687 +0 0:9 1:20 2:3 3:647 4:16 5:48 6:275 7:574 +0 0:2 1:29 2:1 3:930 4:20 5:212 6:78 7:181 +1 0:5 1:29 2:1 3:1102 4:1 5:191 6:170 7:1097 +0 0:9 1:22 2:6 3:1638 4:16 5:233 6:275 7:521 +0 0:6 1:3 2:1 3:1026 4:6 5:140 6:135 7:247 +1 0:3 1:29 2:3 3:1412 4:19 5:65 6:218 7:599 +0 0:5 1:11 2:3 3:1035 4:7 5:49 6:19 7:576 +0 0:7 1:11 3:1712 4:14 5:108 6:89 7:1127 +0 0:8 1:8 2:5 3:823 4:1 5:261 6:83 7:1660 +0 0:8 1:29 2:3 3:1815 4:10 5:182 6:135 7:972 +0 0:9 1:12 2:2 3:1700 4:20 5:209 6:48 7:325 +0 0:2 1:8 2:4 3:1329 4:7 5:82 6:170 7:214 +1 0:10 1:29 3:949 4:1 5:90 6:83 7:987 +0 0:2 1:6 2:1 3:648 4:18 5:274 6:218 7:1726 +0 0:1 1:26 2:3 3:1328 4:1 5:84 6:69 7:592 +0 0:5 1:11 2:2 3:1845 4:7 5:84 6:19 7:732 +0 0:2 1:20 2:1 3:1724 4:20 5:150 6:49 7:220 +0 0:8 1:21 2:2 3:1000 4:20 5:242 6:36 7:443 +0 0:4 1:3 2:5 3:1830 4:6 5:140 6:184 7:758 +0 0:2 1:8 2:4 3:1218 4:7 5:168 6:107 7:1076 +1 0:6 1:28 2:4 3:1854 4:18 5:161 6:267 7:414 +0 0:9 1:15 2:6 3:2117 4:5 5:100 6:19 7:745 +1 0:3 1:14 2:3 3:2050 4:20 5:134 6:297 7:453 +1 0:4 1:18 2:6 3:2046 4:16 5:262 6:260 7:262 +0 0:3 1:3 3:1945 4:20 5:237 6:186 7:842 +0 0:2 1:5 2:1 3:1053 4:1 5:156 6:164 7:2475 +0 0:9 1:1 2:6 3:556 4:18 5:262 6:82 7:967 +0 0:10 1:15 2:2 3:838 4:4 5:156 6:231 7:340 +0 0:4 1:13 3:848 4:15 5:75 6:304 7:226 +1 0:4 2:2 3:2035 4:20 5:134 6:36 7:670 +0 1:4 2:4 3:935 4:20 5:48 6:227 7:369 +0 0:8 1:7 2:3 3:1701 4:1 5:84 6:146 7:762 +0 0:9 1:20 2:3 3:1558 4:19 5:65 6:89 7:500 +1 0:9 1:11 2:6 3:1205 4:8 5:89 6:19 7:743 +0 0:10 1:18 2:5 3:1825 4:8 5:22 6:19 7:164 +0 0:1 1:23 3:1510 4:7 5:154 6:19 7:341 +1 0:9 1:30 2:5 3:1930 4:20 5:204 6:184 7:550 +0 0:7 1:2 2:3 3:1450 4:15 5:75 6:293 7:181 +0 0:5 1:21 2:1 3:935 4:7 5:224 6:19 7:665 +0 0:5 1:13 2:1 3:1657 4:16 5:292 6:275 7:926 +0 0:9 1:23 2:5 3:2040 4:20 5:161 6:257 7:258 +0 0:10 1:11 2:1 3:839 4:21 5:100 6:64 7:529 +0 1:14 2:5 3:1625 4:14 5:36 6:188 7:200 +0 0:11 1:17 2:6 3:934 4:4 5:156 6:256 7:1074 +0 0:9 1:26 2:2 3:2105 4:22 5:216 6:64 7:599 +0 0:7 1:25 2:2 3:656 4:19 5:82 6:170 7:214 +0 0:5 1:22 2:4 3:2040 4:19 5:225 6:162 7:256 +0 0:1 1:6 2:5 3:1200 4:5 5:95 6:141 7:667 +0 0:3 1:28 2:2 3:730 4:20 5:48 6:272 7:296 +0 0:5 1:8 2:4 3:724 4:7 5:252 6:275 7:626 +1 0:11 1:20 2:2 3:1325 4:14 5:203 6:82 7:680 +0 1:9 2:2 3:1837 4:19 5:215 6:227 7:325 +0 0:8 1:16 2:5 3:807 4:1 5:294 6:83 7:813 +0 0:10 1:10 2:4 3:1900 4:20 5:79 6:134 7:239 +0 0:7 1:18 2:4 3:1629 4:7 5:19 6:107 7:581 +0 0:7 1:6 2:6 3:1502 4:1 5:204 6:83 7:448 +0 0:4 1:5 3:1313 4:16 5:216 6:146 7:177 +1 0:10 1:24 2:3 3:1559 4:22 5:25 6:218 7:783 +0 0:8 1:7 2:3 3:817 4:13 5:216 6:60 7:196 +1 0:9 1:14 2:5 3:1931 4:16 5:270 6:279 7:588 +1 1:27 2:3 3:1541 4:18 5:248 6:218 7:528 +0 0:10 1:23 2:1 3:1857 4:1 5:216 6:275 7:1249 +1 0:10 1:20 3:2209 4:13 5:168 6:89 7:501 +0 0:1 1:6 2:6 3:1044 4:10 5:248 6:294 7:1079 +0 0:11 2:3 3:2007 4:13 5:216 6:124 7:577 +0 0:10 1:5 3:1450 4:13 5:84 6:234 7:604 +0 0:10 1:5 3:1042 4:13 5:79 6:258 7:248 +0 0:8 1:5 2:1 3:1124 4:10 5:19 6:182 7:692 +0 0:5 1:12 2:6 3:1924 4:11 5:211 6:133 7:100 +0 0:2 1:22 2:4 3:1317 4:1 5:19 6:83 7:732 +0 0:5 1:24 2:3 3:702 4:19 5:224 6:182 7:1038 +1 0:6 1:12 2:3 3:704 4:14 5:216 6:188 7:491 +0 0:3 1:4 2:2 3:1329 4:14 5:108 6:205 7:1487 +0 0:11 1:28 2:3 3:2052 4:22 5:225 6:87 7:351 +1 0:7 1:23 2:1 3:2019 4:13 5:84 6:304 7:772 +0 0:6 1:6 2:5 3:2054 4:18 5:133 6:164 7:2556 +0 0:1 1:27 2:3 3:1023 4:6 5:260 6:140 7:451 +0 0:7 1:11 2:1 3:1359 4:15 5:75 6:215 7:614 +0 0:8 2:2 3:1337 4:13 5:267 6:257 7:417 +0 1:4 2:3 3:1901 4:1 5:82 6:193 7:920 +0 0:7 1:13 2:6 3:1000 4:15 5:288 6:74 7:181 +0 0:6 1:10 2:1 3:1830 4:20 5:184 6:266 7:1733 +0 0:2 1:13 2:1 3:949 4:13 5:75 6:218 7:264 +1 0:3 1:6 2:4 3:1445 4:19 5:65 6:294 7:508 +1 0:8 1:23 2:4 3:1759 4:14 5:90 6:146 7:231 +0 0:5 1:19 2:6 3:1700 4:20 5:224 6:240 7:238 +0 0:5 1:18 2:5 3:1040 4:20 5:184 6:227 7:1444 +1 0:9 1:21 2:5 3:810 4:4 5:156 6:38 7:187 +0 0:10 1:9 2:4 3:2113 4:21 5:141 6:136 7:295 +1 0:4 1:1 2:3 3:1954 4:19 5:65 6:240 7:683 +0 0:11 1:30 2:5 3:1916 4:5 5:141 6:89 7:1076 +0 0:8 1:15 2:3 3:2134 4:8 5:270 6:251 7:422 +0 0:3 1:10 2:1 3:1544 4:20 5:49 6:192 7:377 +0 0:2 1:18 2:5 3:1126 4:10 5:182 6:19 7:403 +0 0:1 2:6 3:1716 4:7 5:229 6:19 7:526 +0 0:10 1:12 2:5 3:1751 4:7 5:19 6:275 7:1589 +0 0:8 2:2 3:717 4:14 5:141 6:89 7:1076 +0 0:2 1:20 2:1 3:1922 4:1 5:253 6:83 7:247 +0 0:5 1:8 2:4 3:1029 4:7 5:168 6:81 7:214 +0 0:10 1:7 2:2 3:1520 4:5 5:63 6:184 7:895 +1 0:3 1:9 3:1154 4:20 5:180 6:78 7:461 +0 0:7 1:27 2:4 3:1656 4:5 5:63 6:266 7:2021 +0 0:3 1:2 2:6 3:935 4:21 5:141 6:27 7:1428 +0 0:9 1:30 2:6 3:1326 4:3 5:221 6:227 7:1009 +1 0:9 1:26 2:1 3:2140 4:19 5:229 6:64 7:366 +1 0:7 1:19 2:5 3:1802 4:9 5:163 6:82 7:862 +0 0:2 1:29 2:2 3:1150 4:20 5:36 6:227 7:1448 +1 0:1 1:15 2:6 3:1256 4:16 5:262 6:245 7:199 +0 0:11 1:26 3:813 4:7 5:156 6:184 7:944 +0 0:11 2:4 3:659 4:20 5:2 6:162 7:487 +0 0:3 1:3 3:615 4:7 5:220 6:19 7:545 +1 0:1 2:6 3:1230 4:8 5:105 6:19 7:331 +0 0:8 1:7 2:3 3:1848 4:18 5:84 6:218 7:802 +1 0:8 1:19 2:1 3:1851 4:7 5:100 6:275 7:1968 +0 0:7 3:758 4:18 5:229 6:218 7:412 +0 1:30 3:1341 4:10 5:19 6:81 7:547 +1 0:1 1:28 2:4 3:2034 4:7 5:19 6:275 7:1589 +0 0:5 1:22 2:4 3:2031 4:19 5:161 6:257 7:258 +0 0:8 1:14 2:3 3:1236 4:20 5:49 6:36 7:588 +0 0:9 1:10 2:2 3:1215 4:18 5:83 6:99 7:1605 +0 0:7 1:12 2:4 3:1600 4:21 5:141 6:231 7:1117 +0 0:6 1:25 3:1145 4:18 5:83 6:277 7:910 +0 0:4 1:4 2:6 3:1200 4:17 5:184 6:162 7:1521 +0 0:6 1:29 2:4 3:2020 4:20 5:225 6:48 7:369 +0 0:11 1:17 2:6 3:1706 4:16 5:163 6:227 7:370 +0 0:4 1:26 2:6 3:922 4:16 5:282 6:275 7:223 +0 0:9 1:7 2:6 3:1027 4:20 5:279 6:173 7:296 +0 0:7 1:18 2:4 3:814 4:7 5:163 6:133 7:2556 +0 1:12 2:3 3:1126 4:5 5:278 6:99 7:1034 +0 0:7 1:24 2:2 3:1654 4:1 5:82 6:193 7:920 +1 0:7 1:27 2:5 3:2129 4:5 5:141 6:2 7:744 +0 0:8 1:16 2:4 3:1632 4:10 5:49 6:83 7:1217 +1 0:9 1:28 2:3 3:2132 4:12 5:225 6:251 7:601 +0 0:1 1:12 2:3 3:1037 4:4 5:269 6:156 7:1597 +1 0:10 1:3 2:4 3:1500 4:16 5:216 6:69 7:911 +0 0:5 1:29 2:2 3:1507 4:14 5:83 6:89 7:1123 +0 1:15 3:2104 4:10 5:19 6:184 7:403 +0 0:1 1:27 2:4 3:1108 4:20 5:252 6:211 7:446 +1 0:4 1:15 2:3 3:1407 4:20 5:209 6:182 7:1489 +0 0:9 1:30 2:6 3:2039 4:19 5:229 6:38 7:496 +1 0:10 1:11 2:2 3:2345 4:18 5:211 6:164 7:2486 +0 0:2 1:29 2:1 3:1629 4:21 5:70 6:141 7:809 +0 0:3 1:20 2:3 3:730 4:8 5:85 6:19 7:171 +1 0:3 1:17 2:6 3:1404 4:3 5:274 6:211 7:371 +0 0:4 1:10 2:6 3:812 4:18 5:140 6:227 7:1956 +0 0:8 1:9 2:6 3:1605 4:19 5:280 6:64 7:1520 +0 0:3 1:18 2:1 3:1919 4:19 5:225 6:146 7:1489 +0 0:6 1:1 3:1850 4:1 5:21 6:218 7:978 +0 0:8 1:18 2:6 3:1032 4:9 5:180 6:82 7:533 +1 0:3 1:18 3:1850 4:20 5:36 6:182 7:491 +0 0:7 1:9 2:3 3:2050 4:3 5:252 6:266 7:1050 +0 0:3 1:22 2:5 3:2007 4:1 5:216 6:49 7:622 +0 0:4 1:30 2:2 3:851 4:5 5:274 6:141 7:1347 +0 0:8 1:26 2:6 3:1050 4:5 5:141 6:164 7:1379 +0 0:4 1:27 2:6 3:1039 4:13 5:252 6:164 7:109 +0 0:6 1:25 3:1332 4:21 5:42 6:141 7:308 +1 0:11 1:10 2:1 3:810 4:19 5:224 6:184 7:861 +1 1:26 2:2 3:1226 4:18 5:83 6:258 7:794 +0 0:6 1:27 2:2 3:740 4:10 5:192 6:19 7:669 +0 0:1 1:27 2:3 3:1411 4:13 5:66 6:218 7:296 +0 0:1 1:7 2:6 3:1457 4:16 5:252 6:275 7:626 +0 0:5 1:21 2:2 3:724 4:16 5:262 6:103 7:158 +0 0:4 1:5 2:1 3:1928 4:3 5:16 6:101 7:261 +0 0:9 1:13 2:4 3:1856 4:20 5:134 6:206 7:303 +0 0:3 1:24 2:6 3:1555 4:18 5:163 6:156 7:2475 +0 0:4 1:10 2:5 3:805 4:20 5:225 6:182 7:1044 +1 0:9 1:8 2:6 3:1922 4:6 5:13 6:140 7:325 +1 0:10 1:29 3:1410 4:8 5:98 6:19 7:350 +0 0:2 1:27 3:656 4:20 5:2 6:78 7:580 +1 0:3 1:4 2:1 3:1100 4:4 5:161 6:156 7:2248 +0 0:1 1:24 2:1 3:714 4:20 5:36 6:134 7:670 +0 0:4 1:7 2:2 3:1256 4:1 5:252 6:83 7:1171 +0 0:11 1:18 3:955 4:12 5:161 6:218 7:1515 +0 0:8 1:26 3:1005 4:18 5:83 6:215 7:472 +0 0:11 1:20 2:3 3:1827 4:9 5:83 6:227 7:602 +0 0:10 1:20 3:1755 4:19 5:161 6:82 7:629 +0 0:2 1:5 3:621 4:21 5:124 6:141 7:838 +0 0:9 1:4 2:3 3:750 4:21 5:79 6:141 7:217 +1 0:3 1:4 2:1 3:1829 4:18 5:262 6:156 7:2586 +0 0:9 1:15 2:5 3:2106 4:15 5:75 6:194 7:318 +0 0:5 1:29 2:2 3:1626 4:10 5:142 6:19 7:781 +0 0:5 1:30 2:2 3:835 4:20 5:79 6:258 7:248 +0 0:5 1:13 2:1 3:1155 4:5 5:182 6:141 7:853 +0 0:6 1:11 2:5 3:556 4:14 5:13 6:89 7:488 +0 0:8 1:8 2:4 3:1749 4:3 5:261 6:277 7:605 +0 0:8 1:29 2:3 3:1033 4:13 5:242 6:25 7:532 +0 0:7 1:19 2:4 3:628 4:13 5:163 6:257 7:109 +0 0:5 1:9 2:5 3:943 4:1 5:289 6:193 7:204 +0 0:1 1:11 3:818 4:18 5:279 6:218 7:258 +1 0:4 1:15 2:2 3:926 4:15 5:75 6:226 7:507 +1 0:9 2:4 3:1540 4:12 5:225 6:162 7:256 +0 0:10 2:1 3:1651 4:20 5:252 6:227 7:304 +0 0:1 1:20 2:5 3:810 4:13 5:89 6:218 7:299 +1 0:10 1:29 3:549 4:14 5:45 6:188 7:319 +0 0:8 1:8 2:5 3:1200 4:1 5:84 6:223 7:1616 +0 0:2 1:22 2:3 3:1015 4:19 5:182 6:64 7:468 +0 0:9 1:7 2:6 3:1320 4:14 5:203 6:231 7:726 +0 0:2 1:20 3:1350 4:3 5:16 6:101 7:261 +0 0:11 1:30 2:4 3:1704 4:18 5:216 6:81 7:612 +0 0:7 1:30 3:1647 4:14 5:90 6:247 7:501 +0 0:6 1:26 2:1 3:2040 4:20 5:279 6:186 7:251 +0 0:2 1:25 2:4 3:819 4:16 5:270 6:299 7:601 +0 0:8 1:15 2:3 3:805 4:8 5:180 6:275 7:919 +0 0:11 1:13 2:3 3:745 4:8 5:292 6:19 7:674 +1 0:9 1:24 3:2057 4:5 5:141 6:267 7:1635 +0 0:7 1:26 2:4 3:2210 4:15 5:19 6:62 7:554 +1 0:4 1:17 2:5 3:1457 4:16 5:200 6:164 7:267 +0 0:11 1:26 2:1 3:1457 4:18 5:216 6:205 7:334 +0 0:8 1:1 2:4 3:750 4:20 5:184 6:49 7:611 +0 0:2 1:15 2:2 3:1503 4:16 5:216 6:69 7:911 +1 0:10 1:5 3:1140 4:16 5:294 6:275 7:601 +0 0:7 1:23 3:1619 4:16 5:262 6:245 7:199 +0 0:8 1:11 2:3 3:825 4:20 5:184 6:107 7:1166 +0 0:5 1:1 2:3 3:925 4:14 5:90 6:186 7:229 +0 0:10 1:15 2:2 3:637 4:4 5:38 6:21 7:1698 +0 0:8 1:2 2:6 3:2104 4:7 5:19 6:107 7:581 +1 0:11 2:4 3:1708 4:19 5:25 6:64 7:644 +0 0:9 1:16 2:6 3:1142 4:7 5:19 6:206 7:425 +0 0:4 1:6 2:2 3:1827 4:1 5:161 6:83 7:1055 +0 0:4 1:3 2:5 3:1445 4:13 5:216 6:142 7:588 +0 0:7 1:1 2:2 3:915 4:14 5:38 6:89 7:632 +1 0:7 1:7 3:1033 4:19 5:224 6:267 7:2521 +0 0:1 1:15 3:1851 4:19 5:163 6:227 7:370 +0 1:25 2:2 3:1900 4:3 5:274 6:211 7:371 +0 0:2 1:20 3:840 4:20 5:272 6:257 7:480 +0 0:10 1:4 2:6 3:941 4:14 5:186 6:36 7:200 +0 0:5 1:17 2:5 3:1356 4:7 5:155 6:19 7:270 +0 0:10 1:10 2:4 3:738 4:19 5:65 6:141 7:913 +1 0:5 1:11 2:2 3:839 4:14 5:65 6:205 7:930 +0 0:8 1:28 2:1 3:1045 4:16 5:163 6:223 7:834 +0 0:5 1:29 2:1 3:1535 4:20 5:154 6:186 7:666 +0 0:2 1:3 2:6 3:1427 4:14 5:186 6:49 7:787 +0 1:22 3:924 4:21 5:100 6:231 7:319 +0 1:1 3:1234 4:18 5:89 6:82 7:589 +0 0:3 1:14 2:4 3:2014 4:19 5:209 6:162 7:407 +0 0:2 1:15 2:2 3:1348 4:12 5:216 6:227 7:1440 +0 0:2 1:14 2:2 3:1547 4:16 5:216 6:155 7:865 +0 0:7 1:24 2:2 3:1123 4:16 5:163 6:299 7:451 +0 0:8 1:2 2:6 3:1540 4:1 5:49 6:83 7:1217 +0 0:5 1:26 2:6 3:1042 4:21 5:180 6:99 7:1092 +0 0:6 1:19 2:2 3:1823 4:1 5:84 6:275 7:988 +1 0:1 1:19 2:4 3:2115 4:9 5:163 6:267 7:337 +0 0:11 1:16 2:5 3:924 4:19 5:25 6:64 7:644 +0 0:2 1:19 3:820 4:22 5:242 6:140 7:224 +0 0:8 1:12 2:1 3:939 4:8 5:19 6:77 7:366 +0 0:6 1:14 2:4 3:1045 4:20 5:180 6:284 7:237 +0 0:3 1:7 2:5 3:1706 4:19 5:289 6:226 7:920 +0 0:2 1:13 3:800 4:20 5:246 6:223 7:444 +0 0:8 1:2 2:6 3:906 4:5 5:63 6:141 7:1091 +0 0:8 1:8 2:5 3:1725 4:20 5:154 6:134 7:359 +0 0:5 1:3 2:6 3:1747 4:1 5:191 6:64 7:650 +0 0:2 1:9 2:5 3:600 4:18 5:262 6:140 7:2419 +0 0:10 1:5 2:6 3:1752 4:7 5:156 6:164 7:2475 +0 0:5 1:25 2:5 3:940 4:20 5:114 6:223 7:279 +0 1:21 2:5 3:725 4:20 5:182 6:49 7:787 +0 0:9 1:23 2:6 3:2046 4:1 5:84 6:223 7:1616 +1 0:9 1:1 3:1631 4:19 5:161 6:267 7:414 +0 0:9 1:5 2:4 3:1630 4:3 5:221 6:272 7:569 +0 0:3 1:19 2:1 3:1430 4:20 5:163 6:186 7:1750 +0 0:9 1:22 2:6 3:1029 4:14 5:261 6:205 7:1399 +0 0:8 1:7 2:4 3:2213 4:18 5:133 6:82 7:3365 +0 0:6 1:5 2:4 3:2040 4:21 5:63 6:79 7:162 +0 0:7 1:7 2:1 3:944 4:7 5:289 6:19 7:406 +0 0:10 1:16 2:2 3:1640 4:18 5:262 6:279 7:372 +1 0:1 1:30 2:6 3:1722 4:1 5:84 6:146 7:762 +0 0:5 1:12 3:1343 4:20 5:163 6:251 7:390 +0 0:4 1:18 2:6 3:1251 4:13 5:29 6:38 7:201 +0 0:6 1:29 2:5 3:740 4:20 5:49 6:240 7:328 +1 0:10 1:6 3:1450 4:20 5:48 6:211 7:325 +0 0:3 1:21 2:4 3:727 4:11 5:211 6:133 7:100 +0 0:11 1:2 3:1921 4:20 5:209 6:257 7:446 +0 0:9 1:22 2:6 3:1724 4:13 5:298 6:83 7:103 +0 0:5 1:3 2:5 3:749 4:8 5:19 6:47 7:712 +0 1:17 2:1 3:1302 4:12 5:225 6:107 7:1972 +1 0:4 1:10 2:5 3:1605 4:1 5:84 6:2 7:569 +0 0:9 1:5 2:4 3:1137 4:1 5:84 6:69 7:592 +1 0:3 1:18 2:1 3:1804 4:7 5:108 6:74 7:932 +0 0:3 1:13 2:3 3:1622 4:20 5:225 6:162 7:256 +0 0:6 1:22 2:6 3:912 4:13 5:252 6:272 7:417 +1 0:8 1:23 2:4 3:2103 4:16 5:83 6:18 7:125 +0 0:10 1:2 2:4 3:2058 4:20 5:182 6:25 7:1050 +0 0:10 1:21 2:1 3:1906 4:3 5:274 6:211 7:371 +0 0:3 2:3 3:1112 4:19 5:65 6:231 7:366 +0 0:8 1:13 2:2 3:554 4:18 5:100 6:218 7:719 +0 0:1 1:2 2:1 3:1857 4:20 5:272 6:279 7:404 +1 0:8 1:22 2:5 3:2045 4:20 5:163 6:277 7:373 +0 0:9 1:18 2:2 3:648 4:4 5:169 6:211 7:353 +0 0:10 1:24 2:2 3:1140 4:20 5:155 6:49 7:663 +0 0:1 1:28 2:5 3:1755 4:21 5:63 6:79 7:162 +0 0:1 1:3 2:2 3:1638 4:21 5:63 6:124 7:450 +0 0:8 1:30 2:3 3:1546 4:19 5:65 6:259 7:213 +0 0:6 1:30 2:5 3:754 4:1 5:224 6:218 7:678 +0 0:8 1:8 2:4 3:955 4:7 5:19 6:94 7:1282 +0 0:8 1:13 2:2 3:1338 4:20 5:161 6:272 7:386 +1 0:5 1:2 2:4 3:1509 4:19 5:82 6:247 7:227 +0 0:5 1:15 2:3 3:2033 4:16 5:83 6:238 7:776 +0 0:3 1:21 2:4 3:817 4:13 5:201 6:218 7:109 +0 0:5 1:11 2:3 3:607 4:1 5:221 6:83 7:1616 +0 0:9 1:18 2:1 3:950 4:20 5:270 6:266 7:689 +0 0:11 1:27 2:2 3:1736 4:16 5:252 6:164 7:109 +1 0:9 1:8 2:6 3:1938 4:20 5:220 6:49 7:883 +0 1:21 2:5 3:1322 4:21 5:203 6:62 7:622 +1 0:2 1:19 3:1745 4:13 5:216 6:137 7:510 +0 0:6 1:13 2:4 3:834 4:1 5:216 6:256 7:1120 +0 0:4 1:17 2:5 3:1425 4:15 5:180 6:184 7:1072 +0 0:3 1:26 3:726 4:19 5:224 6:155 7:742 +0 0:3 1:16 2:6 3:1229 4:14 5:203 6:186 7:349 +0 0:6 1:23 2:6 3:1854 4:5 5:141 6:226 7:1324 +1 0:9 1:5 2:4 3:2330 4:10 5:19 6:205 7:906 +0 0:10 1:9 2:4 3:1740 4:22 5:158 6:133 7:163 +0 0:6 1:10 2:1 3:1030 4:20 5:161 6:215 7:1099 +0 0:7 1:15 2:1 3:1708 4:7 5:19 6:258 7:874 +0 0:6 1:22 2:6 3:1002 4:3 5:252 6:223 7:933 +1 0:10 1:22 2:3 3:1000 4:22 5:216 6:253 7:528 +1 0:9 1:23 2:5 3:1004 4:1 5:84 6:153 7:1046 +0 1:10 2:3 3:1627 4:13 5:292 6:83 7:237 +0 0:5 1:20 3:902 4:5 5:274 6:141 7:1347 +1 0:9 1:12 2:3 3:1757 4:3 5:102 6:266 7:1533 +0 0:9 1:19 2:2 3:1806 4:16 5:262 6:69 7:963 +0 0:11 1:6 2:4 3:1524 4:10 5:19 6:184 7:403 +0 0:3 1:5 2:3 3:1719 4:1 5:84 6:275 7:988 +0 0:5 1:8 2:4 3:1040 4:15 5:75 6:123 7:330 +0 0:8 1:22 2:5 3:910 4:20 5:279 6:215 7:342 +0 0:9 1:5 2:3 3:1455 4:1 5:269 6:107 7:1046 +0 0:1 1:22 2:1 3:1752 4:20 5:182 6:49 7:787 +0 0:3 1:15 2:4 3:2305 4:10 5:19 6:83 7:732 +0 0:7 1:26 2:4 3:1545 4:18 5:216 6:231 7:412 +0 0:7 2:6 3:2037 4:19 5:229 6:184 7:834 +0 0:6 1:5 2:4 3:832 4:13 5:216 6:74 7:264 +0 0:10 1:1 2:2 3:916 4:14 5:65 6:89 7:500 +0 0:9 1:4 2:3 3:1108 4:15 5:220 6:74 7:892 +0 0:9 1:19 2:2 3:1549 4:12 5:274 6:227 7:338 +1 0:9 1:21 2:5 3:1330 4:16 5:163 6:269 7:348 +0 0:7 2:6 3:1539 4:13 5:84 6:270 7:190 +0 0:10 1:21 2:1 3:1420 4:18 5:224 6:82 7:1557 +0 0:10 1:8 2:2 3:1735 4:21 5:154 6:141 7:351 +1 0:5 1:17 2:5 3:945 4:22 5:140 6:226 7:134 +0 0:11 1:2 2:6 3:941 4:3 5:157 6:312 7:199 +0 0:7 1:24 2:1 3:1505 4:18 5:161 6:82 7:629 +0 0:2 1:6 2:2 3:1214 4:10 5:182 6:205 7:1310 +0 0:11 1:6 2:3 3:629 4:13 5:245 6:193 7:826 +0 1:17 2:1 3:950 4:8 5:19 6:141 7:689 +0 0:2 1:26 2:6 3:757 4:18 5:163 6:267 7:337 +0 1:17 2:2 3:730 4:20 5:279 6:49 7:737 +0 0:1 1:10 2:3 3:2029 4:7 5:168 6:81 7:214 +0 0:4 1:30 2:2 3:635 4:21 6:62 7:339 +0 1:26 2:3 3:1822 4:19 5:82 6:231 7:205 +0 0:5 1:26 2:6 3:850 4:22 5:51 6:226 7:522 +0 0:9 1:10 2:1 3:2300 4:15 5:75 6:140 7:388 +0 0:9 1:6 2:4 3:1105 4:8 5:75 6:304 7:226 +0 0:11 1:8 2:5 3:1144 4:5 5:163 6:99 7:2454 +0 0:2 1:27 2:6 3:1210 4:20 5:261 6:227 7:1107 +0 0:6 1:27 2:2 3:1156 4:7 5:49 6:19 7:576 +0 0:8 1:6 2:2 3:925 4:20 5:2 6:266 7:1180 +1 0:1 1:18 2:2 3:805 4:7 5:78 6:19 7:366 +0 0:8 1:9 2:6 3:601 4:18 5:163 6:140 7:2288 +1 0:8 1:18 2:6 3:2049 4:15 5:75 6:150 7:626 +0 0:5 1:15 2:3 3:1444 4:13 5:113 6:83 7:859 +0 0:4 1:25 2:5 3:2123 4:10 5:19 6:164 7:1946 +0 0:9 1:27 2:2 3:1656 4:14 5:90 6:231 7:201 +0 0:4 1:26 2:5 3:2012 4:18 5:83 6:279 7:846 +0 0:6 1:25 3:1247 4:15 5:75 6:64 7:335 +1 0:10 1:26 2:4 3:1945 4:7 5:19 6:38 7:946 +0 0:3 1:8 2:6 3:1408 4:20 5:261 6:2 7:1180 +1 0:3 1:29 2:4 3:1955 4:8 5:11 6:19 7:143 +0 0:7 1:12 2:4 3:1513 4:14 5:90 6:205 7:528 +0 0:10 1:16 2:3 3:1934 4:19 5:82 6:107 7:899 +0 0:2 1:7 2:2 3:1305 4:20 5:49 6:25 7:283 +0 0:3 1:2 2:6 3:1946 4:14 5:90 6:266 7:1927 +0 0:5 1:1 2:3 3:740 4:8 5:223 6:19 7:508 +0 1:25 2:1 3:1744 4:7 5:84 6:267 7:1464 +1 0:7 1:5 2:6 3:1934 4:14 5:186 6:170 7:963 +0 0:3 1:4 2:1 3:1725 4:20 5:114 6:37 7:287 +0 0:5 1:9 2:5 3:932 4:20 5:272 6:279 7:404 +0 0:11 1:10 3:1622 4:18 5:75 6:218 7:264 +0 0:7 1:29 2:6 3:825 4:7 5:262 6:19 7:2139 +0 0:7 1:1 2:1 3:1252 4:10 5:110 6:19 7:644 +0 0:10 1:9 2:3 3:645 4:20 5:66 6:49 7:336 +0 0:4 1:14 2:1 3:728 4:21 5:63 6:205 7:622 +0 0:8 1:1 2:5 3:810 4:20 5:134 6:258 7:192 +0 0:1 1:15 3:2053 4:9 5:163 6:267 7:337 +0 0:11 1:28 2:2 3:833 4:14 5:221 6:89 7:1953 +0 0:10 1:21 2:1 3:952 4:18 5:216 6:81 7:612 +0 0:9 1:16 3:1709 4:7 5:108 6:74 7:932 +0 0:7 1:20 2:5 3:941 4:16 5:27 6:164 7:109 +0 0:5 1:21 2:1 3:956 4:3 5:157 6:312 7:199 +0 0:10 1:9 2:3 3:1214 4:13 5:121 6:83 7:134 +0 0:7 1:22 2:1 3:2054 4:5 5:141 6:82 7:861 +0 0:10 1:24 2:3 3:1856 4:18 5:216 6:192 7:843 +1 0:10 1:1 2:3 3:2545 4:8 5:19 6:308 7:264 +0 0:2 1:20 2:1 3:1545 4:9 5:83 6:21 7:775 +0 0:10 1:7 2:2 3:1310 4:16 5:262 6:6 7:250 +0 0:3 1:25 3:621 4:14 5:100 6:205 7:1008 +0 0:1 1:15 2:6 3:603 4:13 5:229 6:218 7:412 +1 1:16 3:1741 4:5 5:141 6:21 7:140 +1 0:3 1:14 2:4 3:916 4:1 5:84 6:189 7:468 +0 0:7 1:8 2:2 3:1046 4:5 5:141 6:49 7:1235 +0 0:10 1:14 3:729 4:19 5:38 6:226 7:280 +0 0:9 1:24 2:6 3:1416 4:5 5:141 6:81 7:1208 +1 1:22 2:1 3:1956 4:1 5:146 6:83 7:762 +1 0:7 1:19 2:4 3:1713 4:1 5:216 6:182 7:403 +1 0:7 1:29 2:6 3:1535 4:20 5:163 6:2 7:677 +1 0:1 1:20 2:5 3:1012 4:20 5:184 6:182 7:405 +0 0:11 1:5 2:2 3:1252 4:7 5:220 6:38 7:1197 +0 0:9 1:15 2:6 3:2223 4:18 5:221 6:140 7:2327 +1 0:9 1:20 2:3 3:800 4:19 5:65 6:266 7:2279 +0 0:7 1:26 2:4 3:1329 4:1 5:84 6:274 7:2165 +0 0:2 1:6 2:2 3:842 4:20 5:13 6:49 7:288 +0 0:11 1:15 2:4 3:813 4:14 5:111 6:205 7:197 +1 0:5 1:4 2:6 3:1439 4:13 5:216 6:56 7:501 +0 0:1 1:8 2:1 3:735 4:18 5:37 6:82 7:649 +0 0:7 1:23 2:1 3:929 4:16 5:252 6:275 7:626 +1 0:9 2:5 3:731 4:20 5:289 6:186 7:997 +0 0:6 1:21 2:4 3:2058 4:16 5:163 6:103 7:209 +1 0:3 1:30 2:5 3:1729 4:16 5:309 6:148 7:58 +0 1:19 2:3 3:1103 4:18 5:221 6:218 7:1739 +0 0:10 2:1 3:701 4:20 5:252 6:49 7:2295 +0 0:10 1:20 3:814 4:1 5:191 6:274 7:1045 +0 0:11 2:3 3:2127 4:19 5:65 6:140 7:321 +0 0:11 1:21 2:4 3:1938 4:20 5:134 6:258 7:192 +0 0:8 1:7 2:4 3:1016 4:16 5:48 6:82 7:850 +0 0:9 1:12 2:2 3:655 4:5 5:100 6:81 7:199 +0 1:2 2:1 3:1257 4:1 5:84 6:218 7:802 +0 0:4 1:2 2:4 3:1200 4:8 5:19 6:22 7:164 +1 0:4 1:3 2:5 3:1725 4:20 5:2 6:227 7:328 +0 0:9 1:15 2:6 3:915 4:3 5:16 6:101 7:261 +0 0:7 1:16 2:1 3:2129 4:6 5:140 6:290 7:296 +0 0:4 1:15 2:2 3:1518 4:6 5:140 6:253 7:289 +0 0:5 1:8 2:4 3:1805 4:8 5:51 6:19 7:191 +0 0:10 1:30 2:1 3:905 4:5 5:63 6:162 7:1825 +0 0:9 1:1 2:6 3:806 4:10 5:163 6:83 7:1235 +0 0:4 1:8 2:3 3:1624 4:19 5:278 6:64 7:547 +0 0:5 1:9 2:4 3:1257 4:13 5:82 6:170 7:214 +0 0:5 1:12 2:6 3:859 4:5 5:141 6:49 7:1235 +1 0:4 1:16 2:4 3:2256 4:7 5:163 6:107 7:2342 +0 0:10 3:1542 4:21 5:141 6:154 7:351 +0 0:9 1:25 2:1 3:821 4:16 5:270 6:277 7:532 +0 0:5 1:17 2:5 3:736 4:20 5:289 6:49 7:842 +0 0:11 1:7 2:5 3:1051 4:16 5:216 6:62 7:316 +0 0:9 1:11 2:5 3:743 4:19 5:65 6:266 7:2279 +0 0:5 1:23 2:2 3:1242 4:7 5:161 6:19 7:1747 +0 0:3 1:16 2:6 3:1807 4:13 5:191 6:64 7:650 +0 0:4 1:26 2:6 3:1728 4:10 5:84 6:19 7:732 +0 0:7 1:25 2:2 3:830 4:6 5:140 6:47 7:284 +0 0:10 1:26 2:4 3:1348 4:7 5:47 6:19 7:712 +1 0:7 1:8 2:2 3:1556 4:21 5:49 6:62 7:314 +0 0:10 1:24 2:2 3:854 4:6 5:229 6:140 7:183 +0 0:7 1:9 2:2 3:1725 4:18 5:83 6:277 7:910 +0 0:3 1:2 2:6 3:1755 4:19 5:224 6:250 7:198 +0 0:7 1:15 3:1254 4:13 5:84 6:1 7:158 +0 0:11 1:3 3:1916 4:7 5:75 6:19 7:373 +0 0:6 2:4 3:1742 4:1 5:163 6:172 7:2615 +0 0:4 1:20 2:1 3:651 4:21 5:51 6:99 7:602 +0 0:9 1:25 3:1435 4:10 5:207 6:19 7:317 +0 0:11 1:9 3:915 4:20 5:108 6:155 7:318 +0 0:3 1:4 2:2 3:2109 4:10 5:19 6:234 7:272 +1 1:26 2:3 3:1535 4:20 5:221 6:272 7:569 +0 0:3 1:1 2:5 3:839 4:16 5:216 6:88 7:299 +1 0:10 1:26 2:4 3:2324 4:6 5:140 6:99 7:213 +0 0:5 1:15 2:2 3:1230 4:19 5:161 6:231 7:1910 +1 0:8 1:19 3:1437 4:7 5:100 6:19 7:745 +0 0:7 1:18 2:3 3:1123 4:7 5:269 6:156 7:1597 +0 0:10 1:8 2:2 3:1537 4:7 5:251 6:74 7:879 +0 0:7 1:20 2:5 3:1712 4:14 5:203 6:164 7:1536 +0 0:2 1:26 2:6 3:601 4:13 5:183 6:218 7:594 +1 0:2 1:5 3:1545 4:20 5:2 6:227 7:328 +0 0:11 1:25 3:842 4:16 5:215 6:164 7:47 +0 0:1 1:8 3:647 4:13 5:268 6:83 7:228 +0 0:3 1:3 3:1501 4:19 5:82 6:242 7:482 +0 1:5 2:4 3:647 4:16 5:257 6:164 7:155 +0 0:2 1:6 2:1 3:1246 4:7 5:38 6:107 7:1237 +0 0:1 1:5 2:4 3:2042 4:13 5:84 6:134 7:247 +0 0:11 1:3 2:1 3:1523 4:2 5:170 6:213 7:202 +0 0:6 2:4 3:921 4:5 5:168 6:141 7:1416 +0 0:1 1:30 2:6 3:706 4:13 5:66 6:218 7:296 +0 1:7 3:1400 4:21 5:183 6:62 7:280 +1 0:9 1:25 3:2252 4:7 5:75 6:65 7:116 +0 0:9 1:28 2:4 3:831 4:18 5:48 6:267 7:326 +1 0:8 1:11 2:3 3:2315 4:8 5:19 6:36 7:214 +0 0:7 1:6 3:814 4:1 5:274 6:218 7:1726 +0 0:5 1:23 2:2 3:1140 4:1 5:294 6:218 7:1437 +0 0:8 1:16 2:4 3:2011 4:11 5:211 6:133 7:100 +0 0:5 1:17 2:4 3:903 4:7 5:242 6:19 7:356 +0 0:8 1:28 2:1 3:643 4:1 5:274 6:83 7:1205 +0 0:3 1:26 2:1 3:800 4:18 5:216 6:82 7:888 +0 0:5 1:5 2:1 3:2151 4:7 5:19 6:194 7:669 +0 0:9 1:17 3:1148 4:19 5:182 6:81 7:759 +1 0:1 1:2 2:1 3:1418 4:8 5:19 6:169 7:503 +0 0:10 1:24 2:3 3:907 4:5 5:191 6:141 7:964 +1 0:4 1:26 2:6 3:2000 4:3 5:157 6:266 7:909 +1 1:25 2:1 3:949 4:1 5:161 6:284 7:1372 +0 0:5 1:20 3:616 4:13 5:171 6:218 7:552 +1 0:9 1:14 2:4 3:1300 4:8 5:19 6:209 7:317 +1 0:8 1:7 2:4 3:2034 4:20 5:95 6:134 7:677 +1 0:11 1:15 2:5 3:1155 4:8 5:142 6:19 7:781 +0 0:9 1:9 3:2057 4:18 5:83 6:31 7:455 +0 0:8 2:3 3:2023 4:22 5:83 6:153 7:406 +0 0:9 1:30 2:6 3:1304 4:14 5:203 6:226 7:980 +0 0:10 1:20 2:6 3:1740 4:15 5:75 6:215 7:614 +0 0:2 1:18 2:6 3:825 4:18 5:83 6:227 7:602 +0 0:10 1:23 2:2 3:1156 4:20 5:178 6:78 7:319 +0 0:11 1:4 2:2 3:722 4:10 5:192 6:184 7:1066 +1 0:3 1:11 2:4 3:1920 4:10 5:19 6:170 7:761 +0 0:11 1:7 2:5 3:1537 4:18 5:83 6:64 7:1337 +0 0:6 1:6 2:5 3:1304 4:7 5:37 6:275 7:291 +0 0:9 1:5 2:4 3:849 4:18 5:262 6:158 7:2367 +0 0:4 1:3 2:5 3:2202 4:16 5:163 6:272 7:308 +0 0:9 1:15 2:6 3:1846 4:22 5:117 6:82 7:212 +0 0:11 1:22 2:5 3:1235 4:20 5:246 6:257 7:488 +0 0:4 1:7 2:2 3:710 4:20 5:270 6:164 7:590 +1 0:8 1:28 2:2 3:1931 4:1 5:180 6:83 7:460 +0 1:10 2:2 3:705 4:4 5:209 6:156 7:2576 +0 0:9 1:8 2:6 3:705 4:15 5:98 6:74 7:170 +1 0:11 1:9 2:6 3:1301 4:3 5:209 6:223 7:543 +1 0:6 1:28 2:4 3:1914 4:18 5:216 6:240 7:849 +0 0:2 1:5 3:2017 4:21 5:90 6:99 7:487 +0 0:1 1:24 3:633 4:10 5:223 6:170 7:288 +0 0:7 1:24 2:2 3:1206 4:7 5:84 6:19 7:732 +0 0:4 1:22 2:4 3:1538 4:5 5:100 6:164 7:2454 +0 0:10 1:17 2:4 3:849 4:14 5:221 6:89 7:1953 +0 0:5 1:30 2:2 3:900 4:10 5:182 6:19 7:403 +0 0:1 1:20 2:5 3:1122 4:14 5:203 6:257 7:1532 +0 0:3 1:21 2:4 3:1030 4:15 5:82 6:156 7:213 +0 0:9 1:11 2:6 3:1756 4:9 5:83 6:277 7:910 +0 0:5 1:15 2:3 3:1907 4:20 5:134 6:78 7:239 +0 0:9 1:3 2:1 3:827 4:7 5:270 6:164 7:590 +0 0:7 1:5 2:6 3:909 4:3 5:157 6:271 7:95 +0 0:6 1:19 2:2 3:1629 4:18 5:224 6:82 7:1557 +0 0:6 1:30 2:5 3:1835 4:6 5:124 6:140 7:383 +0 0:9 1:4 2:2 3:1323 4:12 5:225 6:277 7:647 +1 0:6 2:5 3:2133 4:5 5:141 6:2 7:744 +1 0:2 1:3 2:6 3:1538 4:9 5:83 6:170 7:1619 +0 0:9 1:13 2:3 3:1627 4:19 5:108 6:64 7:631 +1 0:9 1:14 2:5 3:2122 4:7 5:156 6:162 7:2248 +0 1:2 2:1 3:1029 4:1 5:168 6:182 7:1107 +0 0:4 1:29 2:2 3:624 4:1 5:279 6:83 7:551 +1 0:9 1:20 2:3 3:1943 4:20 5:134 6:136 7:276 +1 0:10 1:27 2:5 3:2345 4:1 5:191 6:274 7:1045 +0 0:6 1:13 2:3 3:1537 4:16 5:270 6:92 7:200 +0 0:2 1:3 2:5 3:1310 4:8 5:19 6:293 7:549 +0 0:6 1:16 2:6 3:904 4:14 5:225 6:205 7:1276 +1 0:5 1:15 2:3 3:1919 4:16 5:262 6:238 7:421 +1 0:5 1:20 2:1 3:1620 4:10 5:182 6:19 7:403 +1 1:11 3:1632 4:13 5:263 6:218 7:438 +0 0:11 1:21 2:3 3:1036 4:14 5:203 6:99 7:1008 +0 0:4 1:1 2:4 3:1545 4:20 5:225 6:164 7:370 +0 0:8 1:28 2:2 3:1922 4:7 5:19 6:231 7:526 +0 0:11 1:21 2:3 3:1717 4:18 5:83 6:37 7:649 +0 0:5 1:20 3:2108 4:1 5:84 6:146 7:762 +0 0:7 1:30 3:855 4:14 5:203 6:200 7:449 +0 0:9 1:10 2:2 3:607 4:16 5:255 6:164 7:89 +0 0:2 1:15 2:2 3:1555 4:19 5:65 6:107 7:631 +0 0:5 1:14 2:1 3:723 4:17 5:227 6:146 7:840 +0 0:2 1:11 2:3 3:2141 4:14 5:90 6:231 7:201 +0 0:3 1:29 2:3 3:1059 4:18 5:267 6:218 7:1829 +0 0:11 1:28 2:2 3:1626 4:14 5:224 6:188 7:874 +0 1:30 3:833 4:15 5:75 6:22 7:275 +0 1:5 2:4 3:1105 4:20 5:292 6:78 7:237 +0 0:9 1:9 3:515 4:7 5:204 6:19 7:425 +0 0:6 1:1 3:1515 4:20 5:161 6:150 7:2283 +0 0:9 1:4 2:3 3:630 4:21 5:70 6:141 7:809 +0 0:8 1:2 2:5 3:1129 4:1 5:140 6:164 7:2288 +0 0:10 1:6 2:1 3:2020 4:21 5:63 6:47 7:192 +1 0:6 1:9 2:1 3:2252 4:1 5:84 6:94 7:551 +0 0:11 1:20 2:3 3:1631 4:8 5:19 6:192 7:952 +1 0:9 1:29 2:4 3:1856 4:16 5:83 6:268 7:637 +0 0:11 1:23 2:4 3:1454 4:11 5:133 6:158 7:163 +0 0:8 1:13 2:2 3:914 4:21 5:141 6:180 7:429 +0 0:11 1:17 2:6 3:547 4:10 5:182 6:19 7:403 +0 0:8 2:3 3:1203 4:7 5:19 6:155 7:270 +0 1:27 2:4 3:836 4:19 5:229 6:64 7:366 +0 0:9 1:3 2:2 3:1123 4:1 5:216 6:164 7:1745 +0 0:4 1:27 2:6 3:1959 4:19 5:224 6:164 7:2401 +0 0:2 1:23 2:3 3:2002 4:20 5:2 6:15 7:277 +0 0:4 1:8 2:3 3:850 4:20 5:261 6:114 7:224 +0 0:10 1:13 2:6 3:1520 4:4 5:48 6:156 7:2465 +0 0:8 1:3 3:951 4:3 5:221 6:164 7:834 +0 0:8 1:17 2:5 3:1241 4:3 5:261 6:16 7:1449 +0 0:9 1:6 2:4 3:1100 4:20 5:48 6:211 7:325 +0 0:4 1:17 2:5 3:814 4:18 5:267 6:218 7:1829 +0 0:10 1:23 2:2 3:1653 4:18 5:216 6:65 7:296 +0 0:11 1:27 2:2 3:942 4:19 5:163 6:227 7:370 +0 0:6 1:2 2:1 3:2005 4:8 5:19 6:203 7:707 +0 1:27 2:4 3:1107 4:14 5:108 6:89 7:1127 +0 0:11 1:11 2:4 3:1500 4:20 5:2 6:227 7:328 +0 0:4 1:26 2:5 3:2045 4:8 5:19 6:187 7:267 +0 0:9 2:4 3:758 4:5 5:270 6:141 7:1195 +0 0:8 1:19 2:1 3:1617 4:7 5:66 6:19 7:446 +0 0:6 1:2 3:1326 4:1 5:191 6:164 7:2342 +0 0:1 1:4 2:3 3:1921 4:7 5:75 6:240 7:722 +1 0:7 1:21 3:2309 4:1 5:216 6:279 7:1726 +0 0:1 1:8 3:1700 4:10 5:19 6:107 7:581 +0 0:11 1:19 2:2 3:555 4:13 5:257 6:164 7:155 +0 0:4 1:10 2:6 3:1815 4:7 5:19 6:30 7:134 +1 0:10 1:10 2:4 3:1852 4:3 5:215 6:266 7:956 +0 0:7 1:10 2:4 3:1751 4:10 5:184 6:38 7:861 +0 1:21 2:5 3:2130 4:4 5:156 6:274 7:1597 +0 0:8 1:26 3:1927 4:20 5:66 6:36 7:338 +1 0:9 1:26 2:2 3:2200 4:5 5:100 6:193 7:1086 +0 0:10 1:3 2:5 3:2105 4:8 5:19 6:253 7:749 +1 0:7 1:20 2:6 3:1712 4:20 5:225 6:162 7:256 +0 2:6 3:1547 4:1 5:84 6:19 7:732 +0 0:10 1:15 2:2 3:1330 4:16 5:228 6:275 7:150 +1 0:3 1:13 2:2 3:706 4:9 5:90 6:82 7:1123 +0 0:1 1:3 2:3 3:1023 4:1 5:38 6:83 7:1562 +0 0:5 1:16 2:4 3:1004 4:7 5:270 6:38 7:2105 +1 0:9 1:14 2:5 3:815 4:20 5:134 6:49 7:1246 +0 0:3 1:24 2:6 3:1645 4:5 5:187 6:141 7:316 +0 0:5 1:25 2:5 3:1306 4:1 5:269 6:184 7:1189 +1 0:2 1:19 2:6 3:1744 4:8 5:203 6:19 7:906 +0 0:7 1:19 2:5 3:1324 4:8 5:19 6:72 7:363 +0 0:10 1:25 2:4 3:2301 4:10 5:19 6:64 7:227 +0 0:8 1:21 2:2 3:1033 4:14 5:203 6:49 7:936 +1 0:9 1:8 2:6 3:2055 4:20 5:252 6:211 7:446 +1 0:8 1:5 2:1 3:1752 4:3 5:235 6:309 7:31 +0 0:11 1:18 2:1 3:1116 4:20 5:225 6:162 7:256 +0 0:10 1:9 2:4 3:1714 4:16 5:270 6:102 7:865 +0 1:27 2:3 3:1707 4:14 5:49 6:188 7:787 +1 0:9 1:25 3:2156 4:1 5:163 6:193 7:2342 +1 0:7 1:9 2:2 3:1425 4:20 5:48 6:211 7:325 +0 0:1 1:1 3:617 4:1 5:82 6:193 7:920 +0 0:6 1:13 2:3 3:1215 4:18 5:203 6:82 7:680 +0 0:4 1:12 3:1806 4:13 5:203 6:83 7:852 +0 0:4 1:7 2:2 3:1315 4:14 5:38 6:205 7:1124 +0 0:6 1:19 2:2 3:1910 4:16 5:216 6:52 7:344 +0 0:5 1:6 2:1 3:1224 4:13 5:168 6:247 7:431 +0 1:6 2:6 3:1541 4:14 5:274 6:205 7:1522 +0 0:10 1:11 2:2 3:1104 4:7 5:232 6:19 7:272 +0 0:3 1:27 2:2 3:601 4:10 5:100 6:19 7:745 +0 0:2 1:16 2:3 3:1105 4:15 5:75 6:182 7:539 +0 0:10 1:11 2:2 3:1526 4:5 5:63 6:25 7:475 +0 0:4 1:16 2:4 3:845 4:15 5:49 6:38 7:370 +1 0:8 1:27 3:1511 4:16 5:97 6:223 7:106 +0 0:2 1:12 2:6 3:1019 4:13 5:84 6:1 7:158 +0 0:11 1:20 2:3 3:1205 4:7 5:19 6:140 7:533 +1 0:7 1:6 3:2020 4:20 5:49 6:25 7:283 +0 0:1 1:26 2:2 3:1118 4:3 5:261 6:83 7:1660 +0 0:3 1:18 2:1 3:839 4:1 5:216 6:81 7:612 +1 0:6 1:10 2:2 3:1103 4:17 5:134 6:170 7:1428 +0 0:5 1:4 2:6 3:807 4:13 5:84 6:142 7:328 +0 0:6 1:8 3:1143 4:20 5:252 6:182 7:1333 +0 1:15 2:6 3:2000 4:16 5:219 6:164 7:49 +0 0:4 1:1 2:3 3:832 4:3 5:102 6:16 7:261 +1 0:10 1:9 2:4 3:1659 4:1 5:216 6:81 7:612 +0 0:3 1:13 2:2 3:2049 4:14 5:90 6:205 7:528 +0 0:2 1:26 2:5 3:1447 4:1 5:155 6:83 7:919 +0 0:10 1:23 2:2 3:1728 4:7 5:278 6:19 7:445 +0 0:7 1:29 3:1355 4:20 5:272 6:257 7:480 +1 0:8 1:3 3:2019 4:18 5:83 6:206 7:1062 +1 0:11 1:3 2:1 3:1448 4:7 5:261 6:19 7:2182 +0 0:10 1:13 2:6 3:1344 4:13 5:36 6:218 7:409 +0 0:8 1:18 2:6 3:1047 4:13 5:84 6:173 7:304 +0 0:10 1:23 2:2 3:558 4:21 5:165 6:141 7:127 +0 0:7 1:13 2:5 3:1627 4:12 5:225 6:217 7:325 +0 0:1 1:1 3:28 4:5 5:261 6:141 7:1874 +0 0:9 1:4 2:2 3:937 4:1 5:84 6:36 7:631 +1 0:5 1:2 2:4 3:2228 4:7 5:19 6:47 7:712 +0 0:9 1:1 3:857 4:20 5:49 6:107 7:925 +0 0:4 1:30 2:3 3:700 4:9 5:213 6:82 7:472 +0 0:9 1:26 2:1 3:1117 4:7 5:217 6:19 7:516 +0 0:10 1:21 2:1 3:1910 4:16 5:216 6:58 7:760 +0 0:9 2:4 3:531 4:11 5:133 6:158 7:163 +0 1:25 2:1 3:701 4:3 5:261 6:16 7:1449 +0 0:2 1:2 2:4 3:605 4:17 5:184 6:170 7:725 +0 0:6 1:30 2:5 3:1214 4:16 5:282 6:275 7:223 +0 0:2 1:2 2:4 3:1638 4:15 5:75 6:284 7:307 +1 1:25 2:1 3:1217 4:1 5:294 6:218 7:1437 +0 0:11 1:18 2:1 3:1822 4:16 5:216 6:160 7:179 +0 0:3 1:7 2:5 3:1600 4:8 5:142 6:19 7:781 +1 0:4 1:3 2:5 3:951 4:4 5:156 6:46 7:267 +0 0:3 1:4 2:1 3:2035 4:21 5:63 6:170 7:418 +1 0:1 1:19 2:4 3:1310 4:4 5:47 6:156 7:301 +0 0:10 1:9 2:4 3:554 4:16 5:201 6:218 7:109 +0 0:4 1:28 3:1340 4:20 5:209 6:251 7:180 +0 0:4 1:9 2:4 3:1013 4:7 5:19 6:222 7:545 +1 0:8 1:27 3:2019 4:20 5:180 6:2 7:718 +0 0:9 1:13 2:3 3:1503 4:21 5:90 6:99 7:487 +0 0:3 1:30 2:5 3:1254 4:7 5:220 6:156 7:1028 +1 0:7 1:16 2:2 3:2332 4:8 5:19 6:304 7:152 +0 0:5 1:13 2:1 3:700 4:20 5:209 6:164 7:337 +0 0:2 1:22 2:4 3:2007 4:5 5:100 6:184 7:938 +0 0:3 1:6 2:3 3:1336 4:11 5:133 6:164 7:2556 +0 1:5 2:4 3:700 4:8 5:75 6:240 7:722 +0 0:10 1:15 2:1 3:1238 4:15 5:182 6:219 7:655 +0 0:7 1:19 2:4 3:751 4:16 5:252 6:164 7:109 +0 0:8 1:8 2:4 3:1323 4:15 5:51 6:74 7:404 +1 0:9 1:13 2:4 3:1135 4:5 5:262 6:99 7:2565 +0 0:3 1:18 3:1903 4:16 5:201 6:218 7:109 +0 0:6 1:10 2:2 3:1914 4:14 5:90 6:81 7:405 +1 0:2 1:3 2:6 3:1057 4:16 5:83 6:74 7:1069 +1 0:9 1:8 3:1752 4:21 5:190 6:99 7:209 +0 0:7 1:15 2:1 3:2019 4:21 5:63 6:95 7:98 +0 0:10 1:5 3:1805 4:15 5:160 6:74 7:258 +1 1:18 2:2 3:1640 4:3 5:261 6:227 7:1107 +1 0:5 1:20 2:1 3:2001 4:1 5:279 6:218 7:258 +0 0:9 1:13 2:3 3:825 4:14 5:120 6:205 7:252 +0 0:7 1:15 3:1554 4:19 5:65 6:49 7:361 +0 1:1 2:1 3:1432 4:7 5:83 6:74 7:1069 +0 0:2 1:14 2:1 3:1444 4:1 5:253 6:83 7:247 +0 0:9 1:17 3:1218 4:12 5:225 6:294 7:1788 +0 0:5 1:2 2:5 3:810 4:1 5:216 6:272 7:1829 +1 0:10 3:911 4:13 5:84 6:79 7:861 +0 0:6 1:28 2:4 3:636 4:16 5:114 6:275 7:546 +0 0:9 1:22 2:6 3:843 4:18 5:140 6:170 7:229 +0 0:1 1:10 2:2 3:1138 4:19 5:229 6:81 7:205 +0 0:2 1:5 2:1 3:1550 4:8 5:124 6:19 7:153 +0 0:5 1:9 2:4 3:1221 4:7 5:66 6:19 7:446 +1 0:2 1:30 2:3 3:1040 4:8 5:19 6:270 7:552 +0 0:3 1:13 2:3 3:1912 4:20 5:178 6:21 7:294 +1 0:6 1:1 3:2334 4:7 5:261 6:19 7:2182 +0 0:5 1:14 2:1 3:643 4:16 5:221 6:266 7:129 +0 0:7 1:21 2:6 3:1421 4:21 5:180 6:141 7:643 +0 0:6 1:13 2:4 3:1320 4:22 5:225 6:299 7:110 +0 0:7 1:30 3:1020 4:20 5:184 6:49 7:611 +0 0:7 1:1 2:2 3:1211 4:11 5:151 6:133 7:216 +0 0:4 1:29 2:1 3:2139 4:19 5:182 6:226 7:861 +0 0:1 1:30 3:1805 4:22 5:225 6:299 7:110 +0 0:7 1:23 3:1736 4:10 5:248 6:49 7:277 +0 0:9 1:21 2:5 3:1422 4:16 5:243 6:82 7:829 +0 0:8 1:5 2:1 3:703 4:21 5:100 6:74 7:569 +0 0:7 1:19 2:4 3:2032 4:8 5:19 6:209 7:317 +0 0:11 2:4 3:1919 4:13 5:265 6:83 7:190 +0 0:8 1:25 2:5 3:845 4:13 5:242 6:65 7:361 +1 0:5 1:12 3:2300 4:8 5:19 6:228 7:564 +1 0:7 1:24 2:1 3:1651 4:1 5:156 6:274 7:1597 +0 0:2 1:28 2:1 3:1702 4:19 5:38 6:226 7:280 +0 0:9 1:25 2:1 3:1924 4:14 5:90 6:110 7:56 +0 0:8 1:2 2:6 3:1215 4:14 5:90 6:194 7:238 +0 0:3 1:3 2:1 3:2105 4:15 5:75 6:56 7:278 +0 0:1 1:25 2:2 3:1130 4:20 5:251 6:186 7:1105 +0 0:4 1:4 2:6 3:907 4:20 5:36 6:49 7:588 +0 1:18 2:2 3:1344 4:11 5:158 6:213 7:84 +0 0:4 1:2 2:4 3:2157 4:13 5:38 6:29 7:201 +0 0:5 1:27 3:1639 4:15 5:29 6:74 7:906 +0 0:3 1:27 2:1 3:1405 4:20 5:292 6:78 7:237 +0 0:6 1:20 2:4 3:720 4:8 5:51 6:170 7:617 +1 2:6 3:1215 4:14 5:203 6:162 7:1300 +0 0:11 1:27 2:1 3:1610 4:20 5:184 6:62 7:307 +0 0:9 1:21 2:4 3:1342 4:14 5:38 6:205 7:1124 +0 0:9 1:24 2:6 3:1154 4:1 5:168 6:83 7:1389 +0 0:2 2:2 3:1430 4:13 5:232 6:83 7:604 +0 0:4 1:2 2:5 3:1051 4:21 5:279 6:99 7:872 +0 0:3 1:12 2:2 3:613 4:16 5:201 6:82 7:826 +0 0:2 1:14 2:2 3:1939 4:20 5:261 6:277 7:605 +0 0:2 1:28 2:1 3:1630 4:15 5:75 6:36 7:230 +0 0:6 1:29 2:4 3:752 4:19 5:224 6:184 7:861 +1 0:10 1:10 2:5 3:839 4:16 5:262 6:262 7:191 +0 0:7 1:30 3:1210 4:20 5:49 6:240 7:328 +0 0:1 1:25 2:2 3:1115 4:22 5:180 6:82 7:533 +0 0:8 1:6 2:2 3:1822 4:20 5:79 6:258 7:248 +0 0:1 1:10 2:3 3:1258 4:20 5:184 6:266 7:1733 +0 0:4 1:1 2:4 3:1325 4:7 5:19 6:107 7:581 +1 0:10 1:6 3:1400 4:1 5:269 6:184 7:1189 +0 0:9 1:13 2:3 3:809 4:1 5:83 6:83 7:641 +1 0:7 1:19 2:4 3:1009 4:3 5:157 6:312 7:199 +0 1:15 3:1405 4:8 5:19 6:247 7:356 +0 1:20 2:4 3:1147 4:13 5:268 6:83 7:228 +0 1:23 2:6 3:854 4:14 5:186 6:182 7:392 +0 0:5 1:22 2:4 3:830 4:20 5:161 6:164 7:236 +0 0:3 1:14 2:4 3:746 4:20 5:215 6:162 7:197 +0 0:2 1:8 2:4 3:1841 4:7 5:19 6:94 7:1282 +0 0:4 1:8 2:3 3:705 4:20 5:215 6:162 7:197 +0 0:4 1:27 3:1630 4:20 5:49 6:184 7:787 +1 0:4 1:14 2:2 3:950 4:22 5:217 6:64 7:290 +0 1:26 2:3 3:650 4:19 5:274 6:227 7:338 +0 0:9 1:29 2:4 3:1600 4:20 5:134 6:71 7:187 +0 0:10 1:16 2:2 3:1136 4:4 5:108 6:38 7:1237 +0 0:10 1:4 2:5 3:936 4:18 5:49 6:218 7:622 +0 0:7 3:1930 4:13 5:84 6:142 7:328 +0 0:4 1:2 2:5 3:653 4:13 5:67 6:218 7:135 +0 0:9 1:12 2:3 3:1609 4:8 5:114 6:275 7:546 +0 0:3 1:2 3:725 4:19 5:36 6:226 7:675 +0 0:8 1:4 2:1 3:1116 4:1 5:84 6:214 7:175 +0 0:4 1:19 3:835 4:20 5:270 6:162 7:368 +0 0:10 1:5 3:1510 4:19 5:225 6:162 7:256 +0 0:10 1:4 2:6 3:1251 4:20 5:161 6:279 7:226 +0 0:10 1:6 2:1 3:650 4:7 5:82 6:170 7:214 +0 2:6 3:846 4:11 5:158 6:133 7:163 +0 0:9 1:8 2:6 3:1123 4:16 5:83 6:235 7:853 +0 0:2 1:11 2:2 3:1114 4:7 5:2 6:19 7:1269 +0 0:11 1:26 3:1220 4:20 5:79 6:173 7:296 +0 1:29 2:5 3:906 4:11 5:211 6:172 7:202 +0 0:6 1:17 3:1658 4:7 5:80 6:19 7:432 +0 0:6 1:7 2:5 3:2016 4:1 5:191 6:89 7:1145 +0 0:11 1:28 2:2 3:1057 4:14 5:83 6:89 7:1123 +0 0:1 1:1 2:1 3:1503 4:20 5:225 6:94 7:347 +0 0:7 1:3 2:4 3:1324 4:16 5:296 6:275 7:175 +1 0:4 1:19 2:6 3:1943 4:20 5:161 6:266 7:866 +0 0:6 1:5 2:4 3:901 4:18 5:262 6:38 7:2704 +0 0:1 1:18 2:2 3:700 4:1 5:163 6:193 7:2342 +1 0:1 1:25 2:2 3:1239 4:20 5:90 6:36 7:457 +0 0:10 1:4 2:5 3:842 4:16 5:163 6:69 7:833 +0 1:6 2:6 3:1609 4:10 5:19 6:231 7:526 +0 0:5 1:4 2:6 3:2014 4:7 5:58 6:19 7:259 +0 0:1 1:11 2:6 3:1346 4:3 5:209 6:279 7:371 +0 1:15 3:1004 4:13 5:120 6:218 7:174 +0 1:2 2:1 3:1300 4:15 5:89 6:74 7:505 +0 0:2 1:20 2:1 3:800 4:16 5:270 6:287 7:223 +0 0:10 1:27 2:5 3:1355 4:20 5:161 6:94 7:584 +1 0:8 1:19 3:1059 4:19 5:224 6:294 7:920 +0 0:7 1:26 2:4 3:1806 4:11 5:211 6:133 7:100 +0 0:9 1:1 3:1348 4:1 5:216 6:284 7:258 +0 0:4 1:6 2:2 3:1554 4:19 5:224 6:247 7:336 +0 0:11 1:8 2:5 3:830 4:8 5:21 6:275 7:1085 +0 0:5 1:6 2:1 3:1545 4:21 5:100 6:188 7:946 +0 0:8 1:22 2:5 3:1825 4:20 5:252 6:186 7:1728 +1 0:5 2:2 3:1842 4:16 5:216 6:89 7:235 +0 0:2 1:8 2:3 3:1648 4:13 5:166 6:83 7:785 +0 0:10 1:28 2:6 3:1805 4:21 5:141 6:136 7:295 +0 0:10 1:21 3:1745 4:20 5:161 6:251 7:345 +0 0:5 1:12 3:622 4:1 5:108 6:83 7:1119 +0 0:11 1:6 2:3 3:700 4:20 5:184 6:89 7:229 +0 0:7 1:9 2:2 3:1129 4:8 5:19 6:284 7:483 +0 0:7 1:5 2:6 3:1710 4:18 5:262 6:164 7:337 +0 0:9 1:10 2:1 3:642 4:14 5:66 6:89 7:155 +0 0:6 1:21 2:5 3:1255 4:21 5:100 6:89 7:487 +0 0:11 1:28 2:3 3:1730 4:8 5:290 6:19 7:227 +0 0:4 1:13 2:1 3:1942 4:20 5:180 6:186 7:405 +0 0:1 1:21 2:5 3:852 4:16 5:13 6:218 7:723 +0 1:3 2:2 3:1326 4:14 5:203 6:25 7:1050 +0 0:8 1:28 2:2 3:1610 4:19 5:269 6:64 7:1474 +0 0:7 1:10 2:4 3:1416 4:19 5:65 6:226 7:448 +1 0:3 1:24 2:6 3:913 4:21 5:141 6:297 7:429 +0 0:10 1:29 2:1 3:1140 4:3 5:267 6:266 7:697 +0 0:7 1:7 2:1 3:807 4:14 5:146 6:188 7:381 +0 0:8 1:6 2:3 3:1336 4:19 5:140 6:64 7:321 +0 0:5 1:28 3:1814 4:14 5:186 6:81 7:762 +0 0:2 1:2 2:4 3:1012 4:18 5:262 6:218 7:1846 +0 0:2 1:28 2:1 3:1457 4:16 5:143 6:275 7:188 +1 0:4 1:20 3:2033 4:14 5:224 6:89 7:453 +1 0:11 1:6 2:4 3:2150 4:20 5:182 6:36 7:616 +0 0:7 1:26 2:4 3:640 4:15 5:146 6:156 7:664 +0 0:2 1:30 2:3 3:1735 4:20 5:83 6:162 7:629 +1 0:10 1:23 2:2 3:1038 4:13 5:216 6:234 7:794 +0 0:3 1:14 2:4 3:939 4:8 5:47 6:19 7:712 +1 0:6 1:23 2:5 3:1130 4:18 5:19 6:218 7:606 +0 0:6 1:18 2:1 3:737 4:20 5:209 6:266 7:671 +1 0:1 1:14 2:5 3:2005 4:7 5:19 6:218 7:606 +0 1:30 3:1247 4:13 5:84 6:137 7:603 +0 0:1 1:8 3:1657 4:15 5:36 6:74 7:230 +0 0:4 1:1 2:3 3:1319 4:5 5:141 6:155 7:817 +0 0:6 1:8 3:1754 4:13 5:82 6:247 7:227 +0 0:8 1:9 2:5 3:1725 4:20 5:161 6:65 7:1772 +1 0:11 1:18 3:2014 4:21 5:167 6:141 7:201 +0 0:5 1:3 2:5 3:1442 4:7 5:204 6:19 7:425 +0 0:9 1:14 2:5 3:1601 4:18 5:161 6:82 7:629 +0 0:9 1:29 2:4 3:1445 4:1 5:269 6:294 7:1237 +0 0:7 3:2102 4:8 5:19 6:110 7:644 +0 0:4 1:16 2:4 3:1329 4:14 5:90 6:205 7:528 +0 0:1 1:16 2:1 3:915 4:13 5:72 6:83 7:354 +0 0:2 1:7 2:2 3:1941 4:13 5:84 6:116 7:140 +0 1:28 2:5 3:556 4:1 5:21 6:83 7:190 +1 0:8 2:2 3:1138 4:12 5:225 6:83 7:868 +1 0:4 1:2 2:4 3:731 4:13 5:38 6:65 7:640 +0 0:10 1:3 2:5 3:2034 4:5 5:182 6:99 7:938 +0 0:9 1:5 2:4 3:1047 4:1 5:216 6:38 7:867 +0 0:11 1:30 2:4 3:2203 4:8 5:19 6:290 7:793 +0 0:1 1:11 3:936 4:5 5:84 6:141 7:224 +0 0:1 1:3 2:3 3:1451 4:20 5:225 6:284 7:1262 +0 0:3 1:25 3:853 4:14 5:272 6:205 7:1518 +0 0:7 1:19 2:4 3:1400 4:20 5:225 6:164 7:370 +0 0:7 1:24 2:1 3:550 4:15 5:299 6:170 7:647 +0 0:9 1:10 2:1 3:1814 4:1 5:182 6:193 7:193 +1 0:3 1:26 2:1 3:2008 4:13 5:100 6:38 7:200 +0 0:7 1:27 2:4 3:1326 4:21 5:141 6:64 7:913 +1 0:8 1:11 2:3 3:1958 4:1 5:267 6:133 7:2416 +0 0:1 1:19 2:3 3:1659 4:1 5:84 6:2 7:569 +0 0:2 1:17 2:5 3:918 4:18 5:262 6:48 7:326 +0 0:7 1:22 2:1 3:2153 4:1 5:84 6:227 7:868 +1 0:8 1:1 2:4 3:2118 4:1 5:156 6:274 7:1597 +0 0:4 1:4 2:6 3:1550 4:15 5:38 6:46 7:181 +0 0:5 1:7 2:3 3:1435 4:20 5:63 6:186 7:307 +0 0:2 1:26 2:5 3:1355 4:15 5:65 6:74 7:335 +0 1:11 3:924 4:21 5:212 6:141 7:395 +0 0:11 2:4 3:1020 4:13 5:274 6:272 7:342 +0 0:2 1:6 2:1 3:655 4:4 5:156 6:222 7:1028 +0 0:7 1:7 3:1555 4:1 5:100 6:83 7:1372 +0 0:9 1:23 2:6 3:1424 4:10 5:182 6:35 7:936 +1 0:8 1:25 2:6 3:1909 4:13 5:84 6:1 7:158 +0 0:5 1:5 3:1416 4:18 5:163 6:82 7:862 +0 0:7 1:27 2:5 3:615 4:7 5:237 6:19 7:903 +0 0:3 1:22 2:6 3:1544 4:20 5:171 6:134 7:393 +0 0:9 1:8 3:1947 4:21 5:141 6:154 7:351 +0 1:28 2:4 3:2130 4:18 5:216 6:82 7:888 +0 0:1 1:27 2:3 3:1118 4:1 5:95 6:83 7:551 +0 0:4 1:6 2:1 3:639 4:19 5:66 6:81 7:322 +1 1:2 2:2 3:1836 4:18 5:38 6:218 7:867 +0 0:2 1:2 2:4 3:758 4:1 5:245 6:83 7:1158 +0 0:3 1:26 2:1 3:551 4:19 5:82 6:64 7:331 +0 0:10 1:20 3:5 4:14 5:163 6:89 7:1979 +0 0:10 1:16 2:3 3:1730 4:20 5:225 6:182 7:1044 +0 0:3 1:28 2:2 3:925 4:7 5:19 6:290 7:793 +0 0:4 1:30 2:3 3:1220 4:22 5:200 6:227 7:598 +0 0:3 1:26 2:1 3:1100 4:22 5:225 6:238 7:261 +0 0:6 1:15 2:6 3:858 4:18 5:161 6:82 7:629 +1 0:3 1:16 2:5 3:817 4:14 5:180 6:188 7:392 +0 0:9 1:6 2:5 3:657 4:7 5:261 6:19 7:2182 +1 0:5 1:29 2:2 3:1503 4:8 5:19 6:56 7:106 +0 0:2 1:25 2:4 3:857 4:7 5:90 6:19 7:594 +0 0:5 1:18 2:6 3:1714 4:15 5:75 6:261 7:203 +0 0:1 1:7 3:1850 4:20 5:252 6:162 7:258 +1 0:10 1:14 2:1 3:1700 4:13 5:84 6:173 7:304 +1 0:8 1:23 2:3 3:2045 4:1 5:38 6:193 7:1258 +0 1:23 3:725 4:20 5:47 6:184 7:1011 +0 0:5 1:20 2:1 3:1803 4:15 5:122 6:74 7:268 +0 0:1 1:2 2:2 3:1153 4:22 5:117 6:227 7:438 +0 0:10 1:24 2:2 3:1635 4:13 5:84 6:270 7:190 +0 0:9 1:28 2:4 3:821 4:18 5:38 6:267 7:2704 +0 0:7 1:3 2:3 3:1456 4:13 5:38 6:170 7:185 +1 0:1 1:13 2:4 3:950 4:8 5:19 6:188 7:332 +0 0:2 1:3 2:6 3:2045 4:5 5:100 6:19 7:745 +0 0:8 1:5 2:1 3:2026 4:16 5:262 6:103 7:158 +0 0:6 1:8 2:6 3:2328 4:8 5:19 6:154 7:341 +0 0:11 1:30 2:4 3:1915 4:7 5:75 6:250 7:413 +0 0:6 1:23 2:6 3:755 4:20 5:161 6:223 7:762 +0 0:1 1:16 2:1 3:2224 4:8 5:19 6:145 7:377 +0 0:3 1:1 2:6 3:1754 4:9 5:83 6:162 7:629 +0 0:6 1:8 3:835 4:15 5:75 6:218 7:264 +0 0:10 1:29 3:1211 4:16 5:2 6:275 7:493 +1 1:25 2:2 3:1909 4:21 5:63 6:25 7:475 +0 0:3 1:8 2:6 3:907 4:7 5:75 6:184 7:756 +1 0:10 1:30 2:2 3:1948 4:13 5:84 6:51 7:922 +0 0:4 1:6 2:1 3:1430 4:20 5:225 6:2 7:328 +0 0:6 1:30 2:6 3:1515 4:15 5:38 6:156 7:187 +0 0:3 1:14 2:4 3:1611 4:3 5:163 6:223 7:834 +1 0:1 1:24 3:1525 4:20 5:253 6:78 7:248 +0 0:7 1:15 3:1019 4:18 5:163 6:82 7:862 +0 0:5 1:10 2:5 3:828 4:14 5:160 6:89 7:74 +0 0:5 1:16 2:4 3:1330 4:20 5:225 6:164 7:370 +0 0:9 1:16 3:1643 4:11 5:211 6:133 7:100 +0 0:11 1:29 2:4 3:725 4:20 5:225 6:2 7:328 +0 0:5 1:25 2:4 3:1644 4:16 5:114 6:275 7:546 +0 0:5 1:25 2:5 3:1557 4:10 5:155 6:19 7:270 +0 0:4 1:3 2:5 3:1447 4:1 5:292 6:218 7:585 +0 0:2 1:8 2:4 3:1330 4:20 5:79 6:165 7:293 +1 0:7 1:29 2:6 3:740 4:17 5:146 6:164 7:1814 +1 0:9 2:4 3:2115 4:20 5:272 6:162 7:397 +0 0:7 1:20 2:5 3:700 4:17 5:184 6:267 7:1855 +0 0:8 1:13 2:1 3:1827 4:16 5:270 6:235 7:521 +0 1:9 2:2 3:1447 4:13 5:121 6:83 7:134 +0 0:1 1:13 2:5 3:1759 4:19 5:224 6:222 7:951 +1 1:26 2:2 3:1327 4:13 5:84 6:268 7:364 +0 0:1 1:26 2:3 3:642 4:19 5:38 6:81 7:399 +0 0:10 1:8 2:3 3:1735 4:10 5:19 6:247 7:356 +1 0:2 1:7 2:3 3:2215 4:20 5:237 6:184 7:1073 +1 0:8 1:3 2:6 3:1302 4:13 5:84 6:123 7:999 +0 0:3 1:10 3:600 4:8 5:248 6:19 7:749 +0 0:2 1:23 2:2 3:1744 4:1 5:84 6:267 7:1464 +0 0:1 1:28 2:5 3:703 4:20 5:182 6:36 7:616 +0 0:7 1:16 2:1 3:1929 4:17 5:184 6:38 7:861 +0 0:1 1:15 3:757 4:7 5:38 6:19 7:946 +0 1:19 2:3 3:2047 4:1 5:191 6:247 7:700 +0 0:11 1:2 2:6 3:2011 4:15 5:290 6:74 7:216 +1 0:4 1:20 2:1 3:1952 4:18 5:216 6:170 7:733 +0 0:10 2:1 3:1550 4:13 5:163 6:202 7:267 +1 0:8 1:18 2:6 3:1532 4:13 5:245 6:193 7:826 +0 0:8 1:14 2:3 3:1757 4:3 5:261 6:114 7:224 +0 0:3 1:21 2:3 3:1310 4:16 5:132 6:275 7:402 +0 0:8 1:17 2:5 3:1814 4:19 5:229 6:164 7:2136 +0 0:3 1:20 2:3 3:946 4:19 5:161 6:277 7:397 +0 1:3 2:3 3:730 4:20 5:190 6:184 7:1142 +0 0:3 1:10 2:1 3:1406 4:13 5:89 6:218 7:299 +1 0:2 1:5 3:1628 4:13 5:216 6:19 7:606 +0 0:8 1:1 2:5 3:651 4:19 5:262 6:162 7:414 +0 0:11 1:21 2:4 3:1438 4:20 5:279 6:186 7:251 +0 0:5 1:10 2:6 3:1751 4:15 5:183 6:74 7:429 +0 0:10 1:9 2:4 3:738 4:21 5:141 6:71 7:201 +0 0:2 1:22 2:4 3:1041 4:10 5:49 6:79 7:406 +0 0:5 1:6 2:2 3:1409 4:16 5:83 6:188 7:872 +0 0:8 1:23 2:3 3:655 4:7 5:168 6:19 7:761 +0 0:10 1:7 2:2 3:1145 4:21 5:229 6:99 7:319 +0 0:3 1:29 2:4 3:804 4:10 5:19 6:222 7:545 +0 0:6 1:13 2:3 3:1048 4:17 5:146 6:162 7:1591 +1 0:5 1:17 2:4 3:1259 4:19 5:65 6:294 7:508 +0 0:2 1:4 2:6 3:656 4:21 5:299 6:99 7:631 +0 0:2 1:3 2:5 3:706 4:14 5:203 6:156 7:1028 +0 0:5 1:17 2:4 3:1225 4:14 5:262 6:188 7:1807 +1 0:5 1:14 2:1 3:1416 4:14 5:108 6:205 7:1487 +0 0:3 2:3 3:1051 4:5 5:141 6:38 7:1597 +0 0:5 1:24 2:3 3:1337 4:5 5:2 6:141 7:744 +0 0:8 1:14 2:3 3:805 4:16 5:270 6:37 7:291 +0 0:5 1:15 2:2 3:1929 4:21 5:84 6:62 7:1021 +0 0:4 1:4 3:1113 4:13 5:306 6:83 7:281 +0 0:3 2:3 3:1231 4:14 5:163 6:89 7:1979 +0 0:6 1:15 2:5 3:2108 4:7 5:19 6:36 7:214 +0 0:1 1:2 2:2 3:1859 4:21 5:45 6:141 7:253 +1 0:7 1:14 3:636 4:16 5:104 6:82 7:844 +0 0:7 1:30 2:1 3:1832 4:10 5:84 6:186 7:797 +0 1:19 2:4 3:1340 4:18 5:83 6:186 7:895 +0 0:5 2:2 3:1142 4:13 5:84 6:268 7:364 +0 0:11 1:28 2:2 3:658 4:3 5:236 6:267 7:421 +0 0:10 1:17 2:3 3:1547 4:20 5:184 6:231 7:402 +0 0:6 1:20 2:4 3:1853 4:8 5:105 6:19 7:331 +0 0:7 1:13 2:5 3:1820 4:20 5:252 6:277 7:480 +0 0:2 1:11 2:2 3:1222 4:14 5:90 6:205 7:528 +0 0:2 1:26 2:6 3:2110 4:20 5:209 6:257 7:446 +1 1:7 2:6 3:2304 4:7 5:19 6:184 7:403 +0 0:11 1:14 2:4 3:1850 4:16 5:252 6:164 7:109 +0 0:6 1:3 2:1 3:736 4:1 5:161 6:193 7:2175 +0 0:10 1:4 2:6 3:748 4:22 5:129 6:82 7:142 +0 0:8 1:9 2:6 3:815 4:20 5:63 6:284 7:487 +0 0:6 1:29 2:4 3:919 4:19 5:65 6:274 7:1474 +1 1:21 2:5 3:1813 4:15 5:75 6:122 7:268 +0 0:9 1:1 3:728 4:18 5:133 6:164 7:2556 +0 0:10 1:18 2:4 3:1302 4:16 5:262 6:202 7:77 +0 0:10 1:16 2:3 3:1231 4:14 5:156 6:89 7:508 +1 0:3 1:29 2:4 3:2300 4:4 5:156 6:290 7:209 +0 0:5 1:27 2:6 3:1905 4:18 5:216 6:140 7:589 +0 0:3 1:16 2:6 3:556 4:18 5:168 6:218 7:733 +0 0:9 1:13 2:4 3:1659 4:1 5:141 6:83 7:224 +0 1:7 2:6 3:1121 4:7 5:19 6:155 7:270 +0 1:6 2:5 3:2310 4:15 5:75 6:205 7:596 +0 0:5 1:28 2:1 3:750 4:15 5:168 6:123 7:461 +0 0:1 1:23 2:6 3:1101 4:15 5:253 6:74 7:1024 +0 0:1 1:25 2:1 3:2020 4:20 5:279 6:182 7:237 +0 1:2 2:2 3:833 4:1 5:269 6:156 7:1597 +1 0:8 1:17 2:6 3:1335 4:15 5:19 6:215 7:821 +0 0:5 1:23 2:3 3:1946 4:18 5:82 6:218 7:612 +0 0:6 1:21 2:5 3:1149 4:10 5:19 6:259 7:215 +0 0:3 1:20 2:2 3:1915 4:20 5:48 6:162 7:223 +1 0:2 1:26 2:6 3:1618 4:7 5:19 6:223 7:2172 +0 0:9 1:23 2:5 3:1123 4:20 5:163 6:277 7:373 +0 0:7 1:11 2:1 3:910 4:20 5:49 6:21 7:1342 +0 1:18 2:2 3:1500 4:8 5:19 6:108 7:273 +0 0:3 1:13 2:2 3:1112 4:19 5:224 6:49 7:90 +1 0:1 1:18 2:2 3:1246 4:12 5:225 6:205 7:1276 +0 0:2 1:9 2:5 3:1015 4:20 5:79 6:165 7:293 +0 0:1 1:6 2:6 3:1229 4:1 5:261 6:83 7:1660 +0 0:5 1:25 2:4 3:1119 4:16 5:270 6:44 7:357 +0 0:11 2:4 3:1323 4:21 5:25 6:99 7:116 +1 0:5 1:26 2:6 3:831 4:19 5:38 6:170 7:185 +0 0:10 1:2 2:4 3:1344 4:7 5:163 6:19 7:1946 +0 0:11 1:6 2:3 3:715 4:7 5:100 6:19 7:745 +0 1:20 2:5 3:1340 4:8 5:242 6:19 7:356 +0 0:1 1:14 2:5 3:835 4:20 5:182 6:186 7:989 +0 0:2 1:11 2:2 3:1602 4:19 5:224 6:240 7:238 +0 0:10 1:15 2:2 3:939 4:14 5:90 6:192 7:609 +0 0:5 1:28 3:1600 4:20 5:79 6:21 7:189 +0 0:2 1:3 2:6 3:734 4:18 5:261 6:267 7:679 +0 1:9 2:2 3:741 4:16 5:262 6:275 7:599 +1 0:8 1:29 2:2 3:923 4:1 5:191 6:164 7:2342 +0 0:11 1:14 2:4 3:1759 4:18 5:163 6:267 7:337 +0 0:6 1:22 3:1905 4:20 5:184 6:154 7:666 +0 0:9 1:29 2:5 3:737 4:16 5:262 6:190 7:329 +0 0:6 1:1 3:1723 4:20 5:270 6:164 7:590 +0 0:11 1:20 2:3 3:1718 4:14 5:203 6:257 7:1532 +0 0:10 1:21 2:1 3:2055 4:15 5:19 6:242 7:1027 +0 0:4 1:22 2:3 3:1240 4:20 5:95 6:2 7:223 +0 0:3 1:14 2:3 3:1356 4:7 5:19 6:134 7:696 +0 0:2 1:16 2:3 3:1217 4:19 5:242 6:64 7:130 +0 0:4 1:27 2:6 3:1950 4:20 5:161 6:182 7:1140 +0 0:7 1:21 2:6 3:822 4:1 5:216 6:272 7:1829 +0 0:1 1:2 2:2 3:1228 4:1 5:84 6:182 7:460 +0 0:7 1:21 3:828 4:19 5:216 6:162 7:1515 +1 0:7 1:3 2:4 3:1927 4:20 5:79 6:173 7:296 +1 0:9 1:24 2:6 3:1956 4:18 5:216 6:82 7:888 +0 0:7 1:24 2:2 3:657 4:3 5:252 6:223 7:933 +0 1:20 2:4 3:1410 4:20 5:79 6:21 7:189 +1 0:10 1:22 2:3 3:2144 4:20 5:184 6:240 7:842 +0 0:10 1:11 2:2 3:807 4:20 5:134 6:49 7:1246 +1 0:2 1:20 3:1033 4:1 5:274 6:83 7:1205 +0 0:7 1:26 2:3 3:756 4:19 5:224 6:64 7:448 +1 0:10 1:12 2:5 3:1550 4:20 5:270 6:182 7:919 +0 0:6 1:2 3:659 4:19 5:25 6:107 7:1173 +1 0:8 1:28 2:2 3:2029 4:1 5:216 6:170 7:733 +0 0:2 1:21 2:1 3:653 4:16 5:217 6:218 7:717 +0 0:3 1:11 2:5 3:924 4:22 5:20 6:218 7:160 +0 0:8 1:28 2:2 3:1651 4:16 5:270 6:266 7:689 +0 0:5 1:28 3:800 4:20 5:49 6:155 7:663 +1 0:8 1:8 2:5 3:1945 4:14 5:186 6:205 7:700 +0 0:5 1:3 2:5 3:1815 4:20 5:184 6:211 7:1844 +0 0:9 1:4 2:3 3:626 4:16 5:239 6:218 7:780 +1 0:8 2:2 3:2142 4:10 5:84 6:164 7:1235 +0 0:2 1:19 2:6 3:839 4:19 5:229 6:64 7:366 +0 0:10 1:17 2:4 3:1116 4:20 5:267 6:251 7:188 +0 0:2 1:19 3:2112 4:18 5:83 6:218 7:888 +0 0:2 1:17 2:5 3:1138 4:18 5:262 6:164 7:337 +0 0:11 1:18 3:1850 4:14 5:122 6:89 7:120 +0 1:3 2:3 3:1750 4:22 5:88 6:227 7:351 +0 0:6 1:4 2:2 3:1242 4:18 5:83 6:140 7:1452 +1 0:4 1:19 2:6 3:1423 4:20 5:209 6:164 7:337 +0 0:10 1:15 2:2 3:1815 4:20 5:209 6:279 7:371 +0 1:21 2:6 3:1233 4:1 5:21 6:83 7:190 +0 0:3 1:11 2:5 3:633 4:16 5:201 6:82 7:826 +0 0:8 1:12 2:1 3:1504 4:20 5:252 6:211 7:446 +0 0:8 1:29 2:2 3:1613 4:3 5:261 6:37 7:399 +0 0:1 1:7 2:6 3:1910 4:15 5:224 6:74 7:507 +1 0:3 1:18 2:1 3:854 4:18 5:216 6:266 7:1721 +0 0:3 1:17 2:6 3:2118 4:5 5:141 6:297 7:429 +0 1:26 2:3 3:1307 4:5 5:84 6:99 7:1372 +0 0:4 1:30 2:3 3:1805 4:22 5:140 6:252 7:177 +0 0:5 1:29 2:2 3:921 4:21 5:141 6:69 7:809 +0 0:8 2:2 3:2043 4:15 5:75 6:81 7:411 +0 0:9 1:10 2:2 3:1119 4:20 5:204 6:184 7:550 +0 0:1 1:11 2:6 3:959 4:18 5:216 6:266 7:1721 +0 0:6 1:14 2:5 3:1755 4:8 5:186 6:19 7:332 +0 0:9 1:29 2:4 3:641 4:18 5:203 6:82 7:680 +1 0:9 2:4 3:1637 4:1 5:84 6:218 7:802 +1 0:8 1:19 2:1 3:1204 4:3 5:261 6:48 7:937 +0 0:7 1:20 2:5 3:1810 4:20 5:21 6:78 7:189 +0 0:11 1:10 3:757 4:15 5:36 6:74 7:230 +0 1:12 2:4 3:1312 4:13 5:124 6:83 7:862 +0 0:6 1:12 2:3 3:1758 4:21 5:232 6:141 7:489 +0 0:7 1:1 2:1 3:1905 4:21 5:192 6:62 7:328 +1 0:3 1:25 2:6 3:1716 4:10 5:182 6:226 7:861 +0 0:5 1:30 2:3 3:1123 4:10 5:224 6:294 7:920 +1 0:5 1:3 2:6 3:1045 4:1 5:163 6:83 7:1235 +0 0:1 1:16 3:1825 4:20 5:225 6:279 7:338 +0 0:3 1:4 2:1 3:2318 4:14 5:133 6:267 7:2398 +0 0:5 1:1 2:4 3:1334 4:16 5:221 6:246 7:116 +0 0:4 1:14 2:1 3:1620 4:14 5:190 6:89 7:609 +0 0:5 1:11 2:2 3:1345 4:14 5:90 6:256 7:1085 +0 0:6 1:12 2:3 3:2058 4:14 5:90 6:242 7:668 +1 0:5 1:19 2:6 3:854 4:19 5:182 6:64 7:468 +0 0:1 1:3 2:3 3:714 4:16 5:262 6:27 7:238 +0 0:10 1:28 3:1045 4:1 5:19 6:193 7:595 +0 0:1 1:22 2:1 3:703 4:14 5:203 6:82 7:680 +0 0:7 1:15 2:1 3:1145 4:20 5:184 6:277 7:1790 +0 0:10 1:20 2:6 3:1000 4:15 5:82 6:38 7:399 +1 0:7 3:1150 4:16 5:215 6:164 7:47 +0 0:9 1:4 2:3 3:1126 4:8 5:306 6:19 7:589 +1 0:10 1:29 3:1930 4:20 5:204 6:30 7:321 +0 0:9 1:21 2:4 3:2132 4:5 5:252 6:99 7:2425 +0 0:10 1:27 2:5 3:857 4:19 5:38 6:231 7:496 +0 0:2 1:21 2:2 3:854 4:16 5:215 6:164 7:47 +0 0:3 1:5 2:3 3:1159 4:20 5:154 6:134 7:359 +0 0:7 1:22 2:1 3:1615 4:14 5:21 6:188 7:559 +0 1:11 3:710 4:15 5:186 6:74 7:403 +0 0:3 1:3 2:1 3:1341 4:3 5:221 6:279 7:859 +0 0:6 1:7 2:5 3:1751 4:3 5:16 6:26 7:399 +1 0:1 1:20 2:5 3:1614 4:7 5:19 6:81 7:547 +0 0:5 1:25 2:4 3:955 4:15 5:248 6:74 7:461 +0 0:4 1:29 2:1 3:1913 4:19 5:65 7:481 +1 0:3 1:15 2:5 3:912 4:20 5:184 6:227 7:1444 +0 0:7 1:13 2:5 3:1030 4:20 5:2 6:94 7:223 +0 1:21 2:6 3:2120 4:20 5:224 6:240 7:238 +1 0:11 1:7 2:5 3:1105 4:18 5:216 6:25 7:783 +0 0:1 1:1 2:1 3:820 4:1 5:203 6:218 7:334 +1 0:3 1:30 2:4 3:2009 4:1 5:108 6:274 7:1046 +0 0:10 1:17 2:4 3:2021 4:13 5:216 6:255 7:268 +0 0:6 1:17 2:1 3:1325 4:1 5:84 6:274 7:2165 +0 1:6 2:5 3:1839 4:21 5:141 6:188 7:469 +0 0:5 1:24 2:4 3:627 4:4 5:156 6:38 7:187 +1 0:5 1:29 2:2 3:1617 4:5 5:141 6:38 7:1597 +0 0:9 1:10 2:2 3:1442 4:20 5:279 6:186 7:251 +0 0:2 1:25 2:4 3:1519 4:1 5:279 6:279 7:1570 +0 0:10 1:10 2:5 3:915 4:22 5:78 6:140 7:703 +0 0:2 1:1 2:4 3:1046 4:21 5:100 6:173 7:1069 +0 0:6 1:6 2:5 3:2014 4:10 5:19 6:107 7:581 +0 0:3 1:5 2:3 3:1259 4:18 5:163 6:218 7:1745 +0 0:4 1:3 2:5 3:1720 4:20 5:150 6:36 7:803 +0 0:3 1:1 2:6 3:1558 4:20 5:246 6:164 7:390 +0 0:6 1:9 2:1 3:603 4:1 5:253 6:83 7:247 +0 0:1 1:18 2:3 3:1645 4:21 5:63 6:284 7:487 +0 0:7 1:13 2:6 3:602 4:18 5:261 6:267 7:679 +0 0:6 1:27 2:2 3:749 4:16 5:221 6:266 7:129 +0 1:21 2:6 3:1351 4:1 5:36 6:83 7:631 +1 0:7 1:25 2:3 3:2035 4:13 5:84 6:142 7:328 +0 0:2 1:10 2:6 3:1442 4:7 5:66 6:19 7:446 +0 0:8 1:19 3:1255 4:13 5:89 6:83 7:624 +0 0:2 1:8 2:3 3:830 4:18 5:246 6:267 7:192 +0 0:2 1:16 2:4 3:1055 4:13 5:216 6:140 7:589 +0 0:2 1:26 2:5 3:918 4:13 5:84 6:51 7:922 +1 0:5 1:17 2:4 3:1214 4:18 5:83 6:267 7:967 +0 0:4 1:19 2:6 3:1100 4:20 5:49 6:184 7:787 +1 0:9 1:11 2:6 3:1250 4:5 5:274 6:141 7:1347 +0 0:8 1:21 2:3 3:733 4:9 5:83 6:215 7:472 +0 0:8 1:26 3:1630 4:22 5:203 6:64 7:930 +0 0:9 1:3 2:2 3:629 4:19 5:163 6:226 7:2401 +1 0:3 1:17 3:1908 4:1 5:84 6:223 7:1616 +1 0:9 1:17 2:1 3:2046 4:22 5:140 6:38 7:413 +1 0:3 1:6 2:4 3:1838 4:1 5:216 6:21 7:978 +0 0:10 1:24 2:2 3:625 4:16 5:240 6:267 7:199 +0 1:5 2:4 3:1730 4:16 5:83 6:208 7:197 +0 0:4 1:30 2:2 3:1125 4:12 5:225 6:266 7:1107 +1 0:6 2:4 3:1635 4:20 5:184 6:36 7:395 +0 0:2 1:22 2:4 3:1900 4:5 5:141 6:170 7:1416 +0 0:7 1:7 2:1 3:1447 4:8 5:19 6:185 7:619 +0 0:1 1:16 2:1 3:1450 4:8 5:28 6:19 7:749 +0 0:10 1:27 2:5 3:1436 4:14 5:49 6:205 7:936 +1 0:8 1:28 2:1 3:1605 4:19 5:224 6:294 7:920 +0 0:10 1:23 2:2 3:1213 4:1 5:216 6:297 7:585 +0 0:2 1:21 2:1 3:1709 4:17 5:269 6:184 7:1189 +1 0:1 1:4 2:3 3:1910 4:20 5:79 6:173 7:296 +0 0:10 1:13 2:6 3:857 4:21 5:189 6:141 7:554 +0 0:5 1:25 2:4 3:1800 4:20 5:184 6:25 7:777 +0 0:11 1:7 2:5 3:1455 4:21 5:137 6:141 7:595 +1 0:1 1:18 2:3 3:1718 4:8 5:303 6:19 7:264 +0 0:6 1:17 3:1233 4:12 5:163 6:227 7:370 +0 0:1 1:1 3:630 4:1 5:225 6:218 7:1440 +0 0:7 1:24 2:1 3:2353 4:12 5:161 6:81 7:2089 +0 0:6 1:16 3:1520 4:1 5:84 6:99 7:1372 +0 0:6 1:27 2:2 3:657 4:1 5:84 6:164 7:1235 +0 1:19 2:4 3:1042 4:20 5:79 6:2 7:580 +0 1:2 2:2 3:1900 4:1 5:83 6:164 7:862 +0 0:7 1:16 2:1 3:1252 4:18 5:225 6:267 7:651 +1 0:3 1:12 2:1 3:2045 4:8 5:19 7:692 +0 0:7 1:19 2:5 3:921 4:1 5:168 6:222 7:1035 +0 0:8 1:23 2:3 3:805 4:20 5:134 6:206 7:303 +0 0:7 1:12 2:4 3:2237 4:16 5:262 6:245 7:199 +1 0:9 1:9 2:1 3:1855 4:20 5:279 6:30 7:410 +0 0:8 1:26 2:6 3:1245 4:20 5:279 6:134 7:687 +0 0:7 1:22 2:2 3:850 4:21 5:100 6:205 7:1008 +0 1:6 2:5 3:1802 4:8 5:19 6:118 7:300 +0 1:1 2:1 3:1124 4:19 5:225 6:217 7:325 +1 0:2 1:23 2:2 3:1800 4:20 5:163 6:251 7:390 +0 0:7 1:5 2:6 3:1430 4:15 5:168 6:259 7:722 +0 0:11 1:21 2:4 3:1803 4:19 5:49 6:226 7:90 +0 0:10 1:19 2:5 3:1043 4:18 5:216 6:47 7:473 +0 0:10 1:4 2:6 3:856 4:7 5:84 6:19 7:732 +0 0:3 1:21 2:4 3:742 4:1 5:221 6:218 7:1739 +0 0:9 1:6 2:4 3:1030 4:13 5:212 6:218 7:693 +0 0:9 1:27 2:2 3:1824 4:1 5:163 6:82 7:862 +0 0:5 1:12 2:6 3:1043 4:1 5:83 6:218 7:888 +1 0:2 1:21 2:1 3:1831 4:18 5:100 6:218 7:719 +1 0:9 1:22 3:1527 4:14 5:90 6:205 7:528 +0 0:5 1:20 3:700 4:18 5:213 6:82 7:472 +0 0:4 1:20 2:1 3:1001 4:18 5:216 6:83 7:802 +0 0:6 1:11 2:6 3:1214 4:18 5:182 6:218 7:1005 +0 0:3 1:28 2:3 3:804 4:16 5:18 6:275 7:291 +0 0:2 1:4 3:1143 4:21 5:141 6:36 7:657 +0 0:2 2:1 3:1205 4:20 5:225 6:94 7:347 +0 0:11 1:16 2:6 3:755 4:1 5:262 6:83 7:1464 +1 0:10 1:22 2:3 3:1305 4:15 5:254 6:170 7:722 +0 0:3 1:26 3:843 4:18 5:262 6:158 7:2367 +0 0:2 1:1 2:3 3:1052 4:18 5:272 6:218 7:1781 +0 1:20 2:4 3:915 4:15 5:38 6:46 7:181 +1 0:2 1:12 2:6 3:1441 4:20 5:161 6:164 7:236 +0 0:1 1:20 2:5 3:1442 4:1 5:84 6:81 7:1192 +0 0:10 1:23 2:2 3:1445 4:10 5:110 6:19 7:644 +0 0:1 1:27 2:4 3:1517 4:7 5:19 6:223 7:2172 +0 0:2 1:26 2:6 3:1916 4:19 5:225 6:83 7:868 +0 0:10 1:2 2:4 3:617 4:13 5:56 6:218 7:501 +0 0:7 1:17 2:2 3:659 4:16 5:272 6:267 7:86 +0 0:6 1:20 2:3 3:1310 4:20 5:279 6:186 7:251 +1 0:6 1:26 2:2 3:1532 4:13 5:84 6:45 7:383 +1 0:5 1:7 2:3 3:1500 4:16 5:257 6:267 7:191 +0 0:6 1:1 2:6 3:1351 4:16 5:270 6:70 7:320 +0 0:10 1:7 2:1 3:1649 4:7 5:19 6:64 7:227 +0 0:3 1:18 2:1 3:2009 4:19 5:182 6:162 7:2039 +0 0:5 1:4 3:2016 4:1 5:191 6:81 7:920 +1 0:11 1:29 2:3 3:1745 4:1 5:216 6:247 7:647 +0 0:10 1:14 2:1 3:1936 4:4 5:140 6:156 7:228 +0 0:2 1:15 2:2 3:1923 4:18 5:229 6:218 7:412 +0 0:1 1:4 2:4 3:2304 4:8 5:19 6:11 7:143 +0 1:10 2:2 3:1505 4:20 5:90 6:284 7:440 +1 0:6 1:17 2:1 3:1323 4:20 5:289 6:47 7:1054 +0 0:11 1:11 2:4 3:1843 4:20 5:289 6:107 7:197 +0 0:5 1:2 2:4 3:1655 4:14 5:216 6:188 7:491 +0 0:4 1:8 2:4 3:931 4:5 5:168 6:141 7:1416 +0 0:9 1:1 2:6 3:830 4:20 5:171 6:78 7:296 +0 0:8 1:17 2:5 3:1520 4:20 5:161 6:186 7:1521 +1 0:3 1:8 2:6 3:1242 4:3 5:261 6:267 7:679 +0 0:7 1:1 2:1 3:1500 4:20 5:134 6:184 7:848 +0 0:7 1:19 2:4 3:1923 4:14 5:186 6:206 7:349 +0 0:7 1:8 2:1 3:1542 4:10 5:19 6:64 7:227 +0 0:11 1:3 3:1010 4:20 5:252 6:94 7:636 +0 0:1 1:9 2:1 3:1555 4:20 5:225 6:49 7:1999 +0 0:11 1:9 2:6 3:1015 4:9 5:83 6:182 7:533 +0 0:4 1:9 2:4 3:2321 4:18 5:261 6:218 7:1721 +0 1:2 2:1 3:1450 4:14 5:186 6:164 7:1619 +0 0:5 1:27 2:6 3:555 4:10 5:289 6:19 7:406 +0 0:8 1:7 2:3 3:911 4:8 5:256 6:74 7:203 +1 0:3 1:10 2:1 3:1038 4:15 5:38 6:29 7:201 +0 0:5 2:1 3:1254 4:10 5:49 6:19 7:576 +0 0:6 1:25 2:1 3:1205 4:10 5:182 6:19 7:403 +0 0:9 1:24 3:727 4:13 5:279 6:38 7:1046 +1 0:9 1:13 2:4 3:1207 4:18 5:82 6:218 7:612 +0 0:11 1:30 2:4 3:631 4:7 5:19 6:64 7:227 +0 0:4 1:15 2:3 3:1925 4:4 5:108 6:156 7:1069 +1 0:8 1:18 2:6 3:1125 4:14 5:203 6:188 7:700 +0 1:13 2:5 3:2351 4:19 5:161 6:227 7:256 +0 0:2 1:12 2:6 3:1121 4:18 5:215 6:82 7:819 +0 0:10 1:11 2:1 3:1340 4:10 5:186 6:19 7:332 +1 0:1 1:17 2:2 3:1851 4:14 5:163 6:205 7:1536 +0 0:8 1:25 2:5 3:1251 4:7 5:272 6:19 7:2092 +0 0:2 1:12 2:6 3:1405 4:20 5:49 6:184 7:787 +0 0:1 1:25 2:1 3:2038 4:13 5:84 6:1 7:158 +1 1:15 2:6 3:2021 4:12 5:161 6:83 7:1055 +0 0:9 1:25 2:1 3:820 4:19 5:82 6:107 7:899 +0 0:2 2:2 3:1930 4:13 5:216 6:56 7:501 +0 0:2 1:7 2:3 3:1243 4:20 5:225 6:217 7:325 +0 0:6 1:28 2:3 3:1515 4:8 5:270 6:251 7:422 +0 0:7 1:20 2:5 3:1354 4:7 5:182 6:156 7:944 +0 0:2 1:17 2:4 3:1504 4:15 5:98 6:74 7:170 +0 0:10 1:11 2:2 3:555 4:14 5:183 6:89 7:370 +0 0:7 1:16 2:2 3:751 4:1 5:261 6:284 7:1710 +0 0:11 1:12 2:1 3:753 4:10 5:182 6:49 7:787 +1 0:6 1:28 2:4 3:1205 4:20 5:30 6:134 7:570 +0 0:11 1:20 2:3 3:917 4:5 5:141 6:162 7:1222 +0 0:3 1:6 2:3 3:1916 4:16 5:225 6:164 7:370 +0 0:4 1:22 2:3 3:1914 4:19 5:216 6:231 7:412 +0 0:9 1:21 2:5 3:1856 4:16 5:2 6:82 7:349 +0 0:2 1:9 2:5 3:1345 4:3 5:209 6:279 7:371 +0 0:7 1:20 2:5 3:1920 4:20 5:95 6:227 7:347 +0 0:2 1:30 2:3 3:600 4:8 5:167 6:19 7:503 diff --git a/tests/distributed/_test_distributed.py b/tests/distributed/_test_distributed.py new file mode 100644 index 0000000..2753077 --- /dev/null +++ b/tests/distributed/_test_distributed.py @@ -0,0 +1,196 @@ +import copy +import io +import socket +import subprocess +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path +from typing import Any, Dict, Generator, List + +import numpy as np +import pytest +from sklearn.datasets import make_blobs, make_regression +from sklearn.metrics import accuracy_score + +TESTS_DIR = Path(__file__).absolute().parent + + +@pytest.fixture(scope="module") +def executable(pytestconfig) -> str: + """Returns the path to the lightgbm executable.""" + return pytestconfig.getoption("execfile") + + +def _find_random_open_port() -> int: + """Find a random open port on localhost.""" + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("", 0)) + port = s.getsockname()[1] + return port # noqa: RET504 + + +def _generate_n_ports(n: int) -> Generator[int, None, None]: + return (_find_random_open_port() for _ in range(n)) + + +def _write_dict(d: Dict, file: io.TextIOWrapper) -> None: + for k, v in d.items(): + file.write(f"{k} = {v}\n") + + +def create_data(task: str, n_samples: int = 1_000) -> np.ndarray: + """Create the appropriate data for the task. + + The data is returned as a numpy array with the label as the first column. + """ + if task == "binary-classification": + centers = [[-4, -4], [4, 4]] + X, y = make_blobs(n_samples, centers=centers, random_state=42) + elif task == "regression": + X, y = make_regression(n_samples, n_features=4, n_informative=2, random_state=42) + return np.hstack([y.reshape(-1, 1), X]) + + +class DistributedMockup: + """Simulate distributed training.""" + + default_train_config = { + "task": "train", + "pre_partition": True, + "machine_list_file": TESTS_DIR / "mlist.txt", + "tree_learner": "data", + "force_row_wise": True, + "verbose": 0, + "num_boost_round": 20, + "num_leaves": 15, + "num_threads": 2, + } + + default_predict_config = { + "task": "predict", + "data": TESTS_DIR / "train.txt", + "input_model": TESTS_DIR / "model0.txt", + "output_result": TESTS_DIR / "predictions.txt", + } + + def __init__(self, executable: str): + self.executable = executable + + def worker_train(self, i: int) -> subprocess.CompletedProcess: + """Start the training process on the `i`-th worker.""" + config_path = TESTS_DIR / f"train{i}.conf" + cmd = [self.executable, f"config={config_path}"] + return subprocess.run(cmd, check=True) + + def _set_ports(self) -> None: + """Randomly assign a port for training to each worker and save all ports to mlist.txt.""" + ports = set(_generate_n_ports(self.n_workers)) + i = 0 + max_tries = 100 + while i < max_tries and len(ports) < self.n_workers: + n_ports_left = self.n_workers - len(ports) + candidates = _generate_n_ports(n_ports_left) + ports.update(candidates) + i += 1 + if i == max_tries: + raise RuntimeError("Unable to find non-colliding ports.") + self.listen_ports = list(ports) + with open(TESTS_DIR / "mlist.txt", "wt") as f: + for port in self.listen_ports: + f.write(f"127.0.0.1 {port}\n") + + def _write_data(self, partitions: List[np.ndarray]) -> None: + """Write all training data as train.txt and each training partition as train{i}.txt.""" + all_data = np.vstack(partitions) + np.savetxt(str(TESTS_DIR / "train.txt"), all_data, delimiter=",") + for i, partition in enumerate(partitions): + np.savetxt(str(TESTS_DIR / f"train{i}.txt"), partition, delimiter=",") + + def fit(self, partitions: List[np.ndarray], train_config: Dict) -> None: + """Run the distributed training process on a single machine. + + For each worker i: + 1. The i-th partition is saved as train{i}.txt. + 2. A random port is assigned for training. + 3. A configuration file train{i}.conf is created. + 4. The lightgbm binary is called with config=train{i}.conf in another thread. + 5. The trained model is saved as model{i}.txt. Each model file only differs in data and local_listen_port. + The whole training set is saved as train.txt. + """ + self.train_config = copy.deepcopy(self.default_train_config) + self.train_config.update(train_config) + self.n_workers = self.train_config["num_machines"] + self._set_ports() + self._write_data(partitions) + self.label_ = np.hstack([partition[:, 0] for partition in partitions]) + futures = [] + with ThreadPoolExecutor(max_workers=self.n_workers) as executor: + for i in range(self.n_workers): + self.write_train_config(i) + train_future = executor.submit(self.worker_train, i) + futures.append(train_future) + results = [f.result() for f in futures] + for result in results: + if result.returncode != 0: + raise RuntimeError("Error in training") + + def predict(self, predict_config: Dict[str, Any]) -> np.ndarray: + """Compute the predictions using the model created in the fit step. + + predict_config is used to predict the training set train.txt + The predictions are saved as predictions.txt and are then loaded to return them as a numpy array. + """ + self.predict_config = copy.deepcopy(self.default_predict_config) + self.predict_config.update(predict_config) + config_path = TESTS_DIR / "predict.conf" + with open(config_path, "wt") as file: + _write_dict(self.predict_config, file) + cmd = [self.executable, f"config={config_path}"] + result = subprocess.run(cmd, check=True) + if result.returncode != 0: + raise RuntimeError("Error in prediction") + return np.loadtxt(str(TESTS_DIR / "predictions.txt")) + + def write_train_config(self, i: int) -> None: + """Create a file train{i}.conf with the required configuration to train. + + Each worker gets a different port and piece of the data, the rest are the + model parameters contained in `self.config`. + """ + with open(TESTS_DIR / f"train{i}.conf", "wt") as file: + output_model = TESTS_DIR / f"model{i}.txt" + data = TESTS_DIR / f"train{i}.txt" + file.write(f"output_model = {output_model}\n") + file.write(f"local_listen_port = {self.listen_ports[i]}\n") + file.write(f"data = {data}\n") + _write_dict(self.train_config, file) + + +def test_classifier(executable): + """Test the classification task.""" + num_machines = 2 + data = create_data(task="binary-classification") + partitions = np.array_split(data, num_machines) + train_params = { + "objective": "binary", + "num_machines": num_machines, + } + clf = DistributedMockup(executable) + clf.fit(partitions, train_params) + y_probas = clf.predict(predict_config={}) + y_pred = y_probas > 0.5 + assert accuracy_score(clf.label_, y_pred) == 1.0 + + +def test_regressor(executable): + """Test the regression task.""" + num_machines = 2 + data = create_data(task="regression") + partitions = np.array_split(data, num_machines) + train_params = { + "objective": "regression", + "num_machines": num_machines, + } + reg = DistributedMockup(executable) + reg.fit(partitions, train_params) + y_pred = reg.predict(predict_config={}) + np.testing.assert_allclose(y_pred, reg.label_, rtol=0.2, atol=50.0) diff --git a/tests/distributed/conftest.py b/tests/distributed/conftest.py new file mode 100644 index 0000000..ef62f3f --- /dev/null +++ b/tests/distributed/conftest.py @@ -0,0 +1,7 @@ +from pathlib import Path + +default_exec_file = Path(__file__).absolute().parents[2] / "lightgbm" + + +def pytest_addoption(parser): + parser.addoption("--execfile", action="store", default=str(default_exec_file)) diff --git a/tests/python_package_test/__init__.py b/tests/python_package_test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/python_package_test/conftest.py b/tests/python_package_test/conftest.py new file mode 100644 index 0000000..7d9c5b2 --- /dev/null +++ b/tests/python_package_test/conftest.py @@ -0,0 +1,12 @@ +import numpy as np +import pytest + + +@pytest.fixture(scope="function") +def rng(): + return np.random.default_rng() + + +@pytest.fixture(scope="function") +def rng_fixed_seed(): + return np.random.default_rng(seed=42) diff --git a/tests/python_package_test/test_arrow.py b/tests/python_package_test/test_arrow.py new file mode 100644 index 0000000..e997597 --- /dev/null +++ b/tests/python_package_test/test_arrow.py @@ -0,0 +1,512 @@ +# coding: utf-8 +import filecmp +from pathlib import Path +from typing import Any, Dict, Optional + +import numpy as np +import pytest + +import lightgbm as lgb + +from .utils import np_assert_array_equal + +pa = pytest.importorskip("pyarrow") + + +# ----------------------------------------------------------------------------------------------- # +# UTILITIES # +# ----------------------------------------------------------------------------------------------- # + +_INTEGER_TYPES = [ + pa.int8(), + pa.int16(), + pa.int32(), + pa.int64(), + pa.uint8(), + pa.uint16(), + pa.uint32(), + pa.uint64(), +] +_FLOAT_TYPES = [ + pa.float32(), + pa.float64(), +] + + +def generate_simple_arrow_table(empty_chunks: bool = False) -> pa.Table: + c: list[list[int]] = [[]] if empty_chunks else [] + columns = [ + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.uint8()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.int8()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.uint16()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.int16()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.uint32()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.int32()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.uint64()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.int64()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.float32()), + pa.chunked_array(c + [[1, 2, 3]] + c + [[4, 5]] + c, type=pa.float64()), + pa.chunked_array(c + [[True, True, False]] + c + [[False, True]] + c, type=pa.bool_()), + ] + return pa.Table.from_arrays(columns, names=[f"col_{i}" for i in range(len(columns))]) + + +def generate_nullable_arrow_table(dtype: Any) -> pa.Table: + columns = [ + pa.chunked_array([[1, None, 3, 4, 5]], type=dtype), + pa.chunked_array([[None, 2, 3, 4, 5]], type=dtype), + pa.chunked_array([[1, 2, 3, 4, None]], type=dtype), + pa.chunked_array([[None, None, None, None, None]], type=dtype), + ] + return pa.Table.from_arrays(columns, names=[f"col_{i}" for i in range(len(columns))]) + + +def generate_dummy_arrow_table() -> pa.Table: + col1 = pa.chunked_array([[1, 2, 3], [4, 5]], type=pa.uint8()) + col2 = pa.chunked_array([[0.5, 0.6], [0.1, 0.8, 1.5]], type=pa.float32()) + return pa.Table.from_arrays([col1, col2], names=["a", "b"]) + + +def generate_random_arrow_table( + num_columns: int, + num_datapoints: int, + seed: int, + generate_nulls: bool = True, + values: Optional[np.ndarray] = None, +) -> pa.Table: + columns = [ + generate_random_arrow_array(num_datapoints, seed + i, generate_nulls=generate_nulls, values=values) + for i in range(num_columns) + ] + names = [f"col_{i}" for i in range(num_columns)] + return pa.Table.from_arrays(columns, names=names) + + +def generate_random_arrow_array( + num_datapoints: int, + seed: int, + generate_nulls: bool = True, + values: Optional[np.ndarray] = None, +) -> pa.ChunkedArray: + generator = np.random.default_rng(seed) + data = ( + generator.standard_normal(num_datapoints) + if values is None + else generator.choice(values, size=num_datapoints, replace=True) + ) + + # Set random nulls + if generate_nulls: + indices = generator.choice(len(data), size=num_datapoints // 10) + data[indices] = None + + # Split data into <=2 random chunks + split_points = np.sort(generator.choice(np.arange(1, num_datapoints), 2, replace=False)) + split_points = np.concatenate([[0], split_points, [num_datapoints]]) + chunks = [data[split_points[i] : split_points[i + 1]] for i in range(len(split_points) - 1)] + chunks = [chunk for chunk in chunks if len(chunk) > 0] + + # Turn chunks into array + return pa.chunked_array(chunks, type=pa.float32()) + + +def dummy_dataset_params() -> Dict[str, Any]: + return { + "min_data_in_bin": 1, + "min_data_in_leaf": 1, + } + + +# ----------------------------------------------------------------------------------------------- # +# UNIT TESTS # +# ----------------------------------------------------------------------------------------------- # + +# ------------------------------------------- DATASET ------------------------------------------- # + + +def assert_datasets_equal(tmp_path: Path, lhs: lgb.Dataset, rhs: lgb.Dataset): + lhs._dump_text(tmp_path / "arrow.txt") + rhs._dump_text(tmp_path / "pandas.txt") + assert filecmp.cmp(tmp_path / "arrow.txt", tmp_path / "pandas.txt") + + +@pytest.mark.parametrize( + ("arrow_table_fn", "dataset_params"), + [ # Use lambda functions here to minimize memory consumption + (generate_simple_arrow_table, dummy_dataset_params()), + (lambda: generate_simple_arrow_table(empty_chunks=True), dummy_dataset_params()), + (generate_dummy_arrow_table, dummy_dataset_params()), + (lambda: generate_nullable_arrow_table(pa.float32()), dummy_dataset_params()), + (lambda: generate_nullable_arrow_table(pa.int32()), dummy_dataset_params()), + (lambda: generate_random_arrow_table(3, 1000, 42), {}), + (lambda: generate_random_arrow_table(100, 10000, 43), {}), + ], +) +def test_dataset_construct_fuzzy(tmp_path, arrow_table_fn, dataset_params): + arrow_table = arrow_table_fn() + + arrow_dataset = lgb.Dataset(arrow_table, params=dataset_params) + arrow_dataset.construct() + + pandas_dataset = lgb.Dataset(arrow_table.to_pandas(), params=dataset_params) + pandas_dataset.construct() + + assert_datasets_equal(tmp_path, arrow_dataset, pandas_dataset) + + +def test_dataset_construct_fuzzy_boolean(tmp_path): + boolean_data = generate_random_arrow_table(10, 10000, 42, generate_nulls=False, values=np.array([True, False])) + + float_schema = pa.schema([pa.field(f"col_{i}", pa.float32()) for i in range(len(boolean_data.columns))]) + float_data = boolean_data.cast(float_schema) + + arrow_dataset = lgb.Dataset(boolean_data) + arrow_dataset.construct() + + pandas_dataset = lgb.Dataset(float_data.to_pandas()) + pandas_dataset.construct() + + assert_datasets_equal(tmp_path, arrow_dataset, pandas_dataset) + + +# -------------------------------------------- FIELDS ------------------------------------------- # + + +def test_dataset_construct_fields_fuzzy(): + arrow_table = generate_random_arrow_table(3, 1000, 42) + arrow_labels = generate_random_arrow_array(1000, 42, generate_nulls=False) + arrow_weights = generate_random_arrow_array(1000, 42, generate_nulls=False) + arrow_groups = pa.chunked_array([[300, 400, 50], [250]], type=pa.int32()) + + arrow_dataset = lgb.Dataset(arrow_table, label=arrow_labels, weight=arrow_weights, group=arrow_groups) + arrow_dataset.construct() + + pandas_dataset = lgb.Dataset( + arrow_table.to_pandas(), + label=arrow_labels.to_numpy(), + weight=arrow_weights.to_numpy(), + group=arrow_groups.to_numpy(), + ) + pandas_dataset.construct() + + # Check for equality + for field in ("label", "weight", "group"): + np_assert_array_equal(arrow_dataset.get_field(field), pandas_dataset.get_field(field), strict=True) + np_assert_array_equal(arrow_dataset.get_label(), pandas_dataset.get_label(), strict=True) + np_assert_array_equal(arrow_dataset.get_weight(), pandas_dataset.get_weight(), strict=True) + + +# -------------------------------------------- LABELS ------------------------------------------- # + + +@pytest.mark.parametrize( + "label_data", + [ + [[0, 1, 0, 0, 1]], + [[0], [1, 0, 0, 1]], + [[], [0], [1, 0, 0, 1]], + [[0], [], [1, 0], [], [], [0, 1], []], + ], +) +@pytest.mark.parametrize("arrow_type", _INTEGER_TYPES + _FLOAT_TYPES) +def test_dataset_construct_labels(label_data, arrow_type): + data = generate_dummy_arrow_table() + labels = pa.chunked_array(label_data, type=arrow_type) + dataset = lgb.Dataset(data, label=labels, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 1, 0, 0, 1], dtype=np.float32) + np_assert_array_equal(expected, dataset.get_label(), strict=True) + + +@pytest.mark.parametrize( + "label_data", + [ + [[False, True, False, False, True]], + [[False], [True, False, False, True]], + [[], [False], [True, False, False, True]], + [[False], [], [True, False], [], [], [False, True], []], + ], +) +def test_dataset_construct_labels_boolean(label_data): + data = generate_dummy_arrow_table() + labels = pa.chunked_array(label_data, type=pa.bool_()) + dataset = lgb.Dataset(data, label=labels, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 1, 0, 0, 1], dtype=np.float32) + np_assert_array_equal(expected, dataset.get_label(), strict=True) + + +# ------------------------------------------- WEIGHTS ------------------------------------------- # + + +def test_dataset_construct_weights_none(): + data = generate_dummy_arrow_table() + weight = pa.chunked_array([[1, 1, 1, 1, 1]]) + dataset = lgb.Dataset(data, weight=weight, params=dummy_dataset_params()) + dataset.construct() + assert dataset.get_weight() is None + assert dataset.get_field("weight") is None + + +@pytest.mark.parametrize( + "weight_data", + [ + [[3, 0.7, 1.5, 0.5, 0.1]], + [[3], [0.7, 1.5, 0.5, 0.1]], + [[], [3], [0.7, 1.5, 0.5, 0.1]], + [[3], [0.7], [], [], [1.5, 0.5, 0.1], []], + ], +) +@pytest.mark.parametrize("arrow_type", _FLOAT_TYPES) +def test_dataset_construct_weights(weight_data, arrow_type): + data = generate_dummy_arrow_table() + weights = pa.chunked_array(weight_data, type=arrow_type) + dataset = lgb.Dataset(data, weight=weights, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([3, 0.7, 1.5, 0.5, 0.1], dtype=np.float32) + np_assert_array_equal(expected, dataset.get_weight(), strict=True) + + +# -------------------------------------------- GROUPS ------------------------------------------- # + + +@pytest.mark.parametrize( + "group_data", + [ + [[2, 3]], + [[2], [3]], + [[], [2, 3]], + [[2], [], [3], []], + ], +) +@pytest.mark.parametrize("arrow_type", _INTEGER_TYPES) +def test_dataset_construct_groups(group_data, arrow_type): + data = generate_dummy_arrow_table() + groups = pa.chunked_array(group_data, type=arrow_type) + dataset = lgb.Dataset(data, group=groups, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 2, 5], dtype=np.int32) + np_assert_array_equal(expected, dataset.get_field("group"), strict=True) + + +# ----------------------------------------- INIT SCORES ----------------------------------------- # + + +@pytest.mark.parametrize( + "init_score_data", + [ + [[0, 1, 2, 3, 3]], + [[0, 1, 2], [3, 3]], + [[], [0, 1, 2], [3, 3]], + [[0, 1], [], [], [2], [3, 3], []], + ], +) +@pytest.mark.parametrize("arrow_type", _INTEGER_TYPES + _FLOAT_TYPES) +def test_dataset_construct_init_scores_array(init_score_data, arrow_type): + data = generate_dummy_arrow_table() + init_scores = pa.chunked_array(init_score_data, type=arrow_type) + dataset = lgb.Dataset(data, init_score=init_scores, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 1, 2, 3, 3], dtype=np.float64) + np_assert_array_equal(expected, dataset.get_init_score(), strict=True) + + +def test_dataset_construct_init_scores_table(): + data = generate_dummy_arrow_table() + init_scores = pa.Table.from_arrays( + [ + generate_random_arrow_array(5, seed=1, generate_nulls=False), + generate_random_arrow_array(5, seed=2, generate_nulls=False), + generate_random_arrow_array(5, seed=3, generate_nulls=False), + ], + names=["a", "b", "c"], + ) + dataset = lgb.Dataset(data, init_score=init_scores, params=dummy_dataset_params()) + dataset.construct() + + actual = dataset.get_init_score() + expected = init_scores.to_pandas().to_numpy().astype(np.float64) + np_assert_array_equal(expected, actual, strict=True) + + +# ------------------------------------------ PREDICTION ----------------------------------------- # + + +def assert_equal_predict_arrow_pandas(booster: lgb.Booster, data: pa.Table): + p_arrow = booster.predict(data) + p_pandas = booster.predict(data.to_pandas()) + np_assert_array_equal(p_arrow, p_pandas, strict=True) + + p_raw_arrow = booster.predict(data, raw_score=True) + p_raw_pandas = booster.predict(data.to_pandas(), raw_score=True) + np_assert_array_equal(p_raw_arrow, p_raw_pandas, strict=True) + + p_leaf_arrow = booster.predict(data, pred_leaf=True) + p_leaf_pandas = booster.predict(data.to_pandas(), pred_leaf=True) + np_assert_array_equal(p_leaf_arrow, p_leaf_pandas, strict=True) + + p_pred_contrib_arrow = booster.predict(data, pred_contrib=True) + p_pred_contrib_pandas = booster.predict(data.to_pandas(), pred_contrib=True) + np_assert_array_equal(p_pred_contrib_arrow, p_pred_contrib_pandas, strict=True) + + p_first_iter_arrow = booster.predict(data, start_iteration=0, num_iteration=1, raw_score=True) + p_first_iter_pandas = booster.predict(data.to_pandas(), start_iteration=0, num_iteration=1, raw_score=True) + np_assert_array_equal(p_first_iter_arrow, p_first_iter_pandas, strict=True) + + +def test_predict_regression(): + data_float = generate_random_arrow_table(10, 10000, 42) + data_bool = generate_random_arrow_table(1, 10000, 42, generate_nulls=False, values=np.array([True, False])) + data = pa.Table.from_arrays(data_float.columns + data_bool.columns, names=data_float.schema.names + ["col_bool"]) + + dataset = lgb.Dataset( + data, + label=generate_random_arrow_array(10000, 43, generate_nulls=False), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "regression", "num_leaves": 7}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_arrow_pandas(booster, data) + + +def test_predict_binary_classification(): + data = generate_random_arrow_table(10, 10000, 42) + dataset = lgb.Dataset( + data, + label=generate_random_arrow_array(10000, 43, generate_nulls=False, values=np.arange(2)), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "binary", "num_leaves": 7}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_arrow_pandas(booster, data) + + +def test_predict_multiclass_classification(): + data = generate_random_arrow_table(10, 10000, 42) + dataset = lgb.Dataset( + data, + label=generate_random_arrow_array(10000, 43, generate_nulls=False, values=np.arange(5)), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "multiclass", "num_leaves": 7, "num_class": 5}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_arrow_pandas(booster, data) + + +def test_predict_ranking(): + data = generate_random_arrow_table(10, 10000, 42) + dataset = lgb.Dataset( + data, + label=generate_random_arrow_array(10000, 43, generate_nulls=False, values=np.arange(4)), + group=np.array([1000, 2000, 3000, 4000]), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "lambdarank", "num_leaves": 7}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_arrow_pandas(booster, data) + + +def test_arrow_feature_name_auto(): + data = generate_dummy_arrow_table() + dataset = lgb.Dataset( + data, + label=pa.chunked_array([[0, 1, 0, 0, 1]]), + params=dummy_dataset_params(), + categorical_feature=["a"], + ) + booster = lgb.train({"num_leaves": 7}, dataset, num_boost_round=5) + assert booster.feature_name() == ["a", "b"] + + +def test_arrow_feature_name_manual(): + data = generate_dummy_arrow_table() + dataset = lgb.Dataset( + data, + label=pa.chunked_array([[0, 1, 0, 0, 1]]), + params=dummy_dataset_params(), + feature_name=["c", "d"], + categorical_feature=["c"], + ) + booster = lgb.train({"num_leaves": 7}, dataset, num_boost_round=5) + assert booster.feature_name() == ["c", "d"] + + +def pyarrow_array_equal(arr1: pa.ChunkedArray, arr2: pa.ChunkedArray) -> bool: + """Similar to ``np.array_equal()``, but for ``pyarrow.Array`` objects. + + ``pyarrow.Array`` objects with identical values do not compare equal if any of those + values are nulls. This function treats them as equal. + """ + if len(arr1) != len(arr2): + return False + + np1 = arr1.to_numpy() + np2 = arr2.to_numpy() + return np.array_equal(np1, np2, equal_nan=True) + + +def test_get_data_arrow_table(): + original_table = generate_simple_arrow_table() + dataset = lgb.Dataset(original_table, free_raw_data=False) + dataset.construct() + + returned_data = dataset.get_data() + assert isinstance(returned_data, pa.Table) + assert returned_data.schema == original_table.schema + assert returned_data.shape == original_table.shape + + for column_name in original_table.column_names: + original_column = original_table[column_name] + returned_column = returned_data[column_name] + + assert original_column.type == returned_column.type + assert original_column.num_chunks == returned_column.num_chunks + assert pyarrow_array_equal(original_column, returned_column) + + for i in range(original_column.num_chunks): + original_chunk_array = pa.chunked_array([original_column.chunk(i)]) + returned_chunk_array = pa.chunked_array([returned_column.chunk(i)]) + assert pyarrow_array_equal(original_chunk_array, returned_chunk_array) + + +def test_get_data_arrow_table_subset(rng): + original_table = generate_random_arrow_table(num_columns=3, num_datapoints=1000, seed=42) + dataset = lgb.Dataset(original_table, free_raw_data=False) + dataset.construct() + + subset_size = 100 + used_indices = rng.choice(a=original_table.shape[0], size=subset_size, replace=False) + used_indices = sorted(used_indices) + + subset_dataset = dataset.subset(used_indices).construct() + expected_subset = original_table.take(used_indices) + subset_data = subset_dataset.get_data() + + assert isinstance(subset_data, pa.Table) + assert subset_data.schema == expected_subset.schema + assert subset_data.shape == expected_subset.shape + assert len(subset_data) == len(used_indices) + assert subset_data.shape == (subset_size, 3) + + for column_name in expected_subset.column_names: + expected_col = expected_subset[column_name] + returned_col = subset_data[column_name] + assert expected_col.type == returned_col.type + assert pyarrow_array_equal(expected_col, returned_col) diff --git a/tests/python_package_test/test_basic.py b/tests/python_package_test/test_basic.py new file mode 100644 index 0000000..37f541e --- /dev/null +++ b/tests/python_package_test/test_basic.py @@ -0,0 +1,1277 @@ +# coding: utf-8 +import filecmp +import numbers +import re +import signal +import warnings +from copy import deepcopy +from pathlib import Path + +import numpy as np +import pytest +from scipy import sparse +from sklearn.datasets import dump_svmlight_file, load_svmlight_file, make_blobs +from sklearn.model_selection import train_test_split + +import lightgbm as lgb + +from .utils import BuildInfo, dummy_obj, load_breast_cancer, mse_obj, np_assert_array_equal + + +def test_basic(tmp_path): + X_train, X_test, y_train, y_test = train_test_split( + *load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2 + ) + feature_names = [f"Column_{i}" for i in range(X_train.shape[1])] + feature_names[1] = "a" * 1000 # set one name to a value longer than default buffer size + train_data = lgb.Dataset(X_train, label=y_train, feature_name=feature_names) + valid_data = train_data.create_valid(X_test, label=y_test) + + params = { + "objective": "binary", + "metric": "auc", + "min_data": 10, + "num_leaves": 15, + "verbose": -1, + "num_threads": 1, + "max_bin": 255, + "gpu_use_dp": True, + } + bst = lgb.Booster(params, train_data) + bst.add_valid(valid_data, "valid_1") + + for i in range(20): + bst.update() + if i % 10 == 0: + print(bst.eval_train(), bst.eval_valid()) + + assert train_data.get_feature_name() == feature_names + + assert bst.current_iteration() == 20 + assert bst.num_trees() == 20 + assert bst.num_model_per_iteration() == 1 + if not BuildInfo.has_cuda: + assert bst.lower_bound() == pytest.approx(-2.9040190126976606) + assert bst.upper_bound() == pytest.approx(3.3182142872462883) + + tname = tmp_path / "svm_light.dat" + model_file = tmp_path / "model.txt" + + bst.save_model(model_file) + pred_from_matr = bst.predict(X_test) + with open(tname, "w+b") as f: + dump_svmlight_file(X_test, y_test, f) + pred_from_file = bst.predict(tname) + np.testing.assert_allclose(pred_from_matr, pred_from_file) + + # check saved model persistence + bst = lgb.Booster(params, model_file=model_file) + assert bst.feature_name() == feature_names + pred_from_model_file = bst.predict(X_test) + # we need to check the consistency of model file here, so test for exact equal + np_assert_array_equal(pred_from_matr, pred_from_model_file, strict=True) + + # check early stopping is working. Make it stop very early, so the scores should be very close to zero + pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5} + pred_early_stopping = bst.predict(X_test, **pred_parameter) + # scores likely to be different, but prediction should still be the same + np_assert_array_equal(np.sign(pred_from_matr), np.sign(pred_early_stopping), strict=True) + + # test that shape is checked during prediction + bad_X_test = X_test[:, 1:] + bad_shape_error_msg = "The number of features in data*" + np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg, bst.predict, bad_X_test) + np.testing.assert_raises_regex( + lgb.basic.LightGBMError, bad_shape_error_msg, bst.predict, sparse.csr_matrix(bad_X_test) + ) + np.testing.assert_raises_regex( + lgb.basic.LightGBMError, bad_shape_error_msg, bst.predict, sparse.csc_matrix(bad_X_test) + ) + with open(tname, "w+b") as f: + dump_svmlight_file(bad_X_test, y_test, f) + np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg, bst.predict, tname) + with open(tname, "w+b") as f: + dump_svmlight_file(X_test, y_test, f, zero_based=False) + np.testing.assert_raises_regex(lgb.basic.LightGBMError, bad_shape_error_msg, bst.predict, tname) + + +def test_booster_rollback_one_iter(rng): + """Test that Booster.rollback_one_iter() correctly rolls back one boosting iteration.""" + X = rng.uniform(size=(100, 5)) + y = rng.integers(0, 2, size=(100,)) + X_test = rng.uniform(size=(10, 5)) + + train_data = lgb.Dataset(X, label=y) + params = { + "objective": "binary", + "verbose": -1, + } + bst = lgb.Booster(params, train_data) + + # Train for 10 iterations + num_iterations = 10 + for _ in range(num_iterations): + bst.update() + + assert bst.current_iteration() == num_iterations + assert bst.num_trees() == num_iterations + + # Get predictions before rollback + pred_before = bst.predict(X_test) + + # Rollback one iteration + result = bst.rollback_one_iter() + + # Verify rollback decremented both iteration count and tree count + assert bst.current_iteration() == num_iterations - 1 + assert bst.num_trees() == num_iterations - 1 + # Verify it returns self for method chaining + assert result is bst + + # Verify predictions actually changed (proves tree was removed, not just counter) + pred_after = bst.predict(X_test) + assert not np.allclose(pred_before, pred_after) + + # Verify multiple rollbacks work + bst.rollback_one_iter() + assert bst.current_iteration() == num_iterations - 2 + assert bst.num_trees() == num_iterations - 2 + + +class NumpySequence(lgb.Sequence): + def __init__(self, ndarray, batch_size): + self.ndarray = ndarray + self.batch_size = batch_size + + def __getitem__(self, idx): + # The simple implementation is just a single "return self.ndarray[idx]" + # The following is for demo and testing purpose. + if isinstance(idx, numbers.Integral): + return self.ndarray[idx] + elif isinstance(idx, slice): + if not (idx.step is None or idx.step == 1): + raise NotImplementedError("No need to implement, caller will not set step by now") + return self.ndarray[idx.start : idx.stop] + elif isinstance(idx, list): + return self.ndarray[idx] + else: + raise TypeError(f"Sequence Index must be an integer/list/slice, got {type(idx).__name__}") + + def __len__(self): + return len(self.ndarray) + + +def _create_sequence_from_ndarray(data, num_seq, batch_size): + if num_seq == 1: + return NumpySequence(data, batch_size) + + nrow = data.shape[0] + seqs = [] + seq_size = nrow // num_seq + for start in range(0, nrow, seq_size): + end = min(start + seq_size, nrow) + seq = NumpySequence(data[start:end], batch_size) + seqs.append(seq) + return seqs + + +@pytest.mark.parametrize("sample_count", [11, 100, None]) +@pytest.mark.parametrize("batch_size", [3, None]) +@pytest.mark.parametrize("include_0_and_nan", [False, True]) +@pytest.mark.parametrize("num_seq", [1, 3]) +def test_sequence(tmpdir, sample_count, batch_size, include_0_and_nan, num_seq, rng): + params = {"bin_construct_sample_cnt": sample_count} + + nrow = 50 + half_nrow = nrow // 2 + ncol = 11 + data = np.arange(nrow * ncol, dtype=np.float64).reshape((nrow, ncol)) + + if include_0_and_nan: + # whole col + data[:, 0] = 0 + data[:, 1] = np.nan + + # half col + data[:half_nrow, 3] = 0 + data[:half_nrow, 2] = np.nan + + data[half_nrow:-2, 4] = 0 + data[:half_nrow, 4] = np.nan + + X = data[:, :-1] + Y = data[:, -1] + + npy_bin_fname = tmpdir / "data_from_npy.bin" + seq_bin_fname = tmpdir / "data_from_seq.bin" + + # Create dataset from numpy array directly. + ds = lgb.Dataset(X, label=Y, params=params) + ds.save_binary(npy_bin_fname) + + # Create dataset using Sequence. + seqs = _create_sequence_from_ndarray(X, num_seq, batch_size) + seq_ds = lgb.Dataset(seqs, label=Y, params=params) + seq_ds.save_binary(seq_bin_fname) + + assert filecmp.cmp(npy_bin_fname, seq_bin_fname) + + # Test for validation set. + # Select some random rows as valid data. + valid_idx = (rng.random(10) * nrow).astype(np.int32) + valid_data = data[valid_idx, :] + valid_X = valid_data[:, :-1] + valid_Y = valid_data[:, -1] + + valid_npy_bin_fname = tmpdir / "valid_data_from_npy.bin" + valid_seq_bin_fname = tmpdir / "valid_data_from_seq.bin" + valid_seq2_bin_fname = tmpdir / "valid_data_from_seq2.bin" + + valid_ds = lgb.Dataset(valid_X, label=valid_Y, params=params, reference=ds) + valid_ds.save_binary(valid_npy_bin_fname) + + # From Dataset constructor, with dataset from numpy array. + valid_seqs = _create_sequence_from_ndarray(valid_X, num_seq, batch_size) + valid_seq_ds = lgb.Dataset(valid_seqs, label=valid_Y, params=params, reference=ds) + valid_seq_ds.save_binary(valid_seq_bin_fname) + assert filecmp.cmp(valid_npy_bin_fname, valid_seq_bin_fname) + + # From Dataset.create_valid, with dataset from sequence. + valid_seq_ds2 = seq_ds.create_valid(valid_seqs, label=valid_Y, params=params) + valid_seq_ds2.save_binary(valid_seq2_bin_fname) + assert filecmp.cmp(valid_npy_bin_fname, valid_seq2_bin_fname) + + +@pytest.mark.parametrize("num_seq", [1, 2]) +def test_sequence_get_data(num_seq, rng): + nrow = 20 + ncol = 11 + data = np.arange(nrow * ncol, dtype=np.float64).reshape((nrow, ncol)) + X = data[:, :-1] + Y = data[:, -1] + + seqs = _create_sequence_from_ndarray(data=X, num_seq=num_seq, batch_size=6) + seq_ds = lgb.Dataset(seqs, label=Y, params=None, free_raw_data=False).construct() + assert seq_ds.get_data() == seqs + + used_indices = rng.choice(a=np.arange(nrow), size=nrow // 3, replace=False) + subset_data = seq_ds.subset(used_indices).construct() + np_assert_array_equal(subset_data.get_data(), X[sorted(used_indices)], strict=True) + + +def test_chunked_dataset(): + X_train, X_test, y_train, y_test = train_test_split( + *load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2 + ) + + chunk_size = X_train.shape[0] // 10 + 1 + X_train = [X_train[i * chunk_size : (i + 1) * chunk_size, :] for i in range(X_train.shape[0] // chunk_size + 1)] + X_test = [X_test[i * chunk_size : (i + 1) * chunk_size, :] for i in range(X_test.shape[0] // chunk_size + 1)] + + train_data = lgb.Dataset(X_train, label=y_train, params={"bin_construct_sample_cnt": 100}) + valid_data = train_data.create_valid(X_test, label=y_test, params={"bin_construct_sample_cnt": 100}) + train_data.construct() + valid_data.construct() + + +def test_chunked_dataset_linear(): + X_train, X_test, y_train, y_test = train_test_split( + *load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2 + ) + chunk_size = X_train.shape[0] // 10 + 1 + X_train = [X_train[i * chunk_size : (i + 1) * chunk_size, :] for i in range(X_train.shape[0] // chunk_size + 1)] + X_test = [X_test[i * chunk_size : (i + 1) * chunk_size, :] for i in range(X_test.shape[0] // chunk_size + 1)] + params = {"bin_construct_sample_cnt": 100, "linear_tree": True} + train_data = lgb.Dataset(X_train, label=y_train, params=params) + valid_data = train_data.create_valid(X_test, label=y_test, params=params) + train_data.construct() + valid_data.construct() + + +def test_save_dataset_subset_and_load_from_file(tmp_path, rng): + data = rng.standard_normal(size=(100, 2)) + params = {"max_bin": 50, "min_data_in_bin": 10} + ds = lgb.Dataset(data, params=params) + ds.subset([1, 2, 3, 5, 8]).save_binary(tmp_path / "subset.bin") + lgb.Dataset(tmp_path / "subset.bin", params=params).construct() + + +def test_save_binary_raises_on_truncated_write(tmp_path, rng): + resource = pytest.importorskip("resource") + if not hasattr(signal, "SIGXFSZ"): + pytest.skip("SIGXFSZ is not available on this platform") + + data = rng.standard_normal(size=(1000, 20)) + ds = lgb.Dataset(data).construct() + original_limit = resource.getrlimit(resource.RLIMIT_FSIZE) + original_signal_handler = signal.getsignal(signal.SIGXFSZ) + try: + signal.signal(signal.SIGXFSZ, signal.SIG_IGN) + resource.setrlimit(resource.RLIMIT_FSIZE, (4096, original_limit[1])) + with pytest.raises(lgb.basic.LightGBMError, match="Cannot write binary data"): + ds.save_binary(tmp_path / "truncated.bin") + finally: + resource.setrlimit(resource.RLIMIT_FSIZE, original_limit) + signal.signal(signal.SIGXFSZ, original_signal_handler) + + +def test_subset_group(): + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train")) + q_train = np.loadtxt(str(rank_example_dir / "rank.train.query")) + lgb_train = lgb.Dataset(X_train, y_train, group=q_train) + assert len(lgb_train.get_group()) == 201 + subset = lgb_train.subset(list(range(10))).construct() + subset_group = subset.get_group() + assert len(subset_group) == 2 + assert subset_group[0] == 1 + assert subset_group[1] == 9 + + +def test_add_features_throws_if_num_data_unequal(rng): + X1 = rng.uniform(size=(100, 1)) + X2 = rng.uniform(size=(10, 1)) + d1 = lgb.Dataset(X1).construct() + d2 = lgb.Dataset(X2).construct() + with pytest.raises( + lgb.basic.LightGBMError, match="Cannot add features from other Dataset with a different number of rows" + ): + d1.add_features_from(d2) + + +def test_add_features_throws_if_datasets_unconstructed(rng): + X1 = rng.uniform(size=(100, 1)) + X2 = rng.uniform(size=(100, 1)) + err_msg = "Both source and target Datasets must be constructed before adding features" + d1 = lgb.Dataset(X1) + d2 = lgb.Dataset(X2) + with pytest.raises(ValueError, match=err_msg): + d1.add_features_from(d2) + d1 = lgb.Dataset(X1).construct() + d2 = lgb.Dataset(X2) + with pytest.raises(ValueError, match=err_msg): + d1.add_features_from(d2) + d1 = lgb.Dataset(X1) + d2 = lgb.Dataset(X2).construct() + with pytest.raises(ValueError, match=err_msg): + d1.add_features_from(d2) + + +def test_add_features_equal_data_on_alternating_used_unused(tmp_path, rng): + X = rng.uniform(size=(100, 5)) + X[:, [1, 3]] = 0 + names = [f"col_{i}" for i in range(5)] + for j in range(1, 5): + d1 = lgb.Dataset(X[:, :j], feature_name=names[:j]).construct() + d2 = lgb.Dataset(X[:, j:], feature_name=names[j:]).construct() + d1.add_features_from(d2) + d1name = tmp_path / "d1.txt" + d1._dump_text(d1name) + d = lgb.Dataset(X, feature_name=names).construct() + dname = tmp_path / "d.txt" + d._dump_text(dname) + with open(d1name, "rt") as d1f: + d1txt = d1f.read() + with open(dname, "rt") as df: + dtxt = df.read() + assert dtxt == d1txt + + +def test_add_features_same_booster_behaviour(tmp_path, rng): + X = rng.uniform(size=(100, 5)) + X[:, [1, 3]] = 0 + names = [f"col_{i}" for i in range(5)] + for j in range(1, 5): + d1 = lgb.Dataset(X[:, :j], feature_name=names[:j]).construct() + d2 = lgb.Dataset(X[:, j:], feature_name=names[j:]).construct() + d1.add_features_from(d2) + d = lgb.Dataset(X, feature_name=names).construct() + y = rng.uniform(size=(100,)) + d1.set_label(y) + d.set_label(y) + b1 = lgb.Booster(train_set=d1) + b = lgb.Booster(train_set=d) + for _ in range(10): + b.update() + b1.update() + dname = tmp_path / "d.txt" + d1name = tmp_path / "d1.txt" + b1.save_model(d1name) + b.save_model(dname) + with open(dname, "rt") as df: + dtxt = df.read() + with open(d1name, "rt") as d1f: + d1txt = d1f.read() + assert dtxt == d1txt + + +def test_add_features_from_different_sources(rng): + pd = pytest.importorskip("pandas") + n_row = 100 + n_col = 5 + X = rng.uniform(size=(n_row, n_col)) + xxs = [X, sparse.csr_matrix(X), pd.DataFrame(X)] + names = [f"col_{i}" for i in range(n_col)] + seq = _create_sequence_from_ndarray(X, 1, 30) + seq_ds = lgb.Dataset(seq, feature_name=names, free_raw_data=False).construct() + npy_list_ds = lgb.Dataset( + [X[: n_row // 2, :], X[n_row // 2 :, :]], feature_name=names, free_raw_data=False + ).construct() + immergeable_dds = [seq_ds, npy_list_ds] + for x_1 in xxs: + # test that method works even with free_raw_data=True + d1 = lgb.Dataset(x_1, feature_name=names, free_raw_data=True).construct() + d2 = lgb.Dataset(x_1, feature_name=names, free_raw_data=True).construct() + d1.add_features_from(d2) + assert d1.data is None + + # test that method works but sets raw data to None in case of immergeable data types + d1 = lgb.Dataset(x_1, feature_name=names, free_raw_data=False).construct() + for d2 in immergeable_dds: + d1.add_features_from(d2) + assert d1.data is None + + # test that method works for different data types + d1 = lgb.Dataset(x_1, feature_name=names, free_raw_data=False).construct() + res_feature_names = deepcopy(names) + for idx, x_2 in enumerate(xxs, 2): + original_type = type(d1.get_data()) + d2 = lgb.Dataset(x_2, feature_name=names, free_raw_data=False).construct() + d1.add_features_from(d2) + assert isinstance(d1.get_data(), original_type) + assert d1.get_data().shape == (n_row, n_col * idx) + res_feature_names += [f"D{idx}_{name}" for name in names] + assert d1.feature_name == res_feature_names + + +def test_add_features_does_not_fail_if_initial_dataset_has_zero_informative_features(capsys, rng): + arr_a = np.zeros((100, 1), dtype=np.float32) + arr_b = rng.uniform(size=(100, 5)) + + dataset_a = lgb.Dataset(arr_a, params={"verbose": 0}).construct() + expected_msg = ( + "[LightGBM] [Warning] There are no meaningful features which satisfy " + "the provided configuration. Decreasing Dataset parameters min_data_in_bin " + "or min_data_in_leaf and re-constructing Dataset might resolve this warning.\n" + ) + log_lines = capsys.readouterr().out + assert expected_msg in log_lines + + dataset_b = lgb.Dataset(arr_b).construct() + + original_handle = dataset_a._handle.value + dataset_a.add_features_from(dataset_b) + assert dataset_a.num_feature() == 6 + assert dataset_a.num_data() == 100 + assert dataset_a._handle.value == original_handle + + +def test_cegb_affects_behavior(tmp_path, rng): + X = rng.uniform(size=(100, 5)) + X[:, [1, 3]] = 0 + y = rng.uniform(size=(100,)) + names = [f"col_{i}" for i in range(5)] + ds = lgb.Dataset(X, feature_name=names).construct() + ds.set_label(y) + base = lgb.Booster(train_set=ds) + for _ in range(10): + base.update() + basename = tmp_path / "basename.txt" + base.save_model(basename) + with open(basename, "rt") as f: + basetxt = f.read() + # Set extremely harsh penalties, so CEGB will block most splits. + cases = [ + {"cegb_penalty_feature_coupled": [50, 100, 10, 25, 30]}, + {"cegb_penalty_feature_lazy": [1, 2, 3, 4, 5]}, + {"cegb_penalty_split": 1}, + ] + for case in cases: + booster = lgb.Booster(train_set=ds, params=case) + for _ in range(10): + booster.update() + casename = tmp_path / "casename.txt" + booster.save_model(casename) + with open(casename, "rt") as f: + casetxt = f.read() + assert basetxt != casetxt + + +def test_cegb_scaling_equalities(tmp_path, rng): + X = rng.uniform(size=(100, 5)) + X[:, [1, 3]] = 0 + y = rng.uniform(size=(100,)) + names = [f"col_{i}" for i in range(5)] + ds = lgb.Dataset(X, feature_name=names).construct() + ds.set_label(y) + # Compare pairs of penalties, to ensure scaling works as intended + pairs = [ + ( + {"cegb_penalty_feature_coupled": [1, 2, 1, 2, 1]}, + {"cegb_penalty_feature_coupled": [0.5, 1, 0.5, 1, 0.5], "cegb_tradeoff": 2}, + ), + ( + {"cegb_penalty_feature_lazy": [0.01, 0.02, 0.03, 0.04, 0.05]}, + {"cegb_penalty_feature_lazy": [0.005, 0.01, 0.015, 0.02, 0.025], "cegb_tradeoff": 2}, + ), + ({"cegb_penalty_split": 1}, {"cegb_penalty_split": 2, "cegb_tradeoff": 0.5}), + ] + for p1, p2 in pairs: + booster1 = lgb.Booster(train_set=ds, params=p1) + booster2 = lgb.Booster(train_set=ds, params=p2) + for _ in range(10): + booster1.update() + booster2.update() + p1name = tmp_path / "p1.txt" + # Reset booster1's parameters to p2, so the parameter section of the file matches. + booster1.reset_parameter(p2) + booster1.save_model(p1name) + with open(p1name, "rt") as f: + p1txt = f.read() + p2name = tmp_path / "p2.txt" + booster2.save_model(p2name) + with open(p2name, "rt") as f: + p2txt = f.read() + assert p1txt == p2txt + + +def test_consistent_state_for_dataset_fields(): + def check_asserts(data): + np.testing.assert_allclose(data.label, data.get_label()) + np.testing.assert_allclose(data.label, data.get_field("label")) + assert not np.isnan(data.label[0]) + assert not np.isinf(data.label[1]) + np.testing.assert_allclose(data.weight, data.get_weight()) + np.testing.assert_allclose(data.weight, data.get_field("weight")) + assert not np.isnan(data.weight[0]) + assert not np.isinf(data.weight[1]) + np.testing.assert_allclose(data.init_score, data.get_init_score()) + np.testing.assert_allclose(data.init_score, data.get_field("init_score")) + assert not np.isnan(data.init_score[0]) + assert not np.isinf(data.init_score[1]) + assert np.all(np.isclose([data.label[0], data.weight[0], data.init_score[0]], data.label[0])) + assert data.label[1] == pytest.approx(data.weight[1]) + assert data.feature_name == data.get_feature_name() + + X, y = load_breast_cancer(return_X_y=True) + sequence = np.ones(y.shape[0]) + sequence[0] = np.nan + sequence[1] = np.inf + feature_names = [f"f{i}" for i in range(X.shape[1])] + lgb_data = lgb.Dataset(X, sequence, weight=sequence, init_score=sequence, feature_name=feature_names).construct() + check_asserts(lgb_data) + lgb_data = lgb.Dataset(X, y).construct() + lgb_data.set_label(sequence) + lgb_data.set_weight(sequence) + lgb_data.set_init_score(sequence) + lgb_data.set_feature_name(feature_names) + check_asserts(lgb_data) + + +def test_dataset_construction_overwrites_user_provided_metadata_fields(): + X = np.array([[1.0, 2.0], [3.0, 4.0]]) + + position = np.array([0.0, 1.0], dtype=np.float32) + if BuildInfo.has_cuda: + position = None + + dtrain = lgb.Dataset( + X, + params={"min_data_in_bin": 1, "min_data_in_leaf": 1, "verbosity": -1}, + group=[1, 1], + init_score=[0.312, 0.708], + label=[1, 2], + position=position, + weight=[0.5, 1.5], + ) + + # unconstructed, get_* methods should return whatever was provided + assert dtrain.group == [1, 1] + assert dtrain.get_group() == [1, 1] + assert dtrain.init_score == [0.312, 0.708] + assert dtrain.get_init_score() == [0.312, 0.708] + assert dtrain.label == [1, 2] + assert dtrain.get_label() == [1, 2] + if not BuildInfo.has_cuda: + np_assert_array_equal(dtrain.position, np.array([0.0, 1.0], dtype=np.float32), strict=True) + np_assert_array_equal(dtrain.get_position(), np.array([0.0, 1.0], dtype=np.float32), strict=True) + assert dtrain.weight == [0.5, 1.5] + assert dtrain.get_weight() == [0.5, 1.5] + + # before construction, get_field() should raise an exception + for field_name in ["group", "init_score", "label", "position", "weight"]: + with pytest.raises(Exception, match=f"Cannot get {field_name} before construct Dataset"): + dtrain.get_field(field_name) + + # constructed, get_* methods should return numpy arrays, even when the provided + # input was a list of floats or ints + dtrain.construct() + expected_group = np.array([1, 1], dtype=np.int32) + np_assert_array_equal(dtrain.group, expected_group, strict=True) + np_assert_array_equal(dtrain.get_group(), expected_group, strict=True) + # get_field("group") returns a numpy array with boundaries, instead of size + np_assert_array_equal(dtrain.get_field("group"), np.array([0, 1, 2], dtype=np.int32), strict=True) + + expected_init_score = np.array( + [0.312, 0.708], + ) + np_assert_array_equal(dtrain.init_score, expected_init_score, strict=True) + np_assert_array_equal(dtrain.get_init_score(), expected_init_score, strict=True) + np_assert_array_equal(dtrain.get_field("init_score"), expected_init_score, strict=True) + + expected_label = np.array([1, 2], dtype=np.float32) + np_assert_array_equal(dtrain.label, expected_label, strict=True) + np_assert_array_equal(dtrain.get_label(), expected_label, strict=True) + np_assert_array_equal(dtrain.get_field("label"), expected_label, strict=True) + + if not BuildInfo.has_cuda: + # NOTE: "position" is converted to int32 on the C++ side and remapped to dense + # internal indices in encounter order. Here the input [0, 1] is already dense + # starting from 0 in encounter order, so the remap is the identity. + expected_position = np.array([0, 1], dtype=np.int32) + np_assert_array_equal(dtrain.position, expected_position, strict=True) + np_assert_array_equal(dtrain.get_position(), expected_position, strict=True) + np_assert_array_equal(dtrain.get_field("position"), expected_position, strict=True) + + expected_weight = np.array([0.5, 1.5], dtype=np.float32) + np_assert_array_equal(dtrain.weight, expected_weight, strict=True) + np_assert_array_equal(dtrain.get_weight(), expected_weight, strict=True) + np_assert_array_equal(dtrain.get_field("weight"), expected_weight, strict=True) + + +@pytest.mark.skipif( + BuildInfo.has_cuda, + reason="Positions in learning to rank is not supported in CUDA version yet", +) +def test_set_position_updates_self_position_with_remapped_int32_values(): + # Position values are remapped to dense int32 indices in the order they are first + # encountered. With input [3, 1, 0, 2, 4, 3, 1, 0, 2, 4]: + # 3 -> 0 (first encountered), 1 -> 1, 0 -> 2, 2 -> 3, 4 -> 4 + X = np.arange(20, dtype=np.float64).reshape(10, 2) + y = np.arange(10, dtype=np.float64) + position = np.array([3, 1, 0, 2, 4, 3, 1, 0, 2, 4], dtype=np.int64) + expected = np.array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4], dtype=np.int32) + + # set via constructor + dtrain = lgb.Dataset( + X, + label=y, + position=position, + params={"min_data_in_bin": 1, "min_data_in_leaf": 1, "verbosity": -1}, + ).construct() + np_assert_array_equal(dtrain.position, expected, strict=True) + np_assert_array_equal(dtrain.get_position(), expected, strict=True) + np_assert_array_equal(dtrain.get_field("position"), expected, strict=True) + + # set via set_position() on an already-constructed Dataset + dtrain2 = lgb.Dataset( + X, + label=y, + params={"min_data_in_bin": 1, "min_data_in_leaf": 1, "verbosity": -1}, + ).construct() + dtrain2.set_position(position) + np_assert_array_equal(dtrain2.position, expected, strict=True) + np_assert_array_equal(dtrain2.get_position(), expected, strict=True) + np_assert_array_equal(dtrain2.get_field("position"), expected, strict=True) + + +def test_dataset_construction_with_high_cardinality_categorical_succeeds(rng): + pd = pytest.importorskip("pandas") + X = pd.DataFrame({"x1": rng.integers(low=0, high=5_000, size=(10_000,))}) + y = rng.uniform(size=(10_000,)) + ds = lgb.Dataset(X, y, categorical_feature=["x1"]) + ds.construct() + assert ds.num_data() == 10_000 + assert ds.num_feature() == 1 + + +def test_choose_param_value(): + original_params = { + "local_listen_port": 1234, + "port": 2222, + "metric": "auc", + "num_trees": 81, + "n_iter": 13, + } + + # should resolve duplicate aliases, and prefer the main parameter + params = lgb.basic._choose_param_value( + main_param_name="local_listen_port", params=original_params, default_value=5555 + ) + assert params["local_listen_port"] == 1234 + assert "port" not in params + + # should choose the highest priority alias and set that value on main param + # if only aliases are used + params = lgb.basic._choose_param_value(main_param_name="num_iterations", params=params, default_value=17) + assert params["num_iterations"] == 13 + assert "num_trees" not in params + assert "n_iter" not in params + + # should use the default if main param and aliases are missing + params = lgb.basic._choose_param_value(main_param_name="learning_rate", params=params, default_value=0.789) + assert params["learning_rate"] == 0.789 + + # all changes should be made on copies and not modify the original + expected_params = { + "local_listen_port": 1234, + "port": 2222, + "metric": "auc", + "num_trees": 81, + "n_iter": 13, + } + assert original_params == expected_params + + +def test_choose_param_value_preserves_nones(): + # preserves None found for main param and still removes aliases + params = lgb.basic._choose_param_value( + main_param_name="num_threads", + params={"num_threads": None, "n_jobs": 4, "objective": "regression"}, + default_value=2, + ) + assert params == {"num_threads": None, "objective": "regression"} + + # correctly chooses value when only an alias is provided + params = lgb.basic._choose_param_value( + main_param_name="num_threads", params={"n_jobs": None, "objective": "regression"}, default_value=2 + ) + assert params == {"num_threads": None, "objective": "regression"} + + # adds None if that's given as the default and param not found + params = lgb.basic._choose_param_value( + main_param_name="min_data_in_leaf", params={"objective": "regression"}, default_value=None + ) + assert params == {"objective": "regression", "min_data_in_leaf": None} + + +@pytest.mark.parametrize("objective_alias", lgb.basic._ConfigAliases.get("objective")) +def test_choose_param_value_objective(objective_alias): + # If callable is found in objective + params = {objective_alias: dummy_obj} + params = lgb.basic._choose_param_value(main_param_name="objective", params=params, default_value=None) + assert params["objective"] == dummy_obj + + # Value in params should be preferred to the default_value passed from keyword arguments + params = {objective_alias: dummy_obj} + params = lgb.basic._choose_param_value(main_param_name="objective", params=params, default_value=mse_obj) + assert params["objective"] == dummy_obj + + # None of objective or its aliases in params, but default_value is callable. + params = {} + params = lgb.basic._choose_param_value(main_param_name="objective", params=params, default_value=mse_obj) + assert params["objective"] == mse_obj + + +@pytest.mark.parametrize("collection", ["1d_np", "2d_np", "pd_float", "pd_str", "1d_list", "2d_list"]) +@pytest.mark.parametrize("dtype", [np.float32, np.float64]) +def test_list_to_1d_numpy(collection, dtype, rng): + collection2y = { + "1d_np": rng.uniform(size=(10,)), + "2d_np": rng.uniform(size=(10, 1)), + "pd_float": rng.uniform(size=(10,)), + "pd_str": ["a", "b"], + "1d_list": [1] * 10, + "2d_list": [[1], [2]], + } + y = collection2y[collection] + custom_name = "my_custom_variable" + + if collection.startswith("pd"): + pd = pytest.importorskip("pandas") + y = pd.Series(y) + if pd.api.types.is_object_dtype(y): + with pytest.raises( + ValueError, + match=r"pandas dtypes must be int, float or bool\.\nFields with bad pandas dtypes: 0: object", + ): + lgb.basic._list_to_1d_numpy(data=y, dtype=np.float32, name=custom_name) + return + elif pd.api.types.is_string_dtype(y): + with pytest.raises( + ValueError, match=r"pandas dtypes must be int, float or bool\.\nFields with bad pandas dtypes: 0: str" + ): + lgb.basic._list_to_1d_numpy(data=y, dtype=np.float32, name=custom_name) + return + + if isinstance(y, np.ndarray) and len(y.shape) == 2: + with pytest.warns(UserWarning, match="column-vector"): + lgb.basic._list_to_1d_numpy(data=y, dtype=np.float32, name=custom_name) + return + elif isinstance(y, list) and isinstance(y[0], list): + err_msg = ( + rf"Wrong type\(list\) for {custom_name}.\n" + r"It should be list, numpy 1-D array or pandas Series" + ) + with pytest.raises(TypeError, match=err_msg): + lgb.basic._list_to_1d_numpy(data=y, dtype=np.float32, name=custom_name) + return + + result = lgb.basic._list_to_1d_numpy(data=y, dtype=dtype, name=custom_name) + assert result.size == 10 + assert result.dtype == dtype + + +@pytest.mark.parametrize("init_score_type", ["array", "dataframe", "list"]) +def test_init_score_for_multiclass_classification(init_score_type, rng): + init_score = [[i * 10 + j for j in range(3)] for i in range(10)] + if init_score_type == "array": + init_score = np.array(init_score) + elif init_score_type == "dataframe": + pd = pytest.importorskip("pandas") + init_score = pd.DataFrame(init_score) + data = rng.uniform(size=(10, 2)) + ds = lgb.Dataset(data, init_score=init_score).construct() + np.testing.assert_equal(ds.get_field("init_score"), init_score) + np.testing.assert_equal(ds.init_score, init_score) + + +def test_smoke_custom_parser(tmp_path): + data_path = Path(__file__).absolute().parents[2] / "examples" / "binary_classification" / "binary.train" + parser_config_file = tmp_path / "parser.ini" + with open(parser_config_file, "w") as fout: + fout.write('{"className": "dummy", "id": "1"}') + + data = lgb.Dataset(data_path, params={"parser_config_file": parser_config_file}) + with pytest.raises( + lgb.basic.LightGBMError, match="Cannot find parser class 'dummy', please register first or check config format" + ): + data.construct() + + +def test_param_aliases(): + aliases = lgb.basic._ConfigAliases.aliases + assert isinstance(aliases, dict) + assert len(aliases) > 100 + assert all(isinstance(i, list) for i in aliases.values()) + assert all(len(i) >= 1 for i in aliases.values()) + assert all(k in v for k, v in aliases.items()) + assert lgb.basic._ConfigAliases.get("config", "task") == {"config", "config_file", "task", "task_type"} + assert lgb.basic._ConfigAliases.get_sorted("min_data_in_leaf") == [ + "min_data_in_leaf", + "min_data", + "min_samples_leaf", + "min_child_samples", + "min_data_per_leaf", + ] + + +def _bad_gradients(preds, _): + rng = np.random.default_rng() + # "bad" = 1 element too many + size = (len(preds) + 1,) + return rng.standard_normal(size=size), rng.uniform(size=size) + + +def _good_gradients(preds, _): + rng = np.random.default_rng() + return rng.standard_normal(size=preds.shape), rng.uniform(size=preds.shape) + + +def test_custom_objective_safety(rng): + nrows = 100 + X = rng.standard_normal(size=(nrows, 5)) + y_binary = np.arange(nrows) % 2 + classes = [0, 1, 2] + nclass = len(classes) + y_multiclass = np.arange(nrows) % nclass + ds_binary = lgb.Dataset(X, y_binary).construct() + ds_multiclass = lgb.Dataset(X, y_multiclass).construct() + bad_bst_binary = lgb.Booster({"objective": "none"}, ds_binary) + good_bst_binary = lgb.Booster({"objective": "none"}, ds_binary) + bad_bst_multi = lgb.Booster({"objective": "none", "num_class": nclass}, ds_multiclass) + good_bst_multi = lgb.Booster({"objective": "none", "num_class": nclass}, ds_multiclass) + good_bst_binary.update(fobj=_good_gradients) + with pytest.raises(ValueError, match=re.escape("number of models per one iteration (1)")): + bad_bst_binary.update(fobj=_bad_gradients) + good_bst_multi.update(fobj=_good_gradients) + with pytest.raises(ValueError, match=re.escape(f"number of models per one iteration ({nclass})")): + bad_bst_multi.update(fobj=_bad_gradients) + + +@pytest.mark.parametrize("dtype", [np.float32, np.float64]) +@pytest.mark.parametrize("feature_name", [["x1", "x2"], "auto"]) +def test_no_copy_when_single_float_dtype_dataframe(dtype, feature_name, rng): + pd = pytest.importorskip("pandas") + X = rng.uniform(size=(10, 2)).astype(dtype) + # copy=False is necessary because starting with pandas 3.0, pd.DataFrame() creates + # a copy of the input numpy array by default + # ref: https://github.com/pandas-dev/pandas/issues/58913 + df = pd.DataFrame(X, copy=False) + built_data = lgb.basic._data_from_pandas( + data=df, feature_name=feature_name, categorical_feature="auto", pandas_categorical=None + )[0] + assert built_data.dtype == dtype + assert np.shares_memory(X, built_data) + + +@pytest.mark.parametrize("feature_name", [["x1"], [42], "auto"]) +@pytest.mark.parametrize("categories", ["seen", "unseen"]) +def test_categorical_code_conversion_doesnt_modify_original_data(feature_name, categories, rng): + pd = pytest.importorskip("pandas") + X = rng.choice(a=["a", "b"], size=(100, 1)) + column_name = "a" if feature_name == "auto" else feature_name[0] + df = pd.DataFrame(X.copy(), columns=[column_name], dtype="category") + if categories == "seen": + pandas_categorical = [["a", "b"]] + else: + pandas_categorical = [["a"]] + data = lgb.basic._data_from_pandas( + data=df, + feature_name=feature_name, + categorical_feature="auto", + pandas_categorical=pandas_categorical, + )[0] + # check that the original data wasn't modified + np.testing.assert_equal(df[column_name], X[:, 0]) + # check that the built data has the codes + if categories == "seen": + # if all categories were seen during training we just take the codes + codes = df[column_name].cat.codes + else: + # if we only saw 'a' during training we just replace its code + # and leave the rest as nan + a_code = df[column_name].cat.categories.get_loc("a") + codes = np.where(df[column_name] == "a", a_code, np.nan) + np.testing.assert_equal(codes, data[:, 0]) + + +@pytest.mark.parametrize("min_data_in_bin", [2, 10]) +def test_feature_num_bin(min_data_in_bin, rng): + X = np.vstack( + [ + rng.uniform(size=(100,)), + np.array([1, 2] * 50), + np.array([0, 1, 2] * 33 + [0]), + np.array([1, 2] * 49 + 2 * [np.nan]), + np.zeros(100), + rng.choice(a=[0, 1], size=(100,)), + ] + ).T + n_continuous = X.shape[1] - 1 + feature_name = [f"x{i}" for i in range(n_continuous)] + ["cat1"] + ds_kwargs = { + "params": {"min_data_in_bin": min_data_in_bin}, + "categorical_feature": [n_continuous], # last feature + } + ds = lgb.Dataset(X, feature_name=feature_name, **ds_kwargs).construct() + expected_num_bins = [ + 100 // min_data_in_bin + 1, # extra bin for zero + 3, # 0, 1, 2 + 3, # 0, 1, 2 + 4, # 0, 1, 2 + nan + 0, # unused + 3, # 0, 1 + nan + ] + actual_num_bins = [ds.feature_num_bin(i) for i in range(X.shape[1])] + assert actual_num_bins == expected_num_bins + # test using defined feature names + bins_by_name = [ds.feature_num_bin(name) for name in feature_name] + assert bins_by_name == expected_num_bins + # test using default feature names + ds_no_names = lgb.Dataset(X, **ds_kwargs).construct() + default_names = [f"Column_{i}" for i in range(X.shape[1])] + bins_by_default_name = [ds_no_names.feature_num_bin(name) for name in default_names] + assert bins_by_default_name == expected_num_bins + # check for feature indices outside of range + num_features = X.shape[1] + with pytest.raises( + lgb.basic.LightGBMError, + match=( + f"Tried to retrieve number of bins for feature index {num_features}, " + f"but the valid feature indices are \\[0, {num_features - 1}\\]." + ), + ): + ds.feature_num_bin(num_features) + + +def test_feature_num_bin_with_max_bin_by_feature(rng): + X = rng.uniform(size=(100, 3)) + max_bin_by_feature = rng.integers(low=3, high=30, size=X.shape[1]) + ds = lgb.Dataset(X, params={"max_bin_by_feature": max_bin_by_feature}).construct() + actual_num_bins = [ds.feature_num_bin(i) for i in range(X.shape[1])] + np.testing.assert_equal(actual_num_bins, max_bin_by_feature) + + +def test_set_leaf_output(): + X, y = load_breast_cancer(return_X_y=True) + ds = lgb.Dataset(X, y) + bst = lgb.Booster({"num_leaves": 2}, ds) + bst.update() + y_pred = bst.predict(X) + for leaf_id in range(2): + leaf_output = bst.get_leaf_output(tree_id=0, leaf_id=leaf_id) + bst.set_leaf_output(tree_id=0, leaf_id=leaf_id, value=leaf_output + 1) + np.testing.assert_allclose(bst.predict(X), y_pred + 1) + + +def test_feature_names_are_set_correctly_when_no_feature_names_passed_into_Dataset(rng): + ds = lgb.Dataset( + data=rng.standard_normal(size=(100, 3)), + ) + assert ds.construct().feature_name == ["Column_0", "Column_1", "Column_2"] + + +def test_set_feature_name_updates_has_non_default_feature_names(rng): + ds = lgb.Dataset(data=rng.standard_normal(size=(100, 3)), label=rng.integers(0, 2, size=100)) + assert ds._has_non_default_feature_names is False + ds.construct() + assert ds._has_non_default_feature_names is False + assert ds.get_feature_name() == ["Column_0", "Column_1", "Column_2"] + ds.set_feature_name(["a", "b", "c"]) + assert ds._has_non_default_feature_names is True + assert ds.get_feature_name() == ["a", "b", "c"] + + +# NOTE: this intentionally contains values where num_leaves <, ==, and > (max_depth^2) +@pytest.mark.parametrize(("max_depth", "num_leaves"), [(-1, 3), (-1, 50), (5, 3), (5, 31), (5, 32), (8, 3), (8, 31)]) +def test_max_depth_warning_is_not_raised_if_num_leaves_is_also_provided(capsys, num_leaves, max_depth): + X, y = make_blobs(n_samples=1_000, n_features=1, centers=2) + lgb.Booster( + params={ + "objective": "binary", + "max_depth": max_depth, + "num_leaves": num_leaves, + "num_iterations": 1, + "verbose": 0, + }, + train_set=lgb.Dataset(X, label=y), + ) + assert "Provided parameters constrain tree depth" not in capsys.readouterr().out + + +# NOTE: max_depth < 5 is significant here because the default for num_leaves=31. With max_depth=5, +# a full depth-wise tree would have 2^5 = 32 leaves. +@pytest.mark.parametrize("max_depth", [1, 2, 3, 4]) +def test_max_depth_warning_is_not_raised_if_max_depth_gt_1_and_lt_5_and_num_leaves_omitted(capsys, max_depth): + X, y = make_blobs(n_samples=1_000, n_features=1, centers=2) + lgb.Booster( + params={ + "objective": "binary", + "max_depth": max_depth, + "num_iterations": 1, + "verbose": 0, + }, + train_set=lgb.Dataset(X, label=y), + ) + assert "Provided parameters constrain tree depth" not in capsys.readouterr().out + + +@pytest.mark.parametrize("max_depth", [5, 6, 7, 8, 9]) +def test_max_depth_warning_is_raised_if_max_depth_gte_5_and_num_leaves_omitted(capsys, max_depth): + X, y = make_blobs(n_samples=1_000, n_features=1, centers=2) + lgb.Booster( + params={ + "objective": "binary", + "max_depth": max_depth, + "num_iterations": 1, + "verbose": 0, + }, + train_set=lgb.Dataset(X, label=y), + ) + expected_warning = ( + f"[LightGBM] [Warning] Provided parameters constrain tree depth (max_depth={max_depth}) without explicitly " + f"setting 'num_leaves'. This can lead to underfitting. To resolve this warning, pass 'num_leaves' (<={2**max_depth}) " + "in params. Alternatively, pass (max_depth=-1) and just use 'num_leaves' to constrain model complexity." + ) + assert expected_warning in capsys.readouterr().out + + +@pytest.mark.parametrize("order", ["C", "F"]) +@pytest.mark.parametrize("dtype", ["float32", "int64"]) +def test_no_copy_in_dataset_from_numpy_2d(rng, order, dtype): + X = rng.random(size=(100, 3)) + X = np.require(X, dtype=dtype, requirements=order) + X1d, layout = lgb.basic._np2d_to_np1d(X) + if order == "F": + assert layout == lgb.basic._C_API_IS_COL_MAJOR + else: + assert layout == lgb.basic._C_API_IS_ROW_MAJOR + if dtype == "float32": + assert np.shares_memory(X, X1d) + else: + # makes a copy + assert not np.shares_memory(X, X1d) + + +def test_equal_datasets_from_row_major_and_col_major_data(tmp_path): + # row-major dataset + X_row, y = make_blobs(n_samples=1_000, n_features=3, centers=2) + assert X_row.flags["C_CONTIGUOUS"] + assert not X_row.flags["F_CONTIGUOUS"] + ds_row = lgb.Dataset(X_row, y) + ds_row_path = tmp_path / "ds_row.txt" + ds_row._dump_text(ds_row_path) + + # col-major dataset + X_col = np.asfortranarray(X_row) + assert X_col.flags["F_CONTIGUOUS"] + assert not X_col.flags["C_CONTIGUOUS"] + ds_col = lgb.Dataset(X_col, y) + ds_col_path = tmp_path / "ds_col.txt" + ds_col._dump_text(ds_col_path) + + # check datasets are equal + assert filecmp.cmp(ds_row_path, ds_col_path) + + +def test_equal_datasets_from_one_and_several_matrices_w_different_layouts(rng, tmp_path): + # several matrices + mats = [np.require(rng.random(size=(100, 2)), requirements=order) for order in ("C", "F", "F", "C")] + several_path = tmp_path / "several.txt" + lgb.Dataset(mats)._dump_text(several_path) + + # one matrix + mat = np.vstack(mats) + one_path = tmp_path / "one.txt" + lgb.Dataset(mat)._dump_text(one_path) + + assert filecmp.cmp(one_path, several_path) + + +@pytest.mark.parametrize( + "field_name", + [ + "group", + "init_score", + pytest.param( + "position", + marks=pytest.mark.skipif( + BuildInfo.has_cuda, + reason="Positions in learning to rank is not supported in CUDA version yet", + ), + ), + "weight", + ], +) +def test_set_field_none_removes_field(rng, field_name): + X = rng.uniform(size=(10, 1)) + d = lgb.Dataset(X).construct() + + if field_name == "group": + field = [5, 5] + expected = np.array([0, 5, 10], dtype=np.int32) + elif field_name == "position": + field = [100, 20, 100, 10, 30, 10, 30, 10, 30, 30] + expected = np.array([0, 1, 0, 2, 3, 2, 3, 2, 3, 3], dtype=np.int32) + else: + field = rng.uniform(size=10) + expected = field.astype(np.float64 if field_name == "init_score" else np.float32) + + out = d.set_field(field_name, field) + assert out is d + + np_assert_array_equal(d.get_field(field_name), expected, strict=True) + + d.set_field(field_name, None) + assert d.get_field(field_name) is None + + +def test_booster_eval_adds_new_valid_dataset() -> None: + X_train, X_test, y_train, y_test = train_test_split( + *load_breast_cancer(return_X_y=True), + test_size=0.1, + random_state=42, + ) + train_set = lgb.Dataset(X_train, label=y_train) + valid_set = lgb.Dataset(X_test, label=y_test, reference=train_set) + booster = lgb.Booster( + params={ + "deterministic": True, + "force_row_wise": True, + "objective": "binary", + "metric": ["auc", "binary_error"], + "num_iterations": 2, + "num_leaves": 3, + "num_threads": 1, + "seed": 708, + "verbose": -1, + }, + train_set=train_set, + ) + assert booster._Booster__num_dataset == 1 + assert booster.valid_sets == [] + + result = booster.eval(valid_set, name="test") + + assert booster._Booster__num_dataset == 2 + assert booster.valid_sets == [valid_set] + assert len(result) == 2 + assert isinstance(result, list) + + # first metric - AUC + dataset_name, metric_name, metric_value, maximize = result[0] + assert dataset_name == "test" + assert metric_name == "auc" + assert metric_value >= 0.50 + assert maximize is True + + # second metric - binary error + dataset_name, metric_name, metric_value, maximize = result[1] + assert dataset_name == "test" + assert metric_name == "binary_error" + assert metric_value >= 0.40 + assert maximize is False + + +def test_refit_correctly_handles_categorical_features_in_params(rng) -> None: + rng = np.random.default_rng() + X = rng.integers(1, 10, size=(1_000, 3)) + y = rng.uniform(size=(X.shape[0],)) + + # Dataset with 'categorical_feature" keyword arg + dtrain = lgb.Dataset(X, label=y, categorical_feature=[0, 2]) + bst = lgb.train( + params={ + "num_leaves": 7, + "verbose": -1, + }, + train_set=dtrain, + num_boost_round=2, + ) + + # 'categorical_column' is correctly set in params + assert bst.params["categorical_column"] == [0, 2] + + # refit() should not raise a warning + X_new = rng.integers(1, 10, size=(10, 3)) + y_new = rng.uniform(size=(X_new.shape[0],)) + with warnings.catch_warnings() as w: + warnings.simplefilter("always") + bst.refit(X_new, y_new) + if w: + assert not any( + re.search(r"has been found in .*params.* and will be ignored", str(warning.message)) for warning in w + ) + + # round-trip to and from a model string + loaded_bst = lgb.Booster(model_str=bst.model_to_string()) + + # that round-trip sets Booster.params to all model parameters, using the "main" + # ones, not any aliases + assert loaded_bst.params["categorical_feature"] == [0, 2] + assert "categorical_column" not in loaded_bst.params + + # case 1: 'categorical_feature' keyword arg not passed + # result: should succeed and not warn + loaded_bst_new = loaded_bst.refit(X_new, y_new) + assert loaded_bst_new.params["categorical_column"] == [0, 2] + + # case 2: 'categorical_feature' keyword arg passed, but identical to what's in params + # result: should succeed and not warn + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + loaded_bst_new = loaded_bst.refit(X_new, y_new, categorical_feature=[0, 2]) + assert loaded_bst_new.params["categorical_column"] == [0, 2] + if w: + assert not any( + re.search(r"has been found in .*params.* and will be ignored", str(warning.message)) for warning in w + ) + + # case 3: 'categorical_feature' keyword arg passed, different value + # result: informative error + with pytest.raises( + lgb.basic.LightGBMError, + match=re.escape("Using refit() to change which columns are treated as categorical is not supported"), + ): + loaded_bst_new = loaded_bst.refit(X_new, y_new, categorical_feature=[0, 1]) diff --git a/tests/python_package_test/test_callback.py b/tests/python_package_test/test_callback.py new file mode 100644 index 0000000..48c7a29 --- /dev/null +++ b/tests/python_package_test/test_callback.py @@ -0,0 +1,65 @@ +# coding: utf-8 +import pytest + +import lightgbm as lgb + +from .utils import SERIALIZERS, pickle_and_unpickle_object + + +def reset_feature_fraction(boosting_round): + return 0.6 if boosting_round < 15 else 0.8 + + +@pytest.mark.parametrize("serializer", SERIALIZERS) +def test_early_stopping_callback_is_picklable(serializer): + rounds = 5 + callback = lgb.early_stopping(stopping_rounds=rounds) + callback_from_disk = pickle_and_unpickle_object(obj=callback, serializer=serializer) + assert callback_from_disk.order == 30 + assert callback_from_disk.before_iteration is False + assert callback.stopping_rounds == callback_from_disk.stopping_rounds + assert callback.stopping_rounds == rounds + + +def test_early_stopping_callback_rejects_invalid_stopping_rounds_with_informative_errors(): + with pytest.raises(TypeError, match="early_stopping_round should be an integer. Got 'str'"): + lgb.early_stopping(stopping_rounds="neverrrr") + + +@pytest.mark.parametrize("stopping_rounds", [-10, -1, 0]) +def test_early_stopping_callback_accepts_non_positive_stopping_rounds(stopping_rounds): + cb = lgb.early_stopping(stopping_rounds=stopping_rounds) + assert cb.enabled is False + + +@pytest.mark.parametrize("serializer", SERIALIZERS) +def test_log_evaluation_callback_is_picklable(serializer): + periods = 42 + callback = lgb.log_evaluation(period=periods) + callback_from_disk = pickle_and_unpickle_object(obj=callback, serializer=serializer) + assert callback_from_disk.order == 10 + assert callback_from_disk.before_iteration is False + assert callback.period == callback_from_disk.period + assert callback.period == periods + + +@pytest.mark.parametrize("serializer", SERIALIZERS) +def test_record_evaluation_callback_is_picklable(serializer): + results = {} + callback = lgb.record_evaluation(eval_result=results) + callback_from_disk = pickle_and_unpickle_object(obj=callback, serializer=serializer) + assert callback_from_disk.order == 20 + assert callback_from_disk.before_iteration is False + assert callback.eval_result == callback_from_disk.eval_result + assert callback.eval_result is results + + +@pytest.mark.parametrize("serializer", SERIALIZERS) +def test_reset_parameter_callback_is_picklable(serializer): + params = {"bagging_fraction": [0.7] * 5 + [0.6] * 5, "feature_fraction": reset_feature_fraction} + callback = lgb.reset_parameter(**params) + callback_from_disk = pickle_and_unpickle_object(obj=callback, serializer=serializer) + assert callback_from_disk.order == 10 + assert callback_from_disk.before_iteration is True + assert callback.kwargs == callback_from_disk.kwargs + assert callback.kwargs == params diff --git a/tests/python_package_test/test_consistency.py b/tests/python_package_test/test_consistency.py new file mode 100644 index 0000000..4f5bca2 --- /dev/null +++ b/tests/python_package_test/test_consistency.py @@ -0,0 +1,143 @@ +# coding: utf-8 +from pathlib import Path + +import numpy as np +from sklearn.datasets import load_svmlight_file + +import lightgbm as lgb + +EXAMPLES_DIR = Path(__file__).absolute().parents[2] / "examples" + + +class FileLoader: + def __init__(self, directory, prefix, config_file="train.conf"): + self.directory = directory + self.prefix = prefix + self.params = {"gpu_use_dp": True} + with open(self.directory / config_file, "r") as f: + for line in f.readlines(): + line = line.strip() + if line and not line.startswith("#"): + key, value = [token.strip() for token in line.split("=")] + if "early_stopping" not in key: # disable early_stopping + self.params[key] = value if key not in {"num_trees", "num_threads"} else int(value) + + def load_dataset(self, suffix, is_sparse=False): + filename = str(self.path(suffix)) + if is_sparse: + X, Y = load_svmlight_file(filename, dtype=np.float64, zero_based=True) + return X, Y, filename + else: + mat = np.loadtxt(filename, dtype=np.float64) + return mat[:, 1:], mat[:, 0], filename + + def load_field(self, suffix): + return np.loadtxt(str(self.directory / f"{self.prefix}{suffix}")) + + def load_cpp_result(self, result_file="LightGBM_predict_result.txt"): + return np.loadtxt(str(self.directory / result_file)) + + def train_predict_check(self, lgb_train, X_test, X_test_fn, sk_pred): + params = dict(self.params) + params["force_row_wise"] = True + gbm = lgb.train(params, lgb_train) + y_pred = gbm.predict(X_test) + cpp_pred = gbm.predict(X_test_fn) + np.testing.assert_allclose(y_pred, cpp_pred) + np.testing.assert_allclose(y_pred, sk_pred) + + def file_load_check(self, lgb_train, name): + lgb_train_f = lgb.Dataset(self.path(name), params=self.params).construct() + for f in ("num_data", "num_feature", "get_label", "get_weight", "get_init_score", "get_group"): + a = getattr(lgb_train, f)() + b = getattr(lgb_train_f, f)() + if a is None and b is None: + pass + elif a is None: + assert np.all(b == 1), f + elif isinstance(b, (list, np.ndarray)): + np.testing.assert_allclose(a, b) + else: + assert a == b, f + + def path(self, suffix): + return self.directory / f"{self.prefix}{suffix}" + + +def test_binary(): + fd = FileLoader(EXAMPLES_DIR / "binary_classification", "binary") + X_train, y_train, _ = fd.load_dataset(".train") + X_test, _, X_test_fn = fd.load_dataset(".test") + weight_train = fd.load_field(".train.weight") + lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train) + gbm = lgb.LGBMClassifier(**fd.params) + gbm.fit(X_train, y_train, sample_weight=weight_train) + sk_pred = gbm.predict_proba(X_test)[:, 1] + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, ".train") + + +def test_binary_linear(): + fd = FileLoader(EXAMPLES_DIR / "binary_classification", "binary", "train_linear.conf") + X_train, y_train, _ = fd.load_dataset(".train") + X_test, _, X_test_fn = fd.load_dataset(".test") + weight_train = fd.load_field(".train.weight") + lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train) + gbm = lgb.LGBMClassifier(**fd.params) + gbm.fit(X_train, y_train, sample_weight=weight_train) + sk_pred = gbm.predict_proba(X_test)[:, 1] + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, ".train") + + +def test_multiclass(): + fd = FileLoader(EXAMPLES_DIR / "multiclass_classification", "multiclass") + X_train, y_train, _ = fd.load_dataset(".train") + X_test, _, X_test_fn = fd.load_dataset(".test") + lgb_train = lgb.Dataset(X_train, y_train) + gbm = lgb.LGBMClassifier(**fd.params) + gbm.fit(X_train, y_train) + sk_pred = gbm.predict_proba(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, ".train") + + +def test_regression(): + fd = FileLoader(EXAMPLES_DIR / "regression", "regression") + X_train, y_train, _ = fd.load_dataset(".train") + X_test, _, X_test_fn = fd.load_dataset(".test") + init_score_train = fd.load_field(".train.init") + lgb_train = lgb.Dataset(X_train, y_train, init_score=init_score_train) + gbm = lgb.LGBMRegressor(**fd.params) + gbm.fit(X_train, y_train, init_score=init_score_train) + sk_pred = gbm.predict(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, ".train") + + +def test_lambdarank(): + fd = FileLoader(EXAMPLES_DIR / "lambdarank", "rank") + X_train, y_train, _ = fd.load_dataset(".train", is_sparse=True) + X_test, _, X_test_fn = fd.load_dataset(".test", is_sparse=True) + group_train = fd.load_field(".train.query") + lgb_train = lgb.Dataset(X_train, y_train, group=group_train) + params = dict(fd.params) + params["force_col_wise"] = True + gbm = lgb.LGBMRanker(**params) + gbm.fit(X_train, y_train, group=group_train) + sk_pred = gbm.predict(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, ".train") + + +def test_xendcg(): + fd = FileLoader(EXAMPLES_DIR / "xendcg", "rank") + X_train, y_train, _ = fd.load_dataset(".train", is_sparse=True) + X_test, _, X_test_fn = fd.load_dataset(".test", is_sparse=True) + group_train = fd.load_field(".train.query") + lgb_train = lgb.Dataset(X_train, y_train, group=group_train) + gbm = lgb.LGBMRanker(**fd.params) + gbm.fit(X_train, y_train, group=group_train) + sk_pred = gbm.predict(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, ".train") diff --git a/tests/python_package_test/test_dask.py b/tests/python_package_test/test_dask.py new file mode 100644 index 0000000..ef7339e --- /dev/null +++ b/tests/python_package_test/test_dask.py @@ -0,0 +1,1701 @@ +# coding: utf-8 +"""Tests for lightgbm.dask module""" + +import inspect +import re +import socket +from itertools import groupby +from sys import platform +from urllib.parse import urlparse + +import pytest +from sklearn.metrics import accuracy_score, r2_score + +import lightgbm as lgb + +from .utils import ( + BuildInfo, + np_assert_array_equal, + sklearn_multiclass_custom_objective, +) + +if platform in {"cygwin", "win32"}: + pytest.skip("lightgbm.dask is not currently supported on Windows", allow_module_level=True) + +dask = pytest.importorskip("dask") + +import dask.array as da +import dask.dataframe as dd +import numpy as np +import pandas as pd +import sklearn.utils.estimator_checks as sklearn_checks +from dask.array.utils import assert_eq +from dask.distributed import Client, LocalCluster, default_client, wait +from scipy.sparse import csc_matrix, csr_matrix +from scipy.stats import spearmanr +from sklearn.datasets import make_blobs, make_regression + +from .utils import make_ranking, pickle_obj, unpickle_obj + +tasks = ["binary-classification", "multiclass-classification", "regression", "ranking"] +distributed_training_algorithms = ["data", "voting"] +data_output = ["array", "scipy_csr_matrix", "dataframe", "dataframe-with-categorical"] +boosting_types = ["gbdt", "dart", "goss", "rf"] +group_sizes = [5, 5, 5, 10, 10, 10, 20, 20, 20, 50, 50] +task_to_dask_factory = { + "regression": lgb.DaskLGBMRegressor, + "binary-classification": lgb.DaskLGBMClassifier, + "multiclass-classification": lgb.DaskLGBMClassifier, + "ranking": lgb.DaskLGBMRanker, +} +task_to_local_factory = { + "regression": lgb.LGBMRegressor, + "binary-classification": lgb.LGBMClassifier, + "multiclass-classification": lgb.LGBMClassifier, + "ranking": lgb.LGBMRanker, +} + +pytestmark = [ + pytest.mark.skipif(BuildInfo.has_cuda, reason="Fails to run with CUDA interface"), + pytest.mark.skipif(BuildInfo.has_gpu, reason="Fails to run with GPU interface"), + pytest.mark.skipif(BuildInfo.has_mpi, reason="Fails to run with MPI interface"), +] + + +@pytest.fixture(scope="module") +def cluster(): + dask_cluster = LocalCluster(n_workers=2, threads_per_worker=2, dashboard_address=None) + yield dask_cluster + dask_cluster.close() + + +@pytest.fixture(scope="module") +def cluster2(): + dask_cluster = LocalCluster(n_workers=2, threads_per_worker=2, dashboard_address=None) + yield dask_cluster + dask_cluster.close() + + +@pytest.fixture(scope="module") +def cluster_three_workers(): + dask_cluster = LocalCluster(n_workers=3, threads_per_worker=1, dashboard_address=None) + yield dask_cluster + dask_cluster.close() + + +@pytest.fixture +def listen_port(): + listen_port.port += 10 + return listen_port.port + + +listen_port.port = 13000 + + +def _get_workers_hostname(cluster: LocalCluster) -> str: + one_worker_address = next(iter(cluster.scheduler_info["workers"])) + return urlparse(one_worker_address).hostname + + +def _create_ranking_data(n_samples=100, output="array", chunk_size=50, **kwargs): + X, y, g = make_ranking(n_samples=n_samples, random_state=42, **kwargs) + rnd = np.random.RandomState(42) + w = rnd.rand(X.shape[0]) * 0.01 + g_rle = np.array([len(list(grp)) for _, grp in groupby(g)]) + + if output.startswith("dataframe"): + # add target, weight, and group to DataFrame so that partitions abide by group boundaries. + X_df = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(X.shape[1])]) + if output == "dataframe-with-categorical": + for i in range(5): + col_name = f"cat_col{i}" + cat_values = rnd.choice(["a", "b"], X.shape[0]) + cat_series = pd.Series(cat_values, dtype="category") + X_df[col_name] = cat_series + X = X_df.copy() + X_df = X_df.assign(y=y, g=g, w=w) + + # set_index ensures partitions are based on group id. + # See https://stackoverflow.com/questions/49532824/dask-dataframe-split-partitions-based-on-a-column-or-function. + X_df.set_index("g", inplace=True) + dX = dd.from_pandas(X_df, chunksize=chunk_size) + + # separate target, weight from features. + dy = dX["y"] + dw = dX["w"] + dX = dX.drop(columns=["y", "w"]) + dg = dX.index.to_series() + + # encode group identifiers into run-length encoding, the format LightGBMRanker is expecting + # so that within each partition, sum(g) = n_samples. + dg = dg.map_partitions(lambda p: p.groupby("g", sort=False).apply(lambda z: z.shape[0])) + elif output == "array": + # ranking arrays: one chunk per group. Each chunk must include all columns. + p = X.shape[1] + dX, dy, dw, dg = [], [], [], [] + for g_idx, rhs in enumerate(np.cumsum(g_rle)): + lhs = rhs - g_rle[g_idx] + dX.append(da.from_array(X[lhs:rhs, :], chunks=(rhs - lhs, p))) + dy.append(da.from_array(y[lhs:rhs])) + dw.append(da.from_array(w[lhs:rhs])) + dg.append(da.from_array(np.array([g_rle[g_idx]]))) + + dX = da.concatenate(dX, axis=0) + dy = da.concatenate(dy, axis=0) + dw = da.concatenate(dw, axis=0) + dg = da.concatenate(dg, axis=0) + else: + raise ValueError("Ranking data creation only supported for Dask arrays and dataframes") + + return X, y, w, g_rle, dX, dy, dw, dg + + +def _create_data(objective, n_samples=1_000, output="array", chunk_size=500, **kwargs): + if objective.endswith("classification"): + if objective == "binary-classification": + centers = [[-4, -4], [4, 4]] + elif objective == "multiclass-classification": + centers = [[-4, -4], [4, 4], [-4, 4]] + else: + raise ValueError(f"Unknown classification task '{objective}'") + X, y = make_blobs(n_samples=n_samples, centers=centers, random_state=42) + elif objective == "regression": + X, y = make_regression(n_samples=n_samples, n_features=4, n_informative=2, random_state=42) + elif objective == "ranking": + return _create_ranking_data(n_samples=n_samples, output=output, chunk_size=chunk_size, **kwargs) + else: + raise ValueError(f"Unknown objective '{objective}'") + rnd = np.random.RandomState(42) + weights = rnd.random(X.shape[0]) * 0.01 + + if output == "array": + dX = da.from_array(X, (chunk_size, X.shape[1])) + dy = da.from_array(y, chunk_size) + dw = da.from_array(weights, chunk_size) + elif output.startswith("dataframe"): + X_df = pd.DataFrame(X, columns=[f"feature_{i}" for i in range(X.shape[1])]) + if output == "dataframe-with-categorical": + num_cat_cols = 2 + for i in range(num_cat_cols): + col_name = f"cat_col{i}" + cat_values = rnd.choice(["a", "b"], X.shape[0]) + cat_series = pd.Series(cat_values, dtype="category") + X_df[col_name] = cat_series + X = np.hstack((X, cat_series.cat.codes.values.reshape(-1, 1))) + + # make one categorical feature relevant to the target + cat_col_is_a = X_df["cat_col0"] == "a" + if objective == "regression": + y = np.where(cat_col_is_a, y, 2 * y) + elif objective == "binary-classification": + y = np.where(cat_col_is_a, y, 1 - y) + elif objective == "multiclass-classification": + n_classes = 3 + y = np.where(cat_col_is_a, y, (1 + y) % n_classes) + y_df = pd.Series(y, name="target") + dX = dd.from_pandas(X_df, chunksize=chunk_size) + dy = dd.from_pandas(y_df, chunksize=chunk_size) + dw = dd.from_array(weights, chunksize=chunk_size) + elif output == "scipy_csr_matrix": + dX = da.from_array(X, chunks=(chunk_size, X.shape[1])).map_blocks(csr_matrix) + dy = da.from_array(y, chunks=chunk_size) + dw = da.from_array(weights, chunk_size) + X = csr_matrix(X) + elif output == "scipy_csc_matrix": + dX = da.from_array(X, chunks=(chunk_size, X.shape[1])).map_blocks(csc_matrix) + dy = da.from_array(y, chunks=chunk_size) + dw = da.from_array(weights, chunk_size) + X = csc_matrix(X) + else: + raise ValueError(f"Unknown output type '{output}'") + + return X, y, weights, None, dX, dy, dw, None + + +def _r2_score(dy_true, dy_pred): + y_true = dy_true.compute() + y_pred = dy_pred.compute() + numerator = ((y_true - y_pred) ** 2).sum(axis=0) + denominator = ((y_true - y_true.mean(axis=0)) ** 2).sum(axis=0) + return 1 - numerator / denominator + + +def _accuracy_score(dy_true, dy_pred): + y_true = dy_true.compute() + y_pred = dy_pred.compute() + return (y_true == y_pred).mean() + + +def _constant_metric(y_true, y_pred): + metric_name = "constant_metric" + value = 0.708 + maximize = False + return metric_name, value, maximize + + +def _objective_least_squares(y_true, y_pred): + grad = y_pred - y_true + hess = np.ones(len(y_true)) + return grad, hess + + +def _objective_logistic_regression(y_true, y_pred): + y_pred = 1.0 / (1.0 + np.exp(-y_pred)) + grad = y_pred - y_true + hess = y_pred * (1.0 - y_pred) + return grad, hess + + +@pytest.mark.parametrize("output", data_output) +@pytest.mark.parametrize("task", ["binary-classification", "multiclass-classification"]) +@pytest.mark.parametrize("boosting_type", boosting_types) +@pytest.mark.parametrize("tree_learner", distributed_training_algorithms) +def test_classifier(output, task, boosting_type, tree_learner, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data(objective=task, output=output) + + params = {"boosting_type": boosting_type, "tree_learner": tree_learner, "n_estimators": 50, "num_leaves": 31} + if boosting_type == "rf": + params.update( + { + "bagging_freq": 1, + "bagging_fraction": 0.9, + } + ) + elif boosting_type == "goss": + params["top_rate"] = 0.5 + + dask_classifier = lgb.DaskLGBMClassifier(client=client, time_out=5, **params) + dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw) + p1 = dask_classifier.predict(dX) + p1_raw = dask_classifier.predict(dX, raw_score=True).compute() + p1_first_iter_raw = dask_classifier.predict(dX, start_iteration=0, num_iteration=1, raw_score=True).compute() + p1_early_stop_raw = dask_classifier.predict( + dX, pred_early_stop=True, pred_early_stop_margin=1.0, pred_early_stop_freq=2, raw_score=True + ).compute() + p1_proba = dask_classifier.predict_proba(dX).compute() + p1_pred_leaf = dask_classifier.predict(dX, pred_leaf=True) + p1_local = dask_classifier.to_local().predict(X) + s1 = _accuracy_score(dy, p1) + p1 = p1.compute() + + local_classifier = lgb.LGBMClassifier(**params) + local_classifier.fit(X, y, sample_weight=w) + p2 = local_classifier.predict(X) + p2_proba = local_classifier.predict_proba(X) + s2 = local_classifier.score(X, y) + + if boosting_type == "rf": + # https://github.com/lightgbm-org/LightGBM/issues/4118 + assert_eq(s1, s2, atol=0.01) + assert_eq(p1_proba, p2_proba, atol=0.8) + else: + assert_eq(s1, s2) + assert_eq(p1, p2) + assert_eq(p1, y) + assert_eq(p2, y) + assert_eq(p1_proba, p2_proba, atol=0.03) + assert_eq(p1_local, p2) + assert_eq(p1_local, y) + + # extra predict() parameters should be passed through correctly + with pytest.raises(AssertionError): # noqa: PT011 + assert_eq(p1_raw, p1_first_iter_raw) + + with pytest.raises(AssertionError): # noqa: PT011 + assert_eq(p1_raw, p1_early_stop_raw) + + # pref_leaf values should have the right shape + # and values that look like valid tree nodes + pred_leaf_vals = p1_pred_leaf.compute() + assert pred_leaf_vals.shape == (X.shape[0], dask_classifier.booster_.num_trees()) + assert np.max(pred_leaf_vals) <= params["num_leaves"] + assert np.min(pred_leaf_vals) >= 0 + assert len(np.unique(pred_leaf_vals)) <= params["num_leaves"] + + # be sure LightGBM actually used at least one categorical column, + # and that it was correctly treated as a categorical feature + if output == "dataframe-with-categorical": + cat_cols = [col for col in dX.columns if dX.dtypes[col].name == "category"] + tree_df = dask_classifier.booster_.trees_to_dataframe() + node_uses_cat_col = tree_df["split_feature"].isin(cat_cols) + assert node_uses_cat_col.sum() > 0 + assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == "==" + + +@pytest.mark.parametrize("output", data_output + ["scipy_csc_matrix"]) +@pytest.mark.parametrize("task", ["binary-classification", "multiclass-classification"]) +def test_classifier_pred_contrib(output, task, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data(objective=task, output=output) + + params = {"n_estimators": 10, "num_leaves": 10} + + dask_classifier = lgb.DaskLGBMClassifier(client=client, time_out=5, tree_learner="data", **params) + dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw) + preds_with_contrib = dask_classifier.predict(dX, pred_contrib=True) + + local_classifier = lgb.LGBMClassifier(**params) + local_classifier.fit(X, y, sample_weight=w) + local_preds_with_contrib = local_classifier.predict(X, pred_contrib=True) + + # shape depends on whether it is binary or multiclass classification + num_features = dask_classifier.n_features_ + num_classes = dask_classifier.n_classes_ + if num_classes == 2: + expected_num_cols = num_features + 1 + else: + expected_num_cols = (num_features + 1) * num_classes + + # in the special case of multi-class classification using scipy sparse matrices, + # the output of `.predict(..., pred_contrib=True)` is a list of sparse matrices (one per class) + # + # since that case is so different than all other cases, check the relevant things here + # and then return early + if output.startswith("scipy") and task == "multiclass-classification": + if output == "scipy_csr_matrix": + expected_type = csr_matrix + elif output == "scipy_csc_matrix": + expected_type = csc_matrix + else: + raise ValueError(f"Unrecognized output type: {output}") + assert isinstance(preds_with_contrib, list) + assert all(isinstance(arr, da.Array) for arr in preds_with_contrib) + assert all(isinstance(arr._meta, expected_type) for arr in preds_with_contrib) + assert len(preds_with_contrib) == num_classes + assert len(preds_with_contrib) == len(local_preds_with_contrib) + for i in range(num_classes): + computed_preds = preds_with_contrib[i].compute() + assert isinstance(computed_preds, expected_type) + assert computed_preds.shape[1] == num_classes + assert computed_preds.shape == local_preds_with_contrib[i].shape + assert len(np.unique(computed_preds[:, -1])) == 1 + # raw scores will probably be different, but at least check that all predicted classes are the same + pred_classes = np.argmax(computed_preds.toarray(), axis=1) + local_pred_classes = np.argmax(local_preds_with_contrib[i].toarray(), axis=1) + np_assert_array_equal(pred_classes, local_pred_classes, strict=True) + return + + preds_with_contrib = preds_with_contrib.compute() + if output.startswith("scipy"): + preds_with_contrib = preds_with_contrib.toarray() + + # be sure LightGBM actually used at least one categorical column, + # and that it was correctly treated as a categorical feature + if output == "dataframe-with-categorical": + cat_cols = [col for col in dX.columns if dX.dtypes[col].name == "category"] + tree_df = dask_classifier.booster_.trees_to_dataframe() + node_uses_cat_col = tree_df["split_feature"].isin(cat_cols) + assert node_uses_cat_col.sum() > 0 + assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == "==" + + # * shape depends on whether it is binary or multiclass classification + # * matrix for binary classification is of the form [feature_contrib, base_value], + # for multi-class it's [feat_contrib_class1, base_value_class1, feat_contrib_class2, base_value_class2, etc.] + # * contrib outputs for distributed training are different than from local training, so we can just test + # that the output has the right shape and base values are in the right position + assert preds_with_contrib.shape[1] == expected_num_cols + assert preds_with_contrib.shape == local_preds_with_contrib.shape + + if num_classes == 2: + assert len(np.unique(preds_with_contrib[:, num_features])) == 1 + else: + for i in range(num_classes): + base_value_col = num_features * (i + 1) + i + assert len(np.unique(preds_with_contrib[:, base_value_col]) == 1) + + +@pytest.mark.parametrize("output", data_output) +@pytest.mark.parametrize("task", ["binary-classification", "multiclass-classification"]) +def test_classifier_custom_objective(output, task, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data( + objective=task, + output=output, + ) + + params = { + "n_estimators": 50, + "num_leaves": 31, + "verbose": -1, + "seed": 708, + "deterministic": True, + "force_col_wise": True, + } + + if task == "binary-classification": + params.update( + { + "objective": _objective_logistic_regression, + } + ) + elif task == "multiclass-classification": + params.update({"objective": sklearn_multiclass_custom_objective, "num_classes": 3}) + + dask_classifier = lgb.DaskLGBMClassifier(client=client, time_out=5, tree_learner="data", **params) + dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw) + dask_classifier_local = dask_classifier.to_local() + p1_raw = dask_classifier.predict(dX, raw_score=True).compute() + p1_raw_local = dask_classifier_local.predict(X, raw_score=True) + + local_classifier = lgb.LGBMClassifier(**params) + local_classifier.fit(X, y, sample_weight=w) + p2_raw = local_classifier.predict(X, raw_score=True) + + # with a custom objective, prediction result is a raw score instead of predicted class + if task == "binary-classification": + p1_proba = 1.0 / (1.0 + np.exp(-p1_raw)) + p1_class = (p1_proba > 0.5).astype(np.int64) + p1_proba_local = 1.0 / (1.0 + np.exp(-p1_raw_local)) + p1_class_local = (p1_proba_local > 0.5).astype(np.int64) + p2_proba = 1.0 / (1.0 + np.exp(-p2_raw)) + p2_class = (p2_proba > 0.5).astype(np.int64) + elif task == "multiclass-classification": + p1_proba = np.exp(p1_raw) / np.sum(np.exp(p1_raw), axis=1).reshape(-1, 1) + p1_class = p1_proba.argmax(axis=1) + p1_proba_local = np.exp(p1_raw_local) / np.sum(np.exp(p1_raw_local), axis=1).reshape(-1, 1) + p1_class_local = p1_proba_local.argmax(axis=1) + p2_proba = np.exp(p2_raw) / np.sum(np.exp(p2_raw), axis=1).reshape(-1, 1) + p2_class = p2_proba.argmax(axis=1) + + # function should have been preserved + assert callable(dask_classifier.objective_) + assert callable(dask_classifier_local.objective_) + + # should correctly classify every sample + assert_eq(p1_class, y) + assert_eq(p1_class_local, y) + assert_eq(p2_class, y) + + # probability estimates should be similar + assert_eq(p1_proba, p2_proba, atol=0.03) + assert_eq(p1_proba, p1_proba_local) + + +def test_machines_to_worker_map_unparsable_host_names(): + workers = {"0.0.0.1:80": {}, "0.0.0.2:80": {}} + machines = "0.0.0.1:80,0.0.0.2:80" + with pytest.raises(ValueError, match="Could not parse host name from worker address '0.0.0.1:80'"): + lgb.dask._machines_to_worker_map(machines=machines, worker_addresses=workers.keys()) + + +def test_training_does_not_fail_on_port_conflicts(cluster): + with Client(cluster) as client: + _, _, _, _, dX, dy, dw, _ = _create_data("binary-classification", output="array") + + lightgbm_default_port = 12400 + workers_hostname = _get_workers_hostname(cluster) + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind((workers_hostname, lightgbm_default_port)) + dask_classifier = lgb.DaskLGBMClassifier(client=client, time_out=5, n_estimators=5, num_leaves=5) + for _ in range(5): + dask_classifier.fit( + X=dX, + y=dy, + sample_weight=dw, + ) + assert dask_classifier.booster_ + + +@pytest.mark.parametrize("output", data_output) +@pytest.mark.parametrize("boosting_type", boosting_types) +@pytest.mark.parametrize("tree_learner", distributed_training_algorithms) +def test_regressor(output, boosting_type, tree_learner, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data(objective="regression", output=output) + + params = { + "boosting_type": boosting_type, + "random_state": 42, + "num_leaves": 31, + "n_estimators": 20, + } + if boosting_type == "rf": + params.update( + { + "bagging_freq": 1, + "bagging_fraction": 0.9, + } + ) + + dask_regressor = lgb.DaskLGBMRegressor(client=client, time_out=5, tree=tree_learner, **params) + dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw) + p1 = dask_regressor.predict(dX) + p1_pred_leaf = dask_regressor.predict(dX, pred_leaf=True) + + s1 = _r2_score(dy, p1) + p1 = p1.compute() + p1_raw = dask_regressor.predict(dX, raw_score=True).compute() + p1_first_iter_raw = dask_regressor.predict(dX, start_iteration=0, num_iteration=1, raw_score=True).compute() + p1_local = dask_regressor.to_local().predict(X) + s1_local = dask_regressor.to_local().score(X, y) + + local_regressor = lgb.LGBMRegressor(**params) + local_regressor.fit(X, y, sample_weight=w) + s2 = local_regressor.score(X, y) + p2 = local_regressor.predict(X) + + # Scores should be the same + assert_eq(s1, s2, atol=0.01) + assert_eq(s1, s1_local) + + # Predictions should be roughly the same. + assert_eq(p1, p1_local) + + # pref_leaf values should have the right shape + # and values that look like valid tree nodes + pred_leaf_vals = p1_pred_leaf.compute() + assert pred_leaf_vals.shape == (X.shape[0], dask_regressor.booster_.num_trees()) + assert np.max(pred_leaf_vals) <= params["num_leaves"] + assert np.min(pred_leaf_vals) >= 0 + assert len(np.unique(pred_leaf_vals)) <= params["num_leaves"] + + assert_eq(p1, y, rtol=0.5, atol=50.0) + assert_eq(p2, y, rtol=0.5, atol=50.0) + + # extra predict() parameters should be passed through correctly + with pytest.raises(AssertionError): # noqa: PT011 + assert_eq(p1_raw, p1_first_iter_raw) + + # be sure LightGBM actually used at least one categorical column, + # and that it was correctly treated as a categorical feature + if output == "dataframe-with-categorical": + cat_cols = [col for col in dX.columns if dX.dtypes[col].name == "category"] + tree_df = dask_regressor.booster_.trees_to_dataframe() + node_uses_cat_col = tree_df["split_feature"].isin(cat_cols) + assert node_uses_cat_col.sum() > 0 + assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == "==" + + +@pytest.mark.parametrize("output", data_output) +def test_regressor_pred_contrib(output, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data(objective="regression", output=output) + + params = {"n_estimators": 10, "num_leaves": 10} + + dask_regressor = lgb.DaskLGBMRegressor(client=client, time_out=5, tree_learner="data", **params) + dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw) + preds_with_contrib = dask_regressor.predict(dX, pred_contrib=True).compute() + + local_regressor = lgb.LGBMRegressor(**params) + local_regressor.fit(X, y, sample_weight=w) + local_preds_with_contrib = local_regressor.predict(X, pred_contrib=True) + + if output == "scipy_csr_matrix": + preds_with_contrib = preds_with_contrib.toarray() + + # contrib outputs for distributed training are different than from local training, so we can just test + # that the output has the right shape and base values are in the right position + num_features = dX.shape[1] + assert preds_with_contrib.shape[1] == num_features + 1 + assert preds_with_contrib.shape == local_preds_with_contrib.shape + + # be sure LightGBM actually used at least one categorical column, + # and that it was correctly treated as a categorical feature + if output == "dataframe-with-categorical": + cat_cols = [col for col in dX.columns if dX.dtypes[col].name == "category"] + tree_df = dask_regressor.booster_.trees_to_dataframe() + node_uses_cat_col = tree_df["split_feature"].isin(cat_cols) + assert node_uses_cat_col.sum() > 0 + assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == "==" + + +@pytest.mark.parametrize("output", data_output) +@pytest.mark.parametrize("alpha", [0.1, 0.5, 0.9]) +def test_regressor_quantile(output, alpha, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data(objective="regression", output=output) + + params = {"objective": "quantile", "alpha": alpha, "random_state": 42, "n_estimators": 10, "num_leaves": 10} + + dask_regressor = lgb.DaskLGBMRegressor(client=client, tree_learner_type="data_parallel", **params) + dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw) + p1 = dask_regressor.predict(dX).compute() + q1 = np.count_nonzero(y < p1) / y.shape[0] + + local_regressor = lgb.LGBMRegressor(**params) + local_regressor.fit(X, y, sample_weight=w) + p2 = local_regressor.predict(X) + q2 = np.count_nonzero(y < p2) / y.shape[0] + + # Quantiles should be right + np.testing.assert_allclose(q1, alpha, atol=0.2) + np.testing.assert_allclose(q2, alpha, atol=0.2) + + # be sure LightGBM actually used at least one categorical column, + # and that it was correctly treated as a categorical feature + if output == "dataframe-with-categorical": + cat_cols = [col for col in dX.columns if dX.dtypes[col].name == "category"] + tree_df = dask_regressor.booster_.trees_to_dataframe() + node_uses_cat_col = tree_df["split_feature"].isin(cat_cols) + assert node_uses_cat_col.sum() > 0 + assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == "==" + + +@pytest.mark.parametrize("output", data_output) +def test_regressor_custom_objective(output, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data(objective="regression", output=output) + + params = {"n_estimators": 10, "num_leaves": 10, "objective": _objective_least_squares} + + dask_regressor = lgb.DaskLGBMRegressor(client=client, time_out=5, tree_learner="data", **params) + dask_regressor = dask_regressor.fit(dX, dy, sample_weight=dw) + dask_regressor_local = dask_regressor.to_local() + p1 = dask_regressor.predict(dX) + p1_local = dask_regressor_local.predict(X) + s1_local = dask_regressor_local.score(X, y) + s1 = _r2_score(dy, p1) + p1 = p1.compute() + + local_regressor = lgb.LGBMRegressor(**params) + local_regressor.fit(X, y, sample_weight=w) + p2 = local_regressor.predict(X) + s2 = local_regressor.score(X, y) + + # function should have been preserved + assert callable(dask_regressor.objective_) + assert callable(dask_regressor_local.objective_) + + # Scores should be the same + assert_eq(s1, s2, atol=0.01) + assert_eq(s1, s1_local) + + # local and Dask predictions should be the same + assert_eq(p1, p1_local) + + # predictions should be better than random + assert_precision = {"rtol": 0.5, "atol": 50.0} + assert_eq(p1, y, **assert_precision) + assert_eq(p2, y, **assert_precision) + + +@pytest.mark.xfail( + platform.lower().startswith("darwin"), + reason=( + "learning-to-rank Dask tests are unreliable on macOS. " + "See https://github.com/lightgbm-org/LightGBM/issues/4074#issuecomment-3124996317" + ), +) +@pytest.mark.parametrize("output", ["array", "dataframe", "dataframe-with-categorical"]) +@pytest.mark.parametrize("group", [None, group_sizes]) +@pytest.mark.parametrize("boosting_type", boosting_types) +@pytest.mark.parametrize("tree_learner", distributed_training_algorithms) +def test_ranker(output, group, boosting_type, tree_learner, cluster): + with Client(cluster) as client: + if output == "dataframe-with-categorical": + X, y, w, g, dX, dy, dw, dg = _create_data( + objective="ranking", output=output, group=group, n_features=1, n_informative=1 + ) + else: + X, y, w, g, dX, dy, dw, dg = _create_data(objective="ranking", output=output, group=group) + + # rebalance small dask.Array dataset for better performance. + if output == "array": + dX = dX.persist() + dy = dy.persist() + dw = dw.persist() + dg = dg.persist() + _ = wait([dX, dy, dw, dg]) + client.rebalance() + + # use many trees + leaves to overfit, help ensure that Dask data-parallel strategy matches that of + # serial learner. See https://github.com/lightgbm-org/LightGBM/issues/3292#issuecomment-671288210. + params = { + "boosting_type": boosting_type, + "random_state": 42, + "n_estimators": 50, + "num_leaves": 20, + "min_child_samples": 1, + } + if boosting_type == "rf": + params.update( + { + "bagging_freq": 1, + "bagging_fraction": 0.9, + } + ) + + dask_ranker = lgb.DaskLGBMRanker(client=client, time_out=5, tree_learner_type=tree_learner, **params) + dask_ranker = dask_ranker.fit(dX, dy, sample_weight=dw, group=dg) + rnkvec_dask = dask_ranker.predict(dX) + rnkvec_dask = rnkvec_dask.compute() + p1_pred_leaf = dask_ranker.predict(dX, pred_leaf=True) + p1_raw = dask_ranker.predict(dX, raw_score=True).compute() + p1_first_iter_raw = dask_ranker.predict(dX, start_iteration=0, num_iteration=1, raw_score=True).compute() + p1_early_stop_raw = dask_ranker.predict( + dX, pred_early_stop=True, pred_early_stop_margin=1.0, pred_early_stop_freq=2, raw_score=True + ).compute() + rnkvec_dask_local = dask_ranker.to_local().predict(X) + + local_ranker = lgb.LGBMRanker(**params) + local_ranker.fit(X, y, sample_weight=w, group=g) + rnkvec_local = local_ranker.predict(X) + + # distributed ranker should be able to rank decently well and should + # have high rank correlation with scores from serial ranker. + dcor = spearmanr(rnkvec_dask, y).correlation + assert dcor > 0.6 + assert spearmanr(rnkvec_dask, rnkvec_local).correlation > 0.8 + assert_eq(rnkvec_dask, rnkvec_dask_local) + + # extra predict() parameters should be passed through correctly + with pytest.raises(AssertionError): # noqa: PT011 + assert_eq(p1_raw, p1_first_iter_raw) + + with pytest.raises(AssertionError): # noqa: PT011 + assert_eq(p1_raw, p1_early_stop_raw) + + # pref_leaf values should have the right shape + # and values that look like valid tree nodes + pred_leaf_vals = p1_pred_leaf.compute() + assert pred_leaf_vals.shape == (X.shape[0], dask_ranker.booster_.num_trees()) + assert np.max(pred_leaf_vals) <= params["num_leaves"] + assert np.min(pred_leaf_vals) >= 0 + assert len(np.unique(pred_leaf_vals)) <= params["num_leaves"] + + # be sure LightGBM actually used at least one categorical column, + # and that it was correctly treated as a categorical feature + if output == "dataframe-with-categorical": + cat_cols = [col for col in dX.columns if dX.dtypes[col].name == "category"] + tree_df = dask_ranker.booster_.trees_to_dataframe() + node_uses_cat_col = tree_df["split_feature"].isin(cat_cols) + assert node_uses_cat_col.sum() > 0 + assert tree_df.loc[node_uses_cat_col, "decision_type"].unique()[0] == "==" + + +@pytest.mark.parametrize("output", ["array", "dataframe", "dataframe-with-categorical"]) +def test_ranker_custom_objective(output, cluster): + with Client(cluster) as client: + if output == "dataframe-with-categorical": + X, y, w, g, dX, dy, dw, dg = _create_data( + objective="ranking", output=output, group=group_sizes, n_features=1, n_informative=1 + ) + else: + X, y, w, g, dX, dy, dw, dg = _create_data(objective="ranking", output=output, group=group_sizes) + + # rebalance small dask.Array dataset for better performance. + if output == "array": + dX = dX.persist() + dy = dy.persist() + dw = dw.persist() + dg = dg.persist() + _ = wait([dX, dy, dw, dg]) + client.rebalance() + + params = { + "random_state": 42, + "n_estimators": 50, + "num_leaves": 20, + "min_child_samples": 1, + "objective": _objective_least_squares, + } + + dask_ranker = lgb.DaskLGBMRanker(client=client, time_out=5, tree_learner_type="data", **params) + dask_ranker = dask_ranker.fit(dX, dy, sample_weight=dw, group=dg) + rnkvec_dask = dask_ranker.predict(dX).compute() + dask_ranker_local = dask_ranker.to_local() + rnkvec_dask_local = dask_ranker_local.predict(X) + + local_ranker = lgb.LGBMRanker(**params) + local_ranker.fit(X, y, sample_weight=w, group=g) + rnkvec_local = local_ranker.predict(X) + + # distributed ranker should be able to rank decently well with the least-squares objective + # and should have high rank correlation with scores from serial ranker. + assert spearmanr(rnkvec_dask, y).correlation > 0.6 + assert spearmanr(rnkvec_dask, rnkvec_local).correlation > 0.8 + assert_eq(rnkvec_dask, rnkvec_dask_local) + + # function should have been preserved + assert callable(dask_ranker.objective_) + assert callable(dask_ranker_local.objective_) + + +@pytest.mark.parametrize("task", tasks) +@pytest.mark.parametrize("output", data_output) +@pytest.mark.parametrize("eval_sizes", [[0.5, 1, 1.5], [0]]) +@pytest.mark.parametrize("eval_names_prefix", ["specified", None]) +def test_eval_set_no_early_stopping(task, output, eval_sizes, eval_names_prefix, cluster): + if task == "ranking" and output == "scipy_csr_matrix": + pytest.skip("LGBMRanker is not currently tested on sparse matrices") + + with Client(cluster) as client: + # Use larger trainset to prevent premature stopping due to zero loss, causing num_trees() < n_estimators. + # Use small chunk_size to avoid single-worker allocation of eval data partitions. + n_samples = 1000 + chunk_size = 10 + n_eval_sets = len(eval_sizes) + eval_set = [] + eval_sample_weight = [] + eval_class_weight = None + eval_init_score = None + + if eval_names_prefix: + eval_names = [f"{eval_names_prefix}_{i}" for i in range(len(eval_sizes))] + else: + eval_names = None + + X, y, w, g, dX, dy, dw, dg = _create_data( + objective=task, n_samples=n_samples, output=output, chunk_size=chunk_size + ) + + if task == "ranking": + eval_metrics = ["ndcg"] + eval_at = (5, 6) + eval_metric_names = [f"ndcg@{k}" for k in eval_at] + eval_group = [] + else: + # test eval_class_weight, eval_init_score on binary-classification task. + # Note: objective's default `metric` will be evaluated in evals_result_ in addition to all eval_metrics. + if task == "binary-classification": + eval_metrics = ["binary_error", "auc"] + eval_metric_names = ["binary_logloss", "binary_error", "auc"] + eval_class_weight = [] + eval_init_score = [] + elif task == "multiclass-classification": + eval_metrics = ["multi_error"] + eval_metric_names = ["multi_logloss", "multi_error"] + elif task == "regression": + eval_metrics = ["l1"] + eval_metric_names = ["l2", "l1"] + + # create eval_sets by creating new datasets or copying training data. + for eval_size in eval_sizes: + if eval_size == 1: + y_e = y + dX_e = dX + dy_e = dy + dw_e = dw + dg_e = dg + else: + n_eval_samples = max(chunk_size, int(n_samples * eval_size)) + _, y_e, _, _, dX_e, dy_e, dw_e, dg_e = _create_data( + objective=task, n_samples=n_eval_samples, output=output, chunk_size=chunk_size + ) + + eval_set.append((dX_e, dy_e)) + eval_sample_weight.append(dw_e) + if task == "ranking": + eval_group.append(dg_e) + + if task == "binary-classification": + n_neg = np.sum(y_e == 0) + n_pos = np.sum(y_e == 1) + eval_class_weight.append({0: n_neg / n_pos, 1: n_pos / n_neg}) + init_score_value = np.log(np.mean(y_e) / (1 - np.mean(y_e))) + if "dataframe" in output: + d_init_score = dy_e.map_partitions(lambda x, val=init_score_value: pd.Series([val] * x.size)) + else: + d_init_score = dy_e.map_blocks(lambda x, val=init_score_value: np.repeat(val, x.size)) + + eval_init_score.append(d_init_score) + + fit_trees = 50 + params = {"random_state": 42, "n_estimators": fit_trees, "num_leaves": 2} + + model_factory = task_to_dask_factory[task] + dask_model = model_factory(client=client, **params) + + fit_params = { + "X": dX, + "y": dy, + "eval_set": eval_set, + "eval_names": eval_names, + "eval_sample_weight": eval_sample_weight, + "eval_init_score": eval_init_score, + "eval_metric": eval_metrics, + } + if task == "ranking": + fit_params.update({"group": dg, "eval_group": eval_group, "eval_at": eval_at}) + elif task == "binary-classification": + fit_params.update({"eval_class_weight": eval_class_weight}) + + if eval_sizes == [0]: + with pytest.warns( + UserWarning, + match="Worker (.*) was not allocated eval_set data. Therefore evals_result_ and best_score_ data may be unreliable.", + ): + dask_model.fit(**fit_params) + else: + dask_model = dask_model.fit(**fit_params) + + # total number of trees scales up for ova classifier. + if task == "multiclass-classification": + model_trees = fit_trees * dask_model.n_classes_ + else: + model_trees = fit_trees + + # check that early stopping was not applied. + assert dask_model.booster_.num_trees() == model_trees + assert dask_model.best_iteration_ == 0 + + # checks that evals_result_ and best_score_ contain expected data and eval_set names. + evals_result = dask_model.evals_result_ + best_scores = dask_model.best_score_ + assert len(evals_result) == n_eval_sets + assert len(best_scores) == n_eval_sets + + for eval_name in evals_result: + assert eval_name in dask_model.best_score_ + if eval_names: + assert eval_name in eval_names + + # check that each eval_name and metric exists for all eval sets, allowing for the + # case when a worker receives a fully-padded eval_set component which is not evaluated. + if evals_result[eval_name] != {}: + for metric in eval_metric_names: + assert metric in evals_result[eval_name] + assert metric in best_scores[eval_name] + assert len(evals_result[eval_name][metric]) == fit_trees + + +@pytest.mark.parametrize("task", ["binary-classification", "regression", "ranking"]) +def test_eval_set_with_custom_eval_metric(task, cluster): + with Client(cluster) as client: + n_samples = 1000 + n_eval_samples = int(n_samples * 0.5) + chunk_size = 10 + output = "array" + + X, y, w, g, dX, dy, dw, dg = _create_data( + objective=task, n_samples=n_samples, output=output, chunk_size=chunk_size + ) + _, _, _, _, dX_e, dy_e, _, dg_e = _create_data( + objective=task, n_samples=n_eval_samples, output=output, chunk_size=chunk_size + ) + + if task == "ranking": + eval_at = (5, 6) + eval_metrics = ["ndcg", _constant_metric] + eval_metric_names = [f"ndcg@{k}" for k in eval_at] + ["constant_metric"] + elif task == "binary-classification": + eval_metrics = ["binary_error", "auc", _constant_metric] + eval_metric_names = ["binary_logloss", "binary_error", "auc", "constant_metric"] + else: + eval_metrics = ["l1", _constant_metric] + eval_metric_names = ["l2", "l1", "constant_metric"] + + fit_trees = 50 + params = {"random_state": 42, "n_estimators": fit_trees, "num_leaves": 2} + model_factory = task_to_dask_factory[task] + dask_model = model_factory(client=client, **params) + + eval_set = [(dX_e, dy_e)] + fit_params = {"X": dX, "y": dy, "eval_set": eval_set, "eval_metric": eval_metrics} + if task == "ranking": + fit_params.update({"group": dg, "eval_group": [dg_e], "eval_at": eval_at}) + + dask_model = dask_model.fit(**fit_params) + + eval_name = "valid_0" + evals_result = dask_model.evals_result_ + assert len(evals_result) == 1 + assert eval_name in evals_result + + for metric in eval_metric_names: + assert metric in evals_result[eval_name] + assert len(evals_result[eval_name][metric]) == fit_trees + + np.testing.assert_allclose(evals_result[eval_name]["constant_metric"], 0.708) + + +@pytest.mark.parametrize("task", tasks) +def test_training_works_if_client_not_provided_or_set_after_construction(task, cluster): + with Client(cluster) as client: + _, _, _, _, dX, dy, _, dg = _create_data(objective=task, output="array", group=None) + model_factory = task_to_dask_factory[task] + + params = {"time_out": 5, "n_estimators": 1, "num_leaves": 2} + + # should be able to use the class without specifying a client + dask_model = model_factory(**params) + assert dask_model.client is None + with pytest.raises(lgb.compat.LGBMNotFittedError, match="Cannot access property client_ before calling fit"): + dask_model.client_ + + dask_model.fit(dX, dy, group=dg) + assert dask_model.fitted_ + assert dask_model.client is None + assert dask_model.client_ == client + + preds = dask_model.predict(dX) + assert isinstance(preds, da.Array) + assert dask_model.fitted_ + assert dask_model.client is None + assert dask_model.client_ == client + + local_model = dask_model.to_local() + no_client_attr_msg = re.compile( + f"{repr(type(local_model).__name__)} object has no attribute '(client|client_)'" + ) + + with pytest.raises(AttributeError, match=no_client_attr_msg): + local_model.client + with pytest.raises(AttributeError, match=no_client_attr_msg): + local_model.client_ + + # should be able to set client after construction + dask_model = model_factory(**params) + dask_model.set_params(client=client) + assert dask_model.client == client + + with pytest.raises(lgb.compat.LGBMNotFittedError, match="Cannot access property client_ before calling fit"): + dask_model.client_ + + dask_model.fit(dX, dy, group=dg) + assert dask_model.fitted_ + assert dask_model.client == client + assert dask_model.client_ == client + + preds = dask_model.predict(dX) + assert isinstance(preds, da.Array) + assert dask_model.fitted_ + assert dask_model.client == client + assert dask_model.client_ == client + + local_model = dask_model.to_local() + with pytest.raises(AttributeError, match=no_client_attr_msg): + local_model.client + with pytest.raises(AttributeError, match=no_client_attr_msg): + local_model.client_ + + +@pytest.mark.parametrize("serializer", ["pickle", "joblib", "cloudpickle"]) +@pytest.mark.parametrize("task", tasks) +@pytest.mark.parametrize("set_client", [True, False]) +def test_model_and_local_version_are_picklable_whether_or_not_client_set_explicitly( + serializer, task, set_client, tmp_path, cluster, cluster2 +): + with Client(cluster) as client1: + # data on cluster1 + X_1, _, _, _, dX_1, dy_1, _, dg_1 = _create_data(objective=task, output="array", group=None) + + with Client(cluster2) as client2: + # create identical data on cluster2 + X_2, _, _, _, dX_2, dy_2, _, dg_2 = _create_data(objective=task, output="array", group=None) + + model_factory = task_to_dask_factory[task] + + params = {"time_out": 5, "n_estimators": 1, "num_leaves": 2} + + # at this point, the result of default_client() is client2 since it was the most recently + # created. So setting client to client1 here to test that you can select a non-default client + assert default_client() == client2 + if set_client: + params.update({"client": client1}) + + # unfitted model should survive pickling round trip, and pickling + # shouldn't have side effects on the model object + dask_model = model_factory(**params) + local_model = dask_model.to_local() + if set_client: + assert dask_model.client == client1 + else: + assert dask_model.client is None + + with pytest.raises( + lgb.compat.LGBMNotFittedError, match="Cannot access property client_ before calling fit" + ): + dask_model.client_ + + assert "client" not in local_model.get_params() + assert getattr(local_model, "client", None) is None + + tmp_file = tmp_path / "model-1.pkl" + pickle_obj(obj=dask_model, filepath=tmp_file, serializer=serializer) + model_from_disk = unpickle_obj(filepath=tmp_file, serializer=serializer) + + local_tmp_file = tmp_path / "local-model-1.pkl" + pickle_obj(obj=local_model, filepath=local_tmp_file, serializer=serializer) + local_model_from_disk = unpickle_obj(filepath=local_tmp_file, serializer=serializer) + + assert model_from_disk.client is None + + if set_client: + assert dask_model.client == client1 + else: + assert dask_model.client is None + + with pytest.raises( + lgb.compat.LGBMNotFittedError, match="Cannot access property client_ before calling fit" + ): + dask_model.client_ + + # client will always be None after unpickling + if set_client: + from_disk_params = model_from_disk.get_params() + from_disk_params.pop("client", None) + dask_params = dask_model.get_params() + dask_params.pop("client", None) + assert from_disk_params == dask_params + else: + assert model_from_disk.get_params() == dask_model.get_params() + assert local_model_from_disk.get_params() == local_model.get_params() + + # fitted model should survive pickling round trip, and pickling + # shouldn't have side effects on the model object + if set_client: + dask_model.fit(dX_1, dy_1, group=dg_1) + else: + dask_model.fit(dX_2, dy_2, group=dg_2) + local_model = dask_model.to_local() + + assert "client" not in local_model.get_params() + no_client_attr_msg = re.compile( + f"{repr(type(local_model).__name__)} object has no attribute '(client|client_)'" + ) + with pytest.raises(AttributeError, match=no_client_attr_msg): + local_model.client + with pytest.raises(AttributeError, match=no_client_attr_msg): + local_model.client_ + + tmp_file2 = tmp_path / "model-2.pkl" + pickle_obj(obj=dask_model, filepath=tmp_file2, serializer=serializer) + fitted_model_from_disk = unpickle_obj(filepath=tmp_file2, serializer=serializer) + + local_tmp_file2 = tmp_path / "local-model-2.pkl" + pickle_obj(obj=local_model, filepath=local_tmp_file2, serializer=serializer) + local_fitted_model_from_disk = unpickle_obj(filepath=local_tmp_file2, serializer=serializer) + + if set_client: + assert dask_model.client == client1 + assert dask_model.client_ == client1 + else: + assert dask_model.client is None + assert dask_model.client_ == default_client() + assert dask_model.client_ == client2 + + assert isinstance(fitted_model_from_disk, model_factory) + assert fitted_model_from_disk.client is None + assert fitted_model_from_disk.client_ == default_client() + assert fitted_model_from_disk.client_ == client2 + + # client will always be None after unpickling + if set_client: + from_disk_params = fitted_model_from_disk.get_params() + from_disk_params.pop("client", None) + dask_params = dask_model.get_params() + dask_params.pop("client", None) + assert from_disk_params == dask_params + else: + assert fitted_model_from_disk.get_params() == dask_model.get_params() + assert local_fitted_model_from_disk.get_params() == local_model.get_params() + + if set_client: + preds_orig = dask_model.predict(dX_1).compute() + preds_loaded_model = fitted_model_from_disk.predict(dX_1).compute() + preds_orig_local = local_model.predict(X_1) + preds_loaded_model_local = local_fitted_model_from_disk.predict(X_1) + else: + preds_orig = dask_model.predict(dX_2).compute() + preds_loaded_model = fitted_model_from_disk.predict(dX_2).compute() + preds_orig_local = local_model.predict(X_2) + preds_loaded_model_local = local_fitted_model_from_disk.predict(X_2) + + assert_eq(preds_orig, preds_loaded_model) + assert_eq(preds_orig_local, preds_loaded_model_local) + + +def test_warns_and_continues_on_unrecognized_tree_learner(cluster): + with Client(cluster) as client: + X = da.random.random((1e3, 10)) + y = da.random.random((1e3, 1)) + dask_regressor = lgb.DaskLGBMRegressor( + client=client, time_out=5, tree_learner="some-nonsense-value", n_estimators=1, num_leaves=2 + ) + with pytest.warns(UserWarning, match="Parameter tree_learner set to some-nonsense-value"): + dask_regressor = dask_regressor.fit(X, y) + + assert dask_regressor.fitted_ + + +@pytest.mark.parametrize("tree_learner", ["data_parallel", "voting_parallel"]) +def test_training_respects_tree_learner_aliases(tree_learner, cluster): + with Client(cluster) as client: + task = "regression" + _, _, _, _, dX, dy, dw, dg = _create_data(objective=task, output="array") + dask_factory = task_to_dask_factory[task] + dask_model = dask_factory(client=client, tree_learner=tree_learner, time_out=5, n_estimators=10, num_leaves=15) + dask_model.fit(dX, dy, sample_weight=dw, group=dg) + + assert dask_model.fitted_ + assert dask_model.get_params()["tree_learner"] == tree_learner + + +def test_error_on_feature_parallel_tree_learner(cluster): + with Client(cluster) as client: + X = da.random.random((100, 10), chunks=(50, 10)) + y = da.random.random(100, chunks=50) + X, y = client.persist([X, y]) + _ = wait([X, y]) + client.rebalance() + dask_regressor = lgb.DaskLGBMRegressor( + client=client, time_out=5, tree_learner="feature_parallel", n_estimators=1, num_leaves=2 + ) + with pytest.raises(lgb.basic.LightGBMError, match="Do not support feature parallel in c api"): + dask_regressor = dask_regressor.fit(X, y) + + +def test_errors(cluster): + with Client(cluster) as client: + + def f(part): + raise Exception("foo") + + df = dd.demo.make_timeseries() + df = df.map_partitions(f, meta=df._meta) + with pytest.raises(Exception) as info: # noqa: PT011, PT012 # error message needs to be coerced to a string + lgb.dask._train(client=client, data=df, label=df.x, params={}, model_factory=lgb.LGBMClassifier) + assert "foo" in str(info.value) + + +@pytest.mark.parametrize("task", tasks) +@pytest.mark.parametrize("output", data_output) +def test_training_succeeds_even_if_some_workers_do_not_have_any_data(task, output, cluster_three_workers): + if task == "ranking" and output == "scipy_csr_matrix": + pytest.skip("LGBMRanker is not currently tested on sparse matrices") + + with Client(cluster_three_workers) as client: + _, y, _, _, dX, dy, dw, dg = _create_data( + objective=task, + output=output, + group=None, + n_samples=1_000, + chunk_size=200, + ) + + dask_model_factory = task_to_dask_factory[task] + + workers = list(client.scheduler_info()["workers"].keys()) + assert len(workers) == 3 + first_two_workers = workers[:2] + + dX = client.persist(dX, workers=first_two_workers) + dy = client.persist(dy, workers=first_two_workers) + dw = client.persist(dw, workers=first_two_workers) + wait([dX, dy, dw]) + + workers_with_data = set() + for coll in (dX, dy, dw): + for with_data in client.who_has(coll).values(): + workers_with_data.update(with_data) + assert workers[2] not in with_data + assert len(workers_with_data) == 2 + + params = { + "time_out": 5, + "random_state": 42, + "num_leaves": 10, + "n_estimators": 20, + } + + dask_model = dask_model_factory(tree="data", client=client, **params) + dask_model.fit(dX, dy, group=dg, sample_weight=dw) + dask_preds = dask_model.predict(dX).compute() + if task == "regression": + score = r2_score(y, dask_preds) + elif task.endswith("classification"): + score = accuracy_score(y, dask_preds) + else: + score = spearmanr(dask_preds, y).correlation + assert score > 0.9 + + +@pytest.mark.parametrize("task", tasks) +def test_network_params_not_required_but_respected_if_given(task, listen_port, cluster): + with Client(cluster) as client: + _, _, _, _, dX, dy, _, dg = _create_data(objective=task, output="array", chunk_size=10, group=None) + + dask_model_factory = task_to_dask_factory[task] + + # rebalance data to be sure that each worker has a piece of the data + client.rebalance() + + # model 1 - no network parameters given + dask_model1 = dask_model_factory( + n_estimators=5, + num_leaves=5, + ) + dask_model1.fit(dX, dy, group=dg) + assert dask_model1.fitted_ + params = dask_model1.get_params() + assert "local_listen_port" not in params + assert "machines" not in params + + # model 2 - machines given + workers = list(client.scheduler_info()["workers"]) + workers_hostname = _get_workers_hostname(cluster) + remote_sockets, open_ports = lgb.dask._assign_open_ports_to_workers( + client=client, + workers=workers, + ) + for s in remote_sockets.values(): + s.release() + dask_model2 = dask_model_factory( + n_estimators=5, + num_leaves=5, + machines=",".join([f"{workers_hostname}:{port}" for port in open_ports.values()]), + ) + + dask_model2.fit(dX, dy, group=dg) + assert dask_model2.fitted_ + params = dask_model2.get_params() + assert "local_listen_port" not in params + assert "machines" in params + + # model 3 - local_listen_port given + # training should fail because LightGBM will try to use the same + # port for multiple worker processes on the same machine + dask_model3 = dask_model_factory(n_estimators=5, num_leaves=5, local_listen_port=listen_port) + error_msg = "has multiple Dask worker processes running on it" + with pytest.raises(lgb.basic.LightGBMError, match=error_msg): + dask_model3.fit(dX, dy, group=dg) + + +@pytest.mark.parametrize("task", tasks) +def test_machines_should_be_used_if_provided(task, cluster): + pytest.skip("skipping due to timeout issues discussed in https://github.com/lightgbm-org/LightGBM/issues/5390") + with Client(cluster) as client: + _, _, _, _, dX, dy, _, dg = _create_data(objective=task, output="array", chunk_size=10, group=None) + + dask_model_factory = task_to_dask_factory[task] + + # rebalance data to be sure that each worker has a piece of the data + client.rebalance() + + n_workers = len(client.scheduler_info()["workers"]) + assert n_workers > 1 + workers_hostname = _get_workers_hostname(cluster) + open_ports = lgb.dask._find_n_open_ports(n_workers) + dask_model = dask_model_factory( + n_estimators=5, + num_leaves=5, + machines=",".join([f"{workers_hostname}:{port}" for port in open_ports]), + ) + + # test that "machines" is actually respected by creating a socket that uses + # one of the ports mentioned in "machines" + error_msg = f"Binding port {open_ports[0]} failed" + with pytest.raises(lgb.basic.LightGBMError, match=error_msg): # noqa: PT012 + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind((workers_hostname, open_ports[0])) + dask_model.fit(dX, dy, group=dg) + + # The above error leaves a worker waiting + client.restart() + + # an informative error should be raised if "machines" has duplicates + one_open_port = lgb.dask._find_n_open_ports(1) + dask_model.set_params(machines=",".join([f"127.0.0.1:{one_open_port}" for _ in range(n_workers)])) + with pytest.raises(ValueError, match="Found duplicates in 'machines'"): + dask_model.fit(dX, dy, group=dg) + + +@pytest.mark.parametrize( + ("dask_est", "sklearn_est"), + [ + (lgb.DaskLGBMClassifier, lgb.LGBMClassifier), + (lgb.DaskLGBMRegressor, lgb.LGBMRegressor), + (lgb.DaskLGBMRanker, lgb.LGBMRanker), + ], +) +def test_dask_classes_and_sklearn_equivalents_have_identical_constructors_except_client_arg(dask_est, sklearn_est): + dask_spec = inspect.getfullargspec(dask_est) + sklearn_spec = inspect.getfullargspec(sklearn_est) + + # should not allow for any varargs + assert dask_spec.varargs == sklearn_spec.varargs + assert dask_spec.varargs is None + + # the only varkw should be **kwargs, + # for pass-through to parent classes' __init__() + assert dask_spec.varkw == sklearn_spec.varkw + assert dask_spec.varkw == "kwargs" + + # "client" should be the only different, and the final argument + assert dask_spec.kwonlyargs == [*sklearn_spec.kwonlyargs, "client"] + + # default values for all constructor arguments should be identical + # + # NOTE: if LGBMClassifier / LGBMRanker / LGBMRegressor ever override + # any of LGBMModel's constructor arguments, this will need to be updated + assert dask_spec.kwonlydefaults == {**sklearn_spec.kwonlydefaults, "client": None} + + # only positional argument should be 'self' + assert dask_spec.args == sklearn_spec.args + assert dask_spec.args == ["self"] + assert dask_spec.defaults is None + + # get_params() should be identical, except for "client" + assert dask_est().get_params() == {**sklearn_est().get_params(), "client": None} + + +@pytest.mark.parametrize( + "methods", + [ + (lgb.DaskLGBMClassifier.fit, lgb.LGBMClassifier.fit), + (lgb.DaskLGBMClassifier.predict, lgb.LGBMClassifier.predict), + (lgb.DaskLGBMClassifier.predict_proba, lgb.LGBMClassifier.predict_proba), + (lgb.DaskLGBMRegressor.fit, lgb.LGBMRegressor.fit), + (lgb.DaskLGBMRegressor.predict, lgb.LGBMRegressor.predict), + (lgb.DaskLGBMRanker.fit, lgb.LGBMRanker.fit), + (lgb.DaskLGBMRanker.predict, lgb.LGBMRanker.predict), + ], +) +def test_dask_methods_and_sklearn_equivalents_have_similar_signatures(methods): + dask_spec = inspect.getfullargspec(methods[0]) + sklearn_spec = inspect.getfullargspec(methods[1]) + dask_params = inspect.signature(methods[0]).parameters + sklearn_params = inspect.signature(methods[1]).parameters + assert dask_spec.args == sklearn_spec.args[: len(dask_spec.args)] + assert dask_spec.varargs == sklearn_spec.varargs + if sklearn_spec.varkw: + assert dask_spec.varkw == sklearn_spec.varkw[: len(dask_spec.varkw)] + assert dask_spec.kwonlyargs == sklearn_spec.kwonlyargs + assert dask_spec.kwonlydefaults == sklearn_spec.kwonlydefaults + for param in dask_spec.args: + error_msg = f"param '{param}' has different default values in the methods" + assert dask_params[param].default == sklearn_params[param].default, error_msg + + +@pytest.mark.parametrize("task", tasks) +def test_training_succeeds_when_data_is_dataframe_and_label_is_column_array(task, cluster): + with Client(cluster): + _, _, _, _, dX, dy, dw, dg = _create_data(objective=task, output="dataframe", group=None) + + model_factory = task_to_dask_factory[task] + + dy = dy.to_dask_array(lengths=True) + dy_col_array = dy.reshape(-1, 1) + assert len(dy_col_array.shape) == 2 + assert dy_col_array.shape[1] == 1 + + params = {"n_estimators": 1, "num_leaves": 3, "random_state": 0, "time_out": 5} + model = model_factory(**params) + model.fit(dX, dy_col_array, sample_weight=dw, group=dg) + assert model.fitted_ + + +@pytest.mark.parametrize("task", tasks) +@pytest.mark.parametrize("output", data_output) +def test_init_score(task, output, cluster, rng): + if task == "ranking" and output == "scipy_csr_matrix": + pytest.skip("LGBMRanker is not currently tested on sparse matrices") + + with Client(cluster) as client: + _, _, _, _, dX, dy, dw, dg = _create_data(objective=task, output=output, group=None) + + model_factory = task_to_dask_factory[task] + + params = { + "n_estimators": 1, + "num_leaves": 2, + "time_out": 5, + "seed": 708, + "deterministic": True, + "force_row_wise": True, + "num_thread": 1, + } + num_classes = 1 + if task == "multiclass-classification": + num_classes = 3 + + if output.startswith("dataframe"): + init_scores = dy.map_partitions(lambda x: pd.DataFrame(rng.uniform(size=(x.size, num_classes)))) + else: + init_scores = dy.map_blocks(lambda x: rng.uniform(size=(x.size, num_classes))) + + model = model_factory(client=client, **params) + model.fit(dX, dy, sample_weight=dw, group=dg) + pred = model.predict(dX, raw_score=True) + + model_init_score = model_factory(client=client, **params) + model_init_score.fit(dX, dy, sample_weight=dw, init_score=init_scores, group=dg) + pred_init_score = model_init_score.predict(dX, raw_score=True) + + # check if init score changes predictions + with pytest.raises(AssertionError): # noqa: PT011 + assert_eq(pred, pred_init_score) + + +def sklearn_checks_to_run(): + check_names = ["check_estimator_get_tags_default_keys", "check_get_params_invariance", "check_set_params"] + for check_name in check_names: + check_func = getattr(sklearn_checks, check_name, None) + if check_func: + yield check_func + + +def _tested_estimators(): + for Estimator in [lgb.DaskLGBMClassifier, lgb.DaskLGBMRegressor]: + yield Estimator() + + +@pytest.mark.parametrize("estimator", _tested_estimators()) +@pytest.mark.parametrize("check", sklearn_checks_to_run()) +def test_sklearn_integration(estimator, check, cluster): + with Client(cluster): + estimator.set_params(local_listen_port=18000, time_out=5) + name = type(estimator).__name__ + check(name, estimator) + + +# this test is separate because it takes a not-yet-constructed estimator +@pytest.mark.parametrize("estimator", list(_tested_estimators())) +def test_parameters_default_constructible(estimator): + name = estimator.__class__.__name__ + Estimator = estimator + sklearn_checks.check_parameters_default_constructible(name, Estimator) + + +@pytest.mark.parametrize("task", tasks) +@pytest.mark.parametrize("output", data_output) +def test_predict_with_raw_score(task, output, cluster): + if task == "ranking" and output == "scipy_csr_matrix": + pytest.skip("LGBMRanker is not currently tested on sparse matrices") + + with Client(cluster) as client: + _, _, _, _, dX, dy, _, dg = _create_data(objective=task, output=output, group=None) + + model_factory = task_to_dask_factory[task] + params = {"client": client, "n_estimators": 1, "num_leaves": 2, "time_out": 5, "min_sum_hessian": 0} + model = model_factory(**params) + model.fit(dX, dy, group=dg) + raw_predictions = model.predict(dX, raw_score=True).compute() + + trees_df = model.booster_.trees_to_dataframe() + leaves_df = trees_df[trees_df.node_depth == 2] + if task == "multiclass-classification": + for i in range(model.n_classes_): + class_df = leaves_df[leaves_df.tree_index == i] + assert set(raw_predictions[:, i]) == set(class_df["value"]) + else: + assert set(raw_predictions) == set(leaves_df["value"]) + + if task.endswith("classification"): + pred_proba_raw = model.predict_proba(dX, raw_score=True).compute() + assert_eq(raw_predictions, pred_proba_raw) + + +@pytest.mark.parametrize("output", data_output) +@pytest.mark.parametrize("task", tasks) +def test_predict_returns_expected_dtypes(task, output, cluster): + if task == "ranking" and output == "scipy_csr_matrix": + pytest.skip("LGBMRanker is not currently tested on sparse matrices") + + with Client(cluster) as client: + _, _, _, _, dX, dy, _, dg = _create_data(objective=task, output=output, group=None) + + model_factory = task_to_dask_factory[task] + params = { + "client": client, + "n_estimators": 1, + "num_leaves": 2, + "time_out": 5, + "verbose": -1, + } + model = model_factory(**params) + model.fit(dX, dy, group=dg) + + # use a small sub-sample (to keep the tests fast) + if output.startswith("dataframe"): + dX_sample = dX.sample(frac=0.001) + else: + dX_sample = dX[:1,] + dX_sample.persist() + + # default predictions: + # + # * classification: int64 + # * ranking: float64 + # * regression: float64 + # + preds = model.predict(dX_sample).compute() + if task.endswith("classification"): + # preds go through LabelEncoder.inverse_transform() and have the same + # dtype as model.classes_ (expected to be an integer type, but exact size + # varies across numpy versions and operating systems) + assert preds.dtype == model.classes_.dtype + assert preds.dtype in (np.int32, np.int64) + else: + assert preds.dtype == np.float64 + + # raw predictions: always float64 + preds_raw = model.predict(dX_sample, raw_score=True).compute() + assert preds_raw.dtype == np.float64 + + # pred_contrib: always float64 + if output.startswith("scipy"): + preds_contrib = [arr.compute() for arr in model.predict(dX_sample, pred_contrib=True)] + assert all(arr.dtype == np.float64 for arr in preds_contrib) + else: + preds_contrib = model.predict(dX_sample, pred_contrib=True).compute() + assert preds_contrib.dtype == np.float64 + + # pred_leavs: always int32 + preds_leaves = model.predict(dX_sample, pred_leaf=True).compute() + assert preds_leaves.dtype == np.int32 + + +@pytest.mark.parametrize("output", data_output) +@pytest.mark.parametrize("use_init_score", [False, True]) +def test_predict_stump(output, use_init_score, cluster, rng): + with Client(cluster) as client: + _, _, _, _, dX, dy, _, _ = _create_data(objective="binary-classification", n_samples=1_000, output=output) + + params = {"objective": "binary", "n_estimators": 5, "min_data_in_leaf": 1_000} + + if not use_init_score: + init_scores = None + elif output.startswith("dataframe"): + init_scores = dy.map_partitions(lambda x: pd.DataFrame(rng.uniform(size=x.size))) + else: + init_scores = dy.map_blocks(lambda x: rng.uniform(size=x.size)) + + model = lgb.DaskLGBMClassifier(client=client, **params) + model.fit(dX, dy, init_score=init_scores) + preds_1 = model.predict(dX, raw_score=True, num_iteration=1).compute() + preds_all = model.predict(dX, raw_score=True).compute() + + if use_init_score: + # if init_score was provided, a model of stumps should predict all 0s + all_zeroes = np.full_like(preds_1, fill_value=0.0) + assert_eq(preds_1, all_zeroes) + assert_eq(preds_all, all_zeroes) + else: + # if init_score was not provided, prediction for a model of stumps should be + # the "average" of the labels + y_avg = np.log(dy.mean() / (1.0 - dy.mean())) + assert_eq(preds_1, np.full_like(preds_1, fill_value=y_avg)) + assert_eq(preds_all, np.full_like(preds_all, fill_value=y_avg)) + + +def test_distributed_quantized_training(tmp_path, cluster): + with Client(cluster) as client: + X, y, w, _, dX, dy, dw, _ = _create_data(objective="regression", output="array") + + np.savetxt(tmp_path / "data_dask.csv", np.hstack([np.array([y]).T, X]), fmt="%f,%f,%f,%f,%f") + + params = { + "boosting_type": "gbdt", + "n_estimators": 50, + "num_leaves": 31, + "use_quantized_grad": True, + "num_grad_quant_bins": 30, + "quant_train_renew_leaf": True, + "verbose": -1, + } + + quant_dask_classifier = lgb.DaskLGBMRegressor(client=client, time_out=5, **params) + quant_dask_classifier = quant_dask_classifier.fit(dX, dy, sample_weight=dw) + quant_p1 = quant_dask_classifier.predict(dX) + quant_rmse = np.sqrt(np.mean((quant_p1.compute() - y) ** 2)) + + params["use_quantized_grad"] = False + dask_classifier = lgb.DaskLGBMRegressor(client=client, time_out=5, **params) + dask_classifier = dask_classifier.fit(dX, dy, sample_weight=dw) + p1 = dask_classifier.predict(dX) + rmse = np.sqrt(np.mean((p1.compute() - y) ** 2)) + assert quant_rmse < rmse + 7.0 diff --git a/tests/python_package_test/test_dual.py b/tests/python_package_test/test_dual.py new file mode 100644 index 0000000..7834723 --- /dev/null +++ b/tests/python_package_test/test_dual.py @@ -0,0 +1,37 @@ +# coding: utf-8 +"""Tests for dual GPU+CPU support.""" + +import os +import platform + +import pytest +from sklearn.metrics import log_loss + +import lightgbm as lgb + +from .utils import load_breast_cancer + + +@pytest.mark.skipif( + os.environ.get("LIGHTGBM_TEST_DUAL_CPU_GPU", "0") != "1", + reason="Set LIGHTGBM_TEST_DUAL_CPU_GPU=1 to test using CPU and GPU training from the same package.", +) +def test_cpu_and_gpu_work(): + # If compiled appropriately, the same installation will support both GPU and CPU. + X, y = load_breast_cancer(return_X_y=True) + data = lgb.Dataset(X, y) + + params_cpu = {"verbosity": -1, "num_leaves": 31, "objective": "binary", "device": "cpu"} + cpu_bst = lgb.train(params_cpu, data, num_boost_round=10) + cpu_score = log_loss(y, cpu_bst.predict(X)) + + params_gpu = params_cpu.copy() + params_gpu["device"] = "gpu" + # Double-precision floats are only supported on x86_64 with PoCL + params_gpu["gpu_use_dp"] = platform.machine() == "x86_64" + gpu_bst = lgb.train(params_gpu, data, num_boost_round=10) + gpu_score = log_loss(y, gpu_bst.predict(X)) + + rel = 1e-6 if params_gpu["gpu_use_dp"] else 1e-4 + assert cpu_score == pytest.approx(gpu_score, rel=rel) + assert gpu_score < 0.242 diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py new file mode 100644 index 0000000..1598e74 --- /dev/null +++ b/tests/python_package_test/test_engine.py @@ -0,0 +1,4910 @@ +# coding: utf-8 +import copy +import itertools +import json +import math +import pickle +import platform +import random +import re +from pathlib import Path +from shutil import copyfile + +import numpy as np +import psutil +import pytest +from scipy.sparse import csr_matrix, isspmatrix_csc, isspmatrix_csr +from sklearn.datasets import load_svmlight_file, make_blobs, make_classification, make_multilabel_classification +from sklearn.metrics import ( + average_precision_score, + log_loss, + mean_absolute_error, + mean_squared_error, + r2_score, + roc_auc_score, +) +from sklearn.model_selection import GroupKFold, TimeSeriesSplit, train_test_split + +import lightgbm as lgb + +from .utils import ( + SERIALIZERS, + BuildInfo, + assert_all_trees_valid, + assert_silent, + dummy_obj, + load_breast_cancer, + load_digits, + load_iris, + logistic_sigmoid, + make_synthetic_regression, + mse_obj, + np_assert_array_equal, + pickle_and_unpickle_object, + sklearn_multiclass_custom_objective, + softmax, +) + +decreasing_generator = itertools.count(0, -1) + + +def logloss_obj(preds, train_data): + y_true = train_data.get_label() + y_pred = logistic_sigmoid(preds) + grad = y_pred - y_true + hess = y_pred * (1.0 - y_pred) + return grad, hess + + +def multi_logloss(y_true, y_pred): + return np.mean([-math.log(y_pred[i][y]) for i, y in enumerate(y_true)]) + + +def top_k_error(y_true, y_pred, k): + if k == y_pred.shape[1]: + return 0 + max_rest = np.max(-np.partition(-y_pred, k)[:, k:], axis=1) + return 1 - np.mean((y_pred[np.arange(len(y_true)), y_true] > max_rest)) + + +def constant_metric(preds, train_data): + return ("error", 0.0, False) + + +def constant_metric_multi(preds, train_data): + return [ + ("important_metric", 1.5, False), + ("irrelevant_metric", 7.8, False), + ] + + +def decreasing_metric(preds, train_data): + return ("decreasing_metric", next(decreasing_generator), False) + + +def categorize(continuous_x): + return np.digitize(continuous_x, bins=np.arange(0, 1, 0.01)) + + +def test_binary(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "binary", + "metric": "binary_logloss", + "verbose": -1, + "num_iteration": 50, # test num_iteration in dict here + } + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = log_loss(y_test, gbm.predict(X_test)) + assert ret < 0.14 + assert len(evals_result["valid_0"]["binary_logloss"]) == 50 + assert evals_result["valid_0"]["binary_logloss"][-1] == pytest.approx(ret) + + +def test_rf(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "boosting_type": "rf", + "objective": "binary", + "bagging_freq": 1, + "bagging_fraction": 0.5, + "feature_fraction": 0.5, + "num_leaves": 50, + "metric": "binary_logloss", + "verbose": -1, + } + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = log_loss(y_test, gbm.predict(X_test)) + assert ret < 0.19 + assert evals_result["valid_0"]["binary_logloss"][-1] == pytest.approx(ret) + + +@pytest.mark.parametrize("objective", ["regression", "regression_l1", "huber", "fair", "poisson", "quantile"]) +def test_regression(objective): + X, y = make_synthetic_regression() + y = np.abs(y) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"objective": objective, "metric": "l2", "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = mean_squared_error(y_test, gbm.predict(X_test)) + if objective == "huber": + assert ret < 430 + elif objective == "fair": + assert ret < 296 + elif objective == "poisson": + assert ret < 193 + elif objective == "quantile": + assert ret < 1311 + else: + assert ret < 343 + assert evals_result["valid_0"]["l2"][-1] == pytest.approx(ret) + + +@pytest.mark.skipif( + BuildInfo.has_cuda, reason="CUDA version has a different implementation of WeightedPercentileFun, not tested here" +) +@pytest.mark.parametrize("objective", ["regression_l1", "quantile", "mape"]) +def test_weighted_percentile_inside_label_range(objective): + # Regression test for https://github.com/lightgbm-org/LightGBM/issues/7151 + # WeightedPercentileFun used the wrong + # CDF segment for linear interpolation and could return values outside + # [min(y), max(y)]. The pre-fix implementation produced a "weighted + # median" of 1.0 for y=[2,3,4,5], w=[4,3,2,1], far below min(y)=2. + # + # The correct weighted median for that example is 2 + 1/3 = 2.333..., and + # any weighted percentile with non-negative weights must lie in the label + # range. We train a model that cannot learn any structure from X (a single + # constant feature) so BoostFromScore dominates the prediction. + X = np.zeros((4, 1)) + y = np.array([2.0, 3.0, 4.0, 5.0]) + w = np.array([4.0, 3.0, 2.0, 1.0]) + params = { + "objective": objective, + "verbose": -1, + "num_leaves": 2, + "min_data_in_leaf": 1, + "min_sum_hessian_in_leaf": 0.0, + "learning_rate": 1.0, + "feature_fraction": 1.0, + "bagging_fraction": 1.0, + } + if objective == "quantile": + params["alpha"] = 0.5 + train = lgb.Dataset(X, label=y, weight=w) + gbm = lgb.train(params, train, num_boost_round=1) + preds = gbm.predict(X) + assert np.all(preds >= y.min() - 1e-6), f"{objective}: prediction {preds.min()} fell below min(y)={y.min()}" + assert np.all(preds <= y.max() + 1e-6), f"{objective}: prediction {preds.max()} rose above max(y)={y.max()}" + # For regression_l1 the single-leaf prediction is exactly the weighted + # median of y; verify it matches the expected 2 + 1/3. + if objective == "regression_l1": + np.testing.assert_allclose(preds, 2.0 + 1.0 / 3.0, rtol=1e-6) + + +def test_missing_value_handle(): + X_train = np.zeros((100, 1)) + y_train = np.zeros(100) + trues = random.sample(range(100), 20) + for idx in trues: + X_train[idx, 0] = np.nan + y_train[idx] = 1 + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = {"metric": "l2", "verbose": -1, "boost_from_average": False} + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = mean_squared_error(y_train, gbm.predict(X_train)) + assert ret < 0.005 + assert evals_result["valid_0"]["l2"][-1] == pytest.approx(ret) + + +def test_missing_value_handle_more_na(): + X_train = np.ones((100, 1)) + y_train = np.ones(100) + trues = random.sample(range(100), 80) + for idx in trues: + X_train[idx, 0] = np.nan + y_train[idx] = 0 + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = {"metric": "l2", "verbose": -1, "boost_from_average": False} + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = mean_squared_error(y_train, gbm.predict(X_train)) + assert ret < 0.005 + assert evals_result["valid_0"]["l2"][-1] == pytest.approx(ret) + + +def test_missing_value_handle_na(): + x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan] + y = [1, 1, 1, 1, 0, 0, 0, 0, 1] + + X_train = np.array(x).reshape(len(x), 1) + y_train = np.array(y) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = { + "objective": "regression", + "metric": "auc", + "verbose": -1, + "boost_from_average": False, + "min_data": 1, + "num_leaves": 2, + "learning_rate": 1, + "min_data_in_bin": 1, + "zero_as_missing": False, + } + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + pred = gbm.predict(X_train) + np.testing.assert_allclose(pred, y) + ret = roc_auc_score(y_train, pred) + assert ret > 0.999 + assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret) + + +def test_missing_value_handle_zero(): + x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan] + y = [0, 1, 1, 1, 0, 0, 0, 0, 0] + + X_train = np.array(x).reshape(len(x), 1) + y_train = np.array(y) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = { + "objective": "regression", + "metric": "auc", + "verbose": -1, + "boost_from_average": False, + "min_data": 1, + "num_leaves": 2, + "learning_rate": 1, + "min_data_in_bin": 1, + "zero_as_missing": True, + } + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + pred = gbm.predict(X_train) + np.testing.assert_allclose(pred, y) + ret = roc_auc_score(y_train, pred) + assert ret > 0.999 + assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret) + + +def test_missing_value_handle_none(): + x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan] + y = [0, 1, 1, 1, 0, 0, 0, 0, 0] + + X_train = np.array(x).reshape(len(x), 1) + y_train = np.array(y) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = { + "objective": "regression", + "metric": "auc", + "verbose": -1, + "boost_from_average": False, + "min_data": 1, + "num_leaves": 2, + "learning_rate": 1, + "min_data_in_bin": 1, + "use_missing": False, + } + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + pred = gbm.predict(X_train) + assert pred[0] == pytest.approx(pred[1]) + assert pred[-1] == pytest.approx(pred[0]) + ret = roc_auc_score(y_train, pred) + assert ret > 0.83 + assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret) + + +@pytest.mark.parametrize( + "use_quantized_grad", + [ + pytest.param( + True, + marks=pytest.mark.skipif( + BuildInfo.has_cuda, + reason="Skip because quantized training with categorical features is not supported for cuda version", + ), + ), + False, + ], +) +def test_categorical_handle(use_quantized_grad): + x = [0, 1, 2, 3, 4, 5, 6, 7] + y = [0, 1, 0, 1, 0, 1, 0, 1] + + X_train = np.array(x).reshape(len(x), 1) + y_train = np.array(y) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = { + "objective": "regression", + "metric": "auc", + "verbose": -1, + "boost_from_average": False, + "min_data": 1, + "num_leaves": 2, + "learning_rate": 1, + "min_data_in_bin": 1, + "min_data_per_group": 1, + "cat_smooth": 1, + "cat_l2": 0, + "max_cat_to_onehot": 1, + "zero_as_missing": True, + "categorical_column": 0, + "use_quantized_grad": use_quantized_grad, + } + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + pred = gbm.predict(X_train) + np.testing.assert_allclose(pred, y) + ret = roc_auc_score(y_train, pred) + assert ret > 0.999 + assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret) + + +@pytest.mark.parametrize( + "use_quantized_grad", + [ + pytest.param( + True, + marks=pytest.mark.skipif( + BuildInfo.has_cuda, + reason="Skip because quantized training with categorical features is not supported for cuda version", + ), + ), + False, + ], +) +def test_categorical_handle_na(use_quantized_grad): + x = [0, np.nan, 0, np.nan, 0, np.nan] + y = [0, 1, 0, 1, 0, 1] + + X_train = np.array(x).reshape(len(x), 1) + y_train = np.array(y) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = { + "objective": "regression", + "metric": "auc", + "verbose": -1, + "boost_from_average": False, + "min_data": 1, + "num_leaves": 2, + "learning_rate": 1, + "min_data_in_bin": 1, + "min_data_per_group": 1, + "cat_smooth": 1, + "cat_l2": 0, + "max_cat_to_onehot": 1, + "zero_as_missing": False, + "categorical_column": 0, + "use_quantized_grad": use_quantized_grad, + } + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + pred = gbm.predict(X_train) + np.testing.assert_allclose(pred, y) + ret = roc_auc_score(y_train, pred) + assert ret > 0.999 + assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret) + + +@pytest.mark.parametrize( + "use_quantized_grad", + [ + pytest.param( + True, + marks=pytest.mark.skipif( + BuildInfo.has_cuda, + reason="Skip because quantized training with categorical features is not supported for cuda version", + ), + ), + False, + ], +) +def test_categorical_non_zero_inputs(use_quantized_grad): + x = [1, 1, 1, 1, 1, 1, 2, 2] + y = [1, 1, 1, 1, 1, 1, 0, 0] + + X_train = np.array(x).reshape(len(x), 1) + y_train = np.array(y) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_train, y_train) + + params = { + "objective": "regression", + "metric": "auc", + "verbose": -1, + "boost_from_average": False, + "min_data": 1, + "num_leaves": 2, + "learning_rate": 1, + "min_data_in_bin": 1, + "min_data_per_group": 1, + "cat_smooth": 1, + "cat_l2": 0, + "max_cat_to_onehot": 1, + "zero_as_missing": False, + "categorical_column": 0, + "use_quantized_grad": use_quantized_grad, + } + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + pred = gbm.predict(X_train) + np.testing.assert_allclose(pred, y) + ret = roc_auc_score(y_train, pred) + assert ret > 0.999 + assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret) + + +def test_multiclass(): + X, y = load_digits(n_class=10, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 10, "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train, params=params) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params) + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = multi_logloss(y_test, gbm.predict(X_test)) + assert ret < 0.16 + assert evals_result["valid_0"]["multi_logloss"][-1] == pytest.approx(ret) + + +def test_multiclass_rf(): + X, y = load_digits(n_class=10, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "boosting_type": "rf", + "objective": "multiclass", + "metric": "multi_logloss", + "bagging_freq": 1, + "bagging_fraction": 0.6, + "feature_fraction": 0.6, + "num_class": 10, + "num_leaves": 50, + "min_data": 1, + "verbose": -1, + "gpu_use_dp": True, + } + lgb_train = lgb.Dataset(X_train, y_train, params=params) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params) + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = multi_logloss(y_test, gbm.predict(X_test)) + assert ret < 0.23 + assert evals_result["valid_0"]["multi_logloss"][-1] == pytest.approx(ret) + + +def test_multiclass_prediction_early_stopping(): + X, y = load_digits(n_class=10, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 10, "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train, params=params) + gbm = lgb.train(params, lgb_train, num_boost_round=50) + + pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5} + ret = multi_logloss(y_test, gbm.predict(X_test, **pred_parameter)) + assert ret < 0.8 + assert ret > 0.6 # loss will be higher than when evaluating the full model + + pred_parameter["pred_early_stop_margin"] = 5.5 + ret = multi_logloss(y_test, gbm.predict(X_test, **pred_parameter)) + assert ret < 0.2 + + +def test_multi_class_error(): + X, y = load_digits(n_class=10, return_X_y=True) + params = {"objective": "multiclass", "num_classes": 10, "metric": "multi_error", "num_leaves": 4, "verbose": -1} + lgb_data = lgb.Dataset(X, label=y) + est = lgb.train(params, lgb_data, num_boost_round=10) + predict_default = est.predict(X) + results = {} + est = lgb.train( + dict(params, multi_error_top_k=1), + lgb_data, + num_boost_round=10, + valid_sets=[lgb_data], + callbacks=[lgb.record_evaluation(results)], + ) + predict_1 = est.predict(X) + # check that default gives same result as k = 1 + np.testing.assert_allclose(predict_1, predict_default) + # check against independent calculation for k = 1 + err = top_k_error(y, predict_1, 1) + assert results["training"]["multi_error"][-1] == pytest.approx(err) + # check against independent calculation for k = 2 + results = {} + est = lgb.train( + dict(params, multi_error_top_k=2), + lgb_data, + num_boost_round=10, + valid_sets=[lgb_data], + callbacks=[lgb.record_evaluation(results)], + ) + predict_2 = est.predict(X) + err = top_k_error(y, predict_2, 2) + assert results["training"]["multi_error@2"][-1] == pytest.approx(err) + # check against independent calculation for k = 10 + results = {} + est = lgb.train( + dict(params, multi_error_top_k=10), + lgb_data, + num_boost_round=10, + valid_sets=[lgb_data], + callbacks=[lgb.record_evaluation(results)], + ) + predict_3 = est.predict(X) + err = top_k_error(y, predict_3, 10) + assert results["training"]["multi_error@10"][-1] == pytest.approx(err) + # check cases where predictions are equal + X = np.array([[0, 0], [0, 0]]) + y = np.array([0, 1]) + lgb_data = lgb.Dataset(X, label=y) + params["num_classes"] = 2 + results = {} + lgb.train(params, lgb_data, num_boost_round=10, valid_sets=[lgb_data], callbacks=[lgb.record_evaluation(results)]) + assert results["training"]["multi_error"][-1] == pytest.approx(1) + results = {} + lgb.train( + dict(params, multi_error_top_k=2), + lgb_data, + num_boost_round=10, + valid_sets=[lgb_data], + callbacks=[lgb.record_evaluation(results)], + ) + assert results["training"]["multi_error@2"][-1] == pytest.approx(0) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Skip due to differences in implementation details of CUDA version") +def test_auc_mu(rng): + # should give same result as binary auc for 2 classes + X, y = load_digits(n_class=10, return_X_y=True) + y_new = np.zeros((len(y))) + y_new[y != 0] = 1 + lgb_X = lgb.Dataset(X, label=y_new) + params = {"objective": "multiclass", "metric": "auc_mu", "verbose": -1, "num_classes": 2, "seed": 0} + results_auc_mu = {} + lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_auc_mu)]) + params = {"objective": "binary", "metric": "auc", "verbose": -1, "seed": 0} + results_auc = {} + lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_auc)]) + np.testing.assert_allclose(results_auc_mu["training"]["auc_mu"], results_auc["training"]["auc"]) + # test the case where all predictions are equal + lgb_X = lgb.Dataset(X[:10], label=y_new[:10]) + params = { + "objective": "multiclass", + "metric": "auc_mu", + "verbose": -1, + "num_classes": 2, + "min_data_in_leaf": 20, + "seed": 0, + } + results_auc_mu = {} + lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_auc_mu)]) + assert results_auc_mu["training"]["auc_mu"][-1] == pytest.approx(0.5) + # test that weighted data gives different auc_mu + lgb_X = lgb.Dataset(X, label=y) + lgb_X_weighted = lgb.Dataset(X, label=y, weight=np.abs(rng.standard_normal(size=y.shape))) + results_unweighted = {} + results_weighted = {} + params = dict(params, num_classes=10, num_leaves=5) + lgb.train( + params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_unweighted)] + ) + lgb.train( + params, + lgb_X_weighted, + num_boost_round=10, + valid_sets=[lgb_X_weighted], + callbacks=[lgb.record_evaluation(results_weighted)], + ) + assert results_weighted["training"]["auc_mu"][-1] < 1 + assert results_unweighted["training"]["auc_mu"][-1] != results_weighted["training"]["auc_mu"][-1] + # test that equal data weights give same auc_mu as unweighted data + lgb_X_weighted = lgb.Dataset(X, label=y, weight=np.ones(y.shape) * 0.5) + lgb.train( + params, + lgb_X_weighted, + num_boost_round=10, + valid_sets=[lgb_X_weighted], + callbacks=[lgb.record_evaluation(results_weighted)], + ) + assert results_unweighted["training"]["auc_mu"][-1] == pytest.approx( + results_weighted["training"]["auc_mu"][-1], abs=1e-5 + ) + # should give 1 when accuracy = 1 + X = X[:10, :] + y = y[:10] + lgb_X = lgb.Dataset(X, label=y) + params = {"objective": "multiclass", "metric": "auc_mu", "num_classes": 10, "min_data_in_leaf": 1, "verbose": -1} + results = {} + lgb.train(params, lgb_X, num_boost_round=100, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results)]) + assert results["training"]["auc_mu"][-1] == pytest.approx(1) + # test loading class weights + Xy = np.loadtxt( + str(Path(__file__).absolute().parents[2] / "examples" / "multiclass_classification" / "multiclass.train") + ) + y = Xy[:, 0] + X = Xy[:, 1:] + lgb_X = lgb.Dataset(X, label=y) + params = { + "objective": "multiclass", + "metric": "auc_mu", + "auc_mu_weights": [0, 2, 2, 2, 2, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0], + "num_classes": 5, + "verbose": -1, + "seed": 0, + } + results_weight = {} + lgb.train(params, lgb_X, num_boost_round=5, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_weight)]) + params["auc_mu_weights"] = [] + results_no_weight = {} + lgb.train( + params, lgb_X, num_boost_round=5, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_no_weight)] + ) + assert results_weight["training"]["auc_mu"][-1] != results_no_weight["training"]["auc_mu"][-1] + + +def test_ranking_prediction_early_stopping(): + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train")) + q_train = np.loadtxt(str(rank_example_dir / "rank.train.query")) + X_test, _ = load_svmlight_file(str(rank_example_dir / "rank.test")) + params = {"objective": "rank_xendcg", "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train, group=q_train, params=params) + gbm = lgb.train(params, lgb_train, num_boost_round=50) + + pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5} + ret_early = gbm.predict(X_test, **pred_parameter) + + pred_parameter["pred_early_stop_margin"] = 5.5 + ret_early_more_strict = gbm.predict(X_test, **pred_parameter) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(ret_early, ret_early_more_strict) + + +# Simulates position bias for a given ranking dataset. +# The output dataset is identical to the input one with the exception for the relevance labels. +# The new labels are generated according to an instance of a cascade user model: +# for each query, the user is simulated to be traversing the list of documents ranked by a baseline ranker +# (in our example it is simply the ordering by some feature correlated with relevance, e.g., 34) +# and clicks on that document (new_label=1) with some probability 'pclick' depending on its true relevance; +# at each position the user may stop the traversal with some probability pstop. For the non-clicked documents, +# new_label=0. Thus the generated new labels are biased towards the baseline ranker. +# The positions of the documents in the ranked lists produced by the baseline, are returned. +def simulate_position_bias(file_dataset_in, file_query_in, file_dataset_out, baseline_feature): + # a mapping of a document's true relevance (defined on a 5-grade scale) into the probability of clicking it + def get_pclick(label): + if label == 0: + return 0.4 + elif label == 1: + return 0.6 + elif label == 2: + return 0.7 + elif label == 3: + return 0.8 + else: + return 0.9 + + # an instantiation of a cascade model where the user stops with probability 0.2 after observing each document + pstop = 0.2 + + f_dataset_in = open(file_dataset_in, "r") + f_dataset_out = open(file_dataset_out, "w") + random.seed(10) + positions_all = [] + for line in open(file_query_in): + docs_num = int(line) + lines = [] + index_values = [] + positions = [0] * docs_num + for index in range(docs_num): + features = f_dataset_in.readline().split() + lines.append(features) + val = 0.0 + for feature_val in features: + feature_val_split = feature_val.split(":") + if int(feature_val_split[0]) == baseline_feature: + val = float(feature_val_split[1]) + index_values.append([index, val]) + index_values.sort(key=lambda x: -x[1]) + stop = False + for pos in range(docs_num): + index = index_values[pos][0] + new_label = 0 + if not stop: + label = int(lines[index][0]) + pclick = get_pclick(label) + if random.random() < pclick: + new_label = 1 + stop = random.random() < pstop + lines[index][0] = str(new_label) + positions[index] = pos + for features in lines: + f_dataset_out.write(" ".join(features) + "\n") + positions_all.extend(positions) + f_dataset_out.close() + return positions_all + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Positions in learning to rank is not supported in CUDA version yet") +def test_ranking_with_position_information_with_file(tmp_path): + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + params = { + "objective": "lambdarank", + "verbose": -1, + "eval_at": [3], + "metric": "ndcg", + "bagging_freq": 1, + "bagging_fraction": 0.9, + "min_data_in_leaf": 50, + "min_sum_hessian_in_leaf": 5.0, + } + + # simulate position bias for the train dataset and put the train dataset with biased labels to temp directory + positions = simulate_position_bias( + str(rank_example_dir / "rank.train"), + str(rank_example_dir / "rank.train.query"), + str(tmp_path / "rank.train"), + baseline_feature=34, + ) + copyfile(str(rank_example_dir / "rank.train.query"), str(tmp_path / "rank.train.query")) + copyfile(str(rank_example_dir / "rank.test"), str(tmp_path / "rank.test")) + copyfile(str(rank_example_dir / "rank.test.query"), str(tmp_path / "rank.test.query")) + + lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params) + lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))] + gbm_baseline = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50) + + f_positions_out = open(str(tmp_path / "rank.train.position"), "w") + for pos in positions: + f_positions_out.write(str(pos) + "\n") + f_positions_out.close() + + lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params) + lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))] + gbm_unbiased_with_file = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50) + + # the performance of the unbiased LambdaMART should outperform the plain LambdaMART on the dataset with position bias + assert gbm_baseline.best_score["valid_0"]["ndcg@3"] + 0.03 <= gbm_unbiased_with_file.best_score["valid_0"]["ndcg@3"] + + # add extra row to position file + with open(str(tmp_path / "rank.train.position"), "a") as file: + file.write("pos_1000\n") + file.close() + lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params) + lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))] + with pytest.raises(lgb.basic.LightGBMError, match=r"Positions size \(3006\) doesn't match data size"): + lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Positions in learning to rank is not supported in CUDA version yet") +def test_ranking_with_position_information_with_dataset_constructor(tmp_path): + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + params = { + "objective": "lambdarank", + "verbose": -1, + "eval_at": [3], + "metric": "ndcg", + "bagging_freq": 1, + "bagging_fraction": 0.9, + "min_data_in_leaf": 50, + "min_sum_hessian_in_leaf": 5.0, + "num_threads": 1, + "deterministic": True, + "seed": 0, + } + + # simulate position bias for the train dataset and put the train dataset with biased labels to temp directory + positions = simulate_position_bias( + str(rank_example_dir / "rank.train"), + str(rank_example_dir / "rank.train.query"), + str(tmp_path / "rank.train"), + baseline_feature=34, + ) + copyfile(str(rank_example_dir / "rank.train.query"), str(tmp_path / "rank.train.query")) + copyfile(str(rank_example_dir / "rank.test"), str(tmp_path / "rank.test")) + copyfile(str(rank_example_dir / "rank.test.query"), str(tmp_path / "rank.test.query")) + + lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params) + lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))] + gbm_baseline = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50) + + positions = np.array(positions) + + # test setting positions through Dataset constructor with numpy array + lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params, position=positions) + lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))] + gbm_unbiased = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50) + + # the performance of the unbiased LambdaMART should outperform the plain LambdaMART on the dataset with position bias + assert gbm_baseline.best_score["valid_0"]["ndcg@3"] + 0.03 <= gbm_unbiased.best_score["valid_0"]["ndcg@3"] + + try: + import pandas as pd # noqa: PLC0415 + + # test setting positions through Dataset constructor with pandas Series + lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params, position=pd.Series(positions)) + lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))] + gbm_unbiased_pandas_series = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50) + assert ( + gbm_unbiased.best_score["valid_0"]["ndcg@3"] == gbm_unbiased_pandas_series.best_score["valid_0"]["ndcg@3"] + ) + except ImportError: + pass + + # test setting positions through set_position + lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params) + lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))] + lgb_train.set_position(positions) + gbm_unbiased_set_position = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50) + assert gbm_unbiased.best_score["valid_0"]["ndcg@3"] == gbm_unbiased_set_position.best_score["valid_0"]["ndcg@3"] + + # test get_position works (positions are remapped to dense int32 indices on the C++ + # side, so compare against get_field("position") rather than the original input) + positions_from_get = lgb_train.get_position() + np_assert_array_equal(positions_from_get, lgb_train.get_field("position"), strict=True) + assert positions_from_get.dtype == np.int32 + assert positions_from_get.shape == positions.shape + + +def test_early_stopping(): + X, y = load_breast_cancer(return_X_y=True) + params = {"objective": "binary", "metric": "binary_logloss", "verbose": -1} + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + valid_set_name = "valid_set" + # no early stopping + gbm = lgb.train( + params, + lgb_train, + num_boost_round=10, + valid_sets=lgb_eval, + valid_names=valid_set_name, + callbacks=[lgb.early_stopping(stopping_rounds=5)], + ) + assert gbm.best_iteration == 10 + assert valid_set_name in gbm.best_score + assert "binary_logloss" in gbm.best_score[valid_set_name] + # early stopping occurs + gbm = lgb.train( + params, + lgb_train, + num_boost_round=40, + valid_sets=lgb_eval, + valid_names=valid_set_name, + callbacks=[lgb.early_stopping(stopping_rounds=5)], + ) + assert gbm.best_iteration <= 39 + assert valid_set_name in gbm.best_score + assert "binary_logloss" in gbm.best_score[valid_set_name] + + +@pytest.mark.parametrize("use_valid", [True, False]) +def test_early_stopping_ignores_training_set(use_valid): + x = np.linspace(-1, 1, 100) + X = x.reshape(-1, 1) + y = x**2 + X_train, X_valid = X[:80], X[80:] + y_train, y_valid = y[:80], y[80:] + train_ds = lgb.Dataset(X_train, y_train) + valid_ds = lgb.Dataset(X_valid, y_valid) + valid_sets = [train_ds] + valid_names = ["train"] + if use_valid: + valid_sets.append(valid_ds) + valid_names.append("valid") + eval_result = {} + + def train_fn(): + return lgb.train( + {"num_leaves": 5}, + train_ds, + num_boost_round=2, + valid_sets=valid_sets, + valid_names=valid_names, + callbacks=[lgb.early_stopping(1), lgb.record_evaluation(eval_result)], + ) + + if use_valid: + bst = train_fn() + assert bst.best_iteration == 1 + assert eval_result["train"]["l2"][1] < eval_result["train"]["l2"][0] # train improved + assert eval_result["valid"]["l2"][1] > eval_result["valid"]["l2"][0] # valid didn't + else: + with pytest.warns(UserWarning, match="Only training set found, disabling early stopping."): + bst = train_fn() + assert bst.current_iteration() == 2 + assert bst.best_iteration == 0 + + +@pytest.mark.parametrize("first_metric_only", [True, False]) +def test_early_stopping_via_global_params(first_metric_only): + X, y = load_breast_cancer(return_X_y=True) + num_trees = 5 + params = { + "num_trees": num_trees, + "objective": "binary", + "metric": "None", + "verbose": -1, + "early_stopping_round": 2, + "first_metric_only": first_metric_only, + } + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + valid_set_name = "valid_set" + gbm = lgb.train( + params, lgb_train, feval=[decreasing_metric, constant_metric], valid_sets=lgb_eval, valid_names=valid_set_name + ) + if first_metric_only: + assert gbm.best_iteration == num_trees + else: + assert gbm.best_iteration == 1 + assert valid_set_name in gbm.best_score + assert "decreasing_metric" in gbm.best_score[valid_set_name] + assert "error" in gbm.best_score[valid_set_name] + + +@pytest.mark.parametrize("early_stopping_round", [-10, -1, 0, None, "None"]) +def test_early_stopping_is_not_enabled_for_non_positive_stopping_rounds(early_stopping_round): + X, y = load_breast_cancer(return_X_y=True) + num_trees = 5 + params = { + "num_trees": num_trees, + "objective": "binary", + "metric": "None", + "verbose": -1, + "early_stopping_round": early_stopping_round, + "first_metric_only": True, + } + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + valid_set_name = "valid_set" + + if early_stopping_round is None: + gbm = lgb.train( + params, + lgb_train, + feval=[constant_metric], + valid_sets=lgb_eval, + valid_names=valid_set_name, + ) + assert "early_stopping_round" not in gbm.params + assert gbm.num_trees() == num_trees + elif early_stopping_round == "None": + with pytest.raises(TypeError, match="early_stopping_round should be an integer. Got 'str'"): + gbm = lgb.train( + params, + lgb_train, + feval=[constant_metric], + valid_sets=lgb_eval, + valid_names=valid_set_name, + ) + elif early_stopping_round <= 0: + gbm = lgb.train( + params, + lgb_train, + feval=[constant_metric], + valid_sets=lgb_eval, + valid_names=valid_set_name, + ) + assert gbm.params["early_stopping_round"] == early_stopping_round + assert gbm.num_trees() == num_trees + + +@pytest.mark.parametrize("first_only", [True, False]) +@pytest.mark.parametrize("single_metric", [True, False]) +@pytest.mark.parametrize("greater_is_better", [True, False]) +def test_early_stopping_min_delta(first_only, single_metric, greater_is_better): + if single_metric and not first_only: + pytest.skip("first_metric_only doesn't affect single metric.") + metric2min_delta = { + "auc": 0.001, + "binary_logloss": 0.01, + "average_precision": 0.001, + "mape": 0.01, + } + if single_metric: + if greater_is_better: + metric = "auc" + else: + metric = "binary_logloss" + else: + if first_only: + if greater_is_better: + metric = ["auc", "binary_logloss"] + else: + metric = ["binary_logloss", "auc"] + else: + if greater_is_better: + metric = ["auc", "average_precision"] + else: + metric = ["binary_logloss", "mape"] + + X, y = load_breast_cancer(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=0) + train_ds = lgb.Dataset(X_train, y_train) + valid_ds = lgb.Dataset(X_valid, y_valid, reference=train_ds) + + params = {"objective": "binary", "metric": metric, "verbose": -1} + if isinstance(metric, str): + min_delta = metric2min_delta[metric] + elif first_only: + min_delta = metric2min_delta[metric[0]] + else: + min_delta = [metric2min_delta[m] for m in metric] + train_kwargs = { + "params": params, + "train_set": train_ds, + "num_boost_round": 50, + "valid_sets": [train_ds, valid_ds], + "valid_names": ["training", "valid"], + } + + # regular early stopping + evals_result = {} + train_kwargs["callbacks"] = [ + lgb.callback.early_stopping(10, first_only, verbose=False), + lgb.record_evaluation(evals_result), + ] + bst = lgb.train(**train_kwargs) + scores = np.vstack(list(evals_result["valid"].values())).T + + # positive min_delta + delta_result = {} + train_kwargs["callbacks"] = [ + lgb.callback.early_stopping(10, first_only, verbose=False, min_delta=min_delta), + lgb.record_evaluation(delta_result), + ] + delta_bst = lgb.train(**train_kwargs) + delta_scores = np.vstack(list(delta_result["valid"].values())).T + + if first_only: + scores = scores[:, 0] + delta_scores = delta_scores[:, 0] + + assert delta_bst.num_trees() < bst.num_trees() + np.testing.assert_allclose(scores[: len(delta_scores)], delta_scores) + last_score = delta_scores[-1] + best_score = delta_scores[delta_bst.num_trees() - 1] + if greater_is_better: + assert np.less_equal(last_score, best_score + min_delta).any() + else: + assert np.greater_equal(last_score, best_score - min_delta).any() + + +@pytest.mark.parametrize("early_stopping_min_delta", [1e3, 0.0]) +def test_early_stopping_min_delta_via_global_params(early_stopping_min_delta): + X, y = load_breast_cancer(return_X_y=True) + num_trees = 5 + params = { + "num_trees": num_trees, + "num_leaves": 5, + "objective": "binary", + "metric": "None", + "verbose": -1, + "early_stopping_round": 2, + "early_stopping_min_delta": early_stopping_min_delta, + } + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + gbm = lgb.train(params, lgb_train, feval=decreasing_metric, valid_sets=lgb_eval) + if early_stopping_min_delta == 0: + assert gbm.best_iteration == num_trees + else: + assert gbm.best_iteration == 1 + + +def test_early_stopping_can_be_triggered_via_custom_callback(): + X, y = make_synthetic_regression() + + def _early_stop_after_seventh_iteration(env): + if env.iteration == 6: + exc = lgb.EarlyStopException( + best_iteration=6, best_score=[("some_validation_set", "some_metric", 0.708, True)] + ) + raise exc + + bst = lgb.train( + params={"objective": "regression", "verbose": -1, "num_leaves": 2}, + train_set=lgb.Dataset(X, label=y), + num_boost_round=23, + callbacks=[_early_stop_after_seventh_iteration], + ) + assert bst.num_trees() == 7 + assert bst.best_score["some_validation_set"]["some_metric"] == 0.708 + assert bst.best_iteration == 7 + assert bst.current_iteration() == 7 + + +def test_continue_train(tmp_path): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"objective": "regression", "metric": "l1", "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, free_raw_data=False) + init_gbm = lgb.train(params, lgb_train, num_boost_round=20) + model_path = tmp_path / "model.txt" + init_gbm.save_model(model_path) + evals_result = {} + gbm = lgb.train( + params, + lgb_train, + num_boost_round=30, + valid_sets=lgb_eval, + # test custom eval metrics + feval=(lambda p, d: ("custom_mae", mean_absolute_error(p, d.get_label()), False)), + callbacks=[lgb.record_evaluation(evals_result)], + init_model=model_path, + ) + ret = mean_absolute_error(y_test, gbm.predict(X_test)) + assert ret < 13.6 + assert evals_result["valid_0"]["l1"][-1] == pytest.approx(ret) + np.testing.assert_allclose(evals_result["valid_0"]["l1"], evals_result["valid_0"]["custom_mae"]) + + +def test_continue_train_reused_dataset(): + X, y = make_synthetic_regression() + params = {"objective": "regression", "verbose": -1} + lgb_train = lgb.Dataset(X, y, free_raw_data=False) + init_gbm = lgb.train(params, lgb_train, num_boost_round=5) + init_gbm_2 = lgb.train(params, lgb_train, num_boost_round=5, init_model=init_gbm) + init_gbm_3 = lgb.train(params, lgb_train, num_boost_round=5, init_model=init_gbm_2) + gbm = lgb.train(params, lgb_train, num_boost_round=5, init_model=init_gbm_3) + assert gbm.current_iteration() == 20 + + +def test_continue_train_dart(): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"boosting_type": "dart", "objective": "regression", "metric": "l1", "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, free_raw_data=False) + init_gbm = lgb.train(params, lgb_train, num_boost_round=50) + evals_result = {} + gbm = lgb.train( + params, + lgb_train, + num_boost_round=50, + valid_sets=lgb_eval, + callbacks=[lgb.record_evaluation(evals_result)], + init_model=init_gbm, + ) + ret = mean_absolute_error(y_test, gbm.predict(X_test)) + assert ret < 13.6 + assert evals_result["valid_0"]["l1"][-1] == pytest.approx(ret) + + +def test_continue_train_multiclass(): + X, y = load_iris(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 3, "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train, params=params, free_raw_data=False) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params, free_raw_data=False) + init_gbm = lgb.train(params, lgb_train, num_boost_round=20) + evals_result = {} + gbm = lgb.train( + params, + lgb_train, + num_boost_round=30, + valid_sets=lgb_eval, + callbacks=[lgb.record_evaluation(evals_result)], + init_model=init_gbm, + ) + ret = multi_logloss(y_test, gbm.predict(X_test)) + assert ret < 0.1 + assert evals_result["valid_0"]["multi_logloss"][-1] == pytest.approx(ret) + + +def test_cv(): + X_train, y_train = make_synthetic_regression() + params = {"verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train) + # shuffle = False, override metric in params + params_with_metric = {"metric": "l2", "verbose": -1} + cv_res = lgb.cv( + params_with_metric, lgb_train, num_boost_round=10, nfold=3, stratified=False, shuffle=False, metrics="l1" + ) + assert "valid l1-mean" in cv_res + assert "valid l2-mean" not in cv_res + assert len(cv_res["valid l1-mean"]) == 10 + # shuffle = True, callbacks + cv_res = lgb.cv( + params, + lgb_train, + num_boost_round=10, + nfold=3, + stratified=False, + shuffle=True, + metrics="l1", + callbacks=[lgb.reset_parameter(learning_rate=lambda i: 0.1 - 0.001 * i)], + ) + assert "valid l1-mean" in cv_res + assert len(cv_res["valid l1-mean"]) == 10 + # enable display training loss + cv_res = lgb.cv( + params_with_metric, + lgb_train, + num_boost_round=10, + nfold=3, + stratified=False, + shuffle=False, + metrics="l1", + eval_train_metric=True, + ) + assert "train l1-mean" in cv_res + assert "valid l1-mean" in cv_res + assert "train l2-mean" not in cv_res + assert "valid l2-mean" not in cv_res + assert len(cv_res["train l1-mean"]) == 10 + assert len(cv_res["valid l1-mean"]) == 10 + # self defined folds + tss = TimeSeriesSplit(3) + folds = tss.split(X_train) + cv_res_gen = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=folds) + cv_res_obj = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=tss) + np.testing.assert_allclose(cv_res_gen["valid l2-mean"], cv_res_obj["valid l2-mean"]) + # LambdaRank + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train")) + q_train = np.loadtxt(str(rank_example_dir / "rank.train.query")) + params_lambdarank = {"objective": "lambdarank", "verbose": -1, "eval_at": 3} + lgb_train = lgb.Dataset(X_train, y_train, group=q_train) + # ... with l2 metric + cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, metrics="l2") + assert len(cv_res_lambda) == 2 + assert not np.isnan(cv_res_lambda["valid l2-mean"]).any() + # ... with NDCG (default) metric + cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3) + assert len(cv_res_lambda) == 2 + assert not np.isnan(cv_res_lambda["valid ndcg@3-mean"]).any() + # self defined folds with lambdarank + cv_res_lambda_obj = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, folds=GroupKFold(n_splits=3)) + np.testing.assert_allclose(cv_res_lambda["valid ndcg@3-mean"], cv_res_lambda_obj["valid ndcg@3-mean"]) + + +def test_cv_works_with_init_model(tmp_path): + X, y = make_synthetic_regression() + params = {"objective": "regression", "verbose": -1} + num_train_rounds = 2 + lgb_train = lgb.Dataset(X, y, free_raw_data=False) + bst = lgb.train(params=params, train_set=lgb_train, num_boost_round=num_train_rounds) + preds_raw = bst.predict(X, raw_score=True) + model_path_txt = str(tmp_path / "lgb.model") + bst.save_model(model_path_txt) + + num_cv_rounds = 5 + cv_kwargs = { + "num_boost_round": num_cv_rounds, + "nfold": 3, + "stratified": False, + "shuffle": False, + "seed": 708, + "return_cvbooster": True, + "params": params, + } + + # init_model from an in-memory Booster + cv_res = lgb.cv(train_set=lgb_train, init_model=bst, **cv_kwargs) + cv_bst_w_in_mem_init_model = cv_res["cvbooster"] + assert cv_bst_w_in_mem_init_model.current_iteration() == [num_train_rounds + num_cv_rounds] * 3 + for booster in cv_bst_w_in_mem_init_model.boosters: + np.testing.assert_allclose(preds_raw, booster.predict(X, raw_score=True, num_iteration=num_train_rounds)) + + # init_model from a text file + cv_res = lgb.cv(train_set=lgb_train, init_model=model_path_txt, **cv_kwargs) + cv_bst_w_file_init_model = cv_res["cvbooster"] + assert cv_bst_w_file_init_model.current_iteration() == [num_train_rounds + num_cv_rounds] * 3 + for booster in cv_bst_w_file_init_model.boosters: + np.testing.assert_allclose(preds_raw, booster.predict(X, raw_score=True, num_iteration=num_train_rounds)) + + # predictions should be identical + for i in range(3): + np.testing.assert_allclose( + cv_bst_w_in_mem_init_model.boosters[i].predict(X), cv_bst_w_file_init_model.boosters[i].predict(X) + ) + + +def test_cvbooster(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "binary", + "metric": "binary_logloss", + "verbose": -1, + } + nfold = 3 + lgb_train = lgb.Dataset(X_train, y_train) + # with early stopping + cv_res = lgb.cv( + params, + lgb_train, + num_boost_round=25, + nfold=nfold, + callbacks=[lgb.early_stopping(stopping_rounds=5)], + return_cvbooster=True, + ) + assert "cvbooster" in cv_res + cvb = cv_res["cvbooster"] + assert isinstance(cvb, lgb.CVBooster) + assert isinstance(cvb.boosters, list) + assert len(cvb.boosters) == nfold + assert all(isinstance(bst, lgb.Booster) for bst in cvb.boosters) + assert cvb.best_iteration > 0 + # predict by each fold booster + preds = cvb.predict(X_test) + assert isinstance(preds, list) + assert len(preds) == nfold + # check that each booster predicted using the best iteration + for fold_preds, bst in zip(preds, cvb.boosters, strict=True): + assert bst.best_iteration == cvb.best_iteration + expected = bst.predict(X_test, num_iteration=cvb.best_iteration) + np.testing.assert_allclose(fold_preds, expected) + # fold averaging + avg_pred = np.mean(preds, axis=0) + ret = log_loss(y_test, avg_pred) + assert ret < 0.13 + # without early stopping + cv_res = lgb.cv(params, lgb_train, num_boost_round=20, nfold=3, return_cvbooster=True) + cvb = cv_res["cvbooster"] + assert cvb.best_iteration == -1 + preds = cvb.predict(X_test) + avg_pred = np.mean(preds, axis=0) + ret = log_loss(y_test, avg_pred) + assert ret < 0.15 + + +def test_cvbooster_save_load(tmp_path): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "binary", + "metric": "binary_logloss", + "verbose": -1, + } + nfold = 3 + lgb_train = lgb.Dataset(X_train, y_train) + + cv_res = lgb.cv( + params, + lgb_train, + num_boost_round=10, + nfold=nfold, + callbacks=[lgb.early_stopping(stopping_rounds=5)], + return_cvbooster=True, + ) + cvbooster = cv_res["cvbooster"] + preds = cvbooster.predict(X_test) + best_iteration = cvbooster.best_iteration + + model_path_txt = str(tmp_path / "lgb.model") + + cvbooster.save_model(model_path_txt) + model_string = cvbooster.model_to_string() + del cvbooster + + cvbooster_from_txt_file = lgb.CVBooster(model_file=model_path_txt) + cvbooster_from_string = lgb.CVBooster().model_from_string(model_string) + for cvbooster_loaded in [cvbooster_from_txt_file, cvbooster_from_string]: + assert best_iteration == cvbooster_loaded.best_iteration + np_assert_array_equal(preds, cvbooster_loaded.predict(X_test), strict=True) + + +@pytest.mark.parametrize("serializer", SERIALIZERS) +def test_cvbooster_picklable(serializer): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "binary", + "metric": "binary_logloss", + "verbose": -1, + } + nfold = 3 + lgb_train = lgb.Dataset(X_train, y_train) + + cv_res = lgb.cv( + params, + lgb_train, + num_boost_round=10, + nfold=nfold, + callbacks=[lgb.early_stopping(stopping_rounds=5)], + return_cvbooster=True, + ) + cvbooster = cv_res["cvbooster"] + preds = cvbooster.predict(X_test) + best_iteration = cvbooster.best_iteration + + cvbooster_from_disk = pickle_and_unpickle_object(obj=cvbooster, serializer=serializer) + del cvbooster + + assert best_iteration == cvbooster_from_disk.best_iteration + + preds_from_disk = cvbooster_from_disk.predict(X_test) + np_assert_array_equal(preds, preds_from_disk, strict=True) + + +def test_feature_name(): + X_train, y_train = make_synthetic_regression() + params = {"verbose": -1} + feature_names = [f"f_{i}" for i in range(X_train.shape[-1])] + lgb_train = lgb.Dataset(X_train, y_train, feature_name=feature_names) + gbm = lgb.train(params, lgb_train, num_boost_round=5) + assert feature_names == gbm.feature_name() + # test feature_names with whitespaces + feature_names_with_space = [f"f {i}" for i in range(X_train.shape[-1])] + lgb_train.set_feature_name(feature_names_with_space) + gbm = lgb.train(params, lgb_train, num_boost_round=5) + assert feature_names == gbm.feature_name() + + +def test_feature_name_with_non_ascii(rng, tmp_path): + X_train = rng.normal(size=(100, 4)) + y_train = rng.normal(size=(100,)) + # This has non-ascii strings. + feature_names = ["F_零", "F_一", "F_二", "F_三"] + params = {"verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train, feature_name=feature_names) + + gbm = lgb.train(params, lgb_train, num_boost_round=5) + assert feature_names == gbm.feature_name() + model_path_txt = str(tmp_path / "lgb.model") + gbm.save_model(model_path_txt) + + gbm2 = lgb.Booster(model_file=model_path_txt) + assert feature_names == gbm2.feature_name() + + +def test_parameters_are_loaded_from_model_file(tmp_path, capsys, rng): + X = np.hstack( + [ + rng.uniform(size=(100, 1)), + rng.integers(low=0, high=5, size=(100, 2)), + ] + ) + y = rng.uniform(size=(100,)) + ds = lgb.Dataset(X, y, categorical_feature=[1, 2]) + params = { + "bagging_fraction": 0.8, + "bagging_freq": 2, + "boosting": "rf", + "feature_contri": [0.5, 0.5, 0.5], + "feature_fraction": 0.7, + "boost_from_average": False, + "interaction_constraints": [[0, 1], [0]], + "metric": ["l2", "rmse"], + "num_leaves": 5, + "num_threads": 1, + "verbosity": 0, + } + model_file = tmp_path / "model.txt" + orig_bst = lgb.train(params, ds, num_boost_round=1) + orig_bst.save_model(model_file) + with model_file.open("rt") as f: + model_contents = f.readlines() + params_start = model_contents.index("parameters:\n") + model_contents.insert(params_start + 1, "[max_conflict_rate: 0]\n") + with model_file.open("wt") as f: + f.writelines(model_contents) + bst = lgb.Booster(model_file=model_file) + expected_msg = "[LightGBM] [Warning] Ignoring unrecognized parameter 'max_conflict_rate' found in model string." + stdout = capsys.readouterr().out + assert expected_msg in stdout + set_params = {k: bst.params[k] for k in params.keys()} + assert set_params == params + assert bst.params["categorical_feature"] == [1, 2] + + # check that passing parameters to the constructor raises warning and ignores them + with pytest.warns(UserWarning, match="Ignoring params argument, using parameters from model file."): + bst2 = lgb.Booster(params={"num_leaves": 7}, model_file=model_file) + assert bst.params == bst2.params + + # check inference isn't affected by unknown parameter + orig_preds = orig_bst.predict(X) + preds = bst.predict(X) + np.testing.assert_allclose(preds, orig_preds) + + +def test_string_serialized_params_retrieval(rng): + # Random train data + train_x = rng.random((500, 3)) + train_y = rng.integers(0, 1, 500) + train_data = lgb.Dataset(train_x, train_y) + + # Parameters + params = { + "boosting": "gbdt", + "deterministic": True, + "feature_contri": [0.5] * train_x.shape[1], + "interaction_constraints": [[0, 1], [0]], + "objective": "binary", + "metric": ["auc"], + "num_leaves": 7, + "learning_rate": 0.05, + "feature_fraction": 0.9, + "bagging_fraction": 0.8, + "bagging_freq": 5, + "verbosity": -100, + } + + # train a model and serialize it to a string in memory + model = lgb.train(params, train_data, num_boost_round=2) + model_serialized = model.model_to_string() + + # load a new model with the string + with pytest.warns(UserWarning, match="Ignoring params argument, using parameters from model string."): + new_model = lgb.Booster(params={"num_leaves": 32}, model_str=model_serialized) + + assert new_model.params["boosting"] == "gbdt" + assert new_model.params["deterministic"] is True + assert new_model.params["feature_contri"] == [0.5] * train_x.shape[1] + assert new_model.params["interaction_constraints"] == [[0, 1], [0]] + assert new_model.params["objective"] == "binary" + assert new_model.params["metric"] == ["auc"] + assert new_model.params["num_leaves"] == 7 + assert new_model.params["learning_rate"] == 0.05 + assert new_model.params["feature_fraction"] == 0.9 + assert new_model.params["bagging_fraction"] == 0.8 + assert new_model.params["bagging_freq"] == 5 + assert new_model.params["verbosity"] == -100 + + +def test_save_load_copy_pickle(tmp_path): + def train_and_predict(init_model=None, return_model=False): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"objective": "regression", "metric": "l2", "verbose": -1} + lgb_train = lgb.Dataset(X_train, y_train) + gbm_template = lgb.train(params, lgb_train, num_boost_round=10, init_model=init_model) + return gbm_template if return_model else mean_squared_error(y_test, gbm_template.predict(X_test)) + + gbm = train_and_predict(return_model=True) + ret_origin = train_and_predict(init_model=gbm) + other_ret = [] + model_path_txt = str(tmp_path / "lgb.model") + gbm.save_model(model_path_txt) + with open(model_path_txt) as f: # check all params are logged into model file correctly + assert f.read().find("[num_iterations: 10]") != -1 + other_ret.append(train_and_predict(init_model=model_path_txt)) + gbm_load = lgb.Booster(model_file=model_path_txt) + other_ret.append(train_and_predict(init_model=gbm_load)) + other_ret.append(train_and_predict(init_model=copy.copy(gbm))) + other_ret.append(train_and_predict(init_model=copy.deepcopy(gbm))) + model_path_pkl = str(tmp_path / "lgb.pkl") + with open(model_path_pkl, "wb") as f: + pickle.dump(gbm, f) + with open(model_path_pkl, "rb") as f: + gbm_pickle = pickle.load(f) + other_ret.append(train_and_predict(init_model=gbm_pickle)) + gbm_pickles = pickle.loads(pickle.dumps(gbm)) + other_ret.append(train_and_predict(init_model=gbm_pickles)) + for ret in other_ret: + assert ret_origin == pytest.approx(ret) + + +def test_all_expected_params_are_written_out_to_model_text(tmp_path): + X, y = make_synthetic_regression() + params = { + "objective": "mape", + "metric": ["l2", "mae"], + "seed": 708, + "data_sample_strategy": "bagging", + "sub_row": 0.8234, + "verbose": -1, + } + dtrain = lgb.Dataset(data=X, label=y) + gbm = lgb.train(params=params, train_set=dtrain, num_boost_round=3) + + model_txt_from_memory = gbm.model_to_string() + model_file = tmp_path / "out.model" + gbm.save_model(filename=model_file) + with open(model_file, "r") as f: + model_txt_from_file = f.read() + + assert model_txt_from_memory == model_txt_from_file + + # entries whose values should reflect params passed to lgb.train() + non_default_param_entries = [ + "[objective: mape]", + # 'l1' was passed in with alias 'mae' + "[metric: l2,l1]", + "[data_sample_strategy: bagging]", + "[seed: 708]", + # NOTE: this was passed in with alias 'sub_row' + "[bagging_fraction: 0.8234]", + "[num_iterations: 3]", + ] + + # entries with default values of params + default_param_entries = [ + "[boosting: gbdt]", + "[tree_learner: serial]", + "[data: ]", + "[valid: ]", + "[learning_rate: 0.1]", + "[num_leaves: 31]", + "[num_threads: 0]", + "[deterministic: 0]", + "[histogram_pool_size: -1]", + "[max_depth: -1]", + "[min_data_in_leaf: 20]", + "[min_sum_hessian_in_leaf: 0.001]", + "[pos_bagging_fraction: 1]", + "[neg_bagging_fraction: 1]", + "[bagging_freq: 0]", + "[bagging_seed: 15415]", + "[feature_fraction: 1]", + "[feature_fraction_bynode: 1]", + "[feature_fraction_seed: 32671]", + "[extra_trees: 0]", + "[extra_seed: 6642]", + "[early_stopping_round: 0]", + "[early_stopping_min_delta: 0]", + "[first_metric_only: 0]", + "[max_delta_step: 0]", + "[lambda_l1: 0]", + "[lambda_l2: 0]", + "[linear_lambda: 0]", + "[min_gain_to_split: 0]", + "[drop_rate: 0.1]", + "[max_drop: 50]", + "[skip_drop: 0.5]", + "[xgboost_dart_mode: 0]", + "[uniform_drop: 0]", + "[drop_seed: 20623]", + "[top_rate: 0.2]", + "[other_rate: 0.1]", + "[min_data_per_group: 100]", + "[max_cat_threshold: 32]", + "[cat_l2: 10]", + "[cat_smooth: 10]", + "[max_cat_to_onehot: 4]", + "[top_k: 20]", + "[monotone_constraints: ]", + "[monotone_constraints_method: basic]", + "[monotone_penalty: 0]", + "[feature_contri: ]", + "[forcedsplits_filename: ]", + "[refit_decay_rate: 0.9]", + "[cegb_tradeoff: 1]", + "[cegb_penalty_split: 0]", + "[cegb_penalty_feature_lazy: ]", + "[cegb_penalty_feature_coupled: ]", + "[path_smooth: 0]", + "[interaction_constraints: ]", + "[verbosity: -1]", + "[saved_feature_importance_type: 0]", + "[use_quantized_grad: 0]", + "[num_grad_quant_bins: 4]", + "[quant_train_renew_leaf: 0]", + "[stochastic_rounding: 1]", + "[linear_tree: 0]", + "[max_bin: 255]", + "[max_bin_by_feature: ]", + "[min_data_in_bin: 3]", + "[bin_construct_sample_cnt: 200000]", + "[data_random_seed: 2350]", + "[is_enable_sparse: 1]", + "[enable_bundle: 1]", + "[use_missing: 1]", + "[zero_as_missing: 0]", + "[feature_pre_filter: 1]", + "[pre_partition: 0]", + "[two_round: 0]", + "[header: 0]", + "[label_column: ]", + "[weight_column: ]", + "[group_column: ]", + "[ignore_column: ]", + "[categorical_feature: ]", + "[forcedbins_filename: ]", + "[precise_float_parser: 0]", + "[parser_config_file: ]", + "[objective_seed: 4309]", + "[num_class: 1]", + "[is_unbalance: 0]", + "[scale_pos_weight: 1]", + "[sigmoid: 1]", + "[boost_from_average: 1]", + "[reg_sqrt: 0]", + "[alpha: 0.9]", + "[fair_c: 1]", + "[poisson_max_delta_step: 0.7]", + "[tweedie_variance_power: 1.5]", + "[lambdarank_truncation_level: 30]", + "[lambdarank_norm: 1]", + "[label_gain: ]", + "[lambdarank_position_bias_regularization: 0]", + "[eval_at: ]", + "[multi_error_top_k: 1]", + "[auc_mu_weights: ]", + "[num_machines: 1]", + "[local_listen_port: 12400]", + "[time_out: 120]", + "[machine_list_filename: ]", + "[machines: ]", + "[gpu_platform_id: -1]", + "[gpu_device_id: -1]", + "[num_gpu: 1]", + ] + all_param_entries = non_default_param_entries + default_param_entries + + # add device-specific entries + # + # passed-in force_col_wise / force_row_wise parameters are ignored on CUDA and GPU builds... + # https://github.com/lightgbm-org/LightGBM/blob/1d7ee63686272bceffd522284127573b511df6be/src/io/config.cpp#L375-L377 + if BuildInfo.has_cuda: + device_entries = ["[force_col_wise: 0]", "[force_row_wise: 1]", "[device_type: cuda]", "[gpu_use_dp: 1]"] + elif BuildInfo.has_gpu: + device_entries = ["[force_col_wise: 1]", "[force_row_wise: 0]", "[device_type: gpu]", "[gpu_use_dp: 0]"] + else: + device_entries = ["[force_col_wise: 0]", "[force_row_wise: 0]", "[device_type: cpu]", "[gpu_use_dp: 0]"] + + all_param_entries += device_entries + + # check that model text has all expected param entries + for param_str in all_param_entries: + assert param_str in model_txt_from_file + assert param_str in model_txt_from_memory + + # since Booster.model_to_string() is used when pickling, check that parameters all + # roundtrip pickling successfully too + gbm_pkl = pickle_and_unpickle_object(gbm, serializer="joblib") + model_txt_from_memory = gbm_pkl.model_to_string() + model_file = tmp_path / "out-pkl.model" + gbm_pkl.save_model(filename=model_file) + with open(model_file, "r") as f: + model_txt_from_file = f.read() + + for param_str in all_param_entries: + assert param_str in model_txt_from_file + assert param_str in model_txt_from_memory + + +# why fixed seed? +# sometimes there is no difference how cols are treated (cat or not cat) +def test_pandas_categorical(rng_fixed_seed, tmp_path): + pd = pytest.importorskip("pandas") + X = pd.DataFrame( + { + "A": rng_fixed_seed.permutation(["a", "b", "c", "d"] * 75), # str + "B": rng_fixed_seed.permutation([1, 2, 3] * 100), # int + "C": rng_fixed_seed.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60), # float + "D": rng_fixed_seed.permutation([True, False] * 150), # bool + "E": pd.Categorical(rng_fixed_seed.permutation(["z", "y", "x", "w", "v"] * 60), ordered=True), + } + ) # str and ordered categorical + y = rng_fixed_seed.permutation([0, 1] * 150) + X_test = pd.DataFrame( + { + "A": rng_fixed_seed.permutation(["a", "b", "e"] * 20), # unseen category + "B": rng_fixed_seed.permutation([1, 3] * 30), + "C": rng_fixed_seed.permutation([0.1, -0.1, 0.2, 0.2] * 15), + "D": rng_fixed_seed.permutation([True, False] * 30), + "E": pd.Categorical(rng_fixed_seed.permutation(["z", "y"] * 30), ordered=True), + } + ) + cat_cols_actual = ["A", "B", "C", "D"] + cat_cols_to_store = cat_cols_actual + ["E"] + X[cat_cols_actual] = X[cat_cols_actual].astype("category") + X_test[cat_cols_actual] = X_test[cat_cols_actual].astype("category") + cat_values = [X[col].cat.categories.tolist() for col in cat_cols_to_store] + params = {"objective": "binary", "metric": "binary_logloss", "verbose": -1} + lgb_train = lgb.Dataset(X, y) + gbm0 = lgb.train(params, lgb_train, num_boost_round=10) + pred0 = gbm0.predict(X_test) + assert lgb_train.categorical_feature == "auto" + lgb_train = lgb.Dataset( + X, pd.DataFrame(y), categorical_feature=[0] + ) # also test that label can be one-column pd.DataFrame + gbm1 = lgb.train(params, lgb_train, num_boost_round=10) + pred1 = gbm1.predict(X_test) + assert lgb_train.categorical_feature == [0] + lgb_train = lgb.Dataset(X, pd.Series(y), categorical_feature=["A"]) # also test that label can be pd.Series + gbm2 = lgb.train(params, lgb_train, num_boost_round=10) + pred2 = gbm2.predict(X_test) + assert lgb_train.categorical_feature == ["A"] + lgb_train = lgb.Dataset(X, y, categorical_feature=["A", "B", "C", "D"]) + gbm3 = lgb.train(params, lgb_train, num_boost_round=10) + pred3 = gbm3.predict(X_test) + assert lgb_train.categorical_feature == ["A", "B", "C", "D"] + categorical_model_path = tmp_path / "categorical.model" + gbm3.save_model(categorical_model_path) + gbm4 = lgb.Booster(model_file=categorical_model_path) + pred4 = gbm4.predict(X_test) + model_str = gbm4.model_to_string() + gbm4.model_from_string(model_str) + pred5 = gbm4.predict(X_test) + gbm5 = lgb.Booster(model_str=model_str) + pred6 = gbm5.predict(X_test) + lgb_train = lgb.Dataset(X, y, categorical_feature=["A", "B", "C", "D", "E"]) + gbm6 = lgb.train(params, lgb_train, num_boost_round=10) + pred7 = gbm6.predict(X_test) + assert lgb_train.categorical_feature == ["A", "B", "C", "D", "E"] + lgb_train = lgb.Dataset(X, y, categorical_feature=[]) + gbm7 = lgb.train(params, lgb_train, num_boost_round=10) + pred8 = gbm7.predict(X_test) + assert lgb_train.categorical_feature == [] + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred1) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred2) + np.testing.assert_allclose(pred1, pred2) + np.testing.assert_allclose(pred0, pred3) + np.testing.assert_allclose(pred0, pred4) + np.testing.assert_allclose(pred0, pred5) + np.testing.assert_allclose(pred0, pred6) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred7) # ordered cat features aren't treated as cat features by default + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred8) + assert gbm0.pandas_categorical == cat_values + assert gbm1.pandas_categorical == cat_values + assert gbm2.pandas_categorical == cat_values + assert gbm3.pandas_categorical == cat_values + assert gbm4.pandas_categorical == cat_values + assert gbm5.pandas_categorical == cat_values + assert gbm6.pandas_categorical == cat_values + assert gbm7.pandas_categorical == cat_values + + +def test_pandas_sparse(rng): + pd = pytest.importorskip("pandas") + X = pd.DataFrame( + { + "A": pd.arrays.SparseArray(rng.permutation([0, 1, 2] * 100)), + "B": pd.arrays.SparseArray(rng.permutation([0.0, 0.1, 0.2, -0.1, 0.2] * 60)), + "C": pd.arrays.SparseArray(rng.permutation([True, False] * 150)), + } + ) + y = pd.Series(pd.arrays.SparseArray(rng.permutation([0, 1] * 150))) + X_test = pd.DataFrame( + { + "A": pd.arrays.SparseArray(rng.permutation([0, 2] * 30)), + "B": pd.arrays.SparseArray(rng.permutation([0.0, 0.1, 0.2, -0.1] * 15)), + "C": pd.arrays.SparseArray(rng.permutation([True, False] * 30)), + } + ) + for dtype in pd.concat([X.dtypes, X_test.dtypes, pd.Series(y.dtypes)]): + assert isinstance(dtype, pd.SparseDtype) + params = {"objective": "binary", "verbose": -1} + lgb_train = lgb.Dataset(X, y) + gbm = lgb.train(params, lgb_train, num_boost_round=10) + pred_sparse = gbm.predict(X_test, raw_score=True) + if hasattr(X_test, "sparse"): + pred_dense = gbm.predict(X_test.sparse.to_dense(), raw_score=True) + else: + pred_dense = gbm.predict(X_test.to_dense(), raw_score=True) + np.testing.assert_allclose(pred_sparse, pred_dense) + + +def test_reference_chain(rng): + X = rng.normal(size=(100, 2)) + y = rng.normal(size=(100,)) + tmp_dat = lgb.Dataset(X, y) + # take subsets and train + tmp_dat_train = tmp_dat.subset(np.arange(80)) + tmp_dat_val = tmp_dat.subset(np.arange(80, 100)).subset(np.arange(18)) + params = {"objective": "regression_l2", "metric": "rmse"} + evals_result = {} + lgb.train( + params, + tmp_dat_train, + num_boost_round=20, + valid_sets=[tmp_dat_train, tmp_dat_val], + callbacks=[lgb.record_evaluation(evals_result)], + ) + assert len(evals_result["training"]["rmse"]) == 20 + assert len(evals_result["valid_1"]["rmse"]) == 20 + + +def test_contribs(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "binary", + "metric": "binary_logloss", + "verbose": -1, + } + lgb_train = lgb.Dataset(X_train, y_train) + gbm = lgb.train(params, lgb_train, num_boost_round=20) + + assert ( + np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(gbm.predict(X_test, pred_contrib=True), axis=1)) + < 1e-4 + ) + + +def test_contribs_sparse(): + n_features = 20 + n_samples = 100 + # generate CSR sparse dataset + X, y = make_multilabel_classification( + n_samples=n_samples, sparse=True, n_features=n_features, n_classes=1, n_labels=2 + ) + y = y.flatten() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "binary", + "verbose": -1, + } + lgb_train = lgb.Dataset(X_train, y_train) + gbm = lgb.train(params, lgb_train, num_boost_round=20) + contribs_csr = gbm.predict(X_test, pred_contrib=True) + assert isspmatrix_csr(contribs_csr) + # convert data to dense and get back same contribs + contribs_dense = gbm.predict(X_test.toarray(), pred_contrib=True) + # validate the values are the same + if platform.machine() == "aarch64": + np.testing.assert_allclose(contribs_csr.toarray(), contribs_dense, rtol=1, atol=1e-12) + else: + np.testing.assert_allclose(contribs_csr.toarray(), contribs_dense) + assert np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(contribs_dense, axis=1)) < 1e-4 + # validate using CSC matrix + X_test_csc = X_test.tocsc() + contribs_csc = gbm.predict(X_test_csc, pred_contrib=True) + assert isspmatrix_csc(contribs_csc) + # validate the values are the same + if platform.machine() == "aarch64": + np.testing.assert_allclose(contribs_csc.toarray(), contribs_dense, rtol=1, atol=1e-12) + else: + np.testing.assert_allclose(contribs_csc.toarray(), contribs_dense) + + +def test_contribs_sparse_multiclass(): + n_features = 20 + n_samples = 100 + n_labels = 4 + # generate CSR sparse dataset + X, y = make_multilabel_classification( + n_samples=n_samples, sparse=True, n_features=n_features, n_classes=1, n_labels=n_labels + ) + y = y.flatten() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "multiclass", + "num_class": n_labels, + "verbose": -1, + } + lgb_train = lgb.Dataset(X_train, y_train) + gbm = lgb.train(params, lgb_train, num_boost_round=20) + contribs_csr = gbm.predict(X_test, pred_contrib=True) + assert isinstance(contribs_csr, list) + for perclass_contribs_csr in contribs_csr: + assert isspmatrix_csr(perclass_contribs_csr) + # convert data to dense and get back same contribs + contribs_dense = gbm.predict(X_test.toarray(), pred_contrib=True) + # validate the values are the same + contribs_csr_array = np.swapaxes(np.array([sparse_array.toarray() for sparse_array in contribs_csr]), 0, 1) + contribs_csr_arr_re = contribs_csr_array.reshape( + (contribs_csr_array.shape[0], contribs_csr_array.shape[1] * contribs_csr_array.shape[2]) + ) + if platform.machine() == "aarch64": + np.testing.assert_allclose(contribs_csr_arr_re, contribs_dense, rtol=1, atol=1e-12) + else: + np.testing.assert_allclose(contribs_csr_arr_re, contribs_dense) + contribs_dense_re = contribs_dense.reshape(contribs_csr_array.shape) + assert np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(contribs_dense_re, axis=2)) < 1e-4 + # validate using CSC matrix + X_test_csc = X_test.tocsc() + contribs_csc = gbm.predict(X_test_csc, pred_contrib=True) + assert isinstance(contribs_csc, list) + for perclass_contribs_csc in contribs_csc: + assert isspmatrix_csc(perclass_contribs_csc) + # validate the values are the same + contribs_csc_array = np.swapaxes(np.array([sparse_array.toarray() for sparse_array in contribs_csc]), 0, 1) + contribs_csc_array = contribs_csc_array.reshape( + (contribs_csc_array.shape[0], contribs_csc_array.shape[1] * contribs_csc_array.shape[2]) + ) + if platform.machine() == "aarch64": + np.testing.assert_allclose(contribs_csc_array, contribs_dense, rtol=1, atol=1e-12) + else: + np.testing.assert_allclose(contribs_csc_array, contribs_dense) + + +@pytest.mark.skipif( + BuildInfo.has_cuda, reason="Skip because int64 sparse matrix indices are not supported for CUDA version" +) +def test_predict_contrib_int64(): + X, y = make_multilabel_classification(n_samples=100, sparse=True, n_features=5, n_classes=1, n_labels=2) + y = y.flatten() + X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42) + X_test.indptr = X_test.indptr.astype(np.int64) + + train_data = lgb.Dataset(X_train, label=y_train) + params = { + "objective": "binary", + "num_leaves": 7, + "min_data_in_bin": 1, + "min_data_in_leaf": 1, + "seed": 708, + "verbose": -1, + } + booster = lgb.train(params, train_set=train_data, num_boost_round=5) + + preds = booster.predict(X_test, pred_contrib=True) + + assert preds is not None + assert preds.shape[0] == X_test.shape[0] + assert preds.shape[1] == X_test.shape[1] + 1 + + +@pytest.mark.skipif(psutil.virtual_memory().available / 1024 / 1024 / 1024 < 3, reason="not enough RAM") +def test_int32_max_sparse_contribs(rng): + params = {"objective": "binary"} + train_features = rng.uniform(size=(100, 1000)) + train_targets = [0] * 50 + [1] * 50 + lgb_train = lgb.Dataset(train_features, train_targets) + gbm = lgb.train(params, lgb_train, num_boost_round=2) + csr_input_shape = (3000000, 1000) + test_features = csr_matrix(csr_input_shape) + for i in range(0, csr_input_shape[0], csr_input_shape[0] // 6): + for j in range(0, 1000, 100): + test_features[i, j] = random.random() + y_pred_csr = gbm.predict(test_features, pred_contrib=True) + # Note there is an extra column added to the output for the expected value + csr_output_shape = (csr_input_shape[0], csr_input_shape[1] + 1) + assert y_pred_csr.shape == csr_output_shape + y_pred_csc = gbm.predict(test_features.tocsc(), pred_contrib=True) + # Note output CSC shape should be same as CSR output shape + assert y_pred_csc.shape == csr_output_shape + + +def test_sliced_data(rng): + def train_and_get_predictions(features, labels): + dataset = lgb.Dataset(features, label=labels) + lgb_params = { + "application": "binary", + "verbose": -1, + "min_data": 5, + } + gbm = lgb.train( + params=lgb_params, + train_set=dataset, + num_boost_round=10, + ) + return gbm.predict(features) + + num_samples = 100 + features = rng.uniform(size=(num_samples, 5)) + positive_samples = int(num_samples * 0.25) + labels = np.append( + np.ones(positive_samples, dtype=np.float32), np.zeros(num_samples - positive_samples, dtype=np.float32) + ) + # test sliced labels + origin_pred = train_and_get_predictions(features, labels) + stacked_labels = np.column_stack((labels, np.ones(num_samples, dtype=np.float32))) + sliced_labels = stacked_labels[:, 0] + sliced_pred = train_and_get_predictions(features, sliced_labels) + np.testing.assert_allclose(origin_pred, sliced_pred) + # append some columns + stacked_features = np.column_stack((np.ones(num_samples, dtype=np.float32), features)) + stacked_features = np.column_stack((np.ones(num_samples, dtype=np.float32), stacked_features)) + stacked_features = np.column_stack((stacked_features, np.ones(num_samples, dtype=np.float32))) + stacked_features = np.column_stack((stacked_features, np.ones(num_samples, dtype=np.float32))) + # append some rows + stacked_features = np.concatenate((np.ones(9, dtype=np.float32).reshape((1, 9)), stacked_features), axis=0) + stacked_features = np.concatenate((np.ones(9, dtype=np.float32).reshape((1, 9)), stacked_features), axis=0) + stacked_features = np.concatenate((stacked_features, np.ones(9, dtype=np.float32).reshape((1, 9))), axis=0) + stacked_features = np.concatenate((stacked_features, np.ones(9, dtype=np.float32).reshape((1, 9))), axis=0) + # test sliced 2d matrix + sliced_features = stacked_features[2:102, 2:7] + assert np.all(sliced_features == features) + sliced_pred = train_and_get_predictions(sliced_features, sliced_labels) + np.testing.assert_allclose(origin_pred, sliced_pred) + # test sliced CSR + stacked_csr = csr_matrix(stacked_features) + sliced_csr = stacked_csr[2:102, 2:7] + assert np.all(sliced_csr == features) + sliced_pred = train_and_get_predictions(sliced_csr, sliced_labels) + np.testing.assert_allclose(origin_pred, sliced_pred) + + +def test_init_with_subset(tmp_path, rng): + data = rng.uniform(size=(50, 2)) + y = [1] * 25 + [0] * 25 + lgb_train = lgb.Dataset(data, y, free_raw_data=False) + subset_index_1 = rng.choice(a=np.arange(50), size=30, replace=False) + subset_data_1 = lgb_train.subset(subset_index_1) + subset_index_2 = rng.choice(a=np.arange(50), size=20, replace=False) + subset_data_2 = lgb_train.subset(subset_index_2) + params = {"objective": "binary", "verbose": -1} + init_gbm = lgb.train(params=params, train_set=subset_data_1, num_boost_round=10, keep_training_booster=True) + lgb.train(params=params, train_set=subset_data_2, num_boost_round=10, init_model=init_gbm) + assert lgb_train.get_data().shape[0] == 50 + assert subset_data_1.get_data().shape[0] == 30 + assert subset_data_2.get_data().shape[0] == 20 + lgb_train_data = str(tmp_path / "lgb_train_data.bin") + lgb_train.save_binary(lgb_train_data) + lgb_train_from_file = lgb.Dataset(lgb_train_data, free_raw_data=False) + subset_data_3 = lgb_train_from_file.subset(subset_index_1) + subset_data_4 = lgb_train_from_file.subset(subset_index_2) + init_gbm_2 = lgb.train(params=params, train_set=subset_data_3, num_boost_round=10, keep_training_booster=True) + with np.testing.assert_raises_regex(lgb.basic.LightGBMError, "Unknown format of training data"): + lgb.train(params=params, train_set=subset_data_4, num_boost_round=10, init_model=init_gbm_2) + assert lgb_train_from_file.get_data() == lgb_train_data + assert subset_data_3.get_data() == lgb_train_data + assert subset_data_4.get_data() == lgb_train_data + + +def test_training_on_constructed_subset_without_params(rng): + X = rng.uniform(size=(100, 10)) + y = rng.uniform(size=(100,)) + lgb_data = lgb.Dataset(X, y) + subset_indices = [1, 2, 3, 4] + subset = lgb_data.subset(subset_indices).construct() + bst = lgb.train({}, subset, num_boost_round=1) + assert subset.get_params() == {} + assert subset.num_data() == len(subset_indices) + assert bst.current_iteration() == 1 + + +def generate_trainset_for_monotone_constraints_tests(x3_to_category=True): + number_of_dpoints = 3000 + rng = np.random.default_rng() + x1_positively_correlated_with_y = rng.uniform(size=number_of_dpoints) + x2_negatively_correlated_with_y = rng.uniform(size=number_of_dpoints) + x3_negatively_correlated_with_y = rng.uniform(size=number_of_dpoints) + x = np.column_stack( + ( + x1_positively_correlated_with_y, + x2_negatively_correlated_with_y, + categorize(x3_negatively_correlated_with_y) if x3_to_category else x3_negatively_correlated_with_y, + ) + ) + + zs = rng.normal(loc=0.0, scale=0.01, size=number_of_dpoints) + scales = 10.0 * (rng.uniform(size=6) + 0.5) + y = ( + scales[0] * x1_positively_correlated_with_y + + np.sin(scales[1] * np.pi * x1_positively_correlated_with_y) + - scales[2] * x2_negatively_correlated_with_y + - np.cos(scales[3] * np.pi * x2_negatively_correlated_with_y) + - scales[4] * x3_negatively_correlated_with_y + - np.cos(scales[5] * np.pi * x3_negatively_correlated_with_y) + + zs + ) + categorical_features = [] + if x3_to_category: + categorical_features = [2] + return lgb.Dataset(x, label=y, categorical_feature=categorical_features, free_raw_data=False) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Monotone constraints are not yet supported by CUDA version") +@pytest.mark.parametrize("test_with_categorical_variable", [True, False]) +def test_monotone_constraints(test_with_categorical_variable): + def is_increasing(y): + return (np.diff(y) >= 0.0).all() + + def is_decreasing(y): + return (np.diff(y) <= 0.0).all() + + def is_non_monotone(y): + return (np.diff(y) < 0.0).any() and (np.diff(y) > 0.0).any() + + def is_correctly_constrained(learner, x3_to_category=True): + iterations = 10 + n = 1000 + variable_x = np.linspace(0, 1, n).reshape((n, 1)) + fixed_xs_values = np.linspace(0, 1, n) + for i in range(iterations): + fixed_x = fixed_xs_values[i] * np.ones((n, 1)) + monotonically_increasing_x = np.column_stack((variable_x, fixed_x, fixed_x)) + monotonically_increasing_y = learner.predict(monotonically_increasing_x) + monotonically_decreasing_x = np.column_stack((fixed_x, variable_x, fixed_x)) + monotonically_decreasing_y = learner.predict(monotonically_decreasing_x) + non_monotone_x = np.column_stack( + ( + fixed_x, + fixed_x, + categorize(variable_x) if x3_to_category else variable_x, + ) + ) + non_monotone_y = learner.predict(non_monotone_x) + if not ( + is_increasing(monotonically_increasing_y) + and is_decreasing(monotonically_decreasing_y) + and is_non_monotone(non_monotone_y) + ): + return False + return True + + def are_interactions_enforced(gbm, feature_sets): + def parse_tree_features(gbm): + # trees start at position 1. + tree_str = gbm.model_to_string().split("Tree")[1:] + feature_sets = [] + for tree in tree_str: + # split_features are in 4th line. + features = tree.splitlines()[3].split("=")[1].split(" ") + features = {f"Column_{f}" for f in features} + feature_sets.append(features) + return np.array(feature_sets) + + def has_interaction(treef): + n = 0 + for fs in feature_sets: + if len(treef.intersection(fs)) > 0: + n += 1 + return n > 1 + + tree_features = parse_tree_features(gbm) + has_interaction_flag = np.array([has_interaction(treef) for treef in tree_features]) + + return not has_interaction_flag.any() + + trainset = generate_trainset_for_monotone_constraints_tests(test_with_categorical_variable) + for test_with_interaction_constraints in [True, False]: + error_msg = ( + f"Model not correctly constrained (test_with_interaction_constraints={test_with_interaction_constraints})" + ) + for monotone_constraints_method in ["basic", "intermediate", "advanced"]: + params = { + "min_data": 20, + "num_leaves": 20, + "monotone_constraints": [1, -1, 0], + "monotone_constraints_method": monotone_constraints_method, + "use_missing": False, + } + if test_with_interaction_constraints: + params["interaction_constraints"] = [[0], [1], [2]] + constrained_model = lgb.train(params, trainset) + assert is_correctly_constrained(constrained_model, test_with_categorical_variable), error_msg + if test_with_interaction_constraints: + feature_sets = [["Column_0"], ["Column_1"], "Column_2"] + assert are_interactions_enforced(constrained_model, feature_sets) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Monotone constraints are not yet supported by CUDA version") +def test_monotone_penalty(): + def are_first_splits_non_monotone(tree, n, monotone_constraints): + if n <= 0: + return True + if "leaf_value" in tree: + return True + if monotone_constraints[tree["split_feature"]] != 0: + return False + return are_first_splits_non_monotone( + tree["left_child"], n - 1, monotone_constraints + ) and are_first_splits_non_monotone(tree["right_child"], n - 1, monotone_constraints) + + def are_there_monotone_splits(tree, monotone_constraints): + if "leaf_value" in tree: + return False + if monotone_constraints[tree["split_feature"]] != 0: + return True + return are_there_monotone_splits(tree["left_child"], monotone_constraints) or are_there_monotone_splits( + tree["right_child"], monotone_constraints + ) + + max_depth = 5 + monotone_constraints = [1, -1, 0] + penalization_parameter = 2.0 + trainset = generate_trainset_for_monotone_constraints_tests(x3_to_category=False) + for monotone_constraints_method in ["basic", "intermediate", "advanced"]: + params = { + "max_depth": max_depth, + "monotone_constraints": monotone_constraints, + "monotone_penalty": penalization_parameter, + "monotone_constraints_method": monotone_constraints_method, + } + constrained_model = lgb.train(params, trainset, 10) + dumped_model = constrained_model.dump_model()["tree_info"] + for tree in dumped_model: + assert are_first_splits_non_monotone( + tree["tree_structure"], int(penalization_parameter), monotone_constraints + ) + assert are_there_monotone_splits(tree["tree_structure"], monotone_constraints) + + +# test if a penalty as high as the depth indeed prohibits all monotone splits +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Monotone constraints are not yet supported by CUDA version") +def test_monotone_penalty_max(): + max_depth = 5 + monotone_constraints = [1, -1, 0] + penalization_parameter = max_depth + trainset_constrained_model = generate_trainset_for_monotone_constraints_tests(x3_to_category=False) + x = trainset_constrained_model.data + y = trainset_constrained_model.label + x3_negatively_correlated_with_y = x[:, 2] + trainset_unconstrained_model = lgb.Dataset(x3_negatively_correlated_with_y.reshape(-1, 1), label=y) + params_constrained_model = { + "monotone_constraints": monotone_constraints, + "monotone_penalty": penalization_parameter, + "max_depth": max_depth, + "gpu_use_dp": True, + } + params_unconstrained_model = { + "max_depth": max_depth, + "gpu_use_dp": True, + } + + unconstrained_model = lgb.train(params_unconstrained_model, trainset_unconstrained_model, 10) + unconstrained_model_predictions = unconstrained_model.predict(x3_negatively_correlated_with_y.reshape(-1, 1)) + + for monotone_constraints_method in ["basic", "intermediate", "advanced"]: + params_constrained_model["monotone_constraints_method"] = monotone_constraints_method + # The penalization is so high that the first 2 features should not be used here + constrained_model = lgb.train(params_constrained_model, trainset_constrained_model, 10) + + # Check that a very high penalization is the same as not using the features at all + np_assert_array_equal(constrained_model.predict(x), unconstrained_model_predictions, strict=True) + + +def test_max_bin_by_feature(): + col1 = np.arange(0, 100)[:, np.newaxis] + col2 = np.zeros((100, 1)) + col2[20:] = 1 + X = np.concatenate([col1, col2], axis=1) + y = np.arange(0, 100) + params = { + "objective": "regression_l2", + "verbose": -1, + "num_leaves": 100, + "min_data_in_leaf": 1, + "min_sum_hessian_in_leaf": 0, + "min_data_in_bin": 1, + "max_bin_by_feature": [100, 2], + } + lgb_data = lgb.Dataset(X, label=y) + est = lgb.train(params, lgb_data, num_boost_round=1) + assert len(np.unique(est.predict(X))) == 100 + params["max_bin_by_feature"] = [2, 100] + lgb_data = lgb.Dataset(X, label=y) + est = lgb.train(params, lgb_data, num_boost_round=1) + assert len(np.unique(est.predict(X))) == 3 + + +def test_small_max_bin(rng_fixed_seed): + y = rng_fixed_seed.choice([0, 1], 100) + x = np.ones((100, 1)) + x[:30, 0] = -1 + x[60:, 0] = 2 + params = {"objective": "binary", "seed": 0, "min_data_in_leaf": 1, "verbose": -1, "max_bin": 2} + lgb_x = lgb.Dataset(x, label=y) + lgb.train(params, lgb_x, num_boost_round=5) + x[0, 0] = np.nan + params["max_bin"] = 3 + lgb_x = lgb.Dataset(x, label=y) + lgb.train(params, lgb_x, num_boost_round=5) + + +def test_refit(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = {"objective": "binary", "metric": "binary_logloss", "verbose": -1, "min_data": 10} + lgb_train = lgb.Dataset(X_train, y_train) + gbm = lgb.train(params, lgb_train, num_boost_round=20) + err_pred = log_loss(y_test, gbm.predict(X_test)) + new_gbm = gbm.refit(X_test, y_test) + new_err_pred = log_loss(y_test, new_gbm.predict(X_test)) + assert err_pred > new_err_pred + + +def test_refit_with_one_tree_regression(): + X, y = make_synthetic_regression(n_samples=1_000, n_features=2) + lgb_train = lgb.Dataset(X, label=y) + params = {"objective": "regression", "verbosity": -1} + model = lgb.train(params, lgb_train, num_boost_round=1) + model_refit = model.refit(X, y) + assert isinstance(model_refit, lgb.Booster) + + +def test_refit_with_one_tree_binary_classification(): + X, y = load_breast_cancer(return_X_y=True) + lgb_train = lgb.Dataset(X, label=y) + params = {"objective": "binary", "verbosity": -1} + model = lgb.train(params, lgb_train, num_boost_round=1) + model_refit = model.refit(X, y) + assert isinstance(model_refit, lgb.Booster) + + +def test_refit_with_one_tree_multiclass_classification(): + X, y = load_iris(return_X_y=True) + lgb_train = lgb.Dataset(X, y) + params = {"objective": "multiclass", "num_class": 3, "verbose": -1} + model = lgb.train(params, lgb_train, num_boost_round=1) + model_refit = model.refit(X, y) + assert isinstance(model_refit, lgb.Booster) + + +def test_refit_dataset_params(rng): + # check refit accepts dataset_params + X, y = load_breast_cancer(return_X_y=True) + lgb_train = lgb.Dataset(X, y, init_score=np.zeros(y.size)) + train_params = {"objective": "binary", "verbose": -1, "seed": 123} + gbm = lgb.train(train_params, lgb_train, num_boost_round=10) + non_weight_err_pred = log_loss(y, gbm.predict(X)) + refit_weight = rng.uniform(size=(y.shape[0],)) + dataset_params = { + "max_bin": 260, + "min_data_in_bin": 5, + "data_random_seed": 123, + } + new_gbm = gbm.refit( + data=X, + label=y, + weight=refit_weight, + dataset_params=dataset_params, + decay_rate=0.0, + ) + weight_err_pred = log_loss(y, new_gbm.predict(X)) + train_set_params = new_gbm.train_set.get_params() + stored_weights = new_gbm.train_set.get_weight() + assert weight_err_pred != non_weight_err_pred + assert train_set_params["max_bin"] == 260 + assert train_set_params["min_data_in_bin"] == 5 + assert train_set_params["data_random_seed"] == 123 + np.testing.assert_allclose(stored_weights, refit_weight) + + +@pytest.mark.parametrize("boosting_type", ["rf", "dart"]) +def test_mape_for_specific_boosting_types(boosting_type): + X, y = make_synthetic_regression() + y = abs(y) + params = { + "boosting_type": boosting_type, + "objective": "mape", + "verbose": -1, + "bagging_freq": 1, + "bagging_fraction": 0.8, + "feature_fraction": 0.8, + "boost_from_average": True, + } + lgb_train = lgb.Dataset(X, y) + gbm = lgb.train(params, lgb_train, num_boost_round=20) + pred = gbm.predict(X) + pred_mean = pred.mean() + # the following checks that dart and rf with mape can predict outside the 0-1 range + # https://github.com/lightgbm-org/LightGBM/issues/1579 + # Threshold is intentionally loose (>5) because fixing the + # WeightedPercentileFun segment bug (#7151) shifted the output of MAPE + # training. The intent of this assertion is to guard against predictions + # being stuck inside [0, 1]; a mean around 6-8 satisfies that. + assert pred_mean > 5 + + +def check_constant_features(y_true, expected_pred, more_params): + X_train = np.ones((len(y_true), 1)) + y_train = np.array(y_true) + params = { + "objective": "regression", + "num_class": 1, + "verbose": -1, + "min_data": 1, + "num_leaves": 2, + "learning_rate": 1, + "min_data_in_bin": 1, + "boost_from_average": True, + } + params.update(more_params) + lgb_train = lgb.Dataset(X_train, y_train, params=params) + gbm = lgb.train(params, lgb_train, num_boost_round=2) + pred = gbm.predict(X_train) + assert np.allclose(pred, expected_pred) + + +def test_constant_features_regression(): + params = {"objective": "regression"} + check_constant_features([0.0, 10.0, 0.0, 10.0], 5.0, params) + check_constant_features([0.0, 1.0, 2.0, 3.0], 1.5, params) + check_constant_features([-1.0, 1.0, -2.0, 2.0], 0.0, params) + + +def test_constant_features_binary(): + params = {"objective": "binary"} + check_constant_features([0.0, 10.0, 0.0, 10.0], 0.5, params) + check_constant_features([0.0, 1.0, 2.0, 3.0], 0.75, params) + + +def test_constant_features_multiclass(): + params = {"objective": "multiclass", "num_class": 3} + check_constant_features([0.0, 1.0, 2.0, 0.0], [0.5, 0.25, 0.25], params) + check_constant_features([0.0, 1.0, 2.0, 1.0], [0.25, 0.5, 0.25], params) + + +def test_constant_features_multiclassova(): + params = {"objective": "multiclassova", "num_class": 3} + check_constant_features([0.0, 1.0, 2.0, 0.0], [0.5, 0.25, 0.25], params) + check_constant_features([0.0, 1.0, 2.0, 1.0], [0.25, 0.5, 0.25], params) + + +def test_fpreproc(): + def preprocess_data(dtrain, dtest, params): + train_data = dtrain.construct().get_data() + test_data = dtest.construct().get_data() + train_data[:, 0] += 1 + test_data[:, 0] += 1 + dtrain.label[-5:] = 3 + dtest.label[-5:] = 3 + dtrain = lgb.Dataset(train_data, dtrain.label) + dtest = lgb.Dataset(test_data, dtest.label, reference=dtrain) + params["num_class"] = 4 + return dtrain, dtest, params + + X, y = load_iris(return_X_y=True) + dataset = lgb.Dataset(X, y, free_raw_data=False) + params = {"objective": "multiclass", "num_class": 3, "verbose": -1} + results = lgb.cv(params, dataset, num_boost_round=10, fpreproc=preprocess_data) + assert "valid multi_logloss-mean" in results + assert len(results["valid multi_logloss-mean"]) == 10 + + +def test_metrics(): + X, y = load_digits(n_class=2, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_valid = lgb.Dataset(X_test, y_test, reference=lgb_train) + + evals_result = {} + params_dummy_obj_verbose = {"verbose": -1, "objective": dummy_obj} + params_obj_verbose = {"objective": "binary", "verbose": -1} + params_obj_metric_log_verbose = {"objective": "binary", "metric": "binary_logloss", "verbose": -1} + params_obj_metric_err_verbose = {"objective": "binary", "metric": "binary_error", "verbose": -1} + params_obj_metric_inv_verbose = {"objective": "binary", "metric": "invalid_metric", "verbose": -1} + params_obj_metric_quant_verbose = {"objective": "regression", "metric": "quantile", "verbose": 2} + params_obj_metric_multi_verbose = { + "objective": "binary", + "metric": ["binary_logloss", "binary_error"], + "verbose": -1, + } + params_obj_metric_none_verbose = {"objective": "binary", "metric": "None", "verbose": -1} + params_dummy_obj_metric_log_verbose = {"objective": dummy_obj, "metric": "binary_logloss", "verbose": -1} + params_dummy_obj_metric_err_verbose = {"objective": dummy_obj, "metric": "binary_error", "verbose": -1} + params_dummy_obj_metric_inv_verbose = {"objective": dummy_obj, "metric_types": "invalid_metric", "verbose": -1} + params_dummy_obj_metric_multi_verbose = { + "objective": dummy_obj, + "metric": ["binary_logloss", "binary_error"], + "verbose": -1, + } + params_dummy_obj_metric_none_verbose = {"objective": dummy_obj, "metric": "None", "verbose": -1} + + def get_cv_result(params=params_obj_verbose, **kwargs): + return lgb.cv(params, lgb_train, num_boost_round=2, **kwargs) + + def train_booster(params=params_obj_verbose, **kwargs): + lgb.train( + params, + lgb_train, + num_boost_round=2, + valid_sets=[lgb_valid], + callbacks=[lgb.record_evaluation(evals_result)], + **kwargs, + ) + + # no custom objective, no feval + # default metric + res = get_cv_result() + assert len(res) == 2 + assert "valid binary_logloss-mean" in res + + # non-default metric in params + res = get_cv_result(params=params_obj_metric_err_verbose) + assert len(res) == 2 + assert "valid binary_error-mean" in res + + # default metric in args + res = get_cv_result(metrics="binary_logloss") + assert len(res) == 2 + assert "valid binary_logloss-mean" in res + + # non-default metric in args + res = get_cv_result(metrics="binary_error") + assert len(res) == 2 + assert "valid binary_error-mean" in res + + # metric in args overwrites one in params + res = get_cv_result(params=params_obj_metric_inv_verbose, metrics="binary_error") + assert len(res) == 2 + assert "valid binary_error-mean" in res + + # metric in args overwrites one in params + res = get_cv_result(params=params_obj_metric_quant_verbose) + assert len(res) == 2 + assert "valid quantile-mean" in res + + # multiple metrics in params + res = get_cv_result(params=params_obj_metric_multi_verbose) + assert len(res) == 4 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + + # multiple metrics in args + res = get_cv_result(metrics=["binary_logloss", "binary_error"]) + assert len(res) == 4 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + + # remove default metric by 'None' in list + res = get_cv_result(metrics=["None"]) + assert len(res) == 0 + + # remove default metric by 'None' aliases + for na_alias in ("None", "na", "null", "custom"): + res = get_cv_result(metrics=na_alias) + assert len(res) == 0 + + # custom objective, no feval + # no default metric + res = get_cv_result(params=params_dummy_obj_verbose) + assert len(res) == 0 + + # metric in params + res = get_cv_result(params=params_dummy_obj_metric_err_verbose) + assert len(res) == 2 + assert "valid binary_error-mean" in res + + # metric in args + res = get_cv_result(params=params_dummy_obj_verbose, metrics="binary_error") + assert len(res) == 2 + assert "valid binary_error-mean" in res + + # metric in args overwrites its' alias in params + res = get_cv_result(params=params_dummy_obj_metric_inv_verbose, metrics="binary_error") + assert len(res) == 2 + assert "valid binary_error-mean" in res + + # multiple metrics in params + res = get_cv_result(params=params_dummy_obj_metric_multi_verbose) + assert len(res) == 4 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + + # multiple metrics in args + res = get_cv_result(params=params_dummy_obj_verbose, metrics=["binary_logloss", "binary_error"]) + assert len(res) == 4 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + + # no custom objective, feval + # default metric with custom one + res = get_cv_result(feval=constant_metric) + assert len(res) == 4 + assert "valid binary_logloss-mean" in res + assert "valid error-mean" in res + + # non-default metric in params with custom one + res = get_cv_result(params=params_obj_metric_err_verbose, feval=constant_metric) + assert len(res) == 4 + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # default metric in args with custom one + res = get_cv_result(metrics="binary_logloss", feval=constant_metric) + assert len(res) == 4 + assert "valid binary_logloss-mean" in res + assert "valid error-mean" in res + + # default metric in args with 1 custom function returning a list of 2 metrics + res = get_cv_result(metrics="binary_logloss", feval=constant_metric_multi) + assert len(res) == 6 + assert "valid binary_logloss-mean" in res + assert res["valid important_metric-mean"] == [1.5, 1.5] + assert res["valid irrelevant_metric-mean"] == [7.8, 7.8] + + # non-default metric in args with custom one + res = get_cv_result(metrics="binary_error", feval=constant_metric) + assert len(res) == 4 + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # metric in args overwrites one in params, custom one is evaluated too + res = get_cv_result(params=params_obj_metric_inv_verbose, metrics="binary_error", feval=constant_metric) + assert len(res) == 4 + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # multiple metrics in params with custom one + res = get_cv_result(params=params_obj_metric_multi_verbose, feval=constant_metric) + assert len(res) == 6 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # multiple metrics in args with custom one + res = get_cv_result(metrics=["binary_logloss", "binary_error"], feval=constant_metric) + assert len(res) == 6 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # custom metric is evaluated despite 'None' is passed + res = get_cv_result(metrics=["None"], feval=constant_metric) + assert len(res) == 2 + assert "valid error-mean" in res + + # custom objective, feval + # no default metric, only custom one + res = get_cv_result(params=params_dummy_obj_verbose, feval=constant_metric) + assert len(res) == 2 + assert "valid error-mean" in res + + # metric in params with custom one + res = get_cv_result(params=params_dummy_obj_metric_err_verbose, feval=constant_metric) + assert len(res) == 4 + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # metric in args with custom one + res = get_cv_result(params=params_dummy_obj_verbose, feval=constant_metric, metrics="binary_error") + assert len(res) == 4 + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # metric in args overwrites one in params, custom one is evaluated too + res = get_cv_result(params=params_dummy_obj_metric_inv_verbose, feval=constant_metric, metrics="binary_error") + assert len(res) == 4 + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # multiple metrics in params with custom one + res = get_cv_result(params=params_dummy_obj_metric_multi_verbose, feval=constant_metric) + assert len(res) == 6 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # multiple metrics in args with custom one + res = get_cv_result( + params=params_dummy_obj_verbose, feval=constant_metric, metrics=["binary_logloss", "binary_error"] + ) + assert len(res) == 6 + assert "valid binary_logloss-mean" in res + assert "valid binary_error-mean" in res + assert "valid error-mean" in res + + # custom metric is evaluated despite 'None' is passed + res = get_cv_result(params=params_dummy_obj_metric_none_verbose, feval=constant_metric) + assert len(res) == 2 + assert "valid error-mean" in res + + # no custom objective, no feval + # default metric + train_booster() + assert len(evals_result["valid_0"]) == 1 + assert "binary_logloss" in evals_result["valid_0"] + + # default metric in params + train_booster(params=params_obj_metric_log_verbose) + assert len(evals_result["valid_0"]) == 1 + assert "binary_logloss" in evals_result["valid_0"] + + # non-default metric in params + train_booster(params=params_obj_metric_err_verbose) + assert len(evals_result["valid_0"]) == 1 + assert "binary_error" in evals_result["valid_0"] + + # multiple metrics in params + train_booster(params=params_obj_metric_multi_verbose) + assert len(evals_result["valid_0"]) == 2 + assert "binary_logloss" in evals_result["valid_0"] + assert "binary_error" in evals_result["valid_0"] + + # remove default metric by 'None' aliases + for na_alias in ("None", "na", "null", "custom"): + params = {"objective": "binary", "metric": na_alias, "verbose": -1} + train_booster(params=params) + assert len(evals_result) == 0 + + # custom objective, no feval + # no default metric + train_booster(params=params_dummy_obj_verbose) + assert len(evals_result) == 0 + + # metric in params + train_booster(params=params_dummy_obj_metric_log_verbose) + assert len(evals_result["valid_0"]) == 1 + assert "binary_logloss" in evals_result["valid_0"] + + # multiple metrics in params + train_booster(params=params_dummy_obj_metric_multi_verbose) + assert len(evals_result["valid_0"]) == 2 + assert "binary_logloss" in evals_result["valid_0"] + assert "binary_error" in evals_result["valid_0"] + + # no custom objective, feval + # default metric with custom one + train_booster(feval=constant_metric) + assert len(evals_result["valid_0"]) == 2 + assert "binary_logloss" in evals_result["valid_0"] + assert "error" in evals_result["valid_0"] + + # default metric in params with custom one + train_booster(params=params_obj_metric_log_verbose, feval=constant_metric) + assert len(evals_result["valid_0"]) == 2 + assert "binary_logloss" in evals_result["valid_0"] + assert "error" in evals_result["valid_0"] + + # default metric in params with custom function returning a list of 2 metrics + train_booster(params=params_obj_metric_log_verbose, feval=constant_metric_multi) + assert len(evals_result["valid_0"]) == 3 + assert "binary_logloss" in evals_result["valid_0"] + assert evals_result["valid_0"]["important_metric"] == [1.5, 1.5] + assert evals_result["valid_0"]["irrelevant_metric"] == [7.8, 7.8] + + # non-default metric in params with custom one + train_booster(params=params_obj_metric_err_verbose, feval=constant_metric) + assert len(evals_result["valid_0"]) == 2 + assert "binary_error" in evals_result["valid_0"] + assert "error" in evals_result["valid_0"] + + # multiple metrics in params with custom one + train_booster(params=params_obj_metric_multi_verbose, feval=constant_metric) + assert len(evals_result["valid_0"]) == 3 + assert "binary_logloss" in evals_result["valid_0"] + assert "binary_error" in evals_result["valid_0"] + assert "error" in evals_result["valid_0"] + + # custom metric is evaluated despite 'None' is passed + train_booster(params=params_obj_metric_none_verbose, feval=constant_metric) + assert len(evals_result) == 1 + assert "error" in evals_result["valid_0"] + + # custom objective, feval + # no default metric, only custom one + train_booster(params=params_dummy_obj_verbose, feval=constant_metric) + assert len(evals_result["valid_0"]) == 1 + assert "error" in evals_result["valid_0"] + + # metric in params with custom one + train_booster(params=params_dummy_obj_metric_log_verbose, feval=constant_metric) + assert len(evals_result["valid_0"]) == 2 + assert "binary_logloss" in evals_result["valid_0"] + assert "error" in evals_result["valid_0"] + + # multiple metrics in params with custom one + train_booster(params=params_dummy_obj_metric_multi_verbose, feval=constant_metric) + assert len(evals_result["valid_0"]) == 3 + assert "binary_logloss" in evals_result["valid_0"] + assert "binary_error" in evals_result["valid_0"] + assert "error" in evals_result["valid_0"] + + # custom metric is evaluated despite 'None' is passed + train_booster(params=params_dummy_obj_metric_none_verbose, feval=constant_metric) + assert len(evals_result) == 1 + assert "error" in evals_result["valid_0"] + + X, y = load_digits(n_class=3, return_X_y=True) + lgb_train = lgb.Dataset(X, y) + + obj_multi_aliases = ["multiclass", "softmax", "multiclassova", "multiclass_ova", "ova", "ovr"] + for obj_multi_alias in obj_multi_aliases: + # Custom objective replaces multiclass + params_obj_class_3_verbose = {"objective": obj_multi_alias, "num_class": 3, "verbose": -1} + params_dummy_obj_class_3_verbose = {"objective": dummy_obj, "num_class": 3, "verbose": -1} + params_dummy_obj_class_1_verbose = {"objective": dummy_obj, "num_class": 1, "verbose": -1} + params_obj_verbose = {"objective": obj_multi_alias, "verbose": -1} + params_dummy_obj_verbose = {"objective": dummy_obj, "verbose": -1} + # multiclass default metric + res = get_cv_result(params_obj_class_3_verbose) + assert len(res) == 2 + assert "valid multi_logloss-mean" in res + # multiclass default metric with custom one + res = get_cv_result(params_obj_class_3_verbose, feval=constant_metric) + assert len(res) == 4 + assert "valid multi_logloss-mean" in res + assert "valid error-mean" in res + # multiclass metric alias with custom one for custom objective + res = get_cv_result(params_dummy_obj_class_3_verbose, feval=constant_metric) + assert len(res) == 2 + assert "valid error-mean" in res + # no metric for invalid class_num + res = get_cv_result(params_dummy_obj_class_1_verbose) + assert len(res) == 0 + # custom metric for invalid class_num + res = get_cv_result(params_dummy_obj_class_1_verbose, feval=constant_metric) + assert len(res) == 2 + assert "valid error-mean" in res + # multiclass metric alias with custom one with invalid class_num + with pytest.raises(lgb.basic.LightGBMError, match="Multiclass objective and metrics don't match"): + get_cv_result(params_dummy_obj_class_1_verbose, metrics=obj_multi_alias, feval=constant_metric) + # multiclass default metric without num_class + with pytest.raises( + lgb.basic.LightGBMError, + match="Number of classes should be specified and greater than 1 for multiclass training", + ): + get_cv_result(params_obj_verbose) + for metric_multi_alias in obj_multi_aliases + ["multi_logloss"]: + # multiclass metric alias + res = get_cv_result(params_obj_class_3_verbose, metrics=metric_multi_alias) + assert len(res) == 2 + assert "valid multi_logloss-mean" in res + # multiclass metric + res = get_cv_result(params_obj_class_3_verbose, metrics="multi_error") + assert len(res) == 2 + assert "valid multi_error-mean" in res + # non-valid metric for multiclass objective + with pytest.raises(lgb.basic.LightGBMError, match="Multiclass objective and metrics don't match"): + get_cv_result(params_obj_class_3_verbose, metrics="binary_logloss") + params_class_3_verbose = {"num_class": 3, "verbose": -1} + # non-default num_class for default objective + with pytest.raises(lgb.basic.LightGBMError, match="Number of classes must be 1 for non-multiclass training"): + get_cv_result(params_class_3_verbose) + # no metric with non-default num_class for custom objective + res = get_cv_result(params_dummy_obj_class_3_verbose) + assert len(res) == 0 + for metric_multi_alias in obj_multi_aliases + ["multi_logloss"]: + # multiclass metric alias for custom objective + res = get_cv_result(params_dummy_obj_class_3_verbose, metrics=metric_multi_alias) + assert len(res) == 2 + assert "valid multi_logloss-mean" in res + # multiclass metric for custom objective + res = get_cv_result(params_dummy_obj_class_3_verbose, metrics="multi_error") + assert len(res) == 2 + assert "valid multi_error-mean" in res + # binary metric with non-default num_class for custom objective + with pytest.raises(lgb.basic.LightGBMError, match="Multiclass objective and metrics don't match"): + get_cv_result(params_dummy_obj_class_3_verbose, metrics="binary_error") + + +def test_multiple_feval_train(): + X, y = load_breast_cancer(return_X_y=True) + + params = {"verbose": -1, "objective": "binary", "metric": "binary_logloss"} + + X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size=0.2) + + train_dataset = lgb.Dataset(data=X_train, label=y_train) + validation_dataset = lgb.Dataset(data=X_validation, label=y_validation, reference=train_dataset) + evals_result = {} + lgb.train( + params=params, + train_set=train_dataset, + valid_sets=validation_dataset, + num_boost_round=5, + feval=[constant_metric, decreasing_metric], + callbacks=[lgb.record_evaluation(evals_result)], + ) + + assert len(evals_result["valid_0"]) == 3 + assert "binary_logloss" in evals_result["valid_0"] + assert "error" in evals_result["valid_0"] + assert "decreasing_metric" in evals_result["valid_0"] + + +def test_objective_callable_train_binary_classification(): + X, y = load_breast_cancer(return_X_y=True) + params = {"verbose": -1, "objective": logloss_obj, "learning_rate": 0.01} + train_dataset = lgb.Dataset(X, y) + booster = lgb.train(params=params, train_set=train_dataset, num_boost_round=20) + y_pred = logistic_sigmoid(booster.predict(X)) + logloss_error = log_loss(y, y_pred) + rocauc_error = roc_auc_score(y, y_pred) + assert booster.params["objective"] == "none" + assert logloss_error == pytest.approx(0.547907) + assert rocauc_error == pytest.approx(0.995944) + + +def test_objective_callable_train_regression(): + X, y = make_synthetic_regression() + params = {"verbose": -1, "objective": mse_obj} + lgb_train = lgb.Dataset(X, y) + booster = lgb.train(params, lgb_train, num_boost_round=20) + y_pred = booster.predict(X) + mse_error = mean_squared_error(y, y_pred) + assert booster.params["objective"] == "none" + assert mse_error == pytest.approx(286.724194) + + +def test_objective_callable_cv_binary_classification(): + X, y = load_breast_cancer(return_X_y=True) + params = {"verbose": -1, "objective": logloss_obj, "learning_rate": 0.01} + train_dataset = lgb.Dataset(X, y) + cv_res = lgb.cv(params, train_dataset, num_boost_round=20, nfold=3, return_cvbooster=True) + cv_booster = cv_res["cvbooster"].boosters + cv_logloss_errors = [log_loss(y, logistic_sigmoid(cb.predict(X))) < 0.56 for cb in cv_booster] + cv_objs = [cb.params["objective"] == "none" for cb in cv_booster] + assert all(cv_objs) + assert all(cv_logloss_errors) + + +def test_objective_callable_cv_regression(): + X, y = make_synthetic_regression() + lgb_train = lgb.Dataset(X, y) + params = {"verbose": -1, "objective": mse_obj} + cv_res = lgb.cv(params, lgb_train, num_boost_round=20, nfold=3, stratified=False, return_cvbooster=True) + cv_booster = cv_res["cvbooster"].boosters + cv_mse_errors = [mean_squared_error(y, cb.predict(X)) < 504 for cb in cv_booster] + cv_objs = [cb.params["objective"] == "none" for cb in cv_booster] + assert all(cv_objs) + assert all(cv_mse_errors) + + +def test_multiple_feval_cv(): + X, y = load_breast_cancer(return_X_y=True) + + params = {"verbose": -1, "objective": "binary", "metric": "binary_logloss"} + + train_dataset = lgb.Dataset(data=X, label=y) + + cv_results = lgb.cv( + params=params, train_set=train_dataset, num_boost_round=5, feval=[constant_metric, decreasing_metric] + ) + + # Expect three metrics but mean and stdv for each metric + assert len(cv_results) == 6 + assert "valid binary_logloss-mean" in cv_results + assert "valid error-mean" in cv_results + assert "valid decreasing_metric-mean" in cv_results + assert "valid binary_logloss-stdv" in cv_results + assert "valid error-stdv" in cv_results + assert "valid decreasing_metric-stdv" in cv_results + + +def test_default_objective_and_metric(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + train_dataset = lgb.Dataset(data=X_train, label=y_train) + validation_dataset = lgb.Dataset(data=X_test, label=y_test, reference=train_dataset) + evals_result = {} + params = {"verbose": -1} + lgb.train( + params=params, + train_set=train_dataset, + valid_sets=validation_dataset, + num_boost_round=5, + callbacks=[lgb.record_evaluation(evals_result)], + ) + + assert "valid_0" in evals_result + assert len(evals_result["valid_0"]) == 1 + assert "l2" in evals_result["valid_0"] + assert len(evals_result["valid_0"]["l2"]) == 5 + + +@pytest.mark.parametrize("use_weight", [True, False]) +def test_multiclass_custom_objective(use_weight): + def custom_obj(y_pred, ds): + y_true = ds.get_label() + weight = ds.get_weight() + grad, hess = sklearn_multiclass_custom_objective(y_true, y_pred, weight) + return grad, hess + + centers = [[-4, -4], [4, 4], [-4, 4]] + X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42) + weight = np.full_like(y, 2) + ds = lgb.Dataset(X, y) + if use_weight: + ds.set_weight(weight) + params = {"objective": "multiclass", "num_class": 3, "num_leaves": 7} + builtin_obj_bst = lgb.train(params, ds, num_boost_round=10) + builtin_obj_preds = builtin_obj_bst.predict(X) + + params["objective"] = custom_obj + custom_obj_bst = lgb.train(params, ds, num_boost_round=10) + custom_obj_preds = softmax(custom_obj_bst.predict(X)) + + np.testing.assert_allclose(builtin_obj_preds, custom_obj_preds, rtol=0.01) + + +@pytest.mark.parametrize("use_weight", [True, False]) +def test_multiclass_custom_eval(use_weight): + def custom_eval(y_pred, ds): + y_true = ds.get_label() + weight = ds.get_weight() # weight is None when not set + loss = log_loss(y_true, y_pred, sample_weight=weight) + return "custom_logloss", loss, False + + centers = [[-4, -4], [4, 4], [-4, 4]] + X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42) + weight = np.full_like(y, 2) + X_train, X_valid, y_train, y_valid, weight_train, weight_valid = train_test_split( + X, y, weight, test_size=0.2, random_state=0 + ) + train_ds = lgb.Dataset(X_train, y_train) + valid_ds = lgb.Dataset(X_valid, y_valid, reference=train_ds) + if use_weight: + train_ds.set_weight(weight_train) + valid_ds.set_weight(weight_valid) + params = {"objective": "multiclass", "num_class": 3, "num_leaves": 7} + eval_result = {} + bst = lgb.train( + params, + train_ds, + num_boost_round=10, + valid_sets=[train_ds, valid_ds], + valid_names=["train", "valid"], + feval=custom_eval, + callbacks=[lgb.record_evaluation(eval_result)], + keep_training_booster=True, + ) + + for key, ds in zip(["train", "valid"], [train_ds, valid_ds], strict=True): + np.testing.assert_allclose(eval_result[key]["multi_logloss"], eval_result[key]["custom_logloss"]) + _, metric, value, _ = bst.eval(ds, key, feval=custom_eval)[1] # first element is multi_logloss + assert metric == "custom_logloss" + np.testing.assert_allclose(value, eval_result[key][metric][-1]) + + +@pytest.mark.skipif(psutil.virtual_memory().available / 1024 / 1024 / 1024 < 3, reason="not enough RAM") +def test_model_size(): + X, y = make_synthetic_regression() + data = lgb.Dataset(X, y) + bst = lgb.train({"verbose": -1}, data, num_boost_round=2) + y_pred = bst.predict(X) + model_str = bst.model_to_string() + one_tree = model_str[model_str.find("Tree=1") : model_str.find("end of trees")] + one_tree_size = len(one_tree) + one_tree = one_tree.replace("Tree=1", "Tree={}") + multiplier = 100 + total_trees = multiplier + 2 + try: + before_tree_sizes = model_str[: model_str.find("tree_sizes")] + trees = model_str[model_str.find("Tree=0") : model_str.find("end of trees")] + more_trees = (one_tree * multiplier).format(*range(2, total_trees)) + after_trees = model_str[model_str.find("end of trees") :] + num_end_spaces = 2**31 - one_tree_size * total_trees + new_model_str = f"{before_tree_sizes}\n\n{trees}{more_trees}{after_trees}{'':{num_end_spaces}}" + assert len(new_model_str) > 2**31 + bst.model_from_string(new_model_str) + assert bst.num_trees() == total_trees + y_pred_new = bst.predict(X, num_iteration=2) + np.testing.assert_allclose(y_pred, y_pred_new) + except MemoryError: + pytest.skipTest("not enough RAM") + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Skip due to differences in implementation details of CUDA version") +def test_get_split_value_histogram(rng_fixed_seed): + X, y = make_synthetic_regression() + X = np.repeat(X, 3, axis=0) + y = np.repeat(y, 3, axis=0) + X[:, 2] = np.random.default_rng(0).integers(0, 20, size=X.shape[0]) + lgb_train = lgb.Dataset(X, y, categorical_feature=[2]) + gbm = lgb.train({"verbose": -1}, lgb_train, num_boost_round=20) + # test XGBoost-style return value + params = {"feature": 0, "xgboost_style": True} + assert gbm.get_split_value_histogram(**params).shape == (12, 2) + assert gbm.get_split_value_histogram(bins=999, **params).shape == (12, 2) + assert gbm.get_split_value_histogram(bins=-1, **params).shape == (1, 2) + assert gbm.get_split_value_histogram(bins=0, **params).shape == (1, 2) + assert gbm.get_split_value_histogram(bins=1, **params).shape == (1, 2) + assert gbm.get_split_value_histogram(bins=2, **params).shape == (2, 2) + assert gbm.get_split_value_histogram(bins=6, **params).shape == (6, 2) + assert gbm.get_split_value_histogram(bins=7, **params).shape == (7, 2) + if lgb.compat.PANDAS_INSTALLED: + np.testing.assert_allclose( + gbm.get_split_value_histogram(0, xgboost_style=True).values, + gbm.get_split_value_histogram(gbm.feature_name()[0], xgboost_style=True).values, + ) + np.testing.assert_allclose( + gbm.get_split_value_histogram(X.shape[-1] - 1, xgboost_style=True).values, + gbm.get_split_value_histogram(gbm.feature_name()[X.shape[-1] - 1], xgboost_style=True).values, + ) + else: + np.testing.assert_allclose( + gbm.get_split_value_histogram(0, xgboost_style=True), + gbm.get_split_value_histogram(gbm.feature_name()[0], xgboost_style=True), + ) + np.testing.assert_allclose( + gbm.get_split_value_histogram(X.shape[-1] - 1, xgboost_style=True), + gbm.get_split_value_histogram(gbm.feature_name()[X.shape[-1] - 1], xgboost_style=True), + ) + # test numpy-style return value + hist, bins = gbm.get_split_value_histogram(0) + assert len(hist) == 20 + assert len(bins) == 21 + hist, bins = gbm.get_split_value_histogram(0, bins=999) + assert len(hist) == 999 + assert len(bins) == 1000 + with pytest.raises(ValueError, match="`bins` must be positive, when an integer"): + gbm.get_split_value_histogram(0, bins=-1) + with pytest.raises(ValueError, match="`bins` must be positive, when an integer"): + gbm.get_split_value_histogram(0, bins=0) + hist, bins = gbm.get_split_value_histogram(0, bins=1) + assert len(hist) == 1 + assert len(bins) == 2 + hist, bins = gbm.get_split_value_histogram(0, bins=2) + assert len(hist) == 2 + assert len(bins) == 3 + hist, bins = gbm.get_split_value_histogram(0, bins=6) + assert len(hist) == 6 + assert len(bins) == 7 + hist, bins = gbm.get_split_value_histogram(0, bins=7) + assert len(hist) == 7 + assert len(bins) == 8 + hist_idx, bins_idx = gbm.get_split_value_histogram(0) + hist_name, bins_name = gbm.get_split_value_histogram(gbm.feature_name()[0]) + np_assert_array_equal(hist_idx, hist_name, strict=True) + np.testing.assert_allclose(bins_idx, bins_name) + hist_idx, bins_idx = gbm.get_split_value_histogram(X.shape[-1] - 1) + hist_name, bins_name = gbm.get_split_value_histogram(gbm.feature_name()[X.shape[-1] - 1]) + np_assert_array_equal(hist_idx, hist_name, strict=True) + np.testing.assert_allclose(bins_idx, bins_name) + # test bins string type + hist_vals, bin_edges = gbm.get_split_value_histogram(0, bins="auto") + hist = gbm.get_split_value_histogram(0, bins="auto", xgboost_style=True) + if lgb.compat.PANDAS_INSTALLED: + mask = hist_vals > 0 + # strict=False due to dtype mismatch: 'int64' and 'float64' + np_assert_array_equal(hist_vals[mask], hist["Count"].values, strict=False) + np.testing.assert_allclose(bin_edges[1:][mask], hist["SplitValue"].values) + else: + mask = hist_vals > 0 + # strict=False due to dtype mismatch: 'int64' and 'float64' + np_assert_array_equal(hist_vals[mask], hist[:, 1], strict=False) + np.testing.assert_allclose(bin_edges[1:][mask], hist[:, 0]) + # test histogram is disabled for categorical features + with pytest.raises( + lgb.basic.LightGBMError, match="Cannot compute split value histogram for the categorical feature" + ): + gbm.get_split_value_histogram(2) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Skip due to differences in implementation details of CUDA version") +def test_early_stopping_for_only_first_metric(): + def metrics_combination_train_regression(valid_sets, metric_list, assumed_iteration, first_metric_only, feval=None): + params = { + "objective": "regression", + "learning_rate": 1.1, + "num_leaves": 10, + "metric": metric_list, + "verbose": -1, + "seed": 123, + } + gbm = lgb.train( + params, + lgb_train, + num_boost_round=25, + valid_sets=valid_sets, + feval=feval, + callbacks=[lgb.early_stopping(stopping_rounds=5, first_metric_only=first_metric_only)], + ) + assert assumed_iteration == gbm.best_iteration + + def metrics_combination_cv_regression( + metric_list, assumed_iteration, first_metric_only, eval_train_metric, feval=None + ): + params = { + "objective": "regression", + "learning_rate": 0.9, + "num_leaves": 10, + "metric": metric_list, + "verbose": -1, + "seed": 123, + "gpu_use_dp": True, + } + ret = lgb.cv( + params, + train_set=lgb_train, + num_boost_round=25, + stratified=False, + feval=feval, + callbacks=[lgb.early_stopping(stopping_rounds=5, first_metric_only=first_metric_only)], + eval_train_metric=eval_train_metric, + ) + assert assumed_iteration == len(ret[list(ret.keys())[0]]) + + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + X_test1, X_test2, y_test1, y_test2 = train_test_split(X_test, y_test, test_size=0.5, random_state=73) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_valid1 = lgb.Dataset(X_test1, y_test1, reference=lgb_train) + lgb_valid2 = lgb.Dataset(X_test2, y_test2, reference=lgb_train) + + iter_valid1_l1 = 3 + iter_valid1_l2 = 3 + iter_valid2_l1 = 3 + iter_valid2_l2 = 15 + assert len({iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2}) == 2 + iter_min_l1 = min([iter_valid1_l1, iter_valid2_l1]) + iter_min_l2 = min([iter_valid1_l2, iter_valid2_l2]) + iter_min_valid1 = min([iter_valid1_l1, iter_valid1_l2]) + + iter_cv_l1 = 15 + iter_cv_l2 = 13 + assert len({iter_cv_l1, iter_cv_l2}) == 2 + iter_cv_min = min([iter_cv_l1, iter_cv_l2]) + + # test for lgb.train + metrics_combination_train_regression(lgb_valid1, [], iter_valid1_l2, False) + metrics_combination_train_regression(lgb_valid1, [], iter_valid1_l2, True) + metrics_combination_train_regression(lgb_valid1, None, iter_valid1_l2, False) + metrics_combination_train_regression(lgb_valid1, None, iter_valid1_l2, True) + metrics_combination_train_regression(lgb_valid1, "l2", iter_valid1_l2, True) + metrics_combination_train_regression(lgb_valid1, "l1", iter_valid1_l1, True) + metrics_combination_train_regression(lgb_valid1, ["l2", "l1"], iter_valid1_l2, True) + metrics_combination_train_regression(lgb_valid1, ["l1", "l2"], iter_valid1_l1, True) + metrics_combination_train_regression(lgb_valid1, ["l2", "l1"], iter_min_valid1, False) + metrics_combination_train_regression(lgb_valid1, ["l1", "l2"], iter_min_valid1, False) + + # test feval for lgb.train + metrics_combination_train_regression( + lgb_valid1, + "None", + 1, + False, + feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)], + ) + metrics_combination_train_regression( + lgb_valid1, + "None", + 25, + True, + feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)], + ) + metrics_combination_train_regression( + lgb_valid1, + "None", + 1, + True, + feval=lambda preds, train_data: [constant_metric(preds, train_data), decreasing_metric(preds, train_data)], + ) + + # test with two valid data for lgb.train + metrics_combination_train_regression([lgb_valid1, lgb_valid2], ["l2", "l1"], iter_min_l2, True) + metrics_combination_train_regression([lgb_valid2, lgb_valid1], ["l2", "l1"], iter_min_l2, True) + metrics_combination_train_regression([lgb_valid1, lgb_valid2], ["l1", "l2"], iter_min_l1, True) + metrics_combination_train_regression([lgb_valid2, lgb_valid1], ["l1", "l2"], iter_min_l1, True) + + # test for lgb.cv + metrics_combination_cv_regression(None, iter_cv_l2, True, False) + metrics_combination_cv_regression("l2", iter_cv_l2, True, False) + metrics_combination_cv_regression("l1", iter_cv_l1, True, False) + metrics_combination_cv_regression(["l2", "l1"], iter_cv_l2, True, False) + metrics_combination_cv_regression(["l1", "l2"], iter_cv_l1, True, False) + metrics_combination_cv_regression(["l2", "l1"], iter_cv_min, False, False) + metrics_combination_cv_regression(["l1", "l2"], iter_cv_min, False, False) + metrics_combination_cv_regression(None, iter_cv_l2, True, True) + metrics_combination_cv_regression("l2", iter_cv_l2, True, True) + metrics_combination_cv_regression("l1", iter_cv_l1, True, True) + metrics_combination_cv_regression(["l2", "l1"], iter_cv_l2, True, True) + metrics_combination_cv_regression(["l1", "l2"], iter_cv_l1, True, True) + metrics_combination_cv_regression(["l2", "l1"], iter_cv_min, False, True) + metrics_combination_cv_regression(["l1", "l2"], iter_cv_min, False, True) + + # test feval for lgb.cv + metrics_combination_cv_regression( + "None", + 1, + False, + False, + feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)], + ) + metrics_combination_cv_regression( + "None", + 25, + True, + False, + feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)], + ) + metrics_combination_cv_regression( + "None", + 1, + True, + False, + feval=lambda preds, train_data: [constant_metric(preds, train_data), decreasing_metric(preds, train_data)], + ) + + +def test_node_level_subcol(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + params = { + "objective": "binary", + "metric": "binary_logloss", + "feature_fraction_bynode": 0.8, + "feature_fraction": 1.0, + "verbose": -1, + } + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + evals_result = {} + gbm = lgb.train( + params, lgb_train, num_boost_round=25, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + ret = log_loss(y_test, gbm.predict(X_test)) + assert ret < 0.14 + assert evals_result["valid_0"]["binary_logloss"][-1] == pytest.approx(ret) + params["feature_fraction"] = 0.5 + gbm2 = lgb.train(params, lgb_train, num_boost_round=25) + ret2 = log_loss(y_test, gbm2.predict(X_test)) + assert ret != ret2 + + +def test_forced_split_feature_indices(tmp_path): + X, y = make_synthetic_regression() + forced_split = { + "feature": 0, + "threshold": 0.5, + "left": {"feature": X.shape[1], "threshold": 0.5}, + } + tmp_split_file = tmp_path / "forced_split.json" + with open(tmp_split_file, "w") as f: + f.write(json.dumps(forced_split)) + lgb_train = lgb.Dataset(X, y) + params = {"objective": "regression", "forcedsplits_filename": tmp_split_file} + with pytest.raises(lgb.basic.LightGBMError, match="Forced splits file includes feature index"): + lgb.train(params, lgb_train) + + +def test_forced_bins(): + x = np.empty((100, 2)) + x[:, 0] = np.arange(0, 1, 0.01) + x[:, 1] = -np.arange(0, 1, 0.01) + y = np.arange(0, 1, 0.01) + forcedbins_filename = Path(__file__).absolute().parents[2] / "examples" / "regression" / "forced_bins.json" + params = { + "objective": "regression_l1", + "max_bin": 5, + "forcedbins_filename": forcedbins_filename, + "num_leaves": 2, + "min_data_in_leaf": 1, + "verbose": -1, + } + lgb_x = lgb.Dataset(x, label=y) + est = lgb.train(params, lgb_x, num_boost_round=20) + new_x = np.zeros((3, x.shape[1])) + new_x[:, 0] = [0.31, 0.37, 0.41] + predicted = est.predict(new_x) + assert len(np.unique(predicted)) == 3 + new_x[:, 0] = [0, 0, 0] + new_x[:, 1] = [-0.9, -0.6, -0.3] + predicted = est.predict(new_x) + assert len(np.unique(predicted)) == 1 + params["forcedbins_filename"] = "" + lgb_x = lgb.Dataset(x, label=y) + est = lgb.train(params, lgb_x, num_boost_round=20) + predicted = est.predict(new_x) + assert len(np.unique(predicted)) == 3 + params["forcedbins_filename"] = ( + Path(__file__).absolute().parents[2] / "examples" / "regression" / "forced_bins2.json" + ) + params["max_bin"] = 11 + lgb_x = lgb.Dataset(x[:, :1], label=y) + est = lgb.train(params, lgb_x, num_boost_round=50) + predicted = est.predict(x[1:, :1]) + _, counts = np.unique(predicted, return_counts=True) + assert min(counts) >= 9 + assert max(counts) <= 11 + + +def test_binning_same_sign(): + # test that binning works properly for features with only positive or only negative values + x = np.empty((99, 2)) + x[:, 0] = np.arange(0.01, 1, 0.01) + x[:, 1] = -np.arange(0.01, 1, 0.01) + y = np.arange(0.01, 1, 0.01) + params = { + "objective": "regression_l1", + "max_bin": 5, + "num_leaves": 2, + "min_data_in_leaf": 1, + "verbose": -1, + "seed": 0, + } + lgb_x = lgb.Dataset(x, label=y) + est = lgb.train(params, lgb_x, num_boost_round=20) + new_x = np.zeros((3, 2)) + new_x[:, 0] = [-1, 0, 1] + predicted = est.predict(new_x) + assert predicted[0] == pytest.approx(predicted[1]) + assert predicted[1] != pytest.approx(predicted[2]) + new_x = np.zeros((3, 2)) + new_x[:, 1] = [-1, 0, 1] + predicted = est.predict(new_x) + assert predicted[0] != pytest.approx(predicted[1]) + assert predicted[1] == pytest.approx(predicted[2]) + + +def test_dataset_update_params(rng): + default_params = { + "max_bin": 100, + "max_bin_by_feature": [20, 10], + "bin_construct_sample_cnt": 10000, + "min_data_in_bin": 1, + "use_missing": False, + "zero_as_missing": False, + "categorical_feature": [0], + "feature_pre_filter": True, + "pre_partition": False, + "enable_bundle": True, + "data_random_seed": 0, + "is_enable_sparse": True, + "header": True, + "two_round": True, + "label_column": 0, + "weight_column": 0, + "group_column": 0, + "ignore_column": 0, + "min_data_in_leaf": 10, + "linear_tree": False, + "precise_float_parser": True, + "verbose": -1, + } + unchangeable_params = { + "max_bin": 150, + "max_bin_by_feature": [30, 5], + "bin_construct_sample_cnt": 5000, + "min_data_in_bin": 2, + "use_missing": True, + "zero_as_missing": True, + "categorical_feature": [0, 1], + "feature_pre_filter": False, + "pre_partition": True, + "enable_bundle": False, + "data_random_seed": 1, + "is_enable_sparse": False, + "header": False, + "two_round": False, + "label_column": 1, + "weight_column": 1, + "group_column": 1, + "ignore_column": 1, + "forcedbins_filename": "/some/path/forcedbins.json", + "min_data_in_leaf": 2, + "linear_tree": True, + "precise_float_parser": False, + } + X = rng.uniform(size=(100, 2)) + y = rng.uniform(size=(100,)) + + # decreasing without freeing raw data is allowed + lgb_data = lgb.Dataset(X, y, params=default_params, free_raw_data=False).construct() + default_params["min_data_in_leaf"] -= 1 + lgb.train(default_params, lgb_data, num_boost_round=3) + + # decreasing before lazy init is allowed + lgb_data = lgb.Dataset(X, y, params=default_params) + default_params["min_data_in_leaf"] -= 1 + lgb.train(default_params, lgb_data, num_boost_round=3) + + # increasing is allowed + default_params["min_data_in_leaf"] += 2 + lgb.train(default_params, lgb_data, num_boost_round=3) + + # decreasing with disabled filter is allowed + default_params["feature_pre_filter"] = False + lgb_data = lgb.Dataset(X, y, params=default_params).construct() + default_params["min_data_in_leaf"] -= 4 + lgb.train(default_params, lgb_data, num_boost_round=3) + + # decreasing with enabled filter is disallowed; + # also changes of other params are disallowed + default_params["feature_pre_filter"] = True + lgb_data = lgb.Dataset(X, y, params=default_params).construct() + for key, value in unchangeable_params.items(): + new_params = default_params.copy() + new_params[key] = value + if key != "forcedbins_filename": + param_name = key + else: + param_name = "forced bins" + err_msg = ( + "Reducing `min_data_in_leaf` with `feature_pre_filter=true` may cause *" + if key == "min_data_in_leaf" + else f"Cannot change {param_name} *" + ) + with np.testing.assert_raises_regex(lgb.basic.LightGBMError, err_msg): + lgb.train(new_params, lgb_data, num_boost_round=3) + + +def test_dataset_params_with_reference(rng): + default_params = {"max_bin": 100} + X = rng.uniform(size=(100, 2)) + y = rng.uniform(size=(100,)) + X_val = rng.uniform(size=(100, 2)) + y_val = rng.uniform(size=(100,)) + lgb_train = lgb.Dataset(X, y, params=default_params, free_raw_data=False).construct() + lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train, free_raw_data=False).construct() + assert lgb_train.get_params() == default_params + assert lgb_val.get_params() == default_params + lgb.train(default_params, lgb_train, valid_sets=[lgb_val]) + + +def test_extra_trees(): + # check extra trees increases regularization + X, y = make_synthetic_regression() + lgb_x = lgb.Dataset(X, label=y) + params = {"objective": "regression", "num_leaves": 32, "verbose": -1, "extra_trees": False, "seed": 0} + est = lgb.train(params, lgb_x, num_boost_round=10) + predicted = est.predict(X) + err = mean_squared_error(y, predicted) + params["extra_trees"] = True + est = lgb.train(params, lgb_x, num_boost_round=10) + predicted_new = est.predict(X) + err_new = mean_squared_error(y, predicted_new) + assert err < err_new + + +def test_path_smoothing(): + # check path smoothing increases regularization + X, y = make_synthetic_regression() + lgb_x = lgb.Dataset(X, label=y) + params = {"objective": "regression", "num_leaves": 32, "verbose": -1, "seed": 0} + est = lgb.train(params, lgb_x, num_boost_round=10) + predicted = est.predict(X) + err = mean_squared_error(y, predicted) + params["path_smooth"] = 1 + est = lgb.train(params, lgb_x, num_boost_round=10) + predicted_new = est.predict(X) + err_new = mean_squared_error(y, predicted_new) + assert err < err_new + + +def test_trees_to_dataframe(rng): + pytest.importorskip("pandas") + + def _imptcs_to_numpy(X, impcts_dict): + cols = [f"Column_{i}" for i in range(X.shape[1])] + return [impcts_dict.get(col, 0.0) for col in cols] + + X, y = load_breast_cancer(return_X_y=True) + data = lgb.Dataset(X, label=y) + num_trees = 10 + bst = lgb.train({"objective": "binary", "verbose": -1}, data, num_trees) + tree_df = bst.trees_to_dataframe() + split_dict = tree_df[~tree_df["split_gain"].isnull()].groupby("split_feature").size().to_dict() + + gains_dict = tree_df.groupby("split_feature")["split_gain"].sum().to_dict() + + tree_split = _imptcs_to_numpy(X, split_dict) + tree_gains = _imptcs_to_numpy(X, gains_dict) + mod_split = bst.feature_importance("split") + mod_gains = bst.feature_importance("gain") + num_trees_from_df = tree_df["tree_index"].nunique() + obs_counts_from_df = tree_df.loc[tree_df["node_depth"] == 1, "count"].values + + np.testing.assert_equal(tree_split, mod_split) + np.testing.assert_allclose(tree_gains, mod_gains) + assert num_trees_from_df == num_trees + np.testing.assert_equal(obs_counts_from_df, len(y)) + + # test edge case with one leaf + X = np.ones((10, 2)) + y = rng.uniform(size=(10,)) + data = lgb.Dataset(X, label=y) + bst = lgb.train({"objective": "binary", "verbose": -1}, data, num_trees) + tree_df = bst.trees_to_dataframe() + + assert len(tree_df) == 1 + assert tree_df.loc[0, "tree_index"] == 0 + assert tree_df.loc[0, "node_depth"] == 1 + assert tree_df.loc[0, "node_index"] == "0-L0" + assert tree_df.loc[0, "value"] is not None + for col in ( + "left_child", + "right_child", + "parent_index", + "split_feature", + "split_gain", + "threshold", + "decision_type", + "missing_direction", + "missing_type", + "weight", + "count", + ): + assert tree_df.loc[0, col] is None + + +def test_interaction_constraints(): + X, y = make_synthetic_regression(n_samples=200) + num_features = X.shape[1] + train_data = lgb.Dataset(X, label=y) + # check that constraint containing all features is equivalent to no constraint + params = {"verbose": -1, "seed": 0} + est = lgb.train(params, train_data, num_boost_round=10) + pred1 = est.predict(X) + est = lgb.train(dict(params, interaction_constraints=[list(range(num_features))]), train_data, num_boost_round=10) + pred2 = est.predict(X) + np.testing.assert_allclose(pred1, pred2) + # check that constraint partitioning the features reduces train accuracy + est = lgb.train(dict(params, interaction_constraints=[[0, 2], [1, 3]]), train_data, num_boost_round=10) + pred3 = est.predict(X) + assert mean_squared_error(y, pred1) < mean_squared_error(y, pred3) + # check that constraints consisting of single features reduce accuracy further + est = lgb.train( + dict(params, interaction_constraints=[[i] for i in range(num_features)]), train_data, num_boost_round=10 + ) + pred4 = est.predict(X) + assert mean_squared_error(y, pred3) < mean_squared_error(y, pred4) + # test that interaction constraints work when not all features are used + X = np.concatenate([np.zeros((X.shape[0], 1)), X], axis=1) + num_features = X.shape[1] + train_data = lgb.Dataset(X, label=y) + est = lgb.train( + dict(params, interaction_constraints=[[0] + list(range(2, num_features)), [1] + list(range(2, num_features))]), + train_data, + num_boost_round=10, + ) + + +def test_linear_trees_num_threads(rng_fixed_seed): + # check that number of threads does not affect result + x = np.arange(0, 1000, 0.1) + y = 2 * x + rng_fixed_seed.normal(loc=0, scale=0.1, size=(len(x),)) + x = x[:, np.newaxis] + lgb_train = lgb.Dataset(x, label=y) + params = {"verbose": -1, "objective": "regression", "seed": 0, "linear_tree": True, "num_threads": 2} + est = lgb.train(params, lgb_train, num_boost_round=100) + pred1 = est.predict(x) + params["num_threads"] = 4 + est = lgb.train(params, lgb_train, num_boost_round=100) + pred2 = est.predict(x) + np.testing.assert_allclose(pred1, pred2) + + +def test_linear_trees(tmp_path, rng_fixed_seed): + # check that setting linear_tree=True fits better than ordinary trees when data has linear relationship + x = np.arange(0, 100, 0.1) + y = 2 * x + rng_fixed_seed.normal(0, 0.1, len(x)) + x = x[:, np.newaxis] + lgb_train = lgb.Dataset(x, label=y) + params = {"verbose": -1, "metric": "mse", "seed": 0, "num_leaves": 2} + est = lgb.train(params, lgb_train, num_boost_round=10) + pred1 = est.predict(x) + lgb_train = lgb.Dataset(x, label=y) + res = {} + est = lgb.train( + dict(params, linear_tree=True), + lgb_train, + num_boost_round=10, + valid_sets=[lgb_train], + valid_names=["train"], + callbacks=[lgb.record_evaluation(res)], + ) + pred2 = est.predict(x) + assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred2), abs=1e-1) + assert mean_squared_error(y, pred2) < mean_squared_error(y, pred1) + # test again with nans in data + x[:10] = np.nan + lgb_train = lgb.Dataset(x, label=y) + est = lgb.train(params, lgb_train, num_boost_round=10) + pred1 = est.predict(x) + lgb_train = lgb.Dataset(x, label=y) + res = {} + est = lgb.train( + dict(params, linear_tree=True), + lgb_train, + num_boost_round=10, + valid_sets=[lgb_train], + valid_names=["train"], + callbacks=[lgb.record_evaluation(res)], + ) + pred2 = est.predict(x) + assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred2), abs=1e-1) + assert mean_squared_error(y, pred2) < mean_squared_error(y, pred1) + # test again with bagging + res = {} + est = lgb.train( + dict(params, linear_tree=True, subsample=0.8, bagging_freq=1), + lgb_train, + num_boost_round=10, + valid_sets=[lgb_train], + valid_names=["train"], + callbacks=[lgb.record_evaluation(res)], + ) + pred = est.predict(x) + assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred), abs=1e-1) + # test with a feature that has only one non-nan value + x = np.concatenate([np.ones([x.shape[0], 1]), x], 1) + x[500:, 1] = np.nan + y[500:] += 10 + lgb_train = lgb.Dataset(x, label=y) + res = {} + est = lgb.train( + dict(params, linear_tree=True, subsample=0.8, bagging_freq=1), + lgb_train, + num_boost_round=10, + valid_sets=[lgb_train], + valid_names=["train"], + callbacks=[lgb.record_evaluation(res)], + ) + pred = est.predict(x) + assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred), abs=1e-1) + # test with a categorical feature + x[:250, 0] = 0 + y[:250] += 10 + lgb_train = lgb.Dataset(x, label=y, categorical_feature=[0]) + est = lgb.train( + dict(params, linear_tree=True, subsample=0.8, bagging_freq=1), + lgb_train, + num_boost_round=10, + ) + # test refit: same results on same data + est2 = est.refit(x, label=y) + p1 = est.predict(x) + p2 = est2.predict(x) + assert np.mean(np.abs(p1 - p2)) < 2 + + # test refit with save and load + temp_model = str(tmp_path / "temp_model.txt") + est.save_model(temp_model) + est2 = lgb.Booster(model_file=temp_model) + est2 = est2.refit(x, label=y) + p1 = est.predict(x) + p2 = est2.predict(x) + assert np.mean(np.abs(p1 - p2)) < 2 + # test refit: different results training on different data + est3 = est.refit(x[:100, :], label=y[:100]) + p3 = est3.predict(x) + assert np.mean(np.abs(p2 - p1)) > np.abs(np.max(p3 - p1)) + # test when num_leaves - 1 < num_features and when num_leaves - 1 > num_features + X_train, _, y_train, _ = train_test_split(*load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2) + params = {"linear_tree": True, "verbose": -1, "metric": "mse", "seed": 0} + train_data = lgb.Dataset( + X_train, + label=y_train, + params=dict(params, num_leaves=2), + categorical_feature=[0], + ) + est = lgb.train(params, train_data, num_boost_round=10) + train_data = lgb.Dataset( + X_train, + label=y_train, + params=dict(params, num_leaves=60), + categorical_feature=[0], + ) + est = lgb.train(params, train_data, num_boost_round=10) + + +def test_save_and_load_linear(tmp_path): + X_train, X_test, y_train, y_test = train_test_split( + *load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2 + ) + X_train = np.concatenate([np.ones((X_train.shape[0], 1)), X_train], 1) + X_train[: X_train.shape[0] // 2, 0] = 0 + y_train[: X_train.shape[0] // 2] = 1 + params = {"linear_tree": True} + train_data_1 = lgb.Dataset(X_train, label=y_train, params=params, categorical_feature=[0]) + est_1 = lgb.train(params, train_data_1, num_boost_round=10) + pred_1 = est_1.predict(X_train) + + tmp_dataset = str(tmp_path / "temp_dataset.bin") + train_data_1.save_binary(tmp_dataset) + train_data_2 = lgb.Dataset(tmp_dataset) + est_2 = lgb.train(params, train_data_2, num_boost_round=10) + pred_2 = est_2.predict(X_train) + np.testing.assert_allclose(pred_1, pred_2) + + model_file = str(tmp_path / "model.txt") + est_2.save_model(model_file) + est_3 = lgb.Booster(model_file=model_file) + pred_3 = est_3.predict(X_train) + np.testing.assert_allclose(pred_2, pred_3) + + +def test_linear_single_leaf(): + X_train, y_train = load_breast_cancer(return_X_y=True) + train_data = lgb.Dataset(X_train, label=y_train) + params = {"objective": "binary", "linear_tree": True, "min_sum_hessian": 5000} + bst = lgb.train(params, train_data, num_boost_round=5) + y_pred = bst.predict(X_train) + assert log_loss(y_train, y_pred) < 0.661 + + +def test_linear_raises_informative_errors_on_unsupported_params(): + X, y = make_synthetic_regression() + with pytest.raises(lgb.basic.LightGBMError, match="Cannot use regression_l1 objective when fitting linear trees"): + lgb.train( + train_set=lgb.Dataset(X, label=y), + params={"linear_tree": True, "objective": "regression_l1"}, + num_boost_round=1, + ) + with pytest.raises(lgb.basic.LightGBMError, match="zero_as_missing must be false when fitting linear trees"): + lgb.train( + train_set=lgb.Dataset(X, label=y), + params={"linear_tree": True, "zero_as_missing": True}, + num_boost_round=1, + ) + + +def test_predict_with_start_iteration(): + def inner_test(X, y, params, early_stopping_rounds): + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + train_data = lgb.Dataset(X_train, label=y_train) + valid_data = lgb.Dataset(X_test, label=y_test) + callbacks = [lgb.early_stopping(early_stopping_rounds)] if early_stopping_rounds is not None else [] + booster = lgb.train(params, train_data, num_boost_round=50, valid_sets=[valid_data], callbacks=callbacks) + + # test that the predict once with all iterations equals summed results with start_iteration and num_iteration + all_pred = booster.predict(X, raw_score=True) + all_pred_contrib = booster.predict(X, pred_contrib=True) + steps = [10, 12] + for step in steps: + pred = np.zeros_like(all_pred) + pred_contrib = np.zeros_like(all_pred_contrib) + for start_iter in range(0, 50, step): + pred += booster.predict(X, start_iteration=start_iter, num_iteration=step, raw_score=True) + pred_contrib += booster.predict(X, start_iteration=start_iter, num_iteration=step, pred_contrib=True) + np.testing.assert_allclose(all_pred, pred) + np.testing.assert_allclose(all_pred_contrib, pred_contrib) + # test the case where start_iteration <= 0, and num_iteration is None + pred1 = booster.predict(X, start_iteration=-1) + pred2 = booster.predict(X, num_iteration=booster.best_iteration) + np.testing.assert_allclose(pred1, pred2) + + # test the case where start_iteration > 0, and num_iteration <= 0 + pred4 = booster.predict(X, start_iteration=10, num_iteration=-1) + pred5 = booster.predict(X, start_iteration=10, num_iteration=90) + pred6 = booster.predict(X, start_iteration=10, num_iteration=0) + np.testing.assert_allclose(pred4, pred5) + np.testing.assert_allclose(pred4, pred6) + + # test the case where start_iteration > 0, and num_iteration <= 0, with pred_leaf=True + pred4 = booster.predict(X, start_iteration=10, num_iteration=-1, pred_leaf=True) + pred5 = booster.predict(X, start_iteration=10, num_iteration=40, pred_leaf=True) + pred6 = booster.predict(X, start_iteration=10, num_iteration=0, pred_leaf=True) + np.testing.assert_allclose(pred4, pred5) + np.testing.assert_allclose(pred4, pred6) + + # test the case where start_iteration > 0, and num_iteration <= 0, with pred_contrib=True + pred4 = booster.predict(X, start_iteration=10, num_iteration=-1, pred_contrib=True) + pred5 = booster.predict(X, start_iteration=10, num_iteration=40, pred_contrib=True) + pred6 = booster.predict(X, start_iteration=10, num_iteration=0, pred_contrib=True) + np.testing.assert_allclose(pred4, pred5) + np.testing.assert_allclose(pred4, pred6) + + # test for regression + X, y = make_synthetic_regression() + params = {"objective": "regression", "verbose": -1, "metric": "l2", "learning_rate": 0.5} + # test both with and without early stopping + inner_test(X, y, params, early_stopping_rounds=1) + inner_test(X, y, params, early_stopping_rounds=5) + inner_test(X, y, params, early_stopping_rounds=None) + + # test for multi-class + X, y = load_iris(return_X_y=True) + params = {"objective": "multiclass", "num_class": 3, "verbose": -1, "metric": "multi_error"} + # test both with and without early stopping + inner_test(X, y, params, early_stopping_rounds=1) + inner_test(X, y, params, early_stopping_rounds=5) + inner_test(X, y, params, early_stopping_rounds=None) + + # test for binary + X, y = load_breast_cancer(return_X_y=True) + params = {"objective": "binary", "verbose": -1, "metric": "auc"} + # test both with and without early stopping + inner_test(X, y, params, early_stopping_rounds=1) + inner_test(X, y, params, early_stopping_rounds=5) + inner_test(X, y, params, early_stopping_rounds=None) + + +@pytest.mark.parametrize("use_init_score", [False, True]) +def test_predict_stump(rng, use_init_score): + X, y = load_breast_cancer(return_X_y=True) + dataset_kwargs = {"data": X, "label": y} + if use_init_score: + dataset_kwargs.update({"init_score": rng.uniform(size=y.shape)}) + bst = lgb.train( + train_set=lgb.Dataset(**dataset_kwargs), + params={"objective": "binary", "min_data_in_leaf": X.shape[0]}, + num_boost_round=5, + ) + # checking prediction from 1 iteration and the whole model, to prevent bugs + # of the form "a model of n stumps predicts n * initial_score" + preds_1 = bst.predict(X, raw_score=True, num_iteration=1) + preds_all = bst.predict(X, raw_score=True) + if use_init_score: + # if init_score was provided, a model of stumps should predict all 0s + all_zeroes = np.full_like(preds_1, fill_value=0.0) + np.testing.assert_allclose(preds_1, all_zeroes) + np.testing.assert_allclose(preds_all, all_zeroes) + else: + # if init_score was not provided, prediction for a model of stumps should be + # the "average" of the labels + y_avg = np.log(y.mean() / (1.0 - y.mean())) + np.testing.assert_allclose(preds_1, np.full_like(preds_1, fill_value=y_avg)) + np.testing.assert_allclose(preds_all, np.full_like(preds_all, fill_value=y_avg)) + + +def test_predict_regression_output_shape(): + n_samples = 1_000 + n_features = 4 + X, y = make_synthetic_regression(n_samples=n_samples, n_features=n_features) + dtrain = lgb.Dataset(X, label=y) + params = {"objective": "regression", "verbosity": -1} + + # 1-round model + bst = lgb.train(params, dtrain, num_boost_round=1) + assert bst.predict(X).shape == (n_samples,) + assert bst.predict(X, raw_score=True).shape == (n_samples,) + assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1) + assert bst.predict(X, pred_leaf=True).shape == (n_samples, 1) + + # 2-round model + bst = lgb.train(params, dtrain, num_boost_round=2) + assert bst.predict(X).shape == (n_samples,) + assert bst.predict(X, raw_score=True).shape == (n_samples,) + assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1) + assert bst.predict(X, pred_leaf=True).shape == (n_samples, 2) + + +def test_predict_binary_classification_output_shape(): + n_samples = 1_000 + n_features = 4 + X, y = make_classification(n_samples=n_samples, n_features=n_features, n_classes=2) + dtrain = lgb.Dataset(X, label=y) + params = {"objective": "binary", "verbosity": -1} + + # 1-round model + bst = lgb.train(params, dtrain, num_boost_round=1) + assert bst.predict(X).shape == (n_samples,) + assert bst.predict(X, raw_score=True).shape == (n_samples,) + assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1) + assert bst.predict(X, pred_leaf=True).shape == (n_samples, 1) + + # 2-round model + bst = lgb.train(params, dtrain, num_boost_round=2) + assert bst.predict(X).shape == (n_samples,) + assert bst.predict(X, raw_score=True).shape == (n_samples,) + assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_features + 1) + assert bst.predict(X, pred_leaf=True).shape == (n_samples, 2) + + +def test_predict_multiclass_classification_output_shape(): + n_samples = 1_000 + n_features = 10 + n_classes = 3 + X, y = make_classification(n_samples=n_samples, n_features=n_features, n_classes=n_classes, n_informative=6) + dtrain = lgb.Dataset(X, label=y) + params = {"objective": "multiclass", "verbosity": -1, "num_class": n_classes} + + # 1-round model + bst = lgb.train(params, dtrain, num_boost_round=1) + assert bst.predict(X).shape == (n_samples, n_classes) + assert bst.predict(X, raw_score=True).shape == (n_samples, n_classes) + assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_classes * (n_features + 1)) + assert bst.predict(X, pred_leaf=True).shape == (n_samples, n_classes) + + # 2-round model + bst = lgb.train(params, dtrain, num_boost_round=2) + assert bst.predict(X).shape == (n_samples, n_classes) + assert bst.predict(X, raw_score=True).shape == (n_samples, n_classes) + assert bst.predict(X, pred_contrib=True).shape == (n_samples, n_classes * (n_features + 1)) + assert bst.predict(X, pred_leaf=True).shape == (n_samples, n_classes * 2) + + +def test_average_precision_metric(): + # test against sklearn average precision metric + X, y = load_breast_cancer(return_X_y=True) + params = {"objective": "binary", "metric": "average_precision", "verbose": -1} + res = {} + lgb_X = lgb.Dataset(X, label=y) + est = lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(res)]) + ap = res["training"]["average_precision"][-1] + pred = est.predict(X) + sklearn_ap = average_precision_score(y, pred) + assert ap == pytest.approx(sklearn_ap) + # test that average precision is 1 where model predicts perfectly + y = y.copy() + y[:] = 1 + lgb_X = lgb.Dataset(X, label=y) + lgb.train(params, lgb_X, num_boost_round=1, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(res)]) + assert res["training"]["average_precision"][-1] == pytest.approx(1) + + +def test_r2_metric(): + # test against sklearn R2 metric + X, y = make_synthetic_regression() + params = {"objective": "regression", "metric": "r2", "verbose": -1} + res = {} + train_data = lgb.Dataset(X, label=y) + est = lgb.train( + params, train_data, num_boost_round=1, valid_sets=[train_data], callbacks=[lgb.record_evaluation(res)] + ) + r2 = res["training"]["r2"][-1] + pred = est.predict(X) + sklearn_r2 = r2_score(y, pred) + assert r2 == pytest.approx(sklearn_r2) + assert r2 != 0 + assert r2 != 1 + # test that R2 is 1 when y has no variance and the model predicts perfectly + y = y.copy() + y[:] = 1 + lgb_X = lgb.Dataset(X, label=y) + lgb.train(params, lgb_X, num_boost_round=1, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(res)]) + assert res["training"]["r2"][-1] == pytest.approx(1) + + +def test_reset_params_works_with_metric_num_class_and_boosting(): + X, y = load_breast_cancer(return_X_y=True) + dataset_params = {"max_bin": 150} + booster_params = { + "objective": "multiclass", + "max_depth": 4, + "bagging_fraction": 0.8, + "metric": ["multi_logloss", "multi_error"], + "boosting": "gbdt", + "num_class": 5, + } + dtrain = lgb.Dataset(X, y, params=dataset_params) + bst = lgb.Booster(params=booster_params, train_set=dtrain) + + expected_params = dict(dataset_params, **booster_params) + assert bst.params == expected_params + + booster_params["bagging_fraction"] += 0.1 + new_bst = bst.reset_parameter(booster_params) + + expected_params = dict(dataset_params, **booster_params) + assert bst.params == expected_params + assert new_bst.params == expected_params + + +@pytest.mark.parametrize("linear_tree", [False, True]) +def test_dump_model_stump(linear_tree): + X, y = load_breast_cancer(return_X_y=True) + + train_data = lgb.Dataset(X, label=y) + params = {"objective": "binary", "verbose": -1, "linear_tree": linear_tree, "min_data_in_leaf": len(y)} + bst = lgb.train(params, train_data, num_boost_round=5) + dumped_model = bst.dump_model(num_iteration=5, start_iteration=0) + tree_structure = dumped_model["tree_info"][0]["tree_structure"] + assert len(dumped_model["tree_info"]) == 1 + assert "leaf_value" in tree_structure + assert tree_structure["leaf_count"] == len(y) + + +def test_dump_model(): + initial_score_offset = 57.5 + X, y = make_synthetic_regression() + train_data = lgb.Dataset(X, label=y + initial_score_offset) + + params = { + "objective": "regression", + "verbose": -1, + "boost_from_average": True, + } + bst = lgb.train(params, train_data, num_boost_round=5) + dumped_model = bst.dump_model(num_iteration=5, start_iteration=0) + dumped_model_str = str(dumped_model) + assert "leaf_features" not in dumped_model_str + assert "leaf_coeff" not in dumped_model_str + assert "leaf_const" not in dumped_model_str + assert "leaf_value" in dumped_model_str + assert "leaf_count" in dumped_model_str + + for tree in dumped_model["tree_info"]: + assert tree["tree_structure"]["internal_value"] != 0 + + assert dumped_model["tree_info"][0]["tree_structure"]["internal_value"] == pytest.approx( + initial_score_offset, abs=1 + ) + assert_all_trees_valid(dumped_model) + + +def test_dump_model_linear(): + X, y = load_breast_cancer(return_X_y=True) + params = { + "objective": "binary", + "verbose": -1, + "linear_tree": True, + } + train_data = lgb.Dataset(X, label=y) + bst = lgb.train(params, train_data, num_boost_round=5) + dumped_model = bst.dump_model(num_iteration=5, start_iteration=0) + assert_all_trees_valid(dumped_model) + dumped_model_str = str(dumped_model) + assert "leaf_features" in dumped_model_str + assert "leaf_coeff" in dumped_model_str + assert "leaf_const" in dumped_model_str + assert "leaf_value" in dumped_model_str + assert "leaf_count" in dumped_model_str + + +def test_dump_model_hook(): + def hook(obj): + if "leaf_value" in obj: + obj["LV"] = obj["leaf_value"] + del obj["leaf_value"] + return obj + + X, y = load_breast_cancer(return_X_y=True) + train_data = lgb.Dataset(X, label=y) + params = {"objective": "binary", "verbose": -1} + bst = lgb.train(params, train_data, num_boost_round=5) + dumped_model_str = str(bst.dump_model(5, 0, object_hook=hook)) + assert "leaf_value" not in dumped_model_str + assert "LV" in dumped_model_str + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Forced splits are not yet supported by CUDA version") +def test_force_split_with_feature_fraction(tmp_path): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + + forced_split = {"feature": 0, "threshold": 0.5, "right": {"feature": 2, "threshold": 10.0}} + + tmp_split_file = tmp_path / "forced_split.json" + with open(tmp_split_file, "w") as f: + f.write(json.dumps(forced_split)) + + params = { + "objective": "regression", + "feature_fraction": 0.6, + "force_col_wise": True, + "feature_fraction_seed": 1, + "forcedsplits_filename": tmp_split_file, + } + + gbm = lgb.train(params, lgb_train) + ret = mean_absolute_error(y_test, gbm.predict(X_test)) + assert ret < 15.7 + + tree_info = gbm.dump_model()["tree_info"] + assert len(tree_info) > 1 + for tree in tree_info: + tree_structure = tree["tree_structure"] + assert tree_structure["split_feature"] == 0 + + +def test_goss_boosting_and_strategy_equivalent(): + X, y = make_synthetic_regression(n_samples=10_000, n_features=10, n_informative=5, random_state=42) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + base_params = { + "metric": "l2", + "verbose": -1, + "bagging_seed": 0, + "learning_rate": 0.05, + "num_threads": 1, + "force_row_wise": True, + "gpu_use_dp": True, + } + params1 = {**base_params, "boosting": "goss"} + evals_result1 = {} + lgb.train( + params1, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result1)] + ) + params2 = {**base_params, "data_sample_strategy": "goss"} + evals_result2 = {} + lgb.train( + params2, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result2)] + ) + assert evals_result1["valid_0"]["l2"] == evals_result2["valid_0"]["l2"] + + +def test_sample_strategy_with_boosting(): + X, y = make_synthetic_regression(n_samples=10_000, n_features=10, n_informative=5, random_state=42) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + lgb_train = lgb.Dataset(X_train, y_train) + lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) + + base_params = { + "metric": "l2", + "verbose": -1, + "num_threads": 1, + "force_row_wise": True, + "gpu_use_dp": True, + } + + params1 = {**base_params, "boosting": "dart", "data_sample_strategy": "goss"} + evals_result = {} + gbm = lgb.train( + params1, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + eval_res1 = evals_result["valid_0"]["l2"][-1] + test_res1 = mean_squared_error(y_test, gbm.predict(X_test)) + assert test_res1 == pytest.approx(3149.393862, abs=1.0) + assert eval_res1 == pytest.approx(test_res1) + + params2 = {**base_params, "boosting": "gbdt", "data_sample_strategy": "goss"} + evals_result = {} + gbm = lgb.train( + params2, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + eval_res2 = evals_result["valid_0"]["l2"][-1] + test_res2 = mean_squared_error(y_test, gbm.predict(X_test)) + assert test_res2 == pytest.approx(2547.715968, abs=1.0) + assert eval_res2 == pytest.approx(test_res2) + + params3 = {**base_params, "boosting": "goss", "data_sample_strategy": "goss"} + evals_result = {} + gbm = lgb.train( + params3, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + eval_res3 = evals_result["valid_0"]["l2"][-1] + test_res3 = mean_squared_error(y_test, gbm.predict(X_test)) + assert test_res3 == pytest.approx(2547.715968, abs=1.0) + assert eval_res3 == pytest.approx(test_res3) + + params4 = {**base_params, "boosting": "rf", "data_sample_strategy": "goss"} + evals_result = {} + gbm = lgb.train( + params4, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + eval_res4 = evals_result["valid_0"]["l2"][-1] + test_res4 = mean_squared_error(y_test, gbm.predict(X_test)) + assert test_res4 == pytest.approx(2095.538735, abs=1.0) + assert eval_res4 == pytest.approx(test_res4) + + assert test_res1 != test_res2 + assert eval_res1 != eval_res2 + assert test_res2 == test_res3 + assert eval_res2 == eval_res3 + assert eval_res1 != eval_res4 + assert test_res1 != test_res4 + assert eval_res2 != eval_res4 + assert test_res2 != test_res4 + + params5 = { + **base_params, + "boosting": "dart", + "data_sample_strategy": "bagging", + "bagging_freq": 1, + "bagging_fraction": 0.5, + } + evals_result = {} + gbm = lgb.train( + params5, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + eval_res5 = evals_result["valid_0"]["l2"][-1] + test_res5 = mean_squared_error(y_test, gbm.predict(X_test)) + assert test_res5 == pytest.approx(3134.866931, abs=1.0) + assert eval_res5 == pytest.approx(test_res5) + + params6 = { + **base_params, + "boosting": "gbdt", + "data_sample_strategy": "bagging", + "bagging_freq": 1, + "bagging_fraction": 0.5, + } + evals_result = {} + gbm = lgb.train( + params6, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + eval_res6 = evals_result["valid_0"]["l2"][-1] + test_res6 = mean_squared_error(y_test, gbm.predict(X_test)) + assert test_res6 == pytest.approx(2539.792378, abs=1.0) + assert eval_res6 == pytest.approx(test_res6) + assert test_res5 != test_res6 + assert eval_res5 != eval_res6 + + params7 = { + **base_params, + "boosting": "rf", + "data_sample_strategy": "bagging", + "bagging_freq": 1, + "bagging_fraction": 0.5, + } + evals_result = {} + gbm = lgb.train( + params7, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)] + ) + eval_res7 = evals_result["valid_0"]["l2"][-1] + test_res7 = mean_squared_error(y_test, gbm.predict(X_test)) + assert test_res7 == pytest.approx(1518.704481, abs=1.0) + assert eval_res7 == pytest.approx(test_res7) + assert test_res5 != test_res7 + assert eval_res5 != eval_res7 + assert test_res6 != test_res7 + assert eval_res6 != eval_res7 + + +def test_record_evaluation_with_train(): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, y) + eval_result = {} + callbacks = [lgb.record_evaluation(eval_result)] + params = {"objective": "l2", "num_leaves": 3} + num_boost_round = 5 + bst = lgb.train(params, ds, num_boost_round=num_boost_round, valid_sets=[ds], callbacks=callbacks) + assert list(eval_result.keys()) == ["training"] + train_mses = [] + for i in range(num_boost_round): + pred = bst.predict(X, num_iteration=i + 1) + mse = mean_squared_error(y, pred) + train_mses.append(mse) + np.testing.assert_allclose(eval_result["training"]["l2"], train_mses) + + +@pytest.mark.parametrize("train_metric", [False, True]) +def test_record_evaluation_with_cv(train_metric): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, y) + eval_result = {} + callbacks = [lgb.record_evaluation(eval_result)] + metrics = ["l2", "rmse"] + params = {"objective": "l2", "num_leaves": 3, "metric": metrics} + cv_hist = lgb.cv( + params, ds, num_boost_round=5, stratified=False, callbacks=callbacks, eval_train_metric=train_metric + ) + expected_datasets = {"valid"} + if train_metric: + expected_datasets.add("train") + assert set(eval_result.keys()) == expected_datasets + for dataset in expected_datasets: + for metric in metrics: + for agg in ("mean", "stdv"): + key = f"{dataset} {metric}-{agg}" + np.testing.assert_allclose(cv_hist[key], eval_result[dataset][f"{metric}-{agg}"]) + + +def test_pandas_with_numpy_regular_dtypes(rng_fixed_seed): + pd = pytest.importorskip("pandas") + uints = ["uint8", "uint16", "uint32", "uint64"] + ints = ["int8", "int16", "int32", "int64"] + bool_and_floats = ["bool", "float16", "float32", "float64"] + + n_samples = 100 + # data as float64 + df = pd.DataFrame( + { + "x1": rng_fixed_seed.integers(low=0, high=2, size=n_samples), + "x2": rng_fixed_seed.integers(low=1, high=3, size=n_samples), + "x3": 10 * rng_fixed_seed.integers(low=1, high=3, size=n_samples), + "x4": 100 * rng_fixed_seed.integers(low=1, high=3, size=n_samples), + } + ) + df = df.astype(np.float64) + y = df["x1"] * (df["x2"] + df["x3"] + df["x4"]) + ds = lgb.Dataset(df, y) + params = {"objective": "l2", "num_leaves": 31, "min_child_samples": 1} + bst = lgb.train(params, ds, num_boost_round=5) + preds = bst.predict(df) + + # test all features were used + assert bst.trees_to_dataframe()["split_feature"].nunique() == df.shape[1] + # test the score is better than predicting the mean + baseline = np.full_like(y, y.mean()) + assert mean_squared_error(y, preds) < mean_squared_error(y, baseline) + + # test all predictions are equal using different input dtypes + for target_dtypes in [uints, ints, bool_and_floats]: + df2 = df.astype({f"x{i}": dtype for i, dtype in enumerate(target_dtypes, start=1)}) + assert df2.dtypes.tolist() == target_dtypes + ds2 = lgb.Dataset(df2, y) + bst2 = lgb.train(params, ds2, num_boost_round=5) + preds2 = bst2.predict(df2) + np.testing.assert_allclose(preds, preds2) + + +def test_pandas_nullable_dtypes(rng_fixed_seed): + pd = pytest.importorskip("pandas") + df = pd.DataFrame( + { + "x1": rng_fixed_seed.integers(low=1, high=3, size=100), + "x2": np.linspace(-1, 1, 100), + "x3": pd.arrays.SparseArray(rng_fixed_seed.integers(low=0, high=11, size=100)), + "x4": rng_fixed_seed.uniform(size=(100,)) < 0.5, + } + ) + # introduce some missing values + df.loc[1, "x1"] = np.nan + df.loc[2, "x2"] = np.nan + # in recent versions of pandas, type 'bool' is incompatible with nan values in x4 + df["x4"] = df["x4"].astype(np.float64) + df.loc[3, "x4"] = np.nan + y = df["x1"] * df["x2"] + df["x3"] * (1 + df["x4"]) + y = y.fillna(0) + + # train with regular dtypes + params = {"objective": "l2", "num_leaves": 31, "min_child_samples": 1} + ds = lgb.Dataset(df, y) + bst = lgb.train(params, ds, num_boost_round=5) + preds = bst.predict(df) + + # convert to nullable dtypes + df2 = df.copy() + df2["x1"] = df2["x1"].astype("Int32") + df2["x2"] = df2["x2"].astype("Float64") + df2["x4"] = df2["x4"].astype("boolean") + + # test training succeeds + ds_nullable_dtypes = lgb.Dataset(df2, y) + bst_nullable_dtypes = lgb.train(params, ds_nullable_dtypes, num_boost_round=5) + preds_nullable_dtypes = bst_nullable_dtypes.predict(df2) + + trees_df = bst_nullable_dtypes.trees_to_dataframe() + # test all features were used + assert trees_df["split_feature"].nunique() == df.shape[1] + # test the score is better than predicting the mean + baseline = np.full_like(y, y.mean()) + assert mean_squared_error(y, preds) < mean_squared_error(y, baseline) + + # test equal predictions + np.testing.assert_allclose(preds, preds_nullable_dtypes) + + +def test_boost_from_average_with_single_leaf_trees(): + # test data are taken from bug report + # https://github.com/lightgbm-org/LightGBM/issues/4708 + X = np.array( + [ + [1021.0589, 1018.9578], + [1023.85754, 1018.7854], + [1024.5468, 1018.88513], + [1019.02954, 1018.88513], + [1016.79926, 1018.88513], + [1007.6, 1018.88513], + ], + dtype=np.float32, + ) + y = np.array([1023.8, 1024.6, 1024.4, 1023.8, 1022.0, 1014.4], dtype=np.float32) + params = { + "extra_trees": True, + "min_data_in_bin": 1, + "extra_seed": 7, + "objective": "regression", + "verbose": -1, + "boost_from_average": True, + "min_data_in_leaf": 1, + } + train_set = lgb.Dataset(X, y) + model = lgb.train(params=params, train_set=train_set, num_boost_round=10) + + preds = model.predict(X) + mean_preds = np.mean(preds) + assert y.min() <= mean_preds <= y.max() + + +def test_cegb_split_buffer_clean(rng_fixed_seed): + # modified from https://github.com/lightgbm-org/LightGBM/issues/3679#issuecomment-938652811 + # and https://github.com/lightgbm-org/LightGBM/pull/5087 + # test that the ``splits_per_leaf_`` of CEGB is cleaned before training a new tree + # which is done in the fix #5164 + # without the fix: + # Check failed: (best_split_info.left_count) > (0) + + R, C = 1000, 100 + data = rng_fixed_seed.standard_normal(size=(R, C)) + for i in range(1, C): + data[i] += data[0] * rng_fixed_seed.standard_normal() + + N = int(0.8 * len(data)) + train_data = data[:N] + test_data = data[N:] + train_y = np.sum(train_data, axis=1) + test_y = np.sum(test_data, axis=1) + + train = lgb.Dataset(train_data, train_y, free_raw_data=True) + + params = { + "boosting_type": "gbdt", + "objective": "regression", + "max_bin": 255, + "num_leaves": 31, + "seed": 0, + "learning_rate": 0.1, + "min_data_in_leaf": 0, + "verbose": -1, + "min_split_gain": 1000.0, + "cegb_penalty_feature_coupled": 5 * np.arange(C), + "cegb_penalty_split": 0.0002, + "cegb_tradeoff": 10.0, + "force_col_wise": True, + } + + model = lgb.train(params, train, num_boost_round=10) + predicts = model.predict(test_data) + rmse = np.sqrt(mean_squared_error(test_y, predicts)) + assert rmse < 10.0 + + +def test_verbosity_and_verbose(capsys): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, y) + params = { + "num_leaves": 3, + "verbose": 1, + "verbosity": 0, + } + lgb.train(params, ds, num_boost_round=1) + expected_msg = "[LightGBM] [Warning] verbosity is set=0, verbose=1 will be ignored. Current value: verbosity=0" + stdout = capsys.readouterr().out + assert expected_msg in stdout + + +def test_verbosity_is_respected_when_using_custom_objective(capsys): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, y) + params = { + "objective": mse_obj, + "nonsense": 123, + "num_leaves": 3, + } + lgb.train({**params, "verbosity": -1}, ds, num_boost_round=1) + assert_silent(capsys) + lgb.train({**params, "verbosity": 0}, ds, num_boost_round=1) + assert "[LightGBM] [Warning] Unknown parameter: nonsense" in capsys.readouterr().out + + +@pytest.mark.parametrize("verbosity_param", lgb.basic._ConfigAliases.get("verbosity")) +@pytest.mark.parametrize("verbosity", [-1, 0]) +def test_verbosity_can_suppress_alias_warnings(capsys, verbosity_param, verbosity): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, y) + params = { + "num_leaves": 3, + "subsample": 0.75, + "bagging_fraction": 0.8, + "force_col_wise": True, + verbosity_param: verbosity, + } + lgb.train(params, ds, num_boost_round=1) + expected_msg = ( + "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=0.75 will be ignored. " + "Current value: bagging_fraction=0.8" + ) + stdout = capsys.readouterr().out + if verbosity >= 0: + assert expected_msg in stdout + else: + assert re.search(r"\[LightGBM\]", stdout) is None + + +def test_cv_only_raises_num_rounds_warning_when_expected(capsys): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, y) + base_params = { + "num_leaves": 5, + "objective": "regression", + "verbosity": -1, + } + additional_kwargs = {"return_cvbooster": True, "stratified": False} + + # no warning: no aliases, all defaults + cv_bst = lgb.cv({**base_params}, ds, **additional_kwargs) + assert all(t == 100 for t in cv_bst["cvbooster"].num_trees()) + assert_silent(capsys) + + # no warning: no aliases, just num_boost_round + cv_bst = lgb.cv({**base_params}, ds, num_boost_round=2, **additional_kwargs) + assert all(t == 2 for t in cv_bst["cvbooster"].num_trees()) + assert_silent(capsys) + + # no warning: 1 alias + num_boost_round (both same value) + cv_bst = lgb.cv({**base_params, "n_iter": 3}, ds, num_boost_round=3, **additional_kwargs) + assert all(t == 3 for t in cv_bst["cvbooster"].num_trees()) + assert_silent(capsys) + + # no warning: 1 alias + num_boost_round (different values... value from params should win) + cv_bst = lgb.cv({**base_params, "n_iter": 4}, ds, num_boost_round=3, **additional_kwargs) + assert all(t == 4 for t in cv_bst["cvbooster"].num_trees()) + assert_silent(capsys) + + # no warning: 2 aliases (both same value) + cv_bst = lgb.cv({**base_params, "n_iter": 3, "num_iterations": 3}, ds, **additional_kwargs) + assert all(t == 3 for t in cv_bst["cvbooster"].num_trees()) + assert_silent(capsys) + + # no warning: 4 aliases (all same value) + cv_bst = lgb.cv({**base_params, "n_iter": 3, "num_trees": 3, "nrounds": 3, "max_iter": 3}, ds, **additional_kwargs) + assert all(t == 3 for t in cv_bst["cvbooster"].num_trees()) + assert_silent(capsys) + + # warning: 2 aliases (different values... "num_iterations" wins because it's the main param name) + with pytest.warns(UserWarning, match="LightGBM will perform up to 5 boosting rounds"): + cv_bst = lgb.cv({**base_params, "n_iter": 6, "num_iterations": 5}, ds, **additional_kwargs) + assert all(t == 5 for t in cv_bst["cvbooster"].num_trees()) + # should not be any other logs (except the warning, intercepted by pytest) + assert_silent(capsys) + + # warning: 2 aliases (different values... first one in the order from Config::parameter2aliases() wins) + with pytest.warns(UserWarning, match="LightGBM will perform up to 4 boosting rounds"): + cv_bst = lgb.cv({**base_params, "n_iter": 4, "max_iter": 5}, ds, **additional_kwargs)["cvbooster"] + assert all(t == 4 for t in cv_bst.num_trees()) + # should not be any other logs (except the warning, intercepted by pytest) + assert_silent(capsys) + + +def test_train_only_raises_num_rounds_warning_when_expected(capsys): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, y) + base_params = { + "num_leaves": 5, + "objective": "regression", + "verbosity": -1, + } + + # no warning: no aliases, all defaults + bst = lgb.train({**base_params}, ds) + assert bst.num_trees() == 100 + assert_silent(capsys) + + # no warning: no aliases, just num_boost_round + bst = lgb.train({**base_params}, ds, num_boost_round=2) + assert bst.num_trees() == 2 + assert_silent(capsys) + + # no warning: 1 alias + num_boost_round (both same value) + bst = lgb.train({**base_params, "n_iter": 3}, ds, num_boost_round=3) + assert bst.num_trees() == 3 + assert_silent(capsys) + + # no warning: 1 alias + num_boost_round (different values... value from params should win) + bst = lgb.train({**base_params, "n_iter": 4}, ds, num_boost_round=3) + assert bst.num_trees() == 4 + assert_silent(capsys) + + # no warning: 2 aliases (both same value) + bst = lgb.train({**base_params, "n_iter": 3, "num_iterations": 3}, ds) + assert bst.num_trees() == 3 + assert_silent(capsys) + + # no warning: 4 aliases (all same value) + bst = lgb.train({**base_params, "n_iter": 3, "num_trees": 3, "nrounds": 3, "max_iter": 3}, ds) + assert bst.num_trees() == 3 + assert_silent(capsys) + + # warning: 2 aliases (different values... "num_iterations" wins because it's the main param name) + with pytest.warns(UserWarning, match="LightGBM will perform up to 5 boosting rounds"): + bst = lgb.train({**base_params, "n_iter": 6, "num_iterations": 5}, ds) + assert bst.num_trees() == 5 + # should not be any other logs (except the warning, intercepted by pytest) + assert_silent(capsys) + + # warning: 2 aliases (different values... first one in the order from Config::parameter2aliases() wins) + with pytest.warns(UserWarning, match="LightGBM will perform up to 4 boosting rounds"): + bst = lgb.train({**base_params, "n_iter": 4, "max_iter": 5}, ds) + assert bst.num_trees() == 4 + # should not be any other logs (except the warning, intercepted by pytest) + assert_silent(capsys) + + +def test_validate_features(): + pd = pytest.importorskip("pandas") + X, y = make_synthetic_regression() + features = ["x1", "x2", "x3", "x4"] + df = pd.DataFrame(X, columns=features) + ds = lgb.Dataset(df, y) + bst = lgb.train({"num_leaves": 15, "verbose": -1}, ds, num_boost_round=10) + assert bst.feature_name() == features + + # try to predict with a different feature + df2 = df.rename(columns={"x3": "z"}) + with pytest.raises(lgb.basic.LightGBMError, match="Expected 'x3' at position 2 but found 'z'"): + bst.predict(df2, validate_features=True) + + # check that disabling the check doesn't raise the error + bst.predict(df2, validate_features=False) + + # try to refit with a different feature + with pytest.raises(lgb.basic.LightGBMError, match="Expected 'x3' at position 2 but found 'z'"): + bst.refit(df2, y, validate_features=True) + + # check that disabling the check doesn't raise the error + bst.refit(df2, y, validate_features=False) + + +def test_train_and_cv_raise_informative_error_for_train_set_of_wrong_type(): + with pytest.raises(TypeError, match=r"train\(\) only accepts Dataset object, train_set has type 'list'\."): + lgb.train({}, train_set=[]) + with pytest.raises(TypeError, match=r"cv\(\) only accepts Dataset object, train_set has type 'list'\."): + lgb.cv({}, train_set=[]) + + +@pytest.mark.parametrize("num_boost_round", [-7, -1, 0]) +def test_train_and_cv_raise_informative_error_for_impossible_num_boost_round(num_boost_round): + X, y = make_synthetic_regression(n_samples=100) + error_msg = rf"Number of boosting rounds must be greater than 0\. Got {num_boost_round}\." + with pytest.raises(ValueError, match=error_msg): + lgb.train({}, train_set=lgb.Dataset(X, y), num_boost_round=num_boost_round) + with pytest.raises(ValueError, match=error_msg): + lgb.cv({}, train_set=lgb.Dataset(X, y), num_boost_round=num_boost_round) + + +def test_train_raises_informative_error_if_any_valid_sets_are_not_dataset_objects(): + X, y = make_synthetic_regression(n_samples=100) + X_valid = X * 2.0 + with pytest.raises( + TypeError, match=r"Every item in valid_sets must be a Dataset object\. Item 1 has type 'tuple'\." + ): + lgb.train( + params={}, + train_set=lgb.Dataset(X, y), + valid_sets=[lgb.Dataset(X_valid, y), ([1.0], [2.0]), [5.6, 5.7, 5.8]], + ) + + +def test_train_raises_informative_error_for_params_of_wrong_type(): + X, y = make_synthetic_regression() + params = {"num_leaves": "too-many"} + dtrain = lgb.Dataset(X, label=y) + with pytest.raises(lgb.basic.LightGBMError, match='Parameter num_leaves should be of type int, got "too-many"'): + lgb.train(params, dtrain) + + +def test_quantized_training(): + X, y = make_synthetic_regression() + ds = lgb.Dataset(X, label=y) + bst_params = {"num_leaves": 15, "verbose": -1, "seed": 0} + bst = lgb.train(bst_params, ds, num_boost_round=10) + rmse = np.sqrt(np.mean((bst.predict(X) - y) ** 2)) + bst_params.update( + { + "use_quantized_grad": True, + "num_grad_quant_bins": 30, + "quant_train_renew_leaf": True, + } + ) + quant_bst = lgb.train(bst_params, ds, num_boost_round=10) + quant_rmse = np.sqrt(np.mean((quant_bst.predict(X) - y) ** 2)) + assert quant_rmse < rmse + 6.0 + + +def test_bagging_by_query_in_lambdarank(): + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train")) + q_train = np.loadtxt(str(rank_example_dir / "rank.train.query")) + X_test, y_test = load_svmlight_file(str(rank_example_dir / "rank.test")) + q_test = np.loadtxt(str(rank_example_dir / "rank.test.query")) + params = {"objective": "lambdarank", "verbose": -1, "metric": "ndcg", "ndcg_eval_at": [5]} + lgb_train = lgb.Dataset(X_train, y_train, group=q_train, params=params) + lgb_test = lgb.Dataset(X_test, y_test, group=q_test, params=params) + gbm = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=[lgb_test]) + ndcg_score = gbm.best_score["valid_0"]["ndcg@5"] + + params.update({"bagging_by_query": True, "bagging_fraction": 0.1, "bagging_freq": 1}) + gbm_bagging_by_query = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=[lgb_test]) + ndcg_score_bagging_by_query = gbm_bagging_by_query.best_score["valid_0"]["ndcg@5"] + + params.update({"bagging_by_query": False, "bagging_fraction": 0.1, "bagging_freq": 1}) + gbm_no_bagging_by_query = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=[lgb_test]) + ndcg_score_no_bagging_by_query = gbm_no_bagging_by_query.best_score["valid_0"]["ndcg@5"] + assert ndcg_score_bagging_by_query >= ndcg_score - 0.1 + assert ndcg_score_no_bagging_by_query >= ndcg_score - 0.1 + + +def test_equal_predict_from_row_major_and_col_major_data(): + X_row, y = make_synthetic_regression() + assert X_row.flags["C_CONTIGUOUS"] + assert not X_row.flags["F_CONTIGUOUS"] + ds = lgb.Dataset(X_row, y) + params = {"num_leaves": 8, "verbose": -1} + bst = lgb.train(params, ds, num_boost_round=5) + preds_row = bst.predict(X_row) + + X_col = np.asfortranarray(X_row) + assert X_col.flags["F_CONTIGUOUS"] + assert not X_col.flags["C_CONTIGUOUS"] + preds_col = bst.predict(X_col) + + np.testing.assert_allclose(preds_row, preds_col) diff --git a/tests/python_package_test/test_plotting.py b/tests/python_package_test/test_plotting.py new file mode 100644 index 0000000..0bfbe4b --- /dev/null +++ b/tests/python_package_test/test_plotting.py @@ -0,0 +1,631 @@ +# coding: utf-8 +import numpy as np +import pandas as pd +import pytest +from sklearn.model_selection import train_test_split + +import lightgbm as lgb + +from .utils import load_breast_cancer, make_synthetic_regression + + +@pytest.fixture(scope="function") +def matplotlib(): + mpl = pytest.importorskip("matplotlib") + # use non-interactive, in-memory renderer + mpl.use("Agg") + return mpl + + +@pytest.fixture(scope="module") +def breast_cancer_split(): + return train_test_split(*load_breast_cancer(return_X_y=True), test_size=0.1, random_state=1) + + +def _categorical_data(category_values_lower_bound, category_values_upper_bound): + X, y = load_breast_cancer(return_X_y=True) + X_df = pd.DataFrame() + rnd = np.random.RandomState(0) + n_cat_values = rnd.randint(category_values_lower_bound, category_values_upper_bound, size=X.shape[1]) + for i in range(X.shape[1]): + bins = np.linspace(0, 1, num=n_cat_values[i] + 1) + X_df[f"cat_col_{i}"] = pd.qcut(X[:, i], q=bins, labels=range(n_cat_values[i])).as_unordered() + return X_df, y + + +@pytest.fixture(scope="module") +def train_data(breast_cancer_split): + X_train, _, y_train, _ = breast_cancer_split + return lgb.Dataset(X_train, y_train) + + +@pytest.fixture +def params(): + return {"objective": "binary", "verbose": -1, "num_leaves": 3} + + +def test_plot_importance(params, breast_cancer_split, train_data, matplotlib): + X_train, _, y_train, _ = breast_cancer_split + + gbm0 = lgb.train(params, train_data, num_boost_round=10) + ax0 = lgb.plot_importance(gbm0) + assert isinstance(ax0, matplotlib.axes.Axes) + assert ax0.get_title() == "Feature importance" + assert ax0.get_xlabel() == "Feature importance" + assert ax0.get_ylabel() == "Features" + assert len(ax0.patches) <= 30 + + gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1) + gbm1.fit(X_train, y_train) + + ax1 = lgb.plot_importance(gbm1, color="r", title="t", xlabel="x", ylabel="y") + assert isinstance(ax1, matplotlib.axes.Axes) + assert ax1.get_title() == "t" + assert ax1.get_xlabel() == "x" + assert ax1.get_ylabel() == "y" + assert len(ax1.patches) <= 30 + for patch in ax1.patches: + assert patch.get_facecolor() == (1.0, 0, 0, 1.0) # red + + ax2 = lgb.plot_importance(gbm0, color=["r", "y", "g", "b"], title=None, xlabel=None, ylabel=None) + assert isinstance(ax2, matplotlib.axes.Axes) + assert ax2.get_title() == "" + assert ax2.get_xlabel() == "" + assert ax2.get_ylabel() == "" + assert len(ax2.patches) <= 30 + assert ax2.patches[0].get_facecolor() == (1.0, 0, 0, 1.0) # r + assert ax2.patches[1].get_facecolor() == (0.75, 0.75, 0, 1.0) # y + assert ax2.patches[2].get_facecolor() == (0, 0.5, 0, 1.0) # g + assert ax2.patches[3].get_facecolor() == (0, 0, 1.0, 1.0) # b + + ax3 = lgb.plot_importance( + gbm0, title="t @importance_type@", xlabel="x @importance_type@", ylabel="y @importance_type@" + ) + assert isinstance(ax3, matplotlib.axes.Axes) + assert ax3.get_title() == "t @importance_type@" + assert ax3.get_xlabel() == "x split" + assert ax3.get_ylabel() == "y @importance_type@" + assert len(ax3.patches) <= 30 + + ax4 = lgb.plot_importance(gbm0, title=None, xlabel=None, ylabel=None, xlim=(0, 30)) + assert isinstance(ax4, matplotlib.axes.Axes) + assert ax4.get_title() == "" + assert ax4.get_xlabel() == "" + assert ax4.get_ylabel() == "" + assert ax4.get_xlim() == (0, 30) + assert len(ax4.patches) <= 30 + + with pytest.raises(TypeError, match="xlim must be a tuple of 2 elements."): + lgb.plot_importance(gbm0, title=None, xlabel=None, ylabel=None, xlim="not a tuple") + + ax5 = lgb.plot_importance(gbm0, title=None, xlabel=None, ylabel=None, ylim=(0, 30)) + assert isinstance(ax5, matplotlib.axes.Axes) + assert ax5.get_title() == "" + assert ax5.get_xlabel() == "" + assert ax5.get_ylabel() == "" + assert ax5.get_ylim() == (0, 30) + assert len(ax5.patches) <= 30 + + with pytest.raises(TypeError, match="ylim must be a tuple of 2 elements."): + lgb.plot_importance(gbm0, title=None, xlabel=None, ylabel=None, ylim="not a tuple") + + ax6 = lgb.plot_importance(gbm0, title=None, xlabel=None, ylabel=None, figsize=(0, 30)) + assert isinstance(ax6, matplotlib.axes.Axes) + assert ax6.get_title() == "" + assert ax6.get_xlabel() == "" + assert ax6.get_ylabel() == "" + assert list(ax6.get_figure().get_size_inches()) == [0, 30] + assert len(ax6.patches) <= 30 + + with pytest.raises(TypeError, match="figsize must be a tuple of 2 elements."): + lgb.plot_importance(gbm0, title=None, xlabel=None, ylabel=None, figsize="not a tuple") + + # test max_num_features parameter + total_features = len(gbm0.feature_importance()) + assert total_features > 5, "model must have more than 5 features to test max_num_features" + ax7 = lgb.plot_importance(gbm0, max_num_features=5) + assert isinstance(ax7, matplotlib.axes.Axes) + assert len(ax7.patches) == 5 + # verify the 5 displayed features are the top 5 by importance + importance = gbm0.feature_importance() + feature_names = gbm0.feature_name() + sorted_pairs = sorted(zip(feature_names, importance, strict=True), key=lambda x: x[1]) + top5_names = [name for name, _ in sorted_pairs[-5:]] + displayed_labels = [label.get_text() for label in ax7.get_yticklabels()] + assert displayed_labels == top5_names + + gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1, importance_type="gain") + gbm2.fit(X_train, y_train) + + def get_bounds_of_first_patch(axes): + return axes.patches[0].get_extents().bounds + + first_bar1 = get_bounds_of_first_patch(lgb.plot_importance(gbm1)) + first_bar2 = get_bounds_of_first_patch(lgb.plot_importance(gbm1, importance_type="split")) + first_bar3 = get_bounds_of_first_patch(lgb.plot_importance(gbm1, importance_type="gain")) + first_bar4 = get_bounds_of_first_patch(lgb.plot_importance(gbm2)) + first_bar5 = get_bounds_of_first_patch(lgb.plot_importance(gbm2, importance_type="split")) + first_bar6 = get_bounds_of_first_patch(lgb.plot_importance(gbm2, importance_type="gain")) + + assert first_bar1 == first_bar2 + assert first_bar1 == first_bar5 + assert first_bar3 == first_bar4 + assert first_bar3 == first_bar6 + assert first_bar1 != first_bar3 + + +def test_plot_importance_zero_splits(matplotlib): + X, y = load_breast_cancer(return_X_y=True) + model = lgb.train( + params={ + "min_data_in_bin": X.shape[0] + 1, + "objective": "regression", + "verbose": -1, + }, + train_set=lgb.Dataset(X, label=y), + num_boost_round=1, + ) + with pytest.raises(ValueError, match="No non-zero feature importances found"): + lgb.plot_importance(model) + # ignore_zero=False should still produce a valid plot + ax = lgb.plot_importance(model, ignore_zero=False) + assert isinstance(ax, matplotlib.axes.Axes) + assert len(ax.patches) == X.shape[1] + + +def test_plot_split_value_histogram(params, breast_cancer_split, train_data, matplotlib): + X_train, _, y_train, _ = breast_cancer_split + + gbm0 = lgb.train(params, train_data, num_boost_round=10) + ax0 = lgb.plot_split_value_histogram(gbm0, 27) + assert isinstance(ax0, matplotlib.axes.Axes) + assert ax0.get_title() == "Split value histogram for feature with index 27" + assert ax0.get_xlabel() == "Feature split value" + assert ax0.get_ylabel() == "Count" + assert len(ax0.patches) <= 2 + + gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1) + gbm1.fit(X_train, y_train) + + ax1 = lgb.plot_split_value_histogram( + gbm1, + gbm1.booster_.feature_name()[27], + figsize=(10, 5), + title="Histogram for feature @index/name@ @feature@", + xlabel="x", + ylabel="y", + color="r", + ) + assert isinstance(ax1, matplotlib.axes.Axes) + title = f"Histogram for feature name {gbm1.booster_.feature_name()[27]}" + assert ax1.get_title() == title + assert ax1.get_xlabel() == "x" + assert ax1.get_ylabel() == "y" + assert len(ax1.patches) <= 2 + for patch in ax1.patches: + assert patch.get_facecolor() == (1.0, 0, 0, 1.0) # red + + ax2 = lgb.plot_split_value_histogram( + gbm0, 27, bins=10, color=["r", "y", "g", "b"], title=None, xlabel=None, ylabel=None + ) + assert isinstance(ax2, matplotlib.axes.Axes) + assert ax2.get_title() == "" + assert ax2.get_xlabel() == "" + assert ax2.get_ylabel() == "" + assert len(ax2.patches) == 10 + assert ax2.patches[0].get_facecolor() == (1.0, 0, 0, 1.0) # r + assert ax2.patches[1].get_facecolor() == (0.75, 0.75, 0, 1.0) # y + assert ax2.patches[2].get_facecolor() == (0, 0.5, 0, 1.0) # g + assert ax2.patches[3].get_facecolor() == (0, 0, 1.0, 1.0) # b + + # test xlim parameter + ax3 = lgb.plot_split_value_histogram(gbm0, 27, xlim=(0, 100), title=None, xlabel=None, ylabel=None) + assert isinstance(ax3, matplotlib.axes.Axes) + assert ax3.get_title() == "" + assert ax3.get_xlabel() == "" + assert ax3.get_ylabel() == "" + assert ax3.get_xlim() == (0, 100) + + with pytest.raises(TypeError, match="xlim must be a tuple of 2 elements."): + lgb.plot_split_value_histogram(gbm0, 27, xlim="not a tuple") + + ax4 = lgb.plot_split_value_histogram(gbm0, 27, ylim=(0, 100), title=None, xlabel=None, ylabel=None) + assert isinstance(ax4, matplotlib.axes.Axes) + assert ax4.get_title() == "" + assert ax4.get_xlabel() == "" + assert ax4.get_ylabel() == "" + assert ax4.get_ylim() == (0, 100) + + with pytest.raises(TypeError, match="ylim must be a tuple of 2 elements."): + lgb.plot_split_value_histogram(gbm0, 27, ylim="not a tuple") + + with pytest.raises( + ValueError, match="Cannot plot split value histogram, because feature 0 was not used in splitting" + ): + lgb.plot_split_value_histogram(gbm0, 0) # was not used in splitting + + +def test_plot_tree(breast_cancer_split, matplotlib): + pytest.importorskip("graphviz") + X_train, _, y_train, _ = breast_cancer_split + gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1) + gbm.fit(X_train, y_train) + + with pytest.raises(IndexError, match="tree_index is out of range."): + lgb.plot_tree(gbm, tree_index=83) + + ax = lgb.plot_tree(gbm, tree_index=3, figsize=(15, 8), show_info=["split_gain"]) + assert isinstance(ax, matplotlib.axes.Axes) + w, h = ax.axes.get_figure().get_size_inches() + assert int(w) == 15 + assert int(h) == 8 + + +def test_create_tree_digraph(tmp_path, breast_cancer_split): + graphviz = pytest.importorskip("graphviz") + X_train, _, y_train, _ = breast_cancer_split + + constraints = [-1, 1] * int(X_train.shape[1] / 2) + gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1, monotone_constraints=constraints) + gbm.fit(X_train, y_train) + + with pytest.raises(IndexError, match="tree_index is out of range."): + lgb.create_tree_digraph(gbm, tree_index=83) + + graph = lgb.create_tree_digraph( + gbm, + tree_index=3, + show_info=["split_gain", "internal_value", "internal_weight"], + name="Tree4", + node_attr={"color": "red"}, + directory=tmp_path, + ) + graph.render(view=False) + assert isinstance(graph, graphviz.Digraph) + assert graph.name == "Tree4" + assert len(graph.node_attr) == 1 + assert graph.node_attr["color"] == "red" + assert len(graph.graph_attr) == 0 + assert len(graph.edge_attr) == 0 + graph_body = "".join(graph.body) + assert "leaf" in graph_body + assert "gain" in graph_body + assert "value" in graph_body + assert "weight" in graph_body + assert "#ffdddd" in graph_body + assert "#ddffdd" in graph_body + assert "data" not in graph_body + assert "count" not in graph_body + + +def test_tree_with_categories_below_max_category_values(tmp_path): + graphviz = pytest.importorskip("graphviz") + X_train, y_train = _categorical_data(2, 10) + params = { + "n_estimators": 10, + "num_leaves": 3, + "min_data_in_bin": 1, + "force_col_wise": True, + "deterministic": True, + "num_threads": 1, + "seed": 708, + "verbose": -1, + } + gbm = lgb.LGBMClassifier(**params) + gbm.fit(X_train, y_train) + + with pytest.raises(IndexError, match="tree_index is out of range."): + lgb.create_tree_digraph(gbm, tree_index=83) + + graph = lgb.create_tree_digraph( + gbm, + tree_index=3, + show_info=["split_gain", "internal_value", "internal_weight"], + name="Tree4", + node_attr={"color": "red"}, + max_category_values=10, + directory=tmp_path, + ) + graph.render(view=False) + assert isinstance(graph, graphviz.Digraph) + assert graph.name == "Tree4" + assert len(graph.node_attr) == 1 + assert graph.node_attr["color"] == "red" + assert len(graph.graph_attr) == 0 + assert len(graph.edge_attr) == 0 + graph_body = "".join(graph.body) + assert "leaf" in graph_body + assert "gain" in graph_body + assert "value" in graph_body + assert "weight" in graph_body + assert "data" not in graph_body + assert "count" not in graph_body + assert "||...||" not in graph_body + + +def test_tree_with_categories_above_max_category_values(tmp_path): + graphviz = pytest.importorskip("graphviz") + X_train, y_train = _categorical_data(20, 30) + params = { + "n_estimators": 10, + "num_leaves": 3, + "min_data_in_bin": 1, + "force_col_wise": True, + "deterministic": True, + "num_threads": 1, + "seed": 708, + "verbose": -1, + } + gbm = lgb.LGBMClassifier(**params) + gbm.fit(X_train, y_train) + + with pytest.raises(IndexError, match="tree_index is out of range."): + lgb.create_tree_digraph(gbm, tree_index=83) + + graph = lgb.create_tree_digraph( + gbm, + tree_index=9, + show_info=["split_gain", "internal_value", "internal_weight"], + name="Tree4", + node_attr={"color": "red"}, + max_category_values=4, + directory=tmp_path, + ) + graph.render(view=False) + assert isinstance(graph, graphviz.Digraph) + assert graph.name == "Tree4" + assert len(graph.node_attr) == 1 + assert graph.node_attr["color"] == "red" + assert len(graph.graph_attr) == 0 + assert len(graph.edge_attr) == 0 + graph_body = "".join(graph.body) + assert "leaf" in graph_body + assert "gain" in graph_body + assert "value" in graph_body + assert "weight" in graph_body + assert "data" not in graph_body + assert "count" not in graph_body + assert "||...||" in graph_body + + +@pytest.mark.parametrize("use_missing", [True, False]) +@pytest.mark.parametrize("zero_as_missing", [True, False]) +def test_numeric_split_direction(use_missing, zero_as_missing): + X, y = make_synthetic_regression() + rng = np.random.RandomState(0) + zero_mask = rng.rand(X.shape[0]) < 0.05 + X[zero_mask, :] = 0 + if use_missing: + nan_mask = ~zero_mask & (rng.rand(X.shape[0]) < 0.1) + X[nan_mask, :] = np.nan + ds = lgb.Dataset(X, y) + params = { + "num_leaves": 127, + "min_child_samples": 1, + "use_missing": use_missing, + "zero_as_missing": zero_as_missing, + } + bst = lgb.train(params, ds, num_boost_round=1) + + case_with_zero = X[zero_mask][[0]] + expected_leaf_zero = bst.predict(case_with_zero, pred_leaf=True)[0] + node = bst.dump_model()["tree_info"][0]["tree_structure"] + while "decision_type" in node: + direction = lgb.plotting._determine_direction_for_numeric_split( + fval=case_with_zero[0][node["split_feature"]], + threshold=node["threshold"], + missing_type_str=node["missing_type"], + default_left=node["default_left"], + ) + node = node["left_child"] if direction == "left" else node["right_child"] + assert node["leaf_index"] == expected_leaf_zero + + if use_missing: + case_with_nan = X[nan_mask][[0]] + expected_leaf_nan = bst.predict(case_with_nan, pred_leaf=True)[0] + node = bst.dump_model()["tree_info"][0]["tree_structure"] + while "decision_type" in node: + direction = lgb.plotting._determine_direction_for_numeric_split( + fval=case_with_nan[0][node["split_feature"]], + threshold=node["threshold"], + missing_type_str=node["missing_type"], + default_left=node["default_left"], + ) + node = node["left_child"] if direction == "left" else node["right_child"] + assert node["leaf_index"] == expected_leaf_nan + if zero_as_missing: + # zeros treated as missing -> same leaf as NaN + assert expected_leaf_zero == expected_leaf_nan + else: + # zeros are regular values -> different leaf from NaN + assert expected_leaf_zero != expected_leaf_nan + + +def test_example_case_in_tree_digraph(): + pytest.importorskip("graphviz") + rng = np.random.RandomState(0) + x1 = rng.rand(100) + cat = rng.randint(1, 3, size=x1.size) + X = np.vstack([x1, cat]).T + y = x1 + 2 * cat + feature_name = ["x1", "cat"] + ds = lgb.Dataset(X, y, feature_name=feature_name, categorical_feature=["cat"]) + + num_round = 3 + bst = lgb.train({"num_leaves": 7}, ds, num_boost_round=num_round) + mod = bst.dump_model() + example_case = X[[0]] + makes_categorical_splits = False + seen_indices = set() + for i in range(num_round): + graph = lgb.create_tree_digraph(bst, example_case=example_case, tree_index=i) + gbody = graph.body + node = mod["tree_info"][i]["tree_structure"] + while "decision_type" in node: # iterate through the splits + split_index = node["split_index"] + + node_in_graph = [n for n in gbody if f"split{split_index}" in n and "->" not in n] + assert len(node_in_graph) == 1 + seen_indices.add(gbody.index(node_in_graph[0])) + + edge_to_node = [e for e in gbody if f"-> split{split_index}" in e] + if node["decision_type"] == "<=": + direction = lgb.plotting._determine_direction_for_numeric_split( + fval=example_case[0][node["split_feature"]], + threshold=node["threshold"], + missing_type_str=node["missing_type"], + default_left=node["default_left"], + ) + else: + makes_categorical_splits = True + direction = lgb.plotting._determine_direction_for_categorical_split( + example_case[0][node["split_feature"]], node["threshold"] + ) + node = node["left_child"] if direction == "left" else node["right_child"] + assert "color=blue" in node_in_graph[0] + if edge_to_node: + assert len(edge_to_node) == 1 + assert "color=blue" in edge_to_node[0] + seen_indices.add(gbody.index(edge_to_node[0])) + # we're in a leaf now + leaf_index = node["leaf_index"] + leaf_in_graph = [n for n in gbody if f"leaf{leaf_index}" in n and "->" not in n] + edge_to_leaf = [e for e in gbody if f"-> leaf{leaf_index}" in e] + assert len(leaf_in_graph) == 1 + assert "color=blue" in leaf_in_graph[0] + assert len(edge_to_leaf) == 1 + assert "color=blue" in edge_to_leaf[0] + seen_indices.update([gbody.index(leaf_in_graph[0]), gbody.index(edge_to_leaf[0])]) + + # check that the rest of the elements have black color + remaining_elements = [e for i, e in enumerate(graph.body) if i not in seen_indices and "graph" not in e] + assert all("color=black" in e for e in remaining_elements) + + # check that we got to the expected leaf + expected_leaf = bst.predict(example_case, start_iteration=i, num_iteration=1, pred_leaf=True)[0] + assert leaf_index == expected_leaf + assert makes_categorical_splits + + +@pytest.mark.parametrize("input_type", ["array", "dataframe"]) +def test_empty_example_case_on_tree_digraph_raises_error(input_type): + pytest.importorskip("graphviz") + X, y = make_synthetic_regression() + if input_type == "dataframe": + pd = pytest.importorskip("pandas") + X = pd.DataFrame(X) + example_case = pd.DataFrame(X[:0]) + else: + example_case = X[:0] + ds = lgb.Dataset(X, y) + bst = lgb.train({"num_leaves": 3}, ds, num_boost_round=1) + with pytest.raises(ValueError, match="example_case must have a single row."): + lgb.create_tree_digraph(bst, tree_index=0, example_case=example_case) + + +def test_plot_metrics(params, breast_cancer_split, train_data, matplotlib): + X_train, X_test, y_train, y_test = breast_cancer_split + test_data = lgb.Dataset(X_test, y_test, reference=train_data) + params.update({"metric": {"binary_logloss", "binary_error"}}) + + evals_result0 = {} + lgb.train( + params, + train_data, + valid_sets=[train_data, test_data], + valid_names=["v1", "v2"], + num_boost_round=10, + callbacks=[lgb.record_evaluation(evals_result0)], + ) + with pytest.warns(UserWarning, match="More than one metric available, picking one to plot."): + ax0 = lgb.plot_metric(evals_result0) + assert isinstance(ax0, matplotlib.axes.Axes) + assert ax0.get_title() == "Metric during training" + assert ax0.get_xlabel() == "Iterations" + assert ax0.get_ylabel() in {"binary_logloss", "binary_error"} + legend_items = ax0.get_legend().get_texts() + assert len(legend_items) == 2 + assert legend_items[0].get_text() == "v1" + assert legend_items[1].get_text() == "v2" + + ax1 = lgb.plot_metric(evals_result0, metric="binary_error") + assert isinstance(ax1, matplotlib.axes.Axes) + assert ax1.get_title() == "Metric during training" + assert ax1.get_xlabel() == "Iterations" + assert ax1.get_ylabel() == "binary_error" + legend_items = ax1.get_legend().get_texts() + assert len(legend_items) == 2 + assert legend_items[0].get_text() == "v1" + assert legend_items[1].get_text() == "v2" + + ax2 = lgb.plot_metric(evals_result0, metric="binary_logloss", dataset_names=["v2"]) + assert isinstance(ax2, matplotlib.axes.Axes) + assert ax2.get_title() == "Metric during training" + assert ax2.get_xlabel() == "Iterations" + assert ax2.get_ylabel() == "binary_logloss" + legend_items = ax2.get_legend().get_texts() + assert len(legend_items) == 1 + assert legend_items[0].get_text() == "v2" + + ax3 = lgb.plot_metric( + evals_result0, + metric="binary_logloss", + dataset_names=["v1"], + title="Metric @metric@", + xlabel="Iterations @metric@", + ylabel='Value of "@metric@"', + figsize=(5, 5), + dpi=600, + grid=False, + ) + assert isinstance(ax3, matplotlib.axes.Axes) + assert ax3.get_title() == "Metric @metric@" + assert ax3.get_xlabel() == "Iterations @metric@" + assert ax3.get_ylabel() == 'Value of "binary_logloss"' + legend_items = ax3.get_legend().get_texts() + assert len(legend_items) == 1 + assert legend_items[0].get_text() == "v1" + assert ax3.get_figure().get_figheight() == 5 + assert ax3.get_figure().get_figwidth() == 5 + assert ax3.get_figure().get_dpi() == 600 + for grid_line in ax3.get_xgridlines(): + assert not grid_line.get_visible() + for grid_line in ax3.get_ygridlines(): + assert not grid_line.get_visible() + + evals_result1 = {} + lgb.train(params, train_data, num_boost_round=10, callbacks=[lgb.record_evaluation(evals_result1)]) + with pytest.raises(ValueError, match="eval results cannot be empty."): + lgb.plot_metric(evals_result1) + + gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1) + gbm2.fit(X_train, y_train, eval_set=[(X_test, y_test)]) + ax4 = lgb.plot_metric(gbm2, title=None, xlabel=None, ylabel=None) + assert isinstance(ax4, matplotlib.axes.Axes) + assert ax4.get_title() == "" + assert ax4.get_xlabel() == "" + assert ax4.get_ylabel() == "" + legend_items = ax4.get_legend().get_texts() + assert len(legend_items) == 1 + assert legend_items[0].get_text() == "valid_0" + + # test xlim parameter + ax5 = lgb.plot_metric(evals_result0, metric="binary_logloss", xlim=(0, 15), title=None, xlabel=None, ylabel=None) + assert isinstance(ax5, matplotlib.axes.Axes) + assert ax5.get_title() == "" + assert ax5.get_xlabel() == "" + assert ax5.get_ylabel() == "" + assert ax5.get_xlim() == (0, 15) + + with pytest.raises(TypeError, match="xlim must be a tuple of 2 elements."): + lgb.plot_metric(evals_result0, metric="binary_logloss", xlim="not a tuple") + + ax6 = lgb.plot_metric(evals_result0, metric="binary_logloss", ylim=(0, 15), title=None, xlabel=None, ylabel=None) + assert isinstance(ax6, matplotlib.axes.Axes) + assert ax6.get_title() == "" + assert ax6.get_xlabel() == "" + assert ax6.get_ylabel() == "" + assert ax6.get_ylim() == (0, 15) + + with pytest.raises(TypeError, match="ylim must be a tuple of 2 elements."): + lgb.plot_metric(evals_result0, metric="binary_logloss", ylim="not a tuple") diff --git a/tests/python_package_test/test_polars.py b/tests/python_package_test/test_polars.py new file mode 100644 index 0000000..555b932 --- /dev/null +++ b/tests/python_package_test/test_polars.py @@ -0,0 +1,413 @@ +# coding: utf-8 +import filecmp +from pathlib import Path +from typing import Any, Dict, Optional + +import numpy as np +import pytest + +import lightgbm as lgb + +from .utils import np_assert_array_equal + +pl = pytest.importorskip("polars") + + +# ----------------------------------------------------------------------------------------------- # +# UTILITIES # +# ----------------------------------------------------------------------------------------------- # + +_INTEGER_TYPES = [pl.Int8, pl.Int16, pl.Int32, pl.Int64, pl.UInt8, pl.UInt16, pl.UInt32, pl.UInt64] +_FLOAT_TYPES = [pl.Float32, pl.Float64] + + +def generate_simple_polars_frame() -> pl.DataFrame: + values = [1, 2, 3, 4, 5] + bool_values = [True, True, False, False, True] + columns = {f"col_{i}": pl.Series(values, dtype=dtype) for i, dtype in enumerate(_INTEGER_TYPES + _FLOAT_TYPES)} + columns[f"col_{len(columns)}"] = pl.Series(bool_values, dtype=pl.Boolean) + return pl.DataFrame(columns) + + +def generate_nullable_polars_frame(dtype: Any) -> pl.DataFrame: + return pl.DataFrame( + { + "col_0": pl.Series([1, None, 3, 4, 5], dtype=dtype), + "col_1": pl.Series([None, 2, 3, 4, 5], dtype=dtype), + "col_2": pl.Series([1, 2, 3, 4, None], dtype=dtype), + "col_3": pl.Series([None, None, None, None, None], dtype=dtype), + } + ) + + +def generate_dummy_polars_frame() -> pl.DataFrame: + return pl.DataFrame( + { + "a": pl.Series([1, 2, 3, 4, 5], dtype=pl.UInt8), + "b": pl.Series([0.5, 0.6, 0.1, 0.8, 1.5], dtype=pl.Float32), + } + ) + + +def generate_random_polars_frame( + num_columns: int, + num_datapoints: int, + seed: int, + generate_nulls: bool = True, + values: Optional[np.ndarray] = None, +) -> pl.DataFrame: + return pl.DataFrame( + { + f"col_{i}": generate_random_polars_series( + num_datapoints, seed + i, generate_nulls=generate_nulls, values=values + ) + for i in range(num_columns) + } + ) + + +def generate_random_polars_series( + num_datapoints: int, + seed: int, + generate_nulls: bool = True, + values: Optional[np.ndarray] = None, +) -> pl.Series: + generator = np.random.default_rng(seed) + data = ( + generator.standard_normal(num_datapoints).astype(np.float32) + if values is None + else generator.choice(values, size=num_datapoints, replace=True) + ) + series = pl.Series("col", data, dtype=pl.Float32) + if generate_nulls: + indices = generator.choice(len(data), size=num_datapoints // 10) + series = series.scatter(indices, None) + return series + + +def dummy_dataset_params() -> Dict[str, Any]: + return { + "min_data_in_bin": 1, + "min_data_in_leaf": 1, + } + + +# ----------------------------------------------------------------------------------------------- # +# UNIT TESTS # +# ----------------------------------------------------------------------------------------------- # + +# ------------------------------------------- DATASET ------------------------------------------- # + + +def assert_datasets_equal(tmp_path: Path, lhs: lgb.Dataset, rhs: lgb.Dataset): + lhs._dump_text(tmp_path / "polars.txt") + rhs._dump_text(tmp_path / "pandas.txt") + assert filecmp.cmp(tmp_path / "polars.txt", tmp_path / "pandas.txt") + + +@pytest.mark.parametrize( + ("polars_frame_fn", "dataset_params"), + [ # Use lambda functions here to minimize memory consumption + (generate_simple_polars_frame, dummy_dataset_params()), + (generate_dummy_polars_frame, dummy_dataset_params()), + (lambda: generate_nullable_polars_frame(pl.Float32), dummy_dataset_params()), + (lambda: generate_nullable_polars_frame(pl.Int32), dummy_dataset_params()), + (lambda: generate_random_polars_frame(3, 1000, 42), {}), + (lambda: generate_random_polars_frame(100, 10000, 43), {}), + ], +) +def test_dataset_construct_fuzzy(tmp_path, polars_frame_fn, dataset_params): + polars_frame = polars_frame_fn() + + polars_dataset = lgb.Dataset(polars_frame, params=dataset_params) + polars_dataset.construct() + + pandas_dataset = lgb.Dataset(polars_frame.to_pandas(), params=dataset_params) + pandas_dataset.construct() + + assert_datasets_equal(tmp_path, polars_dataset, pandas_dataset) + + +def test_dataset_construct_fuzzy_boolean(tmp_path): + boolean_data = generate_random_polars_frame(10, 10000, 42, generate_nulls=False, values=np.array([True, False])) + float_data = boolean_data.cast(pl.Float32) + + polars_dataset = lgb.Dataset(boolean_data) + polars_dataset.construct() + + pandas_dataset = lgb.Dataset(float_data.to_pandas()) + pandas_dataset.construct() + + assert_datasets_equal(tmp_path, polars_dataset, pandas_dataset) + + +# -------------------------------------------- FIELDS ------------------------------------------- # + + +def test_dataset_construct_fields_fuzzy(): + polars_frame = generate_random_polars_frame(3, 1000, 42) + polars_labels = generate_random_polars_series(1000, 42, generate_nulls=False) + polars_weights = generate_random_polars_series(1000, 42, generate_nulls=False) + polars_groups = pl.Series("group", [300, 400, 50, 250], dtype=pl.Int32) + + polars_dataset = lgb.Dataset(polars_frame, label=polars_labels, weight=polars_weights, group=polars_groups) + polars_dataset.construct() + + pandas_dataset = lgb.Dataset( + polars_frame.to_pandas(), + label=polars_labels.to_numpy(), + weight=polars_weights.to_numpy(), + group=polars_groups.to_numpy(), + ) + pandas_dataset.construct() + + # Check for equality + for field in ("label", "weight", "group"): + np_assert_array_equal(polars_dataset.get_field(field), pandas_dataset.get_field(field), strict=True) + np_assert_array_equal(polars_dataset.get_label(), pandas_dataset.get_label(), strict=True) + np_assert_array_equal(polars_dataset.get_weight(), pandas_dataset.get_weight(), strict=True) + + +# -------------------------------------------- LABELS ------------------------------------------- # + + +@pytest.mark.parametrize("polars_type", _INTEGER_TYPES + _FLOAT_TYPES) +def test_dataset_construct_labels(polars_type): + data = generate_dummy_polars_frame() + labels = pl.Series("label", [0, 1, 0, 0, 1], dtype=polars_type) + dataset = lgb.Dataset(data, label=labels, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 1, 0, 0, 1], dtype=np.float32) + np_assert_array_equal(expected, dataset.get_label(), strict=True) + + +def test_dataset_construct_labels_boolean(): + data = generate_dummy_polars_frame() + labels = pl.Series("label", [False, True, False, False, True], dtype=pl.Boolean) + dataset = lgb.Dataset(data, label=labels, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 1, 0, 0, 1], dtype=np.float32) + np_assert_array_equal(expected, dataset.get_label(), strict=True) + + +# ------------------------------------------- WEIGHTS ------------------------------------------- # + + +def test_dataset_construct_weights_none(): + data = generate_dummy_polars_frame() + weight = pl.Series("weight", [1, 1, 1, 1, 1], dtype=pl.Float32) + dataset = lgb.Dataset(data, weight=weight, params=dummy_dataset_params()) + dataset.construct() + assert dataset.get_weight() is None + assert dataset.get_field("weight") is None + + +@pytest.mark.parametrize("polars_type", _FLOAT_TYPES) +def test_dataset_construct_weights(polars_type): + data = generate_dummy_polars_frame() + weights = pl.Series("weight", [3, 0.7, 1.5, 0.5, 0.1], dtype=polars_type) + dataset = lgb.Dataset(data, weight=weights, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([3, 0.7, 1.5, 0.5, 0.1], dtype=np.float32) + np_assert_array_equal(expected, dataset.get_weight(), strict=True) + + +# -------------------------------------------- GROUPS ------------------------------------------- # + + +@pytest.mark.parametrize("polars_type", _INTEGER_TYPES) +def test_dataset_construct_groups(polars_type): + data = generate_dummy_polars_frame() + groups = pl.Series("group", [2, 3], dtype=polars_type) + dataset = lgb.Dataset(data, group=groups, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 2, 5], dtype=np.int32) + np_assert_array_equal(expected, dataset.get_field("group"), strict=True) + + +# ----------------------------------------- INIT SCORES ----------------------------------------- # + + +@pytest.mark.parametrize("polars_type", _INTEGER_TYPES + _FLOAT_TYPES) +def test_dataset_construct_init_scores_array(polars_type): + data = generate_dummy_polars_frame() + init_scores = pl.Series("init_score", [0, 1, 2, 3, 3], dtype=polars_type) + dataset = lgb.Dataset(data, init_score=init_scores, params=dummy_dataset_params()) + dataset.construct() + + expected = np.array([0, 1, 2, 3, 3], dtype=np.float64) + np_assert_array_equal(expected, dataset.get_init_score(), strict=True) + + +def test_dataset_construct_init_scores_table(): + data = generate_dummy_polars_frame() + init_scores = pl.DataFrame( + { + "a": generate_random_polars_series(5, seed=1, generate_nulls=False), + "b": generate_random_polars_series(5, seed=2, generate_nulls=False), + "c": generate_random_polars_series(5, seed=3, generate_nulls=False), + } + ) + dataset = lgb.Dataset(data, init_score=init_scores, params=dummy_dataset_params()) + dataset.construct() + + actual = dataset.get_init_score() + expected = init_scores.to_numpy().astype(np.float64) + np_assert_array_equal(expected, actual, strict=True) + + +# ------------------------------------------ PREDICTION ----------------------------------------- # + + +def assert_equal_predict_polars_pandas(booster: lgb.Booster, data: pl.DataFrame): + pandas_data = data.to_pandas() + + p_polars = booster.predict(data) + p_pandas = booster.predict(pandas_data) + np_assert_array_equal(p_polars, p_pandas, strict=True) + + p_raw_polars = booster.predict(data, raw_score=True) + p_raw_pandas = booster.predict(pandas_data, raw_score=True) + np_assert_array_equal(p_raw_polars, p_raw_pandas, strict=True) + + p_leaf_polars = booster.predict(data, pred_leaf=True) + p_leaf_pandas = booster.predict(pandas_data, pred_leaf=True) + np_assert_array_equal(p_leaf_polars, p_leaf_pandas, strict=True) + + p_pred_contrib_polars = booster.predict(data, pred_contrib=True) + p_pred_contrib_pandas = booster.predict(pandas_data, pred_contrib=True) + np_assert_array_equal(p_pred_contrib_polars, p_pred_contrib_pandas, strict=True) + + p_first_iter_polars = booster.predict(data, start_iteration=0, num_iteration=1, raw_score=True) + p_first_iter_pandas = booster.predict(pandas_data, start_iteration=0, num_iteration=1, raw_score=True) + np_assert_array_equal(p_first_iter_polars, p_first_iter_pandas, strict=True) + + +def test_predict_regression(): + data_float = generate_random_polars_frame(10, 10000, 42) + data_bool = generate_random_polars_frame(1, 10000, 42, generate_nulls=False, values=np.array([True, False])) + data = data_float.with_columns(data_bool["col_0"].alias("col_bool")) + + dataset = lgb.Dataset( + data, + label=generate_random_polars_series(10000, 43, generate_nulls=False), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "regression", "num_leaves": 7}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_polars_pandas(booster, data) + + +def test_predict_binary_classification(): + data = generate_random_polars_frame(10, 10000, 42) + dataset = lgb.Dataset( + data, + label=generate_random_polars_series(10000, 43, generate_nulls=False, values=np.arange(2)), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "binary", "num_leaves": 7}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_polars_pandas(booster, data) + + +def test_predict_multiclass_classification(): + data = generate_random_polars_frame(10, 10000, 42) + dataset = lgb.Dataset( + data, + label=generate_random_polars_series(10000, 43, generate_nulls=False, values=np.arange(5)), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "multiclass", "num_leaves": 7, "num_class": 5}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_polars_pandas(booster, data) + + +def test_predict_ranking(): + data = generate_random_polars_frame(10, 10000, 42) + dataset = lgb.Dataset( + data, + label=generate_random_polars_series(10000, 43, generate_nulls=False, values=np.arange(4)), + group=np.array([1000, 2000, 3000, 4000]), + params=dummy_dataset_params(), + ) + booster = lgb.train( + {"objective": "lambdarank", "num_leaves": 7}, + dataset, + num_boost_round=5, + ) + assert_equal_predict_polars_pandas(booster, data) + + +def test_polars_feature_name_auto(): + data = generate_dummy_polars_frame() + dataset = lgb.Dataset( + data, + label=pl.Series("label", [0, 1, 0, 0, 1]), + params=dummy_dataset_params(), + categorical_feature=["a"], + ) + booster = lgb.train({"num_leaves": 7}, dataset, num_boost_round=5) + assert booster.feature_name() == ["a", "b"] + + +def test_polars_feature_name_manual(): + data = generate_dummy_polars_frame() + dataset = lgb.Dataset( + data, + label=pl.Series("label", [0, 1, 0, 0, 1]), + params=dummy_dataset_params(), + feature_name=["c", "d"], + categorical_feature=["c"], + ) + booster = lgb.train({"num_leaves": 7}, dataset, num_boost_round=5) + assert booster.feature_name() == ["c", "d"] + + +def test_get_data_polars_frame(): + from polars.testing import assert_frame_equal # noqa: PLC0415 + + original_frame = generate_simple_polars_frame() + dataset = lgb.Dataset(original_frame, free_raw_data=False) + dataset.construct() + + returned_data = dataset.get_data() + assert isinstance(returned_data, pl.DataFrame) + assert returned_data.schema == original_frame.schema + assert returned_data.shape == original_frame.shape + assert_frame_equal(returned_data, original_frame) + + +def test_get_data_polars_frame_subset(rng): + from polars.testing import assert_frame_equal # noqa: PLC0415 + + original_frame = generate_random_polars_frame(num_columns=3, num_datapoints=1000, seed=42) + dataset = lgb.Dataset(original_frame, free_raw_data=False) + dataset.construct() + + subset_size = 100 + used_indices = rng.choice(a=original_frame.shape[0], size=subset_size, replace=False) + used_indices = sorted(used_indices) + + subset_dataset = dataset.subset(used_indices).construct() + expected_subset = original_frame[used_indices] + subset_data = subset_dataset.get_data() + + assert isinstance(subset_data, pl.DataFrame) + assert subset_data.schema == expected_subset.schema + assert subset_data.shape == expected_subset.shape + assert len(subset_data) == len(used_indices) + assert subset_data.shape == (subset_size, 3) + assert_frame_equal(subset_data, expected_subset) diff --git a/tests/python_package_test/test_sklearn.py b/tests/python_package_test/test_sklearn.py new file mode 100644 index 0000000..a1b50b5 --- /dev/null +++ b/tests/python_package_test/test_sklearn.py @@ -0,0 +1,2367 @@ +# coding: utf-8 +import inspect +import itertools +import math +import re +import sys +import warnings +from functools import partial +from pathlib import Path + +import joblib +import numpy as np +import pytest +import scipy.sparse +from scipy.stats import spearmanr +from sklearn.base import clone +from sklearn.calibration import CalibratedClassifierCV +from sklearn.datasets import load_svmlight_file, make_blobs, make_multilabel_classification +from sklearn.ensemble import StackingClassifier, StackingRegressor +from sklearn.metrics import accuracy_score, log_loss, mean_squared_error, r2_score +from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split +from sklearn.multioutput import ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, RegressorChain +from sklearn.utils.estimator_checks import parametrize_with_checks as sklearn_parametrize_with_checks +from sklearn.utils.validation import check_is_fitted + +import lightgbm as lgb +from lightgbm.basic import LGBMDeprecationWarning +from lightgbm.compat import ( + PANDAS_INSTALLED, + _sklearn_version, + pd_DataFrame, + pd_Series, +) + +from .utils import ( + BuildInfo, + assert_silent, + load_breast_cancer, + load_digits, + load_iris, + load_linnerud, + logistic_sigmoid, + make_ranking, + make_synthetic_regression, + np_assert_array_equal, + sklearn_multiclass_custom_objective, + softmax, +) + +SKLEARN_MAJOR, SKLEARN_MINOR, *_ = _sklearn_version.split(".") +SKLEARN_VERSION_GTE_1_6 = (int(SKLEARN_MAJOR), int(SKLEARN_MINOR)) >= (1, 6) +SKLEARN_VERSION_GTE_1_7 = (int(SKLEARN_MAJOR), int(SKLEARN_MINOR)) >= (1, 7) + +decreasing_generator = itertools.count(0, -1) +estimator_classes = (lgb.LGBMModel, lgb.LGBMClassifier, lgb.LGBMRegressor, lgb.LGBMRanker) +task_to_model_factory = { + "ranking": lgb.LGBMRanker, + "binary-classification": lgb.LGBMClassifier, + "multiclass-classification": lgb.LGBMClassifier, + "regression": lgb.LGBMRegressor, +} +all_tasks = tuple(task_to_model_factory.keys()) +all_x_types = ("list2d", "numpy", "pd_DataFrame", "pa_Table", "pl_DataFrame", "scipy_csc", "scipy_csr") +all_y_types = ("list1d", "numpy", "pd_Series", "pd_DataFrame", "pa_ChunkedArray", "pl_Series") +all_group_types = ("list1d_float", "list1d_int", "numpy", "pd_Series", "pa_ChunkedArray", "pl_Series") + + +def _create_data(task, n_samples=100, n_features=4): + if task == "ranking": + X, y, g = make_ranking(n_features=4, n_samples=n_samples) + g = np.bincount(g) + elif task.endswith("classification"): + if task == "binary-classification": + centers = 2 + elif task == "multiclass-classification": + centers = 3 + else: + raise ValueError(f"Unknown classification task '{task}'") + X, y = make_blobs(n_samples=n_samples, n_features=n_features, centers=centers, random_state=42) + g = None + elif task == "regression": + X, y = make_synthetic_regression(n_samples=n_samples, n_features=n_features) + g = None + return X, y, g + + +class UnpicklableCallback: + def __reduce__(self): + raise Exception("This class in not picklable") + + def __call__(self, env): + env.model.attr_set_inside_callback = env.iteration * 10 + + +class ExtendedLGBMClassifier(lgb.LGBMClassifier): + """Class for testing that inheriting from LGBMClassifier works""" + + def __init__(self, *, some_other_param: str = "lgbm-classifier", **kwargs): + self.some_other_param = some_other_param + super().__init__(**kwargs) + + +class ExtendedLGBMRanker(lgb.LGBMRanker): + """Class for testing that inheriting from LGBMRanker works""" + + def __init__(self, *, some_other_param: str = "lgbm-ranker", **kwargs): + self.some_other_param = some_other_param + super().__init__(**kwargs) + + +class ExtendedLGBMRegressor(lgb.LGBMRegressor): + """Class for testing that inheriting from LGBMRegressor works""" + + def __init__(self, *, some_other_param: str = "lgbm-regressor", **kwargs): + self.some_other_param = some_other_param + super().__init__(**kwargs) + + +def custom_asymmetric_obj(y_true, y_pred): + residual = (y_true - y_pred).astype(np.float64) + grad = np.where(residual < 0, -2 * 10.0 * residual, -2 * residual) + hess = np.where(residual < 0, 2 * 10.0, 2.0) + return grad, hess + + +def objective_ls(y_true, y_pred): + grad = y_pred - y_true + hess = np.ones(len(y_true)) + return grad, hess + + +def logregobj(y_true, y_pred): + y_pred = 1.0 / (1.0 + np.exp(-y_pred)) + grad = y_pred - y_true + hess = y_pred * (1.0 - y_pred) + return grad, hess + + +def custom_dummy_obj(y_true, y_pred): + return np.ones(y_true.shape), np.ones(y_true.shape) + + +def constant_metric(y_true, y_pred): + return "error", 0, False + + +def decreasing_metric(y_true, y_pred): + return ("decreasing_metric", next(decreasing_generator), False) + + +def mse(y_true, y_pred): + return "custom MSE", mean_squared_error(y_true, y_pred), False + + +def binary_error(y_true, y_pred): + return np.mean((y_pred > 0.5) != y_true) + + +def multi_error(y_true, y_pred): + return np.mean(y_true != y_pred) + + +def multi_logloss(y_true, y_pred): + return np.mean([-math.log(y_pred[i][y]) for i, y in enumerate(y_true)]) + + +def test_binary(): + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1) + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)]) + ret = log_loss(y_test, gbm.predict_proba(X_test)) + assert ret < 0.12 + assert gbm.evals_result_["valid_0"]["binary_logloss"][gbm.best_iteration_ - 1] == pytest.approx(ret) + + +def test_regression(): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1) + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)]) + ret = mean_squared_error(y_test, gbm.predict(X_test)) + assert ret < 174 + assert gbm.evals_result_["valid_0"]["l2"][gbm.best_iteration_ - 1] == pytest.approx(ret) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Skip due to differences in implementation details of CUDA version") +def test_multiclass(): + X, y = load_digits(n_class=10, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1) + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)]) + ret = multi_error(y_test, gbm.predict(X_test)) + assert ret < 0.05 + ret = multi_logloss(y_test, gbm.predict_proba(X_test)) + assert ret < 0.16 + assert gbm.evals_result_["valid_0"]["multi_logloss"][gbm.best_iteration_ - 1] == pytest.approx(ret) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Skip due to differences in implementation details of CUDA version") +def test_lambdarank(): + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train")) + X_test, y_test = load_svmlight_file(str(rank_example_dir / "rank.test")) + q_train = np.loadtxt(str(rank_example_dir / "rank.train.query")) + q_test = np.loadtxt(str(rank_example_dir / "rank.test.query")) + gbm = lgb.LGBMRanker(n_estimators=50) + gbm.fit( + X_train, + y_train, + group=q_train, + eval_set=[(X_test, y_test)], + eval_group=[q_test], + eval_at=[1, 3], + callbacks=[lgb.early_stopping(10), lgb.reset_parameter(learning_rate=lambda x: max(0.01, 0.1 - 0.01 * x))], + ) + assert gbm.best_iteration_ <= 24 + assert gbm.best_score_["valid_0"]["ndcg@1"] > 0.5674 + assert gbm.best_score_["valid_0"]["ndcg@3"] > 0.578 + + +def test_xendcg(): + xendcg_example_dir = Path(__file__).absolute().parents[2] / "examples" / "xendcg" + X_train, y_train = load_svmlight_file(str(xendcg_example_dir / "rank.train")) + X_test, y_test = load_svmlight_file(str(xendcg_example_dir / "rank.test")) + q_train = np.loadtxt(str(xendcg_example_dir / "rank.train.query")) + q_test = np.loadtxt(str(xendcg_example_dir / "rank.test.query")) + gbm = lgb.LGBMRanker(n_estimators=50, objective="rank_xendcg", random_state=5, n_jobs=1) + gbm.fit( + X_train, + y_train, + group=q_train, + eval_set=[(X_test, y_test)], + eval_group=[q_test], + eval_at=[1, 3], + eval_metric="ndcg", + callbacks=[lgb.early_stopping(10), lgb.reset_parameter(learning_rate=lambda x: max(0.01, 0.1 - 0.01 * x))], + ) + assert gbm.best_iteration_ <= 24 + assert gbm.best_score_["valid_0"]["ndcg@1"] > 0.6211 + assert gbm.best_score_["valid_0"]["ndcg@3"] > 0.6253 + + +def test_eval_at_aliases(): + rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank" + X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train")) + X_test, y_test = load_svmlight_file(str(rank_example_dir / "rank.test")) + q_train = np.loadtxt(str(rank_example_dir / "rank.train.query")) + q_test = np.loadtxt(str(rank_example_dir / "rank.test.query")) + for alias in lgb.basic._ConfigAliases.get("eval_at"): + gbm = lgb.LGBMRanker(n_estimators=5, **{alias: [1, 2, 3, 9]}) + with pytest.warns(UserWarning, match=f"Found '{alias}' in params. Will use it instead of 'eval_at' argument"): + gbm.fit(X_train, y_train, group=q_train, eval_set=[(X_test, y_test)], eval_group=[q_test]) + assert list(gbm.evals_result_["valid_0"].keys()) == ["ndcg@1", "ndcg@2", "ndcg@3", "ndcg@9"] + + +@pytest.mark.parametrize("custom_objective", [True, False]) +def test_objective_aliases(custom_objective): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + if custom_objective: + obj = custom_dummy_obj + metric_name = "l2" # default one + else: + obj = "mape" + metric_name = "mape" + evals = [] + for alias in lgb.basic._ConfigAliases.get("objective"): + gbm = lgb.LGBMRegressor(n_estimators=5, **{alias: obj}) + if alias != "objective": + with pytest.warns( + UserWarning, match=f"Found '{alias}' in params. Will use it instead of 'objective' argument" + ): + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)]) + else: + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)]) + assert list(gbm.evals_result_["valid_0"].keys()) == [metric_name] + evals.append(gbm.evals_result_["valid_0"][metric_name]) + evals_t = np.array(evals).T + for i in range(evals_t.shape[0]): + np.testing.assert_allclose(evals_t[i], evals_t[i][0]) + # check that really dummy objective was used and estimator didn't learn anything + if custom_objective: + np.testing.assert_allclose(evals_t, evals_t[0][0]) + + +def test_regression_with_custom_objective(): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1, objective=objective_ls) + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)]) + ret = mean_squared_error(y_test, gbm.predict(X_test)) + assert ret < 174 + assert gbm.evals_result_["valid_0"]["l2"][gbm.best_iteration_ - 1] == pytest.approx(ret) + + +def test_binary_classification_with_custom_objective(): + X, y = load_digits(n_class=2, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1, objective=logregobj) + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=[lgb.early_stopping(5)]) + # prediction result is actually not transformed (is raw) due to custom objective + y_pred_raw = gbm.predict_proba(X_test) + assert not np.all(y_pred_raw >= 0) + y_pred = 1.0 / (1.0 + np.exp(-y_pred_raw)) + ret = binary_error(y_test, y_pred) + assert ret < 0.05 + + +def test_dart(): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMRegressor(boosting_type="dart", n_estimators=50) + gbm.fit(X_train, y_train) + score = gbm.score(X_test, y_test) + assert 0.8 <= score <= 1.0 + + +def test_stacking_classifier(): + X, y = load_iris(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) + classifiers = [("gbm1", lgb.LGBMClassifier(n_estimators=3)), ("gbm2", lgb.LGBMClassifier(n_estimators=3))] + clf = StackingClassifier( + estimators=classifiers, final_estimator=lgb.LGBMClassifier(n_estimators=3), passthrough=True + ) + clf.fit(X_train, y_train) + score = clf.score(X_test, y_test) + assert score >= 0.8 + assert score <= 1.0 + assert clf.n_features_in_ == 4 # number of input features + assert len(clf.named_estimators_["gbm1"].feature_importances_) == 4 + assert clf.named_estimators_["gbm1"].n_features_in_ == clf.named_estimators_["gbm2"].n_features_in_ + assert clf.final_estimator_.n_features_in_ == 10 # number of concatenated features + assert len(clf.final_estimator_.feature_importances_) == 10 + assert all(clf.named_estimators_["gbm1"].classes_ == clf.named_estimators_["gbm2"].classes_) + assert all(clf.classes_ == clf.named_estimators_["gbm1"].classes_) + + +def test_stacking_regressor(): + X, y = make_synthetic_regression(n_samples=200) + n_features = X.shape[1] + n_input_models = 2 + X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) + regressors = [("gbm1", lgb.LGBMRegressor(n_estimators=3)), ("gbm2", lgb.LGBMRegressor(n_estimators=3))] + reg = StackingRegressor(estimators=regressors, final_estimator=lgb.LGBMRegressor(n_estimators=3), passthrough=True) + reg.fit(X_train, y_train) + score = reg.score(X_test, y_test) + assert score >= 0.2 + assert score <= 1.0 + assert reg.n_features_in_ == n_features # number of input features + assert len(reg.named_estimators_["gbm1"].feature_importances_) == n_features + assert reg.named_estimators_["gbm1"].n_features_in_ == reg.named_estimators_["gbm2"].n_features_in_ + assert reg.final_estimator_.n_features_in_ == n_features + n_input_models # number of concatenated features + assert len(reg.final_estimator_.feature_importances_) == n_features + n_input_models + + +def test_grid_search(): + X, y = load_iris(return_X_y=True) + y = y.astype(str) # utilize label encoder at it's max power + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42) + params = {"subsample": 0.8, "subsample_freq": 1} + grid_params = {"boosting_type": ["rf", "gbdt"], "n_estimators": [4, 6], "reg_alpha": [0.01, 0.005]} + evals_result = {} + fit_params = { + "eval_set": [(X_val, y_val)], + "eval_metric": constant_metric, + "callbacks": [lgb.early_stopping(2), lgb.record_evaluation(evals_result)], + } + grid = GridSearchCV(estimator=lgb.LGBMClassifier(**params), param_grid=grid_params, cv=2) + grid.fit(X_train, y_train, **fit_params) + score = grid.score(X_test, y_test) # utilizes GridSearchCV default refit=True + assert grid.best_params_["boosting_type"] in ["rf", "gbdt"] + assert grid.best_params_["n_estimators"] in [4, 6] + assert grid.best_params_["reg_alpha"] in [0.01, 0.005] + assert grid.best_score_ <= 1.0 + assert grid.best_estimator_.best_iteration_ == 1 + assert grid.best_estimator_.best_score_["valid_0"]["multi_logloss"] < 0.25 + assert grid.best_estimator_.best_score_["valid_0"]["error"] == 0 + assert score >= 0.2 + assert score <= 1.0 + assert evals_result == grid.best_estimator_.evals_result_ + + +def test_random_search(rng): + X, y = load_iris(return_X_y=True) + y = y.astype(str) # utilize label encoder at it's max power + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42) + n_iter = 3 # Number of samples + params = {"subsample": 0.8, "subsample_freq": 1} + param_dist = { + "boosting_type": ["rf", "gbdt"], + "n_estimators": rng.integers(low=3, high=10, size=(n_iter,)).tolist(), + "reg_alpha": rng.uniform(low=0.01, high=0.06, size=(n_iter,)).tolist(), + } + fit_params = {"eval_set": [(X_val, y_val)], "eval_metric": constant_metric, "callbacks": [lgb.early_stopping(2)]} + rand = RandomizedSearchCV( + estimator=lgb.LGBMClassifier(**params), param_distributions=param_dist, cv=2, n_iter=n_iter, random_state=42 + ) + rand.fit(X_train, y_train, **fit_params) + score = rand.score(X_test, y_test) # utilizes RandomizedSearchCV default refit=True + assert rand.best_params_["boosting_type"] in ["rf", "gbdt"] + assert rand.best_params_["n_estimators"] in list(range(3, 10)) + assert rand.best_params_["reg_alpha"] >= 0.01 # Left-closed boundary point + assert rand.best_params_["reg_alpha"] <= 0.06 # Right-closed boundary point + assert rand.best_score_ <= 1.0 + assert rand.best_estimator_.best_score_["valid_0"]["multi_logloss"] < 0.25 + assert rand.best_estimator_.best_score_["valid_0"]["error"] == 0 + assert score >= 0.2 + assert score <= 1.0 + + +def test_multioutput_classifier(): + n_outputs = 3 + X, y = make_multilabel_classification(n_samples=100, n_features=20, n_classes=n_outputs, random_state=0) + y = y.astype(str) # utilize label encoder at it's max power + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + clf = MultiOutputClassifier(estimator=lgb.LGBMClassifier(n_estimators=10)) + clf.fit(X_train, y_train) + score = clf.score(X_test, y_test) + assert score >= 0.2 + assert score <= 1.0 + np_assert_array_equal(np.tile(np.unique(y_train), n_outputs), np.concatenate(clf.classes_), strict=True) + for classifier in clf.estimators_: + assert isinstance(classifier, lgb.LGBMClassifier) + assert isinstance(classifier.booster_, lgb.Booster) + + +def test_multioutput_regressor(): + bunch = load_linnerud(as_frame=True) # returns a Bunch instance + X, y = bunch["data"], bunch["target"] + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + reg = MultiOutputRegressor(estimator=lgb.LGBMRegressor(n_estimators=10)) + reg.fit(X_train, y_train) + y_pred = reg.predict(X_test) + _, score, _ = mse(y_test, y_pred) + assert score >= 0.2 + assert score <= 120.0 + for regressor in reg.estimators_: + assert isinstance(regressor, lgb.LGBMRegressor) + assert isinstance(regressor.booster_, lgb.Booster) + + +def test_classifier_chain(): + n_outputs = 3 + X, y = make_multilabel_classification(n_samples=100, n_features=20, n_classes=n_outputs, random_state=0) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + order = [2, 0, 1] + # 'base_estimator' parameter was deprecated in scikit-learn 1.7 and removed in 1.9 + # + # * https://github.com/scikit-learn/scikit-learn/pull/30152 + # * https://github.com/scikit-learn/scikit-learn/pull/33750 + # + if SKLEARN_VERSION_GTE_1_7: + clf = ClassifierChain(estimator=lgb.LGBMClassifier(n_estimators=10), order=order, random_state=42) + else: + clf = ClassifierChain(base_estimator=lgb.LGBMClassifier(n_estimators=10), order=order, random_state=42) + clf.fit(X_train, y_train) + score = clf.score(X_test, y_test) + assert score >= 0.2 + assert score <= 1.0 + np_assert_array_equal(np.tile(np.unique(y_train), n_outputs), np.concatenate(clf.classes_), strict=True) + assert order == clf.order_ + for classifier in clf.estimators_: + assert isinstance(classifier, lgb.LGBMClassifier) + assert isinstance(classifier.booster_, lgb.Booster) + + +def test_regressor_chain(): + bunch = load_linnerud(as_frame=True) # returns a Bunch instance + X, y = bunch["data"], bunch["target"] + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + order = [2, 0, 1] + # 'base_estimator' parameter was deprecated in scikit-learn 1.7 and removed in 1.9 + # + # * https://github.com/scikit-learn/scikit-learn/pull/30152 + # * https://github.com/scikit-learn/scikit-learn/pull/33750 + # + if SKLEARN_VERSION_GTE_1_7: + reg = RegressorChain(estimator=lgb.LGBMRegressor(n_estimators=10), order=order, random_state=42) + else: + reg = RegressorChain(base_estimator=lgb.LGBMRegressor(n_estimators=10), order=order, random_state=42) + reg.fit(X_train, y_train) + y_pred = reg.predict(X_test) + _, score, _ = mse(y_test, y_pred) + assert score >= 0.2 + assert score <= 120.0 + assert order == reg.order_ + for regressor in reg.estimators_: + assert isinstance(regressor, lgb.LGBMRegressor) + assert isinstance(regressor.booster_, lgb.Booster) + + +def test_clone_and_property(): + X, y = make_synthetic_regression() + gbm = lgb.LGBMRegressor(n_estimators=10, verbose=-1) + gbm.fit(X, y) + + gbm_clone = clone(gbm) + + # original estimator is unaffected + assert gbm.n_estimators == 10 + assert gbm.verbose == -1 + assert isinstance(gbm.booster_, lgb.Booster) + assert isinstance(gbm.feature_importances_, np.ndarray) + + # new estimator is unfitted, but has the same parameters + assert gbm_clone.__sklearn_is_fitted__() is False + assert gbm_clone.n_estimators == 10 + assert gbm_clone.verbose == -1 + assert gbm_clone.get_params() == gbm.get_params() + + X, y = load_digits(n_class=2, return_X_y=True) + clf = lgb.LGBMClassifier(n_estimators=10, verbose=-1) + clf.fit(X, y) + assert sorted(clf.classes_) == [0, 1] + assert clf.n_classes_ == 2 + assert isinstance(clf.booster_, lgb.Booster) + assert isinstance(clf.feature_importances_, np.ndarray) + + +@pytest.mark.parametrize("estimator", (lgb.LGBMClassifier, lgb.LGBMRegressor, lgb.LGBMRanker)) # noqa: PT007 +def test_estimators_all_have_the_same_kwargs_and_defaults(estimator): + base_spec = inspect.getfullargspec(lgb.LGBMModel) + subclass_spec = inspect.getfullargspec(estimator) + + # should not allow for any varargs + assert subclass_spec.varargs == base_spec.varargs + assert subclass_spec.varargs is None + + # the only varkw should be **kwargs, + assert subclass_spec.varkw == base_spec.varkw + assert subclass_spec.varkw == "kwargs" + + # default values for all constructor arguments should be identical + # + # NOTE: if LGBMClassifier / LGBMRanker / LGBMRegressor ever override + # any of LGBMModel's constructor arguments, this will need to be updated + assert subclass_spec.kwonlydefaults == base_spec.kwonlydefaults + + # only positional argument should be 'self' + assert subclass_spec.args == base_spec.args + assert subclass_spec.args == ["self"] + assert subclass_spec.defaults is None + + # get_params() should be identical + assert estimator().get_params() == lgb.LGBMModel().get_params() + + +def test_subclassing_get_params_works(): + expected_params = { + "boosting_type": "gbdt", + "class_weight": None, + "colsample_bytree": 1.0, + "importance_type": "split", + "learning_rate": 0.1, + "max_depth": -1, + "min_child_samples": 20, + "min_child_weight": 0.001, + "min_split_gain": 0.0, + "n_estimators": 100, + "n_jobs": None, + "num_leaves": 31, + "objective": None, + "random_state": None, + "reg_alpha": 0.0, + "reg_lambda": 0.0, + "subsample": 1.0, + "subsample_for_bin": 200000, + "subsample_freq": 0, + } + + # Overrides, used to test that passing through **kwargs works as expected. + # + # why these? + # + # - 'n_estimators' directly matches a keyword arg for the scikit-learn estimators + # - 'eta' is a parameter alias for 'learning_rate' + overrides = {"n_estimators": 13, "eta": 0.07} + + # lightgbm-official classes + for est in [lgb.LGBMModel, lgb.LGBMClassifier, lgb.LGBMRanker, lgb.LGBMRegressor]: + assert est().get_params() == expected_params + assert est(**overrides).get_params() == { + **expected_params, + "eta": 0.07, + "n_estimators": 13, + "learning_rate": 0.1, + } + + try: + import dask # noqa: F401,PLC0415 + except lgb.dask._DaskImportErrorTypes: + pass + + if "dask" in sys.modules: + for est in [lgb.DaskLGBMClassifier, lgb.DaskLGBMRanker, lgb.DaskLGBMRegressor]: + assert est().get_params() == { + **expected_params, + "client": None, + } + assert est(**overrides).get_params() == { + **expected_params, + "eta": 0.07, + "n_estimators": 13, + "learning_rate": 0.1, + "client": None, + } + + # custom sub-classes + assert ExtendedLGBMClassifier().get_params() == {**expected_params, "some_other_param": "lgbm-classifier"} + assert ExtendedLGBMClassifier(**overrides).get_params() == { + **expected_params, + "eta": 0.07, + "n_estimators": 13, + "learning_rate": 0.1, + "some_other_param": "lgbm-classifier", + } + assert ExtendedLGBMRanker().get_params() == { + **expected_params, + "some_other_param": "lgbm-ranker", + } + assert ExtendedLGBMRanker(**overrides).get_params() == { + **expected_params, + "eta": 0.07, + "n_estimators": 13, + "learning_rate": 0.1, + "some_other_param": "lgbm-ranker", + } + assert ExtendedLGBMRegressor().get_params() == { + **expected_params, + "some_other_param": "lgbm-regressor", + } + assert ExtendedLGBMRegressor(**overrides).get_params() == { + **expected_params, + "eta": 0.07, + "n_estimators": 13, + "learning_rate": 0.1, + "some_other_param": "lgbm-regressor", + } + + +@pytest.mark.parametrize("task", all_tasks) +def test_subclassing_works(task): + # param values to make training deterministic and + # just train a small, cheap model + params = { + "deterministic": True, + "force_row_wise": True, + "n_jobs": 1, + "n_estimators": 5, + "num_leaves": 11, + "random_state": 708, + } + + X, y, g = _create_data(task=task) + if task == "ranking": + est = lgb.LGBMRanker(**params).fit(X, y, group=g) + est_sub = ExtendedLGBMRanker(**params).fit(X, y, group=g) + elif task.endswith("classification"): + est = lgb.LGBMClassifier(**params).fit(X, y) + est_sub = ExtendedLGBMClassifier(**params).fit(X, y) + else: + est = lgb.LGBMRegressor(**params).fit(X, y) + est_sub = ExtendedLGBMRegressor(**params).fit(X, y) + + np.testing.assert_allclose(est.predict(X), est_sub.predict(X)) + + +@pytest.mark.parametrize( + "estimator_to_task", + [ + (lgb.LGBMClassifier, "binary-classification"), + (ExtendedLGBMClassifier, "binary-classification"), + (lgb.LGBMRanker, "ranking"), + (ExtendedLGBMRanker, "ranking"), + (lgb.LGBMRegressor, "regression"), + (ExtendedLGBMRegressor, "regression"), + ], +) +def test_parameter_aliases_are_handled_correctly(estimator_to_task): + estimator, task = estimator_to_task + # scikit-learn estimators should remember every parameter passed + # via keyword arguments in the estimator constructor, but then + # only pass the correct value down to LightGBM's C++ side + params = { + "eta": 0.08, + "num_iterations": 3, + "num_leaves": 5, + } + X, y, g = _create_data(task=task) + mod = estimator(**params) + if task == "ranking": + mod.fit(X, y, group=g) + else: + mod.fit(X, y) + + # scikit-learn get_params() + p = mod.get_params() + assert p["eta"] == 0.08 + assert p["learning_rate"] == 0.1 + + # lgb.Booster's 'params' attribute + p = mod.booster_.params + assert p["eta"] == 0.08 + assert p["learning_rate"] == 0.1 + + # Config in the 'LightGBM::Booster' on the C++ side + p = mod.booster_._get_loaded_param() + assert p["learning_rate"] == 0.1 + assert "eta" not in p + + +def test_joblib(tmp_path): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMRegressor(n_estimators=10, objective=custom_asymmetric_obj, verbose=-1, importance_type="split") + gbm.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_test, y_test)], + eval_metric=mse, + callbacks=[lgb.early_stopping(5), lgb.reset_parameter(learning_rate=list(np.arange(1, 0, -0.1)))], + ) + model_path_pkl = str(tmp_path / "lgb.pkl") + joblib.dump(gbm, model_path_pkl) # test model with custom functions + gbm_pickle = joblib.load(model_path_pkl) + assert isinstance(gbm_pickle.booster_, lgb.Booster) + assert gbm.get_params() == gbm_pickle.get_params() + np_assert_array_equal(gbm.feature_importances_, gbm_pickle.feature_importances_, strict=True) + assert gbm_pickle.learning_rate == pytest.approx(0.1) + assert callable(gbm_pickle.objective) + + for eval_set in gbm.evals_result_: + for metric in gbm.evals_result_[eval_set]: + np.testing.assert_allclose(gbm.evals_result_[eval_set][metric], gbm_pickle.evals_result_[eval_set][metric]) + pred_origin = gbm.predict(X_test) + pred_pickle = gbm_pickle.predict(X_test) + np.testing.assert_allclose(pred_origin, pred_pickle) + + +def test_non_serializable_objects_in_callbacks(tmp_path): + unpicklable_callback = UnpicklableCallback() + + with pytest.raises(Exception, match="This class in not picklable"): + joblib.dump(unpicklable_callback, tmp_path / "tmp.joblib") + + X, y = make_synthetic_regression() + gbm = lgb.LGBMRegressor(n_estimators=5) + gbm.fit(X, y, callbacks=[unpicklable_callback]) + assert gbm.booster_.attr_set_inside_callback == 40 + + +@pytest.mark.parametrize("rng_constructor", [np.random.RandomState, np.random.default_rng]) +def test_random_state_object(rng_constructor): + X, y = load_iris(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + state1 = rng_constructor(123) + state2 = rng_constructor(123) + clf1 = lgb.LGBMClassifier(n_estimators=10, subsample=0.5, subsample_freq=1, random_state=state1) + clf2 = lgb.LGBMClassifier(n_estimators=10, subsample=0.5, subsample_freq=1, random_state=state2) + # Test if random_state is properly stored + assert clf1.random_state is state1 + assert clf2.random_state is state2 + # Test if two random states produce identical models + clf1.fit(X_train, y_train) + clf2.fit(X_train, y_train) + y_pred1 = clf1.predict(X_test, raw_score=True) + y_pred2 = clf2.predict(X_test, raw_score=True) + np.testing.assert_allclose(y_pred1, y_pred2) + np_assert_array_equal(clf1.feature_importances_, clf2.feature_importances_, strict=True) + df1 = clf1.booster_.model_to_string(num_iteration=0) + df2 = clf2.booster_.model_to_string(num_iteration=0) + assert df1 == df2 + # Test if subsequent fits sample from random_state object and produce different models + clf1.fit(X_train, y_train) + y_pred1_refit = clf1.predict(X_test, raw_score=True) + df3 = clf1.booster_.model_to_string(num_iteration=0) + assert clf1.random_state is state1 + assert clf2.random_state is state2 + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(y_pred1, y_pred1_refit) + assert df1 != df3 + + +def test_feature_importances_single_leaf(): + data = load_iris(return_X_y=False) + clf = lgb.LGBMClassifier(n_estimators=10) + clf.fit(data.data, data.target) + importances = clf.feature_importances_ + assert len(importances) == 4 + + +def test_feature_importances_type(): + data = load_iris(return_X_y=False) + clf = lgb.LGBMClassifier(n_estimators=10) + clf.fit(data.data, data.target) + clf.set_params(importance_type="split") + importances_split = clf.feature_importances_ + clf.set_params(importance_type="gain") + importances_gain = clf.feature_importances_ + # Test that the largest element is NOT the same, the smallest can be the same, i.e. zero + importance_split_top1 = sorted(importances_split, reverse=True)[0] + importance_gain_top1 = sorted(importances_gain, reverse=True)[0] + assert importance_split_top1 != importance_gain_top1 + + +# why fixed seed? +# sometimes there is no difference how cols are treated (cat or not cat) +def test_pandas_categorical(rng_fixed_seed, tmp_path): + pd = pytest.importorskip("pandas") + X = pd.DataFrame( + { + "A": rng_fixed_seed.permutation(["a", "b", "c", "d"] * 75), # str + "B": rng_fixed_seed.permutation([1, 2, 3] * 100), # int + "C": rng_fixed_seed.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60), # float + "D": rng_fixed_seed.permutation([True, False] * 150), # bool + "E": pd.Categorical(rng_fixed_seed.permutation(["z", "y", "x", "w", "v"] * 60), ordered=True), + } + ) # str and ordered categorical + y = rng_fixed_seed.permutation([0, 1] * 150) + X_test = pd.DataFrame( + { + "A": rng_fixed_seed.permutation(["a", "b", "e"] * 20), # unseen category + "B": rng_fixed_seed.permutation([1, 3] * 30), + "C": rng_fixed_seed.permutation([0.1, -0.1, 0.2, 0.2] * 15), + "D": rng_fixed_seed.permutation([True, False] * 30), + "E": pd.Categorical(rng_fixed_seed.permutation(["z", "y"] * 30), ordered=True), + } + ) + cat_cols_actual = ["A", "B", "C", "D"] + cat_cols_to_store = cat_cols_actual + ["E"] + X[cat_cols_actual] = X[cat_cols_actual].astype("category") + X_test[cat_cols_actual] = X_test[cat_cols_actual].astype("category") + cat_values = [X[col].cat.categories.tolist() for col in cat_cols_to_store] + gbm0 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y) + pred0 = gbm0.predict(X_test, raw_score=True) + pred_prob = gbm0.predict_proba(X_test)[:, 1] + gbm1 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, pd.Series(y), categorical_feature=[0]) + pred1 = gbm1.predict(X_test, raw_score=True) + gbm2 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A"]) + pred2 = gbm2.predict(X_test, raw_score=True) + gbm3 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A", "B", "C", "D"]) + pred3 = gbm3.predict(X_test, raw_score=True) + categorical_model_path = tmp_path / "categorical.model" + gbm3.booster_.save_model(categorical_model_path) + gbm4 = lgb.Booster(model_file=categorical_model_path) + pred4 = gbm4.predict(X_test) + gbm5 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=["A", "B", "C", "D", "E"]) + pred5 = gbm5.predict(X_test, raw_score=True) + gbm6 = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y, categorical_feature=[]) + pred6 = gbm6.predict(X_test, raw_score=True) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred1) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred2) + np.testing.assert_allclose(pred1, pred2) + np.testing.assert_allclose(pred0, pred3) + np.testing.assert_allclose(pred_prob, pred4) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred5) # ordered cat features aren't treated as cat features by default + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(pred0, pred6) + assert gbm0.booster_.pandas_categorical == cat_values + assert gbm1.booster_.pandas_categorical == cat_values + assert gbm2.booster_.pandas_categorical == cat_values + assert gbm3.booster_.pandas_categorical == cat_values + assert gbm4.pandas_categorical == cat_values + assert gbm5.booster_.pandas_categorical == cat_values + assert gbm6.booster_.pandas_categorical == cat_values + + +def test_pandas_sparse(rng): + pd = pytest.importorskip("pandas") + X = pd.DataFrame( + { + "A": pd.arrays.SparseArray(rng.permutation([0, 1, 2] * 100)), + "B": pd.arrays.SparseArray(rng.permutation([0.0, 0.1, 0.2, -0.1, 0.2] * 60)), + "C": pd.arrays.SparseArray(rng.permutation([True, False] * 150)), + } + ) + y = pd.Series(pd.arrays.SparseArray(rng.permutation([0, 1] * 150))) + X_test = pd.DataFrame( + { + "A": pd.arrays.SparseArray(rng.permutation([0, 2] * 30)), + "B": pd.arrays.SparseArray(rng.permutation([0.0, 0.1, 0.2, -0.1] * 15)), + "C": pd.arrays.SparseArray(rng.permutation([True, False] * 30)), + } + ) + for dtype in pd.concat([X.dtypes, X_test.dtypes, pd.Series(y.dtypes)]): + assert isinstance(dtype, pd.SparseDtype) + gbm = lgb.sklearn.LGBMClassifier(n_estimators=10).fit(X, y) + pred_sparse = gbm.predict(X_test, raw_score=True) + if hasattr(X_test, "sparse"): + pred_dense = gbm.predict(X_test.sparse.to_dense(), raw_score=True) + else: + pred_dense = gbm.predict(X_test.to_dense(), raw_score=True) + np.testing.assert_allclose(pred_sparse, pred_dense) + + +def test_predict(): + # With default params + iris = load_iris(return_X_y=False) + X_train, X_test, y_train, _ = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42) + + gbm = lgb.train({"objective": "multiclass", "num_class": 3, "verbose": -1}, lgb.Dataset(X_train, y_train)) + clf = lgb.LGBMClassifier(verbose=-1).fit(X_train, y_train) + + # Tests same probabilities + res_engine = gbm.predict(X_test) + res_sklearn = clf.predict_proba(X_test) + np.testing.assert_allclose(res_engine, res_sklearn) + + # Tests same predictions + res_engine = np.argmax(gbm.predict(X_test), axis=1) + res_sklearn = clf.predict(X_test) + np.testing.assert_equal(res_engine, res_sklearn) + + # Tests same raw scores + res_engine = gbm.predict(X_test, raw_score=True) + res_sklearn = clf.predict(X_test, raw_score=True) + np.testing.assert_allclose(res_engine, res_sklearn) + + # Tests same leaf indices + res_engine = gbm.predict(X_test, pred_leaf=True) + res_sklearn = clf.predict(X_test, pred_leaf=True) + np.testing.assert_equal(res_engine, res_sklearn) + + # Tests same feature contributions + res_engine = gbm.predict(X_test, pred_contrib=True) + res_sklearn = clf.predict(X_test, pred_contrib=True) + np.testing.assert_allclose(res_engine, res_sklearn) + + # Tests other parameters for the prediction works + res_engine = gbm.predict(X_test) + res_sklearn_params = clf.predict_proba(X_test, pred_early_stop=True, pred_early_stop_margin=1.0) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(res_engine, res_sklearn_params) + + # Tests start_iteration + # Tests same probabilities, starting from iteration 10 + res_engine = gbm.predict(X_test, start_iteration=10) + res_sklearn = clf.predict_proba(X_test, start_iteration=10) + np.testing.assert_allclose(res_engine, res_sklearn) + + # Tests same predictions, starting from iteration 10 + res_engine = np.argmax(gbm.predict(X_test, start_iteration=10), axis=1) + res_sklearn = clf.predict(X_test, start_iteration=10) + np.testing.assert_equal(res_engine, res_sklearn) + + # Tests same raw scores, starting from iteration 10 + res_engine = gbm.predict(X_test, raw_score=True, start_iteration=10) + res_sklearn = clf.predict(X_test, raw_score=True, start_iteration=10) + np.testing.assert_allclose(res_engine, res_sklearn) + + # Tests same leaf indices, starting from iteration 10 + res_engine = gbm.predict(X_test, pred_leaf=True, start_iteration=10) + res_sklearn = clf.predict(X_test, pred_leaf=True, start_iteration=10) + np.testing.assert_equal(res_engine, res_sklearn) + + # Tests same feature contributions, starting from iteration 10 + res_engine = gbm.predict(X_test, pred_contrib=True, start_iteration=10) + res_sklearn = clf.predict(X_test, pred_contrib=True, start_iteration=10) + np.testing.assert_allclose(res_engine, res_sklearn) + + # Tests other parameters for the prediction works, starting from iteration 10 + res_engine = gbm.predict(X_test, start_iteration=10) + res_sklearn_params = clf.predict_proba(X_test, pred_early_stop=True, pred_early_stop_margin=1.0, start_iteration=10) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(res_engine, res_sklearn_params) + + # Test multiclass binary classification + num_samples = 100 + num_classes = 2 + X_train = np.linspace(start=0, stop=10, num=num_samples * 3).reshape(num_samples, 3) + y_train = np.concatenate([np.zeros(int(num_samples / 2 - 10)), np.ones(int(num_samples / 2 + 10))]) + + gbm = lgb.train({"objective": "multiclass", "num_class": num_classes, "verbose": -1}, lgb.Dataset(X_train, y_train)) + clf = lgb.LGBMClassifier(objective="multiclass", num_classes=num_classes).fit(X_train, y_train) + + res_engine = gbm.predict(X_train) + res_sklearn = clf.predict_proba(X_train) + + assert res_engine.shape == (num_samples, num_classes) + assert res_sklearn.shape == (num_samples, num_classes) + np.testing.assert_allclose(res_engine, res_sklearn) + + res_class_sklearn = clf.predict(X_train) + np.testing.assert_allclose(res_class_sklearn, y_train) + + +def test_decision_function_and_predict_proba_consistency(): + # binary + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42) + clf = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbose=-1).fit(X_train, y_train) + preds_raw = clf.decision_function(X_test) + np.testing.assert_allclose(preds_raw, clf.predict(X_test, raw_score=True)) + np.testing.assert_allclose(logistic_sigmoid(preds_raw), clf.predict_proba(X_test)[:, 1]) + + # multiclass + X, y = load_iris(return_X_y=True) + X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42) + clf = lgb.LGBMClassifier(n_estimators=10, random_state=42, verbose=-1).fit(X_train, y_train) + preds_raw = clf.decision_function(X_test) + np.testing.assert_allclose(preds_raw, clf.predict(X_test, raw_score=True)) + np.testing.assert_allclose(softmax(preds_raw), clf.predict_proba(X_test)) + + +@pytest.mark.parametrize("method", ["sigmoid", "isotonic"]) +def test_calibrated_classifier_cv(method): + # binary + X, y = load_breast_cancer(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + deterministic_params = { + "deterministic": True, + "force_col_wise": True, + "n_jobs": 1, + "seed": 312, + } + clf = CalibratedClassifierCV( + lgb.LGBMClassifier(n_estimators=10, verbose=-1, **deterministic_params), + method=method, + cv=3, + ) + clf.fit(X_train, y_train) + proba = clf.predict_proba(X_test) + assert proba.shape == (X_test.shape[0], 2) + np.testing.assert_array_less(proba, 1.0 + 1e-9) + np.testing.assert_array_less(-1e-9, proba) + np.testing.assert_allclose(proba.sum(axis=1), 1.0) + score = accuracy_score(y_test, clf.predict(X_test)) + assert 0.8 <= score <= 1.0 + + # multiclass + X, y = load_iris(return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + clf = CalibratedClassifierCV( + lgb.LGBMClassifier(n_estimators=10, verbose=-1, **deterministic_params), + method=method, + cv=3, + ) + clf.fit(X_train, y_train) + proba = clf.predict_proba(X_test) + assert proba.shape == (X_test.shape[0], 3) + np.testing.assert_array_less(proba, 1.0 + 1e-9) + np.testing.assert_array_less(-1e-9, proba) + np.testing.assert_allclose(proba.sum(axis=1), 1.0) + score = accuracy_score(y_test, clf.predict(X_test)) + assert 0.8 <= score <= 1.0 + + +def test_predict_with_params_from_init(): + X, y = load_iris(return_X_y=True) + X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42) + + predict_params = {"pred_early_stop": True, "pred_early_stop_margin": 1.0} + + y_preds_no_params = lgb.LGBMClassifier(verbose=-1).fit(X_train, y_train).predict(X_test, raw_score=True) + + y_preds_params_in_predict = ( + lgb.LGBMClassifier(verbose=-1).fit(X_train, y_train).predict(X_test, raw_score=True, **predict_params) + ) + with pytest.raises(AssertionError): # noqa: PT011 + np.testing.assert_allclose(y_preds_no_params, y_preds_params_in_predict) + + y_preds_params_in_set_params_before_fit = ( + lgb.LGBMClassifier(verbose=-1) + .set_params(**predict_params) + .fit(X_train, y_train) + .predict(X_test, raw_score=True) + ) + np.testing.assert_allclose(y_preds_params_in_predict, y_preds_params_in_set_params_before_fit) + + y_preds_params_in_set_params_after_fit = ( + lgb.LGBMClassifier(verbose=-1) + .fit(X_train, y_train) + .set_params(**predict_params) + .predict(X_test, raw_score=True) + ) + np.testing.assert_allclose(y_preds_params_in_predict, y_preds_params_in_set_params_after_fit) + + y_preds_params_in_init = ( + lgb.LGBMClassifier(verbose=-1, **predict_params).fit(X_train, y_train).predict(X_test, raw_score=True) + ) + np.testing.assert_allclose(y_preds_params_in_predict, y_preds_params_in_init) + + # test that params passed in predict have higher priority + y_preds_params_overwritten = ( + lgb.LGBMClassifier(verbose=-1, **predict_params) + .fit(X_train, y_train) + .predict(X_test, raw_score=True, pred_early_stop=False) + ) + np.testing.assert_allclose(y_preds_no_params, y_preds_params_overwritten) + + +def test_evaluate_train_set(): + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + gbm = lgb.LGBMRegressor(n_estimators=10, verbose=-1) + gbm.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)]) + assert len(gbm.evals_result_) == 2 + assert "training" in gbm.evals_result_ + assert len(gbm.evals_result_["training"]) == 1 + assert "l2" in gbm.evals_result_["training"] + assert "valid_1" in gbm.evals_result_ + assert len(gbm.evals_result_["valid_1"]) == 1 + assert "l2" in gbm.evals_result_["valid_1"] + + +def test_metrics(): + X, y = make_synthetic_regression() + y = abs(y) + params = {"n_estimators": 2, "verbose": -1} + params_fit = {"X": X, "y": y, "eval_set": (X, y)} + + # no custom objective, no custom metric + # default metric + gbm = lgb.LGBMRegressor(**params).fit(**params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "l2" in gbm.evals_result_["training"] + + # non-default metric + gbm = lgb.LGBMRegressor(metric="mape", **params).fit(**params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "mape" in gbm.evals_result_["training"] + + # no metric + gbm = lgb.LGBMRegressor(metric="None", **params).fit(**params_fit) + assert gbm.evals_result_ == {} + + # non-default metric in eval_metric + gbm = lgb.LGBMRegressor(**params).fit(eval_metric="mape", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "l2" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # non-default metric with non-default metric in eval_metric + gbm = lgb.LGBMRegressor(metric="gamma", **params).fit(eval_metric="mape", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "gamma" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # non-default metric with multiple metrics in eval_metric + gbm = lgb.LGBMRegressor(metric="gamma", **params).fit(eval_metric=["l2", "mape"], **params_fit) + assert len(gbm.evals_result_["training"]) == 3 + assert "gamma" in gbm.evals_result_["training"] + assert "l2" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # non-default metric with multiple metrics in eval_metric for LGBMClassifier + X_classification, y_classification = load_breast_cancer(return_X_y=True) + params_classification = {"n_estimators": 2, "verbose": -1, "objective": "binary", "metric": "binary_logloss"} + params_fit_classification = { + "X": X_classification, + "y": y_classification, + "eval_set": (X_classification, y_classification), + } + gbm = lgb.LGBMClassifier(**params_classification).fit(eval_metric=["fair", "error"], **params_fit_classification) + assert len(gbm.evals_result_["training"]) == 3 + assert "fair" in gbm.evals_result_["training"] + assert "binary_error" in gbm.evals_result_["training"] + assert "binary_logloss" in gbm.evals_result_["training"] + + # default metric for non-default objective + gbm = lgb.LGBMRegressor(objective="regression_l1", **params).fit(**params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "l1" in gbm.evals_result_["training"] + + # non-default metric for non-default objective + gbm = lgb.LGBMRegressor(objective="regression_l1", metric="mape", **params).fit(**params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "mape" in gbm.evals_result_["training"] + + # no metric + gbm = lgb.LGBMRegressor(objective="regression_l1", metric="None", **params).fit(**params_fit) + assert gbm.evals_result_ == {} + + # non-default metric in eval_metric for non-default objective + gbm = lgb.LGBMRegressor(objective="regression_l1", **params).fit(eval_metric="mape", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "l1" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # non-default metric with non-default metric in eval_metric for non-default objective + gbm = lgb.LGBMRegressor(objective="regression_l1", metric="gamma", **params).fit(eval_metric="mape", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "gamma" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # non-default metric with multiple metrics in eval_metric for non-default objective + gbm = lgb.LGBMRegressor(objective="regression_l1", metric="gamma", **params).fit( + eval_metric=["l2", "mape"], **params_fit + ) + assert len(gbm.evals_result_["training"]) == 3 + assert "gamma" in gbm.evals_result_["training"] + assert "l2" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # custom objective, no custom metric + # default regression metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, **params).fit(**params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "l2" in gbm.evals_result_["training"] + + # non-default regression metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric="mape", **params).fit(**params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "mape" in gbm.evals_result_["training"] + + # multiple regression metrics for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=["l1", "gamma"], **params).fit(**params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "l1" in gbm.evals_result_["training"] + assert "gamma" in gbm.evals_result_["training"] + + # no metric + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric="None", **params).fit(**params_fit) + assert gbm.evals_result_ == {} + + # default regression metric with non-default metric in eval_metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, **params).fit(eval_metric="mape", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "l2" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # non-default regression metric with metric in eval_metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric="mape", **params).fit(eval_metric="gamma", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "mape" in gbm.evals_result_["training"] + assert "gamma" in gbm.evals_result_["training"] + + # multiple regression metrics with metric in eval_metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=["l1", "gamma"], **params).fit( + eval_metric="l2", **params_fit + ) + assert len(gbm.evals_result_["training"]) == 3 + assert "l1" in gbm.evals_result_["training"] + assert "gamma" in gbm.evals_result_["training"] + assert "l2" in gbm.evals_result_["training"] + + # multiple regression metrics with multiple metrics in eval_metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=["l1", "gamma"], **params).fit( + eval_metric=["l2", "mape"], **params_fit + ) + assert len(gbm.evals_result_["training"]) == 4 + assert "l1" in gbm.evals_result_["training"] + assert "gamma" in gbm.evals_result_["training"] + assert "l2" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + + # no custom objective, custom metric + # default metric with custom metric + gbm = lgb.LGBMRegressor(**params).fit(eval_metric=constant_metric, **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "l2" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + # non-default metric with custom metric + gbm = lgb.LGBMRegressor(metric="mape", **params).fit(eval_metric=constant_metric, **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "mape" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + # multiple metrics with custom metric + gbm = lgb.LGBMRegressor(metric=["l1", "gamma"], **params).fit(eval_metric=constant_metric, **params_fit) + assert len(gbm.evals_result_["training"]) == 3 + assert "l1" in gbm.evals_result_["training"] + assert "gamma" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + # custom metric (disable default metric) + gbm = lgb.LGBMRegressor(metric="None", **params).fit(eval_metric=constant_metric, **params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "error" in gbm.evals_result_["training"] + + # default metric for non-default objective with custom metric + gbm = lgb.LGBMRegressor(objective="regression_l1", **params).fit(eval_metric=constant_metric, **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "l1" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + # non-default metric for non-default objective with custom metric + gbm = lgb.LGBMRegressor(objective="regression_l1", metric="mape", **params).fit( + eval_metric=constant_metric, **params_fit + ) + assert len(gbm.evals_result_["training"]) == 2 + assert "mape" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + # multiple metrics for non-default objective with custom metric + gbm = lgb.LGBMRegressor(objective="regression_l1", metric=["l1", "gamma"], **params).fit( + eval_metric=constant_metric, **params_fit + ) + assert len(gbm.evals_result_["training"]) == 3 + assert "l1" in gbm.evals_result_["training"] + assert "gamma" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + # custom metric (disable default metric for non-default objective) + gbm = lgb.LGBMRegressor(objective="regression_l1", metric="None", **params).fit( + eval_metric=constant_metric, **params_fit + ) + assert len(gbm.evals_result_["training"]) == 1 + assert "error" in gbm.evals_result_["training"] + + # custom objective, custom metric + # custom metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, **params).fit(eval_metric=constant_metric, **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "error" in gbm.evals_result_["training"] + + # non-default regression metric with custom metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric="mape", **params).fit( + eval_metric=constant_metric, **params_fit + ) + assert len(gbm.evals_result_["training"]) == 2 + assert "mape" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + # multiple regression metrics with custom metric for custom objective + gbm = lgb.LGBMRegressor(objective=custom_dummy_obj, metric=["l2", "mape"], **params).fit( + eval_metric=constant_metric, **params_fit + ) + assert len(gbm.evals_result_["training"]) == 3 + assert "l2" in gbm.evals_result_["training"] + assert "mape" in gbm.evals_result_["training"] + assert "error" in gbm.evals_result_["training"] + + X, y = load_digits(n_class=3, return_X_y=True) + params_fit = {"X": X, "y": y, "eval_set": (X, y)} + + # default metric and invalid binary metric is replaced with multiclass alternative + gbm = lgb.LGBMClassifier(**params).fit(eval_metric="binary_error", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "multi_logloss" in gbm.evals_result_["training"] + assert "multi_error" in gbm.evals_result_["training"] + + # invalid binary metric is replaced with multiclass alternative + gbm = lgb.LGBMClassifier(**params).fit(eval_metric="binary_error", **params_fit) + assert gbm.objective_ == "multiclass" + assert len(gbm.evals_result_["training"]) == 2 + assert "multi_logloss" in gbm.evals_result_["training"] + assert "multi_error" in gbm.evals_result_["training"] + + # default metric for non-default multiclass objective + # and invalid binary metric is replaced with multiclass alternative + gbm = lgb.LGBMClassifier(objective="ovr", **params).fit(eval_metric="binary_error", **params_fit) + assert gbm.objective_ == "ovr" + assert len(gbm.evals_result_["training"]) == 2 + assert "multi_logloss" in gbm.evals_result_["training"] + assert "multi_error" in gbm.evals_result_["training"] + + X, y = load_digits(n_class=2, return_X_y=True) + params_fit = {"X": X, "y": y, "eval_set": (X, y)} + + # default metric and invalid multiclass metric is replaced with binary alternative + gbm = lgb.LGBMClassifier(**params).fit(eval_metric="multi_error", **params_fit) + assert len(gbm.evals_result_["training"]) == 2 + assert "binary_logloss" in gbm.evals_result_["training"] + assert "binary_error" in gbm.evals_result_["training"] + + # invalid multiclass metric is replaced with binary alternative for custom objective + gbm = lgb.LGBMClassifier(objective=custom_dummy_obj, **params).fit(eval_metric="multi_logloss", **params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "binary_logloss" in gbm.evals_result_["training"] + + # the evaluation metric changes to multiclass metric even num classes is 2 for multiclass objective + gbm = lgb.LGBMClassifier(objective="multiclass", num_classes=2, **params).fit( + eval_metric="binary_logloss", **params_fit + ) + assert len(gbm._evals_result["training"]) == 1 + assert "multi_logloss" in gbm.evals_result_["training"] + + # the evaluation metric changes to multiclass metric even num classes is 2 for ovr objective + gbm = lgb.LGBMClassifier(objective="ovr", num_classes=2, **params).fit(eval_metric="binary_error", **params_fit) + assert gbm.objective_ == "ovr" + assert len(gbm.evals_result_["training"]) == 2 + assert "multi_logloss" in gbm.evals_result_["training"] + assert "multi_error" in gbm.evals_result_["training"] + + +def test_multiple_eval_metrics(): + X, y = load_breast_cancer(return_X_y=True) + + params = {"n_estimators": 2, "verbose": -1, "objective": "binary", "metric": "binary_logloss"} + params_fit = {"X": X, "y": y, "eval_set": (X, y)} + + # Verify that can receive a list of metrics, only callable + gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[constant_metric, decreasing_metric], **params_fit) + assert len(gbm.evals_result_["training"]) == 3 + assert "error" in gbm.evals_result_["training"] + assert "decreasing_metric" in gbm.evals_result_["training"] + assert "binary_logloss" in gbm.evals_result_["training"] + + # Verify that can receive a list of custom and built-in metrics + gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[constant_metric, decreasing_metric, "fair"], **params_fit) + assert len(gbm.evals_result_["training"]) == 4 + assert "error" in gbm.evals_result_["training"] + assert "decreasing_metric" in gbm.evals_result_["training"] + assert "binary_logloss" in gbm.evals_result_["training"] + assert "fair" in gbm.evals_result_["training"] + + # Verify that works as expected when eval_metric is empty + gbm = lgb.LGBMClassifier(**params).fit(eval_metric=[], **params_fit) + assert len(gbm.evals_result_["training"]) == 1 + assert "binary_logloss" in gbm.evals_result_["training"] + + # Verify that can receive a list of metrics, only built-in + gbm = lgb.LGBMClassifier(**params).fit(eval_metric=["fair", "error"], **params_fit) + assert len(gbm.evals_result_["training"]) == 3 + assert "binary_logloss" in gbm.evals_result_["training"] + + # Verify that eval_metric is robust to receiving a list with None + gbm = lgb.LGBMClassifier(**params).fit(eval_metric=["fair", "error", None], **params_fit) + assert len(gbm.evals_result_["training"]) == 3 + assert "binary_logloss" in gbm.evals_result_["training"] + + +def test_nan_handle(rng): + nrows = 100 + ncols = 10 + X = rng.standard_normal(size=(nrows, ncols)) + y = rng.standard_normal(size=(nrows,)) + np.full(nrows, 1e30) + weight = np.zeros(nrows) + params = {"n_estimators": 20, "verbose": -1} + params_fit = {"X": X, "y": y, "sample_weight": weight, "eval_set": (X, y), "callbacks": [lgb.early_stopping(5)]} + gbm = lgb.LGBMRegressor(**params).fit(**params_fit) + np.testing.assert_allclose(gbm.evals_result_["training"]["l2"], np.nan) + + +@pytest.mark.skipif(BuildInfo.has_cuda, reason="Skip due to differences in implementation details of CUDA version") +def test_first_metric_only(): + def fit_and_check(eval_set_names, metric_names, assumed_iteration, first_metric_only): + params["first_metric_only"] = first_metric_only + gbm = lgb.LGBMRegressor(**params).fit(**params_fit) + assert len(gbm.evals_result_) == len(eval_set_names) + for eval_set_name in eval_set_names: + assert eval_set_name in gbm.evals_result_ + assert len(gbm.evals_result_[eval_set_name]) == len(metric_names) + for metric_name in metric_names: + assert metric_name in gbm.evals_result_[eval_set_name] + + actual = len(gbm.evals_result_[eval_set_name][metric_name]) + expected = assumed_iteration + ( + params["early_stopping_rounds"] + if eval_set_name != "training" and assumed_iteration != gbm.n_estimators + else 0 + ) + assert expected == actual + if eval_set_name != "training": + assert assumed_iteration == gbm.best_iteration_ + else: + assert gbm.n_estimators == gbm.best_iteration_ + + X, y = make_synthetic_regression(n_samples=300) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + X_test1, X_test2, y_test1, y_test2 = train_test_split(X_test, y_test, test_size=0.5, random_state=72) + params = { + "n_estimators": 30, + "learning_rate": 0.8, + "num_leaves": 15, + "verbose": -1, + "seed": 123, + "early_stopping_rounds": 5, + } # early stop should be supported via global LightGBM parameter + params_fit = {"X": X_train, "y": y_train} + + iter_valid1_l1 = 4 + iter_valid1_l2 = 4 + iter_valid2_l1 = 2 + iter_valid2_l2 = 2 + assert len({iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2}) == 2 + iter_min_l1 = min([iter_valid1_l1, iter_valid2_l1]) + iter_min_l2 = min([iter_valid1_l2, iter_valid2_l2]) + iter_min = min([iter_min_l1, iter_min_l2]) + iter_min_valid1 = min([iter_valid1_l1, iter_valid1_l2]) + + # feval + params["metric"] = "None" + params_fit["eval_metric"] = lambda preds, train_data: [ + decreasing_metric(preds, train_data), + constant_metric(preds, train_data), + ] + params_fit["eval_set"] = (X_test1, y_test1) + fit_and_check(["valid_0"], ["decreasing_metric", "error"], 1, False) + fit_and_check(["valid_0"], ["decreasing_metric", "error"], 30, True) + params_fit["eval_metric"] = lambda preds, train_data: [ + constant_metric(preds, train_data), + decreasing_metric(preds, train_data), + ] + fit_and_check(["valid_0"], ["decreasing_metric", "error"], 1, True) + + # single eval_set + params.pop("metric") + params_fit.pop("eval_metric") + fit_and_check(["valid_0"], ["l2"], iter_valid1_l2, False) + fit_and_check(["valid_0"], ["l2"], iter_valid1_l2, True) + + params_fit["eval_metric"] = "l2" + fit_and_check(["valid_0"], ["l2"], iter_valid1_l2, False) + fit_and_check(["valid_0"], ["l2"], iter_valid1_l2, True) + + params_fit["eval_metric"] = "l1" + fit_and_check(["valid_0"], ["l1", "l2"], iter_min_valid1, False) + fit_and_check(["valid_0"], ["l1", "l2"], iter_valid1_l1, True) + + params_fit["eval_metric"] = ["l1", "l2"] + fit_and_check(["valid_0"], ["l1", "l2"], iter_min_valid1, False) + fit_and_check(["valid_0"], ["l1", "l2"], iter_valid1_l1, True) + + params_fit["eval_metric"] = ["l2", "l1"] + fit_and_check(["valid_0"], ["l1", "l2"], iter_min_valid1, False) + fit_and_check(["valid_0"], ["l1", "l2"], iter_valid1_l2, True) + + params_fit["eval_metric"] = ["l2", "regression", "mse"] # test aliases + fit_and_check(["valid_0"], ["l2"], iter_valid1_l2, False) + fit_and_check(["valid_0"], ["l2"], iter_valid1_l2, True) + + # two eval_set + params_fit["eval_set"] = [(X_test1, y_test1), (X_test2, y_test2)] + params_fit["eval_metric"] = ["l1", "l2"] + fit_and_check(["valid_0", "valid_1"], ["l1", "l2"], iter_min_l1, True) + params_fit["eval_metric"] = ["l2", "l1"] + fit_and_check(["valid_0", "valid_1"], ["l1", "l2"], iter_min_l2, True) + + params_fit["eval_set"] = [(X_test2, y_test2), (X_test1, y_test1)] + params_fit["eval_metric"] = ["l1", "l2"] + fit_and_check(["valid_0", "valid_1"], ["l1", "l2"], iter_min, False) + fit_and_check(["valid_0", "valid_1"], ["l1", "l2"], iter_min_l1, True) + params_fit["eval_metric"] = ["l2", "l1"] + fit_and_check(["valid_0", "valid_1"], ["l1", "l2"], iter_min, False) + fit_and_check(["valid_0", "valid_1"], ["l1", "l2"], iter_min_l2, True) + + +def test_class_weight(): + X, y = load_digits(n_class=10, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + y_train_str = y_train.astype("str") + y_test_str = y_test.astype("str") + gbm = lgb.LGBMClassifier(n_estimators=10, class_weight="balanced", verbose=-1) + gbm.fit( + X_train, + y_train, + eval_set=[(X_train, y_train), (X_test, y_test), (X_test, y_test), (X_test, y_test), (X_test, y_test)], + eval_class_weight=["balanced", None, "balanced", {1: 10, 4: 20}, {5: 30, 2: 40}], + ) + for eval_set1, eval_set2 in itertools.combinations(gbm.evals_result_.keys(), 2): + for metric in gbm.evals_result_[eval_set1]: + np.testing.assert_raises( + AssertionError, + np.testing.assert_allclose, + gbm.evals_result_[eval_set1][metric], + gbm.evals_result_[eval_set2][metric], + ) + gbm_str = lgb.LGBMClassifier(n_estimators=10, class_weight="balanced", verbose=-1) + gbm_str.fit( + X_train, + y_train_str, + eval_set=[ + (X_train, y_train_str), + (X_test, y_test_str), + (X_test, y_test_str), + (X_test, y_test_str), + (X_test, y_test_str), + ], + eval_class_weight=["balanced", None, "balanced", {"1": 10, "4": 20}, {"5": 30, "2": 40}], + ) + for eval_set1, eval_set2 in itertools.combinations(gbm_str.evals_result_.keys(), 2): + for metric in gbm_str.evals_result_[eval_set1]: + np.testing.assert_raises( + AssertionError, + np.testing.assert_allclose, + gbm_str.evals_result_[eval_set1][metric], + gbm_str.evals_result_[eval_set2][metric], + ) + for eval_set in gbm.evals_result_: + for metric in gbm.evals_result_[eval_set]: + np.testing.assert_allclose(gbm.evals_result_[eval_set][metric], gbm_str.evals_result_[eval_set][metric]) + + +def test_continue_training_with_model(): + X, y = load_digits(n_class=3, return_X_y=True) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) + init_gbm = lgb.LGBMClassifier(n_estimators=5).fit(X_train, y_train, eval_set=(X_test, y_test)) + gbm = lgb.LGBMClassifier(n_estimators=5).fit(X_train, y_train, eval_set=(X_test, y_test), init_model=init_gbm) + assert len(init_gbm.evals_result_["valid_0"]["multi_logloss"]) == len(gbm.evals_result_["valid_0"]["multi_logloss"]) + assert len(init_gbm.evals_result_["valid_0"]["multi_logloss"]) == 5 + assert gbm.evals_result_["valid_0"]["multi_logloss"][-1] < init_gbm.evals_result_["valid_0"]["multi_logloss"][-1] + + +def test_booster_does_not_hold_datasets(): + """fit() should clear the Dataset objects stored on the Booster""" + data = load_iris(return_X_y=False) + clf = lgb.LGBMClassifier(n_estimators=2, max_depth=2) + clf.fit(data.data, data.target) + assert not hasattr(clf.booster_, "train_set") + assert not hasattr(clf.booster_, "valid_sets") + + +def test_actual_number_of_trees(): + X = [[1, 2, 3], [1, 2, 3]] + y = [1.0, 1.0] + n_estimators = 5 + gbm = lgb.LGBMRegressor(n_estimators=n_estimators).fit(X, y) + assert gbm.n_estimators == n_estimators + assert gbm.n_estimators_ == 1 + assert gbm.n_iter_ == 1 + np_assert_array_equal(gbm.predict(np.array(X) * 10), y, strict=True) + + +def test_check_is_fitted(): + X, y = load_digits(n_class=2, return_X_y=True) + est = lgb.LGBMModel(n_estimators=5, objective="binary") + clf = lgb.LGBMClassifier(n_estimators=5) + reg = lgb.LGBMRegressor(n_estimators=5) + rnk = lgb.LGBMRanker(n_estimators=5) + models = (est, clf, reg, rnk) + for model in models: + err_msg = f"This {type(model).__name__} instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator." + with pytest.raises(lgb.compat.LGBMNotFittedError, match=err_msg): + check_is_fitted(model) + est.fit(X, y) + clf.fit(X, y) + reg.fit(X, y) + rnk.fit(X, y, group=np.ones(X.shape[0])) + for model in models: + check_is_fitted(model) + + +@pytest.mark.parametrize("estimator_class", estimator_classes) +@pytest.mark.parametrize("max_depth", [3, 4, 5, 8]) +def test_max_depth_warning_is_never_raised(capsys, estimator_class, max_depth): + X, y = make_blobs(n_samples=1_000, n_features=1, centers=2) + params = {"n_estimators": 1, "max_depth": max_depth, "verbose": 0} + if estimator_class is lgb.LGBMModel: + estimator_class(**{**params, "objective": "binary"}).fit(X, y) + elif estimator_class is lgb.LGBMRanker: + estimator_class(**params).fit(X, y, group=np.ones(X.shape[0])) + else: + estimator_class(**params).fit(X, y) + assert "Provided parameters constrain tree depth" not in capsys.readouterr().out + + +def test_verbosity_is_respected_when_using_custom_objective(capsys): + X, y = make_synthetic_regression() + params = { + "objective": objective_ls, + "nonsense": 123, + "num_leaves": 3, + } + lgb.LGBMRegressor(**params, verbosity=-1, n_estimators=1).fit(X, y) + assert capsys.readouterr().out == "" + lgb.LGBMRegressor(**params, verbosity=0, n_estimators=1).fit(X, y) + assert "[LightGBM] [Warning] Unknown parameter: nonsense" in capsys.readouterr().out + + +def test_fit_only_raises_num_rounds_warning_when_expected(capsys): + X, y = make_synthetic_regression() + base_kwargs = { + "num_leaves": 5, + "verbosity": -1, + } + + # no warning: no aliases, all defaults + reg = lgb.LGBMRegressor(**base_kwargs).fit(X, y) + assert reg.n_estimators_ == 100 + assert_silent(capsys) + + # no warning: no aliases, just n_estimators + reg = lgb.LGBMRegressor(**base_kwargs, n_estimators=2).fit(X, y) + assert reg.n_estimators_ == 2 + assert_silent(capsys) + + # no warning: 1 alias + n_estimators (both same value) + reg = lgb.LGBMRegressor(**base_kwargs, n_estimators=3, n_iter=3).fit(X, y) + assert reg.n_estimators_ == 3 + assert_silent(capsys) + + # no warning: 1 alias + n_estimators (different values... value from params should win) + reg = lgb.LGBMRegressor(**base_kwargs, n_estimators=3, n_iter=4).fit(X, y) + assert reg.n_estimators_ == 4 + assert_silent(capsys) + + # no warning: 2 aliases (both same value) + reg = lgb.LGBMRegressor(**base_kwargs, n_iter=3, num_iterations=3).fit(X, y) + assert reg.n_estimators_ == 3 + assert_silent(capsys) + + # no warning: 4 aliases (all same value) + reg = lgb.LGBMRegressor(**base_kwargs, n_iter=3, num_trees=3, nrounds=3, max_iter=3).fit(X, y) + assert reg.n_estimators_ == 3 + assert_silent(capsys) + + # warning: 2 aliases (different values... "num_iterations" wins because it's the main param name) + with pytest.warns(UserWarning, match="LightGBM will perform up to 5 boosting rounds"): + reg = lgb.LGBMRegressor(**base_kwargs, num_iterations=5, n_iter=6).fit(X, y) + assert reg.n_estimators_ == 5 + # should not be any other logs (except the warning, intercepted by pytest) + assert_silent(capsys) + + # warning: 2 aliases (different values... first one in the order from Config::parameter2aliases() wins) + with pytest.warns(UserWarning, match="LightGBM will perform up to 4 boosting rounds"): + reg = lgb.LGBMRegressor(**base_kwargs, n_iter=4, max_iter=5).fit(X, y) + assert reg.n_estimators_ == 4 + # should not be any other logs (except the warning, intercepted by pytest) + assert_silent(capsys) + + +@pytest.mark.parametrize("estimator_class", estimator_classes) +def test_cannot_access_feature_names_before_fitting(estimator_class): + model = estimator_class() + with pytest.raises(lgb.compat.LGBMNotFittedError): # noqa: PT011 + model.feature_name_ + with pytest.raises(lgb.compat.LGBMNotFittedError): # noqa: PT011 + model.feature_names_in_ + with pytest.raises(lgb.compat.LGBMNotFittedError): # noqa: PT011 + model.n_features_ + with pytest.raises(lgb.compat.LGBMNotFittedError): # noqa: PT011 + model.n_features_in_ + + +@pytest.mark.parametrize( + "predict_X_type", + [ + pytest.param("numpy", id="predict=numpy"), + pytest.param("pd_DataFrame", id="predict=pd_DataFrame"), + pytest.param("pa_Table", id="predict=pa_Table"), + pytest.param("pl_DataFrame", id="predict=pl_DataFrame"), + ], +) +@pytest.mark.parametrize( + "fit_X_type", + [ + pytest.param("numpy", id="fit=numpy"), + pytest.param("pd_DataFrame", id="fit=pd_DataFrame"), + pytest.param("pa_Table", id="fit=pa_Table"), + pytest.param("pl_DataFrame", id="fit=pl_DataFrame"), + ], +) +@pytest.mark.filterwarnings("error:.*feature name.*:UserWarning:sklearn") +def test_feature_names_in_and_predict_warning( + predict_X_type, + fit_X_type, +): + """Test feature_names_in_ behavior and predict()-time feature name warnings. + + Should cover all combinations of fit X type, feature_name argument, and predict X type. + Regression test for https://github.com/lightgbm-org/LightGBM/issues/6798. + """ + if fit_X_type.startswith("pa_") or predict_X_type.startswith("pa_"): + pa = pytest.importorskip("pyarrow") + pd = pytest.importorskip("pandas") + if fit_X_type.startswith("pd_") or predict_X_type.startswith("pd_"): + pd = pytest.importorskip("pandas") + if fit_X_type.startswith("pl_") or predict_X_type.startswith("pl_"): + pl = pytest.importorskip("polars") + + X_np = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]]) + y = np.array([0, 1, 0, 1]) + n_features = X_np.shape[1] + col_names = ["feat_0", "feat_1"] + default_names = ["Column_0", "Column_1"] + + if fit_X_type == "numpy": + X_fit = X_np + elif fit_X_type == "pd_DataFrame": + X_fit = pd.DataFrame(X_np, columns=col_names) + elif fit_X_type == "pl_DataFrame": + X_fit = pl.DataFrame(X_np, schema=col_names) + else: + X_fit = pa.Table.from_pandas(pd.DataFrame(X_np, columns=col_names)) + + if predict_X_type == "numpy": + X_predict = X_np[:2] + elif predict_X_type == "pd_DataFrame": + X_predict = pd.DataFrame(X_np[:2], columns=col_names) + elif predict_X_type == "pl_DataFrame": + X_predict = pl.DataFrame(X_np[:2], schema=col_names) + else: + X_predict = pa.Table.from_pandas(pd.DataFrame(X_np[:2], columns=col_names)) + + # arguments to warnings.filterwarnings() that match scikit-learn's warning + warning_kwargs = { + "category": UserWarning, + "module": "sklearn", + "message": ".*feature names.*", + } + + # input types where LightGBM supports 'feature_name="auto"' + types_with_feat_names = {"pa_Table", "pd_DataFrame", "pl_DataFrame"} + + # case 1: no 'feature_names' passed to fit() and "feature_name='auto'" should have identical behavior + for fit_kwargs in ({}, {"feature_name": "auto"}): + model = lgb.LGBMClassifier(n_estimators=2, num_leaves=3).fit(X_fit, y, **fit_kwargs) + + # n_features_in_: always set after fit + assert model.n_features_in_ == n_features + + # feature_name_: always accessible, reflects actual names used internally + # feature_names_in_: absent when no named features, present otherwise + if fit_X_type in types_with_feat_names: + np_assert_array_equal(model.feature_names_in_, np.array(col_names), strict=True) + assert model.feature_name_ == col_names + else: + assert model.feature_name_ == default_names + with pytest.raises(AttributeError, match="The training data did not have feature names"): + model.feature_names_in_ + + # predict() should not raise a warning if the input did not have feature names + if SKLEARN_VERSION_GTE_1_6: + # fmt:off + if ( + fit_X_type in types_with_feat_names + and + predict_X_type not in types_with_feat_names + ): + # fmt:on + # warning may be raised (and was from at least scikit-learn 1.6) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", **warning_kwargs) + model.predict(X_predict) + else: + # expect no warning + with warnings.catch_warnings(): + warnings.filterwarnings("error", **warning_kwargs) + model.predict(X_predict) + + # case 2: 'feature_names=custom_names' (different from column names) passed to fit() + custom_names = ["custom_0", "custom_1"] + model = lgb.LGBMClassifier(n_estimators=2, num_leaves=3).fit(X_fit, y, feature_name=custom_names) + + # feature names from keyword arg should be used, not any from the input data + np_assert_array_equal(model.feature_names_in_, np.array(custom_names), strict=True) + assert model.feature_name_ == custom_names + np_assert_array_equal(model.feature_names_in_, np.array(custom_names), strict=True) + assert model.n_features_in_ == n_features + + # predict() should not raise a warning if input has feature names + if SKLEARN_VERSION_GTE_1_6: + if predict_X_type in types_with_feat_names: + # expect no warning + with warnings.catch_warnings(): + warnings.filterwarnings("error", **warning_kwargs) + model.predict(X_predict) + else: + # warning may be raised (and was from at least scikit-learn 1.6) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", **warning_kwargs) + model.predict(X_predict) + + +# Starting with scikit-learn 1.6 (https://github.com/scikit-learn/scikit-learn/pull/30149), +# the only API for marking estimator tests as expected to fail is to pass a keyword argument +# to parametrize_with_checks(). That function didn't accept additional arguments in earlier +# versions. +# +# This block defines a patched version of parametrize_with_checks() so lightgbm's tests +# can be compatible with scikit-learn <1.6 and >=1.6. +# +# This should be removed once minimum supported scikit-learn version is at least 1.6. +if SKLEARN_VERSION_GTE_1_6: + parametrize_with_checks = sklearn_parametrize_with_checks +else: + + def parametrize_with_checks(estimator, *args, **kwargs): + return sklearn_parametrize_with_checks(estimator) + + +def _get_expected_failed_tests(estimator): + return estimator._more_tags()["_xfail_checks"] + + +@parametrize_with_checks( + [ExtendedLGBMClassifier(), ExtendedLGBMRegressor(), lgb.LGBMClassifier(), lgb.LGBMRegressor()], + expected_failed_checks=_get_expected_failed_tests, +) +def test_sklearn_integration(estimator, check): + estimator.set_params(min_child_samples=1, min_data_in_bin=1) + check(estimator) + + +@pytest.mark.parametrize("estimator_class", estimator_classes) +def test_sklearn_tags_should_correctly_reflect_lightgbm_specific_values(estimator_class): + est = estimator_class() + more_tags = est._more_tags() + err_msg = "List of supported X_types has changed. Update LGBMModel.__sklearn_tags__() to match." + assert more_tags["X_types"] == ["2darray", "sparse", "1dlabels"], err_msg + # the try-except part of this should be removed once lightgbm's + # minimum supported scikit-learn version is at least 1.6 + try: + sklearn_tags = est.__sklearn_tags__() + except AttributeError: + # only the exact error we expected to be raised should be raised + with pytest.raises(AttributeError, match=r"__sklearn_tags__.* should not be called"): + est.__sklearn_tags__() + else: + # if no AttributeError was thrown, we must be using scikit-learn>=1.6, + # and so the actual effects of __sklearn_tags__() should be tested + assert sklearn_tags.input_tags.allow_nan is True + assert sklearn_tags.input_tags.sparse is True + assert sklearn_tags.target_tags.one_d_labels is True + if estimator_class is lgb.LGBMClassifier: + assert sklearn_tags.estimator_type == "classifier" + assert sklearn_tags.classifier_tags.multi_class is True + assert sklearn_tags.classifier_tags.multi_label is False + elif estimator_class is lgb.LGBMRegressor: + assert sklearn_tags.estimator_type == "regressor" + + +@pytest.mark.parametrize("task", all_tasks) +def test_training_succeeds_when_data_is_dataframe_and_label_is_column_array(task): + pd = pytest.importorskip("pandas") + X, y, g = _create_data(task) + X = pd.DataFrame(X) + y_col_array = y.reshape(-1, 1) + params = {"n_estimators": 1, "num_leaves": 3, "random_state": 0} + model_factory = task_to_model_factory[task] + if task == "ranking": + model_1d = model_factory(**params).fit(X, y, group=g) + with pytest.warns(UserWarning, match="column-vector"): + model_2d = model_factory(**params).fit(X, y_col_array, group=g) + else: + model_1d = model_factory(**params).fit(X, y) + with pytest.warns(UserWarning, match="column-vector"): + model_2d = model_factory(**params).fit(X, y_col_array) + + preds_1d = model_1d.predict(X) + preds_2d = model_2d.predict(X) + np_assert_array_equal(preds_1d, preds_2d, strict=True) + + +@pytest.mark.parametrize("use_weight", [True, False]) +def test_multiclass_custom_objective(use_weight): + centers = [[-4, -4], [4, 4], [-4, 4]] + X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42) + weight = np.full_like(y, 2) if use_weight else None + params = {"n_estimators": 10, "num_leaves": 7} + builtin_obj_model = lgb.LGBMClassifier(**params) + builtin_obj_model.fit(X, y, sample_weight=weight) + builtin_obj_preds = builtin_obj_model.predict_proba(X) + + custom_obj_model = lgb.LGBMClassifier(objective=sklearn_multiclass_custom_objective, **params) + custom_obj_model.fit(X, y, sample_weight=weight) + custom_obj_preds = softmax(custom_obj_model.predict(X, raw_score=True)) + + np.testing.assert_allclose(builtin_obj_preds, custom_obj_preds, rtol=0.01) + assert not callable(builtin_obj_model.objective_) + assert callable(custom_obj_model.objective_) + + +@pytest.mark.parametrize("use_weight", [True, False]) +def test_multiclass_custom_eval(use_weight): + def custom_eval(y_true, y_pred, weight): + loss = log_loss(y_true, y_pred, sample_weight=weight) + return "custom_logloss", loss, False + + centers = [[-4, -4], [4, 4], [-4, 4]] + X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42) + train_test_split_func = partial(train_test_split, test_size=0.2, random_state=0) + X_train, X_valid, y_train, y_valid = train_test_split_func(X, y) + if use_weight: + weight = np.full_like(y, 2) + weight_train, weight_valid = train_test_split_func(weight) + else: + weight_train = None + weight_valid = None + params = {"objective": "multiclass", "num_class": 3, "num_leaves": 7} + model = lgb.LGBMClassifier(**params) + model.fit( + X_train, + y_train, + sample_weight=weight_train, + eval_set=[(X_train, y_train), (X_valid, y_valid)], + eval_names=["train", "valid"], + eval_sample_weight=[weight_train, weight_valid], + eval_metric=custom_eval, + ) + eval_result = model.evals_result_ + train_ds = (X_train, y_train, weight_train) + valid_ds = (X_valid, y_valid, weight_valid) + for key, (X, y_true, weight) in zip(["train", "valid"], [train_ds, valid_ds], strict=True): + np.testing.assert_allclose(eval_result[key]["multi_logloss"], eval_result[key]["custom_logloss"]) + y_pred = model.predict_proba(X) + _, metric_value, _ = custom_eval(y_true, y_pred, weight) + np.testing.assert_allclose(metric_value, eval_result[key]["custom_logloss"][-1]) + + +def test_negative_n_jobs(tmp_path): + n_threads = joblib.cpu_count() + if n_threads <= 1: + return None + # 'val_minus_two' here is the expected number of threads for n_jobs=-2 + val_minus_two = n_threads - 1 + X, y = load_breast_cancer(return_X_y=True) + # Note: according to joblib's formula, a value of n_jobs=-2 means + # "use all but one thread" (formula: n_cpus + 1 + n_jobs) + gbm = lgb.LGBMClassifier(n_estimators=2, verbose=-1, n_jobs=-2).fit(X, y) + gbm.booster_.save_model(tmp_path / "model.txt") + with open(tmp_path / "model.txt", "r") as f: + model_txt = f.read() + assert bool(re.search(rf"\[num_threads: {val_minus_two}\]", model_txt)) + + +def test_default_n_jobs(tmp_path): + n_cores = joblib.cpu_count(only_physical_cores=True) + X, y = load_breast_cancer(return_X_y=True) + gbm = lgb.LGBMClassifier(n_estimators=2, verbose=-1, n_jobs=None).fit(X, y) + gbm.booster_.save_model(tmp_path / "model.txt") + with open(tmp_path / "model.txt", "r") as f: + model_txt = f.read() + assert bool(re.search(rf"\[num_threads: {n_cores}\]", model_txt)) + + +@pytest.mark.skipif(not PANDAS_INSTALLED, reason="pandas is not installed") +@pytest.mark.parametrize("task", all_tasks) +def test_validate_features(task): + X, y, g = _create_data(task, n_features=4) + features = ["x1", "x2", "x3", "x4"] + df = pd_DataFrame(X, columns=features) + model = task_to_model_factory[task](n_estimators=10, num_leaves=15, verbose=-1) + if task == "ranking": + model.fit(df, y, group=g) + else: + model.fit(df, y) + assert model.feature_name_ == features + + # try to predict with a different feature + df2 = df.rename(columns={"x2": "z"}) + with pytest.raises(lgb.basic.LightGBMError, match="Expected 'x2' at position 1 but found 'z'"): + model.predict(df2, validate_features=True) + + # check that disabling the check doesn't raise the error + model.predict(df2, validate_features=False) + + +# LightGBM's 'predict_disable_shape_check' mechanism is intentionally not respected by +# its scikit-learn estimators, for consistency with scikit-learn's own behavior. +@pytest.mark.parametrize("task", all_tasks) +@pytest.mark.parametrize("predict_disable_shape_check", [True, False]) +def test_predict_rejects_inputs_with_incorrect_number_of_features(predict_disable_shape_check, task): + X, y, g = _create_data(task, n_features=4) + model_factory = task_to_model_factory[task] + fit_kwargs = {"X": X[:, :-1], "y": y} + if task == "ranking": + estimator_name = "LGBMRanker" + fit_kwargs.update({"group": g}) + elif task == "regression": + estimator_name = "LGBMRegressor" + else: + estimator_name = "LGBMClassifier" + + # train on the first 3 features + model = model_factory(n_estimators=5, num_leaves=7, verbose=-1).fit(**fit_kwargs) + + # more cols in X than features: error + err_msg = f"X has 4 features, but {estimator_name} is expecting 3 features as input" + with pytest.raises(ValueError, match=err_msg): + model.predict(X, predict_disable_shape_check=predict_disable_shape_check) + + if estimator_name == "LGBMClassifier": + with pytest.raises(ValueError, match=err_msg): + model.predict_proba(X, predict_disable_shape_check=predict_disable_shape_check) + + # fewer cols in X than features: error + err_msg = f"X has 2 features, but {estimator_name} is expecting 3 features as input" + with pytest.raises(ValueError, match=err_msg): + model.predict(X[:, :-2], predict_disable_shape_check=predict_disable_shape_check) + + if estimator_name == "LGBMClassifier": + with pytest.raises(ValueError, match=err_msg): + model.predict_proba(X[:, :-2], predict_disable_shape_check=predict_disable_shape_check) + + # same number of columns in both: no error + preds = model.predict(X[:, :-1], predict_disable_shape_check=predict_disable_shape_check) + assert preds.shape == y.shape + + if estimator_name == "LGBMClassifier": + preds = model.predict_proba(X[:, :-1], predict_disable_shape_check=predict_disable_shape_check) + assert preds.shape[0] == y.shape[0] + + +def _run_minimal_test(*, X_type, y_type, g_type, task, rng): + if any(t.startswith("pa_") for t in [X_type, y_type, g_type]): + pa = pytest.importorskip("pyarrow") + if any(t.startswith("pl_") for t in [X_type, y_type, g_type]): + pl = pytest.importorskip("polars") + + X, y, g = _create_data(task, n_samples=2_000) + weights = np.abs(rng.standard_normal(size=(y.shape[0],))) + + if task in {"binary-classification", "regression", "ranking"}: + init_score = np.full_like(y, np.mean(y)) + elif task == "multiclass-classification": + init_score = np.outer(y, np.array([0.1, 0.2, 0.7])) + else: + raise ValueError(f"Unrecognized task '{task}'") + + X_valid = X * 2 + if X_type == "list2d": + X = X.tolist() + elif X_type == "scipy_csc": + X = scipy.sparse.csc_matrix(X) + elif X_type == "scipy_csr": + X = scipy.sparse.csr_matrix(X) + elif X_type == "pd_DataFrame": + X = pd_DataFrame(X) + elif X_type == "pa_Table": + X = pa.Table.from_pandas(pd_DataFrame(X)) + elif X_type == "pl_DataFrame": + X = pl.DataFrame(X) + elif X_type != "numpy": + raise ValueError(f"Unrecognized X_type: '{X_type}'") + + # make weights and init_score same types as y, just to avoid + # a huge number of combinations and therefore test cases + if y_type == "list1d": + y = y.tolist() + weights = weights.tolist() + init_score = init_score.tolist() + elif y_type == "pd_DataFrame": + y = pd_DataFrame(y) + weights = pd_Series(weights) + if task == "multiclass-classification": + init_score = pd_DataFrame(init_score) + else: + init_score = pd_Series(init_score) + elif y_type == "pd_Series": + y = pd_Series(y) + weights = pd_Series(weights) + if task == "multiclass-classification": + init_score = pd_DataFrame(init_score) + else: + init_score = pd_Series(init_score) + elif y_type == "pa_ChunkedArray": + y = pa.chunked_array([y]) + weights = pa.chunked_array([weights]) + if task == "multiclass-classification": + init_score = pa.Table.from_pandas(pd_DataFrame(init_score)) + else: + init_score = pa.chunked_array([init_score]) + elif y_type == "pl_Series": + y = pl.Series(y) + weights = pl.Series(weights) + if task == "multiclass-classification": + init_score = pl.DataFrame(init_score) + else: + init_score = pl.Series(init_score) + elif y_type != "numpy": + raise ValueError(f"Unrecognized y_type: '{y_type}'") + + if g_type == "list1d_float": + g = g.astype("float").tolist() + elif g_type == "list1d_int": + g = g.astype("int").tolist() + elif g_type == "pd_Series": + g = pd_Series(g) + elif g_type == "pa_ChunkedArray": + g = pa.chunked_array([g]) + elif g_type == "pl_Series": + g = pl.Series(g) + elif g_type != "numpy": + raise ValueError(f"Unrecognized g_type: '{g_type}'") + + model = task_to_model_factory[task](n_estimators=10, verbose=-1) + params_fit = { + "X": X, + "y": y, + "sample_weight": weights, + "init_score": init_score, + "eval_set": [(X_valid, y)], + "eval_sample_weight": [weights], + "eval_init_score": [init_score], + } + if task == "ranking": + params_fit["group"] = g + params_fit["eval_group"] = [g] + model.fit(**params_fit) + + # --- prediction accuracy --# + preds = model.predict(X) + if task == "binary-classification": + assert accuracy_score(y, preds) >= 0.99 + elif task == "multiclass-classification": + assert accuracy_score(y, preds) >= 0.99 + elif task == "regression": + assert r2_score(y, preds) > 0.86 + elif task == "ranking": + assert spearmanr(preds, y).correlation >= 0.99 + else: + raise ValueError(f"Unrecognized task: '{task}'") + + # --- prediction dtypes ---# + + # default predictions: + # + # * classification: int32 or int64 + # * ranking: float64 + # * regression: float64 + # + if task.endswith("classification"): + # preds go through LabelEncoder.inverse_transform() and have the same + # dtype as model.classes_ (expected to be an integer type, but exact size + # varies across numpy versions and operating systems) + assert preds.dtype == model.classes_.dtype + assert preds.dtype in (np.int32, np.int64) + else: + assert preds.dtype == np.float64 + + # raw predictions: always float64 + preds_raw = model.predict(X, raw_score=True) + assert preds_raw.dtype == np.float64 + + # pred_contrib: always float64 + if X_type.startswith("scipy"): + assert all(arr.dtype == np.float64 for arr in model.predict(X, pred_contrib=True)) + else: + preds_contrib = model.predict(X, pred_contrib=True) + assert preds_contrib.dtype == np.float64 + + # pred_leavs: always int32 + preds_leaves = model.predict(X, pred_leaf=True) + assert preds_leaves.dtype == np.int32 + + +@pytest.mark.parametrize("X_type", all_x_types) +@pytest.mark.parametrize("y_type", all_y_types) +@pytest.mark.parametrize("task", [t for t in all_tasks if t != "ranking"]) +def test_classification_and_regression_minimally_work_with_all_accepted_data_types( + X_type, + y_type, + task, + rng, +): + if any(t.startswith("pd_") for t in [X_type, y_type]) and not PANDAS_INSTALLED: + pytest.skip("pandas is not installed") + _run_minimal_test(X_type=X_type, y_type=y_type, g_type="numpy", task=task, rng=rng) + + +@pytest.mark.parametrize("X_type", all_x_types) +@pytest.mark.parametrize("y_type", all_y_types) +@pytest.mark.parametrize("g_type", all_group_types) +def test_ranking_minimally_works_with_all_accepted_data_types( + X_type, + y_type, + g_type, + rng, +): + if any(t.startswith("pd_") for t in [X_type, y_type, g_type]) and not PANDAS_INSTALLED: + pytest.skip("pandas is not installed") + _run_minimal_test(X_type=X_type, y_type=y_type, g_type=g_type, task="ranking", rng=rng) + + +def test_classifier_fit_detects_classes_every_time(): + rng = np.random.default_rng(seed=123) + nrows = 1000 + ncols = 20 + + X = rng.standard_normal(size=(nrows, ncols)) + y_bin = (rng.random(size=nrows) <= 0.3).astype(np.float64) + y_multi = rng.integers(4, size=nrows) + + model = lgb.LGBMClassifier(verbose=-1) + for _ in range(2): + model.fit(X, y_multi) + assert model.objective_ == "multiclass" + model.fit(X, y_bin) + assert model.objective_ == "binary" + + +def test_eval_set_deprecation(): + """Test use of eval_set raises deprecation warning.""" + X, y = make_synthetic_regression(n_samples=10) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) + gbm = lgb.LGBMRegressor() + msg = "The argument 'eval_set' is deprecated.*" + with pytest.warns(LGBMDeprecationWarning, match=msg): + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)]) + + +def test_eval_set_raises(): + """Test that eval_set and eval_X raise errors where appropriate.""" + X, y = make_synthetic_regression(n_samples=10) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42) + gbm = lgb.LGBMRegressor() + + msg = "Specify either 'eval_set' or 'eval_X'.*" + with pytest.raises(ValueError, match=msg): + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_X=X_test) + with pytest.raises(ValueError, match=msg): + gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_y=y_test) + + msg = "You must specify eval_X and eval_y, not just one of them." + with pytest.raises(ValueError, match=msg): + gbm.fit(X_train, y_train, eval_X=X_test) + with pytest.raises(ValueError, match=msg): + gbm.fit(X_train, y_train, eval_y=y_test) + + msg = "If eval_X is a tuple, y_val must be a tuple of same length, and vice versa." + with pytest.raises(ValueError, match=msg): + gbm.fit(X_train, y_train, eval_X=(X_test, X_test), eval_y=y_test) + with pytest.raises(ValueError, match=msg): + gbm.fit(X_train, y_train, eval_X=X_test, eval_y=(y_test, y_test)) + with pytest.raises(ValueError, match=msg): + gbm.fit(X_train, y_train, eval_X=(X_test,) * 3, eval_y=(y_test,) * 2) + + +def test_ranker_eval_set_raises(): + """Test that LGBMRanker raises expected errors from validating eval_group.""" + X, y, g = _create_data(task="ranking", n_samples=1_000) + X_test, y_test, g_test = _create_data(task="ranking", n_samples=100) + gbm = lgb.LGBMRanker() + + msg = "eval_group cannot be None if any of eval_set, eval_X, or eval_y are provided" + with pytest.raises(ValueError, match=msg): + gbm.fit(X, y, group=g, eval_set=[(X_test, y_test)]) + with pytest.raises(ValueError, match=msg): + gbm.fit(X, y, group=g, eval_X=(X_test,)) + with pytest.raises(ValueError, match=msg): + gbm.fit(X, y, group=g, eval_y=(y_test,)) + with pytest.raises(ValueError, match=msg): + gbm.fit(X, y, group=g, eval_X=(X_test,), eval_y=(y_test,)) + + msg = re.escape("Length of eval_group (1) not equal to length of eval_set (2)") + with pytest.raises(ValueError, match=msg): + gbm.fit(X, y, group=g, eval_X=(X_test, X_test), eval_y=(y_test, y_test), eval_group=[g_test]) + with pytest.raises(ValueError, match=msg): + gbm.fit(X, y, group=g, eval_set=[(X_test, y_test), (X_test, y_test)], eval_group=[g_test]) + + +def test_eval_X_eval_y_eval_set_equivalence(): + """Test that eval_X and eval_y are equivalent to eval_set.""" + X, y = make_synthetic_regression() + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42) + params = { + "deterministic": True, + "force_row_wise": True, + "n_jobs": 1, + "seed": 708, + } + cbs = [lgb.early_stopping(2)] + gbm1 = lgb.LGBMRegressor(**params) + with pytest.warns(LGBMDeprecationWarning, match="The argument 'eval_set' is deprecated.*"): + gbm1.fit(X_train, y_train, eval_set=[(X_test, y_test)], callbacks=cbs) + gbm2 = lgb.LGBMRegressor(**params) + gbm2.fit(X_train, y_train, eval_X=X_test, eval_y=y_test, callbacks=cbs) + np.testing.assert_allclose(gbm1.predict(X), gbm2.predict(X)) + assert gbm1.evals_result_["valid_0"]["l2"][0] == pytest.approx(gbm2.evals_result_["valid_0"]["l2"][0]) + # 2 evaluation sets + n = X_test.shape[0] + X_test1, X_test2 = X_test[: n // 2], X_test[n // 2 :] + y_test1, y_test2 = y_test[: n // 2], y_test[n // 2 :] + gbm1 = lgb.LGBMRegressor(**params) + with pytest.warns(LGBMDeprecationWarning, match="The argument 'eval_set' is deprecated.*"): + gbm1.fit(X_train, y_train, eval_set=[(X_test1, y_test1), (X_test2, y_test2)], callbacks=cbs) + gbm2 = lgb.LGBMRegressor(**params) + gbm2.fit(X_train, y_train, eval_X=(X_test1, X_test2), eval_y=(y_test1, y_test2), callbacks=cbs) + np.testing.assert_allclose(gbm1.predict(X), gbm2.predict(X)) + assert set(gbm2.evals_result_.keys()) == {"valid_0", "valid_1"}, ( + f"expected 2 validation sets, 'valid_0' and 'valid_1', in evals_result_, got {gbm2.evals_result_.keys()}" + ) + assert gbm1.evals_result_["valid_0"]["l2"][0] == pytest.approx(gbm2.evals_result_["valid_0"]["l2"][0]) + assert gbm1.evals_result_["valid_1"]["l2"][0] == pytest.approx(gbm2.evals_result_["valid_1"]["l2"][0]) + assert gbm2.evals_result_["valid_0"]["l2"] != gbm2.evals_result_["valid_1"]["l2"], ( + "Evaluation results for the 2 validation sets are not different. This might mean they weren't both used." + ) diff --git a/tests/python_package_test/test_utilities.py b/tests/python_package_test/test_utilities.py new file mode 100644 index 0000000..440a717 --- /dev/null +++ b/tests/python_package_test/test_utilities.py @@ -0,0 +1,160 @@ +# coding: utf-8 +import logging + +import numpy as np +import pytest + +import lightgbm as lgb + + +def test_register_logger(tmp_path): + logger = logging.getLogger("LightGBM") + logger.setLevel(logging.DEBUG) + formatter = logging.Formatter("%(levelname)s | %(message)s") + log_filename = tmp_path / "LightGBM_test_logger.log" + file_handler = logging.FileHandler(log_filename, mode="w", encoding="utf-8") + file_handler.setLevel(logging.DEBUG) + file_handler.setFormatter(formatter) + logger.addHandler(file_handler) + + def dummy_metric(_, __): + logger.debug("In dummy_metric") + return "dummy_metric", 1, True + + lgb.register_logger(logger) + + X = np.array([[1, 2, 3], [1, 2, 4], [1, 2, 4], [1, 2, 3]], dtype=np.float32) + y = np.array([0, 1, 1, 0]) + lgb_train = lgb.Dataset(X, y, categorical_feature=[1]) + lgb_valid = lgb.Dataset(X, y, categorical_feature=[1]) # different object for early-stopping + + eval_records = {} + callbacks = [lgb.record_evaluation(eval_records), lgb.log_evaluation(2), lgb.early_stopping(10)] + lgb.train( + {"objective": "binary", "metric": ["auc", "binary_error"], "verbose": 1}, + lgb_train, + num_boost_round=10, + feval=dummy_metric, + valid_sets=[lgb_valid], + callbacks=callbacks, + ) + + lgb.plot_metric(eval_records) + + expected_log = r""" +INFO | [LightGBM] [Warning] There are no meaningful features which satisfy the provided configuration. Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing Dataset might resolve this warning. +INFO | [LightGBM] [Info] Number of positive: 2, number of negative: 2 +INFO | [LightGBM] [Info] Total Bins 0 +INFO | [LightGBM] [Info] Number of data points in the train set: 4, number of used features: 0 +INFO | [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | Training until validation scores don't improve for 10 rounds +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [2] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [4] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [6] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [8] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1 +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements +DEBUG | In dummy_metric +INFO | [10] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1 +INFO | Did not meet early stopping. Best iteration is: +[1] valid_0's auc: 0.5 valid_0's binary_error: 0.5 valid_0's dummy_metric: 1 +WARNING | More than one metric available, picking one to plot. +""".strip() + + gpu_lines = [ + "INFO | [LightGBM] [Info] This is the GPU trainer", + "INFO | [LightGBM] [Info] Using GPU Device:", + "INFO | [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...", + "INFO | [LightGBM] [Info] GPU programs have been built", + "INFO | [LightGBM] [Warning] GPU acceleration is disabled because no non-trivial dense features can be found", + "INFO | [LightGBM] [Warning] Using sparse features with CUDA is currently not supported.", + "INFO | [LightGBM] [Warning] CUDA currently requires double precision calculations.", + "INFO | [LightGBM] [Info] LightGBM using CUDA trainer with DP float!!", + ] + cuda_lines = [ + "INFO | [LightGBM] [Warning] Metric auc is not implemented in cuda version. Fall back to evaluation on CPU.", + "INFO | [LightGBM] [Warning] Metric binary_error is not implemented in cuda version. Fall back to evaluation on CPU.", + ] + with open(log_filename, "rt", encoding="utf-8") as f: + actual_log = f.read().strip() + actual_log_wo_gpu_stuff = [] + for line in actual_log.split("\n"): + if not any(line.startswith(gpu_or_cuda_line) for gpu_or_cuda_line in gpu_lines + cuda_lines): + actual_log_wo_gpu_stuff.append(line) + + assert "\n".join(actual_log_wo_gpu_stuff) == expected_log + + +def test_register_invalid_logger(): + class LoggerWithoutInfoMethod: + def warning(self, msg: str) -> None: + print(msg) + + class LoggerWithoutWarningMethod: + def info(self, msg: str) -> None: + print(msg) + + class LoggerWithAttributeNotCallable: + def __init__(self): + self.info = 1 + self.warning = 2 + + expected_error_message = "Logger must provide 'info' and 'warning' method" + + with pytest.raises(TypeError, match=expected_error_message): + lgb.register_logger(LoggerWithoutInfoMethod()) + + with pytest.raises(TypeError, match=expected_error_message): + lgb.register_logger(LoggerWithoutWarningMethod()) + + with pytest.raises(TypeError, match=expected_error_message): + lgb.register_logger(LoggerWithAttributeNotCallable()) + + +def test_register_custom_logger(): + logged_messages = [] + + class CustomLogger: + def custom_info(self, msg: str) -> None: + logged_messages.append(msg) + + def custom_warning(self, msg: str) -> None: + logged_messages.append(msg) + + custom_logger = CustomLogger() + lgb.register_logger(custom_logger, info_method_name="custom_info", warning_method_name="custom_warning") + + lgb.basic._log_info("info message") + lgb.basic._log_warning("warning message") + + expected_log = ["info message", "warning message"] + assert logged_messages == expected_log + + logged_messages = [] + X = np.array([[1, 2, 3], [1, 2, 4], [1, 2, 4], [1, 2, 3]], dtype=np.float32) + y = np.array([0, 1, 1, 0]) + lgb_data = lgb.Dataset(X, y, categorical_feature=[1]) + lgb.train( + {"objective": "binary", "metric": "auc"}, + lgb_data, + num_boost_round=10, + valid_sets=[lgb_data], + ) + assert logged_messages, "custom logger was not called" diff --git a/tests/python_package_test/utils.py b/tests/python_package_test/utils.py new file mode 100644 index 0000000..ecaff9e --- /dev/null +++ b/tests/python_package_test/utils.py @@ -0,0 +1,279 @@ +# coding: utf-8 +import os +import pickle +from functools import lru_cache +from inspect import getfullargspec + +import cloudpickle +import joblib +import numpy as np +import sklearn.datasets +from sklearn.utils import check_random_state + +import lightgbm as lgb + +SERIALIZERS = ["pickle", "joblib", "cloudpickle"] + + +@lru_cache(maxsize=None) +def load_breast_cancer(**kwargs): + return sklearn.datasets.load_breast_cancer(**kwargs) + + +@lru_cache(maxsize=None) +def load_digits(**kwargs): + return sklearn.datasets.load_digits(**kwargs) + + +@lru_cache(maxsize=None) +def load_iris(**kwargs): + return sklearn.datasets.load_iris(**kwargs) + + +@lru_cache(maxsize=None) +def load_linnerud(**kwargs): + return sklearn.datasets.load_linnerud(**kwargs) + + +def make_ranking( + *, n_samples=100, n_features=20, n_informative=5, gmax=2, group=None, random_gs=False, avg_gs=10, random_state=0 +): + """Generate a learning-to-rank dataset - feature vectors grouped together with + integer-valued graded relevance scores. Replace this with a sklearn.datasets function + if ranking objective becomes supported in sklearn.datasets module. + + Parameters + ---------- + n_samples : int, optional (default=100) + Total number of documents (records) in the dataset. + n_features : int, optional (default=20) + Total number of features in the dataset. + n_informative : int, optional (default=5) + Number of features that are "informative" for ranking, as they are bias + beta * y + where bias and beta are standard normal variates. If this is greater than n_features, the dataset will have + n_features features, all will be informative. + gmax : int, optional (default=2) + Maximum graded relevance value for creating relevance/target vector. If you set this to 2, for example, all + documents in a group will have relevance scores of either 0, 1, or 2. + group : array-like, optional (default=None) + 1-d array or list of group sizes. When `group` is specified, this overrides n_samples, random_gs, and + avg_gs by simply creating groups with sizes group[0], ..., group[-1]. + random_gs : bool, optional (default=False) + True will make group sizes ~ Poisson(avg_gs), False will make group sizes == avg_gs. + avg_gs : int, optional (default=10) + Average number of documents (records) in each group. + random_state : int, optional (default=0) + Random seed. + + Returns + ------- + X : 2-d np.ndarray of shape = [n_samples (or np.sum(group)), n_features] + Input feature matrix for ranking objective. + y : 1-d np.array of shape = [n_samples (or np.sum(group))] + Integer-graded relevance scores. + group_ids : 1-d np.array of shape = [n_samples (or np.sum(group))] + Array of group ids, each value indicates to which group each record belongs. + """ + rnd_generator = check_random_state(random_state) + + y_vec, group_id_vec = np.empty((0,), dtype=int), np.empty((0,), dtype=int) + gid = 0 + + # build target, group ID vectors. + relvalues = range(gmax + 1) + + # build y/target and group-id vectors with user-specified group sizes. + if group is not None and hasattr(group, "__len__"): + n_samples = np.sum(group) + + for i, gsize in enumerate(group): + y_vec = np.concatenate((y_vec, rnd_generator.choice(relvalues, size=gsize, replace=True))) + group_id_vec = np.concatenate((group_id_vec, [i] * gsize)) + + # build y/target and group-id vectors according to n_samples, avg_gs, and random_gs. + else: + while len(y_vec) < n_samples: + gsize = avg_gs if not random_gs else rnd_generator.poisson(avg_gs) + + # groups should contain > 1 element for pairwise learning objective. + if gsize < 1: + continue + + y_vec = np.append(y_vec, rnd_generator.choice(relvalues, size=gsize, replace=True)) + group_id_vec = np.append(group_id_vec, [gid] * gsize) + gid += 1 + + y_vec, group_id_vec = y_vec[:n_samples], group_id_vec[:n_samples] + + # build feature data, X. Transform first few into informative features. + n_informative = max(min(n_features, n_informative), 0) + X = rnd_generator.uniform(size=(n_samples, n_features)) + + for j in range(n_informative): + bias, coef = rnd_generator.normal(size=2) + X[:, j] = bias + coef * y_vec + + return X, y_vec, group_id_vec + + +@lru_cache(maxsize=None) +def make_synthetic_regression(*, n_samples=100, n_features=4, n_informative=2, random_state=42): + return sklearn.datasets.make_regression( + n_samples=n_samples, n_features=n_features, n_informative=n_informative, random_state=random_state + ) + + +def dummy_obj(preds, train_data): + return np.ones(preds.shape), np.ones(preds.shape) + + +def mse_obj(y_pred, dtrain): + y_true = dtrain.get_label() + grad = y_pred - y_true + hess = np.ones(len(grad)) + return grad, hess + + +def softmax(x): + row_wise_max = np.max(x, axis=1).reshape(-1, 1) + exp_x = np.exp(x - row_wise_max) + return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1) + + +def logistic_sigmoid(x): + return 1.0 / (1.0 + np.exp(-x)) + + +def sklearn_multiclass_custom_objective(y_true, y_pred, weight=None): + num_rows, num_class = y_pred.shape + prob = softmax(y_pred) + grad_update = np.zeros_like(prob) + grad_update[np.arange(num_rows), y_true.astype(np.int32)] = -1.0 + grad = prob + grad_update + factor = num_class / (num_class - 1) + hess = factor * prob * (1 - prob) + if weight is not None: + weight2d = weight.reshape(-1, 1) + grad *= weight2d + hess *= weight2d + return grad, hess + + +def pickle_obj(obj, filepath, serializer): + if serializer == "pickle": + with open(filepath, "wb") as f: + pickle.dump(obj, f) + elif serializer == "joblib": + joblib.dump(obj, filepath) + elif serializer == "cloudpickle": + with open(filepath, "wb") as f: + cloudpickle.dump(obj, f) + else: + raise ValueError(f"Unrecognized serializer type: {serializer}") + + +def unpickle_obj(filepath, serializer): + if serializer == "pickle": + with open(filepath, "rb") as f: + return pickle.load(f) + elif serializer == "joblib": + return joblib.load(filepath) + elif serializer == "cloudpickle": + with open(filepath, "rb") as f: + return cloudpickle.load(f) + else: + raise ValueError(f"Unrecognized serializer type: {serializer}") + + +def pickle_and_unpickle_object(obj, serializer): + with lgb.basic._TempFile() as tmp_file: + pickle_obj(obj=obj, filepath=tmp_file.name, serializer=serializer) + obj_from_disk = unpickle_obj(filepath=tmp_file.name, serializer=serializer) + return obj_from_disk # noqa: RET504 + + +def assert_silent(capsys) -> None: + """ + Given a ``CaptureFixture`` instance (from the ``pytest`` built-in ``capsys`` fixture), + read the recently-captured data into a variable and assert that nothing was written + to stdout or stderr. + + This is just here to turn 3 lines of repetitive code into 1. + + Note that this does have a side effect... ``capsys.readouterr()`` copies + from a buffer then frees it. So it will only store into ``.out`` and ``.err`` the + captured output since the last time that ``.readouterr()`` was called. + + ref: https://docs.pytest.org/en/stable/how-to/capture-stdout-stderr.html + """ + captured = capsys.readouterr() + assert captured.out == "", captured.out + assert captured.err == "", captured.err + + +# doing this here, at import time, to ensure it only runs once_per import +# instead of once per assertion +_numpy_testing_supports_strict_kwarg = "strict" in getfullargspec(np.testing.assert_array_equal).kwonlyargs + + +def np_assert_array_equal(*args, **kwargs): + """ + np.testing.assert_array_equal() only got the kwarg ``strict`` in June 2022: + https://github.com/numpy/numpy/pull/21595 + + This function is here for testing on older Python (and therefore ``numpy``) + """ + if not _numpy_testing_supports_strict_kwarg: + kwargs.pop("strict") + np.testing.assert_array_equal(*args, **kwargs) + + +def assert_subtree_valid(root): + """Recursively checks the validity of a subtree rooted at `root`. + + Currently it only checks whether weights and counts are consistent between + all parent nodes and their children. + + Parameters + ---------- + root : dict + A dictionary representing the root of the subtree. + It should be produced by dump_model() + + Returns + ------- + tuple + A tuple containing the weight and count of the subtree rooted at `root`. + """ + if "leaf_count" in root: + return (root["leaf_weight"], root["leaf_count"]) + + left_child = root["left_child"] + right_child = root["right_child"] + (l_w, l_c) = assert_subtree_valid(left_child) + (r_w, r_c) = assert_subtree_valid(right_child) + assert abs(root["internal_weight"] - (l_w + r_w)) <= 1e-3, ( + "root node's internal weight should be approximately the sum of its child nodes' internal weights" + ) + assert root["internal_count"] == l_c + r_c, ( + "root node's internal count should be exactly the sum of its child nodes' internal counts" + ) + return (root["internal_weight"], root["internal_count"]) + + +def assert_all_trees_valid(model_dump): + for idx, tree in enumerate(model_dump["tree_info"]): + assert tree["tree_index"] == idx, f"tree {idx} should have tree_index={idx}. Full tree: {tree}" + assert_subtree_valid(tree["tree_structure"]) + + +# This mapping from CI-time environment variables is a placeholder +# until there is a more reliable way to detect which customizations +# LightGBM was built with. +# +# see https://github.com/lightgbm-org/LightGBM/issues/7273 +# +class BuildInfo: + has_cuda = os.getenv("TASK", "") == "cuda" + has_gpu = os.getenv("TASK", "") == "gpu" + has_mpi = os.getenv("TASK", "") == "mpi" diff --git a/windows/LightGBM.sln b/windows/LightGBM.sln new file mode 100644 index 0000000..29e7a35 --- /dev/null +++ b/windows/LightGBM.sln @@ -0,0 +1,33 @@ +Microsoft Visual Studio Solution File, Format Version 12.00 +# Visual Studio 15 +VisualStudioVersion = 15.0 +MinimumVisualStudioVersion = 15.0 +Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "LightGBM", "LightGBM.vcxproj", "{F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}" +EndProject +Global + GlobalSection(SolutionConfigurationPlatforms) = preSolution + Debug_DLL|x64 = Debug_DLL|x64 + Debug_mpi|x64 = Debug_mpi|x64 + Debug|x64 = Debug|x64 + DLL|x64 = DLL|x64 + Release_mpi|x64 = Release_mpi|x64 + Release|x64 = Release|x64 + EndGlobalSection + GlobalSection(ProjectConfigurationPlatforms) = postSolution + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Debug_DLL|x64.ActiveCfg = Debug_DLL|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Debug_DLL|x64.Build.0 = Debug_DLL|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Debug_mpi|x64.ActiveCfg = Debug_mpi|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Debug_mpi|x64.Build.0 = Debug_mpi|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Debug|x64.ActiveCfg = Debug|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Debug|x64.Build.0 = Debug|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.DLL|x64.ActiveCfg = DLL|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.DLL|x64.Build.0 = DLL|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Release_mpi|x64.ActiveCfg = Release_mpi|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Release_mpi|x64.Build.0 = Release_mpi|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Release|x64.ActiveCfg = Release|x64 + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C}.Release|x64.Build.0 = Release|x64 + EndGlobalSection + GlobalSection(SolutionProperties) = preSolution + HideSolutionNode = FALSE + EndGlobalSection +EndGlobal diff --git a/windows/LightGBM.vcxproj b/windows/LightGBM.vcxproj new file mode 100644 index 0000000..5b4ad5a --- /dev/null +++ b/windows/LightGBM.vcxproj @@ -0,0 +1,358 @@ + + + + + Debug_DLL + x64 + + + Debug_mpi + x64 + + + Debug + x64 + + + DLL + x64 + + + Release_mpi + x64 + + + Release + x64 + + + + {F31C0B5D-715E-4953-AA1B-8D2AEEE4344C} + LightGBM + SAK + SAK + SAK + SAK + LightGBM + 10.0.19041.0 + + + + v141 + + + v141 + + + v141 + DynamicLibrary + + + v141 + + + v141 + DynamicLibrary + + + v141 + + + + + + + + + + + + + + + + ..\include;$(VC_IncludePath);$(WindowsSDK_IncludePath); + $(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64); + lightgbm + + + ..\include;$(VC_IncludePath);$(WindowsSDK_IncludePath); + $(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64); + lib_lightgbm + + + $(MSMPI_INC);..\include;$(VC_IncludePath);$(WindowsSDK_IncludePath); + $(MSMPI_LIB64);$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64); + lightgbm + + + ..\include;$(VC_IncludePath);$(WindowsSDK_IncludePath); + $(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64); + lightgbm + + + $(MSMPI_INC);..\include;$(VC_IncludePath);$(WindowsSDK_IncludePath); + $(MSMPI_LIB64);$(VC_LibraryPath_x64);$(WindowsSDK_LibraryPath_x64); + lightgbm + + + ..\include;$(VC_IncludePath);$(WindowsSDK_IncludePath); + lib_lightgbm + + + + EIGEN_MPL2_ONLY;EIGEN_DONT_PARALLELIZE;WIN_HAS_INET_PTON; + + + + + USE_MPI;USE_DEBUG;%(PreprocessorDefinitions) + Level4 + true + Neither + Default + false + false + false + Disabled + MultiThreadedDebugDLL + true + $(ProjectDir)\..\external_libs\eigen;$(ProjectDir)\..\external_libs\fast_double_parser\include;$(ProjectDir)\..\external_libs\fmt\include;%(AdditionalIncludeDirectories) + /utf-8 %(AdditionalOptions) + + + + + + + msmpi.lib + + + + + USE_SOCKET;USE_DEBUG;%(PreprocessorDefinitions) + Level4 + true + Neither + Default + false + false + false + Disabled + MultiThreadedDebugDLL + true + $(ProjectDir)\..\external_libs\eigen;$(ProjectDir)\..\external_libs\fast_double_parser\include;$(ProjectDir)\..\external_libs\fmt\include;%(AdditionalIncludeDirectories) + /utf-8 %(AdditionalOptions) + + + + + + + + + USE_SOCKET;USE_DEBUG;%(PreprocessorDefinitions) + Level4 + true + Neither + Default + false + false + false + Disabled + MultiThreadedDebugDLL + true + $(ProjectDir)\..\external_libs\eigen;$(ProjectDir)\..\external_libs\fast_double_parser\include;$(ProjectDir)\..\external_libs\fmt\include;%(AdditionalIncludeDirectories) + /utf-8 %(AdditionalOptions) + + + + + + + + + Level4 + MaxSpeed + true + true + true + USE_MPI;_MBCS;_CRT_SECURE_NO_WARNINGS;%(PreprocessorDefinitions) + true + Speed + AnySuitable + false + false + true + MultiThreadedDLL + true + $(ProjectDir)\..\external_libs\eigen;$(ProjectDir)\..\external_libs\fast_double_parser\include;$(ProjectDir)\..\external_libs\fmt\include;%(AdditionalIncludeDirectories) + /utf-8 %(AdditionalOptions) + + + + + + + true + true + true + msmpi.lib + + + + + USE_SOCKET;_MBCS;_CRT_SECURE_NO_WARNINGS;%(PreprocessorDefinitions) + Level4 + true + Speed + AnySuitable + true + false + false + MultiThreadedDLL + true + true + true + $(ProjectDir)\..\external_libs\eigen;$(ProjectDir)\..\external_libs\fast_double_parser\include;$(ProjectDir)\..\external_libs\fmt\include;%(AdditionalIncludeDirectories) + /utf-8 %(AdditionalOptions) + + + + true + + + + + USE_SOCKET;_MBCS;_CRT_SECURE_NO_WARNINGS;%(PreprocessorDefinitions) + Level4 + true + Speed + AnySuitable + true + false + false + MultiThreadedDLL + true + true + true + $(ProjectDir)\..\external_libs\eigen;$(ProjectDir)\..\external_libs\fast_double_parser\include;$(ProjectDir)\..\external_libs\fmt\include;%(AdditionalIncludeDirectories) + /utf-8 %(AdditionalOptions) + + + + + true + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/windows/LightGBM.vcxproj.filters b/windows/LightGBM.vcxproj.filters new file mode 100644 index 0000000..a2b5a96 --- /dev/null +++ b/windows/LightGBM.vcxproj.filters @@ -0,0 +1,352 @@ + + + + + {6e213f6b-b843-4469-bc8c-56c1ffe7f195} + + + {29082261-e6cd-40b2-b30c-c4cb70f23339} + + + {3a703e42-6f06-4ab1-8e46-0dfb07407d9e} + + + {43be32f9-227b-4a15-9c0e-38dbf9747aeb} + + + {6fcdaf19-880a-45b0-80db-344be9498017} + + + {8bacb16c-7f31-494f-94df-8ccc6c3e3894} + + + {93db474b-4ab8-406b-99ec-eb8e40f97593} + + + {34d576af-dec6-4cad-90bd-f8d0e95ec614} + + + {16638c37-41bd-4124-8b80-befbca2f969f} + + + {37b41659-26e2-4b2f-ac0c-7b52d8bd53da} + + + {bf66b9f7-015e-404d-8098-4353abc46956} + + + {fa3b4166-a8fd-4030-b80d-c167089332c7} + + + + + src\boosting + + + src\network + + + src\treelearner + + + src\treelearner + + + src\treelearner + + + src\application + + + src\boosting + + + src\io + + + src\io + + + src\io + + + src\metric + + + src\metric + + + src\metric + + + src\metric + + + src\network + + + src\objective + + + src\objective + + + src\objective + + + src\objective + + + src\treelearner + + + src\treelearner + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM + + + include\LightGBM\utils + + + include\LightGBM\utils + + + include\LightGBM\utils + + + include\LightGBM\utils + + + include\LightGBM\utils + + + include\LightGBM\utils + + + include\LightGBM\utils + + + include\LightGBM\utils + + + include\LightGBM + + + include\LightGBM + + + src\boosting + + + include\LightGBM + + + src\treelearner + + + src\treelearner + + + src\boosting + + + include\LightGBM\utils + + + src\metric + + + src\boosting + + + src\objective + + + src\metric + + + include\LightGBM\utils + + + src\treelearner + + + src\io + + + src\io + + + include\LightGBM\utils + + + src\treelearner + + + include\LightGBM\utils\yamc + + + include\LightGBM\utils\yamc + + + include\LightGBM\utils\yamc + + + src\treelearner + + + src\treelearner + + + include\LightGBM\utils + + + include\LightGBM\utils + + + + + src\application + + + src\network + + + src\network + + + src\treelearner + + + src\treelearner + + + src\boosting + + + src\io + + + src\io + + + src\io + + + src\objective + + + src\boosting + + + src\io + + + src\metric + + + src\treelearner + + + src\treelearner + + + src\treelearner + + + src\network + + + src\network + + + src\io + + + src\metric + + + src\io + + + src + + + src + + + src\io + + + src\treelearner + + + src\boosting + + + src\boosting + + + src\boosting + + + src\boosting + + + src\io + + + src\io + + + src\io + + + src\io + + + src\treelearner + + + src\treelearner + + + +